diff --git a/.coveragerc b/.coveragerc index 94ecfe88ffc..dffe22a6f46 100644 --- a/.coveragerc +++ b/.coveragerc @@ -1,6 +1,6 @@ # -*- coding: utf-8 -*- # -# Copyright 2024 Google LLC +# Copyright 2023 Google LLC # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -34,5 +34,6 @@ exclude_lines = omit = */gapic/*.py */proto/*.py + */core/*.py */site-packages/*.py google/cloud/__init__.py diff --git a/.flake8 b/.flake8 index 32986c79287..87f6e408c47 100644 --- a/.flake8 +++ b/.flake8 @@ -1,6 +1,6 @@ # -*- coding: utf-8 -*- # -# Copyright 2024 Google LLC +# Copyright 2023 Google LLC # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/.gemini/common/constraints.md b/.gemini/common/constraints.md deleted file mode 100644 index 1b563eab3b8..00000000000 --- a/.gemini/common/constraints.md +++ /dev/null @@ -1,8 +0,0 @@ -## Constraints - -- Only add git commits. Do not change git history. -- Follow the spec file for development. - - Check off items in the "Acceptance - criteria" and "Detailed steps" sections with `[x]`. - - Please do this as they are completed. - - Refer back to the spec after each step. diff --git a/.gemini/common/docs.md b/.gemini/common/docs.md deleted file mode 100644 index 1a718005ad7..00000000000 --- a/.gemini/common/docs.md +++ /dev/null @@ -1,9 +0,0 @@ -## Documentation - -If a method or property is implementing the same interface as a third-party -package such as pandas or scikit-learn, place the relevant docstring in the -corresponding `third_party/bigframes_vendored/package_name` directory, not in -the `bigframes` directory. Implementations may be placed in the `bigframes` -directory, though. - -@../tools/test_docs.md diff --git a/.gemini/tasks/scalar_op.md b/.gemini/tasks/scalar_op.md deleted file mode 100644 index a9318d54824..00000000000 --- a/.gemini/tasks/scalar_op.md +++ /dev/null @@ -1,67 +0,0 @@ -## Adding a scalar operator - -For an example, see commit -[c5b7fdae74a22e581f7705bc0cf5390e928f4425](https://github.com/googleapis/python-bigquery-dataframes/commit/c5b7fdae74a22e581f7705bc0cf5390e928f4425). - -To add a new scalar operator, follow these steps: - -1. **Define the operation dataclass:** - - In `bigframes/operations/`, find the relevant file (e.g., `geo_ops.py` for geography functions) or create a new one. - - Create a new dataclass inheriting from `base_ops.UnaryOp` for unary - operators, `base_ops.BinaryOp` for binary operators, `base_ops.TernaryOp` - for ternary operators, or `base_ops.NaryOp for operators with many - arguments. Note that these operators are counting the number column-like - arguments. A function that takes only a single column but several literal - values would still be a `UnaryOp`. - - Define the `name` of the operation and any parameters it requires. - - Implement the `output_type` method to specify the data type of the result. - -2. **Export the new operation:** - - In `bigframes/operations/__init__.py`, import your new operation dataclass and add it to the `__all__` list. - -3. **Implement the user-facing function (pandas-like):** - - - Identify the canonical function from pandas / geopandas / awkward array / - other popular Python package that this operator implements. - - Find the corresponding class in BigFrames. For example, the implementation - for most geopandas.GeoSeries methods is in - `bigframes/geopandas/geoseries.py`. Pandas Series methods are implemented - in `bigframes/series.py` or one of the accessors, such as `StringMethods` - in `bigframes/operations/strings.py`. - - Create the user-facing function that will be called by users (e.g., `length`). - - If the SQL method differs from pandas or geopandas in a way that can't be - made the same, raise a `NotImplementedError` with an appropriate message and - link to the feedback form. - - Add the docstring to the corresponding file in - `third_party/bigframes_vendored`, modeled after pandas / geopandas. - -4. **Implement the user-facing function (SQL-like):** - - - In `bigframes/bigquery/_operations/`, find the relevant file (e.g., `geo.py`) or create a new one. - - Create the user-facing function that will be called by users (e.g., `st_length`). - - This function should take a `Series` for any column-like inputs, plus any other parameters. - - Inside the function, call `series._apply_unary_op`, - `series._apply_binary_op`, or similar passing the operation dataclass you - created. - - Add a comprehensive docstring with examples. - - In `bigframes/bigquery/__init__.py`, import your new user-facing function and add it to the `__all__` list. - -5. **Implement the compilation logic:** - - In `bigframes/core/compile/scalar_op_compiler.py`: - - If the BigQuery function has a direct equivalent in Ibis, you can often reuse an existing Ibis method. - - If not, define a new Ibis UDF using `@ibis_udf.scalar.builtin` to map to the specific BigQuery function signature. - - Create a new compiler implementation function (e.g., `geo_length_op_impl`). - - Register this function to your operation dataclass using `@scalar_op_compiler.register_unary_op` or `@scalar_op_compiler.register_binary_op`. - - This implementation will translate the BigQuery DataFrames operation into the appropriate Ibis expression. - -6. **Add Tests:** - - Add system tests in the `tests/system/` directory to verify the end-to-end - functionality of the new operator. Test various inputs, including edge cases - and `NULL` values. - - Where possible, run the same test code against pandas or GeoPandas and - compare that the outputs are the same (except for dtypes if BigFrames - differs from pandas). - - If you are overriding a pandas or GeoPandas property, add a unit test to - ensure the correct behavior (e.g., raising `NotImplementedError` if the - functionality is not supported). diff --git a/.gemini/tools/style_nox.md b/.gemini/tools/style_nox.md deleted file mode 100644 index 894fd102363..00000000000 --- a/.gemini/tools/style_nox.md +++ /dev/null @@ -1,18 +0,0 @@ -## Code Style with nox - -- We use the automatic code formatter `black`. You can run it using - the nox session `format`. This will eliminate many lint errors. Run via: - - ```bash - nox -r -s format - ``` - -- PEP8 compliance is required, with exceptions defined in the linter configuration. - If you have ``nox`` installed, you can test that you have not introduced - any non-compliant code via: - - ``` - nox -r -s lint - ``` - -- When writing tests, use the idiomatic "pytest" style. diff --git a/.gemini/tools/test_docs.md b/.gemini/tools/test_docs.md deleted file mode 100644 index 5cb988186c7..00000000000 --- a/.gemini/tools/test_docs.md +++ /dev/null @@ -1,10 +0,0 @@ -## Testing code samples - -Code samples are very important for accurate documentation. We use the "doctest" -framework to ensure the samples are functioning as expected. After adding a code -sample, please ensure it is correct by running doctest. To run the samples -doctests for just a single method, refer to the following example: - -```bash -pytest --doctest-modules bigframes/pandas/__init__.py::bigframes.pandas.cut -``` diff --git a/.gemini/tools/test_nox.md b/.gemini/tools/test_nox.md deleted file mode 100644 index 023ada1b61f..00000000000 --- a/.gemini/tools/test_nox.md +++ /dev/null @@ -1,28 +0,0 @@ -## Testing with nox - -Use `nox` to instrument our tests. - -- To test your changes, run unit tests with `nox`: - - ```bash - nox -r -s unit - ``` - -- To run a single unit test: - - ```bash - nox -r -s unit-3.14 -- -k - ``` - -- Ignore this step if you lack access to Google Cloud resources. To run system - tests, you can execute:: - - # Run all system tests - $ nox -r -s system - - # Run a single system test - $ nox -r -s system-3.14 -- -k - -- The codebase must have better coverage than it had previously after each - change. You can test coverage via `nox -s unit system cover` (takes a long - time). Omit `system` if you lack access to cloud resources. diff --git a/.gemini/tools/test_pytest.md b/.gemini/tools/test_pytest.md deleted file mode 100644 index 5228ae06ba8..00000000000 --- a/.gemini/tools/test_pytest.md +++ /dev/null @@ -1,9 +0,0 @@ -## Testing with pytest - -Use `pytest` to instrument our tests. - -- To test your changes, run `pytest`: - - ```bash - pytest :: - ``` diff --git a/.github/.OwlBot.lock.yaml b/.github/.OwlBot.lock.yaml new file mode 100644 index 00000000000..ec696b558c3 --- /dev/null +++ b/.github/.OwlBot.lock.yaml @@ -0,0 +1,17 @@ +# Copyright 2023 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +docker: + image: gcr.io/cloud-devrel-public-resources/owlbot-python:latest + digest: sha256:30470597773378105e239b59fce8eb27cc97375580d592699206d17d117143d0 +# created: 2023-11-03T00:57:07.335914631Z diff --git a/.github/.OwlBot.yaml b/.github/.OwlBot.yaml new file mode 100644 index 00000000000..c379bd3092d --- /dev/null +++ b/.github/.OwlBot.yaml @@ -0,0 +1,18 @@ +# Copyright 2021 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +docker: + image: gcr.io/cloud-devrel-public-resources/owlbot-python:latest + +begin-after-commit-hash: 92006bb3cdc84677aa93c7f5235424ec2b157146 diff --git a/.github/CODEOWNERS b/.github/CODEOWNERS new file mode 100644 index 00000000000..7686a50da62 --- /dev/null +++ b/.github/CODEOWNERS @@ -0,0 +1,12 @@ +# Code owners file. +# This file controls who is tagged for review for any given pull request. +# +# For syntax help see: +# https://help.github.com/en/github/creating-cloning-and-archiving-repositories/about-code-owners#codeowners-syntax +# Note: This file is autogenerated. To make changes to the codeowner team, please update .repo-metadata.json. + +# @googleapis/yoshi-python @googleapis/api-bigquery-dataframe are the default owners for changes in this repo +* @googleapis/yoshi-python @googleapis/api-bigquery-dataframe + +# @googleapis/python-samples-reviewers @googleapis/api-bigquery-dataframe are the default owners for samples changes +/samples/ @googleapis/python-samples-reviewers @googleapis/api-bigquery-dataframe diff --git a/.github/CONTRIBUTING.md b/.github/CONTRIBUTING.md new file mode 100644 index 00000000000..939e5341e74 --- /dev/null +++ b/.github/CONTRIBUTING.md @@ -0,0 +1,28 @@ +# How to Contribute + +We'd love to accept your patches and contributions to this project. There are +just a few small guidelines you need to follow. + +## Contributor License Agreement + +Contributions to this project must be accompanied by a Contributor License +Agreement. You (or your employer) retain the copyright to your contribution; +this simply gives us permission to use and redistribute your contributions as +part of the project. Head over to to see +your current agreements on file or to sign a new one. + +You generally only need to submit a CLA once, so if you've already submitted one +(even if it was for a different project), you probably don't need to do it +again. + +## Code reviews + +All submissions, including submissions by project members, require review. We +use GitHub pull requests for this purpose. Consult +[GitHub Help](https://help.github.com/articles/about-pull-requests/) for more +information on using pull requests. + +## Community Guidelines + +This project follows [Google's Open Source Community +Guidelines](https://opensource.google.com/conduct/). diff --git a/.github/ISSUE_TEMPLATE/bug_report.md b/.github/ISSUE_TEMPLATE/bug_report.md new file mode 100644 index 00000000000..7b0900728e4 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/bug_report.md @@ -0,0 +1,43 @@ +--- +name: Bug report +about: Create a report to help us improve + +--- + +Thanks for stopping by to let us know something could be better! + +**PLEASE READ**: If you have a support contract with Google, please create an issue in the [support console](https://cloud.google.com/support/) instead of filing on GitHub. This will ensure a timely response. + +Please run down the following list and make sure you've tried the usual "quick fixes": + + - Search the issues already opened: https://github.com/googleapis/python-bigquery-dataframes/issues + - Search StackOverflow: https://stackoverflow.com/questions/tagged/google-cloud-platform+python + +If you are still having issues, please be sure to include as much information as possible: + +#### Environment details + + - OS type and version: + - Python version: `python --version` + - pip version: `pip --version` + - `bigframes` version: `pip show bigframes` + +#### Steps to reproduce + + 1. ? + 2. ? + +#### Code example + +```python +# example +``` + +#### Stack trace +``` +# example +``` + +Making sure to follow these steps will guarantee the quickest resolution possible. + +Thanks! diff --git a/.github/ISSUE_TEMPLATE/feature_request.md b/.github/ISSUE_TEMPLATE/feature_request.md new file mode 100644 index 00000000000..6365857f33c --- /dev/null +++ b/.github/ISSUE_TEMPLATE/feature_request.md @@ -0,0 +1,18 @@ +--- +name: Feature request +about: Suggest an idea for this library + +--- + +Thanks for stopping by to let us know something could be better! + +**PLEASE READ**: If you have a support contract with Google, please create an issue in the [support console](https://cloud.google.com/support/) instead of filing on GitHub. This will ensure a timely response. + + **Is your feature request related to a problem? Please describe.** +A clear and concise description of what the problem is. Ex. I'm always frustrated when [...] + **Describe the solution you'd like** +A clear and concise description of what you want to happen. + **Describe alternatives you've considered** +A clear and concise description of any alternative solutions or features you've considered. + **Additional context** +Add any other context or screenshots about the feature request here. diff --git a/.github/ISSUE_TEMPLATE/support_request.md b/.github/ISSUE_TEMPLATE/support_request.md new file mode 100644 index 00000000000..99586903212 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/support_request.md @@ -0,0 +1,7 @@ +--- +name: Support request +about: If you have a support contract with Google, please create an issue in the Google Cloud Support console. + +--- + +**PLEASE READ**: If you have a support contract with Google, please create an issue in the [support console](https://cloud.google.com/support/) instead of filing on GitHub. This will ensure a timely response. diff --git a/.github/PULL_REQUEST_TEMPLATE.md b/.github/PULL_REQUEST_TEMPLATE.md new file mode 100644 index 00000000000..3e59d9a70d1 --- /dev/null +++ b/.github/PULL_REQUEST_TEMPLATE.md @@ -0,0 +1,7 @@ +Thank you for opening a Pull Request! Before submitting your PR, there are a few things you can do to make sure it goes smoothly: +- [ ] Make sure to open an issue as a [bug/issue](https://github.com/googleapis/python-bigquery-dataframes/issues/new/choose) before writing your code! That way we can discuss the change, evaluate designs, and agree on the general idea +- [ ] Ensure the tests and linter pass +- [ ] Code coverage does not decrease (if any source code was changed) +- [ ] Appropriate docs were updated (if necessary) + +Fixes # 🦕 diff --git a/.github/auto-approve.yml b/.github/auto-approve.yml new file mode 100644 index 00000000000..311ebbb853a --- /dev/null +++ b/.github/auto-approve.yml @@ -0,0 +1,3 @@ +# https://github.com/googleapis/repo-automation-bots/tree/main/packages/auto-approve +processes: + - "OwlBotTemplateChanges" diff --git a/.github/auto-label.yaml b/.github/auto-label.yaml new file mode 100644 index 00000000000..b2016d119b4 --- /dev/null +++ b/.github/auto-label.yaml @@ -0,0 +1,15 @@ +# Copyright 2023 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +requestsize: + enabled: true diff --git a/.github/header-checker-lint.yml b/.github/header-checker-lint.yml new file mode 100644 index 00000000000..6fe78aa7987 --- /dev/null +++ b/.github/header-checker-lint.yml @@ -0,0 +1,15 @@ +{"allowedCopyrightHolders": ["Google LLC"], + "allowedLicenses": ["Apache-2.0", "MIT", "BSD-3"], + "ignoreFiles": ["**/requirements.txt", "**/requirements-test.txt", "**/__init__.py", "samples/**/constraints.txt", "samples/**/constraints-test.txt"], + "sourceFileExtensions": [ + "ts", + "js", + "java", + "sh", + "Dockerfile", + "yaml", + "py", + "html", + "txt" + ] +} \ No newline at end of file diff --git a/.github/release-please.yml b/.github/release-please.yml new file mode 100644 index 00000000000..466597e5b19 --- /dev/null +++ b/.github/release-please.yml @@ -0,0 +1,2 @@ +releaseType: python +handleGHRelease: true diff --git a/.github/release-trigger.yml b/.github/release-trigger.yml new file mode 100644 index 00000000000..4fbd4aa427b --- /dev/null +++ b/.github/release-trigger.yml @@ -0,0 +1,2 @@ +enabled: true +multiScmName: python-bigquery-dataframes diff --git a/bigframes/py.typed b/.github/snippet-bot.yml similarity index 100% rename from bigframes/py.typed rename to .github/snippet-bot.yml diff --git a/.github/sync-repo-settings.yaml b/.github/sync-repo-settings.yaml new file mode 100644 index 00000000000..cfa62f787c9 --- /dev/null +++ b/.github/sync-repo-settings.yaml @@ -0,0 +1,32 @@ +# https://github.com/googleapis/repo-automation-bots/tree/main/packages/sync-repo-settings +# Rules for main branch protection +branchProtectionRules: +# Identifies the protection rule pattern. Name of the branch to be protected. +# Defaults to `main` +- pattern: main + requiresCodeOwnerReviews: true + requiresStrictStatusChecks: true + requiredStatusCheckContexts: + - 'conventionalcommits.org' + - 'cla/google' + - 'OwlBot Post Processor' + - 'docs' + - 'lint' + - 'unit (3.9)' + - 'unit (3.10)' + - 'unit (3.11)' + - 'cover' + - 'Kokoro presubmit' +permissionRules: + - team: actools-python + permission: admin + - team: actools + permission: admin + - team: api-bigquery-dataframe + permission: push + - team: yoshi-python + permission: push + - team: python-samples-owners + permission: push + - team: python-samples-reviewers + permission: push diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml new file mode 100644 index 00000000000..221806cedf5 --- /dev/null +++ b/.github/workflows/docs.yml @@ -0,0 +1,38 @@ +on: + pull_request: + branches: + - main +name: docs +jobs: + docs: + runs-on: ubuntu-latest + steps: + - name: Checkout + uses: actions/checkout@v3 + - name: Setup Python + uses: actions/setup-python@v4 + with: + python-version: "3.9" + - name: Install nox + run: | + python -m pip install --upgrade setuptools pip wheel + python -m pip install nox + - name: Run docs + run: | + nox -s docs + docfx: + runs-on: ubuntu-latest + steps: + - name: Checkout + uses: actions/checkout@v3 + - name: Setup Python + uses: actions/setup-python@v4 + with: + python-version: "3.10" + - name: Install nox + run: | + python -m pip install --upgrade setuptools pip wheel + python -m pip install nox + - name: Run docfx + run: | + nox -s docfx diff --git a/.github/workflows/lint.yml b/.github/workflows/lint.yml new file mode 100644 index 00000000000..16d5a9e90f6 --- /dev/null +++ b/.github/workflows/lint.yml @@ -0,0 +1,25 @@ +on: + pull_request: + branches: + - main +name: lint +jobs: + lint: + runs-on: ubuntu-latest + steps: + - name: Checkout + uses: actions/checkout@v3 + - name: Setup Python + uses: actions/setup-python@v4 + with: + python-version: "3.8" + - name: Install nox + run: | + python -m pip install --upgrade setuptools pip wheel + python -m pip install nox + - name: Run lint + run: | + nox -s lint + - name: Run lint_setup_py + run: | + nox -s lint_setup_py diff --git a/.github/workflows/unittest.yml b/.github/workflows/unittest.yml new file mode 100644 index 00000000000..465199fc9a9 --- /dev/null +++ b/.github/workflows/unittest.yml @@ -0,0 +1,57 @@ +on: + pull_request: + branches: + - main +name: unittest +jobs: + unit: + runs-on: ubuntu-latest + strategy: + matrix: + python: ['3.9', '3.10', '3.11'] + steps: + - name: Checkout + uses: actions/checkout@v3 + - name: Setup Python + uses: actions/setup-python@v4 + with: + python-version: ${{ matrix.python }} + - name: Install nox + run: | + python -m pip install --upgrade setuptools pip wheel + python -m pip install nox + - name: Run unit tests + env: + COVERAGE_FILE: .coverage-${{ matrix.python }} + run: | + nox -s unit-${{ matrix.python }} + - name: Upload coverage results + uses: actions/upload-artifact@v3 + with: + name: coverage-artifacts + path: .coverage-${{ matrix.python }} + + cover: + runs-on: ubuntu-latest + needs: + - unit + steps: + - name: Checkout + uses: actions/checkout@v3 + - name: Setup Python + uses: actions/setup-python@v4 + with: + python-version: "3.8" + - name: Install coverage + run: | + python -m pip install --upgrade setuptools pip wheel + python -m pip install coverage + - name: Download coverage results + uses: actions/download-artifact@v3 + with: + name: coverage-artifacts + path: .coverage-results/ + - name: Report coverage results + run: | + coverage combine .coverage-results/.coverage* + coverage report --show-missing --fail-under=35 diff --git a/.gitignore b/.gitignore new file mode 100644 index 00000000000..d083ea1ddc3 --- /dev/null +++ b/.gitignore @@ -0,0 +1,64 @@ +*.py[cod] +*.sw[op] + +# C extensions +*.so + +# Packages +*.egg +*.egg-info +dist +build +eggs +.eggs +parts +bin +var +sdist +develop-eggs +.installed.cfg +lib +lib64 +__pycache__ + +# Installer logs +pip-log.txt + +# Unit test / coverage reports +.coverage +.nox +.cache +.pytest_cache + + +# Mac +.DS_Store + +# JetBrains +.idea + +# VS Code +.vscode + +# emacs +*~ + +# Built documentation +docs/_build +bigquery/docs/generated +docs.metadata + +# Virtual environment +env/ +venv/ + +# Test logs +coverage.xml +*sponge_log.xml + +# System test environment variables. +system_tests/local_test_setup + +# Make sure a generated file isn't accidentally committed. +pylintrc +pylintrc.test diff --git a/.kokoro/build.sh b/.kokoro/build.sh index 01b0af912ee..58eaa7fedf9 100755 --- a/.kokoro/build.sh +++ b/.kokoro/build.sh @@ -1,11 +1,11 @@ #!/bin/bash -# Copyright 2022 Google LLC +# Copyright 2023 Google LLC # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # -# http://www.apache.org/licenses/LICENSE-2.0 +# https://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, @@ -13,24 +13,40 @@ # See the License for the specific language governing permissions and # limitations under the License. -# `-e` enables the script to automatically fail when a command fails -# `-o pipefail` sets the exit code to non-zero if any command fails, -# or zero if all commands in the pipeline exit successfully. set -eo pipefail -cd "${KOKORO_ARTIFACTS_DIR}/git/bigframes" -pwd +PROJECT_SCM="github/python-bigquery-dataframes" -# If NOX_SESSION is set, it only runs the specified session, -# otherwise run all the sessions. -NOX_SESSION_ARG="" +if [[ -z "${PROJECT_ROOT:-}" ]]; then + PROJECT_ROOT="${KOKORO_ARTIFACTS_DIR}/${PROJECT_SCM}" +fi + +cd "${PROJECT_ROOT}" + +# Disable buffering, so that the logs stream through. +export PYTHONUNBUFFERED=1 -# IF NOX_FILE is set, it runs the specific nox file, -# otherwise it runs noxfile.py in the package directory. -NOX_FILE_ARG="" +# Workaround https://github.com/pytest-dev/pytest/issues/9567 +export PY_IGNORE_IMPORTMISMATCH=1 -[[ -z "${NOX_SESSION}" ]] || NOX_SESSION_ARG="-s ${NOX_SESSION}" +# Debug: show build environment +env | grep KOKORO -[[ -z "${NOX_FILE}" ]] || NOX_FILE_ARG="-f ${NOX_FILE}" +# Install pip +python3 -m pip install --upgrade --quiet pip +python3 -m pip --version -python3 -m nox ${NOX_SESSION_ARG} $NOX_FILE_ARG +# Remove old nox +python3 -m pip uninstall --yes --quiet nox-automation + +# Install nox +python3 -m pip install --upgrade --quiet nox +python3 -m nox --version + +# If NOX_SESSION is set, it only runs the specified session, +# otherwise run all the sessions. +if [[ -n "${NOX_SESSION:-}" ]]; then + python3 -m nox --stop-on-first-error -s ${NOX_SESSION:-} +else + python3 -m nox --stop-on-first-error +fi diff --git a/.kokoro/buildwheel.cfg b/.kokoro/buildwheel.cfg deleted file mode 100644 index af89c99e419..00000000000 --- a/.kokoro/buildwheel.cfg +++ /dev/null @@ -1,22 +0,0 @@ -# -*- protobuffer -*- -# proto-file: google3/devtools/kokoro/config/proto/build.proto -# proto-message: BuildConfig - -build_file: "bigframes-internal/bigframes/.kokoro/buildwheel.sh" -container_properties { - docker_image: "us-docker.pkg.dev/artifact-foundry-prod/docker-3p-trusted/python@sha256:0b3498e251759df85a00474be7d3b791d6abe1600ce3531a649e42964749655f" -} - -fileset_artifacts { - name: "artifacts" - artifact_globs: "artifacts/*" - error_if_missing: true - destinations { - store_attestation: true - gcs { - gcs_root_path: "oss-exit-gate-prod-projects-bucket/bigframes/pypi/attestations" - } - } - generate_sbom_from_fileset: true - generate_attestation: true -} diff --git a/.kokoro/buildwheel.sh b/.kokoro/buildwheel.sh deleted file mode 100755 index b64b6412b02..00000000000 --- a/.kokoro/buildwheel.sh +++ /dev/null @@ -1,48 +0,0 @@ -#!/bin/bash -set -euo pipefail - -cd "${KOKORO_ARTIFACTS_DIR}/git/bigframes-internal/bigframes" - -### Set up Airlock for Bookworm -rm -f /etc/apt/sources.list.d/* /etc/apt/sources.list -echo 'deb https://us-apt.pkg.dev/remote/artifact-foundry-prod/debian-3p-remote-bookworm bookworm main' | \ - tee -a /etc/apt/sources.list.d/artifact-registry.list - -# Set up Airlock for Python -cat > "$HOME/.pypirc" < "$HOME/.pip/pip.conf" <&2 ;} +function println { printf '%s\n' "$(now) $*" ;} + + +# Populates requested secrets set in SECRET_MANAGER_KEYS from service account: +# kokoro-trampoline@cloud-devrel-kokoro-resources.iam.gserviceaccount.com +SECRET_LOCATION="${KOKORO_GFILE_DIR}/secret_manager" +msg "Creating folder on disk for secrets: ${SECRET_LOCATION}" +mkdir -p ${SECRET_LOCATION} +for key in $(echo ${SECRET_MANAGER_KEYS} | sed "s/,/ /g") +do + msg "Retrieving secret ${key}" + docker run --entrypoint=gcloud \ + --volume=${KOKORO_GFILE_DIR}:${KOKORO_GFILE_DIR} \ + gcr.io/google.com/cloudsdktool/cloud-sdk \ + secrets versions access latest \ + --project cloud-devrel-kokoro-resources \ + --secret ${key} > \ + "${SECRET_LOCATION}/${key}" + if [[ $? == 0 ]]; then + msg "Secret written to ${SECRET_LOCATION}/${key}" + else + msg "Error retrieving secret ${key}" + fi +done diff --git a/.kokoro/presubmit/common.cfg b/.kokoro/presubmit/common.cfg index 5d40578ac79..97e0651aa92 100644 --- a/.kokoro/presubmit/common.cfg +++ b/.kokoro/presubmit/common.cfg @@ -7,4 +7,4 @@ action { } } -build_file: "bigframes/.kokoro/build.sh" +build_file: "python-bigquery-dataframes/.kokoro/build.sh" diff --git a/.kokoro/presubmit/e2e-gerrit.cfg b/.kokoro/presubmit/e2e-gerrit.cfg new file mode 100644 index 00000000000..d875f360603 --- /dev/null +++ b/.kokoro/presubmit/e2e-gerrit.cfg @@ -0,0 +1,7 @@ +# Format: //devtools/kokoro/config/proto/build.proto + +# Only run this nox session. +env_vars: { + key: "NOX_SESSION" + value: "system_noextras e2e notebook samples" +} diff --git a/.kokoro/presubmit/e2e.cfg b/.kokoro/presubmit/e2e.cfg new file mode 100644 index 00000000000..d875f360603 --- /dev/null +++ b/.kokoro/presubmit/e2e.cfg @@ -0,0 +1,7 @@ +# Format: //devtools/kokoro/config/proto/build.proto + +# Only run this nox session. +env_vars: { + key: "NOX_SESSION" + value: "system_noextras e2e notebook samples" +} diff --git a/.kokoro/presubmit/prerelease-deps.cfg b/.kokoro/presubmit/prerelease-deps.cfg new file mode 100644 index 00000000000..3595fb43f5c --- /dev/null +++ b/.kokoro/presubmit/prerelease-deps.cfg @@ -0,0 +1,7 @@ +# Format: //devtools/kokoro/config/proto/build.proto + +# Only run this nox session. +env_vars: { + key: "NOX_SESSION" + value: "prerelease_deps" +} diff --git a/.kokoro/presubmit/presubmit-docs-linux.cfg b/.kokoro/presubmit/presubmit-docs-linux.cfg deleted file mode 100644 index 93832f9e5aa..00000000000 --- a/.kokoro/presubmit/presubmit-docs-linux.cfg +++ /dev/null @@ -1,6 +0,0 @@ -# Format: //devtools/kokoro/config/proto/build.proto - -env_vars: { - key: "NOX_SESSION" - value: "docs docfx" -} diff --git a/.kokoro/presubmit/presubmit-doctest-linux.cfg b/.kokoro/presubmit/presubmit-doctest-linux.cfg deleted file mode 100644 index af74ca0fbcd..00000000000 --- a/.kokoro/presubmit/presubmit-doctest-linux.cfg +++ /dev/null @@ -1,6 +0,0 @@ -# Format: //devtools/kokoro/config/proto/build.proto - -env_vars: { - key: "NOX_SESSION" - value: "doctest" -} diff --git a/.kokoro/presubmit/presubmit-e2e-linux.cfg b/.kokoro/presubmit/presubmit-e2e-linux.cfg deleted file mode 100644 index ed73a083ee0..00000000000 --- a/.kokoro/presubmit/presubmit-e2e-linux.cfg +++ /dev/null @@ -1,6 +0,0 @@ -# Format: //devtools/kokoro/config/proto/build.proto - -env_vars: { - key: "NOX_SESSION" - value: "e2e" -} diff --git a/.kokoro/presubmit/presubmit-gerrit.cfg b/.kokoro/presubmit/presubmit-gerrit.cfg new file mode 100644 index 00000000000..18a4c35325b --- /dev/null +++ b/.kokoro/presubmit/presubmit-gerrit.cfg @@ -0,0 +1 @@ +# Format: //devtools/kokoro/config/proto/build.proto diff --git a/.kokoro/presubmit/presubmit-lint-linux.cfg b/.kokoro/presubmit/presubmit-lint-linux.cfg deleted file mode 100644 index 490cafab878..00000000000 --- a/.kokoro/presubmit/presubmit-lint-linux.cfg +++ /dev/null @@ -1,6 +0,0 @@ -# Format: //devtools/kokoro/config/proto/build.proto - -env_vars: { - key: "NOX_SESSION" - value: "lint mypy" -} diff --git a/.kokoro/presubmit/presubmit-prerelease-linux.cfg b/.kokoro/presubmit/presubmit-prerelease-linux.cfg deleted file mode 100644 index bfc635fbf30..00000000000 --- a/.kokoro/presubmit/presubmit-prerelease-linux.cfg +++ /dev/null @@ -1,6 +0,0 @@ -# Format: //devtools/kokoro/config/proto/build.proto - -env_vars: { - key: "NOX_SESSION" - value: "unit_prerelease system_prerelease" -} diff --git a/.kokoro/presubmit/presubmit-system-linux.cfg b/.kokoro/presubmit/presubmit-system-linux.cfg deleted file mode 100644 index 9fa4e1b73b2..00000000000 --- a/.kokoro/presubmit/presubmit-system-linux.cfg +++ /dev/null @@ -1,6 +0,0 @@ -# Format: //devtools/kokoro/config/proto/build.proto - -env_vars: { - key: "NOX_SESSION" - value: "system" -} diff --git a/.kokoro/presubmit/presubmit-unit-linux.cfg b/.kokoro/presubmit/presubmit-unit-linux.cfg deleted file mode 100644 index 09f4f4b8311..00000000000 --- a/.kokoro/presubmit/presubmit-unit-linux.cfg +++ /dev/null @@ -1,6 +0,0 @@ -# Format: //devtools/kokoro/config/proto/build.proto - -env_vars: { - key: "NOX_SESSION" - value: "unit" -} diff --git a/.kokoro/presubmit/presubmit.cfg b/.kokoro/presubmit/presubmit.cfg new file mode 100644 index 00000000000..8f43917d92f --- /dev/null +++ b/.kokoro/presubmit/presubmit.cfg @@ -0,0 +1 @@ +# Format: //devtools/kokoro/config/proto/build.proto \ No newline at end of file diff --git a/.kokoro/publish-docs.sh b/.kokoro/publish-docs.sh new file mode 100755 index 00000000000..7700c90ee92 --- /dev/null +++ b/.kokoro/publish-docs.sh @@ -0,0 +1,65 @@ +#!/bin/bash +# Copyright 2023 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +set -eo pipefail + +# Disable buffering, so that the logs stream through. +export PYTHONUNBUFFERED=1 + +export PATH="${HOME}/.local/bin:${PATH}" + +# Install nox +python3 -m pip install --require-hashes -r .kokoro/requirements.txt +python3 -m nox --version + +# build docs +nox -s docs + +# create metadata +python3 -m docuploader create-metadata \ + --name=$(jq --raw-output '.name // empty' .repo-metadata.json) \ + --version=$(python3 setup.py --version) \ + --language=$(jq --raw-output '.language // empty' .repo-metadata.json) \ + --distribution-name=$(python3 setup.py --name) \ + --product-page=$(jq --raw-output '.product_documentation // empty' .repo-metadata.json) \ + --github-repository=$(jq --raw-output '.repo // empty' .repo-metadata.json) \ + --issue-tracker=$(jq --raw-output '.issue_tracker // empty' .repo-metadata.json) + +cat docs.metadata + +# upload docs +python3 -m docuploader upload docs/_build/html --metadata-file docs.metadata --staging-bucket "${STAGING_BUCKET}" + + +# docfx yaml files +nox -s docfx + +# create metadata. +python3 -m docuploader create-metadata \ + --name=$(jq --raw-output '.name // empty' .repo-metadata.json) \ + --version=$(python3 setup.py --version) \ + --language=$(jq --raw-output '.language // empty' .repo-metadata.json) \ + --distribution-name=$(python3 setup.py --name) \ + --product-page=$(jq --raw-output '.product_documentation // empty' .repo-metadata.json) \ + --github-repository=$(jq --raw-output '.repo // empty' .repo-metadata.json) \ + --issue-tracker=$(jq --raw-output '.issue_tracker // empty' .repo-metadata.json) + +cat docs.metadata + +# Replace toc.yml template file +mv docs/templates/toc.yml docs/_build/html/docfx_yaml/toc.yml + +# upload docs +python3 -m docuploader upload docs/_build/html/docfx_yaml --metadata-file docs.metadata --destination-prefix docfx --staging-bucket "${V2_STAGING_BUCKET}" diff --git a/.kokoro/release-nightly.sh b/.kokoro/release-nightly.sh new file mode 100755 index 00000000000..0751cf2502c --- /dev/null +++ b/.kokoro/release-nightly.sh @@ -0,0 +1,121 @@ +#!/bin/bash +# Copyright 2020 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# Based loosely on +# https://github.com/googleapis/python-bigquery/blob/main/.kokoro/release.sh + +set -eo pipefail +set -x + +# Parse command line arguments +DRY_RUN= +while [ $# -gt 0 ] ; do + case "$1" in + -d | --dry-run ) + DRY_RUN=true + ;; + -h | --help ) + echo -e "USAGE: `basename $0` [ -d | --dry-run ]" + exit + ;; + esac + shift 1; +done + +if [[ -z "${KOKORO_GOB_COMMIT}" ]]; then + PROJECT_SCM="github/python-bigquery-dataframes" +else + PROJECT_SCM="git/bigframes" +fi + +if [ -z "${PROJECT_ROOT:-}" ]; then + PROJECT_ROOT="${KOKORO_ARTIFACTS_DIR}/${PROJECT_SCM}" +fi + +# Move into the package, build the distribution and upload to shared bucket. +# See internal bug 274624240 for details. + +cd "${PROJECT_ROOT}" +rm -rf build dist + +# Workaround the fact that the repository that has been fetched before the +# build script. See: go/kokoro-native-docker-migration#known-issues and +# internal issue b/261050975. +git config --global --add safe.directory "${PROJECT_ROOT}" + +python3.10 -m pip install --require-hashes -r .kokoro/requirements.txt + +# Disable buffering, so that the logs stream through. +export PYTHONUNBUFFERED=1 + +# Install dependencies, as the following steps depend on it +python3.10 -m pip install -e .[all] + +# Update version string to include git hash and date +CURRENT_DATE=$(date '+%Y%m%d') +GIT_HASH=$(git rev-parse --short HEAD) +BIGFRAMES_VERSION=$(python3.10 -c "import bigframes; print(bigframes.__version__)") +RELEASE_VERSION=${BIGFRAMES_VERSION}dev${CURRENT_DATE}+${GIT_HASH} +sed -i -e "s/$BIGFRAMES_VERSION/$RELEASE_VERSION/g" bigframes/version.py + +# Generate the package wheel +python3.10 setup.py sdist bdist_wheel + +# Make sure that the wheel file is generated +VERSION_WHEEL=`ls dist/bigframes-*.whl` +num_wheel_files=`echo $VERSION_WHEEL | wc -w` +if [ $num_wheel_files -ne 1 ] ; then + echo "Exactly one wheel file should have been generated, found $num_wheel_files: $VERSION_WHEEL" + exit -1 +fi + +# Create a copy of the wheel with a well known, version agnostic name +LATEST_WHEEL=dist/bigframes-latest-py2.py3-none-any.whl +cp $VERSION_WHEEL $LATEST_WHEEL +cp dist/bigframes-*.tar.gz dist/bigframes-latest.tar.gz + +if ! [ ${DRY_RUN} ]; then +for gcs_path in gs://vertex_sdk_private_releases/bigframe/ \ + gs://dl-platform-colab/bigframes/ \ + gs://bigframes-wheels/; + do + gsutil cp -v dist/* ${gcs_path} + gsutil cp -v LICENSE ${gcs_path} + gsutil -m cp -r -v "notebooks/" ${gcs_path}notebooks/ + + done + + # publish API coverage information to BigQuery + # Note: only the kokoro service account has permission to write to this + # table, if you want to test this step, point it to a table you have + # write access to + COVERAGE_TABLE=bigframes-metrics.coverage_report.bigframes_coverage_nightly + python3.10 scripts/publish_api_coverage.py \ + --bigframes_version=$BIGFRAMES_VERSION \ + --release_version=$RELEASE_VERSION \ + --bigquery_table=$COVERAGE_TABLE +fi + +# Undo the file changes, in case this script is running on a +# non-temporary instance of the bigframes repo +# TODO: This doesn't work with (set -eo pipefail) if the failure happened after +# the changes were made but before this cleanup, because the script would +# terminate with the failure itself. See if we can ensure the cleanup. +sed -i -e "s/$RELEASE_VERSION/$BIGFRAMES_VERSION/g" bigframes/version.py + +if ! [ ${DRY_RUN} ]; then + # Copy docs and wheels to Google Drive + python3.10 scripts/upload_to_google_drive.py +fi diff --git a/.kokoro/release.cfg b/.kokoro/release.cfg deleted file mode 100644 index 18c85a69d63..00000000000 --- a/.kokoro/release.cfg +++ /dev/null @@ -1,23 +0,0 @@ -# -*- protobuffer -*- -# proto-file: google3/devtools/kokoro/config/proto/build.proto -# proto-message: BuildConfig - -build_file: "bigframes-internal/bigframes/.kokoro/release.sh" -container_properties { - docker_image: "us-docker.pkg.dev/artifact-foundry-prod/docker-3p-trusted/ubuntu:22.04" -} - -fileset_artifacts { - name: "manifest" - artifact_globs: "manifest.json" - error_if_missing: true - destinations { - store_attestation: false - gcs { - gcs_root_path: "oss-exit-gate-prod-projects-bucket/bigframes/pypi/manifests" - populate_content_type: true - } - } - generate_sbom_from_fileset: false - generate_attestation: false -} diff --git a/.kokoro/release.sh b/.kokoro/release.sh index 58b865a6f54..320ac51271e 100755 --- a/.kokoro/release.sh +++ b/.kokoro/release.sh @@ -1,10 +1,29 @@ #!/bin/bash -set -euo pipefail +# Copyright 2023 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. -cd "${KOKORO_ARTIFACTS_DIR}" +set -eo pipefail -cat > manifest.json <<'EOF' -{ - "publish_all": true -} -EOF +# Start the releasetool reporter +python3 -m pip install --require-hashes -r github/python-bigquery-dataframes/.kokoro/requirements.txt +python3 -m releasetool publish-reporter-script > /tmp/publisher-script; source /tmp/publisher-script + +# Disable buffering, so that the logs stream through. +export PYTHONUNBUFFERED=1 + +# Move into the package, build the distribution and upload. +TWINE_PASSWORD=$(cat "${KOKORO_KEYSTORE_DIR}/73713_google-cloud-pypi-token-keystore-1") +cd github/python-bigquery-dataframes +python3 setup.py sdist bdist_wheel +twine upload --username __token__ --password "${TWINE_PASSWORD}" dist/* diff --git a/.kokoro/release/common.cfg b/.kokoro/release/common.cfg new file mode 100644 index 00000000000..a0c39946cf1 --- /dev/null +++ b/.kokoro/release/common.cfg @@ -0,0 +1,49 @@ +# Format: //devtools/kokoro/config/proto/build.proto + +# Build logs will be here +action { + define_artifacts { + regex: "**/*sponge_log.xml" + } +} + +# Download trampoline resources. +gfile_resources: "/bigstore/cloud-devrel-kokoro-resources/trampoline" + +# Use the trampoline script to run in docker. +build_file: "python-bigquery-dataframes/.kokoro/trampoline.sh" + +# Configure the docker image for kokoro-trampoline. +env_vars: { + key: "TRAMPOLINE_IMAGE" + value: "gcr.io/cloud-devrel-kokoro-resources/python-multi" +} +env_vars: { + key: "TRAMPOLINE_BUILD_FILE" + value: "github/python-bigquery-dataframes/.kokoro/release.sh" +} + +# Fetch PyPI password +before_action { + fetch_keystore { + keystore_resource { + keystore_config_id: 73713 + keyname: "google-cloud-pypi-token-keystore-1" + } + } +} + +# Tokens needed to report release status back to GitHub +env_vars: { + key: "SECRET_MANAGER_KEYS" + value: "releasetool-publish-reporter-app,releasetool-publish-reporter-googleapis-installation,releasetool-publish-reporter-pem" +} + +# Store the packages we uploaded to PyPI. That way, we have a record of exactly +# what we published, which we can use to generate SBOMs and attestations. +action { + define_artifacts { + regex: "github/python-bigquery-dataframes/**/*.tar.gz" + strip_prefix: "github/python-bigquery-dataframes" + } +} diff --git a/.kokoro/release/release.cfg b/.kokoro/release/release.cfg new file mode 100644 index 00000000000..8f43917d92f --- /dev/null +++ b/.kokoro/release/release.cfg @@ -0,0 +1 @@ +# Format: //devtools/kokoro/config/proto/build.proto \ No newline at end of file diff --git a/.kokoro/requirements.in b/.kokoro/requirements.in new file mode 100644 index 00000000000..ec867d9fd65 --- /dev/null +++ b/.kokoro/requirements.in @@ -0,0 +1,10 @@ +gcp-docuploader +gcp-releasetool>=1.10.5 # required for compatibility with cryptography>=39.x +importlib-metadata +typing-extensions +twine +wheel +setuptools +nox>=2022.11.21 # required to remove dependency on py +charset-normalizer<3 +click<8.1.0 diff --git a/.kokoro/requirements.txt b/.kokoro/requirements.txt new file mode 100644 index 00000000000..16170d0ca7b --- /dev/null +++ b/.kokoro/requirements.txt @@ -0,0 +1,497 @@ +# +# This file is autogenerated by pip-compile with Python 3.9 +# by the following command: +# +# pip-compile --allow-unsafe --generate-hashes requirements.in +# +argcomplete==2.0.0 \ + --hash=sha256:6372ad78c89d662035101418ae253668445b391755cfe94ea52f1b9d22425b20 \ + --hash=sha256:cffa11ea77999bb0dd27bb25ff6dc142a6796142f68d45b1a26b11f58724561e + # via nox +attrs==22.1.0 \ + --hash=sha256:29adc2665447e5191d0e7c568fde78b21f9672d344281d0c6e1ab085429b22b6 \ + --hash=sha256:86efa402f67bf2df34f51a335487cf46b1ec130d02b8d39fd248abfd30da551c + # via gcp-releasetool +bleach==5.0.1 \ + --hash=sha256:085f7f33c15bd408dd9b17a4ad77c577db66d76203e5984b1bd59baeee948b2a \ + --hash=sha256:0d03255c47eb9bd2f26aa9bb7f2107732e7e8fe195ca2f64709fcf3b0a4a085c + # via readme-renderer +cachetools==5.2.0 \ + --hash=sha256:6a94c6402995a99c3970cc7e4884bb60b4a8639938157eeed436098bf9831757 \ + --hash=sha256:f9f17d2aec496a9aa6b76f53e3b614c965223c061982d434d160f930c698a9db + # via google-auth +certifi==2023.7.22 \ + --hash=sha256:539cc1d13202e33ca466e88b2807e29f4c13049d6d87031a3c110744495cb082 \ + --hash=sha256:92d6037539857d8206b8f6ae472e8b77db8058fec5937a1ef3f54304089edbb9 + # via requests +cffi==1.15.1 \ + --hash=sha256:00a9ed42e88df81ffae7a8ab6d9356b371399b91dbdf0c3cb1e84c03a13aceb5 \ + --hash=sha256:03425bdae262c76aad70202debd780501fabeaca237cdfddc008987c0e0f59ef \ + --hash=sha256:04ed324bda3cda42b9b695d51bb7d54b680b9719cfab04227cdd1e04e5de3104 \ + --hash=sha256:0e2642fe3142e4cc4af0799748233ad6da94c62a8bec3a6648bf8ee68b1c7426 \ + --hash=sha256:173379135477dc8cac4bc58f45db08ab45d228b3363adb7af79436135d028405 \ + --hash=sha256:198caafb44239b60e252492445da556afafc7d1e3ab7a1fb3f0584ef6d742375 \ + --hash=sha256:1e74c6b51a9ed6589199c787bf5f9875612ca4a8a0785fb2d4a84429badaf22a \ + --hash=sha256:2012c72d854c2d03e45d06ae57f40d78e5770d252f195b93f581acf3ba44496e \ + --hash=sha256:21157295583fe8943475029ed5abdcf71eb3911894724e360acff1d61c1d54bc \ + --hash=sha256:2470043b93ff09bf8fb1d46d1cb756ce6132c54826661a32d4e4d132e1977adf \ + --hash=sha256:285d29981935eb726a4399badae8f0ffdff4f5050eaa6d0cfc3f64b857b77185 \ + --hash=sha256:30d78fbc8ebf9c92c9b7823ee18eb92f2e6ef79b45ac84db507f52fbe3ec4497 \ + --hash=sha256:320dab6e7cb2eacdf0e658569d2575c4dad258c0fcc794f46215e1e39f90f2c3 \ + --hash=sha256:33ab79603146aace82c2427da5ca6e58f2b3f2fb5da893ceac0c42218a40be35 \ + --hash=sha256:3548db281cd7d2561c9ad9984681c95f7b0e38881201e157833a2342c30d5e8c \ + --hash=sha256:3799aecf2e17cf585d977b780ce79ff0dc9b78d799fc694221ce814c2c19db83 \ + --hash=sha256:39d39875251ca8f612b6f33e6b1195af86d1b3e60086068be9cc053aa4376e21 \ + --hash=sha256:3b926aa83d1edb5aa5b427b4053dc420ec295a08e40911296b9eb1b6170f6cca \ + --hash=sha256:3bcde07039e586f91b45c88f8583ea7cf7a0770df3a1649627bf598332cb6984 \ + --hash=sha256:3d08afd128ddaa624a48cf2b859afef385b720bb4b43df214f85616922e6a5ac \ + --hash=sha256:3eb6971dcff08619f8d91607cfc726518b6fa2a9eba42856be181c6d0d9515fd \ + --hash=sha256:40f4774f5a9d4f5e344f31a32b5096977b5d48560c5592e2f3d2c4374bd543ee \ + --hash=sha256:4289fc34b2f5316fbb762d75362931e351941fa95fa18789191b33fc4cf9504a \ + --hash=sha256:470c103ae716238bbe698d67ad020e1db9d9dba34fa5a899b5e21577e6d52ed2 \ + --hash=sha256:4f2c9f67e9821cad2e5f480bc8d83b8742896f1242dba247911072d4fa94c192 \ + --hash=sha256:50a74364d85fd319352182ef59c5c790484a336f6db772c1a9231f1c3ed0cbd7 \ + --hash=sha256:54a2db7b78338edd780e7ef7f9f6c442500fb0d41a5a4ea24fff1c929d5af585 \ + --hash=sha256:5635bd9cb9731e6d4a1132a498dd34f764034a8ce60cef4f5319c0541159392f \ + --hash=sha256:59c0b02d0a6c384d453fece7566d1c7e6b7bae4fc5874ef2ef46d56776d61c9e \ + --hash=sha256:5d598b938678ebf3c67377cdd45e09d431369c3b1a5b331058c338e201f12b27 \ + --hash=sha256:5df2768244d19ab7f60546d0c7c63ce1581f7af8b5de3eb3004b9b6fc8a9f84b \ + --hash=sha256:5ef34d190326c3b1f822a5b7a45f6c4535e2f47ed06fec77d3d799c450b2651e \ + --hash=sha256:6975a3fac6bc83c4a65c9f9fcab9e47019a11d3d2cf7f3c0d03431bf145a941e \ + --hash=sha256:6c9a799e985904922a4d207a94eae35c78ebae90e128f0c4e521ce339396be9d \ + --hash=sha256:70df4e3b545a17496c9b3f41f5115e69a4f2e77e94e1d2a8e1070bc0c38c8a3c \ + --hash=sha256:7473e861101c9e72452f9bf8acb984947aa1661a7704553a9f6e4baa5ba64415 \ + --hash=sha256:8102eaf27e1e448db915d08afa8b41d6c7ca7a04b7d73af6514df10a3e74bd82 \ + --hash=sha256:87c450779d0914f2861b8526e035c5e6da0a3199d8f1add1a665e1cbc6fc6d02 \ + --hash=sha256:8b7ee99e510d7b66cdb6c593f21c043c248537a32e0bedf02e01e9553a172314 \ + --hash=sha256:91fc98adde3d7881af9b59ed0294046f3806221863722ba7d8d120c575314325 \ + --hash=sha256:94411f22c3985acaec6f83c6df553f2dbe17b698cc7f8ae751ff2237d96b9e3c \ + --hash=sha256:98d85c6a2bef81588d9227dde12db8a7f47f639f4a17c9ae08e773aa9c697bf3 \ + --hash=sha256:9ad5db27f9cabae298d151c85cf2bad1d359a1b9c686a275df03385758e2f914 \ + --hash=sha256:a0b71b1b8fbf2b96e41c4d990244165e2c9be83d54962a9a1d118fd8657d2045 \ + --hash=sha256:a0f100c8912c114ff53e1202d0078b425bee3649ae34d7b070e9697f93c5d52d \ + --hash=sha256:a591fe9e525846e4d154205572a029f653ada1a78b93697f3b5a8f1f2bc055b9 \ + --hash=sha256:a5c84c68147988265e60416b57fc83425a78058853509c1b0629c180094904a5 \ + --hash=sha256:a66d3508133af6e8548451b25058d5812812ec3798c886bf38ed24a98216fab2 \ + --hash=sha256:a8c4917bd7ad33e8eb21e9a5bbba979b49d9a97acb3a803092cbc1133e20343c \ + --hash=sha256:b3bbeb01c2b273cca1e1e0c5df57f12dce9a4dd331b4fa1635b8bec26350bde3 \ + --hash=sha256:cba9d6b9a7d64d4bd46167096fc9d2f835e25d7e4c121fb2ddfc6528fb0413b2 \ + --hash=sha256:cc4d65aeeaa04136a12677d3dd0b1c0c94dc43abac5860ab33cceb42b801c1e8 \ + --hash=sha256:ce4bcc037df4fc5e3d184794f27bdaab018943698f4ca31630bc7f84a7b69c6d \ + --hash=sha256:cec7d9412a9102bdc577382c3929b337320c4c4c4849f2c5cdd14d7368c5562d \ + --hash=sha256:d400bfb9a37b1351253cb402671cea7e89bdecc294e8016a707f6d1d8ac934f9 \ + --hash=sha256:d61f4695e6c866a23a21acab0509af1cdfd2c013cf256bbf5b6b5e2695827162 \ + --hash=sha256:db0fbb9c62743ce59a9ff687eb5f4afbe77e5e8403d6697f7446e5f609976f76 \ + --hash=sha256:dd86c085fae2efd48ac91dd7ccffcfc0571387fe1193d33b6394db7ef31fe2a4 \ + --hash=sha256:e00b098126fd45523dd056d2efba6c5a63b71ffe9f2bbe1a4fe1716e1d0c331e \ + --hash=sha256:e229a521186c75c8ad9490854fd8bbdd9a0c9aa3a524326b55be83b54d4e0ad9 \ + --hash=sha256:e263d77ee3dd201c3a142934a086a4450861778baaeeb45db4591ef65550b0a6 \ + --hash=sha256:ed9cb427ba5504c1dc15ede7d516b84757c3e3d7868ccc85121d9310d27eed0b \ + --hash=sha256:fa6693661a4c91757f4412306191b6dc88c1703f780c8234035eac011922bc01 \ + --hash=sha256:fcd131dd944808b5bdb38e6f5b53013c5aa4f334c5cad0c72742f6eba4b73db0 + # via cryptography +charset-normalizer==2.1.1 \ + --hash=sha256:5a3d016c7c547f69d6f81fb0db9449ce888b418b5b9952cc5e6e66843e9dd845 \ + --hash=sha256:83e9a75d1911279afd89352c68b45348559d1fc0506b054b346651b5e7fee29f + # via + # -r requirements.in + # requests +click==8.0.4 \ + --hash=sha256:6a7a62563bbfabfda3a38f3023a1db4a35978c0abd76f6c9605ecd6554d6d9b1 \ + --hash=sha256:8458d7b1287c5fb128c90e23381cf99dcde74beaf6c7ff6384ce84d6fe090adb + # via + # -r requirements.in + # gcp-docuploader + # gcp-releasetool +colorlog==6.7.0 \ + --hash=sha256:0d33ca236784a1ba3ff9c532d4964126d8a2c44f1f0cb1d2b0728196f512f662 \ + --hash=sha256:bd94bd21c1e13fac7bd3153f4bc3a7dc0eb0974b8bc2fdf1a989e474f6e582e5 + # via + # gcp-docuploader + # nox +commonmark==0.9.1 \ + --hash=sha256:452f9dc859be7f06631ddcb328b6919c67984aca654e5fefb3914d54691aed60 \ + --hash=sha256:da2f38c92590f83de410ba1a3cbceafbc74fee9def35f9251ba9a971d6d66fd9 + # via rich +cryptography==41.0.4 \ + --hash=sha256:004b6ccc95943f6a9ad3142cfabcc769d7ee38a3f60fb0dddbfb431f818c3a67 \ + --hash=sha256:047c4603aeb4bbd8db2756e38f5b8bd7e94318c047cfe4efeb5d715e08b49311 \ + --hash=sha256:0d9409894f495d465fe6fda92cb70e8323e9648af912d5b9141d616df40a87b8 \ + --hash=sha256:23a25c09dfd0d9f28da2352503b23e086f8e78096b9fd585d1d14eca01613e13 \ + --hash=sha256:2ed09183922d66c4ec5fdaa59b4d14e105c084dd0febd27452de8f6f74704143 \ + --hash=sha256:35c00f637cd0b9d5b6c6bd11b6c3359194a8eba9c46d4e875a3660e3b400005f \ + --hash=sha256:37480760ae08065437e6573d14be973112c9e6dcaf5f11d00147ee74f37a3829 \ + --hash=sha256:3b224890962a2d7b57cf5eeb16ccaafba6083f7b811829f00476309bce2fe0fd \ + --hash=sha256:5a0f09cefded00e648a127048119f77bc2b2ec61e736660b5789e638f43cc397 \ + --hash=sha256:5b72205a360f3b6176485a333256b9bcd48700fc755fef51c8e7e67c4b63e3ac \ + --hash=sha256:7e53db173370dea832190870e975a1e09c86a879b613948f09eb49324218c14d \ + --hash=sha256:7febc3094125fc126a7f6fb1f420d0da639f3f32cb15c8ff0dc3997c4549f51a \ + --hash=sha256:80907d3faa55dc5434a16579952ac6da800935cd98d14dbd62f6f042c7f5e839 \ + --hash=sha256:86defa8d248c3fa029da68ce61fe735432b047e32179883bdb1e79ed9bb8195e \ + --hash=sha256:8ac4f9ead4bbd0bc8ab2d318f97d85147167a488be0e08814a37eb2f439d5cf6 \ + --hash=sha256:93530900d14c37a46ce3d6c9e6fd35dbe5f5601bf6b3a5c325c7bffc030344d9 \ + --hash=sha256:9eeb77214afae972a00dee47382d2591abe77bdae166bda672fb1e24702a3860 \ + --hash=sha256:b5f4dfe950ff0479f1f00eda09c18798d4f49b98f4e2006d644b3301682ebdca \ + --hash=sha256:c3391bd8e6de35f6f1140e50aaeb3e2b3d6a9012536ca23ab0d9c35ec18c8a91 \ + --hash=sha256:c880eba5175f4307129784eca96f4e70b88e57aa3f680aeba3bab0e980b0f37d \ + --hash=sha256:cecfefa17042941f94ab54f769c8ce0fe14beff2694e9ac684176a2535bf9714 \ + --hash=sha256:e40211b4923ba5a6dc9769eab704bdb3fbb58d56c5b336d30996c24fcf12aadb \ + --hash=sha256:efc8ad4e6fc4f1752ebfb58aefece8b4e3c4cae940b0994d43649bdfce8d0d4f + # via + # gcp-releasetool + # secretstorage +distlib==0.3.6 \ + --hash=sha256:14bad2d9b04d3a36127ac97f30b12a19268f211063d8f8ee4f47108896e11b46 \ + --hash=sha256:f35c4b692542ca110de7ef0bea44d73981caeb34ca0b9b6b2e6d7790dda8f80e + # via virtualenv +docutils==0.19 \ + --hash=sha256:33995a6753c30b7f577febfc2c50411fec6aac7f7ffeb7c4cfe5991072dcf9e6 \ + --hash=sha256:5e1de4d849fee02c63b040a4a3fd567f4ab104defd8a5511fbbc24a8a017efbc + # via readme-renderer +filelock==3.8.0 \ + --hash=sha256:55447caa666f2198c5b6b13a26d2084d26fa5b115c00d065664b2124680c4edc \ + --hash=sha256:617eb4e5eedc82fc5f47b6d61e4d11cb837c56cb4544e39081099fa17ad109d4 + # via virtualenv +gcp-docuploader==0.6.4 \ + --hash=sha256:01486419e24633af78fd0167db74a2763974765ee8078ca6eb6964d0ebd388af \ + --hash=sha256:70861190c123d907b3b067da896265ead2eeb9263969d6955c9e0bb091b5ccbf + # via -r requirements.in +gcp-releasetool==1.10.5 \ + --hash=sha256:174b7b102d704b254f2a26a3eda2c684fd3543320ec239baf771542a2e58e109 \ + --hash=sha256:e29d29927fe2ca493105a82958c6873bb2b90d503acac56be2c229e74de0eec9 + # via -r requirements.in +google-api-core==2.10.2 \ + --hash=sha256:10c06f7739fe57781f87523375e8e1a3a4674bf6392cd6131a3222182b971320 \ + --hash=sha256:34f24bd1d5f72a8c4519773d99ca6bf080a6c4e041b4e9f024fe230191dda62e + # via + # google-cloud-core + # google-cloud-storage +google-auth==2.14.1 \ + --hash=sha256:ccaa901f31ad5cbb562615eb8b664b3dd0bf5404a67618e642307f00613eda4d \ + --hash=sha256:f5d8701633bebc12e0deea4df8abd8aff31c28b355360597f7f2ee60f2e4d016 + # via + # gcp-releasetool + # google-api-core + # google-cloud-core + # google-cloud-storage +google-cloud-core==2.3.2 \ + --hash=sha256:8417acf6466be2fa85123441696c4badda48db314c607cf1e5d543fa8bdc22fe \ + --hash=sha256:b9529ee7047fd8d4bf4a2182de619154240df17fbe60ead399078c1ae152af9a + # via google-cloud-storage +google-cloud-storage==2.6.0 \ + --hash=sha256:104ca28ae61243b637f2f01455cc8a05e8f15a2a18ced96cb587241cdd3820f5 \ + --hash=sha256:4ad0415ff61abdd8bb2ae81c1f8f7ec7d91a1011613f2db87c614c550f97bfe9 + # via gcp-docuploader +google-crc32c==1.5.0 \ + --hash=sha256:024894d9d3cfbc5943f8f230e23950cd4906b2fe004c72e29b209420a1e6b05a \ + --hash=sha256:02c65b9817512edc6a4ae7c7e987fea799d2e0ee40c53ec573a692bee24de876 \ + --hash=sha256:02ebb8bf46c13e36998aeaad1de9b48f4caf545e91d14041270d9dca767b780c \ + --hash=sha256:07eb3c611ce363c51a933bf6bd7f8e3878a51d124acfc89452a75120bc436289 \ + --hash=sha256:1034d91442ead5a95b5aaef90dbfaca8633b0247d1e41621d1e9f9db88c36298 \ + --hash=sha256:116a7c3c616dd14a3de8c64a965828b197e5f2d121fedd2f8c5585c547e87b02 \ + --hash=sha256:19e0a019d2c4dcc5e598cd4a4bc7b008546b0358bd322537c74ad47a5386884f \ + --hash=sha256:1c7abdac90433b09bad6c43a43af253e688c9cfc1c86d332aed13f9a7c7f65e2 \ + --hash=sha256:1e986b206dae4476f41bcec1faa057851f3889503a70e1bdb2378d406223994a \ + --hash=sha256:272d3892a1e1a2dbc39cc5cde96834c236d5327e2122d3aaa19f6614531bb6eb \ + --hash=sha256:278d2ed7c16cfc075c91378c4f47924c0625f5fc84b2d50d921b18b7975bd210 \ + --hash=sha256:2ad40e31093a4af319dadf503b2467ccdc8f67c72e4bcba97f8c10cb078207b5 \ + --hash=sha256:2e920d506ec85eb4ba50cd4228c2bec05642894d4c73c59b3a2fe20346bd00ee \ + --hash=sha256:3359fc442a743e870f4588fcf5dcbc1bf929df1fad8fb9905cd94e5edb02e84c \ + --hash=sha256:37933ec6e693e51a5b07505bd05de57eee12f3e8c32b07da7e73669398e6630a \ + --hash=sha256:398af5e3ba9cf768787eef45c803ff9614cc3e22a5b2f7d7ae116df8b11e3314 \ + --hash=sha256:3b747a674c20a67343cb61d43fdd9207ce5da6a99f629c6e2541aa0e89215bcd \ + --hash=sha256:461665ff58895f508e2866824a47bdee72497b091c730071f2b7575d5762ab65 \ + --hash=sha256:4c6fdd4fccbec90cc8a01fc00773fcd5fa28db683c116ee3cb35cd5da9ef6c37 \ + --hash=sha256:5829b792bf5822fd0a6f6eb34c5f81dd074f01d570ed7f36aa101d6fc7a0a6e4 \ + --hash=sha256:596d1f98fc70232fcb6590c439f43b350cb762fb5d61ce7b0e9db4539654cc13 \ + --hash=sha256:5ae44e10a8e3407dbe138984f21e536583f2bba1be9491239f942c2464ac0894 \ + --hash=sha256:635f5d4dd18758a1fbd1049a8e8d2fee4ffed124462d837d1a02a0e009c3ab31 \ + --hash=sha256:64e52e2b3970bd891309c113b54cf0e4384762c934d5ae56e283f9a0afcd953e \ + --hash=sha256:66741ef4ee08ea0b2cc3c86916ab66b6aef03768525627fd6a1b34968b4e3709 \ + --hash=sha256:67b741654b851abafb7bc625b6d1cdd520a379074e64b6a128e3b688c3c04740 \ + --hash=sha256:6ac08d24c1f16bd2bf5eca8eaf8304812f44af5cfe5062006ec676e7e1d50afc \ + --hash=sha256:6f998db4e71b645350b9ac28a2167e6632c239963ca9da411523bb439c5c514d \ + --hash=sha256:72218785ce41b9cfd2fc1d6a017dc1ff7acfc4c17d01053265c41a2c0cc39b8c \ + --hash=sha256:74dea7751d98034887dbd821b7aae3e1d36eda111d6ca36c206c44478035709c \ + --hash=sha256:759ce4851a4bb15ecabae28f4d2e18983c244eddd767f560165563bf9aefbc8d \ + --hash=sha256:77e2fd3057c9d78e225fa0a2160f96b64a824de17840351b26825b0848022906 \ + --hash=sha256:7c074fece789b5034b9b1404a1f8208fc2d4c6ce9decdd16e8220c5a793e6f61 \ + --hash=sha256:7c42c70cd1d362284289c6273adda4c6af8039a8ae12dc451dcd61cdabb8ab57 \ + --hash=sha256:7f57f14606cd1dd0f0de396e1e53824c371e9544a822648cd76c034d209b559c \ + --hash=sha256:83c681c526a3439b5cf94f7420471705bbf96262f49a6fe546a6db5f687a3d4a \ + --hash=sha256:8485b340a6a9e76c62a7dce3c98e5f102c9219f4cfbf896a00cf48caf078d438 \ + --hash=sha256:84e6e8cd997930fc66d5bb4fde61e2b62ba19d62b7abd7a69920406f9ecca946 \ + --hash=sha256:89284716bc6a5a415d4eaa11b1726d2d60a0cd12aadf5439828353662ede9dd7 \ + --hash=sha256:8b87e1a59c38f275c0e3676fc2ab6d59eccecfd460be267ac360cc31f7bcde96 \ + --hash=sha256:8f24ed114432de109aa9fd317278518a5af2d31ac2ea6b952b2f7782b43da091 \ + --hash=sha256:98cb4d057f285bd80d8778ebc4fde6b4d509ac3f331758fb1528b733215443ae \ + --hash=sha256:998679bf62b7fb599d2878aa3ed06b9ce688b8974893e7223c60db155f26bd8d \ + --hash=sha256:9ba053c5f50430a3fcfd36f75aff9caeba0440b2d076afdb79a318d6ca245f88 \ + --hash=sha256:9c99616c853bb585301df6de07ca2cadad344fd1ada6d62bb30aec05219c45d2 \ + --hash=sha256:a1fd716e7a01f8e717490fbe2e431d2905ab8aa598b9b12f8d10abebb36b04dd \ + --hash=sha256:a2355cba1f4ad8b6988a4ca3feed5bff33f6af2d7f134852cf279c2aebfde541 \ + --hash=sha256:b1f8133c9a275df5613a451e73f36c2aea4fe13c5c8997e22cf355ebd7bd0728 \ + --hash=sha256:b8667b48e7a7ef66afba2c81e1094ef526388d35b873966d8a9a447974ed9178 \ + --hash=sha256:ba1eb1843304b1e5537e1fca632fa894d6f6deca8d6389636ee5b4797affb968 \ + --hash=sha256:be82c3c8cfb15b30f36768797a640e800513793d6ae1724aaaafe5bf86f8f346 \ + --hash=sha256:c02ec1c5856179f171e032a31d6f8bf84e5a75c45c33b2e20a3de353b266ebd8 \ + --hash=sha256:c672d99a345849301784604bfeaeba4db0c7aae50b95be04dd651fd2a7310b93 \ + --hash=sha256:c6c777a480337ac14f38564ac88ae82d4cd238bf293f0a22295b66eb89ffced7 \ + --hash=sha256:cae0274952c079886567f3f4f685bcaf5708f0a23a5f5216fdab71f81a6c0273 \ + --hash=sha256:cd67cf24a553339d5062eff51013780a00d6f97a39ca062781d06b3a73b15462 \ + --hash=sha256:d3515f198eaa2f0ed49f8819d5732d70698c3fa37384146079b3799b97667a94 \ + --hash=sha256:d5280312b9af0976231f9e317c20e4a61cd2f9629b7bfea6a693d1878a264ebd \ + --hash=sha256:de06adc872bcd8c2a4e0dc51250e9e65ef2ca91be023b9d13ebd67c2ba552e1e \ + --hash=sha256:e1674e4307fa3024fc897ca774e9c7562c957af85df55efe2988ed9056dc4e57 \ + --hash=sha256:e2096eddb4e7c7bdae4bd69ad364e55e07b8316653234a56552d9c988bd2d61b \ + --hash=sha256:e560628513ed34759456a416bf86b54b2476c59144a9138165c9a1575801d0d9 \ + --hash=sha256:edfedb64740750e1a3b16152620220f51d58ff1b4abceb339ca92e934775c27a \ + --hash=sha256:f13cae8cc389a440def0c8c52057f37359014ccbc9dc1f0827936bcd367c6100 \ + --hash=sha256:f314013e7dcd5cf45ab1945d92e713eec788166262ae8deb2cfacd53def27325 \ + --hash=sha256:f583edb943cf2e09c60441b910d6a20b4d9d626c75a36c8fcac01a6c96c01183 \ + --hash=sha256:fd8536e902db7e365f49e7d9029283403974ccf29b13fc7028b97e2295b33556 \ + --hash=sha256:fe70e325aa68fa4b5edf7d1a4b6f691eb04bbccac0ace68e34820d283b5f80d4 + # via google-resumable-media +google-resumable-media==2.4.0 \ + --hash=sha256:2aa004c16d295c8f6c33b2b4788ba59d366677c0a25ae7382436cb30f776deaa \ + --hash=sha256:8d5518502f92b9ecc84ac46779bd4f09694ecb3ba38a3e7ca737a86d15cbca1f + # via google-cloud-storage +googleapis-common-protos==1.57.0 \ + --hash=sha256:27a849d6205838fb6cc3c1c21cb9800707a661bb21c6ce7fb13e99eb1f8a0c46 \ + --hash=sha256:a9f4a1d7f6d9809657b7f1316a1aa527f6664891531bcfcc13b6696e685f443c + # via google-api-core +idna==3.4 \ + --hash=sha256:814f528e8dead7d329833b91c5faa87d60bf71824cd12a7530b5526063d02cb4 \ + --hash=sha256:90b77e79eaa3eba6de819a0c442c0b4ceefc341a7a2ab77d7562bf49f425c5c2 + # via requests +importlib-metadata==5.0.0 \ + --hash=sha256:da31db32b304314d044d3c12c79bd59e307889b287ad12ff387b3500835fc2ab \ + --hash=sha256:ddb0e35065e8938f867ed4928d0ae5bf2a53b7773871bfe6bcc7e4fcdc7dea43 + # via + # -r requirements.in + # keyring + # twine +jaraco-classes==3.2.3 \ + --hash=sha256:2353de3288bc6b82120752201c6b1c1a14b058267fa424ed5ce5984e3b922158 \ + --hash=sha256:89559fa5c1d3c34eff6f631ad80bb21f378dbcbb35dd161fd2c6b93f5be2f98a + # via keyring +jeepney==0.8.0 \ + --hash=sha256:5efe48d255973902f6badc3ce55e2aa6c5c3b3bc642059ef3a91247bcfcc5806 \ + --hash=sha256:c0a454ad016ca575060802ee4d590dd912e35c122fa04e70306de3d076cce755 + # via + # keyring + # secretstorage +jinja2==3.1.2 \ + --hash=sha256:31351a702a408a9e7595a8fc6150fc3f43bb6bf7e319770cbc0db9df9437e852 \ + --hash=sha256:6088930bfe239f0e6710546ab9c19c9ef35e29792895fed6e6e31a023a182a61 + # via gcp-releasetool +keyring==23.11.0 \ + --hash=sha256:3dd30011d555f1345dec2c262f0153f2f0ca6bca041fb1dc4588349bb4c0ac1e \ + --hash=sha256:ad192263e2cdd5f12875dedc2da13534359a7e760e77f8d04b50968a821c2361 + # via + # gcp-releasetool + # twine +markupsafe==2.1.1 \ + --hash=sha256:0212a68688482dc52b2d45013df70d169f542b7394fc744c02a57374a4207003 \ + --hash=sha256:089cf3dbf0cd6c100f02945abeb18484bd1ee57a079aefd52cffd17fba910b88 \ + --hash=sha256:10c1bfff05d95783da83491be968e8fe789263689c02724e0c691933c52994f5 \ + --hash=sha256:33b74d289bd2f5e527beadcaa3f401e0df0a89927c1559c8566c066fa4248ab7 \ + --hash=sha256:3799351e2336dc91ea70b034983ee71cf2f9533cdff7c14c90ea126bfd95d65a \ + --hash=sha256:3ce11ee3f23f79dbd06fb3d63e2f6af7b12db1d46932fe7bd8afa259a5996603 \ + --hash=sha256:421be9fbf0ffe9ffd7a378aafebbf6f4602d564d34be190fc19a193232fd12b1 \ + --hash=sha256:43093fb83d8343aac0b1baa75516da6092f58f41200907ef92448ecab8825135 \ + --hash=sha256:46d00d6cfecdde84d40e572d63735ef81423ad31184100411e6e3388d405e247 \ + --hash=sha256:4a33dea2b688b3190ee12bd7cfa29d39c9ed176bda40bfa11099a3ce5d3a7ac6 \ + --hash=sha256:4b9fe39a2ccc108a4accc2676e77da025ce383c108593d65cc909add5c3bd601 \ + --hash=sha256:56442863ed2b06d19c37f94d999035e15ee982988920e12a5b4ba29b62ad1f77 \ + --hash=sha256:671cd1187ed5e62818414afe79ed29da836dde67166a9fac6d435873c44fdd02 \ + --hash=sha256:694deca8d702d5db21ec83983ce0bb4b26a578e71fbdbd4fdcd387daa90e4d5e \ + --hash=sha256:6a074d34ee7a5ce3effbc526b7083ec9731bb3cbf921bbe1d3005d4d2bdb3a63 \ + --hash=sha256:6d0072fea50feec76a4c418096652f2c3238eaa014b2f94aeb1d56a66b41403f \ + --hash=sha256:6fbf47b5d3728c6aea2abb0589b5d30459e369baa772e0f37a0320185e87c980 \ + --hash=sha256:7f91197cc9e48f989d12e4e6fbc46495c446636dfc81b9ccf50bb0ec74b91d4b \ + --hash=sha256:86b1f75c4e7c2ac2ccdaec2b9022845dbb81880ca318bb7a0a01fbf7813e3812 \ + --hash=sha256:8dc1c72a69aa7e082593c4a203dcf94ddb74bb5c8a731e4e1eb68d031e8498ff \ + --hash=sha256:8e3dcf21f367459434c18e71b2a9532d96547aef8a871872a5bd69a715c15f96 \ + --hash=sha256:8e576a51ad59e4bfaac456023a78f6b5e6e7651dcd383bcc3e18d06f9b55d6d1 \ + --hash=sha256:96e37a3dc86e80bf81758c152fe66dbf60ed5eca3d26305edf01892257049925 \ + --hash=sha256:97a68e6ada378df82bc9f16b800ab77cbf4b2fada0081794318520138c088e4a \ + --hash=sha256:99a2a507ed3ac881b975a2976d59f38c19386d128e7a9a18b7df6fff1fd4c1d6 \ + --hash=sha256:a49907dd8420c5685cfa064a1335b6754b74541bbb3706c259c02ed65b644b3e \ + --hash=sha256:b09bf97215625a311f669476f44b8b318b075847b49316d3e28c08e41a7a573f \ + --hash=sha256:b7bd98b796e2b6553da7225aeb61f447f80a1ca64f41d83612e6139ca5213aa4 \ + --hash=sha256:b87db4360013327109564f0e591bd2a3b318547bcef31b468a92ee504d07ae4f \ + --hash=sha256:bcb3ed405ed3222f9904899563d6fc492ff75cce56cba05e32eff40e6acbeaa3 \ + --hash=sha256:d4306c36ca495956b6d568d276ac11fdd9c30a36f1b6eb928070dc5360b22e1c \ + --hash=sha256:d5ee4f386140395a2c818d149221149c54849dfcfcb9f1debfe07a8b8bd63f9a \ + --hash=sha256:dda30ba7e87fbbb7eab1ec9f58678558fd9a6b8b853530e176eabd064da81417 \ + --hash=sha256:e04e26803c9c3851c931eac40c695602c6295b8d432cbe78609649ad9bd2da8a \ + --hash=sha256:e1c0b87e09fa55a220f058d1d49d3fb8df88fbfab58558f1198e08c1e1de842a \ + --hash=sha256:e72591e9ecd94d7feb70c1cbd7be7b3ebea3f548870aa91e2732960fa4d57a37 \ + --hash=sha256:e8c843bbcda3a2f1e3c2ab25913c80a3c5376cd00c6e8c4a86a89a28c8dc5452 \ + --hash=sha256:efc1913fd2ca4f334418481c7e595c00aad186563bbc1ec76067848c7ca0a933 \ + --hash=sha256:f121a1420d4e173a5d96e47e9a0c0dcff965afdf1626d28de1460815f7c4ee7a \ + --hash=sha256:fc7b548b17d238737688817ab67deebb30e8073c95749d55538ed473130ec0c7 + # via jinja2 +more-itertools==9.0.0 \ + --hash=sha256:250e83d7e81d0c87ca6bd942e6aeab8cc9daa6096d12c5308f3f92fa5e5c1f41 \ + --hash=sha256:5a6257e40878ef0520b1803990e3e22303a41b5714006c32a3fd8304b26ea1ab + # via jaraco-classes +nox==2022.11.21 \ + --hash=sha256:0e41a990e290e274cb205a976c4c97ee3c5234441a8132c8c3fd9ea3c22149eb \ + --hash=sha256:e21c31de0711d1274ca585a2c5fde36b1aa962005ba8e9322bf5eeed16dcd684 + # via -r requirements.in +packaging==21.3 \ + --hash=sha256:dd47c42927d89ab911e606518907cc2d3a1f38bbd026385970643f9c5b8ecfeb \ + --hash=sha256:ef103e05f519cdc783ae24ea4e2e0f508a9c99b2d4969652eed6a2e1ea5bd522 + # via + # gcp-releasetool + # nox +pkginfo==1.8.3 \ + --hash=sha256:848865108ec99d4901b2f7e84058b6e7660aae8ae10164e015a6dcf5b242a594 \ + --hash=sha256:a84da4318dd86f870a9447a8c98340aa06216bfc6f2b7bdc4b8766984ae1867c + # via twine +platformdirs==2.5.4 \ + --hash=sha256:1006647646d80f16130f052404c6b901e80ee4ed6bef6792e1f238a8969106f7 \ + --hash=sha256:af0276409f9a02373d540bf8480021a048711d572745aef4b7842dad245eba10 + # via virtualenv +protobuf==3.20.3 \ + --hash=sha256:03038ac1cfbc41aa21f6afcbcd357281d7521b4157926f30ebecc8d4ea59dcb7 \ + --hash=sha256:28545383d61f55b57cf4df63eebd9827754fd2dc25f80c5253f9184235db242c \ + --hash=sha256:2e3427429c9cffebf259491be0af70189607f365c2f41c7c3764af6f337105f2 \ + --hash=sha256:398a9e0c3eaceb34ec1aee71894ca3299605fa8e761544934378bbc6c97de23b \ + --hash=sha256:44246bab5dd4b7fbd3c0c80b6f16686808fab0e4aca819ade6e8d294a29c7050 \ + --hash=sha256:447d43819997825d4e71bf5769d869b968ce96848b6479397e29fc24c4a5dfe9 \ + --hash=sha256:67a3598f0a2dcbc58d02dd1928544e7d88f764b47d4a286202913f0b2801c2e7 \ + --hash=sha256:74480f79a023f90dc6e18febbf7b8bac7508420f2006fabd512013c0c238f454 \ + --hash=sha256:819559cafa1a373b7096a482b504ae8a857c89593cf3a25af743ac9ecbd23480 \ + --hash=sha256:899dc660cd599d7352d6f10d83c95df430a38b410c1b66b407a6b29265d66469 \ + --hash=sha256:8c0c984a1b8fef4086329ff8dd19ac77576b384079247c770f29cc8ce3afa06c \ + --hash=sha256:9aae4406ea63d825636cc11ffb34ad3379335803216ee3a856787bcf5ccc751e \ + --hash=sha256:a7ca6d488aa8ff7f329d4c545b2dbad8ac31464f1d8b1c87ad1346717731e4db \ + --hash=sha256:b6cc7ba72a8850621bfec987cb72623e703b7fe2b9127a161ce61e61558ad905 \ + --hash=sha256:bf01b5720be110540be4286e791db73f84a2b721072a3711efff6c324cdf074b \ + --hash=sha256:c02ce36ec760252242a33967d51c289fd0e1c0e6e5cc9397e2279177716add86 \ + --hash=sha256:d9e4432ff660d67d775c66ac42a67cf2453c27cb4d738fc22cb53b5d84c135d4 \ + --hash=sha256:daa564862dd0d39c00f8086f88700fdbe8bc717e993a21e90711acfed02f2402 \ + --hash=sha256:de78575669dddf6099a8a0f46a27e82a1783c557ccc38ee620ed8cc96d3be7d7 \ + --hash=sha256:e64857f395505ebf3d2569935506ae0dfc4a15cb80dc25261176c784662cdcc4 \ + --hash=sha256:f4bd856d702e5b0d96a00ec6b307b0f51c1982c2bf9c0052cf9019e9a544ba99 \ + --hash=sha256:f4c42102bc82a51108e449cbb32b19b180022941c727bac0cfd50170341f16ee + # via + # gcp-docuploader + # gcp-releasetool + # google-api-core + # googleapis-common-protos +pyasn1==0.4.8 \ + --hash=sha256:39c7e2ec30515947ff4e87fb6f456dfc6e84857d34be479c9d4a4ba4bf46aa5d \ + --hash=sha256:aef77c9fb94a3ac588e87841208bdec464471d9871bd5050a287cc9a475cd0ba + # via + # pyasn1-modules + # rsa +pyasn1-modules==0.2.8 \ + --hash=sha256:905f84c712230b2c592c19470d3ca8d552de726050d1d1716282a1f6146be65e \ + --hash=sha256:a50b808ffeb97cb3601dd25981f6b016cbb3d31fbf57a8b8a87428e6158d0c74 + # via google-auth +pycparser==2.21 \ + --hash=sha256:8ee45429555515e1f6b185e78100aea234072576aa43ab53aefcae078162fca9 \ + --hash=sha256:e644fdec12f7872f86c58ff790da456218b10f863970249516d60a5eaca77206 + # via cffi +pygments==2.15.0 \ + --hash=sha256:77a3299119af881904cd5ecd1ac6a66214b6e9bed1f2db16993b54adede64094 \ + --hash=sha256:f7e36cffc4c517fbc252861b9a6e4644ca0e5abadf9a113c72d1358ad09b9500 + # via + # readme-renderer + # rich +pyjwt==2.6.0 \ + --hash=sha256:69285c7e31fc44f68a1feb309e948e0df53259d579295e6cfe2b1792329f05fd \ + --hash=sha256:d83c3d892a77bbb74d3e1a2cfa90afaadb60945205d1095d9221f04466f64c14 + # via gcp-releasetool +pyparsing==3.0.9 \ + --hash=sha256:2b020ecf7d21b687f219b71ecad3631f644a47f01403fa1d1036b0c6416d70fb \ + --hash=sha256:5026bae9a10eeaefb61dab2f09052b9f4307d44aee4eda64b309723d8d206bbc + # via packaging +pyperclip==1.8.2 \ + --hash=sha256:105254a8b04934f0bc84e9c24eb360a591aaf6535c9def5f29d92af107a9bf57 + # via gcp-releasetool +python-dateutil==2.8.2 \ + --hash=sha256:0123cacc1627ae19ddf3c27a5de5bd67ee4586fbdd6440d9748f8abb483d3e86 \ + --hash=sha256:961d03dc3453ebbc59dbdea9e4e11c5651520a876d0f4db161e8674aae935da9 + # via gcp-releasetool +readme-renderer==37.3 \ + --hash=sha256:cd653186dfc73055656f090f227f5cb22a046d7f71a841dfa305f55c9a513273 \ + --hash=sha256:f67a16caedfa71eef48a31b39708637a6f4664c4394801a7b0d6432d13907343 + # via twine +requests==2.31.0 \ + --hash=sha256:58cd2187c01e70e6e26505bca751777aa9f2ee0b7f4300988b709f44e013003f \ + --hash=sha256:942c5a758f98d790eaed1a29cb6eefc7ffb0d1cf7af05c3d2791656dbd6ad1e1 + # via + # gcp-releasetool + # google-api-core + # google-cloud-storage + # requests-toolbelt + # twine +requests-toolbelt==0.10.1 \ + --hash=sha256:18565aa58116d9951ac39baa288d3adb5b3ff975c4f25eee78555d89e8f247f7 \ + --hash=sha256:62e09f7ff5ccbda92772a29f394a49c3ad6cb181d568b1337626b2abb628a63d + # via twine +rfc3986==2.0.0 \ + --hash=sha256:50b1502b60e289cb37883f3dfd34532b8873c7de9f49bb546641ce9cbd256ebd \ + --hash=sha256:97aacf9dbd4bfd829baad6e6309fa6573aaf1be3f6fa735c8ab05e46cecb261c + # via twine +rich==12.6.0 \ + --hash=sha256:a4eb26484f2c82589bd9a17c73d32a010b1e29d89f1604cd9bf3a2097b81bb5e \ + --hash=sha256:ba3a3775974105c221d31141f2c116f4fd65c5ceb0698657a11e9f295ec93fd0 + # via twine +rsa==4.9 \ + --hash=sha256:90260d9058e514786967344d0ef75fa8727eed8a7d2e43ce9f4bcf1b536174f7 \ + --hash=sha256:e38464a49c6c85d7f1351b0126661487a7e0a14a50f1675ec50eb34d4f20ef21 + # via google-auth +secretstorage==3.3.3 \ + --hash=sha256:2403533ef369eca6d2ba81718576c5e0f564d5cca1b58f73a8b23e7d4eeebd77 \ + --hash=sha256:f356e6628222568e3af06f2eba8df495efa13b3b63081dafd4f7d9a7b7bc9f99 + # via keyring +six==1.16.0 \ + --hash=sha256:1e61c37477a1626458e36f7b1d82aa5c9b094fa4802892072e49de9c60c4c926 \ + --hash=sha256:8abb2f1d86890a2dfb989f9a77cfcfd3e47c2a354b01111771326f8aa26e0254 + # via + # bleach + # gcp-docuploader + # google-auth + # python-dateutil +twine==4.0.1 \ + --hash=sha256:42026c18e394eac3e06693ee52010baa5313e4811d5a11050e7d48436cf41b9e \ + --hash=sha256:96b1cf12f7ae611a4a40b6ae8e9570215daff0611828f5fe1f37a16255ab24a0 + # via -r requirements.in +typing-extensions==4.4.0 \ + --hash=sha256:1511434bb92bf8dd198c12b1cc812e800d4181cfcb867674e0f8279cc93087aa \ + --hash=sha256:16fa4864408f655d35ec496218b85f79b3437c829e93320c7c9215ccfd92489e + # via -r requirements.in +urllib3==1.26.18 \ + --hash=sha256:34b97092d7e0a3a8cf7cd10e386f401b3737364026c45e622aa02903dffe0f07 \ + --hash=sha256:f8ecc1bba5667413457c529ab955bf8c67b45db799d159066261719e328580a0 + # via + # requests + # twine +virtualenv==20.16.7 \ + --hash=sha256:8691e3ff9387f743e00f6bb20f70121f5e4f596cae754531f2b3b3a1b1ac696e \ + --hash=sha256:efd66b00386fdb7dbe4822d172303f40cd05e50e01740b19ea42425cbe653e29 + # via nox +webencodings==0.5.1 \ + --hash=sha256:a0af1213f3c2226497a97e2b3aa01a7e4bee4f403f95be16fc9acd2947514a78 \ + --hash=sha256:b36a1c245f2d304965eb4e0a82848379241dc04b865afcc4aab16748587e1923 + # via bleach +wheel==0.38.4 \ + --hash=sha256:965f5259b566725405b05e7cf774052044b1ed30119b5d586b2703aafe8719ac \ + --hash=sha256:b60533f3f5d530e971d6737ca6d58681ee434818fab630c83a734bb10c083ce8 + # via -r requirements.in +zipp==3.10.0 \ + --hash=sha256:4fcb6f278987a6605757302a6e40e896257570d11c51628968ccb2a47e80c6c1 \ + --hash=sha256:7a7262fd930bd3e36c50b9a64897aec3fafff3dfdeec9623ae22b40e93f99bb8 + # via importlib-metadata + +# The following packages are considered to be unsafe in a requirements file: +setuptools==65.5.1 \ + --hash=sha256:d0b9a8433464d5800cbe05094acf5c6d52a91bfac9b52bcfc4d41382be5d5d31 \ + --hash=sha256:e197a19aa8ec9722928f2206f8de752def0e4c9fc6953527360d1c36d94ddb2f + # via -r requirements.in diff --git a/.kokoro/requirements/build.in b/.kokoro/requirements/build.in deleted file mode 100644 index a9455578618..00000000000 --- a/.kokoro/requirements/build.in +++ /dev/null @@ -1,6 +0,0 @@ ---only-binary :all: -twine>=6.2.0 -build>=1.3.0 -wheel>=0.45.1 -keyring>=25.7.0 -keyrings.google-artifactregistry-auth>=1.1.2 diff --git a/.kokoro/requirements/build.txt b/.kokoro/requirements/build.txt deleted file mode 100644 index 375d78c0f81..00000000000 --- a/.kokoro/requirements/build.txt +++ /dev/null @@ -1,408 +0,0 @@ -# -# This file is autogenerated by pip-compile with Python 3.13 -# by the following command: -# -# pip-compile --generate-hashes --output-file=.kokoro/requirements/build.txt .kokoro/requirements/build.in -# ---only-binary :all: - -build==1.3.0 \ - --hash=sha256:7145f0b5061ba90a1500d60bd1b13ca0a8a4cebdd0cc16ed8adf1c0e739f43b4 - # via -r .kokoro/requirements/build.in -cachetools==6.2.2 \ - --hash=sha256:6c09c98183bf58560c97b2abfcedcbaf6a896a490f534b031b661d3723b45ace - # via google-auth -certifi==2025.11.12 \ - --hash=sha256:97de8790030bbd5c2d96b7ec782fc2f7820ef8dba6db909ccf95449f2d062d4b - # via requests -cffi==2.0.0 \ - --hash=sha256:00bdf7acc5f795150faa6957054fbbca2439db2f775ce831222b66f192f03beb \ - --hash=sha256:07b271772c100085dd28b74fa0cd81c8fb1a3ba18b21e03d7c27f3436a10606b \ - --hash=sha256:087067fa8953339c723661eda6b54bc98c5625757ea62e95eb4898ad5e776e9f \ - --hash=sha256:0a1527a803f0a659de1af2e1fd700213caba79377e27e4693648c2923da066f9 \ - --hash=sha256:0cf2d91ecc3fcc0625c2c530fe004f82c110405f101548512cce44322fa8ac44 \ - --hash=sha256:0f6084a0ea23d05d20c3edcda20c3d006f9b6f3fefeac38f59262e10cef47ee2 \ - --hash=sha256:12873ca6cb9b0f0d3a0da705d6086fe911591737a59f28b7936bdfed27c0d47c \ - --hash=sha256:19f705ada2530c1167abacb171925dd886168931e0a7b78f5bffcae5c6b5be75 \ - --hash=sha256:1cd13c99ce269b3ed80b417dcd591415d3372bcac067009b6e0f59c7d4015e65 \ - --hash=sha256:1e3a615586f05fc4065a8b22b8152f0c1b00cdbc60596d187c2a74f9e3036e4e \ - --hash=sha256:1f72fb8906754ac8a2cc3f9f5aaa298070652a0ffae577e0ea9bd480dc3c931a \ - --hash=sha256:1fc9ea04857caf665289b7a75923f2c6ed559b8298a1b8c49e59f7dd95c8481e \ - --hash=sha256:203a48d1fb583fc7d78a4c6655692963b860a417c0528492a6bc21f1aaefab25 \ - --hash=sha256:2081580ebb843f759b9f617314a24ed5738c51d2aee65d31e02f6f7a2b97707a \ - --hash=sha256:21d1152871b019407d8ac3985f6775c079416c282e431a4da6afe7aefd2bccbe \ - --hash=sha256:24b6f81f1983e6df8db3adc38562c83f7d4a0c36162885ec7f7b77c7dcbec97b \ - --hash=sha256:256f80b80ca3853f90c21b23ee78cd008713787b1b1e93eae9f3d6a7134abd91 \ - --hash=sha256:28a3a209b96630bca57cce802da70c266eb08c6e97e5afd61a75611ee6c64592 \ - --hash=sha256:2c8f814d84194c9ea681642fd164267891702542f028a15fc97d4674b6206187 \ - --hash=sha256:2de9a304e27f7596cd03d16f1b7c72219bd944e99cc52b84d0145aefb07cbd3c \ - --hash=sha256:38100abb9d1b1435bc4cc340bb4489635dc2f0da7456590877030c9b3d40b0c1 \ - --hash=sha256:3925dd22fa2b7699ed2617149842d2e6adde22b262fcbfada50e3d195e4b3a94 \ - --hash=sha256:3e17ed538242334bf70832644a32a7aae3d83b57567f9fd60a26257e992b79ba \ - --hash=sha256:3e837e369566884707ddaf85fc1744b47575005c0a229de3327f8f9a20f4efeb \ - --hash=sha256:3f4d46d8b35698056ec29bca21546e1551a205058ae1a181d871e278b0b28165 \ - --hash=sha256:45d5e886156860dc35862657e1494b9bae8dfa63bf56796f2fb56e1679fc0bca \ - --hash=sha256:4647afc2f90d1ddd33441e5b0e85b16b12ddec4fca55f0d9671fef036ecca27c \ - --hash=sha256:4671d9dd5ec934cb9a73e7ee9676f9362aba54f7f34910956b84d727b0d73fb6 \ - --hash=sha256:53f77cbe57044e88bbd5ed26ac1d0514d2acf0591dd6bb02a3ae37f76811b80c \ - --hash=sha256:5eda85d6d1879e692d546a078b44251cdd08dd1cfb98dfb77b670c97cee49ea0 \ - --hash=sha256:5fed36fccc0612a53f1d4d9a816b50a36702c28a2aa880cb8a122b3466638743 \ - --hash=sha256:61d028e90346df14fedc3d1e5441df818d095f3b87d286825dfcbd6459b7ef63 \ - --hash=sha256:66f011380d0e49ed280c789fbd08ff0d40968ee7b665575489afa95c98196ab5 \ - --hash=sha256:6824f87845e3396029f3820c206e459ccc91760e8fa24422f8b0c3d1731cbec5 \ - --hash=sha256:6c6c373cfc5c83a975506110d17457138c8c63016b563cc9ed6e056a82f13ce4 \ - --hash=sha256:6d02d6655b0e54f54c4ef0b94eb6be0607b70853c45ce98bd278dc7de718be5d \ - --hash=sha256:6d50360be4546678fc1b79ffe7a66265e28667840010348dd69a314145807a1b \ - --hash=sha256:730cacb21e1bdff3ce90babf007d0a0917cc3e6492f336c2f0134101e0944f93 \ - --hash=sha256:737fe7d37e1a1bffe70bd5754ea763a62a066dc5913ca57e957824b72a85e205 \ - --hash=sha256:74a03b9698e198d47562765773b4a8309919089150a0bb17d829ad7b44b60d27 \ - --hash=sha256:7553fb2090d71822f02c629afe6042c299edf91ba1bf94951165613553984512 \ - --hash=sha256:7a66c7204d8869299919db4d5069a82f1561581af12b11b3c9f48c584eb8743d \ - --hash=sha256:7cc09976e8b56f8cebd752f7113ad07752461f48a58cbba644139015ac24954c \ - --hash=sha256:81afed14892743bbe14dacb9e36d9e0e504cd204e0b165062c488942b9718037 \ - --hash=sha256:8941aaadaf67246224cee8c3803777eed332a19d909b47e29c9842ef1e79ac26 \ - --hash=sha256:89472c9762729b5ae1ad974b777416bfda4ac5642423fa93bd57a09204712322 \ - --hash=sha256:8ea985900c5c95ce9db1745f7933eeef5d314f0565b27625d9a10ec9881e1bfb \ - --hash=sha256:8eca2a813c1cb7ad4fb74d368c2ffbbb4789d377ee5bb8df98373c2cc0dee76c \ - --hash=sha256:92b68146a71df78564e4ef48af17551a5ddd142e5190cdf2c5624d0c3ff5b2e8 \ - --hash=sha256:9332088d75dc3241c702d852d4671613136d90fa6881da7d770a483fd05248b4 \ - --hash=sha256:94698a9c5f91f9d138526b48fe26a199609544591f859c870d477351dc7b2414 \ - --hash=sha256:9a67fc9e8eb39039280526379fb3a70023d77caec1852002b4da7e8b270c4dd9 \ - --hash=sha256:9de40a7b0323d889cf8d23d1ef214f565ab154443c42737dfe52ff82cf857664 \ - --hash=sha256:a05d0c237b3349096d3981b727493e22147f934b20f6f125a3eba8f994bec4a9 \ - --hash=sha256:afb8db5439b81cf9c9d0c80404b60c3cc9c3add93e114dcae767f1477cb53775 \ - --hash=sha256:b18a3ed7d5b3bd8d9ef7a8cb226502c6bf8308df1525e1cc676c3680e7176739 \ - --hash=sha256:b1e74d11748e7e98e2f426ab176d4ed720a64412b6a15054378afdb71e0f37dc \ - --hash=sha256:b21e08af67b8a103c71a250401c78d5e0893beff75e28c53c98f4de42f774062 \ - --hash=sha256:b4c854ef3adc177950a8dfc81a86f5115d2abd545751a304c5bcf2c2c7283cfe \ - --hash=sha256:b882b3df248017dba09d6b16defe9b5c407fe32fc7c65a9c69798e6175601be9 \ - --hash=sha256:baf5215e0ab74c16e2dd324e8ec067ef59e41125d3eade2b863d294fd5035c92 \ - --hash=sha256:c649e3a33450ec82378822b3dad03cc228b8f5963c0c12fc3b1e0ab940f768a5 \ - --hash=sha256:c654de545946e0db659b3400168c9ad31b5d29593291482c43e3564effbcee13 \ - --hash=sha256:c6638687455baf640e37344fe26d37c404db8b80d037c3d29f58fe8d1c3b194d \ - --hash=sha256:c8d3b5532fc71b7a77c09192b4a5a200ea992702734a2e9279a37f2478236f26 \ - --hash=sha256:cb527a79772e5ef98fb1d700678fe031e353e765d1ca2d409c92263c6d43e09f \ - --hash=sha256:cf364028c016c03078a23b503f02058f1814320a56ad535686f90565636a9495 \ - --hash=sha256:d48a880098c96020b02d5a1f7d9251308510ce8858940e6fa99ece33f610838b \ - --hash=sha256:d68b6cef7827e8641e8ef16f4494edda8b36104d79773a334beaa1e3521430f6 \ - --hash=sha256:d9b29c1f0ae438d5ee9acb31cadee00a58c46cc9c0b2f9038c6b0b3470877a8c \ - --hash=sha256:d9b97165e8aed9272a6bb17c01e3cc5871a594a446ebedc996e2397a1c1ea8ef \ - --hash=sha256:da68248800ad6320861f129cd9c1bf96ca849a2771a59e0344e88681905916f5 \ - --hash=sha256:da902562c3e9c550df360bfa53c035b2f241fed6d9aef119048073680ace4a18 \ - --hash=sha256:dbd5c7a25a7cb98f5ca55d258b103a2054f859a46ae11aaf23134f9cc0d356ad \ - --hash=sha256:dd4f05f54a52fb558f1ba9f528228066954fee3ebe629fc1660d874d040ae5a3 \ - --hash=sha256:de8dad4425a6ca6e4e5e297b27b5c824ecc7581910bf9aee86cb6835e6812aa7 \ - --hash=sha256:e11e82b744887154b182fd3e7e8512418446501191994dbf9c9fc1f32cc8efd5 \ - --hash=sha256:e6e73b9e02893c764e7e8d5bb5ce277f1a009cd5243f8228f75f842bf937c534 \ - --hash=sha256:f73b96c41e3b2adedc34a7356e64c8eb96e03a3782b535e043a986276ce12a49 \ - --hash=sha256:f93fd8e5c8c0a4aa1f424d6173f14a892044054871c771f8566e4008eaa359d2 \ - --hash=sha256:fc33c5141b55ed366cfaad382df24fe7dcbc686de5be719b207bb248e3053dc5 \ - --hash=sha256:fc7de24befaeae77ba923797c7c87834c73648a05a4bde34b3b7e5588973a453 \ - --hash=sha256:fe562eb1a64e67dd297ccc4f5addea2501664954f2692b69a76449ec7913ecbf - # via cryptography -charset-normalizer==3.4.4 \ - --hash=sha256:027f6de494925c0ab2a55eab46ae5129951638a49a34d87f4c3eda90f696b4ad \ - --hash=sha256:077fbb858e903c73f6c9db43374fd213b0b6a778106bc7032446a8e8b5b38b93 \ - --hash=sha256:0a98e6759f854bd25a58a73fa88833fba3b7c491169f86ce1180c948ab3fd394 \ - --hash=sha256:0d3d8f15c07f86e9ff82319b3d9ef6f4bf907608f53fe9d92b28ea9ae3d1fd89 \ - --hash=sha256:0f04b14ffe5fdc8c4933862d8306109a2c51e0704acfa35d51598eb45a1e89fc \ - --hash=sha256:11d694519d7f29d6cd09f6ac70028dba10f92f6cdd059096db198c283794ac86 \ - --hash=sha256:194f08cbb32dc406d6e1aea671a68be0823673db2832b38405deba2fb0d88f63 \ - --hash=sha256:1bee1e43c28aa63cb16e5c14e582580546b08e535299b8b6158a7c9c768a1f3d \ - --hash=sha256:21d142cc6c0ec30d2efee5068ca36c128a30b0f2c53c1c07bd78cb6bc1d3be5f \ - --hash=sha256:2437418e20515acec67d86e12bf70056a33abdacb5cb1655042f6538d6b085a8 \ - --hash=sha256:244bfb999c71b35de57821b8ea746b24e863398194a4014e4c76adc2bbdfeff0 \ - --hash=sha256:2677acec1a2f8ef614c6888b5b4ae4060cc184174a938ed4e8ef690e15d3e505 \ - --hash=sha256:277e970e750505ed74c832b4bf75dac7476262ee2a013f5574dd49075879e161 \ - --hash=sha256:2aaba3b0819274cc41757a1da876f810a3e4d7b6eb25699253a4effef9e8e4af \ - --hash=sha256:2b7d8f6c26245217bd2ad053761201e9f9680f8ce52f0fcd8d0755aeae5b2152 \ - --hash=sha256:2c9d3c380143a1fedbff95a312aa798578371eb29da42106a29019368a475318 \ - --hash=sha256:3162d5d8ce1bb98dd51af660f2121c55d0fa541b46dff7bb9b9f86ea1d87de72 \ - --hash=sha256:31fd66405eaf47bb62e8cd575dc621c56c668f27d46a61d975a249930dd5e2a4 \ - --hash=sha256:362d61fd13843997c1c446760ef36f240cf81d3ebf74ac62652aebaf7838561e \ - --hash=sha256:376bec83a63b8021bb5c8ea75e21c4ccb86e7e45ca4eb81146091b56599b80c3 \ - --hash=sha256:44c2a8734b333e0578090c4cd6b16f275e07aa6614ca8715e6c038e865e70576 \ - --hash=sha256:47cc91b2f4dd2833fddaedd2893006b0106129d4b94fdb6af1f4ce5a9965577c \ - --hash=sha256:4902828217069c3c5c71094537a8e623f5d097858ac6ca8252f7b4d10b7560f1 \ - --hash=sha256:4bd5d4137d500351a30687c2d3971758aac9a19208fc110ccb9d7188fbe709e8 \ - --hash=sha256:4fe7859a4e3e8457458e2ff592f15ccb02f3da787fcd31e0183879c3ad4692a1 \ - --hash=sha256:542d2cee80be6f80247095cc36c418f7bddd14f4a6de45af91dfad36d817bba2 \ - --hash=sha256:554af85e960429cf30784dd47447d5125aaa3b99a6f0683589dbd27e2f45da44 \ - --hash=sha256:5833d2c39d8896e4e19b689ffc198f08ea58116bee26dea51e362ecc7cd3ed26 \ - --hash=sha256:5947809c8a2417be3267efc979c47d76a079758166f7d43ef5ae8e9f92751f88 \ - --hash=sha256:5ae497466c7901d54b639cf42d5b8c1b6a4fead55215500d2f486d34db48d016 \ - --hash=sha256:5bd2293095d766545ec1a8f612559f6b40abc0eb18bb2f5d1171872d34036ede \ - --hash=sha256:5bfbb1b9acf3334612667b61bd3002196fe2a1eb4dd74d247e0f2a4d50ec9bbf \ - --hash=sha256:5cb4d72eea50c8868f5288b7f7f33ed276118325c1dfd3957089f6b519e1382a \ - --hash=sha256:5dbe56a36425d26d6cfb40ce79c314a2e4dd6211d51d6d2191c00bed34f354cc \ - --hash=sha256:5f819d5fe9234f9f82d75bdfa9aef3a3d72c4d24a6e57aeaebba32a704553aa0 \ - --hash=sha256:64b55f9dce520635f018f907ff1b0df1fdc31f2795a922fb49dd14fbcdf48c84 \ - --hash=sha256:6515f3182dbe4ea06ced2d9e8666d97b46ef4c75e326b79bb624110f122551db \ - --hash=sha256:65e2befcd84bc6f37095f5961e68a6f077bf44946771354a28ad434c2cce0ae1 \ - --hash=sha256:6aee717dcfead04c6eb1ce3bd29ac1e22663cdea57f943c87d1eab9a025438d7 \ - --hash=sha256:6b39f987ae8ccdf0d2642338faf2abb1862340facc796048b604ef14919e55ed \ - --hash=sha256:6e1fcf0720908f200cd21aa4e6750a48ff6ce4afe7ff5a79a90d5ed8a08296f8 \ - --hash=sha256:74018750915ee7ad843a774364e13a3db91682f26142baddf775342c3f5b1133 \ - --hash=sha256:74664978bb272435107de04e36db5a9735e78232b85b77d45cfb38f758efd33e \ - --hash=sha256:74bb723680f9f7a6234dcf67aea57e708ec1fbdf5699fb91dfd6f511b0a320ef \ - --hash=sha256:752944c7ffbfdd10c074dc58ec2d5a8a4cd9493b314d367c14d24c17684ddd14 \ - --hash=sha256:778d2e08eda00f4256d7f672ca9fef386071c9202f5e4607920b86d7803387f2 \ - --hash=sha256:780236ac706e66881f3b7f2f32dfe90507a09e67d1d454c762cf642e6e1586e0 \ - --hash=sha256:798d75d81754988d2565bff1b97ba5a44411867c0cf32b77a7e8f8d84796b10d \ - --hash=sha256:799a7a5e4fb2d5898c60b640fd4981d6a25f1c11790935a44ce38c54e985f828 \ - --hash=sha256:7a32c560861a02ff789ad905a2fe94e3f840803362c84fecf1851cb4cf3dc37f \ - --hash=sha256:7c308f7e26e4363d79df40ca5b2be1c6ba9f02bdbccfed5abddb7859a6ce72cf \ - --hash=sha256:7fa17817dc5625de8a027cb8b26d9fefa3ea28c8253929b8d6649e705d2835b6 \ - --hash=sha256:81d5eb2a312700f4ecaa977a8235b634ce853200e828fbadf3a9c50bab278328 \ - --hash=sha256:82004af6c302b5d3ab2cfc4cc5f29db16123b1a8417f2e25f9066f91d4411090 \ - --hash=sha256:837c2ce8c5a65a2035be9b3569c684358dfbf109fd3b6969630a87535495ceaa \ - --hash=sha256:840c25fb618a231545cbab0564a799f101b63b9901f2569faecd6b222ac72381 \ - --hash=sha256:8a6562c3700cce886c5be75ade4a5db4214fda19fede41d9792d100288d8f94c \ - --hash=sha256:8af65f14dc14a79b924524b1e7fffe304517b2bff5a58bf64f30b98bbc5079eb \ - --hash=sha256:8ef3c867360f88ac904fd3f5e1f902f13307af9052646963ee08ff4f131adafc \ - --hash=sha256:99ae2cffebb06e6c22bdc25801d7b30f503cc87dbd283479e7b606f70aff57ec \ - --hash=sha256:9a26f18905b8dd5d685d6d07b0cdf98a79f3c7a918906af7cc143ea2e164c8bc \ - --hash=sha256:9b35f4c90079ff2e2edc5b26c0c77925e5d2d255c42c74fdb70fb49b172726ac \ - --hash=sha256:9cd98cdc06614a2f768d2b7286d66805f94c48cde050acdbbb7db2600ab3197e \ - --hash=sha256:9d1bb833febdff5c8927f922386db610b49db6e0d4f4ee29601d71e7c2694313 \ - --hash=sha256:9f7fcd74d410a36883701fafa2482a6af2ff5ba96b9a620e9e0721e28ead5569 \ - --hash=sha256:a59cb51917aa591b1c4e6a43c132f0cdc3c76dbad6155df4e28ee626cc77a0a3 \ - --hash=sha256:a61900df84c667873b292c3de315a786dd8dac506704dea57bc957bd31e22c7d \ - --hash=sha256:a79cfe37875f822425b89a82333404539ae63dbdddf97f84dcbc3d339aae9525 \ - --hash=sha256:a8a8b89589086a25749f471e6a900d3f662d1d3b6e2e59dcecf787b1cc3a1894 \ - --hash=sha256:a8bf8d0f749c5757af2142fe7903a9df1d2e8aa3841559b2bad34b08d0e2bcf3 \ - --hash=sha256:a9768c477b9d7bd54bc0c86dbaebdec6f03306675526c9927c0e8a04e8f94af9 \ - --hash=sha256:ac1c4a689edcc530fc9d9aa11f5774b9e2f33f9a0c6a57864e90908f5208d30a \ - --hash=sha256:af2d8c67d8e573d6de5bc30cdb27e9b95e49115cd9baad5ddbd1a6207aaa82a9 \ - --hash=sha256:b435cba5f4f750aa6c0a0d92c541fb79f69a387c91e61f1795227e4ed9cece14 \ - --hash=sha256:b5b290ccc2a263e8d185130284f8501e3e36c5e02750fc6b6bdeb2e9e96f1e25 \ - --hash=sha256:b5d84d37db046c5ca74ee7bb47dd6cbc13f80665fdde3e8040bdd3fb015ecb50 \ - --hash=sha256:b7cf1017d601aa35e6bb650b6ad28652c9cd78ee6caff19f3c28d03e1c80acbf \ - --hash=sha256:bc7637e2f80d8530ee4a78e878bce464f70087ce73cf7c1caf142416923b98f1 \ - --hash=sha256:c0463276121fdee9c49b98908b3a89c39be45d86d1dbaa22957e38f6321d4ce3 \ - --hash=sha256:c4ef880e27901b6cc782f1b95f82da9313c0eb95c3af699103088fa0ac3ce9ac \ - --hash=sha256:c8ae8a0f02f57a6e61203a31428fa1d677cbe50c93622b4149d5c0f319c1d19e \ - --hash=sha256:ca5862d5b3928c4940729dacc329aa9102900382fea192fc5e52eb69d6093815 \ - --hash=sha256:cb01158d8b88ee68f15949894ccc6712278243d95f344770fa7593fa2d94410c \ - --hash=sha256:cb6254dc36b47a990e59e1068afacdcd02958bdcce30bb50cc1700a8b9d624a6 \ - --hash=sha256:cc00f04ed596e9dc0da42ed17ac5e596c6ccba999ba6bd92b0e0aef2f170f2d6 \ - --hash=sha256:cd09d08005f958f370f539f186d10aec3377d55b9eeb0d796025d4886119d76e \ - --hash=sha256:cd4b7ca9984e5e7985c12bc60a6f173f3c958eae74f3ef6624bb6b26e2abbae4 \ - --hash=sha256:ce8a0633f41a967713a59c4139d29110c07e826d131a316b50ce11b1d79b4f84 \ - --hash=sha256:cead0978fc57397645f12578bfd2d5ea9138ea0fac82b2f63f7f7c6877986a69 \ - --hash=sha256:d055ec1e26e441f6187acf818b73564e6e6282709e9bcb5b63f5b23068356a15 \ - --hash=sha256:d1f13550535ad8cff21b8d757a3257963e951d96e20ec82ab44bc64aeb62a191 \ - --hash=sha256:d9c7f57c3d666a53421049053eaacdd14bbd0a528e2186fcb2e672effd053bb0 \ - --hash=sha256:d9e45d7faa48ee908174d8fe84854479ef838fc6a705c9315372eacbc2f02897 \ - --hash=sha256:da3326d9e65ef63a817ecbcc0df6e94463713b754fe293eaa03da99befb9a5bd \ - --hash=sha256:de00632ca48df9daf77a2c65a484531649261ec9f25489917f09e455cb09ddb2 \ - --hash=sha256:e1f185f86a6f3403aa2420e815904c67b2f9ebc443f045edd0de921108345794 \ - --hash=sha256:e824f1492727fa856dd6eda4f7cee25f8518a12f3c4a56a74e8095695089cf6d \ - --hash=sha256:e912091979546adf63357d7e2ccff9b44f026c075aeaf25a52d0e95ad2281074 \ - --hash=sha256:eaabd426fe94daf8fd157c32e571c85cb12e66692f15516a83a03264b08d06c3 \ - --hash=sha256:ebf3e58c7ec8a8bed6d66a75d7fb37b55e5015b03ceae72a8e7c74495551e224 \ - --hash=sha256:ecaae4149d99b1c9e7b88bb03e3221956f68fd6d50be2ef061b2381b61d20838 \ - --hash=sha256:eecbc200c7fd5ddb9a7f16c7decb07b566c29fa2161a16cf67b8d068bd21690a \ - --hash=sha256:f155a433c2ec037d4e8df17d18922c3a0d9b3232a396690f17175d2946f0218d \ - --hash=sha256:f1e34719c6ed0b92f418c7c780480b26b5d9c50349e9a9af7d76bf757530350d \ - --hash=sha256:f34be2938726fc13801220747472850852fe6b1ea75869a048d6f896838c896f \ - --hash=sha256:f820802628d2694cb7e56db99213f930856014862f3fd943d290ea8438d07ca8 \ - --hash=sha256:f8bf04158c6b607d747e93949aa60618b61312fe647a6369f88ce2ff16043490 \ - --hash=sha256:f8e160feb2aed042cd657a72acc0b481212ed28b1b9a95c0cee1621b524e1966 \ - --hash=sha256:f9d332f8c2a2fcbffe1378594431458ddbef721c1769d78e2cbc06280d8155f9 \ - --hash=sha256:fa09f53c465e532f4d3db095e0c55b615f010ad81803d383195b6b5ca6cbf5f3 \ - --hash=sha256:faa3a41b2b66b6e50f84ae4a68c64fcd0c44355741c6374813a800cd6695db9e \ - --hash=sha256:fd44c878ea55ba351104cb93cc85e74916eb8fa440ca7903e57575e97394f608 - # via requests -cryptography==46.0.3 \ - --hash=sha256:00a5e7e87938e5ff9ff5447ab086a5706a957137e6e433841e9d24f38a065217 \ - --hash=sha256:01ca9ff2885f3acc98c29f1860552e37f6d7c7d013d7334ff2a9de43a449315d \ - --hash=sha256:09859af8466b69bc3c27bdf4f5d84a665e0f7ab5088412e9e2ec49758eca5cbc \ - --hash=sha256:0abf1ffd6e57c67e92af68330d05760b7b7efb243aab8377e583284dbab72c71 \ - --hash=sha256:1000713389b75c449a6e979ffc7dcc8ac90b437048766cef052d4d30b8220971 \ - --hash=sha256:109d4ddfadf17e8e7779c39f9b18111a09efb969a301a31e987416a0191ed93a \ - --hash=sha256:10b01676fc208c3e6feeb25a8b83d81767e8059e1fe86e1dc62d10a3018fa926 \ - --hash=sha256:10ca84c4668d066a9878890047f03546f3ae0a6b8b39b697457b7757aaf18dbc \ - --hash=sha256:15ab9b093e8f09daab0f2159bb7e47532596075139dd74365da52ecc9cb46c5d \ - --hash=sha256:191bb60a7be5e6f54e30ba16fdfae78ad3a342a0599eb4193ba88e3f3d6e185b \ - --hash=sha256:22d7e97932f511d6b0b04f2bfd818d73dcd5928db509460aaf48384778eb6d20 \ - --hash=sha256:23b1a8f26e43f47ceb6d6a43115f33a5a37d57df4ea0ca295b780ae8546e8044 \ - --hash=sha256:36e627112085bb3b81b19fed209c05ce2a52ee8b15d161b7c643a7d5a88491f3 \ - --hash=sha256:39b6755623145ad5eff1dab323f4eae2a32a77a7abef2c5089a04a3d04366715 \ - --hash=sha256:3b51b8ca4f1c6453d8829e1eb7299499ca7f313900dd4d89a24b8b87c0a780d4 \ - --hash=sha256:402b58fc32614f00980b66d6e56a5b4118e6cb362ae8f3fda141ba4689bd4506 \ - --hash=sha256:416260257577718c05135c55958b674000baef9a1c7d9e8f306ec60d71db850f \ - --hash=sha256:46acf53b40ea38f9c6c229599a4a13f0d46a6c3fa9ef19fc1a124d62e338dfa0 \ - --hash=sha256:4b7387121ac7d15e550f5cb4a43aef2559ed759c35df7336c402bb8275ac9683 \ - --hash=sha256:50fc3343ac490c6b08c0cf0d704e881d0d660be923fd3076db3e932007e726e3 \ - --hash=sha256:516ea134e703e9fe26bcd1277a4b59ad30586ea90c365a87781d7887a646fe21 \ - --hash=sha256:549e234ff32571b1f4076ac269fcce7a808d3bf98b76c8dd560e42dbc66d7d91 \ - --hash=sha256:5d7f93296ee28f68447397bf5198428c9aeeab45705a55d53a6343455dcb2c3c \ - --hash=sha256:5ecfccd2329e37e9b7112a888e76d9feca2347f12f37918facbb893d7bb88ee8 \ - --hash=sha256:6276eb85ef938dc035d59b87c8a7dc559a232f954962520137529d77b18ff1df \ - --hash=sha256:6b5063083824e5509fdba180721d55909ffacccc8adbec85268b48439423d78c \ - --hash=sha256:6eae65d4c3d33da080cff9c4ab1f711b15c1d9760809dad6ea763f3812d254cb \ - --hash=sha256:6f61efb26e76c45c4a227835ddeae96d83624fb0d29eb5df5b96e14ed1a0afb7 \ - --hash=sha256:71e842ec9bc7abf543b47cf86b9a743baa95f4677d22baa4c7d5c69e49e9bc04 \ - --hash=sha256:760f83faa07f8b64e9c33fc963d790a2edb24efb479e3520c14a45741cd9b2db \ - --hash=sha256:78a97cf6a8839a48c49271cdcbd5cf37ca2c1d6b7fdd86cc864f302b5e9bf459 \ - --hash=sha256:7ce938a99998ed3c8aa7e7272dca1a610401ede816d36d0693907d863b10d9ea \ - --hash=sha256:8a6e050cb6164d3f830453754094c086ff2d0b2f3a897a1d9820f6139a1f0914 \ - --hash=sha256:9394673a9f4de09e28b5356e7fff97d778f8abad85c9d5ac4a4b7e25a0de7717 \ - --hash=sha256:94cd0549accc38d1494e1f8de71eca837d0509d0d44bf11d158524b0e12cebf9 \ - --hash=sha256:a04bee9ab6a4da801eb9b51f1b708a1b5b5c9eb48c03f74198464c66f0d344ac \ - --hash=sha256:a23582810fedb8c0bc47524558fb6c56aac3fc252cb306072fd2815da2a47c32 \ - --hash=sha256:a2c0cd47381a3229c403062f764160d57d4d175e022c1df84e168c6251a22eec \ - --hash=sha256:a9a3008438615669153eb86b26b61e09993921ebdd75385ddd748702c5adfddb \ - --hash=sha256:b02cf04496f6576afffef5ddd04a0cb7d49cf6be16a9059d793a30b035f6b6ac \ - --hash=sha256:b419ae593c86b87014b9be7396b385491ad7f320bde96826d0dd174459e54665 \ - --hash=sha256:c0a7bb1a68a5d3471880e264621346c48665b3bf1c3759d682fc0864c540bd9e \ - --hash=sha256:c70cc23f12726be8f8bc72e41d5065d77e4515efae3690326764ea1b07845cfb \ - --hash=sha256:c8daeb2d2174beb4575b77482320303f3d39b8e81153da4f0fb08eb5fe86a6c5 \ - --hash=sha256:cb3d760a6117f621261d662bccc8ef5bc32ca673e037c83fbe565324f5c46936 \ - --hash=sha256:d55f3dffadd674514ad19451161118fd010988540cee43d8bc20675e775925de \ - --hash=sha256:d89c3468de4cdc4f08a57e214384d0471911a3830fcdaf7a8cc587e42a866372 \ - --hash=sha256:db391fa7c66df6762ee3f00c95a89e6d428f4d60e7abc8328f4fe155b5ac6e54 \ - --hash=sha256:dfb781ff7eaa91a6f7fd41776ec37c5853c795d3b358d4896fdbb5df168af422 \ - --hash=sha256:e5bf0ed4490068a2e72ac03d786693adeb909981cc596425d09032d372bcc849 \ - --hash=sha256:e7aec276d68421f9574040c26e2a7c3771060bc0cff408bae1dcb19d3ab1e63c \ - --hash=sha256:ef639cb3372f69ec44915fafcd6698b6cc78fbe0c2ea41be867f6ed612811963 \ - --hash=sha256:f260d0d41e9b4da1ed1e0f1ce571f97fe370b152ab18778e9e8f67d6af432018 - # via secretstorage -docutils==0.22.3 \ - --hash=sha256:bd772e4aca73aff037958d44f2be5229ded4c09927fcf8690c577b66234d6ceb - # via readme-renderer -google-auth==2.43.0 \ - --hash=sha256:af628ba6fa493f75c7e9dbe9373d148ca9f4399b5ea29976519e0a3848eddd16 - # via keyrings-google-artifactregistry-auth -id==1.5.0 \ - --hash=sha256:f1434e1cef91f2cbb8a4ec64663d5a23b9ed43ef44c4c957d02583d61714c658 - # via twine -idna==3.11 \ - --hash=sha256:771a87f49d9defaf64091e6e6fe9c18d4833f140bd19464795bc32d966ca37ea - # via requests -jaraco-classes==3.4.0 \ - --hash=sha256:f662826b6bed8cace05e7ff873ce0f9283b5c924470fe664fff1c2f00f581790 - # via keyring -jaraco-context==6.0.1 \ - --hash=sha256:f797fc481b490edb305122c9181830a3a5b76d84ef6d1aef2fb9b47ab956f9e4 - # via keyring -jaraco-functools==4.3.0 \ - --hash=sha256:227ff8ed6f7b8f62c56deff101545fa7543cf2c8e7b82a7c2116e672f29c26e8 - # via keyring -jeepney==0.9.0 \ - --hash=sha256:97e5714520c16fc0a45695e5365a2e11b81ea79bba796e26f9f1d178cb182683 - # via - # keyring - # secretstorage -keyring==25.7.0 \ - --hash=sha256:be4a0b195f149690c166e850609a477c532ddbfbaed96a404d4e43f8d5e2689f - # via - # -r .kokoro/requirements/build.in - # keyrings-google-artifactregistry-auth - # twine -keyrings-google-artifactregistry-auth==1.1.2 \ - --hash=sha256:e3f18b50fa945c786593014dc225810d191671d4f5f8e12d9259e39bad3605a3 - # via -r .kokoro/requirements/build.in -markdown-it-py==4.0.0 \ - --hash=sha256:87327c59b172c5011896038353a81343b6754500a08cd7a4973bb48c6d578147 - # via rich -mdurl==0.1.2 \ - --hash=sha256:84008a41e51615a49fc9966191ff91509e3c40b939176e643fd50a5c2196b8f8 - # via markdown-it-py -more-itertools==10.8.0 \ - --hash=sha256:52d4362373dcf7c52546bc4af9a86ee7c4579df9a8dc268be0a2f949d376cc9b - # via - # jaraco-classes - # jaraco-functools -nh3==0.3.2 \ - --hash=sha256:019ecbd007536b67fdf76fab411b648fb64e2257ca3262ec80c3425c24028c80 \ - --hash=sha256:03d617e5c8aa7331bd2659c654e021caf9bba704b109e7b2b28b039a00949fe5 \ - --hash=sha256:0dca4365db62b2d71ff1620ee4f800c4729849906c5dd504ee1a7b2389558e31 \ - --hash=sha256:0fe7ee035dd7b2290715baf29cb27167dddd2ff70ea7d052c958dbd80d323c99 \ - --hash=sha256:13398e676a14d6233f372c75f52d5ae74f98210172991f7a3142a736bd92b131 \ - --hash=sha256:169db03df90da63286e0560ea0efa9b6f3b59844a9735514a1d47e6bb2c8c61b \ - --hash=sha256:1710f3901cd6440ca92494ba2eb6dc260f829fa8d9196b659fa10de825610ce0 \ - --hash=sha256:1f9ba555a797dbdcd844b89523f29cdc90973d8bd2e836ea6b962cf567cadd93 \ - --hash=sha256:2ab70e8c6c7d2ce953d2a58102eefa90c2d0a5ed7aa40c7e29a487bc5e613131 \ - --hash=sha256:2c9850041b77a9147d6bbd6dbbf13eeec7009eb60b44e83f07fcb2910075bf9b \ - --hash=sha256:403c11563e50b915d0efdb622866d1d9e4506bce590ef7da57789bf71dd148b5 \ - --hash=sha256:45c953e57028c31d473d6b648552d9cab1efe20a42ad139d78e11d8f42a36130 \ - --hash=sha256:562da3dca7a17f9077593214a9781a94b8d76de4f158f8c895e62f09573945fe \ - --hash=sha256:6d66f41672eb4060cf87c037f760bdbc6847852ca9ef8e9c5a5da18f090abf87 \ - --hash=sha256:7064ccf5ace75825bd7bf57859daaaf16ed28660c1c6b306b649a9eda4b54b1e \ - --hash=sha256:72d67c25a84579f4a432c065e8b4274e53b7cf1df8f792cf846abfe2c3090866 \ - --hash=sha256:7bb18403f02b655a1bbe4e3a4696c2ae1d6ae8f5991f7cacb684b1ae27e6c9f7 \ - --hash=sha256:91e9b001101fb4500a2aafe3e7c92928d85242d38bf5ac0aba0b7480da0a4cd6 \ - --hash=sha256:a40202fd58e49129764f025bbaae77028e420f1d5b3c8e6f6fd3a6490d513868 \ - --hash=sha256:c8745454cdd28bbbc90861b80a0111a195b0e3961b9fa2e672be89eb199fa5d8 \ - --hash=sha256:cf5964d54edd405e68583114a7cba929468bcd7db5e676ae38ee954de1cfc104 \ - --hash=sha256:d18957a90806d943d141cc5e4a0fefa1d77cf0d7a156878bf9a66eed52c9cc7d \ - --hash=sha256:dce4248edc427c9b79261f3e6e2b3ecbdd9b88c267012168b4a7b3fc6fd41d13 \ - --hash=sha256:f2f55c4d2d5a207e74eefe4d828067bbb01300e06e2a7436142f915c5928de07 \ - --hash=sha256:f97f8b25cb2681d25e2338148159447e4d689aafdccfcf19e61ff7db3905768a - # via readme-renderer -packaging==25.0 \ - --hash=sha256:29572ef2b1f17581046b3a2227d5c611fb25ec70ca1ba8554b24b0e69331a484 - # via - # build - # twine - # wheel -pluggy==1.6.0 \ - --hash=sha256:e920276dd6813095e9377c0bc5566d94c932c33b27a3e3945d8389c374dd4746 - # via keyrings-google-artifactregistry-auth -pyasn1==0.6.1 \ - --hash=sha256:0d632f46f2ba09143da3a8afe9e33fb6f92fa2320ab7e886e2d0f7672af84629 - # via - # pyasn1-modules - # rsa -pyasn1-modules==0.4.2 \ - --hash=sha256:29253a9207ce32b64c3ac6600edc75368f98473906e8fd1043bd6b5b1de2c14a - # via google-auth -pycparser==2.23 \ - --hash=sha256:e5c6e8d3fbad53479cab09ac03729e0a9faf2bee3db8208a550daf5af81a5934 - # via cffi -pygments==2.19.2 \ - --hash=sha256:86540386c03d588bb81d44bc3928634ff26449851e99741617ecb9037ee5ec0b - # via - # readme-renderer - # rich -pyproject-hooks==1.2.0 \ - --hash=sha256:9e5c6bfa8dcc30091c74b0cf803c81fdd29d94f01992a7707bc97babb1141913 - # via build -readme-renderer==44.0 \ - --hash=sha256:2fbca89b81a08526aadf1357a8c2ae889ec05fb03f5da67f9769c9a592166151 - # via twine -requests==2.32.5 \ - --hash=sha256:2462f94637a34fd532264295e186976db0f5d453d1cdd31473c85a6a161affb6 - # via - # id - # keyrings-google-artifactregistry-auth - # requests-toolbelt - # twine -requests-toolbelt==1.0.0 \ - --hash=sha256:cccfdd665f0a24fcf4726e690f65639d272bb0637b9b92dfd91a5568ccf6bd06 - # via twine -rfc3986==2.0.0 \ - --hash=sha256:50b1502b60e289cb37883f3dfd34532b8873c7de9f49bb546641ce9cbd256ebd - # via twine -rich==14.2.0 \ - --hash=sha256:76bc51fe2e57d2b1be1f96c524b890b816e334ab4c1e45888799bfaab0021edd - # via twine -rsa==4.9.1 \ - --hash=sha256:68635866661c6836b8d39430f97a996acbd61bfa49406748ea243539fe239762 - # via google-auth -secretstorage==3.5.0 \ - --hash=sha256:0ce65888c0725fcb2c5bc0fdb8e5438eece02c523557ea40ce0703c266248137 - # via keyring -twine==6.2.0 \ - --hash=sha256:418ebf08ccda9a8caaebe414433b0ba5e25eb5e4a927667122fbe8f829f985d8 - # via -r .kokoro/requirements/build.in -urllib3==2.6.3 \ - --hash=sha256:bf272323e553dfb2e87d9bfd225ca7b0f467b919d7bbd355436d3fd37cb0acd4 - # via - # requests - # twine -wheel==0.46.1 \ - --hash=sha256:f796f65d72750ccde090663e466d0ca37cd72b62870f7520b96d34cdc07d86d8 - # via -r .kokoro/requirements/build.in diff --git a/.kokoro/samples/lint/common.cfg b/.kokoro/samples/lint/common.cfg new file mode 100644 index 00000000000..b4d26c1f982 --- /dev/null +++ b/.kokoro/samples/lint/common.cfg @@ -0,0 +1,34 @@ +# Format: //devtools/kokoro/config/proto/build.proto + +# Build logs will be here +action { + define_artifacts { + regex: "**/*sponge_log.xml" + } +} + +# Specify which tests to run +env_vars: { + key: "RUN_TESTS_SESSION" + value: "lint" +} + +env_vars: { + key: "TRAMPOLINE_BUILD_FILE" + value: "github/python-bigquery-dataframes/.kokoro/test-samples.sh" +} + +# Configure the docker image for kokoro-trampoline. +env_vars: { + key: "TRAMPOLINE_IMAGE" + value: "gcr.io/cloud-devrel-kokoro-resources/python-samples-testing-docker" +} + +# Download secrets for samples +gfile_resources: "/bigstore/cloud-devrel-kokoro-resources/python-docs-samples" + +# Download trampoline resources. +gfile_resources: "/bigstore/cloud-devrel-kokoro-resources/trampoline" + +# Use the trampoline script to run in docker. +build_file: "python-bigquery-dataframes/.kokoro/trampoline_v2.sh" \ No newline at end of file diff --git a/.kokoro/samples/lint/continuous.cfg b/.kokoro/samples/lint/continuous.cfg new file mode 100644 index 00000000000..a1c8d9759c8 --- /dev/null +++ b/.kokoro/samples/lint/continuous.cfg @@ -0,0 +1,6 @@ +# Format: //devtools/kokoro/config/proto/build.proto + +env_vars: { + key: "INSTALL_LIBRARY_FROM_SOURCE" + value: "True" +} \ No newline at end of file diff --git a/.kokoro/samples/lint/periodic.cfg b/.kokoro/samples/lint/periodic.cfg new file mode 100644 index 00000000000..50fec964973 --- /dev/null +++ b/.kokoro/samples/lint/periodic.cfg @@ -0,0 +1,6 @@ +# Format: //devtools/kokoro/config/proto/build.proto + +env_vars: { + key: "INSTALL_LIBRARY_FROM_SOURCE" + value: "False" +} \ No newline at end of file diff --git a/.kokoro/samples/lint/presubmit.cfg b/.kokoro/samples/lint/presubmit.cfg new file mode 100644 index 00000000000..a1c8d9759c8 --- /dev/null +++ b/.kokoro/samples/lint/presubmit.cfg @@ -0,0 +1,6 @@ +# Format: //devtools/kokoro/config/proto/build.proto + +env_vars: { + key: "INSTALL_LIBRARY_FROM_SOURCE" + value: "True" +} \ No newline at end of file diff --git a/.kokoro/samples/python3.10/common.cfg b/.kokoro/samples/python3.10/common.cfg new file mode 100644 index 00000000000..8f9c66c571b --- /dev/null +++ b/.kokoro/samples/python3.10/common.cfg @@ -0,0 +1,40 @@ +# Format: //devtools/kokoro/config/proto/build.proto + +# Build logs will be here +action { + define_artifacts { + regex: "**/*sponge_log.xml" + } +} + +# Specify which tests to run +env_vars: { + key: "RUN_TESTS_SESSION" + value: "py-3.10" +} + +# Declare build specific Cloud project. +env_vars: { + key: "BUILD_SPECIFIC_GCLOUD_PROJECT" + value: "python-docs-samples-tests-310" +} + +env_vars: { + key: "TRAMPOLINE_BUILD_FILE" + value: "github/python-bigquery-dataframes/.kokoro/test-samples.sh" +} + +# Configure the docker image for kokoro-trampoline. +env_vars: { + key: "TRAMPOLINE_IMAGE" + value: "gcr.io/cloud-devrel-kokoro-resources/python-samples-testing-docker" +} + +# Download secrets for samples +gfile_resources: "/bigstore/cloud-devrel-kokoro-resources/python-docs-samples" + +# Download trampoline resources. +gfile_resources: "/bigstore/cloud-devrel-kokoro-resources/trampoline" + +# Use the trampoline script to run in docker. +build_file: "python-bigquery-dataframes/.kokoro/trampoline_v2.sh" \ No newline at end of file diff --git a/.kokoro/samples/python3.10/continuous.cfg b/.kokoro/samples/python3.10/continuous.cfg new file mode 100644 index 00000000000..a1c8d9759c8 --- /dev/null +++ b/.kokoro/samples/python3.10/continuous.cfg @@ -0,0 +1,6 @@ +# Format: //devtools/kokoro/config/proto/build.proto + +env_vars: { + key: "INSTALL_LIBRARY_FROM_SOURCE" + value: "True" +} \ No newline at end of file diff --git a/.kokoro/samples/python3.10/periodic-head.cfg b/.kokoro/samples/python3.10/periodic-head.cfg new file mode 100644 index 00000000000..123a35fbd3d --- /dev/null +++ b/.kokoro/samples/python3.10/periodic-head.cfg @@ -0,0 +1,11 @@ +# Format: //devtools/kokoro/config/proto/build.proto + +env_vars: { + key: "INSTALL_LIBRARY_FROM_SOURCE" + value: "True" +} + +env_vars: { + key: "TRAMPOLINE_BUILD_FILE" + value: "github/python-bigquery-dataframes/.kokoro/test-samples-against-head.sh" +} diff --git a/.kokoro/samples/python3.10/periodic.cfg b/.kokoro/samples/python3.10/periodic.cfg new file mode 100644 index 00000000000..71cd1e597e3 --- /dev/null +++ b/.kokoro/samples/python3.10/periodic.cfg @@ -0,0 +1,6 @@ +# Format: //devtools/kokoro/config/proto/build.proto + +env_vars: { + key: "INSTALL_LIBRARY_FROM_SOURCE" + value: "False" +} diff --git a/.kokoro/samples/python3.10/presubmit.cfg b/.kokoro/samples/python3.10/presubmit.cfg new file mode 100644 index 00000000000..a1c8d9759c8 --- /dev/null +++ b/.kokoro/samples/python3.10/presubmit.cfg @@ -0,0 +1,6 @@ +# Format: //devtools/kokoro/config/proto/build.proto + +env_vars: { + key: "INSTALL_LIBRARY_FROM_SOURCE" + value: "True" +} \ No newline at end of file diff --git a/.kokoro/samples/python3.11/common.cfg b/.kokoro/samples/python3.11/common.cfg new file mode 100644 index 00000000000..1bba39114aa --- /dev/null +++ b/.kokoro/samples/python3.11/common.cfg @@ -0,0 +1,40 @@ +# Format: //devtools/kokoro/config/proto/build.proto + +# Build logs will be here +action { + define_artifacts { + regex: "**/*sponge_log.xml" + } +} + +# Specify which tests to run +env_vars: { + key: "RUN_TESTS_SESSION" + value: "py-3.11" +} + +# Declare build specific Cloud project. +env_vars: { + key: "BUILD_SPECIFIC_GCLOUD_PROJECT" + value: "python-docs-samples-tests-311" +} + +env_vars: { + key: "TRAMPOLINE_BUILD_FILE" + value: "github/python-bigquery-dataframes/.kokoro/test-samples.sh" +} + +# Configure the docker image for kokoro-trampoline. +env_vars: { + key: "TRAMPOLINE_IMAGE" + value: "gcr.io/cloud-devrel-kokoro-resources/python-samples-testing-docker" +} + +# Download secrets for samples +gfile_resources: "/bigstore/cloud-devrel-kokoro-resources/python-docs-samples" + +# Download trampoline resources. +gfile_resources: "/bigstore/cloud-devrel-kokoro-resources/trampoline" + +# Use the trampoline script to run in docker. +build_file: "python-bigquery-dataframes/.kokoro/trampoline_v2.sh" \ No newline at end of file diff --git a/.kokoro/samples/python3.11/continuous.cfg b/.kokoro/samples/python3.11/continuous.cfg new file mode 100644 index 00000000000..a1c8d9759c8 --- /dev/null +++ b/.kokoro/samples/python3.11/continuous.cfg @@ -0,0 +1,6 @@ +# Format: //devtools/kokoro/config/proto/build.proto + +env_vars: { + key: "INSTALL_LIBRARY_FROM_SOURCE" + value: "True" +} \ No newline at end of file diff --git a/.kokoro/samples/python3.11/periodic-head.cfg b/.kokoro/samples/python3.11/periodic-head.cfg new file mode 100644 index 00000000000..123a35fbd3d --- /dev/null +++ b/.kokoro/samples/python3.11/periodic-head.cfg @@ -0,0 +1,11 @@ +# Format: //devtools/kokoro/config/proto/build.proto + +env_vars: { + key: "INSTALL_LIBRARY_FROM_SOURCE" + value: "True" +} + +env_vars: { + key: "TRAMPOLINE_BUILD_FILE" + value: "github/python-bigquery-dataframes/.kokoro/test-samples-against-head.sh" +} diff --git a/.kokoro/samples/python3.11/periodic.cfg b/.kokoro/samples/python3.11/periodic.cfg new file mode 100644 index 00000000000..71cd1e597e3 --- /dev/null +++ b/.kokoro/samples/python3.11/periodic.cfg @@ -0,0 +1,6 @@ +# Format: //devtools/kokoro/config/proto/build.proto + +env_vars: { + key: "INSTALL_LIBRARY_FROM_SOURCE" + value: "False" +} diff --git a/.kokoro/samples/python3.11/presubmit.cfg b/.kokoro/samples/python3.11/presubmit.cfg new file mode 100644 index 00000000000..a1c8d9759c8 --- /dev/null +++ b/.kokoro/samples/python3.11/presubmit.cfg @@ -0,0 +1,6 @@ +# Format: //devtools/kokoro/config/proto/build.proto + +env_vars: { + key: "INSTALL_LIBRARY_FROM_SOURCE" + value: "True" +} \ No newline at end of file diff --git a/.kokoro/samples/python3.7/common.cfg b/.kokoro/samples/python3.7/common.cfg new file mode 100644 index 00000000000..09d7af02ba9 --- /dev/null +++ b/.kokoro/samples/python3.7/common.cfg @@ -0,0 +1,40 @@ +# Format: //devtools/kokoro/config/proto/build.proto + +# Build logs will be here +action { + define_artifacts { + regex: "**/*sponge_log.xml" + } +} + +# Specify which tests to run +env_vars: { + key: "RUN_TESTS_SESSION" + value: "py-3.7" +} + +# Declare build specific Cloud project. +env_vars: { + key: "BUILD_SPECIFIC_GCLOUD_PROJECT" + value: "python-docs-samples-tests-py37" +} + +env_vars: { + key: "TRAMPOLINE_BUILD_FILE" + value: "github/python-bigquery-dataframes/.kokoro/test-samples.sh" +} + +# Configure the docker image for kokoro-trampoline. +env_vars: { + key: "TRAMPOLINE_IMAGE" + value: "gcr.io/cloud-devrel-kokoro-resources/python-samples-testing-docker" +} + +# Download secrets for samples +gfile_resources: "/bigstore/cloud-devrel-kokoro-resources/python-docs-samples" + +# Download trampoline resources. +gfile_resources: "/bigstore/cloud-devrel-kokoro-resources/trampoline" + +# Use the trampoline script to run in docker. +build_file: "python-bigquery-dataframes/.kokoro/trampoline_v2.sh" \ No newline at end of file diff --git a/.kokoro/samples/python3.7/continuous.cfg b/.kokoro/samples/python3.7/continuous.cfg new file mode 100644 index 00000000000..a1c8d9759c8 --- /dev/null +++ b/.kokoro/samples/python3.7/continuous.cfg @@ -0,0 +1,6 @@ +# Format: //devtools/kokoro/config/proto/build.proto + +env_vars: { + key: "INSTALL_LIBRARY_FROM_SOURCE" + value: "True" +} \ No newline at end of file diff --git a/.kokoro/samples/python3.7/periodic-head.cfg b/.kokoro/samples/python3.7/periodic-head.cfg new file mode 100644 index 00000000000..123a35fbd3d --- /dev/null +++ b/.kokoro/samples/python3.7/periodic-head.cfg @@ -0,0 +1,11 @@ +# Format: //devtools/kokoro/config/proto/build.proto + +env_vars: { + key: "INSTALL_LIBRARY_FROM_SOURCE" + value: "True" +} + +env_vars: { + key: "TRAMPOLINE_BUILD_FILE" + value: "github/python-bigquery-dataframes/.kokoro/test-samples-against-head.sh" +} diff --git a/.kokoro/samples/python3.7/periodic.cfg b/.kokoro/samples/python3.7/periodic.cfg new file mode 100644 index 00000000000..71cd1e597e3 --- /dev/null +++ b/.kokoro/samples/python3.7/periodic.cfg @@ -0,0 +1,6 @@ +# Format: //devtools/kokoro/config/proto/build.proto + +env_vars: { + key: "INSTALL_LIBRARY_FROM_SOURCE" + value: "False" +} diff --git a/.kokoro/samples/python3.7/presubmit.cfg b/.kokoro/samples/python3.7/presubmit.cfg new file mode 100644 index 00000000000..a1c8d9759c8 --- /dev/null +++ b/.kokoro/samples/python3.7/presubmit.cfg @@ -0,0 +1,6 @@ +# Format: //devtools/kokoro/config/proto/build.proto + +env_vars: { + key: "INSTALL_LIBRARY_FROM_SOURCE" + value: "True" +} \ No newline at end of file diff --git a/.kokoro/samples/python3.8/common.cfg b/.kokoro/samples/python3.8/common.cfg new file mode 100644 index 00000000000..976d9ce8c5c --- /dev/null +++ b/.kokoro/samples/python3.8/common.cfg @@ -0,0 +1,40 @@ +# Format: //devtools/kokoro/config/proto/build.proto + +# Build logs will be here +action { + define_artifacts { + regex: "**/*sponge_log.xml" + } +} + +# Specify which tests to run +env_vars: { + key: "RUN_TESTS_SESSION" + value: "py-3.8" +} + +# Declare build specific Cloud project. +env_vars: { + key: "BUILD_SPECIFIC_GCLOUD_PROJECT" + value: "python-docs-samples-tests-py38" +} + +env_vars: { + key: "TRAMPOLINE_BUILD_FILE" + value: "github/python-bigquery-dataframes/.kokoro/test-samples.sh" +} + +# Configure the docker image for kokoro-trampoline. +env_vars: { + key: "TRAMPOLINE_IMAGE" + value: "gcr.io/cloud-devrel-kokoro-resources/python-samples-testing-docker" +} + +# Download secrets for samples +gfile_resources: "/bigstore/cloud-devrel-kokoro-resources/python-docs-samples" + +# Download trampoline resources. +gfile_resources: "/bigstore/cloud-devrel-kokoro-resources/trampoline" + +# Use the trampoline script to run in docker. +build_file: "python-bigquery-dataframes/.kokoro/trampoline_v2.sh" \ No newline at end of file diff --git a/.kokoro/samples/python3.8/continuous.cfg b/.kokoro/samples/python3.8/continuous.cfg new file mode 100644 index 00000000000..a1c8d9759c8 --- /dev/null +++ b/.kokoro/samples/python3.8/continuous.cfg @@ -0,0 +1,6 @@ +# Format: //devtools/kokoro/config/proto/build.proto + +env_vars: { + key: "INSTALL_LIBRARY_FROM_SOURCE" + value: "True" +} \ No newline at end of file diff --git a/.kokoro/samples/python3.8/periodic-head.cfg b/.kokoro/samples/python3.8/periodic-head.cfg new file mode 100644 index 00000000000..123a35fbd3d --- /dev/null +++ b/.kokoro/samples/python3.8/periodic-head.cfg @@ -0,0 +1,11 @@ +# Format: //devtools/kokoro/config/proto/build.proto + +env_vars: { + key: "INSTALL_LIBRARY_FROM_SOURCE" + value: "True" +} + +env_vars: { + key: "TRAMPOLINE_BUILD_FILE" + value: "github/python-bigquery-dataframes/.kokoro/test-samples-against-head.sh" +} diff --git a/.kokoro/samples/python3.8/periodic.cfg b/.kokoro/samples/python3.8/periodic.cfg new file mode 100644 index 00000000000..71cd1e597e3 --- /dev/null +++ b/.kokoro/samples/python3.8/periodic.cfg @@ -0,0 +1,6 @@ +# Format: //devtools/kokoro/config/proto/build.proto + +env_vars: { + key: "INSTALL_LIBRARY_FROM_SOURCE" + value: "False" +} diff --git a/.kokoro/samples/python3.8/presubmit.cfg b/.kokoro/samples/python3.8/presubmit.cfg new file mode 100644 index 00000000000..a1c8d9759c8 --- /dev/null +++ b/.kokoro/samples/python3.8/presubmit.cfg @@ -0,0 +1,6 @@ +# Format: //devtools/kokoro/config/proto/build.proto + +env_vars: { + key: "INSTALL_LIBRARY_FROM_SOURCE" + value: "True" +} \ No newline at end of file diff --git a/.kokoro/samples/python3.9/common.cfg b/.kokoro/samples/python3.9/common.cfg new file mode 100644 index 00000000000..603cfffa280 --- /dev/null +++ b/.kokoro/samples/python3.9/common.cfg @@ -0,0 +1,40 @@ +# Format: //devtools/kokoro/config/proto/build.proto + +# Build logs will be here +action { + define_artifacts { + regex: "**/*sponge_log.xml" + } +} + +# Specify which tests to run +env_vars: { + key: "RUN_TESTS_SESSION" + value: "py-3.9" +} + +# Declare build specific Cloud project. +env_vars: { + key: "BUILD_SPECIFIC_GCLOUD_PROJECT" + value: "python-docs-samples-tests-py39" +} + +env_vars: { + key: "TRAMPOLINE_BUILD_FILE" + value: "github/python-bigquery-dataframes/.kokoro/test-samples.sh" +} + +# Configure the docker image for kokoro-trampoline. +env_vars: { + key: "TRAMPOLINE_IMAGE" + value: "gcr.io/cloud-devrel-kokoro-resources/python-samples-testing-docker" +} + +# Download secrets for samples +gfile_resources: "/bigstore/cloud-devrel-kokoro-resources/python-docs-samples" + +# Download trampoline resources. +gfile_resources: "/bigstore/cloud-devrel-kokoro-resources/trampoline" + +# Use the trampoline script to run in docker. +build_file: "python-bigquery-dataframes/.kokoro/trampoline_v2.sh" \ No newline at end of file diff --git a/.kokoro/samples/python3.9/continuous.cfg b/.kokoro/samples/python3.9/continuous.cfg new file mode 100644 index 00000000000..a1c8d9759c8 --- /dev/null +++ b/.kokoro/samples/python3.9/continuous.cfg @@ -0,0 +1,6 @@ +# Format: //devtools/kokoro/config/proto/build.proto + +env_vars: { + key: "INSTALL_LIBRARY_FROM_SOURCE" + value: "True" +} \ No newline at end of file diff --git a/.kokoro/samples/python3.9/periodic-head.cfg b/.kokoro/samples/python3.9/periodic-head.cfg new file mode 100644 index 00000000000..123a35fbd3d --- /dev/null +++ b/.kokoro/samples/python3.9/periodic-head.cfg @@ -0,0 +1,11 @@ +# Format: //devtools/kokoro/config/proto/build.proto + +env_vars: { + key: "INSTALL_LIBRARY_FROM_SOURCE" + value: "True" +} + +env_vars: { + key: "TRAMPOLINE_BUILD_FILE" + value: "github/python-bigquery-dataframes/.kokoro/test-samples-against-head.sh" +} diff --git a/.kokoro/samples/python3.9/periodic.cfg b/.kokoro/samples/python3.9/periodic.cfg new file mode 100644 index 00000000000..71cd1e597e3 --- /dev/null +++ b/.kokoro/samples/python3.9/periodic.cfg @@ -0,0 +1,6 @@ +# Format: //devtools/kokoro/config/proto/build.proto + +env_vars: { + key: "INSTALL_LIBRARY_FROM_SOURCE" + value: "False" +} diff --git a/.kokoro/samples/python3.9/presubmit.cfg b/.kokoro/samples/python3.9/presubmit.cfg new file mode 100644 index 00000000000..a1c8d9759c8 --- /dev/null +++ b/.kokoro/samples/python3.9/presubmit.cfg @@ -0,0 +1,6 @@ +# Format: //devtools/kokoro/config/proto/build.proto + +env_vars: { + key: "INSTALL_LIBRARY_FROM_SOURCE" + value: "True" +} \ No newline at end of file diff --git a/.kokoro/test-samples-against-head.sh b/.kokoro/test-samples-against-head.sh new file mode 100755 index 00000000000..63ac41dfae1 --- /dev/null +++ b/.kokoro/test-samples-against-head.sh @@ -0,0 +1,26 @@ +#!/bin/bash +# Copyright 2023 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# A customized test runner for samples. +# +# For periodic builds, you can specify this file for testing against head. + +# `-e` enables the script to automatically fail when a command fails +# `-o pipefail` sets the exit code to the rightmost comment to exit with a non-zero +set -eo pipefail +# Enables `**` to include files nested inside sub-folders +shopt -s globstar + +exec .kokoro/test-samples-impl.sh diff --git a/.kokoro/test-samples-impl.sh b/.kokoro/test-samples-impl.sh new file mode 100755 index 00000000000..5a0f5fab6a8 --- /dev/null +++ b/.kokoro/test-samples-impl.sh @@ -0,0 +1,102 @@ +#!/bin/bash +# Copyright 2023 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +# `-e` enables the script to automatically fail when a command fails +# `-o pipefail` sets the exit code to the rightmost comment to exit with a non-zero +set -eo pipefail +# Enables `**` to include files nested inside sub-folders +shopt -s globstar + +# Exit early if samples don't exist +if ! find samples -name 'requirements.txt' | grep -q .; then + echo "No tests run. './samples/**/requirements.txt' not found" + exit 0 +fi + +# Disable buffering, so that the logs stream through. +export PYTHONUNBUFFERED=1 + +# Debug: show build environment +env | grep KOKORO + +# Install nox +python3.9 -m pip install --upgrade --quiet nox + +# Use secrets acessor service account to get secrets +if [[ -f "${KOKORO_GFILE_DIR}/secrets_viewer_service_account.json" ]]; then + gcloud auth activate-service-account \ + --key-file="${KOKORO_GFILE_DIR}/secrets_viewer_service_account.json" \ + --project="cloud-devrel-kokoro-resources" +fi + +# This script will create 3 files: +# - testing/test-env.sh +# - testing/service-account.json +# - testing/client-secrets.json +./scripts/decrypt-secrets.sh + +source ./testing/test-env.sh +export GOOGLE_APPLICATION_CREDENTIALS=$(pwd)/testing/service-account.json + +# For cloud-run session, we activate the service account for gcloud sdk. +gcloud auth activate-service-account \ + --key-file "${GOOGLE_APPLICATION_CREDENTIALS}" + +export GOOGLE_CLIENT_SECRETS=$(pwd)/testing/client-secrets.json + +echo -e "\n******************** TESTING PROJECTS ********************" + +# Switch to 'fail at end' to allow all tests to complete before exiting. +set +e +# Use RTN to return a non-zero value if the test fails. +RTN=0 +ROOT=$(pwd) +# Find all requirements.txt in the samples directory (may break on whitespace). +for file in samples/**/requirements.txt; do + cd "$ROOT" + # Navigate to the project folder. + file=$(dirname "$file") + cd "$file" + + echo "------------------------------------------------------------" + echo "- testing $file" + echo "------------------------------------------------------------" + + # Use nox to execute the tests for the project. + python3.9 -m nox -s "$RUN_TESTS_SESSION" + EXIT=$? + + # If this is a periodic build, send the test log to the FlakyBot. + # See https://github.com/googleapis/repo-automation-bots/tree/main/packages/flakybot. + if [[ $KOKORO_BUILD_ARTIFACTS_SUBDIR = *"periodic"* ]]; then + chmod +x $KOKORO_GFILE_DIR/linux_amd64/flakybot + $KOKORO_GFILE_DIR/linux_amd64/flakybot + fi + + if [[ $EXIT -ne 0 ]]; then + RTN=1 + echo -e "\n Testing failed: Nox returned a non-zero exit code. \n" + else + echo -e "\n Testing completed.\n" + fi + +done +cd "$ROOT" + +# Workaround for Kokoro permissions issue: delete secrets +rm testing/{test-env.sh,client-secrets.json,service-account.json} + +exit "$RTN" diff --git a/.kokoro/test-samples.sh b/.kokoro/test-samples.sh new file mode 100755 index 00000000000..50b35a48c19 --- /dev/null +++ b/.kokoro/test-samples.sh @@ -0,0 +1,44 @@ +#!/bin/bash +# Copyright 2023 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# The default test runner for samples. +# +# For periodic builds, we rewinds the repo to the latest release, and +# run test-samples-impl.sh. + +# `-e` enables the script to automatically fail when a command fails +# `-o pipefail` sets the exit code to the rightmost comment to exit with a non-zero +set -eo pipefail +# Enables `**` to include files nested inside sub-folders +shopt -s globstar + +# Run periodic samples tests at latest release +if [[ $KOKORO_BUILD_ARTIFACTS_SUBDIR = *"periodic"* ]]; then + # preserving the test runner implementation. + cp .kokoro/test-samples-impl.sh "${TMPDIR}/test-samples-impl.sh" + echo "--- IMPORTANT IMPORTANT IMPORTANT ---" + echo "Now we rewind the repo back to the latest release..." + LATEST_RELEASE=$(git describe --abbrev=0 --tags) + git checkout $LATEST_RELEASE + echo "The current head is: " + echo $(git rev-parse --verify HEAD) + echo "--- IMPORTANT IMPORTANT IMPORTANT ---" + # move back the test runner implementation if there's no file. + if [ ! -f .kokoro/test-samples-impl.sh ]; then + cp "${TMPDIR}/test-samples-impl.sh" .kokoro/test-samples-impl.sh + fi +fi + +exec .kokoro/test-samples-impl.sh diff --git a/.kokoro/trampoline.sh b/.kokoro/trampoline.sh new file mode 100755 index 00000000000..d85b1f26769 --- /dev/null +++ b/.kokoro/trampoline.sh @@ -0,0 +1,28 @@ +#!/bin/bash +# Copyright 2023 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +set -eo pipefail + +# Always run the cleanup script, regardless of the success of bouncing into +# the container. +function cleanup() { + chmod +x ${KOKORO_GFILE_DIR}/trampoline_cleanup.sh + ${KOKORO_GFILE_DIR}/trampoline_cleanup.sh + echo "cleanup"; +} +trap cleanup EXIT + +$(dirname $0)/populate-secrets.sh # Secret Manager secrets. +python3 "${KOKORO_GFILE_DIR}/trampoline_v1.py" \ No newline at end of file diff --git a/.kokoro/trampoline_v2.sh b/.kokoro/trampoline_v2.sh new file mode 100755 index 00000000000..59a7cf3a937 --- /dev/null +++ b/.kokoro/trampoline_v2.sh @@ -0,0 +1,487 @@ +#!/usr/bin/env bash +# Copyright 2023 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# trampoline_v2.sh +# +# This script does 3 things. +# +# 1. Prepare the Docker image for the test +# 2. Run the Docker with appropriate flags to run the test +# 3. Upload the newly built Docker image +# +# in a way that is somewhat compatible with trampoline_v1. +# +# To run this script, first download few files from gcs to /dev/shm. +# (/dev/shm is passed into the container as KOKORO_GFILE_DIR). +# +# gsutil cp gs://cloud-devrel-kokoro-resources/python-docs-samples/secrets_viewer_service_account.json /dev/shm +# gsutil cp gs://cloud-devrel-kokoro-resources/python-docs-samples/automl_secrets.txt /dev/shm +# +# Then run the script. +# .kokoro/trampoline_v2.sh +# +# These environment variables are required: +# TRAMPOLINE_IMAGE: The docker image to use. +# TRAMPOLINE_DOCKERFILE: The location of the Dockerfile. +# +# You can optionally change these environment variables: +# TRAMPOLINE_IMAGE_UPLOAD: +# (true|false): Whether to upload the Docker image after the +# successful builds. +# TRAMPOLINE_BUILD_FILE: The script to run in the docker container. +# TRAMPOLINE_WORKSPACE: The workspace path in the docker container. +# Defaults to /workspace. +# Potentially there are some repo specific envvars in .trampolinerc in +# the project root. + + +set -euo pipefail + +TRAMPOLINE_VERSION="2.0.5" + +if command -v tput >/dev/null && [[ -n "${TERM:-}" ]]; then + readonly IO_COLOR_RED="$(tput setaf 1)" + readonly IO_COLOR_GREEN="$(tput setaf 2)" + readonly IO_COLOR_YELLOW="$(tput setaf 3)" + readonly IO_COLOR_RESET="$(tput sgr0)" +else + readonly IO_COLOR_RED="" + readonly IO_COLOR_GREEN="" + readonly IO_COLOR_YELLOW="" + readonly IO_COLOR_RESET="" +fi + +function function_exists { + [ $(LC_ALL=C type -t $1)"" == "function" ] +} + +# Logs a message using the given color. The first argument must be one +# of the IO_COLOR_* variables defined above, such as +# "${IO_COLOR_YELLOW}". The remaining arguments will be logged in the +# given color. The log message will also have an RFC-3339 timestamp +# prepended (in UTC). You can disable the color output by setting +# TERM=vt100. +function log_impl() { + local color="$1" + shift + local timestamp="$(date -u "+%Y-%m-%dT%H:%M:%SZ")" + echo "================================================================" + echo "${color}${timestamp}:" "$@" "${IO_COLOR_RESET}" + echo "================================================================" +} + +# Logs the given message with normal coloring and a timestamp. +function log() { + log_impl "${IO_COLOR_RESET}" "$@" +} + +# Logs the given message in green with a timestamp. +function log_green() { + log_impl "${IO_COLOR_GREEN}" "$@" +} + +# Logs the given message in yellow with a timestamp. +function log_yellow() { + log_impl "${IO_COLOR_YELLOW}" "$@" +} + +# Logs the given message in red with a timestamp. +function log_red() { + log_impl "${IO_COLOR_RED}" "$@" +} + +readonly tmpdir=$(mktemp -d -t ci-XXXXXXXX) +readonly tmphome="${tmpdir}/h" +mkdir -p "${tmphome}" + +function cleanup() { + rm -rf "${tmpdir}" +} +trap cleanup EXIT + +RUNNING_IN_CI="${RUNNING_IN_CI:-false}" + +# The workspace in the container, defaults to /workspace. +TRAMPOLINE_WORKSPACE="${TRAMPOLINE_WORKSPACE:-/workspace}" + +pass_down_envvars=( + # TRAMPOLINE_V2 variables. + # Tells scripts whether they are running as part of CI or not. + "RUNNING_IN_CI" + # Indicates which CI system we're in. + "TRAMPOLINE_CI" + # Indicates the version of the script. + "TRAMPOLINE_VERSION" +) + +log_yellow "Building with Trampoline ${TRAMPOLINE_VERSION}" + +# Detect which CI systems we're in. If we're in any of the CI systems +# we support, `RUNNING_IN_CI` will be true and `TRAMPOLINE_CI` will be +# the name of the CI system. Both envvars will be passing down to the +# container for telling which CI system we're in. +if [[ -n "${KOKORO_BUILD_ID:-}" ]]; then + # descriptive env var for indicating it's on CI. + RUNNING_IN_CI="true" + TRAMPOLINE_CI="kokoro" + if [[ "${TRAMPOLINE_USE_LEGACY_SERVICE_ACCOUNT:-}" == "true" ]]; then + if [[ ! -f "${KOKORO_GFILE_DIR}/kokoro-trampoline.service-account.json" ]]; then + log_red "${KOKORO_GFILE_DIR}/kokoro-trampoline.service-account.json does not exist. Did you forget to mount cloud-devrel-kokoro-resources/trampoline? Aborting." + exit 1 + fi + # This service account will be activated later. + TRAMPOLINE_SERVICE_ACCOUNT="${KOKORO_GFILE_DIR}/kokoro-trampoline.service-account.json" + else + if [[ "${TRAMPOLINE_VERBOSE:-}" == "true" ]]; then + gcloud auth list + fi + log_yellow "Configuring Container Registry access" + gcloud auth configure-docker --quiet + fi + pass_down_envvars+=( + # KOKORO dynamic variables. + "KOKORO_BUILD_NUMBER" + "KOKORO_BUILD_ID" + "KOKORO_JOB_NAME" + "KOKORO_GIT_COMMIT" + "KOKORO_GITHUB_COMMIT" + "KOKORO_GITHUB_PULL_REQUEST_NUMBER" + "KOKORO_GITHUB_PULL_REQUEST_COMMIT" + # For FlakyBot + "KOKORO_GITHUB_COMMIT_URL" + "KOKORO_GITHUB_PULL_REQUEST_URL" + ) +elif [[ "${TRAVIS:-}" == "true" ]]; then + RUNNING_IN_CI="true" + TRAMPOLINE_CI="travis" + pass_down_envvars+=( + "TRAVIS_BRANCH" + "TRAVIS_BUILD_ID" + "TRAVIS_BUILD_NUMBER" + "TRAVIS_BUILD_WEB_URL" + "TRAVIS_COMMIT" + "TRAVIS_COMMIT_MESSAGE" + "TRAVIS_COMMIT_RANGE" + "TRAVIS_JOB_NAME" + "TRAVIS_JOB_NUMBER" + "TRAVIS_JOB_WEB_URL" + "TRAVIS_PULL_REQUEST" + "TRAVIS_PULL_REQUEST_BRANCH" + "TRAVIS_PULL_REQUEST_SHA" + "TRAVIS_PULL_REQUEST_SLUG" + "TRAVIS_REPO_SLUG" + "TRAVIS_SECURE_ENV_VARS" + "TRAVIS_TAG" + ) +elif [[ -n "${GITHUB_RUN_ID:-}" ]]; then + RUNNING_IN_CI="true" + TRAMPOLINE_CI="github-workflow" + pass_down_envvars+=( + "GITHUB_WORKFLOW" + "GITHUB_RUN_ID" + "GITHUB_RUN_NUMBER" + "GITHUB_ACTION" + "GITHUB_ACTIONS" + "GITHUB_ACTOR" + "GITHUB_REPOSITORY" + "GITHUB_EVENT_NAME" + "GITHUB_EVENT_PATH" + "GITHUB_SHA" + "GITHUB_REF" + "GITHUB_HEAD_REF" + "GITHUB_BASE_REF" + ) +elif [[ "${CIRCLECI:-}" == "true" ]]; then + RUNNING_IN_CI="true" + TRAMPOLINE_CI="circleci" + pass_down_envvars+=( + "CIRCLE_BRANCH" + "CIRCLE_BUILD_NUM" + "CIRCLE_BUILD_URL" + "CIRCLE_COMPARE_URL" + "CIRCLE_JOB" + "CIRCLE_NODE_INDEX" + "CIRCLE_NODE_TOTAL" + "CIRCLE_PREVIOUS_BUILD_NUM" + "CIRCLE_PROJECT_REPONAME" + "CIRCLE_PROJECT_USERNAME" + "CIRCLE_REPOSITORY_URL" + "CIRCLE_SHA1" + "CIRCLE_STAGE" + "CIRCLE_USERNAME" + "CIRCLE_WORKFLOW_ID" + "CIRCLE_WORKFLOW_JOB_ID" + "CIRCLE_WORKFLOW_UPSTREAM_JOB_IDS" + "CIRCLE_WORKFLOW_WORKSPACE_ID" + ) +fi + +# Configure the service account for pulling the docker image. +function repo_root() { + local dir="$1" + while [[ ! -d "${dir}/.git" ]]; do + dir="$(dirname "$dir")" + done + echo "${dir}" +} + +# Detect the project root. In CI builds, we assume the script is in +# the git tree and traverse from there, otherwise, traverse from `pwd` +# to find `.git` directory. +if [[ "${RUNNING_IN_CI:-}" == "true" ]]; then + PROGRAM_PATH="$(realpath "$0")" + PROGRAM_DIR="$(dirname "${PROGRAM_PATH}")" + PROJECT_ROOT="$(repo_root "${PROGRAM_DIR}")" +else + PROJECT_ROOT="$(repo_root $(pwd))" +fi + +log_yellow "Changing to the project root: ${PROJECT_ROOT}." +cd "${PROJECT_ROOT}" + +# To support relative path for `TRAMPOLINE_SERVICE_ACCOUNT`, we need +# to use this environment variable in `PROJECT_ROOT`. +if [[ -n "${TRAMPOLINE_SERVICE_ACCOUNT:-}" ]]; then + + mkdir -p "${tmpdir}/gcloud" + gcloud_config_dir="${tmpdir}/gcloud" + + log_yellow "Using isolated gcloud config: ${gcloud_config_dir}." + export CLOUDSDK_CONFIG="${gcloud_config_dir}" + + log_yellow "Using ${TRAMPOLINE_SERVICE_ACCOUNT} for authentication." + gcloud auth activate-service-account \ + --key-file "${TRAMPOLINE_SERVICE_ACCOUNT}" + log_yellow "Configuring Container Registry access" + gcloud auth configure-docker --quiet +fi + +required_envvars=( + # The basic trampoline configurations. + "TRAMPOLINE_IMAGE" + "TRAMPOLINE_BUILD_FILE" +) + +if [[ -f "${PROJECT_ROOT}/.trampolinerc" ]]; then + source "${PROJECT_ROOT}/.trampolinerc" +fi + +log_yellow "Checking environment variables." +for e in "${required_envvars[@]}" +do + if [[ -z "${!e:-}" ]]; then + log "Missing ${e} env var. Aborting." + exit 1 + fi +done + +# We want to support legacy style TRAMPOLINE_BUILD_FILE used with V1 +# script: e.g. "github/repo-name/.kokoro/run_tests.sh" +TRAMPOLINE_BUILD_FILE="${TRAMPOLINE_BUILD_FILE#github/*/}" +log_yellow "Using TRAMPOLINE_BUILD_FILE: ${TRAMPOLINE_BUILD_FILE}" + +# ignore error on docker operations and test execution +set +e + +log_yellow "Preparing Docker image." +# We only download the docker image in CI builds. +if [[ "${RUNNING_IN_CI:-}" == "true" ]]; then + # Download the docker image specified by `TRAMPOLINE_IMAGE` + + # We may want to add --max-concurrent-downloads flag. + + log_yellow "Start pulling the Docker image: ${TRAMPOLINE_IMAGE}." + if docker pull "${TRAMPOLINE_IMAGE}"; then + log_green "Finished pulling the Docker image: ${TRAMPOLINE_IMAGE}." + has_image="true" + else + log_red "Failed pulling the Docker image: ${TRAMPOLINE_IMAGE}." + has_image="false" + fi +else + # For local run, check if we have the image. + if docker images "${TRAMPOLINE_IMAGE}:latest" | grep "${TRAMPOLINE_IMAGE}"; then + has_image="true" + else + has_image="false" + fi +fi + + +# The default user for a Docker container has uid 0 (root). To avoid +# creating root-owned files in the build directory we tell docker to +# use the current user ID. +user_uid="$(id -u)" +user_gid="$(id -g)" +user_name="$(id -un)" + +# To allow docker in docker, we add the user to the docker group in +# the host os. +docker_gid=$(cut -d: -f3 < <(getent group docker)) + +update_cache="false" +if [[ "${TRAMPOLINE_DOCKERFILE:-none}" != "none" ]]; then + # Build the Docker image from the source. + context_dir=$(dirname "${TRAMPOLINE_DOCKERFILE}") + docker_build_flags=( + "-f" "${TRAMPOLINE_DOCKERFILE}" + "-t" "${TRAMPOLINE_IMAGE}" + "--build-arg" "UID=${user_uid}" + "--build-arg" "USERNAME=${user_name}" + ) + if [[ "${has_image}" == "true" ]]; then + docker_build_flags+=("--cache-from" "${TRAMPOLINE_IMAGE}") + fi + + log_yellow "Start building the docker image." + if [[ "${TRAMPOLINE_VERBOSE:-false}" == "true" ]]; then + echo "docker build" "${docker_build_flags[@]}" "${context_dir}" + fi + + # ON CI systems, we want to suppress docker build logs, only + # output the logs when it fails. + if [[ "${RUNNING_IN_CI:-}" == "true" ]]; then + if docker build "${docker_build_flags[@]}" "${context_dir}" \ + > "${tmpdir}/docker_build.log" 2>&1; then + if [[ "${TRAMPOLINE_VERBOSE:-}" == "true" ]]; then + cat "${tmpdir}/docker_build.log" + fi + + log_green "Finished building the docker image." + update_cache="true" + else + log_red "Failed to build the Docker image, aborting." + log_yellow "Dumping the build logs:" + cat "${tmpdir}/docker_build.log" + exit 1 + fi + else + if docker build "${docker_build_flags[@]}" "${context_dir}"; then + log_green "Finished building the docker image." + update_cache="true" + else + log_red "Failed to build the Docker image, aborting." + exit 1 + fi + fi +else + if [[ "${has_image}" != "true" ]]; then + log_red "We do not have ${TRAMPOLINE_IMAGE} locally, aborting." + exit 1 + fi +fi + +# We use an array for the flags so they are easier to document. +docker_flags=( + # Remove the container after it exists. + "--rm" + + # Use the host network. + "--network=host" + + # Run in priviledged mode. We are not using docker for sandboxing or + # isolation, just for packaging our dev tools. + "--privileged" + + # Run the docker script with the user id. Because the docker image gets to + # write in ${PWD} you typically want this to be your user id. + # To allow docker in docker, we need to use docker gid on the host. + "--user" "${user_uid}:${docker_gid}" + + # Pass down the USER. + "--env" "USER=${user_name}" + + # Mount the project directory inside the Docker container. + "--volume" "${PROJECT_ROOT}:${TRAMPOLINE_WORKSPACE}" + "--workdir" "${TRAMPOLINE_WORKSPACE}" + "--env" "PROJECT_ROOT=${TRAMPOLINE_WORKSPACE}" + + # Mount the temporary home directory. + "--volume" "${tmphome}:/h" + "--env" "HOME=/h" + + # Allow docker in docker. + "--volume" "/var/run/docker.sock:/var/run/docker.sock" + + # Mount the /tmp so that docker in docker can mount the files + # there correctly. + "--volume" "/tmp:/tmp" + # Pass down the KOKORO_GFILE_DIR and KOKORO_KEYSTORE_DIR + # TODO(tmatsuo): This part is not portable. + "--env" "TRAMPOLINE_SECRET_DIR=/secrets" + "--volume" "${KOKORO_GFILE_DIR:-/dev/shm}:/secrets/gfile" + "--env" "KOKORO_GFILE_DIR=/secrets/gfile" + "--volume" "${KOKORO_KEYSTORE_DIR:-/dev/shm}:/secrets/keystore" + "--env" "KOKORO_KEYSTORE_DIR=/secrets/keystore" +) + +# Add an option for nicer output if the build gets a tty. +if [[ -t 0 ]]; then + docker_flags+=("-it") +fi + +# Passing down env vars +for e in "${pass_down_envvars[@]}" +do + if [[ -n "${!e:-}" ]]; then + docker_flags+=("--env" "${e}=${!e}") + fi +done + +# If arguments are given, all arguments will become the commands run +# in the container, otherwise run TRAMPOLINE_BUILD_FILE. +if [[ $# -ge 1 ]]; then + log_yellow "Running the given commands '" "${@:1}" "' in the container." + readonly commands=("${@:1}") + if [[ "${TRAMPOLINE_VERBOSE:-}" == "true" ]]; then + echo docker run "${docker_flags[@]}" "${TRAMPOLINE_IMAGE}" "${commands[@]}" + fi + docker run "${docker_flags[@]}" "${TRAMPOLINE_IMAGE}" "${commands[@]}" +else + log_yellow "Running the tests in a Docker container." + docker_flags+=("--entrypoint=${TRAMPOLINE_BUILD_FILE}") + if [[ "${TRAMPOLINE_VERBOSE:-}" == "true" ]]; then + echo docker run "${docker_flags[@]}" "${TRAMPOLINE_IMAGE}" + fi + docker run "${docker_flags[@]}" "${TRAMPOLINE_IMAGE}" +fi + + +test_retval=$? + +if [[ ${test_retval} -eq 0 ]]; then + log_green "Build finished with ${test_retval}" +else + log_red "Build finished with ${test_retval}" +fi + +# Only upload it when the test passes. +if [[ "${update_cache}" == "true" ]] && \ + [[ $test_retval == 0 ]] && \ + [[ "${TRAMPOLINE_IMAGE_UPLOAD:-false}" == "true" ]]; then + log_yellow "Uploading the Docker image." + if docker push "${TRAMPOLINE_IMAGE}"; then + log_green "Finished uploading the Docker image." + else + log_red "Failed uploading the Docker image." + fi + # Call trampoline_after_upload_hook if it's defined. + if function_exists trampoline_after_upload_hook; then + trampoline_after_upload_hook + fi + +fi + +exit "${test_retval}" diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 00000000000..6e0fd8b98fb --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,41 @@ +# Copyright 2023 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# See https://pre-commit.com for more information +# See https://pre-commit.com/hooks.html for more hooks +repos: +- repo: https://github.com/pre-commit/pre-commit-hooks + rev: v4.0.1 + hooks: + - id: trailing-whitespace + - id: end-of-file-fixer + - id: check-yaml +- repo: https://github.com/pycqa/isort + rev: 5.12.0 + hooks: + - id: isort + name: isort (python) +- repo: https://github.com/psf/black + rev: 22.3.0 + hooks: + - id: black +- repo: https://github.com/pycqa/flake8 + rev: 6.1.0 + hooks: + - id: flake8 +- repo: https://github.com/pre-commit/mirrors-mypy + rev: v1.1.1 + hooks: + - id: mypy + additional_dependencies: [types-requests] diff --git a/.repo-metadata.json b/.repo-metadata.json index b988476c181..0efaa967d2c 100644 --- a/.repo-metadata.json +++ b/.repo-metadata.json @@ -1,9 +1,16 @@ { - "client_documentation": "https://googleapis.dev/python/bigframes/latest", - "distribution_name": "bigframes", + "name": "bigframes", + "name_pretty": "A unified Python API in BigQuery", + "product_documentation": "https://cloud.google.com/bigquery", + "client_documentation": "https://cloud.google.com/python/docs/reference/bigframes/latest", + "issue_tracker": "https://github.com/googleapis/python-bigquery-dataframes/issues", + "release_level": "preview", "language": "python", "library_type": "INTEGRATION", - "name": "bigframes", - "release_level": "stable", - "repo": "googleapis/google-cloud-python" -} \ No newline at end of file + "repo": "googleapis/python-bigquery-dataframes", + "distribution_name": "bigframes", + "api_id": "bigquery.googleapis.com", + "default_version": "", + "codeowner_team": "@googleapis/api-bigquery-dataframe", + "api_shortname": "bigquery" +} diff --git a/.trampolinerc b/.trampolinerc new file mode 100644 index 00000000000..a7dfeb42c6d --- /dev/null +++ b/.trampolinerc @@ -0,0 +1,61 @@ +# Copyright 2023 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# Add required env vars here. +required_envvars+=( +) + +# Add env vars which are passed down into the container here. +pass_down_envvars+=( + "NOX_SESSION" + ############### + # Docs builds + ############### + "STAGING_BUCKET" + "V2_STAGING_BUCKET" + ################## + # Samples builds + ################## + "INSTALL_LIBRARY_FROM_SOURCE" + "RUN_TESTS_SESSION" + "BUILD_SPECIFIC_GCLOUD_PROJECT" + # Target directories. + "RUN_TESTS_DIRS" + # The nox session to run. + "RUN_TESTS_SESSION" +) + +# Prevent unintentional override on the default image. +if [[ "${TRAMPOLINE_IMAGE_UPLOAD:-false}" == "true" ]] && \ + [[ -z "${TRAMPOLINE_IMAGE:-}" ]]; then + echo "Please set TRAMPOLINE_IMAGE if you want to upload the Docker image." + exit 1 +fi + +# Define the default value if it makes sense. +if [[ -z "${TRAMPOLINE_IMAGE_UPLOAD:-}" ]]; then + TRAMPOLINE_IMAGE_UPLOAD="" +fi + +if [[ -z "${TRAMPOLINE_IMAGE:-}" ]]; then + TRAMPOLINE_IMAGE="" +fi + +if [[ -z "${TRAMPOLINE_DOCKERFILE:-}" ]]; then + TRAMPOLINE_DOCKERFILE="" +fi + +if [[ -z "${TRAMPOLINE_BUILD_FILE:-}" ]]; then + TRAMPOLINE_BUILD_FILE="" +fi diff --git a/CHANGELOG.md b/CHANGELOG.md index 86f91791fb3..fc327b2e966 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -4,2903 +4,6 @@ [1]: https://pypi.org/project/bigframes/#history -## [2.47.0](https://github.com/googleapis/google-cloud-python/compare/bigframes-v2.46.0...bigframes-v2.47.0) (2026-08-03) - - -### Features - -* **bigframes:** add ai.embed and ai.similarity to bigquery accessor ([#17927](https://github.com/googleapis/google-cloud-python/issues/17927)) ([2d1372b](https://github.com/googleapis/google-cloud-python/commit/2d1372b25b871aaebba0296f516dbbdd9ef43a37)) - - -### Bug Fixes - -* **bigframes:** fix mypy errors in _magics.py related to get_ipython ([#17979](https://github.com/googleapis/google-cloud-python/issues/17979)) ([24d955e](https://github.com/googleapis/google-cloud-python/commit/24d955e7573acf5e2941f9d10b342b171d9def24)) -* bump brace-expansion from 5.0.6 to 5.0.7 in /packages/bigframes/bigframes/display/table_widget_angular ([#17794](https://github.com/googleapis/google-cloud-python/issues/17794)) ([2df1bb5](https://github.com/googleapis/google-cloud-python/commit/2df1bb5cb53906bfc41de92c10fdb5a8920ce36f)) -* bump fast-uri from 3.1.1 to 3.1.4 in /packages/bigframes/bigframes/display/table_widget_angular ([#17828](https://github.com/googleapis/google-cloud-python/issues/17828)) ([8ce495a](https://github.com/googleapis/google-cloud-python/commit/8ce495ab51d35cc56332f27589ce33b035215abf)) -* bump hono from 4.12.16 to 4.12.31 in /packages/bigframes/bigframes/display/table_widget_angular ([#17829](https://github.com/googleapis/google-cloud-python/issues/17829)) ([6d6fa39](https://github.com/googleapis/google-cloud-python/commit/6d6fa399b1578dd6c0ed7b2284b35f6bb0922da8)) -* bump immutable from 5.1.7 to 5.1.9 in /packages/bigframes/bigframes/display/table_widget_angular ([#17830](https://github.com/googleapis/google-cloud-python/issues/17830)) ([6abb1da](https://github.com/googleapis/google-cloud-python/commit/6abb1da519e7d4acdbdd531982530047fdafe854)) -* bump postcss from 8.5.14 to 8.5.23 in /packages/bigframes/bigframes/display/table_widget_angular ([#17908](https://github.com/googleapis/google-cloud-python/issues/17908)) ([e68eb8f](https://github.com/googleapis/google-cloud-python/commit/e68eb8ff03948b4a694aca465b69543ae0be6c27)) -* bump tar from 7.5.16 to 7.5.20 in /packages/bigframes/bigframes/display/table_widget_angular ([#17793](https://github.com/googleapis/google-cloud-python/issues/17793)) ([3502d41](https://github.com/googleapis/google-cloud-python/commit/3502d4183d9864e4310b3daf063ec90be9969e25)) -* require Protobuf 6.33.5+ ([#17743](https://github.com/googleapis/google-cloud-python/issues/17743)) ([d267342](https://github.com/googleapis/google-cloud-python/commit/d26734293c23f06ccce048f7d9b0fa365e813410)) - -## [2.46.0](https://github.com/googleapis/google-cloud-python/compare/bigframes-v2.45.0...bigframes-v2.46.0) (2026-07-16) - - -### Features - -* **bigframes:** Support groupby.agg/transform with udf transpiler ([#17613](https://github.com/googleapis/google-cloud-python/issues/17613)) ([cae94f9](https://github.com/googleapis/google-cloud-python/commit/cae94f99121d7708671a35acf82616cfe378cccb)) -* **bigframes:** support offset-based column access via iloc ([#17367](https://github.com/googleapis/google-cloud-python/issues/17367)) ([4253fab](https://github.com/googleapis/google-cloud-python/commit/4253fab07ccdb2b94e247f8dade793828754b88b)) - - -### Bug Fixes - -* **bigframes:** Fix sqlglot backend regressions ([#17655](https://github.com/googleapis/google-cloud-python/issues/17655)) ([91f93bc](https://github.com/googleapis/google-cloud-python/commit/91f93bcd7b71b6cea62f506ed684500cec1eb6bb)) -* bump gradio from 6.15.0 to 6.15.1 in /packages/bigframes ([#17712](https://github.com/googleapis/google-cloud-python/issues/17712)) ([a85d59f](https://github.com/googleapis/google-cloud-python/commit/a85d59f39998d94cbac8d5e98547f18b3cc5e5be)) -* bump mistune from 3.2.1 to 3.3.0 in /packages/bigframes ([#17694](https://github.com/googleapis/google-cloud-python/issues/17694)) ([e5f7fef](https://github.com/googleapis/google-cloud-python/commit/e5f7fef31c2bbe5f559f4c79fdaf4ebcf6e1bd3f)) -* bump soupsieve from 2.7 to 2.8.4 in /packages/bigframes ([#17695](https://github.com/googleapis/google-cloud-python/issues/17695)) ([635da34](https://github.com/googleapis/google-cloud-python/commit/635da3453b2ba78b8abea43c554a055257f33aa1)) -* bump transformers from 5.3.0 to 5.5.0 in /packages/bigframes ([#17700](https://github.com/googleapis/google-cloud-python/issues/17700)) ([4b049c4](https://github.com/googleapis/google-cloud-python/commit/4b049c4eb8dc1ec91320b55fe515c339cd448af3)) -* emit bracketed inline array syntax for scalar subquery expressions ([#17716](https://github.com/googleapis/google-cloud-python/issues/17716)) ([ce5fd50](https://github.com/googleapis/google-cloud-python/commit/ce5fd500b68c16f56ea8066d8a6fa4b0b8d92081)) - - -### Documentation - -* make landing page quickstart runnable ([fc423c8](https://github.com/googleapis/google-cloud-python/commit/fc423c809cc80168f45fee795d5db5dc7a571fb1)) -* make landing page quickstart runnable ([#17687](https://github.com/googleapis/google-cloud-python/issues/17687)) ([fc423c8](https://github.com/googleapis/google-cloud-python/commit/fc423c809cc80168f45fee795d5db5dc7a571fb1)) - -## [2.45.0](https://github.com/googleapis/google-cloud-python/compare/bigframes-v2.44.0...bigframes-v2.45.0) (2026-07-08) - - -### Features - -* **bigframes:** add ai.classify, ai.score, ai.if_ to the df bq accessor ([#17569](https://github.com/googleapis/google-cloud-python/issues/17569)) ([4f94be8](https://github.com/googleapis/google-cloud-python/commit/4f94be8f01971380f0fb5b433ab33d7b4cb7176d)) -* **bigframes:** Enable local udf execution ([#17588](https://github.com/googleapis/google-cloud-python/issues/17588)) ([b8ed34c](https://github.com/googleapis/google-cloud-python/commit/b8ed34cc05101c58ef285822d86298cd0f56613c)) -* **bigframes:** UDF transpiler handles some control flow ([#17558](https://github.com/googleapis/google-cloud-python/issues/17558)) ([a8cbde3](https://github.com/googleapis/google-cloud-python/commit/a8cbde39199f838a43ebc8b938ad722595655abd)) -* support gemini-3.x models ([#17615](https://github.com/googleapis/google-cloud-python/issues/17615)) ([5d0efa3](https://github.com/googleapis/google-cloud-python/commit/5d0efa3cb86568a33a5b3097f30733d39fcbef66)) - - -### Bug Fixes - -* bump gdal from 3.13.0 to 3.13.1 in /packages/bigframes ([#17609](https://github.com/googleapis/google-cloud-python/issues/17609)) ([0f4bfed](https://github.com/googleapis/google-cloud-python/commit/0f4bfed4685a362f6487cd4cb02ead3c0dde85c9)) -* bump gradio from 5.39.0 to 6.15.0 in /packages/bigframes ([#17619](https://github.com/googleapis/google-cloud-python/issues/17619)) ([bddda6a](https://github.com/googleapis/google-cloud-python/commit/bddda6a11a9c9bcce2d9e8b665b63d47f49f894f)) -* bump transformers from 4.54.1 to 5.3.0 in /packages/bigframes ([#17610](https://github.com/googleapis/google-cloud-python/issues/17610)) ([10eca3f](https://github.com/googleapis/google-cloud-python/commit/10eca3f4b6578c9451b06cdb2889561563fa8d0d)) - -## [2.44.0](https://github.com/googleapis/google-cloud-python/compare/bigframes-v2.43.0...bigframes-v2.44.0) (2026-06-25) - - -### Features - -* add date functions to `bigframes.bigquery` module ([#17514](https://github.com/googleapis/google-cloud-python/issues/17514)) ([e5d2e35](https://github.com/googleapis/google-cloud-python/commit/e5d2e35db94373ca395976fd755c2bc7e0a060bd)) -* **bigframes:** add AI TVFs to the pandas bq accessor ([#17402](https://github.com/googleapis/google-cloud-python/issues/17402)) ([ee74e31](https://github.com/googleapis/google-cloud-python/commit/ee74e3140a2e11936c36714a27393c3072bed6c7)) -* Experimental transpilation of unannotated python callables ([#17419](https://github.com/googleapis/google-cloud-python/issues/17419)) ([ea9aad9](https://github.com/googleapis/google-cloud-python/commit/ea9aad9a43c306ab109054183b257e6c41a1b2e6)) -* support gemini-3.x models in loader and update default model to gemini-3.5-flash ([#17557](https://github.com/googleapis/google-cloud-python/issues/17557)) ([3619b29](https://github.com/googleapis/google-cloud-python/commit/3619b29e10ae04623d101808cb98be5edbb483b4)) -* support interactive execution of deferred DataFrames in TableWidget ([#17486](https://github.com/googleapis/google-cloud-python/issues/17486)) ([421eebd](https://github.com/googleapis/google-cloud-python/commit/421eebdb31d526a6d5ba27c433cf2803d7619be3)) - - -### Bug Fixes - -* avoid invalid CAST(NULL AS NULL) in SQLGlot compiler ([#17487](https://github.com/googleapis/google-cloud-python/issues/17487)) ([3b79caa](https://github.com/googleapis/google-cloud-python/commit/3b79caa8f40f61ccd7c655542e9f242f34e068e2)) -* **bigframes:** world-readable temp zip in create_cloud_function ([#17522](https://github.com/googleapis/google-cloud-python/issues/17522)) ([e726878](https://github.com/googleapis/google-cloud-python/commit/e7268785c6736c10c1337160b4d8606975062637)) -* bump @angular/common, @angular/forms, @angular/platform-browser and @angular/router in /packages/bigframes/bigframes/display/table_widget_angular ([#17525](https://github.com/googleapis/google-cloud-python/issues/17525)) ([2f893b1](https://github.com/googleapis/google-cloud-python/commit/2f893b1b53e7394655fd204d1f8a138212ad8227)) -* bump langsmith from 0.8.0 to 0.8.18 in /packages/bigframes ([#17518](https://github.com/googleapis/google-cloud-python/issues/17518)) ([f23063f](https://github.com/googleapis/google-cloud-python/commit/f23063f9182cdec868c16afb80304892850fbe88)) -* bump msgpack from 1.1.1 to 1.2.1 in /packages/bigframes ([#17520](https://github.com/googleapis/google-cloud-python/issues/17520)) ([36b5b7e](https://github.com/googleapis/google-cloud-python/commit/36b5b7ebb01030a2d0f10d49fe4827ddc79dde9a)) -* bump undici and @angular/build in /packages/bigframes/bigframes/display/table_widget_angular ([#17519](https://github.com/googleapis/google-cloud-python/issues/17519)) ([6fc45e3](https://github.com/googleapis/google-cloud-python/commit/6fc45e3790c5a248dcec4b74799834c7b9219ef0)) -* handle empty endpoints during cloud function reuse ([#17501](https://github.com/googleapis/google-cloud-python/issues/17501)) ([4f5593a](https://github.com/googleapis/google-cloud-python/commit/4f5593a520b5afdeb02cc28f19a9596dbc35a90f)) - - -### Documentation - -* ensure that PlotAccessor is included in the API reference ([#17513](https://github.com/googleapis/google-cloud-python/issues/17513)) ([6febabf](https://github.com/googleapis/google-cloud-python/commit/6febabf795106a0c336dc905fc23da88d8cc94a0)) - -## [2.43.0](https://github.com/googleapis/google-cloud-python/compare/bigframes-v2.42.0...bigframes-v2.43.0) (2026-06-12) - - -### Documentation - -* add a notebook explaining bqsql magics cell chaining (#17216) ([1a0de4a7701b7fdf4c2593b1960f1194ebc49793](https://github.com/googleapis/google-cloud-python/commit/1a0de4a7701b7fdf4c2593b1960f1194ebc49793)) - - -### Features - -* add `bigframes.bigquery.bit_count` and conversion scalar function (#17433) ([7f29823fadb3cff42dbe666f8c7aa33bab3c7021](https://github.com/googleapis/google-cloud-python/commit/7f29823fadb3cff42dbe666f8c7aa33bab3c7021)) - - -### Bug Fixes - -* preserve aliases on cast columns and fix star selection in sqlglot (#17394) (#17455) ([145034a345eb3e14ea3f23dfcafa3d2409a09067](https://github.com/googleapis/google-cloud-python/commit/145034a345eb3e14ea3f23dfcafa3d2409a09067)) -* bump pyarrow from 15.0.2 to 23.0.1 in /packages/bigframes (#17386) ([f59c2b2aa61316cf04b650933036ef50f6a1f08c](https://github.com/googleapis/google-cloud-python/commit/f59c2b2aa61316cf04b650933036ef50f6a1f08c)) -* improve error message when unescaped `{` are found in SQL cells (#17346) ([3a90cc8e867c8a2d2f8060858fde9eda94f80a54](https://github.com/googleapis/google-cloud-python/commit/3a90cc8e867c8a2d2f8060858fde9eda94f80a54)) - -## [2.42.0](https://github.com/googleapis/google-cloud-python/compare/bigframes-v2.41.0...bigframes-v2.42.0) (2026-06-08) - - -### Features - -* create `Series.bigquery.function_name` accessors for array and AEAD functions (#17279) ([d01a4ba30040cfcb6498d0e9ef3ed3a54d56239d](https://github.com/googleapis/google-cloud-python/commit/d01a4ba30040cfcb6498d0e9ef3ed3a54d56239d)) -* support automatic per-cell execution history filtering and isolated callbacks (#17144) ([7d440111d836b94f0ce22f6b08c7ce0e7bf4a38a](https://github.com/googleapis/google-cloud-python/commit/7d440111d836b94f0ce22f6b08c7ce0e7bf4a38a)) -* Add ai_generate functions to the dataframe bq accessor (#17302) ([6b62cb6fb3de94326b8944ae08a400c12529cad2](https://github.com/googleapis/google-cloud-python/commit/6b62cb6fb3de94326b8944ae08a400c12529cad2)) - - -### Bug Fixes - -* nameless column to_frame bug for pandas 3.0 (#17371) ([b23bfa4ceb819bca8201a7fe8b64a9bed56733f0](https://github.com/googleapis/google-cloud-python/commit/b23bfa4ceb819bca8201a7fe8b64a9bed56733f0)) -* include pyopenssl as a dependency (#17362) ([1f6205ee5a370249ece2c2cc7131a47830ef00ea](https://github.com/googleapis/google-cloud-python/commit/1f6205ee5a370249ece2c2cc7131a47830ef00ea)) -* Fix IsInOp literal bug with sqlglot (#17356) ([a3d93afe74dd2b5ec8a2ae92f91c95962764debe](https://github.com/googleapis/google-cloud-python/commit/a3d93afe74dd2b5ec8a2ae92f91c95962764debe)) - -## [2.41.0](https://github.com/googleapis/google-cloud-python/compare/bigframes-v2.40.0...bigframes-v2.41.0) (2026-05-28) - - -### Documentation - -* modernize multimodal tutorials and migrate legacy blob APIs (#16918) ([05d80c3cccc237480dc5f589b7768b57a147cb0e](https://github.com/googleapis/google-cloud-python/commit/05d80c3cccc237480dc5f589b7768b57a147cb0e)) - - -### Features - -* Defer unnamed @udf deployment until needed (#17217) ([ad3b8fa9693b7d23859c417f2f5954ea946f2bb8](https://github.com/googleapis/google-cloud-python/commit/ad3b8fa9693b7d23859c417f2f5954ea946f2bb8)) -* set up Angular infrastructure for TableWidget (#16934) ([4d20bab8ce15c31e5832789e6a5d306983b8584a](https://github.com/googleapis/google-cloud-python/commit/4d20bab8ce15c31e5832789e6a5d306983b8584a)) -* support pandas inputs in more bigframes.bigquery functions (#17224) ([d4d885547f99c08caabad5e715aecd8c6f1fb4d6](https://github.com/googleapis/google-cloud-python/commit/d4d885547f99c08caabad5e715aecd8c6f1fb4d6)) -* add more scalar array functions to `bigframes.bigquery` (#17213) ([4f8a6c81797204f4334c7251c244bc0b6bd568e2](https://github.com/googleapis/google-cloud-python/commit/4f8a6c81797204f4334c7251c244bc0b6bd568e2)) -* add `bigframes.bigquery.deterministic_decrypt*` and `bigframes.bigquery.deterministic_encrypt` functions (#17212) ([85f36725802f3e7ad16156ca9e957e61d57d3112](https://github.com/googleapis/google-cloud-python/commit/85f36725802f3e7ad16156ca9e957e61d57d3112)) -* add `bigframes.bigquery.aead.*` scalar functions (#17168) ([a7e4d048e254cdb723df0a47ccbd8d09aed00c7a](https://github.com/googleapis/google-cloud-python/commit/a7e4d048e254cdb723df0a47ccbd8d09aed00c7a)) -* complete deprecation and cleanup of multimodal blob APIs (#16618) ([3624f3bb102e7d599097975db5cdaee508c9549a](https://github.com/googleapis/google-cloud-python/commit/3624f3bb102e7d599097975db5cdaee508c9549a)) -* support output_mode for ai.classify (#17097) ([098c35c5a8383d1585848e10806f9914b2ef4f97](https://github.com/googleapis/google-cloud-python/commit/098c35c5a8383d1585848e10806f9914b2ef4f97)) - - -### Bug Fixes - -* cast JSON and nested struct columns to string for anywidget rendering (#17189) ([994a22d64856b436d196743d16c3fd1967b20784](https://github.com/googleapis/google-cloud-python/commit/994a22d64856b436d196743d16c3fd1967b20784)) -* Respect display.progress_bar=None in background threads (#16715) ([07dd3315447d2feb6de8d53e0915798da9c04151](https://github.com/googleapis/google-cloud-python/commit/07dd3315447d2feb6de8d53e0915798da9c04151)) - - -### Dependencies - -* bump mistune from 3.1.3 to 3.2.1 in /packages/bigframes (#17202) ([52f21788f76575036624c9163b63620a1bb92a83](https://github.com/googleapis/google-cloud-python/commit/52f21788f76575036624c9163b63620a1bb92a83)) -* bump langsmith from 0.4.10 to 0.8.0 in /packages/bigframes (#17210) ([9dd0c02c585f7fda34d6e2199ab2bc7c0b5a246a](https://github.com/googleapis/google-cloud-python/commit/9dd0c02c585f7fda34d6e2199ab2bc7c0b5a246a)) -* bump gdal from 3.8.4 to 3.13.0 in /packages/bigframes (#17204) ([900007bab07feb7580cb7e8a36a5d4ee4cce14ab](https://github.com/googleapis/google-cloud-python/commit/900007bab07feb7580cb7e8a36a5d4ee4cce14ab)) - -## [2.40.0](https://github.com/googleapis/google-cloud-python/compare/bigframes-v2.39.0...bigframes-v2.40.0) (2026-05-13) - - -### Documentation - -* Add docs to the to_csv methods of dataframe and series (#16570) ([a8fccefd868e3474d3a2cfbabc03364891e05824](https://github.com/googleapis/google-cloud-python/commit/a8fccefd868e3474d3a2cfbabc03364891e05824)) - - -### Features - -* add more params to ai.classify (#16990) ([e9c52b12c02f8b15e43b62e6f3fb7617ac3bdfd9](https://github.com/googleapis/google-cloud-python/commit/e9c52b12c02f8b15e43b62e6f3fb7617ac3bdfd9)) -* add support for `hparam_range` and `hparam_candidates` to `bigframes.bigquery.create_model` (#16640) ([ca47835ce0e381c0833545ca1cf7734c3c34ceb5](https://github.com/googleapis/google-cloud-python/commit/ca47835ce0e381c0833545ca1cf7734c3c34ceb5)) -* update ai.score to match its SQL version (#16919) ([9f42fe1436df61ca0abad77bb4b51ed983a85a48](https://github.com/googleapis/google-cloud-python/commit/9f42fe1436df61ca0abad77bb4b51ed983a85a48)) -* update ai.if_() params to match the SQL version (#16857) ([f3cb4ad04a15a58a931d4feb43b172805209cf58](https://github.com/googleapis/google-cloud-python/commit/f3cb4ad04a15a58a931d4feb43b172805209cf58)) -* Support unstable sort_values, sort_index (#16665) ([bbdeb70fff766dc51bcac32b5312c13ce16764d4](https://github.com/googleapis/google-cloud-python/commit/bbdeb70fff766dc51bcac32b5312c13ce16764d4)) -* Support Expression objects in create_model options (#16606) ([cf12ffd858bdba0a95dba8fd591ed9adcf8c0e8a](https://github.com/googleapis/google-cloud-python/commit/cf12ffd858bdba0a95dba8fd591ed9adcf8c0e8a)) -* implement ai.similarity (#16771) ([d4afa2c835d53983ecd22e2f9835107791cde65f](https://github.com/googleapis/google-cloud-python/commit/d4afa2c835d53983ecd22e2f9835107791cde65f)) -* implement ai.embed (#16759) ([fcb4579b9e273c3ad43ed150f4ef0fbb7daeef2c](https://github.com/googleapis/google-cloud-python/commit/fcb4579b9e273c3ad43ed150f4ef0fbb7daeef2c)) -* Add bigframes.execution_history API to track BigQuery jobs (#16588) ([fa20a740b15accf2b1ae18a9ac20b75f006dbcad](https://github.com/googleapis/google-cloud-python/commit/fa20a740b15accf2b1ae18a9ac20b75f006dbcad)) -* Support loading avro, orc data (#16555) ([6d46cba3777c1b2adf6f1f86f6d3db3ea30c55d2](https://github.com/googleapis/google-cloud-python/commit/6d46cba3777c1b2adf6f1f86f6d3db3ea30c55d2)) -* Add numpy ufunc support to col expressions (#16554) ([2f792abd5d48ec680305e1e4ec9136360e16c9a5](https://github.com/googleapis/google-cloud-python/commit/2f792abd5d48ec680305e1e4ec9136360e16c9a5)) - - -### Bug Fixes - -* avoid `copy` argument warning in `to_pandas` (#16917) ([fe5245b8f20dd94231e72e2572609e029ee137c7](https://github.com/googleapis/google-cloud-python/commit/fe5245b8f20dd94231e72e2572609e029ee137c7)) -* BigFrames respects bq default region (#16933) ([ef9945a5d6296e6bbf00b6ef980462f5a0b91b20](https://github.com/googleapis/google-cloud-python/commit/ef9945a5d6296e6bbf00b6ef980462f5a0b91b20)) -* Fix bugs compiling ambiguous ids and in subqueries (#16617) ([479e44ddb8ba7515797f062064c4ebf2db5d09f2](https://github.com/googleapis/google-cloud-python/commit/479e44ddb8ba7515797f062064c4ebf2db5d09f2)) -* avoid views when querying BigLake tables from SQL cells (#16562) ([fdd3e0de66377d75ec235e4fc071e4ecc33a35c7](https://github.com/googleapis/google-cloud-python/commit/fdd3e0de66377d75ec235e4fc071e4ecc33a35c7)) - -## [2.39.0](https://github.com/googleapis/python-bigquery-dataframes/compare/v2.38.0...v2.39.0) (2026-03-31) - - -### Documentation - -* Rename Blob column references to ObjectRef column (#2535) ([44e0ffd947e9db66ab612f92de6e31f1085e7968](https://github.com/googleapis/python-bigquery-dataframes/commit/44e0ffd947e9db66ab612f92de6e31f1085e7968)) -* gemini retouch of the index page for seo (#2514) ([2e5311e2242b039da4c8e37b7b48942fa8ed34c2](https://github.com/googleapis/python-bigquery-dataframes/commit/2e5311e2242b039da4c8e37b7b48942fa8ed34c2)) - - -### Features - -* expose DataFrame.bigquery in both pandas and bigframes DataFrames (#2533) ([69fe317612a69aa92f06f0c418c67aa1f9488bd2](https://github.com/googleapis/python-bigquery-dataframes/commit/69fe317612a69aa92f06f0c418c67aa1f9488bd2)) -* support full round-trip persistence for multimodal reference cols (#2511) ([494a0a113b1ba6dcdc9f9b85a4f750d093f5652f](https://github.com/googleapis/python-bigquery-dataframes/commit/494a0a113b1ba6dcdc9f9b85a4f750d093f5652f)) -* add `df.bigquery.ai.forecast` method to pandas dataframe accessor (#2518) ([1126cec9cdfcc1ec1062c60e5affbe1b60223767](https://github.com/googleapis/python-bigquery-dataframes/commit/1126cec9cdfcc1ec1062c60e5affbe1b60223767)) - - -### Bug Fixes - -* handle aggregate operations on empty selections (#2510) ([34fb5daa93726d0d3ff364912a3c1de0fc535fb2](https://github.com/googleapis/python-bigquery-dataframes/commit/34fb5daa93726d0d3ff364912a3c1de0fc535fb2)) -* Localize BigQuery log suppression for gbq.py (#2541) ([af49ca29399aa2c63753d9045fd382e30334d134](https://github.com/googleapis/python-bigquery-dataframes/commit/af49ca29399aa2c63753d9045fd382e30334d134)) -* to_gbq may swap data columns when replace table (#2532) ([17ecc65e1c0397ef349fca4afcf5a77af72aa798](https://github.com/googleapis/python-bigquery-dataframes/commit/17ecc65e1c0397ef349fca4afcf5a77af72aa798)) -* Respect remote function config changes even if logic unchanged (#2512) ([b9524284ad3b457b15598f546bac04c76b3e27b8](https://github.com/googleapis/python-bigquery-dataframes/commit/b9524284ad3b457b15598f546bac04c76b3e27b8)) -* support melting empty DataFrames without crashing (#2509) ([e8c46032154e186042314d97aa813301413d8a13](https://github.com/googleapis/python-bigquery-dataframes/commit/e8c46032154e186042314d97aa813301413d8a13)) - -## [2.38.0](https://github.com/googleapis/python-bigquery-dataframes/compare/v2.37.0...v2.38.0) (2026-03-16) - - -### Documentation - -* add notebooks to user guide page (#2505) ([5cf37888bc0b4b1b0993dadd1e0fe5ee08341ef4](https://github.com/googleapis/python-bigquery-dataframes/commit/5cf37888bc0b4b1b0993dadd1e0fe5ee08341ef4)) -* Fix typo in ExperimentOptions class docstring (#2498) ([077cb2ebe515fc5e07bcbb5dc663edd28d3eaf00](https://github.com/googleapis/python-bigquery-dataframes/commit/077cb2ebe515fc5e07bcbb5dc663edd28d3eaf00)) - - -### Features - -* add `df.bigquery` pandas accessor (#2513) ([91b6c245521218bb78b543885e1b9424278ce2ab](https://github.com/googleapis/python-bigquery-dataframes/commit/91b6c245521218bb78b543885e1b9424278ce2ab)) -* use EUC for AI IF, CLASSIFY, and SCORE when connection is not provided (#2507) ([fe94910abff28e244dd79e1540a6c2184a12eb44](https://github.com/googleapis/python-bigquery-dataframes/commit/fe94910abff28e244dd79e1540a6c2184a12eb44)) -* Add `bigframes.bigquery.rand()` function (#2501) ([5c43efb745118f506ecc30196da68e9d6f4346dc](https://github.com/googleapis/python-bigquery-dataframes/commit/5c43efb745118f506ecc30196da68e9d6f4346dc)) -* add bigquery.ml.get_insights function (#2493) ([d29a60953ac989bb2c95e6eec3010620ac776a3c](https://github.com/googleapis/python-bigquery-dataframes/commit/d29a60953ac989bb2c95e6eec3010620ac776a3c)) -* Add str, dt accessors to pd.col Expression objects (#2488) ([ce5de57019449ca77d308946df72f04289343b51](https://github.com/googleapis/python-bigquery-dataframes/commit/ce5de57019449ca77d308946df72f04289343b51)) - - -### Bug Fixes - -* handle unsupported types and empty results in describe (#2506) ([2326ad6aec15c20a66756eff093b50be484b3ba8](https://github.com/googleapis/python-bigquery-dataframes/commit/2326ad6aec15c20a66756eff093b50be484b3ba8)) -* no longer automatically use anywidget in the `%%bqsql` magics (#2504) ([43353e2bc9ffbc38b7383c24ecaac80d3b8bab32](https://github.com/googleapis/python-bigquery-dataframes/commit/43353e2bc9ffbc38b7383c24ecaac80d3b8bab32)) - -## [2.37.0](https://github.com/googleapis/python-bigquery-dataframes/compare/v2.36.0...v2.37.0) (2026-03-03) - - -### Documentation - -* Fix recall_score doc example (#2477) ([a6f499c1e225a962b53621158f9d4a19ca220ccd](https://github.com/googleapis/python-bigquery-dataframes/commit/a6f499c1e225a962b53621158f9d4a19ca220ccd)) -* add code sample and docstring for bpd.options.experiments.sql_compiler (#2474) ([867951bcabcff12e2fce88143b45d929d3237088](https://github.com/googleapis/python-bigquery-dataframes/commit/867951bcabcff12e2fce88143b45d929d3237088)) -* use direct API for image (#2465) ([8a1a82f7a0fd224f2b075c68ab116d1f580d1d82](https://github.com/googleapis/python-bigquery-dataframes/commit/8a1a82f7a0fd224f2b075c68ab116d1f580d1d82)) -* add bigframes default connection warning (#2471) ([f1bbba23667f01d3b8e7c51b18fe64641a4b135f](https://github.com/googleapis/python-bigquery-dataframes/commit/f1bbba23667f01d3b8e7c51b18fe64641a4b135f)) -* Move readme content to new User Guide section (#2464) ([61a948451baeb1caa323e721ad88b31c7cd0b3cb](https://github.com/googleapis/python-bigquery-dataframes/commit/61a948451baeb1caa323e721ad88b31c7cd0b3cb)) -* Skip inherited methods, use autosummary only for big classes (#2470) ([a9512498ef39b9d5260cad2ca0513c701a6d3592](https://github.com/googleapis/python-bigquery-dataframes/commit/a9512498ef39b9d5260cad2ca0513c701a6d3592)) -* Add code examples to configuration docstrings (#2352) ([3c21993e6fca474c32f3c2371c41ef2be146267e](https://github.com/googleapis/python-bigquery-dataframes/commit/3c21993e6fca474c32f3c2371c41ef2be146267e)) - - -### Features - -* Add cloud_function_cpus option to remote_function (#2475) ([4caf74ccaeb9608d91da864bb80eddf1148a1502](https://github.com/googleapis/python-bigquery-dataframes/commit/4caf74ccaeb9608d91da864bb80eddf1148a1502)) -* Support pd.col simple aggregates (#2480) ([cb00daabce49f067be8e16627166dda00d5d8134](https://github.com/googleapis/python-bigquery-dataframes/commit/cb00daabce49f067be8e16627166dda00d5d8134)) -* add display.render_mode to control DataFrame/Series visualization (#2413) ([7813eaa6fa2ae42943b90583e600c95beaf5d75e](https://github.com/googleapis/python-bigquery-dataframes/commit/7813eaa6fa2ae42943b90583e600c95beaf5d75e)) -* add support for Python 3.14 (#2232) ([c25a6d0151380dde74368a35e13deb7a930b494f](https://github.com/googleapis/python-bigquery-dataframes/commit/c25a6d0151380dde74368a35e13deb7a930b494f)) -* Support pd.col expressions with .loc and getitem (#2473) ([ae5c8b322765aef51eed016bfacaff5a7a917a7b](https://github.com/googleapis/python-bigquery-dataframes/commit/ae5c8b322765aef51eed016bfacaff5a7a917a7b)) -* add dt.tz_localize() (#2469) ([f70f93a1227add1627d522d7e55a37f42fc3549e](https://github.com/googleapis/python-bigquery-dataframes/commit/f70f93a1227add1627d522d7e55a37f42fc3549e)) -* Update bigquery.ai.generate_table output_schema to allow Mapping type (#2463) ([f7fd1895e64a133fe63eddeb90f57a42a35c29b2](https://github.com/googleapis/python-bigquery-dataframes/commit/f7fd1895e64a133fe63eddeb90f57a42a35c29b2)) - - -### Bug Fixes - -* upload local data through write API if nested JSONs detected (#2478) ([01dc5a34e09171351575d5cbdc9f301e505e1567](https://github.com/googleapis/python-bigquery-dataframes/commit/01dc5a34e09171351575d5cbdc9f301e505e1567)) -* allow IsInOp with same dtypes regardless nullable (#2466) ([1d81b414acbc964502ca624eae72cdb8c14e1576](https://github.com/googleapis/python-bigquery-dataframes/commit/1d81b414acbc964502ca624eae72cdb8c14e1576)) - -## [2.36.0](https://github.com/googleapis/python-bigquery-dataframes/compare/v2.35.0...v2.36.0) (2026-02-17) - - -### Documentation - -* update multimodal dataframe notebook to use public APIs (#2456) ([342fa723c4631d371364a87ae0ddd6fa03360a4b](https://github.com/googleapis/python-bigquery-dataframes/commit/342fa723c4631d371364a87ae0ddd6fa03360a4b)) -* use direct API for pdf chunk and pdf extract (#2452) ([543ce52c18269eab2a89886f226d1478dbabf9ba](https://github.com/googleapis/python-bigquery-dataframes/commit/543ce52c18269eab2a89886f226d1478dbabf9ba)) -* fix generate_text and generate_table input docs (#2455) ([078bd32ebd28af0d2cfba6bb874ba79e904183e2](https://github.com/googleapis/python-bigquery-dataframes/commit/078bd32ebd28af0d2cfba6bb874ba79e904183e2)) -* Update multimodal notebook to use public runtime helpers (#2451) ([e36dd8b492fd7ab433fa4cac732b31774c1e428b](https://github.com/googleapis/python-bigquery-dataframes/commit/e36dd8b492fd7ab433fa4cac732b31774c1e428b)) -* use direct API for audio transcription (#2447) ([59cbc5db66fd178ecce03bf4b8b4a504d7ef3e9f](https://github.com/googleapis/python-bigquery-dataframes/commit/59cbc5db66fd178ecce03bf4b8b4a504d7ef3e9f)) -* Add EXIF metadata extraction example to multimodal notebook (#2429) ([84c6f883aef8048e7013a8b3c03a1bde47e94eea](https://github.com/googleapis/python-bigquery-dataframes/commit/84c6f883aef8048e7013a8b3c03a1bde47e94eea)) - - -### Features - -* Initial support for biglake iceberg tables (#2409) ([ae35a9890a2f9903b12e431488362c091118bbdd](https://github.com/googleapis/python-bigquery-dataframes/commit/ae35a9890a2f9903b12e431488362c091118bbdd)) -* add bigquery.ai.generate_table function (#2453) ([b925aa243dad0e42ad126c9397f42be0aad7152d](https://github.com/googleapis/python-bigquery-dataframes/commit/b925aa243dad0e42ad126c9397f42be0aad7152d)) - -## [2.35.0](https://github.com/googleapis/python-bigquery-dataframes/compare/v2.34.0...v2.35.0) (2026-02-07) - - -### Documentation - -* fix cast method shown on public docs (#2436) ([ad0f33c65ee01409826c381ae0f70aad65bb6a27](https://github.com/googleapis/python-bigquery-dataframes/commit/ad0f33c65ee01409826c381ae0f70aad65bb6a27)) - - -### Features - -* remove redundant "started." messages from progress output (#2440) ([2017cc2f27f0a432af46f60b3286b231caa4a98b](https://github.com/googleapis/python-bigquery-dataframes/commit/2017cc2f27f0a432af46f60b3286b231caa4a98b)) -* Add bigframes.pandas.col with basic operators (#2405) ([12741677c0391efb5d05281fc756445ccbb1387e](https://github.com/googleapis/python-bigquery-dataframes/commit/12741677c0391efb5d05281fc756445ccbb1387e)) -* Disable progress bars in Anywidget mode (#2444) ([4e2689a1c975c4cabaf36b7d0817dcbedc926853](https://github.com/googleapis/python-bigquery-dataframes/commit/4e2689a1c975c4cabaf36b7d0817dcbedc926853)) -* Disable progress bars in Anywidget mode to reduce notebook clutter (#2437) ([853240daf45301ad534c635c8955cb6ce91d23c2](https://github.com/googleapis/python-bigquery-dataframes/commit/853240daf45301ad534c635c8955cb6ce91d23c2)) -* add bigquery.ai.generate_text function (#2433) ([5bd0029a99e7653843de4ac7d57370c9dffeed4d](https://github.com/googleapis/python-bigquery-dataframes/commit/5bd0029a99e7653843de4ac7d57370c9dffeed4d)) -* Add a bigframes cell magic for ipython (#2395) ([e6de52ded6c5091275a936dec36f01a6cf701233](https://github.com/googleapis/python-bigquery-dataframes/commit/e6de52ded6c5091275a936dec36f01a6cf701233)) -* add `bigframes.bigquery.ai.generate_embedding` (#2343) ([e91536c8a5b2d8d896767510ced80c6fd2a68a97](https://github.com/googleapis/python-bigquery-dataframes/commit/e91536c8a5b2d8d896767510ced80c6fd2a68a97)) -* add bigframe.bigquery.load_data function (#2426) ([4b0f13b2fe10fa5b07d3ca3b7cb1ae1cb95030c7](https://github.com/googleapis/python-bigquery-dataframes/commit/4b0f13b2fe10fa5b07d3ca3b7cb1ae1cb95030c7)) - - -### Bug Fixes - -* suppress JSONDtypeWarning in Anywidget mode and clean up progress output (#2441) ([e0d185ad2c0245b17eac315f71152a46c6da41bb](https://github.com/googleapis/python-bigquery-dataframes/commit/e0d185ad2c0245b17eac315f71152a46c6da41bb)) -* exlcude gcsfs 2026.2.0 (#2445) ([311de31e79227408515f087dafbab7edc54ddf1b](https://github.com/googleapis/python-bigquery-dataframes/commit/311de31e79227408515f087dafbab7edc54ddf1b)) -* always display the results in the `%%bqsql` cell magics output (#2439) ([2d973b54550f30429dbd10894f78db7bb0c57345](https://github.com/googleapis/python-bigquery-dataframes/commit/2d973b54550f30429dbd10894f78db7bb0c57345)) - -## [2.34.0](https://github.com/googleapis/python-bigquery-dataframes/compare/v2.33.0...v2.34.0) (2026-02-02) - - -### Features - -* add `bigframes.pandas.options.experiments.sql_compiler` for switching the backend compiler (#2417) ([7eba6ee03f07938315d99e2aeaf72368c02074cf](https://github.com/googleapis/python-bigquery-dataframes/commit/7eba6ee03f07938315d99e2aeaf72368c02074cf)) -* add bigquery.ml.generate_embedding function (#2422) ([35f3f5e6f8c64b47e6e7214034f96f047785e647](https://github.com/googleapis/python-bigquery-dataframes/commit/35f3f5e6f8c64b47e6e7214034f96f047785e647)) -* add bigquery.create_external_table method (#2415) ([76db2956e505aec4f1055118ac7ca523facc10ff](https://github.com/googleapis/python-bigquery-dataframes/commit/76db2956e505aec4f1055118ac7ca523facc10ff)) -* add deprecation warnings for .blob accessor and read_gbq_object_table (#2408) ([7261a4ea5cdab6b30f5bc333501648c60e70be59](https://github.com/googleapis/python-bigquery-dataframes/commit/7261a4ea5cdab6b30f5bc333501648c60e70be59)) -* add bigquery.ml.generate_text function (#2403) ([5ac681028624de15e31f0c2ae360b47b2dcf1e8d](https://github.com/googleapis/python-bigquery-dataframes/commit/5ac681028624de15e31f0c2ae360b47b2dcf1e8d)) - - -### Bug Fixes - -* broken job url (#2411) ([fcb5bc1761c656e1aec61dbcf96a36d436833b7a](https://github.com/googleapis/python-bigquery-dataframes/commit/fcb5bc1761c656e1aec61dbcf96a36d436833b7a)) - -## [2.33.0](https://github.com/googleapis/python-bigquery-dataframes/compare/v2.32.0...v2.33.0) (2026-01-22) - - -### Features - -* add bigquery.ml.transform function (#2394) ([1f9ee373c1f1d0cd08b80169c3063b862ea46465](https://github.com/googleapis/python-bigquery-dataframes/commit/1f9ee373c1f1d0cd08b80169c3063b862ea46465)) -* Add BigQuery ObjectRef functions to `bigframes.bigquery.obj` (#2380) ([9c3bbc36983dffb265454f27b37450df8c5fbc71](https://github.com/googleapis/python-bigquery-dataframes/commit/9c3bbc36983dffb265454f27b37450df8c5fbc71)) -* Stabilize interactive table height to prevent notebook layout shifts (#2378) ([a634e976c0f44087ca2a65f68cf2775ae6f04024](https://github.com/googleapis/python-bigquery-dataframes/commit/a634e976c0f44087ca2a65f68cf2775ae6f04024)) -* Add max_columns control for anywidget mode (#2374) ([34b5975f6911c5aa5ffc64a2fe6967a9f3d86f78](https://github.com/googleapis/python-bigquery-dataframes/commit/34b5975f6911c5aa5ffc64a2fe6967a9f3d86f78)) -* Add dark mode to anywidget mode (#2365) ([2763b41d4b86939e389f76789f5b2acd44f18169](https://github.com/googleapis/python-bigquery-dataframes/commit/2763b41d4b86939e389f76789f5b2acd44f18169)) -* Configure Biome for Consistent Code Style (#2364) ([81e27b3d81da9b1684eae0b7f0b9abfd7badcc4f](https://github.com/googleapis/python-bigquery-dataframes/commit/81e27b3d81da9b1684eae0b7f0b9abfd7badcc4f)) - - -### Bug Fixes - -* Throw if write api commit op has stream_errors (#2385) ([7abfef0598d476ef233364a01f72d73291983c30](https://github.com/googleapis/python-bigquery-dataframes/commit/7abfef0598d476ef233364a01f72d73291983c30)) -* implement retry logic for cloud function endpoint fetching (#2369) ([0f593c27bfee89fe1bdfc880504f9ab0ac28a24e](https://github.com/googleapis/python-bigquery-dataframes/commit/0f593c27bfee89fe1bdfc880504f9ab0ac28a24e)) - -## [2.32.0](https://github.com/googleapis/google-cloud-python/compare/bigframes-v2.31.0...bigframes-v2.32.0) (2026-01-05) - - -### Documentation - -* generate sitemap.xml for better search indexing (#2351) ([7d2990f1c48c6d74e2af6bee3af87f90189a3d9b](https://github.com/googleapis/google-cloud-python/commit/7d2990f1c48c6d74e2af6bee3af87f90189a3d9b)) -* update supported pandas APIs documentation links (#2330) ([ea71936ce240b2becf21b552d4e41e8ef4418e2d](https://github.com/googleapis/google-cloud-python/commit/ea71936ce240b2becf21b552d4e41e8ef4418e2d)) -* Add time series analysis notebook (#2328) ([369f1c0aff29d197b577ec79e401b107985fe969](https://github.com/googleapis/google-cloud-python/commit/369f1c0aff29d197b577ec79e401b107985fe969)) - - -### Features - -* Enable multi-column sorting in anywidget mode (#2360) ([1feb956e4762e30276e5b380c0633e6ed7881357](https://github.com/googleapis/google-cloud-python/commit/1feb956e4762e30276e5b380c0633e6ed7881357)) -* display series in anywidget mode (#2346) ([7395d418550058c516ad878e13567256f4300a37](https://github.com/googleapis/google-cloud-python/commit/7395d418550058c516ad878e13567256f4300a37)) -* Refactor TableWidget and to_pandas_batches (#2250) ([b8f09015a7c8e6987dc124e6df925d4f6951b1da](https://github.com/googleapis/google-cloud-python/commit/b8f09015a7c8e6987dc124e6df925d4f6951b1da)) -* Auto-plan complex reduction expressions (#2298) ([4d5de14ccdd05b1ac8f50c3fe71c35ab9e5150c1](https://github.com/googleapis/google-cloud-python/commit/4d5de14ccdd05b1ac8f50c3fe71c35ab9e5150c1)) -* Display custom single index column in anywidget mode (#2311) ([f27196260743883ed8131d5fd33a335e311177e4](https://github.com/googleapis/google-cloud-python/commit/f27196260743883ed8131d5fd33a335e311177e4)) -* add fit_predict method to ml unsupervised models (#2320) ([59df7f70a12ef702224ad61e597bd775208dac45](https://github.com/googleapis/google-cloud-python/commit/59df7f70a12ef702224ad61e597bd775208dac45)) - - -### Bug Fixes - -* vendor sqlglot bigquery dialect and remove package dependency (#2354) ([b321d72d5eb005b6e9295541a002540f05f72209](https://github.com/googleapis/google-cloud-python/commit/b321d72d5eb005b6e9295541a002540f05f72209)) -* bigframes.ml fit with eval data in partial mode avoids join on null index (#2355) ([7171d21b8c8d5a2d61081f41fa1109b5c9c4bc5f](https://github.com/googleapis/google-cloud-python/commit/7171d21b8c8d5a2d61081f41fa1109b5c9c4bc5f)) -* Improve strictness of nan vs None usage (#2326) ([481d938fb0b840e17047bc4b57e61af15b976e54](https://github.com/googleapis/google-cloud-python/commit/481d938fb0b840e17047bc4b57e61af15b976e54)) -* Correct DataFrame widget rendering in Colab (#2319) ([7f1d3df3839ec58f52e48df088057fc0df967da9](https://github.com/googleapis/google-cloud-python/commit/7f1d3df3839ec58f52e48df088057fc0df967da9)) -* Fix pd.timedelta handling in polars comipler with polars 1.36 (#2325) ([252644826289d9db7a8548884de880b3a4fccafd](https://github.com/googleapis/google-cloud-python/commit/252644826289d9db7a8548884de880b3a4fccafd)) - -## [2.31.0](https://github.com/googleapis/google-cloud-python/compare/bigframes-v2.30.0...bigframes-v2.31.0) (2025-12-10) - - -### Features - -* add `bigframes.bigquery.ml` methods (#2300) ([719b278c844ca80c1bec741873b30a9ee4fd6c56](https://github.com/googleapis/google-cloud-python/commit/719b278c844ca80c1bec741873b30a9ee4fd6c56)) -* add 'weekday' property to DatatimeMethod (#2304) ([fafd7c732d434eca3f8b5d849a87149f106e3d5d](https://github.com/googleapis/google-cloud-python/commit/fafd7c732d434eca3f8b5d849a87149f106e3d5d)) - - -### Bug Fixes - -* cache DataFrames to temp tables in bigframes.bigquery.ml methods to avoid time travel (#2318) ([d99383195ac3f1683842cfe472cca5a914b04d8e](https://github.com/googleapis/google-cloud-python/commit/d99383195ac3f1683842cfe472cca5a914b04d8e)) - -## [2.30.0](https://github.com/googleapis/google-cloud-python/compare/bigframes-v2.29.0...bigframes-v2.30.0) (2025-12-03) - - -### Documentation - -* Add Google Analytics configuration to conf.py (#2301) ([0b266da10f4d3d0ef9b4dd71ddadebfc7d5064ca](https://github.com/googleapis/google-cloud-python/commit/0b266da10f4d3d0ef9b4dd71ddadebfc7d5064ca)) -* fix LogisticRegression docs rendering (#2295) ([32e531343c764156b45c6fb9de49793d26c19f02](https://github.com/googleapis/google-cloud-python/commit/32e531343c764156b45c6fb9de49793d26c19f02)) -* update API reference to new `dataframes.bigquery.dev` location (#2293) ([da064397acd2358c16fdd9659edf23afde5c882a](https://github.com/googleapis/google-cloud-python/commit/da064397acd2358c16fdd9659edf23afde5c882a)) -* use autosummary to split documentation pages (#2251) ([f7fd2d20896fe3e0e210c3833b6a4c3913270ebc](https://github.com/googleapis/google-cloud-python/commit/f7fd2d20896fe3e0e210c3833b6a4c3913270ebc)) -* update docs and tests for Gemini 2.5 models (#2279) ([08c0c0c8fe8f806f6224dc403a3f1d4db708573a](https://github.com/googleapis/google-cloud-python/commit/08c0c0c8fe8f806f6224dc403a3f1d4db708573a)) - - -### Features - -* Allow drop_duplicates over unordered dataframe (#2303) ([52665fa57ef13c58254bfc8736afcc521f7f0f11](https://github.com/googleapis/google-cloud-python/commit/52665fa57ef13c58254bfc8736afcc521f7f0f11)) -* Add agg/aggregate methods to windows (#2288) ([c4cb39dcbd388356f5f1c48ff28b19b79b996485](https://github.com/googleapis/google-cloud-python/commit/c4cb39dcbd388356f5f1c48ff28b19b79b996485)) -* Implement single-column sorting for interactive table widget (#2255) ([d1ecc61bf448651a0cca0fc760673da54f5c2183](https://github.com/googleapis/google-cloud-python/commit/d1ecc61bf448651a0cca0fc760673da54f5c2183)) -* add bigquery.json_keys (#2286) ([b487cf1f6ecacb1ee3b35ffdd934221516bbd558](https://github.com/googleapis/google-cloud-python/commit/b487cf1f6ecacb1ee3b35ffdd934221516bbd558)) -* use end user credentials for `bigframes.bigquery.ai` functions when `connection_id` is not present (#2272) ([7c062a68c6a3c9737865985b4f1fd80117490c73](https://github.com/googleapis/google-cloud-python/commit/7c062a68c6a3c9737865985b4f1fd80117490c73)) -* pivot_table supports fill_value arg (#2257) ([8f490e68a9a2584236486060ad3b55923781d975](https://github.com/googleapis/google-cloud-python/commit/8f490e68a9a2584236486060ad3b55923781d975)) -* Support mixed scalar-analytic expressions (#2239) ([20ab469d29767a2f04fe02aa66797893ecd1c539](https://github.com/googleapis/google-cloud-python/commit/20ab469d29767a2f04fe02aa66797893ecd1c539)) -* Support builtins funcs for df.agg (#2256) ([956a5b00dff55b73e3cbebb4e6e81672680f1f63](https://github.com/googleapis/google-cloud-python/commit/956a5b00dff55b73e3cbebb4e6e81672680f1f63)) -* Preserve source names better for more readable sql (#2243) ([64995d659837a8576b2ee9335921904e577c7014](https://github.com/googleapis/google-cloud-python/commit/64995d659837a8576b2ee9335921904e577c7014)) -* Add bigframes.pandas.crosstab (#2231) ([c62e5535ed4c19b6d65f9a46cb1531e8099621b2](https://github.com/googleapis/google-cloud-python/commit/c62e5535ed4c19b6d65f9a46cb1531e8099621b2)) - - -### Bug Fixes - -* Update max_instances default to reflect actual value (#2302) ([4489687eafc9a1ea1b985600010296a4245cef94](https://github.com/googleapis/google-cloud-python/commit/4489687eafc9a1ea1b985600010296a4245cef94)) -* Fix issue with stream upload batch size upload limit (#2290) ([6cdf64b0674d0e673f86362032d549316850837b](https://github.com/googleapis/google-cloud-python/commit/6cdf64b0674d0e673f86362032d549316850837b)) -* Pass credentials properly for read api instantiation (#2280) ([3e3fe259567d249d91f90786a577b05577e2b9fd](https://github.com/googleapis/google-cloud-python/commit/3e3fe259567d249d91f90786a577b05577e2b9fd)) -* Improve Anywidget pagination and display for unknown row counts (#2258) ([508deae5869e06cdad7bb94537c9c58d8f083d86](https://github.com/googleapis/google-cloud-python/commit/508deae5869e06cdad7bb94537c9c58d8f083d86)) -* calling info() on empty dataframes no longer leads to errors (#2267) ([95a83f7774766cd19cb583dfaa3417882b5c9b1e](https://github.com/googleapis/google-cloud-python/commit/95a83f7774766cd19cb583dfaa3417882b5c9b1e)) -* do not warn with DefaultIndexWarning in partial ordering mode (#2230) ([cc2dbae684103a21fe8838468f7eb8267188780d](https://github.com/googleapis/google-cloud-python/commit/cc2dbae684103a21fe8838468f7eb8267188780d)) - -## [2.29.0](https://github.com/googleapis/python-bigquery-dataframes/compare/v2.28.0...v2.29.0) (2025-11-10) - - -### Features - -* Add bigframes.bigquery.st_regionstats to join raster data from Earth Engine ([#2228](https://github.com/googleapis/python-bigquery-dataframes/issues/2228)) ([10ec52f](https://github.com/googleapis/python-bigquery-dataframes/commit/10ec52f30a0a9c61b9eda9cf4f9bd6aa0cd95db5)) -* Add DataFrame.resample and Series.resample ([#2213](https://github.com/googleapis/python-bigquery-dataframes/issues/2213)) ([c9ca02c](https://github.com/googleapis/python-bigquery-dataframes/commit/c9ca02c5194c8b8e9b940eddd2224efd2ff0d5d9)) -* SQL Cell no longer escapes formatted string values ([#2245](https://github.com/googleapis/python-bigquery-dataframes/issues/2245)) ([d2d38f9](https://github.com/googleapis/python-bigquery-dataframes/commit/d2d38f94ed8333eae6f9cff3833177756eefe85a)) -* Support left_index and right_index for merge ([#2220](https://github.com/googleapis/python-bigquery-dataframes/issues/2220)) ([da9ba26](https://github.com/googleapis/python-bigquery-dataframes/commit/da9ba267812c01ffa6fa0b09943d7a4c63b8f187)) - - -### Bug Fixes - -* Correctly iterate over null struct values in ManagedArrowTable ([#2209](https://github.com/googleapis/python-bigquery-dataframes/issues/2209)) ([12e04d5](https://github.com/googleapis/python-bigquery-dataframes/commit/12e04d55f0d6aef1297b7ca773935aecf3313ee7)) -* Simplify UnsupportedTypeError message ([#2212](https://github.com/googleapis/python-bigquery-dataframes/issues/2212)) ([6c9a18d](https://github.com/googleapis/python-bigquery-dataframes/commit/6c9a18d7e67841c6fe6c1c6f34f80b950815141f)) -* Support results with STRUCT and ARRAY columns containing JSON subfields in `to_pandas_batches()` ([#2216](https://github.com/googleapis/python-bigquery-dataframes/issues/2216)) ([3d8b17f](https://github.com/googleapis/python-bigquery-dataframes/commit/3d8b17fa5eb9bbfc9e151031141a419f2dc3acb4)) - - -### Documentation - -* Switch API reference docs to pydata theme ([#2237](https://github.com/googleapis/python-bigquery-dataframes/issues/2237)) ([9b86dcf](https://github.com/googleapis/python-bigquery-dataframes/commit/9b86dcf87929648bf5ab565dfd46a23b639f01ac)) -* Update notebook for JSON subfields support in to_pandas_batches() ([#2138](https://github.com/googleapis/python-bigquery-dataframes/issues/2138)) ([5663d2a](https://github.com/googleapis/python-bigquery-dataframes/commit/5663d2a18064589596558af109e915f87d426eb0)) - -## [2.28.0](https://github.com/googleapis/python-bigquery-dataframes/compare/v2.27.0...v2.28.0) (2025-11-03) - - -### Features - -* Add bigframes.bigquery.st_simplify ([#2210](https://github.com/googleapis/python-bigquery-dataframes/issues/2210)) ([ecee2bc](https://github.com/googleapis/python-bigquery-dataframes/commit/ecee2bc6ada0bc968fc56ed7194dc8c043547e93)) -* Add Series.dt.day_name ([#2218](https://github.com/googleapis/python-bigquery-dataframes/issues/2218)) ([5e006e4](https://github.com/googleapis/python-bigquery-dataframes/commit/5e006e404b65c32e5b1d342ebfcfce59ee592c8c)) -* Polars engine supports std, var ([#2215](https://github.com/googleapis/python-bigquery-dataframes/issues/2215)) ([ef5e83a](https://github.com/googleapis/python-bigquery-dataframes/commit/ef5e83acedf005cbe1e6ad174bec523ac50517d7)) -* Support INFORMATION_SCHEMA views in `read_gbq` ([#1895](https://github.com/googleapis/python-bigquery-dataframes/issues/1895)) ([d97cafc](https://github.com/googleapis/python-bigquery-dataframes/commit/d97cafcb5921fca2351b18011b0e54e2631cc53d)) -* Support some python standard lib callables in apply/combine ([#2187](https://github.com/googleapis/python-bigquery-dataframes/issues/2187)) ([86a2756](https://github.com/googleapis/python-bigquery-dataframes/commit/86a27564b48b854a32b3d11cd2105aa0fa496279)) - - -### Bug Fixes - -* Correct connection normalization in blob system tests ([#2222](https://github.com/googleapis/python-bigquery-dataframes/issues/2222)) ([a0e1e50](https://github.com/googleapis/python-bigquery-dataframes/commit/a0e1e50e47c758bdceb54d04180ed36b35cf2e35)) -* Improve error handling in blob operations ([#2194](https://github.com/googleapis/python-bigquery-dataframes/issues/2194)) ([d410046](https://github.com/googleapis/python-bigquery-dataframes/commit/d4100466612df0523d01ed01ca1e115dabd6ef45)) -* Resolve AttributeError in TableWidget and improve initialization ([#1937](https://github.com/googleapis/python-bigquery-dataframes/issues/1937)) ([4c4c9b1](https://github.com/googleapis/python-bigquery-dataframes/commit/4c4c9b14657b7cda1940ef39e7d4db20a9ff5308)) - - -### Documentation - -* Update bq_dataframes_llm_output_schema.ipynb ([#2004](https://github.com/googleapis/python-bigquery-dataframes/issues/2004)) ([316ba9f](https://github.com/googleapis/python-bigquery-dataframes/commit/316ba9f557d792117d5a7845d7567498f78dd513)) - -## [2.27.0](https://github.com/googleapis/python-bigquery-dataframes/compare/v2.26.0...v2.27.0) (2025-10-24) - - -### Features - -* Add __abs__ to dataframe ([#2186](https://github.com/googleapis/python-bigquery-dataframes/issues/2186)) ([c331dfe](https://github.com/googleapis/python-bigquery-dataframes/commit/c331dfed59174962fbdc8ace175dd00fcc3d5d50)) -* Add df.groupby().corr()/cov() support ([#2190](https://github.com/googleapis/python-bigquery-dataframes/issues/2190)) ([ccd7c07](https://github.com/googleapis/python-bigquery-dataframes/commit/ccd7c0774a65d09e6cf31d2b62d0bc64bd7c4248)) -* Add str accessor to index ([#2179](https://github.com/googleapis/python-bigquery-dataframes/issues/2179)) ([cd87ce0](https://github.com/googleapis/python-bigquery-dataframes/commit/cd87ce0d504747f44d1b5a55f869a2e0fca6df17)) -* Add support for `np.isnan` and `np.isfinite` ufuncs ([#2188](https://github.com/googleapis/python-bigquery-dataframes/issues/2188)) ([68723bc](https://github.com/googleapis/python-bigquery-dataframes/commit/68723bc1f08013e43a8b11752f908bf8fd6d51f5)) -* Include local data bytes in the dry run report when available ([#2185](https://github.com/googleapis/python-bigquery-dataframes/issues/2185)) ([ee2c40c](https://github.com/googleapis/python-bigquery-dataframes/commit/ee2c40c6789535e259fb6a9774831d6913d16212)) -* Support len() on Groupby objects ([#2183](https://github.com/googleapis/python-bigquery-dataframes/issues/2183)) ([4191821](https://github.com/googleapis/python-bigquery-dataframes/commit/4191821b0976281a96c8965336ef51f061b0c481)) -* Support pa.json_(pa.string()) in struct/list if available ([#2180](https://github.com/googleapis/python-bigquery-dataframes/issues/2180)) ([5ec3cc0](https://github.com/googleapis/python-bigquery-dataframes/commit/5ec3cc0298c7a6195d5bd12a08d996e7df57fc5f)) - - -### Documentation - -* Update AI operators deprecation notice ([#2182](https://github.com/googleapis/python-bigquery-dataframes/issues/2182)) ([2c50310](https://github.com/googleapis/python-bigquery-dataframes/commit/2c503107e17c59232b14b0d7bc40c350bb087d6f)) - -## [2.26.0](https://github.com/googleapis/python-bigquery-dataframes/compare/v2.25.0...v2.26.0) (2025-10-17) - - -### ⚠ BREAKING CHANGES - -* turn Series.struct.dtypes into a property to match pandas (https://github.com/googleapis/python-bigquery-dataframes/pull/2169) - -### Features - -* Add df.sort_index(axis=1) ([#2173](https://github.com/googleapis/python-bigquery-dataframes/issues/2173)) ([ebf95e3](https://github.com/googleapis/python-bigquery-dataframes/commit/ebf95e3ef77822650f2e190df7b868011174d412)) -* Enhanced multimodal error handling with verbose mode for blob image functions ([#2024](https://github.com/googleapis/python-bigquery-dataframes/issues/2024)) ([f9e28fe](https://github.com/googleapis/python-bigquery-dataframes/commit/f9e28fe3f883cc4d486178fe241bc8b76473700f)) -* Implement cos, sin, and log operations for polars compiler ([#2170](https://github.com/googleapis/python-bigquery-dataframes/issues/2170)) ([5613e44](https://github.com/googleapis/python-bigquery-dataframes/commit/5613e4454f198691209ec28e58ce652104ac2de4)) -* Make `all` and `any` compatible with integer columns on Polars session ([#2154](https://github.com/googleapis/python-bigquery-dataframes/issues/2154)) ([6353d6e](https://github.com/googleapis/python-bigquery-dataframes/commit/6353d6ecad5139551ef68376c08f8749dd440014)) - - -### Bug Fixes - -* `blob.display()` shows <NA> for null rows ([#2158](https://github.com/googleapis/python-bigquery-dataframes/issues/2158)) ([ddb4df0](https://github.com/googleapis/python-bigquery-dataframes/commit/ddb4df0dd991bef051e2a365c5cacf502803014d)) -* Turn Series.struct.dtypes into a property to match pandas (https://github.com/googleapis/python-bigquery-dataframes/pull/2169) ([62f7e9f](https://github.com/googleapis/python-bigquery-dataframes/commit/62f7e9f38f26b6eb549219a4cbf2c9b9023c9c35)) - - -### Documentation - -* Clarify that only NULL values are handled by fillna/isna, not NaN ([#2176](https://github.com/googleapis/python-bigquery-dataframes/issues/2176)) ([8f27e73](https://github.com/googleapis/python-bigquery-dataframes/commit/8f27e737fc78a182238090025d09479fac90b326)) -* Remove import bigframes.pandas as bpd boilerplate from many samples ([#2147](https://github.com/googleapis/python-bigquery-dataframes/issues/2147)) ([1a01ab9](https://github.com/googleapis/python-bigquery-dataframes/commit/1a01ab97f103361f489f37b0af8c4b4d7806707c)) - -## [2.25.0](https://github.com/googleapis/python-bigquery-dataframes/compare/v2.24.0...v2.25.0) (2025-10-13) - - -### Features - -* Add barh, pie plot types ([#2146](https://github.com/googleapis/python-bigquery-dataframes/issues/2146)) ([5cc3c5b](https://github.com/googleapis/python-bigquery-dataframes/commit/5cc3c5b1391a7dfa062b1d77f001726b013f6337)) -* Add Index.__eq__ for consts, aligned objects ([#2141](https://github.com/googleapis/python-bigquery-dataframes/issues/2141)) ([8514200](https://github.com/googleapis/python-bigquery-dataframes/commit/85142008ec895fa078d192bbab942d0257f70df3)) -* Add output_schema parameter to ai.generate() ([#2139](https://github.com/googleapis/python-bigquery-dataframes/issues/2139)) ([ef0b0b7](https://github.com/googleapis/python-bigquery-dataframes/commit/ef0b0b73843da2a93baf08e4cd5457fbb590b89c)) -* Create session-scoped `cut`, `DataFrame`, `MultiIndex`, `Index`, `Series`, `to_datetime`, and `to_timedelta` methods ([#2157](https://github.com/googleapis/python-bigquery-dataframes/issues/2157)) ([5e1e809](https://github.com/googleapis/python-bigquery-dataframes/commit/5e1e8098ecf212c91d73fa80d722d1cb3e46668b)) -* Replace ML.GENERATE_TEXT with AI.GENERATE for audio transcription ([#2151](https://github.com/googleapis/python-bigquery-dataframes/issues/2151)) ([a410d0a](https://github.com/googleapis/python-bigquery-dataframes/commit/a410d0ae43ef3b053b650804156eda0b1f569da9)) -* Support string literal inputs for AI functions ([#2152](https://github.com/googleapis/python-bigquery-dataframes/issues/2152)) ([7600001](https://github.com/googleapis/python-bigquery-dataframes/commit/760000122dc190ac8a3303234cf4cbee1bbb9493)) - - -### Bug Fixes - -* Address typo in error message ([#2142](https://github.com/googleapis/python-bigquery-dataframes/issues/2142)) ([cdf2dd5](https://github.com/googleapis/python-bigquery-dataframes/commit/cdf2dd55a0c03da50ab92de09788cafac0abf6f6)) -* Avoid possible circular imports in global session ([#2115](https://github.com/googleapis/python-bigquery-dataframes/issues/2115)) ([095c0b8](https://github.com/googleapis/python-bigquery-dataframes/commit/095c0b85a25a2e51087880909597cc62a0341c93)) -* Fix too many cluster columns requested by caching ([#2155](https://github.com/googleapis/python-bigquery-dataframes/issues/2155)) ([35c1c33](https://github.com/googleapis/python-bigquery-dataframes/commit/35c1c33b85d1b92e402aab73677df3ffe43a51b4)) -* Show progress even in job optional queries ([#2119](https://github.com/googleapis/python-bigquery-dataframes/issues/2119)) ([1f48d3a](https://github.com/googleapis/python-bigquery-dataframes/commit/1f48d3a62e7e6dac4acb39e911daf766b8e2fe62)) -* Yield row count from read session if otherwise unknown ([#2148](https://github.com/googleapis/python-bigquery-dataframes/issues/2148)) ([8997d4d](https://github.com/googleapis/python-bigquery-dataframes/commit/8997d4d7d9965e473195f98c550c80657035b7e1)) - - -### Documentation - -* Add a brief intro notebook for bbq AI functions ([#2150](https://github.com/googleapis/python-bigquery-dataframes/issues/2150)) ([1f434fb](https://github.com/googleapis/python-bigquery-dataframes/commit/1f434fb5c7c00601654b3ab19c6ad7fceb258bd6)) -* Fix ai function related docs ([#2149](https://github.com/googleapis/python-bigquery-dataframes/issues/2149)) ([93a0749](https://github.com/googleapis/python-bigquery-dataframes/commit/93a0749392b84f27162654fe5ea5baa329a23f99)) -* Remove progress bar from getting started template ([#2143](https://github.com/googleapis/python-bigquery-dataframes/issues/2143)) ([d13abad](https://github.com/googleapis/python-bigquery-dataframes/commit/d13abadbcd68d03997e8dc11bb7a2b14bbd57fcc)) - -## [2.24.0](https://github.com/googleapis/python-bigquery-dataframes/compare/v2.23.0...v2.24.0) (2025-10-07) - - -### Features - -* Add ai.classify() to bigframes.bigquery package ([#2137](https://github.com/googleapis/python-bigquery-dataframes/issues/2137)) ([56e5033](https://github.com/googleapis/python-bigquery-dataframes/commit/56e50331d198b7f517f85695c208f893ab9389d2)) -* Add ai.generate() to bigframes.bigquery module ([#2128](https://github.com/googleapis/python-bigquery-dataframes/issues/2128)) ([3810452](https://github.com/googleapis/python-bigquery-dataframes/commit/3810452f16d8d6c9d3eb9075f1537177d98b4725)) -* Add ai.if_() and ai.score() to bigframes.bigquery package ([#2132](https://github.com/googleapis/python-bigquery-dataframes/issues/2132)) ([32502f4](https://github.com/googleapis/python-bigquery-dataframes/commit/32502f4195306d262788f39d1ab4206fc84ae50e)) - - -### Bug Fixes - -* Fix internal type errors with temporal accessors ([#2125](https://github.com/googleapis/python-bigquery-dataframes/issues/2125)) ([c390da1](https://github.com/googleapis/python-bigquery-dataframes/commit/c390da11b7c2aa710bc2fbc692efb9f06059e4c4)) -* Fix row count local execution bug ([#2133](https://github.com/googleapis/python-bigquery-dataframes/issues/2133)) ([ece0762](https://github.com/googleapis/python-bigquery-dataframes/commit/ece07623e354a1dde2bd37020349e13f682e863f)) -* Join on, how args are now positional ([#2140](https://github.com/googleapis/python-bigquery-dataframes/issues/2140)) ([b711815](https://github.com/googleapis/python-bigquery-dataframes/commit/b7118152bfecc6ecf67aa4df23ec3f0a2b08aa30)) -* Only show JSON dtype warning when accessing dtypes directly ([#2136](https://github.com/googleapis/python-bigquery-dataframes/issues/2136)) ([eca22ee](https://github.com/googleapis/python-bigquery-dataframes/commit/eca22ee3104104cea96189391e527cad09bd7509)) -* Remove noisy AmbiguousWindowWarning from partial ordering mode ([#2129](https://github.com/googleapis/python-bigquery-dataframes/issues/2129)) ([4607f86](https://github.com/googleapis/python-bigquery-dataframes/commit/4607f86ebd77b916aafc37f69725b676e203b332)) - - -### Performance Improvements - -* Scale read stream workers to cpu count ([#2135](https://github.com/googleapis/python-bigquery-dataframes/issues/2135)) ([67e46cd](https://github.com/googleapis/python-bigquery-dataframes/commit/67e46cd47933b84b55808003ed344b559e47c498)) - -## [2.23.0](https://github.com/googleapis/python-bigquery-dataframes/compare/v2.22.0...v2.23.0) (2025-09-29) - - -### Features - -* Add ai.generate_double to bigframes.bigquery package ([#2111](https://github.com/googleapis/python-bigquery-dataframes/issues/2111)) ([6b8154c](https://github.com/googleapis/python-bigquery-dataframes/commit/6b8154c578bb1a276e9cf8fe494d91f8cd6260f2)) - - -### Bug Fixes - -* Prevent invalid syntax for no-op .replace ops ([#2112](https://github.com/googleapis/python-bigquery-dataframes/issues/2112)) ([c311876](https://github.com/googleapis/python-bigquery-dataframes/commit/c311876b2adbc0b66ae5e463c6e56466c6a6a495)) - - -### Documentation - -* Add timedelta notebook sample ([#2124](https://github.com/googleapis/python-bigquery-dataframes/issues/2124)) ([d1a9888](https://github.com/googleapis/python-bigquery-dataframes/commit/d1a9888a2b47de6aca5dddc94d0c8f280344b58a)) - -## [2.22.0](https://github.com/googleapis/python-bigquery-dataframes/compare/v2.21.0...v2.22.0) (2025-09-25) - - -### Features - -* Add `GroupBy.__iter__` ([#1394](https://github.com/googleapis/python-bigquery-dataframes/issues/1394)) ([c56a78c](https://github.com/googleapis/python-bigquery-dataframes/commit/c56a78cd509a535d4998d5b9a99ec3ecd334b883)) -* Add ai.generate_int to bigframes.bigquery package ([#2109](https://github.com/googleapis/python-bigquery-dataframes/issues/2109)) ([af6b862](https://github.com/googleapis/python-bigquery-dataframes/commit/af6b862de5c3921684210ec169338815f45b19dd)) -* Add Groupby.describe() ([#2088](https://github.com/googleapis/python-bigquery-dataframes/issues/2088)) ([328a765](https://github.com/googleapis/python-bigquery-dataframes/commit/328a765e746138806a021bea22475e8c03512aeb)) -* Implement `Index.to_list()` ([#2106](https://github.com/googleapis/python-bigquery-dataframes/issues/2106)) ([60056ca](https://github.com/googleapis/python-bigquery-dataframes/commit/60056ca06511f99092647fe55fc02eeab486b4ca)) -* Implement inplace parameter for `DataFrame.drop` ([#2105](https://github.com/googleapis/python-bigquery-dataframes/issues/2105)) ([3487f13](https://github.com/googleapis/python-bigquery-dataframes/commit/3487f13d12e34999b385c2e11551b5e27bfbf4ff)) -* Support callable for series map method ([#2100](https://github.com/googleapis/python-bigquery-dataframes/issues/2100)) ([ac25618](https://github.com/googleapis/python-bigquery-dataframes/commit/ac25618feed2da11fe4fb85058d498d262c085c0)) -* Support df.info() with null index ([#2094](https://github.com/googleapis/python-bigquery-dataframes/issues/2094)) ([fb81eea](https://github.com/googleapis/python-bigquery-dataframes/commit/fb81eeaf13af059f32cb38e7f117fb3504243d51)) - - -### Bug Fixes - -* Avoid ibis fillna warning in compiler ([#2113](https://github.com/googleapis/python-bigquery-dataframes/issues/2113)) ([7ef667b](https://github.com/googleapis/python-bigquery-dataframes/commit/7ef667b0f46f13bcc8ad4f2ed8f81278132b5aec)) -* Negative start and stop parameter values in Series.str.slice() ([#2104](https://github.com/googleapis/python-bigquery-dataframes/issues/2104)) ([f57a348](https://github.com/googleapis/python-bigquery-dataframes/commit/f57a348f1935a4e2bb14c501bb4c47cd552d102a)) -* Throw type error for incomparable join keys ([#2098](https://github.com/googleapis/python-bigquery-dataframes/issues/2098)) ([9dc9695](https://github.com/googleapis/python-bigquery-dataframes/commit/9dc96959a84b751d18b290129c2926df6e50b3f5)) -* Transformers with non-standard column names throw errors ([#2089](https://github.com/googleapis/python-bigquery-dataframes/issues/2089)) ([a2daa3f](https://github.com/googleapis/python-bigquery-dataframes/commit/a2daa3fffe6743327edb9f4c74db93198bd12f8e)) - -## [2.21.0](https://github.com/googleapis/python-bigquery-dataframes/compare/v2.20.0...v2.21.0) (2025-09-17) - - -### Features - -* Add bigframes.bigquery.to_json ([#2078](https://github.com/googleapis/python-bigquery-dataframes/issues/2078)) ([0fc795a](https://github.com/googleapis/python-bigquery-dataframes/commit/0fc795a9fb56f469b62603462c3f0f56f52bfe04)) -* Support average='binary' in precision_score() ([#2080](https://github.com/googleapis/python-bigquery-dataframes/issues/2080)) ([920f381](https://github.com/googleapis/python-bigquery-dataframes/commit/920f381aec7e0a0b986886cdbc333e86335c6d7d)) -* Support pandas series in ai.generate_bool ([#2086](https://github.com/googleapis/python-bigquery-dataframes/issues/2086)) ([a3de53f](https://github.com/googleapis/python-bigquery-dataframes/commit/a3de53f68b2a24f4ed85a474dfaff9b59570a2f1)) - - -### Bug Fixes - -* Allow bigframes.options.bigquery.credentials to be `None` ([#2092](https://github.com/googleapis/python-bigquery-dataframes/issues/2092)) ([78f4001](https://github.com/googleapis/python-bigquery-dataframes/commit/78f4001e8fcfc77fc82f3893d58e0d04c0f6d3db)) - -## [2.20.0](https://github.com/googleapis/python-bigquery-dataframes/compare/v2.19.0...v2.20.0) (2025-09-16) - - -### Features - -* Add `__dataframe__` interchange support ([#2063](https://github.com/googleapis/python-bigquery-dataframes/issues/2063)) ([3b46a0d](https://github.com/googleapis/python-bigquery-dataframes/commit/3b46a0d91eb379c61ced45ae0b25339281326c3d)) -* Add ai_generate_bool to the bigframes.bigquery package ([#2060](https://github.com/googleapis/python-bigquery-dataframes/issues/2060)) ([70d6562](https://github.com/googleapis/python-bigquery-dataframes/commit/70d6562df64b2aef4ff0024df6f57702d52dcaf8)) -* Add bigframes.bigquery.to_json_string ([#2076](https://github.com/googleapis/python-bigquery-dataframes/issues/2076)) ([41e8f33](https://github.com/googleapis/python-bigquery-dataframes/commit/41e8f33ceb46a7c2a75d1c59a4a3f2f9413d281d)) -* Add rank(pct=True) support ([#2084](https://github.com/googleapis/python-bigquery-dataframes/issues/2084)) ([c1e871d](https://github.com/googleapis/python-bigquery-dataframes/commit/c1e871d9327bf6c920d17e1476fed3088d506f5f)) -* Add StreamingDataFrame.to_bigtable and .to_pubsub start_timestamp parameter ([#2066](https://github.com/googleapis/python-bigquery-dataframes/issues/2066)) ([a63cbae](https://github.com/googleapis/python-bigquery-dataframes/commit/a63cbae24ff2dc191f0a53dced885bc95f38ec96)) -* Can call agg with some callables ([#2055](https://github.com/googleapis/python-bigquery-dataframes/issues/2055)) ([17a1ed9](https://github.com/googleapis/python-bigquery-dataframes/commit/17a1ed99ec8c6d3215d3431848814d5d458d4ff1)) -* Support astype to json ([#2073](https://github.com/googleapis/python-bigquery-dataframes/issues/2073)) ([6bd6738](https://github.com/googleapis/python-bigquery-dataframes/commit/6bd67386341de7a92ada948381702430c399406e)) -* Support pandas.Index as key for DataFrame.__setitem__() ([#2062](https://github.com/googleapis/python-bigquery-dataframes/issues/2062)) ([b3cf824](https://github.com/googleapis/python-bigquery-dataframes/commit/b3cf8248e3b8ea76637ded64fb12028d439448d1)) -* Support pd.cut() for array-like type ([#2064](https://github.com/googleapis/python-bigquery-dataframes/issues/2064)) ([21eb213](https://github.com/googleapis/python-bigquery-dataframes/commit/21eb213c5f0e0f696f2d1ca1f1263678d791cf7c)) -* Support to cast struct to json ([#2067](https://github.com/googleapis/python-bigquery-dataframes/issues/2067)) ([b0ff718](https://github.com/googleapis/python-bigquery-dataframes/commit/b0ff718a04fadda33cfa3613b1d02822cde34bc2)) - - -### Bug Fixes - -* Deflake ai_gen_bool multimodel test ([#2085](https://github.com/googleapis/python-bigquery-dataframes/issues/2085)) ([566a37a](https://github.com/googleapis/python-bigquery-dataframes/commit/566a37a30ad5677aef0c5f79bdd46bca2139cc1e)) -* Do not scroll page selector in anywidget `repr_mode` ([#2082](https://github.com/googleapis/python-bigquery-dataframes/issues/2082)) ([5ce5d63](https://github.com/googleapis/python-bigquery-dataframes/commit/5ce5d63fcb51bfb3df2769108b7486287896ccb9)) -* Fix the potential invalid VPC egress configuration ([#2068](https://github.com/googleapis/python-bigquery-dataframes/issues/2068)) ([cce4966](https://github.com/googleapis/python-bigquery-dataframes/commit/cce496605385f2ac7ab0becc0773800ed5901aa5)) -* Return a DataFrame containing query stats for all non-SELECT statements ([#2071](https://github.com/googleapis/python-bigquery-dataframes/issues/2071)) ([a52b913](https://github.com/googleapis/python-bigquery-dataframes/commit/a52b913d9d8794b4b959ea54744a38d9f2f174e7)) -* Use the remote and managed functions for bigframes results ([#2079](https://github.com/googleapis/python-bigquery-dataframes/issues/2079)) ([49b91e8](https://github.com/googleapis/python-bigquery-dataframes/commit/49b91e878de651de23649756259ee35709e3f5a8)) - - -### Performance Improvements - -* Avoid re-authenticating if credentials have already been fetched ([#2058](https://github.com/googleapis/python-bigquery-dataframes/issues/2058)) ([913de1b](https://github.com/googleapis/python-bigquery-dataframes/commit/913de1b31f3bb0b306846fddae5dcaff6be3cec4)) -* Improve apply axis=1 performance ([#2077](https://github.com/googleapis/python-bigquery-dataframes/issues/2077)) ([12e4380](https://github.com/googleapis/python-bigquery-dataframes/commit/12e438051134577e911c1a6ce9d5a5885a0b45ad)) - -## [2.19.0](https://github.com/googleapis/python-bigquery-dataframes/compare/v2.18.0...v2.19.0) (2025-09-09) - - -### Features - -* Add str.join method ([#2054](https://github.com/googleapis/python-bigquery-dataframes/issues/2054)) ([8804ada](https://github.com/googleapis/python-bigquery-dataframes/commit/8804adaf8ba23fdcad6e42a7bf034bd0a11c890f)) -* Support display.max_colwidth option ([#2053](https://github.com/googleapis/python-bigquery-dataframes/issues/2053)) ([5229e07](https://github.com/googleapis/python-bigquery-dataframes/commit/5229e07b4535c01b0cdbd731455ff225a373b5c8)) -* Support VPC egress setting in remote function ([#2059](https://github.com/googleapis/python-bigquery-dataframes/issues/2059)) ([5df779d](https://github.com/googleapis/python-bigquery-dataframes/commit/5df779d4f421d3ba777cfd928d99ca2e8a3f79ad)) - - -### Bug Fixes - -* Fix issue mishandling chunked array while loading data ([#2051](https://github.com/googleapis/python-bigquery-dataframes/issues/2051)) ([873d0ee](https://github.com/googleapis/python-bigquery-dataframes/commit/873d0eee474ed34f1d5164c37383f2737dbec4db)) -* Remove warning for slot_millis_sum ([#2047](https://github.com/googleapis/python-bigquery-dataframes/issues/2047)) ([425a691](https://github.com/googleapis/python-bigquery-dataframes/commit/425a6917d5442eeb4df486c6eed1fd136bbcedfb)) - -## [2.18.0](https://github.com/googleapis/python-bigquery-dataframes/compare/v2.17.0...v2.18.0) (2025-09-03) - - -### ⚠ BREAKING CHANGES - -* add `allow_large_results` option to `read_gbq_query`, aligning with `bpd.options.compute.allow_large_results` option ([#1935](https://github.com/googleapis/python-bigquery-dataframes/issues/1935)) - -### Features - -* Add `allow_large_results` option to `read_gbq_query`, aligning with `bpd.options.compute.allow_large_results` option ([#1935](https://github.com/googleapis/python-bigquery-dataframes/issues/1935)) ([a7963fe](https://github.com/googleapis/python-bigquery-dataframes/commit/a7963fe57a0e141debf726f0bc7b0e953ebe9634)) -* Add parameter shuffle for ml.model_selection.train_test_split ([#2030](https://github.com/googleapis/python-bigquery-dataframes/issues/2030)) ([2c72c56](https://github.com/googleapis/python-bigquery-dataframes/commit/2c72c56fb5893eb01d5aec6273d11945c9c532c5)) -* Can pivot unordered, unindexed dataframe ([#2040](https://github.com/googleapis/python-bigquery-dataframes/issues/2040)) ([1a0f710](https://github.com/googleapis/python-bigquery-dataframes/commit/1a0f710ac11418fd71ab3373f3f6002fa581b180)) -* Local date accessor execution support ([#2034](https://github.com/googleapis/python-bigquery-dataframes/issues/2034)) ([7ac6fe1](https://github.com/googleapis/python-bigquery-dataframes/commit/7ac6fe16f7f2c09d2efac6ab813ec841c21baef8)) -* Support args in dataframe apply method ([#2026](https://github.com/googleapis/python-bigquery-dataframes/issues/2026)) ([164c481](https://github.com/googleapis/python-bigquery-dataframes/commit/164c4818bc4ff2990dca16b9f22a798f47e0a60b)) -* Support args in series apply method ([#2013](https://github.com/googleapis/python-bigquery-dataframes/issues/2013)) ([d9d725c](https://github.com/googleapis/python-bigquery-dataframes/commit/d9d725cfbc3dca9e66b460cae4084e25162f2acf)) -* Support callable for dataframe mask method ([#2020](https://github.com/googleapis/python-bigquery-dataframes/issues/2020)) ([9d4504b](https://github.com/googleapis/python-bigquery-dataframes/commit/9d4504be310d38b63515d67c0f60d2e48e68c7b5)) -* Support multi-column assignment for DataFrame ([#2028](https://github.com/googleapis/python-bigquery-dataframes/issues/2028)) ([ba0d23b](https://github.com/googleapis/python-bigquery-dataframes/commit/ba0d23b59c44ba5a46ace8182ad0e0cfc703b3ab)) -* Support string matching in local executor ([#2032](https://github.com/googleapis/python-bigquery-dataframes/issues/2032)) ([c0b54f0](https://github.com/googleapis/python-bigquery-dataframes/commit/c0b54f03849ee3115413670e690e68f3ef10f2ec)) - - -### Bug Fixes - -* Fix scalar op lowering tree walk ([#2029](https://github.com/googleapis/python-bigquery-dataframes/issues/2029)) ([935af10](https://github.com/googleapis/python-bigquery-dataframes/commit/935af107ef98837fb2b81d72185d0b6a9e09fbcf)) -* Read_csv fails when check file size for wildcard gcs files ([#2019](https://github.com/googleapis/python-bigquery-dataframes/issues/2019)) ([b0d620b](https://github.com/googleapis/python-bigquery-dataframes/commit/b0d620bbe8227189bbdc2ba5a913b03c70575296)) -* Resolve the validation issue for other arg in dataframe where method ([#2042](https://github.com/googleapis/python-bigquery-dataframes/issues/2042)) ([8689199](https://github.com/googleapis/python-bigquery-dataframes/commit/8689199aa82212ed300fff592097093812e0290e)) - - -### Performance Improvements - -* Improve axis=1 aggregation performance ([#2036](https://github.com/googleapis/python-bigquery-dataframes/issues/2036)) ([fbb2094](https://github.com/googleapis/python-bigquery-dataframes/commit/fbb209468297a8057d9d49c40e425c3bfdeb92bd)) -* Improve iter_nodes_topo performance using Kahn's algorithm ([#2038](https://github.com/googleapis/python-bigquery-dataframes/issues/2038)) ([3961637](https://github.com/googleapis/python-bigquery-dataframes/commit/39616374bba424996ebeb9a12096bfaf22660b44)) - -## [2.17.0](https://github.com/googleapis/python-bigquery-dataframes/compare/v2.16.0...v2.17.0) (2025-08-22) - - -### Features - -* Add isin local execution impl ([#1993](https://github.com/googleapis/python-bigquery-dataframes/issues/1993)) ([26df6e6](https://github.com/googleapis/python-bigquery-dataframes/commit/26df6e691bb27ed09322a81214faedbf3639b32e)) -* Add reset_index names, col_level, col_fill, allow_duplicates args ([#2017](https://github.com/googleapis/python-bigquery-dataframes/issues/2017)) ([c02a1b6](https://github.com/googleapis/python-bigquery-dataframes/commit/c02a1b67d27758815430bb8006ac3a72cea55a89)) -* Support callable for series mask method ([#2014](https://github.com/googleapis/python-bigquery-dataframes/issues/2014)) ([5ac32eb](https://github.com/googleapis/python-bigquery-dataframes/commit/5ac32ebe17cfda447870859f5dd344b082b4d3d0)) - -## [2.16.0](https://github.com/googleapis/python-bigquery-dataframes/compare/v2.15.0...v2.16.0) (2025-08-20) - - -### Features - -* Add `bigframes.pandas.options.display.precision` option ([#1979](https://github.com/googleapis/python-bigquery-dataframes/issues/1979)) ([15e6175](https://github.com/googleapis/python-bigquery-dataframes/commit/15e6175ec0aeb1b7b02d0bba9e8e1e018bd11c31)) -* Add level, inplace params to reset_index ([#1988](https://github.com/googleapis/python-bigquery-dataframes/issues/1988)) ([3446950](https://github.com/googleapis/python-bigquery-dataframes/commit/34469504b79a082d3380f9f25c597483aef2068a)) -* Add ML code samples from dbt blog post ([#1978](https://github.com/googleapis/python-bigquery-dataframes/issues/1978)) ([ebaa244](https://github.com/googleapis/python-bigquery-dataframes/commit/ebaa244a9eb7b87f7f9fd9c3bebe5c7db24cd013)) -* Add where, coalesce, fillna, casewhen, invert local impl ([#1976](https://github.com/googleapis/python-bigquery-dataframes/issues/1976)) ([f7f686c](https://github.com/googleapis/python-bigquery-dataframes/commit/f7f686cf85ab7e265d9c07ebc7f0cd59babc5357)) -* Adjust anywidget CSS to prevent overflow ([#1981](https://github.com/googleapis/python-bigquery-dataframes/issues/1981)) ([204f083](https://github.com/googleapis/python-bigquery-dataframes/commit/204f083a2f00fcc9fd1500dcd7a738eda3904d2f)) -* Format page number in table widget ([#1992](https://github.com/googleapis/python-bigquery-dataframes/issues/1992)) ([e83836e](https://github.com/googleapis/python-bigquery-dataframes/commit/e83836e8e1357f009f3f95666f1661bdbe0d3751)) -* Or, And, Xor can execute locally ([#1994](https://github.com/googleapis/python-bigquery-dataframes/issues/1994)) ([59c52a5](https://github.com/googleapis/python-bigquery-dataframes/commit/59c52a55ebea697855eb4c70529e226cc077141f)) -* Support callable bigframes function for dataframe where ([#1990](https://github.com/googleapis/python-bigquery-dataframes/issues/1990)) ([44c1ec4](https://github.com/googleapis/python-bigquery-dataframes/commit/44c1ec48cc4db1c4c9c15ec1fab43d4ef0758e56)) -* Support callable for series where method ([#2005](https://github.com/googleapis/python-bigquery-dataframes/issues/2005)) ([768b82a](https://github.com/googleapis/python-bigquery-dataframes/commit/768b82af96a5dd0c434edcb171036eb42cfb9b41)) -* When using `repr_mode = "anywidget"`, numeric values align right ([15e6175](https://github.com/googleapis/python-bigquery-dataframes/commit/15e6175ec0aeb1b7b02d0bba9e8e1e018bd11c31)) - - -### Bug Fixes - -* Address the packages issue for bigframes function ([#1991](https://github.com/googleapis/python-bigquery-dataframes/issues/1991)) ([68f1d22](https://github.com/googleapis/python-bigquery-dataframes/commit/68f1d22d5ed8457a5cabc7751ed1d178063dd63e)) -* Correct pypdf dependency specifier for remote PDF functions ([#1980](https://github.com/googleapis/python-bigquery-dataframes/issues/1980)) ([0bd5e1b](https://github.com/googleapis/python-bigquery-dataframes/commit/0bd5e1b3c004124d2100c3fbec2fbe1e965d1e96)) -* Enable default retries in calls to BQ Storage Read API ([#1985](https://github.com/googleapis/python-bigquery-dataframes/issues/1985)) ([f25d7bd](https://github.com/googleapis/python-bigquery-dataframes/commit/f25d7bd30800dffa65b6c31b0b7ac711a13d790f)) -* Fix the copyright year in dbt sample files ([#1996](https://github.com/googleapis/python-bigquery-dataframes/issues/1996)) ([fad5722](https://github.com/googleapis/python-bigquery-dataframes/commit/fad57223d129f0c95d0c6a066179bb66880edd06)) - - -### Performance Improvements - -* Faster session startup by defering anon dataset fetch ([#1982](https://github.com/googleapis/python-bigquery-dataframes/issues/1982)) ([2720c4c](https://github.com/googleapis/python-bigquery-dataframes/commit/2720c4cf070bf57a0930d7623bfc41d89cc053ee)) - - -### Documentation - -* Add examples of running bigframes in kaggle ([#2002](https://github.com/googleapis/python-bigquery-dataframes/issues/2002)) ([7d89d76](https://github.com/googleapis/python-bigquery-dataframes/commit/7d89d76976595b75cb0105fbe7b4f7ca2fdf49f2)) -* Remove preview warning from partial ordering mode sample notebook ([#1986](https://github.com/googleapis/python-bigquery-dataframes/issues/1986)) ([132e0ed](https://github.com/googleapis/python-bigquery-dataframes/commit/132e0edfe9f96c15753649d77fcb6edd0b0708a3)) - -## [2.15.0](https://github.com/googleapis/python-bigquery-dataframes/compare/v2.14.0...v2.15.0) (2025-08-11) - - -### Features - -* Add `st_buffer`, `st_centroid`, and `st_convexhull` and their corresponding GeoSeries methods ([#1963](https://github.com/googleapis/python-bigquery-dataframes/issues/1963)) ([c4c7fa5](https://github.com/googleapis/python-bigquery-dataframes/commit/c4c7fa578e135e7f0e31ad3063db379514957acc)) -* Add first, last support to GroupBy ([#1969](https://github.com/googleapis/python-bigquery-dataframes/issues/1969)) ([41dda88](https://github.com/googleapis/python-bigquery-dataframes/commit/41dda889860c0ed8ca2eab81b34a9d71372c69f7)) -* Add value_counts to GroupBy classes ([#1974](https://github.com/googleapis/python-bigquery-dataframes/issues/1974)) ([82175a4](https://github.com/googleapis/python-bigquery-dataframes/commit/82175a4d0fa41d8aee11efdf8778a21bb70b1c0f)) -* Allow callable as a conditional or replacement input in DataFrame.where ([#1971](https://github.com/googleapis/python-bigquery-dataframes/issues/1971)) ([a8d57d2](https://github.com/googleapis/python-bigquery-dataframes/commit/a8d57d2f7075158eff69ec65a14c232756ab72a6)) -* Can cast locally in hybrid engine ([#1944](https://github.com/googleapis/python-bigquery-dataframes/issues/1944)) ([d9bc4a5](https://github.com/googleapis/python-bigquery-dataframes/commit/d9bc4a5940e9930d5e3c3bfffdadd2f91f96b53b)) -* Df.join lsuffix and rsuffix support ([#1857](https://github.com/googleapis/python-bigquery-dataframes/issues/1857)) ([26515c3](https://github.com/googleapis/python-bigquery-dataframes/commit/26515c34c4f0a5e4602d2f59bf229d41e0fc9196)) - - -### Bug Fixes - -* Add warnings for duplicated or conflicting type hints in bigfram… ([#1956](https://github.com/googleapis/python-bigquery-dataframes/issues/1956)) ([d38e42c](https://github.com/googleapis/python-bigquery-dataframes/commit/d38e42ce689e65f57223e9a8b14c4262cba08966)) -* Make `remote_function` more robust when there are `create_function` retries ([#1973](https://github.com/googleapis/python-bigquery-dataframes/issues/1973)) ([cd954ac](https://github.com/googleapis/python-bigquery-dataframes/commit/cd954ac07ad5e5820a20b941d3c6cab7cfcc1f29)) -* Make ExecutionMetrics stats tracking more robust to missing stats ([#1977](https://github.com/googleapis/python-bigquery-dataframes/issues/1977)) ([feb3ff4](https://github.com/googleapis/python-bigquery-dataframes/commit/feb3ff4b543eb8acbf6adf335b67a266a1cf4297)) - - -### Performance Improvements - -* Remove an unnecessary extra `dry_run` query from `read_gbq_table` ([#1972](https://github.com/googleapis/python-bigquery-dataframes/issues/1972)) ([d17b711](https://github.com/googleapis/python-bigquery-dataframes/commit/d17b711750d281ef3efd42c160f3784cd60021ae)) - - -### Documentation - -* Divide BQ DataFrames quickstart code cell ([#1975](https://github.com/googleapis/python-bigquery-dataframes/issues/1975)) ([fedb8f2](https://github.com/googleapis/python-bigquery-dataframes/commit/fedb8f23120aa315c7e9dd6f1bf1255ccf1ebc48)) - -## [2.14.0](https://github.com/googleapis/python-bigquery-dataframes/compare/v2.13.0...v2.14.0) (2025-08-05) - - -### Features - -* Dynamic table width for better display across devices (https://github.com/googleapis/python-bigquery-dataframes/issues/1948) ([a6d30ae](https://github.com/googleapis/python-bigquery-dataframes/commit/a6d30ae3f4358925c999c53b558c1ecd3ee03e6c)) ([a6d30ae](https://github.com/googleapis/python-bigquery-dataframes/commit/a6d30ae3f4358925c999c53b558c1ecd3ee03e6c)) -* Retry AI/ML jobs that fail more often ([#1965](https://github.com/googleapis/python-bigquery-dataframes/issues/1965)) ([25bde9f](https://github.com/googleapis/python-bigquery-dataframes/commit/25bde9f9b89112db0efcc119bf29b6d1f3896c33)) -* Support series input in managed function ([#1920](https://github.com/googleapis/python-bigquery-dataframes/issues/1920)) ([62a189f](https://github.com/googleapis/python-bigquery-dataframes/commit/62a189f4d69f6c05fe348a1acd1fbac364fa60b9)) - - -### Bug Fixes - -* Enhance type error messages for bigframes functions ([#1958](https://github.com/googleapis/python-bigquery-dataframes/issues/1958)) ([770918e](https://github.com/googleapis/python-bigquery-dataframes/commit/770918e998bf1fde7a656e8f8a0ff0a8c68509f2)) - - -### Performance Improvements - -* Use promote_offsets for consistent row number generation for index.get_loc ([#1957](https://github.com/googleapis/python-bigquery-dataframes/issues/1957)) ([c67a25a](https://github.com/googleapis/python-bigquery-dataframes/commit/c67a25a879ab2a35ca9053a81c9c85b5660206ae)) - - -### Documentation - -* Add code snippet for storing dataframes to a CSV file ([#1943](https://github.com/googleapis/python-bigquery-dataframes/issues/1943)) ([a511e09](https://github.com/googleapis/python-bigquery-dataframes/commit/a511e09e6924d2e8302af2eb4a602c6b9e5d2d72)) -* Add code snippet for storing dataframes to a CSV file ([#1953](https://github.com/googleapis/python-bigquery-dataframes/issues/1953)) ([a298a02](https://github.com/googleapis/python-bigquery-dataframes/commit/a298a02b451f03ca200fe0756b9a7b57e3d1bf0e)) - -## [2.13.0](https://github.com/googleapis/python-bigquery-dataframes/compare/v2.12.0...v2.13.0) (2025-07-25) - - -### Features - -* _read_gbq_colab creates hybrid session ([#1901](https://github.com/googleapis/python-bigquery-dataframes/issues/1901)) ([31b17b0](https://github.com/googleapis/python-bigquery-dataframes/commit/31b17b01706ccfcee9a2d838c43a9609ec4dc218)) -* Add CSS styling for TableWidget pagination interface ([#1934](https://github.com/googleapis/python-bigquery-dataframes/issues/1934)) ([5b232d7](https://github.com/googleapis/python-bigquery-dataframes/commit/5b232d7e33563196316f5dbb50b28c6be388d440)) -* Add row numbering local pushdown in hybrid execution ([#1932](https://github.com/googleapis/python-bigquery-dataframes/issues/1932)) ([92a2377](https://github.com/googleapis/python-bigquery-dataframes/commit/92a237712aa4ce516b1a44748127b34d7780fff6)) -* Implement Index.get_loc ([#1921](https://github.com/googleapis/python-bigquery-dataframes/issues/1921)) ([bbbcaf3](https://github.com/googleapis/python-bigquery-dataframes/commit/bbbcaf35df113617fd6bb8ae36468cf3f7ab493b)) - - -### Bug Fixes - -* Add license header and correct issues in dbt sample ([#1931](https://github.com/googleapis/python-bigquery-dataframes/issues/1931)) ([ab01b0a](https://github.com/googleapis/python-bigquery-dataframes/commit/ab01b0a236ffc7b667f258e0497105ea5c3d3aab)) - - -### Dependencies - -* Replace `google-cloud-iam` with `grpc-google-iam-v1` ([#1864](https://github.com/googleapis/python-bigquery-dataframes/issues/1864)) ([e5ff8f7](https://github.com/googleapis/python-bigquery-dataframes/commit/e5ff8f7d9fdac3ea47dabcc80a2598d601f39e64)) - -## [2.12.0](https://github.com/googleapis/python-bigquery-dataframes/compare/v2.11.0...v2.12.0) (2025-07-23) - - -### Features - -* Add code samples for dbt bigframes integration ([#1898](https://github.com/googleapis/python-bigquery-dataframes/issues/1898)) ([7e03252](https://github.com/googleapis/python-bigquery-dataframes/commit/7e03252d31e505731db113eb38af77842bf29b9b)) -* Add isin local execution to hybrid engine ([#1915](https://github.com/googleapis/python-bigquery-dataframes/issues/1915)) ([c0cefd3](https://github.com/googleapis/python-bigquery-dataframes/commit/c0cefd36cfd55962b86178d2a612d625ed17f79c)) -* Add ml.metrics.mean_absolute_error method ([#1910](https://github.com/googleapis/python-bigquery-dataframes/issues/1910)) ([15b8449](https://github.com/googleapis/python-bigquery-dataframes/commit/15b8449dc5ad0c8190a5cbf47894436de18c8e88)) -* Allow local arithmetic execution in hybrid engine ([#1906](https://github.com/googleapis/python-bigquery-dataframes/issues/1906)) ([ebdcd02](https://github.com/googleapis/python-bigquery-dataframes/commit/ebdcd0240f0d8edaef3094b3a4e664b4a84d4a25)) -* Provide day_of_year and day_of_week for dt accessor ([#1911](https://github.com/googleapis/python-bigquery-dataframes/issues/1911)) ([40e7638](https://github.com/googleapis/python-bigquery-dataframes/commit/40e76383948a79bde48108f6180fd6ae2b3d0875)) -* Support params `max_batching_rows`, `container_cpu`, and `container_memory` for `udf` ([#1897](https://github.com/googleapis/python-bigquery-dataframes/issues/1897)) ([8baa912](https://github.com/googleapis/python-bigquery-dataframes/commit/8baa9126e595ae682469a6bb462244240699f57f)) -* Support typed pyarrow.Scalar in assignment ([#1930](https://github.com/googleapis/python-bigquery-dataframes/issues/1930)) ([cd28e12](https://github.com/googleapis/python-bigquery-dataframes/commit/cd28e12b3f70a6934a68963a7f25dbd5e3c67335)) - - -### Bug Fixes - -* Correct min field from max() to min() in remote function tests ([#1917](https://github.com/googleapis/python-bigquery-dataframes/issues/1917)) ([d5c54fc](https://github.com/googleapis/python-bigquery-dataframes/commit/d5c54fca32ed75c1aef52c99781db7f8ac7426e1)) -* Resolve location reset issue in bigquery options ([#1914](https://github.com/googleapis/python-bigquery-dataframes/issues/1914)) ([c15cb8a](https://github.com/googleapis/python-bigquery-dataframes/commit/c15cb8a1a9c834c2c1c2984930415b246f3f948b)) -* Series.str.isdigit in unicode superscripts and fractions ([#1924](https://github.com/googleapis/python-bigquery-dataframes/issues/1924)) ([8d46c36](https://github.com/googleapis/python-bigquery-dataframes/commit/8d46c36da7881a99861166c03a0831beff8ee0dd)) - - -### Documentation - -* Add code snippets for session and IO public docs ([#1919](https://github.com/googleapis/python-bigquery-dataframes/issues/1919)) ([6e01cbe](https://github.com/googleapis/python-bigquery-dataframes/commit/6e01cbec0dcf40e528b4a96e944681df18773c11)) -* Add snippets for performance optimization doc ([#1923](https://github.com/googleapis/python-bigquery-dataframes/issues/1923)) ([4da309e](https://github.com/googleapis/python-bigquery-dataframes/commit/4da309e27bd58a685e8aca953717da75d4ba5305)) - -## [2.11.0](https://github.com/googleapis/python-bigquery-dataframes/compare/v2.10.0...v2.11.0) (2025-07-15) - - -### Features - -* Add `__contains__` to Index, Series, DataFrame ([#1899](https://github.com/googleapis/python-bigquery-dataframes/issues/1899)) ([07222bf](https://github.com/googleapis/python-bigquery-dataframes/commit/07222bfe2f6ae60859d33eb366598d7dee5c0572)) -* Add `thresh` param for Dataframe.dropna ([#1885](https://github.com/googleapis/python-bigquery-dataframes/issues/1885)) ([1395a50](https://github.com/googleapis/python-bigquery-dataframes/commit/1395a502ffa0faf4b7462045dcb0657485c7ce26)) -* Add concat pushdown for hybrid engine ([#1891](https://github.com/googleapis/python-bigquery-dataframes/issues/1891)) ([813624d](https://github.com/googleapis/python-bigquery-dataframes/commit/813624dddfd4f2396c8b1c9768c0c831bb0681ac)) -* Add pagination buttons (prev/next) to anywidget mode for DataFrames ([#1841](https://github.com/googleapis/python-bigquery-dataframes/issues/1841)) ([8eca767](https://github.com/googleapis/python-bigquery-dataframes/commit/8eca767425c7910c8f907747a8a8b335df0caa1a)) -* Add total_rows property to pandas batches iterator ([#1888](https://github.com/googleapis/python-bigquery-dataframes/issues/1888)) ([e3f5e65](https://github.com/googleapis/python-bigquery-dataframes/commit/e3f5e6539d220f8da57f08f67863ade29df4ad16)) -* Hybrid engine local join support ([#1900](https://github.com/googleapis/python-bigquery-dataframes/issues/1900)) ([1aa7950](https://github.com/googleapis/python-bigquery-dataframes/commit/1aa7950334bdc826a9a0a1894dad67ca6f755425)) -* Support `date` data type for to_datetime() ([#1902](https://github.com/googleapis/python-bigquery-dataframes/issues/1902)) ([24050cb](https://github.com/googleapis/python-bigquery-dataframes/commit/24050cb00247f68eb4ece827fd31ee1dd8b25380)) -* Support bpd.Series(json_data, dtype="json") ([#1882](https://github.com/googleapis/python-bigquery-dataframes/issues/1882)) ([05cb7d0](https://github.com/googleapis/python-bigquery-dataframes/commit/05cb7d0bc3599054acf8ecb8b15eb2045b9bf463)) - - -### Bug Fixes - -* Bpd.merge on common columns ([#1905](https://github.com/googleapis/python-bigquery-dataframes/issues/1905)) ([a1fa112](https://github.com/googleapis/python-bigquery-dataframes/commit/a1fa11291305a1da0d6a4121436c09ed04b224b5)) -* DataFrame string addition respects order ([#1894](https://github.com/googleapis/python-bigquery-dataframes/issues/1894)) ([52c8233](https://github.com/googleapis/python-bigquery-dataframes/commit/52c82337bcc9f2b6cfc1c6ac14deb83b693d114d)) -* Show slot_millis_sum warning only when `allow_large_results=False` ([#1892](https://github.com/googleapis/python-bigquery-dataframes/issues/1892)) ([25efabc](https://github.com/googleapis/python-bigquery-dataframes/commit/25efabc4897e0692725618ce43134127a7f2c2ee)) -* Used query row count metadata instead of table metadata ([#1893](https://github.com/googleapis/python-bigquery-dataframes/issues/1893)) ([e1ebc53](https://github.com/googleapis/python-bigquery-dataframes/commit/e1ebc5369a416280cec0ab1513e763b7a2fe3c20)) - -## [2.10.0](https://github.com/googleapis/python-bigquery-dataframes/compare/v2.9.0...v2.10.0) (2025-07-08) - - -### Features - -* `df.to_pandas_batches()` returns one empty DataFrame if `df` is empty ([#1878](https://github.com/googleapis/python-bigquery-dataframes/issues/1878)) ([e43d15d](https://github.com/googleapis/python-bigquery-dataframes/commit/e43d15d535d6d5fd73c33967271f3591c41dffb3)) -* Add filter pushdown to hybrid engine ([#1871](https://github.com/googleapis/python-bigquery-dataframes/issues/1871)) ([6454aff](https://github.com/googleapis/python-bigquery-dataframes/commit/6454aff726dee791acbac98f893075ee5ee6d9a1)) -* Add simple stats support to hybrid local pushdown ([#1873](https://github.com/googleapis/python-bigquery-dataframes/issues/1873)) ([8715105](https://github.com/googleapis/python-bigquery-dataframes/commit/8715105239216bffe899ddcbb15805f2e3063af4)) - - -### Bug Fixes - -* Fix issues where duration type returned as int ([#1875](https://github.com/googleapis/python-bigquery-dataframes/issues/1875)) ([f30f750](https://github.com/googleapis/python-bigquery-dataframes/commit/f30f75053a6966abd1a6a644c23efb86b2ac568d)) - - -### Documentation - -* Update gsutil commands to gcloud commands ([#1876](https://github.com/googleapis/python-bigquery-dataframes/issues/1876)) ([c289f70](https://github.com/googleapis/python-bigquery-dataframes/commit/c289f7061320ec6d9de099cab2416cc9f289baac)) - -## [2.9.0](https://github.com/googleapis/python-bigquery-dataframes/compare/v2.8.0...v2.9.0) (2025-06-30) - - -### Features - -* Add `bpd.read_arrow` to convert an Arrow object into a bigframes DataFrame ([#1855](https://github.com/googleapis/python-bigquery-dataframes/issues/1855)) ([633bf98](https://github.com/googleapis/python-bigquery-dataframes/commit/633bf98fde33264be4fc9d7454e541c560589152)) -* Add experimental polars execution ([#1747](https://github.com/googleapis/python-bigquery-dataframes/issues/1747)) ([daf0c3b](https://github.com/googleapis/python-bigquery-dataframes/commit/daf0c3b349fb1e85e7070c54a2d3f5460f5e40c9)) -* Add size op support in local engine ([#1865](https://github.com/googleapis/python-bigquery-dataframes/issues/1865)) ([942e66c](https://github.com/googleapis/python-bigquery-dataframes/commit/942e66c483c9afbb680a7af56c9e9a76172a33e1)) -* Create `deploy_remote_function` and `deploy_udf` functions to immediately deploy functions to BigQuery ([#1832](https://github.com/googleapis/python-bigquery-dataframes/issues/1832)) ([c706759](https://github.com/googleapis/python-bigquery-dataframes/commit/c706759b85359b6d23ce3449f6ab138ad2d22f9d)) -* Support index item assign in Series ([#1868](https://github.com/googleapis/python-bigquery-dataframes/issues/1868)) ([c5d251a](https://github.com/googleapis/python-bigquery-dataframes/commit/c5d251a1d454bb4ef55ea9905faeadd646a23b14)) -* Support item assignment in series ([#1859](https://github.com/googleapis/python-bigquery-dataframes/issues/1859)) ([25684ff](https://github.com/googleapis/python-bigquery-dataframes/commit/25684ff60367f49dd318d4677a7438abdc98bff9)) -* Support local execution of comparison ops ([#1849](https://github.com/googleapis/python-bigquery-dataframes/issues/1849)) ([1c45ccb](https://github.com/googleapis/python-bigquery-dataframes/commit/1c45ccb133091aa85bc34450704fc8cab3d9296b)) - - -### Bug Fixes - -* Fix bug selecting column repeatedly ([#1858](https://github.com/googleapis/python-bigquery-dataframes/issues/1858)) ([cc339e9](https://github.com/googleapis/python-bigquery-dataframes/commit/cc339e9938129cac896460e3a794b3ec8479fa4a)) -* Fix bug with DataFrame.agg for string values ([#1870](https://github.com/googleapis/python-bigquery-dataframes/issues/1870)) ([81e4d64](https://github.com/googleapis/python-bigquery-dataframes/commit/81e4d64c5a3bd8d30edaf909d0bef2d1d1a51c01)) -* Generate GoogleSQL instead of legacy SQL data types for `dry_run=True` from `bpd._read_gbq_colab` with local pandas DataFrame ([#1867](https://github.com/googleapis/python-bigquery-dataframes/issues/1867)) ([fab3c38](https://github.com/googleapis/python-bigquery-dataframes/commit/fab3c387b2ad66043244fa813a366e613b41c60f)) -* Revert dict back to protobuf in the iam binding update ([#1838](https://github.com/googleapis/python-bigquery-dataframes/issues/1838)) ([9fb3cb4](https://github.com/googleapis/python-bigquery-dataframes/commit/9fb3cb444607df6736d383a2807059bca470c453)) - - -### Documentation - -* Add data visualization samples for public doc ([#1847](https://github.com/googleapis/python-bigquery-dataframes/issues/1847)) ([15e1277](https://github.com/googleapis/python-bigquery-dataframes/commit/15e1277b1413de18a5e36f72959a99701d6df08b)) -* Changed broken logo ([#1866](https://github.com/googleapis/python-bigquery-dataframes/issues/1866)) ([e3c06b4](https://github.com/googleapis/python-bigquery-dataframes/commit/e3c06b4a07d0669a42460d081f1582b681ae3dd5)) -* Update ai.forecast notebook ([#1844](https://github.com/googleapis/python-bigquery-dataframes/issues/1844)) ([1863538](https://github.com/googleapis/python-bigquery-dataframes/commit/186353888db537b561ee994256f998df361b4071)) - -## [2.8.0](https://github.com/googleapis/python-bigquery-dataframes/compare/v2.7.0...v2.8.0) (2025-06-23) - - -### ⚠ BREAKING CHANGES - -* add required param 'engine' to multimodal functions ([#1834](https://github.com/googleapis/python-bigquery-dataframes/issues/1834)) - -### Features - -* Add `bpd.options.compute.maximum_result_rows` option to limit client data download ([#1829](https://github.com/googleapis/python-bigquery-dataframes/issues/1829)) ([e22a3f6](https://github.com/googleapis/python-bigquery-dataframes/commit/e22a3f61a02cc1b7a5155556e5a07a1a2fea1d82)) -* Add `bpd.options.display.repr_mode = "anywidget"` to create an interactive display of the results ([#1820](https://github.com/googleapis/python-bigquery-dataframes/issues/1820)) ([be0a3cf](https://github.com/googleapis/python-bigquery-dataframes/commit/be0a3cf7711dadc68d8366ea90b99855773e2a2e)) -* Add DataFrame.ai.forecast() support ([#1828](https://github.com/googleapis/python-bigquery-dataframes/issues/1828)) ([7bc7f36](https://github.com/googleapis/python-bigquery-dataframes/commit/7bc7f36fc20d233f4cf5ed688cc5dcaf100ce4fb)) -* Add describe() method to Series ([#1827](https://github.com/googleapis/python-bigquery-dataframes/issues/1827)) ([a4205f8](https://github.com/googleapis/python-bigquery-dataframes/commit/a4205f882012820c034cb15d73b2768ec4ad3ac8)) -* Add required param 'engine' to multimodal functions ([#1834](https://github.com/googleapis/python-bigquery-dataframes/issues/1834)) ([37666e4](https://github.com/googleapis/python-bigquery-dataframes/commit/37666e4c137d52c28ab13477dfbcc6e92b913334)) - - -### Performance Improvements - -* Produce simpler sql ([#1836](https://github.com/googleapis/python-bigquery-dataframes/issues/1836)) ([cf9c22a](https://github.com/googleapis/python-bigquery-dataframes/commit/cf9c22a09c4e668a598fa1dad0f6a07b59bc6524)) - - -### Documentation - -* Add ai.forecast notebook ([#1840](https://github.com/googleapis/python-bigquery-dataframes/issues/1840)) ([2430497](https://github.com/googleapis/python-bigquery-dataframes/commit/24304972fdbdfd12c25c7f4ef5a7b280f334801a)) - -## [2.7.0](https://github.com/googleapis/python-bigquery-dataframes/compare/v2.6.0...v2.7.0) (2025-06-16) - - -### Features - -* Add bbq.json_query_array and warn bbq.json_extract_array deprecated ([#1811](https://github.com/googleapis/python-bigquery-dataframes/issues/1811)) ([dc9eb27](https://github.com/googleapis/python-bigquery-dataframes/commit/dc9eb27fa75e90c2c95a0619551bf67aea6ef63b)) -* Add bbq.json_value_array and deprecate bbq.json_extract_string_array ([#1818](https://github.com/googleapis/python-bigquery-dataframes/issues/1818)) ([019051e](https://github.com/googleapis/python-bigquery-dataframes/commit/019051e453d81769891aa398475ebd04d1826e81)) -* Add groupby cumcount ([#1798](https://github.com/googleapis/python-bigquery-dataframes/issues/1798)) ([18f43e8](https://github.com/googleapis/python-bigquery-dataframes/commit/18f43e8b58e03a27b021bce07566a3d006ac3679)) -* Support custom build service account in `remote_function` ([#1796](https://github.com/googleapis/python-bigquery-dataframes/issues/1796)) ([e586151](https://github.com/googleapis/python-bigquery-dataframes/commit/e586151df81917b49f702ae496aaacbd02931636)) - - -### Bug Fixes - -* Correct read_csv behaviours with use_cols, names, index_col ([#1804](https://github.com/googleapis/python-bigquery-dataframes/issues/1804)) ([855031a](https://github.com/googleapis/python-bigquery-dataframes/commit/855031a316a6957731a5d1c5e59dedb9757d9f7a)) -* Fix single row broadcast with null index ([#1803](https://github.com/googleapis/python-bigquery-dataframes/issues/1803)) ([080eb7b](https://github.com/googleapis/python-bigquery-dataframes/commit/080eb7be3cde591e08cad0d5c52c68cc0b25ade8)) - - -### Documentation - -* Document how to use ai.map() for information extraction ([#1808](https://github.com/googleapis/python-bigquery-dataframes/issues/1808)) ([b586746](https://github.com/googleapis/python-bigquery-dataframes/commit/b5867464a5bf30300dcfc069eda546b11f03146c)) -* Rearrange README.rst to include a short code sample ([#1812](https://github.com/googleapis/python-bigquery-dataframes/issues/1812)) ([f6265db](https://github.com/googleapis/python-bigquery-dataframes/commit/f6265dbb8e22de81bb59c7def175cd325e85c041)) -* Use pandas API instead of pandas-like or pandas-compatible ([#1825](https://github.com/googleapis/python-bigquery-dataframes/issues/1825)) ([aa32369](https://github.com/googleapis/python-bigquery-dataframes/commit/aa323694e161f558bc5e60490c2f21008961e2ca)) - -## [2.6.0](https://github.com/googleapis/python-bigquery-dataframes/compare/v2.5.0...v2.6.0) (2025-06-09) - - -### Features - -* Add blob.transcribe function ([#1773](https://github.com/googleapis/python-bigquery-dataframes/issues/1773)) ([86159a7](https://github.com/googleapis/python-bigquery-dataframes/commit/86159a7d24102574c26764a056478757844e2eca)) -* Implement ai.classify() ([#1781](https://github.com/googleapis/python-bigquery-dataframes/issues/1781)) ([8af26d0](https://github.com/googleapis/python-bigquery-dataframes/commit/8af26d07cf3e8b22e0c69dd0172352fadc1857d8)) -* Implement item() for Series and Index ([#1792](https://github.com/googleapis/python-bigquery-dataframes/issues/1792)) ([d2154c8](https://github.com/googleapis/python-bigquery-dataframes/commit/d2154c82fa0fed6e89c47db747d3c9cd57f9c618)) -* Implement ST_ISCLOSED geography function ([#1789](https://github.com/googleapis/python-bigquery-dataframes/issues/1789)) ([36bc179](https://github.com/googleapis/python-bigquery-dataframes/commit/36bc179ee7ef9b0b6799f98f8fac3f64d91412af)) -* Implement ST_LENGTH geography function ([#1791](https://github.com/googleapis/python-bigquery-dataframes/issues/1791)) ([c5b7fda](https://github.com/googleapis/python-bigquery-dataframes/commit/c5b7fdae74a22e581f7705bc0cf5390e928f4425)) -* Support isin with bigframes.pandas.Index arg ([#1779](https://github.com/googleapis/python-bigquery-dataframes/issues/1779)) ([e480d29](https://github.com/googleapis/python-bigquery-dataframes/commit/e480d29f03636fa9824404ef90c510701e510195)) - - -### Bug Fixes - -* Address `read_csv` with both `index_col` and `use_cols` behavior inconsistency with pandas ([#1785](https://github.com/googleapis/python-bigquery-dataframes/issues/1785)) ([ba7c313](https://github.com/googleapis/python-bigquery-dataframes/commit/ba7c313c8d308e3ff3f736b60978cb7a51715209)) -* Allow KMeans model init parameter as k-means++ alias ([#1790](https://github.com/googleapis/python-bigquery-dataframes/issues/1790)) ([0b59cf1](https://github.com/googleapis/python-bigquery-dataframes/commit/0b59cf1008613770fa1433c6da395e755c86fe22)) -* Replace function now can handle pd.NA value. ([#1786](https://github.com/googleapis/python-bigquery-dataframes/issues/1786)) ([7269512](https://github.com/googleapis/python-bigquery-dataframes/commit/7269512a28eb42029447d5380c764353278a74e1)) - - -### Documentation - -* Adjust strip method examples to match latest pandas ([#1797](https://github.com/googleapis/python-bigquery-dataframes/issues/1797)) ([817b0c0](https://github.com/googleapis/python-bigquery-dataframes/commit/817b0c0c5dc481598fbfdbe40fd925fb38f3a066)) -* Fix docstrings to improve html rendering of code examples ([#1788](https://github.com/googleapis/python-bigquery-dataframes/issues/1788)) ([38d9b73](https://github.com/googleapis/python-bigquery-dataframes/commit/38d9b7376697f8e19124e5d1f5fccda82d920b92)) - -## [2.5.0](https://github.com/googleapis/python-bigquery-dataframes/compare/v2.4.0...v2.5.0) (2025-05-30) - - -### ⚠ BREAKING CHANGES - -* the updated `ai.map()` parameter list is not backward-compatible - -### Features - -* Add `bpd.options.bigquery.requests_transport_adapters` option ([#1755](https://github.com/googleapis/python-bigquery-dataframes/issues/1755)) ([bb45db8](https://github.com/googleapis/python-bigquery-dataframes/commit/bb45db8afdffa1417f11c050d40d4ec6d15b8654)) -* Add bbq.json_query and warn bbq.json_extract deprecated ([#1756](https://github.com/googleapis/python-bigquery-dataframes/issues/1756)) ([ec81dd2](https://github.com/googleapis/python-bigquery-dataframes/commit/ec81dd2228697d5bf193d86396cf7f3212e0289d)) -* Add bpd.options.reset() method ([#1743](https://github.com/googleapis/python-bigquery-dataframes/issues/1743)) ([36c359d](https://github.com/googleapis/python-bigquery-dataframes/commit/36c359d2521089e186a412d353daf9de6cfbc8f4)) -* Add DataFrame.round method ([#1742](https://github.com/googleapis/python-bigquery-dataframes/issues/1742)) ([3ea6043](https://github.com/googleapis/python-bigquery-dataframes/commit/3ea6043be7025fa7a11cca27b02f5505bbc9b129)) -* Add deferred data uploading ([#1720](https://github.com/googleapis/python-bigquery-dataframes/issues/1720)) ([1f6442e](https://github.com/googleapis/python-bigquery-dataframes/commit/1f6442e576c35ec784ccf9cab3d081d46e45a5ce)) -* Add deprecation warning to Gemini-1.5-X, text-embedding-004, and remove remove legacy models in notebooks and docs ([#1723](https://github.com/googleapis/python-bigquery-dataframes/issues/1723)) ([80aad9a](https://github.com/googleapis/python-bigquery-dataframes/commit/80aad9af794c2e06d1608c879f459a836fd4448b)) -* Add structured output for ai map, ai filter and ai join ([#1746](https://github.com/googleapis/python-bigquery-dataframes/issues/1746)) ([133ac6b](https://github.com/googleapis/python-bigquery-dataframes/commit/133ac6b0e1f1e7a12844a4b6fd5b26df59f7ef37)) -* Add support for df.loc[list, column(s)] ([#1761](https://github.com/googleapis/python-bigquery-dataframes/issues/1761)) ([768a757](https://github.com/googleapis/python-bigquery-dataframes/commit/768a7570845c4eb88f495d7f3c0f3158accdc231)) -* Include bq schema and query string in dry run results ([#1752](https://github.com/googleapis/python-bigquery-dataframes/issues/1752)) ([bb51147](https://github.com/googleapis/python-bigquery-dataframes/commit/bb511475b74cc253230725846098a9045be2e324)) -* Support `inplace=True` in `rename` and `rename_axis` ([#1744](https://github.com/googleapis/python-bigquery-dataframes/issues/1744)) ([734cc65](https://github.com/googleapis/python-bigquery-dataframes/commit/734cc652e435dc5d97a23411735aa51b7824e381)) -* Support `unique()` for Index ([#1750](https://github.com/googleapis/python-bigquery-dataframes/issues/1750)) ([27fac78](https://github.com/googleapis/python-bigquery-dataframes/commit/27fac78cb5654e5655aec861062837a7d4f3f679)) -* Support astype conversions to and from JSON dtypes ([#1716](https://github.com/googleapis/python-bigquery-dataframes/issues/1716)) ([8ef4de1](https://github.com/googleapis/python-bigquery-dataframes/commit/8ef4de10151717f88364a909b29fa7600e959ada)) -* Support dict param for dataframe.agg() ([#1772](https://github.com/googleapis/python-bigquery-dataframes/issues/1772)) ([f9c29c8](https://github.com/googleapis/python-bigquery-dataframes/commit/f9c29c85053d8111a74ce382490daed36f8bb35b)) -* Support dtype parameter in read_csv for bigquery engine ([#1749](https://github.com/googleapis/python-bigquery-dataframes/issues/1749)) ([50dca4c](https://github.com/googleapis/python-bigquery-dataframes/commit/50dca4c706d78673b03f90eccf776118247ba30b)) -* Use read api for some peek ops ([#1731](https://github.com/googleapis/python-bigquery-dataframes/issues/1731)) ([108f4d2](https://github.com/googleapis/python-bigquery-dataframes/commit/108f4d259e1bcfbe6c7aa3c3c3f8f605cf7615ee)) - - -### Bug Fixes - -* Fix clip int series with float bounds ([#1739](https://github.com/googleapis/python-bigquery-dataframes/issues/1739)) ([d451aef](https://github.com/googleapis/python-bigquery-dataframes/commit/d451aefd2181aef250c3b48cceac09063081cab2)) -* Fix error with self-merge operations ([#1774](https://github.com/googleapis/python-bigquery-dataframes/issues/1774)) ([e5fe143](https://github.com/googleapis/python-bigquery-dataframes/commit/e5fe14339b4a40ab4a25657ee0453e4108cf8bba)) -* Fix the default value for na_value for numpy conversions ([#1766](https://github.com/googleapis/python-bigquery-dataframes/issues/1766)) ([0629cac](https://github.com/googleapis/python-bigquery-dataframes/commit/0629cac7f9a9370a72c1ae25e014eb478a4c8c08)) -* Include location in Session-based temporary storage manager DDL queries ([#1780](https://github.com/googleapis/python-bigquery-dataframes/issues/1780)) ([acba032](https://github.com/googleapis/python-bigquery-dataframes/commit/acba0321cafeb49f3e560a364ebbf3d15fb8af88)) -* Prevent creating unnecessary client objects in multithreaded environments ([#1757](https://github.com/googleapis/python-bigquery-dataframes/issues/1757)) ([1cf9f5e](https://github.com/googleapis/python-bigquery-dataframes/commit/1cf9f5e8dba733ee26d15fc5edc44c81e094e9a0)) -* Reduce bigquery table modification via DML for to_gbq ([#1737](https://github.com/googleapis/python-bigquery-dataframes/issues/1737)) ([545cdca](https://github.com/googleapis/python-bigquery-dataframes/commit/545cdcac1361607678c2574f0f31eb43950073e5)) -* Stop ignoring arguments to `MatrixFactorization.score(X, y)` ([#1726](https://github.com/googleapis/python-bigquery-dataframes/issues/1726)) ([55c07e9](https://github.com/googleapis/python-bigquery-dataframes/commit/55c07e9d4315949c37ffa3e03c8fedc6daf17faf)) -* Support JSON and STRUCT for bbq.sql_scalar ([#1754](https://github.com/googleapis/python-bigquery-dataframes/issues/1754)) ([190390b](https://github.com/googleapis/python-bigquery-dataframes/commit/190390b804c2131c2eaa624d7f025febb7784b01)) -* Support str.replace re.compile with flags ([#1736](https://github.com/googleapis/python-bigquery-dataframes/issues/1736)) ([f8d2cd2](https://github.com/googleapis/python-bigquery-dataframes/commit/f8d2cd24281415f4a8f9193b676f5483128cd173)) - - -### Performance Improvements - -* Faster local data comparison using idenitity ([#1738](https://github.com/googleapis/python-bigquery-dataframes/issues/1738)) ([2858b1e](https://github.com/googleapis/python-bigquery-dataframes/commit/2858b1efb4fe74097dcb17c086ee1dc18e53053c)) -* Optimize repr for unordered gbq table ([#1778](https://github.com/googleapis/python-bigquery-dataframes/issues/1778)) ([2bc4fbc](https://github.com/googleapis/python-bigquery-dataframes/commit/2bc4fbc78eba4bb2ee335e0475700a7ca5bc84d7)) -* Use JOB_CREATION_OPTIONAL when `allow_large_results=False` ([#1763](https://github.com/googleapis/python-bigquery-dataframes/issues/1763)) ([15f3f2a](https://github.com/googleapis/python-bigquery-dataframes/commit/15f3f2aa42cfe4a2233f62c5f8906e7f7658f9fa)) - - -### Dependencies - -* Avoid `gcsfs==2025.5.0` ([#1762](https://github.com/googleapis/python-bigquery-dataframes/issues/1762)) ([68d5e2c](https://github.com/googleapis/python-bigquery-dataframes/commit/68d5e2cbef3510cadc7e9dd199117c1e3b02d19f)) - - -### Documentation - -* Add llm output_schema notebook ([#1732](https://github.com/googleapis/python-bigquery-dataframes/issues/1732)) ([b2261cc](https://github.com/googleapis/python-bigquery-dataframes/commit/b2261cc07cd58b51d212f9bf495c5022e587f816)) -* Add MatrixFactorization to the table of contents ([#1725](https://github.com/googleapis/python-bigquery-dataframes/issues/1725)) ([611e43b](https://github.com/googleapis/python-bigquery-dataframes/commit/611e43b156483848a5470f889fb7b2b473ecff4d)) -* Fix typo for "population" in the `GeminiTextGenerator.predict(..., output_schema={...})` sample notebook ([#1748](https://github.com/googleapis/python-bigquery-dataframes/issues/1748)) ([bd07e05](https://github.com/googleapis/python-bigquery-dataframes/commit/bd07e05d26820313c052eaf41c267a1ab20b4fc6)) -* Integrations notebook extracts token from `bqclient._http.credentials` instead of `bqclient._credentials` ([#1784](https://github.com/googleapis/python-bigquery-dataframes/issues/1784)) ([6e63eca](https://github.com/googleapis/python-bigquery-dataframes/commit/6e63eca29f20d83435878273604816ce7595c396)) -* Updated multimodal notebook instructions ([#1745](https://github.com/googleapis/python-bigquery-dataframes/issues/1745)) ([1df8ca6](https://github.com/googleapis/python-bigquery-dataframes/commit/1df8ca6312ee428d55c2091a00c73b13d9a6b193)) -* Use partial ordering mode in the quickstart sample ([#1734](https://github.com/googleapis/python-bigquery-dataframes/issues/1734)) ([476b7dd](https://github.com/googleapis/python-bigquery-dataframes/commit/476b7dd7c2639cb6804272d06aa5c1db666819da)) - -## [2.4.0](https://github.com/googleapis/python-bigquery-dataframes/compare/v2.3.0...v2.4.0) (2025-05-12) - - -### Features - -* Add "dayofyear" property for `dt` accessors ([#1692](https://github.com/googleapis/python-bigquery-dataframes/issues/1692)) ([9d4a59d](https://github.com/googleapis/python-bigquery-dataframes/commit/9d4a59ddf22793d4e0587ea2f8648fae937875f3)) -* Add `.dt.days`, `.dt.seconds`, `dt.microseconds`, and `dt.total_seconds()` for timedelta series. ([#1713](https://github.com/googleapis/python-bigquery-dataframes/issues/1713)) ([2b3a45f](https://github.com/googleapis/python-bigquery-dataframes/commit/2b3a45f8c1fd299ee97cf1c343df7c80175b4287)) -* Add `DatetimeIndex` class ([#1719](https://github.com/googleapis/python-bigquery-dataframes/issues/1719)) ([c3c830c](https://github.com/googleapis/python-bigquery-dataframes/commit/c3c830cf20397830d531a89edf5302aede5d48a0)) -* Add `isocalendar()` for dt accessor" ([#1717](https://github.com/googleapis/python-bigquery-dataframes/issues/1717)) ([0479763](https://github.com/googleapis/python-bigquery-dataframes/commit/047976315dcbaed86e50d47f545b76c3a513dafb)) -* Add bigframes.bigquery.json_value ([#1697](https://github.com/googleapis/python-bigquery-dataframes/issues/1697)) ([46a9c53](https://github.com/googleapis/python-bigquery-dataframes/commit/46a9c53256be2a293f96122ba6b330564383bcd5)) -* Add blob.exif function support ([#1703](https://github.com/googleapis/python-bigquery-dataframes/issues/1703)) ([3f79528](https://github.com/googleapis/python-bigquery-dataframes/commit/3f79528781abe9bfc122f6f6e26bfa08b029265a)) -* Add inplace arg support to sort methods ([#1710](https://github.com/googleapis/python-bigquery-dataframes/issues/1710)) ([d1ccb52](https://github.com/googleapis/python-bigquery-dataframes/commit/d1ccb524ea26deac1cf9e481e9d55f9ae166247b)) -* Improve error message in `Series.apply` for direct udfs ([#1673](https://github.com/googleapis/python-bigquery-dataframes/issues/1673)) ([1a658b2](https://github.com/googleapis/python-bigquery-dataframes/commit/1a658b2aa43c4a7a7f2007a509b0e1401f925dab)) -* Publish bigframes blob(Multimodal) to preview ([#1693](https://github.com/googleapis/python-bigquery-dataframes/issues/1693)) ([e4c85ba](https://github.com/googleapis/python-bigquery-dataframes/commit/e4c85ba4813469d39edd7352201aefc26642d14c)) -* Support () operator between timedeltas ([#1702](https://github.com/googleapis/python-bigquery-dataframes/issues/1702)) ([edaac89](https://github.com/googleapis/python-bigquery-dataframes/commit/edaac89c03db1ffc93b56275c765d8a964f7d02d)) -* Support forecast_limit_lower_bound and forecast_limit_upper_bound in ARIMA_PLUS (and ARIMA_PLUS_XREG) models ([#1305](https://github.com/googleapis/python-bigquery-dataframes/issues/1305)) ([b16740e](https://github.com/googleapis/python-bigquery-dataframes/commit/b16740ef4ad7b1fbf731595238cf087c93c93066)) -* Support to_strip parameter for str.strip, str.lstrip and str.rstrip ([#1705](https://github.com/googleapis/python-bigquery-dataframes/issues/1705)) ([a84ee75](https://github.com/googleapis/python-bigquery-dataframes/commit/a84ee75ddd4d9dae1463e505549d74eb4f819338)) - - -### Bug Fixes - -* Fix dayofyear doc test ([#1701](https://github.com/googleapis/python-bigquery-dataframes/issues/1701)) ([9b777a0](https://github.com/googleapis/python-bigquery-dataframes/commit/9b777a019aa31a115a22289f21c7cd9df07aa8b9)) -* Fix issues with chunked arrow data ([#1700](https://github.com/googleapis/python-bigquery-dataframes/issues/1700)) ([e3289b7](https://github.com/googleapis/python-bigquery-dataframes/commit/e3289b7a64ee1400c6cb78e75cff4759d8da8b7a)) -* Rename columns with protected names such as `_TABLE_SUFFIX` in `to_gbq()` ([#1691](https://github.com/googleapis/python-bigquery-dataframes/issues/1691)) ([8ec6079](https://github.com/googleapis/python-bigquery-dataframes/commit/8ec607986fd38f357746fbaeabef2ce7ab3e501f)) - - -### Performance Improvements - -* Defer query in `read_gbq` with wildcard tables ([#1661](https://github.com/googleapis/python-bigquery-dataframes/issues/1661)) ([5c125c9](https://github.com/googleapis/python-bigquery-dataframes/commit/5c125c99d4632c617425c2ef5c399d17878c0043)) -* Rechunk result pages client side ([#1680](https://github.com/googleapis/python-bigquery-dataframes/issues/1680)) ([67d8760](https://github.com/googleapis/python-bigquery-dataframes/commit/67d876076027b6123e49d1d8ddee4e45eaa28f5d)) - - -### Dependencies - -* Move bigtable and pubsub to extras ([#1696](https://github.com/googleapis/python-bigquery-dataframes/issues/1696)) ([597d817](https://github.com/googleapis/python-bigquery-dataframes/commit/597d8178048b203cea4777f29b1ce95de7b0670e)) - - -### Documentation - -* Add snippets for Matrix Factorization tutorials ([#1630](https://github.com/googleapis/python-bigquery-dataframes/issues/1630)) ([24b37ae](https://github.com/googleapis/python-bigquery-dataframes/commit/24b37aece60460aabecce306397eb1bf6686f8a7)) -* Deprecate `bpd.options.bigquery.allow_large_results` in favor of `bpd.options.compute.allow_large_results` ([#1597](https://github.com/googleapis/python-bigquery-dataframes/issues/1597)) ([18780b4](https://github.com/googleapis/python-bigquery-dataframes/commit/18780b48a17dba2b3b3542500f027ae9527f6bee)) -* Include import statement in the bigframes code snippet ([#1699](https://github.com/googleapis/python-bigquery-dataframes/issues/1699)) ([08d70b6](https://github.com/googleapis/python-bigquery-dataframes/commit/08d70b6ad3ab3ac7b9a57d93da00168a8de7df9a)) -* Include the clean-up step in the udf code snippet ([#1698](https://github.com/googleapis/python-bigquery-dataframes/issues/1698)) ([48992e2](https://github.com/googleapis/python-bigquery-dataframes/commit/48992e26d460832704401bd2a3eedb800c5061cc)) -* Move multimodal notebook out of experimental folder ([#1712](https://github.com/googleapis/python-bigquery-dataframes/issues/1712)) ([68b6532](https://github.com/googleapis/python-bigquery-dataframes/commit/68b6532a780d6349a4b65994b696c8026457eb94)) -* Update blob_display option in snippets ([#1714](https://github.com/googleapis/python-bigquery-dataframes/issues/1714)) ([8b30143](https://github.com/googleapis/python-bigquery-dataframes/commit/8b30143e3320a730df168b5a72e6d18e631135ee)) - -## [2.3.0](https://github.com/googleapis/python-bigquery-dataframes/compare/v2.2.0...v2.3.0) (2025-05-06) - - -### Features - -* Add dry_run parameter to `read_gbq()`, `read_gbq_table()` and `read_gbq_query()` ([#1674](https://github.com/googleapis/python-bigquery-dataframes/issues/1674)) ([4c5dee5](https://github.com/googleapis/python-bigquery-dataframes/commit/4c5dee5e6f4b30deb01e258670aa21dbf3ac9aa5)) - - -### Bug Fixes - -* Guarantee guid thread safety across threads ([#1684](https://github.com/googleapis/python-bigquery-dataframes/issues/1684)) ([cb0267d](https://github.com/googleapis/python-bigquery-dataframes/commit/cb0267deea227ea85f20d6dbef8c29cf03526d7a)) -* Support large lists of lists in bpd.Series() constructor ([#1662](https://github.com/googleapis/python-bigquery-dataframes/issues/1662)) ([0f4024c](https://github.com/googleapis/python-bigquery-dataframes/commit/0f4024c84508c17657a9104ef1f8718094827ada)) -* Use value equality to check types for unix epoch functions and timestamp diff ([#1690](https://github.com/googleapis/python-bigquery-dataframes/issues/1690)) ([81e8fb8](https://github.com/googleapis/python-bigquery-dataframes/commit/81e8fb8627f1d35423dbbdcc99d02ab0ad362d11)) - - -### Performance Improvements - -* `to_datetime()` now avoids caching inputs unless data is inspected to infer format ([#1667](https://github.com/googleapis/python-bigquery-dataframes/issues/1667)) ([dd08857](https://github.com/googleapis/python-bigquery-dataframes/commit/dd08857f65140cbe5c524050d2d538949897c3cc)) - - -### Documentation - -* Add a visualization notebook to BigFrame samples ([#1675](https://github.com/googleapis/python-bigquery-dataframes/issues/1675)) ([ee062bf](https://github.com/googleapis/python-bigquery-dataframes/commit/ee062bfc29c27949205ca21d6c1dcd6125300e5e)) -* Fix spacing of k-means code snippet ([#1687](https://github.com/googleapis/python-bigquery-dataframes/issues/1687)) ([99f45dd](https://github.com/googleapis/python-bigquery-dataframes/commit/99f45dd14bd9632d209389a5fef009f18c57adbf)) -* Update snippet for `Create a k-means` model tutorial ([#1664](https://github.com/googleapis/python-bigquery-dataframes/issues/1664)) ([761c364](https://github.com/googleapis/python-bigquery-dataframes/commit/761c364f4df045b9e9d8d3d5fee91d9a87b772db)) - -## [2.2.0](https://github.com/googleapis/python-bigquery-dataframes/compare/v2.1.0...v2.2.0) (2025-04-30) - - -### Features - -* Add gemini-2.0-flash-001 and gemini-2.0-flash-lite-001 to fine tune score endponts and multimodal endpoints ([#1650](https://github.com/googleapis/python-bigquery-dataframes/issues/1650)) ([4fb54df](https://github.com/googleapis/python-bigquery-dataframes/commit/4fb54dfe448604a90fc1818cf18b1e77e1e7227b)) -* Add GeminiTextGenerator.predict structured output ([#1653](https://github.com/googleapis/python-bigquery-dataframes/issues/1653)) ([6199023](https://github.com/googleapis/python-bigquery-dataframes/commit/6199023a6a71e72e926f5879e74a15215bc6e4a0)) -* DataFrames.__getitem__ support for slice input ([#1668](https://github.com/googleapis/python-bigquery-dataframes/issues/1668)) ([563f0cb](https://github.com/googleapis/python-bigquery-dataframes/commit/563f0cbdf4a18c3cd1bd2a4b52de823165638911)) -* Print right origin of `PreviewWarning` for the `bpd.udf` ([#1629](https://github.com/googleapis/python-bigquery-dataframes/issues/1629)) ([48d10d1](https://github.com/googleapis/python-bigquery-dataframes/commit/48d10d1f0150a29dd3b91f505f8d3874e0b88c42)) -* Session.bytes_processed_sum will be updated when allow_large_re… ([#1669](https://github.com/googleapis/python-bigquery-dataframes/issues/1669)) ([ae312db](https://github.com/googleapis/python-bigquery-dataframes/commit/ae312dbed25da6da5e2817d5c9838654c2a1ad1c)) -* Short circuit query for local scan ([#1618](https://github.com/googleapis/python-bigquery-dataframes/issues/1618)) ([e84f232](https://github.com/googleapis/python-bigquery-dataframes/commit/e84f232b0fc5e2167a7cddb355cf0c8837ae5422)) -* Support names parameter in read_csv for bigquery engine ([#1659](https://github.com/googleapis/python-bigquery-dataframes/issues/1659)) ([3388191](https://github.com/googleapis/python-bigquery-dataframes/commit/33881914ab5b8d0e701eabd9c731aed1deab3d49)) -* Support passing list of values to bigframes.core.sql.simple_literal ([#1641](https://github.com/googleapis/python-bigquery-dataframes/issues/1641)) ([102d363](https://github.com/googleapis/python-bigquery-dataframes/commit/102d363aa7e3245ff262c817bc756ea0eaee57e7)) -* Support write api as loading option ([#1617](https://github.com/googleapis/python-bigquery-dataframes/issues/1617)) ([c46ad06](https://github.com/googleapis/python-bigquery-dataframes/commit/c46ad0647785a9207359eba0fb5b6f7a16610f2a)) - - -### Bug Fixes - -* DataFrame accessors is not pupulated ([#1639](https://github.com/googleapis/python-bigquery-dataframes/issues/1639)) ([28afa2c](https://github.com/googleapis/python-bigquery-dataframes/commit/28afa2c73c0517f9365fab05193706631b656551)) -* Prefer remote schema instead of throwing on materialize conflicts ([#1644](https://github.com/googleapis/python-bigquery-dataframes/issues/1644)) ([53fc25b](https://github.com/googleapis/python-bigquery-dataframes/commit/53fc25bfc86e166b91e5001506051b1cac34c996)) -* Remove itertools.pairwise usage ([#1638](https://github.com/googleapis/python-bigquery-dataframes/issues/1638)) ([9662745](https://github.com/googleapis/python-bigquery-dataframes/commit/9662745265c8c6e42f372629bd2c7806542cee1a)) -* Resolve issue where pre-release versions of google-auth are installed ([#1491](https://github.com/googleapis/python-bigquery-dataframes/issues/1491)) ([ebb7a5e](https://github.com/googleapis/python-bigquery-dataframes/commit/ebb7a5e2b24fa57d6fe6a76d9b857ad44c67d194)) -* Resolve some of the typo errors ([#1655](https://github.com/googleapis/python-bigquery-dataframes/issues/1655)) ([cd7fbde](https://github.com/googleapis/python-bigquery-dataframes/commit/cd7fbde026522f53a23a4bb6585ad8629769fad1)) - - -### Performance Improvements - -* Fold row count ops when known ([#1656](https://github.com/googleapis/python-bigquery-dataframes/issues/1656)) ([c958dbe](https://github.com/googleapis/python-bigquery-dataframes/commit/c958dbea32b77cec9fddfc09e3b40d1da220a42c)) -* Use flyweight for node fields ([#1654](https://github.com/googleapis/python-bigquery-dataframes/issues/1654)) ([8482bfc](https://github.com/googleapis/python-bigquery-dataframes/commit/8482bfc1d4caa91a35c4fbf0be420301d05ad544)) - - -### Dependencies - -* Support shapely 1.8.5+ again ([#1651](https://github.com/googleapis/python-bigquery-dataframes/issues/1651)) ([ae83e61](https://github.com/googleapis/python-bigquery-dataframes/commit/ae83e61c49ade64d6f727e9f364bd2f1aeec6e19)) - - -### Documentation - -* Add JSON data types notebook ([#1647](https://github.com/googleapis/python-bigquery-dataframes/issues/1647)) ([9128c4a](https://github.com/googleapis/python-bigquery-dataframes/commit/9128c4a31dab487bc23f67c43380abd0beda5b1c)) -* Add sample code snippets for `udf` ([#1649](https://github.com/googleapis/python-bigquery-dataframes/issues/1649)) ([53caa8d](https://github.com/googleapis/python-bigquery-dataframes/commit/53caa8d689e64436f5313095ee27479a06d8e8a8)) -* Fix `bq_dataframes_template` notebook to work if partial ordering mode is enabled ([#1665](https://github.com/googleapis/python-bigquery-dataframes/issues/1665)) ([f442e7a](https://github.com/googleapis/python-bigquery-dataframes/commit/f442e7a07ff273ba3af74eeabafb62110b78f692)) -* Note that `udf` is in preview and must be python 3.11 compatible ([#1629](https://github.com/googleapis/python-bigquery-dataframes/issues/1629)) ([48d10d1](https://github.com/googleapis/python-bigquery-dataframes/commit/48d10d1f0150a29dd3b91f505f8d3874e0b88c42)) - -## [2.1.0](https://github.com/googleapis/python-bigquery-dataframes/compare/v2.0.0...v2.1.0) (2025-04-22) - - -### Features - -* Add `bigframes.bigquery.st_distance` function ([#1637](https://github.com/googleapis/python-bigquery-dataframes/issues/1637)) ([bf1ae70](https://github.com/googleapis/python-bigquery-dataframes/commit/bf1ae7091a02ad28d222fa63d311ed5ef3800807)) -* Enable local json string validations ([#1614](https://github.com/googleapis/python-bigquery-dataframes/issues/1614)) ([233347a](https://github.com/googleapis/python-bigquery-dataframes/commit/233347aca0ac55b2407e0f49430bf13536986e25)) -* Enhance `read_csv` `index_col` parameter support ([#1631](https://github.com/googleapis/python-bigquery-dataframes/issues/1631)) ([f4e5b26](https://github.com/googleapis/python-bigquery-dataframes/commit/f4e5b26b7b7b00ef807987c4b9c5fded56ad883f)) - - -### Bug Fixes - -* Add retry for test_clean_up_via_context_manager ([#1627](https://github.com/googleapis/python-bigquery-dataframes/issues/1627)) ([58e7cb0](https://github.com/googleapis/python-bigquery-dataframes/commit/58e7cb025a86959164643cebb725c853dc2ebc34)) -* Improve robustness of managed udf code extraction ([#1634](https://github.com/googleapis/python-bigquery-dataframes/issues/1634)) ([8cc56d5](https://github.com/googleapis/python-bigquery-dataframes/commit/8cc56d5118017beb2931519ddd1eb8e151852849)) - - -### Documentation - -* Add code samples in the `udf` API docstring ([#1632](https://github.com/googleapis/python-bigquery-dataframes/issues/1632)) ([f68b80c](https://github.com/googleapis/python-bigquery-dataframes/commit/f68b80cce2451a8c8d931a54e0cb69e02f34ce10)) - -## [2.0.0](https://github.com/googleapis/python-bigquery-dataframes/compare/v1.42.0...v2.0.0) (2025-04-17) - - -### ⚠ BREAKING CHANGES - -* make `dataset` and `name` params mandatory in `udf` ([#1619](https://github.com/googleapis/python-bigquery-dataframes/issues/1619)) -* Locational endpoints support is not available in BigFrames 2.0. -* change default LLM model to gemini-2.0-flash-001, drop PaLM2TextGenerator and PaLM2TextEmbeddingGenerator ([#1558](https://github.com/googleapis/python-bigquery-dataframes/issues/1558)) -* change default ingress setting for `remote_function` to internal-only ([#1544](https://github.com/googleapis/python-bigquery-dataframes/issues/1544)) -* make `remote_function` params keyword only ([#1537](https://github.com/googleapis/python-bigquery-dataframes/issues/1537)) -* make `remote_function` default service account explicit ([#1537](https://github.com/googleapis/python-bigquery-dataframes/issues/1537)) -* set `allow_large_results=False` by default ([#1541](https://github.com/googleapis/python-bigquery-dataframes/issues/1541)) - -### Features - -* Add `on` parameter in `dataframe.rolling()` and `dataframe.groupby.rolling()` ([#1556](https://github.com/googleapis/python-bigquery-dataframes/issues/1556)) ([45c9d9f](https://github.com/googleapis/python-bigquery-dataframes/commit/45c9d9fd1c5c13a8692435aa22861820fc11e347)) -* Add component to manage temporary tables ([#1559](https://github.com/googleapis/python-bigquery-dataframes/issues/1559)) ([0a4e245](https://github.com/googleapis/python-bigquery-dataframes/commit/0a4e245670e678f4ead0aec8f8b534e7fe97d112)) -* Add Series.to_pandas_batches() method ([#1592](https://github.com/googleapis/python-bigquery-dataframes/issues/1592)) ([09ce979](https://github.com/googleapis/python-bigquery-dataframes/commit/09ce97999cfc1ded72906b1c7307da5950978ae6)) -* Add support for creating a Matrix Factorization model ([#1330](https://github.com/googleapis/python-bigquery-dataframes/issues/1330)) ([b5297f9](https://github.com/googleapis/python-bigquery-dataframes/commit/b5297f909b08928b97d887764d6e5142c763a5a3)) -* Allow `input_types`, `output_type`, and `dataset` to be used positionally in `remote_function` ([#1560](https://github.com/googleapis/python-bigquery-dataframes/issues/1560)) ([bcac8c6](https://github.com/googleapis/python-bigquery-dataframes/commit/bcac8c6ed0b40902d0ccaef3f907e6acbe6a52ed)) -* Allow pandas.cut 'labels' parameter to accept a list of string ([#1549](https://github.com/googleapis/python-bigquery-dataframes/issues/1549)) ([af842b1](https://github.com/googleapis/python-bigquery-dataframes/commit/af842b174de7eef4908b397d6a745caf8eda7b3d)) -* Change default ingress setting for `remote_function` to internal-only ([#1544](https://github.com/googleapis/python-bigquery-dataframes/issues/1544)) ([c848a80](https://github.com/googleapis/python-bigquery-dataframes/commit/c848a80766ff68ea92c05a5dc5c26508e6755381)) -* Detect duplicate column/index names in read_gbq before send query. ([#1615](https://github.com/googleapis/python-bigquery-dataframes/issues/1615)) ([40d6960](https://github.com/googleapis/python-bigquery-dataframes/commit/40d696088114fb08e68df74be261144350b785c8)) -* Drop support for locational endpoints ([#1542](https://github.com/googleapis/python-bigquery-dataframes/issues/1542)) ([4bf2e43](https://github.com/googleapis/python-bigquery-dataframes/commit/4bf2e43ef4498b11f32086231fc4cc749fde966a)) -* Enable time range rolling for DataFrame, DataFrameGroupBy and SeriesGroupBy ([#1605](https://github.com/googleapis/python-bigquery-dataframes/issues/1605)) ([b4b7073](https://github.com/googleapis/python-bigquery-dataframes/commit/b4b7073da8348b6597bd3d90d1a758cd29586533)) -* Improve local data validation ([#1598](https://github.com/googleapis/python-bigquery-dataframes/issues/1598)) ([815e471](https://github.com/googleapis/python-bigquery-dataframes/commit/815e471b904d4bd708afc4bfbf1db945e76f75c9)) -* Make `remote_function` default service account explicit ([#1537](https://github.com/googleapis/python-bigquery-dataframes/issues/1537)) ([9eb9089](https://github.com/googleapis/python-bigquery-dataframes/commit/9eb9089ce3f1dad39761ba8ebc2d6f76261bd243)) -* Set `allow_large_results=False` by default ([#1541](https://github.com/googleapis/python-bigquery-dataframes/issues/1541)) ([e9fb712](https://github.com/googleapis/python-bigquery-dataframes/commit/e9fb7129a05e8ac7c938ffe30e86902950316f20)) -* Support bigquery connection in managed function ([#1554](https://github.com/googleapis/python-bigquery-dataframes/issues/1554)) ([f6f697a](https://github.com/googleapis/python-bigquery-dataframes/commit/f6f697afc167e0fa7ea923c0aed85a9ef257d61f)) -* Support bq connection path format ([#1550](https://github.com/googleapis/python-bigquery-dataframes/issues/1550)) ([e7eb918](https://github.com/googleapis/python-bigquery-dataframes/commit/e7eb918dd9df3569febe695f57c1a5909844fd3c)) -* Support gemini-2.0-X models ([#1558](https://github.com/googleapis/python-bigquery-dataframes/issues/1558)) ([3104fab](https://github.com/googleapis/python-bigquery-dataframes/commit/3104fab019d20b0cbc06cd81d43b3f34fd1dd987)) -* Support inlining small list, struct, json data ([#1589](https://github.com/googleapis/python-bigquery-dataframes/issues/1589)) ([2ce891f](https://github.com/googleapis/python-bigquery-dataframes/commit/2ce891fcd5bfd9f093fbcbb1ea35158d2bf9d8b9)) -* Support time range rolling on Series. ([#1590](https://github.com/googleapis/python-bigquery-dataframes/issues/1590)) ([6e98a2c](https://github.com/googleapis/python-bigquery-dataframes/commit/6e98a2cf53dd130963a9c5ba07e21ce6c32b7c6d)) -* Use session temp tables for all ephemeral storage ([#1569](https://github.com/googleapis/python-bigquery-dataframes/issues/1569)) ([9711b83](https://github.com/googleapis/python-bigquery-dataframes/commit/9711b830a7bdc6740f4ebeaaab6f37082ae5dfd9)) -* Use validated local storage for data uploads ([#1612](https://github.com/googleapis/python-bigquery-dataframes/issues/1612)) ([aee4159](https://github.com/googleapis/python-bigquery-dataframes/commit/aee4159807401d7432bb8c0c41859ada3291599b)) -* Warn the deprecated `max_download_size`, `random_state` and `sampling_method` parameters in `(DataFrame|Series).to_pandas()` ([#1573](https://github.com/googleapis/python-bigquery-dataframes/issues/1573)) ([b9623da](https://github.com/googleapis/python-bigquery-dataframes/commit/b9623daa847805abf420f0f11e173674fb147193)) - - -### Bug Fixes - -* `to_pandas_batches()` respects `page_size` and `max_results` again ([#1572](https://github.com/googleapis/python-bigquery-dataframes/issues/1572)) ([27c5905](https://github.com/googleapis/python-bigquery-dataframes/commit/27c59051549b83fdac954eaa3d257803c6f9133d)) -* Ensure `page_size` works correctly in `to_pandas_batches` when `max_results` is not set ([#1588](https://github.com/googleapis/python-bigquery-dataframes/issues/1588)) ([570cff3](https://github.com/googleapis/python-bigquery-dataframes/commit/570cff3c2efe3a47535bb3c931a345856d256a19)) -* Include role and service account in IAM exception ([#1564](https://github.com/googleapis/python-bigquery-dataframes/issues/1564)) ([8c50755](https://github.com/googleapis/python-bigquery-dataframes/commit/8c507556c5f61fab95c6389a8ad04d731df1df7b)) -* Make `dataset` and `name` params mandatory in `udf` ([#1619](https://github.com/googleapis/python-bigquery-dataframes/issues/1619)) ([637e860](https://github.com/googleapis/python-bigquery-dataframes/commit/637e860d3cea0a36b1e58a45ec9b9ab0059fb3b1)) -* Pandas.cut returns labels index for numeric breaks when labels=False ([#1548](https://github.com/googleapis/python-bigquery-dataframes/issues/1548)) ([b2375de](https://github.com/googleapis/python-bigquery-dataframes/commit/b2375decedbf1a793eedbbc9dc2efc2296f8cc6e)) -* Prevent `KeyError` in `bpd.concat` with empty DF and struct/array types DF ([#1568](https://github.com/googleapis/python-bigquery-dataframes/issues/1568)) ([b4da1cf](https://github.com/googleapis/python-bigquery-dataframes/commit/b4da1cf3c0fb94a2bb21e6039896accab85742d4)) -* Read_csv supports for tilde local paths and includes index for bigquery_stream write engine ([#1580](https://github.com/googleapis/python-bigquery-dataframes/issues/1580)) ([352e8e4](https://github.com/googleapis/python-bigquery-dataframes/commit/352e8e4b05cf19e970b47b017f958a1c6fc89bea)) -* Use dictionaries to avoid problematic google.iam namespace ([#1611](https://github.com/googleapis/python-bigquery-dataframes/issues/1611)) ([b03e44f](https://github.com/googleapis/python-bigquery-dataframes/commit/b03e44f7fca429a6de41c42ec28504b688cd84f0)) - - -### Performance Improvements - -* Directly read gbq table for simple plans ([#1607](https://github.com/googleapis/python-bigquery-dataframes/issues/1607)) ([6ad38e8](https://github.com/googleapis/python-bigquery-dataframes/commit/6ad38e8287354f62b0c5cad1f3d5b897256860ca)) - - -### Dependencies - -* Remove jellyfish dependency ([#1604](https://github.com/googleapis/python-bigquery-dataframes/issues/1604)) ([1ac0e1e](https://github.com/googleapis/python-bigquery-dataframes/commit/1ac0e1e82c097717338a6816f27c01b67736f51c)) -* Remove parsy dependency ([#1610](https://github.com/googleapis/python-bigquery-dataframes/issues/1610)) ([293f676](https://github.com/googleapis/python-bigquery-dataframes/commit/293f676e98446c417c12c345d5db875dd4c438df)) -* Remove test dependency on pytest-mock package ([#1622](https://github.com/googleapis/python-bigquery-dataframes/issues/1622)) ([1ba72ea](https://github.com/googleapis/python-bigquery-dataframes/commit/1ba72ead256178afee6f1d3303b0556bec1c4a9b)) -* Support a shapely versions 1.8.5+ ([#1621](https://github.com/googleapis/python-bigquery-dataframes/issues/1621)) ([e39ee3b](https://github.com/googleapis/python-bigquery-dataframes/commit/e39ee3bcf37f2a4f5e6ce981d248c24c6f5d770b)) - - -### Documentation - -* Add details for `bigquery_connection` in `[@bpd](https://github.com/bpd).udf` docstring ([#1609](https://github.com/googleapis/python-bigquery-dataframes/issues/1609)) ([ef63772](https://github.com/googleapis/python-bigquery-dataframes/commit/ef6377277bc9c354385c83ceba9e00094c0a6cc6)) -* Add explain forecast snippet to multiple time series tutorial ([#1586](https://github.com/googleapis/python-bigquery-dataframes/issues/1586)) ([40c55a0](https://github.com/googleapis/python-bigquery-dataframes/commit/40c55a06a529ca49d203227ccf36c12427d0cd5b)) -* Add message to remove default model for version 3.0 ([#1563](https://github.com/googleapis/python-bigquery-dataframes/issues/1563)) ([910be2b](https://github.com/googleapis/python-bigquery-dataframes/commit/910be2b5b2bfaf0e21cdc4fd775c1605a864c1aa)) -* Add samples for ArimaPlus `time_series_id_col` feature ([#1577](https://github.com/googleapis/python-bigquery-dataframes/issues/1577)) ([1e4cd9c](https://github.com/googleapis/python-bigquery-dataframes/commit/1e4cd9cf69f98d4af6b2a70bd8189c619b19baaa)) -* Add warning for bigframes 2.0 ([#1557](https://github.com/googleapis/python-bigquery-dataframes/issues/1557)) ([3f0eaa1](https://github.com/googleapis/python-bigquery-dataframes/commit/3f0eaa1c6b02d086270421f91dbb6aa2f117317d)) -* Deprecate default model in `TextEmbedddingGenerator`, `GeminiTextGenerator`, and other `bigframes.ml.llm` classes ([#1570](https://github.com/googleapis/python-bigquery-dataframes/issues/1570)) ([89ab33e](https://github.com/googleapis/python-bigquery-dataframes/commit/89ab33e1179aef142415fd5c9073671903bf1d45)) -* Include all licenses for vendored packages in the root LICENSE file ([#1626](https://github.com/googleapis/python-bigquery-dataframes/issues/1626)) ([8116ed0](https://github.com/googleapis/python-bigquery-dataframes/commit/8116ed0938634d301a153613f8a9cd8053ddf026)) -* Remove gemini-1.5 deprecation warning for `GeminiTextGenerator` ([#1562](https://github.com/googleapis/python-bigquery-dataframes/issues/1562)) ([0cc6784](https://github.com/googleapis/python-bigquery-dataframes/commit/0cc678448fdec1eaa3acfbb563a018325a8c85bc)) -* Use restructured text to allow publishing to PyPI ([#1565](https://github.com/googleapis/python-bigquery-dataframes/issues/1565)) ([d1e9ec2](https://github.com/googleapis/python-bigquery-dataframes/commit/d1e9ec2936d270ec4035014ea3ddd335a5747ade)) - - -### Miscellaneous Chores - -* Make `remote_function` params keyword only ([#1537](https://github.com/googleapis/python-bigquery-dataframes/issues/1537)) ([9eb9089](https://github.com/googleapis/python-bigquery-dataframes/commit/9eb9089ce3f1dad39761ba8ebc2d6f76261bd243)) - -## [1.42.0](https://github.com/googleapis/python-bigquery-dataframes/compare/v1.41.0...v1.42.0) (2025-03-27) - - -### Features - -* Add `closed` parameter in rolling() ([#1539](https://github.com/googleapis/python-bigquery-dataframes/issues/1539)) ([8bcc89b](https://github.com/googleapis/python-bigquery-dataframes/commit/8bcc89b30022f5ccf9ced80676a279c261c2f697)) -* Add `GeoSeries.difference()` and `bigframes.bigquery.st_difference()` ([#1471](https://github.com/googleapis/python-bigquery-dataframes/issues/1471)) ([e9fe815](https://github.com/googleapis/python-bigquery-dataframes/commit/e9fe8154d83e2674a05d7b670e949368b175ec8b)) -* Add `GeoSeries.intersection()` and `bigframes.bigquery.st_intersection()` ([#1529](https://github.com/googleapis/python-bigquery-dataframes/issues/1529)) ([8542bd4](https://github.com/googleapis/python-bigquery-dataframes/commit/8542bd469ff8775a9073f5a040b4117facfd8513)) -* Add df.take and series.take ([#1509](https://github.com/googleapis/python-bigquery-dataframes/issues/1509)) ([7d00be6](https://github.com/googleapis/python-bigquery-dataframes/commit/7d00be67cf50fdf713c40912f207d14f0f65538f)) -* Add Linear_Regression.global_explain() ([#1446](https://github.com/googleapis/python-bigquery-dataframes/issues/1446)) ([7e5b6a8](https://github.com/googleapis/python-bigquery-dataframes/commit/7e5b6a873d00162ffca3d254d3af276c5f06d866)) -* Allow iloc to support lists of negative indices ([#1497](https://github.com/googleapis/python-bigquery-dataframes/issues/1497)) ([a9cf215](https://github.com/googleapis/python-bigquery-dataframes/commit/a9cf215fb1403fda4ab2b58252f5fedc33aba3e1)) -* Support dry_run in `to_pandas()` ([#1436](https://github.com/googleapis/python-bigquery-dataframes/issues/1436)) ([75fc7e0](https://github.com/googleapis/python-bigquery-dataframes/commit/75fc7e0268dc5b10bdbc33dcf28db97dce62e41c)) -* Support window partition by geo column ([#1512](https://github.com/googleapis/python-bigquery-dataframes/issues/1512)) ([bdcb1e7](https://github.com/googleapis/python-bigquery-dataframes/commit/bdcb1e7929dc2f24c642ddb052629da394f45876)) -* Upgrade BQ managed `udf` to preview ([#1536](https://github.com/googleapis/python-bigquery-dataframes/issues/1536)) ([4a7fe4d](https://github.com/googleapis/python-bigquery-dataframes/commit/4a7fe4d75724e734634d41f18b4957e0877becc3)) - - -### Bug Fixes - -* Add deprecation warning to TextEmbeddingGenerator model, espeically gemini-1.0-X and gemini-1.5-X ([#1534](https://github.com/googleapis/python-bigquery-dataframes/issues/1534)) ([c93e720](https://github.com/googleapis/python-bigquery-dataframes/commit/c93e7204758435b0306699d3a1332aaf522f576b)) -* Change the default value for pdf extract/chunk ([#1517](https://github.com/googleapis/python-bigquery-dataframes/issues/1517)) ([a70a607](https://github.com/googleapis/python-bigquery-dataframes/commit/a70a607512797463f70ed529f078fcb2d40c85a1)) -* Local data always has sequential index ([#1514](https://github.com/googleapis/python-bigquery-dataframes/issues/1514)) ([014bd33](https://github.com/googleapis/python-bigquery-dataframes/commit/014bd33317966e15d05617c978e847de8c953453)) -* Read_pandas inline returns None when exceeds limit ([#1525](https://github.com/googleapis/python-bigquery-dataframes/issues/1525)) ([578081e](https://github.com/googleapis/python-bigquery-dataframes/commit/578081e978f2cca21ddae8b3ee371972ba723777)) -* Temporary fix for StreamingDataFrame not working backend bug ([#1533](https://github.com/googleapis/python-bigquery-dataframes/issues/1533)) ([6ab4ffd](https://github.com/googleapis/python-bigquery-dataframes/commit/6ab4ffd33d4900da833020ffa7ffc03a93a2b4b2)) -* Tolerate BQ connection service account propagation delay ([#1505](https://github.com/googleapis/python-bigquery-dataframes/issues/1505)) ([6681f1f](https://github.com/googleapis/python-bigquery-dataframes/commit/6681f1f9e30ed2325b85668de8a0b1d3d0e2858b)) - - -### Performance Improvements - -* Update shape to use quer_and_wait ([#1519](https://github.com/googleapis/python-bigquery-dataframes/issues/1519)) ([34ab9b8](https://github.com/googleapis/python-bigquery-dataframes/commit/34ab9b8abd2c632c806afe69f00d9e7dddb6a8b5)) - - -### Documentation - -* Update `GeoSeries.difference()` and `bigframes.bigquery.st_difference()` docs ([#1526](https://github.com/googleapis/python-bigquery-dataframes/issues/1526)) ([d553fa2](https://github.com/googleapis/python-bigquery-dataframes/commit/d553fa25fe85b3590269ed2ce08d5dff3bd22dfc)) - -## [1.41.0](https://github.com/googleapis/python-bigquery-dataframes/compare/v1.40.0...v1.41.0) (2025-03-19) - - -### Features - -* Add support for the 'right' parameter in 'pandas.cut' ([#1496](https://github.com/googleapis/python-bigquery-dataframes/issues/1496)) ([8aff128](https://github.com/googleapis/python-bigquery-dataframes/commit/8aff1285b26754118cc8ee906c4ac3076456a791)) -* Support BQ managed functions through `read_gbq_function` ([#1476](https://github.com/googleapis/python-bigquery-dataframes/issues/1476)) ([802183d](https://github.com/googleapis/python-bigquery-dataframes/commit/802183dc000ad2ce5559d14181dd3f7d036b3fed)) -* Warn when the BigFrames version is more than a year old ([#1455](https://github.com/googleapis/python-bigquery-dataframes/issues/1455)) ([00e0750](https://github.com/googleapis/python-bigquery-dataframes/commit/00e07508cfb0d8798e079b86a14834b3b593aa54)) - - -### Bug Fixes - -* Fix pandas.cut errors with empty bins ([#1499](https://github.com/googleapis/python-bigquery-dataframes/issues/1499)) ([434fb5d](https://github.com/googleapis/python-bigquery-dataframes/commit/434fb5dd60d11f09b808ea656394790aba43fdde)) -* Fix read_gbq with ORDER BY query and index_col set ([#963](https://github.com/googleapis/python-bigquery-dataframes/issues/963)) ([de46d2f](https://github.com/googleapis/python-bigquery-dataframes/commit/de46d2fdf7a1a30b2be07dbaa1cb127f10f5fe30)) - - -### Performance Improvements - -* Eliminate count queries in llm retry ([#1489](https://github.com/googleapis/python-bigquery-dataframes/issues/1489)) ([1c934c2](https://github.com/googleapis/python-bigquery-dataframes/commit/1c934c2fe2374c9abaaa79696f5e5f349248f3b7)) - - -### Documentation - -* Add a sample notebook for vector search ([#1500](https://github.com/googleapis/python-bigquery-dataframes/issues/1500)) ([f3bf139](https://github.com/googleapis/python-bigquery-dataframes/commit/f3bf139d33ed00ca3081e4e0315f409fdb2ad84d)) - -## [1.40.0](https://github.com/googleapis/python-bigquery-dataframes/compare/v1.39.0...v1.40.0) (2025-03-11) - - -### ⚠ BREAKING CHANGES - -* reading JSON data as a custom arrow extension type ([#1458](https://github.com/googleapis/python-bigquery-dataframes/issues/1458)) - -### Features - -* Reading JSON data as a custom arrow extension type ([#1458](https://github.com/googleapis/python-bigquery-dataframes/issues/1458)) ([e720f41](https://github.com/googleapis/python-bigquery-dataframes/commit/e720f41ef643ac14ae94fa98de5ef4a3fd6dde93)) -* Support list output for managed function ([#1457](https://github.com/googleapis/python-bigquery-dataframes/issues/1457)) ([461e9e0](https://github.com/googleapis/python-bigquery-dataframes/commit/461e9e017d513376fc623a5ee47f8b9dd002b452)) - - -### Bug Fixes - -* Fix list-like indexers in partial ordering mode ([#1456](https://github.com/googleapis/python-bigquery-dataframes/issues/1456)) ([fe72ada](https://github.com/googleapis/python-bigquery-dataframes/commit/fe72ada9cebb32947560c97567d7937c8b618f0d)) -* Fix the merge issue between 1424 and 1373 ([#1461](https://github.com/googleapis/python-bigquery-dataframes/issues/1461)) ([7b6e361](https://github.com/googleapis/python-bigquery-dataframes/commit/7b6e3615f8d4531beb4b59ca1223927112e713da)) -* Use `==` instead of `is` for timedelta type equality checks ([#1480](https://github.com/googleapis/python-bigquery-dataframes/issues/1480)) ([0db248b](https://github.com/googleapis/python-bigquery-dataframes/commit/0db248b5597a3966ac3dee1cca849509e48f4648)) - - -### Performance Improvements - -* Compilation no longer bounded by recursion ([#1464](https://github.com/googleapis/python-bigquery-dataframes/issues/1464)) ([27ab028](https://github.com/googleapis/python-bigquery-dataframes/commit/27ab028cdc45296923b12446c77b344af4208a3a)) - -## [1.39.0](https://github.com/googleapis/python-bigquery-dataframes/compare/v1.38.0...v1.39.0) (2025-03-05) - - -### Features - -* (Preview) Support `diff()` for date series ([#1423](https://github.com/googleapis/python-bigquery-dataframes/issues/1423)) ([521e987](https://github.com/googleapis/python-bigquery-dataframes/commit/521e9874f1c7dcd80e10bfd86f1b467b0f6d6d6e)) -* (Preview) Support aggregations over timedeltas ([#1418](https://github.com/googleapis/python-bigquery-dataframes/issues/1418)) ([1251ded](https://github.com/googleapis/python-bigquery-dataframes/commit/1251dedac8faf383c931185a057a8bb26afb4b8f)) -* (Preview) Support arithmetics between dates and timedeltas ([#1413](https://github.com/googleapis/python-bigquery-dataframes/issues/1413)) ([962b152](https://github.com/googleapis/python-bigquery-dataframes/commit/962b152ce5a368132d1ac14f6d8348b7ba285694)) -* (Preview) Support automatic load of timedelta from BQ tables. ([#1429](https://github.com/googleapis/python-bigquery-dataframes/issues/1429)) ([b2917bb](https://github.com/googleapis/python-bigquery-dataframes/commit/b2917bb57212ac399c20356755c878d179454bfe)) -* Add `allow_large_results` option to many I/O methods. Set to `False` to reduce latency ([#1428](https://github.com/googleapis/python-bigquery-dataframes/issues/1428)) ([dd2f488](https://github.com/googleapis/python-bigquery-dataframes/commit/dd2f48893eced458afecc93dc17b7e22735c39b9)) -* Add `GeoSeries.boundary()` ([#1435](https://github.com/googleapis/python-bigquery-dataframes/issues/1435)) ([32cddfe](https://github.com/googleapis/python-bigquery-dataframes/commit/32cddfecd25ff4208473574df09a8010f8be0de9)) -* Add allow_large_results to peek ([#1448](https://github.com/googleapis/python-bigquery-dataframes/issues/1448)) ([67487b9](https://github.com/googleapis/python-bigquery-dataframes/commit/67487b9a3bbe07f1b76e0332fab693b4c4022529)) -* Add groupby.rank() ([#1433](https://github.com/googleapis/python-bigquery-dataframes/issues/1433)) ([3a633d5](https://github.com/googleapis/python-bigquery-dataframes/commit/3a633d5cc9c3e6a2bd8311c8834b406db5cb8699)) -* Iloc multiple columns selection. ([#1437](https://github.com/googleapis/python-bigquery-dataframes/issues/1437)) ([ddfd02a](https://github.com/googleapis/python-bigquery-dataframes/commit/ddfd02a83040847f6d4642420d3bd32a4a855001)) -* Support interface for BigQuery managed functions ([#1373](https://github.com/googleapis/python-bigquery-dataframes/issues/1373)) ([2bbf53f](https://github.com/googleapis/python-bigquery-dataframes/commit/2bbf53f0d92dc669e1d775fafc54199f582d9059)) -* Warn if default ingress_settings is used in remote_functions ([#1419](https://github.com/googleapis/python-bigquery-dataframes/issues/1419)) ([dfd891a](https://github.com/googleapis/python-bigquery-dataframes/commit/dfd891a0102314e7542d0b0057442dcde3d9a4a1)) - - -### Bug Fixes - -* Do not compare schema description during schema validation ([#1452](https://github.com/googleapis/python-bigquery-dataframes/issues/1452)) ([03a3a56](https://github.com/googleapis/python-bigquery-dataframes/commit/03a3a5632ab187e1208cdc7133acfe0214243832)) -* Remove warnings for null index and partial ordering mode in prep for GA ([#1431](https://github.com/googleapis/python-bigquery-dataframes/issues/1431)) ([6785aee](https://github.com/googleapis/python-bigquery-dataframes/commit/6785aee97f4ee0c122d83e78409f9d6cc361b6d8)) -* Warn if default `cloud_function_service_account` is used in `remote_function` ([#1424](https://github.com/googleapis/python-bigquery-dataframes/issues/1424)) ([fe7463a](https://github.com/googleapis/python-bigquery-dataframes/commit/fe7463a69e616776df3f1b3bce4abdeaf7579f9b)) -* Window operations over JSON columns ([#1451](https://github.com/googleapis/python-bigquery-dataframes/issues/1451)) ([0070e77](https://github.com/googleapis/python-bigquery-dataframes/commit/0070e77579d0d0535d9f9a6c12641128e8a6dfbc)) -* Write chunked text instead of dummy text for pdf chunk ([#1444](https://github.com/googleapis/python-bigquery-dataframes/issues/1444)) ([96b0e8a](https://github.com/googleapis/python-bigquery-dataframes/commit/96b0e8a7a9d405c895ffd8ece56f4e3d04e0fbe5)) - - -### Performance Improvements - -* Speed up DataFrame corr, cov ([#1309](https://github.com/googleapis/python-bigquery-dataframes/issues/1309)) ([c598c0a](https://github.com/googleapis/python-bigquery-dataframes/commit/c598c0a1694ebc5a49bd92c837e4aaf1c311a899)) - - -### Documentation - -* Add snippet for explaining the linear regression model prediction ([#1427](https://github.com/googleapis/python-bigquery-dataframes/issues/1427)) ([7c37c7d](https://github.com/googleapis/python-bigquery-dataframes/commit/7c37c7d81c0cdc4647667daeebf13d47dabf3972)) - -## [1.38.0](https://github.com/googleapis/python-bigquery-dataframes/compare/v1.37.0...v1.38.0) (2025-02-24) - - -### Features - -* (Preview) Support diff aggregation for timestamp series. ([#1405](https://github.com/googleapis/python-bigquery-dataframes/issues/1405)) ([abe48d6](https://github.com/googleapis/python-bigquery-dataframes/commit/abe48d6f13a954534460fa14c9337e1085d9fbb3)) -* Add `GeoSeries.from_wkt() `and `GeoSeries.to_wkt()` ([#1401](https://github.com/googleapis/python-bigquery-dataframes/issues/1401)) ([2993b28](https://github.com/googleapis/python-bigquery-dataframes/commit/2993b283966960430ad8482f40f177e276db2d64)) -* Support DF.__array__(copy=True) ([#1403](https://github.com/googleapis/python-bigquery-dataframes/issues/1403)) ([693ed8c](https://github.com/googleapis/python-bigquery-dataframes/commit/693ed8cfb1ecc3af161801225d3e9cda489c29dd)) -* Support routines with ARRAY return type in `read_gbq_function` ([#1412](https://github.com/googleapis/python-bigquery-dataframes/issues/1412)) ([4b60049](https://github.com/googleapis/python-bigquery-dataframes/commit/4b60049e8362bfb07c136d8b2eb02b984d71f084)) - - -### Bug Fixes - -* Calling to_timdelta() over timedeltas no longer changes their values ([#1411](https://github.com/googleapis/python-bigquery-dataframes/issues/1411)) ([650a190](https://github.com/googleapis/python-bigquery-dataframes/commit/650a1907fdf84897eb7aa288863ee27d938e0879)) -* Replace empty dict with None to avoid mutable default arguments ([#1416](https://github.com/googleapis/python-bigquery-dataframes/issues/1416)) ([fa4e3ad](https://github.com/googleapis/python-bigquery-dataframes/commit/fa4e3ad8bcd5db56fa26b26609cc7e58b1edf498)) - - -### Performance Improvements - -* Avoid redundant SQL casts ([#1399](https://github.com/googleapis/python-bigquery-dataframes/issues/1399)) ([6ee48d5](https://github.com/googleapis/python-bigquery-dataframes/commit/6ee48d5c16870f1caa99c3f658c2c1a0e14be749)) - - -### Dependencies - -* Remove scikit-learn and sqlalchemy as required dependencies ([#1296](https://github.com/googleapis/python-bigquery-dataframes/issues/1296)) ([fd8bc89](https://github.com/googleapis/python-bigquery-dataframes/commit/fd8bc894bdbdf551ebbec1fb93832588371ae6af)) - - -### Documentation - -* Add samples using SQL methods via the `bigframes.bigquery` module ([#1358](https://github.com/googleapis/python-bigquery-dataframes/issues/1358)) ([f54e768](https://github.com/googleapis/python-bigquery-dataframes/commit/f54e7688fda6372c6decc9b61796b0272d803c79)) -* Add snippets for visualizing a time series and creating a time series model for the Limit forecasted values in time series model tutorial ([#1310](https://github.com/googleapis/python-bigquery-dataframes/issues/1310)) ([c6c9120](https://github.com/googleapis/python-bigquery-dataframes/commit/c6c9120e839647e5b3cb97f04a8d90cc8690b8a3)) - -## [1.37.0](https://github.com/googleapis/python-bigquery-dataframes/compare/v1.36.0...v1.37.0) (2025-02-19) - - -### Features - -* (Preview) Support add, sub, mult, div, and more between timedeltas ([#1396](https://github.com/googleapis/python-bigquery-dataframes/issues/1396)) ([ffa63d4](https://github.com/googleapis/python-bigquery-dataframes/commit/ffa63d47ca1dd1a18617f44d9b3bc33419656a20)) -* (Preview) Support comparison, ordering, and filtering for timedeltas ([#1387](https://github.com/googleapis/python-bigquery-dataframes/issues/1387)) ([34d01b2](https://github.com/googleapis/python-bigquery-dataframes/commit/34d01b27f867abf10bddffdf4f88fa7052cd237c)) -* (Preview) Support subtraction in DATETIME/TIMESTAMP columns with timedelta columns ([#1390](https://github.com/googleapis/python-bigquery-dataframes/issues/1390)) ([50ad3a5](https://github.com/googleapis/python-bigquery-dataframes/commit/50ad3a56e9bd77bb77d60d7d5ec497e3335a7177)) -* JSON dtype support for read_pandas and Series constructor ([#1391](https://github.com/googleapis/python-bigquery-dataframes/issues/1391)) ([44f4137](https://github.com/googleapis/python-bigquery-dataframes/commit/44f4137adb02790e07c696f0641bc58390857210)) - - -### Bug Fixes - -* Ensure binops with pandas objects returns bigquery dataframes ([#1404](https://github.com/googleapis/python-bigquery-dataframes/issues/1404)) ([3cee24b](https://github.com/googleapis/python-bigquery-dataframes/commit/3cee24bae1d352015a5b6a8c18d5c394293d08fd)) - - -### Performance Improvements - -* Prune projections more aggressively ([#1398](https://github.com/googleapis/python-bigquery-dataframes/issues/1398)) ([7990262](https://github.com/googleapis/python-bigquery-dataframes/commit/7990262cf09e97c0739be922ede151d616655726)) -* Simplify sum aggregate SQL text ([#1395](https://github.com/googleapis/python-bigquery-dataframes/issues/1395)) ([0145656](https://github.com/googleapis/python-bigquery-dataframes/commit/0145656e5e378442f2f38f9f04e87e33ddf345f5)) -* Use simple null constraints to simplify queries ([#1381](https://github.com/googleapis/python-bigquery-dataframes/issues/1381)) ([00611d4](https://github.com/googleapis/python-bigquery-dataframes/commit/00611d4d697a8b74451375f5a7700b92a4410295)) - - -### Documentation - -* Add DataFrame.struct docs ([#1348](https://github.com/googleapis/python-bigquery-dataframes/issues/1348)) ([7e9e93a](https://github.com/googleapis/python-bigquery-dataframes/commit/7e9e93aafd26cbfec9a1710caaf97937bcb6ee05)) - -## [1.36.0](https://github.com/googleapis/python-bigquery-dataframes/compare/v1.35.0...v1.36.0) (2025-02-11) - - -### Features - -* (Preview) Support addition between a timestamp and a timedelta ([#1369](https://github.com/googleapis/python-bigquery-dataframes/issues/1369)) ([b598aa8](https://github.com/googleapis/python-bigquery-dataframes/commit/b598aa8ef4f6dd0cbca7629d290c5e511cdc86fc)) -* (Preview) Support casting floats and list-likes to timedelta series ([#1362](https://github.com/googleapis/python-bigquery-dataframes/issues/1362)) ([65933b6](https://github.com/googleapis/python-bigquery-dataframes/commit/65933b6b7608ec52717e818d8ec1732fb756b67b)) -* (Preview) Support timestamp subtractions ([#1346](https://github.com/googleapis/python-bigquery-dataframes/issues/1346)) ([86b7e72](https://github.com/googleapis/python-bigquery-dataframes/commit/86b7e72097ce67d88b72cfe031080d5af22f65cd)) -* Add `bigframes.bigquery.st_area` and suggest it from `GeoSeries.area` ([#1318](https://github.com/googleapis/python-bigquery-dataframes/issues/1318)) ([8b5ffa8](https://github.com/googleapis/python-bigquery-dataframes/commit/8b5ffa8893b51016c51794865c40def74ea6716b)) -* Add `GeoSeries.from_xy()` ([#1364](https://github.com/googleapis/python-bigquery-dataframes/issues/1364)) ([3c3e14c](https://github.com/googleapis/python-bigquery-dataframes/commit/3c3e14c715f476ca44f254c0d53d639ea5988a8d)) - - -### Bug Fixes - -* Dtype parameter ineffective in Series/DataFrame construction ([#1354](https://github.com/googleapis/python-bigquery-dataframes/issues/1354)) ([b9bdca8](https://github.com/googleapis/python-bigquery-dataframes/commit/b9bdca8285ee54fecf3795fbf3cbea6f878ee8ca)) -* Translate labels to col ids when copying dataframes ([#1372](https://github.com/googleapis/python-bigquery-dataframes/issues/1372)) ([0c55b07](https://github.com/googleapis/python-bigquery-dataframes/commit/0c55b07dc001b568875f06d578ca7d59409f2a11)) - - -### Performance Improvements - -* Prune unused operations from sql ([#1365](https://github.com/googleapis/python-bigquery-dataframes/issues/1365)) ([923da03](https://github.com/googleapis/python-bigquery-dataframes/commit/923da037ef6e4e7f8b54924ea5644c2c5ceb2234)) -* Simplify merge join key coalescing ([#1361](https://github.com/googleapis/python-bigquery-dataframes/issues/1361)) ([7ae565d](https://github.com/googleapis/python-bigquery-dataframes/commit/7ae565d9e0e59fdf75c7659c0263562688ccc1e8)) - -## [1.35.0](https://github.com/googleapis/python-bigquery-dataframes/compare/v1.34.0...v1.35.0) (2025-02-04) - - -### Features - -* (Preview) Support timedeltas for read_pandas() ([#1349](https://github.com/googleapis/python-bigquery-dataframes/issues/1349)) ([866ba9e](https://github.com/googleapis/python-bigquery-dataframes/commit/866ba9efb54f11c1fc2ced0d7995fff86277b049)) -* Add Series.keys() ([#1342](https://github.com/googleapis/python-bigquery-dataframes/issues/1342)) ([deb015d](https://github.com/googleapis/python-bigquery-dataframes/commit/deb015dc1276549519d51363501355272f8976d8)) -* Allow `case_when` to change dtypes if case list contains the condition `(True, some_default_value)` ([#1311](https://github.com/googleapis/python-bigquery-dataframes/issues/1311)) ([5c2a2c6](https://github.com/googleapis/python-bigquery-dataframes/commit/5c2a2c6086be20cba7da08ecd37899699aab518f)) -* Support python type as astype arg ([#1316](https://github.com/googleapis/python-bigquery-dataframes/issues/1316)) ([b26e135](https://github.com/googleapis/python-bigquery-dataframes/commit/b26e13570f198ec4d252590a8c07253624db667a)) -* Support time_series_id_col in ARIMAPlus ([#1282](https://github.com/googleapis/python-bigquery-dataframes/issues/1282)) ([97532c9](https://github.com/googleapis/python-bigquery-dataframes/commit/97532c9ba02cd709d69666dd0afca5c1df8b9faf)) - - -### Bug Fixes - -* Exclude `DataFrame` and `Series` `__call__` from unimplemented API metrics ([#1351](https://github.com/googleapis/python-bigquery-dataframes/issues/1351)) ([f2d5264](https://github.com/googleapis/python-bigquery-dataframes/commit/f2d526445da7dae29c49c8d6dacdfee7d2fa9d79)) -* Make `DataFrame` `__getattr__` and `__setattr__` more robust to subclassing ([#1352](https://github.com/googleapis/python-bigquery-dataframes/issues/1352)) ([417de3a](https://github.com/googleapis/python-bigquery-dataframes/commit/417de3a449e5d0748831b502f4f5b9fb9ba38714)) - - -### Performance Improvements - -* Fall back to ordering by bq pk when possible ([#1350](https://github.com/googleapis/python-bigquery-dataframes/issues/1350)) ([3c4abf2](https://github.com/googleapis/python-bigquery-dataframes/commit/3c4abf24ea186e98f629b6f83c0f3e36dc0571c6)) -* Improve isin performance ([#1203](https://github.com/googleapis/python-bigquery-dataframes/issues/1203)) ([db087b0](https://github.com/googleapis/python-bigquery-dataframes/commit/db087b0bfe4b3ba965682d620079c923e098e362)) -* Prevent inlining of remote ops ([#1347](https://github.com/googleapis/python-bigquery-dataframes/issues/1347)) ([012081a](https://github.com/googleapis/python-bigquery-dataframes/commit/012081af9ef825ced96ec1e772b9646cbe09d9a1)) - - -### Dependencies - -* Add support for Python 3.13 for everything but remote functions ([#1307](https://github.com/googleapis/python-bigquery-dataframes/issues/1307)) ([533db96](https://github.com/googleapis/python-bigquery-dataframes/commit/533db9685d159de2bc76307b0e0add676bd679a0)) - - -### Documentation - -* Add `GeoSeries` docs ([#1327](https://github.com/googleapis/python-bigquery-dataframes/issues/1327)) ([05f83d1](https://github.com/googleapis/python-bigquery-dataframes/commit/05f83d18d276091a1549dbba1f2baf8c91c8c37e)) -* Add link to DataFrames intro to improve SEO ([#1176](https://github.com/googleapis/python-bigquery-dataframes/issues/1176)) ([aafb5be](https://github.com/googleapis/python-bigquery-dataframes/commit/aafb5be3e9c50f477fca2a1ebb5338194672913f)) -* Add snippet to explain the univariate model's forecast result in the Forecast a single time series with a univariate model tutorial ([#1272](https://github.com/googleapis/python-bigquery-dataframes/issues/1272)) ([c22126b](https://github.com/googleapis/python-bigquery-dataframes/commit/c22126b846db428d21c0f5cbd2d439ecc56365b2)) - -## [1.34.0](https://github.com/googleapis/python-bigquery-dataframes/compare/v1.33.0...v1.34.0) (2025-01-27) - - -### ⚠ BREAKING CHANGES - -* Enable reading JSON data with `dbjson` extension dtype ([#1139](https://github.com/googleapis/python-bigquery-dataframes/issues/1139)) - -### Features - -* (df|s).hist(), (df|s).line(), (df|s).area(), (df|s).bar(), df.scatter() ([#1320](https://github.com/googleapis/python-bigquery-dataframes/issues/1320)) ([bd3f584](https://github.com/googleapis/python-bigquery-dataframes/commit/bd3f584a7eab5d01dedebb7ca2485942ef5b5ebe)) -* (Preview) Define timedelta type and to_timedelta function ([#1317](https://github.com/googleapis/python-bigquery-dataframes/issues/1317)) ([3901951](https://github.com/googleapis/python-bigquery-dataframes/commit/39019510d0c2758096589ecd0d83175f313a8cf5)) -* Add DataFrame.corrwith method ([#1315](https://github.com/googleapis/python-bigquery-dataframes/issues/1315)) ([b503355](https://github.com/googleapis/python-bigquery-dataframes/commit/b5033559a77a9bc5ffb7dc1e44e02aaaaf1e051e)) -* Add DataFrame.mask method ([#1302](https://github.com/googleapis/python-bigquery-dataframes/issues/1302)) ([8b8155f](https://github.com/googleapis/python-bigquery-dataframes/commit/8b8155fef9c5cd36cfabf728ccebf6a14a1cbbda)) -* Enable reading JSON data with `dbjson` extension dtype ([#1139](https://github.com/googleapis/python-bigquery-dataframes/issues/1139)) ([f672262](https://github.com/googleapis/python-bigquery-dataframes/commit/f6722629fb47eed5befb0ecae2e6b5ec9042d669)) - -## [1.33.0](https://github.com/googleapis/python-bigquery-dataframes/compare/v1.32.0...v1.33.0) (2025-01-22) - - -### Features - -* Add `bigframes.bigquery.sql_scalar()` to apply SQL syntax on Series objects ([#1293](https://github.com/googleapis/python-bigquery-dataframes/issues/1293)) ([aa2f73a](https://github.com/googleapis/python-bigquery-dataframes/commit/aa2f73ad86e42c37d85ac867a3702eb6f2724b11)) -* Add unix_seconds, unix_millis and unix_micros for timestamp series. ([#1297](https://github.com/googleapis/python-bigquery-dataframes/issues/1297)) ([e4b0c8d](https://github.com/googleapis/python-bigquery-dataframes/commit/e4b0c8dd9edda48e07c433b99f44db82e1ea2054)) -* DataFrame.join supports Series other ([#1303](https://github.com/googleapis/python-bigquery-dataframes/issues/1303)) ([ee37a0a](https://github.com/googleapis/python-bigquery-dataframes/commit/ee37a0ab84e9415046e0e15955c14a1965b3a904)) -* Support array output in `remote_function` ([#1057](https://github.com/googleapis/python-bigquery-dataframes/issues/1057)) ([bdee173](https://github.com/googleapis/python-bigquery-dataframes/commit/bdee1734809589e5a7a3c23ee9cd2f967adf346f)) - - -### Bug Fixes - -* Dataframe sort_values Series input keyerror. ([#1285](https://github.com/googleapis/python-bigquery-dataframes/issues/1285)) ([5a2731b](https://github.com/googleapis/python-bigquery-dataframes/commit/5a2731bda8b2e9ea54bf582f823acdb6153dbb8f)) -* Fix read_gbq_function issue in dataframe apply method ([#1174](https://github.com/googleapis/python-bigquery-dataframes/issues/1174)) ([0318764](https://github.com/googleapis/python-bigquery-dataframes/commit/0318764030f6753a4e925c62612aabbb8e192fdf)) -* Series sort_index and sort_values now raises when axis!=0 ([#1294](https://github.com/googleapis/python-bigquery-dataframes/issues/1294)) ([94bc2f2](https://github.com/googleapis/python-bigquery-dataframes/commit/94bc2f2dc3514fffeac625592ec4b28c32957723)) - - -### Documentation - -* Add snippet to forecast future time series in the Forecast a single time series with a univariate model tutorial ([#1271](https://github.com/googleapis/python-bigquery-dataframes/issues/1271)) ([a687050](https://github.com/googleapis/python-bigquery-dataframes/commit/a687050b2a92bed1af9cb86a812b62f9a69cf959)) -* Update `bigframes.pandas.Series` docs ([#1273](https://github.com/googleapis/python-bigquery-dataframes/issues/1273)) ([0cac64f](https://github.com/googleapis/python-bigquery-dataframes/commit/0cac64f5ba3f3c9e8495fc5acb09d81c39d36de0)) - -## [1.32.0](https://github.com/googleapis/python-bigquery-dataframes/compare/v1.31.0...v1.32.0) (2025-01-13) - - -### Features - -* Add max_retries to TextEmbeddingGenerator and Claude3TextGenerator ([#1259](https://github.com/googleapis/python-bigquery-dataframes/issues/1259)) ([8077ff4](https://github.com/googleapis/python-bigquery-dataframes/commit/8077ff49426b103dc5a52eeb86a2c6a869c99825)) -* Bigframes.bigquery.parse_json ([#1265](https://github.com/googleapis/python-bigquery-dataframes/issues/1265)) ([27bbd80](https://github.com/googleapis/python-bigquery-dataframes/commit/27bbd8085ccac175f113afbd6c94b52c034a3d97)) -* Support DataFrame.astype(dict) ([#1262](https://github.com/googleapis/python-bigquery-dataframes/issues/1262)) ([5934f8e](https://github.com/googleapis/python-bigquery-dataframes/commit/5934f8ee0a1c950a820d1911d73a46f6891a40bb)) - - -### Bug Fixes - -* Avoid global mutation in `BigQueryOptions.client_endpoints_override` ([#1280](https://github.com/googleapis/python-bigquery-dataframes/issues/1280)) ([788f6e9](https://github.com/googleapis/python-bigquery-dataframes/commit/788f6e94a1e80f0ba8741a53a05a467e7b18e902)) -* Fix erroneous window bounds removal during compilation ([#1163](https://github.com/googleapis/python-bigquery-dataframes/issues/1163)) ([f91756a](https://github.com/googleapis/python-bigquery-dataframes/commit/f91756a4413b10f1072c0ae96301fe854bb1ba4e)) - - -### Dependencies - -* Relax sqlglot upper bound ([#1278](https://github.com/googleapis/python-bigquery-dataframes/issues/1278)) ([c71ec09](https://github.com/googleapis/python-bigquery-dataframes/commit/c71ec093314409cd4c7a52a713dbd6164fbbd792)) - - -### Documentation - -* Add bq studio links that allows users to generate Jupiter notebooks in bq studio with github contents ([#1266](https://github.com/googleapis/python-bigquery-dataframes/issues/1266)) ([58f13cb](https://github.com/googleapis/python-bigquery-dataframes/commit/58f13cb9ef8bac3222e5013d8ae77dd20f886e30)) -* Add snippet to evaluate ARIMA plus model in the Forecast a single time series with a univariate model tutorial ([#1267](https://github.com/googleapis/python-bigquery-dataframes/issues/1267)) ([3dcae2d](https://github.com/googleapis/python-bigquery-dataframes/commit/3dcae2dca45efdd4493cf3f367bf025ea291f4df)) -* Add snippet to see the ARIMA coefficients in the Forecast a single time series with a univariate model tutorial ([#1268](https://github.com/googleapis/python-bigquery-dataframes/issues/1268)) ([059a564](https://github.com/googleapis/python-bigquery-dataframes/commit/059a564095dfea0518982f13c8118d3807861ccf)) -* Update `bigframes.pandas.pandas` docstrings ([#1247](https://github.com/googleapis/python-bigquery-dataframes/issues/1247)) ([c4bffc3](https://github.com/googleapis/python-bigquery-dataframes/commit/c4bffc3e8ec630a362c94f9d269a66073a14ad04)) -* Use 002 model for better scalability in text generation ([#1270](https://github.com/googleapis/python-bigquery-dataframes/issues/1270)) ([bb7a850](https://github.com/googleapis/python-bigquery-dataframes/commit/bb7a85005ebebfbcb0d2a4d5c4c27b354f38d3d1)) - -## [1.31.0](https://github.com/googleapis/python-bigquery-dataframes/compare/v1.30.0...v1.31.0) (2025-01-05) - - -### Features - -* Implement confirmation threshold for semantic operators ([#1251](https://github.com/googleapis/python-bigquery-dataframes/issues/1251)) ([5ba4511](https://github.com/googleapis/python-bigquery-dataframes/commit/5ba4511ad85cf02f0e5ad4e33ea3826b19527293)) - - -### Bug Fixes - -* Raise if trying to change `ordering_mode` after session has started ([#1252](https://github.com/googleapis/python-bigquery-dataframes/issues/1252)) ([8cfaae8](https://github.com/googleapis/python-bigquery-dataframes/commit/8cfaae8718f3c4c6739b7155a02ef13dbed73425)) -* Reduce the number of labels added to query jobs ([#1245](https://github.com/googleapis/python-bigquery-dataframes/issues/1245)) ([fdcdc18](https://github.com/googleapis/python-bigquery-dataframes/commit/fdcdc189e5fcae9de68bf8fb3872136f55be36cb)) - - -### Documentation - -* Remove bq studio link ([#1258](https://github.com/googleapis/python-bigquery-dataframes/issues/1258)) ([dd4fd2e](https://github.com/googleapis/python-bigquery-dataframes/commit/dd4fd2e8bafa73b4b5d99f095943bd9a757cd5b5)) -* Update bigframes.pandas.DatetimeMethods docstrings ([#1246](https://github.com/googleapis/python-bigquery-dataframes/issues/1246)) ([10f08da](https://github.com/googleapis/python-bigquery-dataframes/commit/10f08daec6034aafe48096be56683c953accc79a)) -* Update semantic_operators.ipynb ([#1260](https://github.com/googleapis/python-bigquery-dataframes/issues/1260)) ([a2ed989](https://github.com/googleapis/python-bigquery-dataframes/commit/a2ed989fac789b0debacc0ec8a044b473cc6112c)) - -## [1.30.0](https://github.com/googleapis/python-bigquery-dataframes/compare/v1.29.0...v1.30.0) (2024-12-30) - - -### Features - -* Add `GeoSeries.x` and `GeoSeries.y` ([#1126](https://github.com/googleapis/python-bigquery-dataframes/issues/1126)) ([4c3548f](https://github.com/googleapis/python-bigquery-dataframes/commit/4c3548f060ba7ce649aa368fa9367dfc769ae0c3)) -* Add `LinearRegression.predict_explain()` to generate `ML.EXPLAIN_PREDICT` columns ([#1190](https://github.com/googleapis/python-bigquery-dataframes/issues/1190)) ([e13eca2](https://github.com/googleapis/python-bigquery-dataframes/commit/e13eca2128b2bf8a914a5ce781b82dffb95563a8)) -* Add `LogisticRegression.predict_explain()` to generate `ML.EXPLAIN_PREDICT` columns ([#1222](https://github.com/googleapis/python-bigquery-dataframes/issues/1222)) ([bcbc732](https://github.com/googleapis/python-bigquery-dataframes/commit/bcbc732f321ab31f8fb6b995aeb908ac87750587)) -* Add `write_engine` parameter to `read_FORMATNAME` methods to control how data is written to BigQuery ([#371](https://github.com/googleapis/python-bigquery-dataframes/issues/371)) ([ed47ef1](https://github.com/googleapis/python-bigquery-dataframes/commit/ed47ef16ba6f4ae67a128712fd67113aefe08467)) -* Add client side retry to GeminiTextGenerator ([#1242](https://github.com/googleapis/python-bigquery-dataframes/issues/1242)) ([8193abe](https://github.com/googleapis/python-bigquery-dataframes/commit/8193abe395c5648db8169818eca29aee76c46478)) -* Add Gemini-pro-1.5 to GeminiTextGenerator Tuning and Support score() method in Gemini-pro-1.5 ([#1208](https://github.com/googleapis/python-bigquery-dataframes/issues/1208)) ([298fc73](https://github.com/googleapis/python-bigquery-dataframes/commit/298fc73985daf565033347dcf40afd0d5560c717)) -* Add support for `LinearRegression.predict_explain` and `LogisticRegression.predict_explain` parameter, `top_k_features` ([#1228](https://github.com/googleapis/python-bigquery-dataframes/issues/1228)) ([3068e19](https://github.com/googleapis/python-bigquery-dataframes/commit/3068e19495f99d2d7c39c67672350d0b411f79b7)) -* Support dataframe where method ([#1166](https://github.com/googleapis/python-bigquery-dataframes/issues/1166)) ([71b4053](https://github.com/googleapis/python-bigquery-dataframes/commit/71b4053f855239cc3b2f659a6bfa776e38a1d4d3)) - - -### Bug Fixes - -* Arima model series input. ([#1237](https://github.com/googleapis/python-bigquery-dataframes/issues/1237)) ([f7d52d9](https://github.com/googleapis/python-bigquery-dataframes/commit/f7d52d916e8fb6362abc56b3a27cdd994e994214)) -* Json in struct destination type ([#1187](https://github.com/googleapis/python-bigquery-dataframes/issues/1187)) ([200c9bb](https://github.com/googleapis/python-bigquery-dataframes/commit/200c9bbcf020913710de86822e2e2917484932fa)) -* Throw an error message when setting is_row_processor=True to read a multi param function ([#1160](https://github.com/googleapis/python-bigquery-dataframes/issues/1160)) ([b2816a5](https://github.com/googleapis/python-bigquery-dataframes/commit/b2816a5df2d03b97757b46a004ac54d86d1e26a1)) - - -### Documentation - -* Add an "open in BQ Studio" link to all BigFrames sample notebooks ([#1223](https://github.com/googleapis/python-bigquery-dataframes/issues/1223)) ([e0a8288](https://github.com/googleapis/python-bigquery-dataframes/commit/e0a82888cd34fa2404ac68229dc38496cb22c67b)) -* Add bq studio link for a new ipynb file called "bq_dataframes_template.ipynb" ([#1239](https://github.com/googleapis/python-bigquery-dataframes/issues/1239)) ([840aaff](https://github.com/googleapis/python-bigquery-dataframes/commit/840aaff6d5895ef0594a4f02bde03143c36e7d82)) -* Add example for logistic regression ([#1240](https://github.com/googleapis/python-bigquery-dataframes/issues/1240)) ([4d854fd](https://github.com/googleapis/python-bigquery-dataframes/commit/4d854fd6c7b6b7c2322032d720befc773cc56412)) -* Add examples for ml PCA and SimpleImputer ([#1236](https://github.com/googleapis/python-bigquery-dataframes/issues/1236)) ([0d84459](https://github.com/googleapis/python-bigquery-dataframes/commit/0d84459a083bbad2cb694da0256c4ff4a2438d4e)) -* Add KMeans example ([#1234](https://github.com/googleapis/python-bigquery-dataframes/issues/1234)) ([d87ab97](https://github.com/googleapis/python-bigquery-dataframes/commit/d87ab97011d09784ab528ec1ab1df7f3591502a6)) -* Add linear model example ([#1235](https://github.com/googleapis/python-bigquery-dataframes/issues/1235)) ([2c3e1fd](https://github.com/googleapis/python-bigquery-dataframes/commit/2c3e1fde7614057ac3deb637993134e7a9661c3d)) -* Add ml.model_selection examples ([#1238](https://github.com/googleapis/python-bigquery-dataframes/issues/1238)) ([50648e4](https://github.com/googleapis/python-bigquery-dataframes/commit/50648e4d5d7c0b8b41d9a9605a9923ead73a7831)) -* Add python snippet for "Create the time series model" section of the Forecast a single time series with a univariate model tutorial ([#1227](https://github.com/googleapis/python-bigquery-dataframes/issues/1227)) ([20f3190](https://github.com/googleapis/python-bigquery-dataframes/commit/20f3190d2fc26846f55328a7481de70e9fe3f84b)) - -## [1.29.0](https://github.com/googleapis/python-bigquery-dataframes/compare/v1.28.0...v1.29.0) (2024-12-12) - - -### Features - -* Add Gemini 2.0 preview text model support ([#1209](https://github.com/googleapis/python-bigquery-dataframes/issues/1209)) ([1021d57](https://github.com/googleapis/python-bigquery-dataframes/commit/1021d5761a291f2327fc10216e938826e53dbcc4)) - - -### Documentation - -* Add Gemini 2.0 text gen sample notebook ([#1211](https://github.com/googleapis/python-bigquery-dataframes/issues/1211)) ([9596b66](https://github.com/googleapis/python-bigquery-dataframes/commit/9596b66a8a41f5e5db6fa5f87b01c5363ffa89c4)) -* Update bigframes.pandas.index docs return types ([#1191](https://github.com/googleapis/python-bigquery-dataframes/issues/1191)) ([c63e7da](https://github.com/googleapis/python-bigquery-dataframes/commit/c63e7dad6fe67f5769ddcdd1730666580a7e7a05)) - -## [1.28.0](https://github.com/googleapis/python-bigquery-dataframes/compare/v1.27.0...v1.28.0) (2024-12-11) - - -### Features - -* (Series | DataFrame).plot.bar ([#1152](https://github.com/googleapis/python-bigquery-dataframes/issues/1152)) ([0fae2e0](https://github.com/googleapis/python-bigquery-dataframes/commit/0fae2e0291ec8d22341b5b543e8f1b384f83cd3c)) -* `bigframes.bigquery.vector_search` supports `use_brute_force` and `fraction_lists_to_search` parameters ([#1158](https://github.com/googleapis/python-bigquery-dataframes/issues/1158)) ([131edc3](https://github.com/googleapis/python-bigquery-dataframes/commit/131edc3d79f46d35a25422f0db7f150e63e8f561)) -* Add `ARIMAPlus.predict_explain()` to generate forecasts with explanation columns ([#1177](https://github.com/googleapis/python-bigquery-dataframes/issues/1177)) ([05f8b4d](https://github.com/googleapis/python-bigquery-dataframes/commit/05f8b4d2b2b5f624097228e65a3c42364fc40d36)) -* Add client_endpoints_override to bq options ([#1167](https://github.com/googleapis/python-bigquery-dataframes/issues/1167)) ([be74b99](https://github.com/googleapis/python-bigquery-dataframes/commit/be74b99977cfbd513def5b7e439de6b7706c0712)) -* Add support for temporal types in dataframe's describe() method ([#1189](https://github.com/googleapis/python-bigquery-dataframes/issues/1189)) ([2d564a6](https://github.com/googleapis/python-bigquery-dataframes/commit/2d564a6a9925b69c7e9a15b532fb66ad68c3e264)) -* Allow join-free alignment of analytic expressions ([#1168](https://github.com/googleapis/python-bigquery-dataframes/issues/1168)) ([daef4f0](https://github.com/googleapis/python-bigquery-dataframes/commit/daef4f0c7c5ff2d0a4e9a6ffefeb81f43780ac8b)) -* Series.isin supports bigframes.Series arg ([#1195](https://github.com/googleapis/python-bigquery-dataframes/issues/1195)) ([0d8a16b](https://github.com/googleapis/python-bigquery-dataframes/commit/0d8a16ba77a66dce544d0a7cf411fca0adc2a694)) -* Update llm.TextEmbeddingGenerator to 005 ([#1186](https://github.com/googleapis/python-bigquery-dataframes/issues/1186)) ([3072d38](https://github.com/googleapis/python-bigquery-dataframes/commit/3072d382c6ff57bdb37d7e080c794c67dbf6e701)) - - -### Bug Fixes - -* Fix error loading local dataframes into bigquery ([#1165](https://github.com/googleapis/python-bigquery-dataframes/issues/1165)) ([5b355ef](https://github.com/googleapis/python-bigquery-dataframes/commit/5b355efde122ed76b1cff39900ab8f94f5a13a30)) -* Fix null index join with 'on' arg ([#1153](https://github.com/googleapis/python-bigquery-dataframes/issues/1153)) ([9015c33](https://github.com/googleapis/python-bigquery-dataframes/commit/9015c33e73675ebb2299487dce3295732ea0527e)) -* Fix series.isin using local path always ([#1202](https://github.com/googleapis/python-bigquery-dataframes/issues/1202)) ([a44eafd](https://github.com/googleapis/python-bigquery-dataframes/commit/a44eafdd95eb1b994dc82411640b61fd0a78a492)) - - -### Performance Improvements - -* Update df.corr, df.cov to be used with more than 30 columns case. ([#1161](https://github.com/googleapis/python-bigquery-dataframes/issues/1161)) ([9dcf1aa](https://github.com/googleapis/python-bigquery-dataframes/commit/9dcf1aa918919704dcf4d12b05935b22fb502fc6)) - - -### Dependencies - -* Remove `ibis-framework` by vendoring a fork of the package to `bigframes_vendored`. ([#1170](https://github.com/googleapis/python-bigquery-dataframes/pull/1170)) ([421d24d](https://github.com/googleapis/python-bigquery-dataframes/commit/421d24d6e61d557aa696fc701c08c84389f72ed2)) - - -### Documentation - -* Add a code sample using `bpd.options.bigquery.ordering_mode = "partial"` ([#909](https://github.com/googleapis/python-bigquery-dataframes/issues/909)) ([f80d705](https://github.com/googleapis/python-bigquery-dataframes/commit/f80d70503b80559a0b1fe64434383aa3e028bf9b)) -* Add snippet for creating boosted tree model ([#1142](https://github.com/googleapis/python-bigquery-dataframes/issues/1142)) ([a972668](https://github.com/googleapis/python-bigquery-dataframes/commit/a972668833a454fb18e6cb148697165edd46e8cc)) -* Add snippet for evaluating a boosted tree model ([#1154](https://github.com/googleapis/python-bigquery-dataframes/issues/1154)) ([9d8970a](https://github.com/googleapis/python-bigquery-dataframes/commit/9d8970ac1f18b2520a061ac743e767ca8593cc8c)) -* Add snippet for predicting classifications using a boosted tree model ([#1156](https://github.com/googleapis/python-bigquery-dataframes/issues/1156)) ([e7b83f1](https://github.com/googleapis/python-bigquery-dataframes/commit/e7b83f166ef56e631120050103c2f43f454fce44)) -* Add third party `pandas.Index methods` and docstrings ([#1171](https://github.com/googleapis/python-bigquery-dataframes/issues/1171)) ([a970294](https://github.com/googleapis/python-bigquery-dataframes/commit/a9702945286fbe500ade4d0f0c14cc60a8aa00eb)) -* Fix Bigframes.Pandas.General_Function missing docs ([#1164](https://github.com/googleapis/python-bigquery-dataframes/issues/1164)) ([de923d0](https://github.com/googleapis/python-bigquery-dataframes/commit/de923d01b904b96cc51dfd526b6a412f28ff10c4)) -* Update `bigframes.pandas.Index` docstrings ([#1144](https://github.com/googleapis/python-bigquery-dataframes/issues/1144)) ([557ab8d](https://github.com/googleapis/python-bigquery-dataframes/commit/557ab8df526fcf743af0a609ec7ec636b00d0c0b)) - -## [1.27.0](https://github.com/googleapis/python-bigquery-dataframes/compare/v1.26.0...v1.27.0) (2024-11-16) - - -### Features - -* Add astype(type, errors='null') to cast safely ([#1122](https://github.com/googleapis/python-bigquery-dataframes/issues/1122)) ([b4d17ff](https://github.com/googleapis/python-bigquery-dataframes/commit/b4d17ffdd891da266ad9765a087d3512c0e056fc)) - - -### Bug Fixes - -* Dataframe fillna with scalar. ([#1132](https://github.com/googleapis/python-bigquery-dataframes/issues/1132)) ([37f8c32](https://github.com/googleapis/python-bigquery-dataframes/commit/37f8c32a541565208602f3f6ed37dded13e16b9b)) -* Exclude index columns from model fitting processes. ([#1138](https://github.com/googleapis/python-bigquery-dataframes/issues/1138)) ([8d4da15](https://github.com/googleapis/python-bigquery-dataframes/commit/8d4da1582a5965e6a1f9732ec0ce592ea47ce5fa)) -* Unordered mode too many labels issue. ([#1148](https://github.com/googleapis/python-bigquery-dataframes/issues/1148)) ([7216b21](https://github.com/googleapis/python-bigquery-dataframes/commit/7216b21abd01bc61878bb5686f83ee13ef297912)) - - -### Documentation - -* Document groupby.head and groupby.size methods ([#1111](https://github.com/googleapis/python-bigquery-dataframes/issues/1111)) ([a61eb4d](https://github.com/googleapis/python-bigquery-dataframes/commit/a61eb4d6e323e5001715d402e0e67054df6e62af)) - -## [1.26.0](https://github.com/googleapis/python-bigquery-dataframes/compare/v1.25.0...v1.26.0) (2024-11-12) - - -### Features - -* Add basic geopandas functionality ([#962](https://github.com/googleapis/python-bigquery-dataframes/issues/962)) ([3759c63](https://github.com/googleapis/python-bigquery-dataframes/commit/3759c6397eaa3c46c4142aa51ca22be3dc8e4971)) -* Support `json_extract_string_array` in the `bigquery` module ([#1131](https://github.com/googleapis/python-bigquery-dataframes/issues/1131)) ([4ef8bac](https://github.com/googleapis/python-bigquery-dataframes/commit/4ef8bacdcc5447ba53c0f354526346f4dec7c5a1)) - - -### Bug Fixes - -* Fix Series.to_frame generating string label instead of int where name is None ([#1118](https://github.com/googleapis/python-bigquery-dataframes/issues/1118)) ([14e32b5](https://github.com/googleapis/python-bigquery-dataframes/commit/14e32b51c11c1718128f49ef94e754afc0ac0618)) -* Update the API documentation with newly added rep ([#1120](https://github.com/googleapis/python-bigquery-dataframes/issues/1120)) ([72c228b](https://github.com/googleapis/python-bigquery-dataframes/commit/72c228b15627e6047d60ae42740563a6dfea73da)) - - -### Performance Improvements - -* Reduce CURRENT_TIMESTAMP queries ([#1114](https://github.com/googleapis/python-bigquery-dataframes/issues/1114)) ([32274b1](https://github.com/googleapis/python-bigquery-dataframes/commit/32274b130849b37d7e587643cf7b6d109455ff38)) -* Reduce dry runs from read_gbq with table ([#1129](https://github.com/googleapis/python-bigquery-dataframes/issues/1129)) ([f7e4354](https://github.com/googleapis/python-bigquery-dataframes/commit/f7e435488d630cf4cf493c89ecdde94a95a7a0d7)) - - -### Documentation - -* Add file for Classification with a Boosted Treed Model and snippet for preparing sample data ([#1135](https://github.com/googleapis/python-bigquery-dataframes/issues/1135)) ([7ac6639](https://github.com/googleapis/python-bigquery-dataframes/commit/7ac6639fb0e8baf5fb3adf5785dffd8cf9b06702)) -* Add snippet for Linear Regression tutorial Predict Outcomes section ([#1101](https://github.com/googleapis/python-bigquery-dataframes/issues/1101)) ([108f4a9](https://github.com/googleapis/python-bigquery-dataframes/commit/108f4a98463596d8df6d381b3580eb72eab41b6e)) -* Update `DataFrame` docstrings to include the errors section ([#1127](https://github.com/googleapis/python-bigquery-dataframes/issues/1127)) ([a38d4c4](https://github.com/googleapis/python-bigquery-dataframes/commit/a38d4c422b6b312f6a54d7b1dd105a474ec2e91a)) -* Update GroupBy docstrings ([#1103](https://github.com/googleapis/python-bigquery-dataframes/issues/1103)) ([9867a78](https://github.com/googleapis/python-bigquery-dataframes/commit/9867a788e7c46bf0850cacbe7cd41a11fea32d6b)) -* Update Session doctrings to include exceptions ([#1130](https://github.com/googleapis/python-bigquery-dataframes/issues/1130)) ([a870421](https://github.com/googleapis/python-bigquery-dataframes/commit/a87042158b181dceee31124fe208926a3bb1071f)) - -## [1.25.0](https://github.com/googleapis/python-bigquery-dataframes/compare/v1.24.0...v1.25.0) (2024-10-29) - - -### Features - -* Add the `ground_with_google_search` option for GeminiTextGenerator predict ([#1119](https://github.com/googleapis/python-bigquery-dataframes/issues/1119)) ([ca02cd4](https://github.com/googleapis/python-bigquery-dataframes/commit/ca02cd4b87d354c1e01c670cd9d4e36fa74896f5)) -* Add warning when user tries to access struct series fields with `__getitem__` ([#1082](https://github.com/googleapis/python-bigquery-dataframes/issues/1082)) ([20e5c58](https://github.com/googleapis/python-bigquery-dataframes/commit/20e5c58868af8b18595d5635cb7722da4f622eb5)) -* Allow `fit` to take additional eval data in linear and ensemble models ([#1096](https://github.com/googleapis/python-bigquery-dataframes/issues/1096)) ([254875c](https://github.com/googleapis/python-bigquery-dataframes/commit/254875c25f39df4bc477e1ed7339ecb30b395ab6)) -* Support context manager for bigframes session ([#1107](https://github.com/googleapis/python-bigquery-dataframes/issues/1107)) ([5f7b8b1](https://github.com/googleapis/python-bigquery-dataframes/commit/5f7b8b189c093629d176ffc99364767dc766397a)) - - -### Performance Improvements - -* Improve series.unique performance and replace drop_duplicates i… ([#1108](https://github.com/googleapis/python-bigquery-dataframes/issues/1108)) ([499f24a](https://github.com/googleapis/python-bigquery-dataframes/commit/499f24a5f22ce484db96eb09cd3a0ce972398d81)) - -## [1.24.0](https://github.com/googleapis/python-bigquery-dataframes/compare/v1.23.0...v1.24.0) (2024-10-24) - - -### Features - -* Support series items method ([#1089](https://github.com/googleapis/python-bigquery-dataframes/issues/1089)) ([245a89c](https://github.com/googleapis/python-bigquery-dataframes/commit/245a89c36544faf2bcecb5735abbc00c0b4dd687)) - - -### Documentation - -* Update docstrings of DataFrame and related files ([#1092](https://github.com/googleapis/python-bigquery-dataframes/issues/1092)) ([15e9fd5](https://github.com/googleapis/python-bigquery-dataframes/commit/15e9fd547a01572cbda3d21de04d5548c7a4a82c)) - -## [1.23.0](https://github.com/googleapis/python-bigquery-dataframes/compare/v1.22.0...v1.23.0) (2024-10-23) - - -### Features - -* Add `bigframes.bigquery.create_vector_index` to assist in creating vector index on `ARRAY` columns ([#1024](https://github.com/googleapis/python-bigquery-dataframes/issues/1024)) ([863d694](https://github.com/googleapis/python-bigquery-dataframes/commit/863d6942eaf0cc435c3b76dc5d579c68fd478aa4)) -* Add gemini-1.5-pro-002 and gemini-1.5-flash-002 to known Gemini model list. ([#1105](https://github.com/googleapis/python-bigquery-dataframes/issues/1105)) ([7094c85](https://github.com/googleapis/python-bigquery-dataframes/commit/7094c85945efeb57067640404f7b98969401191b)) -* Add support for pandas series & data frames as inputs for ml models. ([#1088](https://github.com/googleapis/python-bigquery-dataframes/issues/1088)) ([30c8883](https://github.com/googleapis/python-bigquery-dataframes/commit/30c8883ff19db2c223d84c099c7b822467e9eb9a)) -* Cleanup temp resources with session deletion ([#1068](https://github.com/googleapis/python-bigquery-dataframes/issues/1068)) ([1d5373d](https://github.com/googleapis/python-bigquery-dataframes/commit/1d5373dd531c95b4a6a4132ef9b0ead0ecab14b4)) -* Show possible correct key(s) in `.__getitem__` KeyError message ([#1097](https://github.com/googleapis/python-bigquery-dataframes/issues/1097)) ([32fab96](https://github.com/googleapis/python-bigquery-dataframes/commit/32fab9626b9278e20c70c2ada8702e28e167a539)) -* Support uploading local geo data ([#1036](https://github.com/googleapis/python-bigquery-dataframes/issues/1036)) ([51cdd33](https://github.com/googleapis/python-bigquery-dataframes/commit/51cdd33e9f8377b3b992e0392eeb212aed499e3b)) - - -### Bug Fixes - -* Escape ids more consistently in ml module ([#1074](https://github.com/googleapis/python-bigquery-dataframes/issues/1074)) ([103e998](https://github.com/googleapis/python-bigquery-dataframes/commit/103e99823d442a36b2aaa5113950b988f6d3ba1e)) -* Model.fit metric not collected issue. ([#1085](https://github.com/googleapis/python-bigquery-dataframes/issues/1085)) ([06cec00](https://github.com/googleapis/python-bigquery-dataframes/commit/06cec00c51ba4b8df591e0988379db75b20c450b)) -* Remove index requirement from some dataframe APIs ([#1073](https://github.com/googleapis/python-bigquery-dataframes/issues/1073)) ([2d16f6d](https://github.com/googleapis/python-bigquery-dataframes/commit/2d16f6d1e9519e228533a67084000568a61c086e)) -* Update session metrics in `read_gbq_query` ([#1084](https://github.com/googleapis/python-bigquery-dataframes/issues/1084)) ([dced460](https://github.com/googleapis/python-bigquery-dataframes/commit/dced46070ee4212b5585a1eb53ae341dc0bf63ba)) - - -### Performance Improvements - -* Speed up tree transforms during sql compile ([#1071](https://github.com/googleapis/python-bigquery-dataframes/issues/1071)) ([d73fe9d](https://github.com/googleapis/python-bigquery-dataframes/commit/d73fe9d5fd2907aeaaa892a329221c10bb390da0)) -* Utilize ORDER BY LIMIT over ROW_NUMBER where possible ([#1077](https://github.com/googleapis/python-bigquery-dataframes/issues/1077)) ([7003d1a](https://github.com/googleapis/python-bigquery-dataframes/commit/7003d1ae6fddd535f6c206081e85f82bb6006f17)) - - -### Documentation - -* Add ml tutorial for Evaluate the model ([#1038](https://github.com/googleapis/python-bigquery-dataframes/issues/1038)) ([a120bae](https://github.com/googleapis/python-bigquery-dataframes/commit/a120bae2a8039d6115369b1f4a9047d4f0586120)) -* Show best practice of closing the session to cleanup resources in sample notebooks ([#1095](https://github.com/googleapis/python-bigquery-dataframes/issues/1095)) ([62a88e8](https://github.com/googleapis/python-bigquery-dataframes/commit/62a88e87f55f9cc109aa38f4b7ac10dd45ca41fd)) -* Update docstrings of Session and related files ([#1087](https://github.com/googleapis/python-bigquery-dataframes/issues/1087)) ([bf93e80](https://github.com/googleapis/python-bigquery-dataframes/commit/bf93e808daad2454e5c1aa933e0d2164d63084e7)) - -## [1.22.0](https://github.com/googleapis/python-bigquery-dataframes/compare/v1.21.0...v1.22.0) (2024-10-09) - - -### Features - -* Support regional endpoints for more bigquery locations ([#1061](https://github.com/googleapis/python-bigquery-dataframes/issues/1061)) ([45b672a](https://github.com/googleapis/python-bigquery-dataframes/commit/45b672a9a6359ec8c4755d94e63e5ae77a39754b)) -* Update LLM generators to warn user about model name instead of raising error. ([#1048](https://github.com/googleapis/python-bigquery-dataframes/issues/1048)) ([650d80d](https://github.com/googleapis/python-bigquery-dataframes/commit/650d80d1ad90927068cdb71efbfc548b416641a6)) - - -### Bug Fixes - -* Access MATERIALIZED_VIEW with read_gbq ([#1070](https://github.com/googleapis/python-bigquery-dataframes/issues/1070)) ([601e984](https://github.com/googleapis/python-bigquery-dataframes/commit/601e984aeb3ebf1dcf9cb3f1c34b7f0e4ec7cd16)) -* Correct zero row count in DataFrame from table view ([#1062](https://github.com/googleapis/python-bigquery-dataframes/issues/1062)) ([b536070](https://github.com/googleapis/python-bigquery-dataframes/commit/b53607015abb79be0aa5666681f1c53b5b1bc2b5)) -* Fix generic error message when entering an incorrect column name ([#1031](https://github.com/googleapis/python-bigquery-dataframes/issues/1031)) ([5ac217d](https://github.com/googleapis/python-bigquery-dataframes/commit/5ac217d650bc4f5576ba2b6595a3c0b1d88813ad)) -* Make `explode` respect the index labels ([#1064](https://github.com/googleapis/python-bigquery-dataframes/issues/1064)) ([99ca0df](https://github.com/googleapis/python-bigquery-dataframes/commit/99ca0df90acbbd81197c9b6718b7de7e4dfb86cc)) -* Make invalid location warning case-insensitive ([#1044](https://github.com/googleapis/python-bigquery-dataframes/issues/1044)) ([b6cd55a](https://github.com/googleapis/python-bigquery-dataframes/commit/b6cd55afc49b522904a13a7fd34d40201d176588)) -* Remove palm2 test case from llm load test ([#1063](https://github.com/googleapis/python-bigquery-dataframes/issues/1063)) ([575a10a](https://github.com/googleapis/python-bigquery-dataframes/commit/575a10a7ba0fbac76867f02da1dd65355f00d7aa)) -* Show warning for unknown location set through .ctor ([#1052](https://github.com/googleapis/python-bigquery-dataframes/issues/1052)) ([02c2da7](https://github.com/googleapis/python-bigquery-dataframes/commit/02c2da733b834b99d8044f3c5cac3ac9a85802a6)) - - -### Performance Improvements - -* Reduce schema tracking overhead ([#1056](https://github.com/googleapis/python-bigquery-dataframes/issues/1056)) ([1c3879d](https://github.com/googleapis/python-bigquery-dataframes/commit/1c3879df2d6925e17e2cdca827db8ec919471f72)) -* Repr generates fewer queries ([#1046](https://github.com/googleapis/python-bigquery-dataframes/issues/1046)) ([d204603](https://github.com/googleapis/python-bigquery-dataframes/commit/d204603fdc024823421397dbe514f1f7ced1bc2c)) -* Speedup internal tree comparisons ([#1060](https://github.com/googleapis/python-bigquery-dataframes/issues/1060)) ([4379438](https://github.com/googleapis/python-bigquery-dataframes/commit/4379438fc4f44ea847fd2c00a82af544265a30d2)) - - -### Documentation - -* Add docstring return type section to BigQueryOptions class ([#964](https://github.com/googleapis/python-bigquery-dataframes/issues/964)) ([307385f](https://github.com/googleapis/python-bigquery-dataframes/commit/307385f5295ae6918e7d42dcca2c0e0c32e82446)) - -## [1.21.0](https://github.com/googleapis/python-bigquery-dataframes/compare/v1.20.0...v1.21.0) (2024-10-02) - - -### Features - -* Add deprecation warning to PaLM2TextGenerator model ([#1035](https://github.com/googleapis/python-bigquery-dataframes/issues/1035)) ([1183b0f](https://github.com/googleapis/python-bigquery-dataframes/commit/1183b0fb2be7af7386e4bd0d0d1312433db60454)) -* Add DeprecationWarning for PaLM2TextEmbeddingGenerator ([#1018](https://github.com/googleapis/python-bigquery-dataframes/issues/1018)) ([4af5bbb](https://github.com/googleapis/python-bigquery-dataframes/commit/4af5bbb9e42fdb0add17308475c7881d7035fbfd)) -* Add ml.model_selection.cross_validate support ([#1020](https://github.com/googleapis/python-bigquery-dataframes/issues/1020)) ([1a38063](https://github.com/googleapis/python-bigquery-dataframes/commit/1a380631f793f82637cd384601956ee4457dc58a)) -* Allow access of struct fields with dot operators on `Series` ([#1019](https://github.com/googleapis/python-bigquery-dataframes/issues/1019)) ([ef76f13](https://github.com/googleapis/python-bigquery-dataframes/commit/ef76f137fbbf9e8f8c5a63023554d22059ab4fbd)) - - -### Bug Fixes - -* Ensure no double execution for to_pandas ([#1032](https://github.com/googleapis/python-bigquery-dataframes/issues/1032)) ([4992cc2](https://github.com/googleapis/python-bigquery-dataframes/commit/4992cc27e46bc2b0a908c7d521785989735186f4)) -* Remove pre-caching of remote function results ([#1028](https://github.com/googleapis/python-bigquery-dataframes/issues/1028)) ([0359bc8](https://github.com/googleapis/python-bigquery-dataframes/commit/0359bc85839c37b5cd10c0c418b275ac0dc29c4a)) - - -### Documentation - -* Add ml cross-validation notebook ([#1037](https://github.com/googleapis/python-bigquery-dataframes/issues/1037)) ([057f3f0](https://github.com/googleapis/python-bigquery-dataframes/commit/057f3f0d694ddffe8745443a85b4fb43081893bb)) - -## [1.20.0](https://github.com/googleapis/python-bigquery-dataframes/compare/v1.19.0...v1.20.0) (2024-09-25) - - -### Features - -* Add bigframes.bigquery.approx_top_count ([#1010](https://github.com/googleapis/python-bigquery-dataframes/issues/1010)) ([3263bd7](https://github.com/googleapis/python-bigquery-dataframes/commit/3263bd70cff01bc18f1ae4ac3d5aa7f9d70fd4b7)) -* Add bigframes.ml.compose.SQLScalarColumnTransformer to create custom SQL-based transformations ([#955](https://github.com/googleapis/python-bigquery-dataframes/issues/955)) ([1930b4e](https://github.com/googleapis/python-bigquery-dataframes/commit/1930b4efe60295751ceef89c2a824923a35b19af)) -* Allow multiple columns input for llm models ([#998](https://github.com/googleapis/python-bigquery-dataframes/issues/998)) ([2fe5e48](https://github.com/googleapis/python-bigquery-dataframes/commit/2fe5e48c56bbc359d3769824c83745d65a001dd7)) - - -### Bug Fixes - -* Fix __repr__ caching with partial ordering ([#1016](https://github.com/googleapis/python-bigquery-dataframes/issues/1016)) ([208a984](https://github.com/googleapis/python-bigquery-dataframes/commit/208a98475389f59d4e32e0cfbcc46824cac278a6)) - - -### Documentation - -* Limit pypi notebook to 7 days and add more info about differences with partial ordering mode ([#1013](https://github.com/googleapis/python-bigquery-dataframes/issues/1013)) ([3c54399](https://github.com/googleapis/python-bigquery-dataframes/commit/3c543990297ec3be0e30425ee841546217e26d2a)) -* Move and edit existing linear-regression tutorial snippet ([#991](https://github.com/googleapis/python-bigquery-dataframes/issues/991)) ([4cb62fd](https://github.com/googleapis/python-bigquery-dataframes/commit/4cb62fd74fc1ac3bb21da23b8639464a9ae3525d)) - -## [1.19.0](https://github.com/googleapis/python-bigquery-dataframes/compare/v1.18.0...v1.19.0) (2024-09-24) - - -### Features - -* Add ml.model_selection.KFold class ([#1001](https://github.com/googleapis/python-bigquery-dataframes/issues/1001)) ([952cab9](https://github.com/googleapis/python-bigquery-dataframes/commit/952cab92e548b70d077b20bf10f5307751d2ae76)) -* Support bool and bytes types in `describe(include='all')` ([#994](https://github.com/googleapis/python-bigquery-dataframes/issues/994)) ([cc48f58](https://github.com/googleapis/python-bigquery-dataframes/commit/cc48f58cbd94f8110ee863eb57d3fe8dc5a17778)) -* Support ingress settings in `remote_function` ([#1011](https://github.com/googleapis/python-bigquery-dataframes/issues/1011)) ([8e9919b](https://github.com/googleapis/python-bigquery-dataframes/commit/8e9919b53899b6951a10d02643d1d0e53e15665f)) - - -### Bug Fixes - -* Fix miscasting issues with case_when ([#1003](https://github.com/googleapis/python-bigquery-dataframes/issues/1003)) ([038139d](https://github.com/googleapis/python-bigquery-dataframes/commit/038139dfa4fa89167c52c1cb559c2eb5fe2f0411)) - - -### Performance Improvements - -* Join op discards child ordering in unordered mode ([#923](https://github.com/googleapis/python-bigquery-dataframes/issues/923)) ([1b5b0ee](https://github.com/googleapis/python-bigquery-dataframes/commit/1b5b0eea92631b7dd1b688cf1da617fc7ce862dc)) - - -### Dependencies - -* Update ibis version in prerelease tests ([#1012](https://github.com/googleapis/python-bigquery-dataframes/issues/1012)) ([f89785f](https://github.com/googleapis/python-bigquery-dataframes/commit/f89785fcfc51c541253ca8c1e8baf80fbfaea3b6)) - -## [1.18.0](https://github.com/googleapis/python-bigquery-dataframes/compare/v1.17.0...v1.18.0) (2024-09-18) - - -### Features - -* Add "include" param to describe for string types ([#973](https://github.com/googleapis/python-bigquery-dataframes/issues/973)) ([deac6d2](https://github.com/googleapis/python-bigquery-dataframes/commit/deac6d2d6e459b26c05f6e5ff328ea03a3cff45f)) -* Add `subset` parameter to `DataFrame.dropna` to select which columns to consider ([#981](https://github.com/googleapis/python-bigquery-dataframes/issues/981)) ([f7c03dc](https://github.com/googleapis/python-bigquery-dataframes/commit/f7c03dcaf7ee4d62497f6653851e390795fc60a2)) - - -### Bug Fixes - -* DataFrameGroupby.agg now works with unnamed tuples ([#985](https://github.com/googleapis/python-bigquery-dataframes/issues/985)) ([0f047b4](https://github.com/googleapis/python-bigquery-dataframes/commit/0f047b4fae2a10b2a465c506bea561f8bb8d4262)) -* Fix a bug that raises exception when re-indexing columns with their original order ([#988](https://github.com/googleapis/python-bigquery-dataframes/issues/988)) ([596b03b](https://github.com/googleapis/python-bigquery-dataframes/commit/596b03bb3ea27cead9b90200b9ef3cdcd99ca184)) -* Make the `Series.apply` outcome `assign`able to the original dataframe in partial ordering mode ([#874](https://github.com/googleapis/python-bigquery-dataframes/issues/874)) ([c94ead9](https://github.com/googleapis/python-bigquery-dataframes/commit/c94ead996e3bfa98edd51ff678a3d43a10ee980f)) - - -### Dependencies - -* Limit ibis-framework version to 9.2.0 ([#989](https://github.com/googleapis/python-bigquery-dataframes/issues/989)) ([06c1b33](https://github.com/googleapis/python-bigquery-dataframes/commit/06c1b3396d77d1de4f927328bae70cd7b3eb0b0b)) -* Update to ibis-framework 9.x and newer sqlglot ([#827](https://github.com/googleapis/python-bigquery-dataframes/issues/827)) ([89ea44f](https://github.com/googleapis/python-bigquery-dataframes/commit/89ea44fb66314b134fc0a10d816c1659978d4182)) - -## [1.17.0](https://github.com/googleapis/python-bigquery-dataframes/compare/v1.16.0...v1.17.0) (2024-09-11) - - -### Features - -* Add `__version__` alias to bigframes.pandas ([#967](https://github.com/googleapis/python-bigquery-dataframes/issues/967)) ([9ce10b4](https://github.com/googleapis/python-bigquery-dataframes/commit/9ce10b4248f106ac9e09fc0fe686cece86827337)) -* Add Gemini 1.5 stable models support ([#945](https://github.com/googleapis/python-bigquery-dataframes/issues/945)) ([c1cde19](https://github.com/googleapis/python-bigquery-dataframes/commit/c1cde19769c169b962b58b25f0be61c8c41edb95)) -* Allow setting table labels in `to_gbq` ([#941](https://github.com/googleapis/python-bigquery-dataframes/issues/941)) ([cccc6ca](https://github.com/googleapis/python-bigquery-dataframes/commit/cccc6ca8c1271097bbe15e3d9ccdcfd7c633227a)) -* Define list accessor for bigframes Series ([#946](https://github.com/googleapis/python-bigquery-dataframes/issues/946)) ([8e8279d](https://github.com/googleapis/python-bigquery-dataframes/commit/8e8279d4da90feb5766f266b49cb417f8cbec6c9)) -* Enable read_csv() to process other files ([#940](https://github.com/googleapis/python-bigquery-dataframes/issues/940)) ([3b35860](https://github.com/googleapis/python-bigquery-dataframes/commit/3b35860776033fc8e71e471422c6d2b9366a7c9f)) -* Include the bigframes package version alongside the feedback link in error messages ([#936](https://github.com/googleapis/python-bigquery-dataframes/issues/936)) ([7b59b6d](https://github.com/googleapis/python-bigquery-dataframes/commit/7b59b6dc6f0cedfee713b5b273d46fa84b70bfa4)) - - -### Bug Fixes - -* Astype Decimal to Int64 conversion. ([#957](https://github.com/googleapis/python-bigquery-dataframes/issues/957)) ([27764a6](https://github.com/googleapis/python-bigquery-dataframes/commit/27764a64f90092374458fafbe393bc6c30c85681)) -* Make `read_gbq_function` work for multi-param functions ([#947](https://github.com/googleapis/python-bigquery-dataframes/issues/947)) ([c750be6](https://github.com/googleapis/python-bigquery-dataframes/commit/c750be6093941677572a10c36a92984e954de32c)) -* Support `read_gbq_function` for axis=1 application ([#950](https://github.com/googleapis/python-bigquery-dataframes/issues/950)) ([86e54b1](https://github.com/googleapis/python-bigquery-dataframes/commit/86e54b13d2b91517b1df2d9c1f852a8e1925309a)) - - -### Documentation - -* Add docstring returns section to Options ([#937](https://github.com/googleapis/python-bigquery-dataframes/issues/937)) ([a2640a2](https://github.com/googleapis/python-bigquery-dataframes/commit/a2640a2d731c8d0aba1307311092f5e85b8ba077)) -* Update title of pypi notebook example to reflect use of the PyPI public dataset ([#952](https://github.com/googleapis/python-bigquery-dataframes/issues/952)) ([cd62e60](https://github.com/googleapis/python-bigquery-dataframes/commit/cd62e604967adac0c2f8600408bd9ce7886f2f98)) - -## [1.16.0](https://github.com/googleapis/python-bigquery-dataframes/compare/v1.15.0...v1.16.0) (2024-09-04) - - -### Features - -* Add `DataFrame.struct.explode` to add struct subfields to a DataFrame ([#916](https://github.com/googleapis/python-bigquery-dataframes/issues/916)) ([ad2f75e](https://github.com/googleapis/python-bigquery-dataframes/commit/ad2f75ecbc3660459814716eec7d1f88d1188942)) -* Implement `bigframes.bigquery.json_extract_array` ([#910](https://github.com/googleapis/python-bigquery-dataframes/issues/910)) ([575a29e](https://github.com/googleapis/python-bigquery-dataframes/commit/575a29e77d50d60d7e9a84ebb87abcdb993adef1)) -* Recover struct column from exploded Series ([#904](https://github.com/googleapis/python-bigquery-dataframes/issues/904)) ([7dd304c](https://github.com/googleapis/python-bigquery-dataframes/commit/7dd304cc7168fac222fa1330f868677818d10903)) - - -### Bug Fixes - -* Fix issue with iterating on >10gb dataframes ([#949](https://github.com/googleapis/python-bigquery-dataframes/issues/949)) ([2b0f0fa](https://github.com/googleapis/python-bigquery-dataframes/commit/2b0f0faf840a1ec43d007827bbbf908df62ce9d3)) -* Improve `Series.replace` for dict input ([#907](https://github.com/googleapis/python-bigquery-dataframes/issues/907)) ([4208044](https://github.com/googleapis/python-bigquery-dataframes/commit/4208044222c6a8494004ec6f511a3b85f4eb4180)) -* NullIndex in ML model.predict error ([#917](https://github.com/googleapis/python-bigquery-dataframes/issues/917)) ([612271d](https://github.com/googleapis/python-bigquery-dataframes/commit/612271d35675353effa465a797d6e3a1285d4d37)) -* Struct field non-nullable type issue. ([#914](https://github.com/googleapis/python-bigquery-dataframes/issues/914)) ([149d5ff](https://github.com/googleapis/python-bigquery-dataframes/commit/149d5ff822da3d7fda18dbed4814e0406708cf07)) -* Unordered mode errors in ml train_test_split ([#925](https://github.com/googleapis/python-bigquery-dataframes/issues/925)) ([85d7c21](https://github.com/googleapis/python-bigquery-dataframes/commit/85d7c21b4bd5dc669098342fc60d66d89ef06b2b)) - - -### Performance Improvements - -* Improve repr performance ([#918](https://github.com/googleapis/python-bigquery-dataframes/issues/918)) ([46f2dd7](https://github.com/googleapis/python-bigquery-dataframes/commit/46f2dd79f59131bbb98fe4ae3780b98cb4d50646)) - - -### Dependencies - -* Re-introduce support for numpy 1.24.x ([#931](https://github.com/googleapis/python-bigquery-dataframes/issues/931)) ([3d71913](https://github.com/googleapis/python-bigquery-dataframes/commit/3d71913b3cf357fc9e94304ca0c94070e0a16f92)) -* Update minimum support to Pandas 1.5.3 and Pyarrow 10.0.1 ([#903](https://github.com/googleapis/python-bigquery-dataframes/issues/903)) ([7ed3962](https://github.com/googleapis/python-bigquery-dataframes/commit/7ed39629c638874d8e9cc3c7a9b3ec92ad480eca)) - - -### Documentation - -* Add Claude3 ML and RemoteFunc notebooks ([#930](https://github.com/googleapis/python-bigquery-dataframes/issues/930)) ([cfd16c1](https://github.com/googleapis/python-bigquery-dataframes/commit/cfd16c1278023bd2c3dce9c0cb378615aa00e58d)) -* Create sample notebook to manipulate struct and array data ([#883](https://github.com/googleapis/python-bigquery-dataframes/issues/883)) ([3031903](https://github.com/googleapis/python-bigquery-dataframes/commit/303190331d3194562c5ed44fefc2c9fd1d73bedd)) -* Update struct examples. ([#953](https://github.com/googleapis/python-bigquery-dataframes/issues/953)) ([d632cd0](https://github.com/googleapis/python-bigquery-dataframes/commit/d632cd03e3e3ea6dfa7c56dd459c422e95be906e)) -* Use unstack() from BigQuery DataFrames instead of pandas in the PyPI sample notebook ([#890](https://github.com/googleapis/python-bigquery-dataframes/issues/890)) ([d1883cc](https://github.com/googleapis/python-bigquery-dataframes/commit/d1883cc04ce5b2944d87a00c79b99a406001ba8f)) - -## [1.15.0](https://github.com/googleapis/python-bigquery-dataframes/compare/v1.14.0...v1.15.0) (2024-08-20) - - -### Features - -* Add llm.TextEmbeddingGenerator to support new embedding models ([#905](https://github.com/googleapis/python-bigquery-dataframes/issues/905)) ([6bc6a41](https://github.com/googleapis/python-bigquery-dataframes/commit/6bc6a41426fbbb60e77cd77f80860f88a1751a4b)) -* Add ml.llm.Claude3TextGenerator model ([#901](https://github.com/googleapis/python-bigquery-dataframes/issues/901)) ([7050038](https://github.com/googleapis/python-bigquery-dataframes/commit/7050038eeee258452860941aa6b01d6a8ae10c6f)) - - -### Documentation - -* Add columns for "requires ordering/index" to supported APIs summary ([#892](https://github.com/googleapis/python-bigquery-dataframes/issues/892)) ([d2fc51a](https://github.com/googleapis/python-bigquery-dataframes/commit/d2fc51a30c4fff6fe0b98df61eec70ddb28b37ec)) -* Remove duplicate description for `kms_key_name` ([#898](https://github.com/googleapis/python-bigquery-dataframes/issues/898)) ([1053d56](https://github.com/googleapis/python-bigquery-dataframes/commit/1053d56260eef1cff6e7c419f6c86be8f7e74373)) -* Update embedding model notebooks ([#906](https://github.com/googleapis/python-bigquery-dataframes/issues/906)) ([d9b8ef5](https://github.com/googleapis/python-bigquery-dataframes/commit/d9b8ef56deb0c776edeeb0112bd9d35d5ed1b70e)) - -## [1.14.0](https://github.com/googleapis/python-bigquery-dataframes/compare/v1.13.0...v1.14.0) (2024-08-14) - - -### Features - -* Implement `bigframes.bigquery.json_extract` ([#868](https://github.com/googleapis/python-bigquery-dataframes/issues/868)) ([3dbf84b](https://github.com/googleapis/python-bigquery-dataframes/commit/3dbf84bd1531c1f8d41ba57c2c38b3ba6abfb812)) -* Implement `Series.str.__getitem__` ([#897](https://github.com/googleapis/python-bigquery-dataframes/issues/897)) ([e027b7e](https://github.com/googleapis/python-bigquery-dataframes/commit/e027b7e9d29f628d058611106014a1790459958c)) - - -### Bug Fixes - -* Fix caching from generating row numbers in partial ordering mode ([#872](https://github.com/googleapis/python-bigquery-dataframes/issues/872)) ([52b7786](https://github.com/googleapis/python-bigquery-dataframes/commit/52b7786c3a28da6c29e3ddf12629802215194ad9)) - - -### Performance Improvements - -* Generate SQL with fewer CTEs ([#877](https://github.com/googleapis/python-bigquery-dataframes/issues/877)) ([eb60804](https://github.com/googleapis/python-bigquery-dataframes/commit/eb6080460344aff2fabb7864536ea4fe24c5fbef)) -* Speed up compilation by reducing redundant type normalization ([#896](https://github.com/googleapis/python-bigquery-dataframes/issues/896)) ([e0b11bc](https://github.com/googleapis/python-bigquery-dataframes/commit/e0b11bc8c038db7b950b1653ed4cd44a6246c713)) - - -### Documentation - -* Add streaming html docs ([#884](https://github.com/googleapis/python-bigquery-dataframes/issues/884)) ([171da6c](https://github.com/googleapis/python-bigquery-dataframes/commit/171da6cb33165b49d46ea6528038342abd89e9fa)) -* Fix the `DisplayOptions` doc rendering ([#893](https://github.com/googleapis/python-bigquery-dataframes/issues/893)) ([3eb6a17](https://github.com/googleapis/python-bigquery-dataframes/commit/3eb6a17a5823faf5ecba92cb9a554df74477871d)) -* Update streaming notebook ([#887](https://github.com/googleapis/python-bigquery-dataframes/issues/887)) ([6e6f9df](https://github.com/googleapis/python-bigquery-dataframes/commit/6e6f9df55d435afe0b3ade728ca06826e92a6ee6)) - -## [1.13.0](https://github.com/googleapis/python-bigquery-dataframes/compare/v1.12.0...v1.13.0) (2024-08-05) - - -### Features - -* `df.apply(axis=1)` to support remote function with mutiple params ([#851](https://github.com/googleapis/python-bigquery-dataframes/issues/851)) ([2158818](https://github.com/googleapis/python-bigquery-dataframes/commit/2158818e53e09e55c87ffd574e3ebc2e201285fb)) -* Allow windowing in 'partial' ordering mode ([#861](https://github.com/googleapis/python-bigquery-dataframes/issues/861)) ([ca26fe5](https://github.com/googleapis/python-bigquery-dataframes/commit/ca26fe5f9edec519788c276a09eaff33ecd87434)) -* Create a separate OrderingModePartialPreviewWarning for more fine-grained warning filters ([#879](https://github.com/googleapis/python-bigquery-dataframes/issues/879)) ([8753bdd](https://github.com/googleapis/python-bigquery-dataframes/commit/8753bdd1e44701e56eae914ebc0e91d9b1a6adf1)) - - -### Bug Fixes - -* Fix issue with invalid sql generated by ml distance functions ([#865](https://github.com/googleapis/python-bigquery-dataframes/issues/865)) ([9959fc8](https://github.com/googleapis/python-bigquery-dataframes/commit/9959fc8fcba93441fdd3d9c17e8fdbe6e6a7b504)) - - -### Documentation - -* Create sample notebook using `ordering_mode="partial"` ([#880](https://github.com/googleapis/python-bigquery-dataframes/issues/880)) ([c415eb9](https://github.com/googleapis/python-bigquery-dataframes/commit/c415eb91eb71dea53d245ba2bce416062e3f02f8)) -* Update streaming notebook ([#875](https://github.com/googleapis/python-bigquery-dataframes/issues/875)) ([e9b0557](https://github.com/googleapis/python-bigquery-dataframes/commit/e9b05571123cf13079772856317ca3cd3d564c5a)) - -## [1.12.0](https://github.com/googleapis/python-bigquery-dataframes/compare/v1.11.1...v1.12.0) (2024-07-31) - - -### Features - -* Add bigframes-mode label to query jobs ([#832](https://github.com/googleapis/python-bigquery-dataframes/issues/832)) ([c9eaff0](https://github.com/googleapis/python-bigquery-dataframes/commit/c9eaff0a1a0731b28f4c67bca5606db12a47c8c0)) -* Add config option to set partial ordering mode ([#855](https://github.com/googleapis/python-bigquery-dataframes/issues/855)) ([823c0ce](https://github.com/googleapis/python-bigquery-dataframes/commit/823c0ce57611c0918a9e9999638d7393337fe9af)) -* Add stratify param support to ml.model_selection.train_test_split method ([#815](https://github.com/googleapis/python-bigquery-dataframes/issues/815)) ([27f8631](https://github.com/googleapis/python-bigquery-dataframes/commit/27f8631be81a3e136cfeb8904558bb4f3f5caa05)) -* Add streaming.StreamingDataFrame class ([#864](https://github.com/googleapis/python-bigquery-dataframes/issues/864)) ([a7d7197](https://github.com/googleapis/python-bigquery-dataframes/commit/a7d7197a32c55b989ae4ea8f6cf6e1c0f7184cd4)) -* Allow DataFrame.join for self-join on Null index ([#860](https://github.com/googleapis/python-bigquery-dataframes/issues/860)) ([e950533](https://github.com/googleapis/python-bigquery-dataframes/commit/e95053372c36ea5a91a2d7295c1a3a3671181670)) -* Support remote function cleanup with `session.close` ([#818](https://github.com/googleapis/python-bigquery-dataframes/issues/818)) ([ed06436](https://github.com/googleapis/python-bigquery-dataframes/commit/ed06436612c0d46f190f79721416d473bde7e2f4)) -* Support to_csv/parquet/json to local files/objects ([#858](https://github.com/googleapis/python-bigquery-dataframes/issues/858)) ([d0ab9cc](https://github.com/googleapis/python-bigquery-dataframes/commit/d0ab9cc47298bdde638299baecac9dffd7841ede)) - - -### Bug Fixes - -* Fewer relation joins from df self-operations ([#823](https://github.com/googleapis/python-bigquery-dataframes/issues/823)) ([0d24f73](https://github.com/googleapis/python-bigquery-dataframes/commit/0d24f737041c7dd70253ebb4baa8d8ef67bd4f1d)) -* Fix 'sql' property for null index ([#844](https://github.com/googleapis/python-bigquery-dataframes/issues/844)) ([1b6a556](https://github.com/googleapis/python-bigquery-dataframes/commit/1b6a556206a7a66283339d827ab12db2753521e2)) -* Fix unordered mode using ordered path to print frame ([#839](https://github.com/googleapis/python-bigquery-dataframes/issues/839)) ([93785cb](https://github.com/googleapis/python-bigquery-dataframes/commit/93785cb48be4a2eb8770129148bd0b897fed4ee7)) -* Reduce redundant `remote_function` deployments ([#856](https://github.com/googleapis/python-bigquery-dataframes/issues/856)) ([cbf2d42](https://github.com/googleapis/python-bigquery-dataframes/commit/cbf2d42e4d961a7537381a9c3b28a8b463ad8f74)) - - -### Documentation - -* Add partner attribution steps to integrations sample notebook ([#835](https://github.com/googleapis/python-bigquery-dataframes/issues/835)) ([d7b333f](https://github.com/googleapis/python-bigquery-dataframes/commit/d7b333fa26acddaeb5ccca4f81b1d624dff03ba2)) -* Make `get_global_session`/`close_session`/`reset_session` appears in the docs ([#847](https://github.com/googleapis/python-bigquery-dataframes/issues/847)) ([01d6bbb](https://github.com/googleapis/python-bigquery-dataframes/commit/01d6bbb7479da706dc62bb5e7d51dc28a4042812)) - -## [1.11.1](https://github.com/googleapis/python-bigquery-dataframes/compare/v1.11.0...v1.11.1) (2024-07-08) - - -### Documentation - -* Remove session and connection in llm notebook ([#821](https://github.com/googleapis/python-bigquery-dataframes/issues/821)) ([74170da](https://github.com/googleapis/python-bigquery-dataframes/commit/74170dabd323f1b08ad76241e37ff9f2a5b67ab5)) -* Remove the experimental flask icon from the public docs ([#820](https://github.com/googleapis/python-bigquery-dataframes/issues/820)) ([067ff17](https://github.com/googleapis/python-bigquery-dataframes/commit/067ff173f0abfcf5bf06d3fbdb6d12e0fa5283c3)) - -## [1.11.0](https://github.com/googleapis/python-bigquery-dataframes/compare/v1.10.0...v1.11.0) (2024-07-01) - - -### Features - -* Add .agg support for size ([#792](https://github.com/googleapis/python-bigquery-dataframes/issues/792)) ([87e6018](https://github.com/googleapis/python-bigquery-dataframes/commit/87e60182c964c369079165e87ce73dd0c0481a5a)) -* Add `bigframes.bigquery.json_set` ([#782](https://github.com/googleapis/python-bigquery-dataframes/issues/782)) ([1b613e0](https://github.com/googleapis/python-bigquery-dataframes/commit/1b613e00eddf18fa40ed1d08ff19c4ebeeac2197)) -* Add `bigframes.streaming.to_pubsub` method to create continuous query that writes to Pub/Sub ([#801](https://github.com/googleapis/python-bigquery-dataframes/issues/801)) ([b47f32d](https://github.com/googleapis/python-bigquery-dataframes/commit/b47f32d74a0c9eb908be690b2dd56b0f5579b133)) -* Add `DataFrame.to_arrow` to create Arrow Table from DataFrame ([#807](https://github.com/googleapis/python-bigquery-dataframes/issues/807)) ([1e3feda](https://github.com/googleapis/python-bigquery-dataframes/commit/1e3feda9e8fe9d08a0e3838066f6414f8015197d)) -* Add `PolynomialFeatures` support to `to_gbq` and pipelines ([#805](https://github.com/googleapis/python-bigquery-dataframes/issues/805)) ([57d98b9](https://github.com/googleapis/python-bigquery-dataframes/commit/57d98b9e3298583ec40c04665ab84e6ad2b948fb)) -* Add Series.peek to preview data efficiently ([#727](https://github.com/googleapis/python-bigquery-dataframes/issues/727)) ([580e1b9](https://github.com/googleapis/python-bigquery-dataframes/commit/580e1b9e965d883a67f91a6db8311c2416ca8fe5)) -* Expose gcf memory param in `remote_function` ([#803](https://github.com/googleapis/python-bigquery-dataframes/issues/803)) ([014765c](https://github.com/googleapis/python-bigquery-dataframes/commit/014765c22410a0b4559896d163c440f46f7ce98f)) -* More informative error when query plan too complex ([#811](https://github.com/googleapis/python-bigquery-dataframes/issues/811)) ([136dc24](https://github.com/googleapis/python-bigquery-dataframes/commit/136dc24e160339d27f6335e7b28f08cd95d2c67d)) - - -### Bug Fixes - -* Include internally required packages in `remote_function` hash ([#799](https://github.com/googleapis/python-bigquery-dataframes/issues/799)) ([4b8fc15](https://github.com/googleapis/python-bigquery-dataframes/commit/4b8fc15ec2c126566269f84d75289198fee2c655)) - - -### Documentation - -* Document dtype limitation on row processing `remote_function` ([#800](https://github.com/googleapis/python-bigquery-dataframes/issues/800)) ([487dff6](https://github.com/googleapis/python-bigquery-dataframes/commit/487dff6ac147683aef529e1ff8c197dce3fb437c)) - -## [1.10.0](https://github.com/googleapis/python-bigquery-dataframes/compare/v1.9.0...v1.10.0) (2024-06-21) - - -### Features - -* Add dataframe.insert ([#770](https://github.com/googleapis/python-bigquery-dataframes/issues/770)) ([e8bab68](https://github.com/googleapis/python-bigquery-dataframes/commit/e8bab681a2d07636e5809e804f4fd81b0d582685)) -* Add groupby head API ([#791](https://github.com/googleapis/python-bigquery-dataframes/issues/791)) ([44202bc](https://github.com/googleapis/python-bigquery-dataframes/commit/44202bc3541df03154ea0b2cca8eac18094a91a9)) -* Add ml.preprocessing.PolynomialFeatures class ([#793](https://github.com/googleapis/python-bigquery-dataframes/issues/793)) ([b4fbb51](https://github.com/googleapis/python-bigquery-dataframes/commit/b4fbb518711922c09ac6f55f3b8f6ab57c89114b)) -* Bigframes.streaming module for continuous queries ([#703](https://github.com/googleapis/python-bigquery-dataframes/issues/703)) ([0433a1c](https://github.com/googleapis/python-bigquery-dataframes/commit/0433a1cff57fddda26b2c57adc0ea71f3fdd3201)) -* Include index columns in DataFrame.sql if they are named ([#788](https://github.com/googleapis/python-bigquery-dataframes/issues/788)) ([c8d16c0](https://github.com/googleapis/python-bigquery-dataframes/commit/c8d16c0f72a25bce854b80be517114e1603c947e)) - - -### Bug Fixes - -* Allow `__repr__` to work with uninitialed DataFrame/Series/Index ([#778](https://github.com/googleapis/python-bigquery-dataframes/issues/778)) ([e14c7a9](https://github.com/googleapis/python-bigquery-dataframes/commit/e14c7a9e7a9cb8847e0382b135fc06c7b82b872a)) -* Df.loc with the 2nd input as bigframes boolean Series ([#789](https://github.com/googleapis/python-bigquery-dataframes/issues/789)) ([a4ac82e](https://github.com/googleapis/python-bigquery-dataframes/commit/a4ac82e06221581ddfcfc1246a3e3cd65a8bb00e)) -* Ensure numpy version matches in `remote_function` deployment ([#798](https://github.com/googleapis/python-bigquery-dataframes/issues/798)) ([324d93c](https://github.com/googleapis/python-bigquery-dataframes/commit/324d93cb31191520b790bbbc501468b8d1d8467d)) -* Fix temp table creation retries by now throwing if table already exists. ([#787](https://github.com/googleapis/python-bigquery-dataframes/issues/787)) ([0e57d1f](https://github.com/googleapis/python-bigquery-dataframes/commit/0e57d1f1f8a150ba6faac5f667bb5b4c78f4c0a3)) -* Self-join optimization doesn't needlessly invalidate caching ([#797](https://github.com/googleapis/python-bigquery-dataframes/issues/797)) ([1b96b80](https://github.com/googleapis/python-bigquery-dataframes/commit/1b96b8027a550e1601a5360f2af35d24a8806da9)) - -## [1.9.0](https://github.com/googleapis/python-bigquery-dataframes/compare/v1.8.0...v1.9.0) (2024-06-10) - - -### Features - -* Allow functions returned from `bpd.read_gbq_function` to execute outside of `apply` ([#706](https://github.com/googleapis/python-bigquery-dataframes/issues/706)) ([ad7d8ac](https://github.com/googleapis/python-bigquery-dataframes/commit/ad7d8ac1247ec3b9532dd5375265c36907f50da2)) -* Support `bigquery.vector_search()` ([#736](https://github.com/googleapis/python-bigquery-dataframes/issues/736)) ([dad66fd](https://github.com/googleapis/python-bigquery-dataframes/commit/dad66fdd22bb2d507e7f366c970d971554598cf3)) -* Support `score()` in GeminiTextGenerator ([#740](https://github.com/googleapis/python-bigquery-dataframes/issues/740)) ([b2c7d8b](https://github.com/googleapis/python-bigquery-dataframes/commit/b2c7d8b28e235c839370818137fba71796c9f02a)) -* Support bytes type in `remote_function` ([#761](https://github.com/googleapis/python-bigquery-dataframes/issues/761)) ([4915424](https://github.com/googleapis/python-bigquery-dataframes/commit/4915424a68f36542e901a0ac27946f1ecb2d05ab)) -* Support fit() in GeminiTextGenerator ([#758](https://github.com/googleapis/python-bigquery-dataframes/issues/758)) ([d751f5c](https://github.com/googleapis/python-bigquery-dataframes/commit/d751f5cd1cf578618eabbb992cfb6b0a3c36608c)) - - -### Bug Fixes - -* ARIMAPlus loads auto_arima_min_order param ([#752](https://github.com/googleapis/python-bigquery-dataframes/issues/752)) ([39d7013](https://github.com/googleapis/python-bigquery-dataframes/commit/39d7013a8a8d2908f20bfe54a7dc8de166323b90)) -* Improve to_pandas_batches for large results ([#746](https://github.com/googleapis/python-bigquery-dataframes/issues/746)) ([61f18cb](https://github.com/googleapis/python-bigquery-dataframes/commit/61f18cb63f2785c03dc612a34c030079fc8f4172)) -* Resolve issue with unset thread-local options ([#741](https://github.com/googleapis/python-bigquery-dataframes/issues/741)) ([d93dbaf](https://github.com/googleapis/python-bigquery-dataframes/commit/d93dbafe2bb405c60f7141d9ae4135db4ffdb702)) - - -### Documentation - -* Fix ML.EVALUATE spelling ([#749](https://github.com/googleapis/python-bigquery-dataframes/issues/749)) ([7899749](https://github.com/googleapis/python-bigquery-dataframes/commit/7899749505a75ed89c68e9df64124a153644de96)) -* Remove LogisticRegression normal_equation strategy ([#753](https://github.com/googleapis/python-bigquery-dataframes/issues/753)) ([ea5d367](https://github.com/googleapis/python-bigquery-dataframes/commit/ea5d367d5ecc6826d30082e75c957af8362c9e61)) - -## [1.8.0](https://github.com/googleapis/python-bigquery-dataframes/compare/v1.7.0...v1.8.0) (2024-05-31) - - -### Features - -* `merge` only generates a default index if both inputs already have an index ([#733](https://github.com/googleapis/python-bigquery-dataframes/issues/733)) ([25d049c](https://github.com/googleapis/python-bigquery-dataframes/commit/25d049c078693466905a19cc0954fafcac6c414c)) -* Add `+`, `-` as unary ops, `^` binary op ([#724](https://github.com/googleapis/python-bigquery-dataframes/issues/724)) ([968d825](https://github.com/googleapis/python-bigquery-dataframes/commit/968d8257edbfcb6d437c6203c7c0078ba782cfed)) -* Add `GroupBy.size()` to get number of rows in each group ([#479](https://github.com/googleapis/python-bigquery-dataframes/issues/479)) ([1fca588](https://github.com/googleapis/python-bigquery-dataframes/commit/1fca588e4398baa0dae61bdea0d3bff17e3971b5)) -* Add DataFrame `~` operator ([#721](https://github.com/googleapis/python-bigquery-dataframes/issues/721)) ([354abc1](https://github.com/googleapis/python-bigquery-dataframes/commit/354abc17b5bd55d70d47f893cfccd7cd0ac9794a)) -* Add GeminiText 1.5 Preview models ([#737](https://github.com/googleapis/python-bigquery-dataframes/issues/737)) ([56cbd3b](https://github.com/googleapis/python-bigquery-dataframes/commit/56cbd3b6f17c5ac22572e872b270ac7e3636675a)) -* Add slot_millis and add stats to session object ([#725](https://github.com/googleapis/python-bigquery-dataframes/issues/725)) ([72e9583](https://github.com/googleapis/python-bigquery-dataframes/commit/72e95834f8755760f3529d38f340703f3b971f0a)) -* Adds bigframes.bigquery.array_to_string to convert array elements to delimited strings ([#731](https://github.com/googleapis/python-bigquery-dataframes/issues/731)) ([f12c906](https://github.com/googleapis/python-bigquery-dataframes/commit/f12c90611adb4741069ec32840ebbf2aea83a9f3)) -* Allow functions decorated with `bpd.remote_function()` to execute locally ([#704](https://github.com/googleapis/python-bigquery-dataframes/issues/704)) ([d850da6](https://github.com/googleapis/python-bigquery-dataframes/commit/d850da6364b98c4e01120725e1e609ad8f6c1263)) -* Ensure `"bigframes-api"` label is always set on jobs, even if the API is unknown ([#722](https://github.com/googleapis/python-bigquery-dataframes/issues/722)) ([1832778](https://github.com/googleapis/python-bigquery-dataframes/commit/1832778cfc4f29fdab1b22380f03b192eb8aebb9)) -* Support `ml.SimpleImputer` in bigframes ([#708](https://github.com/googleapis/python-bigquery-dataframes/issues/708)) ([4c4415f](https://github.com/googleapis/python-bigquery-dataframes/commit/4c4415fb137e3baedc4b2d77ec146827b003557e)) -* Support type annotations to supply input and output types to `bpd.remote_function()` decorator ([#717](https://github.com/googleapis/python-bigquery-dataframes/issues/717)) ([4a12e3c](https://github.com/googleapis/python-bigquery-dataframes/commit/4a12e3c6d49d78fc2b51d783cc8de5d09e7c9995)) -* Support type annotations with `bpd.remote_function()` and `axis=1` (a preview feature) ([#730](https://github.com/googleapis/python-bigquery-dataframes/issues/730)) ([e5a2992](https://github.com/googleapis/python-bigquery-dataframes/commit/e5a299271e3bcf94c66fb6ef70393071c1b7dc69)) - - -### Bug Fixes - -* Correct index labels in multiple aggregations for DataFrameGroupBy ([#723](https://github.com/googleapis/python-bigquery-dataframes/issues/723)) ([6a78c89](https://github.com/googleapis/python-bigquery-dataframes/commit/6a78c89a3a766b747b03c8a739760db1c79f533f)) -* Fix Null index assign series to column ([#711](https://github.com/googleapis/python-bigquery-dataframes/issues/711)) ([ffb4b57](https://github.com/googleapis/python-bigquery-dataframes/commit/ffb4b5712a1a07c703ea88f66ba3f43dd2f98197)) -* Set `bpd.remote_function()`s `input_types` and `output_types` default to `None` to allow omitting them when type annotations are present ([#729](https://github.com/googleapis/python-bigquery-dataframes/issues/729)) ([0e25a3b](https://github.com/googleapis/python-bigquery-dataframes/commit/0e25a3b3ae704bf75b752c57f613e778af58bac3)) -* Warn and disable time travel for linked datasets ([#712](https://github.com/googleapis/python-bigquery-dataframes/issues/712)) ([085fa9d](https://github.com/googleapis/python-bigquery-dataframes/commit/085fa9d8fe1ea4cd02a3d25d443beaa697e10784)) - - -### Performance Improvements - -* Optimize dataframe-series alignment on axis=1 ([#732](https://github.com/googleapis/python-bigquery-dataframes/issues/732)) ([3d39221](https://github.com/googleapis/python-bigquery-dataframes/commit/3d39221526df82617a8560fd2ab7ea13bc3c03d9)) - - -### Documentation - -* Add examples to DataFrameGroupBy and SeriesGroupBy ([#701](https://github.com/googleapis/python-bigquery-dataframes/issues/701)) ([e7da0f0](https://github.com/googleapis/python-bigquery-dataframes/commit/e7da0f085eb9b9cec06e5de972f07d9c1d545ac7)) - -## [1.7.0](https://github.com/googleapis/python-bigquery-dataframes/compare/v1.6.0...v1.7.0) (2024-05-20) - - -### Features - -* `read_gbq_query` supports `filters` ([9386373](https://github.com/googleapis/python-bigquery-dataframes/commit/9386373538c1e7827e2210c4fd9946312821b54d)) -* `read_gbq` suggests a correct column name when one is not found ([9386373](https://github.com/googleapis/python-bigquery-dataframes/commit/9386373538c1e7827e2210c4fd9946312821b54d)) -* Add `DefaultIndexKind.NULL` to use as `index_col` in `read_gbq*`, creating an indexless DataFrame/Series ([#662](https://github.com/googleapis/python-bigquery-dataframes/issues/662)) ([29e4886](https://github.com/googleapis/python-bigquery-dataframes/commit/29e4886d41e3d615bc493cf3a104ef1b0698ece8)) -* Bigframes.bigquery.array_agg(SeriesGroupBy|DataFrameGroupby) ([#663](https://github.com/googleapis/python-bigquery-dataframes/issues/663)) ([412f28b](https://github.com/googleapis/python-bigquery-dataframes/commit/412f28bf7551430473690160a2a1c4c2f133539e)) -* To_datetime supports utc=False for string inputs ([#579](https://github.com/googleapis/python-bigquery-dataframes/issues/579)) ([adf9889](https://github.com/googleapis/python-bigquery-dataframes/commit/adf98892e499f4a9c85162c38f56ca5634a1ba6d)) - - -### Bug Fixes - -* `read_gbq_table` respects primary keys even when `filters` are set ([#689](https://github.com/googleapis/python-bigquery-dataframes/issues/689)) ([9386373](https://github.com/googleapis/python-bigquery-dataframes/commit/9386373538c1e7827e2210c4fd9946312821b54d)) -* Fix type error in test_cluster ([#698](https://github.com/googleapis/python-bigquery-dataframes/issues/698)) ([14d81c1](https://github.com/googleapis/python-bigquery-dataframes/commit/14d81c17505f9a09439a874ff855aec6f95fc0d1)) -* Improve escaping of literals and identifiers ([#682](https://github.com/googleapis/python-bigquery-dataframes/issues/682)) ([da9b136](https://github.com/googleapis/python-bigquery-dataframes/commit/da9b136df08b243c8515946f7c0d7b591b8fcbdc)) -* Properly identify non-unique index in tables without primary keys ([#699](https://github.com/googleapis/python-bigquery-dataframes/issues/699)) ([6e0f4d8](https://github.com/googleapis/python-bigquery-dataframes/commit/6e0f4d8c76f78dc26f4aa1880dd67ebdb638bb5e)) -* Remove a usage of the `resource` package when not available, such as on Windows ([#681](https://github.com/googleapis/python-bigquery-dataframes/issues/681)) ([96243f2](https://github.com/googleapis/python-bigquery-dataframes/commit/96243f23a1571001509d0d01c16c1e72e47e0d23)) -* The imported samples error and use peek() ([#688](https://github.com/googleapis/python-bigquery-dataframes/issues/688)) ([1a0b744](https://github.com/googleapis/python-bigquery-dataframes/commit/1a0b744c5aacdd8ba4eececf7b0a374808e8672c)) - - -### Performance Improvements - -* Don't run query immediately from `read_gbq_table` if `filters` is set ([9386373](https://github.com/googleapis/python-bigquery-dataframes/commit/9386373538c1e7827e2210c4fd9946312821b54d)) -* Use a `LIMIT` clause when `max_results` is set ([9386373](https://github.com/googleapis/python-bigquery-dataframes/commit/9386373538c1e7827e2210c4fd9946312821b54d)) - - -### Documentation - -* Add code snippets for imported onnx tutorials ([#684](https://github.com/googleapis/python-bigquery-dataframes/issues/684)) ([cb36e46](https://github.com/googleapis/python-bigquery-dataframes/commit/cb36e468d1c2a34c2231638124f3c8d9052f032b)) -* Add code snippets for imported tensorflow model ([#679](https://github.com/googleapis/python-bigquery-dataframes/issues/679)) ([b02c401](https://github.com/googleapis/python-bigquery-dataframes/commit/b02c401614eeab9cbf2e9a7c648b3d0a4e741b97)) -* Use `class_weight="balanced"` in the logistic regression prediction tutorial ([#678](https://github.com/googleapis/python-bigquery-dataframes/issues/678)) ([b951549](https://github.com/googleapis/python-bigquery-dataframes/commit/b95154908fd7838e499a2af0fc3760c5ab33358f)) - -## [1.6.0](https://github.com/googleapis/python-bigquery-dataframes/compare/v1.5.0...v1.6.0) (2024-05-13) - - -### Features - -* Add `DataFrame.__delitem__` ([#673](https://github.com/googleapis/python-bigquery-dataframes/issues/673)) ([2218c21](https://github.com/googleapis/python-bigquery-dataframes/commit/2218c21b5bb0f9e54a365ba1ada0203cbc4c9efc)) -* Add `Series.case_when()` ([#673](https://github.com/googleapis/python-bigquery-dataframes/issues/673)) ([2218c21](https://github.com/googleapis/python-bigquery-dataframes/commit/2218c21b5bb0f9e54a365ba1ada0203cbc4c9efc)) -* Add `strategy="quantile"` in KBinsDiscretizer ([#654](https://github.com/googleapis/python-bigquery-dataframes/issues/654)) ([c6c487f](https://github.com/googleapis/python-bigquery-dataframes/commit/c6c487fb3e39a980a05ff2dab5fb2b528d44016a)) -* Add Series.combine ([#680](https://github.com/googleapis/python-bigquery-dataframes/issues/680)) ([2fd1b81](https://github.com/googleapis/python-bigquery-dataframes/commit/2fd1b8117bda0dee5d8fc0924c80ce257fa9e3f1)) -* Series.str.split ([#675](https://github.com/googleapis/python-bigquery-dataframes/issues/675)) ([6eb19a7](https://github.com/googleapis/python-bigquery-dataframes/commit/6eb19a7288155b093aa7cc9bcbc710b31e7dc87a)) -* Suggest correct options in bpd.options.bigquery.location ([#666](https://github.com/googleapis/python-bigquery-dataframes/issues/666)) ([57ccabc](https://github.com/googleapis/python-bigquery-dataframes/commit/57ccabcd1402b7938e2c7068e5b4880ef018f39c)) -* Support `axis=1` in `df.apply` for scalar outputs ([#629](https://github.com/googleapis/python-bigquery-dataframes/issues/629)) ([f6bdc4a](https://github.com/googleapis/python-bigquery-dataframes/commit/f6bdc4aeb3f81a1e0b955521c04ac0dd22981c76)) -* Support gcf vpc connector in `remote_function` ([#677](https://github.com/googleapis/python-bigquery-dataframes/issues/677)) ([9ca92d0](https://github.com/googleapis/python-bigquery-dataframes/commit/9ca92d09e9c56db408350b35ec698152c13954ed)) -* Warn with a more specific `DefaultLocationWarning` category when no location can be detected ([#648](https://github.com/googleapis/python-bigquery-dataframes/issues/648)) ([e084e54](https://github.com/googleapis/python-bigquery-dataframes/commit/e084e54557addff78522bbd710637ecb4b46d23e)) - - -### Bug Fixes - -* Include `index_col` when selecting `columns` and `filters` in `read_gbq_table` ([#648](https://github.com/googleapis/python-bigquery-dataframes/issues/648)) ([e084e54](https://github.com/googleapis/python-bigquery-dataframes/commit/e084e54557addff78522bbd710637ecb4b46d23e)) - - -### Dependencies - -* Add jellyfish as a dependency for spelling correction ([57ccabc](https://github.com/googleapis/python-bigquery-dataframes/commit/57ccabcd1402b7938e2c7068e5b4880ef018f39c)) - - -### Documentation - -* Add code snippets for llm text generatiion ([#669](https://github.com/googleapis/python-bigquery-dataframes/issues/669)) ([93416ed](https://github.com/googleapis/python-bigquery-dataframes/commit/93416ed2f8353c12eb162e21e9bf155312b0ed8c)) -* Add logistic regression samples ([#673](https://github.com/googleapis/python-bigquery-dataframes/issues/673)) ([2218c21](https://github.com/googleapis/python-bigquery-dataframes/commit/2218c21b5bb0f9e54a365ba1ada0203cbc4c9efc)) -* Address lint errors in code samples ([#665](https://github.com/googleapis/python-bigquery-dataframes/issues/665)) ([4fc8964](https://github.com/googleapis/python-bigquery-dataframes/commit/4fc89644e47a6da9367b54826b25c6abbe97327b)) -* Document inlining of small data in `read_*` APIs ([#670](https://github.com/googleapis/python-bigquery-dataframes/issues/670)) ([306953a](https://github.com/googleapis/python-bigquery-dataframes/commit/306953aaae69e57c7c2f5eefb88d55a35bdcca9d)) - -## [1.5.0](https://github.com/googleapis/python-bigquery-dataframes/compare/v1.4.0...v1.5.0) (2024-05-07) - - -### Features - -* `bigframes.options` and `bigframes.option_context` now uses thread-local variables to prevent context managers in separate threads from affecting each other ([#652](https://github.com/googleapis/python-bigquery-dataframes/issues/652)) ([651fd7d](https://github.com/googleapis/python-bigquery-dataframes/commit/651fd7daf14273f172c6c55e5d6c374eb590a22d)) -* Add `ARIMAPlus.coef_` property exposing `ML.ARIMA_COEFFICIENTS` functionality ([#585](https://github.com/googleapis/python-bigquery-dataframes/issues/585)) ([81d1262](https://github.com/googleapis/python-bigquery-dataframes/commit/81d1262a40c133017c6debe89506d66aab7bb0c5)) -* Add a unique session_id to Session and allow cleaning up sessions ([#553](https://github.com/googleapis/python-bigquery-dataframes/issues/553)) ([c8d4e23](https://github.com/googleapis/python-bigquery-dataframes/commit/c8d4e231fe8263f5b10fae9b879ff82df58da534)) -* Add the `bigframes.bigquery` sub-package with a `bigframes.bigquery.array_length` function ([#630](https://github.com/googleapis/python-bigquery-dataframes/issues/630)) ([9963f85](https://github.com/googleapis/python-bigquery-dataframes/commit/9963f85b84c3b3c681447ab79e22ac93ac48349c)) -* Always do a query dry run when `option.repr_mode == "deferred"` ([#652](https://github.com/googleapis/python-bigquery-dataframes/issues/652)) ([651fd7d](https://github.com/googleapis/python-bigquery-dataframes/commit/651fd7daf14273f172c6c55e5d6c374eb590a22d)) -* Custom query labels for compute options ([#638](https://github.com/googleapis/python-bigquery-dataframes/issues/638)) ([f561799](https://github.com/googleapis/python-bigquery-dataframes/commit/f5617994bc136de5caa72719b8c3c297c512cb36)) -* Warn with `DefaultIndexWarning` from `read_gbq` on clustered/partitioned tables with no `index_col` or `filters` set ([#631](https://github.com/googleapis/python-bigquery-dataframes/issues/631), [#658](https://github.com/googleapis/python-bigquery-dataframes/issues/658)) ([2715d2b](https://github.com/googleapis/python-bigquery-dataframes/commit/2715d2b4a353710175a66a4f6149356f583f2c45), [73064dd](https://github.com/googleapis/python-bigquery-dataframes/commit/73064dd2aa1ece5de8f5849a0fd337d0ba677404)) -* Support `index_col=False` in `read_csv` and `engine="bigquery"` ([73064dd](https://github.com/googleapis/python-bigquery-dataframes/commit/73064dd2aa1ece5de8f5849a0fd337d0ba677404)) -* Support gcf max instance count in `remote_function` ([#657](https://github.com/googleapis/python-bigquery-dataframes/issues/657)) ([36578ab](https://github.com/googleapis/python-bigquery-dataframes/commit/36578ab431119f71dda746de415d0c6417bb4de2)) - - -### Bug Fixes - -* Don't raise UnknownLocationWarning for US or EU multi-regions ([#653](https://github.com/googleapis/python-bigquery-dataframes/issues/653)) ([8e4616b](https://github.com/googleapis/python-bigquery-dataframes/commit/8e4616b896f4e0d13d8bb0424c89335d3a1fe697)) -* Fix bug with na in the column labels in stack ([#659](https://github.com/googleapis/python-bigquery-dataframes/issues/659)) ([4a34293](https://github.com/googleapis/python-bigquery-dataframes/commit/4a342933559fba417fe42e2bd386838defdb2778)) -* Use explicit session in `PaLM2TextGenerator` ([#651](https://github.com/googleapis/python-bigquery-dataframes/issues/651)) ([e4f13c3](https://github.com/googleapis/python-bigquery-dataframes/commit/e4f13c3633b90e32d3171976d8b27ed10049882f)) - - -### Documentation - -* Add python code sample for multiple forecasting time series ([#531](https://github.com/googleapis/python-bigquery-dataframes/issues/531)) ([16866d2](https://github.com/googleapis/python-bigquery-dataframes/commit/16866d2bbd4901b1bf57f7e8cfbdb444d63fee6c)) -* Fix the Palm2TextGenerator output token size ([#649](https://github.com/googleapis/python-bigquery-dataframes/issues/649)) ([c67e501](https://github.com/googleapis/python-bigquery-dataframes/commit/c67e501a4958ac097216cc1c0a9d5c1530c87ae5)) - -## [1.4.0](https://github.com/googleapis/python-bigquery-dataframes/compare/v1.3.0...v1.4.0) (2024-04-29) - - -### Features - -* Add .cache() method to persist intermediate dataframe ([#626](https://github.com/googleapis/python-bigquery-dataframes/issues/626)) ([a5c94ec](https://github.com/googleapis/python-bigquery-dataframes/commit/a5c94ec90dcf2c541d7d4b9558a629f935649dd2)) -* Add transpose support for small homogeneously typed DataFrames. ([#621](https://github.com/googleapis/python-bigquery-dataframes/issues/621)) ([054075d](https://github.com/googleapis/python-bigquery-dataframes/commit/054075d448f7de1b3bc1a4631b4e2340643de4ef)) -* Allow single input type in `remote_function` ([#641](https://github.com/googleapis/python-bigquery-dataframes/issues/641)) ([3aa643f](https://github.com/googleapis/python-bigquery-dataframes/commit/3aa643f7ab6dd0ff826ca2aafbeef29035d7c912)) -* Expose gcf max timeout in `remote_function` ([#639](https://github.com/googleapis/python-bigquery-dataframes/issues/639)) ([dfeaad0](https://github.com/googleapis/python-bigquery-dataframes/commit/dfeaad0ae3b3557a9e8ccb21ddbdc55cfd611e0f)) -* Series binary ops compatible with more types ([#618](https://github.com/googleapis/python-bigquery-dataframes/issues/618)) ([518d315](https://github.com/googleapis/python-bigquery-dataframes/commit/518d315487f351c227070c0127382d11381c5e88)) -* Support the `score` method for `PaLM2TextGenerator` ([#634](https://github.com/googleapis/python-bigquery-dataframes/issues/634)) ([3ffc1d2](https://github.com/googleapis/python-bigquery-dataframes/commit/3ffc1d275ae110bffea2f08e63ef75b053764a0c)) - - -### Bug Fixes - -* Allow to_pandas to download more than 10GB ([#637](https://github.com/googleapis/python-bigquery-dataframes/issues/637)) ([ce56495](https://github.com/googleapis/python-bigquery-dataframes/commit/ce5649513b66c5191a56fc1fd29240b5dbe02394)) -* Extend row hash to 128 bits to guarantee unique row id ([#632](https://github.com/googleapis/python-bigquery-dataframes/issues/632)) ([9005c6e](https://github.com/googleapis/python-bigquery-dataframes/commit/9005c6e79297d7130e93a0e632eb3936aa145efe)) -* Llm fine tuning tests ([#627](https://github.com/googleapis/python-bigquery-dataframes/issues/627)) ([4724a1a](https://github.com/googleapis/python-bigquery-dataframes/commit/4724a1a456076d003613d2e964a8dd2d80a09ad9)) -* Llm palm score tests ([#643](https://github.com/googleapis/python-bigquery-dataframes/issues/643)) ([cf4ec3a](https://github.com/googleapis/python-bigquery-dataframes/commit/cf4ec3af96c28d42e76868c6230a38511052c44e)) - - -### Performance Improvements - -* Automatically condense internal expression representation ([#516](https://github.com/googleapis/python-bigquery-dataframes/issues/516)) ([03c1b0d](https://github.com/googleapis/python-bigquery-dataframes/commit/03c1b0d8122afe9e56b480100d6207d1228ca576)) -* Cache transpose to allow performant retranspose ([#635](https://github.com/googleapis/python-bigquery-dataframes/issues/635)) ([44b738d](https://github.com/googleapis/python-bigquery-dataframes/commit/44b738df07d0ee9d9ae2ced339a123f31139f887)) - - -### Documentation - -* Add supported pandas apis on the main page ([#628](https://github.com/googleapis/python-bigquery-dataframes/issues/628)) ([8d2a51c](https://github.com/googleapis/python-bigquery-dataframes/commit/8d2a51c4079844daba20f414b6c0c0ca030ba1f9)) -* Add the first sample for the Single time-series forecasting from Google Analytics data tutorial ([#623](https://github.com/googleapis/python-bigquery-dataframes/issues/623)) ([2b84c4f](https://github.com/googleapis/python-bigquery-dataframes/commit/2b84c4f173e956ba2c7fcc0ad92785ae95161d8e)) -* Address more technical writers' feedback ([#640](https://github.com/googleapis/python-bigquery-dataframes/issues/640)) ([1e7793c](https://github.com/googleapis/python-bigquery-dataframes/commit/1e7793cdcb56b8c0bcccc1c1ab356bac44454592)) - -## [1.3.0](https://github.com/googleapis/python-bigquery-dataframes/compare/v1.2.0...v1.3.0) (2024-04-22) - - -### Features - -* Add `Series.struct.dtypes` property ([#599](https://github.com/googleapis/python-bigquery-dataframes/issues/599)) ([d924ec2](https://github.com/googleapis/python-bigquery-dataframes/commit/d924ec2937c158644b5d1bbae4f82476de2c1655)) -* Add fine tuning `fit()` for Palm2TextGenerator ([#616](https://github.com/googleapis/python-bigquery-dataframes/issues/616)) ([9c106bd](https://github.com/googleapis/python-bigquery-dataframes/commit/9c106bd24482620ef5ff3c85f94be9da76c49716)) -* Add quantile statistic ([#613](https://github.com/googleapis/python-bigquery-dataframes/issues/613)) ([bc82804](https://github.com/googleapis/python-bigquery-dataframes/commit/bc82804da43c03c2311cd56f47a2316d3aae93d2)) -* Expose `max_batching_rows` in `remote_function` ([#622](https://github.com/googleapis/python-bigquery-dataframes/issues/622)) ([240a1ac](https://github.com/googleapis/python-bigquery-dataframes/commit/240a1ac6fa914550bb6216cd5d179a36009f2657)) -* Support primary key(s) in `read_gbq` by using as the `index_col` by default ([#625](https://github.com/googleapis/python-bigquery-dataframes/issues/625)) ([75bb240](https://github.com/googleapis/python-bigquery-dataframes/commit/75bb2409532e80de742030d05ffcbacacf5ffba2)) -* Warn if location is set to unknown location ([#609](https://github.com/googleapis/python-bigquery-dataframes/issues/609)) ([3706b4f](https://github.com/googleapis/python-bigquery-dataframes/commit/3706b4f9dde65788b5e6343a6428fb1866499461)) - - -### Bug Fixes - -* Address technical writers fb ([#611](https://github.com/googleapis/python-bigquery-dataframes/issues/611)) ([9f8f181](https://github.com/googleapis/python-bigquery-dataframes/commit/9f8f181279133abdb7da3aa045df6fa278587013)) -* Infer narrowest numeric type when combining numeric columns ([#602](https://github.com/googleapis/python-bigquery-dataframes/issues/602)) ([8f9ece6](https://github.com/googleapis/python-bigquery-dataframes/commit/8f9ece6d13f57f02d677bf0e3fea97dea94ae240)) -* Use exact median implementation by default ([#619](https://github.com/googleapis/python-bigquery-dataframes/issues/619)) ([9d205ae](https://github.com/googleapis/python-bigquery-dataframes/commit/9d205aecb77f35baeec82a8f6e1b72c2d852ca46)) - - -### Documentation - -* Fix rendering of examples for multiple apis ([#620](https://github.com/googleapis/python-bigquery-dataframes/issues/620)) ([9665e39](https://github.com/googleapis/python-bigquery-dataframes/commit/9665e39ef288841f03a9d823bd2210ef58394ad3)) -* Set `index_cols` in `read_gbq` as a best practice ([#624](https://github.com/googleapis/python-bigquery-dataframes/issues/624)) ([70015b7](https://github.com/googleapis/python-bigquery-dataframes/commit/70015b79e8cff16ff1b36c5e3f019fe099750a9d)) - -## [1.2.0](https://github.com/googleapis/python-bigquery-dataframes/compare/v1.1.0...v1.2.0) (2024-04-15) - - -### Features - -* Add hasnans, combine_first, update to Series ([#600](https://github.com/googleapis/python-bigquery-dataframes/issues/600)) ([86e0f38](https://github.com/googleapis/python-bigquery-dataframes/commit/86e0f38adc71d76e09dd832e5e33cb7c1aab02ac)) -* Add MultiIndex subclass. ([#596](https://github.com/googleapis/python-bigquery-dataframes/issues/596)) ([5d0f149](https://github.com/googleapis/python-bigquery-dataframes/commit/5d0f149dce5425098fcd154d96a302c1661ce5d3)) -* Add pivot_table for DataFrame. ([#473](https://github.com/googleapis/python-bigquery-dataframes/issues/473)) ([5f1d670](https://github.com/googleapis/python-bigquery-dataframes/commit/5f1d670e6b839a30acdb495a05011c2ce4e0c7a4)) -* Add Series.autocorr ([#605](https://github.com/googleapis/python-bigquery-dataframes/issues/605)) ([4ec8034](https://github.com/googleapis/python-bigquery-dataframes/commit/4ec80340459e675b82b437f6c48b2872d362bafe)) -* Support list of numerics in pandas.cut ([#580](https://github.com/googleapis/python-bigquery-dataframes/issues/580)) ([290f95d](https://github.com/googleapis/python-bigquery-dataframes/commit/290f95dc5198f9ab7cd9d726d40af704250c0449)) - - -### Bug Fixes - -* Address more technical writers feedback ([#581](https://github.com/googleapis/python-bigquery-dataframes/issues/581)) ([4b08d92](https://github.com/googleapis/python-bigquery-dataframes/commit/4b08d9243272229f71688152dbeb69d0ab7c68b4)) -* Error for object dtype on read_pandas ([#570](https://github.com/googleapis/python-bigquery-dataframes/issues/570)) ([8702dcf](https://github.com/googleapis/python-bigquery-dataframes/commit/8702dcf54c0f2073e21df42eaef51927481da421)) -* Inverting int now does bitwise inversion rather than sign flip ([#574](https://github.com/googleapis/python-bigquery-dataframes/issues/574)) ([5f1db8b](https://github.com/googleapis/python-bigquery-dataframes/commit/5f1db8b270b32ab366be3690761da137d9fe65f5)) -* Loc setitem dtype issue. ([#603](https://github.com/googleapis/python-bigquery-dataframes/issues/603)) ([b94bae9](https://github.com/googleapis/python-bigquery-dataframes/commit/b94bae9892e0fa79dc4bde0f4f1427d00accda6d)) -* Toc menu missing plotting name ([#591](https://github.com/googleapis/python-bigquery-dataframes/issues/591)) ([eed12c1](https://github.com/googleapis/python-bigquery-dataframes/commit/eed12c181ff8724333b1c426a0eb442c627528b8)) - - -### Documentation - -* (Series|Dataframe).dtypes ([#598](https://github.com/googleapis/python-bigquery-dataframes/issues/598)) ([edef48f](https://github.com/googleapis/python-bigquery-dataframes/commit/edef48f7a93e19bc1f6d37fb041dfd6314d881d5)) -* Add code samples for `str` accessor methdos ([#594](https://github.com/googleapis/python-bigquery-dataframes/issues/594)) ([a557ea2](https://github.com/googleapis/python-bigquery-dataframes/commit/a557ea2b64633932f730b56688f76806da6195fb)) -* Add docs for `DataFrame` and `Series` dunder methods ([#562](https://github.com/googleapis/python-bigquery-dataframes/issues/562)) ([8fc26c4](https://github.com/googleapis/python-bigquery-dataframes/commit/8fc26c424b29a8b78542372e402fcc4e8fface7b)) -* Add examples for at/iat ([#582](https://github.com/googleapis/python-bigquery-dataframes/issues/582)) ([3be4a2e](https://github.com/googleapis/python-bigquery-dataframes/commit/3be4a2e784e046ca9a1fac8d386d072537b6c4de)) - -## [1.1.0](https://github.com/googleapis/python-bigquery-dataframes/compare/v1.0.0...v1.1.0) (2024-04-04) - - -### Features - -* (Series|DataFrame).explode ([#556](https://github.com/googleapis/python-bigquery-dataframes/issues/556)) ([9e32f57](https://github.com/googleapis/python-bigquery-dataframes/commit/9e32f570b42c8ddae0c9b281b25beff91f0c922c)) -* Add `DataFrame.eval` and `DataFrame.query` ([#361](https://github.com/googleapis/python-bigquery-dataframes/issues/361)) ([5e28ebd](https://github.com/googleapis/python-bigquery-dataframes/commit/5e28ebd1ba3a5559e093c2ea676c0714c1434ba9)) -* Add ColumnTransformer save/load ([#541](https://github.com/googleapis/python-bigquery-dataframes/issues/541)) ([9d8cf67](https://github.com/googleapis/python-bigquery-dataframes/commit/9d8cf6792a8dbe03e03b102c454d15fcde7986af)) -* Add ml.metrics.mean_squared_error ([#559](https://github.com/googleapis/python-bigquery-dataframes/issues/559)) ([853c25e](https://github.com/googleapis/python-bigquery-dataframes/commit/853c25e8023bf877f28cda4dade0694d0299a83e)) -* Add support for numpy expm1, log1p, floor, ceil, arctan2 ops ([#505](https://github.com/googleapis/python-bigquery-dataframes/issues/505)) ([e8e66cf](https://github.com/googleapis/python-bigquery-dataframes/commit/e8e66cf25887f64d2a7cb26081c2ef3cea10827d)) -* Add transformers save/load ([#552](https://github.com/googleapis/python-bigquery-dataframes/issues/552)) ([d805241](https://github.com/googleapis/python-bigquery-dataframes/commit/d805241b7ec99fcb7579dce778d4b04778a72002)) -* Allow DataFrame binary ops to align on either axis and with loc… ([#544](https://github.com/googleapis/python-bigquery-dataframes/issues/544)) ([6d8f3af](https://github.com/googleapis/python-bigquery-dataframes/commit/6d8f3afe28d39eb15b969f50d37c58a2c3ff1967)) -* Expose `DataFrame.bqclient` to assist in integrations ([#519](https://github.com/googleapis/python-bigquery-dataframes/issues/519)) ([0be8911](https://github.com/googleapis/python-bigquery-dataframes/commit/0be891191ed89be77494e4dcda30fb37836842ac)) -* Read_pandas accepts pandas Series and Index objects ([#573](https://github.com/googleapis/python-bigquery-dataframes/issues/573)) ([f8821fe](https://github.com/googleapis/python-bigquery-dataframes/commit/f8821fe7ecf8a80532a6aab98044fad601ff939c)) -* Support `ML.GENERATE_EMBEDDING` in `PaLM2TextEmbeddingGenerator` ([#539](https://github.com/googleapis/python-bigquery-dataframes/issues/539)) ([1156c1e](https://github.com/googleapis/python-bigquery-dataframes/commit/1156c1e3ce8c1e62898dbe68ccd6c5ab3cd4068f)) -* Support max_columns in repr and make repr more efficient ([#515](https://github.com/googleapis/python-bigquery-dataframes/issues/515)) ([54e49cf](https://github.com/googleapis/python-bigquery-dataframes/commit/54e49cff89bd329852a823cd5cf5c5b41b7f9e32)) - - -### Bug Fixes - -* Assign NaN scalar to column error. ([#513](https://github.com/googleapis/python-bigquery-dataframes/issues/513)) ([0a4153c](https://github.com/googleapis/python-bigquery-dataframes/commit/0a4153cc71a44c09b8d691897f1e5afa58c69f25)) -* Don't download 100gb onto local python machine in load test ([#537](https://github.com/googleapis/python-bigquery-dataframes/issues/537)) ([082c58b](https://github.com/googleapis/python-bigquery-dataframes/commit/082c58bbe76821b90337dc5af0ab5fa7515682c2)) -* Exclude list-like s parameter in plot.scatter ([#568](https://github.com/googleapis/python-bigquery-dataframes/issues/568)) ([1caac27](https://github.com/googleapis/python-bigquery-dataframes/commit/1caac27fe95ef3eb36bad2ac351090891922858c)) -* Fix case where df.peek would fail to execute even with force=True ([#511](https://github.com/googleapis/python-bigquery-dataframes/issues/511)) ([8eca99a](https://github.com/googleapis/python-bigquery-dataframes/commit/8eca99a03bc4bdaccf15a979b5382f3659f2aac5)) -* Fix error in `Series.drop(0)` ([#575](https://github.com/googleapis/python-bigquery-dataframes/issues/575)) ([75dd786](https://github.com/googleapis/python-bigquery-dataframes/commit/75dd7862e60502c97f7defe5dfefb044ea74bae8)) -* Include all names in MultiIndex repr ([#564](https://github.com/googleapis/python-bigquery-dataframes/issues/564)) ([b188146](https://github.com/googleapis/python-bigquery-dataframes/commit/b188146466780e6f7a041f51f5be51a7d60719c9)) -* Plot.scatter s parameter cannot accept float-like column ([#563](https://github.com/googleapis/python-bigquery-dataframes/issues/563)) ([8d39187](https://github.com/googleapis/python-bigquery-dataframes/commit/8d3918761a17649180aa806d7b01aa103f69b4fe)) -* Product operation produces float result for all input types ([#501](https://github.com/googleapis/python-bigquery-dataframes/issues/501)) ([6873b30](https://github.com/googleapis/python-bigquery-dataframes/commit/6873b30b691a11a368308825a72013d8ec1408ed)) -* Reloaded transformer .transform error ([#569](https://github.com/googleapis/python-bigquery-dataframes/issues/569)) ([39fe474](https://github.com/googleapis/python-bigquery-dataframes/commit/39fe47451d24a8cf55d7dbb15c6d3b176d25ab18)) -* Rename PaLM2TextEmbeddingGenerator.predict output columns to be backward compatible ([#561](https://github.com/googleapis/python-bigquery-dataframes/issues/561)) ([4995c00](https://github.com/googleapis/python-bigquery-dataframes/commit/4995c0046265463bc5c502cbeb34c7632d5a255e)) -* Respect hard stack size limit and swallow limit change exception. ([#558](https://github.com/googleapis/python-bigquery-dataframes/issues/558)) ([4833908](https://github.com/googleapis/python-bigquery-dataframes/commit/483390830ae0ee2fe0fb47dc7d2aea143b2dc7d8)) -* Restore string to date/time type coercion ([#565](https://github.com/googleapis/python-bigquery-dataframes/issues/565)) ([4ae0262](https://github.com/googleapis/python-bigquery-dataframes/commit/4ae0262a2b1dfc35c1e4c3392b9e21456d6e964e)) -* Sync the notebook with embedding changes ([#550](https://github.com/googleapis/python-bigquery-dataframes/issues/550)) ([347f2dd](https://github.com/googleapis/python-bigquery-dataframes/commit/347f2dda2298e17cd44a298f04a723f2d20c080a)) -* Use bytes limit on frame inlining rather than element count ([#576](https://github.com/googleapis/python-bigquery-dataframes/issues/576)) ([659a161](https://github.com/googleapis/python-bigquery-dataframes/commit/659a161a53e93f66334cd04d1c3dc1f1f47ecc16)) - - -### Performance Improvements - -* Add multi-query execution capability for complex dataframes ([#427](https://github.com/googleapis/python-bigquery-dataframes/issues/427)) ([d2d7e33](https://github.com/googleapis/python-bigquery-dataframes/commit/d2d7e33b1f8b4e184ef3e76eedbd673a8fcee60e)) - - -### Dependencies - -* Include `pyarrow` as a dependency ([#529](https://github.com/googleapis/python-bigquery-dataframes/issues/529)) ([9b1525a](https://github.com/googleapis/python-bigquery-dataframes/commit/9b1525a0c359455160bfbc0dc1366e37982ad01f)) - - -### Documentation - -* `bigframes.options.bigquery.project` and `location` are optional in some circumstances ([#548](https://github.com/googleapis/python-bigquery-dataframes/issues/548)) ([90bcec5](https://github.com/googleapis/python-bigquery-dataframes/commit/90bcec5c73f7eefeff14bbd8bdcad3a4c9d91d8f)) -* Add "Supported pandas APIs" reference to the documentation ([#542](https://github.com/googleapis/python-bigquery-dataframes/issues/542)) ([74c3915](https://github.com/googleapis/python-bigquery-dataframes/commit/74c391586280b55c35d66c697167122d72c13386)) -* Add General Availability banner to README ([#507](https://github.com/googleapis/python-bigquery-dataframes/issues/507)) ([262ff59](https://github.com/googleapis/python-bigquery-dataframes/commit/262ff5922643039e037bd9b6c0a91b5bd20a4e08)) -* Add opeartions in API docs ([#557](https://github.com/googleapis/python-bigquery-dataframes/issues/557)) ([ea95761](https://github.com/googleapis/python-bigquery-dataframes/commit/ea9576125d46f3912372f75ebe51196ba83e96db)) -* Add progress_bar code sample ([#508](https://github.com/googleapis/python-bigquery-dataframes/issues/508)) ([92a1af3](https://github.com/googleapis/python-bigquery-dataframes/commit/92a1af35b8de4afb6cdb5b5e89facdceb5c151d2)) -* Add the code samples for metrics{auc, roc_auc_score, roc_curve} ([#520](https://github.com/googleapis/python-bigquery-dataframes/issues/520)) ([5f37b09](https://github.com/googleapis/python-bigquery-dataframes/commit/5f37b0902fae2c099207acf3ce2e251c09ac889d)) -* Address more comments from technical writers to meet legal purposes ([#571](https://github.com/googleapis/python-bigquery-dataframes/issues/571)) ([9084df3](https://github.com/googleapis/python-bigquery-dataframes/commit/9084df369bc6819edf5f57ceba85667a14371ac5)) -* Fix docs of ARIMAPlus.predict ([#512](https://github.com/googleapis/python-bigquery-dataframes/issues/512)) ([3b80f95](https://github.com/googleapis/python-bigquery-dataframes/commit/3b80f956755c9d7043138aab6e5687cba50be8cb)) -* Include Index in table-of-contents ([#564](https://github.com/googleapis/python-bigquery-dataframes/issues/564)) ([b188146](https://github.com/googleapis/python-bigquery-dataframes/commit/b188146466780e6f7a041f51f5be51a7d60719c9)) -* Mark Gemini model as Pre-GA ([#543](https://github.com/googleapis/python-bigquery-dataframes/issues/543)) ([769868b](https://github.com/googleapis/python-bigquery-dataframes/commit/769868b9fc7dfff2e7b1ed5cec52a5dd3dfd6ff2)) -* Migrate the overview page to Bigframes official landing page ([#536](https://github.com/googleapis/python-bigquery-dataframes/issues/536)) ([a0fb8bb](https://github.com/googleapis/python-bigquery-dataframes/commit/a0fb8bbfddd07f1e0ef03eeb4be653d1e9f06772)) - -## [1.0.0](https://github.com/googleapis/python-bigquery-dataframes/compare/v0.26.0...v1.0.0) (2024-03-25) - - -### ⚠ BREAKING CHANGES - -* rename model parameter `min_rel_progress` to `tol` -* `early_stop` setting no longer supported, always uses `True` -* rename model parameter `n_parallell_trees` to `n_estimators` -* rename `class_weights` to `class_weight` -* rename `learn_rate` to `learning_rate` -* PCA `n_components` supports float value and `None`, default to `None` -* rename various ml model parameters for consistency with sklearn (https://github.com/googleapis/python-bigquery-dataframes/pull/491) - -### Features - -* Add configuration option to read_gbq ([#401](https://github.com/googleapis/python-bigquery-dataframes/issues/401)) ([85cede2](https://github.com/googleapis/python-bigquery-dataframes/commit/85cede22587a9fe1dae888721492f9390dc46d70)) -* Add ml ARIMAPlus model params ([#488](https://github.com/googleapis/python-bigquery-dataframes/issues/488)) ([352cb85](https://github.com/googleapis/python-bigquery-dataframes/commit/352cb850d23e41a2278edf0df584b89ee9619aab)) -* Add ml KMeans model params ([#477](https://github.com/googleapis/python-bigquery-dataframes/issues/477)) ([23a8d9a](https://github.com/googleapis/python-bigquery-dataframes/commit/23a8d9a32e1619aff92c8dfabb7bcdd54c314bd5)) -* Add ml LogisticRegression model params ([#481](https://github.com/googleapis/python-bigquery-dataframes/issues/481)) ([f959b65](https://github.com/googleapis/python-bigquery-dataframes/commit/f959b653a0e82b5bfd21f9e994031cf6d25c281a)) -* Add ml PCA model params ([#474](https://github.com/googleapis/python-bigquery-dataframes/issues/474)) ([fb5d83b](https://github.com/googleapis/python-bigquery-dataframes/commit/fb5d83b1e35c465cff486e6cf7862e5b32e3c65a)) -* Add params for LinearRegression model ([#464](https://github.com/googleapis/python-bigquery-dataframes/issues/464)) ([21b2188](https://github.com/googleapis/python-bigquery-dataframes/commit/21b2188cd0ca85485b5171ee9e46da4c924e2ff8)) -* Add support for Python 3.12 ([#231](https://github.com/googleapis/python-bigquery-dataframes/issues/231)) ([df2976f](https://github.com/googleapis/python-bigquery-dataframes/commit/df2976fa9fd0319b824128d0ccf2ebb20f381caa)) -* Allow assigning directly to Series.name property ([#495](https://github.com/googleapis/python-bigquery-dataframes/issues/495)) ([ad0e99e](https://github.com/googleapis/python-bigquery-dataframes/commit/ad0e99eddb1dddd3d439cea7db1e4f222b45c6b9)) -* Ensure `Series.str.len()` can get length of array columns ([#497](https://github.com/googleapis/python-bigquery-dataframes/issues/497)) ([10c0446](https://github.com/googleapis/python-bigquery-dataframes/commit/10c044686228e5c6f3868c1eb10454f6a086ac8b)) -* Option to use bq connection without check ([#460](https://github.com/googleapis/python-bigquery-dataframes/issues/460)) ([0b3f8e5](https://github.com/googleapis/python-bigquery-dataframes/commit/0b3f8e5ce63f75ba99ee8cf29226a0fd38bef99f)) -* PCA `n_components` supports float value and `None`, default to `None` ([65c6f47](https://github.com/googleapis/python-bigquery-dataframes/commit/65c6f4736d1a5552835e4cec8b777b2c0f3dd8da)) -* Rename `class_weights` to `class_weight` ([65c6f47](https://github.com/googleapis/python-bigquery-dataframes/commit/65c6f4736d1a5552835e4cec8b777b2c0f3dd8da)) -* Rename `learn_rate` to `learning_rate` ([65c6f47](https://github.com/googleapis/python-bigquery-dataframes/commit/65c6f4736d1a5552835e4cec8b777b2c0f3dd8da)) -* Rename model parameter `min_rel_progress` to `tol` ([65c6f47](https://github.com/googleapis/python-bigquery-dataframes/commit/65c6f4736d1a5552835e4cec8b777b2c0f3dd8da)) -* Rename model parameter `n_parallell_trees` to `n_estimators` ([65c6f47](https://github.com/googleapis/python-bigquery-dataframes/commit/65c6f4736d1a5552835e4cec8b777b2c0f3dd8da)) -* Rename various ml model parameters for consistency with sklearn (https://github.com/googleapis/python-bigquery-dataframes/pull/491) ([65c6f47](https://github.com/googleapis/python-bigquery-dataframes/commit/65c6f4736d1a5552835e4cec8b777b2c0f3dd8da)) -* Support BQ regional endpoints for europe-west9, europe-west3, us-east4, and us-west1 ([#504](https://github.com/googleapis/python-bigquery-dataframes/issues/504)) ([fbada4a](https://github.com/googleapis/python-bigquery-dataframes/commit/fbada4a70688c5d13fa35d1843b0c4252c5ced72)) -* Support dataframe.cov ([#498](https://github.com/googleapis/python-bigquery-dataframes/issues/498)) ([c4beafd](https://github.com/googleapis/python-bigquery-dataframes/commit/c4beafdf0c1ba88b306ca96fa3ca46b86debaa4c)) -* Support Series.dt.floor ([#493](https://github.com/googleapis/python-bigquery-dataframes/issues/493)) ([2dd01c2](https://github.com/googleapis/python-bigquery-dataframes/commit/2dd01c25e9f01c03979c61e71d3c5cd9f0bd4c96)) -* Support Series.dt.normalize ([#483](https://github.com/googleapis/python-bigquery-dataframes/issues/483)) ([0bf1e91](https://github.com/googleapis/python-bigquery-dataframes/commit/0bf1e916c2b636ec02ac010190e89d38e88fce4b)) -* Update plot sample to 1000 rows ([#458](https://github.com/googleapis/python-bigquery-dataframes/issues/458)) ([60d4a7b](https://github.com/googleapis/python-bigquery-dataframes/commit/60d4a7bbac867256f8bbfd3053c7dd2645c1b062)) - - -### Bug Fixes - -* `early_stop` setting no longer supported, always uses `True` ([65c6f47](https://github.com/googleapis/python-bigquery-dataframes/commit/65c6f4736d1a5552835e4cec8b777b2c0f3dd8da)) -* Fix -1 offset lookups failing ([#463](https://github.com/googleapis/python-bigquery-dataframes/issues/463)) ([2dfb9c2](https://github.com/googleapis/python-bigquery-dataframes/commit/2dfb9c24d07841d785e41b33573c5f3a218efeea)) -* Plot.scatter `c` argument functionalities ([#494](https://github.com/googleapis/python-bigquery-dataframes/issues/494)) ([d6ee994](https://github.com/googleapis/python-bigquery-dataframes/commit/d6ee994c17e0b1dd6768b09ee81d2c902f601b76)) -* Properly support format param for numerical input. ([#486](https://github.com/googleapis/python-bigquery-dataframes/issues/486)) ([ae20c35](https://github.com/googleapis/python-bigquery-dataframes/commit/ae20c3583d5526777548b5d594ecca6034bb49ec)) -* Renable to_csv and to_json related tests ([#468](https://github.com/googleapis/python-bigquery-dataframes/issues/468)) ([2b9a01d](https://github.com/googleapis/python-bigquery-dataframes/commit/2b9a01de0adb8d41fbe73ce94b1acc8d22f507b5)) -* Sampling plot cannot preserve ordering if index is not ordered ([#475](https://github.com/googleapis/python-bigquery-dataframes/issues/475)) ([a5345fe](https://github.com/googleapis/python-bigquery-dataframes/commit/a5345fe8943667a89fcba48ce31aa8ecfc283f92)) -* Use actual BigQuery types rather than ibis types in to_pandas ([#500](https://github.com/googleapis/python-bigquery-dataframes/issues/500)) ([82b4f91](https://github.com/googleapis/python-bigquery-dataframes/commit/82b4f91db365fe06d8bd0bf938f880a48091104e)) - - -### Dependencies - -* Support pandas 2.2 ([#492](https://github.com/googleapis/python-bigquery-dataframes/issues/492)) ([e2cf50e](https://github.com/googleapis/python-bigquery-dataframes/commit/e2cf50e053f7163d1654c4b5621cc93e922d5148)) - - -### Documentation - -* Add code samples for metrics.{accuracy_score, confusion_matrix} ([#478](https://github.com/googleapis/python-bigquery-dataframes/issues/478)) ([3e3329a](https://github.com/googleapis/python-bigquery-dataframes/commit/3e3329a37c1020bd3e6d4d5e980103c63ab0c337)) -* Add code samples for metrics.{recall_score, precision_score, f11_score} ([#502](https://github.com/googleapis/python-bigquery-dataframes/issues/502)) ([370fe90](https://github.com/googleapis/python-bigquery-dataframes/commit/370fe9087848862d02f0e5a333fcb4cd37cf5ca0)) -* Improve API documentation ([#489](https://github.com/googleapis/python-bigquery-dataframes/issues/489)) ([751266e](https://github.com/googleapis/python-bigquery-dataframes/commit/751266e056ac566ef5b6e40fbbca84ed95e7a7a9)) -* Update bigquery connection documentation ([#499](https://github.com/googleapis/python-bigquery-dataframes/issues/499)) ([4bfe094](https://github.com/googleapis/python-bigquery-dataframes/commit/4bfe094fdf2f7e1af72cc939558713a499760129)) -* Update LLM + K-means notebook to handle partial failures ([#496](https://github.com/googleapis/python-bigquery-dataframes/issues/496)) ([97afad9](https://github.com/googleapis/python-bigquery-dataframes/commit/97afad96f80c1815db8ad34f0ff62095631036c2)) - -## [0.26.0](https://github.com/googleapis/python-bigquery-dataframes/compare/v0.25.0...v0.26.0) (2024-03-20) - - -### ⚠ BREAKING CHANGES - -* exclude remote models for .register() ([#465](https://github.com/googleapis/python-bigquery-dataframes/issues/465)) - -### Features - -* (Series|DataFrame).plot ([#438](https://github.com/googleapis/python-bigquery-dataframes/issues/438)) ([1c3e668](https://github.com/googleapis/python-bigquery-dataframes/commit/1c3e668ceb26fd0f1377acbf6b95e8f4bcef40d6)) -* `read_gbq_table` supports `LIKE` as a operator in `filters` ([#454](https://github.com/googleapis/python-bigquery-dataframes/issues/454)) ([d2d425a](https://github.com/googleapis/python-bigquery-dataframes/commit/d2d425a93aa9e96f3b71c3ca3b185f4b5eaf32ef)) -* Add DataFrame.pipe() method ([#421](https://github.com/googleapis/python-bigquery-dataframes/issues/421)) ([95f5a6e](https://github.com/googleapis/python-bigquery-dataframes/commit/95f5a6e749468743af65062e559bc35ac56f3c24)) -* Set `force=True` by default in `DataFrame.peek()` ([#469](https://github.com/googleapis/python-bigquery-dataframes/issues/469)) ([4e8e97d](https://github.com/googleapis/python-bigquery-dataframes/commit/4e8e97d661078ed38d77be93b0bc1ad0fd52949c)) -* Support datetime related casting in (Series|DataFrame|Index).astype ([#442](https://github.com/googleapis/python-bigquery-dataframes/issues/442)) ([fde339b](https://github.com/googleapis/python-bigquery-dataframes/commit/fde339b71c754e617c61052940215b77890b59e4)) -* Support Series.dt.strftime ([#453](https://github.com/googleapis/python-bigquery-dataframes/issues/453)) ([8f6e955](https://github.com/googleapis/python-bigquery-dataframes/commit/8f6e955fc946db97c95ea012659432355b0cd12c)) - - -### Bug Fixes - -* Any() on empty set now correctly returns False ([#471](https://github.com/googleapis/python-bigquery-dataframes/issues/471)) ([f55680c](https://github.com/googleapis/python-bigquery-dataframes/commit/f55680cd0eed46ee06cd9baf658de792f4a27f31)) -* Df.drop_na preserves columns dtype ([#457](https://github.com/googleapis/python-bigquery-dataframes/issues/457)) ([3bab1a9](https://github.com/googleapis/python-bigquery-dataframes/commit/3bab1a917a5833bd58b20071a229ee95cf86a251)) -* Disable to_json and to_csv related tests ([#462](https://github.com/googleapis/python-bigquery-dataframes/issues/462)) ([874026d](https://github.com/googleapis/python-bigquery-dataframes/commit/874026da612bf08fbaf6d7dbfaa3325dc8a61500)) -* Exclude remote models for .register() ([#465](https://github.com/googleapis/python-bigquery-dataframes/issues/465)) ([73fe0f8](https://github.com/googleapis/python-bigquery-dataframes/commit/73fe0f89a96557afc4225521654978b96a2291b3)) -* Fix broken link in covid notebook ([#450](https://github.com/googleapis/python-bigquery-dataframes/issues/450)) ([adadb06](https://github.com/googleapis/python-bigquery-dataframes/commit/adadb0658c35142fed228abbd9baa42f9372f44b)) -* Fix broken multiindex loc cases ([#467](https://github.com/googleapis/python-bigquery-dataframes/issues/467)) ([b519197](https://github.com/googleapis/python-bigquery-dataframes/commit/b519197d51cc098ac4981a9a57a9d6988ba07d03)) -* Fix grouping series on multiple other series ([#455](https://github.com/googleapis/python-bigquery-dataframes/issues/455)) ([3971bd2](https://github.com/googleapis/python-bigquery-dataframes/commit/3971bd27c96b68b859399564dbb6abdb93de5f14)) -* Groupby aggregates no longer check if grouping keys are numeric ([#472](https://github.com/googleapis/python-bigquery-dataframes/issues/472)) ([4fbf938](https://github.com/googleapis/python-bigquery-dataframes/commit/4fbf938c200a3e0e6b592aa4a4e18b59f2f34082)) -* Raise `ValueError` when `read_pandas()` receives a bigframes `DataFrame` ([#447](https://github.com/googleapis/python-bigquery-dataframes/issues/447)) ([b28f9fd](https://github.com/googleapis/python-bigquery-dataframes/commit/b28f9fdd9681b3c9783a6e52322b70093e0283ec)) -* Series.(to_csv|to_json) leverages bq export ([#452](https://github.com/googleapis/python-bigquery-dataframes/issues/452)) ([718a00c](https://github.com/googleapis/python-bigquery-dataframes/commit/718a00c1fa8ac44b0d3a79a2217e5b12690785fb)) -* Warn when `read_gbq` / `read_gbq_table` uses the snapshot time cache ([#441](https://github.com/googleapis/python-bigquery-dataframes/issues/441)) ([e16a8c0](https://github.com/googleapis/python-bigquery-dataframes/commit/e16a8c0a6fb46cf1a7be12eec9471ae95d6f2c44)) - - -### Documentation - -* Add code samples for `ml.metrics.r2_score` ([#459](https://github.com/googleapis/python-bigquery-dataframes/issues/459)) ([85fefa2](https://github.com/googleapis/python-bigquery-dataframes/commit/85fefa2f1d4dbe3e0c9d4ab8124cea88eb5df38f)) -* Add the docs for loc and iloc indexers ([#446](https://github.com/googleapis/python-bigquery-dataframes/issues/446)) ([14ab8d8](https://github.com/googleapis/python-bigquery-dataframes/commit/14ab8d834d793ac7644f066145912e6d50966881)) -* Add the pages for at and iat indexers ([#456](https://github.com/googleapis/python-bigquery-dataframes/issues/456)) ([340f0b5](https://github.com/googleapis/python-bigquery-dataframes/commit/340f0b5b41fc5150d73890c7f27ae68dc308e160)) -* Add version information to bug template ([#437](https://github.com/googleapis/python-bigquery-dataframes/issues/437)) ([91bd39e](https://github.com/googleapis/python-bigquery-dataframes/commit/91bd39e8b194ddad09d53fca96201eee58063bb9)) -* Indicate that project and location are optional in example notebooks ([#451](https://github.com/googleapis/python-bigquery-dataframes/issues/451)) ([1df0140](https://github.com/googleapis/python-bigquery-dataframes/commit/1df014010652e7827a2720a906d0afe482a30ca9)) - -## [0.25.0](https://github.com/googleapis/python-bigquery-dataframes/compare/v0.24.0...v0.25.0) (2024-03-14) - - -### Features - -* (Series|DataFrame).plot.(line|area|scatter) ([#431](https://github.com/googleapis/python-bigquery-dataframes/issues/431)) ([0772510](https://github.com/googleapis/python-bigquery-dataframes/commit/077251084e3121019c56e5d6c16aebab16be8dc7)) -* Support CMEK for `remote_function` cloud functions ([#430](https://github.com/googleapis/python-bigquery-dataframes/issues/430)) ([2fd69f4](https://github.com/googleapis/python-bigquery-dataframes/commit/2fd69f4bed143fc8c040dac1c55288c1cb660f6e)) - -## [0.24.0](https://github.com/googleapis/python-bigquery-dataframes/compare/v0.23.0...v0.24.0) (2024-03-12) - - -### ⚠ BREAKING CHANGES - -* `read_parquet` uses a "pandas" engine to parse files by default. Use `engine="bigquery"` for the previous behavior - -### Features - -* (Series|Dataframe).plot.hist() ([#420](https://github.com/googleapis/python-bigquery-dataframes/issues/420)) ([4aadff4](https://github.com/googleapis/python-bigquery-dataframes/commit/4aadff4db59243b4510a874fef2bdb17402d1674)) -* Add detect_anomalies to ml ARIMAPlus and KMeans models ([#426](https://github.com/googleapis/python-bigquery-dataframes/issues/426)) ([6df28ed](https://github.com/googleapis/python-bigquery-dataframes/commit/6df28ed704552ebec7869e1f2034614cb6407098)) -* Add engine parameter to `read_parquet` ([#413](https://github.com/googleapis/python-bigquery-dataframes/issues/413)) ([31325a1](https://github.com/googleapis/python-bigquery-dataframes/commit/31325a190320bf01ced53d9f4cdb94462daaa06b)) -* Add ml PCA.detect_anomalies method ([#422](https://github.com/googleapis/python-bigquery-dataframes/issues/422)) ([8d82945](https://github.com/googleapis/python-bigquery-dataframes/commit/8d8294544ac7fedaca753c5473e3ca2a27868420)) -* Support BYOSA in `remote_function` ([#407](https://github.com/googleapis/python-bigquery-dataframes/issues/407)) ([d92ced2](https://github.com/googleapis/python-bigquery-dataframes/commit/d92ced2adaa30a0405ace9ca6cd70a8e217f13d0)) -* Support CMEK for BQ tables ([#403](https://github.com/googleapis/python-bigquery-dataframes/issues/403)) ([9a678e3](https://github.com/googleapis/python-bigquery-dataframes/commit/9a678e35201d935e1d93875429005033cfe7cff6)) - - -### Bug Fixes - -* Move `third_party.bigframes_vendored` to `bigframes_vendored` ([#424](https://github.com/googleapis/python-bigquery-dataframes/issues/424)) ([763edeb](https://github.com/googleapis/python-bigquery-dataframes/commit/763edeb4f4e8bc4b8bb05a992dae80c49c245e25)) -* Only do row identity based joins when joining by index ([#356](https://github.com/googleapis/python-bigquery-dataframes/issues/356)) ([76b252f](https://github.com/googleapis/python-bigquery-dataframes/commit/76b252f907055d72556e3e95f6cb5ee41de5b1c2)) -* Read_pandas inline respects location ([#412](https://github.com/googleapis/python-bigquery-dataframes/issues/412)) ([ae0e3ea](https://github.com/googleapis/python-bigquery-dataframes/commit/ae0e3eaca49171fd449de4d43ddc3e3ce9fdc2ce)) - - -### Documentation - -* Add predict sample to samples/snippets/bqml_getting_started_test.py ([#388](https://github.com/googleapis/python-bigquery-dataframes/issues/388)) ([6a3b0cc](https://github.com/googleapis/python-bigquery-dataframes/commit/6a3b0cc7f84120fc5978ce11b6b7c55e89654304)) -* Document minimum IAM requirement ([#416](https://github.com/googleapis/python-bigquery-dataframes/issues/416)) ([36173b0](https://github.com/googleapis/python-bigquery-dataframes/commit/36173b0c14747fb52909bbedd93249024bae9ac1)) -* Fix the note rendering for DataFrames methods: nlargest, nsmallest ([#417](https://github.com/googleapis/python-bigquery-dataframes/issues/417)) ([38bd2ba](https://github.com/googleapis/python-bigquery-dataframes/commit/38bd2ba21bc1a3222635de22eecd97930bf5b1de)) - -## [0.23.0](https://github.com/googleapis/python-bigquery-dataframes/compare/v0.22.0...v0.23.0) (2024-03-05) - - -### Features - -* Add ml.metrics.pairwise.euclidean_distance ([#397](https://github.com/googleapis/python-bigquery-dataframes/issues/397)) ([1726588](https://github.com/googleapis/python-bigquery-dataframes/commit/1726588beb8894bc08c272d718ca8e3a9451d0c2)) -* Add TextEmbedding model version support ([#394](https://github.com/googleapis/python-bigquery-dataframes/issues/394)) ([e0f1ab0](https://github.com/googleapis/python-bigquery-dataframes/commit/e0f1ab07cbc81034e24767baff54560561950e67)) - - -### Bug Fixes - -* Code exception in `remote_function` now prevents retry and surfaces in the client ([#387](https://github.com/googleapis/python-bigquery-dataframes/issues/387)) ([dd3643d](https://github.com/googleapis/python-bigquery-dataframes/commit/dd3643d3733ca1c2a18352bafac7d32fbdfa2a25)) -* Docs link for metrics.pairwise ([#400](https://github.com/googleapis/python-bigquery-dataframes/issues/400)) ([a60aba7](https://github.com/googleapis/python-bigquery-dataframes/commit/a60aba712576e2e4e14cfcfffe9349d6972716a5)) - - -### Dependencies - -* Update ibis to version 8.0.0 and refactor `remote_function` to use ibis UDF method ([#277](https://github.com/googleapis/python-bigquery-dataframes/issues/277)) ([350499b](https://github.com/googleapis/python-bigquery-dataframes/commit/350499bccb62e22169ab2f2e1400175b2179ef85)) - - -### Documentation - -* Update README to point to new summary pages ([#402](https://github.com/googleapis/python-bigquery-dataframes/issues/402)) ([bfe2b23](https://github.com/googleapis/python-bigquery-dataframes/commit/bfe2b23e2dea0cdf1e1b6ff5b17f6759d73c3e24)) - -## [0.22.0](https://github.com/googleapis/python-bigquery-dataframes/compare/v0.21.0...v0.22.0) (2024-02-27) - - -### ⚠ BREAKING CHANGES - -* rename cosine_similarity to paired_cosine_distances ([#393](https://github.com/googleapis/python-bigquery-dataframes/issues/393)) -* move model optional args to kwargs ([#381](https://github.com/googleapis/python-bigquery-dataframes/issues/381)) - -### Features - -* Add `DataFrames.corr()` method ([#379](https://github.com/googleapis/python-bigquery-dataframes/issues/379)) ([67fd434](https://github.com/googleapis/python-bigquery-dataframes/commit/67fd434bbb1c73f9013f65252d1ecc8da79542f6)) -* Add ml.metrics.pairwise.manhattan_distance ([#392](https://github.com/googleapis/python-bigquery-dataframes/issues/392)) ([9d31865](https://github.com/googleapis/python-bigquery-dataframes/commit/9d318653c001287bcc8ae9d8e09d0187413cbed6)) -* Enable regional endpoints for me-central2 ([#386](https://github.com/googleapis/python-bigquery-dataframes/issues/386)) ([469674d](https://github.com/googleapis/python-bigquery-dataframes/commit/469674d64f6ad5dac0f24ad450a7b8b6998fdf68)) - - -### Bug Fixes - -* Avoid ibis warning for "database" table() method argument ([#390](https://github.com/googleapis/python-bigquery-dataframes/issues/390)) ([a0490a4](https://github.com/googleapis/python-bigquery-dataframes/commit/a0490a492a43db24a314b3f42bfac61da7683151)) -* Correct the numeric literal dtype ([#365](https://github.com/googleapis/python-bigquery-dataframes/issues/365)) ([93b02cd](https://github.com/googleapis/python-bigquery-dataframes/commit/93b02cd8bc620823563f8214b43bc5f2f35c155b)) -* Rename cosine_similarity to paired_cosine_distances ([#393](https://github.com/googleapis/python-bigquery-dataframes/issues/393)) ([81ece46](https://github.com/googleapis/python-bigquery-dataframes/commit/81ece463b69765b0f93585d6b866fb642ddc65dc)) - - -### Performance Improvements - -* Inline read_pandas for small data ([#383](https://github.com/googleapis/python-bigquery-dataframes/issues/383)) ([59b446b](https://github.com/googleapis/python-bigquery-dataframes/commit/59b446bad8d2c5fca791c384616cfa7e54d54c09)) - - -### Dependencies - -* Add minimum version constraint for sqlglot to 19.9.0 ([#389](https://github.com/googleapis/python-bigquery-dataframes/issues/389)) ([8b62d77](https://github.com/googleapis/python-bigquery-dataframes/commit/8b62d77d8274cff2842c98b032bf98d69c483482)) - - -### Documentation - -* Add a code sample for creating a kmeans model ([#267](https://github.com/googleapis/python-bigquery-dataframes/issues/267)) ([4291d65](https://github.com/googleapis/python-bigquery-dataframes/commit/4291d656f30dc50b8ffcdd10ccbfa7f327711100)) -* Fix `bigframes.pandas.concat` documentation ([#382](https://github.com/googleapis/python-bigquery-dataframes/issues/382)) ([234b61c](https://github.com/googleapis/python-bigquery-dataframes/commit/234b61cdfe75b402adf1b56f53b5f06934777f95)) - - -### Miscellaneous Chores - -* Release 0.22.0 ([#396](https://github.com/googleapis/python-bigquery-dataframes/issues/396)) ([8f73d9e](https://github.com/googleapis/python-bigquery-dataframes/commit/8f73d9e37827ecdc90683313000364922ae61dab)) - - -### Code Refactoring - -* Move model optional args to kwargs ([#381](https://github.com/googleapis/python-bigquery-dataframes/issues/381)) ([4037992](https://github.com/googleapis/python-bigquery-dataframes/commit/4037992b61ff352320d5dfb87dcf5f274791ace1)) - -## [0.21.0](https://github.com/googleapis/python-bigquery-dataframes/compare/v0.20.1...v0.21.0) (2024-02-13) - - -### Features - -* Add `Series.cov` method ([#368](https://github.com/googleapis/python-bigquery-dataframes/issues/368)) ([443db22](https://github.com/googleapis/python-bigquery-dataframes/commit/443db228375da9b232376140c9d5b0db14895eae)) -* Add ml.llm.GeminiTextGenerator model ([#370](https://github.com/googleapis/python-bigquery-dataframes/issues/370)) ([de1e0a4](https://github.com/googleapis/python-bigquery-dataframes/commit/de1e0a451785e679f37b083be6d58c267319f56a)) -* Add ml.metrics.pairwise.cosine_similarity function ([#374](https://github.com/googleapis/python-bigquery-dataframes/issues/374)) ([126f566](https://github.com/googleapis/python-bigquery-dataframes/commit/126f5660bd61bd8998e5f17ca0cbd39959590367)) -* Add XGBoostModel ([#363](https://github.com/googleapis/python-bigquery-dataframes/issues/363)) ([d5518b2](https://github.com/googleapis/python-bigquery-dataframes/commit/d5518b28509be0ce070b22d9134a6a662412010a)) -* Limited support of lambdas in `Series.apply` ([#345](https://github.com/googleapis/python-bigquery-dataframes/issues/345)) ([208e081](https://github.com/googleapis/python-bigquery-dataframes/commit/208e081fa99e17b8085e83c111c07eb6fc5c4730)) -* Support bigframes.pandas.to_datetime for scalars, iterables and series. ([#372](https://github.com/googleapis/python-bigquery-dataframes/issues/372)) ([ffb0d15](https://github.com/googleapis/python-bigquery-dataframes/commit/ffb0d15602fe4d86e7a1aad72bba0a7049193a14)) -* Support read_gbq wildcard table path ([#377](https://github.com/googleapis/python-bigquery-dataframes/issues/377)) ([90caf86](https://github.com/googleapis/python-bigquery-dataframes/commit/90caf865efc940f94e16643bda7ba261c2f2e473)) - - -### Bug Fixes - -* Error message fix. ([#375](https://github.com/googleapis/python-bigquery-dataframes/issues/375)) ([930cf6b](https://github.com/googleapis/python-bigquery-dataframes/commit/930cf6b9ae8a48f422586dbd21b52e15c9ef9492)) - - -### Documentation - -* Clarify ADC pre-auth in a non-interactive environment ([#348](https://github.com/googleapis/python-bigquery-dataframes/issues/348)) ([99a9e6e](https://github.com/googleapis/python-bigquery-dataframes/commit/99a9e6e15c6eef4297035ce89bb619f8e4ca54ff)) - -## [0.20.1](https://github.com/googleapis/python-bigquery-dataframes/compare/v0.20.0...v0.20.1) (2024-02-06) - - -### Performance Improvements - -* Make repr cache the block where appropriate ([#350](https://github.com/googleapis/python-bigquery-dataframes/issues/350)) ([068879f](https://github.com/googleapis/python-bigquery-dataframes/commit/068879f97fb1626aca081106150803f832a0cf81)) - - -### Documentation - -* Add a sample to demonstrate the evaluation results ([#364](https://github.com/googleapis/python-bigquery-dataframes/issues/364)) ([cff0919](https://github.com/googleapis/python-bigquery-dataframes/commit/cff09194b2c3a96a1f50e86a38ee59783c2a343b)) -* Fix the `DataFrame.apply` code sample ([#366](https://github.com/googleapis/python-bigquery-dataframes/issues/366)) ([1866a26](https://github.com/googleapis/python-bigquery-dataframes/commit/1866a266f0fa40882b589579654c1ad428b036d8)) - -## [0.20.0](https://github.com/googleapis/python-bigquery-dataframes/compare/v0.19.2...v0.20.0) (2024-01-30) - - -### Features - -* Add `DataFrame.peek()` as an efficient alternative to `head()` results preview ([#318](https://github.com/googleapis/python-bigquery-dataframes/issues/318)) ([9c34d83](https://github.com/googleapis/python-bigquery-dataframes/commit/9c34d834e83ca5514bee723ebb9a7ad1ad50e88d)) -* Add ARIMA_EVAULATE options in forecasting models ([#336](https://github.com/googleapis/python-bigquery-dataframes/issues/336)) ([73e997b](https://github.com/googleapis/python-bigquery-dataframes/commit/73e997b3e80f844a8120b52ed2ece8b046cf4ca9)) -* Add Index constructor, repr, copy, get_level_values, to_series ([#334](https://github.com/googleapis/python-bigquery-dataframes/issues/334)) ([e5d054e](https://github.com/googleapis/python-bigquery-dataframes/commit/e5d054e93a05f5c504e8db57b954c07d33e5f5b9)) -* Improve error message for drive based BQ table reads ([#344](https://github.com/googleapis/python-bigquery-dataframes/issues/344)) ([0794788](https://github.com/googleapis/python-bigquery-dataframes/commit/0794788a2d232d795d803cd0c5b3f7d51c562cf1)) -* Update cut to work without labels = False and show intervals as dict ([#335](https://github.com/googleapis/python-bigquery-dataframes/issues/335)) ([4ff53db](https://github.com/googleapis/python-bigquery-dataframes/commit/4ff53db48133b817bec5f123b634690244a610d3)) - - -### Bug Fixes - -* Chance default connection name in getting_started.ipnyb ([#347](https://github.com/googleapis/python-bigquery-dataframes/issues/347)) ([677f014](https://github.com/googleapis/python-bigquery-dataframes/commit/677f0146acf19def88fddbeb0527a078458948ae)) -* Series iteration correctly returns values instead of index ([#339](https://github.com/googleapis/python-bigquery-dataframes/issues/339)) ([2c6af9b](https://github.com/googleapis/python-bigquery-dataframes/commit/2c6af9ba8b362dae39a6e082cdc816c955c73517)) - - -### Documentation - -* Add code samples for `Series.{between, cumprod}` ([#353](https://github.com/googleapis/python-bigquery-dataframes/issues/353)) ([09a52fd](https://github.com/googleapis/python-bigquery-dataframes/commit/09a52fda19cde8efa6b20731d5b8e21f50b18a9a)) - -## [0.19.2](https://github.com/googleapis/python-bigquery-dataframes/compare/v0.19.1...v0.19.2) (2024-01-22) - - -### Bug Fixes - -* Read_gbq large response issue ([#332](https://github.com/googleapis/python-bigquery-dataframes/issues/332)) ([b8178b9](https://github.com/googleapis/python-bigquery-dataframes/commit/b8178b9a47958d9176d99dfd8833556a64d9724d)) -* Use object dtype for ARRAY columns in `to_pandas()` with pandas 1.x ([#329](https://github.com/googleapis/python-bigquery-dataframes/issues/329)) ([374ddb5](https://github.com/googleapis/python-bigquery-dataframes/commit/374ddb534777895d93a1e2ae2f9c6dbe5f10bf8c)) - - -### Documentation - -* Add `DataFrame.applymap` documentation ([#326](https://github.com/googleapis/python-bigquery-dataframes/issues/326)) ([bd531a1](https://github.com/googleapis/python-bigquery-dataframes/commit/bd531a1557c08bcee6a0d275747f0939cdd33e81)) -* Add code samples for series methods ([#323](https://github.com/googleapis/python-bigquery-dataframes/issues/323)) ([32cc6fa](https://github.com/googleapis/python-bigquery-dataframes/commit/32cc6fa73dea80e31985d380d550d8042e5f5566)) -* Add remote model requirements ([#333](https://github.com/googleapis/python-bigquery-dataframes/issues/333)) ([c91f70c](https://github.com/googleapis/python-bigquery-dataframes/commit/c91f70ca7b9793cc62578d7845c3aa31cf8a4507)) - -## [0.19.1](https://github.com/googleapis/python-bigquery-dataframes/compare/v0.19.0...v0.19.1) (2024-01-17) - - -### Bug Fixes - -* Handle multi-level columns for df aggregates properly ([#305](https://github.com/googleapis/python-bigquery-dataframes/issues/305)) ([5bb45ba](https://github.com/googleapis/python-bigquery-dataframes/commit/5bb45ba5560f178438d490a62520ccd36fd2f284)) -* Update max_output_token limitation. ([#308](https://github.com/googleapis/python-bigquery-dataframes/issues/308)) ([5cccd36](https://github.com/googleapis/python-bigquery-dataframes/commit/5cccd36fd2081becd741541c4ac8d5cf53c076f2)) - - -### Documentation - -* Add code samples for Series.corr ([#316](https://github.com/googleapis/python-bigquery-dataframes/issues/316)) ([9150c16](https://github.com/googleapis/python-bigquery-dataframes/commit/9150c16e951fb757547721e0003910c7c49e3d27)) - -## [0.19.0](https://github.com/googleapis/python-bigquery-dataframes/compare/v0.18.0...v0.19.0) (2024-01-09) - - -### Features - -* Add 'columns' as an alias for 'col_order' ([#298](https://github.com/googleapis/python-bigquery-dataframes/issues/298)) ([a01b271](https://github.com/googleapis/python-bigquery-dataframes/commit/a01b271e76d05459f531cd83c6e93a2d13bfa061)) -* Add Series dt.tz and dt.unit properties ([#303](https://github.com/googleapis/python-bigquery-dataframes/issues/303)) ([2e1a403](https://github.com/googleapis/python-bigquery-dataframes/commit/2e1a4036e58fb6b35aa68ac6d121cb0d04f4f369)) -* Add to_gbq() method for LLM models ([#299](https://github.com/googleapis/python-bigquery-dataframes/issues/299)) ([dafbc1b](https://github.com/googleapis/python-bigquery-dataframes/commit/dafbc1bdb225c7132cdf7191792fde785947c7a1)) -* Allow manually set clustering_columns in dataframe.to_gbq ([#302](https://github.com/googleapis/python-bigquery-dataframes/issues/302)) ([9c21323](https://github.com/googleapis/python-bigquery-dataframes/commit/9c213239a73b5cd0ca7b647a86238263d3947431)) -* Support assigning to columns like a property ([#304](https://github.com/googleapis/python-bigquery-dataframes/issues/304)) ([f645c56](https://github.com/googleapis/python-bigquery-dataframes/commit/f645c56e5436adb100018afbf9ef18003a1a6ed9)) -* Support upcasting numeric columns in concat ([#294](https://github.com/googleapis/python-bigquery-dataframes/issues/294)) ([e3a056a](https://github.com/googleapis/python-bigquery-dataframes/commit/e3a056a301e99c4c3d2a2ecdcbcaf8804be8089f)) - - -### Bug Fixes - -* DF.drop tuple input as multi-index ([#301](https://github.com/googleapis/python-bigquery-dataframes/issues/301)) ([21391a9](https://github.com/googleapis/python-bigquery-dataframes/commit/21391a9d07bb0dc6b6f900f1b069350d6232bd92)) -* Fix bug converting non-string labels to sql ids ([#296](https://github.com/googleapis/python-bigquery-dataframes/issues/296)) ([a61c5fe](https://github.com/googleapis/python-bigquery-dataframes/commit/a61c5fef1e3b88f38269ee5bfd50886b8d2908ae)) - - -### Documentation - -* Add code samples for `Series.ffill` and `DataFrame.ffill` ([#307](https://github.com/googleapis/python-bigquery-dataframes/issues/307)) ([1c63b45](https://github.com/googleapis/python-bigquery-dataframes/commit/1c63b451bb057e5b6470d63d4b44c090d7172aa5)) - -## [0.18.0](https://github.com/googleapis/python-bigquery-dataframes/compare/v0.17.0...v0.18.0) (2024-01-02) - - -### Features - -* Add dataframe.to_html ([#259](https://github.com/googleapis/python-bigquery-dataframes/issues/259)) ([2cd6489](https://github.com/googleapis/python-bigquery-dataframes/commit/2cd64891170dcd4f2a709024a2993e36db210976)) -* Add IntervalIndex support to bigframes.pandas.cut ([#254](https://github.com/googleapis/python-bigquery-dataframes/issues/254)) ([6c1969a](https://github.com/googleapis/python-bigquery-dataframes/commit/6c1969a35fe720cf3a804006bcc9046ba554fcc3)) -* Add replace method to DataFrame ([#261](https://github.com/googleapis/python-bigquery-dataframes/issues/261)) ([5092215](https://github.com/googleapis/python-bigquery-dataframes/commit/5092215767d77c90b132e9cd6b3e3749827ebe09)) -* Specific pyarrow mappings for decimal, bytes types ([#283](https://github.com/googleapis/python-bigquery-dataframes/issues/283)) ([a1c0631](https://github.com/googleapis/python-bigquery-dataframes/commit/a1c06319ab0e3697c3175112490488002bb344c0)) - - -### Bug Fixes - -* Dataframes to_gbq now creates dataset if it doesn't exist ([#222](https://github.com/googleapis/python-bigquery-dataframes/issues/222)) ([bac62f7](https://github.com/googleapis/python-bigquery-dataframes/commit/bac62f76af1af6ca8834c3690c7c79aeb12dd331)) -* Exclude pandas 2.2.0rc0 to unblock prerelease tests ([#292](https://github.com/googleapis/python-bigquery-dataframes/issues/292)) ([ac1a745](https://github.com/googleapis/python-bigquery-dataframes/commit/ac1a745ddce9865f4585777b43c2234b9bf2841d)) -* Fix DataFrameGroupby.agg() issue with as_index=False ([#273](https://github.com/googleapis/python-bigquery-dataframes/issues/273)) ([ab49350](https://github.com/googleapis/python-bigquery-dataframes/commit/ab493506e71ed8970a11fe2f88b2145150e09291)) -* Make `Series.str.replace` work for simple strings ([#285](https://github.com/googleapis/python-bigquery-dataframes/issues/285)) ([ad67465](https://github.com/googleapis/python-bigquery-dataframes/commit/ad6746569b3af11be9d40805a1449ee1e89288dc)) -* Update dataframe.to_gbq to dedup column names. ([#286](https://github.com/googleapis/python-bigquery-dataframes/issues/286)) ([746115d](https://github.com/googleapis/python-bigquery-dataframes/commit/746115d5564c95bc3c4a5309c99e7a29e535e6fe)) -* Use setuptools.find_namespace_packages ([#246](https://github.com/googleapis/python-bigquery-dataframes/issues/246)) ([9ec352a](https://github.com/googleapis/python-bigquery-dataframes/commit/9ec352a338f11d82aee9cd665ffb0e6e97cb391b)) - - -### Dependencies - -* Migrate to `ibis-framework >= "7.1.0"` ([#53](https://github.com/googleapis/python-bigquery-dataframes/issues/53)) ([9798a2b](https://github.com/googleapis/python-bigquery-dataframes/commit/9798a2b14dffb20432f732343cac92341e42fe09)) - - -### Documentation - -* Add code snippets for explore query result page ([#278](https://github.com/googleapis/python-bigquery-dataframes/issues/278)) ([7cbbb7d](https://github.com/googleapis/python-bigquery-dataframes/commit/7cbbb7d4608d8b7d1a360b2fe2d39d89a52f9546)) -* Code samples for `astype` common to DataFrame and Series ([#280](https://github.com/googleapis/python-bigquery-dataframes/issues/280)) ([95b673a](https://github.com/googleapis/python-bigquery-dataframes/commit/95b673aeb1545744e4b1a353cf1f4d0202d8a1b2)) -* Code samples for `DataFrame.copy` and `Series.copy` ([#290](https://github.com/googleapis/python-bigquery-dataframes/issues/290)) ([7cbc2b0](https://github.com/googleapis/python-bigquery-dataframes/commit/7cbc2b0ba572d11778ba7caf7c95b7fb8f3a31a7)) -* Code samples for `drop` and `fillna` ([#284](https://github.com/googleapis/python-bigquery-dataframes/issues/284)) ([9c5012e](https://github.com/googleapis/python-bigquery-dataframes/commit/9c5012ec68275db83d1f6f7e743f5edaaaacd8cb)) -* Code samples for `isna`, `isnull`, `dropna`, `isin` ([#289](https://github.com/googleapis/python-bigquery-dataframes/issues/289)) ([ad51035](https://github.com/googleapis/python-bigquery-dataframes/commit/ad51035bcf80d6a49f134df26624b578010b5b12)) -* Code samples for `rename` , `size` ([#293](https://github.com/googleapis/python-bigquery-dataframes/issues/293)) ([eb69f60](https://github.com/googleapis/python-bigquery-dataframes/commit/eb69f60db52544882fb06c2d5fa0e41226dfe93f)) -* Code samples for `reset_index` and `sort_values` ([#282](https://github.com/googleapis/python-bigquery-dataframes/issues/282)) ([acc0eb7](https://github.com/googleapis/python-bigquery-dataframes/commit/acc0eb7010951c8cfb91aecc45268b041217dd09)) -* Code samples for `sample`, `get`, `Series.round` ([#295](https://github.com/googleapis/python-bigquery-dataframes/issues/295)) ([c2b1892](https://github.com/googleapis/python-bigquery-dataframes/commit/c2b1892825545a34ce4ed5b0ef99e99348466108)) -* Code samples for `Series.{add, replace, unique, T, transpose}` ([#287](https://github.com/googleapis/python-bigquery-dataframes/issues/287)) ([0e1bbfc](https://github.com/googleapis/python-bigquery-dataframes/commit/0e1bbfc1055aff9757b5138907c11caab2f3965a)) -* Code samples for `Series.{map, to_list, count}` ([#290](https://github.com/googleapis/python-bigquery-dataframes/issues/290)) ([7cbc2b0](https://github.com/googleapis/python-bigquery-dataframes/commit/7cbc2b0ba572d11778ba7caf7c95b7fb8f3a31a7)) -* Code samples for `Series.{name, std, agg}` ([#293](https://github.com/googleapis/python-bigquery-dataframes/issues/293)) ([eb69f60](https://github.com/googleapis/python-bigquery-dataframes/commit/eb69f60db52544882fb06c2d5fa0e41226dfe93f)) -* Code samples for `Series.groupby` and `Series.{sum,mean,min,max}` ([#280](https://github.com/googleapis/python-bigquery-dataframes/issues/280)) ([95b673a](https://github.com/googleapis/python-bigquery-dataframes/commit/95b673aeb1545744e4b1a353cf1f4d0202d8a1b2)) -* Code samples for DataFrame `set_index`, `items` ([#295](https://github.com/googleapis/python-bigquery-dataframes/issues/295)) ([c2b1892](https://github.com/googleapis/python-bigquery-dataframes/commit/c2b1892825545a34ce4ed5b0ef99e99348466108)) -* Fix the rendering for `get_dummies` ([#291](https://github.com/googleapis/python-bigquery-dataframes/issues/291)) ([252f3a2](https://github.com/googleapis/python-bigquery-dataframes/commit/252f3a2a0e1296c7d786acdc0bdebe9e4a9ae1be)) - -## [0.17.0](https://github.com/googleapis/python-bigquery-dataframes/compare/v0.16.0...v0.17.0) (2023-12-14) - - -### Features - -* Add `filters` argument to `read_gbq` for enhanced data querying ([#198](https://github.com/googleapis/python-bigquery-dataframes/issues/198)) ([034f71f](https://github.com/googleapis/python-bigquery-dataframes/commit/034f71f113235f2218223e43f129507c1ec3f6ff)) -* Add module/class level api tracking ([#272](https://github.com/googleapis/python-bigquery-dataframes/issues/272)) ([4f3db3d](https://github.com/googleapis/python-bigquery-dataframes/commit/4f3db3d50fb782dbe03051ed024d03e19944d775)) -* Deprecate `use_regional_endpoints` ([#199](https://github.com/googleapis/python-bigquery-dataframes/issues/199)) ([319a1f2](https://github.com/googleapis/python-bigquery-dataframes/commit/319a1f27be5bd96ebbe29f11a00a5a62d2b4237f)) - - -### Bug Fixes - -* Increase recursion limit, cache compilation tree hashes ([#184](https://github.com/googleapis/python-bigquery-dataframes/issues/184)) ([b54791c](https://github.com/googleapis/python-bigquery-dataframes/commit/b54791c820f56c578a0bd9883489de9b9c7eb3a2)) -* Replaced raise `NotImplementedError` with return `NotImplemented` ([#258](https://github.com/googleapis/python-bigquery-dataframes/issues/258)) ([a133822](https://github.com/googleapis/python-bigquery-dataframes/commit/a133822974229f70529a414a682b6d98770d1846)) - - -### Documentation - -* Add code samples for `values` and `value_counts` ([#249](https://github.com/googleapis/python-bigquery-dataframes/issues/249)) ([f247d95](https://github.com/googleapis/python-bigquery-dataframes/commit/f247d957a12a119ce8a263df215e8a9ef7310ef6)) -* Add sample for getting started with BQML ([#141](https://github.com/googleapis/python-bigquery-dataframes/issues/141)) ([fb14f54](https://github.com/googleapis/python-bigquery-dataframes/commit/fb14f54548e988c6c226753fcca162cf15b5c8d7)) - -## [0.16.0](https://github.com/googleapis/python-bigquery-dataframes/compare/v0.15.0...v0.16.0) (2023-12-12) - - -### Features - -* Add ARIMAPlus.predict parameters ([#264](https://github.com/googleapis/python-bigquery-dataframes/issues/264)) ([99598c7](https://github.com/googleapis/python-bigquery-dataframes/commit/99598c7d359f1d1e0671dcf27a5c77094f3c7f67)) -* Add DataFrame from_dict and from_records methods ([#244](https://github.com/googleapis/python-bigquery-dataframes/issues/244)) ([8d81e24](https://github.com/googleapis/python-bigquery-dataframes/commit/8d81e24677613dcf4d275c27a327384b8c17bc85)) -* Add DataFrame.select_dtypes method ([#242](https://github.com/googleapis/python-bigquery-dataframes/issues/242)) ([1737acc](https://github.com/googleapis/python-bigquery-dataframes/commit/1737acc51b4fdd9b385bbf91a758efd2e7ead11a)) -* Add nunique method to Series/DataFrameGroupby ([#256](https://github.com/googleapis/python-bigquery-dataframes/issues/256)) ([c8ec245](https://github.com/googleapis/python-bigquery-dataframes/commit/c8ec245070402aa0770bc9b2375693de674ca925)) -* Support dataframe.loc with conditional columns selection ([#233](https://github.com/googleapis/python-bigquery-dataframes/issues/233)) ([3febea9](https://github.com/googleapis/python-bigquery-dataframes/commit/3febea99358d10f823d43c3af83ea30458e579a2)) - - -### Bug Fixes - -* Enfore pandas version requirement <2.1.4 ([#265](https://github.com/googleapis/python-bigquery-dataframes/issues/265)) ([9dd63f6](https://github.com/googleapis/python-bigquery-dataframes/commit/9dd63f6dcb6234e1f3aebd63c59e1e5c717099dc)) -* Exclude pandas 2.1.4 from prerelease tests to unblock e2e tests ([b02fc2c](https://github.com/googleapis/python-bigquery-dataframes/commit/b02fc2c1843e18d3a8d6894c64763f53e6af1b73)) -* Fix value_counts column label for normalize=True ([#245](https://github.com/googleapis/python-bigquery-dataframes/issues/245)) ([d3fa6f2](https://github.com/googleapis/python-bigquery-dataframes/commit/d3fa6f26931d5d0f0ae3fa49baccfc148f870417)) -* Migrate e2e tests to bigframes-load-testing project ([8766ac6](https://github.com/googleapis/python-bigquery-dataframes/commit/8766ac63f501929577f71e6bd2b523e92c43ba66)) -* Ml.sql logic ([#262](https://github.com/googleapis/python-bigquery-dataframes/issues/262)) ([68c6fdf](https://github.com/googleapis/python-bigquery-dataframes/commit/68c6fdf78af8b87fa4ef4f832631f24d7433a4d8)) -* Update the llm_kmeans notebook ([#247](https://github.com/googleapis/python-bigquery-dataframes/issues/247)) ([66d1839](https://github.com/googleapis/python-bigquery-dataframes/commit/66d1839c3e9a3011c7feb13a59d966b64cf8313f)) - - -### Documentation - -* Add code samples for `shape` and `head` ([#257](https://github.com/googleapis/python-bigquery-dataframes/issues/257)) ([5bdcc65](https://github.com/googleapis/python-bigquery-dataframes/commit/5bdcc6594ef2e99e96636341d286ea70420858fe)) -* Add example for dataframe.melt, dataframe.pivot, dataframe.stac… ([#252](https://github.com/googleapis/python-bigquery-dataframes/issues/252)) ([8c63697](https://github.com/googleapis/python-bigquery-dataframes/commit/8c636978f4a21eda2856862100b7a8272797fe42)) -* Add example to dataframe.nlargest, dataframe.nsmallest, datafra… ([#234](https://github.com/googleapis/python-bigquery-dataframes/issues/234)) ([e735412](https://github.com/googleapis/python-bigquery-dataframes/commit/e735412fdc52d034df92dd5462d6956bdc0167be)) -* Add examples for dataframe.cummin, dataframe.cummax, dataframe.cumsum, dataframe.cumprod ([#243](https://github.com/googleapis/python-bigquery-dataframes/issues/243)) ([0523a31](https://github.com/googleapis/python-bigquery-dataframes/commit/0523a31fa0b589f88afe0ad5b447634409ddeb86)) -* Add examples for dataframe.nunique, dataframe.diff, dataframe.a… ([#251](https://github.com/googleapis/python-bigquery-dataframes/issues/251)) ([77074ec](https://github.com/googleapis/python-bigquery-dataframes/commit/77074ecbe7f52d1d7d1d1dc537fbe4062b407672)) -* Correct the docs for `option_context` ([#263](https://github.com/googleapis/python-bigquery-dataframes/issues/263)) ([d21c6dd](https://github.com/googleapis/python-bigquery-dataframes/commit/d21c6dd26eadd64c526b0fd35b977a74b8334562)) -* Correct the params rendering for `ml.remote` and `ml.ensemble` modules ([#248](https://github.com/googleapis/python-bigquery-dataframes/issues/248)) ([c2829e3](https://github.com/googleapis/python-bigquery-dataframes/commit/c2829e3d976a43c53251c9288266e3a8ec5304c5)) -* Fix return annotation in API docstrings ([#253](https://github.com/googleapis/python-bigquery-dataframes/issues/253)) ([89a1c67](https://github.com/googleapis/python-bigquery-dataframes/commit/89a1c67fa5cbb76c1cc6ae24d5f919e22514705c)) - -## [0.15.0](https://github.com/googleapis/python-bigquery-dataframes/compare/v0.14.1...v0.15.0) (2023-11-29) - - -### ⚠ BREAKING CHANGES - -* model.predict returns all the columns ([#204](https://github.com/googleapis/python-bigquery-dataframes/issues/204)) - -### Features - -* Add info and memory_usage methods to dataframe ([#219](https://github.com/googleapis/python-bigquery-dataframes/issues/219)) ([9d6613d](https://github.com/googleapis/python-bigquery-dataframes/commit/9d6613d318b558722b7bab12773efdea4bbe9931)) -* Add remote vertex model support ([#237](https://github.com/googleapis/python-bigquery-dataframes/issues/237)) ([0bfc4fb](https://github.com/googleapis/python-bigquery-dataframes/commit/0bfc4fb117686c734d4a2503d5a6de0e64e9f9b9)) -* Add the recent api method for ML component ([#225](https://github.com/googleapis/python-bigquery-dataframes/issues/225)) ([ed8876d](https://github.com/googleapis/python-bigquery-dataframes/commit/ed8876d3439a3b45b65e8789737c3c2e3a7f1adb)) -* Model.predict returns all the columns ([#204](https://github.com/googleapis/python-bigquery-dataframes/issues/204)) ([416171a](https://github.com/googleapis/python-bigquery-dataframes/commit/416171a70d91d4a6b71622ba72685147ab7d6186)) -* Send warnings on LLM prediction partial failures ([#216](https://github.com/googleapis/python-bigquery-dataframes/issues/216)) ([81125f9](https://github.com/googleapis/python-bigquery-dataframes/commit/81125f9505ad98e89939769a8e1fcf30518705f0)) - - -### Bug Fixes - -* Add df snapshots lookup for `read_gbq` ([#229](https://github.com/googleapis/python-bigquery-dataframes/issues/229)) ([d0d9b84](https://github.com/googleapis/python-bigquery-dataframes/commit/d0d9b84b101eb03c499d85e74dcfc900dedd4137)) -* Avoid unnecessary row_number() on sort key for io ([#211](https://github.com/googleapis/python-bigquery-dataframes/issues/211)) ([a18d40e](https://github.com/googleapis/python-bigquery-dataframes/commit/a18d40e808ee0822d21715cc3e8f794c418aeebc)) -* Dedup special character ([#209](https://github.com/googleapis/python-bigquery-dataframes/issues/209)) ([dd78acb](https://github.com/googleapis/python-bigquery-dataframes/commit/dd78acb174545ba292776a642afcec46f8ee4a2a)) -* Invalid JSON type of the notebook ([#215](https://github.com/googleapis/python-bigquery-dataframes/issues/215)) ([a729831](https://github.com/googleapis/python-bigquery-dataframes/commit/a7298317ea2604faa6ae31817f1f729d7e0b9818)) -* Make to_pandas override enable_downsampling when sampling_method is manually set. ([#200](https://github.com/googleapis/python-bigquery-dataframes/issues/200)) ([ae03756](https://github.com/googleapis/python-bigquery-dataframes/commit/ae03756f5ee45e0e74e0c0bdd4777e018eba2273)) -* Polish the llm+kmeans notebook ([#208](https://github.com/googleapis/python-bigquery-dataframes/issues/208)) ([e8532b1](https://github.com/googleapis/python-bigquery-dataframes/commit/e8532b1d999d26ea1ebdd30efb8f2c0a93a6a28d)) -* Update the llm+kmeans notebook with recent change ([#236](https://github.com/googleapis/python-bigquery-dataframes/issues/236)) ([f8917ab](https://github.com/googleapis/python-bigquery-dataframes/commit/f8917abc094e222e0435891d4d184b77bfe67722)) -* Use anonymous dataset to create `remote_function` ([#205](https://github.com/googleapis/python-bigquery-dataframes/issues/205)) ([69b016e](https://github.com/googleapis/python-bigquery-dataframes/commit/69b016eae7ea97d84ceeb22ba09f5472841db072)) - - -### Documentation - -* Add code samples for `index` and `column` properties ([#212](https://github.com/googleapis/python-bigquery-dataframes/issues/212)) ([c88d38e](https://github.com/googleapis/python-bigquery-dataframes/commit/c88d38e69682f4c620174086b8f16f4780c04811)) -* Add code samples for df reshaping, function, merge, and join methods ([#203](https://github.com/googleapis/python-bigquery-dataframes/issues/203)) ([010486c](https://github.com/googleapis/python-bigquery-dataframes/commit/010486c3494e05d714da6cc7d51514518d9ae1ea)) -* Add examples for dataframe.kurt, dataframe.std, dataframe.count ([#232](https://github.com/googleapis/python-bigquery-dataframes/issues/232)) ([f9c6e72](https://github.com/googleapis/python-bigquery-dataframes/commit/f9c6e727e2b901310bb5301da449d616ea85e135)) -* Add examples for dataframe.mean, dataframe.median, dataframe.va… ([#228](https://github.com/googleapis/python-bigquery-dataframes/issues/228)) ([edd0522](https://github.com/googleapis/python-bigquery-dataframes/commit/edd0522747eadb74780124fb18ed7face251441d)) -* Add examples for dataframe.min, dataframe.max and dataframe.sum ([#227](https://github.com/googleapis/python-bigquery-dataframes/issues/227)) ([3a375e8](https://github.com/googleapis/python-bigquery-dataframes/commit/3a375e87b64b8fb51370bfec8f2cfdbcd8fe960a)) -* Code samples for `Series.dot` and `DataFrame.dot` ([#226](https://github.com/googleapis/python-bigquery-dataframes/issues/226)) ([b62a07a](https://github.com/googleapis/python-bigquery-dataframes/commit/b62a07a95cd60f995a48825c9874822d0eb02483)) -* Code samples for `Series.where` and `Series.mask` ([#217](https://github.com/googleapis/python-bigquery-dataframes/issues/217)) ([52dfad2](https://github.com/googleapis/python-bigquery-dataframes/commit/52dfad281def82548751a276ce42b087dbb09f9a)) -* Code samples for dataframe.any, dataframe.all and dataframe.prod ([#223](https://github.com/googleapis/python-bigquery-dataframes/issues/223)) ([d7957fa](https://github.com/googleapis/python-bigquery-dataframes/commit/d7957fad071d223ef8f6fb8f3de395c865ff60aa)) -* Make the code samples reflect default bq connection usage ([#206](https://github.com/googleapis/python-bigquery-dataframes/issues/206)) ([71844b0](https://github.com/googleapis/python-bigquery-dataframes/commit/71844b03cdbfe684320c186a0488c8c7fb4fcd6e)) - - -### Miscellaneous Chores - -* Release 0.15.0 ([#241](https://github.com/googleapis/python-bigquery-dataframes/issues/241)) ([6c899be](https://github.com/googleapis/python-bigquery-dataframes/commit/6c899be2989e24f697d72fe1bb92ebbf7dec84cb)) - -## [0.14.1](https://github.com/googleapis/python-bigquery-dataframes/compare/v0.14.0...v0.14.1) (2023-11-16) - - -### Bug Fixes - -* Correctly handle null values when initializing fingerprint ordering ([#210](https://github.com/googleapis/python-bigquery-dataframes/issues/210)) ([8324f13](https://github.com/googleapis/python-bigquery-dataframes/commit/8324f133547ec35da5eefc0a8b02fe0f3887d81d)) - - -### Documentation - -* Add an example notebook about line graphs ([#197](https://github.com/googleapis/python-bigquery-dataframes/issues/197)) ([f957b27](https://github.com/googleapis/python-bigquery-dataframes/commit/f957b278b39e0a472a3153e9e1906c2d5f2ac2e5)) - -## [0.14.0](https://github.com/googleapis/python-bigquery-dataframes/compare/v0.13.0...v0.14.0) (2023-11-14) - - -### Features - -* Add 'cross' join support ([#176](https://github.com/googleapis/python-bigquery-dataframes/issues/176)) ([765446a](https://github.com/googleapis/python-bigquery-dataframes/commit/765446a929abe1ac076c3037afa7892f64105356)) -* Add 'index', 'pad', 'nearest' interpolate methods ([#162](https://github.com/googleapis/python-bigquery-dataframes/issues/162)) ([6a28403](https://github.com/googleapis/python-bigquery-dataframes/commit/6a2840349a23035bdfdabacd1e231b41bbb5ed7a)) -* Add series.sample (identical to existing dataframe.sample) ([#187](https://github.com/googleapis/python-bigquery-dataframes/issues/187)) ([37914a4](https://github.com/googleapis/python-bigquery-dataframes/commit/37914a4077c681881491f5c36d1a9c9f4255e18f)) -* Add unordered sql compilation ([#156](https://github.com/googleapis/python-bigquery-dataframes/issues/156)) ([58f420c](https://github.com/googleapis/python-bigquery-dataframes/commit/58f420c91d94ca085e9810f36513ffe772bfddcf)) -* Log most recent API calls as `recent-bigframes-api-xx` labels on BigQuery jobs ([#145](https://github.com/googleapis/python-bigquery-dataframes/issues/145)) ([4ea33b7](https://github.com/googleapis/python-bigquery-dataframes/commit/4ea33b7433532ae3a386a6ffa9eb57360ea39526)) -* Read_gbq creates order deterministically without table copy ([#191](https://github.com/googleapis/python-bigquery-dataframes/issues/191)) ([8ab81de](https://github.com/googleapis/python-bigquery-dataframes/commit/8ab81dee4d0eee499094f2dd576550f0c59d7551)) -* Support `date_series.astype("string[pyarrow]")` to cast DATE to STRING ([#186](https://github.com/googleapis/python-bigquery-dataframes/issues/186)) ([aee0e8e](https://github.com/googleapis/python-bigquery-dataframes/commit/aee0e8e2518c59bd1e0b07940c3309871fde8899)) -* Support `series.at[row_label] = scalar` ([#173](https://github.com/googleapis/python-bigquery-dataframes/issues/173)) ([0c8bd33](https://github.com/googleapis/python-bigquery-dataframes/commit/0c8bd33806bb99206b8b12dbdf7d7485c6ffb759)) -* Temporary resources no longer use BigQuery Sessions ([#194](https://github.com/googleapis/python-bigquery-dataframes/issues/194)) ([4a02cac](https://github.com/googleapis/python-bigquery-dataframes/commit/4a02cac88c7d7b46bed1fa813a862fc2ef9ef084)) - - -### Bug Fixes - -* All sort operation are now stable ([#195](https://github.com/googleapis/python-bigquery-dataframes/issues/195)) ([3a2761f](https://github.com/googleapis/python-bigquery-dataframes/commit/3a2761f3c38d0de8b8eda47fffa15b8412aa84b0)) -* Default to 7 days expiration for `read_csv`, `read_json`, `read_parquet` ([#193](https://github.com/googleapis/python-bigquery-dataframes/issues/193)) ([03606cd](https://github.com/googleapis/python-bigquery-dataframes/commit/03606cda30eb7645bfd4534460112dcca56b0ab0)) -* Deprecate the `remote_service_type` in llm model ([#180](https://github.com/googleapis/python-bigquery-dataframes/issues/180)) ([a8a409a](https://github.com/googleapis/python-bigquery-dataframes/commit/a8a409ab0bd1f99dfb442df0703bf8786e0fe58e)) -* For reset_index on unnamed multiindex, always use level_[n] label ([#182](https://github.com/googleapis/python-bigquery-dataframes/issues/182)) ([f95000d](https://github.com/googleapis/python-bigquery-dataframes/commit/f95000d3f88662be4d88c8b0152f1b838e99ec55)) -* Match pandas behavior when assigning listlike to empty dfs ([#172](https://github.com/googleapis/python-bigquery-dataframes/issues/172)) ([c1d1f42](https://github.com/googleapis/python-bigquery-dataframes/commit/c1d1f42a21cc089877f79ebb46a39ddef6958e04)) -* Use anonymous dataset instead of session dataset for temp tables ([#181](https://github.com/googleapis/python-bigquery-dataframes/issues/181)) ([800d44e](https://github.com/googleapis/python-bigquery-dataframes/commit/800d44eb5eb77da5d87b2e005f5a2ed53842e7b5)) -* Use random table for `read_pandas` ([#192](https://github.com/googleapis/python-bigquery-dataframes/issues/192)) ([741c75e](https://github.com/googleapis/python-bigquery-dataframes/commit/741c75e5797e26a1487ff3da76a07953d9537f3f)) -* Use random table when loading data for `read_csv`, `read_json`, `read_parquet` ([#175](https://github.com/googleapis/python-bigquery-dataframes/issues/175)) ([9d2e6dc](https://github.com/googleapis/python-bigquery-dataframes/commit/9d2e6dc1ae4e11e80da4aabe0daa3a6044137cc6)) - - -### Documentation - -* Add code samples for `read_gbq_function` using community UDFs ([#188](https://github.com/googleapis/python-bigquery-dataframes/issues/188)) ([7506eab](https://github.com/googleapis/python-bigquery-dataframes/commit/7506eabf2e58159507809e36abfe90c417dfe92f)) -* Add docstring code samples for `Series.apply` and `DataFrame.map` ([#185](https://github.com/googleapis/python-bigquery-dataframes/issues/185)) ([c816d84](https://github.com/googleapis/python-bigquery-dataframes/commit/c816d843e6f3c5a944cd4395ed0e1e91cec49812)) -* Add llm kmeans notebook as an included example ([#177](https://github.com/googleapis/python-bigquery-dataframes/issues/177)) ([d49ae42](https://github.com/googleapis/python-bigquery-dataframes/commit/d49ae42a379fafd601cc94227e7f8f14b3d5f8c3)) -* Use `head()` to get top `n` results, not to preview results ([#190](https://github.com/googleapis/python-bigquery-dataframes/issues/190)) ([87f84c9](https://github.com/googleapis/python-bigquery-dataframes/commit/87f84c9e58e7d0ea521ac386c9f02791cdddd19f)) - ## [0.13.0](https://github.com/googleapis/python-bigquery-dataframes/compare/v0.12.0...v0.13.0) (2023-11-07) diff --git a/CODE_OF_CONDUCT.md b/CODE_OF_CONDUCT.md new file mode 100644 index 00000000000..039f4368120 --- /dev/null +++ b/CODE_OF_CONDUCT.md @@ -0,0 +1,95 @@ + +# Code of Conduct + +## Our Pledge + +In the interest of fostering an open and welcoming environment, we as +contributors and maintainers pledge to making participation in our project and +our community a harassment-free experience for everyone, regardless of age, body +size, disability, ethnicity, gender identity and expression, level of +experience, education, socio-economic status, nationality, personal appearance, +race, religion, or sexual identity and orientation. + +## Our Standards + +Examples of behavior that contributes to creating a positive environment +include: + +* Using welcoming and inclusive language +* Being respectful of differing viewpoints and experiences +* Gracefully accepting constructive criticism +* Focusing on what is best for the community +* Showing empathy towards other community members + +Examples of unacceptable behavior by participants include: + +* The use of sexualized language or imagery and unwelcome sexual attention or + advances +* Trolling, insulting/derogatory comments, and personal or political attacks +* Public or private harassment +* Publishing others' private information, such as a physical or electronic + address, without explicit permission +* Other conduct which could reasonably be considered inappropriate in a + professional setting + +## Our Responsibilities + +Project maintainers are responsible for clarifying the standards of acceptable +behavior and are expected to take appropriate and fair corrective action in +response to any instances of unacceptable behavior. + +Project maintainers have the right and responsibility to remove, edit, or reject +comments, commits, code, wiki edits, issues, and other contributions that are +not aligned to this Code of Conduct, or to ban temporarily or permanently any +contributor for other behaviors that they deem inappropriate, threatening, +offensive, or harmful. + +## Scope + +This Code of Conduct applies both within project spaces and in public spaces +when an individual is representing the project or its community. Examples of +representing a project or community include using an official project e-mail +address, posting via an official social media account, or acting as an appointed +representative at an online or offline event. Representation of a project may be +further defined and clarified by project maintainers. + +This Code of Conduct also applies outside the project spaces when the Project +Steward has a reasonable belief that an individual's behavior may have a +negative impact on the project or its community. + +## Conflict Resolution + +We do not believe that all conflict is bad; healthy debate and disagreement +often yield positive results. However, it is never okay to be disrespectful or +to engage in behavior that violates the project’s code of conduct. + +If you see someone violating the code of conduct, you are encouraged to address +the behavior directly with those involved. Many issues can be resolved quickly +and easily, and this gives people more control over the outcome of their +dispute. If you are unable to resolve the matter for any reason, or if the +behavior is threatening or harassing, report it. We are dedicated to providing +an environment where participants feel welcome and safe. + + +Reports should be directed to *googleapis-stewards@google.com*, the +Project Steward(s) for *Google Cloud Client Libraries*. It is the Project Steward’s duty to +receive and address reported violations of the code of conduct. They will then +work with a committee consisting of representatives from the Open Source +Programs Office and the Google Open Source Strategy team. If for any reason you +are uncomfortable reaching out to the Project Steward, please email +opensource@google.com. + +We will investigate every complaint, but you may not receive a direct response. +We will use our discretion in determining when and how to follow up on reported +incidents, which may range from not taking action to permanent expulsion from +the project and project-sponsored spaces. We will notify the accused of the +report and provide them an opportunity to discuss it before any action is taken. +The identity of the reporter will be omitted from the details of the report +supplied to the accused. In potentially harmful situations, such as ongoing +harassment or threats to anyone's safety, we may take action without notice. + +## Attribution + +This Code of Conduct is adapted from the Contributor Covenant, version 1.4, +available at +https://www.contributor-covenant.org/version/1/4/code-of-conduct.html \ No newline at end of file diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md new file mode 100644 index 00000000000..b16bd944285 --- /dev/null +++ b/CONTRIBUTING.md @@ -0,0 +1,33 @@ +# How to contribute + +We'd love to accept your patches and contributions to this project. + +## Before you begin + +### Sign our Contributor License Agreement + +Contributions to this project must be accompanied by a +[Contributor License Agreement](https://cla.developers.google.com/about) (CLA). +You (or your employer) retain the copyright to your contribution; this simply +gives us permission to use and redistribute your contributions as part of the +project. + +If you or your current employer have already signed the Google CLA (even if it +was for a different project), you probably don't need to do it again. + +Visit to see your current agreements or to +sign a new one. + +### Review our community guidelines + +This project follows +[Google's Open Source Community Guidelines](https://opensource.google/conduct/). + +## Contribution process + +### Code reviews + +All submissions, including submissions by project members, require review. We +use GitHub pull requests for this purpose. Consult +[GitHub Help](https://help.github.com/articles/about-pull-requests/) for more +information on using pull requests. diff --git a/CONTRIBUTING.rst b/CONTRIBUTING.rst index ba8400eb866..3933152cf78 100644 --- a/CONTRIBUTING.rst +++ b/CONTRIBUTING.rst @@ -22,7 +22,7 @@ In order to add a feature: documentation. - The feature must work fully on the following CPython versions: - 3.10, 3.11, 3.12, 3.13 and 3.14 on both UNIX and Windows. + 3.9, 3.10 and 3.11 on both UNIX and Windows. - The feature must not add unnecessary dependencies (where "unnecessary" is of course subjective, but new dependencies should @@ -42,14 +42,14 @@ You'll have to create a development environment using a Git checkout: - Clone your fork of ``python-bigquery-dataframes`` from your GitHub account to your local computer, substituting your account username and specifying the destination - as ``hack-on-google-cloud-python``. E.g.:: + as ``hack-on-python-bigquery-dataframes``. E.g.:: $ cd ${HOME} - $ git clone git@github.com:USERNAME/google-cloud-python.git hack-on-google-cloud-python - $ cd hack-on-google-cloud-python - # Configure remotes such that you can pull changes from the googleapis/google-cloud-python + $ git clone git@github.com:USERNAME/python-bigquery-dataframes.git hack-on-python-bigquery-dataframes + $ cd hack-on-python-bigquery-dataframes + # Configure remotes such that you can pull changes from the googleapis/python-bigquery-dataframes # repository into your local repository. - $ git remote add upstream git@github.com:googleapis/google-cloud-python.git + $ git remote add upstream git@github.com:googleapis/python-bigquery-dataframes.git # fetch and merge changes from upstream into main $ git fetch upstream $ git merge upstream/main @@ -60,7 +60,7 @@ repo, from which you can submit a pull request. To work on the codebase and run the tests, we recommend using ``nox``, but you can also use a ``virtualenv`` of your own creation. -.. _repo: https://github.com/googleapis/google-cloud-python/tree/main/packages/bigframes +.. _repo: https://github.com/googleapis/python-bigquery-dataframes Using ``nox`` ============= @@ -72,7 +72,7 @@ We use `nox `__ to instrument our tests. - To run a single unit test:: - $ nox -s unit-3.14 -- -k + $ nox -s unit-3.11 -- -k .. note:: @@ -96,9 +96,9 @@ On Debian/Ubuntu:: Coding Style ************ - We use the automatic code formatter ``black``. You can run it using - the nox session ``format``. This will eliminate many lint errors. Run via:: + the nox session ``blacken``. This will eliminate many lint errors. Run via:: - $ nox -s format + $ nox -s blacken - PEP8 compliance is required, with exceptions defined in the linter configuration. If you have ``nox`` installed, you can test that you have not introduced @@ -143,56 +143,19 @@ Running System Tests $ nox -s system # Run a single system test - $ nox -s system-3.14 -- -k + $ nox -s system-3.11 -- -k .. note:: - System tests are only configured to run under Python 3.10, 3.12 and 3.14. + System tests are only configured to run under Python 3.9 and 3.11. For expediency, we do not run them in older versions of Python 3. This alone will not run the tests. You'll need to change some local auth settings and change some configuration in your project to run all the tests. -- System tests will be run against an actual project. A project can be set in - the environment variable ``$GOOGLE_CLOUD_PROJECT``. If not, the project property - set in the `Google Cloud CLI `__ - will be effective, which can be peeked into via ``gcloud config get project``, - or set via ``gcloud config set project ``. The following roles - carry the permissions to run the system tests in the project: - - - `BigQuery User `__ - to be able to create test datasets and run BigQuery jobs in the project. - - - `BigQuery Connection Admin `__ - to be able to use BigQuery connections in the project. - - - `BigQuery Data Editor `__ - to be able to create BigQuery remote functions in the project. - - - `Browser `__ - to be able to get current IAM policy for the service accounts of the BigQuery connections in the project. - - - `Cloud Functions Developer `__ - to be able to create cloud functions to support BigQuery DataFrames remote functions. - - - `Service Account User `__ - to be able to use the project's service accounts. - - - `Vertex AI User `__ - to be able to use the BigQuery DataFrames' ML integration with Vertex AI. - -- You can run the script ``scripts/setup-project-for-testing.sh []`` - to set up a project for running system tests and optionally set up necessary - IAM roles for a principal (user/group/service-account). You need to have the following - IAM permission to be able to run the set up script successfully: - - - ``serviceusage.services.enable`` - - ``bigquery.connections.create`` - - ``resourcemanager.projects.setIamPolicy`` - -- You should use local credentials from gcloud when possible. See `Best practices for application authentication `__. Some tests require a service account. For those tests see `Authenticating as a service account `__. +- System tests will be run against an actual project. You should use local credentials from gcloud when possible. See `Best practices for application authentication `__. Some tests require a service account. For those tests see `Authenticating as a service account `__. ************* Test Coverage @@ -232,11 +195,11 @@ configure them just like the System Tests. # Run all tests in a folder $ cd samples/snippets - $ nox -s py-3.10 + $ nox -s py-3.8 # Run a single sample test $ cd samples/snippets - $ nox -s py-3.10 -- -k + $ nox -s py-3.8 -- -k ******************************************** Note About ``README`` as it pertains to PyPI @@ -246,7 +209,7 @@ The `description on PyPI`_ for the project comes directly from the ``README``. Due to the reStructuredText (``rst``) parser used by PyPI, relative links which will work on GitHub (e.g. ``CONTRIBUTING.rst`` instead of -``https://github.com/googleapis/google-cloud-python/blob/main/packages/bigframes/CONTRIBUTING.rst``) +``https://github.com/googleapis/python-bigquery-dataframes/blob/main/CONTRIBUTING.rst``) may cause problems creating links or rendering the description. .. _description on PyPI: https://pypi.org/project/bigframes @@ -258,25 +221,31 @@ Supported Python Versions We support: +- `Python 3.9`_ - `Python 3.10`_ - `Python 3.11`_ -- `Python 3.12`_ -- `Python 3.13`_ -- `Python 3.14`_ +.. _Python 3.9: https://docs.python.org/3.9/ .. _Python 3.10: https://docs.python.org/3.10/ .. _Python 3.11: https://docs.python.org/3.11/ -.. _Python 3.12: https://docs.python.org/3.12/ -.. _Python 3.13: https://docs.python.org/3.13/ -.. _Python 3.14: https://docs.python.org/3.14/ Supported versions can be found in our ``noxfile.py`` `config`_. -.. _config: https://github.com/googleapis/google-cloud-python/blob/main/packages/bigframes/noxfile.py +.. _config: https://github.com/googleapis/python-bigquery-dataframes/blob/main/noxfile.py + +We also explicitly decided to support Python 3 beginning with version 3.9. +Reasons for this include: +- Encouraging use of newest versions of Python 3 +- Taking the lead of `prominent`_ open-source `projects`_ +- `Unicode literal support`_ which allows for a cleaner codebase that + works in both Python 2 and Python 3 +.. _prominent: https://docs.djangoproject.com/en/1.9/faq/install/#what-python-version-can-i-use-with-django +.. _projects: http://flask.pocoo.org/docs/0.10/python3/ +.. _Unicode literal support: https://www.python.org/dev/peps/pep-0414/ ********** Versioning diff --git a/LICENSE b/LICENSE index 4f29daf576c..d6456956733 100644 --- a/LICENSE +++ b/LICENSE @@ -1,6 +1,3 @@ -Files: All files not covered by another license. Notably: the bigframes module, -tests/*, bigframes_vendored.google_cloud_bigquery module, -bigframes_vendored.ibis module, and bigframes_vendored.xgboost module. Apache License Version 2.0, January 2004 @@ -203,144 +200,3 @@ bigframes_vendored.ibis module, and bigframes_vendored.xgboost module. WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. - ---- - -Files: For the bigframes_vendored.cpython module. - -PYTHON SOFTWARE FOUNDATION LICENSE VERSION 2 - -1. This LICENSE AGREEMENT is between the Python Software Foundation ("PSF"), and the Individual or Organization ("Licensee") accessing and otherwise using this software ("Python") in source or binary form and its associated documentation. -2. 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By copying, installing or otherwise using Python, Licensee agrees to be bound by the terms and conditions of this License Agreement. - ---- - -Files: for the bigframes_vendored.geopandas module. - -Copyright (c) 2013-2022, GeoPandas developers. -All rights reserved. - -Redistribution and use in source and binary forms, with or without -modification, are permitted provided that the following conditions are met: - - * Redistributions of source code must retain the above copyright notice, this - list of conditions and the following disclaimer. - * Redistributions in binary form must reproduce the above copyright notice, - this list of conditions and the following disclaimer in the documentation - and/or other materials provided with the distribution. - * Neither the name of GeoPandas nor the names of its contributors may - be used to endorse or promote products derived from this software without - specific prior written permission. - -THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND -ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED -WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE -DISCLAIMED. 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IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE -FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL -DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR -SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER -CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, -OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE -OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - ---- - -Files: The bigframes_vendored.sqlglot module. - -MIT License - -Copyright (c) 2025 Toby Mao - -Permission is hereby granted, free of charge, to any person obtaining a copy -of this software and associated documentation files (the "Software"), to deal -in the Software without restriction, including without limitation the rights -to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -copies of the Software, and to permit persons to whom the Software is -furnished to do so, subject to the following conditions: - -The above copyright notice and this permission notice shall be included in all -copies or substantial portions of the Software. - -THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -SOFTWARE. diff --git a/MANIFEST.in b/MANIFEST.in index c8555a39bf8..b422266a96a 100644 --- a/MANIFEST.in +++ b/MANIFEST.in @@ -1,6 +1,6 @@ # -*- coding: utf-8 -*- # -# Copyright 2024 Google LLC +# Copyright 2023 Google LLC # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -16,8 +16,8 @@ # Generated by synthtool. DO NOT EDIT! include README.rst LICENSE -recursive-include third_party/bigframes_vendored * -recursive-include bigframes *.json *.proto *.js *.css py.typed +recursive-include third_party * +recursive-include bigframes *.json *.proto py.typed recursive-include tests * global-exclude *.py[co] global-exclude __pycache__ diff --git a/OWNERS b/OWNERS index 562ee0f19b9..f86ad551efa 100644 --- a/OWNERS +++ b/OWNERS @@ -1,8 +1,12 @@ +ashleyxu@google.com +bmil@google.com chelsealin@google.com garrettwu@google.com +henryjsolberg@google.com +hormati@google.com huanc@google.com jiaxun@google.com -mlaurencechen@google.com +kemppeterson@google.com shobs@google.com swast@google.com -tbergeron@google.com \ No newline at end of file +tbergeron@google.com diff --git a/README.rst b/README.rst index a3aef5380bb..5ddb4a7639a 100644 --- a/README.rst +++ b/README.rst @@ -1,78 +1,368 @@ -BigQuery DataFrames (BigFrames) -=============================== +BigQuery DataFrames +=================== +BigQuery DataFrames provides a Pythonic DataFrame and machine learning (ML) API +powered by the BigQuery engine. -|GA| |pypi| |versions| +* ``bigframes.pandas`` provides a pandas-compatible API for analytics. +* ``bigframes.ml`` provides a scikit-learn-like API for ML. -BigQuery DataFrames (also known as BigFrames) provides a Pythonic DataFrame -and machine learning (ML) API powered by the BigQuery engine. It provides modules -for many use cases, including: +BigQuery DataFrames is an open-source package. You can run +``pip install --upgrade bigframes`` to install the latest version. -* `bigframes.pandas `_ - is a pandas API for analytics. Many workloads can be - migrated from pandas to bigframes by just changing a few imports. -* `bigframes.ml `_ - is a scikit-learn-like API for ML. -* `bigframes.bigquery.ai `_ - are a collection of powerful AI methods, powered by Gemini. +Documentation +------------- -BigQuery DataFrames is an `open-source package `_. +* `BigQuery DataFrames source code (GitHub) `_ +* `BigQuery DataFrames sample notebooks `_ +* `BigQuery DataFrames API reference `_ +* `BigQuery documentation `_ -.. |GA| image:: https://img.shields.io/badge/support-GA-gold.svg - :target: https://github.com/googleapis/google-cloud-python/blob/main/README.rst#general-availability -.. |pypi| image:: https://img.shields.io/pypi/v/bigframes.svg - :target: https://pypi.org/project/bigframes/ -.. |versions| image:: https://img.shields.io/pypi/pyversions/bigframes.svg - :target: https://pypi.org/project/bigframes/ -Getting started with BigQuery DataFrames ----------------------------------------- +Quickstart +---------- -The easiest way to get started is to try the -`BigFrames quickstart `_ -in a `notebook in BigQuery Studio `_. +Prerequisites +^^^^^^^^^^^^^ -To use BigFrames in your local development environment, +* Install the ``bigframes`` package. +* Create a Google Cloud project and billing account. +* When running locally, authenticate with application default credentials. See + the `gcloud auth application-default login + `_ + reference. -1. Run ``pip install --upgrade bigframes`` to install the latest version. +Code sample +^^^^^^^^^^^ -2. Setup `Application default credentials `_ - for your local development environment enviroment. +Import ``bigframes.pandas`` for a pandas-like interface. The ``read_gbq`` +method accepts either a fully-qualified table ID or a SQL query. -3. Create a `GCP project with the BigQuery API enabled `_. +.. code-block:: python -4. Use the ``bigframes`` package to query data. + import bigframes.pandas as bpd -.. code-block:: python + bpd.options.bigquery.project = your_gcp_project_id + df1 = bpd.read_gbq("project.dataset.table") + df2 = bpd.read_gbq("SELECT a, b, c, FROM `project.dataset.table`") - import bigframes.pandas as bpd +* `More code samples `_ - bpd.options.bigquery.project = your_gcp_project_id # Optional in BQ Studio. - bpd.options.bigquery.ordering_mode = "partial" # Recommended for performance. - df = bpd.read_gbq("bigquery-public-data.usa_names.usa_1910_2013") - print( - df.groupby("name") - .agg({"number": "sum"}) - .sort_values("number", ascending=False) - .head(10) - .to_pandas() - ) -Documentation -------------- +Locations +--------- +BigQuery DataFrames uses a +`BigQuery session `_ +internally to manage metadata on the service side. This session is tied to a +`location `_ . +BigQuery DataFrames uses the US multi-region as the default location, but you +can use ``session_options.location`` to set a different location. Every query +in a session is executed in the location where the session was created. +BigQuery DataFrames +auto-populates ``bf.options.bigquery.location`` if the user starts with +``read_gbq/read_gbq_table/read_gbq_query()`` and specifies a table, either +directly or in a SQL statement. + +If you want to reset the location of the created DataFrame or Series objects, +you can close the session by executing ``bigframes.pandas.close_session()``. +After that, you can reuse ``bigframes.pandas.options.bigquery.location`` to +specify another location. + + +``read_gbq()`` requires you to specify a location if the dataset you are +querying is not in the US multi-region. If you try to read a table from another +location, you get a NotFound exception. + +Project +------- +If ``bf.options.bigquery.project`` is not set, the ``$GOOGLE_CLOUD_PROJECT`` +environment variable is used, which is set in the notebook runtime serving the +BigQuery Studio/Vertex Notebooks. + +ML Capabilities +--------------- + +The ML capabilities in BigQuery DataFrames let you preprocess data, and +then train models on that data. You can also chain these actions together to +create data pipelines. + +Preprocess data +^^^^^^^^^^^^^^^^^^^^^^^^ + +Create transformers to prepare data for use in estimators (models) by +using the +`bigframes.ml.preprocessing module `_ +and the `bigframes.ml.compose module `_. +BigQuery DataFrames offers the following transformations: + +* Use the `KBinsDiscretizer class `_ + in the ``bigframes.ml.preprocessing`` module to bin continuous data into intervals. +* Use the `LabelEncoder class `_ + in the ``bigframes.ml.preprocessing`` module to normalize the target labels as integer values. +* Use the `MaxAbsScaler class `_ + in the ``bigframes.ml.preprocessing`` module to scale each feature to the range ``[-1, 1]`` by its maximum absolute value. +* Use the `MinMaxScaler class `_ + in the ``bigframes.ml.preprocessing`` module to standardize features by scaling each feature to the range ``[0, 1]``. +* Use the `StandardScaler class `_ + in the ``bigframes.ml.preprocessing`` module to standardize features by removing the mean and scaling to unit variance. +* Use the `OneHotEncoder class `_ + in the ``bigframes.ml.preprocessing`` module to transform categorical values into numeric format. +* Use the `ColumnTransformer class `_ + in the ``bigframes.ml.compose`` module to apply transformers to DataFrames columns. + + +Train models +^^^^^^^^^^^^ + +Create estimators to train models in BigQuery DataFrames. + +**Clustering models** + +Create estimators for clustering models by using the +`bigframes.ml.cluster module `_. + +* Use the `KMeans class `_ + to create K-means clustering models. Use these models for + data segmentation. For example, identifying customer segments. K-means is an + unsupervised learning technique, so model training doesn't require labels or split + data for training or evaluation. + +**Decomposition models** + +Create estimators for decomposition models by using the `bigframes.ml.decomposition module `_. + +* Use the `PCA class `_ + to create principal component analysis (PCA) models. Use these + models for computing principal components and using them to perform a change of + basis on the data. This provides dimensionality reduction by projecting each data + point onto only the first few principal components to obtain lower-dimensional + data while preserving as much of the data's variation as possible. + + +**Ensemble models** + +Create estimators for ensemble models by using the `bigframes.ml.ensemble module `_. + +* Use the `RandomForestClassifier class `_ + to create random forest classifier models. Use these models for constructing multiple + learning method decision trees for classification. +* Use the `RandomForestRegressor class `_ + to create random forest regression models. Use + these models for constructing multiple learning method decision trees for regression. +* Use the `XGBClassifier class `_ + to create gradient boosted tree classifier models. Use these models for additively + constructing multiple learning method decision trees for classification. +* Use the `XGBRegressor class `_ + to create gradient boosted tree regression models. Use these models for additively + constructing multiple learning method decision trees for regression. + + +**Forecasting models** + +Create estimators for forecasting models by using the `bigframes.ml.forecasting module `_. + +* Use the `ARIMAPlus class `_ + to create time series forecasting models. + +**Imported models** + +Create estimators for imported models by using the `bigframes.ml.imported module `_. + +* Use the `ONNXModel class `_ + to import Open Neural Network Exchange (ONNX) models. +* Use the `TensorFlowModel class `_ + to import TensorFlow models. + +**Linear models** + +Create estimators for linear models by using the `bigframes.ml.linear_model module `_. + +* Use the `LinearRegression class `_ + to create linear regression models. Use these models for forecasting. For example, + forecasting the sales of an item on a given day. +* Use the `LogisticRegression class `_ + to create logistic regression models. Use these models for the classification of two + or more possible values such as whether an input is ``low-value``, ``medium-value``, + or ``high-value``. + +**Large language models** + +Create estimators for LLMs by using the `bigframes.ml.llm module `_. + +* Use the `PaLM2TextGenerator class `_ to create PaLM2 text generator models. Use these models + for text generation tasks. +* Use the `PaLM2TextEmbeddingGenerator class `_ to create PaLM2 text embedding generator models. + Use these models for text embedding generation tasks. + + +Create pipelines +^^^^^^^^^^^^^^^^ + +Create ML pipelines by using +`bigframes.ml.pipeline module `_. +Pipelines let you assemble several ML steps to be cross-validated together while setting +different parameters. This simplifies your code, and allows you to deploy data preprocessing +steps and an estimator together. + +* Use the `Pipeline class `_ + to create a pipeline of transforms with a final estimator. + + +ML locations +------------ + +``bigframes.ml`` supports the same locations as BigQuery ML. BigQuery ML model +prediction and other ML functions are supported in all BigQuery regions. Support +for model training varies by region. For more information, see +`BigQuery ML locations `_. + + +Data types +---------- + +BigQuery DataFrames supports the following numpy and pandas dtypes: + +* ``numpy.dtype("O")`` +* ``pandas.BooleanDtype()`` +* ``pandas.Float64Dtype()`` +* ``pandas.Int64Dtype()`` +* ``pandas.StringDtype(storage="pyarrow")`` +* ``pandas.ArrowDtype(pa.date32())`` +* ``pandas.ArrowDtype(pa.time64("us"))`` +* ``pandas.ArrowDtype(pa.timestamp("us"))`` +* ``pandas.ArrowDtype(pa.timestamp("us", tz="UTC"))`` + +BigQuery DataFrames doesn’t support the following BigQuery data types: + +* ``ARRAY`` +* ``NUMERIC`` +* ``BIGNUMERIC`` +* ``INTERVAL`` +* ``STRUCT`` +* ``JSON`` + +All other BigQuery data types display as the object type. + + +Remote functions +---------------- + +BigQuery DataFrames gives you the ability to turn your custom scalar functions +into `BigQuery remote functions +`_ . Creating a remote +function in BigQuery DataFrames (See `code samples +`_) +creates a BigQuery remote function, a `BigQuery +connection +`_ , +and a `Cloud Functions (2nd gen) function +`_ . + +BigQuery connections are created in the same location as the BigQuery +DataFrames session, using the name you provide in the custom function +definition. To view and manage connections, do the following: + +1. Go to `BigQuery in the Google Cloud Console `__. +2. Select the project in which you created the remote function. +3. In the Explorer pane, expand that project and then expand External connections. + +BigQuery remote functions are created in the dataset you specify, or +in a dataset with the name ``bigframes_temp_location``, where location is +the location used by the BigQuery DataFrames session. For example, +``bigframes_temp_us_central1``. To view and manage remote functions, do +the following: + +1. Go to `BigQuery in the Google Cloud Console `__. +2. Select the project in which you created the remote function. +3. In the Explorer pane, expand that project, expand the dataset in which you + created the remote function, and then expand Routines. + +To view and manage Cloud Functions functions, use the +`Functions `_ +page and use the project picker to select the project in which you +created the function. For easy identification, the names of the functions +created by BigQuery DataFrames are prefixed by ``bigframes``. + +**Requirements** + +BigQuery DataFrames uses the ``gcloud`` command-line interface internally, +so you must run ``gcloud auth login`` before using remote functions. + +To use BigQuery DataFrames remote functions, you must enable the following APIs: + +* The BigQuery API (bigquery.googleapis.com) +* The BigQuery Connection API (bigqueryconnection.googleapis.com) +* The Cloud Functions API (cloudfunctions.googleapis.com) +* The Cloud Run API (run.googleapis.com) +* The Artifact Registry API (artifactregistry.googleapis.com) +* The Cloud Build API (cloudbuild.googleapis.com ) +* The Cloud Resource Manager API (cloudresourcemanager.googleapis.com) + +To use BigQuery DataFrames remote functions, you must be granted the +following IAM roles: + +* BigQuery Data Editor (roles/bigquery.dataEditor) +* BigQuery Connection Admin (roles/bigquery.connectionAdmin) +* Cloud Functions Developer (roles/cloudfunctions.developer) +* Service Account User (roles/iam.serviceAccountUser) on the + `service account ` + ``PROJECT_NUMBER-compute@developer.gserviceaccount.com`` +* Storage Object Viewer (roles/storage.objectViewer) +* Project IAM Admin (roles/resourcemanager.projectIamAdmin) + +**Limitations** + +* Remote functions take about 90 seconds to become available when you first create them. +* Trivial changes in the notebook, such as inserting a new cell or renaming a variable, + might cause the remote function to be re-created, even if these changes are unrelated + to the remote function code. +* BigQuery DataFrames does not differentiate any personal data you include in the remote + function code. The remote function code is serialized as an opaque box to deploy it as a + Cloud Functions function. +* The Cloud Functions (2nd gen) functions, BigQuery connections, and BigQuery remote + functions created by BigQuery DataFrames persist in Google Cloud. If you don’t want to + keep these resources, you must delete them separately using an appropriate Cloud Functions + or BigQuery interface. +* A project can have up to 1000 Cloud Functions (2nd gen) functions at a time. See Cloud + Functions quotas for all the limits. + + +Quotas and limits +------------------ + +`BigQuery quotas `_ +including hardware, software, and network components. + + +Session termination +------------------- + +Each BigQuery DataFrames DataFrame or Series object is tied to a BigQuery +DataFrames session, which is in turn based on a BigQuery session. BigQuery +sessions +`auto-terminate `_ +; when this happens, you can’t use previously +created DataFrame or Series objects and must re-create them using a new +BigQuery DataFrames session. You can do this by running +``bigframes.pandas.close_session()`` and then re-running the BigQuery +DataFrames expressions. + + +Data processing location +------------------------ -To learn more about BigQuery DataFrames, visit these pages +BigQuery DataFrames is designed for scale, which it achieves by keeping data +and processing on the BigQuery service. However, you can bring data into the +memory of your client machine by calling ``.to_pandas()`` on a DataFrame or Series +object. If you choose to do this, the memory limitation of your client machine +applies. -* `Introduction to BigQuery DataFrames (BigFrames) `_ -* `Sample notebooks `_ -* `API reference `_ -* `Source code (GitHub) `_ License ------- BigQuery DataFrames is distributed with the `Apache-2.0 license -`_. +`_. It also contains code derived from the following third-party packages: @@ -81,10 +371,9 @@ It also contains code derived from the following third-party packages: * `Python `_ * `scikit-learn `_ * `XGBoost `_ -* `SQLGlot `_ For details, see the `third_party -`_ +`_ directory. diff --git a/SECURITY.md b/SECURITY.md new file mode 100644 index 00000000000..8b58ae9c01a --- /dev/null +++ b/SECURITY.md @@ -0,0 +1,7 @@ +# Security Policy + +To report a security issue, please use [g.co/vulnz](https://g.co/vulnz). + +The Google Security Team will respond within 5 working days of your report on g.co/vulnz. + +We use g.co/vulnz for our intake, and do coordination and disclosure here using GitHub Security Advisory to privately discuss and fix the issue. diff --git a/bigframes/__init__.py b/bigframes/__init__.py index 533726343a5..bd1476957bf 100644 --- a/bigframes/__init__.py +++ b/bigframes/__init__.py @@ -14,66 +14,17 @@ """BigQuery DataFrames provides a DataFrame API scaled by the BigQuery engine.""" -import warnings - -# Suppress Python version support warnings from google-cloud libraries. -# These are particularly noisy in Colab which still uses Python 3.10. -warnings.filterwarnings( - "ignore", - category=FutureWarning, - message=".*Google will stop supporting.*Python.*", -) - -# import configuration and types. -# This ensures that when the deeper 'core' modules ask for 'dtypes','options', et. al., -# they are already defined and available. -import bigframes.dtypes # noqa: E402 # isort: skip -import bigframes._config # noqa: E402 # isort: skip -from bigframes._config import option_context, options # noqa: E402 # isort: skip - -import bigframes.enums as enums # noqa: E402 -import bigframes.exceptions as exceptions # noqa: E402 - -# We import operations early to resolve a circular dependency between -# bigframes.core.expression and bigframes.operations. -# This ensures the 'Expression' base class is defined before 'Aggregation' -# subclasses attempt to inherit from it. -import bigframes.operations # noqa: E402 # isort: skip - -# Register pandas extensions -import bigframes.extensions.pandas.dataframe_accessor # noqa: F401, E402 -import bigframes.extensions.pandas.series_accessor # noqa: F401, E402 -from bigframes._config.bigquery_options import BigQueryOptions # noqa: E402 -from bigframes.core.global_session import ( # noqa: E402 - close_session, - execution_history, - get_global_session, -) -from bigframes.session import Session, connect # noqa: E402 -from bigframes.version import __version__ # noqa: E402 - -_MAGIC_NAMES = ["bqsql"] - - -def load_ipython_extension(ipython): - """Called by IPython when this module is loaded as an IPython extension.""" - # Requires IPython to be installed for import to succeed - from bigframes._magics import _cell_magic - - for magic_name in _MAGIC_NAMES: - ipython.register_magic_function( - _cell_magic, magic_kind="cell", magic_name=magic_name - ) - +from bigframes._config import option_context, options +from bigframes._config.bigquery_options import BigQueryOptions +from bigframes.core.global_session import close_session, get_global_session +from bigframes.session import connect, Session +from bigframes.version import __version__ __all__ = [ "options", "BigQueryOptions", "get_global_session", "close_session", - "execution_history", - "enums", - "exceptions", "connect", "Session", "__version__", diff --git a/bigframes/_config/__init__.py b/bigframes/_config/__init__.py index cbe369ad58c..8dcebfce6a2 100644 --- a/bigframes/_config/__init__.py +++ b/bigframes/_config/__init__.py @@ -17,24 +17,55 @@ DataFrames from this package. """ -import bigframes._config.global_options as global_options -from bigframes._config.bigquery_options import BigQueryOptions -from bigframes._config.compute_options import ComputeOptions -from bigframes._config.display_options import DisplayOptions -from bigframes._config.experiment_options import ExperimentOptions -from bigframes._config.global_options import Options, option_context -from bigframes._config.sampling_options import SamplingOptions +import bigframes._config.bigquery_options as bigquery_options +import bigframes._config.compute_options as compute_options +import bigframes._config.display_options as display_options +import bigframes._config.sampling_options as sampling_options +import third_party.bigframes_vendored.pandas._config.config as pandas_config + + +class Options: + """Global options affecting BigQuery DataFrames behavior.""" + + def __init__(self): + self._bigquery_options = bigquery_options.BigQueryOptions() + self._display_options = display_options.DisplayOptions() + self._sampling_options = sampling_options.SamplingOptions() + self._compute_options = compute_options.ComputeOptions() + + @property + def bigquery(self) -> bigquery_options.BigQueryOptions: + """Options to use with the BigQuery engine.""" + return self._bigquery_options + + @property + def display(self) -> display_options.DisplayOptions: + """Options controlling object representation.""" + return self._display_options + + @property + def sampling(self) -> sampling_options.SamplingOptions: + """Options controlling downsampling when downloading data + to memory. The data will be downloaded into memory explicitly + (e.g., to_pandas, to_numpy, values) or implicitly (e.g., + matplotlib plotting). This option can be overriden by + parameters in specific functions.""" + return self._sampling_options + + @property + def compute(self) -> compute_options.ComputeOptions: + """Options controlling object computation.""" + return self._compute_options + + +options = Options() +"""Global options for default session.""" -options = global_options.options -"""Global options for the default session.""" __all__ = ( "Options", "options", - "option_context", - "BigQueryOptions", - "ComputeOptions", - "DisplayOptions", - "ExperimentOptions", - "SamplingOptions", ) + + +option_context = pandas_config.option_context diff --git a/bigframes/_config/auth.py b/bigframes/_config/auth.py deleted file mode 100644 index f1c069b5310..00000000000 --- a/bigframes/_config/auth.py +++ /dev/null @@ -1,86 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from __future__ import annotations - -import os -import threading -from typing import Optional - -import google.auth.credentials -import google.auth.transport.requests -import pydata_google_auth - -import bigframes._config.bigquery_options as bigquery_options - -_SCOPES = ["https://www.googleapis.com/auth/cloud-platform"] - -# Put the lock here rather than in BigQueryOptions so that BigQueryOptions -# remains deepcopy-able. -_AUTH_LOCK = threading.Lock() -_cached_credentials: Optional[google.auth.credentials.Credentials] = None -_cached_project_default: Optional[str] = None - - -_GOOGLE_CLOUD_PROJECT = "GOOGLE_CLOUD_PROJECT" - - -def resolve_credentials_and_project( - options: bigquery_options.BigQueryOptions, -) -> tuple[google.auth.credentials.Credentials, str]: - project = options.project - credentials = options.credentials - if project is None: - project = os.getenv(_GOOGLE_CLOUD_PROJECT) - - if credentials is None: - credentials, cred_project = _get_default_credentials_with_project() - # This might conflict with explicit project, which will be ignored, credentials project - # only used if nothing else specified - if project is None: - project = cred_project - - if project is None: - raise ValueError( - "Project must be set to initialize BigQuery client. " - "Try setting `bigframes.options.bigquery.project` first." - ) - return credentials, project - - -def _get_default_credentials_with_project() -> tuple[ - google.auth.credentials.Credentials, Optional[str] -]: - global _AUTH_LOCK, _cached_credentials, _cached_project_default - - with _AUTH_LOCK: - if _cached_credentials is not None: - return _cached_credentials, _cached_project_default - - _cached_credentials, _cached_project_default = pydata_google_auth.default( - scopes=_SCOPES, use_local_webserver=False - ) - - # Ensure an access token is available. - _cached_credentials.refresh(google.auth.transport.requests.Request()) - - return _cached_credentials, _cached_project_default - - -def reset_default_credentials_and_project(): - global _AUTH_LOCK, _cached_credentials, _cached_project_default - - with _AUTH_LOCK: - _cached_credentials = None - _cached_project_default = None diff --git a/bigframes/_config/bigquery_options.py b/bigframes/_config/bigquery_options.py index 6c5c424240d..d0cce9492bb 100644 --- a/bigframes/_config/bigquery_options.py +++ b/bigframes/_config/bigquery_options.py @@ -16,15 +16,10 @@ from __future__ import annotations -import warnings -from typing import Literal, Optional, Sequence, Tuple +from typing import Optional +import google.api_core.exceptions import google.auth.credentials -import requests.adapters - -import bigframes._importing -import bigframes.enums -import bigframes.exceptions as bfe SESSION_STARTED_MESSAGE = ( "Cannot change '{attribute}' once a session has started. " @@ -32,49 +27,6 @@ ) -UNKNOWN_LOCATION_MESSAGE = "The location '{location}' is set to an unknown value. Did you mean '{possibility}'?" - - -def _get_validated_location(value: Optional[str]) -> Optional[str]: - import bigframes._tools.strings - - if value is None or value in bigframes.constants.ALL_BIGQUERY_LOCATIONS: - return value - - location = str(value) - - location_lowercase = location.lower() - if location_lowercase in bigframes.constants.BIGQUERY_REGIONS: - return location_lowercase - - location_uppercase = location.upper() - if location_uppercase in bigframes.constants.BIGQUERY_MULTIREGIONS: - return location_uppercase - - possibility = min( - bigframes.constants.ALL_BIGQUERY_LOCATIONS, - key=lambda item: bigframes._tools.strings.levenshtein_distance(location, item), - ) - # There are many layers before we get to (possibly) the user's code: - # -> bpd.options.bigquery.location = "us-central-1" - # -> location.setter - # -> _get_validated_location - msg = bfe.format_message( - UNKNOWN_LOCATION_MESSAGE.format(location=location, possibility=possibility) - ) - warnings.warn(msg, stacklevel=3, category=bfe.UnknownLocationWarning) - - return value - - -def _validate_ordering_mode(value: str) -> bigframes.enums.OrderingMode: - if value.casefold() == bigframes.enums.OrderingMode.STRICT.value.casefold(): - return bigframes.enums.OrderingMode.STRICT - if value.casefold() == bigframes.enums.OrderingMode.PARTIAL.value.casefold(): - return bigframes.enums.OrderingMode.PARTIAL - raise ValueError("Ordering mode must be one of 'strict' or 'partial'.") - - class BigQueryOptions: """Encapsulates configuration for working with a session.""" @@ -86,55 +38,21 @@ def __init__( bq_connection: Optional[str] = None, use_regional_endpoints: bool = False, application_name: Optional[str] = None, - kms_key_name: Optional[str] = None, - skip_bq_connection_check: bool = False, - *, - allow_large_results: bool = False, - ordering_mode: Literal["strict", "partial"] = "strict", - client_endpoints_override: Optional[dict] = None, - requests_transport_adapters: Sequence[ - Tuple[str, requests.adapters.BaseAdapter] - ] = (), - enable_polars_execution: bool = False, ): self._credentials = credentials self._project = project - self._location = _get_validated_location(location) + self._location = location self._bq_connection = bq_connection self._use_regional_endpoints = use_regional_endpoints self._application_name = application_name - self._kms_key_name = kms_key_name - self._skip_bq_connection_check = skip_bq_connection_check - self._allow_large_results = allow_large_results - self._requests_transport_adapters = requests_transport_adapters self._session_started = False - # Determines the ordering strictness for the session. - self._ordering_mode = _validate_ordering_mode(ordering_mode) - - if client_endpoints_override is None: - client_endpoints_override = {} - - self._client_endpoints_override = client_endpoints_override - if enable_polars_execution: - bigframes._importing.import_polars() - self._enable_polars_execution = enable_polars_execution @property def application_name(self) -> Optional[str]: """The application name to amend to the user-agent sent to Google APIs. - The application name to amend to the user agent sent to Google APIs. - The recommended format is ``"application-name/major.minor.patch_version"`` + Recommended format is ``"appplication-name/major.minor.patch_version"`` or ``"(gpn:PartnerName;)"`` for official Google partners. - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> bpd.options.bigquery.application_name = "my-app/1.0.0" # doctest: +SKIP - - Returns: - None or str: - Application name as a string if exists; otherwise None. """ return self._application_name @@ -148,19 +66,7 @@ def application_name(self, value: Optional[str]): @property def credentials(self) -> Optional[google.auth.credentials.Credentials]: - """The OAuth2 credentials to use for this client. - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> import google.auth - >>> credentials, project = google.auth.default() # doctest: +SKIP - >>> bpd.options.bigquery.credentials = credentials # doctest: +SKIP - - Returns: - None or google.auth.credentials.Credentials: - google.auth.credentials.Credentials if exists; otherwise None. - """ + """The OAuth2 Credentials to use for this client.""" return self._credentials @credentials.setter @@ -173,38 +79,19 @@ def credentials(self, value: Optional[google.auth.credentials.Credentials]): def location(self) -> Optional[str]: """Default location for job, datasets, and tables. - For more information, see https://cloud.google.com/bigquery/docs/locations BigQuery locations. - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> bpd.options.bigquery.location = "US" # doctest: +SKIP - - Returns: - None or str: - Default location as a string; otherwise None. + See: https://cloud.google.com/bigquery/docs/locations """ return self._location @location.setter def location(self, value: Optional[str]): - if self._session_started and self._location != _get_validated_location(value): + if self._session_started and self._location != value: raise ValueError(SESSION_STARTED_MESSAGE.format(attribute="location")) - self._location = _get_validated_location(value) + self._location = value @property def project(self) -> Optional[str]: - """Google Cloud project ID to use for billing and as the default project. - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> bpd.options.bigquery.project = "my-project" # doctest: +SKIP - - Returns: - None or str: - Google Cloud project ID as a string; otherwise None. - """ + """Google Cloud project ID to use for billing and as the default project.""" return self._project @project.setter @@ -215,27 +102,14 @@ def project(self, value: Optional[str]): @property def bq_connection(self) -> Optional[str]: - """Name of the BigQuery connection to use in the form - ... - - You either need to create the connection in a location of your choice, or - you need the Project Admin IAM role to enable the service to create the - connection for you. - - If this option isn't available, or the project or location isn't provided, - then the default connection project/location/connection_id is used in the session. + """Name of the BigQuery connection to use. Should be of the form ... - If this option isn't provided, or project or location aren't provided, - session will use its default project/location/connection_id as default connection. + You should either have the connection already created in the + location you have chosen, or you should have the Project IAM + Admin role to enable the service to create the connection for you if you + need it. - **Examples:** - - >>> import bigframes.pandas as bpd - >>> bpd.options.bigquery.bq_connection = "my-project.us.my-connection" # doctest: +SKIP - - Returns: - None or str: - Name of the BigQuery connection as a string; otherwise None. + If this option isn't provided, or project or location aren't provided, session will use its default project/location/connection_id as default connection. """ return self._bq_connection @@ -245,104 +119,13 @@ def bq_connection(self, value: Optional[str]): raise ValueError(SESSION_STARTED_MESSAGE.format(attribute="bq_connection")) self._bq_connection = value - @property - def skip_bq_connection_check(self) -> bool: - """Forcibly use the BigQuery connection. - - Setting this flag to True would avoid creating the BigQuery connection - and checking or setting IAM permissions on it. So if the BigQuery - connection (default or user-provided) does not exist, or it does not have - necessary permissions set up to support BigQuery DataFrames operations, - then a runtime error will be reported. - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> bpd.options.bigquery.skip_bq_connection_check = True # doctest: +SKIP - - Returns: - bool: - A boolean value, where True indicates a BigQuery connection is - not created or the connection does not have necessary - permissions set up; otherwise False. - """ - return self._skip_bq_connection_check - - @skip_bq_connection_check.setter - def skip_bq_connection_check(self, value: bool): - if self._session_started and self._skip_bq_connection_check != value: - raise ValueError( - SESSION_STARTED_MESSAGE.format(attribute="skip_bq_connection_check") - ) - self._skip_bq_connection_check = value - - @property - def allow_large_results(self) -> bool: - """ - DEPRECATED: Checks the legacy global setting for allowing large results. - Use ``bpd.options.compute.allow_large_results`` instead. - - Warning: Accessing ``bpd.options.bigquery.allow_large_results`` is deprecated - and this property will be removed in a future version. The configuration for - handling large results has moved. - - Returns: - bool: The value of the deprecated setting. - """ - return self._allow_large_results - - @allow_large_results.setter - def allow_large_results(self, value: bool): - warnings.warn( - "Setting `bpd.options.bigquery.allow_large_results` is deprecated, " - "and will be removed in the future. " - "Please use `bpd.options.compute.allow_large_results = ` instead. " - "The `bpd.options.bigquery.allow_large_results` option is ignored if " - "`bpd.options.compute.allow_large_results` is set.", - FutureWarning, - stacklevel=2, - ) - if self._session_started and self._allow_large_results != value: - raise ValueError( - SESSION_STARTED_MESSAGE.format(attribute="allow_large_results") - ) - - self._allow_large_results = value - @property def use_regional_endpoints(self) -> bool: - """Flag to connect to regional API endpoints for BigQuery API and - BigQuery Storage API. - - .. note:: - Use of regional endpoints is a feature in Preview and available only - in regions "europe-west3", "europe-west8", "europe-west9", - "me-central2", "us-central1", "us-central2", "us-east1", "us-east4", - "us-east5", "us-east7", "us-south1", "us-west1", "us-west2", "us-west3" - and "us-west4". + """Flag to connect to regional API endpoints. - Requires that ``location`` is set. For [supported regions](https://cloud.google.com/bigquery/docs/regional-endpoints), - for example ``europe-west3``, you need to specify - ``location='europe-west3'`` and ``use_regional_endpoints=True``, and - then BigQuery DataFrames would connect to the BigQuery endpoint - ``bigquery.europe-west3.rep.googleapis.com``. For not supported regions, - for example ``asia-northeast1``, when you specify - ``location='asia-northeast1'`` and ``use_regional_endpoints=True``, - the global endpoint ``bigquery.googleapis.com`` would be used, which - does not promise any guarantee on the request remaining within the - location during transit. - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> bpd.options.bigquery.location = "europe-west3" # doctest: +SKIP - >>> bpd.options.bigquery.use_regional_endpoints = True # doctest: +SKIP - - Returns: - bool: - A boolean value, where True indicates that regional endpoints - would be used for BigQuery and BigQuery storage APIs; otherwise - global endpoints would be used. + Requires ``location`` to also be set. For example, set + ``location='asia-northeast1'`` and ``use_regional_endpoints=True`` to + connect to asia-northeast1-bigquery.googleapis.com. """ return self._use_regional_endpoints @@ -352,154 +135,4 @@ def use_regional_endpoints(self, value: bool): raise ValueError( SESSION_STARTED_MESSAGE.format(attribute="use_regional_endpoints") ) - - if value: - msg = bfe.format_message( - "Use of regional endpoints is a feature in preview and " - "available only in selected regions and projects. " - ) - warnings.warn(msg, category=bfe.PreviewWarning, stacklevel=2) - self._use_regional_endpoints = value - - @property - def kms_key_name(self) -> Optional[str]: - """ - Customer managed encryption key used to control encryption of the - data-at-rest in BigQuery. This is of the format - projects/PROJECT_ID/locations/LOCATION/keyRings/KEYRING/cryptoKeys/KEY. - - For more information, see https://cloud.google.com/bigquery/docs/customer-managed-encryption - Customer-managed Cloud KMS keys - - Make sure the project used for Bigquery DataFrames has the - Cloud KMS CryptoKey Encrypter/Decrypter IAM role in the key's project. - For more information, see https://cloud.google.com/bigquery/docs/customer-managed-encryption#assign_role - Assign the Encrypter/Decrypter. - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> bpd.options.bigquery.kms_key_name = "projects/my-project/locations/us/keyRings/my-ring/cryptoKeys/my-key" # doctest: +SKIP - - Returns: - None or str: - Name of the customer managed encryption key as a string; otherwise None. - """ - return self._kms_key_name - - @kms_key_name.setter - def kms_key_name(self, value: str): - if self._session_started and self._kms_key_name != value: - raise ValueError(SESSION_STARTED_MESSAGE.format(attribute="kms_key_name")) - - self._kms_key_name = value - - @property - def ordering_mode(self) -> Literal["strict", "partial"]: - """Controls whether total row order is always maintained for DataFrame/Series. - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> bpd.options.bigquery.ordering_mode = "partial" # doctest: +SKIP - - Returns: - Literal: - A literal string value of either strict or partial ordering mode. - """ - return self._ordering_mode.value - - @ordering_mode.setter - def ordering_mode(self, value: Literal["strict", "partial"]) -> None: - ordering_mode = _validate_ordering_mode(value) - if self._session_started and self._ordering_mode != ordering_mode: - raise ValueError(SESSION_STARTED_MESSAGE.format(attribute="ordering_mode")) - self._ordering_mode = ordering_mode - - @property - def client_endpoints_override(self) -> dict: - """Option that sets the BQ client endpoints addresses directly as a dict. Possible keys are "bqclient", "bqconnectionclient", "bqstoragereadclient".""" - return self._client_endpoints_override - - @client_endpoints_override.setter - def client_endpoints_override(self, value: dict): - msg = bfe.format_message( - "This is an advanced configuration option for directly setting endpoints. " - "Incorrect use may lead to unexpected behavior or system instability. " - "Proceed only if you fully understand its implications." - ) - warnings.warn(msg) - - if self._session_started and self._client_endpoints_override != value: - raise ValueError( - SESSION_STARTED_MESSAGE.format(attribute="client_endpoints_override") - ) - - self._client_endpoints_override = value - - @property - def requests_transport_adapters( - self, - ) -> Sequence[Tuple[str, requests.adapters.BaseAdapter]]: - """Transport adapters for requests-based REST clients such as the - google-cloud-bigquery package. - - For more details, see the explanation in `requests guide to transport - adapters - `_. - - **Examples:** - - Increase the connection pool size using the requests `HTTPAdapter - `_. - - >>> import bigframes.pandas as bpd - >>> bpd.options.bigquery.requests_transport_adapters = ( - ... ("http://", requests.adapters.HTTPAdapter(pool_maxsize=100)), - ... ("https://", requests.adapters.HTTPAdapter(pool_maxsize=100)), - ... ) # doctest: +SKIP - - Returns: - Sequence[Tuple[str, requests.adapters.BaseAdapter]]: - Prefixes and corresponding transport adapters to `mount - `_ - in requests-based REST clients. - """ - return self._requests_transport_adapters - - @requests_transport_adapters.setter - def requests_transport_adapters( - self, value: Sequence[Tuple[str, requests.adapters.BaseAdapter]] - ) -> None: - if self._session_started and self._requests_transport_adapters != value: - raise ValueError( - SESSION_STARTED_MESSAGE.format(attribute="requests_transport_adapters") - ) - self._requests_transport_adapters = value - - @property - def enable_polars_execution(self) -> bool: - """If True, will use polars to execute some simple query plans locally. - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> bpd.options.bigquery.enable_polars_execution = True # doctest: +SKIP - - """ - return self._enable_polars_execution - - @enable_polars_execution.setter - def enable_polars_execution(self, value: bool): - if self._session_started and self._enable_polars_execution != value: - raise ValueError( - SESSION_STARTED_MESSAGE.format(attribute="enable_polars_execution") - ) - if value is True: - msg = bfe.format_message( - "Polars execution is an experimental feature, and may not be stable. Must have polars installed." - ) - warnings.warn(msg, category=bfe.PreviewWarning) - bigframes._importing.import_polars() - self._enable_polars_execution = value diff --git a/bigframes/_config/compute_options.py b/bigframes/_config/compute_options.py index 2ef1e5b7213..20c31d39066 100644 --- a/bigframes/_config/compute_options.py +++ b/bigframes/_config/compute_options.py @@ -15,181 +15,21 @@ """Options for displaying objects.""" import dataclasses -from typing import Any, Dict, Optional +from typing import Optional @dataclasses.dataclass class ComputeOptions: """ - Encapsulates the configuration for compute options. + Encapsulates configuration for compute options. - **Examples:** + Attributes: + maximum_bytes_billed (int, Options): + Limits the bytes billed for query jobs. Queries that will have + bytes billed beyond this limit will fail (without incurring a + charge). If unspecified, this will be set to your project default. + See `maximum_bytes_billed `_. - >>> import bigframes.pandas as bpd - >>> df = bpd.read_gbq("bigquery-public-data.ml_datasets.penguins") - - >>> bpd.options.compute.maximum_bytes_billed = 500 # doctest: +SKIP - >>> df.to_pandas() # this should fail # doctest: +SKIP - google.api_core.exceptions.InternalServerError: 500 Query exceeded limit for bytes billed: 500. 10485760 or higher required. - - >>> bpd.options.compute.maximum_bytes_billed = None # reset option # doctest: +SKIP - - To add multiple extra labels to a query configuration, use the `assign_extra_query_labels` - method with keyword arguments: - - >>> bpd.options.compute.assign_extra_query_labels(test1=1, test2="abc") # doctest: +SKIP - >>> bpd.options.compute.extra_query_labels # doctest: +SKIP - {'test1': 1, 'test2': 'abc'} - - Alternatively, you can add labels individually by directly accessing the `extra_query_labels` - dictionary: - - >>> bpd.options.compute.extra_query_labels["test3"] = False # doctest: +SKIP - >>> bpd.options.compute.extra_query_labels # doctest: +SKIP - {'test1': 1, 'test2': 'abc', 'test3': False} - - To remove a label from the configuration, use the `del` keyword on the desired label key: - - >>> del bpd.options.compute.extra_query_labels["test1"] # doctest: +SKIP - >>> bpd.options.compute.extra_query_labels # doctest: +SKIP - {'test2': 'abc', 'test3': False} - """ - - ai_ops_confirmation_threshold: Optional[int] = 0 - """ - Guards against unexpected processing of large amount of rows by semantic operators. - - If the number of rows exceeds the threshold, the user will be asked to confirm - their operations to resume. The default value is 0. Set the value to None - to turn off the guard. - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> bpd.options.compute.ai_ops_confirmation_threshold = 100 # doctest: +SKIP - - Returns: - Optional[int]: Number of rows. - """ - - ai_ops_threshold_autofail: bool = False - """ - Guards against unexpected processing of large amount of rows by semantic operators. - - When set to True, the operation automatically fails without asking for user inputs. - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> bpd.options.compute.ai_ops_threshold_autofail = True # doctest: +SKIP - - Returns: - bool: True if the guard is enabled. - """ - - allow_large_results: Optional[bool] = None - """ - Specifies whether query results can exceed 10 GB. - - Defaults to False. Setting this to False (the default) restricts results to - 10 GB for potentially faster execution; BigQuery will raise an error if this - limit is exceeded. Setting to True removes this result size limit. - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> bpd.options.compute.allow_large_results = True # doctest: +SKIP - - Returns: - bool | None: True if results > 10 GB are enabled. - """ - enable_multi_query_execution: bool = False - """ - If enabled, large queries may be factored into multiple smaller queries. - - This is in order to avoid generating queries that are too complex for the - query engine to handle. However this comes at the cost of increase cost and - latency. - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> bpd.options.compute.enable_multi_query_execution = True # doctest: +SKIP - - Returns: - bool | None: True if enabled. - """ - - extra_query_labels: Dict[str, Any] = dataclasses.field( - default_factory=dict, init=False - ) - """ - Stores additional custom labels for query configuration. - - Returns: - Dict[str, Any] | None: Additional labels. """ maximum_bytes_billed: Optional[int] = None - """ - Limits the bytes billed for query jobs. - - Queries that will have bytes billed beyond this limit will fail (without - incurring a charge). If unspecified, this will be set to your project - default. See `maximum_bytes_billed`: - https://cloud.google.com/python/docs/reference/bigquery/latest/google.cloud.bigquery.job.QueryJobConfig#google_cloud_bigquery_job_QueryJobConfig_maximum_bytes_billed. - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> bpd.options.compute.maximum_bytes_billed = 1000 # doctest: +SKIP - - Returns: - int | None: Number of bytes, if set. - """ - - maximum_result_rows: Optional[int] = None - """ - Limits the number of rows in an execution result. - - When converting a BigQuery DataFrames object to a pandas DataFrame or Series - (e.g., using ``.to_pandas()``, ``.peek()``, ``.__repr__()``, direct - iteration), the data is downloaded from BigQuery to the client machine. This - option restricts the number of rows that can be downloaded. If the number - of rows to be downloaded exceeds this limit, a - ``bigframes.exceptions.MaximumResultRowsExceeded`` exception is raised. - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> bpd.options.compute.maximum_result_rows = 1000 # doctest: +SKIP - - Returns: - int | None: Number of rows, if set. - """ - - def assign_extra_query_labels(self, **kwargs: Any) -> None: - """ - Assigns additional custom labels for query configuration. The method updates the - `extra_query_labels` dictionary with new labels provided through keyword arguments. - - Args: - kwargs (Any): - Custom labels provided as keyword arguments. Each key-value pair - in `kwargs` represents a label name and its value. - - Raises: - ValueError: If a key matches one of the reserved attribute names, - specifically 'maximum_bytes_billed' or 'enable_multi_query_execution', - to prevent conflicts with built-in settings. - """ - reserved_keys = ["maximum_bytes_billed", "enable_multi_query_execution"] - for key in kwargs: - if key in reserved_keys: - raise ValueError( - f"'{key}' is a reserved attribute name. Please use " - "a different key for your custom labels to avoid " - "conflicts with built-in settings." - ) - - self.extra_query_labels.update(kwargs) diff --git a/bigframes/_config/display_options.py b/bigframes/_config/display_options.py index 34c5c77d57d..ad3ea3f68cc 100644 --- a/bigframes/_config/display_options.py +++ b/bigframes/_config/display_options.py @@ -15,29 +15,36 @@ """Options for displaying objects.""" import contextlib +import dataclasses +from typing import Literal, Optional -import bigframes_vendored.pandas.core.config_init as vendored_pandas_config import pandas as pd -DisplayOptions = vendored_pandas_config.DisplayOptions +import third_party.bigframes_vendored.pandas.core.config_init as vendored_pandas_config + + +@dataclasses.dataclass +class DisplayOptions: + __doc__ = vendored_pandas_config.display_options_doc + + max_columns: int = 20 + max_rows: int = 25 + progress_bar: Optional[str] = "auto" + repr_mode: Literal["head", "deferred"] = "head" @contextlib.contextmanager -def pandas_repr(display_options: vendored_pandas_config.DisplayOptions): +def pandas_repr(display_options: DisplayOptions): """Use this when visualizing with pandas. This context manager makes sure we reset the pandas options when we're done so that we don't override pandas behavior. """ with pd.option_context( - "display.max_colwidth", - display_options.max_colwidth, "display.max_columns", display_options.max_columns, "display.max_rows", display_options.max_rows, - "display.precision", - display_options.precision, "display.show_dimensions", True, ) as pandas_context: diff --git a/bigframes/_config/experiment_options.py b/bigframes/_config/experiment_options.py deleted file mode 100644 index 8f70c8952f6..00000000000 --- a/bigframes/_config/experiment_options.py +++ /dev/null @@ -1,140 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import warnings -from typing import Literal, Optional - -import bigframes -import bigframes.exceptions as bfe - - -class ExperimentOptions: - """ - Encapsulates the configuration for experiments - """ - - def __init__(self): - self._sql_compiler: Literal["legacy", "stable", "experimental"] = "stable" - self._enable_python_transpiler: bool = False - - @property - def sql_compiler(self) -> Literal["legacy", "stable", "experimental"]: - """Set to 'experimental' to try out the latest in compilation experiments.. - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> bpd.options.experiments.sql_compiler = 'experimental' # doctest: +SKIP - """ - return self._sql_compiler - - @sql_compiler.setter - def sql_compiler(self, value: Literal["legacy", "stable", "experimental"]): - if value not in ["legacy", "stable", "experimental"]: - raise ValueError( - "sql_compiler must be one of 'legacy', 'stable', or 'experimental'" - ) - if value == "experimental": - msg = bfe.format_message( - "The experimental SQL compiler is still under experiments, and is subject " - "to change in the future." - ) - warnings.warn(msg, category=FutureWarning) - self._sql_compiler = value - - @property - def blob(self) -> bool: - msg = bfe.format_message( - "BigFrames Blob is in preview now. This flag is no longer needed." - ) - warnings.warn(msg, category=bfe.ApiDeprecationWarning) - return True - - @blob.setter - def blob(self, value: bool): - msg = bfe.format_message( - "BigFrames Blob is in preview now. This flag is no longer needed." - ) - warnings.warn(msg, category=bfe.ApiDeprecationWarning) - - @property - def blob_display(self) -> bool: - """Whether to display the blob content in notebook DataFrame preview. Default True.""" - msg = bfe.format_message( - "BigFrames Blob is in preview now. The option has been moved to bigframes.options.display.blob_display." - ) - warnings.warn(msg, category=bfe.ApiDeprecationWarning) - - return bigframes.options.display.blob_display - - @blob_display.setter - def blob_display(self, value: bool): - msg = bfe.format_message( - "BigFrames Blob is in preview now. The option has been moved to bigframes.options.display.blob_display." - ) - warnings.warn(msg, category=bfe.ApiDeprecationWarning) - - bigframes.options.display.blob_display = value - - @property - def blob_display_width(self) -> Optional[int]: - """Width in pixels that the blob constrained to.""" - msg = bfe.format_message( - "BigFrames Blob is in preview now. The option has been moved to bigframes.options.display.blob_display_width." - ) - warnings.warn(msg, category=bfe.ApiDeprecationWarning) - - return bigframes.options.display.blob_display_width - - @blob_display_width.setter - def blob_display_width(self, value: Optional[int]): - msg = bfe.format_message( - "BigFrames Blob is in preview now. The option has been moved to bigframes.options.display.blob_display_width." - ) - warnings.warn(msg, category=bfe.ApiDeprecationWarning) - - bigframes.options.display.blob_display_width = value - - @property - def blob_display_height(self) -> Optional[int]: - """Height in pixels that the blob constrained to.""" - msg = bfe.format_message( - "BigFrames Blob is in preview now. The option has been moved to bigframes.options.display.blob_display_height." - ) - warnings.warn(msg, category=bfe.ApiDeprecationWarning) - - return bigframes.options.display.blob_display_height - - @blob_display_height.setter - def blob_display_height(self, value: Optional[int]): - msg = bfe.format_message( - "BigFrames Blob is in preview now. The option has been moved to bigframes.options.display.blob_display_height." - ) - warnings.warn(msg, category=bfe.ApiDeprecationWarning) - - bigframes.options.display.blob_display_height = value - - @property - def enable_python_transpiler(self) -> bool: - return self._enable_python_transpiler - - @enable_python_transpiler.setter - def enable_python_transpiler(self, value: bool): - if value: - msg = bfe.format_message( - "Python transpiler is an unstable, experimental feature, and not yet fully " - "validated, use at your own risk." - ) - warnings.warn(msg, category=bfe.PythonTranspilerPreviewWarning) - self._enable_python_transpiler = value diff --git a/bigframes/_config/global_options.py b/bigframes/_config/global_options.py deleted file mode 100644 index 8f742608292..00000000000 --- a/bigframes/_config/global_options.py +++ /dev/null @@ -1,186 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -""" -Configuration for BigQuery DataFrames. Do not depend on other parts of BigQuery -DataFrames from this package. -""" - -from __future__ import annotations - -import copy -import threading -from dataclasses import dataclass, field -from typing import Optional - -import bigframes_vendored.pandas._config.config as pandas_config - -import bigframes._config.bigquery_options as bigquery_options -import bigframes._config.compute_options as compute_options -import bigframes._config.display_options as display_options -import bigframes._config.experiment_options as experiment_options -import bigframes._config.sampling_options as sampling_options - - -@dataclass -class ThreadLocalConfig(threading.local): - # If unset, global settings will be used - bigquery_options: Optional[bigquery_options.BigQueryOptions] = None - # Note: use default factory instead of default instance so each thread initializes to default values - display_options: display_options.DisplayOptions = field( - default_factory=display_options.DisplayOptions - ) - sampling_options: sampling_options.SamplingOptions = field( - default_factory=sampling_options.SamplingOptions - ) - compute_options: compute_options.ComputeOptions = field( - default_factory=compute_options.ComputeOptions - ) - experiment_options: experiment_options.ExperimentOptions = field( - default_factory=experiment_options.ExperimentOptions - ) - - -class Options: - """Global options affecting BigQuery DataFrames behavior. - - Do not construct directly. Instead, refer to - :attr:`bigframes.pandas.options`. - """ - - def __init__(self): - self.reset() - - def reset(self) -> Options: - """Reset the option settings to defaults. - - Returns: - bigframes._config.Options: Options object with default values. - """ - self._local = ThreadLocalConfig() - - # BigQuery options are special because they can only be set once per - # session, so we need an indicator as to whether we are using the - # thread-local session or the global session. - self._bigquery_options = bigquery_options.BigQueryOptions() - return self - - def _init_bigquery_thread_local(self): - """Initialize thread-local options, based on current global options.""" - - # Already thread-local, so don't reset any options that have been set - # already. No locks needed since this only modifies thread-local - # variables. - if self._local.bigquery_options is not None: - return - - self._local.bigquery_options = copy.deepcopy(self._bigquery_options) - self._local.bigquery_options._session_started = False - - @property - def bigquery(self) -> bigquery_options.BigQueryOptions: - """Options to use with the BigQuery engine. - - Returns: - bigframes._config.bigquery_options.BigQueryOptions: - Options for BigQuery engine. - """ - if self._local.bigquery_options is not None: - # The only way we can get here is if someone called - # _init_bigquery_thread_local. - return self._local.bigquery_options - - return self._bigquery_options - - @property - def display(self) -> display_options.DisplayOptions: - """Options controlling object representation. - - Returns: - bigframes._config.display_options.DisplayOptions: - Options for controlling object representation. - """ - return self._local.display_options - - @property - def sampling(self) -> sampling_options.SamplingOptions: - """Options controlling downsampling when downloading data - to memory. - - The data can be downloaded into memory explicitly - (e.g., to_pandas, to_numpy, values) or implicitly (e.g., - matplotlib plotting). This option can be overridden by - parameters in specific functions. - - Returns: - bigframes._config.sampling_options.SamplingOptions: - Options for controlling downsampling. - """ - return self._local.sampling_options - - @property - def compute(self) -> compute_options.ComputeOptions: - """Thread-local options controlling object computation. - - Returns: - bigframes._config.compute_options.ComputeOptions: - Thread-local options for controlling object computation - """ - return self._local.compute_options - - @property - def experiments(self) -> experiment_options.ExperimentOptions: - """Options controlling experiments - - Returns: - bigframes._config.experiment_options.ExperimentOptions: - Thread-local options for controlling experiments - """ - return self._local.experiment_options - - @property - def is_bigquery_thread_local(self) -> bool: - """Indicator that we're using a thread-local session. - - A thread-local session can be started by using - `with bigframes.option_context("bigquery.some_option", "some-value"):`. - - Returns: - bool: - A boolean value, where a value is True if a thread-local session - is in use; otherwise False. - """ - return self._local.bigquery_options is not None - - @property - def _allow_large_results(self) -> bool: - """The effective 'allow_large_results' setting. - - This value is `self.compute.allow_large_results` if set (not `None`), - otherwise it defaults to `self.bigquery.allow_large_results`. - - Returns: - bool: - Whether large query results are permitted. - - `True`: The BigQuery result size limit (e.g., 10 GB) is removed. - - `False`: Results are restricted to this limit (potentially faster). - BigQuery will raise an error if this limit is exceeded. - """ - if self.compute.allow_large_results is None: - return self.bigquery.allow_large_results - return self.compute.allow_large_results - - -options = Options() -option_context = pandas_config.option_context diff --git a/bigframes/_config/sampling_options.py b/bigframes/_config/sampling_options.py index 9746e01f31d..1742dabe17a 100644 --- a/bigframes/_config/sampling_options.py +++ b/bigframes/_config/sampling_options.py @@ -14,125 +14,17 @@ """Options for downsampling.""" -from __future__ import annotations - import dataclasses from typing import Literal, Optional +import third_party.bigframes_vendored.pandas.core.config_init as vendored_pandas_config + @dataclasses.dataclass class SamplingOptions: - """ - Encapsulates the configuration for data sampling. - """ + __doc__ = vendored_pandas_config.sampling_options_doc max_download_size: Optional[int] = 500 - """ - Download size threshold in MB. Default 500. - - If value set to None, the download size won't be checked. - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> bpd.options.sampling.max_download_size = 1000 # doctest: +SKIP - """ - enable_downsampling: bool = False - """ - Whether to enable downsampling. Default False. - - If max_download_size is exceeded when downloading data (e.g., to_pandas()), - the data will be downsampled if enable_downsampling is True, otherwise, an - error will be raised. - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> bpd.options.sampling.enable_downsampling = True # doctest: +SKIP - """ - sampling_method: Literal["head", "uniform"] = "uniform" - """ - Downsampling algorithms to be chosen from. Default "uniform". - - The choices are: "head": This algorithm returns a portion of the data from - the beginning. It is fast and requires minimal computations to perform the - downsampling.; "uniform": This algorithm returns uniform random samples of - the data. - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> bpd.options.sampling.sampling_method = "head" # doctest: +SKIP - """ - random_state: Optional[int] = None - """ - The seed for the uniform downsampling algorithm. Default None. - - If provided, the uniform method may take longer to execute and require more - computation. - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> bpd.options.sampling.random_state = 42 # doctest: +SKIP - """ - - def with_max_download_size(self, max_rows: Optional[int]) -> SamplingOptions: - """Configures the maximum download size for data sampling in MB - - Args: - max_rows (None or int): - An int value for the maximum row size. - - Returns: - bigframes._config.sampling_options.SamplingOptions: - The configuration for data sampling. - """ - return SamplingOptions( - max_rows, self.enable_downsampling, self.sampling_method, self.random_state - ) - - def with_method(self, method: Literal["head", "uniform"]) -> SamplingOptions: - """Configures the downsampling algorithms to be chosen from - - Args: - method (None or Literal): - A literal string value of either head or uniform data sampling method. - - Returns: - bigframes._config.sampling_options.SamplingOptions: - The configuration for data sampling. - """ - return SamplingOptions(self.max_download_size, True, method, self.random_state) - - def with_random_state(self, state: Optional[int]) -> SamplingOptions: - """Configures the seed for the uniform downsampling algorithm - - Args: - state (None or int): - An int value for the data sampling random state - - Returns: - bigframes._config.sampling_options.SamplingOptions: - The configuration for data sampling. - """ - return SamplingOptions( - self.max_download_size, - self.enable_downsampling, - self.sampling_method, - state, - ) - - def with_disabled(self) -> SamplingOptions: - """Configures whether to disable downsampling - - Returns: - bigframes._config.sampling_options.SamplingOptions: - The configuration for data sampling. - """ - return SamplingOptions( - self.max_download_size, False, self.sampling_method, self.random_state - ) diff --git a/bigframes/_importing.py b/bigframes/_importing.py deleted file mode 100644 index e88bd77fe86..00000000000 --- a/bigframes/_importing.py +++ /dev/null @@ -1,35 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -import importlib -from types import ModuleType - -import numpy -from packaging import version - -# Keep this in sync with setup.py -POLARS_MIN_VERSION = version.Version("1.7.0") - - -def import_polars() -> ModuleType: - polars_module = importlib.import_module("polars") - # Check for necessary methods instead of the version number because we - # can't trust the polars version until - # https://github.com/pola-rs/polars/issues/23940 is fixed. - try: - polars_module.lit(numpy.int64(100), dtype=polars_module.Int64()) - except TypeError: - raise ImportError( - f"Imported polars version is likely below the minimum version: {POLARS_MIN_VERSION}" - ) - return polars_module diff --git a/bigframes/_magics.py b/bigframes/_magics.py deleted file mode 100644 index f6b69f35ff5..00000000000 --- a/bigframes/_magics.py +++ /dev/null @@ -1,54 +0,0 @@ -# Copyright 2026 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from IPython.core import magic_arguments # type: ignore -from IPython.core.getipython import get_ipython -from IPython.display import display - -import bigframes.pandas - - -@magic_arguments.magic_arguments() -@magic_arguments.argument( - "destination_var", - nargs="?", - help=("If provided, save the output to this variable instead of displaying it."), -) -@magic_arguments.argument( - "--dry_run", - action="store_true", - default=False, - help=( - "Sets query to be a dry run to estimate costs. " - "Defaults to executing the query instead of dry run if this argument is not used." - "Does not work with engine 'bigframes'. " - ), -) -def _cell_magic(line, cell): - ipython = get_ipython() - if ipython is None: - raise RuntimeError("BigQuery magic must be run in an IPython environment.") - - args = magic_arguments.parse_argstring(_cell_magic, line) - if not cell: - print("Query is missing.") - return - pyformat_args = ipython.user_ns - dataframe = bigframes.pandas._read_gbq_colab( - cell, pyformat_args=pyformat_args, dry_run=args.dry_run - ) - if args.destination_var: - ipython.push({args.destination_var: dataframe}) - - display(dataframe) diff --git a/bigframes/_tools/__init__.py b/bigframes/_tools/__init__.py deleted file mode 100644 index ea3bc209d00..00000000000 --- a/bigframes/_tools/__init__.py +++ /dev/null @@ -1,19 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -"""_tools is a collection of helper functions with minimal dependencies. - -Please keep the dependencies used in this subpackage to a minimum to avoid the -risk of circular dependencies. -""" diff --git a/bigframes/_tools/docs.py b/bigframes/_tools/docs.py deleted file mode 100644 index 9ecfd61b3c9..00000000000 --- a/bigframes/_tools/docs.py +++ /dev/null @@ -1,53 +0,0 @@ -# Copyright 2026 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -def inherit_docs(source_class): - """ - A class decorator that copies docstrings from source_class to the - decorated class for any methods or attributes that match names. - """ - - def decorator(target_class): - if not target_class.__doc__ and source_class.__doc__: - target_class.__doc__ = source_class.__doc__ - - for name, source_item in vars(source_class).items(): - if name in vars(target_class): - target_item = getattr(target_class, name) - - if hasattr(target_item, "__doc__") and not target_item.__doc__: - if hasattr(source_item, "__doc__") and source_item.__doc__: - try: - target_item.__doc__ = source_item.__doc__ - except AttributeError: - pass - - underlying = None - if isinstance(target_item, property): - underlying = target_item.fget - elif hasattr(target_item, "__func__"): - underlying = target_item.__func__ - elif hasattr(target_item, "func"): - underlying = getattr(target_item, "func", None) - - if underlying is not None: - try: - underlying.__doc__ = source_item.__doc__ - except AttributeError: - pass - - return target_class - - return decorator diff --git a/bigframes/_tools/strings.py b/bigframes/_tools/strings.py deleted file mode 100644 index 3d9402c68fb..00000000000 --- a/bigframes/_tools/strings.py +++ /dev/null @@ -1,66 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -"""Helper methods for processing strings with minimal dependencies. - -Please keep the dependencies used in this subpackage to a minimum to avoid the -risk of circular dependencies. -""" - -import numpy - - -def levenshtein_distance(left: str, right: str) -> int: - """Compute the edit distance between two strings. - - This is the minumum number of substitutions, insertions, deletions - to get from left string to right string. See: - https://en.wikipedia.org/wiki/Levenshtein_distance - """ - # TODO(tswast): accelerate with numba (if available) if we end up using this - # function in contexts other than when raising an exception or there are too - # many values to compare even in that context. - - distances0 = numpy.zeros(len(right) + 1) - distances1 = numpy.zeros(len(right) + 1) - - # Maximum distance is to drop all characters and then add the other string. - distances0[:] = range(len(right) + 1) - - for left_index in range(len(left)): - # Calculate distance from distances0 to distances1. - - # Edit distance is to delete (i + 1) chars from left to match empty right - distances1[0] = left_index + 1 - # "ab" - for right_index in range(len(right)): - left_char = left[left_index] - right_char = right[right_index] - - deletion_cost = distances0[right_index + 1] + 1 - insertion_cost = distances1[right_index] + 1 - if left_char == right_char: - substitution_cost = distances0[right_index] - else: - substitution_cost = distances0[right_index] + 1 - - distances1[right_index + 1] = min( - deletion_cost, insertion_cost, substitution_cost - ) - - temp = distances0 - distances0 = distances1 - distances1 = temp - - return distances0[len(right)] diff --git a/bigframes/bigquery/__init__.py b/bigframes/bigquery/__init__.py deleted file mode 100644 index ade7535c32b..00000000000 --- a/bigframes/bigquery/__init__.py +++ /dev/null @@ -1,339 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -""" -Access BigQuery-specific operations and namespaces within BigQuery DataFrames. - -This module provides specialized functions and sub-modules that expose BigQuery's -advanced capabilities to DataFrames and Series. It acts as a bridge between the -pandas-compatible API and the full power of BigQuery SQL. - -Key sub-modules include: - -* :mod:`bigframes.bigquery.ai`: Generative and predictive AI functions (Gemini, BQML). -* :mod:`bigframes.bigquery.ml`: Direct access to BigQuery ML model operations. -* :mod:`bigframes.bigquery.obj`: Support for BigQuery object tables. - -This module also provides direct access to optimized BigQuery functions for: - -* **JSON Processing:** High-performance functions like ``json_extract``, ``json_value``, - and ``parse_json`` for handling semi-structured data. -* **Geospatial Analysis:** Comprehensive geographic functions such as ``st_area``, - ``st_distance``, and ``st_centroid`` (``ST_`` prefixed functions). -* **Array Operations:** Tools for working with BigQuery arrays, including ``array_agg`` - and ``array_length``. -* **Vector Search:** Integration with BigQuery's vector search and indexing - capabilities for high-dimensional data. -* **Custom SQL:** The ``sql_scalar`` function allows embedding raw SQL snippets for - advanced operations not yet directly mapped in the API. - -By using these functions, you can leverage BigQuery's high-performance engine for -domain-specific tasks while maintaining a Python-centric development experience. - -For the full list of BigQuery standard SQL functions, see: -https://cloud.google.com/bigquery/docs/reference/standard-sql/functions-reference -""" - -import sys - -from bigframes.bigquery import aead, ai, ml, obj -from bigframes.bigquery._operations.approx_agg import approx_top_count -from bigframes.bigquery._operations.array import array_agg -from bigframes.bigquery._operations.datetime import ( - unix_micros, - unix_millis, - unix_seconds, -) -from bigframes.bigquery._operations.geo import ( - st_area, - st_buffer, - st_centroid, - st_convexhull, - st_difference, - st_distance, - st_intersection, - st_isclosed, - st_length, - st_regionstats, - st_simplify, -) -from bigframes.bigquery._operations.io import load_data -from bigframes.bigquery._operations.json import ( - json_extract, - json_extract_array, - json_extract_string_array, - json_keys, - json_query, - json_query_array, - json_set, - json_value, - json_value_array, - parse_json, - to_json, - to_json_string, -) -from bigframes.bigquery._operations.mathematical import ( - hparam_candidates, - hparam_range, - rand, -) -from bigframes.bigquery._operations.search import create_vector_index, vector_search -from bigframes.bigquery._operations.sql import sql_scalar -from bigframes.bigquery._operations.struct import struct -from bigframes.bigquery._operations.table import create_external_table -from bigframes.core.logging import log_adapter -from bigframes.operations.googlesql.global_namespace.aead_encryption import ( - deterministic_decrypt_bytes, - deterministic_decrypt_string, - deterministic_encrypt, -) -from bigframes.operations.googlesql.global_namespace.array import ( - array_concat, - array_first, - array_first_n, - array_includes, - array_includes_all, - array_includes_any, - array_is_distinct, - array_last, - array_length, - array_reverse, - array_slice, - array_to_string, - flatten, - generate_array, -) -from bigframes.operations.googlesql.global_namespace.bit import ( - bit_count, -) -from bigframes.operations.googlesql.global_namespace.conversion import ( - bool_, - double, - float64, - int64, - parse_bignumeric, - parse_numeric, - string, -) -from bigframes.operations.googlesql.global_namespace.date import ( - current_date, - date, - date_add, - date_diff, - date_from_unix_date, - date_sub, - date_trunc, - extract, - format_date, - generate_date_array, - last_day, - parse_date, - unix_date, -) - -_functions = [ - # approximate aggregate ops - approx_top_count, - # array ops - array_agg, - array_concat, - array_first, - array_first_n, - array_includes, - array_includes_all, - array_includes_any, - array_is_distinct, - array_last, - array_length, - array_reverse, - array_slice, - array_to_string, - flatten, - generate_array, - # bit ops - bit_count, - # conversion ops - bool_, - double, - float64, - int64, - parse_bignumeric, - parse_numeric, - string, - # date ops - current_date, - date, - date_add, - date_diff, - date_from_unix_date, - date_sub, - date_trunc, - extract, - format_date, - generate_date_array, - last_day, - parse_date, - unix_date, - # datetime ops - unix_micros, - unix_millis, - unix_seconds, - # geo ops - st_area, - st_buffer, - st_centroid, - st_convexhull, - st_difference, - st_distance, - st_intersection, - st_isclosed, - st_length, - st_regionstats, - st_simplify, - # deterministic encryption ops - deterministic_decrypt_bytes, - deterministic_decrypt_string, - deterministic_encrypt, - # json ops - json_extract, - json_extract_array, - json_extract_string_array, - json_query, - json_query_array, - json_set, - json_value, - json_value_array, - parse_json, - to_json, - to_json_string, - # mathematical ops - hparam_candidates, - hparam_range, - rand, - # search ops - create_vector_index, - vector_search, - # sql ops - sql_scalar, - # struct ops - struct, - # table ops - create_external_table, - # io ops - load_data, -] - -_module = sys.modules[__name__] -for f in _functions: - _decorated_object = log_adapter.method_logger(f, custom_base_name="bigquery") - setattr(_module, f.__name__, _decorated_object) - del f - -__all__ = [ - # approximate aggregate ops - "approx_top_count", - # array ops - "array_agg", - "array_concat", - "array_first", - "array_first_n", - "array_includes", - "array_includes_all", - "array_includes_any", - "array_is_distinct", - "array_last", - "array_length", - "array_reverse", - "array_slice", - "array_to_string", - "flatten", - "generate_array", - # bit ops - "bit_count", - # conversion ops - "bool_", - "double", - "float64", - "int64", - "parse_bignumeric", - "parse_numeric", - "string", - # date ops - "current_date", - "date", - "date_add", - "date_diff", - "date_from_unix_date", - "date_sub", - "date_trunc", - "extract", - "format_date", - "generate_date_array", - "last_day", - "parse_date", - "unix_date", - # datetime ops - "unix_micros", - "unix_millis", - "unix_seconds", - # geo ops - "st_area", - "st_buffer", - "st_centroid", - "st_convexhull", - "st_difference", - "st_distance", - "st_intersection", - "st_isclosed", - "st_length", - "st_regionstats", - "st_simplify", - # deterministic encryption ops - "deterministic_decrypt_bytes", - "deterministic_decrypt_string", - "deterministic_encrypt", - # json ops - "json_extract", - "json_extract_array", - "json_extract_string_array", - "json_keys", - "json_query", - "json_query_array", - "json_set", - "json_value", - "json_value_array", - "parse_json", - "to_json", - "to_json_string", - # mathematical ops - "hparam_candidates", - "hparam_range", - "rand", - # search ops - "create_vector_index", - "vector_search", - # sql ops - "sql_scalar", - # struct ops - "struct", - # table ops - "create_external_table", - # io ops - "load_data", - # Modules / SQL namespaces - "aead", - "ai", - "ml", - "obj", -] diff --git a/bigframes/bigquery/_operations/__init__.py b/bigframes/bigquery/_operations/__init__.py deleted file mode 100644 index 6d5e14bcf4a..00000000000 --- a/bigframes/bigquery/_operations/__init__.py +++ /dev/null @@ -1,13 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. diff --git a/bigframes/bigquery/_operations/ai.py b/bigframes/bigquery/_operations/ai.py deleted file mode 100644 index 40d5556de40..00000000000 --- a/bigframes/bigquery/_operations/ai.py +++ /dev/null @@ -1,1254 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -"""This module integrates BigQuery built-in AI functions for use with Series/DataFrame objects, -such as AI.GENERATE_BOOL: -https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-ai-generate-bool""" - -from __future__ import annotations - -import json -from typing import Any, Dict, Iterable, List, Literal, Mapping, Optional, Tuple, Union - -import pandas as pd - -from bigframes import dataframe, dtypes, series, session -from bigframes import pandas as bpd -from bigframes.bigquery._operations import obj as bq_obj -from bigframes.bigquery._operations import utils as bq_utils -from bigframes.core import convert -from bigframes.core.compile.sqlglot import sql as sg_sql -from bigframes.core.logging import log_adapter -from bigframes.ml import base as ml_base -from bigframes.ml import core as ml_core -from bigframes.operations import ai_ops, output_schemas - -PROMPT_TYPE = Union[ - str, - series.Series, - pd.Series, - List[Union[str, series.Series, pd.Series]], - Tuple[Union[str, series.Series, pd.Series], ...], -] - - -@log_adapter.method_logger(custom_base_name="bigquery_ai") -def generate( - prompt: PROMPT_TYPE, - *, - connection_id: str | None = None, - endpoint: str | None = None, - request_type: Literal["dedicated", "shared", "unspecified"] | None = None, - model_params: Mapping[Any, Any] | None = None, - output_schema: Mapping[str, str] | None = None, -) -> series.Series: - """ - Returns the AI analysis based on the prompt, which can be any combination of text and unstructured data. - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> import bigframes.bigquery as bbq - >>> country = bpd.Series(["Japan", "Canada"]) - >>> bbq.ai.generate(("What's the capital city of ", country, " one word only")) # doctest: +ELLIPSIS - 0 {'result': 'Tokyo', 'full_response': '{"cand... - 1 {'result': 'Ottawa', 'full_response': '{"can... - dtype: struct>, status: string>[pyarrow] - - >>> bbq.ai.generate(("What's the capital city of ", country, " one word only")).struct.field("result") - 0 Tokyo - 1 Ottawa - Name: result, dtype: string - - You get structured output when the ``output_schema`` parameter is set: - - >>> animals = bpd.Series(["Rabbit", "Spider"]) - >>> bbq.ai.generate(animals, output_schema={"number_of_legs": "INT64", "is_herbivore": "BOOL"}) - 0 {'is_herbivore': True, 'number_of_legs': 4, 'f... - 1 {'is_herbivore': False, 'number_of_legs': 8, '... - dtype: struct>, status: string>[pyarrow] - - Args: - prompt (str | Series | List[str|Series] | Tuple[str|Series, ...]): - A mixture of Series and string literals that specifies the prompt to send to the model. The Series can be BigFrames Series - or pandas Series. - connection_id (str, optional): - Specifies the connection to use to communicate with the model. For example, ``myproject.us.myconnection``. - If not provided, the query uses your end-user credential. - endpoint (str, optional): - Specifies the Vertex AI endpoint to use for the model. For example ``"gemini-2.5-flash"``. You can specify any - generally available or preview Gemini model. If you specify the model name, BigQuery ML automatically identifies and - uses the full endpoint of the model. If you don't specify an ENDPOINT value, BigQuery ML selects a recent stable - version of Gemini to use. - request_type (Literal["dedicated", "shared", "unspecified"]): - Specifies the type of inference request to send to the Gemini model. The request type determines what quota the request uses. - * "dedicated": function only uses Provisioned Throughput quota. The function returns the error Provisioned throughput is not - purchased or is not active if Provisioned Throughput quota isn't available. - * "shared": the function only uses dynamic shared quota (DSQ), even if you have purchased Provisioned Throughput quota. - * "unspecified": If you haven't purchased Provisioned Throughput quota, the function uses DSQ quota. - If you have purchased Provisioned Throughput quota, the function uses the Provisioned Throughput quota first. - If requests exceed the Provisioned Throughput quota, the overflow traffic uses DSQ quota. - model_params (Mapping[Any, Any]): - Provides additional parameters to the model. The MODEL_PARAMS value must conform to the generateContent request body format. - output_schema (Mapping[str, str]): - A mapping value that specifies the schema of the output, in the form {field_name: data_type}. Supported data types include - ``STRING``, ``INT64``, ``FLOAT64``, ``BOOL``, ``ARRAY``, and ``STRUCT``. - - Returns: - bigframes.series.Series: A new struct Series with the result data. The struct contains these fields: - * "result": a STRING value containing the model's response to the prompt. The result is None if the request fails or is filtered by responsible AI. - If you specify an output schema then result is replaced by your custom schema. - * "full_response": a JSON value containing the response from the projects.locations.endpoints.generateContent call to the model. - The generated text is in the text element. - * "status": a STRING value that contains the API response status for the corresponding row. This value is empty if the operation was successful. - """ - - prompt_context, series_list = _separate_context_and_series(prompt) - assert len(series_list) > 0 - - if output_schema is None: - output_schema_str = None - else: - output_schema_str = ", ".join( - [f"{name} {sql_type}" for name, sql_type in output_schema.items()] - ) - # Validate user input - output_schemas.parse_sql_fields(output_schema_str) - - operator = ai_ops.AIGenerate( - prompt_context=tuple(prompt_context), - connection_id=connection_id, - endpoint=endpoint, - request_type=_upper_optional(request_type), - model_params=json.dumps(model_params) if model_params else None, - output_schema=output_schema_str, - ) - - return series_list[0]._apply_nary_op(operator, series_list[1:]) - - -@log_adapter.method_logger(custom_base_name="bigquery_ai") -def generate_bool( - prompt: PROMPT_TYPE, - *, - connection_id: str | None = None, - endpoint: str | None = None, - request_type: Literal["dedicated", "shared", "unspecified"] | None = None, - model_params: Mapping[Any, Any] | None = None, -) -> series.Series: - """ - Returns the AI analysis based on the prompt, which can be any combination of text and unstructured data. - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> import bigframes.bigquery as bbq - >>> df = bpd.DataFrame({ - ... "col_1": ["apple", "bear", "pear"], - ... "col_2": ["fruit", "animal", "animal"] - ... }) - >>> bbq.ai.generate_bool((df["col_1"], " is a ", df["col_2"])) - 0 {'result': True, 'full_response': '{"candidate... - 1 {'result': True, 'full_response': '{"candidate... - 2 {'result': False, 'full_response': '{"candidat... - dtype: struct>, status: string>[pyarrow] - - >>> bbq.ai.generate_bool((df["col_1"], " is a ", df["col_2"])).struct.field("result") - 0 True - 1 True - 2 False - Name: result, dtype: boolean - - Args: - prompt (str | Series | List[str|Series] | Tuple[str|Series, ...]): - A mixture of Series and string literals that specifies the prompt to send to the model. The Series can be BigFrames Series - or pandas Series. - connection_id (str, optional): - Specifies the connection to use to communicate with the model. For example, ``myproject.us.myconnection``. - If not provided, the query uses your end-user credential. - endpoint (str, optional): - Specifies the Vertex AI endpoint to use for the model. For example ``"gemini-2.5-flash"``. You can specify any - generally available or preview Gemini model. If you specify the model name, BigQuery ML automatically identifies and - uses the full endpoint of the model. If you don't specify an ENDPOINT value, BigQuery ML selects a recent stable - version of Gemini to use. - request_type (Literal["dedicated", "shared", "unspecified"]): - Specifies the type of inference request to send to the Gemini model. The request type determines what quota the request uses. - * "dedicated": function only uses Provisioned Throughput quota. The function returns the error Provisioned throughput is not - purchased or is not active if Provisioned Throughput quota isn't available. - * "shared": the function only uses dynamic shared quota (DSQ), even if you have purchased Provisioned Throughput quota. - * "unspecified": If you haven't purchased Provisioned Throughput quota, the function uses DSQ quota. - If you have purchased Provisioned Throughput quota, the function uses the Provisioned Throughput quota first. - If requests exceed the Provisioned Throughput quota, the overflow traffic uses DSQ quota. - model_params (Mapping[Any, Any]): - Provides additional parameters to the model. The MODEL_PARAMS value must conform to the generateContent request body format. - - Returns: - bigframes.series.Series: A new struct Series with the result data. The struct contains these fields: - * "result": a BOOL value containing the model's response to the prompt. The result is None if the request fails or is filtered by responsible AI. - * "full_response": a JSON value containing the response from the projects.locations.endpoints.generateContent call to the model. - The generated text is in the text element. - * "status": a STRING value that contains the API response status for the corresponding row. This value is empty if the operation was successful. - """ - - prompt_context, series_list = _separate_context_and_series(prompt) - assert len(series_list) > 0 - - operator = ai_ops.AIGenerateBool( - prompt_context=tuple(prompt_context), - connection_id=connection_id, - endpoint=endpoint, - request_type=_upper_optional(request_type), - model_params=json.dumps(model_params) if model_params else None, - ) - - return series_list[0]._apply_nary_op(operator, series_list[1:]) - - -@log_adapter.method_logger(custom_base_name="bigquery_ai") -def generate_int( - prompt: PROMPT_TYPE, - *, - connection_id: str | None = None, - endpoint: str | None = None, - request_type: Literal["dedicated", "shared", "unspecified"] | None = None, - model_params: Mapping[Any, Any] | None = None, -) -> series.Series: - """ - Returns the AI analysis based on the prompt, which can be any combination of text and unstructured data. - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> import bigframes.bigquery as bbq - >>> animal = bpd.Series(["Ostrich", "Rabbit", "Spider"]) - >>> bbq.ai.generate_int(("How many legs does a ", animal, " have?")) # doctest: +ELLIPSIS - 0 {'result': 2, 'full_response': '{"candidates":... - 1 {'result': 4, 'full_response': '{"candidates":... - 2 {'result': 8, 'full_response': '{"candidates":... - dtype: struct>, status: string>[pyarrow] - - >>> bbq.ai.generate_int(("How many legs does a ", animal, " have?")).struct.field("result") - 0 2 - 1 4 - 2 8 - Name: result, dtype: Int64 - - Args: - prompt (str | Series | List[str|Series] | Tuple[str|Series, ...]): - A mixture of Series and string literals that specifies the prompt to send to the model. The Series can be BigFrames Series - or pandas Series. - connection_id (str, optional): - Specifies the connection to use to communicate with the model. For example, ``myproject.us.myconnection``. - If not provided, the query uses your end-user credential. - endpoint (str, optional): - Specifies the Vertex AI endpoint to use for the model. For example ``"gemini-2.5-flash"``. You can specify any - generally available or preview Gemini model. If you specify the model name, BigQuery ML automatically identifies and - uses the full endpoint of the model. If you don't specify an ENDPOINT value, BigQuery ML selects a recent stable - version of Gemini to use. - request_type (Literal["dedicated", "shared", "unspecified"]): - Specifies the type of inference request to send to the Gemini model. The request type determines what quota the request uses. - * "dedicated": function only uses Provisioned Throughput quota. The function returns the error Provisioned throughput is not - purchased or is not active if Provisioned Throughput quota isn't available. - * "shared": the function only uses dynamic shared quota (DSQ), even if you have purchased Provisioned Throughput quota. - * "unspecified": If you haven't purchased Provisioned Throughput quota, the function uses DSQ quota. - If you have purchased Provisioned Throughput quota, the function uses the Provisioned Throughput quota first. - If requests exceed the Provisioned Throughput quota, the overflow traffic uses DSQ quota. - model_params (Mapping[Any, Any]): - Provides additional parameters to the model. The MODEL_PARAMS value must conform to the generateContent request body format. - - Returns: - bigframes.series.Series: A new struct Series with the result data. The struct contains these fields: - * "result": an integer (INT64) value containing the model's response to the prompt. The result is None if the request fails or is filtered by responsible AI. - * "full_response": a JSON value containing the response from the projects.locations.endpoints.generateContent call to the model. - The generated text is in the text element. - * "status": a STRING value that contains the API response status for the corresponding row. This value is empty if the operation was successful. - """ - - prompt_context, series_list = _separate_context_and_series(prompt) - assert len(series_list) > 0 - - operator = ai_ops.AIGenerateInt( - prompt_context=tuple(prompt_context), - connection_id=connection_id, - endpoint=endpoint, - request_type=_upper_optional(request_type), - model_params=json.dumps(model_params) if model_params else None, - ) - - return series_list[0]._apply_nary_op(operator, series_list[1:]) - - -@log_adapter.method_logger(custom_base_name="bigquery_ai") -def generate_double( - prompt: PROMPT_TYPE, - *, - connection_id: str | None = None, - endpoint: str | None = None, - request_type: Literal["dedicated", "shared", "unspecified"] | None = None, - model_params: Mapping[Any, Any] | None = None, -) -> series.Series: - """ - Returns the AI analysis based on the prompt, which can be any combination of text and unstructured data. - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> import bigframes.bigquery as bbq - >>> animal = bpd.Series(["Ostrich", "Rabbit", "Spider"]) - >>> bbq.ai.generate_double(("How many legs does a ", animal, " have?")) # doctest: +ELLIPSIS - 0 {'result': 2.0, 'full_response': '{"candidates... - 1 {'result': 4.0, 'full_response': '{"candidates... - 2 {'result': 8.0, 'full_response': '{"candidates... - dtype: struct>, status: string>[pyarrow] - - >>> bbq.ai.generate_double(("How many legs does a ", animal, " have?")).struct.field("result") - 0 2.0 - 1 4.0 - 2 8.0 - Name: result, dtype: Float64 - - Args: - prompt (str | Series | List[str|Series] | Tuple[str|Series, ...]): - A mixture of Series and string literals that specifies the prompt to send to the model. The Series can be BigFrames Series - or pandas Series. - connection_id (str, optional): - Specifies the connection to use to communicate with the model. For example, ``myproject.us.myconnection``. - If not provided, the query uses your end-user credential. - endpoint (str, optional): - Specifies the Vertex AI endpoint to use for the model. For example ``"gemini-2.5-flash"``. You can specify any - generally available or preview Gemini model. If you specify the model name, BigQuery ML automatically identifies and - uses the full endpoint of the model. If you don't specify an ENDPOINT value, BigQuery ML selects a recent stable - version of Gemini to use. - request_type (Literal["dedicated", "shared", "unspecified"]): - Specifies the type of inference request to send to the Gemini model. The request type determines what quota the request uses. - * "dedicated": function only uses Provisioned Throughput quota. The function returns the error Provisioned throughput is not - purchased or is not active if Provisioned Throughput quota isn't available. - * "shared": the function only uses dynamic shared quota (DSQ), even if you have purchased Provisioned Throughput quota. - * "unspecified": If you haven't purchased Provisioned Throughput quota, the function uses DSQ quota. - If you have purchased Provisioned Throughput quota, the function uses the Provisioned Throughput quota first. - If requests exceed the Provisioned Throughput quota, the overflow traffic uses DSQ quota. - model_params (Mapping[Any, Any]): - Provides additional parameters to the model. The MODEL_PARAMS value must conform to the generateContent request body format. - - Returns: - bigframes.series.Series: A new struct Series with the result data. The struct contains these fields: - * "result": an DOUBLE value containing the model's response to the prompt. The result is None if the request fails or is filtered by responsible AI. - * "full_response": a JSON value containing the response from the projects.locations.endpoints.generateContent call to the model. - The generated text is in the text element. - * "status": a STRING value that contains the API response status for the corresponding row. This value is empty if the operation was successful. - """ - - prompt_context, series_list = _separate_context_and_series(prompt) - assert len(series_list) > 0 - - operator = ai_ops.AIGenerateDouble( - prompt_context=tuple(prompt_context), - connection_id=connection_id, - endpoint=endpoint, - request_type=_upper_optional(request_type), - model_params=json.dumps(model_params) if model_params else None, - ) - - return series_list[0]._apply_nary_op(operator, series_list[1:]) - - -@log_adapter.method_logger(custom_base_name="bigquery_ai") -def generate_embedding( - model: Union[ml_base.BaseEstimator, str, pd.Series], - data: Union[dataframe.DataFrame, series.Series, pd.DataFrame, pd.Series], - *, - output_dimensionality: Optional[int] = None, - task_type: Optional[str] = None, - start_second: Optional[float] = None, - end_second: Optional[float] = None, - interval_seconds: Optional[float] = None, - trial_id: Optional[int] = None, -) -> dataframe.DataFrame: - """ - Creates embeddings that describe an entity—for example, a piece of text or an image. - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> import bigframes.bigquery as bbq - >>> df = bpd.DataFrame({"content": ["apple", "bear", "pear"]}) - >>> bbq.ai.generate_embedding( # doctest: +SKIP - ... "project.dataset.model_name", - ... df - ... ) - - Args: - model (ml_base.BaseEstimator or str): - The model to use for text embedding. - data (bigframes.pandas.DataFrame or bigframes.pandas.Series): - The data to generate embeddings for. If a Series is provided, it is - treated as the 'content' column. If a DataFrame is provided, it - must contain a 'content' column, or you must rename the column you - wish to embed to 'content'. - output_dimensionality (int, optional): - An INT64 value that specifies the number of dimensions to use when - generating embeddings. For example, if you specify 256 AS - output_dimensionality, then the embedding output column contains a - 256-dimensional embedding for each input value. To find the - supported range of output dimensions, read about the available - `Google text embedding models `_. - task_type (str, optional): - A STRING literal that specifies the intended downstream application to - help the model produce better quality embeddings. For a list of - supported task types and how to choose which one to use, see `Choose an - embeddings task type `_. - start_second (float, optional): - The second in the video at which to start the embedding. The default value is 0. - end_second (float, optional): - The second in the video at which to end the embedding. The default value is 120. - interval_seconds (float, optional): - The interval to use when creating embeddings. The default value is 16. - trial_id (int, optional): - An INT64 value that identifies the hyperparameter tuning trial that - you want the function to evaluate. The function uses the optimal - trial by default. Only specify this argument if you ran - hyperparameter tuning when creating the model. - - Returns: - bigframes.pandas.DataFrame: - A new DataFrame with the generated embeddings. See the `SQL - reference for AI.GENERATE_EMBEDDING - `_ - for details. - """ - data = _to_dataframe(data, series_rename="content") - model_name, session = bq_utils.get_model_name_and_session(model, data) - table_sql = bq_utils.to_sql(data) - - struct_fields: Dict[str, Any] = {} - if output_dimensionality is not None: - struct_fields["OUTPUT_DIMENSIONALITY"] = output_dimensionality - if task_type is not None: - struct_fields["TASK_TYPE"] = task_type - if start_second is not None: - struct_fields["START_SECOND"] = start_second - if end_second is not None: - struct_fields["END_SECOND"] = end_second - if interval_seconds is not None: - struct_fields["INTERVAL_SECONDS"] = interval_seconds - if trial_id is not None: - struct_fields["TRIAL_ID"] = trial_id - - # Construct the TVF query - query = f""" - SELECT * - FROM AI.GENERATE_EMBEDDING( - MODEL `{model_name}`, - ({table_sql}), - {sg_sql.to_sql(sg_sql.literal(struct_fields))} - ) - """ - - if session is None: - return bpd.read_gbq_query(query) - else: - return session.read_gbq_query(query) - - -@log_adapter.method_logger(custom_base_name="bigquery_ai") -def generate_text( - model: Union[ml_base.BaseEstimator, str, pd.Series], - data: Union[dataframe.DataFrame, series.Series, pd.DataFrame, pd.Series], - *, - temperature: Optional[float] = None, - max_output_tokens: Optional[int] = None, - top_k: Optional[int] = None, - top_p: Optional[float] = None, - stop_sequences: Optional[List[str]] = None, - ground_with_google_search: Optional[bool] = None, - request_type: Optional[str] = None, -) -> dataframe.DataFrame: - """ - Generates text using a BigQuery ML model. - - See the `BigQuery ML GENERATE_TEXT function syntax - `_ - for additional reference. - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> import bigframes.bigquery as bbq - >>> df = bpd.DataFrame({"prompt": ["write a poem about apples"]}) - >>> bbq.ai.generate_text( # doctest: +SKIP - ... "project.dataset.model_name", - ... df - ... ) - - Args: - model (ml_base.BaseEstimator or str): - The model to use for text generation. - data (bigframes.pandas.DataFrame or bigframes.pandas.Series): - The data to generate text for. If a Series is provided, it is - treated as the 'prompt' column. If a DataFrame is provided, it - must contain a 'prompt' column, or you must rename the column you - wish to generate text to 'prompt'. - temperature (float, optional): - A FLOAT64 value that is used for sampling promiscuity. The value - must be in the range ``[0.0, 1.0]``. A lower temperature works well - for prompts that expect a more deterministic and less open-ended - or creative response, while a higher temperature can lead to more - diverse or creative results. A temperature of ``0`` is - deterministic, meaning that the highest probability response is - always selected. - max_output_tokens (int, optional): - An INT64 value that sets the maximum number of tokens in the - generated text. - top_k (int, optional): - An INT64 value that changes how the model selects tokens for - output. A ``top_k`` of ``1`` means the next selected token is the - most probable among all tokens in the model's vocabulary. A - ``top_k`` of ``3`` means that the next token is selected from - among the three most probable tokens by using temperature. The - default value is ``40``. - top_p (float, optional): - A FLOAT64 value that changes how the model selects tokens for - output. Tokens are selected from most probable to least probable - until the sum of their probabilities equals the ``top_p`` value. - For example, if tokens A, B, and C have a probability of 0.3, 0.2, - and 0.1 and the ``top_p`` value is ``0.5``, then the model will - select either A or B as the next token by using temperature. The - default value is ``0.95``. - stop_sequences (List[str], optional): - An ARRAY value that contains the stop sequences for the model. - ground_with_google_search (bool, optional): - A BOOL value that determines whether to ground the model with Google Search. - request_type (str, optional): - A STRING value that contains the request type for the model. - - Returns: - bigframes.pandas.DataFrame: - The generated text. - """ - data = _to_dataframe(data, series_rename="prompt") - model_name, session = bq_utils.get_model_name_and_session(model, data) - table_sql = bq_utils.to_sql(data) - - struct_fields: Dict[ - str, - Union[str, int, float, bool, Mapping[str, str], List[str], Mapping[str, Any]], - ] = {} - if temperature is not None: - struct_fields["TEMPERATURE"] = temperature - if max_output_tokens is not None: - struct_fields["MAX_OUTPUT_TOKENS"] = max_output_tokens - if top_k is not None: - struct_fields["TOP_K"] = top_k - if top_p is not None: - struct_fields["TOP_P"] = top_p - if stop_sequences is not None: - struct_fields["STEP_SEQUENCES"] = stop_sequences - if ground_with_google_search is not None: - struct_fields["GROUND_WITH_GOOGLE_SEARCH"] = ground_with_google_search - if request_type is not None: - struct_fields["REQUEST_TYPE"] = request_type - - query = f""" - SELECT * - FROM AI.GENERATE_TEXT( - MODEL `{model_name}`, - ({table_sql}), - {sg_sql.to_sql(sg_sql.literal(struct_fields))} - ) - """ - - if session is None: - return bpd.read_gbq_query(query) - else: - return session.read_gbq_query(query) - - -@log_adapter.method_logger(custom_base_name="bigquery_ai") -def generate_table( - model: Union[ml_base.BaseEstimator, str, pd.Series], - data: Union[dataframe.DataFrame, series.Series, pd.DataFrame, pd.Series], - *, - output_schema: Union[str, Mapping[str, str]], - temperature: Optional[float] = None, - top_p: Optional[float] = None, - max_output_tokens: Optional[int] = None, - stop_sequences: Optional[List[str]] = None, - request_type: Optional[str] = None, -) -> dataframe.DataFrame: - """ - Generates a table using a BigQuery ML model. - - See the `AI.GENERATE_TABLE function syntax - `_ - for additional reference. - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> import bigframes.bigquery as bbq - >>> # The user is responsible for constructing a DataFrame that contains - >>> # the necessary columns for the model's prompt. For example, a - >>> # DataFrame with a 'prompt' column for text classification. - >>> df = bpd.DataFrame({'prompt': ["some text to classify"]}) - >>> result = bbq.ai.generate_table( # doctest: +SKIP - ... "project.dataset.model_name", - ... data=df, - ... output_schema="category STRING" - ... ) - - Args: - model (ml_base.BaseEstimator or str): - The model to use for table generation. - data (bigframes.pandas.DataFrame or bigframes.pandas.Series): - The data to generate table for. If a Series is provided, it is - treated as the 'prompt' column. If a DataFrame is provided, it - must contain a 'prompt' column, or you must rename the column you - wish to generate table to 'prompt'. - output_schema (str | Mapping[str, str]): - A string defining the output schema (e.g., "col1 STRING, col2 INT64"), - or a mapping value that specifies the schema of the output, in the form {field_name: data_type}. - Supported data types include ``STRING``, ``INT64``, ``FLOAT64``, ``BOOL``, ``ARRAY``, and ``STRUCT``. - temperature (float, optional): - A FLOAT64 value that is used for sampling promiscuity. The value - must be in the range ``[0.0, 1.0]``. - top_p (float, optional): - A FLOAT64 value that changes how the model selects tokens for - output. - max_output_tokens (int, optional): - An INT64 value that sets the maximum number of tokens in the - generated table. - stop_sequences (List[str], optional): - An ARRAY value that contains the stop sequences for the model. - request_type (str, optional): - A STRING value that contains the request type for the model. - - Returns: - bigframes.pandas.DataFrame: - The generated table. - """ - data = _to_dataframe(data, series_rename="prompt") - model_name, session = bq_utils.get_model_name_and_session(model, data) - table_sql = bq_utils.to_sql(data) - - if isinstance(output_schema, Mapping): - output_schema_str = ", ".join( - [f"{name} {sql_type}" for name, sql_type in output_schema.items()] - ) - # Validate user input - output_schemas.parse_sql_fields(output_schema_str) - else: - output_schema_str = output_schema - - struct_fields_bq: Dict[str, Any] = {"output_schema": output_schema_str} - if temperature is not None: - struct_fields_bq["temperature"] = temperature - if top_p is not None: - struct_fields_bq["top_p"] = top_p - if max_output_tokens is not None: - struct_fields_bq["max_output_tokens"] = max_output_tokens - if stop_sequences is not None: - struct_fields_bq["stop_sequences"] = stop_sequences - if request_type is not None: - struct_fields_bq["request_type"] = request_type - - struct_sql = sg_sql.to_sql(sg_sql.literal(struct_fields_bq)) - query = f""" - SELECT * - FROM AI.GENERATE_TABLE( - MODEL `{model_name}`, - ({table_sql}), - {struct_sql} - ) - """ - - if session is None: - return bpd.read_gbq_query(query) - else: - return session.read_gbq_query(query) - - -@log_adapter.method_logger(custom_base_name="bigquery_ai") -def embed( - content: str | series.Series | pd.Series, - *, - endpoint: str | None = None, - model: str | None = None, - task_type: ( - Literal[ - "retrieval_query", - "retrieval_document", - "semantic_similarity", - "classification", - "clustering", - "question_answering", - "fact_verification", - "code_retrieval_query", - ] - | None - ) = None, - title: str | None = None, - model_params: Mapping[Any, Any] | None = None, - connection_id: str | None = None, -) -> series.Series: - """ - Creates embeddings from text or image data in BigQuery. - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> import bigframes.bigquery as bbq - >>> bbq.ai.embed("dog", endpoint="text-embedding-005") # doctest: +ELLIPSIS - 0 {'result': array([ 1.78243860e-03, -1.10658340... - dtype: struct, status: string>[pyarrow] - - >>> s = bpd.Series(['dog']) - >>> bbq.ai.embed(s, endpoint='text-embedding-005') # doctest: +ELLIPSIS - 0 {'result': array([ 1.78243860e-03, -1.10658340... - dtype: struct, status: string>[pyarrow] - - Args: - content (str | Series): - A string literal or a Series (either BigFrames series or pandas Series) that provides the text or image to embed. - endpoint (str, optional): - A string value that specifies a supported Vertex AI embedding model endpoint to use. - The endpoint value that you specify must include the model version, for example, - ``"text-embedding-005"``. If you specify this parameter, you can't specify the - ``model`` parameter. - model (str, optional): - A string value that specifies a built-in embedding model. The only supported value is - ``"embeddinggemma-300m"``. If you specify this parameter, you can't specify the ``endpoint``, - ``title``, ``model_params``, or ``connection_id`` parameters. - task_type (str, optional): - A string literal that specifies the intended downstream application to help the model - produce better quality embeddings. Accepts ``"retrieval_query"``, ``"retrieval_document"``, - ``"semantic_similarity"``, ``"classification"``, ``"clustering"``, ``"question_answering"``, - ``"fact_verification"``, ``"code_retrieval_query"``. - title (str, optional): - A string value that specifies the document title, which the model uses to improve - embedding quality. You can only use this parameter if you specify ``"retrieval_document"`` - for the ``task_type`` value. - model_params (Mapping[Any, Any], optional): - A JSON literal that provides additional parameters to the model. For example, - ``{"outputDimensionality": 768}`` lets you specify the number of dimensions to use when - generating embeddings. - connection_id (str, optional): - A STRING value specifying the connection to use to communicate with the model, in the - format ``PROJECT_ID.LOCATION.CONNECTION_ID``. For example, ``myproject.us.myconnection``. - If not provided, the query uses your end-user credential. - - Returns: - bigframes.series.Series: A new struct Series with the result data. The struct contains these fields: - * "result": an ARRAY value containing the generated embeddings. - * "status": a STRING value that contains the API response status for the corresponding row. This value is empty if the operation was successful. - """ - - operator = ai_ops.AIEmbed( - endpoint=endpoint, - model=model, - task_type=_upper_optional(task_type), - title=title, - model_params=json.dumps(model_params) if model_params else None, - connection_id=connection_id, - ) - - if isinstance(content, str): - return series.Series([content])._apply_unary_op(operator) - elif isinstance(content, pd.Series): - return series.Series(content)._apply_unary_op(operator) - elif isinstance(content, series.Series): - return content._apply_unary_op(operator) - else: - raise ValueError(f"Unsupported 'content' parameter type: {type(content)}") - - -@log_adapter.method_logger(custom_base_name="bigquery_ai") -def if_( - prompt: PROMPT_TYPE, - *, - connection_id: str | None = None, - endpoint: str | None = None, - optimization_mode: Literal["minimize_cost", "maximize_quality"] | None = None, - max_error_ratio: float | None = None, -) -> series.Series: - """ - Evaluates the prompt to True or False. Compared to ``ai.generate_bool()``, this function - provides optimization such that not all rows are evaluated with the LLM. - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> import bigframes.bigquery as bbq - >>> us_state = bpd.Series(["Massachusetts", "Illinois", "Hawaii"]) - >>> bbq.ai.if_((us_state, " has a city called Springfield")) - 0 True - 1 True - 2 False - dtype: boolean - - >>> us_state[bbq.ai.if_((us_state, " has a city called Springfield"))] - 0 Massachusetts - 1 Illinois - dtype: string - - Args: - prompt (str | Series | List[str|Series] | Tuple[str|Series, ...]): - A mixture of Series and string literals that specifies the prompt to send to the model. The Series can be BigFrames Series - or pandas Series. - connection_id (str, optional): - Specifies the connection to use to communicate with the model. For example, ``myproject.us.myconnection``. - If not provided, the query uses your end-user credential. - endpoint (str, optional): - Specifies the Vertex AI endpoint to use for the model. For example ``"gemini-2.5-flash"``. You can specify any - generally available or preview Gemini model. If you specify the model name, BigQuery ML automatically identifies and - uses the full endpoint of the model. If you don't specify an ENDPOINT value, BigQuery ML dynamically chooses a model based on your query to have the - best cost to quality tradeoff for the task. - optimization_mode (Literal["minimize_cost", "maximize_quality"]): - Specifies the optimization strategy to use. Supported values are: - * "minimize_cost" (default): uses a local, distilled model to process the majority of rows, reducing latency and cost. - * "maximize_quality": always uses the remote LLM for inference. - max_error_ratio (float): - A float value between 0.0 and 1.0 that contains the maximum acceptable ratio of row-level inference failures to - rows processed on this function. If this value is exceeded, then the query fails. The default value is 1.0. - This argument isn't supported when ``optimization_mode`` is set to "minimize_cost". - - Returns: - bigframes.series.Series: A new series of bools. - """ - - prompt_context, series_list = _separate_context_and_series(prompt) - assert len(series_list) > 0 - - operator = ai_ops.AIIf( - prompt_context=tuple(prompt_context), - connection_id=connection_id, - endpoint=endpoint, - optimization_mode=_upper_optional(optimization_mode), - max_error_ratio=max_error_ratio, - ) - - return series_list[0]._apply_nary_op(operator, series_list[1:]) - - -@log_adapter.method_logger(custom_base_name="bigquery_ai") -def classify( - input: PROMPT_TYPE, - categories: tuple[str, ...] | list[str], - *, - examples: list[tuple[str, str]] - | list[tuple[str, list[str] | tuple[str, ...]]] - | None = None, - connection_id: str | None = None, - endpoint: str | None = None, - output_mode: Literal["single", "multi"] | None = None, - optimization_mode: Literal["minimize_cost", "maximize_quality"] | None = None, - max_error_ratio: float | None = None, -) -> series.Series: - """ - Classifies a given input into one of the specified categories. It will always return one of the provided categories best fit the prompt input. - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> import bigframes.bigquery as bbq - >>> df = bpd.DataFrame({'creature': ['Cat', 'Salmon']}) - >>> df['type'] = bbq.ai.classify(df['creature'], ['Mammal', 'Fish']) - >>> df - creature type - 0 Cat Mammal - 1 Salmon Fish - - [2 rows x 2 columns] - - Args: - input (str | Series | List[str|Series] | Tuple[str|Series, ...]): - A mixture of Series and string literals that specifies the input to send to the model. The Series can be BigFrames Series - or pandas Series. - categories (tuple[str, ...] | list[str]): - Categories to classify the input into. - examples (list[tuple[str, str]] | list[tuple[str, list[str] | tuple[str, ...]]], optional): - An array that contains representative examples of input strings and the output category - that you expect. If ``output_mode`` is ``multi``, each example output must be a list or tuple of strings. - You can provide examples to help the model understand your intended threshold for a condition with nuanced - or subjective logic. We recommend providing at most 5 examples. - connection_id (str, optional): - Specifies the connection to use to communicate with the model. For example, ``myproject.us.myconnection``. - If not provided, the query uses your end-user credential. - endpoint (str, optional): - A STRING value that specifies the Vertex AI endpoint to use for the model. You can specify any - generally available or preview Gemini model. If you specify the model name, BigQuery ML automatically - identifies and uses the full endpoint of the model. - output_mode (Literal["single", "multi"], optional): - A STRING value that indicates whether a single input can be classified into multiple categories. - Supported values are ``single`` and ``multi``. - optimization_mode (Literal["minimize_cost", "maximize_quality"], optional): - A STRING value that specifies the optimization strategy to use. Supported values are ``minimize_cost`` - and ``maximize_quality``. - max_error_ratio (float, optional): - A value between ``0.0`` and ``1.0`` that contains the maximum acceptable ratio of row-level - inference failures to rows processed on this function. The default value is 1.0. - This argument isn't supported when ``optimization_mode`` is set to ``minimize_cost``. - - Returns: - bigframes.series.Series: A new series of strings (or a series of arrays of strings if ``output_mode`` is specified). - """ - - prompt_context, series_list = _separate_context_and_series(input) - assert len(series_list) > 0 - - if examples is not None: - example_tuples: Any = tuple( - (ex[0], tuple(ex[1]) if isinstance(ex[1], (list, tuple)) else ex[1]) - for ex in examples - ) - else: - example_tuples = None - - operator = ai_ops.AIClassify( - prompt_context=tuple(prompt_context), - categories=tuple(categories), - examples=example_tuples, - connection_id=connection_id, - endpoint=endpoint, - output_mode=output_mode, - optimization_mode=_upper_optional(optimization_mode), - max_error_ratio=max_error_ratio, - ) - - return series_list[0]._apply_nary_op(operator, series_list[1:]) - - -@log_adapter.method_logger(custom_base_name="bigquery_ai") -def score( - prompt: PROMPT_TYPE, - *, - connection_id: str | None = None, - endpoint: str | None = None, - max_error_ratio: float | None = None, -) -> series.Series: - """ - Computes a score based on rubrics described in natural language. It will return a double value. - There is no fixed range for the score returned. To get high quality results, provide a scoring - rubric with examples in the prompt. - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> import bigframes.bigquery as bbq - >>> animal = bpd.Series(["Tiger", "Rabbit", "Blue Whale"]) - >>> bbq.ai.score(("Rank the relative weights of ", animal, " on the scale from 1 to 3")) - 0 2.0 - 1 1.0 - 2 3.0 - dtype: Float64 - - Args: - prompt (str | Series | List[str|Series] | Tuple[str|Series, ...]): - A mixture of Series and string literals that specifies the prompt to send to the model. The Series can be BigFrames Series - or pandas Series. - connection_id (str, optional): - Specifies the connection to use to communicate with the model. For example, ``myproject.us.myconnection``. - If not provided, the query uses your end-user credential. - endpoint (str, optional): - Specifies the Vertex AI endpoint to use for the model. For example ``"gemini-2.5-flash"``. You can specify any - generally available or preview Gemini model. If you specify the model name, BigQuery ML automatically identifies and - uses the full endpoint of the model. If you don't specify an endpoint value, BigQuery ML dynamically chooses a model - based on your query to have the best cost to quality tradeoff for the task. - max_error_ratio (float, optional): - A value between ``0.0`` and ``1.0`` that contains the maximum acceptable ratio of row-level inference failures to - rows processed on this function. If this value is exceeded, then the query fails. - - Returns: - bigframes.series.Series: A new series of double (float) values. - """ - - prompt_context, series_list = _separate_context_and_series(prompt) - assert len(series_list) > 0 - - operator = ai_ops.AIScore( - prompt_context=tuple(prompt_context), - connection_id=connection_id, - endpoint=endpoint, - max_error_ratio=max_error_ratio, - ) - - return series_list[0]._apply_nary_op(operator, series_list[1:]) - - -@log_adapter.method_logger(custom_base_name="bigquery_ai") -def similarity( - content1: str | series.Series | pd.Series, - content2: str | series.Series | pd.Series, - *, - endpoint: str | None = None, - model: str | None = None, - model_params: Mapping[Any, Any] | None = None, - connection_id: str | None = None, -) -> series.Series: - """ - Returns a FLOAT64 value that represents the cosine similarity between the two inputs. - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> import bigframes.bigquery as bbq - >>> df = bpd.DataFrame({'word': ['happy', 'sad']}) - >>> bbq.ai.similarity(df['word'], 'glad', endpoint='text-embedding-005') - 0 0.916601 - 1 0.660579 - Name: word, dtype: Float64 - - Args: - content1 (str | Series): - A string or series that provides the first value to compare. Both a BigFrames Series or a pandas Series are allowed. - content2 (str | Series): - A string or series that provides the second value to compare. Both a BigFrames Series or a pandas Series are allowed. - endpoint (str, optional): - Specifies the Vertex AI endpoint to use for the text embedding model. - If you specify the model name, such as ``'text-embedding-005'``, rather than a URL, then BigQuery ML automatically identifies the model and uses the model's full endpoint. - model (str, optional): - Specifies a built-in text embedding model. The only supported value is the embeddinggemma-300m model. - If you specify this parameter, you can't specify the ``endpoint``, ``model_params``, or ``connection_id`` parameters. - model_params (Mapping[Any, Any], optional): - Provides additional parameters to the model. You can use any of the parameters object fields. - One of these fields, ``outputDimensionality``, lets you specify the number of dimensions to use when generating embeddings. - connection_id (str, optional): - Specifies the connection to use to communicate with the model. For example, ``myproject.us.myconnection``. - - Returns: - bigframes.series.Series: A new series of FLOAT64 values representing the cosine similarity. - """ - - operator = ai_ops.AISimilarity( - endpoint=endpoint, - model=model, - model_params=json.dumps(model_params) if model_params else None, - connection_id=connection_id, - ) - - # Find a unifying session for the subsequent operations. - bf_session = None - if isinstance(content1, series.Series): - bf_session = content1._session - elif isinstance(content2, series.Series): - bf_session = content2._session - - if isinstance(content1, str) and isinstance(content2, str): - content1 = series.Series([content1], session=bf_session) - return content1._apply_binary_op(content2, operator) - elif isinstance(content1, str): - # content2 must be a series - content2 = convert.to_bf_series( - content2, default_index=None, session=bf_session - ) - return content2._apply_binary_op(content1, operator) - else: - # content1 must be a series. - content1 = convert.to_bf_series( - content1, default_index=None, session=bf_session - ) - return content1._apply_binary_op(content2, operator) - - -@log_adapter.method_logger(custom_base_name="bigquery_ai") -def forecast( - df: dataframe.DataFrame | pd.DataFrame, - *, - data_col: str, - timestamp_col: str, - model: str = "TimesFM 2.0", - id_cols: Iterable[str] | None = None, - horizon: int = 10, - confidence_level: float = 0.95, - output_historical_time_series: bool = False, - context_window: int | None = None, -) -> dataframe.DataFrame: - """ - Forecast time series at future horizon. Using Google Research's open source TimesFM(https://github.com/google-research/timesfm) model. - - **Examples:** - - Forecast using a pandas DataFrame: - - >>> import pandas as pd - >>> import bigframes.pandas as bpd - >>> df = pd.DataFrame({"value": [1, 2, 3], "time": pd.to_datetime(["2020-01-01", "2020-01-02", "2020-01-03"])}) - >>> bpd.options.display.progress_bar = None - >>> forecasted_pandas_df = df.bigquery.ai.forecast(data_col="value", timestamp_col="time", horizon=2) - >>> type(forecasted_pandas_df) # doctest: +ELLIPSIS - - - Forecast using a BigFrames DataFrame: - - >>> bf_df = bpd.DataFrame({"value": [1, 2, 3], "time": pd.to_datetime(["2020-01-01", "2020-01-02", "2020-01-03"])}) - >>> forecasted_bf_df = bf_df.bigquery.ai.forecast(data_col="value", timestamp_col="time", horizon=2) - >>> type(forecasted_bf_df) - - - Args: - df (DataFrame): - The dataframe that contains the data that you want to forecast. It could be either a BigFrames Dataframe or - a pandas DataFrame. If it's a pandas DataFrame, the global BigQuery session will be used to load the data. - data_col (str): - A str value that specifies the name of the data column. The data column contains the data to forecast. - The data column must use one of the following data types: INT64, NUMERIC and FLOAT64 - timestamp_col (str): - A str value that specified the name of the time points column. - The time points column provides the time points used to generate the forecast. - The time points column must use one of the following data types: TIMESTAMP, DATE and DATETIME - model (str, default "TimesFM 2.0"): - A str value that specifies the name of the model. TimesFM 2.0 is the only supported value, and is the default value. - id_cols (Iterable[str], optional): - An iterable of str value that specifies the names of one or more ID columns. Each ID identifies a unique time series to forecast. - Specify one or more values for this argument in order to forecast multiple time series using a single query. - The columns that you specify must use one of the following data types: STRING, INT64, ARRAY and ARRAY - horizon (int, default 10): - An int value that specifies the number of time points to forecast. The default value is 10. The valid input range is [1, 10,000]. - confidence_level (float, default 0.95): - A FLOAT64 value that specifies the percentage of the future values that fall in the prediction interval. - The default value is 0.95. The valid input range is [0, 1). - output_historical_time_series (bool, default False): - A BOOL value that determines whether the input data is returned - along with the forecasted data. Set this argument to TRUE to return - input data. The default value is FALSE. - - Returning the input data along with the forecasted data lets you - compare the historical value of the data column with the forecasted - value of the data column, or chart the change in the data column - values over time. - context_window (int, optional): - An int value that specifies the context window length used by BigQuery ML's built-in TimesFM model. - The context window length determines how many of the most recent data points from the input time series are use by the model. - If you don't specify a value, the AI.FORECAST function automatically chooses the smallest possible context window length to use - that is still large enough to cover the number of time series data points in your input data. - - Returns: - DataFrame: - The forecast dataframe matches that of the BigQuery AI.FORECAST function. - See: https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-ai-forecast - - Raises: - ValueError: when any column ID does not exist in the dataframe. - """ - - if isinstance(df, pd.DataFrame): - # Load the pandas DataFrame with global session - df = bpd.read_pandas(df) - - columns = [timestamp_col, data_col] - if id_cols: - columns += id_cols - for column in columns: - if column not in df.columns: - raise ValueError(f"Column `{column}` not found") - - options: dict[str, Union[int, float, str, Iterable[str]]] = { - "data_col": data_col, - "timestamp_col": timestamp_col, - "model": model, - "horizon": horizon, - "output_historical_time_series": output_historical_time_series, - "confidence_level": confidence_level, - } - if id_cols: - options["id_cols"] = id_cols - if context_window: - options["context_window"] = context_window - - return ml_core.BaseBqml(df._session).ai_forecast(input_data=df, options=options) - - -def _separate_context_and_series( - prompt: PROMPT_TYPE, -) -> Tuple[List[str | None], List[series.Series]]: - """ - Returns the two values. The first value is the prompt with all series replaced by None. The second value is all the series - in the prompt. The original item order is kept. - For example: - Input: ("str1", series1, "str2", "str3", series2) - Output: ["str1", None, "str2", "str3", None], [series1, series2] - """ - if not isinstance(prompt, (str, list, tuple, series.Series, pd.Series)): - raise ValueError(f"Unsupported prompt type: {type(prompt)}") - - if isinstance(prompt, str): - return [None], [series.Series([prompt])] - - if isinstance(prompt, pd.Series): - return [None], [bpd.read_pandas(prompt)] - - if isinstance(prompt, series.Series): - if prompt.dtype == dtypes.OBJ_REF_DTYPE: - # Multi-model support - return [None], [bq_obj.get_access_url(prompt, mode="R")] - return [None], [prompt] - - prompt_context: List[str | None] = [] - series_list: List[series.Series | pd.Series] = [] - - session = None - for item in prompt: - if isinstance(item, str): - prompt_context.append(item) - - elif isinstance(item, (series.Series, pd.Series)): - prompt_context.append(None) - - if isinstance(item, series.Series) and session is None: - # Use the first available BF session if there's any. - session = item._session - series_list.append(item) - - else: - raise TypeError(f"Unsupported type in prompt: {type(item)}") - - if not series_list: - raise ValueError("Please provide at least one Series in the prompt") - - converted_list = [_convert_series(s, session) for s in series_list] - - return prompt_context, converted_list - - -def _convert_series( - s: series.Series | pd.Series, session: session.Session | None -) -> series.Series: - result = convert.to_bf_series(s, default_index=None, session=session) - - if result.dtype == dtypes.OBJ_REF_DTYPE: - # Support multimodal - return bq_obj.get_access_url(result, mode="R") - return result - - -def _to_dataframe( - data: Union[dataframe.DataFrame, series.Series, pd.DataFrame, pd.Series], - series_rename: str, -) -> dataframe.DataFrame: - if isinstance(data, (pd.DataFrame, pd.Series)): - data = bpd.read_pandas(data) - - if isinstance(data, series.Series): - data = data.copy() - data.name = series_rename - return data.to_frame() - elif isinstance(data, dataframe.DataFrame): - return data - - raise ValueError(f"Unsupported data type: {type(data)}") - - -def _upper_optional(value: str | None) -> str | None: - if value is None: - return None - return value.upper() diff --git a/bigframes/bigquery/_operations/approx_agg.py b/bigframes/bigquery/_operations/approx_agg.py deleted file mode 100644 index 73b6fdbb73b..00000000000 --- a/bigframes/bigquery/_operations/approx_agg.py +++ /dev/null @@ -1,58 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from __future__ import annotations - -import bigframes.operations.aggregations as agg_ops -import bigframes.series as series - -""" -Approximate functions defined from -https://cloud.google.com/bigquery/docs/reference/standard-sql/approximate_aggregate_functions -""" - - -def approx_top_count( - series: series.Series, - number: int, -) -> series.Series: - """Returns the approximate top elements of `expression` as an array of STRUCTs. - The number parameter specifies the number of elements returned. - - Each `STRUCT` contains two fields. The first field (named `value`) contains an input - value. The second field (named `count`) contains an `INT64` specifying the number - of times the value was returned. - - Returns `NULL` if there are zero input rows. - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> import bigframes.bigquery as bbq - >>> s = bpd.Series(["apple", "apple", "pear", "pear", "pear", "banana"]) - >>> bbq.approx_top_count(s, number=2) - [{'value': 'pear', 'count': 3}, {'value': 'apple', 'count': 2}] - - Args: - series (bigframes.series.Series): - The Series with any data type that the `GROUP BY` clause supports. - number (int): - An integer specifying the number of times the value was returned. - - Returns: - bigframes.series.Series: A new Series with the result data. - """ - if number < 1: - raise ValueError("The number of approx_top_count must be at least 1") - return series._apply_aggregation(agg_ops.ApproxTopCountOp(number=number)) diff --git a/bigframes/bigquery/_operations/array.py b/bigframes/bigquery/_operations/array.py deleted file mode 100644 index 0a3c5d66217..00000000000 --- a/bigframes/bigquery/_operations/array.py +++ /dev/null @@ -1,82 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -""" -Array functions defined from -https://cloud.google.com/bigquery/docs/reference/standard-sql/array_functions -""" - -from __future__ import annotations - -import typing - -import bigframes_vendored.constants as constants - -import bigframes.core.groupby as groupby -import bigframes.operations.aggregations as agg_ops -import bigframes.series as series - -if typing.TYPE_CHECKING: - import bigframes.dataframe as dataframe - - -def array_agg( - obj: groupby.SeriesGroupBy | groupby.DataFrameGroupBy, -) -> series.Series | dataframe.DataFrame: - """Group data and create arrays from selected columns, omitting NULLs to avoid - BigQuery errors (NULLs not allowed in arrays). - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> import bigframes.bigquery as bbq - - For a SeriesGroupBy object: - - >>> lst = ['a', 'a', 'b', 'b', 'a'] - >>> s = bpd.Series([1, 2, 3, 4, np.nan], index=lst) - >>> bbq.array_agg(s.groupby(level=0)) - a [1. 2.] - b [3. 4.] - dtype: list[pyarrow] - - For a DataFrameGroupBy object: - - >>> l = [[1, 2, 3], [1, None, 4], [2, 1, 3], [1, 2, 2]] - >>> df = bpd.DataFrame(l, columns=["a", "b", "c"]) - >>> bbq.array_agg(df.groupby(by=["b"])) - a c - b - 1.0 [2] [3] - 2.0 [1 1] [3 2] - - [2 rows x 2 columns] - - Args: - obj (groupby.SeriesGroupBy | groupby.DataFrameGroupBy): - A GroupBy object to be applied the function. - - Returns: - bigframes.series.Series | bigframes.dataframe.DataFrame: A Series or - DataFrame containing aggregated array columns, and indexed by the - original group columns. - """ - if isinstance(obj, groupby.SeriesGroupBy): - return obj._aggregate(agg_ops.ArrayAggOp()) - elif isinstance(obj, groupby.DataFrameGroupBy): - return obj._aggregate_all(agg_ops.ArrayAggOp(), numeric_only=False) - else: - raise ValueError( - f"Unsupported type {type(obj)} to apply `array_agg` function. {constants.FEEDBACK_LINK}" - ) diff --git a/bigframes/bigquery/_operations/datetime.py b/bigframes/bigquery/_operations/datetime.py deleted file mode 100644 index 99467beb066..00000000000 --- a/bigframes/bigquery/_operations/datetime.py +++ /dev/null @@ -1,91 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from bigframes import operations as ops -from bigframes import series - - -def unix_seconds(input: series.Series) -> series.Series: - """Converts a timestmap series to unix epoch seconds - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> import bigframes.bigquery as bbq - - >>> s = bpd.Series([pd.Timestamp("1970-01-02", tz="UTC"), pd.Timestamp("1970-01-03", tz="UTC")]) - >>> bbq.unix_seconds(s) - 0 86400 - 1 172800 - dtype: Int64 - - Args: - input (bigframes.pandas.Series): - A timestamp series. - - Returns: - bigframes.pandas.Series: A new series of unix epoch in seconds. - - """ - return input._apply_unary_op(ops.UnixSeconds()) - - -def unix_millis(input: series.Series) -> series.Series: - """Converts a timestmap series to unix epoch milliseconds - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> import bigframes.bigquery as bbq - - >>> s = bpd.Series([pd.Timestamp("1970-01-02", tz="UTC"), pd.Timestamp("1970-01-03", tz="UTC")]) - >>> bbq.unix_millis(s) - 0 86400000 - 1 172800000 - dtype: Int64 - - Args: - input (bigframes.pandas.Series): - A timestamp series. - - Returns: - bigframes.pandas.Series: A new series of unix epoch in milliseconds. - - """ - return input._apply_unary_op(ops.UnixMillis()) - - -def unix_micros(input: series.Series) -> series.Series: - """Converts a timestmap series to unix epoch microseconds - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> import bigframes.bigquery as bbq - - >>> s = bpd.Series([pd.Timestamp("1970-01-02", tz="UTC"), pd.Timestamp("1970-01-03", tz="UTC")]) - >>> bbq.unix_micros(s) - 0 86400000000 - 1 172800000000 - dtype: Int64 - - Args: - input (bigframes.pandas.Series): - A timestamp series. - - Returns: - bigframes.pandas.Series: A new series of unix epoch in microseconds. - - """ - return input._apply_unary_op(ops.UnixMicros()) diff --git a/bigframes/bigquery/_operations/geo.py b/bigframes/bigquery/_operations/geo.py deleted file mode 100644 index e9ea711c969..00000000000 --- a/bigframes/bigquery/_operations/geo.py +++ /dev/null @@ -1,756 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from __future__ import annotations - -import json -from typing import Mapping, Optional, Union - -import shapely # type: ignore - -import bigframes.dataframe -import bigframes.geopandas -import bigframes.series -from bigframes import operations as ops - -""" -Search functions defined from -https://cloud.google.com/bigquery/docs/reference/standard-sql/geography_functions -""" - - -def st_area( - series: Union[bigframes.series.Series, bigframes.geopandas.GeoSeries], -) -> bigframes.series.Series: - """ - Returns the area in square meters covered by the polygons in the input - `GEOGRAPHY`. - - If geography_expression is a point or a line, returns zero. If - geography_expression is a collection, returns the area of the polygons - in the collection; if the collection doesn't contain polygons, returns zero. - - - .. note:: - BigQuery's Geography functions, like `st_area`, interpret the geometry - data type as a point set on the Earth's surface. A point set is a set - of points, lines, and polygons on the WGS84 reference spheroid, with - geodesic edges. See: https://cloud.google.com/bigquery/docs/geospatial-data - - - **Examples:** - - >>> import bigframes.geopandas - >>> import bigframes.pandas as bpd - >>> import bigframes.bigquery as bbq - >>> from shapely.geometry import Polygon, LineString, Point - - >>> series = bigframes.geopandas.GeoSeries( - ... [ - ... Polygon([(0.0, 0.0), (0.1, 0.1), (0.0, 0.1)]), - ... Polygon([(0.10, 0.4), (0.9, 0.5), (0.10, 0.5)]), - ... Polygon([(0.1, 0.1), (0.2, 0.1), (0.2, 0.2)]), - ... LineString([(0, 0), (1, 1), (0, 1)]), - ... Point(0, 1), - ... ] - ... ) - >>> series - 0 POLYGON ((0 0, 0.1 0.1, 0 0.1, 0 0)) - 1 POLYGON ((0.1 0.4, 0.9 0.5, 0.1 0.5, 0.1 0.4)) - 2 POLYGON ((0.1 0.1, 0.2 0.1, 0.2 0.2, 0.1 0.1)) - 3 LINESTRING (0 0, 1 1, 0 1) - 4 POINT (0 1) - dtype: geometry - - >>> bbq.st_area(series) - 0 61821689.855985 - 1 494563347.88721 - 2 61821689.855841 - 3 0.0 - 4 0.0 - dtype: Float64 - - Use `round()` to round the outputed areas to the neares ten millions - - >>> bbq.st_area(series).round(-7) - 0 60000000.0 - 1 490000000.0 - 2 60000000.0 - 3 0.0 - 4 0.0 - dtype: Float64 - - Args: - series (bigframes.pandas.Series | bigframes.geopandas.GeoSeries): - A series containing geography objects. - - Returns: - bigframes.pandas.Series: - Series of float representing the areas. - """ - series = series._apply_nary_op(ops.googlesql.ST_AREA, []) - series.name = None - return series - - -def st_buffer( - series: Union[bigframes.series.Series, bigframes.geopandas.GeoSeries], - buffer_radius: float, - num_seg_quarter_circle: float = 8.0, - use_spheroid: bool = False, -) -> bigframes.series.Series: - """ - Computes a `GEOGRAPHY` that represents all points whose distance from the - input `GEOGRAPHY` is less than or equal to `distance` meters. - - .. note:: - BigQuery's Geography functions, like `st_buffer`, interpret the geometry - data type as a point set on the Earth's surface. A point set is a set - of points, lines, and polygons on the WGS84 reference spheroid, with - geodesic edges. See: https://cloud.google.com/bigquery/docs/geospatial-data - - **Examples:** - - >>> import bigframes.geopandas - >>> import bigframes.pandas as bpd - >>> import bigframes.bigquery as bbq - >>> from shapely.geometry import Point - - >>> series = bigframes.geopandas.GeoSeries( - ... [ - ... Point(0, 0), - ... Point(1, 1), - ... ] - ... ) - >>> series - 0 POINT (0 0) - 1 POINT (1 1) - dtype: geometry - - >>> buffer = bbq.st_buffer(series, 100) - >>> bbq.st_area(buffer) > 0 - 0 True - 1 True - dtype: boolean - - Args: - series (bigframes.pandas.Series | bigframes.geopandas.GeoSeries): - A series containing geography objects. - buffer_radius (float): - The distance in meters. - num_seg_quarter_circle (float, optional): - Specifies the number of segments that are used to approximate a - quarter circle. The default value is 8.0. - use_spheroid (bool, optional): - Determines how this function measures distance. If use_spheroid is - FALSE, the function measures distance on the surface of a perfect - sphere. The use_spheroid parameter currently only supports the - value FALSE. The default value of use_spheroid is FALSE. - - Returns: - bigframes.pandas.Series: - A series of geography objects representing the buffered geometries. - """ - op = ops.GeoStBufferOp( - buffer_radius=buffer_radius, - num_seg_quarter_circle=num_seg_quarter_circle, - use_spheroid=use_spheroid, - ) - series = series._apply_unary_op(op) - series.name = None - return series - - -def st_centroid( - series: Union[bigframes.series.Series, bigframes.geopandas.GeoSeries], -) -> bigframes.series.Series: - """ - Computes the geometric centroid of a `GEOGRAPHY` type. - - For `POINT` and `MULTIPOINT` types, this is the arithmetic mean of the - input coordinates. For `LINESTRING` and `POLYGON` types, this is the - center of mass. For `GEOMETRYCOLLECTION` types, this is the center of - mass of the collection's elements. - - .. note:: - BigQuery's Geography functions, like `st_centroid`, interpret the geometry - data type as a point set on the Earth's surface. A point set is a set - of points, lines, and polygons on the WGS84 reference spheroid, with - geodesic edges. See: https://cloud.google.com/bigquery/docs/geospatial-data - - **Examples:** - - >>> import bigframes.geopandas - >>> import bigframes.pandas as bpd - >>> import bigframes.bigquery as bbq - >>> from shapely.geometry import Polygon, LineString, Point - - >>> series = bigframes.geopandas.GeoSeries( - ... [ - ... Polygon([(0.0, 0.0), (0.1, 0.1), (0.0, 0.1)]), - ... LineString([(0, 0), (1, 1), (0, 1)]), - ... Point(0, 1), - ... ] - ... ) - >>> series - 0 POLYGON ((0 0, 0.1 0.1, 0 0.1, 0 0)) - 1 LINESTRING (0 0, 1 1, 0 1) - 2 POINT (0 1) - dtype: geometry - - >>> bbq.st_centroid(series) - 0 POINT (0.03333 0.06667) - 1 POINT (0.49998 0.70712) - 2 POINT (0 1) - dtype: geometry - - Args: - series (bigframes.pandas.Series | bigframes.geopandas.GeoSeries): - A series containing geography objects. - - Returns: - bigframes.pandas.Series: - A series of geography objects representing the centroids. - """ - series = series._apply_nary_op(ops.googlesql.ST_CENTROID, []) - series.name = None - return series - - -def st_convexhull( - series: Union[bigframes.series.Series, bigframes.geopandas.GeoSeries], -) -> bigframes.series.Series: - """ - Computes the convex hull of a `GEOGRAPHY` type. - - The convex hull is the smallest convex set that contains all of the - points in the input `GEOGRAPHY`. - - .. note:: - BigQuery's Geography functions, like `st_convexhull`, interpret the geometry - data type as a point set on the Earth's surface. A point set is a set - of points, lines, and polygons on the WGS84 reference spheroid, with - geodesic edges. See: https://cloud.google.com/bigquery/docs/geospatial-data - - **Examples:** - - >>> import bigframes.geopandas - >>> import bigframes.pandas as bpd - >>> import bigframes.bigquery as bbq - >>> from shapely.geometry import Polygon, LineString, Point - - >>> series = bigframes.geopandas.GeoSeries( - ... [ - ... Polygon([(0.0, 0.0), (0.1, 0.1), (0.0, 0.1)]), - ... LineString([(0, 0), (1, 1), (0, 1)]), - ... Point(0, 1), - ... ] - ... ) - >>> series - 0 POLYGON ((0 0, 0.1 0.1, 0 0.1, 0 0)) - 1 LINESTRING (0 0, 1 1, 0 1) - 2 POINT (0 1) - dtype: geometry - - >>> bbq.st_convexhull(series) - 0 POLYGON ((0 0, 0.1 0.1, 0 0.1, 0 0)) - 1 POLYGON ((0 0, 1 1, 0 1, 0 0)) - 2 POINT (0 1) - dtype: geometry - - Args: - series (bigframes.pandas.Series | bigframes.geopandas.GeoSeries): - A series containing geography objects. - - Returns: - bigframes.pandas.Series: - A series of geography objects representing the convex hulls. - """ - series = series._apply_unary_op(ops.geo_st_convexhull_op) - series.name = None - return series - - -def st_difference( - series: Union[bigframes.series.Series, bigframes.geopandas.GeoSeries], - other: Union[ - bigframes.series.Series, - bigframes.geopandas.GeoSeries, - shapely.geometry.base.BaseGeometry, - ], -) -> bigframes.series.Series: - """ - Returns a `GEOGRAPHY` that represents the point set difference of - `geography_1` and `geography_2`. Therefore, the result consists of the part - of `geography_1` that doesn't intersect with `geography_2`. - - If `geometry_1` is completely contained in `geometry_2`, then `ST_DIFFERENCE` - returns an empty `GEOGRAPHY`. - - .. note:: - BigQuery's Geography functions, like `st_difference`, interpret the geometry - data type as a point set on the Earth's surface. A point set is a set - of points, lines, and polygons on the WGS84 reference spheroid, with - geodesic edges. See: https://cloud.google.com/bigquery/docs/geospatial-data - - **Examples:** - - >>> import bigframes as bpd - >>> import bigframes.bigquery as bbq - >>> import bigframes.geopandas - >>> from shapely.geometry import Polygon, LineString, Point - - We can check two GeoSeries against each other, row by row: - - >>> s1 = bigframes.geopandas.GeoSeries( - ... [ - ... Polygon([(0, 0), (2, 2), (0, 2)]), - ... Polygon([(0, 0), (2, 2), (0, 2)]), - ... LineString([(0, 0), (2, 2)]), - ... LineString([(2, 0), (0, 2)]), - ... Point(0, 1), - ... ], - ... ) - >>> s2 = bigframes.geopandas.GeoSeries( - ... [ - ... Polygon([(0, 0), (1, 1), (0, 1)]), - ... LineString([(1, 0), (1, 3)]), - ... LineString([(2, 0), (0, 2)]), - ... Point(1, 1), - ... Point(0, 1), - ... ], - ... index=range(1, 6), - ... ) - - >>> s1 - 0 POLYGON ((0 0, 2 2, 0 2, 0 0)) - 1 POLYGON ((0 0, 2 2, 0 2, 0 0)) - 2 LINESTRING (0 0, 2 2) - 3 LINESTRING (2 0, 0 2) - 4 POINT (0 1) - dtype: geometry - - >>> s2 - 1 POLYGON ((0 0, 1 1, 0 1, 0 0)) - 2 LINESTRING (1 0, 1 3) - 3 LINESTRING (2 0, 0 2) - 4 POINT (1 1) - 5 POINT (0 1) - dtype: geometry - - >>> bbq.st_difference(s1, s2) - 0 None - 1 POLYGON ((0.99954 1, 2 2, 0 2, 0 1, 0.99954 1)) - 2 LINESTRING (0 0, 1 1.00046, 2 2) - 3 GEOMETRYCOLLECTION EMPTY - 4 POINT (0 1) - 5 None - dtype: geometry - - Additionally, we can check difference of a GeoSeries against a single shapely geometry: - - >>> polygon = Polygon([(0, 0), (10, 0), (10, 10), (0, 0)]) - >>> bbq.st_difference(s1, polygon) - 0 POLYGON ((1.97082 2.00002, 0 2, 0 0, 1.97082 2... - 1 POLYGON ((1.97082 2.00002, 0 2, 0 0, 1.97082 2... - 2 GEOMETRYCOLLECTION EMPTY - 3 LINESTRING (0.99265 1.00781, 0 2) - 4 POINT (0 1) - dtype: geometry - - Args: - series (bigframes.pandas.Series | bigframes.geopandas.GeoSeries): - A series containing geography objects. - other (bigframes.pandas.Series | bigframes.geopandas.GeoSeries | shapely.Geometry): - The series or geometric object to subtract from the geography - objects in ``series``. - - Returns: - bigframes.series.Series: - A GeoSeries of the points in each aligned geometry that are not - in other. - """ - return series._apply_binary_op(other, ops.geo_st_difference_op) - - -def st_distance( - series: Union[bigframes.series.Series, bigframes.geopandas.GeoSeries], - other: Union[ - bigframes.series.Series, - bigframes.geopandas.GeoSeries, - shapely.geometry.base.BaseGeometry, - ], - *, - use_spheroid: bool = False, -) -> bigframes.series.Series: - """ - Returns the shortest distance in meters between two non-empty - ``GEOGRAPHY`` objects. - - **Examples:** - - >>> import bigframes as bpd - >>> import bigframes.bigquery as bbq - >>> import bigframes.geopandas - >>> from shapely.geometry import Polygon, LineString, Point - - We can check two GeoSeries against each other, row by row. - - >>> s1 = bigframes.geopandas.GeoSeries( - ... [ - ... Point(0, 0), - ... Point(0.00001, 0), - ... Point(0.00002, 0), - ... ], - ... ) - >>> s2 = bigframes.geopandas.GeoSeries( - ... [ - ... Point(0.00001, 0), - ... Point(0.00003, 0), - ... Point(0.00005, 0), - ... ], - ... ) - - >>> bbq.st_distance(s1, s2, use_spheroid=True) - 0 1.113195 - 1 2.22639 - 2 3.339585 - dtype: Float64 - - We can also calculate the distance of each geometry and a single shapely geometry: - - >>> bbq.st_distance(s2, Point(0.00001, 0)) - 0 0.0 - 1 2.223902 - 2 4.447804 - dtype: Float64 - - Args: - series (bigframes.pandas.Series | bigframes.geopandas.GeoSeries): - A series containing geography objects. - other (bigframes.pandas.Series | bigframes.geopandas.GeoSeries | shapely.Geometry): - The series or geometric object to calculate the distance in meters - to form the geography objects in ``series``. - use_spheroid (optional, default ``False``): - Determines how this function measures distance. If ``use_spheroid`` - is False, the function measures distance on the surface of a perfect - sphere. If ``use_spheroid`` is True, the function measures distance - on the surface of the `WGS84 spheroid - `_. The - default value of ``use_spheroid`` is False. - - Returns: - bigframes.pandas.Series: - The Series (elementwise) of the smallest distance between - each aligned geometry with other. - """ - return series._apply_binary_op( - other, ops.GeoStDistanceOp(use_spheroid=use_spheroid) - ) - - -def st_intersection( - series: Union[bigframes.series.Series, bigframes.geopandas.GeoSeries], - other: Union[ - bigframes.series.Series, - bigframes.geopandas.GeoSeries, - shapely.geometry.base.BaseGeometry, - ], -) -> bigframes.series.Series: - """ - Returns a `GEOGRAPHY` that represents the point set intersection of the two - input `GEOGRAPHYs`. Thus, every point in the intersection appears in both - `geography_1` and `geography_2`. - - .. note:: - BigQuery's Geography functions, like `st_intersection`, interpret the geometry - data type as a point set on the Earth's surface. A point set is a set - of points, lines, and polygons on the WGS84 reference spheroid, with - geodesic edges. See: https://cloud.google.com/bigquery/docs/geospatial-data - - **Examples:** - - >>> import bigframes as bpd - >>> import bigframes.bigquery as bbq - >>> import bigframes.geopandas - >>> from shapely.geometry import Polygon, LineString, Point - - We can check two GeoSeries against each other, row by row. - - >>> s1 = bigframes.geopandas.GeoSeries( - ... [ - ... Polygon([(0, 0), (2, 2), (0, 2)]), - ... Polygon([(0, 0), (2, 2), (0, 2)]), - ... LineString([(0, 0), (2, 2)]), - ... LineString([(2, 0), (0, 2)]), - ... Point(0, 1), - ... ], - ... ) - >>> s2 = bigframes.geopandas.GeoSeries( - ... [ - ... Polygon([(0, 0), (1, 1), (0, 1)]), - ... LineString([(1, 0), (1, 3)]), - ... LineString([(2, 0), (0, 2)]), - ... Point(1, 1), - ... Point(0, 1), - ... ], - ... index=range(1, 6), - ... ) - - >>> s1 - 0 POLYGON ((0 0, 2 2, 0 2, 0 0)) - 1 POLYGON ((0 0, 2 2, 0 2, 0 0)) - 2 LINESTRING (0 0, 2 2) - 3 LINESTRING (2 0, 0 2) - 4 POINT (0 1) - dtype: geometry - - >>> s2 - 1 POLYGON ((0 0, 1 1, 0 1, 0 0)) - 2 LINESTRING (1 0, 1 3) - 3 LINESTRING (2 0, 0 2) - 4 POINT (1 1) - 5 POINT (0 1) - dtype: geometry - - >>> bbq.st_intersection(s1, s2) - 0 None - 1 POLYGON ((0 0, 0.99954 1, 0 1, 0 0)) - 2 POINT (1 1.00046) - 3 LINESTRING (2 0, 0 2) - 4 GEOMETRYCOLLECTION EMPTY - 5 None - dtype: geometry - - We can also do intersection of each geometry and a single shapely geometry: - - >>> bbq.st_intersection(s1, Polygon([(0, 0), (1, 1), (0, 1)])) - 0 POLYGON ((0 0, 0.99954 1, 0 1, 0 0)) - 1 POLYGON ((0 0, 0.99954 1, 0 1, 0 0)) - 2 LINESTRING (0 0, 0.99954 1) - 3 GEOMETRYCOLLECTION EMPTY - 4 POINT (0 1) - dtype: geometry - - Args: - series (bigframes.pandas.Series | bigframes.geopandas.GeoSeries): - A series containing geography objects. - other (bigframes.pandas.Series | bigframes.geopandas.GeoSeries | shapely.Geometry): - The series or geometric object to intersect with the geography - objects in ``series``. - - Returns: - bigframes.geopandas.GeoSeries: - The Geoseries (elementwise) of the intersection of points in - each aligned geometry with other. - """ - return series._apply_binary_op(other, ops.geo_st_intersection_op) - - -def st_isclosed( - series: Union[bigframes.series.Series, bigframes.geopandas.GeoSeries], -) -> bigframes.series.Series: - """ - Returns TRUE for a non-empty Geography, where each element in the - Geography has an empty boundary. - - .. note:: - BigQuery's Geography functions, like `st_isclosed`, interpret the geometry - data type as a point set on the Earth's surface. A point set is a set - of points, lines, and polygons on the WGS84 reference spheroid, with - geodesic edges. See: https://cloud.google.com/bigquery/docs/geospatial-data - - **Examples:** - - >>> import bigframes.geopandas - >>> import bigframes.pandas as bpd - >>> import bigframes.bigquery as bbq - - >>> from shapely.geometry import Point, LineString, Polygon - - >>> series = bigframes.geopandas.GeoSeries( - ... [ - ... Point(0, 0), # Point - ... LineString([(0, 0), (1, 1)]), # Open LineString - ... LineString([(0, 0), (1, 1), (0, 1), (0, 0)]), # Closed LineString - ... Polygon([(0, 0), (1, 1), (0, 1), (0, 0)]), - ... None, - ... ] - ... ) - >>> series - 0 POINT (0 0) - 1 LINESTRING (0 0, 1 1) - 2 LINESTRING (0 0, 1 1, 0 1, 0 0) - 3 POLYGON ((0 0, 1 1, 0 1, 0 0)) - 4 None - dtype: geometry - - >>> bbq.st_isclosed(series) - 0 True - 1 False - 2 True - 3 False - 4 - dtype: boolean - - Args: - series (bigframes.pandas.Series | bigframes.geopandas.GeoSeries): - A series containing geography objects. - - Returns: - bigframes.pandas.Series: - Series of booleans indicating whether each geometry is closed. - """ - series = series._apply_unary_op(ops.geo_st_isclosed_op) - series.name = None - return series - - -def st_length( - series: Union[bigframes.series.Series, bigframes.geopandas.GeoSeries], - *, - use_spheroid: bool = False, -) -> bigframes.series.Series: - """Returns the total length in meters of the lines in the input GEOGRAPHY. - - If a series element is a point or a polygon, returns zero for that row. - If a series element is a collection, returns the length of the lines - in the collection; if the collection doesn't contain lines, returns - zero. - - The optional use_spheroid parameter determines how this function - measures distance. If use_spheroid is FALSE, the function measures - distance on the surface of a perfect sphere. - - The use_spheroid parameter currently only supports the value FALSE. The - default value of use_spheroid is FALSE. See: - https://cloud.google.com/bigquery/docs/reference/standard-sql/geography_functions#st_length - - **Examples:** - - >>> import bigframes.geopandas - >>> import bigframes.pandas as bpd - >>> import bigframes.bigquery as bbq - - >>> from shapely.geometry import Polygon, LineString, Point, GeometryCollection - - >>> series = bigframes.geopandas.GeoSeries( - ... [ - ... LineString([(0, 0), (1, 0)]), # Length will be approx 1 degree in meters - ... Polygon([(0.0, 0.0), (0.1, 0.1), (0.0, 0.1)]), # Length is 0 - ... Point(0, 1), # Length is 0 - ... GeometryCollection([LineString([(0,0),(0,1)]), Point(1,1)]) # Length of LineString only - ... ] - ... ) - - >>> result = bbq.st_length(series) - >>> result - 0 111195.101177 - 1 0.0 - 2 0.0 - 3 111195.101177 - dtype: Float64 - - Args: - series (bigframes.series.Series | bigframes.geopandas.GeoSeries): - A series containing geography objects. - use_spheroid (bool, optional): - Determines how this function measures distance. - If FALSE (default), measures distance on a perfect sphere. - Currently, only FALSE is supported. - - Returns: - bigframes.series.Series: - Series of floats representing the lengths in meters. - """ - series = series._apply_unary_op(ops.GeoStLengthOp(use_spheroid=use_spheroid)) - series.name = None - return series - - -def st_regionstats( - geography: Union[bigframes.series.Series, bigframes.geopandas.GeoSeries], - raster_id: str, - band: Optional[str] = None, - include: Optional[str] = None, - options: Optional[Mapping[str, Union[str, int, float]]] = None, -) -> bigframes.series.Series: - """Returns statistics summarizing the pixel values of the raster image - referenced by raster_id that intersect with geography. - - The statistics include the count, minimum, maximum, sum, standard - deviation, mean, and area of the valid pixels of the raster band named - band_name. Google Earth Engine computes the results of the function call. - - See: https://cloud.google.com/bigquery/docs/reference/standard-sql/geography_functions#st_regionstats - - Args: - geography (bigframes.series.Series | bigframes.geopandas.GeoSeries): - A series of geography objects to intersect with the raster image. - raster_id (str): - A string that identifies a raster image. The following formats are - supported. A URI from an image table provided by Google Earth Engine - in BigQuery sharing (formerly Analytics Hub). A URI for a readable - GeoTIFF raster file. A Google Earth Engine asset path that - references public catalog data or project-owned assets with read - access. - band (Optional[str]): - A string in one of the following formats: - A single band within the raster image specified by raster_id. A - formula to compute a value from the available bands in the raster - image. The formula uses the Google Earth Engine image expression - syntax. Bands can be referenced by their name, band_name, in - expressions. If you don't specify a band, the first band of the - image is used. - include (Optional[str]): - An optional string formula that uses the Google Earth Engine image - expression syntax to compute a pixel weight. The formula should - return values from 0 to 1. Values outside this range are set to the - nearest limit, either 0 or 1. A value of 0 means that the pixel is - invalid and it's excluded from analysis. A positive value means that - a pixel is valid. Values between 0 and 1 represent proportional - weights for calculations, such as weighted means. - options (Mapping[str, Union[str, int, float]], optional): - A dictionary of options to pass to the function. See the BigQuery - documentation for a list of available options. - - Returns: - bigframes.pandas.Series: - A STRUCT Series containing the computed statistics. - """ - op = ops.GeoStRegionStatsOp( - raster_id=raster_id, - band=band, - include=include, - options=json.dumps(options) if options else None, - ) - return geography._apply_unary_op(op) - - -def st_simplify( - geography: "bigframes.series.Series", - tolerance_meters: float, -) -> "bigframes.series.Series": - """Returns a simplified version of the input geography. - - Args: - geography (bigframes.series.Series): - A Series containing GEOGRAPHY data. - tolerance_meters (float): - A float64 value indicating the tolerance in meters. - - Returns: - a Series containing the simplified GEOGRAPHY data. - """ - return geography._apply_nary_op(ops.googlesql.ST_SIMPLIFY, [tolerance_meters]) diff --git a/bigframes/bigquery/_operations/io.py b/bigframes/bigquery/_operations/io.py deleted file mode 100644 index bf9eae95660..00000000000 --- a/bigframes/bigquery/_operations/io.py +++ /dev/null @@ -1,95 +0,0 @@ -# Copyright 2026 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from __future__ import annotations - -from typing import Mapping, Optional, Union - -import pandas as pd - -import bigframes.core.compile.sqlglot.sql as sql -import bigframes.core.logging.log_adapter as log_adapter -import bigframes.session -from bigframes.bigquery._operations.table import _get_table_metadata - - -@log_adapter.method_logger(custom_base_name="bigquery_io") -def load_data( - table_name: str, - *, - write_disposition: str = "INTO", - columns: Optional[Mapping[str, str]] = None, - partition_by: Optional[list[str]] = None, - cluster_by: Optional[list[str]] = None, - table_options: Optional[Mapping[str, Union[str, int, float, bool, list]]] = None, - from_files_options: Mapping[str, Union[str, int, float, bool, list]], - with_partition_columns: Optional[Mapping[str, str]] = None, - connection_name: Optional[str] = None, - session: Optional[bigframes.session.Session] = None, -) -> pd.Series: - """ - Loads data into a BigQuery table. - See the `BigQuery LOAD DATA DDL syntax - `_ - for additional reference. - Args: - table_name (str): - The name of the table in BigQuery. - write_disposition (str, default "INTO"): - Whether to replace the table if it already exists ("OVERWRITE") or append to it ("INTO"). - columns (Mapping[str, str], optional): - The table's schema. - partition_by (list[str], optional): - A list of partition expressions to partition the table by. See https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/load-statements#partition_expression. - cluster_by (list[str], optional): - A list of columns to cluster the table by. - table_options (Mapping[str, Union[str, int, float, bool, list]], optional): - The table options. - from_files_options (Mapping[str, Union[str, int, float, bool, list]]): - The options for loading data from files. - with_partition_columns (Mapping[str, str], optional): - The table's partition columns. - connection_name (str, optional): - The connection to use for the table. - session (bigframes.session.Session, optional): - The session to use. If not provided, the default session is used. - Returns: - pandas.Series: - A Series with object dtype containing the table metadata. Reference - the `BigQuery Table REST API reference - `_ - for available fields. - """ - import bigframes.pandas as bpd - - load_data_expr = sql.load_data( - table_name=table_name, - write_disposition=write_disposition, - columns=columns, - partition_by=partition_by, - cluster_by=cluster_by, - table_options=table_options, - from_files_options=from_files_options, - with_partition_columns=with_partition_columns, - connection_name=connection_name, - ) - sql_text = sql.to_sql(load_data_expr) - - if session is None: - bpd.read_gbq_query(sql_text) - session = bpd.get_global_session() - else: - session.read_gbq_query(sql_text) - - return _get_table_metadata(bqclient=session.bqclient, table_name=table_name) diff --git a/bigframes/bigquery/_operations/json.py b/bigframes/bigquery/_operations/json.py deleted file mode 100644 index 8afb234b719..00000000000 --- a/bigframes/bigquery/_operations/json.py +++ /dev/null @@ -1,548 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -""" -JSON functions defined from -https://cloud.google.com/bigquery/docs/reference/standard-sql/json_functions -""" - -from __future__ import annotations - -import warnings -from typing import Any, Optional, Sequence, Tuple, Union, cast - -import bigframes.core.utils as utils -import bigframes.dtypes -import bigframes.exceptions as bfe -import bigframes.operations as ops -import bigframes.series as series - -from . import array - - -@utils.preview(name="The JSON-related API `json_set`") -def json_set( - input: series.Series, - json_path_value_pairs: Sequence[Tuple[str, Any]], -) -> series.Series: - """Produces a new JSON value within a Series by inserting or replacing values at - specified paths. - - .. warning:: - The JSON-related API `parse_json` is in preview. Its behavior may change in - future versions. - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> import bigframes.bigquery as bbq - - >>> s = bpd.read_gbq("SELECT JSON '{\\\"a\\\": 1}' AS data")["data"] - >>> bbq.json_set(s, json_path_value_pairs=[("$.a", 100), ("$.b", "hi")]) - 0 {"a":100,"b":"hi"} - Name: data, dtype: extension>[pyarrow] - - Args: - input (bigframes.series.Series): - The Series containing JSON data (as native JSON objects or JSON-formatted strings). - json_path_value_pairs (Sequence[Tuple[str, Any]]): - Pairs of JSON path and the new value to insert/replace. - - Returns: - bigframes.series.Series: A new Series with the transformed JSON data. - - """ - # SQLGlot parser does not support the "create_if_missing => true" syntax, so - # create_if_missing is not currently implemented. - - result = input - for json_path_value_pair in json_path_value_pairs: - if len(json_path_value_pair) != 2: - raise ValueError( - "Incorrect format: Expected (, ), but found: " - + f"{json_path_value_pair}" - ) - - json_path, json_value = json_path_value_pair - result = result._apply_binary_op( - json_value, ops.JSONSet(json_path=json_path), alignment="left" - ) - return result - - -def json_extract( - input: series.Series, - json_path: str, -) -> series.Series: - """Extracts a JSON value and converts it to a SQL JSON-formatted ``STRING`` or - ``JSON`` value. This function uses single quotes and brackets to escape invalid - JSONPath characters in JSON keys. - - .. deprecated:: 2.5.0 - The ``json_extract`` is deprecated and will be removed in a future version. - Use ``json_query`` instead. - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> import bigframes.bigquery as bbq - - >>> s = bpd.Series(['{"class": {"students": [{"id": 5}, {"id": 12}]}}']) - >>> bbq.json_extract(s, json_path="$.class") - 0 {"students":[{"id":5},{"id":12}]} - dtype: string - - Args: - input (bigframes.series.Series): - The Series containing JSON data (as native JSON objects or JSON-formatted strings). - json_path (str): - The JSON path identifying the data that you want to obtain from the input. - - Returns: - bigframes.series.Series: A new Series with the JSON or JSON-formatted STRING. - """ - msg = ( - "The `json_extract` is deprecated and will be removed in a future version. " - "Use `json_query` instead." - ) - warnings.warn(bfe.format_message(msg), category=UserWarning) - return input._apply_unary_op(ops.JSONExtract(json_path=json_path)) - - -def json_extract_array( - input: series.Series, - json_path: str = "$", -) -> series.Series: - """Extracts a JSON array and converts it to a SQL array of JSON-formatted - `STRING` or `JSON` values. This function uses single quotes and brackets to - escape invalid JSONPath characters in JSON keys. - - .. deprecated:: 2.5.0 - The ``json_extract_array`` is deprecated and will be removed in a future version. - Use ``json_query_array`` instead. - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> import bigframes.bigquery as bbq - - >>> s = bpd.Series(['[1, 2, 3]', '[4, 5]']) - >>> bbq.json_extract_array(s) - 0 ['1' '2' '3'] - 1 ['4' '5'] - dtype: list[pyarrow] - - >>> s = bpd.Series([ - ... '{"fruits": [{"name": "apple"}, {"name": "cherry"}]}', - ... '{"fruits": [{"name": "guava"}, {"name": "grapes"}]}' - ... ]) - >>> bbq.json_extract_array(s, "$.fruits") - 0 ['{"name":"apple"}' '{"name":"cherry"}'] - 1 ['{"name":"guava"}' '{"name":"grapes"}'] - dtype: list[pyarrow] - - >>> s = bpd.Series([ - ... '{"fruits": {"color": "red", "names": ["apple","cherry"]}}', - ... '{"fruits": {"color": "green", "names": ["guava", "grapes"]}}' - ... ]) - >>> bbq.json_extract_array(s, "$.fruits.names") - 0 ['"apple"' '"cherry"'] - 1 ['"guava"' '"grapes"'] - dtype: list[pyarrow] - - Args: - input (bigframes.series.Series): - The Series containing JSON data (as native JSON objects or JSON-formatted strings). - json_path (str): - The JSON path identifying the data that you want to obtain from the input. - - Returns: - bigframes.series.Series: A new Series with the parsed arrays from the input. - """ - msg = ( - "The `json_extract_array` is deprecated and will be removed in a future version. " - "Use `json_query_array` instead." - ) - warnings.warn(bfe.format_message(msg), category=UserWarning) - return input._apply_unary_op(ops.JSONExtractArray(json_path=json_path)) - - -def json_extract_string_array( - input: series.Series, - json_path: str = "$", - value_dtype: Optional[ - Union[bigframes.dtypes.Dtype, bigframes.dtypes.DtypeString] - ] = None, -) -> series.Series: - """Extracts a JSON array and converts it to a SQL array of `STRING` values. - A `value_dtype` can be provided to further coerce the data type of the - values in the array. This function uses single quotes and brackets to escape - invalid JSONPath characters in JSON keys. - - .. deprecated:: 2.6.0 - The ``json_extract_string_array`` is deprecated and will be removed in a future version. - Use ``json_value_array`` instead. - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> import bigframes.bigquery as bbq - - >>> s = bpd.Series(['[1, 2, 3]', '[4, 5]']) - >>> bbq.json_extract_string_array(s) - 0 ['1' '2' '3'] - 1 ['4' '5'] - dtype: list[pyarrow] - - >>> bbq.json_extract_string_array(s, value_dtype='Int64') - 0 [1 2 3] - 1 [4 5] - dtype: list[pyarrow] - - >>> s = bpd.Series([ - ... '{"fruits": {"color": "red", "names": ["apple","cherry"]}}', - ... '{"fruits": {"color": "green", "names": ["guava", "grapes"]}}' - ... ]) - >>> bbq.json_extract_string_array(s, "$.fruits.names") - 0 ['apple' 'cherry'] - 1 ['guava' 'grapes'] - dtype: list[pyarrow] - - Args: - input (bigframes.series.Series): - The Series containing JSON data (as native JSON objects or JSON-formatted strings). - json_path (str): - The JSON path identifying the data that you want to obtain from the input. - value_dtype (dtype, Optional): - The data type supported by BigFrames DataFrame. - - Returns: - bigframes.series.Series: A new Series with the parsed arrays from the input. - """ - msg = ( - "The `json_extract_string_array` is deprecated and will be removed in a future version. " - "Use `json_value_array` instead." - ) - warnings.warn(bfe.format_message(msg), category=UserWarning) - array_series = input._apply_unary_op( - ops.JSONExtractStringArray(json_path=json_path) - ) - if value_dtype not in [None, bigframes.dtypes.STRING_DTYPE]: - array_items_series = array_series.explode() - if value_dtype == bigframes.dtypes.BOOL_DTYPE: - array_items_series = array_items_series.str.lower() == "true" - else: - array_items_series = array_items_series.astype(value_dtype) - array_series = cast( - series.Series, - array.array_agg( - array_items_series.groupby(level=input.index.names, dropna=False) - ), - ) - return array_series - - -def json_query( - input: series.Series, - json_path: str, -) -> series.Series: - """Extracts a JSON value and converts it to a SQL JSON-formatted ``STRING`` - or ``JSON`` value. This function uses double quotes to escape invalid JSONPath - characters in JSON keys. For example: ``"a.b"``. - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> import bigframes.bigquery as bbq - - >>> s = bpd.Series(['{"class": {"students": [{"id": 5}, {"id": 12}]}}']) - >>> bbq.json_query(s, json_path="$.class") - 0 {"students":[{"id":5},{"id":12}]} - dtype: string - - Args: - input (bigframes.series.Series): - The Series containing JSON data (as native JSON objects or JSON-formatted strings). - json_path (str): - The JSON path identifying the data that you want to obtain from the input. - - Returns: - bigframes.series.Series: A new Series with the JSON or JSON-formatted STRING. - """ - return input._apply_unary_op(ops.JSONQuery(json_path=json_path)) - - -def json_query_array( - input: series.Series, - json_path: str = "$", -) -> series.Series: - """Extracts a JSON array and converts it to a SQL array of JSON-formatted - `STRING` or `JSON` values. This function uses double quotes to escape invalid - JSONPath characters in JSON keys. For example: `"a.b"`. - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> import bigframes.bigquery as bbq - - >>> s = bpd.Series(['[1, 2, 3]', '[4, 5]']) - >>> bbq.json_query_array(s) - 0 ['1' '2' '3'] - 1 ['4' '5'] - dtype: list[pyarrow] - - >>> s = bpd.Series([ - ... '{"fruits": [{"name": "apple"}, {"name": "cherry"}]}', - ... '{"fruits": [{"name": "guava"}, {"name": "grapes"}]}' - ... ]) - >>> bbq.json_query_array(s, "$.fruits") - 0 ['{"name":"apple"}' '{"name":"cherry"}'] - 1 ['{"name":"guava"}' '{"name":"grapes"}'] - dtype: list[pyarrow] - - >>> s = bpd.Series([ - ... '{"fruits": {"color": "red", "names": ["apple","cherry"]}}', - ... '{"fruits": {"color": "green", "names": ["guava", "grapes"]}}' - ... ]) - >>> bbq.json_query_array(s, "$.fruits.names") - 0 ['"apple"' '"cherry"'] - 1 ['"guava"' '"grapes"'] - dtype: list[pyarrow] - - Args: - input (bigframes.series.Series): - The Series containing JSON data (as native JSON objects or JSON-formatted strings). - json_path (str): - The JSON path identifying the data that you want to obtain from the input. - - Returns: - bigframes.series.Series: A new Series with the parsed arrays from the input. - """ - return input._apply_unary_op(ops.JSONQueryArray(json_path=json_path)) - - -def json_value( - input: series.Series, - json_path: str = "$", -) -> series.Series: - """Extracts a JSON scalar value and converts it to a SQL ``STRING`` value. In - addtion, this function: - - Removes the outermost quotes and unescapes the values. - - Returns a SQL ``NULL`` if a non-scalar value is selected. - - Uses double quotes to escape invalid ``JSON_PATH`` characters in JSON keys. - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> import bigframes.bigquery as bbq - - >>> s = bpd.Series(['{"name": "Jakob", "age": "6"}', '{"name": "Jakob", "age": []}']) - >>> bbq.json_value(s, json_path="$.age") - 0 6 - 1 - dtype: string - - Args: - input (bigframes.series.Series): - The Series containing JSON data (as native JSON objects or JSON-formatted strings). - json_path (str): - The JSON path identifying the data that you want to obtain from the input. - - Returns: - bigframes.series.Series: A new Series with the JSON-formatted STRING. - """ - return input._apply_unary_op(ops.JSONValue(json_path=json_path)) - - -def json_value_array( - input: series.Series, - json_path: str = "$", -) -> series.Series: - """ - Extracts a JSON array of scalar values and converts it to a SQL ``ARRAY`` - value. In addition, this function: - - - Removes the outermost quotes and unescapes the values. - - Returns a SQL ``NULL`` if the selected value isn't an array or not an array - containing only scalar values. - - Uses double quotes to escape invalid ``JSON_PATH`` characters in JSON keys. - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> import bigframes.bigquery as bbq - - >>> s = bpd.Series(['[1, 2, 3]', '[4, 5]']) - >>> bbq.json_value_array(s) - 0 ['1' '2' '3'] - 1 ['4' '5'] - dtype: list[pyarrow] - - >>> s = bpd.Series([ - ... '{"fruits": ["apples", "oranges", "grapes"]', - ... '{"fruits": ["guava", "grapes"]}' - ... ]) - >>> bbq.json_value_array(s, "$.fruits") - 0 ['apples' 'oranges' 'grapes'] - 1 ['guava' 'grapes'] - dtype: list[pyarrow] - - >>> s = bpd.Series([ - ... '{"fruits": {"color": "red", "names": ["apple","cherry"]}}', - ... '{"fruits": {"color": "green", "names": ["guava", "grapes"]}}' - ... ]) - >>> bbq.json_value_array(s, "$.fruits.names") - 0 ['apple' 'cherry'] - 1 ['guava' 'grapes'] - dtype: list[pyarrow] - - Args: - input (bigframes.series.Series): - The Series containing JSON data (as native JSON objects or JSON-formatted strings). - json_path (str): - The JSON path identifying the data that you want to obtain from the input. - - Returns: - bigframes.series.Series: A new Series with the parsed arrays from the input. - """ - return input._apply_unary_op(ops.JSONValueArray(json_path=json_path)) - - -def json_keys( - input: series.Series, - max_depth: Optional[int] = None, -) -> series.Series: - """Returns all keys in the root of a JSON object as an ARRAY of STRINGs. - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> import bigframes.bigquery as bbq - - >>> s = bpd.Series(['{"b": {"c": 2}, "a": 1}'], dtype="json") - >>> bbq.json_keys(s) - 0 ['a' 'b' 'b.c'] - dtype: list[pyarrow] - - Args: - input (bigframes.series.Series): - The Series containing JSON data. - max_depth (int, optional): - Specifies the maximum depth of nested fields to search for keys. If not - provided, searched keys at all levels. - - Returns: - bigframes.series.Series: A new Series containing arrays of keys from the input JSON. - """ - return input._apply_unary_op(ops.JSONKeys(max_depth=max_depth)) - - -def to_json( - input: series.Series, -) -> series.Series: - """Converts a series with a JSON value to a JSON-formatted STRING value. - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> import bigframes.bigquery as bbq - - >>> s = bpd.Series([1, 2, 3]) - >>> bbq.to_json(s) - 0 1 - 1 2 - 2 3 - dtype: extension>[pyarrow] - - >>> s = bpd.Series([{"int": 1, "str": "pandas"}, {"int": 2, "str": "numpy"}]) - >>> bbq.to_json(s) - 0 {"int":1,"str":"pandas"} - 1 {"int":2,"str":"numpy"} - dtype: extension>[pyarrow] - - Args: - input (bigframes.series.Series): - The Series containing JSON or JSON-formatted string values. - - Returns: - bigframes.series.Series: A new Series with the JSON value. - """ - return input._apply_unary_op(ops.ToJSON()) - - -def to_json_string( - input: series.Series, -) -> series.Series: - """Converts a series to a JSON-formatted STRING value. - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> import bigframes.bigquery as bbq - - >>> s = bpd.Series([1, 2, 3]) - >>> bbq.to_json_string(s) - 0 1 - 1 2 - 2 3 - dtype: string - - >>> s = bpd.Series([{"int": 1, "str": "pandas"}, {"int": 2, "str": "numpy"}]) - >>> bbq.to_json_string(s) - 0 {"int":1,"str":"pandas"} - 1 {"int":2,"str":"numpy"} - dtype: string - - Args: - input (bigframes.series.Series): - The Series to be converted. - - Returns: - bigframes.series.Series: A new Series with the JSON-formatted STRING value. - """ - return input._apply_unary_op(ops.ToJSONString()) - - -@utils.preview(name="The JSON-related API `parse_json`") -def parse_json( - input: series.Series, -) -> series.Series: - """Converts a series with a JSON-formatted STRING value to a JSON value. - - .. warning:: - The JSON-related API `parse_json` is in preview. Its behavior may change in - future versions. - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> import bigframes.bigquery as bbq - - >>> s = bpd.Series(['{"class": {"students": [{"id": 5}, {"id": 12}]}}']) - >>> s - 0 {"class": {"students": [{"id": 5}, {"id": 12}]}} - dtype: string - >>> bbq.parse_json(s) - 0 {"class":{"students":[{"id":5},{"id":12}]}} - dtype: extension>[pyarrow] - - Args: - input (bigframes.series.Series): - The Series containing JSON-formatted strings). - - Returns: - bigframes.series.Series: A new Series with the JSON value. - """ - return input._apply_unary_op(ops.ParseJSON()) diff --git a/bigframes/bigquery/_operations/mathematical.py b/bigframes/bigquery/_operations/mathematical.py deleted file mode 100644 index 5e6a299f83f..00000000000 --- a/bigframes/bigquery/_operations/mathematical.py +++ /dev/null @@ -1,121 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from __future__ import annotations - -from typing import Sequence - -import bigframes.core.col -import bigframes.core.expression -from bigframes import dtypes -from bigframes import operations as ops -from bigframes.operations import googlesql - - -def rand() -> bigframes.core.col.Expression: - """ - Generates a pseudo-random value of type FLOAT64 in the range of [0, 1), - inclusive of 0 and exclusive of 1. - - .. warning:: - This method introduces non-determinism to the expression. Reading the - same column twice may result in different results. The value might - change. Do not use this value or any value derived from it as a join - key. - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> import bigframes.bigquery as bbq - >>> df = bpd.DataFrame({"a": [1, 2, 3]}) - >>> df['random'] = bbq.rand() - >>> # Resulting column 'random' will contain random floats between 0 and 1. - - Returns: - bigframes.pandas.api.typing.Expression: - An expression that can be used in - :func:`~bigframes.pandas.DataFrame.assign` and other methods. See - :func:`bigframes.pandas.col`. - """ - return bigframes.core.col.Expression( - bigframes.core.expression.OpExpression(googlesql.RAND, ()) - ) - - -def hparam_range(min: float, max: float) -> bigframes.core.col.Expression: - """ - Defines the minimum and maximum bounds of the search space of continuous - values for a hyperparameter. - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> import bigframes.bigquery as bbq - >>> # Specify a range of values for a hyperparameter. - >>> learn_rate = bbq.hparam_range(0.0001, 1.0) - - Args: - min (float or int): - The minimum bound of the search space. - max (float or int): - The maximum bound of the search space. - - Returns: - bigframes.pandas.api.typing.Expression: - An expression that can be used in model options. - """ - min_expr = bigframes.core.expression.const(min) - max_expr = bigframes.core.expression.const(max) - - op = ops.SqlScalarOp( - _output_type=dtypes.FLOAT_DTYPE, - sql_template="HPARAM_RANGE({0}, {1})", - is_deterministic=True, - ) - return bigframes.core.col.Expression( - bigframes.core.expression.OpExpression(op, (min_expr, max_expr)) - ) - - -def hparam_candidates( - candidates: Sequence[float | str], -) -> bigframes.core.col.Expression: - """ - Specifies the set of discrete values for the hyperparameter. - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> import bigframes.bigquery as bbq - >>> # Specify a set of values for a hyperparameter. - >>> optimizer = bbq.hparam_candidates(['ADAGRAD', 'SGD', 'FTRL']) - - Args: - candidates (Sequence[float | str]): - The set of discrete values for the hyperparameter. - - Returns: - bigframes.pandas.api.typing.Expression: - An expression that can be used in model options. - """ - candidates_expr = bigframes.core.expression.const(tuple(candidates)) - - op = ops.SqlScalarOp( - _output_type=dtypes.STRING_DTYPE, - sql_template="HPARAM_CANDIDATES({0})", - is_deterministic=True, - ) - return bigframes.core.col.Expression( - bigframes.core.expression.OpExpression(op, (candidates_expr,)) - ) diff --git a/bigframes/bigquery/_operations/ml.py b/bigframes/bigquery/_operations/ml.py deleted file mode 100644 index c6ef1f8bb7a..00000000000 --- a/bigframes/bigquery/_operations/ml.py +++ /dev/null @@ -1,576 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from __future__ import annotations - -from typing import List, Mapping, Optional, Union - -import bigframes_vendored.constants -import google.cloud.bigquery -import pandas as pd - -import bigframes.core.col as col -import bigframes.core.logging.log_adapter as log_adapter -import bigframes.core.sql.ml -import bigframes.dataframe as dataframe -import bigframes.ml.base -import bigframes.session -from bigframes.bigquery._operations import utils - - -def _get_model_metadata( - *, - bqclient: google.cloud.bigquery.Client, - model_name: str, -) -> pd.Series: - model_metadata = bqclient.get_model(model_name) - model_dict = model_metadata.to_api_repr() - return pd.Series(model_dict) - - -@log_adapter.method_logger(custom_base_name="bigquery_ml") -def create_model( - model_name: str, - *, - replace: bool = False, - if_not_exists: bool = False, - # TODO(tswast): Also support bigframes.ml transformer classes and/or - # bigframes.pandas functions? - transform: Optional[list[str]] = None, - input_schema: Optional[Mapping[str, str]] = None, - output_schema: Optional[Mapping[str, str]] = None, - connection_name: Optional[str] = None, - options: Optional[ - Mapping[str, Union[str, int, float, bool, list, "col.Expression"]] - ] = None, - training_data: Optional[Union[pd.DataFrame, dataframe.DataFrame, str]] = None, - custom_holiday: Optional[Union[pd.DataFrame, dataframe.DataFrame, str]] = None, - session: Optional[bigframes.session.Session] = None, -) -> pd.Series: - """ - Creates a BigQuery ML model. - - See the `BigQuery ML CREATE MODEL DDL syntax - `_ - for additional reference. - - Args: - model_name (str): - The name of the model in BigQuery. - replace (bool, default False): - Whether to replace the model if it already exists. - if_not_exists (bool, default False): - Whether to ignore the error if the model already exists. - transform (list[str], optional): - A list of SQL transformations for the TRANSFORM clause, which - specifies the preprocessing steps to apply to the input data. - input_schema (Mapping[str, str], optional): - The INPUT clause, which specifies the schema of the input data. - output_schema (Mapping[str, str], optional): - The OUTPUT clause, which specifies the schema of the output data. - connection_name (str, optional): - The connection to use for the model. - options (Mapping[str, Union[str, int, float, bool, list, bigframes.core.col.Expression]], optional): - The OPTIONS clause, which specifies the model options. - training_data (Union[bigframes.pandas.DataFrame, str], optional): - The query or DataFrame to use for training the model. - custom_holiday (Union[bigframes.pandas.DataFrame, str], optional): - The query or DataFrame to use for custom holiday data. - session (bigframes.session.Session, optional): - The session to use. If not provided, the default session is used. - - Returns: - pandas.Series: - A Series with object dtype containing the model metadata. Reference - the `BigQuery Model REST API reference - `_ - for available fields. - - """ - import bigframes.pandas as bpd - - training_data_sql = ( - utils.to_sql(training_data) if training_data is not None else None - ) - custom_holiday_sql = ( - utils.to_sql(custom_holiday) if custom_holiday is not None else None - ) - - # Determine session from DataFrames if not provided - if session is None: - # Try to get session from inputs - dfs = [ - obj - for obj in [training_data, custom_holiday] - if isinstance(obj, dataframe.DataFrame) - ] - if dfs: - session = dfs[0]._session - - sql = bigframes.core.sql.ml.create_model_ddl( - model_name=model_name, - replace=replace, - if_not_exists=if_not_exists, - transform=transform, - input_schema=input_schema, - output_schema=output_schema, - connection_name=connection_name, - options=options, - training_data=training_data_sql, - custom_holiday=custom_holiday_sql, - ) - - if session is None: - bpd.read_gbq_query(sql) - session = bpd.get_global_session() - assert session is not None, ( - f"Missing connection to BigQuery. Please report how you encountered this error at {bigframes_vendored.constants.FEEDBACK_LINK}." - ) - else: - session.read_gbq_query(sql) - - return _get_model_metadata(bqclient=session.bqclient, model_name=model_name) - - -@log_adapter.method_logger(custom_base_name="bigquery_ml") -def evaluate( - model: Union[bigframes.ml.base.BaseEstimator, str, pd.Series], - input_: Optional[Union[pd.DataFrame, dataframe.DataFrame, str]] = None, - *, - perform_aggregation: Optional[bool] = None, - horizon: Optional[int] = None, - confidence_level: Optional[float] = None, -) -> dataframe.DataFrame: - """ - Evaluates a BigQuery ML model. - - See the `BigQuery ML EVALUATE function syntax - `_ - for additional reference. - - Args: - model (bigframes.ml.base.BaseEstimator or str): - The model to evaluate. - input_ (Union[bigframes.pandas.DataFrame, str], optional): - The DataFrame or query to use for evaluation. If not provided, the - evaluation data from training is used. - perform_aggregation (bool, optional): - A BOOL value that indicates the level of evaluation for forecasting - accuracy. If you specify TRUE, then the forecasting accuracy is on - the time series level. If you specify FALSE, the forecasting - accuracy is on the timestamp level. The default value is TRUE. - horizon (int, optional): - An INT64 value that specifies the number of forecasted time points - against which the evaluation metrics are computed. The default value - is the horizon value specified in the CREATE MODEL statement for the - time series model, or 1000 if unspecified. When evaluating multiple - time series at the same time, this parameter applies to each time - series. - confidence_level (float, optional): - A FLOAT64 value that specifies the percentage of the future values - that fall in the prediction interval. The default value is 0.95. The - valid input range is ``[0, 1)``. - - Returns: - bigframes.pandas.DataFrame: - The evaluation results. - """ - import bigframes.pandas as bpd - - model_name, session = utils.get_model_name_and_session(model, input_) - table_sql = utils.to_sql(input_) if input_ is not None else None - - sql = bigframes.core.sql.ml.evaluate( - model_name=model_name, - table=table_sql, - perform_aggregation=perform_aggregation, - horizon=horizon, - confidence_level=confidence_level, - ) - - if session is None: - return bpd.read_gbq_query(sql) - else: - return session.read_gbq_query(sql) - - -@log_adapter.method_logger(custom_base_name="bigquery_ml") -def predict( - model: Union[bigframes.ml.base.BaseEstimator, str, pd.Series], - input_: Union[pd.DataFrame, dataframe.DataFrame, str], - *, - threshold: Optional[float] = None, - keep_original_columns: Optional[bool] = None, - trial_id: Optional[int] = None, -) -> dataframe.DataFrame: - """ - Runs prediction on a BigQuery ML model. - - See the `BigQuery ML PREDICT function syntax - `_ - for additional reference. - - Args: - model (bigframes.ml.base.BaseEstimator or str): - The model to use for prediction. - input_ (Union[bigframes.pandas.DataFrame, str]): - The DataFrame or query to use for prediction. - threshold (float, optional): - The threshold to use for classification models. - keep_original_columns (bool, optional): - Whether to keep the original columns in the output. - trial_id (int, optional): - An INT64 value that identifies the hyperparameter tuning trial that - you want the function to evaluate. The function uses the optimal - trial by default. Only specify this argument if you ran - hyperparameter tuning when creating the model. - - Returns: - bigframes.pandas.DataFrame: - The prediction results. - """ - import bigframes.pandas as bpd - - model_name, session = utils.get_model_name_and_session(model, input_) - table_sql = utils.to_sql(input_) - - sql = bigframes.core.sql.ml.predict( - model_name=model_name, - table=table_sql, - threshold=threshold, - keep_original_columns=keep_original_columns, - trial_id=trial_id, - ) - - if session is None: - return bpd.read_gbq_query(sql) - else: - return session.read_gbq_query(sql) - - -@log_adapter.method_logger(custom_base_name="bigquery_ml") -def explain_predict( - model: Union[bigframes.ml.base.BaseEstimator, str, pd.Series], - input_: Union[pd.DataFrame, dataframe.DataFrame, str], - *, - top_k_features: Optional[int] = None, - threshold: Optional[float] = None, - integrated_gradients_num_steps: Optional[int] = None, - approx_feature_contrib: Optional[bool] = None, -) -> dataframe.DataFrame: - """ - Runs explainable prediction on a BigQuery ML model. - - See the `BigQuery ML EXPLAIN_PREDICT function syntax - `_ - for additional reference. - - Args: - model (bigframes.ml.base.BaseEstimator or str): - The model to use for prediction. - input_ (Union[bigframes.pandas.DataFrame, str]): - The DataFrame or query to use for prediction. - top_k_features (int, optional): - The number of top features to return. - threshold (float, optional): - The threshold for binary classification models. - integrated_gradients_num_steps (int, optional): - an INT64 value that specifies the number of steps to sample between - the example being explained and its baseline. This value is used to - approximate the integral in integrated gradients attribution - methods. Increasing the value improves the precision of feature - attributions, but can be slower and more computationally expensive. - approx_feature_contrib (bool, optional): - A BOOL value that indicates whether to use an approximate feature - contribution method in the XGBoost model explanation. - - Returns: - bigframes.pandas.DataFrame: - The prediction results with explanations. - """ - import bigframes.pandas as bpd - - model_name, session = utils.get_model_name_and_session(model, input_) - table_sql = utils.to_sql(input_) - - sql = bigframes.core.sql.ml.explain_predict( - model_name=model_name, - table=table_sql, - top_k_features=top_k_features, - threshold=threshold, - integrated_gradients_num_steps=integrated_gradients_num_steps, - approx_feature_contrib=approx_feature_contrib, - ) - - if session is None: - return bpd.read_gbq_query(sql) - else: - return session.read_gbq_query(sql) - - -@log_adapter.method_logger(custom_base_name="bigquery_ml") -def global_explain( - model: Union[bigframes.ml.base.BaseEstimator, str, pd.Series], - *, - class_level_explain: Optional[bool] = None, -) -> dataframe.DataFrame: - """ - Gets global explanations for a BigQuery ML model. - - See the `BigQuery ML GLOBAL_EXPLAIN function syntax - `_ - for additional reference. - - Args: - model (bigframes.ml.base.BaseEstimator or str): - The model to get explanations from. - class_level_explain (bool, optional): - Whether to return class-level explanations. - - Returns: - bigframes.pandas.DataFrame: - The global explanation results. - """ - import bigframes.pandas as bpd - - model_name, session = utils.get_model_name_and_session(model) - sql = bigframes.core.sql.ml.global_explain( - model_name=model_name, - class_level_explain=class_level_explain, - ) - - if session is None: - return bpd.read_gbq_query(sql) - else: - return session.read_gbq_query(sql) - - -@log_adapter.method_logger(custom_base_name="bigquery_ml") -def transform( - model: Union[bigframes.ml.base.BaseEstimator, str, pd.Series], - input_: Union[pd.DataFrame, dataframe.DataFrame, str], -) -> dataframe.DataFrame: - """ - Transforms input data using a BigQuery ML model. - - See the `BigQuery ML TRANSFORM function syntax - `_ - for additional reference. - - Args: - model (bigframes.ml.base.BaseEstimator or str): - The model to use for transformation. - input_ (Union[bigframes.pandas.DataFrame, str]): - The DataFrame or query to use for transformation. - - Returns: - bigframes.pandas.DataFrame: - The transformed data. - """ - import bigframes.pandas as bpd - - model_name, session = utils.get_model_name_and_session(model, input_) - table_sql = utils.to_sql(input_) - - sql = bigframes.core.sql.ml.transform( - model_name=model_name, - table=table_sql, - ) - - if session is None: - return bpd.read_gbq_query(sql) - else: - return session.read_gbq_query(sql) - - -@log_adapter.method_logger(custom_base_name="bigquery_ml") -def generate_text( - model: Union[bigframes.ml.base.BaseEstimator, str, pd.Series], - input_: Union[pd.DataFrame, dataframe.DataFrame, str], - *, - temperature: Optional[float] = None, - max_output_tokens: Optional[int] = None, - top_k: Optional[int] = None, - top_p: Optional[float] = None, - flatten_json_output: Optional[bool] = None, - stop_sequences: Optional[List[str]] = None, - ground_with_google_search: Optional[bool] = None, - request_type: Optional[str] = None, -) -> dataframe.DataFrame: - """ - Generates text using a BigQuery ML model. - - See the `BigQuery ML GENERATE_TEXT function syntax - `_ - for additional reference. - - Args: - model (bigframes.ml.base.BaseEstimator or str): - The model to use for text generation. - input_ (Union[bigframes.pandas.DataFrame, str]): - The DataFrame or query to use for text generation. - temperature (float, optional): - A FLOAT64 value that is used for sampling promiscuity. The value - must be in the range ``[0.0, 1.0]``. A lower temperature works well - for prompts that expect a more deterministic and less open-ended - or creative response, while a higher temperature can lead to more - diverse or creative results. A temperature of ``0`` is - deterministic, meaning that the highest probability response is - always selected. - max_output_tokens (int, optional): - An INT64 value that sets the maximum number of tokens in the - generated text. - top_k (int, optional): - An INT64 value that changes how the model selects tokens for - output. A ``top_k`` of ``1`` means the next selected token is the - most probable among all tokens in the model's vocabulary. A - ``top_k`` of ``3`` means that the next token is selected from - among the three most probable tokens by using temperature. The - default value is ``40``. - top_p (float, optional): - A FLOAT64 value that changes how the model selects tokens for - output. Tokens are selected from most probable to least probable - until the sum of their probabilities equals the ``top_p`` value. - For example, if tokens A, B, and C have a probability of 0.3, 0.2, - and 0.1 and the ``top_p`` value is ``0.5``, then the model will - select either A or B as the next token by using temperature. The - default value is ``0.95``. - flatten_json_output (bool, optional): - A BOOL value that determines the content of the generated JSON column. - stop_sequences (List[str], optional): - An ARRAY value that contains the stop sequences for the model. - ground_with_google_search (bool, optional): - A BOOL value that determines whether to ground the model with Google Search. - request_type (str, optional): - A STRING value that contains the request type for the model. - - Returns: - bigframes.pandas.DataFrame: - The generated text. - """ - import bigframes.pandas as bpd - - model_name, session = utils.get_model_name_and_session(model, input_) - table_sql = utils.to_sql(input_) - - sql = bigframes.core.sql.ml.generate_text( - model_name=model_name, - table=table_sql, - temperature=temperature, - max_output_tokens=max_output_tokens, - top_k=top_k, - top_p=top_p, - flatten_json_output=flatten_json_output, - stop_sequences=stop_sequences, - ground_with_google_search=ground_with_google_search, - request_type=request_type, - ) - - if session is None: - return bpd.read_gbq_query(sql) - else: - return session.read_gbq_query(sql) - - -@log_adapter.method_logger(custom_base_name="bigquery_ml") -def get_insights( - model: Union[bigframes.ml.base.BaseEstimator, str, pd.Series], -) -> dataframe.DataFrame: - """ - Gets insights from a BigQuery ML model. - - See the `BigQuery ML GET_INSIGHTS function syntax - `_ - for additional reference. - - Args: - model (bigframes.ml.base.BaseEstimator, str, or pd.Series): - The model to get insights from. - - Returns: - bigframes.pandas.DataFrame: - The insights. - """ - import bigframes.pandas as bpd - - model_name, session = utils.get_model_name_and_session(model) - - sql = bigframes.core.sql.ml.get_insights( - model_name=model_name, - ) - - if session is None: - return bpd.read_gbq_query(sql) - else: - return session.read_gbq_query(sql) - - -@log_adapter.method_logger(custom_base_name="bigquery_ml") -def generate_embedding( - model: Union[bigframes.ml.base.BaseEstimator, str, pd.Series], - input_: Union[pd.DataFrame, dataframe.DataFrame, str], - *, - flatten_json_output: Optional[bool] = None, - task_type: Optional[str] = None, - output_dimensionality: Optional[int] = None, -) -> dataframe.DataFrame: - """ - Generates text embedding using a BigQuery ML model. - - See the `BigQuery ML GENERATE_EMBEDDING function syntax - `_ - for additional reference. - - Args: - model (bigframes.ml.base.BaseEstimator or str): - The model to use for text embedding. - input_ (Union[bigframes.pandas.DataFrame, str]): - The DataFrame or query to use for text embedding. - flatten_json_output (bool, optional): - A BOOL value that determines the content of the generated JSON column. - task_type (str, optional): - A STRING value that specifies the intended downstream application task. - Supported values are: - - `RETRIEVAL_QUERY` - - `RETRIEVAL_DOCUMENT` - - `SEMANTIC_SIMILARITY` - - `CLASSIFICATION` - - `CLUSTERING` - - `QUESTION_ANSWERING` - - `FACT_VERIFICATION` - - `CODE_RETRIEVAL_QUERY` - output_dimensionality (int, optional): - An INT64 value that specifies the size of the output embedding. - - Returns: - bigframes.pandas.DataFrame: - The generated text embedding. - """ - import bigframes.pandas as bpd - - model_name, session = utils.get_model_name_and_session(model, input_) - table_sql = utils.to_sql(input_) - - sql = bigframes.core.sql.ml.generate_embedding( - model_name=model_name, - table=table_sql, - flatten_json_output=flatten_json_output, - task_type=task_type, - output_dimensionality=output_dimensionality, - ) - - if session is None: - return bpd.read_gbq_query(sql) - else: - return session.read_gbq_query(sql) diff --git a/bigframes/bigquery/_operations/obj.py b/bigframes/bigquery/_operations/obj.py deleted file mode 100644 index ca09d7ab1ce..00000000000 --- a/bigframes/bigquery/_operations/obj.py +++ /dev/null @@ -1,114 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -"""This module exposes BigQuery ObjectRef functions. - -See bigframes.bigquery.obj for public docs. -""" - -from __future__ import annotations - -import datetime -from typing import Optional, Sequence, Union - -import numpy as np -import pandas as pd - -import bigframes.core.utils as utils -import bigframes.operations as ops -import bigframes.series as series -from bigframes.core import convert -from bigframes.core.logging import log_adapter - - -@log_adapter.method_logger(custom_base_name="bigquery_obj") -def fetch_metadata( - objectref: series.Series, -) -> series.Series: - """[Preview] The OBJ.FETCH_METADATA function returns Cloud Storage metadata for a partially populated ObjectRef value. - - Args: - objectref (bigframes.pandas.Series): - A partially populated ObjectRef value, in which the uri and authorizer fields are populated and the details field isn't. - - Returns: - bigframes.pandas.Series: A fully populated ObjectRef value. The metadata is provided in the details field of the returned ObjectRef value. - """ - objectref = convert.to_bf_series(objectref, default_index=None) - return objectref._apply_unary_op(ops.obj_fetch_metadata_op) - - -@log_adapter.method_logger(custom_base_name="bigquery_obj") -def get_access_url( - objectref: series.Series, - mode: str, - duration: Optional[Union[datetime.timedelta, pd.Timedelta, np.timedelta64]] = None, -) -> series.Series: - """[Preview] The OBJ.GET_ACCESS_URL function returns JSON that contains reference information for the input ObjectRef value, and also access URLs that you can use to read or modify the Cloud Storage object. - - Args: - objectref (bigframes.pandas.Series): - An ObjectRef value that represents a Cloud Storage object. - mode (str): - A STRING value that identifies the type of URL that you want to be returned. The following values are supported: - 'r': Returns a URL that lets you read the object. - 'rw': Returns two URLs, one that lets you read the object, and one that lets you modify the object. - duration (Union[datetime.timedelta, pandas.Timedelta, numpy.timedelta64], optional): - An optional INTERVAL value that specifies how long the generated access URLs remain valid. You can specify a value between 30 minutes and 6 hours. For example, you could specify INTERVAL 2 HOUR to generate URLs that expire after 2 hours. The default value is 6 hours. - - Returns: - bigframes.pandas.Series: A JSON value that contains the Cloud Storage object reference information from the input ObjectRef value, and also one or more URLs that you can use to access the Cloud Storage object. - """ - objectref = convert.to_bf_series(objectref, default_index=None) - - duration_micros = None - if duration is not None: - duration_micros = utils.timedelta_to_micros(duration) - - return objectref._apply_unary_op( - ops.ObjGetAccessUrl(mode=mode, duration=duration_micros) - ) - - -@log_adapter.method_logger(custom_base_name="bigquery_obj") -def make_ref( - uri_or_json: Union[series.Series, Sequence[str]], - authorizer: Union[series.Series, str, None] = None, -) -> series.Series: - """[Preview] Use the OBJ.MAKE_REF function to create an ObjectRef value that contains reference information for a Cloud Storage object. - - Args: - uri_or_json (bigframes.pandas.Series or str): - A series of STRING values that contains the URI for the Cloud Storage object, for example, gs://mybucket/flowers/12345.jpg. - OR - A series of JSON value that represents a Cloud Storage object. - authorizer (bigframes.pandas.Series or str, optional): - A STRING value that contains the Cloud Resource connection used to access the Cloud Storage object. - Required if ``uri_or_json`` is a URI string. - - Returns: - bigframes.pandas.Series: An ObjectRef value. - """ - uri_or_json = convert.to_bf_series(uri_or_json, default_index=None) - - if authorizer is not None: - # Avoid join problems encountered if we try to convert a literal into Series. - if not isinstance(authorizer, str): - authorizer = convert.to_bf_series(authorizer, default_index=None) - - return uri_or_json._apply_binary_op(authorizer, ops.obj_make_ref_op) - - # If authorizer is not provided, we assume uri_or_json is a JSON objectref - return uri_or_json._apply_unary_op(ops.obj_make_ref_json_op) diff --git a/bigframes/bigquery/_operations/search.py b/bigframes/bigquery/_operations/search.py deleted file mode 100644 index b65eed24753..00000000000 --- a/bigframes/bigquery/_operations/search.py +++ /dev/null @@ -1,249 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from __future__ import annotations - -import json -import typing -from typing import Collection, Literal, Mapping, Optional, Union - -import google.cloud.bigquery as bigquery - -import bigframes.ml.utils as utils - -if typing.TYPE_CHECKING: - import bigframes.dataframe as dataframe - import bigframes.series as series - import bigframes.session - -""" -Search functions defined from -https://cloud.google.com/bigquery/docs/reference/standard-sql/search_functions -""" - - -def create_vector_index( - table_id: str, - column_name: str, - *, - replace: bool = False, - index_name: Optional[str] = None, - distance_type="cosine", - stored_column_names: Collection[str] = (), - index_type: str = "ivf", - ivf_options: Optional[Mapping] = None, - tree_ah_options: Optional[Mapping] = None, - session: Optional[bigframes.session.Session] = None, -) -> None: - """ - Creates a new vector index on a column of a table. - - This method calls the `CREATE VECTOR INDEX DDL statement - `_. - - """ - import bigframes.pandas - - if index_name is None: - table_ref = bigquery.TableReference.from_string(table_id) - index_name = table_ref.table_id - - options = { - "index_type": index_type.upper(), - "distance_type": distance_type.upper(), - } - - if ivf_options is not None: - options["ivf_options"] = json.dumps(ivf_options) - - if tree_ah_options is not None: - options["tree_ah_options"] = json.dumps(tree_ah_options) - - sql = bigframes.core.sql.create_vector_index_ddl( - replace=replace, - index_name=index_name, - table_name=table_id, - column_name=column_name, - stored_column_names=stored_column_names, - options=options, - ) - - # Use global read_gbq to execute this for better location autodetection. - if session is None: - read_gbq_query = bigframes.pandas.read_gbq_query - else: - read_gbq_query = session.read_gbq_query - - read_gbq_query(sql) - - -def vector_search( - base_table: str, - column_to_search: str, - query: Union[dataframe.DataFrame, series.Series], - *, - query_column_to_search: Optional[str] = None, - top_k: Optional[int] = None, - distance_type: Optional[Literal["euclidean", "cosine", "dot_product"]] = None, - fraction_lists_to_search: Optional[float] = None, - use_brute_force: Optional[bool] = None, - allow_large_results: Optional[bool] = None, -) -> dataframe.DataFrame: - """ - Conduct vector search which searches embeddings to find semantically similar entities. - - This method calls the `VECTOR_SEARCH() SQL function - `_. - - **Examples:** - - - >>> import bigframes.pandas as bpd - >>> import bigframes.bigquery as bbq - - DataFrame embeddings for which to find nearest neighbors. The ``ARRAY`` column - is used as the search query: - - >>> search_query = bpd.DataFrame({"query_id": ["dog", "cat"], - ... "embedding": [[1.0, 2.0], [3.0, 5.2]]}) - >>> bbq.vector_search( - ... base_table="bigframes-dev.bigframes_tests_sys.base_table", - ... column_to_search="my_embedding", - ... query=search_query, - ... top_k=2).sort_values("id") - query_id embedding id my_embedding distance - 0 dog [1. 2.] 1 [1. 2.] 0.0 - 1 cat [3. 5.2] 2 [2. 4.] 1.56205 - 0 dog [1. 2.] 4 [1. 3.2] 1.2 - 1 cat [3. 5.2] 5 [5. 5.4] 2.009975 - - [4 rows x 5 columns] - - Series embeddings for which to find nearest neighbors: - - >>> search_query = bpd.Series([[1.0, 2.0], [3.0, 5.2]], - ... index=["dog", "cat"], - ... name="embedding") - >>> bbq.vector_search( - ... base_table="bigframes-dev.bigframes_tests_sys.base_table", - ... column_to_search="my_embedding", - ... query=search_query, - ... top_k=2, - ... use_brute_force=True).sort_values("id") - embedding id my_embedding distance - dog [1. 2.] 1 [1. 2.] 0.0 - cat [3. 5.2] 2 [2. 4.] 1.56205 - dog [1. 2.] 4 [1. 3.2] 1.2 - cat [3. 5.2] 5 [5. 5.4] 2.009975 - - [4 rows x 4 columns] - - You can specify the name of the column in the query DataFrame embeddings and distance type. - If you specify query_column_to_search_value, it will use the provided column which contains - the embeddings for which to find nearest neighbors. Otherwiese, it uses the column_to_search value. - - >>> search_query = bpd.DataFrame({"query_id": ["dog", "cat"], - ... "embedding": [[1.0, 2.0], [3.0, 5.2]], - ... "another_embedding": [[0.7, 2.2], [3.3, 5.2]]}) - >>> bbq.vector_search( - ... base_table="bigframes-dev.bigframes_tests_sys.base_table", - ... column_to_search="my_embedding", - ... query=search_query, - ... distance_type="cosine", - ... query_column_to_search="another_embedding", - ... top_k=2).sort_values("id") - query_id embedding another_embedding id my_embedding distance - 1 cat [3. 5.2] [3.3 5.2] 1 [1. 2.] 0.005181 - 1 cat [3. 5.2] [3.3 5.2] 2 [2. 4.] 0.005181 - 0 dog [1. 2.] [0.7 2.2] 3 [1.5 7. ] 0.004697 - 0 dog [1. 2.] [0.7 2.2] 4 [1. 3.2] 0.000013 - - [4 rows x 6 columns] - - Args: - base_table (str): - The table to search for nearest neighbor embeddings. - column_to_search (str): - The name of the base table column to search for nearest neighbor embeddings. - The column must have a type of ``ARRAY``. All elements in the array must be non-NULL. - query (bigframes.dataframe.DataFrame | bigframes.dataframe.Series): - A Series or DataFrame that provides the embeddings for which to find nearest neighbors. - query_column_to_search (str): - Specifies the name of the column in the query that contains the embeddings for which to - find nearest neighbors. The column must have a type of ``ARRAY``. All elements in - the array must be non-NULL and all values in the column must have the same array dimensions - as the values in the ``column_to_search`` column. Can only be set when query is a DataFrame. - top_k (int): - Sepecifies the number of nearest neighbors to return. Default to 10. - distance_type (str, defalt "euclidean"): - Specifies the type of metric to use to compute the distance between two vectors. - Possible values are "euclidean", "cosine" and "dot_product". - Default to "euclidean". - fraction_lists_to_search (float, range in [0.0, 1.0]): - Specifies the percentage of lists to search. Specifying a higher percentage leads to - higher recall and slower performance, and the converse is true when specifying a lower - percentage. It is only used when a vector index is also used. You can only specify - ``fraction_lists_to_search`` when ``use_brute_force`` is set to False. - use_brute_force (bool): - Determines whether to use brute force search by skipping the vector index if one is available. - Default to False. - allow_large_results (bool, optional): - Whether to allow large query results. If ``True``, the query - results can be larger than the maximum response size. - Defaults to ``bpd.options.compute.allow_large_results``. - - Returns: - bigframes.dataframe.DataFrame: A DataFrame containing vector search result. - """ - import bigframes.series - - if ( - isinstance(query, bigframes.series.Series) - and query_column_to_search is not None - ): - raise ValueError( - "You can't specify query_column_to_search when query is a Series." - ) - - # Only populate options if not set to the default value. - # This avoids accidentally setting options that are mutually exclusive. - options = None - if fraction_lists_to_search is not None: - options = {} if options is None else options - options["fraction_lists_to_search"] = fraction_lists_to_search - if use_brute_force is not None: - options = {} if options is None else options - options["use_brute_force"] = use_brute_force - - (query,) = utils.batch_convert_to_dataframe(query) - sql_string, index_col_ids, index_labels = query._to_sql_query(include_index=True) - - sql = bigframes.core.sql.create_vector_search_sql( - sql_string=sql_string, - base_table=base_table, - column_to_search=column_to_search, - query_column_to_search=query_column_to_search, - top_k=top_k, - distance_type=distance_type, - options=options, - ) - if index_col_ids is not None: - df = query._session.read_gbq_query( - sql, index_col=index_col_ids, allow_large_results=allow_large_results - ) - df.index.names = index_labels - else: - df = query._session.read_gbq_query(sql, allow_large_results=allow_large_results) - - return df diff --git a/bigframes/bigquery/_operations/sql.py b/bigframes/bigquery/_operations/sql.py deleted file mode 100644 index 332d558866b..00000000000 --- a/bigframes/bigquery/_operations/sql.py +++ /dev/null @@ -1,147 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -"""SQL escape hatch features.""" - -from __future__ import annotations - -from typing import Optional, Sequence, Union, cast - -import google.cloud.bigquery - -import bigframes.dataframe -import bigframes.dtypes -import bigframes.operations -import bigframes.series -from bigframes.core.compile.sqlglot import sql - - -def _format_names(sql_template: str, dataframe: bigframes.dataframe.DataFrame): - """Turn sql_template from a template that uses names to one that uses - numbers. - """ - names_to_numbers = {name: f"{{{i}}}" for i, name in enumerate(dataframe.columns)} - numbers = [f"{{{i}}}" for i in range(len(dataframe.columns))] - return sql_template.format(*numbers, **names_to_numbers) - - -def sql_scalar( - sql_template: str, - columns: Union[bigframes.dataframe.DataFrame, Sequence[bigframes.series.Series]], - *, - output_dtype: Optional[bigframes.dtypes.Dtype] = None, -) -> bigframes.series.Series: - """Create a Series from a SQL template. - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> import bigframes.bigquery as bbq - - Either pass in a sequence of series, in which case use integers in the - format strings. - - >>> s = bpd.Series(["1.5", "2.5", "3.5"]) - >>> s = s.astype(pd.ArrowDtype(pa.decimal128(38, 9))) - >>> bbq.sql_scalar("ROUND({0}, 0, 'ROUND_HALF_EVEN')", [s]) - 0 2.000000000 - 1 2.000000000 - 2 4.000000000 - dtype: decimal128(38, 9)[pyarrow] - - Or pass in a DataFrame, in which case use the column names in the format - strings. - - >>> df = bpd.DataFrame({"a": ["1.5", "2.5", "3.5"]}) - >>> df = df.astype({"a": pd.ArrowDtype(pa.decimal128(38, 9))}) - >>> bbq.sql_scalar("ROUND({a}, 0, 'ROUND_HALF_EVEN')", df) - 0 2.000000000 - 1 2.000000000 - 2 4.000000000 - dtype: decimal128(38, 9)[pyarrow] - - You can also use the `.bigquery` DataFrame accessor to apply a SQL scalar function. - - Compute SQL scalar using a pandas DataFrame: - - >>> import pandas as pd - >>> df = pd.DataFrame({"x": [1, 2, 3]}) - >>> bpd.options.display.progress_bar = None # doctest: +SKIP - >>> pandas_s = df.bigquery.sql_scalar("POW({0}, 2)") # doctest: +SKIP - >>> type(pandas_s) # doctest: +SKIP - - - Compute SQL scalar using a BigFrames DataFrame: - - >>> bf_df = bpd.DataFrame({"x": [1, 2, 3]}) - >>> bf_s = bf_df.bigquery.sql_scalar("POW({0}, 2)") # doctest: +SKIP - >>> type(bf_s) # doctest: +SKIP - - - - Args: - sql_template (str): - A SQL format string with Python-style {0} placeholders for each of - the Series objects in ``columns``. - columns ( - Sequence[bigframes.pandas.Series] | bigframes.pandas.DataFrame - ): - Series objects representing the column inputs to the - ``sql_template``. Must contain at least one Series. - output_dtype (a BigQuery DataFrames compatible dtype, optional): - If provided, BigQuery DataFrames uses this to determine the output - of the returned Series. This avoids a dry run query. - - Returns: - bigframes.pandas.Series: - A Series with the SQL applied. - - Raises: - ValueError: If ``columns`` is empty. - """ - if isinstance(columns, bigframes.dataframe.DataFrame): - sql_template = _format_names(sql_template, columns) - columns = [ - cast(bigframes.series.Series, columns[column]) for column in columns.columns - ] - - if len(columns) == 0: - raise ValueError("Must provide at least one column in columns") - - base_series = columns[0] - - # To integrate this into our expression trees, we need to get the output - # type, so we do some manual compilation and a dry run query to get that. - # Another benefit of this is that if there is a syntax error in the SQL - # template, then this will fail with an error earlier in the process, - # aiding users in debugging. - if output_dtype is None: - literals_sql = [ - sql.to_sql(sql.literal(None, column.dtype)) for column in columns - ] - select_sql = sql_template.format(*literals_sql) - dry_run_sql = f"SELECT {select_sql}" - - # Use the executor directly, because we want the original column IDs, not - # the user-friendly column names that block.to_sql_query() would produce. - bqclient = base_series._session.bqclient - job = bqclient.query( - dry_run_sql, job_config=google.cloud.bigquery.QueryJobConfig(dry_run=True) - ) - _, output_dtype = bigframes.dtypes.convert_schema_field(job.schema[0]) - - op = bigframes.operations.SqlScalarOp( - _output_type=output_dtype, sql_template=sql_template - ) - return base_series._apply_nary_op(op, columns[1:]) diff --git a/bigframes/bigquery/_operations/struct.py b/bigframes/bigquery/_operations/struct.py deleted file mode 100644 index 2ee760fb8e5..00000000000 --- a/bigframes/bigquery/_operations/struct.py +++ /dev/null @@ -1,61 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -"""This module integrates BigQuery built-in functions for use with DataFrame objects, -such as array functions: -https://cloud.google.com/bigquery/docs/reference/standard-sql/array_functions.""" - -from __future__ import annotations - -import typing - -import bigframes.operations as ops -import bigframes.series as series - -if typing.TYPE_CHECKING: - import bigframes.dataframe as dataframe - - -def struct(value: dataframe.DataFrame) -> series.Series: - """Takes a DataFrame and converts it into a Series of structs with each - struct entry corresponding to a DataFrame row and each struct field - corresponding to a DataFrame column - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> import bigframes.bigquery as bbq - >>> import bigframes.series as series - - >>> srs = series.Series([{"version": 1, "project": "pandas"}, {"version": 2, "project": "numpy"},]) - >>> df = srs.struct.explode() - >>> bbq.struct(df) - 0 {'version': 1, 'project': 'pandas'} - 1 {'version': 2, 'project': 'numpy'} - dtype: struct[pyarrow] - - Args: - value (bigframes.dataframe.DataFrame): - The DataFrame to be converted to a Series of structs - - Returns: - bigframes.series.Series: A new Series with struct entries representing rows of the original DataFrame - """ - block = value._block - block, result_id = block.apply_nary_op( - block.value_columns, ops.StructOp(column_names=tuple(block.column_labels)) - ) - block = block.select_column(result_id).with_column_labels([None]) - return series.Series(block) diff --git a/bigframes/bigquery/_operations/table.py b/bigframes/bigquery/_operations/table.py deleted file mode 100644 index cad025412d5..00000000000 --- a/bigframes/bigquery/_operations/table.py +++ /dev/null @@ -1,101 +0,0 @@ -# Copyright 2026 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from __future__ import annotations - -from typing import Mapping, Optional, Union - -import google.cloud.bigquery -import pandas as pd - -import bigframes.core.compile.sqlglot.sql as sg_sql -import bigframes.core.logging.log_adapter as log_adapter -import bigframes.session - - -def _get_table_metadata( - *, - bqclient: google.cloud.bigquery.Client, - table_name: str, -) -> pd.Series: - table_metadata = bqclient.get_table(table_name) - table_dict = table_metadata.to_api_repr() - return pd.Series(table_dict) - - -@log_adapter.method_logger(custom_base_name="bigquery_table") -def create_external_table( - table_name: str, - *, - replace: bool = False, - if_not_exists: bool = False, - columns: Optional[Mapping[str, str]] = None, - partition_columns: Optional[Mapping[str, str]] = None, - connection_name: Optional[str] = None, - options: Mapping[str, Union[str, int, float, bool, list]], - session: Optional[bigframes.session.Session] = None, -) -> pd.Series: - """ - Creates a BigQuery external table. - - See the `BigQuery CREATE EXTERNAL TABLE DDL syntax - `_ - for additional reference. - - Args: - table_name (str): - The name of the table in BigQuery. - replace (bool, default False): - Whether to replace the table if it already exists. - if_not_exists (bool, default False): - Whether to ignore the error if the table already exists. - columns (Mapping[str, str], optional): - The table's schema. - partition_columns (Mapping[str, str], optional): - The table's partition columns. - connection_name (str, optional): - The connection to use for the table. - options (Mapping[str, Union[str, int, float, bool, list]]): - The OPTIONS clause, which specifies the table options. - session (bigframes.session.Session, optional): - The session to use. If not provided, the default session is used. - - Returns: - pandas.Series: - A Series with object dtype containing the table metadata. Reference - the `BigQuery Table REST API reference - `_ - for available fields. - """ - import bigframes.pandas as bpd - - sql = sg_sql.to_sql( - sg_sql.create_external_table( - table_name=table_name, - replace=replace, - if_not_exists=if_not_exists, - columns=columns, - partition_columns=partition_columns, - connection_name=connection_name, - options=options, - ) - ) - - if session is None: - bpd.read_gbq_query(sql) - session = bpd.get_global_session() - else: - session.read_gbq_query(sql) - - return _get_table_metadata(bqclient=session.bqclient, table_name=table_name) diff --git a/bigframes/bigquery/_operations/utils.py b/bigframes/bigquery/_operations/utils.py deleted file mode 100644 index 0bae8f47c7a..00000000000 --- a/bigframes/bigquery/_operations/utils.py +++ /dev/null @@ -1,70 +0,0 @@ -# Copyright 2026 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from typing import Optional, Union, cast - -import pandas as pd - -import bigframes -from bigframes import dataframe -from bigframes.ml import base as ml_base - - -def get_model_name_and_session( - model: Union[ml_base.BaseEstimator, str, pd.Series], - # Other dataframe arguments to extract session from - *dataframes: Optional[Union[pd.DataFrame, dataframe.DataFrame, str]], -) -> tuple[str, Optional[bigframes.session.Session]]: - if isinstance(model, pd.Series): - try: - model_ref = model["modelReference"] - model_name = f"{model_ref['projectId']}.{model_ref['datasetId']}.{model_ref['modelId']}" # type: ignore - except KeyError: - raise ValueError("modelReference must be present in the pandas Series.") - elif isinstance(model, str): - model_name = model - else: - if model._bqml_model is None: - raise ValueError("Model must be fitted to be used in ML operations.") - return model._bqml_model.model_name, model._bqml_model.session - - session = None - for df in dataframes: - if isinstance(df, dataframe.DataFrame): - session = df._session - break - - return model_name, session - - -def to_sql(df_or_sql: Union[pd.DataFrame, dataframe.DataFrame, str]) -> str: - """ - Helper to convert DataFrame to SQL string - """ - import bigframes.pandas as bpd - - if isinstance(df_or_sql, str): - return df_or_sql - - if isinstance(df_or_sql, pd.DataFrame): - bf_df = bpd.read_pandas(df_or_sql) - else: - bf_df = cast(dataframe.DataFrame, df_or_sql) - - # Cache dataframes to make sure base table is not a snapshot. - # Cached dataframe creates a full copy, never uses snapshot. - # This is a workaround for internal issue b/310266666. - bf_df.cache() - sql, _, _ = bf_df._to_sql_query(include_index=False) - return sql diff --git a/bigframes/bigquery/aead.py b/bigframes/bigquery/aead.py deleted file mode 100644 index c4243a5c010..00000000000 --- a/bigframes/bigquery/aead.py +++ /dev/null @@ -1,25 +0,0 @@ -# Copyright 2026 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -"""AEAD encryption functions""" - -from __future__ import annotations - -from bigframes.operations.googlesql.aead import decrypt_bytes, decrypt_string, encrypt - -__all__ = [ - "decrypt_bytes", - "decrypt_string", - "encrypt", -] diff --git a/bigframes/bigquery/ai.py b/bigframes/bigquery/ai.py deleted file mode 100644 index 6dd3d116635..00000000000 --- a/bigframes/bigquery/ai.py +++ /dev/null @@ -1,89 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -""" -Integrate BigQuery built-in AI functions into your BigQuery DataFrames workflow. - -The ``bigframes.bigquery.ai`` module provides a Pythonic interface to leverage BigQuery ML's -generative AI and predictive functions directly on BigQuery DataFrames and Series objects. -These functions enable you to perform advanced AI tasks at scale without moving data -out of BigQuery. - -Key capabilities include: - -* **Generative AI:** Use :func:`bigframes.bigquery.ai.generate` (Gemini) to - perform text analysis, translation, or - content generation. Specialized versions like - :func:`~bigframes.bigquery.ai.generate_bool`, - :func:`~bigframes.bigquery.ai.generate_int`, and - :func:`~bigframes.bigquery.ai.generate_double` are available for structured - outputs. -* **Embeddings:** Generate vector embeddings for text using - :func:`~bigframes.bigquery.ai.generate_embedding`, which are essential for - semantic search and retrieval-augmented generation (RAG) workflows. -* **Classification and Scoring:** Apply machine learning models to your data for - predictive tasks with :func:`~bigframes.bigquery.ai.classify` and - :func:`~bigframes.bigquery.ai.score`. -* **Forecasting:** Predict future values in time-series data using - :func:`~bigframes.bigquery.ai.forecast`. - -**Example usage:** - - >>> import bigframes.pandas as bpd - >>> import bigframes.bigquery as bbq - - >>> df = bpd.DataFrame({ - ... "text_input": [ - ... "Is this a positive review? The food was terrible.", - ... ], - ... }) # doctest: +SKIP - - >>> # Assuming a Gemini model has been created in BigQuery as 'my_gemini_model' - >>> result = bq.ai.generate_text("my_gemini_model", df["text_input"]) # doctest: +SKIP - -For more information on the underlying BigQuery ML syntax, see: -https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-ai-generate-bool -""" - -from bigframes.bigquery._operations.ai import ( - classify, - embed, - forecast, - generate, - generate_bool, - generate_double, - generate_embedding, - generate_int, - generate_table, - generate_text, - if_, - score, - similarity, -) - -__all__ = [ - "classify", - "embed", - "forecast", - "generate", - "generate_bool", - "generate_double", - "generate_embedding", - "generate_int", - "generate_table", - "generate_text", - "if_", - "score", - "similarity", -] diff --git a/bigframes/bigquery/ml.py b/bigframes/bigquery/ml.py deleted file mode 100644 index 9b0d77d5b89..00000000000 --- a/bigframes/bigquery/ml.py +++ /dev/null @@ -1,44 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -"""This module exposes `BigQuery ML -`_ functions -by directly mapping to the equivalent function names in SQL syntax. - -For an interface more familiar to Scikit-Learn users, see :mod:`bigframes.ml`. -""" - -from bigframes.bigquery._operations.ml import ( - create_model, - evaluate, - explain_predict, - generate_embedding, - generate_text, - get_insights, - global_explain, - predict, - transform, -) - -__all__ = [ - "create_model", - "evaluate", - "predict", - "explain_predict", - "global_explain", - "transform", - "generate_text", - "generate_embedding", - "get_insights", -] diff --git a/bigframes/bigquery/obj.py b/bigframes/bigquery/obj.py deleted file mode 100644 index dc2c29e1f3d..00000000000 --- a/bigframes/bigquery/obj.py +++ /dev/null @@ -1,41 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -"""This module integrates BigQuery built-in 'ObjectRef' functions for use with Series/DataFrame objects, -such as OBJ.FETCH_METADATA: -https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/objectref_functions - - -.. warning:: - - This product or feature is subject to the "Pre-GA Offerings Terms" in the - General Service Terms section of the `Service Specific Terms - `_. Pre-GA products and - features are available "as is" and might have limited support. For more - information, see the `launch stage descriptions - `_. - -.. note:: - - To provide feedback or request support for this feature, send an email to - bq-objectref-feedback@google.com. -""" - -from bigframes.bigquery._operations.obj import fetch_metadata, get_access_url, make_ref - -__all__ = [ - "fetch_metadata", - "get_access_url", - "make_ref", -] diff --git a/bigframes/clients.py b/bigframes/clients.py index b724843c133..de2421e499f 100644 --- a/bigframes/clients.py +++ b/bigframes/clients.py @@ -17,60 +17,16 @@ from __future__ import annotations import logging -import textwrap import time -from typing import Optional, cast +from typing import cast, Optional import google.api_core.exceptions -import google.api_core.retry from google.cloud import bigquery_connection_v1, resourcemanager_v3 -from google.iam.v1 import policy_pb2 +from google.iam.v1 import iam_policy_pb2, policy_pb2 logger = logging.getLogger(__name__) -def get_canonical_bq_connection_id( - connection_id: str, default_project: str, default_location: str -) -> str: - """ - Retrieve the full connection id of the form - ... - Use default project, location or connection_id when any of them are missing. - """ - - if "/" in connection_id: - fields = connection_id.split("/") - if ( - len(fields) == 6 - and fields[0] == "projects" - and fields[2] == "locations" - and fields[4] == "connections" - ): - return ".".join((fields[1], fields[3], fields[5])) - else: - if connection_id.count(".") == 2: - return connection_id - - if connection_id.count(".") == 1: - return f"{default_project}.{connection_id}" - - if connection_id.count(".") == 0: - return f"{default_project}.{default_location}.{connection_id}" - - raise ValueError( - textwrap.dedent( - f""" - Invalid connection id format: {connection_id}. - Only the following formats are supported: - .., - ., - , - projects//locations//connections/ - """ - ).strip() - ) - - class BqConnectionManager: """Manager to handle operations with BQ connections.""" @@ -85,12 +41,25 @@ def __init__( self._bq_connection_client = bq_connection_client self._cloud_resource_manager_client = cloud_resource_manager_client + @classmethod + def resolve_full_connection_name( + cls, connection_name: str, default_project: str, default_location: str + ) -> str: + """Retrieve the full connection name of the form ... + Use default project, location or connection_id when any of them are missing.""" + if connection_name.count(".") == 2: + return connection_name + + if connection_name.count(".") == 1: + return f"{default_project}.{connection_name}" + + if connection_name.count(".") == 0: + return f"{default_project}.{default_location}.{connection_name}" + + raise ValueError(f"Invalid connection name format: {connection_name}.") + def create_bq_connection( - self, - project_id: str, - location: str, - connection_id: str, - iam_role: Optional[str] = None, + self, project_id: str, location: str, connection_id: str, iam_role: str ): """Create the BQ connection if not exist. In addition, try to add the IAM role to the connection to ensure required permissions. @@ -104,13 +73,19 @@ def create_bq_connection( iam_role: str of the IAM role that the service account of the created connection needs to aquire. E.g. 'run.invoker', 'aiplatform.user' """ + # TODO(shobs): The below command to enable BigQuery Connection API needs + # to be automated. Disabling for now since most target users would not + # have the privilege to enable API in a project. + # log("Making sure BigQuery Connection API is enabled") + # if os.system("gcloud services enable bigqueryconnection.googleapis.com"): + # raise ValueError("Failed to enable BigQuery Connection API") # If the intended connection does not exist then create it service_account_id = self._get_service_account_if_connection_exists( project_id, location, connection_id ) if service_account_id: logger.info( - f"BQ connection {project_id}.{location}.{connection_id} already exists" + f"Connector {project_id}.{location}.{connection_id} already exists" ) else: connection_name, service_account_id = self._create_bq_connection( @@ -120,34 +95,20 @@ def create_bq_connection( f"Created BQ connection {connection_name} with service account id: {service_account_id}" ) service_account_id = cast(str, service_account_id) - # Ensure IAM role on the BQ connection # https://cloud.google.com/bigquery/docs/reference/standard-sql/remote-functions#grant_permission_on_function - if iam_role: - try: - self._ensure_iam_binding(project_id, service_account_id, iam_role) - except google.api_core.exceptions.PermissionDenied as ex: - ex.message = f"Failed ensuring IAM binding (role={iam_role}, service-account={service_account_id}). {ex.message}" - raise - - # Introduce retries to accommodate transient errors like: - # (1) Etag mismatch, - # which can be caused by concurrent operation on the same resource, and - # manifests with message like: - # google.api_core.exceptions.Aborted: 409 There were concurrent policy - # changes. Please retry the whole read-modify-write with exponential - # backoff. The request's ETag '\007\006\003,\264\304\337\272' did not - # match the current policy's ETag '\007\006\003,\3750&\363'. - # (2) Connection creation, - # for which sometimes it takes a bit for its service account to reflect - # across APIs (e.g. b/397662004, b/386838767), before which, an attempt - # to set an IAM policy for the service account may throw an error like: - # google.api_core.exceptions.InvalidArgument: 400 Service account - # bqcx-*@gcp-sa-bigquery-condel.iam.gserviceaccount.com does not exist. + self._ensure_iam_binding(project_id, service_account_id, iam_role) + + # Introduce retries to accommodate transient errors like etag mismatch, + # which can be caused by concurrent operation on the same resource, and + # manifests with message like: + # google.api_core.exceptions.Aborted: 409 There were concurrent policy + # changes. Please retry the whole read-modify-write with exponential + # backoff. The request's ETag '\007\006\003,\264\304\337\272' did not match + # the current policy's ETag '\007\006\003,\3750&\363'. @google.api_core.retry.Retry( predicate=google.api_core.retry.if_exception_type( - google.api_core.exceptions.Aborted, - google.api_core.exceptions.InvalidArgument, + google.api_core.exceptions.Aborted ), initial=10, maximum=20, @@ -161,9 +122,7 @@ def _ensure_iam_binding( project = f"projects/{project_id}" service_account = f"serviceAccount:{service_account_id}" role = f"roles/{iam_role}" - request = { - "resource": project - } # Use a dictionary to avoid problematic google.iam namespace package. + request = iam_policy_pb2.GetIamPolicyRequest(resource=project) policy = self._cloud_resource_manager_client.get_iam_policy(request=request) # Check if the binding already exists, and if does, do nothing more @@ -175,10 +134,7 @@ def _ensure_iam_binding( # Create a new binding new_binding = policy_pb2.Binding(role=role, members=[service_account]) policy.bindings.append(new_binding) - request = { - "resource": project, - "policy": policy, - } # Use a dictionary to avoid problematic google.iam namespace package. + request = iam_policy_pb2.SetIamPolicyRequest(resource=project, policy=policy) self._cloud_resource_manager_client.set_iam_policy(request=request) # We would wait for the IAM policy change to take effect diff --git a/bigframes/constants.py b/bigframes/constants.py index b6e0b8b2211..a1ffd2b755c 100644 --- a/bigframes/constants.py +++ b/bigframes/constants.py @@ -12,124 +12,18 @@ # See the License for the specific language governing permissions and # limitations under the License. +import datetime + """Constants used across BigQuery DataFrames. This module should not depend on any others in the package. """ -import datetime -import textwrap - -DEFAULT_EXPIRATION = datetime.timedelta(days=7) - -# https://cloud.google.com/bigquery/docs/locations -BIGQUERY_REGIONS = frozenset( - { - "africa-south1", - "asia-east1", - "asia-east2", - "asia-northeast1", - "asia-northeast2", - "asia-northeast3", - "asia-south1", - "asia-south2", - "asia-southeast1", - "asia-southeast2", - "australia-southeast1", - "australia-southeast2", - "europe-central2", - "europe-north1", - "europe-southwest1", - "europe-west1", - "europe-west10", - "europe-west12", - "europe-west2", - "europe-west3", - "europe-west4", - "europe-west6", - "europe-west8", - "europe-west9", - "me-central1", - "me-central2", - "me-west1", - "northamerica-northeast1", - "northamerica-northeast2", - "southamerica-east1", - "southamerica-west1", - "us-central1", - "us-east1", - "us-east4", - "us-east5", - "us-south1", - "us-west1", - "us-west2", - "us-west3", - "us-west4", - } +FEEDBACK_LINK = ( + "Share your usecase with the BigQuery DataFrames team at the " + "https://bit.ly/bigframes-feedback survey." ) -BIGQUERY_MULTIREGIONS = frozenset( - { - "EU", - "US", - } -) -ALL_BIGQUERY_LOCATIONS = frozenset(BIGQUERY_REGIONS.union(BIGQUERY_MULTIREGIONS)) -# https://cloud.google.com/storage/docs/regional-endpoints -REP_ENABLED_BIGQUERY_LOCATIONS = frozenset( - { - "europe-west3", - "europe-west8", - "europe-west9", - "me-central2", - "us-central1", - "us-central2", - "us-east1", - "us-east4", - "us-east5", - "us-east7", - "us-south1", - "us-west1", - "us-west2", - "us-west3", - "us-west4", - } -) +ABSTRACT_METHOD_ERROR_MESSAGE = f"Abstract method. You have likely encountered a bug. Please share this stacktrace and how you reached it with the BigQuery DataFrames team. {FEEDBACK_LINK}" -REP_NOT_ENABLED_BIGQUERY_LOCATIONS = frozenset( - ALL_BIGQUERY_LOCATIONS - REP_ENABLED_BIGQUERY_LOCATIONS -) - -LOCATION_NEEDED_FOR_REP_MESSAGE = textwrap.dedent( - """ - Must set location to use regional endpoints. - You can do it via bigframaes.pandas.options.bigquery.location. - The supported locations can be found at - https://cloud.google.com/bigquery/docs/regional-endpoints#supported-locations. - """ -).strip() - -REP_NOT_SUPPORTED_MESSAGE = textwrap.dedent( - """ - Support for regional endpoints for BigQuery and BigQuery Storage APIs may - not be available in the location {location}. For the supported APIs and - locations see https://cloud.google.com/bigquery/docs/regional-endpoints. - If you have the (deprecated) locational endpoints enabled in your project - (which requires your project to be allowlisted), you can override the - endpoints directly by doing the following: - bigframes.pandas.options.bigquery.client_endpoints_override = {{ - "bqclient": "https://{location}-bigquery.googleapis.com", - "bqconnectionclient": "{location}-bigqueryconnection.googleapis.com", - "bqstoragereadclient": "{location}-bigquerystorage.googleapis.com" - }} - """ -).strip() - -# BigQuery default is 10000, leave 100 for overhead -MAX_COLUMNS = 9900 - -# BigQuery has 1 MB query size limit. Don't want to take up more than a few % of that inlining a table. -# Also must assume that text encoding as literals is much less efficient than in-memory representation. -MAX_INLINE_BYTES = 5000 - -SUGGEST_PEEK_PREVIEW = "Use .peek(n) to preview n arbitrary rows." +DEFAULT_EXPIRATION = datetime.timedelta(days=7) diff --git a/bigframes/core/__init__.py b/bigframes/core/__init__.py index 2f3f15953cd..866be9c4003 100644 --- a/bigframes/core/__init__.py +++ b/bigframes/core/__init__.py @@ -11,7 +11,430 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. +from __future__ import annotations -from bigframes.core.array_value import ArrayValue +from dataclasses import dataclass +import io +import typing +from typing import Iterable, Literal, Optional, Sequence, Tuple -__all__ = ["ArrayValue"] +from google.cloud import bigquery +import ibis +import ibis.expr.types as ibis_types +import pandas + +import bigframes.core.compile as compiled +import bigframes.core.guid +import bigframes.core.nodes as nodes +from bigframes.core.ordering import OrderingColumnReference +import bigframes.core.ordering as orderings +from bigframes.core.window_spec import WindowSpec +import bigframes.dtypes +import bigframes.operations as ops +import bigframes.operations.aggregations as agg_ops +import bigframes.session._io.bigquery + +if typing.TYPE_CHECKING: + from bigframes.session import Session + +ORDER_ID_COLUMN = "bigframes_ordering_id" +PREDICATE_COLUMN = "bigframes_predicate" + + +@dataclass(frozen=True) +class ArrayValue: + """ + ArrayValue is an immutable type representing a 2D array with per-column types. + """ + + node: nodes.BigFrameNode + + @classmethod + def from_ibis( + cls, + session: Session, + table: ibis_types.Table, + columns: Sequence[ibis_types.Value], + hidden_ordering_columns: Sequence[ibis_types.Value], + ordering: orderings.ExpressionOrdering, + ): + node = nodes.ReadGbqNode( + table=table, + table_session=session, + columns=tuple(columns), + hidden_ordering_columns=tuple(hidden_ordering_columns), + ordering=ordering, + ) + return cls(node) + + @classmethod + def from_pandas(cls, pd_df: pandas.DataFrame): + iobytes = io.BytesIO() + # Discard row labels and use simple string ids for columns + column_ids = tuple(str(label) for label in pd_df.columns) + pd_df.reset_index(drop=True).set_axis(column_ids, axis=1).to_feather(iobytes) + node = nodes.ReadLocalNode(iobytes.getvalue(), column_ids=column_ids) + return cls(node) + + @property + def column_ids(self) -> typing.Sequence[str]: + return self.compile().column_ids + + @property + def session(self) -> Session: + required_session = self.node.session + from bigframes import get_global_session + + return self.node.session[0] if required_session else get_global_session() + + def get_column_type(self, key: str) -> bigframes.dtypes.Dtype: + return self.compile().get_column_type(key) + + def compile(self) -> compiled.CompiledArrayValue: + return compiled.compile_node(self.node) + + def shape(self) -> typing.Tuple[int, int]: + """Returns dimensions as (length, width) tuple.""" + width = len(self.compile().columns) + count_expr = self.compile()._to_ibis_expr("unordered").count() + + # Support in-memory engines for hermetic unit tests. + if not self.node.session: + try: + length = ibis.pandas.connect({}).execute(count_expr) + return (length, width) + except Exception: + # Not all cases can be handled by pandas engine + pass + + sql = self.session.ibis_client.compile(count_expr) + row_iterator, _ = self.session._start_query( + sql=sql, + max_results=1, + ) + length = next(row_iterator)[0] + return (length, width) + + def to_sql( + self, + offset_column: typing.Optional[str] = None, + col_id_overrides: typing.Mapping[str, str] = {}, + sorted: bool = False, + ) -> str: + return self.compile().to_sql( + offset_column=offset_column, + col_id_overrides=col_id_overrides, + sorted=sorted, + ) + + def start_query( + self, + job_config: Optional[bigquery.job.QueryJobConfig] = None, + max_results: Optional[int] = None, + *, + sorted: bool = True, + ) -> Tuple[bigquery.table.RowIterator, bigquery.QueryJob]: + """Execute a query and return metadata about the results.""" + # TODO(swast): Cache the job ID so we can look it up again if they ask + # for the results? We'd need a way to invalidate the cache if DataFrame + # becomes mutable, though. Or move this method to the immutable + # expression class. + # TODO(swast): We might want to move this method to Session and/or + # provide our own minimal metadata class. Tight coupling to the + # BigQuery client library isn't ideal, especially if we want to support + # a LocalSession for unit testing. + # TODO(swast): Add a timeout here? If the query is taking a long time, + # maybe we just print the job metadata that we have so far? + sql = self.to_sql(sorted=sorted) # type:ignore + return self.session._start_query( + sql=sql, + job_config=job_config, + max_results=max_results, + ) + + def cached(self, cluster_cols: typing.Sequence[str]) -> ArrayValue: + """Write the ArrayValue to a session table and create a new block object that references it.""" + compiled_value = self.compile() + ibis_expr = compiled_value._to_ibis_expr( + ordering_mode="unordered", expose_hidden_cols=True + ) + tmp_table = self.session._ibis_to_session_table( + ibis_expr, cluster_cols=cluster_cols, api_name="cached" + ) + + table_expression = self.session.ibis_client.table( + f"{tmp_table.project}.{tmp_table.dataset_id}.{tmp_table.table_id}" + ) + new_columns = [table_expression[column] for column in compiled_value.column_ids] + new_hidden_columns = [ + table_expression[column] + for column in compiled_value._hidden_ordering_column_names + ] + return ArrayValue.from_ibis( + self.session, + table_expression, + columns=new_columns, + hidden_ordering_columns=new_hidden_columns, + ordering=compiled_value._ordering, + ) + + # Operations + + def drop_columns(self, columns: Iterable[str]) -> ArrayValue: + return ArrayValue( + nodes.DropColumnsNode(child=self.node, columns=tuple(columns)) + ) + + def filter(self, predicate_id: str, keep_null: bool = False) -> ArrayValue: + """Filter the table on a given expression, the predicate must be a boolean series aligned with the table expression.""" + return ArrayValue( + nodes.FilterNode( + child=self.node, predicate_id=predicate_id, keep_null=keep_null + ) + ) + + def order_by( + self, by: Sequence[OrderingColumnReference], stable: bool = False + ) -> ArrayValue: + return ArrayValue( + nodes.OrderByNode(child=self.node, by=tuple(by), stable=stable) + ) + + def reversed(self) -> ArrayValue: + return ArrayValue(nodes.ReversedNode(child=self.node)) + + def promote_offsets(self, col_id: str) -> ArrayValue: + """ + Convenience function to promote copy of column offsets to a value column. Can be used to reset index. + """ + return ArrayValue(nodes.PromoteOffsetsNode(child=self.node, col_id=col_id)) + + def select_columns(self, column_ids: typing.Sequence[str]) -> ArrayValue: + return ArrayValue( + nodes.SelectNode(child=self.node, column_ids=tuple(column_ids)) + ) + + def concat(self, other: typing.Sequence[ArrayValue]) -> ArrayValue: + """Append together multiple ArrayValue objects.""" + return ArrayValue( + nodes.ConcatNode(children=tuple([self.node, *[val.node for val in other]])) + ) + + def project_unary_op( + self, column_name: str, op: ops.UnaryOp, output_name=None + ) -> ArrayValue: + """Creates a new expression based on this expression with unary operation applied to one column.""" + return ArrayValue( + nodes.ProjectUnaryOpNode( + child=self.node, input_id=column_name, op=op, output_id=output_name + ) + ) + + def project_binary_op( + self, + left_column_id: str, + right_column_id: str, + op: ops.BinaryOp, + output_column_id: str, + ) -> ArrayValue: + """Creates a new expression based on this expression with binary operation applied to two columns.""" + return ArrayValue( + nodes.ProjectBinaryOpNode( + child=self.node, + left_input_id=left_column_id, + right_input_id=right_column_id, + op=op, + output_id=output_column_id, + ) + ) + + def project_ternary_op( + self, + col_id_1: str, + col_id_2: str, + col_id_3: str, + op: ops.TernaryOp, + output_column_id: str, + ) -> ArrayValue: + """Creates a new expression based on this expression with ternary operation applied to three columns.""" + return ArrayValue( + nodes.ProjectTernaryOpNode( + child=self.node, + input_id1=col_id_1, + input_id2=col_id_2, + input_id3=col_id_3, + op=op, + output_id=output_column_id, + ) + ) + + def aggregate( + self, + aggregations: typing.Sequence[typing.Tuple[str, agg_ops.AggregateOp, str]], + by_column_ids: typing.Sequence[str] = (), + dropna: bool = True, + ) -> ArrayValue: + """ + Apply aggregations to the expression. + Arguments: + aggregations: input_column_id, operation, output_column_id tuples + by_column_id: column id of the aggregation key, this is preserved through the transform + dropna: whether null keys should be dropped + """ + return ArrayValue( + nodes.AggregateNode( + child=self.node, + aggregations=tuple(aggregations), + by_column_ids=tuple(by_column_ids), + dropna=dropna, + ) + ) + + def corr_aggregate( + self, corr_aggregations: typing.Sequence[typing.Tuple[str, str, str]] + ) -> ArrayValue: + """ + Get correlations between each lef_column_id and right_column_id, stored in the respective output_column_id. + This uses BigQuery's CORR under the hood, and thus only Pearson's method is used. + Arguments: + corr_aggregations: left_column_id, right_column_id, output_column_id tuples + """ + return ArrayValue( + nodes.CorrNode(child=self.node, corr_aggregations=tuple(corr_aggregations)) + ) + + def project_window_op( + self, + column_name: str, + op: agg_ops.WindowOp, + window_spec: WindowSpec, + output_name=None, + *, + never_skip_nulls=False, + skip_reproject_unsafe: bool = False, + ) -> ArrayValue: + """ + Creates a new expression based on this expression with unary operation applied to one column. + column_name: the id of the input column present in the expression + op: the windowable operator to apply to the input column + window_spec: a specification of the window over which to apply the operator + output_name: the id to assign to the output of the operator, by default will replace input col if distinct output id not provided + never_skip_nulls: will disable null skipping for operators that would otherwise do so + skip_reproject_unsafe: skips the reprojection step, can be used when performing many non-dependent window operations, user responsible for not nesting window expressions, or using outputs as join, filter or aggregation keys before a reprojection + """ + return ArrayValue( + nodes.WindowOpNode( + child=self.node, + column_name=column_name, + op=op, + window_spec=window_spec, + output_name=output_name, + never_skip_nulls=never_skip_nulls, + skip_reproject_unsafe=skip_reproject_unsafe, + ) + ) + + def _reproject_to_table(self) -> ArrayValue: + """ + Internal operators that projects the internal representation into a + new ibis table expression where each value column is a direct + reference to a column in that table expression. Needed after + some operations such as window operations that cannot be used + recursively in projections. + """ + return ArrayValue( + nodes.ReprojectOpNode( + child=self.node, + ) + ) + + def unpivot( + self, + row_labels: typing.Sequence[typing.Hashable], + unpivot_columns: typing.Sequence[ + typing.Tuple[str, typing.Tuple[typing.Optional[str], ...]] + ], + *, + passthrough_columns: typing.Sequence[str] = (), + index_col_ids: typing.Sequence[str] = ["index"], + dtype: typing.Union[ + bigframes.dtypes.Dtype, typing.Tuple[bigframes.dtypes.Dtype, ...] + ] = pandas.Float64Dtype(), + how: typing.Literal["left", "right"] = "left", + ) -> ArrayValue: + """ + Unpivot ArrayValue columns. + + Args: + row_labels: Identifies the source of the row. Must be equal to length to source column list in unpivot_columns argument. + unpivot_columns: Mapping of column id to list of input column ids. Lists of input columns may use None. + passthrough_columns: Columns that will not be unpivoted. Column id will be preserved. + index_col_id (str): The column id to be used for the row labels. + dtype (dtype or list of dtype): Dtype to use for the unpivot columns. If list, must be equal in number to unpivot_columns. + + Returns: + ArrayValue: The unpivoted ArrayValue + """ + return ArrayValue( + nodes.UnpivotNode( + child=self.node, + row_labels=tuple(row_labels), + unpivot_columns=tuple(unpivot_columns), + passthrough_columns=tuple(passthrough_columns), + index_col_ids=tuple(index_col_ids), + dtype=dtype, + how=how, + ) + ) + + def assign(self, source_id: str, destination_id: str) -> ArrayValue: + return ArrayValue( + nodes.AssignNode( + child=self.node, source_id=source_id, destination_id=destination_id + ) + ) + + def assign_constant( + self, + destination_id: str, + value: typing.Any, + dtype: typing.Optional[bigframes.dtypes.Dtype], + ) -> ArrayValue: + return ArrayValue( + nodes.AssignConstantNode( + child=self.node, destination_id=destination_id, value=value, dtype=dtype + ) + ) + + def join( + self, + self_column_ids: typing.Sequence[str], + other: ArrayValue, + other_column_ids: typing.Sequence[str], + *, + how: Literal[ + "inner", + "left", + "outer", + "right", + ], + allow_row_identity_join: bool = True, + ): + return ArrayValue( + nodes.JoinNode( + left_child=self.node, + right_child=other.node, + left_column_ids=tuple(self_column_ids), + right_column_ids=tuple(other_column_ids), + how=how, + allow_row_identity_join=allow_row_identity_join, + ) + ) + + def _uniform_sampling(self, fraction: float) -> ArrayValue: + """Sampling the table on given fraction. + + .. warning:: + The row numbers of result is non-deterministic, avoid to use. + """ + return ArrayValue(nodes.RandomSampleNode(self.node, fraction)) diff --git a/bigframes/core/agg_expressions.py b/bigframes/core/agg_expressions.py deleted file mode 100644 index 6d126c92420..00000000000 --- a/bigframes/core/agg_expressions.py +++ /dev/null @@ -1,231 +0,0 @@ -# Copyright 2023 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from __future__ import annotations - -import abc -import dataclasses -import functools -import itertools -import typing -from typing import Callable, Hashable, Mapping, Tuple, TypeVar - -import bigframes.core.identifiers as ids -import bigframes.operations.aggregations as agg_ops -from bigframes import dtypes -from bigframes.core import expression, window_spec - -TExpression = TypeVar("TExpression", bound="Aggregation") - - -@dataclasses.dataclass(frozen=True) -class Aggregation(expression.Expression): - """Represents windowing or aggregation over a column.""" - - op: agg_ops.WindowOp = dataclasses.field() - - @property - def column_references(self) -> typing.Tuple[ids.ColumnId, ...]: - return tuple( - itertools.chain.from_iterable( - map(lambda x: x.column_references, self.inputs) - ) - ) - - @functools.cached_property - def is_resolved(self) -> bool: - return all(input.is_resolved for input in self.inputs) - - @functools.cached_property - def output_type(self) -> dtypes.ExpressionType: - if not self.is_resolved: - raise ValueError(f"Type of expression {self.op} has not been fixed.") - - input_types = [input.output_type for input in self.inputs] - - return self.op.output_type(*input_types) - - @property - @abc.abstractmethod - def inputs( - self, - ) -> typing.Tuple[expression.Expression, ...]: ... - - @property - def children(self) -> Tuple[expression.Expression, ...]: - return self.inputs - - @property - def free_variables(self) -> typing.Tuple[Hashable, ...]: - return tuple( - itertools.chain.from_iterable(map(lambda x: x.free_variables, self.inputs)) - ) - - @property - def is_const(self) -> bool: - return all(child.is_const for child in self.inputs) - - @functools.cached_property - def is_scalar_expr(self) -> bool: - return False - - @abc.abstractmethod - def replace_args(self: TExpression, *arg) -> TExpression: ... - - def transform_children( - self: TExpression, t: Callable[[expression.Expression], expression.Expression] - ) -> TExpression: - return self.replace_args(*(t(arg) for arg in self.inputs)) - - def bind_variables( - self: TExpression, - bindings: Mapping[Hashable, expression.Expression], - allow_partial_bindings: bool = False, - ) -> TExpression: - return self.transform_children( - lambda x: x.bind_variables(bindings, allow_partial_bindings) - ) - - def bind_refs( - self: TExpression, - bindings: Mapping[ids.ColumnId, expression.Expression], - allow_partial_bindings: bool = False, - ) -> TExpression: - return self.transform_children( - lambda x: x.bind_refs(bindings, allow_partial_bindings) - ) - - -@dataclasses.dataclass(frozen=True) -class NullaryAggregation(Aggregation): - op: agg_ops.NullaryWindowOp = dataclasses.field() - - @property - def inputs( - self, - ) -> typing.Tuple[expression.Expression, ...]: - return () - - def replace_args(self, *arg) -> NullaryAggregation: - return self - - -@dataclasses.dataclass(frozen=True) -class UnaryAggregation(Aggregation): - op: agg_ops.UnaryWindowOp - arg: expression.Expression - - @property - def inputs( - self, - ) -> typing.Tuple[expression.Expression, ...]: - return (self.arg,) - - def replace_args(self, arg: expression.Expression) -> UnaryAggregation: - return UnaryAggregation( - self.op, - arg, - ) - - -@dataclasses.dataclass(frozen=True) -class BinaryAggregation(Aggregation): - op: agg_ops.BinaryAggregateOp = dataclasses.field() - left: expression.Expression = dataclasses.field() - right: expression.Expression = dataclasses.field() - - @property - def inputs( - self, - ) -> typing.Tuple[expression.Expression, ...]: - return (self.left, self.right) - - def replace_args( - self, larg: expression.Expression, rarg: expression.Expression - ) -> BinaryAggregation: - return BinaryAggregation(self.op, larg, rarg) - - -@dataclasses.dataclass(frozen=True) -class WindowExpression(expression.Expression): - analytic_expr: Aggregation - window: window_spec.WindowSpec - - @property - def column_references(self) -> typing.Tuple[ids.ColumnId, ...]: - return tuple( - itertools.chain.from_iterable( - map(lambda x: x.column_references, self.inputs) - ) - ) - - @functools.cached_property - def is_resolved(self) -> bool: - return all(input.is_resolved for input in self.inputs) - - @property - def output_type(self) -> dtypes.ExpressionType: - return self.analytic_expr.output_type - - @property - def inputs( - self, - ) -> typing.Tuple[expression.Expression, ...]: - # TODO: Maybe make the window spec itself an expression? - return (self.analytic_expr, *self.window.expressions) - - @property - def children(self) -> Tuple[expression.Expression, ...]: - return self.inputs - - @property - def free_variables(self) -> typing.Tuple[Hashable, ...]: - return tuple( - itertools.chain.from_iterable(map(lambda x: x.free_variables, self.inputs)) - ) - - @property - def is_const(self) -> bool: - return all(child.is_const for child in self.inputs) - - @functools.cached_property - def is_scalar_expr(self) -> bool: - return False - - def transform_children( - self: WindowExpression, - t: Callable[[expression.Expression], expression.Expression], - ) -> WindowExpression: - return WindowExpression( - t(self.analytic_expr), # type: ignore - self.window.transform_exprs(t), - ) - - def bind_variables( - self: WindowExpression, - bindings: Mapping[Hashable, expression.Expression], - allow_partial_bindings: bool = False, - ) -> WindowExpression: - return self.transform_children( - lambda x: x.bind_variables(bindings, allow_partial_bindings) - ) - - def bind_refs( - self: WindowExpression, - bindings: Mapping[ids.ColumnId, expression.Expression], - allow_partial_bindings: bool = False, - ) -> WindowExpression: - return self.transform_children( - lambda x: x.bind_refs(bindings, allow_partial_bindings) - ) diff --git a/bigframes/core/array_value.py b/bigframes/core/array_value.py deleted file mode 100644 index d7fb186ae91..00000000000 --- a/bigframes/core/array_value.py +++ /dev/null @@ -1,645 +0,0 @@ -# Copyright 2023 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -from __future__ import annotations - -import datetime -import functools -import typing -from dataclasses import dataclass -from typing import Iterable, List, Mapping, Optional, Sequence, Tuple, Union - -import pandas -import pyarrow as pa - -import bigframes.core.expression as ex -import bigframes.core.guid -import bigframes.core.identifiers as ids -import bigframes.core.nodes as nodes -import bigframes.core.ordering as orderings -import bigframes.core.schema as schemata -import bigframes.core.tree_properties -import bigframes.dtypes -import bigframes.operations as ops -import bigframes.operations.aggregations as agg_ops -from bigframes.core import ( - agg_expressions, - bq_data, - expression_factoring, - join_def, - local_data, -) -from bigframes.core.ordering import OrderingExpression -from bigframes.core.window_spec import WindowSpec - -if typing.TYPE_CHECKING: - from bigframes.session import Session - -ORDER_ID_COLUMN = "bigframes_ordering_id" -PREDICATE_COLUMN = "bigframes_predicate" - - -@dataclass(frozen=True) -class ArrayValue: - """ - ArrayValue is an immutable type representing a 2D array with per-column types. - """ - - node: nodes.BigFrameNode - - @classmethod - def from_pyarrow(cls, arrow_table: pa.Table, session: Session): - data_source = local_data.ManagedArrowTable.from_pyarrow(arrow_table) - return cls.from_managed(source=data_source, session=session) - - @classmethod - def from_managed(cls, source: local_data.ManagedArrowTable, session: Session): - scan_list = nodes.ScanList( - tuple( - nodes.ScanItem(ids.ColumnId(item.column), item.column) - for item in source.schema.items - ) - ) - node = nodes.ReadLocalNode( - source, - session=session, - scan_list=scan_list, - ) - return cls(node) - - @classmethod - def from_range(cls, start, end, step): - return cls( - nodes.FromRangeNode( - start=start.node, - end=end.node, - step=step, - ) - ) - - @classmethod - def from_table( - cls, - table: Union[bq_data.BiglakeIcebergTable, bq_data.GbqNativeTable], - session: Session, - *, - columns: Optional[Sequence[str]] = None, - predicate: Optional[str] = None, - at_time: Optional[datetime.datetime] = None, - primary_key: Sequence[str] = (), - offsets_col: Optional[str] = None, - n_rows: Optional[int] = None, - ): - if offsets_col and primary_key: - raise ValueError("must set at most one of 'offests', 'primary_key'") - - # create ordering from info - ordering = None - if offsets_col: - ordering = orderings.TotalOrdering.from_offset_col(offsets_col) - elif primary_key: - ordering = orderings.TotalOrdering.from_primary_key( - [ids.ColumnId(key_part) for key_part in primary_key] - ) - - bf_schema = schemata.ArraySchema.from_bq_schema( - table.physical_schema, columns=columns - ) - # Scan all columns by default, we define this list as it can be pruned while preserving source_def - scan_list = nodes.ScanList( - tuple( - nodes.ScanItem(ids.ColumnId(item.column), item.column) - for item in bf_schema.items - ) - ) - source_def = bq_data.BigqueryDataSource( - table=table, - schema=bf_schema, - at_time=at_time, - sql_predicate=predicate, - ordering=ordering, - n_rows=n_rows, - ) - return cls.from_bq_data_source(source_def, scan_list, session) - - @classmethod - def from_bq_data_source( - cls, - source: bq_data.BigqueryDataSource, - scan_list: nodes.ScanList, - session: Session, - ): - node = nodes.ReadTableNode( - source=source, - scan_list=scan_list, - table_session=session, - ) - return cls(node) - - @property - def column_ids(self) -> typing.Sequence[str]: - """Returns column ids as strings.""" - return self.schema.names - - @property - def session(self) -> Session: - required_session = self.node.session - from bigframes import get_global_session - - return ( - required_session if (required_session is not None) else get_global_session() - ) - - @functools.cached_property - def schema(self) -> schemata.ArraySchema: - return self.node.schema - - @property - def explicitly_ordered(self) -> bool: - # see BigFrameNode.explicitly_ordered - return self.node.explicitly_ordered - - @property - def order_ambiguous(self) -> bool: - # see BigFrameNode.order_ambiguous - return self.node.order_ambiguous - - @property - def supports_fast_peek(self) -> bool: - return bigframes.core.tree_properties.can_fast_peek(self.node) - - def get_column_type(self, key: str) -> bigframes.dtypes.Dtype: - return self.schema.get_type(key) - - def row_count(self) -> ArrayValue: - """Get number of rows in ArrayValue as a single-entry ArrayValue.""" - return ArrayValue( - nodes.AggregateNode( - child=self.node, - aggregations=( - ( - agg_expressions.NullaryAggregation(agg_ops.size_op), - ids.ColumnId(bigframes.core.guid.generate_guid()), - ), - ), - ) - ) - - # Operations - def filter_by_id(self, predicate_id: str, keep_null: bool = False) -> ArrayValue: - """Filter the table on a given expression, the predicate must be a boolean series aligned with the table expression.""" - predicate: ex.Expression = ex.deref(predicate_id) - if keep_null: - predicate = ops.fillna_op.as_expr(predicate, ex.const(True)) - return self.filter(predicate) - - def filter(self, predicate: ex.Expression): - if predicate.is_scalar_expr: - return ArrayValue(nodes.FilterNode(child=self.node, predicate=predicate)) - else: - arr, filter_ids = self.compute_general_expression([predicate]) - arr = arr.filter_by_id(filter_ids[0]) - return arr.drop_columns(filter_ids) - - def order_by( - self, - by: Sequence[OrderingExpression], - is_total_order: bool = False, - stable: bool = True, - ) -> ArrayValue: - return ArrayValue( - nodes.OrderByNode( - child=self.node, - by=tuple(by), - is_total_order=is_total_order, - stable=stable, - ) - ) - - def reversed(self) -> ArrayValue: - return ArrayValue(nodes.ReversedNode(child=self.node)) - - def slice( - self, start: Optional[int], stop: Optional[int], step: Optional[int] - ) -> ArrayValue: - return ArrayValue( - nodes.SliceNode( - self.node, - start=start, - stop=stop, - step=step if (step is not None) else 1, - ) - ) - - def promote_offsets(self) -> Tuple[ArrayValue, str]: - """ - Convenience function to promote copy of column offsets to a value column. Can be used to reset index. - """ - col_id = self._gen_namespaced_uid() - return ( - ArrayValue( - nodes.PromoteOffsetsNode(child=self.node, col_id=ids.ColumnId(col_id)) - ), - col_id, - ) - - def concat(self, other: typing.Sequence[ArrayValue]) -> ArrayValue: - """Append together multiple ArrayValue objects.""" - return ArrayValue( - nodes.ConcatNode( - children=tuple([self.node, *[val.node for val in other]]), - output_ids=tuple( - ids.ColumnId(bigframes.core.guid.generate_guid()) - for id in self.column_ids - ), - ) - ) - - def compute_values(self, assignments: Sequence[ex.Expression]): - col_ids = self._gen_namespaced_uids(len(assignments)) - ex_id_pairs = tuple( - (ex, ids.ColumnId(id)) for ex, id in zip(assignments, col_ids) - ) - return ( - ArrayValue(nodes.ProjectionNode(child=self.node, assignments=ex_id_pairs)), - col_ids, - ) - - def compute_general_expression(self, assignments: Sequence[ex.Expression]): - """ - Applies arbitrary column expressions to the current execution block. - - This method transforms the logical plan by applying a sequence of expressions that - preserve the length of the input columns. It supports both scalar operations - and window functions. Each expression is assigned a unique internal column identifier. - - Args: - assignments (Sequence[ex.Expression]): A sequence of expression objects - representing the transformations to apply to the columns. - - Returns: - Tuple[ArrayValue, Tuple[str, ...]]: A tuple containing: - - An `ArrayValue` wrapping the new root node of the updated logical plan. - - A tuple of strings representing the unique column IDs generated for - each expression in the assignments. - """ - named_exprs = [ - nodes.ColumnDef(expr, ids.ColumnId.unique()) for expr in assignments - ] - # TODO: Push this to rewrite later to go from block expression to planning form - new_root = expression_factoring.apply_col_exprs_to_plan(self.node, named_exprs) - - target_ids = tuple(named_expr.id for named_expr in named_exprs) - return (ArrayValue(new_root), target_ids) - - def compute_general_reduction( - self, - assignments: Sequence[ex.Expression], - by_column_ids: typing.Sequence[str] = (), - *, - dropna: bool = False, - ): - """ - Applies arbitrary aggregation expressions to the block, optionally grouped by keys. - - This method handles reduction operations (e.g., sum, mean, count) that collapse - multiple input rows into a single scalar value per group. If grouping keys are - provided, the operation is performed per group; otherwise, it is a global reduction. - - Note: Intermediate aggregations (those that are inputs to further aggregations) - must be windowizable. Notably excluded are approx quantile, top count ops. - - Args: - assignments (Sequence[ex.Expression]): A sequence of aggregation expressions - to be calculated. - by_column_ids (typing.Sequence[str], optional): A sequence of column IDs - to use as grouping keys. Defaults to an empty tuple (global reduction). - dropna (bool, optional): If True, rows containing null values in the - `by_column_ids` columns will be filtered out before the reduction - is applied. Defaults to False. - - Returns: - ArrayValue: - The new root node representing the aggregation/group-by result. - """ - plan = self.node - - # shortcircuit to keep things simple if all aggs are simple - # TODO: Fully unify paths once rewriters are strong enough to simplify complexity from full path - def _is_direct_agg(agg_expr): - return isinstance(agg_expr, agg_expressions.Aggregation) and all( - isinstance(child, (ex.DerefOp, ex.ScalarConstantExpression)) - for child in agg_expr.children - ) - - if all(_is_direct_agg(agg) for agg in assignments): - agg_defs = tuple((agg, ids.ColumnId.unique()) for agg in assignments) - return ArrayValue( - nodes.AggregateNode( - child=self.node, - aggregations=agg_defs, # type: ignore - by_column_ids=tuple(map(ex.deref, by_column_ids)), - dropna=dropna, - ) - ) - - if dropna: - for col_id in by_column_ids: - plan = nodes.FilterNode(plan, ops.notnull_op.as_expr(col_id)) - - named_exprs = [ - nodes.ColumnDef(expr, ids.ColumnId.unique()) for expr in assignments - ] - # TODO: Push this to rewrite later to go from block expression to planning form - new_root = expression_factoring.apply_agg_exprs_to_plan( - plan, named_exprs, grouping_keys=[ex.deref(by) for by in by_column_ids] - ) - return ArrayValue(new_root) - - def project_to_id(self, expression: ex.Expression): - array_val, ids = self.compute_values( - [expression], - ) - return array_val, ids[0] - - def assign(self, source_id: str, destination_id: str) -> ArrayValue: - if destination_id in self.column_ids: # Mutate case - exprs = [ - ( - bigframes.core.nodes.AliasedRef( - ex.deref(source_id if (col_id == destination_id) else col_id), - ids.ColumnId(col_id), - ) - ) - for col_id in self.column_ids - ] - else: # append case - self_projection = ( - bigframes.core.nodes.AliasedRef.identity(ids.ColumnId(col_id)) - for col_id in self.column_ids - ) - exprs = [ - *self_projection, - ( - bigframes.core.nodes.AliasedRef( - ex.deref(source_id), ids.ColumnId(destination_id) - ) - ), - ] - return ArrayValue( - nodes.SelectionNode( - child=self.node, - input_output_pairs=tuple(exprs), - ) - ) - - def create_constant( - self, - value: typing.Any, - dtype: typing.Optional[bigframes.dtypes.Dtype], - ) -> Tuple[ArrayValue, str]: - if pandas.isna(value): - # Need to assign a data type when value is NaN. - dtype = dtype or bigframes.dtypes.DEFAULT_DTYPE - - return self.project_to_id(ex.const(value, dtype)) - - def select_columns( - self, column_ids: typing.Sequence[str], allow_renames: bool = False - ) -> ArrayValue: - # This basically just drops and reorders columns - logically a no-op except as a final step - selections = [] - seen = set() - - for id in column_ids: - if id not in seen: - ref = nodes.AliasedRef.identity(ids.ColumnId(id)) - elif allow_renames: - ref = nodes.AliasedRef( - ex.deref(id), ids.ColumnId(bigframes.core.guid.generate_guid()) - ) - else: - raise ValueError( - "Must set allow_renames=True to select columns repeatedly" - ) - selections.append(ref) - seen.add(id) - - return ArrayValue( - nodes.SelectionNode( - child=self.node, - input_output_pairs=tuple(selections), - ) - ) - - def rename_columns(self, col_id_overrides: Mapping[str, str]) -> ArrayValue: - if not col_id_overrides: - return self - output_ids = [col_id_overrides.get(id, id) for id in self.node.schema.names] - return ArrayValue( - nodes.SelectionNode( - self.node, - tuple( - nodes.AliasedRef(ex.DerefOp(old_id), ids.ColumnId(out_id)) - for old_id, out_id in zip(self.node.ids, output_ids) - ), - ) - ) - - def drop_columns(self, columns: Iterable[str]) -> ArrayValue: - return self.select_columns( - [col_id for col_id in self.column_ids if col_id not in columns] - ) - - def aggregate( - self, - aggregations: typing.Sequence[typing.Tuple[agg_expressions.Aggregation, str]], - by_column_ids: typing.Sequence[str] = (), - dropna: bool = True, - ) -> ArrayValue: - """ - Apply aggregations to the expression. - Arguments: - aggregations: input_column_id, operation, output_column_id tuples - by_column_id: column id of the aggregation key, this is preserved through the transform - dropna: whether null keys should be dropped - """ - agg_defs = tuple((agg, ids.ColumnId(name)) for agg, name in aggregations) - return ArrayValue( - nodes.AggregateNode( - child=self.node, - aggregations=agg_defs, - by_column_ids=tuple(map(ex.deref, by_column_ids)), - dropna=dropna, - ) - ) - - def project_window_expr( - self, - expressions: Sequence[agg_expressions.Aggregation], - window: WindowSpec, - ): - id_strings = [self._gen_namespaced_uid() for _ in expressions] - agg_exprs = tuple( - nodes.ColumnDef(expression, ids.ColumnId(id_str)) - for expression, id_str in zip(expressions, id_strings) - ) - - return ( - ArrayValue( - nodes.WindowOpNode( - child=self.node, - agg_exprs=agg_exprs, - window_spec=window, - ) - ), - id_strings, - ) - - def isin( - self, - other: ArrayValue, - lcol: str, - ) -> typing.Tuple[ArrayValue, str]: - assert len(other.column_ids) == 1 - node = nodes.InNode( - self.node, - other.node, - ex.deref(lcol), - indicator_col=ids.ColumnId.unique(), - ) - return ArrayValue(node), node.indicator_col.name - - def relational_join( - self, - other: ArrayValue, - conditions: typing.Tuple[typing.Tuple[str, str], ...] = (), - type: typing.Literal["inner", "outer", "left", "right", "cross"] = "inner", - propogate_order: Optional[bool] = None, - ) -> typing.Tuple[ArrayValue, typing.Tuple[dict[str, str], dict[str, str]]]: - for lcol, rcol in conditions: - ltype = self.get_column_type(lcol) - rtype = other.get_column_type(rcol) - if not bigframes.dtypes.can_compare(ltype, rtype): - raise TypeError( - f"Cannot join with non-comparable join key types: {ltype}, {rtype}" - ) - - l_mapping = { # Identity mapping, only rename right side - lcol.name: lcol.name for lcol in self.node.ids - } - other_node, r_mapping = self.prepare_join_names(other) - join_node = nodes.JoinNode( - left_child=self.node, - right_child=other_node, - conditions=tuple( - (ex.deref(l_mapping[l_col]), ex.deref(r_mapping[r_col])) - for l_col, r_col in conditions - ), - type=type, - nulls_equal=True, # pandas semantics - propogate_order=propogate_order or self.session._strictly_ordered, - ) - return ArrayValue(join_node), (l_mapping, r_mapping) - - def try_row_join( - self, - other: ArrayValue, - conditions: typing.Tuple[typing.Tuple[str, str], ...] = (), - ) -> Optional[ - typing.Tuple[ArrayValue, typing.Tuple[dict[str, str], dict[str, str]]] - ]: - l_mapping = { # Identity mapping, only rename right side - lcol.name: lcol.name for lcol in self.node.ids - } - other_node, r_mapping = self.prepare_join_names(other) - import bigframes.core.rewrite - - result_node = bigframes.core.rewrite.try_row_join( - self.node, other_node, conditions - ) - if result_node is None: - return None - - return ( - ArrayValue(result_node), - (l_mapping, r_mapping), - ) - - def prepare_join_names( - self, other: ArrayValue - ) -> Tuple[bigframes.core.nodes.BigFrameNode, dict[str, str]]: - if set(other.node.ids) & set(self.node.ids): - r_mapping = { # Rename conflicting names - rcol.name: rcol.name - if (rcol.name not in self.column_ids) - else bigframes.core.guid.generate_guid() - for rcol in other.node.ids - } - return ( - nodes.SelectionNode( - other.node, - tuple( - bigframes.core.nodes.AliasedRef( - ex.deref(old_id), ids.ColumnId(new_id) - ) - for old_id, new_id in r_mapping.items() - ), - ), - r_mapping, - ) - else: - return other.node, {id: id for id in other.column_ids} - - def try_legacy_row_join( - self, - other: ArrayValue, - join_type: join_def.JoinType, - join_keys: typing.Tuple[join_def.CoalescedColumnMapping, ...], - mappings: typing.Tuple[join_def.JoinColumnMapping, ...], - ) -> typing.Optional[ArrayValue]: - import bigframes.core.rewrite - - result = bigframes.core.rewrite.legacy_join_as_projection( - self.node, other.node, join_keys, mappings, join_type - ) - if result is not None: - return ArrayValue(result) - return None - - def explode(self, column_ids: typing.Sequence[str]) -> ArrayValue: - assert len(column_ids) > 0 - for column_id in column_ids: - assert bigframes.dtypes.is_array_like(self.get_column_type(column_id)) - - offsets = tuple(ex.deref(id) for id in column_ids) - return ArrayValue(nodes.ExplodeNode(child=self.node, column_ids=offsets)) - - def _uniform_sampling(self, fraction: float) -> ArrayValue: - """Sampling the table on given fraction. - - .. warning:: - The row numbers of result is non-deterministic, avoid to use. - """ - return ArrayValue(nodes.RandomSampleNode(self.node, fraction)) - - # Deterministically generate namespaced ids for new variables - # These new ids are only unique within the current namespace. - # Many operations, such as joins, create new namespaces. See: BigFrameNode.defines_namespace - # When migrating to integer ids, these will generate the next available integer, in order to densely pack ids - # this will help represent variables sets as compact bitsets - def _gen_namespaced_uid(self) -> str: - return self._gen_namespaced_uids(1)[0] - - def _gen_namespaced_uids(self, n: int) -> List[str]: - return [ids.ColumnId.unique().name for _ in range(n)] diff --git a/bigframes/core/backports.py b/bigframes/core/backports.py deleted file mode 100644 index 09ba09731c2..00000000000 --- a/bigframes/core/backports.py +++ /dev/null @@ -1,33 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -"""Helpers for working across versions of different depenencies.""" - -from typing import List - -import pyarrow - - -def pyarrow_struct_type_fields(struct_type: pyarrow.StructType) -> List[pyarrow.Field]: - """StructType.fields was added in pyarrow 18. - - See: https://arrow.apache.org/docs/18.0/python/generated/pyarrow.StructType.html - """ - - if hasattr(struct_type, "fields"): - return struct_type.fields - - return [ - struct_type.field(field_index) for field_index in range(struct_type.num_fields) - ] diff --git a/bigframes/core/bigframe_node.py b/bigframes/core/bigframe_node.py deleted file mode 100644 index c48605dc248..00000000000 --- a/bigframes/core/bigframe_node.py +++ /dev/null @@ -1,390 +0,0 @@ -# Copyright 2023 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from __future__ import annotations - -import abc -import collections -import dataclasses -import functools -import itertools -import typing -from typing import Callable, Dict, Generator, Iterable, Mapping, Sequence, Tuple - -import bigframes.core.schema as schemata -import bigframes.dtypes -from bigframes.core import expression, field, identifiers - -COLUMN_SET = frozenset[identifiers.ColumnId] - -T = typing.TypeVar("T") - - -@dataclasses.dataclass(eq=False, frozen=True) -class BigFrameNode: - """ - Immutable node for representing 2D typed array as a tree of operators. - - All subclasses must be hashable so as to be usable as caching key. - """ - - @property - def deterministic(self) -> bool: - """Whether this node will evaluates deterministically.""" - return True - - @property - def row_preserving(self) -> bool: - """Whether this node preserves input rows.""" - return True - - @property - def non_local(self) -> bool: - """ - Whether this node combines information across multiple rows instead of processing rows independently. - Used as an approximation for whether the expression may require shuffling to execute (and therefore be expensive). - """ - return False - - @property - def child_nodes(self) -> typing.Sequence[BigFrameNode]: - """Direct children of this node""" - return tuple([]) - - @property - @abc.abstractmethod - def row_count(self) -> typing.Optional[int]: - return None - - @abc.abstractmethod - def remap_refs( - self, mappings: Mapping[identifiers.ColumnId, identifiers.ColumnId] - ) -> BigFrameNode: - """Remap variable references""" - ... - - @property - @abc.abstractmethod - def node_defined_ids(self) -> Tuple[identifiers.ColumnId, ...]: - """The variables defined in this node (as opposed to by child nodes).""" - ... - - @functools.cached_property - def session(self): - sessions = [] - for child in self.child_nodes: - if child.session is not None: - sessions.append(child.session) - unique_sessions = len(set(sessions)) - if unique_sessions > 1: - raise ValueError("Cannot use combine sources from multiple sessions.") - elif unique_sessions == 1: - return sessions[0] - return None - - def _validate(self): - """Validate the local data in the node.""" - return - - @functools.cache - def validate_tree(self) -> bool: - for child in self.child_nodes: - child.validate_tree() - self._validate() - field_list = list(self.fields) - if len(set(field_list)) != len(field_list): - raise ValueError(f"Non unique field ids {list(self.fields)}") - return True - - def _as_tuple(self) -> Tuple: - """Get all fields as tuple.""" - return tuple(getattr(self, field.name) for field in dataclasses.fields(self)) - - def __hash__(self) -> int: - # Custom hash that uses cache to avoid costly recomputation - return self._cached_hash - - def __eq__(self, other) -> bool: - # Custom eq that tries to short-circuit full structural comparison - if not isinstance(other, self.__class__): - return False - if self is other: - return True - if hash(self) != hash(other): - return False - return self._as_tuple() == other._as_tuple() - - # BigFrameNode trees can be very deep so its important avoid recalculating the hash from scratch - # Each subclass of BigFrameNode should use this property to implement __hash__ - # The default dataclass-generated __hash__ method is not cached - @functools.cached_property - def _cached_hash(self): - return hash(self._as_tuple()) - - @property - def roots(self) -> typing.Set[BigFrameNode]: - roots = itertools.chain.from_iterable( - map(lambda child: child.roots, self.child_nodes) - ) - return set(roots) - - # TODO: Store some local data lazily for select, aggregate nodes. - @property - @abc.abstractmethod - def fields(self) -> Sequence[field.Field]: ... - - @property - def ids(self) -> Iterable[identifiers.ColumnId]: - """All output ids from the node.""" - return (field.id for field in self.fields) - - @property - @abc.abstractmethod - def variables_introduced(self) -> int: - """ - Defines number of values created by the current node. Helps represent the "width" of a query - """ - ... - - @property - def relation_ops_created(self) -> int: - """ - Defines the number of relational ops generated by the current node. Used to estimate query planning complexity. - """ - return 1 - - @property - def joins(self) -> bool: - """ - Defines whether the node joins data. - """ - return False - - @property - @abc.abstractmethod - def order_ambiguous(self) -> bool: - """ - Whether row ordering is potentially ambiguous. For example, ReadTable (without a primary key) could be ordered in different ways. - """ - ... - - @property - @abc.abstractmethod - def explicitly_ordered(self) -> bool: - """ - Whether row ordering is potentially ambiguous. For example, ReadTable (without a primary key) could be ordered in different ways. - """ - ... - - @functools.cached_property - def height(self) -> int: - if len(self.child_nodes) == 0: - return 0 - return max(child.height for child in self.child_nodes) + 1 - - @functools.cached_property - def total_variables(self) -> int: - return self.variables_introduced + sum( - map(lambda x: x.total_variables, self.child_nodes) - ) - - @functools.cached_property - def total_relational_ops(self) -> int: - return self.relation_ops_created + sum( - map(lambda x: x.total_relational_ops, self.child_nodes) - ) - - @functools.cached_property - def total_joins(self) -> int: - return int(self.joins) + sum(map(lambda x: x.total_joins, self.child_nodes)) - - @functools.cached_property - def schema(self) -> schemata.ArraySchema: - # TODO: Make schema just a view on fields - return schemata.ArraySchema( - tuple(schemata.SchemaItem(i.id.name, i.dtype) for i in self.fields) - ) - - @property - def planning_complexity(self) -> int: - """ - Empirical heuristic measure of planning complexity. - - Used to determine when to decompose overly complex computations. May require tuning. - """ - return self.total_variables * self.total_relational_ops * (1 + self.total_joins) - - @abc.abstractmethod - def transform_children( - self, t: Callable[[BigFrameNode], BigFrameNode] - ) -> BigFrameNode: - """Apply a function to each child node.""" - ... - - @abc.abstractmethod - def remap_vars( - self, mappings: Mapping[identifiers.ColumnId, identifiers.ColumnId] - ) -> BigFrameNode: - """Remap defined (in this node only) variables.""" - ... - - @property - def defines_namespace(self) -> bool: - """ - If true, this node establishes a new column id namespace. - - If false, this node consumes and produces ids in the namespace - """ - return False - - @property - def referenced_ids(self) -> COLUMN_SET: - return frozenset() - - @functools.cached_property - def defined_variables(self) -> set[str]: - """Full set of variables defined in the namespace, even if not selected.""" - self_defined_variables = set(self.schema.names) - if self.defines_namespace: - return self_defined_variables - return self_defined_variables.union( - *(child.defined_variables for child in self.child_nodes) - ) - - def get_type(self, id: identifiers.ColumnId) -> bigframes.dtypes.Dtype: - return self._dtype_lookup[id] - - # TODO: Deprecate in favor of field_by_id, and eventually, by rich references - @functools.cached_property - def _dtype_lookup(self) -> dict[identifiers.ColumnId, bigframes.dtypes.Dtype]: - return {field.id: field.dtype for field in self.fields} - - @functools.cached_property - def field_by_id(self) -> Mapping[identifiers.ColumnId, field.Field]: - return {field.id: field for field in self.fields} - - @property - def _node_expressions( - self, - ) -> Sequence[expression.Expression]: - """List of expressions. Intended for checking engine compatibility with used ops.""" - return () - - # Plan algorithms - def unique_nodes( - self: BigFrameNode, - ) -> Generator[BigFrameNode, None, None]: - """Walks the tree for unique nodes""" - seen = set() - stack: list[BigFrameNode] = [self] - while stack: - item = stack.pop() - if item not in seen: - yield item - seen.add(item) - stack.extend(item.child_nodes) - - def iter_nodes_topo( - self: BigFrameNode, - ) -> Generator[BigFrameNode, None, None]: - """Returns nodes in reverse topological order, using Kahn's algorithm.""" - child_to_parents: Dict[BigFrameNode, list[BigFrameNode]] = ( - collections.defaultdict(list) - ) - out_degree: Dict[BigFrameNode, int] = collections.defaultdict(int) - - queue: collections.deque["BigFrameNode"] = collections.deque() - for node in list(self.unique_nodes()): - num_children = len(node.child_nodes) - out_degree[node] = num_children - if num_children == 0: - queue.append(node) - for child in node.child_nodes: - child_to_parents[child].append(node) - - while queue: - item = queue.popleft() - yield item - parents = child_to_parents.get(item, []) - for parent in parents: - out_degree[parent] -= 1 - if out_degree[parent] == 0: - queue.append(parent) - - def top_down( - self: BigFrameNode, - transform: Callable[[BigFrameNode], BigFrameNode], - ) -> BigFrameNode: - """ - Perform a top-down transformation of the BigFrameNode tree. - """ - results: Dict[BigFrameNode, BigFrameNode] = {} - # Each stack entry is (node, t_node). t_node is None until transform(node) is called. - stack: list[tuple[BigFrameNode, typing.Optional[BigFrameNode]]] = [(self, None)] - - while stack: - node, t_node = stack[-1] - - if t_node is None: - if node in results: - stack.pop() - continue - t_node = transform(node) - stack[-1] = (node, t_node) - - all_done = True - for child in reversed(t_node.child_nodes): - if child not in results: - stack.append((child, None)) - all_done = False - break - - if all_done: - results[node] = t_node.transform_children(lambda x: results[x]) - stack.pop() - - return results[self] - - def bottom_up( - self: BigFrameNode, - transform: Callable[[BigFrameNode], BigFrameNode], - ) -> BigFrameNode: - """ - Perform a bottom-up transformation of the BigFrameNode tree. - - The `transform` function is applied to each node *after* its children - have been transformed. This allows for transformations that depend - on the results of transforming subtrees. - - Returns the transformed root node. - """ - results: dict[BigFrameNode, BigFrameNode] = {} - for node in list(self.iter_nodes_topo()): - # child nodes have already been transformed - result = node.transform_children(lambda x: results[x]) - result = transform(result) - results[node] = result - - return results[self] - - def reduce_up(self, reduction: Callable[[BigFrameNode, Tuple[T, ...]], T]) -> T: - """Apply a bottom-up reduction to the tree.""" - results: dict[BigFrameNode, T] = {} - for node in list(self.iter_nodes_topo()): - # child nodes have already been transformed - child_results = tuple(results[child] for child in node.child_nodes) - result = reduction(node, child_results) - results[node] = result - - return results[self] diff --git a/bigframes/core/block_transforms.py b/bigframes/core/block_transforms.py index c919b88614d..917edac0ded 100644 --- a/bigframes/core/block_transforms.py +++ b/bigframes/core/block_transforms.py @@ -13,123 +13,17 @@ # limitations under the License. from __future__ import annotations -import functools -import inspect import typing -from typing import Callable, Hashable, Optional, Sequence -import bigframes_vendored.constants as constants import pandas as pd -import bigframes.constants +import bigframes.constants as constants import bigframes.core as core import bigframes.core.blocks as blocks -import bigframes.core.bytecode as bytecode -import bigframes.core.expression as ex import bigframes.core.ordering as ordering -import bigframes.core.window_spec as window_specs -import bigframes.dtypes as dtypes -import bigframes.functions +import bigframes.core.window_spec as windows import bigframes.operations as ops import bigframes.operations.aggregations as agg_ops -from bigframes._config import options -from bigframes.core import agg_expressions, py_expressions - - -def compile_udf( - block: blocks.Block, - func: Callable, - args: tuple = (), - kwargs: dict | None = None, - col_series_args: typing.Mapping[str, str] | None = None, - window_spec: Optional[window_specs.WindowSpec] = None, -) -> ex.Expression: - """Compile a python function to a BigFrames expression in the context of a block.""" - if kwargs is None: - kwargs = {} - expr = bytecode._compile_bytecode_to_py_expr(func) - sig = inspect.signature(func) - - bindings: dict[Hashable, ex.Expression] = {} - - bound_args = sig.bind(*(None, *args), **kwargs) - bound_args.apply_defaults() - bound_params = bound_args.arguments - for name, value in bound_params.items(): - bindings[name] = ex.const(value) - - series_arg = next(iter(sig.parameters.keys())) - - if col_series_args is not None: - expr = py_expressions.resolve_py_exprs( - expr, - series_arg=series_arg, - col_series_args=col_series_args, - window_spec=window_spec, - ) - else: - series_attrs: dict = {} - for i, (col_id, label) in enumerate( - zip(block.value_columns, block.column_labels) - ): - series_attrs[i] = col_id - if label is not None: - series_attrs[label] = col_id - - expr = py_expressions.resolve_py_exprs( - expr, - series_arg=series_arg, - series_attrs=series_attrs, - window_spec=window_spec, - ) - - expr = expr.bind_variables(bindings) - return expr - - -def is_transpiler_eligible(func: typing.Any) -> bool: - """Return True if func is eligible for Python transpilation.""" - return ( - options.experiments.enable_python_transpiler - and callable(func) - and not isinstance(func, bigframes.functions.Udf) - ) - - -def compile_column_udf( - block: blocks.Block, - func: Callable, - column_id: str, - args: tuple = (), - kwargs: dict | None = None, - window_spec: Optional[window_specs.WindowSpec] = None, -) -> tuple[ex.Expression, str]: - """Compile a column-wise python UDF in block context and return (expr, name).""" - sig = inspect.signature(func) - series_arg = next(iter(sig.parameters.keys())) - expr = compile_udf( - block, - func, - args=args, - kwargs=kwargs, - col_series_args={series_arg: column_id}, - window_spec=window_spec, - ) - name = getattr(func, "__name__", "") - return expr, name - - -def apply_to_block_rows( - func: Callable, block: blocks.Block, *args, **kwargs -) -> blocks.Block: - """ - Apply the given function to each row of the block. - - The function is applied to each row of the block, and the result is returned - as a new block with the same index. - """ - expr = compile_udf(block, func, args, kwargs) - return block.project_exprs([expr], labels=[None], drop=True) def equals(block1: blocks.Block, block2: blocks.Block) -> bool: @@ -142,18 +36,23 @@ def equals(block1: blocks.Block, block2: blocks.Block) -> bool: block1 = block1.reset_index(drop=False) block2 = block2.reset_index(drop=False) - joined_block, (lmap, rmap) = block1.join(block2, how="outer") + joined, (lmap, rmap) = block1.index.join(block2.index, how="outer") + joined_block = joined._block - exprs = [] + equality_ids = [] for lcol, rcol in zip(block1.value_columns, block2.value_columns): - exprs.append( - ops.fillna_op.as_expr( - ops.eq_null_match_op.as_expr(lmap[lcol], rmap[rcol]), ex.const(False) - ) + lcolmapped = lmap[lcol] + rcolmapped = rmap[rcol] + joined_block, result_id = joined_block.apply_binary_op( + lcolmapped, rcolmapped, ops.eq_nulls_match_op + ) + joined_block, result_id = joined_block.apply_unary_op( + result_id, ops.partial_right(ops.fillna_op, False) ) + equality_ids.append(result_id) - joined_block = joined_block.project_exprs( - exprs, labels=list(range(len(exprs))), drop=True + joined_block = joined_block.select_columns(equality_ids).with_column_labels( + list(range(len(equality_ids))) ) stacked_block = joined_block.stack() result = stacked_block.get_stat(stacked_block.value_columns[0], agg_ops.all_op) @@ -167,108 +66,58 @@ def indicate_duplicates( if keep not in ["first", "last", False]: raise ValueError("keep must be one of 'first', 'last', or False'") - rownums = agg_expressions.WindowExpression( - agg_expressions.NullaryAggregation( - agg_ops.RowNumberOp(), - ), - window=window_specs.unbound(grouping_keys=tuple(columns)), - ) - count = agg_expressions.WindowExpression( - agg_expressions.NullaryAggregation( - agg_ops.SizeOp(), - ), - window=window_specs.unbound(grouping_keys=tuple(columns)), - ) - if keep == "first": # Count how many copies occur up to current copy of value # Discard this value if there are copies BEFORE - predicate = ops.gt_op.as_expr(rownums, ex.const(0)) + window_spec = windows.WindowSpec( + grouping_keys=tuple(columns), + following=0, + ) elif keep == "last": # Count how many copies occur up to current copy of values # Discard this value if there are copies AFTER - predicate = ops.lt_op.as_expr(rownums, ops.sub_op.as_expr(count, ex.const(1))) + window_spec = windows.WindowSpec( + grouping_keys=tuple(columns), + preceding=0, + ) else: # keep == False # Count how many copies of the value occur in entire series. # Discard this value if there are copies ANYWHERE - predicate = ops.gt_op.as_expr(count, ex.const(1)) - - block = block.project_block_exprs( - [predicate], - labels=[None], - ) - return ( - block, - block.value_columns[-1], + window_spec = windows.WindowSpec(grouping_keys=tuple(columns)) + block, dummy = block.create_constant(1) + block, val_count_col_id = block.apply_window_op( + dummy, + agg_ops.count_op, + window_spec=window_spec, ) - - -def quantile( - block: blocks.Block, - columns: Sequence[str], - qs: Sequence[float], - grouping_column_ids: Sequence[str] = (), - dropna: bool = False, -) -> blocks.Block: - # TODO: handle windowing and more interpolation methods - window = window_specs.unbound( - grouping_keys=tuple(grouping_column_ids), + block, duplicate_indicator = block.apply_unary_op( + val_count_col_id, + ops.partial_right(ops.gt_op, 1), ) - quantile_cols = [] - labels = [] - if len(columns) * len(qs) > bigframes.constants.MAX_COLUMNS: - raise NotImplementedError("Too many aggregates requested.") - for col in columns: - for q in qs: - label = block.col_id_to_label[col] - new_label = (*label, q) if isinstance(label, tuple) else (label, q) - labels.append(new_label) - block, quantile_col = block.apply_window_op( - col, - agg_ops.QuantileOp(q), - window_spec=window, + return ( + block.drop_columns( + ( + dummy, + val_count_col_id, ) - quantile_cols.append(quantile_col) - block = block.aggregate( - tuple( - agg_expressions.UnaryAggregation(agg_ops.AnyValueOp(), ex.deref(col)) - for col in quantile_cols ), - grouping_column_ids, - column_labels=pd.Index(labels), - dropna=dropna, + duplicate_indicator, ) - return block def interpolate(block: blocks.Block, method: str = "linear") -> blocks.Block: - supported_methods = [ - "linear", - "values", - "index", - "nearest", - "zero", - "slinear", - ] - if method not in supported_methods: + if method != "linear": raise NotImplementedError( - f"Method {method} not supported, following interpolate methods supported: {', '.join(supported_methods)}. {constants.FEEDBACK_LINK}" + f"Only 'linear' interpolate method supported. {constants.FEEDBACK_LINK}" ) + backwards_window = windows.WindowSpec(following=0) + forwards_window = windows.WindowSpec(preceding=0) + output_column_ids = [] original_columns = block.value_columns original_labels = block.column_labels - - if method == "linear": # Assumes evenly spaced, ignore index - block, xvalues = block.promote_offsets() - else: - index_columns = block.index_columns - if len(index_columns) != 1: - raise ValueError("only method 'linear' supports multi-index") - xvalues = block.index_columns[0] - if block.index.dtypes[0] not in dtypes.NUMERIC_BIGFRAMES_TYPES_PERMISSIVE: - raise ValueError("Can only interpolate on numeric index.") - + block, offsets = block.promote_offsets() for column in original_columns: # null in same places column is null should_interpolate = block._column_type(column) in [ @@ -276,104 +125,57 @@ def interpolate(block: blocks.Block, method: str = "linear") -> blocks.Block: pd.Int64Dtype(), ] if should_interpolate: - interpolate_method_map = { - "linear": "linear", - "values": "linear", - "index": "linear", - "slinear": "linear", - "zero": "ffill", - "nearest": "nearest", - } - extrapolating_methods = ["linear", "values", "index"] - interpolate_method = interpolate_method_map[method] - do_extrapolate = method in extrapolating_methods - block, interpolated = _interpolate_column( - block, - column, - xvalues, - interpolate_method=interpolate_method, - do_extrapolate=do_extrapolate, + block, notnull = block.apply_unary_op(column, ops.notnull_op) + block, masked_offsets = block.apply_binary_op( + offsets, notnull, ops.partial_arg3(ops.where_op, None) ) - output_column_ids.append(interpolated) - else: - output_column_ids.append(column) - - block = block.select_columns(output_column_ids) - return block.with_column_labels(original_labels) + block, previous_value = block.apply_window_op( + column, agg_ops.LastNonNullOp(), backwards_window + ) + block, next_value = block.apply_window_op( + column, agg_ops.FirstNonNullOp(), forwards_window + ) + block, previous_value_offset = block.apply_window_op( + masked_offsets, + agg_ops.LastNonNullOp(), + backwards_window, + skip_reproject_unsafe=True, + ) + block, next_value_offset = block.apply_window_op( + masked_offsets, + agg_ops.FirstNonNullOp(), + forwards_window, + skip_reproject_unsafe=True, + ) -def _interpolate_column( - block: blocks.Block, - column: str, - x_values: str, - interpolate_method: str, - do_extrapolate: bool = True, -) -> typing.Tuple[blocks.Block, str]: - if interpolate_method not in ["linear", "nearest", "ffill"]: - raise ValueError("interpolate method not supported") - window_ordering = (ordering.OrderingExpression(ex.deref(x_values)),) - backwards_window = window_specs.rows(end=0, ordering=window_ordering) - forwards_window = window_specs.rows(start=0, ordering=window_ordering) - - # Note, this method may - block, notnull = block.apply_unary_op(column, ops.notnull_op) - block, masked_offsets = block.project_expr( - ops.where_op.as_expr(x_values, notnull, ex.const(None)) - ) + block, prediction_id = _interpolate( + block, + previous_value_offset, + previous_value, + next_value_offset, + next_value, + offsets, + ) - block, previous_value = block.apply_window_op( - column, agg_ops.LastNonNullOp(), backwards_window - ) - block, next_value = block.apply_window_op( - column, agg_ops.FirstNonNullOp(), forwards_window - ) - block, previous_value_offset = block.apply_window_op( - masked_offsets, - agg_ops.LastNonNullOp(), - backwards_window, - ) - block, next_value_offset = block.apply_window_op( - masked_offsets, - agg_ops.FirstNonNullOp(), - forwards_window, - ) + block, interpolated_column = block.apply_binary_op( + column, prediction_id, ops.fillna_op + ) + # Pandas performs ffill-like behavior to extrapolate forwards + block, interpolated_and_ffilled = block.apply_binary_op( + interpolated_column, previous_value, ops.fillna_op + ) - if interpolate_method == "linear": - block, prediction_id = _interpolate_points_linear( - block, - previous_value_offset, - previous_value, - next_value_offset, - next_value, - x_values, - ) - elif interpolate_method == "nearest": - block, prediction_id = _interpolate_points_nearest( - block, - previous_value_offset, - previous_value, - next_value_offset, - next_value, - x_values, - ) - else: # interpolate_method == 'ffill': - block, prediction_id = _interpolate_points_ffill( - block, - previous_value_offset, - previous_value, - next_value_offset, - next_value, - x_values, - ) - if do_extrapolate: - block, prediction_id = block.apply_binary_op( - prediction_id, previous_value, ops.fillna_op - ) + output_column_ids.append(interpolated_and_ffilled) + else: + output_column_ids.append(column) - return block.apply_binary_op(column, prediction_id, ops.fillna_op) + # Force reproject since used `skip_project_unsafe` perviously + block = block.select_columns(output_column_ids)._force_reproject() + return block.with_column_labels(original_labels) -def _interpolate_points_linear( +def _interpolate( block: blocks.Block, x0_id: str, y0_id: str, @@ -394,54 +196,12 @@ def _interpolate_points_linear( return block, prediction_id -def _interpolate_points_nearest( - block: blocks.Block, - x0_id: str, - y0_id: str, - x1_id: str, - y1_id: str, - xpredict_id: str, -) -> typing.Tuple[blocks.Block, str]: - """Interpolate by taking the y value of the nearest x value""" - left_diff = ops.sub_op.as_expr(xpredict_id, x0_id) - right_diff = ops.sub_op.as_expr(x1_id, xpredict_id) - # If diffs equal, choose left - choose_left = ops.fillna_op.as_expr( - ops.le_op.as_expr(left_diff, right_diff), ex.const(False) - ) - - nearest = ops.where_op.as_expr(y0_id, choose_left, y1_id) - - is_interpolation = ops.and_op.as_expr( - ops.notnull_op.as_expr(y0_id), ops.notnull_op.as_expr(y1_id) - ) - - return block.project_expr( - ops.where_op.as_expr(nearest, is_interpolation, ex.const(None)) - ) - - -def _interpolate_points_ffill( - block: blocks.Block, - x0_id: str, - y0_id: str, - x1_id: str, - y1_id: str, - xpredict_id: str, -) -> typing.Tuple[blocks.Block, str]: - """Interpolates by using the preceding values""" - # check for existance of y1, otherwise we are extrapolating instead of interpolating - return block.project_expr( - ops.where_op.as_expr(y0_id, ops.notnull_op.as_expr(y1_id), ex.const(None)) - ) - - def drop_duplicates( block: blocks.Block, columns: typing.Sequence[str], keep: str = "first" ) -> blocks.Block: block, dupe_indicator_id = indicate_duplicates(block, columns, keep) block, keep_indicator_id = block.apply_unary_op(dupe_indicator_id, ops.invert_op) - return block.filter_by_id(keep_indicator_id).drop_columns( + return block.filter(keep_indicator_id).drop_columns( (dupe_indicator_id, keep_indicator_id) ) @@ -452,62 +212,54 @@ def value_counts( normalize: bool = False, sort: bool = True, ascending: bool = False, - drop_na: bool = True, - grouping_keys: typing.Sequence[str] = (), + dropna: bool = True, ): - if grouping_keys and drop_na: - # only need this if grouping_keys is involved, otherwise the drop_na in the aggregation will handle it for us - block = dropna(block, columns, how="any") - block = block.aggregate( - aggregations=[agg_expressions.NullaryAggregation(agg_ops.size_op)], - by_column_ids=(*grouping_keys, *columns), - dropna=drop_na and not grouping_keys, + block, dummy = block.create_constant(1) + block, agg_ids = block.aggregate( + by_column_ids=columns, + aggregations=[(dummy, agg_ops.count_op)], + dropna=dropna, + as_index=True, ) - count_id = block.value_columns[0] + count_id = agg_ids[0] if normalize: - unbound_window = window_specs.unbound(grouping_keys=tuple(grouping_keys)) + unbound_window = windows.WindowSpec() block, total_count_id = block.apply_window_op( count_id, agg_ops.sum_op, unbound_window ) block, count_id = block.apply_binary_op(count_id, total_count_id, ops.div_op) if sort: - order_parts = [ordering.ascending_over(id) for id in grouping_keys] - order_parts.extend( + block = block.order_by( [ - ordering.OrderingExpression( - ex.deref(count_id), + ordering.OrderingColumnReference( + count_id, direction=ordering.OrderingDirection.ASC if ascending else ordering.OrderingDirection.DESC, ) ] ) - block = block.order_by(order_parts) - return block.select_column(count_id).with_column_labels( - ["proportion" if normalize else "count"] - ) + return block.select_column(count_id).with_column_labels(["count"]) def pct_change(block: blocks.Block, periods: int = 1) -> blocks.Block: column_labels = block.column_labels - - # Window framing clause is not allowed for analytic function lag. - window_spec = window_specs.unbound() + window_spec = windows.WindowSpec( + preceding=periods if periods > 0 else None, + following=-periods if periods < 0 else None, + ) original_columns = block.value_columns - exprs = [] - for original_col in original_columns: - shift_expr = agg_expressions.WindowExpression( - agg_expressions.UnaryAggregation( - agg_ops.ShiftOp(periods), ex.deref(original_col) - ), - window_spec, - ) - change_expr = ops.sub_op.as_expr(original_col, shift_expr) - pct_change_expr = ops.div_op.as_expr(change_expr, shift_expr) - exprs.append(pct_change_expr) - return block.project_block_exprs(exprs, labels=column_labels, drop=True) + block, shift_columns = block.multi_apply_window_op( + original_columns, agg_ops.ShiftOp(periods), window_spec=window_spec + ) + result_ids = [] + for original_col, shifted_col in zip(original_columns, shift_columns): + block, change_id = block.apply_binary_op(original_col, shifted_col, ops.sub_op) + block, pct_change_id = block.apply_binary_op(change_id, shifted_col, ops.div_op) + result_ids.append(pct_change_id) + return block.select_columns(result_ids).with_column_labels(column_labels) def rank( @@ -515,9 +267,6 @@ def rank( method: str = "average", na_option: str = "keep", ascending: bool = True, - grouping_cols: tuple[str, ...] = (), - columns: tuple[str, ...] = (), - pct: bool = False, ): if method not in ["average", "min", "max", "first", "dense"]: raise ValueError( @@ -526,131 +275,109 @@ def rank( if na_option not in ["keep", "top", "bottom"]: raise ValueError("na_option must be one of 'keep', 'top', or 'bottom'") - columns = columns or tuple(col for col in block.value_columns) - labels = [block.col_id_to_label[id] for id in columns] - - result_exprs = [] + columns = block.value_columns + labels = block.column_labels + # Step 1: Calculate row numbers for each row + # Identify null values to be treated according to na_option param + rownum_col_ids = [] + nullity_col_ids = [] for col in columns: - # Step 1: Calculate row numbers for each row - # Identify null values to be treated according to na_option param - window_ordering = ( - ordering.OrderingExpression( - ex.deref(col), - ordering.OrderingDirection.ASC - if ascending - else ordering.OrderingDirection.DESC, - na_last=(na_option in ["bottom", "keep"]), + block, nullity_col_id = block.apply_unary_op( + col, + ops.isnull_op, + ) + nullity_col_ids.append(nullity_col_id) + window = windows.WindowSpec( + # BigQuery has syntax to reorder nulls with "NULLS FIRST/LAST", but that is unavailable through ibis presently, so must order on a separate nullity expression first. + ordering=( + ordering.OrderingColumnReference( + col, + ordering.OrderingDirection.ASC + if ascending + else ordering.OrderingDirection.DESC, + na_last=(na_option in ["bottom", "keep"]), + ), ), ) # Count_op ignores nulls, so if na_option is "top" or "bottom", we instead count the nullity columns, where nulls have been mapped to bools - target_expr = ( - ex.deref(col) if na_option == "keep" else ops.isnull_op.as_expr(col) + block, rownum_id = block.apply_window_op( + col if na_option == "keep" else nullity_col_id, + agg_ops.dense_rank_op if method == "dense" else agg_ops.count_op, + window_spec=window, + skip_reproject_unsafe=(col != columns[-1]), ) - window_op = agg_ops.dense_rank_op if method == "dense" else agg_ops.count_op - window_spec = ( - window_specs.unbound(grouping_keys=grouping_cols, ordering=window_ordering) - if method == "dense" - else window_specs.rows( - end=0, ordering=window_ordering, grouping_keys=grouping_cols + rownum_col_ids.append(rownum_id) + + # Step 2: Apply aggregate to groups of like input values. + # This step is skipped for method=='first' or 'dense' + if method in ["average", "min", "max"]: + agg_op = { + "average": agg_ops.mean_op, + "min": agg_ops.min_op, + "max": agg_ops.max_op, + }[method] + post_agg_rownum_col_ids = [] + for i in range(len(columns)): + block, result_id = block.apply_window_op( + rownum_col_ids[i], + agg_op, + window_spec=windows.WindowSpec(grouping_keys=(columns[i],)), + skip_reproject_unsafe=(i < (len(columns) - 1)), ) + post_agg_rownum_col_ids.append(result_id) + rownum_col_ids = post_agg_rownum_col_ids + + # Step 3: post processing: mask null values and cast to float + if method in ["min", "max", "first", "dense"]: + # Pandas rank always produces Float64, so must cast for aggregation types that produce ints + block = block.multi_apply_unary_op( + rownum_col_ids, ops.AsTypeOp(pd.Float64Dtype()) ) - result_expr: ex.Expression = agg_expressions.WindowExpression( - agg_expressions.UnaryAggregation(window_op, target_expr), window_spec - ) - if pct: - result_expr = ops.div_op.as_expr( - result_expr, - agg_expressions.WindowExpression( - agg_expressions.UnaryAggregation(agg_ops.max_op, result_expr), - window_specs.unbound(grouping_keys=grouping_cols), - ), - ) - # Step 2: Apply aggregate to groups of like input values. - # This step is skipped for method=='first' or 'dense' - if method in ["average", "min", "max"]: - agg_op = { - "average": agg_ops.mean_op, - "min": agg_ops.min_op, - "max": agg_ops.max_op, - }[method] - result_expr = agg_expressions.WindowExpression( - agg_expressions.UnaryAggregation(agg_op, result_expr), - window_specs.unbound(grouping_keys=(col, *grouping_cols)), - ) - # Pandas masks all values where any grouping column is null - # Note: we use pd.NA instead of float('nan') - if grouping_cols: - predicate = functools.reduce( - ops.and_op.as_expr, - [ops.notnull_op.as_expr(column_id) for column_id in grouping_cols], - ) - result_expr = ops.where_op.as_expr( - result_expr, - predicate, - ex.const(None), + if na_option == "keep": + # For na_option "keep", null inputs must produce null outputs + for i in range(len(columns)): + block, null_const = block.create_constant(pd.NA, dtype=pd.Float64Dtype()) + block, rownum_col_ids[i] = block.apply_ternary_op( + null_const, nullity_col_ids[i], rownum_col_ids[i], ops.where_op ) - # Step 3: post processing: mask null values and cast to float - if method in ["min", "max", "first", "dense"]: - # Pandas rank always produces Float64, so must cast for aggregation types that produce ints - result_expr = ops.AsTypeOp(pd.Float64Dtype()).as_expr(result_expr) - elif na_option == "keep": - # For na_option "keep", null inputs must produce null outputs - result_expr = ops.where_op.as_expr( - ex.const(pd.NA, dtype=pd.Float64Dtype()), - ops.isnull_op.as_expr(col), - result_expr, - ) - result_exprs.append(result_expr) - return block.project_block_exprs(result_exprs, labels=labels, drop=True) + return block.select_columns(rownum_col_ids).with_column_labels(labels) def dropna( block: blocks.Block, column_ids: typing.Sequence[str], - how: str = "any", - thresh: typing.Optional[int] = None, - subset: Optional[typing.Sequence[str]] = None, + how: typing.Literal["all", "any"] = "any", ): """ Drop na entries from block """ - if subset is None: - subset = column_ids - - # Predicates to check for non-null values in the subset of columns - predicates = [ - ops.notnull_op.as_expr(column_id) - for column_id in column_ids - if column_id in subset - ] - - if len(predicates) == 0: - return block - - if thresh is not None: - # Handle single predicate case - if len(predicates) == 1: - count_expr = ops.AsTypeOp(pd.Int64Dtype()).as_expr(predicates[0]) - else: - # Sum the boolean expressions to count non-null values - count_expr = functools.reduce( - lambda a, b: ops.add_op.as_expr( - ops.AsTypeOp(pd.Int64Dtype()).as_expr(a), - ops.AsTypeOp(pd.Int64Dtype()).as_expr(b), - ), - predicates, + if how == "any": + filtered_block = block + for column in column_ids: + filtered_block, result_id = filtered_block.apply_unary_op( + column, ops.notnull_op ) - # Filter rows where count >= thresh - predicate = ops.ge_op.as_expr(count_expr, ex.const(thresh)) - else: - # Only handle 'how' parameter when thresh is not specified - if how == "any": - predicate = functools.reduce(ops.and_op.as_expr, predicates) - else: # "all" - predicate = functools.reduce(ops.or_op.as_expr, predicates) - - return block.filter(predicate) + filtered_block = filtered_block.filter(result_id) + filtered_block = filtered_block.drop_columns([result_id]) + return filtered_block + else: # "all" + filtered_block = block + predicate = None + for column in column_ids: + filtered_block, partial_predicate = filtered_block.apply_unary_op( + column, ops.notnull_op + ) + if predicate: + filtered_block, predicate = filtered_block.apply_binary_op( + partial_predicate, predicate, ops.or_op + ) + else: + predicate = partial_predicate + if predicate: + filtered_block = filtered_block.filter(predicate) + filtered_block = filtered_block.select_columns(block.value_columns) + return filtered_block def nsmallest( @@ -664,22 +391,24 @@ def nsmallest( if keep == "last": block = block.reversed() order_refs = [ - ordering.OrderingExpression( - ex.deref(col_id), direction=ordering.OrderingDirection.ASC + ordering.OrderingColumnReference( + col_id, direction=ordering.OrderingDirection.ASC ) for col_id in column_ids ] - block = block.order_by(order_refs) + block = block.order_by(order_refs, stable=True) if keep in ("first", "last"): return block.slice(0, n) else: # keep == "all": block, counter = block.apply_window_op( column_ids[0], agg_ops.rank_op, - window_spec=window_specs.unbound(ordering=tuple(order_refs)), + window_spec=windows.WindowSpec(ordering=tuple(order_refs)), + ) + block, condition = block.apply_unary_op( + counter, ops.partial_right(ops.le_op, n) ) - block, condition = block.project_expr(ops.le_op.as_expr(counter, ex.const(n))) - block = block.filter_by_id(condition) + block = block.filter(condition) return block.drop_columns([counter, condition]) @@ -694,22 +423,24 @@ def nlargest( if keep == "last": block = block.reversed() order_refs = [ - ordering.OrderingExpression( - ex.deref(col_id), direction=ordering.OrderingDirection.DESC + ordering.OrderingColumnReference( + col_id, direction=ordering.OrderingDirection.DESC ) for col_id in column_ids ] - block = block.order_by(order_refs) + block = block.order_by(order_refs, stable=True) if keep in ("first", "last"): return block.slice(0, n) else: # keep == "all": block, counter = block.apply_window_op( column_ids[0], agg_ops.rank_op, - window_spec=window_specs.unbound(ordering=tuple(order_refs)), + window_spec=windows.WindowSpec(ordering=tuple(order_refs)), ) - block, condition = block.project_expr(ops.le_op.as_expr(counter, ex.const(n))) - block = block.filter_by_id(condition) + block, condition = block.apply_unary_op( + counter, ops.partial_right(ops.le_op, n) + ) + block = block.filter(condition) return block.drop_columns([counter, condition]) @@ -718,23 +449,39 @@ def skew( skew_column_ids: typing.Sequence[str], grouping_column_ids: typing.Sequence[str] = (), ) -> blocks.Block: + original_columns = skew_column_ids column_labels = block.select_columns(original_columns).column_labels + block, delta3_ids = _mean_delta_to_power( + block, 3, original_columns, grouping_column_ids + ) # counts, moment3 for each column aggregations = [] - for col in original_columns: - aggregations.append(skew_expr(ex.deref(col))) + for i, col in enumerate(original_columns): + count_agg = (col, agg_ops.count_op) + moment3_agg = (delta3_ids[i], agg_ops.mean_op) + variance_agg = (col, agg_ops.PopVarOp()) + aggregations.extend([count_agg, moment3_agg, variance_agg]) + + block, agg_ids = block.aggregate( + by_column_ids=grouping_column_ids, aggregations=aggregations + ) + + skew_ids = [] + for i, col in enumerate(original_columns): + # Corresponds to order of aggregations in preceding loop + count_id, moment3_id, var_id = agg_ids[i * 3 : (i * 3) + 3] + block, skew_id = _skew_from_moments_and_count( + block, count_id, moment3_id, var_id + ) + skew_ids.append(skew_id) - block = block.aggregate( - aggregations, grouping_column_ids, column_labels=column_labels - ) + block = block.select_columns(skew_ids).with_column_labels(column_labels) if not grouping_column_ids: - # When ungrouped, transpose result row into a series - # perform transpose last, so as to not invalidate cache - block, index_col = block.create_constant(None, None) - block = block.set_index([index_col]) - return block.transpose(original_row_index=pd.Index([None])) + # When ungrouped, stack everything into single column so can be returned as series + block = block.stack() + block = block.drop_levels([block.index_columns[0]]) return block @@ -745,89 +492,94 @@ def kurt( ) -> blocks.Block: original_columns = skew_column_ids column_labels = block.select_columns(original_columns).column_labels - # counts, moment4 for each column - kurt_exprs = [] - for col in original_columns: - kurt_exprs.append(kurt_expr(ex.deref(col))) - block = block.aggregate( - kurt_exprs, grouping_column_ids, column_labels=column_labels + block, delta4_ids = _mean_delta_to_power( + block, 4, original_columns, grouping_column_ids ) + # counts, moment4 for each column + aggregations = [] + for i, col in enumerate(original_columns): + count_agg = (col, agg_ops.count_op) + moment4_agg = (delta4_ids[i], agg_ops.mean_op) + variance_agg = (col, agg_ops.PopVarOp()) + aggregations.extend([count_agg, moment4_agg, variance_agg]) + + block, agg_ids = block.aggregate( + by_column_ids=grouping_column_ids, aggregations=aggregations + ) + + kurt_ids = [] + for i, col in enumerate(original_columns): + # Corresponds to order of aggregations in preceding loop + count_id, moment4_id, var_id = agg_ids[i * 3 : (i * 3) + 3] + block, kurt_id = _kurt_from_moments_and_count( + block, count_id, moment4_id, var_id + ) + kurt_ids.append(kurt_id) + + block = block.select_columns(kurt_ids).with_column_labels(column_labels) if not grouping_column_ids: - # When ungrouped, transpose result row into a series - # perform transpose last, so as to not invalidate cache - block, index_col = block.create_constant(None, None) - block = block.set_index([index_col]) - return block.transpose(original_row_index=pd.Index([None])) + # When ungrouped, stack everything into single column so can be returned as series + block = block.stack() + block = block.drop_levels([block.index_columns[0]]) return block -def skew_expr(expr: ex.Expression) -> ex.Expression: - delta3_expr = _mean_delta_to_power(3, expr) - count_agg = agg_expressions.UnaryAggregation( - agg_ops.count_op, - expr, - ) - moment3_agg = agg_expressions.UnaryAggregation( - agg_ops.mean_op, - delta3_expr, - ) - variance_agg = agg_expressions.UnaryAggregation( - agg_ops.PopVarOp(), - expr, - ) - return _skew_from_moments_and_count(count_agg, moment3_agg, variance_agg) - - -def kurt_expr(expr: ex.Expression) -> ex.Expression: - delta_4_expr = _mean_delta_to_power(4, expr) - count_agg = agg_expressions.UnaryAggregation(agg_ops.count_op, expr) - moment4_agg = agg_expressions.UnaryAggregation(agg_ops.mean_op, delta_4_expr) - variance_agg = agg_expressions.UnaryAggregation(agg_ops.PopVarOp(), expr) - return _kurt_from_moments_and_count(count_agg, moment4_agg, variance_agg) - - def _mean_delta_to_power( - n_power: int, - col_expr: ex.Expression, -) -> ex.Expression: + block: blocks.Block, + n_power, + column_ids: typing.Sequence[str], + grouping_column_ids: typing.Sequence[str], +) -> typing.Tuple[blocks.Block, typing.Sequence[str]]: """Calculate (x-mean(x))^n. Useful for calculating moment statistics such as skew and kurtosis.""" - mean_expr = agg_expressions.UnaryAggregation(agg_ops.mean_op, col_expr) - delta = ops.sub_op.as_expr(col_expr, mean_expr) - return ops.pow_op.as_expr(delta, ex.const(n_power)) + window = windows.WindowSpec(grouping_keys=tuple(grouping_column_ids)) + block, mean_ids = block.multi_apply_window_op(column_ids, agg_ops.mean_op, window) + delta_ids = [] + cube_op = ops.partial_right(ops.pow_op, n_power) + for val_id, mean_val_id in zip(column_ids, mean_ids): + block, delta_id = block.apply_binary_op(val_id, mean_val_id, ops.sub_op) + block, delta_power_id = block.apply_unary_op(delta_id, cube_op) + block = block.drop_columns([delta_id]) + delta_ids.append(delta_power_id) + return block, delta_ids def _skew_from_moments_and_count( - count: ex.Expression, moment3: ex.Expression, moment2: ex.Expression -) -> ex.Expression: + block: blocks.Block, count_id: str, moment3_id: str, moment2_id: str +) -> typing.Tuple[blocks.Block, str]: # Calculate skew using count, third moment and population variance # See G1 estimator: # https://en.wikipedia.org/wiki/Skewness#Sample_skewness - moments_estimator = ops.div_op.as_expr( - moment3, ops.pow_op.as_expr(moment2, ex.const(3 / 2)) + block, denominator_id = block.apply_unary_op( + moment2_id, ops.partial_right(ops.unsafe_pow_op, 3 / 2) ) - - countminus1 = ops.sub_op.as_expr(count, ex.const(1)) - countminus2 = ops.sub_op.as_expr(count, ex.const(2)) - adjustment = ops.div_op.as_expr( - ops.unsafe_pow_op.as_expr( - ops.mul_op.as_expr(count, countminus1), ex.const(1 / 2) - ), - countminus2, + block, base_id = block.apply_binary_op(moment3_id, denominator_id, ops.div_op) + block, countminus1_id = block.apply_unary_op( + count_id, ops.partial_right(ops.sub_op, 1) ) - - skew = ops.mul_op.as_expr(moments_estimator, adjustment) + block, countminus2_id = block.apply_unary_op( + count_id, ops.partial_right(ops.sub_op, 2) + ) + block, adjustment_id = block.apply_binary_op(count_id, countminus1_id, ops.mul_op) + block, adjustment_id = block.apply_unary_op( + adjustment_id, ops.partial_right(ops.unsafe_pow_op, 1 / 2) + ) + block, adjustment_id = block.apply_binary_op( + adjustment_id, countminus2_id, ops.div_op + ) + block, skew_id = block.apply_binary_op(base_id, adjustment_id, ops.mul_op) # Need to produce NA if have less than 3 data points - cleaned_skew = ops.where_op.as_expr( - skew, ops.ge_op.as_expr(count, ex.const(3)), ex.const(None) + block, na_cond_id = block.apply_unary_op(count_id, ops.partial_right(ops.ge_op, 3)) + block, skew_id = block.apply_binary_op( + skew_id, na_cond_id, ops.partial_arg3(ops.where_op, None) ) - return cleaned_skew + return block, skew_id def _kurt_from_moments_and_count( - count: ex.Expression, moment4: ex.Expression, moment2: ex.Expression -) -> ex.Expression: + block: blocks.Block, count_id: str, moment4_id: str, moment2_id: str +) -> typing.Tuple[blocks.Block, str]: # Kurtosis is often defined as the second standardize moment: moment(4)/moment(2)**2 # Pandas however uses Fisher’s estimator, implemented below # numerator = (count + 1) * (count - 1) * moment4 @@ -835,40 +587,49 @@ def _kurt_from_moments_and_count( # adjustment = 3 * (count - 1) ** 2 / ((count - 2) * (count - 3)) # kurtosis = (numerator / denominator) - adjustment - numerator = ops.mul_op.as_expr( - moment4, - ops.mul_op.as_expr( - ops.sub_op.as_expr(count, ex.const(1)), - ops.add_op.as_expr(count, ex.const(1)), - ), + # Numerator + block, countminus1_id = block.apply_unary_op( + count_id, ops.partial_right(ops.sub_op, 1) ) + block, countplus1_id = block.apply_unary_op( + count_id, ops.partial_right(ops.add_op, 1) + ) + block, num_adj = block.apply_binary_op(countplus1_id, countminus1_id, ops.mul_op) + block, numerator_id = block.apply_binary_op(moment4_id, num_adj, ops.mul_op) # Denominator - countminus2 = ops.sub_op.as_expr(count, ex.const(2)) - countminus3 = ops.sub_op.as_expr(count, ex.const(3)) - - # Denominator - denominator = ops.mul_op.as_expr( - ops.unsafe_pow_op.as_expr(moment2, ex.const(2)), - ops.mul_op.as_expr(countminus2, countminus3), + block, countminus2_id = block.apply_unary_op( + count_id, ops.partial_right(ops.sub_op, 2) + ) + block, countminus3_id = block.apply_unary_op( + count_id, ops.partial_right(ops.sub_op, 3) ) + block, denom_adj = block.apply_binary_op(countminus2_id, countminus3_id, ops.mul_op) + block, popvar_squared = block.apply_unary_op( + moment2_id, ops.partial_right(ops.unsafe_pow_op, 2) + ) + block, denominator_id = block.apply_binary_op(popvar_squared, denom_adj, ops.mul_op) # Adjustment - adj_num = ops.mul_op.as_expr( - ops.unsafe_pow_op.as_expr(ops.sub_op.as_expr(count, ex.const(1)), ex.const(2)), - ex.const(3), + block, countminus1_square = block.apply_unary_op( + countminus1_id, ops.partial_right(ops.unsafe_pow_op, 2) + ) + block, adj_num = block.apply_unary_op( + countminus1_square, ops.partial_right(ops.mul_op, 3) ) - adj_denom = ops.mul_op.as_expr(countminus2, countminus3) - adjustment = ops.div_op.as_expr(adj_num, adj_denom) + block, adj_denom = block.apply_binary_op(countminus2_id, countminus3_id, ops.mul_op) + block, adjustment_id = block.apply_binary_op(adj_num, adj_denom, ops.div_op) # Combine - kurt = ops.sub_op.as_expr(ops.div_op.as_expr(numerator, denominator), adjustment) + block, base_id = block.apply_binary_op(numerator_id, denominator_id, ops.div_op) + block, kurt_id = block.apply_binary_op(base_id, adjustment_id, ops.sub_op) # Need to produce NA if have less than 4 data points - cleaned_kurt = ops.where_op.as_expr( - kurt, ops.ge_op.as_expr(count, ex.const(4)), ex.const(None) + block, na_cond_id = block.apply_unary_op(count_id, ops.partial_right(ops.ge_op, 4)) + block, kurt_id = block.apply_binary_op( + kurt_id, na_cond_id, ops.partial_arg3(ops.where_op, None) ) - return cleaned_kurt + return block, kurt_id def align( @@ -891,14 +652,14 @@ def align_rows( right_block: blocks.Block, join: str = "outer", ): - joined_block, (get_column_left, get_column_right) = left_block.join( - right_block, how=join + joined_index, (get_column_left, get_column_right) = left_block.index.join( + right_block.index, how=join ) left_columns = [get_column_left[col] for col in left_block.value_columns] right_columns = [get_column_right[col] for col in right_block.value_columns] - left_block = joined_block.select_columns(left_columns) - right_block = joined_block.select_columns(right_columns) + left_block = joined_index._block.select_columns(left_columns) + right_block = joined_index._block.select_columns(right_columns) return left_block, right_block @@ -954,8 +715,7 @@ def idxmax(block: blocks.Block) -> blocks.Block: def _idx_extrema( block: blocks.Block, min_or_max: typing.Literal["min", "max"] ) -> blocks.Block: - block._throw_if_null_index("idx") - if len(block.index_columns) > 1: + if len(block.index_columns) != 1: # TODO: Need support for tuple dtype raise NotImplementedError( f"idxmin not support for multi-index. {constants.FEEDBACK_LINK}" @@ -971,13 +731,13 @@ def _idx_extrema( ) # Have to find the min for each order_refs = [ - ordering.OrderingExpression(ex.deref(value_col), direction), + ordering.OrderingColumnReference(value_col, direction), *[ - ordering.OrderingExpression(ex.deref(idx_col)) + ordering.OrderingColumnReference(idx_col) for idx_col in original_block.index_columns ], ] - window_spec = window_specs.unbound(ordering=tuple(order_refs)) + window_spec = windows.WindowSpec(ordering=tuple(order_refs)) idx_col = original_block.index_columns[0] block, result_col = block.apply_window_op( idx_col, agg_ops.first_op, window_spec @@ -990,5 +750,5 @@ def _idx_extrema( # Stack the entire column axis to produce single-column result # Assumption: uniform dtype for stackability return block.aggregate_all_and_stack( - agg_ops.AnyValueOp(), + agg_ops.AnyValueOp(), dtype=block.dtypes[0] ).with_column_labels([original_block.index.name]) diff --git a/bigframes/core/blocks.py b/bigframes/core/blocks.py index 8522a4d97be..e831b42752f 100644 --- a/bigframes/core/blocks.py +++ b/bigframes/core/blocks.py @@ -21,54 +21,29 @@ from __future__ import annotations -import ast -import dataclasses -import datetime import functools import itertools import random import typing +from typing import Iterable, List, Optional, Sequence, Tuple import warnings -from typing import ( - Iterable, - Iterator, - List, - Literal, - Mapping, - Optional, - Sequence, - Tuple, - Union, -) - -import bigframes_vendored.constants as constants + import google.cloud.bigquery as bigquery -import numpy import pandas as pd -import pyarrow as pa -import bigframes.constants +import bigframes.constants as constants import bigframes.core as core -import bigframes.core.agg_expressions as ex_types -import bigframes.core.expression as ex -import bigframes.core.expression as scalars import bigframes.core.guid as guid -import bigframes.core.identifiers -import bigframes.core.join_def as join_defs +import bigframes.core.indexes as indexes +import bigframes.core.joins.name_resolution as join_names import bigframes.core.ordering as ordering -import bigframes.core.pyarrow_utils as pyarrow_utils +import bigframes.core.utils import bigframes.core.utils as utils -import bigframes.core.window_spec as windows import bigframes.dtypes -import bigframes.exceptions as bfe import bigframes.operations as ops import bigframes.operations.aggregations as agg_ops -from bigframes import session -from bigframes._config import sampling_options -from bigframes.core import agg_expressions, local_data -from bigframes.session import dry_runs, execution_spec -from bigframes.session import executor as executors -from bigframes.session._io import pandas as io_pandas +import bigframes.session._io.pandas +import third_party.bigframes_vendored.pandas.io.common as vendored_pandas_io_common # Type constraint for wherever column labels are used Label = typing.Hashable @@ -91,43 +66,18 @@ _MONOTONIC_DECREASING = "monotonic_decreasing" -LevelType = typing.Hashable +LevelType = typing.Union[str, int] LevelsType = typing.Union[LevelType, typing.Sequence[LevelType]] -class PandasBatches(Iterator[pd.DataFrame]): +class BlockHolder(typing.Protocol): """Interface for mutable objects with state represented by a block value object.""" - def __init__( - self, - pandas_batches: Iterator[pd.DataFrame], - total_rows: Optional[int] = 0, - *, - total_bytes_processed: Optional[int] = 0, - ): - self._dataframes: Iterator[pd.DataFrame] = pandas_batches - self._total_rows: Optional[int] = total_rows - self._total_bytes_processed: Optional[int] = total_bytes_processed - - @property - def total_rows(self) -> Optional[int]: - return self._total_rows - - @property - def total_bytes_processed(self) -> Optional[int]: - return self._total_bytes_processed - - def __next__(self) -> pd.DataFrame: - return next(self._dataframes) + def _set_block(self, block: Block): + """Set the underlying block value of the object""" - -@dataclasses.dataclass() -class MaterializationOptions: - downsampling: sampling_options.SamplingOptions = dataclasses.field( - default_factory=sampling_options.SamplingOptions - ) - allow_large_results: Optional[bool] = None - ordered: bool = True + def _get_block(self) -> Block: + """Get the underlying block value of the object""" class Block: @@ -139,19 +89,19 @@ def __init__( index_columns: Iterable[str], column_labels: typing.Union[pd.Index, typing.Iterable[Label]], index_labels: typing.Union[pd.Index, typing.Iterable[Label], None] = None, - *, - value_columns: Optional[Iterable[str]] = None, - transpose_cache: Optional[Block] = None, ): """Construct a block object, will create default index if no index columns specified.""" index_columns = list(index_columns) - if index_labels is not None: + if index_labels: index_labels = list(index_labels) if len(index_labels) != len(index_columns): raise ValueError( - f"'index_columns' (size {len(index_columns)}) and 'index_labels' (size {len(index_labels)}) must have equal length" + "'index_columns' and 'index_labels' must have equal length" ) - + if len(index_columns) == 0: + new_index_col_id = guid.generate_guid() + expr = expr.promote_offsets(new_index_col_id) + index_columns = [new_index_col_id] self._index_columns = tuple(index_columns) # Index labels don't need complicated hierarchical access so can store as tuple self._index_labels = ( @@ -159,13 +109,7 @@ def __init__( if index_labels else tuple([None for _ in index_columns]) ) - if value_columns is None: - value_columns = [ - col_id for col_id in expr.column_ids if col_id not in index_columns - ] - self._expr = self._normalize_expression( - expr, self._index_columns, value_columns - ) + self._expr = self._normalize_expression(expr, self._index_columns) # Use pandas index to more easily replicate column indexing, especially for hierarchical column index self._column_labels = ( column_labels.copy() @@ -184,111 +128,28 @@ def __init__( # TODO(kemppeterson) Add a cache for corr to parallel the single-column stats. self._stats_cache[" ".join(self.index_columns)] = {} - self._transpose_cache: Optional[Block] = transpose_cache - self._view_ref: Optional[bigquery.TableReference] = None - self._view_ref_dry_run: Optional[bigquery.TableReference] = None - - @classmethod - def from_pyarrow( - cls, - data: pa.Table, - session: bigframes.Session, - ) -> Block: - column_labels = data.column_names - - # TODO(tswast): Use array_value.promote_offsets() instead once that node is - # supported by the local engine. - offsets_col = bigframes.core.guid.generate_guid() - index_ids = [offsets_col] - index_labels = [None] - - # TODO(https://github.com/googleapis/python-bigquery-dataframes/issues/859): - # Allow users to specify the "total ordering" column(s) or allow multiple - # such columns. - data = pyarrow_utils.append_offsets(data, offsets_col=offsets_col) - - # from_pyarrow will normalize the types for us. - managed_data = local_data.ManagedArrowTable.from_pyarrow(data) - array_value = core.ArrayValue.from_managed(managed_data, session=session) - block = cls( - array_value, - column_labels=column_labels, - index_columns=index_ids, - index_labels=index_labels, - ) - return block - - @classmethod - def from_local( - cls, - data: pd.DataFrame, - session: bigframes.Session, - *, - cache_transpose: bool = True, - ) -> Block: - # Assumes caller has already converted datatypes to bigframes ones. - pd_data = data - column_labels = pd_data.columns - index_labels = list(pd_data.index.names) - - # unique internal ids - column_ids = [f"column_{i}" for i in range(len(pd_data.columns))] - index_ids = [f"level_{level}" for level in range(pd_data.index.nlevels)] - - pd_data = pd_data.set_axis(column_ids, axis=1) - pd_data = pd_data.reset_index(names=index_ids) - managed_data = local_data.ManagedArrowTable.from_pandas(pd_data) - array_value = core.ArrayValue.from_managed(managed_data, session=session) - block = cls( - array_value, - column_labels=column_labels, - index_columns=index_ids, - index_labels=index_labels, - ) - if cache_transpose: - try: - # this cache will help when aligning on axis=1 - block = block.with_transpose_cache( - cls.from_local(data.T, session, cache_transpose=False) - ) - except Exception: - pass - return block @property - def has_index(self) -> bool: - return len(self._index_columns) > 0 - - @property - def index(self) -> BlockIndexProperties: + def index(self) -> indexes.IndexValue: """Row identities for values in the Block.""" - return BlockIndexProperties(self) + return indexes.IndexValue(self) @functools.cached_property def shape(self) -> typing.Tuple[int, int]: """Returns dimensions as (length, width) tuple.""" - # Support zero-query for hermetic unit tests. - if self.expr.session is None and self.expr.node.row_count: - try: - return self.expr.node.row_count - except Exception: - pass - - row_count = ( - self.session._executor.execute( - self.expr.row_count(), - execution_spec.ExecutionSpec(promise_under_10gb=True, ordered=False), - ) - .batches() - .to_py_scalar() - ) - return (row_count, len(self.value_columns)) + impl_length, _ = self._expr.shape() + return (impl_length, len(self.value_columns)) @property def index_columns(self) -> Sequence[str]: """Column(s) to use as row labels.""" return self._index_columns + @property + def index_labels(self) -> Sequence[Label]: + """Name of column(s) to use as row labels.""" + return self._index_labels + @property def value_columns(self) -> Sequence[str]: """All value columns, mutually exclusive with index columns.""" @@ -315,8 +176,11 @@ def dtypes( return [self.expr.get_column_type(col) for col in self.value_columns] @property - def session(self) -> session.Session: - return self._expr.session + def index_dtypes( + self, + ) -> Sequence[bigframes.dtypes.Dtype]: + """Returns the dtypes of the index columns.""" + return [self.expr.get_column_type(col) for col in self.index_columns] @functools.cached_property def col_id_to_label(self) -> typing.Mapping[str, Label]: @@ -334,26 +198,6 @@ def label_to_col_id(self) -> typing.Mapping[Label, typing.Sequence[str]]: mapping[label] = (*mapping.get(label, ()), id) return mapping - def resolve_label_exact(self, label: Label) -> Optional[str]: - """Returns the column id matching the label if there is exactly - one such column. If there are multiple columns with the same name, - raises an error. If there is no such a column, returns None.""" - matches = self.label_to_col_id.get(label, []) - if len(matches) > 1: - raise ValueError( - f"Multiple columns matching id {label} were found. {constants.FEEDBACK_LINK}" - ) - return matches[0] if len(matches) != 0 else None - - def resolve_label_exact_or_error(self, label: Label) -> str: - """Returns the column id matching the label if there is exactly - one such column. If there are multiple columns with the same name, - raises an error. If there is no such a column, raises an error too.""" - col_id = self.resolve_label_exact(label) - if col_id is None: - raise ValueError(f"Label {label} not found. {constants.FEEDBACK_LINK}") - return col_id - @functools.cached_property def col_id_to_index_name(self) -> typing.Mapping[str, Label]: """Get column label for value columns, or index name for index columns""" @@ -370,10 +214,6 @@ def index_name_to_col_id(self) -> typing.Mapping[Label, typing.Sequence[str]]: mapping[label] = (*mapping.get(label, ()), id) return mapping - @property - def explicitly_ordered(self) -> bool: - return self.expr.explicitly_ordered - def cols_matching_label(self, partial_label: Label) -> typing.Sequence[str]: """ Unlike label_to_col_id, this works with partial labels for multi-index. @@ -394,8 +234,8 @@ def cols_matching_label(self, partial_label: Label) -> typing.Sequence[str]: def order_by( self, - by: typing.Sequence[ordering.OrderingExpression], - stable: bool = True, + by: typing.Sequence[ordering.OrderingColumnReference], + stable: bool = False, ) -> Block: return Block( self._expr.order_by(by, stable=stable), @@ -412,97 +252,57 @@ def reversed(self) -> Block: index_labels=self.index.names, ) - def reset_index( - self, - level: LevelsType = None, - drop: bool = True, - *, - col_level: Union[str, int] = 0, - col_fill: typing.Hashable = "", - allow_duplicates: bool = False, - replacement: Optional[bigframes.enums.DefaultIndexKind] = None, - ) -> Block: + def reset_index(self, drop: bool = True) -> Block: """Reset the index of the block, promoting the old index to a value column. Arguments: - level: the label or index level of the index levels to remove. name: this is the column id for the new value id derived from the old index - allow_duplicates: if false, duplicate col labels will result in error - replacement: if not null, will override default index replacement type Returns: A new Block because dropping index columns can break references from Index classes that point to this block. """ - if level is not None: - # preserve original order, not user provided order - level_ids: Sequence[str] = [ - id for id in self.index_columns if id in self.index.resolve_level(level) - ] - else: - level_ids = self.index_columns - - expr = self._expr - replacement_idx_type = replacement or self.session._default_index_type - if set(self.index_columns) > set(level_ids): - new_index_cols = [col for col in self.index_columns if col not in level_ids] - new_index_labels = [self.col_id_to_index_name[id] for id in new_index_cols] - elif replacement_idx_type == bigframes.enums.DefaultIndexKind.SEQUENTIAL_INT64: - expr, new_index_col_id = expr.promote_offsets() - new_index_cols = [new_index_col_id] - new_index_labels = [None] - elif replacement_idx_type == bigframes.enums.DefaultIndexKind.NULL: - new_index_cols = [] - new_index_labels = [] - else: - raise ValueError(f"Unrecognized default index kind: {replacement_idx_type}") - + block = self + new_index_col_id = guid.generate_guid() + expr = self._expr.promote_offsets(new_index_col_id) if drop: # Even though the index might be part of the ordering, keep that # ordering expression as reset_index shouldn't change the row # order. - expr = expr.drop_columns(level_ids) - return Block( + expr = expr.drop_columns(self.index_columns) + block = Block( expr, - index_columns=new_index_cols, - index_labels=new_index_labels, + index_columns=[new_index_col_id], column_labels=self.column_labels, + index_labels=[None], ) else: # Add index names to column index - col_level_n = ( - col_level - if isinstance(col_level, int) - else self.column_labels.names.index(col_level) - ) + index_labels = self.index.names column_labels_modified = self.column_labels - for position, level_id in enumerate(level_ids): - label = self.col_id_to_index_name[level_id] + for level, label in enumerate(index_labels): if label is None: - if "index" not in self.column_labels and self.index.nlevels <= 1: + if "index" not in self.column_labels and len(index_labels) <= 1: label = "index" else: - label = f"level_{self.index_columns.index(level_id)}" + label = f"level_{level}" - if (not allow_duplicates) and (label in self.column_labels): + if label in self.column_labels: raise ValueError(f"cannot insert {label}, already exists") - if isinstance(self.column_labels, pd.MultiIndex): nlevels = self.column_labels.nlevels - label = tuple( - label if i == col_level_n else col_fill for i in range(nlevels) - ) - + label = tuple(label if i == 0 else "" for i in range(nlevels)) # Create index copy with label inserted # See: https://pandas.pydata.org/docs/reference/api/pandas.Index.insert.html - column_labels_modified = column_labels_modified.insert(position, label) + column_labels_modified = column_labels_modified.insert(level, label) - return Block( - expr.select_columns((*new_index_cols, *level_ids, *self.value_columns)), - index_columns=new_index_cols, - index_labels=new_index_labels, + block = Block( + expr, + index_columns=[new_index_col_id], column_labels=column_labels_modified, + index_labels=[None], ) + return block def set_index( self, @@ -573,243 +373,99 @@ def reorder_levels(self, ids: typing.Sequence[str]): level_names = [self.col_id_to_index_name[index_id] for index_id in ids] return Block(self.expr, ids, self.column_labels, level_names) - def to_arrow( - self, - *, - ordered: bool = True, - allow_large_results: Optional[bool] = None, - ) -> Tuple[pa.Table, Optional[bigquery.QueryJob]]: - """Run query and download results as a pyarrow Table.""" - under_10gb = ( - (not allow_large_results) - if (allow_large_results is not None) - else not bigframes.options._allow_large_results - ) - execute_result = self.session._executor.execute( - self.expr, - execution_spec.ExecutionSpec( - promise_under_10gb=under_10gb, - ordered=ordered, - ), - ) - pa_table = execute_result.batches().to_arrow_table() - - pa_index_labels = [] - for index_level, index_label in enumerate(self._index_labels): - if isinstance(index_label, str): - pa_index_labels.append(index_label) - else: - pa_index_labels.append(f"__index_level_{index_level}__") - - # pa.Table.from_pandas puts index columns last, so update to match. - pa_table = pa_table.select([*self.value_columns, *self.index_columns]) - pa_table = pa_table.rename_columns(list(self.column_labels) + pa_index_labels) - return pa_table, execute_result.query_job + def _to_dataframe(self, result) -> pd.DataFrame: + """Convert BigQuery data to pandas DataFrame with specific dtypes.""" + dtypes = dict(zip(self.index_columns, self.index_dtypes)) + dtypes.update(zip(self.value_columns, self.dtypes)) + return self._expr.session._rows_to_dataframe(result, dtypes) def to_pandas( self, + value_keys: Optional[Iterable[str]] = None, + max_results: Optional[int] = None, max_download_size: Optional[int] = None, sampling_method: Optional[str] = None, random_state: Optional[int] = None, - *, - ordered: bool = True, - allow_large_results: Optional[bool] = None, - ) -> Tuple[pd.DataFrame, Optional[bigquery.QueryJob]]: - """Run query and download results as a pandas DataFrame. - - Args: - max_download_size (int, default None): - Download size threshold in MB. If max_download_size is exceeded when downloading data - (e.g., to_pandas()), the data will be downsampled if - bigframes.options.sampling.enable_downsampling is True, otherwise, an error will be - raised. If set to a value other than None, this will supersede the global config. - sampling_method (str, default None): - Downsampling algorithms to be chosen from, the choices are: "head": This algorithm - returns a portion of the data from the beginning. It is fast and requires minimal - computations to perform the downsampling; "uniform": This algorithm returns uniform - random samples of the data. If set to a value other than None, this will supersede - the global config. - random_state (int, default None): - The seed for the uniform downsampling algorithm. If provided, the uniform method may - take longer to execute and require more computation. If set to a value other than - None, this will supersede the global config. - ordered (bool, default True): - Determines whether the resulting pandas dataframe will be ordered. - Whether the row ordering is deterministics depends on whether session ordering is strict. - - Returns: - pandas.DataFrame, QueryJob - """ - sampling = self._get_sampling_option( - max_download_size, sampling_method, random_state - ) - - return self._materialize_local( - materialize_options=MaterializationOptions( - downsampling=sampling, - allow_large_results=allow_large_results, - ordered=ordered, + ) -> Tuple[pd.DataFrame, bigquery.QueryJob]: + """Run query and download results as a pandas DataFrame.""" + if max_download_size is None: + max_download_size = bigframes.options.sampling.max_download_size + if sampling_method is None: + sampling_method = ( + bigframes.options.sampling.sampling_method + if bigframes.options.sampling.sampling_method is not None + else _UNIFORM ) - ) + if random_state is None: + random_state = bigframes.options.sampling.random_state - def _get_sampling_option( - self, - max_download_size: Optional[int] = None, - sampling_method: Optional[str] = None, - random_state: Optional[int] = None, - ) -> sampling_options.SamplingOptions: - if (sampling_method is not None) and (sampling_method not in _SAMPLING_METHODS): + sampling_method = sampling_method.lower() + if sampling_method not in _SAMPLING_METHODS: raise NotImplementedError( f"The downsampling method {sampling_method} is not implemented, " f"please choose from {','.join(_SAMPLING_METHODS)}." ) - sampling = bigframes.options.sampling.with_max_download_size(max_download_size) - if sampling_method is None: - return sampling.with_disabled() - - return sampling.with_method(sampling_method).with_random_state( # type: ignore - random_state + df, _, query_job = self._compute_and_count( + value_keys=value_keys, + max_results=max_results, + max_download_size=max_download_size, + sampling_method=sampling_method, + random_state=random_state, ) + return df, query_job - def try_peek( - self, n: int = 20, force: bool = False, allow_large_results=None - ) -> typing.Optional[pd.DataFrame]: - if force or self.expr.supports_fast_peek: - # really, we should just block insane peek values and always assume <10gb - under_10gb = ( - (not allow_large_results) - if (allow_large_results is not None) - else not bigframes.options._allow_large_results - ) - result = self.session._executor.execute( - self.expr, - execution_spec.ExecutionSpec(promise_under_10gb=under_10gb, peek=n), - ) - df = result.batches().to_pandas() - return self._copy_index_to_pandas(df) - else: - return None + def to_pandas_batches(self): + """Download results one message at a time.""" + dtypes = dict(zip(self.index_columns, self.index_dtypes)) + dtypes.update(zip(self.value_columns, self.dtypes)) + results_iterator, _ = self._expr.start_query() + for arrow_table in results_iterator.to_arrow_iterable( + bqstorage_client=self._expr.session.bqstoragereadclient + ): + df = bigframes.session._io.pandas.arrow_to_pandas(arrow_table, dtypes) + self._copy_index_to_pandas(df) + yield df - def to_pandas_batches( - self, - page_size: Optional[int] = None, - max_results: Optional[int] = None, - allow_large_results: Optional[bool] = None, - cell_execution_count: Optional[int] = None, - ) -> PandasBatches: - """Download results one message at a time. + def _copy_index_to_pandas(self, df: pd.DataFrame): + """Set the index on pandas DataFrame to match this block. - page_size and max_results determine the size and number of batches, - see https://cloud.google.com/python/docs/reference/bigquery/latest/google.cloud.bigquery.job.QueryJob#google_cloud_bigquery_job_QueryJob_result + Warning: This method modifies ``df`` inplace. """ - - under_10gb = ( - (not allow_large_results) - if (allow_large_results is not None) - else not bigframes.options._allow_large_results - ) - execution_result = self.session._executor.execute( - self.expr, - execution_spec.ExecutionSpec( - promise_under_10gb=under_10gb, - ordered=True, - cell_execution_count=cell_execution_count, - ), - ) - result_batches = execution_result.batches() - - # To reduce the number of edge cases to consider when working with the - # results of this, always return at least one DataFrame. See: - # b/428918844. - try: - empty_arrow_table = self.expr.schema.to_pyarrow().empty_table() - except pa.ArrowNotImplementedError: - # Bug with some pyarrow versions(https://github.com/apache/arrow/issues/45262), - # empty_table only supports base storage types, not extension types. - empty_arrow_table = self.expr.schema.to_pyarrow( - use_storage_types=True - ).empty_table() - empty_val = io_pandas.arrow_to_pandas(empty_arrow_table, self.expr.schema) - dfs = map( - lambda a: a[0], - itertools.zip_longest( - result_batches.to_pandas_batches(page_size, max_results), - [0], - fillvalue=empty_val, - ), - ) - dfs = iter(map(self._copy_index_to_pandas, dfs)) - - total_rows = result_batches.approx_total_rows - if (total_rows is not None) and (max_results is not None): - total_rows = min(total_rows, max_results) - - return PandasBatches( - dfs, - total_rows, - total_bytes_processed=execution_result.total_bytes_processed, - ) - - def _copy_index_to_pandas(self, df: pd.DataFrame) -> pd.DataFrame: - """Set the index on pandas DataFrame to match this block.""" - # Note: If BigQuery DataFrame has null index, a default one will be created for the local materialization. - new_df = df.copy() - if len(self.index_columns) > 0: - new_df.set_index(list(self.index_columns), inplace=True) + if self.index_columns: + df.set_index(list(self.index_columns), inplace=True) # Pandas names is annotated as list[str] rather than the more # general Sequence[Label] that BigQuery DataFrames has. # See: https://github.com/pandas-dev/pandas-stubs/issues/804 - new_df.index.names = self.index.names # type: ignore - new_df.columns = self.column_labels - return new_df + df.index.names = self.index.names # type: ignore - def _materialize_local( - self, materialize_options: MaterializationOptions = MaterializationOptions() - ) -> tuple[pd.DataFrame, Optional[bigquery.QueryJob]]: + def _compute_and_count( + self, + value_keys: Optional[Iterable[str]] = None, + max_results: Optional[int] = None, + max_download_size: Optional[int] = None, + sampling_method: Optional[str] = None, + random_state: Optional[int] = None, + ) -> Tuple[pd.DataFrame, int, bigquery.QueryJob]: """Run query and download results as a pandas DataFrame. Return the total number of results as well.""" # TODO(swast): Allow for dry run and timeout. - under_10gb = ( - (not materialize_options.allow_large_results) - if (materialize_options.allow_large_results is not None) - else (not bigframes.options._allow_large_results) + expr = self._apply_value_keys_to_expr(value_keys=value_keys) + + results_iterator, query_job = expr.start_query(max_results=max_results) + + table_size = ( + expr.session._get_table_size(query_job.destination) / _BYTES_TO_MEGABYTES ) - execute_result = self.session._executor.execute( - self.expr, - execution_spec.ExecutionSpec( - promise_under_10gb=under_10gb, - ordered=materialize_options.ordered, - ), + fraction = ( + max_download_size / table_size + if (max_download_size is not None) and (table_size != 0) + else 2 ) - result_batches = execute_result.batches() - - sample_config = materialize_options.downsampling - if result_batches.approx_total_bytes is not None: - table_mb = result_batches.approx_total_bytes / _BYTES_TO_MEGABYTES - max_download_size = sample_config.max_download_size - fraction = ( - max_download_size / table_mb - if (max_download_size is not None) and (table_mb != 0) - else 2 - ) - else: - # Since we cannot acquire the table size without a query_job, - # we skip the sampling. - if sample_config.enable_downsampling: - msg = bfe.format_message( - "Sampling is disabled and there is no download size limit when 'allow_large_results' is set to " - "False. To prevent downloading excessive data, it is recommended to use the peek() method, or " - "limit the data with methods like .head() or .sample() before proceeding with downloads." - ) - warnings.warn(msg, category=UserWarning) - fraction = 2 - # TODO: Maybe materialize before downsampling - # Some downsampling methods - if fraction < 1 and (result_batches.approx_total_rows is not None): - if not sample_config.enable_downsampling: + if fraction < 1: + if not bigframes.options.sampling.enable_downsampling: raise RuntimeError( - f"The data size ({table_mb:.2f} MB) exceeds the maximum download limit of " + f"The data size ({table_size:.2f} MB) exceeds the maximum download limit of " f"{max_download_size} MB. You can:\n\t* Enable downsampling in global options:\n" "\t\t`bigframes.options.sampling.enable_downsampling = True`\n" "\t* Update the global `max_download_size` option. Please make sure " @@ -818,48 +474,57 @@ def _materialize_local( " # Setting it to None will download all the data\n" f"{constants.FEEDBACK_LINK}" ) - msg = bfe.format_message( - f"The data size ({table_mb:.2f} MB) exceeds the maximum download limit of" - f"({max_download_size} MB). It will be downsampled to {max_download_size} " - "MB for download.\nPlease refer to the documentation for configuring " - "the downloading limit." - ) - warnings.warn(msg, category=UserWarning) - total_rows = result_batches.approx_total_rows - # Remove downsampling config from subsequent invocations, as otherwise could result in many - # iterations if downsampling undershoots - if sample_config.sampling_method == "head": - # Just truncates the result iterator without a follow-up query - raw_df = result_batches.to_pandas(limit=int(total_rows * fraction)) - elif ( - sample_config.sampling_method == "uniform" - and sample_config.random_state is None - ): - # Pushes sample into result without new query - sampled_batches = execute_result.batches(sample_rate=fraction) - raw_df = sampled_batches.to_pandas() - else: # uniform sample with random state requires a full follow-up query - down_sampled_block = self.split( - fracs=(fraction,), - random_state=sample_config.random_state, - sort=False, + + warnings.warn( + f"The data size ({table_size:.2f} MB) exceeds the maximum download limit of" + f"({max_download_size} MB). It will be downsampled to {max_download_size} MB for download." + "\nPlease refer to the documentation for configuring the downloading limit.", + UserWarning, + ) + if sampling_method == _HEAD: + total_rows = int(results_iterator.total_rows * fraction) + results_iterator.max_results = total_rows + df = self._to_dataframe(results_iterator) + + if self.index_columns: + df.set_index(list(self.index_columns), inplace=True) + df.index.names = self.index.names # type: ignore + elif (sampling_method == _UNIFORM) and (random_state is None): + filtered_expr = self.expr._uniform_sampling(fraction) + block = Block( + filtered_expr, + index_columns=self.index_columns, + column_labels=self.column_labels, + index_labels=self.index.names, + ) + df, total_rows, _ = block._compute_and_count(max_download_size=None) + elif sampling_method == _UNIFORM: + block = self._split( + fracs=(max_download_size / table_size,), + random_state=random_state, + preserve_order=True, )[0] - return down_sampled_block._materialize_local( - MaterializationOptions(ordered=materialize_options.ordered) + df, total_rows, _ = block._compute_and_count(max_download_size=None) + else: + # This part should never be called, just in case. + raise NotImplementedError( + f"The downsampling method {sampling_method} is not implemented, " + f"please choose from {','.join(_SAMPLING_METHODS)}." ) else: - raw_df = result_batches.to_pandas() - df = self._copy_index_to_pandas(raw_df) - df.columns = self.column_labels - return df, execute_result.query_job + total_rows = results_iterator.total_rows + df = self._to_dataframe(results_iterator) + self._copy_index_to_pandas(df) + + return df, total_rows, query_job - def split( + def _split( self, ns: Iterable[int] = (), fracs: Iterable[float] = (), *, random_state: Optional[int] = None, - sort: Optional[bool | Literal["random"]] = "random", + preserve_order: Optional[bool] = False, ) -> List[Block]: """Internal function to support splitting Block to multiple parts along index axis. @@ -890,17 +555,15 @@ def split( # Create an ordering col and convert to string block, ordering_col = block.promote_offsets() block, string_ordering_col = block.apply_unary_op( - ordering_col, ops.AsTypeOp(to_type=bigframes.dtypes.STRING_DTYPE) + ordering_col, ops.AsTypeOp("string[pyarrow]") ) # Apply hash method to sum col and order by it. block, string_sum_col = block.apply_binary_op( - string_ordering_col, random_state_col, ops.strconcat_op + string_ordering_col, random_state_col, ops.concat_op ) block, hash_string_sum_col = block.apply_unary_op(string_sum_col, ops.hash_op) - block = block.order_by( - [ordering.OrderingExpression(ex.deref(hash_string_sum_col))] - ) + block = block.order_by([ordering.OrderingColumnReference(hash_string_sum_col)]) intervals = [] cur = 0 @@ -913,22 +576,9 @@ def split( typing.cast(Block, block.slice(start=lower, stop=upper)) for lower, upper in intervals ] - - if sort is True: + if preserve_order: sliced_blocks = [ - sliced_block.order_by( - [ - ordering.OrderingExpression(ex.deref(idx_col)) - for idx_col in sliced_block.index_columns - ] - ) - for sliced_block in sliced_blocks - ] - elif sort is False: - sliced_blocks = [ - sliced_block.order_by( - [ordering.OrderingExpression(ex.deref(ordering_col))] - ) + sliced_block.order_by([ordering.OrderingColumnReference(ordering_col)]) for sliced_block in sliced_blocks ] @@ -942,32 +592,12 @@ def split( return [sliced_block.drop_columns(drop_cols) for sliced_block in sliced_blocks] def _compute_dry_run( - self, - value_keys: Optional[Iterable[str]] = None, - *, - ordered: bool = True, - max_download_size: Optional[int] = None, - sampling_method: Optional[str] = None, - random_state: Optional[int] = None, - ) -> typing.Tuple[pd.Series, bigquery.QueryJob]: - sampling = self._get_sampling_option( - max_download_size, sampling_method, random_state - ) - if sampling.enable_downsampling: - raise NotImplementedError("Dry run with sampling is not supported") - + self, value_keys: Optional[Iterable[str]] = None + ) -> bigquery.QueryJob: expr = self._apply_value_keys_to_expr(value_keys=value_keys) - query_job = self.session._executor.dry_run(expr, ordered) - - column_dtypes = { - col: self.expr.get_column_type(self.resolve_label_exact_or_error(col)) - for col in self.column_labels - } - - dry_run_stats = dry_runs.get_query_stats_with_dtypes( - query_job, column_dtypes, self.index.dtypes, self.expr.node - ) - return dry_run_stats, query_job + job_config = bigquery.QueryJobConfig(dry_run=True) + _, query_job = expr.start_query(job_config=job_config) + return query_job def _apply_value_keys_to_expr(self, value_keys: Optional[Iterable[str]] = None): expr = self._expr @@ -985,26 +615,11 @@ def with_column_labels( f"The column labels size `{len(label_list)} ` should equal to the value" + f"columns size: {len(self.value_columns)}." ) - block = Block( - self._expr, - index_columns=self.index_columns, - column_labels=label_list, - index_labels=self.index.names, - ) - singleton_label = len(list(value)) == 1 and list(value)[0] - if singleton_label is not None and self._transpose_cache is not None: - new_cache, label_id = self._transpose_cache.create_constant(singleton_label) - new_cache = new_cache.set_index([label_id]) - block = block.with_transpose_cache(new_cache) - return block - - def with_transpose_cache(self, transposed: Block): return Block( self._expr, index_columns=self.index_columns, - column_labels=self._column_labels, + column_labels=label_list, index_labels=self.index.names, - transpose_cache=transposed, ) def with_index_labels(self, value: typing.Sequence[Label]) -> Block: @@ -1020,30 +635,23 @@ def with_index_labels(self, value: typing.Sequence[Label]) -> Block: index_labels=tuple(value), ) - def project_expr( - self, expr: ex.Expression, label: Label = None + def apply_unary_op( + self, column: str, op: ops.UnaryOp, result_label: Label = None ) -> typing.Tuple[Block, str]: """ - Apply a scalar expression to the block. Creates a new column to store the result. + Apply a unary op to the block. Creates a new column to store the result. """ - array_val, result_id = self._expr.project_to_id(expr) + # TODO(tbergeron): handle labels safely so callers don't need to + result_id = guid.generate_guid() + expr = self._expr.project_unary_op(column, op, result_id) block = Block( - array_val, + expr, index_columns=self.index_columns, - column_labels=self.column_labels.insert(len(self.column_labels), label), + column_labels=[*self.column_labels, result_label], index_labels=self.index.names, ) return (block, result_id) - def apply_unary_op( - self, column: str, op: ops.UnaryOp, result_label: Label = None - ) -> typing.Tuple[Block, str]: - """ - Apply a unary op to the block. Creates a new column to store the result. - """ - expr = op.as_expr(column) - return self.project_expr(expr, result_label) - def apply_binary_op( self, left_column_id: str, @@ -1051,8 +659,17 @@ def apply_binary_op( op: ops.BinaryOp, result_label: Label = None, ) -> typing.Tuple[Block, str]: - expr = op.as_expr(left_column_id, right_column_id) - return self.project_expr(expr, result_label) + result_id = guid.generate_guid() + expr = self._expr.project_binary_op( + left_column_id, right_column_id, op, result_id + ) + block = Block( + expr, + index_columns=self.index_columns, + column_labels=[*self.column_labels, result_label], + index_labels=self.index.names, + ) + return (block, result_id) def apply_ternary_op( self, @@ -1062,196 +679,92 @@ def apply_ternary_op( op: ops.TernaryOp, result_label: Label = None, ) -> typing.Tuple[Block, str]: - expr = op.as_expr(col_id_1, col_id_2, col_id_3) - return self.project_expr(expr, result_label) - - def apply_nary_op( - self, - columns: Iterable[str], - op: ops.NaryOp, - result_label: Label = None, - ) -> typing.Tuple[Block, str]: - expr = op.as_expr(*columns) - return self.project_expr(expr, result_label) + result_id = guid.generate_guid() + expr = self._expr.project_ternary_op( + col_id_1, col_id_2, col_id_3, op, result_id + ) + block = Block( + expr, + index_columns=self.index_columns, + column_labels=[*self.column_labels, result_label], + index_labels=self.index.names, + ) + return (block, result_id) def multi_apply_window_op( self, columns: typing.Sequence[str], - op: agg_ops.UnaryWindowOp, - window_spec: windows.WindowSpec, + op: agg_ops.WindowOp, + window_spec: core.WindowSpec, *, skip_null_groups: bool = False, + never_skip_nulls: bool = False, ) -> typing.Tuple[Block, typing.Sequence[str]]: - return self.apply_analytic( - agg_exprs=( - agg_expressions.UnaryAggregation(op, ex.deref(col)) for col in columns - ), - window=window_spec, - result_labels=self._get_labels_for_columns(columns), - skip_null_groups=skip_null_groups, - ) + block = self + result_ids = [] + for i, col_id in enumerate(columns): + label = self.col_id_to_label[col_id] + block, result_id = block.apply_window_op( + col_id, + op, + window_spec=window_spec, + skip_reproject_unsafe=(i + 1) < len(columns), + result_label=label, + skip_null_groups=skip_null_groups, + never_skip_nulls=never_skip_nulls, + ) + result_ids.append(result_id) + return block, result_ids def multi_apply_unary_op( self, - op: Union[ops.UnaryOp, ops.NaryOp, ex.Expression], + columns: typing.Sequence[str], + op: ops.UnaryOp, ) -> Block: - if isinstance(op, (ops.UnaryOp, ops.NaryOp)): - input_varname = guid.generate_guid() - expr = op.as_expr(ex.free_var(input_varname)) - else: - input_varnames = op.free_variables - assert len(set(input_varnames)) == 1 - expr = op - input_varname = input_varnames[0] - block = self - - exprs = [ - expr.bind_variables({input_varname: ex.deref(col_id)}) - for col_id in self.value_columns - ] - block = self.project_exprs(exprs, labels=self.column_labels, drop=True) - - # Special case, we can preserve transpose cache for full-frame unary ops - if self._transpose_cache is not None: - new_transpose_cache = self._transpose_cache.multi_apply_unary_op(op) - block = block.with_transpose_cache(new_transpose_cache) + for i, col_id in enumerate(columns): + label = self.col_id_to_label[col_id] + block, result_id = block.apply_unary_op( + col_id, + op, + result_label=label, + ) + block = block.copy_values(result_id, col_id) + block = block.drop_columns([result_id]) return block - def project_exprs( - self, - exprs: Sequence[ex.Expression], - labels: Union[Sequence[Label], pd.Index], - drop=False, - ) -> Block: - new_array, new_cols = self.expr.compute_values(exprs) - if drop: - new_array = new_array.drop_columns(self.value_columns) - - new_val_cols = new_cols if drop else (*self.value_columns, *new_cols) - return Block( - new_array, - index_columns=self.index_columns, - value_columns=new_val_cols, - column_labels=labels - if drop - else self.column_labels.append(pd.Index(labels)), - index_labels=self._index_labels, - ) - - def project_block_exprs( - self, - exprs: Sequence[ex.Expression], - labels: Union[Sequence[Label], pd.Index], - drop=False, - ) -> Block: - """ - Version of the project_exprs that supports mixing analytic and scalar expressions - """ - new_array, _ = self.expr.compute_general_expression(exprs) - if drop: - new_array = new_array.drop_columns(self.value_columns) - - new_array.node.validate_tree() - return Block( - new_array, - index_columns=self.index_columns, - column_labels=labels - if drop - else self.column_labels.append(pd.Index(labels)), - index_labels=self._index_labels, - ) - - def aggregate( - self, - aggregations: typing.Sequence[ex.Expression] = (), - by_column_ids: typing.Sequence[str] = (), - column_labels: Optional[pd.Index] = None, - *, - dropna: bool = True, - ) -> Block: - """ - Apply aggregations to the block. - - Grouping columns will form the index of the result block. - - Arguments: - aggregations: Aggregation expressions to apply - by_column_id: column id of the aggregation key, this is preserved through the transform and used as index. - dropna: whether null keys should be dropped - - Returns: - Block - """ - if column_labels is None: - column_labels = pd.Index(range(len(aggregations))) - - result_expr = self.expr.compute_general_reduction( - aggregations, by_column_ids, dropna=dropna - ) - - grouping_col_labels: typing.List[Label] = [] - if len(by_column_ids) == 0: - # in the absence of grouping columns, there will be a single row output, assign 0 as its row label. - result_expr, label_id = result_expr.create_constant(0, pd.Int64Dtype()) - index_columns = (label_id,) - grouping_col_labels = [None] - else: - index_columns = tuple(by_column_ids) # type: ignore - for by_col_id in by_column_ids: - if by_col_id in self.value_columns: - grouping_col_labels.append(self.col_id_to_label[by_col_id]) - else: - grouping_col_labels.append(self.col_id_to_index_name[by_col_id]) - - return Block( - result_expr, - index_columns=index_columns, - column_labels=column_labels, - index_labels=grouping_col_labels, - ) - def apply_window_op( self, column: str, - op: agg_ops.UnaryWindowOp, - window_spec: windows.WindowSpec, + op: agg_ops.WindowOp, + window_spec: core.WindowSpec, *, result_label: Label = None, skip_null_groups: bool = False, + skip_reproject_unsafe: bool = False, + never_skip_nulls: bool = False, ) -> typing.Tuple[Block, str]: - agg_expr = agg_expressions.UnaryAggregation(op, ex.deref(column)) - block, ids = self.apply_analytic( - [agg_expr], - window_spec, - [result_label], - skip_null_groups=skip_null_groups, - ) - return block, ids[0] - - def apply_analytic( - self, - agg_exprs: Iterable[agg_expressions.Aggregation], - window: windows.WindowSpec, - result_labels: Iterable[Label], - *, - skip_null_groups: bool = False, - ) -> typing.Tuple[Block, Sequence[str]]: block = self if skip_null_groups: - for key in window.grouping_keys: - block = block.filter(ops.notnull_op.as_expr(key)) - expr, result_ids = block._expr.project_window_expr( - tuple(agg_exprs), - window, + for key in window_spec.grouping_keys: + block, not_null_id = block.apply_unary_op(key, ops.notnull_op) + block = block.filter(not_null_id).drop_columns([not_null_id]) + result_id = guid.generate_guid() + expr = block._expr.project_window_op( + column, + op, + window_spec, + result_id, + skip_reproject_unsafe=skip_reproject_unsafe, + never_skip_nulls=never_skip_nulls, ) block = Block( expr, index_columns=self.index_columns, - column_labels=self.column_labels.append(pd.Index(result_labels)), + column_labels=[*self.column_labels, result_label], index_labels=self._index_labels, ) - return (block, result_ids) + return (block, result_id) def copy_values(self, source_column_id: str, destination_column_id: str) -> Block: expr = self.expr.assign(source_column_id, destination_column_id) @@ -1268,7 +781,8 @@ def create_constant( label: Label = None, dtype: typing.Optional[bigframes.dtypes.Dtype] = None, ) -> typing.Tuple[Block, str]: - expr, result_id = self.expr.create_constant(scalar_constant, dtype=dtype) + result_id = guid.generate_guid() + expr = self.expr.assign_constant(result_id, scalar_constant, dtype=dtype) # Create index copy with label inserted # See: https://pandas.pydata.org/docs/reference/api/pandas.Index.insert.html labels = self.column_labels.insert(len(self.column_labels), label) @@ -1291,17 +805,9 @@ def assign_label(self, column_id: str, new_label: Label) -> Block: ) return self.with_column_labels(new_labels) - def filter_by_id(self, column_id: str, keep_null: bool = False): + def filter(self, column_id: str, keep_null: bool = False): return Block( - self._expr.filter_by_id(column_id, keep_null), - index_columns=self.index_columns, - column_labels=self.column_labels, - index_labels=self.index.names, - ) - - def filter(self, predicate: scalars.Expression): - return Block( - self._expr.filter(predicate), + self._expr.filter(column_id, keep_null), index_columns=self.index_columns, column_labels=self.column_labels, index_labels=self.index.names, @@ -1309,46 +815,61 @@ def filter(self, predicate: scalars.Expression): def aggregate_all_and_stack( self, - operation: typing.Union[agg_ops.UnaryAggregateOp, agg_ops.NullaryAggregateOp], + operation: agg_ops.AggregateOp, *, axis: int | str = 0, + value_col_id: str = "values", dropna: bool = True, + dtype: typing.Union[ + bigframes.dtypes.Dtype, typing.Tuple[bigframes.dtypes.Dtype, ...] + ] = pd.Float64Dtype(), ) -> Block: axis_n = utils.get_axis_number(axis) if axis_n == 0: aggregations = [ - ( - agg_expressions.UnaryAggregation(operation, ex.deref(col_id)) - if isinstance(operation, agg_ops.UnaryAggregateOp) - else agg_expressions.NullaryAggregation(operation), - col_id, - ) - for col_id in self.value_columns + (col_id, operation, col_id) for col_id in self.value_columns ] - result_expr, index_id = self.expr.aggregate( - aggregations, dropna=dropna - ).create_constant(None, None) - # Transpose as last operation so that final block has valid transpose cache - return Block( - result_expr, - index_columns=[index_id], - column_labels=self.column_labels, - index_labels=[None], - ).transpose(original_row_index=pd.Index([None]), single_row_mode=True) + result_expr = self.expr.aggregate(aggregations, dropna=dropna).unpivot( + row_labels=self.column_labels.to_list(), + index_col_ids=["index"], + unpivot_columns=tuple([(value_col_id, tuple(self.value_columns))]), + dtype=dtype, + ) + return Block(result_expr, index_columns=["index"], column_labels=[None]) else: # axis_n == 1 - as_array = ops.ToArrayOp().as_expr(*(col for col in self.value_columns)) - reduced = ops.ArrayReduceOp(operation).as_expr(as_array) - block, id = self.project_expr(reduced, None) - return block.select_column(id).with_column_labels(pd.Index([None])) + # using offsets as identity to group on. + # TODO: Allow to promote identity/total_order columns instead for better perf + offset_col = guid.generate_guid() + expr_with_offsets = self.expr.promote_offsets(offset_col) + stacked_expr = expr_with_offsets.unpivot( + row_labels=self.column_labels.to_list(), + index_col_ids=[guid.generate_guid()], + unpivot_columns=[(value_col_id, tuple(self.value_columns))], + passthrough_columns=[*self.index_columns, offset_col], + dtype=dtype, + ) + index_aggregations = [ + (col_id, agg_ops.AnyValueOp(), col_id) + for col_id in [*self.index_columns] + ] + main_aggregation = (value_col_id, operation, value_col_id) + result_expr = stacked_expr.aggregate( + [*index_aggregations, main_aggregation], + by_column_ids=[offset_col], + dropna=dropna, + ) + return Block( + result_expr.drop_columns([offset_col]), + self.index_columns, + column_labels=[None], + index_labels=self.index_labels, + ) def select_column(self, id: str) -> Block: return self.select_columns([id]) def select_columns(self, ids: typing.Sequence[str]) -> Block: - # Allow renames as may end up selecting same columns multiple times - expr = self._expr.select_columns( - [*self.index_columns, *ids], allow_renames=True - ) + expr = self._expr.select_columns([*self.index_columns, *ids]) col_labels = self._get_labels_for_columns(ids) return Block(expr, self.index_columns, col_labels, self.index.names) @@ -1389,36 +910,81 @@ def remap_f(x): col_labels.append(remap_f(col_label)) return self.with_column_labels(col_labels) - def get_stat( + def aggregate( self, - column_id: str, - stat: typing.Union[agg_ops.UnaryAggregateOp, agg_ops.NullaryAggregateOp], - ): + by_column_ids: typing.Sequence[str] = (), + aggregations: typing.Sequence[typing.Tuple[str, agg_ops.AggregateOp]] = (), + *, + as_index: bool = True, + dropna: bool = True, + ) -> typing.Tuple[Block, typing.Sequence[str]]: + """ + Apply aggregations to the block. Callers responsible for setting index column(s) after. + Arguments: + by_column_id: column id of the aggregation key, this is preserved through the transform and used as index. + aggregations: input_column_id, operation tuples + as_index: if True, grouping keys will be index columns in result, otherwise they will be non-index columns. + dropna: whether null keys should be dropped + """ + agg_specs = [ + (input_id, operation, guid.generate_guid()) + for input_id, operation in aggregations + ] + output_col_ids = [agg_spec[2] for agg_spec in agg_specs] + result_expr = self.expr.aggregate(agg_specs, by_column_ids, dropna=dropna) + + aggregate_labels = self._get_labels_for_columns( + [agg[0] for agg in aggregations] + ) + if as_index: + names: typing.List[Label] = [] + for by_col_id in by_column_ids: + if by_col_id in self.value_columns: + names.append(self.col_id_to_label[by_col_id]) + else: + names.append(self.col_id_to_index_name[by_col_id]) + return ( + Block( + result_expr, + index_columns=by_column_ids, + column_labels=aggregate_labels, + index_labels=names, + ), + output_col_ids, + ) + else: # as_index = False + # If as_index=False, drop grouping levels, but keep grouping value columns + by_value_columns = [ + col for col in by_column_ids if col in self.value_columns + ] + by_column_labels = self._get_labels_for_columns(by_value_columns) + labels = (*by_column_labels, *aggregate_labels) + offsets_id = guid.generate_guid() + result_expr_pruned = result_expr.select_columns( + [*by_value_columns, *output_col_ids] + ).promote_offsets(offsets_id) + + return ( + Block( + result_expr_pruned, index_columns=[offsets_id], column_labels=labels + ), + output_col_ids, + ) + + def get_stat(self, column_id: str, stat: agg_ops.AggregateOp): """Gets aggregates immediately, and caches it""" if stat.name in self._stats_cache[column_id]: return self._stats_cache[column_id][stat.name] # TODO: Convert nonstandard stats into standard stats where possible (popvar, etc.) # if getting a standard stat, just go get the rest of them - standard_stats = typing.cast( - typing.Sequence[ - typing.Union[agg_ops.UnaryAggregateOp, agg_ops.NullaryAggregateOp] - ], - self._standard_stats(column_id), - ) + standard_stats = self._standard_stats(column_id) stats_to_fetch = standard_stats if stat in standard_stats else [stat] - aggregations = [ - ( - agg_expressions.UnaryAggregation(stat, ex.deref(column_id)) - if isinstance(stat, agg_ops.UnaryAggregateOp) - else agg_expressions.NullaryAggregation(stat), - stat.name, - ) - for stat in stats_to_fetch - ] + aggregations = [(column_id, stat, stat.name) for stat in stats_to_fetch] expr = self.expr.aggregate(aggregations) - expr, offset_index_id = expr.promote_offsets() + offset_index_id = guid.generate_guid() + expr = expr.promote_offsets(offset_index_id) block = Block( expr, index_columns=[offset_index_id], @@ -1431,95 +997,53 @@ def get_stat( self._stats_cache[column_id].update(stats_map) return stats_map[stat.name] - def get_binary_stat( - self, column_id_left: str, column_id_right: str, stat: agg_ops.BinaryAggregateOp - ): + def get_corr_stat(self, column_id_left: str, column_id_right: str): # TODO(kemppeterson): Clean up the column names for DataFrames.corr support # TODO(kemppeterson): Add a cache here. - aggregations = [ + corr_aggregations = [ ( - agg_expressions.BinaryAggregation( - stat, ex.deref(column_id_left), ex.deref(column_id_right) - ), - f"{stat.name}_{column_id_left}{column_id_right}", + column_id_left, + column_id_right, + "corr_" + column_id_left + column_id_right, ) ] - expr = self.expr.aggregate(aggregations) - expr, offset_index_id = expr.promote_offsets() + expr = self.expr.corr_aggregate(corr_aggregations) + offset_index_id = guid.generate_guid() + expr = expr.promote_offsets(offset_index_id) block = Block( expr, index_columns=[offset_index_id], - column_labels=[a[1] for a in aggregations], + column_labels=[a[2] for a in corr_aggregations], ) df, _ = block.to_pandas() - return df.loc[0, f"{stat.name}_{column_id_left}{column_id_right}"] + return df.loc[0, "corr_" + column_id_left + column_id_right] def summarize( self, column_ids: typing.Sequence[str], - stats: typing.Sequence[ - typing.Union[agg_ops.UnaryAggregateOp, agg_ops.NullaryAggregateOp] - ], + stats: typing.Sequence[agg_ops.AggregateOp], ): """Get a list of stats as a deferred block object.""" - labels = pd.Index([stat.name for stat in stats]) + label_col_id = guid.generate_guid() + labels = [stat.name for stat in stats] aggregations = [ - ( - agg_expressions.UnaryAggregation(stat, ex.deref(col_id)) - if isinstance(stat, agg_ops.UnaryAggregateOp) - else agg_expressions.NullaryAggregation(stat), - f"{col_id}-{stat.name}", - ) + (col_id, stat, f"{col_id}-{stat.name}") for stat in stats for col_id in column_ids ] columns = [ - (tuple(f"{col_id}-{stat.name}" for stat in stats)) for col_id in column_ids + (col_id, tuple(f"{col_id}-{stat.name}" for stat in stats)) + for col_id in column_ids ] - expr, (index_cols, _, _) = unpivot( - self.expr.aggregate(aggregations), + expr = self.expr.aggregate(aggregations).unpivot( labels, unpivot_columns=tuple(columns), + index_col_ids=tuple([label_col_id]), ) - return Block( - expr, - column_labels=self._get_labels_for_columns(column_ids), - index_columns=index_cols, - ) - - def explode( - self, - column_ids: typing.Sequence[str], - ignore_index: Optional[bool], - ) -> Block: - column_ids = [ - column_id - for column_id in column_ids - if bigframes.dtypes.is_array_like(self.expr.get_column_type(column_id)) - ] - if len(column_ids) == 0: - expr = self.expr - else: - expr = self.expr.explode(column_ids) - - if ignore_index: - expr = expr.drop_columns(self.index_columns) - expr, new_index_ids = expr.promote_offsets() - return Block( - expr, - column_labels=self.column_labels, - # Initiates default index creation using the block constructor. - index_columns=[new_index_ids], - ) - else: - return Block( - expr, - column_labels=self.column_labels, - index_columns=self.index_columns, - index_labels=self._index_labels, - ) + labels = self._get_labels_for_columns(column_ids) + return Block(expr, column_labels=labels, index_columns=[label_col_id]) - def _standard_stats(self, column_id) -> typing.Sequence[agg_ops.UnaryAggregateOp]: + def _standard_stats(self, column_id) -> typing.Sequence[agg_ops.AggregateOp]: """ Gets a standard set of stats to preemptively fetch for a column if any other stat is fetched. @@ -1530,10 +1054,10 @@ def _standard_stats(self, column_id) -> typing.Sequence[agg_ops.UnaryAggregateOp """ # TODO: annotate aggregations themself with this information dtype = self.expr.get_column_type(column_id) - stats: list[agg_ops.UnaryAggregateOp] = [agg_ops.count_op] - if bigframes.dtypes.is_orderable(dtype): + stats: list[agg_ops.AggregateOp] = [agg_ops.count_op] + if dtype not in bigframes.dtypes.UNORDERED_DTYPES: stats += [agg_ops.min_op, agg_ops.max_op] - if dtype in bigframes.dtypes.NUMERIC_BIGFRAMES_TYPES_PERMISSIVE: + if dtype in bigframes.dtypes.NUMERIC_BIGFRAMES_TYPES: # Notable exclusions: # prod op tends to cause overflows # Also, var_op is redundant as can be derived from std @@ -1546,57 +1070,114 @@ def _standard_stats(self, column_id) -> typing.Sequence[agg_ops.UnaryAggregateOp return stats - def _get_labels_for_columns(self, column_ids: typing.Sequence[str]) -> pd.Index: + def _get_labels_for_columns(self, column_ids: typing.Sequence[str]): """Get column label for value columns, or index name for index columns""" - indices = [self.value_columns.index(col_id) for col_id in column_ids] - return self.column_labels.take(indices, allow_fill=False) + lookup = self.col_id_to_label + return [lookup.get(col_id, None) for col_id in column_ids] def _normalize_expression( self, expr: core.ArrayValue, - index_columns: Iterable[str], - value_columns: Iterable[str], + index_columns: typing.Sequence[str], + assert_value_size: typing.Optional[int] = None, ): """Normalizes expression by moving index columns to left.""" - normalized_ids = (*index_columns, *value_columns) - if tuple(expr.column_ids) == normalized_ids: - return expr + value_columns = [ + col_id for col_id in expr.column_ids if col_id not in index_columns + ] + if (assert_value_size is not None) and ( + len(value_columns) != assert_value_size + ): + raise ValueError("Unexpected number of value columns.") return expr.select_columns([*index_columns, *value_columns]) - def grouped_head( - self, - by_column_ids: typing.Sequence[str], - value_columns: typing.Sequence[str], - n: int, - ): - window_spec = windows.cumulative_rows(grouping_keys=tuple(by_column_ids)) - - block, result_id = self.apply_window_op( - value_columns[0], - agg_ops.count_op, - window_spec=window_spec, - ) - - cond = ops.lt_op.as_expr(result_id, ex.const(n + 1)) - block, cond_id = block.project_expr(cond) - block = block.filter_by_id(cond_id) - if value_columns: - return block.select_columns(value_columns) - def slice( self, start: typing.Optional[int] = None, stop: typing.Optional[int] = None, - step: int = 1, - ) -> Block: + step: typing.Optional[int] = None, + ) -> bigframes.core.blocks.Block: + if step is None: + step = 1 if step == 0: - raise ValueError("Slice step size must be non-zero") - return Block( - self.expr.slice(start, stop, step), - index_columns=self.index_columns, - column_labels=self.column_labels, - index_labels=self._index_labels, + raise ValueError("slice step cannot be zero") + if step < 0: + reverse_start = (-start - 1) if start else 0 + reverse_stop = (-stop - 1) if stop else None + reverse_step = -step + return self.reversed()._forward_slice( + reverse_start, reverse_stop, reverse_step + ) + return self._forward_slice(start or 0, stop, step) + + def _forward_slice(self, start: int = 0, stop=None, step: int = 1): + """Performs slice but only for positive step size.""" + if step <= 0: + raise ValueError("forward_slice only supports positive step size") + + use_postive_offsets = ( + (start > 0) + or ((stop is not None) and (stop >= 0)) + or ((step > 1) and (start >= 0)) ) + use_negative_offsets = ( + (start < 0) or (stop and (stop < 0)) or ((step > 1) and (start < 0)) + ) + + block = self + + # only generate offsets that are used + positive_offsets = None + negative_offsets = None + + if use_postive_offsets: + block, positive_offsets = self.promote_offsets() + if use_negative_offsets: + block, negative_offsets = block.reversed().promote_offsets() + block = block.reversed() + + conditions = [] + if start != 0: + if start > 0: + op = ops.partial_right(ops.ge_op, start) + assert positive_offsets + block, start_cond = block.apply_unary_op(positive_offsets, op) + else: + op = ops.partial_right(ops.le_op, -start - 1) + assert negative_offsets + block, start_cond = block.apply_unary_op(negative_offsets, op) + conditions.append(start_cond) + if stop is not None: + if stop >= 0: + op = ops.partial_right(ops.lt_op, stop) + assert positive_offsets + block, stop_cond = block.apply_unary_op(positive_offsets, op) + else: + op = ops.partial_right(ops.gt_op, -stop - 1) + assert negative_offsets + block, stop_cond = block.apply_unary_op(negative_offsets, op) + conditions.append(stop_cond) + + if step > 1: + op = ops.partial_right(ops.mod_op, step) + if start >= 0: + op = ops.partial_right(ops.sub_op, start) + assert positive_offsets + block, start_diff = block.apply_unary_op(positive_offsets, op) + else: + op = ops.partial_right(ops.sub_op, -start + 1) + assert negative_offsets + block, start_diff = block.apply_unary_op(negative_offsets, op) + modulo_op = ops.partial_right(ops.mod_op, step) + block, mod = block.apply_unary_op(start_diff, modulo_op) + is_zero_op = ops.partial_right(ops.eq_op, 0) + block, step_cond = block.apply_unary_op(mod, is_zero_op) + conditions.append(step_cond) + + for cond in conditions: + block = block.filter(cond) + + return block.select_columns(self.value_columns) # Using cache to optimize for Jupyter Notebook's behavior where both '__repr__' # and '__repr_html__' are called in a single display action, reducing redundant @@ -1604,74 +1185,53 @@ def slice( @functools.cache def retrieve_repr_request_results( self, max_results: int - ) -> Tuple[pd.DataFrame, int, Optional[bigquery.QueryJob]]: + ) -> Tuple[pd.DataFrame, int, bigquery.QueryJob]: """ Retrieves a pandas dataframe containing only max_results many rows for use with printing methods. Returns a tuple of the dataframe and the overall number of rows of the query. """ - - # head caches full underlying expression, so row_count will be free after - executor = self.session._executor - executor.cached( - array_value=self.expr, - config=executors.CacheConfig(optimize_for="head", if_cached="reuse-strict"), - ) - head_result = self.session._executor.execute( - self.expr.slice(start=None, stop=max_results, step=None), - execution_spec.ExecutionSpec( - promise_under_10gb=True, - ordered=True, - ), - ) - row_count = ( - self.session._executor.execute( - self.expr.row_count(), - execution_spec.ExecutionSpec( - promise_under_10gb=True, - ordered=False, - ), - ) - .batches() - .to_py_scalar() - ) - - head_df = head_result.batches().to_pandas() - return self._copy_index_to_pandas(head_df), row_count, head_result.query_job + # TODO(swast): Select a subset of columns if max_columns is less than the + # number of columns in the schema. + count = self.shape[0] + if count > max_results: + head_block = self.slice(0, max_results) + computed_df, query_job = head_block.to_pandas(max_results=max_results) + else: + head_block = self + computed_df, query_job = head_block.to_pandas() + formatted_df = computed_df.set_axis(self.column_labels, axis=1) + # we reset the axis and substitute the bf index name for the default + formatted_df.index.name = self.index.name + return formatted_df, count, query_job def promote_offsets(self, label: Label = None) -> typing.Tuple[Block, str]: - expr, result_id = self._expr.promote_offsets() + result_id = guid.generate_guid() + expr = self._expr.promote_offsets(result_id) return ( Block( expr, index_columns=self.index_columns, - column_labels=self.column_labels.insert(len(self.column_labels), label), + column_labels=[label, *self.column_labels], index_labels=self._index_labels, ), result_id, ) def add_prefix(self, prefix: str, axis: str | int | None = None) -> Block: - axis_number = utils.get_axis_number("rows" if (axis is None) else axis) + axis_number = bigframes.core.utils.get_axis_number( + "rows" if (axis is None) else axis + ) if axis_number == 0: expr = self._expr - new_index_cols = [] for index_col in self._index_columns: - expr, new_col = expr.project_to_id( - expression=ops.add_op.as_expr( - ex.const(prefix), - ops.AsTypeOp(to_type=bigframes.dtypes.STRING_DTYPE).as_expr( - index_col - ), - ), - ) - new_index_cols.append(new_col) - expr = expr.select_columns((*new_index_cols, *self.value_columns)) - + expr = expr.project_unary_op(index_col, ops.AsTypeOp("string")) + prefix_op = ops.BinopPartialLeft(ops.add_op, prefix) + expr = expr.project_unary_op(index_col, prefix_op) return Block( expr, - index_columns=new_index_cols, + index_columns=self.index_columns, column_labels=self.column_labels, index_labels=self.index.names, ) @@ -1679,24 +1239,18 @@ def add_prefix(self, prefix: str, axis: str | int | None = None) -> Block: return self.rename(columns=lambda label: f"{prefix}{label}") def add_suffix(self, suffix: str, axis: str | int | None = None) -> Block: - axis_number = utils.get_axis_number("rows" if (axis is None) else axis) + axis_number = bigframes.core.utils.get_axis_number( + "rows" if (axis is None) else axis + ) if axis_number == 0: expr = self._expr - new_index_cols = [] for index_col in self._index_columns: - expr, new_col = expr.project_to_id( - expression=ops.add_op.as_expr( - ops.AsTypeOp(to_type=bigframes.dtypes.STRING_DTYPE).as_expr( - index_col - ), - ex.const(suffix), - ), - ) - new_index_cols.append(new_col) - expr = expr.select_columns((*new_index_cols, *self.value_columns)) + expr = expr.project_unary_op(index_col, ops.AsTypeOp("string")) + prefix_op = ops.BinopPartialRight(ops.add_op, suffix) + expr = expr.project_unary_op(index_col, prefix_op) return Block( expr, - index_columns=new_index_cols, + index_columns=self.index_columns, column_labels=self.column_labels, index_labels=self.index.names, ) @@ -1740,26 +1294,23 @@ def pivot( column_ids.append(masked_id) block = block.select_columns(column_ids) - aggregations = [ - agg_expressions.UnaryAggregation(agg_ops.AnyValueOp(), ex.deref(col_id)) - for col_id in column_ids - ] - result_block = block.aggregate( + aggregations = [(col_id, agg_ops.AnyValueOp()) for col_id in column_ids] + result_block, _ = block.aggregate( by_column_ids=self.index_columns, aggregations=aggregations, + as_index=True, dropna=True, ) if values_in_index or len(values) > 1: value_labels = self._get_labels_for_columns(values) column_index = self._create_pivot_column_index(value_labels, columns_values) - return result_block.with_column_labels(column_index) else: - return result_block.with_column_labels(columns_values) + column_index = columns_values - def stack( - self, how="left", levels: int = 1, *, override_labels: Optional[pd.Index] = None - ): + return result_block.with_column_labels(column_index) + + def stack(self, how="left", levels: int = 1): """Unpivot last column axis level into row axis""" if levels == 0: return self @@ -1767,41 +1318,42 @@ def stack( # These are the values that will be turned into rows col_labels, row_labels = utils.split_index(self.column_labels, levels=levels) - row_labels = ( - row_labels.drop_duplicates() if override_labels is None else override_labels - ) + row_labels = row_labels.drop_duplicates() - if col_labels is None: - result_index: pd.Index = pd.Index([None]) - result_col_labels: Sequence[Tuple] = list([()]) - elif (col_labels.nlevels == 1) and all( - col_labels.isna() - ): # isna not implemented for MultiIndex for newer pandas versions - result_index = pd.Index([None]) - result_col_labels = utils.index_as_tuples(col_labels.drop_duplicates()) - else: + row_label_tuples = utils.index_as_tuples(row_labels) + + if col_labels is not None: result_index = col_labels.drop_duplicates().dropna(how="all") result_col_labels = utils.index_as_tuples(result_index) + else: + result_index = pd.Index([None]) + result_col_labels = list([()]) # Get matching columns - unpivot_columns: List[Tuple[Optional[str], ...]] = [] + unpivot_columns: List[Tuple[str, List[str]]] = [] + dtypes = [] for val in result_col_labels: - input_columns, _ = self._create_stack_column(val, row_labels) - unpivot_columns.append(input_columns) - - unpivot_expr, (added_index_columns, _, passthrough_cols) = unpivot( - self._expr, - row_labels=row_labels, + col_id = guid.generate_guid("unpivot_") + input_columns, dtype = self._create_stack_column(val, row_label_tuples) + unpivot_columns.append((col_id, input_columns)) + if dtype: + dtypes.append(dtype or pd.Float64Dtype()) + + added_index_columns = [guid.generate_guid() for _ in range(row_labels.nlevels)] + unpivot_expr = self._expr.unpivot( + row_labels=row_label_tuples, passthrough_columns=self.index_columns, unpivot_columns=unpivot_columns, - join_side=how, + index_col_ids=added_index_columns, + dtype=tuple(dtypes), + how=how, ) new_index_level_names = self.column_labels.names[-levels:] if how == "left": - index_columns = [*passthrough_cols, *added_index_columns] + index_columns = [*self.index_columns, *added_index_columns] index_labels = [*self._index_labels, *new_index_level_names] else: - index_columns = [*added_index_columns, *passthrough_cols] + index_columns = [*added_index_columns, *self.index_columns] index_labels = [*new_index_level_names, *self._index_labels] return Block( @@ -1817,319 +1369,44 @@ def melt( value_vars=typing.Sequence[str], var_names=typing.Sequence[typing.Hashable], value_name: typing.Hashable = "value", - *, - create_offsets_index: bool = True, ): - """ - Unpivot columns to produce longer, narrower dataframe. - Arguments correspond to pandas.melt arguments. - """ # TODO: Implement col_level and ignore_index - value_labels: pd.Index = self.column_labels[ - [self.value_columns.index(col_id) for col_id in value_vars] - ] + unpivot_col_id = guid.generate_guid() + var_col_ids = tuple([guid.generate_guid() for _ in var_names]) + # single unpivot col + unpivot_col = (unpivot_col_id, tuple(value_vars)) + value_labels = [self.col_id_to_label[col_id] for col_id in value_vars] id_labels = [self.col_id_to_label[col_id] for col_id in id_vars] - unpivot_expr, (var_col_ids, unpivot_out, passthrough_cols) = unpivot( - self._expr, + dtype = self._expr.get_column_type(value_vars[0]) + + unpivot_expr = self._expr.unpivot( row_labels=value_labels, passthrough_columns=id_vars, - unpivot_columns=(tuple(value_vars),), # single unpivot col - join_side="right", + unpivot_columns=(unpivot_col,), + index_col_ids=var_col_ids, + dtype=dtype, + how="right", ) - - if create_offsets_index: - unpivot_expr, index_id = unpivot_expr.promote_offsets() - index_cols = [index_id] - else: - index_cols = [] - + index_id = guid.generate_guid() + unpivot_expr = unpivot_expr.promote_offsets(index_id) # Need to reorder to get id_vars before var_col and unpivot_col unpivot_expr = unpivot_expr.select_columns( - [*index_cols, *passthrough_cols, *var_col_ids, *unpivot_out] + [index_id, *id_vars, *var_col_ids, unpivot_col_id] ) return Block( unpivot_expr, column_labels=[*id_labels, *var_names, value_name], - index_columns=index_cols, - ) - - def transpose( - self, - *, - original_row_index: Optional[pd.Index] = None, - single_row_mode: bool = False, - ) -> Block: - """Transpose the block. Will fail if dtypes aren't coercible to a common type or too many rows. - Can provide the original_row_index directly if it is already known, otherwise a query is needed. - """ - if self._transpose_cache is not None: - return self._transpose_cache.with_transpose_cache(self) - - original_col_index = self.column_labels - original_row_index = ( - original_row_index - if original_row_index is not None - else self.index.to_pandas(ordered=True)[0] - ) - original_row_count = len(original_row_index) - if original_row_count > bigframes.constants.MAX_COLUMNS: - raise NotImplementedError( - f"Object has {original_row_count} rows and is too large to transpose." - ) - - # Add row numbers to both axes to disambiguate, clean them up later - block = self - numbered_block = block.with_column_labels( - utils.combine_indices( - block.column_labels, pd.Index(range(len(block.column_labels))) - ) - ) - # TODO: Determine if single row from expression tree (after aggregation without groupby) - if single_row_mode: - numbered_block, offsets = numbered_block.create_constant(0) - else: - numbered_block, offsets = numbered_block.promote_offsets() - - stacked_block = numbered_block.melt( - id_vars=(offsets,), - var_names=( - *[name for name in original_col_index.names], - "col_offset", - ), - value_vars=block.value_columns, - create_offsets_index=False, - ) - row_offset = stacked_block.value_columns[0] - col_labels = stacked_block.value_columns[-2 - original_col_index.nlevels : -2] - col_offset = stacked_block.value_columns[-2] # disambiguator we created earlier - cell_values = stacked_block.value_columns[-1] - # Groupby source column - stacked_block = stacked_block.set_index( - [*col_labels, col_offset] - ) # col index is now row index - result = stacked_block.pivot( - columns=[row_offset], - values=[cell_values], - columns_unique_values=tuple(range(original_row_count)), - ) - # Drop the offsets from both axes before returning - return ( - result.with_column_labels(original_row_index) - .order_by([ordering.ascending_over(result.index_columns[-1])]) - .drop_levels([result.index_columns[-1]]) - .with_transpose_cache(self) + index_columns=[index_id], ) - def _generate_sequence( - self, - start, - stop, - step: int = 1, + def _create_stack_column( + self, col_label: typing.Tuple, stack_labels: typing.Sequence[typing.Tuple] ): - range_expr = self.expr.from_range( - start, - stop, - step, - ) - - return Block( - range_expr, - column_labels=["min"], - index_columns=[], - ) - - def _generate_resample_label( - self, - rule: str, - closed: Optional[Literal["right", "left"]] = None, - label: Optional[Literal["right", "left"]] = None, - on: Optional[Label] = None, - level: typing.Union[LevelType, typing.Sequence[LevelType]] = None, - origin: Union[ - Union[pd.Timestamp, datetime.datetime, numpy.datetime64, int, float, str], - Literal["epoch", "start", "start_day", "end", "end_day"], - ] = "start_day", - ) -> Block: - if not isinstance(rule, str): - raise NotImplementedError( - f"Only offset strings are currently supported for rule, but got {repr(rule)}. {constants.FEEDBACK_LINK}" - ) - - if rule in ("ME", "YE", "QE", "BME", "BA", "BQE", "W"): - raise NotImplementedError( - f"Offset strings 'ME', 'YE', 'QE', 'BME', 'BA', 'BQE', 'W' are not currently supported for rule, but got {repr(rule)}. {constants.FEEDBACK_LINK}" - ) - - if closed == "right": - raise NotImplementedError( - f"Only closed='left' is currently supported. {constants.FEEDBACK_LINK}", - ) - - if label == "right": - raise NotImplementedError( - f"Only label='left' is currently supported. {constants.FEEDBACK_LINK}", - ) - - if origin not in ("epoch", "start", "start_day"): - raise NotImplementedError( - f"Only origin='epoch', 'start', 'start_day' are currently supported, but got {repr(origin)}. {constants.FEEDBACK_LINK}" - ) - - # Validate and resolve the index or column to use for grouping - if on is None: - if len(self.index_columns) == 0: - raise ValueError( - f"No index for resampling. Expected {bigframes.dtypes.DATETIME_DTYPE} or " - f"{bigframes.dtypes.TIMESTAMP_DTYPE} index or 'on' parameter specifying a column." - ) - if len(self.index_columns) > 1 and (level is None): - raise ValueError( - "Multiple indices are not supported for this operation" - " when 'level' is not set." - ) - level = level or 0 - col_id = self.index.resolve_level(level)[0] - if isinstance(level, int): - resample_label = self.index.names[level] - else: - resample_label = level - # Reset index to make the resampling level a column, then drop all other index columns. - # This simplifies processing by focusing solely on the column required for resampling. - block = self.reset_index(drop=False) - block = block.drop_columns( - [col for col in self.index.column_ids if col != col_id] - ) - elif level is not None: - raise ValueError("The Grouper cannot specify both a key and a level!") - else: - matches = self.label_to_col_id.get(on, []) - if len(matches) > 1: - raise ValueError( - f"Multiple columns matching id {on} were found. {constants.FEEDBACK_LINK}" - ) - if len(matches) == 0: - raise KeyError(f"The grouper name {on} is not found") - - col_id = matches[0] - resample_label = on - block = self - if level is None: - dtype = self._column_type(col_id) - elif isinstance(level, int): - dtype = self.index.dtypes[level] - else: - dtype = self.index.dtypes[self.index.names.index(level)] - - if dtype not in ( - bigframes.dtypes.DATETIME_DTYPE, - bigframes.dtypes.TIMESTAMP_DTYPE, - ): - raise TypeError( - f"Invalid column type: {dtype}. Expected types are " - f"{bigframes.dtypes.DATETIME_DTYPE}, or " - f"{bigframes.dtypes.TIMESTAMP_DTYPE}." - ) - - freq = pd.tseries.frequencies.to_offset(rule) - assert freq is not None - - if origin not in ("epoch", "start", "start_day"): - raise ValueError( - "'origin' should be equal to 'epoch', 'start' or 'start_day'" - f". Got '{origin}' instead." - ) - - agg_specs = [ - ( - agg_expressions.UnaryAggregation(agg_ops.min_op, ex.deref(col_id)), - guid.generate_guid(), - ), - ] - origin_block = Block( - block.expr.aggregate(agg_specs, dropna=True), - column_labels=["origin"], - index_columns=[], - ) - - col_level = block.value_columns.index(col_id) - - block = block.merge( - origin_block, how="cross", left_join_ids=[], right_join_ids=[], sort=True - ) - - # After merging, the original column ids are altered. 'col_level' is the index of - # the datetime column used for resampling. 'block.value_columns[-1]' is the - # 'origin' column, which is the minimum datetime value. - block, label_col_id = block.apply_binary_op( - block.value_columns[col_level], - block.value_columns[-1], - op=ops.DatetimeToIntegerLabelOp(freq=freq, closed=closed, origin=origin), - ) - block = block.drop_columns([block.value_columns[-2]]) - - # Generate integer label sequence. - min_agg_specs = [ - ( - ex_types.UnaryAggregation(agg_ops.min_op, ex.deref(label_col_id)), - guid.generate_guid(), - ), - ] - max_agg_specs = [ - ( - ex_types.UnaryAggregation(agg_ops.max_op, ex.deref(label_col_id)), - guid.generate_guid(), - ), - ] - label_start = block.expr.aggregate(min_agg_specs, dropna=True) - label_stop = block.expr.aggregate(max_agg_specs, dropna=True) - - label_block = block._generate_sequence( - start=label_start, - stop=label_stop, - ) - - label_block = label_block.merge( - origin_block, how="cross", left_join_ids=[], right_join_ids=[], sort=True - ) - - block = label_block.merge( - block, - how="left", - left_join_ids=[label_block.value_columns[0]], - right_join_ids=[label_col_id], - sort=True, - ) - - block, resample_label_id = block.apply_binary_op( - block.value_columns[0], - block.value_columns[1], - op=ops.IntegerLabelToDatetimeOp(freq=freq, label=label, origin=origin), - result_label=resample_label, - ) - - # After multiple merges, the columns: - # - block.value_columns[0] is the integer label sequence, - # - block.value_columns[1] is the origin column (minimum datetime value), - # - col_level+2 represents the datetime column used for resampling, - # - block.value_columns[-2] is the integer label column derived from the datetime column. - # These columns are no longer needed. - block = block.drop_columns( - [ - block.value_columns[0], - block.value_columns[1], - block.value_columns[col_level + 2], - block.value_columns[-2], - ] - ) - - return block.set_index([resample_label_id]) - - def _create_stack_column(self, col_label: typing.Tuple, stack_labels: pd.Index): - input_dtypes = [] + dtype = None input_columns: list[Optional[str]] = [] - for uvalue in utils.index_as_tuples(stack_labels): + for uvalue in stack_labels: label_to_match = (*col_label, *uvalue) label_to_match = ( label_to_match[0] if len(label_to_match) == 1 else label_to_match @@ -2137,18 +1414,15 @@ def _create_stack_column(self, col_label: typing.Tuple, stack_labels: pd.Index): matching_ids = self.label_to_col_id.get(label_to_match, []) input_id = matching_ids[0] if len(matching_ids) > 0 else None if input_id: - input_dtypes.append(self._column_type(input_id)) + if dtype and dtype != self._column_type(input_id): + raise NotImplementedError( + "Cannot stack columns with non-matching dtypes." + ) + else: + dtype = self._column_type(input_id) input_columns.append(input_id) # Input column i is the first one that - if len(input_dtypes) > 0: - output_dtype = bigframes.dtypes.lcd_type(*input_dtypes) - if output_dtype is None: - raise NotImplementedError( - "Cannot stack columns with non-matching dtypes." - ) - else: - output_dtype = pd.Float64Dtype() - return tuple(input_columns), output_dtype + return tuple(input_columns), dtype or pd.Float64Dtype() def _column_type(self, col_id: str) -> bigframes.dtypes.Dtype: col_offset = self.value_columns.index(col_id) @@ -2157,8 +1431,8 @@ def _column_type(self, col_id: str) -> bigframes.dtypes.Dtype: @staticmethod def _create_pivot_column_index( - value_labels: pd.Index, columns_values: pd.Index - ) -> pd.Index: + value_labels: Sequence[typing.Hashable], columns_values: pd.Index + ): index_parts = [] for value in value_labels: as_frame = columns_values.to_frame() @@ -2173,24 +1447,29 @@ def _create_pivot_column_index( def _create_pivot_col( block: Block, columns: typing.Sequence[str], value_col: str, value ) -> typing.Tuple[Block, str]: - condition: typing.Optional[ex.Expression] = None + cond_id = "" nlevels = len(columns) for i in range(len(columns)): uvalue_level = value[i] if nlevels > 1 else value if pd.isna(uvalue_level): - equality = ops.isnull_op.as_expr(columns[i]) + block, eq_id = block.apply_unary_op( + columns[i], + ops.isnull_op, + ) else: - equality = ops.eq_op.as_expr(columns[i], ex.const(uvalue_level)) - if condition is not None: - condition = ops.and_op.as_expr(equality, condition) + block, eq_id = block.apply_unary_op( + columns[i], ops.partial_right(ops.eq_op, uvalue_level) + ) + if cond_id: + block, cond_id = block.apply_binary_op(eq_id, cond_id, ops.and_op) else: - condition = equality - - assert condition is not None - return block.project_expr( - ops.where_op.as_expr(value_col, condition, ex.const(None)) + cond_id = eq_id + block, masked_id = block.apply_binary_op( + value_col, cond_id, ops.partial_arg3(ops.where_op, None) ) + return block, masked_id + def _get_unique_values( self, columns: Sequence[str], max_unique_values: int ) -> pd.Index: @@ -2199,17 +1478,9 @@ def _get_unique_values( import bigframes.core.block_transforms as block_tf import bigframes.dataframe as df - if self.explicitly_ordered: - unique_value_block = block_tf.drop_duplicates( - self.select_columns(columns), columns - ) - else: - unique_value_block = self.aggregate(by_column_ids=columns, dropna=False) - col_labels = self._get_labels_for_columns(columns) - unique_value_block = unique_value_block.reset_index( - drop=False - ).with_column_labels(col_labels) - + unique_value_block = block_tf.drop_duplicates( + self.select_columns(columns), columns + ) pd_values = ( df.DataFrame(unique_value_block).head(max_unique_values + 1).to_pandas() ) @@ -2230,10 +1501,8 @@ def concat( blocks: typing.List[Block] = [self, *other] if ignore_index: blocks = [block.reset_index() for block in blocks] - level_names = None - else: - level_names, level_types = _align_indices(blocks) - blocks = [_cast_index(block, level_types) for block in blocks] + + result_labels = _align_indices(blocks) index_nlevels = blocks[0].index.nlevels @@ -2248,38 +1517,12 @@ def concat( result_expr, index_columns=list(result_expr.column_ids)[:index_nlevels], column_labels=aligned_blocks[0].column_labels, - index_labels=level_names, + index_labels=result_labels, ) if ignore_index: result_block = result_block.reset_index() return result_block - def isin(self, other: Block): - # TODO: Support multiple other columns and match on label - assert len(other.value_columns) == 1 - unique_other_values = other.expr.select_columns( - [other.value_columns[0]] - ).aggregate((), by_column_ids=(other.value_columns[0],), dropna=False) - block = self - # for each original column, join with other - for i in range(len(self.value_columns)): - block = block._isin_inner(block.value_columns[i], unique_other_values) - return block - - def _isin_inner(self: Block, col: str, unique_values: core.ArrayValue) -> Block: - expr, matches = self._expr.isin(unique_values, col) - - new_value_cols = tuple( - val_col if val_col != col else matches for val_col in self.value_columns - ) - expr = expr.select_columns((*self.index_columns, *new_value_cols)) - return Block( - expr, - index_columns=self.index_columns, - column_labels=self.column_labels, - index_labels=self._index_labels, - ) - def merge( self, other: Block, @@ -2288,67 +1531,54 @@ def merge( "left", "outer", "right", - "cross", ], left_join_ids: typing.Sequence[str], right_join_ids: typing.Sequence[str], sort: bool, suffixes: tuple[str, str] = ("_x", "_y"), - left_index: bool = False, - right_index: bool = False, ) -> Block: - conditions = tuple( - (lid, rid) for lid, rid in zip(left_join_ids, right_join_ids) + joined_expr = self.expr.join( + left_join_ids, + other.expr, + right_join_ids, + how=how, ) - joined_expr, (get_column_left, get_column_right) = self.expr.relational_join( - other.expr, type=how, conditions=conditions + get_column_left, get_column_right = join_names.JOIN_NAME_REMAPPER( + self.expr.column_ids, other.expr.column_ids ) + result_columns = [] + matching_join_labels = [] - left_post_join_ids = tuple(get_column_left[id] for id in left_join_ids) - right_post_join_ids = tuple(get_column_right[id] for id in right_join_ids) - - if left_index or right_index: - # For some reason pandas coalesces two joining columns if one side is an index. - joined_expr, resolved_join_ids = coalesce_columns( - joined_expr, left_post_join_ids, right_post_join_ids - ) - else: - joined_expr, resolved_join_ids = resolve_col_join_ids( # type: ignore - joined_expr, - left_post_join_ids, - right_post_join_ids, - how=how, - drop=False, + coalesced_ids = [] + for left_id, right_id in zip(left_join_ids, right_join_ids): + coalesced_id = guid.generate_guid() + joined_expr = joined_expr.project_binary_op( + get_column_left[left_id], + get_column_right[right_id], + ops.coalesce_op, + coalesced_id, ) + coalesced_ids.append(coalesced_id) - result_columns = [] - matching_join_labels = [] - - # Select left value columns for col_id in self.value_columns: if col_id in left_join_ids: key_part = left_join_ids.index(col_id) matching_right_id = right_join_ids[key_part] if ( - right_index - or self.col_id_to_label[col_id] + self.col_id_to_label[col_id] == other.col_id_to_label[matching_right_id] ): matching_join_labels.append(self.col_id_to_label[col_id]) - result_columns.append(resolved_join_ids[key_part]) + result_columns.append(coalesced_ids[key_part]) else: result_columns.append(get_column_left[col_id]) else: result_columns.append(get_column_left[col_id]) - - # Select right value columns for col_id in other.value_columns: if col_id in right_join_ids: - if other.col_id_to_label[col_id] in matching_join_labels: + key_part = right_join_ids.index(col_id) + if other.col_id_to_label[matching_right_id] in matching_join_labels: pass - elif left_index: - key_part = right_join_ids.index(col_id) - result_columns.append(resolved_join_ids[key_part]) else: result_columns.append(get_column_right[col_id]) else: @@ -2357,271 +1587,31 @@ def merge( if sort: # sort uses coalesced join keys always joined_expr = joined_expr.order_by( - [ - ordering.OrderingExpression(ex.deref(col_id)) - for col_id in resolved_join_ids - ], + [ordering.OrderingColumnReference(col_id) for col_id in coalesced_ids], + stable=True, ) - left_idx_id_post_join = [get_column_left[id] for id in self.index_columns] - right_idx_id_post_join = [get_column_right[id] for id in other.index_columns] - index_cols = _resolve_index_col( - left_idx_id_post_join, - right_idx_id_post_join, - resolved_join_ids, - left_index, - right_index, - how, - ) - - joined_expr = joined_expr.select_columns(result_columns + index_cols) + joined_expr = joined_expr.select_columns(result_columns) labels = utils.merge_column_labels( self.column_labels, other.column_labels, coalesce_labels=matching_join_labels, suffixes=suffixes, ) + # Constructs default index + offset_index_id = guid.generate_guid() + expr = joined_expr.promote_offsets(offset_index_id) + return Block(expr, index_columns=[offset_index_id], column_labels=labels) - # Construct a default index only if this object and the other both have - # indexes. In other words, joining anything to a NULL index object - # keeps everything as a NULL index. - # - # This keeps us from generating an index if the user joins a large - # BigQuery table against small local data, for example. - if ( - self.index.is_null - or other.index.is_null - or self.session._default_index_type == bigframes.enums.DefaultIndexKind.NULL - ): - return Block(joined_expr, index_columns=[], column_labels=labels) - elif index_cols: - return Block(joined_expr, index_columns=index_cols, column_labels=labels) - else: - expr, offset_index_id = joined_expr.promote_offsets() - index_columns = [offset_index_id] - return Block(expr, index_columns=index_columns, column_labels=labels) - - def _align_both_axes( - self, other: Block, how: str - ) -> Tuple[Block, pd.Index, Sequence[Tuple[ex.RefOrConstant, ex.RefOrConstant]]]: - # Join rows - aligned_block, (get_column_left, get_column_right) = self.join(other, how=how) - # join columns schema - # indexers will be none for exact match - if self.column_labels.equals(other.column_labels): - columns, lcol_indexer, rcol_indexer = self.column_labels, None, None - else: - columns, lcol_indexer, rcol_indexer = self.column_labels.join( - other.column_labels, how=how, return_indexers=True - ) - lcol_indexer = ( - lcol_indexer if (lcol_indexer is not None) else range(len(columns)) - ) - rcol_indexer = ( - rcol_indexer if (rcol_indexer is not None) else range(len(columns)) - ) - - left_input_lookup = lambda index: ( - ex.deref(get_column_left[self.value_columns[index]]) - if index != -1 - else ex.const(None) - ) - righ_input_lookup = lambda index: ( - ex.deref(get_column_right[other.value_columns[index]]) - if index != -1 - else ex.const(None) - ) - - left_inputs = [left_input_lookup(i) for i in lcol_indexer] - right_inputs = [righ_input_lookup(i) for i in rcol_indexer] - return aligned_block, columns, tuple(zip(left_inputs, right_inputs)) # type: ignore - - def _align_axis_0( - self, other: Block, how: str - ) -> Tuple[Block, pd.Index, Sequence[Tuple[ex.DerefOp, ex.DerefOp]]]: - assert len(other.value_columns) == 1 - aligned_block, (get_column_left, get_column_right) = self.join(other, how=how) - - series_column_id = other.value_columns[0] - inputs = tuple( - ( - ex.deref(get_column_left[col]), - ex.deref(get_column_right[series_column_id]), - ) - for col in self.value_columns - ) - return aligned_block, self.column_labels, inputs - - def _align_series_block_axis_1( - self, other: Block, how: str - ) -> Tuple[Block, pd.Index, Sequence[Tuple[ex.RefOrConstant, ex.RefOrConstant]]]: - assert len(other.value_columns) == 1 - if other._transpose_cache is None: - raise ValueError( - "Wrong align method, this approach requires transpose cache" - ) - - # Join rows - aligned_block, (get_column_left, get_column_right) = join_with_single_row( - self, other.transpose() - ) - # join columns schema - # indexers will be none for exact match - if self.column_labels.equals(other.transpose().column_labels): - columns, lcol_indexer, rcol_indexer = self.column_labels, None, None - else: - columns, lcol_indexer, rcol_indexer = self.column_labels.join( - other.transpose().column_labels, how=how, return_indexers=True - ) - lcol_indexer = ( - lcol_indexer if (lcol_indexer is not None) else range(len(columns)) - ) - rcol_indexer = ( - rcol_indexer if (rcol_indexer is not None) else range(len(columns)) - ) - - left_input_lookup = lambda index: ( - ex.deref(get_column_left[self.value_columns[index]]) - if index != -1 - else ex.const(None) - ) - righ_input_lookup = lambda index: ( - ex.deref(get_column_right[other.transpose().value_columns[index]]) - if index != -1 - else ex.const(None) - ) - - left_inputs = [left_input_lookup(i) for i in lcol_indexer] - right_inputs = [righ_input_lookup(i) for i in rcol_indexer] - return aligned_block, columns, tuple(zip(left_inputs, right_inputs)) # type: ignore - - def _align_pd_series_axis_1( - self, other: pd.Series, how: str - ) -> Tuple[Block, pd.Index, Sequence[Tuple[ex.RefOrConstant, ex.RefOrConstant]]]: - if self.column_labels.equals(other.index): - columns, lcol_indexer, rcol_indexer = self.column_labels, None, None - else: - if not (self.column_labels.is_unique and other.index.is_unique): - raise ValueError("Cannot align non-unique indices") - columns, lcol_indexer, rcol_indexer = self.column_labels.join( - other.index, how=how, return_indexers=True - ) - lcol_indexer = ( - lcol_indexer if (lcol_indexer is not None) else range(len(columns)) - ) - rcol_indexer = ( - rcol_indexer if (rcol_indexer is not None) else range(len(columns)) - ) - - left_input_lookup = lambda index: ( - ex.deref(self.value_columns[index]) if index != -1 else ex.const(None) - ) - righ_input_lookup = lambda index: ( - ex.const(other.iloc[index]) if index != -1 else ex.const(None) + def _force_reproject(self) -> Block: + """Forces a reprojection of the underlying tables expression. Used to force predicate/order application before subsequent operations.""" + return Block( + self._expr._reproject_to_table(), + index_columns=self.index_columns, + column_labels=self.column_labels, + index_labels=self.index.names, ) - left_inputs = [left_input_lookup(i) for i in lcol_indexer] - right_inputs = [righ_input_lookup(i) for i in rcol_indexer] - return self, columns, tuple(zip(left_inputs, right_inputs)) # type: ignore - - def _apply_binop( - self, - op: ops.BinaryOp, - inputs: Sequence[Tuple[ex.Expression, ex.Expression]], - labels: pd.Index, - reverse: bool = False, - ) -> Block: - exprs = [] - for left_input, right_input in inputs: - exprs.append( - op.as_expr(right_input, left_input) - if reverse - else op.as_expr(left_input, right_input) - ) - - return self.project_exprs(exprs, labels=labels, drop=True) - - # TODO: Re-implement join in terms of merge (requires also adding remaining merge args) - def join( - self, - other: Block, - *, - how="left", - sort: bool = False, - block_identity_join: bool = False, - always_order: bool = False, - ) -> Tuple[ - Block, - Tuple[Mapping[str, str], Mapping[str, str]], - ]: - """ - Join two blocks objects together, and provide mappings between source columns and output columns. - - Args: - other (Block): - The right operand of the join operation - how (str): - Describes the join type. 'inner', 'outer', 'left', or 'right' - sort (bool): - if true will sort result by index - block_identity_join (bool): - If true, will not convert join to a projection (implicitly assuming unique indices) - always_order (bool): - If true, will always preserve input ordering, even if ordering mode is partial - - Returns: - Block, (left_mapping, right_mapping): Result block and mappers from input column ids to result column ids. - """ - - if not isinstance(other, Block): - # TODO(swast): We need to improve this error message to be more - # actionable for the user. For example, it's possible they - # could call set_index and try again to resolve this error. - raise ValueError( - f"Tried to join with an unexpected type: {type(other)}. {constants.FEEDBACK_LINK}" - ) - - # TODO(swast): Support cross-joins (requires reindexing). - if how not in {"outer", "left", "right", "inner"}: - raise NotImplementedError( - f"Only how='outer','left','right','inner' currently supported. {constants.FEEDBACK_LINK}" - ) - # Handle null index, which only supports row join - # This is the canonical way of aligning on null index, so always allow (ignore block_identity_join) - if self.index.nlevels == other.index.nlevels == 0: - result = try_legacy_row_join(self, other, how=how) or try_new_row_join( - self, other - ) - if result is not None: - return result - raise bigframes.exceptions.NullIndexError( - "Cannot implicitly align objects. Set an explicit index using set_index." - ) - - # Oddly, pandas row-wise join ignores right index names - if ( - not block_identity_join - and (self.index.nlevels == other.index.nlevels) - and (self.index.dtypes == other.index.dtypes) - ): - result = try_legacy_row_join(self, other, how=how) or try_new_row_join( - self, other - ) - if result is not None: - return result - - self._throw_if_null_index("join") - other._throw_if_null_index("join") - if self.index.nlevels == other.index.nlevels == 1: - return join_mono_indexed( - self, other, how=how, sort=sort, propogate_order=always_order - ) - else: # Handles cases where one or both sides are multi-indexed - # Always sort mult-index join - return join_multi_indexed( - self, other, how=how, sort=sort, propogate_order=always_order - ) - def is_monotonic_increasing( self, column_id: typing.Union[str, Sequence[str]] ) -> bool: @@ -2632,24 +1622,24 @@ def is_monotonic_decreasing( ) -> bool: return self._is_monotonic(column_id, increasing=False) - def _array_value_for_output( - self, *, include_index: bool - ) -> Tuple[bigframes.core.ArrayValue, list[str], list[Label]]: + def to_sql_query( + self, include_index: bool + ) -> typing.Tuple[str, list[str], list[Label]]: """ - Creates the expression tree with user-visible column names, such as for - SQL output. + Compiles this DataFrame's expression tree to SQL, optionally + including index columns. Args: include_index (bool): whether to include index columns. Returns: - a tuple of (ArrayValue, index_column_id_list, index_column_label_list). + a tuple of (sql_string, index_column_id_list, index_column_label_list). If include_index is set to False, index_column_id_list and index_column_label_list return empty lists. """ array_value = self._expr - col_labels, idx_labels = list(self.column_labels), list(self.index.names) + col_labels, idx_labels = list(self.column_labels), list(self.index_labels) old_col_ids, old_idx_ids = list(self.value_columns), list(self.index_columns) if not include_index: @@ -2667,88 +1657,39 @@ def _array_value_for_output( # the BigQuery unicode column name feature? substitutions[old_id] = new_id + sql = array_value.to_sql(col_id_overrides=substitutions) return ( - array_value.rename_columns(substitutions), + sql, new_ids[: len(idx_labels)], idx_labels, ) - def to_sql_query( - self, include_index: bool, enable_cache: bool = True - ) -> Tuple[str, list[str], list[Label]]: - """ - Compiles this DataFrame's expression tree to SQL, optionally - including index columns. - - Args: - include_index (bool): - whether to include index columns. - - Returns: - a tuple of (sql_string, index_column_id_list, index_column_label_list). - If include_index is set to False, index_column_id_list and index_column_label_list - return empty lists. - """ - array_value, idx_ids, idx_labels = self._array_value_for_output( - include_index=include_index - ) - - # Note: this uses the sql from the executor, so is coupled tightly to execution - # implementaton. It will reference cached tables instead of original data sources. - # Maybe should just compile raw BFET? Depends on user intent. - sql = self.session._executor.to_sql(array_value, enable_cache=enable_cache) - return ( - sql, - idx_ids, - idx_labels, + def cached(self) -> Block: + """Write the block to a session table and create a new block object that references it.""" + return Block( + self.expr.cached(cluster_cols=self.index_columns), + index_columns=self.index_columns, + column_labels=self.column_labels, + index_labels=self.index_labels, ) - def to_placeholder_table( - self, include_index: bool, *, dry_run: bool = False - ) -> bigquery.TableReference: - """ - Creates a temporary BigQuery VIEW (or empty table if dry_run) with the - SQL corresponding to this block. - """ - if self._view_ref is not None: - return self._view_ref - - # Prefer the real view if it exists, but since dry_run might be called - # many times before the real query, we cache that empty table reference - # with the correct schema too. - if dry_run: - if self._view_ref_dry_run is not None: - return self._view_ref_dry_run - - # Create empty temp table with the right schema. - array_value, _, _ = self._array_value_for_output( - include_index=include_index - ) - temp_table_schema = array_value.schema.to_bigquery() - self._view_ref_dry_run = self.session._create_temp_table( - schema=temp_table_schema - ) - return self._view_ref_dry_run - - # We shouldn't run `to_sql_query` if we have a `dry_run`, because it - # could cause us to make unnecessary API calls to upload local node - # data. - sql, _, _ = self.to_sql_query(include_index=include_index) - self._view_ref = self.session._create_temp_view(sql) - return self._view_ref - - def cached(self, *, force: bool = False, session_aware: bool = False) -> None: - """Write the block to a session table.""" - # use a heuristic for whether something needs to be cached - self.session._executor.cached( - self.expr, - config=executors.CacheConfig( - optimize_for="auto" - if session_aware - else executors.HierarchicalKey(tuple(self.index_columns)), - if_cached="replace" if force else "reuse-any", - ), - ) + def resolve_index_level(self, level: LevelsType) -> typing.Sequence[str]: + if utils.is_list_like(level): + levels = list(level) + else: + levels = [level] + resolved_level_ids = [] + for level_ref in levels: + if isinstance(level_ref, int): + resolved_level_ids.append(self.index_columns[level_ref]) + elif isinstance(level_ref, typing.Hashable): + matching_ids = self.index_name_to_col_id.get(level_ref, []) + if len(matching_ids) != 1: + raise ValueError("level name cannot be found or is ambiguous") + resolved_level_ids.append(matching_ids[0]) + else: + raise ValueError(f"Unexpected level: {level_ref}") + return resolved_level_ids def _is_monotonic( self, column_ids: typing.Union[str, Sequence[str]], increasing: bool @@ -2763,564 +1704,90 @@ def _is_monotonic( return self._stats_cache[column_name][op_name] period = 1 - window_spec = windows.rows() + window = bigframes.core.WindowSpec( + preceding=period, + following=None, + ) # any NaN value means not monotonic block, last_notna_id = self.apply_unary_op(column_ids[0], ops.notnull_op) for column_id in column_ids[1:]: block, notna_id = block.apply_unary_op(column_id, ops.notnull_op) - old_last_notna_id = last_notna_id block, last_notna_id = block.apply_binary_op( - old_last_notna_id, notna_id, ops.and_op + last_notna_id, notna_id, ops.and_op ) - block.drop_columns([notna_id, old_last_notna_id]) # loop over all columns to check monotonicity last_result_id = None for column_id in column_ids[::-1]: block, lag_result_id = block.apply_window_op( - column_id, agg_ops.ShiftOp(period), window_spec + column_id, agg_ops.ShiftOp(period), window ) block, strict_monotonic_id = block.apply_binary_op( column_id, lag_result_id, ops.gt_op if increasing else ops.lt_op ) block, equal_id = block.apply_binary_op(column_id, lag_result_id, ops.eq_op) - block = block.drop_columns([lag_result_id]) if last_result_id is None: block, last_result_id = block.apply_binary_op( equal_id, strict_monotonic_id, ops.or_op ) - block = block.drop_columns([equal_id, strict_monotonic_id]) - else: - block, equal_monotonic_id = block.apply_binary_op( - equal_id, last_result_id, ops.and_op - ) - block = block.drop_columns([equal_id, last_result_id]) - block, last_result_id = block.apply_binary_op( - equal_monotonic_id, strict_monotonic_id, ops.or_op - ) - block = block.drop_columns([equal_monotonic_id, strict_monotonic_id]) + continue + block, equal_monotonic_id = block.apply_binary_op( + equal_id, last_result_id, ops.and_op + ) + block, last_result_id = block.apply_binary_op( + equal_monotonic_id, strict_monotonic_id, ops.or_op + ) - assert last_result_id is not None block, monotonic_result_id = block.apply_binary_op( - last_result_id, - last_notna_id, - ops.and_op, # type: ignore + last_result_id, last_notna_id, ops.and_op # type: ignore ) - if last_result_id is not None: - block = block.drop_columns([last_result_id, last_notna_id]) result = block.get_stat(monotonic_result_id, agg_ops.all_op) self._stats_cache[column_name].update({op_name: result}) return result - def _throw_if_null_index(self, opname: str): - if len(self.index_columns) == 0: - raise bigframes.exceptions.NullIndexError( - f"Cannot do {opname} without an index. Set an index using set_index." - ) - - def _get_rows_as_json_values(self) -> Block: - # Names of the columns to serialize for the row. - # We will use the repr-eval pattern to serialize a value here and - # deserialize in the cloud function. Let's make sure that would work. - column_names = [] - for col in list(self.index_columns) + [col for col in self.column_labels]: - serialized_column_name = repr(col) - try: - ast.literal_eval(serialized_column_name) - except Exception: - raise NameError( - f"Column name type '{type(col).__name__}' is not supported for row serialization." - " Please consider using a name for which literal_eval(repr(name)) works." - ) - - column_names.append(serialized_column_name) - - # column references to form the array of values for the row - column_types = list(self.index.dtypes) + list(self.dtypes) - column_references = [] - for type_, col in zip(column_types, self.expr.column_ids): - if type_ == bigframes.dtypes.BYTES_DTYPE: - column_references.append(ops.ToJSONString().as_expr(col)) - elif type_ == bigframes.dtypes.BOOL_DTYPE: - # cast operator produces True/False, but function template expects lower case - column_references.append( - ops.lower_op.as_expr( - ops.AsTypeOp(bigframes.dtypes.STRING_DTYPE).as_expr(col) - ) - ) - else: - column_references.append( - ops.AsTypeOp(bigframes.dtypes.STRING_DTYPE).as_expr(col) - ) - - # row dtype to use for deserializing the row as pandas series - pandas_row_dtype = bigframes.dtypes.lcd_type(*column_types) - if pandas_row_dtype is None: - pandas_row_dtype = "object" - pandas_row_dtype = str(pandas_row_dtype) - - struct_op = ops.StructOp( - column_names=("names", "types", "values", "indexlength", "dtype") - ) - names_val = ex.const(tuple(column_names)) - types_val = ex.const(tuple(map(str, column_types))) - values_val = ops.ToArrayOp().as_expr(*column_references) - indexlength_val = ex.const(len(self.index_columns)) - dtype_val = ex.const(str(pandas_row_dtype)) - struct_expr = struct_op.as_expr( - names_val, types_val, values_val, indexlength_val, dtype_val - ) - block, col_id = self.project_expr(ops.ToJSONString().as_expr(struct_expr)) - return block.select_column(col_id) - - -class BlockIndexProperties: - """Accessor for the index-related block properties.""" - - def __init__(self, block: Block): - self._block = block - - @property - def _expr(self) -> core.ArrayValue: - return self._block.expr - - @property - def name(self) -> Label: - return self._block._index_labels[0] - - @property - def names(self) -> typing.Sequence[Label]: - return self._block._index_labels - - @property - def nlevels(self) -> int: - return len(self._block._index_columns) - - @property - def dtypes( - self, - ) -> typing.Sequence[bigframes.dtypes.Dtype]: - return [ - self._block.expr.get_column_type(col) for col in self._block.index_columns - ] - - @property - def session(self) -> session.Session: - return self._expr.session - - @property - def column_ids(self) -> Sequence[str]: - """Column(s) to use as row labels.""" - return self._block._index_columns - - @property - def is_null(self) -> bool: - return len(self._block._index_columns) == 0 - - def to_pandas( - self, - *, - ordered: Optional[bool] = None, - allow_large_results: Optional[bool] = None, - ) -> Tuple[pd.Index, Optional[bigquery.QueryJob]]: - """Executes deferred operations and downloads the results.""" - if len(self.column_ids) == 0: - raise bigframes.exceptions.NullIndexError( - "Cannot materialize index, as this object does not have an index. Set index column(s) using set_index." - ) - ordered = ordered if ordered is not None else True - - df, query_job = self._block.select_columns([]).to_pandas( - ordered=ordered, - allow_large_results=allow_large_results, - ) - return df.index, query_job - - def _compute_dry_run( - self, *, ordered: bool = True - ) -> Tuple[pd.Series, bigquery.QueryJob]: - return self._block.select_columns([])._compute_dry_run(ordered=ordered) - - def resolve_level(self, level: LevelsType) -> typing.Sequence[str]: - if utils.is_list_like(level): - levels = list(level) - else: - levels = [level] - resolved_level_ids = [] - for level_ref in levels: - if isinstance(level_ref, int): - resolved_level_ids.append(self._block.index_columns[level_ref]) - elif isinstance(level_ref, typing.Hashable): - matching_ids = self._block.index_name_to_col_id.get(level_ref, []) - if len(matching_ids) != 1: - raise ValueError("level name cannot be found or is ambiguous") - resolved_level_ids.append(matching_ids[0]) - else: - raise ValueError(f"Unexpected level: {level_ref}") - return resolved_level_ids - - def resolve_level_exact(self: BlockIndexProperties, label: Label) -> str: - matches = self._block.index_name_to_col_id.get(label, []) - if len(matches) > 1: - raise ValueError(f"Ambiguous index level name {label}") - if len(matches) == 0: - raise ValueError(f"Cannot resolve index level name {label}") - return matches[0] - - def is_uniquely_named(self: BlockIndexProperties): - return len(set(self.names)) == len(self.names) - - -def try_new_row_join( - left: Block, right: Block -) -> Optional[ - Tuple[ - Block, - Tuple[Mapping[str, str], Mapping[str, str]], - ] -]: - join_keys = tuple( - (left_id, right_id) - for left_id, right_id in zip(left.index_columns, right.index_columns) - ) - join_result = left.expr.try_row_join(right.expr, join_keys) - if join_result is None: # did not succeed - return None - combined_expr, (get_column_left, get_column_right) = join_result - # Keep the left index column, and drop the matching right column - index_cols_post_join = [get_column_left[id] for id in left.index_columns] - combined_expr = combined_expr.drop_columns( - [get_column_right[id] for id in right.index_columns] - ) - block = Block( - combined_expr, - index_columns=index_cols_post_join, - column_labels=left.column_labels.append(right.column_labels), - index_labels=left.index.names, - ) - return ( - block, - (get_column_left, get_column_right), - ) - -def try_legacy_row_join( - left: Block, - right: Block, - *, - how="left", -) -> Optional[ - Tuple[ - Block, - Tuple[Mapping[str, str], Mapping[str, str]], - ] -]: - """Joins two blocks that have a common root expression by merging the projections.""" - left_expr = left.expr - right_expr = right.expr - # Create a new array value, mapping from both, then left, and then right - join_keys = tuple( - join_defs.CoalescedColumnMapping( - left_source_id=left_id, - right_source_id=right_id, - destination_id=guid.generate_guid(), - ) - for left_id, right_id in zip(left.index_columns, right.index_columns) - ) - left_mappings = [ - join_defs.JoinColumnMapping( - source_table=join_defs.JoinSide.LEFT, - source_id=id, - destination_id=guid.generate_guid(), - ) - for id in left.value_columns - ] - right_mappings = [ - join_defs.JoinColumnMapping( - source_table=join_defs.JoinSide.RIGHT, - source_id=id, - destination_id=guid.generate_guid(), - ) - for id in right.value_columns - ] - combined_expr = left_expr.try_legacy_row_join( - right_expr, - join_type=how, - join_keys=join_keys, - mappings=(*left_mappings, *right_mappings), - ) - if combined_expr is None: - return None - get_column_left = {m.source_id: m.destination_id for m in left_mappings} - get_column_right = {m.source_id: m.destination_id for m in right_mappings} - block = Block( - combined_expr, - column_labels=[*left.column_labels, *right.column_labels], - index_columns=(key.destination_id for key in join_keys), - index_labels=left.index.names, - ) - return ( - block, - (get_column_left, get_column_right), - ) +def block_from_local(data) -> Block: + pd_data = pd.DataFrame(data) + columns = pd_data.columns + # Make a flattened version to treat as a table. + if len(pd_data.columns.names) > 1: + pd_data.columns = columns.to_flat_index() -def join_with_single_row( - left: Block, - single_row_block: Block, -) -> Tuple[ - Block, - Tuple[Mapping[str, str], Mapping[str, str]], -]: - """ - Special join case where other is a single row block. - This property is not validated, caller responsible for not passing multi-row block. - Preserves index of the left block, ignoring label of other. - """ - left_expr = left.expr - # ignore index columns by dropping them - right_expr = single_row_block.expr.select_columns(single_row_block.value_columns) - combined_expr, (get_column_left, get_column_right) = left_expr.relational_join( - right_expr, - type="cross", - ) - # Drop original indices from each side. and used the coalesced combination generated by the join. - index_cols_post_join = [get_column_left[id] for id in left.index_columns] - - block = Block( - combined_expr, - index_columns=index_cols_post_join, - column_labels=left.column_labels.append(single_row_block.column_labels), - index_labels=left.index.names, - ) - return ( - block, - (get_column_left, get_column_right), + index_labels = list(pd_data.index.names) + # The ArrayValue layer doesn't know about indexes, so make sure indexes + # are real columns with unique IDs. + pd_data = pd_data.reset_index( + names=[f"level_{level}" for level in range(len(index_labels))] ) - - -def join_mono_indexed( - left: Block, - right: Block, - *, - how="left", - sort: bool = False, - propogate_order: bool = False, -) -> Tuple[ - Block, - Tuple[Mapping[str, str], Mapping[str, str]], -]: - left_expr = left.expr - right_expr = right.expr - - combined_expr, (get_column_left, get_column_right) = left_expr.relational_join( - right_expr, - type=how, - conditions=( - join_defs.JoinCondition(left.index_columns[0], right.index_columns[0]), + pd_data = pd_data.set_axis( + vendored_pandas_io_common.dedup_names( + list(pd_data.columns), is_potential_multiindex=False ), - propogate_order=propogate_order, - ) - - left_index = get_column_left[left.index_columns[0]] - right_index = get_column_right[right.index_columns[0]] - # Drop original indices from each side. and used the coalesced combination generated by the join. - combined_expr, coalesced_join_cols = resolve_col_join_ids( - combined_expr, [left_index], [right_index], how=how - ) - if sort: - combined_expr = combined_expr.order_by( - [ - ordering.OrderingExpression(ex.deref(col_id)) - for col_id in coalesced_join_cols - ] - ) - block = Block( - combined_expr, - index_columns=coalesced_join_cols, - column_labels=[*left.column_labels, *right.column_labels], - index_labels=[left.index.name] - if left.index.name == right.index.name - else [None], - ) - return ( - block, - (get_column_left, get_column_right), + axis="columns", ) + index_ids = pd_data.columns[: len(index_labels)] - -def join_multi_indexed( - left: Block, - right: Block, - *, - how="left", - sort: bool = False, - propogate_order: bool = False, -) -> Tuple[ - Block, - Tuple[Mapping[str, str], Mapping[str, str]], -]: - if not (left.index.is_uniquely_named() and right.index.is_uniquely_named()): - raise ValueError("Joins not supported on indices with non-unique level names") - - common_names = [name for name in left.index.names if name in right.index.names] - if len(common_names) == 0: - raise ValueError("Cannot join without a index level in common.") - - left_only_names = [ - name for name in left.index.names if name not in right.index.names - ] - right_only_names = [ - name for name in right.index.names if name not in left.index.names - ] - - left_join_ids = [left.index.resolve_level_exact(name) for name in common_names] - right_join_ids = [right.index.resolve_level_exact(name) for name in common_names] - - left_expr = left.expr - right_expr = right.expr - - combined_expr, (get_column_left, get_column_right) = left_expr.relational_join( - right_expr, - type=how, - conditions=tuple( - join_defs.JoinCondition(left, right) - for left, right in zip(left_join_ids, right_join_ids) - ), - propogate_order=propogate_order, - ) - - left_ids_post_join = [get_column_left[id] for id in left_join_ids] - right_ids_post_join = [get_column_right[id] for id in right_join_ids] - # Drop original indices from each side. and used the coalesced combination generated by the join. - combined_expr, coalesced_join_cols = resolve_col_join_ids( - combined_expr, left_ids_post_join, right_ids_post_join, how=how - ) - if sort: - combined_expr = combined_expr.order_by( - [ - ordering.OrderingExpression(ex.deref(col_id)) - for col_id in coalesced_join_cols - ] - ) - - if left.index.nlevels == 1: - index_labels = right.index.names - elif right.index.nlevels == 1: - index_labels = left.index.names - else: - index_labels = [*common_names, *left_only_names, *right_only_names] - - def resolve_label_id(label: Label) -> str: - # if name is shared between both blocks, coalesce the values - if label in common_names: - return coalesced_join_cols[common_names.index(label)] - if label in left_only_names: - return get_column_left[left.index.resolve_level_exact(label)] - if label in right_only_names: - return get_column_right[right.index.resolve_level_exact(label)] - raise ValueError(f"Unexpected label: {label}") - - index_columns = [resolve_label_id(label) for label in index_labels] - - block = Block( - combined_expr, - index_columns=index_columns, - column_labels=[*left.column_labels, *right.column_labels], - index_labels=index_labels, - ) - return ( - block, - (get_column_left, get_column_right), - ) - - -# TODO: Rewrite just to return expressions -def resolve_col_join_ids( - expr: core.ArrayValue, - left_ids: typing.Sequence[str], - right_ids: typing.Sequence[str], - how: str, - drop: bool = True, -) -> Tuple[core.ArrayValue, Sequence[str]]: - """ - Collapses and selects the joining column IDs, with the assumption that - the ids are all belong to value columns. - """ - result_ids = [] - for left_id, right_id in zip(left_ids, right_ids): - if how == "left" or how == "inner" or how == "cross": - result_ids.append(left_id) - if drop: - expr = expr.drop_columns([right_id]) - elif how == "right": - result_ids.append(right_id) - if drop: - expr = expr.drop_columns([left_id]) - elif how == "outer": - expr, coalesced_id = expr.project_to_id( - ops.coalesce_op.as_expr(left_id, right_id) - ) - if drop: - expr = expr.drop_columns([left_id, right_id]) - result_ids.append(coalesced_id) - else: - raise ValueError(f"Unexpected join type: {how}. {constants.FEEDBACK_LINK}") - return expr, result_ids - - -def coalesce_columns( - expr: core.ArrayValue, - left_ids: typing.Sequence[str], - right_ids: typing.Sequence[str], -) -> tuple[core.ArrayValue, list[str]]: - result_ids = [] - for left_id, right_id in zip(left_ids, right_ids): - expr, coalesced_id = expr.project_to_id( - ops.coalesce_op.as_expr(left_id, right_id) - ) - result_ids.append(coalesced_id) - - return expr, result_ids - - -def _cast_index(block: Block, dtypes: typing.Sequence[bigframes.dtypes.Dtype]): - original_block = block - result_ids = [] - for idx_id, idx_dtype, target_dtype in zip( - block.index_columns, block.index.dtypes, dtypes - ): - if idx_dtype != target_dtype: - block, result_id = block.apply_unary_op(idx_id, ops.AsTypeOp(target_dtype)) - result_ids.append(result_id) - else: - result_ids.append(idx_id) - - expr = block.expr.select_columns((*result_ids, *original_block.value_columns)) + keys_expr = core.ArrayValue.from_pandas(pd_data) return Block( - expr, - index_columns=result_ids, - column_labels=original_block.column_labels, - index_labels=original_block.index.names, + keys_expr, + column_labels=columns, + index_columns=index_ids, + index_labels=index_labels, ) -### Schema alignment Utils -### TODO: Pull out to separate module? def _align_block_to_schema( block: Block, schema: dict[Label, bigframes.dtypes.Dtype] ) -> Block: - """For a given schema, remap block to schema by reordering columns, and inserting nulls.""" + """For a given schema, remap block to schema by reordering columns and inserting nulls.""" col_ids: typing.Tuple[str, ...] = () for label, dtype in schema.items(): + # TODO: Support casting to lcd type - requires mixed type support matching_ids: typing.Sequence[str] = block.label_to_col_id.get(label, ()) if len(matching_ids) > 0: col_id = matching_ids[-1] - col_dtype = block.expr.get_column_type(col_id) - if dtype != col_dtype: - # If _align_schema worked properly, this should always be an upcast - block, col_id = block.apply_unary_op(col_id, ops.AsTypeOp(dtype)) col_ids = (*col_ids, col_id) else: block, null_column = block.create_constant(None, dtype=dtype) @@ -3338,28 +1805,24 @@ def _align_schema( return functools.reduce(reduction, schemas) -def _align_indices( - blocks: typing.Sequence[Block], -) -> typing.Tuple[typing.Sequence[Label], typing.Sequence[bigframes.dtypes.Dtype]]: - """Validates that the blocks have compatible indices and returns the resulting label names and dtypes.""" +def _align_indices(blocks: typing.Sequence[Block]) -> typing.Sequence[Label]: + """Validates that the blocks have compatible indices and returns the resulting label names.""" names = blocks[0].index.names types = blocks[0].index.dtypes - for block in blocks[1:]: if len(names) != block.index.nlevels: raise NotImplementedError( f"Cannot combine indices with different number of levels. Use 'ignore_index'=True. {constants.FEEDBACK_LINK}" ) + if block.index.dtypes != types: + raise NotImplementedError( + f"Cannot combine different index dtypes. Use 'ignore_index'=True. {constants.FEEDBACK_LINK}" + ) names = [ lname if lname == rname else None for lname, rname in zip(names, block.index.names) ] - types = [ - bigframes.dtypes.lcd_type_or_throw(ltype, rtype) - for ltype, rtype in zip(types, block.index.dtypes) - ] - types = typing.cast(typing.Sequence[bigframes.dtypes.Dtype], types) - return names, types + return names def _combine_schema_inner( @@ -3367,15 +1830,13 @@ def _combine_schema_inner( right: typing.Dict[Label, bigframes.dtypes.Dtype], ) -> typing.Dict[Label, bigframes.dtypes.Dtype]: result = dict() - for label, left_type in left.items(): + for label, type in left.items(): if label in right: - right_type = right[label] - output_type = bigframes.dtypes.lcd_type(left_type, right_type) - if output_type is None: + if type != right[label]: raise ValueError( f"Cannot concat rows with label {label} due to mismatched types. {constants.FEEDBACK_LINK}" ) - result[label] = output_type + result[label] = type return result @@ -3384,20 +1845,15 @@ def _combine_schema_outer( right: typing.Dict[Label, bigframes.dtypes.Dtype], ) -> typing.Dict[Label, bigframes.dtypes.Dtype]: result = dict() - for label, left_type in left.items(): - if label not in right: - result[label] = left_type - else: - right_type = right[label] - output_type = bigframes.dtypes.lcd_type(left_type, right_type) - if output_type is None: - raise NotImplementedError( - f"Cannot concat rows with label {label} due to mismatched types. {constants.FEEDBACK_LINK}" - ) - result[label] = output_type - for label, right_type in right.items(): + for label, type in left.items(): + if (label in right) and (type != right[label]): + raise ValueError( + f"Cannot concat rows with label {label} due to mismatched types. {constants.FEEDBACK_LINK}" + ) + result[label] = type + for label, type in right.items(): if label not in left: - result[label] = right_type + result[label] = type return result @@ -3409,155 +1865,3 @@ def _get_block_schema( for label, dtype in zip(block.column_labels, block.dtypes): result[label] = typing.cast(bigframes.dtypes.Dtype, dtype) return result - - -## Unpivot helpers -def unpivot( - array_value: core.ArrayValue, - row_labels: pd.Index, - unpivot_columns: Sequence[Tuple[Optional[str], ...]], - *, - passthrough_columns: typing.Sequence[str] = (), - join_side: Literal["left", "right"] = "left", -) -> Tuple[core.ArrayValue, Tuple[Tuple[str, ...], Tuple[str, ...], Tuple[str, ...]]]: - """ - Unpivot ArrayValue columns. - - Args: - row_labels: Identifies the source of the row. Must be equal to length to source column list in unpivot_columns argument. - unpivot_columns: Sequence of column ids tuples. Each tuple of columns will be combined into a single output column - passthrough_columns: Columns that will not be unpivoted. Column id will be preserved. - index_col_id (str): The column id to be used for the row labels. - - Returns: - ArrayValue, (index_cols, unpivot_cols, passthrough_cols): The unpivoted ArrayValue and resulting column ids. - """ - # There will be N labels, used to disambiguate which of N source columns produced each output row - labels_array = _pd_index_to_array_value( - session=array_value.session, index=row_labels - ) - - # Unpivot creates N output rows for each input row, labels disambiguate these N rows - # Join_side is necessary to produce desired row ordering - if join_side == "left": - joined_array, (column_mapping, labels_mapping) = array_value.relational_join( - labels_array, type="cross" - ) - else: - joined_array, (labels_mapping, column_mapping) = labels_array.relational_join( - array_value, type="cross" - ) - - new_passthrough_cols = [column_mapping[col] for col in passthrough_columns] - # Last column is offsets - index_col_ids = [labels_mapping[col] for col in labels_array.column_ids[:-1]] - explode_offsets_id = labels_mapping[labels_array.column_ids[-1]] - - # Build the output rows as a case statment that selects between the N input columns - unpivot_exprs: List[ex.Expression] = [] - # Supports producing multiple stacked ouput columns for stacking only part of hierarchical index - for input_ids in unpivot_columns: - col_expr: ex.Expression - if not input_ids: - col_expr = ex.const(None, dtype=bigframes.dtypes.INT_DTYPE) - else: - # row explode offset used to choose the input column - # we use offset instead of label as labels are not necessarily unique - cases = itertools.chain( - *( - ( - ops.eq_op.as_expr(explode_offsets_id, ex.const(i)), - ex.deref(column_mapping[id_or_null]) - if (id_or_null is not None) - else ex.const(None), - ) - for i, id_or_null in enumerate(input_ids) - ) - ) - col_expr = ops.case_when_op.as_expr(*cases) - unpivot_exprs.append(col_expr) - - joined_array, unpivot_col_ids = joined_array.compute_values(unpivot_exprs) - - return joined_array.select_columns( - [*index_col_ids, *unpivot_col_ids, *new_passthrough_cols] - ), (tuple(index_col_ids), tuple(unpivot_col_ids), tuple(new_passthrough_cols)) - - -def _pd_index_to_array_value( - session: session.Session, - index: pd.Index, -) -> core.ArrayValue: - """ - Create an ArrayValue from a list of label tuples. - The last column will be row offsets. - """ - id_gen = bigframes.core.identifiers.standard_id_strings() - col_ids = [next(id_gen) for _ in range(index.nlevels)] - offset_id = next(id_gen) - - rows = [] - labels_as_tuples = utils.index_as_tuples(index) - for row_offset in range(len(index)): - row_label = labels_as_tuples[row_offset] - row_label = (row_label,) if not isinstance(row_label, tuple) else row_label - row = {} - for label_part, col_id in zip(row_label, col_ids): - row[col_id] = label_part if pd.notnull(label_part) else None - row[offset_id] = row_offset - rows.append(row) - - if not rows: - dtypes_list = getattr(index, "dtypes", None) - if dtypes_list is None: - dtypes_list = ( - [index.dtype] if hasattr(index, "dtype") else [pd.Float64Dtype()] - ) - - fields = [] - for col_id, dtype in zip(col_ids, dtypes_list): - try: - pa_type = bigframes.dtypes.bigframes_dtype_to_arrow_dtype(dtype) - except Exception: - pa_type = pa.string() - fields.append(pa.field(col_id, pa_type)) - fields.append(pa.field(offset_id, pa.int64())) - schema = pa.schema(fields) - pt = pa.Table.from_pylist([], schema=schema) - else: - pt = pa.Table.from_pylist(rows) - pt = pt.rename_columns([*col_ids, offset_id]) - - return core.ArrayValue.from_pyarrow(pt, session=session) - - -def _resolve_index_col( - left_index_cols: list[str], - right_index_cols: list[str], - resolved_join_ids: list[str], - left_index: bool, - right_index: bool, - how: typing.Literal[ - "inner", - "left", - "outer", - "right", - "cross", - ], -) -> list[str]: - if left_index and right_index: - if how == "inner" or how == "left": - return left_index_cols - if how == "right": - return right_index_cols - if how == "outer": - return resolved_join_ids - else: - return [] - elif left_index and not right_index: - return right_index_cols - elif right_index and not left_index: - return left_index_cols - else: - # Joining with value columns only. Existing indices will be discarded. - return [] diff --git a/bigframes/core/bq_data.py b/bigframes/core/bq_data.py deleted file mode 100644 index 55ac1270b6c..00000000000 --- a/bigframes/core/bq_data.py +++ /dev/null @@ -1,410 +0,0 @@ -# Copyright 2023 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from __future__ import annotations - -import concurrent.futures -import dataclasses -import datetime -import functools -import os -import queue -import threading -import typing -from typing import Any, Iterator, List, Literal, Optional, Sequence, Tuple, Union - -import google.cloud.bigquery as bq -import google.cloud.bigquery_storage_v1.types as bq_storage_types -import pyarrow as pa -from google.cloud import bigquery_storage_v1 -from google.protobuf import timestamp_pb2 - -import bigframes.constants -import bigframes.core.schema -from bigframes.core import pyarrow_utils - -if typing.TYPE_CHECKING: - import bigframes.core.ordering as orderings - - -def _resolve_standard_gcp_region(bq_region: str): - """ - Resolve bq regions to standardized - """ - if bq_region.casefold() == "US": - return "us-central1" - elif bq_region.casefold() == "EU": - return "europe-west4" - return bq_region - - -def is_irc_table(table_id: str): - """ - Determines if a table id should be resolved through the iceberg rest catalog. - """ - return len(table_id.split(".")) == 4 - - -def is_compatible( - data_region: Union[GcsRegion, BigQueryRegion], session_location: str -) -> bool: - # based on https://docs.cloud.google.com/bigquery/docs/locations#storage-location-considerations - if isinstance(data_region, BigQueryRegion): - return data_region.name == session_location - else: - assert isinstance(data_region, GcsRegion) - # TODO(b/463675088): Multi-regions don't yet support rest catalog tables - if session_location in bigframes.constants.BIGQUERY_MULTIREGIONS: - return False - return _resolve_standard_gcp_region(session_location) in data_region.included - - -def get_default_bq_region(data_region: Union[GcsRegion, BigQueryRegion]) -> str: - if isinstance(data_region, BigQueryRegion): - return data_region.name - elif isinstance(data_region, GcsRegion): - # should maybe try to track and prefer primary replica? - return data_region.included[0] - - -@dataclasses.dataclass(frozen=True) -class BigQueryRegion: - name: str - - -@dataclasses.dataclass(frozen=True) -class GcsRegion: - # this is the name of gcs regions, which may be names for multi-regions, so shouldn't be compared with non-gcs locations - storage_regions: tuple[str, ...] - # this tracks all the included standard, specific regions (eg us-east1), and should be comparable to bq regions (except non-standard US, EU, omni regions) - included: tuple[str, ...] - - -# what is the line between metadata and core fields? Mostly metadata fields are optional or unreliable, but its fuzzy -@dataclasses.dataclass(frozen=True) -class TableMetadata: - # this size metadata might be stale, don't use where strict correctness is needed - location: Union[BigQueryRegion, GcsRegion] - type: Literal["TABLE", "EXTERNAL", "VIEW", "MATERIALIZE_VIEW", "SNAPSHOT"] - numBytes: Optional[int] = None - numRows: Optional[int] = None - created_time: Optional[datetime.datetime] = None - modified_time: Optional[datetime.datetime] = None - - -@dataclasses.dataclass(frozen=True) -class GbqNativeTable: - project_id: str = dataclasses.field() - dataset_id: str = dataclasses.field() - table_id: str = dataclasses.field() - physical_schema: Tuple[bq.SchemaField, ...] = dataclasses.field() - metadata: TableMetadata = dataclasses.field() - partition_col: Optional[str] = None - cluster_cols: typing.Optional[Tuple[str, ...]] = None - primary_key: Optional[Tuple[str, ...]] = None - - @staticmethod - def from_table(table: bq.Table, columns: Sequence[str] = ()) -> GbqNativeTable: - # Subsetting fields with columns can reduce cost of row-hash default ordering - if columns: - schema = tuple(item for item in table.schema if item.name in columns) - else: - schema = tuple(table.schema) - - metadata = TableMetadata( - numBytes=table.num_bytes, - numRows=table.num_rows, - location=BigQueryRegion(table.location), # type: ignore - type=table.table_type or "TABLE", # type: ignore - created_time=table.created, - modified_time=table.modified, - ) - partition_col = None - if table.range_partitioning: - partition_col = table.range_partitioning.field - elif table.time_partitioning: - partition_col = table.time_partitioning.field - - return GbqNativeTable( - project_id=table.project, - dataset_id=table.dataset_id, - table_id=table.table_id, - physical_schema=schema, - partition_col=partition_col, - cluster_cols=None - if (table.clustering_fields is None) - else tuple(table.clustering_fields), - primary_key=tuple(_get_primary_keys(table)), - metadata=metadata, - ) - - @staticmethod - def from_ref_and_schema( - table_ref: bq.TableReference, - schema: Sequence[bq.SchemaField], - location: str, - table_type: Literal["TABLE"] = "TABLE", - cluster_cols: Optional[Sequence[str]] = None, - ) -> GbqNativeTable: - return GbqNativeTable( - project_id=table_ref.project, - dataset_id=table_ref.dataset_id, - table_id=table_ref.table_id, - metadata=TableMetadata(location=BigQueryRegion(location), type=table_type), - physical_schema=tuple(schema), - cluster_cols=tuple(cluster_cols) if cluster_cols else None, - ) - - @property - def is_physically_stored(self) -> bool: - return self.metadata.type in ["TABLE", "MATERIALIZED_VIEW"] - - def get_table_ref(self) -> bq.TableReference: - return bq.TableReference( - bq.DatasetReference(self.project_id, self.dataset_id), self.table_id - ) - - def get_full_id(self, quoted: bool = False) -> str: - if quoted: - return f"`{self.project_id}`.`{self.dataset_id}`.`{self.table_id}`" - return f"{self.project_id}.{self.dataset_id}.{self.table_id}" - - @property - @functools.cache - def schema_by_id(self): - return {col.name: col for col in self.physical_schema} - - -@dataclasses.dataclass(frozen=True) -class BiglakeIcebergTable: - project_id: str = dataclasses.field() - catalog_id: str = dataclasses.field() - namespace_id: str = dataclasses.field() - table_id: str = dataclasses.field() - physical_schema: Tuple[bq.SchemaField, ...] = dataclasses.field() - cluster_cols: typing.Optional[Tuple[str, ...]] - metadata: TableMetadata - - def get_full_id(self, quoted: bool = False) -> str: - if quoted: - return f"`{self.project_id}`.`{self.catalog_id}`.`{self.namespace_id}`.`{self.table_id}`" - return ( - f"{self.project_id}.{self.catalog_id}.{self.namespace_id}.{self.table_id}" - ) - - @property - @functools.cache - def schema_by_id(self): - return {col.name: col for col in self.physical_schema} - - @property - def partition_col(self) -> Optional[str]: - # TODO: Use iceberg partition metadata - return None - - @property - def dataset_id(self) -> str: - """ - Not a true dataset, but serves as the dataset component of the identifer in sql queries - """ - return f"{self.catalog_id}.{self.namespace_id}" - - @property - def primary_key(self) -> Optional[Tuple[str, ...]]: - return None - - def get_table_ref(self) -> bq.TableReference: - return bq.TableReference( - bq.DatasetReference(self.project_id, self.dataset_id), self.table_id - ) - - -@dataclasses.dataclass(frozen=True) -class BigqueryDataSource: - """ - Google BigQuery Data source. - - This should not be modified once defined, as all attributes contribute to the default ordering. - """ - - def __post_init__(self): - # not all columns need be in schema, eg so can exclude unsupported column types (eg RANGE) - assert set(field.name for field in self.table.physical_schema).issuperset( - self.schema.names - ) - - table: Union[GbqNativeTable, BiglakeIcebergTable] - schema: bigframes.core.schema.ArraySchema - at_time: typing.Optional[datetime.datetime] = None - # Added for backwards compatibility, not validated - sql_predicate: typing.Optional[str] = None - ordering: typing.Optional[orderings.RowOrdering] = None - # Optimization field, must be correct if set, don't put maybe-stale number here - n_rows: Optional[int] = None - - def with_ordering(self, ordering: orderings.RowOrdering) -> BigqueryDataSource: - return dataclasses.replace(self, ordering=ordering) - - -_WORKER_TIME_INCREMENT = 0.05 - - -def _iter_stream( - stream_name: str, - storage_read_client: bigquery_storage_v1.BigQueryReadClient, - result_queue: queue.Queue, - stop_event: threading.Event, -): - reader = storage_read_client.read_rows(stream_name) - for page in reader.rows().pages: - while True: # Alternate between put attempt and checking stop event - try: - result_queue.put(page.to_arrow(), timeout=_WORKER_TIME_INCREMENT) - break - except queue.Full: - if stop_event.is_set(): - return - continue - - -def _iter_streams( - streams: Sequence[bq_storage_types.ReadStream], - storage_read_client: bigquery_storage_v1.BigQueryReadClient, -) -> Iterator[pa.RecordBatch]: - stop_event = threading.Event() - result_queue: queue.Queue = queue.Queue( - len(streams) - ) # each response is large, so small queue is appropriate - - in_progress: list[concurrent.futures.Future] = [] - with concurrent.futures.ThreadPoolExecutor(max_workers=len(streams)) as pool: - try: - for stream in streams: - in_progress.append( - pool.submit( - _iter_stream, - stream.name, - storage_read_client, - result_queue, - stop_event, - ) - ) - - while in_progress: - try: - yield result_queue.get(timeout=0.1) - except queue.Empty: - new_in_progress = [] - for future in in_progress: - if future.done(): - # Call to raise any exceptions - future.result() - else: - new_in_progress.append(future) - in_progress = new_in_progress - finally: - stop_event.set() - - -@dataclasses.dataclass -class ReadResult: - iter: Iterator[pa.RecordBatch] - approx_rows: int - approx_bytes: int - - -def get_arrow_batches( - data: BigqueryDataSource, - columns: Sequence[str], - storage_read_client: bigquery_storage_v1.BigQueryReadClient, - project_id: str, - sample_rate: Optional[float] = None, -) -> ReadResult: - assert isinstance(data.table, GbqNativeTable) - - table_mod_options = {} - read_options_dict: dict[str, Any] = {"selected_fields": list(columns)} - - predicates = [] - if data.sql_predicate: - predicates.append(data.sql_predicate) - if sample_rate is not None: - assert isinstance(sample_rate, float) - predicates.append(f"RAND() < {sample_rate}") - - if predicates: - full_predicates = " AND ".join(f"( {pred} )" for pred in predicates) - read_options_dict["row_restriction"] = full_predicates - - read_options = bq_storage_types.ReadSession.TableReadOptions(**read_options_dict) - - if data.at_time: - snapshot_time = timestamp_pb2.Timestamp() - snapshot_time.FromDatetime(data.at_time) - table_mod_options["snapshot_time"] = snapshot_time - table_mods = bq_storage_types.ReadSession.TableModifiers(**table_mod_options) - - requested_session = bq_storage_types.stream.ReadSession( - table=data.table.get_table_ref().to_bqstorage(), - data_format=bq_storage_types.DataFormat.ARROW, - read_options=read_options, - table_modifiers=table_mods, - ) - if data.ordering is not None: - max_streams = 1 - else: - max_streams = os.cpu_count() or 8 - - # Single stream to maintain ordering - request = bq_storage_types.CreateReadSessionRequest( - parent=f"projects/{project_id}", - read_session=requested_session, - max_stream_count=max_streams, - ) - - session = storage_read_client.create_read_session(request=request) - - if not session.streams: - batches: Iterator[pa.RecordBatch] = iter([]) - else: - batches = _iter_streams(session.streams, storage_read_client) - - def process_batch(pa_batch): - return pyarrow_utils.cast_batch( - pa_batch.select(columns), data.schema.select(columns).to_pyarrow() - ) - - batches = map(process_batch, batches) - - return ReadResult( - batches, session.estimated_row_count, session.estimated_total_bytes_scanned - ) - - -def _get_primary_keys( - table: bq.Table, -) -> List[str]: - """Get primary keys from table if they are set.""" - - primary_keys: List[str] = [] - if ( - (table_constraints := getattr(table, "table_constraints", None)) is not None - and (primary_key := table_constraints.primary_key) is not None - # This will be False for either None or empty list. - # We want primary_keys = None if no primary keys are set. - and (columns := primary_key.columns) - ): - primary_keys = columns if columns is not None else [] - - return primary_keys diff --git a/bigframes/core/bytecode.py b/bigframes/core/bytecode.py deleted file mode 100644 index f657ce707ea..00000000000 --- a/bigframes/core/bytecode.py +++ /dev/null @@ -1,913 +0,0 @@ -# Copyright 2026 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import dataclasses -import dis -import operator -import sys -from types import ModuleType -from typing import Callable - -import bigframes.core.py_expressions as py_exprs -from bigframes.core import expression -from bigframes.operations import generic_ops - -_BINARY_OP_MAP = { - "+": operator.add, - "-": operator.sub, - "*": operator.mul, - "/": operator.truediv, - "//": operator.floordiv, - "%": operator.mod, - "**": operator.pow, - "[]": operator.getitem, -} - -_COMPARE_OP_MAP = { - "==": operator.eq, - "!=": operator.ne, - "<": operator.lt, - "<=": operator.le, - ">": operator.gt, - ">=": operator.ge, -} - -_OLD_BINARY_OP_MAP = { - "BINARY_ADD": operator.add, - "INPLACE_ADD": operator.add, - "BINARY_SUBTRACT": operator.sub, - "INPLACE_SUBTRACT": operator.sub, - "BINARY_MULTIPLY": operator.mul, - "INPLACE_MULTIPLY": operator.mul, - "BINARY_TRUE_DIVIDE": operator.truediv, - "INPLACE_TRUE_DIVIDE": operator.truediv, - "BINARY_FLOOR_DIVIDE": operator.floordiv, - "INPLACE_FLOOR_DIVIDE": operator.floordiv, - "BINARY_MODULO": operator.mod, - "INPLACE_MODULO": operator.mod, - "BINARY_POWER": operator.pow, - "INPLACE_POWER": operator.pow, -} - - -_NULL = py_exprs.PyObject(None) - - -_RETURN_OPNAMES = {"RETURN_VALUE", "RETURN_CONST"} - -_UNCONDITIONAL_JUMP_OPNAMES = { - "JUMP_FORWARD", - "JUMP_ABSOLUTE", - "JUMP_BACKWARD", - "JUMP_BACKWARD_NO_INTERRUPT", - "JUMP", - "JUMP_NO_INTERRUPT", -} - -_JUMP_IF_FALSE_OPNAMES = { - "POP_JUMP_IF_FALSE", - "POP_JUMP_FORWARD_IF_FALSE", - "POP_JUMP_BACKWARD_IF_FALSE", -} - -_JUMP_IF_TRUE_OPNAMES = { - "POP_JUMP_IF_TRUE", - "POP_JUMP_FORWARD_IF_TRUE", - "POP_JUMP_BACKWARD_IF_TRUE", -} - -_JUMP_IF_NONE_OPNAMES = { - "POP_JUMP_IF_NONE", - "POP_JUMP_FORWARD_IF_NONE", - "POP_JUMP_BACKWARD_IF_NONE", -} - -_JUMP_IF_NOT_NONE_OPNAMES = { - "POP_JUMP_IF_NOT_NONE", - "POP_JUMP_FORWARD_IF_NOT_NONE", - "POP_JUMP_BACKWARD_IF_NOT_NONE", -} - -_CONDITIONAL_JUMP_OPNAMES = ( - _JUMP_IF_FALSE_OPNAMES - | _JUMP_IF_TRUE_OPNAMES - | _JUMP_IF_NONE_OPNAMES - | _JUMP_IF_NOT_NONE_OPNAMES - | { - "JUMP_IF_FALSE_OR_POP", - "JUMP_IF_TRUE_OR_POP", - } -) - -_ALL_JUMP_OPNAMES = _UNCONDITIONAL_JUMP_OPNAMES | _CONDITIONAL_JUMP_OPNAMES - - -@dataclasses.dataclass -class BasicBlock: - start_offset: int - instructions: list[dis.Instruction] - successors: list[int] = dataclasses.field(default_factory=list) - predecessors: list[int] = dataclasses.field(default_factory=list) - - -def get_block_starts(instructions: list[dis.Instruction]) -> set[int]: - starts = {0} - for i, inst in enumerate(instructions): - opname = inst.opname - if opname in _ALL_JUMP_OPNAMES: - if isinstance(inst.argval, int): - starts.add(inst.argval) - if i + 1 < len(instructions): - starts.add(instructions[i + 1].offset) - elif opname in _RETURN_OPNAMES: - if i + 1 < len(instructions): - starts.add(instructions[i + 1].offset) - return starts - - -def get_block_successors(block: BasicBlock, next_offsets: dict[int, int]) -> list[int]: - if not block.instructions: - return [] - last_inst = block.instructions[-1] - opname = last_inst.opname - offset = last_inst.offset - - next_offset = next_offsets.get(offset) - - if opname in _RETURN_OPNAMES: - return [] - - if opname in _UNCONDITIONAL_JUMP_OPNAMES: - return [last_inst.argval] - - if opname in _CONDITIONAL_JUMP_OPNAMES: - successors = [last_inst.argval] - if next_offset is not None: - successors.append(next_offset) - return successors - - if next_offset is not None: - return [next_offset] - return [] - - -def build_cfg( - instructions: list[dis.Instruction], next_offsets: dict[int, int] -) -> dict[int, BasicBlock]: - starts = sorted(list(get_block_starts(instructions))) - - blocks: dict[int, BasicBlock] = {} - for i, start in enumerate(starts): - end = starts[i + 1] if i + 1 < len(starts) else None - block_insts = [ - inst - for inst in instructions - if start <= inst.offset and (end is None or inst.offset < end) - ] - blocks[start] = BasicBlock(start_offset=start, instructions=block_insts) - - for block in blocks.values(): - successors = get_block_successors(block, next_offsets) - block.successors = successors - for succ in successors: - blocks[succ].predecessors.append(block.start_offset) - - return blocks - - -def topological_sort(blocks: dict[int, BasicBlock]) -> list[int]: - in_degree = {offset: len(block.predecessors) for offset, block in blocks.items()} - queue = [offset for offset, deg in in_degree.items() if deg == 0] - order = [] - - while queue: - queue.sort() - curr = queue.pop(0) - order.append(curr) - for succ in blocks[curr].successors: - in_degree[succ] -= 1 - if in_degree[succ] == 0: - queue.append(succ) - - # TODO(b/521549179): Support limited loop analysis (eg unroll loops over a constant range). - if len(order) != len(blocks): - raise ValueError( - "Loops are not supported in the Python function for transpilation." - ) - - return order - - -def merge_values( - pairs: list[tuple[expression.Expression, expression.Expression]], -) -> expression.Expression: - if not pairs: - raise ValueError("Cannot merge empty list of values") - if len(pairs) == 1: - return pairs[0][0] - - val = pairs[-1][0] - for next_val, next_cond in reversed(pairs[:-1]): - val = py_exprs.Call( - py_exprs.PyObject(generic_ops.where_op), (next_val, next_cond, val) - ) - return val - - -def _compile_bytecode_to_py_expr(func: Callable) -> expression.Expression: - instructions = list(dis.get_instructions(func)) - next_offsets = { - inst.offset: next_inst.offset - for inst, next_inst in zip(instructions, instructions[1:]) - } - - blocks = build_cfg(instructions, next_offsets) - order = topological_sort(blocks) - - stack: list[expression.Expression] - local_vars: dict[str, expression.Expression] - - globals_dict = func.__globals__ - import builtins - - builtins_dict = builtins.__dict__ - closure_dict = {} - if func.__closure__: - free_vars = func.__code__.co_freevars - for var, cell in zip(free_vars, func.__closure__): - try: - closure_dict[var] = cell.cell_contents - except ValueError: - pass - - block_outputs: dict[ - int, tuple[list[expression.Expression], dict[str, expression.Expression]] - ] = {} - block_reach_conditions: dict[int, expression.Expression] = { - 0: py_exprs.PyObject(True) - } - edge_conditions: dict[tuple[int, int], expression.Expression] = {} - edge_stacks: dict[tuple[int, int], list[expression.Expression]] = {} - returns: list[tuple[expression.Expression, expression.Expression]] = [] - - co = func.__code__ - param_names = list(co.co_varnames[: co.co_argcount]) - kwonly_argcount = co.co_kwonlyargcount - param_names.extend( - co.co_varnames[co.co_argcount : co.co_argcount + kwonly_argcount] - ) - - initial_local_vars: dict[str, expression.Expression] = { - name: expression.UnboundVariableExpression(name) for name in param_names - } - - for offset in order: - block = blocks[offset] - - reach_cond: expression.Expression - if offset == 0: - reach_cond = py_exprs.PyObject(True) - else: - incoming = [ - edge_conditions[(pred, offset)] - for pred in block.predecessors - if (pred, offset) in edge_conditions - ] - if not incoming: - continue - - reach_cond = incoming[0] - for cond in incoming[1:]: - reach_cond = py_exprs.Call( - py_exprs.PyObject(operator.or_), (reach_cond, cond) - ) - - block_reach_conditions[offset] = reach_cond - - if offset == 0: - stack = [] - local_vars = initial_local_vars.copy() - else: - reachable_preds = [ - pred for pred in block.predecessors if (pred, offset) in edge_stacks - ] - if not reachable_preds: - continue - - h = len(edge_stacks[(reachable_preds[0], offset)]) - stack = [] - for i in range(h): - pairs = [ - (edge_stacks[(p, offset)][i], edge_conditions[(p, offset)]) - for p in reachable_preds - ] - stack.append(merge_values(pairs)) - - all_vars: set[str] = set() - for p in reachable_preds: - all_vars.update(block_outputs[p][1].keys()) - - local_vars = {} - for var in all_vars: - pairs = [ - ( - block_outputs[p][1].get( - var, expression.UnboundVariableExpression(var) - ), - edge_conditions[(p, offset)], - ) - for p in reachable_preds - ] - local_vars[var] = merge_values(pairs) - - jumped = False - for inst in block.instructions: - opname = inst.opname - - match opname: - case "RESUME" | "PRECALL" | "COPY_FREE_VARS" | "NOT_TAKEN" | "NOP": - continue - - case "LOAD_FAST_LOAD_FAST" | "LOAD_FAST_BORROW_LOAD_FAST_BORROW": - var1, var2 = inst.argval - stack.append( - local_vars.get(var1, expression.UnboundVariableExpression(var1)) - ) - stack.append( - local_vars.get(var2, expression.UnboundVariableExpression(var2)) - ) - - case ( - "LOAD_FAST" - | "LOAD_FAST_CHECK" - | "LOAD_FAST_AND_CLEAR" - | "LOAD_FAST_BORROW" - ): - stack.append( - local_vars.get( - inst.argval, - expression.UnboundVariableExpression(inst.argval), - ) - ) - - case "STORE_FAST": - if not stack: - raise ValueError("Stack is empty") - local_vars[inst.argval] = stack.pop() - - case "LOAD_CONST" | "LOAD_SMALL_INT": - stack.append(py_exprs.PyObject(inst.argval)) - - case "LOAD_DEREF" | "LOAD_FROM_DICT_OR_DEREF": - name = inst.argval - found = False - val = None - if name in closure_dict: - val = closure_dict[name] - found = True - elif name in globals_dict: - val = globals_dict[name] - found = True - elif name in builtins_dict: - val = builtins_dict[name] - found = True - - if found: - if isinstance(val, ModuleType): - stack.append(py_exprs.Module(val)) - else: - stack.append(py_exprs.PyObject(val)) - else: - stack.append(expression.UnboundVariableExpression(name)) - - case "LOAD_GLOBAL": - if ( - sys.version_info >= (3, 11) - and inst.arg is not None - and (inst.arg & 1) - ): - stack.append(_NULL) - name = inst.argval - found = False - val = None - if name in closure_dict: - val = closure_dict[name] - found = True - elif name in globals_dict: - val = globals_dict[name] - found = True - elif name in builtins_dict: - val = builtins_dict[name] - found = True - - if found: - if isinstance(val, ModuleType): - stack.append(py_exprs.Module(val)) - else: - stack.append(py_exprs.PyObject(val)) - else: - stack.append(expression.UnboundVariableExpression(name)) - - case "LOAD_ATTR" | "LOAD_METHOD": - if not stack: - raise ValueError("Stack is empty") - target = stack.pop() - stack.append(py_exprs.GetAttr(target, inst.argval)) - - is_method_lookup = (opname == "LOAD_METHOD") or ( - opname == "LOAD_ATTR" - and sys.version_info >= (3, 12) - and inst.arg is not None - and (inst.arg & 1) - ) - if is_method_lookup: - if isinstance(target, py_exprs.Module) or ( - isinstance(target, py_exprs.PyObject) - and isinstance(target.value, type) - ): - stack.append(_NULL) - else: - stack.append(target) - - case "PUSH_NULL": - stack.append(_NULL) - - case "TO_BOOL": - if not stack: - raise ValueError("Stack is empty") - val = stack.pop() - stack.append( - py_exprs.Call( - py_exprs.PyObject(generic_ops.coerce_to_bool_op), - (val,), - ) - ) - - case "FORMAT_SIMPLE": - if not stack: - raise ValueError("Stack is empty") - value = stack.pop() - stack.append(py_exprs.Call(py_exprs.PyObject(str), (value,))) - - case "CONVERT_VALUE": - flags = inst.arg - assert flags is not None - value = stack.pop() - if flags == 1: - stack.append(py_exprs.Call(py_exprs.PyObject(str), (value,))) - else: - raise NotImplementedError( - "repr() and ascii() conversions are not supported" - ) - - case "FORMAT_VALUE": - flags = inst.arg - assert flags is not None - if (flags & 0x04) == 0x04: - stack.pop() - raise NotImplementedError( - "Formatting with specifier is not supported" - ) - - value = stack.pop() - conversion = flags & 0x03 - if conversion == 0 or conversion == 1: - stack.append(py_exprs.Call(py_exprs.PyObject(str), (value,))) - else: - raise NotImplementedError( - "repr() and ascii() conversions are not supported" - ) - - case "FORMAT_WITH_SPEC": - raise NotImplementedError( - "Formatting with specifier is not supported" - ) - - case "BUILD_STRING": - count = inst.arg - assert count is not None - if len(stack) < count: - raise ValueError( - "Stack has fewer elements than BUILD_STRING count" - ) - - if count == 0: - stack.append(py_exprs.PyObject("")) - else: - strings = [stack.pop() for _ in range(count)][::-1] - result = strings[0] - for s in strings[1:]: - result = py_exprs.Call( - py_exprs.PyObject(operator.add), - (result, s), - ) - stack.append(result) - - case "COPY": - idx = inst.arg - if idx is None or idx < 1 or len(stack) < idx: - raise ValueError( - f"Invalid COPY index or stack too small: {idx}" - ) - stack.append(stack[-idx]) - - case "UNARY_NOT": - if not stack: - raise ValueError("Stack is empty") - val = stack.pop() - val_bool = py_exprs.Call( - py_exprs.PyObject(generic_ops.coerce_to_bool_op), - (val,), - ) - stack.append( - py_exprs.Call( - py_exprs.PyObject(operator.not_), - (val_bool,), - ) - ) - - case "SWAP": - idx = inst.arg - if idx is None or idx < 1 or len(stack) < idx: - raise ValueError( - f"Invalid SWAP index or stack too small: {idx}" - ) - stack[-1], stack[-idx] = stack[-idx], stack[-1] - - case "ROT_TWO": - if len(stack) < 2: - raise ValueError("Stack has < 2 elements") - stack[-1], stack[-2] = stack[-2], stack[-1] - - case "ROT_THREE": - if len(stack) < 3: - raise ValueError("Stack has < 3 elements") - stack[-1], stack[-2], stack[-3] = stack[-2], stack[-3], stack[-1] - - case "DUP_TOP": - if not stack: - raise ValueError("Stack is empty") - stack.append(stack[-1]) - - case "BINARY_OP": - if len(stack) < 2: - raise ValueError("Stack is empty") - right = stack.pop() - left = stack.pop() - op_symbol = inst.argrepr - if not op_symbol and isinstance(inst.argval, str): - op_symbol = inst.argval - if op_symbol and op_symbol.endswith("="): - op_symbol = op_symbol[:-1] - - if op_symbol not in _BINARY_OP_MAP: - raise ValueError(f"Unsupported binary operator: {op_symbol}") - stack.append( - py_exprs.Call( - py_exprs.PyObject(_BINARY_OP_MAP[op_symbol]), - (left, right), - ) - ) - - case "BINARY_SUBSCR": - if len(stack) < 2: - raise ValueError("Stack has < 2 elements") - key = stack.pop() - container = stack.pop() - stack.append( - py_exprs.Call( - py_exprs.PyObject(operator.getitem), - (container, key), - ) - ) - - case name if name in _OLD_BINARY_OP_MAP: - if len(stack) < 2: - raise ValueError("Stack has < 2 elements") - right = stack.pop() - left = stack.pop() - stack.append( - py_exprs.Call( - py_exprs.PyObject(_OLD_BINARY_OP_MAP[opname]), - (left, right), - ) - ) - - case "IS_OP": - if len(stack) < 2: - raise ValueError("Stack has < 2 elements") - right = stack.pop() - left = stack.pop() - invert = inst.arg - - def is_none_const(expr) -> bool: - if isinstance(expr, py_exprs.PyObject) and expr.value is None: - return True - if ( - isinstance(expr, expression.ScalarConstantExpression) - and expr.value is None - ): - return True - return False - - if is_none_const(right): - op = ( - generic_ops.isnull_op - if not invert - else generic_ops.notnull_op - ) - stack.append(py_exprs.Call(py_exprs.PyObject(op), (left,))) - elif is_none_const(left): - op = ( - generic_ops.isnull_op - if not invert - else generic_ops.notnull_op - ) - stack.append(py_exprs.Call(py_exprs.PyObject(op), (right,))) - else: - raise NotImplementedError( - "Identity comparison (is/is not) is only supported for None" - ) - - case "COMPARE_OP": - if len(stack) < 2: - raise ValueError("Stack has < 2 elements") - right = stack.pop() - left = stack.pop() - op_symbol = inst.argval - if op_symbol not in _COMPARE_OP_MAP: - raise ValueError(f"Unsupported compare operator: {op_symbol}") - stack.append( - py_exprs.Call( - py_exprs.PyObject(_COMPARE_OP_MAP[op_symbol]), - (left, right), - ) - ) - - case "UNARY_NEGATIVE" | "UNARY_INVERT": - if not stack: - raise ValueError("Stack is empty") - target = stack.pop() - stack.append( - py_exprs.Call( - py_exprs.PyObject( - operator.neg - if opname == "UNARY_NEGATIVE" - else operator.invert - ), - (target,), - ) - ) - - case "UNARY_POSITIVE": - if not stack: - raise ValueError("Stack is empty") - target = stack.pop() - stack.append( - py_exprs.Call(py_exprs.PyObject(operator.pos), (target,)) - ) - - case "CALL_INTRINSIC_1": - if inst.argrepr == "INTRINSIC_UNARY_POSITIVE": - if not stack: - raise ValueError("Stack is empty") - target = stack.pop() - stack.append( - py_exprs.Call(py_exprs.PyObject(operator.pos), (target,)) - ) - else: - raise ValueError(f"Unsupported intrinsic: {inst.argrepr}") - - case "CALL" | "CALL_FUNCTION" | "CALL_METHOD": - num_args = inst.arg - assert num_args is not None - if len(stack) < num_args: - raise ValueError(f"Stack has fewer than {num_args} elements") - args = [stack.pop() for _ in range(num_args)][::-1] - - is_method_call = False - if opname == "CALL" or opname == "CALL_METHOD": - if len(stack) >= 2 and stack[-2] == _NULL: - stack[-1], stack[-2] = stack[-2], stack[-1] - if stack and stack[-1] == _NULL: - stack.pop() - is_method_call = False - else: - is_method_call = True - elif opname == "CALL_FUNCTION": - is_method_call = False - - if is_method_call: - if ( - stack - and stack[-1] != _NULL - and isinstance(stack[-1], expression.Expression) - ): - self_arg = stack.pop() - args = [self_arg] + args - - if not stack: - raise ValueError("Stack is empty") - callable_expr = stack.pop() - stack.append(py_exprs.Call(callable_expr, tuple(args))) - - case "RETURN_VALUE": - if not stack: - raise ValueError("Stack is empty") - returns.append((stack[-1], reach_cond)) - jumped = True - break - - case "RETURN_CONST": - returns.append((py_exprs.PyObject(inst.argval), reach_cond)) - jumped = True - break - - case "POP_TOP": - if stack: - stack.pop() - - case name if name in _UNCONDITIONAL_JUMP_OPNAMES: - dest = inst.argval - edge_conditions[(offset, dest)] = reach_cond - edge_stacks[(offset, dest)] = stack.copy() - jumped = True - break - - case "JUMP_IF_FALSE_OR_POP" | "JUMP_IF_TRUE_OR_POP": - if not stack: - raise ValueError("Stack is empty") - cond_expr = stack[-1] - cond_bool = py_exprs.Call( - py_exprs.PyObject(generic_ops.coerce_to_bool_op), - (cond_expr,), - ) - dest = inst.argval - next_offset = next_offsets.get(inst.offset) - if opname == "JUMP_IF_FALSE_OR_POP": - not_cond_bool = py_exprs.Call( - py_exprs.PyObject(operator.not_), (cond_bool,) - ) - edge_conditions[(offset, dest)] = py_exprs.Call( - py_exprs.PyObject(operator.and_), - (reach_cond, not_cond_bool), - ) - edge_stacks[(offset, dest)] = stack.copy() - if next_offset is not None: - edge_conditions[(offset, next_offset)] = py_exprs.Call( - py_exprs.PyObject(operator.and_), - (reach_cond, cond_bool), - ) - edge_stacks[(offset, next_offset)] = stack[:-1] - else: # JUMP_IF_TRUE_OR_POP - edge_conditions[(offset, dest)] = py_exprs.Call( - py_exprs.PyObject(operator.and_), - (reach_cond, cond_bool), - ) - edge_stacks[(offset, dest)] = stack.copy() - if next_offset is not None: - not_cond_bool = py_exprs.Call( - py_exprs.PyObject(operator.not_), (cond_bool,) - ) - edge_conditions[(offset, next_offset)] = py_exprs.Call( - py_exprs.PyObject(operator.and_), - (reach_cond, not_cond_bool), - ) - edge_stacks[(offset, next_offset)] = stack[:-1] - jumped = True - break - - case name if ( - name in _JUMP_IF_FALSE_OPNAMES or name in _JUMP_IF_TRUE_OPNAMES - ): - if not stack: - raise ValueError("Stack is empty") - cond_expr = stack.pop() - cond_expr = py_exprs.Call( - py_exprs.PyObject(generic_ops.coerce_to_bool_op), - (cond_expr,), - ) - - dest = inst.argval - next_offset = next_offsets.get(inst.offset) - - if opname in _JUMP_IF_FALSE_OPNAMES: - not_cond_expr = py_exprs.Call( - py_exprs.PyObject(operator.not_), (cond_expr,) - ) - edge_conditions[(offset, dest)] = py_exprs.Call( - py_exprs.PyObject(operator.and_), - (reach_cond, not_cond_expr), - ) - edge_stacks[(offset, dest)] = stack.copy() - if next_offset is not None: - edge_conditions[(offset, next_offset)] = py_exprs.Call( - py_exprs.PyObject(operator.and_), - (reach_cond, cond_expr), - ) - edge_stacks[(offset, next_offset)] = stack.copy() - else: # opname in _JUMP_IF_TRUE_OPNAMES - not_cond_expr = py_exprs.Call( - py_exprs.PyObject(operator.not_), (cond_expr,) - ) - edge_conditions[(offset, dest)] = py_exprs.Call( - py_exprs.PyObject(operator.and_), - (reach_cond, cond_expr), - ) - edge_stacks[(offset, dest)] = stack.copy() - if next_offset is not None: - edge_conditions[(offset, next_offset)] = py_exprs.Call( - py_exprs.PyObject(operator.and_), - (reach_cond, not_cond_expr), - ) - edge_stacks[(offset, next_offset)] = stack.copy() - jumped = True - break - - case name if ( - name in _JUMP_IF_NONE_OPNAMES or name in _JUMP_IF_NOT_NONE_OPNAMES - ): - if not stack: - raise ValueError("Stack is empty") - cond_expr = stack.pop() - cond_bool = py_exprs.Call( - py_exprs.PyObject(generic_ops.isnull_op), - (cond_expr,), - ) - - dest = inst.argval - next_offset = next_offsets.get(inst.offset) - - if opname in _JUMP_IF_NONE_OPNAMES: - not_cond_bool = py_exprs.Call( - py_exprs.PyObject(operator.not_), (cond_bool,) - ) - edge_conditions[(offset, dest)] = py_exprs.Call( - py_exprs.PyObject(operator.and_), - (reach_cond, cond_bool), - ) - edge_stacks[(offset, dest)] = stack.copy() - if next_offset is not None: - edge_conditions[(offset, next_offset)] = py_exprs.Call( - py_exprs.PyObject(operator.and_), - (reach_cond, not_cond_bool), - ) - edge_stacks[(offset, next_offset)] = stack.copy() - else: # opname in _JUMP_IF_NOT_NONE_OPNAMES - not_cond_bool = py_exprs.Call( - py_exprs.PyObject(operator.not_), (cond_bool,) - ) - edge_conditions[(offset, dest)] = py_exprs.Call( - py_exprs.PyObject(operator.and_), - (reach_cond, not_cond_bool), - ) - edge_stacks[(offset, dest)] = stack.copy() - if next_offset is not None: - edge_conditions[(offset, next_offset)] = py_exprs.Call( - py_exprs.PyObject(operator.and_), - (reach_cond, cond_bool), - ) - edge_stacks[(offset, next_offset)] = stack.copy() - jumped = True - break - - case name if name in _ALL_JUMP_OPNAMES: - raise ValueError(f"Unsupported jump opcode: {opname}") - - case _: - raise ValueError(f"Unsupported opcode: {opname}") - - if not jumped: - next_offset = next_offsets.get(block.instructions[-1].offset) - if next_offset is not None: - edge_conditions[(offset, next_offset)] = reach_cond - edge_stacks[(offset, next_offset)] = stack.copy() - - block_outputs[offset] = (stack, local_vars) - - if not returns: - raise ValueError("No return value found") - - return merge_values(returns) - - -def py_to_expression(func: Callable) -> expression.Expression: - """ - Try to convert a python function to a BigQuery expression. - - This is "best effort" - if the function contains operations that cannot - be converted to BigQuery expressions, it will raise an Exception. - """ - py_expr = _compile_bytecode_to_py_expr(func) - return py_exprs.resolve_py_exprs(py_expr) diff --git a/bigframes/core/col.py b/bigframes/core/col.py deleted file mode 100644 index 50968dfbf94..00000000000 --- a/bigframes/core/col.py +++ /dev/null @@ -1,204 +0,0 @@ -# Copyright 2026 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -from __future__ import annotations - -import dataclasses -from typing import TYPE_CHECKING, Any, Hashable, Literal - -import bigframes_vendored.pandas.core.col as pd_col -import numpy - -import bigframes.core.expression as bf_expression -import bigframes.operations as bf_ops -import bigframes.operations.aggregations as agg_ops -from bigframes.core import agg_expressions, window_spec - -if TYPE_CHECKING: - import bigframes.operations.datetimes as datetimes - import bigframes.operations.strings as strings - - -# Not to be confused with the Expression class in `bigframes.core.expressions` -# Name collision unintended -@dataclasses.dataclass(frozen=True) -class Expression: - __doc__ = pd_col.Expression.__doc__ - - _value: bf_expression.Expression - - def _apply_unary_op(self, op: bf_ops.UnaryOp) -> Expression: - return Expression(op.as_expr(self._value)) - - def _apply_unary_agg(self, op: agg_ops.UnaryAggregateOp) -> Expression: - # We probably shouldn't need to windowize here, but block apis expect pre-windowized expressions - # Later on, we will probably have col expressions in windowed context, so will need to defer windowization - # instead of automatically applying the default unbound window - agg_expr = op.as_expr(self._value) - return Expression( - agg_expressions.WindowExpression(agg_expr, window_spec.unbound()) - ) - - # alignment is purely for series compatibility, and is ignored here - def _apply_binary_op( - self, - other: Any, - op: bf_ops.BinaryOp, - alignment: Literal["outer", "left"] = "outer", - reverse: bool = False, - ): - if reverse: - return Expression(op.as_expr(_as_bf_expr(other), self._value)) - else: - return Expression(op.as_expr(self._value, _as_bf_expr(other))) - - def __add__(self, other: Any) -> Expression: - return self._apply_binary_op(other, bf_ops.add_op) - - def __radd__(self, other: Any) -> Expression: - return self._apply_binary_op(other, bf_ops.add_op, reverse=True) - - def __sub__(self, other: Any) -> Expression: - return self._apply_binary_op(other, bf_ops.sub_op) - - def __rsub__(self, other: Any) -> Expression: - return self._apply_binary_op(other, bf_ops.sub_op, reverse=True) - - def __mul__(self, other: Any) -> Expression: - return self._apply_binary_op(other, bf_ops.mul_op) - - def __rmul__(self, other: Any) -> Expression: - return self._apply_binary_op(other, bf_ops.mul_op, reverse=True) - - def __truediv__(self, other: Any) -> Expression: - return self._apply_binary_op(other, bf_ops.div_op) - - def __rtruediv__(self, other: Any) -> Expression: - return self._apply_binary_op(other, bf_ops.div_op, reverse=True) - - def __floordiv__(self, other: Any) -> Expression: - return self._apply_binary_op(other, bf_ops.floordiv_op) - - def __rfloordiv__(self, other: Any) -> Expression: - return self._apply_binary_op(other, bf_ops.floordiv_op, reverse=True) - - def __ge__(self, other: Any) -> Expression: - return self._apply_binary_op(other, bf_ops.ge_op) - - def __gt__(self, other: Any) -> Expression: - return self._apply_binary_op(other, bf_ops.gt_op) - - def __le__(self, other: Any) -> Expression: - return self._apply_binary_op(other, bf_ops.le_op) - - def __lt__(self, other: Any) -> Expression: - return self._apply_binary_op(other, bf_ops.lt_op) - - def __eq__(self, other: object) -> Expression: # type: ignore - return self._apply_binary_op(other, bf_ops.eq_op) - - def __ne__(self, other: object) -> Expression: # type: ignore - return self._apply_binary_op(other, bf_ops.ne_op) - - def __mod__(self, other: Any) -> Expression: - return self._apply_binary_op(other, bf_ops.mod_op) - - def __rmod__(self, other: Any) -> Expression: - return self._apply_binary_op(other, bf_ops.mod_op, reverse=True) - - def __and__(self, other: Any) -> Expression: - return self._apply_binary_op(other, bf_ops.and_op) - - def __rand__(self, other: Any) -> Expression: - return self._apply_binary_op(other, bf_ops.and_op, reverse=True) - - def __or__(self, other: Any) -> Expression: - return self._apply_binary_op(other, bf_ops.or_op) - - def __ror__(self, other: Any) -> Expression: - return self._apply_binary_op(other, bf_ops.or_op, reverse=True) - - def __xor__(self, other: Any) -> Expression: - return self._apply_binary_op(other, bf_ops.xor_op) - - def __rxor__(self, other: Any) -> Expression: - return self._apply_binary_op(other, bf_ops.xor_op, reverse=True) - - def __invert__(self) -> Expression: - return self._apply_unary_op(bf_ops.invert_op) - - def sum(self) -> Expression: - return self._apply_unary_agg(agg_ops.sum_op) - - def mean(self) -> Expression: - return self._apply_unary_agg(agg_ops.mean_op) - - def var(self) -> Expression: - return self._apply_unary_agg(agg_ops.var_op) - - def std(self) -> Expression: - return self._apply_unary_agg(agg_ops.std_op) - - def min(self) -> Expression: - return self._apply_unary_agg(agg_ops.min_op) - - def max(self) -> Expression: - return self._apply_unary_agg(agg_ops.max_op) - - @property - def dt(self) -> datetimes.DatetimeSimpleMethods: - import bigframes.operations.datetimes as datetimes - - return datetimes.DatetimeSimpleMethods(self) - - def __array_ufunc__( - self, ufunc: numpy.ufunc, method: str, *inputs, **kwargs - ) -> Expression: - """Used to support numpy ufuncs. - See: https://numpy.org/doc/stable/reference/ufuncs.html - """ - # Only __call__ supported with zero arguments - if method != "__call__" or len(inputs) > 2 or len(kwargs) > 0: - return NotImplemented - - if len(inputs) == 1 and ufunc in bf_ops.NUMPY_TO_OP: - op = bf_ops.NUMPY_TO_OP[ufunc] - return Expression(op.as_expr(self._value)) - if len(inputs) == 2 and ufunc in bf_ops.NUMPY_TO_BINOP: - binop = bf_ops.NUMPY_TO_BINOP[ufunc] - if inputs[0] is self: - return Expression(binop.as_expr(self._value, _as_bf_expr(inputs[1]))) - else: - return Expression(binop.as_expr(_as_bf_expr(inputs[0]), self._value)) - - return NotImplemented - - # keep this last as str declaration can shadow builtins.str - @property - def str(self) -> strings.StringMethods: - import bigframes.operations.strings as strings - - return strings.StringMethods(self) - - -def _as_bf_expr(arg: Any) -> bf_expression.Expression: - if isinstance(arg, Expression): - return arg._value - return bf_expression.const(arg) - - -def col(col_name: Hashable) -> Expression: - return Expression(bf_expression.free_var(col_name)) - - -col.__doc__ = pd_col.col.__doc__ diff --git a/bigframes/core/compile/__init__.py b/bigframes/core/compile/__init__.py index c1b9c5d9022..c86f4463dc0 100644 --- a/bigframes/core/compile/__init__.py +++ b/bigframes/core/compile/__init__.py @@ -11,32 +11,11 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. -from __future__ import annotations - -from typing import Literal - -from bigframes.core.compile.api import test_only_ibis_inferred_schema -from bigframes.core.compile.configs import CompileRequest, CompileResult - - -def compile_sql( - request: CompileRequest, - compiler_name: Literal["sqlglot", "ibis"] = "sqlglot", -) -> CompileResult: - """Compiles a BigFrameNode according to the request into SQL.""" - if compiler_name == "sqlglot": - import bigframes.core.compile.sqlglot.compiler as sqlglot_compiler - - return sqlglot_compiler.compile_sql(request) - else: - import bigframes.core.compile.ibis_compiler.ibis_compiler as ibis_compiler - - return ibis_compiler.compile_sql(request) +from bigframes.core.compile.compiled import CompiledArrayValue +from bigframes.core.compile.compiler import compile_node __all__ = [ - "test_only_ibis_inferred_schema", - "CompileRequest", - "CompileResult", - "compile_sql", + "compile_node", + "CompiledArrayValue", ] diff --git a/bigframes/core/compile/api.py b/bigframes/core/compile/api.py deleted file mode 100644 index 82672fc95b5..00000000000 --- a/bigframes/core/compile/api.py +++ /dev/null @@ -1,35 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -from __future__ import annotations - -from typing import TYPE_CHECKING - -if TYPE_CHECKING: - import bigframes.core.nodes - - -def test_only_ibis_inferred_schema(node: bigframes.core.nodes.BigFrameNode): - """Use only for testing paths to ensure ibis inferred schema does not diverge from bigframes inferred schema.""" - import bigframes.core.rewrite - import bigframes.core.schema - from bigframes.core.compile.ibis_compiler import ibis_compiler - - node = ibis_compiler._replace_unsupported_ops(node) - node = bigframes.core.rewrite.bake_order(node) - ir = ibis_compiler.compile_node(node) - items = tuple( - bigframes.core.schema.SchemaItem(name, ir.get_column_type(ibis_id)) - for name, ibis_id in zip(node.schema.names, ir.column_ids) - ) - return bigframes.core.schema.ArraySchema(items) diff --git a/bigframes/core/compile/compiled.py b/bigframes/core/compile/compiled.py index fea94f6e6ed..1134f1aab01 100644 --- a/bigframes/core/compile/compiled.py +++ b/bigframes/core/compile/compiled.py @@ -13,106 +13,168 @@ # limitations under the License. from __future__ import annotations -import itertools +import functools +import math +import textwrap import typing -from typing import Literal, Optional, Sequence - -import bigframes_vendored.ibis -import bigframes_vendored.ibis.backends.bigquery.backend as ibis_bigquery -import bigframes_vendored.ibis.common.deferred as ibis_deferred # type: ignore -import bigframes_vendored.ibis.expr.datatypes as ibis_dtypes -import bigframes_vendored.ibis.expr.operations as ibis_ops -import bigframes_vendored.ibis.expr.types as ibis_types -import bigframes_vendored.sqlglot.expressions as sge -import pyarrow as pa -from google.cloud import bigquery - -import bigframes.core.agg_expressions as ex_types -import bigframes.core.compile.ibis_compiler.aggregate_compiler as agg_compiler -import bigframes.core.compile.ibis_compiler.scalar_op_compiler as op_compilers -import bigframes.core.compile.ibis_types -import bigframes.core.expression as ex -import bigframes.core.sql -import bigframes.dtypes -from bigframes.core import agg_expressions, rewrite -from bigframes.core.ordering import OrderingExpression +from typing import Collection, Iterable, Literal, Optional, Sequence + +import ibis +import ibis.backends.bigquery as ibis_bigquery +import ibis.expr.datatypes as ibis_dtypes +import ibis.expr.types as ibis_types +import pandas + +import bigframes.constants as constants +import bigframes.core.guid +from bigframes.core.ordering import ( + encode_order_string, + ExpressionOrdering, + IntegerEncoding, + OrderingColumnReference, + reencode_order_string, + StringEncoding, +) +import bigframes.core.utils as utils from bigframes.core.window_spec import WindowSpec +import bigframes.dtypes +import bigframes.operations as ops +import bigframes.operations.aggregations as agg_ops + +ORDER_ID_COLUMN = "bigframes_ordering_id" +PREDICATE_COLUMN = "bigframes_predicate" -op_compiler = op_compilers.scalar_op_compiler +class CompiledArrayValue: + """Immutable BigQuery DataFrames expression tree. + + Note: Usage of this class is considered to be private and subject to change + at any time. + + This class is a wrapper around Ibis expressions. Its purpose is to defer + Ibis projection operations to keep generated SQL small and correct when + mixing and matching columns from different versions of a DataFrame. + + Args: + table: An Ibis table expression. + columns: Ibis value expressions that can be projected as columns. + hidden_ordering_columns: Ibis value expressions to store ordering. + ordering: An ordering property of the data frame. + predicates: A list of filters on the data frame. + """ -# Ibis Implementations -class UnorderedIR: def __init__( self, table: ibis_types.Table, columns: Sequence[ibis_types.Value], + hidden_ordering_columns: Optional[Sequence[ibis_types.Value]] = None, + ordering: ExpressionOrdering = ExpressionOrdering(), + predicates: Optional[Collection[ibis_types.BooleanValue]] = None, ): self._table = table + self._predicates = tuple(predicates) if predicates is not None else () + # TODO: Validate ordering + if not ordering.total_ordering_columns: + raise ValueError("Must have total ordering defined by one or more columns") + self._ordering = ordering # Allow creating a DataFrame directly from an Ibis table expression. # TODO(swast): Validate that each column references the same table (or # no table for literal values). - self._columns = tuple( - column.resolve(table) # type:ignore - # TODO(https://github.com/ibis-project/ibis/issues/7613): use - # public API to refer to Deferred type. - if isinstance(column, ibis_deferred.Deferred) - else column - for column in columns + self._columns = tuple(columns) + + # Meta columns store ordering, or other data that doesn't correspond to dataframe columns + self._hidden_ordering_columns = ( + tuple(hidden_ordering_columns) + if hidden_ordering_columns is not None + else () ) + # To allow for more efficient lookup by column name, create a # dictionary mapping names to column values. self._column_names = {column.get_name(): column for column in self._columns} + self._hidden_ordering_column_names = { + column.get_name(): column for column in self._hidden_ordering_columns + } + ### Validation + value_col_ids = self._column_names.keys() + hidden_col_ids = self._hidden_ordering_column_names.keys() - def to_sql( - self, - order_by: Sequence[OrderingExpression], - limit: Optional[int], - selections: tuple[tuple[ex.DerefOp, str], ...], - ) -> str: - ibis_table = self._to_ibis_expr() - # This set of output transforms maybe should be its own output node?? + all_columns = value_col_ids | hidden_col_ids + ordering_valid = all( + col.column_id in all_columns for col in ordering.all_ordering_columns + ) + if value_col_ids & hidden_col_ids: + raise ValueError( + f"Keys in both hidden and exposed list: {value_col_ids & hidden_col_ids}" + ) + if not ordering_valid: + raise ValueError(f"Illegal ordering keys: {ordering.all_ordering_columns}") - selection_strings = tuple((ref.id.sql, name) for ref, name in selections) + @classmethod + def mem_expr_from_pandas( + cls, + pd_df: pandas.DataFrame, + ) -> CompiledArrayValue: + """ + Builds an in-memory only (SQL only) expr from a pandas dataframe. + """ + # We can't include any hidden columns in the ArrayValue constructor, so + # grab the column names before we add the hidden ordering column. + column_names = [str(column) for column in pd_df.columns] + # Make sure column names are all strings. + pd_df = pd_df.set_axis(column_names, axis="columns") + pd_df = pd_df.assign(**{ORDER_ID_COLUMN: range(len(pd_df))}) - names_preserved = tuple(name for _, name in selections) == tuple( - self.column_ids - ) - is_noop_selection = ( - all((i[0] == i[1] for i in selection_strings)) and names_preserved - ) + # ibis memtable cannot handle NA, must convert to None + pd_df = pd_df.astype("object") # type: ignore + pd_df = pd_df.where(pandas.notnull(pd_df), None) - if order_by or limit or not is_noop_selection: - # selections are (ref.id.sql, name) where ref.id.sql is escaped identifier - to_select = [ - sge.Alias( - this=sge.to_identifier(src, quoted=True), - alias=sge.to_identifier(alias, quoted=True), + # NULL type isn't valid in BigQuery, so retry with an explicit schema in these cases. + keys_memtable = ibis.memtable(pd_df) + schema = keys_memtable.schema() + new_schema = [] + for column_index, column in enumerate(schema): + if column == ORDER_ID_COLUMN: + new_type: ibis_dtypes.DataType = ibis_dtypes.int64 + else: + column_type = schema[column] + # The autodetected type might not be one we can support, such + # as NULL type for empty rows, so convert to a type we do + # support. + new_type = bigframes.dtypes.bigframes_dtype_to_ibis_dtype( + bigframes.dtypes.ibis_dtype_to_bigframes_dtype(column_type) ) - if src != alias - else sge.to_identifier(src, quoted=True) - for src, alias in selection_strings - ] - # Use string formatting for FROM clause to avoid re-parsing potentially complex SQL (like ARRAY>) - # that sqlglot might not handle perfectly when parsing BigQuery dialect strings. - select_sql = sge.Select().select(*to_select).sql(dialect="bigquery") - ibis_sql = ibis_bigquery.Backend().compile(ibis_table) - sql = f"{select_sql} FROM ({ibis_sql}) AS `t`" - - # Single row frames may not have any ordering columns - if len(order_by) > 0: - order_by_clause = bigframes.core.sql.ordering_clause(order_by) - sql += f"\n{order_by_clause}" - if limit is not None: - if not isinstance(limit, int): - raise TypeError(f"Limit param: {limit} must be an int.") - sql += f"\nLIMIT {limit}" - else: - sql = ibis_bigquery.Backend().compile(ibis_table) - return typing.cast(str, sql) + # TODO(swast): Ibis memtable doesn't use backticks in struct + # field names, so spaces and other characters aren't allowed in + # the memtable context. Blocked by + # https://github.com/ibis-project/ibis/issues/7187 + column = f"col_{column_index}" + new_schema.append((column, new_type)) + + # must set non-null column labels. these are not the user-facing labels + pd_df = pd_df.set_axis( + [column for column, _ in new_schema], + axis="columns", + ) + keys_memtable = ibis.memtable(pd_df, schema=ibis.schema(new_schema)) + + return cls( + keys_memtable, + columns=[ + keys_memtable[f"col_{column_index}"].name(column) + for column_index, column in enumerate(column_names) + ], + ordering=ExpressionOrdering( + ordering_value_columns=tuple( + [OrderingColumnReference(ORDER_ID_COLUMN)] + ), + total_ordering_columns=frozenset([ORDER_ID_COLUMN]), + ), + hidden_ordering_columns=(keys_memtable[ORDER_ID_COLUMN],), + ) @property - def columns(self) -> tuple[ibis_types.Value, ...]: + def columns(self) -> typing.Tuple[ibis_types.Value, ...]: return self._columns @property @@ -120,39 +182,62 @@ def column_ids(self) -> typing.Sequence[str]: return tuple(self._column_names.keys()) @property - def _ibis_bindings(self) -> dict[str, ibis_types.Value]: - return {col: self._get_ibis_column(col) for col in self.column_ids} + def _hidden_column_ids(self) -> typing.Sequence[str]: + return tuple(self._hidden_ordering_column_names.keys()) - def projection( - self, - expression_id_pairs: tuple[tuple[ex.Expression, str], ...], - ) -> UnorderedIR: - """Apply an expression to the ArrayValue and assign the output to a column.""" - cannot_inline = any(expr.expensive for expr, _ in expression_id_pairs) - - bindings = {col: self._get_ibis_column(col) for col in self.column_ids} - new_values = [ - op_compiler.compile_expression(expression, bindings).name(id) - for expression, id in expression_id_pairs - ] - result = UnorderedIR(self._table, (*self._columns, *new_values)) - if cannot_inline: - return result._reproject_to_table() - else: - # Cheap ops can defer "SELECT" and inline into later ops - return result + @property + def _reduced_predicate(self) -> typing.Optional[ibis_types.BooleanValue]: + """Returns the frame's predicates as an equivalent boolean value, useful where a single predicate value is preferred.""" + return ( + _reduce_predicate_list(self._predicates).name(PREDICATE_COLUMN) + if self._predicates + else None + ) - def selection( - self, - input_output_pairs: tuple[tuple[ex.DerefOp, str], ...], - ) -> UnorderedIR: - """Apply an expression to the ArrayValue and assign the output to a column.""" - bindings = {col: self._get_ibis_column(col) for col in self.column_ids} - values = [ - op_compiler.compile_expression(input, bindings).name(id) - for input, id in input_output_pairs + @property + def _ibis_order(self) -> Sequence[ibis_types.Value]: + """Returns a sequence of ibis values which can be directly used to order a table expression. Has direction modifiers applied.""" + return _convert_ordering_to_table_values( + {**self._column_names, **self._hidden_ordering_column_names}, + self._ordering.all_ordering_columns, + ) + + def builder(self) -> ArrayValueBuilder: + """Creates a mutable builder for expressions.""" + # Since ArrayValue is intended to be immutable (immutability offers + # potential opportunities for caching, though we might need to introduce + # more node types for that to be useful), we create a builder class. + return ArrayValueBuilder( + self._table, + columns=self._columns, + hidden_ordering_columns=self._hidden_ordering_columns, + ordering=self._ordering, + predicates=self._predicates, + ) + + def drop_columns(self, columns: Iterable[str]) -> CompiledArrayValue: + # Must generate offsets if we are dropping a column that ordering depends on + expr = self + for ordering_column in set(columns).intersection( + [col.column_id for col in self._ordering.ordering_value_columns] + ): + expr = self._hide_column(ordering_column) + + expr_builder = expr.builder() + remain_cols = [ + column for column in expr.columns if column.get_name() not in columns ] - return UnorderedIR(self._table, tuple(values)) + expr_builder.columns = remain_cols + return expr_builder.build() + + def get_column_type(self, key: str) -> bigframes.dtypes.Dtype: + ibis_type = typing.cast( + bigframes.dtypes.IbisDtype, self._get_any_column(key).type() + ) + return typing.cast( + bigframes.dtypes.Dtype, + bigframes.dtypes.ibis_dtype_to_bigframes_dtype(ibis_type), + ) def _get_ibis_column(self, key: str) -> ibis_types.Value: """Gets the Ibis expression for a given column.""" @@ -162,109 +247,549 @@ def _get_ibis_column(self, key: str) -> ibis_types.Value: ) return typing.cast(ibis_types.Value, self._column_names[key]) - def get_column_type(self, key: str) -> bigframes.dtypes.Dtype: - ibis_type = typing.cast( - bigframes.core.compile.ibis_types.IbisDtype, - self._get_ibis_column(key).type(), - ) - return typing.cast( - bigframes.dtypes.Dtype, - bigframes.core.compile.ibis_types.ibis_dtype_to_bigframes_dtype(ibis_type), + def _get_any_column(self, key: str) -> ibis_types.Value: + """Gets the Ibis expression for a given column. Will also get hidden columns.""" + all_columns = {**self._column_names, **self._hidden_ordering_column_names} + if key not in all_columns.keys(): + raise ValueError( + "Column name {} not in set of values: {}".format( + key, all_columns.keys() + ) + ) + return typing.cast(ibis_types.Value, all_columns[key]) + + def _get_hidden_ordering_column(self, key: str) -> ibis_types.Column: + """Gets the Ibis expression for a given hidden column.""" + if key not in self._hidden_ordering_column_names.keys(): + raise ValueError( + "Column name {} not in set of values: {}".format( + key, self._hidden_ordering_column_names.keys() + ) + ) + return typing.cast(ibis_types.Column, self._hidden_ordering_column_names[key]) + + def filter(self, predicate_id: str, keep_null: bool = False) -> CompiledArrayValue: + """Filter the table on a given expression, the predicate must be a boolean series aligned with the table expression.""" + condition = typing.cast( + ibis_types.BooleanValue, self._get_ibis_column(predicate_id) ) + if keep_null: + condition = typing.cast( + ibis_types.BooleanValue, + condition.fillna( + typing.cast(ibis_types.BooleanScalar, ibis_types.literal(True)) + ), + ) + return self._filter(condition) - def _to_ibis_expr( - self, - *, - fraction: Optional[float] = None, - ): + def _filter(self, predicate_value: ibis_types.BooleanValue) -> CompiledArrayValue: + """Filter the table on a given expression, the predicate must be a boolean series aligned with the table expression.""" + expr = self.builder() + expr.ordering = expr.ordering.with_non_sequential() + expr.predicates = [*self._predicates, predicate_value] + return expr.build() + + def order_by( + self, by: Sequence[OrderingColumnReference], stable: bool = False + ) -> CompiledArrayValue: + expr_builder = self.builder() + expr_builder.ordering = self._ordering.with_ordering_columns(by, stable=stable) + return expr_builder.build() + + def reversed(self) -> CompiledArrayValue: + expr_builder = self.builder() + expr_builder.ordering = self._ordering.with_reverse() + return expr_builder.build() + + def _uniform_sampling(self, fraction: float) -> CompiledArrayValue: + """Sampling the table on given fraction. + + .. warning:: + The row numbers of result is non-deterministic, avoid to use. """ - Creates an Ibis table expression representing the DataFrame. + table = self._to_ibis_expr( + "unordered", expose_hidden_cols=True, fraction=fraction + ) + columns = [table[column_name] for column_name in self._column_names] + hidden_ordering_columns = [ + table[column_name] for column_name in self._hidden_ordering_column_names + ] + return CompiledArrayValue( + table, + columns=columns, + hidden_ordering_columns=hidden_ordering_columns, + ordering=self._ordering, + ) - Args: - expose_hidden_cols: - If True, include the hidden ordering columns in the results. + @property + def _offsets(self) -> ibis_types.IntegerColumn: + if not self._ordering.is_sequential: + raise ValueError( + "Expression does not have offsets. Generate them first using project_offsets." + ) + if not self._ordering.total_order_col: + raise ValueError( + "Ordering is invalid. Marked as sequential but no total order columns." + ) + column = self._get_any_column(self._ordering.total_order_col.column_id) + return typing.cast(ibis_types.IntegerColumn, column) - Returns: - An ibis expression representing the data help by the ArrayValue object. + def _project_offsets(self) -> CompiledArrayValue: + """Create a new expression that contains offsets. Should only be executed when offsets are needed for an operations. Has no effect on expression semantics.""" + if self._ordering.is_sequential: + return self + # TODO(tbergeron): Enforce total ordering + table = self._to_ibis_expr( + ordering_mode="offset_col", order_col_name=ORDER_ID_COLUMN + ) + columns = [table[column_name] for column_name in self._column_names] + ordering = ExpressionOrdering( + ordering_value_columns=tuple([OrderingColumnReference(ORDER_ID_COLUMN)]), + total_ordering_columns=frozenset([ORDER_ID_COLUMN]), + integer_encoding=IntegerEncoding(True, is_sequential=True), + ) + return CompiledArrayValue( + table, + columns=columns, + hidden_ordering_columns=[table[ORDER_ID_COLUMN]], + ordering=ordering, + ) + + def _hide_column(self, column_id) -> CompiledArrayValue: + """Pushes columns to hidden columns list. Used to hide ordering columns that have been dropped or destructively mutated.""" + expr_builder = self.builder() + # Need to rename column as caller might be creating a new row with the same name but different values. + # Can avoid this if don't allow callers to determine ids and instead generate unique ones in this class. + new_name = bigframes.core.guid.generate_guid(prefix="bigframes_hidden_") + expr_builder.hidden_ordering_columns = [ + *self._hidden_ordering_columns, + self._get_ibis_column(column_id).name(new_name), + ] + expr_builder.ordering = self._ordering.with_column_remap({column_id: new_name}) + return expr_builder.build() + + def promote_offsets(self, col_id: str) -> CompiledArrayValue: """ - # Special case for empty tables, since we can't create an empty - # projection. - if not self._columns: - return self._table.select([bigframes_vendored.ibis.literal(1)]) + Convenience function to promote copy of column offsets to a value column. Can be used to reset index. + """ + # Special case: offsets already exist + ordering = self._ordering - table = self._table.select(self._columns) - if fraction is not None: - table = table.filter( - bigframes_vendored.ibis.random() < ibis_types.literal(fraction) + if (not ordering.is_sequential) or (not ordering.total_order_col): + return self._project_offsets().promote_offsets(col_id) + expr_builder = self.builder() + expr_builder.columns = [ + self._get_any_column(ordering.total_order_col.column_id).name(col_id), + *self.columns, + ] + return expr_builder.build() + + def select_columns(self, column_ids: typing.Sequence[str]) -> CompiledArrayValue: + """Creates a new expression based on this expression with new columns.""" + columns = [self._get_ibis_column(col_id) for col_id in column_ids] + expr = self + for ordering_column in set(self.column_ids).intersection( + [col_ref.column_id for col_ref in self._ordering.ordering_value_columns] + ): + # Need to hide ordering columns that are being dropped. Alternatively, could project offsets + expr = expr._hide_column(ordering_column) + builder = expr.builder() + builder.columns = list(columns) + new_expr = builder.build() + return new_expr + + def concat(self, other: typing.Sequence[CompiledArrayValue]) -> CompiledArrayValue: + """Append together multiple ArrayValue objects.""" + if len(other) == 0: + return self + tables = [] + prefix_base = 10 + prefix_size = math.ceil(math.log(len(other) + 1, prefix_base)) + # Must normalize all ids to the same encoding size + max_encoding_size = max( + self._ordering.string_encoding.length, + *[expression._ordering.string_encoding.length for expression in other], + ) + for i, expr in enumerate([self, *other]): + ordering_prefix = str(i).zfill(prefix_size) + table = expr._to_ibis_expr( + ordering_mode="string_encoded", order_col_name=ORDER_ID_COLUMN ) - return table + # Rename the value columns based on horizontal offset before applying union. + table = table.select( + [ + table[col].name(f"column_{i}") + if col != ORDER_ID_COLUMN + else ( + ordering_prefix + + reencode_order_string( + table[ORDER_ID_COLUMN], max_encoding_size + ) + ).name(ORDER_ID_COLUMN) + for i, col in enumerate(table.columns) + ] + ) + tables.append(table) + combined_table = ibis.union(*tables) + ordering = ExpressionOrdering( + ordering_value_columns=tuple([OrderingColumnReference(ORDER_ID_COLUMN)]), + total_ordering_columns=frozenset([ORDER_ID_COLUMN]), + string_encoding=StringEncoding(True, prefix_size + max_encoding_size), + ) + return CompiledArrayValue( + combined_table, + columns=[ + combined_table[col] + for col in combined_table.columns + if col != ORDER_ID_COLUMN + ], + hidden_ordering_columns=[combined_table[ORDER_ID_COLUMN]], + ordering=ordering, + ) - def filter(self, predicate: ex.Expression) -> UnorderedIR: - table = self._to_ibis_expr() - condition = op_compiler.compile_expression(predicate, table) - table = table.filter(condition) - return UnorderedIR( - table, tuple(table[column_name] for column_name in self._column_names) + def project_unary_op( + self, column_name: str, op: ops.UnaryOp, output_name=None + ) -> CompiledArrayValue: + """Creates a new expression based on this expression with unary operation applied to one column.""" + value = op._as_ibis(self._get_ibis_column(column_name)).name( + output_name or column_name ) + return self._set_or_replace_by_id(output_name or column_name, value) + + def project_binary_op( + self, + left_column_id: str, + right_column_id: str, + op: ops.BinaryOp, + output_column_id: str, + ) -> CompiledArrayValue: + """Creates a new expression based on this expression with binary operation applied to two columns.""" + value = op( + self._get_ibis_column(left_column_id), + self._get_ibis_column(right_column_id), + ).name(output_column_id) + return self._set_or_replace_by_id(output_column_id, value) + + def project_ternary_op( + self, + col_id_1: str, + col_id_2: str, + col_id_3: str, + op: ops.TernaryOp, + output_column_id: str, + ) -> CompiledArrayValue: + """Creates a new expression based on this expression with ternary operation applied to three columns.""" + value = op( + self._get_ibis_column(col_id_1), + self._get_ibis_column(col_id_2), + self._get_ibis_column(col_id_3), + ).name(output_column_id) + return self._set_or_replace_by_id(output_column_id, value) def aggregate( self, - aggregations: typing.Sequence[tuple[ex_types.Aggregation, str]], - by_column_ids: typing.Sequence[ex.DerefOp] = (), - order_by: typing.Sequence[OrderingExpression] = (), - ) -> UnorderedIR: + aggregations: typing.Sequence[typing.Tuple[str, agg_ops.AggregateOp, str]], + by_column_ids: typing.Sequence[str] = (), + dropna: bool = True, + ) -> CompiledArrayValue: """ Apply aggregations to the expression. Arguments: aggregations: input_column_id, operation, output_column_id tuples - by_column_ids: column ids of the aggregation key, this is preserved through - the transform + by_column_id: column id of the aggregation key, this is preserved through the transform dropna: whether null keys should be dropped - Returns: - OrderedIR: the grouping key is a unique-valued column and has ordering - information. """ - table = self._to_ibis_expr() - bindings = {col: table[col] for col in self.column_ids} + table = self._to_ibis_expr("unordered") stats = { - col_out: agg_compiler.compile_aggregate( - aggregate, - bindings, - order_by=op_compiler._convert_row_ordering_to_table_values( - table, order_by - ), - ) - for aggregate, col_out in aggregations + col_out: agg_op._as_ibis(table[col_in]) + for col_in, agg_op, col_out in aggregations } if by_column_ids: - result = table.group_by((ref.id.sql for ref in by_column_ids)).aggregate( - **stats - ) - return UnorderedIR( - result, columns=tuple(result[key] for key in result.columns) + result = table.group_by(by_column_ids).aggregate(**stats) + # Must have deterministic ordering, so order by the unique "by" column + ordering = ExpressionOrdering( + tuple( + [ + OrderingColumnReference(column_id=column_id) + for column_id in by_column_ids + ] + ), + total_ordering_columns=frozenset(by_column_ids), ) + columns = tuple(result[key] for key in result.columns) + expr = CompiledArrayValue(result, columns=columns, ordering=ordering) + if dropna: + for column_id in by_column_ids: + expr = expr._filter( + ops.notnull_op._as_ibis(expr._get_ibis_column(column_id)) + ) + # Can maybe remove this as Ordering id is redundant as by_column is unique after aggregation + return expr._project_offsets() else: - result = table.aggregate(**stats) - return UnorderedIR( + aggregates = {**stats, ORDER_ID_COLUMN: ibis_types.literal(0)} + result = table.aggregate(**aggregates) + # Ordering is irrelevant for single-row output, but set ordering id regardless as other ops(join etc.) expect it. + ordering = ExpressionOrdering( + ordering_value_columns=tuple( + [OrderingColumnReference(ORDER_ID_COLUMN)] + ), + total_ordering_columns=frozenset([ORDER_ID_COLUMN]), + integer_encoding=IntegerEncoding(is_encoded=True, is_sequential=True), + ) + return CompiledArrayValue( result, columns=[result[col_id] for col_id in [*stats.keys()]], + hidden_ordering_columns=[result[ORDER_ID_COLUMN]], + ordering=ordering, ) - def _uniform_sampling(self, fraction: float) -> UnorderedIR: - """Sampling the table on given fraction. + def corr_aggregate( + self, corr_aggregations: typing.Sequence[typing.Tuple[str, str, str]] + ) -> CompiledArrayValue: + """ + Get correlations between each lef_column_id and right_column_id, stored in the respective output_column_id. + This uses BigQuery's CORR under the hood, and thus only Pearson's method is used. + Arguments: + corr_aggregations: left_column_id, right_column_id, output_column_id tuples + """ + table = self._to_ibis_expr("unordered") + stats = { + col_out: table[col_left].corr(table[col_right], how="pop") + for col_left, col_right, col_out in corr_aggregations + } + aggregates = {**stats, ORDER_ID_COLUMN: ibis_types.literal(0)} + result = table.aggregate(**aggregates) + # Ordering is irrelevant for single-row output, but set ordering id regardless as other ops(join etc.) expect it. + ordering = ExpressionOrdering( + ordering_value_columns=tuple([OrderingColumnReference(ORDER_ID_COLUMN)]), + total_ordering_columns=frozenset([ORDER_ID_COLUMN]), + integer_encoding=IntegerEncoding(is_encoded=True, is_sequential=True), + ) + return CompiledArrayValue( + result, + columns=[result[col_id] for col_id in [*stats.keys()]], + hidden_ordering_columns=[result[ORDER_ID_COLUMN]], + ordering=ordering, + ) - .. warning:: - The row numbers of result is non-deterministic, avoid to use. + def project_window_op( + self, + column_name: str, + op: agg_ops.WindowOp, + window_spec: WindowSpec, + output_name=None, + *, + never_skip_nulls=False, + skip_reproject_unsafe: bool = False, + ) -> CompiledArrayValue: """ - table = self._to_ibis_expr(fraction=fraction) - columns = [table[column_name] for column_name in self._column_names] - return UnorderedIR( - table, - columns=columns, + Creates a new expression based on this expression with unary operation applied to one column. + column_name: the id of the input column present in the expression + op: the windowable operator to apply to the input column + window_spec: a specification of the window over which to apply the operator + output_name: the id to assign to the output of the operator, by default will replace input col if distinct output id not provided + never_skip_nulls: will disable null skipping for operators that would otherwise do so + skip_reproject_unsafe: skips the reprojection step, can be used when performing many non-dependent window operations, user responsible for not nesting window expressions, or using outputs as join, filter or aggregation keys before a reprojection + """ + column = typing.cast(ibis_types.Column, self._get_ibis_column(column_name)) + window = self._ibis_window_from_spec(window_spec, allow_ties=op.handles_ties) + + window_op = op._as_ibis(column, window) + + clauses = [] + if op.skips_nulls and not never_skip_nulls: + clauses.append((column.isnull(), ibis.NA)) + if window_spec.min_periods: + if op.skips_nulls: + # Most operations do not count NULL values towards min_periods + observation_count = agg_ops.count_op._as_ibis(column, window) + else: + # Operations like count treat even NULLs as valid observations for the sake of min_periods + # notnull is just used to convert null values to non-null (FALSE) values to be counted + denulled_value = typing.cast(ibis_types.BooleanColumn, column.notnull()) + observation_count = agg_ops.count_op._as_ibis(denulled_value, window) + clauses.append( + ( + observation_count < ibis_types.literal(window_spec.min_periods), + ibis.NA, + ) + ) + if clauses: + case_statement = ibis.case() + for clause in clauses: + case_statement = case_statement.when(clause[0], clause[1]) + case_statement = case_statement.else_(window_op).end() + window_op = case_statement + + result = self._set_or_replace_by_id(output_name or column_name, window_op) + # TODO(tbergeron): Automatically track analytic expression usage and defer reprojection until required for valid query generation. + return result._reproject_to_table() if not skip_reproject_unsafe else result + + def to_sql( + self, + offset_column: typing.Optional[str] = None, + col_id_overrides: typing.Mapping[str, str] = {}, + sorted: bool = False, + ) -> str: + offsets_id = offset_column or ORDER_ID_COLUMN + + sql = ibis_bigquery.Backend().compile( + self._to_ibis_expr( + ordering_mode="offset_col" + if (offset_column or sorted) + else "unordered", + order_col_name=offsets_id, + col_id_overrides=col_id_overrides, + ) + ) + if sorted: + sql = textwrap.dedent( + f""" + SELECT * EXCEPT (`{offsets_id}`) + FROM ({sql}) + ORDER BY `{offsets_id}` + """ + ) + return typing.cast(str, sql) + + def _to_ibis_expr( + self, + ordering_mode: Literal["string_encoded", "offset_col", "unordered"], + order_col_name: Optional[str] = ORDER_ID_COLUMN, + expose_hidden_cols: bool = False, + fraction: Optional[float] = None, + col_id_overrides: typing.Mapping[str, str] = {}, + ): + """ + Creates an Ibis table expression representing the DataFrame. + + ArrayValue objects are sorted, so the following options are available + to reflect this in the ibis expression. + + * "offset_col": Zero-based offsets are generated as a column, this will + not sort the rows however. + * "string_encoded": An ordered string column is provided in output table. + * "unordered": No ordering information will be provided in output. Only + value columns are projected. + + For offset or ordered column, order_col_name can be used to assign the + output label for the ordering column. If none is specified, the default + column name will be 'bigframes_ordering_id' + + Args: + ordering_mode: + How to construct the Ibis expression from the ArrayValue. See + above for details. + order_col_name: + If the ordering mode outputs a single ordering or offsets + column, use this as the column name. + expose_hidden_cols: + If True, include the hidden ordering columns in the results. + Only compatible with `order_by` and `unordered` + ``ordering_mode``. + col_id_overrides: + overrides the column ids for the result + Returns: + An ibis expression representing the data help by the ArrayValue object. + """ + assert ordering_mode in ( + "string_encoded", + "offset_col", + "unordered", ) + if expose_hidden_cols and ordering_mode in ("ordered_col", "offset_col"): + raise ValueError( + f"Cannot expose hidden ordering columns with ordering_mode {ordering_mode}" + ) + + columns = list(self._columns) + columns_to_drop: list[ + str + ] = [] # Ordering/Filtering columns that will be dropped at end + + if self._reduced_predicate is not None: + columns.append(self._reduced_predicate) + # Usually drop predicate as it is will be all TRUE after filtering + if not expose_hidden_cols: + columns_to_drop.append(self._reduced_predicate.get_name()) + + order_columns = self._create_order_columns( + ordering_mode, order_col_name, expose_hidden_cols + ) + columns.extend(order_columns) + + # Special case for empty tables, since we can't create an empty + # projection. + if not columns: + return ibis.memtable([]) - ## Helpers - def _reproject_to_table(self) -> UnorderedIR: + # Make sure all dtypes are the "canonical" ones for BigFrames. This is + # important for operations like UNION where the schema must match. + table = self._table.select( + bigframes.dtypes.ibis_value_to_canonical_type(column) for column in columns + ) + base_table = table + if self._reduced_predicate is not None: + table = table.filter(base_table[PREDICATE_COLUMN]) + table = table.drop(*columns_to_drop) + if col_id_overrides: + table = table.relabel(col_id_overrides) + if fraction is not None: + table = table.filter(ibis.random() < ibis.literal(fraction)) + return table + + def _create_order_columns( + self, + ordering_mode: str, + order_col_name: Optional[str], + expose_hidden_cols: bool, + ) -> typing.Sequence[ibis_types.Value]: + # Generate offsets if current ordering id semantics are not sufficiently strict + if ordering_mode == "offset_col": + return (self._create_offset_column().name(order_col_name),) + elif ordering_mode == "string_encoded": + return (self._create_string_ordering_column().name(order_col_name),) + elif expose_hidden_cols: + return self._hidden_ordering_columns + return () + + def _create_offset_column(self) -> ibis_types.IntegerColumn: + if self._ordering.total_order_col and self._ordering.is_sequential: + offsets = self._get_any_column(self._ordering.total_order_col.column_id) + return typing.cast(ibis_types.IntegerColumn, offsets) + else: + window = ibis.window(order_by=self._ibis_order) + if self._predicates: + window = window.group_by(self._reduced_predicate) + offsets = ibis.row_number().over(window) + return typing.cast(ibis_types.IntegerColumn, offsets) + + def _create_string_ordering_column(self) -> ibis_types.StringColumn: + if self._ordering.total_order_col and self._ordering.is_string_encoded: + string_order_ids = self._get_any_column( + self._ordering.total_order_col.column_id + ) + return typing.cast(ibis_types.StringColumn, string_order_ids) + if ( + self._ordering.total_order_col + and self._ordering.integer_encoding.is_encoded + ): + # Special case: non-negative integer ordering id can be converted directly to string without regenerating row numbers + int_values = self._get_any_column(self._ordering.total_order_col.column_id) + return encode_order_string( + typing.cast(ibis_types.IntegerColumn, int_values), + ) + else: + # Have to build string from scratch + window = ibis.window(order_by=self._ibis_order) + if self._predicates: + window = window.group_by(self._reduced_predicate) + row_nums = typing.cast( + ibis_types.IntegerColumn, ibis.row_number().over(window) + ) + return encode_order_string(row_nums) + + def _reproject_to_table(self) -> CompiledArrayValue: """ Internal operators that projects the internal representation into a new ibis table expression where each value column is a direct @@ -272,249 +797,325 @@ def _reproject_to_table(self) -> UnorderedIR: some operations such as window operations that cannot be used recursively in projections. """ - table = self._to_ibis_expr() + table = self._to_ibis_expr( + "unordered", + expose_hidden_cols=True, + ) columns = [table[column_name] for column_name in self._column_names] - return UnorderedIR( + ordering_col_ids = [ + ref.column_id for ref in self._ordering.all_ordering_columns + ] + hidden_ordering_columns = [ + table[column_name] + for column_name in self._hidden_ordering_column_names + if column_name in ordering_col_ids + ] + return CompiledArrayValue( table, columns=columns, + hidden_ordering_columns=hidden_ordering_columns, + ordering=self._ordering, ) - @classmethod - def from_polars( - cls, pa_table: pa.Table, schema: Sequence[bigquery.SchemaField] - ) -> UnorderedIR: - """Builds an in-memory only (SQL only) expr from a pyarrow table.""" - import bigframes_vendored.ibis.backends.bigquery.datatypes as third_party_ibis_bqtypes - - # derive the ibis schema from the original pandas schema - keys_memtable = bigframes_vendored.ibis.memtable( - pa_table, - schema=third_party_ibis_bqtypes.BigQuerySchema.to_ibis(list(schema)), + def _ibis_window_from_spec(self, window_spec: WindowSpec, allow_ties: bool = False): + group_by: typing.List[ibis_types.Value] = ( + [ + typing.cast( + ibis_types.Column, _as_identity(self._get_ibis_column(column)) + ) + for column in window_spec.grouping_keys + ] + if window_spec.grouping_keys + else [] ) - return cls( - keys_memtable, - columns=tuple(keys_memtable[key] for key in keys_memtable.columns), + if self._reduced_predicate is not None: + group_by.append(self._reduced_predicate) + if window_spec.ordering: + order_by = _convert_ordering_to_table_values( + {**self._column_names, **self._hidden_ordering_column_names}, + window_spec.ordering, + ) + if not allow_ties: + # Most operator need an unambiguous ordering, so the table's total ordering is appended + order_by = tuple([*order_by, *self._ibis_order]) + elif (window_spec.following is not None) or (window_spec.preceding is not None): + # If window spec has following or preceding bounds, we need to apply an unambiguous ordering. + order_by = tuple(self._ibis_order) + else: + # Unbound grouping window. Suitable for aggregations but not for analytic function application. + order_by = None + return ibis.window( + preceding=window_spec.preceding, + following=window_spec.following, + order_by=order_by, + group_by=group_by, ) - def join( - self: UnorderedIR, - right: UnorderedIR, - conditions: tuple[tuple[str, str], ...], - type: Literal["inner", "outer", "left", "right", "cross"], + def unpivot( + self, + row_labels: typing.Sequence[typing.Hashable], + unpivot_columns: typing.Sequence[ + typing.Tuple[str, typing.Sequence[typing.Optional[str]]] + ], *, - join_nulls: bool = True, - ) -> UnorderedIR: - """Join two expressions by column equality. + passthrough_columns: typing.Sequence[str] = (), + index_col_ids: typing.Sequence[str] = ["index"], + dtype: typing.Union[ + bigframes.dtypes.Dtype, typing.Sequence[bigframes.dtypes.Dtype] + ] = pandas.Float64Dtype(), + how="left", + ) -> CompiledArrayValue: + """ + Unpivot ArrayValue columns. + + Args: + row_labels: Identifies the source of the row. Must be equal to length to source column list in unpivot_columns argument. + unpivot_columns: Mapping of column id to list of input column ids. Lists of input columns may use None. + passthrough_columns: Columns that will not be unpivoted. Column id will be preserved. + index_col_id (str): The column id to be used for the row labels. + dtype (dtype or list of dtype): Dtype to use for the unpivot columns. If list, must be equal in number to unpivot_columns. - Arguments: - left: Expression for left table to join. - left_column_ids: Column IDs (not label) to join by. - right: Expression for right table to join. - right_column_ids: Column IDs (not label) to join by. - how: The type of join to perform. - join_nulls (bool): - If True, will joins NULL keys to each other. Returns: - The joined expression. The resulting columns will be, in order, - first the coalesced join keys, then, all the left columns, and - finally, all the right columns. + ArrayValue: The unpivoted ArrayValue """ - # Shouldn't need to select the column ids explicitly, but it seems that ibis has some - # bug resolving column ids otherwise, potentially because of the "JoinChain" op - left_table = self._to_ibis_expr().select(self.column_ids) - right_table = right._to_ibis_expr().select(right.column_ids) - - join_conditions = [ - _join_condition( - left_table[left_index], right_table[right_index], nullsafe=join_nulls - ) - for left_index, right_index in conditions - ] + if how not in ("left", "right"): + raise ValueError("'how' must be 'left' or 'right'") + table = self._to_ibis_expr("unordered", expose_hidden_cols=True) + row_n = len(row_labels) + hidden_col_ids = self._hidden_ordering_column_names.keys() + if not all( + len(source_columns) == row_n for _, source_columns in unpivot_columns + ): + raise ValueError("Columns and row labels must all be same length.") - combined_table = bigframes_vendored.ibis.join( - left_table, - right_table, - predicates=join_conditions, - how=type, # type: ignore + unpivot_offset_id = bigframes.core.guid.generate_guid("unpivot_offsets_") + unpivot_table = table.cross_join( + ibis.memtable({unpivot_offset_id: range(row_n)}) ) - columns = [combined_table[col.get_name()] for col in self.columns] + [ - combined_table[col.get_name()] for col in right.columns + # Use ibis memtable to infer type of rowlabels (if possible) + # TODO: Allow caller to specify dtype + if isinstance(row_labels[0], tuple): + labels_table = ibis.memtable(row_labels) + labels_ibis_types = [ + labels_table[col].type() for col in labels_table.columns + ] + else: + labels_ibis_types = [ibis.memtable({"col": row_labels})["col"].type()] + labels_dtypes = [ + bigframes.dtypes.ibis_dtype_to_bigframes_dtype(ibis_type) + for ibis_type in labels_ibis_types ] - return UnorderedIR( - combined_table, - columns=columns, - ) - - def isin_join( - self: UnorderedIR, - right: UnorderedIR, - indicator_col: str, - conditions: tuple[str, str], - *, - join_nulls: bool = True, - ) -> UnorderedIR: - """Join two expressions by column equality. - Arguments: - left: Expression for left table to join. - right: Expression for right table to join. - conditions: Id pairs to compare - Returns: - The joined expression. - """ - left_table = self._to_ibis_expr() - right_table = right._to_ibis_expr() - if join_nulls: # nullsafe isin join must actually use "exists" subquery - new_column = ( + label_columns = [] + for label_part, (col_id, label_dtype) in enumerate( + zip(index_col_ids, labels_dtypes) + ): + # interpret as tuples even if it wasn't originally so can apply same logic for multi-column labels + labels_as_tuples = [ + label if isinstance(label, tuple) else (label,) for label in row_labels + ] + cases = [ ( - _join_condition( - left_table[conditions[0]], - right_table[conditions[1]], - nullsafe=True, - ) + i, + bigframes.dtypes.literal_to_ibis_scalar( + label_tuple[label_part], # type:ignore + force_dtype=label_dtype, # type:ignore + ), ) - .any() - .name(indicator_col) + for i, label_tuple in enumerate(labels_as_tuples) + ] + labels_value = ( + typing.cast(ibis_types.IntegerColumn, unpivot_table[unpivot_offset_id]) + .cases(cases, default=None) # type:ignore + .name(col_id) ) + label_columns.append(labels_value) - else: # Can do simpler "in" subquery - new_column = ( - (left_table[conditions[0]]) - .isin((right_table[conditions[1]])) - .fillna(False) - .name(indicator_col) + unpivot_values = [] + for j in range(len(unpivot_columns)): + col_dtype = dtype[j] if utils.is_list_like(dtype) else dtype + result_col, source_cols = unpivot_columns[j] + null_value = bigframes.dtypes.literal_to_ibis_scalar( + None, force_dtype=col_dtype ) - - columns = tuple( - itertools.chain( - (left_table[col.get_name()] for col in self.columns), (new_column,) + ibis_values = [ + ops.AsTypeOp(col_dtype)._as_ibis(unpivot_table[col]) + if col is not None + else null_value + for col in source_cols + ] + cases = [(i, ibis_values[i]) for i in range(len(ibis_values))] + unpivot_value = typing.cast( + ibis_types.IntegerColumn, unpivot_table[unpivot_offset_id] + ).cases( + cases, default=null_value # type:ignore ) - ) + unpivot_values.append(unpivot_value.name(result_col)) - return UnorderedIR( - left_table, - columns=columns, + unpivot_table = unpivot_table.select( + passthrough_columns, + *label_columns, + *unpivot_values, + *hidden_col_ids, + unpivot_offset_id, ) - def project_window_op( - self, - expression: ex_types.Aggregation, - window_spec: WindowSpec, - output_name: str, - ) -> UnorderedIR: - """ - Creates a new expression based on this expression with unary operation applied to one column. - column_name: the id of the input column present in the expression - op: the windowable operator to apply to the input column - window_spec: a specification of the window over which to apply the operator - output_name: the id to assign to the output of the operator - """ - # Cannot nest analytic expressions, so reproject to cte first if needed. - # Also ibis cannot window literals, so need to reproject those (even though this is legal in googlesql) - # See: https://github.com/ibis-project/ibis/issues/9773 - used_exprs = map( - self._compile_expression, - map( - ex.DerefOp, - itertools.chain( - expression.column_references, window_spec.all_referenced_columns + # Extend the original ordering using unpivot_offset_id + old_ordering = self._ordering + if how == "left": + new_ordering = ExpressionOrdering( + ordering_value_columns=tuple( + [ + *old_ordering.ordering_value_columns, + OrderingColumnReference(unpivot_offset_id), + ] ), - ), + total_ordering_columns=frozenset( + [*old_ordering.total_ordering_columns, unpivot_offset_id] + ), + ) + else: # how=="right" + new_ordering = ExpressionOrdering( + ordering_value_columns=tuple( + [ + OrderingColumnReference(unpivot_offset_id), + *old_ordering.ordering_value_columns, + ] + ), + total_ordering_columns=frozenset( + [*old_ordering.total_ordering_columns, unpivot_offset_id] + ), + ) + value_columns = [ + unpivot_table[value_col_id] for value_col_id, _ in unpivot_columns + ] + passthrough_values = [unpivot_table[col] for col in passthrough_columns] + hidden_ordering_columns = [ + unpivot_table[unpivot_offset_id], + *[unpivot_table[hidden_col] for hidden_col in hidden_col_ids], + ] + return CompiledArrayValue( + table=unpivot_table, + columns=[ + *[unpivot_table[col_id] for col_id in index_col_ids], + *value_columns, + *passthrough_values, + ], + hidden_ordering_columns=hidden_ordering_columns, + ordering=new_ordering, ) - can_directly_window = not any( - map(lambda x: is_literal(x) or is_window(x), used_exprs) + + def assign(self, source_id: str, destination_id: str) -> CompiledArrayValue: + return self._set_or_replace_by_id( + destination_id, self._get_ibis_column(source_id) ) - if not can_directly_window: - return self._reproject_to_table().project_window_op( - expression, - window_spec, - output_name, + + def assign_constant( + self, + destination_id: str, + value: typing.Any, + dtype: typing.Optional[bigframes.dtypes.Dtype], + ) -> CompiledArrayValue: + # TODO(b/281587571): Solve scalar constant aggregation problem w/Ibis. + ibis_value = bigframes.dtypes.literal_to_ibis_scalar(value, dtype) + if ibis_value is None: + raise NotImplementedError( + f"Type not supported as scalar value {type(value)}. {constants.FEEDBACK_LINK}" ) + expr = self._set_or_replace_by_id(destination_id, ibis_value) + return expr._reproject_to_table() + + def _set_or_replace_by_id( + self, id: str, new_value: ibis_types.Value + ) -> CompiledArrayValue: + """Safely assign by id while maintaining ordering integrity.""" + # TODO: Split into explicit set and replace methods + ordering_col_ids = [ + col_ref.column_id for col_ref in self._ordering.ordering_value_columns + ] + if id in ordering_col_ids: + return self._hide_column(id)._set_or_replace_by_id(id, new_value) - rewritten_expr = rewrite.simplify_complex_windows( - agg_expressions.WindowExpression(expression, window_spec) - ) - - ibis_expr = op_compiler.compile_expression(rewritten_expr, self._ibis_bindings) - - return UnorderedIR(self._table, (*self.columns, ibis_expr.name(output_name))) - - def _compile_expression(self, expr: ex.Expression): - return op_compiler.compile_expression(expr, self._ibis_bindings) - - -def is_literal(column: ibis_types.Value) -> bool: - # Unfortunately, Literals in ibis are not "Columns"s and therefore can't be aggregated. - return not isinstance(column, ibis_types.Column) - - -def is_window(column: ibis_types.Value) -> bool: - matches = ( - (column) - .op() - .find_topmost( - lambda x: isinstance(x, (ibis_ops.WindowFunction, ibis_ops.Relation)) - ) - ) - return any(isinstance(op, ibis_ops.WindowFunction) for op in matches) - - -def _string_cast_join_cond( - lvalue: ibis_types.Column, rvalue: ibis_types.Column -) -> ibis_types.BooleanColumn: - result = ( - lvalue.cast(ibis_dtypes.str).fill_null(ibis_types.literal("0")) - == rvalue.cast(ibis_dtypes.str).fill_null(ibis_types.literal("0")) - ) & ( - lvalue.cast(ibis_dtypes.str).fill_null(ibis_types.literal("1")) - == rvalue.cast(ibis_dtypes.str).fill_null(ibis_types.literal("1")) - ) - return typing.cast(ibis_types.BooleanColumn, result) - - -def _numeric_join_cond( - lvalue: ibis_types.Column, rvalue: ibis_types.Column -) -> ibis_types.BooleanColumn: - lvalue1 = lvalue.fill_null(ibis_types.literal(0)) - lvalue2 = lvalue.fill_null(ibis_types.literal(1)) - rvalue1 = rvalue.fill_null(ibis_types.literal(0)) - rvalue2 = rvalue.fill_null(ibis_types.literal(1)) - if lvalue.type().is_floating() and rvalue.type().is_floating(): - # NaN aren't equal so need to coalesce as well with diff constants - lvalue1 = ( - typing.cast(ibis_types.FloatingColumn, lvalue) - .isnan() - .ifelse(ibis_types.literal(2), lvalue1) - ) - lvalue2 = ( - typing.cast(ibis_types.FloatingColumn, lvalue) - .isnan() - .ifelse(ibis_types.literal(3), lvalue2) - ) - rvalue1 = ( - typing.cast(ibis_types.FloatingColumn, rvalue) - .isnan() - .ifelse(ibis_types.literal(2), rvalue1) - ) - rvalue2 = ( - typing.cast(ibis_types.FloatingColumn, rvalue) - .isnan() - .ifelse(ibis_types.literal(3), rvalue2) - ) - result = (lvalue1 == rvalue1) & (lvalue2 == rvalue2) - return typing.cast(ibis_types.BooleanColumn, result) - - -def _join_condition( - lvalue: ibis_types.Column, rvalue: ibis_types.Column, nullsafe: bool -) -> ibis_types.BooleanColumn: - if (lvalue.type().is_floating()) and (lvalue.type().is_floating()): - # Need to always make safe join condition to handle nan, even if no nulls - return _numeric_join_cond(lvalue, rvalue) - if nullsafe: - # TODO: Define more coalesce constants for non-numeric types to avoid cast - if (lvalue.type().is_numeric()) and (lvalue.type().is_numeric()): - return _numeric_join_cond(lvalue, rvalue) + builder = self.builder() + if id in self.column_ids: + builder.columns = [ + val if (col_id != id) else new_value.name(id) + for col_id, val in zip(self.column_ids, self._columns) + ] else: - return _string_cast_join_cond(lvalue, rvalue) - return typing.cast(ibis_types.BooleanColumn, lvalue == rvalue) + builder.columns = [*self.columns, new_value.name(id)] + return builder.build() + + +class ArrayValueBuilder: + """Mutable expression class. + Use ArrayValue.builder() to create from a ArrayValue object. + """ + + def __init__( + self, + table: ibis_types.Table, + ordering: ExpressionOrdering, + columns: Collection[ibis_types.Value] = (), + hidden_ordering_columns: Collection[ibis_types.Value] = (), + predicates: Optional[Collection[ibis_types.BooleanValue]] = None, + ): + self.table = table + self.columns = list(columns) + self.hidden_ordering_columns = list(hidden_ordering_columns) + self.ordering = ordering + self.predicates = list(predicates) if predicates is not None else None + + def build(self) -> CompiledArrayValue: + return CompiledArrayValue( + table=self.table, + columns=self.columns, + hidden_ordering_columns=self.hidden_ordering_columns, + ordering=self.ordering, + predicates=self.predicates, + ) + + +def _reduce_predicate_list( + predicate_list: typing.Collection[ibis_types.BooleanValue], +) -> ibis_types.BooleanValue: + """Converts a list of predicates BooleanValues into a single BooleanValue.""" + if len(predicate_list) == 0: + raise ValueError("Cannot reduce empty list of predicates") + if len(predicate_list) == 1: + (item,) = predicate_list + return item + return functools.reduce(lambda acc, pred: acc.__and__(pred), predicate_list) + + +def _convert_ordering_to_table_values( + value_lookup: typing.Mapping[str, ibis_types.Value], + ordering_columns: typing.Sequence[OrderingColumnReference], +) -> typing.Sequence[ibis_types.Value]: + column_refs = ordering_columns + ordering_values = [] + for ordering_col in column_refs: + column = typing.cast(ibis_types.Column, value_lookup[ordering_col.column_id]) + ordering_value = ( + ibis.asc(column) + if ordering_col.direction.is_ascending + else ibis.desc(column) + ) + # Bigquery SQL considers NULLS to be "smallest" values, but we need to override in these cases. + if (not ordering_col.na_last) and (not ordering_col.direction.is_ascending): + # Force nulls to be first + is_null_val = typing.cast(ibis_types.Column, column.isnull()) + ordering_values.append(ibis.desc(is_null_val)) + elif (ordering_col.na_last) and (ordering_col.direction.is_ascending): + # Force nulls to be last + is_null_val = typing.cast(ibis_types.Column, column.isnull()) + ordering_values.append(ibis.asc(is_null_val)) + ordering_values.append(ordering_value) + return ordering_values + + +def _as_identity(value: ibis_types.Value): + # Some types need to be converted to string to enable groupby + if value.type().is_float64() or value.type().is_geospatial(): + return value.cast(ibis_dtypes.str) + return value diff --git a/bigframes/core/compile/compiler.py b/bigframes/core/compile/compiler.py new file mode 100644 index 00000000000..195d830122a --- /dev/null +++ b/bigframes/core/compile/compiler.py @@ -0,0 +1,185 @@ +# Copyright 2023 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from __future__ import annotations + +import functools +import io +import typing + +import pandas as pd + +import bigframes.core.compile as compiled +import bigframes.core.compile.single_column +import bigframes.core.nodes as nodes + +if typing.TYPE_CHECKING: + import bigframes.core + import bigframes.session + + +@functools.cache +def compile_node(node: nodes.BigFrameNode) -> compiled.CompiledArrayValue: + """Compile node into CompileArrayValue. Caches result.""" + return _compile_node(node) + + +@functools.singledispatch +def _compile_node(node: nodes.BigFrameNode) -> compiled.CompiledArrayValue: + """Defines transformation but isn't cached, always use compile_node instead""" + raise ValueError(f"Can't compile unnrecognized node: {node}") + + +@_compile_node.register +def compile_join(node: nodes.JoinNode): + compiled_left = compile_node(node.left_child) + compiled_right = compile_node(node.right_child) + return bigframes.core.compile.single_column.join_by_column( + compiled_left, + node.left_column_ids, + compiled_right, + node.right_column_ids, + how=node.how, + allow_row_identity_join=node.allow_row_identity_join, + ) + + +@_compile_node.register +def compile_select(node: nodes.SelectNode): + return compile_node(node.child).select_columns(node.column_ids) + + +@_compile_node.register +def compile_drop(node: nodes.DropColumnsNode): + return compile_node(node.child).drop_columns(node.columns) + + +@_compile_node.register +def compile_readlocal(node: nodes.ReadLocalNode): + array_as_pd = pd.read_feather(io.BytesIO(node.feather_bytes)) + return compiled.CompiledArrayValue.mem_expr_from_pandas(array_as_pd) + + +@_compile_node.register +def compile_readgbq(node: nodes.ReadGbqNode): + return compiled.CompiledArrayValue( + node.table, + node.columns, + node.hidden_ordering_columns, + node.ordering, + ) + + +@_compile_node.register +def compile_promote_offsets(node: nodes.PromoteOffsetsNode): + return compile_node(node.child).promote_offsets(node.col_id) + + +@_compile_node.register +def compile_filter(node: nodes.FilterNode): + return compile_node(node.child).filter(node.predicate_id, node.keep_null) + + +@_compile_node.register +def compile_orderby(node: nodes.OrderByNode): + return compile_node(node.child).order_by(node.by, node.stable) + + +@_compile_node.register +def compile_reversed(node: nodes.ReversedNode): + return compile_node(node.child).reversed() + + +@_compile_node.register +def compile_project_unary(node: nodes.ProjectUnaryOpNode): + return compile_node(node.child).project_unary_op( + node.input_id, node.op, node.output_id + ) + + +@_compile_node.register +def compile_project_binary(node: nodes.ProjectBinaryOpNode): + return compile_node(node.child).project_binary_op( + node.left_input_id, node.right_input_id, node.op, node.output_id + ) + + +@_compile_node.register +def compile_project_ternary(node: nodes.ProjectTernaryOpNode): + return compile_node(node.child).project_ternary_op( + node.input_id1, node.input_id2, node.input_id3, node.op, node.output_id + ) + + +@_compile_node.register +def compile_concat(node: nodes.ConcatNode): + compiled_nodes = [compile_node(node) for node in node.children] + return compiled_nodes[0].concat(compiled_nodes[1:]) + + +@_compile_node.register +def compile_aggregate(node: nodes.AggregateNode): + return compile_node(node.child).aggregate( + node.aggregations, node.by_column_ids, node.dropna + ) + + +@_compile_node.register +def compile_corr(node: nodes.CorrNode): + return compile_node(node.child).corr_aggregate(node.corr_aggregations) + + +@_compile_node.register +def compile_window(node: nodes.WindowOpNode): + return compile_node(node.child).project_window_op( + node.column_name, + node.op, + node.window_spec, + node.output_name, + never_skip_nulls=node.never_skip_nulls, + skip_reproject_unsafe=node.skip_reproject_unsafe, + ) + + +@_compile_node.register +def compile_reproject(node: nodes.ReprojectOpNode): + return compile_node(node.child)._reproject_to_table() + + +@_compile_node.register +def compile_unpivot(node: nodes.UnpivotNode): + return compile_node(node.child).unpivot( + node.row_labels, + node.unpivot_columns, + passthrough_columns=node.passthrough_columns, + index_col_ids=node.index_col_ids, + dtype=node.dtype, + how=node.how, + ) + + +@_compile_node.register +def compile_assign(node: nodes.AssignNode): + return compile_node(node.child).assign(node.source_id, node.destination_id) + + +@_compile_node.register +def compile_assign_constant(node: nodes.AssignConstantNode): + return compile_node(node.child).assign_constant( + node.destination_id, node.value, node.dtype + ) + + +@_compile_node.register +def compiler_random_sample(node: nodes.RandomSampleNode): + return compile_node(node.child)._uniform_sampling(node.fraction) diff --git a/bigframes/core/compile/concat.py b/bigframes/core/compile/concat.py deleted file mode 100644 index 742f429f547..00000000000 --- a/bigframes/core/compile/concat.py +++ /dev/null @@ -1,41 +0,0 @@ -# Copyright 2023 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -from __future__ import annotations - -import typing - -import bigframes_vendored.ibis.expr.api as ibis_api - -import bigframes.core.compile.compiled as compiled - - -def concat_unordered( - items: typing.Sequence[compiled.UnorderedIR], - output_ids: typing.Sequence[str], -) -> compiled.UnorderedIR: - """Append together multiple ArrayValue objects.""" - if len(items) == 1: - return items[0] - tables = [] - for expr in items: - table = expr._to_ibis_expr() - table = table.select( - [table[col].name(id) for id, col in zip(output_ids, table.columns)] - ) - tables.append(table) - combined_table = ibis_api.union(*tables) - return compiled.UnorderedIR( - combined_table, - columns=[combined_table[col] for col in combined_table.columns], - ) diff --git a/bigframes/core/compile/configs.py b/bigframes/core/compile/configs.py deleted file mode 100644 index 62c28f87cae..00000000000 --- a/bigframes/core/compile/configs.py +++ /dev/null @@ -1,37 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -from __future__ import annotations - -import dataclasses -import typing - -import google.cloud.bigquery - -from bigframes.core import nodes, ordering - - -@dataclasses.dataclass(frozen=True) -class CompileRequest: - node: nodes.BigFrameNode - sort_rows: bool - materialize_all_order_keys: bool = False - peek_count: typing.Optional[int] = None - - -@dataclasses.dataclass(frozen=True) -class CompileResult: - sql: str - sql_schema: typing.Sequence[google.cloud.bigquery.SchemaField] - row_order: typing.Optional[ordering.RowOrdering] - encoded_type_refs: str diff --git a/bigframes/core/compile/constants.py b/bigframes/core/compile/constants.py deleted file mode 100644 index 9c307125ab2..00000000000 --- a/bigframes/core/compile/constants.py +++ /dev/null @@ -1,27 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -# Datetime constants -UNIT_TO_US_CONVERSION_FACTORS = { - "W": 7 * 24 * 60 * 60 * 1000 * 1000, - "d": 24 * 60 * 60 * 1000 * 1000, - "D": 24 * 60 * 60 * 1000 * 1000, - "h": 60 * 60 * 1000 * 1000, - "m": 60 * 1000 * 1000, - "s": 1000 * 1000, - "ms": 1000, - "us": 1, - "ns": 1e-3, -} diff --git a/bigframes/core/compile/explode.py b/bigframes/core/compile/explode.py deleted file mode 100644 index 59e3a13d023..00000000000 --- a/bigframes/core/compile/explode.py +++ /dev/null @@ -1,73 +0,0 @@ -# Copyright 2023 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -from __future__ import annotations - -import typing - -import bigframes_vendored.ibis - -import bigframes.core.compile.compiled as compiled -import bigframes.core.expression as ex -import bigframes.core.guid -import bigframes.core.ordering - - -def explode_unordered( - input: compiled.UnorderedIR, - columns: typing.Sequence[ex.DerefOp], - offsets_id: typing.Optional[str], -) -> compiled.UnorderedIR: - table = input._to_ibis_expr() - column_ids = tuple(ref.id.sql for ref in columns) - - # The offset array ensures null represents empty arrays after unnesting. - offset_array_id = bigframes.core.guid.generate_guid("offset_array_") - offset_array = bigframes_vendored.ibis.range( - 0, - bigframes_vendored.ibis.greatest( - 1, # We always want at least 1 element to fill in NULLs for empty arrays. - bigframes_vendored.ibis.least( - *[table[column_id].length() for column_id in column_ids] - ), - ), - 1, - ).name(offset_array_id) - table_w_offset_array = table.select( - offset_array, - *input._column_names, - ) - - unnest_offset_id = offsets_id or bigframes.core.guid.generate_guid("unnest_offset_") - unnest_offset = ( - table_w_offset_array[offset_array_id].unnest().name(unnest_offset_id) - ) - table_w_offset = table_w_offset_array.select( - unnest_offset, - *input._column_names, - ) - - output_cols = tuple(input.column_ids) + ((offsets_id,) if offsets_id else ()) - unnested_columns = [ - table_w_offset[column_id][table_w_offset[unnest_offset_id]].name(column_id) - if column_id in column_ids - else table_w_offset[column_id] - for column_id in output_cols - ] - table_w_unnest = table_w_offset.select(*unnested_columns) - - columns = [table_w_unnest[column_name] for column_name in output_cols] - return compiled.UnorderedIR( - table_w_unnest, - columns=columns, # type: ignore - ) diff --git a/bigframes/core/compile/ibis_compiler/__init__.py b/bigframes/core/compile/ibis_compiler/__init__.py deleted file mode 100644 index 6b9d284c536..00000000000 --- a/bigframes/core/compile/ibis_compiler/__init__.py +++ /dev/null @@ -1,25 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -"""Compiler for BigFrames expression to Ibis expression. - -Make sure to import all ibis_compiler implementations here so that they get -registered. -""" - -from __future__ import annotations - -import bigframes.core.compile.ibis_compiler.operations.generic_ops # noqa: F401 -import bigframes.core.compile.ibis_compiler.operations.geo_ops # noqa: F401 -import bigframes.core.compile.ibis_compiler.scalar_op_registry # noqa: F401 diff --git a/bigframes/core/compile/ibis_compiler/aggregate_compiler.py b/bigframes/core/compile/ibis_compiler/aggregate_compiler.py deleted file mode 100644 index 94607bf04bc..00000000000 --- a/bigframes/core/compile/ibis_compiler/aggregate_compiler.py +++ /dev/null @@ -1,868 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from __future__ import annotations - -import functools -import typing -from typing import List, Optional, cast - -import bigframes_vendored.constants as constants -import bigframes_vendored.ibis -import bigframes_vendored.ibis.expr.api as ibis_api -import bigframes_vendored.ibis.expr.datatypes as ibis_dtypes -import bigframes_vendored.ibis.expr.operations as ibis_ops -import bigframes_vendored.ibis.expr.operations.udf as ibis_udf -import bigframes_vendored.ibis.expr.types as ibis_types -import pandas as pd -from bigframes_vendored.ibis.expr import builders as ibis_expr_builders -from bigframes_vendored.ibis.expr.operations import window as ibis_expr_window - -import bigframes.core.compile.ibis_compiler.scalar_op_compiler as scalar_compilers -import bigframes.core.compile.ibis_types as compile_ibis_types -import bigframes.core.utils -import bigframes.core.window_spec as window_spec -import bigframes.operations.aggregations as agg_ops -from bigframes.core import agg_expressions -from bigframes.core.compile import constants as compiler_constants -from bigframes.core.window_spec import RangeWindowBounds, RowsWindowBounds, WindowSpec - -scalar_compiler = scalar_compilers.scalar_op_compiler - - -# TODO(swast): We can remove this if ibis adds general approx_quantile -# See: https://github.com/ibis-project/ibis/issues/9541 -@ibis_udf.agg.builtin -def approx_quantiles(expression: float, number) -> List[float]: - """APPROX_QUANTILES - - https://cloud.google.com/bigquery/docs/reference/standard-sql/approximate_aggregate_functions#approx_quantiles - """ - return [] # pragma: NO COVER - - -def compile_aggregate( - aggregate: agg_expressions.Aggregation, - bindings: typing.Dict[str, ibis_types.Value], - order_by: typing.Sequence[ibis_types.Value] = [], -) -> ibis_types.Value: - if isinstance(aggregate, agg_expressions.NullaryAggregation): - return compile_nullary_agg(aggregate.op) - if isinstance(aggregate, agg_expressions.UnaryAggregation): - input = scalar_compiler.compile_expression(aggregate.arg, bindings=bindings) - if not aggregate.op.order_independent: - return compile_ordered_unary_agg(aggregate.op, input, order_by=order_by) # type: ignore - else: - return compile_unary_agg(aggregate.op, input) # type: ignore - elif isinstance(aggregate, agg_expressions.BinaryAggregation): - left = scalar_compiler.compile_expression(aggregate.left, bindings=bindings) - right = scalar_compiler.compile_expression(aggregate.right, bindings=bindings) - return compile_binary_agg(aggregate.op, left, right) # type: ignore - else: - raise ValueError(f"Unexpected aggregation: {aggregate}") - - -def compile_analytic( - aggregate: agg_expressions.Aggregation, - window: window_spec.WindowSpec, - bindings: typing.Dict[str, ibis_types.Value], -) -> ibis_types.Value: - ibis_window = _ibis_window_from_spec(window, bindings=bindings) - if isinstance(aggregate, agg_expressions.NullaryAggregation): - return compile_nullary_agg(aggregate.op, ibis_window) - elif isinstance(aggregate, agg_expressions.UnaryAggregation): - input = scalar_compiler.compile_expression(aggregate.arg, bindings=bindings) - return compile_unary_agg(aggregate.op, input, ibis_window) # type: ignore - elif isinstance(aggregate, agg_expressions.BinaryAggregation): - raise NotImplementedError("binary analytic operations not yet supported") - else: - raise ValueError(f"Unexpected analytic operation: {aggregate}") - - -@functools.singledispatch -def compile_binary_agg( - op: agg_ops.WindowOp, - left: ibis_types.Column, - right: ibis_types.Column, - window: Optional[window_spec.WindowSpec] = None, -) -> ibis_types.Value: - raise ValueError(f"Can't compile unrecognized operation: {op}") - - -@functools.singledispatch -def compile_unary_agg( - op: agg_ops.WindowOp, - input: ibis_types.Column, - window: Optional[window_spec.WindowSpec] = None, -) -> ibis_types.Value: - raise ValueError(f"Can't compile unrecognized operation: {op}") - - -@functools.singledispatch -def compile_ordered_unary_agg( - op: agg_ops.WindowOp, - input: ibis_types.Column, - window: Optional[window_spec.WindowSpec] = None, - order_by: typing.Sequence[ibis_types.Value] = [], -) -> ibis_types.Value: - raise ValueError(f"Can't compile unrecognized operation: {op}") - - -@functools.singledispatch -def compile_nullary_agg( - op: agg_ops.WindowOp, - window: Optional[window_spec.WindowSpec] = None, -) -> ibis_types.Value: - raise ValueError(f"Can't compile unrecognized operation: {op}") - - -def numeric_op(operation): - @functools.wraps(operation) - def constrained_op( - op, - column: ibis_types.Column, - window=None, - order_by: typing.Sequence[ibis_types.Value] = [], - ): - if column.type().is_boolean(): - column = typing.cast( - ibis_types.NumericColumn, column.cast(ibis_dtypes.int64) - ) - if column.type().is_numeric(): - return operation(op, column, window) - else: - raise ValueError( - f"Numeric operation cannot be applied to type {column.type()}. {constants.FEEDBACK_LINK}" - ) - - return constrained_op - - -### Specific Op implementations Below - - -@compile_nullary_agg.register -def _(op: agg_ops.SizeOp, window=None) -> ibis_types.NumericValue: - return _apply_window_if_present(ibis_ops.count(1), window) - - -@compile_unary_agg.register -def _(op: agg_ops.SizeUnaryOp, _, window=None) -> ibis_types.NumericValue: - return _apply_window_if_present(ibis_ops.count(1), window) - - -@compile_unary_agg.register -@numeric_op -def _( - op: agg_ops.SumOp, - column: ibis_types.NumericColumn, - window=None, -) -> ibis_types.NumericValue: - # Will be null if all inputs are null. Pandas defaults to zero sum though. - bq_sum = _apply_window_if_present(column.sum(), window) - return bq_sum.coalesce(ibis_types.literal(0)) - - -@compile_unary_agg.register -def _( - op: agg_ops.MedianOp, - column: ibis_types.NumericColumn, - window=None, -) -> ibis_types.NumericValue: - return cast(ibis_types.NumericValue, column.approx_median()) - - -@compile_unary_agg.register -@numeric_op -def _( - op: agg_ops.ApproxQuartilesOp, - column: ibis_types.NumericColumn, - window=None, -) -> ibis_types.NumericValue: - # APPROX_QUANTILES has very few allowed windows. - if window is not None: - raise NotImplementedError( - f"Approx Quartiles with windowing is not supported. {constants.FEEDBACK_LINK}" - ) - value = approx_quantiles(column, 4)[op.quartile] # type: ignore - return cast(ibis_types.NumericValue, value) - - -@compile_unary_agg.register -def _( - op: agg_ops.ApproxTopCountOp, - column: ibis_types.Column, - window=None, -) -> ibis_types.ArrayColumn: - # APPROX_TOP_COUNT has very few allowed windows. - if window is not None: - raise NotImplementedError( - f"Approx top count with windowing is not supported. {constants.FEEDBACK_LINK}" - ) - - # Define a user-defined function (UDF) that approximates the top counts of an expression. - # The type of value is dynamically matching the input column. - def approx_top_count(expression, number: ibis_dtypes.int64): # type: ignore - ... - - ibis_return_type = ibis_dtypes.Array( - value_type=ibis_dtypes.Struct.from_tuples( - [("value", column.type()), ("count", ibis_dtypes.int64)] - ) - ) # type: ignore - approx_top_count.__annotations__["return"] = ibis_return_type - udf_op = ibis_ops.udf.agg.builtin(approx_top_count) - - return udf_op(expression=column, number=op.number) # type: ignore - - -@compile_unary_agg.register -@numeric_op -def _( - op: agg_ops.QuantileOp, - column: ibis_types.NumericColumn, - window=None, -) -> ibis_types.NumericValue: - result = column.quantile(op.q) - if op.should_floor_result: - result = result.floor() # type:ignore - - return _apply_window_if_present(result, window) - - -@compile_unary_agg.register -@numeric_op -def _( - op: agg_ops.MeanOp, - column: ibis_types.NumericColumn, - window=None, - # order_by: typing.Sequence[ibis_types.Value] = [], -) -> ibis_types.NumericValue: - result = column.mean().floor() if op.should_floor_result else column.mean() - return _apply_window_if_present(result, window) - - -@compile_unary_agg.register -@numeric_op -def _( - op: agg_ops.ProductOp, - column: ibis_types.NumericColumn, - window=None, -) -> ibis_types.NumericValue: - # Need to short-circuit as log with zeroes is illegal sql - is_zero = cast(ibis_types.BooleanColumn, (column == 0)) - - # There is no product sql aggregate function, so must implement as a sum of logs, and then - # apply power after. Note, log and power base must be equal! This impl uses base 2. - logs = cast( - ibis_types.NumericColumn, - ibis_api.case().when(is_zero, 0).else_(column.abs().log2()).end(), - ) - logs_sum = _apply_window_if_present(logs.sum(), window) - magnitude = cast(ibis_types.NumericValue, ibis_types.literal(2)).pow(logs_sum) - - # Can't determine sign from logs, so have to determine parity of count of negative inputs - is_negative = cast( - ibis_types.NumericColumn, - ibis_api.case().when(column.sign() == -1, 1).else_(0).end(), - ) - negative_count = _apply_window_if_present(is_negative.sum(), window) - negative_count_parity = negative_count % cast( - ibis_types.NumericValue, ibis_types.literal(2) - ) # 1 if result should be negative, otherwise 0 - - any_zeroes = _apply_window_if_present(is_zero.any(), window) - float_result = ( - ibis_api.case() - .when(any_zeroes, ibis_types.literal(0)) - .else_(magnitude * pow(-1, negative_count_parity)) - .end() - ) - return cast(ibis_types.NumericValue, float_result) - - -@compile_unary_agg.register -def _( - op: agg_ops.MaxOp, - column: ibis_types.Column, - window=None, -) -> ibis_types.Value: - return _apply_window_if_present(column.max(), window) - - -@compile_unary_agg.register -def _( - op: agg_ops.MinOp, - column: ibis_types.Column, - window=None, -) -> ibis_types.Value: - return _apply_window_if_present(column.min(), window) - - -@compile_unary_agg.register -@numeric_op -def _( - op: agg_ops.StdOp, - x: ibis_types.NumericColumn, - window=None, -) -> ibis_types.Value: - result = x.std().floor() if op.should_floor_result else x.std() - return _apply_window_if_present(result, window) - - -@compile_unary_agg.register -@numeric_op -def _( - op: agg_ops.VarOp, - x: ibis_types.Column, - window=None, -) -> ibis_types.Value: - return _apply_window_if_present(cast(ibis_types.NumericColumn, x).var(), window) - - -@compile_unary_agg.register -@numeric_op -def _( - op: agg_ops.PopVarOp, - x: ibis_types.Column, - window=None, -) -> ibis_types.Value: - return _apply_window_if_present( - cast(ibis_types.NumericColumn, x).var(how="pop"), window - ) - - -@compile_unary_agg.register -def _( - op: agg_ops.CountOp, - column: ibis_types.Column, - window=None, -) -> ibis_types.IntegerValue: - return _apply_window_if_present(column.count(), window) - - -@compile_unary_agg.register -def _( - op: agg_ops.CutOp, - x: ibis_types.Column, - window=None, -): - out = ibis_api.case() - if isinstance(op.bins, int): - col_min = _apply_window_if_present(x.min(), window) - col_max = _apply_window_if_present(x.max(), window) - adj = (col_max - col_min) * 0.001 - bin_width = (col_max - col_min) / op.bins - - for this_bin in range(op.bins): - if op.labels is False: - value = compile_ibis_types.literal_to_ibis_scalar( - this_bin, - force_dtype=pd.Int64Dtype(), - ) - elif isinstance(op.labels, typing.Iterable): - value = compile_ibis_types.literal_to_ibis_scalar( - list(op.labels)[this_bin], - force_dtype=pd.StringDtype(storage="pyarrow"), - ) - else: - left_adj = adj if this_bin == 0 and op.right else 0 - right_adj = adj if this_bin == op.bins - 1 and not op.right else 0 - - left = col_min + this_bin * bin_width - left_adj - right = col_min + (this_bin + 1) * bin_width + right_adj - - if op.right: - value = ibis_types.struct( - {"left_exclusive": left, "right_inclusive": right} - ) - else: - value = ibis_types.struct( - {"left_inclusive": left, "right_exclusive": right} - ) - if this_bin == op.bins - 1: - case_expr = x.notnull() - else: - if op.right: - case_expr = x <= (col_min + (this_bin + 1) * bin_width) - else: - case_expr = x < (col_min + (this_bin + 1) * bin_width) - out = out.when(case_expr, value) - else: # Interpret as intervals - for this_bin, interval in enumerate(op.bins): - left = compile_ibis_types.literal_to_ibis_scalar(interval[0]) - right = compile_ibis_types.literal_to_ibis_scalar(interval[1]) - if op.right: - condition = (x > left) & (x <= right) - else: - condition = (x >= left) & (x < right) - - if op.labels is False: - value = compile_ibis_types.literal_to_ibis_scalar( - this_bin, - force_dtype=pd.Int64Dtype(), - ) - elif isinstance(op.labels, typing.Iterable): - value = compile_ibis_types.literal_to_ibis_scalar( - list(op.labels)[this_bin], - force_dtype=pd.StringDtype(storage="pyarrow"), - ) - else: - if op.right: - value = ibis_types.struct( - {"left_exclusive": left, "right_inclusive": right} - ) - else: - value = ibis_types.struct( - {"left_inclusive": left, "right_exclusive": right} - ) - - out = out.when(condition, value) - return out.end() - - -@compile_unary_agg.register -@numeric_op -def _( - self: agg_ops.QcutOp, - column: ibis_types.Column, - window=None, -) -> ibis_types.IntegerValue: - if isinstance(self.quantiles, int): - quantiles_ibis = compile_ibis_types.literal_to_ibis_scalar(self.quantiles) - percent_ranks = cast( - ibis_types.FloatingColumn, - _apply_window_if_present(column.percent_rank(), window), - ) - float_bucket = cast(ibis_types.FloatingColumn, (percent_ranks * quantiles_ibis)) - return float_bucket.ceil().clip(lower=_ibis_num(1)) - _ibis_num(1) - else: - percent_ranks = cast( - ibis_types.FloatingColumn, - _apply_window_if_present(column.percent_rank(), window), - ) - out = ibis_api.case() - first_ibis_quantile = compile_ibis_types.literal_to_ibis_scalar( - self.quantiles[0] - ) - out = out.when(percent_ranks < first_ibis_quantile, None) - for bucket_n in range(len(self.quantiles) - 1): - ibis_quantile = compile_ibis_types.literal_to_ibis_scalar( - self.quantiles[bucket_n + 1] - ) - out = out.when( - percent_ranks <= ibis_quantile, - compile_ibis_types.literal_to_ibis_scalar( - bucket_n, force_dtype=pd.Int64Dtype() - ), - ) - out = out.else_(None) - return out.end() # type: ignore - - -@compile_unary_agg.register -def _( - op: agg_ops.NuniqueOp, - column: ibis_types.Column, - window=None, -) -> ibis_types.IntegerValue: - return _apply_window_if_present(column.nunique(), window) - - -@compile_unary_agg.register -def _( - op: agg_ops.AnyValueOp, - column: ibis_types.Column, - window=None, -) -> ibis_types.IntegerValue: - return _apply_window_if_present(column.arbitrary(), window) - - -@compile_unary_agg.register -def _( - op: agg_ops.RankOp, - column: ibis_types.Column, - window=None, -) -> ibis_types.IntegerValue: - # Ibis produces 0-based ranks, while pandas creates 1-based ranks - return _apply_window_if_present(ibis_api.rank(), window) + 1 - - -@compile_unary_agg.register -def _( - op: agg_ops.DenseRankOp, - column: ibis_types.Column, - window=None, -) -> ibis_types.IntegerValue: - # Ibis produces 0-based ranks, while pandas creates 1-based ranks - return _apply_window_if_present(column.dense_rank(), window) + 1 - - -@compile_nullary_agg.register -def _( - op: agg_ops.RowNumberOp, - window=None, -) -> ibis_types.IntegerValue: - return _apply_window_if_present(ibis_api.row_number(), window) - - -@compile_unary_agg.register -def _(op: agg_ops.FirstOp, column: ibis_types.Column, window=None) -> ibis_types.Value: - return _apply_window_if_present(column.first(), window) - - -@compile_unary_agg.register -def _( - op: agg_ops.FirstNonNullOp, - column: ibis_types.Column, - window=None, -) -> ibis_types.Value: - # Ibis FirstNonNullValue expects Value[Any, Columnar], Mypy struggles to see Column as compatible. - return _apply_window_if_present( - ibis_ops.FirstNonNullValue(column).to_expr(), # type: ignore[arg-type] - window, # type: ignore - ) - - -@compile_unary_agg.register -def _( - op: agg_ops.LastOp, - column: ibis_types.Column, - window=None, -) -> ibis_types.Value: - return _apply_window_if_present(column.last(), window) - - -@compile_unary_agg.register -def _( - op: agg_ops.LastNonNullOp, - column: ibis_types.Column, - window=None, -) -> ibis_types.Value: - # Ibis LastNonNullValue expects Value[Any, Columnar], Mypy struggles to see Column as compatible. - return _apply_window_if_present( - ibis_ops.LastNonNullValue(column).to_expr(), # type: ignore[arg-type] - window, # type: ignore - ) - - -@compile_unary_agg.register -def _( - op: agg_ops.ShiftOp, - column: ibis_types.Column, - window=None, -) -> ibis_types.Value: - if op.periods == 0: # No-op - return column - if op.periods > 0: - return _apply_window_if_present(column.lag(op.periods), window) - return _apply_window_if_present(column.lead(-op.periods), window) - - -@compile_unary_agg.register -def _( - op: agg_ops.DiffOp, - column: ibis_types.Column, - window=None, -) -> ibis_types.Value: - shifted = compile_unary_agg(agg_ops.ShiftOp(op.periods), column, window) - if column.type().is_boolean(): - return cast(ibis_types.BooleanColumn, column) != cast( - ibis_types.BooleanColumn, shifted - ) - elif column.type().is_numeric(): - return cast(ibis_types.NumericColumn, column) - cast( - ibis_types.NumericColumn, shifted - ) - else: - raise TypeError(f"Cannot perform diff on type{column.type()}") - - -@compile_unary_agg.register -def _( - op: agg_ops.TimeSeriesDiffOp, - column: ibis_types.Column, - window=None, -) -> ibis_types.Value: - if not column.type().is_timestamp(): - raise TypeError(f"Cannot perform time series diff on type{column.type()}") - - original_column = cast(ibis_types.TimestampColumn, column) - shifted_column = cast( - ibis_types.TimestampColumn, - compile_unary_agg(agg_ops.ShiftOp(op.periods), column, window), - ) - - return original_column.delta(shifted_column, part="microsecond") - - -@compile_unary_agg.register -def _( - op: agg_ops.DateSeriesDiffOp, - column: ibis_types.Column, - window=None, -) -> ibis_types.Value: - if not column.type().is_date(): - raise TypeError(f"Cannot perform date series diff on type{column.type()}") - - original_column = cast(ibis_types.DateColumn, column) - shifted_column = cast( - ibis_types.DateColumn, - compile_unary_agg(agg_ops.ShiftOp(op.periods), column, window), - ) - - conversion_factor = typing.cast( - ibis_types.IntegerValue, compiler_constants.UNIT_TO_US_CONVERSION_FACTORS["D"] - ) - - return ( - original_column.delta(shifted_column, part="day") * conversion_factor - ).floor() - - -@compile_unary_agg.register -def _( - op: agg_ops.AllOp, - column: ibis_types.Column, - window=None, -) -> ibis_types.BooleanValue: - # BQ will return null for empty column, result would be false in pandas. - result = _apply_window_if_present(_is_true(column).all(), window) - literal = ibis_types.literal(True) - - return cast(ibis_types.BooleanScalar, result.fill_null(literal)) - - -@compile_unary_agg.register -def _( - op: agg_ops.AnyOp, - column: ibis_types.Column, - window=None, -) -> ibis_types.BooleanValue: - # BQ will return null for empty column, result would be false in pandas. - result = _apply_window_if_present(_is_true(column).any(), window) - literal = ibis_types.literal(False) - - return cast(ibis_types.BooleanScalar, result.fill_null(literal)) - - -@compile_ordered_unary_agg.register -def _( - op: agg_ops.ArrayAggOp, - column: ibis_types.Column, - window=None, - order_by: typing.Sequence[ibis_types.Value] = [], -) -> ibis_types.ArrayValue: - # BigQuery doesn't currently support using ARRAY_AGG with both window and aggregate - # functions simultaneously. Some aggregate functions (or its equivalent syntax) - # are more important, such as: - # - `IGNORE NULLS` are required to avoid an raised error if the final result - # contains a NULL element. - # - `ORDER BY` are required for the default ordering mode. - # To keep things simpler, windowing support is skipped for now. - if window is not None: - raise NotImplementedError( - f"ArrayAgg with windowing is not supported. {constants.FEEDBACK_LINK}" - ) - - return ibis_ops.ArrayAggregate( - column, # type: ignore - order_by=order_by, # type: ignore - ).to_expr() - - -@compile_ordered_unary_agg.register -def _( - op: agg_ops.StringAggOp, - column: ibis_types.Column, - window=None, - order_by: typing.Sequence[ibis_types.Value] = [], -) -> ibis_types.ArrayValue: - if window is not None: - raise NotImplementedError( - f"StringAgg with windowing is not supported. {constants.FEEDBACK_LINK}" - ) - - return ( - ibis_ops.StringAgg( - column, # type: ignore - sep=op.sep, # type: ignore - order_by=order_by, # type: ignore - ) - .to_expr() - .fill_null(ibis_types.literal("")) - ) - - -@compile_binary_agg.register -def _( - op: agg_ops.CorrOp, left: ibis_types.Column, right: ibis_types.Column, window=None -) -> ibis_types.NumericValue: - # Will be null if all inputs are null. Pandas defaults to zero sum though. - left_numeric = cast(ibis_types.NumericColumn, left) - right_numeric = cast(ibis_types.NumericColumn, right) - bq_corr = _apply_window_if_present( - left_numeric.corr(right_numeric, how="pop"), window - ) - return cast(ibis_types.NumericColumn, bq_corr) - - -@compile_binary_agg.register -def _( - op: agg_ops.CovOp, left: ibis_types.Column, right: ibis_types.Column, window=None -) -> ibis_types.NumericValue: - # Will be null if all inputs are null. Pandas defaults to zero sum though. - left_numeric = cast(ibis_types.NumericColumn, left) - right_numeric = cast(ibis_types.NumericColumn, right) - bq_cov = _apply_window_if_present( - left_numeric.cov(right_numeric, how="sample"), window - ) - return cast(ibis_types.NumericColumn, bq_cov) - - -def _apply_window_if_present(value: ibis_types.Value, window): - return value.over(window) if (window is not None) else value - - -def _ibis_window_from_spec( - window_spec: WindowSpec, bindings: typing.Dict[str, ibis_types.Value] -): - group_by: typing.List[ibis_types.Value] = ( - [ - typing.cast( - ibis_types.Column, - _as_groupable(scalar_compiler.compile_expression(column, bindings)), - ) - for column in window_spec.grouping_keys - ] - if window_spec.grouping_keys - else [] - ) - - # Construct ordering. There are basically 3 main cases - # 1. Order-independent op (aggregation, cut, rank) with unbound window - no ordering clause needed - # 2. Order-independent op (aggregation, cut, rank) with range window - use ordering clause, ties allowed - # 3. Order-depedenpent op (navigation functions, array_agg) or rows bounds - use total row order to break ties. - if window_spec.is_row_bounded: - if not window_spec.ordering: - # If window spec has following or preceding bounds, we need to apply an unambiguous ordering. - raise ValueError("No ordering provided for ordered analytic function") - order_by = scalar_compiler._convert_row_ordering_to_table_values( - bindings, - window_spec.ordering, - ) - - elif window_spec.is_range_bounded: - order_by = [ - scalar_compiler._convert_range_ordering_to_table_value( - bindings, - window_spec.ordering[0], - ) - ] - # The rest if branches are for unbounded windows - elif window_spec.ordering: - # Unbound grouping window. Suitable for aggregations but not for analytic function application. - order_by = scalar_compiler._convert_row_ordering_to_table_values( - bindings, - window_spec.ordering, - ) - else: - order_by = None - - window = bigframes_vendored.ibis.window(order_by=order_by, group_by=group_by) - if window_spec.bounds is not None: - return _add_boundary(window_spec.bounds, window) - return window - - -def _as_groupable(value: ibis_types.Value): - from bigframes.core.compile.ibis_compiler import scalar_op_registry - - # Some types need to be converted to another type to enable groupby - if value.type().is_float64(): - return value.cast(ibis_dtypes.str) - elif value.type().is_geospatial(): - return typing.cast(ibis_types.GeoSpatialColumn, value).as_binary() - elif value.type().is_json(): - return scalar_op_registry.to_json_string(value) - else: - return value - - -def _to_ibis_boundary( - boundary: Optional[int], -) -> Optional[ibis_expr_window.WindowBoundary]: - if boundary is None: - return None - # WindowBoundary expects Value[Any, Any], ibis_types.literal returns Scalar which Mypy doesn't see as compatible. - return ibis_expr_window.WindowBoundary( - ibis_types.literal(boundary if boundary >= 0 else -boundary), # type: ignore[arg-type] - preceding=boundary <= 0, # type:ignore - ) - - -def _add_boundary( - bounds: typing.Union[RowsWindowBounds, RangeWindowBounds], - ibis_window: ibis_expr_builders.LegacyWindowBuilder, -) -> ibis_expr_builders.LegacyWindowBuilder: - if isinstance(bounds, RangeWindowBounds): - return ibis_window.range( - start=_to_ibis_boundary( - None - if bounds.start is None - else bigframes.core.utils.timedelta_to_micros(bounds.start) - ), - end=_to_ibis_boundary( - None - if bounds.end is None - else bigframes.core.utils.timedelta_to_micros(bounds.end) - ), - ) - if isinstance(bounds, RowsWindowBounds): - if bounds.start is not None or bounds.end is not None: - return ibis_window.rows( - start=_to_ibis_boundary(bounds.start), - end=_to_ibis_boundary(bounds.end), - ) - return ibis_window - else: - raise ValueError(f"unrecognized window bounds {bounds}") - - -def _map_to_literal( - original: ibis_types.Value, literal: ibis_types.Scalar -) -> ibis_types.Column: - # Hack required to perform aggregations on literals in ibis, even though bigquery - # will let you directly aggregate literals (eg. 'SELECT COUNT(1) from table1') - return ibis_api.ifelse(original.isnull(), literal, literal) # type: ignore - - -def _ibis_num(number: float): - return typing.cast(ibis_types.NumericValue, ibis_types.literal(number)) - - -def _is_true(column: ibis_types.Column) -> ibis_types.BooleanColumn: - if column.type().is_boolean(): - return cast(ibis_types.BooleanColumn, column) - elif column.type().is_numeric(): - result = cast(ibis_types.NumericColumn, column).__ne__(ibis_types.literal(0)) - return cast(ibis_types.BooleanColumn, result) - elif column.type().is_string(): - result = cast(ibis_types.StringValue, column).length() > ibis_types.literal(0) - return cast(ibis_types.BooleanColumn, result) - else: - # Time and geo values don't have a 'False' value - return cast( - ibis_types.BooleanColumn, _map_to_literal(column, ibis_types.literal(True)) - ) diff --git a/bigframes/core/compile/ibis_compiler/default_ordering.py b/bigframes/core/compile/ibis_compiler/default_ordering.py deleted file mode 100644 index 84ce52851c4..00000000000 --- a/bigframes/core/compile/ibis_compiler/default_ordering.py +++ /dev/null @@ -1,91 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -""" -Private helpers for loading a BigQuery table as a BigQuery DataFrames DataFrame. -""" - -from __future__ import annotations - -from typing import Sequence, cast - -import bigframes_vendored.ibis -import bigframes_vendored.ibis.expr.datatypes as ibis_dtypes -import bigframes_vendored.ibis.expr.operations as ibis_ops -import bigframes_vendored.ibis.expr.types as ibis_types - -import bigframes.core.guid as guid - - -def _convert_to_nonnull_string(column: ibis_types.Value) -> ibis_types.StringValue: - col_type = column.type() - if ( - col_type.is_numeric() - or col_type.is_boolean() - or col_type.is_binary() - or col_type.is_temporal() - ): - result = column.cast(ibis_dtypes.String(nullable=True)) - elif col_type.is_geospatial(): - result = cast(ibis_types.GeoSpatialColumn, column).as_text() - elif col_type.is_string(): - result = column - else: - # TO_JSON_STRING works with all data types, but isn't the most efficient - # Needed for JSON, STRUCT and ARRAY datatypes - result = ibis_ops.ToJsonString(column).to_expr() # type: ignore - # Escape backslashes and use backslash as delineator - escaped = cast( - ibis_types.StringColumn, result.fill_null(ibis_types.literal("")) - ).replace( - "\\", # type: ignore - "\\\\", # type: ignore - ) - return cast(ibis_types.StringColumn, bigframes_vendored.ibis.literal("\\")).concat( - escaped - ) - - -def gen_row_key( - columns: Sequence[ibis_types.Value], -) -> bigframes_vendored.ibis.Value: - ordering_hash_part = guid.generate_guid("bigframes_ordering_") - ordering_hash_part2 = guid.generate_guid("bigframes_ordering_") - ordering_rand_part = guid.generate_guid("bigframes_ordering_") - - # All inputs into hash must be non-null or resulting hash will be null - str_values = list(map(_convert_to_nonnull_string, columns)) - full_row_str = ( - str_values[0].concat(*str_values[1:]) if len(str_values) > 1 else str_values[0] - ) - full_row_hash = ( - full_row_str.hash() - .name(ordering_hash_part) - .cast(ibis_dtypes.String(nullable=True)) - ) - # By modifying value slightly, we get another hash uncorrelated with the first - full_row_hash_p2 = ( - (full_row_str + "_") - .hash() - .name(ordering_hash_part2) - .cast(ibis_dtypes.String(nullable=True)) - ) - # Used to disambiguate between identical rows (which will have identical hash) - random_value = ( - bigframes_vendored.ibis.random() - .name(ordering_rand_part) - .cast(ibis_dtypes.String(nullable=True)) - ) - - return full_row_hash.concat(full_row_hash_p2, random_value) diff --git a/bigframes/core/compile/ibis_compiler/ibis_compiler.py b/bigframes/core/compile/ibis_compiler/ibis_compiler.py deleted file mode 100644 index 938759ae181..00000000000 --- a/bigframes/core/compile/ibis_compiler/ibis_compiler.py +++ /dev/null @@ -1,307 +0,0 @@ -# Copyright 2023 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -from __future__ import annotations - -import dataclasses -import functools -import typing -from typing import Optional, cast - -import bigframes_vendored.ibis.backends.bigquery as ibis_bigquery -import bigframes_vendored.ibis.expr.api as ibis_api -import bigframes_vendored.ibis.expr.datatypes as ibis_dtypes -import bigframes_vendored.ibis.expr.types as ibis_types - -import bigframes.core.compile.compiled as compiled -import bigframes.core.compile.concat as concat_impl -import bigframes.core.compile.configs as configs -import bigframes.core.compile.explode -import bigframes.core.nodes as nodes -import bigframes.core.ordering as bf_ordering -import bigframes.core.rewrite as rewrites -import bigframes.core.rewrite.schema_binding as schema_binding -from bigframes import dtypes, operations -from bigframes.core import bq_data, expression, pyarrow_utils -from bigframes.core.logging import data_types as data_type_logger - -if typing.TYPE_CHECKING: - import bigframes.core - - -def compile_sql(request: configs.CompileRequest) -> configs.CompileResult: - output_names = tuple((expression.DerefOp(id), id.sql) for id in request.node.ids) - result_node = nodes.ResultNode( - request.node, - output_cols=output_names, - limit=request.peek_count, - ) - if request.sort_rows: - # Can only pullup slice if we are doing ORDER BY in outermost SELECT - # Need to do this before replacing unsupported ops, as that will rewrite slice ops - result_node = rewrites.pull_up_limits(result_node) - result_node = cast(nodes.ResultNode, _replace_unsupported_ops(result_node)) - result_node = cast(nodes.ResultNode, result_node.bottom_up(rewrites.simplify_join)) - # prune before pulling up order to avoid unnnecessary row_number() ops - result_node = cast(nodes.ResultNode, rewrites.column_pruning(result_node)) - result_node = rewrites.defer_order( - result_node, output_hidden_row_keys=request.materialize_all_order_keys - ) - if request.sort_rows: - result_node = cast(nodes.ResultNode, rewrites.column_pruning(result_node)) - encoded_type_refs = data_type_logger.encode_type_refs(result_node) - # Have to bind schema as the final step before compilation. - # Probably, should defer even further - result_node = typing.cast( - nodes.ResultNode, schema_binding.bind_schema_to_tree(result_node) - ) - sql = compile_result_node(result_node) - return configs.CompileResult( - sql, - result_node.schema.to_bigquery(), - result_node.order_by, - encoded_type_refs, - ) - - ordering: Optional[bf_ordering.RowOrdering] = result_node.order_by - result_node = dataclasses.replace(result_node, order_by=None) - result_node = cast(nodes.ResultNode, rewrites.column_pruning(result_node)) - result_node = cast(nodes.ResultNode, rewrites.defer_selection(result_node)) - encoded_type_refs = data_type_logger.encode_type_refs(result_node) - # Have to bind schema as the final step before compilation. - # Probably, should defer even further - result_node = typing.cast( - nodes.ResultNode, schema_binding.bind_schema_to_tree(result_node) - ) - sql = compile_result_node(result_node) - # Return the ordering iff no extra columns are needed to define the row order - if ordering is not None: - output_order = ( - ordering if ordering.referenced_columns.issubset(result_node.ids) else None - ) - assert (not request.materialize_all_order_keys) or (output_order is not None) - return configs.CompileResult( - sql, result_node.schema.to_bigquery(), output_order, encoded_type_refs - ) - - -def _replace_unsupported_ops(node: nodes.BigFrameNode): - # TODO: Run all replacement rules as single bottom-up pass - node = nodes.bottom_up(node, rewrites.rewrite_slice) - node = nodes.bottom_up(node, rewrites.rewrite_timedelta_expressions) - node = nodes.bottom_up(node, rewrites.rewrite_range_rolling) - node = nodes.bottom_up(node, rewrites.lower_udfs) - return node - - -def compile_result_node(root: nodes.ResultNode) -> str: - return compile_node(root.child).to_sql( - order_by=root.order_by.all_ordering_columns if root.order_by else (), - limit=root.limit, - selections=root.output_cols, - ) - - -# TODO: Remove cache when schema no longer requires compilation to derive schema (and therefor only compiles for execution) -@functools.lru_cache(maxsize=5000) -def compile_node(node: nodes.BigFrameNode) -> compiled.UnorderedIR: - """Compile node into CompileArrayValue. Caches result.""" - return node.reduce_up(lambda node, children: _compile_node(node, *children)) - - -@functools.singledispatch -def _compile_node( - node: nodes.BigFrameNode, *compiled_children: compiled.UnorderedIR -) -> compiled.UnorderedIR: - """Defines transformation but isn't cached, always use compile_node instead""" - raise ValueError(f"Can't compile unrecognized node: {node}") - - -@_compile_node.register -def compile_join( - node: nodes.JoinNode, left: compiled.UnorderedIR, right: compiled.UnorderedIR -): - condition_pairs = tuple( - (left.id.sql, right.id.sql) for left, right in node.conditions - ) - return left.join( - right=right, - type=node.type, - conditions=condition_pairs, - join_nulls=node.joins_nulls, - ) - - -@_compile_node.register -def compile_isin( - node: nodes.InNode, left: compiled.UnorderedIR, right: compiled.UnorderedIR -): - return left.isin_join( - right=right, - indicator_col=node.indicator_col.sql, - conditions=(node.left_col.id.sql, list(node.right_child.ids)[0].sql), - join_nulls=node.joins_nulls, - ) - - -@_compile_node.register -def compile_fromrange( - node: nodes.FromRangeNode, start: compiled.UnorderedIR, end: compiled.UnorderedIR -): - # Both start and end are single elements and do not inherently have an order) - start_table = start._to_ibis_expr() - end_table = end._to_ibis_expr() - - start_column = start_table.schema().names[0] - end_column = end_table.schema().names[0] - - # Perform a cross join to avoid errors - joined_table = start_table.cross_join(end_table) - - labels_array_table = ibis_api.range( - joined_table[start_column], joined_table[end_column] + node.step, node.step - ).name(node.output_id.sql) - labels = ( - typing.cast(ibis_types.ArrayValue, labels_array_table) - .as_table() - .unnest([node.output_id.sql]) - ) - return compiled.UnorderedIR( - labels, - columns=[labels[labels.columns[0]]], - ) - - -@_compile_node.register -def compile_readlocal(node: nodes.ReadLocalNode, *args): - offsets = node.offsets_col.sql if node.offsets_col else None - pa_table = node.local_data_source.data - bq_schema = node.schema.to_bigquery() - - pa_table = pa_table.select([item.source_id for item in node.scan_list.items]) - pa_table = pa_table.rename_columns([item.id.sql for item in node.scan_list.items]) - - if offsets: - pa_table = pyarrow_utils.append_offsets(pa_table, offsets) - return compiled.UnorderedIR.from_polars(pa_table, bq_schema) - - -@_compile_node.register -def compile_readtable(node: nodes.ReadTableNode, *args): - from bigframes.core.compile.ibis_compiler import scalar_op_registry - - ibis_table = _table_to_ibis( - node.source, scan_cols=[col.source_id for col in node.scan_list.items] - ) - - # TODO(b/395912450): Remove workaround solution once b/374784249 got resolved. - for scan_item in node.scan_list.items: - if ( - node.source.schema.get_type(scan_item.source_id) == dtypes.JSON_DTYPE - and ibis_table[scan_item.source_id].type() == ibis_dtypes.string - ): - json_column = scalar_op_registry.parse_json( - ibis_table[scan_item.source_id] - ).name(scan_item.source_id) - ibis_table = ibis_table.mutate(json_column) - - return compiled.UnorderedIR( - ibis_table, - tuple( - ibis_table[scan_item.source_id].name(scan_item.id.sql) - for scan_item in node.scan_list.items - ), - ) - - -def _table_to_ibis( - source: bq_data.BigqueryDataSource, - scan_cols: typing.Sequence[str], -) -> ibis_types.Table: - full_table_name = source.table.get_full_id(quoted=False) - # Physical schema might include unused columns, unsupported datatypes like JSON - physical_schema = ibis_bigquery.BigQuerySchema.to_ibis( - list(source.table.physical_schema) - ) - if source.at_time is not None or source.sql_predicate is not None: - import bigframes.session._io.bigquery - - sql = bigframes.session._io.bigquery.to_query( - full_table_name, - columns=scan_cols, - sql_predicate=source.sql_predicate, - time_travel_timestamp=source.at_time, - ) - return ibis_bigquery.Backend().sql(schema=physical_schema, query=sql) - else: - return ibis_api.table(physical_schema, full_table_name).select(scan_cols) - - -@_compile_node.register -def compile_filter(node: nodes.FilterNode, child: compiled.UnorderedIR): - return child.filter(node.predicate) - - -@_compile_node.register -def compile_selection(node: nodes.SelectionNode, child: compiled.UnorderedIR): - selection = tuple((ref, id.sql) for ref, id in node.input_output_pairs) - return child.selection(selection) - - -@_compile_node.register -def compile_projection(node: nodes.ProjectionNode, child: compiled.UnorderedIR): - projections = ((expr, id.sql) for expr, id in node.assignments) - return child.projection(tuple(projections)) - - -@_compile_node.register -def compile_concat(node: nodes.ConcatNode, *children: compiled.UnorderedIR): - output_ids = [id.sql for id in node.output_ids] - return concat_impl.concat_unordered(children, output_ids) - - -@_compile_node.register -def compile_aggregate(node: nodes.AggregateNode, child: compiled.UnorderedIR): - aggs = tuple((agg, id.sql) for agg, id in node.aggregations) - result = child.aggregate(aggs, node.by_column_ids, order_by=node.order_by) - # TODO: Remove dropna field and use filter node instead - if node.dropna: - for key in node.by_column_ids: - if node.child.field_by_id[key.id].nullable: - result = result.filter(operations.notnull_op.as_expr(key)) - return result - - -@_compile_node.register -def compile_window(node: nodes.WindowOpNode, child: compiled.UnorderedIR): - result = child - for cdef in node.agg_exprs: - result = result.project_window_op( - cdef.expression, # type: ignore - node.window_spec, - cdef.id.sql, - ) - return result - - -@_compile_node.register -def compile_explode(node: nodes.ExplodeNode, child: compiled.UnorderedIR): - offsets_col = node.offsets_col.sql if (node.offsets_col is not None) else None - return bigframes.core.compile.explode.explode_unordered( - child, node.column_ids, offsets_col - ) - - -@_compile_node.register -def compile_random_sample(node: nodes.RandomSampleNode, child: compiled.UnorderedIR): - return child._uniform_sampling(node.fraction) diff --git a/bigframes/core/compile/ibis_compiler/operations/__init__.py b/bigframes/core/compile/ibis_compiler/operations/__init__.py deleted file mode 100644 index 9d9f3849abe..00000000000 --- a/bigframes/core/compile/ibis_compiler/operations/__init__.py +++ /dev/null @@ -1,21 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -"""Operation implementations for the Ibis-based compiler. - -This directory structure should reflect the same layout as the -`bigframes/operations` directory where the operations are defined. - -Prefer a few ops per file to keep file sizes manageable for text editors and LLMs. -""" diff --git a/bigframes/core/compile/ibis_compiler/operations/generic_ops.py b/bigframes/core/compile/ibis_compiler/operations/generic_ops.py deleted file mode 100644 index 78f6a0c4de8..00000000000 --- a/bigframes/core/compile/ibis_compiler/operations/generic_ops.py +++ /dev/null @@ -1,38 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -""" -BigFrames -> Ibis compilation for the operations in bigframes.operations.generic_ops. - -Please keep implementations in sequential order by op name. -""" - -from __future__ import annotations - -from bigframes_vendored.ibis.expr import types as ibis_types - -from bigframes.core.compile.ibis_compiler import scalar_op_compiler -from bigframes.operations import generic_ops - -register_unary_op = scalar_op_compiler.scalar_op_compiler.register_unary_op - - -@register_unary_op(generic_ops.notnull_op) -def notnull_op_impl(x: ibis_types.Value): - return x.notnull() - - -@register_unary_op(generic_ops.isnull_op) -def isnull_op_impl(x: ibis_types.Value): - return x.isnull() diff --git a/bigframes/core/compile/ibis_compiler/operations/geo_ops.py b/bigframes/core/compile/ibis_compiler/operations/geo_ops.py deleted file mode 100644 index 772752112a4..00000000000 --- a/bigframes/core/compile/ibis_compiler/operations/geo_ops.py +++ /dev/null @@ -1,192 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from __future__ import annotations - -from typing import cast - -import bigframes_vendored.ibis.expr.datatypes as ibis_dtypes -import bigframes_vendored.ibis.expr.operations.geospatial as ibis_geo -import bigframes_vendored.ibis.expr.operations.udf as ibis_udf -from bigframes_vendored import ibis -from bigframes_vendored.ibis.expr import types as ibis_types - -from bigframes.core.compile.ibis_compiler import scalar_op_compiler -from bigframes.operations import geo_ops as ops - -register_unary_op = scalar_op_compiler.scalar_op_compiler.register_unary_op -register_binary_op = scalar_op_compiler.scalar_op_compiler.register_binary_op - - -# Geo Ops -@register_unary_op(ops.geo_st_astext_op) -def geo_st_astext_op_impl(x: ibis_types.Value): - return cast(ibis_types.GeoSpatialValue, x).as_text() - - -@register_unary_op(ops.geo_st_boundary_op, pass_op=False) -def geo_st_boundary_op_impl(x: ibis_types.Value): - return st_boundary(x) - - -@register_unary_op(ops.GeoStBufferOp, pass_op=True) -def geo_st_buffer_op_impl(x: ibis_types.Value, op: ops.GeoStBufferOp): - return st_buffer( - x, - op.buffer_radius, - op.num_seg_quarter_circle, - op.use_spheroid, - ) - - -@register_unary_op(ops.geo_st_convexhull_op, pass_op=False) -def geo_st_convexhull_op_impl(x: ibis_types.Value): - return st_convexhull(x) - - -@register_binary_op(ops.geo_st_difference_op, pass_op=False) -def geo_st_difference_op_impl(x: ibis_types.Value, y: ibis_types.Value): - return cast(ibis_types.GeoSpatialValue, x).difference( - cast(ibis_types.GeoSpatialValue, y) - ) - - -@register_binary_op(ops.GeoStDistanceOp, pass_op=True) -def geo_st_distance_op_impl( - x: ibis_types.Value, y: ibis_types.Value, op: ops.GeoStDistanceOp -): - return st_distance(x, y, op.use_spheroid) - - -@register_unary_op(ops.geo_st_geogfromtext_op) -def geo_st_geogfromtext_op_impl(x: ibis_types.Value): - # Ibis doesn't seem to provide a dedicated method to cast from string to geography, - # so we use a BigQuery scalar function, st_geogfromtext(), directly. - return st_geogfromtext(x) - - -@register_binary_op(ops.geo_st_geogpoint_op, pass_op=False) -def geo_st_geogpoint_op_impl(x: ibis_types.Value, y: ibis_types.Value): - return cast(ibis_types.NumericValue, x).point(cast(ibis_types.NumericValue, y)) - - -@register_binary_op(ops.geo_st_intersection_op, pass_op=False) -def geo_st_intersection_op_impl(x: ibis_types.Value, y: ibis_types.Value): - return cast(ibis_types.GeoSpatialValue, x).intersection( - cast(ibis_types.GeoSpatialValue, y) - ) - - -@register_unary_op(ops.geo_st_isclosed_op, pass_op=False) -def geo_st_isclosed_op_impl(x: ibis_types.Value): - return st_isclosed(x) - - -@register_unary_op(ops.GeoStRegionStatsOp, pass_op=True) -def geo_st_regionstats_op_impl( - geography: ibis_types.Value, - op: ops.GeoStRegionStatsOp, -): - if op.band: - band = ibis.literal(op.band, type=ibis_dtypes.string()) - else: - band = None - - if op.include: - include = ibis.literal(op.include, type=ibis_dtypes.string()) - else: - include = None - - if op.options: - options = ibis.literal(op.options, type=ibis_dtypes.json()) - else: - options = None - - return ibis_geo.GeoRegionStats( - arg=geography, # type: ignore - raster_id=ibis.literal(op.raster_id, type=ibis_dtypes.string()), # type: ignore - band=band, # type: ignore - include=include, # type: ignore - options=options, # type: ignore - ).to_expr() - - -@register_unary_op(ops.geo_x_op) -def geo_x_op_impl(x: ibis_types.Value): - return cast(ibis_types.GeoSpatialValue, x).x() - - -@register_unary_op(ops.GeoStLengthOp, pass_op=True) -def geo_length_op_impl(x: ibis_types.Value, op: ops.GeoStLengthOp): - # Call the st_length UDF defined in this file (or imported) - return st_length(x, op.use_spheroid) - - -@register_unary_op(ops.geo_y_op) -def geo_y_op_impl(x: ibis_types.Value): - return cast(ibis_types.GeoSpatialValue, x).y() - - -@ibis_udf.scalar.builtin -def st_convexhull(x: ibis_dtypes.geography) -> ibis_dtypes.geography: # type: ignore - """ST_CONVEXHULL""" - ... - - -@ibis_udf.scalar.builtin -def st_geogfromtext(a: str) -> ibis_dtypes.geography: # type: ignore - """Convert string to geography.""" - - -@ibis_udf.scalar.builtin -def st_boundary(a: ibis_dtypes.geography) -> ibis_dtypes.geography: # type: ignore - """Find the boundary of a geography.""" - - -@ibis_udf.scalar.builtin -def st_buffer( - geography: ibis_dtypes.geography, # type: ignore - buffer_radius: ibis_dtypes.Float64, - num_seg_quarter_circle: ibis_dtypes.Float64, - use_spheroid: ibis_dtypes.Boolean, -) -> ibis_dtypes.geography: # type: ignore - ... - - -@ibis_udf.scalar.builtin -def st_distance( - a: ibis_dtypes.geography, # type: ignore - b: ibis_dtypes.geography, # type: ignore - use_spheroid: bool, # type: ignore -) -> ibis_dtypes.float: # type: ignore - """Convert string to geography.""" - - -@ibis_udf.scalar.builtin -def st_length(geog: ibis_dtypes.geography, use_spheroid: bool) -> ibis_dtypes.float: # type: ignore - """ST_LENGTH BQ builtin. This body is never executed.""" - pass - - -@ibis_udf.scalar.builtin -def st_isclosed(a: ibis_dtypes.geography) -> ibis_dtypes.boolean: # type: ignore - """Checks if a geography is closed.""" - - -@ibis_udf.scalar.builtin -def st_simplify( - geography: ibis_dtypes.geography, # type: ignore - tolerance_meters: ibis_dtypes.float, # type: ignore -) -> ibis_dtypes.geography: # type: ignore - ... diff --git a/bigframes/core/compile/ibis_compiler/scalar_op_compiler.py b/bigframes/core/compile/ibis_compiler/scalar_op_compiler.py deleted file mode 100644 index 31a8459923c..00000000000 --- a/bigframes/core/compile/ibis_compiler/scalar_op_compiler.py +++ /dev/null @@ -1,330 +0,0 @@ -# Copyright 2023 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -"""To avoid circular imports, this module should _not_ depend on any ops.""" - -from __future__ import annotations - -import functools -import typing -from typing import TYPE_CHECKING - -import bigframes_vendored.ibis -import bigframes_vendored.ibis.expr.operations.generic as ibis_generic -import bigframes_vendored.ibis.expr.types as ibis_types - -import bigframes.core.compile.ibis_types -import bigframes.core.expression as ex -from bigframes.core import agg_expressions, ordering -from bigframes.operations import googlesql as gsql_ops -from bigframes.operations import numeric_ops - -if TYPE_CHECKING: - import bigframes.operations as ops - - -class ExpressionCompiler: - # Mapping of operation name to implemenations - _registry: dict[ - str, - typing.Callable[ - [typing.Sequence[ibis_types.Value], ops.RowOp], ibis_types.Value - ], - ] = {} - - @functools.singledispatchmethod - def compile_expression( - self, - expression: ex.Expression, - bindings: typing.Dict[str, ibis_types.Value], - ) -> ibis_types.Value: - raise NotImplementedError(f"Unrecognized expression: {expression}") - - @compile_expression.register - def _( - self, - expression: ex.ScalarConstantExpression, - bindings: typing.Dict[str, ibis_types.Value], - ) -> ibis_types.Value: - return bigframes.core.compile.ibis_types.literal_to_ibis_scalar( - expression.value, expression.dtype - ) - - @compile_expression.register - def _( - self, - expression: ex.DerefOp, - bindings: typing.Dict[str, ibis_types.Value], - ) -> ibis_types.Value: - if expression.id.sql not in bindings: - raise ValueError(f"Could not resolve unbound variable {expression.id}") - else: - return bindings[expression.id.sql] - - @compile_expression.register - def _( - self, - expression: agg_expressions.WindowExpression, - bindings: typing.Dict[str, ibis_types.Value], - ) -> ibis_types.Value: - import bigframes.core.compile.ibis_compiler.aggregate_compiler as agg_compile - - return agg_compile.compile_analytic( - expression.analytic_expr, expression.window, bindings - ) - - @compile_expression.register - def _( - self, - expression: ex.OpExpression, - bindings: typing.Dict[str, ibis_types.Value], - ) -> ibis_types.Value: - inputs = [ - self.compile_expression(sub_expr, bindings) - for sub_expr in expression.inputs - ] - if isinstance(expression.op, gsql_ops.GoogleSqlScalarOp): - return googlesql_scalar_op_impl( - *inputs, op=expression.op, output_type=expression.output_type - ) - return self.compile_row_op(expression.op, inputs) - - @compile_expression.register - def _( - self, - expression: ex.OmittedArg, - bindings: typing.Dict[str, ibis_types.Value], - ) -> ibis_types.Value: - return bigframes_vendored.ibis.omitted() - - def compile_row_op( - self, op: ops.RowOp, inputs: typing.Sequence[ibis_types.Value] - ) -> ibis_types.Value: - impl = self._registry[op.name] - return impl(inputs, op) - - def register_unary_op( - self, - op_ref: typing.Union[ops.UnaryOp, type[ops.UnaryOp]], - pass_op: bool = False, - ): - """ - Decorator to register a unary op implementation. - - Args: - op_ref (UnaryOp or UnaryOp type): - Class or instance of operator that is implemented by the decorated function. - pass_op (bool): - Set to true if implementation takes the operator object as the last argument. - This is needed for parameterized ops where parameters are part of op object. - """ - key = typing.cast(str, op_ref.name) - - def decorator(impl: typing.Callable[..., ibis_types.Value]): - def normalized_impl(args: typing.Sequence[ibis_types.Value], op: ops.RowOp): - if pass_op: - return impl(args[0], op) - else: - return impl(args[0]) - - self._register(key, normalized_impl) - return impl - - return decorator - - def register_binary_op( - self, - op_ref: typing.Union[ops.BinaryOp, type[ops.BinaryOp]], - pass_op: bool = False, - ): - """ - Decorator to register a binary op implementation. - - Args: - op_ref (BinaryOp or BinaryOp type): - Class or instance of operator that is implemented by the decorated function. - pass_op (bool): - Set to true if implementation takes the operator object as the last argument. - This is needed for parameterized ops where parameters are part of op object. - """ - key = typing.cast(str, op_ref.name) - - def decorator(impl: typing.Callable[..., ibis_types.Value]): - def normalized_impl(args: typing.Sequence[ibis_types.Value], op: ops.RowOp): - if pass_op: - return impl(args[0], args[1], op) - else: - return impl(args[0], args[1]) - - self._register(key, normalized_impl) - return impl - - return decorator - - def register_ternary_op( - self, op_ref: typing.Union[ops.TernaryOp, type[ops.TernaryOp]] - ): - """ - Decorator to register a ternary op implementation. - - Args: - op_ref (TernaryOp or TernaryOp type): - Class or instance of operator that is implemented by the decorated function. - """ - key = typing.cast(str, op_ref.name) - - def decorator(impl: typing.Callable[..., ibis_types.Value]): - def normalized_impl(args: typing.Sequence[ibis_types.Value], op: ops.RowOp): - return impl(args[0], args[1], args[2]) - - self._register(key, normalized_impl) - return impl - - return decorator - - def register_nary_op( - self, op_ref: typing.Union[ops.NaryOp, type[ops.NaryOp]], pass_op: bool = False - ): - """ - Decorator to register a nary op implementation. - - Args: - op_ref (NaryOp or NaryOp type): - Class or instance of operator that is implemented by the decorated function. - pass_op (bool): - Set to true if implementation takes the operator object as the last argument. - This is needed for parameterized ops where parameters are part of op object. - """ - key = typing.cast(str, op_ref.name) - - def decorator(impl: typing.Callable[..., ibis_types.Value]): - def normalized_impl(args: typing.Sequence[ibis_types.Value], op: ops.RowOp): - if pass_op: - return impl(*args, op=op) - else: - return impl(*args) - - self._register(key, normalized_impl) - return impl - - return decorator - - def _register( - self, - op_name: str, - impl: typing.Callable[ - [typing.Sequence[ibis_types.Value], ops.RowOp], ibis_types.Value - ], - ): - if op_name in self._registry: - raise ValueError(f"Operation name {op_name} already registered") - self._registry[op_name] = impl - - def _convert_row_ordering_to_table_values( - self, - value_lookup: typing.Mapping[str, ibis_types.Value], - ordering_columns: typing.Sequence[ordering.OrderingExpression], - ) -> typing.Sequence[ibis_types.Value]: - column_refs = ordering_columns - ordering_values = [] - for ordering_col in column_refs: - expr = self.compile_expression(ordering_col.scalar_expression, value_lookup) - ordering_value = ( - bigframes_vendored.ibis.asc(expr) # type: ignore - if ordering_col.direction.is_ascending - else bigframes_vendored.ibis.desc(expr) # type: ignore - ) - # Bigquery SQL considers NULLS to be "smallest" values, but we need to override in these cases. - if (not ordering_col.na_last) and (not ordering_col.direction.is_ascending): - # Force nulls to be first - is_null_val = typing.cast(ibis_types.Column, expr.isnull()) - ordering_values.append(bigframes_vendored.ibis.desc(is_null_val)) - elif (ordering_col.na_last) and (ordering_col.direction.is_ascending): - # Force nulls to be last - is_null_val = typing.cast(ibis_types.Column, expr.isnull()) - ordering_values.append(bigframes_vendored.ibis.asc(is_null_val)) - ordering_values.append(ordering_value) - return ordering_values - - def _convert_range_ordering_to_table_value( - self, - value_lookup: typing.Mapping[str, ibis_types.Value], - ordering_column: ordering.OrderingExpression, - ) -> ibis_types.Value: - """Converts the ordering for range windows to Ibis references. - - Note that this method is different from `_convert_row_ordering_to_table_values` in - that it does not arrange null values. There are two reasons: - 1. Manipulating null positions requires more than one ordering key, which is forbidden - by SQL window syntax for range rolling. - 2. Pandas does not allow range rolling on timeseries with nulls. - - Therefore, we opt for the simplest approach here: generate the simplest SQL and follow - the BigQuery engine behavior. - """ - expr = self.compile_expression(ordering_column.scalar_expression, value_lookup) - - if ordering_column.direction.is_ascending: - return bigframes_vendored.ibis.asc(expr) # type: ignore - return bigframes_vendored.ibis.desc(expr) # type: ignore - - -# Singleton compiler -scalar_op_compiler = ExpressionCompiler() - - -@scalar_op_compiler.register_unary_op(numeric_ops.isnan_op) -def isnanornull(arg): - return arg.isnan() - - -@scalar_op_compiler.register_unary_op(numeric_ops.isfinite_op) -def isfinite(arg): - return arg.isinf().negate() & arg.isnan().negate() - - -def googlesql_scalar_op_impl( - *operands: ibis_types.Value, op: ops.GoogleSqlScalarOp, output_type -): - final_operands: list[ibis_types.Value] = [] - arg_templates = [] - for i, operand in enumerate(operands): - if i < len(op.args): - arg_spec = op.args[i] - else: - assert op.args[-1].is_vararg, ( - f"Too many arguments, for {op.sql_name}, expected {len(op.args)}" - ) - arg_spec = op.args[-1] - if isinstance(operand.op(), ibis_generic.OmittedArg): - assert arg_spec.optional, "Argument omitted, but not optional" - continue - - target_idx = len(final_operands) - final_operands.append(operand) - if arg_spec.arg_name: - arg_templates.append(f"{arg_spec.arg_name} => {{{target_idx}}}") - else: - arg_templates.append(f"{{{target_idx}}}") - args_template = ", ".join(arg_templates) - sql_template = f"{op.sql_name}({args_template})" - return ibis_generic.SqlScalar( - sql_template, - values=tuple( - typing.cast(ibis_generic.Value, expr.op()) for expr in final_operands - ), - output_type=bigframes.core.compile.ibis_types.bigframes_dtype_to_ibis_dtype( - output_type - ), - ).to_expr() diff --git a/bigframes/core/compile/ibis_compiler/scalar_op_registry.py b/bigframes/core/compile/ibis_compiler/scalar_op_registry.py deleted file mode 100644 index 530d23a8b06..00000000000 --- a/bigframes/core/compile/ibis_compiler/scalar_op_registry.py +++ /dev/null @@ -1,2248 +0,0 @@ -# Copyright 2023 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from __future__ import annotations - -import functools -import typing -from typing import Any, cast - -import bigframes_vendored.ibis.expr.api as ibis_api -import bigframes_vendored.ibis.expr.datatypes as ibis_dtypes -import bigframes_vendored.ibis.expr.operations.ai_ops as ai_ops -import bigframes_vendored.ibis.expr.operations.generic as ibis_generic -import bigframes_vendored.ibis.expr.operations.udf as ibis_udf -import bigframes_vendored.ibis.expr.types as ibis_types -import numpy as np -import pandas as pd -from bigframes_vendored import ibis - -import bigframes.core.compile.ibis_compiler.default_ordering -import bigframes.core.compile.ibis_types -import bigframes.operations as ops -from bigframes.core.compile.constants import UNIT_TO_US_CONVERSION_FACTORS -from bigframes.core.compile.ibis_compiler.scalar_op_compiler import ( - scalar_op_compiler, # TODO(tswast): avoid import of variables -) - -_ZERO = typing.cast(ibis_types.NumericValue, ibis_types.literal(0)) -_NAN = typing.cast(ibis_types.NumericValue, ibis_types.literal(np.nan)) -_INF = typing.cast(ibis_types.NumericValue, ibis_types.literal(np.inf)) -_NEG_INF = typing.cast(ibis_types.NumericValue, ibis_types.literal(-np.inf)) - -# Approx Highest number you can pass in to EXP function and get a valid FLOAT64 result -# FLOAT64 has 11 exponent bits, so max values is about 2**(2**10) -# ln(2**(2**10)) == (2**10)*ln(2) ~= 709.78, so EXP(x) for x>709.78 will overflow. -_FLOAT64_EXP_BOUND = typing.cast(ibis_types.NumericValue, ibis_types.literal(709.78)) - -_OBJ_REF_STRUCT_SCHEMA = ( - ("uri", ibis_dtypes.String), - ("version", ibis_dtypes.String), - ("authorizer", ibis_dtypes.String), - ("details", ibis_dtypes.JSON), -) -_OBJ_REF_IBIS_DTYPE = ibis_dtypes.Struct.from_tuples(_OBJ_REF_STRUCT_SCHEMA) # type: ignore - - -### Unary Ops -@scalar_op_compiler.register_unary_op(ops.hash_op) -def hash_op_impl(x: ibis_types.Value): - return typing.cast(ibis_types.IntegerValue, x).hash() - - -# Trig Functions -@scalar_op_compiler.register_unary_op(ops.sin_op) -def sin_op_impl(x: ibis_types.Value): - return typing.cast(ibis_types.NumericValue, x).sin() - - -@scalar_op_compiler.register_unary_op(ops.cos_op) -def cos_op_impl(x: ibis_types.Value): - return typing.cast(ibis_types.NumericValue, x).cos() - - -@scalar_op_compiler.register_unary_op(ops.tan_op) -def tan_op_impl(x: ibis_types.Value): - return typing.cast(ibis_types.NumericValue, x).tan() - - -# Inverse trig functions -@scalar_op_compiler.register_unary_op(ops.arcsin_op) -def arcsin_op_impl(x: ibis_types.Value): - numeric_value = typing.cast(ibis_types.NumericValue, x) - domain = numeric_value.abs() <= _ibis_num(1) - return (~domain).ifelse(_NAN, numeric_value.asin()) - - -@scalar_op_compiler.register_unary_op(ops.arccos_op) -def arccos_op_impl(x: ibis_types.Value): - numeric_value = typing.cast(ibis_types.NumericValue, x) - domain = numeric_value.abs() <= _ibis_num(1) - return (~domain).ifelse(_NAN, numeric_value.acos()) - - -@scalar_op_compiler.register_unary_op(ops.arctan_op) -def arctan_op_impl(x: ibis_types.Value): - return typing.cast(ibis_types.NumericValue, x).atan() - - -@scalar_op_compiler.register_binary_op(ops.arctan2_op) -def arctan2_op_impl(x: ibis_types.Value, y: ibis_types.Value): - return typing.cast(ibis_types.NumericValue, x).atan2( - typing.cast(ibis_types.NumericValue, y) - ) - - -# Hyperbolic trig functions -# BQ has these functions, but Ibis doesn't -@scalar_op_compiler.register_unary_op(ops.sinh_op) -def sinh_op_impl(x: ibis_types.Value): - numeric_value = typing.cast(ibis_types.NumericValue, x) - sinh_result = (numeric_value.exp() - (numeric_value.negate()).exp()) / _ibis_num(2) - domain = numeric_value.abs() < _FLOAT64_EXP_BOUND - return (~domain).ifelse(_INF * numeric_value.sign(), sinh_result) - - -@scalar_op_compiler.register_unary_op(ops.cosh_op) -def cosh_op_impl(x: ibis_types.Value): - numeric_value = typing.cast(ibis_types.NumericValue, x) - cosh_result = (numeric_value.exp() + (numeric_value.negate()).exp()) / _ibis_num(2) - domain = numeric_value.abs() < _FLOAT64_EXP_BOUND - return (~domain).ifelse(_INF, cosh_result) - - -@scalar_op_compiler.register_unary_op(ops.tanh_op) -def tanh_op_impl(x: ibis_types.Value): - numeric_value = typing.cast(ibis_types.NumericValue, x) - tanh_result = (numeric_value.exp() - (numeric_value.negate()).exp()) / ( - numeric_value.exp() + (numeric_value.negate()).exp() - ) - # Beyond +-20, is effectively just the sign function - domain = numeric_value.abs() < _ibis_num(20) - return (~domain).ifelse(numeric_value.sign(), tanh_result) - - -@scalar_op_compiler.register_unary_op(ops.arcsinh_op) -def arcsinh_op_impl(x: ibis_types.Value): - numeric_value = typing.cast(ibis_types.NumericValue, x) - sqrt_part = ((numeric_value * numeric_value) + _ibis_num(1)).sqrt() - return (numeric_value.abs() + sqrt_part).ln() * numeric_value.sign() - - -@scalar_op_compiler.register_unary_op(ops.arccosh_op) -def arccosh_op_impl(x: ibis_types.Value): - numeric_value = typing.cast(ibis_types.NumericValue, x) - sqrt_part = ((numeric_value * numeric_value) - _ibis_num(1)).sqrt() - acosh_result = (numeric_value + sqrt_part).ln() - domain = numeric_value >= _ibis_num(1) - return (~domain).ifelse(_NAN, acosh_result) - - -@scalar_op_compiler.register_unary_op(ops.arctanh_op) -def arctanh_op_impl(x: ibis_types.Value): - numeric_value = typing.cast(ibis_types.NumericValue, x) - domain = numeric_value.abs() < _ibis_num(1) - numerator = numeric_value + _ibis_num(1) - denominator = _ibis_num(1) - numeric_value - ln_input = typing.cast(ibis_types.NumericValue, numerator.div(denominator)) - atanh_result = ln_input.ln().div(2) - - out_of_domain = (numeric_value.abs() == _ibis_num(1)).ifelse( - _INF * numeric_value, _NAN - ) - - return (~domain).ifelse(out_of_domain, atanh_result) - - -# Numeric Ops -@scalar_op_compiler.register_unary_op(ops.floor_op) -def floor_op_impl(x: ibis_types.Value): - x_numeric = typing.cast(ibis_types.NumericValue, x) - if x_numeric.type().is_boolean(): - return x_numeric.cast(ibis_dtypes.Int64()).cast(ibis_dtypes.Float64()) - if x_numeric.type().is_integer(): - return x_numeric.cast(ibis_dtypes.Float64()) - if x_numeric.type().is_floating(): - # Default ibis impl tries to cast to integer, which doesn't match pandas and can overflow - return float_floor(x_numeric) - else: # numeric - return x_numeric.floor() - - -@scalar_op_compiler.register_unary_op(ops.ceil_op) -def ceil_op_impl(x: ibis_types.Value): - x_numeric = typing.cast(ibis_types.NumericValue, x) - if x_numeric.type().is_boolean(): - return x_numeric.cast(ibis_dtypes.Int64()).cast(ibis_dtypes.Float64()) - if x_numeric.type().is_integer(): - return x_numeric.cast(ibis_dtypes.Float64()) - if x_numeric.type().is_floating(): - # Default ibis impl tries to cast to integer, which doesn't match pandas and can overflow - return float_ceil(x_numeric) - else: # numeric - return x_numeric.ceil() - - -@scalar_op_compiler.register_unary_op(ops.abs_op) -def abs_op_impl(x: ibis_types.Value): - return typing.cast(ibis_types.NumericValue, x).abs() - - -@scalar_op_compiler.register_unary_op(ops.pos_op) -def pos_op_impl(x: ibis_types.Value): - return typing.cast(ibis_types.NumericValue, x) - - -@scalar_op_compiler.register_unary_op(ops.neg_op) -def neg_op_impl(x: ibis_types.Value): - return typing.cast(ibis_types.NumericValue, x).negate() - - -@scalar_op_compiler.register_unary_op(ops.sqrt_op) -def sqrt_op_impl(x: ibis_types.Value): - numeric_value = typing.cast(ibis_types.NumericValue, x) - domain = numeric_value >= _ZERO - return (~domain).ifelse(_NAN, numeric_value.sqrt()) - - -@scalar_op_compiler.register_unary_op(ops.log10_op) -def log10_op_impl(x: ibis_types.Value): - numeric_value = typing.cast(ibis_types.NumericValue, x) - domain = numeric_value > _ZERO - out_of_domain = (numeric_value == _ZERO).ifelse(_NEG_INF, _NAN) - return (~domain).ifelse(out_of_domain, numeric_value.log10()) - - -@scalar_op_compiler.register_unary_op(ops.ln_op) -def ln_op_impl(x: ibis_types.Value): - numeric_value = typing.cast(ibis_types.NumericValue, x) - domain = numeric_value > _ZERO - out_of_domain = (numeric_value == _ZERO).ifelse(_NEG_INF, _NAN) - return (~domain).ifelse(out_of_domain, numeric_value.ln()) - - -@scalar_op_compiler.register_unary_op(ops.log1p_op) -def log1p_op_impl(x: ibis_types.Value): - return ln_op_impl(_ibis_num(1) + x) - - -@scalar_op_compiler.register_unary_op(ops.exp_op) -def exp_op_impl(x: ibis_types.Value): - numeric_value = typing.cast(ibis_types.NumericValue, x) - domain = numeric_value < _FLOAT64_EXP_BOUND - return (~domain).ifelse(_INF, numeric_value.exp()) - - -@scalar_op_compiler.register_unary_op(ops.expm1_op) -def expm1_op_impl(x: ibis_types.Value): - return exp_op_impl(x) - _ibis_num(1) - - -@scalar_op_compiler.register_unary_op(ops.invert_op) -def invert_op_impl(x: ibis_types.Value): - return x.__invert__() # type: ignore - - -## String Operation -@scalar_op_compiler.register_unary_op(ops.len_op) -def len_op_impl(x: ibis_types.Value): - return typing.cast(ibis_types.StringValue, x).length().cast(ibis_dtypes.int64) - - -@scalar_op_compiler.register_unary_op(ops.reverse_op) -def reverse_op_impl(x: ibis_types.Value): - return typing.cast(ibis_types.StringValue, x).reverse() - - -@scalar_op_compiler.register_unary_op(ops.lower_op) -def lower_op_impl(x: ibis_types.Value): - return typing.cast(ibis_types.StringValue, x).lower() - - -@scalar_op_compiler.register_unary_op(ops.upper_op) -def upper_op_impl(x: ibis_types.Value): - return typing.cast(ibis_types.StringValue, x).upper() - - -@scalar_op_compiler.register_unary_op(ops.StrLstripOp, pass_op=True) -def str_lstrip_op_impl(x: ibis_types.Value, op: ops.StrStripOp): - return str_lstrip_op(x, to_strip=op.to_strip) - - -@scalar_op_compiler.register_unary_op(ops.StrRstripOp, pass_op=True) -def str_rstrip_op_impl(x: ibis_types.Value, op: ops.StrRstripOp): - return str_rstrip_op(x, to_strip=op.to_strip) - - -@scalar_op_compiler.register_unary_op(ops.StrStripOp, pass_op=True) -def str_strip_op_impl(x: ibis_types.Value, op: ops.StrStripOp): - return str_strip_op(x, to_strip=op.to_strip) - - -@scalar_op_compiler.register_unary_op(ops.isnumeric_op) -def isnumeric_op_impl(x: ibis_types.Value): - # catches all members of the Unicode number class, which matches pandas isnumeric - # see https://cloud.google.com/bigquery/docs/reference/standard-sql/string_functions#regexp_contains - # TODO: Validate correctness, my miss eg ⅕ character - return typing.cast(ibis_types.StringValue, x).re_search(r"^(\pN+)$") - - -@scalar_op_compiler.register_unary_op(ops.isalpha_op) -def isalpha_op_impl(x: ibis_types.Value): - return typing.cast(ibis_types.StringValue, x).re_search( - r"^(\p{Lm}|\p{Lt}|\p{Lu}|\p{Ll}|\p{Lo})+$" - ) - - -@scalar_op_compiler.register_unary_op(ops.isdigit_op) -def isdigit_op_impl(x: ibis_types.Value): - return typing.cast(ibis_types.StringValue, x).re_search( - r"^[\p{Nd}\x{00B9}\x{00B2}\x{00B3}\x{2070}\x{2074}-\x{2079}\x{2080}-\x{2089}]+$" - ) - - -@scalar_op_compiler.register_unary_op(ops.isdecimal_op) -def isdecimal_op_impl(x: ibis_types.Value): - return typing.cast(ibis_types.StringValue, x).re_search(r"^(\p{Nd})+$") - - -@scalar_op_compiler.register_unary_op(ops.isalnum_op) -def isalnum_op_impl(x: ibis_types.Value): - return typing.cast(ibis_types.StringValue, x).re_search( - r"^(\p{N}|\p{Lm}|\p{Lt}|\p{Lu}|\p{Ll}|\p{Lo})+$" - ) - - -@scalar_op_compiler.register_unary_op(ops.isspace_op) -def isspace_op_impl(x: ibis_types.Value): - # All characters are whitespace characters, False for empty string - return typing.cast(ibis_types.StringValue, x).re_search(r"^\s+$") - - -@scalar_op_compiler.register_unary_op(ops.islower_op) -def islower_op_impl(x: ibis_types.Value): - # No upper case characters, min one cased character - # See: https://docs.python.org/3/library/stdtypes.html#str - return typing.cast(ibis_types.StringValue, x).re_search(r"\p{Ll}") & ~typing.cast( - ibis_types.StringValue, x - ).re_search(r"\p{Lu}|\p{Lt}") - - -@scalar_op_compiler.register_unary_op(ops.isupper_op) -def isupper_op_impl(x: ibis_types.Value): - # No lower case characters, min one cased character - # See: https://docs.python.org/3/library/stdtypes.html#str - return typing.cast(ibis_types.StringValue, x).re_search(r"\p{Lu}") & ~typing.cast( - ibis_types.StringValue, x - ).re_search(r"\p{Ll}|\p{Lt}") - - -@scalar_op_compiler.register_unary_op(ops.capitalize_op) -def capitalize_op_impl(x: ibis_types.Value): - return typing.cast(ibis_types.StringValue, x).capitalize() - - -@scalar_op_compiler.register_unary_op(ops.StrContainsOp, pass_op=True) -def strcontains_op(x: ibis_types.Value, op: ops.StrContainsOp): - return typing.cast(ibis_types.StringValue, x).contains(op.pat) - - -@scalar_op_compiler.register_unary_op(ops.StrContainsRegexOp, pass_op=True) -def contains_regex_op_impl(x: ibis_types.Value, op: ops.StrContainsRegexOp): - return typing.cast(ibis_types.StringValue, x).re_search(op.pat) - - -@scalar_op_compiler.register_unary_op(ops.StrPadOp, pass_op=True) -def strpad_op_impl(x: ibis_types.Value, op: ops.StrPadOp): - str_val = typing.cast(ibis_types.StringValue, x) - - # SQL pad operations will truncate, we do not want to truncate though. - pad_length = typing.cast( - ibis_types.IntegerValue, ibis_api.greatest(str_val.length(), op.length) - ) - if op.side == "left": - return str_val.lpad(pad_length, op.fillchar) - elif op.side == "right": - return str_val.rpad(pad_length, op.fillchar) - else: # side == both - # Pad more on right side if can't pad both sides equally - two = typing.cast(ibis_types.IntegerValue, 2) - lpad_amount = ((pad_length - str_val.length()) // two) + str_val.length() - return str_val.lpad( - length=typing.cast(ibis_types.IntegerValue, lpad_amount), pad=op.fillchar - ).rpad(pad_length, op.fillchar) - - -@scalar_op_compiler.register_unary_op(ops.ReplaceStrOp, pass_op=True) -def replacestring_op_impl(x: ibis_types.Value, op: ops.ReplaceStrOp): - pat_str_value = typing.cast(ibis_types.StringValue, ibis_types.literal(op.pat)) - repl_str_value = typing.cast(ibis_types.StringValue, ibis_types.literal(op.repl)) - return typing.cast(ibis_types.StringValue, x).replace(pat_str_value, repl_str_value) - - -@scalar_op_compiler.register_unary_op(ops.RegexReplaceStrOp, pass_op=True) -def replaceregex_op_impl(x: ibis_types.Value, op: ops.RegexReplaceStrOp): - return typing.cast(ibis_types.StringValue, x).re_replace(op.pat, op.repl) - - -@scalar_op_compiler.register_unary_op(ops.StartsWithOp, pass_op=True) -def startswith_op_impl(x: ibis_types.Value, op: ops.StartsWithOp): - any_match = None - for pat in op.pat: - pat_match = typing.cast(ibis_types.StringValue, x).startswith(pat) - if any_match is not None: - any_match = any_match | pat_match - else: - any_match = pat_match - return any_match if any_match is not None else ibis_types.literal(False) - - -@scalar_op_compiler.register_unary_op(ops.EndsWithOp, pass_op=True) -def endswith_op_impl(x: ibis_types.Value, op: ops.EndsWithOp): - any_match = None - for pat in op.pat: - pat_match = typing.cast(ibis_types.StringValue, x).endswith(pat) - if any_match is not None: - any_match = any_match | pat_match - else: - any_match = pat_match - return any_match if any_match is not None else ibis_types.literal(False) - - -@scalar_op_compiler.register_unary_op(ops.StringSplitOp, pass_op=True) -def stringsplit_op_impl(x: ibis_types.Value, op: ops.StringSplitOp): - return typing.cast(ibis_types.StringValue, x).split(delimiter=op.pat) # type: ignore - - -@scalar_op_compiler.register_unary_op(ops.ZfillOp, pass_op=True) -def zfill_op_impl(x: ibis_types.Value, op: ops.ZfillOp): - str_value = typing.cast(ibis_types.StringValue, x) - return ( - ibis_api.case() - .when( - str_value[0] == "-", - "-" - + strpad_op_impl( - str_value.substr(1), - ops.StrPadOp(length=op.width - 1, fillchar="0", side="left"), - ), - ) - .else_( - strpad_op_impl( - str_value, ops.StrPadOp(length=op.width, fillchar="0", side="left") - ) - ) - .end() - ) - - -@scalar_op_compiler.register_unary_op(ops.StrFindOp, pass_op=True) -def find_op_impl(x: ibis_types.Value, op: ops.StrFindOp): - return typing.cast(ibis_types.StringValue, x).find(op.substr, op.start, op.end) - - -@scalar_op_compiler.register_unary_op(ops.StrExtractOp, pass_op=True) -def extract_op_impl(x: ibis_types.Value, op: ops.StrExtractOp): - return typing.cast(ibis_types.StringValue, x).re_extract(op.pat, op.n) - - -@scalar_op_compiler.register_unary_op(ops.StrSliceOp, pass_op=True) -def slice_op_impl(x: ibis_types.Value, op: ops.StrSliceOp): - return typing.cast(ibis_types.StringValue, x)[op.start : op.end] - - -@scalar_op_compiler.register_unary_op(ops.StrRepeatOp, pass_op=True) -def repeat_op_impl(x: ibis_types.Value, op: ops.StrRepeatOp): - return typing.cast(ibis_types.StringValue, x).repeat(op.repeats) - - -## Datetime Ops -@scalar_op_compiler.register_unary_op(ops.day_op) -def day_op_impl(x: ibis_types.Value): - return typing.cast(ibis_types.TimestampValue, x).day().cast(ibis_dtypes.int64) - - -@scalar_op_compiler.register_unary_op(ops.date_op) -def date_op_impl(x: ibis_types.Value): - return typing.cast(ibis_types.TimestampValue, x).date() - - -@scalar_op_compiler.register_unary_op(ops.iso_day_op) -def iso_day_op_impl(x: ibis_types.Value): - # Plus 1 because iso day of week uses 1-based indexing - return dayofweek_op_impl(x) + 1 - - -@scalar_op_compiler.register_unary_op(ops.iso_week_op) -def iso_week_op_impl(x: ibis_types.Value): - return typing.cast(ibis_types.TimestampValue, x).week_of_year() - - -@scalar_op_compiler.register_unary_op(ops.iso_year_op) -def iso_year_op_impl(x: ibis_types.Value): - return typing.cast(ibis_types.TimestampValue, x).iso_year() - - -@scalar_op_compiler.register_unary_op(ops.dayofweek_op) -def dayofweek_op_impl(x: ibis_types.Value): - return ( - typing.cast(ibis_types.TimestampValue, x) - .day_of_week.index() - .cast(ibis_dtypes.int64) - ) - - -@scalar_op_compiler.register_unary_op(ops.dayofyear_op) -def dayofyear_op_impl(x: ibis_types.Value): - return ( - typing.cast(ibis_types.TimestampValue, x).day_of_year().cast(ibis_dtypes.int64) - ) - - -@scalar_op_compiler.register_unary_op(ops.hour_op) -def hour_op_impl(x: ibis_types.Value): - return typing.cast(ibis_types.TimestampValue, x).hour().cast(ibis_dtypes.int64) - - -@scalar_op_compiler.register_unary_op(ops.minute_op) -def minute_op_impl(x: ibis_types.Value): - return typing.cast(ibis_types.TimestampValue, x).minute().cast(ibis_dtypes.int64) - - -@scalar_op_compiler.register_unary_op(ops.month_op) -def month_op_impl(x: ibis_types.Value): - return typing.cast(ibis_types.TimestampValue, x).month().cast(ibis_dtypes.int64) - - -@scalar_op_compiler.register_unary_op(ops.quarter_op) -def quarter_op_impl(x: ibis_types.Value): - return typing.cast(ibis_types.TimestampValue, x).quarter().cast(ibis_dtypes.int64) - - -@scalar_op_compiler.register_unary_op(ops.second_op) -def second_op_impl(x: ibis_types.Value): - return typing.cast(ibis_types.TimestampValue, x).second().cast(ibis_dtypes.int64) - - -@scalar_op_compiler.register_unary_op(ops.StrftimeOp, pass_op=True) -def strftime_op_impl(x: ibis_types.Value, op: ops.StrftimeOp): - return ( - typing.cast(ibis_types.TimestampValue, x) - .strftime(op.date_format) - .cast(ibis_dtypes.str) - ) - - -@scalar_op_compiler.register_unary_op(ops.UnixSeconds) -def unix_seconds_op_impl(x: ibis_types.TimestampValue): - return x.epoch_seconds() - - -@scalar_op_compiler.register_unary_op(ops.UnixMicros) -def unix_micros_op_impl(x: ibis_types.TimestampValue): - return unix_micros(x) - - -@scalar_op_compiler.register_unary_op(ops.UnixMillis) -def unix_millis_op_impl(x: ibis_types.TimestampValue): - return unix_millis(x) - - -@scalar_op_compiler.register_binary_op(ops.timestamp_diff_op) -def timestamp_diff_op_impl(x: ibis_types.TimestampValue, y: ibis_types.TimestampValue): - return x.delta(y, "microsecond") - - -@scalar_op_compiler.register_binary_op(ops.timestamp_add_op) -def timestamp_add_op_impl(x: ibis_types.TimestampValue, y: ibis_types.IntegerValue): - return x + y.to_interval("us") - - -@scalar_op_compiler.register_binary_op(ops.timestamp_sub_op) -def timestamp_sub_op_impl(x: ibis_types.TimestampValue, y: ibis_types.IntegerValue): - return x - y.to_interval("us") - - -@scalar_op_compiler.register_binary_op(ops.date_diff_op) -def date_diff_op_impl(x: ibis_types.DateValue, y: ibis_types.DateValue): - return x.delta(y, "day") * int(UNIT_TO_US_CONVERSION_FACTORS["d"]) # type: ignore - - -@scalar_op_compiler.register_binary_op(ops.date_add_op) -def date_add_op_impl(x: ibis_types.DateValue, y: ibis_types.IntegerValue): - return x.cast(ibis_dtypes.timestamp()) + y.to_interval("us") # type: ignore - - -@scalar_op_compiler.register_binary_op(ops.date_sub_op) -def date_sub_op_impl(x: ibis_types.DateValue, y: ibis_types.IntegerValue): - return x.cast(ibis_dtypes.timestamp()) - y.to_interval("us") # type: ignore - - -@scalar_op_compiler.register_unary_op(ops.FloorDtOp, pass_op=True) -def floor_dt_op_impl(x: ibis_types.Value, op: ops.FloorDtOp): - supported_freqs = ["Y", "Q", "M", "W", "D", "h", "min", "s", "ms", "us", "ns"] - pandas_to_ibis_freqs = {"min": "m"} - if op.freq not in supported_freqs: - raise NotImplementedError( - f"Unsupported freq paramater: {op.freq}" - + " Supported freq parameters are: " - + ",".join(supported_freqs) - ) - if op.freq in pandas_to_ibis_freqs: - ibis_freq = pandas_to_ibis_freqs[op.freq] - else: - ibis_freq = op.freq - result_type = x.type() - result = typing.cast(ibis_types.TimestampValue, x) - result = result.truncate(ibis_freq) # type: ignore - return result.cast(result_type) - - -@scalar_op_compiler.register_binary_op(ops.DatetimeToIntegerLabelOp, pass_op=True) -def datetime_to_integer_label_op_impl( - x: ibis_types.Value, y: ibis_types.Value, op: ops.DatetimeToIntegerLabelOp -): - # Determine if the frequency is fixed by checking if 'op.freq.nanos' is defined. - try: - return datetime_to_integer_label_fixed_frequency(x, y, op) - except ValueError: - return datetime_to_integer_label_non_fixed_frequency(x, y, op) - - -def datetime_to_integer_label_fixed_frequency( - x: ibis_types.Value, y: ibis_types.Value, op: ops.DatetimeToIntegerLabelOp -): - """ - This function handles fixed frequency conversions where the unit can range - from microseconds (us) to days. - """ - us = op.freq.nanos / 1000 - x_int = x.cast(ibis_dtypes.Timestamp(timezone="UTC")).cast(ibis_dtypes.int64) - first = calculate_resample_first(y, op.origin) - x_int_label = (x_int - first) // us - return x_int_label - - -def datetime_to_integer_label_non_fixed_frequency( - x: ibis_types.Value, y: ibis_types.Value, op: ops.DatetimeToIntegerLabelOp -): - """ - This function handles non-fixed frequency conversions for units ranging - from weeks to years. - """ - rule_code = op.freq.rule_code - n = op.freq.n - if rule_code == "W-SUN": # Weekly - us = n * 7 * 24 * 60 * 60 * 1000000 - x = x.truncate("week") + ibis_api.interval(days=6) # type: ignore - y = y.truncate("week") + ibis_api.interval(days=6) # type: ignore - x_int = x.cast(ibis_dtypes.Timestamp(timezone="UTC")).cast(ibis_dtypes.int64) - first = y.cast(ibis_dtypes.Timestamp(timezone="UTC")).cast(ibis_dtypes.int64) - x_int_label = ( - ibis_api.case() - .when(x_int == first, 0) - .else_((x_int - first - 1) // us + 1) # type: ignore - .end() - ) - elif rule_code in ("M", "ME"): # Monthly - x_int = x.year() * 12 + x.month() - 1 # type: ignore - first = y.year() * 12 + y.month() - 1 # type: ignore - x_int_label = ( - ibis_api.case() - .when(x_int == first, 0) - .else_((x_int - first - 1) // n + 1) # type: ignore - .end() - ) - elif rule_code in ("Q-DEC", "QE-DEC"): # Quarterly - x_int = x.year() * 4 + x.quarter() - 1 # type: ignore - first = y.year() * 4 + y.quarter() - 1 # type: ignore - x_int_label = ( - ibis_api.case() - .when(x_int == first, 0) - .else_((x_int - first - 1) // n + 1) # type: ignore - .end() - ) - elif rule_code in ("A-DEC", "Y-DEC", "YE-DEC"): # Yearly - x_int = x.year() # type: ignore - first = y.year() # type: ignore - x_int_label = ( - ibis_api.case() - .when(x_int == first, 0) - .else_((x_int - first - 1) // n + 1) # type: ignore - .end() - ) - else: - raise ValueError(rule_code) - return x_int_label - - -@scalar_op_compiler.register_binary_op(ops.IntegerLabelToDatetimeOp, pass_op=True) -def integer_label_to_datetime_op_impl( - x: ibis_types.Value, y: ibis_types.Value, op: ops.IntegerLabelToDatetimeOp -): - # Determine if the frequency is fixed by checking if 'op.freq.nanos' is defined. - try: - return integer_label_to_datetime_op_fixed_frequency(x, y, op) - except ValueError: - return integer_label_to_datetime_op_non_fixed_frequency(x, y, op) - - -def integer_label_to_datetime_op_fixed_frequency( - x: ibis_types.Value, y: ibis_types.Value, op: ops.IntegerLabelToDatetimeOp -): - """ - This function handles fixed frequency conversions where the unit can range - from microseconds (us) to days. - """ - us = op.freq.nanos / 1000 - - first = calculate_resample_first(y, op.origin) - - x_label = ( - (x * us + first) # type: ignore - .cast(ibis_dtypes.int64) - .to_timestamp(unit="us") - .cast(ibis_dtypes.Timestamp(timezone="UTC")) - .cast(y.type()) - ) - return x_label - - -def integer_label_to_datetime_op_non_fixed_frequency( - x: ibis_types.Value, y: ibis_types.Value, op: ops.IntegerLabelToDatetimeOp -): - """ - This function handles non-fixed frequency conversions for units ranging - from weeks to years. - """ - rule_code = op.freq.rule_code - n = op.freq.n - if rule_code == "W-SUN": # Weekly - us = n * 7 * 24 * 60 * 60 * 1000000 - first = ( - y.cast(ibis_dtypes.Timestamp(timezone="UTC")).truncate("week") # type: ignore - + ibis_api.interval(days=6) - ).cast(ibis_dtypes.int64) - x_label = ( - (x * us + first) # type: ignore - .cast(ibis_dtypes.int64) - .to_timestamp(unit="us") - .cast(ibis_dtypes.Timestamp(timezone="UTC")) - .cast(y.type()) - ) - elif rule_code in ("M", "ME"): # Monthly - one = ibis_types.literal(1) - twelve = ibis_types.literal(12) - first = y.year() * twelve + y.month() - one # type: ignore - - x = x * n + first # type: ignore - year = x // twelve # type: ignore - month = (x % twelve) + one # type: ignore - - next_year = (month == twelve).ifelse(year + one, year) - next_month = (month == twelve).ifelse(one, month + one) - next_month_date = ibis_api.timestamp( - typing.cast(ibis_types.IntegerValue, next_year), - typing.cast(ibis_types.IntegerValue, next_month), - 1, - 0, - 0, - 0, - ) - x_label = next_month_date - ibis_api.interval(days=1) - elif rule_code in ("Q-DEC", "QE-DEC"): # Quarterly - one = ibis_types.literal(1) - three = ibis_types.literal(3) - four = ibis_types.literal(4) - twelve = ibis_types.literal(12) - first = y.year() * four + y.quarter() - one # type: ignore - - x = x * n + first # type: ignore - year = x // four # type: ignore - month = ((x % four) + one) * three # type: ignore - - next_year = (month == twelve).ifelse(year + one, year) - next_month = (month == twelve).ifelse(one, month + one) - next_month_date = ibis_api.timestamp( - typing.cast(ibis_types.IntegerValue, next_year), - typing.cast(ibis_types.IntegerValue, next_month), - 1, - 0, - 0, - 0, - ) - - x_label = next_month_date - ibis_api.interval(days=1) - elif rule_code in ("A-DEC", "Y-DEC", "YE-DEC"): # Yearly - one = ibis_types.literal(1) - first = y.year() # type: ignore - x = x * n + first # type: ignore - next_year = x + one # type: ignore - next_month_date = ibis_api.timestamp( - typing.cast(ibis_types.IntegerValue, next_year), - 1, - 1, - 0, - 0, - 0, - ) - x_label = next_month_date - ibis_api.interval(days=1) - - return x_label.cast(ibis_dtypes.Timestamp(timezone="UTC")).cast(y.type()) - - -def calculate_resample_first(y: ibis_types.Value, origin): - if origin == "epoch": - return ibis_types.literal(0) - elif origin == "start_day": - return ( - y.cast(ibis_dtypes.date) - .cast(ibis_dtypes.Timestamp(timezone="UTC")) - .cast(ibis_dtypes.int64) - ) - elif origin == "start": - return y.cast(ibis_dtypes.Timestamp(timezone="UTC")).cast(ibis_dtypes.int64) - else: - raise ValueError(f"Origin {origin} not supported") - - -@scalar_op_compiler.register_unary_op(ops.time_op) -def time_op_impl(x: ibis_types.Value): - return typing.cast(ibis_types.TimestampValue, x).time() - - -@scalar_op_compiler.register_unary_op(ops.year_op) -def year_op_impl(x: ibis_types.Value): - return typing.cast(ibis_types.TimestampValue, x).year().cast(ibis_dtypes.int64) - - -@scalar_op_compiler.register_unary_op(ops.normalize_op) -def normalize_op_impl(x: ibis_types.Value): - result_type = x.type() - result = x.truncate("D") # type: ignore - return result.cast(result_type) - - -# Parameterized ops -@scalar_op_compiler.register_unary_op(ops.StructFieldOp, pass_op=True) -def struct_field_op_impl(x: ibis_types.Value, op: ops.StructFieldOp): - struct_value = typing.cast(ibis_types.StructValue, x) - if isinstance(op.name_or_index, str): - name = op.name_or_index - else: - name = struct_value.names[op.name_or_index] - - result = struct_value[name] - return result.cast(result.type()(nullable=True)).name(name) - - -def numeric_to_datetime( - x: ibis_types.Value, unit: str, safe: bool = False -) -> ibis_types.TimestampValue: - if not isinstance(x, ibis_types.IntegerValue) and not isinstance( - x, ibis_types.FloatingValue - ): - raise TypeError("Non-numerical types are not supposed to reach this function.") - - if unit not in UNIT_TO_US_CONVERSION_FACTORS: - raise ValueError(f"Cannot convert input with unit '{unit}'.") - x_converted = x * typing.cast( - ibis_types.IntegerValue, UNIT_TO_US_CONVERSION_FACTORS[unit] - ) - x_converted = ( - x_converted.try_cast(ibis_dtypes.int64) # type: ignore - if safe - else x_converted.cast(ibis_dtypes.int64) - ) - - # Note: Due to an issue where casting directly to a timestamp - # without a timezone does not work, we first cast to UTC. This - # approach appears to bypass a potential bug in Ibis's cast function, - # allowing for subsequent casting to a timestamp type without timezone - # information. Further investigation is needed to confirm this behavior. - return x_converted.to_timestamp(unit="us").cast( # type: ignore - ibis_dtypes.Timestamp(timezone="UTC") - ) - - -@scalar_op_compiler.register_unary_op(ops.coerce_to_bool_op) -def coerce_to_bool_op_impl(x: ibis_types.Value): - x_type = x.type() - if x_type.is_boolean(): - res = x - elif x_type.is_numeric(): - res = x != 0 # type: ignore - elif x_type.is_string(): - res = x.length() > 0 # type: ignore - elif x_type.is_binary(): - res = x.length() > 0 # type: ignore - elif isinstance(x_type, ibis_dtypes.Array): - res = x.length() > 0 # type: ignore - else: - res = x.notnull() - - return res.fill_null(False) # type: ignore - - -@scalar_op_compiler.register_unary_op(ops.AsTypeOp, pass_op=True) -def astype_op_impl(x: ibis_types.Value, op: ops.AsTypeOp): - to_type = bigframes.core.compile.ibis_types.bigframes_dtype_to_ibis_dtype( - op.to_type - ) - if isinstance(x, ibis_types.NullScalar): - return ibis_types.null().cast(to_type) - - # When casting DATETIME column into INT column, we need to convert the column into TIMESTAMP first. - if to_type == ibis_dtypes.int64 and x.type() == ibis_dtypes.timestamp: - utc_time_type = ibis_dtypes.Timestamp(timezone="UTC") - x_converted = x.try_cast(utc_time_type) if op.safe else x.cast(utc_time_type) - return bigframes.core.compile.ibis_types.cast_ibis_value( - x_converted, to_type, safe=op.safe - ) - - if to_type == ibis_dtypes.int64 and x.type() == ibis_dtypes.time: - # The conversion unit is set to "us" (microseconds) for consistency - # with pandas converting time64[us][pyarrow] to int64[pyarrow]. - return x.delta(ibis_api.time("00:00:00"), part="microsecond") # type: ignore - - if x.type() == ibis_dtypes.int64: - # The conversion unit is set to "us" (microseconds) for consistency - # with pandas converting int64[pyarrow] to timestamp[us][pyarrow], - # timestamp[us, tz=UTC][pyarrow], and time64[us][pyarrow]. - unit = "us" - x_converted = numeric_to_datetime(x, unit, safe=op.safe) - if to_type == ibis_dtypes.timestamp: - return ( - x_converted.try_cast(ibis_dtypes.Timestamp()) - if op.safe - else x_converted.cast(ibis_dtypes.Timestamp()) - ) - elif to_type == ibis_dtypes.Timestamp(timezone="UTC"): - return x_converted - elif to_type == ibis_dtypes.time: - return x_converted.time() - - # TODO: either inline this function, or push rest of this op into the function - return bigframes.core.compile.ibis_types.cast_ibis_value(x, to_type, safe=op.safe) - - -@scalar_op_compiler.register_unary_op(ops.IsInOp, pass_op=True) -def isin_op_impl(x: ibis_types.Value, op: ops.IsInOp): - contains_nulls = any(is_null(value) for value in op.values) - matchable_ibis_values = [] - for item in op.values: - if not is_null(item): - try: - # we want values that *could* be cast to the dtype, but we don't want - # to actually cast it, as that could be lossy (eg float -> int) - item_inferred_type = ibis_types.literal(item).type() - if ( - x.type().name == item_inferred_type.name - or x.type().is_numeric() - and item_inferred_type.is_numeric() - ): - matchable_ibis_values.append(item) - except TypeError: - pass - - if op.match_nulls and contains_nulls: - return x.isnull() | x.isin(matchable_ibis_values) - else: - return x.isin(matchable_ibis_values).fill_null(ibis.literal(False)) - - -@scalar_op_compiler.register_unary_op(ops.ToDatetimeOp, pass_op=True) -def to_datetime_op_impl(x: ibis_types.Value, op: ops.ToDatetimeOp): - if x.type() == ibis_dtypes.Timestamp(None): # type: ignore - return x # already a timestamp, no-op - elif x.type() in (ibis_dtypes.str, ibis_dtypes.Timestamp("UTC")): # type: ignore - return x.try_cast(ibis_dtypes.Timestamp(None)) # type: ignore - else: - # Numerical inputs. - if op.format: - x = x.cast(ibis_dtypes.str).to_timestamp(op.format) # type: ignore - else: - # The default unit is set to "ns" (nanoseconds) for consistency - # with pandas, where "ns" is the default unit for datetime operations. - unit = op.unit or "ns" - x = numeric_to_datetime(x, unit) - - return x.cast(ibis_dtypes.Timestamp(None)) # type: ignore - - -@scalar_op_compiler.register_unary_op(ops.ToTimestampOp, pass_op=True) -def to_timestamp_op_impl(x: ibis_types.Value, op: ops.ToTimestampOp): - if x.type() == ibis_dtypes.str: - x = ( - typing.cast(ibis_types.StringValue, x).to_timestamp(op.format) - if op.format - else timestamp(x) - ) - elif x.type() == ibis_dtypes.Timestamp(None): # type: ignore - return timestamp(x) - else: - # Numerical inputs. - if op.format: - x = x.cast(ibis_dtypes.str).to_timestamp(op.format) # type: ignore - else: - # The default unit is set to "ns" (nanoseconds) for consistency - # with pandas, where "ns" is the default unit for datetime operations. - unit = op.unit or "ns" - x = numeric_to_datetime(x, unit) - - return x.cast(ibis_dtypes.Timestamp(timezone="UTC")) - - -@scalar_op_compiler.register_unary_op(ops.ToTimedeltaOp, pass_op=True) -def to_timedelta_op_impl(x: ibis_types.Value, op: ops.ToTimedeltaOp): - return ( - typing.cast(ibis_types.NumericValue, x) * UNIT_TO_US_CONVERSION_FACTORS[op.unit] # type: ignore - ).floor() - - -@scalar_op_compiler.register_unary_op(ops.timedelta_floor_op) -def timedelta_floor_op_impl(x: ibis_types.NumericValue): - return ibis_api.case().when(x > ibis.literal(0), x.floor()).else_(x.ceil()).end() - - -@scalar_op_compiler.register_nary_op(ops.RemoteFunctionOp, pass_op=True) -def remote_function_op_impl(*values: ibis_types.Value, op: ops.RemoteFunctionOp): - udf_sig = op.function_def.signature - assert not udf_sig.is_virtual # should have been devirtualized in lowering pass - ibis_py_sig = (tuple(arg.py_type for arg in udf_sig.inputs), udf_sig.output.py_type) - arg_names = tuple(arg.name for arg in udf_sig.inputs) - - @ibis_udf.scalar.builtin( - name=str(op.function_def.routine_ref), - signature=ibis_py_sig, - param_name_overrides=arg_names, - ) - def udf(*inputs): ... - - return udf(*values) - - -@scalar_op_compiler.register_unary_op(ops.MapOp, pass_op=True) -def map_op_impl(x: ibis_types.Value, op: ops.MapOp): - # this should probably be handled by a rewriter - if len(op.mappings) == 0: - return x - - case = ibis_api.case() - for mapping in op.mappings: - case = case.when(x == mapping[0], mapping[1]) - return case.else_(x).end() - - -# Array Ops -@scalar_op_compiler.register_unary_op(ops.ArrayToStringOp, pass_op=True) -def array_to_string_op_impl(x: ibis_types.Value, op: ops.ArrayToStringOp): - return typing.cast(ibis_types.ArrayValue, x).join(op.delimiter) - - -@scalar_op_compiler.register_unary_op(ops.GetItemOp, pass_op=True) -def getitem_op_impl(x: ibis_types.Value, op: ops.GetItemOp): - if x.type().is_struct(): - struct_value = typing.cast(ibis_types.StructValue, x) - if isinstance(op.key, str): - name = op.key - else: - name = struct_value.names[op.key] - result = struct_value[name] - return result.cast(result.type()(nullable=True)).name(name) - elif x.type().is_array(): - key = typing.cast(int, op.key) - res = typing.cast(ibis_types.ArrayValue, x)[key] - return res - elif x.type().is_string(): - key = typing.cast(int, op.key) - res = typing.cast(ibis_types.StringValue, x)[key] - return _null_or_value(res, res != ibis_types.literal("")) - else: - raise TypeError(f"Cannot subscript input of type {x.type()}") - - -@scalar_op_compiler.register_binary_op(ops.DynamicGetItemOp) -def dynamic_getitem_op_impl(left: ibis_types.Value, right: ibis_types.Value): - if left.type().is_array(): - int_right = typing.cast(ibis_types.IntegerValue, right) - return typing.cast(ibis_types.ArrayValue, left)[int_right] - elif left.type().is_string(): - scalar_right = typing.cast(ibis_types.IntegerScalar, right) - res = typing.cast(ibis_types.StringValue, left)[scalar_right] - return _null_or_value(res, res != ibis_types.literal("")) - else: - raise TypeError(f"Cannot dynamically subscript input of type {left.type()}") - - -@scalar_op_compiler.register_unary_op(ops.ArraySliceOp, pass_op=True) -def array_slice_op_impl(x: ibis_types.Value, op: ops.ArraySliceOp): - res = typing.cast(ibis_types.ArrayValue, x)[op.start : op.stop : op.step] - if x.type().is_string(): - return _null_or_value(res, res != ibis_types.literal("")) - else: - return res - - -@scalar_op_compiler.register_nary_op(ops.ToArrayOp, pass_op=False) -def to_arry_op_impl(*values: ibis_types.Value): - do_upcast_bool = any(t.type().is_numeric() for t in values) - if do_upcast_bool: - values = tuple( - val.cast(ibis_dtypes.int64) if val.type().is_boolean() else val - for val in values - ) - return ibis_api.array(values) - - -@scalar_op_compiler.register_unary_op(ops.ArrayReduceOp, pass_op=True) -def array_reduce_op_impl(x: ibis_types.Value, op: ops.ArrayReduceOp): - import bigframes.core.compile.ibis_compiler.aggregate_compiler as agg_compilers - - if op.aggregation.order_independent: - return typing.cast(ibis_types.ArrayValue, x).reduce( - lambda arr_vals: agg_compilers.compile_unary_agg( - op.aggregation, typing.cast(ibis_types.Column, arr_vals) - ) - ) - else: - return typing.cast(ibis_types.ArrayValue, x).reduce( - lambda arr_vals: agg_compilers.compile_ordered_unary_agg( - op.aggregation, typing.cast(ibis_types.Column, arr_vals) - ) - ) - - -@scalar_op_compiler.register_unary_op(ops.ArrayMapOp, pass_op=True) -def array_map_op_impl(x: ibis_types.Value, op: ops.ArrayMapOp): - return typing.cast(ibis_types.ArrayValue, x).map( - lambda arr_vals: scalar_op_compiler.compile_row_op(op.map_op, (arr_vals,)) - ) - - -# JSON Ops -@scalar_op_compiler.register_binary_op(ops.JSONSet, pass_op=True) -def json_set_op_impl(x: ibis_types.Value, y: ibis_types.Value, op: ops.JSONSet): - return json_set(json_obj=x, json_path=op.json_path, json_value=y) - - -@scalar_op_compiler.register_unary_op(ops.JSONExtract, pass_op=True) -def json_extract_op_impl(x: ibis_types.Value, op: ops.JSONExtract): - # Define a user-defined function whose returned type is dynamically matching the input. - def json_extract(json_or_json_string, json_path: ibis_dtypes.str): # type: ignore - """Extracts a JSON value and converts it to a SQL JSON-formatted STRING or JSON value.""" - ... - - return_type = x.type() - json_extract.__annotations__["return"] = return_type - json_extract_op = ibis_udf.scalar.builtin(json_extract) - return json_extract_op(json_or_json_string=x, json_path=op.json_path) - - -@scalar_op_compiler.register_unary_op(ops.JSONExtractArray, pass_op=True) -def json_extract_array_op_impl(x: ibis_types.Value, op: ops.JSONExtractArray): - # Define a user-defined function whose returned type is dynamically matching the input. - def json_extract_array(json_or_json_string, json_path: ibis_dtypes.str): # type: ignore - """Extracts a JSON value and converts it to a SQL JSON-formatted STRING or JSON value.""" - ... - - return_type = x.type() - json_extract_array.__annotations__["return"] = ibis_dtypes.Array[return_type] # type: ignore - json_extract_op = ibis_udf.scalar.builtin(json_extract_array) - return json_extract_op(json_or_json_string=x, json_path=op.json_path) - - -@scalar_op_compiler.register_unary_op(ops.JSONExtractStringArray, pass_op=True) -def json_extract_string_array_op_impl( - x: ibis_types.Value, op: ops.JSONExtractStringArray -): - return json_extract_string_array(json_obj=x, json_path=op.json_path) - - -@scalar_op_compiler.register_unary_op(ops.JSONQuery, pass_op=True) -def json_query_op_impl(x: ibis_types.Value, op: ops.JSONQuery): - # Define a user-defined function whose returned type is dynamically matching the input. - def json_query(json_or_json_string, json_path: ibis_dtypes.str): # type: ignore - """Extracts a JSON value and converts it to a SQL JSON-formatted STRING or JSON value.""" - ... - - return_type = x.type() - json_query.__annotations__["return"] = return_type - json_query_op = ibis_udf.scalar.builtin(json_query) - return json_query_op(json_or_json_string=x, json_path=op.json_path) - - -@scalar_op_compiler.register_unary_op(ops.JSONQueryArray, pass_op=True) -def json_query_array_op_impl(x: ibis_types.Value, op: ops.JSONQueryArray): - # Define a user-defined function whose returned type is dynamically matching the input. - def json_query_array(json_or_json_string, json_path: ibis_dtypes.str): # type: ignore - """Extracts a JSON value and converts it to a SQL JSON-formatted STRING or JSON value.""" - ... - - return_type = x.type() - json_query_array.__annotations__["return"] = ibis_dtypes.Array[return_type] # type: ignore - json_query_op = ibis_udf.scalar.builtin(json_query_array) - return json_query_op(json_or_json_string=x, json_path=op.json_path) - - -@scalar_op_compiler.register_unary_op(ops.ParseJSON, pass_op=True) -def parse_json_op_impl(x: ibis_types.Value, op: ops.ParseJSON): - return parse_json(json_str=x) - - -@scalar_op_compiler.register_unary_op(ops.ToJSON, pass_op=True) -def to_json_op_impl(x: ibis_types.Value, op: ops.ToJSON): - if x.type() == ibis_dtypes.string: - return parse_json_in_safe(x) if op.safe else parse_json(x) - return x.isnull().ifelse(ibis.null().cast(ibis_dtypes.json), to_json(x)) - - -@scalar_op_compiler.register_unary_op(ops.JSONDecode, pass_op=True) -def json_decode_op_impl(x: ibis_types.Value, op: ops.JSONDecode): - to_type = bigframes.core.compile.ibis_types.bigframes_dtype_to_ibis_dtype( - op.to_type - ) - if to_type == ibis_dtypes.int64: - return cast_json_to_int64_in_safe(x) if op.safe else cast_json_to_int64(x) - if to_type == ibis_dtypes.float64: - return cast_json_to_float64_in_safe(x) if op.safe else cast_json_to_float64(x) - if to_type == ibis_dtypes.bool: - return cast_json_to_bool_in_safe(x) if op.safe else cast_json_to_bool(x) - if to_type == ibis_dtypes.string: - return cast_json_to_string_in_safe(x) if op.safe else cast_json_to_string(x) - raise TypeError(f"Cannot cast from JSON to type {to_type}") - - -@scalar_op_compiler.register_unary_op(ops.ToJSONString) -def to_json_string_op_impl(x: ibis_types.Value): - return to_json_string(value=x) - - -@scalar_op_compiler.register_unary_op(ops.JSONValue, pass_op=True) -def json_value_op_impl(x: ibis_types.Value, op: ops.JSONValue): - return json_value(json_obj=x, json_path=op.json_path) - - -@scalar_op_compiler.register_unary_op(ops.JSONValueArray, pass_op=True) -def json_value_array_op_impl(x: ibis_types.Value, op: ops.JSONValueArray): - return json_value_array(json_obj=x, json_path=op.json_path) - - -@scalar_op_compiler.register_unary_op(ops.JSONKeys, pass_op=True) -def json_keys_op_impl(x: ibis_types.Value, op: ops.JSONKeys): - return json_keys(x, op.max_depth) - - -# Blob Ops -@scalar_op_compiler.register_unary_op(ops.obj_fetch_metadata_op) -def obj_fetch_metadata_op_impl(obj_ref: ibis_types.Value): - return obj_fetch_metadata(obj_ref=obj_ref) - - -@scalar_op_compiler.register_unary_op(ops.ObjGetAccessUrl, pass_op=True) -def obj_get_access_url_op_impl(obj_ref: ibis_types.Value, op: ops.ObjGetAccessUrl): - if op.duration is not None: - duration_value = cast( - ibis_types.IntegerValue, ibis_types.literal(op.duration) - ).to_interval("us") - return obj_get_access_url_with_duration( - obj_ref=obj_ref, mode=op.mode, duration=duration_value - ) - return obj_get_access_url(obj_ref=obj_ref, mode=op.mode) - - -### Binary Ops -def short_circuit_nulls(type_override: typing.Optional[ibis_dtypes.DataType] = None): - """Wraps a binary operator to generate nulls of the expected type if either input is a null scalar.""" - - def short_circuit_nulls_inner(binop): - @functools.wraps(binop) - def wrapped_binop(x: ibis_types.Value, y: ibis_types.Value): - if isinstance(x, ibis_types.NullScalar): - return ibis_types.null().cast(type_override or y.type()) - elif isinstance(y, ibis_types.NullScalar): - return ibis_types.null().cast(type_override or x.type()) - else: - return binop(x, y) - - return wrapped_binop - - return short_circuit_nulls_inner - - -@scalar_op_compiler.register_binary_op(ops.strconcat_op) -def concat_op( - x: ibis_types.Value, - y: ibis_types.Value, -): - x_string = typing.cast(ibis_types.StringValue, x) - y_string = typing.cast(ibis_types.StringValue, y) - return x_string.concat(y_string) - - -@scalar_op_compiler.register_binary_op(ops.eq_op) -def eq_op( - x: ibis_types.Value, - y: ibis_types.Value, -): - x, y = _coerce_bools(x, y) - return x == y - - -@scalar_op_compiler.register_binary_op(ops.eq_null_match_op) -def eq_nulls_match_op( - x: ibis_types.Value, - y: ibis_types.Value, -): - """Variant of eq_op where nulls match each other. Only use where dtypes are known to be same.""" - x, y = _coerce_bools(x, y) - literal = ibis_types.literal("$NULL_SENTINEL$") - if hasattr(x, "fill_null"): - left = x.cast(ibis_dtypes.str).fill_null(literal) - right = y.cast(ibis_dtypes.str).fill_null(literal) - else: - left = x.cast(ibis_dtypes.str).fill_null(literal) - right = y.cast(ibis_dtypes.str).fill_null(literal) - - return left == right - - -@scalar_op_compiler.register_binary_op(ops.ne_op) -def ne_op( - x: ibis_types.Value, - y: ibis_types.Value, -): - x, y = _coerce_bools(x, y) - return x != y - - -def _null_or_value(value: ibis_types.Value, where_value: ibis_types.BooleanValue): - return ibis_api.ifelse( - where_value, - value, - ibis_types.null(), - ) - - -def _coerce_bools(x: ibis_types.Value, y: ibis_types.Value, *, always: bool = False): - if x.type().is_boolean() and (always or not y.type().is_boolean()): - x = x.cast(ibis_dtypes.int64) - if y.type().is_boolean() and (always or not x.type().is_boolean()): - y = y.cast(ibis_dtypes.int64) - return x, y - - -@scalar_op_compiler.register_binary_op(ops.and_op) -def and_op( - x: ibis_types.Value, - y: ibis_types.Value, -): - # Workaround issue https://github.com/ibis-project/ibis/issues/7775 by - # implementing three-valued logic ourselves. For AND, when we encounter a - # NULL value, we only know when the result is FALSE, otherwise the result - # is unknown (NULL). See: truth table at - # https://en.wikibooks.org/wiki/Structured_Query_Language/NULLs_and_the_Three_Valued_Logic#AND,_OR - if isinstance(x, ibis_types.NullScalar): - return _null_or_value(y, y == ibis_types.literal(False)) - - if isinstance(y, ibis_types.NullScalar): - return _null_or_value(x, x == ibis_types.literal(False)) - return typing.cast(ibis_types.BooleanValue, x) & typing.cast( - ibis_types.BooleanValue, y - ) - - -@scalar_op_compiler.register_binary_op(ops.or_op) -def or_op( - x: ibis_types.Value, - y: ibis_types.Value, -): - # Workaround issue https://github.com/ibis-project/ibis/issues/7775 by - # implementing three-valued logic ourselves. For OR, when we encounter a - # NULL value, we only know when the result is TRUE, otherwise the result - # is unknown (NULL). See: truth table at - # https://en.wikibooks.org/wiki/Structured_Query_Language/NULLs_and_the_Three_Valued_Logic#AND,_OR - if isinstance(x, ibis_types.NullScalar): - return _null_or_value(y, y == ibis_types.literal(True)) - - if isinstance(y, ibis_types.NullScalar): - return _null_or_value(x, x == ibis_types.literal(True)) - return typing.cast(ibis_types.BooleanValue, x) | typing.cast( - ibis_types.BooleanValue, y - ) - - -@scalar_op_compiler.register_binary_op(ops.xor_op) -def xor_op( - x: ibis_types.Value, - y: ibis_types.Value, -): - return typing.cast(ibis_types.BooleanValue, x) ^ typing.cast( - ibis_types.BooleanValue, y - ) - - -@scalar_op_compiler.register_binary_op(ops.add_op) -@short_circuit_nulls() -def add_op( - x: ibis_types.Value, - y: ibis_types.Value, -): - x, y = _coerce_bools(x, y) - if isinstance(x, ibis_types.NullScalar) or isinstance(x, ibis_types.NullScalar): - return ibis_types.null() - - if x.type().is_boolean() and y.type().is_boolean(): - x, y = _coerce_bools(x, y, always=True) - return ( - typing.cast(ibis_types.NumericValue, x) - + typing.cast(ibis_types.NumericValue, x) - ).cast(ibis_dtypes.Boolean) - - x, y = _coerce_bools(x, y) - return x + y # type: ignore - - -@scalar_op_compiler.register_binary_op(ops.sub_op) -@short_circuit_nulls() -def sub_op( - x: ibis_types.Value, - y: ibis_types.Value, -): - x, y = _coerce_bools(x, y) - return typing.cast(ibis_types.NumericValue, x) - typing.cast( - ibis_types.NumericValue, y - ) - - -@scalar_op_compiler.register_binary_op(ops.mul_op) -@short_circuit_nulls() -def mul_op( - x: ibis_types.Value, - y: ibis_types.Value, -): - if x.type().is_boolean() and y.type().is_boolean(): - x, y = _coerce_bools(x, y, always=True) - return ( - typing.cast(ibis_types.NumericValue, x) - * typing.cast(ibis_types.NumericValue, x) - ).cast(ibis_dtypes.Boolean) - x, y = _coerce_bools(x, y) - return typing.cast(ibis_types.NumericValue, x) * typing.cast( - ibis_types.NumericValue, y - ) - - -@scalar_op_compiler.register_binary_op(ops.div_op) -@short_circuit_nulls(ibis_dtypes.float) -def div_op( - x: ibis_types.Value, - y: ibis_types.Value, -): - x, y = _coerce_bools(x, y) - return typing.cast(ibis_types.NumericValue, x) / typing.cast( - ibis_types.NumericValue, y - ) - - -@scalar_op_compiler.register_binary_op(ops.pow_op) -@short_circuit_nulls(ibis_dtypes.float) -def pow_op( - x: ibis_types.Value, - y: ibis_types.Value, -): - x, y = _coerce_bools(x, y) - if x.type().is_integer() and y.type().is_integer(): - return _int_pow_op(x, y) - else: - return _float_pow_op(x, y) - - -@scalar_op_compiler.register_binary_op(ops.unsafe_pow_op) -@short_circuit_nulls(ibis_dtypes.float) -def unsafe_pow_op( - x: ibis_types.Value, - y: ibis_types.Value, -): - """For internal use only - where domain and overflow checks are not needed.""" - x, y = _coerce_bools(x, y) - return typing.cast(ibis_types.NumericValue, x) ** typing.cast( - ibis_types.NumericValue, y - ) - - -def _int_pow_op( - x: ibis_types.Value, - y: ibis_types.Value, -): - # Need to avoid any error cases - should produce NaN instead - # See: https://cloud.google.com/bigquery/docs/reference/standard-sql/mathematical_functions#pow - x_as_decimal = typing.cast( - ibis_types.NumericValue, - x.cast(ibis_dtypes.Decimal(precision=38, scale=9, nullable=True)), - ) - y_val = typing.cast(ibis_types.NumericValue, y) - - # BQ POW() function outputs FLOAT64, which can lose precision. - # Therefore, we do math in NUMERIC and cast back down after. - # Also, explicit bounds checks, pandas will silently overflow. - pow_result = x_as_decimal**y_val - overflow_cond = (pow_result > _ibis_num((2**63) - 1)) | ( - pow_result < _ibis_num(-(2**63)) - ) - - return ( - ibis_api.case() - .when((overflow_cond), ibis_types.null()) - .else_(pow_result.cast(ibis_dtypes.int64)) - .end() - ) - - -def _float_pow_op( - x: ibis_types.Value, - y: ibis_types.Value, -): - # Most conditions here seek to prevent calling BQ POW with inputs that would generate errors. - # See: https://cloud.google.com/bigquery/docs/reference/standard-sql/mathematical_functions#pow - x_val = typing.cast(ibis_types.NumericValue, x) - y_val = typing.cast(ibis_types.NumericValue, y) - - overflow_cond = (x_val != _ZERO) & ((y_val * x_val.abs().ln()) > _FLOAT64_EXP_BOUND) - - # Float64 lose integer precision beyond 2**53, beyond this insufficient precision to get parity - exp_too_big = y_val.abs() > _ibis_num(2**53) - # Treat very large exponents as +=INF - norm_exp = exp_too_big.ifelse(_INF * y_val.sign(), y_val) - - pow_result = x_val**norm_exp - - # This cast is dangerous, need to only excuted where y_val has been bounds-checked - # Ibis needs try_cast binding to bq safe_cast - exponent_is_whole = y_val.cast(ibis_dtypes.int64) == y_val - odd_exponent = (x_val < _ZERO) & ( - y_val.cast(ibis_dtypes.int64) % _ibis_num(2) == _ibis_num(1) - ) - infinite_base = x_val.abs() == _INF - - return ( - ibis_api.case() - # Might be able to do something more clever with x_val==0 case - .when(y_val == _ZERO, _ibis_num(1)) - .when( - x_val == _ibis_num(1), _ibis_num(1) - ) # Need to ignore exponent, even if it is NA - .when( - (x_val == _ZERO) & (y_val < _ZERO), _INF - ) # This case would error POW function in BQ - .when(infinite_base, pow_result) - .when( - exp_too_big, pow_result - ) # Bigquery can actually handle the +-inf cases gracefully - .when((x_val < _ZERO) & (~exponent_is_whole), _NAN) - .when( - overflow_cond, _INF * odd_exponent.ifelse(_ibis_num(-1), _ibis_num(1)) - ) # finite overflows would cause bq to error - .else_(pow_result) - .end() - ) - - -@scalar_op_compiler.register_binary_op(ops.lt_op) -@short_circuit_nulls(ibis_dtypes.bool) -def lt_op( - x: ibis_types.Value, - y: ibis_types.Value, -): - x, y = _coerce_bools(x, y) - return x < y - - -@scalar_op_compiler.register_binary_op(ops.le_op) -@short_circuit_nulls(ibis_dtypes.bool) -def le_op( - x: ibis_types.Value, - y: ibis_types.Value, -): - x, y = _coerce_bools(x, y) - return x <= y - - -@scalar_op_compiler.register_binary_op(ops.gt_op) -@short_circuit_nulls(ibis_dtypes.bool) -def gt_op( - x: ibis_types.Value, - y: ibis_types.Value, -): - x, y = _coerce_bools(x, y) - return x > y - - -@scalar_op_compiler.register_binary_op(ops.ge_op) -@short_circuit_nulls(ibis_dtypes.bool) -def ge_op( - x: ibis_types.Value, - y: ibis_types.Value, -): - x, y = _coerce_bools(x, y) - return x >= y - - -@scalar_op_compiler.register_binary_op(ops.floordiv_op) -@short_circuit_nulls(ibis_dtypes.int) -def floordiv_op( - x: ibis_types.Value, - y: ibis_types.Value, -): - if x.type().is_boolean(): - x = x.cast(ibis_dtypes.int64) - elif y.type().is_boolean(): - y = y.cast(ibis_dtypes.int64) - x_numeric = typing.cast(ibis_types.NumericValue, x) - y_numeric = typing.cast(ibis_types.NumericValue, y) - floordiv_expr = x_numeric // y_numeric - - # DIV(N, 0) will error in bigquery, but needs to return 0 for int, and inf for float in BQ so we short-circuit in this case. - # Multiplying left by zero propogates nulls. - zero_result = _INF if (x.type().is_floating() or y.type().is_floating()) else _ZERO - return ( - ibis_api.case() - .when(y_numeric == _ZERO, zero_result * x_numeric) - .else_(floordiv_expr) - .end() - ) - - -def _is_bignumeric(x: ibis_types.Value): - if not isinstance(x, ibis_types.DecimalValue): - return False - # Should be exactly 76 for bignumeric - return x.precision > 70 # type: ignore - - -def _is_numeric(x: ibis_types.Value): - # either big-numeric or numeric - return isinstance(x, ibis_types.DecimalValue) - - -@scalar_op_compiler.register_binary_op(ops.mod_op) -@short_circuit_nulls() -def mod_op( - x: ibis_types.Value, - y: ibis_types.Value, -): - # Hacky short-circuit to avoid passing zero-literal to sql backend, evaluate locally instead to null. - op = y.op() - if isinstance(op, ibis_generic.Literal) and op.value == 0: - return ibis_types.null().cast(x.type()) - - x, y = _coerce_bools(x, y) - if x.type().is_integer() and y.type().is_integer(): - # both are ints, no casting necessary - return _int_mod( - typing.cast(ibis_types.IntegerValue, x), - typing.cast(ibis_types.IntegerValue, y), - ) - - else: - # bigquery doens't support float mod, so just cast to bignumeric and hope for the best - x_numeric = typing.cast( - ibis_types.DecimalValue, - x.cast(ibis_dtypes.Decimal(precision=76, scale=38, nullable=True)), - ) - y_numeric = typing.cast( - ibis_types.DecimalValue, - y.cast(ibis_dtypes.Decimal(precision=76, scale=38, nullable=True)), - ) - mod_numeric = _bignumeric_mod(x_numeric, y_numeric) # type: ignore - - # Cast back down based on original types - if _is_bignumeric(x) or _is_bignumeric(y): - return mod_numeric - if _is_numeric(x) or _is_numeric(y): - return mod_numeric.cast(ibis_dtypes.Decimal(38, 9)) - else: - return mod_numeric.cast(ibis_dtypes.float64) - - -def _bignumeric_mod( - x: ibis_types.IntegerValue, - y: ibis_types.IntegerValue, -): - # Hacky short-circuit to avoid passing zero-literal to sql backend, evaluate locally instead to null. - op = y.op() - if isinstance(op, ibis_generic.Literal) and op.value == 0: - return ibis_types.null().cast(x.type()) - - bq_mod = x % y # Bigquery will maintain x sign here - - # In BigQuery returned value has the same sign as X. In pandas, the sign of y is used, so we need to flip the result if sign(x) != sign(y) - return ( - ibis_api.case() - .when( - y == _ZERO, - _NAN * x, - ) # Dummy op to propogate nulls and type from x arg - .when( - (y < _ZERO) & (bq_mod > _ZERO), (y + bq_mod) - ) # Convert positive result to negative - .when( - (y > _ZERO) & (bq_mod < _ZERO), (y + bq_mod) - ) # Convert negative result to positive - .else_(bq_mod) - .end() - ) - - -def _int_mod( - x: ibis_types.IntegerValue, - y: ibis_types.IntegerValue, -): - # Hacky short-circuit to avoid passing zero-literal to sql backend, evaluate locally instead to null. - op = y.op() - if isinstance(op, ibis_generic.Literal) and op.value == 0: - return ibis_types.null().cast(x.type()) - - bq_mod = x % y # Bigquery will maintain x sign here - - # In BigQuery returned value has the same sign as X. In pandas, the sign of y is used, so we need to flip the result if sign(x) != sign(y) - return ( - ibis_api.case() - .when( - y == _ZERO, - _ZERO * x, - ) # Dummy op to propogate nulls and type from x arg - .when( - (y < _ZERO) & (bq_mod > _ZERO), (y + bq_mod) - ) # Convert positive result to negative - .when( - (y > _ZERO) & (bq_mod < _ZERO), (y + bq_mod) - ) # Convert negative result to positive - .else_(bq_mod) - .end() - ) - - -@scalar_op_compiler.register_binary_op(ops.fillna_op) -def fillna_op( - x: ibis_types.Value, - y: ibis_types.Value, -): - return x.fill_null(typing.cast(ibis_types.Scalar, y)) - - -@scalar_op_compiler.register_binary_op(ops.round_op) -def round_op(x: ibis_types.Value, y: ibis_types.Value): - if x.type().is_integer(): - # bq produces float64, but pandas returns int - return ( - typing.cast(ibis_types.NumericValue, x) - .round(digits=typing.cast(ibis_types.IntegerValue, y)) - .cast(ibis_dtypes.int64) - ) - return typing.cast(ibis_types.NumericValue, x).round( - digits=typing.cast(ibis_types.IntegerValue, y) - ) - - -@scalar_op_compiler.register_binary_op(ops.coalesce_op) -def coalesce_impl( - x: ibis_types.Value, - y: ibis_types.Value, -): - if x.name("name").equals(y.name("name")): - return x - else: - return ibis_api.coalesce(x, y) - - -@scalar_op_compiler.register_binary_op(ops.maximum_op) -def maximum_impl( - value: ibis_types.Value, - lower: ibis_types.Value, -): - # Note: propagates nulls - return ( - ibis_api.case().when(lower.isnull() | (value < lower), lower).else_(value).end() - ) - - -@scalar_op_compiler.register_binary_op(ops.minimum_op) -def minimum_impl( - value: ibis_types.Value, - upper: ibis_types.Value, -): - # Note: propagates nulls - return ( - ibis_api.case().when(upper.isnull() | (value > upper), upper).else_(value).end() - ) - - -@scalar_op_compiler.register_binary_op(ops.cosine_distance_op) -def cosine_distance_impl( - vector1: ibis_types.Value, - vector2: ibis_types.Value, -): - return vector_distance(vector1, vector2, "COSINE") - - -@scalar_op_compiler.register_binary_op(ops.euclidean_distance_op) -def euclidean_distance_impl( - vector1: ibis_types.Value, - vector2: ibis_types.Value, -): - return vector_distance(vector1, vector2, "EUCLIDEAN") - - -@scalar_op_compiler.register_binary_op(ops.manhattan_distance_op) -def manhattan_distance_impl( - vector1: ibis_types.Value, - vector2: ibis_types.Value, -): - return vector_distance(vector1, vector2, "MANHATTAN") - - -# Blob Ops -@scalar_op_compiler.register_binary_op(ops.obj_make_ref_op) -def obj_make_ref_op(x: ibis_types.Value, y: ibis_types.Value): - return obj_make_ref(uri=x, authorizer=y) - - -@scalar_op_compiler.register_unary_op(ops.obj_make_ref_json_op) -def obj_make_ref_json_op(x: ibis_types.Value): - return obj_make_ref_json(objectref_json=x) - - -# Ternary Operations -@scalar_op_compiler.register_ternary_op(ops.where_op) -def where_op( - original: ibis_types.Value, - condition: ibis_types.Value, - replacement: ibis_types.Value, -) -> ibis_types.Value: - """Returns x if y is true, otherwise returns z.""" - return ibis_api.case().when(condition, original).else_(replacement).end() # type: ignore - - -@scalar_op_compiler.register_ternary_op(ops.clip_op) -def clip_op( - original: ibis_types.Value, - lower: ibis_types.Value, - upper: ibis_types.Value, -) -> ibis_types.Value: - """Clips value to lower and upper bounds.""" - if isinstance(lower, ibis_types.NullScalar) and ( - not isinstance(upper, ibis_types.NullScalar) - ): - return ibis_api.least(original, upper) - elif (not isinstance(lower, ibis_types.NullScalar)) and isinstance( - upper, ibis_types.NullScalar - ): - return ibis_api.greatest(original, lower) - elif isinstance(lower, ibis_types.NullScalar) and ( - isinstance(upper, ibis_types.NullScalar) - ): - return original - else: - # Note: Pandas has unchanged behavior when upper bound and lower bound are flipped. This implementation requires that lower_bound < upper_bound - return ibis_api.greatest(ibis_api.least(original, upper), lower) - - -# N-ary Operations -@scalar_op_compiler.register_nary_op(ops.case_when_op) -def case_when_op(*cases_and_outputs: ibis_types.Value) -> ibis_types.Value: - # ibis can handle most type coercions, but we need to force bool -> int - # TODO: dispatch coercion depending on bigframes dtype schema - result_values = cases_and_outputs[1::2] - do_upcast_bool = any(t.type().is_numeric() for t in result_values) - if do_upcast_bool: - # Just need to upcast to int, ibis can handle further coercion - result_values = tuple( - val.cast(ibis_dtypes.int64) if val.type().is_boolean() else val - for val in result_values - ) - - case_val = ibis_api.case() - for predicate, output in zip(cases_and_outputs[::2], result_values): - case_val = case_val.when(predicate, output) - return case_val.end() # type: ignore - - -@scalar_op_compiler.register_nary_op(ops.SqlScalarOp, pass_op=True) -def sql_scalar_op_impl(*operands: ibis_types.Value, op: ops.SqlScalarOp): - return ibis_generic.SqlScalar( - op.sql_template, - values=tuple(typing.cast(ibis_generic.Value, expr.op()) for expr in operands), - output_type=bigframes.core.compile.ibis_types.bigframes_dtype_to_ibis_dtype( - op.output_type() - ), - ).to_expr() - - -@scalar_op_compiler.register_nary_op(ops.StructOp, pass_op=True) -def struct_op_impl( - *values: ibis_types.Value, op: ops.StructOp -) -> ibis_types.StructValue: - data = {} - for i, value in enumerate(values): - data[op.column_names[i]] = value - - return ibis_types.struct(data) - - -@scalar_op_compiler.register_nary_op(ops.AIGenerate, pass_op=True) -def ai_generate( - *values: ibis_types.Value, op: ops.AIGenerate -) -> ibis_types.StructValue: - return ai_ops.AIGenerate( - _construct_prompt(values, op.prompt_context), # type: ignore - op.connection_id, # type: ignore - op.endpoint, # type: ignore - op.request_type, # type: ignore - op.model_params, # type: ignore - op.output_schema, # type: ignore - ).to_expr() - - -@scalar_op_compiler.register_nary_op(ops.AIGenerateBool, pass_op=True) -def ai_generate_bool( - *values: ibis_types.Value, op: ops.AIGenerateBool -) -> ibis_types.StructValue: - return ai_ops.AIGenerateBool( - _construct_prompt(values, op.prompt_context), # type: ignore - op.connection_id, # type: ignore - op.endpoint, # type: ignore - op.request_type, # type: ignore - op.model_params, # type: ignore - ).to_expr() - - -@scalar_op_compiler.register_nary_op(ops.AIGenerateInt, pass_op=True) -def ai_generate_int( - *values: ibis_types.Value, op: ops.AIGenerateInt -) -> ibis_types.StructValue: - return ai_ops.AIGenerateInt( - _construct_prompt(values, op.prompt_context), # type: ignore - op.connection_id, # type: ignore - op.endpoint, # type: ignore - op.request_type, # type: ignore - op.model_params, # type: ignore - ).to_expr() - - -@scalar_op_compiler.register_nary_op(ops.AIGenerateDouble, pass_op=True) -def ai_generate_double( - *values: ibis_types.Value, op: ops.AIGenerateDouble -) -> ibis_types.StructValue: - return ai_ops.AIGenerateDouble( - _construct_prompt(values, op.prompt_context), # type: ignore - op.connection_id, # type: ignore - op.endpoint, # type: ignore - op.request_type, # type: ignore - op.model_params, # type: ignore - ).to_expr() - - -@scalar_op_compiler.register_unary_op(ops.AIEmbed, pass_op=True) -def ai_embed(value: ibis_types.Value, op: ops.AIEmbed) -> ibis_types.StructValue: - return ai_ops.AIEmbed( - value, # type: ignore - connection_id=op.connection_id, # type: ignore - endpoint=op.endpoint, # type: ignore - model=op.model, # type: ignore - task_type=op.task_type, # type: ignore - title=op.title, # type: ignore - model_params=op.model_params, # type: ignore - ).to_expr() - - -@scalar_op_compiler.register_nary_op(ops.AIIf, pass_op=True) -def ai_if(*values: ibis_types.Value, op: ops.AIIf) -> ibis_types.StructValue: - return ai_ops.AIIf( - _construct_prompt(values, op.prompt_context), # type: ignore - op.connection_id, # type: ignore - op.endpoint, # type: ignore - op.optimization_mode, # type: ignore - op.max_error_ratio, # type: ignore - ).to_expr() - - -@scalar_op_compiler.register_nary_op(ops.AIClassify, pass_op=True) -def ai_classify( - *values: ibis_types.Value, op: ops.AIClassify -) -> ibis_types.StructValue: - return ai_ops.AIClassify( - _construct_prompt(values, op.prompt_context), # type: ignore - op.categories, # type: ignore - _construct_examples(op.examples), # type: ignore - op.connection_id, # type: ignore - op.endpoint, # type: ignore - op.output_mode, # type: ignore - op.optimization_mode, # type: ignore - op.max_error_ratio, # type: ignore - ).to_expr() - - -@scalar_op_compiler.register_nary_op(ops.AIScore, pass_op=True) -def ai_score(*values: ibis_types.Value, op: ops.AIScore) -> ibis_types.StructValue: - return ai_ops.AIScore( - _construct_prompt(values, op.prompt_context), # type: ignore - op.connection_id, # type: ignore - op.endpoint, # type: ignore - op.max_error_ratio, # type: ignore - ).to_expr() - - -@scalar_op_compiler.register_binary_op(ops.AISimilarity, pass_op=True) -def ai_similarity( - content1: ibis_types.Value, content2: ibis_types.Value, op: ops.AISimilarity -) -> ibis_types.Value: - return ai_ops.AISimilarity( - content1, # type: ignore - content2, # type: ignore - op.endpoint, # type: ignore - op.model, # type: ignore - op.model_params, # type: ignore - op.connection_id, # type: ignore - ).to_expr() - - -def _construct_prompt( - col_refs: tuple[ibis_types.Value], prompt_context: tuple[str | None] -) -> ibis_types.StructValue: - prompt: dict[str, ibis_types.Value | str] = {} - column_ref_idx = 0 - - for idx, elem in enumerate(prompt_context): - if elem is None: - prompt[f"_field_{idx + 1}"] = col_refs[column_ref_idx] - column_ref_idx += 1 - else: - prompt[f"_field_{idx + 1}"] = elem - - return ibis.struct(prompt) - - -def _construct_examples( - examples: tuple[tuple[str, str | tuple[str, ...]], ...] | None, -) -> ibis_types.ArrayValue | None: - if examples is None: - return None - - results: list[ibis_types.StructValue] = [] - - for example in examples: - value: Any = example[1] - if isinstance(example[1], (list, tuple)): - value = list(example[1]) - - ibis_example = ibis.struct({"_field_1": example[0], "_field_2": value}) - results.append(ibis_example) - - return ibis.array(results) - - -@scalar_op_compiler.register_nary_op(ops.RowKey, pass_op=True) -def rowkey_op_impl(*values: ibis_types.Value, op: ops.RowKey) -> ibis_types.Value: - return bigframes.core.compile.ibis_compiler.default_ordering.gen_row_key(values) - - -# Helpers -def is_null(value) -> bool: - # float NaN/inf should be treated as distinct from 'true' null values - return typing.cast(bool, pd.isna(value)) and not isinstance(value, float) - - -def _ibis_num(number: float): - return typing.cast(ibis_types.NumericValue, ibis_types.literal(number)) - - -@ibis_udf.scalar.builtin -def timestamp(a) -> ibis_dtypes.timestamp: # type: ignore - """Convert string or a datetime to timestamp.""" - - -@ibis_udf.scalar.builtin -def unix_millis(a: ibis_dtypes.timestamp) -> int: # type: ignore - """Convert a timestamp to milliseconds""" - - -@ibis_udf.scalar.builtin -def unix_micros(a: ibis_dtypes.timestamp) -> int: # type: ignore - """Convert a timestamp to microseconds""" - - -# Need these because ibis otherwise tries to do casts to int that can fail -@ibis_udf.scalar.builtin(name="floor") -def float_floor(a: float) -> float: - """Convert string to timestamp.""" - return 0 # pragma: NO COVER - - -@ibis_udf.scalar.builtin(name="ceil") -def float_ceil(a: float) -> float: - """Convert string to timestamp.""" - return 0 # pragma: NO COVER - - -@ibis_udf.scalar.builtin(name="parse_json") -def parse_json(json_str: str) -> ibis_dtypes.JSON: # type: ignore[empty-body] - """Converts a JSON-formatted STRING value to a JSON value.""" - - -@ibis_udf.scalar.builtin(name="SAFE.PARSE_JSON") -def parse_json_in_safe(json_str: str) -> ibis_dtypes.JSON: # type: ignore[empty-body] - """Converts a JSON-formatted STRING value to a JSON value in the safe mode.""" - - -@ibis_udf.scalar.builtin(name="json_set") -def json_set( # type: ignore[empty-body] - json_obj: ibis_dtypes.JSON, json_path: ibis_dtypes.String, json_value -) -> ibis_dtypes.JSON: - """Produces a new SQL JSON value with the specified JSON data inserted or replaced.""" - - -@ibis_udf.scalar.builtin(name="json_extract_string_array") -def json_extract_string_array( # type: ignore[empty-body] - json_obj: ibis_dtypes.JSON, json_path: ibis_dtypes.String -) -> ibis_dtypes.Array[ibis_dtypes.String]: - """Extracts a JSON array and converts it to a SQL ARRAY of STRINGs.""" - - -@ibis_udf.scalar.builtin(name="to_json") -def to_json(json_obj) -> ibis_dtypes.JSON: # type: ignore[empty-body] - """Convert to JSON.""" - - -@ibis_udf.scalar.builtin(name="to_json_string") -def to_json_string(value) -> ibis_dtypes.String: # type: ignore[empty-body] - """Convert value to JSON-formatted string.""" - - -@ibis_udf.scalar.builtin(name="json_keys") -def json_keys( # type: ignore[empty-body] - json_obj: ibis_dtypes.JSON, - max_depth: ibis_dtypes.Int64, -) -> ibis_dtypes.Array[ibis_dtypes.String]: - """Extracts unique JSON keys from a JSON expression.""" - - -@ibis_udf.scalar.builtin(name="json_value") -def json_value( # type: ignore[empty-body] - json_obj: ibis_dtypes.JSON, json_path: ibis_dtypes.String -) -> ibis_dtypes.String: - """Retrieve value of a JSON field as plain STRING.""" - - -@ibis_udf.scalar.builtin(name="json_value_array") -def json_value_array( # type: ignore[empty-body] - json_obj: ibis_dtypes.JSON, json_path: ibis_dtypes.String -) -> ibis_dtypes.Array[ibis_dtypes.String]: - """Extracts a JSON array and converts it to a SQL ARRAY of STRINGs.""" - - -@ibis_udf.scalar.builtin(name="INT64") -def cast_json_to_int64(json_str: ibis_dtypes.JSON) -> ibis_dtypes.Int64: # type: ignore[empty-body] - """Converts a JSON number to a SQL INT64 value.""" - - -@ibis_udf.scalar.builtin(name="SAFE.INT64") -def cast_json_to_int64_in_safe(json_str: ibis_dtypes.JSON) -> ibis_dtypes.Int64: # type: ignore[empty-body] - """Converts a JSON number to a SQL INT64 value in the safe mode.""" - - -@ibis_udf.scalar.builtin(name="FLOAT64") -def cast_json_to_float64(json_str: ibis_dtypes.JSON) -> ibis_dtypes.Float64: # type: ignore[empty-body] - """Attempts to convert a JSON value to a SQL FLOAT64 value.""" - - -@ibis_udf.scalar.builtin(name="SAFE.FLOAT64") -def cast_json_to_float64_in_safe(json_str: ibis_dtypes.JSON) -> ibis_dtypes.Float64: # type: ignore[empty-body] - """Attempts to convert a JSON value to a SQL FLOAT64 value.""" - - -@ibis_udf.scalar.builtin(name="BOOL") -def cast_json_to_bool(json_str: ibis_dtypes.JSON) -> ibis_dtypes.Boolean: # type: ignore[empty-body] - """Attempts to convert a JSON value to a SQL BOOL value.""" - - -@ibis_udf.scalar.builtin(name="SAFE.BOOL") -def cast_json_to_bool_in_safe(json_str: ibis_dtypes.JSON) -> ibis_dtypes.Boolean: # type: ignore[empty-body] - """Attempts to convert a JSON value to a SQL BOOL value.""" - - -@ibis_udf.scalar.builtin(name="STRING") -def cast_json_to_string(json_str: ibis_dtypes.JSON) -> ibis_dtypes.String: # type: ignore[empty-body] - """Attempts to convert a JSON value to a SQL STRING value.""" - - -@ibis_udf.scalar.builtin(name="SAFE.STRING") -def cast_json_to_string_in_safe(json_str: ibis_dtypes.JSON) -> ibis_dtypes.String: # type: ignore[empty-body] - """Attempts to convert a JSON value to a SQL STRING value.""" - - -@ibis_udf.scalar.builtin(name="ML.DISTANCE") -def vector_distance(vector1, vector2, type: str) -> ibis_dtypes.Float64: # type: ignore[empty-body] - """Computes the distance between two vectors using specified type ("EUCLIDEAN", "MANHATTAN", or "COSINE")""" - - -@ibis_udf.scalar.builtin(name="OBJ.FETCH_METADATA") -def obj_fetch_metadata(obj_ref: _OBJ_REF_IBIS_DTYPE) -> _OBJ_REF_IBIS_DTYPE: # type: ignore - """Fetch metadata from ObjectRef Struct.""" - - -@ibis_udf.scalar.builtin(name="OBJ.MAKE_REF") -def obj_make_ref(uri: str, authorizer: str) -> _OBJ_REF_IBIS_DTYPE: # type: ignore - """Make ObjectRef Struct from uri and connection.""" - - -@ibis_udf.scalar.builtin(name="OBJ.MAKE_REF") -def obj_make_ref_json(objectref_json: ibis_dtypes.JSON) -> _OBJ_REF_IBIS_DTYPE: # type: ignore - """Make ObjectRef Struct from json.""" - - -@ibis_udf.scalar.builtin(name="OBJ.GET_ACCESS_URL") -# Stub for BigQuery UDF, empty body is intentional. -# _OBJ_REF_IBIS_DTYPE is a variable holding a type, Mypy complains about it being used as type hint. -def obj_get_access_url( # type: ignore[empty-body] - obj_ref: _OBJ_REF_IBIS_DTYPE, # type: ignore[valid-type] - mode: ibis_dtypes.String, -) -> ibis_dtypes.JSON: - """Get access url (as ObjectRefRumtime JSON) from ObjectRef.""" - - -@ibis_udf.scalar.builtin(name="OBJ.GET_ACCESS_URL") -def obj_get_access_url_with_duration(obj_ref, mode, duration) -> ibis_dtypes.JSON: # type: ignore - """Get access url (as ObjectRefRumtime JSON) from ObjectRef.""" - - -@ibis_udf.scalar.builtin(name="ltrim") -def str_lstrip_op( # type: ignore[empty-body] - x: ibis_dtypes.String, to_strip: ibis_dtypes.String -) -> ibis_dtypes.String: - """Remove leading and trailing characters.""" - - -@ibis_udf.scalar.builtin(name="rtrim") -def str_rstrip_op( # type: ignore[empty-body] - x: ibis_dtypes.String, to_strip: ibis_dtypes.String -) -> ibis_dtypes.String: - """Remove leading and trailing characters.""" - - -@ibis_udf.scalar.builtin(name="trim") -def str_strip_op( # type: ignore[empty-body] - x: ibis_dtypes.String, to_strip: ibis_dtypes.String -) -> ibis_dtypes.String: - """Remove leading and trailing characters.""" diff --git a/bigframes/core/compile/ibis_types.py b/bigframes/core/compile/ibis_types.py deleted file mode 100644 index 788f6db4435..00000000000 --- a/bigframes/core/compile/ibis_types.py +++ /dev/null @@ -1,431 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -from __future__ import annotations - -from typing import Dict, Iterable, Optional, Tuple, Union, cast - -import bigframes_vendored.constants as constants -import bigframes_vendored.ibis -import bigframes_vendored.ibis.expr.datatypes as ibis_dtypes -import bigframes_vendored.ibis.expr.types as ibis_types -import db_dtypes # type: ignore -import geopandas as gpd # type: ignore -import pandas as pd -import pyarrow as pa - -import bigframes.dtypes - -# Type hints for Ibis data types supported by BigQuery DataFrame -IbisDtype = Union[ - ibis_dtypes.Boolean, - ibis_dtypes.Float64, - ibis_dtypes.Int64, - ibis_dtypes.String, - ibis_dtypes.Date, - ibis_dtypes.Time, - ibis_dtypes.Timestamp, - ibis_dtypes.Binary, - ibis_dtypes.Decimal, - ibis_dtypes.GeoSpatial, - ibis_dtypes.JSON, -] - -IBIS_GEO_TYPE = ibis_dtypes.GeoSpatial(geotype="geography", srid=4326, nullable=True) - - -BIDIRECTIONAL_MAPPINGS: Iterable[Tuple[IbisDtype, bigframes.dtypes.Dtype]] = ( - (ibis_dtypes.boolean, pd.BooleanDtype()), - (ibis_dtypes.date, pd.ArrowDtype(pa.date32())), - (ibis_dtypes.float64, pd.Float64Dtype()), - (ibis_dtypes.int64, pd.Int64Dtype()), - (ibis_dtypes.string, pd.StringDtype(storage="pyarrow")), - (ibis_dtypes.time, pd.ArrowDtype(pa.time64("us"))), - (ibis_dtypes.Timestamp(timezone=None), pd.ArrowDtype(pa.timestamp("us"))), - ( - ibis_dtypes.Timestamp(timezone="UTC"), - pd.ArrowDtype(pa.timestamp("us", tz="UTC")), - ), - (ibis_dtypes.binary, pd.ArrowDtype(pa.binary())), - ( - ibis_dtypes.Decimal(precision=38, scale=9, nullable=True), - pd.ArrowDtype(pa.decimal128(38, 9)), - ), - ( - ibis_dtypes.Decimal(precision=76, scale=38, nullable=True), - pd.ArrowDtype(pa.decimal256(76, 38)), - ), - ( - IBIS_GEO_TYPE, - gpd.array.GeometryDtype(), - ), - (ibis_dtypes.json, pd.ArrowDtype(db_dtypes.JSONArrowType())), -) - -BIGFRAMES_TO_IBIS: Dict[bigframes.dtypes.Dtype, ibis_dtypes.DataType] = { - pandas: ibis for ibis, pandas in BIDIRECTIONAL_MAPPINGS -} -BIGFRAMES_TO_IBIS.update({bigframes.dtypes.TIMEDELTA_DTYPE: ibis_dtypes.int64}) -IBIS_TO_BIGFRAMES: Dict[ibis_dtypes.DataType, bigframes.dtypes.Dtype] = { - ibis: pandas for ibis, pandas in BIDIRECTIONAL_MAPPINGS -} -# Allow REQUIRED fields to map correctly. -IBIS_TO_BIGFRAMES.update( - {ibis.copy(nullable=False): pandas for ibis, pandas in BIDIRECTIONAL_MAPPINGS} -) -IBIS_TO_BIGFRAMES.update( - { - # TODO: Interval - } -) - - -def cast_ibis_value( - value: ibis_types.Value, to_type: ibis_dtypes.DataType, safe: bool = False -) -> ibis_types.Value: - """Perform compatible type casts of ibis values - - Args: - value: - Ibis value, which could be a literal, scalar, or column - - to_type: - The Ibis type to cast to - - Returns: - A new Ibis value of type to_type - - Raises: - TypeError: if the type cast cannot be executed""" - # normalize to nullable, which doesn't impact compatibility - value_type = value.type().copy(nullable=True) - if value_type == to_type: - return value - # casts that just work - # TODO(bmil): add to this as more casts are verified - good_casts = { - ibis_dtypes.bool: (ibis_dtypes.int64,), - ibis_dtypes.int64: ( - ibis_dtypes.bool, - ibis_dtypes.float64, - ibis_dtypes.string, - ibis_dtypes.Decimal(precision=38, scale=9), - ibis_dtypes.Decimal(precision=76, scale=38), - ibis_dtypes.time, - ibis_dtypes.timestamp, - ibis_dtypes.Timestamp(timezone="UTC"), - ), - ibis_dtypes.float64: ( - ibis_dtypes.string, - ibis_dtypes.int64, - ibis_dtypes.Decimal(precision=38, scale=9), - ibis_dtypes.Decimal(precision=76, scale=38), - ), - ibis_dtypes.string: ( - ibis_dtypes.int64, - ibis_dtypes.float64, - ibis_dtypes.Decimal(precision=38, scale=9), - ibis_dtypes.Decimal(precision=76, scale=38), - ibis_dtypes.binary, - ibis_dtypes.date, - ibis_dtypes.timestamp, - ibis_dtypes.Timestamp(timezone="UTC"), - ), - ibis_dtypes.date: ( - ibis_dtypes.string, - ibis_dtypes.timestamp, - ibis_dtypes.Timestamp(timezone="UTC"), - ), - ibis_dtypes.Decimal(precision=38, scale=9): ( - ibis_dtypes.float64, - ibis_dtypes.int64, - ibis_dtypes.Decimal(precision=76, scale=38), - ), - ibis_dtypes.Decimal(precision=76, scale=38): ( - ibis_dtypes.float64, - ibis_dtypes.int64, - ibis_dtypes.Decimal(precision=38, scale=9), - ), - ibis_dtypes.time: ( - ibis_dtypes.int64, - ibis_dtypes.string, - ), - ibis_dtypes.timestamp: ( - ibis_dtypes.date, - ibis_dtypes.int64, - ibis_dtypes.string, - ibis_dtypes.time, - ibis_dtypes.Timestamp(timezone="UTC"), - ), - ibis_dtypes.Timestamp(timezone="UTC"): ( - ibis_dtypes.date, - ibis_dtypes.int64, - ibis_dtypes.string, - ibis_dtypes.time, - ibis_dtypes.timestamp, - ), - ibis_dtypes.binary: (ibis_dtypes.string,), - ibis_dtypes.point: (IBIS_GEO_TYPE,), - ibis_dtypes.geometry: (IBIS_GEO_TYPE,), - ibis_dtypes.geography: (IBIS_GEO_TYPE,), - ibis_dtypes.linestring: (IBIS_GEO_TYPE,), - ibis_dtypes.polygon: (IBIS_GEO_TYPE,), - ibis_dtypes.multilinestring: (IBIS_GEO_TYPE,), - ibis_dtypes.multipoint: (IBIS_GEO_TYPE,), - ibis_dtypes.multipolygon: (IBIS_GEO_TYPE,), - } - - if value_type in good_casts: - if to_type in good_casts[value_type]: - return value.try_cast(to_type) if safe else value.cast(to_type) - else: - # this should never happen - raise TypeError( - f"Unexpected value type {value_type}. {constants.FEEDBACK_LINK}" - ) - - # casts that need some encouragement - - # BigQuery casts bools to lower case strings. Capitalize the result to match Pandas - # TODO(bmil): remove this workaround after fixing Ibis - if value_type == ibis_dtypes.bool and to_type == ibis_dtypes.string: - if safe: - return cast(ibis_types.StringValue, value.try_cast(to_type)).capitalize() - else: - return cast(ibis_types.StringValue, value.cast(to_type)).capitalize() - - if value_type == ibis_dtypes.bool and to_type == ibis_dtypes.float64: - if safe: - return value.try_cast(ibis_dtypes.int64).try_cast(ibis_dtypes.float64) - else: - return value.cast(ibis_dtypes.int64).cast(ibis_dtypes.float64) - - if value_type == ibis_dtypes.float64 and to_type == ibis_dtypes.bool: - return value != ibis_types.literal(0) - - raise TypeError( - f"Unsupported cast {value_type} to {to_type}. {constants.FEEDBACK_LINK}" - ) - - -def bigframes_dtype_to_ibis_dtype( - bigframes_dtype: bigframes.dtypes.Dtype, -) -> ibis_dtypes.DataType: - """Converts a BigQuery DataFrames supported dtype to an Ibis dtype. - - Args: - bigframes_dtype: - A dtype supported by BigQuery DataFrame - - Returns: - IbisDtype: The corresponding Ibis type - - Raises: - ValueError: If passed a dtype not supported by BigQuery DataFrames. - """ - if bigframes_dtype in BIGFRAMES_TO_IBIS.keys(): - return BIGFRAMES_TO_IBIS[bigframes_dtype] - - elif isinstance(bigframes_dtype, pd.ArrowDtype) and bigframes_dtype.pyarrow_dtype: - return _arrow_dtype_to_ibis_dtype(bigframes_dtype.pyarrow_dtype) - - else: - raise ValueError(f"Datatype has no ibis type mapping: {bigframes_dtype}") - - -def ibis_dtype_to_bigframes_dtype( - ibis_dtype: ibis_dtypes.DataType, -) -> bigframes.dtypes.Dtype: - """Converts an Ibis dtype to a BigQuery DataFrames dtype - - Args: - ibis_dtype: The ibis dtype used to represent this type, which - should in turn correspond to an underlying BigQuery type - - Returns: - The supported BigQuery DataFrames dtype, which may be provided by - pandas, numpy, or db_types - - Raises: - ValueError: if passed an unexpected type - """ - # Special cases: Ibis supports variations on these types, but currently - # our IO returns them as objects. Eventually, we should support them as - # ArrowDType (and update the IO accordingly) - if isinstance(ibis_dtype, ibis_dtypes.Array): - return pd.ArrowDtype(_ibis_dtype_to_arrow_dtype(ibis_dtype)) - - if isinstance(ibis_dtype, ibis_dtypes.Struct): - return pd.ArrowDtype(_ibis_dtype_to_arrow_dtype(ibis_dtype)) - - # BigQuery only supports integers of size 64 bits. - if isinstance(ibis_dtype, ibis_dtypes.Integer): - return pd.Int64Dtype() - - if isinstance(ibis_dtype, ibis_dtypes.JSON): - return bigframes.dtypes.JSON_DTYPE - - if isinstance(ibis_dtype, ibis_dtypes.GeoSpatial): - return gpd.array.GeometryDtype() - - if ibis_dtype in IBIS_TO_BIGFRAMES: - return IBIS_TO_BIGFRAMES[ibis_dtype] - elif isinstance(ibis_dtype, ibis_dtypes.Decimal): - # Temporary workaround for ibis decimal issue (b/323387826) - if ibis_dtype.precision is not None and ibis_dtype.precision >= 76: - return pd.ArrowDtype(pa.decimal256(76, 38)) - else: - return pd.ArrowDtype(pa.decimal128(38, 9)) - elif isinstance(ibis_dtype, ibis_dtypes.Null): - # Fallback to STRING for NULL values for most flexibility in SQL. - return IBIS_TO_BIGFRAMES[ibis_dtypes.string] - else: - raise ValueError( - f"Unexpected Ibis data type {ibis_dtype}. {constants.FEEDBACK_LINK}" - ) - - -def _ibis_dtype_to_arrow_dtype(ibis_dtype: ibis_dtypes.DataType) -> pa.DataType: - """Private utility to convert ibis dtype to equivalent arrow type.""" - if isinstance(ibis_dtype, ibis_dtypes.Array): - return pa.list_( - _ibis_dtype_to_arrow_dtype(ibis_dtype.value_type.copy(nullable=True)) - ) - - if isinstance(ibis_dtype, ibis_dtypes.Struct): - return pa.struct( - [ - pa.field( - name, - _ibis_dtype_to_arrow_dtype(dtype), - nullable=not pa.types.is_list(_ibis_dtype_to_arrow_dtype(dtype)), - ) - for name, dtype in ibis_dtype.fields.items() - ] - ) - - if ibis_dtype in IBIS_TO_BIGFRAMES: - dtype = IBIS_TO_BIGFRAMES[ibis_dtype] - # Note: arrow mappings are incomplete, no geography type - return bigframes.dtypes.bigframes_dtype_to_arrow_dtype(dtype) - else: - raise ValueError( - f"Unexpected Ibis data type {ibis_dtype}. {constants.FEEDBACK_LINK}" - ) - - -_ARROW_TO_IBIS = { - mapping.arrow_dtype: bigframes_dtype_to_ibis_dtype(mapping.dtype) - for mapping in bigframes.dtypes.SIMPLE_TYPES - if mapping.arrow_dtype is not None -} - - -def _arrow_dtype_to_ibis_dtype(arrow_dtype: pa.DataType) -> ibis_dtypes.DataType: - if arrow_dtype == pa.null(): - # Used for empty local dataframes where pyarrow has null type - return ibis_dtypes.float64 - if pa.types.is_struct(arrow_dtype): - struct_dtype = cast(pa.StructType, arrow_dtype) - return ibis_dtypes.Struct.from_tuples( - [ - (field.name, _arrow_dtype_to_ibis_dtype(field.type)) - for field in struct_dtype - ] - ) - if pa.types.is_list(arrow_dtype): - list_dtype = cast(pa.ListType, arrow_dtype) - value_dtype = list_dtype.value_type - value_ibis_type = _arrow_dtype_to_ibis_dtype(value_dtype) - return ibis_dtypes.Array(value_type=value_ibis_type) - elif arrow_dtype in _ARROW_TO_IBIS: - return _ARROW_TO_IBIS[arrow_dtype] - else: - raise ValueError(f"Unexpected arrow type: {arrow_dtype}") - - -def literal_to_ibis_scalar( - literal, force_dtype: Optional[bigframes.dtypes.Dtype] = None, validate: bool = True -): - """Accept any literal and, if possible, return an Ibis Scalar - expression with a BigQuery DataFrames compatible data type - - Args: - literal: - any value accepted by Ibis - force_dtype: - force the value to a specific dtype - validate: - If true, will raise ValueError if type cannot be stored in a - BigQuery DataFrames object. If used as a subexpression, this should - be disabled. - - Returns: - An ibis Scalar supported by BigQuery DataFrame - - Raises: - ValueError: if passed literal cannot be coerced to a - BigQuery DataFrames compatible scalar - """ - # Special case: Can create nulls for non-bidirectional types - if (force_dtype == gpd.array.GeometryDtype()) and pd.isna(literal): - # Ibis has bug for casting nulltype to geospatial, so we perform intermediate cast first - geotype = ibis_dtypes.GeoSpatial(geotype="geography", srid=4326, nullable=True) - return bigframes_vendored.ibis.literal(None, geotype) - - ibis_dtype = bigframes_dtype_to_ibis_dtype(force_dtype) if force_dtype else None - - if pd.api.types.is_list_like(literal): - # "correct" way would be to use ibis.array, but this produces invalid BQ SQL syntax - return tuple(literal) - - if not pd.api.types.is_list_like(literal) and pd.isna(literal): - if ibis_dtype: - return bigframes_vendored.ibis.null().cast(ibis_dtype) - else: - return bigframes_vendored.ibis.null() - - scalar_expr = bigframes_vendored.ibis.literal(literal) - if ibis_dtype: - scalar_expr = bigframes_vendored.ibis.literal(literal, ibis_dtype) - elif scalar_expr.type().is_floating(): - scalar_expr = bigframes_vendored.ibis.literal(literal, ibis_dtypes.float64) - elif scalar_expr.type().is_integer(): - scalar_expr = bigframes_vendored.ibis.literal(literal, ibis_dtypes.int64) - elif scalar_expr.type().is_decimal(): - scalar_expr_type = cast(ibis_dtypes.Decimal, scalar_expr.type()) - precision = scalar_expr_type.precision - scale = scalar_expr_type.scale - if (not precision and not scale) or ( - precision and scale and scale <= 9 and precision + (9 - scale) <= 38 - ): - scalar_expr = bigframes_vendored.ibis.literal( - literal, ibis_dtypes.decimal(precision=38, scale=9) - ) - elif precision and scale and scale <= 38 and precision + (38 - scale) <= 76: - scalar_expr = bigframes_vendored.ibis.literal( - literal, ibis_dtypes.decimal(precision=76, scale=38) - ) - else: - raise TypeError( - "BigQuery's decimal data type supports a maximum precision of 76 and a maximum scale of 38." - f"Current precision: {precision}. Current scale: {scale}" - ) - - # TODO(bmil): support other literals that can be coerced to compatible types - if validate and (scalar_expr.type() not in BIGFRAMES_TO_IBIS.values()): - raise ValueError( - f"Literal did not coerce to a supported data type: {scalar_expr.type()}. {constants.FEEDBACK_LINK}" - ) - - return scalar_expr diff --git a/bigframes/core/compile/polars/__init__.py b/bigframes/core/compile/polars/__init__.py deleted file mode 100644 index 027582c7ded..00000000000 --- a/bigframes/core/compile/polars/__init__.py +++ /dev/null @@ -1,44 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -"""Compiler for BigFrames expression to Polars LazyFrame expression. - -Make sure to import all polars implementations here so that they get registered. -""" - -from __future__ import annotations - -import warnings - -# The ops imports appear first so that the implementations can be registered. -# polars shouldn't be needed at import time, as register is a no-op if polars -# isn't installed. -import bigframes.core.compile.polars.operations.array_ops # noqa: F401 -import bigframes.core.compile.polars.operations.generic_ops # noqa: F401 -import bigframes.core.compile.polars.operations.numeric_ops # noqa: F401 -import bigframes.core.compile.polars.operations.struct_ops # noqa: F401 - -try: - import bigframes._importing - - # Use import_polars() instead of importing directly so that we check the - # version numbers. - bigframes._importing.import_polars() - - from bigframes.core.compile.polars.compiler import PolarsCompiler - - __all__ = ["PolarsCompiler"] -except Exception as exc: - msg = f"Polars compiler not available as there was an exception importing polars. Details: {str(exc)}" - warnings.warn(msg) diff --git a/bigframes/core/compile/polars/compiler.py b/bigframes/core/compile/polars/compiler.py deleted file mode 100644 index 65b217602b9..00000000000 --- a/bigframes/core/compile/polars/compiler.py +++ /dev/null @@ -1,1148 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -from __future__ import annotations - -import dataclasses -import functools -import itertools -from typing import TYPE_CHECKING, Literal, Optional, Sequence, Tuple, Type, cast - -import pandas as pd - -import bigframes.core -import bigframes.core.expression as ex -import bigframes.core.guid as guid -import bigframes.core.rewrite -import bigframes.core.rewrite.schema_binding -import bigframes.dtypes -import bigframes.operations as ops -import bigframes.operations.aggregations as agg_ops -import bigframes.operations.array_ops as arr_ops -import bigframes.operations.bool_ops as bool_ops -import bigframes.operations.comparison_ops as comp_ops -import bigframes.operations.date_ops as date_ops -import bigframes.operations.datetime_ops as dt_ops -import bigframes.operations.frequency_ops as freq_ops -import bigframes.operations.generic_ops as gen_ops -import bigframes.operations.json_ops as json_ops -import bigframes.operations.numeric_ops as num_ops -import bigframes.operations.remote_function_ops as remote_function_ops -import bigframes.operations.string_ops as string_ops -import bigframes.operations.struct_ops as struct_ops -from bigframes.core import agg_expressions, identifiers, nodes, ordering, window_spec -from bigframes.core.compile.polars import lowering - -polars_installed = True -if TYPE_CHECKING: - import polars as pl -else: - try: - import bigframes._importing - - # Use import_polars() instead of importing directly so that we check - # the version numbers. - pl = bigframes._importing.import_polars() - except Exception: - polars_installed = False - - -def register_op(op: Type): - """Register a compilation from BigFrames to Ibis. - - This decorator can be used, even if Polars is not installed. - - Args: - op: The type of the operator the wrapped function compiles. - """ - - def decorator(func): - if polars_installed: - # Ignore the type because compile_op is a generic Callable, so - # register isn't available according to mypy. - return PolarsExpressionCompiler.compile_op.register(op)(func) # type: ignore - else: - return func - - return decorator - - -if polars_installed: - _FREQ_MAPPING = { - "Y": "1y", - "Q": "1q", - "M": "1mo", - "W": "1w", - "D": "1d", - "h": "1h", - "min": "1m", - "s": "1s", - "ms": "1ms", - "us": "1us", - "ns": "1ns", - } - - _DTYPE_MAPPING = { - # Direct mappings - bigframes.dtypes.INT_DTYPE: pl.Int64(), - bigframes.dtypes.FLOAT_DTYPE: pl.Float64(), - bigframes.dtypes.BOOL_DTYPE: pl.Boolean(), - bigframes.dtypes.STRING_DTYPE: pl.String(), - bigframes.dtypes.NUMERIC_DTYPE: pl.Decimal(38, 9), - bigframes.dtypes.BIGNUMERIC_DTYPE: pl.Decimal(76, 38), - bigframes.dtypes.BYTES_DTYPE: pl.Binary(), - bigframes.dtypes.DATE_DTYPE: pl.Date(), - bigframes.dtypes.DATETIME_DTYPE: pl.Datetime(time_zone=None), - bigframes.dtypes.TIMESTAMP_DTYPE: pl.Datetime(time_zone="UTC"), - bigframes.dtypes.TIME_DTYPE: pl.Time(), - bigframes.dtypes.TIMEDELTA_DTYPE: pl.Duration(), - # Indirect mappings - bigframes.dtypes.GEO_DTYPE: pl.String(), - bigframes.dtypes.JSON_DTYPE: pl.String(), - } - - def _bigframes_dtype_to_polars_dtype( - dtype: bigframes.dtypes.ExpressionType, - ) -> pl.DataType: - if dtype is None: - return pl.Null() - if bigframes.dtypes.is_struct_like(dtype): - return pl.Struct( - [ - pl.Field(name, _bigframes_dtype_to_polars_dtype(type)) - for name, type in bigframes.dtypes.get_struct_fields(dtype).items() - ] - ) - if bigframes.dtypes.is_array_like(dtype): - return pl.List( - inner=_bigframes_dtype_to_polars_dtype( - bigframes.dtypes.get_array_inner_type(dtype) - ) - ) - else: - return _DTYPE_MAPPING[dtype] - - @dataclasses.dataclass(frozen=True) - class PolarsExpressionCompiler: - """ - Simple compiler for converting bigframes expressions to polars expressions. - - Should be extended to dispatch based on bigframes schema types. - """ - - _expr_types: dict[int, bigframes.dtypes.ExpressionType] = dataclasses.field( - default_factory=dict, init=False, compare=False - ) - - def compile_expression(self, expression: ex.Expression) -> pl.Expr: - res = self._compile_expression(expression) - self._expr_types[id(res)] = expression.output_type - return res - - @functools.singledispatchmethod - def _compile_expression(self, expression: ex.Expression) -> pl.Expr: - raise NotImplementedError(f"Cannot compile expression: {expression}") - - @_compile_expression.register - def _( - self, - expression: ex.ScalarConstantExpression, - ) -> pl.Expr: - value = expression.value - if not isinstance(value, float) and pd.isna(value): # type: ignore - value = None - if expression.dtype is None: - return pl.lit(None) - - # Polars lit does not handle pandas timedelta well at v1.36 - if isinstance(value, pd.Timedelta): - value = value.to_pytimedelta() - - return pl.lit(value, _bigframes_dtype_to_polars_dtype(expression.dtype)) - - @_compile_expression.register - def _( - self, - expression: ex.DerefOp, - ) -> pl.Expr: - return pl.col(expression.id.sql) - - @_compile_expression.register - def _( - self, - expression: ex.ResolvedDerefOp, - ) -> pl.Expr: - return pl.col(expression.id.sql) - - @_compile_expression.register - def _( - self, - expression: ex.OpExpression, - ) -> pl.Expr: - import datetime - - import pyarrow as pa - - op = expression.op - - # Polars panics on nulls from pandas objects in timezone-aware - # datetimes for certain ops. Convert to timezone-naive temporarily - # to avoid this issue. - # TODO(tswast): Remove workaround when - # https://github.com/pola-rs/polars/issues/27862 has been fixed. - is_problematic_op = type(op) in ( - date_ops.YearOp, - date_ops.QuarterOp, - date_ops.MonthOp, - date_ops.DayOp, - date_ops.IsoWeekOp, - ) - - if is_problematic_op and len(expression.inputs) == 1: - input_expr = expression.inputs[0] - if ( - input_expr.is_resolved - and isinstance(input_expr.output_type, pd.ArrowDtype) - and isinstance( - input_expr.output_type.pyarrow_dtype, pa.TimestampType - ) - and input_expr.output_type.pyarrow_dtype.tz is not None - ): - tz_str = input_expr.output_type.pyarrow_dtype.tz - if tz_str == "UTC": - dummy_tz = datetime.timezone.utc - else: - try: - from zoneinfo import ZoneInfo - - dummy_tz = ZoneInfo(tz_str) # type: ignore - except Exception: - dummy_tz = datetime.timezone.utc - - dummy_val = datetime.datetime(1970, 1, 1, tzinfo=dummy_tz) - - compiled_input = self.compile_expression(input_expr) - filled_input = compiled_input.fill_null(dummy_val) - compiled_op_with_fill = self.compile_op(op, filled_input) - - return ( - pl.when(compiled_input.is_null()) - .then(None) - .otherwise(compiled_op_with_fill) - ) - - # TODO: Complete the implementation - args = tuple(map(self.compile_expression, expression.inputs)) - return self.compile_op(op, *args) - - @functools.singledispatchmethod - def compile_op(self, op: ops.ScalarOp, *args: pl.Expr) -> pl.Expr: - raise NotImplementedError(f"Polars compiler hasn't implemented {op}") - - @compile_op.register(gen_ops.InvertOp) - def _(self, op: ops.ScalarOp, input: pl.Expr) -> pl.Expr: - return input.not_() - - @compile_op.register(num_ops.AbsOp) - def _(self, op: ops.ScalarOp, input: pl.Expr) -> pl.Expr: - return input.abs() - - @compile_op.register(num_ops.FloorOp) - def _(self, op: ops.ScalarOp, input: pl.Expr) -> pl.Expr: - return input.floor() - - @compile_op.register(num_ops.CeilOp) - def _(self, op: ops.ScalarOp, input: pl.Expr) -> pl.Expr: - return input.ceil() - - @compile_op.register(num_ops.PosOp) - def _(self, op: ops.ScalarOp, input: pl.Expr) -> pl.Expr: - return input.__pos__() - - @compile_op.register(num_ops.NegOp) - def _(self, op: ops.ScalarOp, input: pl.Expr) -> pl.Expr: - return input.__neg__() - - @compile_op.register(bool_ops.AndOp) - def _(self, op: ops.ScalarOp, l_input: pl.Expr, r_input: pl.Expr) -> pl.Expr: - return l_input & r_input - - @compile_op.register(bool_ops.OrOp) - def _(self, op: ops.ScalarOp, l_input: pl.Expr, r_input: pl.Expr) -> pl.Expr: - return l_input | r_input - - @compile_op.register(bool_ops.XorOp) - def _(self, op: ops.ScalarOp, l_input: pl.Expr, r_input: pl.Expr) -> pl.Expr: - return l_input ^ r_input - - @compile_op.register(num_ops.AddOp) - def _(self, op: ops.ScalarOp, l_input: pl.Expr, r_input: pl.Expr) -> pl.Expr: - return l_input + r_input - - @compile_op.register(num_ops.SubOp) - def _(self, op: ops.ScalarOp, l_input: pl.Expr, r_input: pl.Expr) -> pl.Expr: - return l_input - r_input - - @compile_op.register(num_ops.MulOp) - def _(self, op: ops.ScalarOp, l_input: pl.Expr, r_input: pl.Expr) -> pl.Expr: - return l_input * r_input - - @compile_op.register(num_ops.DivOp) - def _(self, op: ops.ScalarOp, l_input: pl.Expr, r_input: pl.Expr) -> pl.Expr: - return l_input / r_input - - @compile_op.register(num_ops.FloorDivOp) - def _(self, op: ops.ScalarOp, l_input: pl.Expr, r_input: pl.Expr) -> pl.Expr: - return l_input // r_input - - @compile_op.register(num_ops.ModOp) - def _(self, op: ops.ScalarOp, l_input: pl.Expr, r_input: pl.Expr) -> pl.Expr: - return l_input % r_input - - @compile_op.register(num_ops.PowOp) - @compile_op.register(num_ops.UnsafePowOp) - def _(self, op: ops.ScalarOp, l_input: pl.Expr, r_input: pl.Expr) -> pl.Expr: - return l_input**r_input - - @compile_op.register(comp_ops.EqOp) - def _(self, op: ops.ScalarOp, l_input: pl.Expr, r_input: pl.Expr) -> pl.Expr: - return l_input.eq(r_input) - - @compile_op.register(comp_ops.EqNullsMatchOp) - def _(self, op: ops.ScalarOp, l_input: pl.Expr, r_input: pl.Expr) -> pl.Expr: - return l_input.eq_missing(r_input) - - @compile_op.register(comp_ops.NeOp) - def _(self, op: ops.ScalarOp, l_input: pl.Expr, r_input: pl.Expr) -> pl.Expr: - return l_input.ne(r_input) - - @compile_op.register(comp_ops.GtOp) - def _(self, op: ops.ScalarOp, l_input: pl.Expr, r_input: pl.Expr) -> pl.Expr: - return l_input > r_input - - @compile_op.register(comp_ops.GeOp) - def _(self, op: ops.ScalarOp, l_input: pl.Expr, r_input: pl.Expr) -> pl.Expr: - return l_input >= r_input - - @compile_op.register(comp_ops.LtOp) - def _(self, op: ops.ScalarOp, l_input: pl.Expr, r_input: pl.Expr) -> pl.Expr: - return l_input < r_input - - @compile_op.register(comp_ops.LeOp) - def _(self, op: ops.ScalarOp, l_input: pl.Expr, r_input: pl.Expr) -> pl.Expr: - return l_input <= r_input - - @compile_op.register(gen_ops.IsInOp) - def _(self, op: ops.ScalarOp, input: pl.Expr) -> pl.Expr: - # TODO: Filter out types that can't be coerced to right type - assert isinstance(op, gen_ops.IsInOp) - assert not op.match_nulls # should be stripped by a lowering step rn - values = pl.Series(op.values, strict=False) - return input.is_in(values) - - @compile_op.register(gen_ops.FillNaOp) - @compile_op.register(gen_ops.CoalesceOp) - def _(self, op: ops.ScalarOp, l_input: pl.Expr, r_input: pl.Expr) -> pl.Expr: - return pl.coalesce(l_input, r_input) - - @compile_op.register(gen_ops.CaseWhenOp) - def _(self, op: ops.ScalarOp, *inputs: pl.Expr) -> pl.Expr: - expr = pl.when(inputs[0]).then(inputs[1]) - for pred, result in zip(inputs[2::2], inputs[3::2]): - expr = expr.when(pred).then(result) # type: ignore - return expr - - @compile_op.register(gen_ops.WhereOp) - def _( - self, - op: ops.ScalarOp, - original: pl.Expr, - condition: pl.Expr, - otherwise: pl.Expr, - ) -> pl.Expr: - return pl.when(condition).then(original).otherwise(otherwise) - - @compile_op.register(gen_ops.CoerceToBoolOp) - def _(self, op: ops.ScalarOp, input: pl.Expr) -> pl.Expr: - assert isinstance(op, gen_ops.CoerceToBoolOp) - from_type = self._expr_types.get(id(input)) - if from_type is None: - return input.cast(pl.Boolean).fill_null(False) - - if from_type == bigframes.dtypes.BOOL_DTYPE: - res = input - elif bigframes.dtypes.is_numeric(from_type): - res = input != 0 - elif from_type == bigframes.dtypes.BYTES_DTYPE: - res = input.bin.size() > 0 - elif bigframes.dtypes.is_string_like(from_type): - res = input.str.len_chars() > 0 - elif bigframes.dtypes.is_array_like(from_type): - res = input.list.len() > 0 - else: - res = input.is_not_null() - - return res.fill_null(False) - - @compile_op.register(gen_ops.AsTypeOp) - def _(self, op: ops.ScalarOp, input: pl.Expr) -> pl.Expr: - assert isinstance(op, gen_ops.AsTypeOp) - # TODO: Polars casting works differently, need to lower instead to specific conversion ops. - # eg. We want "True" instead of "true" for bool to strin - return input.cast(_DTYPE_MAPPING[op.to_type], strict=not op.safe) - - @compile_op.register(string_ops.StrConcatOp) - def _(self, op: ops.ScalarOp, l_input: pl.Expr, r_input: pl.Expr) -> pl.Expr: - assert isinstance(op, string_ops.StrConcatOp) - return pl.concat_str(l_input, r_input) - - @compile_op.register(string_ops.StrContainsOp) - def _(self, op: ops.ScalarOp, input: pl.Expr) -> pl.Expr: - assert isinstance(op, string_ops.StrContainsOp) - return input.str.contains(pattern=op.pat, literal=True) - - @compile_op.register(string_ops.StrContainsRegexOp) - def _(self, op: ops.ScalarOp, input: pl.Expr) -> pl.Expr: - assert isinstance(op, string_ops.StrContainsRegexOp) - return input.str.contains(pattern=op.pat, literal=False) - - @compile_op.register(string_ops.UpperOp) - def _(self, op: ops.ScalarOp, input: pl.Expr) -> pl.Expr: - assert isinstance(op, string_ops.UpperOp) - return input.str.to_uppercase() - - @compile_op.register(string_ops.LowerOp) - def _(self, op: ops.ScalarOp, input: pl.Expr) -> pl.Expr: - assert isinstance(op, string_ops.LowerOp) - return input.str.to_lowercase() - - @compile_op.register(string_ops.ArrayLenOp) - def _(self, op: ops.ScalarOp, input: pl.Expr) -> pl.Expr: - assert isinstance(op, string_ops.ArrayLenOp) - return input.list.len() - - @compile_op.register(string_ops.StrLenOp) - def _(self, op: ops.ScalarOp, input: pl.Expr) -> pl.Expr: - assert isinstance(op, string_ops.StrLenOp) - return input.str.len_chars() - - @compile_op.register(string_ops.StartsWithOp) - def _(self, op: ops.ScalarOp, input: pl.Expr) -> pl.Expr: - assert isinstance(op, string_ops.StartsWithOp) - if len(op.pat) == 1: - return input.str.starts_with(op.pat[0]) - else: - return pl.any_horizontal( - *(input.str.starts_with(pat) for pat in op.pat) - ) - - @compile_op.register(string_ops.EndsWithOp) - def _(self, op: ops.ScalarOp, input: pl.Expr) -> pl.Expr: - assert isinstance(op, string_ops.EndsWithOp) - if len(op.pat) == 1: - return input.str.ends_with(op.pat[0]) - else: - return pl.any_horizontal(*(input.str.ends_with(pat) for pat in op.pat)) - - @compile_op.register(string_ops.CapitalizeOp) - def _(self, op: ops.ScalarOp, input: pl.Expr) -> pl.Expr: - assert isinstance(op, string_ops.CapitalizeOp) - return ( - input.str.slice(0, 1).str.to_uppercase() - + input.str.slice(1).str.to_lowercase() - ) - - @compile_op.register(string_ops.IsAlnumOp) - def _(self, op: ops.ScalarOp, input: pl.Expr) -> pl.Expr: - assert isinstance(op, string_ops.IsAlnumOp) - return input.str.contains(r"^[a-zA-Z0-9]+$") - - @compile_op.register(string_ops.IsAlphaOp) - def _(self, op: ops.ScalarOp, input: pl.Expr) -> pl.Expr: - assert isinstance(op, string_ops.IsAlphaOp) - return input.str.contains(r"^[a-zA-Z]+$") - - @compile_op.register(string_ops.IsDigitOp) - def _(self, op: ops.ScalarOp, input: pl.Expr) -> pl.Expr: - assert isinstance(op, string_ops.IsDigitOp) - return input.str.contains(r"^[0-9]+$") - - @compile_op.register(string_ops.IsSpaceOp) - def _(self, op: ops.ScalarOp, input: pl.Expr) -> pl.Expr: - assert isinstance(op, string_ops.IsSpaceOp) - return input.str.contains(r"^\s+$") - - @compile_op.register(string_ops.IsDecimalOp) - def _(self, op: ops.ScalarOp, input: pl.Expr) -> pl.Expr: - assert isinstance(op, string_ops.IsDecimalOp) - return input.str.contains(r"^[0-9]+$") - - @compile_op.register(string_ops.IsNumericOp) - def _(self, op: ops.ScalarOp, input: pl.Expr) -> pl.Expr: - assert isinstance(op, string_ops.IsNumericOp) - return input.str.contains(r"^[0-9]+$") - - @compile_op.register(string_ops.IsLowerOp) - def _(self, op: ops.ScalarOp, input: pl.Expr) -> pl.Expr: - assert isinstance(op, string_ops.IsLowerOp) - return input.str.contains(r"[a-z]") & ~input.str.contains(r"[A-Z]") - - @compile_op.register(string_ops.IsUpperOp) - def _(self, op: ops.ScalarOp, input: pl.Expr) -> pl.Expr: - assert isinstance(op, string_ops.IsUpperOp) - return input.str.contains(r"[A-Z]") & ~input.str.contains(r"[a-z]") - - @compile_op.register(freq_ops.FloorDtOp) - def _(self, op: ops.ScalarOp, input: pl.Expr) -> pl.Expr: - assert isinstance(op, freq_ops.FloorDtOp) - return input.dt.truncate(every=_FREQ_MAPPING[op.freq]) - - @compile_op.register(dt_ops.StrftimeOp) - def _(self, op: ops.ScalarOp, input: pl.Expr) -> pl.Expr: - assert isinstance(op, dt_ops.StrftimeOp) - return input.dt.strftime(op.date_format) - - @compile_op.register(date_ops.YearOp) - def _(self, op: ops.ScalarOp, input: pl.Expr) -> pl.Expr: - return input.dt.year() - - @compile_op.register(date_ops.QuarterOp) - def _(self, op: ops.ScalarOp, input: pl.Expr) -> pl.Expr: - return input.dt.quarter() - - @compile_op.register(date_ops.MonthOp) - def _(self, op: ops.ScalarOp, input: pl.Expr) -> pl.Expr: - return input.dt.month() - - @compile_op.register(date_ops.DayOfWeekOp) - def _(self, op: ops.ScalarOp, input: pl.Expr) -> pl.Expr: - return input.dt.weekday() - 1 - - @compile_op.register(date_ops.DayOp) - def _(self, op: ops.ScalarOp, input: pl.Expr) -> pl.Expr: - return input.dt.day() - - @compile_op.register(date_ops.IsoYearOp) - def _(self, op: ops.ScalarOp, input: pl.Expr) -> pl.Expr: - return input.dt.iso_year() - - @compile_op.register(date_ops.IsoWeekOp) - def _(self, op: ops.ScalarOp, input: pl.Expr) -> pl.Expr: - return input.dt.week() - - @compile_op.register(date_ops.IsoDayOp) - def _(self, op: ops.ScalarOp, input: pl.Expr) -> pl.Expr: - return input.dt.weekday() - - @compile_op.register(dt_ops.ParseDatetimeOp) - def _(self, op: ops.ScalarOp, input: pl.Expr) -> pl.Expr: - assert isinstance(op, dt_ops.ParseDatetimeOp) - return input.str.to_datetime( - time_unit="us", time_zone=None, ambiguous="earliest" - ) - - @compile_op.register(dt_ops.ParseTimestampOp) - def _(self, op: ops.ScalarOp, input: pl.Expr) -> pl.Expr: - assert isinstance(op, dt_ops.ParseTimestampOp) - return input.str.to_datetime( - time_unit="us", time_zone="UTC", ambiguous="earliest" - ) - - @compile_op.register(json_ops.JSONDecode) - def _(self, op: json_ops.JSONDecode, input: pl.Expr) -> pl.Expr: - assert isinstance(op, json_ops.JSONDecode) - return input.str.json_decode(_DTYPE_MAPPING[op.to_type]) - - @compile_op.register(json_ops.ToJSON) - def _(self, op: json_ops.ToJSON, input: pl.Expr) -> pl.Expr: - from_type = self._expr_types.get(id(input)) - if from_type in ( - bigframes.dtypes.STRING_DTYPE, - bigframes.dtypes.JSON_DTYPE, - ): - return input - else: - return input.cast(pl.String()) - - @compile_op.register(json_ops.ToJSONString) - def _(self, op: json_ops.ToJSONString, input: pl.Expr) -> pl.Expr: - from_type = self._expr_types.get(id(input)) - - def preprocess_binary( - expr: pl.Expr, dtype: bigframes.dtypes.ExpressionType - ) -> pl.Expr: - if dtype == bigframes.dtypes.BYTES_DTYPE: - return expr.bin.encode("base64") - if bigframes.dtypes.is_struct_like(dtype): - fields = bigframes.dtypes.get_struct_fields(dtype) - return pl.struct( - *[ - preprocess_binary( - expr.struct.field(name), field_type - ).alias(name) - for name, field_type in fields.items() - ] - ) - if bigframes.dtypes.is_array_like(dtype): - inner_type = bigframes.dtypes.get_array_inner_type(dtype) - return expr.list.eval(preprocess_binary(pl.element(), inner_type)) - return expr - - preprocessed = preprocess_binary(input, from_type) - - if bigframes.dtypes.is_struct_like(from_type): - result = preprocessed.struct.json_encode() - elif from_type == bigframes.dtypes.INT_DTYPE: - result = preprocessed.cast(pl.String) - elif from_type == bigframes.dtypes.BOOL_DTYPE: - result = ( - pl.when(preprocessed) - .then(pl.lit("true")) - .otherwise(pl.lit("false")) - ) - elif from_type == bigframes.dtypes.BYTES_DTYPE: - result = pl.lit('"') + preprocessed + pl.lit('"') - else: - wrapped = pl.struct(value=preprocessed).struct.json_encode() - result = wrapped.str.slice(9, wrapped.str.len_chars() - 10) - - return pl.when(input.is_null()).then(pl.lit("null")).otherwise(result) - - @compile_op.register(arr_ops.ToArrayOp) - def _(self, op: ops.ToArrayOp, *inputs: pl.Expr) -> pl.Expr: - return pl.concat_list(*inputs) - - @compile_op.register(arr_ops.ArrayReduceOp) - def _(self, op: ops.ArrayReduceOp, input: pl.Expr) -> pl.Expr: - # TODO: Unify this with general aggregation compilation? - if isinstance(op.aggregation, agg_ops.MinOp): - return input.list.min() - if isinstance(op.aggregation, agg_ops.MaxOp): - return input.list.max() - if isinstance(op.aggregation, agg_ops.SumOp): - return input.list.sum() - if isinstance(op.aggregation, agg_ops.MeanOp): - return input.list.mean() - if isinstance(op.aggregation, agg_ops.CountOp): - return input.list.len() - if isinstance(op.aggregation, agg_ops.StdOp): - return input.list.std() - if isinstance(op.aggregation, agg_ops.VarOp): - return input.list.var() - if isinstance(op.aggregation, agg_ops.AnyOp): - return input.list.any() - if isinstance(op.aggregation, agg_ops.AllOp): - return input.list.all() - else: - raise NotImplementedError( - f"Haven't implemented array aggregation: {op.aggregation}" - ) - - @compile_op.register(struct_ops.StructOp) - def _(self, op: struct_ops.StructOp, *inputs: pl.Expr) -> pl.Expr: - return pl.struct(**{col: inp for col, inp in zip(op.column_names, inputs)}) # type: ignore - - @compile_op.register(struct_ops.StructFieldOp) - def _(self, op: struct_ops.StructFieldOp, *inputs: pl.Expr) -> pl.Expr: - return inputs[0].struct[op.name_or_index] - - @compile_op.register(remote_function_ops.PythonUdfOp) - def _(self, op: ops.PythonUdfOp, *inputs: pl.Expr) -> pl.Expr: - from bigframes.functions import function_template - - code = op.function_def.code.to_callable() - if op.function_def.signature.is_row_processor: - - def handler(py_struct): - args = list(py_struct.values()) - series_arg = function_template.get_pd_series(args[0]) - return code(series_arg, *args[1:]) - else: - - def handler(py_struct): - return code(*(field for field in py_struct.values())) - - return pl.struct(*inputs).map_elements( - handler, - return_dtype=_bigframes_dtype_to_polars_dtype(op.output_type()), - skip_nulls=False, - ) - - @dataclasses.dataclass(frozen=True) - class PolarsAggregateCompiler: - scalar_compiler = PolarsExpressionCompiler() - - def get_args( - self, - agg: agg_expressions.Aggregation, - ) -> Sequence[pl.Expr]: - """Prepares arguments for aggregation by compiling them.""" - if isinstance(agg, agg_expressions.NullaryAggregation): - return [] - elif isinstance(agg, agg_expressions.UnaryAggregation): - arg = self.scalar_compiler.compile_expression(agg.arg) - return [arg] - elif isinstance(agg, agg_expressions.BinaryAggregation): - larg = self.scalar_compiler.compile_expression(agg.left) - rarg = self.scalar_compiler.compile_expression(agg.right) - return [larg, rarg] - - raise NotImplementedError( - f"Aggregation {agg} not yet supported in polars engine." - ) - - def compile_agg_expr(self, expr: agg_expressions.Aggregation): - if isinstance(expr, agg_expressions.NullaryAggregation): - inputs: Tuple = () - elif isinstance(expr, agg_expressions.UnaryAggregation): - assert isinstance(expr.arg, ex.DerefOp) - inputs = (expr.arg.id.sql,) - elif isinstance(expr, agg_expressions.BinaryAggregation): - assert isinstance(expr.left, ex.DerefOp) - assert isinstance(expr.right, ex.DerefOp) - inputs = ( - expr.left.id.sql, - expr.right.id.sql, - ) - else: - raise ValueError(f"Unexpected aggregation: {expr.op}") - - return self.compile_agg_op(expr.op, inputs) - - def compile_agg_op( - self, op: agg_ops.WindowOp, inputs: Sequence[str] = [] - ) -> pl.Expr: - if isinstance(op, agg_ops.ProductOp): - # TODO: Fix datatype inconsistency with float/int - return pl.col(*inputs).product() - if isinstance(op, agg_ops.SumOp): - return pl.sum(*inputs) - if isinstance(op, (agg_ops.SizeOp, agg_ops.SizeUnaryOp)): - return pl.len() - if isinstance(op, agg_ops.MeanOp): - return pl.mean(*inputs) - if isinstance(op, agg_ops.MedianOp): - return pl.median(*inputs) - if isinstance(op, agg_ops.AllOp): - return pl.col(inputs).cast(pl.Boolean).all() - if isinstance(op, agg_ops.AnyOp): - return pl.col(inputs).cast(pl.Boolean).any() - if isinstance(op, agg_ops.NuniqueOp): - return pl.col(*inputs).drop_nulls().n_unique() - if isinstance(op, agg_ops.MinOp): - return pl.min(*inputs) - if isinstance(op, agg_ops.MaxOp): - return pl.max(*inputs) - if isinstance(op, agg_ops.CountOp): - return pl.count(*inputs) - if isinstance(op, agg_ops.CorrOp): - return pl.corr( - pl.col(inputs[0]).fill_nan(None), pl.col(inputs[1]).fill_nan(None) - ) - if isinstance(op, agg_ops.CovOp): - return pl.cov( - pl.col(inputs[0]).fill_nan(None), pl.col(inputs[1]).fill_nan(None) - ) - if isinstance(op, agg_ops.StdOp): - return pl.std(inputs[0]) - if isinstance(op, agg_ops.VarOp): - # polars var doesnt' support decimal, so use std instead - return pl.std(inputs[0]).pow(2) - if isinstance(op, agg_ops.PopVarOp): - # polars var doesnt' support decimal, so use std instead - return pl.std(inputs[0], ddof=0).pow(2) - if isinstance(op, agg_ops.FirstNonNullOp): - return pl.col(*inputs).drop_nulls().first() - if isinstance(op, agg_ops.LastNonNullOp): - return pl.col(*inputs).drop_nulls().last() - if isinstance(op, agg_ops.FirstOp): - return pl.col(*inputs).first() - if isinstance(op, agg_ops.LastOp): - return pl.col(*inputs).last() - if isinstance(op, agg_ops.RowNumberOp): - # pl.row_index is not yet stable enough to use here, and only supports polars>=1.32 - return pl.int_range(pl.len(), dtype=pl.Int64) - if isinstance(op, agg_ops.ShiftOp): - return pl.col(*inputs).shift(op.periods) - if isinstance(op, agg_ops.DiffOp): - return pl.col(*inputs) - pl.col(*inputs).shift(op.periods) - if isinstance(op, agg_ops.AnyValueOp): - return pl.max( - *inputs - ) # probably something faster? maybe just get first item? - raise NotImplementedError( - f"Aggregate op {op} not yet supported in polars engine." - ) - - @dataclasses.dataclass(frozen=True) - class PolarsCompiler: - """ - Compiles ArrayValue to polars LazyFrame and executes. - - This feature is in development and is incomplete. - While most node types are supported, this has the following limitations: - 1. GBQ data sources not supported. - 2. Joins do not order rows correctly - 3. Incomplete scalar op support - 4. Incomplete aggregate op support - 5. Incomplete analytic op support - 6. Some complex windowing types not supported (eg. groupby + rolling) - 7. UDFs are not supported. - 8. Returned types may not be entirely consistent with BigQuery backend - 9. Some operations are not entirely lazy - sampling and somse windowing. - """ - - expr_compiler = PolarsExpressionCompiler() - agg_compiler = PolarsAggregateCompiler() - - def compile(self, plan: nodes.BigFrameNode) -> pl.LazyFrame: - if not polars_installed: - raise ValueError( - "Polars is not installed, cannot compile to polars engine." - ) - - # TODO: Create standard way to configure BFET -> BFET rewrites - # Polars has incomplete slice support in lazy mode - node = plan - node = bigframes.core.rewrite.column_pruning(node) - node = nodes.bottom_up(node, bigframes.core.rewrite.rewrite_slice) - node = bigframes.core.rewrite.pull_out_window_order(node) - node = bigframes.core.rewrite.schema_binding.bind_schema_to_tree(node) - node = lowering.lower_ops_to_polars(node) - return self.compile_node(node) - - @functools.singledispatchmethod - def compile_node(self, node: nodes.BigFrameNode) -> pl.LazyFrame: - """Defines transformation but isn't cached, always use compile_node instead""" - raise ValueError(f"Can't compile unrecognized node: {node}") - - @compile_node.register - def compile_readlocal(self, node: nodes.ReadLocalNode): - cols_to_read = { - scan_item.source_id: scan_item.id.sql - for scan_item in node.scan_list.items - } - lazy_frame = cast( - pl.DataFrame, pl.from_arrow(node.local_data_source.data) - ).lazy() - lazy_frame = lazy_frame.select(cols_to_read.keys()).rename(cols_to_read) - if node.offsets_col: - lazy_frame = lazy_frame.with_columns( - [pl.int_range(pl.len(), dtype=pl.Int64).alias(node.offsets_col.sql)] - ) - return lazy_frame - - @compile_node.register - def compile_filter(self, node: nodes.FilterNode): - return self.compile_node(node.child).filter( - self.expr_compiler.compile_expression(node.predicate) - ) - - @compile_node.register - def compile_orderby(self, node: nodes.OrderByNode): - frame = self.compile_node(node.child) - if len(node.by) == 0: - # pragma: no cover - return frame - return self._sort(frame, node.by) - - def _sort( - self, frame: pl.LazyFrame, by: Sequence[ordering.OrderingExpression] - ) -> pl.LazyFrame: - sorted = frame.sort( - [ - self.expr_compiler.compile_expression(by.scalar_expression) - for by in by - ], - descending=[not by.direction.is_ascending for by in by], - nulls_last=[by.na_last for by in by], - maintain_order=True, - ) - return sorted - - @compile_node.register - def compile_reversed(self, node: nodes.ReversedNode): - return self.compile_node(node.child).reverse() - - @compile_node.register - def compile_selection(self, node: nodes.SelectionNode): - return self.compile_node(node.child).select( - **{new.sql: orig.id.sql for orig, new in node.input_output_pairs} - ) - - @compile_node.register - def compile_projection(self, node: nodes.ProjectionNode): - new_cols = [] - for proj_expr, name in node.assignments: - bound_expr = ex.bind_schema_fields(proj_expr, node.child.field_by_id) - new_col = self.expr_compiler.compile_expression(bound_expr).alias( - name.sql - ) - if bound_expr.output_type is None: - new_col = new_col.cast( - _bigframes_dtype_to_polars_dtype(bigframes.dtypes.DEFAULT_DTYPE) - ) - new_cols.append(new_col) - return self.compile_node(node.child).with_columns(new_cols) - - @compile_node.register - def compile_offsets(self, node: nodes.PromoteOffsetsNode): - return self.compile_node(node.child).with_columns( - [pl.int_range(pl.len(), dtype=pl.Int64).alias(node.col_id.sql)] - ) - - @compile_node.register - def compile_join(self, node: nodes.JoinNode): - left = self.compile_node(node.left_child) - right = self.compile_node(node.right_child) - - left_on = [] - right_on = [] - for left_ex, right_ex in node.conditions: - left_ex, right_ex = lowering._coerce_comparables(left_ex, right_ex) - left_on.append(self.expr_compiler.compile_expression(left_ex)) - right_on.append(self.expr_compiler.compile_expression(right_ex)) - - if node.type == "right": - return self._ordered_join( - right, left, "left", right_on, left_on, node.joins_nulls - ).select([id.sql for id in node.ids]) - return self._ordered_join( - left, right, node.type, left_on, right_on, node.joins_nulls - ) - - @compile_node.register - def compile_isin(self, node: nodes.InNode): - left = self.compile_node(node.left_child) - right = self.compile_node(node.right_child).unique() - right = right.with_columns(pl.lit(True).alias(node.indicator_col.sql)) - - right_col = ex.ResolvedDerefOp.from_field(node.right_child.fields[0]) - left_ex, right_ex = lowering._coerce_comparables(node.left_col, right_col) - - left_pl_ex = self.expr_compiler.compile_expression(left_ex) - right_pl_ex = self.expr_compiler.compile_expression(right_ex) - - joined = left.join( - right, - how="left", - left_on=left_pl_ex, - right_on=right_pl_ex, - # Note: join_nulls renamed to nulls_equal for polars 1.24 - join_nulls=node.joins_nulls, # type: ignore - coalesce=False, - ) - passthrough = [pl.col(id) for id in left.columns] - indicator = pl.col(node.indicator_col.sql).fill_null(False) - return joined.select((*passthrough, indicator)) - - def _ordered_join( - self, - left_frame: pl.LazyFrame, - right_frame: pl.LazyFrame, - how: Literal["inner", "outer", "left", "cross"], - left_on: Sequence[pl.Expr], - right_on: Sequence[pl.Expr], - join_nulls: bool, - ): - if how == "right": - # seems to cause seg faults as of v1.30 for no apparent reason - raise ValueError("right join not supported") - left = left_frame.with_columns( - [ - pl.int_range(pl.len()).alias("_bf_join_l"), - ] - ) - right = right_frame.with_columns( - [ - pl.int_range(pl.len()).alias("_bf_join_r"), - ] - ) - if how != "cross": - joined = left.join( - right, - how=how, - left_on=left_on, - right_on=right_on, - # Note: join_nulls renamed to nulls_equal for polars 1.24 - join_nulls=join_nulls, # type: ignore - coalesce=False, - ) - else: - joined = left.join(right, how=how, coalesce=False) - - join_order = ( - ["_bf_join_l", "_bf_join_r"] - if how != "right" - else ["_bf_join_r", "_bf_join_l"] - ) - return joined.sort(join_order, nulls_last=True).drop( - ["_bf_join_l", "_bf_join_r"] - ) - - @compile_node.register - def compile_concat(self, node: nodes.ConcatNode): - child_frames = [self.compile_node(child) for child in node.child_nodes] - child_frames = [ - frame.rename( - {col: id.sql for col, id in zip(frame.columns, node.output_ids)} - ).cast( - { - field.id.sql: _bigframes_dtype_to_polars_dtype(field.dtype) - for field in node.fields - } - ) - for frame in child_frames - ] - df = pl.concat(child_frames) - return df - - @compile_node.register - def compile_agg(self, node: nodes.AggregateNode): - df = self.compile_node(node.child) - if node.dropna and len(node.by_column_ids) > 0: - df = df.filter( - [pl.col(ref.id.sql).is_not_null() for ref in node.by_column_ids] - ) - if node.order_by: - df = self._sort(df, node.order_by) - return self._aggregate(df, node.aggregations, node.by_column_ids) - - def _aggregate( - self, - df: pl.LazyFrame, - aggregations: Sequence[ - Tuple[agg_expressions.Aggregation, identifiers.ColumnId] - ], - grouping_keys: Tuple[ex.DerefOp, ...], - ) -> pl.LazyFrame: - # Need to materialize columns to broadcast constants - agg_inputs = [ - list( - map( - lambda x: x.alias(guid.generate_guid()), - self.agg_compiler.get_args(agg), - ) - ) - for agg, _ in aggregations - ] - - df_agg_inputs = df.with_columns(itertools.chain(*agg_inputs)) - - agg_exprs = [ - self.agg_compiler.compile_agg_op( - agg.op, list(map(lambda x: x.meta.output_name(), inputs)) - ).alias(id.sql) - for (agg, id), inputs in zip(aggregations, agg_inputs) - ] - - if len(grouping_keys) > 0: - group_exprs = [pl.col(ref.id.sql) for ref in grouping_keys] - grouped_df = df_agg_inputs.group_by(group_exprs) - return grouped_df.agg(agg_exprs).sort(group_exprs, nulls_last=True) - else: - return df_agg_inputs.select(agg_exprs) - - @compile_node.register - def compile_explode(self, node: nodes.ExplodeNode): - assert node.offsets_col is None - df = self.compile_node(node.child) - cols = [col.id.sql for col in node.column_ids] - return df.explode(cols) - - @compile_node.register - def compile_sample(self, node: nodes.RandomSampleNode): - df = self.compile_node(node.child) - # Sample is not available on lazyframe - return df.collect().sample(fraction=node.fraction).lazy() - - @compile_node.register - def compile_window(self, node: nodes.WindowOpNode): - df = self.compile_node(node.child) - - window = node.window_spec - # Should have been handled by reweriter - assert len(window.ordering) == 0 - if window.min_periods > 0: - raise NotImplementedError( - "min_period not yet supported for polars engine" - ) - - result = df - for cdef in node.agg_exprs: - assert isinstance(cdef.expression, agg_expressions.Aggregation) - if (window.bounds is None) or (window.is_unbounded): - # polars will automatically broadcast the aggregate to the matching input rows - agg_pl = self.agg_compiler.compile_agg_expr(cdef.expression) - if window.grouping_keys: - agg_pl = agg_pl.over( - self.expr_compiler.compile_expression(key) - for key in window.grouping_keys - ) - result = result.with_columns(agg_pl.alias(cdef.id.sql)) - else: # row-bounded window - window_result = self._calc_row_analytic_func( - result, cdef.expression, node.window_spec, cdef.id.sql - ) - result = pl.concat([result, window_result], how="horizontal") - return result - - def _calc_row_analytic_func( - self, - frame: pl.LazyFrame, - agg_expr: agg_expressions.Aggregation, - window: window_spec.WindowSpec, - name: str, - ) -> pl.LazyFrame: - if not isinstance(window.bounds, window_spec.RowsWindowBounds): - raise NotImplementedError("Only row bounds supported by polars engine") - groupby = None - if len(window.grouping_keys) > 0: - groupby = [ - self.expr_compiler.compile_expression(ref) - for ref in window.grouping_keys - ] - - # Polars API semi-bounded, and any grouped rolling window challenging - # https://github.com/pola-rs/polars/issues/4799 - # https://github.com/pola-rs/polars/issues/8976 - pl_agg_expr = self.agg_compiler.compile_agg_expr(agg_expr).alias(name) - index_col_name = "_bf_pl_engine_offsets" - indexed_df = frame.with_row_index(index_col_name) - # https://docs.pola.rs/api/python/stable/reference/dataframe/api/polars.DataFrame.rolling.html - period_n, offset_n = _get_period_and_offset(window.bounds) - return ( - indexed_df.rolling( - index_column=index_col_name, - period=f"{period_n}i", - offset=f"{offset_n}i" if (offset_n is not None) else None, - group_by=groupby, - ) - .agg(pl_agg_expr) - .select(name) - ) - - -def _get_period_and_offset( - bounds: window_spec.RowsWindowBounds, -) -> tuple[int, Optional[int]]: - # fixed size window - if (bounds.start is not None) and (bounds.end is not None): - return ((bounds.end - bounds.start + 1), bounds.start - 1) - - LARGE_N = 1000000000 - if bounds.start is not None: - return (LARGE_N, bounds.start - 1) - if bounds.end is not None: - return (LARGE_N, None) - raise ValueError("Not a bounded window") diff --git a/bigframes/core/compile/polars/lowering.py b/bigframes/core/compile/polars/lowering.py deleted file mode 100644 index 5b3d9154b73..00000000000 --- a/bigframes/core/compile/polars/lowering.py +++ /dev/null @@ -1,504 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import dataclasses -from typing import cast - -import numpy as np -import pandas as pd - -import bigframes.operations as ops -from bigframes import dtypes -from bigframes.core import bigframe_node, expression -from bigframes.core.rewrite import op_lowering -from bigframes.operations import ( - comparison_ops, - datetime_ops, - generic_ops, - numeric_ops, - string_ops, -) - -# TODO: Would be more precise to actually have separate op set for polars ops (where they diverge from the original ops) - - -@dataclasses.dataclass -class CoerceArgsRule(op_lowering.OpLoweringRule): - op_type: type[ops.BinaryOp] - - @property - def op(self) -> type[ops.ScalarOp]: - return self.op_type - - def lower(self, expr: expression.OpExpression) -> expression.Expression: - assert isinstance(expr.op, self.op_type) - larg, rarg = _coerce_comparables(expr.children[0], expr.children[1]) - return expr.op.as_expr(larg, rarg) - - -class LowerAddRule(op_lowering.OpLoweringRule): - @property - def op(self) -> type[ops.ScalarOp]: - return numeric_ops.AddOp - - def lower(self, expr: expression.OpExpression) -> expression.Expression: - assert isinstance(expr.op, numeric_ops.AddOp) - larg, rarg = expr.children[0], expr.children[1] - - if ( - larg.output_type == dtypes.BOOL_DTYPE - and rarg.output_type == dtypes.BOOL_DTYPE - ): - int_result = expr.op.as_expr( - ops.AsTypeOp(to_type=dtypes.INT_DTYPE).as_expr(larg), - ops.AsTypeOp(to_type=dtypes.INT_DTYPE).as_expr(rarg), - ) - return ops.AsTypeOp(to_type=dtypes.BOOL_DTYPE).as_expr(int_result) - - if dtypes.is_string_like(larg.output_type) and dtypes.is_string_like( - rarg.output_type - ): - return ops.strconcat_op.as_expr(larg, rarg) - - if larg.output_type == dtypes.BOOL_DTYPE: - larg = ops.AsTypeOp(to_type=dtypes.INT_DTYPE).as_expr(larg) - if rarg.output_type == dtypes.BOOL_DTYPE: - rarg = ops.AsTypeOp(to_type=dtypes.INT_DTYPE).as_expr(rarg) - - if ( - larg.output_type == dtypes.DATE_DTYPE - and rarg.output_type == dtypes.TIMEDELTA_DTYPE - ): - larg = ops.AsTypeOp(to_type=dtypes.DATETIME_DTYPE).as_expr(larg) - - if ( - larg.output_type == dtypes.TIMEDELTA_DTYPE - and rarg.output_type == dtypes.DATE_DTYPE - ): - rarg = ops.AsTypeOp(to_type=dtypes.DATETIME_DTYPE).as_expr(rarg) - - return expr.op.as_expr(larg, rarg) - - -class LowerSubRule(op_lowering.OpLoweringRule): - @property - def op(self) -> type[ops.ScalarOp]: - return numeric_ops.SubOp - - def lower(self, expr: expression.OpExpression) -> expression.Expression: - assert isinstance(expr.op, numeric_ops.SubOp) - larg, rarg = expr.children[0], expr.children[1] - - if ( - larg.output_type == dtypes.BOOL_DTYPE - and rarg.output_type == dtypes.BOOL_DTYPE - ): - int_result = expr.op.as_expr( - ops.AsTypeOp(to_type=dtypes.INT_DTYPE).as_expr(larg), - ops.AsTypeOp(to_type=dtypes.INT_DTYPE).as_expr(rarg), - ) - return ops.AsTypeOp(to_type=dtypes.BOOL_DTYPE).as_expr(int_result) - - if larg.output_type == dtypes.BOOL_DTYPE: - larg = ops.AsTypeOp(to_type=dtypes.INT_DTYPE).as_expr(larg) - if rarg.output_type == dtypes.BOOL_DTYPE: - rarg = ops.AsTypeOp(to_type=dtypes.INT_DTYPE).as_expr(rarg) - - if ( - larg.output_type == dtypes.DATE_DTYPE - and rarg.output_type == dtypes.TIMEDELTA_DTYPE - ): - larg = ops.AsTypeOp(to_type=dtypes.DATETIME_DTYPE).as_expr(larg) - - return expr.op.as_expr(larg, rarg) - - -@dataclasses.dataclass -class LowerMulRule(op_lowering.OpLoweringRule): - @property - def op(self) -> type[ops.ScalarOp]: - return numeric_ops.MulOp - - def lower(self, expr: expression.OpExpression) -> expression.Expression: - assert isinstance(expr.op, numeric_ops.MulOp) - larg, rarg = expr.children[0], expr.children[1] - - if ( - larg.output_type == dtypes.BOOL_DTYPE - and rarg.output_type == dtypes.BOOL_DTYPE - ): - int_result = expr.op.as_expr( - ops.AsTypeOp(to_type=dtypes.INT_DTYPE).as_expr(larg), - ops.AsTypeOp(to_type=dtypes.INT_DTYPE).as_expr(rarg), - ) - return ops.AsTypeOp(to_type=dtypes.BOOL_DTYPE).as_expr(int_result) - - if ( - larg.output_type == dtypes.BOOL_DTYPE - and rarg.output_type != dtypes.BOOL_DTYPE - ): - larg = ops.AsTypeOp(to_type=dtypes.INT_DTYPE).as_expr(larg) - if ( - rarg.output_type == dtypes.BOOL_DTYPE - and larg.output_type != dtypes.BOOL_DTYPE - ): - rarg = ops.AsTypeOp(to_type=dtypes.INT_DTYPE).as_expr(rarg) - - return expr.op.as_expr(larg, rarg) - - -class LowerDivRule(op_lowering.OpLoweringRule): - @property - def op(self) -> type[ops.ScalarOp]: - return numeric_ops.DivOp - - def lower(self, expr: expression.OpExpression) -> expression.Expression: - assert isinstance(expr.op, numeric_ops.DivOp) - - dividend = expr.children[0] - divisor = expr.children[1] - - if dividend.output_type == dtypes.TIMEDELTA_DTYPE and dtypes.is_numeric( - divisor.output_type - ): - # exact same as floordiv impl for timedelta - numeric_result = ops.div_op.as_expr( - ops.AsTypeOp(to_type=dtypes.INT_DTYPE).as_expr(dividend), divisor - ) - return _numeric_to_timedelta(numeric_result) - if ( - dividend.output_type == dtypes.BOOL_DTYPE - and divisor.output_type == dtypes.BOOL_DTYPE - ): - int_result = expr.op.as_expr( - ops.AsTypeOp(to_type=dtypes.INT_DTYPE).as_expr(dividend), - ops.AsTypeOp(to_type=dtypes.INT_DTYPE).as_expr(divisor), - ) - return ops.AsTypeOp(to_type=dtypes.BOOL_DTYPE).as_expr(int_result) - - # polars divide doesn't like bools, convert to int always - # convert numerics to float always - if dividend.output_type == dtypes.BOOL_DTYPE: - dividend = ops.AsTypeOp(to_type=dtypes.INT_DTYPE).as_expr(dividend) - elif dividend.output_type in (dtypes.BIGNUMERIC_DTYPE, dtypes.NUMERIC_DTYPE): - dividend = ops.AsTypeOp(to_type=dtypes.FLOAT_DTYPE).as_expr(dividend) - if divisor.output_type == dtypes.BOOL_DTYPE: - divisor = ops.AsTypeOp(to_type=dtypes.INT_DTYPE).as_expr(divisor) - - return numeric_ops.div_op.as_expr(dividend, divisor) - - -class LowerFloorDivRule(op_lowering.OpLoweringRule): - @property - def op(self) -> type[ops.ScalarOp]: - return numeric_ops.FloorDivOp - - def lower(self, expr: expression.OpExpression) -> expression.Expression: - assert isinstance(expr.op, numeric_ops.FloorDivOp) - - dividend = expr.children[0] - divisor = expr.children[1] - - if ( - dividend.output_type == dtypes.TIMEDELTA_DTYPE - and divisor.output_type == dtypes.TIMEDELTA_DTYPE - ): - int_result = expr.op.as_expr( - ops.AsTypeOp(to_type=dtypes.INT_DTYPE).as_expr(dividend), - ops.AsTypeOp(to_type=dtypes.INT_DTYPE).as_expr(divisor), - ) - return int_result - if dividend.output_type == dtypes.TIMEDELTA_DTYPE and dtypes.is_numeric( - divisor.output_type - ): - # this is pretty fragile as zero will break it, and must fit back into int - numeric_result = ops.div_op.as_expr( - ops.AsTypeOp(to_type=dtypes.INT_DTYPE).as_expr(dividend), divisor - ) - return _numeric_to_timedelta(numeric_result) - - if dividend.output_type == dtypes.BOOL_DTYPE: - dividend = ops.AsTypeOp(to_type=dtypes.INT_DTYPE).as_expr(dividend) - if divisor.output_type == dtypes.BOOL_DTYPE: - divisor = ops.AsTypeOp(to_type=dtypes.INT_DTYPE).as_expr(divisor) - - if expr.output_type != dtypes.FLOAT_DTYPE: - # need to guard against zero divisor - # multiply dividend in this case to propagate nulls - return ops.where_op.as_expr( - ops.mul_op.as_expr(dividend, expression.const(0)), - ops.eq_op.as_expr(divisor, expression.const(0)), - numeric_ops.floordiv_op.as_expr(dividend, divisor), - ) - else: - return expr.op.as_expr(dividend, divisor) - - -class LowerModRule(op_lowering.OpLoweringRule): - @property - def op(self) -> type[ops.ScalarOp]: - return numeric_ops.ModOp - - def lower(self, expr: expression.OpExpression) -> expression.Expression: - og_expr = expr - assert isinstance(expr.op, numeric_ops.ModOp) - larg, rarg = expr.children[0], expr.children[1] - - if ( - larg.output_type == dtypes.TIMEDELTA_DTYPE - and rarg.output_type == dtypes.TIMEDELTA_DTYPE - ): - larg_int = ops.AsTypeOp(to_type=dtypes.INT_DTYPE).as_expr(larg) - rarg_int = ops.AsTypeOp(to_type=dtypes.INT_DTYPE).as_expr(rarg) - int_result = expr.op.as_expr(larg_int, rarg_int) - w_zero_handling = ops.where_op.as_expr( - int_result, - ops.ne_op.as_expr(rarg_int, expression.const(0)), - ops.mul_op.as_expr(rarg_int, expression.const(0)), - ) - return ops.AsTypeOp(to_type=dtypes.TIMEDELTA_DTYPE).as_expr(w_zero_handling) - - if larg.output_type == dtypes.BOOL_DTYPE: - larg = ops.AsTypeOp(to_type=dtypes.INT_DTYPE).as_expr(larg) - if rarg.output_type == dtypes.BOOL_DTYPE: - rarg = ops.AsTypeOp(to_type=dtypes.INT_DTYPE).as_expr(rarg) - - wo_bools = expr.op.as_expr(larg, rarg) - - if og_expr.output_type == dtypes.INT_DTYPE: - return ops.where_op.as_expr( - wo_bools, - ops.ne_op.as_expr(rarg, expression.const(0)), - ops.mul_op.as_expr(rarg, expression.const(0)), - ) - return wo_bools - - -class LowerAsTypeRule(op_lowering.OpLoweringRule): - @property - def op(self) -> type[ops.ScalarOp]: - return ops.AsTypeOp - - def lower(self, expr: expression.OpExpression) -> expression.Expression: - assert isinstance(expr.op, ops.AsTypeOp) - return _lower_cast(expr.op, expr.inputs[0]) - - -def invert_bytes(byte_string): - inverted_bytes = ~np.frombuffer(byte_string, dtype=np.uint8) - return inverted_bytes.tobytes() - - -class LowerInvertOp(op_lowering.OpLoweringRule): - @property - def op(self) -> type[ops.ScalarOp]: - return generic_ops.InvertOp - - def lower(self, expr: expression.OpExpression) -> expression.Expression: - assert isinstance(expr.op, generic_ops.InvertOp) - arg = expr.children[0] - if arg.output_type == dtypes.BYTES_DTYPE: - return generic_ops.PyUdfOp(invert_bytes, dtypes.BYTES_DTYPE).as_expr( - expr.inputs[0] - ) - return expr - - -class LowerCeilOp(op_lowering.OpLoweringRule): - @property - def op(self) -> type[ops.ScalarOp]: - return numeric_ops.CeilOp - - def lower(self, expr: expression.OpExpression) -> expression.Expression: - assert isinstance(expr.op, numeric_ops.CeilOp) - arg = expr.children[0] - if arg.output_type in (dtypes.INT_DTYPE, dtypes.BOOL_DTYPE): - return expr.op.as_expr(ops.AsTypeOp(dtypes.FLOAT_DTYPE).as_expr(arg)) - return expr - - -class LowerFloorOp(op_lowering.OpLoweringRule): - @property - def op(self) -> type[ops.ScalarOp]: - return numeric_ops.FloorOp - - def lower(self, expr: expression.OpExpression) -> expression.Expression: - assert isinstance(expr.op, numeric_ops.FloorOp) - arg = expr.children[0] - if arg.output_type in (dtypes.INT_DTYPE, dtypes.BOOL_DTYPE): - return expr.op.as_expr(ops.AsTypeOp(dtypes.FLOAT_DTYPE).as_expr(arg)) - return expr - - -class LowerIsinOp(op_lowering.OpLoweringRule): - @property - def op(self) -> type[ops.ScalarOp]: - return generic_ops.IsInOp - - def lower(self, expr: expression.OpExpression) -> expression.Expression: - assert isinstance(expr.op, generic_ops.IsInOp) - arg = expr.children[0] - new_values = [] - match_nulls = False - for val in expr.op.values: - # coercible, non-coercible - # float NaN/inf should be treated as distinct from 'true' null values - if cast(bool, pd.isna(val)) and not isinstance(val, float): - if expr.op.match_nulls: - match_nulls = True - elif dtypes.is_compatible(val, arg.output_type): - new_values.append(val) - else: - pass - - new_isin = ops.IsInOp(tuple(new_values), match_nulls=False).as_expr(arg) - if match_nulls: - return ops.coalesce_op.as_expr(new_isin, expression.const(True)) - else: - # polars propagates nulls, so need to coalesce to false - return ops.coalesce_op.as_expr(new_isin, expression.const(False)) - - -class LowerLenOp(op_lowering.OpLoweringRule): - @property - def op(self) -> type[ops.ScalarOp]: - return string_ops.LenOp - - def lower(self, expr: expression.OpExpression) -> expression.Expression: - assert isinstance(expr.op, string_ops.LenOp) - arg = expr.children[0] - - if dtypes.is_string_like(arg.output_type): - return string_ops.StrLenOp().as_expr(arg) - elif dtypes.is_array_like(arg.output_type): - return string_ops.ArrayLenOp().as_expr(arg) - else: - raise ValueError(f"Unexpected type: {arg.output_type}") - - -def _coerce_comparables( - expr1: expression.Expression, - expr2: expression.Expression, - *, - bools_only: bool = False, -): - if bools_only: - if ( - expr1.output_type != dtypes.BOOL_DTYPE - and expr2.output_type != dtypes.BOOL_DTYPE - ): - return expr1, expr2 - - target_type = dtypes.coerce_to_common(expr1.output_type, expr2.output_type) - if expr1.output_type != target_type: - expr1 = _lower_cast(ops.AsTypeOp(target_type), expr1) - if expr2.output_type != target_type: - expr2 = _lower_cast(ops.AsTypeOp(target_type), expr2) - return expr1, expr2 - - -def _lower_cast(cast_op: ops.AsTypeOp, arg: expression.Expression): - if arg.output_type == cast_op.to_type: - return arg - if ( - arg.output_type == dtypes.STRING_DTYPE - and cast_op.to_type == dtypes.DATETIME_DTYPE - ): - return datetime_ops.ParseDatetimeOp().as_expr(arg) - if ( - arg.output_type == dtypes.STRING_DTYPE - and cast_op.to_type == dtypes.TIMESTAMP_DTYPE - ): - return datetime_ops.ParseTimestampOp().as_expr(arg) - # date -> string casting - if ( - arg.output_type == dtypes.DATETIME_DTYPE - and cast_op.to_type == dtypes.STRING_DTYPE - ): - return datetime_ops.StrftimeOp("%Y-%m-%d %H:%M:%S").as_expr(arg) - if arg.output_type == dtypes.TIME_DTYPE and cast_op.to_type == dtypes.STRING_DTYPE: - return datetime_ops.StrftimeOp("%H:%M:%S.%6f").as_expr(arg) - if ( - arg.output_type == dtypes.TIMESTAMP_DTYPE - and cast_op.to_type == dtypes.STRING_DTYPE - ): - return datetime_ops.StrftimeOp("%Y-%m-%d %H:%M:%S%.6f%:::z").as_expr(arg) - if arg.output_type == dtypes.BOOL_DTYPE and cast_op.to_type == dtypes.STRING_DTYPE: - # bool -> decimal needs two-step cast - new_arg = ops.AsTypeOp(to_type=dtypes.INT_DTYPE).as_expr(arg) - is_true_cond = ops.eq_op.as_expr(arg, expression.const(True)) - is_false_cond = ops.eq_op.as_expr(arg, expression.const(False)) - return ops.CaseWhenOp().as_expr( - is_true_cond, - expression.const("True"), - is_false_cond, - expression.const("False"), - ) - if arg.output_type == dtypes.BOOL_DTYPE and dtypes.is_numeric(cast_op.to_type): - # bool -> decimal needs two-step cast - new_arg = ops.AsTypeOp(to_type=dtypes.INT_DTYPE).as_expr(arg) - return cast_op.as_expr(new_arg) - if arg.output_type == dtypes.TIME_DTYPE and dtypes.is_numeric(cast_op.to_type): - # polars cast gives nanoseconds, so convert to microseconds - return numeric_ops.floordiv_op.as_expr( - cast_op.as_expr(arg), expression.const(1000) - ) - if dtypes.is_numeric(arg.output_type) and cast_op.to_type == dtypes.TIME_DTYPE: - return cast_op.as_expr(ops.mul_op.as_expr(expression.const(1000), arg)) - return cast_op.as_expr(arg) - - -LOWER_COMPARISONS = tuple( - CoerceArgsRule(op) - for op in ( - comparison_ops.EqOp, - comparison_ops.EqNullsMatchOp, - comparison_ops.NeOp, - comparison_ops.LtOp, - comparison_ops.GtOp, - comparison_ops.LeOp, - comparison_ops.GeOp, - ) -) - -POLARS_LOWERING_RULES = ( - *LOWER_COMPARISONS, - LowerAddRule(), - LowerSubRule(), - LowerMulRule(), - LowerDivRule(), - LowerFloorDivRule(), - LowerModRule(), - LowerAsTypeRule(), - LowerInvertOp(), - LowerIsinOp(), - LowerLenOp(), - LowerCeilOp(), - LowerFloorOp(), -) - - -def lower_ops_to_polars(root: bigframe_node.BigFrameNode) -> bigframe_node.BigFrameNode: - return op_lowering.lower_ops(root, rules=POLARS_LOWERING_RULES) - - -def _numeric_to_timedelta(expr: expression.Expression) -> expression.Expression: - """rounding logic used for emulating timedelta ops""" - rounded_value = ops.where_op.as_expr( - ops.floor_op.as_expr(expr), - ops.gt_op.as_expr(expr, expression.const(0)), - ops.ceil_op.as_expr(expr), - ) - int_value = ops.AsTypeOp(to_type=dtypes.INT_DTYPE).as_expr(rounded_value) - return ops.AsTypeOp(to_type=dtypes.TIMEDELTA_DTYPE).as_expr(int_value) diff --git a/bigframes/core/compile/polars/operations/__init__.py b/bigframes/core/compile/polars/operations/__init__.py deleted file mode 100644 index 26444dcb670..00000000000 --- a/bigframes/core/compile/polars/operations/__init__.py +++ /dev/null @@ -1,21 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -"""Operation implementations for the Polars LazyFrame compiler. - -This directory structure should reflect the same layout as the -`bigframes/operations` directory where the operations are defined. - -Prefer small groups of ops per file to keep file sizes manageable for text editors and LLMs. -""" diff --git a/bigframes/core/compile/polars/operations/array_ops.py b/bigframes/core/compile/polars/operations/array_ops.py deleted file mode 100644 index 1f2960471db..00000000000 --- a/bigframes/core/compile/polars/operations/array_ops.py +++ /dev/null @@ -1,62 +0,0 @@ -# Copyright 2026 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -""" -BigFrames -> Polars compilation for the operations in bigframes.operations.array_ops. -""" - -from __future__ import annotations - -from typing import TYPE_CHECKING - -import bigframes.core.compile.polars.compiler as polars_compiler -import bigframes.dtypes as dtypes -from bigframes.operations import generic_ops - -if TYPE_CHECKING: - import polars as pl - - -@polars_compiler.register_op(generic_ops.GetItemOp) -def getitem_op_impl( - compiler: polars_compiler.PolarsExpressionCompiler, - op: generic_ops.GetItemOp, # type: ignore - input: pl.Expr, -) -> pl.Expr: - input_type = compiler._expr_types.get(id(input)) - if input_type is not None and dtypes.is_struct_like(input_type): - if isinstance(op.key, str): - return input.struct.field(op.key) - else: - raise NotImplementedError( - "Referencing a struct field by number not implemented in polars compiler." - ) - elif input_type is not None and dtypes.is_string_like(input_type): - return input.str.slice(op.key, 1) - else: - return input.list.get(op.key) - - -@polars_compiler.register_op(generic_ops.DynamicGetItemOp) -def dynamic_getitem_op_impl( - compiler: polars_compiler.PolarsExpressionCompiler, - op: generic_ops.DynamicGetItemOp, # type: ignore - left: pl.Expr, - right: pl.Expr, -) -> pl.Expr: - left_type = compiler._expr_types.get(id(left)) - if left_type is not None and dtypes.is_string_like(left_type): - return left.str.slice(right, 1) - else: - return left.list.get(right) diff --git a/bigframes/core/compile/polars/operations/generic_ops.py b/bigframes/core/compile/polars/operations/generic_ops.py deleted file mode 100644 index 4051fa49953..00000000000 --- a/bigframes/core/compile/polars/operations/generic_ops.py +++ /dev/null @@ -1,58 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -""" -BigFrames -> Polars compilation for the operations in bigframes.operations.generic_ops. - -Please keep implementations in sequential order by op name. -""" - -from __future__ import annotations - -from typing import TYPE_CHECKING - -import bigframes.core.compile.polars.compiler as polars_compiler -from bigframes.operations import generic_ops - -if TYPE_CHECKING: - import polars as pl - - -@polars_compiler.register_op(generic_ops.NotNullOp) -def notnull_op_impl( - compiler: polars_compiler.PolarsExpressionCompiler, - op: generic_ops.NotNullOp, # type: ignore - input: pl.Expr, -) -> pl.Expr: - return input.is_not_null() - - -@polars_compiler.register_op(generic_ops.IsNullOp) -def isnull_op_impl( - compiler: polars_compiler.PolarsExpressionCompiler, - op: generic_ops.IsNullOp, # type: ignore - input: pl.Expr, -) -> pl.Expr: - return input.is_null() - - -@polars_compiler.register_op(generic_ops.PyUdfOp) -def py_udf_op_impl( - compiler: polars_compiler.PolarsExpressionCompiler, - op: generic_ops.PyUdfOp, # type: ignore - input: pl.Expr, -) -> pl.Expr: - return input.map_elements( - op.fn, return_dtype=polars_compiler._DTYPE_MAPPING[op._output_type] - ) diff --git a/bigframes/core/compile/polars/operations/numeric_ops.py b/bigframes/core/compile/polars/operations/numeric_ops.py deleted file mode 100644 index 440415014e9..00000000000 --- a/bigframes/core/compile/polars/operations/numeric_ops.py +++ /dev/null @@ -1,172 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -""" -BigFrames -> Polars compilation for the operations in bigframes.operations.numeric_ops. - -Please keep implementations in sequential order by op name. -""" - -from __future__ import annotations - -from typing import TYPE_CHECKING - -import bigframes.core.compile.polars.compiler as polars_compiler -from bigframes.operations import numeric_ops - -if TYPE_CHECKING: - import polars as pl - - -@polars_compiler.register_op(numeric_ops.LnOp) -def ln_op_impl( - compiler: polars_compiler.PolarsExpressionCompiler, - op: numeric_ops.LnOp, # type: ignore - input: pl.Expr, -) -> pl.Expr: - import polars as pl - - return pl.when(input <= 0).then(float("nan")).otherwise(input.log()) - - -@polars_compiler.register_op(numeric_ops.Log10Op) -def log10_op_impl( - compiler: polars_compiler.PolarsExpressionCompiler, - op: numeric_ops.Log10Op, # type: ignore - input: pl.Expr, -) -> pl.Expr: - import polars as pl - - return pl.when(input <= 0).then(float("nan")).otherwise(input.log(base=10)) - - -@polars_compiler.register_op(numeric_ops.Log1pOp) -def log1p_op_impl( - compiler: polars_compiler.PolarsExpressionCompiler, - op: numeric_ops.Log1pOp, # type: ignore - input: pl.Expr, -) -> pl.Expr: - import polars as pl - - return pl.when(input <= -1).then(float("nan")).otherwise((input + 1).log()) - - -@polars_compiler.register_op(numeric_ops.SinOp) -def sin_op_impl( - compiler: polars_compiler.PolarsExpressionCompiler, - op: numeric_ops.SinOp, # type: ignore - input: pl.Expr, -) -> pl.Expr: - return input.sin() - - -@polars_compiler.register_op(numeric_ops.CosOp) -def cos_op_impl( - compiler: polars_compiler.PolarsExpressionCompiler, - op: numeric_ops.CosOp, # type: ignore - input: pl.Expr, -) -> pl.Expr: - return input.cos() - - -@polars_compiler.register_op(numeric_ops.TanOp) -def tan_op_impl( - compiler: polars_compiler.PolarsExpressionCompiler, - op: numeric_ops.SinOp, # type: ignore - input: pl.Expr, -) -> pl.Expr: - return input.tan() - - -@polars_compiler.register_op(numeric_ops.SinhOp) -def sinh_op_impl( - compiler: polars_compiler.PolarsExpressionCompiler, - op: numeric_ops.SinOp, # type: ignore - input: pl.Expr, -) -> pl.Expr: - return input.sinh() - - -@polars_compiler.register_op(numeric_ops.CoshOp) -def cosh_op_impl( - compiler: polars_compiler.PolarsExpressionCompiler, - op: numeric_ops.CosOp, # type: ignore - input: pl.Expr, -) -> pl.Expr: - return input.cosh() - - -@polars_compiler.register_op(numeric_ops.TanhOp) -def tanh_op_impl( - compiler: polars_compiler.PolarsExpressionCompiler, - op: numeric_ops.SinOp, # type: ignore - input: pl.Expr, -) -> pl.Expr: - return input.tanh() - - -@polars_compiler.register_op(numeric_ops.ArcsinOp) -def asin_op_impl( - compiler: polars_compiler.PolarsExpressionCompiler, - op: numeric_ops.ArcsinOp, # type: ignore - input: pl.Expr, -) -> pl.Expr: - return input.arcsin() - - -@polars_compiler.register_op(numeric_ops.ArccosOp) -def acos_op_impl( - compiler: polars_compiler.PolarsExpressionCompiler, - op: numeric_ops.ArccosOp, # type: ignore - input: pl.Expr, -) -> pl.Expr: - return input.arccos() - - -@polars_compiler.register_op(numeric_ops.ArctanOp) -def atan_op_impl( - compiler: polars_compiler.PolarsExpressionCompiler, - op: numeric_ops.ArctanOp, # type: ignore - input: pl.Expr, -) -> pl.Expr: - return input.arctan() - - -@polars_compiler.register_op(numeric_ops.SqrtOp) -def sqrt_op_impl( - compiler: polars_compiler.PolarsExpressionCompiler, - op: numeric_ops.SqrtOp, # type: ignore - input: pl.Expr, -) -> pl.Expr: - import polars as pl - - return pl.when(input < 0).then(float("nan")).otherwise(input.sqrt()) - - -@polars_compiler.register_op(numeric_ops.IsNanOp) -def is_nan_op_impl( - compiler: polars_compiler.PolarsExpressionCompiler, - op: numeric_ops.IsNanOp, # type: ignore - input: pl.Expr, -) -> pl.Expr: - return input.is_nan() - - -@polars_compiler.register_op(numeric_ops.IsFiniteOp) -def is_finite_op_impl( - compiler: polars_compiler.PolarsExpressionCompiler, - op: numeric_ops.IsFiniteOp, # type: ignore - input: pl.Expr, -) -> pl.Expr: - return input.is_finite() diff --git a/bigframes/core/compile/polars/operations/struct_ops.py b/bigframes/core/compile/polars/operations/struct_ops.py deleted file mode 100644 index 1573d4aa9b4..00000000000 --- a/bigframes/core/compile/polars/operations/struct_ops.py +++ /dev/null @@ -1,48 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -""" -BigFrames -> Polars compilation for the operations in bigframes.operations.generic_ops. - -Please keep implementations in sequential order by op name. -""" - -from __future__ import annotations - -from typing import TYPE_CHECKING - -import bigframes_vendored.constants - -import bigframes.core.compile.polars.compiler as polars_compiler -from bigframes.operations import struct_ops - -if TYPE_CHECKING: - import polars as pl - - -@polars_compiler.register_op(struct_ops.StructFieldOp) -def struct_field_op_impl( - compiler: polars_compiler.PolarsExpressionCompiler, - op: struct_ops.StructFieldOp, # type: ignore - input: pl.Expr, -) -> pl.Expr: - if isinstance(op.name_or_index, str): - name = op.name_or_index - else: - raise NotImplementedError( - "Referencing a struct field by number not implemented in polars compiler. " - f"{bigframes_vendored.constants.FEEDBACK_LINK}" - ) - - return input.struct.field(name) diff --git a/bigframes/core/compile/row_identity.py b/bigframes/core/compile/row_identity.py new file mode 100644 index 00000000000..2e9bc0527ca --- /dev/null +++ b/bigframes/core/compile/row_identity.py @@ -0,0 +1,200 @@ +# Copyright 2023 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Helpers to join ArrayValue objects.""" + +from __future__ import annotations + +import functools +import typing + +import ibis +import ibis.expr.types as ibis_types + +import bigframes.constants as constants +import bigframes.core.compile as compiled +import bigframes.core.joins.name_resolution as naming +import bigframes.core.ordering as orderings + +SUPPORTED_ROW_IDENTITY_HOW = {"outer", "left", "inner"} + + +def join_by_row_identity( + left: compiled.CompiledArrayValue, right: compiled.CompiledArrayValue, *, how: str +) -> compiled.CompiledArrayValue: + """Compute join when we are joining by row identity not a specific column.""" + if how not in SUPPORTED_ROW_IDENTITY_HOW: + raise NotImplementedError( + f"Only how='outer','left','inner' currently supported. {constants.FEEDBACK_LINK}" + ) + + if not left._table.equals(right._table): + raise ValueError( + "Cannot combine objects without an explicit join/merge key. " + f"Left based on: {left._table.compile()}, but " + f"right based on: {right._table.compile()}" + ) + + left_predicates = left._predicates + right_predicates = right._predicates + # TODO(tbergeron): Skip generating these for inner part of join + ( + left_relative_predicates, + right_relative_predicates, + ) = _get_relative_predicates(left_predicates, right_predicates) + + combined_predicates = [] + if left_predicates or right_predicates: + joined_predicates = _join_predicates( + left_predicates, right_predicates, join_type=how + ) + combined_predicates = list(joined_predicates) # builder expects mutable list + + left_mask = left_relative_predicates if how in ["right", "outer"] else None + right_mask = right_relative_predicates if how in ["left", "outer"] else None + + # Public mapping must use JOIN_NAME_REMAPPER to stay in sync with consumers of join result + lpublicmapping, rpublicmapping = naming.JOIN_NAME_REMAPPER( + left.column_ids, right.column_ids + ) + lhiddenmapping, rhiddenmapping = naming.JoinNameRemapper(namespace="hidden")( + left._hidden_column_ids, right._hidden_column_ids + ) + map_left_id = {**lpublicmapping, **lhiddenmapping} + map_right_id = {**rpublicmapping, **rhiddenmapping} + + joined_columns = [ + _mask_value(left._get_ibis_column(key), left_mask).name(map_left_id[key]) + for key in left.column_ids + ] + [ + _mask_value(right._get_ibis_column(key), right_mask).name(map_right_id[key]) + for key in right.column_ids + ] + + # If left isn't being masked, can just use left ordering + if not left_mask: + col_mapping = { + order_ref.column_id: map_left_id[order_ref.column_id] + for order_ref in left._ordering.ordering_value_columns + } + new_ordering = left._ordering.with_column_remap(col_mapping) + else: + ordering_columns = [ + col_ref.with_name(map_left_id[col_ref.column_id]) + for col_ref in left._ordering.ordering_value_columns + ] + [ + col_ref.with_name(map_right_id[col_ref.column_id]) + for col_ref in right._ordering.ordering_value_columns + ] + left_total_order_cols = frozenset( + map_left_id[col] for col in left._ordering.total_ordering_columns + ) + # Assume that left ordering is sufficient since 1:1 join over same base table + join_total_order_cols = left_total_order_cols + new_ordering = orderings.ExpressionOrdering( + tuple(ordering_columns), total_ordering_columns=join_total_order_cols + ) + + hidden_ordering_columns = [ + left._get_hidden_ordering_column(key.column_id).name(map_left_id[key.column_id]) + for key in left._ordering.ordering_value_columns + if key.column_id in left._hidden_ordering_column_names.keys() + ] + [ + right._get_hidden_ordering_column(key.column_id).name( + map_right_id[key.column_id] + ) + for key in right._ordering.ordering_value_columns + if key.column_id in right._hidden_ordering_column_names.keys() + ] + + joined_expr = compiled.CompiledArrayValue( + left._table, + columns=joined_columns, + hidden_ordering_columns=hidden_ordering_columns, + ordering=new_ordering, + predicates=combined_predicates, + ) + return joined_expr + + +def _mask_value( + value: ibis_types.Value, + predicates: typing.Optional[typing.Sequence[ibis_types.BooleanValue]] = None, +): + if predicates: + return ( + ibis.case() + .when(_reduce_predicate_list(predicates), value) + .else_(ibis.null()) + .end() + ) + return value + + +def _join_predicates( + left_predicates: typing.Collection[ibis_types.BooleanValue], + right_predicates: typing.Collection[ibis_types.BooleanValue], + join_type: str = "outer", +) -> typing.Tuple[ibis_types.BooleanValue, ...]: + """Combines predicates lists for each side of a join.""" + if join_type == "outer": + if not left_predicates: + return () + if not right_predicates: + return () + # TODO(tbergeron): Investigate factoring out common predicates + joined_predicates = _reduce_predicate_list(left_predicates).__or__( + _reduce_predicate_list(right_predicates) + ) + return (joined_predicates,) + if join_type == "left": + return tuple(left_predicates) + if join_type == "inner": + _, right_relative_predicates = _get_relative_predicates( + left_predicates, right_predicates + ) + return (*left_predicates, *right_relative_predicates) + else: + raise ValueError( + f"Unsupported join_type: {join_type}. {constants.FEEDBACK_LINK}" + ) + + +def _get_relative_predicates( + left_predicates: typing.Collection[ibis_types.BooleanValue], + right_predicates: typing.Collection[ibis_types.BooleanValue], +) -> tuple[ + typing.Tuple[ibis_types.BooleanValue, ...], + typing.Tuple[ibis_types.BooleanValue, ...], +]: + """Get predicates that apply to only one side of the join. Not strictly necessary but simplifies resulting query.""" + left_relative_predicates = tuple(left_predicates) or () + right_relative_predicates = tuple(right_predicates) or () + if left_predicates and right_predicates: + # Factor out common predicates needed for left/right column masking + left_relative_predicates = tuple(set(left_predicates) - set(right_predicates)) + right_relative_predicates = tuple(set(right_predicates) - set(left_predicates)) + return (left_relative_predicates, right_relative_predicates) + + +def _reduce_predicate_list( + predicate_list: typing.Collection[ibis_types.BooleanValue], +) -> ibis_types.BooleanValue: + """Converts a list of predicates BooleanValues into a single BooleanValue.""" + if len(predicate_list) == 0: + raise ValueError("Cannot reduce empty list of predicates") + if len(predicate_list) == 1: + (item,) = predicate_list + return item + return functools.reduce(lambda acc, pred: acc.__and__(pred), predicate_list) diff --git a/bigframes/core/compile/schema_translator.py b/bigframes/core/compile/schema_translator.py deleted file mode 100644 index 428f94035d6..00000000000 --- a/bigframes/core/compile/schema_translator.py +++ /dev/null @@ -1,35 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -from __future__ import annotations - -import bigframes_vendored.ibis.expr.api as ibis_api -import bigframes_vendored.ibis.expr.schema as ibis_sch - -import bigframes.core.compile.ibis_types -import bigframes.core.schema as bf_schema -import bigframes.dtypes - - -def convert_bf_schema(schema: bf_schema.ArraySchema) -> ibis_sch.Schema: - """ - Convert bigframes schema to ibis schema. This is unambigous as every bigframes type is backed by a specific SQL/ibis dtype. - """ - names = schema.names - types = [ - bigframes.core.compile.ibis_types.bigframes_dtype_to_ibis_dtype(bf_type) - for bf_type in schema.dtypes - ] - return ibis_api.schema(names=names, types=types) diff --git a/bigframes/core/compile/single_column.py b/bigframes/core/compile/single_column.py new file mode 100644 index 00000000000..b992aa1d1d9 --- /dev/null +++ b/bigframes/core/compile/single_column.py @@ -0,0 +1,181 @@ +# Copyright 2023 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Helpers to join ArrayValue objects.""" + +from __future__ import annotations + +import typing +from typing import Literal, Mapping + +import ibis +import ibis.expr.datatypes as ibis_dtypes +import ibis.expr.types as ibis_types + +import bigframes.core.compile as compiled +import bigframes.core.compile.row_identity +import bigframes.core.joins as joining +import bigframes.core.ordering as orderings + + +def join_by_column( + left: compiled.CompiledArrayValue, + left_column_ids: typing.Sequence[str], + right: compiled.CompiledArrayValue, + right_column_ids: typing.Sequence[str], + *, + how: Literal[ + "inner", + "left", + "outer", + "right", + ], + allow_row_identity_join: bool = True, +) -> compiled.CompiledArrayValue: + """Join two expressions by column equality. + + Arguments: + left: Expression for left table to join. + left_column_ids: Column IDs (not label) to join by. + right: Expression for right table to join. + right_column_ids: Column IDs (not label) to join by. + how: The type of join to perform. + allow_row_identity_join (bool): + If True, allow matching by row identity. Set to False to always + perform a true JOIN in generated SQL. + Returns: + The joined expression. The resulting columns will be, in order, + first the coalesced join keys, then, all the left columns, and + finally, all the right columns. + """ + if ( + allow_row_identity_join + and how in bigframes.core.compile.row_identity.SUPPORTED_ROW_IDENTITY_HOW + and left._table.equals(right._table) + # Make sure we're joining on exactly the same column(s), at least with + # regards to value its possible that they both have the same names but + # were modified in different ways. Ignore differences in the names. + and all( + left._get_any_column(lcol) + .name("index") + .equals(right._get_any_column(rcol).name("index")) + for lcol, rcol in zip(left_column_ids, right_column_ids) + ) + ): + return bigframes.core.compile.row_identity.join_by_row_identity( + left, right, how=how + ) + else: + # Value column mapping must use JOIN_NAME_REMAPPER to stay in sync with consumers of join result + l_public_mapping, r_public_mapping = joining.JOIN_NAME_REMAPPER( + left.column_ids, right.column_ids + ) + l_hidden_mapping, r_hidden_mapping = joining.JoinNameRemapper( + namespace="hidden" + )(left._hidden_column_ids, right._hidden_column_ids) + l_mapping = {**l_public_mapping, **l_hidden_mapping} + r_mapping = {**r_public_mapping, **r_hidden_mapping} + + left_table = left._to_ibis_expr( + "unordered", + expose_hidden_cols=True, + col_id_overrides=l_mapping, + ) + right_table = right._to_ibis_expr( + "unordered", + expose_hidden_cols=True, + col_id_overrides=r_mapping, + ) + join_conditions = [ + value_to_join_key(left_table[l_mapping[left_index]]) + == value_to_join_key(right_table[r_mapping[right_index]]) + for left_index, right_index in zip(left_column_ids, right_column_ids) + ] + + combined_table = ibis.join( + left_table, + right_table, + predicates=join_conditions, + how=how, + ) + + # Preserve ordering accross joins. + ordering = join_orderings( + left._ordering, + right._ordering, + l_mapping, + r_mapping, + left_order_dominates=(how != "right"), + ) + + # We could filter out the original join columns, but predicates/ordering + # might still reference them in implicit joins. + columns = [ + combined_table[l_mapping[col.get_name()]] for col in left.columns + ] + [combined_table[r_mapping[col.get_name()]] for col in right.columns] + hidden_ordering_columns = [ + *[ + combined_table[l_hidden_mapping[col.get_name()]] + for col in left._hidden_ordering_columns + ], + *[ + combined_table[r_hidden_mapping[col.get_name()]] + for col in right._hidden_ordering_columns + ], + ] + return compiled.CompiledArrayValue( + combined_table, + columns=columns, + hidden_ordering_columns=hidden_ordering_columns, + ordering=ordering, + ) + + +def value_to_join_key(value: ibis_types.Value): + """Converts nullable values to non-null string SQL will not match null keys together - but pandas does.""" + if not value.type().is_string(): + value = value.cast(ibis_dtypes.str) + return value.fillna(ibis_types.literal("$NULL_SENTINEL$")) + + +def join_orderings( + left: orderings.ExpressionOrdering, + right: orderings.ExpressionOrdering, + left_id_mapping: Mapping[str, str], + right_id_mapping: Mapping[str, str], + left_order_dominates: bool = True, +) -> orderings.ExpressionOrdering: + left_ordering_refs = [ + ref.with_name(left_id_mapping[ref.column_id]) + for ref in left.all_ordering_columns + ] + right_ordering_refs = [ + ref.with_name(right_id_mapping[ref.column_id]) + for ref in right.all_ordering_columns + ] + if left_order_dominates: + joined_refs = [*left_ordering_refs, *right_ordering_refs] + else: + joined_refs = [*right_ordering_refs, *left_ordering_refs] + + left_total_order_cols = frozenset( + [left_id_mapping[id] for id in left.total_ordering_columns] + ) + right_total_order_cols = frozenset( + [right_id_mapping[id] for id in right.total_ordering_columns] + ) + return orderings.ExpressionOrdering( + ordering_value_columns=tuple(joined_refs), + total_ordering_columns=left_total_order_cols | right_total_order_cols, + ) diff --git a/bigframes/core/compile/sqlglot/__init__.py b/bigframes/core/compile/sqlglot/__init__.py deleted file mode 100644 index fa515e4f15a..00000000000 --- a/bigframes/core/compile/sqlglot/__init__.py +++ /dev/null @@ -1,32 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -from __future__ import annotations - -import bigframes.core.compile.sqlglot.expressions.ai_ops # noqa: F401 -import bigframes.core.compile.sqlglot.expressions.array_ops # noqa: F401 -import bigframes.core.compile.sqlglot.expressions.blob_ops # noqa: F401 -import bigframes.core.compile.sqlglot.expressions.bool_ops # noqa: F401 -import bigframes.core.compile.sqlglot.expressions.comparison_ops # noqa: F401 -import bigframes.core.compile.sqlglot.expressions.date_ops # noqa: F401 -import bigframes.core.compile.sqlglot.expressions.datetime_ops # noqa: F401 -import bigframes.core.compile.sqlglot.expressions.generic_ops # noqa: F401 -import bigframes.core.compile.sqlglot.expressions.geo_ops # noqa: F401 -import bigframes.core.compile.sqlglot.expressions.json_ops # noqa: F401 -import bigframes.core.compile.sqlglot.expressions.numeric_ops # noqa: F401 -import bigframes.core.compile.sqlglot.expressions.string_ops # noqa: F401 -import bigframes.core.compile.sqlglot.expressions.struct_ops # noqa: F401 -import bigframes.core.compile.sqlglot.expressions.timedelta_ops # noqa: F401 -from bigframes.core.compile.sqlglot.compiler import compile_sql - -__all__ = ["compile_sql"] diff --git a/bigframes/core/compile/sqlglot/aggregate_compiler.py b/bigframes/core/compile/sqlglot/aggregate_compiler.py deleted file mode 100644 index c0781e260c6..00000000000 --- a/bigframes/core/compile/sqlglot/aggregate_compiler.py +++ /dev/null @@ -1,74 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -from __future__ import annotations - -import bigframes_vendored.sqlglot.expressions as sge - -import bigframes.core.compile.sqlglot.expression_compiler as expression_compiler -from bigframes.core import agg_expressions, window_spec -from bigframes.core.compile.sqlglot.aggregations import ( - binary_compiler, - nullary_compiler, - ordered_unary_compiler, - unary_compiler, -) -from bigframes.core.compile.sqlglot.expressions import typed_expr - - -def compile_aggregate( - aggregate: agg_expressions.Aggregation, - order_by: tuple[sge.Expression, ...], -) -> sge.Expression: - """Compiles BigFrames aggregation expression into SQLGlot expression.""" - if isinstance(aggregate, agg_expressions.NullaryAggregation): - return nullary_compiler.compile(aggregate.op) - if isinstance(aggregate, agg_expressions.UnaryAggregation): - column = typed_expr.TypedExpr( - expression_compiler.expression_compiler.compile_expression(aggregate.arg), - aggregate.arg.output_type, - ) - if not aggregate.op.order_independent: - return ordered_unary_compiler.compile( - aggregate.op, column, order_by=order_by - ) - else: - return unary_compiler.compile(aggregate.op, column) - elif isinstance(aggregate, agg_expressions.BinaryAggregation): - left = typed_expr.TypedExpr( - expression_compiler.expression_compiler.compile_expression(aggregate.left), - aggregate.left.output_type, - ) - right = typed_expr.TypedExpr( - expression_compiler.expression_compiler.compile_expression(aggregate.right), - aggregate.right.output_type, - ) - return binary_compiler.compile(aggregate.op, left, right) - else: - raise ValueError(f"Unexpected aggregation: {aggregate}") - - -def compile_analytic( - aggregate: agg_expressions.Aggregation, - window: window_spec.WindowSpec, -) -> sge.Expression: - if isinstance(aggregate, agg_expressions.NullaryAggregation): - return nullary_compiler.compile(aggregate.op, window) - if isinstance(aggregate, agg_expressions.UnaryAggregation): - column = typed_expr.TypedExpr( - expression_compiler.expression_compiler.compile_expression(aggregate.arg), - aggregate.arg.output_type, - ) - return unary_compiler.compile(aggregate.op, column, window) - else: - raise ValueError(f"Unexpected analytic operation: {aggregate}") diff --git a/bigframes/core/compile/sqlglot/aggregations/__init__.py b/bigframes/core/compile/sqlglot/aggregations/__init__.py deleted file mode 100644 index 0a2669d7a25..00000000000 --- a/bigframes/core/compile/sqlglot/aggregations/__init__.py +++ /dev/null @@ -1,13 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. diff --git a/bigframes/core/compile/sqlglot/aggregations/binary_compiler.py b/bigframes/core/compile/sqlglot/aggregations/binary_compiler.py deleted file mode 100644 index df8437fe76f..00000000000 --- a/bigframes/core/compile/sqlglot/aggregations/binary_compiler.py +++ /dev/null @@ -1,60 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from __future__ import annotations - -import typing - -import bigframes_vendored.sqlglot.expressions as sge - -import bigframes.core.compile.sqlglot.aggregations.op_registration as reg -import bigframes.core.compile.sqlglot.expressions.typed_expr as typed_expr -from bigframes.core import window_spec -from bigframes.core.compile.sqlglot.aggregations.windows import apply_window_if_present -from bigframes.operations import aggregations as agg_ops - -BINARY_OP_REGISTRATION = reg.OpRegistration() - - -def compile( - op: agg_ops.WindowOp, - left: typed_expr.TypedExpr, - right: typed_expr.TypedExpr, - window: typing.Optional[window_spec.WindowSpec] = None, -) -> sge.Expression: - if op.order_independent and (window is not None) and window.is_unbounded: - window = window.without_order() - return BINARY_OP_REGISTRATION[op](op, left, right, window=window) - - -@BINARY_OP_REGISTRATION.register(agg_ops.CorrOp) -def _( - op: agg_ops.CorrOp, - left: typed_expr.TypedExpr, - right: typed_expr.TypedExpr, - window: typing.Optional[window_spec.WindowSpec] = None, -) -> sge.Expression: - result = sge.func("CORR", left.expr, right.expr) - return apply_window_if_present(result, window) - - -@BINARY_OP_REGISTRATION.register(agg_ops.CovOp) -def _( - op: agg_ops.CovOp, - left: typed_expr.TypedExpr, - right: typed_expr.TypedExpr, - window: typing.Optional[window_spec.WindowSpec] = None, -) -> sge.Expression: - result = sge.func("COVAR_SAMP", left.expr, right.expr) - return apply_window_if_present(result, window) diff --git a/bigframes/core/compile/sqlglot/aggregations/nullary_compiler.py b/bigframes/core/compile/sqlglot/aggregations/nullary_compiler.py deleted file mode 100644 index f2f1978908f..00000000000 --- a/bigframes/core/compile/sqlglot/aggregations/nullary_compiler.py +++ /dev/null @@ -1,55 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from __future__ import annotations - -import typing - -import bigframes_vendored.sqlglot.expressions as sge - -import bigframes.core.compile.sqlglot.aggregations.op_registration as reg -from bigframes.core import window_spec -from bigframes.core.compile.sqlglot.aggregations.windows import apply_window_if_present -from bigframes.operations import aggregations as agg_ops - -NULLARY_OP_REGISTRATION = reg.OpRegistration() - - -def compile( - op: agg_ops.WindowOp, - window: typing.Optional[window_spec.WindowSpec] = None, -) -> sge.Expression: - if op.order_independent and (window is not None) and window.is_unbounded: - window = window.without_order() - return NULLARY_OP_REGISTRATION[op](op, window=window) - - -@NULLARY_OP_REGISTRATION.register(agg_ops.SizeOp) -def _( - op: agg_ops.SizeOp, - window: typing.Optional[window_spec.WindowSpec] = None, -) -> sge.Expression: - return apply_window_if_present(sge.func("COUNT", sge.convert(1)), window) - - -@NULLARY_OP_REGISTRATION.register(agg_ops.RowNumberOp) -def _( - op: agg_ops.RowNumberOp, - window: typing.Optional[window_spec.WindowSpec] = None, -) -> sge.Expression: - result: sge.Expression = sge.func("ROW_NUMBER") - if window is None: - # ROW_NUMBER always needs an OVER clause. - return sge.Window(this=result) - 1 - return apply_window_if_present(result, window, include_framing_clauses=False) - 1 diff --git a/bigframes/core/compile/sqlglot/aggregations/op_registration.py b/bigframes/core/compile/sqlglot/aggregations/op_registration.py deleted file mode 100644 index 2b3ba20ef09..00000000000 --- a/bigframes/core/compile/sqlglot/aggregations/op_registration.py +++ /dev/null @@ -1,56 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from __future__ import annotations - -import typing - -from bigframes_vendored.sqlglot import expressions as sge - -from bigframes.operations import aggregations as agg_ops - -# We should've been more specific about input types. Unfortunately, -# MyPy doesn't support more rigorous checks. -CompilationFunc = typing.Callable[..., sge.Expression] - - -class OpRegistration: - def __init__(self) -> None: - self._registered_ops: dict[str, CompilationFunc] = {} - - def register( - self, op: agg_ops.WindowOp | type[agg_ops.WindowOp] - ) -> typing.Callable[[CompilationFunc], CompilationFunc]: - def decorator(item: CompilationFunc): - def arg_checker(*args, **kwargs): - if not isinstance(args[0], agg_ops.WindowOp): - raise ValueError( - "The first parameter must be a window operator. " - f"Got {type(args[0])}" - ) - return item(*args, **kwargs) - - key = str(op) - if key in self._registered_ops: - raise ValueError(f"{key} is already registered") - self._registered_ops[key] = item - return arg_checker - - return decorator - - def __getitem__(self, op: str | agg_ops.WindowOp) -> CompilationFunc: - key = op if isinstance(op, type) else type(op) - if str(key) not in self._registered_ops: - raise ValueError(f"{key} is not registered") - return self._registered_ops[str(key)] diff --git a/bigframes/core/compile/sqlglot/aggregations/ordered_unary_compiler.py b/bigframes/core/compile/sqlglot/aggregations/ordered_unary_compiler.py deleted file mode 100644 index 5feaf794e0b..00000000000 --- a/bigframes/core/compile/sqlglot/aggregations/ordered_unary_compiler.py +++ /dev/null @@ -1,60 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from __future__ import annotations - -import bigframes_vendored.sqlglot.expressions as sge - -import bigframes.core.compile.sqlglot.aggregations.op_registration as reg -import bigframes.core.compile.sqlglot.expressions.typed_expr as typed_expr -from bigframes.operations import aggregations as agg_ops - -ORDERED_UNARY_OP_REGISTRATION = reg.OpRegistration() - - -def compile( - op: agg_ops.WindowOp, - column: typed_expr.TypedExpr, - *, - order_by: tuple[sge.Expression, ...] = (), -) -> sge.Expression: - return ORDERED_UNARY_OP_REGISTRATION[op](op, column, order_by=order_by) - - -@ORDERED_UNARY_OP_REGISTRATION.register(agg_ops.ArrayAggOp) -def _( - op: agg_ops.ArrayAggOp, - column: typed_expr.TypedExpr, - *, - order_by: tuple[sge.Expression, ...], -) -> sge.Expression: - expr = column.expr - if len(order_by) > 0: - expr = sge.Order(this=column.expr, expressions=list(order_by)) - return sge.IgnoreNulls(this=sge.ArrayAgg(this=expr)) - - -@ORDERED_UNARY_OP_REGISTRATION.register(agg_ops.StringAggOp) -def _( - op: agg_ops.StringAggOp, - column: typed_expr.TypedExpr, - *, - order_by: tuple[sge.Expression, ...], -) -> sge.Expression: - expr = column.expr - if len(order_by) > 0: - expr = sge.Order(this=expr, expressions=list(order_by)) - - expr = sge.GroupConcat(this=expr, separator=sge.convert(op.sep)) - return sge.func("COALESCE", expr, sge.convert("")) diff --git a/bigframes/core/compile/sqlglot/aggregations/unary_compiler.py b/bigframes/core/compile/sqlglot/aggregations/unary_compiler.py deleted file mode 100644 index 417faef34aa..00000000000 --- a/bigframes/core/compile/sqlglot/aggregations/unary_compiler.py +++ /dev/null @@ -1,626 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from __future__ import annotations - -import typing - -import bigframes_vendored.sqlglot as sg -import bigframes_vendored.sqlglot.expressions as sge -import pandas as pd - -import bigframes.core.compile.sqlglot.aggregations.op_registration as reg -import bigframes.core.compile.sqlglot.expressions.typed_expr as typed_expr -from bigframes import dtypes -from bigframes.core import window_spec -from bigframes.core.compile.sqlglot import sql -from bigframes.core.compile.sqlglot.aggregations.windows import apply_window_if_present -from bigframes.core.compile.sqlglot.expressions import constants -from bigframes.operations import aggregations as agg_ops - -UNARY_OP_REGISTRATION = reg.OpRegistration() - - -def compile( - op: agg_ops.WindowOp, - column: typed_expr.TypedExpr, - window: typing.Optional[window_spec.WindowSpec] = None, -) -> sge.Expression: - if op.order_independent and (window is not None) and window.is_unbounded: - window = window.without_order() - return UNARY_OP_REGISTRATION[op](op, column, window=window) - - -@UNARY_OP_REGISTRATION.register(agg_ops.AllOp) -def _( - op: agg_ops.AllOp, - column: typed_expr.TypedExpr, - window: typing.Optional[window_spec.WindowSpec] = None, -) -> sge.Expression: - expr = column.expr - if column.dtype != dtypes.BOOL_DTYPE: - expr = sge.NEQ(this=expr, expression=sge.convert(0)) - expr = apply_window_if_present(sge.func("LOGICAL_AND", expr), window) - - # BQ will return null for empty column, result would be true in pandas. - return sge.func("COALESCE", expr, sge.convert(True)) - - -@UNARY_OP_REGISTRATION.register(agg_ops.AnyOp) -def _( - op: agg_ops.AnyOp, - column: typed_expr.TypedExpr, - window: typing.Optional[window_spec.WindowSpec] = None, -) -> sge.Expression: - expr = column.expr - if column.dtype != dtypes.BOOL_DTYPE: - expr = sge.NEQ(this=expr, expression=sge.convert(0)) - expr = apply_window_if_present(sge.func("LOGICAL_OR", expr), window) - - # BQ will return null for empty column, result would be false in pandas. - return sge.func("COALESCE", expr, sge.convert(False)) - - -@UNARY_OP_REGISTRATION.register(agg_ops.ApproxQuartilesOp) -def _( - op: agg_ops.ApproxQuartilesOp, - column: typed_expr.TypedExpr, - window: typing.Optional[window_spec.WindowSpec] = None, -) -> sge.Expression: - if window is not None: - raise NotImplementedError("Approx Quartiles with windowing is not supported.") - # APPROX_QUANTILES returns an array of the quartiles, so we need to index it. - # The op.quartile is 1-based for the quartile, but array is 0-indexed. - # The quartiles are Q0, Q1, Q2, Q3, Q4. op.quartile is 1, 2, or 3. - # The array has 5 elements (for N=4 intervals). - # So we want the element at index `op.quartile`. - approx_quantiles_expr = sge.func("APPROX_QUANTILES", column.expr, sge.convert(4)) - return sge.Bracket( - this=approx_quantiles_expr, - expressions=[sge.func("OFFSET", sge.convert(op.quartile))], - ) - - -@UNARY_OP_REGISTRATION.register(agg_ops.ApproxTopCountOp) -def _( - op: agg_ops.ApproxTopCountOp, - column: typed_expr.TypedExpr, - window: typing.Optional[window_spec.WindowSpec] = None, -) -> sge.Expression: - if window is not None: - raise NotImplementedError("Approx top count with windowing is not supported.") - return sge.func("APPROX_TOP_COUNT", column.expr, sge.convert(op.number)) - - -@UNARY_OP_REGISTRATION.register(agg_ops.AnyValueOp) -def _( - op: agg_ops.AnyValueOp, - column: typed_expr.TypedExpr, - window: typing.Optional[window_spec.WindowSpec] = None, -) -> sge.Expression: - return apply_window_if_present(sge.func("ANY_VALUE", column.expr), window) - - -@UNARY_OP_REGISTRATION.register(agg_ops.CountOp) -def _( - op: agg_ops.CountOp, - column: typed_expr.TypedExpr, - window: typing.Optional[window_spec.WindowSpec] = None, -) -> sge.Expression: - return apply_window_if_present(sge.func("COUNT", column.expr), window) - - -@UNARY_OP_REGISTRATION.register(agg_ops.CutOp) -def _( - op: agg_ops.CutOp, - column: typed_expr.TypedExpr, - window: typing.Optional[window_spec.WindowSpec] = None, -) -> sge.Expression: - if isinstance(op.bins, int): - case_expr = _cut_ops_w_int_bins(op, column, op.bins, window) - else: # Interpret as intervals - case_expr = _cut_ops_w_intervals(op, column, op.bins, window) - return case_expr - - -def _cut_ops_w_int_bins( - op: agg_ops.CutOp, - column: typed_expr.TypedExpr, - bins: int, - window: typing.Optional[window_spec.WindowSpec] = None, -) -> sge.Case: - case_expr = sge.Case() - col_min = apply_window_if_present( - sge.func("MIN", column.expr), window or window_spec.WindowSpec() - ) - col_max = apply_window_if_present( - sge.func("MAX", column.expr), window or window_spec.WindowSpec() - ) - adj: sge.Expression = sge.Sub(this=col_max, expression=col_min) * sge.convert(0.001) - bin_width: sge.Expression = sge.func( - "IEEE_DIVIDE", - sge.Sub(this=col_max, expression=col_min), - sge.convert(bins), - ) - - for this_bin in range(bins): - value: sge.Expression - if op.labels is False: - value = sql.literal(this_bin, dtypes.INT_DTYPE) - elif isinstance(op.labels, typing.Iterable): - value = sql.literal(list(op.labels)[this_bin], dtypes.STRING_DTYPE) - else: - left_adj: sge.Expression = ( - adj if this_bin == 0 and op.right else sge.convert(0) - ) - right_adj: sge.Expression = ( - adj if this_bin == bins - 1 and not op.right else sge.convert(0) - ) - - left: sge.Expression = ( - col_min + sge.convert(this_bin) * bin_width - left_adj - ) - right: sge.Expression = ( - col_min + sge.convert(this_bin + 1) * bin_width + right_adj - ) - if op.right: - left_identifier = sge.Identifier(this="left_exclusive", quoted=True) - right_identifier = sge.Identifier(this="right_inclusive", quoted=True) - else: - left_identifier = sge.Identifier(this="left_inclusive", quoted=True) - right_identifier = sge.Identifier(this="right_exclusive", quoted=True) - - value = sge.Struct( - expressions=[ - sge.PropertyEQ(this=left_identifier, expression=left), - sge.PropertyEQ(this=right_identifier, expression=right), - ] - ) - - condition: sge.Expression - if this_bin == bins - 1: - condition = sge.Is( - this=sge.paren(column.expr, copy=False), - expression=sg.not_(sge.Null(), copy=False), - ) - else: - if op.right: - condition = sge.LTE( - this=column.expr, - expression=(col_min + sge.convert(this_bin + 1) * bin_width), - ) - else: - condition = sge.LT( - this=column.expr, - expression=(col_min + sge.convert(this_bin + 1) * bin_width), - ) - case_expr = case_expr.when(condition, value) - return case_expr - - -def _cut_ops_w_intervals( - op: agg_ops.CutOp, - column: typed_expr.TypedExpr, - bins: typing.Iterable[typing.Tuple[typing.Any, typing.Any]], - window: typing.Optional[window_spec.WindowSpec] = None, -) -> sge.Case: - case_expr = sge.Case() - for this_bin, interval in enumerate(bins): - left: sge.Expression = sql.literal( - interval[0], dtypes.infer_literal_type(interval[0]) - ) - right: sge.Expression = sql.literal( - interval[1], dtypes.infer_literal_type(interval[1]) - ) - condition: sge.Expression - if op.right: - condition = sge.And( - this=sge.GT(this=column.expr, expression=left), - expression=sge.LTE(this=column.expr, expression=right), - ) - else: - condition = sge.And( - this=sge.GTE(this=column.expr, expression=left), - expression=sge.LT(this=column.expr, expression=right), - ) - - value: sge.Expression - if op.labels is False: - value = sql.literal(this_bin, dtypes.INT_DTYPE) - elif isinstance(op.labels, typing.Iterable): - value = sql.literal(list(op.labels)[this_bin], dtypes.STRING_DTYPE) - else: - if op.right: - left_identifier = sge.Identifier(this="left_exclusive", quoted=True) - right_identifier = sge.Identifier(this="right_inclusive", quoted=True) - else: - left_identifier = sge.Identifier(this="left_inclusive", quoted=True) - right_identifier = sge.Identifier(this="right_exclusive", quoted=True) - - value = sge.Struct( - expressions=[ - sge.PropertyEQ(this=left_identifier, expression=left), - sge.PropertyEQ(this=right_identifier, expression=right), - ] - ) - case_expr = case_expr.when(condition, value) - return case_expr - - -@UNARY_OP_REGISTRATION.register(agg_ops.DenseRankOp) -def _( - op: agg_ops.DenseRankOp, - column: typed_expr.TypedExpr, - window: typing.Optional[window_spec.WindowSpec] = None, -) -> sge.Expression: - return apply_window_if_present( - sge.func("DENSE_RANK"), window, include_framing_clauses=False - ) - - -@UNARY_OP_REGISTRATION.register(agg_ops.FirstOp) -def _( - op: agg_ops.FirstOp, - column: typed_expr.TypedExpr, - window: typing.Optional[window_spec.WindowSpec] = None, -) -> sge.Expression: - # FIRST_VALUE in BQ respects nulls by default. - return apply_window_if_present(sge.FirstValue(this=column.expr), window) - - -@UNARY_OP_REGISTRATION.register(agg_ops.FirstNonNullOp) -def _( - op: agg_ops.FirstNonNullOp, - column: typed_expr.TypedExpr, - window: typing.Optional[window_spec.WindowSpec] = None, -) -> sge.Expression: - return apply_window_if_present( - sge.IgnoreNulls(this=sge.FirstValue(this=column.expr)), window - ) - - -@UNARY_OP_REGISTRATION.register(agg_ops.LastOp) -def _( - op: agg_ops.LastOp, - column: typed_expr.TypedExpr, - window: typing.Optional[window_spec.WindowSpec] = None, -) -> sge.Expression: - # LAST_VALUE in BQ respects nulls by default. - return apply_window_if_present(sge.LastValue(this=column.expr), window) - - -@UNARY_OP_REGISTRATION.register(agg_ops.LastNonNullOp) -def _( - op: agg_ops.LastNonNullOp, - column: typed_expr.TypedExpr, - window: typing.Optional[window_spec.WindowSpec] = None, -) -> sge.Expression: - return apply_window_if_present( - sge.IgnoreNulls(this=sge.LastValue(this=column.expr)), window - ) - - -@UNARY_OP_REGISTRATION.register(agg_ops.DiffOp) -def _( - op: agg_ops.DiffOp, - column: typed_expr.TypedExpr, - window: typing.Optional[window_spec.WindowSpec] = None, -) -> sge.Expression: - shift_op_impl = UNARY_OP_REGISTRATION[agg_ops.ShiftOp(0)] - shifted = shift_op_impl(agg_ops.ShiftOp(op.periods), column, window) - if column.dtype == dtypes.BOOL_DTYPE: - return sge.NEQ(this=column.expr, expression=shifted) - - if column.dtype in (dtypes.INT_DTYPE, dtypes.FLOAT_DTYPE): - return sge.Sub(this=column.expr, expression=shifted) - - if column.dtype == dtypes.TIMESTAMP_DTYPE: - return sge.TimestampDiff( - this=column.expr, - expression=shifted, - unit=sge.Identifier(this="MICROSECOND"), - ) - - if column.dtype == dtypes.DATETIME_DTYPE: - return sge.DatetimeDiff( - this=column.expr, - expression=shifted, - unit=sge.Identifier(this="MICROSECOND"), - ) - - if column.dtype == dtypes.DATE_DTYPE: - date_diff = sge.DateDiff( - this=column.expr, expression=shifted, unit=sge.Identifier(this="DAY") - ) - return sge.Cast( - this=sge.Floor(this=date_diff * constants._DAY_TO_MICROSECONDS), - to="INT64", - ) - - raise TypeError(f"Cannot perform diff on type {column.dtype}") - - -@UNARY_OP_REGISTRATION.register(agg_ops.MaxOp) -def _( - op: agg_ops.MaxOp, - column: typed_expr.TypedExpr, - window: typing.Optional[window_spec.WindowSpec] = None, -) -> sge.Expression: - return apply_window_if_present(sge.func("MAX", column.expr), window) - - -@UNARY_OP_REGISTRATION.register(agg_ops.MeanOp) -def _( - op: agg_ops.MeanOp, - column: typed_expr.TypedExpr, - window: typing.Optional[window_spec.WindowSpec] = None, -) -> sge.Expression: - expr = column.expr - if column.dtype == dtypes.BOOL_DTYPE: - expr = sge.Cast(this=expr, to="INT64") - - expr = sge.func("AVG", expr) - - should_floor_result = ( - op.should_floor_result or column.dtype == dtypes.TIMEDELTA_DTYPE - ) - if should_floor_result: - expr = sge.Cast(this=sge.func("FLOOR", expr), to="INT64") - return apply_window_if_present(expr, window) - - -@UNARY_OP_REGISTRATION.register(agg_ops.MedianOp) -def _( - op: agg_ops.MedianOp, - column: typed_expr.TypedExpr, - window: typing.Optional[window_spec.WindowSpec] = None, -) -> sge.Expression: - approx_quantiles = sge.func("APPROX_QUANTILES", column.expr, sge.convert(2)) - return sge.Bracket( - this=approx_quantiles, expressions=[sge.func("OFFSET", sge.convert(1))] - ) - - -@UNARY_OP_REGISTRATION.register(agg_ops.MinOp) -def _( - op: agg_ops.MinOp, - column: typed_expr.TypedExpr, - window: typing.Optional[window_spec.WindowSpec] = None, -) -> sge.Expression: - return apply_window_if_present(sge.func("MIN", column.expr), window) - - -@UNARY_OP_REGISTRATION.register(agg_ops.NuniqueOp) -def _( - op: agg_ops.NuniqueOp, - column: typed_expr.TypedExpr, - window: typing.Optional[window_spec.WindowSpec] = None, -) -> sge.Expression: - return apply_window_if_present( - sge.func("COUNT", sge.Distinct(expressions=[column.expr])), window - ) - - -@UNARY_OP_REGISTRATION.register(agg_ops.PopVarOp) -def _( - op: agg_ops.PopVarOp, - column: typed_expr.TypedExpr, - window: typing.Optional[window_spec.WindowSpec] = None, -) -> sge.Expression: - expr = column.expr - if column.dtype == dtypes.BOOL_DTYPE: - expr = sge.Cast(this=expr, to="INT64") - - expr = sge.func("VAR_POP", expr) - return apply_window_if_present(expr, window) - - -@UNARY_OP_REGISTRATION.register(agg_ops.ProductOp) -def _( - op: agg_ops.ProductOp, - column: typed_expr.TypedExpr, - window: typing.Optional[window_spec.WindowSpec] = None, -) -> sge.Expression: - expr = column.expr - if column.dtype == dtypes.BOOL_DTYPE: - expr = sge.Cast(this=expr, to="INT64") - - # Need to short-circuit as log with zeroes is illegal sql - is_zero = sge.EQ(this=expr, expression=sge.convert(0)) - - # There is no product sql aggregate function, so must implement as a sum of logs, and then - # apply power after. Note, log and power base must be equal! This impl uses natural log. - logs = sge.If( - this=is_zero, - true=sge.convert(0), - false=sge.func("LOG", sge.convert(2), sge.func("ABS", expr)), - ) - logs_sum = apply_window_if_present(sge.func("SUM", logs), window) - magnitude = sge.func("POWER", sge.convert(2), logs_sum) - - # Can't determine sign from logs, so have to determine parity of count of negative inputs - is_negative = ( - sge.Case() - .when( - sge.EQ(this=sge.func("SIGN", expr), expression=sge.convert(-1)), - sge.convert(1), - ) - .else_(sge.convert(0)) - ) - negative_count = apply_window_if_present(sge.func("SUM", is_negative), window) - negative_count_parity = sge.Mod( - this=negative_count, expression=sge.convert(2) - ) # 1 if result should be negative, otherwise 0 - - any_zeroes = apply_window_if_present(sge.func("LOGICAL_OR", is_zero), window) - - float_result = ( - sge.Case() - .when(any_zeroes, sge.convert(0)) - .else_( - sge.Mul( - this=magnitude, - expression=sge.func("POWER", sge.convert(-1), negative_count_parity), - ) - ) - ) - return float_result - - -@UNARY_OP_REGISTRATION.register(agg_ops.QcutOp) -def _( - op: agg_ops.QcutOp, - column: typed_expr.TypedExpr, - window: typing.Optional[window_spec.WindowSpec] = None, -) -> sge.Expression: - percent_ranks_order_by = sge.Ordered(this=column.expr, desc=False) - percent_ranks = apply_window_if_present( - sge.func("PERCENT_RANK"), - window, - include_framing_clauses=False, - order_by_override=[percent_ranks_order_by], - ) - if isinstance(op.quantiles, int): - scaled_rank = percent_ranks * sge.convert(op.quantiles) - # Calculate the 0-based bucket index. - bucket_index = sge.func("CEIL", scaled_rank) - sge.convert(1) - safe_bucket_index = sge.func("GREATEST", bucket_index, 0) - - return sge.If( - this=sge.Is(this=column.expr, expression=sge.Null()), - true=sge.Null(), - false=sge.Cast(this=safe_bucket_index, to="INT64"), - ) - else: - case = sge.Case() - first_quantile = sge.convert(op.quantiles[0]) - case = case.when( - sge.LT(this=percent_ranks, expression=first_quantile), sge.Null() - ) - for bucket_n in range(len(op.quantiles) - 1): - quantile = sge.convert(op.quantiles[bucket_n + 1]) - bucket = sge.convert(bucket_n) - case = case.when(sge.LTE(this=percent_ranks, expression=quantile), bucket) - return case.else_(sge.Null()) - - -@UNARY_OP_REGISTRATION.register(agg_ops.QuantileOp) -def _( - op: agg_ops.QuantileOp, - column: typed_expr.TypedExpr, - window: typing.Optional[window_spec.WindowSpec] = None, -) -> sge.Expression: - expr = column.expr - if column.dtype == dtypes.BOOL_DTYPE: - expr = sge.Cast(this=expr, to="INT64") - - result: sge.Expression = sge.func("PERCENTILE_CONT", expr, sge.convert(op.q)) - if window is None: - # PERCENTILE_CONT is a navigation function, not an aggregate function, - # so it always needs an OVER clause. - result = sge.Window(this=result) - else: - result = apply_window_if_present(result, window) - - if op.should_floor_result or column.dtype == dtypes.TIMEDELTA_DTYPE: - result = sge.Cast(this=sge.func("FLOOR", result), to="INT64") - return result - - -@UNARY_OP_REGISTRATION.register(agg_ops.RankOp) -def _( - op: agg_ops.RankOp, - column: typed_expr.TypedExpr, - window: typing.Optional[window_spec.WindowSpec] = None, -) -> sge.Expression: - return apply_window_if_present( - sge.func("RANK"), window, include_framing_clauses=False - ) - - -@UNARY_OP_REGISTRATION.register(agg_ops.SizeUnaryOp) -def _( - op: agg_ops.SizeUnaryOp, - _, - window: typing.Optional[window_spec.WindowSpec] = None, -) -> sge.Expression: - return apply_window_if_present(sge.func("COUNT", sge.convert(1)), window) - - -@UNARY_OP_REGISTRATION.register(agg_ops.StdOp) -def _( - op: agg_ops.StdOp, - column: typed_expr.TypedExpr, - window: typing.Optional[window_spec.WindowSpec] = None, -) -> sge.Expression: - expr = column.expr - if column.dtype == dtypes.BOOL_DTYPE: - expr = sge.Cast(this=expr, to="INT64") - - expr = sge.func("STDDEV", expr) - if op.should_floor_result or column.dtype == dtypes.TIMEDELTA_DTYPE: - expr = sge.Cast(this=sge.func("FLOOR", expr), to="INT64") - return apply_window_if_present(expr, window) - - -@UNARY_OP_REGISTRATION.register(agg_ops.ShiftOp) -def _( - op: agg_ops.ShiftOp, - column: typed_expr.TypedExpr, - window: typing.Optional[window_spec.WindowSpec] = None, -) -> sge.Expression: - if op.periods == 0: # No-op - return column.expr - if op.periods > 0: - return apply_window_if_present( - sge.func("LAG", column.expr, sge.convert(op.periods)), - window, - include_framing_clauses=False, - ) - return apply_window_if_present( - sge.func("LEAD", column.expr, sge.convert(-op.periods)), - window, - include_framing_clauses=False, - ) - - -@UNARY_OP_REGISTRATION.register(agg_ops.SumOp) -def _( - op: agg_ops.SumOp, - column: typed_expr.TypedExpr, - window: typing.Optional[window_spec.WindowSpec] = None, -) -> sge.Expression: - expr = column.expr - if column.dtype == dtypes.BOOL_DTYPE: - expr = sge.Cast(this=column.expr, to="INT64") - - expr = apply_window_if_present(sge.func("SUM", expr), window) - - # Will be null if all inputs are null. Pandas defaults to zero sum though. - zero = pd.to_timedelta(0) if column.dtype == dtypes.TIMEDELTA_DTYPE else 0 - return sge.func("IFNULL", expr, sql.literal(zero, column.dtype)) - - -@UNARY_OP_REGISTRATION.register(agg_ops.VarOp) -def _( - op: agg_ops.VarOp, - column: typed_expr.TypedExpr, - window: typing.Optional[window_spec.WindowSpec] = None, -) -> sge.Expression: - expr = column.expr - if column.dtype == dtypes.BOOL_DTYPE: - expr = sge.Cast(this=expr, to="INT64") - - expr = sge.func("VAR_SAMP", expr) - return apply_window_if_present(expr, window) diff --git a/bigframes/core/compile/sqlglot/aggregations/windows.py b/bigframes/core/compile/sqlglot/aggregations/windows.py deleted file mode 100644 index cb4a2e70edd..00000000000 --- a/bigframes/core/compile/sqlglot/aggregations/windows.py +++ /dev/null @@ -1,205 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -from __future__ import annotations - -import typing - -import bigframes_vendored.sqlglot.expressions as sge - -import bigframes.core.compile.sqlglot.expression_compiler as expression_compiler -import bigframes.core.expression as ex -import bigframes.core.ordering as ordering_spec -import bigframes.dtypes as dtypes -from bigframes.core import utils, window_spec - - -def apply_window_if_present( - value: sge.Expression, - window: typing.Optional[window_spec.WindowSpec] = None, - include_framing_clauses: bool = True, - order_by_override: typing.Optional[typing.List[sge.Ordered]] = None, -) -> sge.Expression: - if window is None: - return value - - if window.is_row_bounded and not window.ordering: - raise ValueError("No ordering provided for ordered analytic function") - elif ( - not window.is_row_bounded - and not window.is_range_bounded - and not window.ordering - ): - # Unbound grouping window. - order_by = None - elif window.is_range_bounded: - order_by = get_window_order_by((window.ordering[0],)) - order_by = remove_null_ordering_for_range_windows(order_by) - else: - order_by = get_window_order_by(window.ordering) - - order = None - if order_by_override is not None and len(order_by_override) > 0: - order = sge.Order(expressions=order_by_override) - elif order_by: - order = sge.Order(expressions=order_by) - - group_by = ( - [_compile_group_by_key(key) for key in window.grouping_keys] - if window.grouping_keys - else None - ) - - # This is the key change. Don't create a spec for the default window frame - # if there's no ordering. This avoids generating an `ORDER BY NULL` clause. - if window.is_unbounded and not order: - return sge.Window(this=value, partition_by=group_by) - - if window.is_unbounded and not include_framing_clauses: - return sge.Window(this=value, partition_by=group_by, order=order) - - kind = ( - "RANGE" if isinstance(window.bounds, window_spec.RangeWindowBounds) else "ROWS" - ) - - start: typing.Union[int, float, None] = None - end: typing.Union[int, float, None] = None - if isinstance(window.bounds, window_spec.RangeWindowBounds): - if window.bounds.start is not None: - start = utils.timedelta_to_micros(window.bounds.start) - if window.bounds.end is not None: - end = utils.timedelta_to_micros(window.bounds.end) - elif window.bounds: - start = window.bounds.start - end = window.bounds.end - - start_value, start_side = _get_window_bounds(start, is_preceding=True) - end_value, end_side = _get_window_bounds(end, is_preceding=False) - - spec = sge.WindowSpec( - kind=kind, - start=start_value, - start_side=start_side, - end=end_value, - end_side=end_side, - over="OVER", - ) - - return sge.Window(this=value, partition_by=group_by, order=order, spec=spec) - - -def get_window_order_by( - ordering: typing.Tuple[ordering_spec.OrderingExpression, ...], - override_null_order: bool = False, -) -> typing.Optional[tuple[sge.Ordered, ...]]: - """Returns the SQL order by clause for a window specification. - Args: - ordering (Tuple[ordering_spec.OrderingExpression, ...]): - A tuple of ordering specification objects. - override_null_order (bool): - If True, overrides BigQuery's default null ordering behavior, which - is sometimes incompatible with ordered aggregations. The generated SQL - will include extra expressions to correctly enforce NULL FIRST/LAST. - """ - if not ordering: - return None - - order_by = [] - for ordering_spec_item in ordering: - expr = expression_compiler.expression_compiler.compile_expression( - ordering_spec_item.scalar_expression - ) - desc = not ordering_spec_item.direction.is_ascending - nulls_first = not ordering_spec_item.na_last - - if override_null_order: - is_null_expr = sge.Is(this=expr, expression=sge.Null()) - if nulls_first and desc: - order_by.append( - sge.Ordered( - this=is_null_expr, - desc=desc, - nulls_first=nulls_first, - ) - ) - elif (not nulls_first) and (not desc): - order_by.append( - sge.Ordered( - this=is_null_expr, - desc=desc, - nulls_first=nulls_first, - ) - ) - - order_by.append( - sge.Ordered( - this=expr, - desc=desc, - nulls_first=nulls_first, - ) - ) - return tuple(order_by) - - -def remove_null_ordering_for_range_windows( - order_by: typing.Optional[tuple[sge.Ordered, ...]], -) -> typing.Optional[tuple[sge.Ordered, ...]]: - """Removes NULL FIRST/LAST from ORDER BY expressions in RANGE windows. - Here's the support matrix: - ✅ sum(x) over (order by y desc nulls last) - 🚫 sum(x) over (order by y asc nulls last) - ✅ sum(x) over (order by y asc nulls first) - 🚫 sum(x) over (order by y desc nulls first) - """ - if order_by is None: - return None - - new_order_by = [] - for key in order_by: - kargs = key.args - if kargs.get("desc") is True and kargs.get("nulls_first", False): - kargs["nulls_first"] = False - elif kargs.get("desc") is False and not kargs.setdefault("nulls_first", True): - kargs["nulls_first"] = True - new_order_by.append(sge.Ordered(**kargs)) - return tuple(new_order_by) - - -def _get_window_bounds( - value, is_preceding: bool -) -> tuple[typing.Union[str, sge.Expression], typing.Optional[str]]: - """Compiles a single boundary value into its SQL components.""" - if value is None: - side = "PRECEDING" if is_preceding else "FOLLOWING" - return "UNBOUNDED", side - - if value == 0: - return "CURRENT ROW", None - - side = "PRECEDING" if value < 0 else "FOLLOWING" - return sge.convert(abs(value)), side - - -def _compile_group_by_key(key: ex.Expression) -> sge.Expression: - expr = expression_compiler.expression_compiler.compile_expression(key) - # The group_by keys has been rewritten by bind_schema_to_node - assert key.is_scalar_expr and key.is_resolved - - # Some types need to be converted to another type to enable groupby - if key.output_type == dtypes.FLOAT_DTYPE: - expr = sge.Cast(this=expr, to="STRING") - elif key.output_type == dtypes.GEO_DTYPE: - expr = sge.func("ST_ASBINARY", expr) - elif key.output_type == dtypes.JSON_DTYPE: - expr = sge.func("TO_JSON_STRING", expr) - return expr diff --git a/bigframes/core/compile/sqlglot/compiler.py b/bigframes/core/compile/sqlglot/compiler.py deleted file mode 100644 index 393d10ec825..00000000000 --- a/bigframes/core/compile/sqlglot/compiler.py +++ /dev/null @@ -1,378 +0,0 @@ -# Copyright 2023 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -from __future__ import annotations - -import dataclasses -import functools -import typing - -import bigframes_vendored.sqlglot.expressions as sge - -import bigframes.core.compile.sqlglot.aggregate_compiler as aggregate_compiler -import bigframes.core.compile.sqlglot.expression_compiler as expression_compiler -import bigframes.core.ordering as bf_ordering -from bigframes import dtypes -from bigframes.core import ( - expression, - guid, - identifiers, - nodes, - pyarrow_utils, - rewrite, - sql_nodes, -) -from bigframes.core.compile import configs -from bigframes.core.compile.sqlglot import sql, sqlglot_ir -from bigframes.core.compile.sqlglot.aggregations import windows -from bigframes.core.compile.sqlglot.expressions import typed_expr -from bigframes.core.logging import data_types as data_type_logger -from bigframes.core.rewrite import schema_binding - - -def compile_sql(request: configs.CompileRequest) -> configs.CompileResult: - """Compiles a BigFrameNode according to the request into SQL using SQLGlot.""" - - output_names = tuple((expression.DerefOp(id), id.sql) for id in request.node.ids) - result_node = nodes.ResultNode( - request.node, - output_cols=output_names, - limit=request.peek_count, - ) - if request.sort_rows: - # Can only pullup slice if we are doing ORDER BY in outermost SELECT - # Need to do this before replacing unsupported ops, as that will rewrite slice ops - result_node = rewrite.pull_up_limits(result_node) - result_node = typing.cast(nodes.ResultNode, _replace_unsupported_ops(result_node)) - result_node = typing.cast( - nodes.ResultNode, result_node.bottom_up(rewrite.simplify_join) - ) - # prune before pulling up order to avoid unnnecessary row_number() ops - result_node = typing.cast(nodes.ResultNode, rewrite.column_pruning(result_node)) - result_node = rewrite.defer_order( - result_node, output_hidden_row_keys=request.materialize_all_order_keys - ) - if request.sort_rows: - result_node = typing.cast(nodes.ResultNode, rewrite.column_pruning(result_node)) - encoded_type_refs = data_type_logger.encode_type_refs(result_node) - # TODO: Extract CTEs earlier - result_node = typing.cast(nodes.ResultNode, rewrite.extract_ctes(result_node)) - sql = _compile_result_node(result_node) - return configs.CompileResult( - sql, - result_node.schema.to_bigquery(), - result_node.order_by, - encoded_type_refs, - ) - - ordering: typing.Optional[bf_ordering.RowOrdering] = result_node.order_by - result_node = dataclasses.replace(result_node, order_by=None) - result_node = typing.cast(nodes.ResultNode, rewrite.column_pruning(result_node)) - encoded_type_refs = data_type_logger.encode_type_refs(result_node) - # TODO: Extract CTEs earlier - result_node = typing.cast(nodes.ResultNode, rewrite.extract_ctes(result_node)) - sql = _compile_result_node(result_node) - # Return the ordering iff no extra columns are needed to define the row order - if ordering is not None: - output_order = ( - ordering if ordering.referenced_columns.issubset(result_node.ids) else None - ) - assert (not request.materialize_all_order_keys) or (output_order is not None) - return configs.CompileResult( - sql, result_node.schema.to_bigquery(), output_order, encoded_type_refs - ) - - -def _remap_variables( - node: nodes.ResultNode, uid_gen: guid.SequentialUIDGenerator -) -> nodes.ResultNode: - """Remaps `ColumnId`s in the BFET of a `ResultNode` to produce deterministic UIDs.""" - - result_node, _ = rewrite.remap_variables( - node, map(identifiers.ColumnId, uid_gen.get_uid_stream("bfcol_")) - ) - result_node.validate_tree() - return typing.cast(nodes.ResultNode, result_node) - - -def _compile_result_node(root: nodes.ResultNode) -> str: - # Create UIDs to standardize variable names and ensure consistent compilation - # of nodes using the same generator. - uid_gen = guid.SequentialUIDGenerator() - root = _remap_variables(root, uid_gen) - # Remap variables creates too mayn new - # root = rewrite.select_pullup(root, prefer_source_names=False) - root = typing.cast(nodes.ResultNode, rewrite.defer_selection(root)) - - # Have to bind schema as the final step before compilation. - # Probably, should defer even further - root = typing.cast(nodes.ResultNode, schema_binding.bind_schema_to_tree(root)) - - # TODO: Bake all IDs in tree, stop passing uid_gen to emitters - sqlglot_ir_obj = compile_node(rewrite.as_sql_nodes(root, uid_gen), uid_gen) - return sqlglot_ir_obj.sql - - -def compile_node( - node: nodes.BigFrameNode, uid_gen: guid.SequentialUIDGenerator -) -> sqlglot_ir.SQLGlotIR: - """Compiles the given BigFrameNode from bottem-up into SQLGlotIR.""" - bf_to_sqlglot: dict[nodes.BigFrameNode, sqlglot_ir.SQLGlotIR] = {} - child_results: tuple[sqlglot_ir.SQLGlotIR, ...] = () - for current_node in list(node.iter_nodes_topo()): - if current_node.child_nodes == (): - # For leaf node, generates a dumpy child to pass the UID generator. - child_results = tuple([sqlglot_ir.SQLGlotIR.empty(uid_gen=uid_gen)]) - else: - # Child nodes should have been compiled in the reverse topological order. - child_results = tuple( - bf_to_sqlglot[child] for child in current_node.child_nodes - ) - result = _compile_node(current_node, *child_results) - bf_to_sqlglot[current_node] = result - - return bf_to_sqlglot[node] - - -@functools.singledispatch -def _compile_node( - node: nodes.BigFrameNode, *compiled_children: sqlglot_ir.SQLGlotIR -) -> sqlglot_ir.SQLGlotIR: - """Defines transformation but isn't cached, always use compile_node instead""" - raise ValueError(f"Can't compile unrecognized node: {node}") - - -@_compile_node.register -def compile_sql_select(node: sql_nodes.SqlSelectNode, child: sqlglot_ir.SQLGlotIR): - ordering_cols = tuple( - sge.Ordered( - this=expression_compiler.expression_compiler.compile_expression( - ordering.scalar_expression - ), - desc=ordering.direction.is_ascending is False, - nulls_first=ordering.na_last is False, - ) - for ordering in node.sorting - ) - - projected_cols: tuple[tuple[str, sge.Expression], ...] = tuple() - if not node.is_star_selection: - projected_cols = tuple( - ( - cdef.id.sql, - expression_compiler.expression_compiler.compile_expression( - cdef.expression - ), - ) - for cdef in node.selections - ) - - sge_predicates = tuple( - expression_compiler.expression_compiler.compile_expression(expression) - for expression in node.predicates - ) - - return child.select(projected_cols, sge_predicates, ordering_cols, node.limit) - - -@_compile_node.register -def compile_readlocal( - node: nodes.ReadLocalNode, child: sqlglot_ir.SQLGlotIR -) -> sqlglot_ir.SQLGlotIR: - pa_table = node.local_data_source.data - pa_table = pa_table.select([item.source_id for item in node.scan_list.items]) - pa_table = pa_table.rename_columns([item.id.sql for item in node.scan_list.items]) - - offsets = node.offsets_col.sql if node.offsets_col else None - if offsets: - pa_table = pyarrow_utils.append_offsets(pa_table, offsets) - - return sqlglot_ir.SQLGlotIR.from_pyarrow( - pa_table, node.schema, uid_gen=child.uid_gen - ) - - -@_compile_node.register -def compile_readtable(node: sql_nodes.SqlDataSource, child: sqlglot_ir.SQLGlotIR): - table_obj = node.source.table - columns = () if node.is_star_selection else node.source.schema.names - return sqlglot_ir.SQLGlotIR.from_table( - table_obj.project_id, - table_obj.dataset_id, - table_obj.table_id, - uid_gen=child.uid_gen, - columns=columns, - sql_predicate=node.source.sql_predicate, - system_time=node.source.at_time, - ) - - -@_compile_node.register -def compile_join( - node: nodes.JoinNode, left: sqlglot_ir.SQLGlotIR, right: sqlglot_ir.SQLGlotIR -) -> sqlglot_ir.SQLGlotIR: - conditions = tuple( - ( - typed_expr.TypedExpr( - expression_compiler.expression_compiler.compile_expression(left_expr), - left_expr.output_type, - ), - typed_expr.TypedExpr( - expression_compiler.expression_compiler.compile_expression(right_expr), - right_expr.output_type, - ), - ) - for left_expr, right_expr in node.conditions - ) - - return left.join( - right, - join_type=node.type, - conditions=conditions, - joins_nulls=node.joins_nulls, - ) - - -@_compile_node.register -def compile_isin_join( - node: nodes.InNode, left: sqlglot_ir.SQLGlotIR, right: sqlglot_ir.SQLGlotIR -) -> sqlglot_ir.SQLGlotIR: - right_field = node.right_child.fields[0] - conditions = ( - typed_expr.TypedExpr( - expression_compiler.expression_compiler.compile_expression(node.left_col), - node.left_col.output_type, - ), - typed_expr.TypedExpr( - expression_compiler.expression_compiler.compile_expression( - expression.DerefOp(right_field.id) - ), - right_field.dtype, - ), - ) - - return left.isin_join( - right, - indicator_col=node.indicator_col.sql, - conditions=conditions, - joins_nulls=node.joins_nulls, - ) - - -@_compile_node.register -def compile_cte_ref_node(node: sql_nodes.SqlCteRefNode, child: sqlglot_ir.SQLGlotIR): - return sqlglot_ir.SQLGlotIR.from_cte_ref( - node.cte_name, - uid_gen=child.uid_gen, - ) - - -@_compile_node.register -def compile_with_ctes_node( - node: sql_nodes.SqlWithCtesNode, - child: sqlglot_ir.SQLGlotIR, - *ctes: sqlglot_ir.SQLGlotIR, -): - return child.with_ctes(tuple(zip(node.cte_names, ctes))) - - -@_compile_node.register -def compile_concat( - node: nodes.ConcatNode, *children: sqlglot_ir.SQLGlotIR -) -> sqlglot_ir.SQLGlotIR: - assert len(children) >= 1 - uid_gen = children[0].uid_gen - - # BigQuery `UNION` query takes the column names from the first `SELECT` clause. - default_output_ids = [field.id.sql for field in node.child_nodes[0].fields] - output_aliases = [ - (default_output_id, output_id.sql) - for default_output_id, output_id in zip(default_output_ids, node.output_ids) - ] - - return sqlglot_ir.SQLGlotIR.from_union( - [child.expr.as_select_all() for child in children], - output_aliases=output_aliases, - uid_gen=uid_gen, - ) - - -@_compile_node.register -def compile_explode( - node: nodes.ExplodeNode, child: sqlglot_ir.SQLGlotIR -) -> sqlglot_ir.SQLGlotIR: - offsets_col = node.offsets_col.sql if (node.offsets_col is not None) else None - columns = tuple(ref.id.sql for ref in node.column_ids) - return child.explode(columns, offsets_col) - - -@_compile_node.register -def compile_fromrange( - node: nodes.FromRangeNode, start: sqlglot_ir.SQLGlotIR, end: sqlglot_ir.SQLGlotIR -) -> sqlglot_ir.SQLGlotIR: - start_col_id = node.start.fields[0].id - end_col_id = node.end.fields[0].id - - start_expr = expression_compiler.expression_compiler.compile_expression( - expression.DerefOp(start_col_id) - ) - end_expr = expression_compiler.expression_compiler.compile_expression( - expression.DerefOp(end_col_id) - ) - step_expr = sql.literal(node.step, dtypes.INT_DTYPE) - - return start.resample(end, node.output_id.sql, start_expr, end_expr, step_expr) - - -@_compile_node.register -def compile_random_sample( - node: nodes.RandomSampleNode, child: sqlglot_ir.SQLGlotIR -) -> sqlglot_ir.SQLGlotIR: - return child.sample(node.fraction) - - -@_compile_node.register -def compile_aggregate( - node: nodes.AggregateNode, child: sqlglot_ir.SQLGlotIR -) -> sqlglot_ir.SQLGlotIR: - # The BigQuery ordered aggregation cannot support for NULL FIRST/LAST, - # so we need to add extra expressions to enforce the null ordering. - ordering_cols = windows.get_window_order_by(node.order_by, override_null_order=True) - aggregations: tuple[tuple[str, sge.Expression], ...] = tuple( - ( - id.sql, - aggregate_compiler.compile_aggregate( - agg, order_by=ordering_cols if ordering_cols else () - ), - ) - for agg, id in node.aggregations - ) - by_cols: tuple[sge.Expression, ...] = tuple( - expression_compiler.expression_compiler.compile_expression(by_col) - for by_col in node.by_column_ids - ) - - dropna_cols = [] - if node.dropna: - for key, by_col in zip(node.by_column_ids, by_cols): - if node.child.field_by_id[key.id].nullable: - dropna_cols.append(by_col) - - return child.aggregate(aggregations, by_cols, tuple(dropna_cols)) - - -def _replace_unsupported_ops(node: nodes.BigFrameNode): - node = nodes.bottom_up(node, rewrite.rewrite_slice) - node = nodes.bottom_up(node, rewrite.rewrite_range_rolling) - node = nodes.bottom_up(node, rewrite.lower_udfs) - return node diff --git a/bigframes/core/compile/sqlglot/expression_compiler.py b/bigframes/core/compile/sqlglot/expression_compiler.py deleted file mode 100644 index b412249a39f..00000000000 --- a/bigframes/core/compile/sqlglot/expression_compiler.py +++ /dev/null @@ -1,236 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -from __future__ import annotations - -import functools -import typing - -import bigframes_vendored.sqlglot.expressions as sge - -import bigframes.core.agg_expressions as agg_exprs -import bigframes.core.expression as ex -import bigframes.operations as ops -from bigframes.core.compile.sqlglot import sql -from bigframes.core.compile.sqlglot.expressions.typed_expr import TypedExpr - - -class ExpressionCompiler: - # Mapping of operation name to implemenations - _registry: dict[ - str, - typing.Callable[[typing.Sequence[TypedExpr], ops.RowOp], sge.Expression], - ] = {} - - # A set of SQLGlot classes that may need to be parenthesized - SQLGLOT_NEEDS_PARENS = { - # Numeric operations - sge.Add, - sge.Sub, - sge.Mul, - sge.Div, - sge.Mod, - sge.Pow, - # Comparison operations - sge.GTE, - sge.GT, - sge.LTE, - sge.LT, - sge.EQ, - sge.NEQ, - sge.Like, - sge.RegexpLike, - sge.In, - sge.Between, - # Logical operations - sge.And, - sge.Or, - sge.Xor, - # Bitwise operations - sge.BitwiseAnd, - sge.BitwiseOr, - sge.BitwiseXor, - sge.BitwiseLeftShift, - sge.BitwiseRightShift, - sge.BitwiseNot, - # Other operations - sge.Is, - } - - @functools.singledispatchmethod - def compile_expression( - self, - expression: ex.Expression, - ) -> sge.Expression: - """Compiles BigFrames scalar expression into SQLGlot expression.""" - raise NotImplementedError(f"Unrecognized expression: {expression}") - - @compile_expression.register - def _(self, expr: ex.DerefOp) -> sge.Expression: - return sge.Column(this=sge.to_identifier(expr.id.sql, quoted=True)) - - @compile_expression.register - def _(self, expr: ex.ScalarConstantExpression) -> sge.Expression: - return sql.literal(expr.value, expr.dtype) - - @compile_expression.register - def _(self, expr: agg_exprs.WindowExpression) -> sge.Expression: - import bigframes.core.compile.sqlglot.aggregate_compiler as agg_compile - - return agg_compile.compile_analytic( - expr.analytic_expr, - expr.window, - ) - - @compile_expression.register - def _(self, expr: ex.OpExpression) -> sge.Expression: - inputs = tuple( - TypedExpr(self.compile_expression(sub_expr), sub_expr.output_type) - if not isinstance(sub_expr, ex.OmittedArg) - else TypedExpr(sge.Null(), None, is_omitted=True) - for sub_expr in expr.inputs - ) - return self.compile_row_op(expr.op, inputs) - - def compile_row_op( - self, op: ops.RowOp, inputs: typing.Sequence[TypedExpr] - ) -> sge.Expression: - impl = self._registry[op.name] - return impl(inputs, op) - - def register_unary_op( - self, - op_ref: typing.Union[ops.UnaryOp, type[ops.UnaryOp]], - pass_op: bool = False, - ): - """ - Decorator to register a unary op implementation. - - Args: - op_ref (UnaryOp or UnaryOp type): - Class or instance of operator that is implemented by the decorated function. - pass_op (bool): - Set to true if implementation takes the operator object as the last argument. - This is needed for parameterized ops where parameters are part of op object. - """ - key = typing.cast(str, op_ref.name) - - def decorator(impl: typing.Callable[..., sge.Expression]): - def normalized_impl(args: typing.Sequence[TypedExpr], op: ops.RowOp): - if pass_op: - return impl(args[0], op) - else: - return impl(args[0]) - - self._register(key, normalized_impl) - return impl - - return decorator - - def register_binary_op( - self, - op_ref: typing.Union[ops.BinaryOp, type[ops.BinaryOp]], - pass_op: bool = False, - ): - """ - Decorator to register a binary op implementation. - - Args: - op_ref (BinaryOp or BinaryOp type): - Class or instance of operator that is implemented by the decorated function. - pass_op (bool): - Set to true if implementation takes the operator object as the last argument. - This is needed for parameterized ops where parameters are part of op object. - """ - key = typing.cast(str, op_ref.name) - - def decorator(impl: typing.Callable[..., sge.Expression]): - def normalized_impl(args: typing.Sequence[TypedExpr], op: ops.RowOp): - left = self._add_parentheses(args[0]) - right = self._add_parentheses(args[1]) - if pass_op: - return impl(left, right, op) - else: - return impl(left, right) - - self._register(key, normalized_impl) - return impl - - return decorator - - def register_ternary_op( - self, op_ref: typing.Union[ops.TernaryOp, type[ops.TernaryOp]] - ): - """ - Decorator to register a ternary op implementation. - - Args: - op_ref (TernaryOp or TernaryOp type): - Class or instance of operator that is implemented by the decorated function. - """ - key = typing.cast(str, op_ref.name) - - def decorator(impl: typing.Callable[..., sge.Expression]): - def normalized_impl(args: typing.Sequence[TypedExpr], op: ops.RowOp): - return impl(args[0], args[1], args[2]) - - self._register(key, normalized_impl) - return impl - - return decorator - - def register_nary_op( - self, op_ref: typing.Union[ops.NaryOp, type[ops.NaryOp]], pass_op: bool = False - ): - """ - Decorator to register a nary op implementation. - - Args: - op_ref (NaryOp or NaryOp type): - Class or instance of operator that is implemented by the decorated function. - pass_op (bool): - Set to true if implementation takes the operator object as the last argument. - This is needed for parameterized ops where parameters are part of op object. - """ - key = typing.cast(str, op_ref.name) - - def decorator(impl: typing.Callable[..., sge.Expression]): - def normalized_impl(args: typing.Sequence[TypedExpr], op: ops.RowOp): - if pass_op: - return impl(*args, op=op) - else: - return impl(*args) - - self._register(key, normalized_impl) - return impl - - return decorator - - def _register( - self, - op_name: str, - impl: typing.Callable[[typing.Sequence[TypedExpr], ops.RowOp], sge.Expression], - ): - if op_name in self._registry: - raise ValueError(f"Operation name {op_name} already registered") - self._registry[op_name] = impl - - @classmethod - def _add_parentheses(cls, expr: TypedExpr) -> TypedExpr: - if type(expr.expr) in cls.SQLGLOT_NEEDS_PARENS: - return TypedExpr(sge.paren(expr.expr, copy=False), expr.dtype) - return expr - - -# Singleton compiler -expression_compiler = ExpressionCompiler() diff --git a/bigframes/core/compile/sqlglot/expressions/__init__.py b/bigframes/core/compile/sqlglot/expressions/__init__.py deleted file mode 100644 index f42d5c7d99e..00000000000 --- a/bigframes/core/compile/sqlglot/expressions/__init__.py +++ /dev/null @@ -1,21 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -"""Expression implementations for the SQLGlot-based compiler. - -This directory structure should reflect the same layout as the -`bigframes/operations` directory where the expressions are defined. - -Prefer a few ops per file to keep file sizes manageable for text editors and LLMs. -""" diff --git a/bigframes/core/compile/sqlglot/expressions/ai_ops.py b/bigframes/core/compile/sqlglot/expressions/ai_ops.py deleted file mode 100644 index d092f662f0f..00000000000 --- a/bigframes/core/compile/sqlglot/expressions/ai_ops.py +++ /dev/null @@ -1,161 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from __future__ import annotations - -from dataclasses import asdict -from typing import Any - -import bigframes_vendored.sqlglot.expressions as sge - -from bigframes import operations as ops -from bigframes.core.compile.sqlglot import expression_compiler -from bigframes.core.compile.sqlglot.expressions.typed_expr import TypedExpr - -register_nary_op = expression_compiler.expression_compiler.register_nary_op -register_binary_op = expression_compiler.expression_compiler.register_binary_op -register_unary_op = expression_compiler.expression_compiler.register_unary_op - - -@register_nary_op(ops.AIGenerate, pass_op=True) -def _(*exprs: TypedExpr, op: ops.AIGenerate) -> sge.Expression: - args = [_construct_prompt(exprs, op.prompt_context)] + _construct_named_args(op) - - return sge.func("AI.GENERATE", *args) - - -@register_nary_op(ops.AIGenerateBool, pass_op=True) -def _(*exprs: TypedExpr, op: ops.AIGenerateBool) -> sge.Expression: - args = [_construct_prompt(exprs, op.prompt_context)] + _construct_named_args(op) - - return sge.func("AI.GENERATE_BOOL", *args) - - -@register_nary_op(ops.AIGenerateInt, pass_op=True) -def _(*exprs: TypedExpr, op: ops.AIGenerateInt) -> sge.Expression: - args = [_construct_prompt(exprs, op.prompt_context)] + _construct_named_args(op) - - return sge.func("AI.GENERATE_INT", *args) - - -@register_nary_op(ops.AIGenerateDouble, pass_op=True) -def _(*exprs: TypedExpr, op: ops.AIGenerateDouble) -> sge.Expression: - args = [_construct_prompt(exprs, op.prompt_context)] + _construct_named_args(op) - - return sge.func("AI.GENERATE_DOUBLE", *args) - - -@register_unary_op(ops.AIEmbed, pass_op=True) -def _(expr: TypedExpr, op: ops.AIEmbed) -> sge.Expression: - args: list[Any] = [expr.expr] + _construct_named_args(op) - - return sge.func("AI.EMBED", *args) - - -@register_nary_op(ops.AIIf, pass_op=True) -def _(*exprs: TypedExpr, op: ops.AIIf) -> sge.Expression: - args = [_construct_prompt(exprs, op.prompt_context)] + _construct_named_args(op) - - return sge.func("AI.IF", *args) - - -@register_nary_op(ops.AIClassify, pass_op=True) -def _(*exprs: TypedExpr, op: ops.AIClassify) -> sge.Expression: - args = [ - _construct_prompt(exprs, op.prompt_context, param_name="input"), - ] + _construct_named_args(op) - - return sge.func("AI.CLASSIFY", *args) - - -@register_nary_op(ops.AIScore, pass_op=True) -def _(*exprs: TypedExpr, op: ops.AIScore) -> sge.Expression: - args = [_construct_prompt(exprs, op.prompt_context)] + _construct_named_args(op) - - return sge.func("AI.SCORE", *args) - - -@register_binary_op(ops.AISimilarity, pass_op=True) -def _(content1: TypedExpr, content2: TypedExpr, op: ops.AISimilarity) -> sge.Expression: - args = [ - sge.Kwarg(this="content1", expression=content1.expr), - sge.Kwarg(this="content2", expression=content2.expr), - ] + _construct_named_args(op) - - return sge.func("AI.SIMILARITY", *args) - - -def _construct_prompt( - exprs: tuple[TypedExpr, ...], - prompt_context: tuple[str | None, ...], - param_name: str = "prompt", -) -> sge.Kwarg: - prompt: list[str | sge.Expression] = [] - column_ref_idx = 0 - - for elem in prompt_context: - if elem is None: - prompt.append(exprs[column_ref_idx].expr) - column_ref_idx += 1 - else: - prompt.append(sge.Literal.string(elem)) - - # Need Struct rather than tuple syntax, as tuple syntax is ambiguous for single arg - return sge.Kwarg(this=param_name, expression=sge.Struct(expressions=prompt)) - - -def _construct_named_args(op: ops.ScalarOp) -> list[sge.Kwarg]: - args = [] - - op_args = asdict(op) - - for field, value in op_args.items(): - if value is None or field == "prompt_context": - continue - - if field == "categories": - category_literals = [sge.Literal.string(cat) for cat in value] - categories_arg = sge.Kwarg( - this="categories", expression=sge.array(*category_literals) - ) - args.append(categories_arg) - elif field == "model_params": - # model_params is a JSON string, so we need to use the JSON function to pass it as a named argument. - args.append( - sge.Kwarg( - this="model_params", - # sge.JSON requires the SQLGlot version to be at least 25.18.0 - # PARSE_JSON won't work as the function requires a JSON literal. - expression=sge.JSON(this=sge.Literal.string(value)), - ) - ) - elif field == "examples": - example_expressions = [] - for key, val in value: - if isinstance(val, (list, tuple)): - val_expr: sge.Array | sge.Literal = sge.array( - *[sge.Literal.string(v) for v in val] - ) - else: - val_expr = sge.Literal.string(val) - example_expressions.append( - sge.Tuple(expressions=[sge.Literal.string(key), val_expr]) - ) - args.append( - sge.Kwarg(this=field, expression=sge.array(*example_expressions)) - ) - else: - args.append(sge.Kwarg(this=field, expression=sge.convert(value))) - - return args diff --git a/bigframes/core/compile/sqlglot/expressions/array_ops.py b/bigframes/core/compile/sqlglot/expressions/array_ops.py deleted file mode 100644 index 56ffbf24cb3..00000000000 --- a/bigframes/core/compile/sqlglot/expressions/array_ops.py +++ /dev/null @@ -1,193 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from __future__ import annotations - -import typing - -import bigframes_vendored.sqlglot as sg -import bigframes_vendored.sqlglot.expressions as sge -import pandas as pd -import pyarrow as pa - -import bigframes.core.compile.sqlglot.expression_compiler as expression_compiler -import bigframes.dtypes as dtypes -from bigframes import operations as ops -from bigframes.core.compile.sqlglot.expressions.string_ops import ( - string_index, - string_slice, -) -from bigframes.core.compile.sqlglot.expressions.typed_expr import TypedExpr - -register_unary_op = expression_compiler.expression_compiler.register_unary_op -register_nary_op = expression_compiler.expression_compiler.register_nary_op - - -@register_unary_op(ops.GetItemOp, pass_op=True) -def _(expr: TypedExpr, op: ops.GetItemOp) -> sge.Expression: - if dtypes.is_struct_like(expr.dtype): - if isinstance(op.key, str): - name = op.key - else: - pa_type = typing.cast(pd.ArrowDtype, expr.dtype) - pa_struct_type = typing.cast(pa.StructType, pa_type.pyarrow_dtype) - name = pa_struct_type.field(op.key).name - - return sge.Column( - this=sge.to_identifier(name, quoted=True), - catalog=expr.expr, - ) - elif dtypes.is_array_like(expr.dtype): - return sge.Bracket( - this=expr.expr, - expressions=[sge.convert(op.key)], - safe=True, - offset=False, - ) - elif expr.dtype == dtypes.STRING_DTYPE: - return string_index(expr, typing.cast(int, op.key)) - else: - raise TypeError(f"Cannot subscript input of type {expr.dtype}") - - -@register_nary_op(ops.DynamicGetItemOp) # type: ignore[arg-type] -def _(left: TypedExpr, right: TypedExpr) -> sge.Expression: - if dtypes.is_array_like(left.dtype): - return sge.Bracket( - this=left.expr, - expressions=[right.expr], - safe=True, - offset=False, - ) - elif left.dtype == dtypes.STRING_DTYPE: - start_expr = sge.Add(this=right.expr, expression=sge.convert(1)) - sub_str = sge.Substring( - this=left.expr, - start=start_expr, - length=sge.convert(1), - ) - return sge.If( - this=sge.NEQ(this=sub_str, expression=sge.convert("")), - true=sub_str, - false=sge.Null(), - ) - else: - raise TypeError(f"Cannot dynamically subscript input of type {left.dtype}") - - -@register_unary_op(ops.ArrayReduceOp, pass_op=True) -def _(expr: TypedExpr, op: ops.ArrayReduceOp) -> sge.Expression: - sub_expr = sg.to_identifier("bf_arr_reduce_uid") - sub_type = dtypes.get_array_inner_type(expr.dtype) - - if op.aggregation.order_independent: - from bigframes.core.compile.sqlglot.aggregations import unary_compiler - - agg_expr = unary_compiler.compile(op.aggregation, TypedExpr(sub_expr, sub_type)) - else: - from bigframes.core.compile.sqlglot.aggregations import ordered_unary_compiler - - agg_expr = ordered_unary_compiler.compile( - op.aggregation, TypedExpr(sub_expr, sub_type) - ) - - return ( - sge.select(agg_expr) - .from_( - sge.Unnest( - expressions=[expr.expr], - alias=sge.TableAlias(columns=[sub_expr]), - ) - ) - .subquery() - ) - - -@register_unary_op(ops.ArrayMapOp, pass_op=True) -def _(expr: TypedExpr, op: ops.ArrayMapOp) -> sge.Expression: - sub_expr = sg.to_identifier("bf_arr_map_uid") - sub_type = dtypes.get_array_inner_type(expr.dtype) - - # TODO: Expression should be provided instead of invoking compiler manually - map_expr = expression_compiler.expression_compiler.compile_row_op( - op.map_op, (TypedExpr(sub_expr, sub_type),) - ) - - return sge.array( - sge.select(map_expr) - .from_( - sge.Unnest( - expressions=[expr.expr], - alias=sge.TableAlias(columns=[sub_expr]), - ) - ) - .subquery() - ) - - -@register_unary_op(ops.ArraySliceOp, pass_op=True) -def _(expr: TypedExpr, op: ops.ArraySliceOp) -> sge.Expression: - if expr.dtype == dtypes.STRING_DTYPE: - return string_slice(expr, op.start, op.stop) - else: - return _array_slice(expr, op) - - -@register_unary_op(ops.ArrayToStringOp, pass_op=True) -def _(expr: TypedExpr, op: ops.ArrayToStringOp) -> sge.Expression: - return sge.ArrayToString(this=expr.expr, expression=sge.convert(op.delimiter)) - - -@register_nary_op(ops.ToArrayOp) -def _(*exprs: TypedExpr) -> sge.Expression: - do_upcast_bool = any( - dtypes.is_numeric(expr.dtype, include_bool=False) for expr in exprs - ) - if do_upcast_bool: - sg_exprs = [_coerce_bool_to_int(expr) for expr in exprs] - else: - sg_exprs = [expr.expr for expr in exprs] - return sge.Array(expressions=sg_exprs) - - -def _coerce_bool_to_int(typed_expr: TypedExpr) -> sge.Expression: - """Coerce boolean expression to integer.""" - if typed_expr.dtype == dtypes.BOOL_DTYPE: - return sge.Cast(this=typed_expr.expr, to="INT64") - return typed_expr.expr - - -def _array_slice(expr: TypedExpr, op: ops.ArraySliceOp) -> sge.Expression: - # local name for each element in the array - el = sg.to_identifier("el") - # local name for the index in the array - slice_idx = sg.to_identifier("slice_idx") - - conditions: typing.List[sge.Predicate] = [slice_idx >= op.start] - if op.stop is not None: - conditions.append(slice_idx < op.stop) - - selected_elements = ( - sge.select(el) - .from_( - sge.Unnest( - expressions=[expr.expr], - alias=sge.TableAlias(columns=[el]), - offset=slice_idx, - ) - ) - .where(*conditions) - ) - - return sge.array(selected_elements) diff --git a/bigframes/core/compile/sqlglot/expressions/blob_ops.py b/bigframes/core/compile/sqlglot/expressions/blob_ops.py deleted file mode 100644 index 01b4f7a1617..00000000000 --- a/bigframes/core/compile/sqlglot/expressions/blob_ops.py +++ /dev/null @@ -1,52 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from __future__ import annotations - -import bigframes_vendored.sqlglot.expressions as sge - -import bigframes.core.compile.sqlglot.expression_compiler as expression_compiler -from bigframes import operations as ops -from bigframes.core.compile.sqlglot.expressions.typed_expr import TypedExpr - -register_unary_op = expression_compiler.expression_compiler.register_unary_op -register_binary_op = expression_compiler.expression_compiler.register_binary_op - - -@register_unary_op(ops.obj_fetch_metadata_op) -def _(expr: TypedExpr) -> sge.Expression: - return sge.func("OBJ.FETCH_METADATA", expr.expr) - - -@register_unary_op(ops.ObjGetAccessUrl, pass_op=True) -def _(expr: TypedExpr, op: ops.ObjGetAccessUrl) -> sge.Expression: - args = [expr.expr, sge.Literal.string(op.mode)] - if op.duration is not None: - args.append( - sge.Interval( - this=sge.Literal.number(op.duration), - unit=sge.Var(this="MICROSECOND"), - ) - ) - return sge.func("OBJ.GET_ACCESS_URL", *args) - - -@register_binary_op(ops.obj_make_ref_op) -def _(left: TypedExpr, right: TypedExpr) -> sge.Expression: - return sge.func("OBJ.MAKE_REF", left.expr, right.expr) - - -@register_unary_op(ops.obj_make_ref_json_op) -def _(expr: TypedExpr) -> sge.Expression: - return sge.func("OBJ.MAKE_REF", expr.expr) diff --git a/bigframes/core/compile/sqlglot/expressions/bool_ops.py b/bigframes/core/compile/sqlglot/expressions/bool_ops.py deleted file mode 100644 index 7e31646b295..00000000000 --- a/bigframes/core/compile/sqlglot/expressions/bool_ops.py +++ /dev/null @@ -1,86 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from __future__ import annotations - -import bigframes_vendored.sqlglot.expressions as sge - -import bigframes.core.compile.sqlglot.expression_compiler as expression_compiler -from bigframes import dtypes -from bigframes import operations as ops -from bigframes.core.compile.sqlglot import sql -from bigframes.core.compile.sqlglot.expressions.typed_expr import TypedExpr - -register_binary_op = expression_compiler.expression_compiler.register_binary_op - - -@register_binary_op(ops.and_op) -def _(left: TypedExpr, right: TypedExpr) -> sge.Expression: - # For AND, when we encounter a NULL value, we only know when the result is FALSE, - # otherwise the result is unknown (NULL). See: truth table at - # https://en.wikibooks.org/wiki/Structured_Query_Language/NULLs_and_the_Three_Valued_Logic#AND,_OR - if sql.is_null_literal(left.expr): - condition = sge.EQ(this=right.expr, expression=sge.convert(False)) - return sge.If(this=condition, true=right.expr, false=sge.null()) - if sql.is_null_literal(right.expr): - condition = sge.EQ(this=left.expr, expression=sge.convert(False)) - return sge.If(this=condition, true=left.expr, false=sge.null()) - - if left.dtype == dtypes.BOOL_DTYPE and right.dtype == dtypes.BOOL_DTYPE: - return sge.And(this=left.expr, expression=right.expr) - return sge.BitwiseAnd(this=left.expr, expression=right.expr) - - -@register_binary_op(ops.or_op) -def _(left: TypedExpr, right: TypedExpr) -> sge.Expression: - # For OR, when we encounter a NULL value, we only know when the result is TRUE, - # otherwise the result is unknown (NULL). See: truth table at - # https://en.wikibooks.org/wiki/Structured_Query_Language/NULLs_and_the_Three_Valued_Logic#AND,_OR - if sql.is_null_literal(left.expr): - condition = sge.EQ(this=right.expr, expression=sge.convert(True)) - return sge.If(this=condition, true=right.expr, false=sge.null()) - if sql.is_null_literal(right.expr): - condition = sge.EQ(this=left.expr, expression=sge.convert(True)) - return sge.If(this=condition, true=left.expr, false=sge.null()) - - if left.dtype == dtypes.BOOL_DTYPE and right.dtype == dtypes.BOOL_DTYPE: - return sge.Or(this=left.expr, expression=right.expr) - return sge.BitwiseOr(this=left.expr, expression=right.expr) - - -@register_binary_op(ops.xor_op) -def _(left: TypedExpr, right: TypedExpr) -> sge.Expression: - # For XOR, cast NULL operands to BOOLEAN to ensure the resulting expression - # maintains the boolean data type. - left_expr = left.expr - left_dtype = left.dtype - if sql.is_null_literal(left_expr): - left_expr = sge.Cast(this=sge.convert(None), to="BOOLEAN") - left_dtype = dtypes.BOOL_DTYPE - right_expr = right.expr - right_dtype = right.dtype - if sql.is_null_literal(right_expr): - right_expr = sge.Cast(this=sge.convert(None), to="BOOLEAN") - right_dtype = dtypes.BOOL_DTYPE - - if left_dtype == dtypes.BOOL_DTYPE and right_dtype == dtypes.BOOL_DTYPE: - return sge.Or( - this=sge.paren( - sge.And(this=left_expr, expression=sge.Not(this=right_expr)) - ), - expression=sge.paren( - sge.And(this=sge.Not(this=left_expr), expression=right_expr) - ), - ) - return sge.BitwiseXor(this=left.expr, expression=right.expr) diff --git a/bigframes/core/compile/sqlglot/expressions/common.py b/bigframes/core/compile/sqlglot/expressions/common.py deleted file mode 100644 index 067ca070edf..00000000000 --- a/bigframes/core/compile/sqlglot/expressions/common.py +++ /dev/null @@ -1,33 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from __future__ import annotations - -import bigframes_vendored.sqlglot.expressions as sge - - -def round_towards_zero(expr: sge.Expression): - """ - Round a float value to to an integer, always rounding towards zero. - - This is used to handle duration/timedelta emulation mostly. - """ - return sge.Cast( - this=sge.If( - this=sge.GT(this=expr, expression=sge.convert(0)), - true=sge.Floor(this=expr), - false=sge.Ceil(this=expr), - ), - to="INT64", - ) diff --git a/bigframes/core/compile/sqlglot/expressions/comparison_ops.py b/bigframes/core/compile/sqlglot/expressions/comparison_ops.py deleted file mode 100644 index a3331ce6fb5..00000000000 --- a/bigframes/core/compile/sqlglot/expressions/comparison_ops.py +++ /dev/null @@ -1,181 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from __future__ import annotations - -import typing - -import bigframes_vendored.sqlglot as sg -import bigframes_vendored.sqlglot.expressions as sge -import pandas as pd - -import bigframes.core.compile.sqlglot.expression_compiler as expression_compiler -from bigframes import dtypes -from bigframes import operations as ops -from bigframes.core.compile.sqlglot import sql -from bigframes.core.compile.sqlglot.expressions.typed_expr import TypedExpr - -register_unary_op = expression_compiler.expression_compiler.register_unary_op -register_binary_op = expression_compiler.expression_compiler.register_binary_op - - -@register_unary_op(ops.IsInOp, pass_op=True) -def _(expr: TypedExpr, op: ops.IsInOp) -> sge.Expression: - values = [] - # bools are not comparable to non-bools in SQL, so we need to cast the expression to INT64 if the values contain non-bools. - must_upcast_bools = dtypes.is_numeric(expr.dtype, include_bool=False) or any( - dtypes.is_numeric(dtypes.bigframes_type(type(value)), include_bool=False) - for value in op.values - if not _is_null(value) - ) - for value in op.values: - if _is_null(value): - continue - dtype = dtypes.bigframes_type(type(value)) - if dtypes.can_compare(expr.dtype, dtype): - if must_upcast_bools and dtype == dtypes.BOOL_DTYPE: - value = int(value) - values.append(sql.literal(value)) - - sg_lexpr: sge.Expression = expr.expr - if expr.dtype == dtypes.BOOL_DTYPE and must_upcast_bools: - sg_lexpr = sge.cast(expr.expr, "INT64") - - if op.match_nulls: - contains_nulls = any(_is_null(value) for value in op.values) - if contains_nulls: - if len(values) == 0: - return sge.Is(this=sg_lexpr, expression=sge.Null()) - return sge.Is(this=sg_lexpr, expression=sge.Null()) | sge.In( - this=sg_lexpr, expressions=values - ) - - if len(values) == 0: - return sge.convert(False) - - return sge.func( - "COALESCE", sge.In(this=sg_lexpr, expressions=values), sge.convert(False) - ) - - -@register_binary_op(ops.eq_op) -def _(left: TypedExpr, right: TypedExpr) -> sge.Expression: - if sql.is_null_literal(left.expr): - return sge.Is(this=right.expr, expression=sge.Null()) - if sql.is_null_literal(right.expr): - return sge.Is(this=left.expr, expression=sge.Null()) - left_expr = _coerce_bool_to_int(left) - right_expr = _coerce_bool_to_int(right) - return sge.EQ(this=left_expr, expression=right_expr) - - -@register_binary_op(ops.eq_null_match_op) -def _(left: TypedExpr, right: TypedExpr) -> sge.Expression: - left_expr = left.expr - if right.dtype != dtypes.BOOL_DTYPE: - left_expr = _coerce_bool_to_int(left) - - right_expr = right.expr - if left.dtype != dtypes.BOOL_DTYPE: - right_expr = _coerce_bool_to_int(right) - - sentinel = sge.convert("$NULL_SENTINEL$") - left_coalesce = sge.Coalesce( - this=sge.Cast(this=left_expr, to="STRING"), expressions=[sentinel] - ) - right_coalesce = sge.Coalesce( - this=sge.Cast(this=right_expr, to="STRING"), expressions=[sentinel] - ) - return sge.EQ(this=left_coalesce, expression=right_coalesce) - - -@register_binary_op(ops.ge_op) -def _(left: TypedExpr, right: TypedExpr) -> sge.Expression: - if sql.is_null_literal(left.expr) or sql.is_null_literal(right.expr): - return sge.null() - - left_expr = _coerce_bool_to_int(left) - right_expr = _coerce_bool_to_int(right) - return sge.GTE(this=left_expr, expression=right_expr) - - -@register_binary_op(ops.gt_op) -def _(left: TypedExpr, right: TypedExpr) -> sge.Expression: - if sql.is_null_literal(left.expr) or sql.is_null_literal(right.expr): - return sge.null() - - left_expr = _coerce_bool_to_int(left) - right_expr = _coerce_bool_to_int(right) - return sge.GT(this=left_expr, expression=right_expr) - - -@register_binary_op(ops.lt_op) -def _(left: TypedExpr, right: TypedExpr) -> sge.Expression: - if sql.is_null_literal(left.expr) or sql.is_null_literal(right.expr): - return sge.null() - - left_expr = _coerce_bool_to_int(left) - right_expr = _coerce_bool_to_int(right) - return sge.LT(this=left_expr, expression=right_expr) - - -@register_binary_op(ops.le_op) -def _(left: TypedExpr, right: TypedExpr) -> sge.Expression: - if sql.is_null_literal(left.expr) or sql.is_null_literal(right.expr): - return sge.null() - - left_expr = _coerce_bool_to_int(left) - right_expr = _coerce_bool_to_int(right) - return sge.LTE(this=left_expr, expression=right_expr) - - -@register_binary_op(ops.maximum_op) -def _(left: TypedExpr, right: TypedExpr) -> sge.Expression: - return sge.Greatest(expressions=[left.expr, right.expr]) - - -@register_binary_op(ops.minimum_op) -def _(left: TypedExpr, right: TypedExpr) -> sge.Expression: - return sge.Least(this=left.expr, expressions=right.expr) - - -@register_binary_op(ops.ne_op) -def _(left: TypedExpr, right: TypedExpr) -> sge.Expression: - if sql.is_null_literal(left.expr): - return sge.Is( - this=sge.paren(right.expr, copy=False), - expression=sg.not_(sge.Null(), copy=False), - ) - if sql.is_null_literal(right.expr): - return sge.Is( - this=sge.paren(left.expr, copy=False), - expression=sg.not_(sge.Null(), copy=False), - ) - - left_expr = _coerce_bool_to_int(left) - right_expr = _coerce_bool_to_int(right) - return sge.NEQ(this=left_expr, expression=right_expr) - - -# Helpers -def _is_null(value) -> bool: - # float NaN/inf should be treated as distinct from 'true' null values - return typing.cast(bool, pd.isna(value)) and not isinstance(value, float) - - -def _coerce_bool_to_int(typed_expr: TypedExpr) -> sge.Expression: - """Coerce boolean expression to integer.""" - if typed_expr.dtype == dtypes.BOOL_DTYPE: - return sge.Cast(this=typed_expr.expr, to="INT64") - return typed_expr.expr diff --git a/bigframes/core/compile/sqlglot/expressions/constants.py b/bigframes/core/compile/sqlglot/expressions/constants.py deleted file mode 100644 index 5ba4a72279f..00000000000 --- a/bigframes/core/compile/sqlglot/expressions/constants.py +++ /dev/null @@ -1,38 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import math - -import bigframes_vendored.sqlglot.expressions as sge - -_ZERO = sge.Cast(this=sge.convert(0), to="INT64") -_NAN = sge.Cast(this=sge.convert("NaN"), to="FLOAT64") -_INF = sge.Cast(this=sge.convert("Infinity"), to="FLOAT64") -_NEG_INF = sge.Cast(this=sge.convert("-Infinity"), to="FLOAT64") -_DAY_TO_MICROSECONDS = sge.convert(86400000000) - -# Approx Highest number you can pass in to EXP function and get a valid FLOAT64 result -# FLOAT64 has 11 exponent bits, so max values is about 2**(2**10) -# ln(2**(2**10)) == (2**10)*ln(2) ~= 709.78, so EXP(x) for x>709.78 will overflow. -_FLOAT64_EXP_BOUND = sge.convert(709.78) - -# The natural logarithm of the maximum value for a signed 64-bit integer. -# This is used to check for potential overflows in power operations involving integers -# by checking if `exponent * log(base)` exceeds this value. -_INT64_LOG_BOUND = math.log(2**63 - 1) - -# Represents the largest integer N where all integers from -N to N can be -# represented exactly as a float64. Float64 types have a 53-bit significand precision, -# so integers beyond this value may lose precision. -_FLOAT64_MAX_INT_PRECISION = 2**53 diff --git a/bigframes/core/compile/sqlglot/expressions/date_ops.py b/bigframes/core/compile/sqlglot/expressions/date_ops.py deleted file mode 100644 index 2410926887b..00000000000 --- a/bigframes/core/compile/sqlglot/expressions/date_ops.py +++ /dev/null @@ -1,72 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from __future__ import annotations - -import bigframes_vendored.sqlglot.expressions as sge - -import bigframes.core.compile.sqlglot.expression_compiler as expression_compiler -from bigframes import operations as ops -from bigframes.core.compile.sqlglot.expressions.typed_expr import TypedExpr - -register_unary_op = expression_compiler.expression_compiler.register_unary_op - - -@register_unary_op(ops.date_op) -def _(expr: TypedExpr) -> sge.Expression: - return sge.Date(this=expr.expr) - - -@register_unary_op(ops.day_op) -def _(expr: TypedExpr) -> sge.Expression: - return sge.Extract(this=sge.Identifier(this="DAY"), expression=expr.expr) - - -@register_unary_op(ops.dayofweek_op) -def _(expr: TypedExpr) -> sge.Expression: - return dayofweek_op_impl(expr) - - -@register_unary_op(ops.dayofyear_op) -def _(expr: TypedExpr) -> sge.Expression: - return sge.Extract(this=sge.Identifier(this="DAYOFYEAR"), expression=expr.expr) - - -@register_unary_op(ops.iso_day_op) -def _(expr: TypedExpr) -> sge.Expression: - # Plus 1 because iso day of week uses 1-based indexing - return dayofweek_op_impl(expr) + sge.convert(1) - - -@register_unary_op(ops.iso_week_op) -def _(expr: TypedExpr) -> sge.Expression: - return sge.Extract(this=sge.Identifier(this="ISOWEEK"), expression=expr.expr) - - -@register_unary_op(ops.iso_year_op) -def _(expr: TypedExpr) -> sge.Expression: - return sge.Extract(this=sge.Identifier(this="ISOYEAR"), expression=expr.expr) - - -# Helpers -def dayofweek_op_impl(expr: TypedExpr) -> sge.Expression: - # BigQuery SQL Extract(DAYOFWEEK) returns 1 for Sunday through 7 for Saturday. - # We want 0 for Monday through 6 for Sunday to be compatible with Pandas. - extract_expr = sge.Extract( - this=sge.Identifier(this="DAYOFWEEK"), expression=expr.expr - ) - return sge.Cast( - this=sge.Mod(this=extract_expr + sge.convert(5), expression=sge.convert(7)), - to="INT64", - ) diff --git a/bigframes/core/compile/sqlglot/expressions/datetime_ops.py b/bigframes/core/compile/sqlglot/expressions/datetime_ops.py deleted file mode 100644 index 399b3062273..00000000000 --- a/bigframes/core/compile/sqlglot/expressions/datetime_ops.py +++ /dev/null @@ -1,734 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from __future__ import annotations - -import bigframes_vendored.sqlglot.expressions as sge - -import bigframes.core.compile.sqlglot.expression_compiler as expression_compiler -from bigframes import dtypes -from bigframes import operations as ops -from bigframes.core.compile.constants import UNIT_TO_US_CONVERSION_FACTORS -from bigframes.core.compile.sqlglot import sqlglot_types -from bigframes.core.compile.sqlglot.expressions.typed_expr import TypedExpr - -register_unary_op = expression_compiler.expression_compiler.register_unary_op -register_binary_op = expression_compiler.expression_compiler.register_binary_op - - -@register_binary_op(ops.DatetimeToIntegerLabelOp, pass_op=True) -def datetime_to_integer_label_op( - x: TypedExpr, y: TypedExpr, op: ops.DatetimeToIntegerLabelOp -) -> sge.Expression: - # Determine if the frequency is fixed by checking if 'op.freq.nanos' is defined. - try: - return _datetime_to_integer_label_fixed_frequency(x, y, op) - except ValueError: - return _datetime_to_integer_label_non_fixed_frequency(x, y, op) - - -def _datetime_to_integer_label_fixed_frequency( - x: TypedExpr, y: TypedExpr, op: ops.DatetimeToIntegerLabelOp -) -> sge.Expression: - """ - This function handles fixed frequency conversions where the unit can range - from microseconds (us) to days. - """ - us = op.freq.nanos / 1000 - x_int = sge.func( - "UNIX_MICROS", - sge.Cast(this=x.expr, to=sge.DataType(this=sge.DataType.Type.TIMESTAMPTZ)), - ) - first = _calculate_resample_first(y, op.origin) # type: ignore - x_int_label = sge.Cast( - this=sge.Floor( - this=sge.func( - "IEEE_DIVIDE", - sge.Sub(this=x_int, expression=first), - sge.convert(int(us)), - ) - ), - to=sge.DataType.build("INT64"), - ) - return x_int_label - - -def _datetime_to_integer_label_non_fixed_frequency( - x: TypedExpr, y: TypedExpr, op: ops.DatetimeToIntegerLabelOp -) -> sge.Expression: - """ - This function handles non-fixed frequency conversions for units ranging - from weeks to years. - """ - rule_code = op.freq.rule_code - n = op.freq.n - if rule_code == "W-SUN": # Weekly - us = n * 7 * 24 * 60 * 60 * 1000000 - x_trunc = sge.TimestampTrunc(this=x.expr, unit=sge.Var(this="WEEK(MONDAY)")) - y_trunc = sge.TimestampTrunc(this=y.expr, unit=sge.Var(this="WEEK(MONDAY)")) - x_plus_6 = sge.Add( - this=x_trunc, - expression=sge.Interval( - this=sge.convert(6), unit=sge.Identifier(this="DAY") - ), - ) - y_plus_6 = sge.Add( - this=y_trunc, - expression=sge.Interval( - this=sge.convert(6), unit=sge.Identifier(this="DAY") - ), - ) - x_int = sge.func( - "UNIX_MICROS", - sge.Cast( - this=x_plus_6, to=sge.DataType(this=sge.DataType.Type.TIMESTAMPTZ) - ), - ) - first = sge.func( - "UNIX_MICROS", - sge.Cast( - this=y_plus_6, to=sge.DataType(this=sge.DataType.Type.TIMESTAMPTZ) - ), - ) - return sge.Case( - ifs=[ - sge.If( - this=sge.EQ(this=x_int, expression=first), - true=sge.convert(0), - ) - ], - default=sge.Add( - this=sge.Cast( - this=sge.Floor( - this=sge.func( - "IEEE_DIVIDE", - sge.Sub( - this=sge.Sub(this=x_int, expression=first), - expression=sge.convert(1), - ), - sge.convert(us), - ) - ), - to=sge.DataType.build("INT64"), - ), - expression=sge.convert(1), - ), - ) - elif rule_code in ("M", "ME"): # Monthly - x_int = sge.Paren( # type: ignore - this=sge.Add( - this=sge.Mul( - this=sge.Extract( - this=sge.Identifier(this="YEAR"), expression=x.expr - ), - expression=sge.convert(12), - ), - expression=sge.Sub( - this=sge.Extract( - this=sge.Identifier(this="MONTH"), expression=x.expr - ), - expression=sge.convert(1), - ), - ) - ) - first = sge.Paren( # type: ignore - this=sge.Add( - this=sge.Mul( - this=sge.Extract( - this=sge.Identifier(this="YEAR"), expression=y.expr - ), - expression=sge.convert(12), - ), - expression=sge.Sub( - this=sge.Extract( - this=sge.Identifier(this="MONTH"), expression=y.expr - ), - expression=sge.convert(1), - ), - ) - ) - return sge.Case( - ifs=[ - sge.If( - this=sge.EQ(this=x_int, expression=first), - true=sge.convert(0), - ) - ], - default=sge.Add( - this=sge.Cast( - this=sge.Floor( - this=sge.func( - "IEEE_DIVIDE", - sge.Sub( - this=sge.Sub(this=x_int, expression=first), - expression=sge.convert(1), - ), - sge.convert(n), - ) - ), - to=sge.DataType.build("INT64"), - ), - expression=sge.convert(1), - ), - ) - elif rule_code in ("Q-DEC", "QE-DEC"): # Quarterly - x_int = sge.Paren( # type: ignore - this=sge.Add( - this=sge.Mul( - this=sge.Extract( - this=sge.Identifier(this="YEAR"), expression=x.expr - ), - expression=sge.convert(4), - ), - expression=sge.Sub( - this=sge.Extract( - this=sge.Identifier(this="QUARTER"), expression=x.expr - ), - expression=sge.convert(1), - ), - ) - ) - first = sge.Paren( # type: ignore - this=sge.Add( - this=sge.Mul( - this=sge.Extract( - this=sge.Identifier(this="YEAR"), expression=y.expr - ), - expression=sge.convert(4), - ), - expression=sge.Sub( - this=sge.Extract( - this=sge.Identifier(this="QUARTER"), expression=y.expr - ), - expression=sge.convert(1), - ), - ) - ) - return sge.Case( - ifs=[ - sge.If( - this=sge.EQ(this=x_int, expression=first), - true=sge.convert(0), - ) - ], - default=sge.Add( - this=sge.Cast( - this=sge.Floor( - this=sge.func( - "IEEE_DIVIDE", - sge.Sub( - this=sge.Sub(this=x_int, expression=first), - expression=sge.convert(1), - ), - sge.convert(n), - ) - ), - to=sge.DataType.build("INT64"), - ), - expression=sge.convert(1), - ), - ) - elif rule_code in ("A-DEC", "Y-DEC", "YE-DEC"): # Yearly - x_int = sge.Extract(this=sge.Identifier(this="YEAR"), expression=x.expr) - first = sge.Extract(this=sge.Identifier(this="YEAR"), expression=y.expr) - return sge.Case( - ifs=[ - sge.If( - this=sge.EQ(this=x_int, expression=first), - true=sge.convert(0), - ) - ], - default=sge.Add( - this=sge.Cast( - this=sge.Floor( - this=sge.func( - "IEEE_DIVIDE", - sge.Sub( - this=sge.Sub(this=x_int, expression=first), - expression=sge.convert(1), - ), - sge.convert(n), - ) - ), - to=sge.DataType.build("INT64"), - ), - expression=sge.convert(1), - ), - ) - else: - raise ValueError(rule_code) - - -@register_unary_op(ops.FloorDtOp, pass_op=True) -def _(expr: TypedExpr, op: ops.FloorDtOp) -> sge.Expression: - pandas_to_bq_freq_map = { - "Y": "YEAR", - "Q": "QUARTER", - "M": "MONTH", - "W": "WEEK(MONDAY)", - "D": "DAY", - "h": "HOUR", - "min": "MINUTE", - "s": "SECOND", - "ms": "MILLISECOND", - "us": "MICROSECOND", - "ns": "NANOSECOND", - } - if op.freq not in pandas_to_bq_freq_map.keys(): - raise NotImplementedError( - f"Unsupported freq paramater: {op.freq}" - + " Supported freq parameters are: " - + ",".join(pandas_to_bq_freq_map.keys()) - ) - - bq_freq = pandas_to_bq_freq_map[op.freq] - return sge.TimestampTrunc(this=expr.expr, unit=sge.Identifier(this=bq_freq)) - - -def _calculate_resample_first(y: TypedExpr, origin: str) -> sge.Expression: - if origin == "epoch": - return sge.convert(0) - elif origin == "start_day": - return sge.func( - "UNIX_MICROS", - sge.Cast(this=sge.Cast(this=y.expr, to="DATE"), to="TIMESTAMP"), - ) - elif origin == "start": - return sge.func("UNIX_MICROS", sge.Cast(this=y.expr, to="TIMESTAMP")) - else: - raise ValueError(f"Origin {origin} not supported") - - -@register_unary_op(ops.hour_op) -def _(expr: TypedExpr) -> sge.Expression: - return sge.Extract(this=sge.Identifier(this="HOUR"), expression=expr.expr) - - -@register_unary_op(ops.minute_op) -def _(expr: TypedExpr) -> sge.Expression: - return sge.Extract(this=sge.Identifier(this="MINUTE"), expression=expr.expr) - - -@register_unary_op(ops.month_op) -def _(expr: TypedExpr) -> sge.Expression: - return sge.Extract(this=sge.Identifier(this="MONTH"), expression=expr.expr) - - -@register_unary_op(ops.normalize_op) -def _(expr: TypedExpr) -> sge.Expression: - return sge.TimestampTrunc(this=expr.expr, unit=sge.Identifier(this="DAY")) - - -@register_unary_op(ops.quarter_op) -def _(expr: TypedExpr) -> sge.Expression: - return sge.Extract(this=sge.Identifier(this="QUARTER"), expression=expr.expr) - - -@register_unary_op(ops.second_op) -def _(expr: TypedExpr) -> sge.Expression: - return sge.Extract(this=sge.Identifier(this="SECOND"), expression=expr.expr) - - -@register_unary_op(ops.StrftimeOp, pass_op=True) -def _(expr: TypedExpr, op: ops.StrftimeOp) -> sge.Expression: - func_name = "" - if expr.dtype == dtypes.DATE_DTYPE: - func_name = "FORMAT_DATE" - elif expr.dtype == dtypes.DATETIME_DTYPE: - func_name = "FORMAT_DATETIME" - elif expr.dtype == dtypes.TIME_DTYPE: - func_name = "FORMAT_TIME" - elif expr.dtype == dtypes.TIMESTAMP_DTYPE: - func_name = "FORMAT_TIMESTAMP" - - return sge.func(func_name, sge.convert(op.date_format), expr.expr) - - -@register_unary_op(ops.time_op) -def _(expr: TypedExpr) -> sge.Expression: - return sge.func("TIME", expr.expr) - - -@register_unary_op(ops.ToDatetimeOp, pass_op=True) -def _(expr: TypedExpr, op: ops.ToDatetimeOp) -> sge.Expression: - if op.format: - result = expr.expr - if expr.dtype == dtypes.STRING_DTYPE: - return sge.TryCast(this=result, to="DATETIME") - else: - result = sge.Cast(this=result, to="STRING") - result = sge.func( - "PARSE_TIMESTAMP", sge.convert(op.format), result, sge.convert("UTC") - ) - return sge.Cast(this=result, to="DATETIME") - - if expr.dtype == dtypes.TIMESTAMP_DTYPE: - return sge.func("DATETIME", expr.expr, sge.convert("UTC")) - - if expr.dtype in ( - dtypes.STRING_DTYPE, - dtypes.DATETIME_DTYPE, - dtypes.DATE_DTYPE, - ): - return sge.TryCast(this=expr.expr, to="DATETIME") - - value = expr.expr - unit = op.unit or "ns" - factor = UNIT_TO_US_CONVERSION_FACTORS[unit] - if factor != 1: - value = sge.Mul(this=value, expression=sge.convert(factor)) - value = sge.func("TRUNC", value) - return sge.func( - "DATETIME", - sge.func("TIMESTAMP_MICROS", sge.Cast(this=value, to="INT64")), - sge.convert("UTC"), - ) - - -@register_unary_op(ops.ToTimestampOp, pass_op=True) -def _(expr: TypedExpr, op: ops.ToTimestampOp) -> sge.Expression: - if op.format: - result = expr.expr - if expr.dtype != dtypes.STRING_DTYPE: - result = sge.Cast(this=result, to="STRING") - return sge.func( - "PARSE_TIMESTAMP", sge.convert(op.format), result, sge.convert("UTC") - ) - - if expr.dtype in ( - dtypes.STRING_DTYPE, - dtypes.DATETIME_DTYPE, - dtypes.TIMESTAMP_DTYPE, - dtypes.DATE_DTYPE, - ): - return sge.func("TIMESTAMP", expr.expr) - - value = expr.expr - unit = op.unit or "ns" - factor = UNIT_TO_US_CONVERSION_FACTORS[unit] - if factor != 1: - value = sge.Mul(this=value, expression=sge.convert(factor)) - value = sge.func("TRUNC", value) - return sge.Cast( - this=sge.func("TIMESTAMP_MICROS", sge.Cast(this=value, to="INT64")), - to="TIMESTAMP", - ) - - -@register_unary_op(ops.UnixMicros) -def _(expr: TypedExpr) -> sge.Expression: - return sge.func("UNIX_MICROS", expr.expr) - - -@register_unary_op(ops.UnixMillis) -def _(expr: TypedExpr) -> sge.Expression: - return sge.func("UNIX_MILLIS", expr.expr) - - -@register_unary_op(ops.UnixSeconds, pass_op=True) -def _(expr: TypedExpr, op: ops.UnixSeconds) -> sge.Expression: - return sge.func("UNIX_SECONDS", expr.expr) - - -@register_unary_op(ops.year_op) -def _(expr: TypedExpr) -> sge.Expression: - return sge.Extract(this=sge.Identifier(this="YEAR"), expression=expr.expr) - - -@register_binary_op(ops.IntegerLabelToDatetimeOp, pass_op=True) -def integer_label_to_datetime_op( - x: TypedExpr, y: TypedExpr, op: ops.IntegerLabelToDatetimeOp -) -> sge.Expression: - # Determine if the frequency is fixed by checking if 'op.freq.nanos' is defined. - try: - return _integer_label_to_datetime_op_fixed_frequency(x, y, op) - - except ValueError: - # Non-fixed frequency conversions for units ranging from weeks to years. - rule_code = op.freq.rule_code - - if rule_code == "W-SUN": - return _integer_label_to_datetime_op_weekly_freq(x, y, op) - - if rule_code in ("ME", "M"): - return _integer_label_to_datetime_op_monthly_freq(x, y, op) - - if rule_code in ("QE-DEC", "Q-DEC"): - return _integer_label_to_datetime_op_quarterly_freq(x, y, op) - - if rule_code in ("YE-DEC", "A-DEC", "Y-DEC"): - return _integer_label_to_datetime_op_yearly_freq(x, y, op) - - # If the rule_code is not recognized, raise an error here. - raise ValueError(f"Unsupported frequency rule code: {rule_code}") - - -def _integer_label_to_datetime_op_fixed_frequency( - x: TypedExpr, y: TypedExpr, op: ops.IntegerLabelToDatetimeOp -) -> sge.Expression: - """ - This function handles fixed frequency conversions where the unit can range - from microseconds (us) to days. - """ - us = op.freq.nanos / 1000 - first = _calculate_resample_first(y, op.origin) # type: ignore - x_label = sge.Cast( - this=sge.func( - "TIMESTAMP_MICROS", - sge.Cast( - this=sge.Add( - this=sge.Mul( - this=sge.Cast(this=x.expr, to="BIGNUMERIC"), - expression=sge.convert(int(us)), - ), - expression=sge.Cast(this=first, to="BIGNUMERIC"), - ), - to="INT64", - ), - ), - to=sqlglot_types.from_bigframes_dtype(y.dtype), - ) - return x_label - - -def _integer_label_to_datetime_op_weekly_freq( - x: TypedExpr, y: TypedExpr, op: ops.IntegerLabelToDatetimeOp -) -> sge.Expression: - n = op.freq.n - # Calculate microseconds for the weekly interval. - us = n * 7 * 24 * 60 * 60 * 1000000 - first = sge.func( - "UNIX_MICROS", - sge.Add( - this=sge.TimestampTrunc( - this=sge.Cast(this=y.expr, to="TIMESTAMP"), - unit=sge.Var(this="WEEK(MONDAY)"), - ), - expression=sge.Interval( - this=sge.convert(6), unit=sge.Identifier(this="DAY") - ), - ), - ) - return sge.Cast( - this=sge.func( - "TIMESTAMP_MICROS", - sge.Cast( - this=sge.Add( - this=sge.Mul( - this=sge.Cast(this=x.expr, to="BIGNUMERIC"), - expression=sge.convert(us), - ), - expression=sge.Cast(this=first, to="BIGNUMERIC"), - ), - to="INT64", - ), - ), - to=sqlglot_types.from_bigframes_dtype(y.dtype), - ) - - -def _integer_label_to_datetime_op_monthly_freq( - x: TypedExpr, y: TypedExpr, op: ops.IntegerLabelToDatetimeOp -) -> sge.Expression: - n = op.freq.n - one = sge.convert(1) - twelve = sge.convert(12) - first = sge.Sub( # type: ignore - this=sge.Add( - this=sge.Mul( - this=sge.Extract(this="YEAR", expression=y.expr), - expression=twelve, - ), - expression=sge.Extract(this="MONTH", expression=y.expr), - ), - expression=one, - ) - x_val = sge.Add( - this=sge.Mul(this=x.expr, expression=sge.convert(n)), expression=first - ) - year = sge.Cast( - this=sge.Floor(this=sge.func("IEEE_DIVIDE", x_val, twelve)), - to="INT64", - ) - month = sge.Add(this=sge.Mod(this=x_val, expression=twelve), expression=one) - - next_year = sge.Case( - ifs=[ - sge.If( - this=sge.EQ(this=month, expression=twelve), - true=sge.Add(this=year, expression=one), - ) - ], - default=year, - ) - next_month = sge.Case( - ifs=[sge.If(this=sge.EQ(this=month, expression=twelve), true=one)], - default=sge.Add(this=month, expression=one), - ) - next_month_date = sge.func( - "TIMESTAMP", - sge.Anonymous( - this="DATETIME", - expressions=[ - next_year, - next_month, - one, - sge.convert(0), - sge.convert(0), - sge.convert(0), - ], - ), - ) - x_label = sge.Sub( # type: ignore - this=next_month_date, expression=sge.Interval(this=one, unit="DAY") - ) - return sge.Cast(this=x_label, to=sqlglot_types.from_bigframes_dtype(y.dtype)) - - -def _integer_label_to_datetime_op_quarterly_freq( - x: TypedExpr, y: TypedExpr, op: ops.IntegerLabelToDatetimeOp -) -> sge.Expression: - n = op.freq.n - one = sge.convert(1) - three = sge.convert(3) - four = sge.convert(4) - twelve = sge.convert(12) - first = sge.Sub( # type: ignore - this=sge.Add( - this=sge.Mul( - this=sge.Extract(this="YEAR", expression=y.expr), - expression=four, - ), - expression=sge.Extract(this="QUARTER", expression=y.expr), - ), - expression=one, - ) - x_val = sge.Add( - this=sge.Mul(this=x.expr, expression=sge.convert(n)), expression=first - ) - year = sge.Cast( - this=sge.Floor(this=sge.func("IEEE_DIVIDE", x_val, four)), - to="INT64", - ) - month = sge.Mul( # type: ignore - this=sge.Paren( - this=sge.Add(this=sge.Mod(this=x_val, expression=four), expression=one) - ), - expression=three, - ) - - next_year = sge.Case( - ifs=[ - sge.If( - this=sge.EQ(this=month, expression=twelve), - true=sge.Add(this=year, expression=one), - ) - ], - default=year, - ) - next_month = sge.Case( - ifs=[sge.If(this=sge.EQ(this=month, expression=twelve), true=one)], - default=sge.Add(this=month, expression=one), - ) - next_month_date = sge.Anonymous( - this="DATETIME", - expressions=[ - next_year, - next_month, - one, - sge.convert(0), - sge.convert(0), - sge.convert(0), - ], - ) - x_label = sge.Sub( # type: ignore - this=next_month_date, expression=sge.Interval(this=one, unit="DAY") - ) - return sge.Cast(this=x_label, to=sqlglot_types.from_bigframes_dtype(y.dtype)) - - -def _integer_label_to_datetime_op_yearly_freq( - x: TypedExpr, y: TypedExpr, op: ops.IntegerLabelToDatetimeOp -) -> sge.Expression: - n = op.freq.n - one = sge.convert(1) - first = sge.Extract(this="YEAR", expression=y.expr) - x_val = sge.Add( - this=sge.Mul(this=x.expr, expression=sge.convert(n)), expression=first - ) - next_year = sge.Add(this=x_val, expression=one) # type: ignore - next_month_date = sge.func( - "TIMESTAMP", - sge.Anonymous( - this="DATETIME", - expressions=[ - next_year, - one, - one, - sge.convert(0), - sge.convert(0), - sge.convert(0), - ], - ), - ) - x_label = sge.Sub( # type: ignore - this=next_month_date, expression=sge.Interval(this=one, unit="DAY") - ) - return sge.Cast(this=x_label, to=sqlglot_types.from_bigframes_dtype(y.dtype)) - - -@register_binary_op(ops.timestamp_add_op) -def _(left: TypedExpr, right: TypedExpr) -> sge.Expression: - return sge.TimestampAdd( - this=left.expr, expression=right.expr, unit=sge.Var(this="MICROSECOND") - ) - - -@register_binary_op(ops.timestamp_sub_op) -def _(left: TypedExpr, right: TypedExpr) -> sge.Expression: - return sge.TimestampSub( - this=left.expr, expression=right.expr, unit=sge.Var(this="MICROSECOND") - ) - - -@register_binary_op(ops.timestamp_diff_op) -def _(left: TypedExpr, right: TypedExpr) -> sge.Expression: - return sge.TimestampDiff( - this=left.expr, expression=right.expr, unit=sge.Var(this="MICROSECOND") - ) - - -@register_binary_op(ops.date_add_op) -def _(left: TypedExpr, right: TypedExpr) -> sge.Expression: - left_expr = sge.Cast(this=left.expr, to="TIMESTAMP") - return sge.TimestampAdd( - this=left_expr, expression=right.expr, unit=sge.Var(this="MICROSECOND") - ) - - -@register_binary_op(ops.date_sub_op) -def _(left: TypedExpr, right: TypedExpr) -> sge.Expression: - left_expr = sge.Cast(this=left.expr, to="TIMESTAMP") - return sge.TimestampSub( - this=left_expr, expression=right.expr, unit=sge.Var(this="MICROSECOND") - ) - - -@register_binary_op(ops.date_diff_op) -def _(left: TypedExpr, right: TypedExpr) -> sge.Expression: - diff = sge.DateDiff(this=left.expr, expression=right.expr, unit=sge.Var(this="DAY")) - return sge.Mul( - this=diff, - expression=sge.convert(int(UNIT_TO_US_CONVERSION_FACTORS["d"])), - ) diff --git a/bigframes/core/compile/sqlglot/expressions/generic_ops.py b/bigframes/core/compile/sqlglot/expressions/generic_ops.py deleted file mode 100644 index 90c8270ae1d..00000000000 --- a/bigframes/core/compile/sqlglot/expressions/generic_ops.py +++ /dev/null @@ -1,320 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from __future__ import annotations - -import bigframes_vendored.sqlglot as sg -import bigframes_vendored.sqlglot.expressions as sge - -import bigframes.core.compile.sqlglot.expression_compiler as expression_compiler -from bigframes import dtypes -from bigframes import operations as ops -from bigframes.core.compile.sqlglot import sql, sqlglot_types -from bigframes.core.compile.sqlglot.expressions.typed_expr import TypedExpr - -register_unary_op = expression_compiler.expression_compiler.register_unary_op -register_binary_op = expression_compiler.expression_compiler.register_binary_op -register_nary_op = expression_compiler.expression_compiler.register_nary_op -register_ternary_op = expression_compiler.expression_compiler.register_ternary_op - - -@register_unary_op(ops.AsTypeOp, pass_op=True) -def _(expr: TypedExpr, op: ops.AsTypeOp) -> sge.Expression: - from_type = expr.dtype - to_type = op.to_type - sg_to_type = sqlglot_types.from_bigframes_dtype(to_type) - sg_expr = expr.expr - - if to_type == dtypes.INT_DTYPE: - result = _cast_to_int(expr, op) - if result is not None: - return result - - if to_type == dtypes.FLOAT_DTYPE and from_type == dtypes.BOOL_DTYPE: - sg_expr = sql.cast(sg_expr, "INT64", op.safe) - return sql.cast(sg_expr, sg_to_type, op.safe) - - if to_type == dtypes.BOOL_DTYPE: - if from_type == dtypes.BOOL_DTYPE: - return sg_expr - else: - return sge.NEQ(this=sg_expr, expression=sge.convert(0)) - - if to_type == dtypes.STRING_DTYPE: - sg_expr = sql.cast(sg_expr, sg_to_type, op.safe) - if from_type == dtypes.BOOL_DTYPE: - sg_expr = sge.func("INITCAP", sg_expr) - return sg_expr - - if dtypes.is_time_like(to_type) and from_type == dtypes.INT_DTYPE: - sg_expr = sge.func("TIMESTAMP_MICROS", sg_expr) - return sql.cast(sg_expr, sg_to_type, op.safe) - - return sql.cast(sg_expr, sg_to_type, op.safe) - - -@register_unary_op(ops.hash_op) -def _(expr: TypedExpr) -> sge.Expression: - return sge.func("FARM_FINGERPRINT", expr.expr) - - -@register_unary_op(ops.invert_op) -def _(expr: TypedExpr) -> sge.Expression: - if expr.dtype == dtypes.BOOL_DTYPE: - return sge.Not(this=sge.paren(expr.expr)) - return sge.BitwiseNot(this=sge.paren(expr.expr)) - - -@register_nary_op(ops.GoogleSqlScalarOp, pass_op=True) -def _(*operands: TypedExpr, op: ops.GoogleSqlScalarOp) -> sge.Expression: - args: list[sge.Expression] = [] - for i, operand in enumerate(operands): - if i < len(op.args): - arg_spec = op.args[i] - else: - assert op.args[-1].is_vararg, ( - f"Too many arguments, for {op.sql_name}, expected {len(op.args)}" - ) - arg_spec = op.args[-1] - if operand.is_omitted: - assert arg_spec.optional, "Argument omitted, but not optional" - continue - elif arg_spec.arg_name: - args.append(sge.Kwarg(this=arg_spec.arg_name, expression=operand.expr)) - else: - args.append(operand.expr) - return sg.func(op.sql_name, *args) - - -@register_nary_op(ops.SqlScalarOp, pass_op=True) -def _(*operands: TypedExpr, op: ops.SqlScalarOp) -> sge.Expression: - return sg.parse_one( - op.sql_template.format( - *[operand.expr.sql(dialect="bigquery") for operand in operands] - ), - dialect="bigquery", - ) - - -@register_unary_op(ops.isnull_op) -def _(expr: TypedExpr) -> sge.Expression: - return sge.Is(this=sge.paren(expr.expr), expression=sge.Null()) - - -@register_unary_op(ops.MapOp, pass_op=True) -def _(expr: TypedExpr, op: ops.MapOp) -> sge.Expression: - if len(op.mappings) == 0: - return expr.expr - - mappings = [ - ( - sql.literal(key, dtypes.is_compatible(key, expr.dtype)), - sql.literal(value, dtypes.is_compatible(value, expr.dtype)), - ) - for key, value in op.mappings - ] - return sge.Case( - ifs=[ - sge.If( - this=( - sge.EQ(this=expr.expr, expression=key) - if not sql.is_null_literal(key) - else sge.Is(this=expr.expr, expression=sge.Null()) - ), - true=value, - ) - for key, value in mappings - ], - default=expr.expr, - ) - - -@register_unary_op(ops.notnull_op) -def _(expr: TypedExpr) -> sge.Expression: - return sge.Is( - this=sge.paren(expr.expr, copy=False), - expression=sg.not_(sge.Null(), copy=False), - ) - - -@register_unary_op(ops.coerce_to_bool_op) -def _(expr: TypedExpr) -> sge.Expression: - from_type = expr.dtype - sg_expr = expr.expr - - if from_type == dtypes.BOOL_DTYPE: - res = sg_expr - elif dtypes.is_numeric(from_type): - res = sge.NEQ(this=sg_expr, expression=sge.convert(0)) - elif dtypes.is_string_like(from_type): - res = sge.GT(this=sge.func("LENGTH", sg_expr), expression=sge.convert(0)) - elif dtypes.is_array_like(from_type): - res = sge.GT(this=sge.func("ARRAY_LENGTH", sg_expr), expression=sge.convert(0)) - else: - res = sge.Is( - this=sge.paren(sg_expr, copy=False), - expression=sg.not_(sge.Null(), copy=False), - ) - - return sge.Coalesce(this=res, expressions=[sge.convert(False)]) - - -@register_ternary_op(ops.where_op) -def _( - original: TypedExpr, condition: TypedExpr, replacement: TypedExpr -) -> sge.Expression: - return sge.If(this=condition.expr, true=original.expr, false=replacement.expr) - - -@register_ternary_op(ops.clip_op) -def _( - original: TypedExpr, - lower: TypedExpr, - upper: TypedExpr, -) -> sge.Expression: - return sge.Greatest( - this=sge.Least(this=original.expr, expressions=[upper.expr]), - expressions=[lower.expr], - ) - - -@register_binary_op(ops.fillna_op) -def _(left: TypedExpr, right: TypedExpr) -> sge.Expression: - return sge.Coalesce(this=left.expr, expressions=[right.expr]) - - -def _get_remote_function_name(op): - routine_ref = op.function_def.routine_ref - # Quote project, dataset, and routine IDs to avoid keyword clashes. - return ( - f"`{routine_ref.project}`.`{routine_ref.dataset_id}`.`{routine_ref.routine_id}`" - ) - - -@register_nary_op(ops.RemoteFunctionOp, pass_op=True) -def _(*values: TypedExpr, op: ops.RemoteFunctionOp) -> sge.Expression: - return sge.func(_get_remote_function_name(op), *(value.expr for value in values)) - - -@register_nary_op(ops.case_when_op) -def _(*cases_and_outputs: TypedExpr) -> sge.Expression: - # Need to upcast BOOL to INT if any output is numeric - result_values = cases_and_outputs[1::2] - do_upcast_bool = any( - dtypes.is_numeric(t.dtype, include_bool=False) for t in result_values - ) - if do_upcast_bool: - result_values = tuple( - ( - TypedExpr( - sge.Cast(this=val.expr, to="INT64"), - dtypes.INT_DTYPE, - ) - if val.dtype == dtypes.BOOL_DTYPE - else val - ) - for val in result_values - ) - - return sge.Case( - ifs=[ - sge.If(this=predicate.expr, true=output.expr) - for predicate, output in zip(cases_and_outputs[::2], result_values) - ], - ) - - -@register_binary_op(ops.coalesce_op) -def _(left: TypedExpr, right: TypedExpr) -> sge.Expression: - if left.expr == right.expr: - return left.expr - return sge.Coalesce(this=left.expr, expressions=[right.expr]) - - -@register_nary_op(ops.RowKey) -def _(*values: TypedExpr) -> sge.Expression: - # All inputs into hash must be non-null or resulting hash will be null - str_values = [_convert_to_nonnull_string_sqlglot(value) for value in values] - - full_row_hash_p1 = sge.func("FARM_FINGERPRINT", sge.Concat(expressions=str_values)) - - # By modifying value slightly, we get another hash uncorrelated with the first - full_row_hash_p2 = sge.func( - "FARM_FINGERPRINT", sge.Concat(expressions=[*str_values, sge.convert("_")]) - ) - - # Used to disambiguate between identical rows (which will have identical hash) - random_hash_p3 = sge.func("RAND") - - return sge.Concat( - expressions=[ - sge.Cast(this=full_row_hash_p1, to="STRING"), - sge.Cast(this=full_row_hash_p2, to="STRING"), - sge.Cast(this=random_hash_p3, to="STRING"), - ] - ) - - -# Helper functions - - -def _cast_to_int(expr: TypedExpr, op: ops.AsTypeOp) -> sge.Expression | None: - from_type = expr.dtype - sg_expr = expr.expr - # Cannot cast DATETIME to INT directly so need to convert to TIMESTAMP first. - if from_type == dtypes.DATETIME_DTYPE: - sg_expr = sql.cast(sg_expr, "TIMESTAMP", op.safe) - return sge.func("UNIX_MICROS", sg_expr) - if from_type == dtypes.TIMESTAMP_DTYPE: - return sge.func("UNIX_MICROS", sg_expr) - if from_type == dtypes.TIME_DTYPE: - return sge.func( - "TIME_DIFF", - sql.cast(sg_expr, "TIME", op.safe), - sge.convert("00:00:00"), - "MICROSECOND", - ) - if from_type == dtypes.NUMERIC_DTYPE or from_type == dtypes.FLOAT_DTYPE: - sg_expr = sge.func("TRUNC", sg_expr) - return sql.cast(sg_expr, "INT64", op.safe) - return None - - -def _convert_to_nonnull_string_sqlglot(expr: TypedExpr) -> sge.Expression: - col_type = expr.dtype - sg_expr = expr.expr - - if col_type == dtypes.STRING_DTYPE: - result = sg_expr - elif ( - dtypes.is_numeric(col_type) - or dtypes.is_time_or_date_like(col_type) - or col_type == dtypes.BYTES_DTYPE - ): - result = sge.Cast(this=sg_expr, to="STRING") - elif col_type == dtypes.GEO_DTYPE: - result = sge.func("ST_ASTEXT", sg_expr) - else: - # TO_JSON_STRING works with all data types, but isn't the most efficient - # Needed for JSON, STRUCT and ARRAY datatypes - result = sge.func("TO_JSON_STRING", sg_expr) - - # Escape backslashes and use backslash as delineator - escaped = sge.func( - "REPLACE", - sge.func("COALESCE", result, sge.convert("")), - sge.convert("\\"), - sge.convert("\\\\"), - ) - return sge.Concat(expressions=[sge.convert("\\"), escaped]) diff --git a/bigframes/core/compile/sqlglot/expressions/geo_ops.py b/bigframes/core/compile/sqlglot/expressions/geo_ops.py deleted file mode 100644 index 8c353988ae3..00000000000 --- a/bigframes/core/compile/sqlglot/expressions/geo_ops.py +++ /dev/null @@ -1,112 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from __future__ import annotations - -import bigframes_vendored.sqlglot.expressions as sge - -import bigframes.core.compile.sqlglot.expression_compiler as expression_compiler -from bigframes import operations as ops -from bigframes.core.compile.sqlglot.expressions.typed_expr import TypedExpr - -register_unary_op = expression_compiler.expression_compiler.register_unary_op -register_binary_op = expression_compiler.expression_compiler.register_binary_op - - -@register_unary_op(ops.geo_st_astext_op) -def _(expr: TypedExpr) -> sge.Expression: - return sge.func("ST_ASTEXT", expr.expr) - - -@register_unary_op(ops.geo_st_boundary_op) -def _(expr: TypedExpr) -> sge.Expression: - return sge.func("ST_BOUNDARY", expr.expr) - - -@register_unary_op(ops.GeoStBufferOp, pass_op=True) -def _(expr: TypedExpr, op: ops.GeoStBufferOp) -> sge.Expression: - return sge.func( - "ST_BUFFER", - expr.expr, - sge.convert(op.buffer_radius), - sge.convert(op.num_seg_quarter_circle), - sge.convert(op.use_spheroid), - ) - - -@register_unary_op(ops.geo_st_convexhull_op) -def _(expr: TypedExpr) -> sge.Expression: - return sge.func("ST_CONVEXHULL", expr.expr) - - -@register_binary_op(ops.geo_st_geogpoint_op) -def _(left: TypedExpr, right: TypedExpr) -> sge.Expression: - return sge.func("ST_GEOGPOINT", left.expr, right.expr) - - -@register_unary_op(ops.geo_st_geogfromtext_op) -def _(expr: TypedExpr) -> sge.Expression: - return sge.func("SAFE.ST_GEOGFROMTEXT", expr.expr) - - -@register_unary_op(ops.geo_st_isclosed_op) -def _(expr: TypedExpr) -> sge.Expression: - return sge.func("ST_ISCLOSED", expr.expr) - - -@register_unary_op(ops.GeoStLengthOp, pass_op=True) -def _(expr: TypedExpr, op: ops.GeoStLengthOp) -> sge.Expression: - return sge.func("ST_LENGTH", expr.expr) - - -@register_unary_op(ops.GeoStRegionStatsOp, pass_op=True) -def _( - geography: TypedExpr, - op: ops.GeoStRegionStatsOp, -): - args = [geography.expr, sge.convert(op.raster_id)] - if op.band: - args.append(sge.Kwarg(this="band", expression=sge.convert(op.band))) - if op.include: - args.append(sge.Kwarg(this="include", expression=sge.convert(op.include))) - if op.options: - args.append( - sge.Kwarg(this="options", expression=sge.JSON(this=sge.convert(op.options))) - ) - return sge.func("ST_REGIONSTATS", *args) - - -@register_unary_op(ops.geo_x_op) -def _(expr: TypedExpr) -> sge.Expression: - return sge.func("ST_X", expr.expr) - - -@register_unary_op(ops.geo_y_op) -def _(expr: TypedExpr) -> sge.Expression: - return sge.func("ST_Y", expr.expr) - - -@register_binary_op(ops.GeoStDistanceOp, pass_op=True) -def _(left: TypedExpr, right: TypedExpr, op: ops.GeoStDistanceOp) -> sge.Expression: - return sge.func("ST_DISTANCE", left.expr, right.expr, sge.convert(op.use_spheroid)) - - -@register_binary_op(ops.geo_st_difference_op) -def _(left: TypedExpr, right: TypedExpr) -> sge.Expression: - return sge.func("ST_DIFFERENCE", left.expr, right.expr) - - -@register_binary_op(ops.geo_st_intersection_op) -def _(left: TypedExpr, right: TypedExpr) -> sge.Expression: - return sge.func("ST_INTERSECTION", left.expr, right.expr) diff --git a/bigframes/core/compile/sqlglot/expressions/json_ops.py b/bigframes/core/compile/sqlglot/expressions/json_ops.py deleted file mode 100644 index f9a92d3d7a6..00000000000 --- a/bigframes/core/compile/sqlglot/expressions/json_ops.py +++ /dev/null @@ -1,115 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from __future__ import annotations - -import bigframes_vendored.sqlglot.expressions as sge - -import bigframes.core.compile.sqlglot.expression_compiler as expression_compiler -from bigframes import dtypes -from bigframes import operations as ops -from bigframes.core.compile.sqlglot.expressions.typed_expr import TypedExpr - -register_unary_op = expression_compiler.expression_compiler.register_unary_op -register_binary_op = expression_compiler.expression_compiler.register_binary_op - - -@register_unary_op(ops.JSONExtract, pass_op=True) -def _(expr: TypedExpr, op: ops.JSONExtract) -> sge.Expression: - return sge.func("JSON_EXTRACT", expr.expr, sge.convert(op.json_path)) - - -@register_unary_op(ops.JSONExtractArray, pass_op=True) -def _(expr: TypedExpr, op: ops.JSONExtractArray) -> sge.Expression: - return sge.func("JSON_EXTRACT_ARRAY", expr.expr, sge.convert(op.json_path)) - - -@register_unary_op(ops.JSONExtractStringArray, pass_op=True) -def _(expr: TypedExpr, op: ops.JSONExtractStringArray) -> sge.Expression: - return sge.func("JSON_EXTRACT_STRING_ARRAY", expr.expr, sge.convert(op.json_path)) - - -@register_unary_op(ops.JSONKeys, pass_op=True) -def _(expr: TypedExpr, op: ops.JSONKeys) -> sge.Expression: - return sge.func("JSON_KEYS", expr.expr, sge.convert(op.max_depth)) - - -@register_unary_op(ops.JSONQuery, pass_op=True) -def _(expr: TypedExpr, op: ops.JSONQuery) -> sge.Expression: - return sge.func("JSON_QUERY", expr.expr, sge.convert(op.json_path)) - - -@register_unary_op(ops.JSONQueryArray, pass_op=True) -def _(expr: TypedExpr, op: ops.JSONQueryArray) -> sge.Expression: - return sge.func("JSON_QUERY_ARRAY", expr.expr, sge.convert(op.json_path)) - - -@register_unary_op(ops.JSONValue, pass_op=True) -def _(expr: TypedExpr, op: ops.JSONValue) -> sge.Expression: - return sge.func("JSON_VALUE", expr.expr, sge.convert(op.json_path)) - - -@register_unary_op(ops.JSONValueArray, pass_op=True) -def _(expr: TypedExpr, op: ops.JSONValueArray) -> sge.Expression: - return sge.func("JSON_VALUE_ARRAY", expr.expr, sge.convert(op.json_path)) - - -@register_unary_op(ops.ParseJSON) -def _(expr: TypedExpr) -> sge.Expression: - return sge.func("PARSE_JSON", expr.expr) - - -@register_unary_op(ops.ToJSON, pass_op=True) -def _(expr: TypedExpr, op: ops.ToJSON) -> sge.Expression: - from_type = expr.dtype - sg_expr = expr.expr - - # Parsing really should be a distinct operation from serialization, but - # this was the way things were intially launched. - if from_type == dtypes.STRING_DTYPE: - func_name = "SAFE.PARSE_JSON" if op.safe else "PARSE_JSON" - return sge.func(func_name, sg_expr) - else: - return sge.func( - "IF", sg_expr.is_(sge.Null()), sge.Null(), sge.func("TO_JSON", sg_expr) - ) - - -@register_unary_op(ops.JSONDecode, pass_op=True) -def _(expr: TypedExpr, op: ops.JSONDecode) -> sge.Expression: - to_type = op.to_type - sg_expr = expr.expr - func_name = "" - if to_type == dtypes.INT_DTYPE: - func_name = "INT64" - elif to_type == dtypes.FLOAT_DTYPE: - func_name = "FLOAT64" - elif to_type == dtypes.BOOL_DTYPE: - func_name = "BOOL" - elif to_type == dtypes.STRING_DTYPE: - func_name = "STRING" - if func_name: - func_name = "SAFE." + func_name if op.safe else func_name - return sge.func(func_name, sg_expr) - raise TypeError(f"Cannot cast from {dtypes.JSON_DTYPE} to {to_type}") - - -@register_unary_op(ops.ToJSONString) -def _(expr: TypedExpr) -> sge.Expression: - return sge.func("TO_JSON_STRING", expr.expr) - - -@register_binary_op(ops.JSONSet, pass_op=True) -def _(left: TypedExpr, right: TypedExpr, op) -> sge.Expression: - return sge.func("JSON_SET", left.expr, sge.convert(op.json_path), right.expr) diff --git a/bigframes/core/compile/sqlglot/expressions/numeric_ops.py b/bigframes/core/compile/sqlglot/expressions/numeric_ops.py deleted file mode 100644 index d62a93111be..00000000000 --- a/bigframes/core/compile/sqlglot/expressions/numeric_ops.py +++ /dev/null @@ -1,677 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from __future__ import annotations - -import bigframes_vendored.constants as bf_constants -import bigframes_vendored.sqlglot.expressions as sge - -import bigframes.core.compile.sqlglot.expression_compiler as expression_compiler -import bigframes.core.compile.sqlglot.expressions.constants as constants -from bigframes import dtypes -from bigframes import operations as ops -from bigframes.core.compile.sqlglot import sql -from bigframes.core.compile.sqlglot.expressions.common import round_towards_zero -from bigframes.core.compile.sqlglot.expressions.typed_expr import TypedExpr -from bigframes.operations import numeric_ops - -register_unary_op = expression_compiler.expression_compiler.register_unary_op -register_binary_op = expression_compiler.expression_compiler.register_binary_op - - -@register_unary_op(ops.abs_op) -def _(expr: TypedExpr) -> sge.Expression: - return sge.Abs(this=expr.expr) - - -@register_unary_op(ops.arccosh_op) -def _(expr: TypedExpr) -> sge.Expression: - return sge.Case( - ifs=[ - sge.If( - this=expr.expr < sge.convert(1), - true=constants._NAN, - ) - ], - default=sge.func("ACOSH", expr.expr), - ) - - -@register_unary_op(ops.arccos_op) -def _(expr: TypedExpr) -> sge.Expression: - return sge.Case( - ifs=[ - sge.If( - this=sge.func("ABS", expr.expr) > sge.convert(1), - true=constants._NAN, - ) - ], - default=sge.func("ACOS", expr.expr), - ) - - -@register_unary_op(ops.arcsin_op) -def _(expr: TypedExpr) -> sge.Expression: - return sge.Case( - ifs=[ - sge.If( - this=sge.func("ABS", expr.expr) > sge.convert(1), - true=constants._NAN, - ) - ], - default=sge.func("ASIN", expr.expr), - ) - - -@register_unary_op(ops.arcsinh_op) -def _(expr: TypedExpr) -> sge.Expression: - return sge.func("ASINH", expr.expr) - - -@register_binary_op(ops.arctan2_op) -def _(left: TypedExpr, right: TypedExpr) -> sge.Expression: - left_expr = _coerce_bool_to_int(left) - right_expr = _coerce_bool_to_int(right) - return sge.func("ATAN2", left_expr, right_expr) - - -@register_unary_op(ops.arctan_op) -def _(expr: TypedExpr) -> sge.Expression: - return sge.func("ATAN", expr.expr) - - -@register_unary_op(ops.arctanh_op) -def _(expr: TypedExpr) -> sge.Expression: - return sge.Case( - ifs=[ - # |x| < 1: The standard formula - sge.If( - this=sge.func("ABS", expr.expr) < sge.convert(1), - true=sge.func("ATANH", expr.expr), - ), - # |x| > 1: Returns NaN - sge.If( - this=sge.func("ABS", expr.expr) > sge.convert(1), - true=constants._NAN, - ), - ], - # |x| = 1: Returns Infinity or -Infinity - default=sge.Mul(this=constants._INF, expression=expr.expr), - ) - - -@register_unary_op(ops.ceil_op) -def _(expr: TypedExpr) -> sge.Expression: - return sge.Ceil(this=expr.expr) - - -@register_unary_op(ops.cos_op) -def _(expr: TypedExpr) -> sge.Expression: - return sge.func("COS", expr.expr) - - -@register_unary_op(ops.cosh_op) -def _(expr: TypedExpr) -> sge.Expression: - return sge.Case( - ifs=[ - sge.If( - this=sge.func("ABS", expr.expr) > sge.convert(709.78), - true=constants._INF, - ) - ], - default=sge.func("COSH", expr.expr), - ) - - -@register_binary_op(ops.cosine_distance_op) -def _(left: TypedExpr, right: TypedExpr) -> sge.Expression: - return sge.func("ML.DISTANCE", left.expr, right.expr, sge.Literal.string("COSINE")) - - -@register_unary_op(ops.exp_op) -def _(expr: TypedExpr) -> sge.Expression: - return sge.Case( - ifs=[ - sge.If( - this=expr.expr > constants._FLOAT64_EXP_BOUND, - true=constants._INF, - ) - ], - default=sge.func("EXP", expr.expr), - ) - - -@register_unary_op(ops.expm1_op) -def _(expr: TypedExpr) -> sge.Expression: - return sge.If( - this=expr.expr > constants._FLOAT64_EXP_BOUND, - true=constants._INF, - false=sge.func("EXP", expr.expr) - sge.convert(1), - ) - - -@register_unary_op(ops.floor_op) -def _(expr: TypedExpr) -> sge.Expression: - return sge.Floor(this=expr.expr) - - -@register_unary_op(ops.ln_op) -def _(expr: TypedExpr) -> sge.Expression: - return sge.Case( - ifs=[ - sge.If( - this=sge.Is(this=expr.expr, expression=sge.Null()), - true=sge.null(), - ), - # |x| > 0: The standard formula - sge.If( - this=expr.expr > sge.convert(0), - true=sge.Ln(this=expr.expr), - ), - # |x| < 0: Returns NaN - sge.If( - this=expr.expr < sge.convert(0), - true=constants._NAN, - ), - ], - # |x| == 0: Returns -Infinity - default=constants._NEG_INF, - ) - - -@register_unary_op(ops.log10_op) -def _(expr: TypedExpr) -> sge.Expression: - return sge.Case( - ifs=[ - sge.If( - this=sge.Is(this=expr.expr, expression=sge.Null()), - true=sge.null(), - ), - # |x| > 0: The standard formula - sge.If( - this=expr.expr > sge.convert(0), - true=sge.Log(this=sge.convert(10), expression=expr.expr), - ), - # |x| < 0: Returns NaN - sge.If( - this=expr.expr < sge.convert(0), - true=constants._NAN, - ), - ], - # |x| == 0: Returns -Infinity - default=constants._NEG_INF, - ) - - -@register_unary_op(ops.log1p_op) -def _(expr: TypedExpr) -> sge.Expression: - return sge.Case( - ifs=[ - sge.If( - this=sge.Is(this=expr.expr, expression=sge.Null()), - true=sge.null(), - ), - # Domain: |x| > -1 (The standard formula) - sge.If( - this=expr.expr > sge.convert(-1), - true=sge.Ln(this=sge.convert(1) + expr.expr), - ), - # Out of Domain: |x| < -1 (Returns NaN) - sge.If( - this=expr.expr < sge.convert(-1), - true=constants._NAN, - ), - ], - # Boundary: |x| == -1 (Returns -Infinity) - default=constants._NEG_INF, - ) - - -@register_unary_op(ops.neg_op) -def _(expr: TypedExpr) -> sge.Expression: - return sge.Neg(this=sge.paren(expr.expr)) - - -@register_unary_op(ops.pos_op) -def _(expr: TypedExpr) -> sge.Expression: - return expr.expr - - -@register_binary_op(ops.pow_op) -def _(left: TypedExpr, right: TypedExpr) -> sge.Expression: - left_expr = _coerce_bool_to_int(left) - right_expr = _coerce_bool_to_int(right) - if left.dtype == dtypes.INT_DTYPE and right.dtype == dtypes.INT_DTYPE: - return _int_pow_op(left_expr, right_expr) - else: - return _float_pow_op(left_expr, right_expr) - - -def _int_pow_op( - left_expr: sge.Expression, right_expr: sge.Expression -) -> sge.Expression: - if sql.is_null_literal(left_expr) or sql.is_null_literal(right_expr): - return sge.null() - - overflow_cond = sge.and_( - sge.NEQ(this=left_expr, expression=sge.convert(0)), - sge.GT( - this=sge.Mul( - this=right_expr, expression=sge.Ln(this=sge.Abs(this=left_expr)) - ), - expression=sge.convert(constants._INT64_LOG_BOUND), - ), - ) - - return sge.Case( - ifs=[ - sge.If( - this=overflow_cond, - true=sge.Null(), - ) - ], - default=sge.Cast( - this=sge.Pow( - this=sge.Cast( - this=left_expr, to=sge.DataType(this=sge.DataType.Type.DECIMAL) - ), - expression=right_expr, - ), - to="INT64", - ), - ) - - -def _float_pow_op( - left_expr: sge.Expression, right_expr: sge.Expression -) -> sge.Expression: - if sql.is_null_literal(left_expr) or sql.is_null_literal(right_expr): - return sge.null() - - # Most conditions here seek to prevent calling BQ POW with inputs that would generate errors. - # See: https://cloud.google.com/bigquery/docs/reference/standard-sql/mathematical_functions#pow - overflow_cond = sge.and_( - sge.NEQ(this=left_expr, expression=constants._ZERO), - sge.GT( - this=sge.Mul( - this=right_expr, expression=sge.Ln(this=sge.Abs(this=left_expr)) - ), - expression=constants._FLOAT64_EXP_BOUND, - ), - ) - - # Float64 lose integer precision beyond 2**53, beyond this insufficient precision to get parity - exp_too_big = sge.GT( - this=sge.Abs(this=right_expr), - expression=sge.convert(constants._FLOAT64_MAX_INT_PRECISION), - ) - # Treat very large exponents as +=INF - norm_exp = sge.Case( - ifs=[ - sge.If( - this=exp_too_big, - true=sge.Mul(this=constants._INF, expression=sge.Sign(this=right_expr)), - ) - ], - default=right_expr, - ) - - pow_result = sge.Pow(this=left_expr, expression=norm_exp) - - # This cast is dangerous, need to only excuted where y_val has been bounds-checked - # Ibis needs try_cast binding to bq safe_cast - exponent_is_whole = sge.EQ( - this=sge.Cast(this=right_expr, to="INT64"), expression=right_expr - ) - odd_exponent = sge.and_( - sge.LT(this=left_expr, expression=constants._ZERO), - sge.EQ( - this=sge.Mod( - this=sge.Cast(this=right_expr, to="INT64"), expression=sge.convert(2) - ), - expression=sge.convert(1), - ), - ) - infinite_base = sge.EQ(this=sge.Abs(this=left_expr), expression=constants._INF) - - return sge.Case( - ifs=[ - # Might be able to do something more clever with x_val==0 case - sge.If( - this=sge.EQ(this=right_expr, expression=constants._ZERO), - true=sge.convert(1), - ), - sge.If( - this=sge.EQ(this=left_expr, expression=sge.convert(1)), - true=sge.convert(1), - ), # Need to ignore exponent, even if it is NA - sge.If( - this=sge.and_( - sge.EQ(this=left_expr, expression=constants._ZERO), - sge.LT(this=right_expr, expression=constants._ZERO), - ), - true=constants._INF, - ), # This case would error POW function in BQ - sge.If(this=infinite_base, true=pow_result), - sge.If( - this=exp_too_big, true=pow_result - ), # Bigquery can actually handle the +-inf cases gracefully - sge.If( - this=sge.and_( - sge.LT(this=left_expr, expression=constants._ZERO), - sge.Not(this=sge.paren(exponent_is_whole)), - ), - true=constants._NAN, - ), - sge.If( - this=overflow_cond, - true=sge.Mul( - this=constants._INF, - expression=sge.Case( - ifs=[sge.If(this=odd_exponent, true=sge.convert(-1))], - default=sge.convert(1), - ), - ), - ), # finite overflows would cause bq to error - ], - default=pow_result, - ) - - -@register_unary_op(ops.sqrt_op) -def _(expr: TypedExpr) -> sge.Expression: - return sge.Case( - ifs=[ - sge.If( - this=expr.expr < sge.convert(0), - true=constants._NAN, - ) - ], - default=sge.Sqrt(this=expr.expr), - ) - - -@register_unary_op(ops.sin_op) -def _(expr: TypedExpr) -> sge.Expression: - return sge.func("SIN", expr.expr) - - -@register_unary_op(ops.sinh_op) -def _(expr: TypedExpr) -> sge.Expression: - return sge.Case( - ifs=[ - sge.If( - this=sge.func("ABS", expr.expr) > constants._FLOAT64_EXP_BOUND, - true=sge.func("SIGN", expr.expr) * constants._INF, - ) - ], - default=sge.func("SINH", expr.expr), - ) - - -@register_unary_op(ops.tan_op) -def _(expr: TypedExpr) -> sge.Expression: - return sge.func("TAN", expr.expr) - - -@register_unary_op(ops.tanh_op) -def _(expr: TypedExpr) -> sge.Expression: - return sge.func("TANH", expr.expr) - - -@register_binary_op(ops.add_op) -def _(left: TypedExpr, right: TypedExpr) -> sge.Expression: - if sql.is_null_literal(left.expr) or sql.is_null_literal(right.expr): - return sge.null() - - if left.dtype == dtypes.STRING_DTYPE and right.dtype == dtypes.STRING_DTYPE: - # String addition - return sge.Concat(expressions=[left.expr, right.expr]) - - if dtypes.is_numeric(left.dtype) and dtypes.is_numeric(right.dtype): - left_expr = _coerce_bool_to_int(left) - right_expr = _coerce_bool_to_int(right) - return sge.Add(this=left_expr, expression=right_expr) - - if ( - dtypes.is_time_or_date_like(left.dtype) - and right.dtype == dtypes.TIMEDELTA_DTYPE - ): - left_expr = _coerce_date_to_datetime(left) - return sge.TimestampAdd( - this=left_expr, expression=right.expr, unit=sge.Var(this="MICROSECOND") - ) - if ( - dtypes.is_time_or_date_like(right.dtype) - and left.dtype == dtypes.TIMEDELTA_DTYPE - ): - right_expr = _coerce_date_to_datetime(right) - return sge.TimestampAdd( - this=right_expr, expression=left.expr, unit=sge.Var(this="MICROSECOND") - ) - if left.dtype == dtypes.TIMEDELTA_DTYPE and right.dtype == dtypes.TIMEDELTA_DTYPE: - return sge.Add(this=left.expr, expression=right.expr) - - raise TypeError( - f"Cannot add type {left.dtype} and {right.dtype}. {bf_constants.FEEDBACK_LINK}" - ) - - -@register_binary_op(ops.div_op) -def _(left: TypedExpr, right: TypedExpr) -> sge.Expression: - if sql.is_null_literal(left.expr) or sql.is_null_literal(right.expr): - return sge.null() - - left_expr = _coerce_bool_to_int(left) - right_expr = _coerce_bool_to_int(right) - - result = sge.func("IEEE_DIVIDE", left_expr, right_expr) - if left.dtype == dtypes.TIMEDELTA_DTYPE and dtypes.is_numeric(right.dtype): - return round_towards_zero(result) - else: - return result - - -@register_binary_op(ops.euclidean_distance_op) -def _(left: TypedExpr, right: TypedExpr) -> sge.Expression: - return sge.func( - "ML.DISTANCE", left.expr, right.expr, sge.Literal.string("EUCLIDEAN") - ) - - -@register_binary_op(ops.floordiv_op) -def _(left: TypedExpr, right: TypedExpr) -> sge.Expression: - if sql.is_null_literal(left.expr) or sql.is_null_literal(right.expr): - return sge.null() - - left_expr = _coerce_bool_to_int(left) - right_expr = _coerce_bool_to_int(right) - - result: sge.Expression = sge.Cast( - this=sge.Floor(this=sge.func("IEEE_DIVIDE", left_expr, right_expr)), to="INT64" - ) - - # DIV(N, 0) will error in bigquery, but needs to return `0` for int, and - # `inf`` for float in BQ so we short-circuit in this case. - # Multiplying left by zero propogates nulls. - zero_result = ( - constants._INF - if (left.dtype == dtypes.FLOAT_DTYPE or right.dtype == dtypes.FLOAT_DTYPE) - else constants._ZERO - ) - result = sge.Case( - ifs=[ - sge.If( - this=sge.EQ(this=right_expr, expression=constants._ZERO), - true=zero_result * left_expr, - ) - ], - default=result, - ) - - if dtypes.is_numeric(right.dtype) and left.dtype == dtypes.TIMEDELTA_DTYPE: - result = round_towards_zero(sge.func("IEEE_DIVIDE", left_expr, right_expr)) - - return result - - -@register_binary_op(ops.manhattan_distance_op) -def _(left: TypedExpr, right: TypedExpr) -> sge.Expression: - return sge.func( - "ML.DISTANCE", left.expr, right.expr, sge.Literal.string("MANHATTAN") - ) - - -@register_binary_op(ops.mod_op) -def _(left: TypedExpr, right: TypedExpr) -> sge.Expression: - if sql.is_null_literal(left.expr) or sql.is_null_literal(right.expr): - return sge.null() - - # In BigQuery returned value has the same sign as X. In pandas, the sign of y is used, so we need to flip the result if sign(x) != sign(y) - left_expr = _coerce_bool_to_int(left) - right_expr = _coerce_bool_to_int(right) - - # BigQuery MOD function doesn't support float types, so cast to BIGNUMERIC - if left.dtype == dtypes.FLOAT_DTYPE or right.dtype == dtypes.FLOAT_DTYPE: - left_expr = sge.Cast(this=left_expr, to="BIGNUMERIC") - right_expr = sge.Cast(this=right_expr, to="BIGNUMERIC") - - # MOD(N, 0) will error in bigquery, but needs to return null - bq_mod = sge.Mod(this=left_expr, expression=right_expr) - zero_result = ( - constants._NAN - if (left.dtype == dtypes.FLOAT_DTYPE or right.dtype == dtypes.FLOAT_DTYPE) - else constants._ZERO - ) - return sge.Case( - ifs=[ - sge.If( - this=sge.EQ(this=right_expr, expression=constants._ZERO), - true=zero_result * left_expr, - ), - sge.If( - this=sge.and_( - right_expr < constants._ZERO, - bq_mod > constants._ZERO, - ), - true=right_expr + bq_mod, - ), - sge.If( - this=sge.and_( - right_expr > constants._ZERO, - bq_mod < constants._ZERO, - ), - true=right_expr + bq_mod, - ), - ], - default=bq_mod, - ) - - -@register_binary_op(ops.mul_op) -def _(left: TypedExpr, right: TypedExpr) -> sge.Expression: - if sql.is_null_literal(left.expr) or sql.is_null_literal(right.expr): - return sge.null() - - left_expr = _coerce_bool_to_int(left) - right_expr = _coerce_bool_to_int(right) - - result = sge.Mul(this=left_expr, expression=right_expr) - - if (dtypes.is_numeric(left.dtype) and right.dtype == dtypes.TIMEDELTA_DTYPE) or ( - left.dtype == dtypes.TIMEDELTA_DTYPE and dtypes.is_numeric(right.dtype) - ): - return round_towards_zero(result) - else: - return result - - -@register_binary_op(ops.round_op) -def _(expr: TypedExpr, n_digits: TypedExpr) -> sge.Expression: - rounded = sge.Round(this=expr.expr, decimals=n_digits.expr) - if expr.dtype == dtypes.INT_DTYPE: - return sge.Cast(this=rounded, to="INT64") - return rounded - - -@register_binary_op(ops.sub_op) -def _(left: TypedExpr, right: TypedExpr) -> sge.Expression: - if sql.is_null_literal(left.expr) or sql.is_null_literal(right.expr): - return sge.null() - - if dtypes.is_numeric(left.dtype) and dtypes.is_numeric(right.dtype): - left_expr = _coerce_bool_to_int(left) - right_expr = _coerce_bool_to_int(right) - return sge.Sub(this=left_expr, expression=right_expr) - - if ( - dtypes.is_time_or_date_like(left.dtype) - and right.dtype == dtypes.TIMEDELTA_DTYPE - ): - left_expr = _coerce_date_to_datetime(left) - return sge.TimestampSub( - this=left_expr, expression=right.expr, unit=sge.Var(this="MICROSECOND") - ) - if dtypes.is_time_or_date_like(left.dtype) and dtypes.is_time_or_date_like( - right.dtype - ): - left_expr = _coerce_date_to_datetime(left) - right_expr = _coerce_date_to_datetime(right) - return sge.TimestampDiff( - this=left_expr, expression=right_expr, unit=sge.Var(this="MICROSECOND") - ) - - if left.dtype == dtypes.TIMEDELTA_DTYPE and right.dtype == dtypes.TIMEDELTA_DTYPE: - return sge.Sub(this=left.expr, expression=right.expr) - - raise TypeError( - f"Cannot subtract type {left.dtype} and {right.dtype}. {bf_constants.FEEDBACK_LINK}" - ) - - -@register_binary_op(ops.unsafe_pow_op) -def _(left: TypedExpr, right: TypedExpr) -> sge.Expression: - """For internal use only - where domain and overflow checks are not needed.""" - left_expr = _coerce_bool_to_int(left) - right_expr = _coerce_bool_to_int(right) - return sge.Pow(this=left_expr, expression=right_expr) - - -@register_unary_op(numeric_ops.isnan_op) -def isnan(arg: TypedExpr) -> sge.Expression: - return sge.IsNan(this=arg.expr) - - -@register_unary_op(numeric_ops.isfinite_op) -def isfinite(arg: TypedExpr) -> sge.Expression: - return sge.Not( - this=sge.Or( - this=sge.IsInf(this=arg.expr), - expression=sge.IsNan(this=arg.expr), - ), - ) - - -def _coerce_bool_to_int(typed_expr: TypedExpr) -> sge.Expression: - """Coerce boolean expression to integer.""" - if typed_expr.dtype == dtypes.BOOL_DTYPE: - return sge.Cast(this=typed_expr.expr, to="INT64") - return typed_expr.expr - - -def _coerce_date_to_datetime(typed_expr: TypedExpr) -> sge.Expression: - """Coerce date expression to datetime.""" - if typed_expr.dtype == dtypes.DATE_DTYPE: - return sge.Cast(this=typed_expr.expr, to="DATETIME") - return typed_expr.expr diff --git a/bigframes/core/compile/sqlglot/expressions/string_ops.py b/bigframes/core/compile/sqlglot/expressions/string_ops.py deleted file mode 100644 index 65a13a45f8b..00000000000 --- a/bigframes/core/compile/sqlglot/expressions/string_ops.py +++ /dev/null @@ -1,383 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from __future__ import annotations - -import functools -import typing - -import bigframes_vendored.sqlglot.expressions as sge - -import bigframes.core.compile.sqlglot.expression_compiler as expression_compiler -from bigframes import dtypes -from bigframes import operations as ops -from bigframes.core.compile.sqlglot.expressions.typed_expr import TypedExpr - -register_unary_op = expression_compiler.expression_compiler.register_unary_op -register_binary_op = expression_compiler.expression_compiler.register_binary_op - - -@register_unary_op(ops.capitalize_op) -def _(expr: TypedExpr) -> sge.Expression: - return sge.Initcap(this=expr.expr, expression=sge.convert("")) - - -@register_unary_op(ops.StrContainsOp, pass_op=True) -def _(expr: TypedExpr, op: ops.StrContainsOp) -> sge.Expression: - return sge.Like(this=expr.expr, expression=sge.convert(f"%{op.pat}%")) - - -@register_unary_op(ops.StrContainsRegexOp, pass_op=True) -def _(expr: TypedExpr, op: ops.StrContainsRegexOp) -> sge.Expression: - return sge.RegexpLike(this=expr.expr, expression=sge.convert(op.pat)) - - -@register_unary_op(ops.StrExtractOp, pass_op=True) -def _(expr: TypedExpr, op: ops.StrExtractOp) -> sge.Expression: - # Cannot use BigQuery's REGEXP_EXTRACT function, which only allows one - # capturing group. - pat_expr = sge.convert(op.pat) - if op.n == 0: - pat_expr = sge.func("CONCAT", sge.convert(".*?("), pat_expr, sge.convert(").*")) - n = 1 - else: - pat_expr = sge.func("CONCAT", sge.convert(".*?"), pat_expr, sge.convert(".*")) - n = op.n - - rex_replace = sge.func("REGEXP_REPLACE", expr.expr, pat_expr, sge.convert(f"\\{n}")) - rex_contains = sge.func("REGEXP_CONTAINS", expr.expr, sge.convert(op.pat)) - return sge.If(this=rex_contains, true=rex_replace, false=sge.null()) - - -@register_unary_op(ops.StrFindOp, pass_op=True) -def _(expr: TypedExpr, op: ops.StrFindOp) -> sge.Expression: - # INSTR is 1-based, so we need to adjust the start position. - start = sge.convert(op.start + 1) if op.start is not None else sge.convert(1) - if op.end is not None: - # BigQuery's INSTR doesn't support `end`, so we need to use SUBSTR. - return sge.func( - "INSTR", - sge.Substring( - this=expr.expr, - start=start, - length=sge.convert(op.end - (op.start or 0)), - ), - sge.convert(op.substr), - ) - sge.convert(1) - else: - return sge.func( - "INSTR", - expr.expr, - sge.convert(op.substr), - start, - ) - sge.convert(1) - - -@register_unary_op(ops.StrLstripOp, pass_op=True) -def _(expr: TypedExpr, op: ops.StrLstripOp) -> sge.Expression: - return sge.func("LTRIM", expr.expr, sge.convert(op.to_strip)) - - -@register_unary_op(ops.StrRstripOp, pass_op=True) -def _(expr: TypedExpr, op: ops.StrRstripOp) -> sge.Expression: - return sge.func("RTRIM", expr.expr, sge.convert(op.to_strip)) - - -@register_unary_op(ops.StrPadOp, pass_op=True) -def _(expr: TypedExpr, op: ops.StrPadOp) -> sge.Expression: - expr_length = sge.Length(this=expr.expr) - fillchar = sge.convert(op.fillchar) - pad_length = sge.func("GREATEST", expr_length, sge.convert(op.length)) - - if op.side == "left": - return sge.func("LPAD", expr.expr, pad_length, fillchar) - elif op.side == "right": - return sge.func("RPAD", expr.expr, pad_length, fillchar) - else: # side == both - lpad_amount = ( - sge.Cast( - this=sge.Floor( - this=sge.func( - "SAFE_DIVIDE", - sge.Sub(this=pad_length, expression=expr_length), - sge.convert(2), - ) - ), - to="INT64", - ) - + expr_length - ) - return sge.func( - "RPAD", - sge.func("LPAD", expr.expr, lpad_amount, fillchar), - pad_length, - fillchar, - ) - - -@register_unary_op(ops.StrRepeatOp, pass_op=True) -def _(expr: TypedExpr, op: ops.StrRepeatOp) -> sge.Expression: - return sge.Repeat(this=expr.expr, times=sge.convert(op.repeats)) - - -@register_unary_op(ops.EndsWithOp, pass_op=True) -def _(expr: TypedExpr, op: ops.EndsWithOp) -> sge.Expression: - if not op.pat: - return sge.false() - - def to_endswith(pat: str) -> sge.Expression: - return sge.func("ENDS_WITH", expr.expr, sge.convert(pat)) - - conditions = [to_endswith(pat) for pat in op.pat] - return functools.reduce(lambda x, y: sge.Or(this=x, expression=y), conditions) - - -@register_unary_op(ops.isalnum_op) -def _(expr: TypedExpr) -> sge.Expression: - return sge.RegexpLike(this=expr.expr, expression=sge.convert(r"^(\p{N}|\p{L})+$")) - - -@register_unary_op(ops.isalpha_op) -def _(expr: TypedExpr) -> sge.Expression: - return sge.RegexpLike(this=expr.expr, expression=sge.convert(r"^\p{L}+$")) - - -@register_unary_op(ops.isdecimal_op) -def _(expr: TypedExpr) -> sge.Expression: - return sge.RegexpLike(this=expr.expr, expression=sge.convert(r"^(\p{Nd})+$")) - - -@register_unary_op(ops.isdigit_op) -def _(expr: TypedExpr) -> sge.Expression: - regexp_pattern = ( - r"^[\p{Nd}\x{00B9}\x{00B2}\x{00B3}\x{2070}\x{2074}-\x{2079}\x{2080}-\x{2089}]+$" - ) - return sge.RegexpLike(this=expr.expr, expression=sge.convert(regexp_pattern)) - - -@register_unary_op(ops.islower_op) -def _(expr: TypedExpr) -> sge.Expression: - return sge.And( - this=sge.EQ( - this=sge.Lower(this=expr.expr), - expression=expr.expr, - ), - expression=sge.NEQ( - this=sge.Upper(this=expr.expr), - expression=expr.expr, - ), - ) - - -@register_unary_op(ops.isnumeric_op) -def _(expr: TypedExpr) -> sge.Expression: - return sge.RegexpLike(this=expr.expr, expression=sge.convert(r"^\pN+$")) - - -@register_unary_op(ops.isspace_op) -def _(expr: TypedExpr) -> sge.Expression: - return sge.RegexpLike(this=expr.expr, expression=sge.convert(r"^\s+$")) - - -@register_unary_op(ops.isupper_op) -def _(expr: TypedExpr) -> sge.Expression: - return sge.And( - this=sge.EQ( - this=sge.Upper(this=expr.expr), - expression=expr.expr, - ), - expression=sge.NEQ( - this=sge.Lower(this=expr.expr), - expression=expr.expr, - ), - ) - - -@register_unary_op(ops.len_op) -def _(expr: TypedExpr) -> sge.Expression: - if dtypes.is_array_like(expr.dtype): - return sge.func("ARRAY_LENGTH", expr.expr) - - return sge.Length(this=expr.expr) - - -@register_unary_op(ops.lower_op) -def _(expr: TypedExpr) -> sge.Expression: - return sge.Lower(this=expr.expr) - - -@register_unary_op(ops.ReplaceStrOp, pass_op=True) -def _(expr: TypedExpr, op: ops.ReplaceStrOp) -> sge.Expression: - return sge.func("REPLACE", expr.expr, sge.convert(op.pat), sge.convert(op.repl)) - - -@register_unary_op(ops.RegexReplaceStrOp, pass_op=True) -def _(expr: TypedExpr, op: ops.RegexReplaceStrOp) -> sge.Expression: - return sge.func( - "REGEXP_REPLACE", expr.expr, sge.convert(op.pat), sge.convert(op.repl) - ) - - -@register_unary_op(ops.reverse_op) -def _(expr: TypedExpr) -> sge.Expression: - return sge.func("REVERSE", expr.expr) - - -@register_unary_op(ops.StartsWithOp, pass_op=True) -def _(expr: TypedExpr, op: ops.StartsWithOp) -> sge.Expression: - if not op.pat: - return sge.false() - - def to_startswith(pat: str) -> sge.Expression: - return sge.func("STARTS_WITH", expr.expr, sge.convert(pat)) - - conditions = [to_startswith(pat) for pat in op.pat] - return functools.reduce(lambda x, y: sge.Or(this=x, expression=y), conditions) - - -@register_unary_op(ops.StrStripOp, pass_op=True) -def _(expr: TypedExpr, op: ops.StrStripOp) -> sge.Expression: - return sge.Trim(this=expr.expr, expression=sge.convert(op.to_strip)) - - -@register_unary_op(ops.StringSplitOp, pass_op=True) -def _(expr: TypedExpr, op: ops.StringSplitOp) -> sge.Expression: - return sge.Split(this=expr.expr, expression=sge.convert(op.pat)) - - -@register_unary_op(ops.StrSliceOp, pass_op=True) -def _(expr: TypedExpr, op: ops.StrSliceOp) -> sge.Expression: - return string_slice(expr, op.start, op.end) - - -@register_unary_op(ops.upper_op) -def _(expr: TypedExpr) -> sge.Expression: - return sge.Upper(this=expr.expr) - - -@register_binary_op(ops.strconcat_op) -def _(left: TypedExpr, right: TypedExpr) -> sge.Expression: - return sge.Concat(expressions=[left.expr, right.expr]) - - -@register_unary_op(ops.ZfillOp, pass_op=True) -def _(expr: TypedExpr, op: ops.ZfillOp) -> sge.Expression: - length_expr = sge.Greatest( - expressions=[sge.Length(this=expr.expr), sge.convert(op.width)] - ) - return sge.Case( - ifs=[ - sge.If( - this=sge.func( - "STARTS_WITH", - expr.expr, - sge.convert("-"), - ), - true=sge.Concat( - expressions=[ - sge.convert("-"), - sge.func( - "LPAD", - sge.Substring(this=expr.expr, start=sge.convert(2)), - length_expr - 1, - sge.convert("0"), - ), - ] - ), - ) - ], - default=sge.func("LPAD", expr.expr, length_expr, sge.convert("0")), - ) - - -def string_index(expr: TypedExpr, index: int) -> sge.Expression: - sub_str = sge.Substring( - this=expr.expr, - start=sge.convert(index + 1), - length=sge.convert(1), - ) - return sge.If( - this=sge.NEQ(this=sub_str, expression=sge.convert("")), - true=sub_str, - false=sge.Null(), - ) - - -def string_slice( - expr: TypedExpr, op_start: typing.Optional[int], op_end: typing.Optional[int] -) -> sge.Expression: - column_length = sge.Length(this=expr.expr) - if op_start is None: - start = 0 - else: - start = op_start - - start_expr = sge.convert(start) if start < 0 else sge.convert(start + 1) - length_expr: typing.Optional[sge.Expression] - if op_end is None: - length_expr = None - elif op_end < 0: - if start < 0: - start_expr = sge.Greatest( - expressions=[ - sge.convert(1), - column_length + sge.convert(start + 1), - ] - ) - length_expr = sge.Greatest( - expressions=[ - sge.convert(0), - column_length + sge.convert(op_end), - ] - ) - sge.Greatest( - expressions=[ - sge.convert(0), - column_length + sge.convert(start), - ] - ) - else: - length_expr = sge.Greatest( - expressions=[ - sge.convert(0), - column_length + sge.convert(op_end - start), - ] - ) - else: # op.end >= 0 - if start < 0: - start_expr = sge.Greatest( - expressions=[ - sge.convert(1), - column_length + sge.convert(start + 1), - ] - ) - length_expr = sge.Greatest( - expressions=[ - sge.convert(0), - sge.convert(op_end) - - sge.Greatest( - expressions=[ - sge.convert(0), - column_length + sge.convert(start), - ] - ), - ] - ) - else: - length_expr = sge.convert(op_end - start) - - return sge.Substring( - this=expr.expr, - start=start_expr, - length=length_expr, - ) diff --git a/bigframes/core/compile/sqlglot/expressions/struct_ops.py b/bigframes/core/compile/sqlglot/expressions/struct_ops.py deleted file mode 100644 index 01022210182..00000000000 --- a/bigframes/core/compile/sqlglot/expressions/struct_ops.py +++ /dev/null @@ -1,53 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from __future__ import annotations - -import typing - -import bigframes_vendored.sqlglot.expressions as sge -import pandas as pd -import pyarrow as pa - -import bigframes.core.compile.sqlglot.expression_compiler as expression_compiler -from bigframes import operations as ops -from bigframes.core.compile.sqlglot.expressions.typed_expr import TypedExpr - -register_nary_op = expression_compiler.expression_compiler.register_nary_op -register_unary_op = expression_compiler.expression_compiler.register_unary_op - - -@register_unary_op(ops.StructFieldOp, pass_op=True) -def _(expr: TypedExpr, op: ops.StructFieldOp) -> sge.Expression: - if isinstance(op.name_or_index, str): - name = op.name_or_index - else: - pa_type = typing.cast(pd.ArrowDtype, expr.dtype) - pa_struct_type = typing.cast(pa.StructType, pa_type.pyarrow_dtype) - name = pa_struct_type.field(op.name_or_index).name - - return sge.Column( - this=sge.to_identifier(name, quoted=True), - catalog=expr.expr, - ) - - -@register_nary_op(ops.StructOp, pass_op=True) -def _(*exprs: TypedExpr, op: ops.StructOp) -> sge.Struct: - return sge.Struct( - expressions=[ - sge.PropertyEQ(this=sge.to_identifier(col), expression=expr.expr) - for col, expr in zip(op.column_names, exprs) - ] - ) diff --git a/bigframes/core/compile/sqlglot/expressions/timedelta_ops.py b/bigframes/core/compile/sqlglot/expressions/timedelta_ops.py deleted file mode 100644 index fbc982829ca..00000000000 --- a/bigframes/core/compile/sqlglot/expressions/timedelta_ops.py +++ /dev/null @@ -1,44 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from __future__ import annotations - -import bigframes_vendored.sqlglot.expressions as sge - -import bigframes.core.compile.sqlglot.expression_compiler as expression_compiler -from bigframes import dtypes -from bigframes import operations as ops -from bigframes.core.compile.constants import UNIT_TO_US_CONVERSION_FACTORS -from bigframes.core.compile.sqlglot.expressions.typed_expr import TypedExpr - -register_unary_op = expression_compiler.expression_compiler.register_unary_op - - -@register_unary_op(ops.timedelta_floor_op) -def _(expr: TypedExpr) -> sge.Expression: - return sge.Floor(this=expr.expr) - - -@register_unary_op(ops.ToTimedeltaOp, pass_op=True) -def _(expr: TypedExpr, op: ops.ToTimedeltaOp) -> sge.Expression: - value = expr.expr - if expr.dtype == dtypes.TIMEDELTA_DTYPE: - return value - - factor = UNIT_TO_US_CONVERSION_FACTORS[op.unit] - if factor != 1: - value = sge.Mul(this=value, expression=sge.convert(factor)) - if expr.dtype == dtypes.FLOAT_DTYPE: - value = sge.Cast(this=sge.Floor(this=value), to=sge.DataType(this="INT64")) - return value diff --git a/bigframes/core/compile/sqlglot/expressions/typed_expr.py b/bigframes/core/compile/sqlglot/expressions/typed_expr.py deleted file mode 100644 index d8c38c2e718..00000000000 --- a/bigframes/core/compile/sqlglot/expressions/typed_expr.py +++ /dev/null @@ -1,30 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import dataclasses - -import bigframes_vendored.sqlglot.expressions as sge - -from bigframes import dtypes - - -@dataclasses.dataclass(frozen=True) -class TypedExpr: - """SQLGlot expression with type.""" - - expr: sge.Expression - dtype: dtypes.ExpressionType - - # kludge to support optional args in argument lists - is_omitted: bool = False diff --git a/bigframes/core/compile/sqlglot/sql/__init__.py b/bigframes/core/compile/sqlglot/sql/__init__.py deleted file mode 100644 index 751c3cfc3a5..00000000000 --- a/bigframes/core/compile/sqlglot/sql/__init__.py +++ /dev/null @@ -1,41 +0,0 @@ -# Copyright 2026 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -from __future__ import annotations - -from bigframes.core.compile.sqlglot.sql.base import ( - cast, - identifier, - is_null_literal, - literal, - table, - to_sql, -) -from bigframes.core.compile.sqlglot.sql.ddl import create_external_table, load_data -from bigframes.core.compile.sqlglot.sql.dml import insert, replace - -__all__ = [ - # From base.py - "cast", - "identifier", - "is_null_literal", - "literal", - "table", - "to_sql", - # From ddl.py - "create_external_table", - "load_data", - # From dml.py - "insert", - "replace", -] diff --git a/bigframes/core/compile/sqlglot/sql/base.py b/bigframes/core/compile/sqlglot/sql/base.py deleted file mode 100644 index f77dcbee4d9..00000000000 --- a/bigframes/core/compile/sqlglot/sql/base.py +++ /dev/null @@ -1,147 +0,0 @@ -# Copyright 2026 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from __future__ import annotations - -import typing - -import bigframes_vendored.sqlglot as sg -import bigframes_vendored.sqlglot.expressions as sge -import numpy as np -import pandas as pd -import pyarrow as pa -from google.cloud import bigquery - -import bigframes.core.compile.sqlglot.sqlglot_types as sgt -from bigframes import dtypes -from bigframes.core import utils -from bigframes.core.compile.sqlglot.expressions import constants - -# shapely.wkt.dumps was moved to shapely.io.to_wkt in 2.0. -try: - from shapely.io import to_wkt # type: ignore -except ImportError: - from shapely.wkt import dumps # type: ignore - - to_wkt = dumps - - -QUOTED: bool = True -"""Whether to quote identifiers in the generated SQL.""" - -PRETTY: bool = True -"""Whether to pretty-print the generated SQL.""" - -DIALECT = sg.dialects.bigquery.BigQuery -"""The SQL dialect used for generation.""" - - -def to_sql(expr: sge.Expression) -> str: - """Generate SQL string from the given expression.""" - return expr.sql(dialect=DIALECT, pretty=PRETTY) - - -def identifier(id: str) -> sge.Identifier: - """Return a string representing column reference in a SQL.""" - return sge.to_identifier(id, quoted=QUOTED) - - -def literal(value: typing.Any, dtype: dtypes.Dtype | None = None) -> sge.Expression: - """Return a string representing column reference in a SQL.""" - if dtype is None: - dtype = dtypes.infer_literal_type(value) - - sqlglot_type = sgt.from_bigframes_dtype(dtype) if dtype else None - if sqlglot_type is None: - if not pd.isna(value): - raise ValueError(f"Cannot infer SQLGlot type from None dtype: {value}") - return sge.Null() - - if value is None: - if str(sqlglot_type).upper() == "NULL": - return sge.Null() - return cast(sge.Null(), sqlglot_type) - if dtypes.is_struct_like(dtype): - items = [ - literal(value=value[field_name], dtype=field_dtype).as_( - field_name, quoted=True - ) - for field_name, field_dtype in dtypes.get_struct_fields(dtype).items() - ] - return sge.Struct.from_arg_list(items) - elif dtypes.is_array_like(dtype): - value_type = dtypes.get_array_inner_type(dtype) - values = sge.Array( - expressions=[literal(value=v, dtype=value_type) for v in value] - ) - return values if len(value) > 0 else cast(values, sqlglot_type) - elif dtype == dtypes.FLOAT_DTYPE: - if pd.isna(value): - if isinstance(value, (float, np.floating)) and np.isnan(value): - return constants._NAN - return cast(sge.Null(), sqlglot_type) - if np.isinf(value): - return constants._INF if value > 0 else constants._NEG_INF - return sge.convert(value) - elif pd.isna(value) or (isinstance(value, pa.Scalar) and not value.is_valid): - return cast(sge.Null(), sqlglot_type) - elif dtype == dtypes.JSON_DTYPE: - return sge.ParseJSON(this=sge.convert(str(value))) - elif dtype == dtypes.BYTES_DTYPE: - return cast(str(value), sqlglot_type) - elif dtypes.is_time_like(dtype): - if isinstance(value, str): - return cast(sge.convert(value), sqlglot_type) - if isinstance(value, np.generic): - value = value.item() - return cast(sge.convert(value.isoformat()), sqlglot_type) - elif dtype in (dtypes.NUMERIC_DTYPE, dtypes.BIGNUMERIC_DTYPE): - return cast(sge.convert(value), sqlglot_type) - elif dtypes.is_geo_like(dtype): - wkt = value if isinstance(value, str) else to_wkt(value) - return sge.func("ST_GEOGFROMTEXT", sge.convert(wkt)) - elif dtype == dtypes.TIMEDELTA_DTYPE: - return sge.convert(utils.timedelta_to_micros(value)) - else: - if isinstance(value, np.generic): - value = value.item() - if isinstance(value, pa.Scalar): - value = value.as_py() - return sge.convert(value) - - -def cast(arg: typing.Any, to: str, safe: bool = False) -> sge.Cast | sge.TryCast: - """Return a SQL expression that casts the given argument to the specified type.""" - if safe: - return sge.TryCast(this=arg, to=to) - else: - return sge.Cast(this=arg, to=to) - - -def table(table: bigquery.TableReference) -> sge.Table: - """Return a SQLGlot Table expression representing the given BigQuery table reference.""" - return sge.Table( - this=sge.to_identifier(table.table_id, quoted=True), - db=sge.to_identifier(table.dataset_id, quoted=True), - catalog=sge.to_identifier(table.project, quoted=True), - ) - - -def is_null_literal(expr: sge.Expression) -> bool: - """Checks if the given expression is a NULL literal.""" - if isinstance(expr, sge.Null): - return True - if isinstance(expr, sge.Cast) and isinstance(expr.this, sge.Null): - return True - return False diff --git a/bigframes/core/compile/sqlglot/sql/ddl.py b/bigframes/core/compile/sqlglot/sql/ddl.py deleted file mode 100644 index 1a63d016d5e..00000000000 --- a/bigframes/core/compile/sqlglot/sql/ddl.py +++ /dev/null @@ -1,220 +0,0 @@ -# Copyright 2026 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from __future__ import annotations - -from typing import Mapping, Optional, Union - -import bigframes_vendored.sqlglot as sg -import bigframes_vendored.sqlglot.expressions as sge - -from bigframes.core.compile.sqlglot.sql import base - - -def load_data( - table_name: str, - *, - write_disposition: str = "INTO", - columns: Optional[Mapping[str, str]] = None, - partition_by: Optional[list[str]] = None, - cluster_by: Optional[list[str]] = None, - table_options: Optional[Mapping[str, Union[str, int, float, bool, list]]] = None, - from_files_options: Mapping[str, Union[str, int, float, bool, list]], - with_partition_columns: Optional[Mapping[str, str]] = None, - connection_name: Optional[str] = None, -) -> sge.LoadData: - """Generates the LOAD DATA DDL statement.""" - # We use a Table with a simple identifier for the table name. - # Quoting is handled by the dialect. - table_expr = sge.Table(this=base.identifier(table_name)) - - sge_partition_by = ( - sge.PartitionedByProperty( - this=base.identifier(partition_by[0]) - if len(partition_by) == 1 - else sge.Tuple(expressions=[base.identifier(col) for col in partition_by]) - ) - if partition_by - else None - ) - - sge_cluster_by = ( - sge.Cluster(expressions=[base.identifier(col) for col in cluster_by]) - if cluster_by - else None - ) - - sge_from_files = sge.Tuple( - expressions=[ - sge.Property(this=base.identifier(k), value=base.literal(v)) - for k, v in from_files_options.items() - ] - ) - - sge_connection = base.identifier(connection_name) if connection_name else None - - return sge.LoadData( - this=table_expr, - overwrite=(write_disposition == "OVERWRITE"), - inpath=sge.convert("fake"), # satisfy sqlglot's required inpath arg - columns=_get_sge_schema(columns), - partition_by=sge_partition_by, - cluster_by=sge_cluster_by, - options=_get_sge_properties(table_options), - from_files=sge_from_files, - with_partition_columns=_get_sge_schema(with_partition_columns), - connection=sge_connection, - ) - - -def create_external_table( - table_name: str, - *, - replace: bool = False, - if_not_exists: bool = False, - columns: Optional[Mapping[str, str]] = None, - partition_columns: Optional[Mapping[str, str]] = None, - connection_name: Optional[str] = None, - options: Optional[Mapping[str, Union[str, int, float, bool, list]]] = None, -) -> sge.Create: - """Generates the CREATE EXTERNAL TABLE DDL statement.""" - sge_connection = base.identifier(connection_name) if connection_name else None - - table_expr = sge.Table(this=base.identifier(table_name)) - - # sqlglot.expressions.Create usually takes 'this' (Table or Schema) - sge_schema = _get_sge_schema(columns) - this: sge.Table | sge.Schema - if sge_schema: - sge_schema.set("this", table_expr) - this = sge_schema - else: - this = table_expr - - return sge.Create( - this=this, - kind="EXTERNAL TABLE", - replace=replace, - exists_ok=if_not_exists, - properties=_get_sge_properties(options), - connection=sge_connection, - partition_columns=_get_sge_schema(partition_columns), - ) - - -def _get_sge_schema( - columns: Optional[Mapping[str, str]] = None, -) -> Optional[sge.Schema]: - if not columns: - return None - - return sge.Schema( - this=None, - expressions=[ - sge.ColumnDef( - this=base.identifier(name), - kind=sge.DataType.build(typ, dialect=base.DIALECT), - ) - for name, typ in columns.items() - ], - ) - - -def _get_sge_properties( - options: Optional[Mapping[str, Union[str, int, float, bool, list]]] = None, -) -> Optional[sge.Properties]: - if not options: - return None - - return sge.Properties( - expressions=[ - sge.Property(this=base.identifier(k), value=base.literal(v)) - for k, v in options.items() - ] - ) - - -def _loaddata_sql(self: sg.Generator, expression: sge.LoadData) -> str: - out = ["LOAD DATA"] - if expression.args.get("overwrite"): - out.append("OVERWRITE") - - out.append(f"INTO {self.sql(expression, 'this').strip()}") - - # We ignore inpath as it's just a dummy to satisfy sqlglot requirements - # but BigQuery uses FROM FILES instead. - - columns = self.sql(expression, "columns").strip() - if columns: - out.append(columns) - - partition_by = self.sql(expression, "partition_by").strip() - if partition_by: - out.append(partition_by) - - cluster_by = self.sql(expression, "cluster_by").strip() - if cluster_by: - out.append(cluster_by) - - options = self.sql(expression, "options").strip() - if options: - out.append(options) - - from_files = self.sql(expression, "from_files").strip() - if from_files: - out.append(f"FROM FILES {from_files}") - - with_partition_columns = self.sql(expression, "with_partition_columns").strip() - if with_partition_columns: - out.append(f"WITH PARTITION COLUMNS {with_partition_columns}") - - connection = self.sql(expression, "connection").strip() - if connection: - out.append(f"WITH CONNECTION {connection}") - - return " ".join(out) - - -def _create_sql(self: sg.Generator, expression: sge.Create) -> str: - kind = expression.args.get("kind") - if kind != "EXTERNAL TABLE": - return self.create_sql(expression) - - out = ["CREATE"] - if expression.args.get("replace"): - out.append("OR REPLACE") - out.append("EXTERNAL TABLE") - if expression.args.get("exists_ok"): - out.append("IF NOT EXISTS") - - out.append(self.sql(expression, "this")) - - connection = self.sql(expression, "connection").strip() - if connection: - out.append(f"WITH CONNECTION {connection}") - - partition_columns = self.sql(expression, "partition_columns").strip() - if partition_columns: - out.append(f"WITH PARTITION COLUMNS {partition_columns}") - - properties = self.sql(expression, "properties").strip() - if properties: - out.append(properties) - - return " ".join(out) - - -# Register the transform for BigQuery generator -base.DIALECT.Generator.TRANSFORMS[sge.LoadData] = _loaddata_sql -base.DIALECT.Generator.TRANSFORMS[sge.Create] = _create_sql diff --git a/bigframes/core/compile/sqlglot/sql/dml.py b/bigframes/core/compile/sqlglot/sql/dml.py deleted file mode 100644 index 0f0ae9dff2b..00000000000 --- a/bigframes/core/compile/sqlglot/sql/dml.py +++ /dev/null @@ -1,59 +0,0 @@ -# Copyright 2026 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from __future__ import annotations - -import typing - -import bigframes_vendored.sqlglot.expressions as sge -from google.cloud import bigquery - -from bigframes import dtypes -from bigframes.core.compile.sqlglot.sql import base - - -def insert( - query_or_table: typing.Union[sge.Select, sge.Table], - destination: bigquery.TableReference, -) -> sge.Insert: - """Generates an INSERT INTO SQL statement from the given SELECT statement or - table reference.""" - return sge.insert(_as_from_item(query_or_table), base.table(destination)) - - -def replace( - query_or_table: typing.Union[sge.Select, sge.Table], - destination: bigquery.TableReference, -) -> sge.Merge: - """Generates a MERGE statement to replace the contents of the destination table.""" - return sge.Merge( - this=base.table(destination), - using=_as_from_item(query_or_table), - on=base.literal(False, dtypes.BOOL_DTYPE), - whens=sge.Whens( - expressions=[ - sge.When(matched=False, source=True, then=sge.Delete()), - sge.When(matched=False, then=sge.Insert(this=sge.Var(this="ROW"))), - ] - ), - ) - - -def _as_from_item( - query_or_table: typing.Union[sge.Select, sge.Table], -) -> typing.Union[sge.Subquery, sge.Table]: - if isinstance(query_or_table, sge.Select): - return query_or_table.subquery() - else: # table - return query_or_table diff --git a/bigframes/core/compile/sqlglot/sqlglot_ir.py b/bigframes/core/compile/sqlglot/sqlglot_ir.py deleted file mode 100644 index b29a23cd84b..00000000000 --- a/bigframes/core/compile/sqlglot/sqlglot_ir.py +++ /dev/null @@ -1,676 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from __future__ import annotations - -import dataclasses -import datetime -import functools -import typing - -import bigframes_vendored.sqlglot as sg -import bigframes_vendored.sqlglot.expressions as sge -import pyarrow as pa - -import bigframes.core.compile.sqlglot.sqlglot_types as sgt -from bigframes import dtypes -from bigframes.core import guid, local_data, schema -from bigframes.core.compile.sqlglot import sql -from bigframes.core.compile.sqlglot.expressions import typed_expr - -# shapely.wkt.dumps was moved to shapely.io.to_wkt in 2.0. -try: - from shapely.io import to_wkt # type: ignore -except ImportError: - from shapely.wkt import dumps # type: ignore - - to_wkt = dumps - - -class SelectFragment: - def __init__(self, select_expr: sge.Select): - self.select_expr = select_expr - - def as_select_all(self) -> sge.Select: - return self.select_expr - - def select(self, *items: sge.Expression) -> sge.Select: - return sge.Select().select(*items).from_(self.select_expr.subquery()) - - def as_from_item(self) -> sge.Expression: - return self.select_expr.subquery() - - -class TableFragment: - def __init__(self, table: sge.Table | sge.Unnest): - self.table = table - - def as_select_all(self) -> sge.Select: - return sge.Select().select(sge.Star()).from_(self.table) - - def select(self, *items: sge.Expression) -> sge.Select: - return sge.Select().select(*items).from_(self.table) - - def as_from_item(self) -> sge.Expression: - return self.table - - -class DeferredSelectFragment: - def __init__(self, select_supplier: typing.Callable[[sge.Select], sge.Select]): - self.select_supplier = select_supplier - - def as_select_all(self) -> sge.Select: - return self.select_supplier(sge.Select().select(sge.Star())) - - def select(self, *items: sge.Expression) -> sge.Select: - return self.select_supplier(sge.Select().select(*items)) - - def as_from_item(self) -> sge.Expression: - return self.select_supplier(sge.Select().select(sge.Star())).subquery() - - -ExprT = SelectFragment | TableFragment | DeferredSelectFragment - - -@dataclasses.dataclass(frozen=True) -class SQLGlotIR: - """Helper class to build SQLGlot Query and generate SQL string.""" - - expr: ExprT - """The SQLGlot expression representing the query.""" - - uid_gen: guid.SequentialUIDGenerator = guid.SequentialUIDGenerator() - """Generator for unique identifiers.""" - - @property - def sql(self) -> str: - """Generate SQL string from the given expression.""" - return sql.to_sql(self.expr.as_select_all()) - - @classmethod - def empty( - cls, uid_gen: guid.SequentialUIDGenerator = guid.SequentialUIDGenerator() - ) -> SQLGlotIR: - return cls(expr=SelectFragment(sge.select()), uid_gen=uid_gen) - - @classmethod - def from_expr( - cls, - expr: sge.Expression, - uid_gen: guid.SequentialUIDGenerator = guid.SequentialUIDGenerator(), - ) -> SQLGlotIR: - if isinstance(expr, sge.Select): - return cls(expr=SelectFragment(expr), uid_gen=uid_gen) - elif isinstance(expr, (sge.Table, sge.Unnest)): - return cls(expr=TableFragment(expr), uid_gen=uid_gen) - else: - raise ValueError(f"Unsupported expression type: {type(expr)}") - - @classmethod - def from_func( - cls, - select_handler: typing.Callable[[sge.Select], sge.Select], - uid_gen: guid.SequentialUIDGenerator = guid.SequentialUIDGenerator(), - ): - return cls(expr=DeferredSelectFragment(select_handler), uid_gen=uid_gen) - - @classmethod - def from_pyarrow( - cls, - pa_table: pa.Table, - schema: schema.ArraySchema, - uid_gen: guid.SequentialUIDGenerator, - ) -> SQLGlotIR: - """Builds SQLGlot expression from a pyarrow table. - - This is used to represent in-memory data as a SQL query. - """ - dtype_expr = sge.DataType( - this=sge.DataType.Type.STRUCT, - expressions=[ - sge.ColumnDef( - this=sge.to_identifier(field.column, quoted=True), - kind=sgt.from_bigframes_dtype(field.dtype), - ) - for field in schema.items - ], - nested=True, - ) - data_expr = [ - sge.Struct( - expressions=tuple( - sql.literal( - value=value, - dtype=field.dtype, - ) - for value, field in zip(tuple(row_dict.values()), schema.items) - ) - ) - for row_dict in local_data._iter_table(pa_table, schema) - ] - expr = sge.Unnest( - expressions=[ - sge.DataType( - this=sge.DataType.Type.ARRAY, - expressions=[dtype_expr], - nested=True, - values=data_expr, - ), - ], - ) - return cls.from_expr(expr=expr, uid_gen=uid_gen) - - @classmethod - def from_table( - cls, - project_id: str, - dataset_id: str, - table_id: str, - uid_gen: guid.SequentialUIDGenerator | None = None, - columns: typing.Sequence[str] = (), - sql_predicate: typing.Optional[str] = None, - system_time: typing.Optional[datetime.datetime] = None, - ) -> SQLGlotIR: - """Builds a SQLGlotIR expression from a BigQuery table. - - Args: - project_id (str): The project ID of the BigQuery table. - dataset_id (str): The dataset ID of the BigQuery table. - table_id (str): The table ID of the BigQuery table. - uid_gen (guid.SequentialUIDGenerator): A generator for unique identifiers. - columns (typing.Sequence[str]): The names of the columns to select. - sql_predicate (typing.Optional[str]): An optional SQL predicate for filtering. - system_time (typing.Optional[str]): An optional system time for time-travel queries. - """ - version = ( - sge.Version( - this=sge.Identifier(this="SYSTEM_TIME", quoted=False), - expression=sge.Literal.string(system_time.isoformat()), - kind="AS OF", - ) - if system_time - else None - ) - if uid_gen is None: - uid_gen = guid.SequentialUIDGenerator() - table_alias = next(uid_gen.get_uid_stream("bft_")) - table_expr = sge.Table( - this=sql.identifier(table_id), - db=sql.identifier(dataset_id), - catalog=sql.identifier(project_id), - version=version, - alias=sql.identifier(table_alias), - ) - - if not columns and not sql_predicate: - return cls.from_expr(expr=table_expr, uid_gen=uid_gen) - - select_items: list[sge.Identifier | sge.Star] = ( - [sql.identifier(col) for col in columns] if columns else [sge.Star()] - ) - select_expr = sge.Select().select(*select_items).from_(table_expr) - - if sql_predicate: - select_expr = select_expr.where( - sg.parse_one(sql_predicate, dialect=sql.base.DIALECT), append=False - ) - - return cls.from_expr(expr=select_expr, uid_gen=uid_gen) - - @classmethod - def from_cte_ref( - cls, - cte_ref: str, - uid_gen: guid.SequentialUIDGenerator, - ) -> SQLGlotIR: - table_expr = sge.Table( - this=sql.identifier(cte_ref), - ) - return cls.from_expr(expr=table_expr, uid_gen=uid_gen) - - def select( - self, - selections: tuple[tuple[str, sge.Expression], ...] = (), - predicates: tuple[sge.Expression, ...] = (), - sorting: tuple[sge.Ordered, ...] = (), - limit: typing.Optional[int] = None, - ) -> SQLGlotIR: - # TODO: Explicitly insert CTEs into plan - if len(selections) > 0: - to_select = [ - expr - if (isinstance(expr, sge.Alias) and expr.alias == id) - or (isinstance(expr, sge.Column) and expr.name == id) - else sge.Alias( - this=expr.this if isinstance(expr, sge.Alias) else expr, - alias=sql.identifier(id), - ) - for id, expr in selections - ] - new_expr = self.expr.select(*to_select) - else: - new_expr = self.expr.as_select_all() - - if len(sorting) > 0: - new_expr = new_expr.order_by(*sorting) - - if len(predicates) > 0: - condition = _and(predicates) - new_expr = new_expr.where(condition, append=False) - if limit is not None: - new_expr = new_expr.limit(limit) - - return SQLGlotIR.from_expr(expr=new_expr, uid_gen=self.uid_gen) - - @classmethod - def from_unparsed_query( - cls, - query_string: str, - ) -> SQLGlotIR: - """Builds a SQLGlot expression from a query string. Wrapping the query - in a CTE can avoid the query parsing issue for unsupported syntax in - SQLGlot.""" - uid_gen: guid.SequentialUIDGenerator = guid.SequentialUIDGenerator() - cte_name = sql.identifier(next(uid_gen.get_uid_stream("bfcte_"))) - cte = sge.CTE( - this=query_string, - alias=cte_name, - ) - select_expr = sge.Select().select(sge.Star()).from_(sge.Table(this=cte_name)) - select_expr = _set_query_ctes(select_expr, [cte]) - return cls.from_expr(expr=select_expr, uid_gen=uid_gen) - - @classmethod - def from_union( - cls, - selects: typing.Sequence[sge.Select], - output_aliases: typing.Sequence[typing.Tuple[str, str]], - uid_gen: guid.SequentialUIDGenerator, - ) -> SQLGlotIR: - """Builds a SQLGlot expression by unioning of multiple select expressions.""" - assert len(list(selects)) >= 2, ( - f"At least two select expressions must be provided, but got {selects}." - ) - union_expr: sge.Query = selects[0].subquery() - for select in selects[1:]: - union_expr = sge.Union( - this=union_expr, - expression=select.subquery(), - distinct=False, - copy=False, - ) - - selections = [ - sge.Alias( - this=sql.identifier(old_name), - alias=sql.identifier(new_name), - ) - for old_name, new_name in output_aliases - ] - final_select_expr = ( - sge.Select().select(*selections).from_(union_expr.subquery()) - ) - return cls.from_expr(expr=final_select_expr, uid_gen=uid_gen) - - def join( - self, - right: SQLGlotIR, - join_type: typing.Literal["inner", "outer", "left", "right", "cross"], - conditions: tuple[tuple[typed_expr.TypedExpr, typed_expr.TypedExpr], ...], - *, - joins_nulls: bool = True, - ) -> SQLGlotIR: - """Joins the current query with another SQLGlotIR instance.""" - left_from = self.expr.as_from_item() - right_from = right.expr.as_from_item() - - join_on = _and( - tuple( - _join_condition(left, right, joins_nulls) for left, right in conditions - ) - ) - - join_type_str = join_type if join_type != "outer" else "full outer" - return SQLGlotIR.from_func( - lambda select: select.from_(left_from).join( - right_from, on=join_on, join_type=join_type_str - ), - uid_gen=self.uid_gen, - ) - - def isin_join( - self, - right: SQLGlotIR, - indicator_col: str, - conditions: tuple[typed_expr.TypedExpr, typed_expr.TypedExpr], - joins_nulls: bool = True, - ) -> SQLGlotIR: - """Joins the current query with another SQLGlotIR instance.""" - left_from = self.expr.as_from_item() - - new_column: sge.Expression - if joins_nulls: - force_float_domain = False - if ( - conditions[0].dtype == dtypes.FLOAT_DTYPE - or conditions[1].dtype == dtypes.FLOAT_DTYPE - ): - force_float_domain = True - left_expr1, left_expr2 = _value_to_non_null_identity( - conditions[0], force_float_domain - ) - right_expr1, right_expr2 = _value_to_non_null_identity( - conditions[1], force_float_domain - ) - - # Use EXISTS for better performance. - # We use COALESCE on both sides in the WHERE clause as requested. - new_column = sge.Exists( - this=sge.Select() - .select(sge.convert(1)) - .from_(right.expr.as_from_item()) - .where( - sge.and_( - sge.EQ(this=left_expr1, expression=right_expr1), - sge.EQ(this=left_expr2, expression=right_expr2), - ) - ) - ) - else: - new_column = sge.func( - "COALESCE", - sge.In( - this=conditions[0].expr, - expressions=[right._as_subquery()], - ), - sql.literal(False, dtypes.BOOL_DTYPE), - ) - - new_column = sge.Alias( - this=new_column, - alias=sql.identifier(indicator_col), - ) - - new_expr = sge.Select().select(sge.Star(), new_column).from_(left_from) - return SQLGlotIR.from_expr(expr=new_expr, uid_gen=self.uid_gen) - - def explode( - self, - column_names: tuple[str, ...], - offsets_col: typing.Optional[str], - ) -> SQLGlotIR: - """Unnests one or more array columns.""" - num_columns = len(list(column_names)) - assert num_columns > 0, "At least one column must be provided for explode." - if num_columns == 1: - return self._explode_single_column(column_names[0], offsets_col) - else: - return self._explode_multiple_columns(column_names, offsets_col) - - def sample(self, fraction: float) -> SQLGlotIR: - """Uniform samples a fraction of the rows.""" - condition = sge.LT( - this=sge.func("RAND"), - expression=sql.literal(fraction, dtypes.FLOAT_DTYPE), - ) - - new_expr = self.expr.as_select_all().where(condition, append=False) - return SQLGlotIR.from_expr(expr=new_expr, uid_gen=self.uid_gen) - - def aggregate( - self, - aggregations: tuple[tuple[str, sge.Expression], ...], - by_cols: tuple[sge.Expression, ...], - dropna_cols: tuple[sge.Expression, ...], - ) -> SQLGlotIR: - """Applies the aggregation expressions. - - Args: - aggregations: output_column_id, aggregation_expr tuples - by_cols: column expressions for aggregation - dropna_cols: columns whether null keys should be dropped - """ - aggregations_expr = [ - sge.Alias( - this=expr, - alias=sql.identifier(id), - ) - for id, expr in aggregations - ] - - new_expr = self.expr.select(*[*by_cols, *aggregations_expr]).group_by(*by_cols) - - condition = _and( - tuple( - sg.not_(sge.Is(this=drop_col, expression=sge.Null())) - for drop_col in dropna_cols - ) - ) - if condition is not None: - new_expr = new_expr.where(condition, append=False) - return SQLGlotIR.from_expr(expr=new_expr, uid_gen=self.uid_gen) - - def with_ctes( - self, - ctes: tuple[tuple[str, SQLGlotIR], ...], - ) -> SQLGlotIR: - sge_ctes = [ - sge.CTE( - this=cte.expr.as_select_all(), - alias=sql.identifier(cte_name), - ) - for cte_name, cte in ctes - ] - select_expr = _set_query_ctes(self.expr.as_select_all(), sge_ctes) - return SQLGlotIR.from_expr(expr=select_expr, uid_gen=self.uid_gen) - - def resample( - self, - right: SQLGlotIR, - array_col_name: str, - start_expr: sge.Expression, - stop_expr: sge.Expression, - step_expr: sge.Expression, - ) -> SQLGlotIR: - generate_array = sge.func( - "GENERATE_ARRAY", - start_expr, - stop_expr, - step_expr, - ) - - unnested_column_alias = sql.identifier( - next(self.uid_gen.get_uid_stream("bfcol_")) - ) - unnest_expr = sge.Unnest( - expressions=[generate_array], - alias=sge.TableAlias(columns=[unnested_column_alias]), - ) - - final_col_id = sql.identifier(array_col_name) - - # Build final expression by joining everything directly in a single SELECT - new_expr = ( - sge.Select() - .select(unnested_column_alias.as_(final_col_id)) - .from_(self.expr.as_from_item()) - .join(right.expr.as_from_item(), join_type="cross") - .join(unnest_expr, join_type="cross") - ) - - return SQLGlotIR.from_expr(expr=new_expr, uid_gen=self.uid_gen) - - def _explode_single_column( - self, column_name: str, offsets_col: typing.Optional[str] - ) -> SQLGlotIR: - """Helper method to handle the case of exploding a single column.""" - offset = sql.identifier(offsets_col) if offsets_col else None - column = sql.identifier(column_name) - unnested_column_alias = sql.identifier( - next(self.uid_gen.get_uid_stream("bfcol_")) - ) - unnest_expr = sge.Unnest( - expressions=[column], - alias=sge.TableAlias(columns=[unnested_column_alias]), - offset=offset, - ) - selection = sge.Star(replace=[unnested_column_alias.as_(column)]) - - # Use LEFT JOIN to preserve rows when unnesting empty arrays. - new_expr = self.expr.select(selection).join(unnest_expr, join_type="LEFT") - return SQLGlotIR.from_expr(expr=new_expr, uid_gen=self.uid_gen) - - def _explode_multiple_columns( - self, - column_names: tuple[str, ...], - offsets_col: typing.Optional[str], - ) -> SQLGlotIR: - """Helper method to handle the case of exploding multiple columns.""" - offset = sql.identifier(offsets_col) if offsets_col else None - columns = [sql.identifier(column_name) for column_name in column_names] - - # If there are multiple columns, we need to unnest by zipping the arrays: - # https://cloud.google.com/bigquery/docs/arrays#zipping_arrays - column_lengths = [sge.func("ARRAY_LENGTH", column) - 1 for column in columns] - generate_array = sge.func( - "GENERATE_ARRAY", - sge.convert(0), - sge.func("LEAST", *column_lengths), - ) - unnested_offset_alias = sql.identifier( - next(self.uid_gen.get_uid_stream("bfcol_")) - ) - unnest_expr = sge.Unnest( - expressions=[generate_array], - alias=sge.TableAlias(columns=[unnested_offset_alias]), - offset=offset, - ) - selection = sge.Star( - replace=[ - sge.Bracket( - this=column, - expressions=[unnested_offset_alias], - safe=True, - offset=False, - ).as_(column) - for column in columns - ] - ) - # Use LEFT JOIN to preserve rows when unnesting empty arrays. - new_expr = self.expr.select(selection).join(unnest_expr, join_type="LEFT") - return SQLGlotIR.from_expr(expr=new_expr, uid_gen=self.uid_gen) - - def _as_subquery(self) -> sge.Subquery: - # Sometimes explicitly need a subquery, e.g. for IN expressions. - return self.expr.as_select_all().subquery() - - -def _and(conditions: tuple[sge.Expression, ...]) -> typing.Optional[sge.Expression]: - """Chains multiple expressions together using a logical AND.""" - if not conditions: - return None - - return functools.reduce( - lambda left, right: sge.And(this=left, expression=right), conditions - ) - - -def _join_condition( - left: typed_expr.TypedExpr, - right: typed_expr.TypedExpr, - joins_nulls: bool, -) -> typing.Union[sge.EQ, sge.And]: - """Generates a join condition to match pandas's null-handling logic. - - Pandas treats null values as distinct from each other, leading to a - cross-join-like behavior for null keys. In contrast, BigQuery SQL treats - null values as equal, leading to a inner-join-like behavior. - - This function generates the appropriate SQL condition to replicate the - desired pandas behavior in BigQuery. - - Args: - left: The left-side join key. - right: The right-side join key. - joins_nulls: If True, generates complex logic to handle nulls/NaNs. - Otherwise, uses a simple equality check where appropriate. - """ - if not joins_nulls: - return sge.EQ(this=left.expr, expression=right.expr) - - force_float_domain = False - if left.dtype == dtypes.FLOAT_DTYPE or right.dtype == dtypes.FLOAT_DTYPE: - force_float_domain = True - left_expr1, left_expr2 = _value_to_non_null_identity(left, force_float_domain) - right_expr1, right_expr2 = _value_to_non_null_identity(right, force_float_domain) - return sge.And( - this=sge.EQ(this=left_expr1, expression=right_expr1), - expression=sge.EQ(this=left_expr2, expression=right_expr2), - ) - - -def _value_to_non_null_identity( - value: typed_expr.TypedExpr, force_float_domain: bool = False -) -> tuple[sge.Expression, sge.Expression]: - # normal_value -> (normal_value, normal_value) - # null_value -> (0, 1) - # nan_value -> (2, 3) - if dtypes.is_numeric(value.dtype, include_bool=False): - dtype = dtypes.FLOAT_DTYPE if force_float_domain else value.dtype - expr1 = sge.func( - "COALESCE", value.expr, sql.literal(0.0 if force_float_domain else 0, dtype) - ) - expr2 = sge.func( - "COALESCE", value.expr, sql.literal(1.0 if force_float_domain else 1, dtype) - ) - if value.dtype == dtypes.FLOAT_DTYPE: - expr1 = sge.If( - this=sge.IsNan(this=value.expr), - true=sql.literal(2.0, value.dtype), - false=expr1, - ) - expr2 = sge.If( - this=sge.IsNan(this=value.expr), - true=sql.literal(3, value.dtype), - false=expr2, - ) - else: # general case, convert to string and coalesce - expr1 = sge.func( - "COALESCE", - sql.cast(value.expr, "STRING"), - sql.literal("0", dtypes.STRING_DTYPE), - ) - expr2 = sge.func( - "COALESCE", - sql.cast(value.expr, "STRING"), - sql.literal("1", dtypes.STRING_DTYPE), - ) - return expr1, expr2 - - -def _set_query_ctes( - expr: sge.Select, - ctes: list[sge.CTE], -) -> sge.Select: - """Sets the CTEs of a given sge.Select expression.""" - new_expr = expr.copy() - with_expr = sge.With(expressions=ctes) if len(ctes) > 0 else None - - if "with" in new_expr.arg_types.keys(): - new_expr.set("with", with_expr) - elif "with_" in new_expr.arg_types.keys(): - new_expr.set("with_", with_expr) - else: - raise ValueError("The expression does not support CTEs.") - return new_expr diff --git a/bigframes/core/compile/sqlglot/sqlglot_types.py b/bigframes/core/compile/sqlglot/sqlglot_types.py deleted file mode 100644 index d22373b303f..00000000000 --- a/bigframes/core/compile/sqlglot/sqlglot_types.py +++ /dev/null @@ -1,81 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from __future__ import annotations - -import typing - -import bigframes_vendored.constants as constants -import bigframes_vendored.sqlglot as sg -import numpy as np -import pandas as pd -import pyarrow as pa - -import bigframes.dtypes - - -def from_bigframes_dtype( - bigframes_dtype: typing.Union[ - bigframes.dtypes.DtypeString, bigframes.dtypes.Dtype, np.dtype[typing.Any] - ], -) -> str: - if bigframes_dtype == bigframes.dtypes.INT_DTYPE: - return "INT64" - elif bigframes_dtype == bigframes.dtypes.FLOAT_DTYPE: - return "FLOAT64" - elif bigframes_dtype == bigframes.dtypes.STRING_DTYPE: - return "STRING" - elif bigframes_dtype == bigframes.dtypes.BOOL_DTYPE: - return "BOOLEAN" - elif bigframes_dtype == bigframes.dtypes.DATE_DTYPE: - return "DATE" - elif bigframes_dtype == bigframes.dtypes.TIME_DTYPE: - return "TIME" - elif bigframes_dtype == bigframes.dtypes.DATETIME_DTYPE: - return "DATETIME" - elif bigframes_dtype == bigframes.dtypes.TIMESTAMP_DTYPE: - return "TIMESTAMP" - elif bigframes_dtype == bigframes.dtypes.BYTES_DTYPE: - return "BYTES" - elif bigframes_dtype == bigframes.dtypes.NUMERIC_DTYPE: - return "NUMERIC" - elif bigframes_dtype == bigframes.dtypes.BIGNUMERIC_DTYPE: - return "BIGNUMERIC" - elif bigframes_dtype == bigframes.dtypes.JSON_DTYPE: - return "JSON" - elif bigframes_dtype == bigframes.dtypes.GEO_DTYPE: - return "GEOGRAPHY" - elif bigframes_dtype == bigframes.dtypes.TIMEDELTA_DTYPE: - return "INT64" - elif isinstance(bigframes_dtype, pd.ArrowDtype): - if pa.types.is_list(bigframes_dtype.pyarrow_dtype): - inner_bigframes_dtype = bigframes.dtypes.arrow_dtype_to_bigframes_dtype( - bigframes_dtype.pyarrow_dtype.value_type - ) - return f"ARRAY<{from_bigframes_dtype(inner_bigframes_dtype)}>" - elif pa.types.is_struct(bigframes_dtype.pyarrow_dtype): - struct_type = typing.cast(pa.StructType, bigframes_dtype.pyarrow_dtype) - inner_fields: list[str] = [] - for i in range(struct_type.num_fields): - field = struct_type.field(i) - key = sg.to_identifier(field.name).sql("bigquery") - dtype = from_bigframes_dtype( - bigframes.dtypes.arrow_dtype_to_bigframes_dtype(field.type) - ) - inner_fields.append(f"{key} {dtype}") - return "STRUCT<{}>".format(", ".join(inner_fields)) - - raise ValueError( - f"Unsupported type for {bigframes_dtype}. {constants.FEEDBACK_LINK}" - ) diff --git a/bigframes/core/convert.py b/bigframes/core/convert.py deleted file mode 100644 index 1546c2f87ee..00000000000 --- a/bigframes/core/convert.py +++ /dev/null @@ -1,131 +0,0 @@ -# Copyright 2023 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -from __future__ import annotations - -from typing import Optional - -import pandas as pd - -from bigframes import dataframe, series, session -from bigframes.core import global_session, indexes - - -def can_convert_to_series(obj) -> bool: - if isinstance(obj, series.Series): - return True - if isinstance(obj, pd.Series): - return True - if isinstance(obj, indexes.Index): - return True - if isinstance(obj, pd.Index): - return True - if pd.api.types.is_list_like(obj): - return True - - return False - - -def to_bf_series( - obj, - default_index: Optional[indexes.Index], - session: Optional[session.Session] = None, -) -> series.Series: - """ - Convert a an object to a bigframes series - - Args: - obj (list-like or Series): - Object to convert to bigframes Series - default_index (list-like or Index or None): - Index to use if obj has no index - - Returns - bigframes.pandas.Series - """ - if isinstance(obj, series.Series): - return obj.copy() - - if session is None: - session = global_session.get_global_session() - - if isinstance(obj, pd.Series): - return series.Series(obj, session=session) - if isinstance(obj, indexes.Index): - return series.Series(obj, default_index, session=session) - if isinstance(obj, pd.Index): - return series.Series(obj, default_index, session=session) - if pd.api.types.is_dict_like(obj): - return series.Series(obj, session=session) - if pd.api.types.is_list_like(obj): - return series.Series(obj, default_index, session=session) - - raise TypeError(f"Cannot interpret {obj} as series.") - - -def to_pd_series(obj, default_index: pd.Index) -> pd.Series: - """ - Convert a an object to a pandas series - - Args: - obj (list-like or Series): - Object to convert to pandas Series - default_index (list-like or Index or None): - Index to use if obj has no index - - Returns - pandas.Series - """ - if isinstance(obj, series.Series): - return obj.to_pandas() - if isinstance(obj, pd.Series): - return obj - if isinstance(obj, indexes.Index): - return pd.Series(obj.to_pandas(), default_index) - if isinstance(obj, pd.Index): - return pd.Series(obj, default_index) - if pd.api.types.is_dict_like(obj): - return pd.Series(obj) - if pd.api.types.is_list_like(obj): - return pd.Series(obj, default_index) - - raise TypeError(f"Cannot interpret {obj} as series.") - - -def can_convert_to_dataframe(obj) -> bool: - if can_convert_to_series(obj): - return True - - if isinstance(obj, dataframe.DataFrame) or isinstance(obj, pd.DataFrame): - return True - - return False - - -def to_bf_dataframe( - obj, - default_index: Optional[indexes.Index], - session: Optional[session.Session] = None, -) -> dataframe.DataFrame: - if isinstance(obj, dataframe.DataFrame): - return obj.copy() - - if isinstance(obj, pd.DataFrame): - if session is None: - session = global_session.get_global_session() - return dataframe.DataFrame(obj, session=session) - - if can_convert_to_series(obj): - return to_bf_series(obj, default_index, session).to_frame() - - raise TypeError(f"Cannot interpret {obj} as a dataframe.") diff --git a/bigframes/core/eval.py b/bigframes/core/eval.py deleted file mode 100644 index aba0f836b7a..00000000000 --- a/bigframes/core/eval.py +++ /dev/null @@ -1,77 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import dataclasses -from typing import Optional - -import bigframes_vendored.pandas.core.computation.eval as vendored_pandas_eval -import bigframes_vendored.pandas.core.computation.parsing as vendored_pandas_eval_parsing - -import bigframes.dataframe as dataframe -import bigframes.dtypes -import bigframes.series as series - - -def eval(df: dataframe.DataFrame, expr: str, target: Optional[dataframe.DataFrame]): - """ - Evaluate the given python expression - - Args: - df (DataFrame): - Columns of this dataframe will be used to resolve variables in expression. - expr (str): - One or more python expression to evaluate. - target (DataFrame or None): - The evaluation result will be written to the target if provided. - - Returns: - Result of evaluation. - """ - if df._has_index: - index_resolver = { - vendored_pandas_eval_parsing.clean_column_name(str(name)): EvalSeries( - df.index.get_level_values(level).to_series() - ) - for level, name in enumerate(df.index.names) - } - else: - index_resolver = {} - column_resolver = { - vendored_pandas_eval_parsing.clean_column_name(str(name)): EvalSeries(series) - for name, series in df.items() - } - # 3 Levels: user -> logging wrapper -> dataframe -> eval helper (this) - return vendored_pandas_eval.eval( - expr=expr, - level=3, - target=target, - resolvers=(index_resolver, column_resolver), # type: ignore - ) - - -@dataclasses.dataclass -class FakeNumpyArray: - dtype: bigframes.dtypes.Dtype - - -class EvalSeries(series.Series): - """Slight modified series that works better with pandas.eval""" - - def __init__(self, underlying: series.Series): - super().__init__(data=underlying._block) - - @property - def values(self): - """Returns fake numpy array with only dtype property so that eval can determine schema without actually downloading the data.""" - return FakeNumpyArray(self.dtype) diff --git a/bigframes/core/events.py b/bigframes/core/events.py deleted file mode 100644 index d6cef860f6d..00000000000 --- a/bigframes/core/events.py +++ /dev/null @@ -1,299 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from __future__ import annotations - -import asyncio -import concurrent.futures -import dataclasses -import datetime -import threading -import uuid -from typing import Any, Callable, Literal, Optional, Set - -import google.cloud.bigquery._job_helpers -import google.cloud.bigquery.job.query -import google.cloud.bigquery.table - -import bigframes.session.executor - -_DEFAULT: Literal["default"] = "default" - -ProgressBarType = Literal["default", "auto", "notebook", "terminal"] | None -QueryPlanType = list[google.cloud.bigquery.job.query.QueryPlanEntry] | None - - -class Subscriber: - def __init__( - self, - callback: Callable[[EventEnvelope], None], - *, - publisher: Publisher, - ): - self._publisher = publisher - self._callback = callback - self._subscriber_id = uuid.uuid4() - - def __call__(self, *args, **kwargs): - return self._callback(*args, **kwargs) - - def __hash__(self) -> int: - return hash(self._subscriber_id) - - def __eq__(self, value: object): - if not isinstance(value, Subscriber): - return NotImplemented - return value._subscriber_id == self._subscriber_id - - def close(self): - self._publisher.unsubscribe(self) - del self._publisher - del self._callback - - def __enter__(self): - return self - - def __exit__(self, exc_type, exc_value, traceback): - if exc_value is not None: - self( - EventEnvelope( - UnknownErrorEvent( - exc_type=exc_type, - exc_value=exc_value, - traceback=traceback, - ) - ) - ) - self.close() - - -class Publisher: - def __init__(self): - self._subscribers_lock = threading.Lock() - self._subscribers: Set[Subscriber] = set() - self._executor: concurrent.futures.Executor = ( - concurrent.futures.ThreadPoolExecutor() - ) - - def subscribe( - self, - callback: Callable[[EventEnvelope], None], - ) -> Subscriber: - # TODO(b/448176657): figure out how to handle subscribers/publishers in - # a background thread. Maybe subscribers should be thread-local? - subscriber = Subscriber(callback, publisher=self) - with self._subscribers_lock: - self._subscribers.add(subscriber) - return subscriber - - def unsubscribe(self, subscriber: Subscriber): - with self._subscribers_lock: - self._subscribers.remove(subscriber) - - def publish(self, envelope: EventEnvelope | Event): - if not isinstance(envelope, EventEnvelope): - envelope = EventEnvelope(event=envelope) - with self._subscribers_lock: - for subscriber in self._subscribers: - subscriber(envelope) - - async def publish_async(self, envelope: EventEnvelope | Event): - if not isinstance(envelope, EventEnvelope): - envelope = EventEnvelope(event=envelope) - with self._subscribers_lock: - subscribers_snapshot = list(self._subscribers) - loop = asyncio.get_running_loop() - tasks = [ - loop.run_in_executor(self._executor, subscriber, envelope) - for subscriber in subscribers_snapshot - ] - return await asyncio.gather(*tasks, return_exceptions=True) - - -class Event: - pass - - -@dataclasses.dataclass(frozen=True) -class EventEnvelope: - """An envelope that wraps an execution event with metadata and display options. - - Attributes: - event: - The actual execution event details (e.g., ExecutionStarted, BigQuerySentEvent). - progress_bar: - Specifies the style of progress bar to display during execution. - cell_execution_count: - The 1-indexed IPython/Jupyter notebook cell execution number (e.g. the 'x' in 'In [x]'). - This is NOT a job count, but rather the sequential number of the cell execution in the - current notebook session, used to group and filter execution history on a per-cell basis. - """ - - event: Event - progress_bar: ProgressBarType = _DEFAULT - cell_execution_count: Optional[int] = None - - -@dataclasses.dataclass(frozen=True) -class SessionClosed(Event): - session_id: str - - -class ExecutionStarted(Event): - pass - - -class ExecutionRunning(Event): - pass - - -@dataclasses.dataclass(frozen=True) -class ExecutionFinished(Event): - result: bigframes.session.executor.ExecuteResult | None = None - - -@dataclasses.dataclass(frozen=True) -class UnknownErrorEvent(Event): - exc_type: Any - exc_value: Any - traceback: Any - - -@dataclasses.dataclass(frozen=True) -class BigQuerySentEvent(ExecutionRunning): - """Query sent to BigQuery.""" - - query: str - billing_project: str | None = None - location: str | None = None - job_id: str | None = None - request_id: str | None = None - - @classmethod - def from_bqclient( - cls, - event: google.cloud.bigquery._job_helpers.QuerySentEvent, - ): - return cls( - query=event.query, - billing_project=event.billing_project, - location=event.location, - job_id=event.job_id, - request_id=event.request_id, - ) - - -@dataclasses.dataclass(frozen=True) -class BigQueryRetryEvent(ExecutionRunning): - """Query sent another time because the previous attempt failed.""" - - query: str - billing_project: str | None = None - location: str | None = None - job_id: str | None = None - request_id: str | None = None - - @classmethod - def from_bqclient( - cls, - event: google.cloud.bigquery._job_helpers.QueryRetryEvent, - ): - return cls( - query=event.query, - billing_project=event.billing_project, - location=event.location, - job_id=event.job_id, - request_id=event.request_id, - ) - - -@dataclasses.dataclass(frozen=True) -class BigQueryReceivedEvent(ExecutionRunning): - """Query received and acknowledged by the BigQuery API.""" - - billing_project: str | None = None - location: str | None = None - job_id: str | None = None - statement_type: str | None = None - state: str | None = None - query_plan: QueryPlanType = None - created: datetime.datetime | None = None - started: datetime.datetime | None = None - ended: datetime.datetime | None = None - - @classmethod - def from_bqclient( - cls, - event: google.cloud.bigquery._job_helpers.QueryReceivedEvent, - ): - return cls( - billing_project=event.billing_project, - location=event.location, - job_id=event.job_id, - statement_type=event.statement_type, - state=event.state, - query_plan=event.query_plan, - created=event.created, - started=event.started, - ended=event.ended, - ) - - -@dataclasses.dataclass(frozen=True) -class BigQueryFinishedEvent(ExecutionRunning): - """Query finished successfully.""" - - billing_project: str | None = None - location: str | None = None - query_id: str | None = None - job_id: str | None = None - destination: google.cloud.bigquery.table.TableReference | None = None - total_rows: int | None = None - total_bytes_processed: int | None = None - slot_millis: int | None = None - created: datetime.datetime | None = None - started: datetime.datetime | None = None - ended: datetime.datetime | None = None - - @classmethod - def from_bqclient( - cls, - event: google.cloud.bigquery._job_helpers.QueryFinishedEvent, - ): - return cls( - billing_project=event.billing_project, - location=event.location, - query_id=event.query_id, - job_id=event.job_id, - destination=event.destination, - total_rows=event.total_rows, - total_bytes_processed=event.total_bytes_processed, - slot_millis=event.slot_millis, - created=event.created, - started=event.started, - ended=event.ended, - ) - - -@dataclasses.dataclass(frozen=True) -class BigQueryUnknownEvent(ExecutionRunning): - """Got unknown event from the BigQuery client library.""" - - # TODO: should we just skip sending unknown events? - - event: object - - @classmethod - def from_bqclient(cls, event): - return cls(event) diff --git a/bigframes/core/explode.py b/bigframes/core/explode.py deleted file mode 100644 index ddd290b0f84..00000000000 --- a/bigframes/core/explode.py +++ /dev/null @@ -1,36 +0,0 @@ -# Copyright 2023 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -"""Utility functions for implementing 'explode' functions.""" - -from typing import Sequence, Union, cast - -import bigframes.core.blocks as blocks -import bigframes.core.utils as utils - - -def check_column( - column: Union[blocks.Label, Sequence[blocks.Label]], -) -> Sequence[blocks.Label]: - if not utils.is_list_like(column): - column_labels = cast(Sequence[blocks.Label], (column,)) - else: - column_labels = cast(Sequence[blocks.Label], tuple(column)) - - if not column_labels: - raise ValueError("column must be nonempty") - if len(column_labels) > len(set(column_labels)): - raise ValueError("column must be unique") - - return column_labels diff --git a/bigframes/core/expression.py b/bigframes/core/expression.py deleted file mode 100644 index 6c27dfc120b..00000000000 --- a/bigframes/core/expression.py +++ /dev/null @@ -1,533 +0,0 @@ -# Copyright 2023 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from __future__ import annotations - -import abc -import dataclasses -import functools -import itertools -import typing -from typing import Callable, Generator, Hashable, Mapping, TypeVar, Union - -import pandas as pd - -import bigframes.core.identifiers as ids -import bigframes.operations -from bigframes import dtypes -from bigframes.core import field - -if typing.TYPE_CHECKING: - import bigframes.operations - - -def const( - value: typing.Hashable, dtype: dtypes.ExpressionType = None -) -> ScalarConstantExpression: - return ScalarConstantExpression(value, dtype or dtypes.infer_literal_type(value)) - - -def deref(name: str) -> DerefOp: - return DerefOp(ids.ColumnId(name)) - - -def free_var(id: Hashable) -> UnboundVariableExpression: - return UnboundVariableExpression(id) - - -T = TypeVar("T") -TExpression = TypeVar("TExpression", bound="Expression") - - -@dataclasses.dataclass(frozen=True) -class Expression(abc.ABC): - """An expression represents a computation taking N scalar inputs and producing a single output scalar.""" - - @property - def free_variables(self) -> typing.Tuple[Hashable, ...]: - return () - - @property - def children(self) -> typing.Tuple[Expression, ...]: - return () - - @property - def expensive(self) -> bool: - return any( - isinstance(ex, OpExpression) and ex.op.expensive for ex in self.walk() - ) - - @property - def nullable(self) -> bool: - return True - - @property - @abc.abstractmethod - def column_references(self) -> typing.Tuple[ids.ColumnId, ...]: ... - - def remap_column_refs( - self: TExpression, - name_mapping: Mapping[ids.ColumnId, ids.ColumnId], - allow_partial_bindings: bool = False, - ) -> TExpression: - return self.bind_refs( - {old_id: DerefOp(new_id) for old_id, new_id in name_mapping.items()}, # type: ignore - allow_partial_bindings=allow_partial_bindings, - ) - - @property - @abc.abstractmethod - def is_const(self) -> bool: ... - - @property - @abc.abstractmethod - def is_resolved(self) -> bool: - """ - Returns true if and only if the expression's output type and nullability is available. - """ - ... - - @property - @abc.abstractmethod - def output_type(self) -> dtypes.ExpressionType: ... - - @abc.abstractmethod - def bind_refs( - self, - bindings: Mapping[ids.ColumnId, Expression], - allow_partial_bindings: bool = False, - ) -> Expression: - """Replace variables with expression given in `bindings`. - - If allow_partial_bindings is False, validate that all free variables are bound to a new value. - """ - ... - - @abc.abstractmethod - def bind_variables( - self, - bindings: Mapping[Hashable, Expression], - allow_partial_bindings: bool = False, - ) -> Expression: - """Replace variables with expression given in `bindings`. - - If allow_partial_bindings is False, validate that all free variables are bound to a new value. - """ - ... - - @property - def is_bijective(self) -> bool: - return False - - @property - def deterministic(self) -> bool: - return True - - @property - def is_identity(self) -> bool: - """True for identity operation that does not transform input.""" - return False - - @functools.cached_property - def is_scalar_expr(self) -> bool: - """True if expression represents scalar value or expression over scalar values (no windows or aggregations)""" - return all(expr.is_scalar_expr for expr in self.children) - - @abc.abstractmethod - def transform_children( - self, t: Callable[[Expression], Expression] - ) -> Expression: ... - - def bottom_up(self, t: Callable[[Expression], Expression]) -> Expression: - expr = self.transform_children(lambda child: child.bottom_up(t)) - expr = t(expr) - return expr - - def top_down(self, t: Callable[[Expression], Expression]) -> Expression: - expr = t(self) - expr = expr.transform_children(lambda child: child.top_down(t)) - return expr - - def walk(self) -> Generator[Expression, None, None]: - yield self - for child in self.children: - yield from child.children - - -@dataclasses.dataclass(frozen=True) -class ScalarConstantExpression(Expression): - """An expression representing a scalar constant.""" - - # TODO: Further constrain? - value: typing.Hashable - dtype: dtypes.ExpressionType = None - - @property - def is_const(self) -> bool: - return True - - @property - def column_references(self) -> typing.Tuple[ids.ColumnId, ...]: - return () - - @property - def nullable(self) -> bool: - return pd.isna(self.value) # type: ignore - - @property - def is_resolved(self) -> bool: - return True - - @property - def output_type(self) -> dtypes.ExpressionType: - return self.dtype - - def bind_variables( - self, - bindings: Mapping[Hashable, Expression], - allow_partial_bindings: bool = False, - ) -> Expression: - return self - - def bind_refs( - self, - bindings: Mapping[ids.ColumnId, Expression], - allow_partial_bindings: bool = False, - ) -> ScalarConstantExpression: - return self - - @property - def is_bijective(self) -> bool: - # () <-> value - return True - - def __eq__(self, other): - if not isinstance(other, ScalarConstantExpression): - return False - - # With python 3.13 and the pre-release version of pandas, - # NA == NA is NA instead of True - if pd.isna(self.value) and pd.isna(other.value): # type: ignore - return self.dtype == other.dtype - - return self.value == other.value and self.dtype == other.dtype - - def transform_children(self, t: Callable[[Expression], Expression]) -> Expression: - return self - - -@dataclasses.dataclass(frozen=True) -class UnboundVariableExpression(Expression): - """A variable expression representing an unbound variable.""" - - id: Hashable - - @property - def free_variables(self) -> typing.Tuple[Hashable, ...]: - return (self.id,) - - @property - def is_const(self) -> bool: - return False - - @property - def column_references(self) -> typing.Tuple[ids.ColumnId, ...]: - return () - - @property - def is_resolved(self): - return False - - @property - def output_type(self) -> dtypes.ExpressionType: - raise ValueError(f"Type of variable {self.id} has not been fixed.") - - def bind_refs( - self, - bindings: Mapping[ids.ColumnId, Expression], - allow_partial_bindings: bool = False, - ) -> UnboundVariableExpression: - return self - - def bind_variables( - self, - bindings: Mapping[Hashable, Expression], - allow_partial_bindings: bool = False, - ) -> Expression: - if self.id in bindings.keys(): - return bindings[self.id] - elif not allow_partial_bindings: - raise ValueError(f"Variable {self.id} remains unbound") - return self - - @property - def is_bijective(self) -> bool: - return True - - @property - def is_identity(self) -> bool: - return True - - def transform_children(self, t: Callable[[Expression], Expression]) -> Expression: - return self - - -@dataclasses.dataclass(frozen=True) -class DerefOp(Expression): - """An expression that refers to a column by ID.""" - - id: ids.ColumnId - - @property - def column_references(self) -> typing.Tuple[ids.ColumnId, ...]: - return (self.id,) - - @property - def is_const(self) -> bool: - return False - - @property - def nullable(self) -> bool: - # Safe default, need to actually bind input schema to determine - return True - - @property - def is_resolved(self) -> bool: - return False - - @property - def output_type(self) -> dtypes.ExpressionType: - raise ValueError(f"Type of variable {self.id} has not been fixed.") - - def bind_variables( - self, - bindings: Mapping[Hashable, Expression], - allow_partial_bindings: bool = False, - ) -> Expression: - return self - - def bind_refs( - self, - bindings: Mapping[ids.ColumnId, Expression], - allow_partial_bindings: bool = False, - ) -> Expression: - if self.id in bindings.keys(): - return bindings[self.id] - elif not allow_partial_bindings: - raise ValueError(f"Variable {self.id} remains unbound") - return self - - @property - def is_bijective(self) -> bool: - return True - - @property - def is_identity(self) -> bool: - return True - - def transform_children(self, t: Callable[[Expression], Expression]) -> Expression: - return self - - -@dataclasses.dataclass(frozen=True) -class ResolvedDerefOp(DerefOp): - """An expression that refers to a column by ID and resolved with schema bound.""" - - dtype: dtypes.Dtype - is_nullable: bool - - @classmethod - def from_field(cls, f: field.Field): - return cls(id=f.id, dtype=f.dtype, is_nullable=f.nullable) - - @property - def is_resolved(self) -> bool: - return True - - @property - def nullable(self) -> bool: - return self.is_nullable - - @property - def output_type(self) -> dtypes.ExpressionType: - return self.dtype - - -@dataclasses.dataclass(frozen=True) -class OmittedArg(Expression): - """Represents an omitted optional arg used calling a function.""" - - @property - def free_variables(self) -> typing.Tuple[Hashable, ...]: - return () - - @property - def is_const(self) -> bool: - return True - - @property - def column_references(self) -> typing.Tuple[ids.ColumnId, ...]: - return () - - @property - def is_resolved(self): - return True # vacuously - - @property - def output_type(self) -> dtypes.ExpressionType: - return None - - def bind_refs( - self, - bindings: Mapping[ids.ColumnId, Expression], - allow_partial_bindings: bool = False, - ) -> OmittedArg: - return self - - def bind_variables( - self, - bindings: Mapping[Hashable, Expression], - allow_partial_bindings: bool = False, - ) -> Expression: - return self - - @property - def is_bijective(self) -> bool: - return True - - @property - def is_identity(self) -> bool: - return True - - def transform_children(self, t: Callable[[Expression], Expression]) -> Expression: - return self - - -@dataclasses.dataclass(frozen=True) -class OpExpression(Expression): - """An expression representing a scalar operation applied to 1 or more argument sub-expressions.""" - - op: bigframes.operations.ScalarOp - inputs: typing.Tuple[Expression, ...] - - @property - def column_references( - self, - ) -> typing.Tuple[bigframes.core.identifiers.ColumnId, ...]: - return tuple( - itertools.chain.from_iterable( - map(lambda x: x.column_references, self.inputs) - ) - ) - - @property - def free_variables(self) -> typing.Tuple[Hashable, ...]: - return tuple( - itertools.chain.from_iterable(map(lambda x: x.free_variables, self.inputs)) - ) - - @property - def is_const(self) -> bool: - return all(child.is_const for child in self.inputs) - - @property - def children(self): - return self.inputs - - @property - def nullable(self) -> bool: - # This is very conservative, need to label null properties of individual ops to get more precise - null_free = self.is_identity and not any( - child.nullable for child in self.inputs - ) - return not null_free - - @functools.cached_property - def is_resolved(self) -> bool: - return all(input.is_resolved for input in self.inputs) - - @functools.cached_property - def output_type(self) -> dtypes.ExpressionType: - if not self.is_resolved: - raise ValueError(f"Type of expression {self.op.name} has not been fixed.") - - input_types = [input.output_type for input in self.inputs] - - return self.op.output_type(*input_types) - - def bind_variables( - self, - bindings: Mapping[Hashable, Expression], - allow_partial_bindings: bool = False, - ) -> OpExpression: - return OpExpression( - self.op, - tuple( - input.bind_variables( - bindings, allow_partial_bindings=allow_partial_bindings - ) - for input in self.inputs - ), - ) - - def bind_refs( - self, - bindings: Mapping[ids.ColumnId, Expression], - allow_partial_bindings: bool = False, - ) -> OpExpression: - return OpExpression( - self.op, - tuple( - input.bind_refs(bindings, allow_partial_bindings=allow_partial_bindings) - for input in self.inputs - ), - ) - - @property - def is_bijective(self) -> bool: - # TODO: Mark individual functions as bijective? - return all(input.is_bijective for input in self.inputs) and self.op.is_bijective - - @property - def deterministic(self) -> bool: - return ( - all(input.deterministic for input in self.inputs) and self.op.deterministic - ) - - def transform_children(self, t: Callable[[Expression], Expression]) -> Expression: - new_inputs = tuple(t(input) for input in self.inputs) - if new_inputs != self.inputs: - return dataclasses.replace(self, inputs=new_inputs) - return self - - -def bind_schema_fields( - expr: Expression, field_by_id: Mapping[ids.ColumnId, field.Field] -) -> Expression: - """ - Updates `DerefOp` expressions by replacing column IDs with actual schema fields(columns). - - We can only deduct an expression's output type and nullability after binding schema fields to - all its deref expressions. - """ - if expr.is_resolved: - return expr - - expr_by_id = { - id: ResolvedDerefOp.from_field(field) for id, field in field_by_id.items() - } - return expr.bind_refs(expr_by_id) - - -RefOrConstant = Union[DerefOp, ScalarConstantExpression] diff --git a/bigframes/core/expression_factoring.py b/bigframes/core/expression_factoring.py deleted file mode 100644 index 22f1433c8a4..00000000000 --- a/bigframes/core/expression_factoring.py +++ /dev/null @@ -1,467 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -import collections -import dataclasses -import functools -import itertools -from typing import ( - Callable, - Dict, - Generator, - Hashable, - Iterable, - Iterator, - Mapping, - Optional, - Sequence, - Tuple, - TypeVar, - cast, -) - -from bigframes.core import ( - agg_expressions, - expression, - graphs, - identifiers, - nodes, - window_spec, -) - -_MAX_INLINE_COMPLEXITY = 10 - -T = TypeVar("T") - - -def unique_nodes( - roots: Sequence[expression.Expression], -) -> Generator[expression.Expression, None, None]: - """Walks the tree for unique nodes""" - seen = set() - stack: list[expression.Expression] = list(roots) - while stack: - item = stack.pop() - if item not in seen: - yield item - seen.add(item) - stack.extend(item.children) - - -def iter_nodes_topo( - roots: Sequence[expression.Expression], -) -> Generator[expression.Expression, None, None]: - """Returns nodes in reverse topological order, using Kahn's algorithm.""" - child_to_parents: Dict[expression.Expression, list[expression.Expression]] = ( - collections.defaultdict(list) - ) - out_degree: Dict[expression.Expression, int] = collections.defaultdict(int) - - queue: collections.deque[expression.Expression] = collections.deque() - for node in unique_nodes(roots): - num_children = len(node.children) - out_degree[node] = num_children - if num_children == 0: - queue.append(node) - for child in node.children: - child_to_parents[child].append(node) - - while queue: - item = queue.popleft() - yield item - parents = child_to_parents.get(item, []) - for parent in parents: - out_degree[parent] -= 1 - if out_degree[parent] == 0: - queue.append(parent) - - -def reduce_up( - roots: Sequence[expression.Expression], - reduction: Callable[[expression.Expression, Tuple[T, ...]], T], -) -> Tuple[T, ...]: - """Apply a bottom-up reduction to the forest.""" - results: dict[expression.Expression, T] = {} - for node in list(iter_nodes_topo(roots)): - # child nodes have already been transformed - child_results = tuple(results[child] for child in node.children) - result = reduction(node, child_results) - results[node] = result - - return tuple(results[root] for root in roots) - - -def apply_col_exprs_to_plan( - plan: nodes.BigFrameNode, col_exprs: Sequence[nodes.ColumnDef] -) -> nodes.BigFrameNode: - target_ids = tuple(named_expr.id for named_expr in col_exprs) - - fragments = fragmentize_expression(col_exprs) - return push_into_tree(plan, fragments, target_ids) - - -def apply_agg_exprs_to_plan( - plan: nodes.BigFrameNode, - agg_defs: Sequence[nodes.ColumnDef], - grouping_keys: Sequence[expression.DerefOp], -) -> nodes.BigFrameNode: - factored_aggs = [factor_aggregation(agg_def) for agg_def in agg_defs] - all_inputs = list( - itertools.chain(*(factored_agg.agg_inputs for factored_agg in factored_aggs)) - ) - window_def = window_spec.WindowSpec(grouping_keys=tuple(grouping_keys)) - windowized_inputs = [ - nodes.ColumnDef(windowize(cdef.expression, window_def), cdef.id) - for cdef in all_inputs - ] - plan = apply_col_exprs_to_plan(plan, windowized_inputs) - all_aggs = list( - itertools.chain(*(factored_agg.agg_exprs for factored_agg in factored_aggs)) - ) - plan = nodes.AggregateNode( - plan, - tuple((cdef.expression, cdef.id) for cdef in all_aggs), # type: ignore - by_column_ids=tuple(grouping_keys), - ) - - post_scalar_exprs = tuple( - (factored_agg.root_scalar_expr for factored_agg in factored_aggs) - ) - plan = nodes.ProjectionNode( - plan, tuple((cdef.expression, cdef.id) for cdef in post_scalar_exprs) - ) - final_ids = itertools.chain( - (ref.id for ref in grouping_keys), (cdef.id for cdef in post_scalar_exprs) - ) - plan = nodes.SelectionNode( - plan, tuple(nodes.AliasedRef.identity(ident) for ident in final_ids) - ) - - return plan - - -@dataclasses.dataclass(frozen=True, eq=False) -class FactoredExpression: - root_expr: expression.Expression - sub_exprs: Tuple[nodes.ColumnDef, ...] - - -def fragmentize_expression( - roots: Sequence[nodes.ColumnDef], -) -> Sequence[nodes.ColumnDef]: - """ - The goal of this functions is to factor out an expression into multiple sub-expressions. - """ - # TODO: Fragmentize a bit less aggressively - factored_exprs = reduce_up([root.expression for root in roots], gather_fragments) - root_exprs = ( - nodes.ColumnDef(factored.root_expr, root.id) - for factored, root in zip(factored_exprs, roots) - ) - return ( - *root_exprs, - *dedupe( - itertools.chain.from_iterable( - factored_expr.sub_exprs for factored_expr in factored_exprs - ) - ), - ) - - -@dataclasses.dataclass(frozen=True, eq=False) -class FactoredAggregation: - """ - A three part recomposition of a general aggregating expression. - - 1. agg_inputs: This is a set of (*col) -> col transformation that preprocess inputs for the aggregations ops - 2. agg_exprs: This is a set of pure aggregations (eg sum, mean, min, max) ops referencing the outputs of (1) - 3. root_scalar_expr: This is the final set, takes outputs of (2), applies scalar expression to produce final result. - """ - - # pure scalar expression - root_scalar_expr: nodes.ColumnDef - # pure agg expression, only refs cols and consts - agg_exprs: Tuple[nodes.ColumnDef, ...] - # can be analytic, scalar op, const, col refs - agg_inputs: Tuple[nodes.ColumnDef, ...] - - -def windowize( - root: expression.Expression, window: window_spec.WindowSpec -) -> expression.Expression: - def windowize_local(expr: expression.Expression): - if isinstance(expr, agg_expressions.Aggregation): - if not expr.op.can_be_windowized: - raise ValueError(f"Op: {expr.op} cannot be windowized.") - return agg_expressions.WindowExpression(expr, window) - if isinstance(expr, agg_expressions.WindowExpression): - raise ValueError(f"Expression {expr} already windowed!") - return expr - - return root.bottom_up(windowize_local) - - -def factor_aggregation(root: nodes.ColumnDef) -> FactoredAggregation: - """ - Factor an aggregation def into three components. - 1. Input column expressions (includes analytic expressions) - 2. The set of underlying primitive aggregations - 3. A final post-aggregate scalar expression - """ - final_aggs = list(dedupe(find_final_aggregations(root.expression))) - agg_inputs = list( - dedupe(itertools.chain.from_iterable(map(find_agg_inputs, final_aggs))) - ) - - agg_input_defs = tuple( - nodes.ColumnDef(expr, identifiers.ColumnId.unique()) for expr in agg_inputs - ) - agg_inputs_dict = { - cdef.expression: expression.DerefOp(cdef.id) for cdef in agg_input_defs - } - - agg_expr_to_ids = {expr: identifiers.ColumnId.unique() for expr in final_aggs} - - isolated_aggs = tuple( - nodes.ColumnDef(sub_expressions(expr, agg_inputs_dict), agg_expr_to_ids[expr]) - for expr in final_aggs - ) - agg_outputs_dict = { - expr: expression.DerefOp(id) for expr, id in agg_expr_to_ids.items() - } - - root_scalar_expr = nodes.ColumnDef( - sub_expressions( - root.expression, - cast( - Mapping[expression.Expression, expression.Expression], agg_outputs_dict - ), - ), - root.id, # type: ignore - ) - - return FactoredAggregation( - root_scalar_expr=root_scalar_expr, - agg_exprs=isolated_aggs, - agg_inputs=agg_input_defs, - ) - - -def sub_expressions( - root: expression.Expression, - replacements: Mapping[expression.Expression, expression.Expression], -) -> expression.Expression: - return root.top_down(lambda x: replacements.get(x, x)) - - -def find_final_aggregations( - root: expression.Expression, -) -> Iterator[agg_expressions.Aggregation]: - if isinstance(root, agg_expressions.Aggregation): - yield root - elif isinstance(root, expression.OpExpression): - for child in root.children: - yield from find_final_aggregations(child) - elif isinstance(root, expression.ScalarConstantExpression): - return - else: - # eg, window expression, column references not allowed - raise ValueError(f"Unexpected node: {root}") - - -def find_agg_inputs( - root: agg_expressions.Aggregation, -) -> Iterator[expression.Expression]: - for child in root.children: - if not isinstance( - child, (expression.DerefOp, expression.ScalarConstantExpression) - ): - yield child - - -def gather_fragments( - root: expression.Expression, fragmentized_children: Sequence[FactoredExpression] -) -> FactoredExpression: - replacements: list[expression.Expression] = [] - named_exprs = [] # root -> leaf dependency order - for child_result in fragmentized_children: - child_expr = child_result.root_expr - is_leaf = isinstance( - child_expr, (expression.DerefOp, expression.ScalarConstantExpression) - ) - is_window_agg = isinstance( - root, agg_expressions.WindowExpression - ) and isinstance(child_expr, agg_expressions.Aggregation) - do_inline = is_leaf | is_window_agg - if not do_inline: - id = identifiers.ColumnId.unique() - replacements.append(expression.DerefOp(id)) - named_exprs.append(nodes.ColumnDef(child_result.root_expr, id)) - named_exprs.extend(child_result.sub_exprs) - else: - replacements.append(child_result.root_expr) - named_exprs.extend(child_result.sub_exprs) - new_root = replace_children(root, replacements) - return FactoredExpression(new_root, tuple(named_exprs)) - - -def replace_children( - root: expression.Expression, new_children: Sequence[expression.Expression] -): - mapping = {root.children[i]: new_children[i] for i in range(len(root.children))} - return root.transform_children(lambda x: mapping.get(x, x)) - - -def push_into_tree( - root: nodes.BigFrameNode, - exprs: Sequence[nodes.ColumnDef], - target_ids: Sequence[identifiers.ColumnId], -) -> nodes.BigFrameNode: - curr_root = root - by_id = {expr.id: expr for expr in exprs} - # id -> id - graph = graphs.DiGraph( - (expr.id for expr in exprs), - ( - (expr.id, child_id) - for expr in exprs - for child_id in expr.expression.column_references - if child_id in by_id.keys() - ), - ) - # TODO: Also prevent inlining expensive or non-deterministic - # We avoid inlining multi-parent ids, as they would be inlined multiple places, potentially increasing work and/or compiled text size - multi_parent_ids = set(id for id in graph.nodes if len(list(graph.parents(id))) > 2) - scalar_ids = set(expr.id for expr in exprs if expr.expression.is_scalar_expr) - - analytic_defs = filter( - lambda x: isinstance(x.expression, agg_expressions.WindowExpression), exprs - ) - analytic_by_window = grouped( - map( - lambda x: (cast(agg_expressions.WindowExpression, x.expression).window, x), - analytic_defs, - ) - ) - - def graph_extract_scalar_exprs() -> Sequence[nodes.ColumnDef]: - results: dict[identifiers.ColumnId, expression.Expression] = dict() - while True: # Will converge as each loop either reduces graph size, or fails to find any candidate and breaks - candidate_ids = list( - id - for id in graph.sinks - if (id in scalar_ids) - and not any( - ( - child in multi_parent_ids - and id in results.keys() - and not is_simple(results[id]) - ) - for child in graph.children(id) - ) - ) - if len(candidate_ids) == 0: - break - for id in candidate_ids: - graph.remove_node(id) - new_exprs = { - id: by_id[id].expression.bind_refs( - results, allow_partial_bindings=True - ) - } - results.update(new_exprs) - # TODO: We can prune expressions that won't be reused here, - return tuple(nodes.ColumnDef(expr, id) for id, expr in results.items()) - - def graph_extract_window_expr() -> Optional[ - Tuple[Sequence[nodes.ColumnDef], window_spec.WindowSpec] - ]: - for id in graph.sinks: - next_def = by_id[id] - if isinstance(next_def.expression, agg_expressions.WindowExpression): - window = next_def.expression.window - window_exprs = [ - cdef - for cdef in analytic_by_window[window] - if cdef.id in graph.sinks - ] - agg_exprs = tuple( - nodes.ColumnDef( - cast( - agg_expressions.WindowExpression, cdef.expression - ).analytic_expr, - cdef.id, - ) - for cdef in window_exprs - ) - for cdef in window_exprs: - graph.remove_node(cdef.id) - return (agg_exprs, window) - - return None - - while not graph.empty: - pre_size = len(graph.nodes) - scalar_exprs = graph_extract_scalar_exprs() - if scalar_exprs: - curr_root = nodes.ProjectionNode( - curr_root, tuple((x.expression, x.id) for x in scalar_exprs) - ) - while result := graph_extract_window_expr(): - defs, window = result - assert len(defs) > 0 - curr_root = nodes.WindowOpNode( - curr_root, - tuple(defs), - window, - ) - if len(graph.nodes) >= pre_size: - raise ValueError("graph didn't shrink") - # TODO: Try to get the ordering right earlier, so can avoid this extra node. - post_ids = (*root.ids, *target_ids) - if tuple(curr_root.ids) != post_ids: - curr_root = nodes.SelectionNode( - curr_root, tuple(nodes.AliasedRef.identity(id) for id in post_ids) - ) - return curr_root - - -@functools.cache -def is_simple(expr: expression.Expression) -> bool: - count = 0 - for part in expr.walk(): - count += 1 - if count > _MAX_INLINE_COMPLEXITY: - return False - return True - - -K = TypeVar("K", bound=Hashable) -V = TypeVar("V") - - -def grouped(values: Iterable[tuple[K, V]]) -> dict[K, list[V]]: - result = collections.defaultdict(list) - for k, v in values: - result[k].append(v) - return result - - -def dedupe(values: Iterable[K]) -> Iterator[K]: - seen = set() - for k in values: - if k not in seen: - seen.add(k) - yield k diff --git a/bigframes/core/field.py b/bigframes/core/field.py deleted file mode 100644 index c5b7dd35559..00000000000 --- a/bigframes/core/field.py +++ /dev/null @@ -1,37 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from __future__ import annotations - -import dataclasses - -from bigframes import dtypes -from bigframes.core import identifiers - - -@dataclasses.dataclass(frozen=True) -class Field: - id: identifiers.ColumnId - dtype: dtypes.Dtype - # Best effort, nullable=True if not certain - nullable: bool = True - - def with_nullable(self) -> Field: - return Field(self.id, self.dtype, nullable=True) - - def with_nonnull(self) -> Field: - return Field(self.id, self.dtype, nullable=False) - - def with_id(self, id: identifiers.ColumnId) -> Field: - return Field(id, self.dtype, nullable=self.nullable) diff --git a/bigframes/core/global_session.py b/bigframes/core/global_session.py index a38280e6447..1f960839a0a 100644 --- a/bigframes/core/global_session.py +++ b/bigframes/core/global_session.py @@ -14,78 +14,31 @@ """Utilities for managing a default, globally available Session object.""" -from __future__ import annotations - import threading -import traceback -import warnings -from typing import TYPE_CHECKING, Callable, Iterable, Optional, TypeVar - -import google.auth.exceptions - -import bigframes.exceptions as bfe +from typing import Callable, Optional, TypeVar -if TYPE_CHECKING: - import bigframes.session +import bigframes._config +import bigframes.session _global_session: Optional[bigframes.session.Session] = None _global_session_lock = threading.Lock() -_global_session_state = threading.local() -_global_session_state.thread_local_session = None - - -def _try_close_session(session: bigframes.session.Session): - """Try to close the session and warn if couldn't.""" - try: - session.close() - except google.auth.exceptions.RefreshError as e: - session_id = session.session_id - location = session._location - project_id = session._project - msg = bfe.format_message( - f"Session cleanup failed for session with id: {session_id}, " - f"location: {location}, project: {project_id}" - ) - warnings.warn(msg, category=bfe.CleanupFailedWarning) - traceback.print_tb(e.__traceback__) def close_session() -> None: """Start a fresh session the next time a function requires a session. - Closes the current session if it was already started, deleting any - temporary tables that were created. + Closes the current session if it was already started. Returns: None """ - # Avoid troubles with circular imports. - import bigframes._config - - global _global_session, _global_session_lock, _global_session_state - - if bigframes._config.options.is_bigquery_thread_local: - if _global_session_state.thread_local_session is not None: - _try_close_session(_global_session_state.thread_local_session) - _global_session_state.thread_local_session = None - - # Currently using thread-local options, so no global lock needed. - # Don't reset options.bigquery, as that's the responsibility - # of the context manager that started it in the first place. The user - # might have explicitly closed the session in the context manager and - # the thread-locality property needs to be retained. - bigframes._config.options.bigquery._session_started = False - - # Don't close the non-thread-local session. - return + global _global_session with _global_session_lock: if _global_session is not None: - _try_close_session(_global_session) + _global_session.close() _global_session = None - # This should be global, not thread-local because of the if clause - # above. bigframes._config.options.bigquery._session_started = False @@ -94,19 +47,7 @@ def get_global_session(): Creates the global session if it does not exist. """ - # Avoid troubles with circular imports. - import bigframes._config - import bigframes.session - - global _global_session, _global_session_lock, _global_session_state - - if bigframes._config.options.is_bigquery_thread_local: - if _global_session_state.thread_local_session is None: - _global_session_state.thread_local_session = bigframes.session.connect( - bigframes._config.options.bigquery - ) - - return _global_session_state.thread_local_session + global _global_session, _global_session_lock with _global_session_lock: if _global_session is None: @@ -120,41 +61,5 @@ def get_global_session(): _T = TypeVar("_T") -def with_default_session(func_: Callable[..., _T], *args, **kwargs) -> _T: - return func_(get_global_session(), *args, **kwargs) - - -def execution_history( - *, - events: Optional[Iterable[bigframes.core.events.Event]] = None, - job_ids: Optional[Iterable[str]] = None, - all_cells: bool = True, -) -> "bigframes.session._ExecutionHistory": - import bigframes.session - - return with_default_session( - bigframes.session.Session.execution_history, - events=events, - job_ids=job_ids, - all_cells=all_cells, - ) - - -class _GlobalSessionContext: - """ - Context manager for testing that sets global session. - """ - - def __init__(self, session: bigframes.session.Session): - self._session = session - - def __enter__(self): - global _global_session, _global_session_lock - with _global_session_lock: - self._previous_session = _global_session - _global_session = self._session - - def __exit__(self, *exc_details): - global _global_session, _global_session_lock - with _global_session_lock: - _global_session = self._previous_session +def with_default_session(func: Callable[..., _T], *args, **kwargs) -> _T: + return func(get_global_session(), *args, **kwargs) diff --git a/bigframes/core/googlesql.py b/bigframes/core/googlesql.py deleted file mode 100644 index 8869fbff3ef..00000000000 --- a/bigframes/core/googlesql.py +++ /dev/null @@ -1,116 +0,0 @@ -# Copyright 2026 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -"""Utilities for working with GoogleSqlScalarOps.""" - -from __future__ import annotations - -from typing import TYPE_CHECKING, Any, Optional, Union - -import pandas as pd - -import bigframes.core.col -import bigframes.core.expression as ex -import bigframes.core.global_session as global_session -import bigframes.core.sentinels as sentinels -import bigframes.series as series -from bigframes.operations import googlesql - -if TYPE_CHECKING: - import bigframes.session - - -def _is_pandas_series(arg: Any) -> bool: - return isinstance(arg, pd.Series) - - -def _find_session(*args: Any) -> Optional[bigframes.session.Session]: - import bigframes.core.indexes as indexes - import bigframes.dataframe as dataframe - - for arg in args: - if isinstance(arg, (series.Series, dataframe.DataFrame, indexes.Index)): - return arg._session - return None - - -def _get_session(*args: Any) -> bigframes.session.Session: - session = _find_session(*args) - if session is not None: - return session - return global_session.get_global_session() - - -def apply_googlesql_scalar_op( - op: googlesql.GoogleSqlScalarOp, - *args: Any, -) -> Union[series.Series, bigframes.core.col.Expression]: - """Applies a GoogleSQL scalar operator to the given arguments. - - Handles a mix of Series, Expression, and literal inputs. - - Args: - op (googlesql.GoogleSqlScalarOp): - The operator to apply. - *args (Any): - The arguments to apply the operator to. - - Returns: - bigframes.pandas.Series | bigframes.core.col.Expression: - The result of the operation. If any of ``args`` is a Series, returns - a Series. Otherwise, returns an Expression. - """ - has_pandas_series = any(_is_pandas_series(arg) for arg in args) - - if has_pandas_series: - session = _get_session(*args) - args = tuple( - session.read_pandas(arg) if _is_pandas_series(arg) else arg for arg in args - ) - - # Find the first Series to use for alignment - first_series = None - for arg in args: - if isinstance(arg, series.Series): - first_series = arg - break - - if first_series is not None: - processed_args: list[Union[bigframes.core.col.Expression, series.Series]] = [] - block = first_series._block - for arg in args: - if isinstance(arg, bigframes.core.col.Expression): - block, col_id = block.project_expr(bigframes.core.col._as_bf_expr(arg)) - processed_args.append(series.Series(block.select_column(col_id))) - elif arg is sentinels.Sentinel.ARGUMENT_DEFAULT: - processed_args.append(bigframes.core.col.Expression(ex.OmittedArg())) - else: - processed_args.append(arg) - - # Apply the n-ary op. _apply_nary_op handles alignment of Series and literals. - result = first_series._apply_nary_op(op, processed_args, ignore_self=True) - result.name = None - return result - - # No Series, return an Expression - expr_args = [] - for arg in args: - if isinstance(arg, bigframes.core.col.Expression): - expr_args.append(bigframes.core.col._as_bf_expr(arg)) - elif arg is sentinels.Sentinel.ARGUMENT_DEFAULT: - expr_args.append(ex.OmittedArg()) - else: - expr_args.append(ex.const(arg)) - - return bigframes.core.col.Expression(ex.OpExpression(op, tuple(expr_args))) diff --git a/bigframes/core/graphs.py b/bigframes/core/graphs.py deleted file mode 100644 index b7ce80e3cf0..00000000000 --- a/bigframes/core/graphs.py +++ /dev/null @@ -1,76 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import collections -from typing import Dict, Generic, Hashable, Iterable, Iterator, Tuple, TypeVar - -import bigframes.core.ordered_sets as sets - -T = TypeVar("T", bound=Hashable) - - -class DiGraph(Generic[T]): - def __init__(self, nodes: Iterable[T], edges: Iterable[Tuple[T, T]]): - self._parents: Dict[T, sets.InsertionOrderedSet[T]] = collections.defaultdict( - sets.InsertionOrderedSet - ) - self._children: Dict[T, sets.InsertionOrderedSet[T]] = collections.defaultdict( - sets.InsertionOrderedSet - ) - self._sinks: sets.InsertionOrderedSet[T] = sets.InsertionOrderedSet() - for node in nodes: - self._children[node] - self._parents[node] - self._sinks.add(node) - for src, dst in edges: - assert src in self.nodes - assert dst in self.nodes - self._children[src].add(dst) - self._parents[dst].add(src) - # sinks have no children - if src in self._sinks: - self._sinks.remove(src) - - @property - def nodes(self): - # should be the same set of ids as self._parents - return self._children.keys() - - @property - def sinks(self) -> Iterable[T]: - return self._sinks - - @property - def empty(self): - return len(self.nodes) == 0 - - def parents(self, node: T) -> Iterator[T]: - assert node in self._parents - yield from self._parents[node] - - def children(self, node: T) -> Iterator[T]: - assert node in self._children - yield from self._children[node] - - def remove_node(self, node: T) -> None: - for child in self._children[node]: - self._parents[child].remove(node) - for parent in self._parents[node]: - self._children[parent].remove(node) - if len(self._children[parent]) == 0: - self._sinks.add(parent) - del self._children[node] - del self._parents[node] - if node in self._sinks: - self._sinks.remove(node) diff --git a/bigframes/core/groupby/__init__.py b/bigframes/core/groupby/__init__.py index fe44911858f..2a19a83dd5f 100644 --- a/bigframes/core/groupby/__init__.py +++ b/bigframes/core/groupby/__init__.py @@ -12,7 +12,599 @@ # See the License for the specific language governing permissions and # limitations under the License. -from bigframes.core.groupby.dataframe_group_by import DataFrameGroupBy -from bigframes.core.groupby.series_group_by import SeriesGroupBy +from __future__ import annotations -__all__ = ["DataFrameGroupBy", "SeriesGroupBy"] +import typing + +import pandas as pd + +import bigframes.constants as constants +import bigframes.core as core +import bigframes.core.block_transforms as block_ops +import bigframes.core.blocks as blocks +import bigframes.core.ordering as order +import bigframes.core.utils as utils +import bigframes.core.window as windows +import bigframes.dataframe as df +import bigframes.dtypes as dtypes +import bigframes.operations as ops +import bigframes.operations.aggregations as agg_ops +import bigframes.series as series +import third_party.bigframes_vendored.pandas.core.groupby as vendored_pandas_groupby + + +class DataFrameGroupBy(vendored_pandas_groupby.DataFrameGroupBy): + __doc__ = vendored_pandas_groupby.GroupBy.__doc__ + + def __init__( + self, + block: blocks.Block, + by_col_ids: typing.Sequence[str], + *, + selected_cols: typing.Optional[typing.Sequence[str]] = None, + dropna: bool = True, + as_index: bool = True, + ): + # TODO(tbergeron): Support more group-by expression types + self._block = block + self._col_id_labels = { + value_column: column_label + for value_column, column_label in zip( + block.value_columns, block.column_labels + ) + } + self._by_col_ids = by_col_ids + + self._dropna = dropna + self._as_index = as_index + if selected_cols: + for col in selected_cols: + if col not in self._block.value_columns: + raise ValueError(f"Invalid column selection: {col}") + self._selected_cols = selected_cols + else: + self._selected_cols = [ + col_id + for col_id in self._block.value_columns + if col_id not in self._by_col_ids + ] + + def __getitem__( + self, + key: typing.Union[ + blocks.Label, + typing.Sequence[blocks.Label], + ], + ): + if utils.is_list_like(key): + keys = list(key) + else: + keys = [key] + columns = [ + col_id for col_id, label in self._col_id_labels.items() if label in keys + ] + + if len(columns) > 1 or (not self._as_index): + return DataFrameGroupBy( + self._block, + self._by_col_ids, + selected_cols=columns, + dropna=self._dropna, + as_index=self._as_index, + ) + else: + return SeriesGroupBy( + self._block, + columns[0], + self._by_col_ids, + value_name=self._col_id_labels[columns[0]], + dropna=self._dropna, + ) + + def sum(self, numeric_only: bool = False, *args) -> df.DataFrame: + if not numeric_only: + self._raise_on_non_numeric("sum") + return self._aggregate_all(agg_ops.sum_op, numeric_only=True) + + def mean(self, numeric_only: bool = False, *args) -> df.DataFrame: + if not numeric_only: + self._raise_on_non_numeric("mean") + return self._aggregate_all(agg_ops.mean_op, numeric_only=True) + + def median( + self, numeric_only: bool = False, *, exact: bool = False + ) -> df.DataFrame: + if exact: + raise NotImplementedError( + f"Only approximate median is supported. {constants.FEEDBACK_LINK}" + ) + if not numeric_only: + self._raise_on_non_numeric("median") + return self._aggregate_all(agg_ops.median_op, numeric_only=True) + + def min(self, numeric_only: bool = False, *args) -> df.DataFrame: + return self._aggregate_all(agg_ops.min_op, numeric_only=numeric_only) + + def max(self, numeric_only: bool = False, *args) -> df.DataFrame: + return self._aggregate_all(agg_ops.max_op, numeric_only=numeric_only) + + def std( + self, + *, + numeric_only: bool = False, + ) -> df.DataFrame: + if not numeric_only: + self._raise_on_non_numeric("std") + return self._aggregate_all(agg_ops.std_op, numeric_only=True) + + def var( + self, + *, + numeric_only: bool = False, + ) -> df.DataFrame: + if not numeric_only: + self._raise_on_non_numeric("var") + return self._aggregate_all(agg_ops.var_op, numeric_only=True) + + def skew( + self, + *, + numeric_only: bool = False, + ) -> df.DataFrame: + if not numeric_only: + self._raise_on_non_numeric("skew") + block = block_ops.skew(self._block, self._selected_cols, self._by_col_ids) + return df.DataFrame(block) + + def kurt( + self, + *, + numeric_only: bool = False, + ) -> df.DataFrame: + if not numeric_only: + self._raise_on_non_numeric("kurt") + block = block_ops.kurt(self._block, self._selected_cols, self._by_col_ids) + return df.DataFrame(block) + + kurtosis = kurt + + def all(self) -> df.DataFrame: + return self._aggregate_all(agg_ops.all_op) + + def any(self) -> df.DataFrame: + return self._aggregate_all(agg_ops.any_op) + + def count(self) -> df.DataFrame: + return self._aggregate_all(agg_ops.count_op) + + def cumsum(self, *args, numeric_only: bool = False, **kwargs) -> df.DataFrame: + if not numeric_only: + self._raise_on_non_numeric("cumsum") + return self._apply_window_op(agg_ops.sum_op, numeric_only=True) + + def cummin(self, *args, numeric_only: bool = False, **kwargs) -> df.DataFrame: + return self._apply_window_op(agg_ops.min_op, numeric_only=numeric_only) + + def cummax(self, *args, numeric_only: bool = False, **kwargs) -> df.DataFrame: + return self._apply_window_op(agg_ops.max_op, numeric_only=numeric_only) + + def cumprod(self, *args, **kwargs) -> df.DataFrame: + return self._apply_window_op(agg_ops.product_op, numeric_only=True) + + def shift(self, periods=1) -> series.Series: + window = core.WindowSpec( + grouping_keys=tuple(self._by_col_ids), + preceding=periods if periods > 0 else None, + following=-periods if periods < 0 else None, + ) + return self._apply_window_op(agg_ops.ShiftOp(periods), window=window) + + def diff(self, periods=1) -> series.Series: + window = core.WindowSpec( + grouping_keys=tuple(self._by_col_ids), + preceding=periods if periods > 0 else None, + following=-periods if periods < 0 else None, + ) + return self._apply_window_op(agg_ops.DiffOp(periods), window=window) + + def rolling(self, window: int, min_periods=None) -> windows.Window: + # To get n size window, need current row and n-1 preceding rows. + window_spec = core.WindowSpec( + grouping_keys=tuple(self._by_col_ids), + preceding=window - 1, + following=0, + min_periods=min_periods or window, + ) + block = self._block.order_by( + [order.OrderingColumnReference(col) for col in self._by_col_ids], + stable=True, + ) + return windows.Window( + block, window_spec, self._selected_cols, drop_null_groups=self._dropna + ) + + def expanding(self, min_periods: int = 1) -> windows.Window: + window_spec = core.WindowSpec( + grouping_keys=tuple(self._by_col_ids), + following=0, + min_periods=min_periods, + ) + block = self._block.order_by( + [order.OrderingColumnReference(col) for col in self._by_col_ids], + stable=True, + ) + return windows.Window( + block, window_spec, self._selected_cols, drop_null_groups=self._dropna + ) + + def agg(self, func=None, **kwargs) -> df.DataFrame: + if func: + if isinstance(func, str): + return self._agg_string(func) + elif utils.is_dict_like(func): + return self._agg_dict(func) + elif utils.is_list_like(func): + return self._agg_list(func) + else: + raise NotImplementedError( + f"Aggregate with {func} not supported. {constants.FEEDBACK_LINK}" + ) + else: + return self._agg_named(**kwargs) + + def _agg_string(self, func: str) -> df.DataFrame: + aggregations = [ + (col_id, agg_ops.lookup_agg_func(func)) + for col_id in self._aggregated_columns() + ] + agg_block, _ = self._block.aggregate( + by_column_ids=self._by_col_ids, + aggregations=aggregations, + as_index=self._as_index, + dropna=self._dropna, + ) + return df.DataFrame(agg_block) + + def _agg_dict(self, func: typing.Mapping) -> df.DataFrame: + aggregations: typing.List[typing.Tuple[str, agg_ops.AggregateOp]] = [] + column_labels = [] + + want_aggfunc_level = any(utils.is_list_like(aggs) for aggs in func.values()) + + for label, funcs_for_id in func.items(): + col_id = self._resolve_label(label) + func_list = ( + funcs_for_id if utils.is_list_like(funcs_for_id) else [funcs_for_id] + ) + for f in func_list: + aggregations.append((col_id, agg_ops.lookup_agg_func(f))) + column_labels.append(label) + agg_block, _ = self._block.aggregate( + by_column_ids=self._by_col_ids, + aggregations=aggregations, + as_index=self._as_index, + dropna=self._dropna, + ) + if want_aggfunc_level: + agg_block = agg_block.with_column_labels( + utils.combine_indices( + pd.Index(column_labels), + pd.Index(agg[1].name for agg in aggregations), + ) + ) + else: + agg_block = agg_block.with_column_labels(pd.Index(column_labels)) + return df.DataFrame(agg_block) + + def _agg_list(self, func: typing.Sequence) -> df.DataFrame: + aggregations = [ + (col_id, agg_ops.lookup_agg_func(f)) + for col_id in self._aggregated_columns() + for f in func + ] + column_labels = [ + (col_id, f) for col_id in self._aggregated_columns() for f in func + ] + agg_block, _ = self._block.aggregate( + by_column_ids=self._by_col_ids, + aggregations=aggregations, + as_index=self._as_index, + dropna=self._dropna, + ) + agg_block = agg_block.with_column_labels( + pd.MultiIndex.from_tuples( + column_labels, names=[*self._block.column_labels.names, None] + ) + ) + return df.DataFrame(agg_block) + + def _agg_named(self, **kwargs) -> df.DataFrame: + aggregations = [] + column_labels = [] + for k, v in kwargs.items(): + if not isinstance(k, str): + raise NotImplementedError( + f"Only string aggregate names supported. {constants.FEEDBACK_LINK}" + ) + if not hasattr(v, "column") or not hasattr(v, "aggfunc"): + import bigframes.pandas as bpd + + raise TypeError(f"kwargs values must be {bpd.NamedAgg.__qualname__}") + col_id = self._resolve_label(v.column) + aggregations.append((col_id, agg_ops.lookup_agg_func(v.aggfunc))) + column_labels.append(k) + agg_block, _ = self._block.aggregate( + by_column_ids=self._by_col_ids, + aggregations=aggregations, + as_index=self._as_index, + dropna=self._dropna, + ) + agg_block = agg_block.with_column_labels(column_labels) + return df.DataFrame(agg_block) + + aggregate = agg + + def _raise_on_non_numeric(self, op: str): + if not all( + dtype in dtypes.NUMERIC_BIGFRAMES_TYPES for dtype in self._block.dtypes + ): + raise NotImplementedError( + f"'{op}' does not support non-numeric columns. " + "Set 'numeric_only'=True to ignore non-numeric columns. " + f"{constants.FEEDBACK_LINK}" + ) + return self + + def _aggregated_columns(self, numeric_only: bool = False) -> typing.Sequence[str]: + valid_agg_cols: list[str] = [] + for col_id in self._selected_cols: + is_numeric = self._column_type(col_id) in dtypes.NUMERIC_BIGFRAMES_TYPES + if is_numeric or not numeric_only: + valid_agg_cols.append(col_id) + return valid_agg_cols + + def _column_type(self, col_id: str) -> dtypes.Dtype: + col_offset = self._block.value_columns.index(col_id) + dtype = self._block.dtypes[col_offset] + return dtype + + def _aggregate_all( + self, aggregate_op: agg_ops.AggregateOp, numeric_only: bool = False + ) -> df.DataFrame: + aggregated_col_ids = self._aggregated_columns(numeric_only=numeric_only) + aggregations = [(col_id, aggregate_op) for col_id in aggregated_col_ids] + result_block, _ = self._block.aggregate( + by_column_ids=self._by_col_ids, + aggregations=aggregations, + as_index=self._as_index, + dropna=self._dropna, + ) + return df.DataFrame(result_block) + + def _apply_window_op( + self, + op: agg_ops.WindowOp, + window: typing.Optional[core.WindowSpec] = None, + numeric_only: bool = False, + ): + """Apply window op to groupby. Defaults to grouped cumulative window.""" + window_spec = window or core.WindowSpec( + grouping_keys=tuple(self._by_col_ids), following=0 + ) + columns = self._aggregated_columns(numeric_only=numeric_only) + block, result_ids = self._block.multi_apply_window_op( + columns, op, window_spec=window_spec + ) + block = block.select_columns(result_ids) + return df.DataFrame(block) + + def _resolve_label(self, label: blocks.Label) -> str: + """Resolve label to column id.""" + col_ids = self._block.label_to_col_id.get(label, ()) + if len(col_ids) > 1: + raise ValueError(f"Label {label} is ambiguous") + if len(col_ids) == 0: + raise ValueError(f"Label {label} does not match any columns") + return col_ids[0] + + +class SeriesGroupBy(vendored_pandas_groupby.SeriesGroupBy): + __doc__ = vendored_pandas_groupby.GroupBy.__doc__ + + def __init__( + self, + block: blocks.Block, + value_column: str, + by_col_ids: typing.Sequence[str], + value_name: blocks.Label = None, + dropna=True, + ): + # TODO(tbergeron): Support more group-by expression types + self._block = block + self._value_column = value_column + self._by_col_ids = by_col_ids + self._value_name = value_name + self._dropna = dropna # Applies to aggregations but not windowing + + def all(self) -> series.Series: + return self._aggregate(agg_ops.all_op) + + def any(self) -> series.Series: + return self._aggregate(agg_ops.any_op) + + def min(self, *args) -> series.Series: + return self._aggregate(agg_ops.min_op) + + def max(self, *args) -> series.Series: + return self._aggregate(agg_ops.max_op) + + def count(self) -> series.Series: + return self._aggregate(agg_ops.count_op) + + def sum(self, *args) -> series.Series: + return self._aggregate(agg_ops.sum_op) + + def mean(self, *args) -> series.Series: + return self._aggregate(agg_ops.mean_op) + + def median(self, *args, **kwargs) -> series.Series: + return self._aggregate(agg_ops.mean_op) + + def std(self, *args, **kwargs) -> series.Series: + return self._aggregate(agg_ops.std_op) + + def var(self, *args, **kwargs) -> series.Series: + return self._aggregate(agg_ops.var_op) + + def skew(self, *args, **kwargs) -> series.Series: + block = block_ops.skew(self._block, [self._value_column], self._by_col_ids) + return series.Series(block) + + def kurt(self, *args, **kwargs) -> series.Series: + block = block_ops.kurt(self._block, [self._value_column], self._by_col_ids) + return series.Series(block) + + kurtosis = kurt + + def prod(self, *args) -> series.Series: + return self._aggregate(agg_ops.product_op) + + def agg(self, func=None) -> typing.Union[df.DataFrame, series.Series]: + column_names: list[str] = [] + if isinstance(func, str): + aggregations = [(self._value_column, agg_ops.lookup_agg_func(func))] + column_names = [func] + elif utils.is_list_like(func): + aggregations = [ + (self._value_column, agg_ops.lookup_agg_func(f)) for f in func + ] + column_names = list(func) + else: + raise NotImplementedError( + f"Aggregate with {func} not supported. {constants.FEEDBACK_LINK}" + ) + + agg_block, _ = self._block.aggregate( + by_column_ids=self._by_col_ids, + aggregations=aggregations, + dropna=self._dropna, + ) + + if column_names: + agg_block = agg_block.with_column_labels(column_names) + + if len(aggregations) > 1: + return df.DataFrame(agg_block) + return series.Series(agg_block) + + aggregate = agg + + def cumsum(self, *args, **kwargs) -> series.Series: + return self._apply_window_op( + agg_ops.sum_op, + ) + + def cumprod(self, *args, **kwargs) -> series.Series: + return self._apply_window_op( + agg_ops.product_op, + ) + + def cummax(self, *args, **kwargs) -> series.Series: + return self._apply_window_op( + agg_ops.max_op, + ) + + def cummin(self, *args, **kwargs) -> series.Series: + return self._apply_window_op( + agg_ops.min_op, + ) + + def cumcount(self, *args, **kwargs) -> series.Series: + return self._apply_window_op( + agg_ops.rank_op, + discard_name=True, + )._apply_unary_op(ops.partial_right(ops.sub_op, 1)) + + def shift(self, periods=1) -> series.Series: + """Shift index by desired number of periods.""" + window = core.WindowSpec( + grouping_keys=tuple(self._by_col_ids), + preceding=periods if periods > 0 else None, + following=-periods if periods < 0 else None, + ) + return self._apply_window_op(agg_ops.ShiftOp(periods), window=window) + + def diff(self, periods=1) -> series.Series: + window = core.WindowSpec( + grouping_keys=tuple(self._by_col_ids), + preceding=periods if periods > 0 else None, + following=-periods if periods < 0 else None, + ) + return self._apply_window_op(agg_ops.DiffOp(periods), window=window) + + def rolling(self, window: int, min_periods=None) -> windows.Window: + # To get n size window, need current row and n-1 preceding rows. + window_spec = core.WindowSpec( + grouping_keys=tuple(self._by_col_ids), + preceding=window - 1, + following=0, + min_periods=min_periods or window, + ) + block = self._block.order_by( + [order.OrderingColumnReference(col) for col in self._by_col_ids], + stable=True, + ) + return windows.Window( + block, + window_spec, + [self._value_column], + drop_null_groups=self._dropna, + is_series=True, + ) + + def expanding(self, min_periods: int = 1) -> windows.Window: + window_spec = core.WindowSpec( + grouping_keys=tuple(self._by_col_ids), + following=0, + min_periods=min_periods, + ) + block = self._block.order_by( + [order.OrderingColumnReference(col) for col in self._by_col_ids], + stable=True, + ) + return windows.Window( + block, + window_spec, + [self._value_column], + drop_null_groups=self._dropna, + is_series=True, + ) + + def _aggregate(self, aggregate_op: agg_ops.AggregateOp) -> series.Series: + result_block, _ = self._block.aggregate( + self._by_col_ids, + ((self._value_column, aggregate_op),), + dropna=self._dropna, + ) + + return series.Series(result_block.with_column_labels([self._value_name])) + + def _apply_window_op( + self, + op: agg_ops.WindowOp, + discard_name=False, + window: typing.Optional[core.WindowSpec] = None, + ): + """Apply window op to groupby. Defaults to grouped cumulative window.""" + window_spec = window or core.WindowSpec( + grouping_keys=tuple(self._by_col_ids), following=0 + ) + + label = self._value_name if not discard_name else None + block, result_id = self._block.apply_window_op( + self._value_column, + op, + result_label=label, + window_spec=window_spec, + ) + return series.Series(block.select_column(result_id)) diff --git a/bigframes/core/groupby/aggs.py b/bigframes/core/groupby/aggs.py deleted file mode 100644 index 9d8b957d547..00000000000 --- a/bigframes/core/groupby/aggs.py +++ /dev/null @@ -1,26 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from __future__ import annotations - -from bigframes.core import agg_expressions, expression -from bigframes.operations import aggregations as agg_ops - - -def agg(input: str, op: agg_ops.AggregateOp) -> agg_expressions.Aggregation: - if isinstance(op, agg_ops.UnaryAggregateOp): - return agg_expressions.UnaryAggregation(op, expression.deref(input)) - else: - assert isinstance(op, agg_ops.NullaryAggregateOp) - return agg_expressions.NullaryAggregation(op) diff --git a/bigframes/core/groupby/dataframe_group_by.py b/bigframes/core/groupby/dataframe_group_by.py deleted file mode 100644 index 7cc61d43a02..00000000000 --- a/bigframes/core/groupby/dataframe_group_by.py +++ /dev/null @@ -1,834 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from __future__ import annotations - -import datetime -import typing -from typing import Iterable, Literal, Optional, Sequence, Tuple, Union - -import bigframes_vendored.constants as constants -import bigframes_vendored.pandas.core.groupby as vendored_pandas_groupby -import numpy -import pandas as pd - -import bigframes.core.block_transforms as block_ops -import bigframes.core.block_transforms as block_transforms -import bigframes.core.blocks as blocks -import bigframes.core.ordering as order -import bigframes.core.utils as utils -import bigframes.core.validations as validations -import bigframes.core.window as windows -import bigframes.core.window_spec as window_specs -import bigframes.dataframe as df -import bigframes.dtypes as dtypes -import bigframes.operations -import bigframes.operations.aggregations as agg_ops -import bigframes.series as series -from bigframes import session -from bigframes._tools import docs -from bigframes.core import agg_expressions -from bigframes.core import expression as ex -from bigframes.core.groupby import aggs, group_by, series_group_by -from bigframes.core.logging import log_adapter -from bigframes.core.window import rolling - - -@log_adapter.class_logger -@docs.inherit_docs(vendored_pandas_groupby.DataFrameGroupBy) -class DataFrameGroupBy: - def __init__( - self, - block: blocks.Block, - by_col_ids: typing.Sequence[str], - *, - selected_cols: typing.Optional[typing.Sequence[str]] = None, - dropna: bool = True, - as_index: bool = True, - by_key_is_singular: bool = False, - ): - # TODO(tbergeron): Support more group-by expression types - self._block = block - self._col_id_labels = { - value_column: column_label - for value_column, column_label in zip( - block.value_columns, block.column_labels - ) - } - self._by_col_ids = by_col_ids - self._by_key_is_singular = by_key_is_singular - if by_key_is_singular: - assert len(by_col_ids) == 1, "singular key should be exactly one group key" - - self._dropna = dropna - self._as_index = as_index - if selected_cols: - for col in selected_cols: - if col not in self._block.value_columns: - raise ValueError(f"Invalid column selection: {col}") - self._selected_cols = selected_cols - else: - self._selected_cols = [ - col_id - for col_id in self._block.value_columns - if col_id not in self._by_col_ids - ] - - @property - def _session(self) -> session.Session: - return self._block.session - - def __getitem__( - self, - key: typing.Union[ - blocks.Label, - typing.Sequence[blocks.Label], - ], - ): - import bigframes._tools.strings - - if utils.is_list_like(key): - keys = list(key) - else: - keys = [key] - - bad_keys = [key for key in keys if key not in self._block.column_labels] - - # Raise a KeyError message with the possible correct key(s) - if len(bad_keys) > 0: - possible_key = [] - for bad_key in bad_keys: - possible_key.append( - min( - self._block.column_labels, - key=lambda item: bigframes._tools.strings.levenshtein_distance( - bad_key, item - ), - ) - ) - raise KeyError( - f"Columns not found: {str(bad_keys)[1:-1]}. Did you mean {str(possible_key)[1:-1]}?" - ) - - columns = [ - col_id for col_id, label in self._col_id_labels.items() if label in keys - ] - - if len(columns) > 1 or (not self._as_index): - return DataFrameGroupBy( - self._block, - self._by_col_ids, - selected_cols=columns, - dropna=self._dropna, - as_index=self._as_index, - ) - else: - return series_group_by.SeriesGroupBy( - self._block, - columns[0], - self._by_col_ids, - value_name=self._col_id_labels[columns[0]], - dropna=self._dropna, - ) - - @validations.requires_ordering() - def head(self, n: int = 5) -> df.DataFrame: - block = self._block - if self._dropna: - block = block_ops.dropna(self._block, self._by_col_ids, how="any") - return df.DataFrame( - block.grouped_head( - by_column_ids=self._by_col_ids, - value_columns=self._block.value_columns, - n=n, - ) - ) - - def describe(self, include: None | Literal["all"] = None): - from bigframes.pandas.core.methods import describe - - return df.DataFrame( - describe._describe( - self._block, - self._selected_cols, - include, - as_index=self._as_index, - by_col_ids=self._by_col_ids, - dropna=self._dropna, - ) - ) - - def __iter__(self) -> Iterable[Tuple[blocks.Label, df.DataFrame]]: - for group_keys, filtered_block in group_by.block_groupby_iter( - self._block, - by_col_ids=self._by_col_ids, - by_key_is_singular=self._by_key_is_singular, - dropna=self._dropna, - ): - filtered_df = df.DataFrame(filtered_block) - yield group_keys, filtered_df - - def __len__(self) -> int: - return len(self.agg([])) - - def size(self) -> typing.Union[df.DataFrame, series.Series]: - agg_block = self._block.aggregate( - aggregations=[agg_ops.SizeOp().as_expr()], - by_column_ids=self._by_col_ids, - dropna=self._dropna, - ) - agg_block = agg_block.with_column_labels(pd.Index(["size"])) - dataframe = df.DataFrame(agg_block) - - if self._as_index: - series = dataframe["size"] - return series.rename(None) - else: - return self._convert_index(dataframe) - - def sum(self, numeric_only: bool = False, *args) -> df.DataFrame: - if not numeric_only: - self._raise_on_non_numeric("sum") - return self._aggregate_all(agg_ops.sum_op, numeric_only=True) - - def mean(self, numeric_only: bool = False, *args) -> df.DataFrame: - if not numeric_only: - self._raise_on_non_numeric("mean") - return self._aggregate_all(agg_ops.mean_op, numeric_only=True) - - def median(self, numeric_only: bool = False, *, exact: bool = True) -> df.DataFrame: - if not numeric_only: - self._raise_on_non_numeric("median") - if exact: - return self.quantile(0.5) - return self._aggregate_all(agg_ops.median_op, numeric_only=True) - - def rank( - self, - method="average", - ascending: bool = True, - na_option: str = "keep", - pct: bool = False, - ) -> df.DataFrame: - return df.DataFrame( - block_ops.rank( - self._block, - method, - na_option, - ascending, - grouping_cols=tuple(self._by_col_ids), - columns=tuple(self._selected_cols), - pct=pct, - ) - ) - - def quantile( - self, q: Union[float, Sequence[float]] = 0.5, *, numeric_only: bool = False - ) -> df.DataFrame: - if not numeric_only: - self._raise_on_non_numeric("quantile") - q_cols = tuple( - col - for col in self._selected_cols - if self._column_type(col) in dtypes.NUMERIC_BIGFRAMES_TYPES_PERMISSIVE - ) - multi_q = utils.is_list_like(q) - result = block_ops.quantile( - self._block, - q_cols, - qs=tuple(q) if multi_q else (q,), # type: ignore - grouping_column_ids=self._by_col_ids, - dropna=self._dropna, - ) - result_df = df.DataFrame(result) - if multi_q: - return result_df.stack() - else: - return result_df.droplevel(-1, 1) - - def min(self, numeric_only: bool = False, *args) -> df.DataFrame: - return self._aggregate_all(agg_ops.min_op, numeric_only=numeric_only) - - def max(self, numeric_only: bool = False, *args) -> df.DataFrame: - return self._aggregate_all(agg_ops.max_op, numeric_only=numeric_only) - - def std( - self, - *, - numeric_only: bool = False, - ) -> df.DataFrame: - if not numeric_only: - self._raise_on_non_numeric("std") - return self._aggregate_all(agg_ops.std_op, numeric_only=True) - - def var( - self, - *, - numeric_only: bool = False, - ) -> df.DataFrame: - if not numeric_only: - self._raise_on_non_numeric("var") - return self._aggregate_all(agg_ops.var_op, numeric_only=True) - - def corr( - self, - *, - numeric_only: bool = False, - ) -> df.DataFrame: - if not numeric_only: - self._raise_on_non_numeric("corr") - if len(self._selected_cols) > 30: - raise ValueError( - f"Cannot calculate corr on >30 columns, dataframe has {len(self._selected_cols)} selected columns." - ) - - labels = self._block._get_labels_for_columns(self._selected_cols) - block = self._block - aggregations = [ - agg_expressions.BinaryAggregation( - agg_ops.CorrOp(), ex.deref(left_col), ex.deref(right_col) - ) - for left_col in self._selected_cols - for right_col in self._selected_cols - ] - # unique columns stops - uniq_orig_columns = utils.combine_indices(labels, pd.Index(range(len(labels)))) - result_labels = utils.cross_indices(uniq_orig_columns, uniq_orig_columns) - - block = block.aggregate( - by_column_ids=self._by_col_ids, - aggregations=aggregations, - column_labels=result_labels, - ) - - block = block.stack(levels=labels.nlevels + 1) - # Drop the last level of each index, which was created to guarantee uniqueness - return df.DataFrame(block).droplevel(-1, axis=0).droplevel(-1, axis=1) - - def cov( - self, - *, - numeric_only: bool = False, - ) -> df.DataFrame: - if not numeric_only: - self._raise_on_non_numeric("cov") - if len(self._selected_cols) > 30: - raise ValueError( - f"Cannot calculate cov on >30 columns, dataframe has {len(self._selected_cols)} selected columns." - ) - - labels = self._block._get_labels_for_columns(self._selected_cols) - block = self._block - aggregations = [ - agg_expressions.BinaryAggregation( - agg_ops.CovOp(), ex.deref(left_col), ex.deref(right_col) - ) - for left_col in self._selected_cols - for right_col in self._selected_cols - ] - # unique columns stops - uniq_orig_columns = utils.combine_indices(labels, pd.Index(range(len(labels)))) - result_labels = utils.cross_indices(uniq_orig_columns, uniq_orig_columns) - - block = block.aggregate( - by_column_ids=self._by_col_ids, - aggregations=aggregations, - column_labels=result_labels, - ) - - block = block.stack(levels=labels.nlevels + 1) - # Drop the last level of each index, which was created to guarantee uniqueness - return df.DataFrame(block).droplevel(-1, axis=0).droplevel(-1, axis=1) - - def skew( - self, - *, - numeric_only: bool = False, - ) -> df.DataFrame: - if not numeric_only: - self._raise_on_non_numeric("skew") - block = block_ops.skew(self._block, self._selected_cols, self._by_col_ids) - return df.DataFrame(block) - - def kurt( - self, - *, - numeric_only: bool = False, - ) -> df.DataFrame: - if not numeric_only: - self._raise_on_non_numeric("kurt") - block = block_ops.kurt(self._block, self._selected_cols, self._by_col_ids) - return df.DataFrame(block) - - kurtosis = kurt - - @validations.requires_ordering() - def first(self, numeric_only: bool = False, min_count: int = -1) -> df.DataFrame: - window_spec = window_specs.unbound( - grouping_keys=tuple(self._by_col_ids), - min_periods=min_count if min_count >= 0 else 0, - ) - target_cols, index = self._aggregated_columns(numeric_only) - block, firsts_ids = self._block.multi_apply_window_op( - target_cols, - agg_ops.FirstNonNullOp(), - window_spec=window_spec, - ) - block = block.aggregate( - by_column_ids=self._by_col_ids, - aggregations=tuple( - aggs.agg(firsts_id, agg_ops.AnyValueOp()) for firsts_id in firsts_ids - ), - dropna=self._dropna, - column_labels=index, - ) - return df.DataFrame(block) - - @validations.requires_ordering() - def last(self, numeric_only: bool = False, min_count: int = -1) -> df.DataFrame: - window_spec = window_specs.unbound( - grouping_keys=tuple(self._by_col_ids), - min_periods=min_count if min_count >= 0 else 0, - ) - target_cols, index = self._aggregated_columns(numeric_only) - block, lasts_ids = self._block.multi_apply_window_op( - target_cols, - agg_ops.LastNonNullOp(), - window_spec=window_spec, - ) - block = block.aggregate( - by_column_ids=self._by_col_ids, - aggregations=tuple( - aggs.agg(lasts_id, agg_ops.AnyValueOp()) for lasts_id in lasts_ids - ), - dropna=self._dropna, - column_labels=index, - ) - return df.DataFrame(block) - - def all(self) -> df.DataFrame: - return self._aggregate_all(agg_ops.all_op) - - def any(self) -> df.DataFrame: - return self._aggregate_all(agg_ops.any_op) - - def count(self) -> df.DataFrame: - return self._aggregate_all(agg_ops.count_op) - - def nunique(self) -> df.DataFrame: - return self._aggregate_all(agg_ops.nunique_op) - - @validations.requires_ordering() - def cumcount(self, ascending: bool = True) -> series.Series: - window_spec = ( - window_specs.cumulative_rows(grouping_keys=tuple(self._by_col_ids)) - if ascending - else window_specs.inverse_cumulative_rows( - grouping_keys=tuple(self._by_col_ids) - ) - ) - block, result_ids = self._block.apply_analytic( - [agg_expressions.NullaryAggregation(agg_ops.size_op)], - window=window_spec, - result_labels=[None], - ) - result = series.Series(block.select_columns(result_ids)) - 1 - if self._dropna and (len(self._by_col_ids) == 1): - result = result.mask( - series.Series(block.select_column(self._by_col_ids[0])).isna() - ) - return result - - @validations.requires_ordering() - def cumsum(self, *args, numeric_only: bool = False, **kwargs) -> df.DataFrame: - if not numeric_only: - self._raise_on_non_numeric("cumsum") - return self._apply_window_op(agg_ops.sum_op, numeric_only=True) - - @validations.requires_ordering() - def cummin(self, *args, numeric_only: bool = False, **kwargs) -> df.DataFrame: - return self._apply_window_op(agg_ops.min_op, numeric_only=numeric_only) - - @validations.requires_ordering() - def cummax(self, *args, numeric_only: bool = False, **kwargs) -> df.DataFrame: - return self._apply_window_op(agg_ops.max_op, numeric_only=numeric_only) - - @validations.requires_ordering() - def cumprod(self, *args, **kwargs) -> df.DataFrame: - return self._apply_window_op(agg_ops.product_op, numeric_only=True) - - @validations.requires_ordering() - def shift(self, periods=1) -> series.Series: - # Window framing clause is not allowed for analytic function lag. - window = window_specs.unbound( - grouping_keys=tuple(self._by_col_ids), - ) - return self._apply_window_op(agg_ops.ShiftOp(periods), window=window) - - @validations.requires_ordering() - def diff(self, periods=1) -> series.Series: - # Window framing clause is not allowed for analytic function lag. - window = window_specs.rows( - grouping_keys=tuple(self._by_col_ids), - ) - return self._apply_window_op(agg_ops.DiffOp(periods), window=window) - - def value_counts( - self, - subset: Optional[Sequence[blocks.Label]] = None, - normalize: bool = False, - sort: bool = True, - ascending: bool = False, - dropna: bool = True, - ) -> Union[df.DataFrame, series.Series]: - if subset is None: - columns = self._selected_cols - else: - columns = [ - column - for column in self._block.value_columns - if self._block.col_id_to_label[column] in subset - ] - block = self._block - if self._dropna: # this drops null grouping columns - block = block_ops.dropna(block, self._by_col_ids) - block = block_ops.value_counts( - block, - columns, - normalize=normalize, - sort=sort, - ascending=ascending, - drop_na=dropna, # this drops null value columns - grouping_keys=self._by_col_ids, - ) - if self._as_index: - return series.Series(block) - else: - return series.Series(block).to_frame().reset_index(drop=False) - - @validations.requires_ordering() - def rolling( - self, - window: int | pd.Timedelta | numpy.timedelta64 | datetime.timedelta | str, - min_periods=None, - on: str | None = None, - closed: Literal["right", "left", "both", "neither"] = "right", - ) -> windows.Window: - if isinstance(window, int): - window_spec = window_specs.WindowSpec( - bounds=window_specs.RowsWindowBounds.from_window_size(window, closed), - min_periods=min_periods if min_periods is not None else window, - grouping_keys=tuple(ex.deref(col) for col in self._by_col_ids), - ) - block = self._block.order_by( - [order.ascending_over(col) for col in self._by_col_ids], - ) - skip_agg_col_id = ( - None if on is None else self._block.resolve_label_exact_or_error(on) - ) - return windows.Window( - block, - window_spec, - self._selected_cols, - drop_null_groups=self._dropna, - skip_agg_column_id=skip_agg_col_id, - ) - - return rolling.create_range_window( - self._block, - window, - min_periods=min_periods, - value_column_ids=self._selected_cols, - on=on, - closed=closed, - is_series=False, - grouping_keys=self._by_col_ids, - drop_null_groups=self._dropna, - ) - - @validations.requires_ordering() - def expanding(self, min_periods: int = 1) -> windows.Window: - window_spec = window_specs.cumulative_rows( - grouping_keys=tuple(self._by_col_ids), - min_periods=min_periods, - ) - block = self._block.order_by( - [order.ascending_over(col) for col in self._by_col_ids], - ) - return windows.Window( - block, window_spec, self._selected_cols, drop_null_groups=self._dropna - ) - - def agg(self, func=None, **kwargs) -> typing.Union[df.DataFrame, series.Series]: - if func: - if utils.is_dict_like(func): - return self._agg_dict(func) - elif utils.is_list_like(func): - return self._agg_list(func) - else: - return self.size() if func == "size" else self._agg_func(func) - else: - return self._agg_named(**kwargs) - - def transform(self, func, *args, **kwargs) -> df.DataFrame: - if block_transforms.is_transpiler_eligible(func): - window_spec = window_specs.unbound(grouping_keys=tuple(self._by_col_ids)) - target_cols, labels = self._aggregated_columns() - exprs = [] - for col_id in target_cols: - expr, _ = block_transforms.compile_column_udf( - self._block, - func, - col_id, - args=args, - kwargs=kwargs, - window_spec=window_spec, - ) - exprs.append(expr) - - block = self._block.project_block_exprs( - exprs, - labels=labels, - drop=True, - ) - return df.DataFrame(block) - - raise NotImplementedError( - "DataFrameGroupBy.transform is only supported when experiments.enable_python_transpiler is True and a transpiler-compatible python function is provided." - ) - - def _agg_func(self, func) -> df.DataFrame: - ids, labels = self._aggregated_columns() - aggregations = [] - for col_id in ids: - if block_transforms.is_transpiler_eligible(func): - expr, _ = block_transforms.compile_column_udf(self._block, func, col_id) - aggregations.append(expr) - else: - aggregations.append(aggs.agg(col_id, agg_ops.lookup_agg_func(func)[0])) - - agg_block = self._block.aggregate( - by_column_ids=self._by_col_ids, - aggregations=aggregations, - dropna=self._dropna, - column_labels=labels, - ) - dataframe = df.DataFrame(agg_block) - return dataframe if self._as_index else self._convert_index(dataframe) - - def _agg_dict(self, func: typing.Mapping) -> df.DataFrame: - aggregations: typing.List[ex.Expression] = [] - column_labels = [] - function_labels = [] - - want_aggfunc_level = any(utils.is_list_like(aggs) for aggs in func.values()) - - for label, funcs_for_id in func.items(): - col_id = self._resolve_label(label) - func_list = ( - funcs_for_id if utils.is_list_like(funcs_for_id) else [funcs_for_id] - ) - for f in func_list: - if block_transforms.is_transpiler_eligible(f): - expr, name = block_transforms.compile_column_udf( - self._block, f, col_id - ) - aggregations.append(expr) - column_labels.append(label) - function_labels.append(name) - else: - f_op, f_label = agg_ops.lookup_agg_func(f) - aggregations.append(aggs.agg(col_id, f_op)) - column_labels.append(label) - function_labels.append(f_label) - agg_block = self._block.aggregate( - by_column_ids=self._by_col_ids, - aggregations=aggregations, - dropna=self._dropna, - ) - if want_aggfunc_level: - agg_block = agg_block.with_column_labels( - utils.combine_indices( - pd.Index(column_labels), - pd.Index(function_labels), - ) - ) - else: - agg_block = agg_block.with_column_labels(pd.Index(column_labels)) - dataframe = df.DataFrame(agg_block) - return dataframe if self._as_index else self._convert_index(dataframe) - - def _agg_list(self, func: typing.Sequence) -> df.DataFrame: - ids, labels = self._aggregated_columns() - aggregations = [] - fn_labels = [] - - for f in func: - if block_transforms.is_transpiler_eligible(f): - fn_labels.append(getattr(f, "__name__", "")) - else: - fn_labels.append(agg_ops.lookup_agg_func(f)[1]) - - for col_id in ids: - for f in func: - if block_transforms.is_transpiler_eligible(f): - expr, _ = block_transforms.compile_column_udf( - self._block, f, col_id - ) - aggregations.append(expr) - else: - aggregations.append(aggs.agg(col_id, agg_ops.lookup_agg_func(f)[0])) - - if self._block.column_labels.nlevels > 1: - column_labels = [ - tuple(label) + (fn_lbl,) - for label in labels.to_frame(index=False).to_numpy() - for fn_lbl in fn_labels - ] - else: # Single-level index - column_labels = [ - (label, fn_lbl) for label in labels for fn_lbl in fn_labels - ] - - agg_block = self._block.aggregate( - by_column_ids=self._by_col_ids, - aggregations=aggregations, - dropna=self._dropna, - ) - agg_block = agg_block.with_column_labels( - pd.MultiIndex.from_tuples( - column_labels, names=[*self._block.column_labels.names, None] - ) - ) - dataframe = df.DataFrame(agg_block) - return dataframe if self._as_index else self._convert_index(dataframe) - - def _agg_named(self, **kwargs) -> df.DataFrame: - aggregations = [] - column_labels = [] - for k, v in kwargs.items(): - if not isinstance(k, str): - raise NotImplementedError( - f"Only string aggregate names supported. {constants.FEEDBACK_LINK}" - ) - if not isinstance(v, tuple) or (len(v) != 2): - raise TypeError("kwargs values must be 2-tuples of column, aggfunc") - col_id = self._resolve_label(v[0]) - aggregations.append(aggs.agg(col_id, agg_ops.lookup_agg_func(v[1])[0])) - column_labels.append(k) - agg_block = self._block.aggregate( - by_column_ids=self._by_col_ids, - aggregations=aggregations, - dropna=self._dropna, - ) - agg_block = agg_block.with_column_labels(column_labels) - dataframe = df.DataFrame(agg_block) - return dataframe if self._as_index else self._convert_index(dataframe) - - def _convert_index(self, dataframe: df.DataFrame): - """Convert index levels to columns except where names conflict.""" - levels_to_drop = [ - level for level in dataframe.index.names if level in dataframe.columns - ] - - if len(levels_to_drop) == dataframe.index.nlevels: - return dataframe.reset_index(drop=True) - return dataframe.droplevel(levels_to_drop).reset_index(drop=False) - - aggregate = agg - - def _raise_on_non_numeric(self, op: str): - if not all( - self._column_type(col) in dtypes.NUMERIC_BIGFRAMES_TYPES_PERMISSIVE - for col in self._selected_cols - ): - raise NotImplementedError( - f"'{op}' does not support non-numeric columns. " - "Set 'numeric_only'=True to ignore non-numeric columns. " - f"{constants.FEEDBACK_LINK}" - ) - return self - - def _aggregated_columns( - self, numeric_only: bool = False - ) -> Tuple[typing.Sequence[str], pd.Index]: - valid_agg_cols: list[str] = [] - offsets: list[int] = [] - for i, col_id in enumerate(self._block.value_columns): - is_numeric = ( - self._column_type(col_id) in dtypes.NUMERIC_BIGFRAMES_TYPES_PERMISSIVE - ) - if (col_id in self._selected_cols) and (is_numeric or not numeric_only): - offsets.append(i) - valid_agg_cols.append(col_id) - return valid_agg_cols, self._block.column_labels.take(offsets) - - def _column_type(self, col_id: str) -> dtypes.Dtype: - col_offset = self._block.value_columns.index(col_id) - dtype = self._block.dtypes[col_offset] - return dtype - - def _aggregate_all( - self, aggregate_op: agg_ops.UnaryAggregateOp, numeric_only: bool = False - ) -> df.DataFrame: - aggregated_col_ids, labels = self._aggregated_columns(numeric_only=numeric_only) - aggregations = [aggs.agg(col_id, aggregate_op) for col_id in aggregated_col_ids] - result_block = self._block.aggregate( - by_column_ids=self._by_col_ids, - aggregations=aggregations, - column_labels=labels, - dropna=self._dropna, - ) - dataframe = df.DataFrame(result_block) - return dataframe if self._as_index else self._convert_index(dataframe) - - def _apply_window_op( - self, - op: agg_ops.UnaryWindowOp, - window: typing.Optional[window_specs.WindowSpec] = None, - numeric_only: bool = False, - ): - """Apply window op to groupby. Defaults to grouped cumulative window.""" - window_spec = window or window_specs.cumulative_rows( - grouping_keys=tuple(self._by_col_ids) - ) - columns, labels = self._aggregated_columns(numeric_only=numeric_only) - block, result_ids = self._block.multi_apply_window_op( - columns, - op, - window_spec=window_spec, - ) - block = block.project_exprs( - tuple( - bigframes.operations.where_op.as_expr( - r_col, - bigframes.operations.notnull_op.as_expr(og_col), - ex.const(None), - ) - for og_col, r_col in zip(columns, result_ids) - ), - labels=labels, - drop=True, - ) - - return df.DataFrame(block) - - def _resolve_label(self, label: blocks.Label) -> str: - """Resolve label to column id.""" - col_ids = self._block.label_to_col_id.get(label, ()) - if len(col_ids) > 1: - raise ValueError(f"Label {label} is ambiguous") - if len(col_ids) == 0: - raise ValueError(f"Label {label} does not match any columns") - return col_ids[0] diff --git a/bigframes/core/groupby/group_by.py b/bigframes/core/groupby/group_by.py deleted file mode 100644 index 34786e4fd88..00000000000 --- a/bigframes/core/groupby/group_by.py +++ /dev/null @@ -1,91 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from __future__ import annotations - -import functools -from typing import Sequence - -import pandas as pd - -import bigframes.enums -import bigframes.operations as ops -from bigframes.core import blocks -from bigframes.core import expression as ex - - -def block_groupby_iter( - block: blocks.Block, - *, - by_col_ids: Sequence[str], - by_key_is_singular: bool, - dropna: bool, -): - original_index_columns = block._index_columns - original_index_labels = block._index_labels - by_col_ids = by_col_ids - block = block.reset_index( - level=None, - # Keep the original index columns so they can be recovered. - drop=False, - allow_duplicates=True, - replacement=bigframes.enums.DefaultIndexKind.NULL, - ).set_index( - by_col_ids, - # Keep by_col_ids in-place so the ordering doesn't change. - drop=False, - append=False, - ) - block.cached( - force=True, - # All DataFrames will be filtered by by_col_ids, so - # force block.cached() to cluster by the new index by explicitly - # setting `session_aware=False`. This will ensure that the filters - # are more efficient. - session_aware=False, - ) - keys_block = block.aggregate(by_column_ids=by_col_ids, dropna=dropna) - for chunk in keys_block.to_pandas_batches(): - # Convert to MultiIndex to make sure we get tuples, - # even for singular keys. - by_keys_index = chunk.index - if not isinstance(by_keys_index, pd.MultiIndex): - by_keys_index = pd.MultiIndex.from_frame(by_keys_index.to_frame()) - - for by_keys in by_keys_index: - filtered_block = ( - # To ensure the cache is used, filter first, then reset the - # index before yielding the DataFrame. - block.filter( - functools.reduce( - ops.and_op.as_expr, - ( - ops.eq_op.as_expr(by_col, ex.const(by_key)) - for by_col, by_key in zip(by_col_ids, by_keys) - ), - ), - ).set_index( - original_index_columns, - # We retained by_col_ids in the set_index call above, - # so it's safe to drop the duplicates now. - drop=True, - append=False, - index_labels=original_index_labels, - ) - ) - - if by_key_is_singular: - yield by_keys[0], filtered_block - else: - yield by_keys, filtered_block diff --git a/bigframes/core/groupby/series_group_by.py b/bigframes/core/groupby/series_group_by.py deleted file mode 100644 index fb7845f36d2..00000000000 --- a/bigframes/core/groupby/series_group_by.py +++ /dev/null @@ -1,486 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from __future__ import annotations - -import datetime -import typing -from typing import Iterable, Literal, Sequence, Tuple, Union - -import bigframes_vendored.constants as constants -import bigframes_vendored.pandas.core.groupby as vendored_pandas_groupby -import numpy -import pandas - -import bigframes.core.block_transforms as block_ops -import bigframes.core.block_transforms as block_transforms -import bigframes.core.blocks as blocks -import bigframes.core.ordering as order -import bigframes.core.utils as utils -import bigframes.core.validations as validations -import bigframes.core.window as windows -import bigframes.core.window_spec as window_specs -import bigframes.dataframe as df -import bigframes.dtypes -import bigframes.operations -import bigframes.operations.aggregations as agg_ops -import bigframes.series as series -from bigframes import session -from bigframes._tools import docs -from bigframes.core import expression as ex -from bigframes.core.groupby import aggs, group_by -from bigframes.core.logging import log_adapter -from bigframes.core.window import rolling - - -@log_adapter.class_logger -@docs.inherit_docs(vendored_pandas_groupby.SeriesGroupBy) -class SeriesGroupBy(vendored_pandas_groupby.SeriesGroupBy): - def __init__( - self, - block: blocks.Block, - value_column: str, - by_col_ids: typing.Sequence[str], - value_name: blocks.Label = None, - dropna=True, - *, - by_key_is_singular: bool = False, - ): - # TODO(tbergeron): Support more group-by expression types - self._block = block - self._value_column = value_column - self._by_col_ids = by_col_ids - self._value_name = value_name - self._dropna = dropna # Applies to aggregations but not windowing - - self._by_key_is_singular = by_key_is_singular - if by_key_is_singular: - assert len(by_col_ids) == 1, "singular key should be exactly one group key" - - @property - def _session(self) -> session.Session: - return self._block.session - - @validations.requires_ordering() - def head(self, n: int = 5) -> series.Series: - block = self._block - if self._dropna: - block = block_ops.dropna(self._block, self._by_col_ids, how="any") - return series.Series( - block.grouped_head( - by_column_ids=self._by_col_ids, value_columns=[self._value_column], n=n - ) - ) - - def describe(self, include: None | Literal["all"] = None): - from bigframes.pandas.core.methods import describe - - return df.DataFrame( - describe._describe( - self._block, - columns=[self._value_column], - include=include, - as_index=True, - by_col_ids=self._by_col_ids, - dropna=self._dropna, - ) - ).droplevel(level=0, axis=1) - - def __iter__(self) -> Iterable[Tuple[blocks.Label, series.Series]]: - for group_keys, filtered_block in group_by.block_groupby_iter( - self._block, - by_col_ids=self._by_col_ids, - by_key_is_singular=self._by_key_is_singular, - dropna=self._dropna, - ): - filtered_series = series.Series( - filtered_block.select_column(self._value_column) - ) - filtered_series.name = self._value_name - yield group_keys, filtered_series - - def __len__(self) -> int: - return len(self.agg([])) - - def all(self) -> series.Series: - return self._aggregate(agg_ops.all_op) - - def any(self) -> series.Series: - return self._aggregate(agg_ops.any_op) - - def min(self, *args) -> series.Series: - return self._aggregate(agg_ops.min_op) - - def max(self, *args) -> series.Series: - return self._aggregate(agg_ops.max_op) - - def count(self) -> series.Series: - return self._aggregate(agg_ops.count_op) - - def nunique(self) -> series.Series: - return self._aggregate(agg_ops.nunique_op) - - def sum(self, *args) -> series.Series: - return self._aggregate(agg_ops.sum_op) - - def mean(self, *args) -> series.Series: - return self._aggregate(agg_ops.mean_op) - - def rank( - self, - method="average", - ascending: bool = True, - na_option: str = "keep", - pct: bool = False, - ) -> series.Series: - return series.Series( - block_ops.rank( - self._block, - method, - na_option, - ascending, - grouping_cols=tuple(self._by_col_ids), - columns=(self._value_column,), - pct=pct, - ) - ) - - def median( - self, - *args, - exact: bool = True, - **kwargs, - ) -> series.Series: - if exact: - return self.quantile(0.5) - else: - return self._aggregate(agg_ops.median_op) - - def quantile( - self, q: Union[float, Sequence[float]] = 0.5, *, numeric_only: bool = False - ) -> series.Series: - multi_q = utils.is_list_like(q) - result = block_ops.quantile( - self._block, - (self._value_column,), - qs=tuple(q) if multi_q else (q,), # type: ignore - grouping_column_ids=self._by_col_ids, - dropna=self._dropna, - ) - if multi_q: - return series.Series(result.stack()) - else: - return series.Series(result.stack()).droplevel(-1) - - def std(self, *args, **kwargs) -> series.Series: - return self._aggregate(agg_ops.std_op) - - def var(self, *args, **kwargs) -> series.Series: - return self._aggregate(agg_ops.var_op) - - def size(self) -> series.Series: - agg_block = self._block.aggregate( - aggregations=[agg_ops.SizeOp().as_expr()], - by_column_ids=self._by_col_ids, - dropna=self._dropna, - ) - return series.Series(agg_block.with_column_labels([self._value_name])) - - def skew(self, *args, **kwargs) -> series.Series: - block = block_ops.skew(self._block, [self._value_column], self._by_col_ids) - return series.Series(block) - - def kurt(self, *args, **kwargs) -> series.Series: - block = block_ops.kurt(self._block, [self._value_column], self._by_col_ids) - return series.Series(block) - - kurtosis = kurt - - @validations.requires_ordering() - def first(self, numeric_only: bool = False, min_count: int = -1) -> series.Series: - if numeric_only and not bigframes.dtypes.is_numeric( - self._block.expr.get_column_type(self._value_column) - ): - raise TypeError( - f"Cannot use 'numeric_only' with non-numeric column {self._value_name}." - ) - window_spec = window_specs.unbound( - grouping_keys=tuple(self._by_col_ids), - min_periods=min_count if min_count >= 0 else 0, - ) - block, firsts_id = self._block.apply_window_op( - self._value_column, - agg_ops.FirstNonNullOp(), - window_spec=window_spec, - ) - block = block.aggregate( - (aggs.agg(firsts_id, agg_ops.AnyValueOp()),), - self._by_col_ids, - dropna=self._dropna, - ) - return series.Series(block.with_column_labels([self._value_name])) - - @validations.requires_ordering() - def last(self, numeric_only: bool = False, min_count: int = -1) -> series.Series: - if numeric_only and not bigframes.dtypes.is_numeric( - self._block.expr.get_column_type(self._value_column) - ): - raise TypeError( - f"Cannot use 'numeric_only' with non-numeric column {self._value_name}." - ) - window_spec = window_specs.unbound( - grouping_keys=tuple(self._by_col_ids), - min_periods=min_count if min_count >= 0 else 0, - ) - block, firsts_id = self._block.apply_window_op( - self._value_column, - agg_ops.LastNonNullOp(), - window_spec=window_spec, - ) - block = block.aggregate( - (aggs.agg(firsts_id, agg_ops.AnyValueOp()),), - self._by_col_ids, - dropna=self._dropna, - ) - return series.Series(block.with_column_labels([self._value_name])) - - def prod(self, *args) -> series.Series: - return self._aggregate(agg_ops.product_op) - - def agg(self, func=None) -> typing.Union[df.DataFrame, series.Series]: - if utils.is_dict_like(func): - raise NotImplementedError( - f"Aggregate with {func} not supported. {constants.FEEDBACK_LINK}" - ) - is_single_func = not utils.is_list_like(func) - if is_single_func: - func = [func] - - aggregations = [] - column_labels = [] - for f in func: - if block_transforms.is_transpiler_eligible(f): - expr, name = block_transforms.compile_column_udf( - self._block, f, self._value_column - ) - aggregations.append(expr) - column_labels.append(self._value_name if is_single_func else name) - else: - agg_op, label = agg_ops.lookup_agg_func(f) - aggregations.append(aggs.agg(self._value_column, agg_op)) - column_labels.append(label if not is_single_func else self._value_name) - - agg_block = self._block.aggregate( - by_column_ids=self._by_col_ids, - aggregations=aggregations, - dropna=self._dropna, - ) - - if column_labels: - agg_block = agg_block.with_column_labels(column_labels) - - if len(aggregations) == 1: - return series.Series(agg_block) - return df.DataFrame(agg_block) - - aggregate = agg - - def transform(self, func, *args, **kwargs) -> series.Series: - if block_transforms.is_transpiler_eligible(func): - window_spec = window_specs.unbound(grouping_keys=tuple(self._by_col_ids)) - expr, _ = block_transforms.compile_column_udf( - self._block, - func, - self._value_column, - args=args, - kwargs=kwargs, - window_spec=window_spec, - ) - - block = self._block.project_block_exprs( - [expr], - labels=[self._value_name], - drop=True, - ) - return series.Series(block) - - raise NotImplementedError( - "SeriesGroupBy.transform is only supported when experiments.enable_python_transpiler is True and a transpiler-compatible python function is provided." - ) - - def value_counts( - self, - normalize: bool = False, - sort: bool = True, - ascending: bool = False, - dropna: bool = True, - ) -> Union[df.DataFrame, series.Series]: - columns = [self._value_column] - block = self._block - if self._dropna: # this drops null grouping columns - block = block_ops.dropna(block, self._by_col_ids) - block = block_ops.value_counts( - block, - columns, - normalize=normalize, - sort=sort, - ascending=ascending, - drop_na=dropna, # this drops null value columns - grouping_keys=self._by_col_ids, - ) - # TODO: once as_index=Fales supported, return DataFrame instead by resetting index - # with .to_frame().reset_index(drop=False) - return series.Series(block) - - @validations.requires_ordering() - def cumsum(self, *args, **kwargs) -> series.Series: - return self._apply_window_op( - agg_ops.sum_op, - ) - - @validations.requires_ordering() - def cumprod(self, *args, **kwargs) -> series.Series: - return self._apply_window_op( - agg_ops.product_op, - ) - - @validations.requires_ordering() - def cummax(self, *args, **kwargs) -> series.Series: - return self._apply_window_op( - agg_ops.max_op, - ) - - @validations.requires_ordering() - def cummin(self, *args, **kwargs) -> series.Series: - return self._apply_window_op( - agg_ops.min_op, - ) - - @validations.requires_ordering() - def cumcount(self, *args, **kwargs) -> series.Series: - # TODO: Add nullary op support to implement more cleanly - return ( - self._apply_window_op( - agg_ops.SizeUnaryOp(), - discard_name=True, - ) - - 1 - ) - - @validations.requires_ordering() - def shift(self, periods=1) -> series.Series: - """Shift index by desired number of periods.""" - # Window framing clause is not allowed for analytic function lag. - window = window_specs.rows( - grouping_keys=tuple(self._by_col_ids), - ) - return self._apply_window_op(agg_ops.ShiftOp(periods), window=window) - - @validations.requires_ordering() - def diff(self, periods=1) -> series.Series: - window = window_specs.rows( - grouping_keys=tuple(self._by_col_ids), - ) - return self._apply_window_op(agg_ops.DiffOp(periods), window=window) - - @validations.requires_ordering() - def rolling( - self, - window: int | pandas.Timedelta | numpy.timedelta64 | datetime.timedelta | str, - min_periods=None, - closed: Literal["right", "left", "both", "neither"] = "right", - ) -> windows.Window: - if isinstance(window, int): - window_spec = window_specs.WindowSpec( - bounds=window_specs.RowsWindowBounds.from_window_size(window, closed), - min_periods=min_periods if min_periods is not None else window, - grouping_keys=tuple(ex.deref(col) for col in self._by_col_ids), - ) - block = self._block.order_by( - [order.ascending_over(col) for col in self._by_col_ids], - ) - return windows.Window( - block, - window_spec, - [self._value_column], - drop_null_groups=self._dropna, - is_series=True, - ) - - return rolling.create_range_window( - self._block, - window, - min_periods=min_periods, - value_column_ids=[self._value_column], - closed=closed, - is_series=True, - grouping_keys=self._by_col_ids, - drop_null_groups=self._dropna, - ) - - @validations.requires_ordering() - def expanding(self, min_periods: int = 1) -> windows.Window: - window_spec = window_specs.cumulative_rows( - grouping_keys=tuple(self._by_col_ids), - min_periods=min_periods, - ) - block = self._block.order_by( - [order.ascending_over(col) for col in self._by_col_ids], - ) - return windows.Window( - block, - window_spec, - [self._value_column], - drop_null_groups=self._dropna, - is_series=True, - ) - - def _aggregate(self, aggregate_op: agg_ops.UnaryAggregateOp) -> series.Series: - result_block = self._block.aggregate( - (aggs.agg(self._value_column, aggregate_op),), - self._by_col_ids, - dropna=self._dropna, - ) - - return series.Series(result_block.with_column_labels([self._value_name])) - - def _apply_window_op( - self, - op: agg_ops.UnaryWindowOp, - discard_name=False, - window: typing.Optional[window_specs.WindowSpec] = None, - ) -> series.Series: - """Apply window op to groupby. Defaults to grouped cumulative window.""" - window_spec = window or window_specs.cumulative_rows( - grouping_keys=tuple(self._by_col_ids) - ) - - label = self._value_name if not discard_name else None - block, result_id = self._block.apply_window_op( - self._value_column, - op, - result_label=label, - window_spec=window_spec, - ) - if op.skips_nulls: - block, result_id = block.project_expr( - bigframes.operations.where_op.as_expr( - result_id, - bigframes.operations.notnull_op.as_expr(self._value_column), - ex.const(None), - ), - label, - ) - - return series.Series(block.select_column(result_id)) diff --git a/bigframes/core/guid.py b/bigframes/core/guid.py index f9b666d32ba..4eb6c7a9d62 100644 --- a/bigframes/core/guid.py +++ b/bigframes/core/guid.py @@ -11,36 +11,11 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. -import threading -import typing -_GUID_LOCK = threading.Lock() _GUID_COUNTER = 0 def generate_guid(prefix="col_"): - global _GUID_LOCK - with _GUID_LOCK: - global _GUID_COUNTER - _GUID_COUNTER += 1 - return f"bfuid_{prefix}{_GUID_COUNTER}" - - -class SequentialUIDGenerator: - """Produces a sequence of UIDs, such as {"t0", "t1", "c0", "t2", ...}, by - cycling through provided prefixes (e.g., "t" and "c"). - Note: this function is not thread-safe. - """ - - def __init__(self): - self.prefix_counters: typing.Dict[str, int] = {} - - def get_uid_stream(self, prefix: str) -> typing.Generator[str, None, None]: - """Yields a continuous stream of raw UID strings for the given prefix.""" - if prefix not in self.prefix_counters: - self.prefix_counters[prefix] = 0 - - while True: - uid = f"{prefix}{self.prefix_counters[prefix]}" - self.prefix_counters[prefix] += 1 - yield uid + global _GUID_COUNTER + _GUID_COUNTER += 1 + return prefix + str(_GUID_COUNTER) diff --git a/bigframes/core/identifiers.py b/bigframes/core/identifiers.py deleted file mode 100644 index b7ae0e24345..00000000000 --- a/bigframes/core/identifiers.py +++ /dev/null @@ -1,78 +0,0 @@ -# Copyright 2023 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -from __future__ import annotations - -import dataclasses -import functools -import itertools -from typing import Generator - -import bigframes.core.guid - - -def standard_id_strings(prefix: str = "col_") -> Generator[str, None, None]: - i = 0 - while True: - yield f"{prefix}{i}" - i = i + 1 - - -# Used for expression trees -@functools.total_ordering -@dataclasses.dataclass(frozen=True) -class ColumnId: - """Local id without plan-wide id.""" - - name: str - - @property - def sql(self) -> str: - """Returns the unescaped SQL name.""" - return self.name - - @property - def local_normalized(self) -> ColumnId: - """For use in compiler only. Normalizes to ColumnId referring to sql name.""" - return self # == ColumnId(name=self.sql) - - def __lt__(self, other: ColumnId) -> bool: - return self.sql < other.sql - - @classmethod - def unique(cls) -> ColumnId: - return ColumnId(name=bigframes.core.guid.generate_guid()) - - -@dataclasses.dataclass(frozen=True) -class SerialColumnId(ColumnId): - """Id that is assigned a unique serial within the tree.""" - - name: str - id: int - - @property - def sql(self) -> str: - """Returns the unescaped SQL name.""" - return f"{self.name}_{self.id}" - - @property - def local_normalized(self) -> ColumnId: - """For use in compiler only. Normalizes to ColumnId referring to sql name.""" - return ColumnId(name=self.sql) - - -# TODO: Create serial ids locally, so can preserve name info -def anonymous_serial_ids() -> Generator[ColumnId, None, None]: - for i in itertools.count(): - yield SerialColumnId("uid", i) diff --git a/bigframes/core/indexers.py b/bigframes/core/indexers.py index faf76f7d4f0..69048b6845a 100644 --- a/bigframes/core/indexers.py +++ b/bigframes/core/indexers.py @@ -14,45 +14,27 @@ from __future__ import annotations -import numbers import typing -import warnings -from typing import Any, Sequence, Tuple, Union, cast +from typing import List, Tuple, Union -import bigframes_vendored.constants as constants -import bigframes_vendored.ibis.common.exceptions as ibis_exceptions -import numpy as np +import ibis import pandas as pd -import pyarrow as pa -import pyarrow.types # type: ignore +import bigframes.constants as constants import bigframes.core.blocks -import bigframes.core.col -import bigframes.core.expression as ex import bigframes.core.guid as guid import bigframes.core.indexes as indexes import bigframes.core.scalar -import bigframes.core.validations as validations -import bigframes.core.window_spec as windows import bigframes.dataframe -import bigframes.dtypes -import bigframes.exceptions as bfe import bigframes.operations as ops import bigframes.series if typing.TYPE_CHECKING: LocSingleKey = Union[ - bigframes.series.Series, - indexes.Index, - slice, - bigframes.core.scalar.Scalar, - bigframes.core.col.Expression, + bigframes.series.Series, indexes.Index, slice, bigframes.core.scalar.Scalar ] -_DATAFRAME_ILOC_ERROR = "Only DataFrame.iloc[:, col_indexer] = value is supported." - - class LocSeriesIndexer: def __init__(self, series: bigframes.series.Series): self._series = series @@ -81,14 +63,17 @@ def __setitem__(self, key, value) -> None: index_column = block.index_columns[0] # if index == key return value else value_colum - block, result_id = block.project_expr( - ops.where_op.as_expr( - ex.const(value), - ops.eq_op.as_expr(index_column, ex.const(key)), - self._series._value_column, - ) + block, insert_cond = block.apply_unary_op( + index_column, ops.partial_right(ops.eq_op, key) + ) + block, result_id = block.apply_binary_op( + insert_cond, + self._series._value_column, + ops.partial_arg1(ops.where_op, value), + ) + block = block.copy_values(result_id, value_column).drop_columns( + [insert_cond, result_id] ) - block = block.copy_values(result_id, value_column).drop_columns([result_id]) self._series._set_block(block) @@ -110,9 +95,6 @@ def __getitem__( Other key types are not yet supported. """ - if not _is_noop_slice(key): - validations.enforce_ordered(self._series, "iloc") - return _iloc_getitem_series_or_dataframe(self._series, key) @@ -121,9 +103,9 @@ def __init__(self, series: bigframes.series.Series): self._series = series def __getitem__(self, key: int) -> bigframes.core.scalar.Scalar: - if not _is_integer_scalar(key): + if not isinstance(key, int): raise ValueError("Series iAt based indexing can only have integer indexers") - return self._series.iloc[_to_python_int(key)] + return self._series.iloc[key] class AtSeriesIndexer: @@ -155,37 +137,26 @@ def __init__(self, dataframe: bigframes.dataframe.DataFrame): @typing.overload def __getitem__( self, key: LocSingleKey - ) -> Union[bigframes.dataframe.DataFrame, pd.Series]: ... + ) -> Union[bigframes.dataframe.DataFrame, pd.Series]: + ... # Technically this is wrong since we can have duplicate column labels, but # this is expected to be rare. @typing.overload def __getitem__( self, key: Tuple[LocSingleKey, str] - ) -> Union[bigframes.series.Series, bigframes.core.scalar.Scalar]: ... + ) -> Union[bigframes.series.Series, bigframes.core.scalar.Scalar]: + ... def __getitem__(self, key): - # TODO(tbergeron): Pandas will try both splitting 2-tuple into row, index or as 2-part - # row key. We must choose one, so bias towards treating as multi-part row label + # TODO(swast): If the DataFrame has a MultiIndex, we'll need to + # disambiguate this from a single row selection. if isinstance(key, tuple) and len(key) == 2: - is_row_multi_index = self._dataframe.index.nlevels > 1 - is_first_item_list_or_tuple = isinstance(key[0], (tuple, list)) - if not is_row_multi_index or is_first_item_list_or_tuple: - df = typing.cast( - bigframes.dataframe.DataFrame, - _loc_getitem_series_or_dataframe(self._dataframe, key[0]), - ) - - columns = key[1] - if isinstance(columns, bigframes.series.Series): - columns = columns.to_pandas() - if isinstance(columns, pd.Series) and columns.dtype in ( - bool, - pd.BooleanDtype(), - ): - columns = df.columns[typing.cast(pd.Series, columns)] - - return df[columns] + df = typing.cast( + bigframes.dataframe.DataFrame, + _loc_getitem_series_or_dataframe(self._dataframe, key[0]), + ) + return df[key[1]] return typing.cast( bigframes.dataframe.DataFrame, @@ -197,7 +168,14 @@ def __setitem__( key: Tuple[slice, str], value: bigframes.dataframe.SingleItemValue, ): - if isinstance(key, tuple) and len(key) == 2 and _is_noop_slice(key[0]): + if ( + isinstance(key, tuple) + and len(key) == 2 + and isinstance(key[0], slice) + and (key[0].start is None or key[0].start == 0) + and (key[0].step is None or key[0].step == 1) + and key[0].stop is None + ): # TODO(swast): Support setting multiple columns with key[1] as a list # of labels and value as a DataFrame. df = self._dataframe.assign(**{key[1]: value}) @@ -208,15 +186,7 @@ def __setitem__( and isinstance(key[0], bigframes.series.Series) and key[0].dtype == "boolean" ) and pd.api.types.is_scalar(value): - # For integer scalar, if set value to a new column, the dtype would be default to float. - # But if set value to an existing Int64 column, the dtype would still be integer. - # So we need to use different NaN type to match this behavior. - new_column = key[0].map( - { - True: value, - False: pd.NA if key[1] in self._dataframe.columns else None, - } - ) + new_column = key[0].map({True: value, False: None}) try: original_column = self._dataframe[key[1]] except KeyError: @@ -224,7 +194,7 @@ def __setitem__( return try: self._dataframe[key[1]] = new_column.fillna(original_column) - except ibis_exceptions.IbisTypeError: + except ibis.common.exceptions.IbisTypeError: raise TypeError( f"Cannot assign scalar of type {type(value)} to column of type {original_column.dtype}, or index type of series argument does not match dataframe." ) @@ -248,41 +218,8 @@ def __getitem__(self, key) -> Union[bigframes.dataframe.DataFrame, pd.Series]: Other key types are not yet supported. """ - requires_ordering = True - if isinstance(key, tuple): - if len(key) > 0: - row_indexer = key[0] - if _is_noop_slice(row_indexer): - requires_ordering = False - elif _is_noop_slice(key): - requires_ordering = False - - if requires_ordering: - validations.enforce_ordered(self._dataframe, "iloc") - return _iloc_getitem_series_or_dataframe(self._dataframe, key) - def __setitem__( - self, - key: Tuple[ - slice, Union[int, typing.Sequence[int], slice, typing.Sequence[bool]] - ], - value: Union[ - bigframes.dataframe.SingleItemValue, bigframes.dataframe.DataFrame - ], - ): - if not (isinstance(key, tuple) and len(key) == 2): - raise NotImplementedError(_DATAFRAME_ILOC_ERROR) - - row_indexer, col_indexer = key - - if not _is_noop_slice(row_indexer): - raise NotImplementedError(_DATAFRAME_ILOC_ERROR) - - col_offsets = _iloc_col_indexer_to_offsets(self._dataframe, col_indexer) - df = self._dataframe._assign_multi_items_by_offsets(col_offsets, value) - self._dataframe._set_block(df._get_block()) - class IatDataFrameIndexer: def __init__(self, dataframe: bigframes.dataframe.DataFrame): @@ -291,21 +228,19 @@ def __init__(self, dataframe: bigframes.dataframe.DataFrame): def __getitem__(self, key: tuple) -> bigframes.core.scalar.Scalar: error_message = "DataFrame.iat should be indexed by a tuple of exactly 2 ints" # we raise TypeError or ValueError under the same conditions that pandas does - if _is_integer_scalar(key): + if isinstance(key, int): raise TypeError(error_message) if not isinstance(key, tuple): raise ValueError(error_message) - key_values_are_ints = [_is_integer_scalar(key_value) for key_value in key] + key_values_are_ints = [isinstance(key_value, int) for key_value in key] if not all(key_values_are_ints): raise ValueError(error_message) if len(key) != 2: raise TypeError(error_message) - row_idx = _to_python_int(key[0]) - col_idx = _to_python_int(key[1]) - block: bigframes.core.blocks.Block = self._dataframe._block - column_block = block.select_columns([block.value_columns[col_idx]]) + block: bigframes.core.blocks.Block = self._dataframe._block # type: ignore + column_block = block.select_columns([block.value_columns[key[1]]]) column = bigframes.series.Series(column_block) - return column.iloc[row_idx] + return column.iloc[key[0]] class AtDataFrameIndexer: @@ -322,26 +257,18 @@ def __getitem__( return self._dataframe.loc[key] -def _is_noop_slice(key: Any) -> bool: - """Return True if key is a slice selecting all elements in the original order.""" - return ( - isinstance(key, slice) - and (key.start is None or key.start == 0) - and (key.step is None or key.step == 1) - and key.stop is None - ) - - @typing.overload def _loc_getitem_series_or_dataframe( series_or_dataframe: bigframes.series.Series, key -) -> Union[bigframes.core.scalar.Scalar, bigframes.series.Series]: ... +) -> Union[bigframes.core.scalar.Scalar, bigframes.series.Series]: + ... @typing.overload def _loc_getitem_series_or_dataframe( series_or_dataframe: bigframes.dataframe.DataFrame, key -) -> Union[bigframes.dataframe.DataFrame, pd.Series]: ... +) -> Union[bigframes.dataframe.DataFrame, pd.Series]: + ... def _loc_getitem_series_or_dataframe( @@ -353,51 +280,94 @@ def _loc_getitem_series_or_dataframe( pd.Series, bigframes.core.scalar.Scalar, ]: - if _is_noop_slice(key): - return series_or_dataframe.copy() - - if isinstance(key, slice): + if isinstance(key, bigframes.series.Series) and key.dtype == "boolean": + return series_or_dataframe[key] + elif isinstance(key, bigframes.series.Series): + temp_name = guid.generate_guid(prefix="temp_series_name_") + if len(series_or_dataframe.index.names) > 1: + temp_name = series_or_dataframe.index.names[0] + key = key.rename(temp_name) + keys_df = key.to_frame() + keys_df = keys_df.set_index(temp_name, drop=True) + return _perform_loc_list_join(series_or_dataframe, keys_df) + elif isinstance(key, bigframes.core.indexes.Index): + block = key._data._get_block() + block = block.select_columns(()) + keys_df = bigframes.dataframe.DataFrame(block) + return _perform_loc_list_join(series_or_dataframe, keys_df) + elif pd.api.types.is_list_like(key): + key = typing.cast(List, key) + if len(key) == 0: + return typing.cast( + Union[bigframes.dataframe.DataFrame, bigframes.series.Series], + series_or_dataframe.iloc[0:0], + ) + if pd.api.types.is_list_like(key[0]): + original_index_names = series_or_dataframe.index.names + num_index_cols = len(original_index_names) + + entry_col_count_correct = [len(entry) == num_index_cols for entry in key] + if not all(entry_col_count_correct): + # pandas usually throws TypeError in these cases- tuple causes IndexError, but that + # seems like unintended behavior + raise TypeError( + "All entries must be of equal length when indexing by list of listlikes" + ) + temporary_index_names = [ + guid.generate_guid(prefix="temp_loc_index_") + for _ in range(len(original_index_names)) + ] + index_cols_dict = {} + for i in range(num_index_cols): + index_name = temporary_index_names[i] + values = [entry[i] for entry in key] + index_cols_dict[index_name] = values + keys_df = bigframes.dataframe.DataFrame( + index_cols_dict, session=series_or_dataframe._get_block().expr.session + ) + keys_df = keys_df.set_index(temporary_index_names, drop=True) + keys_df = keys_df.rename_axis(original_index_names) + else: + # We can't upload a DataFrame with None as the column name, so set it + # an arbitrary string. + index_name = series_or_dataframe.index.name + index_name_is_none = index_name is None + if index_name_is_none: + index_name = "unnamed_col" + keys_df = bigframes.dataframe.DataFrame( + {index_name: key}, + session=series_or_dataframe._get_block().expr.session, + ) + keys_df = keys_df.set_index(index_name, drop=True) + if index_name_is_none: + keys_df.index.name = None + return _perform_loc_list_join(series_or_dataframe, keys_df) + elif isinstance(key, slice): + if (key.start is None) and (key.stop is None) and (key.step is None): + return series_or_dataframe.copy() raise NotImplementedError( f"loc does not yet support indexing with a slice. {constants.FEEDBACK_LINK}" ) - - if isinstance(key, bigframes.core.col.Expression): - label_to_col_ref = { - label: ex.deref(id) - for id, label in series_or_dataframe._block.col_id_to_label.items() - } - resolved_expr = key._value.bind_variables(label_to_col_ref) - result = series_or_dataframe.copy() - result._set_block(series_or_dataframe._block.filter(resolved_expr)) - return result - if callable(key): + elif callable(key): raise NotImplementedError( f"loc does not yet support indexing with a callable. {constants.FEEDBACK_LINK}" ) - elif isinstance(key, bigframes.series.Series) and key.dtype == "boolean": - return series_or_dataframe[key] - elif ( - isinstance(key, bigframes.series.Series) - or isinstance(key, indexes.Index) - or (pd.api.types.is_list_like(key) and not isinstance(key, tuple)) - ): - index = indexes.Index(key, session=series_or_dataframe._session) - index.names = series_or_dataframe.index.names[: index.nlevels] - return _perform_loc_list_join(series_or_dataframe, index) - elif pd.api.types.is_scalar(key) or isinstance(key, tuple): - index = indexes.Index([key], session=series_or_dataframe._session) - index.names = series_or_dataframe.index.names[: index.nlevels] - result = _perform_loc_list_join(series_or_dataframe, index, drop_levels=True) - - if index.nlevels == series_or_dataframe.index.nlevels: - pandas_result = result.to_pandas() - # although loc[scalar_key] returns multiple results when scalar_key - # is not unique, we download the results here and return the computed - # individual result (as a scalar or pandas series) when the key is unique, - # since we expect unique index keys to be more common. loc[[scalar_key]] - # can be used to retrieve one-item DataFrames or Series. - if len(pandas_result) == 1: - return pandas_result.iloc[0] + elif pd.api.types.is_scalar(key): + index_name = "unnamed_col" + keys_df = bigframes.dataframe.DataFrame( + {index_name: [key]}, session=series_or_dataframe._get_block().expr.session + ) + keys_df = keys_df.set_index(index_name, drop=True) + keys_df.index.name = None + result = _perform_loc_list_join(series_or_dataframe, keys_df) + pandas_result = result.to_pandas() + # although loc[scalar_key] returns multiple results when scalar_key + # is not unique, we download the results here and return the computed + # individual result (as a scalar or pandas series) when the key is unique, + # since we expect unique index keys to be more common. loc[[scalar_key]] + # can be used to retrieve one-item DataFrames or Series. + if len(pandas_result) == 1: + return pandas_result.iloc[0] # when the key is not unique, we return a bigframes data type # as usual for methods that return dataframes/series return result @@ -412,194 +382,54 @@ def _loc_getitem_series_or_dataframe( @typing.overload def _perform_loc_list_join( series_or_dataframe: bigframes.series.Series, - keys_index: indexes.Index, - drop_levels: bool = False, -) -> bigframes.series.Series: ... + keys_df: bigframes.dataframe.DataFrame, +) -> bigframes.series.Series: + ... @typing.overload def _perform_loc_list_join( series_or_dataframe: bigframes.dataframe.DataFrame, - keys_index: indexes.Index, - drop_levels: bool = False, -) -> bigframes.dataframe.DataFrame: ... + keys_df: bigframes.dataframe.DataFrame, +) -> bigframes.dataframe.DataFrame: + ... def _perform_loc_list_join( series_or_dataframe: Union[bigframes.dataframe.DataFrame, bigframes.series.Series], - keys_index: indexes.Index, - drop_levels: bool = False, + keys_df: bigframes.dataframe.DataFrame, ) -> Union[bigframes.series.Series, bigframes.dataframe.DataFrame]: # right join based on the old index so that the matching rows from the user's # original dataframe will be duplicated and reordered appropriately + original_index_names = series_or_dataframe.index.names if isinstance(series_or_dataframe, bigframes.series.Series): - _struct_accessor_check_and_warn(series_or_dataframe, keys_index) original_name = series_or_dataframe.name - name = series_or_dataframe.name if series_or_dataframe.name is not None else 0 + name = series_or_dataframe.name if series_or_dataframe.name is not None else "0" result = typing.cast( bigframes.series.Series, - series_or_dataframe.to_frame()._perform_join_by_index( - keys_index, how="right", always_order=True - )[name], + series_or_dataframe.to_frame()._perform_join_by_index(keys_df, how="right")[ + name + ], ) result = result.rename(original_name) else: - result = series_or_dataframe._perform_join_by_index( - keys_index, how="right", always_order=True - ) - - if drop_levels and series_or_dataframe.index.nlevels > keys_index.nlevels: - # drop common levels - levels_to_drop = [ - name for name in series_or_dataframe.index.names if name in keys_index.names - ] - result = result.droplevel(levels_to_drop) + result = series_or_dataframe._perform_join_by_index(keys_df, how="right") # type: ignore + result = result.rename_axis(original_index_names) return result -def _struct_accessor_check_and_warn( - series: bigframes.series.Series, index: indexes.Index -): - if not bigframes.dtypes.is_struct_like(series.dtype): - # No need to check series that do not have struct values - return - - if not bigframes.dtypes.is_string_like(index.dtype): - # No need to check indexing with non-string values. - return - - if not bigframes.dtypes.is_string_like(series.index.dtype): - msg = bfe.format_message( - "Are you trying to access struct fields? If so, please use Series.struct.field(...) " - "method instead." - ) - # Stack depth from series.__getitem__ to here - warnings.warn(msg, stacklevel=7, category=bfe.BadIndexerKeyWarning) - - -def _to_python_int(value: Any) -> int: - if isinstance(value, pa.Scalar): - return int(value.as_py()) - return int(value) - - -def _iloc_clip_to_offset(index: Any, length: int, name: str) -> int: - """Support negative values for offsets.""" - if not _is_integer_scalar(index): - raise TypeError(f"got unexpected {type(index)} for {name}") - offset = _to_python_int(index) - if offset < 0: - offset += length - - if offset < 0 or offset >= length: - raise IndexError(f"{name} {index} is out-of-bounds") - - return offset - - -def _is_integer_scalar(value: Any) -> bool: - return not ( - isinstance(value, bool) - or isinstance(value, np.bool_) - or (isinstance(value, pa.Scalar) and pyarrow.types.is_boolean(value.type)) - ) and ( - isinstance(value, numbers.Integral) - or (isinstance(value, pa.Scalar) and pyarrow.types.is_integer(value.type)) - ) - - -def _is_boolean_scalar(value: Any) -> bool: - return ( - isinstance(value, bool) - or isinstance(value, np.bool_) - or (isinstance(value, pa.Scalar) and pyarrow.types.is_boolean(value.type)) - ) - - -def _truth_val(value: Any) -> bool: - if value is None or value is pd.NA or pd.isna(value): - return False - if isinstance(value, pa.Scalar): - return bool(value.as_py()) if value.is_valid else False - return bool(value) - - -def _is_boolean_indexer(indexer: Any) -> bool: - if hasattr(indexer, "dtype") and pd.api.types.is_bool_dtype(indexer.dtype): - return True - if ( - hasattr(indexer, "type") - and isinstance(indexer.type, pa.DataType) - and pyarrow.types.is_boolean(indexer.type) - ): - return True - if pd.api.types.is_list_like(indexer): - lst = ( - list(indexer) - if not isinstance(indexer, (bigframes.series.Series, indexes.Index)) - else list(indexer.to_pandas()) - ) - if len(lst) > 0 and all( - _is_boolean_scalar(x) or (x is None) or (x is pd.NA) or pd.isna(x) - for x in lst - ): - return any(_is_boolean_scalar(x) for x in lst) - return False - - -def _iloc_col_indexer_to_offsets( - df: bigframes.dataframe.DataFrame, col_indexer: Any -) -> Sequence[int]: - """Convert col_indexer from one of the many pandas-compatible formats to a list of offsets.""" - n_cols = len(df.columns) - - if _is_integer_scalar(col_indexer): - col_offset = _to_python_int(col_indexer) - return [ - _iloc_clip_to_offset( - col_offset, n_cols, "single positional iloc column indexer" - ) - ] - - elif isinstance(col_indexer, slice): - return list(range(*col_indexer.indices(n_cols))) - - elif _is_boolean_indexer(col_indexer): - col_indexer_list = list(col_indexer) - if len(col_indexer_list) != n_cols: - raise ValueError( - f"Boolean iloc column indexer has wrong length: {len(col_indexer_list)} instead of {n_cols}" - ) - return [i for i, val in enumerate(col_indexer_list) if _truth_val(val)] - - elif pd.api.types.is_list_like(col_indexer): - col_indexer_list = list(col_indexer) - return [ - _iloc_clip_to_offset(idx, n_cols, "iloc column indexer") - for idx in col_indexer_list - ] - - raise TypeError(f"got unexpected {type(col_indexer)} for iloc column indexer") - - -def _iloc_df_from_column_offsets( - df: bigframes.dataframe.DataFrame, key: Sequence[int] -) -> bigframes.dataframe.DataFrame: - block = df._block - selected_ids = tuple(block.value_columns[offset] for offset in key) - return bigframes.dataframe.DataFrame(block.select_columns(selected_ids)) - - @typing.overload def _iloc_getitem_series_or_dataframe( series_or_dataframe: bigframes.series.Series, key -) -> Union[bigframes.series.Series, bigframes.core.scalar.Scalar]: ... +) -> Union[bigframes.series.Series, bigframes.core.scalar.Scalar]: + ... @typing.overload def _iloc_getitem_series_or_dataframe( series_or_dataframe: bigframes.dataframe.DataFrame, key -) -> Union[bigframes.dataframe.DataFrame, pd.Series, bigframes.core.scalar.Scalar]: ... +) -> Union[bigframes.dataframe.DataFrame, pd.Series]: + ... def _iloc_getitem_series_or_dataframe( @@ -611,93 +441,39 @@ def _iloc_getitem_series_or_dataframe( bigframes.core.scalar.Scalar, pd.Series, ]: - if _is_integer_scalar(key): - key_int = _to_python_int(key) - stop_key = key_int + 1 if key_int != -1 else None - internal_slice_result = series_or_dataframe._slice(key_int, stop_key, 1) + if isinstance(key, int): + internal_slice_result = series_or_dataframe._slice(key, key + 1, 1) result_pd_df = internal_slice_result.to_pandas() if result_pd_df.empty: raise IndexError("single positional indexer is out-of-bounds") return result_pd_df.iloc[0] elif isinstance(key, slice): return series_or_dataframe._slice(key.start, key.stop, key.step) + elif isinstance(key, tuple) and len(key) == 0: + return series_or_dataframe + elif isinstance(key, tuple) and len(key) == 1: + return _iloc_getitem_series_or_dataframe(series_or_dataframe, key[0]) + elif ( + isinstance(key, tuple) + and isinstance(series_or_dataframe, bigframes.dataframe.DataFrame) + and len(key) == 2 + ): + return series_or_dataframe.iat[key] elif isinstance(key, tuple): - if len(key) > 2 or ( - len(key) == 2 and isinstance(series_or_dataframe, bigframes.series.Series) - ): - raise pd.errors.IndexingError("Too many indexers") - - if len(key) == 0: - return series_or_dataframe - - if len(key) == 1: - return _iloc_getitem_series_or_dataframe(series_or_dataframe, key[0]) - - # len(key) == 2 - df = typing.cast(bigframes.dataframe.DataFrame, series_or_dataframe) - if _is_integer_scalar(key[0]) and _is_integer_scalar(key[1]): - return df.iat[key] - - row_indexer, column_indexer = key - column_offsets = _iloc_col_indexer_to_offsets(df, column_indexer) - df_subset = _iloc_df_from_column_offsets(df, column_offsets) - - if _is_integer_scalar(column_indexer): - selected_columns = cast( - Union[bigframes.dataframe.DataFrame, bigframes.series.Series], - df_subset[df_subset.columns[0]], - ) - else: - selected_columns = df_subset - - return _iloc_getitem_series_or_dataframe(selected_columns, row_indexer) + raise pd.errors.IndexingError("Too many indexers") elif pd.api.types.is_list_like(key): if len(key) == 0: return typing.cast( Union[bigframes.dataframe.DataFrame, bigframes.series.Series], series_or_dataframe.iloc[0:0], ) - - if _is_boolean_indexer(key): - key_list = ( - list(key) - if not isinstance(key, (bigframes.series.Series, indexes.Index)) - else list(key.to_pandas()) - ) - n_rows = len(series_or_dataframe) - if len(key_list) != n_rows: - raise IndexError( - f"Boolean index has wrong length: {len(key_list)} instead of {n_rows}" - ) - key = [i for i, val in enumerate(key_list) if _truth_val(val)] - if len(key) == 0: - return typing.cast( - Union[bigframes.dataframe.DataFrame, bigframes.series.Series], - series_or_dataframe.iloc[0:0], - ) - else: - key = [_to_python_int(k) for k in list(key)] - - # Check if both positive index and negative index are necessary - if isinstance(key, (bigframes.series.Series, indexes.Index)): - # Avoid data download - is_key_unisigned = False - else: - first_sign = key[0] >= 0 - is_key_unisigned = True - for k in key: - if (k >= 0) != first_sign: - is_key_unisigned = False - break - + df = series_or_dataframe if isinstance(series_or_dataframe, bigframes.series.Series): original_series_name = series_or_dataframe.name series_name = ( - original_series_name if original_series_name is not None else 0 + original_series_name if original_series_name is not None else "0" ) df = series_or_dataframe.to_frame() - else: - df = series_or_dataframe original_index_names = df.index.names temporary_index_names = [ guid.generate_guid(prefix="temp_iloc_index_") @@ -707,32 +483,6 @@ def _iloc_getitem_series_or_dataframe( # set to offset index and use regular loc, then restore index df = df.reset_index(drop=False) - block = df._block - # explicitly set index to offsets, reset_index may not generate offsets in some modes - block, offsets_id = block.promote_offsets("temp_iloc_offsets_") - pos_block = block.set_index([offsets_id]) - - if not is_key_unisigned or key[0] < 0: - neg_block, size_col_id = block.apply_window_op( - offsets_id, - ops.aggregations.SizeUnaryOp(), - window_spec=windows.rows(), - ) - neg_block, neg_index_id = neg_block.apply_binary_op( - offsets_id, size_col_id, ops.SubOp() - ) - - neg_block = neg_block.set_index([neg_index_id]).drop_columns( - [size_col_id, offsets_id] - ) - - if is_key_unisigned: - block = pos_block if key[0] >= 0 else neg_block - else: - block = pos_block.concat([neg_block], how="inner") - - df = bigframes.dataframe.DataFrame(block) - result = df.loc[key] result = result.set_index(temporary_index_names) result = result.rename_axis(original_index_names) @@ -743,6 +493,11 @@ def _iloc_getitem_series_or_dataframe( result = result.rename(original_series_name) return result + + elif isinstance(key, tuple): + raise NotImplementedError( + f"iloc does not yet support indexing with a (row, column) tuple. {constants.FEEDBACK_LINK}" + ) elif callable(key): raise NotImplementedError( f"iloc does not yet support indexing with a callable. {constants.FEEDBACK_LINK}" diff --git a/bigframes/core/indexes/__init__.py b/bigframes/core/indexes/__init__.py index dfe361aa763..184a9ce262f 100644 --- a/bigframes/core/indexes/__init__.py +++ b/bigframes/core/indexes/__init__.py @@ -12,12 +12,9 @@ # See the License for the specific language governing permissions and # limitations under the License. -from bigframes.core.indexes.base import Index -from bigframes.core.indexes.datetimes import DatetimeIndex -from bigframes.core.indexes.multi import MultiIndex +from bigframes.core.indexes.index import Index, IndexValue __all__ = [ "Index", - "MultiIndex", - "DatetimeIndex", + "IndexValue", ] diff --git a/bigframes/core/indexes/base.py b/bigframes/core/indexes/base.py deleted file mode 100644 index 32279d36c9a..00000000000 --- a/bigframes/core/indexes/base.py +++ /dev/null @@ -1,854 +0,0 @@ -# Copyright 2023 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -"""An index based on a single column.""" - -from __future__ import annotations - -import functools -import typing -from typing import Hashable, Literal, Optional, Sequence, Union, cast, overload - -import bigframes_vendored.constants as constants -import bigframes_vendored.pandas.core.indexes.base as vendored_pandas_index -import google.cloud.bigquery as bigquery -import numpy as np -import pandas - -import bigframes.core.agg_expressions as ex_types -import bigframes.core.block_transforms as block_ops -import bigframes.core.blocks as blocks -import bigframes.core.expression as ex -import bigframes.core.ordering as order -import bigframes.core.utils as utils -import bigframes.core.validations as validations -import bigframes.dtypes -import bigframes.formatting_helpers as formatter -import bigframes.operations as ops -import bigframes.operations.aggregations as agg_ops -import bigframes.series -import bigframes.session.execution_spec as ex_spec -from bigframes import dtypes -from bigframes._tools import docs - -if typing.TYPE_CHECKING: - import bigframes.dataframe - import bigframes.operations.strings - import bigframes.series - - -@docs.inherit_docs(vendored_pandas_index.Index) -class Index: - _query_job = None - _block: blocks.Block - _linked_frame: Union[ - bigframes.dataframe.DataFrame, bigframes.series.Series, None - ] = None - # Must be above 5000 for pandas to delegate to bigframes for binops - __pandas_priority__ = 12000 - - # Overrided on __new__ to create subclasses like pandas does - def __new__( - cls, - data=None, - dtype=None, - *, - name=None, - session=None, - ): - import bigframes.dataframe as df - import bigframes.series as series - - if isinstance(data, blocks.Block): - block = data.select_columns([]) - elif isinstance(data, df.DataFrame): - raise ValueError("Cannot construct index from dataframe.") - elif isinstance(data, series.Series) or isinstance(data, Index): - if isinstance(data, series.Series): - block = data._block - block = block.set_index(col_ids=[data._value_column]) - elif isinstance(data, Index): - block = data._block - index = Index(data=block) - name = data.name if name is None else name - if name is not None: - index.name = name - if dtype is not None: - bf_dtype = bigframes.dtypes.bigframes_type(dtype) - index = index.astype(bf_dtype) - block = index._block - elif isinstance(data, pandas.Index): - pd_df = pandas.DataFrame(index=data) - block = df.DataFrame(pd_df, session=session)._block - else: - if isinstance(dtype, str) and dtype.lower() == "json": - dtype = bigframes.dtypes.JSON_DTYPE - pd_index = pandas.Index(data=data, dtype=dtype, name=name) - pd_df = pandas.DataFrame(index=pd_index) - block = df.DataFrame(pd_df, session=session)._block - - # TODO: Support more index subtypes - - if len(block._index_columns) > 1: - from bigframes.core.indexes.multi import MultiIndex - - klass: type[Index] = MultiIndex # type hint to make mypy happy - elif _should_create_datetime_index(block): - from bigframes.core.indexes.datetimes import DatetimeIndex - - klass = DatetimeIndex - else: - klass = cls - - result = typing.cast(Index, object.__new__(klass)) - result._query_job = None - result._block = block - block.session._register_object(result) - return result - - @classmethod - def from_frame( - cls, frame: Union[bigframes.series.Series, bigframes.dataframe.DataFrame] - ) -> Index: - if len(frame._block.index_columns) == 0: - raise bigframes.exceptions.NullIndexError( - "Cannot access index properties with Null Index. Set an index using set_index." - ) - frame._block._throw_if_null_index("from_frame") - index = Index(frame._block) - index._linked_frame = frame - return index - - @property - def _session(self): - return self._block.session - - @property - def name(self) -> blocks.Label: - names = self.names - if len(names) == 1: - return self.names[0] - else: - # pandas returns None for MultiIndex.name. - return None - - @name.setter - def name(self, value: blocks.Label): - self.names = [value] - - @property - def names(self) -> typing.Sequence[blocks.Label]: - return self._block._index_labels - - @names.setter - def names(self, values: typing.Sequence[blocks.Label]): - self.rename(values, inplace=True) - - @property - def nlevels(self) -> int: - return len(self._block.index_columns) - - @property - def values(self) -> np.ndarray: - return self.to_numpy() - - @property - def ndim(self) -> int: - return 1 - - @property - def shape(self) -> typing.Tuple[int]: - return (self._block.shape[0],) - - @property - def dtype(self): - dtype = self._block.index.dtypes[0] if self.nlevels == 1 else np.dtype("O") - bigframes.dtypes.warn_on_db_dtypes_json_dtype([dtype]) - return dtype - - @property - def dtypes(self) -> pandas.Series: - dtypes = self._block.index.dtypes - bigframes.dtypes.warn_on_db_dtypes_json_dtype(dtypes) - return pandas.Series( - data=dtypes, - index=typing.cast(typing.Tuple, self._block.index.names), - ) - - def __setitem__(self, key, value) -> None: - """Index objects are immutable. Use Index constructor to create - modified Index.""" - raise TypeError("Index does not support mutable operations") - - @property - def size(self) -> int: - return self.shape[0] - - @property - def empty(self) -> bool: - """Returns True if the Index is empty, otherwise returns False.""" - return self.shape[0] == 0 - - @property - @validations.requires_ordering() - def is_monotonic_increasing(self) -> bool: - return typing.cast( - bool, - self._block.is_monotonic_increasing(self._block.index_columns), - ) - - @property - @validations.requires_ordering() - def is_monotonic_decreasing(self) -> bool: - return typing.cast( - bool, - self._block.is_monotonic_decreasing(self._block.index_columns), - ) - - @property - def is_unique(self) -> bool: - # TODO: Cache this at block level - # Avoid circular imports - return not self.has_duplicates - - @property - def has_duplicates(self) -> bool: - # TODO: Cache this at block level - # Avoid circular imports - import bigframes.core.block_transforms as block_ops - import bigframes.dataframe as df - - duplicates_block, indicator = block_ops.indicate_duplicates( - self._block, self._block.index_columns - ) - duplicates_block = duplicates_block.select_columns( - [indicator] - ).with_column_labels(["is_duplicate"]) - duplicates_df = df.DataFrame(duplicates_block) - return duplicates_df["is_duplicate"].any() - - @property - def T(self) -> Index: - return self.transpose() - - @property - def query_job(self) -> bigquery.QueryJob: - """BigQuery job metadata for the most recent query. - - Returns: - The most recent `QueryJob - `_. - """ - if self._query_job is None: - _, query_job = self._block._compute_dry_run() - self._query_job = query_job - return self._query_job - - def get_loc(self, key) -> typing.Union[int, slice, "bigframes.series.Series"]: - """Get integer location, slice or boolean mask for requested label. - - Args: - key: - The label to search for in the index. - - Returns: - An integer, slice, or boolean mask representing the location(s) of the key. - - Raises: - NotImplementedError: If the index has more than one level. - KeyError: If the key is not found in the index. - """ - if self.nlevels != 1: - raise NotImplementedError("get_loc only supports single-level indexes") - - # Get the index column from the block - index_column = self._block.index_columns[0] - - # Use promote_offsets to get row numbers (similar to argmax/argmin implementation) - block_with_offsets, offsets_id = self._block.promote_offsets( - "temp_get_loc_offsets_" - ) - - # Create expression to find matching positions - match_expr = ops.eq_op.as_expr(ex.deref(index_column), ex.const(key)) - block_with_offsets, match_col_id = block_with_offsets.project_expr(match_expr) - - # Filter to only rows where the key matches - filtered_block = block_with_offsets.filter_by_id(match_col_id) - - # Check if key exists at all by counting - count_agg = ex_types.UnaryAggregation(agg_ops.count_op, ex.deref(offsets_id)) - count_result = filtered_block._expr.aggregate([(count_agg, "count")]) - - count_scalar = ( - self._block.session._executor.execute( - count_result, - ex_spec.ExecutionSpec(promise_under_10gb=True), - ) - .batches() - .to_py_scalar() - ) - - if count_scalar == 0: - raise KeyError(f"'{key}' is not in index") - - # If only one match, return integer position - if count_scalar == 1: - min_agg = ex_types.UnaryAggregation(agg_ops.min_op, ex.deref(offsets_id)) - position_result = filtered_block._expr.aggregate([(min_agg, "position")]) - position_scalar = ( - self._block.session._executor.execute( - position_result, - ex_spec.ExecutionSpec(promise_under_10gb=True), - ) - .batches() - .to_py_scalar() - ) - return int(position_scalar) - - # Handle multiple matches based on index monotonicity - is_monotonic = self.is_monotonic_increasing or self.is_monotonic_decreasing - if is_monotonic: - return self._get_monotonic_slice(filtered_block, offsets_id) - else: - # Return boolean mask for non-monotonic duplicates - mask_block = block_with_offsets.select_columns([match_col_id]) - mask_block = mask_block.reset_index(drop=True) - mask_block = mask_block.with_column_labels([None]) - result_series = bigframes.series.Series(mask_block) - return result_series.astype("boolean") - - def _get_monotonic_slice( - self, filtered_block, offsets_id: __builtins__.str - ) -> slice: - """Helper method to get a slice for monotonic duplicates with an optimized query.""" - # Combine min and max aggregations into a single query for efficiency - min_max_aggs = [ - ( - ex_types.UnaryAggregation(agg_ops.min_op, ex.deref(offsets_id)), - "min_pos", - ), - ( - ex_types.UnaryAggregation(agg_ops.max_op, ex.deref(offsets_id)), - "max_pos", - ), - ] - combined_result = filtered_block._expr.aggregate(min_max_aggs) - - # Execute query and extract positions - result_df = ( - self._block.session._executor.execute( - combined_result, - execution_spec=ex_spec.ExecutionSpec(promise_under_10gb=True), - ) - .batches() - .to_pandas() - ) - min_pos = int(result_df["min_pos"].iloc[0]) - max_pos = int(result_df["max_pos"].iloc[0]) - - # Create slice (stop is exclusive) - return slice(min_pos, max_pos + 1) - - def __repr__(self) -> __builtins__.str: - # Protect against errors with uninitialized Series. See: - # https://github.com/googleapis/python-bigquery-dataframes/issues/728 - if not hasattr(self, "_block"): - return object.__repr__(self) - - # TODO(swast): Add a timeout here? If the query is taking a long time, - # maybe we just print the job metadata that we have so far? - # TODO(swast): Avoid downloading the whole series by using job - # metadata, like we do with DataFrame. - opts = bigframes.options.display - max_results = opts.max_rows - if opts.repr_mode == "deferred": - _, dry_run_query_job = self._block._compute_dry_run() - return formatter.repr_query_job(dry_run_query_job) - - pandas_df, _, query_job = self._block.retrieve_repr_request_results(max_results) - self._query_job = query_job - return repr(pandas_df.index) - - def copy(self, name: Optional[Hashable] = None): - copy_index = Index(self._block) - if name is not None: - copy_index.name = name - return copy_index - - def to_series( - self, index: Optional[Index] = None, name: Optional[Hashable] = None - ) -> bigframes.series.Series: - if self.nlevels != 1: - NotImplementedError( - f"Converting multi-index to series is not yet supported. {constants.FEEDBACK_LINK}" - ) - - import bigframes.series - - name = self.name if name is None else name - if index is None: - return bigframes.series.Series( - data=self, index=self, name=str(name), session=self._session - ) - else: - return bigframes.series.Series( - data=self, - index=Index(index, session=self._session), - name=str(name), - session=self._session, - ) - - def get_level_values(self, level) -> Index: - level_n = level if isinstance(level, int) else self.names.index(level) - block = self._block.drop_levels( - [self._block.index_columns[i] for i in range(self.nlevels) if i != level_n] - ) - return Index(block) - - def _memory_usage(self) -> int: - (n_rows,) = self.shape - return sum( - self.dtypes.map( - lambda dtype: bigframes.dtypes.DTYPE_BYTE_SIZES.get(dtype, 8) * n_rows - ) - ) - - def transpose(self) -> Index: - return self - - def sort_values( - self, - *, - inplace: bool = False, - ascending: bool = True, - kind: str | None = None, - na_position: str = "last", - ) -> Index: - if na_position not in ["first", "last"]: - raise ValueError("Param na_position must be one of 'first' or 'last'") - na_last = na_position == "last" - index_columns = self._block.index_columns - ordering = [ - order.ascending_over(column, na_last) - if ascending - else order.descending_over(column, na_last) - for column in index_columns - ] - is_stable = (kind or constants.DEFAULT_SORT_KIND) in constants.STABLE_SORT_KINDS - return Index(self._block.order_by(ordering, stable=is_stable)) - - def astype( - self, - dtype, - *, - errors: Literal["raise", "null"] = "raise", - ) -> Index: - if errors not in ["raise", "null"]: - raise ValueError("Argument 'errors' must be one of 'raise' or 'null'") - if self.nlevels > 1: - raise TypeError("Multiindex does not support 'astype'") - dtype = bigframes.dtypes.bigframes_type(dtype) - return self._apply_unary_expr( - ops.AsTypeOp(to_type=dtype, safe=(errors == "null")).as_expr( - ex.free_var("arg") - ) - ) - - def all(self) -> bool: - if self.nlevels > 1: - raise TypeError("Multiindex does not support 'all'") - return typing.cast(bool, self._apply_aggregation(agg_ops.all_op)) - - def any(self) -> bool: - if self.nlevels > 1: - raise TypeError("Multiindex does not support 'any'") - return typing.cast(bool, self._apply_aggregation(agg_ops.any_op)) - - def nunique(self) -> int: - return typing.cast(int, self._apply_aggregation(agg_ops.nunique_op)) - - def max(self) -> typing.Any: - return self._apply_aggregation(agg_ops.max_op) - - def min(self) -> typing.Any: - return self._apply_aggregation(agg_ops.min_op) - - @validations.requires_ordering() - def argmax(self) -> int: - block, row_nums = self._block.promote_offsets() - block = block.order_by( - [ - *[order.descending_over(col) for col in self._block.index_columns], - order.ascending_over(row_nums), - ] - ) - import bigframes.series as series - - return typing.cast(int, series.Series(block.select_column(row_nums)).iloc[0]) - - @validations.requires_ordering() - def argmin(self) -> int: - block, row_nums = self._block.promote_offsets() - block = block.order_by( - [ - *[order.ascending_over(col) for col in self._block.index_columns], - order.ascending_over(row_nums), - ] - ) - import bigframes.series as series - - return typing.cast(int, series.Series(block.select_column(row_nums)).iloc[0]) - - def value_counts( - self, - normalize: bool = False, - sort: bool = True, - ascending: bool = False, - *, - dropna: bool = True, - ): - block = block_ops.value_counts( - self._block, - self._block.index_columns, - normalize=normalize, - ascending=ascending, - drop_na=dropna, - ) - import bigframes.series as series - - return series.Series(block) - - def fillna(self, value=None) -> Index: - if self.nlevels > 1: - raise TypeError("Multiindex does not support 'fillna'") - return self._apply_unary_expr( - ops.fillna_op.as_expr(ex.free_var("arg"), ex.const(value)) - ) - - @overload - def rename( - self, - name: Union[blocks.Label, Sequence[blocks.Label]], - ) -> Index: ... - - @overload - def rename( - self, - name: Union[blocks.Label, Sequence[blocks.Label]], - *, - inplace: Literal[False], - ) -> Index: ... - - @overload - def rename( - self, - name: Union[blocks.Label, Sequence[blocks.Label]], - *, - inplace: Literal[True], - ) -> None: ... - - def rename( - self, - name: Union[blocks.Label, Sequence[blocks.Label]], - *, - inplace: bool = False, - ) -> Optional[Index]: - # Tuples are allowed as a label, but we specifically exclude them here. - # This is because tuples are hashable, but we want to treat them as a - # sequence. If name is iterable, we want to assume we're working with a - # MultiIndex. Unfortunately, strings are iterable and we don't want a - # list of all the characters, so specifically exclude the non-tuple - # hashables. - if isinstance(name, blocks.Label) and not isinstance(name, tuple): - names = [name] - else: - names = list(name) - - if len(names) != self.nlevels: - raise ValueError("'name' must be same length as levels") - - new_block = self._block.with_index_labels(names) - - if inplace: - if self._linked_frame is not None: - self._linked_frame._set_block( - self._linked_frame._block.with_index_labels(names) - ) - self._block = new_block - return None - else: - return Index(new_block) - - def drop( - self, - labels: typing.Any, - ) -> Index: - # ignore axis, columns params - block = self._block - level_id = self._block.index_columns[0] - if utils.is_list_like(labels): - block, inverse_condition_id = block.apply_unary_op( - level_id, ops.IsInOp(values=tuple(labels), match_nulls=True) - ) - block, condition_id = block.apply_unary_op( - inverse_condition_id, ops.invert_op - ) - else: - block, condition_id = block.project_expr( - ops.ne_op.as_expr(level_id, ex.const(labels)) - ) - block = block.filter_by_id(condition_id, keep_null=True) - block = block.drop_columns([condition_id]) - return Index(block) - - def dropna(self, how: typing.Literal["all", "any"] = "any") -> Index: - if how not in ("any", "all"): - raise ValueError("'how' must be one of 'any', 'all'") - result = block_ops.dropna(self._block, self._block.index_columns, how=how) - return Index(result) - - def drop_duplicates(self, *, keep: __builtins__.str = "first") -> Index: - block = block_ops.drop_duplicates(self._block, self._block.index_columns, keep) - return Index(block) - - def unique(self, level: Hashable | int | None = None) -> Index: - if level is None: - return self.drop_duplicates() - - return self.get_level_values(level).drop_duplicates() - - def isin(self, values) -> Index: - import bigframes.series as series - - if isinstance(values, (series.Series, Index)): - return Index(self.to_series().isin(values)) - if not utils.is_list_like(values): - raise TypeError( - "only list-like objects are allowed to be passed to " - f"isin(), you passed a [{type(values).__name__}]" - ) - - return self._apply_unary_expr( - ops.IsInOp(values=tuple(values), match_nulls=True).as_expr( - ex.free_var("arg") - ) - ).fillna(value=False) - - def __contains__(self, key) -> bool: - hash(key) # to throw for unhashable values - if self.nlevels == 0: - return False - - if (not isinstance(key, tuple)) or (self.nlevels == 1): - key = (key,) - - match_exprs = [] - for key_part, index_col, dtype in zip( - key, self._block.index_columns, self._block.index.dtypes - ): - key_type = bigframes.dtypes.is_compatible(key_part, dtype) - if key_type is None: - return False - key_expr = ex.const(key_part, key_type) - match_expr = ops.eq_null_match_op.as_expr(ex.deref(index_col), key_expr) - match_exprs.append(match_expr) - - match_expr_final = functools.reduce(ops.and_op.as_expr, match_exprs) - block, match_col = self._block.project_expr(match_expr_final) - return cast(bool, block.get_stat(match_col, agg_ops.AnyOp())) - - def _apply_unary_op(self, op: ops.UnaryOp) -> Index: - return self._apply_unary_expr(op.as_expr(ex.free_var("input"))) - - def _apply_unary_expr( - self, - op: ex.Expression, - ) -> Index: - """Applies a unary operator to the index.""" - if len(op.free_variables) != 1: - raise ValueError("Expression must have exactly 1 unbound variable.") - unbound_variable = op.free_variables[0] - - block = self._block - result_ids = [] - for col in self._block.index_columns: - block, result_id = block.project_expr( - op.bind_variables({unbound_variable: ex.deref(col)}) - ) - result_ids.append(result_id) - - block = block.set_index(result_ids, index_labels=self._block.index.names) - return Index(block) - - def _apply_aggregation(self, op: agg_ops.UnaryAggregateOp) -> typing.Any: - if self.nlevels > 1: - raise NotImplementedError(f"Multiindex does not yet support {op.name}") - column_id = self._block.index_columns[0] - return self._block.get_stat(column_id, op) - - def __getitem__(self, key: int) -> typing.Any: - if isinstance(key, int): - if key != -1: - result_pd_df, _ = self._block.slice(key, key + 1, 1).to_pandas() - else: # special case, want [-1:] instead of [-1:0] - result_pd_df, _ = self._block.slice(key).to_pandas() - if result_pd_df.index.empty: - raise IndexError("single positional indexer is out-of-bounds") - return result_pd_df.index[0] - else: - raise NotImplementedError(f"Index key not supported {key}") - - @overload - def to_pandas( # type: ignore[overload-overlap] - self, - *, - allow_large_results: Optional[bool] = ..., - dry_run: Literal[False] = ..., - ) -> pandas.Index: ... - - @overload - def to_pandas( - self, *, allow_large_results: Optional[bool] = ..., dry_run: Literal[True] = ... - ) -> pandas.Series: ... - - def to_pandas( - self, - *, - allow_large_results: Optional[bool] = None, - dry_run: bool = False, - ) -> pandas.Index | pandas.Series: - """Gets the Index as a pandas Index. - - Args: - allow_large_results (bool, default None): - If not None, overrides the global setting to allow or disallow large query results - over the default size limit of 10 GB. - dry_run (bool, default False): - If this argument is true, this method will not process the data. Instead, it returns - a Pandas series containing dtype and the amount of bytes to be processed. - - Returns: - pandas.Index | pandas.Series: - A pandas Index with all of the labels from this Index. If dry run is set to True, - returns a Series containing dry run statistics. - """ - if dry_run: - dry_run_stats, dry_run_job = self._block.index._compute_dry_run( - ordered=True - ) - self._query_job = dry_run_job - return dry_run_stats - - df, query_job = self._block.index.to_pandas( - ordered=True, allow_large_results=allow_large_results - ) - if query_job: - self._query_job = query_job - return df - - def to_numpy(self, dtype=None, *, allow_large_results=None, **kwargs) -> np.ndarray: - return self.to_pandas(allow_large_results=allow_large_results).to_numpy( - dtype, **kwargs - ) - - __array__ = to_numpy - - def to_list(self, *, allow_large_results: Optional[bool] = None) -> list: - return self.to_pandas(allow_large_results=allow_large_results).to_list() - - def __len__(self): - return self.shape[0] - - def __bool__(self): - raise ValueError( - "Cannot convert Index into bool. Consider using .empty(), .item(), .any(), or .all() methods." - ) - - def item(self): - # Docstring is in third_party/bigframes_vendored/pandas/core/indexes/base.py - return self.to_series().peek(2).item() - - def __eq__(self, other) -> Index: # type: ignore - return self._apply_binary_op(other, ops.eq_op) - - def _apply_binary_op( - self, - other, - op: ops.BinaryOp, - alignment: typing.Literal["outer", "left"] = "outer", - ) -> Index: - # Note: alignment arg is for compatibility with accessors, is ignored as irrelevant for implicit joins. - # TODO: Handle local objects, or objects not implicitly alignable? Gets ambiguous with partial ordering though - if isinstance(other, (bigframes.series.Series, Index)): - other = Index(other) - if other.nlevels != self.nlevels: - raise ValueError("Dimensions do not match") - - lexpr = self._block.expr - rexpr = other._block.expr - join_result = lexpr.try_row_join(rexpr) - if join_result is None: - raise ValueError("Cannot align objects") - - expr, (lmap, rmap) = join_result - - expr, res_ids = expr.compute_values( - [ - op.as_expr(lmap[lid], rmap[rid]) - for lid, rid in zip(lexpr.column_ids, rexpr.column_ids) - ] - ) - labels = self.names if self.names == other.names else [None] * len(res_ids) - return Index( - blocks.Block( - expr.select_columns(res_ids), - index_columns=res_ids, - column_labels=[], - index_labels=labels, - ) - ) - elif ( - isinstance(other, bigframes.dtypes.LOCAL_SCALAR_TYPES) and self.nlevels == 1 - ): - block, id = self._block.project_expr( - op.as_expr(self._block.index_columns[0], ex.const(other)) - ) - return Index(block.set_index([id], index_labels=self.names)) - elif isinstance(other, tuple) and len(other) == self.nlevels: - block = self._block.project_exprs( - [ - op.as_expr(self._block.index_columns[i], ex.const(other[i])) - for i in range(self.nlevels) - ], - labels=[None] * self.nlevels, - drop=True, - ) - return Index(block.set_index(block.value_columns, index_labels=self.names)) - else: - return NotImplemented - - # last so as to not shadow __builtins__.str - @property - def str(self) -> bigframes.operations.strings.StringMethods: - import bigframes.operations.strings - - return bigframes.operations.strings.StringMethods(self) - - -def _should_create_datetime_index(block: blocks.Block) -> bool: - if len(block.index.dtypes) != 1: - return False - - return dtypes.is_datetime_like(block.index.dtypes[0]) diff --git a/bigframes/core/indexes/datetimes.py b/bigframes/core/indexes/datetimes.py deleted file mode 100644 index 763e44be095..00000000000 --- a/bigframes/core/indexes/datetimes.py +++ /dev/null @@ -1,56 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -"""An index based on a single column with a datetime-like data type.""" - -from __future__ import annotations - -from bigframes_vendored.pandas.core.indexes import ( - datetimes as vendored_pandas_datetime_index, -) - -from bigframes._tools import docs -from bigframes.core import expression as ex -from bigframes.core.indexes.base import Index -from bigframes.operations import date_ops - - -@docs.inherit_docs(vendored_pandas_datetime_index.DatetimeIndex) -class DatetimeIndex(Index): - # Must be above 5000 for pandas to delegate to bigframes for binops - __pandas_priority__ = 12000 - - @property - def year(self) -> Index: - return self._apply_unary_expr(date_ops.year_op.as_expr(ex.free_var("arg"))) - - @property - def month(self) -> Index: - return self._apply_unary_expr(date_ops.month_op.as_expr(ex.free_var("arg"))) - - @property - def day(self) -> Index: - return self._apply_unary_expr(date_ops.day_op.as_expr(ex.free_var("arg"))) - - @property - def dayofweek(self) -> Index: - return self._apply_unary_expr(date_ops.dayofweek_op.as_expr(ex.free_var("arg"))) - - @property - def day_of_week(self) -> Index: - return self.dayofweek - - @property - def weekday(self) -> Index: - return self.dayofweek diff --git a/bigframes/core/indexes/index.py b/bigframes/core/indexes/index.py new file mode 100644 index 00000000000..6c66c36062a --- /dev/null +++ b/bigframes/core/indexes/index.py @@ -0,0 +1,599 @@ +# Copyright 2023 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""An index based on a single column.""" + +from __future__ import annotations + +import typing +from typing import Mapping, Sequence, Tuple, Union + +import numpy as np +import pandas + +import bigframes.constants as constants +import bigframes.core as core +import bigframes.core.block_transforms as block_ops +import bigframes.core.blocks as blocks +import bigframes.core.joins as joining +import bigframes.core.ordering as order +import bigframes.core.utils as utils +import bigframes.dtypes +import bigframes.dtypes as bf_dtypes +import bigframes.operations as ops +import bigframes.operations.aggregations as agg_ops +import third_party.bigframes_vendored.pandas.core.indexes.base as vendored_pandas_index + + +class Index(vendored_pandas_index.Index): + __doc__ = vendored_pandas_index.Index.__doc__ + + def __init__(self, data: blocks.BlockHolder): + self._data = data + + @property + def name(self) -> blocks.Label: + return self.names[0] + + @name.setter + def name(self, value: blocks.Label): + self.names = [value] + + @property + def names(self) -> typing.Sequence[blocks.Label]: + """Returns the names of the Index.""" + return self._data._get_block()._index_labels + + @names.setter + def names(self, values: typing.Sequence[blocks.Label]): + return self._data._set_block(self._block.with_index_labels(values)) + + @property + def nlevels(self) -> int: + return len(self._data._get_block().index_columns) + + @property + def values(self) -> np.ndarray: + return self.to_numpy() + + @property + def ndim(self) -> int: + return 1 + + @property + def shape(self) -> typing.Tuple[int]: + return (self._data._get_block().shape[0],) + + @property + def dtype(self): + return self._block.index_dtypes[0] if self.nlevels == 1 else np.dtype("O") + + @property + def dtypes(self) -> pandas.Series: + return pandas.Series( + data=self._block.index_dtypes, index=self._block.index_labels # type:ignore + ) + + @property + def size(self) -> int: + """Returns the size of the Index.""" + return self.shape[0] + + @property + def empty(self) -> bool: + """Returns True if the Index is empty, otherwise returns False.""" + return self.shape[0] == 0 + + @property + def is_monotonic_increasing(self) -> bool: + """ + Return a boolean if the values are equal or increasing. + + Returns: + bool + """ + return typing.cast( + bool, + self._data._get_block().is_monotonic_increasing( + self._data._get_block().index_columns + ), + ) + + @property + def is_monotonic_decreasing(self) -> bool: + """ + Return a boolean if the values are equal or decreasing. + + Returns: + bool + """ + return typing.cast( + bool, + self._data._get_block().is_monotonic_decreasing( + self._data._get_block().index_columns + ), + ) + + @property + def is_unique(self) -> bool: + # TODO: Cache this at block level + # Avoid circular imports + return not self.has_duplicates + + @property + def has_duplicates(self) -> bool: + # TODO: Cache this at block level + # Avoid circular imports + import bigframes.core.block_transforms as block_ops + import bigframes.dataframe as df + + duplicates_block, indicator = block_ops.indicate_duplicates( + self._block, self._block.index_columns + ) + duplicates_block = duplicates_block.select_columns( + [indicator] + ).with_column_labels(["is_duplicate"]) + duplicates_df = df.DataFrame(duplicates_block) + return duplicates_df["is_duplicate"].any() + + @property + def _block(self) -> blocks.Block: + return self._data._get_block() + + @property + def T(self) -> Index: + return self.transpose() + + def transpose(self) -> Index: + return self + + def sort_values(self, *, ascending: bool = True, na_position: str = "last"): + if na_position not in ["first", "last"]: + raise ValueError("Param na_position must be one of 'first' or 'last'") + direction = ( + order.OrderingDirection.ASC if ascending else order.OrderingDirection.DESC + ) + na_last = na_position == "last" + index_columns = self._block.index_columns + ordering = [ + order.OrderingColumnReference(column, direction=direction, na_last=na_last) + for column in index_columns + ] + return Index._from_block(self._block.order_by(ordering)) + + def astype( + self, + dtype: Union[bigframes.dtypes.DtypeString, bigframes.dtypes.Dtype], + ) -> Index: + if self.nlevels > 1: + raise TypeError("Multiindex does not support 'astype'") + return self._apply_unary_op(ops.AsTypeOp(dtype)) + + def all(self) -> bool: + if self.nlevels > 1: + raise TypeError("Multiindex does not support 'all'") + return typing.cast(bool, self._apply_aggregation(agg_ops.all_op)) + + def any(self) -> bool: + if self.nlevels > 1: + raise TypeError("Multiindex does not support 'any'") + return typing.cast(bool, self._apply_aggregation(agg_ops.any_op)) + + def nunique(self) -> int: + return typing.cast(int, self._apply_aggregation(agg_ops.nunique_op)) + + def max(self) -> typing.Any: + return self._apply_aggregation(agg_ops.max_op) + + def min(self) -> typing.Any: + return self._apply_aggregation(agg_ops.min_op) + + def argmax(self) -> int: + block, row_nums = self._block.promote_offsets() + block = block.order_by( + [ + *[ + order.OrderingColumnReference( + col, direction=order.OrderingDirection.DESC + ) + for col in self._block.index_columns + ], + order.OrderingColumnReference(row_nums), + ] + ) + import bigframes.series as series + + return typing.cast(int, series.Series(block.select_column(row_nums)).iloc[0]) + + def argmin(self) -> int: + block, row_nums = self._block.promote_offsets() + block = block.order_by( + [ + *[ + order.OrderingColumnReference(col) + for col in self._block.index_columns + ], + order.OrderingColumnReference(row_nums), + ] + ) + import bigframes.series as series + + return typing.cast(int, series.Series(block.select_column(row_nums)).iloc[0]) + + def value_counts( + self, + normalize: bool = False, + sort: bool = True, + ascending: bool = False, + *, + dropna: bool = True, + ): + block = block_ops.value_counts( + self._block, + self._block.index_columns, + normalize=normalize, + ascending=ascending, + dropna=dropna, + ) + import bigframes.series as series + + return series.Series(block) + + def fillna(self, value=None) -> Index: + if self.nlevels > 1: + raise TypeError("Multiindex does not support 'fillna'") + return self._apply_unary_op(ops.partial_right(ops.fillna_op, value)) + + def rename(self, name: Union[str, Sequence[str]]) -> Index: + names = [name] if isinstance(name, str) else list(name) + if len(names) != self.nlevels: + raise ValueError("'name' must be same length as levels") + return Index._from_block(self._block.with_index_labels(names)) + + def drop( + self, + labels: typing.Any, + ) -> Index: + # ignore axis, columns params + block = self._block + level_id = self._block.index_columns[0] + if utils.is_list_like(labels): + block, inverse_condition_id = block.apply_unary_op( + level_id, ops.IsInOp(labels, match_nulls=True) + ) + block, condition_id = block.apply_unary_op( + inverse_condition_id, ops.invert_op + ) + else: + block, condition_id = block.apply_unary_op( + level_id, ops.partial_right(ops.ne_op, labels) + ) + block = block.filter(condition_id, keep_null=True) + block = block.drop_columns([condition_id]) + return Index._from_block(block) + + def dropna(self, how: str = "any") -> Index: + if how not in ("any", "all"): + raise ValueError("'how' must be one of 'any', 'all'") + result = block_ops.dropna(self._block, self._block.index_columns, how=how) # type: ignore + return Index._from_block(result) + + def drop_duplicates(self, *, keep: str = "first") -> Index: + block = block_ops.drop_duplicates(self._block, self._block.index_columns, keep) + return Index._from_block(block) + + def isin(self, values) -> Index: + if not utils.is_list_like(values): + raise TypeError( + "only list-like objects are allowed to be passed to " + f"isin(), you passed a [{type(values).__name__}]" + ) + + return self._apply_unary_op(ops.IsInOp(values, match_nulls=True)).fillna( + value=False + ) + + def _apply_unary_op( + self, + op: ops.UnaryOp, + ) -> Index: + """Applies a unary operator to the index.""" + block = self._block + result_ids = [] + for col in self._block.index_columns: + block, result_id = block.apply_unary_op(col, op) + result_ids.append(result_id) + + block = block.set_index(result_ids, index_labels=self._block.index_labels) + return Index._from_block(block) + + def _apply_aggregation(self, op: agg_ops.AggregateOp) -> typing.Any: + if self.nlevels > 1: + raise NotImplementedError(f"Multiindex does not yet support {op.name}") + column_id = self._block.index_columns[0] + return self._block.get_stat(column_id, op) + + def __getitem__(self, key: int) -> typing.Any: + if isinstance(key, int): + result_pd_df, _ = self._block.slice(key, key + 1, 1).to_pandas() + if result_pd_df.empty: + raise IndexError("single positional indexer is out-of-bounds") + return result_pd_df.index[0] + else: + raise NotImplementedError(f"Index key not supported {key}") + + def to_pandas(self) -> pandas.Index: + """Gets the Index as a pandas Index. + + Returns: + pandas.Index: + A pandas Index with all of the labels from this Index. + """ + return IndexValue(self._block).to_pandas() + + def to_numpy(self, dtype=None, **kwargs) -> np.ndarray: + return self.to_pandas().to_numpy(dtype, **kwargs) + + __array__ = to_numpy + + def __len__(self): + return self.shape[0] + + @classmethod + def _from_block(cls, block: blocks.Block) -> Index: + import bigframes.dataframe as df + + return Index(df.DataFrame(block)) + + +class IndexValue: + """An immutable index.""" + + def __init__(self, block: blocks.Block): + self._block = block + + @property + def _expr(self) -> core.ArrayValue: + return self._block.expr + + @property + def name(self) -> blocks.Label: + return self._block._index_labels[0] + + @property + def names(self) -> typing.Sequence[blocks.Label]: + return self._block._index_labels + + @property + def nlevels(self) -> int: + return len(self._block._index_columns) + + @property + def dtypes( + self, + ) -> typing.Sequence[typing.Union[bf_dtypes.Dtype, np.dtype[typing.Any]]]: + return self._block.index_dtypes + + def __repr__(self) -> str: + """Converts an Index to a string.""" + # TODO(swast): Add a timeout here? If the query is taking a long time, + # maybe we just print the job metadata that we have so far? + # TODO(swast): Avoid downloading the whole index by using job + # metadata, like we do with DataFrame. + preview = self.to_pandas() + return repr(preview) + + def to_pandas(self) -> pandas.Index: + """Executes deferred operations and downloads the results.""" + # Project down to only the index column. So the query can be cached to visualize other data. + index_columns = list(self._block.index_columns) + dtypes = dict(zip(index_columns, self.dtypes)) + expr = self._expr.select_columns(index_columns) + results, _ = expr.start_query() + df = expr.session._rows_to_dataframe(results, dtypes) + df = df.set_index(index_columns) + index = df.index + index.names = list(self._block._index_labels) + return index + + def join( + self, + other: IndexValue, + *, + how="left", + sort=False, + block_identity_join: bool = False, + ) -> Tuple[IndexValue, Tuple[Mapping[str, str], Mapping[str, str]],]: + if not isinstance(other, IndexValue): + # TODO(swast): We need to improve this error message to be more + # actionable for the user. For example, it's possible they + # could call set_index and try again to resolve this error. + raise ValueError( + f"Tried to join with an unexpected type: {type(other)}. {constants.FEEDBACK_LINK}" + ) + + # TODO(swast): Support cross-joins (requires reindexing). + if how not in {"outer", "left", "right", "inner"}: + raise NotImplementedError( + f"Only how='outer','left','right','inner' currently supported. {constants.FEEDBACK_LINK}" + ) + if self.nlevels == other.nlevels == 1: + return join_mono_indexed( + self, other, how=how, sort=sort, block_identity_join=block_identity_join + ) + else: + # Always sort mult-index join + return join_multi_indexed( + self, other, how=how, sort=sort, block_identity_join=block_identity_join + ) + + def resolve_level_name(self: IndexValue, label: blocks.Label) -> str: + matches = self._block.index_name_to_col_id.get(label, []) + if len(matches) > 1: + raise ValueError(f"Ambiguous index level name {label}") + if len(matches) == 0: + raise ValueError(f"Cannot resolve index level name {label}") + return matches[0] + + def is_uniquely_named(self: IndexValue): + return len(set(self.names)) == len(self.names) + + +def join_mono_indexed( + left: IndexValue, + right: IndexValue, + *, + how="left", + sort=False, + block_identity_join: bool = False, +) -> Tuple[IndexValue, Tuple[Mapping[str, str], Mapping[str, str]],]: + left_expr = left._block.expr + right_expr = right._block.expr + get_column_left, get_column_right = joining.JOIN_NAME_REMAPPER( + left_expr.column_ids, right_expr.column_ids + ) + combined_expr = left._block.expr.join( + left._block.index_columns, + right._block.expr, + right._block.index_columns, + how=how, + allow_row_identity_join=(not block_identity_join), + ) + # Drop original indices from each side. and used the coalesced combination generated by the join. + left_index = get_column_left[left._block.index_columns[0]] + right_index = get_column_right[right._block.index_columns[0]] + # Drop original indices from each side. and used the coalesced combination generated by the join. + combined_expr, coalesced_join_cols = coalesce_columns( + combined_expr, [left_index], [right_index], how=how + ) + if sort: + combined_expr = combined_expr.order_by( + [order.OrderingColumnReference(col_id) for col_id in coalesced_join_cols] + ) + block = blocks.Block( + combined_expr, + index_columns=coalesced_join_cols, + column_labels=[*left._block.column_labels, *right._block.column_labels], + index_labels=[left.name] if left.name == right.name else [None], + ) + return ( + typing.cast(IndexValue, block.index), + (get_column_left, get_column_right), + ) + + +def join_multi_indexed( + left: IndexValue, + right: IndexValue, + *, + how="left", + sort=False, + block_identity_join: bool = False, +) -> Tuple[IndexValue, Tuple[Mapping[str, str], Mapping[str, str]],]: + if not (left.is_uniquely_named() and right.is_uniquely_named()): + raise ValueError("Joins not supported on indices with non-unique level names") + + common_names = [name for name in left.names if name in right.names] + if len(common_names) == 0: + raise ValueError("Cannot join without a index level in common.") + + left_only_names = [name for name in left.names if name not in right.names] + right_only_names = [name for name in right.names if name not in left.names] + + left_join_ids = [left.resolve_level_name(name) for name in common_names] + right_join_ids = [right.resolve_level_name(name) for name in common_names] + + names_fully_match = len(left_only_names) == 0 and len(right_only_names) == 0 + + left_expr = left._block.expr + right_expr = right._block.expr + get_column_left, get_column_right = joining.JOIN_NAME_REMAPPER( + left_expr.column_ids, right_expr.column_ids + ) + + combined_expr = left_expr.join( + left_join_ids, + right_expr, + right_join_ids, + how=how, + # If we're only joining on a subset of the index columns, we need to + # perform a true join. + allow_row_identity_join=(names_fully_match and not block_identity_join), + ) + left_ids_post_join = [get_column_left[id] for id in left_join_ids] + right_ids_post_join = [get_column_right[id] for id in right_join_ids] + # Drop original indices from each side. and used the coalesced combination generated by the join. + combined_expr, coalesced_join_cols = coalesce_columns( + combined_expr, left_ids_post_join, right_ids_post_join, how=how + ) + if sort: + combined_expr = combined_expr.order_by( + [order.OrderingColumnReference(col_id) for col_id in coalesced_join_cols] + ) + + if left.nlevels == 1: + index_labels = right.names + elif right.nlevels == 1: + index_labels = left.names + else: + index_labels = [*common_names, *left_only_names, *right_only_names] + + def resolve_label_id(label: blocks.Label) -> str: + # if name is shared between both blocks, coalesce the values + if label in common_names: + return coalesced_join_cols[common_names.index(label)] + if label in left_only_names: + return get_column_left[left.resolve_level_name(label)] + if label in right_only_names: + return get_column_right[right.resolve_level_name(label)] + raise ValueError(f"Unexpected label: {label}") + + index_columns = [resolve_label_id(label) for label in index_labels] + + block = blocks.Block( + combined_expr, + index_columns=index_columns, + column_labels=[*left._block.column_labels, *right._block.column_labels], + index_labels=index_labels, + ) + return ( + typing.cast(IndexValue, block.index), + (get_column_left, get_column_right), + ) + + +def coalesce_columns( + expr: core.ArrayValue, + left_ids: typing.Sequence[str], + right_ids: typing.Sequence[str], + how: str, +) -> Tuple[core.ArrayValue, Sequence[str]]: + result_ids = [] + for left_id, right_id in zip(left_ids, right_ids): + if how == "left" or how == "inner": + result_ids.append(left_id) + expr = expr.drop_columns([right_id]) + elif how == "right": + result_ids.append(right_id) + expr = expr.drop_columns([left_id]) + elif how == "outer": + coalesced_id = bigframes.core.guid.generate_guid() + expr = expr.project_binary_op( + left_id, right_id, ops.coalesce_op, coalesced_id + ) + expr = expr.drop_columns([left_id, right_id]) + result_ids.append(coalesced_id) + else: + raise ValueError(f"Unexpected join type: {how}. {constants.FEEDBACK_LINK}") + return expr, result_ids diff --git a/bigframes/core/indexes/multi.py b/bigframes/core/indexes/multi.py deleted file mode 100644 index 0b9681b55f6..00000000000 --- a/bigframes/core/indexes/multi.py +++ /dev/null @@ -1,115 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from __future__ import annotations - -from typing import TYPE_CHECKING, Hashable, Iterable, Optional, Sequence, cast - -import bigframes_vendored.pandas.core.indexes.multi as vendored_pandas_multindex -import pandas - -from bigframes._tools import docs -from bigframes.core import blocks -from bigframes.core import expression as ex -from bigframes.core.indexes.base import Index - -if TYPE_CHECKING: - import bigframes.session - - -@docs.inherit_docs(vendored_pandas_multindex.MultiIndex) -class MultiIndex(Index): - @classmethod - def from_tuples( - cls, - tuples: Iterable[tuple[Hashable, ...]], - sortorder: int | None = None, - names: Sequence[Hashable] | Hashable | None = None, - *, - session: Optional[bigframes.session.Session] = None, - ) -> MultiIndex: - pd_index = pandas.MultiIndex.from_tuples(tuples, sortorder, names) - # Index.__new__ should detect multiple levels and properly create a multiindex - return cast(MultiIndex, Index(pd_index, session=session)) - - @classmethod - def from_arrays( - cls, - arrays, - sortorder: int | None = None, - names=None, - *, - session: Optional[bigframes.session.Session] = None, - ) -> MultiIndex: - pd_index = pandas.MultiIndex.from_arrays(arrays, sortorder, names) - # Index.__new__ should detect multiple levels and properly create a multiindex - return cast(MultiIndex, Index(pd_index, session=session)) - - def __eq__(self, other) -> Index: # type: ignore - import bigframes.operations as ops - import bigframes.operations.aggregations as agg_ops - - eq_result = self._apply_binary_op(other, ops.eq_op)._block.expr - - as_array = ops.ToArrayOp().as_expr( - *( - ops.fillna_op.as_expr(col, ex.const(False)) - for col in eq_result.column_ids - ) - ) - reduced = ops.ArrayReduceOp(agg_ops.all_op).as_expr(as_array) - result_expr, result_ids = eq_result.compute_values([reduced]) - return Index( - blocks.Block( - result_expr.select_columns(result_ids), - index_columns=result_ids, - column_labels=(), - index_labels=[None], - ) - ) - - -class MultiIndexAccessor: - """Proxy to MultiIndex constructors to allow a session to be passed in.""" - - def __init__(self, session: bigframes.session.Session): - self._session = session - - def __call__(self, *args, **kwargs) -> MultiIndex: - """Construct a MultiIndex using the associated Session. - - See :class:`bigframes.pandas.MultiIndex`. - """ - return MultiIndex(*args, session=self._session, **kwargs) - - def from_arrays(self, *args, **kwargs) -> MultiIndex: - """Construct a MultiIndex using the associated Session. - - See :func:`bigframes.pandas.MultiIndex.from_arrays`. - """ - return MultiIndex.from_arrays(*args, session=self._session, **kwargs) - - def from_frame(self, *args, **kwargs) -> MultiIndex: - """Construct a MultiIndex using the associated Session. - - See :func:`bigframes.pandas.MultiIndex.from_frame`. - """ - return cast(MultiIndex, MultiIndex.from_frame(*args, **kwargs)) - - def from_tuples(self, *args, **kwargs) -> MultiIndex: - """Construct a MultiIndex using the associated Session. - - See :func:`bigframes.pandas.MultiIndex.from_tuples`. - """ - return MultiIndex.from_tuples(*args, session=self._session, **kwargs) diff --git a/bigframes/core/interchange.py b/bigframes/core/interchange.py deleted file mode 100644 index 4dacedd0142..00000000000 --- a/bigframes/core/interchange.py +++ /dev/null @@ -1,155 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -from __future__ import annotations - -import dataclasses -import functools -from typing import TYPE_CHECKING, Any, Dict, Iterable, Optional, Sequence - -import bigframes.enums -from bigframes.core import blocks - -if TYPE_CHECKING: - import bigframes.dataframe - - -@dataclasses.dataclass(frozen=True) -class InterchangeColumn: - _dataframe: InterchangeDataFrame - _pos: int - - @functools.cache - def _arrow_column(self): - # Conservatively downloads the whole underlying dataframe - # This is much better if multiple columns end up being used, - # but does incur a lot of overhead otherwise. - return self._dataframe._arrow_dataframe().get_column(self._pos) - - def size(self) -> int: - return self._arrow_column().size() - - @property - def offset(self) -> int: - return self._arrow_column().offset - - @property - def dtype(self): - return self._arrow_column().dtype - - @property - def describe_categorical(self): - raise TypeError(f"Column type {self.dtype} is not categorical") - - @property - def describe_null(self): - return self._arrow_column().describe_null - - @property - def null_count(self): - return self._arrow_column().null_count - - @property - def metadata(self) -> Dict[str, Any]: - return self._arrow_column().metadata - - def num_chunks(self) -> int: - return self._arrow_column().num_chunks() - - def get_chunks(self, n_chunks: Optional[int] = None) -> Iterable: - return self._arrow_column().get_chunks(n_chunks=n_chunks) - - def get_buffers(self): - return self._arrow_column().get_buffers() - - -@dataclasses.dataclass(frozen=True) -class InterchangeDataFrame: - """ - Implements the dataframe interchange format. - - Mostly implemented by downloading result to pyarrow, and using pyarrow interchange implementation. - """ - - _value: blocks.Block - - version: int = 0 # version of the protocol - - def __dataframe__( - self, nan_as_null: bool = False, allow_copy: bool = True - ) -> InterchangeDataFrame: - return self - - @classmethod - def _from_bigframes(cls, df: bigframes.dataframe.DataFrame): - block = df._block.with_column_labels( - [str(label) for label in df._block.column_labels] - ) - return cls(block) - - # In future, could potentially rely on executor to refetch batches efficiently with caching, - # but safest for now to just request a single execution and save the whole table. - @functools.cache - def _arrow_dataframe(self): - arrow_table, _ = self._value.reset_index( - replacement=bigframes.enums.DefaultIndexKind.NULL - ).to_arrow(allow_large_results=False) - return arrow_table.__dataframe__() - - @property - def metadata(self): - # Allows round-trip without materialization - return {"bigframes.block": self._value} - - def num_columns(self) -> int: - """ - Return the number of columns in the DataFrame. - """ - return len(self._value.value_columns) - - def num_rows(self) -> Optional[int]: - return self._value.shape[0] - - def num_chunks(self) -> int: - return self._arrow_dataframe().num_chunks() - - def column_names(self) -> Iterable[str]: - return [col for col in self._value.column_labels] - - def get_column(self, i: int) -> InterchangeColumn: - return InterchangeColumn(self, i) - - # For single column getters, we download the whole dataframe still - # This is inefficient in some cases, but more efficient in other - def get_column_by_name(self, name: str) -> InterchangeColumn: - col_id = self._value.resolve_label_exact(name) - assert col_id is not None - pos = self._value.value_columns.index(col_id) - return InterchangeColumn(self, pos) - - def get_columns(self) -> Iterable[InterchangeColumn]: - return [InterchangeColumn(self, i) for i in range(self.num_columns())] - - def select_columns(self, indices: Sequence[int]) -> InterchangeDataFrame: - col_ids = [self._value.value_columns[i] for i in indices] - new_value = self._value.select_columns(col_ids) - return InterchangeDataFrame(new_value) - - def select_columns_by_name(self, names: Sequence[str]) -> InterchangeDataFrame: - col_ids = [self._value.resolve_label_exact(name) for name in names] - assert all(id is not None for id in col_ids) - new_value = self._value.select_columns(col_ids) # type: ignore - return InterchangeDataFrame(new_value) - - def get_chunks(self, n_chunks: Optional[int] = None) -> Iterable: - return self._arrow_dataframe().get_chunks(n_chunks) diff --git a/bigframes/core/join_def.py b/bigframes/core/join_def.py deleted file mode 100644 index cd9c2acd174..00000000000 --- a/bigframes/core/join_def.py +++ /dev/null @@ -1,52 +0,0 @@ -# Copyright 2023 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -from __future__ import annotations - -import dataclasses -import enum -from typing import Literal, NamedTuple - - -class JoinSide(enum.Enum): - LEFT = 0 - RIGHT = 1 - - def inverse(self) -> JoinSide: - if self == JoinSide.LEFT: - return JoinSide.RIGHT - return JoinSide.LEFT - - -JoinType = Literal["inner", "outer", "left", "right", "cross"] - - -class JoinCondition(NamedTuple): - left_id: str - right_id: str - - -@dataclasses.dataclass(frozen=True) -class JoinColumnMapping: - source_table: JoinSide - source_id: str - destination_id: str - - -@dataclasses.dataclass(frozen=True) -class CoalescedColumnMapping: - """Special column mapping used only by implicit joiner only""" - - left_source_id: str - right_source_id: str - destination_id: str diff --git a/bigframes/core/joins/__init__.py b/bigframes/core/joins/__init__.py new file mode 100644 index 00000000000..5d407ec22b2 --- /dev/null +++ b/bigframes/core/joins/__init__.py @@ -0,0 +1,20 @@ +# Copyright 2023 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Helpers to join ArrayValue objects.""" + +from bigframes.core.joins.merge import merge +from bigframes.core.joins.name_resolution import JOIN_NAME_REMAPPER, JoinNameRemapper + +__all__ = ("merge", "JoinNameRemapper", "JOIN_NAME_REMAPPER") diff --git a/bigframes/core/joins/merge.py b/bigframes/core/joins/merge.py new file mode 100644 index 00000000000..fac16b36078 --- /dev/null +++ b/bigframes/core/joins/merge.py @@ -0,0 +1,67 @@ +# Copyright 2023 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +Functions for Merging Data Structures in BigFrames. +""" + +from __future__ import annotations + +from typing import Literal, Optional + +from bigframes.dataframe import DataFrame +from bigframes.series import Series + + +def merge( + left: DataFrame, + right: DataFrame, + how: Literal[ + "inner", + "left", + "outer", + "right", + ] = "inner", + on: Optional[str] = None, + *, + left_on: Optional[str] = None, + right_on: Optional[str] = None, + sort: bool = False, + suffixes: tuple[str, str] = ("_x", "_y"), +) -> DataFrame: + left = _validate_operand(left) + right = _validate_operand(right) + + return left.merge( + right, + how=how, + on=on, + left_on=left_on, + right_on=right_on, + sort=sort, + suffixes=suffixes, + ) + + +def _validate_operand(obj: DataFrame | Series) -> DataFrame: + if isinstance(obj, DataFrame): + return obj + elif isinstance(obj, Series): + if obj.name is None: + raise ValueError("Cannot merge a Series without a name") + return obj.to_frame() + else: + raise TypeError( + f"Can only merge Series or DataFrame objects, a {type(obj)} was passed" + ) diff --git a/bigframes/core/joins/name_resolution.py b/bigframes/core/joins/name_resolution.py new file mode 100644 index 00000000000..df946b3a590 --- /dev/null +++ b/bigframes/core/joins/name_resolution.py @@ -0,0 +1,46 @@ +# Copyright 2023 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from __future__ import annotations + +from typing import Mapping, Sequence, Tuple + + +class JoinNameRemapper: + def __init__(self, namespace: str) -> None: + self._namespace = namespace + + def __call__( + self, left_column_ids: Sequence[str], right_column_ids: Sequence[str] + ) -> Tuple[Mapping[str, str], Mapping[str, str]]: + """ + When joining column ids from different namespaces, this function defines how names are remapped. + + Take care to map value column ids and hidden column ids in separate namespaces. This is important because value + column ids must be deterministic as they are referenced by dependent operators. The generation of hidden ids is + dependent on compilation context, and should be completely separated from value column id mappings. + """ + # This naming strategy depends on the number of value columns in source tables. + # This means column id mappings must be adjusted if pushing operations above or below join in transformation + new_left_ids = { + col: f"{self._namespace}_l_{i}" for i, col in enumerate(left_column_ids) + } + new_right_ids = { + col: f"{self._namespace}_r_{i}" for i, col in enumerate(right_column_ids) + } + return new_left_ids, new_right_ids + + +# Defines how column ids are remapped, regardless of join strategy or ordering mode +# Use this remapper for all value column remappings. +JOIN_NAME_REMAPPER = JoinNameRemapper("bfjoin") diff --git a/bigframes/core/local_data.py b/bigframes/core/local_data.py deleted file mode 100644 index c05bda7a7fb..00000000000 --- a/bigframes/core/local_data.py +++ /dev/null @@ -1,523 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -"""Methods that deal with local pandas/pyarrow dataframes.""" - -from __future__ import annotations - -import dataclasses -import functools -import io -import itertools -import json -import uuid -from typing import Any, Callable, Generator, Iterable, Literal, Optional, Union, cast - -import geopandas # type: ignore -import numpy -import numpy as np -import pandas as pd -import pyarrow as pa -import pyarrow.parquet # type: ignore - -import bigframes.core.schema as schemata -import bigframes.dtypes -from bigframes.core import identifiers, pyarrow_utils - - -@dataclasses.dataclass(frozen=True) -class LocalTableMetadata: - total_bytes: int - row_count: int - - @classmethod - def from_arrow(cls, table: pa.Table) -> LocalTableMetadata: - return cls(total_bytes=table.nbytes, row_count=table.num_rows) - - -_MANAGED_STORAGE_TYPES_OVERRIDES: dict[bigframes.dtypes.Dtype, pa.DataType] = { - # wkt to be precise - bigframes.dtypes.GEO_DTYPE: pa.string(), - # Just json as string - bigframes.dtypes.JSON_DTYPE: pa.string(), -} - - -@dataclasses.dataclass(frozen=True) -class ManagedArrowTable: - data: pa.Table = dataclasses.field(hash=False, compare=False) - schema: schemata.ArraySchema = dataclasses.field(hash=False, compare=False) - id: uuid.UUID = dataclasses.field(default_factory=uuid.uuid4) - - @functools.cached_property - def metadata(self) -> LocalTableMetadata: - return LocalTableMetadata.from_arrow(self.data) - - @classmethod - def from_pandas(cls, dataframe: pd.DataFrame) -> ManagedArrowTable: - """Creates managed table from pandas. Ignores index, col names must be unique strings""" - columns: list[pa.ChunkedArray] = [] - fields: list[schemata.SchemaItem] = [] - column_names = list(dataframe.columns) - assert len(column_names) == len(set(column_names)) - - for name, col in dataframe.items(): - new_arr, bf_type = _adapt_pandas_series(col) - columns.append(new_arr) - fields.append(schemata.SchemaItem(str(name), bf_type)) - - mat = ManagedArrowTable( - pa.table(columns, names=column_names), schemata.ArraySchema(tuple(fields)) - ) - mat.validate() - return mat - - @classmethod - def from_pyarrow( - cls, table: pa.Table, schema: Optional[schemata.ArraySchema] = None - ) -> ManagedArrowTable: - if schema is not None: - pa_fields = [] - for item in schema.items: - pa_type = _get_managed_storage_type(item.dtype) - pa_fields.append( - pyarrow.field( - item.column, - pa_type, - nullable=not pyarrow.types.is_list(pa_type), - ) - ) - pa_schema = pyarrow.schema(pa_fields) - # assumption: needed transformations can be handled by simple cast. - mat = ManagedArrowTable(table.cast(pa_schema), schema) - mat.validate() - return mat - else: # infer bigframes schema - columns: list[pa.ChunkedArray] = [] - fields: list[schemata.SchemaItem] = [] - for name, arr in zip(table.column_names, table.columns): - new_arr, bf_type = _adapt_chunked_array(arr) - columns.append(new_arr) - fields.append(schemata.SchemaItem(name, bf_type)) - - mat = ManagedArrowTable( - pa.table(columns, names=table.column_names), - schemata.ArraySchema(tuple(fields)), - ) - mat.validate() - return mat - - def to_arrow( - self, - *, - offsets_col: Optional[str] = None, - geo_format: Literal["wkb", "wkt"] = "wkt", - duration_type: Literal["int", "duration"] = "duration", - json_type: Literal["string"] = "string", - sample_rate: Optional[float] = None, - max_chunksize: Optional[int] = None, - ) -> tuple[pa.Schema, Iterable[pa.RecordBatch]]: - if geo_format != "wkt": - raise NotImplementedError(f"geo format {geo_format} not yet implemented") - assert json_type == "string" - - data = self.data - - # This exists for symmetry with remote sources, but sampling local data like this shouldn't really happen - if sample_rate is not None: - to_take = numpy.random.rand(data.num_rows) < sample_rate - data = data.filter(to_take) - - batches = data.to_batches(max_chunksize=max_chunksize) - schema = self.data.schema - if duration_type == "int": - schema = _schema_durations_to_ints(schema) - batches = map( - functools.partial(pyarrow_utils.cast_batch, schema=schema), batches - ) - - if offsets_col is not None: - return schema.append(pa.field(offsets_col, pa.int64())), _append_offsets( - batches, offsets_col - ) - else: - return schema, batches - - def is_nullable(self, column_id: identifiers.ColumnId) -> bool: - return self.data.column(column_id.name).null_count > 0 - - def to_pyarrow_table( - self, - *, - offsets_col: Optional[str] = None, - geo_format: Literal["wkb", "wkt"] = "wkt", - duration_type: Literal["int", "duration"] = "duration", - json_type: Literal["string"] = "string", - ) -> pa.Table: - schema, batches = self.to_arrow( - offsets_col=offsets_col, - geo_format=geo_format, - duration_type=duration_type, - json_type=json_type, - ) - return pa.Table.from_batches(batches, schema) - - def to_parquet( - self, - dst: Union[str, io.IOBase], - *, - offsets_col: Optional[str] = None, - geo_format: Literal["wkb", "wkt"] = "wkt", - duration_type: Literal["int", "duration"] = "duration", - json_type: Literal["string"] = "string", - ): - pa_table = self.to_pyarrow_table( - offsets_col=offsets_col, - geo_format=geo_format, - duration_type=duration_type, - json_type=json_type, - ) - pyarrow.parquet.write_table(pa_table, where=dst) - - def itertuples( - self, - *, - geo_format: Literal["wkb", "wkt"] = "wkt", - duration_type: Literal["int", "timedelta"] = "timedelta", - json_type: Literal["string", "object"] = "string", - ) -> Iterable[tuple]: - """ - Yield each row as an unlabeled tuple. - - Row-wise iteration of columnar data is slow, avoid if possible. - """ - for row_dict in _iter_table( - self.data, - self.schema, - geo_format=geo_format, - duration_type=duration_type, - json_type=json_type, - ): - yield tuple(row_dict.values()) - - def validate(self): - for bf_field, arrow_field in zip(self.schema.items, self.data.schema): - expected_arrow_type = _get_managed_storage_type(bf_field.dtype) - arrow_type = arrow_field.type - if expected_arrow_type != arrow_type: - raise TypeError( - f"Field {bf_field} has arrow array type: {arrow_type}, expected type: {expected_arrow_type}" - ) - - -# Sequential iterator, but could split into batches and leverage parallelism for speed -def _iter_table( - table: pa.Table, - schema: schemata.ArraySchema, - *, - geo_format: Literal["wkb", "wkt"] = "wkt", - duration_type: Literal["int", "timedelta"] = "timedelta", - json_type: Literal["string", "object"] = "string", -) -> Generator[dict[str, Any], None, None]: - """For when you feel like iterating row-wise over a column store. Don't expect speed.""" - - if geo_format != "wkt": - raise NotImplementedError(f"geo format {geo_format} not yet implemented") - - @functools.singledispatch - def iter_array( - array: pa.Array, dtype: bigframes.dtypes.Dtype - ) -> Generator[Any, None, None]: - values = array.to_pylist() - if dtype == bigframes.dtypes.JSON_DTYPE: - if json_type == "object": - yield from map(lambda x: json.loads(x) if x is not None else x, values) - else: - yield from values - elif dtype == bigframes.dtypes.TIMEDELTA_DTYPE: - if duration_type == "int": - yield from map( - lambda x: ( - ((x.days * 3600 * 24) + x.seconds) * 1_000_000 + x.microseconds - if x is not None - else x - ), - values, - ) - else: - yield from values - else: - yield from values - - @iter_array.register - def _( - array: pa.ListArray, dtype: bigframes.dtypes.Dtype - ) -> Generator[Any, None, None]: - value_generator = iter_array( - array.flatten(), bigframes.dtypes.get_array_inner_type(dtype) - ) - offset_generator = iter_array(array.offsets, bigframes.dtypes.INT_DTYPE) - - start_offset = None - end_offset = None - for offset in offset_generator: - start_offset = end_offset - end_offset = offset - if start_offset is not None: - arr_size = end_offset - start_offset - yield list(itertools.islice(value_generator, arr_size)) - - @iter_array.register - def _( - array: pa.StructArray, dtype: bigframes.dtypes.Dtype - ) -> Generator[Any, None, None]: - # yield from each subarray - sub_generators: dict[str, Generator[Any, None, None]] = {} - for field_name, dtype in bigframes.dtypes.get_struct_fields(dtype).items(): - sub_generators[field_name] = iter_array(array.field(field_name), dtype) - - keys = list(sub_generators.keys()) - is_null_generator = iter_array(array.is_null(), bigframes.dtypes.BOOL_DTYPE) - - for values in zip(is_null_generator, *sub_generators.values()): - is_row_null = values[0] - row_values = values[1:] - if not is_row_null: - yield {key: value for key, value in zip(keys, row_values)} - else: - yield None - - for batch in table.to_batches(): - sub_generators: dict[str, Generator[Any, None, None]] = {} - for field in schema.items: - sub_generators[field.column] = iter_array( - batch.column(field.column), field.dtype - ) - - keys = list(sub_generators.keys()) - for row_values in zip(*sub_generators.values()): - yield {key: value for key, value in zip(keys, row_values)} - - -def _adapt_pandas_series( - series: pd.Series, -) -> tuple[Union[pa.ChunkedArray, pa.Array], bigframes.dtypes.Dtype]: - # Mostly rely on pyarrow conversions, but have to convert geo without its help. - if series.dtype == bigframes.dtypes.GEO_DTYPE: - # geoseries produces eg "POINT (1, 1)", while bq uses style "POINT(1, 1)" - # we normalize to bq style for consistency - series = ( - geopandas.GeoSeries(series) - .to_wkt(rounding_precision=-1) - .str.replace(r"(\w+) \(", repl=r"\1(", regex=True) - ) - return pa.array(series, type=pa.string()), bigframes.dtypes.GEO_DTYPE - try: - pa_arr = pa.array(series) - if isinstance(pa_arr, pa.ChunkedArray): - return _adapt_chunked_array(pa_arr) - return _adapt_arrow_array(pa_arr) - except pa.ArrowInvalid as e: - if series.dtype == np.dtype("O"): - try: - return _adapt_pandas_series(series.astype(bigframes.dtypes.GEO_DTYPE)) - except TypeError: - # Prefer original error - pass - raise e - - -def _adapt_chunked_array( - chunked_array: pa.ChunkedArray, -) -> tuple[pa.ChunkedArray, bigframes.dtypes.Dtype]: - if len(chunked_array.chunks) == 0: - return _adapt_arrow_array(chunked_array.combine_chunks()) - dtype = None - arrays = [] - for chunk in chunked_array.chunks: - array, arr_dtype = _adapt_arrow_array(chunk) - arrays.append(array) - dtype = dtype or arr_dtype - assert dtype is not None - return pa.chunked_array(arrays), dtype - - -def _adapt_arrow_array(array: pa.Array) -> tuple[pa.Array, bigframes.dtypes.Dtype]: - """Normalize the array to managed storage types. Preserve shapes, only transforms values.""" - if array.offset != 0: # Offset arrays don't have all operations implemented - return _adapt_arrow_array(pa.concat_arrays([array])) - - if pa.types.is_struct(array.type): - assert isinstance(array, pa.StructArray) - assert isinstance(array.type, pa.StructType) - arrays = [] - dtypes = [] - pa_fields = [] - for i in range(array.type.num_fields): - field_array, field_type = _adapt_arrow_array(array.field(i)) - arrays.append(field_array) - dtypes.append(field_type) - pa_fields.append(pa.field(array.type.field(i).name, field_array.type)) - struct_array = pa.StructArray.from_arrays( - arrays=arrays, fields=pa_fields, mask=array.is_null() - ) - dtype = bigframes.dtypes.struct_type( - [(field.name, dtype) for field, dtype in zip(pa_fields, dtypes)] - ) - return struct_array, dtype - if pa.types.is_list(array.type): - assert isinstance(array, pa.ListArray) - values, values_type = _adapt_arrow_array(array.values) - new_value = pa.ListArray.from_arrays( - array.offsets, values, mask=array.is_null() - ) - return new_value.fill_null([]), bigframes.dtypes.list_type(values_type) - if array.type == bigframes.dtypes.JSON_ARROW_TYPE: - return _canonicalize_json(array), bigframes.dtypes.JSON_DTYPE - target_type = logical_type_replacements(array.type) - if target_type != array.type: - # TODO: Maybe warn if lossy conversion? - array = array.cast(target_type) - bf_type = bigframes.dtypes.arrow_dtype_to_bigframes_dtype( - target_type, allow_lossless_cast=True - ) - - storage_type = _get_managed_storage_type(bf_type) - if storage_type != array.type: - array = array.cast(storage_type) - return array, bf_type - - -def _canonicalize_json(array: pa.Array) -> pa.Array: - def _canonicalize_scalar(json_string): - if json_string is None: - return None - # This is the canonical form that bq uses when emitting json - # The sorted keys and unambiguous whitespace ensures a 1:1 mapping - # between syntax and semantics. - return json.dumps( - json.loads(json_string), sort_keys=True, separators=(",", ":") - ) - - return pa.array( - [_canonicalize_scalar(value) for value in array.to_pylist()], type=pa.string() - ) - - -def _get_managed_storage_type(dtype: bigframes.dtypes.Dtype) -> pa.DataType: - if dtype in _MANAGED_STORAGE_TYPES_OVERRIDES.keys(): - return _MANAGED_STORAGE_TYPES_OVERRIDES[dtype] - return _physical_type_replacements( - bigframes.dtypes.bigframes_dtype_to_arrow_dtype(dtype) - ) - - -def _recursive_map_types( - f: Callable[[pa.DataType], pa.DataType], -) -> Callable[[pa.DataType], pa.DataType]: - @functools.wraps(f) - def recursive_f(type: pa.DataType) -> pa.DataType: - if pa.types.is_list(type): - new_field_t = recursive_f(type.value_type) - if new_field_t != type.value_type: - return pa.list_(new_field_t) - return type - # polars can produce large lists, and we want to map these down to regular lists - if pa.types.is_large_list(type): - new_field_t = recursive_f(type.value_type) - return pa.list_(new_field_t) - if pa.types.is_struct(type): - struct_type = cast(pa.StructType, type) - new_fields: list[pa.Field] = [] - for i in range(struct_type.num_fields): - field = struct_type.field(i) - new_fields.append(field.with_type(recursive_f(field.type))) - return pa.struct(new_fields) - return f(type) - - return recursive_f - - -@_recursive_map_types -def logical_type_replacements(type: pa.DataType) -> pa.DataType: - if pa.types.is_timestamp(type): - # This is potentially lossy, but BigFrames doesn't support ns - new_tz = "UTC" if (type.tz is not None) else None - return pa.timestamp(unit="us", tz=new_tz) - if pa.types.is_time64(type): - # This is potentially lossy, but BigFrames doesn't support ns - return pa.time64("us") - if pa.types.is_duration(type): - # This is potentially lossy, but BigFrames doesn't support ns - return pa.duration("us") - if pa.types.is_decimal128(type): - return pa.decimal128(38, 9) - if pa.types.is_decimal256(type): - return pa.decimal256(76, 38) - if pa.types.is_large_string(type): - # simple string type can handle the largest strings needed - return pa.string() - if pa.types.is_large_binary(type): - # simple string type can handle the largest strings needed - return pa.binary() - if pa.types.is_dictionary(type): - return logical_type_replacements(type.value_type) - if pa.types.is_null(type): - # null as a type not allowed, default type is float64 for bigframes - return pa.float64() - else: - return type - - -_ARROW_MANAGED_STORAGE_OVERRIDES = { - bigframes.dtypes._BIGFRAMES_TO_ARROW[bf_dtype]: arrow_type - for bf_dtype, arrow_type in _MANAGED_STORAGE_TYPES_OVERRIDES.items() - if bf_dtype in bigframes.dtypes._BIGFRAMES_TO_ARROW -} - - -@_recursive_map_types -def _physical_type_replacements(dtype: pa.DataType) -> pa.DataType: - if dtype in _ARROW_MANAGED_STORAGE_OVERRIDES: - return _ARROW_MANAGED_STORAGE_OVERRIDES[dtype] - return dtype - - -def _append_offsets( - batches: Iterable[pa.RecordBatch], offsets_col_name: str -) -> Iterable[pa.RecordBatch]: - offset = 0 - for batch in batches: - offsets = pa.array( - range(offset, offset + batch.num_rows), size=batch.num_rows, type=pa.int64() - ) - batch_w_offsets = pa.record_batch( - [*batch.columns, offsets], - schema=batch.schema.append(pa.field(offsets_col_name, pa.int64())), - ) - offset += batch.num_rows - yield batch_w_offsets - - -@_recursive_map_types -def _durations_to_ints(type: pa.DataType) -> pa.DataType: - if pa.types.is_duration(type): - return pa.int64() - return type - - -def _schema_durations_to_ints(schema: pa.Schema) -> pa.Schema: - return pa.schema( - pa.field(field.name, _durations_to_ints(field.type)) for field in schema - ) diff --git a/bigframes/core/logging/__init__.py b/bigframes/core/logging/__init__.py deleted file mode 100644 index 5d06124efce..00000000000 --- a/bigframes/core/logging/__init__.py +++ /dev/null @@ -1,17 +0,0 @@ -# Copyright 2026 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from bigframes.core.logging import data_types, log_adapter - -__all__ = ["log_adapter", "data_types"] diff --git a/bigframes/core/logging/data_types.py b/bigframes/core/logging/data_types.py deleted file mode 100644 index 3cb65a5c501..00000000000 --- a/bigframes/core/logging/data_types.py +++ /dev/null @@ -1,165 +0,0 @@ -# Copyright 2026 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from __future__ import annotations - -import functools - -from bigframes import dtypes -from bigframes.core import agg_expressions, bigframe_node, expression, nodes -from bigframes.core.rewrite import schema_binding - -IGNORED_NODES = ( - nodes.SelectionNode, - nodes.ReadLocalNode, - nodes.ReadTableNode, - nodes.ConcatNode, - nodes.RandomSampleNode, - nodes.FromRangeNode, - nodes.PromoteOffsetsNode, - nodes.ReversedNode, - nodes.SliceNode, - nodes.ResultNode, -) - - -def encode_type_refs(root: bigframe_node.BigFrameNode) -> str: - return f"{root.reduce_up(_encode_type_refs_from_node):x}" - - -def _encode_type_refs_from_node( - node: bigframe_node.BigFrameNode, child_results: tuple[int, ...] -) -> int: - child_result = functools.reduce(lambda x, y: x | y, child_results, 0) - - curr_result = 0 - if isinstance(node, nodes.FilterNode): - curr_result = _encode_type_refs_from_expr(node.predicate, node.child) - elif isinstance(node, nodes.ProjectionNode): - for assignment in node.assignments: - expr = assignment[0] - if isinstance(expr, (expression.DerefOp)): - # Ignore direct assignments in projection nodes. - continue - curr_result = curr_result | _encode_type_refs_from_expr( - assignment[0], node.child - ) - elif isinstance(node, nodes.OrderByNode): - for by in node.by: - curr_result = curr_result | _encode_type_refs_from_expr( - by.scalar_expression, node.child - ) - elif isinstance(node, nodes.JoinNode): - for left, right in node.conditions: - curr_result = ( - curr_result - | _encode_type_refs_from_expr(left, node.left_child) - | _encode_type_refs_from_expr(right, node.right_child) - ) - elif isinstance(node, nodes.InNode): - curr_result = _encode_type_refs_from_expr(node.left_col, node.left_child) - elif isinstance(node, nodes.AggregateNode): - for agg, _ in node.aggregations: - curr_result = curr_result | _encode_type_refs_from_expr(agg, node.child) - elif isinstance(node, nodes.WindowOpNode): - for grouping_key in node.window_spec.grouping_keys: - curr_result = curr_result | _encode_type_refs_from_expr( - grouping_key, node.child - ) - for ordering_expr in node.window_spec.ordering: - curr_result = curr_result | _encode_type_refs_from_expr( - ordering_expr.scalar_expression, node.child - ) - for col_def in node.agg_exprs: - curr_result = curr_result | _encode_type_refs_from_expr( - col_def.expression, node.child - ) - elif isinstance(node, nodes.ExplodeNode): - for col_id in node.column_ids: - curr_result = curr_result | _encode_type_refs_from_expr(col_id, node.child) - elif isinstance(node, IGNORED_NODES): - # Do nothing - pass - else: - # For unseen nodes, do not raise errors as this is the logging path, but - # we should cover those nodes either in the branches above, or place them - # in the IGNORED_NODES collection. - pass - - return child_result | curr_result - - -def _encode_type_refs_from_expr( - expr: expression.Expression, child_node: bigframe_node.BigFrameNode -) -> int: - # TODO(b/409387790): Remove this branch once SQLGlot compiler fully replaces Ibis compiler - if not expr.is_resolved: - if isinstance(expr, agg_expressions.Aggregation): - expr = schema_binding._bind_schema_to_aggregation_expr(expr, child_node) - else: - expr = expression.bind_schema_fields(expr, child_node.field_by_id) - - result = _get_dtype_mask(expr.output_type) - for child_expr in expr.children: - result = result | _encode_type_refs_from_expr(child_expr, child_node) - - return result - - -def _get_dtype_mask(dtype: dtypes.Dtype | None) -> int: - if dtype is None: - # If the dtype is not given, ignore - return 0 - if dtype == dtypes.INT_DTYPE: - return 1 << 1 - if dtype == dtypes.FLOAT_DTYPE: - return 1 << 2 - if dtype == dtypes.BOOL_DTYPE: - return 1 << 3 - if dtype == dtypes.STRING_DTYPE: - return 1 << 4 - if dtype == dtypes.BYTES_DTYPE: - return 1 << 5 - if dtype == dtypes.DATE_DTYPE: - return 1 << 6 - if dtype == dtypes.TIME_DTYPE: - return 1 << 7 - if dtype == dtypes.DATETIME_DTYPE: - return 1 << 8 - if dtype == dtypes.TIMESTAMP_DTYPE: - return 1 << 9 - if dtype == dtypes.TIMEDELTA_DTYPE: - return 1 << 10 - if dtype == dtypes.NUMERIC_DTYPE: - return 1 << 11 - if dtype == dtypes.BIGNUMERIC_DTYPE: - return 1 << 12 - if dtype == dtypes.GEO_DTYPE: - return 1 << 13 - if dtype == dtypes.JSON_DTYPE: - return 1 << 14 - - if dtypes.is_struct_like(dtype): - mask = 1 << 15 - if dtype == dtypes.OBJ_REF_DTYPE: - # obj_ref is a special struct type for multi-modal data. - # It should be double counted as both "struct" and its own type. - mask = mask | (1 << 17) - return mask - - if dtypes.is_array_like(dtype): - return 1 << 16 - - # If an unknown datat type is present, mark it with the least significant bit. - return 1 << 0 diff --git a/bigframes/core/logging/log_adapter.py b/bigframes/core/logging/log_adapter.py deleted file mode 100644 index f8b890b3bf9..00000000000 --- a/bigframes/core/logging/log_adapter.py +++ /dev/null @@ -1,336 +0,0 @@ -# Copyright 2023 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import functools -import inspect -import threading -from typing import List, Optional - -import pandas -from google.cloud import bigquery - -_lock = threading.Lock() - -# The limit is 64 (https://cloud.google.com/bigquery/docs/labels-intro#requirements), -# but leave a few spare for internal labels to be added. -# See internal issue 386825477. -MAX_LABELS_COUNT = 64 - 8 -PANDAS_API_TRACKING_TASK = "pandas_api_tracking" -PANDAS_PARAM_TRACKING_TASK = "pandas_param_tracking" -LOG_OVERRIDE_NAME = "__log_override_name__" - -_api_methods: List = [] -_excluded_methods = ["__setattr__", "__getattr__"] - -# Stack to track method calls -_call_stack: List = [] - - -def submit_pandas_labels( - bq_client: Optional[bigquery.Client], - base_name: str, - method_name: str, - args=(), - kwargs={}, - task: str = PANDAS_API_TRACKING_TASK, -): - """ - Submits usage of API to BigQuery using a simulated failed query. - - This function is designed to capture and log details about the usage of pandas methods, - including class and method names, the count of positional arguments, and any keyword - arguments that match the method's signature. To avoid incurring costs, it simulates a - query execution using a query with syntax errors. - - Args: - bq_client (bigquery.Client): The client used to interact with BigQuery. - base_name (str): The name of the pandas class/module being used. - method_name (str): The name of the method being invoked. - args (tuple): The positional arguments passed to the method. - kwargs (dict): The keyword arguments passed to the method. - task (str): The specific task type for the logging event: - - 'PANDAS_API_TRACKING_TASK': Indicates that the unimplemented feature is a method. - - 'PANDAS_PARAM_TRACKING_TASK': Indicates that the unimplemented feature is a - parameter of a method. - """ - if bq_client is None or ( - method_name.startswith("_") and not method_name.startswith("__") - ): - return - - labels_dict = { - "task": task, - "class_name": base_name.lower(), - "method_name": method_name.lower(), - "args_count": len(args), - } - - # getattr(pandas, "pandas") returns pandas - # so we can also use this for pandas.function - if hasattr(pandas, base_name): - base = getattr(pandas, base_name) - else: - return - - # Omit __call__, because its not implemented on the actual instances of - # DataFrame/Series, only as the constructor. - if method_name != "__call__" and hasattr(base, method_name): - method = getattr(base, method_name) - else: - return - - if kwargs: - # Iterate through the keyword arguments and add them to the labels dictionary if they - # are parameters that are implemented in pandas and the maximum label count has not been reached. - signature = inspect.signature(method) - param_names = [param.name for param in signature.parameters.values()] - - idx = 0 - for key in kwargs.keys(): - if len(labels_dict) >= MAX_LABELS_COUNT: - break - if key in param_names: - labels_dict[f"kwargs_{idx}"] = key.lower() - idx += 1 - - # If this log is for tracking unimplemented parameters and no keyword arguments were - # provided, skip logging. - if len(labels_dict) == 4 and task == PANDAS_PARAM_TRACKING_TASK: - return - - # Run a query with syntax error to avoid cost. - query = "SELECT COUNT(x FROM data_table—" - job_config = bigquery.QueryJobConfig(labels=labels_dict) - bq_client.query(query, job_config=job_config) - - -def class_logger(decorated_cls=None): - """Decorator that adds logging functionality to each method of the class.""" - - def wrap(cls): - for attr_name, attr_value in cls.__dict__.items(): - if callable(attr_value) and (attr_name not in _excluded_methods): - if isinstance(attr_value, staticmethod): - setattr( - cls, - attr_name, - staticmethod(method_logger(attr_value)), - ) - else: - setattr( - cls, - attr_name, - method_logger(attr_value), - ) - elif isinstance(attr_value, property): - setattr( - cls, - attr_name, - property_logger(attr_value), - ) - return cls - - if decorated_cls is None: - # The logger is used with parentheses - return wrap - - # The logger is used without parentheses - return wrap(decorated_cls) - - -def method_logger(method=None, /, *, custom_base_name: Optional[str] = None): - """Decorator that adds logging functionality to a method.""" - - def outer_wrapper(method): - @functools.wraps(method) - def wrapper(*args, **kwargs): - api_method_name = getattr( - method, LOG_OVERRIDE_NAME, method.__name__ - ).lower() - if custom_base_name is None: - qualname_parts = getattr(method, "__qualname__", method.__name__).split( - "." - ) - class_name = qualname_parts[-2] if len(qualname_parts) > 1 else "" - base_name = ( - class_name - if class_name - else "_".join(method.__module__.split(".")[1:]) - ) - else: - base_name = custom_base_name - - full_method_name = f"{base_name.lower()}-{api_method_name}" - # Track directly called methods - if len(_call_stack) == 0: - session = _find_session(*args, **kwargs) - add_api_method(full_method_name, session=session) - - _call_stack.append(full_method_name) - - try: - return method(*args, **kwargs) - except (NotImplementedError, TypeError) as e: - # Log method parameters that are implemented in pandas but either missing (TypeError) - # or not fully supported (NotImplementedError) in BigFrames. - # Logging is currently supported only when we can access the bqclient through - # _block.session.bqclient. - if len(_call_stack) == 1: - submit_pandas_labels( - _get_bq_client(*args, **kwargs), - base_name, - api_method_name, - args, - kwargs, - task=PANDAS_PARAM_TRACKING_TASK, - ) - raise e - finally: - _call_stack.pop() - - return wrapper - - if method is None: - # Called with parentheses - return outer_wrapper - - # Called without parentheses - return outer_wrapper(method) - - -def property_logger(prop): - """Decorator that adds logging functionality to a property.""" - - def shared_wrapper(prop): - @functools.wraps(prop) - def wrapped(*args, **kwargs): - qualname_parts = getattr(prop, "__qualname__", prop.__name__).split(".") - class_name = qualname_parts[-2] if len(qualname_parts) > 1 else "" - property_name = prop.__name__ - full_property_name = f"{class_name.lower()}-{property_name.lower()}" - - if len(_call_stack) == 0: - session = _find_session(*args, **kwargs) - add_api_method(full_property_name, session=session) - - _call_stack.append(full_property_name) - try: - return prop(*args, **kwargs) - finally: - _call_stack.pop() - - return wrapped - - # Apply the wrapper to the getter, setter, and deleter - return property( - shared_wrapper(prop.fget), - shared_wrapper(prop.fset) if prop.fset else None, - shared_wrapper(prop.fdel) if prop.fdel else None, - ) - - -def log_name_override(name: str): - """ - Attaches a custom name to be used by logger. - """ - - def wrapper(func): - setattr(func, LOG_OVERRIDE_NAME, name) - return func - - return wrapper - - -def add_api_method(api_method_name, session=None): - global _lock - global _api_methods - - clean_method_name = api_method_name.replace("<", "").replace(">", "") - - if session is not None and _is_session_initialized(session): - with session._api_methods_lock: - session._api_methods.insert(0, clean_method_name) - session._api_methods = session._api_methods[:MAX_LABELS_COUNT] - else: - with _lock: - # Push the method to the front of the _api_methods list - _api_methods.insert(0, clean_method_name) - # Keep the list length within the maximum limit (adjust MAX_LABELS_COUNT as needed) - _api_methods = _api_methods[:MAX_LABELS_COUNT] - - -def get_and_reset_api_methods(dry_run: bool = False, session=None): - global _lock - methods = [] - - if session is not None and _is_session_initialized(session): - with session._api_methods_lock: - methods.extend(session._api_methods) - if not dry_run: - session._api_methods.clear() - - with _lock: - methods.extend(_api_methods) - - # dry_run might not make a job resource, so only reset the log on real queries. - if not dry_run: - _api_methods.clear() - return methods - - -def _get_bq_client(*args, **kwargs): - # Assumes that on BigFrames API errors (TypeError/NotImplementedError), - # an input arg (likely the first, e.g., 'self') has `_block.session.bqclient` - for argv in args: - if hasattr(argv, "_block"): - return argv._block.session.bqclient - - for kwargv in kwargs.values(): - if hasattr(kwargv, "_block"): - return kwargv._block.session.bqclient - - return None - - -def _is_session_initialized(session): - """Return True if fully initialized. - - Because the method logger could get called before Session.__init__ has a - chance to run, we use the globals in that case. - """ - return hasattr(session, "_api_methods_lock") and hasattr(session, "_api_methods") - - -def _find_session(*args, **kwargs): - # This function cannot import Session at the top level because Session - # imports log_adapter. - from bigframes.session import Session - - session = args[0] if args else None - if ( - session is not None - and isinstance(session, Session) - and _is_session_initialized(session) - ): - return session - - session = kwargs.get("session") - if ( - session is not None - and isinstance(session, Session) - and _is_session_initialized(session) - ): - return session - - return None diff --git a/bigframes/core/nodes.py b/bigframes/core/nodes.py index e88a78fae5c..7b252b164f6 100644 --- a/bigframes/core/nodes.py +++ b/bigframes/core/nodes.py @@ -11,1770 +11,235 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. - from __future__ import annotations -import abc -import dataclasses +from dataclasses import dataclass, field import functools -import itertools import typing -from typing import ( - AbstractSet, - Callable, - Iterable, - Mapping, - Optional, - Sequence, - Tuple, - cast, -) +from typing import Optional, Tuple + +import pandas -import bigframes.core.expression as ex -import bigframes.core.slices as slices +import bigframes.core.guid +from bigframes.core.ordering import OrderingColumnReference import bigframes.core.window_spec as window import bigframes.dtypes -from bigframes.core import agg_expressions, bq_data, identifiers, local_data, sequences -from bigframes.core.bigframe_node import COLUMN_SET, BigFrameNode -from bigframes.core.field import Field -from bigframes.core.ordering import OrderingExpression, RowOrdering +import bigframes.operations as ops +import bigframes.operations.aggregations as agg_ops if typing.TYPE_CHECKING: - import bigframes.session - - -# A fixed number of variable to assume for overhead on some operations -OVERHEAD_VARIABLES = 5 - - -@dataclasses.dataclass(frozen=True, eq=True) -class ColumnDef: - expression: ex.Expression - id: identifiers.ColumnId - - -class AdditiveNode: - """Definition of additive - if you drop added_fields, you end up with the descendent. - - .. code-block:: text - - AdditiveNode (fields: a, b, c; added_fields: c) - | - | additive_base - V - BigFrameNode (fields: a, b) - - """ - - @property - @abc.abstractmethod - def added_fields(self) -> Tuple[Field, ...]: ... - - @property - @abc.abstractmethod - def additive_base(self) -> BigFrameNode: ... - - @abc.abstractmethod - def replace_additive_base(self, BigFrameNode) -> BigFrameNode: ... - - -@dataclasses.dataclass(frozen=True, eq=False) -class UnaryNode(BigFrameNode): - child: BigFrameNode - - @property - def child_nodes(self) -> typing.Sequence[BigFrameNode]: - return (self.child,) - - @property - def fields(self) -> Sequence[Field]: - return self.child.fields - - @property - def explicitly_ordered(self) -> bool: - return self.child.explicitly_ordered - - def transform_children( - self, t: Callable[[BigFrameNode], BigFrameNode] - ) -> UnaryNode: - transformed = dataclasses.replace(self, child=t(self.child)) - if self == transformed: - # reusing existing object speeds up eq, and saves a small amount of memory - return self - return transformed - - def replace_child(self, new_child: BigFrameNode) -> UnaryNode: - new_self = dataclasses.replace(self, child=new_child) # type: ignore - return new_self + import ibis.expr.types as ibis_types - @property - def order_ambiguous(self) -> bool: - return self.child.order_ambiguous - - -@dataclasses.dataclass(frozen=True, eq=False) -class SliceNode(UnaryNode): - """Logical slice node conditionally becomes limit or filter over row numbers.""" - - start: Optional[int] - stop: Optional[int] - step: int = 1 - - @property - def row_preserving(self) -> bool: - """Whether this node preserves input rows.""" - return False - - @property - def non_local(self) -> bool: - """ - Whether this node combines information across multiple rows instead of processing rows independently. - Used as an approximation for whether the expression may require shuffling to execute (and therefore be expensive). - """ - return True - - # these are overestimates, more accurate numbers available by converting to concrete limit or analytic+filter ops - @property - def variables_introduced(self) -> int: - return 2 - - @property - def relation_ops_created(self) -> int: - return 2 - - @property - def is_limit(self) -> bool: - """Returns whether this is equivalent to a ORDER BY ... LIMIT N.""" - # TODO: Handle tail case. - return ( - (not self.start) - and (self.step == 1) - and (self.stop is not None) - and (self.stop > 0) - ) - - @property - def is_noop(self) -> bool: - """Returns whether this node doesn't actually change the results.""" - # TODO: Handle tail case. - return ( - ((not self.start) or (self.start == 0)) - and (self.step == 1) - and ((self.stop is None) or (self.stop == self.child.row_count)) - ) - - @property - def row_count(self) -> typing.Optional[int]: - child_length = self.child.row_count - if child_length is None: - return None - return slices.slice_output_rows( - (self.start, self.stop, self.step), child_length - ) - - @property - def node_defined_ids(self) -> Tuple[identifiers.ColumnId, ...]: - return () - - @property - def referenced_ids(self) -> COLUMN_SET: - return frozenset() - - def remap_vars( - self, mappings: Mapping[identifiers.ColumnId, identifiers.ColumnId] - ) -> SliceNode: - return self - - def remap_refs( - self, mappings: Mapping[identifiers.ColumnId, identifiers.ColumnId] - ) -> SliceNode: - return self + import bigframes.core.ordering as orderings + import bigframes.session -@dataclasses.dataclass(frozen=True, eq=False) -class InNode(BigFrameNode, AdditiveNode): +@dataclass(frozen=True) +class BigFrameNode: """ - Special Join Type that only returns rows from the left side, as well as adding a bool column indicating whether a match exists on the right side. + Immutable node for representing 2D typed array as a tree of operators. - Modelled separately from join node, as this operation preserves row identity. + All subclasses must be hashable so as to be usable as caching key. """ - left_child: BigFrameNode - right_child: BigFrameNode - left_col: ex.DerefOp - indicator_col: identifiers.ColumnId - # For matching left_col to right_child[0], if true, nulls match nulls, if false, nulls don't match nulls - nulls_equal: bool = True - - def _validate(self): - assert len(self.right_child.fields) == 1 - @property - def row_preserving(self) -> bool: - return False - - @property - def non_local(self) -> bool: + def deterministic(self) -> bool: + """Whether this node will evaluates deterministically.""" return True @property def child_nodes(self) -> typing.Sequence[BigFrameNode]: - return (self.left_child, self.right_child) - - @property - def order_ambiguous(self) -> bool: - return False - - @property - def explicitly_ordered(self) -> bool: - # Preserves left ordering always - return True - - @property - def added_fields(self) -> Tuple[Field, ...]: - return (Field(self.indicator_col, bigframes.dtypes.BOOL_DTYPE, nullable=False),) - - @property - def fields(self) -> Sequence[Field]: - return sequences.ChainedSequence( - self.left_child.fields, - self.added_fields, - ) + """Direct children of this node""" + return tuple([]) @functools.cached_property - def variables_introduced(self) -> int: - """Defines the number of variables generated by the current node. Used to estimate query planning complexity.""" - return 1 - - @property - def joins(self) -> bool: - return True - - @property - def row_count(self) -> Optional[int]: - return self.left_child.row_count - - @property - def node_defined_ids(self) -> Tuple[identifiers.ColumnId, ...]: - return (self.indicator_col,) - - @property - def referenced_ids(self) -> COLUMN_SET: - return frozenset( - { - self.left_col.id, - } - ) + def session(self): + sessions = [] + for child in self.child_nodes: + if child.session is not None: + sessions.append(child.session) + unique_sessions = len(set(sessions)) + if unique_sessions > 1: + raise ValueError("Cannot use combine sources from multiple sessions.") + elif unique_sessions == 1: + return sessions[0] + return None - @property - def additive_base(self) -> BigFrameNode: - return self.left_child - @property - def joins_nulls(self) -> bool: - return self.nulls_equal +@dataclass(frozen=True) +class UnaryNode(BigFrameNode): + child: BigFrameNode @property - def _node_expressions(self): - return (self.left_col,) - - def replace_additive_base(self, node: BigFrameNode): - return dataclasses.replace(self, left_child=node) - - def transform_children(self, t: Callable[[BigFrameNode], BigFrameNode]) -> InNode: - transformed = dataclasses.replace( - self, left_child=t(self.left_child), right_child=t(self.right_child) - ) - if self == transformed: - # reusing existing object speeds up eq, and saves a small amount of memory - return self - return transformed - - def remap_vars( - self, mappings: Mapping[identifiers.ColumnId, identifiers.ColumnId] - ) -> InNode: - return dataclasses.replace( - self, indicator_col=mappings.get(self.indicator_col, self.indicator_col) - ) - - def remap_refs( - self, mappings: Mapping[identifiers.ColumnId, identifiers.ColumnId] - ) -> InNode: - return dataclasses.replace( - self, - left_col=self.left_col.remap_column_refs( - mappings, allow_partial_bindings=True - ), - ) # type: ignore + def child_nodes(self) -> typing.Sequence[BigFrameNode]: + return (self.child,) -@dataclasses.dataclass(frozen=True, eq=False) +@dataclass(frozen=True) class JoinNode(BigFrameNode): left_child: BigFrameNode right_child: BigFrameNode - conditions: typing.Tuple[typing.Tuple[ex.DerefOp, ex.DerefOp], ...] - type: typing.Literal["inner", "outer", "left", "right", "cross"] - # choose to treat nulls as equal or not for purposes of the join - # pandas treats nulls as equal, sql does not - nulls_equal: bool - propogate_order: bool - - def _validate(self): - assert not (set(self.left_child.ids) & set(self.right_child.ids)), ( - "Join ids collide" - ) - - @property - def row_preserving(self) -> bool: - return False - - @property - def non_local(self) -> bool: - return True + left_column_ids: typing.Tuple[str, ...] + right_column_ids: typing.Tuple[str, ...] + how: typing.Literal[ + "inner", + "left", + "outer", + "right", + ] + allow_row_identity_join: bool = True @property def child_nodes(self) -> typing.Sequence[BigFrameNode]: return (self.left_child, self.right_child) - @property - def order_ambiguous(self) -> bool: - return True - - @property - def explicitly_ordered(self) -> bool: - return self.propogate_order - - @functools.cached_property - def fields(self) -> Sequence[Field]: - left_fields: Iterable[Field] = self.left_child.fields - if self.type in ("right", "outer"): - left_fields = map(lambda x: x.with_nullable(), left_fields) - right_fields: Iterable[Field] = self.right_child.fields - if self.type in ("left", "outer"): - right_fields = map(lambda x: x.with_nullable(), right_fields) - return (*left_fields, *right_fields) - - @property - def joins_nulls(self) -> bool: - return self.nulls_equal - - @functools.cached_property - def variables_introduced(self) -> int: - """Defines the number of variables generated by the current node. Used to estimate query planning complexity.""" - return OVERHEAD_VARIABLES - - @property - def joins(self) -> bool: - return True - - @property - def row_count(self) -> Optional[int]: - if self.type == "cross": - if self.left_child.row_count is None or self.right_child.row_count is None: - return None - return self.left_child.row_count * self.right_child.row_count - - return None - - @property - def node_defined_ids(self) -> Tuple[identifiers.ColumnId, ...]: - return () - - @property - def referenced_ids(self) -> COLUMN_SET: - return frozenset( - itertools.chain.from_iterable( - (*l_cond.column_references, *r_cond.column_references) - for l_cond, r_cond in self.conditions - ) - ) - - @property - def consumed_ids(self) -> COLUMN_SET: - return frozenset(*self.ids, *self.referenced_ids) - - @property - def _node_expressions(self): - return tuple(itertools.chain.from_iterable(self.conditions)) - - def transform_children(self, t: Callable[[BigFrameNode], BigFrameNode]) -> JoinNode: - transformed = dataclasses.replace( - self, left_child=t(self.left_child), right_child=t(self.right_child) - ) - if self == transformed: - # reusing existing object speeds up eq, and saves a small amount of memory - return self - return transformed - - def remap_vars( - self, mappings: Mapping[identifiers.ColumnId, identifiers.ColumnId] - ) -> JoinNode: - return self - def remap_refs( - self, mappings: Mapping[identifiers.ColumnId, identifiers.ColumnId] - ) -> JoinNode: - new_conds = tuple( - ( - l_cond.remap_column_refs(mappings, allow_partial_bindings=True), - r_cond.remap_column_refs(mappings, allow_partial_bindings=True), - ) - for l_cond, r_cond in self.conditions - ) - return dataclasses.replace(self, conditions=new_conds) # type: ignore - - -@dataclasses.dataclass(frozen=True, eq=False) +@dataclass(frozen=True) class ConcatNode(BigFrameNode): - # TODO: Explcitly map column ids from each child? children: Tuple[BigFrameNode, ...] - output_ids: Tuple[identifiers.ColumnId, ...] - - def _validate(self): - if len(self.children) == 0: - raise ValueError("Concat requires at least one input table. Zero provided.") - child_schemas = [child.schema.dtypes for child in self.children] - if not len(set(child_schemas)) == 1: - raise ValueError("All inputs must have identical dtypes. {child_schemas}") @property def child_nodes(self) -> typing.Sequence[BigFrameNode]: return self.children - @property - def order_ambiguous(self) -> bool: - return any(child.order_ambiguous for child in self.children) - - @property - def explicitly_ordered(self) -> bool: - # Consider concat as an ordered operations (even though input frames may not be ordered) - return True - - @property - def fields(self) -> Sequence[Field]: - # TODO: Output names should probably be aligned beforehand or be part of concat definition - # TODO: Handle nullability - return tuple( - Field(id, field.dtype) - for id, field in zip(self.output_ids, self.children[0].fields) - ) - - @functools.cached_property - def variables_introduced(self) -> int: - """Defines the number of variables generated by the current node. Used to estimate query planning complexity.""" - return len(self.schema.items) + OVERHEAD_VARIABLES - - @property - def row_count(self) -> Optional[int]: - sub_counts = [node.row_count for node in self.child_nodes] - total = 0 - for count in sub_counts: - if count is None: - return None - total += count - return total - - @property - def node_defined_ids(self) -> Tuple[identifiers.ColumnId, ...]: - return self.output_ids - - def transform_children( - self, t: Callable[[BigFrameNode], BigFrameNode] - ) -> ConcatNode: - transformed = dataclasses.replace( - self, children=tuple(t(child) for child in self.children) - ) - if self == transformed: - # reusing existing object speeds up eq, and saves a small amount of memory - return self - return transformed - - def remap_vars( - self, mappings: Mapping[identifiers.ColumnId, identifiers.ColumnId] - ) -> ConcatNode: - new_ids = tuple(mappings.get(id, id) for id in self.output_ids) - return dataclasses.replace(self, output_ids=new_ids) - - def remap_refs( - self, mappings: Mapping[identifiers.ColumnId, identifiers.ColumnId] - ) -> ConcatNode: - return self - - -@dataclasses.dataclass(frozen=True, eq=False) -class FromRangeNode(BigFrameNode): - # TODO: Enforce single-row, single column constraint - start: BigFrameNode - end: BigFrameNode - step: int - output_id: identifiers.ColumnId = identifiers.ColumnId("labels") - - @property - def roots(self) -> typing.Set[BigFrameNode]: - return {self} - - @property - def child_nodes(self) -> typing.Sequence[BigFrameNode]: - return (self.start, self.end) - - @property - def order_ambiguous(self) -> bool: - return False - - @property - def explicitly_ordered(self) -> bool: - return True - - @functools.cached_property - def fields(self) -> Sequence[Field]: - return ( - Field(self.output_id, next(iter(self.start.fields)).dtype, nullable=False), - ) - - @functools.cached_property - def variables_introduced(self) -> int: - """Defines the number of variables generated by the current node. Used to estimate query planning complexity.""" - return len(self.schema.items) + OVERHEAD_VARIABLES - - @property - def row_count(self) -> Optional[int]: - return None - - @property - def node_defined_ids(self) -> Tuple[identifiers.ColumnId, ...]: - return (self.output_id,) - - @property - def defines_namespace(self) -> bool: - return True - - def transform_children( - self, t: Callable[[BigFrameNode], BigFrameNode] - ) -> FromRangeNode: - transformed = dataclasses.replace(self, start=t(self.start), end=t(self.end)) - if self == transformed: - # reusing existing object speeds up eq, and saves a small amount of memory - return self - return transformed - - def remap_vars( - self, mappings: Mapping[identifiers.ColumnId, identifiers.ColumnId] - ) -> FromRangeNode: - return dataclasses.replace( - self, output_id=mappings.get(self.output_id, self.output_id) - ) - - def remap_refs( - self, mappings: Mapping[identifiers.ColumnId, identifiers.ColumnId] - ) -> FromRangeNode: - return self - # Input Nodex -# TODO: Most leaf nodes produce fixed column names based on the datasource -# They should support renaming -@dataclasses.dataclass(frozen=True, eq=False) -class LeafNode(BigFrameNode): - @property - def roots(self) -> typing.Set[BigFrameNode]: - return {self} - - @property - def fast_offsets(self) -> bool: - return False - - @property - def fast_ordered_limit(self) -> bool: - return False - - def transform_children(self, t: Callable[[BigFrameNode], BigFrameNode]) -> LeafNode: - return self +@dataclass(frozen=True) +class ReadLocalNode(BigFrameNode): + feather_bytes: bytes + column_ids: typing.Tuple[str, ...] -class ScanItem(typing.NamedTuple): - id: identifiers.ColumnId - source_id: str # Flexible enough for both local data and bq data - - def with_id(self, id: identifiers.ColumnId) -> ScanItem: - return ScanItem(id, self.source_id) - - def with_source_id(self, source_id: str) -> ScanItem: - return ScanItem(self.id, source_id) - - -@dataclasses.dataclass(frozen=True) -class ScanList: - """ - Defines the set of columns to scan from a source, along with the variable to bind the columns to. - """ - - items: typing.Tuple[ScanItem, ...] - - @classmethod - def from_items(cls, items: Iterable[ScanItem]) -> ScanList: - return cls(tuple(items)) - - def filter_cols( - self, - ids: AbstractSet[identifiers.ColumnId], - ) -> ScanList: - """Drop columns from the scan that except those in the 'ids' arg.""" - result = ScanList(tuple(item for item in self.items if item.id in ids)) - if len(result.items) == 0: - # We need to select something, or sql syntax breaks - result = ScanList(self.items[:1]) - return result - - def project( - self, - selections: Mapping[identifiers.ColumnId, identifiers.ColumnId], - ) -> ScanList: - """Project given ids from the scanlist, dropping previous bindings.""" - by_id = {item.id: item for item in self.items} - result = ScanList( - tuple( - by_id[old_id].with_id(new_id) for old_id, new_id in selections.items() - ) - ) - if len(result.items) == 0: - # We need to select something, or sql syntax breaks - result = ScanList((self.items[:1])) - return result - - def remap_source_ids( - self, - mapping: Mapping[str, str], - ) -> ScanList: - items = tuple( - item.with_source_id(mapping.get(item.source_id, item.source_id)) - for item in self.items - ) - return ScanList(items) - - def append( - self, source_id: str, dtype: bigframes.dtypes.Dtype, id: identifiers.ColumnId - ) -> ScanList: - return ScanList((*self.items, ScanItem(id, source_id))) - - -@dataclasses.dataclass(frozen=True, eq=False) -class ReadLocalNode(LeafNode): - # TODO: Track nullability for local data - local_data_source: local_data.ManagedArrowTable - # Mapping of local ids to bfet id. - scan_list: ScanList - session: bigframes.session.Session - # Offsets are generated only if this is non-null - offsets_col: Optional[identifiers.ColumnId] = None - - @property - def fields(self) -> Sequence[Field]: - fields = tuple( - Field( - col_id, - self.local_data_source.schema.get_type(source_id), - nullable=self.local_data_source.is_nullable( - identifiers.ColumnId(source_id) - ), - ) - for col_id, source_id in self.scan_list.items - ) - - if self.offsets_col is not None: - return tuple( - itertools.chain( - fields, - ( - Field( - self.offsets_col, bigframes.dtypes.INT_DTYPE, nullable=False - ), - ), - ) - ) - return fields - - @property - def variables_introduced(self) -> int: - """Defines the number of variables generated by the current node. Used to estimate query planning complexity.""" - return len(self.scan_list.items) + 1 - - @property - def fast_offsets(self) -> bool: - return True - - @property - def fast_ordered_limit(self) -> bool: - return True - - @property - def order_ambiguous(self) -> bool: - return False - - @property - def explicitly_ordered(self) -> bool: - return True - - @property - def row_count(self) -> typing.Optional[int]: - return self.local_data_source.metadata.row_count - - @property - def node_defined_ids(self) -> Tuple[identifiers.ColumnId, ...]: - return tuple(item.id for item in self.fields) - - def remap_vars( - self, mappings: Mapping[identifiers.ColumnId, identifiers.ColumnId] - ) -> ReadLocalNode: - new_scan_list = ScanList( - tuple( - ScanItem(mappings.get(item.id, item.id), item.source_id) - for item in self.scan_list.items - ) - ) - new_offsets_col = ( - mappings.get(self.offsets_col, self.offsets_col) - if (self.offsets_col is not None) - else None - ) - return dataclasses.replace( - self, scan_list=new_scan_list, offsets_col=new_offsets_col - ) - - def remap_refs( - self, mappings: Mapping[identifiers.ColumnId, identifiers.ColumnId] - ) -> ReadLocalNode: - return self - - -@dataclasses.dataclass(frozen=True, eq=False) -class ReadTableNode(LeafNode): - source: bq_data.BigqueryDataSource - # Subset of physical schema column - # Mapping of table schema ids to bfet id. - scan_list: ScanList - - table_session: bigframes.session.Session = dataclasses.field() - - def _validate(self): - # enforce invariants - physical_names = set(map(lambda i: i.name, self.source.table.physical_schema)) - if not set(scan.source_id for scan in self.scan_list.items).issubset( - physical_names - ): - raise ValueError( - f"Requested schema {self.scan_list} cannot be derived from table schemal {self.source.table.physical_schema}" - ) +# TODO: Refactor to take raw gbq object reference +@dataclass(frozen=True) +class ReadGbqNode(BigFrameNode): + table: ibis_types.Table = field() + table_session: bigframes.session.Session = field() + columns: Tuple[ibis_types.Value, ...] = field() + hidden_ordering_columns: Tuple[ibis_types.Value, ...] = field() + ordering: orderings.ExpressionOrdering = field() @property def session(self): - return self.table_session - - @property - def fields(self) -> Sequence[Field]: - return tuple( - Field( - col_id, - self.source.schema.get_type(source_id), - self.source.table.schema_by_id[source_id].is_nullable, - ) - for col_id, source_id in self.scan_list.items - ) - - @property - def relation_ops_created(self) -> int: - # Assume worst case, where readgbq actually has baked in analytic operation to generate index - return 3 - - @property - def fast_offsets(self) -> bool: - # Fast head is only supported when row offsets are available or data is clustered over ordering key. - return (self.source.ordering is not None) and self.source.ordering.is_sequential - - @property - def fast_ordered_limit(self) -> bool: - if self.source.ordering is None: - return False - order_cols = self.source.ordering.all_ordering_columns - # monotonicity would probably be fine - if not all(col.scalar_expression.is_identity for col in order_cols): - return False - order_col_ids = tuple( - cast(ex.DerefOp, col.scalar_expression).id.name for col in order_cols - ) - cluster_col_ids = self.source.table.cluster_cols - if cluster_col_ids is None: - return False - - return order_col_ids == cluster_col_ids[: len(order_col_ids)] - - @property - def order_ambiguous(self) -> bool: - return ( - self.source.ordering is None - ) or not self.source.ordering.is_total_ordering - - @property - def explicitly_ordered(self) -> bool: - return self.source.ordering is not None - - @functools.cached_property - def variables_introduced(self) -> int: - return len(self.scan_list.items) + 1 - - @property - def row_count(self) -> typing.Optional[int]: - return self.source.n_rows - - @property - def node_defined_ids(self) -> Tuple[identifiers.ColumnId, ...]: - return tuple(item.id for item in self.scan_list.items) - - def remap_vars( - self, mappings: Mapping[identifiers.ColumnId, identifiers.ColumnId] - ) -> ReadTableNode: - new_scan_list = ScanList( - tuple( - ScanItem(mappings.get(item.id, item.id), item.source_id) - for item in self.scan_list.items - ) - ) - return dataclasses.replace(self, scan_list=new_scan_list) - - def remap_refs( - self, mappings: Mapping[identifiers.ColumnId, identifiers.ColumnId] - ) -> ReadTableNode: - return self - - def pull_out_order(self): - # Maybe the ordering should be required to always be in the scan list, and then we won't need this? - if self.source.ordering is None: - return self, RowOrdering() - - order_cols = {col.sql for col in self.source.ordering.referenced_columns} - scan_cols = {col.source_id for col in self.scan_list.items} - new_scan_cols = [ - ScanItem( - identifiers.ColumnId.unique(), - source_id=field.name, - ) - for field in self.source.table.physical_schema - if (field.name in order_cols) and (field.name not in scan_cols) - ] - new_scan_list = ScanList(items=(*self.scan_list.items, *new_scan_cols)) - new_order = self.source.ordering.remap_column_refs( - { - identifiers.ColumnId(item.source_id): item.id - for item in new_scan_list.items - }, - allow_partial_bindings=True, - ) - new_node = dataclasses.replace( - self, - scan_list=new_scan_list, - source=self.source.with_ordering(RowOrdering()), - ) - return new_node, new_order - - -@dataclasses.dataclass(frozen=True, eq=False) -class CachedTableNode(ReadTableNode): - # The original BFET subtree that was cached - # note: this isn't a "child" node. - original_node: BigFrameNode = dataclasses.field() + return (self.table_session,) # Unary nodes -@dataclasses.dataclass(frozen=True, eq=False) -class PromoteOffsetsNode(UnaryNode, AdditiveNode): - col_id: identifiers.ColumnId - - @property - def non_local(self) -> bool: - return True - - @property - def fields(self) -> Sequence[Field]: - return sequences.ChainedSequence(self.child.fields, self.added_fields) - - @property - def relation_ops_created(self) -> int: - return 2 - - @functools.cached_property - def variables_introduced(self) -> int: - return 1 - - @property - def row_count(self) -> Optional[int]: - return self.child.row_count - - @property - def node_defined_ids(self) -> Tuple[identifiers.ColumnId, ...]: - return (self.col_id,) +@dataclass(frozen=True) +class DropColumnsNode(UnaryNode): + columns: Tuple[str, ...] - @property - def referenced_ids(self) -> COLUMN_SET: - return frozenset() - - @property - def added_fields(self) -> Tuple[Field, ...]: - return (Field(self.col_id, bigframes.dtypes.INT_DTYPE, nullable=False),) - - @property - def additive_base(self) -> BigFrameNode: - return self.child - - def replace_additive_base(self, node: BigFrameNode) -> PromoteOffsetsNode: - return dataclasses.replace(self, child=node) - def remap_vars( - self, mappings: Mapping[identifiers.ColumnId, identifiers.ColumnId] - ) -> PromoteOffsetsNode: - return dataclasses.replace(self, col_id=mappings.get(self.col_id, self.col_id)) +@dataclass(frozen=True) +class PromoteOffsetsNode(UnaryNode): + col_id: str - def remap_refs( - self, mappings: Mapping[identifiers.ColumnId, identifiers.ColumnId] - ) -> PromoteOffsetsNode: - return self - -@dataclasses.dataclass(frozen=True, eq=False) +@dataclass(frozen=True) class FilterNode(UnaryNode): - # TODO: Infer null constraints from predicate - predicate: ex.Expression - - @property - def row_preserving(self) -> bool: - return False - - @property - def variables_introduced(self) -> int: - return 1 + predicate_id: str + keep_null: bool = False - @property - def row_count(self) -> Optional[int]: - return None - - @property - def node_defined_ids(self) -> Tuple[identifiers.ColumnId, ...]: - return () - @property - def consumed_ids(self) -> COLUMN_SET: - return frozenset(self.ids) | self.referenced_ids - - @property - def referenced_ids(self) -> COLUMN_SET: - return frozenset(self.predicate.column_references) - - @property - def _node_expressions(self): - return (self.predicate,) - - def transform_exprs( - self, fn: Callable[[ex.Expression], ex.Expression] - ) -> FilterNode: - return dataclasses.replace( - self, - predicate=fn(self.predicate), - ) - - def remap_vars( - self, mappings: Mapping[identifiers.ColumnId, identifiers.ColumnId] - ) -> FilterNode: - return self - - def remap_refs( - self, mappings: Mapping[identifiers.ColumnId, identifiers.ColumnId] - ) -> FilterNode: - return dataclasses.replace( - self, - predicate=self.predicate.remap_column_refs( - mappings, allow_partial_bindings=True - ), - ) - - -@dataclasses.dataclass(frozen=True, eq=False) +@dataclass(frozen=True) class OrderByNode(UnaryNode): - by: Tuple[OrderingExpression, ...] - stable: bool = True - # This is an optimization, if true, can discard previous orderings, even if doing a stable sort - # might be a total ordering even if false - is_total_order: bool = False - - @property - def variables_introduced(self) -> int: - return 0 + by: Tuple[OrderingColumnReference, ...] + stable: bool = False - @property - def relation_ops_created(self) -> int: - # Doesnt directly create any relational operations - return 0 - - @property - def explicitly_ordered(self) -> bool: - return True - @property - def row_count(self) -> Optional[int]: - return self.child.row_count - - @property - def node_defined_ids(self) -> Tuple[identifiers.ColumnId, ...]: - return () - - @property - def consumed_ids(self) -> COLUMN_SET: - return frozenset(self.ids) | self.referenced_ids - - @property - def referenced_ids(self) -> COLUMN_SET: - return frozenset( - itertools.chain.from_iterable(map(lambda x: x.referenced_columns, self.by)) - ) - - @property - def _node_expressions(self): - return tuple(map(lambda x: x.scalar_expression, self.by)) - - def transform_exprs( - self, fn: Callable[[ex.Expression], ex.Expression] - ) -> OrderByNode: - new_by = cast( - tuple[OrderingExpression, ...], - tuple( - dataclasses.replace( - by_expr, scalar_expression=fn(by_expr.scalar_expression) - ) - for by_expr in self.by - ), - ) - return dataclasses.replace(self, by=new_by) - - def remap_vars( - self, mappings: Mapping[identifiers.ColumnId, identifiers.ColumnId] - ) -> OrderByNode: - return self - - def remap_refs( - self, mappings: Mapping[identifiers.ColumnId, identifiers.ColumnId] - ) -> OrderByNode: - all_refs = set( - itertools.chain.from_iterable(map(lambda x: x.referenced_columns, self.by)) - ) - ref_mapping = {id: ex.DerefOp(mappings[id]) for id in all_refs} - return self.transform_exprs( - lambda ex: ex.bind_refs(ref_mapping, allow_partial_bindings=True) - ) - - -@dataclasses.dataclass(frozen=True, eq=False) +@dataclass(frozen=True) class ReversedNode(UnaryNode): - # useless field to make sure has distinct hash - reversed: bool = True - - @property - def variables_introduced(self) -> int: - return 0 - - @property - def relation_ops_created(self) -> int: - # Doesnt directly create any relational operations - return 0 - - @property - def row_count(self) -> Optional[int]: - return self.child.row_count - - @property - def node_defined_ids(self) -> Tuple[identifiers.ColumnId, ...]: - return () - - @property - def referenced_ids(self) -> COLUMN_SET: - return frozenset() - - def remap_vars( - self, mappings: Mapping[identifiers.ColumnId, identifiers.ColumnId] - ) -> ReversedNode: - return self - - def remap_refs( - self, mappings: Mapping[identifiers.ColumnId, identifiers.ColumnId] - ) -> ReversedNode: - return self - - -class AliasedRef(typing.NamedTuple): - ref: ex.DerefOp - id: identifiers.ColumnId - - @classmethod - def identity(cls, id: identifiers.ColumnId) -> AliasedRef: - return cls(ex.DerefOp(id), id) - - def remap_vars( - self, mappings: Mapping[identifiers.ColumnId, identifiers.ColumnId] - ) -> AliasedRef: - return AliasedRef(self.ref, mappings.get(self.id, self.id)) - - def remap_refs( - self, mappings: Mapping[identifiers.ColumnId, identifiers.ColumnId] - ) -> AliasedRef: - return AliasedRef(ex.DerefOp(mappings.get(self.ref.id, self.ref.id)), self.id) + pass -@dataclasses.dataclass(frozen=True, eq=False) -class SelectionNode(UnaryNode): - input_output_pairs: Tuple[AliasedRef, ...] +@dataclass(frozen=True) +class SelectNode(UnaryNode): + column_ids: typing.Tuple[str, ...] - def _validate(self): - for ref, _ in self.input_output_pairs: - if ref.id not in set(self.child.ids): - raise ValueError(f"Reference to column not in child: {ref.id}") - @functools.cached_property - def fields(self) -> Sequence[Field]: - input_fields_by_id = {field.id: field for field in self.child.fields} - return tuple( - Field( - output, - input_fields_by_id[ref.id].dtype, - input_fields_by_id[ref.id].nullable, - ) - for ref, output in self.input_output_pairs - ) - - @property - def variables_introduced(self) -> int: - # This operation only renames variables, doesn't actually create new ones - return 0 - - @property - def has_multi_referenced_ids(self) -> bool: - referenced = tuple(ref.ref.id for ref in self.input_output_pairs) - return len(referenced) != len(set(referenced)) - - # TODO: Reuse parent namespace - # Currently, Selection node allows renaming an reusing existing names, so it must establish a - # new namespace. - @property - def defines_namespace(self) -> bool: - return True - - @property - def row_count(self) -> Optional[int]: - return self.child.row_count - - @property - def node_defined_ids(self) -> Tuple[identifiers.ColumnId, ...]: - return tuple(id for _, id in self.input_output_pairs) - - @property - def consumed_ids(self) -> COLUMN_SET: - return frozenset(ref.id for ref, id in self.input_output_pairs) - - @property - def _node_expressions(self): - return tuple(ref for ref, id in self.input_output_pairs) +@dataclass(frozen=True) +class ProjectUnaryOpNode(UnaryNode): + input_id: str + op: ops.UnaryOp + output_id: Optional[str] = None - def get_id_mapping(self) -> dict[identifiers.ColumnId, identifiers.ColumnId]: - return {ref.id: id for ref, id in self.input_output_pairs} - def remap_vars( - self, mappings: Mapping[identifiers.ColumnId, identifiers.ColumnId] - ) -> SelectionNode: - new_fields = tuple( - item.remap_vars(mappings) for item in self.input_output_pairs - ) - return dataclasses.replace(self, input_output_pairs=new_fields) +@dataclass(frozen=True) +class ProjectBinaryOpNode(UnaryNode): + left_input_id: str + right_input_id: str + op: ops.BinaryOp + output_id: str - def remap_refs( - self, mappings: Mapping[identifiers.ColumnId, identifiers.ColumnId] - ) -> SelectionNode: - new_fields = tuple( - item.remap_refs(mappings) for item in self.input_output_pairs - ) - return dataclasses.replace(self, input_output_pairs=new_fields) # type: ignore +@dataclass(frozen=True) +class ProjectTernaryOpNode(UnaryNode): + input_id1: str + input_id2: str + input_id3: str + op: ops.TernaryOp + output_id: str -@dataclasses.dataclass(frozen=True, eq=False) -class ProjectionNode(UnaryNode, AdditiveNode): - """Assigns new variables (without modifying existing ones)""" - assignments: typing.Tuple[typing.Tuple[ex.Expression, identifiers.ColumnId], ...] - - def _validate(self): - for expression, _ in self.assignments: - # throws TypeError if invalid - _ = ex.bind_schema_fields(expression, self.child.field_by_id).output_type - assert expression.is_scalar_expr - # Cannot assign to existing variables - append only! - assert all(name not in self.child.schema.names for _, name in self.assignments) - - @functools.cached_property - def added_fields(self) -> Tuple[Field, ...]: - fields = [] - for expr, id in self.assignments: - bound_expr = ex.bind_schema_fields(expr, self.child.field_by_id) - field = Field( - id, - bigframes.dtypes.dtype_for_etype(bound_expr.output_type), - nullable=bound_expr.nullable, - ) - - # Special case until we get better nullability inference in expression objects themselves - if bound_expr.is_identity and not any( - self.child.field_by_id[id].nullable for id in expr.column_references - ): - field = field.with_nonnull() - fields.append(field) - - return tuple(fields) - - @property - def fields(self) -> Sequence[Field]: - return sequences.ChainedSequence(self.child.fields, self.added_fields) - - @property - def variables_introduced(self) -> int: - # ignore passthrough expressions - new_vars = sum(1 for i in self.assignments if not i[0].is_identity) - return new_vars - - @property - def row_count(self) -> Optional[int]: - return self.child.row_count - - @property - def node_defined_ids(self) -> Tuple[identifiers.ColumnId, ...]: - return tuple(id for _, id in self.assignments) - - @property - def consumed_ids(self) -> COLUMN_SET: - return frozenset( - itertools.chain.from_iterable( - i[0].column_references for i in self.assignments - ) - ) - - @property - def referenced_ids(self) -> COLUMN_SET: - return frozenset( - itertools.chain.from_iterable( - ex.column_references for ex, id in self.assignments - ) - ) - - @property - def _node_expressions(self): - return tuple(ex for ex, id in self.assignments) - - @property - def additive_base(self) -> BigFrameNode: - return self.child - - def transform_exprs( - self, fn: Callable[[ex.Expression], ex.Expression] - ) -> ProjectionNode: - new_fields = tuple((fn(ex), id) for ex, id in self.assignments) - return dataclasses.replace(self, assignments=new_fields) - - def replace_additive_base(self, node: BigFrameNode) -> ProjectionNode: - return dataclasses.replace(self, child=node) - - def remap_vars( - self, mappings: Mapping[identifiers.ColumnId, identifiers.ColumnId] - ) -> ProjectionNode: - new_fields = tuple((ex, mappings.get(id, id)) for ex, id in self.assignments) - return dataclasses.replace(self, assignments=new_fields) - - def remap_refs( - self, mappings: Mapping[identifiers.ColumnId, identifiers.ColumnId] - ) -> ProjectionNode: - new_fields = tuple( - (ex.remap_column_refs(mappings, allow_partial_bindings=True), id) - for ex, id in self.assignments - ) - return dataclasses.replace(self, assignments=new_fields) - - -@dataclasses.dataclass(frozen=True, eq=False) +@dataclass(frozen=True) class AggregateNode(UnaryNode): - aggregations: typing.Tuple[ - typing.Tuple[agg_expressions.Aggregation, identifiers.ColumnId], ... - ] - by_column_ids: typing.Tuple[ex.DerefOp, ...] = tuple([]) - order_by: Tuple[OrderingExpression, ...] = () + aggregations: typing.Tuple[typing.Tuple[str, agg_ops.AggregateOp, str], ...] + by_column_ids: typing.Tuple[str, ...] = tuple([]) dropna: bool = True - @property - def row_preserving(self) -> bool: - return False - - @property - def non_local(self) -> bool: - return True - - @functools.cached_property - def fields(self) -> Sequence[Field]: - # TODO: Use child nullability to infer grouping key nullability - by_fields = (self.child.field_by_id[ref.id] for ref in self.by_column_ids) - if self.dropna: - by_fields = (field.with_nonnull() for field in by_fields) - # TODO: Label aggregate ops to determine which are guaranteed non-null - agg_items = ( - Field( - id, - ex.bind_schema_fields(agg, self.child.field_by_id).output_type, - nullable=True, - ) - for agg, id in self.aggregations - ) - return tuple(itertools.chain(by_fields, agg_items)) - - @property - def variables_introduced(self) -> int: - return len(self.aggregations) + len(self.by_column_ids) - - @property - def order_ambiguous(self) -> bool: - return False - - @property - def explicitly_ordered(self) -> bool: - return True - - @property - def row_count(self) -> Optional[int]: - if not self.by_column_ids: - return 1 - return None - - @property - def node_defined_ids(self) -> Tuple[identifiers.ColumnId, ...]: - return tuple(id for _, id in self.aggregations) - - @property - def consumed_ids(self) -> COLUMN_SET: - by_ids = (ref.id for ref in self.by_column_ids) - agg_inputs = itertools.chain.from_iterable( - agg.column_references for agg, _ in self.aggregations - ) - order_ids = itertools.chain.from_iterable( - part.scalar_expression.column_references for part in self.order_by - ) - return frozenset(itertools.chain(by_ids, agg_inputs, order_ids)) - - @property - def has_ordered_ops(self) -> bool: - return not all( - aggregate.op.order_independent for aggregate, _ in self.aggregations - ) - - @property - def _node_expressions(self): - by_ids = (ref for ref in self.by_column_ids) - aggs = tuple(agg for agg, _ in self.aggregations) - order_ids = tuple(part.scalar_expression for part in self.order_by) - return (*by_ids, *aggs, *order_ids) - - def remap_vars( - self, mappings: Mapping[identifiers.ColumnId, identifiers.ColumnId] - ) -> AggregateNode: - new_aggs = tuple((agg, mappings.get(id, id)) for agg, id in self.aggregations) - return dataclasses.replace(self, aggregations=new_aggs) - def remap_refs( - self, mappings: Mapping[identifiers.ColumnId, identifiers.ColumnId] - ) -> AggregateNode: - new_aggs = tuple( - (agg.remap_column_refs(mappings, allow_partial_bindings=True), id) - for agg, id in self.aggregations - ) - new_by_ids = tuple(id.remap_column_refs(mappings) for id in self.by_column_ids) - new_order_by = tuple(part.remap_column_refs(mappings) for part in self.order_by) - return dataclasses.replace( - self, by_column_ids=new_by_ids, aggregations=new_aggs, order_by=new_order_by - ) +# TODO: Unify into aggregate +@dataclass(frozen=True) +class CorrNode(UnaryNode): + corr_aggregations: typing.Tuple[typing.Tuple[str, str, str], ...] -@dataclasses.dataclass(frozen=True, eq=False) -class WindowOpNode(UnaryNode, AdditiveNode): - agg_exprs: tuple[ColumnDef, ...] # must be analytic/aggregation op +@dataclass(frozen=True) +class WindowOpNode(UnaryNode): + column_name: str + op: agg_ops.WindowOp window_spec: window.WindowSpec + output_name: typing.Optional[str] = None + never_skip_nulls: bool = False + skip_reproject_unsafe: bool = False - def _validate(self): - """Validate the local data in the node.""" - # Since inner order and row bounds are coupled, rank ops can't be row bounded - for cdef in self.agg_exprs: - assert isinstance(cdef.expression, agg_expressions.Aggregation) - if self.window_spec.is_row_bounded: - assert cdef.expression.op.implicitly_inherits_order - for agg_child in cdef.expression.children: - assert agg_child.is_scalar_expr - for ref in cdef.expression.column_references: - assert ref in self.child.ids - assert not any(field.dtype is None for field in self.added_fields) +@dataclass(frozen=True) +class ReprojectOpNode(UnaryNode): + pass - for window_expr in self.window_spec.expressions: - assert window_expr.is_scalar_expr - @property - def non_local(self) -> bool: - return True - - @property - def fields(self) -> Sequence[Field]: - return sequences.ChainedSequence(self.child.fields, self.added_fields) - - @property - def variables_introduced(self) -> int: - return 1 - - @property - def added_fields(self) -> Tuple[Field, ...]: - return tuple( - Field( - cdef.id, - ex.bind_schema_fields( - cdef.expression, self.child.field_by_id - ).output_type, - ) - for cdef in self.agg_exprs - ) - - @property - def relation_ops_created(self) -> int: - return 2 - - @property - def row_count(self) -> Optional[int]: - return self.child.row_count - - @property - def node_defined_ids(self) -> Tuple[identifiers.ColumnId, ...]: - return tuple(field.id for field in self.added_fields) - - @property - def consumed_ids(self) -> COLUMN_SET: - return frozenset(self.ids) - - @property - def referenced_ids(self) -> COLUMN_SET: - ids_for_aggs = itertools.chain.from_iterable( - cdef.expression.column_references for cdef in self.agg_exprs - ) - return ( - frozenset() - .union(ids_for_aggs) - .union(self.window_spec.all_referenced_columns) - ) - - @property - def inherits_order(self) -> bool: - # does the op both use ordering at all? and if so, can it inherit order? - aggs = ( - typing.cast(agg_expressions.Aggregation, cdef.expression) - for cdef in self.agg_exprs - ) - op_inherits_order = any( - not agg.op.order_independent and agg.op.implicitly_inherits_order - for agg in aggs - ) - # range-bounded windows do not inherit orders because their ordering are - # already defined before rewrite time. - return op_inherits_order or self.window_spec.is_row_bounded - - @property - def additive_base(self) -> BigFrameNode: - return self.child +@dataclass(frozen=True) +class UnpivotNode(UnaryNode): + row_labels: typing.Tuple[typing.Hashable, ...] + unpivot_columns: typing.Tuple[ + typing.Tuple[str, typing.Tuple[typing.Optional[str], ...]], ... + ] + passthrough_columns: typing.Tuple[str, ...] = () + index_col_ids: typing.Tuple[str, ...] = ("index",) + dtype: typing.Union[ + bigframes.dtypes.Dtype, typing.Tuple[bigframes.dtypes.Dtype, ...] + ] = (pandas.Float64Dtype(),) + how: typing.Literal["left", "right"] = "left" - @property - def _node_expressions(self): - return ( - *(cdef.expression for cdef in self.agg_exprs), - *self.window_spec.expressions, - ) - def replace_additive_base(self, node: BigFrameNode) -> WindowOpNode: - return dataclasses.replace(self, child=node) +@dataclass(frozen=True) +class AssignNode(UnaryNode): + source_id: str + destination_id: str - def remap_vars( - self, mappings: Mapping[identifiers.ColumnId, identifiers.ColumnId] - ) -> WindowOpNode: - return dataclasses.replace( - self, - agg_exprs=tuple( - ColumnDef(cdef.expression, mappings.get(cdef.id, cdef.id)) - for cdef in self.agg_exprs - ), - ) - def remap_refs( - self, mappings: Mapping[identifiers.ColumnId, identifiers.ColumnId] - ) -> WindowOpNode: - return dataclasses.replace( - self, - agg_exprs=tuple( - ColumnDef( - cdef.expression.remap_column_refs( - mappings, allow_partial_bindings=True - ), - cdef.id, - ) - for cdef in self.agg_exprs - ), - window_spec=self.window_spec.remap_column_refs( - mappings, allow_partial_bindings=True - ), - ) +@dataclass(frozen=True) +class AssignConstantNode(UnaryNode): + destination_id: str + value: typing.Hashable + dtype: typing.Optional[bigframes.dtypes.Dtype] -@dataclasses.dataclass(frozen=True, eq=False) +@dataclass(frozen=True) class RandomSampleNode(UnaryNode): fraction: float @property def deterministic(self) -> bool: return False - - @property - def row_preserving(self) -> bool: - return False - - @property - def variables_introduced(self) -> int: - return 1 - - @property - def row_count(self) -> Optional[int]: - return None - - @property - def node_defined_ids(self) -> Tuple[identifiers.ColumnId, ...]: - return () - - @property - def referenced_ids(self) -> COLUMN_SET: - return frozenset() - - def remap_vars( - self, mappings: Mapping[identifiers.ColumnId, identifiers.ColumnId] - ) -> RandomSampleNode: - return self - - def remap_refs( - self, mappings: Mapping[identifiers.ColumnId, identifiers.ColumnId] - ) -> RandomSampleNode: - return self - - -# TODO: Explode should create a new column instead of overriding the existing one -@dataclasses.dataclass(frozen=True, eq=False) -class ExplodeNode(UnaryNode): - column_ids: typing.Tuple[ex.DerefOp, ...] - # Offsets are generated only if this is non-null - offsets_col: Optional[identifiers.ColumnId] = None - - def _validate(self): - for col in self.column_ids: - assert col.id in self.child.ids - - @property - def row_preserving(self) -> bool: - return False - - @property - def fields(self) -> Sequence[Field]: - fields = ( - Field( - field.id, - bigframes.dtypes.arrow_dtype_to_bigframes_dtype( - self.child.get_type(field.id).pyarrow_dtype.value_type # type: ignore - ), - nullable=True, - ) - if field.id in set(map(lambda x: x.id, self.column_ids)) - else field - for field in self.child.fields - ) - if self.offsets_col is not None: - return tuple( - itertools.chain( - fields, - ( - Field( - self.offsets_col, bigframes.dtypes.INT_DTYPE, nullable=False - ), - ), - ) - ) - return tuple(fields) - - @property - def relation_ops_created(self) -> int: - return 3 - - @functools.cached_property - def variables_introduced(self) -> int: - return len(self.column_ids) + 1 - - @property - def row_count(self) -> Optional[int]: - return None - - @property - def node_defined_ids(self) -> Tuple[identifiers.ColumnId, ...]: - return (self.offsets_col,) if (self.offsets_col is not None) else () - - @property - def referenced_ids(self) -> COLUMN_SET: - return frozenset(ref.id for ref in self.column_ids) - - @property - def _node_expressions(self): - return self.column_ids - - def remap_vars( - self, mappings: Mapping[identifiers.ColumnId, identifiers.ColumnId] - ) -> ExplodeNode: - if (self.offsets_col is not None) and self.offsets_col in mappings: - return dataclasses.replace(self, offsets_col=mappings[self.offsets_col]) - return self - - def remap_refs( - self, mappings: Mapping[identifiers.ColumnId, identifiers.ColumnId] - ) -> ExplodeNode: - new_ids = tuple(id.remap_column_refs(mappings) for id in self.column_ids) - return dataclasses.replace(self, column_ids=new_ids) # type: ignore - - -# Introduced during planing/compilation -# TODO: Enforce more strictly that this should never be a child node -@dataclasses.dataclass(frozen=True, eq=False) -class ResultNode(UnaryNode): - output_cols: tuple[tuple[ex.DerefOp, str], ...] - order_by: Optional[RowOrdering] = None - limit: Optional[int] = None - # TODO: CTE definitions - - def _validate(self): - for ref, _ in self.output_cols: - assert ref.id in self.child.ids - - @property - def node_defined_ids(self) -> Tuple[identifiers.ColumnId, ...]: - return () - - def remap_vars( - self, mappings: Mapping[identifiers.ColumnId, identifiers.ColumnId] - ) -> ResultNode: - return self - - def remap_refs( - self, mappings: Mapping[identifiers.ColumnId, identifiers.ColumnId] - ) -> ResultNode: - output_cols = tuple( - (ref.remap_column_refs(mappings), name) for ref, name in self.output_cols - ) - order_by = self.order_by.remap_column_refs(mappings) if self.order_by else None - return dataclasses.replace(self, output_cols=output_cols, order_by=order_by) # type: ignore - - @property - def fields(self) -> Sequence[Field]: - # Fields property here is for output schema, not to be consumed by a parent node. - input_fields_by_id = {field.id: field for field in self.child.fields} - return tuple( - Field( - identifiers.ColumnId(output), - input_fields_by_id[ref.id].dtype, - input_fields_by_id[ref.id].nullable, - ) - for ref, output in self.output_cols - ) - - @property - def consumed_ids(self) -> COLUMN_SET: - out_refs = frozenset(ref.id for ref, _ in self.output_cols) - order_refs = self.order_by.referenced_columns if self.order_by else frozenset() - return out_refs | order_refs - - @property - def row_count(self) -> Optional[int]: - child_count = self.child.row_count - if child_count is None: - return None - if self.limit is None: - return child_count - return min(self.limit, child_count) - - @property - def variables_introduced(self) -> int: - return 0 - - @property - def _node_expressions(self): - return tuple(ref for ref, _ in self.output_cols) - - -@dataclasses.dataclass(frozen=True, eq=False) -class CteNode(UnaryNode): - """ - Semantically a no-op, used to indicate shared subtrees and act as optimization boundary. - """ - - @property - def fields(self) -> Sequence[Field]: - return self.child.fields - - @property - def variables_introduced(self) -> int: - return 0 - - @property - def row_count(self) -> Optional[int]: - return self.child.row_count - - @property - def node_defined_ids(self) -> Tuple[identifiers.ColumnId, ...]: - return () - - def remap_vars( - self, mappings: Mapping[identifiers.ColumnId, identifiers.ColumnId] - ) -> CteNode: - return self - - def remap_refs( - self, mappings: Mapping[identifiers.ColumnId, identifiers.ColumnId] - ) -> CteNode: - return self - - -# Tree operators -def top_down( - root: BigFrameNode, - transform: Callable[[BigFrameNode], BigFrameNode], -) -> BigFrameNode: - """ - Perform a top-down transformation of the BigFrameNode tree. - """ - return root.top_down(transform) - - -def bottom_up( - root: BigFrameNode, - transform: Callable[[BigFrameNode], BigFrameNode], -) -> BigFrameNode: - """ - Perform a bottom-up transformation of the BigFrameNode tree. - - The `transform` function is applied to each node *after* its children - have been transformed. This allows for transformations that depend - on the results of transforming subtrees. - - Returns the transformed root node. - """ - return root.bottom_up(transform) diff --git a/bigframes/core/ordered_sets.py b/bigframes/core/ordered_sets.py deleted file mode 100644 index b09c0ce8e0d..00000000000 --- a/bigframes/core/ordered_sets.py +++ /dev/null @@ -1,139 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -from __future__ import annotations - -from typing import ( - Any, - Dict, - Generic, - Hashable, - Iterable, - Iterator, - MutableSet, - Optional, - TypeVar, -) - -T = TypeVar("T", bound=Hashable) - - -class _ListNode(Generic[T]): - """A private class representing a node in the doubly linked list.""" - - __slots__ = ("value", "prev", "next") - - def __init__( - self, - value: Optional[T], - prev: Optional[_ListNode[T]] = None, - next_node: Optional[_ListNode[T]] = None, - ): - self.value = value - self.prev = prev - self.next = next_node - - -class InsertionOrderedSet(MutableSet[T]): - """ - An ordered set implementation that maintains the order in which elements were - first inserted. It provides O(1) average time complexity for addition, - membership testing, and deletion, similar to Python's built-in set. - """ - - def __init__(self, iterable: Optional[Iterable] = None): - # Dictionary mapping element value -> _ListNode instance for O(1) lookup - self._dict: Dict[T, _ListNode[T]] = {} - - # Sentinel nodes for the doubly linked list. They don't hold actual data. - # head.next is the first element, tail.prev is the last element. - self._head: _ListNode[T] = _ListNode(None) - self._tail: _ListNode[T] = _ListNode(None) - self._head.next = self._tail - self._tail.prev = self._head - - if iterable: - self.update(iterable) - - def __len__(self) -> int: - """Return the number of elements in the set.""" - return len(self._dict) - - def __contains__(self, item: Any) -> bool: - """Check if an item is a member of the set (O(1) average).""" - return item in self._dict - - def __iter__(self) -> Iterator[T]: - """Iterate over the elements in insertion order (O(N)).""" - current = self._head.next - while current is not self._tail: - yield current.value # type: ignore - current = current.next # type: ignore - - def _unlink_node(self, node: _ListNode[T]) -> None: - """Helper to remove a node from the linked list.""" - node.prev.next = node.next # type: ignore - node.next.prev = node.prev # type: ignore - # Clear references to aid garbage collection - node.prev = None - node.next = None - - def _append_node(self, node: _ListNode[T]) -> None: - """Helper to append a node to the end of the linked list.""" - last_node = self._tail.prev - last_node.next = node # type: ignore - node.prev = last_node - node.next = self._tail - self._tail.prev = node - - def add(self, value: T) -> None: - """Add an element to the set. If it exists, its order is unchanged (O(1) average).""" - if value not in self._dict: - new_node = _ListNode(value) - self._dict[value] = new_node - self._append_node(new_node) - - def discard(self, value: T) -> None: - """Remove an element from the set if it is a member (O(1) average).""" - if value in self._dict: - node = self._dict.pop(value) - self._unlink_node(node) - - def remove(self, value: T) -> None: - """Remove an element from the set; raises KeyError if not present (O(1) average).""" - if value not in self._dict: - raise KeyError(f"{value} not found in set") - self.discard(value) - - def update(self, *others: Iterable[T]) -> None: - """Update the set with the union of itself and all others.""" - for other in others: - for item in other: - self.add(item) - - def clear(self) -> None: - """Remove all elements from the set.""" - self._dict.clear() - self._head.next = self._tail - self._tail.prev = self._head - - def _replace_contents(self, source: InsertionOrderedSet) -> InsertionOrderedSet: - """Helper method for inplace operators to transfer content from a result set.""" - self.clear() - for item in source: - self.add(item) - return self - - def __repr__(self) -> str: - """Representation of the set.""" - return f"InsertionOrderedSet({list(self)})" diff --git a/bigframes/core/ordering.py b/bigframes/core/ordering.py index 7ad8b6f567a..2cecd2fe7b2 100644 --- a/bigframes/core/ordering.py +++ b/bigframes/core/ordering.py @@ -14,13 +14,21 @@ from __future__ import annotations -import typing from dataclasses import dataclass, field from enum import Enum -from typing import Callable, Mapping, Optional, Sequence, Set, Union +import math +import typing +from typing import Optional, Sequence + +import ibis.expr.datatypes as ibis_dtypes +import ibis.expr.types as ibis_types + +# TODO(tbergeron): Encode more efficiently +ORDERING_ID_STRING_BASE: int = 10 +# Sufficient to store any value up to 2^63 +DEFAULT_ORDERING_ID_LENGTH: int = math.ceil(63 * math.log(2, ORDERING_ID_STRING_BASE)) -import bigframes.core.expression as expression -import bigframes.core.identifiers as ids +STABLE_SORTS = ["mergesort", "stable"] class OrderingDirection(Enum): @@ -39,60 +47,33 @@ def is_ascending(self) -> bool: @dataclass(frozen=True) -class OrderingExpression: +class OrderingColumnReference: """References a column and how to order with respect to values in that column.""" - scalar_expression: expression.Expression + column_id: str direction: OrderingDirection = OrderingDirection.ASC na_last: bool = True - @property - def referenced_columns(self) -> Set[ids.ColumnId]: - return set(self.scalar_expression.column_references) - - @property - def deterministic(self) -> bool: - return self.scalar_expression.deterministic + def with_name(self, name: str): + return OrderingColumnReference(name, self.direction, self.na_last) - def remap_column_refs( - self, - mapping: Mapping[ids.ColumnId, ids.ColumnId], - allow_partial_bindings: bool = False, - ) -> OrderingExpression: - return self.bind_refs( - {old_id: expression.DerefOp(new_id) for old_id, new_id in mapping.items()}, - allow_partial_bindings=allow_partial_bindings, + def with_reverse(self): + return OrderingColumnReference( + self.column_id, self.direction.reverse(), not self.na_last ) - def bind_refs( - self, - mapping: Mapping[ids.ColumnId, expression.Expression], - allow_partial_bindings: bool = False, - ) -> OrderingExpression: - return OrderingExpression( - self.scalar_expression.bind_refs( - mapping, allow_partial_bindings=allow_partial_bindings - ), - self.direction, - self.na_last, - ) - def with_reverse(self) -> OrderingExpression: - return OrderingExpression( - self.scalar_expression, self.direction.reverse(), not self.na_last - ) +# Encoding classes specify additional properties for some ordering representations +@dataclass(frozen=True) +class StringEncoding: + """String encoded order ids are fixed length and can be concat together in joins.""" - def transform_exprs( - self, t: Callable[[expression.Expression], expression.Expression] - ) -> OrderingExpression: - return OrderingExpression( - t(self.scalar_expression), - self.direction, - self.na_last, - ) + is_encoded: bool = False + # Encoding size must be tracked in order to know what how to combine ordering ids across tables (eg how much to pad when combining different length). + # Also will be needed to determine when length is too large and need to compact ordering id with a ROW_NUMBER operation. + length: int = DEFAULT_ORDERING_ID_LENGTH -# Encoding classes specify additional properties for some ordering representations @dataclass(frozen=True) class IntegerEncoding: """Integer encoded order ids are guaranteed non-negative.""" @@ -102,154 +83,16 @@ class IntegerEncoding: @dataclass(frozen=True) -class RowOrdering: - """Immutable object that holds information about the ordering of rows in a ArrayValue object. May not be unambiguous.""" +class ExpressionOrdering: + """Immutable object that holds information about the ordering of rows in a ArrayValue object.""" - ordering_value_columns: typing.Tuple[OrderingExpression, ...] = () + ordering_value_columns: typing.Tuple[OrderingColumnReference, ...] = () integer_encoding: IntegerEncoding = IntegerEncoding(False) - - @property - def all_ordering_columns(self) -> Sequence[OrderingExpression]: - return list(self.ordering_value_columns) - - @property - def referenced_columns(self) -> Set[ids.ColumnId]: - return set( - col - for part in self.ordering_value_columns - for col in part.referenced_columns - ) - - @property - def is_sequential(self) -> bool: - return self.integer_encoding.is_encoded and self.integer_encoding.is_sequential - - @property - def is_total_ordering(self) -> bool: - return False - - @property - def total_order_col(self) -> Optional[OrderingExpression]: - """Returns column id of columns that defines total ordering, if such as column exists""" - return None - - def with_reverse(self) -> RowOrdering: - """Reverses the ordering.""" - return RowOrdering( - tuple([col.with_reverse() for col in self.ordering_value_columns]), - ) - - def remap_column_refs( - self, - mapping: typing.Mapping[ids.ColumnId, ids.ColumnId], - allow_partial_bindings: bool = False, - ) -> RowOrdering: - new_value_columns = [ - col.remap_column_refs( - mapping, allow_partial_bindings=allow_partial_bindings - ) - for col in self.all_ordering_columns - ] - return RowOrdering( - tuple(new_value_columns), - ) - - def with_non_sequential(self): - """Create a copy that is marked as non-sequential. - - This is useful when filtering, but not sorting, an expression. - """ - if self.integer_encoding.is_sequential: - return RowOrdering( - self.ordering_value_columns, - integer_encoding=IntegerEncoding( - self.integer_encoding.is_encoded, is_sequential=False - ), - ) - - return self - - def with_ordering_columns( - self, - ordering_value_columns: Sequence[OrderingExpression] = (), - ) -> RowOrdering: - """Creates a new ordering that reorders by the given columns. - - Args: - ordering_value_columns: - In decreasing precedence order, the values used to sort the ordering - - Returns: - Modified ExpressionOrdering - """ - - # Truncate to remove any unneded col references after all total order cols included - new_ordering = self._truncate_ordering( - (*ordering_value_columns, *self.ordering_value_columns) - ) - return RowOrdering( - new_ordering, - ) - - def join( - self, - other: RowOrdering, - ) -> RowOrdering: - joined_refs = [*self.all_ordering_columns, *other.all_ordering_columns] - return RowOrdering(tuple(joined_refs)) - - def _truncate_ordering( - self, order_refs: tuple[OrderingExpression, ...] - ) -> tuple[OrderingExpression, ...]: - # Truncate once we refer to a full key in bijective operations - columns_seen: Set[ids.ColumnId] = set() - truncated_refs = [] - for order_part in order_refs: - expr = order_part.scalar_expression - if not set(expr.column_references).issubset(columns_seen): - if expr.is_bijective: - columns_seen.update(expr.column_references) - truncated_refs.append(order_part) - return tuple(truncated_refs) - - -@dataclass(frozen=True) -class TotalOrdering(RowOrdering): - """Immutable object that holds information about the ordering of rows in a ArrayValue object. Guaranteed to be unambiguous.""" - - def __post_init__(self): - assert set(ref.id for ref in self.total_ordering_columns).issubset( - self.referenced_columns - ) - + string_encoding: StringEncoding = StringEncoding(False) # A table has a total ordering defined by the identities of a set of 1 or more columns. # These columns must always be part of the ordering, in order to guarantee that the ordering is total. # Therefore, any modifications(or drops) done to these columns must result in hidden copies being made. - total_ordering_columns: frozenset[expression.DerefOp] = field( - default_factory=frozenset - ) - - @classmethod - def from_offset_col(cls, col: Union[ids.ColumnId, str]) -> TotalOrdering: - col_id = ids.ColumnId(col) if isinstance(col, str) else col - return TotalOrdering( - (ascending_over(col),), - integer_encoding=IntegerEncoding(True, is_sequential=True), - total_ordering_columns=frozenset({expression.DerefOp(col_id)}), - ) - - @classmethod - def from_primary_key(cls, primary_key: Sequence[ids.ColumnId]) -> TotalOrdering: - return TotalOrdering( - tuple(ascending_over(col) for col in primary_key), - total_ordering_columns=frozenset( - {expression.DerefOp(col) for col in primary_key} - ), - ) - - @property - def is_total_ordering(self) -> bool: - return True + total_ordering_columns: frozenset[str] = field(default_factory=frozenset) def with_non_sequential(self): """Create a copy that is marked as non-sequential. @@ -257,7 +100,7 @@ def with_non_sequential(self): This is useful when filtering, but not sorting, an expression. """ if self.integer_encoding.is_sequential: - return TotalOrdering( + return ExpressionOrdering( self.ordering_value_columns, integer_encoding=IntegerEncoding( self.integer_encoding.is_encoded, is_sequential=False @@ -269,103 +112,72 @@ def with_non_sequential(self): def with_ordering_columns( self, - ordering_value_columns: Sequence[OrderingExpression] = (), - ) -> TotalOrdering: + ordering_value_columns: Sequence[OrderingColumnReference] = (), + stable: bool = False, + ) -> ExpressionOrdering: """Creates a new ordering that reorders by the given columns. Args: ordering_value_columns: In decreasing precedence order, the values used to sort the ordering + stable: + If True, will use apply a stable sorting, using the old ordering where + the new ordering produces ties. Otherwise, ties will be resolved in + a performance maximizing way, Returns: Modified ExpressionOrdering """ - - # Truncate to remove any unneded col references after all total order cols included - new_ordering = self._truncate_ordering( - (*ordering_value_columns, *self.ordering_value_columns) - ) - return TotalOrdering( + col_ids_new = [ + ordering_ref.column_id for ordering_ref in ordering_value_columns + ] + if stable: + # Only reference each column once, so discard old referenc if there is a new reference + old_ordering_keep = [ + ordering_ref + for ordering_ref in self.ordering_value_columns + if ordering_ref.column_id not in col_ids_new + ] + else: + # New ordering needs to keep all total ordering columns no matter what. + # All other old ordering references can be discarded as does not need + # to be a stable sort. + old_ordering_keep = [ + ordering_ref + for ordering_ref in self.ordering_value_columns + if (ordering_ref.column_id not in col_ids_new) + and (ordering_ref.column_id in self.total_ordering_columns) + ] + new_ordering = (*ordering_value_columns, *old_ordering_keep) + return ExpressionOrdering( new_ordering, total_ordering_columns=self.total_ordering_columns, ) - def _truncate_ordering( - self, order_refs: tuple[OrderingExpression, ...] - ) -> tuple[OrderingExpression, ...]: - # Truncate once we refer to a full key in bijective operations - must_see = set(ref.id for ref in self.total_ordering_columns) - columns_seen: Set[ids.ColumnId] = set() - truncated_refs = [] - for order_part in order_refs: - expr = order_part.scalar_expression - if not set(expr.column_references).issubset(columns_seen): - if expr.is_bijective: - columns_seen.update(expr.column_references) - truncated_refs.append(order_part) - if columns_seen.issuperset(must_see): - return tuple(truncated_refs) - if len(must_see) == 0: - return () - raise ValueError("Ordering did not contain all total_order_cols") - def with_reverse(self): """Reverses the ordering.""" - return TotalOrdering( + return ExpressionOrdering( tuple([col.with_reverse() for col in self.ordering_value_columns]), total_ordering_columns=self.total_ordering_columns, ) - def remap_column_refs( - self, - mapping: typing.Mapping[ids.ColumnId, ids.ColumnId], - allow_partial_bindings: bool = False, - ): + def with_column_remap(self, mapping: typing.Mapping[str, str]): new_value_columns = [ - col.remap_column_refs( - mapping, allow_partial_bindings=allow_partial_bindings - ) - for col in self.all_ordering_columns + col.with_name(mapping.get(col.column_id, col.column_id)) + for col in self.ordering_value_columns ] new_total_order = frozenset( - expression.DerefOp(mapping.get(col_id.id, col_id.id)) - for col_id in self.total_ordering_columns + mapping.get(col_id, col_id) for col_id in self.total_ordering_columns ) - return TotalOrdering( + return ExpressionOrdering( tuple(new_value_columns), integer_encoding=self.integer_encoding, + string_encoding=self.string_encoding, total_ordering_columns=new_total_order, ) - @typing.overload - def join( - self, - other: TotalOrdering, - ) -> TotalOrdering: ... - - @typing.overload - def join( - self, - other: RowOrdering, - ) -> RowOrdering: ... - - def join( - self, - other: RowOrdering, - ) -> RowOrdering: - joined_refs = [*self.all_ordering_columns, *other.all_ordering_columns] - if isinstance(other, TotalOrdering): - left_total_order_cols = frozenset(self.total_ordering_columns) - right_total_order_cols = frozenset(other.total_ordering_columns) - return TotalOrdering( - ordering_value_columns=tuple(joined_refs), - total_ordering_columns=left_total_order_cols | right_total_order_cols, - ) - else: - return RowOrdering(tuple(joined_refs)) - @property - def total_order_col(self) -> Optional[OrderingExpression]: + def total_order_col(self) -> Optional[OrderingColumnReference]: """Returns column id of columns that defines total ordering, if such as column exists""" if len(self.ordering_value_columns) != 1: return None @@ -374,19 +186,39 @@ def total_order_col(self) -> Optional[OrderingExpression]: return None return order_ref + @property + def is_string_encoded(self) -> bool: + """True if ordering is fully defined by a fixed length string column.""" + return self.string_encoding.is_encoded -# Convenience functions -def ascending_over( - id: Union[ids.ColumnId, str], nulls_last: bool = True -) -> OrderingExpression: - col_id = ids.ColumnId(id) if isinstance(id, str) else id - return OrderingExpression(expression.DerefOp(col_id), na_last=nulls_last) + @property + def is_sequential(self) -> bool: + return self.integer_encoding.is_encoded and self.integer_encoding.is_sequential + + @property + def all_ordering_columns(self) -> Sequence[OrderingColumnReference]: + return list(self.ordering_value_columns) -def descending_over( - id: Union[ids.ColumnId, str], nulls_last: bool = True -) -> OrderingExpression: - col_id = ids.ColumnId(id) if isinstance(id, str) else id - return OrderingExpression( - expression.DerefOp(col_id), direction=OrderingDirection.DESC, na_last=nulls_last +def encode_order_string( + order_id: ibis_types.IntegerColumn, length: int = DEFAULT_ORDERING_ID_LENGTH +) -> ibis_types.StringColumn: + """Converts an order id value to string if it is not already a string. MUST produced fixed-length strings.""" + # This is very inefficient encoding base-10 string uses only 10 characters per byte(out of 256 bit combinations) + # Furthermore, if know tighter bounds on order id are known, can produce smaller strings. + # 19 characters chosen as it can represent any positive Int64 in base-10 + # For missing values, ":" * 19 is used as it is larger than any other value this function produces, so null values will be last. + string_order_id = typing.cast( + ibis_types.StringValue, + order_id.cast(ibis_dtypes.string), + ).lpad(length, "0") + return typing.cast(ibis_types.StringColumn, string_order_id) + + +def reencode_order_string( + order_id: ibis_types.StringColumn, length: int +) -> ibis_types.StringColumn: + return typing.cast( + ibis_types.StringColumn, + (typing.cast(ibis_types.StringValue, order_id).lpad(length, "0")), ) diff --git a/bigframes/core/pruning.py b/bigframes/core/pruning.py deleted file mode 100644 index f98b8eb5d58..00000000000 --- a/bigframes/core/pruning.py +++ /dev/null @@ -1,86 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from typing import TYPE_CHECKING, Set - -import bigframes.core.expression as ex -import bigframes.core.identifiers as ids -import bigframes.core.nodes -import bigframes.dtypes -import bigframes.operations as ops - -if TYPE_CHECKING: - import bigframes.core.nodes - - -LOW_CARDINALITY_TYPES = [bigframes.dtypes.BOOL_DTYPE] - -COMPARISON_OP_TYPES = tuple( - type(i) - for i in ( - ops.eq_op, - ops.eq_null_match_op, - ops.ne_op, - ops.gt_op, - ops.ge_op, - ops.lt_op, - ops.le_op, - ) -) - - -def cluster_cols_for_predicate( - predicate: ex.Expression, clusterable_cols: Set[ids.ColumnId] -) -> list[ids.ColumnId]: - """Try to determine cluster col candidates that work with given predicates.""" - # TODO: Prioritize based on predicted selectivity (eg. equality conditions are probably very selective) - if isinstance(predicate, ex.DerefOp): - cols = [predicate.id] - elif isinstance(predicate, ex.OpExpression): - op = predicate.op - # TODO: Support geo predicates, which support pruning if clustered (other than st_disjoint) - # https://cloud.google.com/bigquery/docs/reference/standard-sql/geography_functions - if isinstance(op, COMPARISON_OP_TYPES): - cols = cluster_cols_for_comparison(predicate.inputs[0], predicate.inputs[1]) - elif isinstance(op, (type(ops.invert_op))): - cols = cluster_cols_for_predicate(predicate.inputs[0], clusterable_cols) - elif isinstance(op, (type(ops.and_op), type(ops.or_op))): - left_cols = cluster_cols_for_predicate( - predicate.inputs[0], clusterable_cols - ) - right_cols = cluster_cols_for_predicate( - predicate.inputs[1], clusterable_cols - ) - cols = [*left_cols, *[col for col in right_cols if col not in left_cols]] - else: - cols = [] - else: - # Constant - cols = [] - return [col for col in cols if col in clusterable_cols] - - -def cluster_cols_for_comparison( - left_ex: ex.Expression, right_ex: ex.Expression -) -> list[ids.ColumnId]: - # TODO: Try to normalize expressions such that one side is a single variable. - # eg. Convert -cola>=3 to cola<-3 and colb+3 < 4 to colb < 1 - if left_ex.is_const: - # There are some invertible ops that would also be ok - if isinstance(right_ex, ex.DerefOp): - return [right_ex.id] - elif right_ex.is_const: - if isinstance(left_ex, ex.DerefOp): - return [left_ex.id] - return [] diff --git a/bigframes/core/py_expressions.py b/bigframes/core/py_expressions.py deleted file mode 100644 index da937677050..00000000000 --- a/bigframes/core/py_expressions.py +++ /dev/null @@ -1,595 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from __future__ import annotations - -import dataclasses -import itertools -import operator -from types import ModuleType -from typing import Callable, Hashable, Mapping, Optional, Tuple - -import bigframes.core.agg_expressions as agg_exprs -import bigframes.operations.aggregations as agg_ops -import bigframes.operations.python_op_maps as python_op_maps -from bigframes import dtypes -from bigframes.core import identifiers -from bigframes.core import window_spec as window_specs -from bigframes.core.expression import ( - Expression, - OpExpression, - ScalarConstantExpression, - UnboundVariableExpression, - const, - deref, -) -from bigframes.operations import ( - NUMPY_TO_BINOP, - NUMPY_TO_OP, - ScalarOp, - generic_ops, - numeric_ops, -) - -_CALLABLE_TO_OP = { - **NUMPY_TO_OP, - **NUMPY_TO_BINOP, -} - -_BUILTIN_CALLABLES = { - str: generic_ops.AsTypeOp(dtypes.STRING_DTYPE), - abs: numeric_ops.abs_op, -} - - -@dataclasses.dataclass(frozen=True) -class GetAttr(Expression): - input: Expression - attr: str - - @property - def column_references( - self, - ) -> Tuple[identifiers.ColumnId, ...]: - return self.input.column_references - - @property - def free_variables(self) -> tuple[Hashable, ...]: - return self.input.free_variables - - @property - def is_const(self) -> bool: - return False - - @property - def children(self): - return (self.input,) - - @property - def nullable(self) -> bool: - return True - - @property - def is_resolved(self) -> bool: - return False - - @property - def output_type(self) -> dtypes.ExpressionType: - raise ValueError(f"Type of expression {self} has not been fixed.") - - @property - def is_bijective(self) -> bool: - # TODO: Mark individual functions as bijective? - return False - - @property - def deterministic(self) -> bool: - return True - - def transform_children(self, t: Callable[[Expression], Expression]) -> Expression: - new_input = t(self.input) - if new_input != self.input: - return dataclasses.replace(self, input=new_input) - return self - - def bind_variables( - self, - bindings: Mapping[Hashable, Expression], - allow_partial_bindings: bool = False, - ) -> GetAttr: - return GetAttr( - self.input.bind_variables( - bindings, allow_partial_bindings=allow_partial_bindings - ), - self.attr, - ) - - def bind_refs( - self, - bindings: Mapping[identifiers.ColumnId, Expression], - allow_partial_bindings: bool = False, - ) -> GetAttr: - return GetAttr( - self.input.bind_refs( - bindings, allow_partial_bindings=allow_partial_bindings - ), - self.attr, - ) - - -@dataclasses.dataclass(frozen=True) -class GetItem(Expression): - input: Expression - key: Expression - - @property - def column_references(self) -> Tuple[identifiers.ColumnId, ...]: - return self.input.column_references + self.key.column_references - - @property - def free_variables(self) -> tuple[Hashable, ...]: - return self.input.free_variables + self.key.free_variables - - @property - def is_const(self) -> bool: - return False - - @property - def children(self): - return (self.input, self.key) - - @property - def nullable(self) -> bool: - return True - - @property - def is_resolved(self) -> bool: - return False - - @property - def output_type(self) -> dtypes.ExpressionType: - raise ValueError(f"Type of expression {self} has not been fixed.") - - @property - def is_bijective(self) -> bool: - return False - - @property - def deterministic(self) -> bool: - return True - - def transform_children(self, t: Callable[[Expression], Expression]) -> Expression: - new_input = t(self.input) - new_key = t(self.key) - if new_input != self.input or new_key != self.key: - return dataclasses.replace(self, input=new_input, key=new_key) - return self - - def bind_variables( - self, - bindings: Mapping[Hashable, Expression], - allow_partial_bindings: bool = False, - ) -> GetItem: - return GetItem( - self.input.bind_variables( - bindings, allow_partial_bindings=allow_partial_bindings - ), - self.key.bind_variables( - bindings, allow_partial_bindings=allow_partial_bindings - ), - ) - - def bind_refs( - self, - bindings: Mapping[identifiers.ColumnId, Expression], - allow_partial_bindings: bool = False, - ) -> GetItem: - return GetItem( - self.input.bind_refs( - bindings, allow_partial_bindings=allow_partial_bindings - ), - self.key.bind_refs(bindings, allow_partial_bindings=allow_partial_bindings), - ) - - -@dataclasses.dataclass(frozen=True) -class Module(Expression): - """An expression representing a module reference.""" - - module: ModuleType - - @property - def is_const(self) -> bool: - return True - - @property - def column_references(self) -> Tuple[identifiers.ColumnId, ...]: - return () - - @property - def nullable(self) -> bool: - return True # type: ignore - - @property - def is_resolved(self) -> bool: - return False - - @property - def output_type(self) -> dtypes.ExpressionType: - raise ValueError("Module expression does not have a type.") - - def bind_variables( - self, - bindings: Mapping[Hashable, Expression], - allow_partial_bindings: bool = False, - ) -> Expression: - return self - - def bind_refs( - self, - bindings: Mapping[identifiers.ColumnId, Expression], - allow_partial_bindings: bool = False, - ) -> Module: - return self - - @property - def is_bijective(self) -> bool: - # () <-> value - return True - - def transform_children(self, t: Callable[[Expression], Expression]) -> Expression: - return self - - -@dataclasses.dataclass(frozen=True) -class PyObject(Expression): - """An expression representing a module reference.""" - - value: Hashable - - @property - def is_const(self) -> bool: - return True - - @property - def column_references(self) -> Tuple[identifiers.ColumnId, ...]: - return () - - @property - def nullable(self) -> bool: - return True # type: ignore - - @property - def is_resolved(self) -> bool: - return False - - @property - def output_type(self) -> dtypes.ExpressionType: - raise ValueError("PyObject expression does not have a type.") - - def bind_variables( - self, - bindings: Mapping[Hashable, Expression], - allow_partial_bindings: bool = False, - ) -> Expression: - return self - - def bind_refs( - self, - bindings: Mapping[identifiers.ColumnId, Expression], - allow_partial_bindings: bool = False, - ) -> PyObject: - return self - - @property - def is_bijective(self) -> bool: - # () <-> value - return True - - def transform_children(self, t: Callable[[Expression], Expression]) -> Expression: - return self - - -@dataclasses.dataclass(frozen=True) -class Call(Expression): - """An expression representing a scalar constant.""" - - # TODO: Further constrain? - callable: Expression - inputs: Tuple[Expression, ...] - - @property - def column_references( - self, - ) -> Tuple[identifiers.ColumnId, ...]: - return tuple( - itertools.chain.from_iterable( - map(lambda x: x.column_references, self.children) - ) - ) - - @property - def free_variables(self) -> tuple[Hashable, ...]: - return tuple( - itertools.chain.from_iterable( - map(lambda x: x.free_variables, self.children) - ) - ) - - @property - def is_const(self) -> bool: - return False - - @property - def children(self): - return (self.callable, *self.inputs) - - @property - def nullable(self) -> bool: - return True - - @property - def is_resolved(self) -> bool: - return False - - @property - def output_type(self) -> dtypes.ExpressionType: - raise ValueError(f"Type of expression {self} has not been fixed.") - - @property - def is_bijective(self) -> bool: - # TODO: Mark individual functions as bijective? - return False - - @property - def deterministic(self) -> bool: - return True - - def transform_children(self, t: Callable[[Expression], Expression]) -> Expression: - return dataclasses.replace( - self, - callable=t(self.callable), - inputs=tuple(t(input) for input in self.inputs), - ) - - def bind_variables( - self, - bindings: Mapping[Hashable, Expression], - allow_partial_bindings: bool = False, - ) -> Call: - return Call( - callable=self.callable.bind_variables( - bindings, allow_partial_bindings=allow_partial_bindings - ), - inputs=tuple( - input.bind_variables( - bindings, allow_partial_bindings=allow_partial_bindings - ) - for input in self.inputs - ), - ) - - def bind_refs( - self, - bindings: Mapping[identifiers.ColumnId, Expression], - allow_partial_bindings: bool = False, - ) -> Call: - return Call( - callable=self.callable.bind_refs( - bindings, allow_partial_bindings=allow_partial_bindings - ), - inputs=tuple( - input.bind_refs(bindings, allow_partial_bindings=allow_partial_bindings) - for input in self.inputs - ), - ) - - -# TODO: Mode that resolves free variable attrs as columns -def resolve_py_exprs( - expression: Expression, - series_arg: Optional[str] = None, - series_attrs: Mapping[Hashable, str] | None = None, - col_series_args: Mapping[str, str] | None = None, - window_spec: window_specs.WindowSpec | None = None, -) -> Expression: - """ - Replace all PyObject, attribute, item, and call expressions bottom-up. - - This function translates unresolved python expressions (like GetAttr, GetItem, - Call, PyObject) into resolved BigQuery expressions (like OpExpression, DerefOp, - Aggregation, ScalarConstantExpression) by binding them to the specified context. - - Args: - expression: The unresolved python expression to translate. - series_arg: The name of the parameter representing the row (for row-wise UDFs like - apply axis=1) or the DataFrame group (for DataFrameGroupBy.apply). - series_attrs: A mapping of attribute/item names to column IDs for the series_arg. - When GetAttr(series_arg, attr) or GetItem(series_arg, key) is encountered, - it is resolved to deref(column_id). - col_series_args: A mapping of parameter names to column IDs for parameters that - represent a single Series/column directly (for SeriesGroupBy.apply). When - UnboundVariableExpression(arg_name) is encountered and arg_name is in - col_series_args, it is resolved directly to deref(column_id). - window_spec: Optional window spec. When provided, aggregations inside calls will - be converted to WindowExpression using this spec. - - Returns: - The resolved BigQuery Expression. - """ - - def resolve_expr_if_call(expr: Expression) -> Expression: - if isinstance(expr, Call): - return resolve_call(expr, window_spec=window_spec) - return expr - - def resolve_attrs(expr: Expression) -> Expression: - if isinstance(expr, GetAttr): - return _resolve_getattr(expr, series_arg, series_attrs) - if isinstance(expr, GetItem): - return _resolve_getitem(expr, series_arg, series_attrs, col_series_args) - return expr - - def resolve_series_var(expr: Expression) -> Expression: - if ( - col_series_args is not None - and isinstance(expr, UnboundVariableExpression) - and isinstance(expr.id, str) - and expr.id in col_series_args - ): - return deref(col_series_args[expr.id]) - return expr - - def resolve_pyobjs(expr: Expression) -> Expression: - if isinstance(expr, PyObject): - return const(expr.value) - return expr - - wo_calls = expression.bottom_up(resolve_expr_if_call) - wo_attrs = wo_calls.bottom_up(resolve_attrs) - wo_vars = wo_attrs.bottom_up(resolve_series_var) - return wo_vars.bottom_up(resolve_pyobjs) - - -def _resolve_getattr( - expression: GetAttr, - series_arg: Optional[str], - series_attrs: Mapping[Hashable, str] | None, -) -> Expression: - if isinstance(expression.input, Module): - # resolves things like Math.pi - return PyObject(getattr(expression.input.module, expression.attr)) - # Resolve attribute access on the series/row argument - if ( - series_arg is not None - and series_attrs is not None - and isinstance(expression.input, UnboundVariableExpression) - and expression.input.id == series_arg - and expression.attr in series_attrs - ): - return deref(series_attrs[expression.attr]) - return expression - - -def _resolve_getitem( - expression: GetItem, - series_arg: Optional[str], - series_attrs: Mapping[Hashable, str] | None, - col_series_args: Mapping[str, str] | None, -) -> Expression: - # Resolve subscript/item access on the series/row argument - key_val = None - if isinstance(expression.key, PyObject): - key_val = expression.key.value - elif isinstance(expression.key, ScalarConstantExpression): - key_val = expression.key.value - - is_series_var = ( - series_arg is not None - and isinstance(expression.input, UnboundVariableExpression) - and expression.input.id == series_arg - ) - - if is_series_var and series_attrs is not None: - if key_val is None: - raise NotImplementedError("Dynamic column lookup is not supported.") - if key_val in series_attrs: - return deref(series_attrs[key_val]) - else: - raise KeyError(f"Column '{key_val}' not found.") - - is_columnar_var = ( - col_series_args is not None - and isinstance(expression.input, UnboundVariableExpression) - and expression.input.id in col_series_args - ) - - if is_columnar_var: - raise NotImplementedError( - "Subscripting a Series/column is not supported in this UDF context." - ) - - if key_val is not None: - if isinstance(key_val, (str, int)): - return OpExpression(generic_ops.GetItemOp(key_val), (expression.input,)) - else: - raise NotImplementedError( - f"Subscript key of type '{type(key_val).__name__}' is not supported." - ) - else: - return OpExpression( - generic_ops.DynamicGetItemOp(), (expression.input, expression.key) - ) - - -def resolve_call( - call: Call, window_spec: window_specs.WindowSpec | None = None -) -> Expression: - callable = call.callable - if isinstance(callable, GetAttr): - attr = callable.attr - if isinstance(callable.input, Module): - fn = getattr(callable.input.module, attr) - if fn in python_op_maps.PYTHON_TO_BIGFRAMES: - op = python_op_maps.PYTHON_TO_BIGFRAMES[fn] - return OpExpression(op, call.inputs) - if fn in _CALLABLE_TO_OP: - op = _CALLABLE_TO_OP[fn] - return OpExpression(op, call.inputs) - elif isinstance(callable.input, PyObject) and isinstance( - callable.input.value, type - ): - fn = getattr(callable.input.value, attr, None) - if fn in python_op_maps.PYTHON_TO_BIGFRAMES: - op = python_op_maps.PYTHON_TO_BIGFRAMES[fn] - return OpExpression(op, call.inputs) - else: - # Method call on an expression (e.g. df.col.sum() or s.mean()) - try: - agg_op, _ = agg_ops.lookup_agg_func(attr) - - if isinstance(agg_op, agg_ops.UnaryAggregateOp): - agg_expr: agg_exprs.Aggregation = agg_exprs.UnaryAggregation( - agg_op, callable.input - ) - if window_spec is not None: - return agg_exprs.WindowExpression(agg_expr, window_spec) - return agg_expr - elif isinstance(agg_op, agg_ops.NullaryAggregateOp): - agg_expr = agg_exprs.NullaryAggregation(agg_op) - if window_spec is not None: - return agg_exprs.WindowExpression(agg_expr, window_spec) - return agg_expr - except ValueError: - pass - - # Support common scalar method calls on Series/expressions - if (method_op := python_op_maps.SERIES_METHOD_TO_OP.get(attr)) is not None: - if isinstance(method_op, ScalarOp): - return OpExpression(method_op, (callable.input,)) - - elif isinstance(callable, PyObject): - if callable.value == operator.getitem: - return GetItem(call.inputs[0], call.inputs[1]) - if isinstance(callable.value, ScalarOp): - return OpExpression(callable.value, call.inputs) - if callable.value in python_op_maps.PYTHON_TO_BIGFRAMES: - op = python_op_maps.PYTHON_TO_BIGFRAMES[callable.value] # type: ignore - return OpExpression(op, call.inputs) - if callable.value in _BUILTIN_CALLABLES: - return OpExpression(_BUILTIN_CALLABLES[callable.value], call.inputs) - - raise NotImplementedError( - f"No implementation available for call expression: {call}" - ) diff --git a/bigframes/core/pyarrow_utils.py b/bigframes/core/pyarrow_utils.py deleted file mode 100644 index bdbb220b953..00000000000 --- a/bigframes/core/pyarrow_utils.py +++ /dev/null @@ -1,119 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -from typing import Iterable, Iterator - -import pyarrow as pa - - -class BatchBuffer: - """ - FIFO buffer of pyarrow Record batches - - Not thread-safe. - """ - - def __init__(self): - self._buffer: list[pa.RecordBatch] = [] - self._buffer_size: int = 0 - - def __len__(self): - return self._buffer_size - - def append_batch(self, batch: pa.RecordBatch) -> None: - self._buffer.append(batch) - self._buffer_size += batch.num_rows - - def take_as_batches(self, n: int) -> tuple[pa.RecordBatch, ...]: - if n > len(self): - raise ValueError(f"Cannot take {n} rows, only {len(self)} rows in buffer.") - rows_taken = 0 - sub_batches: list[pa.RecordBatch] = [] - while rows_taken < n: - batch = self._buffer.pop(0) - if batch.num_rows > (n - rows_taken): - sub_batches.append(batch.slice(length=n - rows_taken)) - self._buffer.insert(0, batch.slice(offset=n - rows_taken)) - rows_taken += n - rows_taken - else: - sub_batches.append(batch) - rows_taken += batch.num_rows - - self._buffer_size -= n - return tuple(sub_batches) - - def take_rechunked(self, n: int) -> pa.RecordBatch: - return ( - pa.Table.from_batches(self.take_as_batches(n)) - .combine_chunks() - .to_batches()[0] - ) - - -def chunk_by_row_count( - batches: Iterable[pa.RecordBatch], page_size: int -) -> Iterator[tuple[pa.RecordBatch, ...]]: - buffer = BatchBuffer() - for batch in batches: - buffer.append_batch(batch) - while len(buffer) >= page_size: - yield buffer.take_as_batches(page_size) - - # emit final page, maybe smaller - if len(buffer) > 0: - yield buffer.take_as_batches(len(buffer)) - - -def cast_batch(batch: pa.RecordBatch, schema: pa.Schema) -> pa.RecordBatch: - if batch.schema == schema: - return batch - # TODO: Use RecordBatch.cast once min pyarrow>=16.0 - return pa.record_batch( - [arr.cast(type) for arr, type in zip(batch.columns, schema.types)], - schema=schema, - ) - - -def rename_batch(batch: pa.RecordBatch, names: list[str]) -> pa.RecordBatch: - if batch.schema.names == names: - return batch - # TODO: Use RecordBatch.rename_columns once min pyarrow>=16.0 - return pa.RecordBatch.from_arrays(batch.columns, names) - - -def truncate_pyarrow_iterable( - batches: Iterable[pa.RecordBatch], max_results: int -) -> Iterator[pa.RecordBatch]: - total_yielded = 0 - for batch in batches: - if batch.num_rows >= (max_results - total_yielded): - yield batch.slice(length=max_results - total_yielded) - return - else: - yield batch - total_yielded += batch.num_rows - - -def append_offsets( - pa_table: pa.Table, - offsets_col: str, -) -> pa.Table: - return pa_table.append_column( - offsets_col, pa.array(range(pa_table.num_rows), type=pa.int64()) - ) - - -def as_nullable(pa_table: pa.Table): - """Normalizes schema to nullable for value-wise comparisons.""" - nullable_schema = pa.schema(field.with_nullable(True) for field in pa_table.schema) - return pa_table.cast(nullable_schema) diff --git a/bigframes/core/pyformat.py b/bigframes/core/pyformat.py deleted file mode 100644 index dfd91ba1ad0..00000000000 --- a/bigframes/core/pyformat.py +++ /dev/null @@ -1,376 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -"""Helpers for the pyformat feature.""" - -# TODO(tswast): consolidate with pandas-gbq and bigquery-magics. See: -# https://github.com/googleapis/python-bigquery-magics/blob/main/bigquery_magics/pyformat.py - -from __future__ import annotations - -import string -import typing -from typing import Any, Optional, Tuple, Union - -import google.cloud.bigquery -import pandas - -import bigframes.core.local_data -import bigframes.session -from bigframes.core import utils -from bigframes.core.tools import bigquery_schema - -_BQ_TABLE_TYPES = Union[ - google.cloud.bigquery.Table, - google.cloud.bigquery.TableReference, - google.cloud.bigquery.table.TableListItem, -] - - -def _table_to_sql(table: _BQ_TABLE_TYPES) -> str: - # BiglakeIcebergTable IDs have 4 parts. BigFrames packs catalog.namespace - # into the dataset_id. - dataset_parts = table.dataset_id.split(".") - dataset_sql = ".".join(f"`{part}`" for part in dataset_parts) - return f"`{table.project}`.{dataset_sql}.`{table.table_id}`" - - -def _pandas_df_to_sql_dry_run(pd_df: pandas.DataFrame) -> str: - # Ensure there are no duplicate column labels. - # - # Please make sure this stays in sync with the logic used to_gbq(). See - # bigframes.dataframe.DataFrame._prepare_export(). - new_col_labels, new_idx_labels = utils.get_standardized_ids( - pd_df.columns, pd_df.index.names - ) - pd_copy = pd_df.copy() - pd_copy.columns = pandas.Index(new_col_labels) - pd_copy.index.names = new_idx_labels - - managed_table = bigframes.core.local_data.ManagedArrowTable.from_pandas(pd_copy) - bqschema = managed_table.schema.to_bigquery() - return bigquery_schema.to_sql_dry_run(bqschema) - - -def _pandas_df_to_sql( - df_pd: pandas.DataFrame, - *, - name: str, - session: Optional[bigframes.session.Session] = None, - dry_run: bool = False, -) -> str: - if session is None: - if not dry_run: - message = ( - f"Can't embed pandas DataFrame {name} in a SQL " - "string without a bigframes session except if for a dry run." - ) - raise ValueError(message) - - return _pandas_df_to_sql_dry_run(df_pd) - - # Use the _deferred engine to avoid loading data too often during dry run. - df = session.read_pandas(df_pd, write_engine="_deferred") - return _table_to_sql(df._to_placeholder_table(dry_run=dry_run)) - - -def _field_to_template_value( - name: str, - value: Any, - *, - session: Optional[bigframes.session.Session] = None, - dry_run: bool = False, -) -> str: - """Convert value to something embeddable in a SQL string.""" - import bigframes.core.compile.sqlglot.sql as sql # Avoid circular imports - import bigframes.dataframe # Avoid circular imports - - _validate_type(name, value) - - table_types = typing.get_args(_BQ_TABLE_TYPES) - if isinstance(value, table_types): - return _table_to_sql(value) - - if isinstance(value, pandas.DataFrame): - return _pandas_df_to_sql(value, session=session, dry_run=dry_run, name=name) - - if isinstance(value, bigframes.dataframe.DataFrame): - import bigframes.core.bq_data as bq_data - import bigframes.core.nodes as nodes - - # TODO(b/493608478): Remove this workaround for BigLake/Iceberg tables, - # which cannot currently be used in views, once a fix rolls out. - def is_biglake( - node: nodes.BigFrameNode, child_results: Tuple[bool, ...] - ) -> bool: - if isinstance(node, nodes.ReadTableNode): - return isinstance(node.source.table, bq_data.BiglakeIcebergTable) - return any(child_results) - - contains_biglake = value._block.expr.node.reduce_up(is_biglake) - - if contains_biglake: - sql_query, _, _ = value._to_sql_query(include_index=True) - return f"({sql_query})" - - return _table_to_sql(value._to_placeholder_table(dry_run=dry_run)) - - if isinstance(value, str): - return value - - return sql.to_sql(sql.literal(value)) - - -def _validate_type(name: str, value: Any): - """Raises TypeError if value is unsupported.""" - import bigframes.dataframe # Avoid circular imports - import bigframes.dtypes # Avoid circular imports - - if value is None: - return # None can't be used in isinstance, but is a valid literal. - - supported_types = ( - typing.get_args(_BQ_TABLE_TYPES) - + bigframes.dtypes.SUPPORTED_LITERAL_TYPES - + (bigframes.dataframe.DataFrame,) - + (pandas.DataFrame,) - ) - - if not isinstance(value, supported_types): - raise TypeError( - f"{name} has unsupported type: {type(value)}. " - f"Only {supported_types} are supported." - ) - - -def _parse_fields(sql_template: str) -> list[str]: - return [ - field_name - for _, field_name, _, _ in string.Formatter().parse(sql_template) - if field_name is not None - ] - - -def _is_escaped_open_brace(sql_template: str, idx: int, literal_char: str) -> bool: - """Checks if the character at idx in sql_template is an escaped open brace '{{'.""" - return sql_template[idx : idx + 2] == "{{" and literal_char == "{" - - -def _is_escaped_close_brace(sql_template: str, idx: int, literal_char: str) -> bool: - """Checks if the character at idx in sql_template is an escaped close brace '}}'.""" - return sql_template[idx : idx + 2] == "}}" and literal_char == "}" - - -def _consume_literal(sql_template: str, current_idx: int, literal_text: str) -> int: - """Advances current_idx past literal_text in sql_template, accounting for escaped braces. - - A **literal** (or literal text) is the static part of the template string that - does not contain formatting placeholders. The string.Formatter parser resolves - escaped braces ('{{' and '}}') into single braces ('{' and '}') in its output - literal_text. - - This function aligns the resolved literal_text back to the original - sql_template by consuming 2 characters from sql_template ('{{' or '}}') for - every single escaped brace character in literal_text, and 1 character for - everything else. - - Returns: - int: the advanced current_idx in sql_template. - """ - lit_idx = 0 - while lit_idx < len(literal_text): - if _is_escaped_open_brace(sql_template, current_idx, literal_text[lit_idx]): - current_idx += 2 - lit_idx += 1 - elif _is_escaped_close_brace(sql_template, current_idx, literal_text[lit_idx]): - current_idx += 2 - lit_idx += 1 - elif ( - current_idx < len(sql_template) - and sql_template[current_idx] == literal_text[lit_idx] - ): - current_idx += 1 - lit_idx += 1 - else: - raise RuntimeError( - "Internal error: failed to align parsed SQL template with original query. " - f"Expected {literal_text[lit_idx]!r} at position {current_idx} in template, " - f"but found {sql_template[current_idx : current_idx + 2]!r}." - ) - return current_idx - - -def _is_escaped_brace(sql_template: str, idx: int) -> bool: - """Checks if the template has an escaped brace ('{{' or '}}') at the given index.""" - return sql_template[idx : idx + 2] in ("{{", "}}") - - -def _advance_past_field(sql_template: str, current_idx: int) -> int: - """Advances current_idx past the format field starting at current_idx. - - A **field** (or replacement field) is a placeholder in the template enclosed - in braces (e.g., "{my_var}" or "{json_col: { "val": 1 } }"). - - This function assumes current_idx points to the opening '{' of a field. - It parses forward, tracking nested braces to find the matching closing '}' - that terminates the field, while ignoring escaped braces ('{{' and '}}') - which do not affect the nesting level. - - Returns: - int: the index immediately after the closing '}' of the field. - """ - assert sql_template[current_idx] == "{" - brace_count = 1 - current_idx += 1 # past '{' - - while brace_count > 0 and current_idx < len(sql_template): - if _is_escaped_brace(sql_template, current_idx): - current_idx += 2 - elif sql_template[current_idx] == "{": - brace_count += 1 - current_idx += 1 - elif sql_template[current_idx] == "}": - brace_count -= 1 - current_idx += 1 - else: - current_idx += 1 - - return current_idx - - -def _find_all_field_positions(sql_template: str) -> dict[tuple[str, int], int]: - """Finds the character positions of all fields in the sql_template. - - Returns: - dict: a dict mapping (field_name, occurrence_idx) to character index. - """ - formatter = string.Formatter() - current_idx = 0 - seen_counts: dict[str, int] = {} - positions: dict[tuple[str, int], int] = {} - - for literal_text, field_name, _, _ in formatter.parse(sql_template): - current_idx = _consume_literal(sql_template, current_idx, literal_text) - - if field_name is not None: - occurrence_idx = seen_counts.get(field_name, 0) - seen_counts[field_name] = occurrence_idx + 1 - - positions[(field_name, occurrence_idx)] = current_idx - - current_idx = _advance_past_field(sql_template, current_idx) - - return positions - - -def get_error_context_at_pos(sql_template: str, pos: int) -> str: - """Create a helpful 'pointer' to where the problematic position is - in the original SQL. - - This should make the error message a lot friendlier, by providing more - context towards the problematic syntax. - """ - if pos == -1: - return "" - - lines = sql_template.splitlines(keepends=True) - - char_count = 0 - target_line_idx = -1 - for i, line in enumerate(lines): - if char_count <= pos < char_count + len(line): - target_line_idx = i - break - char_count += len(line) - - if target_line_idx == -1: - return "" - - col_offset = pos - char_count - - context_lines = [] - start_line = max(0, target_line_idx - 2) - end_line = min(len(lines), target_line_idx + 3) - - for i in range(start_line, end_line): - line_num = i + 1 - line_content = lines[i].rstrip("\r\n") - if i == target_line_idx: - context_lines.append(f"{line_num:4d}: {line_content}") - indent = 6 + col_offset - context_lines.append(" " * indent + "^") - else: - context_lines.append(f"{line_num:4d}: {line_content}") - - return "\n".join(context_lines) - - -def pyformat( - sql_template: str, - *, - pyformat_args: dict, - session: Optional[bigframes.session.Session] = None, - dry_run: bool = False, -) -> str: - """Unsafe Python-style string formatting of SQL string. - - Only some data types supported. - - Warning: strings are **not** escaped. This allows them to be used in - contexts such as table identifiers, where normal query parameters are not - supported. - - Args: - sql_template (str): - SQL string with 0+ {var_name}-style format options. - pyformat_args (dict): - Variable namespace to use for formatting. - - Raises: - TypeError: if a referenced variable is not of a supported type. - ValueError: - if a referenced variable is not found (KeyError is caught and raised - as ValueError with context). - """ - try: - fields = _parse_fields(sql_template) - except ValueError as e: - raise ValueError( - "Failed to parse SQL template. " - "Did you mean to escape '{' and '}' by doubling them?\n" - f"Error details: {e}" - ) from e - - format_kwargs: dict[str, str] = {} - seen_counts: dict[str, int] = {} - for name in fields: - seen_counts[name] = seen_counts.get(name, 0) + 1 - try: - value = pyformat_args[name] - except KeyError as e: - positions = _find_all_field_positions(sql_template) - occurrence_idx = seen_counts[name] - 1 - pos = positions.get((name, occurrence_idx), -1) - context = get_error_context_at_pos(sql_template, pos) - raise ValueError( - f"Undetected variable {name!r} in SQL template. " - "Did you mean to escape '{' and '}' by doubling them?\n" - f"{context}" - ) from e - - format_kwargs[name] = _field_to_template_value( - name, value, session=session, dry_run=dry_run - ) - - return sql_template.format(**format_kwargs) diff --git a/bigframes/core/reshape/__init__.py b/bigframes/core/reshape/__init__.py index 1dc90d18483..dc61c3baada 100644 --- a/bigframes/core/reshape/__init__.py +++ b/bigframes/core/reshape/__init__.py @@ -11,3 +11,143 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. +from __future__ import annotations + +import typing +from typing import Iterable, Literal, Optional, Union + +import bigframes.constants as constants +import bigframes.core as core +import bigframes.core.utils as utils +import bigframes.dataframe +import bigframes.operations as ops +import bigframes.operations.aggregations as agg_ops +import bigframes.series + + +@typing.overload +def concat( + objs: Iterable[bigframes.series.Series], + *, + axis: typing.Literal["index", 0] = ..., + join=..., + ignore_index=..., +) -> bigframes.series.Series: + ... + + +@typing.overload +def concat( + objs: Iterable[bigframes.dataframe.DataFrame], + *, + axis: typing.Literal["index", 0] = ..., + join=..., + ignore_index=..., +) -> bigframes.dataframe.DataFrame: + ... + + +@typing.overload +def concat( + objs: Iterable[Union[bigframes.dataframe.DataFrame, bigframes.series.Series]], + *, + axis: typing.Literal["columns", 1], + join=..., + ignore_index=..., +) -> bigframes.dataframe.DataFrame: + ... + + +@typing.overload +def concat( + objs: Iterable[Union[bigframes.dataframe.DataFrame, bigframes.series.Series]], + *, + axis=..., + join=..., + ignore_index=..., +) -> Union[bigframes.dataframe.DataFrame, bigframes.series.Series]: + ... + + +def concat( + objs: Iterable[Union[bigframes.dataframe.DataFrame, bigframes.series.Series]], + *, + axis: typing.Union[str, int] = 0, + join: Literal["inner", "outer"] = "outer", + ignore_index: bool = False, +) -> Union[bigframes.dataframe.DataFrame, bigframes.series.Series]: + axis_n = utils.get_axis_number(axis) + if axis_n == 0: + contains_dataframes = any( + isinstance(x, bigframes.dataframe.DataFrame) for x in objs + ) + if not contains_dataframes: + # Special case, all series, so align everything into single column even if labels don't match + series = typing.cast(typing.Iterable[bigframes.series.Series], objs) + names = {s.name for s in series} + # For series case, labels are stripped if they don't all match + if len(names) > 1: + blocks = [s._block.with_column_labels([None]) for s in series] + else: + blocks = [s._block for s in series] + block = blocks[0].concat(blocks[1:], how=join, ignore_index=ignore_index) + return bigframes.series.Series(block) + blocks = [obj._block for obj in objs] + block = blocks[0].concat(blocks[1:], how=join, ignore_index=ignore_index) + return bigframes.dataframe.DataFrame(block) + else: + # Note: does not validate inputs + block_list = [obj._block for obj in objs] + block = block_list[0] + for rblock in block_list[1:]: + combined_index, _ = block.index.join(rblock.index, how=join) + block = combined_index._block + return bigframes.dataframe.DataFrame(block) + + +def cut( + x: bigframes.series.Series, + bins: int, + *, + labels: Optional[bool] = None, +) -> bigframes.series.Series: + if bins <= 0: + raise ValueError("`bins` should be a positive integer.") + + if labels is not False: + raise NotImplementedError( + f"Only labels=False is supported in BigQuery DataFrames so far. {constants.FEEDBACK_LINK}" + ) + return x._apply_window_op(agg_ops.CutOp(bins), window_spec=core.WindowSpec()) + + +def qcut( + x: bigframes.series.Series, + q: typing.Union[int, typing.Sequence[float]], + *, + labels: Optional[bool] = None, + duplicates: typing.Literal["drop", "error"] = "error", +) -> bigframes.series.Series: + if isinstance(q, int) and q <= 0: + raise ValueError("`q` should be a positive integer.") + + if labels is not False: + raise NotImplementedError( + f"Only labels=False is supported in BigQuery DataFrames so far. {constants.FEEDBACK_LINK}" + ) + if duplicates != "drop": + raise NotImplementedError( + f"Only duplicates='drop' is supported in BigQuery DataFrames so far. {constants.FEEDBACK_LINK}" + ) + block = x._block + label = block.col_id_to_label[x._value_column] + block, nullity_id = block.apply_unary_op(x._value_column, ops.notnull_op) + block, result = block.apply_window_op( + x._value_column, + agg_ops.QcutOp(q), + window_spec=core.WindowSpec(grouping_keys=(nullity_id,)), + ) + block, result = block.apply_binary_op( + result, nullity_id, ops.partial_arg3(ops.where_op, None), result_label=label + ) + return bigframes.series.Series(block.select_column(result)) diff --git a/bigframes/core/reshape/api.py b/bigframes/core/reshape/api.py deleted file mode 100644 index adb33427f94..00000000000 --- a/bigframes/core/reshape/api.py +++ /dev/null @@ -1,21 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from bigframes.core.reshape.concat import concat -from bigframes.core.reshape.encoding import get_dummies -from bigframes.core.reshape.merge import merge -from bigframes.core.reshape.pivot import crosstab -from bigframes.core.reshape.tile import cut, qcut - -__all__ = ["concat", "get_dummies", "merge", "cut", "qcut", "crosstab"] diff --git a/bigframes/core/reshape/concat.py b/bigframes/core/reshape/concat.py deleted file mode 100644 index cc81319ae68..00000000000 --- a/bigframes/core/reshape/concat.py +++ /dev/null @@ -1,102 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from __future__ import annotations - -import typing -from typing import Iterable, Literal, Union - -import bigframes_vendored.pandas.core.reshape.concat as vendored_pandas_concat - -import bigframes.core.utils as utils -import bigframes.dataframe -import bigframes.series - - -@typing.overload -def concat( - objs: Iterable[bigframes.series.Series], - *, - axis: typing.Literal["index", 0] = ..., - join=..., - ignore_index=..., -) -> bigframes.series.Series: ... - - -@typing.overload -def concat( - objs: Iterable[bigframes.dataframe.DataFrame], - *, - axis: typing.Literal["index", 0] = ..., - join=..., - ignore_index=..., -) -> bigframes.dataframe.DataFrame: ... - - -@typing.overload -def concat( - objs: Iterable[Union[bigframes.dataframe.DataFrame, bigframes.series.Series]], - *, - axis: typing.Literal["columns", 1], - join=..., - ignore_index=..., -) -> bigframes.dataframe.DataFrame: ... - - -@typing.overload -def concat( - objs: Iterable[Union[bigframes.dataframe.DataFrame, bigframes.series.Series]], - *, - axis=..., - join=..., - ignore_index=..., -) -> Union[bigframes.dataframe.DataFrame, bigframes.series.Series]: ... - - -def concat( - objs: Iterable[Union[bigframes.dataframe.DataFrame, bigframes.series.Series]], - *, - axis: typing.Union[str, int] = 0, - join: Literal["inner", "outer"] = "outer", - ignore_index: bool = False, -) -> Union[bigframes.dataframe.DataFrame, bigframes.series.Series]: - axis_n = utils.get_axis_number(axis) - if axis_n == 0: - contains_dataframes = any( - isinstance(x, bigframes.dataframe.DataFrame) for x in objs - ) - if not contains_dataframes: - # Special case, all series, so align everything into single column even if labels don't match - series = typing.cast(typing.Iterable[bigframes.series.Series], objs) - names = {s.name for s in series} - # For series case, labels are stripped if they don't all match - if len(names) > 1: - blocks = [s._block.with_column_labels([None]) for s in series] - else: - blocks = [s._block for s in series] - block = blocks[0].concat(blocks[1:], how=join, ignore_index=ignore_index) - return bigframes.series.Series(block) - blocks = [obj._block for obj in objs] - block = blocks[0].concat(blocks[1:], how=join, ignore_index=ignore_index) - return bigframes.dataframe.DataFrame(block) - else: - # Note: does not validate inputs - block_list = [obj._block for obj in objs] - block = block_list[0] - for rblock in block_list[1:]: - block, _ = block.join(rblock, how=join) - return bigframes.dataframe.DataFrame(block) - - -concat.__doc__ = vendored_pandas_concat.concat.__doc__ diff --git a/bigframes/core/reshape/encoding.py b/bigframes/core/reshape/encoding.py deleted file mode 100644 index 842e4ece264..00000000000 --- a/bigframes/core/reshape/encoding.py +++ /dev/null @@ -1,194 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import typing -from typing import Any, List, Optional, Tuple, Union - -import bigframes_vendored.constants as constants -import bigframes_vendored.pandas.core.reshape.encoding as vendored_pandas_encoding -import pandas - -from bigframes import operations -from bigframes.core import blocks, expression -from bigframes.dataframe import DataFrame -from bigframes.series import Series - - -def get_dummies( - data: Union[DataFrame, Series], - prefix: Union[List, dict, str, None] = None, - prefix_sep: Union[List, dict, str, None] = "_", - dummy_na: bool = False, - columns: Optional[List] = None, - drop_first: bool = False, - dtype: Any = None, -) -> DataFrame: - # simplify input parameters into per-input-label lists - # also raise errors for invalid parameters - column_labels, prefixes, prefix_seps = _standardize_get_dummies_params( - data, prefix, prefix_sep, columns, dtype - ) - - # combine prefixes into per-column-id list - full_columns_prefixes, columns_ids = _determine_get_dummies_columns_from_labels( - data, column_labels, prefix is not None, prefixes, prefix_seps - ) - - # run queries to compute unique values - block = data._block - max_unique_value = ( - blocks._BQ_MAX_COLUMNS - len(block.value_columns) - len(block.index_columns) - 1 - ) // len(column_labels) - columns_values = [ - block._get_unique_values([col_id], max_unique_value) for col_id in columns_ids - ] - - # for each dummified column, add the content of the output columns via block operations - intermediate_col_ids = [] - for i in range(len(columns_values)): - level = columns_values[i].get_level_values(0).sort_values().dropna() - if drop_first: - level = level[1:] - column_label = full_columns_prefixes[i] - column_id = columns_ids[i] - block, new_intermediate_col_ids = _perform_get_dummies_block_operations( - block, level, column_label, column_id, dummy_na - ) - intermediate_col_ids.extend(new_intermediate_col_ids) - - # drop dummified columns (and the intermediate columns we added) - block = block.drop_columns(columns_ids + intermediate_col_ids) - return DataFrame(block) - - -get_dummies.__doc__ = vendored_pandas_encoding.get_dummies.__doc__ - - -def _standardize_get_dummies_params( - data: Union[DataFrame, Series], - prefix: Union[List, dict, str, None], - prefix_sep: Union[List, dict, str, None], - columns: Optional[List], - dtype: Any, -) -> Tuple[List, List[str], List[str]]: - block = data._block - - if isinstance(data, Series): - columns = [block.column_labels[0]] - if columns is not None and not pandas.api.types.is_list_like(columns): - raise TypeError("Input must be a list-like for parameter `columns`") - if dtype is not None and dtype not in [ - pandas.BooleanDtype, - bool, - "Boolean", - "boolean", - "bool", - ]: - raise NotImplementedError( - f"Only Boolean dtype is currently supported. {constants.FEEDBACK_LINK}" - ) - - if columns is None: - default_dummy_types = [pandas.StringDtype, "string[pyarrow]"] - columns = [] - columns_set = set() - for col_id in block.value_columns: - label = block.col_id_to_label[col_id] - if ( - label not in columns_set - and block.expr.get_column_type(col_id) in default_dummy_types - ): - columns.append(label) - columns_set.add(label) - - column_labels: List = typing.cast(List, columns) - - def parse_prefix_kwarg(kwarg, kwarg_name) -> Optional[List[str]]: - if kwarg is None: - return None - if isinstance(kwarg, str): - return [kwarg] * len(column_labels) - if isinstance(kwarg, dict): - return [kwarg[column] for column in column_labels] - kwarg = typing.cast(List, kwarg) - if pandas.api.types.is_list_like(kwarg) and len(kwarg) != len(column_labels): - raise ValueError( - f"Length of '{kwarg_name}' ({len(kwarg)}) did not match " - f"the length of the columns being encoded ({len(column_labels)})." - ) - if pandas.api.types.is_list_like(kwarg): - return list(map(str, kwarg)) - raise TypeError(f"{kwarg_name} kwarg must be a string, list, or dictionary") - - prefix_seps = parse_prefix_kwarg(prefix_sep or "_", "prefix_sep") - prefix_seps = typing.cast(List, prefix_seps) - prefixes = parse_prefix_kwarg(prefix, "prefix") - if prefixes is None: - prefixes = column_labels - prefixes = typing.cast(List, prefixes) - - return column_labels, prefixes, prefix_seps - - -def _determine_get_dummies_columns_from_labels( - data: Union[DataFrame, Series], - column_labels: List, - prefix_given: bool, - prefixes: List[str], - prefix_seps: List[str], -) -> Tuple[List[str], List[str]]: - block = data._block - - columns_ids = [] - columns_prefixes = [] - for i in range(len(column_labels)): - label = column_labels[i] - empty_prefix = label is None or (isinstance(data, Series) and not prefix_given) - full_prefix = "" if empty_prefix else prefixes[i] + prefix_seps[i] - - for col_id in block.label_to_col_id[label]: - columns_ids.append(col_id) - columns_prefixes.append(full_prefix) - - return columns_prefixes, columns_ids - - -def _perform_get_dummies_block_operations( - block: blocks.Block, - level: pandas.Index, - column_label: str, - column_id: str, - dummy_na: bool, -) -> Tuple[blocks.Block, List[str]]: - intermediate_col_ids = [] - for value in level: - new_column_label = f"{column_label}{value}" - if column_label == "": - new_column_label = value - new_block, new_id = block.project_expr( - operations.eq_op.as_expr(column_id, expression.const(value)) - ) - intermediate_col_ids.append(new_id) - block, _ = new_block.project_expr( - operations.fillna_op.as_expr(new_id, expression.const(False)), - label=new_column_label, - ) - if dummy_na: - # dummy column name for na depends on the dtype - na_string = str(pandas.Index([None], dtype=level.dtype)[0]) - new_column_label = f"{column_label}{na_string}" - block, _ = block.apply_unary_op( - column_id, operations.isnull_op, result_label=new_column_label - ) - return block, intermediate_col_ids diff --git a/bigframes/core/reshape/merge.py b/bigframes/core/reshape/merge.py deleted file mode 100644 index 55e3abe0c6e..00000000000 --- a/bigframes/core/reshape/merge.py +++ /dev/null @@ -1,218 +0,0 @@ -# Copyright 2023 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -""" -Functions for Merging Data Structures in BigFrames. -""" - -from __future__ import annotations - -from typing import Literal, Sequence - -import bigframes_vendored.pandas.core.reshape.merge as vendored_pandas_merge -from bigframes_vendored import constants - -from bigframes import dataframe, series -from bigframes.core import blocks, utils - - -def merge( - left: dataframe.DataFrame, - right: dataframe.DataFrame, - how: Literal[ - "inner", - "left", - "outer", - "right", - "cross", - ] = "inner", - on: blocks.Label | Sequence[blocks.Label] | None = None, - *, - left_on: blocks.Label | Sequence[blocks.Label] | None = None, - right_on: blocks.Label | Sequence[blocks.Label] | None = None, - left_index: bool = False, - right_index: bool = False, - sort: bool = False, - suffixes: tuple[str, str] = ("_x", "_y"), -) -> dataframe.DataFrame: - left = _validate_operand(left) - right = _validate_operand(right) - - if how == "cross": - if on is not None: - raise ValueError("'on' is not supported for cross join.") - result_block = left._block.merge( - right._block, - left_join_ids=[], - right_join_ids=[], - suffixes=suffixes, - how=how, - sort=True, - ) - return dataframe.DataFrame(result_block) - - left_join_ids, right_join_ids = _validate_left_right_on( - left, - right, - on, - left_on=left_on, - right_on=right_on, - left_index=left_index, - right_index=right_index, - ) - - block = left._block.merge( - right._block, - how, - left_join_ids, - right_join_ids, - sort=sort, - suffixes=suffixes, - left_index=left_index, - right_index=right_index, - ) - return dataframe.DataFrame(block) - - -merge.__doc__ = vendored_pandas_merge.merge.__doc__ - - -def _validate_operand( - obj: dataframe.DataFrame | series.Series, -) -> dataframe.DataFrame: - import bigframes.dataframe - import bigframes.series - - if isinstance(obj, bigframes.dataframe.DataFrame): - return obj - elif isinstance(obj, bigframes.series.Series): - if obj.name is None: - raise ValueError("Cannot merge a bigframes.series.Series without a name") - return obj.to_frame() - else: - raise TypeError( - f"Can only merge bigframes.series.Series or bigframes.dataframe.DataFrame objects, a {type(obj)} was passed" - ) - - -def _validate_left_right_on( - left: dataframe.DataFrame, - right: dataframe.DataFrame, - on: blocks.Label | Sequence[blocks.Label] | None = None, - *, - left_on: blocks.Label | Sequence[blocks.Label] | None = None, - right_on: blocks.Label | Sequence[blocks.Label] | None = None, - left_index: bool = False, - right_index: bool = False, -) -> tuple[list[str], list[str]]: - # Turn left_on and right_on to lists - if left_on is not None and not isinstance(left_on, (tuple, list)): - left_on = [left_on] - if right_on is not None and not isinstance(right_on, (tuple, list)): - right_on = [right_on] - - if left_index and left.index.nlevels > 1: - raise ValueError( - f"Joining with multi-level index is not supported. {constants.FEEDBACK_LINK}" - ) - if right_index and right.index.nlevels > 1: - raise ValueError( - f"Joining with multi-level index is not supported. {constants.FEEDBACK_LINK}" - ) - - # The following checks are copied from Pandas. - if on is None and left_on is None and right_on is None: - if left_index and right_index: - return list(left._block.index_columns), list(right._block.index_columns) - elif left_index: - raise ValueError("Must pass right_on or right_index=True") - elif right_index: - raise ValueError("Must pass left_on or left_index=True") - else: - # use the common columns - common_cols = left.columns.intersection(right.columns) - if len(common_cols) == 0: - raise ValueError( - "No common columns to perform merge on. " - f"Merge options: left_on={left_on}, " - f"right_on={right_on}, " - f"left_index={left_index}, " - f"right_index={right_index}" - ) - if ( - not left.columns.join(common_cols, how="inner").is_unique - or not right.columns.join(common_cols, how="inner").is_unique - ): - raise ValueError(f"Data columns not unique: {repr(common_cols)}") - return _to_col_ids(left, common_cols.to_list()), _to_col_ids( - right, common_cols.to_list() - ) - - elif on is not None: - if left_on is not None or right_on is not None: - raise ValueError( - 'Can only pass argument "on" OR "left_on" ' - 'and "right_on", not a combination of both.' - ) - if left_index or right_index: - raise ValueError( - 'Can only pass argument "on" OR "left_index" ' - 'and "right_index", not a combination of both.' - ) - return _to_col_ids(left, on), _to_col_ids(right, on) - - elif left_on is not None: - if left_index: - raise ValueError( - 'Can only pass argument "left_on" OR "left_index" not both.' - ) - if not right_index and right_on is None: - raise ValueError('Must pass "right_on" OR "right_index".') - if right_index: - if len(left_on) != right.index.nlevels: - raise ValueError( - "len(left_on) must equal the number " - 'of levels in the index of "right"' - ) - return _to_col_ids(left, left_on), list(right._block.index_columns) - - elif right_on is not None: - if right_index: - raise ValueError( - 'Can only pass argument "right_on" OR "right_index" not both.' - ) - if not left_index and left_on is None: - raise ValueError('Must pass "left_on" OR "left_index".') - if left_index: - if len(right_on) != left.index.nlevels: - raise ValueError( - "len(right_on) must equal the number " - 'of levels in the index of "left"' - ) - return list(left._block.index_columns), _to_col_ids(right, right_on) - - # The user correctly specified left_on and right_on - if len(right_on) != len(left_on): # type: ignore - raise ValueError("len(right_on) must equal len(left_on)") - - return _to_col_ids(left, left_on), _to_col_ids(right, right_on) - - -def _to_col_ids( - df: dataframe.DataFrame, join_cols: blocks.Label | Sequence[blocks.Label] -) -> list[str]: - if utils.is_list_like(join_cols): - return [df._block.resolve_label_exact_or_error(col) for col in join_cols] - - return [df._block.resolve_label_exact_or_error(join_cols)] diff --git a/bigframes/core/reshape/pivot.py b/bigframes/core/reshape/pivot.py deleted file mode 100644 index 082948728f6..00000000000 --- a/bigframes/core/reshape/pivot.py +++ /dev/null @@ -1,88 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -from __future__ import annotations - -from typing import TYPE_CHECKING, Optional - -import bigframes_vendored.pandas.core.reshape.pivot as vendored_pandas_pivot -import pandas as pd - -import bigframes -from bigframes.core import convert, utils -from bigframes.core.reshape import concat -from bigframes.dataframe import DataFrame - -if TYPE_CHECKING: - import bigframes.session - - -def crosstab( - index, - columns, - values=None, - rownames=None, - colnames=None, - aggfunc=None, - *, - session: Optional[bigframes.session.Session] = None, -) -> DataFrame: - if _is_list_of_lists(index): - index = [ - convert.to_bf_series(subindex, default_index=None, session=session) - for subindex in index - ] - else: - index = [convert.to_bf_series(index, default_index=None, session=session)] - if _is_list_of_lists(columns): - columns = [ - convert.to_bf_series(subcol, default_index=None, session=session) - for subcol in columns - ] - else: - columns = [convert.to_bf_series(columns, default_index=None, session=session)] - - df = concat.concat([*index, *columns], join="inner", axis=1) - # for uniqueness - tmp_index_names = [f"_crosstab_index_{i}" for i in range(len(index))] - tmp_col_names = [f"_crosstab_columns_{i}" for i in range(len(columns))] - df.columns = pd.Index([*tmp_index_names, *tmp_col_names]) - - values = ( - convert.to_bf_series(values, default_index=df.index, session=session) - if values is not None - else 0 - ) - - df["_crosstab_values"] = values - pivot_table = df.pivot_table( - values="_crosstab_values", - index=tmp_index_names, - columns=tmp_col_names, - aggfunc=aggfunc or "count", - sort=False, - fill_value=0 if (aggfunc is None) else None, - ) - # Undo temporary unique level labels - pivot_table.index.names = rownames or [i.name for i in index] - pivot_table.columns.names = colnames or [c.name for c in columns] - return pivot_table - - -def _is_list_of_lists(item) -> bool: - if not utils.is_list_like(item): - return False - return all(convert.can_convert_to_series(subitem) for subitem in item) - - -crosstab.__doc__ = vendored_pandas_pivot.crosstab.__doc__ diff --git a/bigframes/core/reshape/tile.py b/bigframes/core/reshape/tile.py deleted file mode 100644 index 61f869f2797..00000000000 --- a/bigframes/core/reshape/tile.py +++ /dev/null @@ -1,184 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from __future__ import annotations - -import typing -from typing import TYPE_CHECKING, Optional - -import bigframes_vendored.constants as constants -import bigframes_vendored.pandas.core.reshape.tile as vendored_pandas_tile -import pandas as pd - -import bigframes -import bigframes.constants -import bigframes.core.expression as ex -import bigframes.core.ordering as order -import bigframes.core.utils as utils -import bigframes.core.window_spec as window_specs -import bigframes.dataframe -import bigframes.operations as ops -import bigframes.operations.aggregations as agg_ops -import bigframes.series - -if TYPE_CHECKING: - import bigframes.session - - -def cut( - x, - bins: typing.Union[ - int, - pd.IntervalIndex, - typing.Iterable, - ], - *, - right: typing.Optional[bool] = True, - labels: typing.Union[typing.Iterable[str], bool, None] = None, - session: Optional[bigframes.session.Session] = None, -) -> bigframes.series.Series: - if ( - labels is not None - and labels is not False - and not isinstance(labels, typing.Iterable) - ): - raise ValueError( - "Bin labels must either be False, None or passed in as a list-like argument" - ) - if ( - isinstance(labels, typing.Iterable) - and len(list(labels)) > 0 - and not isinstance(list(labels)[0], str) - ): - raise NotImplementedError( - "When using an iterable for labels, only iterables of strings are supported " - f"but found {type(list(labels)[0])}. {constants.FEEDBACK_LINK}" - ) - - if len(x) == 0: - raise ValueError("Cannot cut empty array.") - - if not isinstance(x, bigframes.series.Series): - x = bigframes.series.Series(x, session=session) - - if isinstance(bins, int): - if bins <= 0: - raise ValueError("`bins` should be a positive integer.") - if isinstance(labels, typing.Iterable): - labels = tuple(labels) - if len(labels) != bins: - raise ValueError( - f"Bin labels({len(labels)}) must be same as the value of bins({bins})" - ) - - op = agg_ops.CutOp(bins, right=right, labels=labels) - return x._apply_window_op(op, window_spec=window_specs.unbound()) - elif isinstance(bins, typing.Iterable): - if isinstance(bins, pd.IntervalIndex): - as_index: pd.IntervalIndex = bins - bins = tuple((bin.left.item(), bin.right.item()) for bin in bins) - # To maintain consistency with pandas' behavior - right = True - labels = None - elif len(list(bins)) == 0: - as_index = pd.IntervalIndex.from_tuples(list(bins)) - bins = tuple() - elif isinstance(list(bins)[0], tuple): - as_index = pd.IntervalIndex.from_tuples(list(bins)) - bins = tuple(bins) - # To maintain consistency with pandas' behavior - right = True - labels = None - elif pd.api.types.is_number(list(bins)[0]): - bins_list = list(bins) - as_index = pd.IntervalIndex.from_breaks(bins_list) - single_type = all([isinstance(n, type(bins_list[0])) for n in bins_list]) - numeric_type = type(bins_list[0]) if single_type else float - bins = tuple( - [ - (numeric_type(bins_list[i]), numeric_type(bins_list[i + 1])) - for i in range(len(bins_list) - 1) - ] - ) - else: - raise ValueError("`bins` iterable should contain tuples or numerics.") - - if as_index.is_overlapping: - raise ValueError("Overlapping IntervalIndex is not accepted.") # TODO: test - - if isinstance(labels, typing.Iterable): - labels = tuple(labels) - if len(labels) != len(as_index): - raise ValueError( - f"Bin labels({len(labels)}) must be same as the number of bin edges" - f"({len(as_index)})" - ) - - if len(as_index) == 0: - dtype = agg_ops.CutOp(bins, right=right, labels=labels).output_type() - return bigframes.series.Series( - [pd.NA] * len(x), - dtype=dtype, - name=x.name, - index=x.index, - session=x._session, - ) - else: - op = agg_ops.CutOp(bins, right=right, labels=labels) - return x._apply_window_op(op, window_spec=window_specs.unbound()) - else: - raise ValueError("`bins` must be an integer or interable.") - - -cut.__doc__ = vendored_pandas_tile.cut.__doc__ - - -def qcut( - x: bigframes.series.Series, - q: typing.Union[int, typing.Sequence[float]], - *, - labels: typing.Optional[bool] = None, - duplicates: typing.Literal["drop", "error"] = "error", -) -> bigframes.series.Series: - if isinstance(q, int) and q <= 0: - raise ValueError("`q` should be a positive integer.") - if utils.is_list_like(q): - q = tuple(q) - - if labels is not False: - raise NotImplementedError( - f"Only labels=False is supported in BigQuery DataFrames so far. {constants.FEEDBACK_LINK}" - ) - if duplicates != "drop": - raise NotImplementedError( - f"Only duplicates='drop' is supported in BigQuery DataFrames so far. {constants.FEEDBACK_LINK}" - ) - block = x._block - label = block.col_id_to_label[x._value_column] - block, nullity_id = block.apply_unary_op(x._value_column, ops.notnull_op) - block, result = block.apply_window_op( - x._value_column, - agg_ops.QcutOp(q), # type: ignore - window_spec=window_specs.unbound( - grouping_keys=(nullity_id,), - ordering=(order.ascending_over(x._value_column),), - ), - ) - block, result = block.project_expr( - ops.where_op.as_expr(result, nullity_id, ex.const(None)), label=label - ) - return bigframes.series.Series(block.select_column(result)) - - -qcut.__doc__ = vendored_pandas_tile.qcut.__doc__ diff --git a/bigframes/core/rewrite/__init__.py b/bigframes/core/rewrite/__init__.py deleted file mode 100644 index ae4b142b1a4..00000000000 --- a/bigframes/core/rewrite/__init__.py +++ /dev/null @@ -1,61 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from bigframes.core.rewrite.as_sql import as_sql_nodes -from bigframes.core.rewrite.ctes import extract_ctes -from bigframes.core.rewrite.fold_row_count import fold_row_counts -from bigframes.core.rewrite.identifiers import remap_variables -from bigframes.core.rewrite.implicit_align import try_row_join -from bigframes.core.rewrite.legacy_align import legacy_join_as_projection -from bigframes.core.rewrite.nullity import simplify_join -from bigframes.core.rewrite.order import bake_order, defer_order, pull_out_order -from bigframes.core.rewrite.pruning import column_pruning -from bigframes.core.rewrite.scan_reduction import ( - try_reduce_to_local_scan, - try_reduce_to_table_scan, -) -from bigframes.core.rewrite.select_pullup import defer_selection -from bigframes.core.rewrite.slices import pull_out_limit, pull_up_limits, rewrite_slice -from bigframes.core.rewrite.timedeltas import rewrite_timedelta_expressions -from bigframes.core.rewrite.udfs import lower_udfs -from bigframes.core.rewrite.windows import ( - pull_out_window_order, - rewrite_range_rolling, - simplify_complex_windows, -) - -__all__ = [ - "as_sql_nodes", - "extract_ctes", - "legacy_join_as_projection", - "try_row_join", - "rewrite_slice", - "rewrite_timedelta_expressions", - "pull_up_limits", - "pull_out_limit", - "remap_variables", - "defer_order", - "column_pruning", - "rewrite_range_rolling", - "try_reduce_to_table_scan", - "bake_order", - "pull_out_order", - "try_reduce_to_local_scan", - "fold_row_counts", - "pull_out_window_order", - "defer_selection", - "simplify_complex_windows", - "lower_udfs", - "simplify_join", -] diff --git a/bigframes/core/rewrite/as_sql.py b/bigframes/core/rewrite/as_sql.py deleted file mode 100644 index eb823d1fed1..00000000000 --- a/bigframes/core/rewrite/as_sql.py +++ /dev/null @@ -1,308 +0,0 @@ -# Copyright 2026 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -from __future__ import annotations - -import dataclasses -import itertools -from typing import Optional, Sequence, Union - -import bigframes.core.rewrite -from bigframes.core import ( - agg_expressions, - expression, - guid, - identifiers, - nodes, - ordering, - sql_nodes, -) - - -def _limit(select: sql_nodes.SqlSelectNode, limit: int) -> sql_nodes.SqlSelectNode: - new_limit = limit if select.limit is None else min([select.limit, limit]) - return dataclasses.replace(select, limit=new_limit) - - -def _try_sort( - select: sql_nodes.SqlSelectNode, sort_by: Sequence[ordering.OrderingExpression] -) -> Optional[sql_nodes.SqlSelectNode]: - new_order_exprs = [] - for sort_expr in sort_by: - new_expr = _try_bind( - sort_expr.scalar_expression, select.get_id_mapping(), analytic_allowed=False - ) - if new_expr is None: - return None - new_order_exprs.append( - dataclasses.replace(sort_expr, scalar_expression=new_expr) - ) - return dataclasses.replace(select, sorting=tuple(new_order_exprs)) - - -def _sort( - node: nodes.BigFrameNode, sort_by: Sequence[ordering.OrderingExpression] -) -> sql_nodes.SqlSelectNode: - if isinstance(node, sql_nodes.SqlSelectNode): - merged = _try_sort(node, sort_by) - if merged: - return merged - result = _try_sort(_create_noop_select(node), sort_by) - assert result is not None - return result - - -def _try_bind( - expr: expression.Expression, - bindings: dict[identifiers.ColumnId, expression.Expression], - analytic_allowed: bool = False, # means block binding to an analytic even if original is scalar -) -> Optional[expression.Expression]: - if not expr.is_scalar_expr or not analytic_allowed: - for ref in expr.column_references: - if ref in bindings and not bindings[ref].is_scalar_expr: - return None - return expr.bind_refs(bindings) - - -def _try_add_cdefs( - select: sql_nodes.SqlSelectNode, cdefs: Sequence[nodes.ColumnDef] -) -> Optional[sql_nodes.SqlSelectNode]: - # TODO: add up complexity measure while inlining refs - new_defs = [] - for cdef in cdefs: - cdef_expr = cdef.expression - merged_expr = _try_bind( - cdef_expr, select.get_id_mapping(), analytic_allowed=True - ) - if merged_expr is None: - return None - new_defs.append(nodes.ColumnDef(merged_expr, cdef.id)) - - return dataclasses.replace(select, selections=(*select.selections, *new_defs)) - - -def _add_cdefs( - node: nodes.BigFrameNode, cdefs: Sequence[nodes.ColumnDef] -) -> sql_nodes.SqlSelectNode: - if isinstance(node, sql_nodes.SqlSelectNode): - merged = _try_add_cdefs(node, cdefs) - if merged: - return merged - # Otherwise, wrap the child in a SELECT and add the columns - result = _try_add_cdefs(_create_noop_select(node), cdefs) - assert result is not None - return result - - -def _try_add_filter( - select: sql_nodes.SqlSelectNode, predicates: Sequence[expression.Expression] -) -> Optional[sql_nodes.SqlSelectNode]: - # Filter implicitly happens first, so merging it into ths select will modify non-scalar col expressions - if not all(cdef.expression.is_scalar_expr for cdef in select.selections): - return None - if not all( - sort_expr.scalar_expression.is_scalar_expr for sort_expr in select.sorting - ): - return None - # Constraint: filters can only be merged if they are scalar expression after binding - new_predicates = [] - # bind variables, merge predicates - for predicate in predicates: - merged_pred = _try_bind(predicate, select.get_id_mapping()) - if not merged_pred: - return None - new_predicates.append(merged_pred) - return dataclasses.replace(select, predicates=(*select.predicates, *new_predicates)) - - -def _add_filter( - node: nodes.BigFrameNode, predicates: Sequence[expression.Expression] -) -> sql_nodes.SqlSelectNode: - if isinstance(node, sql_nodes.SqlSelectNode): - result = _try_add_filter(node, predicates) - if result: - return result - new_node = _try_add_filter(_create_noop_select(node), predicates) - assert new_node is not None - return new_node - - -def _create_noop_select(node: nodes.BigFrameNode) -> sql_nodes.SqlSelectNode: - return sql_nodes.SqlSelectNode( - node, - selections=tuple( - nodes.ColumnDef(expression.ResolvedDerefOp.from_field(field), field.id) - for field in node.fields - ), - ) - - -def _try_remap_select_cols( - select: sql_nodes.SqlSelectNode, cols: Sequence[nodes.AliasedRef] -): - new_defs = [] - for aliased_ref in cols: - new_defs.append( - nodes.ColumnDef(select.get_id_mapping()[aliased_ref.ref.id], aliased_ref.id) - ) - - return dataclasses.replace(select, selections=tuple(new_defs)) - - -def _remap_select_cols(node: nodes.BigFrameNode, cols: Sequence[nodes.AliasedRef]): - if isinstance(node, sql_nodes.SqlSelectNode): - result = _try_remap_select_cols(node, cols) - if result: - return result - new_node = _try_remap_select_cols(_create_noop_select(node), cols) - assert new_node is not None - return new_node - - -def _get_added_cdefs(node: Union[nodes.ProjectionNode, nodes.WindowOpNode]): - # TODO: InNode - if isinstance(node, nodes.ProjectionNode): - return tuple(nodes.ColumnDef(expr, id) for expr, id in node.assignments) - if isinstance(node, nodes.WindowOpNode): - new_cdefs = [] - for cdef in node.agg_exprs: - assert isinstance(cdef.expression, agg_expressions.Aggregation) - window_expr = agg_expressions.WindowExpression( - cdef.expression, node.window_spec - ) - # TODO: we probably should do this as another step - rewritten_window_expr = bigframes.core.rewrite.simplify_complex_windows( - window_expr - ) - new_cdefs.append(nodes.ColumnDef(rewritten_window_expr, cdef.id)) - return tuple(new_cdefs) - else: - raise ValueError(f"Unexpected node type: {type(node)}") - - -def _as_sql_node(node: nodes.BigFrameNode) -> nodes.BigFrameNode: - # case one, can be converted to select - if isinstance(node, nodes.ReadTableNode): - leaf = sql_nodes.SqlDataSource(source=node.source) - mappings = [ - nodes.AliasedRef(expression.deref(scan_item.source_id), scan_item.id) - for scan_item in node.scan_list.items - ] - return _remap_select_cols(leaf, mappings) - elif isinstance(node, (nodes.ProjectionNode, nodes.WindowOpNode)): - cdefs = _get_added_cdefs(node) - return _add_cdefs(node.child, cdefs) - elif isinstance(node, (nodes.SelectionNode)): - return _remap_select_cols(node.child, node.input_output_pairs) - elif isinstance(node, nodes.FilterNode): - return _add_filter(node.child, [node.predicate]) - elif isinstance(node, nodes.ResultNode): - result = node.child - if node.order_by is not None: - result = _sort(result, node.order_by.all_ordering_columns) - result = _remap_select_cols( - result, - [ - nodes.AliasedRef(ref, identifiers.ColumnId(name)) - for ref, name in node.output_cols - ], - ) - if node.limit is not None: - result = _limit(result, node.limit) # type: ignore - return result - else: - return node - - -# In the future, we will have sql nodes for each of these node types. -_LOGICAL_NODE_TYPES_TO_WRAP = ( - nodes.ReadLocalNode, - nodes.ExplodeNode, - nodes.InNode, - nodes.AggregateNode, - nodes.FromRangeNode, - nodes.ConcatNode, - sql_nodes.SqlSelectNode, -) - - -def _insert_cte_markers(root: nodes.BigFrameNode) -> nodes.BigFrameNode: - # important not to wrap nodes that are already wrapped - wrapped_nodes = set( - node.child for node in root.unique_nodes() if isinstance(node, nodes.CteNode) - ) - # don't wrap child nodes of ConcatNode - union_child_nodes = set( - itertools.chain.from_iterable( - node.child_nodes - for node in root.unique_nodes() - if isinstance(node, nodes.ConcatNode) - ) - ) - - def maybe_insert_cte_marker(node: nodes.BigFrameNode) -> nodes.BigFrameNode: - if node == root: - return node - if ( - isinstance(node, _LOGICAL_NODE_TYPES_TO_WRAP) - and node not in wrapped_nodes - and node not in union_child_nodes - ): - wrapped_nodes.add(node) - return nodes.CteNode(node) - return node - - return root.top_down(maybe_insert_cte_marker) - - -def _extract_ctes_to_with_expr( - root: nodes.BigFrameNode, uid_gen: guid.SequentialUIDGenerator -) -> nodes.BigFrameNode: - topological_ctes = list( - filter( - lambda n: isinstance(n, nodes.CteNode), - root.iter_nodes_topo(), - ) - ) - cte_names = tuple( - next(uid_gen.get_uid_stream("bfcte_")) for _ in range(len(topological_ctes)) - ) - - if len(topological_ctes) == 0: - return root - - mapping = { - cte_node: sql_nodes.SqlCteRefNode(cte_name, tuple(cte_node.fields)) - for cte_node, cte_name in zip(topological_ctes, cte_names) - } - - # Replace all CTEs with CTE references and wrap the new root in a WITH clause - return sql_nodes.SqlWithCtesNode( - root.top_down(lambda x: mapping.get(x, x)), - cte_names, - tuple( - # Mypy loses context that cte_node is a CteNode with a child attribute, despite the isinstance filter above. - cte_node.child.top_down(lambda x: mapping.get(x, x)) # type: ignore[attr-defined] - for cte_node in topological_ctes - ), - ) - - -def as_sql_nodes( - root: nodes.BigFrameNode, uid_gen: guid.SequentialUIDGenerator -) -> nodes.BigFrameNode: - root = nodes.bottom_up(root, _as_sql_node) - # Insert CTE markers to indicate where we want to split the query. - root = _insert_cte_markers(root) - root = _extract_ctes_to_with_expr(root, uid_gen) - return root diff --git a/bigframes/core/rewrite/ctes.py b/bigframes/core/rewrite/ctes.py deleted file mode 100644 index a5afd19bb35..00000000000 --- a/bigframes/core/rewrite/ctes.py +++ /dev/null @@ -1,41 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -from __future__ import annotations - -from collections import defaultdict - -from bigframes.core import nodes - - -def extract_ctes(root: nodes.BigFrameNode) -> nodes.BigFrameNode: - # identify candidates - node_parents: dict[nodes.BigFrameNode, int] = defaultdict(int) - for parent in root.unique_nodes(): - for child in parent.child_nodes: - node_parents[child] += 1 - - # everywhere a multi-parent node is referenced, wrap it in a CTE node - def insert_cte_markers(node: nodes.BigFrameNode) -> nodes.BigFrameNode: - def _add_cte_if_needed(child: nodes.BigFrameNode) -> nodes.BigFrameNode: - if node_parents[child] > 1: - return nodes.CteNode(child) - return child - - if isinstance(node, nodes.CteNode): - # don't re-wrap CTE nodes - return node - - return node.transform_children(_add_cte_if_needed) - - return root.top_down(insert_cte_markers) diff --git a/bigframes/core/rewrite/fold_row_count.py b/bigframes/core/rewrite/fold_row_count.py deleted file mode 100644 index cc0b818fb96..00000000000 --- a/bigframes/core/rewrite/fold_row_count.py +++ /dev/null @@ -1,40 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -from __future__ import annotations - -import pyarrow as pa - -from bigframes.core import local_data, nodes -from bigframes.operations import aggregations - - -def fold_row_counts(node: nodes.BigFrameNode) -> nodes.BigFrameNode: - if not isinstance(node, nodes.AggregateNode): - return node - if len(node.by_column_ids) > 0: - return node - if node.child.row_count is None: - return node - for agg, _ in node.aggregations: - if agg.op != aggregations.size_op: - return node - local_data_source = local_data.ManagedArrowTable.from_pyarrow( - pa.table({"count": pa.array([node.child.row_count], type=pa.int64())}) - ) - scan_list = nodes.ScanList( - tuple(nodes.ScanItem(out_id, "count") for _, out_id in node.aggregations) - ) - return nodes.ReadLocalNode( - local_data_source=local_data_source, scan_list=scan_list, session=node.session - ) diff --git a/bigframes/core/rewrite/identifiers.py b/bigframes/core/rewrite/identifiers.py deleted file mode 100644 index 7b1d1d9a512..00000000000 --- a/bigframes/core/rewrite/identifiers.py +++ /dev/null @@ -1,117 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -from __future__ import annotations - -import typing - -from bigframes.core import identifiers, nodes - - -def _create_mapping_operator( - id_def_remapping_by_node: dict[ - nodes.BigFrameNode, dict[identifiers.ColumnId, identifiers.ColumnId] - ], - id_ref_remapping_by_node: dict[ - nodes.BigFrameNode, dict[identifiers.ColumnId, identifiers.ColumnId] - ], -): - """ - Builds a remapping operator that uses predefined local remappings for ids. - - Args: - id_remapping_by_node: A mapping from nodes to their local remappings. - - Returns: - A remapping operator. - """ - - def _mapping_operator(node: nodes.BigFrameNode) -> nodes.BigFrameNode: - # Step 1: Get the local remapping for the current node. - local_def_remaps = id_def_remapping_by_node[node] - local_ref_remaps = id_ref_remapping_by_node[node] - - result = node.remap_vars(local_def_remaps) - result = result.remap_refs(local_ref_remaps) - - return result - - return _mapping_operator - - -def remap_variables( - root: nodes.BigFrameNode, - id_generator: typing.Iterator[identifiers.ColumnId], -) -> typing.Tuple[ - nodes.BigFrameNode, - dict[identifiers.ColumnId, identifiers.ColumnId], -]: - """Remaps `ColumnId`s in the expression tree to be deterministic and sequential. - - This function performs a post-order traversal. It recursively remaps children - nodes first, then remaps the current node's references and definitions. - - Note: this will convert a DAG to a tree by duplicating shared nodes. - - Args: - root: The root node of the expression tree. - id_generator: An iterator that yields new column IDs. - - Returns: - A tuple of the new root node and a mapping from old to new column IDs - visible to the parent node. - """ - # step 1: defined remappings for each individual unique node - # step 2: top down traversal to apply remappings (mappings are value-based, so bottom-up doesn't work) - - id_def_remaps: dict[ - nodes.BigFrameNode, dict[identifiers.ColumnId, identifiers.ColumnId] - ] = {} - id_ref_remaps: dict[ - nodes.BigFrameNode, dict[identifiers.ColumnId, identifiers.ColumnId] - ] = {} - for node in root.iter_nodes_topo(): # bottom up - local_def_remaps = { - col_id: next(id_generator) for col_id in node.node_defined_ids - } - id_def_remaps[node] = local_def_remaps - - local_ref_remaps = {} - - # InNode is special case as ID scope inherited purely from left side - inheriting_nodes = ( - [node.child_nodes[0]] - if isinstance(node, nodes.InNode) - else node.child_nodes - ) - for child in inheriting_nodes: # inherit ref and def mappings from children - if not child.defines_namespace: # these nodes represent new id spaces - local_ref_remaps.update( - { - old_id: new_id - for old_id, new_id in id_ref_remaps[child].items() - if old_id in child.ids - } - ) - local_ref_remaps.update(id_def_remaps[child]) - id_ref_remaps[node] = local_ref_remaps - - # have to do top down to preserve node identities - return ( - root.top_down(_create_mapping_operator(id_def_remaps, id_ref_remaps)), - # Only used by unit tests - { - old_id: (id_def_remaps[root] | id_ref_remaps[root])[old_id] - for old_id in root.ids - }, - ) diff --git a/bigframes/core/rewrite/implicit_align.py b/bigframes/core/rewrite/implicit_align.py deleted file mode 100644 index ebd48d82362..00000000000 --- a/bigframes/core/rewrite/implicit_align.py +++ /dev/null @@ -1,220 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -from __future__ import annotations - -import dataclasses -import itertools -from typing import Optional, Sequence, Set, Tuple - -import bigframes.core.expression -import bigframes.core.identifiers -import bigframes.core.nodes - -# Combination of selects and additive nodes can be merged as an explicit keyless "row join" -ALIGNABLE_NODES = ( - bigframes.core.nodes.SelectionNode, - bigframes.core.nodes.ProjectionNode, - bigframes.core.nodes.WindowOpNode, - bigframes.core.nodes.PromoteOffsetsNode, - bigframes.core.nodes.InNode, -) - - -@dataclasses.dataclass(frozen=True) -class ExpressionSpec: - expression: bigframes.core.expression.Expression - node: bigframes.core.nodes.BigFrameNode - - -def get_expression_spec( - node: bigframes.core.nodes.BigFrameNode, id: bigframes.core.identifiers.ColumnId -) -> ExpressionSpec: - """Normalizes column value by chaining expressions across multiple selection and projection nodes if possible. - This normalization helps identify whether columns are equivalent. - """ - # TODO: While we chain expression fragments from different nodes - # we could further normalize with constant folding and other scalar expression rewrites - expression: bigframes.core.expression.Expression = ( - bigframes.core.expression.DerefOp(id) - ) - curr_node = node - while True: - if isinstance(curr_node, bigframes.core.nodes.SelectionNode): - select_mappings = { - col_id: ref for ref, col_id in curr_node.input_output_pairs - } - expression = expression.bind_refs( - select_mappings, allow_partial_bindings=True - ) - elif isinstance(curr_node, bigframes.core.nodes.ProjectionNode): - proj_mappings = {col_id: expr for expr, col_id in curr_node.assignments} - expression = expression.bind_refs( - proj_mappings, allow_partial_bindings=True - ) - elif isinstance( - curr_node, - ( - bigframes.core.nodes.WindowOpNode, - bigframes.core.nodes.PromoteOffsetsNode, - bigframes.core.nodes.InNode, - ), - ): - if set(expression.column_references).isdisjoint( - field.id for field in curr_node.added_fields - ): - # we don't yet have a way of normalizing window ops into a ExpressionSpec, which only - # handles normalizing scalar expressions at the moment. - pass - else: - return ExpressionSpec(expression, curr_node) - else: - return ExpressionSpec(expression, curr_node) - curr_node = curr_node.child_nodes[0] - - -def try_row_join( - l_node: bigframes.core.nodes.BigFrameNode, - r_node: bigframes.core.nodes.BigFrameNode, - join_keys: Tuple[Tuple[str, str], ...], -) -> Optional[bigframes.core.nodes.BigFrameNode]: - """Joins the two nodes""" - divergent_node = first_shared_descendent( - {l_node, r_node}, descendable_types=ALIGNABLE_NODES - ) - if divergent_node is None: - return None - # check join keys are equivalent by normalizing the expressions as much as posisble - # instead of just comparing ids - for l_key, r_key in join_keys: - # Caller is block, so they still work with raw strings rather than ids - left_id = bigframes.core.identifiers.ColumnId(l_key) - right_id = bigframes.core.identifiers.ColumnId(r_key) - if get_expression_spec(l_node, left_id) != get_expression_spec( - r_node, right_id - ): - return None - - l_node, l_selection = pull_up_selection(l_node, stop=divergent_node) - r_node, r_selection = pull_up_selection( - r_node, stop=divergent_node, rename_vars=True - ) # Rename only right vars to avoid collisions with left vars - combined_selection = l_selection + r_selection - - def _linearize_trees( - base_tree: bigframes.core.nodes.BigFrameNode, - append_tree: bigframes.core.nodes.BigFrameNode, - ) -> bigframes.core.nodes.BigFrameNode: - """Linearize two divergent tree who only diverge through different additive nodes.""" - # base case: append tree does not have any divergent nodes to linearize - if append_tree == divergent_node: - return base_tree - - assert isinstance(append_tree, bigframes.core.nodes.AdditiveNode) - return append_tree.replace_additive_base( - _linearize_trees(base_tree, append_tree.additive_base) - ) - - merged_node = _linearize_trees(l_node, r_node) - return bigframes.core.nodes.SelectionNode(merged_node, combined_selection) - - -def pull_up_selection( - node: bigframes.core.nodes.BigFrameNode, - stop: bigframes.core.nodes.BigFrameNode, - rename_vars: bool = False, -) -> Tuple[ - bigframes.core.nodes.BigFrameNode, - Tuple[bigframes.core.nodes.AliasedRef, ...], -]: - """Remove all selection nodes above the base node. Returns stripped tree. - - Args: - node (BigFrameNode): - The node from which to pull up SelectionNode ops - rename_vars (bool): - If true, will rename projected columns to new unique ids. - - Returns: - BigFrameNode, Selections - """ - if node == stop: # base case - return node, tuple( - bigframes.core.nodes.AliasedRef.identity(field.id) for field in node.fields - ) - - if isinstance(node, bigframes.core.nodes.AdditiveNode): - child_node, child_selections = pull_up_selection( - node.additive_base, stop, rename_vars=rename_vars - ) - mapping = {out: ref.id for ref, out in child_selections} - new_node: bigframes.core.nodes.BigFrameNode = node.replace_additive_base( - child_node - ) - new_node = new_node.remap_refs(mapping) - if rename_vars: - var_renames = { - field.id: bigframes.core.identifiers.ColumnId.unique() - for field in node.added_fields - } - new_node = new_node.remap_vars(var_renames) - else: - var_renames = {} - assert isinstance(new_node, bigframes.core.nodes.AdditiveNode) - added_selections = tuple( - bigframes.core.nodes.AliasedRef.identity(field.id).remap_refs(var_renames) - for field in node.added_fields - ) - new_selection = child_selections + added_selections - return new_node, new_selection - elif isinstance(node, bigframes.core.nodes.SelectionNode): - child_node, child_selections = pull_up_selection( - node.child, stop, rename_vars=rename_vars - ) - mapping = {out: ref.id for ref, out in child_selections} - return child_node, tuple( - ref.remap_refs(mapping) for ref in node.input_output_pairs - ) - raise ValueError(f"Couldn't pull up select from node: {node}") - - -## Traversal helpers -def first_shared_descendent( - roots: Set[bigframes.core.nodes.BigFrameNode], - descendable_types: Tuple[type[bigframes.core.nodes.BigFrameNode], ...], -) -> Optional[bigframes.core.nodes.BigFrameNode]: - if not roots: - return None - if len(roots) == 1: - return next(iter(roots)) - - min_height = min(root.height for root in roots) - - def descend( - root: bigframes.core.nodes.BigFrameNode, - ) -> Sequence[bigframes.core.nodes.BigFrameNode]: - # Special case to not descend into right side of IsInNode - if isinstance(root, bigframes.core.nodes.AdditiveNode): - return (root.additive_base,) - return root.child_nodes - - roots_to_descend = set(root for root in roots if root.height > min_height) - if not roots_to_descend: - roots_to_descend = roots - if any(not isinstance(root, descendable_types) for root in roots_to_descend): - return None - as_is = roots - roots_to_descend - descended = set( - itertools.chain.from_iterable(descend(root) for root in roots_to_descend) - ) - return first_shared_descendent(as_is.union(descended), descendable_types) diff --git a/bigframes/core/rewrite/legacy_align.py b/bigframes/core/rewrite/legacy_align.py deleted file mode 100644 index 26ee71d2ec5..00000000000 --- a/bigframes/core/rewrite/legacy_align.py +++ /dev/null @@ -1,367 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -from __future__ import annotations - -import dataclasses -import functools -import itertools -from typing import Mapping, Optional, Sequence, Tuple - -import bigframes.core.expression as scalar_exprs -import bigframes.core.identifiers as ids -import bigframes.core.join_def as join_defs -import bigframes.core.nodes as nodes -import bigframes.core.ordering as order -import bigframes.core.rewrite.implicit_align -import bigframes.operations as ops - -Selection = Tuple[Tuple[scalar_exprs.Expression, ids.ColumnId], ...] - -LEGACY_REWRITER_NODES = ( - bigframes.core.nodes.ProjectionNode, - bigframes.core.nodes.SelectionNode, - bigframes.core.nodes.ReversedNode, - bigframes.core.nodes.OrderByNode, - bigframes.core.nodes.FilterNode, -) - - -@dataclasses.dataclass(frozen=True) -class SquashedSelect: - """Squash nodes together until target node, separating out the projection, filter and reordering expressions.""" - - root: nodes.BigFrameNode - columns: Tuple[Tuple[scalar_exprs.Expression, ids.ColumnId], ...] - predicate: Optional[scalar_exprs.Expression] - ordering: Tuple[order.OrderingExpression, ...] - reverse_root: bool = False - - @classmethod - def from_node_span( - cls, node: nodes.BigFrameNode, target: nodes.BigFrameNode - ) -> SquashedSelect: - if node == target: - selection = tuple((scalar_exprs.DerefOp(id), id) for id in node.ids) - return cls(node, selection, None, ()) - - if isinstance(node, nodes.SelectionNode): - return cls.from_node_span(node.child, target).select( - tuple(node.input_output_pairs) - ) - elif isinstance(node, nodes.ProjectionNode): - return cls.from_node_span(node.child, target).project(node.assignments) - elif isinstance(node, nodes.FilterNode): - return cls.from_node_span(node.child, target).filter(node.predicate) - elif isinstance(node, nodes.ReversedNode): - return cls.from_node_span(node.child, target).reverse() - elif isinstance(node, nodes.OrderByNode): - return cls.from_node_span(node.child, target).order_with(node.by) - else: - raise ValueError(f"Cannot rewrite node {node}") - - @property - def column_lookup(self) -> Mapping[ids.ColumnId, scalar_exprs.Expression]: - return {col_id: expr for expr, col_id in self.columns} - - def select( - self, input_output_pairs: Tuple[Tuple[scalar_exprs.DerefOp, ids.ColumnId], ...] - ) -> SquashedSelect: - new_columns = tuple( - ( - input.bind_refs(self.column_lookup), - output, - ) - for input, output in input_output_pairs - ) - return SquashedSelect( - self.root, new_columns, self.predicate, self.ordering, self.reverse_root - ) - - def project( - self, projection: Tuple[Tuple[scalar_exprs.Expression, ids.ColumnId], ...] - ) -> SquashedSelect: - existing_columns = self.columns - new_columns = tuple( - (expr.bind_refs(self.column_lookup), id) for expr, id in projection - ) - return SquashedSelect( - self.root, - (*existing_columns, *new_columns), - self.predicate, - self.ordering, - self.reverse_root, - ) - - def filter(self, predicate: scalar_exprs.Expression) -> SquashedSelect: - if self.predicate is None: - new_predicate = predicate.bind_refs(self.column_lookup) - else: - new_predicate = ops.and_op.as_expr( - self.predicate, predicate.bind_refs(self.column_lookup) - ) - return SquashedSelect( - self.root, self.columns, new_predicate, self.ordering, self.reverse_root - ) - - def reverse(self) -> SquashedSelect: - new_ordering = tuple(expr.with_reverse() for expr in self.ordering) - return SquashedSelect( - self.root, self.columns, self.predicate, new_ordering, not self.reverse_root - ) - - def order_with(self, by: Tuple[order.OrderingExpression, ...]): - adjusted_orderings = [ - order_part.bind_refs(self.column_lookup) for order_part in by - ] - new_ordering = (*adjusted_orderings, *self.ordering) - return SquashedSelect( - self.root, self.columns, self.predicate, new_ordering, self.reverse_root - ) - - def can_merge( - self, - right: SquashedSelect, - join_keys: Tuple[join_defs.CoalescedColumnMapping, ...], - ) -> bool: - """Determines whether the two selections can be merged into a single selection.""" - r_exprs_by_id = {id.name: expr for expr, id in right.columns} - l_exprs_by_id = {id.name: expr for expr, id in self.columns} - l_join_exprs = [ - l_exprs_by_id[join_key.left_source_id] for join_key in join_keys - ] - r_join_exprs = [ - r_exprs_by_id[join_key.right_source_id] for join_key in join_keys - ] - - if self.root != right.root: - return False - if len(l_join_exprs) != len(r_join_exprs): - return False - if any(l_expr != r_expr for l_expr, r_expr in zip(l_join_exprs, r_join_exprs)): - return False - return True - - def merge( - self, - right: SquashedSelect, - join_type: join_defs.JoinType, - join_keys: Tuple[join_defs.CoalescedColumnMapping, ...], - mappings: Tuple[join_defs.JoinColumnMapping, ...], - ) -> SquashedSelect: - if self.root != right.root: - raise ValueError("Cannot merge expressions with different roots") - # Mask columns and remap names to expected schema - lselection = self.columns - rselection = right.columns - if join_type == "inner": - new_predicate = and_predicates(self.predicate, right.predicate) - elif join_type == "outer": - new_predicate = or_predicates(self.predicate, right.predicate) - elif join_type == "left": - new_predicate = self.predicate - elif join_type == "right": - new_predicate = right.predicate - - l_relative, r_relative = relative_predicates(self.predicate, right.predicate) - lmask = l_relative if join_type in {"right", "outer"} else None - rmask = r_relative if join_type in {"left", "outer"} else None - new_columns = merge_expressions( - join_keys, mappings, lselection, rselection, lmask, rmask - ) - - # Reconstruct ordering - reverse_root = self.reverse_root - if join_type == "right": - new_ordering = right.ordering - reverse_root = right.reverse_root - elif join_type == "outer": - if lmask is not None: - prefix = order.OrderingExpression(lmask, order.OrderingDirection.DESC) - left_ordering = tuple( - order.OrderingExpression( - apply_mask(ref.scalar_expression, lmask), - ref.direction, - ref.na_last, - ) - for ref in self.ordering - ) - right_ordering = ( - tuple( - order.OrderingExpression( - apply_mask(ref.scalar_expression, rmask), - ref.direction, - ref.na_last, - ) - for ref in right.ordering - ) - if rmask - else right.ordering - ) - new_ordering = (prefix, *left_ordering, *right_ordering) - else: - new_ordering = self.ordering - elif join_type in {"inner", "left"}: - new_ordering = self.ordering - else: - raise ValueError(f"Unexpected join type {join_type}") - return SquashedSelect( - self.root, new_columns, new_predicate, new_ordering, reverse_root - ) - - def expand(self) -> nodes.BigFrameNode: - # Safest to apply predicates first, as it may filter out inputs that cannot be handled by other expressions - root = self.root - if self.reverse_root: - root = nodes.ReversedNode(child=root) - if self.predicate: - root = nodes.FilterNode(child=root, predicate=self.predicate) - if self.ordering: - root = nodes.OrderByNode(child=root, by=self.ordering) - selection = tuple( - bigframes.core.nodes.AliasedRef.identity(id) for _, id in self.columns - ) - return nodes.SelectionNode( - child=nodes.ProjectionNode(child=root, assignments=self.columns), - input_output_pairs=selection, - ) - - -def legacy_join_as_projection( - l_node: nodes.BigFrameNode, - r_node: nodes.BigFrameNode, - join_keys: Tuple[join_defs.CoalescedColumnMapping, ...], - mappings: Tuple[join_defs.JoinColumnMapping, ...], - how: join_defs.JoinType, -) -> Optional[nodes.BigFrameNode]: - rewrite_common_node = common_selection_root(l_node, r_node) - if rewrite_common_node is not None: - left_side = SquashedSelect.from_node_span(l_node, rewrite_common_node) - right_side = SquashedSelect.from_node_span(r_node, rewrite_common_node) - if not left_side.can_merge(right_side, join_keys): - # Most likely because join keys didn't match - return None - merged = left_side.merge(right_side, how, join_keys, mappings) - assert merged is not None, ( - "Couldn't merge nodes. This shouldn't happen. Please share full stacktrace with the BigQuery DataFrames team at bigframes-feedback@google.com." - ) - return merged.expand() - else: - return None - - -def merge_expressions( - join_keys: Tuple[join_defs.CoalescedColumnMapping, ...], - mappings: Tuple[join_defs.JoinColumnMapping, ...], - lselection: Selection, - rselection: Selection, - lmask: Optional[scalar_exprs.Expression], - rmask: Optional[scalar_exprs.Expression], -) -> Selection: - new_selection: Selection = tuple() - # Assumption is simple ids - l_exprs_by_id = {id.name: expr for expr, id in lselection} - r_exprs_by_id = {id.name: expr for expr, id in rselection} - for key in join_keys: - # Join keys expressions are equivalent on both sides, so can choose either left or right key - assert l_exprs_by_id[key.left_source_id] == r_exprs_by_id[key.right_source_id] - expr = l_exprs_by_id[key.left_source_id] - id = key.destination_id - new_selection = (*new_selection, (expr, ids.ColumnId(id))) - for mapping in mappings: - if mapping.source_table == join_defs.JoinSide.LEFT: - expr = l_exprs_by_id[mapping.source_id] - if lmask is not None: - expr = apply_mask(expr, lmask) - else: # Right - expr = r_exprs_by_id[mapping.source_id] - if rmask is not None: - expr = apply_mask(expr, rmask) - new_selection = (*new_selection, (expr, ids.ColumnId(mapping.destination_id))) - return new_selection - - -def and_predicates( - expr1: Optional[scalar_exprs.Expression], expr2: Optional[scalar_exprs.Expression] -) -> Optional[scalar_exprs.Expression]: - if expr1 is None: - return expr2 - if expr2 is None: - return expr1 - left_predicates = decompose_conjunction(expr1) - right_predicates = decompose_conjunction(expr2) - # remove common predicates - all_predicates = itertools.chain( - left_predicates, [p for p in right_predicates if p not in left_predicates] - ) - return merge_predicates(list(all_predicates)) - - -def or_predicates( - expr1: Optional[scalar_exprs.Expression], expr2: Optional[scalar_exprs.Expression] -) -> Optional[scalar_exprs.Expression]: - if (expr1 is None) or (expr2 is None): - return None - # TODO(tbergeron): Factor out common predicates - return ops.or_op.as_expr(expr1, expr2) - - -def relative_predicates( - expr1: Optional[scalar_exprs.Expression], expr2: Optional[scalar_exprs.Expression] -) -> Tuple[Optional[scalar_exprs.Expression], Optional[scalar_exprs.Expression]]: - left_predicates = decompose_conjunction(expr1) if expr1 else () - right_predicates = decompose_conjunction(expr2) if expr2 else () - left_relative = tuple( - pred for pred in left_predicates if pred not in right_predicates - ) - right_relative = tuple( - pred for pred in right_predicates if pred not in left_predicates - ) - return merge_predicates(left_relative), merge_predicates(right_relative) - - -def apply_mask( - expr: scalar_exprs.Expression, mask: scalar_exprs.Expression -) -> scalar_exprs.Expression: - return ops.where_op.as_expr(expr, mask, scalar_exprs.const(None)) - - -def merge_predicates( - predicates: Sequence[scalar_exprs.Expression], -) -> Optional[scalar_exprs.Expression]: - if len(predicates) == 0: - return None - - return functools.reduce(ops.and_op.as_expr, predicates) - - -def decompose_conjunction( - expr: scalar_exprs.Expression, -) -> Tuple[scalar_exprs.Expression, ...]: - if isinstance(expr, scalar_exprs.OpExpression) and isinstance( - expr.op, type(ops.and_op) - ): - return tuple( - itertools.chain.from_iterable(decompose_conjunction(i) for i in expr.inputs) - ) - else: - return (expr,) - - -def common_selection_root( - l_tree: nodes.BigFrameNode, r_tree: nodes.BigFrameNode -) -> Optional[nodes.BigFrameNode]: - """Find common subtree between join subtrees""" - return bigframes.core.rewrite.implicit_align.first_shared_descendent( - {l_tree, r_tree}, descendable_types=LEGACY_REWRITER_NODES - ) diff --git a/bigframes/core/rewrite/nullity.py b/bigframes/core/rewrite/nullity.py deleted file mode 100644 index 6307b12ec27..00000000000 --- a/bigframes/core/rewrite/nullity.py +++ /dev/null @@ -1,43 +0,0 @@ -# Copyright 2026 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from __future__ import annotations - -import dataclasses - -from bigframes.core import nodes - - -def simplify_join(node: nodes.BigFrameNode) -> nodes.BigFrameNode: - """Simplify a join node by removing nullity checks.""" - # if join conditions are provably non-null, we can set nulls_equal=False - if isinstance(node, nodes.JoinNode): - # even better, we can always make nulls_equal false, but wrap the join keys in coalesce - # to handle nulls correctly, this is more granular than the current implementation - for left_ref, right_ref in node.conditions: - if ( - node.left_child.field_by_id[left_ref.id].nullable - and node.right_child.field_by_id[right_ref.id].nullable - ): - return node - return dataclasses.replace(node, nulls_equal=False) - elif isinstance(node, nodes.InNode): - if ( - node.left_child.field_by_id[node.left_col.id].nullable - and node.right_child.fields[0].nullable - ): - return node - return dataclasses.replace(node, nulls_equal=False) - else: - return node diff --git a/bigframes/core/rewrite/op_lowering.py b/bigframes/core/rewrite/op_lowering.py deleted file mode 100644 index 013fc48c06a..00000000000 --- a/bigframes/core/rewrite/op_lowering.py +++ /dev/null @@ -1,55 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -from __future__ import annotations - -import abc -from typing import Sequence - -import bigframes.operations as ops -from bigframes.core import bigframe_node, expression, nodes - - -class OpLoweringRule(abc.ABC): - @property - @abc.abstractmethod - def op(self) -> type[ops.ScalarOp]: ... - - @abc.abstractmethod - def lower(self, expr: expression.OpExpression) -> expression.Expression: ... - - -def lower_ops( - root: bigframe_node.BigFrameNode, rules: Sequence[OpLoweringRule] -) -> bigframe_node.BigFrameNode: - rules_by_op = {rule.op: rule for rule in rules} - - def lower_expr(expr: expression.Expression): - def lower_expr_step(expr: expression.Expression) -> expression.Expression: - if isinstance(expr, expression.OpExpression): - maybe_rule = rules_by_op.get(expr.op.__class__) - if maybe_rule: - return maybe_rule.lower(expr) - return expr - - return expr.bottom_up(lower_expr_step) - - def lower_node(node: bigframe_node.BigFrameNode) -> bigframe_node.BigFrameNode: - if isinstance( - node, (nodes.ProjectionNode, nodes.FilterNode, nodes.OrderByNode) - ): - return node.transform_exprs(lower_expr) - else: - return node - - return root.bottom_up(lower_node) diff --git a/bigframes/core/rewrite/order.py b/bigframes/core/rewrite/order.py deleted file mode 100644 index b61fca82182..00000000000 --- a/bigframes/core/rewrite/order.py +++ /dev/null @@ -1,461 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -import dataclasses -import functools -from typing import Mapping, Tuple - -import bigframes.core.nodes -import bigframes.core.ordering -import bigframes.core.window_spec -from bigframes.core import agg_expressions, expression, identifiers -from bigframes.operations import aggregations as agg_ops - - -def defer_order( - root: bigframes.core.nodes.ResultNode, output_hidden_row_keys: bool -) -> bigframes.core.nodes.ResultNode: - new_child, order = _pull_up_order(root.child, order_root=True) - order_by = ( - order.with_ordering_columns(root.order_by.all_ordering_columns) - if root.order_by - else order - ) - if output_hidden_row_keys: - output_names = tuple((expression.DerefOp(id), id.sql) for id in new_child.ids) - else: - output_names = root.output_cols - return dataclasses.replace( - root, output_cols=output_names, child=new_child, order_by=order_by - ) - - -def bake_order( - node: bigframes.core.nodes.BigFrameNode, -) -> bigframes.core.nodes.BigFrameNode: - node, _ = _pull_up_order(node, order_root=False) - return node - - -def pull_out_order( - node: bigframes.core.nodes.BigFrameNode, -) -> Tuple[bigframes.core.nodes.BigFrameNode, bigframes.core.ordering.RowOrdering]: - import bigframes.core.rewrite.slices - - node = node.bottom_up(bigframes.core.rewrite.slices.rewrite_slice) - return _pull_up_order(node, order_root=True) - - -# Makes ordering explicit in window definitions -def _pull_up_order( - root: bigframes.core.nodes.BigFrameNode, - *, - order_root: bool = True, -) -> Tuple[bigframes.core.nodes.BigFrameNode, bigframes.core.ordering.RowOrdering]: - """ - Pull the ordering up, putting full order definition into window ops. - - May create extra colums, which must be removed by callers if they want to preserve original schema. - - Requires the following nodes to be removed/rewritten: SliceNode - - """ - - @functools.cache - def pull_up_order_inner( - node: bigframes.core.nodes.BigFrameNode, - ) -> Tuple[bigframes.core.nodes.BigFrameNode, bigframes.core.ordering.RowOrdering]: - """Pull filter nodes out of a tree section.""" - if isinstance(node, bigframes.core.nodes.ReversedNode): - child_result, child_order = pull_up_order_inner(node.child) - return child_result, child_order.with_reverse() - elif isinstance(node, bigframes.core.nodes.OrderByNode): - # unstable sorts don't care about previous order, total orders override previous order - if (not node.stable) or node.is_total_order: - new_node = remove_order(node.child) - else: - new_node, child_order = pull_up_order_inner(node.child) - - new_by = [] - ids: list[identifiers.ColumnId] = [] - for part in node.by: - if not isinstance( - part.scalar_expression, bigframes.core.expression.DerefOp - ): - id = identifiers.ColumnId.unique() - new_node = bigframes.core.nodes.ProjectionNode( - new_node, ((part.scalar_expression, id),) - ) - new_part = bigframes.core.ordering.OrderingExpression( - bigframes.core.expression.DerefOp(id), - part.direction, - part.na_last, - ) - new_by.append(new_part) - ids.append(id) - else: - new_by.append(part) - ids.append(part.scalar_expression.id) - - if node.is_total_order: - new_order: bigframes.core.ordering.RowOrdering = ( - bigframes.core.ordering.TotalOrdering( - ordering_value_columns=tuple(new_by), - total_ordering_columns=frozenset( - map(lambda x: bigframes.core.expression.DerefOp(x), ids) - ), - ) - ) - elif not node.stable: - new_order = bigframes.core.ordering.RowOrdering( - ordering_value_columns=tuple(new_by), - ) - else: - assert child_order - new_order = child_order.with_ordering_columns(new_by) - return new_node, new_order - elif isinstance(node, bigframes.core.nodes.ProjectionNode): - child_result, child_order = pull_up_order_inner(node.child) - return node.replace_child(child_result), child_order - elif isinstance(node, bigframes.core.nodes.JoinNode): - if node.propogate_order: - return pull_order_join(node) - else: - return ( - dataclasses.replace( - node, - left_child=remove_order_strict(node.left_child), - right_child=remove_order_strict(node.right_child), - ), - bigframes.core.ordering.RowOrdering(), - ) - elif isinstance(node, bigframes.core.nodes.ConcatNode): - return pull_order_concat(node) - elif isinstance(node, bigframes.core.nodes.FromRangeNode): - new_start = remove_order_strict(node.start) - new_end = remove_order_strict(node.end) - - new_node = dataclasses.replace(node, start=new_start, end=new_end) - return node, bigframes.core.ordering.TotalOrdering.from_primary_key( - [node.output_id] - ) - elif isinstance(node, bigframes.core.nodes.ReadLocalNode): - if node.offsets_col is None: - offsets_id = identifiers.ColumnId.unique() - new_root = dataclasses.replace(node, offsets_col=offsets_id) - return new_root, bigframes.core.ordering.TotalOrdering.from_offset_col( - offsets_id - ) - else: - return node, bigframes.core.ordering.TotalOrdering.from_offset_col( - node.offsets_col - ) - elif isinstance(node, bigframes.core.nodes.ReadTableNode): - if node.source.ordering is not None: - return node.pull_out_order() - else: - # No defined ordering - return node, bigframes.core.ordering.RowOrdering() - elif isinstance(node, bigframes.core.nodes.PromoteOffsetsNode): - child_result, child_order = pull_up_order_inner(node.child) - if child_order.is_total_ordering and child_order.is_sequential: - # special case, we can just project the ordering - order_expression = child_order.total_order_col - assert order_expression is not None - order_expression.scalar_expression - new_node = bigframes.core.nodes.ProjectionNode( - child_result, ((order_expression.scalar_expression, node.col_id),) - ) - return new_node, bigframes.core.ordering.TotalOrdering.from_offset_col( - node.col_id - ) - else: - # Otherwise we need to generate offsets - agg = agg_expressions.NullaryAggregation(agg_ops.RowNumberOp()) - col_def = bigframes.core.nodes.ColumnDef(agg, node.col_id) - window_spec = bigframes.core.window_spec.unbound( - ordering=tuple(child_order.all_ordering_columns) - ) - new_offsets_node = bigframes.core.nodes.WindowOpNode( - child_result, (col_def,), window_spec - ) - return ( - new_offsets_node, - bigframes.core.ordering.TotalOrdering.from_offset_col(node.col_id), - ) - elif isinstance(node, bigframes.core.nodes.FilterNode): - child_result, child_order = pull_up_order_inner(node.child) - return node.replace_child(child_result), child_order.with_non_sequential() - elif isinstance(node, bigframes.core.nodes.InNode): - child_result, child_order = pull_up_order_inner(node.left_child) - subquery_result = remove_order_strict(node.right_child) - return ( - dataclasses.replace( - node, left_child=child_result, right_child=subquery_result - ), - child_order, - ) - elif isinstance(node, bigframes.core.nodes.SelectionNode): - child_result, child_order = pull_up_order_inner(node.child) - selected_ids = set(ref.id for ref, _ in node.input_output_pairs) - unselected_order_cols = tuple( - col for col in child_order.referenced_columns if col not in selected_ids - ) - # Create unique ids just to be safe - new_selections = { - col: identifiers.ColumnId.unique() for col in unselected_order_cols - } - all_selections = node.input_output_pairs + tuple( - bigframes.core.nodes.AliasedRef(bigframes.core.expression.DerefOp(k), v) - for k, v in new_selections.items() - ) - new_select_node = dataclasses.replace( - node, child=child_result, input_output_pairs=all_selections - ) - new_order = child_order.remap_column_refs(new_select_node.get_id_mapping()) - return new_select_node, new_order - elif isinstance(node, bigframes.core.nodes.AggregateNode): - if node.has_ordered_ops: - child_result, child_order = pull_up_order_inner(node.child) - new_order_by = child_order.with_ordering_columns(node.order_by) - new_order = bigframes.core.ordering.TotalOrdering.from_primary_key( - [ref.id for ref in node.by_column_ids] - ) - return ( - dataclasses.replace( - node, - child=child_result, - order_by=tuple(new_order_by.all_ordering_columns), - ), - new_order, - ) - else: - child_result = remove_order(node.child) - return node.replace_child( - child_result - ), bigframes.core.ordering.TotalOrdering.from_primary_key( - [ref.id for ref in node.by_column_ids] - ) - elif isinstance(node, bigframes.core.nodes.WindowOpNode): - child_result, child_order = pull_up_order_inner(node.child) - if node.inherits_order: - new_window_order = ( - *node.window_spec.ordering, - *child_order.all_ordering_columns, - ) - new_window_spec = dataclasses.replace( - node.window_spec, ordering=new_window_order - ) - else: - new_window_spec = node.window_spec - return ( - dataclasses.replace( - node, child=child_result, window_spec=new_window_spec - ), - child_order, - ) - elif isinstance(node, bigframes.core.nodes.RandomSampleNode): - child_result, child_order = pull_up_order_inner(node.child) - return node.replace_child(child_result), child_order.with_non_sequential() - elif isinstance(node, bigframes.core.nodes.ExplodeNode): - child_result, child_order = pull_up_order_inner(node.child) - if node.offsets_col is None: - offsets_id = identifiers.ColumnId.unique() - new_explode: bigframes.core.nodes.BigFrameNode = dataclasses.replace( - node, child=child_result, offsets_col=offsets_id - ) - else: - offsets_id = node.offsets_col - new_explode = node.replace_child(child_result) - inner_order = bigframes.core.ordering.TotalOrdering.from_offset_col( - offsets_id - ) - return new_explode, child_order.join(inner_order) - raise ValueError(f"Unexpected node type {type(node).__name__}") - - def pull_order_concat( - node: bigframes.core.nodes.ConcatNode, - ) -> Tuple[ - bigframes.core.nodes.BigFrameNode, bigframes.core.ordering.TotalOrdering - ]: - new_sources = [] - for i, source in enumerate(node.child_nodes): - new_source, order = pull_up_order_inner(source) - offsets_id = identifiers.ColumnId.unique() - table_id = identifiers.ColumnId.unique() - if order.is_total_ordering and order.integer_encoding.is_encoded: - order_expression = order.total_order_col - assert order_expression is not None - new_source = bigframes.core.nodes.ProjectionNode( - new_source, ((order_expression.scalar_expression, offsets_id),) - ) - else: - agg = agg_expressions.NullaryAggregation(agg_ops.RowNumberOp()) - window_spec = bigframes.core.window_spec.unbound( - ordering=tuple(order.all_ordering_columns) - ) - col_def = bigframes.core.nodes.ColumnDef(agg, offsets_id) - new_source = bigframes.core.nodes.WindowOpNode( - new_source, (col_def,), window_spec - ) - new_source = bigframes.core.nodes.ProjectionNode( - new_source, ((bigframes.core.expression.const(i), table_id),) - ) - selection = tuple( - ( - bigframes.core.nodes.AliasedRef.identity(id) - for id in (*source.ids, table_id, offsets_id) - ) - ) - new_source = bigframes.core.nodes.SelectionNode(new_source, selection) - new_sources.append(new_source) - - union_offsets_id = identifiers.ColumnId.unique() - union_table_id = identifiers.ColumnId.unique() - new_ids = (*node.output_ids, union_table_id, union_offsets_id) - new_node = dataclasses.replace( - node, children=tuple(new_sources), output_ids=new_ids - ) - new_ordering = bigframes.core.ordering.TotalOrdering.from_primary_key( - (union_table_id, union_offsets_id) - ) - return new_node, new_ordering - - def pull_order_join( - node: bigframes.core.nodes.JoinNode, - ) -> Tuple[bigframes.core.nodes.BigFrameNode, bigframes.core.ordering.RowOrdering]: - left_child, left_order = pull_up_order_inner(node.left_child) - # as tree is a dag, and pull_up_order_inner is memoized, self-joins can create conflicts in new columns - right_child, right_order = pull_up_order_inner(node.right_child) - conflicts = set(left_child.ids) & set(right_child.ids) - if conflicts: - right_child, mapping = rename_cols(right_child, conflicts) - right_order = right_order.remap_column_refs( - mapping, allow_partial_bindings=True - ) - - if node.type in ("right", "outer"): - # right side is nullable - left_indicator = identifiers.ColumnId.unique() - left_child = bigframes.core.nodes.ProjectionNode( - left_child, ((bigframes.core.expression.const(True), left_indicator),) - ) - left_order = left_order.with_ordering_columns( - [bigframes.core.ordering.descending_over(left_indicator)] - ) - if node.type in ("left", "outer"): - # right side is nullable - right_indicator = identifiers.ColumnId.unique() - right_child = bigframes.core.nodes.ProjectionNode( - right_child, ((bigframes.core.expression.const(True), right_indicator),) - ) - right_order = right_order.with_ordering_columns( - [bigframes.core.ordering.descending_over(right_indicator)] - ) - - new_join = dataclasses.replace( - node, left_child=left_child, right_child=right_child - ) - new_order = ( - left_order.join(right_order) - if (node.type != "right") - else right_order.join(left_order) - ) - return new_join, new_order - - @functools.cache - def remove_order( - node: bigframes.core.nodes.BigFrameNode, - ) -> bigframes.core.nodes.BigFrameNode: - if isinstance( - node, (bigframes.core.nodes.OrderByNode, bigframes.core.nodes.ReversedNode) - ): - return remove_order(node.child) - elif isinstance( - node, - ( - bigframes.core.nodes.WindowOpNode, - bigframes.core.nodes.PromoteOffsetsNode, - ), - ): - if isinstance(node, bigframes.core.nodes.PromoteOffsetsNode): - node = rewrite_promote_offsets(node) - if node.inherits_order: - child_result, child_order = pull_up_order_inner(node.child) - new_window_order = ( - *node.window_spec.ordering, - *child_order.all_ordering_columns, - ) - new_window_spec = dataclasses.replace( - node.window_spec, ordering=new_window_order - ) - return dataclasses.replace( - node, child=child_result, window_spec=new_window_spec - ) - elif isinstance(node, bigframes.core.nodes.AggregateNode): - if node.has_ordered_ops: - child_result, child_order = pull_up_order_inner(node.child) - new_order_by = child_order.with_ordering_columns(node.order_by) - return dataclasses.replace( - node, - child=child_result, - order_by=tuple(new_order_by.all_ordering_columns), - ) - - return node.transform_children(remove_order) - - def remove_order_strict( - node: bigframes.core.nodes.BigFrameNode, - ) -> bigframes.core.nodes.BigFrameNode: - result = remove_order(node) - if result.ids != node.ids: - return bigframes.core.nodes.SelectionNode( - result, - tuple(bigframes.core.nodes.AliasedRef.identity(id) for id in node.ids), - ) - return result - - return ( - pull_up_order_inner(root) - if order_root - else (remove_order(root), bigframes.core.ordering.RowOrdering()) - ) - - -def rewrite_promote_offsets( - node: bigframes.core.nodes.PromoteOffsetsNode, -) -> bigframes.core.nodes.WindowOpNode: - agg = agg_expressions.NullaryAggregation(agg_ops.RowNumberOp()) - window_spec = bigframes.core.window_spec.unbound() - return bigframes.core.nodes.WindowOpNode( - node.child, (bigframes.core.nodes.ColumnDef(agg, node.col_id),), window_spec - ) - - -def rename_cols( - node: bigframes.core.nodes.BigFrameNode, cols: set[identifiers.ColumnId] -) -> Tuple[ - bigframes.core.nodes.BigFrameNode, - Mapping[identifiers.ColumnId, identifiers.ColumnId], -]: - mappings = dict((id, identifiers.ColumnId.unique()) for id in cols) - - result_node = bigframes.core.nodes.SelectionNode( - node, - tuple( - bigframes.core.nodes.AliasedRef.identity(id).remap_vars(mappings) - for id in node.ids - ), - ) - - return result_node, dict(mappings) diff --git a/bigframes/core/rewrite/pruning.py b/bigframes/core/rewrite/pruning.py deleted file mode 100644 index 29744d66cd6..00000000000 --- a/bigframes/core/rewrite/pruning.py +++ /dev/null @@ -1,188 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -import dataclasses -import functools -import itertools -import typing - -from bigframes.core import identifiers, nodes - - -def column_pruning( - root: nodes.BigFrameNode, -) -> nodes.BigFrameNode: - return nodes.top_down(root, prune_columns) - - -def to_fixed(max_iterations: int = 100): - def decorator(func): - @functools.wraps(func) - def wrapper(*args, **kwargs): - previous_result = None - current_result = func(*args, **kwargs) - attempts = 1 - - while attempts < max_iterations: - if current_result == previous_result: - return current_result - previous_result = current_result - current_result = func(current_result) - attempts += 1 - - return current_result - - return wrapper - - return decorator - - -@to_fixed(max_iterations=100) -def prune_columns(node: nodes.BigFrameNode): - if isinstance(node, nodes.SelectionNode): - result = prune_selection_child(node) - elif isinstance(node, nodes.ResultNode): - result = node.replace_child(prune_node(node.child, node.consumed_ids)) - elif isinstance(node, nodes.AggregateNode): - result = node.replace_child(prune_node(node.child, node.consumed_ids)) - else: - result = node - return result - - -def prune_selection_child( - selection: nodes.SelectionNode, -) -> nodes.BigFrameNode: - child = selection.child - - # Important to check this first - if list(selection.ids) == list(child.ids): - if all(ref.ref.id == ref.id for ref in selection.input_output_pairs): - # selection is no-op so just remove it entirely - return child - - if isinstance(child, nodes.SelectionNode): - return selection.remap_refs( - {id: ref.id for ref, id in child.input_output_pairs} - ).replace_child(child.child) - - elif isinstance(child, nodes.AdditiveNode): - if not set(field.id for field in child.added_fields) & selection.consumed_ids: - return selection.replace_child(child.additive_base) - needed_ids = selection.consumed_ids | child.referenced_ids - if isinstance(child, nodes.ProjectionNode): - # Projection expressions are independent, so can be individually removed from the node - child = dataclasses.replace( - child, - assignments=tuple( - (ex, id) for (ex, id) in child.assignments if id in needed_ids - ), - ) - return selection.replace_child( - child.replace_additive_base(prune_node(child.additive_base, needed_ids)) - ) - elif isinstance(child, nodes.ConcatNode): - indices = [ - list(child.ids).index(ref.id) for ref, _ in selection.input_output_pairs - ] - if len(indices) == 0: - # pushing zero-column selection into concat messes up emitter for now, which doesn't like zero columns - return selection - new_children = [] - for concat_node in child.child_nodes: - cc_ids = tuple(concat_node.ids) - sub_selection = tuple(nodes.AliasedRef.identity(cc_ids[i]) for i in indices) - new_children.append(nodes.SelectionNode(concat_node, sub_selection)) - return nodes.ConcatNode( - children=tuple(new_children), output_ids=tuple(selection.ids) - ) - # Nodes that pass through input columns - elif isinstance( - child, - ( - nodes.RandomSampleNode, - nodes.ReversedNode, - nodes.OrderByNode, - nodes.FilterNode, - nodes.SliceNode, - nodes.JoinNode, - nodes.ExplodeNode, - ), - ): - ids = selection.consumed_ids | child.referenced_ids - return selection.replace_child( - child.transform_children(lambda x: prune_node(x, ids)) - ) - elif isinstance(child, nodes.AggregateNode): - return selection.replace_child(prune_aggregate(child, selection.consumed_ids)) - elif isinstance(child, nodes.LeafNode): - return selection.replace_child(prune_leaf(child, selection.consumed_ids)) - return selection - - -def prune_node( - node: nodes.BigFrameNode, - ids: typing.AbstractSet[identifiers.ColumnId], -): - # This clause is important, ensures idempotency, so can reach fixed point - if not (set(node.ids) - ids): - return node - else: - # If no child ids are needed, probably a size op or numbering op above, keep a single column always - ids_to_keep = tuple(id for id in node.ids if id in ids) or tuple( - itertools.islice(node.ids, 0, 1) - ) - return nodes.SelectionNode( - node, - tuple(nodes.AliasedRef.identity(id) for id in ids_to_keep), - ) - - -def prune_aggregate( - node: nodes.AggregateNode, - used_cols: typing.AbstractSet[identifiers.ColumnId], -) -> nodes.AggregateNode: - pruned_aggs = ( - tuple(agg for agg in node.aggregations if agg[1] in used_cols) - or node.aggregations[0:1] - ) - return dataclasses.replace(node, aggregations=pruned_aggs) - - -@functools.singledispatch -def prune_leaf( - node: nodes.BigFrameNode, - used_cols: typing.AbstractSet[identifiers.ColumnId], -): ... - - -@prune_leaf.register -def prune_readlocal( - node: nodes.ReadLocalNode, - selection: typing.AbstractSet[identifiers.ColumnId], -) -> nodes.ReadLocalNode: - new_scan_list = node.scan_list.filter_cols(selection) - return dataclasses.replace( - node, - scan_list=new_scan_list, - offsets_col=node.offsets_col if (node.offsets_col in selection) else None, - ) - - -@prune_leaf.register -def prune_readtable( - node: nodes.ReadTableNode, - selection: typing.AbstractSet[identifiers.ColumnId], -) -> nodes.ReadTableNode: - new_scan_list = node.scan_list.filter_cols(selection) - return dataclasses.replace(node, scan_list=new_scan_list) diff --git a/bigframes/core/rewrite/scan_reduction.py b/bigframes/core/rewrite/scan_reduction.py deleted file mode 100644 index da609c1ea1f..00000000000 --- a/bigframes/core/rewrite/scan_reduction.py +++ /dev/null @@ -1,71 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -import dataclasses -import functools -from typing import Optional - -import bigframes.core.rewrite.slices -from bigframes.core import nodes - - -def try_reduce_to_table_scan(root: nodes.BigFrameNode) -> Optional[nodes.ReadTableNode]: - for node in root.unique_nodes(): - if not isinstance(node, (nodes.ReadTableNode, nodes.SelectionNode)): - return None - result = root.bottom_up(merge_scan) - if isinstance(result, nodes.ReadTableNode): - return result - return None - - -def try_reduce_to_local_scan( - node: nodes.BigFrameNode, -) -> Optional[tuple[nodes.ReadLocalNode, Optional[int]]]: - """Create a ReadLocalNode with optional limit, if possible. - - Similar to ReadApiSemiExecutor._try_adapt_plan. - """ - node, limit = bigframes.core.rewrite.slices.pull_out_limit(node) - - if not all( - map( - lambda x: isinstance(x, (nodes.ReadLocalNode, nodes.SelectionNode)), - node.unique_nodes(), - ) - ): - return None - result = node.bottom_up(merge_scan) - if isinstance(result, nodes.ReadLocalNode): - return result, limit - return None - - -@functools.singledispatch -def merge_scan(node: nodes.BigFrameNode) -> nodes.BigFrameNode: - return node - - -@merge_scan.register -def _(node: nodes.SelectionNode) -> nodes.BigFrameNode: - if not isinstance(node.child, (nodes.ReadTableNode, nodes.ReadLocalNode)): - return node - if node.has_multi_referenced_ids: - return node - if isinstance(node, nodes.ReadLocalNode) and node.offsets_col is not None: - return node - selection = { - aliased_ref.ref.id: aliased_ref.id for aliased_ref in node.input_output_pairs - } - new_scan_list = node.child.scan_list.project(selection) - return dataclasses.replace(node.child, scan_list=new_scan_list) diff --git a/bigframes/core/rewrite/schema_binding.py b/bigframes/core/rewrite/schema_binding.py deleted file mode 100644 index 14755d34a41..00000000000 --- a/bigframes/core/rewrite/schema_binding.py +++ /dev/null @@ -1,157 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import dataclasses -import typing - -from bigframes.core import agg_expressions, bigframe_node, nodes, ordering -from bigframes.core import expression as ex - - -def bind_schema_to_tree( - node: bigframe_node.BigFrameNode, -) -> bigframe_node.BigFrameNode: - return nodes.bottom_up(node, bind_schema_to_node) - - -def bind_schema_to_node( - node: bigframe_node.BigFrameNode, -) -> bigframe_node.BigFrameNode: - if isinstance(node, nodes.ProjectionNode): - bound_assignments = tuple( - (ex.bind_schema_fields(expr, node.child.field_by_id), id) - for expr, id in node.assignments - ) - return dataclasses.replace(node, assignments=bound_assignments) - - if isinstance(node, nodes.FilterNode): - bound_predicate = ex.bind_schema_fields(node.predicate, node.child.field_by_id) - return dataclasses.replace(node, predicate=bound_predicate) - - if isinstance(node, nodes.OrderByNode): - bound_bys = [] - for by in node.by: - bound_by = dataclasses.replace( - by, - scalar_expression=ex.bind_schema_fields( - by.scalar_expression, node.child.field_by_id - ), - ) - bound_bys.append(bound_by) - - return dataclasses.replace(node, by=tuple(bound_bys)) - - if isinstance(node, nodes.JoinNode): - conditions = tuple( - ( - ex.ResolvedDerefOp.from_field(node.left_child.field_by_id[left.id]), - ex.ResolvedDerefOp.from_field(node.right_child.field_by_id[right.id]), - ) - for left, right in node.conditions - ) - return dataclasses.replace( - node, - conditions=conditions, - ) - if isinstance(node, nodes.InNode): - return dataclasses.replace( - node, - left_col=ex.ResolvedDerefOp.from_field( - node.left_child.field_by_id[node.left_col.id] - ), - ) - - if isinstance(node, nodes.AggregateNode): - aggregations = [] - for aggregation, id in node.aggregations: - aggregations.append( - (_bind_schema_to_aggregation_expr(aggregation, node.child), id) - ) - - return dataclasses.replace( - node, - aggregations=tuple(aggregations), - ) - - if isinstance(node, nodes.WindowOpNode): - window_spec = dataclasses.replace( - node.window_spec, - grouping_keys=tuple( - typing.cast( - ex.DerefOp, ex.bind_schema_fields(expr, node.child.field_by_id) - ) - for expr in node.window_spec.grouping_keys - ), - ordering=tuple( - ordering.OrderingExpression( - scalar_expression=ex.bind_schema_fields( - expr.scalar_expression, node.child.field_by_id - ), - direction=expr.direction, - na_last=expr.na_last, - ) - for expr in node.window_spec.ordering - ), - ) - return dataclasses.replace( - node, - agg_exprs=tuple( - nodes.ColumnDef( - _bind_schema_to_aggregation_expr(cdef.expression, node.child), # type: ignore - cdef.id, - ) - for cdef in node.agg_exprs - ), - window_spec=window_spec, - ) - - return node - - -def _bind_schema_to_aggregation_expr( - aggregation: agg_expressions.Aggregation, - child: bigframe_node.BigFrameNode, -) -> agg_expressions.Aggregation: - assert isinstance(aggregation, agg_expressions.Aggregation), ( - f"Expected Aggregation, got {type(aggregation)}" - ) - - if isinstance(aggregation, agg_expressions.UnaryAggregation): - return typing.cast( - agg_expressions.Aggregation, - dataclasses.replace( - aggregation, - arg=typing.cast( - ex.RefOrConstant, - ex.bind_schema_fields(aggregation.arg, child.field_by_id), - ), - ), - ) - elif isinstance(aggregation, agg_expressions.BinaryAggregation): - return typing.cast( - agg_expressions.Aggregation, - dataclasses.replace( - aggregation, - left=typing.cast( - ex.RefOrConstant, - ex.bind_schema_fields(aggregation.left, child.field_by_id), - ), - right=typing.cast( - ex.RefOrConstant, - ex.bind_schema_fields(aggregation.right, child.field_by_id), - ), - ), - ) - else: - return aggregation diff --git a/bigframes/core/rewrite/select_pullup.py b/bigframes/core/rewrite/select_pullup.py deleted file mode 100644 index a15aba7663f..00000000000 --- a/bigframes/core/rewrite/select_pullup.py +++ /dev/null @@ -1,177 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import dataclasses -import functools -from typing import cast - -from bigframes.core import expression, identifiers, nodes - - -def defer_selection( - root: nodes.BigFrameNode, -) -> nodes.BigFrameNode: - """ - Defers SelectionNode operations in the tree, pulling them up. - - In many cases, these nodes will be merged or eliminated entirely, simplifying the overall tree. - """ - return nodes.bottom_up( - root, functools.partial(pull_up_select, prefer_source_names=True) - ) - - -def pull_up_select( - node: nodes.BigFrameNode, prefer_source_names: bool -) -> nodes.BigFrameNode: - if isinstance(node, nodes.LeafNode): - if prefer_source_names and isinstance(node, nodes.ReadTableNode): - return pull_up_source_ids(node) - else: - return node - if isinstance(node, nodes.JoinNode): - return pull_up_selects_under_join(node) - if isinstance(node, nodes.ConcatNode): - return handle_selects_under_concat(node) - if isinstance(node, nodes.UnaryNode): - return pull_up_select_unary(node) - # shouldn't hit this, but not worth crashing over - return node - - -def pull_up_source_ids(node: nodes.ReadTableNode) -> nodes.BigFrameNode: - if all(id.sql == source_id for id, source_id in node.scan_list.items): - return node - else: - new_scan_list = nodes.ScanList.from_items( - [ - nodes.ScanItem( - identifiers.ColumnId(scan_item.source_id), scan_item.source_id - ) - for scan_item in node.scan_list.items - ] - ) - new_source = dataclasses.replace(node, scan_list=new_scan_list) - new_selection = nodes.SelectionNode( - new_source, - tuple( - nodes.AliasedRef( - expression.DerefOp(identifiers.ColumnId(source_id)), id - ) - for id, source_id in node.scan_list.items - ), - ) - return new_selection - - -def pull_up_select_unary(node: nodes.UnaryNode) -> nodes.BigFrameNode: - child = node.child - if not isinstance(child, nodes.SelectionNode): - return node - - # Schema-preserving nodes - if isinstance( - node, - ( - nodes.ReversedNode, - nodes.OrderByNode, - nodes.SliceNode, - nodes.FilterNode, - nodes.RandomSampleNode, - ), - ): - pushed_down_node: nodes.BigFrameNode = node.remap_refs( - {id: ref.id for ref, id in child.input_output_pairs} - ).replace_child(child.child) - pulled_up_select = cast( - nodes.SelectionNode, child.replace_child(pushed_down_node) - ) - return pulled_up_select - elif isinstance( - node, - ( - nodes.SelectionNode, - nodes.ResultNode, - ), - ): - return node.remap_refs( - {id: ref.id for ref, id in child.input_output_pairs} - ).replace_child(child.child) - elif isinstance(node, nodes.AggregateNode): - pushed_down_agg = node.remap_refs( - {id: ref.id for ref, id in child.input_output_pairs} - ).replace_child(child.child) - new_selection = tuple( - nodes.AliasedRef.identity(id).remap_refs( - {id: ref.id for ref, id in child.input_output_pairs} - ) - for id in node.ids - ) - return nodes.SelectionNode(pushed_down_agg, new_selection) - elif isinstance(node, nodes.ExplodeNode): - pushed_down_node = node.remap_refs( - {id: ref.id for ref, id in child.input_output_pairs} - ).replace_child(child.child) - pulled_up_select = cast( - nodes.SelectionNode, child.replace_child(pushed_down_node) - ) - if node.offsets_col: - pulled_up_select = dataclasses.replace( - pulled_up_select, - input_output_pairs=( - *pulled_up_select.input_output_pairs, - nodes.AliasedRef( - expression.DerefOp(node.offsets_col), node.offsets_col - ), - ), - ) - return pulled_up_select - elif isinstance(node, nodes.AdditiveNode): - pushed_down_node = node.replace_additive_base(child.child).remap_refs( - {id: ref.id for ref, id in child.input_output_pairs} - ) - new_selection = ( - *child.input_output_pairs, - *( - nodes.AliasedRef(expression.DerefOp(col.id), col.id) - for col in node.added_fields - ), - ) - pulled_up_select = dataclasses.replace( - child, child=pushed_down_node, input_output_pairs=new_selection - ) - return pulled_up_select - # shouldn't hit this, but not worth crashing over - return node - - -def pull_up_selects_under_join(node: nodes.JoinNode) -> nodes.JoinNode: - # Can in theory pull up selects here, but it is a bit dangerous, in particular or self-joins, when there are more transforms to do. - # TODO: Safely pull up selects above join - return node - - -def handle_selects_under_concat(node: nodes.ConcatNode) -> nodes.ConcatNode: - new_children = [] - for child in node.child_nodes: - # remove select if no-op - if not isinstance(child, nodes.SelectionNode): - new_children.append(child) - else: - inputs = (ref.id for ref in child.input_output_pairs) - if inputs == tuple(child.child.ids): - new_children.append(child.child) - else: - new_children.append(child) - return dataclasses.replace(node, children=tuple(new_children)) diff --git a/bigframes/core/rewrite/slices.py b/bigframes/core/rewrite/slices.py deleted file mode 100644 index bed3a8a3f3d..00000000000 --- a/bigframes/core/rewrite/slices.py +++ /dev/null @@ -1,239 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -from __future__ import annotations - -import dataclasses -import functools -from typing import Optional, Sequence, Tuple - -import bigframes.core.expression as scalar_exprs -import bigframes.core.guid as guids -import bigframes.core.identifiers as ids -import bigframes.core.nodes as nodes -import bigframes.core.slices as slices -import bigframes.operations as ops - - -def pull_up_limits(root: nodes.ResultNode) -> nodes.ResultNode: - new_child, pulled_limit = pull_out_limit(root.child) - if new_child == root.child: - return root - elif pulled_limit is None: - return dataclasses.replace(root, child=new_child) - else: - # new child has redundant slice ops removed now - new_limit = min(pulled_limit, root.limit) if root.limit else pulled_limit - return dataclasses.replace(root, child=new_child, limit=new_limit) - - -def pull_out_limit( - root: nodes.BigFrameNode, -) -> Tuple[nodes.BigFrameNode, Optional[int]]: - """ - This is a BQ-sql specific optimization that can be helpful as ORDER BY LIMIT is more efficient than WHERE + ROW_NUMBER(). - - Only use this if writing to an unclustered table. Clustering is not compatible with ORDER BY. - """ - if isinstance(root, nodes.SliceNode): - # head case - # More cases could be handled, but this is by far the most important, as it is used by df.head(), df[:N] - if root.is_limit: - assert not root.start - assert root.step == 1 - assert root.stop is not None - limit = root.stop - new_root, prior_limit = pull_out_limit(root.child) - if (prior_limit is not None) and (prior_limit < limit): - limit = prior_limit - return new_root, limit - if root.is_noop: - new_root, prior_limit = pull_out_limit(root.child) - return new_root, prior_limit - elif ( - isinstance(root, (nodes.SelectionNode, nodes.ProjectionNode)) - and root.row_preserving - ): - new_child, prior_limit = pull_out_limit(root.child) - if prior_limit is not None: - return root.transform_children(lambda _: new_child), prior_limit - # Most ops don't support pulling up slice, like filter, agg, join, etc. - return root, None - - -def rewrite_slice(node: nodes.BigFrameNode): - if not isinstance(node, nodes.SliceNode): - return node - - slice_def = (node.start, node.stop, node.step) - # no-op (eg. df[::1]) - if slices.is_noop(slice_def, node.child.row_count): - return node.child - - # No filtering, just reverse (eg. df[::-1]) - if slices.is_reverse(slice_def, node.child.row_count): - return nodes.ReversedNode(node.child) - - if node.child.row_count: - slice_def = slices.to_forward_offsets(slice_def, node.child.row_count) - return slice_as_filter(node.child, *slice_def) - - -def slice_as_filter( - node: nodes.BigFrameNode, start: Optional[int], stop: Optional[int], step: int -) -> nodes.BigFrameNode: - if ( - ((start is None) or (start >= 0)) - and ((stop is None) or (stop >= 0)) - and (step > 0) - ): - node_w_offset = add_offsets(node) - predicate = convert_simple_slice( - scalar_exprs.DerefOp(node_w_offset.col_id), start or 0, stop, step - ) - filtered = nodes.FilterNode(node_w_offset, predicate) - return drop_cols(filtered, (node_w_offset.col_id,)) - - # fallback cases, generate both forward and backward offsets - if step < 0: - forward_offsets = add_offsets(node) - reversed_offsets = add_offsets(nodes.ReversedNode(forward_offsets)) - dual_indexed = reversed_offsets - else: - reversed_offsets = add_offsets(nodes.ReversedNode(node)) - forward_offsets = add_offsets(nodes.ReversedNode(reversed_offsets)) - dual_indexed = forward_offsets - default_start = 0 if step >= 0 else -1 - predicate = convert_complex_slice( - scalar_exprs.DerefOp(forward_offsets.col_id), - scalar_exprs.DerefOp(reversed_offsets.col_id), - start if (start is not None) else default_start, - stop, - step, - ) - filtered = nodes.FilterNode(dual_indexed, predicate) - return drop_cols(filtered, (forward_offsets.col_id, reversed_offsets.col_id)) - - -def add_offsets(node: nodes.BigFrameNode) -> nodes.PromoteOffsetsNode: - # Allow providing custom id generator? - offsets_id = ids.ColumnId(guids.generate_guid()) - return nodes.PromoteOffsetsNode(node, offsets_id) - - -def drop_cols( - node: nodes.BigFrameNode, drop_cols: Tuple[ids.ColumnId, ...] -) -> nodes.SelectionNode: - # adding a whole node that redefines the schema is a lot of overhead, should do something more efficient - selections = tuple( - nodes.AliasedRef(scalar_exprs.DerefOp(id), id) - for id in node.ids - if id not in drop_cols - ) - return nodes.SelectionNode(node, selections) - - -def convert_simple_slice( - offsets: scalar_exprs.Expression, - start: int = 0, - stop: Optional[int] = None, - step: int = 1, -) -> scalar_exprs.Expression: - """Performs slice but only for positive step size.""" - assert start >= 0 - assert (stop is None) or (stop >= 0) - - conditions = [] - if start > 0: - conditions.append(ops.ge_op.as_expr(offsets, scalar_exprs.const(start))) - if (stop is not None) and (stop >= 0): - conditions.append(ops.lt_op.as_expr(offsets, scalar_exprs.const(stop))) - if step > 1: - start_diff = ops.sub_op.as_expr(offsets, scalar_exprs.const(start)) - step_cond = ops.eq_op.as_expr( - ops.mod_op.as_expr(start_diff, scalar_exprs.const(step)), - scalar_exprs.const(0), - ) - conditions.append(step_cond) - - return merge_predicates(conditions) or scalar_exprs.const(True) - - -def convert_complex_slice( - forward_offsets: scalar_exprs.Expression, - reverse_offsets: scalar_exprs.Expression, - start: int, - stop: Optional[int], - step: int = 1, -) -> scalar_exprs.Expression: - conditions = [] - assert step != 0 - if start or ((start is not None) and step < 0): - if start > 0 and step > 0: - start_cond = ops.ge_op.as_expr(forward_offsets, scalar_exprs.const(start)) - elif start >= 0 and step < 0: - start_cond = ops.le_op.as_expr(forward_offsets, scalar_exprs.const(start)) - elif start < 0 and step > 0: - start_cond = ops.le_op.as_expr( - reverse_offsets, scalar_exprs.const(-start - 1) - ) - else: - assert start < 0 and step < 0 - start_cond = ops.ge_op.as_expr( - reverse_offsets, scalar_exprs.const(-start - 1) - ) - conditions.append(start_cond) - if stop is not None: - if stop >= 0 and step > 0: - stop_cond = ops.lt_op.as_expr(forward_offsets, scalar_exprs.const(stop)) - elif stop >= 0 and step < 0: - stop_cond = ops.gt_op.as_expr(forward_offsets, scalar_exprs.const(stop)) - elif stop < 0 and step > 0: - stop_cond = ops.gt_op.as_expr( - reverse_offsets, scalar_exprs.const(-stop - 1) - ) - else: - assert (stop < 0) and (step < 0) - stop_cond = ops.lt_op.as_expr( - reverse_offsets, scalar_exprs.const(-stop - 1) - ) - conditions.append(stop_cond) - if step != 1: - if step > 1 and start >= 0: - start_diff = ops.sub_op.as_expr(forward_offsets, scalar_exprs.const(start)) - elif step > 1 and start < 0: - start_diff = ops.sub_op.as_expr( - reverse_offsets, scalar_exprs.const(-start + 1) - ) - elif step < 0 and start >= 0: - start_diff = ops.add_op.as_expr(forward_offsets, scalar_exprs.const(start)) - else: - assert step < 0 and start < 0 - start_diff = ops.add_op.as_expr( - reverse_offsets, scalar_exprs.const(-start + 1) - ) - step_cond = ops.eq_op.as_expr( - ops.mod_op.as_expr(start_diff, scalar_exprs.const(step)), - scalar_exprs.const(0), - ) - conditions.append(step_cond) - return merge_predicates(conditions) or scalar_exprs.const(True) - - -def merge_predicates( - predicates: Sequence[scalar_exprs.Expression], -) -> Optional[scalar_exprs.Expression]: - if len(predicates) == 0: - return None - - return functools.reduce(ops.and_op.as_expr, predicates) diff --git a/bigframes/core/rewrite/timedeltas.py b/bigframes/core/rewrite/timedeltas.py deleted file mode 100644 index 7544963732e..00000000000 --- a/bigframes/core/rewrite/timedeltas.py +++ /dev/null @@ -1,266 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from __future__ import annotations - -import dataclasses -import functools -import typing - -from bigframes import dtypes -from bigframes import operations as ops -from bigframes.core import agg_expressions as ex_types -from bigframes.core import expression as ex -from bigframes.core import nodes, schema, utils -from bigframes.operations import aggregations as aggs - - -@dataclasses.dataclass -class _TypedExpr: - expr: ex.Expression - dtype: dtypes.Dtype - - @classmethod - def create_op_expr( - cls, op: typing.Union[ops.ScalarOp, ops.RowOp], *inputs: _TypedExpr - ) -> _TypedExpr: - expr = op.as_expr(*tuple(x.expr for x in inputs)) # type: ignore - dtype = op.output_type(*tuple(x.dtype for x in inputs)) - return cls(expr, dtype) - - -def rewrite_timedelta_expressions(root: nodes.BigFrameNode) -> nodes.BigFrameNode: - """ - Rewrites expressions to properly handle timedelta values, because this type does not exist - in the SQL world. - """ - if isinstance(root, nodes.ProjectionNode): - updated_assignments = tuple( - (_rewrite_expressions(expr, root.schema).expr, column_id) - for expr, column_id in root.assignments - ) - return nodes.ProjectionNode(root.child, updated_assignments) - - if isinstance(root, nodes.FilterNode): - return nodes.FilterNode( - root.child, _rewrite_expressions(root.predicate, root.schema).expr - ) - - if isinstance(root, nodes.OrderByNode): - by = tuple(_rewrite_ordering_expr(x, root.schema) for x in root.by) - return nodes.OrderByNode(root.child, by) - - if isinstance(root, nodes.WindowOpNode): - return nodes.WindowOpNode( - root.child, - tuple( - nodes.ColumnDef( - _rewrite_aggregation(cdef.expression, root.schema), cdef.id - ) - for cdef in root.agg_exprs - ), - root.window_spec, - ) - - if isinstance(root, nodes.AggregateNode): - updated_aggregations = tuple( - (_rewrite_aggregation(agg, root.child.schema), col_id) - for agg, col_id in root.aggregations - ) - return nodes.AggregateNode( - root.child, - updated_aggregations, - root.by_column_ids, - root.order_by, - root.dropna, - ) - - return root - - -def _rewrite_ordering_expr( - expr: nodes.OrderingExpression, schema: schema.ArraySchema -) -> nodes.OrderingExpression: - by = _rewrite_expressions(expr.scalar_expression, schema).expr - return nodes.OrderingExpression(by, expr.direction, expr.na_last) - - -@functools.cache -def _rewrite_expressions(expr: ex.Expression, schema: schema.ArraySchema) -> _TypedExpr: - if isinstance(expr, ex.DerefOp): - return _TypedExpr(expr, schema.get_type(expr.id.sql)) - - if isinstance(expr, ex.ScalarConstantExpression): - return _rewrite_scalar_constant_expr(expr) - - if isinstance(expr, ex.OpExpression): - updated_inputs = tuple( - map(lambda x: _rewrite_expressions(x, schema), expr.inputs) - ) - return _rewrite_op_expr(expr, updated_inputs) - - raise AssertionError(f"Unexpected expression type: {type(expr)}") - - -def _rewrite_scalar_constant_expr(expr: ex.ScalarConstantExpression) -> _TypedExpr: - if expr.value is None: - return _TypedExpr(ex.const(None, expr.dtype), expr.dtype) - if expr.dtype == dtypes.TIMEDELTA_DTYPE: - int_repr = utils.timedelta_to_micros(expr.value) # type: ignore - return _TypedExpr(ex.const(int_repr, expr.dtype), expr.dtype) - - return _TypedExpr(expr, expr.dtype) - - -def _rewrite_op_expr( - expr: ex.OpExpression, inputs: typing.Tuple[_TypedExpr, ...] -) -> _TypedExpr: - if isinstance(expr.op, ops.SubOp): - return _rewrite_sub_op(inputs[0], inputs[1]) - - if isinstance(expr.op, ops.AddOp): - return _rewrite_add_op(inputs[0], inputs[1]) - - if isinstance(expr.op, ops.MulOp): - return _rewrite_mul_op(inputs[0], inputs[1]) - - if isinstance(expr.op, ops.DivOp): - return _rewrite_div_op(inputs[0], inputs[1]) - - if isinstance(expr.op, ops.FloorDivOp): - # We need to re-write floor div because for numerics: int // float => float - # but for timedeltas: int(timedelta) // float => int(timedelta) - return _rewrite_floordiv_op(inputs[0], inputs[1]) - - if isinstance(expr.op, ops.ToTimedeltaOp): - return _rewrite_to_timedelta_op(expr.op, inputs[0]) - - return _TypedExpr.create_op_expr(expr.op, *inputs) - - -def _rewrite_sub_op(left: _TypedExpr, right: _TypedExpr) -> _TypedExpr: - if dtypes.is_datetime_like(left.dtype) and dtypes.is_datetime_like(right.dtype): - return _TypedExpr.create_op_expr(ops.timestamp_diff_op, left, right) - - if dtypes.is_datetime_like(left.dtype) and right.dtype == dtypes.TIMEDELTA_DTYPE: - return _TypedExpr.create_op_expr(ops.timestamp_sub_op, left, right) - - if left.dtype == dtypes.DATE_DTYPE and right.dtype == dtypes.DATE_DTYPE: - return _TypedExpr.create_op_expr(ops.date_diff_op, left, right) - - if left.dtype == dtypes.DATE_DTYPE and right.dtype == dtypes.TIMEDELTA_DTYPE: - return _TypedExpr.create_op_expr(ops.date_sub_op, left, right) - - return _TypedExpr.create_op_expr(ops.sub_op, left, right) - - -def _rewrite_add_op(left: _TypedExpr, right: _TypedExpr) -> _TypedExpr: - if dtypes.is_datetime_like(left.dtype) and right.dtype == dtypes.TIMEDELTA_DTYPE: - return _TypedExpr.create_op_expr(ops.timestamp_add_op, left, right) - - if left.dtype == dtypes.TIMEDELTA_DTYPE and dtypes.is_datetime_like(right.dtype): - # Re-arrange operands such that timestamp is always on the left and timedelta is - # always on the right. - return _TypedExpr.create_op_expr(ops.timestamp_add_op, right, left) - - if left.dtype == dtypes.DATE_DTYPE and right.dtype == dtypes.TIMEDELTA_DTYPE: - return _TypedExpr.create_op_expr(ops.date_add_op, left, right) - - if left.dtype == dtypes.TIMEDELTA_DTYPE and right.dtype == dtypes.DATE_DTYPE: - # Re-arrange operands such that date is always on the left and timedelta is - # always on the right. - return _TypedExpr.create_op_expr(ops.date_add_op, right, left) - - return _TypedExpr.create_op_expr(ops.add_op, left, right) - - -def _rewrite_mul_op(left: _TypedExpr, right: _TypedExpr) -> _TypedExpr: - result = _TypedExpr.create_op_expr(ops.mul_op, left, right) - - if left.dtype == dtypes.TIMEDELTA_DTYPE and dtypes.is_numeric(right.dtype): - return _TypedExpr.create_op_expr(ops.timedelta_floor_op, result) - if dtypes.is_numeric(left.dtype) and right.dtype == dtypes.TIMEDELTA_DTYPE: - return _TypedExpr.create_op_expr(ops.timedelta_floor_op, result) - - return result - - -def _rewrite_div_op(left: _TypedExpr, right: _TypedExpr) -> _TypedExpr: - result = _TypedExpr.create_op_expr(ops.div_op, left, right) - - if left.dtype == dtypes.TIMEDELTA_DTYPE and dtypes.is_numeric(right.dtype): - return _TypedExpr.create_op_expr(ops.timedelta_floor_op, result) - - return result - - -def _rewrite_floordiv_op(left: _TypedExpr, right: _TypedExpr) -> _TypedExpr: - if left.dtype == dtypes.TIMEDELTA_DTYPE and dtypes.is_numeric(right.dtype): - return _TypedExpr.create_op_expr( - ops.timedelta_floor_op, _TypedExpr.create_op_expr(ops.div_op, left, right) - ) - - return _TypedExpr.create_op_expr(ops.floordiv_op, left, right) - - -def _rewrite_to_timedelta_op(op: ops.ToTimedeltaOp, arg: _TypedExpr): - if arg.dtype == dtypes.TIMEDELTA_DTYPE: - # Do nothing for values that are already timedeltas - return arg - - return _TypedExpr.create_op_expr(op, arg) - - -@functools.cache -def _rewrite_aggregation( - aggregation: ex_types.Aggregation, schema: schema.ArraySchema -) -> ex_types.Aggregation: - if not isinstance(aggregation, ex_types.UnaryAggregation): - return aggregation - - if isinstance(aggregation.arg, ex.DerefOp): - input_type = schema.get_type(aggregation.arg.id.sql) - else: - input_type = aggregation.arg.output_type - - if isinstance(aggregation.op, aggs.DiffOp): - if dtypes.is_datetime_like(input_type): - return ex_types.UnaryAggregation( - aggs.TimeSeriesDiffOp(aggregation.op.periods), aggregation.arg - ) - elif input_type == dtypes.DATE_DTYPE: - return ex_types.UnaryAggregation( - aggs.DateSeriesDiffOp(aggregation.op.periods), aggregation.arg - ) - - if isinstance(aggregation.op, aggs.StdOp) and input_type == dtypes.TIMEDELTA_DTYPE: - return ex_types.UnaryAggregation( - aggs.StdOp(should_floor_result=True), aggregation.arg - ) - - if isinstance(aggregation.op, aggs.MeanOp) and input_type == dtypes.TIMEDELTA_DTYPE: - return ex_types.UnaryAggregation( - aggs.MeanOp(should_floor_result=True), aggregation.arg - ) - - if ( - isinstance(aggregation.op, aggs.QuantileOp) - and input_type == dtypes.TIMEDELTA_DTYPE - ): - return ex_types.UnaryAggregation( - aggs.QuantileOp(q=aggregation.op.q, should_floor_result=True), - aggregation.arg, - ) - - return aggregation diff --git a/bigframes/core/rewrite/udfs.py b/bigframes/core/rewrite/udfs.py deleted file mode 100644 index 286a9d9d940..00000000000 --- a/bigframes/core/rewrite/udfs.py +++ /dev/null @@ -1,46 +0,0 @@ -# Copyright 2026 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -from __future__ import annotations - -import dataclasses - -import bigframes.functions.udf_def as udf_def -import bigframes.operations as ops -from bigframes.core import bigframe_node, expression -from bigframes.core.rewrite import op_lowering - - -@dataclasses.dataclass -class LowerRemoteFunctionRule(op_lowering.OpLoweringRule): - @property - def op(self) -> type[ops.ScalarOp]: - return ops.RemoteFunctionOp - - def lower(self, expr: expression.OpExpression) -> expression.Expression: - assert isinstance(expr.op, ops.RemoteFunctionOp) - func_def = expr.op.function_def - devirtualized_expr = ops.RemoteFunctionOp( - func_def.with_devirtualize(), - ).as_expr(*expr.children) - if isinstance(func_def.signature.output, udf_def.VirtualListTypeV1): - return func_def.signature.output.out_expr(devirtualized_expr) - else: - return devirtualized_expr - - -UDF_LOWERING_RULES = (LowerRemoteFunctionRule(),) - - -def lower_udfs(root: bigframe_node.BigFrameNode) -> bigframe_node.BigFrameNode: - return op_lowering.lower_ops(root, rules=UDF_LOWERING_RULES) diff --git a/bigframes/core/rewrite/windows.py b/bigframes/core/rewrite/windows.py deleted file mode 100644 index 4d271a072d4..00000000000 --- a/bigframes/core/rewrite/windows.py +++ /dev/null @@ -1,139 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from __future__ import annotations - -import dataclasses -import functools -import itertools - -import bigframes.dtypes -from bigframes import operations as ops -from bigframes.core import ( - agg_expressions, - expression, - guid, - identifiers, - nodes, - ordering, -) -from bigframes.operations import aggregations as agg_ops - - -def simplify_complex_windows( - window_expr: agg_expressions.WindowExpression, -) -> expression.Expression: - result_expr: expression.Expression = window_expr - agg_expr = window_expr.analytic_expr - window_spec = window_expr.window - clauses: list[tuple[expression.Expression, expression.Expression]] = [] - if window_spec.min_periods and len(agg_expr.inputs) > 0: - if not agg_expr.op.nulls_count_for_min_values: - is_observation = ops.notnull_op.as_expr() - - # Most operations do not count NULL values towards min_periods - per_col_does_count = ( - ops.notnull_op.as_expr(input) for input in agg_expr.inputs - ) - # All inputs must be non-null for observation to count - is_observation = functools.reduce( - lambda x, y: ops.and_op.as_expr(x, y), per_col_does_count - ) - observation_sentinel = ops.AsTypeOp(bigframes.dtypes.INT_DTYPE).as_expr( - is_observation - ) - observation_count_expr = agg_expressions.WindowExpression( - agg_expressions.UnaryAggregation(agg_ops.sum_op, observation_sentinel), - window_spec, - ) - else: - # Operations like count treat even NULLs as valid observations for the sake of min_periods - # notnull is just used to convert null values to non-null (FALSE) values to be counted - is_observation = ops.notnull_op.as_expr(agg_expr.inputs[0]) - observation_count_expr = agg_expressions.WindowExpression( - agg_ops.count_op.as_expr(is_observation), - window_spec, - ) - clauses.append( - ( - ops.lt_op.as_expr( - observation_count_expr, expression.const(window_spec.min_periods) - ), - expression.const(None), - ) - ) - if clauses: - case_inputs = [ - *itertools.chain.from_iterable(clauses), - expression.const(True), - result_expr, - ] - result_expr = ops.CaseWhenOp().as_expr(*case_inputs) - return result_expr - - -def rewrite_range_rolling(node: nodes.BigFrameNode) -> nodes.BigFrameNode: - if not isinstance(node, nodes.WindowOpNode): - return node - - if not node.window_spec.is_range_bounded: - return node - - if len(node.window_spec.ordering) != 1: - raise ValueError( - "Range rolling should only be performed on exactly one column." - ) - - ordering_expr = node.window_spec.ordering[0] - - new_ordering = dataclasses.replace( - ordering_expr, - scalar_expression=ops.UnixMicros().as_expr(ordering_expr.scalar_expression), - ) - - return dataclasses.replace( - node, - window_spec=dataclasses.replace(node.window_spec, ordering=(new_ordering,)), - ) - - -def pull_out_window_order(root: nodes.BigFrameNode) -> nodes.BigFrameNode: - return root.bottom_up(rewrite_window_node) - - -def rewrite_window_node(node: nodes.BigFrameNode) -> nodes.BigFrameNode: - if not isinstance(node, nodes.WindowOpNode): - return node - if len(node.window_spec.ordering) == 0: - return node - else: - offsets_id = guid.generate_guid() - w_offsets = nodes.PromoteOffsetsNode( - node.child, identifiers.ColumnId(offsets_id) - ) - sorted_child = nodes.OrderByNode(w_offsets, node.window_spec.ordering) - new_window_node = dataclasses.replace( - node, - child=sorted_child, - window_spec=node.window_spec.without_order(force=True), - ) - w_resetted_order = nodes.OrderByNode( - new_window_node, - by=(ordering.ascending_over(identifiers.ColumnId(offsets_id)),), - is_total_order=True, - ) - w_offsets_dropped = nodes.SelectionNode( - w_resetted_order, tuple(nodes.AliasedRef.identity(id) for id in node.ids) - ) - return w_offsets_dropped diff --git a/bigframes/core/schema.py b/bigframes/core/schema.py deleted file mode 100644 index ab30b9bff14..00000000000 --- a/bigframes/core/schema.py +++ /dev/null @@ -1,136 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from __future__ import annotations - -import functools -import typing -from dataclasses import dataclass -from typing import Dict, Optional, Sequence - -import google.cloud.bigquery -import pyarrow - -import bigframes.dtypes - -ColumnIdentifierType = str - - -@dataclass(frozen=True) -class SchemaItem: - column: ColumnIdentifierType - dtype: bigframes.dtypes.Dtype - - -@dataclass(frozen=True) -class ArraySchema: - items: tuple[SchemaItem, ...] - - def __iter__(self): - yield from self.items - - @classmethod - def from_bq_schema( - cls, - schema: Sequence[google.cloud.bigquery.SchemaField], - column_type_overrides: Optional[Dict[str, bigframes.dtypes.Dtype]] = None, - columns: Optional[Sequence[str]] = None, - ): - if columns: - lookup = {field.name: field for field in schema} - schema = [lookup[col] for col in columns] - if column_type_overrides is None: - column_type_overrides = {} - items = tuple( - SchemaItem(name, column_type_overrides.get(name, dtype)) - for name, dtype in bigframes.dtypes.bf_type_from_type_kind(schema).items() - ) - return ArraySchema(items) - - @property - def names(self) -> typing.Tuple[str, ...]: - return tuple(item.column for item in self.items) - - @property - def dtypes(self) -> typing.Tuple[bigframes.dtypes.Dtype, ...]: - return tuple(item.dtype for item in self.items) - - @functools.cached_property - def _mapping(self) -> typing.Dict[ColumnIdentifierType, bigframes.dtypes.Dtype]: - return {item.column: item.dtype for item in self.items} - - def to_bigquery( - self, overrides: dict[bigframes.dtypes.Dtype, str] = {} - ) -> typing.Tuple[google.cloud.bigquery.SchemaField, ...]: - return tuple( - bigframes.dtypes.convert_to_schema_field( - item.column, item.dtype, overrides=overrides - ) - for item in self.items - ) - - def to_pyarrow(self, use_storage_types: bool = False) -> pyarrow.Schema: - fields = [] - for item in self.items: - pa_type = bigframes.dtypes.bigframes_dtype_to_arrow_dtype(item.dtype) - if use_storage_types: - pa_type = bigframes.dtypes.to_storage_type(pa_type) - fields.append( - pyarrow.field( - item.column, - type=pa_type, - nullable=not pyarrow.types.is_list(pa_type), - ) - ) - return pyarrow.schema(fields) - - def drop(self, columns: typing.Iterable[str]) -> ArraySchema: - return ArraySchema( - tuple(item for item in self.items if item.column not in columns) - ) - - def select(self, columns: typing.Iterable[str]) -> ArraySchema: - return ArraySchema( - tuple(SchemaItem(name, self.get_type(name)) for name in columns) - ) - - def rename(self, mapping: typing.Mapping[str, str]) -> ArraySchema: - return ArraySchema( - tuple( - SchemaItem(mapping.get(item.column, item.column), item.dtype) - for item in self.items - ) - ) - - def append(self, item: SchemaItem): - return ArraySchema(tuple([*self.items, item])) - - def prepend(self, item: SchemaItem): - return ArraySchema(tuple([item, *self.items])) - - def update_dtype( - self, id: ColumnIdentifierType, dtype: bigframes.dtypes.Dtype - ) -> ArraySchema: - return ArraySchema( - tuple( - SchemaItem(id, dtype) if item.column == id else item - for item in self.items - ) - ) - - def get_type(self, id: ColumnIdentifierType): - return self._mapping[id] - - def __len__(self) -> int: - return len(self.items) diff --git a/bigframes/core/sentinels.py b/bigframes/core/sentinels.py deleted file mode 100644 index ff9913f7c6f..00000000000 --- a/bigframes/core/sentinels.py +++ /dev/null @@ -1,31 +0,0 @@ -# Copyright 2026 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -"""Sentinel values used throughout BigFrames.""" - -from __future__ import annotations - -import enum - - -class Sentinel(enum.Enum): - """Default values used throughout BigFrames.""" - - """Default value for an optional argument. - - When a parameter is set to this, that parameter is explicitly omitted - from the SQL text. This allows for NULL (None in Python) to be explicitly - passed in to optional parameters. - """ - ARGUMENT_DEFAULT = enum.auto() diff --git a/bigframes/core/sequences.py b/bigframes/core/sequences.py deleted file mode 100644 index 6f1b7e455b0..00000000000 --- a/bigframes/core/sequences.py +++ /dev/null @@ -1,105 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from __future__ import annotations - -import collections.abc -import functools -import itertools -from typing import Iterable, Iterator, Sequence, TypeVar - -ColumnIdentifierType = str - - -T = TypeVar("T") - -# Further optimizations possible: -# * Support mapping operators -# * Support insertions and deletions - - -class ChainedSequence(collections.abc.Sequence[T]): - """ - Memory-optimized sequence from composing chain of existing sequences. - - Will use the provided parts as underlying storage - so do not mutate provided parts. - May merge small underlying parts for better access performance. - """ - - def __init__(self, *parts: Sequence[T]): - # Could build an index that makes random access faster? - self._parts: tuple[Sequence[T], ...] = tuple( - _defrag_parts(_flatten_parts(parts)) - ) - - def __getitem__(self, index): - if isinstance(index, slice): - return tuple(self)[index] - if index < 0: - index = len(self) + index - if index < 0: - raise IndexError("Index out of bounds") - - offset = 0 - for part in self._parts: - if (index - offset) < len(part): - return part[index - offset] - offset += len(part) - raise IndexError("Index out of bounds") - - @functools.cache - def __len__(self): - return sum(map(len, self._parts)) - - def __iter__(self): - for part in self._parts: - yield from part - - -def _flatten_parts(parts: Iterable[Sequence[T]]) -> Iterator[Sequence[T]]: - for part in parts: - if isinstance(part, ChainedSequence): - yield from part._parts - else: - yield part - - -# Should be a cache-friendly chunk size? -_TARGET_SIZE = 128 -_MAX_MERGABLE = 32 - - -def _defrag_parts(parts: Iterable[Sequence[T]]) -> Iterator[Sequence[T]]: - """ - Merge small chunks into larger chunks for better performance. - """ - parts_queue: list[Sequence[T]] = [] - queued_items = 0 - for part in parts: - # too big, just yield from the buffer - if len(part) > _MAX_MERGABLE: - yield from parts_queue - parts_queue = [] - queued_items = 0 - yield part - else: # can be merged, so lets add to the queue - parts_queue.append(part) - queued_items += len(part) - # if queue has reached target size, merge, dump and reset queue - if queued_items >= _TARGET_SIZE: - yield tuple(itertools.chain(*parts_queue)) - parts_queue = [] - queued_items = 0 - - yield from parts_queue diff --git a/bigframes/core/slices.py b/bigframes/core/slices.py deleted file mode 100644 index 68ec79f9fb6..00000000000 --- a/bigframes/core/slices.py +++ /dev/null @@ -1,110 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from __future__ import annotations - -from typing import Optional - - -def to_forward_offsets( - slice: tuple[Optional[int], Optional[int], Optional[int]], input_rows: int -) -> tuple[int, Optional[int], int]: - """Redefine the slice to use forward offsets for start and stop indices.""" - step = slice[2] or 1 - stop = slice[1] - start = slice[0] - - # normalize start to positive number - if start is None: - start = 0 if (step > 0) else (input_rows - 1) - elif start < 0: - start = max(0, input_rows + start) - else: # start >= 0 - # Clip start to either beginning or end depending on step direction - start = min(start, input_rows - 1) if step < 0 else start - - if stop is None: - stop = None - elif stop < 0: - if step > 0: - stop = max(0, input_rows + stop) - else: - stop = input_rows + stop if (input_rows + stop >= 0) else None - else: - stop = min(stop, input_rows) - - return (start, stop, step) - - -def remove_unused_parts( - slice: tuple[Optional[int], Optional[int], Optional[int]], input_rows: int -) -> tuple[Optional[int], Optional[int], Optional[int]]: - """Makes a slice component null if it doesn't impact slice semantics.""" - start, stop, step = slice - is_forward = (step is None) or (step > 0) - if start is not None: - if is_forward and ((start == 0) or (start <= -input_rows)): - start = None - elif (not is_forward) and ((start == -1) or (start >= (input_rows - 1))): - start = None - if stop is not None: - if is_forward and (stop >= input_rows): - stop = None - elif (not is_forward) and (stop <= (-input_rows - 1)): - stop = None - if step == 1: - step = None - return start, stop, step - - -def slice_output_rows( - slice: tuple[Optional[int], Optional[int], Optional[int]], input_size: int -) -> int: - """Given input_size, returns the number of rows returned after the slice operation.""" - slice = to_forward_offsets(slice, input_size) - start, stop, step = slice - - if step > 0: - if stop is None: - stop = input_size - length = max(0, (stop - start + step - 1) // step) - else: - if stop is None: - stop = -1 - length = max(0, (start - stop - step - 1) // -step) - return length - - -def is_noop( - slice_def: tuple[Optional[int], Optional[int], Optional[int]], - input_size: Optional[int], -) -> bool: - """Returns true iff the slice op is a no-op returning the input array.""" - if input_size: - start, stop, step = remove_unused_parts(slice_def, input_size) - else: - start, stop, step = slice_def - return (not start) and (stop is None) and ((step is None) or (step == 1)) - - -def is_reverse( - slice_def: tuple[Optional[int], Optional[int], Optional[int]], - input_size: Optional[int], -) -> bool: - """Returns true iff the slice op is a pure reverse op, equivalent to df[::-1]""" - if input_size: - start, stop, step = remove_unused_parts(slice_def, input_size) - else: - start, stop, step = slice_def - return (start is None) and (stop is None) and (step == -1) diff --git a/bigframes/core/sql/__init__.py b/bigframes/core/sql/__init__.py deleted file mode 100644 index b28d5921695..00000000000 --- a/bigframes/core/sql/__init__.py +++ /dev/null @@ -1,244 +0,0 @@ -# Copyright 2023 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -""" -Utility functions for SQL construction. -""" - -from __future__ import annotations - -import json -from typing import ( - TYPE_CHECKING, - Any, - Collection, - Iterable, - Mapping, - Optional, - Union, - cast, -) - -import bigframes_vendored.sqlglot.expressions as sge - -from bigframes.core.compile.sqlglot import sql - -if TYPE_CHECKING: - import google.cloud.bigquery as bigquery - - import bigframes.core.ordering - - -# shapely.wkt.dumps was moved to shapely.io.to_wkt in 2.0. -try: - from shapely.io import to_wkt # type: ignore -except ImportError: - from shapely.wkt import dumps # type: ignore - - to_wkt = dumps - - -def identifier(name: str) -> str: - if len(name) > 256: - raise ValueError("Identifier must be less than 256 characters") - return f"`{escape_chars(name)}`" - - -def escape_chars(value: str): - """Escapes all special characters""" - # TODO: Reuse literal's escaping logic instead of re-implementing it here. - # https://cloud.google.com/bigquery/docs/reference/standard-sql/lexical#string_and_bytes_literals - trans_table = str.maketrans( - { - "\a": r"\a", - "\b": r"\b", - "\f": r"\f", - "\n": r"\n", - "\r": r"\r", - "\t": r"\t", - "\v": r"\v", - "\\": r"\\", - "?": r"\?", - '"': r"\"", - "'": r"\'", - "`": r"\`", - } - ) - return value.translate(trans_table) - - -def multi_literal(*values: Any): - literal_strings = [sql.to_sql(sql.literal(i)) for i in values] - return "(" + ", ".join(literal_strings) + ")" - - -def cast_as_string(column_name: str) -> str: - """Return a string representing string casting of a column.""" - - return sge.Cast(this=sge.to_identifier(column_name, quoted=True), to="STRING").sql( - dialect="bigquery" - ) - - -def to_json_string(column_name: str) -> str: - """Return a string representing JSON version of a column.""" - - return f"TO_JSON_STRING({sql.to_sql(sql.identifier(column_name))})" - - -def csv(values: Iterable[str]) -> str: - """Return a string of comma separated values.""" - return ", ".join(values) - - -def infix_op(opname: str, left_arg: str, right_arg: str): - # Maybe should add parentheses?? - return f"{left_arg} {opname} {right_arg}" - - -def is_distinct_sql(columns: Iterable[str], table_ref: bigquery.TableReference) -> str: - table_expr = sge.Table( - this=sge.Identifier(this=table_ref.table_id, quoted=True), - db=sge.Identifier(this=table_ref.dataset_id, quoted=True), - catalog=sge.Identifier(this=table_ref.project, quoted=True), - ) - to_select = [sge.to_identifier(col, quoted=True) for col in columns] - - full_table_sql = ( - sge.Select().select(*to_select).from_(table_expr).sql(dialect="bigquery") - ) - distinct_table_sql = ( - sge.Select() - .select(*to_select) - .distinct() - .from_(table_expr) - .sql(dialect="bigquery") - ) - - is_unique_sql = f"""WITH full_table AS ( - {full_table_sql} - ), - distinct_table AS ( - {distinct_table_sql} - ) - - SELECT (SELECT COUNT(*) FROM full_table) AS `total_count`, - (SELECT COUNT(*) FROM distinct_table) AS `distinct_count` - """ - return is_unique_sql - - -def ordering_clause( - ordering: Iterable[bigframes.core.ordering.OrderingExpression], -) -> str: - import bigframes.core.expression - - parts = [] - for col_ref in ordering: - asc_desc = "ASC" if col_ref.direction.is_ascending else "DESC" - null_clause = "NULLS LAST" if col_ref.na_last else "NULLS FIRST" - ordering_expr = col_ref.scalar_expression - # We don't know how to compile scalar expressions in isolation - if ordering_expr.is_const: - # Probably shouldn't have constants in ordering definition, but best to ignore if somehow they end up here. - continue - assert isinstance(ordering_expr, bigframes.core.expression.DerefOp) - part = f"`{ordering_expr.id.sql}` {asc_desc} {null_clause}" - parts.append(part) - return f"ORDER BY {' ,'.join(parts)}" - - -def create_vector_index_ddl( - *, - replace: bool, - index_name: str, - table_name: str, - column_name: str, - stored_column_names: Collection[str], - options: Mapping[str, Union[str | int | bool | float]] = {}, -) -> str: - """Encode the VECTOR INDEX statement for BigQuery Vector Search.""" - - if replace: - create = "CREATE OR REPLACE VECTOR INDEX " - else: - create = "CREATE VECTOR INDEX IF NOT EXISTS " - - if len(stored_column_names) > 0: - escaped_stored = [ - f"{sql.to_sql(sql.identifier(name))}" for name in stored_column_names - ] - storing = f"STORING({', '.join(escaped_stored)}) " - else: - storing = "" - - rendered_options = ", ".join( - [ - f"{option_name} = {sql.to_sql(sql.literal(option_value))}" - for option_name, option_value in options.items() - ] - ) - - return f""" - {create} {sql.to_sql(sql.identifier(index_name))} - ON {sql.to_sql(sql.identifier(table_name))}({sql.to_sql(sql.identifier(column_name))}) - {storing} - OPTIONS({rendered_options}); - """ - - -def create_vector_search_sql( - sql_string: str, - *, - base_table: str, - column_to_search: str, - query_column_to_search: Optional[str] = None, - top_k: Optional[int] = None, - distance_type: Optional[str] = None, - options: Optional[Mapping[str, Union[str | int | bool | float]]] = None, -) -> str: - """Encode the VECTOR SEARCH statement for BigQuery Vector Search.""" - - vector_search_args = [ - f"TABLE {sql.to_sql(sql.identifier(cast(str, base_table)))}", - f"{sql.to_sql(sql.literal(column_to_search))}", - f"({sql_string})", - ] - - if query_column_to_search is not None: - vector_search_args.append( - f"query_column_to_search => {sql.to_sql(sql.literal(query_column_to_search))}" - ) - - if top_k is not None: - vector_search_args.append(f"top_k=> {sql.to_sql(sql.literal(top_k))}") - - if distance_type is not None: - vector_search_args.append( - f"distance_type => {sql.to_sql(sql.literal(distance_type))}" - ) - - if options is not None: - vector_search_args.append( - f"options => {sql.to_sql(sql.literal(json.dumps(options, indent=None)))}" - ) - - args_str = ",\n".join(vector_search_args) - return f""" - SELECT - query.*, - base.*, - distance, - FROM VECTOR_SEARCH({args_str}) - """ diff --git a/bigframes/core/sql/ml.py b/bigframes/core/sql/ml.py deleted file mode 100644 index 8d971e6c3e8..00000000000 --- a/bigframes/core/sql/ml.py +++ /dev/null @@ -1,309 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from __future__ import annotations - -from typing import Any, Dict, List, Mapping, Optional, Union - -import bigframes.core.col as col -from bigframes.core.compile.sqlglot import sql as sg_sql -from bigframes.core.compile.sqlglot.expression_compiler import expression_compiler - - -def create_model_ddl( - model_name: str, - *, - replace: bool = False, - if_not_exists: bool = False, - transform: Optional[list[str]] = None, - input_schema: Optional[Mapping[str, str]] = None, - output_schema: Optional[Mapping[str, str]] = None, - connection_name: Optional[str] = None, - options: Optional[ - Mapping[str, Union[str, int, float, bool, list, "col.Expression"]] - ] = None, - training_data: Optional[str] = None, - custom_holiday: Optional[str] = None, -) -> str: - """Encode the CREATE MODEL statement. - - See https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-create for reference. - """ - - if replace: - create = "CREATE OR REPLACE MODEL " - elif if_not_exists: - create = "CREATE MODEL IF NOT EXISTS " - else: - create = "CREATE MODEL " - - ddl = f"{create}{sg_sql.to_sql(sg_sql.identifier(model_name))}\n" - - # [TRANSFORM (select_list)] - if transform: - ddl += f"TRANSFORM ({', '.join(transform)})\n" - - # [INPUT (field_name field_type) OUTPUT (field_name field_type)] - if input_schema: - inputs = [f"{k} {v}" for k, v in input_schema.items()] - ddl += f"INPUT ({', '.join(inputs)})\n" - - if output_schema: - outputs = [f"{k} {v}" for k, v in output_schema.items()] - ddl += f"OUTPUT ({', '.join(outputs)})\n" - - # [REMOTE WITH CONNECTION {connection_name | DEFAULT}] - if connection_name: - if connection_name.upper() == "DEFAULT": - ddl += "REMOTE WITH CONNECTION DEFAULT\n" - else: - ddl += f"REMOTE WITH CONNECTION {sg_sql.to_sql(sg_sql.identifier(connection_name))}\n" - - # [OPTIONS(model_option_list)] - if options: - rendered_options = [] - for option_name, option_value in options.items(): - if isinstance(option_value, col.Expression): - sg_expr = expression_compiler.compile_expression(option_value._value) - rendered_val = sg_sql.to_sql(sg_expr) - elif isinstance(option_value, (list, tuple)): - # Handle list options like model_registry="vertex_ai" - # wait, usually options are key=value. - # if value is list, it is [val1, val2] - rendered_val = sg_sql.to_sql(sg_sql.literal(list(option_value))) - else: - rendered_val = sg_sql.to_sql(sg_sql.literal(option_value)) - - rendered_options.append(f"{option_name} = {rendered_val}") - - ddl += f"OPTIONS({', '.join(rendered_options)})\n" - - # [AS {query_statement | ( training_data AS (query_statement), custom_holiday AS (holiday_statement) )}] - - if training_data: - if custom_holiday: - # When custom_holiday is present, we need named clauses - parts = [] - parts.append(f"training_data AS ({training_data})") - parts.append(f"custom_holiday AS ({custom_holiday})") - ddl += f"AS (\n {', '.join(parts)}\n)" - else: - # Just training_data is treated as the query_statement - ddl += f"AS {training_data}\n" - - return ddl - - -def _build_struct_sql( - struct_options: Mapping[ - str, - Union[str, int, float, bool, Mapping[str, str], List[str], Mapping[str, Any]], - ], -) -> str: - if not struct_options: - return "" - return f", {sg_sql.to_sql(sg_sql.literal(struct_options))}" - - -def evaluate( - model_name: str, - *, - table: Optional[str] = None, - perform_aggregation: Optional[bool] = None, - horizon: Optional[int] = None, - confidence_level: Optional[float] = None, -) -> str: - """Encode the ML.EVAluate statement. - See https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-evaluate for reference. - """ - struct_options: Dict[str, Union[str, int, float, bool]] = {} - if perform_aggregation is not None: - struct_options["perform_aggregation"] = perform_aggregation - if horizon is not None: - struct_options["horizon"] = horizon - if confidence_level is not None: - struct_options["confidence_level"] = confidence_level - - sql = f"SELECT * FROM ML.EVALUATE(MODEL {sg_sql.to_sql(sg_sql.identifier(model_name))}" - if table: - sql += f", ({table})" - - sql += _build_struct_sql(struct_options) - sql += ")\n" - return sql - - -def predict( - model_name: str, - table: str, - *, - threshold: Optional[float] = None, - keep_original_columns: Optional[bool] = None, - trial_id: Optional[int] = None, -) -> str: - """Encode the ML.PREDICT statement. - See https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-predict for reference. - """ - struct_options: Dict[str, Union[str, int, float, bool]] = {} - if threshold is not None: - struct_options["threshold"] = threshold - if keep_original_columns is not None: - struct_options["keep_original_columns"] = keep_original_columns - if trial_id is not None: - struct_options["trial_id"] = trial_id - - sql = f"SELECT * FROM ML.PREDICT(MODEL {sg_sql.to_sql(sg_sql.identifier(model_name))}, ({table})" - sql += _build_struct_sql(struct_options) - sql += ")\n" - return sql - - -def explain_predict( - model_name: str, - table: str, - *, - top_k_features: Optional[int] = None, - threshold: Optional[float] = None, - integrated_gradients_num_steps: Optional[int] = None, - approx_feature_contrib: Optional[bool] = None, -) -> str: - """Encode the ML.EXPLAIN_PREDICT statement. - See https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-explain-predict for reference. - """ - struct_options: Dict[str, Union[str, int, float, bool]] = {} - if top_k_features is not None: - struct_options["top_k_features"] = top_k_features - if threshold is not None: - struct_options["threshold"] = threshold - if integrated_gradients_num_steps is not None: - struct_options["integrated_gradients_num_steps"] = ( - integrated_gradients_num_steps - ) - if approx_feature_contrib is not None: - struct_options["approx_feature_contrib"] = approx_feature_contrib - - sql = f"SELECT * FROM ML.EXPLAIN_PREDICT(MODEL {sg_sql.to_sql(sg_sql.identifier(model_name))}, ({table})" - sql += _build_struct_sql(struct_options) - sql += ")\n" - return sql - - -def global_explain( - model_name: str, - *, - class_level_explain: Optional[bool] = None, -) -> str: - """Encode the ML.GLOBAL_EXPLAIN statement. - See https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-global-explain for reference. - """ - struct_options: Dict[str, Union[str, int, float, bool]] = {} - if class_level_explain is not None: - struct_options["class_level_explain"] = class_level_explain - - sql = f"SELECT * FROM ML.GLOBAL_EXPLAIN(MODEL {sg_sql.to_sql(sg_sql.identifier(model_name))}" - sql += _build_struct_sql(struct_options) - sql += ")\n" - return sql - - -def transform( - model_name: str, - table: str, -) -> str: - """Encode the ML.TRANSFORM statement. - See https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-transform for reference. - """ - sql = f"SELECT * FROM ML.TRANSFORM(MODEL {sg_sql.to_sql(sg_sql.identifier(model_name))}, ({table}))\n" - return sql - - -def generate_text( - model_name: str, - table: str, - *, - temperature: Optional[float] = None, - max_output_tokens: Optional[int] = None, - top_k: Optional[int] = None, - top_p: Optional[float] = None, - flatten_json_output: Optional[bool] = None, - stop_sequences: Optional[List[str]] = None, - ground_with_google_search: Optional[bool] = None, - request_type: Optional[str] = None, -) -> str: - """Encode the ML.GENERATE_TEXT statement. - See https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-generate-text for reference. - """ - struct_options: Dict[ - str, - Union[str, int, float, bool, Mapping[str, str], List[str], Mapping[str, Any]], - ] = {} - if temperature is not None: - struct_options["temperature"] = temperature - if max_output_tokens is not None: - struct_options["max_output_tokens"] = max_output_tokens - if top_k is not None: - struct_options["top_k"] = top_k - if top_p is not None: - struct_options["top_p"] = top_p - if flatten_json_output is not None: - struct_options["flatten_json_output"] = flatten_json_output - if stop_sequences is not None: - struct_options["stop_sequences"] = stop_sequences - if ground_with_google_search is not None: - struct_options["ground_with_google_search"] = ground_with_google_search - if request_type is not None: - struct_options["request_type"] = request_type - - sql = f"SELECT * FROM ML.GENERATE_TEXT(MODEL {sg_sql.to_sql(sg_sql.identifier(model_name))}, ({table})" - sql += _build_struct_sql(struct_options) - sql += ")\n" - return sql - - -def get_insights( - model_name: str, -) -> str: - """Encode the ML.GET_INSIGHTS statement. - See https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-get-insights for reference. - """ - sql = f"SELECT * FROM ML.GET_INSIGHTS(MODEL {sg_sql.to_sql(sg_sql.identifier(model_name))})\n" - return sql - - -def generate_embedding( - model_name: str, - table: str, - *, - flatten_json_output: Optional[bool] = None, - task_type: Optional[str] = None, - output_dimensionality: Optional[int] = None, -) -> str: - """Encode the ML.GENERATE_EMBEDDING statement. - See https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-generate-embedding for reference. - """ - struct_options: Dict[ - str, - Union[str, int, float, bool, Mapping[str, str], List[str], Mapping[str, Any]], - ] = {} - if flatten_json_output is not None: - struct_options["flatten_json_output"] = flatten_json_output - if task_type is not None: - struct_options["task_type"] = task_type - if output_dimensionality is not None: - struct_options["output_dimensionality"] = output_dimensionality - - sql = f"SELECT * FROM ML.GENERATE_EMBEDDING(MODEL {sg_sql.to_sql(sg_sql.identifier(model_name))}, ({table})" - sql += _build_struct_sql(struct_options) - sql += ")\n" - return sql diff --git a/bigframes/core/sql_nodes.py b/bigframes/core/sql_nodes.py deleted file mode 100644 index c7a05a082f2..00000000000 --- a/bigframes/core/sql_nodes.py +++ /dev/null @@ -1,300 +0,0 @@ -# Copyright 2026 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from __future__ import annotations - -import dataclasses -import functools -from typing import Callable, Mapping, Optional, Sequence, Tuple - -import bigframes.core.expression as ex -import bigframes.dtypes -from bigframes.core import bq_data, identifiers, nodes -from bigframes.core.ordering import OrderingExpression - -# SQL Nodes are generally terminal, so don't support rich transformation methods -# like remap_vars, remap_refs, etc. -# Still, fields should be defined on them, as typing info is still used for -# dispatching some operators in the emitter, and for validation. - - -# TODO: Join node, union node -@dataclasses.dataclass(frozen=True) -class SqlDataSource(nodes.LeafNode): - source: bq_data.BigqueryDataSource - - @functools.cached_property - def fields(self) -> Sequence[nodes.Field]: - return tuple( - nodes.Field( - identifiers.ColumnId(source_id), - self.source.schema.get_type(source_id), - self.source.table.schema_by_id[source_id].is_nullable, - ) - for source_id in self.source.schema.names - ) - - @property - def is_star_selection(self) -> bool: - return tuple(self.source.schema.names) == tuple( - field.name for field in self.source.table.physical_schema - ) - - @property - def variables_introduced(self) -> int: - # This operation only renames variables, doesn't actually create new ones - return 0 - - @property - def defines_namespace(self) -> bool: - return True - - @property - def explicitly_ordered(self) -> bool: - return False - - @property - def order_ambiguous(self) -> bool: - return True - - @property - def row_count(self) -> Optional[int]: - return self.source.n_rows - - @property - def node_defined_ids(self) -> Tuple[identifiers.ColumnId, ...]: - return tuple(self.ids) - - @property - def consumed_ids(self): - return () - - @property - def _node_expressions(self): - return () - - def remap_vars( - self, mappings: Mapping[identifiers.ColumnId, identifiers.ColumnId] - ) -> SqlSelectNode: - raise NotImplementedError() - - def remap_refs( - self, mappings: Mapping[identifiers.ColumnId, identifiers.ColumnId] - ) -> SqlSelectNode: - raise NotImplementedError() # type: ignore - - -@dataclasses.dataclass(frozen=True) -class SqlWithCtesNode(nodes.BigFrameNode): - # def, name pairs - child: nodes.BigFrameNode - cte_names: tuple[str, ...] - cte_defs: tuple[nodes.BigFrameNode, ...] - - @property - def child_nodes(self) -> Sequence[nodes.BigFrameNode]: - return (self.child, *self.cte_defs) - - @property - def fields(self) -> Sequence[nodes.Field]: - return self.child.fields - - @property - def variables_introduced(self) -> int: - # This operation only renames variables, doesn't actually create new ones - return 0 - - @property - def defines_namespace(self) -> bool: - return True - - @property - def explicitly_ordered(self) -> bool: - return False - - @property - def order_ambiguous(self) -> bool: - return True - - @property - def row_count(self) -> Optional[int]: - return self.child.row_count - - @property - def node_defined_ids(self) -> Tuple[identifiers.ColumnId, ...]: - return tuple(self.ids) - - @property - def consumed_ids(self): - return () - - @property - def _node_expressions(self): - return () - - def remap_vars( - self, mappings: Mapping[identifiers.ColumnId, identifiers.ColumnId] - ) -> SqlWithCtesNode: - raise NotImplementedError() - - def remap_refs( - self, mappings: Mapping[identifiers.ColumnId, identifiers.ColumnId] - ) -> SqlWithCtesNode: - raise NotImplementedError() # type: ignore - - def transform_children( - self, transform: Callable[[nodes.BigFrameNode], nodes.BigFrameNode] - ) -> SqlWithCtesNode: - return SqlWithCtesNode( - transform(self.child), - self.cte_names, - tuple(transform(cte) for cte in self.cte_defs), - ) - - -@dataclasses.dataclass(frozen=True) -class SqlCteRefNode(nodes.LeafNode): - cte_name: str - cte_schema: tuple[nodes.Field, ...] - - @property - def fields(self) -> Sequence[nodes.Field]: - return self.cte_schema - - @property - def variables_introduced(self) -> int: - # This operation only renames variables, doesn't actually create new ones - return 0 - - @property - def defines_namespace(self) -> bool: - return True - - @property - def explicitly_ordered(self) -> bool: - return False - - @property - def order_ambiguous(self) -> bool: - return True - - @property - def row_count(self) -> Optional[int]: - raise NotImplementedError() - - @property - def node_defined_ids(self) -> Tuple[identifiers.ColumnId, ...]: - return tuple(self.ids) - - @property - def consumed_ids(self): - return () - - @property - def _node_expressions(self): - return () - - def remap_vars( - self, mappings: Mapping[identifiers.ColumnId, identifiers.ColumnId] - ) -> SqlCteRefNode: - raise NotImplementedError() - - def remap_refs( - self, mappings: Mapping[identifiers.ColumnId, identifiers.ColumnId] - ) -> SqlCteRefNode: - raise NotImplementedError() # type: ignore - - -@dataclasses.dataclass(frozen=True) -class SqlSelectNode(nodes.UnaryNode): - selections: tuple[nodes.ColumnDef, ...] = () - predicates: tuple[ex.Expression, ...] = () - sorting: tuple[OrderingExpression, ...] = () - limit: Optional[int] = None - - @functools.cached_property - def fields(self) -> Sequence[nodes.Field]: - fields = [] - for cdef in self.selections: - bound_expr = ex.bind_schema_fields(cdef.expression, self.child.field_by_id) - field = nodes.Field( - cdef.id, - bigframes.dtypes.dtype_for_etype(bound_expr.output_type), - nullable=bound_expr.nullable, - ) - - # Special case until we get better nullability inference in expression objects themselves - if bound_expr.is_identity and not any( - self.child.field_by_id[id].nullable - for id in cdef.expression.column_references - ): - field = field.with_nonnull() - fields.append(field) - - return tuple(fields) - - @property - def variables_introduced(self) -> int: - # This operation only renames variables, doesn't actually create new ones - return 0 - - @property - def defines_namespace(self) -> bool: - return True - - @property - def row_count(self) -> Optional[int]: - if self.child.row_count is not None: - if self.limit is not None: - return min([self.limit, self.child.row_count]) - return self.child.row_count - - return None - - @property - def node_defined_ids(self) -> Tuple[identifiers.ColumnId, ...]: - return tuple(cdef.id for cdef in self.selections) - - @property - def consumed_ids(self): - raise NotImplementedError() - - @property - def _node_expressions(self): - raise NotImplementedError() - - @property - def is_star_selection(self) -> bool: - if tuple(self.ids) != tuple(self.child.ids): - return False - for cdef in self.selections: - if not isinstance(cdef.expression, ex.DerefOp): - return False - if cdef.expression.id != cdef.id: - return False - return True - - @functools.cache - def get_id_mapping(self) -> dict[identifiers.ColumnId, ex.Expression]: - return {cdef.id: cdef.expression for cdef in self.selections} - - def remap_vars( - self, mappings: Mapping[identifiers.ColumnId, identifiers.ColumnId] - ) -> SqlSelectNode: - raise NotImplementedError() - - def remap_refs( - self, mappings: Mapping[identifiers.ColumnId, identifiers.ColumnId] - ) -> SqlSelectNode: - raise NotImplementedError() # type: ignore diff --git a/bigframes/core/tools/__init__.py b/bigframes/core/tools/__init__.py deleted file mode 100644 index 38563510a79..00000000000 --- a/bigframes/core/tools/__init__.py +++ /dev/null @@ -1,19 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from bigframes.core.tools.datetimes import to_datetime - -__all__ = [ - "to_datetime", -] diff --git a/bigframes/core/tools/bigquery_schema.py b/bigframes/core/tools/bigquery_schema.py deleted file mode 100644 index eef7364a1bc..00000000000 --- a/bigframes/core/tools/bigquery_schema.py +++ /dev/null @@ -1,59 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -"""Helpers for working with BigQuery SchemaFields.""" - -from typing import Tuple - -import google.cloud.bigquery - -_LEGACY_TO_GOOGLESQL_TYPES = { - "BOOLEAN": "BOOL", - "INTEGER": "INT64", - "FLOAT": "FLOAT64", -} - - -def _type_to_sql(field: google.cloud.bigquery.SchemaField): - """Turn the type information of the field into SQL. - - Ignores the mode, since this has already been handled by _field_to_sql. - """ - if field.field_type.casefold() in ("record", "struct"): - return _to_struct(field.fields) - - # Map from legacy SQL names (the ones used in the BigQuery schema API) to - # the GoogleSQL types. Importantly, FLOAT is from legacy SQL, but not valid - # in GoogleSQL. See internal issue b/428190014. - type_ = _LEGACY_TO_GOOGLESQL_TYPES.get(field.field_type.upper(), field.field_type) - return type_ - - -def _field_to_sql(field: google.cloud.bigquery.SchemaField): - if field.mode == "REPEATED": - # Unlike other types, ARRAY are represented as mode="REPEATED". To get - # the array type, we use SchemaField object but ignore the mode. - return f"`{field.name}` ARRAY<{_type_to_sql(field)}>" - - return f"`{field.name}` {_type_to_sql(field)}" - - -def _to_struct(bqschema: Tuple[google.cloud.bigquery.SchemaField, ...]): - fields = [_field_to_sql(field) for field in bqschema] - return f"STRUCT<{', '.join(fields)}>" - - -def to_sql_dry_run(bqschema: Tuple[google.cloud.bigquery.SchemaField, ...]): - """Create an empty table expression with the correct schema.""" - return f"UNNEST(ARRAY<{_to_struct(bqschema)}>[])" diff --git a/bigframes/core/tools/datetimes.py b/bigframes/core/tools/datetimes.py deleted file mode 100644 index 0cdda67693d..00000000000 --- a/bigframes/core/tools/datetimes.py +++ /dev/null @@ -1,141 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from __future__ import annotations - -from collections.abc import Mapping -from datetime import date, datetime -from typing import TYPE_CHECKING, Optional, Union - -import bigframes_vendored.constants as constants -import bigframes_vendored.pandas.core.tools.datetimes as vendored_pandas_datetimes -import pandas as pd - -import bigframes.dataframe -import bigframes.dtypes -import bigframes.operations as ops -import bigframes.series - -if TYPE_CHECKING: - import bigframes.session - - -def to_datetime( - arg: Union[ - Union[int, float, str, datetime, date], - vendored_pandas_datetimes.local_iterables, - bigframes.series.Series, - bigframes.dataframe.DataFrame, - ], - *, - utc: bool = False, - format: Optional[str] = None, - unit: Optional[str] = None, - session: Optional[bigframes.session.Session] = None, -) -> Union[pd.Timestamp, datetime, bigframes.series.Series]: - if isinstance(arg, (int, float, str, datetime, date)): - return pd.to_datetime( - arg, - utc=utc, - format=format, - unit=unit, - ) - - if isinstance(arg, (Mapping, pd.DataFrame, bigframes.dataframe.DataFrame)): - raise NotImplementedError( - "Conversion of Mapping, pandas.DataFrame, or bigframes.dataframe.DataFrame " - f"to datetime is not implemented. {constants.FEEDBACK_LINK}" - ) - - arg = bigframes.series.Series(arg, session=session) - - if ( - format - and unit - and arg.dtype in (bigframes.dtypes.INT_DTYPE, bigframes.dtypes.FLOAT_DTYPE) - ): # type: ignore - raise ValueError("cannot specify both format and unit") - - if unit and arg.dtype not in ( - bigframes.dtypes.INT_DTYPE, - bigframes.dtypes.FLOAT_DTYPE, - ): # type: ignore - raise NotImplementedError( - f"Unit parameter is not supported for non-numerical input types. {constants.FEEDBACK_LINK}" - ) - - if arg.dtype in ( - bigframes.dtypes.TIMESTAMP_DTYPE, - bigframes.dtypes.DATETIME_DTYPE, - bigframes.dtypes.DATE_DTYPE, - ): - to_type = ( - bigframes.dtypes.TIMESTAMP_DTYPE if utc else bigframes.dtypes.DATETIME_DTYPE - ) - return arg._apply_unary_op(ops.AsTypeOp(to_type=to_type)) # type: ignore - if (not utc) and arg.dtype == bigframes.dtypes.STRING_DTYPE: - if format: - raise NotImplementedError( - f"Customized formats are not supported for string inputs when utc=False. Please set utc=True if possible. {constants.FEEDBACK_LINK}" - ) - - assert unit is None - - # The following operations evaluate individual values to infer a format, - # so cache if needed. - arg = arg._cached(force=False) - - as_datetime = arg._apply_unary_op( # type: ignore - ops.ToDatetimeOp( - format=format, - unit=unit, - ) - ) - failed_datetime_cast = arg.notnull() & as_datetime.isnull() - is_utc = arg._apply_unary_op( - ops.EndsWithOp( - pat=("Z", "-00:00", "+00:00", "-0000", "+0000", "-00", "+00") - ) - ) - - # Cast to DATETIME shall succeed if all inputs are tz-naive. - if not failed_datetime_cast.any(): - return as_datetime - - if is_utc.all(): - return arg._apply_unary_op( # type: ignore - ops.ToTimestampOp( - format=format, - unit=unit, - ) - ) - - raise NotImplementedError( - f"Non-UTC string inputs are not supported when utc=False. Please set utc=True if possible. {constants.FEEDBACK_LINK}" - ) - # If utc: - elif utc: - return arg._apply_unary_op( # type: ignore - ops.ToTimestampOp( - format=format, - unit=unit, - ) - ) - else: - return arg._apply_unary_op( # type: ignore - ops.ToDatetimeOp( - format=format, - unit=unit, - ) - ) diff --git a/bigframes/core/tree_properties.py b/bigframes/core/tree_properties.py deleted file mode 100644 index 225cfc2f437..00000000000 --- a/bigframes/core/tree_properties.py +++ /dev/null @@ -1,155 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -from __future__ import annotations - -import functools -import itertools -from typing import TYPE_CHECKING, Callable, Dict, Optional, Sequence - -import bigframes.core.nodes as nodes - -if TYPE_CHECKING: - import bigframes.session.execution_cache as execution_cache - - -def is_trivially_executable(node: nodes.BigFrameNode) -> bool: - if local_only(node): - return True - children_trivial = all(is_trivially_executable(child) for child in node.child_nodes) - self_trivial = (not node.non_local) and (node.row_preserving) - return children_trivial and self_trivial - - -def local_only(node: nodes.BigFrameNode) -> bool: - return all(isinstance(node, nodes.ReadLocalNode) for node in node.roots) - - -def can_fast_peek(node: nodes.BigFrameNode) -> bool: - if local_only(node): - return True - children_peekable = all(can_fast_peek(child) for child in node.child_nodes) - self_peekable = not node.non_local - return children_peekable and self_peekable - - -def can_fast_head(node: nodes.BigFrameNode) -> bool: - """Can get head fast if can push head operator down to leafs and operators preserve rows.""" - # To do fast head operation: - # (1) the underlying data must be arranged/indexed according to the logical ordering - # (2) transformations must support pushing down LIMIT or a filter on row numbers - if isinstance(node, nodes.ReadLocalNode): - # always cheap to push slice into local data - return True - if isinstance(node, nodes.ReadTableNode): - return (node.source.ordering is None) or (node.fast_ordered_limit) - if isinstance(node, (nodes.ProjectionNode, nodes.SelectionNode)): - return can_fast_head(node.child) - return False - - -def row_count(node: nodes.BigFrameNode) -> Optional[int]: - """Determine row count from local metadata, return None if unknown.""" - return node.row_count - - -# Replace modified_cost(node) = cost(apply_cache(node)) -def select_cache_target( - root: nodes.BigFrameNode, - min_complexity: float, - max_complexity: float, - cache: execution_cache.ExecutionCache, - heuristic: Callable[[int, int], float], -) -> Optional[nodes.BigFrameNode]: - """Take tree, and return candidate nodes with (# of occurences, post-caching planning complexity). - - heurstic takes two args, node complexity, and node occurence count, in that order - """ - - @functools.cache - def _with_caching(subtree: nodes.BigFrameNode) -> nodes.BigFrameNode: - return cache.subsitute_cached_subplans(subtree) - - def _combine_counts( - left: Dict[nodes.BigFrameNode, int], right: Dict[nodes.BigFrameNode, int] - ) -> Dict[nodes.BigFrameNode, int]: - return { - key: left.get(key, 0) + right.get(key, 0) - for key in itertools.chain(left.keys(), right.keys()) - } - - @functools.cache - def _node_counts_inner( - subtree: nodes.BigFrameNode, - ) -> Dict[nodes.BigFrameNode, int]: - """Helper function to count occurences of duplicate nodes in a subtree. Considers only nodes in a complexity range""" - empty_counts: Dict[nodes.BigFrameNode, int] = {} - subtree_complexity = _with_caching(subtree).planning_complexity - if subtree_complexity >= min_complexity: - child_counts = [_node_counts_inner(child) for child in subtree.child_nodes] - node_counts = functools.reduce(_combine_counts, child_counts, empty_counts) - if subtree_complexity <= max_complexity: - return _combine_counts(node_counts, {subtree: 1}) - else: - return node_counts - return empty_counts - - node_counts = _node_counts_inner(root) - - if len(node_counts) == 0: - raise ValueError("node counts should be non-zero") - - # for each considered node, calculate heuristic value, and return node with max value - return max( - node_counts.keys(), - key=lambda node: heuristic( - _with_caching(node).planning_complexity, node_counts[node] - ), - ) - - -def count_nodes(forest: Sequence[nodes.BigFrameNode]) -> dict[nodes.BigFrameNode, int]: - """ - Counts the number of instances of each subtree present within a forest. - - Memoizes internally to accelerate execution, but cache not persisted (not reused between invocations). - - Args: - forest (Sequence of BigFrameNode): - The roots of each tree in the forest - - Returns: - dict[BigFramesNode, int]: The number of occurences of each subtree. - """ - - def _combine_counts( - left: Dict[nodes.BigFrameNode, int], right: Dict[nodes.BigFrameNode, int] - ) -> Dict[nodes.BigFrameNode, int]: - return { - key: left.get(key, 0) + right.get(key, 0) - for key in itertools.chain(left.keys(), right.keys()) - } - - empty_counts: Dict[nodes.BigFrameNode, int] = {} - - @functools.cache - def _node_counts_inner( - subtree: nodes.BigFrameNode, - ) -> Dict[nodes.BigFrameNode, int]: - """Helper function to count occurences of duplicate nodes in a subtree. Considers only nodes in a complexity range""" - child_counts = [_node_counts_inner(child) for child in subtree.child_nodes] - node_counts = functools.reduce(_combine_counts, child_counts, empty_counts) - return _combine_counts(node_counts, {subtree: 1}) - - counts = [_node_counts_inner(root) for root in forest] - return functools.reduce(_combine_counts, counts, empty_counts) diff --git a/bigframes/core/utils.py b/bigframes/core/utils.py index 641fbcc9ac4..dc7c7090117 100644 --- a/bigframes/core/utils.py +++ b/bigframes/core/utils.py @@ -11,28 +11,18 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. -import datetime -import functools -import re import typing -import warnings from typing import Hashable, Iterable, List -import bigframes_vendored.pandas.io.common as vendored_pandas_io_common -import numpy as np import pandas as pd import typing_extensions -import bigframes.exceptions as bfe +import third_party.bigframes_vendored.pandas.io.common as vendored_pandas_io_common UNNAMED_COLUMN_ID = "bigframes_unnamed_column" UNNAMED_INDEX_ID = "bigframes_unnamed_index" -def is_gcs_path(value) -> typing_extensions.TypeGuard[str]: - return isinstance(value, str) and value.startswith("gs://") - - def get_axis_number(axis: typing.Union[str, int]) -> typing.Literal[0, 1]: if axis in {0, "index", "rows"}: return 0 @@ -41,10 +31,8 @@ def get_axis_number(axis: typing.Union[str, int]) -> typing.Literal[0, 1]: raise ValueError(f"Not a valid axis: {axis}") -def is_list_like( - obj: typing.Any, allow_sets: bool = True -) -> typing_extensions.TypeGuard[typing.Sequence]: - return pd.api.types.is_list_like(obj, allow_sets=allow_sets) +def is_list_like(obj: typing.Any) -> typing_extensions.TypeGuard[typing.Sequence]: + return pd.api.types.is_list_like(obj) def is_dict_like(obj: typing.Any) -> typing_extensions.TypeGuard[typing.Mapping]: @@ -52,7 +40,7 @@ def is_dict_like(obj: typing.Any) -> typing_extensions.TypeGuard[typing.Mapping] def combine_indices(index1: pd.Index, index2: pd.Index) -> pd.MultiIndex: - """Combines indices into multi-index while preserving dtypes, names merging by rows 1:1""" + """Combines indices into multi-index while preserving dtypes, names.""" multi_index = pd.MultiIndex.from_frame( pd.concat([index1.to_frame(index=False), index2.to_frame(index=False)], axis=1) ) @@ -61,20 +49,6 @@ def combine_indices(index1: pd.Index, index2: pd.Index) -> pd.MultiIndex: return multi_index -def cross_indices(index1: pd.Index, index2: pd.Index) -> pd.MultiIndex: - """Combines indices into multi-index while preserving dtypes, names using cross product""" - multi_index = pd.MultiIndex.from_frame( - pd.merge( - left=index1.to_frame(index=False), - right=index2.to_frame(index=False), - how="cross", - ) - ) - # to_frame will produce numbered default names, we don't want these - multi_index.names = [*index1.names, *index2.names] - return multi_index - - def index_as_tuples(index: pd.Index) -> typing.Sequence[typing.Tuple]: if isinstance(index, pd.MultiIndex): return [label for label in index] @@ -96,9 +70,7 @@ def split_index( def get_standardized_ids( - col_labels: Iterable[Hashable], - idx_labels: Iterable[Hashable] = (), - strict: bool = False, + col_labels: Iterable[Hashable], idx_labels: Iterable[Hashable] = () ) -> tuple[list[str], list[str]]: """Get stardardized column ids as column_ids_list, index_ids_list. The standardized_column_id must be valid BQ SQL schema column names, can only be string type and unique. @@ -112,76 +84,26 @@ def get_standardized_ids( Tuple of (standardized_column_ids, standardized_index_ids) """ col_ids = [ - UNNAMED_COLUMN_ID - if pd.isna(col_label) # type: ignore - else label_to_identifier(col_label, strict=strict) + UNNAMED_COLUMN_ID if col_label is None else str(col_label) for col_label in col_labels ] idx_ids = [ - UNNAMED_INDEX_ID - if pd.isna(idx_label) # type: ignore - else label_to_identifier(idx_label, strict=strict) + UNNAMED_INDEX_ID if idx_label is None else str(idx_label) for idx_label in idx_labels ] - ids = disambiguate_ids(idx_ids + col_ids) - - idx_ids, col_ids = ids[: len(idx_ids)], ids[len(idx_ids) :] - - return col_ids, idx_ids - - -def label_to_identifier(label: typing.Hashable, strict: bool = False) -> str: - """ - Convert pandas label to make legal bigquery identifier. May create collisions (should deduplicate after). - Strict mode might not be necessary, but ibis seems to escape non-alphanumeric characters inconsistently. - """ + ids = idx_ids + col_ids # Column values will be loaded as null if the column name has spaces. # https://github.com/googleapis/python-bigquery/issues/1566 - identifier = str(label) - if strict: - identifier = str(label).replace(" ", "_") - identifier = re.sub(r"[^a-zA-Z0-9_]", "", identifier) - if not identifier: - identifier = "id" - elif identifier[0].isdigit(): - # first character must be letter or underscore - identifier = "_" + identifier + ids = [id.replace(" ", "_") for id in ids] - else: - # Even with flexible column names, there are constraints - # Convert illegal characters - # See: https://cloud.google.com/bigquery/docs/schemas#flexible-column-names - identifier = re.sub(r"[!\"$\(\)\*\,\./;\?@[\]^`{}~]", "_", identifier) - - # Except in special circumstances (true anonymous query results tables), - # field names are not allowed to start with these (case-insensitive) - # prefixes. - # _PARTITION, _TABLE_, _FILE_, _ROW_TIMESTAMP, __ROOT__ and _COLIDENTIFIER - if any( - identifier.casefold().startswith(invalid_prefix.casefold()) - for invalid_prefix in ( - "_PARTITION", - "_TABLE_", - "_FILE_", - "_ROW_TIMESTAMP", - "__ROOT__", - "_COLIDENTIFIER", - ) - ): - # Remove leading _ character(s) to avoid collisions with preserved - # prefixes. - identifier = re.sub("^_+", "", identifier) - - return identifier - - -def disambiguate_ids(ids: typing.Sequence[str]) -> typing.List[str]: - """Disambiguate list of ids by adding suffixes where needed. If inputs are legal sql ids, outputs should be as well.""" - return typing.cast( + ids = typing.cast( List[str], vendored_pandas_io_common.dedup_names(ids, is_potential_multiindex=False), ) + idx_ids, col_ids = ids[: len(idx_ids)], ids[len(idx_ids) :] + + return col_ids, idx_ids def merge_column_labels( @@ -215,50 +137,3 @@ def merge_column_labels( result_labels.append(col_label) return pd.Index(result_labels) - - -def preview(*, name: str): - """Decorate to warn of a preview API.""" - - def decorator(func): - msg = f"{name} is in preview. Its behavior may change in future versions." - - @functools.wraps(func) - def wrapper(*args, **kwargs): - warnings.warn(bfe.format_message(msg), category=bfe.PreviewWarning) - return func(*args, **kwargs) - - return wrapper - - return decorator - - -def timedelta_to_micros( - timedelta: typing.Union[pd.Timedelta, datetime.timedelta, np.timedelta64], -) -> int: - if isinstance(timedelta, pd.Timedelta): - # pd.Timedelta.value returns total nanoseconds. - return timedelta.value // 1000 - - if isinstance(timedelta, np.timedelta64): - return timedelta.astype("timedelta64[us]").astype(np.int64) - - if isinstance(timedelta, datetime.timedelta): - return ( - (timedelta.days * 3600 * 24) + timedelta.seconds - ) * 1_000_000 + timedelta.microseconds - - raise TypeError(f"Unrecognized input type: {type(timedelta)}") - - -def get_ipython_execution_count() -> typing.Optional[int]: - """Returns the current IPython cell execution count if running in a notebook, else None.""" - try: - from IPython.core.interactiveshell import InteractiveShell - - if InteractiveShell.initialized(): - ipy = InteractiveShell.instance() - return getattr(ipy, "execution_count", None) - except (ImportError, NameError): - pass - return None diff --git a/bigframes/core/validations.py b/bigframes/core/validations.py deleted file mode 100644 index 84f802cd740..00000000000 --- a/bigframes/core/validations.py +++ /dev/null @@ -1,79 +0,0 @@ -# Copyright 2023 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -"""DataFrame is a two dimensional data structure.""" - -from __future__ import annotations - -import functools -from typing import TYPE_CHECKING, Optional, Protocol, Union - -import bigframes_vendored.constants as constants - -import bigframes.exceptions - -if TYPE_CHECKING: - from bigframes import Session - from bigframes.core.blocks import Block - from bigframes.dataframe import DataFrame - from bigframes.series import Series - - -class HasSession(Protocol): - @property - def _session(self) -> Session: ... - - @property - def _block(self) -> Block: ... - - -def requires_index(meth): - @functools.wraps(meth) - def guarded_meth(df: Union[DataFrame, Series], *args, **kwargs): - df._throw_if_null_index(meth.__name__) - return meth(df, *args, **kwargs) - - guarded_meth._validations_requires_index = True # type: ignore - return guarded_meth - - -def requires_ordering(suggestion: Optional[str] = None): - def decorator(meth): - @functools.wraps(meth) - def guarded_meth(object: HasSession, *args, **kwargs): - enforce_ordered(object, meth.__name__, suggestion) - return meth(object, *args, **kwargs) - - guarded_meth._validations_requires_ordering = True # type: ignore - return guarded_meth - - return decorator - - -def enforce_ordered( - object: HasSession, opname: str, suggestion: Optional[str] = None -) -> None: - session = object._session - if session._strictly_ordered or not object._block.expr.order_ambiguous: - # No ambiguity for how to calculate ordering, so no error or warning - return None - if not session._allows_ambiguity: - suggestion_substr = suggestion + " " if suggestion else "" - raise bigframes.exceptions.OrderRequiredError( - f"Op {opname} not supported when strict ordering is disabled. {suggestion_substr}{constants.FEEDBACK_LINK}" - ) - if not object._block.explicitly_ordered: - raise bigframes.exceptions.OrderRequiredError( - f"Op {opname} requires an ordering. Use .sort_values or .sort_index to provide an ordering. {constants.FEEDBACK_LINK}" - ) diff --git a/bigframes/core/window/__init__.py b/bigframes/core/window/__init__.py index 1d888ca7e6a..d3d081124e9 100644 --- a/bigframes/core/window/__init__.py +++ b/bigframes/core/window/__init__.py @@ -1,4 +1,4 @@ -# Copyright 2025 Google LLC +# Copyright 2023 Google LLC # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -12,6 +12,84 @@ # See the License for the specific language governing permissions and # limitations under the License. -from bigframes.core.window.rolling import Window +from __future__ import annotations -__all__ = ["Window"] +import typing + +import bigframes.core as core +import bigframes.core.blocks as blocks +import bigframes.operations.aggregations as agg_ops +import third_party.bigframes_vendored.pandas.core.window.rolling as vendored_pandas_rolling + + +class Window(vendored_pandas_rolling.Window): + __doc__ = vendored_pandas_rolling.Window.__doc__ + + def __init__( + self, + block: blocks.Block, + window_spec: core.WindowSpec, + value_column_ids: typing.Sequence[str], + drop_null_groups: bool = True, + is_series: bool = False, + ): + self._block = block + self._window_spec = window_spec + self._value_column_ids = value_column_ids + self._drop_null_groups = drop_null_groups + self._is_series = is_series + + def count(self): + return self._apply_aggregate(agg_ops.count_op) + + def sum(self): + return self._apply_aggregate(agg_ops.sum_op) + + def mean(self): + return self._apply_aggregate(agg_ops.mean_op) + + def var(self): + return self._apply_aggregate(agg_ops.var_op) + + def std(self): + return self._apply_aggregate(agg_ops.std_op) + + def max(self): + return self._apply_aggregate(agg_ops.max_op) + + def min(self): + return self._apply_aggregate(agg_ops.min_op) + + def _apply_aggregate( + self, + op: agg_ops.AggregateOp, + ): + block = self._block + labels = [block.col_id_to_label[col] for col in self._value_column_ids] + block, result_ids = block.multi_apply_window_op( + self._value_column_ids, + op, + self._window_spec, + skip_null_groups=self._drop_null_groups, + never_skip_nulls=True, + ) + + if self._window_spec.grouping_keys: + original_index_ids = block.index_columns + block = block.reset_index(drop=False) + index_ids = ( + *[col for col in self._window_spec.grouping_keys], + *original_index_ids, + ) + block = block.set_index(col_ids=index_ids) + + if self._is_series: + from bigframes.series import Series + + return Series(block.select_columns(result_ids).with_column_labels(labels)) + else: + from bigframes.dataframe import DataFrame + + return DataFrame( + block.select_columns(result_ids).with_column_labels(labels) + ) diff --git a/bigframes/core/window/ordering.py b/bigframes/core/window/ordering.py deleted file mode 100644 index 0bea585bb04..00000000000 --- a/bigframes/core/window/ordering.py +++ /dev/null @@ -1,86 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from __future__ import annotations - -from functools import singledispatch - -from bigframes.core import expression as ex -from bigframes.core import nodes, ordering - - -@singledispatch -def find_order_direction( - root: nodes.BigFrameNode, column_id: str -) -> ordering.OrderingDirection | None: - """Returns the order of the given column with tree traversal. If the column cannot be found, - or the ordering information is not available, return None. - """ - return None - - -@find_order_direction.register -def _(root: nodes.OrderByNode, column_id: str): - if len(root.by) == 0: - # This is a no-op - return find_order_direction(root.child, column_id) - - # Make sure the window key is the prefix of sorting keys. - order_expr = root.by[0] - scalar_expr = order_expr.scalar_expression - if isinstance(scalar_expr, ex.DerefOp) and scalar_expr.id.name == column_id: - return order_expr.direction - - return None - - -@find_order_direction.register -def _(root: nodes.ReversedNode, column_id: str): - direction = find_order_direction(root.child, column_id) - - if direction is None: - return None - return direction.reverse() - - -@find_order_direction.register -def _(root: nodes.SelectionNode, column_id: str): - for alias_ref in root.input_output_pairs: - if alias_ref.id.name == column_id: - return find_order_direction(root.child, alias_ref.ref.id.name) - - -@find_order_direction.register -def _(root: nodes.FilterNode, column_id: str): - return find_order_direction(root.child, column_id) - - -@find_order_direction.register -def _(root: nodes.InNode, column_id: str): - return find_order_direction(root.left_child, column_id) - - -@find_order_direction.register -def _(root: nodes.WindowOpNode, column_id: str): - return find_order_direction(root.child, column_id) - - -@find_order_direction.register -def _(root: nodes.ProjectionNode, column_id: str): - for expr, ref in root.assignments: - if ref.name == column_id and isinstance(expr, ex.DerefOp): - # This source column is renamed. - return find_order_direction(root.child, expr.id.name) - - return find_order_direction(root.child, column_id) diff --git a/bigframes/core/window/rolling.py b/bigframes/core/window/rolling.py deleted file mode 100644 index a3660954dfb..00000000000 --- a/bigframes/core/window/rolling.py +++ /dev/null @@ -1,275 +0,0 @@ -# Copyright 2023 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from __future__ import annotations - -import datetime -from typing import TYPE_CHECKING, Literal, Mapping, Sequence, Union - -import bigframes_vendored.pandas.core.window.rolling as vendored_pandas_rolling -import numpy -import pandas - -import bigframes.core.blocks as blocks -import bigframes.operations.aggregations as agg_ops -from bigframes import dtypes -from bigframes._tools import docs -from bigframes.core import agg_expressions, ordering, utils, window_spec -from bigframes.core import expression as ex -from bigframes.core.logging import log_adapter -from bigframes.core.window import ordering as window_ordering - -if TYPE_CHECKING: - import bigframes.dataframe as df - import bigframes.series as series - - -@log_adapter.class_logger -@docs.inherit_docs(vendored_pandas_rolling.Window) -class Window: - def __init__( - self, - block: blocks.Block, - window_spec: window_spec.WindowSpec, - value_column_ids: Sequence[str], - drop_null_groups: bool = True, - is_series: bool = False, - skip_agg_column_id: str | None = None, - ): - self._block = block - self._window_spec = window_spec - self._value_column_ids = value_column_ids - self._drop_null_groups = drop_null_groups - self._is_series = is_series - # The column ID that won't be aggregated on. - # This is equivalent to pandas `on` parameter in rolling() - self._skip_agg_column_id = skip_agg_column_id - - def count(self): - return self._apply_aggregate_op(agg_ops.count_op) - - def sum(self): - return self._apply_aggregate_op(agg_ops.sum_op) - - def mean(self): - return self._apply_aggregate_op(agg_ops.mean_op) - - def var(self): - return self._apply_aggregate_op(agg_ops.var_op) - - def std(self): - return self._apply_aggregate_op(agg_ops.std_op) - - def max(self): - return self._apply_aggregate_op(agg_ops.max_op) - - def min(self): - return self._apply_aggregate_op(agg_ops.min_op) - - def agg(self, func) -> Union[df.DataFrame, series.Series]: - if utils.is_dict_like(func): - return self._agg_dict(func) - elif utils.is_list_like(func): - return self._agg_list(func) - else: - return self._agg_func(func) - - aggregate = agg - - def _agg_func(self, func) -> df.DataFrame: - ids, labels = self._aggregated_columns() - aggregations = [agg(col_id, agg_ops.lookup_agg_func(func)[0]) for col_id in ids] - return self._apply_aggs(aggregations, labels) - - def _agg_dict(self, func: Mapping) -> df.DataFrame: - aggregations: list[agg_expressions.Aggregation] = [] - column_labels = [] - function_labels = [] - - want_aggfunc_level = any(utils.is_list_like(aggs) for aggs in func.values()) - - for label, funcs_for_id in func.items(): - col_id = self._block.label_to_col_id[label][-1] # get last matching column - func_list = ( - funcs_for_id if utils.is_list_like(funcs_for_id) else [funcs_for_id] - ) - for f in func_list: - f_op, f_label = agg_ops.lookup_agg_func(f) - aggregations.append(agg(col_id, f_op)) - column_labels.append(label) - function_labels.append(f_label) - if want_aggfunc_level: - result_labels: pandas.Index = utils.combine_indices( - pandas.Index(column_labels), - pandas.Index(function_labels), - ) - else: - result_labels = pandas.Index(column_labels) - - return self._apply_aggs(aggregations, result_labels) - - def _agg_list(self, func: Sequence) -> df.DataFrame: - ids, labels = self._aggregated_columns() - aggregations = [ - agg(col_id, agg_ops.lookup_agg_func(f)[0]) for col_id in ids for f in func - ] - - if self._is_series: - # if series, no need to rebuild - result_cols_idx = pandas.Index( - [agg_ops.lookup_agg_func(f)[1] for f in func] - ) - else: - if self._block.column_labels.nlevels > 1: - # Restructure MultiIndex for proper format: (idx1, idx2, func) - # rather than ((idx1, idx2), func). - column_labels = [ - tuple(label) + (agg_ops.lookup_agg_func(f)[1],) - for label in labels.to_frame(index=False).to_numpy() - for f in func - ] - else: # Single-level index - column_labels = [ - (label, agg_ops.lookup_agg_func(f)[1]) - for label in labels - for f in func - ] - result_cols_idx = pandas.MultiIndex.from_tuples( - column_labels, names=[*self._block.column_labels.names, None] - ) - return self._apply_aggs(aggregations, result_cols_idx) - - def _apply_aggs( - self, exprs: Sequence[agg_expressions.Aggregation], labels: pandas.Index - ): - block, ids = self._block.apply_analytic( - agg_exprs=exprs, - window=self._window_spec, - result_labels=labels, - skip_null_groups=self._drop_null_groups, - ) - - if self._window_spec.grouping_keys: - original_index_ids = block.index_columns - block = block.reset_index(drop=False) - # grouping keys will always be direct column references, but we should probably - # refactor this class to enforce this statically - index_ids = ( - *[col.id.name for col in self._window_spec.grouping_keys], # type: ignore - *original_index_ids, - ) - block = block.set_index(col_ids=index_ids) - - if self._skip_agg_column_id is not None: - block = block.select_columns([self._skip_agg_column_id, *ids]) - else: - block = block.select_columns(ids).with_column_labels(labels) - - if self._is_series and (len(block.value_columns) == 1): - import bigframes.series as series - - return series.Series(block) - else: - import bigframes.dataframe as df - - return df.DataFrame(block) - - def _apply_aggregate_op( - self, - op: agg_ops.UnaryAggregateOp, - ): - ids, labels = self._aggregated_columns() - aggregations = [agg(col_id, op) for col_id in ids] - return self._apply_aggs(aggregations, labels) - - def _aggregated_columns(self) -> tuple[Sequence[str], pandas.Index]: - agg_col_ids = [ - col_id - for col_id in self._value_column_ids - if col_id != self._skip_agg_column_id - ] - labels: pandas.Index = pandas.Index( - [self._block.col_id_to_label[col] for col in agg_col_ids] - ) - return agg_col_ids, labels - - -def create_range_window( - block: blocks.Block, - window: pandas.Timedelta | numpy.timedelta64 | datetime.timedelta | str, - *, - value_column_ids: Sequence[str] = tuple(), - min_periods: int | None, - on: str | None = None, - closed: Literal["right", "left", "both", "neither"], - is_series: bool, - grouping_keys: Sequence[str] = tuple(), - drop_null_groups: bool = True, -) -> Window: - if on is None: - # Rolling on index - index_dtypes = block.index.dtypes - if len(index_dtypes) > 1: - raise ValueError("Range rolling on MultiIndex is not supported") - if index_dtypes[0] != dtypes.TIMESTAMP_DTYPE: - raise ValueError("Index type should be timestamps with timezones") - rolling_key_col_id = block.index_columns[0] - else: - # Rolling on a specific column - rolling_key_col_id = block.resolve_label_exact_or_error(on) - if block.expr.get_column_type(rolling_key_col_id) != dtypes.TIMESTAMP_DTYPE: - raise ValueError(f"Column {on} type should be timestamps with timezones") - - order_direction = window_ordering.find_order_direction( - block.expr.node, rolling_key_col_id - ) - if order_direction is None: - target_str = "index" if on is None else f"column {on}" - raise ValueError( - f"The {target_str} might not be in a monotonic order. Please sort by {target_str} before rolling." - ) - if isinstance(window, str): - window = pandas.Timedelta(window) - spec = window_spec.WindowSpec( - bounds=window_spec.RangeWindowBounds.from_timedelta_window(window, closed), - min_periods=1 if min_periods is None else min_periods, - ordering=( - ordering.OrderingExpression(ex.deref(rolling_key_col_id), order_direction), - ), - grouping_keys=tuple(ex.deref(col) for col in grouping_keys), - ) - - selected_value_col_ids = ( - value_column_ids if value_column_ids else block.value_columns - ) - # This step must be done after finding the order direction of the window key. - if grouping_keys: - block = block.order_by([ordering.ascending_over(col) for col in grouping_keys]) - - return Window( - block, - spec, - value_column_ids=selected_value_col_ids, - is_series=is_series, - skip_agg_column_id=None if on is None else rolling_key_col_id, - drop_null_groups=drop_null_groups, - ) - - -def agg(input: str, op: agg_ops.AggregateOp) -> agg_expressions.Aggregation: - if isinstance(op, agg_ops.UnaryAggregateOp): - return agg_expressions.UnaryAggregation(op, ex.deref(input)) - else: - assert isinstance(op, agg_ops.NullaryAggregateOp) - return agg_expressions.NullaryAggregation(op) diff --git a/bigframes/core/window_spec.py b/bigframes/core/window_spec.py index 509dd954b9f..3458bfb1b8c 100644 --- a/bigframes/core/window_spec.py +++ b/bigframes/core/window_spec.py @@ -11,305 +11,25 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. -from __future__ import annotations -import datetime -import itertools -from dataclasses import dataclass, replace -from typing import Callable, Literal, Mapping, Optional, Sequence, Set, Tuple, Union +from dataclasses import dataclass +import typing -import numpy as np -import pandas as pd - -import bigframes.core.expression as ex -import bigframes.core.identifiers as ids import bigframes.core.ordering as orderings -# Unbound Windows -def unbound( - grouping_keys: Tuple[str, ...] = (), - min_periods: int = 0, - ordering: Tuple[orderings.OrderingExpression, ...] = (), -) -> WindowSpec: - """ - Create an unbound window. - - Args: - grouping_keys: - Columns ids of grouping keys - min_periods (int, default 0): - Minimum number of input rows to generate output. - ordering: - Orders the rows within the window. - - Returns: - WindowSpec - """ - return WindowSpec( - grouping_keys=tuple(map(ex.deref, grouping_keys)), - min_periods=min_periods, - ordering=ordering, - ) - - -### Rows-based Windows -def rows( - grouping_keys: Tuple[str, ...] = (), - start: Optional[int] = None, - end: Optional[int] = None, - min_periods: int = 0, - ordering: Tuple[orderings.OrderingExpression, ...] = (), -) -> WindowSpec: - """ - Create a row-bounded window. - - Args: - grouping_keys: - Columns ids of grouping keys - start: - The window's starting boundary relative to the current row. For example, "-1" means one row prior - "1" means one row after, and "0" means the current row. If None, the window is unbounded from the start. - following: - The window's ending boundary relative to the current row. For example, "-1" means one row prior - "1" means one row after, and "0" means the current row. If None, the window is unbounded until the end. - min_periods (int, default 0): - Minimum number of input rows to generate output. - ordering: - Ordering to apply on top of based dataframe ordering - Returns: - WindowSpec - """ - bounds = RowsWindowBounds( - start=start, - end=end, - ) - return WindowSpec( - grouping_keys=tuple(map(ex.deref, grouping_keys)), - bounds=bounds, - min_periods=min_periods, - ordering=ordering, - ) - - -def cumulative_rows( - grouping_keys: Tuple[str, ...] = (), min_periods: int = 0 -) -> WindowSpec: - """ - Create a expanding window that includes all preceding rows - - Args: - grouping_keys: - Columns ids of grouping keys - min_periods (int, default 0): - Minimum number of input rows to generate output. - Returns: - WindowSpec - """ - bounds = RowsWindowBounds(end=0) - return WindowSpec( - grouping_keys=tuple(map(ex.deref, grouping_keys)), - bounds=bounds, - min_periods=min_periods, - ) - - -def inverse_cumulative_rows( - grouping_keys: Tuple[str, ...] = (), min_periods: int = 0 -) -> WindowSpec: - """ - Create a shrinking window that includes all following rows - - Args: - grouping_keys: - Columns ids of grouping keys - min_periods (int, default 0): - Minimum number of input rows to generate output. - Returns: - WindowSpec - """ - bounds = RowsWindowBounds(start=0) - return WindowSpec( - grouping_keys=tuple(map(ex.deref, grouping_keys)), - bounds=bounds, - min_periods=min_periods, - ) - - -### Struct Classes - - -@dataclass(frozen=True) -class RowsWindowBounds: - start: Optional[int] = None - end: Optional[int] = None - - @classmethod - def from_window_size( - cls, window: int, closed: Literal["right", "left", "both", "neither"] - ) -> RowsWindowBounds: - if closed == "right": - return cls(-(window - 1), 0) - elif closed == "left": - return cls(-window, -1) - elif closed == "both": - return cls(-window, 0) - elif closed == "neither": - return cls(-(window - 1), -1) - else: - raise ValueError(f"Unsupported value for 'closed' parameter: {closed}") - - def __post_init__(self): - if self.start is None: - return - if self.end is None: - return - if self.start > self.end: - raise ValueError( - f"Invalid window: start({self.start}) is greater than end({self.end})" - ) - - -@dataclass(frozen=True) -class RangeWindowBounds: - """Represents a time range window, inclusively bounded by start and end""" - - start: pd.Timedelta | None = None - end: pd.Timedelta | None = None - - @classmethod - def from_timedelta_window( - cls, - window: pd.Timedelta | np.timedelta64 | datetime.timedelta, - closed: Literal["right", "left", "both", "neither"], - ) -> RangeWindowBounds: - window = pd.Timedelta(window) - tick = pd.Timedelta("1us") - zero = pd.Timedelta(0) - - if closed == "right": - return cls(-(window - tick), zero) - elif closed == "left": - return cls(-window, -tick) - elif closed == "both": - return cls(-window, zero) - elif closed == "neither": - return cls(-(window - tick), -tick) - else: - raise ValueError(f"Unsupported value for 'closed' parameter: {closed}") - - def __post_init__(self): - if self.start is None: - return - if self.end is None: - return - if self.start > self.end: - raise ValueError( - f"Invalid window: start({self.start}) is greater than end({self.end})" - ) - - @dataclass(frozen=True) class WindowSpec: """ Specifies a window over which aggregate and analytic function may be applied. - - Attributes: - grouping_keys: A set of columns to group on - bounds: The window boundaries - ordering: A list of columns ids and ordering direction to override base ordering - min_periods: The minimum number of observations in window required to have a value + grouping_keys: set of column ids to group on + preceding: Number of preceding rows in the window + following: Number of preceding rows in the window + ordering: List of columns ids and ordering direction to override base ordering """ - grouping_keys: Tuple[ex.Expression, ...] = tuple() - ordering: Tuple[orderings.OrderingExpression, ...] = tuple() - bounds: Union[RowsWindowBounds, RangeWindowBounds, None] = None + grouping_keys: typing.Tuple[str, ...] = tuple() + ordering: typing.Tuple[orderings.OrderingColumnReference, ...] = tuple() + preceding: typing.Optional[int] = None + following: typing.Optional[int] = None min_periods: int = 0 - - @property - def is_row_bounded(self): - """ - Whether the window is bounded by row offsets. - - This is relevant for determining whether the window requires a total order - to calculate deterministically. - """ - return isinstance(self.bounds, RowsWindowBounds) and ( - (self.bounds.start is not None) or (self.bounds.end is not None) - ) - - @property - def is_range_bounded(self): - """ - Whether the window is bounded by range offsets. - - This is relevant for determining whether the window requires a total order - to calculate deterministically. - """ - return isinstance(self.bounds, RangeWindowBounds) - - @property - def is_unbounded(self): - """ - Whether the window is unbounded. - - This is relevant for determining whether the window requires a total order - to calculate deterministically. - """ - return self.bounds is None or ( - self.bounds.start is None and self.bounds.end is None - ) - - @property - def expressions(self) -> Sequence[ex.Expression]: - ordering_exprs = (item.scalar_expression for item in self.ordering) - return (*self.grouping_keys, *ordering_exprs) - - @property - def all_referenced_columns(self) -> Set[ids.ColumnId]: - """ - Return list of all variables reference ind the window. - """ - ordering_vars = itertools.chain.from_iterable( - item.scalar_expression.column_references for item in self.ordering - ) - grouping_vars = itertools.chain.from_iterable( - item.column_references for item in self.grouping_keys - ) - return set(itertools.chain(grouping_vars, ordering_vars)) - - def without_order(self, force: bool = False) -> WindowSpec: - """Removes ordering clause if ordering isn't required to define bounds.""" - if self.is_row_bounded and not force: - raise ValueError("Cannot remove order from row-bounded window") - return replace(self, ordering=()) - - def remap_column_refs( - self, - mapping: Mapping[ids.ColumnId, ids.ColumnId], - allow_partial_bindings: bool = False, - ) -> WindowSpec: - return WindowSpec( - grouping_keys=tuple( - key.remap_column_refs(mapping, allow_partial_bindings) - for key in self.grouping_keys - ), - ordering=tuple( - order_part.remap_column_refs(mapping, allow_partial_bindings) - for order_part in self.ordering - ), - bounds=self.bounds, - min_periods=self.min_periods, - ) - - def transform_exprs( - self: WindowSpec, t: Callable[[ex.Expression], ex.Expression] - ) -> WindowSpec: - return WindowSpec( - grouping_keys=tuple(t(key) for key in self.grouping_keys), - ordering=tuple( - order_part.transform_exprs(t) for order_part in self.ordering - ), - bounds=self.bounds, - min_periods=self.min_periods, - ) diff --git a/bigframes/dataframe.py b/bigframes/dataframe.py index 8d05ba4ddb0..c5b48822fb1 100644 --- a/bigframes/dataframe.py +++ b/bigframes/dataframe.py @@ -17,19 +17,12 @@ from __future__ import annotations import datetime -import inspect -import itertools import re -import sys import textwrap import typing -import warnings from typing import ( - TYPE_CHECKING, - Any, Callable, Dict, - Hashable, Iterable, List, Literal, @@ -37,82 +30,47 @@ Optional, Sequence, Tuple, - TypeVar, Union, - cast, - overload, ) -import bigframes_vendored.constants as constants -import bigframes_vendored.pandas.core.frame as vendored_pandas_frame -import bigframes_vendored.pandas.pandas._typing as vendored_pandas_typing -import google.api_core.exceptions import google.cloud.bigquery as bigquery -import google.cloud.bigquery.job -import google.cloud.bigquery.table import numpy import pandas -import pyarrow -import tabulate -from pandas.api import extensions as pd_ext -import bigframes.constants +import bigframes +import bigframes._config.display_options as display_options +import bigframes.constants as constants import bigframes.core import bigframes.core.block_transforms as block_ops import bigframes.core.blocks as blocks -import bigframes.core.col -import bigframes.core.convert -import bigframes.core.explode -import bigframes.core.expression as ex import bigframes.core.groupby as groupby import bigframes.core.guid import bigframes.core.indexers as indexers import bigframes.core.indexes as indexes -import bigframes.core.interchange import bigframes.core.ordering as order import bigframes.core.utils as utils -import bigframes.core.validations as validations import bigframes.core.window -import bigframes.core.window_spec as windows import bigframes.dtypes -import bigframes.exceptions as bfe import bigframes.formatting_helpers as formatter -import bigframes.functions import bigframes.operations as ops import bigframes.operations.aggregations as agg_ops -import bigframes.operations.plotting as plotting -import bigframes.operations.structs import bigframes.series +import bigframes.series as bf_series import bigframes.session._io.bigquery -import bigframes.session.execution_spec as ex_spec -from bigframes._tools import docs -from bigframes.core import agg_expressions -from bigframes.core.logging import log_adapter -from bigframes.core.window import rolling -from bigframes.functions import function_typing +import third_party.bigframes_vendored.pandas.core.frame as vendored_pandas_frame +import third_party.bigframes_vendored.pandas.pandas._typing as vendored_pandas_typing -if TYPE_CHECKING: - from _typeshed import SupportsRichComparison - - import bigframes.extensions.bigframes.dataframe_accessor as bigquery_accessor +if typing.TYPE_CHECKING: import bigframes.session - SingleItemValue = Union[ - bigframes.series.Series, - int, - float, - str, - pandas.Timedelta, - Callable, - bigframes.core.col.Expression, - ] - MultiItemValue = Union[ - "DataFrame", Sequence[int | float | str | pandas.Timedelta | Callable] - ] - -U = TypeVar("U") -LevelType = typing.Hashable + +# BigQuery has 1 MB query size limit, 5000 items shouldn't take more than 10% of this depending on data type. +# TODO(tbergeron): Convert to bytes-based limit +MAX_INLINE_DF_SIZE = 5000 + +LevelType = typing.Union[str, int] LevelsType = typing.Union[LevelType, typing.Sequence[LevelType]] +SingleItemValue = Union[bigframes.series.Series, int, float, Callable] ERROR_IO_ONLY_GS_PATHS = f"Only Google Cloud Storage (gs://...) paths are supported. {constants.FEEDBACK_LINK}" ERROR_IO_REQUIRES_WILDCARD = ( @@ -123,13 +81,8 @@ # Inherits from pandas DataFrame so that we can use the same docstrings. -@log_adapter.class_logger -@docs.inherit_docs(vendored_pandas_frame.DataFrame) -class DataFrame: - # internal flag to disable cache at all - _disable_cache_override: bool = False - # Must be above 5000 for pandas to delegate to bigframes for binops - __pandas_priority__ = 15000 +class DataFrame(vendored_pandas_frame.DataFrame): + __doc__ = vendored_pandas_frame.DataFrame.__doc__ def __init__( self, @@ -143,18 +96,10 @@ def __init__( *, session: typing.Optional[bigframes.session.Session] = None, ): - global bigframes - - self._query_job: Optional[google.cloud.bigquery.job.QueryJob] = None - if copy is not None and not copy: raise ValueError( f"DataFrame constructor only supports copy=True. {constants.FEEDBACK_LINK}" ) - # Ignore object dtype if provided, as it provides no additional - # information about what BigQuery type to use. - if dtype is not None and bigframes.dtypes.is_object_like(dtype): - dtype = None # Check to see if constructing from BigQuery-backed objects before # falling back to pandas constructor @@ -169,13 +114,9 @@ def __init__( elif ( utils.is_dict_like(data) and len(data) >= 1 - and any( - isinstance(data[key], bigframes.series.Series) for key in data.keys() - ) + and any(isinstance(data[key], bf_series.Series) for key in data.keys()) ): - if not all( - isinstance(data[key], bigframes.series.Series) for key in data.keys() - ): + if not all(isinstance(data[key], bf_series.Series) for key in data.keys()): # TODO(tbergeron): Support local list/series data by converting to memtable. raise NotImplementedError( f"Cannot mix Series with other types. {constants.FEEDBACK_LINK}" @@ -183,42 +124,36 @@ def __init__( keys = list(data.keys()) first_label, first_series = keys[0], data[keys[0]] block = ( - typing.cast(bigframes.series.Series, first_series) + typing.cast(bf_series.Series, first_series) ._get_block() .with_column_labels([first_label]) ) for key in keys[1:]: - other = typing.cast(bigframes.series.Series, data[key]) + other = typing.cast(bf_series.Series, data[key]) other_block = other._block.with_column_labels([key]) # Pandas will keep original sorting if all indices are aligned. # We cannot detect this easily however, and so always sort on index - block, _ = block.join( # type:ignore - other_block, how="outer", sort=True + result_index, _ = block.index.join( # type:ignore + other_block.index, how="outer", sort=True ) + block = result_index._block if block: - if index is not None: - bf_index = indexes.Index(index) - idx_block = bf_index._block - idx_cols = idx_block.index_columns - block, (_, r_mapping) = block.reset_index().join( - bf_index._block.reset_index(), how="inner" + if index: + raise NotImplementedError( + "DataFrame 'index' constructor parameter not supported " + f"when passing BigQuery-backed objects. {constants.FEEDBACK_LINK}" ) - block = block.set_index([r_mapping[idx_col] for idx_col in idx_cols]) if columns: - column_ids = [ - block.resolve_label_exact_or_error(label) for label in list(columns) - ] - block = block.select_columns(column_ids) # type:ignore + block = block.select_columns(list(columns)) # type:ignore if dtype: - bf_dtype = bigframes.dtypes.bigframes_type(dtype) - block = block.multi_apply_unary_op(ops.AsTypeOp(to_type=bf_dtype)) + block = block.multi_apply_unary_op( + block.value_columns, ops.AsTypeOp(dtype) + ) + self._block = block else: - if isinstance(dtype, str) and dtype.lower() == "json": - dtype = bigframes.dtypes.JSON_DTYPE - import bigframes.pandas pd_dataframe = pandas.DataFrame( @@ -227,16 +162,21 @@ def __init__( columns=columns, # type:ignore dtype=dtype, # type:ignore ) - if session: - block = session.read_pandas(pd_dataframe)._get_block() + if ( + pd_dataframe.size < MAX_INLINE_DF_SIZE + # TODO(swast): Workaround data types limitation in inline data. + and not any( + dt.pyarrow_dtype + for dt in pd_dataframe.dtypes + if isinstance(dt, pandas.ArrowDtype) + ) + ): + self._block = blocks.block_from_local(pd_dataframe) + elif session: + self._block = session.read_pandas(pd_dataframe)._get_block() else: - block = bigframes.pandas.read_pandas(pd_dataframe)._get_block() - - # We use _block as an indicator in __getattr__ and __setattr__ to see - # if the object is fully initialized, so make sure we set the _block - # attribute last. - self._block = block - self._block.session._register_object(self) + self._block = bigframes.pandas.read_pandas(pd_dataframe)._get_block() + self._query_job: Optional[bigquery.QueryJob] = None def __dir__(self): return dir(type(self)) + [ @@ -271,7 +211,15 @@ def _find_indices( return [self._block.value_columns.index(col_id) for col_id in col_ids] def _resolve_label_exact(self, label) -> Optional[str]: - return self._block.resolve_label_exact(label) + """Returns the column id matching the label if there is exactly + one such column. If there are multiple columns with the same name, + raises an error. If there is no such column, returns None.""" + matches = self._block.label_to_col_id.get(label, []) + if len(matches) > 1: + raise ValueError( + f"Multiple columns matching id {label} were found. {constants.FEEDBACK_LINK}" + ) + return matches[0] if len(matches) != 0 else None def _sql_names( self, @@ -293,23 +241,12 @@ def _sql_names( return results @property - @validations.requires_index def index( self, ) -> indexes.Index: - return indexes.Index.from_frame(self) - - @index.setter - def index(self, value): - # TODO: Handle assigning MultiIndex - result = self._assign_single_item("_new_bf_index", value).set_index( - "_new_bf_index" - ) - self._set_block(result._get_block()) - self.index.name = value.name if hasattr(value, "name") else None + return indexes.Index(self) @property - @validations.requires_index def loc(self) -> indexers.LocDataFrameIndexer: return indexers.LocDataFrameIndexer(self) @@ -318,24 +255,20 @@ def iloc(self) -> indexers.ILocDataFrameIndexer: return indexers.ILocDataFrameIndexer(self) @property - @validations.requires_ordering() def iat(self) -> indexers.IatDataFrameIndexer: return indexers.IatDataFrameIndexer(self) @property - @validations.requires_index def at(self) -> indexers.AtDataFrameIndexer: return indexers.AtDataFrameIndexer(self) @property def dtypes(self) -> pandas.Series: - dtypes = self._block.dtypes - bigframes.dtypes.warn_on_db_dtypes_json_dtype(dtypes) - return pandas.Series(data=dtypes, index=self._block.column_labels) + return pandas.Series(data=self._block.dtypes, index=self._block.column_labels) @property def columns(self) -> pandas.Index: - return self._block.column_labels + return self.dtypes.index @columns.setter def columns(self, labels: pandas.Index): @@ -346,10 +279,6 @@ def columns(self, labels: pandas.Index): def shape(self) -> Tuple[int, int]: return self._block.shape - @property - def axes(self) -> list: - return [self.index, self.columns] - @property def size(self) -> int: rows, cols = self.shape @@ -367,132 +296,25 @@ def empty(self) -> bool: def values(self) -> numpy.ndarray: return self.to_numpy() - @property - def bqclient(self) -> bigframes.Session: - """BigQuery REST API Client the DataFrame uses for operations.""" - return self._session.bqclient - @property def _session(self) -> bigframes.Session: return self._get_block().expr.session - @property - def bigquery( - self, - ) -> bigquery_accessor.BigframesBigQueryDataFrameAccessor: - """ - Accessor for BigQuery functionality. - - Returns: - bigframes.extensions.core.dataframe_accessor.BigQueryDataFrameAccessor: - Accessor that exposes BigQuery functionality on a DataFrame, - with method names closer to SQL. - """ - # Import the accessor here to avoid circular imports. - import bigframes.extensions.bigframes.dataframe_accessor - - return bigframes.extensions.bigframes.dataframe_accessor.BigframesBigQueryDataFrameAccessor( - self - ) - - @property - def _has_index(self) -> bool: - return len(self._block.index_columns) > 0 - - @property - @validations.requires_ordering() - def T(self) -> DataFrame: - return DataFrame(self._get_block().transpose()) - - @validations.requires_index - @validations.requires_ordering() - def transpose(self) -> DataFrame: - return self.T - def __len__(self): rows, _ = self.shape return rows - def __bool__(self): - raise ValueError( - "Cannot convert dataframe into bool. Consider using .empty(), .any(), or .all() methods." - ) - def __iter__(self): return iter(self.columns) - def __contains__(self, key) -> bool: - return key in self.columns - def astype( self, - dtype: Union[ - bigframes.dtypes.DtypeString, - bigframes.dtypes.Dtype, - type, - dict[str, Union[bigframes.dtypes.DtypeString, bigframes.dtypes.Dtype]], - ], - *, - errors: Literal["raise", "null"] = "raise", + dtype: Union[bigframes.dtypes.DtypeString, bigframes.dtypes.Dtype], ) -> DataFrame: - if errors not in ["raise", "null"]: - raise ValueError("Arg 'error' must be one of 'raise' or 'null'") - - if isinstance(dtype, dict): - for col in dtype: - if col not in self.columns: - raise KeyError( - f"Only Column Names are allowed in dtypes dict. '{col}' is not in the columns." - ) - - safe_cast = errors == "null" - - exprs: list[ex.Expression] = [] - for col_id, col_label in zip( - self._block.value_columns, self._block.column_labels - ): - from_type = self._block._column_type(col_id) - - if isinstance(dtype, dict): - if col_label not in dtype: - exprs.append(ex.deref(col_id)) - continue - to_type = bigframes.dtypes.bigframes_type(dtype[col_label]) - else: - to_type = bigframes.dtypes.bigframes_type(dtype) - - op: ops.UnaryOp - if to_type == bigframes.dtypes.JSON_DTYPE: - op = ops.ToJSON(safe=safe_cast) - elif from_type == bigframes.dtypes.JSON_DTYPE: - op = ops.JSONDecode(to_type=to_type, safe=safe_cast) - else: - op = ops.AsTypeOp(to_type=to_type, safe=safe_cast) - - exprs.append(op.as_expr(ex.deref(col_id))) - - block = self._block.project_exprs(exprs, labels=self.columns, drop=True) - return DataFrame(block) - - def _should_sql_have_index(self) -> bool: - """Should the SQL we pass to BQML and other I/O include the index?""" - - return self._has_index and ( - self.index.name is not None or len(self.index.names) > 1 - ) - - def _to_placeholder_table( - self, dry_run: bool = False - ) -> google.cloud.bigquery.table.TableReference: - """Compiles this DataFrame's expression tree to SQL and saves it to a - (temporary) view or table (in the case of a dry run). - """ - return self._block.to_placeholder_table( - include_index=self._should_sql_have_index(), dry_run=dry_run - ) + return self._apply_unary_op(ops.AsTypeOp(dtype)) def _to_sql_query( - self, include_index: bool, enable_cache: bool = True + self, include_index: bool ) -> Tuple[str, list[str], list[blocks.Label]]: """Compiles this DataFrame's expression tree to SQL, optionally including index columns. @@ -502,198 +324,68 @@ def _to_sql_query( whether to include index columns. Returns: - Tuple[sql_string, index_column_id_list, index_column_label_list]: + a tuple of (sql_string, index_column_id_list, index_column_label_list). If include_index is set to False, index_column_id_list and index_column_label_list return empty lists. """ - return self._block.to_sql_query(include_index, enable_cache=enable_cache) + return self._block.to_sql_query(include_index) @property def sql(self) -> str: - """Compiles this DataFrame's expression tree to SQL. - - Returns: - str: - string representing the compiled SQL. - """ - try: - include_index = self._should_sql_have_index() - sql, _, _ = self._to_sql_query(include_index=include_index) - return sql - except AttributeError as e: - # Workaround for a development-mode debugging issue: - # An `AttributeError` originating *inside* this @property getter (e.g., due to - # a typo or referencing a non-existent attribute) can be mistakenly intercepted - # by the class's __getattr__ method if one is defined. - # We catch the AttributeError and raise SyntaxError instead to make it clear - # the error originates *here* in the property implementation. - # See: https://stackoverflow.com/questions/50542177/correct-handling-of-attributeerror-in-getattr-when-using-property - raise SyntaxError( - "AttributeError encountered. Please check the implementation for incorrect attribute access." - ) from e + """Compiles this DataFrame's expression tree to SQL.""" + sql, _, _ = self._to_sql_query(include_index=False) + return sql @property - def query_job(self) -> Optional[google.cloud.bigquery.job.QueryJob]: + def query_job(self) -> Optional[bigquery.QueryJob]: """BigQuery job metadata for the most recent query. Returns: - None or google.cloud.bigquery.job.QueryJob: - The most recent `QueryJob - `_. + The most recent `QueryJob + `_. """ if self._query_job is None: self._set_internal_query_job(self._compute_dry_run()) return self._query_job - def memory_usage(self, index: bool = True): - n_rows, _ = self.shape - # like pandas, treat all variable-size objects as just 8-byte pointers, ignoring actual object - column_sizes = self.dtypes.map( - lambda dtype: bigframes.dtypes.DTYPE_BYTE_SIZES.get(dtype, 8) * n_rows - ) - if index and self._has_index: - index_size = pandas.Series([self.index._memory_usage()], index=["Index"]) - column_sizes = pandas.concat([index_size, column_sizes]) - return column_sizes - - def info( - self, - verbose: Optional[bool] = None, - buf=None, - max_cols: Optional[int] = None, - memory_usage: Optional[bool] = None, - show_counts: Optional[bool] = None, - ): - obuf = buf or sys.stdout - - n_rows, n_columns = self.shape - - max_cols = ( - max_cols - if max_cols is not None - else bigframes.options.display.max_info_columns - ) - - show_all_columns = verbose if verbose is not None else (n_columns < max_cols) - - obuf.write(f"{type(self)}\n") - - if self._block.has_index: - index_type = "MultiIndex" if self.index.nlevels > 1 else "Index" - - index_stats = f"{n_rows} entries" - if n_rows > 0: - # These accessses are kind of expensive, maybe should try to skip? - first_indice = self.index[0] - last_indice = self.index[-1] - index_stats += f", {first_indice} to {last_indice}" - obuf.write(f"{index_type}: {index_stats}\n") - else: - obuf.write("NullIndex\n") - - if n_columns == 0: - # We don't display any more information if the dataframe has no columns - obuf.write("Empty DataFrame\n") - return - - dtype_strings = self.dtypes.astype("string") - if show_all_columns: - obuf.write(f"Data columns (total {n_columns} columns):\n") - column_info = self.columns.to_frame(name="Column") - - max_rows = bigframes.options.display.max_info_rows - too_many_rows = n_rows > max_rows if max_rows is not None else False - - if show_counts if show_counts is not None else (not too_many_rows): - non_null_counts = self.count().to_pandas() - column_info["Non-Null Count"] = non_null_counts.map( - lambda x: f"{int(x)} non-null" - ) - - column_info["Dtype"] = dtype_strings - - column_info = column_info.reset_index(drop=True) - column_info.index.name = "#" - - column_info_formatted = tabulate.tabulate(column_info, headers="keys") # type: ignore - obuf.write(column_info_formatted) - obuf.write("\n") - - else: # Just number of columns and first, last - obuf.write( - f"Columns: {n_columns} entries, {self.columns[0]} to {self.columns[-1]}\n" - ) - dtype_counts = dtype_strings.value_counts().sort_index(ascending=True).items() - dtype_counts_formatted = ", ".join( - f"{dtype}({count})" for dtype, count in dtype_counts - ) - obuf.write(f"dtypes: {dtype_counts_formatted}\n") - - show_memory = ( - memory_usage - if memory_usage is not None - else bigframes.options.display.memory_usage - ) - if show_memory: - # TODO: Convert to different units (kb, mb, etc.) - obuf.write(f"memory usage: {self.memory_usage().sum()} bytes\n") - - def select_dtypes(self, include=None, exclude=None) -> DataFrame: - # Create empty pandas dataframe with same schema and then leverage actual pandas implementation - as_pandas = pandas.DataFrame( - { - col_id: pandas.Series([], dtype=dtype) - for col_id, dtype in zip(self._block.value_columns, self._block.dtypes) - } - ) - selected_columns = tuple( - as_pandas.select_dtypes(include=include, exclude=exclude).columns - ) - return DataFrame(self._block.select_columns(selected_columns)) - - def _set_internal_query_job( - self, query_job: Optional[google.cloud.bigquery.job.QueryJob] - ): + def _set_internal_query_job(self, query_job: bigquery.QueryJob): self._query_job = query_job def __getitem__( self, key: Union[ blocks.Label, - List[str], - List[blocks.Label], + Sequence[blocks.Label], # Index of column labels can be treated the same as a sequence of column labels. pandas.Index, bigframes.series.Series, - slice, ], ): # No return type annotations (like pandas) as type cannot always be determined statically + """Gets the specified column(s) from the DataFrame.""" # NOTE: This implements the operations described in # https://pandas.pydata.org/docs/getting_started/intro_tutorials/03_subset_data.html - import bigframes.core.col - import bigframes.pandas - if isinstance(key, bigframes.pandas.Series): + if isinstance(key, bigframes.series.Series): return self._getitem_bool_series(key) - if isinstance(key, slice): - return self.iloc[key] - - if isinstance(key, bigframes.core.col.Expression): - return self.loc[key] - - # TODO(tswast): Fix this pylance warning: Class overlaps "Hashable" - # unsafely and could produce a match at runtime - if isinstance(key, blocks.Label): + if isinstance(key, typing.Hashable): return self._getitem_label(key) + # Select a subset of columns or re-order columns. + # In Ibis after you apply a projection, any column objects from the + # table before the projection can't be combined with column objects + # from the table after the projection. This is because the table after + # a projection is considered a totally separate table expression. + # + # This is unexpected behavior for a pandas user, who expects their old + # Series objects to still work with the new / mutated DataFrame. We + # avoid applying a projection in Ibis until it's absolutely necessary + # to provide pandas-like semantics. + # TODO(swast): Do we need to apply implicit join when doing a + # projection? - if utils.is_list_like(key): - return self._getitem_columns(key) - else: - # TODO(tswast): What case is this supposed to be handling? - return self._getitem_columns([cast(Hashable, key)]) + # Select a number of columns as DF. + key = key if utils.is_list_like(key) else [key] # type:ignore - def _getitem_columns(self, key: Sequence[blocks.Label]) -> DataFrame: selected_ids: Tuple[str, ...] = () for label in key: col_ids = self._block.label_to_col_id[label] @@ -704,9 +396,7 @@ def _getitem_columns(self, key: Sequence[blocks.Label]) -> DataFrame: def _getitem_label(self, key: blocks.Label): col_ids = self._block.cols_matching_label(key) if len(col_ids) == 0: - raise KeyError( - f"{key} not found in DataFrame columns: {self._block.column_labels}" - ) + raise KeyError(key) block = self._block.select_columns(col_ids) if isinstance(self.columns, pandas.MultiIndex): # Multiindex should drop-level if not selecting entire @@ -730,39 +420,20 @@ def _getitem_bool_series(self, key: bigframes.series.Series) -> DataFrame: f"Only boolean series currently supported for indexing. {constants.FEEDBACK_LINK}" ) # TODO: enforce stricter alignment - ( - combined_index, - ( - get_column_left, - get_column_right, - ), - ) = self._block.join(key._block, how="left") - block = combined_index + combined_index, ( + get_column_left, + get_column_right, + ) = self._block.index.join(key._block.index, how="left") + block = combined_index._block filter_col_id = get_column_right[key._value_column] - block = block.filter_by_id(filter_col_id) + block = block.filter(filter_col_id) block = block.drop_columns([filter_col_id]) return DataFrame(block) def __getattr__(self, key: str): - # To allow subclasses to set private attributes before the class is - # fully initialized, protect against recursion errors with - # uninitialized DataFrame objects. Note: this comes at the downside - # that columns with a leading `_` won't be treated as columns. - # - # See: - # https://github.com/googleapis/python-bigquery-dataframes/issues/728 - # and - # https://nedbatchelder.com/blog/201010/surprising_getattr_recursion.html - if key == "_block": - raise AttributeError(key) - if key in self._block.column_labels: return self.__getitem__(key) - - if hasattr(pandas.DataFrame, key): - log_adapter.submit_pandas_labels( - self._block.expr.session.bqclient, self.__class__.__name__, key - ) + elif hasattr(pandas.DataFrame, key): raise AttributeError( textwrap.dedent( f""" @@ -771,56 +442,18 @@ def __getattr__(self, key: str): """ ) ) - raise AttributeError(key) - - def __setattr__(self, key: str, value): - if key == "_block": - object.__setattr__(self, key, value) - return - - # To allow subclasses to set private attributes before the class is - # fully initialized, assume anything set before `_block` is initialized - # is a regular attribute. - if not hasattr(self, "_block"): - object.__setattr__(self, key, value) - return - - # If someone has a column named the same as a normal attribute - # (e.g. index), we want to set the normal attribute, not the column. - # To do that, check if there is a normal attribute by using - # __getattribute__ (not __getattr__, because that includes columns). - # If that returns a value without raising, then we know this is a - # normal attribute and we should prefer that. - try: - object.__getattribute__(self, key) - return object.__setattr__(self, key, value) - except AttributeError: - pass - - # If we made it here, then we know that it's not a regular attribute - # already, so it might be a column to update. Note: we don't allow - # adding new columns using __setattr__, only __setitem__, that way we - # can still add regular new attributes. - if key in self._block.column_labels: - self[key] = value else: - object.__setattr__(self, key, value) + raise AttributeError(key) def __repr__(self) -> str: """Converts a DataFrame to a string. Calls to_pandas. Only represents the first `bigframes.options.display.max_rows`. """ - # Protect against errors with uninitialized DataFrame. See: - # https://github.com/googleapis/python-bigquery-dataframes/issues/728 - if not hasattr(self, "_block"): - return object.__repr__(self) - opts = bigframes.options.display max_results = opts.max_rows if opts.repr_mode == "deferred": - return formatter.repr_query_job(self._compute_dry_run()) - + return formatter.repr_query_job(self.query_job) # TODO(swast): pass max_columns and get the true column count back. Maybe # get 1 more column than we have requested so that pandas can add the # ... for us? @@ -829,62 +462,60 @@ def __repr__(self) -> str: ) self._set_internal_query_job(query_job) - from bigframes.display import plaintext - - return plaintext.create_text_representation( - pandas_df, - row_count, - is_series=False, - has_index=self._has_index, - column_count=len(self.columns), - ) - def _prepare_display_df(self) -> DataFrame: - """Process ObjectRef and JSON/nested JSON columns for display.""" - import bigframes.bigquery as bbq - - df = self - # Arrow/Pandas to_pandas_batches does not support raw JSON/nested JSON - # columns. Pre-serialize them to string format to bypass this limit. - # Using TO_JSON_STRING via SqlScalarOp handles complex nested STRUCT - # types correctly. Use the offset so that we can handle duplicate and - # non-string column names. - json_col_indexes = [ - col_index - for col_index, col in enumerate(df.columns) - if bigframes.dtypes.contains_db_dtypes_json_dtype(df[col].dtype) - ] - if json_col_indexes: - df.iloc[:, json_col_indexes] = cast( - DataFrame, - df.iloc[:, json_col_indexes].apply(bbq.to_json_string), # type: ignore - ) - return df + column_count = len(pandas_df.columns) + + with display_options.pandas_repr(opts): + repr_string = repr(pandas_df) + + # Modify the end of the string to reflect count. + lines = repr_string.split("\n") + pattern = re.compile("\\[[0-9]+ rows x [0-9]+ columns\\]") + if pattern.match(lines[-1]): + lines = lines[:-2] + + if row_count > len(lines) - 1: + lines.append("...") - def _repr_mimebundle_(self, include=None, exclude=None): + lines.append("") + lines.append(f"[{row_count} rows x {column_count} columns]") + return "\n".join(lines) + + def _repr_html_(self) -> str: """ - Custom display method for IPython/Jupyter environments. - This is called by IPython's display system when the object is displayed. + Returns an html string primarily for use by notebooks for displaying + a representation of the DataFrame. Displays 20 rows by default since + many notebooks are not configured for large tables. """ - # TODO(b/467647693): Anywidget integration has been tested in Jupyter, VS Code, and - # BQ Studio, but there is a known compatibility issue with Marimo that needs to be addressed. - from bigframes.display import html + opts = bigframes.options.display + max_results = bigframes.options.display.max_rows + if opts.repr_mode == "deferred": + return formatter.repr_query_job_html(self.query_job) + # TODO(swast): pass max_columns and get the true column count back. Maybe + # get 1 more column than we have requested so that pandas can add the + # ... for us? + pandas_df, row_count, query_job = self._block.retrieve_repr_request_results( + max_results + ) - return html.repr_mimebundle(self, include=include, exclude=exclude) + self._set_internal_query_job(query_job) - def __delitem__(self, key: str): - df = self.drop(columns=[key]) - self._set_block(df._get_block()) + column_count = len(pandas_df.columns) - def __setitem__( - self, - key: str | list[str] | pandas.Index, - value: SingleItemValue | MultiItemValue, - ): - if isinstance(key, (list, pandas.Index)): - df = self._assign_multi_items(key, value) - else: - df = self._assign_single_item(key, value) + with display_options.pandas_repr(opts): + # _repr_html_ stub is missing so mypy thinks it's a Series. Ignore mypy. + html_string = pandas_df._repr_html_() # type:ignore + + html_string += f"[{row_count} rows x {column_count} columns in total]" + return html_string + + def __setitem__(self, key: str, value: SingleItemValue): + """Modify or insert a column into the DataFrame. + + Note: This does **not** modify the original table the DataFrame was + derived from. + """ + df = self._assign_single_item(key, value) self._set_block(df._get_block()) def _apply_binop( @@ -893,142 +524,146 @@ def _apply_binop( op, axis: str | int = "columns", how: str = "outer", - reverse: bool = False, ): - if isinstance(other, bigframes.dtypes.LOCAL_SCALAR_TYPES): - return self._apply_scalar_binop(other, op, reverse=reverse) + if isinstance(other, (float, int)): + return self._apply_scalar_binop(other, op) + elif isinstance(other, bigframes.series.Series): + return self._apply_series_binop(other, op, axis=axis, how=how) elif isinstance(other, DataFrame): - return self._apply_dataframe_binop(other, op, how=how, reverse=reverse) - elif isinstance(other, pandas.DataFrame): - return self._apply_dataframe_binop( - DataFrame(other), op, how=how, reverse=reverse - ) - elif utils.get_axis_number(axis) == 0: - return self._apply_series_binop_axis_0(other, op, how, reverse) - elif utils.get_axis_number(axis) == 1: - return self._apply_series_binop_axis_1(other, op, how, reverse) + return self._apply_dataframe_binop(other, op, how=how) raise NotImplementedError( f"binary operation is not implemented on the second operand of type {type(other).__name__}." f"{constants.FEEDBACK_LINK}" ) - def _apply_scalar_binop( - self, - other: bigframes.dtypes.LOCAL_SCALAR_TYPE, - op: ops.BinaryOp, - reverse: bool = False, - ) -> DataFrame: - if reverse: - expr = op.as_expr( - left_input=ex.const(other), - right_input=ex.free_var("var1"), - ) - else: - expr = op.as_expr( - left_input=ex.free_var("var1"), - right_input=ex.const(other), - ) - return DataFrame(self._block.multi_apply_unary_op(expr)) + def _apply_scalar_binop(self, other: float | int, op: ops.BinaryOp) -> DataFrame: + block = self._block + partial_op = ops.BinopPartialRight(op, other) + for column_id, label in zip( + self._block.value_columns, self._block.column_labels + ): + block, _ = block.apply_unary_op(column_id, partial_op, result_label=label) + block = block.drop_columns([column_id]) + return DataFrame(block) - def _apply_series_binop_axis_0( + def _apply_series_binop( self, - other, + other: bigframes.series.Series, op: ops.BinaryOp, + axis: str | int = "columns", how: str = "outer", - reverse: bool = False, ) -> DataFrame: - bf_series = bigframes.core.convert.to_bf_series( - other, self.index if self._has_index else None, self._session - ) - aligned_block, columns, expr_pairs = self._block._align_axis_0( - bf_series._block, how=how - ) - result = aligned_block._apply_binop( - op, inputs=expr_pairs, labels=columns, reverse=reverse + if axis not in ("columns", "index", 0, 1): + raise ValueError(f"Invalid input: axis {axis}.") + + if axis in ("columns", 1): + raise NotImplementedError( + f"Row Series operations haven't been supported. {constants.FEEDBACK_LINK}" + ) + + joined_index, (get_column_left, get_column_right) = self._block.index.join( + other._block.index, how=how ) - return DataFrame(result) - def _apply_series_binop_axis_1( - self, - other, - op: ops.BinaryOp, - how: str = "outer", - reverse: bool = False, - ) -> DataFrame: - """Align dataframe with pandas series by inlining series values as literals.""" - # If we already know the transposed schema (from the transpose cache), we don't need to materialize rows from other - # Instead, can fully defer execution (as a cross-join) - if ( - isinstance(other, bigframes.series.Series) - and other._block._transpose_cache is not None + series_column_id = other._value_column + series_col = get_column_right[series_column_id] + block = joined_index._block + for column_id, label in zip( + self._block.value_columns, self._block.column_labels ): - aligned_block, columns, expr_pairs = self._block._align_series_block_axis_1( - other._block, how=how - ) - else: - # Fallback path, materialize `other` locally - pd_series = bigframes.core.convert.to_pd_series(other, self.columns) - aligned_block, columns, expr_pairs = self._block._align_pd_series_axis_1( - pd_series, how=how + block, _ = block.apply_binary_op( + get_column_left[column_id], + series_col, + op, + result_label=label, ) - result = aligned_block._apply_binop( - op, inputs=expr_pairs, labels=columns, reverse=reverse - ) - return DataFrame(result) + block = block.drop_columns([get_column_left[column_id]]) + + block = block.drop_columns([series_col]) + block = block.with_index_labels(self.index.names) + return DataFrame(block) def _apply_dataframe_binop( - self, - other: DataFrame, - op: ops.BinaryOp, - how: str = "outer", - reverse: bool = False, + self, other: DataFrame, op: ops.BinaryOp, how: str = "outer" ) -> DataFrame: - aligned_block, columns, expr_pairs = self._block._align_both_axes( - other._block, how=how + # Join rows + joined_index, (get_column_left, get_column_right) = self._block.index.join( + other._block.index, how=how ) - result = aligned_block._apply_binop( - op, inputs=expr_pairs, labels=columns, reverse=reverse + # join columns schema + # indexers will be none for exact match + columns, lcol_indexer, rcol_indexer = self.columns.join( + other.columns, how=how, return_indexers=True ) - return DataFrame(result) + + binop_result_ids = [] + block = joined_index._block + + column_indices = zip( + lcol_indexer if (lcol_indexer is not None) else range(len(columns)), + rcol_indexer if (lcol_indexer is not None) else range(len(columns)), + ) + + for left_index, right_index in column_indices: + if left_index >= 0 and right_index >= 0: # -1 indices indicate missing + left_col_id = self._block.value_columns[left_index] + right_col_id = other._block.value_columns[right_index] + block, result_col_id = block.apply_binary_op( + get_column_left[left_col_id], + get_column_right[right_col_id], + op, + ) + binop_result_ids.append(result_col_id) + elif left_index >= 0: + left_col_id = self._block.value_columns[left_index] + block, result_col_id = block.apply_unary_op( + get_column_left[left_col_id], + ops.partial_right(op, None), + ) + binop_result_ids.append(result_col_id) + elif right_index >= 0: + right_col_id = other._block.value_columns[right_index] + block, result_col_id = block.apply_unary_op( + get_column_right[right_col_id], + ops.partial_left(op, None), + ) + binop_result_ids.append(result_col_id) + else: + # Should not be possible + raise ValueError("No right or left index.") + + block = block.select_columns(binop_result_ids).with_column_labels(columns) + return DataFrame(block) def eq(self, other: typing.Any, axis: str | int = "columns") -> DataFrame: return self._apply_binop(other, ops.eq_op, axis=axis) - def __eq__(self, other) -> DataFrame: # type: ignore - return self.eq(other) - def ne(self, other: typing.Any, axis: str | int = "columns") -> DataFrame: return self._apply_binop(other, ops.ne_op, axis=axis) - def __ne__(self, other) -> DataFrame: # type: ignore - return self.ne(other) + __eq__ = eq # type: ignore - def __invert__(self) -> DataFrame: - return self._apply_unary_op(ops.invert_op) + __ne__ = ne # type: ignore def le(self, other: typing.Any, axis: str | int = "columns") -> DataFrame: return self._apply_binop(other, ops.le_op, axis=axis) - def __le__(self, other) -> DataFrame: - return self.le(other) - def lt(self, other: typing.Any, axis: str | int = "columns") -> DataFrame: return self._apply_binop(other, ops.lt_op, axis=axis) - def __lt__(self, other) -> DataFrame: - return self.lt(other) - def ge(self, other: typing.Any, axis: str | int = "columns") -> DataFrame: return self._apply_binop(other, ops.ge_op, axis=axis) - def __ge__(self, other) -> DataFrame: - return self.ge(other) - def gt(self, other: typing.Any, axis: str | int = "columns") -> DataFrame: return self._apply_binop(other, ops.gt_op, axis=axis) - def __gt__(self, other) -> DataFrame: - return self.gt(other) + __lt__ = lt + + __le__ = le + + __gt__ = gt + + __ge__ = ge def add( self, @@ -1039,20 +674,7 @@ def add( # TODO(swast): Support level parameter with MultiIndex. return self._apply_binop(other, ops.add_op, axis=axis) - def radd( - self, - other: float | int | bigframes.series.Series | DataFrame, - axis: str | int = "columns", - ) -> DataFrame: - # TODO(swast): Support fill_value parameter. - # TODO(swast): Support level parameter with MultiIndex. - return self._apply_binop(other, ops.add_op, axis=axis, reverse=True) - - def __add__(self, other) -> DataFrame: - return self.add(other) - - def __radd__(self, other) -> DataFrame: - return self.radd(other) + __radd__ = __add__ = radd = add def sub( self, @@ -1061,20 +683,16 @@ def sub( ) -> DataFrame: return self._apply_binop(other, ops.sub_op, axis=axis) - subtract = sub - - def __sub__(self, other): - return self.sub(other) + __sub__ = subtract = sub def rsub( self, other: float | int | bigframes.series.Series | DataFrame, axis: str | int = "columns", ) -> DataFrame: - return self._apply_binop(other, ops.sub_op, axis=axis, reverse=True) + return self._apply_binop(other, ops.reverse(ops.sub_op), axis=axis) - def __rsub__(self, other): - return self.rsub(other) + __rsub__ = rsub def mul( self, @@ -1083,20 +701,7 @@ def mul( ) -> DataFrame: return self._apply_binop(other, ops.mul_op, axis=axis) - multiply = mul - - def __mul__(self, other): - return self.mul(other) - - def rmul( - self, - other: float | int | bigframes.series.Series | DataFrame, - axis: str | int = "columns", - ) -> DataFrame: - return self.mul(other, axis=axis) - - def __rmul__(self, other): - return self.rmul(other) + __rmul__ = __mul__ = rmul = multiply = mul def truediv( self, @@ -1105,22 +710,16 @@ def truediv( ) -> DataFrame: return self._apply_binop(other, ops.div_op, axis=axis) - div = divide = truediv - - def __truediv__(self, other): - return self.truediv(other) + div = divide = __truediv__ = truediv def rtruediv( self, other: float | int | bigframes.series.Series | DataFrame, axis: str | int = "columns", ) -> DataFrame: - return self._apply_binop(other, ops.div_op, axis=axis, reverse=True) - - rdiv = rtruediv + return self._apply_binop(other, ops.reverse(ops.div_op), axis=axis) - def __rtruediv__(self, other): - return self.rtruediv(other) + __rtruediv__ = rdiv = rtruediv def floordiv( self, @@ -1129,78 +728,40 @@ def floordiv( ) -> DataFrame: return self._apply_binop(other, ops.floordiv_op, axis=axis) - def __floordiv__(self, other): - return self.floordiv(other) + __floordiv__ = floordiv def rfloordiv( self, other: float | int | bigframes.series.Series | DataFrame, axis: str | int = "columns", ) -> DataFrame: - return self._apply_binop(other, ops.floordiv_op, axis=axis, reverse=True) + return self._apply_binop(other, ops.reverse(ops.floordiv_op), axis=axis) - def __rfloordiv__(self, other): - return self.rfloordiv(other) + __rfloordiv__ = rfloordiv - def mod( - self, - other: int | bigframes.series.Series | DataFrame, - axis: str | int = "columns", - ) -> DataFrame: # type: ignore + def mod(self, other: int | bigframes.series.Series | DataFrame, axis: str | int = "columns") -> DataFrame: # type: ignore return self._apply_binop(other, ops.mod_op, axis=axis) - def __mod__(self, other): - return self.mod(other) + def rmod(self, other: int | bigframes.series.Series | DataFrame, axis: str | int = "columns") -> DataFrame: # type: ignore + return self._apply_binop(other, ops.reverse(ops.mod_op), axis=axis) - def rmod( - self, - other: int | bigframes.series.Series | DataFrame, - axis: str | int = "columns", - ) -> DataFrame: # type: ignore - return self._apply_binop(other, ops.mod_op, axis=axis, reverse=True) + __mod__ = mod - def __rmod__(self, other): - return self.rmod(other) + __rmod__ = rmod def pow( self, other: int | bigframes.series.Series, axis: str | int = "columns" ) -> DataFrame: return self._apply_binop(other, ops.pow_op, axis=axis) - def __pow__(self, other): - return self.pow(other) - def rpow( self, other: int | bigframes.series.Series, axis: str | int = "columns" ) -> DataFrame: - return self._apply_binop(other, ops.pow_op, axis=axis, reverse=True) - - def __rpow__(self, other): - return self.rpow(other) - - def __and__(self, other: bool | int | bigframes.series.Series) -> DataFrame: - return self._apply_binop(other, ops.and_op) - - __rand__ = __and__ - - def __or__(self, other: bool | int | bigframes.series.Series) -> DataFrame: - return self._apply_binop(other, ops.or_op) - - __ror__ = __or__ - - def __xor__(self, other: bool | int | bigframes.series.Series) -> DataFrame: - return self._apply_binop(other, ops.xor_op) - - __rxor__ = __xor__ + return self._apply_binop(other, ops.reverse(ops.pow_op), axis=axis) - def __pos__(self) -> DataFrame: - return self._apply_unary_op(ops.pos_op) + __pow__ = pow - def __neg__(self) -> DataFrame: - return self._apply_unary_op(ops.neg_op) - - def __abs__(self) -> DataFrame: - return self._apply_unary_op(ops.abs_op) + __rpow__ = rpow def align( self, @@ -1286,7 +847,7 @@ def combine( results.append(result) if all([isinstance(val, bigframes.series.Series) for val in results]): - import bigframes.core.reshape.api as rs + import bigframes.core.reshape as rs return rs.concat(results, axis=1) else: @@ -1295,613 +856,61 @@ def combine( def combine_first(self, other: DataFrame): return self._apply_dataframe_binop(other, ops.fillna_op) - def _fast_stat_matrix(self, op: agg_ops.BinaryAggregateOp) -> DataFrame: - """Faster corr, cov calculations, but creates more sql text, so cannot scale to many columns""" - assert len(self.columns) * len(self.columns) < bigframes.constants.MAX_COLUMNS - orig_columns = self.columns - frame = self.copy() - # Replace column names with 0 to n - 1 to keep order - # and avoid the influence of duplicated column name - frame.columns = pandas.Index(range(len(orig_columns))) - frame = frame.astype(bigframes.dtypes.FLOAT_DTYPE) - block = frame._block - - aggregations = [ - agg_expressions.BinaryAggregation( - op, ex.deref(left_col), ex.deref(right_col) - ) - for left_col in block.value_columns - for right_col in block.value_columns - ] - # unique columns stops - uniq_orig_columns = utils.combine_indices( - orig_columns, pandas.Index(range(len(orig_columns))) + def to_pandas( + self, + max_download_size: Optional[int] = None, + sampling_method: Optional[str] = None, + random_state: Optional[int] = None, + ) -> pandas.DataFrame: + """Write DataFrame to pandas DataFrame. + + Args: + max_download_size (int, default None): + Download size threshold in MB. If max_download_size is exceeded when downloading data + (e.g., to_pandas()), the data will be downsampled if + bigframes.options.sampling.enable_downsampling is True, otherwise, an error will be + raised. If set to a value other than None, this will supersede the global config. + sampling_method (str, default None): + Downsampling algorithms to be chosen from, the choices are: "head": This algorithm + returns a portion of the data from the beginning. It is fast and requires minimal + computations to perform the downsampling; "uniform": This algorithm returns uniform + random samples of the data. If set to a value other than None, this will supersede + the global config. + random_state (int, default None): + The seed for the uniform downsampling algorithm. If provided, the uniform method may + take longer to execute and require more computation. If set to a value other than + None, this will supersede the global config. + + Returns: + pandas.DataFrame: A pandas DataFrame with all rows and columns of this DataFrame if the + data_sampling_threshold_mb is not exceeded; otherwise, a pandas DataFrame with + downsampled rows and all columns of this DataFrame. + """ + # TODO(orrbradford): Optimize this in future. Potentially some cases where we can return the stored query job + df, query_job = self._block.to_pandas( + max_download_size=max_download_size, + sampling_method=sampling_method, + random_state=random_state, ) - labels = utils.cross_indices(uniq_orig_columns, uniq_orig_columns) + self._set_internal_query_job(query_job) + return df.set_axis(self._block.column_labels, axis=1, copy=False) - block = block.aggregate(aggregations=aggregations, column_labels=labels) + def to_pandas_batches(self) -> Iterable[pandas.DataFrame]: + """Stream DataFrame results to an iterable of pandas DataFrame""" + return self._block.to_pandas_batches() - block = block.stack(levels=orig_columns.nlevels + 1) - # The aggregate operation crated a index level with just 0, need to drop it - # Also, drop the last level of each index, which was created to guarantee uniqueness - return DataFrame(block).droplevel(0).droplevel(-1, axis=0).droplevel(-1, axis=1) + def _compute_dry_run(self) -> bigquery.QueryJob: + return self._block._compute_dry_run() - def corr(self, method="pearson", min_periods=None, numeric_only=False) -> DataFrame: - if method != "pearson": - raise NotImplementedError( - f"Only Pearson correlation is currently supported. {constants.FEEDBACK_LINK}" - ) - if min_periods: - raise NotImplementedError( - f"min_periods not yet supported. {constants.FEEDBACK_LINK}" - ) + def copy(self) -> DataFrame: + return DataFrame(self._block) - if not numeric_only: - frame = self._raise_on_non_numeric("corr") - else: - frame = self._drop_non_numeric() - - if len(frame.columns) <= 30: - return frame._fast_stat_matrix(agg_ops.CorrOp()) - - frame = frame.copy() - orig_columns = frame.columns - # Replace column names with 0 to n - 1 to keep order - # and avoid the influence of duplicated column name - frame.columns = pandas.Index(range(len(orig_columns))) - frame = frame.astype(bigframes.dtypes.FLOAT_DTYPE) - block = frame._block - - # A new column that uniquely identifies each row - block, ordering_col = frame._block.promote_offsets(label="_bigframes_idx") - - val_col_ids = [ - col_id for col_id in block.value_columns if col_id != ordering_col - ] - - block = block.melt( - [ordering_col], val_col_ids, ["_bigframes_variable"], "_bigframes_value" - ) - - block = block.merge( - block, - left_join_ids=[ordering_col], - right_join_ids=[ordering_col], - how="inner", - sort=False, - ) - - frame = DataFrame(block).dropna( - subset=["_bigframes_value_x", "_bigframes_value_y"] - ) - - paired_mean_frame = ( - frame.groupby(["_bigframes_variable_x", "_bigframes_variable_y"]) - .agg( - _bigframes_paired_mean_x=bigframes.pandas.NamedAgg( - column="_bigframes_value_x", aggfunc="mean" - ), - _bigframes_paired_mean_y=bigframes.pandas.NamedAgg( - column="_bigframes_value_y", aggfunc="mean" - ), - ) - .reset_index() - ) - - frame = frame.merge( - paired_mean_frame, on=["_bigframes_variable_x", "_bigframes_variable_y"] - ) - frame["_bigframes_value_x"] -= frame["_bigframes_paired_mean_x"] - frame["_bigframes_value_y"] -= frame["_bigframes_paired_mean_y"] - - frame["_bigframes_dividend"] = ( - frame["_bigframes_value_x"] * frame["_bigframes_value_y"] - ) - frame["_bigframes_x_square"] = ( - frame["_bigframes_value_x"] * frame["_bigframes_value_x"] - ) - frame["_bigframes_y_square"] = ( - frame["_bigframes_value_y"] * frame["_bigframes_value_y"] - ) - - result = ( - frame.groupby(["_bigframes_variable_x", "_bigframes_variable_y"]) - .agg( - _bigframes_dividend_sum=bigframes.pandas.NamedAgg( - column="_bigframes_dividend", aggfunc="sum" - ), - _bigframes_x_square_sum=bigframes.pandas.NamedAgg( - column="_bigframes_x_square", aggfunc="sum" - ), - _bigframes_y_square_sum=bigframes.pandas.NamedAgg( - column="_bigframes_y_square", aggfunc="sum" - ), - ) - .reset_index() - ) - result["_bigframes_corr"] = result["_bigframes_dividend_sum"] / ( - ( - result["_bigframes_x_square_sum"] * result["_bigframes_y_square_sum"] - )._apply_unary_op(ops.sqrt_op) - ) - result = result._pivot( - index="_bigframes_variable_x", - columns="_bigframes_variable_y", - values="_bigframes_corr", - ) - - map_data = { - f"_bigframes_level_{i}": orig_columns.get_level_values(i) - for i in range(orig_columns.nlevels) - } - map_data["_bigframes_keys"] = range(len(orig_columns)) - map_df = bigframes.dataframe.DataFrame( - map_data, - session=self._get_block().expr.session, - ).set_index("_bigframes_keys") - result = result.join(map_df).sort_index() - index_columns = [f"_bigframes_level_{i}" for i in range(orig_columns.nlevels)] - result = result.set_index(index_columns) - result.index.names = orig_columns.names - result.columns = orig_columns - - return result - - def cov(self, *, numeric_only: bool = False) -> DataFrame: - if not numeric_only: - frame = self._raise_on_non_numeric("corr") - else: - frame = self._drop_non_numeric() - - if len(frame.columns) <= 30: - return frame._fast_stat_matrix(agg_ops.CovOp()) - - frame = frame.copy() - orig_columns = frame.columns - # Replace column names with 0 to n - 1 to keep order - # and avoid the influence of duplicated column name - frame.columns = pandas.Index(range(len(orig_columns))) - frame = frame.astype(bigframes.dtypes.FLOAT_DTYPE) - block = frame._block - - # A new column that uniquely identifies each row - block, ordering_col = frame._block.promote_offsets(label="_bigframes_idx") - - val_col_ids = [ - col_id for col_id in block.value_columns if col_id != ordering_col - ] - - block = block.melt( - [ordering_col], val_col_ids, ["_bigframes_variable"], "_bigframes_value" - ) - block = block.merge( - block, - left_join_ids=[ordering_col], - right_join_ids=[ordering_col], - how="inner", - sort=False, - ) - - frame = DataFrame(block).dropna( - subset=["_bigframes_value_x", "_bigframes_value_y"] - ) - - paired_mean_frame = ( - frame.groupby(["_bigframes_variable_x", "_bigframes_variable_y"]) - .agg( - _bigframes_paired_mean_x=bigframes.pandas.NamedAgg( - column="_bigframes_value_x", aggfunc="mean" - ), - _bigframes_paired_mean_y=bigframes.pandas.NamedAgg( - column="_bigframes_value_y", aggfunc="mean" - ), - ) - .reset_index() - ) - - frame = frame.merge( - paired_mean_frame, on=["_bigframes_variable_x", "_bigframes_variable_y"] - ) - frame["_bigframes_value_x"] -= frame["_bigframes_paired_mean_x"] - frame["_bigframes_value_y"] -= frame["_bigframes_paired_mean_y"] - - frame["_bigframes_dividend"] = ( - frame["_bigframes_value_x"] * frame["_bigframes_value_y"] - ) - - result = ( - frame.groupby(["_bigframes_variable_x", "_bigframes_variable_y"]) - .agg( - _bigframes_dividend_sum=bigframes.pandas.NamedAgg( - column="_bigframes_dividend", aggfunc="sum" - ), - _bigframes_dividend_count=bigframes.pandas.NamedAgg( - column="_bigframes_dividend", aggfunc="count" - ), - ) - .reset_index() - ) - result["_bigframes_cov"] = result["_bigframes_dividend_sum"] / ( - result["_bigframes_dividend_count"] - 1 - ) - result = result._pivot( - index="_bigframes_variable_x", - columns="_bigframes_variable_y", - values="_bigframes_cov", - ) - - map_data = { - f"_bigframes_level_{i}": orig_columns.get_level_values(i) - for i in range(orig_columns.nlevels) - } - map_data["_bigframes_keys"] = range(len(orig_columns)) - map_df = bigframes.dataframe.DataFrame( - map_data, - session=self._get_block().expr.session, - ).set_index("_bigframes_keys") - result = result.join(map_df).sort_index() - index_columns = [f"_bigframes_level_{i}" for i in range(orig_columns.nlevels)] - result = result.set_index(index_columns) - result.index.names = orig_columns.names - result.columns = orig_columns - - return result - - def corrwith( - self, - other: typing.Union[DataFrame, bigframes.series.Series], - *, - numeric_only: bool = False, - ): - other_frame = other if isinstance(other, DataFrame) else other.to_frame() - if numeric_only: - l_frame = self._drop_non_numeric() - r_frame = other_frame._drop_non_numeric() - else: - l_frame = self._raise_on_non_numeric("corrwith") - r_frame = other_frame._raise_on_non_numeric("corrwith") - - l_block = l_frame.astype(bigframes.dtypes.FLOAT_DTYPE)._block - r_block = r_frame.astype(bigframes.dtypes.FLOAT_DTYPE)._block - - if isinstance(other, DataFrame): - block, labels, expr_pairs = l_block._align_both_axes(r_block, how="inner") - else: - assert isinstance(other, bigframes.series.Series) - block, labels, expr_pairs = l_block._align_axis_0(r_block, how="inner") - - na_cols = l_block.column_labels.join( - r_block.column_labels, how="outer" - ).difference(labels) - - block = block.aggregate( - aggregations=tuple( - agg_expressions.BinaryAggregation(agg_ops.CorrOp(), left_ex, right_ex) - for left_ex, right_ex in expr_pairs - ), - column_labels=labels, - ) - block = block.project_exprs( - (ex.const(float("nan")),) * len(na_cols), labels=na_cols - ) - block = block.transpose( - original_row_index=pandas.Index([None]), single_row_mode=True - ) - return bigframes.pandas.Series(block) - - def __dataframe__( - self, nan_as_null: bool = False, allow_copy: bool = True - ) -> bigframes.core.interchange.InterchangeDataFrame: - return bigframes.core.interchange.InterchangeDataFrame._from_bigframes(self) - - def to_arrow( - self, - *, - ordered: bool = True, - allow_large_results: Optional[bool] = None, - ) -> pyarrow.Table: - """Write DataFrame to an Arrow table / record batch. - - Args: - ordered (bool, default True): - Determines whether the resulting Arrow table will be ordered. - In some cases, unordered may result in a faster-executing query. - allow_large_results (bool, default None): - If not None, overrides the global setting to allow or disallow large query results - over the default size limit of 10 GB. - - Returns: - pyarrow.Table: A pyarrow Table with all rows and columns of this DataFrame. - """ - msg = bfe.format_message( - "to_arrow is in preview. Types and unnamed or duplicate name columns may " - "change in future." - ) - warnings.warn(msg, category=bfe.PreviewWarning) - - pa_table, query_job = self._block.to_arrow( - ordered=ordered, allow_large_results=allow_large_results - ) - if query_job: - self._set_internal_query_job(query_job) - return pa_table - - @overload - def to_pandas( # type: ignore[overload-overlap] - self, - max_download_size: Optional[int] = ..., - sampling_method: Optional[str] = ..., - random_state: Optional[int] = ..., - *, - ordered: bool = ..., - dry_run: Literal[False] = ..., - allow_large_results: Optional[bool] = ..., - ) -> pandas.DataFrame: ... - - @overload - def to_pandas( - self, - max_download_size: Optional[int] = ..., - sampling_method: Optional[str] = ..., - random_state: Optional[int] = ..., - *, - ordered: bool = ..., - dry_run: Literal[True] = ..., - allow_large_results: Optional[bool] = ..., - ) -> pandas.Series: ... - - def to_pandas( - self, - max_download_size: Optional[int] = None, - sampling_method: Optional[str] = None, - random_state: Optional[int] = None, - *, - ordered: bool = True, - dry_run: bool = False, - allow_large_results: Optional[bool] = None, - ) -> pandas.DataFrame | pandas.Series: - """Write DataFrame to pandas DataFrame. - - **Examples:** - - >>> df = bpd.DataFrame({'col': [4, 2, 2]}) - - Download the data from BigQuery and convert it into an in-memory pandas DataFrame. - - >>> df.to_pandas() - col - 0 4 - 1 2 - 2 2 - - Estimate job statistics without processing or downloading data by using `dry_run=True`. - - >>> df.to_pandas(dry_run=True) # doctest: +SKIP - columnCount 1 - columnDtypes {'col': Int64} - indexLevel 1 - indexDtypes [Int64] - projectId bigframes-dev - location US - jobType QUERY - destinationTable {'projectId': 'bigframes-dev', 'datasetId': '_... - useLegacySql False - referencedTables None - totalBytesProcessed 0 - cacheHit False - statementType SELECT - creationTime 2025-04-02 20:17:12.038000+00:00 - dtype: object - - Args: - max_download_size (int, default None): - .. deprecated:: 2.0.0 - ``max_download_size`` parameter is deprecated. Please use ``to_pandas_batches()`` - method instead. - - Download size threshold in MB. If ``max_download_size`` is exceeded when downloading data, - the data will be downsampled if ``bigframes.options.sampling.enable_downsampling`` is - ``True``, otherwise, an error will be raised. If set to a value other than ``None``, - this will supersede the global config. - sampling_method (str, default None): - .. deprecated:: 2.0.0 - ``sampling_method`` parameter is deprecated. Please use ``sample()`` method instead. - - Downsampling algorithms to be chosen from, the choices are: "head": This algorithm - returns a portion of the data from the beginning. It is fast and requires minimal - computations to perform the downsampling; "uniform": This algorithm returns uniform - random samples of the data. If set to a value other than None, this will supersede - the global config. - random_state (int, default None): - .. deprecated:: 2.0.0 - ``random_state`` parameter is deprecated. Please use ``sample()`` method instead. - - The seed for the uniform downsampling algorithm. If provided, the uniform method may - take longer to execute and require more computation. If set to a value other than - None, this will supersede the global config. - ordered (bool, default True): - Determines whether the resulting pandas dataframe will be ordered. - In some cases, unordered may result in a faster-executing query. - dry_run (bool, default False): - If this argument is true, this method will not process the data. Instead, it returns - a Pandas Series containing dry run statistics - allow_large_results (bool, default None): - If not None, overrides the global setting to allow or disallow large query results - over the default size limit of 10 GB. - - Returns: - pandas.DataFrame: A pandas DataFrame with all rows and columns of this DataFrame if the - data_sampling_threshold_mb is not exceeded; otherwise, a pandas DataFrame with - downsampled rows and all columns of this DataFrame. If dry_run is set, a pandas - Series containing dry run statistics will be returned. - """ - if max_download_size is not None: - msg = bfe.format_message( - "DEPRECATED: The `max_download_size` parameters for `DataFrame.to_pandas()` " - "are deprecated and will be removed soon. Please use `DataFrame.to_pandas_batches()`." - ) - warnings.warn(msg, category=FutureWarning) - if sampling_method is not None or random_state is not None: - msg = bfe.format_message( - "DEPRECATED: The `sampling_method` and `random_state` parameters for " - "`DataFrame.to_pandas()` are deprecated and will be removed soon. " - "Please use `DataFrame.sample().to_pandas()` instead for sampling." - ) - warnings.warn(msg, category=FutureWarning, stacklevel=2) - - if dry_run: - dry_run_stats, dry_run_job = self._block._compute_dry_run( - max_download_size=max_download_size, - sampling_method=sampling_method, - random_state=random_state, - ordered=ordered, - ) - self._set_internal_query_job(dry_run_job) - return dry_run_stats - - df, query_job = self._block.to_pandas( - max_download_size=max_download_size, - sampling_method=sampling_method, - random_state=random_state, - ordered=ordered, - allow_large_results=allow_large_results, - ) - if query_job: - self._set_internal_query_job(query_job) - df.columns = self._block.column_labels - return df - - def to_pandas_batches( - self, - page_size: Optional[int] = None, - max_results: Optional[int] = None, - *, - allow_large_results: Optional[bool] = None, - cell_execution_count: Optional[int] = None, - ) -> blocks.PandasBatches: - """Stream DataFrame results to an iterable of pandas DataFrame. - - page_size and max_results determine the size and number of batches, - see https://cloud.google.com/python/docs/reference/bigquery/latest/google.cloud.bigquery.job.QueryJob#google_cloud_bigquery_job_QueryJob_result - - **Examples:** - - >>> df = bpd.DataFrame({'col': [4, 3, 2, 2, 3]}) - - Iterate through the results in batches, limiting the total rows yielded - across all batches via `max_results`: - - >>> for df_batch in df.to_pandas_batches(max_results=3): - ... print(df_batch) - col - 0 4 - 1 3 - 2 2 - - Alternatively, control the approximate size of each batch using `page_size` - and fetch batches manually using `next()`: - - >>> it = df.to_pandas_batches(page_size=2) - >>> next(it) - col - 0 4 - 1 3 - >>> next(it) - col - 2 2 - 3 2 - - Args: - page_size (int, default None): - The maximum number of rows of each batch. Non-positive values are ignored. - max_results (int, default None): - The maximum total number of rows of all batches. - allow_large_results (bool, default None): - If not None, overrides the global setting to allow or disallow large query results - over the default size limit of 10 GB. - - Returns: - Iterable[pandas.DataFrame]: - An iterable of smaller dataframes which combine to - form the original dataframe. Results stream from bigquery, - see https://cloud.google.com/python/docs/reference/bigquery/latest/google.cloud.bigquery.table.RowIterator#google_cloud_bigquery_table_RowIterator_to_arrow_iterable - """ - return self._to_pandas_batches( - page_size=page_size, - max_results=max_results, - allow_large_results=allow_large_results, - cell_execution_count=cell_execution_count, - ) - - def _to_pandas_batches( - self, - page_size: Optional[int] = None, - max_results: Optional[int] = None, - *, - allow_large_results: Optional[bool] = None, - cell_execution_count: Optional[int] = None, - ) -> blocks.PandasBatches: - return self._block.to_pandas_batches( - page_size=page_size, - max_results=max_results, - allow_large_results=allow_large_results, - cell_execution_count=cell_execution_count, - ) - - def _compute_dry_run(self) -> google.cloud.bigquery.job.QueryJob: - _, query_job = self._block._compute_dry_run() - return query_job - - def copy(self) -> DataFrame: - return DataFrame(self._block) - - @validations.requires_ordering(bigframes.constants.SUGGEST_PEEK_PREVIEW) def head(self, n: int = 5) -> DataFrame: return typing.cast(DataFrame, self.iloc[:n]) - @validations.requires_ordering() def tail(self, n: int = 5) -> DataFrame: return typing.cast(DataFrame, self.iloc[-n:]) - def peek( - self, n: int = 5, *, force: bool = True, allow_large_results=None - ) -> pandas.DataFrame: - """ - Preview n arbitrary rows from the dataframe. No guarantees about row selection or ordering. - ``DataFrame.peek(force=False)`` will always be very fast, but will not succeed if data requires - full data scanning. Using ``force=True`` will always succeed, but may be perform queries. - Query results will be cached so that future steps will benefit from these queries. - - Args: - n (int, default 5): - The number of rows to select from the dataframe. Which N rows are returned is non-deterministic. - force (bool, default True): - If the data cannot be peeked efficiently, the dataframe will instead be fully materialized as part - of the operation if ``force=True``. If ``force=False``, the operation will throw a ValueError. - allow_large_results (bool, default None): - If not None, overrides the global setting to allow or disallow large query results - over the default size limit of 10 GB. - Returns: - pandas.DataFrame: A pandas DataFrame with n rows. - - Raises: - ValueError: If force=False and data cannot be efficiently peeked. - """ - maybe_result = self._block.try_peek(n, allow_large_results=allow_large_results) - if maybe_result is None: - if force: - self._cached() - maybe_result = self._block.try_peek( - n, force=True, allow_large_results=allow_large_results - ) - assert maybe_result is not None - else: - raise ValueError( - "Cannot peek efficiently when data has aggregates, joins or window functions applied. Use force=True to fully compute dataframe." - ) - maybe_result.columns = self._block.column_labels - return maybe_result - def nlargest( self, n: int, @@ -1910,8 +919,6 @@ def nlargest( ) -> DataFrame: if keep not in ("first", "last", "all"): raise ValueError("'keep must be one of 'first', 'last', or 'all'") - if keep != "all": - validations.enforce_ordered(self, "nlargest") column_ids = self._sql_names(columns) return DataFrame(block_ops.nlargest(self._block, n, column_ids, keep=keep)) @@ -1923,40 +930,9 @@ def nsmallest( ) -> DataFrame: if keep not in ("first", "last", "all"): raise ValueError("'keep must be one of 'first', 'last', or 'all'") - if keep != "all": - validations.enforce_ordered(self, "nlargest") column_ids = self._sql_names(columns) return DataFrame(block_ops.nsmallest(self._block, n, column_ids, keep=keep)) - def insert( - self, - loc: int, - column: blocks.Label, - value: SingleItemValue, - allow_duplicates: bool = False, - ): - column_count = len(self.columns) - if loc > column_count: - raise IndexError( - f"Column index {loc} is out of bounds with {column_count} total columns." - ) - if (column in self.columns) and not allow_duplicates: - raise ValueError(f"cannot insert {column}, already exists") - - temp_column = bigframes.core.guid.generate_guid(prefix=str(column)) - df = self._assign_single_item(temp_column, value) - - block = df._get_block() - value_columns = typing.cast(List, block.value_columns) - value_columns, new_column = value_columns[:-1], value_columns[-1] - value_columns.insert(loc, new_column) - - block = block.select_columns(value_columns) - block = block.rename(columns={temp_column: column}) - - self._set_block(block) - - @overload def drop( self, labels: typing.Any = None, @@ -1965,31 +941,7 @@ def drop( index: typing.Any = None, columns: Union[blocks.Label, Sequence[blocks.Label]] = None, level: typing.Optional[LevelType] = None, - inplace: Literal[False] = False, - ) -> DataFrame: ... - - @overload - def drop( - self, - labels: typing.Any = None, - *, - axis: typing.Union[int, str] = 0, - index: typing.Any = None, - columns: Union[blocks.Label, Sequence[blocks.Label]] = None, - level: typing.Optional[LevelType] = None, - inplace: Literal[True], - ) -> None: ... - - def drop( - self, - labels: typing.Any = None, - *, - axis: typing.Union[int, str] = 0, - index: typing.Any = None, - columns: Union[blocks.Label, Sequence[blocks.Label]] = None, - level: typing.Optional[LevelType] = None, - inplace: bool = False, - ) -> Optional[DataFrame]: + ) -> DataFrame: if labels: if index or columns: raise ValueError("Cannot specify both 'labels' and 'index'/'columns") @@ -2001,72 +953,45 @@ def drop( block = self._block if index is not None: - self._throw_if_null_index("drop(axis=0)") level_id = self._resolve_levels(level or 0)[0] if utils.is_list_like(index): - # Only tuple is treated as multi-index value combinations - if isinstance(index, tuple): - if level is not None: - raise ValueError("Multi-index tuple can't specify level.") - condition_id = None - for i, idx in enumerate(index): - level_id = self._resolve_levels(i)[0] - block, condition_id_cur = block.project_expr( - ops.ne_op.as_expr(level_id, ex.const(idx)) - ) - if condition_id: - block, condition_id = block.apply_binary_op( - condition_id, condition_id_cur, ops.or_op - ) - else: - condition_id = condition_id_cur - - condition_id = typing.cast(str, condition_id) - else: - block, inverse_condition_id = block.apply_unary_op( - level_id, ops.IsInOp(values=tuple(index), match_nulls=True) - ) - block, condition_id = block.apply_unary_op( - inverse_condition_id, ops.invert_op - ) + block, inverse_condition_id = block.apply_unary_op( + level_id, ops.IsInOp(index, match_nulls=True) + ) + block, condition_id = block.apply_unary_op( + inverse_condition_id, ops.invert_op + ) elif isinstance(index, indexes.Index): - dropped_block = self._drop_by_index(index)._get_block() - if inplace: - self._set_block(dropped_block) - return None - return DataFrame(dropped_block) + return self._drop_by_index(index) else: - block, condition_id = block.project_expr( - ops.ne_op.as_expr(level_id, ex.const(index)) + block, condition_id = block.apply_unary_op( + level_id, ops.partial_right(ops.ne_op, index) ) - block = block.filter_by_id(condition_id, keep_null=True).select_columns( + block = block.filter(condition_id, keep_null=True).select_columns( self._block.value_columns ) if columns: block = block.drop_columns(self._sql_names(columns)) if index is None and not columns: raise ValueError("Must specify 'labels' or 'index'/'columns") - - if inplace: - self._set_block(block) - return None - else: - return DataFrame(block) + return DataFrame(block) def _drop_by_index(self, index: indexes.Index) -> DataFrame: - block = index._block + block = index._data._get_block() block, ordering_col = block.promote_offsets() - joined_index, (get_column_left, get_column_right) = self._block.join(block) + joined_index, (get_column_left, get_column_right) = self._block.index.join( + block.index + ) new_ordering_col = get_column_right[ordering_col] - drop_block = joined_index + drop_block = joined_index._block drop_block, drop_col = drop_block.apply_unary_op( new_ordering_col, ops.isnull_op, ) - drop_block = drop_block.filter_by_id(drop_col) + drop_block = drop_block.filter(drop_col) original_columns = [ get_column_left[column] for column in self._block.value_columns ] @@ -2119,63 +1044,17 @@ def reorder_levels(self, order: LevelsType, axis: int | str = 0): raise ValueError("Columns must be a multiindex to reorder levels.") def _resolve_levels(self, level: LevelsType) -> typing.Sequence[str]: - return self._block.index.resolve_level(level) - - @overload - def rename(self, *, columns: Mapping[blocks.Label, blocks.Label]) -> DataFrame: ... - - @overload - def rename( - self, *, columns: Mapping[blocks.Label, blocks.Label], inplace: Literal[False] - ) -> DataFrame: ... + return self._block.resolve_index_level(level) - @overload - def rename( - self, *, columns: Mapping[blocks.Label, blocks.Label], inplace: Literal[True] - ) -> None: ... - - def rename( - self, *, columns: Mapping[blocks.Label, blocks.Label], inplace: bool = False - ) -> Optional[DataFrame]: + def rename(self, *, columns: Mapping[blocks.Label, blocks.Label]) -> DataFrame: block = self._block.rename(columns=columns) - - if inplace: - self._block = block - return None - else: - return DataFrame(block) - - @overload - def rename_axis( - self, - mapper: typing.Union[blocks.Label, typing.Sequence[blocks.Label]], - ) -> DataFrame: ... - - @overload - def rename_axis( - self, - mapper: typing.Union[blocks.Label, typing.Sequence[blocks.Label]], - *, - inplace: Literal[False], - **kwargs, - ) -> DataFrame: ... - - @overload - def rename_axis( - self, - mapper: typing.Union[blocks.Label, typing.Sequence[blocks.Label]], - *, - inplace: Literal[True], - **kwargs, - ) -> None: ... + return DataFrame(block) def rename_axis( self, mapper: typing.Union[blocks.Label, typing.Sequence[blocks.Label]], - *, - inplace: bool = False, **kwargs, - ) -> Optional[DataFrame]: + ) -> DataFrame: if len(kwargs) != 0: raise NotImplementedError( f"rename_axis does not currently support any keyword arguments. {constants.FEEDBACK_LINK}" @@ -2185,16 +1064,8 @@ def rename_axis( labels = mapper else: labels = [mapper] + return DataFrame(self._block.with_index_labels(labels)) - block = self._block.with_index_labels(labels) - - if inplace: - self._block = block - return None - else: - return DataFrame(block) - - @validations.requires_ordering() def equals(self, other: typing.Union[bigframes.series.Series, DataFrame]) -> bool: # Must be same object type, same column dtypes, and same label values if not isinstance(other, DataFrame): @@ -2214,17 +1085,10 @@ def assign(self, **kwargs) -> DataFrame: def _assign_single_item( self, k: str, - v: SingleItemValue | MultiItemValue, + v: SingleItemValue, ) -> DataFrame: if isinstance(v, bigframes.series.Series): return self._assign_series_join_on_index(k, v) - elif isinstance(v, bigframes.core.col.Expression): - label_to_col_ref = { - label: ex.deref(id) for id, label in self._block.col_id_to_label.items() - } - resolved_expr = v._value.bind_variables(label_to_col_ref) - block = self._block.project_block_exprs([resolved_expr], labels=[k]) - return DataFrame(block) elif isinstance(v, bigframes.dataframe.DataFrame): v_df_col_count = len(v._block.value_columns) if v_df_col_count != 1: @@ -2239,95 +1103,10 @@ def _assign_single_item( elif utils.is_list_like(v): return self._assign_single_item_listlike(k, v) else: - return self._assign_scalar(k, v) # type: ignore - - def _assign_single_item_by_offset( - self, - offset: int, - value: SingleItemValue | MultiItemValue, - ) -> DataFrame: - if isinstance(value, bigframes.series.Series): - return self._assign_series_join_on_index_by_offset(offset, value) - elif isinstance(value, bigframes.core.col.Expression): - label_to_col_ref = { - label: ex.deref(id) for id, label in self._block.col_id_to_label.items() - } - resolved_expr = value._value.bind_variables(label_to_col_ref) - block, new_col_id = self._block.project_expr(resolved_expr) - target_col_id = self._block.value_columns[offset] - block = block.copy_values(new_col_id, target_col_id).drop_columns( - [new_col_id] - ) - return DataFrame(block) - elif isinstance(value, DataFrame): - v_df_col_count = len(value._block.value_columns) - if v_df_col_count != 1: - raise ValueError( - f"Cannot set a DataFrame with {v_df_col_count} columns to the single column at offset {offset}" - ) - return self._assign_series_join_on_index_by_offset( - offset, cast(bigframes.series.Series, value[value.columns[0]]) - ) - elif callable(value): - raise NotImplementedError( - "Callable assignment is not supported by column offset." - ) - elif utils.is_list_like(value): - return self._assign_single_item_listlike_by_offset(offset, value) - else: - return self._assign_scalar_by_offset(offset, value) # type: ignore - - def _assign_multi_items_helper( - self, - k: Sequence[Any] | pandas.Index, - v: SingleItemValue | MultiItemValue, - assign_single_fn: Callable[[DataFrame, Any, Any], DataFrame], - ) -> DataFrame: - value_sources: Sequence[Any] = [] - if isinstance(v, DataFrame): - value_sources = [v[col] for col in v.columns] - elif isinstance(v, bigframes.series.Series): - # For behavior consistency with Pandas. - raise ValueError("Columns must be same length as key") - elif isinstance(v, Sequence): - value_sources = v - else: - # We assign the same scalar value to all target columns. - value_sources = [v] * len(k) - - if len(value_sources) != len(k): - raise ValueError("Columns must be same length as key") - - # Repeatedly assign columns in order. - result = assign_single_fn(self, k[0], value_sources[0]) - for target, source in zip(k[1:], value_sources[1:]): - result = assign_single_fn(result, target, source) - return result - - def _assign_multi_items( - self, - k: list[str] | pandas.Index, - v: SingleItemValue | MultiItemValue, - ) -> DataFrame: - return self._assign_multi_items_helper(k, v, DataFrame._assign_single_item) - - def _assign_multi_items_by_offsets( - self, - k: Sequence[int], - v: SingleItemValue | MultiItemValue, - ) -> DataFrame: - return self._assign_multi_items_helper( - k, v, DataFrame._assign_single_item_by_offset - ) + return self._assign_scalar(k, v) - _assign_multi_items_by_offset = _assign_multi_items_by_offsets - _assign_multi_items_by_label = _assign_multi_items - _assign_multi_items_by_labels = _assign_multi_items - - def _assign_single_item_listlike_to_col_ids( - self, col_ids: Sequence[str], label: Optional[str], value: Sequence - ) -> DataFrame: - given_rows = len(value) + def _assign_single_item_listlike(self, k: str, v: Sequence) -> DataFrame: + given_rows = len(v) actual_rows = len(self) assigning_to_empty_df = len(self.columns) == 0 and actual_rows == 0 if not assigning_to_empty_df and given_rows != actual_rows: @@ -2335,13 +1114,8 @@ def _assign_single_item_listlike_to_col_ids( f"Length of values ({given_rows}) does not match length of index ({actual_rows})" ) - temp_col_name = ( - label - if label is not None - else bigframes.core.guid.generate_guid("listlike_col_") - ) - local_df = DataFrame( - {temp_col_name: value}, session=self._get_block().expr.session + local_df = bigframes.dataframe.DataFrame( + {k: v}, session=self._get_block().expr.session ) # local_df is likely (but not guaranteed) to be cached locally # since the original list came from memory and so is probably < MAX_INLINE_DF_SIZE @@ -2350,55 +1124,27 @@ def _assign_single_item_listlike_to_col_ids( original_index_column_ids = self._block.index_columns self_block = self._block.reset_index(drop=False) if assigning_to_empty_df: - if label is None: - raise ValueError( - "Label required when assigning listlike to empty DataFrame." - ) if len(self._block.index_columns) > 1: # match error raised by pandas here raise ValueError( "Assigning listlike to a first column under multiindex is not supported." ) - result_block = new_column_block.with_index_labels(self._block.index.names) - result_block = result_block.with_column_labels([label]) + result_block = new_column_block.with_index_labels(self._block.index_labels) + result_block = result_block.with_column_labels([k]) else: - ( - result_block, - ( - get_column_left, - get_column_right, - ), - ) = self_block.join(new_column_block, how="left", block_identity_join=True) + result_index, (get_column_left, get_column_right,) = self_block.index.join( + new_column_block.index, how="left", block_identity_join=True + ) + result_block = result_index._block result_block = result_block.set_index( [get_column_left[col_id] for col_id in original_index_column_ids], - index_labels=self._block.index.names, + index_labels=self._block.index_labels, ) - src_col = get_column_right[new_column_block.value_columns[0]] - # Check to see if key exists, and modify in place - for col_id in col_ids: - result_block = result_block.copy_values( - src_col, get_column_left[col_id] - ) - if len(col_ids) > 0: - result_block = result_block.drop_columns([src_col]) return DataFrame(result_block) - def _assign_single_item_listlike(self, k: str, v: Sequence) -> DataFrame: - col_ids = self._block.cols_matching_label(k) - return self._assign_single_item_listlike_to_col_ids(col_ids, k, v) - - def _assign_single_item_listlike_by_offset( - self, offset: int, value: Sequence - ) -> DataFrame: - col_ids = [self._block.value_columns[offset]] - return self._assign_single_item_listlike_to_col_ids(col_ids, None, value) + def _assign_scalar(self, label: str, value: Union[int, float]) -> DataFrame: + col_ids = self._block.cols_matching_label(label) - def _assign_scalar_to_col_ids( - self, - col_ids: Sequence[str], - label: Optional[str], - value: Union[int, float, str], - ) -> DataFrame: block, constant_col_id = self._block.create_constant(value, label) for col_id in col_ids: block = block.copy_values(constant_col_id, col_id) @@ -2408,131 +1154,37 @@ def _assign_scalar_to_col_ids( return DataFrame(block) - def _assign_scalar(self, label: str, value: Union[int, float, str]) -> DataFrame: - col_ids = self._block.cols_matching_label(label) - return self._assign_scalar_to_col_ids(col_ids, label, value) - - def _assign_scalar_by_offset( - self, offset: int, value: Union[int, float, str] - ) -> DataFrame: - col_ids = [self._block.value_columns[offset]] - return self._assign_scalar_to_col_ids(col_ids, None, value) - - def _assign_series_join_on_index_to_col_ids( - self, - column_ids: Sequence[str], - label: Optional[str], - series: bigframes.series.Series, + def _assign_series_join_on_index( + self, label: str, series: bigframes.series.Series ) -> DataFrame: - block, (get_column_left, get_column_right) = self._block.join( - series._block, how="left" + joined_index, (get_column_left, get_column_right) = self._block.index.join( + series._block.index, how="left" ) - mapped_column_ids = [get_column_left[col_id] for col_id in column_ids] + column_ids = [ + get_column_left[col_id] for col_id in self._block.cols_matching_label(label) + ] + block = joined_index._block source_column = get_column_right[series._value_column] - # Replace each column matching the ids - for column_id in mapped_column_ids: - block = block.copy_values(source_column, column_id) - if label is not None: - block = block.assign_label(column_id, label) + # Replace each column matching the label + for column_id in column_ids: + block = block.copy_values(source_column, column_id).assign_label( + column_id, label + ) - if not mapped_column_ids: - if label is None: - raise ValueError( - "Label required when appending a new column from Series." - ) + if not column_ids: # Append case, so new column needs appropriate label block = block.assign_label(source_column, label) else: # Update case, remove after copying into columns block = block.drop_columns([source_column]) - return DataFrame(block.with_index_labels(self._block.index.names)) - - def _assign_series_join_on_index( - self, label: str, series: bigframes.series.Series - ) -> DataFrame: - column_ids = self._block.cols_matching_label(label) - return self._assign_series_join_on_index_to_col_ids(column_ids, label, series) - - def _assign_series_join_on_index_by_offset( - self, offset: int, series: bigframes.series.Series - ) -> DataFrame: - column_ids = [self._block.value_columns[offset]] - return self._assign_series_join_on_index_to_col_ids(column_ids, None, series) + return DataFrame(block.with_index_labels(self.index.names)) - @overload # type: ignore[override] - def reset_index( - self, - level: blocks.LevelsType = ..., - drop: bool = ..., - inplace: Literal[False] = ..., - col_level: Union[int, str] = ..., - col_fill: Hashable = ..., - allow_duplicates: Optional[bool] = ..., - names: Union[None, Hashable, Sequence[Hashable]] = ..., - ) -> DataFrame: ... - - @overload - def reset_index( - self, - level: blocks.LevelsType = ..., - drop: bool = ..., - inplace: Literal[True] = ..., - col_level: Union[int, str] = ..., - col_fill: Hashable = ..., - allow_duplicates: Optional[bool] = ..., - names: Union[None, Hashable, Sequence[Hashable]] = ..., - ) -> None: ... - - @overload - def reset_index( - self, - level: blocks.LevelsType = None, - drop: bool = False, - inplace: bool = ..., - col_level: Union[int, str] = ..., - col_fill: Hashable = ..., - allow_duplicates: Optional[bool] = ..., - names: Union[None, Hashable, Sequence[Hashable]] = ..., - ) -> Optional[DataFrame]: ... - - def reset_index( - self, - level: blocks.LevelsType = None, - drop: bool = False, - inplace: bool = False, - col_level: Union[int, str] = 0, - col_fill: Hashable = "", - allow_duplicates: Optional[bool] = None, - names: Union[None, Hashable, Sequence[Hashable]] = None, - ) -> Optional[DataFrame]: - block = self._block - if names is not None: - if isinstance(names, blocks.Label) and not isinstance(names, tuple): - names = [names] - else: - names = list(names) - - if len(names) != self.index.nlevels: - raise ValueError("'names' must be same length as levels") - - block = block.with_index_labels(names) - if allow_duplicates is None: - allow_duplicates = False - block = block.reset_index( - level, - drop, - col_level=col_level, - col_fill=col_fill, - allow_duplicates=allow_duplicates, - ) - if inplace: - self._set_block(block) - return None - else: - return DataFrame(block) + def reset_index(self, *, drop: bool = False) -> DataFrame: + block = self._block.reset_index(drop) + return DataFrame(block) def set_index( self, @@ -2552,101 +1204,30 @@ def set_index( col_ids_strs: List[str] = [col_id for col_id in col_ids if col_id is not None] return DataFrame(self._block.set_index(col_ids_strs, append=append, drop=drop)) - @overload # type: ignore[override] - def sort_index( - self, - *, - ascending: bool = ..., - inplace: Literal[False] = ..., - kind: str | None = ..., - na_position: Literal["first", "last"] = ..., - ) -> DataFrame: ... - - @overload - def sort_index( - self, - *, - ascending: bool = ..., - inplace: Literal[True] = ..., - kind: str | None = ..., - na_position: Literal["first", "last"] = ..., - ) -> None: ... - def sort_index( - self, - *, - axis: Union[int, str] = 0, - ascending: bool = True, - inplace: bool = False, - kind: str | None = None, - na_position: Literal["first", "last"] = "last", - ) -> Optional[DataFrame]: - if utils.get_axis_number(axis) == 0: - if na_position not in ["first", "last"]: - raise ValueError("Param na_position must be one of 'first' or 'last'") - na_last = na_position == "last" - index_columns = self._block.index_columns - ordering = [ - order.ascending_over(column, na_last) - if ascending - else order.descending_over(column, na_last) - for column in index_columns - ] - is_stable = ( - kind or constants.DEFAULT_SORT_KIND - ) in constants.STABLE_SORT_KINDS - block = self._block.order_by(ordering, stable=is_stable) - else: # axis=1 - _, indexer = self.columns.sort_values( - return_indexer=True, - ascending=ascending, - na_position=na_position, # type: ignore - ) - block = self._block.select_columns( - [self._block.value_columns[i] for i in indexer] - ) - if inplace: - self._set_block(block) - return None - else: - return DataFrame(block) - - @overload # type: ignore[override] - def sort_values( - self, - by: str | typing.Sequence[str], - *, - inplace: Literal[False] = ..., - ascending: bool | typing.Sequence[bool] = ..., - kind: str | None = ..., - na_position: typing.Literal["first", "last"] = ..., - ) -> DataFrame: ... - - @overload - def sort_values( - self, - by: str | typing.Sequence[str], - *, - inplace: Literal[True] = ..., - ascending: bool | typing.Sequence[bool] = ..., - kind: str | None = ..., - na_position: typing.Literal["first", "last"] = ..., - ) -> None: ... + self, ascending: bool = True, na_position: Literal["first", "last"] = "last" + ) -> DataFrame: + if na_position not in ["first", "last"]: + raise ValueError("Param na_position must be one of 'first' or 'last'") + direction = ( + order.OrderingDirection.ASC if ascending else order.OrderingDirection.DESC + ) + na_last = na_position == "last" + index_columns = self._block.index_columns + ordering = [ + order.OrderingColumnReference(column, direction=direction, na_last=na_last) + for column in index_columns + ] + return DataFrame(self._block.order_by(ordering)) def sort_values( self, by: str | typing.Sequence[str], *, - inplace: bool = False, ascending: bool | typing.Sequence[bool] = True, - kind: str | None = None, + kind: str = "quicksort", na_position: typing.Literal["first", "last"] = "last", - ) -> Optional[DataFrame]: - if isinstance(by, (bigframes.series.Series, indexes.Index, DataFrame)): - raise KeyError( - f"Invalid key type: {type(by).__name__}. Please provide valid column name(s)." - ) - + ) -> DataFrame: if na_position not in {"first", "last"}: raise ValueError("Param na_position must be one of 'first' or 'last'") @@ -2664,31 +1245,20 @@ def sort_values( ordering = [] for i in range(len(sort_labels)): column_id = sort_column_ids[i] - is_ascending = sort_directions[i] + direction = ( + order.OrderingDirection.ASC + if sort_directions[i] + else order.OrderingDirection.DESC + ) na_last = na_position == "last" ordering.append( - order.ascending_over(column_id, na_last) - if is_ascending - else order.descending_over(column_id, na_last) + order.OrderingColumnReference( + column_id, direction=direction, na_last=na_last + ) ) - is_stable = (kind or constants.DEFAULT_SORT_KIND) in constants.STABLE_SORT_KINDS - block = self._block.order_by(ordering, stable=is_stable) - if inplace: - self._set_block(block) - return None - else: - return DataFrame(block) - - def eval(self, expr: str) -> DataFrame: - import bigframes.core.eval as bf_eval - - return bf_eval.eval(self, expr, target=self) - - def query(self, expr: str) -> DataFrame: - import bigframes.core.eval as bf_eval - - eval_result = bf_eval.eval(self, expr, target=None) - return self[eval_result] + return DataFrame( + self._block.order_by(ordering, stable=kind in order.STABLE_SORTS) + ) def value_counts( self, @@ -2706,7 +1276,7 @@ def value_counts( normalize=normalize, sort=sort, ascending=ascending, - drop_na=dropna, + dropna=dropna, ) return bigframes.series.Series(block) @@ -2718,18 +1288,6 @@ def add_suffix(self, suffix: str, axis: int | str | None = None) -> DataFrame: axis = 1 if axis is None else axis return DataFrame(self._get_block().add_suffix(suffix, axis)) - def take( - self, indices: typing.Sequence[int], axis: int | str | None = 0, **kwargs - ) -> DataFrame: - if not utils.is_list_like(indices): - raise ValueError("indices should be a list-like object.") - if axis == 0 or axis == "index": - return typing.cast(DataFrame, self.iloc[indices]) - elif axis == 1 or axis == "columns": - return typing.cast(DataFrame, self.iloc[:, indices]) - else: - raise ValueError(f"No axis named {axis} for object type DataFrame") - def filter( self, items: typing.Optional[typing.Iterable] = None, @@ -2755,34 +1313,34 @@ def _filter_rows( ) -> DataFrame: if len(self._block.index_columns) > 1: raise NotImplementedError( - f"Method filter does not support rows multiindex. {constants.FEEDBACK_LINK}" + "Method filter does not support rows multiindex. {constants.FEEDBACK_LINK}" ) if (like is not None) or (regex is not None): block = self._block block, label_string_id = block.apply_unary_op( self._block.index_columns[0], - ops.AsTypeOp(to_type=pandas.StringDtype(storage="pyarrow")), + ops.AsTypeOp(pandas.StringDtype(storage="pyarrow")), ) if like is not None: block, mask_id = block.apply_unary_op( - label_string_id, ops.StrContainsOp(pat=like) + label_string_id, ops.ContainsStringOp(pat=like) ) else: # regex assert regex is not None block, mask_id = block.apply_unary_op( - label_string_id, ops.StrContainsRegexOp(pat=regex) + label_string_id, ops.ContainsRegexOp(pat=regex) ) - block = block.filter_by_id(mask_id) + block = block.filter(mask_id) block = block.select_columns(self._block.value_columns) return DataFrame(block) elif items is not None: # Behavior matches pandas 2.1+, older pandas versions would reindex block = self._block block, mask_id = block.apply_unary_op( - self._block.index_columns[0], ops.IsInOp(values=tuple(items)) + self._block.index_columns[0], ops.IsInOp(values=list(items)) ) - block = block.filter_by_id(mask_id) + block = block.filter(mask_id) block = block.select_columns(self._block.value_columns) return DataFrame(block) else: @@ -2801,8 +1359,7 @@ def label_filter(label): if like: return like in label_str else: # regex - # TODO(b/340891296): fix type error - return re.match(regex, label_str) is not None # type: ignore + return re.match(regex, label_str) is not None cols = [ col_id @@ -2843,7 +1400,6 @@ def reindex( if columns is not None: return self._reindex_columns(columns) - @validations.requires_index def _reindex_rows( self, index, @@ -2854,7 +1410,7 @@ def _reindex_rows( raise ValueError("Original index must be unique to reindex") keep_original_names = False if isinstance(index, indexes.Index): - new_indexer = DataFrame(data=index._block)[[]] + new_indexer = DataFrame(data=index._data._get_block())[[]] else: if not isinstance(index, pandas.Index): keep_original_names = True @@ -2863,7 +1419,7 @@ def _reindex_rows( raise NotImplementedError( "Cannot reindex with index with different nlevels" ) - new_indexer = DataFrame(index=index, session=self._session)[[]] + new_indexer = DataFrame(index=index)[[]] # multiindex join is senstive to index names, so we will set all these result = new_indexer.rename_axis(range(new_indexer.index.nlevels)).join( self.rename_axis(range(self.index.nlevels)), @@ -2877,11 +1433,6 @@ def _reindex_rows( def _reindex_columns(self, columns): block = self._block new_column_index, indexer = self.columns.reindex(columns) - - if indexer is None: - # The new index is the same as the old one. Do nothing. - return self - result_cols = [] for label, index in zip(columns, indexer): if index >= 0: @@ -2895,44 +1446,22 @@ def _reindex_columns(self, columns): result_df.columns = new_column_index return result_df - @validations.requires_index def reindex_like(self, other: DataFrame, *, validate: typing.Optional[bool] = None): return self.reindex(index=other.index, columns=other.columns, validate=validate) - @validations.requires_ordering() - @validations.requires_index def interpolate(self, method: str = "linear") -> DataFrame: - if method == "pad": - return self.ffill() result = block_ops.interpolate(self._block, method) return DataFrame(result) def fillna(self, value=None) -> DataFrame: return self._apply_binop(value, ops.fillna_op, how="left") - def replace( - self, to_replace: typing.Any, value: typing.Any = None, *, regex: bool = False - ): - if utils.is_dict_like(value): - return self.apply( - lambda x: ( - x.replace(to_replace=to_replace, value=value[x.name], regex=regex) - if (x.name in value) - else x - ) - ) - return self.apply( - lambda x: x.replace(to_replace=to_replace, value=value, regex=regex) - ) - - @validations.requires_ordering() def ffill(self, *, limit: typing.Optional[int] = None) -> DataFrame: - window = windows.rows(start=None if limit is None else -limit, end=0) + window = bigframes.core.WindowSpec(preceding=limit, following=0) return self._apply_window_op(agg_ops.LastNonNullOp(), window) - @validations.requires_ordering() def bfill(self, *, limit: typing.Optional[int] = None) -> DataFrame: - window = windows.rows(start=0, end=limit) + window = bigframes.core.WindowSpec(preceding=0, following=limit) return self._apply_window_op(agg_ops.FirstNonNullOp(), window) def isin(self, values) -> DataFrame: @@ -2943,9 +1472,7 @@ def isin(self, values) -> DataFrame: if label in values.keys(): value_for_key = values[label] block, result_id = block.apply_unary_op( - col, - ops.IsInOp(values=tuple(value_for_key), match_nulls=True), - label, + col, ops.IsInOp(value_for_key, match_nulls=True), label ) result_ids.append(result_id) else: @@ -2953,10 +1480,10 @@ def isin(self, values) -> DataFrame: False, label=label, dtype=pandas.BooleanDtype() ) result_ids.append(result_id) - return DataFrame(block.select_columns(result_ids)) + return DataFrame(block.select_columns(result_ids)).fillna(value=False) elif utils.is_list_like(values): - return self._apply_unary_op( - ops.IsInOp(values=tuple(values), match_nulls=True) + return self._apply_unary_op(ops.IsInOp(values, match_nulls=True)).fillna( + value=False ) else: raise TypeError( @@ -2985,163 +1512,41 @@ def itertuples( for item in df.itertuples(index=index, name=name): yield item - def _apply_callable(self, condition): - """Executes the possible callable condition as needed.""" - if callable(condition): - # When it's a bigframes function. - if isinstance(condition, bigframes.functions.Udf): - return self.apply(condition, axis=1) - - # When it's a plain Python function. - return condition(self) - - # When it's not a callable. - return condition - - def where(self, cond, other=None): - if self.columns.nlevels > 1: - raise NotImplementedError( - "The dataframe.where() method does not support multi-column." - ) - - # Execute it with the DataFrame when cond or/and other is callable. - # It can be either a plain python function or remote/managed function. - cond = self._apply_callable(cond) - other = self._apply_callable(other) - - if isinstance(other, bigframes.series.Series): - raise ValueError("Seires is not a supported replacement type!") - - aligned_block, (_, _) = self._block.join(cond._block, how="left") - # No left join is needed when 'other' is None or constant. - if isinstance(other, bigframes.dataframe.DataFrame): - aligned_block, (_, _) = aligned_block.join(other._block, how="left") - self_len = len(self._block.value_columns) - cond_len = len(cond._block.value_columns) - - ids = aligned_block.value_columns[:self_len] - labels = aligned_block.column_labels[:self_len] - self_col = {x: ex.deref(y) for x, y in zip(labels, ids)} - - if isinstance(cond, bigframes.series.Series): - # This is when 'cond' is a valid series. - y = aligned_block.value_columns[self_len] - cond_col = {x: ex.deref(y) for x in self_col.keys()} - else: - # This is when 'cond' is a dataframe. - ids = aligned_block.value_columns[self_len : self_len + cond_len] - labels = aligned_block.column_labels[self_len : self_len + cond_len] - cond_col = {x: ex.deref(y) for x, y in zip(labels, ids)} - - if isinstance(other, DataFrame): - other_len = len(self._block.value_columns) - ids = aligned_block.value_columns[-other_len:] - labels = aligned_block.column_labels[-other_len:] - other_col = {x: ex.deref(y) for x, y in zip(labels, ids)} - else: - # This is when 'other' is None or constant. - labels = aligned_block.column_labels[:self_len] - other_col = {x: ex.const(other) for x in labels} # type: ignore - - result_series = {} - for x, self_id in self_col.items(): - cond_id = cond_col[x] if x in cond_col else ex.const(False) - other_id = other_col[x] if x in other_col else ex.const(None) - result_block, result_id = aligned_block.project_expr( - ops.where_op.as_expr(self_id, cond_id, other_id) - ) - series = bigframes.series.Series( - result_block.select_column(result_id).with_column_labels([x]) - ) - result_series[x] = series - - result = DataFrame(result_series) - result.columns.name = self.columns.name - result.columns.names = self.columns.names - return result - - def mask(self, cond, other=None): - return self.where(~self._apply_callable(cond), other=other) - def dropna( self, *, axis: int | str = 0, - how: str = "any", - thresh: typing.Optional[int] = None, - subset: typing.Union[None, blocks.Label, Sequence[blocks.Label]] = None, inplace: bool = False, + how: str = "any", ignore_index=False, ) -> DataFrame: if inplace: raise NotImplementedError( - f"'inplace'=True not supported. {constants.FEEDBACK_LINK}" + "'inplace'=True not supported. {constants.FEEDBACK_LINK}" ) - - # Check if both thresh and how are explicitly provided - if thresh is not None: - # cannot specify both thresh and how parameters - if how != "any": - raise TypeError( - "You cannot set both the how and thresh arguments at the same time." - ) - else: - # Only validate 'how' when thresh is not provided - if how not in ("any", "all"): - raise ValueError("'how' must be one of 'any', 'all'") + if how not in ("any", "all"): + raise ValueError("'how' must be one of 'any', 'all'") axis_n = utils.get_axis_number(axis) - if subset is not None and axis_n != 0: - raise NotImplementedError( - f"subset only supported when axis=0. {constants.FEEDBACK_LINK}" - ) - if axis_n == 0: - # subset needs to be converted into column IDs, not column labels. - if subset is None: - subset_ids = None - elif not utils.is_list_like(subset): - subset_ids = [id_ for id_ in self._block.label_to_col_id[subset]] - else: - subset_ids = [ - id_ - for label in subset - for id_ in self._block.label_to_col_id[label] - ] - - result = block_ops.dropna( - self._block, - self._block.value_columns, - how=how, - thresh=thresh, - subset=subset_ids, - ) # type: ignore + result = block_ops.dropna(self._block, self._block.value_columns, how=how) # type: ignore if ignore_index: result = result.reset_index() return DataFrame(result) else: - if thresh is not None: - # Keep columns with at least 'thresh' non-null values - notnull_block = self._block.multi_apply_unary_op(ops.notnull_op) - notnull_counts = DataFrame(notnull_block).sum().to_pandas() - - keep_columns = [ - col - for col, count in zip(self._block.value_columns, notnull_counts) - if count >= thresh - ] - else: - isnull_block = self._block.multi_apply_unary_op(ops.isnull_op) - if how == "any": - null_locations = DataFrame(isnull_block).any().to_pandas() - else: # 'all' - null_locations = DataFrame(isnull_block).all().to_pandas() - keep_columns = [ - col - for col, to_drop in zip(self._block.value_columns, null_locations) - if not to_drop - ] + isnull_block = self._block.multi_apply_unary_op( + self._block.value_columns, ops.isnull_op + ) + if how == "any": + null_locations = DataFrame(isnull_block).any().to_pandas() + else: # 'all' + null_locations = DataFrame(isnull_block).all().to_pandas() + keep_columns = [ + col + for col, to_drop in zip(self._block.value_columns, null_locations) + if not to_drop + ] return DataFrame(self._block.select_columns(keep_columns)) def any( @@ -3154,8 +1559,10 @@ def any( frame = self._raise_on_non_boolean("any") else: frame = self._drop_non_bool() - block = frame._block.aggregate_all_and_stack(agg_ops.any_op, axis=axis) - return bigframes.series.Series(block) + block = frame._block.aggregate_all_and_stack( + agg_ops.any_op, dtype=pandas.BooleanDtype(), axis=axis + ) + return bigframes.series.Series(block.select_column("values")) def all( self, axis: typing.Union[str, int] = 0, *, bool_only: bool = False @@ -3164,8 +1571,10 @@ def all( frame = self._raise_on_non_boolean("all") else: frame = self._drop_non_bool() - block = frame._block.aggregate_all_and_stack(agg_ops.all_op, axis=axis) - return bigframes.series.Series(block) + block = frame._block.aggregate_all_and_stack( + agg_ops.all_op, dtype=pandas.BooleanDtype(), axis=axis + ) + return bigframes.series.Series(block.select_column("values")) def sum( self, axis: typing.Union[str, int] = 0, *, numeric_only: bool = False @@ -3175,7 +1584,7 @@ def sum( else: frame = self._drop_non_numeric() block = frame._block.aggregate_all_and_stack(agg_ops.sum_op, axis=axis) - return bigframes.series.Series(block) + return bigframes.series.Series(block.select_column("values")) def mean( self, axis: typing.Union[str, int] = 0, *, numeric_only: bool = False @@ -3185,46 +1594,21 @@ def mean( else: frame = self._drop_non_numeric() block = frame._block.aggregate_all_and_stack(agg_ops.mean_op, axis=axis) - return bigframes.series.Series(block) + return bigframes.series.Series(block.select_column("values")) def median( - self, *, numeric_only: bool = False, exact: bool = True + self, *, numeric_only: bool = False, exact: bool = False ) -> bigframes.series.Series: - if not numeric_only: - frame = self._raise_on_non_numeric("median") - else: - frame = self._drop_non_numeric() if exact: - result = frame.quantile() - result.name = None - return result - else: - block = frame._block.aggregate_all_and_stack(agg_ops.median_op) - return bigframes.series.Series(block) - - def quantile( - self, q: Union[float, Sequence[float]] = 0.5, *, numeric_only: bool = False - ): + raise NotImplementedError( + f"Only approximate median is supported. {constants.FEEDBACK_LINK}" + ) if not numeric_only: frame = self._raise_on_non_numeric("median") else: frame = self._drop_non_numeric() - multi_q = utils.is_list_like(q) - result = block_ops.quantile( - frame._block, - frame._block.value_columns, - qs=tuple(q) if multi_q else (q,), # type: ignore - ) - if multi_q: - return DataFrame(result.stack()).droplevel(0) - else: - # Drop the last level, which contains q, unnecessary since only one q - result = result.with_column_labels(result.column_labels.droplevel(-1)) - result, index_col = result.create_constant(q, None) - result = result.set_index([index_col]) - return bigframes.series.Series( - result.transpose(original_row_index=pandas.Index([q])) - ) + block = frame._block.aggregate_all_and_stack(agg_ops.median_op) + return bigframes.series.Series(block.select_column("values")) def std( self, axis: typing.Union[str, int] = 0, *, numeric_only: bool = False @@ -3234,7 +1618,7 @@ def std( else: frame = self._drop_non_numeric() block = frame._block.aggregate_all_and_stack(agg_ops.std_op, axis=axis) - return bigframes.series.Series(block) + return bigframes.series.Series(block.select_column("values")) def var( self, axis: typing.Union[str, int] = 0, *, numeric_only: bool = False @@ -3244,7 +1628,7 @@ def var( else: frame = self._drop_non_numeric() block = frame._block.aggregate_all_and_stack(agg_ops.var_op, axis=axis) - return bigframes.series.Series(block) + return bigframes.series.Series(block.select_column("values")) def min( self, axis: typing.Union[str, int] = 0, *, numeric_only: bool = False @@ -3254,7 +1638,7 @@ def min( else: frame = self._drop_non_numeric() block = frame._block.aggregate_all_and_stack(agg_ops.min_op, axis=axis) - return bigframes.series.Series(block) + return bigframes.series.Series(block.select_column("values")) def max( self, axis: typing.Union[str, int] = 0, *, numeric_only: bool = False @@ -3264,7 +1648,7 @@ def max( else: frame = self._drop_non_numeric() block = frame._block.aggregate_all_and_stack(agg_ops.max_op, axis=axis) - return bigframes.series.Series(block) + return bigframes.series.Series(block.select_column("values")) def prod( self, axis: typing.Union[str, int] = 0, *, numeric_only: bool = False @@ -3274,7 +1658,7 @@ def prod( else: frame = self._drop_non_numeric() block = frame._block.aggregate_all_and_stack(agg_ops.product_op, axis=axis) - return bigframes.series.Series(block) + return bigframes.series.Series(block.select_column("values")) product = prod @@ -3284,89 +1668,45 @@ def count(self, *, numeric_only: bool = False) -> bigframes.series.Series: else: frame = self._drop_non_numeric() block = frame._block.aggregate_all_and_stack(agg_ops.count_op) - return bigframes.series.Series(block) + return bigframes.series.Series(block.select_column("values")) def nunique(self) -> bigframes.series.Series: block = self._block.aggregate_all_and_stack(agg_ops.nunique_op) - return bigframes.series.Series(block) - - def agg(self, func) -> DataFrame | bigframes.series.Series: - if utils.is_dict_like(func): - # Must check dict-like first because dictionaries are list-like - # according to Pandas. - - aggs = [] - labels = [] - funcnames = [] - for col_label, agg_func in func.items(): - agg_func_list = agg_func if utils.is_list_like(agg_func) else [agg_func] - col_id = self._block.resolve_label_exact(col_label) - if col_id is None: - raise KeyError(f"Column {col_label} does not exist") - for agg_func in agg_func_list: - op_and_label = agg_ops.lookup_agg_func(agg_func) - agg_expr = ( - agg_expressions.UnaryAggregation( - op_and_label[0], ex.deref(col_id) - ) - if isinstance(op_and_label[0], agg_ops.UnaryAggregateOp) - else agg_expressions.NullaryAggregation(op_and_label[0]) - ) - aggs.append(agg_expr) - labels.append(col_label) - funcnames.append(op_and_label[1]) - - # if any list in dict values, format output differently - if any(utils.is_list_like(v) for v in func.values()): - new_index, _ = self.columns.reindex(labels) - new_index = utils.combine_indices(new_index, pandas.Index(funcnames)) - agg_block = self._block.aggregate( - aggregations=aggs, column_labels=new_index - ) - return DataFrame(agg_block).stack().droplevel(0, axis="index") - else: - new_index, _ = self.columns.reindex(labels) - agg_block = self._block.aggregate( - aggregations=aggs, column_labels=new_index - ) - return bigframes.series.Series( - agg_block.transpose( - single_row_mode=True, original_row_index=pandas.Index([None]) - ) + return bigframes.series.Series(block.select_column("values")) + + def agg( + self, func: str | typing.Sequence[str] + ) -> DataFrame | bigframes.series.Series: + if utils.is_list_like(func): + if any( + dtype not in bigframes.dtypes.NUMERIC_BIGFRAMES_TYPES + for dtype in self.dtypes + ): + raise NotImplementedError( + f"Multiple aggregations only supported on numeric columns. {constants.FEEDBACK_LINK}" ) - elif utils.is_list_like(func): - aggregations = [agg_ops.lookup_agg_func(f)[0] for f in func] - - for dtype, agg in itertools.product(self.dtypes, aggregations): - agg.output_type( - dtype - ) # Raises exception if the agg does not support the dtype. - + aggregations = [agg_ops.lookup_agg_func(f) for f in func] return DataFrame( self._block.summarize( self._block.value_columns, aggregations, ) ) - - else: # function name string + else: return bigframes.series.Series( - self._block.aggregate_all_and_stack(agg_ops.lookup_agg_func(func)[0]) + self._block.aggregate_all_and_stack( + agg_ops.lookup_agg_func(typing.cast(str, func)) + ) ) aggregate = agg - @validations.requires_index - @validations.requires_ordering() def idxmin(self) -> bigframes.series.Series: return bigframes.series.Series(block_ops.idxmin(self._block)) - @validations.requires_index - @validations.requires_ordering() def idxmax(self) -> bigframes.series.Series: return bigframes.series.Series(block_ops.idxmax(self._block)) - @validations.requires_ordering() def melt( self, id_vars: typing.Optional[typing.Iterable[typing.Hashable]] = None, @@ -3405,10 +1745,16 @@ def melt( self._block.melt(id_col_ids, val_col_ids, var_name, value_name) ) - def describe(self, include: None | Literal["all"] = None) -> DataFrame: - from bigframes.pandas.core.methods import describe - - return typing.cast(DataFrame, describe.describe(self, include)) + def describe(self) -> DataFrame: + df_numeric = self._drop_non_numeric(keep_bool=False) + if len(df_numeric.columns) == 0: + raise NotImplementedError( + f"df.describe() currently only supports numeric values. {constants.FEEDBACK_LINK}" + ) + result = df_numeric.agg( + ["count", "mean", "std", "min", "25%", "50%", "75%", "max"] + ) + return typing.cast(DataFrame, result) def skew(self, *, numeric_only: bool = False): if not numeric_only: @@ -3476,93 +1822,6 @@ def pivot( ) -> DataFrame: return self._pivot(columns=columns, index=index, values=values) - def pivot_table( - self, - values: typing.Optional[ - typing.Union[blocks.Label, Sequence[blocks.Label]] - ] = None, - index: typing.Optional[ - typing.Union[blocks.Label, Sequence[blocks.Label]] - ] = None, - columns: typing.Union[blocks.Label, Sequence[blocks.Label]] = None, - aggfunc: str = "mean", - fill_value=None, - margins: bool = False, - dropna: bool = True, - margins_name: Hashable = "All", - observed: bool = False, - sort: bool = True, - ) -> DataFrame: - if margins: - raise NotImplementedError( - "DataFrame.pivot_table margins arg not supported. {constants.FEEDBACK_LINK}" - ) - if not dropna: - raise NotImplementedError( - "DataFrame.pivot_table dropna arg not supported. {constants.FEEDBACK_LINK}" - ) - if margins_name != "All": - raise NotImplementedError( - "DataFrame.pivot_table margins_name arg not supported. {constants.FEEDBACK_LINK}" - ) - if observed: - raise NotImplementedError( - "DataFrame.pivot_table observed arg not supported. {constants.FEEDBACK_LINK}" - ) - - if isinstance(index, Iterable) and not ( - isinstance(index, blocks.Label) and index in self.columns - ): - index = list(index) - else: - index = [index] - - if isinstance(columns, Iterable) and not ( - isinstance(columns, blocks.Label) and columns in self.columns - ): - columns = list(columns) - else: - columns = [columns] - - if isinstance(values, Iterable) and not ( - isinstance(values, blocks.Label) and values in self.columns - ): - values = list(values) - else: - values = [values] - - # Unlike pivot, pivot_table has values always ordered. - values.sort(key=lambda val: typing.cast("SupportsRichComparison", val)) - - keys = index + columns - agged = self.groupby(keys, dropna=True)[values].agg(aggfunc) - - if isinstance(agged, bigframes.series.Series): - agged = agged.to_frame() - - agged = agged.dropna(how="all") - - if len(values) == 1: - agged = agged.rename(columns={agged.columns[0]: values[0]}) - - agged = agged.reset_index() - - pivoted = agged.pivot( - columns=columns, - index=index, - values=values if len(values) > 1 else None, - ) - if fill_value is not None: - pivoted = pivoted.fillna(fill_value) - if sort: - pivoted = pivoted.sort_index() - - # TODO: Remove the reordering step once the issue is resolved. - # The pivot_table method results in multi-index columns that are always ordered. - # However, the order of the pivoted result columns is not guaranteed to be sorted. - # Sort and reorder. - return pivoted.sort_index(axis=1) # type: ignore - def stack(self, level: LevelsType = -1): if not isinstance(self.columns, pandas.MultiIndex): if level not in [0, -1, self.columns.name]: @@ -3576,7 +1835,7 @@ def _stack_mono(self): def _stack_multi(self, level: LevelsType = -1): n_levels = self.columns.nlevels - if not utils.is_list_like(level): + if isinstance(level, int) or isinstance(level, str): level = [level] level_indices = [] for level_ref in level: @@ -3586,7 +1845,7 @@ def _stack_multi(self, level: LevelsType = -1): else: level_indices.append(level_ref) else: # str - level_indices.append(self.columns.names.index(level_ref)) # type: ignore + level_indices.append(self.columns.names.index(level_ref)) new_order = [ *[i for i in range(n_levels) if i not in level_indices], @@ -3601,10 +1860,8 @@ def _stack_multi(self, level: LevelsType = -1): block = block.stack(levels=len(level)) return DataFrame(block) - @validations.requires_index - @validations.requires_ordering() def unstack(self, level: LevelsType = -1): - if not utils.is_list_like(level): + if isinstance(level, int) or isinstance(level, str): level = [level] block = self._block @@ -3627,16 +1884,14 @@ def unstack(self, level: LevelsType = -1): ) return DataFrame(pivot_block) - def _drop_non_numeric(self, permissive=True) -> DataFrame: - numeric_types = ( - set(bigframes.dtypes.NUMERIC_BIGFRAMES_TYPES_PERMISSIVE) - if permissive - else set(bigframes.dtypes.NUMERIC_BIGFRAMES_TYPES_RESTRICTIVE) - ) + def _drop_non_numeric(self, keep_bool=True) -> DataFrame: + types_to_keep = set(bigframes.dtypes.NUMERIC_BIGFRAMES_TYPES) + if not keep_bool: + types_to_keep -= set(bigframes.dtypes.BOOL_BIGFRAMES_TYPES) non_numeric_cols = [ col_id for col_id, dtype in zip(self._block.value_columns, self._block.dtypes) - if dtype not in numeric_types + if dtype not in types_to_keep ] return DataFrame(self._block.drop_columns(non_numeric_cols)) @@ -3650,7 +1905,7 @@ def _drop_non_bool(self) -> DataFrame: def _raise_on_non_numeric(self, op: str): if not all( - dtype in bigframes.dtypes.NUMERIC_BIGFRAMES_TYPES_PERMISSIVE + dtype in bigframes.dtypes.NUMERIC_BIGFRAMES_TYPES for dtype in self._block.dtypes ): raise NotImplementedError( @@ -3678,289 +1933,129 @@ def merge( "left", "outer", "right", - "cross", ] = "inner", + # TODO(garrettwu): Currently can take inner, outer, left and right. To support + # cross joins on: Union[blocks.Label, Sequence[blocks.Label], None] = None, *, left_on: Union[blocks.Label, Sequence[blocks.Label], None] = None, right_on: Union[blocks.Label, Sequence[blocks.Label], None] = None, - left_index: bool = False, - right_index: bool = False, sort: bool = False, suffixes: tuple[str, str] = ("_x", "_y"), ) -> DataFrame: - from bigframes.core.reshape import merge + if on is None: + if left_on is None or right_on is None: + raise ValueError("Must specify `on` or `left_on` + `right_on`.") + else: + if left_on is not None or right_on is not None: + raise ValueError( + "Can not pass both `on` and `left_on` + `right_on` params." + ) + left_on, right_on = on, on - return merge.merge( - self, - right, + if utils.is_list_like(left_on): + left_on = list(left_on) # type: ignore + else: + left_on = [left_on] + + if utils.is_list_like(right_on): + right_on = list(right_on) # type: ignore + else: + right_on = [right_on] + + left_join_ids = [] + for label in left_on: # type: ignore + left_col_id = self._resolve_label_exact(label) + # 0 elements already throws an exception + if not left_col_id: + raise ValueError(f"No column {label} found in self.") + left_join_ids.append(left_col_id) + + right_join_ids = [] + for label in right_on: # type: ignore + right_col_id = right._resolve_label_exact(label) + if not right_col_id: + raise ValueError(f"No column {label} found in other.") + right_join_ids.append(right_col_id) + + block = self._block.merge( + right._block, how, - on, - left_on=left_on, - right_on=right_on, - left_index=left_index, - right_index=right_index, + left_join_ids, + right_join_ids, sort=sort, suffixes=suffixes, ) + return DataFrame(block) def join( - self, - other: Union[DataFrame, bigframes.series.Series], - on: Optional[str] = None, - how: str = "left", - lsuffix: str = "", - rsuffix: str = "", + self, other: DataFrame, *, on: Optional[str] = None, how: str = "left" ) -> DataFrame: - if isinstance(other, bigframes.series.Series): - other = other.to_frame() - left, right = self, other - - col_intersection = left.columns.intersection(right.columns) - - if not col_intersection.empty: - if lsuffix == rsuffix == "": - raise ValueError( - f"columns overlap but no suffix specified: {col_intersection}" - ) - - if how == "cross": - if on is not None: - raise ValueError("'on' is not supported for cross join.") - result_block = left._block.merge( - right._block, - left_join_ids=[], - right_join_ids=[], - suffixes=(lsuffix, rsuffix), - how="cross", - sort=True, + if not left.columns.intersection(right.columns).empty: + raise NotImplementedError( + f"Deduping column names is not implemented. {constants.FEEDBACK_LINK}" ) - return DataFrame(result_block) # Join left columns with right index if on is not None: - if left._has_index and (on in left.index.names): - if on in left.columns: - raise ValueError( - f"'{on}' is both an index level and a column label, which is ambiguous." - ) - else: - raise NotImplementedError( - f"Joining on index level '{on}' is not yet supported. {constants.FEEDBACK_LINK}" - ) - if (left.columns == on).sum() > 1: - raise ValueError(f"The column label '{on}' is not unique.") - if other._block.index.nlevels != 1: raise ValueError( "Join on columns must match the index level of the other DataFrame. Join on column with multi-index haven't been supported." ) - - return self._join_on_key( - other, - on=on, - how=how, - lsuffix=lsuffix, - rsuffix=rsuffix, - should_duplicate_on_key=(on in col_intersection), - ) - - # Join left index with right index - if left._block.index.nlevels != right._block.index.nlevels: - raise ValueError("Index to join on must have the same number of levels.") - - return left._perform_join_by_index(right, how=how)._add_join_suffix( - left.columns, right.columns, lsuffix=lsuffix, rsuffix=rsuffix - ) - - def _join_on_key( - self, - other: DataFrame, - on: str, - how: str, - lsuffix: str, - rsuffix: str, - should_duplicate_on_key: bool, - ) -> DataFrame: - left, right = self.copy(), other - # Replace all columns names with unique names for reordering. - left_col_original_names = left.columns - on_col_name = "bigframes_left_col_on" - dup_on_col_name = "bigframes_left_col_on_dup" - left_col_temp_names = [ - f"bigframes_left_col_name_{i}" if col_name != on else on_col_name - for i, col_name in enumerate(left_col_original_names) - ] - left.columns = pandas.Index(left_col_temp_names) - # if on column is also in right df, we need to duplicate the column - # and set it to be the first column - if should_duplicate_on_key: - left[dup_on_col_name] = left[on_col_name] - on_col_name = dup_on_col_name - left_col_temp_names = [on_col_name] + left_col_temp_names - left = left[left_col_temp_names] - - # Switch left index with on column - left_idx_original_names = left.index.names if left._has_index else () - left_idx_names_in_cols = [ - f"bigframes_left_idx_name_{i}" for i in range(len(left_idx_original_names)) - ] - if left._has_index: + # Switch left index with on column + left_columns = left.columns + left_idx_original_names = left.index.names + left_idx_names_in_cols = [ + f"bigframes_left_idx_name_{i}" for i in range(len(left.index.names)) + ] left.index.names = left_idx_names_in_cols - left = left.reset_index(drop=False) - left = left.set_index(on_col_name) - - right_col_original_names = right.columns - right_col_temp_names = [ - f"bigframes_right_col_name_{i}" - for i in range(len(right_col_original_names)) - ] - right.columns = pandas.Index(right_col_temp_names) + left = left.reset_index(drop=False) + left = left.set_index(on) - # Join on index and switch back - combined_df = left._perform_join_by_index(right, how=how) - combined_df.index.name = on_col_name - combined_df = combined_df.reset_index(drop=False) - combined_df = combined_df.set_index(left_idx_names_in_cols) + # Join on index and switch back + combined_df = left._perform_join_by_index(right, how=how) + combined_df.index.name = on + combined_df = combined_df.reset_index(drop=False) + combined_df = combined_df.set_index(left_idx_names_in_cols) - # To be consistent with Pandas - if combined_df._has_index: + # To be consistent with Pandas combined_df.index.names = ( left_idx_original_names if how in ("inner", "left") else ([None] * len(combined_df.index.names)) ) - # Reorder columns - combined_df = combined_df[left_col_temp_names + right_col_temp_names] - return combined_df._add_join_suffix( - left_col_original_names, - right_col_original_names, - lsuffix=lsuffix, - rsuffix=rsuffix, - extra_col=on if on_col_name == dup_on_col_name else None, - ) - - def _perform_join_by_index( - self, - other: Union[DataFrame, indexes.Index], - *, - how: str = "left", - always_order: bool = False, - ): - block, _ = self._block.join( - other._block, how=how, block_identity_join=True, always_order=always_order - ) - return DataFrame(block) - - def _add_join_suffix( - self, - left_columns, - right_columns, - lsuffix: str = "", - rsuffix: str = "", - extra_col: typing.Optional[str] = None, - ): - """Applies suffixes to overlapping column names to mimic a pandas join. - - This method identifies columns that are common to both a "left" and "right" - set of columns and renames them using the provided suffixes. Columns that - are not in the intersection are kept with their original names. - - Args: - left_columns (pandas.Index): - The column labels from the left DataFrame. - right_columns (pandas.Index): - The column labels from the right DataFrame. - lsuffix (str): - The suffix to apply to overlapping column names from the left side. - rsuffix (str): - The suffix to apply to overlapping column names from the right side. - extra_col (typing.Optional[str]): - An optional column name to prepend to the final list of columns. - This argument is used specifically to match the behavior of a - pandas join. When a join key (i.e., the 'on' column) exists - in both the left and right DataFrames, pandas creates two versions - of that column: one copy keeps its original name and is placed as - the first column, while the other instances receive the normal - suffix. Passing the join key's name here replicates that behavior. - - Returns: - DataFrame: - A new DataFrame with the columns renamed to resolve overlaps. - """ - combined_df = self.copy() - col_intersection = left_columns.intersection(right_columns) - final_col_names = [] if extra_col is None else [extra_col] - for col_name in left_columns: - if col_name in col_intersection: - final_col_names.append(f"{col_name}{lsuffix}") - else: - final_col_names.append(col_name) + # Reorder columns + combined_df = combined_df[list(left_columns) + list(right.columns)] + return combined_df - for col_name in right_columns: - if col_name in col_intersection: - final_col_names.append(f"{col_name}{rsuffix}") - else: - final_col_names.append(col_name) - combined_df.columns = pandas.Index(final_col_names) - return combined_df + # Join left index with right index + if left._block.index.nlevels != right._block.index.nlevels: + raise ValueError("Index to join on must have the same number of levels.") - @validations.requires_ordering() - def rolling( - self, - window: int | pandas.Timedelta | numpy.timedelta64 | datetime.timedelta | str, - min_periods=None, - on: str | None = None, - closed: Literal["right", "left", "both", "neither"] = "right", - ) -> bigframes.core.window.Window: - if isinstance(window, int): - window_def = windows.WindowSpec( - bounds=windows.RowsWindowBounds.from_window_size(window, closed), - min_periods=min_periods if min_periods is not None else window, - ) - skip_agg_col_id = ( - None if on is None else self._block.resolve_label_exact_or_error(on) - ) - return bigframes.core.window.Window( - self._block, - window_def, - self._block.value_columns, - skip_agg_column_id=skip_agg_col_id, - ) + return left._perform_join_by_index(right, how=how) - return rolling.create_range_window( - self._block, - window, - min_periods=min_periods, - on=on, - closed=closed, - is_series=False, + def _perform_join_by_index(self, other: DataFrame, *, how: str = "left"): + combined_index, _ = self._block.index.join( + other._block.index, how=how, block_identity_join=True ) + return DataFrame(combined_index._block) - @validations.requires_ordering() - def expanding(self, min_periods: int = 1) -> bigframes.core.window.Window: - window = windows.cumulative_rows(min_periods=min_periods) + def rolling(self, window: int, min_periods=None) -> bigframes.core.window.Window: + # To get n size window, need current row and n-1 preceding rows. + window_spec = bigframes.core.WindowSpec( + preceding=window - 1, following=0, min_periods=min_periods or window + ) return bigframes.core.window.Window( - self._block, window, self._block.value_columns + self._block, window_spec, self._block.value_columns ) - def pipe( - self, - func: Union[Callable[..., U], tuple[Callable[..., U], str]], - *args, - **kwargs, - ) -> U: - import bigframes_vendored.pandas.core.common as common - - return common.pipe(self, func, *args, **kwargs) - - def get(self, key, default=None): - try: - return self[key] - except (KeyError, ValueError, IndexError): - return default + def expanding(self, min_periods: int = 1) -> bigframes.core.window.Window: + window_spec = bigframes.core.WindowSpec(following=0, min_periods=min_periods) + return bigframes.core.window.Window( + self._block, window_spec, self._block.value_columns + ) def groupby( self, @@ -3968,7 +2063,6 @@ def groupby( blocks.Label, bigframes.series.Series, typing.Sequence[typing.Union[blocks.Label, bigframes.series.Series]], - None, ] = None, *, level: typing.Optional[LevelsType] = None, @@ -3984,24 +2078,17 @@ def groupby( else: raise TypeError("You have to supply one of 'by' and 'level'") - @validations.requires_index def _groupby_level( self, level: LevelsType, as_index: bool = True, dropna: bool = True, ): - if utils.is_list_like(level): - by_key_is_singular = False - else: - by_key_is_singular = True - return groupby.DataFrameGroupBy( self._block, by_col_ids=self._resolve_levels(level), as_index=as_index, dropna=dropna, - by_key_is_singular=by_key_is_singular, ) def _groupby_series( @@ -4014,30 +2101,26 @@ def _groupby_series( as_index: bool = True, dropna: bool = True, ): - # Pandas makes a distinction between groupby with a list of keys - # versus groupby with a single item in some methods, like __iter__. if not isinstance(by, bigframes.series.Series) and utils.is_list_like(by): by = list(by) - by_key_is_singular = False else: by = [typing.cast(typing.Union[blocks.Label, bigframes.series.Series], by)] - by_key_is_singular = True block = self._block col_ids: typing.Sequence[str] = [] for key in by: if isinstance(key, bigframes.series.Series): - ( - block, - ( - get_column_left, - get_column_right, - ), - ) = block.join(key._block, how="inner" if dropna else "left") + combined_index, ( + get_column_left, + get_column_right, + ) = block.index.join( + key._block.index, how="inner" if dropna else "left" + ) col_ids = [ *[get_column_left[value] for value in col_ids], get_column_right[key._value_column], ] + block = combined_index._block else: # Interpret as index level or column name col_matches = block.label_to_col_id.get(key, []) @@ -4054,55 +2137,11 @@ def _groupby_series( by_col_ids=col_ids, as_index=as_index, dropna=dropna, - by_key_is_singular=by_key_is_singular, ) def abs(self) -> DataFrame: return self._apply_unary_op(ops.abs_op) - def round(self, decimals: Union[int, dict[Hashable, int]] = 0) -> DataFrame: - is_mapping = utils.is_dict_like(decimals) - if not (is_mapping or isinstance(decimals, int)): - raise TypeError("'decimals' must be either a dict-like or integer.") - block = self._block - exprs = [] - for label, col_id, dtype in zip( - block.column_labels, block.value_columns, block.dtypes - ): - if dtype in set(bigframes.dtypes.NUMERIC_BIGFRAMES_TYPES_PERMISSIVE) - { - bigframes.dtypes.BOOL_DTYPE - }: - if is_mapping: - decimals_dict = typing.cast(dict[typing.Hashable, int], decimals) - if label in decimals_dict: - exprs.append( - ops.round_op.as_expr( - col_id, - ex.const( - decimals_dict[label], - dtype=bigframes.dtypes.INT_DTYPE, # type: ignore - ), - ) - ) - else: - exprs.append(ex.deref(col_id)) - else: - exprs.append( - ops.round_op.as_expr( - col_id, - ex.const( - typing.cast(int, decimals), - dtype=bigframes.dtypes.INT_DTYPE, - ), - ) - ) - else: - exprs.append(ex.deref(col_id)) - - return DataFrame( - block.project_exprs(exprs, labels=block.column_labels, drop=True) - ) - def isna(self) -> DataFrame: return self._apply_unary_op(ops.isnull_op) @@ -4113,57 +2152,56 @@ def notna(self) -> DataFrame: notnull = notna - @validations.requires_ordering() def cumsum(self): is_numeric_types = [ - (dtype in bigframes.dtypes.NUMERIC_BIGFRAMES_TYPES_PERMISSIVE) + (dtype in bigframes.dtypes.NUMERIC_BIGFRAMES_TYPES) for _, dtype in self.dtypes.items() ] if not all(is_numeric_types): raise ValueError("All values must be numeric to apply cumsum.") return self._apply_window_op( agg_ops.sum_op, - windows.cumulative_rows(), + bigframes.core.WindowSpec(following=0), ) - @validations.requires_ordering() def cumprod(self) -> DataFrame: is_numeric_types = [ - (dtype in bigframes.dtypes.NUMERIC_BIGFRAMES_TYPES_PERMISSIVE) + (dtype in bigframes.dtypes.NUMERIC_BIGFRAMES_TYPES) for _, dtype in self.dtypes.items() ] if not all(is_numeric_types): raise ValueError("All values must be numeric to apply cumsum.") return self._apply_window_op( agg_ops.product_op, - windows.cumulative_rows(), + bigframes.core.WindowSpec(following=0), ) - @validations.requires_ordering() def cummin(self) -> DataFrame: return self._apply_window_op( agg_ops.min_op, - windows.cumulative_rows(), + bigframes.core.WindowSpec(following=0), ) - @validations.requires_ordering() def cummax(self) -> DataFrame: return self._apply_window_op( agg_ops.max_op, - windows.cumulative_rows(), + bigframes.core.WindowSpec(following=0), ) - @validations.requires_ordering() def shift(self, periods: int = 1) -> DataFrame: - window_spec = windows.rows() - return self._apply_window_op(agg_ops.ShiftOp(periods), window_spec) + window = bigframes.core.WindowSpec( + preceding=periods if periods > 0 else None, + following=-periods if periods < 0 else None, + ) + return self._apply_window_op(agg_ops.ShiftOp(periods), window) - @validations.requires_ordering() def diff(self, periods: int = 1) -> DataFrame: - window_spec = windows.rows() - return self._apply_window_op(agg_ops.DiffOp(periods), window_spec) + window = bigframes.core.WindowSpec( + preceding=periods if periods > 0 else None, + following=-periods if periods < 0 else None, + ) + return self._apply_window_op(agg_ops.DiffOp(periods), window) - @validations.requires_ordering() def pct_change(self, periods: int = 1) -> DataFrame: # Future versions of pandas will not perfrom ffill automatically df = self.ffill() @@ -4171,39 +2209,22 @@ def pct_change(self, periods: int = 1) -> DataFrame: def _apply_window_op( self, - op: agg_ops.UnaryWindowOp, - window_spec: windows.WindowSpec, + op: agg_ops.WindowOp, + window_spec: bigframes.core.WindowSpec, ): block, result_ids = self._block.multi_apply_window_op( self._block.value_columns, op, window_spec=window_spec, ) - if op.skips_nulls: - block = block.project_exprs( - tuple( - bigframes.operations.where_op.as_expr( - r_col, - bigframes.operations.notnull_op.as_expr(og_col), - ex.const(None), - ) - for og_col, r_col in zip(self._block.value_columns, result_ids) - ), - labels=self._block.column_labels, - drop=True, - ) - else: - block = block.select_columns(result_ids) - return DataFrame(block) + return DataFrame(block.select_columns(result_ids)) - @validations.requires_ordering() def sample( self, n: Optional[int] = None, frac: Optional[float] = None, *, random_state: Optional[int] = None, - sort: Optional[bool | Literal["random"]] = "random", ) -> DataFrame: if n is not None and frac is not None: raise ValueError("Only one of 'n' or 'frac' parameter can be specified.") @@ -4211,31 +2232,7 @@ def sample( ns = (n,) if n is not None else () fracs = (frac,) if frac is not None else () return DataFrame( - self._block.split(ns=ns, fracs=fracs, random_state=random_state, sort=sort)[ - 0 - ] - ) - - def explode( - self, - column: typing.Union[blocks.Label, typing.Sequence[blocks.Label]], - *, - ignore_index: Optional[bool] = False, - ) -> DataFrame: - column_labels = bigframes.core.explode.check_column(column) - - column_ids = [self._resolve_label_exact(label) for label in column_labels] - missing = [ - column_labels[i] for i in range(len(column_ids)) if column_ids[i] is None - ] - if len(missing) > 0: - raise KeyError(f"None of {missing} are in the columns") - - return DataFrame( - self._block.explode( - column_ids=typing.cast(typing.Sequence[str], tuple(column_ids)), - ignore_index=ignore_index, - ) + self._block._split(ns=ns, fracs=fracs, random_state=random_state)[0] ) def _split( @@ -4250,149 +2247,75 @@ def _split( At most one of ns and fracs can be passed in. If neither, default to ns = (1,). Return a list of sampled DataFrames. """ - blocks = self._block.split(ns=ns, fracs=fracs, random_state=random_state) + blocks = self._block._split(ns=ns, fracs=fracs, random_state=random_state) return [DataFrame(block) for block in blocks] - @validations.requires_ordering() - def resample( - self, - rule: str, - *, - closed: Optional[Literal["right", "left"]] = None, - label: Optional[Literal["right", "left"]] = None, - on: blocks.Label = None, - level: Optional[LevelsType] = None, - origin: Union[ - Union[ - pandas.Timestamp, datetime.datetime, numpy.datetime64, int, float, str - ], - Literal["epoch", "start", "start_day", "end", "end_day"], - ] = "start_day", - ) -> bigframes.core.groupby.DataFrameGroupBy: - block = self._block._generate_resample_label( - rule=rule, - closed=closed, - label=label, - on=on, - level=level, - origin=origin, - ) - df = DataFrame(block) - return df.groupby(level=0) - - @classmethod - def from_dict( - cls, - data: dict, - orient: str = "columns", - dtype=None, - columns=None, - ) -> DataFrame: - return cls(pandas.DataFrame.from_dict(data, orient, dtype, columns)) # type: ignore - - @classmethod - def from_records( - cls, - data, - index=None, - exclude=None, - columns=None, - coerce_float: bool = False, - nrows: int | None = None, - ) -> DataFrame: - return cls( - pandas.DataFrame.from_records( - data, index, exclude, columns, coerce_float, nrows - ) - ) - def to_csv( - self, - path_or_buf=None, - sep=",", - *, - header: bool = True, - index: bool = True, - allow_large_results: Optional[bool] = None, - ) -> Optional[str]: + self, path_or_buf: str, sep=",", *, header: bool = True, index: bool = True + ) -> None: # TODO(swast): Can we support partition columns argument? # TODO(chelsealin): Support local file paths. # TODO(swast): Some warning that wildcard is recommended for large # query results? See: # https://cloud.google.com/bigquery/docs/exporting-data#limit_the_exported_file_size - if not utils.is_gcs_path(path_or_buf): - pd_df = self.to_pandas(allow_large_results=allow_large_results) - return pd_df.to_csv(path_or_buf, sep=sep, header=header, index=index) + if not path_or_buf.startswith("gs://"): + raise NotImplementedError(ERROR_IO_ONLY_GS_PATHS) if "*" not in path_or_buf: raise NotImplementedError(ERROR_IO_REQUIRES_WILDCARD) - export_array, id_overrides = self._prepare_export( - index=index and self._has_index, - ordering_id=bigframes.session._io.bigquery.IO_ORDERING_ID, + result_table = self._run_io_query( + index=index, ordering_id=bigframes.session._io.bigquery.IO_ORDERING_ID ) - options: dict[str, Union[bool, str]] = { - "field_delimiter": sep, - "header": header, - } - result = self._session._executor.execute( - export_array.rename_columns(id_overrides), - ex_spec.ExecutionSpec( - ex_spec.GcsOutputSpec( - uri=path_or_buf, format="csv", export_options=tuple(options.items()) - ) - ), + export_data_statement = bigframes.session._io.bigquery.create_export_csv_statement( + f"{result_table.project}.{result_table.dataset_id}.{result_table.table_id}", + uri=path_or_buf, + field_delimiter=sep, + header=header, ) - self._set_internal_query_job(result.query_job) - return None + _, query_job = self._block.expr.session._start_query(export_data_statement) + self._set_internal_query_job(query_job) def to_json( self, - path_or_buf=None, - orient: Optional[ - Literal["split", "records", "index", "columns", "values", "table"] - ] = None, + path_or_buf: str, + orient: Literal[ + "split", "records", "index", "columns", "values", "table" + ] = "columns", *, lines: bool = False, index: bool = True, - allow_large_results: Optional[bool] = None, - ) -> Optional[str]: + ) -> None: # TODO(swast): Can we support partition columns argument? - if not utils.is_gcs_path(path_or_buf): - pd_df = self.to_pandas(allow_large_results=allow_large_results) - return pd_df.to_json( - path_or_buf, - orient=orient, - lines=lines, - index=index, - default_handler=str, - ) + # TODO(chelsealin): Support local file paths. + if not path_or_buf.startswith("gs://"): + raise NotImplementedError(ERROR_IO_ONLY_GS_PATHS) + if "*" not in path_or_buf: raise NotImplementedError(ERROR_IO_REQUIRES_WILDCARD) + if lines is True and orient != "records": + raise ValueError( + "'lines' keyword is only valid when 'orient' is 'records'." + ) + # TODO(ashleyxu) Support lines=False for small tables with arrays and TO_JSON_STRING. # See: https://cloud.google.com/bigquery/docs/reference/standard-sql/json_functions#to_json_string if lines is False: raise NotImplementedError( - f"Only newline-delimited JSON is supported. Add `lines=True` to your function call. {constants.FEEDBACK_LINK}" - ) - - if lines is True and orient != "records": - raise ValueError( - "'lines' keyword is only valid when 'orient' is 'records'." + f"Only newline delimited JSON format is supported. {constants.FEEDBACK_LINK}" ) - export_array, id_overrides = self._prepare_export( - index=index and self._has_index, - ordering_id=bigframes.session._io.bigquery.IO_ORDERING_ID, + result_table = self._run_io_query( + index=index, ordering_id=bigframes.session._io.bigquery.IO_ORDERING_ID ) - result = self._session._executor.execute( - export_array.rename_columns(id_overrides), - ex_spec.ExecutionSpec( - ex_spec.GcsOutputSpec(uri=path_or_buf, format="json", export_options=()) - ), + export_data_statement = bigframes.session._io.bigquery.create_export_data_statement( + f"{result_table.project}.{result_table.dataset_id}.{result_table.table_id}", + uri=path_or_buf, + format="JSON", + export_options={}, ) - self._set_internal_query_job(result.query_job) - return None + _, query_job = self._block.expr.session._start_query(export_data_statement) + self._set_internal_query_job(query_job) def to_gbq( self, @@ -4401,13 +2324,25 @@ def to_gbq( if_exists: Optional[Literal["fail", "replace", "append"]] = None, index: bool = True, ordering_id: Optional[str] = None, - clustering_columns: Union[pandas.Index, Iterable[typing.Hashable]] = (), - labels: dict[str, str] = {}, ) -> str: - index = index and self._has_index - temp_table_ref = None + dispositions = { + "fail": bigquery.WriteDisposition.WRITE_EMPTY, + "replace": bigquery.WriteDisposition.WRITE_TRUNCATE, + "append": bigquery.WriteDisposition.WRITE_APPEND, + } if destination_table is None: + # TODO(swast): If there have been no modifications to the DataFrame + # since the last time it was written (cached), then return that. + # For `read_gbq` nodes, return the underlying table clone. + destination_table = bigframes.session._io.bigquery.create_temp_table( + self._session.bqclient, + self._session._anonymous_dataset, + # TODO(swast): allow custom expiration times, probably via session configuration. + datetime.datetime.now(datetime.timezone.utc) + + constants.DEFAULT_EXPIRATION, + ) + if if_exists is not None and if_exists != "replace": raise ValueError( f"Got invalid value {repr(if_exists)} for if_exists. " @@ -4416,20 +2351,7 @@ def to_gbq( ) if_exists = "replace" - # The client code owns this table reference now - temp_table_ref = ( - self._session._anon_dataset_manager.generate_unique_resource_id() - ) - destination_table = f"{temp_table_ref.project}.{temp_table_ref.dataset_id}.{temp_table_ref.table_id}" - - table_parts = destination_table.split(".") - default_project = self._block.expr.session.bqclient.project - - if len(table_parts) == 2: - destination_dataset = f"{default_project}.{table_parts[0]}" - elif len(table_parts) == 3: - destination_dataset = f"{table_parts[0]}.{table_parts[1]}" - else: + if "." not in destination_table: raise ValueError( f"Got invalid value for destination_table {repr(destination_table)}. " "Should be of the form 'datasetId.tableId' or 'projectId.datasetId.tableId'." @@ -4438,98 +2360,45 @@ def to_gbq( if if_exists is None: if_exists = "fail" - valid_if_exists = ["fail", "replace", "append"] - if if_exists not in valid_if_exists: + if if_exists not in dispositions: raise ValueError( f"Got invalid value {repr(if_exists)} for if_exists. " - f"Valid options include None or one of {valid_if_exists}." + f"Valid options include None or one of {dispositions.keys()}." ) - try: - self._session.bqclient.get_dataset(destination_dataset) - except google.api_core.exceptions.NotFound: - self._session.bqclient.create_dataset(destination_dataset, exists_ok=True) - - clustering_fields = self._map_clustering_columns( - clustering_columns, index=index - ) - - export_array, id_overrides = self._prepare_export( - index=index and self._has_index, ordering_id=ordering_id - ) - destination: bigquery.table.TableReference = ( - bigquery.table.TableReference.from_string( + job_config = bigquery.QueryJobConfig( + write_disposition=dispositions[if_exists], + destination=bigquery.table.TableReference.from_string( destination_table, - default_project=default_project, - ) - ) - - result = self._session._executor.execute( - export_array.rename_columns(id_overrides), - ex_spec.ExecutionSpec( - ex_spec.TableOutputSpec( - destination, - cluster_cols=tuple(clustering_fields), - if_exists=if_exists, - ) + default_project=self._block.expr.session.bqclient.project, ), ) - assert result.query_job is not None - self._set_internal_query_job(result.query_job) - - # The query job should have finished, so there should be always be a result table. - result_table = result.query_job.destination - assert result_table is not None - - if temp_table_ref: - bigframes.session._io.bigquery.set_table_expiration( - self._session.bqclient, - temp_table_ref, - datetime.datetime.now(datetime.timezone.utc) - + bigframes.constants.DEFAULT_EXPIRATION, - ) - - if len(labels) != 0: - table = bigquery.Table(result_table) - table.labels = labels - self._session.bqclient.update_table(table, ["labels"]) + self._run_io_query(index=index, ordering_id=ordering_id, job_config=job_config) return destination_table def to_numpy( - self, - dtype=None, - copy=False, - na_value=pd_ext.no_default, - *, - allow_large_results=None, - **kwargs, + self, dtype=None, copy=False, na_value=None, **kwargs ) -> numpy.ndarray: - return self.to_pandas(allow_large_results=allow_large_results).to_numpy( - dtype, copy, na_value, **kwargs - ) + return self.to_pandas().to_numpy(dtype, copy, na_value, **kwargs) - def __array__(self, dtype=None, copy: Optional[bool] = None) -> numpy.ndarray: - if copy is False: - raise ValueError("Cannot convert to array without copy.") - return self.to_numpy(dtype=dtype) + __array__ = to_numpy def to_parquet( self, - path=None, + path: str, *, compression: Optional[Literal["snappy", "gzip"]] = "snappy", index: bool = True, - allow_large_results: Optional[bool] = None, - ) -> Optional[bytes]: + ) -> None: # TODO(swast): Can we support partition columns argument? # TODO(chelsealin): Support local file paths. # TODO(swast): Some warning that wildcard is recommended for large # query results? See: # https://cloud.google.com/bigquery/docs/exporting-data#limit_the_exported_file_size - if not utils.is_gcs_path(path): - pd_df = self.to_pandas(allow_large_results=allow_large_results) - return pd_df.to_parquet(path, compression=compression, index=index) + if not path.startswith("gs://"): + raise NotImplementedError(ERROR_IO_ONLY_GS_PATHS) + if "*" not in path: raise NotImplementedError(ERROR_IO_REQUIRES_WILDCARD) @@ -4540,22 +2409,17 @@ def to_parquet( if compression: export_options["compression"] = compression.upper() - export_array, id_overrides = self._prepare_export( - index=index and self._has_index, - ordering_id=bigframes.session._io.bigquery.IO_ORDERING_ID, + result_table = self._run_io_query( + index=index, ordering_id=bigframes.session._io.bigquery.IO_ORDERING_ID ) - result = self._session._executor.execute( - export_array.rename_columns(id_overrides), - ex_spec.ExecutionSpec( - ex_spec.GcsOutputSpec( - uri=path, - format="parquet", - export_options=tuple(export_options.items()), - ) - ), + export_data_statement = bigframes.session._io.bigquery.create_export_data_statement( + f"{result_table.project}.{result_table.dataset_id}.{result_table.table_id}", + uri=path, + format="PARQUET", + export_options=export_options, ) - self._set_internal_query_job(result.query_job) - return None + _, query_job = self._block.expr.session._start_query(export_data_statement) + self._set_internal_query_job(query_job) def to_dict( self, @@ -4563,25 +2427,12 @@ def to_dict( "dict", "list", "series", "split", "tight", "records", "index" ] = "dict", into: type[dict] = dict, - *, - allow_large_results: Optional[bool] = None, **kwargs, ) -> dict | list[dict]: - return self.to_pandas(allow_large_results=allow_large_results).to_dict( - orient=orient, into=into, **kwargs - ) # type: ignore + return self.to_pandas().to_dict(orient, into, **kwargs) # type: ignore - def to_excel( - self, - excel_writer, - sheet_name: str = "Sheet1", - *, - allow_large_results: Optional[bool] = None, - **kwargs, - ) -> None: - return self.to_pandas(allow_large_results=allow_large_results).to_excel( - excel_writer, sheet_name=sheet_name, **kwargs - ) + def to_excel(self, excel_writer, sheet_name: str = "Sheet1", **kwargs) -> None: + return self.to_pandas().to_excel(excel_writer, sheet_name, **kwargs) def to_latex( self, @@ -4589,29 +2440,16 @@ def to_latex( columns: Sequence | None = None, header: bool | Sequence[str] = True, index: bool = True, - *, - allow_large_results: Optional[bool] = None, **kwargs, ) -> str | None: - return self.to_pandas(allow_large_results=allow_large_results).to_latex( - buf, - columns=typing.cast(typing.Optional[list[str]], columns), - header=typing.cast(typing.Union[bool, list[str]], header), - index=index, - **kwargs, # type: ignore + return self.to_pandas().to_latex( + buf, columns=columns, header=header, index=index, **kwargs # type: ignore ) def to_records( - self, - index: bool = True, - column_dtypes=None, - index_dtypes=None, - *, - allow_large_results=None, + self, index: bool = True, column_dtypes=None, index_dtypes=None ) -> numpy.recarray: - return self.to_pandas(allow_large_results=allow_large_results).to_records( - index, column_dtypes, index_dtypes - ) + return self.to_pandas().to_records(index, column_dtypes, index_dtypes) def to_string( self, @@ -4634,83 +2472,27 @@ def to_string( min_rows: int | None = None, max_colwidth: int | None = None, encoding: str | None = None, - *, - allow_large_results: Optional[bool] = None, ) -> str | None: - return self.to_pandas(allow_large_results=allow_large_results).to_string( - buf, - columns=columns, # type: ignore - col_space=col_space, - header=header, # type: ignore - index=index, - na_rep=na_rep, - formatters=formatters, - float_format=float_format, - sparsify=sparsify, - index_names=index_names, - justify=justify, - max_rows=max_rows, - max_cols=max_cols, - show_dimensions=show_dimensions, - decimal=decimal, - line_width=line_width, - min_rows=min_rows, - max_colwidth=max_colwidth, - encoding=encoding, - ) - - def to_html( - self, - buf=None, - columns: Sequence[str] | None = None, - col_space=None, - header: bool = True, - index: bool = True, - na_rep: str = "NaN", - formatters=None, - float_format=None, - sparsify: bool | None = None, - index_names: bool = True, - justify: str | None = None, - max_rows: int | None = None, - max_cols: int | None = None, - show_dimensions: bool = False, - decimal: str = ".", - bold_rows: bool = True, - classes: str | list | tuple | None = None, - escape: bool = True, - notebook: bool = False, - border: int | None = None, - table_id: str | None = None, - render_links: bool = False, - encoding: str | None = None, - *, - allow_large_results: bool | None = None, - ) -> str: - return self.to_pandas(allow_large_results=allow_large_results).to_html( + return self.to_pandas().to_string( buf, - columns=columns, # type: ignore - col_space=col_space, - header=header, - index=index, - na_rep=na_rep, - formatters=formatters, - float_format=float_format, - sparsify=sparsify, - index_names=index_names, - justify=justify, # type: ignore - max_rows=max_rows, - max_cols=max_cols, - show_dimensions=show_dimensions, - decimal=decimal, - bold_rows=bold_rows, - classes=classes, - escape=escape, - notebook=notebook, - border=border, - table_id=table_id, - render_links=render_links, - encoding=encoding, + columns, # type: ignore + col_space, + header, # type: ignore + index, + na_rep, + formatters, + float_format, + sparsify, + index_names, + justify, + max_rows, + max_cols, + show_dimensions, + decimal, + line_width, + min_rows, + max_colwidth, + encoding, ) def to_markdown( @@ -4718,302 +2500,97 @@ def to_markdown( buf=None, mode: str = "wt", index: bool = True, - *, - allow_large_results: Optional[bool] = None, **kwargs, ) -> str | None: - return self.to_pandas(allow_large_results=allow_large_results).to_markdown( - buf, mode=mode, index=index, **kwargs - ) # type: ignore + return self.to_pandas().to_markdown(buf, mode, index, **kwargs) # type: ignore - def to_pickle(self, path, *, allow_large_results=None, **kwargs) -> None: - return self.to_pandas(allow_large_results=allow_large_results).to_pickle( - path, **kwargs - ) + def to_pickle(self, path, **kwargs) -> None: + return self.to_pandas().to_pickle(path, **kwargs) - def to_orc(self, path=None, *, allow_large_results=None, **kwargs) -> bytes | None: - as_pandas = self.to_pandas(allow_large_results=allow_large_results) + def to_orc(self, path=None, **kwargs) -> bytes | None: + as_pandas = self.to_pandas() # to_orc only works with default index as_pandas_default_index = as_pandas.reset_index() return as_pandas_default_index.to_orc(path, **kwargs) def _apply_unary_op(self, operation: ops.UnaryOp) -> DataFrame: - block = self._block.multi_apply_unary_op(operation) + block = self._block.multi_apply_unary_op(self._block.value_columns, operation) return DataFrame(block) - def _map_clustering_columns( - self, - clustering_columns: Union[pandas.Index, Iterable[typing.Hashable]], - index: bool, - ) -> List[str]: - """Maps the provided clustering columns to the existing columns in the DataFrame.""" - - def map_columns_on_occurrence(columns): - mapped_columns = [] - for col in clustering_columns: - if col in columns: - count = columns.count(col) - mapped_columns.extend([col] * count) - return mapped_columns - - if not clustering_columns: - return [] - - if len(list(clustering_columns)) != len(set(clustering_columns)): - raise ValueError("Duplicates are not supported in clustering_columns") - - all_possible_columns = ( - (set(self.columns) | set(self.index.names)) if index else set(self.columns) - ) - missing_columns = set(clustering_columns) - all_possible_columns - if missing_columns: - raise ValueError( - f"Clustering columns not found in DataFrame: {missing_columns}" - ) - - clustering_columns_for_df = map_columns_on_occurrence( - list(self._block.column_labels) - ) - clustering_columns_for_index = ( - map_columns_on_occurrence(list(self.index.names)) if index else [] - ) - - ( - clustering_columns_for_df, - clustering_columns_for_index, - ) = utils.get_standardized_ids( - clustering_columns_for_df, clustering_columns_for_index - ) - - return clustering_columns_for_index + clustering_columns_for_df - - def _prepare_export( - self, index: bool, ordering_id: Optional[str] - ) -> Tuple[bigframes.core.ArrayValue, Dict[str, str]]: + def _create_io_query(self, index: bool, ordering_id: Optional[str]) -> str: + """Create query text representing this dataframe for I/O.""" array_value = self._block.expr - - new_col_labels, new_idx_labels = utils.get_standardized_ids( - self._block.column_labels, self._block.index.names - ) - columns = list(self._block.value_columns) - column_labels = new_col_labels + column_labels = list(self._block.column_labels) # This code drops unnamed indexes to keep consistent with the behavior of # most pandas write APIs. The exception is `pandas.to_csv`, which keeps # unnamed indexes as `Unnamed: 0`. # TODO(chelsealin): check if works for multiple indexes. if index and self.index.name is not None: columns.extend(self._block.index_columns) - column_labels.extend(new_idx_labels) + column_labels.extend(self.index.names) else: array_value = array_value.drop_columns(self._block.index_columns) # Make columns in SQL reflect _labels_ not _ids_. Note: This may use # the arbitrary unicode column labels feature in BigQuery, which is # currently (June 2023) in preview. + # TODO(swast): Handle duplicate and NULL labels. id_overrides = { col_id: col_label for col_id, col_label in zip(columns, column_labels) - if (col_id != col_label) + if col_label and isinstance(col_label, str) } if ordering_id is not None: - array_value, internal_ordering_id = array_value.promote_offsets() - id_overrides[internal_ordering_id] = ordering_id - return array_value, id_overrides - - def map(self, func, na_action: Optional[str] = None) -> DataFrame: - from bigframes._config import options - - if not isinstance(func, bigframes.functions.Udf) and not ( - options.experiments.enable_python_transpiler and callable(func) - ): - raise TypeError("the first argument must be callable") - - if na_action not in {None, "ignore"}: - raise ValueError(f"na_action={na_action} not supported") - - expr = ops.func_to_expr(func).apply(ex.free_var("input")) - if na_action == "ignore": - # True case, predicate, False case - expr = ops.where_op.as_expr( - expr, ops.notnull_op.as_expr(ex.free_var("input")), ex.const(None) + return array_value.to_sql( + offset_column=ordering_id, + col_id_overrides=id_overrides, ) - - return DataFrame(self._block.multi_apply_unary_op(expr)) - - def apply(self, func, *, axis=0, args: typing.Tuple = (), **kwargs): - # In Bigframes BigQuery function, DataFrame '.apply' method is specifically - # designed to work with row-wise or column-wise operations, where the input - # to the applied function should be a Series, not a scalar. - - if utils.get_axis_number(axis) == 1: - msg = bfe.format_message( - "DataFrame.apply with parameter axis=1 scenario is in preview." + else: + return array_value.to_sql( + col_id_overrides=id_overrides, ) - warnings.warn(msg, category=bfe.FunctionAxisOnePreviewWarning) - - from bigframes._config import options - if not isinstance(func, bigframes.functions.Udf) and not ( - options.experiments.enable_python_transpiler and callable(func) - ): - raise ValueError( - "For axis=1 a BigFrames BigQuery function must be used." - ) + def _run_io_query( + self, + index: bool, + ordering_id: Optional[str] = None, + job_config: Optional[bigquery.job.QueryJobConfig] = None, + ) -> bigquery.TableReference: + """Executes a query job presenting this dataframe and returns the destination + table.""" + expr = self._block.expr + session = expr.session + sql = self._create_io_query(index=index, ordering_id=ordering_id) + _, query_job = session._start_query( + sql=sql, job_config=job_config # type: ignore + ) + self._set_internal_query_job(query_job) - if ( - not isinstance(func, bigframes.functions.Udf) - and options.experiments.enable_python_transpiler - and callable(func) - ): - result_block = block_ops.apply_to_block_rows( - func, self._block, *args, **kwargs - ) - return bigframes.series.Series(result_block) - - if func.udf_def.signature.is_row_processor: - # Early check whether the dataframe dtypes are currently supported - # in the bigquery function - # NOTE: Keep in sync with the value converters used in the gcf code - # generated in function_template.py - bigquery_function_supported_dtypes = ( - bigframes.dtypes.INT_DTYPE, - bigframes.dtypes.FLOAT_DTYPE, - bigframes.dtypes.BOOL_DTYPE, - bigframes.dtypes.BYTES_DTYPE, - bigframes.dtypes.STRING_DTYPE, - ) - supported_dtypes_types = tuple( - type(dtype) - for dtype in bigquery_function_supported_dtypes - if not isinstance(dtype, pandas.ArrowDtype) - ) - # Check ArrowDtype separately since multiple BigQuery types map to - # ArrowDtype, including BYTES and TIMESTAMP. - supported_arrow_types = tuple( - dtype.pyarrow_dtype - for dtype in bigquery_function_supported_dtypes - if isinstance(dtype, pandas.ArrowDtype) - ) - supported_dtypes_hints = tuple( - str(dtype) for dtype in bigquery_function_supported_dtypes - ) + # The query job should have finished, so there should be always be a result table. + result_table = query_job.destination + assert result_table is not None + return result_table - for dtype in self.dtypes: - if ( - # Not one of the pandas/numpy types. - not isinstance(dtype, supported_dtypes_types) - # And not one of the arrow types. - and not ( - isinstance(dtype, pandas.ArrowDtype) - and any( - dtype.pyarrow_dtype.equals(arrow_type) - for arrow_type in supported_arrow_types - ) - ) - ): - raise NotImplementedError( - f"DataFrame has a column of dtype '{dtype}' which is not supported with axis=1." - f" Supported dtypes are {supported_dtypes_hints}." - ) - - # Serialize the rows as json values - block = self._get_block() - rows_as_json_series = bigframes.series.Series( - block._get_rows_as_json_values() - ) + def map(self, func, na_action: Optional[str] = None) -> DataFrame: + if not callable(func): + raise TypeError("the first argument must be callable") - # Apply the function - expr = ops.func_to_expr(func).expr - if not ( - isinstance(expr, ex.OpExpression) - and isinstance(expr.op, ops.NaryOp) - ): - raise TypeError(f"Expected OpExpression with NaryOp, got {expr}") - result_series = rows_as_json_series._apply_nary_op( - expr.op, - list(args), - ) + if na_action not in {None, "ignore"}: + raise ValueError(f"na_action={na_action} not supported") - else: - # This is a special case where we are providing not-pandas-like - # extension. If the bigquery function can take one or more - # params (excluding the args) then we assume that here the user - # intention is to use the column values of the dataframe as - # arguments to the function. For this to work the following - # condition must be true: - # 1. The number or input params (excluding the args) in the - # function must be same as the number of columns in the - # dataframe. - # 2. The dtypes of the columns in the dataframe must be - # compatible with the data types of the input params. - # 3. The order of the columns in the dataframe must correspond - # to the order of the input params in the function. - udf_input_dtypes = tuple( - arg.bf_type for arg in func.udf_def.signature.inputs - ) - if not args and len(udf_input_dtypes) != len(self.columns): - raise ValueError( - f"Parameter count mismatch: BigFrames BigQuery function" - f" expected {len(udf_input_dtypes)} parameters but" - f" received {len(self.columns)} DataFrame columns." - ) - if args and len(udf_input_dtypes) != len(self.columns) + len(args): - raise ValueError( - f"Parameter count mismatch: BigFrames BigQuery function" - f" expected {len(udf_input_dtypes)} parameters but" - f" received {len(self.columns) + len(args)} values" - f" ({len(self.columns)} DataFrame columns and" - f" {len(args)} args)." - ) - end_slice = -len(args) if args else None - if udf_input_dtypes[:end_slice] != tuple(self.dtypes.to_list()): - raise ValueError( - f"Data type mismatch for DataFrame columns:" - f" Expected {udf_input_dtypes[:end_slice]}" - f" Received {tuple(self.dtypes)}." - ) - if args: - bq_types = ( - function_typing.sdk_type_from_python_type(type(arg)) - for arg in args - ) - args_dtype = tuple( - function_typing.sdk_type_to_bf_type(bq_type) - for bq_type in bq_types - ) - if udf_input_dtypes[end_slice:] != args_dtype: - raise ValueError( - f"Data type mismatch for 'args' parameter:" - f" Expected {udf_input_dtypes[end_slice:]}" - f" Received {args_dtype}." - ) - - series_list = [self[col] for col in self.columns] - op_list = series_list[1:] + list(args) - result_series = series_list[0]._apply_callable_expr( - ops.func_to_expr(func), op_list - ) - result_series.name = None - - return result_series - - # At this point column-wise or element-wise bigquery function operation will - # be performed (not supported). - if isinstance(func, bigframes.functions.Udf): - raise formatter.create_exception_with_feedback_link( - NotImplementedError, - "BigFrames DataFrame '.apply()' does not support BigFrames " - "BigQuery function for column-wise (i.e. with axis=0) " - "operations, please use a regular python function instead. For " - "element-wise operations of the BigFrames BigQuery function, " - "please use '.map()'.", - ) + # TODO(shobs): Support **kwargs + # Reproject as workaround to applying filter too late. This forces the filter + # to be applied before passing data to remote function, protecting from bad + # inputs causing errors. + reprojected_df = DataFrame(self._block._force_reproject()) + return reprojected_df._apply_unary_op( + ops.RemoteFunctionOp(func, apply_on_null=(na_action is None)) + ) - # Per-column apply + def apply(self, func, *, args: typing.Tuple = (), **kwargs): results = {name: func(col, *args, **kwargs) for name, col in self.items()} - if all( [ isinstance(val, bigframes.series.Series) or utils.is_list_like(val) @@ -5053,7 +2630,7 @@ def duplicated(self, subset=None, keep: str = "first") -> bigframes.series.Serie return bigframes.series.Series( block.select_column( indicator, - ).with_column_labels(pandas.Index([None])), + ) ) def rank( @@ -5063,12 +2640,9 @@ def rank( numeric_only=False, na_option: str = "keep", ascending=True, - pct: bool = False, ) -> DataFrame: df = self._drop_non_numeric() if numeric_only else self - return DataFrame( - block_ops.rank(df._block, method, na_option, ascending, pct=pct) - ) + return DataFrame(block_ops.rank(df._block, method, na_option, ascending)) def first_valid_index(self): return @@ -5081,9 +2655,7 @@ def _slice( stop: typing.Optional[int] = None, step: typing.Optional[int] = None, ) -> DataFrame: - block = self._block.slice( - start=start, stop=stop, step=step if (step is not None) else 1 - ) + block = self._block.slice(start=start, stop=stop, step=step) return DataFrame(block) def __array_ufunc__( @@ -5102,7 +2674,7 @@ def __array_ufunc__( if inputs[0] is self: return self._apply_binop(inputs[1], binop) else: - return self._apply_binop(inputs[0], binop, reverse=True) + return self._apply_binop(inputs[0], ops.reverse(binop)) return NotImplemented @@ -5112,32 +2684,13 @@ def _set_block(self, block: blocks.Block): def _get_block(self) -> blocks.Block: return self._block - def cache(self): - """ - Materializes the DataFrame to a temporary table. - - Useful if the dataframe will be used multiple times, as this will avoid recomputating the shared intermediate value. - - Returns: - bigframes.pandas.DataFrame: DataFrame - """ - return self._cached(force=True) - - def _cached(self, *, force: bool = False) -> DataFrame: - """Materialize dataframe to a temporary table. - No-op if the dataframe represents a trivial transformation of an existing materialization. - Force=True is used for BQML integration where need to copy data rather than use snapshot. - """ - if self._disable_cache_override: - return self - self._block.cached(force=force) - return self + def _cached(self) -> DataFrame: + return DataFrame(self._block.cached()) _DataFrameOrSeries = typing.TypeVar("_DataFrameOrSeries") - @validations.requires_ordering() def dot(self, other: _DataFrameOrSeries) -> _DataFrameOrSeries: - if not isinstance(other, (DataFrame, bigframes.series.Series)): + if not isinstance(other, (DataFrame, bf_series.Series)): raise NotImplementedError( f"Only DataFrame or Series operand is supported. {constants.FEEDBACK_LINK}" ) @@ -5212,75 +2765,9 @@ def get_right_id(id): ) result = result[other_frame.columns] - if isinstance(other, bigframes.series.Series): - # There should be exactly one column in the result - result = result[result.columns[0]].rename() + if isinstance(other, bf_series.Series): + result = result[other.name].rename() return result - @property - def plot(self): - return plotting.PlotAccessor(self) - - def hist( - self, by: typing.Optional[typing.Sequence[str]] = None, bins: int = 10, **kwargs - ): - return self.plot.hist(by=by, bins=bins, **kwargs) - - hist.__doc__ = inspect.getdoc(plotting.PlotAccessor.hist) - - def line( - self, - x: typing.Optional[typing.Hashable] = None, - y: typing.Optional[typing.Hashable] = None, - **kwargs, - ): - return self.plot.line(x=x, y=y, **kwargs) - - line.__doc__ = inspect.getdoc(plotting.PlotAccessor.line) - - def area( - self, - x: typing.Optional[typing.Hashable] = None, - y: typing.Optional[typing.Hashable] = None, - stacked: bool = True, - **kwargs, - ): - return self.plot.area(x=x, y=y, stacked=stacked, **kwargs) - - area.__doc__ = inspect.getdoc(plotting.PlotAccessor.area) - - def bar( - self, - x: typing.Optional[typing.Hashable] = None, - y: typing.Optional[typing.Hashable] = None, - **kwargs, - ): - return self.plot.bar(x=x, y=y, **kwargs) - - bar.__doc__ = inspect.getdoc(plotting.PlotAccessor.bar) - - def scatter( - self, - x: typing.Optional[typing.Hashable] = None, - y: typing.Optional[typing.Hashable] = None, - s: typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]] = None, - c: typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]] = None, - **kwargs, - ): - return self.plot.scatter(x=x, y=y, s=s, c=c, **kwargs) - - scatter.__doc__ = inspect.getdoc(plotting.PlotAccessor.scatter) - - def __matmul__(self, other) -> DataFrame: - return self.dot(other) - - @property - def struct(self): - return bigframes.operations.structs.StructFrameAccessor(self) - - def _throw_if_null_index(self, opname: str): - if not self._has_index: - raise bigframes.exceptions.NullIndexError( - f"DataFrame cannot perform {opname} as it has no index. Set an index using set_index." - ) + __matmul__ = dot diff --git a/bigframes/display/__init__.py b/bigframes/display/__init__.py deleted file mode 100644 index aa1371db564..00000000000 --- a/bigframes/display/__init__.py +++ /dev/null @@ -1,48 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -"""Interactive display objects for BigQuery DataFrames.""" - -from __future__ import annotations - -from typing import Any - - -def __getattr__(name: str) -> Any: - """Lazily import TableWidget to avoid ZMQ port conflicts. - - anywidget and traitlets eagerly initialize kernel communication channels on - import. This can lead to race conditions and ZMQ port conflicts when - multiple Jupyter kernels are started in parallel, such as during notebook - tests. By using __getattr__, we defer the import of TableWidget until it is - explicitly accessed, preventing premature initialization and avoiding port - collisions. - """ - if name == "TableWidget": - try: - import anywidget # noqa - - from bigframes.display.anywidget import TableWidget - - return TableWidget - except Exception: - raise AttributeError( - f"module '{__name__}' has no attribute '{name}'. " - "TableWidget requires anywidget and traitlets to be installed. " - "Please `pip install anywidget traitlets` or `pip install 'bigframes[anywidget]'`." - ) - raise AttributeError(f"module '{__name__}' has no attribute '{name}'") - - -__all__ = ["TableWidget"] diff --git a/bigframes/display/anywidget.py b/bigframes/display/anywidget.py deleted file mode 100644 index 01135d6670b..00000000000 --- a/bigframes/display/anywidget.py +++ /dev/null @@ -1,643 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -"""Interactive, paginated table widget for BigFrames DataFrames.""" - -from __future__ import annotations - -import dataclasses -import functools -import logging - -logger = logging.getLogger(__name__) -import math -import threading -import uuid -import warnings -from importlib import resources -from typing import Any, Iterator, Optional - -import pandas as pd - -import bigframes -import bigframes.dataframe -import bigframes.display.html -import bigframes.dtypes as dtypes -from bigframes.core import blocks - -# anywidget and traitlets are optional dependencies. We don't want the import of -# this module to fail if they aren't installed, though. Instead, we try to -# limit the surface that these packages could affect. This makes unit testing -# easier and ensures we don't accidentally make these required packages. -try: - import anywidget - import traitlets - - _ANYWIDGET_INSTALLED = True -except Exception: - _ANYWIDGET_INSTALLED = False - -_WIDGET_BASE: type[Any] -if _ANYWIDGET_INSTALLED: - _WIDGET_BASE = anywidget.AnyWidget -else: - _WIDGET_BASE = object - - -@dataclasses.dataclass(frozen=True) -class _SortState: - columns: tuple[str, ...] - ascending: tuple[bool, ...] - - -@dataclasses.dataclass -class _ExecutionResult: - df_to_set: Optional[bigframes.dataframe.DataFrame] = None - orderable_cols: Optional[list[str]] = None - batches: Optional[blocks.PandasBatches] = None - batch_iter: Optional[Iterator[pd.DataFrame]] = None - cached_batches: Optional[list[pd.DataFrame]] = None - all_data_loaded: bool = False - total_rows: Optional[int] = None - initial_html: Optional[str] = None - error_message: Optional[str] = None - - -class TableWidget(_WIDGET_BASE): - """An interactive, paginated table widget for BigFrames DataFrames. - - This widget provides a user-friendly way to display and navigate through - large BigQuery DataFrames within a Jupyter environment. - """ - - page = traitlets.Int(0).tag(sync=True) - page_size = traitlets.Int(0).tag(sync=True) - max_columns = traitlets.Int(allow_none=True, default_value=None).tag(sync=True) - row_count = traitlets.Int(allow_none=True, default_value=None).tag(sync=True) - table_html = traitlets.Unicode("").tag(sync=True) - sort_context = traitlets.List(traitlets.Dict(), default_value=[]).tag(sync=True) - orderable_columns = traitlets.List(traitlets.Unicode(), []).tag(sync=True) - _initial_load_complete = traitlets.Bool(False).tag(sync=True) - _batches: Optional[blocks.PandasBatches] = None - _error_message = traitlets.Unicode(allow_none=True, default_value=None).tag( - sync=True - ) - start_execution = traitlets.Bool(False).tag(sync=True) - is_deferred_mode = traitlets.Bool(False).tag(sync=True) - dry_run_info = traitlets.Unicode("").tag(sync=True) - ping = traitlets.Int(0).tag(sync=True) - - def __init__( - self, - dataframe: ( - bigframes.dataframe.DataFrame - | bigframes.session.deferred.DeferredBigQueryDataFrame - ), - dry_run_info: Optional[str] = None, - ): - """Initialize the TableWidget. - - Args: - dataframe: The Bigframes Dataframe to display in the widget. - """ - if not _ANYWIDGET_INSTALLED: - raise ImportError( - "Please `pip install anywidget traitlets` or " - "`pip install 'bigframes[anywidget]'` to use TableWidget." - ) - - # Enable third-party widgets manager in Google Colab environment. - try: - import sys - - if "google.colab" in sys.modules: - from google.colab import output - - output.enable_custom_widget_manager() - except Exception: - pass - - from bigframes.session import deferred - - is_deferred = False - deferred_df = None - df = None - - if isinstance(dataframe, deferred.DeferredBigQueryDataFrame): - is_deferred = True - deferred_df = dataframe - elif bigframes.options.display.repr_mode == "deferred": - is_deferred = True - df = dataframe - else: - df = dataframe - - from bigframes.core.utils import get_ipython_execution_count - - self._cell_execution_count = get_ipython_execution_count() - - super().__init__() - - self.is_deferred_mode = is_deferred - self._deferred_dataframe = deferred_df - self._dataframe = df - - if dry_run_info: - self.dry_run_info = dry_run_info - - # Initialize attributes that might be needed by observers first - self._table_id = str(uuid.uuid4()) - self._all_data_loaded = False - self._batch_iter: Optional[Iterator[pd.DataFrame]] = None - self._cached_batches: list[pd.DataFrame] = [] - self._last_sort_state: Optional[_SortState] = None - self._execution_result: Optional[_ExecutionResult] = None - # Lock to ensure only one thread at a time is updating the table HTML. - self._setting_html_lock = threading.Lock() - - # respect display options for initial page size - initial_page_size = bigframes.options.display.max_rows - initial_max_columns = bigframes.options.display.max_columns - - self.page_size = initial_page_size - self.max_columns = initial_max_columns - - if not self.is_deferred_mode: - self._initialize_from_dataframe() - - # Signals to the frontend that the initial data load is complete. - # Also used as a guard to prevent observers from firing during initialization. - self._initial_load_complete = True - - @traitlets.observe("start_execution") - def _on_start_execution(self, change: dict[str, Any]): - if change["new"]: - import asyncio - - try: - loop = asyncio.get_running_loop() - except RuntimeError: - try: - import tornado.ioloop # type: ignore[import-not-found] - - loop = tornado.ioloop.IOLoop.current().asyncio_loop # type: ignore[attr-defined] - except Exception: - loop = None - - def run_execution(): - try: - self._error_message = None - df = None - if self.is_deferred_mode: - if self._deferred_dataframe is not None: - result = self._deferred_dataframe.execute() - if isinstance(result, bigframes.series.Series): - df = result.to_frame() - elif isinstance(result, bigframes.dataframe.DataFrame): - df = result - else: - raise TypeError( - f"Unexpected result type: {type(result)}" - ) - elif self._dataframe is not None: - df = self._dataframe - else: - df = self._dataframe - - if df is None: - raise ValueError("No DataFrame to execute.") - - df_to_set = df._prepare_display_df() - orderable_cols = self._get_orderable_columns(df_to_set) - - with bigframes.option_context("display.progress_bar", None): - batches = df_to_set.to_pandas_batches( - page_size=self.page_size, - cell_execution_count=self._cell_execution_count, - ) - - total_rows = getattr(batches, "total_rows", None) - - # Fetch the first batch - batch_iter = iter(batches) - try: - initial_batch = next(batch_iter) - cached_batches = [initial_batch] - all_data_loaded = False - except StopIteration: - initial_batch = pd.DataFrame(columns=df_to_set.columns) - cached_batches = [] - all_data_loaded = True - - # Render the HTML - page_data = initial_batch.copy() - start = 0 - if df_to_set._block.has_index: - is_unnamed_single_index = ( - page_data.index.name is None - and not isinstance(page_data.index, pd.MultiIndex) - ) - page_data = page_data.reset_index() - if is_unnamed_single_index and "index" in page_data.columns: - page_data.rename(columns={"index": ""}, inplace=True) - else: - page_data.insert( - 0, "Row", range(start + 1, start + len(page_data) + 1) - ) - - initial_html = bigframes.display.html.render_html( - dataframe=page_data, - table_id=f"table-{self._table_id}", - orderable_columns=orderable_cols, - max_columns=self.max_columns, - ) - - self._execution_result = _ExecutionResult( - df_to_set=df_to_set, - orderable_cols=orderable_cols, - batches=batches, - batch_iter=batch_iter, - cached_batches=cached_batches, - all_data_loaded=all_data_loaded, - total_rows=total_rows, - initial_html=initial_html, - ) - except Exception as e: - logger.warning(f"Error in background execution: {e}") - self._execution_result = _ExecutionResult(error_message=str(e)) - - import sys - - is_colab = "google.colab" in sys.modules - - if loop is not None and loop.is_running() and not is_colab: - loop.call_soon_threadsafe(self._apply_execution_result) - elif is_colab: - # In Google Colab, background thread updates to traitlets are not automatically - # synchronized to the frontend. We rely on the frontend's active pinging - # (which triggers `_on_ping` on the main kernel thread) to apply the result. - pass - else: - self._apply_execution_result() - - self._execution_thread = threading.Thread(target=run_execution, daemon=True) - self._execution_thread.start() - - def _apply_execution_result(self) -> None: - if self._execution_result is None: - return - - result = self._execution_result - self._execution_result = None - - with self.hold_sync(): - if result.error_message is not None: - self._error_message = result.error_message - self.start_execution = False - else: - self._dataframe = result.df_to_set - self.orderable_columns = result.orderable_cols or [] - self._batches = result.batches - self._batch_iter = result.batch_iter - self._cached_batches = result.cached_batches or [] - self._all_data_loaded = result.all_data_loaded - self._last_sort_state = _SortState((), ()) - self.row_count = result.total_rows - self.table_html = result.initial_html or "" - self.is_deferred_mode = False - self.start_execution = False - - @traitlets.observe("ping") - def _on_ping(self, _change: dict[str, Any]): - self._apply_execution_result() - - def _initialize_from_dataframe(self): - if self._dataframe is None: - return - - self.orderable_columns = self._get_orderable_columns(self._dataframe) - - self._initial_load() - - def _get_orderable_columns( - self, dataframe: bigframes.dataframe.DataFrame - ) -> list[str]: - """Determine which columns can be used for client-side sorting.""" - # TODO(b/469861913): Nested columns from structs (e.g., 'struct_col.name') are not currently sortable. - # TODO(b/463754889): Support non-string column labels for sorting. - if not all(isinstance(col, str) for col in dataframe.columns): - return [] - - with warnings.catch_warnings(): - warnings.simplefilter("ignore", bigframes.exceptions.JSONDtypeWarning) - warnings.simplefilter("ignore", category=FutureWarning) - return [ - str(col_name) - for col_name, dtype in dataframe.dtypes.items() - if dtypes.is_orderable(dtype) - ] - - def _initial_load(self) -> None: - """Get initial data and row count.""" - # obtain the row counts - # TODO(b/428238610): Start iterating over the result of `to_pandas_batches()` - # before we get here so that the count might already be cached. - with bigframes.option_context("display.progress_bar", None): - self._reset_batches_for_new_page_size() - - if self._batches is None: - self._error_message = ( - "Could not retrieve data batches. Data might be unavailable or " - "an error occurred." - ) - self.row_count = None - elif self._batches.total_rows is None: - # Total rows is unknown, this is an expected state. - # TODO(b/461536343): Cheaply discover if we have exactly 1 page. - # There are cases where total rows is not set, but there are no additional - # pages. We could disable the "next" button in these cases. - self.row_count = None - else: - self.row_count = self._batches.total_rows - - # get the initial page - self._set_table_html() - - @traitlets.observe("_initial_load_complete") - def _on_initial_load_complete(self, change: dict[str, Any]): - if change["new"]: - self._set_table_html() - - @functools.cached_property - def _esm(self): - """Load JavaScript code from the compiled Angular hybrid bundle.""" - return resources.read_text(bigframes.display, "table_widget_angular.js") - - @functools.cached_property - def _css(self): - """Load CSS code from external file.""" - return resources.read_text(bigframes.display, "table_widget.css") - - @traitlets.validate("page") - def _validate_page(self, proposal: dict[str, Any]) -> int: - """Validate and clamp the page number to a valid range. - - Args: - proposal: A dictionary from the traitlets library containing the - proposed change. The new value is in proposal["value"]. - - Returns: - The validated and clamped page number as an integer. - """ - value = proposal["value"] - - if value < 0: - raise ValueError("Page number cannot be negative.") - - # If truly empty or invalid page size, stay on page 0. - # This handles cases where row_count is 0 or page_size is 0, preventing - # division by zero or nonsensical pagination, regardless of row_count being None. - if self.row_count == 0 or self.page_size == 0: - return 0 - - # If row count is unknown, allow any non-negative page. The previous check - # ensures that invalid page_size (0) is already handled. - if self.row_count is None: - return value - - # Calculate the zero-indexed maximum page number. - max_page = max(0, math.ceil(self.row_count / self.page_size) - 1) - - # Clamp the proposed value to the valid range [0, max_page]. - return max(0, min(value, max_page)) - - @traitlets.validate("page_size") - def _validate_page_size(self, proposal: dict[str, Any]) -> int: - """Validate page size to ensure it's positive and reasonable. - - Args: - proposal: A dictionary from the traitlets library containing the - proposed change. The new value is in proposal["value"]. - - Returns: - The validated page size as an integer. - """ - value = proposal["value"] - - # Ensure page size is positive and within reasonable bounds - if value <= 0: - return self.page_size # Keep current value - - # Cap at reasonable maximum to prevent performance issues - max_page_size = 1000 - return min(value, max_page_size) - - @traitlets.validate("max_columns") - def _validate_max_columns(self, proposal: dict[str, Any]) -> int: - """Validate max columns to ensure it's positive or 0 (for all).""" - value = proposal["value"] - if value is None: - return 0 # Normalize None to 0 for traitlet - return max(0, value) - - def _get_next_batch(self) -> bool: - """ - Gets the next batch of data from the generator and appends to cache. - - Returns: - True if a batch was successfully loaded, False otherwise. - """ - if self._all_data_loaded: - return False - - try: - iterator = self._batch_iterator - batch = next(iterator) - self._cached_batches.append(batch) - return True - except StopIteration: - self._all_data_loaded = True - return False - - @property - def _batch_iterator(self) -> Iterator[pd.DataFrame]: - """Lazily initializes and returns the batch iterator.""" - if self._batch_iter is None: - if self._batches is None: - self._batch_iter = iter([]) - else: - self._batch_iter = iter(self._batches) - return self._batch_iter - - @property - def _cached_data(self) -> pd.DataFrame: - """Combine all cached batches into a single DataFrame.""" - if not self._cached_batches: - if self._dataframe is not None: - return pd.DataFrame(columns=self._dataframe.columns) - return pd.DataFrame() - return pd.concat(self._cached_batches) - - def _reset_batch_cache(self) -> None: - """Resets batch caching attributes.""" - self._cached_batches = [] - self._batch_iter = None - self._all_data_loaded = False - - def _reset_batches_for_new_page_size(self) -> None: - """Reset the batch iterator when page size changes.""" - if self._dataframe is None: - return - with bigframes.option_context("display.progress_bar", None): - self._batches = self._dataframe.to_pandas_batches( - page_size=self.page_size, - cell_execution_count=self._cell_execution_count, - ) - - self._reset_batch_cache() - - def _set_table_html(self) -> None: - """Sets the current html data based on the current page and page size.""" - if self.is_deferred_mode: - return - - new_page = None - with ( - self._setting_html_lock, - bigframes.option_context("display.progress_bar", None), - ): - if self._error_message: - self.table_html = ( - f"
{self._error_message}
" - ) - return - - if self._dataframe is None: - self.table_html = "
Internal Error: DataFrame is missing.
" - return - - # Apply sorting if a column is selected - df_to_display = self._dataframe - sort_columns = [item["column"] for item in self.sort_context] - sort_ascending = [item["ascending"] for item in self.sort_context] - - if sort_columns: - # TODO(b/463715504): Support sorting by index columns. - df_to_display = df_to_display.sort_values( - by=sort_columns, ascending=sort_ascending - ) - - # Reset batches when sorting changes - current_sort_state = _SortState(tuple(sort_columns), tuple(sort_ascending)) - if self._last_sort_state != current_sort_state: - self._batches = df_to_display.to_pandas_batches( - page_size=self.page_size, - cell_execution_count=self._cell_execution_count, - ) - self._reset_batch_cache() - self._last_sort_state = current_sort_state - if self.page != 0: - new_page = 0 # Reset to first page - - if new_page is None: - start = self.page * self.page_size - end = start + self.page_size - - # fetch more data if the requested page is outside our cache - cached_data = self._cached_data - while len(cached_data) < end and not self._all_data_loaded: - if self._get_next_batch(): - cached_data = self._cached_data - else: - break - - # Get the data for the current page - page_data = cached_data.iloc[start:end].copy() - - # Handle case where user navigated beyond available data with unknown row count - is_unknown_count = self.row_count is None - is_beyond_data = ( - self._all_data_loaded and len(page_data) == 0 and self.page > 0 - ) - if is_unknown_count and is_beyond_data: - # Calculate the last valid page (zero-indexed) - total_rows = len(cached_data) - last_valid_page = max(0, math.ceil(total_rows / self.page_size) - 1) - if self.page != last_valid_page: - new_page = last_valid_page - - if new_page is None: - # Handle index display - if self._dataframe._block.has_index: - is_unnamed_single_index = ( - page_data.index.name is None - and not isinstance(page_data.index, pd.MultiIndex) - ) - page_data = page_data.reset_index() - if is_unnamed_single_index and "index" in page_data.columns: - page_data.rename(columns={"index": ""}, inplace=True) - - # Default index - include as "Row" column if no index was present originally - if not self._dataframe._block.has_index: - page_data.insert( - 0, "Row", range(start + 1, start + len(page_data) + 1) - ) - - # Generate HTML table - self.table_html = bigframes.display.html.render_html( - dataframe=page_data, - table_id=f"table-{self._table_id}", - orderable_columns=self.orderable_columns, - max_columns=self.max_columns, - ) - - if new_page is not None: - # Navigate to the new page. This triggers the observer, which will - # re-enter _set_table_html. Since we've released the lock, this is safe. - self.page = new_page - - @traitlets.observe("sort_context") - def _sort_changed(self, _change: dict[str, Any]): - """Handler for when sorting parameters change from the frontend.""" - self._set_table_html() - - @traitlets.observe("page") - def _page_changed(self, _change: dict[str, Any]) -> None: - """Handler for when the page number is changed from the frontend.""" - if not self._initial_load_complete: - return - self._set_table_html() - - @traitlets.observe("page_size") - def _page_size_changed(self, _change: dict[str, Any]) -> None: - """Handler for when the page size is changed from the frontend.""" - if not self._initial_load_complete: - return - # Reset the page to 0 when page size changes to avoid invalid page states - self.page = 0 - # Reset the sort state to default (no sort) - self.sort_context = [] - - # Reset batches to use new page size for future data fetching - self._reset_batches_for_new_page_size() - - # Update the table display - self._set_table_html() - - @traitlets.observe("max_columns") - def _max_columns_changed(self, _change: dict[str, Any]) -> None: - """Handler for when max columns is changed from the frontend.""" - if not self._initial_load_complete: - return - self._set_table_html() diff --git a/bigframes/display/html.py b/bigframes/display/html.py deleted file mode 100644 index 603d53e6866..00000000000 --- a/bigframes/display/html.py +++ /dev/null @@ -1,386 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -"""HTML rendering for DataFrames and other objects.""" - -from __future__ import annotations - -import html -import json -import traceback -import typing -import warnings -from typing import Any, Union - -import pandas as pd -import pandas.api.types - -import bigframes -import bigframes.formatting_helpers as formatter -from bigframes._config import display_options, options -from bigframes.display import plaintext - -if typing.TYPE_CHECKING: - import bigframes.dataframe - import bigframes.series - - -def _is_dtype_numeric(dtype: Any) -> bool: - """Check if a dtype is numeric for alignment purposes.""" - return pandas.api.types.is_numeric_dtype(dtype) - - -def render_html( - *, - dataframe: pd.DataFrame, - table_id: str, - orderable_columns: list[str] | None = None, - max_columns: int | None = None, -) -> str: - """Render a pandas DataFrame to HTML with specific styling.""" - orderable_columns = orderable_columns or [] - classes = "dataframe table table-striped table-hover" - table_html_parts = [f''] - - # Handle column truncation - columns = list(dataframe.columns) - if max_columns is not None and max_columns > 0 and len(columns) > max_columns: - half = max_columns // 2 - left_columns = columns[:half] - # Ensure we don't take more than available if half is 0 or calculation is weird, - # but typical case is safe. - right_count = max_columns - half - right_columns = columns[-right_count:] if right_count > 0 else [] - show_ellipsis = True - else: - left_columns = columns - right_columns = [] - show_ellipsis = False - - table_html_parts.append( - _render_table_header( - dataframe, orderable_columns, left_columns, right_columns, show_ellipsis - ) - ) - table_html_parts.append( - _render_table_body(dataframe, left_columns, right_columns, show_ellipsis) - ) - table_html_parts.append("
") - return "".join(table_html_parts) - - -def _render_table_header( - dataframe: pd.DataFrame, - orderable_columns: list[str], - left_columns: list[Any], - right_columns: list[Any], - show_ellipsis: bool, -) -> str: - """Render the header of the HTML table.""" - header_parts = [" ", " "] - - def render_col_header(col): - th_classes = [] - if col in orderable_columns: - th_classes.append("sortable") - class_str = f'class="{" ".join(th_classes)}"' if th_classes else "" - header_parts.append( - f'
' - f"{html.escape(str(col))}
" - ) - - for col in left_columns: - render_col_header(col) - - if show_ellipsis: - header_parts.append( - '
...
' - ) - - for col in right_columns: - render_col_header(col) - - header_parts.extend([" ", " "]) - return "\n".join(header_parts) - - -def _render_table_body( - dataframe: pd.DataFrame, - left_columns: list[Any], - right_columns: list[Any], - show_ellipsis: bool, -) -> str: - """Render the body of the HTML table.""" - body_parts = [" "] - precision = options.display.precision - - for i in range(len(dataframe)): - body_parts.append(" ") - row = dataframe.iloc[i] - - def render_col_cell(col_name): - value = row[col_name] - dtype = dataframe.dtypes.loc[col_name] # type: ignore - align = "right" if _is_dtype_numeric(dtype) else "left" - - # TODO(b/438181139): Consider semi-exploding ARRAY/STRUCT columns - # into multiple rows/columns like the BQ UI does. - if pandas.api.types.is_scalar(value) and pd.isna(value): - body_parts.append( - f' ' - '<NA>' - ) - else: - if isinstance(value, float): - cell_content = f"{value:.{precision}f}" - else: - cell_content = str(value) - body_parts.append( - f' ' - f"{html.escape(cell_content)}" - ) - - for col in left_columns: - render_col_cell(col) - - if show_ellipsis: - # Ellipsis cell - body_parts.append(' ...') - - for col in right_columns: - render_col_cell(col) - - body_parts.append(" ") - body_parts.append(" ") - return "\n".join(body_parts) - - -def _obj_ref_rt_to_html(obj_ref_rt: str) -> str: - obj_ref_rt_json = json.loads(obj_ref_rt) - obj_ref_details = obj_ref_rt_json["objectref"]["details"] - if "gcs_metadata" in obj_ref_details: - gcs_metadata = obj_ref_details["gcs_metadata"] - content_type = typing.cast(str, gcs_metadata.get("content_type", "")) - if content_type.startswith("image"): - size_str = "" - if options.display.blob_display_width: - size_str = f' width="{options.display.blob_display_width}"' - if options.display.blob_display_height: - size_str = size_str + f' height="{options.display.blob_display_height}"' - url = obj_ref_rt_json["access_urls"]["read_url"] - return f'' - - return f"uri: {obj_ref_rt_json['objectref']['uri']}, authorizer: {obj_ref_rt_json['objectref']['authorizer']}" - - -def create_html_representation( - obj: Union[bigframes.dataframe.DataFrame, bigframes.series.Series], - pandas_df: pd.DataFrame, - total_rows: int, - total_columns: int, -) -> str: - """Create an HTML representation of the DataFrame or Series.""" - import bigframes.series - - opts = options.display - with display_options.pandas_repr(opts): - if isinstance(obj, bigframes.series.Series): - pd_series = pandas_df.iloc[:, 0] - try: - html_string = pd_series._repr_html_() - except AttributeError: - html_string = f"
{pd_series.to_string()}
" - - is_truncated = total_rows is not None and total_rows > len(pandas_df) - if is_truncated: - html_string += f"

[{total_rows} rows]

" - return html_string - else: - # _repr_html_ stub is missing so mypy thinks it's a Series. Ignore mypy. - html_string = pandas_df._repr_html_() # type:ignore - - html_string += f"[{total_rows} rows x {total_columns} columns in total]" - return html_string - - -def _get_obj_metadata( - obj: Union[bigframes.dataframe.DataFrame, bigframes.series.Series], -) -> tuple[bool, bool]: - import bigframes.series - - is_series = isinstance(obj, bigframes.series.Series) - if is_series: - has_index = len(obj._block.index_columns) > 0 - else: - has_index = obj._has_index - return is_series, has_index - - -def get_anywidget_bundle( - obj: Union[bigframes.dataframe.DataFrame, bigframes.series.Series], - include=None, - exclude=None, - dry_run_info: str | None = None, -) -> tuple[dict[str, Any], dict[str, Any]]: - """ - Helper method to create and return the anywidget mimebundle. - This function encapsulates the logic for anywidget display. - """ - import bigframes.series - from bigframes import display - - if isinstance(obj, bigframes.series.Series): - df = obj.to_frame() - else: - df = obj - - from bigframes.session import deferred - - if ( - not isinstance(df, deferred.DeferredBigQueryDataFrame) - and bigframes.options.display.repr_mode != "deferred" - ): - display_df = df._prepare_display_df() - else: - display_df = df - - widget = display.TableWidget(display_df, dry_run_info=dry_run_info) - widget_repr_result = widget._repr_mimebundle_(include=include, exclude=exclude) - - if isinstance(widget_repr_result, tuple): - widget_repr, widget_metadata = widget_repr_result - else: - widget_repr = widget_repr_result - widget_metadata = {} - - widget_repr = dict(widget_repr) - - # Use cached data from widget to render HTML and plain text versions. - cached_pd = widget._cached_data - total_rows = widget.row_count - total_columns = len(df.columns) - - if dry_run_info: - widget_repr["text/plain"] = dry_run_info - else: - widget_repr["text/html"] = create_html_representation( - obj, - cached_pd, - total_rows, - total_columns, - ) - is_series, has_index = _get_obj_metadata(obj) - widget_repr["text/plain"] = plaintext.create_text_representation( - cached_pd, - total_rows, - is_series=is_series, - has_index=has_index, - column_count=len(df.columns) if not is_series else 0, - ) - - return widget_repr, widget_metadata - - -def repr_mimebundle_deferred( - obj: Union[bigframes.dataframe.DataFrame, bigframes.series.Series], -) -> dict[str, str]: - return { - "text/plain": formatter.repr_query_job(obj._compute_dry_run()), - "text/html": formatter.repr_query_job_html(obj._compute_dry_run()), - } - - -def repr_mimebundle_head( - obj: Union[bigframes.dataframe.DataFrame, bigframes.series.Series], -) -> dict[str, str]: - import bigframes.series - - opts = options.display - if isinstance(obj, bigframes.series.Series): - df = obj.to_frame() - else: - df = obj - - df = df._prepare_display_df() - pandas_df, row_count, query_job = df._block.retrieve_repr_request_results( - opts.max_rows - ) - - obj._set_internal_query_job(query_job) - column_count = len(pandas_df.columns) - - html_string = create_html_representation(obj, pandas_df, row_count, column_count) - - is_series, has_index = _get_obj_metadata(obj) - text_representation = plaintext.create_text_representation( - pandas_df, - row_count, - is_series=is_series, - has_index=has_index, - column_count=len(pandas_df.columns) if not is_series else 0, - ) - - return {"text/html": html_string, "text/plain": text_representation} - - -def repr_mimebundle( - obj: Union[bigframes.dataframe.DataFrame, bigframes.series.Series], - include=None, - exclude=None, -): - """Custom display method for IPython/Jupyter environments.""" - # TODO(b/467647693): Anywidget integration has been tested in Jupyter, VS Code, and - # BQ Studio, but there is a known compatibility issue with Marimo that needs to be addressed. - - opts = options.display - if ( - opts.render_mode == "anywidget" - or opts.repr_mode == "anywidget" - or opts.repr_mode == "deferred" - ): - try: - with bigframes.option_context("display.progress_bar", None): - with warnings.catch_warnings(): - warnings.simplefilter( - "ignore", category=bigframes.exceptions.JSONDtypeWarning - ) - warnings.simplefilter("ignore", category=FutureWarning) - dry_run_info = None - if opts.repr_mode == "deferred": - dry_run_job = obj._compute_dry_run() - dry_run_info = formatter.repr_query_job(dry_run_job) - return get_anywidget_bundle( - obj, - include=include, - exclude=exclude, - dry_run_info=dry_run_info, - ) - except Exception: - # Anywidget is an optional dependency, so warn rather than fail. - # TODO(shuowei): When Anywidget becomes the default for all repr modes, - # remove this warning. - warnings.warn( - "Anywidget mode is not available or failed to load. " - "Please `pip install anywidget traitlets` or " - "`pip install 'bigframes[anywidget]'` to use interactive tables. " - f"Falling back to static HTML. Error: {traceback.format_exc()}" - ) - if opts.repr_mode == "deferred": - return repr_mimebundle_deferred(obj) - - bundle = repr_mimebundle_head(obj) - if opts.render_mode == "plaintext": - bundle.pop("text/html", None) - - return bundle diff --git a/bigframes/display/plaintext.py b/bigframes/display/plaintext.py deleted file mode 100644 index 2f7bc1df07f..00000000000 --- a/bigframes/display/plaintext.py +++ /dev/null @@ -1,102 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -"""Plaintext display representations.""" - -from __future__ import annotations - -import typing - -import pandas -import pandas.io.formats - -from bigframes._config import display_options, options - -if typing.TYPE_CHECKING: - import pandas as pd - - -def create_text_representation( - pandas_df: pd.DataFrame, - total_rows: typing.Optional[int], - is_series: bool, - has_index: bool = True, - column_count: int = 0, -) -> str: - """Create a text representation of the DataFrame or Series. - - Args: - pandas_df: - The pandas DataFrame containing the data to represent. - total_rows: - The total number of rows in the original BigFrames object. - is_series: - Whether the object being represented is a Series. - has_index: - Whether the object has an index to display. - column_count: - The total number of columns in the original BigFrames object. - Only used for DataFrames. - - Returns: - A plaintext string representation. - """ - opts = options.display - - if is_series: - with display_options.pandas_repr(opts): - pd_series = pandas_df.iloc[:, 0] - if not has_index: - repr_string = pd_series.to_string( - length=False, index=False, name=True, dtype=True - ) - else: - repr_string = pd_series.to_string(length=False, name=True, dtype=True) - - lines = repr_string.split("\n") - is_truncated = total_rows is not None and total_rows > len(pandas_df) - - if is_truncated: - lines.append("...") - lines.append("") # Add empty line for spacing only if truncated - lines.append(f"[{total_rows} rows]") - - return "\n".join(lines) - - else: - # DataFrame - with display_options.pandas_repr(opts): - # safe to mutate this, this dict is owned by this code, and does not affect global config - to_string_kwargs = ( - pandas.io.formats.format.get_dataframe_repr_params() # type: ignore - ) - if not has_index: - to_string_kwargs.update({"index": False}) - - # We add our own dimensions string, so don't want pandas to. - to_string_kwargs.update({"show_dimensions": False}) - repr_string = pandas_df.to_string(**to_string_kwargs) - - lines = repr_string.split("\n") - is_truncated = total_rows is not None and total_rows > len(pandas_df) - - if is_truncated: - lines.append("...") - lines.append("") # Add empty line for spacing only if truncated - lines.append(f"[{total_rows or '?'} rows x {column_count} columns]") - else: - # For non-truncated DataFrames, we still need to add dimensions if show_dimensions was False - lines.append("") - lines.append(f"[{total_rows or '?'} rows x {column_count} columns]") - return "\n".join(lines) diff --git a/bigframes/display/table_widget.css b/bigframes/display/table_widget.css deleted file mode 100644 index da0a701d694..00000000000 --- a/bigframes/display/table_widget.css +++ /dev/null @@ -1,247 +0,0 @@ -/** - * Copyright 2025 Google LLC - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* Increase specificity to override framework styles without !important */ -.bigframes-widget.bigframes-widget { - /* Default Light Mode Variables */ - --bf-bg: white; - --bf-border-color: #ccc; - --bf-error-bg: #fbe; - --bf-error-border: red; - --bf-error-fg: black; - --bf-fg: black; - --bf-header-bg: #f5f5f5; - --bf-null-fg: gray; - --bf-row-even-bg: #f5f5f5; - --bf-row-odd-bg: white; - - background-color: var(--bf-bg); - box-sizing: border-box; - color: var(--bf-fg); - display: flex; - flex-direction: column; - font-family: - '-apple-system', 'BlinkMacSystemFont', 'Segoe UI', 'Roboto', sans-serif; - margin: 0; - padding: 0; -} - -.bigframes-widget * { - box-sizing: border-box; -} - -/* Dark Mode Overrides: - * 1. @media (prefers-color-scheme: dark) - System-wide dark mode - * 2. .bigframes-dark-mode - Explicit class for VSCode theme detection - * 3. html[theme="dark"], body[data-theme="dark"] - Colab/Pantheon manual override - */ -@media (prefers-color-scheme: dark) { - .bigframes-widget.bigframes-widget { - --bf-bg: var(--vscode-editor-background, #202124); - --bf-border-color: #444; - --bf-error-bg: #511; - --bf-error-border: #f88; - --bf-error-fg: #fcc; - --bf-fg: white; - --bf-header-bg: var(--vscode-editor-background, black); - --bf-null-fg: #aaa; - --bf-row-even-bg: #202124; - --bf-row-odd-bg: #383838; - } -} - -.bigframes-widget.bigframes-dark-mode.bigframes-dark-mode, -html[theme='dark'] .bigframes-widget.bigframes-widget, -body[data-theme='dark'] .bigframes-widget.bigframes-widget { - --bf-bg: var(--vscode-editor-background, #202124); - --bf-border-color: #444; - --bf-error-bg: #511; - --bf-error-border: #f88; - --bf-error-fg: #fcc; - --bf-fg: white; - --bf-header-bg: var(--vscode-editor-background, black); - --bf-null-fg: #aaa; - --bf-row-even-bg: #202124; - --bf-row-odd-bg: #383838; -} - -.bigframes-widget .table-container { - background-color: var(--bf-bg); - margin: 0; - max-height: 620px; - overflow: auto; - padding: 0; -} - -.bigframes-widget .footer { - align-items: center; - background-color: var(--bf-bg); - color: var(--bf-fg); - display: flex; - font-size: 0.8rem; - justify-content: space-between; - padding: 8px; -} - -.bigframes-widget .footer > * { - flex: 1; -} - -.bigframes-widget .pagination { - align-items: center; - display: flex; - flex-direction: row; - gap: 4px; - justify-content: center; - padding: 4px; -} - -.bigframes-widget .page-indicator { - margin: 0 8px; -} - -.bigframes-widget .row-count { - margin: 0 8px; -} - -.bigframes-widget .settings { - align-items: center; - display: flex; - flex-direction: row; - gap: 16px; - justify-content: end; -} - -.bigframes-widget .page-size, -.bigframes-widget .max-columns { - align-items: center; - display: flex; - flex-direction: row; - gap: 4px; -} - -.bigframes-widget .page-size label, -.bigframes-widget .max-columns label { - margin-right: 8px; -} - -.bigframes-widget table.bigframes-widget-table, -.bigframes-widget table.dataframe { - background-color: var(--bf-bg); - border: 1px solid var(--bf-border-color); - border-collapse: collapse; - border-spacing: 0; - box-shadow: none; - color: var(--bf-fg); - margin: 0; - outline: none; - text-align: left; - width: auto; /* Fix stretching */ -} - -.bigframes-widget tr { - border: none; -} - -.bigframes-widget th { - background-color: var(--bf-header-bg); - border: 1px solid var(--bf-border-color); - color: var(--bf-fg); - padding: 0; - position: sticky; - text-align: left; - top: 0; - z-index: 1; -} - -.bigframes-widget td { - border: 1px solid var(--bf-border-color); - color: var(--bf-fg); - padding: 0.5em; -} - -.bigframes-widget table tbody tr:nth-child(odd), -.bigframes-widget table tbody tr:nth-child(odd) td { - background-color: var(--bf-row-odd-bg); -} - -.bigframes-widget table tbody tr:nth-child(even), -.bigframes-widget table tbody tr:nth-child(even) td { - background-color: var(--bf-row-even-bg); -} - -.bigframes-widget .bf-header-content { - box-sizing: border-box; - height: 100%; - overflow: auto; - padding: 0.5em; - resize: horizontal; - width: 100%; -} - -.bigframes-widget th .sort-indicator { - padding-left: 4px; - visibility: hidden; -} - -.bigframes-widget th:hover .sort-indicator { - visibility: visible; -} - -.bigframes-widget button { - background-color: transparent; - border: 1px solid currentColor; - border-radius: 4px; - color: inherit; - cursor: pointer; - display: inline-block; - padding: 2px 8px; - text-align: center; - text-decoration: none; - user-select: none; - vertical-align: middle; -} - -.bigframes-widget button:disabled { - opacity: 0.65; - pointer-events: none; -} - -.bigframes-widget .bigframes-error-message { - background-color: var(--bf-error-bg); - border: 1px solid var(--bf-error-border); - border-radius: 4px; - color: var(--bf-error-fg); - font-size: 14px; - margin-bottom: 8px; - padding: 8px; -} - -.bigframes-widget .cell-align-right { - text-align: right; -} - -.bigframes-widget .cell-align-left { - text-align: left; -} - -.bigframes-widget .null-value { - color: var(--bf-null-fg); -} - -.bigframes-widget .debug-info { - border-top: 1px solid var(--bf-border-color); -} diff --git a/bigframes/display/table_widget.js b/bigframes/display/table_widget.js deleted file mode 100644 index 314bf771d0e..00000000000 --- a/bigframes/display/table_widget.js +++ /dev/null @@ -1,350 +0,0 @@ -/* - * Copyright 2025 Google LLC - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -const ModelProperty = { - ERROR_MESSAGE: 'error_message', - ORDERABLE_COLUMNS: 'orderable_columns', - PAGE: 'page', - PAGE_SIZE: 'page_size', - ROW_COUNT: 'row_count', - SORT_CONTEXT: 'sort_context', - TABLE_HTML: 'table_html', - MAX_COLUMNS: 'max_columns', -}; - -const Event = { - CHANGE: 'change', - CHANGE_TABLE_HTML: 'change:table_html', - CLICK: 'click', -}; - -/** - * Renders the interactive table widget. - * @param {{ model: any, el: !HTMLElement }} props - The widget properties. - */ -function render({ model, el }) { - el.classList.add('bigframes-widget'); - - const errorContainer = document.createElement('div'); - errorContainer.classList.add('error-message'); - - const tableContainer = document.createElement('div'); - tableContainer.classList.add('table-container'); - const footer = document.createElement('footer'); - footer.classList.add('footer'); - - /** Detects theme and applies necessary style overrides. */ - function updateTheme() { - const body = document.body; - const isDark = - body.classList.contains('vscode-dark') || - body.classList.contains('theme-dark') || - body.dataset.theme === 'dark' || - body.getAttribute('data-vscode-theme-kind') === 'vscode-dark'; - - if (isDark) { - el.classList.add('bigframes-dark-mode'); - } else { - el.classList.remove('bigframes-dark-mode'); - } - } - - updateTheme(); - // Re-check after mount to ensure parent styling is applied. - setTimeout(updateTheme, 300); - - const observer = new MutationObserver(updateTheme); - observer.observe(document.body, { - attributes: true, - attributeFilter: ['class', 'data-theme', 'data-vscode-theme-kind'], - }); - - // Settings controls container - const settingsContainer = document.createElement('div'); - settingsContainer.classList.add('settings'); - - // Pagination controls - const paginationContainer = document.createElement('div'); - paginationContainer.classList.add('pagination'); - const prevPage = document.createElement('button'); - const pageIndicator = document.createElement('span'); - pageIndicator.classList.add('page-indicator'); - const nextPage = document.createElement('button'); - const rowCountLabel = document.createElement('span'); - rowCountLabel.classList.add('row-count'); - - // Page size controls - const pageSizeContainer = document.createElement('div'); - pageSizeContainer.classList.add('page-size'); - const pageSizeLabel = document.createElement('label'); - const pageSizeInput = document.createElement('select'); - - prevPage.textContent = '<'; - nextPage.textContent = '>'; - pageSizeLabel.textContent = 'Page size:'; - - const pageSizes = [10, 25, 50, 100]; - for (const size of pageSizes) { - const option = document.createElement('option'); - option.value = size; - option.textContent = size; - if (size === model.get(ModelProperty.PAGE_SIZE)) { - option.selected = true; - } - pageSizeInput.appendChild(option); - } - - // Max columns controls - const maxColumnsContainer = document.createElement('div'); - maxColumnsContainer.classList.add('max-columns'); - const maxColumnsLabel = document.createElement('label'); - const maxColumnsInput = document.createElement('select'); - - maxColumnsLabel.textContent = 'Max columns:'; - - // 0 represents "All" (all columns) - const maxColumnOptions = [5, 10, 15, 20, 0]; - for (const cols of maxColumnOptions) { - const option = document.createElement('option'); - option.value = cols; - option.textContent = cols === 0 ? 'All' : cols; - - const currentMax = model.get(ModelProperty.MAX_COLUMNS); - // Handle None/null from python as 0/All - const currentMaxVal = - currentMax === null || currentMax === undefined ? 0 : currentMax; - - if (cols === currentMaxVal) { - option.selected = true; - } - maxColumnsInput.appendChild(option); - } - - function updateButtonStates() { - const currentPage = model.get(ModelProperty.PAGE); - const pageSize = model.get(ModelProperty.PAGE_SIZE); - const rowCount = model.get(ModelProperty.ROW_COUNT); - - if (rowCount === null) { - rowCountLabel.textContent = 'Total rows unknown'; - pageIndicator.textContent = `Page ${(currentPage + 1).toLocaleString()} of many`; - prevPage.disabled = currentPage === 0; - nextPage.disabled = false; - } else if (rowCount === 0) { - rowCountLabel.textContent = '0 total rows'; - pageIndicator.textContent = 'Page 1 of 1'; - prevPage.disabled = true; - nextPage.disabled = true; - } else { - const totalPages = Math.ceil(rowCount / pageSize); - rowCountLabel.textContent = `${rowCount.toLocaleString()} total rows`; - pageIndicator.textContent = `Page ${(currentPage + 1).toLocaleString()} of ${totalPages.toLocaleString()}`; - prevPage.disabled = currentPage === 0; - nextPage.disabled = currentPage >= totalPages - 1; - } - pageSizeInput.value = pageSize; - } - - function handlePageChange(direction) { - const currentPage = model.get(ModelProperty.PAGE); - model.set(ModelProperty.PAGE, currentPage + direction); - model.save_changes(); - } - - function handlePageSizeChange(newSize) { - model.set(ModelProperty.PAGE_SIZE, newSize); - model.set(ModelProperty.PAGE, 0); - model.save_changes(); - } - - let isHeightInitialized = false; - - function handleTableHTMLChange() { - tableContainer.innerHTML = model.get(ModelProperty.TABLE_HTML); - - // After the first render, dynamically set the container height to fit the - // initial page (usually 10 rows) and then lock it. - setTimeout(() => { - if (!isHeightInitialized) { - const table = tableContainer.querySelector('table'); - if (table) { - const tableHeight = table.offsetHeight; - // Add a small buffer(e.g. 2px) for borders to avoid scrollbars. - if (tableHeight > 0) { - tableContainer.style.height = `${tableHeight + 2}px`; - isHeightInitialized = true; - } - } - } - }, 0); - - const sortableColumns = model.get(ModelProperty.ORDERABLE_COLUMNS); - const currentSortContext = model.get(ModelProperty.SORT_CONTEXT) || []; - - const getSortIndex = (colName) => - currentSortContext.findIndex((item) => item.column === colName); - - const headers = tableContainer.querySelectorAll('th'); - headers.forEach((header) => { - const headerDiv = header.querySelector('div'); - const columnName = headerDiv.textContent.trim(); - - if (columnName && sortableColumns.includes(columnName)) { - header.style.cursor = 'pointer'; - - const indicatorSpan = document.createElement('span'); - indicatorSpan.classList.add('sort-indicator'); - indicatorSpan.style.paddingLeft = '5px'; - - // Determine sort indicator and initial visibility - let indicator = '●'; // Default: unsorted (dot) - const sortIndex = getSortIndex(columnName); - - if (sortIndex !== -1) { - const isAscending = currentSortContext[sortIndex].ascending; - indicator = isAscending ? '▲' : '▼'; - indicatorSpan.style.visibility = 'visible'; // Sorted arrows always visible - } else { - indicatorSpan.style.visibility = 'hidden'; - } - indicatorSpan.textContent = indicator; - - const existingIndicator = headerDiv.querySelector('.sort-indicator'); - if (existingIndicator) { - headerDiv.removeChild(existingIndicator); - } - headerDiv.appendChild(indicatorSpan); - - header.addEventListener('mouseover', () => { - if (getSortIndex(columnName) === -1) { - indicatorSpan.style.visibility = 'visible'; - } - }); - header.addEventListener('mouseout', () => { - if (getSortIndex(columnName) === -1) { - indicatorSpan.style.visibility = 'hidden'; - } - }); - - // Add click handler for three-state toggle - header.addEventListener(Event.CLICK, (event) => { - const sortIndex = getSortIndex(columnName); - let newContext = [...currentSortContext]; - - if (event.shiftKey) { - if (sortIndex !== -1) { - // Already sorted. Toggle or Remove. - if (newContext[sortIndex].ascending) { - // Asc -> Desc - // Clone object to avoid mutation issues - newContext[sortIndex] = { - ...newContext[sortIndex], - ascending: false, - }; - } else { - // Desc -> Remove - newContext.splice(sortIndex, 1); - } - } else { - // Not sorted -> Append Asc - newContext.push({ column: columnName, ascending: true }); - } - } else { - // No shift key. Single column mode. - if (sortIndex !== -1 && newContext.length === 1) { - // Already only this column. Toggle or Remove. - if (newContext[sortIndex].ascending) { - newContext[sortIndex] = { - ...newContext[sortIndex], - ascending: false, - }; - } else { - newContext = []; - } - } else { - // Start fresh with this column - newContext = [{ column: columnName, ascending: true }]; - } - } - - model.set(ModelProperty.SORT_CONTEXT, newContext); - model.save_changes(); - }); - } - }); - - updateButtonStates(); - } - - function handleErrorMessageChange() { - const errorMsg = model.get(ModelProperty.ERROR_MESSAGE); - if (errorMsg) { - errorContainer.textContent = errorMsg; - errorContainer.style.display = 'block'; - } else { - errorContainer.style.display = 'none'; - } - } - - prevPage.addEventListener(Event.CLICK, () => handlePageChange(-1)); - nextPage.addEventListener(Event.CLICK, () => handlePageChange(1)); - pageSizeInput.addEventListener(Event.CHANGE, (e) => { - const newSize = Number(e.target.value); - if (newSize) { - handlePageSizeChange(newSize); - } - }); - - maxColumnsInput.addEventListener(Event.CHANGE, (e) => { - const newVal = Number(e.target.value); - model.set(ModelProperty.MAX_COLUMNS, newVal); - model.save_changes(); - }); - - model.on(Event.CHANGE_TABLE_HTML, handleTableHTMLChange); - model.on(`change:${ModelProperty.ROW_COUNT}`, updateButtonStates); - model.on(`change:${ModelProperty.ERROR_MESSAGE}`, handleErrorMessageChange); - model.on(`change:_initial_load_complete`, (val) => { - if (val) updateButtonStates(); - }); - model.on(`change:${ModelProperty.PAGE}`, updateButtonStates); - - paginationContainer.appendChild(prevPage); - paginationContainer.appendChild(pageIndicator); - paginationContainer.appendChild(nextPage); - - pageSizeContainer.appendChild(pageSizeLabel); - pageSizeContainer.appendChild(pageSizeInput); - - maxColumnsContainer.appendChild(maxColumnsLabel); - maxColumnsContainer.appendChild(maxColumnsInput); - - settingsContainer.appendChild(maxColumnsContainer); - settingsContainer.appendChild(pageSizeContainer); - - footer.appendChild(rowCountLabel); - footer.appendChild(paginationContainer); - footer.appendChild(settingsContainer); - - el.appendChild(errorContainer); - el.appendChild(tableContainer); - el.appendChild(footer); - - handleTableHTMLChange(); - handleErrorMessageChange(); -} - -export default { render }; diff --git a/bigframes/display/table_widget_angular.js b/bigframes/display/table_widget_angular.js deleted file mode 100644 index ad1697def54..00000000000 --- a/bigframes/display/table_widget_angular.js +++ /dev/null @@ -1,20 +0,0 @@ -/* - * Copyright 2026 Google LLC - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY 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infinite;border:2px solid currentColor;border-radius:50%;border-top-color:transparent;display:inline-block;height:12px;width:12px}@keyframes _ngcontent-%COMP%_spin{to{transform:rotate(360deg)}}"]})};function $m({model:e,el:t}){let n=document.createElement("div");n.setAttribute("app-root",""),t.appendChild(n);let r={providers:[nd(),Xg(),{provide:"ANYWIDGET_MODEL",useValue:e}]};Tm(r).then(i=>{i.bootstrap(Zm,n),n.removeAttribute("app-root")}).catch(i=>console.error(i))}var Gm={render:$m};export{Gm as default}; diff --git a/bigframes/display/table_widget_angular/.editorconfig b/bigframes/display/table_widget_angular/.editorconfig deleted file mode 100644 index f166060da1c..00000000000 --- a/bigframes/display/table_widget_angular/.editorconfig +++ /dev/null @@ -1,17 +0,0 @@ -# Editor configuration, see https://editorconfig.org -root = true - -[*] -charset = utf-8 -indent_style = space -indent_size = 2 -insert_final_newline = true -trim_trailing_whitespace = true - -[*.ts] -quote_type = single -ij_typescript_use_double_quotes = false - -[*.md] -max_line_length = off -trim_trailing_whitespace = false diff --git a/bigframes/display/table_widget_angular/.gitignore b/bigframes/display/table_widget_angular/.gitignore deleted file mode 100644 index 854acd5fc03..00000000000 --- a/bigframes/display/table_widget_angular/.gitignore +++ /dev/null @@ -1,44 +0,0 @@ -# See https://docs.github.com/get-started/getting-started-with-git/ignoring-files for more about ignoring files. - -# Compiled output -/dist -/tmp -/out-tsc -/bazel-out - -# Node -/node_modules -npm-debug.log -yarn-error.log - -# IDEs and editors -.idea/ -.project -.classpath -.c9/ -*.launch -.settings/ -*.sublime-workspace - -# Visual Studio Code -.vscode/* -!.vscode/settings.json -!.vscode/tasks.json -!.vscode/launch.json -!.vscode/extensions.json -!.vscode/mcp.json -.history/* - -# Miscellaneous -/.angular/cache -.sass-cache/ -/connect.lock -/coverage -/libpeerconnection.log -testem.log -/typings -__screenshots__/ - -# System files -.DS_Store -Thumbs.db diff --git a/bigframes/display/table_widget_angular/.prettierrc b/bigframes/display/table_widget_angular/.prettierrc deleted file mode 100644 index d6c16d7ee77..00000000000 --- a/bigframes/display/table_widget_angular/.prettierrc +++ /dev/null @@ -1,12 +0,0 @@ -{ - "printWidth": 100, - "singleQuote": true, - "overrides": [ - { - "files": "*.html", - "options": { - "parser": "angular" - } - } - ] -} diff --git a/bigframes/display/table_widget_angular/README.md b/bigframes/display/table_widget_angular/README.md deleted file mode 100644 index db09b5b9f56..00000000000 --- a/bigframes/display/table_widget_angular/README.md +++ /dev/null @@ -1,48 +0,0 @@ -# TableWidgetAngular - -This project is the Angular-based interactive Table Widget frontend for BigQuery DataFrames (``bigframes``). It is integrated into the Python backend using ``anywidget``. - -This project was generated using [Angular CLI](https://github.com/angular/angular-cli) version 21.2.9. - -## Getting Started - -Ensure you have [Node.js](https://nodejs.org/) installed. - -1. Install dependencies: - ```bash - npm install - ``` - -2. Start the local development server: - ```bash - npm run start - ``` - Navigate to `http://localhost:4200/`. The application will automatically reload when you modify the source files under `src/`. - -## Development & Code Scaffolding - -To generate a new component, directive, or service: -```bash -ng generate component component-name -``` - -For a complete list of available schematics (such as `components`, `directives`, or `pipes`), run: -```bash -ng generate --help -``` - -## Running Tests - -To execute unit tests: -```bash -npm run test -``` - -## Packaging for Python - -Before testing the widget inside a Jupyter notebook or committing changes, compile the Angular app and bundle it so that the Python backend can load it: -```bash -npm run build:widget -``` - -This command compiles the project in production mode and then triggers `bundle.js` (via `esbuild`) to bundle the browser artifacts into a single unified ES module file at `../table_widget_angular.js`. diff --git a/bigframes/display/table_widget_angular/angular.json b/bigframes/display/table_widget_angular/angular.json deleted file mode 100644 index 497168c4c95..00000000000 --- a/bigframes/display/table_widget_angular/angular.json +++ /dev/null @@ -1,73 +0,0 @@ -{ - "$schema": "./node_modules/@angular/cli/lib/config/schema.json", - "version": 1, - "cli": { - "packageManager": "npm" - }, - "newProjectRoot": "projects", - "projects": { - "table-widget-angular": { - "projectType": "application", - "schematics": {}, - "root": "", - "sourceRoot": "src", - "prefix": "app", - "architect": { - "build": { - "builder": "@angular/build:application", - "options": { - "browser": "src/main.ts", - "tsConfig": "tsconfig.app.json", - "assets": [ - { - "glob": "**/*", - "input": "public" - } - ], - "styles": [ - "src/styles.css" - ] - }, - "configurations": { - "production": { - "budgets": [ - { - "type": "initial", - "maximumWarning": "500kB", - "maximumError": "1MB" - }, - { - "type": "anyComponentStyle", - "maximumWarning": "4kB", - "maximumError": "8kB" - } - ], - "outputHashing": "all" - }, - "development": { - "optimization": false, - "extractLicenses": false, - "sourceMap": true - } - }, - "defaultConfiguration": "production" - }, - "serve": { - "builder": "@angular/build:dev-server", - "configurations": { - "production": { - "buildTarget": "table-widget-angular:build:production" - }, - "development": { - "buildTarget": "table-widget-angular:build:development" - } - }, - "defaultConfiguration": "development" - }, - "test": { - "builder": "@angular/build:unit-test" - } - } - } - } -} diff --git a/bigframes/display/table_widget_angular/bundle.js b/bigframes/display/table_widget_angular/bundle.js deleted file mode 100644 index fb97ab8a376..00000000000 --- a/bigframes/display/table_widget_angular/bundle.js +++ /dev/null @@ -1,47 +0,0 @@ -/* - * Copyright 2026 Google LLC - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -const esbuild = require('esbuild'); -const path = require('path'); - -const banner = `/* - * Copyright 2026 Google LLC - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -`; - -esbuild.build({ - entryPoints: [path.resolve(__dirname, 'dist/table-widget-angular/browser/main.js')], - bundle: true, - outfile: path.resolve(__dirname, '../table_widget_angular.js'), - format: 'esm', - logLevel: 'info', - minify: true, - banner: { - js: banner, - }, -}).catch(() => process.exit(1)); diff --git a/bigframes/display/table_widget_angular/package-lock.json b/bigframes/display/table_widget_angular/package-lock.json deleted file mode 100644 index 33540ab8512..00000000000 --- a/bigframes/display/table_widget_angular/package-lock.json +++ /dev/null @@ -1,9591 +0,0 @@ -{ - "name": "table-widget-angular", - "version": "0.0.0", - "lockfileVersion": 3, - "requires": true, - "packages": { - "": { - "name": "table-widget-angular", - "version": "0.0.0", - "dependencies": { - "@angular/common": "^22.1.0", - "@angular/compiler": "^22.1.0", - "@angular/core": "^22.1.0", - "@angular/forms": "^22.1.0", - "@angular/platform-browser": "^22.1.0", - "@angular/router": "^22.1.0", - "rxjs": "~7.8.0", - "tslib": "^2.3.0" - }, - "devDependencies": { - "@angular/build": "^22.1.2", - "@angular/cli": "^21.2.16", - "@angular/compiler-cli": "^22.1.0", - "esbuild": "^0.28.0", - "jsdom": "^28.0.0", - "prettier": "^3.8.1", - 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"name": "table-widget-angular", - "version": "0.0.0", - "scripts": { - "ng": "ng", - "start": "ng serve", - "build": "ng build", - "watch": "ng build --watch --configuration development", - "test": "ng test", - "build:widget": "ng build --output-hashing none && node bundle.js" - }, - "private": true, - "packageManager": "npm@11.7.0", - "dependencies": { - "@angular/common": "^22.1.0", - "@angular/compiler": "^22.1.0", - "@angular/core": "^22.1.0", - "@angular/forms": "^22.1.0", - "@angular/platform-browser": "^22.1.0", - "@angular/router": "^22.1.0", - "rxjs": "~7.8.0", - "tslib": "^2.3.0" - }, - "devDependencies": { - "@angular/build": "^22.1.2", - "@angular/cli": "^21.2.16", - "@angular/compiler-cli": "^22.1.0", - "esbuild": "^0.28.0", - "jsdom": "^28.0.0", - "prettier": "^3.8.1", - "typescript": "~5.9.2", - "vitest": "^4.0.8" - } -} diff --git a/bigframes/display/table_widget_angular/public/favicon.ico b/bigframes/display/table_widget_angular/public/favicon.ico deleted file mode 100644 index 57614f9c967..00000000000 Binary files a/bigframes/display/table_widget_angular/public/favicon.ico and /dev/null differ diff --git a/bigframes/display/table_widget_angular/src/app/app.spec.ts b/bigframes/display/table_widget_angular/src/app/app.spec.ts deleted file mode 100644 index 75ccf03e436..00000000000 --- a/bigframes/display/table_widget_angular/src/app/app.spec.ts +++ /dev/null @@ -1,40 +0,0 @@ -/* - * Copyright 2026 Google LLC - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -import { TestBed } from '@angular/core/testing'; -import { App } from './app'; - -describe('App', () => { - beforeEach(async () => { - await TestBed.configureTestingModule({ - imports: [App], - providers: [{ provide: 'ANYWIDGET_MODEL', useValue: null }] - }).compileComponents(); - }); - - it('should create the app', () => { - const fixture = TestBed.createComponent(App); - const app = fixture.componentInstance; - expect(app).toBeTruthy(); - }); - - it('should render the table container', async () => { - const fixture = TestBed.createComponent(App); - fixture.detectChanges(); - const compiled = fixture.nativeElement as HTMLElement; - expect(compiled.querySelector('.table-container')).toBeTruthy(); - }); -}); diff --git a/bigframes/display/table_widget_angular/src/app/app.ts b/bigframes/display/table_widget_angular/src/app/app.ts deleted file mode 100644 index 60b94d30e78..00000000000 --- a/bigframes/display/table_widget_angular/src/app/app.ts +++ /dev/null @@ -1,704 +0,0 @@ -/* - * Copyright 2026 Google LLC - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -import { Component, ElementRef, ViewChild, computed, effect, inject, signal } from '@angular/core'; -import { DomSanitizer } from '@angular/platform-browser'; -import { WidgetStateService } from './widget-state.service'; - -@Component({ - selector: '[app-root]', - standalone: true, - imports: [], - providers: [WidgetStateService], - template: ` -
- @if (errorMessage()) { -
{{ errorMessage() }}
- } - - @if (isDeferredMode()) { -
-
-

{{ dryRunInfo() }}

- -
-
- } @else { -
-
- -
- {{ rowCountText() }} - - - -
-
- - -
- -
- - -
-
-
- } -
- `, - styles: [` - /* Increase specificity to override framework styles without !important */ - .bigframes-widget.bigframes-widget { - /* Default Light Mode Variables */ - --bf-bg: white; - --bf-border-color: #ccc; - --bf-error-bg: #fbe; - --bf-error-border: red; - --bf-error-fg: black; - --bf-fg: black; - --bf-header-bg: #f5f5f5; - --bf-null-fg: gray; - --bf-row-even-bg: #f5f5f5; - --bf-row-odd-bg: white; - - background-color: var(--bf-bg); - box-sizing: border-box; - color: var(--bf-fg); - display: flex; - flex-direction: column; - font-family: - '-apple-system', 'BlinkMacSystemFont', 'Segoe UI', 'Roboto', sans-serif; - margin: 0; - padding: 0; - width: 100%; - } - - .bigframes-widget * { - box-sizing: border-box; - } - - /* Dark Mode Overrides */ - @media (prefers-color-scheme: dark) { - .bigframes-widget.bigframes-widget { - --bf-bg: var(--vscode-editor-background, #202124); - --bf-border-color: #444; - --bf-error-bg: #511; - --bf-error-border: #f88; - --bf-error-fg: #fcc; - --bf-fg: white; - --bf-header-bg: var(--vscode-editor-background, black); - --bf-null-fg: #aaa; - --bf-row-even-bg: #202124; - --bf-row-odd-bg: #383838; - } - } - - .bigframes-widget.bigframes-dark-mode.bigframes-dark-mode { - --bf-bg: var(--vscode-editor-background, #202124); - --bf-border-color: #444; - --bf-error-bg: #511; - --bf-error-border: #f88; - --bf-error-fg: #fcc; - --bf-fg: white; - --bf-header-bg: var(--vscode-editor-background, black); - --bf-null-fg: #aaa; - --bf-row-even-bg: #202124; - --bf-row-odd-bg: #383838; - } - - .bigframes-widget .table-container { - background-color: var(--bf-bg); - margin: 0; - overflow: auto; - padding: 0; - } - - .bigframes-widget .footer { - align-items: center; - background-color: var(--bf-bg); - color: var(--bf-fg); - display: flex; - font-size: 0.8rem; - justify-content: space-between; - padding: 8px; - } - - .bigframes-widget .footer > * { - flex: 1; - } - - .bigframes-widget .pagination { - align-items: center; - display: flex; - flex-direction: row; - gap: 4px; - justify-content: center; - padding: 4px; - } - - .bigframes-widget .page-indicator { - margin: 0 8px; - } - - .bigframes-widget .row-count { - margin: 0 8px; - } - - .bigframes-widget .settings { - align-items: center; - display: flex; - flex-direction: row; - gap: 16px; - justify-content: end; - } - - .bigframes-widget .page-size, - .bigframes-widget .max-columns { - align-items: center; - display: flex; - flex-direction: row; - gap: 4px; - } - - .bigframes-widget .page-size label, - .bigframes-widget .max-columns label { - margin-right: 8px; - } - - /* Dynamic internal elements styles */ - .bigframes-widget ::ng-deep table.bigframes-widget-table, - .bigframes-widget ::ng-deep table.dataframe { - background-color: var(--bf-bg); - border: 1px solid var(--bf-border-color); - border-collapse: collapse; - border-spacing: 0; - box-shadow: none; - color: var(--bf-fg); - margin: 0; - outline: none; - text-align: left; - width: auto; - } - - .bigframes-widget ::ng-deep tr { - border: none; - } - - .bigframes-widget ::ng-deep th { - background-color: var(--bf-header-bg); - border: 1px solid var(--bf-border-color); - color: var(--bf-fg); - padding: 0; - position: sticky; - text-align: left; - top: 0; - z-index: 1; - } - - .bigframes-widget ::ng-deep td { - border: 1px solid var(--bf-border-color); - color: var(--bf-fg); - padding: 0.5em; - } - - .bigframes-widget ::ng-deep table tbody tr:nth-child(odd), - .bigframes-widget ::ng-deep table tbody tr:nth-child(odd) td { - background-color: var(--bf-row-odd-bg); - } - - .bigframes-widget ::ng-deep table tbody tr:nth-child(even), - .bigframes-widget ::ng-deep table tbody tr:nth-child(even) td { - background-color: var(--bf-row-even-bg); - } - - .bigframes-widget ::ng-deep .bf-header-content { - box-sizing: border-box; - height: 100%; - overflow: auto; - padding: 0.5em; - resize: horizontal; - width: 100%; - } - - .bigframes-widget ::ng-deep th .sort-indicator { - padding-left: 4px; - visibility: hidden; - } - - .bigframes-widget ::ng-deep th:hover .sort-indicator { - visibility: visible; - } - - .bigframes-widget button { - background-color: transparent; - border: 1px solid currentColor; - border-radius: 4px; - color: inherit; - cursor: pointer; - display: inline-block; - padding: 2px 8px; - text-align: center; - text-decoration: none; - user-select: none; - vertical-align: middle; - } - - .bigframes-widget button:disabled { - opacity: 0.65; - pointer-events: none; - } - - .bigframes-widget .bigframes-error-message { - background-color: var(--bf-error-bg); - border: 1px solid var(--bf-error-border); - border-radius: 4px; - color: var(--bf-error-fg); - font-size: 14px; - margin-bottom: 8px; - padding: 8px; - } - - .bigframes-widget ::ng-deep .cell-align-right { - text-align: right; - } - - .bigframes-widget ::ng-deep .cell-align-left { - text-align: left; - } - - .bigframes-widget ::ng-deep .null-value { - color: var(--bf-null-fg); - } - - .bigframes-widget ::ng-deep .debug-info { - border-top: 1px solid var(--bf-border-color); - } - - .bigframes-widget .deferred-container { - align-items: center; - display: flex; - justify-content: center; - min-height: 220px; - padding: 24px; - width: 100%; - } - - .bigframes-widget .deferred-card { - background: linear-gradient( - 135deg, - rgba(255, 255, 255, 0.6), - rgba(255, 255, 255, 0.3) - ); - border: 1px solid rgba(255, 255, 255, 0.4); - border-radius: 16px; - box-shadow: 0 8px 32px 0 rgba(31, 38, 135, 0.07); - display: flex; - flex-direction: column; - gap: 16px; - max-width: 500px; - padding: 32px; - text-align: center; - transition: all 0.3s ease-in-out; - } - - .bigframes-widget.bigframes-dark-mode .deferred-card { - background: linear-gradient( - 135deg, - rgba(32, 33, 36, 0.6), - rgba(32, 33, 36, 0.3) - ); - border: 1px solid rgba(255, 255, 255, 0.1); - box-shadow: 0 8px 32px 0 rgba(0, 0, 0, 0.3); - } - - @media (prefers-color-scheme: dark) { - .bigframes-widget .deferred-card { - background: linear-gradient( - 135deg, - rgba(32, 33, 36, 0.6), - rgba(32, 33, 36, 0.3) - ); - border: 1px solid rgba(255, 255, 255, 0.1); - box-shadow: 0 8px 32px 0 rgba(0, 0, 0, 0.3); - } - } - - .bigframes-widget .deferred-title { - font-size: 1.1rem; - font-weight: 600; - margin: 0; - } - - .bigframes-widget .deferred-estimate { - color: var(--bf-null-fg); - font-size: 0.9rem; - margin: 0; - } - - .bigframes-widget .run-query-button { - align-items: center; - background-color: var(--bf-fg); - border: 1px solid var(--bf-fg); - border-radius: 8px; - color: var(--bf-bg); - cursor: pointer; - display: inline-flex; - font-size: 14px; - font-weight: 600; - gap: 8px; - justify-content: center; - padding: 10px 20px; - transition: transform 0.20s ease, opacity 0.20s ease; - } - - .bigframes-widget .run-query-button:hover { - opacity: 0.90; - transform: translateY(-1px); - } - - .bigframes-widget .run-query-button:active { - transform: translateY(0); - } - - .bigframes-widget .run-query-button:disabled { - cursor: not-allowed; - opacity: 0.60; - } - - .bigframes-widget .spinner { - animation: spin 1s linear infinite; - border: 2px solid currentColor; - border-radius: 50%; - border-top-color: transparent; - display: inline-block; - height: 12px; - width: 12px; - } - - @keyframes spin { - to { - transform: rotate(360deg); - } - } - `] -}) -export class App { - protected readonly state = inject(WidgetStateService); - private readonly sanitizer = inject(DomSanitizer); - - protected readonly maxColumnOptions = [5, 10, 15, 20, 0]; - protected readonly pageSizeOptions = [10, 25, 50, 100]; - - // State signals - protected readonly errorMessage = this.state.errorMessage; - protected readonly maxColumns = this.state.maxColumns; - protected readonly pageSize = this.state.pageSize; - protected readonly page = this.state.page; - protected readonly rowCount = this.state.rowCount; - protected readonly isDeferredMode = this.state.isDeferredMode; - protected readonly dryRunInfo = this.state.dryRunInfo; - protected readonly isLoading = signal(false); - - // Computed properties for formatting and display states - protected readonly sanitizedHtml = computed(() => - this.sanitizer.bypassSecurityTrustHtml(this.state.tableHtml()) - ); - - protected readonly totalPages = computed(() => { - const count = this.rowCount(); - const size = this.pageSize(); - return count !== null && size > 0 ? Math.ceil(count / size) : null; - }); - - protected readonly pageIndicatorText = computed(() => { - const currentPage = this.page(); - const count = this.rowCount(); - const total = this.totalPages(); - const currentStr = (currentPage + 1).toLocaleString(); - const totalStr = (total ?? 1).toLocaleString(); - return `Page ${currentStr} of ${totalStr}`; - }); - - protected readonly rowCountText = computed(() => { - const count = this.rowCount(); - if (count === null) { - return 'Total rows unknown'; - } - if (count === 0) { - return '0 total rows'; - } - return `${count.toLocaleString()} total rows`; - }); - - protected readonly prevPageDisabled = computed(() => this.page() === 0); - - protected readonly nextPageDisabled = computed(() => { - const currentPage = this.page(); - const count = this.rowCount(); - const total = this.totalPages(); - if (count === null) { - return false; - } - if (count === 0) { - return true; - } - return total !== null && currentPage >= total - 1; - }); - - protected readonly isDarkMode = signal(false); - private themeObserver: MutationObserver | null = null; - - @ViewChild('tableContainer') - tableContainerRef!: ElementRef; - - private isHeightInitialized = false; - - constructor() { - effect(() => { - // Setup dependencies for reactive effect - const _html = this.state.tableHtml(); - const _sort = this.state.sortContext(); - const _orderable = this.state.orderableColumns(); - const deferred = this.isDeferredMode(); - if (deferred) { - this.isHeightInitialized = false; - } - - // Schedule DOM post-processing once the innerHTML render completes - setTimeout(() => { - this.applySortIndicators(); - this.lockInitialHeight(); - }, 0); - }); - - effect(() => { - if (!this.state.startExecution()) { - this.isLoading.set(false); - } - }); - - effect((onCleanup) => { - const executing = this.state.startExecution(); - if (executing) { - const intervalId = setInterval(() => { - if (this.state.startExecution()) { - const currentPing = this.state.ping(); - this.state.setPing(currentPing + 1); - } else { - clearInterval(intervalId); - } - }, 500); - onCleanup(() => { - clearInterval(intervalId); - }); - } - }); - } - - ngOnInit() { - this.initThemeDetection(); - } - - ngOnDestroy() { - this.themeObserver?.disconnect(); - } - - protected handleRunQuery() { - this.isLoading.set(true); - this.state.setStartExecution(true); - } - - protected handlePageChange(direction: number) { - const nextPage = this.page() + direction; - this.state.setPage(nextPage); - } - - protected handlePageSizeChange(event: Event) { - const select = event.target as HTMLSelectElement; - const newSize = Number(select.value); - if (newSize) { - this.state.setPageSize(newSize); - } - } - - protected handleMaxColumnsChange(event: Event) { - const select = event.target as HTMLSelectElement; - const maxCols = Number(select.value); - this.state.setMaxColumns(maxCols); - } - - protected handleTableClick(event: MouseEvent) { - const target = event.target as HTMLElement; - const header = target.closest('th'); - if (!header) return; - - const headerDiv = header.querySelector( - 'div.bf-header-content' - ) as HTMLElement | null; - if (!headerDiv) return; - - const columnName = this.getColumnName(headerDiv); - const sortableColumns = this.state.orderableColumns(); - if (!columnName || !sortableColumns.includes(columnName)) return; - - const currentSortContext = [...this.state.sortContext()]; - const sortIndex = currentSortContext.findIndex( - (item) => item.column === columnName - ); - let newContext = [...currentSortContext]; - - if (event.shiftKey) { - if (sortIndex !== -1) { - // Toggle: Asc -> Desc -> Unsorted - if (newContext[sortIndex].ascending) { - newContext[sortIndex] = { - ...newContext[sortIndex], - ascending: false - }; - } else { - newContext.splice(sortIndex, 1); - } - } else { - newContext.push({ column: columnName, ascending: true }); - } - } else { - // Single column sort mode - if (sortIndex !== -1 && newContext.length === 1) { - // Toggle: Asc -> Desc -> Unsorted - if (newContext[sortIndex].ascending) { - newContext[sortIndex] = { - ...newContext[sortIndex], - ascending: false - }; - } else { - newContext = []; - } - } else { - newContext = [{ column: columnName, ascending: true }]; - } - } - - this.state.setSortContext(newContext); - } - - private getColumnName(headerDiv: HTMLElement): string { - const clone = headerDiv.cloneNode(true) as HTMLElement; - clone.querySelector('.sort-indicator')?.remove(); - return clone.textContent?.trim() || ''; - } - - private applySortIndicators() { - const container = this.tableContainerRef?.nativeElement; - if (!container) return; - - const sortableColumns = this.state.orderableColumns(); - const currentSortContext = this.state.sortContext() || []; - - const getSortIndex = (colName: string) => - currentSortContext.findIndex((item) => item.column === colName); - - const headers = container.querySelectorAll('th'); - headers.forEach((header: HTMLElement) => { - const headerDiv = header.querySelector( - 'div.bf-header-content' - ) as HTMLElement | null; - if (!headerDiv) return; - - const columnName = this.getColumnName(headerDiv); - if (columnName && sortableColumns.includes(columnName)) { - - let indicatorSpan = headerDiv.querySelector( - '.sort-indicator' - ) as HTMLElement; - if (!indicatorSpan) { - indicatorSpan = document.createElement('span'); - indicatorSpan.classList.add('sort-indicator'); - indicatorSpan.style.paddingLeft = '5px'; - headerDiv.appendChild(indicatorSpan); - } - - const sortIndex = getSortIndex(columnName); - if (sortIndex !== -1) { - const isAscending = currentSortContext[sortIndex].ascending; - indicatorSpan.textContent = isAscending ? '▲' : '▼'; - indicatorSpan.style.visibility = 'visible'; - } else { - indicatorSpan.textContent = '●'; - indicatorSpan.style.visibility = 'hidden'; - } - } - }); - } - - private lockInitialHeight() { - if (this.isHeightInitialized) return; - const container = this.tableContainerRef?.nativeElement; - if (!container) return; - - const table = container.querySelector('table'); - if (table && (table as HTMLElement).offsetHeight > 0) { - const currentHeight = container.offsetHeight; - if (currentHeight > 0) { - container.style.height = `${currentHeight}px`; - this.isHeightInitialized = true; - } - } - } - - private initThemeDetection() { - this.updateTheme(); - const observer = new MutationObserver(() => this.updateTheme()); - observer.observe(document.body, { - attributes: true, - attributeFilter: ['class', 'data-theme', 'data-vscode-theme-kind'], - }); - this.themeObserver = observer; - } - - private updateTheme() { - const body = document.body; - const isDark = - body.classList.contains('vscode-dark') || - body.classList.contains('theme-dark') || - body.dataset['theme'] === 'dark' || - body.getAttribute('data-vscode-theme-kind') === 'vscode-dark'; - this.isDarkMode.set(isDark); - } -} diff --git a/bigframes/display/table_widget_angular/src/app/widget-state.service.spec.ts b/bigframes/display/table_widget_angular/src/app/widget-state.service.spec.ts deleted file mode 100644 index 563f9fa75a5..00000000000 --- a/bigframes/display/table_widget_angular/src/app/widget-state.service.spec.ts +++ /dev/null @@ -1,129 +0,0 @@ -/* - * Copyright 2026 Google LLC - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -import { TestBed } from '@angular/core/testing'; -import { vi } from 'vitest'; -import { WidgetStateService } from './widget-state.service'; - -describe('WidgetStateService', () => { - let service: WidgetStateService; - let mockModel: any; - let mockListeners: { [key: string]: Function }; - - beforeEach(() => { - mockListeners = {}; - mockModel = { - get: vi.fn().mockImplementation((prop: string) => { - if (prop === 'page') return 2; - if (prop === 'page_size') return 25; - if (prop === 'max_columns') return 10; - if (prop === 'row_count') return 150; - if (prop === 'table_html') return '
'; - if (prop === 'sort_context') { - return [{ column: 'col1', ascending: true }]; - } - if (prop === 'orderable_columns') { - return ['col1', 'col2']; - } - if (prop === 'error_message') return 'initial error'; - return null; - }), - set: vi.fn(), - save_changes: vi.fn(), - on: vi.fn().mockImplementation( - (event: string, callback: Function) => { - mockListeners[event] = callback; - } - ) - }; - - TestBed.configureTestingModule({ - providers: [ - WidgetStateService, - { provide: 'ANYWIDGET_MODEL', useValue: mockModel } - ] - }); - service = TestBed.inject(WidgetStateService); - }); - - it('should be created', () => { - expect(service).toBeTruthy(); - }); - - it('should initialize signals from model values', () => { - expect(service.page()).toBe(2); - expect(service.pageSize()).toBe(25); - expect(service.maxColumns()).toBe(10); - expect(service.rowCount()).toBe(150); - expect(service.tableHtml()).toBe('
'); - expect(service.sortContext()).toEqual([ - { column: 'col1', ascending: true } - ]); - expect(service.orderableColumns()).toEqual(['col1', 'col2']); - expect(service.errorMessage()).toBe('initial error'); - }); - - it('should update signals when model triggers change events', () => { - mockModel.get.mockImplementation((prop: string) => { - if (prop === 'page') return 5; - if (prop === 'page_size') return 50; - return null; - }); - - mockListeners['change:page'](); - mockListeners['change:page_size'](); - - expect(service.page()).toBe(5); - expect(service.pageSize()).toBe(50); - }); - - it('should support dual-listen pattern for error messages', () => { - // 1. Check error_message change - mockModel.get.mockImplementation((prop: string) => { - if (prop === 'error_message') return 'new error'; - return null; - }); - mockListeners['change:error_message'](); - expect(service.errorMessage()).toBe('new error'); - - // 2. Check _error_message change - mockModel.get.mockImplementation((prop: string) => { - if (prop === '_error_message') return 'new private error'; - return null; - }); - mockListeners['change:_error_message'](); - expect(service.errorMessage()).toBe('new private error'); - }); - - it('should write updates back to model on setter methods', () => { - service.setPage(4); - expect(mockModel.set).toHaveBeenCalledWith('page', 4); - expect(mockModel.save_changes).toHaveBeenCalled(); - - service.setPageSize(100); - expect(mockModel.set).toHaveBeenCalledWith('page_size', 100); - expect(mockModel.set).toHaveBeenCalledWith('page', 0); - - service.setMaxColumns(15); - expect(mockModel.set).toHaveBeenCalledWith('max_columns', 15); - - service.setSortContext([{ column: 'col2', ascending: false }]); - expect(mockModel.set).toHaveBeenCalledWith( - 'sort_context', - [{ column: 'col2', ascending: false }] - ); - }); -}); diff --git a/bigframes/display/table_widget_angular/src/app/widget-state.service.ts b/bigframes/display/table_widget_angular/src/app/widget-state.service.ts deleted file mode 100644 index 54eff6eb948..00000000000 --- a/bigframes/display/table_widget_angular/src/app/widget-state.service.ts +++ /dev/null @@ -1,157 +0,0 @@ -/* - * Copyright 2026 Google LLC - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -import { Injectable, Inject, signal } from '@angular/core'; - -export interface SortItem { - column: string; - ascending: boolean; -} - -@Injectable() -export class WidgetStateService { - readonly page = signal(0); - readonly pageSize = signal(10); - readonly maxColumns = signal(0); - readonly rowCount = signal(null); - readonly tableHtml = signal(''); - readonly sortContext = signal([]); - readonly orderableColumns = signal([]); - readonly errorMessage = signal(null); - readonly startExecution = signal(false); - readonly isDeferredMode = signal(false); - readonly dryRunInfo = signal(''); - readonly ping = signal(0); - - constructor(@Inject('ANYWIDGET_MODEL') private model: any) { - if (model) { - // Initialize from the model - this.page.set(model.get('page') ?? 0); - this.pageSize.set(model.get('page_size') ?? 10); - this.maxColumns.set(model.get('max_columns') ?? 0); - this.rowCount.set(model.get('row_count') ?? null); - this.tableHtml.set(model.get('table_html') ?? ''); - this.sortContext.set(model.get('sort_context') ?? []); - this.orderableColumns.set(model.get('orderable_columns') ?? []); - const initialError = - model.get('error_message') ?? - model.get('_error_message') ?? - null; - this.errorMessage.set(initialError); - this.startExecution.set(model.get('start_execution') ?? false); - this.isDeferredMode.set(model.get('is_deferred_mode') ?? false); - this.dryRunInfo.set(model.get('dry_run_info') ?? ''); - this.ping.set(model.get('ping') ?? 0); - - // Register event listeners for anywidget updates - model.on('change:page', () => { - this.page.set(model.get('page')); - }); - model.on('change:page_size', () => { - this.pageSize.set(model.get('page_size')); - }); - model.on('change:max_columns', () => { - this.maxColumns.set(model.get('max_columns')); - }); - model.on('change:row_count', () => { - this.rowCount.set(model.get('row_count')); - }); - model.on('change:table_html', () => { - this.tableHtml.set(model.get('table_html')); - }); - model.on('change:sort_context', () => { - this.sortContext.set(model.get('sort_context')); - }); - model.on('change:orderable_columns', () => { - this.orderableColumns.set(model.get('orderable_columns')); - }); - model.on('change:start_execution', () => { - this.startExecution.set(model.get('start_execution') ?? false); - }); - model.on('change:is_deferred_mode', () => { - this.isDeferredMode.set(model.get('is_deferred_mode') ?? false); - }); - model.on('change:dry_run_info', () => { - this.dryRunInfo.set(model.get('dry_run_info') ?? ''); - }); - model.on('change:ping', () => { - this.ping.set(model.get('ping') ?? 0); - }); - - // Robust dual-listen pattern for error messages (with/without underscore) - const handleErrorChange = () => { - const err = - model.get('error_message') ?? - model.get('_error_message') ?? - null; - this.errorMessage.set(err); - }; - model.on('change:error_message', handleErrorChange); - model.on('change:_error_message', handleErrorChange); - } - } - - setPage(page: number) { - this.page.set(page); - if (this.model) { - this.model.set('page', page); - this.model.save_changes(); - } - } - - setPageSize(pageSize: number) { - this.pageSize.set(pageSize); - this.page.set(0); - if (this.model) { - this.model.set('page_size', pageSize); - // Reset to page 0 on page size change - this.model.set('page', 0); - this.model.save_changes(); - } - } - - setMaxColumns(maxColumns: number) { - this.maxColumns.set(maxColumns); - if (this.model) { - this.model.set('max_columns', maxColumns); - this.model.save_changes(); - } - } - - setSortContext(context: SortItem[]) { - this.sortContext.set(context); - if (this.model) { - this.model.set('sort_context', context); - this.model.save_changes(); - } - } - - setStartExecution(startExecution: boolean) { - this.startExecution.set(startExecution); - if (this.model) { - this.model.set('start_execution', startExecution); - this.model.save_changes(); - } - } - - setPing(ping: number) { - this.ping.set(ping); - if (this.model) { - this.model.set('ping', ping); - this.model.save_changes(); - } - } -} diff --git a/bigframes/display/table_widget_angular/src/index.html b/bigframes/display/table_widget_angular/src/index.html deleted file mode 100644 index f5dda01b48a..00000000000 --- a/bigframes/display/table_widget_angular/src/index.html +++ /dev/null @@ -1,29 +0,0 @@ - - - - - - - TableWidgetAngular - - - - - -
- - diff --git a/bigframes/display/table_widget_angular/src/main.ts b/bigframes/display/table_widget_angular/src/main.ts deleted file mode 100644 index 3d515bb3d34..00000000000 --- a/bigframes/display/table_widget_angular/src/main.ts +++ /dev/null @@ -1,43 +0,0 @@ -/* - * Copyright 2026 Google LLC - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -import { createApplication } from '@angular/platform-browser'; -import { App } from './app/app'; -import { ApplicationConfig, provideBrowserGlobalErrorListeners, provideZonelessChangeDetection } from '@angular/core'; - -function render({ model, el }: { model: any, el: HTMLElement }) { - // Create a container for the Angular app - const appRoot = document.createElement('div'); - appRoot.setAttribute('app-root', ''); - el.appendChild(appRoot); - - const appConfig: ApplicationConfig = { - providers: [ - provideBrowserGlobalErrorListeners(), - provideZonelessChangeDetection(), - { provide: 'ANYWIDGET_MODEL', useValue: model } - ] - }; - - createApplication(appConfig) - .then((appRef) => { - appRef.bootstrap(App, appRoot); - appRoot.removeAttribute('app-root'); - }) - .catch((err) => console.error(err)); -} - -export default { render }; diff --git a/bigframes/display/table_widget_angular/src/styles.css b/bigframes/display/table_widget_angular/src/styles.css deleted file mode 100644 index 95b248dae0a..00000000000 --- a/bigframes/display/table_widget_angular/src/styles.css +++ /dev/null @@ -1,17 +0,0 @@ -/* - * Copyright 2026 Google LLC - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* You can add global styles to this file, and also import other style files */ diff --git a/bigframes/display/table_widget_angular/tsconfig.app.json b/bigframes/display/table_widget_angular/tsconfig.app.json deleted file mode 100644 index 264f459bf87..00000000000 --- a/bigframes/display/table_widget_angular/tsconfig.app.json +++ /dev/null @@ -1,15 +0,0 @@ -/* To learn more about Typescript configuration file: https://www.typescriptlang.org/docs/handbook/tsconfig-json.html. */ -/* To learn more about Angular compiler options: https://angular.dev/reference/configs/angular-compiler-options. */ -{ - "extends": "./tsconfig.json", - "compilerOptions": { - "outDir": "./out-tsc/app", - "types": [] - }, - "include": [ - "src/**/*.ts" - ], - "exclude": [ - "src/**/*.spec.ts" - ] -} diff --git a/bigframes/display/table_widget_angular/tsconfig.json b/bigframes/display/table_widget_angular/tsconfig.json deleted file mode 100644 index 2ab7442758f..00000000000 --- a/bigframes/display/table_widget_angular/tsconfig.json +++ /dev/null @@ -1,33 +0,0 @@ -/* To learn more about Typescript configuration file: https://www.typescriptlang.org/docs/handbook/tsconfig-json.html. */ -/* To learn more about Angular compiler options: https://angular.dev/reference/configs/angular-compiler-options. */ -{ - "compileOnSave": false, - "compilerOptions": { - "strict": true, - "noImplicitOverride": true, - "noPropertyAccessFromIndexSignature": true, - "noImplicitReturns": true, - "noFallthroughCasesInSwitch": true, - "skipLibCheck": true, - "isolatedModules": true, - "experimentalDecorators": true, - "importHelpers": true, - "target": "ES2022", - "module": "preserve" - }, - "angularCompilerOptions": { - "enableI18nLegacyMessageIdFormat": false, - "strictInjectionParameters": true, - "strictInputAccessModifiers": true, - "strictTemplates": true - }, - "files": [], - "references": [ - { - "path": "./tsconfig.app.json" - }, - { - "path": "./tsconfig.spec.json" - } - ] -} diff --git a/bigframes/display/table_widget_angular/tsconfig.spec.json b/bigframes/display/table_widget_angular/tsconfig.spec.json deleted file mode 100644 index d38370633f6..00000000000 --- a/bigframes/display/table_widget_angular/tsconfig.spec.json +++ /dev/null @@ -1,15 +0,0 @@ -/* To learn more about Typescript configuration file: https://www.typescriptlang.org/docs/handbook/tsconfig-json.html. */ -/* To learn more about Angular compiler options: https://angular.dev/reference/configs/angular-compiler-options. */ -{ - "extends": "./tsconfig.json", - "compilerOptions": { - "outDir": "./out-tsc/spec", - "types": [ - "vitest/globals" - ] - }, - "include": [ - "src/**/*.d.ts", - "src/**/*.spec.ts" - ] -} diff --git a/bigframes/dtypes.py b/bigframes/dtypes.py index 3cc7e918aa0..cd35e380c02 100644 --- a/bigframes/dtypes.py +++ b/bigframes/dtypes.py @@ -14,24 +14,21 @@ """Mappings for Pandas dtypes supported by BigQuery DataFrames package""" -import datetime -import decimal import textwrap import typing -import warnings -from dataclasses import dataclass -from typing import Any, Dict, List, Literal, Sequence, Union +from typing import Any, Dict, Iterable, Literal, Tuple, Union -import bigframes_vendored.constants as constants -import db_dtypes # type: ignore import geopandas as gpd # type: ignore -import google.cloud.bigquery +import google.cloud.bigquery as bigquery +import ibis +import ibis.expr.datatypes as ibis_dtypes +import ibis.expr.types as ibis_types import numpy as np import pandas as pd import pyarrow as pa -import shapely.geometry # type: ignore -import bigframes.exceptions +import bigframes.constants as constants +import third_party.bigframes_vendored.google_cloud_bigquery._pandas_helpers as gcb3p_pandas_helpers # Type hints for Pandas dtypes supported by BigQuery DataFrame Dtype = Union[ @@ -40,1030 +37,390 @@ pd.Int64Dtype, pd.StringDtype, pd.ArrowDtype, - gpd.array.GeometryDtype, ] -DTYPES = typing.get_args(Dtype) -# Represents both column types (dtypes) and local-only types -# None represents the type of a None scalar. -ExpressionType = typing.Optional[Dtype] - -# Convert to arrow when in array or struct -INT_DTYPE = pd.Int64Dtype() -FLOAT_DTYPE = pd.Float64Dtype() -BOOL_DTYPE = pd.BooleanDtype() -# Wrapped arrow dtypes -STRING_DTYPE = pd.StringDtype(storage="pyarrow") -BYTES_DTYPE = pd.ArrowDtype(pa.binary()) -DATE_DTYPE = pd.ArrowDtype(pa.date32()) -TIME_DTYPE = pd.ArrowDtype(pa.time64("us")) -DATETIME_DTYPE = pd.ArrowDtype(pa.timestamp("us")) -TIMESTAMP_DTYPE = pd.ArrowDtype(pa.timestamp("us", tz="UTC")) -TIMEDELTA_DTYPE = pd.ArrowDtype(pa.duration("us")) -NUMERIC_DTYPE = pd.ArrowDtype(pa.decimal128(38, 9)) -BIGNUMERIC_DTYPE = pd.ArrowDtype(pa.decimal256(76, 38)) -# No arrow equivalent -GEO_DTYPE = gpd.array.GeometryDtype() -# JSON -# TODO(https://github.com/pandas-dev/pandas/issues/60958): switch to -# pyarrow.json_(pyarrow.string()) when pandas 3+ and pyarrow 18+ is installed. -JSON_ARROW_TYPE = db_dtypes.JSONArrowType() -JSON_DTYPE = pd.ArrowDtype(JSON_ARROW_TYPE) -OBJ_REF_DTYPE = pd.ArrowDtype( - pa.struct( - ( - pa.field( - "uri", - pa.string(), - ), - pa.field( - "version", - pa.string(), - ), - pa.field( - "authorizer", - pa.string(), - ), - pa.field( - "details", - JSON_ARROW_TYPE, - ), - ) - ) -) - -# Used when storing Null expressions -DEFAULT_DTYPE = FLOAT_DTYPE - -LOCAL_SCALAR_TYPE = Union[ - bool, - np.bool_, - int, - np.integer, - float, - np.floating, - decimal.Decimal, - str, - np.str_, - bytes, - np.bytes_, - datetime.datetime, - pd.Timestamp, - datetime.date, - datetime.time, - pd.Timedelta, - datetime.timedelta, - np.timedelta64, -] -LOCAL_SCALAR_TYPES = typing.get_args(LOCAL_SCALAR_TYPE) - -SUPPORTED_LITERAL_TYPE = typing.Union[ - bytes, - str, - int, - bool, - float, - datetime.datetime, - datetime.date, - datetime.time, - decimal.Decimal, - list, - shapely.geometry.base.BaseGeometry, -] -SUPPORTED_LITERAL_TYPES = typing.get_args(SUPPORTED_LITERAL_TYPE) - - -# Will have a few dtype variants: simple(eg. int, string, bool), complex (eg. list, struct), and virtual (eg. micro intervals, categorical) -@dataclass(frozen=True) -class SimpleDtypeInfo: - """ - A simple dtype maps 1:1 with a database type and is not parameterized. - """ - - dtype: Dtype - arrow_dtype: typing.Optional[pa.DataType] - type_kind: typing.Tuple[ - str, ... - ] # Should all correspond to the same db type. Put preferred canonical sql type name first - logical_bytes: int = ( - 8 # this is approximate only, some types are variably sized, also, compression - ) - orderable: bool = False - clusterable: bool = False - - -# TODO: Missing BQ types: INTERVAL, JSON, RANGE -# TODO: Add mappings to python types -SIMPLE_TYPES = ( - SimpleDtypeInfo( - dtype=INT_DTYPE, - arrow_dtype=pa.int64(), - type_kind=("INTEGER", "INT64"), - orderable=True, - clusterable=True, - ), - SimpleDtypeInfo( - dtype=FLOAT_DTYPE, - arrow_dtype=pa.float64(), - type_kind=("FLOAT", "FLOAT64"), - orderable=True, - ), - SimpleDtypeInfo( - dtype=BOOL_DTYPE, - arrow_dtype=pa.bool_(), - type_kind=( - "BOOLEAN", - "BOOL", - ), - logical_bytes=1, - orderable=True, - clusterable=True, - ), - SimpleDtypeInfo( - dtype=STRING_DTYPE, - arrow_dtype=pa.string(), - type_kind=("STRING",), - orderable=True, - clusterable=True, - ), - SimpleDtypeInfo( - dtype=JSON_DTYPE, - arrow_dtype=db_dtypes.JSONArrowType(), - type_kind=("JSON",), - orderable=False, - clusterable=False, - ), - SimpleDtypeInfo( - dtype=DATE_DTYPE, - arrow_dtype=pa.date32(), - type_kind=("DATE",), - logical_bytes=4, - orderable=True, - clusterable=True, - ), - SimpleDtypeInfo( - dtype=TIME_DTYPE, - arrow_dtype=pa.time64("us"), - type_kind=("TIME",), - orderable=True, - ), - SimpleDtypeInfo( - dtype=DATETIME_DTYPE, - arrow_dtype=pa.timestamp("us"), - type_kind=("DATETIME",), - orderable=True, - clusterable=True, - ), - SimpleDtypeInfo( - dtype=TIMESTAMP_DTYPE, - arrow_dtype=pa.timestamp("us", tz="UTC"), - type_kind=("TIMESTAMP",), - orderable=True, - clusterable=True, - ), - SimpleDtypeInfo( - dtype=BYTES_DTYPE, arrow_dtype=pa.binary(), type_kind=("BYTES",), orderable=True - ), - SimpleDtypeInfo( - dtype=NUMERIC_DTYPE, - arrow_dtype=pa.decimal128(38, 9), - type_kind=("NUMERIC", "DECIMAL"), - logical_bytes=16, - orderable=True, - clusterable=True, - ), - SimpleDtypeInfo( - dtype=BIGNUMERIC_DTYPE, - arrow_dtype=pa.decimal256(76, 38), - type_kind=("BIGNUMERIC", "BIGDECIMAL"), - logical_bytes=32, - orderable=True, - clusterable=True, - ), - # Geo has no corresponding arrow dtype - SimpleDtypeInfo( - dtype=GEO_DTYPE, - arrow_dtype=None, - type_kind=("GEOGRAPHY",), - logical_bytes=40, - clusterable=True, - ), -) +# Corresponds to the pandas concept of numeric type (such as when 'numeric_only' is specified in an operation) +NUMERIC_BIGFRAMES_TYPES = [pd.BooleanDtype(), pd.Float64Dtype(), pd.Int64Dtype()] +# On BQ side, ARRAY, STRUCT, GEOGRAPHY, JSON are not orderable +UNORDERED_DTYPES = [gpd.array.GeometryDtype()] # Type hints for dtype strings supported by BigQuery DataFrame DtypeString = Literal[ "boolean", "Float64", "Int64", - "int64[pyarrow]", "string", "string[pyarrow]", "timestamp[us, tz=UTC][pyarrow]", "timestamp[us][pyarrow]", "date32[day][pyarrow]", "time64[us][pyarrow]", - "decimal128(38, 9)[pyarrow]", - "decimal256(76, 38)[pyarrow]", - "binary[pyarrow]", - "duration[us][pyarrow]", -] - -DTYPE_STRINGS = typing.get_args(DtypeString) - -BOOL_BIGFRAMES_TYPES = [BOOL_DTYPE] - -# Corresponds to the pandas concept of numeric type (such as when 'numeric_only' is specified in an operation) -# Pandas is inconsistent, so two definitions are provided, each used in different contexts -NUMERIC_BIGFRAMES_TYPES_RESTRICTIVE: List[Dtype] = [ - FLOAT_DTYPE, - INT_DTYPE, -] -NUMERIC_BIGFRAMES_TYPES_PERMISSIVE = NUMERIC_BIGFRAMES_TYPES_RESTRICTIVE + [ - BOOL_DTYPE, - NUMERIC_DTYPE, - BIGNUMERIC_DTYPE, ] - -# Temporal types that are considered as "numeric" by Pandas -TEMPORAL_NUMERIC_BIGFRAMES_TYPES: List[Dtype] = [ - DATE_DTYPE, - TIMESTAMP_DTYPE, - DATETIME_DTYPE, +# Type hints for Ibis data types supported by BigQuery DataFrame +IbisDtype = Union[ + ibis_dtypes.Boolean, + ibis_dtypes.Float64, + ibis_dtypes.Int64, + ibis_dtypes.String, + ibis_dtypes.Date, + ibis_dtypes.Time, + ibis_dtypes.Timestamp, ] -TEMPORAL_BIGFRAMES_TYPES = TEMPORAL_NUMERIC_BIGFRAMES_TYPES + [TIME_DTYPE] - - -# dtype predicates - use these to maintain consistency -def is_datetime_like(type_: ExpressionType) -> bool: - return type_ in (DATETIME_DTYPE, TIMESTAMP_DTYPE) +BOOL_BIGFRAMES_TYPES = [pd.BooleanDtype()] -def is_date_like(type_: ExpressionType) -> bool: - return type_ in (DATETIME_DTYPE, TIMESTAMP_DTYPE, DATE_DTYPE) - - -def is_time_like(type_: ExpressionType) -> bool: - return type_ in (DATETIME_DTYPE, TIMESTAMP_DTYPE, TIME_DTYPE) - - -def is_time_or_date_like(type_: ExpressionType) -> bool: - return type_ in (DATE_DTYPE, DATETIME_DTYPE, TIME_DTYPE, TIMESTAMP_DTYPE) - - -def is_geo_like(type_: ExpressionType) -> bool: - return type_ in (GEO_DTYPE,) - - -def is_binary_like(type_: ExpressionType) -> bool: - return type_ in (BOOL_DTYPE, BYTES_DTYPE, INT_DTYPE) - - -def is_object_like(type_: Union[ExpressionType, str]) -> bool: - # See: https://stackoverflow.com/a/40312924/101923 and - # https://numpy.org/doc/stable/reference/generated/numpy.dtype.kind.html - # for the way to identify object type. - return type_ in ("object", "O") or ( - getattr(type_, "kind", None) == "O" - and getattr(type_, "storage", None) != "pyarrow" - ) - - -def is_string_like(type_: ExpressionType) -> bool: - return type_ in (STRING_DTYPE, BYTES_DTYPE) - - -def is_array_like(type_: ExpressionType) -> bool: - return isinstance(type_, pd.ArrowDtype) and isinstance( - type_.pyarrow_dtype, pa.ListType - ) - - -def is_array_string_like(type_: ExpressionType) -> bool: - return ( - isinstance(type_, pd.ArrowDtype) - and isinstance(type_.pyarrow_dtype, pa.ListType) - and pa.types.is_string(type_.pyarrow_dtype.value_type) - ) - - -def is_struct_like(type_: ExpressionType) -> bool: - return isinstance(type_, pd.ArrowDtype) and isinstance( - type_.pyarrow_dtype, pa.StructType - ) - - -def is_json_arrow_type(type_: pa.DataType) -> bool: - return isinstance(type_, db_dtypes.JSONArrowType) or ( - hasattr(pa, "JsonType") and isinstance(type_, pa.JsonType) - ) - - -def is_json_like(type_: ExpressionType) -> bool: - return type_ == JSON_DTYPE or type_ == STRING_DTYPE # Including JSON string - - -def is_json_encoding_type(type_: ExpressionType, strict: bool = False) -> bool: - # Types can be converted into JSON. - # https://cloud.google.com/bigquery/docs/reference/standard-sql/json_functions#json_encodings - if is_array_like(type_): - return is_json_encoding_type(get_array_inner_type(type_), strict=strict) - if is_struct_like(type_): - return all( - is_json_encoding_type(field_type, strict=strict) - for field_type in get_struct_fields(type_).values() - ) - - if strict: - # Strict are the types (mostly) defined by json spec, with no/minimal - # encoding/decoding involved. So no temporal types. - return type_ in ( - INT_DTYPE, - FLOAT_DTYPE, - BOOL_DTYPE, - STRING_DTYPE, - JSON_DTYPE, - ) - else: - # GoogleSQL implementation handles anything but GEO - return type_ != GEO_DTYPE - - -def is_numeric(type_: ExpressionType, include_bool: bool = True) -> bool: - is_numeric = type_ in NUMERIC_BIGFRAMES_TYPES_PERMISSIVE - return is_numeric if include_bool else is_numeric and type_ != BOOL_DTYPE - - -def is_iterable(type_: ExpressionType) -> bool: - return type_ in (STRING_DTYPE, BYTES_DTYPE) or is_array_like(type_) - - -def is_comparable(type_: ExpressionType) -> bool: - return (type_ is not None) and is_orderable(type_) - - -def can_compare(type1: ExpressionType, type2: ExpressionType) -> bool: - try: - coerced_type = coerce_to_common(type1, type2) - return is_comparable(coerced_type) - except TypeError: - return False - - -def get_struct_fields(type_: ExpressionType) -> dict[str, Dtype]: - assert isinstance(type_, pd.ArrowDtype) - assert isinstance(type_.pyarrow_dtype, pa.StructType) - struct_type = type_.pyarrow_dtype - result: dict[str, Dtype] = {} - - # Local import to break circular dependency with core.backports - import bigframes.core.backports - - for field in bigframes.core.backports.pyarrow_struct_type_fields(struct_type): - result[field.name] = arrow_dtype_to_bigframes_dtype(field.type) - return result - - -def get_array_inner_type(type_: ExpressionType) -> Dtype: - assert isinstance(type_, pd.ArrowDtype) - assert isinstance(type_.pyarrow_dtype, pa.ListType) - list_type = type_.pyarrow_dtype - return arrow_dtype_to_bigframes_dtype(list_type.value_type) - - -def list_type(values_type: Dtype) -> Dtype: - """Create a list dtype with given value type.""" - return pd.ArrowDtype(pa.list_(bigframes_dtype_to_arrow_dtype(values_type))) - - -def struct_type(fields: Sequence[tuple[str, Dtype]]) -> Dtype: - """Create a struct dtype with give fields names and types.""" - pa_fields = [ - pa.field(str, bigframes_dtype_to_arrow_dtype(dtype)) for str, dtype in fields - ] - return pd.ArrowDtype(pa.struct(pa_fields)) +# Several operations are restricted to these types. +NUMERIC_BIGFRAMES_TYPES = [pd.BooleanDtype(), pd.Float64Dtype(), pd.Int64Dtype()] +# Type hints for Ibis data types that can be read to Python objects by BigQuery DataFrame +ReadOnlyIbisDtype = Union[ + ibis_dtypes.Binary, + ibis_dtypes.JSON, + ibis_dtypes.Decimal, + ibis_dtypes.GeoSpatial, + ibis_dtypes.Array, + ibis_dtypes.Struct, +] -_ORDERABLE_SIMPLE_TYPES = set( - mapping.dtype for mapping in SIMPLE_TYPES if mapping.orderable +BIDIRECTIONAL_MAPPINGS: Iterable[Tuple[IbisDtype, Dtype]] = ( + (ibis_dtypes.boolean, pd.BooleanDtype()), + (ibis_dtypes.date, pd.ArrowDtype(pa.date32())), + (ibis_dtypes.float64, pd.Float64Dtype()), + (ibis_dtypes.int64, pd.Int64Dtype()), + (ibis_dtypes.string, pd.StringDtype(storage="pyarrow")), + (ibis_dtypes.time, pd.ArrowDtype(pa.time64("us"))), + (ibis_dtypes.Timestamp(timezone=None), pd.ArrowDtype(pa.timestamp("us"))), + ( + ibis_dtypes.Timestamp(timezone="UTC"), + pd.ArrowDtype(pa.timestamp("us", tz="UTC")), + ), ) +BIGFRAMES_TO_IBIS: Dict[Dtype, ibis_dtypes.DataType] = { + pandas: ibis for ibis, pandas in BIDIRECTIONAL_MAPPINGS +} -def is_orderable(type_: ExpressionType) -> bool: - # On BQ side, ARRAY, STRUCT, GEOGRAPHY, JSON are not orderable - return type_ in _ORDERABLE_SIMPLE_TYPES or type_ is TIMEDELTA_DTYPE +IBIS_TO_ARROW: Dict[ibis_dtypes.DataType, pa.DataType] = { + ibis_dtypes.boolean: pa.bool_(), + ibis_dtypes.date: pa.date32(), + ibis_dtypes.float64: pa.float64(), + ibis_dtypes.int64: pa.int64(), + ibis_dtypes.string: pa.string(), + ibis_dtypes.time: pa.time64("us"), + ibis_dtypes.Timestamp(timezone=None): pa.timestamp("us"), + ibis_dtypes.Timestamp(timezone="UTC"): pa.timestamp("us", tz="UTC"), +} +ARROW_TO_IBIS = {arrow: ibis for ibis, arrow in IBIS_TO_ARROW.items()} -_CLUSTERABLE_SIMPLE_TYPES = set( - mapping.dtype for mapping in SIMPLE_TYPES if mapping.clusterable +IBIS_TO_BIGFRAMES: Dict[ibis_dtypes.DataType, Union[Dtype, np.dtype[Any]]] = { + ibis: pandas for ibis, pandas in BIDIRECTIONAL_MAPPINGS +} +# Allow REQUIRED fields to map correctly. +IBIS_TO_BIGFRAMES.update( + {ibis.copy(nullable=False): pandas for ibis, pandas in BIDIRECTIONAL_MAPPINGS} +) +IBIS_TO_BIGFRAMES.update( + { + ibis_dtypes.binary: np.dtype("O"), + ibis_dtypes.json: np.dtype("O"), + ibis_dtypes.Decimal(precision=38, scale=9, nullable=True): np.dtype("O"), + ibis_dtypes.Decimal(precision=76, scale=38, nullable=True): np.dtype("O"), + ibis_dtypes.GeoSpatial( + geotype="geography", srid=4326, nullable=True + ): gpd.array.GeometryDtype(), + # TODO: Interval + } ) - - -def is_clusterable(type_: ExpressionType) -> bool: - # https://cloud.google.com/bigquery/docs/clustered-tables#cluster_column_types - # This is based on default database type mapping, could in theory represent in non-default bq type to cluster. - return type_ in _CLUSTERABLE_SIMPLE_TYPES - - -def is_bool_coercable(type_: ExpressionType) -> bool: - # TODO: Implement more bool coercions - return ( - (type_ is None) - or is_numeric(type_) - or is_string_like(type_) - or is_array_like(type_) - ) - BIGFRAMES_STRING_TO_BIGFRAMES: Dict[DtypeString, Dtype] = { - typing.cast(DtypeString, mapping.dtype.name): mapping.dtype - for mapping in SIMPLE_TYPES + typing.cast(DtypeString, dtype.name): dtype for dtype in BIGFRAMES_TO_IBIS.keys() } # special case - string[pyarrow] doesn't include the storage in its name, and both -# "string" and "string[pyarrow]" are accepted -BIGFRAMES_STRING_TO_BIGFRAMES["string[pyarrow]"] = STRING_DTYPE +# "string" and "string[pyarrow] are accepted" +BIGFRAMES_STRING_TO_BIGFRAMES["string[pyarrow]"] = pd.StringDtype(storage="pyarrow") -# special case - both "Int64" and "int64[pyarrow]" are accepted -BIGFRAMES_STRING_TO_BIGFRAMES["int64[pyarrow]"] = INT_DTYPE -BIGFRAMES_STRING_TO_BIGFRAMES["duration[us][pyarrow]"] = TIMEDELTA_DTYPE +def ibis_dtype_to_bigframes_dtype( + ibis_dtype: ibis_dtypes.DataType, +) -> Union[Dtype, np.dtype[Any]]: + """Converts an Ibis dtype to a BigQuery DataFrames dtype -# For the purposes of dataframe.memory_usage -DTYPE_BYTE_SIZES = { - type_info.dtype: type_info.logical_bytes for type_info in SIMPLE_TYPES -} - -### Conversion Functions + Args: + ibis_dtype: The ibis dtype used to represent this type, which + should in turn correspond to an underlying BigQuery type + Returns: + The supported BigQuery DataFrames dtype, which may be provided by + pandas, numpy, or db_types -def dtype_for_etype(etype: ExpressionType) -> Dtype: - if etype is None: - return DEFAULT_DTYPE + Raises: + ValueError: if passed an unexpected type + """ + # Special cases: Ibis supports variations on these types, but currently + # our IO returns them as objects. Eventually, we should support them as + # ArrowDType (and update the IO accordingly) + if isinstance(ibis_dtype, ibis_dtypes.Array): + return np.dtype("O") + + if isinstance(ibis_dtype, ibis_dtypes.Struct): + return pd.ArrowDtype(ibis_dtype_to_arrow_dtype(ibis_dtype)) + + # BigQuery only supports integers of size 64 bits. + if isinstance(ibis_dtype, ibis_dtypes.Integer): + return pd.Int64Dtype() + + if ibis_dtype in IBIS_TO_BIGFRAMES: + return IBIS_TO_BIGFRAMES[ibis_dtype] + elif isinstance(ibis_dtype, ibis_dtypes.Null): + # Fallback to STRING for NULL values for most flexibility in SQL. + return IBIS_TO_BIGFRAMES[ibis_dtypes.string] else: - return etype - - -# Mapping between arrow and bigframes types are necessary because arrow types are used for structured types, but not all primitive types, -# so conversion are needed when data is nested or unnested. Also, sometimes local data is stored as arrow. -_ARROW_TO_BIGFRAMES = { - mapping.arrow_dtype: mapping.dtype - for mapping in SIMPLE_TYPES - if mapping.arrow_dtype is not None -} - -# Include types that aren't 1:1 to BigQuery but allowed to be loaded in to BigQuery: -_ARROW_TO_BIGFRAMES_LOSSLESS = { - pa.int8(): INT_DTYPE, - pa.int16(): INT_DTYPE, - pa.int32(): INT_DTYPE, - pa.uint8(): INT_DTYPE, - pa.uint16(): INT_DTYPE, - pa.uint32(): INT_DTYPE, - # uint64 is omitted because uint64 -> BigQuery INT64 is a lossy conversion. - pa.float16(): FLOAT_DTYPE, - pa.float32(): FLOAT_DTYPE, - # TODO(tswast): Can we support datetime/timestamp/time with units larger - # than microseconds? -} - + raise ValueError( + f"Unexpected Ibis data type {ibis_dtype}. {constants.FEEDBACK_LINK}" + ) -def arrow_dtype_to_bigframes_dtype( - arrow_dtype: pa.DataType, allow_lossless_cast: bool = False -) -> Dtype: - """ - Convert an arrow type into the pandas-y type used to represent it in BigFrames. - Args: - arrow_dtype: Arrow data type. - allow_lossless_cast: Allow lossless conversions, such as int32 to int64. - """ - if allow_lossless_cast and arrow_dtype in _ARROW_TO_BIGFRAMES_LOSSLESS: - return _ARROW_TO_BIGFRAMES_LOSSLESS[arrow_dtype] +def ibis_dtype_to_arrow_dtype(ibis_dtype: ibis_dtypes.DataType) -> pa.DataType: + if isinstance(ibis_dtype, ibis_dtypes.Array): + return pa.list_(ibis_dtype_to_arrow_dtype(ibis_dtype.value_type)) - if arrow_dtype in _ARROW_TO_BIGFRAMES: - return _ARROW_TO_BIGFRAMES[arrow_dtype] + if isinstance(ibis_dtype, ibis_dtypes.Struct): + return pa.struct( + [ + (name, ibis_dtype_to_arrow_dtype(dtype)) + for name, dtype in ibis_dtype.fields.items() + ] + ) - if pa.types.is_list(arrow_dtype): - return pd.ArrowDtype(arrow_dtype) + if ibis_dtype in IBIS_TO_ARROW: + return IBIS_TO_ARROW[ibis_dtype] + else: + raise ValueError( + f"Unexpected Ibis data type {ibis_dtype}. {constants.FEEDBACK_LINK}" + ) - if pa.types.is_struct(arrow_dtype): - return pd.ArrowDtype(arrow_dtype) - if pa.types.is_duration(arrow_dtype): - return TIMEDELTA_DTYPE +def ibis_value_to_canonical_type(value: ibis_types.Value) -> ibis_types.Value: + """Converts an Ibis expression to canonical type. - # BigFrames doesn't distinguish between string and large_string because the - # largest string (2 GB) is already larger than the largest BigQuery row. - if pa.types.is_string(arrow_dtype) or pa.types.is_large_string(arrow_dtype): - return STRING_DTYPE + This is useful in cases where multiple types correspond to the same BigFrames dtype. + """ + ibis_type = value.type() + # Allow REQUIRED fields to be joined with NULLABLE fields. + nullable_type = ibis_type.copy(nullable=True) + return value.cast(nullable_type).name(value.get_name()) - if arrow_dtype == pa.null(): - return DEFAULT_DTYPE - # Allow both db_dtypes.JSONArrowType() and pa.json_(pa.string()) - if is_json_arrow_type(arrow_dtype): - return JSON_DTYPE +def ibis_table_to_canonical_types(table: ibis_types.Table) -> ibis_types.Table: + """Converts an Ibis table expression to canonical types. - # No other types matched. - raise TypeError( - f"Unexpected Arrow data type {arrow_dtype}. {constants.FEEDBACK_LINK}" - ) - - -_BIGFRAMES_TO_ARROW = { - mapping.dtype: mapping.arrow_dtype - for mapping in SIMPLE_TYPES - if mapping.arrow_dtype is not None -} -# unidirectional mapping -_BIGFRAMES_TO_ARROW[GEO_DTYPE] = pa.string() + This is useful in cases where multiple types correspond to the same BigFrames dtype. + """ + casted_columns = [] + for column_name in table.columns: + column = typing.cast(ibis_types.Value, table[column_name]) + casted_columns.append(ibis_value_to_canonical_type(column)) + return table.select(*casted_columns) -def bigframes_dtype_to_arrow_dtype( - bigframes_dtype: Dtype, -) -> pa.DataType: - if bigframes_dtype in _BIGFRAMES_TO_ARROW: - return _BIGFRAMES_TO_ARROW[bigframes_dtype] - if isinstance(bigframes_dtype, pd.ArrowDtype): - if pa.types.is_duration(bigframes_dtype.pyarrow_dtype): - return bigframes_dtype.pyarrow_dtype - if pa.types.is_list(bigframes_dtype.pyarrow_dtype): - return bigframes_dtype.pyarrow_dtype - if pa.types.is_struct(bigframes_dtype.pyarrow_dtype): - return bigframes_dtype.pyarrow_dtype - else: - raise TypeError( - f"No arrow conversion for {bigframes_dtype}. {constants.FEEDBACK_LINK}" +def arrow_dtype_to_ibis_dtype(arrow_dtype: pa.DataType) -> ibis_dtypes.DataType: + if pa.types.is_struct(arrow_dtype): + struct_dtype = typing.cast(pa.StructType, arrow_dtype) + return ibis_dtypes.Struct.from_tuples( + [ + (field.name, arrow_dtype_to_ibis_dtype(field.type)) + for field in struct_dtype + ] ) - -def to_storage_type( - arrow_type: pa.DataType, -): - """Some pyarrow versions don't support extension types fully, such as for empty table generation.""" - if isinstance(arrow_type, pa.ExtensionType): - return arrow_type.storage_type - if pa.types.is_list(arrow_type): - assert isinstance(arrow_type, pa.ListType) - return pa.list_(to_storage_type(arrow_type.value_type)) - if pa.types.is_struct(arrow_type): - assert isinstance(arrow_type, pa.StructType) - - # Local import to break circular dependency with core.backports - import bigframes.core.backports - - return pa.struct( - field.with_type(to_storage_type(field.type)) - for field in bigframes.core.backports.pyarrow_struct_type_fields(arrow_type) - ) - return arrow_type - - -def arrow_type_to_literal( - arrow_type: pa.DataType, -) -> Any: - """Create a representative literal value for an arrow type.""" - if pa.types.is_list(arrow_type): - return [arrow_type_to_literal(arrow_type.value_type)] - - # Local import to break circular dependency with core.backports - import bigframes.core.backports - - if pa.types.is_struct(arrow_type): - return { - field.name: arrow_type_to_literal(field.type) - for field in bigframes.core.backports.pyarrow_struct_type_fields(arrow_type) - } - if pa.types.is_string(arrow_type): - return "string" - if pa.types.is_binary(arrow_type): - return b"bytes" - if pa.types.is_floating(arrow_type): - return 1.0 - if pa.types.is_integer(arrow_type): - return 1 - if pa.types.is_boolean(arrow_type): - return True - if pa.types.is_date(arrow_type): - return datetime.date(2025, 1, 1) - if pa.types.is_timestamp(arrow_type): - return datetime.datetime( - 2025, - 1, - 1, - 1, - 1, - tzinfo=datetime.timezone.utc if arrow_type.tz is not None else None, + if arrow_dtype in ARROW_TO_IBIS: + return ARROW_TO_IBIS[arrow_dtype] + else: + raise ValueError( + f"Unexpected Arrow data type {arrow_dtype}. {constants.FEEDBACK_LINK}" ) - if pa.types.is_decimal(arrow_type): - return decimal.Decimal("1.0") - if pa.types.is_time(arrow_type): - return datetime.time(1, 1, 1) - - raise TypeError( - f"No literal conversion for {arrow_type}. {constants.FEEDBACK_LINK}" - ) -def bigframes_type(dtype) -> Dtype: - """Convert type object to canoncial bigframes dtype.""" - if _is_bigframes_dtype(dtype): - return dtype - elif isinstance(dtype, str): - return _dtype_from_string(dtype) - elif isinstance(dtype, type): - return _infer_dtype_from_python_type(dtype) - elif isinstance(dtype, pa.DataType): - return arrow_dtype_to_bigframes_dtype(dtype) - else: - raise TypeError( - f"Cannot infer supported datatype for: {dtype}. {constants.FEEDBACK_LINK}" - ) +def bigframes_dtype_to_ibis_dtype( + bigframes_dtype: Union[DtypeString, Dtype, np.dtype[Any]] +) -> ibis_dtypes.DataType: + """Converts a BigQuery DataFrames supported dtype to an Ibis dtype. + Args: + bigframes_dtype: + A dtype supported by BigQuery DataFrame -def _is_bigframes_dtype(dtype) -> bool: - """True iff dtyps is a canonical bigframes dtype""" - # have to be quite strict, as pyarrow dtypes equal their string form, and we don't consider that a canonical form. - if (type(dtype), dtype) in set( - (type(item.dtype), item.dtype) for item in SIMPLE_TYPES - ): - return True - if isinstance(dtype, pd.ArrowDtype): - try: - _ = arrow_dtype_to_bigframes_dtype(dtype.pyarrow_dtype) - return True - except TypeError: - return False - return False - - -def _infer_dtype_from_python_type(type_: type) -> Dtype: - if type_ in (datetime.timedelta, pd.Timedelta, np.timedelta64): - # Must check timedelta type first. Otherwise other branchs will be evaluated to true - # E.g. np.timedelta64 is a sublcass as np.integer - return TIMEDELTA_DTYPE - if issubclass(type_, (bool, np.bool_)): - return BOOL_DTYPE - if issubclass(type_, (int, np.integer)): - return INT_DTYPE - if issubclass(type_, (float, np.floating)): - return FLOAT_DTYPE - if issubclass(type_, decimal.Decimal): - return NUMERIC_DTYPE - if issubclass(type_, (str, np.str_)): - return STRING_DTYPE - if issubclass(type_, (bytes, np.bytes_)): - return BYTES_DTYPE - if issubclass(type_, datetime.date): - return DATE_DTYPE - if issubclass(type_, datetime.time): - return TIME_DTYPE - if issubclass(type_, shapely.geometry.base.BaseGeometry): - return GEO_DTYPE - else: - raise TypeError( - f"No matching datatype for python type: {type_}. {constants.FEEDBACK_LINK}" - ) + Returns: + IbisDtype: The corresponding Ibis type + Raises: + ValueError: If passed a dtype not supported by BigQuery DataFrames. + """ + if isinstance(bigframes_dtype, pd.ArrowDtype): + return arrow_dtype_to_ibis_dtype(bigframes_dtype.pyarrow_dtype) -def _dtype_from_string(dtype_string: str) -> typing.Optional[Dtype]: - if str(dtype_string) in BIGFRAMES_STRING_TO_BIGFRAMES: - return BIGFRAMES_STRING_TO_BIGFRAMES[ - typing.cast(DtypeString, str(dtype_string)) + type_string = str(bigframes_dtype) + if type_string in BIGFRAMES_STRING_TO_BIGFRAMES: + bigframes_dtype = BIGFRAMES_STRING_TO_BIGFRAMES[ + typing.cast(DtypeString, type_string) ] - if isinstance(dtype_string, str) and dtype_string.lower() == "json": - return JSON_DTYPE - - raise TypeError( - textwrap.dedent( - f""" - Unexpected data type string {dtype_string}. The following - dtypes are supppted: 'boolean','Float64','Int64', - 'int64[pyarrow]','string','string[pyarrow]', - 'timestamp[us, tz=UTC][pyarrow]','timestamp[us][pyarrow]', - 'date32[day][pyarrow]','time64[us][pyarrow]'. - The following pandas.ExtensionDtype are supported: - pandas.BooleanDtype(), pandas.Float64Dtype(), + else: + raise ValueError( + textwrap.dedent( + f""" + Unexpected data type {bigframes_dtype}. The following + str dtypes are supppted: 'boolean','Float64','Int64', 'string', + 'tring[pyarrow]','timestamp[us, tz=UTC][pyarrow]', + 'timestamp[us][pyarrow]','date32[day][pyarrow]', + 'time64[us][pyarrow]'. The following pandas.ExtensionDtype are + supported: pandas.BooleanDtype(), pandas.Float64Dtype(), pandas.Int64Dtype(), pandas.StringDtype(storage="pyarrow"), - pandas.ArrowDtype(pa.date32()), pandas.ArrowDtype(pa.time64("us")), - pandas.ArrowDtype(pa.timestamp("us")), - pandas.ArrowDtype(pa.timestamp("us", tz="UTC")). + pd.ArrowDtype(pa.date32()), pd.ArrowDtype(pa.time64("us")), + pd.ArrowDtype(pa.timestamp("us")), + pd.ArrowDtype(pa.timestamp("us", tz="UTC")). {constants.FEEDBACK_LINK} """ + ) ) - ) - -def infer_literal_type(literal) -> typing.Optional[Dtype]: - # Maybe also normalize literal to canonical python representation to remove this burden from compilers? - if isinstance(literal, pa.Scalar): - return arrow_dtype_to_bigframes_dtype(literal.type) - if pd.api.types.is_dict_like(literal): - fields = [] - for key in literal.keys(): - field_type = bigframes_dtype_to_arrow_dtype( - infer_literal_type(literal[key]) - ) - fields.append( - pa.field(key, field_type, nullable=(not pa.types.is_list(field_type))) - ) - return pd.ArrowDtype(pa.struct(fields)) - if pd.api.types.is_list_like(literal): - element_types = [infer_literal_type(i) for i in literal] - common_type = lcd_type(*element_types) - return list_type(common_type) - if pd.isna(literal): - return None # Null value without a definite type - # Make sure to check datetime before date as datetimes are also dates - if isinstance(literal, (datetime.datetime, pd.Timestamp)): - if literal.tzinfo is not None: - return TIMESTAMP_DTYPE - else: - return DATETIME_DTYPE - from_python_type = _infer_dtype_from_python_type(type(literal)) - if from_python_type is not None: - return from_python_type - else: - raise TypeError(f"Unable to infer type for value: {literal}") + return BIGFRAMES_TO_IBIS[bigframes_dtype] -def infer_literal_arrow_type(literal) -> typing.Optional[pa.DataType]: - if pd.isna(literal): - return None # Null value without a definite type - return bigframes_dtype_to_arrow_dtype(infer_literal_type(literal)) +def literal_to_ibis_scalar( + literal, force_dtype: typing.Optional[Dtype] = None, validate: bool = True +): + """Accept any literal and, if possible, return an Ibis Scalar + expression with a BigQuery DataFrames compatible data type + Args: + literal: + any value accepted by Ibis + force_dtype: + force the value to a specific dtype + validate: + If true, will raise ValueError if type cannot be stored in a + BigQuery DataFrames object. If used as a subexpression, this should + be disabled. + + Returns: + An ibis Scalar supported by BigQuery DataFrame + + Raises: + ValueError: if passed literal cannot be coerced to a + BigQuery DataFrames compatible scalar + """ + ibis_dtype = BIGFRAMES_TO_IBIS[force_dtype] if force_dtype else None -_TK_TO_BIGFRAMES = { - type_kind: mapping.dtype - for mapping in SIMPLE_TYPES - for type_kind in mapping.type_kind -} -_BIGFRAMES_TO_TK = {mapping.dtype: mapping.type_kind[0] for mapping in SIMPLE_TYPES} - - -def convert_schema_field( - field: google.cloud.bigquery.SchemaField, -) -> typing.Tuple[str, Dtype]: - is_repeated = field.mode == "REPEATED" - if field.field_type == "RECORD": - if field.description == OBJ_REF_DESCRIPTION_TAG: - bf_dtype = OBJ_REF_DTYPE # type: ignore - if is_repeated: - pa_type = pa.list_(bigframes_dtype_to_arrow_dtype(bf_dtype)) - bf_dtype = pd.ArrowDtype(pa_type) - return field.name, bf_dtype - - mapped_fields = map(convert_schema_field, field.fields) - fields = [] - for name, dtype in mapped_fields: - arrow_type = bigframes_dtype_to_arrow_dtype(dtype) - fields.append( - pa.field(name, arrow_type, nullable=not pa.types.is_list(arrow_type)) - ) - pa_struct = pa.struct(fields) - pa_type = pa.list_(pa_struct) if is_repeated else pa_struct - return field.name, pd.ArrowDtype(pa_type) - elif ( - field.field_type == "INTEGER" - and field.description is not None - and field.description.endswith(TIMEDELTA_DESCRIPTION_TAG) - ): - return field.name, TIMEDELTA_DTYPE - elif field.field_type in _TK_TO_BIGFRAMES: - if is_repeated: - pa_type = pa.list_( - bigframes_dtype_to_arrow_dtype(_TK_TO_BIGFRAMES[field.field_type]) + if pd.api.types.is_list_like(literal): + if validate: + raise ValueError( + f"List types can't be stored in BigQuery DataFrames. {constants.FEEDBACK_LINK}" ) - return field.name, pd.ArrowDtype(pa_type) - return field.name, _TK_TO_BIGFRAMES[field.field_type] - else: - raise TypeError(f"Cannot handle type: {field.field_type}") - - -def convert_to_schema_field( - name: str, bigframes_dtype: Dtype, overrides: dict[Dtype, str] = {} -) -> google.cloud.bigquery.SchemaField: - if bigframes_dtype in overrides: - return google.cloud.bigquery.SchemaField(name, overrides[bigframes_dtype]) - if bigframes_dtype in _BIGFRAMES_TO_TK: - return google.cloud.bigquery.SchemaField( - name, _BIGFRAMES_TO_TK[bigframes_dtype] + # "correct" way would be to use ibis.array, but this produces invalid BQ SQL syntax + return tuple(literal) + if not pd.api.types.is_list_like(literal) and pd.isna(literal): + if ibis_dtype: + return ibis.null().cast(ibis_dtype) + else: + return ibis.null() + + scalar_expr = ibis.literal(literal) + if ibis_dtype: + scalar_expr = ibis.literal(literal, ibis_dtype) + elif scalar_expr.type().is_floating(): + scalar_expr = ibis.literal(literal, ibis_dtypes.float64) + elif scalar_expr.type().is_integer(): + scalar_expr = ibis.literal(literal, ibis_dtypes.int64) + + # TODO(bmil): support other literals that can be coerced to compatible types + if validate and (scalar_expr.type() not in BIGFRAMES_TO_IBIS.values()): + raise ValueError( + f"Literal did not coerce to a supported data type: {literal}. {constants.FEEDBACK_LINK}" ) - if isinstance(bigframes_dtype, pd.ArrowDtype): - if pa.types.is_list(bigframes_dtype.pyarrow_dtype): - inner_type = arrow_dtype_to_bigframes_dtype( - bigframes_dtype.pyarrow_dtype.value_type - ) - inner_field = convert_to_schema_field(name, inner_type, overrides) - return google.cloud.bigquery.SchemaField( - name, - inner_field.field_type, - mode="REPEATED", - fields=inner_field.fields, - description=inner_field.description, - ) - if pa.types.is_struct(bigframes_dtype.pyarrow_dtype): - inner_fields: list[google.cloud.bigquery.SchemaField] = [] - struct_type = typing.cast(pa.StructType, bigframes_dtype.pyarrow_dtype) - for i in range(struct_type.num_fields): - field = struct_type.field(i) - inner_bf_type = arrow_dtype_to_bigframes_dtype(field.type) - inner_fields.append( - convert_to_schema_field(field.name, inner_bf_type, overrides) - ) - - if bigframes_dtype == OBJ_REF_DTYPE: - return google.cloud.bigquery.SchemaField( - name, - "RECORD", - fields=inner_fields, - description=OBJ_REF_DESCRIPTION_TAG, - ) - - return google.cloud.bigquery.SchemaField( - name, "RECORD", fields=inner_fields - ) - if bigframes_dtype.pyarrow_dtype == pa.duration("us"): - # Timedeltas are represented as integers in microseconds. - return google.cloud.bigquery.SchemaField( - name, "INTEGER", description=TIMEDELTA_DESCRIPTION_TAG - ) - raise TypeError( - f"No arrow conversion for {bigframes_dtype}. {constants.FEEDBACK_LINK}" - ) + return scalar_expr -def bf_type_from_type_kind( - bq_schema: Sequence[google.cloud.bigquery.SchemaField], -) -> typing.Dict[str, Dtype]: - """Converts bigquery sql type to the default bigframes dtype.""" - return {name: dtype for name, dtype in map(convert_schema_field, bq_schema)} - - -def is_dtype(scalar: typing.Any, dtype: Dtype) -> bool: - """Captures whether a scalar can be losslessly represented by a dtype.""" - if pd.isna(scalar): - return True - if pd.api.types.is_bool_dtype(dtype): - return pd.api.types.is_bool(scalar) - if pd.api.types.is_float_dtype(dtype): - return pd.api.types.is_float(scalar) - if pd.api.types.is_integer_dtype(dtype): - return pd.api.types.is_integer(scalar) - if isinstance(dtype, pd.StringDtype): - return isinstance(scalar, str) - if isinstance(dtype, pd.ArrowDtype): - pa_type = dtype.pyarrow_dtype - return is_patype(scalar, pa_type) - return False - - -# string is binary -def is_patype(scalar: typing.Any, pa_type: pa.DataType) -> bool: - """Determine whether a scalar's type matches a given pyarrow type.""" - if pa_type == pa.time64("us"): - return isinstance(scalar, datetime.time) - elif pa_type == pa.timestamp("us"): - if isinstance(scalar, datetime.datetime): - return not scalar.tzinfo - if isinstance(scalar, pd.Timestamp): - return not scalar.tzinfo - elif pa_type == pa.timestamp("us", tz="UTC"): - if isinstance(scalar, datetime.datetime): - return scalar.tzinfo == datetime.timezone.utc - if isinstance(scalar, pd.Timestamp): - return scalar.tzinfo == datetime.timezone.utc - elif pa_type == pa.date32(): - return isinstance(scalar, datetime.date) - elif pa_type == pa.binary(): - return isinstance(scalar, bytes) - elif pa_type == pa.decimal128(38, 9): - # decimal.Decimal is a superset, but ibis performs out-of-bounds and loss-of-precision checks - return isinstance(scalar, decimal.Decimal) - elif pa_type == pa.decimal256(76, 38): - # decimal.Decimal is a superset, but ibis performs out-of-bounds and loss-of-precision checks - return isinstance(scalar, decimal.Decimal) - return False - - -# Utilities for type coercion, and compatibility -def is_compatible(scalar: typing.Any, dtype: Dtype) -> typing.Optional[Dtype]: - """Whether scalar can be compare to items of dtype (though maybe requiring coercion). Returns the datatype that must be used for the comparison""" - if is_dtype(scalar, dtype): - return dtype - elif pd.api.types.is_numeric_dtype(dtype): - # Implicit conversion currently only supported for numeric types - if pd.api.types.is_bool(scalar): - return lcd_type(BOOL_DTYPE, dtype) - if pd.api.types.is_float(scalar): - return lcd_type(FLOAT_DTYPE, dtype) - if pd.api.types.is_integer(scalar): - return lcd_type(INT_DTYPE, dtype) - if isinstance(scalar, decimal.Decimal): - # TODO: Check context to see if can use NUMERIC instead of BIGNUMERIC - return lcd_type(BIGNUMERIC_DTYPE, dtype) - return None - - -def lcd_type(*dtypes: Dtype) -> Dtype: - if len(dtypes) < 1: - raise ValueError("at least one dypes should be provided") - - unique_dtypes = set(dtypes) - if None in unique_dtypes: - unique_dtypes.remove(None) - - if len(unique_dtypes) == 0: - return None - if len(unique_dtypes) == 1: - return next(iter(unique_dtypes)) - - # Implicit conversion currently only supported for numeric types - hierarchy: list[Dtype] = [ - BOOL_DTYPE, - INT_DTYPE, - NUMERIC_DTYPE, - BIGNUMERIC_DTYPE, - FLOAT_DTYPE, - ] - if any([dtype not in hierarchy for dtype in unique_dtypes]): - return None - lcd_index = max([hierarchy.index(dtype) for dtype in unique_dtypes]) - return hierarchy[lcd_index] - - -def coerce_to_common(etype1: ExpressionType, etype2: ExpressionType) -> ExpressionType: - """Coerce types to a common type or throw a TypeError""" - if etype1 is not None and etype2 is not None: - common_supertype = lcd_type(etype1, etype2) - if common_supertype is not None: - return common_supertype - if can_coerce(etype1, etype2): - return etype2 - if can_coerce(etype2, etype1): - return etype1 - raise TypeError(f"Cannot coerce {etype1} and {etype2} to a common type.") - - -def can_coerce(source_type: ExpressionType, target_type: ExpressionType) -> bool: - if source_type is None: - return True # None can be coerced to any supported type - else: - return (source_type == STRING_DTYPE) and ( - target_type in TEMPORAL_BIGFRAMES_TYPES + [JSON_DTYPE] - ) +def cast_ibis_value( + value: ibis_types.Value, to_type: ibis_dtypes.DataType +) -> ibis_types.Value: + """Perform compatible type casts of ibis values -def lcd_type_or_throw(dtype1: Dtype, dtype2: Dtype) -> Dtype: - result = lcd_type(dtype1, dtype2) - if result is None: - raise NotImplementedError( - f"BigFrames cannot upcast {dtype1} and {dtype2} to common type. {constants.FEEDBACK_LINK}" + Args: + value: + Ibis value, which could be a literal, scalar, or column + + to_type: + The Ibis type to cast to + + Returns: + A new Ibis value of type to_type + + Raises: + TypeError: if the type cast cannot be executed""" + if value.type() == to_type: + return value + # casts that just work + # TODO(bmil): add to this as more casts are verified + good_casts = { + ibis_dtypes.bool: (ibis_dtypes.int64,), + ibis_dtypes.int64: ( + ibis_dtypes.bool, + ibis_dtypes.float64, + ibis_dtypes.string, + ), + ibis_dtypes.float64: (ibis_dtypes.string, ibis_dtypes.int64), + ibis_dtypes.string: (ibis_dtypes.int64, ibis_dtypes.float64), + ibis_dtypes.date: (ibis_dtypes.string,), + ibis_dtypes.Decimal(precision=38, scale=9): (ibis_dtypes.float64,), + ibis_dtypes.Decimal(precision=76, scale=38): (ibis_dtypes.float64,), + ibis_dtypes.time: (), + ibis_dtypes.timestamp: (ibis_dtypes.Timestamp(timezone="UTC"),), + ibis_dtypes.Timestamp(timezone="UTC"): (ibis_dtypes.timestamp,), + } + + value = ibis_value_to_canonical_type(value) + if value.type() in good_casts: + if to_type in good_casts[value.type()]: + return value.cast(to_type) + else: + # this should never happen + raise TypeError( + f"Unexpected value type {value.type()}. {constants.FEEDBACK_LINK}" ) - return result + # casts that need some encouragement -TIMEDELTA_DESCRIPTION_TAG = "#microseconds" -OBJ_REF_DESCRIPTION_TAG = "bigframes_dtype: OBJ_REF_DTYPE" + # BigQuery casts bools to lower case strings. Capitalize the result to match Pandas + # TODO(bmil): remove this workaround after fixing Ibis + if value.type() == ibis_dtypes.bool and to_type == ibis_dtypes.string: + return typing.cast(ibis_types.StringValue, value.cast(to_type)).capitalize() + if value.type() == ibis_dtypes.bool and to_type == ibis_dtypes.float64: + return value.cast(ibis_dtypes.int64).cast(ibis_dtypes.float64) -def contains_db_dtypes_json_arrow_type(type_): - if isinstance(type_, db_dtypes.JSONArrowType): - return True - - if isinstance(type_, pa.ListType): - return contains_db_dtypes_json_arrow_type(type_.value_type) - - if isinstance(type_, pa.StructType): - # Local import to break circular dependency with core.backports - import bigframes.core.backports - - return any( - contains_db_dtypes_json_arrow_type(field.type) - for field in bigframes.core.backports.pyarrow_struct_type_fields(type_) - ) - return False - - -def contains_db_dtypes_json_dtype(dtype): - if not isinstance(dtype, pd.ArrowDtype): - return False - - return contains_db_dtypes_json_arrow_type(dtype.pyarrow_dtype) + if value.type() == ibis_dtypes.float64 and to_type == ibis_dtypes.bool: + return value != ibis_types.literal(0) + raise TypeError( + f"Unsupported cast {value.type()} to {to_type}. {constants.FEEDBACK_LINK}" + ) -def warn_on_db_dtypes_json_dtype(dtypes): - """Warn that the JSON dtype is changing. - Note: only call this function if the user is explicitly checking the - dtypes. - """ - if any(contains_db_dtypes_json_dtype(dtype) for dtype in dtypes): - msg = bigframes.exceptions.format_message( - "JSON columns will be represented as pandas.ArrowDtype(pyarrow.json_()) " - "instead of using `db_dtypes` in the future when available in pandas " - "(https://github.com/pandas-dev/pandas/issues/60958) and pyarrow." - ) - warnings.warn(msg, bigframes.exceptions.JSONDtypeWarning) +def to_pandas_dtypes_overrides(schema: Iterable[bigquery.SchemaField]) -> Dict: + """For each STRUCT field, make sure we specify the full type to use.""" + # TODO(swast): Also override ARRAY fields. + dtypes = {} + for field in schema: + if field.field_type == "RECORD" and field.mode != "REPEATED": + # TODO(swast): We're using a private API here. Would likely be + # better if we called `to_arrow()` and converted to a pandas + # DataFrame ourselves from that. + dtypes[field.name] = pd.ArrowDtype( + gcb3p_pandas_helpers.bq_to_arrow_data_type(field) + ) + return dtypes diff --git a/bigframes/enums.py b/bigframes/enums.py deleted file mode 100644 index 3aaf6020206..00000000000 --- a/bigframes/enums.py +++ /dev/null @@ -1,41 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -"""Public enums used across BigQuery DataFrames.""" - -# NOTE: This module should not depend on any others in the package. - -import enum - - -class OrderingMode(enum.Enum): - """Values used to determine the ordering mode. - - Default is 'strict'. - """ - - STRICT = "strict" - PARTIAL = "partial" - - -class DefaultIndexKind(enum.Enum): - """Sentinel values used to override default indexing behavior.""" - - #: Use consecutive integers as the index. This is ``0``, ``1``, ``2``, ..., - #: ``n - 3``, ``n - 2``, ``n - 1``, where ``n`` is the number of items in - #: the index. - SEQUENTIAL_INT64 = enum.auto() - - # A completely null index incapable of indexing or alignment. - NULL = enum.auto() diff --git a/bigframes/exceptions.py b/bigframes/exceptions.py deleted file mode 100644 index dea8a55f9b5..00000000000 --- a/bigframes/exceptions.py +++ /dev/null @@ -1,154 +0,0 @@ -# Copyright 2023 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -"""Public exceptions and warnings used across BigQuery DataFrames.""" - -import textwrap - -# NOTE: This module should not depend on any others in the package. - - -# Uses UserWarning for backwards compatibility with warning without a category -# set. -class DefaultLocationWarning(UserWarning): - """No location was specified, so using a default one.""" - - -class UnknownLocationWarning(Warning): - """The location is set to an unknown value.""" - - -class CleanupFailedWarning(Warning): - """Bigframes failed to clean up a table or function resource.""" - - -class DefaultIndexWarning(Warning): - """Default index may cause unexpected costs.""" - - -class PreviewWarning(Warning): - """The feature is in preview.""" - - -class NullIndexPreviewWarning(PreviewWarning): - """Unused. Kept for backwards compatibility. - - Was used when null index feature was in preview. - """ - - -class NullIndexError(ValueError): - """Object has no index.""" - - -class OrderingModePartialPreviewWarning(PreviewWarning): - """Unused. Kept for backwards compatibility. - - Was used when ordering mode 'partial' was in preview. - """ - - -class OrderRequiredError(ValueError): - """Operation requires total row ordering to be enabled.""" - - -class QueryComplexityError(RuntimeError): - """Query plan is too complex to execute.""" - - -class OperationAbortedError(RuntimeError): - """Operation is aborted.""" - - -class MaximumResultRowsExceeded(RuntimeError): - """Maximum number of rows in the result was exceeded.""" - - -class TranspilationError(RuntimeError): - """Failed to transpile a Python function to BigFrames Expression.""" - - -class TimeTravelDisabledWarning(Warning): - """A query was reattempted without time travel.""" - - -class TimeTravelCacheWarning(Warning): - """Reads from the same table twice in the same session pull time travel from cache.""" - - -class AmbiguousWindowWarning(Warning): - """A query may produce nondeterministic results as the window may be ambiguously ordered. - - Deprecated. Kept for backwards compatibility for code that filters warnings - from this category. - """ - - -class UnknownDataTypeWarning(Warning): - """Data type is unknown.""" - - -class ApiDeprecationWarning(FutureWarning): - """The API has been deprecated.""" - - -class BadIndexerKeyWarning(Warning): - """The indexer key is not used correctly.""" - - -class ObsoleteVersionWarning(Warning): - """The BigFrames version is too old.""" - - -class FunctionAxisOnePreviewWarning(PreviewWarning): - """Remote Function and Managed UDF with axis=1 preview.""" - - -class JSONDtypeWarning(PreviewWarning): - """JSON dtype will be pd.ArrowDtype(pa.json_()) in the future.""" - - -class FunctionConflictTypeHintWarning(UserWarning): - """Conflicting type hints in a BigFrames function.""" - - -class FunctionPackageVersionWarning(PreviewWarning): - """ - Warns that package versions in remote function or managed function may not - match local or specified versions, which might cause unexpected behavior. - """ - - -class PythonTranspilerPreviewWarning(PreviewWarning): - """Python Transpiler is a preview feature.""" - - -def format_message(message: str, fill: bool = True): - """[Private] Formats a warning message. - - :meta private: - - Args: - message: The warning message string. - fill: Whether to wrap the message text using `textwrap.fill`. - Defaults to True. Set to False to prevent wrapping, - especially if the message already contains newlines. - - Returns: - The formatted message string. If `fill` is True, the message will be wrapped - to fit the terminal width. - """ - if fill: - message = textwrap.fill(message) - return message diff --git a/bigframes/extensions/__init__.py b/bigframes/extensions/__init__.py deleted file mode 100644 index 58d482ea386..00000000000 --- a/bigframes/extensions/__init__.py +++ /dev/null @@ -1,13 +0,0 @@ -# Copyright 2026 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. diff --git a/bigframes/extensions/bigframes/__init__.py b/bigframes/extensions/bigframes/__init__.py deleted file mode 100644 index 439a8189ded..00000000000 --- a/bigframes/extensions/bigframes/__init__.py +++ /dev/null @@ -1,27 +0,0 @@ -# Copyright 2026 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from bigframes.extensions.bigframes.dataframe_accessor import ( - BigframesAIAccessor, - BigframesBigQueryDataFrameAccessor, -) -from bigframes.extensions.bigframes.series_accessor import ( - BigframesBigQuerySeriesAccessor, -) - -__all__ = [ - "BigframesAIAccessor", - "BigframesBigQueryDataFrameAccessor", - "BigframesBigQuerySeriesAccessor", -] diff --git a/bigframes/extensions/bigframes/dataframe_accessor.py b/bigframes/extensions/bigframes/dataframe_accessor.py deleted file mode 100644 index f706c19ef2d..00000000000 --- a/bigframes/extensions/bigframes/dataframe_accessor.py +++ /dev/null @@ -1,71 +0,0 @@ -# Copyright 2026 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from __future__ import annotations - -from typing import TypeVar, cast - -import bigframes.dataframe -import bigframes.extensions.core.dataframe_accessor as core_accessor -import bigframes.series -from bigframes.core.logging import log_adapter - -T = TypeVar("T", bound="bigframes.dataframe.DataFrame") -S = TypeVar("S", bound="bigframes.series.Series") - - -@log_adapter.class_logger -class BigframesAIAccessor(core_accessor.AIAccessor[T, S]): - """ - BigFrames DataFrame accessor for BigQuery AI functions. - """ - - def __init__(self, bf_obj: T): - super().__init__(bf_obj) - - def _bf_from_dataframe( - self, session: bigframes.session.Session | None - ) -> bigframes.dataframe.DataFrame: - return self._obj - - def _to_dataframe(self, bf_df: bigframes.dataframe.DataFrame) -> T: - return cast(T, bf_df) - - def _to_series(self, bf_series: bigframes.series.Series) -> S: - return cast(S, bf_series) - - -@log_adapter.class_logger -class BigframesBigQueryDataFrameAccessor(core_accessor.BigQueryDataFrameAccessor[T, S]): - """ - BigFrames DataFrame accessor for BigQuery DataFrames functionality. - """ - - def __init__(self, bf_obj: T): - super().__init__(bf_obj) - - @property - def ai(self) -> BigframesAIAccessor: - return BigframesAIAccessor(self._obj) - - def _bf_from_dataframe( - self, session: bigframes.session.Session | None - ) -> bigframes.dataframe.DataFrame: - return self._obj - - def _to_dataframe(self, bf_df: bigframes.dataframe.DataFrame) -> T: - return cast(T, bf_df) - - def _to_series(self, bf_series: bigframes.series.Series) -> S: - return cast(S, bf_series) diff --git a/bigframes/extensions/bigframes/series_accessor.py b/bigframes/extensions/bigframes/series_accessor.py deleted file mode 100644 index c9026595d97..00000000000 --- a/bigframes/extensions/bigframes/series_accessor.py +++ /dev/null @@ -1,87 +0,0 @@ -# Copyright 2026 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# -# DO NOT MODIFY THIS FILE DIRECTLY. -# This file was generated by the script: scripts/generate_bigframes_bigquery.py -# - -from __future__ import annotations - -from typing import Optional, TypeVar, cast - -from bigframes import dataframe, series, session -from bigframes.core.logging import log_adapter -from bigframes.extensions.core import series_accessor as core_accessor - -T = TypeVar("T", bound="dataframe.DataFrame") -S = TypeVar("S", bound="series.Series") - - -@log_adapter.class_logger -class BigframesBigQuerySeriesAccessor(core_accessor.BigQuerySeriesAccessor[T, S]): - def __init__(self, bf_obj: S): - super().__init__(bf_obj) - - def _bf_from_series( - self, session: Optional[session.Session] = None - ) -> series.Series: - return self._obj - - def _to_dataframe(self, bf_df: dataframe.DataFrame) -> T: - return cast(T, bf_df) - - def _to_series(self, bf_series: series.Series) -> S: - return cast(S, bf_series) - - @property - def aead(self) -> BigframesAeadSeriesAccessor[T, S]: - return BigframesAeadSeriesAccessor(self._obj) - - @property - def ai(self) -> BigframesAiSeriesAccessor[T, S]: - return BigframesAiSeriesAccessor(self._obj) - - -@log_adapter.class_logger -class BigframesAeadSeriesAccessor(core_accessor.AeadSeriesAccessor[T, S]): - def __init__(self, bf_obj: S): - super().__init__(bf_obj) - - def _bf_from_series( - self, session: Optional[session.Session] = None - ) -> series.Series: - return self._obj - - def _to_dataframe(self, bf_df: dataframe.DataFrame) -> T: - return cast(T, bf_df) - - def _to_series(self, bf_series: series.Series) -> S: - return cast(S, bf_series) - - -@log_adapter.class_logger -class BigframesAiSeriesAccessor(core_accessor.AiSeriesAccessor[T, S]): - def __init__(self, bf_obj: S): - super().__init__(bf_obj) - - def _bf_from_series( - self, session: Optional[session.Session] = None - ) -> series.Series: - return self._obj - - def _to_dataframe(self, bf_df: dataframe.DataFrame) -> T: - return cast(T, bf_df) - - def _to_series(self, bf_series: series.Series) -> S: - return cast(S, bf_series) diff --git a/bigframes/extensions/core/__init__.py b/bigframes/extensions/core/__init__.py deleted file mode 100644 index 41b554c99ef..00000000000 --- a/bigframes/extensions/core/__init__.py +++ /dev/null @@ -1,20 +0,0 @@ -# Copyright 2026 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from bigframes.extensions.core.dataframe_accessor import ( - AIAccessor, - BigQueryDataFrameAccessor, -) - -__all__ = ["AIAccessor", "BigQueryDataFrameAccessor"] diff --git a/bigframes/extensions/core/abstract_series_accessor.py b/bigframes/extensions/core/abstract_series_accessor.py deleted file mode 100644 index 22d09861877..00000000000 --- a/bigframes/extensions/core/abstract_series_accessor.py +++ /dev/null @@ -1,50 +0,0 @@ -# Copyright 2026 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# -# DO NOT MODIFY THIS FILE DIRECTLY. -# This file was generated by the script: scripts/generate_bigframes_bigquery.py -# - -from __future__ import annotations - -import abc -from typing import ( - Generic, - Optional, - TypeVar, -) - -from bigframes import dataframe, series, session - -T = TypeVar("T") -S = TypeVar("S") - - -class AbstractBigQuerySeriesAccessor(abc.ABC, Generic[T, S]): - def __init__(self, obj: S): - self._obj = obj - - @abc.abstractmethod - def _bf_from_series( - self, session: Optional[session.Session] = None - ) -> series.Series: - """Convert the accessor's object to a BigFrames Series.""" - - @abc.abstractmethod - def _to_dataframe(self, bf_df: dataframe.DataFrame) -> T: - """Convert a BigFrames DataFrame to the accessor's object type.""" - - @abc.abstractmethod - def _to_series(self, bf_series: series.Series) -> S: - """Convert a BigFrames Series to the accessor's object type.""" diff --git a/bigframes/extensions/core/dataframe_accessor.py b/bigframes/extensions/core/dataframe_accessor.py deleted file mode 100644 index e490aa907dc..00000000000 --- a/bigframes/extensions/core/dataframe_accessor.py +++ /dev/null @@ -1,340 +0,0 @@ -# Copyright 2026 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from __future__ import annotations - -import abc -from typing import ( - TYPE_CHECKING, - Any, - Generic, - Iterable, - List, - Literal, - Mapping, - Tuple, - TypeVar, - Union, -) - -if TYPE_CHECKING: - import pandas as pd - - import bigframes.dataframe - import bigframes.series - import bigframes.session - - PROMPT_TYPE = Union[ - str, - bigframes.series.Series, - pd.Series, - List[Union[str, bigframes.series.Series, pd.Series]], - Tuple[Union[str, bigframes.series.Series, pd.Series], ...], - ] -else: - PROMPT_TYPE = Any - -T = TypeVar("T") -S = TypeVar("S") - - -class AbstractBigQueryDataFrameAccessor(abc.ABC, Generic[T, S]): - @abc.abstractmethod - def _bf_from_dataframe( - self, session: bigframes.session.Session | None - ) -> bigframes.dataframe.DataFrame: - """Convert the accessor's object to a BigFrames DataFrame.""" - - @abc.abstractmethod - def _to_dataframe(self, bf_df: bigframes.dataframe.DataFrame) -> T: - """Convert a BigFrames DataFrame to the accessor's object type.""" - - @abc.abstractmethod - def _to_series(self, bf_series: bigframes.series.Series) -> S: - """Convert a BigFrames Series to the accessor's object type.""" - - -class AIAccessor(AbstractBigQueryDataFrameAccessor[T, S]): - """ - DataFrame accessor for BigQuery AI functions. - """ - - def __init__(self, obj: T): - self._obj = obj - - def forecast( - self, - *, - data_col: str, - timestamp_col: str, - model: str = "TimesFM 2.0", - id_cols: Iterable[str] | None = None, - horizon: int = 10, - confidence_level: float = 0.95, - context_window: int | None = None, - output_historical_time_series: bool = False, - session: bigframes.session.Session | None = None, - ) -> T: - """ - Forecast time series at future horizon using BigQuery AI.FORECAST. - - This is an accessor for :func:`bigframes.bigquery.ai.forecast`. See that - function's documentation for detailed parameter descriptions and examples. - """ - import bigframes.bigquery.ai - - bf_df = self._bf_from_dataframe(session) - result = bigframes.bigquery.ai.forecast( - bf_df, - data_col=data_col, - timestamp_col=timestamp_col, - model=model, - id_cols=id_cols, - horizon=horizon, - confidence_level=confidence_level, - context_window=context_window, - output_historical_time_series=output_historical_time_series, - ) - return self._to_dataframe(result) - - def generate( - self, - prompt: PROMPT_TYPE, - *, - connection_id: str | None = None, - endpoint: str | None = None, - request_type: Literal["dedicated", "shared", "unspecified"] | None = None, - model_params: Mapping[Any, Any] | None = None, - output_schema: Mapping[str, str] | None = None, - ) -> S: - """ - Returns the AI analysis based on the prompt, which can be any combination of text and unstructured data. - - This is an accessor for :func:`bigframes.bigquery.ai.generate`. See that - function's documentation for detailed parameter descriptions and examples. - """ - import bigframes.bigquery.ai - - result = bigframes.bigquery.ai.generate( - prompt, - connection_id=connection_id, - endpoint=endpoint, - request_type=request_type, - model_params=model_params, - output_schema=output_schema, - ) - return self._to_series(result) - - def generate_bool( - self, - prompt: PROMPT_TYPE, - *, - connection_id: str | None = None, - endpoint: str | None = None, - request_type: Literal["dedicated", "shared", "unspecified"] | None = None, - model_params: Mapping[Any, Any] | None = None, - ) -> S: - """ - Returns the AI analysis based on the prompt, which can be any combination of text and unstructured data. - - This is an accessor for :func:`bigframes.bigquery.ai.generate_bool`. See that - function's documentation for detailed parameter descriptions and examples. - """ - import bigframes.bigquery.ai - - result = bigframes.bigquery.ai.generate_bool( - prompt, - connection_id=connection_id, - endpoint=endpoint, - request_type=request_type, - model_params=model_params, - ) - return self._to_series(result) - - def generate_int( - self, - prompt: PROMPT_TYPE, - *, - connection_id: str | None = None, - endpoint: str | None = None, - request_type: Literal["dedicated", "shared", "unspecified"] | None = None, - model_params: Mapping[Any, Any] | None = None, - ) -> S: - """ - Returns the AI analysis based on the prompt, which can be any combination of text and unstructured data. - - This is an accessor for :func:`bigframes.bigquery.ai.generate_int`. See that - function's documentation for detailed parameter descriptions and examples. - """ - import bigframes.bigquery.ai - - result = bigframes.bigquery.ai.generate_int( - prompt, - connection_id=connection_id, - endpoint=endpoint, - request_type=request_type, - model_params=model_params, - ) - return self._to_series(result) - - def generate_double( - self, - prompt: PROMPT_TYPE, - *, - connection_id: str | None = None, - endpoint: str | None = None, - request_type: Literal["dedicated", "shared", "unspecified"] | None = None, - model_params: Mapping[Any, Any] | None = None, - ) -> S: - """ - Returns the AI analysis based on the prompt, which can be any combination of text and unstructured data. - - This is an accessor for :func:`bigframes.bigquery.ai.generate_double`. See that - function's documentation for detailed parameter descriptions and examples. - """ - import bigframes.bigquery.ai - - result = bigframes.bigquery.ai.generate_double( - prompt, - connection_id=connection_id, - endpoint=endpoint, - request_type=request_type, - model_params=model_params, - ) - return self._to_series(result) - - def classify( - self, - input: PROMPT_TYPE, - categories: tuple[str, ...] | list[str], - *, - examples: list[tuple[str, str]] - | list[tuple[str, list[str] | tuple[str, ...]]] - | None = None, - connection_id: str | None = None, - endpoint: str | None = None, - output_mode: Literal["single", "multi"] | None = None, - optimization_mode: Literal["minimize_cost", "maximize_quality"] | None = None, - max_error_ratio: float | None = None, - ) -> S: - """ - Classifies a given input into one of the specified categories. It will always return one of the provided categories best fit the prompt input. - - This is an accessor for :func:`bigframes.bigquery.ai.classify`. See that - function's documentation for detailed parameter descriptions and examples. - """ - import bigframes.bigquery.ai - - result = bigframes.bigquery.ai.classify( - input, - categories, - examples=examples, - connection_id=connection_id, - endpoint=endpoint, - output_mode=output_mode, - optimization_mode=optimization_mode, - max_error_ratio=max_error_ratio, - ) - return self._to_series(result) - - def if_( - self, - prompt: PROMPT_TYPE, - *, - connection_id: str | None = None, - endpoint: str | None = None, - optimization_mode: Literal["minimize_cost", "maximize_quality"] | None = None, - max_error_ratio: float | None = None, - ) -> S: - """ - Evaluates the prompt to True or False. Compared to ``ai.generate_bool()``, this function - provides optimization such that not all rows are evaluated with the LLM. - - This is an accessor for :func:`bigframes.bigquery.ai.if_`. See that - function's documentation for detailed parameter descriptions and examples. - """ - import bigframes.bigquery.ai - - result = bigframes.bigquery.ai.if_( - prompt, - connection_id=connection_id, - endpoint=endpoint, - optimization_mode=optimization_mode, - max_error_ratio=max_error_ratio, - ) - return self._to_series(result) - - def score( - self, - prompt: PROMPT_TYPE, - *, - connection_id: str | None = None, - endpoint: str | None = None, - max_error_ratio: float | None = None, - ) -> S: - """ - Computes a score based on rubrics described in natural language. It will return a double value. - - This is an accessor for :func:`bigframes.bigquery.ai.score`. See that - function's documentation for detailed parameter descriptions and examples. - """ - import bigframes.bigquery.ai - - result = bigframes.bigquery.ai.score( - prompt, - connection_id=connection_id, - endpoint=endpoint, - max_error_ratio=max_error_ratio, - ) - return self._to_series(result) - - -class BigQueryDataFrameAccessor(AbstractBigQueryDataFrameAccessor[T, S]): - """ - DataFrame accessor for BigQuery DataFrames functionality. - """ - - def __init__(self, obj: T): - self._obj = obj - - @property - @abc.abstractmethod - def ai(self) -> AIAccessor: - """ - Accessor for BigQuery AI functions. - - Returns: - AIAccessor: Accessor for BigQuery AI functions. - """ - - def sql_scalar( - self, - sql_template: str, - *, - output_dtype=None, - session: bigframes.session.Session | None = None, - ) -> S: - """ - Compute a new Series by applying a SQL scalar function to the DataFrame. - - This is an accessor for :func:`bigframes.bigquery.sql_scalar`. See that - function's documentation for detailed parameter descriptions and examples. - """ - import bigframes.bigquery - - bf_df = self._bf_from_dataframe(session) - result = bigframes.bigquery.sql_scalar( - sql_template, bf_df, output_dtype=output_dtype - ) - return self._to_series(result) diff --git a/bigframes/extensions/core/series_accessor.py b/bigframes/extensions/core/series_accessor.py deleted file mode 100644 index 5aa50905c8f..00000000000 --- a/bigframes/extensions/core/series_accessor.py +++ /dev/null @@ -1,1229 +0,0 @@ -# Copyright 2026 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# -# DO NOT MODIFY THIS FILE DIRECTLY. -# This file was generated by the script: scripts/generate_bigframes_bigquery.py -# - -from __future__ import annotations - -import abc -import datetime -from typing import ( - Any, - Literal, - Optional, - TypeVar, - Union, - cast, -) - -from bigframes import series, session -from bigframes.core import col, sentinels -from bigframes.extensions.core import abstract_series_accessor, series_mixins - -T = TypeVar("T") -S = TypeVar("S") - - -class BigQuerySeriesAccessor( - abstract_series_accessor.AbstractBigQuerySeriesAccessor[T, S] -): - """Series accessor for BigQuery functions.""" - - @property - @abc.abstractmethod - def aead(self) -> AeadSeriesAccessor[T, S]: - """Accessor for BigQuery aead functions.""" - - @property - @abc.abstractmethod - def ai(self) -> AiSeriesAccessor[T, S]: - """Accessor for BigQuery ai functions.""" - - def deterministic_decrypt_bytes( - self, - ciphertext: Union[ - series.Series, - col.Expression, - Union[Literal[sentinels.Sentinel.ARGUMENT_DEFAULT], bytes], - ], - additional_data: Union[ - series.Series, - col.Expression, - Union[Literal[sentinels.Sentinel.ARGUMENT_DEFAULT], bytes], - ], - *, - session: Optional[session.Session] = None, - ) -> S: - """Uses the matching key from `keyset` to decrypt `ciphertext` and verifies the integrity of the data using `additional_data`. Returns an error if decryption fails.""" - from bigframes.operations.googlesql.global_namespace.aead_encryption import ( - deterministic_decrypt_bytes as deterministic_decrypt_bytes_impl, - ) - - # Resolve session from other arguments if not passed - if session is None: - from bigframes.core import googlesql - - session = googlesql._find_session( - ciphertext, - additional_data, - ) - - bf_series = self._bf_from_series(session) - result = deterministic_decrypt_bytes_impl( - bf_series, - ciphertext, - additional_data, - ) - return self._to_series(cast(series.Series, result)) - - def deterministic_decrypt_string( - self, - ciphertext: Union[ - series.Series, - col.Expression, - Union[Literal[sentinels.Sentinel.ARGUMENT_DEFAULT], bytes], - ], - additional_data: Union[ - series.Series, - col.Expression, - Union[Literal[sentinels.Sentinel.ARGUMENT_DEFAULT], str], - ], - *, - session: Optional[session.Session] = None, - ) -> S: - """Like `DETERMINISTIC_DECRYPT_BYTES`, but where plaintext is of type STRING.""" - from bigframes.operations.googlesql.global_namespace.aead_encryption import ( - deterministic_decrypt_string as deterministic_decrypt_string_impl, - ) - - # Resolve session from other arguments if not passed - if session is None: - from bigframes.core import googlesql - - session = googlesql._find_session( - ciphertext, - additional_data, - ) - - bf_series = self._bf_from_series(session) - result = deterministic_decrypt_string_impl( - bf_series, - ciphertext, - additional_data, - ) - return self._to_series(cast(series.Series, result)) - - def deterministic_encrypt( - self, - plaintext: Union[ - series.Series, - col.Expression, - Union[Literal[sentinels.Sentinel.ARGUMENT_DEFAULT], bytes, str], - ], - additional_data: Union[ - series.Series, - col.Expression, - Union[Literal[sentinels.Sentinel.ARGUMENT_DEFAULT], bytes, str], - ], - *, - session: Optional[session.Session] = None, - ) -> S: - """Encrypts `plaintext` using the primary cryptographic key in `keyset` using deterministic AEAD. The algorithm of the primary key must be `DETERMINISTIC_AEAD_AES_SIV_CMAC_256`. Binds the ciphertext to the context defined by `additional_data`. Returns `NULL` if any input is `NULL`.""" - from bigframes.operations.googlesql.global_namespace.aead_encryption import ( - deterministic_encrypt as deterministic_encrypt_impl, - ) - - # Resolve session from other arguments if not passed - if session is None: - from bigframes.core import googlesql - - session = googlesql._find_session( - plaintext, - additional_data, - ) - - bf_series = self._bf_from_series(session) - result = deterministic_encrypt_impl( - bf_series, - plaintext, - additional_data, - ) - return self._to_series(cast(series.Series, result)) - - def array_concat( - self, - array_expression_2: Union[ - series.Series, - col.Expression, - Union[Any, Literal[sentinels.Sentinel.ARGUMENT_DEFAULT]], - ], - *, - session: Optional[session.Session] = None, - ) -> S: - """Concatenates one or more arrays with the same element type into a single array.""" - from bigframes.operations.googlesql.global_namespace.array import ( - array_concat as array_concat_impl, - ) - - # Resolve session from other arguments if not passed - if session is None: - from bigframes.core import googlesql - - session = googlesql._find_session( - array_expression_2, - ) - - bf_series = self._bf_from_series(session) - result = array_concat_impl( - bf_series, - array_expression_2, - ) - return self._to_series(cast(series.Series, result)) - - def array_first( - self, - *, - session: Optional[session.Session] = None, - ) -> S: - """Takes an array and returns the first element in the array.""" - from bigframes.operations.googlesql.global_namespace.array import ( - array_first as array_first_impl, - ) - - bf_series = self._bf_from_series(session) - result = array_first_impl( - bf_series, - ) - return self._to_series(cast(series.Series, result)) - - def array_first_n( - self, - n: Union[ - series.Series, - col.Expression, - Union[Literal[sentinels.Sentinel.ARGUMENT_DEFAULT], int], - ], - *, - session: Optional[session.Session] = None, - ) -> S: - """Returns a prefix of `input_array` consisting of the first `n` elements.""" - from bigframes.operations.googlesql.global_namespace.array import ( - array_first_n as array_first_n_impl, - ) - - # Resolve session from other arguments if not passed - if session is None: - from bigframes.core import googlesql - - session = googlesql._find_session( - n, - ) - - bf_series = self._bf_from_series(session) - result = array_first_n_impl( - bf_series, - n, - ) - return self._to_series(cast(series.Series, result)) - - def array_includes( - self, - search_value: Union[ - series.Series, - col.Expression, - Union[Any, Literal[sentinels.Sentinel.ARGUMENT_DEFAULT]], - ], - *, - session: Optional[session.Session] = None, - ) -> S: - """Takes an array and returns `TRUE` if there is an element in the array that is equal to the search_value.""" - from bigframes.operations.googlesql.global_namespace.array import ( - array_includes as array_includes_impl, - ) - - # Resolve session from other arguments if not passed - if session is None: - from bigframes.core import googlesql - - session = googlesql._find_session( - search_value, - ) - - bf_series = self._bf_from_series(session) - result = array_includes_impl( - bf_series, - search_value, - ) - return self._to_series(cast(series.Series, result)) - - def array_includes_all( - self, - search_values: Union[ - series.Series, - col.Expression, - Union[Any, Literal[sentinels.Sentinel.ARGUMENT_DEFAULT]], - ], - *, - session: Optional[session.Session] = None, - ) -> S: - """Takes an array to search and an array of search values. Returns `TRUE` if all search values are in the array to search, otherwise returns `FALSE`.""" - from bigframes.operations.googlesql.global_namespace.array import ( - array_includes_all as array_includes_all_impl, - ) - - # Resolve session from other arguments if not passed - if session is None: - from bigframes.core import googlesql - - session = googlesql._find_session( - search_values, - ) - - bf_series = self._bf_from_series(session) - result = array_includes_all_impl( - bf_series, - search_values, - ) - return self._to_series(cast(series.Series, result)) - - def array_includes_any( - self, - search_values: Union[ - series.Series, - col.Expression, - Union[Any, Literal[sentinels.Sentinel.ARGUMENT_DEFAULT]], - ], - *, - session: Optional[session.Session] = None, - ) -> S: - """Takes an array to search and an array of search values. Returns `TRUE` if any search values are in the array to search, otherwise returns `FALSE`.""" - from bigframes.operations.googlesql.global_namespace.array import ( - array_includes_any as array_includes_any_impl, - ) - - # Resolve session from other arguments if not passed - if session is None: - from bigframes.core import googlesql - - session = googlesql._find_session( - search_values, - ) - - bf_series = self._bf_from_series(session) - result = array_includes_any_impl( - bf_series, - search_values, - ) - return self._to_series(cast(series.Series, result)) - - def array_is_distinct( - self, - *, - session: Optional[session.Session] = None, - ) -> S: - """Returns `TRUE` if the array contains no repeated elements, using the same equality comparison logic as `SELECT DISTINCT`.""" - from bigframes.operations.googlesql.global_namespace.array import ( - array_is_distinct as array_is_distinct_impl, - ) - - bf_series = self._bf_from_series(session) - result = array_is_distinct_impl( - bf_series, - ) - return self._to_series(cast(series.Series, result)) - - def array_last( - self, - *, - session: Optional[session.Session] = None, - ) -> S: - """Takes an array and returns the last element in the array.""" - from bigframes.operations.googlesql.global_namespace.array import ( - array_last as array_last_impl, - ) - - bf_series = self._bf_from_series(session) - result = array_last_impl( - bf_series, - ) - return self._to_series(cast(series.Series, result)) - - def array_length( - self, - *, - session: Optional[session.Session] = None, - ) -> S: - """Compute the length of each array element in the Series. - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> import bigframes.bigquery as bbq - - >>> s = bpd.Series([[1, 2, 8, 3], [], [3, 4]]) - >>> bbq.array_length(s) - 0 4 - 1 0 - 2 2 - dtype: Int64 - - You can call this function using the Series `bigquery` accessor. - - >>> s.bigquery.array_length() - 0 4 - 1 0 - 2 2 - dtype: Int64 - - You can also use this accessor on a pandas Series after importing bigframes. - - >>> import bigframes - >>> import pandas as pd - >>> ps = pd.Series([[1, 2, 8, 3], [], [3, 4]]) - >>> ps.bigquery.array_length() - 0 4 - 1 0 - 2 2 - dtype: Int64 - - You can also apply this function directly to Series using `apply`. - - >>> s.apply(bbq.array_length, by_row=False) - 0 4 - 1 0 - 2 2 - dtype: Int64 - - Args: - series (bigframes.series.Series): A Series with array columns. - - Returns: - bigframes.series.Series: A Series of integer values indicating - the length of each element in the Series. - """ - from bigframes.operations.googlesql.global_namespace.array import ( - array_length as array_length_impl, - ) - - bf_series = self._bf_from_series(session) - result = array_length_impl( - bf_series, - ) - return self._to_series(cast(series.Series, result)) - - def array_reverse( - self, - *, - session: Optional[session.Session] = None, - ) -> S: - """Returns the input `ARRAY` with elements in reverse order.""" - from bigframes.operations.googlesql.global_namespace.array import ( - array_reverse as array_reverse_impl, - ) - - bf_series = self._bf_from_series(session) - result = array_reverse_impl( - bf_series, - ) - return self._to_series(cast(series.Series, result)) - - def array_slice( - self, - start_offset: Union[ - series.Series, - col.Expression, - Union[Literal[sentinels.Sentinel.ARGUMENT_DEFAULT], int], - ], - end_offset: Union[ - series.Series, - col.Expression, - Union[Literal[sentinels.Sentinel.ARGUMENT_DEFAULT], int], - ], - *, - session: Optional[session.Session] = None, - ) -> S: - """Returns an array containing zero or more consecutive elements from the input array.""" - from bigframes.operations.googlesql.global_namespace.array import ( - array_slice as array_slice_impl, - ) - - # Resolve session from other arguments if not passed - if session is None: - from bigframes.core import googlesql - - session = googlesql._find_session( - start_offset, - end_offset, - ) - - bf_series = self._bf_from_series(session) - result = array_slice_impl( - bf_series, - start_offset, - end_offset, - ) - return self._to_series(cast(series.Series, result)) - - def array_to_string( - self, - delimiter: Union[ - series.Series, - col.Expression, - Union[Literal[sentinels.Sentinel.ARGUMENT_DEFAULT], bytes, str], - ], - null_text: Union[ - series.Series, - col.Expression, - Union[Literal[sentinels.Sentinel.ARGUMENT_DEFAULT], bytes, str], - ] = sentinels.Sentinel.ARGUMENT_DEFAULT, - *, - session: Optional[session.Session] = None, - ) -> S: - """Converts array elements within a Series into delimited strings. - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> import bigframes.bigquery as bbq - - >>> s = bpd.Series([["H", "i", "!"], ["Hello", "World"], np.nan, [], ["Hi"]]) - >>> bbq.array_to_string(s, delimiter=", ") - 0 H, i, ! - 1 Hello, World - 2 - 3 - 4 Hi - dtype: string - - You can call this function using the Series `bigquery` accessor. - - >>> s.bigquery.array_to_string(delimiter=", ") - 0 H, i, ! - 1 Hello, World - 2 - 3 - 4 Hi - dtype: string - - You can also use this accessor on a pandas Series after importing bigframes. - - >>> import bigframes - >>> import pandas as pd - >>> ps = pd.Series([["H", "i", "!"], ["Hello", "World"], None, [], ["Hi"]]) - >>> ps.bigquery.array_to_string(delimiter=", ") - 0 H, i, ! - 1 Hello, World - 2 - 3 - 4 Hi - dtype: string - - Args: - series (bigframes.series.Series): A Series containing arrays. - delimiter (str): The string used to separate array elements. - null_text (str, optional): The string to replace any NULL values in the array with. - - Returns: - bigframes.series.Series: A Series containing delimited strings. - """ - from bigframes.operations.googlesql.global_namespace.array import ( - array_to_string as array_to_string_impl, - ) - - # Resolve session from other arguments if not passed - if session is None: - from bigframes.core import googlesql - - session = googlesql._find_session( - delimiter, - null_text, - ) - - bf_series = self._bf_from_series(session) - result = array_to_string_impl( - bf_series, - delimiter, - null_text, - ) - return self._to_series(cast(series.Series, result)) - - def flatten( - self, - depth: Union[ - series.Series, - col.Expression, - Union[Literal[sentinels.Sentinel.ARGUMENT_DEFAULT], int], - ] = sentinels.Sentinel.ARGUMENT_DEFAULT, - *, - session: Optional[session.Session] = None, - ) -> S: - """Takes an array of nested data and flattens a specific part of it into a single, flat array with the [array elements field access operator][array-el-field-operator]. Returns `NULL` if the input value is `NULL`.""" - from bigframes.operations.googlesql.global_namespace.array import ( - flatten as flatten_impl, - ) - - # Resolve session from other arguments if not passed - if session is None: - from bigframes.core import googlesql - - session = googlesql._find_session( - depth, - ) - - bf_series = self._bf_from_series(session) - result = flatten_impl( - bf_series, - depth, - ) - return self._to_series(cast(series.Series, result)) - - def bit_count( - self, - *, - session: Optional[session.Session] = None, - ) -> S: - """The input, `expression`, must be an integer or `BYTES`. Returns the number of bits that are set in the input expression. For signed integers, this is the number of bits in two's complement form.""" - from bigframes.operations.googlesql.global_namespace.bit import ( - bit_count as bit_count_impl, - ) - - bf_series = self._bf_from_series(session) - result = bit_count_impl( - bf_series, - ) - return self._to_series(cast(series.Series, result)) - - def bool_( - self, - *, - session: Optional[session.Session] = None, - ) -> S: - """Converts a JSON boolean to a SQL BOOL value.""" - from bigframes.operations.googlesql.global_namespace.conversion import ( - bool_ as bool__impl, - ) - - bf_series = self._bf_from_series(session) - result = bool__impl( - bf_series, - ) - return self._to_series(cast(series.Series, result)) - - def double( - self, - wide_number_mode: Union[ - series.Series, - col.Expression, - Union[Literal[sentinels.Sentinel.ARGUMENT_DEFAULT], str], - ] = sentinels.Sentinel.ARGUMENT_DEFAULT, - *, - session: Optional[session.Session] = None, - ) -> S: - """Converts a JSON number to a SQL FLOAT64 value.""" - from bigframes.operations.googlesql.global_namespace.conversion import ( - double as double_impl, - ) - - # Resolve session from other arguments if not passed - if session is None: - from bigframes.core import googlesql - - session = googlesql._find_session( - wide_number_mode, - ) - - bf_series = self._bf_from_series(session) - result = double_impl( - bf_series, - wide_number_mode, - ) - return self._to_series(cast(series.Series, result)) - - def float64( - self, - wide_number_mode: Union[ - series.Series, - col.Expression, - Union[Literal[sentinels.Sentinel.ARGUMENT_DEFAULT], str], - ] = sentinels.Sentinel.ARGUMENT_DEFAULT, - *, - session: Optional[session.Session] = None, - ) -> S: - """Converts a JSON number to a SQL FLOAT64 value.""" - from bigframes.operations.googlesql.global_namespace.conversion import ( - float64 as float64_impl, - ) - - # Resolve session from other arguments if not passed - if session is None: - from bigframes.core import googlesql - - session = googlesql._find_session( - wide_number_mode, - ) - - bf_series = self._bf_from_series(session) - result = float64_impl( - bf_series, - wide_number_mode, - ) - return self._to_series(cast(series.Series, result)) - - def int64( - self, - *, - session: Optional[session.Session] = None, - ) -> S: - """Converts a JSON number to a SQL INT64 value.""" - from bigframes.operations.googlesql.global_namespace.conversion import ( - int64 as int64_impl, - ) - - bf_series = self._bf_from_series(session) - result = int64_impl( - bf_series, - ) - return self._to_series(cast(series.Series, result)) - - def parse_bignumeric( - self, - *, - session: Optional[session.Session] = None, - ) -> S: - """Converts a STRING to a BIGNUMERIC value.""" - from bigframes.operations.googlesql.global_namespace.conversion import ( - parse_bignumeric as parse_bignumeric_impl, - ) - - bf_series = self._bf_from_series(session) - result = parse_bignumeric_impl( - bf_series, - ) - return self._to_series(cast(series.Series, result)) - - def parse_numeric( - self, - *, - session: Optional[session.Session] = None, - ) -> S: - """Converts a STRING to a NUMERIC value.""" - from bigframes.operations.googlesql.global_namespace.conversion import ( - parse_numeric as parse_numeric_impl, - ) - - bf_series = self._bf_from_series(session) - result = parse_numeric_impl( - bf_series, - ) - return self._to_series(cast(series.Series, result)) - - def string( - self, - timezone: Union[ - series.Series, - col.Expression, - Union[Literal[sentinels.Sentinel.ARGUMENT_DEFAULT], str], - ] = sentinels.Sentinel.ARGUMENT_DEFAULT, - *, - session: Optional[session.Session] = None, - ) -> S: - """Converts a value to a STRING value.""" - from bigframes.operations.googlesql.global_namespace.conversion import ( - string as string_impl, - ) - - # Resolve session from other arguments if not passed - if session is None: - from bigframes.core import googlesql - - session = googlesql._find_session( - timezone, - ) - - bf_series = self._bf_from_series(session) - result = string_impl( - bf_series, - timezone, - ) - return self._to_series(cast(series.Series, result)) - - def date( - self, - time_zone_expression: Union[ - series.Series, - col.Expression, - Union[Literal[sentinels.Sentinel.ARGUMENT_DEFAULT], str], - ] = sentinels.Sentinel.ARGUMENT_DEFAULT, - year: Union[ - series.Series, - col.Expression, - Union[Literal[sentinels.Sentinel.ARGUMENT_DEFAULT], int], - ] = sentinels.Sentinel.ARGUMENT_DEFAULT, - month: Union[ - series.Series, - col.Expression, - Union[Literal[sentinels.Sentinel.ARGUMENT_DEFAULT], int], - ] = sentinels.Sentinel.ARGUMENT_DEFAULT, - day: Union[ - series.Series, - col.Expression, - Union[Literal[sentinels.Sentinel.ARGUMENT_DEFAULT], int], - ] = sentinels.Sentinel.ARGUMENT_DEFAULT, - *, - session: Optional[session.Session] = None, - ) -> S: - """Constructs or extracts a date.""" - from bigframes.operations.googlesql.global_namespace.date import ( - date as date_impl, - ) - - # Resolve session from other arguments if not passed - if session is None: - from bigframes.core import googlesql - - session = googlesql._find_session( - time_zone_expression, - year, - month, - day, - ) - - bf_series = self._bf_from_series(session) - result = date_impl( - bf_series, - time_zone_expression, - year, - month, - day, - ) - return self._to_series(cast(series.Series, result)) - - def date_add( - self, - int64_expression: Union[ - series.Series, - col.Expression, - Union[Literal[sentinels.Sentinel.ARGUMENT_DEFAULT], int], - ], - date_part: Union[ - series.Series, - col.Expression, - Union[Any, Literal[sentinels.Sentinel.ARGUMENT_DEFAULT]], - ], - *, - session: Optional[session.Session] = None, - ) -> S: - """Adds a specified time interval to a DATE.""" - from bigframes.operations.googlesql.global_namespace.date import ( - date_add as date_add_impl, - ) - - # Resolve session from other arguments if not passed - if session is None: - from bigframes.core import googlesql - - session = googlesql._find_session( - int64_expression, - date_part, - ) - - bf_series = self._bf_from_series(session) - result = date_add_impl( - bf_series, - int64_expression, - date_part, - ) - return self._to_series(cast(series.Series, result)) - - def date_diff( - self, - start_date: Union[ - series.Series, - col.Expression, - Union[Literal[sentinels.Sentinel.ARGUMENT_DEFAULT], datetime.date], - ], - granularity: Union[ - series.Series, - col.Expression, - Union[Any, Literal[sentinels.Sentinel.ARGUMENT_DEFAULT]], - ], - *, - session: Optional[session.Session] = None, - ) -> S: - """Gets the number of unit boundaries between two DATE values (end_date - start_date) at a particular time granularity.""" - from bigframes.operations.googlesql.global_namespace.date import ( - date_diff as date_diff_impl, - ) - - # Resolve session from other arguments if not passed - if session is None: - from bigframes.core import googlesql - - session = googlesql._find_session( - start_date, - granularity, - ) - - bf_series = self._bf_from_series(session) - result = date_diff_impl( - bf_series, - start_date, - granularity, - ) - return self._to_series(cast(series.Series, result)) - - def date_from_unix_date( - self, - *, - session: Optional[session.Session] = None, - ) -> S: - """Interprets an INT64 expression as the number of days since 1970-01-01.""" - from bigframes.operations.googlesql.global_namespace.date import ( - date_from_unix_date as date_from_unix_date_impl, - ) - - bf_series = self._bf_from_series(session) - result = date_from_unix_date_impl( - bf_series, - ) - return self._to_series(cast(series.Series, result)) - - def date_sub( - self, - int64_expression: Union[ - series.Series, - col.Expression, - Union[Literal[sentinels.Sentinel.ARGUMENT_DEFAULT], int], - ], - date_part: Union[ - series.Series, - col.Expression, - Union[Any, Literal[sentinels.Sentinel.ARGUMENT_DEFAULT]], - ], - *, - session: Optional[session.Session] = None, - ) -> S: - """Subtracts a specified time interval from a DATE.""" - from bigframes.operations.googlesql.global_namespace.date import ( - date_sub as date_sub_impl, - ) - - # Resolve session from other arguments if not passed - if session is None: - from bigframes.core import googlesql - - session = googlesql._find_session( - int64_expression, - date_part, - ) - - bf_series = self._bf_from_series(session) - result = date_sub_impl( - bf_series, - int64_expression, - date_part, - ) - return self._to_series(cast(series.Series, result)) - - def date_trunc( - self, - granularity: Union[ - series.Series, - col.Expression, - Union[Any, Literal[sentinels.Sentinel.ARGUMENT_DEFAULT]], - ], - *, - session: Optional[session.Session] = None, - ) -> S: - """Truncates a DATE, DATETIME, or TIMESTAMP value at a particular granularity.""" - from bigframes.operations.googlesql.global_namespace.date import ( - date_trunc as date_trunc_impl, - ) - - # Resolve session from other arguments if not passed - if session is None: - from bigframes.core import googlesql - - session = googlesql._find_session( - granularity, - ) - - bf_series = self._bf_from_series(session) - result = date_trunc_impl( - bf_series, - granularity, - ) - return self._to_series(cast(series.Series, result)) - - def extract( - self, - part: Union[ - series.Series, - col.Expression, - Union[Any, Literal[sentinels.Sentinel.ARGUMENT_DEFAULT]], - ], - time_zone: Union[ - series.Series, - col.Expression, - Union[Literal[sentinels.Sentinel.ARGUMENT_DEFAULT], str], - ] = sentinels.Sentinel.ARGUMENT_DEFAULT, - *, - session: Optional[session.Session] = None, - ) -> S: - """Returns the value corresponding to the specified date part.""" - from bigframes.operations.googlesql.global_namespace.date import ( - extract as extract_impl, - ) - - # Resolve session from other arguments if not passed - if session is None: - from bigframes.core import googlesql - - session = googlesql._find_session( - part, - time_zone, - ) - - bf_series = self._bf_from_series(session) - result = extract_impl( - bf_series, - part, - time_zone, - ) - return self._to_series(cast(series.Series, result)) - - def format_date( - self, - format_string: Union[ - series.Series, - col.Expression, - Union[Literal[sentinels.Sentinel.ARGUMENT_DEFAULT], str], - ], - *, - session: Optional[session.Session] = None, - ) -> S: - """Formats a DATE value according to a specified format string.""" - from bigframes.operations.googlesql.global_namespace.date import ( - format_date as format_date_impl, - ) - - # Resolve session from other arguments if not passed - if session is None: - from bigframes.core import googlesql - - session = googlesql._find_session( - format_string, - ) - - bf_series = self._bf_from_series(session) - result = format_date_impl( - format_string, - bf_series, - ) - return self._to_series(cast(series.Series, result)) - - def last_day( - self, - date_part: Union[ - series.Series, - col.Expression, - Union[Any, Literal[sentinels.Sentinel.ARGUMENT_DEFAULT]], - ] = sentinels.Sentinel.ARGUMENT_DEFAULT, - *, - session: Optional[session.Session] = None, - ) -> S: - """Returns the last day from a date expression. This is commonly used to return the last day of the month.""" - from bigframes.operations.googlesql.global_namespace.date import ( - last_day as last_day_impl, - ) - - # Resolve session from other arguments if not passed - if session is None: - from bigframes.core import googlesql - - session = googlesql._find_session( - date_part, - ) - - bf_series = self._bf_from_series(session) - result = last_day_impl( - bf_series, - date_part, - ) - return self._to_series(cast(series.Series, result)) - - def parse_date( - self, - format_string: Union[ - series.Series, - col.Expression, - Union[Literal[sentinels.Sentinel.ARGUMENT_DEFAULT], str], - ], - *, - session: Optional[session.Session] = None, - ) -> S: - """Converts a STRING value to a DATE value.""" - from bigframes.operations.googlesql.global_namespace.date import ( - parse_date as parse_date_impl, - ) - - # Resolve session from other arguments if not passed - if session is None: - from bigframes.core import googlesql - - session = googlesql._find_session( - format_string, - ) - - bf_series = self._bf_from_series(session) - result = parse_date_impl( - format_string, - bf_series, - ) - return self._to_series(cast(series.Series, result)) - - def unix_date( - self, - *, - session: Optional[session.Session] = None, - ) -> S: - """Returns the number of days since 1970-01-01.""" - from bigframes.operations.googlesql.global_namespace.date import ( - unix_date as unix_date_impl, - ) - - bf_series = self._bf_from_series(session) - result = unix_date_impl( - bf_series, - ) - return self._to_series(cast(series.Series, result)) - - -class AeadSeriesAccessor(abstract_series_accessor.AbstractBigQuerySeriesAccessor[T, S]): - """Series accessor for BigQuery aead functions.""" - - def decrypt_bytes( - self, - ciphertext: Union[ - series.Series, - col.Expression, - Union[Literal[sentinels.Sentinel.ARGUMENT_DEFAULT], bytes], - ], - additional_data: Union[ - series.Series, - col.Expression, - Union[Literal[sentinels.Sentinel.ARGUMENT_DEFAULT], bytes], - ], - *, - session: Optional[session.Session] = None, - ) -> S: - """Uses the matching key from keyset to decrypt ciphertext and verifies the integrity of the data using additional_data. Returns an error if decryption or verification fails.""" - from bigframes.operations.googlesql.aead import ( - decrypt_bytes as decrypt_bytes_impl, - ) - - # Resolve session from other arguments if not passed - if session is None: - from bigframes.core import googlesql - - session = googlesql._find_session( - ciphertext, - additional_data, - ) - - bf_series = self._bf_from_series(session) - result = decrypt_bytes_impl( - bf_series, - ciphertext, - additional_data, - ) - return self._to_series(cast(series.Series, result)) - - def decrypt_string( - self, - ciphertext: Union[ - series.Series, - col.Expression, - Union[Literal[sentinels.Sentinel.ARGUMENT_DEFAULT], bytes], - ], - additional_data: Union[ - series.Series, - col.Expression, - Union[Literal[sentinels.Sentinel.ARGUMENT_DEFAULT], str], - ], - *, - session: Optional[session.Session] = None, - ) -> S: - """Like AEAD.DECRYPT_BYTES, but where additional_data is of type STRING.""" - from bigframes.operations.googlesql.aead import ( - decrypt_string as decrypt_string_impl, - ) - - # Resolve session from other arguments if not passed - if session is None: - from bigframes.core import googlesql - - session = googlesql._find_session( - ciphertext, - additional_data, - ) - - bf_series = self._bf_from_series(session) - result = decrypt_string_impl( - bf_series, - ciphertext, - additional_data, - ) - return self._to_series(cast(series.Series, result)) - - def encrypt( - self, - plaintext: Union[ - series.Series, - col.Expression, - Union[Literal[sentinels.Sentinel.ARGUMENT_DEFAULT], bytes, str], - ], - additional_data: Union[ - series.Series, - col.Expression, - Union[Literal[sentinels.Sentinel.ARGUMENT_DEFAULT], bytes, str], - ], - *, - session: Optional[session.Session] = None, - ) -> S: - """Encrypts plaintext using the primary cryptographic key in keyset. The algorithm of the primary key must be AEAD_AES_GCM_256. Binds the ciphertext to the context defined by additional_data. Returns NULL if any input is NULL.""" - from bigframes.operations.googlesql.aead import encrypt as encrypt_impl - - # Resolve session from other arguments if not passed - if session is None: - from bigframes.core import googlesql - - session = googlesql._find_session( - plaintext, - additional_data, - ) - - bf_series = self._bf_from_series(session) - result = encrypt_impl( - bf_series, - plaintext, - additional_data, - ) - return self._to_series(cast(series.Series, result)) - - -class AiSeriesAccessor(series_mixins.AIMixin[T, S]): - """Series accessor for BigQuery ai functions.""" diff --git a/bigframes/extensions/core/series_mixins.py b/bigframes/extensions/core/series_mixins.py deleted file mode 100644 index 4d1b61ecb0c..00000000000 --- a/bigframes/extensions/core/series_mixins.py +++ /dev/null @@ -1,196 +0,0 @@ -# Copyright 2026 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from __future__ import annotations - -from typing import Any, List, Literal, Mapping, TypeVar - -import pandas as pd - -from bigframes import series -from bigframes import session as bf_session -from bigframes.bigquery import ai -from bigframes.extensions.core import abstract_series_accessor -from bigframes.ml import base as ml_base - -T = TypeVar("T") -S = TypeVar("S") - - -class AIMixin(abstract_series_accessor.AbstractBigQuerySeriesAccessor[T, S]): - def generate_embedding( - self, - model: ml_base.BaseEstimator | str | pd.Series, - *, - output_dimensionality: int | None = None, - task_type: str | None = None, - start_second: float | None = None, - end_second: float | None = None, - interval_seconds: float | None = None, - trial_id: int | None = None, - session: bf_session.Session | None = None, - ) -> T: - """ - Creates embeddings that describe an entity — for example, a piece of text or an image. - - This is an accessor for :func:`bigframes.bigquery.ai.generate_embedding`. See that - function's documentation for detailed parameter descriptions and examples. - """ - - bf_series = self._bf_from_series(session) - result = ai.generate_embedding( - model, - bf_series, - output_dimensionality=output_dimensionality, - task_type=task_type, - start_second=start_second, - end_second=end_second, - interval_seconds=interval_seconds, - trial_id=trial_id, - ) - return self._to_dataframe(result) - - def generate_text( - self, - model: ml_base.BaseEstimator | str | pd.Series, - *, - temperature: float | None = None, - max_output_tokens: int | None = None, - top_k: int | None = None, - top_p: float | None = None, - stop_sequences: List[str] | None = None, - ground_with_google_search: bool | None = None, - request_type: str | None = None, - session: bf_session.Session | None = None, - ) -> T: - """ - Generates text using a BigQuery ML model. - - This is an accessor for :func:`bigframes.bigquery.ai.generate_text`. See that - function's documentation for detailed parameter descriptions and examples. - """ - bf_series = self._bf_from_series(session) - result = ai.generate_text( - model, - bf_series, - temperature=temperature, - max_output_tokens=max_output_tokens, - top_k=top_k, - top_p=top_p, - stop_sequences=stop_sequences, - ground_with_google_search=ground_with_google_search, - request_type=request_type, - ) - return self._to_dataframe(result) - - def generate_table( - self, - model: ml_base.BaseEstimator | str | pd.Series, - *, - output_schema: str | Mapping[str, str], - temperature: float | None = None, - top_p: float | None = None, - max_output_tokens: int | None = None, - stop_sequences: List[str] | None = None, - request_type: str | None = None, - session: bf_session.Session | None = None, - ) -> T: - """ - Generates a table using a BigQuery ML model. - - This is an accessor for :func:`bigframes.bigquery.ai.generate_table`. See that - function's documentation for detailed parameter descriptions and examples. - """ - bf_series = self._bf_from_series(session) - result = ai.generate_table( - model, - bf_series, - output_schema=output_schema, - temperature=temperature, - top_p=top_p, - max_output_tokens=max_output_tokens, - stop_sequences=stop_sequences, - request_type=request_type, - ) - return self._to_dataframe(result) - - def embed( - self, - *, - endpoint: str | None = None, - model: str | None = None, - task_type: ( - Literal[ - "retrieval_query", - "retrieval_document", - "semantic_similarity", - "classification", - "clustering", - "question_answering", - "fact_verification", - "code_retrieval_query", - ] - | None - ) = None, - title: str | None = None, - model_params: Mapping[Any, Any] | None = None, - connection_id: str | None = None, - session: bf_session.Session | None = None, - ) -> S: - """ - Creates embeddings from text or image data in BigQuery. - - This is an accessor for :func:`bigframes.bigquery.ai.embed`. See that - function's documentation for detailed parameter descriptions and examples. - """ - - bf_series = self._bf_from_series(session) - result = ai.embed( - bf_series, - endpoint=endpoint, - model=model, - task_type=task_type, - title=title, - model_params=model_params, - connection_id=connection_id, - ) - return self._to_series(result) - - def similarity( - self, - other: str | series.Series | pd.Series, - *, - endpoint: str | None = None, - model: str | None = None, - model_params: Mapping[Any, Any] | None = None, - connection_id: str | None = None, - session: bf_session.Session | None = None, - ) -> S: - """ - Returns a FLOAT64 value that represents the cosine similarity between the two inputs. - - This is an accessor for :func:`bigframes.bigquery.ai.similarity`. See that - function's documentation for detailed parameter descriptions and examples. - """ - - bf_series = self._bf_from_series(session) - result = ai.similarity( - bf_series, - other, - endpoint=endpoint, - model=model, - model_params=model_params, - connection_id=connection_id, - ) - return self._to_series(result) diff --git a/bigframes/extensions/pandas/__init__.py b/bigframes/extensions/pandas/__init__.py deleted file mode 100644 index 6af1f769b5b..00000000000 --- a/bigframes/extensions/pandas/__init__.py +++ /dev/null @@ -1,31 +0,0 @@ -# Copyright 2026 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -""" -BigQuery DataFrames automatically registers a pandas extenstion when imported. -This allows you to use the power of the BigQuery engine with pandas objects -directly. -""" - -from bigframes.extensions.pandas.dataframe_accessor import ( - PandasBigQueryDataFrameAccessor, -) -from bigframes.extensions.pandas.series_accessor import ( - PandasBigQuerySeriesAccessor, -) - -__all__ = [ - "PandasBigQueryDataFrameAccessor", - "PandasBigQuerySeriesAccessor", -] diff --git a/bigframes/extensions/pandas/dataframe_accessor.py b/bigframes/extensions/pandas/dataframe_accessor.py deleted file mode 100644 index 512134cac03..00000000000 --- a/bigframes/extensions/pandas/dataframe_accessor.py +++ /dev/null @@ -1,83 +0,0 @@ -# Copyright 2026 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from typing import TypeVar, cast - -import pandas -import pandas.api.extensions - -import bigframes.core.global_session as bf_session -import bigframes.dataframe -import bigframes.pandas as bpd -from bigframes.core.logging import log_adapter -from bigframes.extensions.core.dataframe_accessor import ( - AIAccessor, - BigQueryDataFrameAccessor, -) - -T = TypeVar("T", bound="pandas.DataFrame") -S = TypeVar("S", bound="pandas.Series") - - -@log_adapter.class_logger -class PandasAIAccessor(AIAccessor[T, S]): - """ - Pandas DataFrame accessor for BigQuery AI functions. - """ - - def __init__(self, pandas_obj: T): - super().__init__(pandas_obj) - - def _bf_from_dataframe( - self, session: bigframes.session.Session | None - ) -> bigframes.dataframe.DataFrame: - if session is None: - session = bf_session.get_global_session() - - return cast(bpd.DataFrame, session.read_pandas(self._obj)) - - def _to_dataframe(self, bf_df: bigframes.dataframe.DataFrame) -> T: - return cast(T, bf_df.to_pandas(ordered=True)) - - def _to_series(self, bf_series: bigframes.series.Series) -> S: - return cast(S, bf_series.to_pandas(ordered=True)) - - -@pandas.api.extensions.register_dataframe_accessor("bigquery") -@log_adapter.class_logger -class PandasBigQueryDataFrameAccessor(BigQueryDataFrameAccessor[T, S]): - """ - Pandas DataFrame accessor for BigQuery DataFrames functionality. - - This accessor is registered under the ``bigquery`` namespace on pandas DataFrame objects. - """ - - def __init__(self, pandas_obj: T): - super().__init__(pandas_obj) - - @property - def ai(self) -> PandasAIAccessor: - return PandasAIAccessor(self._obj) - - def _bf_from_dataframe(self, session) -> bigframes.dataframe.DataFrame: - if session is None: - session = bf_session.get_global_session() - - return cast(bpd.DataFrame, session.read_pandas(self._obj)) - - def _to_dataframe(self, bf_df: bigframes.dataframe.DataFrame) -> T: - return cast(T, bf_df.to_pandas(ordered=True)) - - def _to_series(self, bf_series: bigframes.series.Series) -> S: - return cast(S, bf_series.to_pandas(ordered=True)) diff --git a/bigframes/extensions/pandas/series_accessor.py b/bigframes/extensions/pandas/series_accessor.py deleted file mode 100644 index 9c33996c421..00000000000 --- a/bigframes/extensions/pandas/series_accessor.py +++ /dev/null @@ -1,98 +0,0 @@ -# Copyright 2026 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# -# DO NOT MODIFY THIS FILE DIRECTLY. -# This file was generated by the script: scripts/generate_bigframes_bigquery.py -# - -from __future__ import annotations - -from typing import Optional, TypeVar, cast - -import pandas -import pandas.api.extensions - -from bigframes import dataframe, series, session -from bigframes.core import global_session as bf_session -from bigframes.core.logging import log_adapter -from bigframes.extensions.core import series_accessor as core_accessor - -T = TypeVar("T", bound="pandas.DataFrame") -S = TypeVar("S", bound="pandas.Series") - - -@pandas.api.extensions.register_series_accessor("bigquery") -@log_adapter.class_logger -class PandasBigQuerySeriesAccessor(core_accessor.BigQuerySeriesAccessor[T, S]): - def __init__(self, pandas_obj: S): - super().__init__(pandas_obj) - - def _bf_from_series( - self, session: Optional[session.Session] = None - ) -> series.Series: - if session is None: - session = bf_session.get_global_session() - return cast(series.Series, session.read_pandas(self._obj)) - - def _to_dataframe(self, bf_df: dataframe.DataFrame) -> T: - return cast(T, bf_df.to_pandas(ordered=True)) - - def _to_series(self, bf_series: series.Series) -> S: - return cast(S, bf_series.to_pandas(ordered=True)) - - @property - def aead(self) -> PandasAeadSeriesAccessor[T, S]: - return PandasAeadSeriesAccessor(self._obj) - - @property - def ai(self) -> PandasAiSeriesAccessor[T, S]: - return PandasAiSeriesAccessor(self._obj) - - -@log_adapter.class_logger -class PandasAeadSeriesAccessor(core_accessor.AeadSeriesAccessor[T, S]): - def __init__(self, pandas_obj: S): - super().__init__(pandas_obj) - - def _bf_from_series( - self, session: Optional[session.Session] = None - ) -> series.Series: - if session is None: - session = bf_session.get_global_session() - return cast(series.Series, session.read_pandas(self._obj)) - - def _to_dataframe(self, bf_df: dataframe.DataFrame) -> T: - return cast(T, bf_df.to_pandas(ordered=True)) - - def _to_series(self, bf_series: series.Series) -> S: - return cast(S, bf_series.to_pandas(ordered=True)) - - -@log_adapter.class_logger -class PandasAiSeriesAccessor(core_accessor.AiSeriesAccessor[T, S]): - def __init__(self, pandas_obj: S): - super().__init__(pandas_obj) - - def _bf_from_series( - self, session: Optional[session.Session] = None - ) -> series.Series: - if session is None: - session = bf_session.get_global_session() - return cast(series.Series, session.read_pandas(self._obj)) - - def _to_dataframe(self, bf_df: dataframe.DataFrame) -> T: - return cast(T, bf_df.to_pandas(ordered=True)) - - def _to_series(self, bf_series: series.Series) -> S: - return cast(S, bf_series.to_pandas(ordered=True)) diff --git a/bigframes/features.py b/bigframes/features.py deleted file mode 100644 index 287dbcb0a4e..00000000000 --- a/bigframes/features.py +++ /dev/null @@ -1,42 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from __future__ import annotations - -from typing import Tuple - - -class PandasVersions: - """Version comparisons for pandas package""" - - def __init__(self): - self._installed_version = None - - @property - def installed_version(self) -> Tuple[str, ...]: - """pandas version""" - if self._installed_version is None: - import pandas - - self._installed_version = tuple(pandas.__version__.split(".")) - return self._installed_version - - @property - def is_arrow_list_dtype_usable(self): - """True if pandas.ArrowDtype is usable.""" - version = self.installed_version - return version[0] != "1" - - -PANDAS_VERSIONS = PandasVersions() diff --git a/bigframes/formatting_helpers.py b/bigframes/formatting_helpers.py index 9ab25951932..752aeb7a10b 100644 --- a/bigframes/formatting_helpers.py +++ b/bigframes/formatting_helpers.py @@ -13,23 +13,20 @@ # limitations under the License. """Shared helper functions for formatting jobs related info.""" - -from __future__ import annotations +# TODO(orrbradford): cleanup up typings and documenttion in this file import datetime -import html import random -from typing import TYPE_CHECKING, Any, Optional, Type, Union +from typing import Any, Optional, Union -import bigframes_vendored.constants as constants import google.api_core.exceptions as api_core_exceptions import google.cloud.bigquery as bigquery import humanize +import IPython +import IPython.display as display +import ipywidgets as widgets -import bigframes._config - -if TYPE_CHECKING: - import bigframes.core.events +import bigframes.constants as constants GenericJob = Union[ bigquery.LoadJob, bigquery.ExtractJob, bigquery.QueryJob, bigquery.CopyJob @@ -47,194 +44,128 @@ def add_feedback_link( exception: Union[ api_core_exceptions.RetryError, api_core_exceptions.GoogleAPICallError - ], + ] ): exception.message = exception.message + f" {constants.FEEDBACK_LINK}" -def create_exception_with_feedback_link( - exception: Type[Exception], - arg: str = "", -): - if arg: - return exception(arg + f" {constants.FEEDBACK_LINK}") - - return exception(constants.FEEDBACK_LINK) - - -def repr_query_job(query_job: Optional[bigquery.QueryJob]): - """Return query job as a formatted string. +def repr_query_job_html(query_job: Optional[bigquery.QueryJob]): + """Return query job in html format. Args: - query_job: + query_job (bigquery.QueryJob, Optional): The job representing the execution of the query on the server. Returns: - Formatted string. + Pywidget html table. """ if query_job is None: - return "No job information available" + return display.HTML("No job information available") if query_job.dry_run: - return ( - f"Computation deferred. Computation will process " - f"{get_formatted_bytes(query_job.total_bytes_processed)}" + return display.HTML( + f"Computation deferred. Computation will process {get_formatted_bytes(query_job.total_bytes_processed)}" ) - res = "Query Job Info" + table_html = "" + table_html += "" for key, value in query_job_prop_pairs.items(): job_val = getattr(query_job, value) if job_val is not None: - res += "\n" if key == "Job Id": # add link to job - res += f"""Job url: { - get_job_url( - project_id=query_job.project, - location=query_job.location, - job_id=query_job.job_id, - ) - }""" + table_html += f"""""" elif key == "Slot Time": - res += f"""{key}: {get_formatted_time(job_val)}""" + table_html += ( + f"""""" + ) elif key == "Bytes Processed": - res += f"""{key}: {get_formatted_bytes(job_val)}""" + table_html += f"""""" else: - res += f"""{key}: {job_val}""" - return res + table_html += f"""""" + table_html += "
{key}{job_val}
{key}{get_formatted_time(job_val)}
{key}{get_formatted_bytes(job_val)}
{key}{job_val}
" + return widgets.HTML(table_html) -def repr_query_job_html(query_job: Optional[bigquery.QueryJob]): - """Return query job as a formatted html string. +def repr_query_job(query_job: Optional[bigquery.QueryJob]): + """Return query job as a formatted string. Args: query_job: The job representing the execution of the query on the server. Returns: - Html string. + Pywidget html table. """ if query_job is None: return "No job information available" if query_job.dry_run: - return ( - f"Computation deferred. Computation will process " - f"{get_formatted_bytes(query_job.total_bytes_processed)}" - ) - - # We can reuse the plaintext repr for now or make a nicer table. - # For deferred mode consistency, let's just wrap the text in a pre - # block or similar, but the request implies we want a distinct HTML - # representation if possible. - # However, existing repr_query_job returns a simple string. - # Let's format it as a simple table or list. - - res = "

Query Job Info

    " + return f"Computation deferred. Computation will process {get_formatted_bytes(query_job.total_bytes_processed)}" + res = "Query Job Info" for key, value in query_job_prop_pairs.items(): job_val = getattr(query_job, value) if job_val is not None: + res += "\n" if key == "Job Id": # add link to job - url = get_job_url( - project_id=query_job.project, - location=query_job.location, - job_id=query_job.job_id, - ) - res += ( - f'
  • Job: ' - f"{query_job.job_id}
    • " - ) + res += f"""Job url: {get_job_url(query_job)}""" elif key == "Slot Time": - res += f"
  • {key}: {get_formatted_time(job_val)}
    • " + res += f"""{key}: {get_formatted_time(job_val)}""" elif key == "Bytes Processed": - res += f"
  • {key}: {get_formatted_bytes(job_val)}
    • " + res += f"""{key}: {get_formatted_bytes(job_val)}""" else: - res += f"
  • {key}: {job_val}
    • " - res += "
" + res += f"""{key}: {job_val}""" return res -current_display_id: Optional[str] = None - - -def create_progress_callback(): - # bind potentially thread-local config to the callback so that it uses the user thread - # config even if callback is invoked from a worker thread. - display_opts = bigframes._config.options.display - - def progress_callback( - envelope: Any, - ): - """Displays a progress bar while the query is running""" - global current_display_id - - try: - import bigframes._config - import bigframes.core.events - except ImportError: - # Since this gets called from __del__, skip if the import fails to avoid - # ImportError: sys.meta_path is None, Python is likely shutting down. - # This will allow cleanup to continue. - return - - # Publisher.publish automatically wraps raw Event objects in an - # EventEnvelope, ensuring subscribers receive a consistent contract. - assert isinstance(envelope, bigframes.core.events.EventEnvelope) - event = envelope.event - progress_bar = envelope.progress_bar - - if progress_bar == bigframes.core.events._DEFAULT: - progress_bar = display_opts.progress_bar - - if progress_bar == "auto": - progress_bar = "notebook" if in_ipython() else "terminal" +def wait_for_query_job( + query_job: bigquery.QueryJob, + max_results: Optional[int] = None, + progress_bar: Optional[str] = None, +) -> bigquery.table.RowIterator: + """Return query results. Displays a progress bar while the query is running + Args: + query_job (bigquery.QueryJob, Optional): + The job representing the execution of the query on the server. + max_results (int, Optional): + The maximum number of rows the row iterator should return. + progress_bar (str, Optional): + Which progress bar to show. + Returns: + A row iterator over the query results. + """ + if progress_bar == "auto": + progress_bar = "notebook" if in_ipython() else "terminal" + try: if progress_bar == "notebook": - import IPython.display as display - - display_html = None - - if isinstance(event, bigframes.core.events.ExecutionStarted): - # Start a new context for progress output. - current_display_id = None - - elif isinstance(event, bigframes.core.events.BigQuerySentEvent): - display_html = render_bqquery_sent_event_html(event) - - elif isinstance(event, bigframes.core.events.BigQueryRetryEvent): - display_html = render_bqquery_retry_event_html(event) - - elif isinstance(event, bigframes.core.events.BigQueryReceivedEvent): - display_html = render_bqquery_received_event_html(event) - - elif isinstance(event, bigframes.core.events.BigQueryFinishedEvent): - display_html = render_bqquery_finished_event_html(event) - - elif isinstance(event, bigframes.core.events.SessionClosed): - display_html = f"Session {event.session_id} closed." - - if display_html: - if current_display_id: - display.update_display( - display.HTML(display_html), - display_id=current_display_id, - ) - else: - current_display_id = str(random.random()) - display.display( - display.HTML(display_html), - display_id=current_display_id, - ) - + display_id = str(random.random()) + loading_bar = display.HTML(get_query_job_loading_html(query_job)) + display.display(loading_bar, display_id=display_id) + query_result = query_job.result(max_results=max_results) + query_job.reload() + display.update_display( + display.HTML(get_query_job_loading_html(query_job)), + display_id=display_id, + ) elif progress_bar == "terminal": - message = None - - if isinstance(event, bigframes.core.events.BigQuerySentEvent): - message = render_bqquery_sent_event_plaintext(event) - print(message) - elif isinstance(event, bigframes.core.events.BigQueryRetryEvent): - message = render_bqquery_retry_event_plaintext(event) - print(message) - elif isinstance(event, bigframes.core.events.BigQueryReceivedEvent): - message = render_bqquery_received_event_plaintext(event) - print(message) - elif isinstance(event, bigframes.core.events.BigQueryFinishedEvent): - message = render_bqquery_finished_event_plaintext(event) - print(message) - - return progress_callback + initial_loading_bar = get_query_job_loading_string(query_job) + print(initial_loading_bar) + query_result = query_job.result(max_results=max_results) + query_job.reload() + if initial_loading_bar != get_query_job_loading_string(query_job): + print(get_query_job_loading_string(query_job)) + else: + # No progress bar. + query_result = query_job.result(max_results=max_results) + query_job.reload() + return query_result + except api_core_exceptions.RetryError as exc: + add_feedback_link(exc) + raise + except api_core_exceptions.GoogleAPICallError as exc: + add_feedback_link(exc) + raise + except KeyboardInterrupt: + query_job.cancel() + print( + f"Requested cancellation for {query_job.job_type.capitalize()}" + f" job {query_job.job_id} in location {query_job.location}..." + ) + # begin the cancel request before immediately rethrowing + raise def wait_for_job(job: GenericJob, progress_bar: Optional[str] = None): @@ -250,16 +181,13 @@ def wait_for_job(job: GenericJob, progress_bar: Optional[str] = None): try: if progress_bar == "notebook": - import IPython.display as display - display_id = str(random.random()) loading_bar = display.HTML(get_base_job_loading_html(job)) display.display(loading_bar, display_id=display_id) job.result() job.reload() display.update_display( - display.HTML(get_base_job_loading_html(job)), - display_id=display_id, + display.HTML(get_base_job_loading_html(job)), display_id=display_id ) elif progress_bar == "terminal": inital_loading_bar = get_base_job_loading_string(job) @@ -288,80 +216,24 @@ def wait_for_job(job: GenericJob, progress_bar: Optional[str] = None): raise -def render_query_references( - *, - project_id: Optional[str], - location: Optional[str], - job_id: Optional[str], - request_id: Optional[str], -) -> str: - query_id = "" - if request_id and not job_id: - query_id = f" with request ID {project_id}:{location}.{request_id}" - return query_id - - -def render_job_link_html( - *, - project_id: Optional[str], - location: Optional[str], - job_id: Optional[str], -) -> str: - job_url = get_job_url( - project_id=project_id, - location=location, - job_id=job_id, - ) - if job_url: - job_link = ( - f' [' - f"Job {project_id}:{location}.{job_id} details]" - ) - else: - job_link = "" - return job_link - - -def render_job_link_plaintext( - *, - project_id: Optional[str], - location: Optional[str], - job_id: Optional[str], -) -> str: - job_url = get_job_url( - project_id=project_id, - location=location, - job_id=job_id, - ) - if job_url: - job_link = f" Job {project_id}:{location}.{job_id} details: {job_url}" - else: - job_link = "" - return job_link - - -def get_job_url( - *, - project_id: Optional[str], - location: Optional[str], - job_id: Optional[str], -): +def get_job_url(query_job: GenericJob): """Return url to the query job in cloud console. - + Args: + query_job (GenericJob): + The job representing the execution of the query on the server. Returns: String url. """ - if project_id is None or location is None or job_id is None: + if ( + query_job.project is None + or query_job.location is None + or query_job.job_id is None + ): return None - return ( - f"https://console.cloud.google.com/bigquery?project={project_id}" - f"&j=bq:{location}:{job_id}&page=queryresults" - ) + return f"""https://console.cloud.google.com/bigquery?project={query_job.project}&j=bq:{query_job.location}:{query_job.job_id}&page=queryresults""" -def render_bqquery_sent_event_html( - event: bigframes.core.events.BigQuerySentEvent, -) -> str: +def get_query_job_loading_html(query_job: bigquery.QueryJob): """Return progress bar html string Args: query_job (bigquery.QueryJob): @@ -369,205 +241,18 @@ def render_bqquery_sent_event_html( Returns: Html string. """ - - job_link = render_job_link_html( - project_id=event.billing_project, - location=event.location, - job_id=event.job_id, - ) - query_id = render_query_references( - project_id=event.billing_project, - location=event.location, - job_id=event.job_id, - request_id=event.request_id, - ) - query_text_details = ( - f"
SQL
"
-        f"{html.escape(event.query)}
" - ) - - return f""" - Query started{query_id}.{job_link}{query_text_details} - """ + return f"""Query job {query_job.job_id} is {query_job.state}. {get_bytes_processed_string(query_job.total_bytes_processed)}Open Job""" -def render_bqquery_sent_event_plaintext( - event: bigframes.core.events.BigQuerySentEvent, -) -> str: - """Return progress bar html string +def get_query_job_loading_string(query_job: bigquery.QueryJob): + """Return progress bar string Args: query_job (bigquery.QueryJob): The job representing the execution of the query on the server. Returns: - Html string. - """ - - job_link = render_job_link_plaintext( - project_id=event.billing_project, - location=event.location, - job_id=event.job_id, - ) - query_id = render_query_references( - project_id=event.billing_project, - location=event.location, - job_id=event.job_id, - request_id=event.request_id, - ) - - return f"Query started{query_id}.{job_link}" - - -def render_bqquery_retry_event_html( - event: bigframes.core.events.BigQueryRetryEvent, -) -> str: - """Return progress bar html string for retry event.""" - - job_link = render_job_link_html( - project_id=event.billing_project, - location=event.location, - job_id=event.job_id, - ) - query_id = render_query_references( - project_id=event.billing_project, - location=event.location, - job_id=event.job_id, - request_id=event.request_id, - ) - query_text_details = ( - f"
SQL
"
-        f"{html.escape(event.query)}
" - ) - - return f""" - Retrying query{query_id}.{job_link}{query_text_details} - """ - - -def render_bqquery_retry_event_plaintext( - event: bigframes.core.events.BigQueryRetryEvent, -) -> str: - """Return progress bar plaintext string for retry event.""" - - job_link = render_job_link_plaintext( - project_id=event.billing_project, - location=event.location, - job_id=event.job_id, - ) - query_id = render_query_references( - project_id=event.billing_project, - location=event.location, - job_id=event.job_id, - request_id=event.request_id, - ) - return f"Retrying query{query_id}.{job_link}" - - -def render_bqquery_received_event_html( - event: bigframes.core.events.BigQueryReceivedEvent, -) -> str: - """Return progress bar html string for received event.""" - - job_link = render_job_link_html( - project_id=event.billing_project, - location=event.location, - job_id=event.job_id, - ) - query_id = render_query_references( - project_id=event.billing_project, - location=event.location, - job_id=event.job_id, - request_id=None, - ) - - query_plan_details = "" - if event.query_plan: - plan_str = "\n".join([str(entry) for entry in event.query_plan]) - query_plan_details = ( - f"
Query Plan
"
-            f"{html.escape(plan_str)}
" - ) - - return f""" - Query{query_id} is {event.state}.{job_link}{query_plan_details} - """ - - -def render_bqquery_received_event_plaintext( - event: bigframes.core.events.BigQueryReceivedEvent, -) -> str: - """Return progress bar plaintext string for received event.""" - - job_link = render_job_link_plaintext( - project_id=event.billing_project, - location=event.location, - job_id=event.job_id, - ) - query_id = render_query_references( - project_id=event.billing_project, - location=event.location, - job_id=event.job_id, - request_id=None, - ) - return f"Query{query_id} is {event.state}.{job_link}" - - -def render_bqquery_finished_event_html( - event: bigframes.core.events.BigQueryFinishedEvent, -) -> str: - """Return progress bar html string for finished event.""" - - bytes_str = "" - if event.total_bytes_processed is not None: - bytes_str = f" {humanize.naturalsize(event.total_bytes_processed)}" - - slot_time_str = "" - if event.slot_millis is not None: - slot_time = datetime.timedelta(milliseconds=event.slot_millis) - slot_time_str = f" in {humanize.naturaldelta(slot_time)} of slot time" - - job_link = render_job_link_html( - project_id=event.billing_project, - location=event.location, - job_id=event.job_id, - ) - query_id = render_query_references( - project_id=event.billing_project, - location=event.location, - job_id=event.job_id, - request_id=None, - ) - return f""" - Query processed{bytes_str}{slot_time_str}{query_id}.{job_link} + String """ - - -def render_bqquery_finished_event_plaintext( - event: bigframes.core.events.BigQueryFinishedEvent, -) -> str: - """Return progress bar plaintext string for finished event.""" - - bytes_str = "" - if event.total_bytes_processed is not None: - size_str = humanize.naturalsize(event.total_bytes_processed) - bytes_str = f" {size_str} processed." - - slot_time_str = "" - if event.slot_millis is not None: - slot_time = datetime.timedelta(milliseconds=event.slot_millis) - slot_time_str = f" Slot time: {humanize.naturaldelta(slot_time)}." - - job_link = render_job_link_plaintext( - project_id=event.billing_project, - location=event.location, - job_id=event.job_id, - ) - query_id = render_query_references( - project_id=event.billing_project, - location=event.location, - job_id=event.job_id, - request_id=None, - ) - return f"Query{query_id} finished.{bytes_str}{slot_time_str}{job_link}" + return f"""Query job {query_job.job_id} is {query_job.state}.{get_bytes_processed_string(query_job.total_bytes_processed)} \n{get_job_url(query_job)}""" def get_base_job_loading_html(job: GenericJob): @@ -578,15 +263,7 @@ def get_base_job_loading_html(job: GenericJob): Returns: Html string. """ - return f"""{job.job_type.capitalize()} job {job.job_id} is { - job.state - }. Open Job""" + return f"""{job.job_type.capitalize()} job {job.job_id} is {job.state}. Open Job""" def get_base_job_loading_string(job: GenericJob): @@ -597,13 +274,7 @@ def get_base_job_loading_string(job: GenericJob): Returns: String """ - return f"""{job.job_type.capitalize()} job {job.job_id} is {job.state}. \n{ - get_job_url( - project_id=job.job_id, - location=job.location, - job_id=job.job_id, - ) - }""" + return f"""{job.job_type.capitalize()} job {job.job_id} is {job.state}. \n{get_job_url(job)}""" def get_formatted_time(val): @@ -615,8 +286,7 @@ def get_formatted_time(val): Duration string """ try: - delta = datetime.timedelta(milliseconds=float(val)) - return humanize.naturaldelta(delta) + return humanize.naturaldelta(datetime.timedelta(milliseconds=float(val))) except Exception: return val @@ -635,10 +305,7 @@ def get_formatted_bytes(val): def get_bytes_processed_string(val: Any): - """Try to get bytes processed string. - - Return empty if passed non int value. - """ + """Try to get bytes processed string. Return empty if passed non int value""" bytes_processed_string = "" if isinstance(val, int): bytes_processed_string = f"""{get_formatted_bytes(val)} processed. """ @@ -647,8 +314,4 @@ def get_bytes_processed_string(val: Any): def in_ipython(): """Return True iff we're in a colab-like IPython.""" - try: - import IPython - except (ImportError, NameError): - return False return hasattr(IPython.get_ipython(), "kernel") diff --git a/bigframes/functions/__init__.py b/bigframes/functions/__init__.py deleted file mode 100644 index 86119717d7b..00000000000 --- a/bigframes/functions/__init__.py +++ /dev/null @@ -1,18 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -from bigframes.functions.function import Udf - -__all__ = [ - "Udf", -] diff --git a/bigframes/functions/_function_client.py b/bigframes/functions/_function_client.py deleted file mode 100644 index 69f99b50276..00000000000 --- a/bigframes/functions/_function_client.py +++ /dev/null @@ -1,582 +0,0 @@ -# Copyright 2023 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -from __future__ import annotations - -import logging -import os -import re -import shutil -import tempfile -import textwrap -import types -import warnings -from typing import Any, cast - -import google.api_core.exceptions -import google.api_core.retry -import requests -from google.cloud import bigquery, functions_v2 - -import bigframes.exceptions as bfe -import bigframes.formatting_helpers as bf_formatting -import bigframes.functions.function_template as bff_template -import bigframes.functions.udf_def as udf_def -from bigframes.functions import _utils - -logger = logging.getLogger(__name__) - -# https://cloud.google.com/sdk/gcloud/reference/functions/deploy#--ingress-settings -_INGRESS_SETTINGS_MAP = types.MappingProxyType( - { - "all": functions_v2.ServiceConfig.IngressSettings.ALLOW_ALL, - "internal-only": functions_v2.ServiceConfig.IngressSettings.ALLOW_INTERNAL_ONLY, - "internal-and-gclb": functions_v2.ServiceConfig.IngressSettings.ALLOW_INTERNAL_AND_GCLB, - } -) - -# https://cloud.google.com/functions/docs/reference/rest/v2/projects.locations.functions#vpconnectoregresssettings -_VPC_EGRESS_SETTINGS_MAP = types.MappingProxyType( - { - "all": functions_v2.ServiceConfig.VpcConnectorEgressSettings.ALL_TRAFFIC, - "private-ranges-only": functions_v2.ServiceConfig.VpcConnectorEgressSettings.PRIVATE_RANGES_ONLY, - "unspecified": functions_v2.ServiceConfig.VpcConnectorEgressSettings.VPC_CONNECTOR_EGRESS_SETTINGS_UNSPECIFIED, - } -) - -# BQ managed functions (@udf) currently only support Python 3.11. -_MANAGED_FUNC_PYTHON_VERSION = "python-3.11" - - -class FunctionClient: - # TODO(b/392707725): Convert all necessary parameters for cloud function - # deployment into method parameters. - def __init__( - self, - gcp_project_id: str, - bq_location: str, - bq_client: bigquery.Client, - bq_connection_manager, - cloud_functions_client: functions_v2.FunctionServiceClient, - publisher, - ): - self._gcp_project_id = gcp_project_id - self._bq_location = bq_location - self._bq_client = bq_client - self._bq_connection_manager = bq_connection_manager - self._publisher = publisher - self._cloud_functions_client = cloud_functions_client - - self._cf_location = _utils.gcf_location_from_bq_location(bq_location) - - @property - def cloudfunctions_region(self) -> str: - return self._cf_location - - def _create_bq_connection( - self, - connection_id: str, - bq_project_id: str, - ) -> None: - self._bq_connection_manager.create_bq_connection( - bq_project_id, - self._bq_location, - connection_id, - "run.invoker", - ) - - def _ensure_dataset_exists(self, dataset_ref: bigquery.DatasetReference) -> None: - # Make sure the dataset exists, i.e. if it doesn't exist, go ahead and - # create it. - try: - # This check does not require bigquery.datasets.create IAM - # permission. So, if the data set already exists, then user can work - # without having that permission. - self._bq_client.get_dataset(dataset_ref) - except google.api_core.exceptions.NotFound: - # This requires bigquery.datasets.create IAM permission. - dataset = bigquery.Dataset(dataset_ref) - dataset.location = self._bq_location - self._bq_client.create_dataset(dataset, exists_ok=True) - - def _create_bq_function(self, create_function_ddl: str) -> None: - # TODO(swast): plumb through the original, user-facing api_name. - import bigframes.session._io.bigquery - - _, query_job = bigframes.session._io.bigquery.start_query_with_job( - self._bq_client, - create_function_ddl, - job_config=bigquery.QueryJobConfig(), - location=self._bq_location, - project=None, - timeout=None, - metrics=None, - publisher=self._publisher, - ) - logger.info(f"Created bigframes function {query_job.ddl_target_routine}") - - def _format_function_options(self, function_options: dict) -> str: - def format_val(val): - if isinstance(val, str): - return f"'{val}'" - if isinstance(val, (list, tuple)): - return str(list(val)) - return str(val) - - return ", ".join( - [ - f"{key}={format_val(val)}" - for key, val in function_options.items() - if val is not None - ] - ) - - def create_bq_remote_function( - self, - routine_ref: bigquery.RoutineReference, - udf_def: udf_def.RemoteFunctionConfig, - maybe_reuse: bool, - try_create_connection: bool, - ): - """Create a BigQuery remote function given the artifacts of a user defined - function and the http endpoint of a corresponding cloud function.""" - - if maybe_reuse: - existing_rf_spec = self.get_remote_function_specs(routine_ref) - if existing_rf_spec and existing_rf_spec == udf_def: - logger.info(f"Remote function {str(routine_ref)} already exists.") - return - - if try_create_connection: - self._create_bq_connection(udf_def.connection_id, routine_ref.project) - - # Create BQ function - # https://cloud.google.com/bigquery/docs/reference/standard-sql/remote-functions#create_a_remote_function_2 - - remote_function_options = { - "endpoint": udf_def.endpoint, - "max_batching_rows": udf_def.max_batching_rows, - } - - if udf_def.bq_metadata: - # We are using the description field to store this structured - # bigframes specific metadata for the lack of a better option - remote_function_options["description"] = udf_def.bq_metadata - - remote_function_options_str = self._format_function_options( - remote_function_options - ) - - import bigframes.core.sql - import bigframes.core.utils - - # removes anything that isn't letter, number or underscore - _validate_routine_name(routine_ref.routine_id) - bq_function_name_escaped = bigframes.core.sql.identifier(routine_ref.routine_id) - create_function_ddl = f""" - CREATE OR REPLACE FUNCTION `{routine_ref.project}.{routine_ref.dataset_id}`.{bq_function_name_escaped}({udf_def.signature.to_sql_input_signature()}) - RETURNS {udf_def.signature.with_devirtualize().output.sql_type} - REMOTE WITH CONNECTION `{routine_ref.project}.{self._bq_location}.{udf_def.connection_id}` - OPTIONS ({remote_function_options_str})""" - - logger.info(f"Creating BQ remote function: {create_function_ddl}") - - self._ensure_dataset_exists( - bigquery.DatasetReference(routine_ref.project, routine_ref.dataset_id) - ) - self._create_bq_function(create_function_ddl) - - def provision_bq_managed_function( - self, - routine_ref: bigquery.RoutineReference, - config: udf_def.ManagedFunctionConfig, - ): - """Create a BigQuery managed function.""" - - # TODO(b/406283812): Expose the capability to pass down - # capture_references=True in the public udf API. - if ( - config.capture_references - and (python_version := _utils.get_python_version()) - != _MANAGED_FUNC_PYTHON_VERSION - ): - raise bf_formatting.create_exception_with_feedback_link( - NotImplementedError, - f"Capturing references for udf is currently supported only in Python version {_MANAGED_FUNC_PYTHON_VERSION}, you are running {python_version}.", - ) - - # Create BQ managed function. - bq_function_args = config.signature.to_sql_input_signature() - bq_function_return_type = config.signature.with_devirtualize().output.sql_type - - managed_function_options: dict[str, Any] = { - "runtime_version": _MANAGED_FUNC_PYTHON_VERSION, - "entry_point": "bigframes_handler", - } - if config.max_batching_rows: - managed_function_options["max_batching_rows"] = config.max_batching_rows - if config.container_cpu: - managed_function_options["container_cpu"] = config.container_cpu - if config.container_memory: - managed_function_options["container_memory"] = config.container_memory - - # Augment user package requirements with any internal package - # requirements. - packages = _utils.get_updated_package_requirements( - config.code.package_requirements or [], - config.signature.is_row_processor, - config.capture_references, - ignore_package_version=True, - ) - if packages: - managed_function_options["packages"] = packages - managed_function_options_str = self._format_function_options( - managed_function_options - ) - - persistent_func_id = ( - f"`{routine_ref.project}.{routine_ref.dataset_id}.{routine_ref.routine_id}`" - ) - - with_connection_clause = ( - ( - f"WITH CONNECTION `{routine_ref.project}.{self._bq_location}.{config.bq_connection_id}`" - ) - if config.bq_connection_id - else "" - ) - - # Generate the complete Python code block for the managed Python UDF, - # including the user's function, necessary imports, and the BigQuery - # handler wrapper. - python_code_block = bff_template.generate_managed_function_code( - config.code, config.signature, config.capture_references - ) - - create_function_ddl = ( - textwrap.dedent( - f""" - CREATE OR REPLACE FUNCTION {persistent_func_id}({bq_function_args}) - RETURNS {bq_function_return_type} - LANGUAGE python - {with_connection_clause} - OPTIONS ({managed_function_options_str}) - AS r''' - __UDF_PLACE_HOLDER__ - ''' - """ - ) - .strip() - .replace("__UDF_PLACE_HOLDER__", python_code_block) - ) - - self._ensure_dataset_exists( - bigquery.DatasetReference(routine_ref.project, routine_ref.dataset_id) - ) - self._create_bq_function(create_function_ddl) - - def get_cloud_function_fully_qualified_parent(self): - "Get the fully qualilfied parent for a cloud function." - return self._cloud_functions_client.common_location_path( - self._gcp_project_id, self._cf_location - ) - - def get_cloud_function_fully_qualified_name(self, name): - "Get the fully qualilfied name for a cloud function." - return self._cloud_functions_client.function_path( - self._gcp_project_id, self._cf_location, name - ) - - def get_cloud_function_endpoint(self, name) -> str | None: - """Get the http endpoint of a cloud function if it exists.""" - fully_qualified_name = self.get_cloud_function_fully_qualified_name(name) - try: - response = self._cloud_functions_client.get_function( - name=fully_qualified_name - ) - return response.service_config.uri - except google.api_core.exceptions.NotFound: - pass - return None - - def _generate_cloud_function_code( - self, - code_def: udf_def.CodeDef, - directory, - *, - udf_signature: udf_def.UdfSignature, - ): - """Generate the cloud function code for a given user defined function.""" - - # requirements.txt - if code_def.package_requirements: - requirements_txt = os.path.join(directory, "requirements.txt") - with open(requirements_txt, "w") as f: - f.write("\n".join(code_def.package_requirements)) - - # main.py - entry_point = bff_template.generate_cloud_function_main_code( - code_def, - directory, - udf_signature=udf_signature, - ) - return entry_point - - @google.api_core.retry.Retry( - predicate=google.api_core.retry.if_exception_type(ValueError), - initial=1.0, - maximum=10.0, - multiplier=2.0, - deadline=300.0, # Wait up to 5 minutes for propagation - ) - def _get_cloud_function_endpoint_with_retry(self, name): - endpoint = self.get_cloud_function_endpoint(name) - if not endpoint: - # Raising ValueError triggers the retry predicate - raise ValueError(f"Endpoint for {name} not yet available.") - return endpoint - - def create_cloud_function( - self, - name: str, - func_def: udf_def.CloudRunFunctionConfig, - ) -> str: - """Create a cloud function from the given user defined function.""" - - config = func_def - - # Build and deploy folder structure containing cloud function - with tempfile.TemporaryDirectory() as scratch_dir: - # Keep the generated sources in a subdirectory so the archive can be - # written inside the 0700 TemporaryDirectory. shutil.make_archive - # appends ".zip" to base_name, so archiving `directory` into itself - # would leave a world-readable copy of the (pickled) user code as a - # sibling of the temp dir that also survives the cleanup. - directory = os.path.join(scratch_dir, "src") - os.mkdir(directory) - entry_point = self._generate_cloud_function_code( - config.code, - directory, - udf_signature=config.signature, - ) - archive_path = shutil.make_archive( - os.path.join(scratch_dir, "source"), "zip", directory - ) - - # We are creating cloud function source code from the currently running - # python version. Use the same version to deploy. This is necessary - # because cloudpickle serialization done in one python version and - # deserialization done in another python version doesn't work. - # TODO(shobs): Figure out how to achieve version compatibility, specially - # when pickle (internally used by cloudpickle) guarantees that: - # https://docs.python.org/3/library/pickle.html#:~:text=The%20pickle%20serialization%20format%20is,unique%20breaking%20change%20language%20boundary. - python_version = _utils.get_python_version(is_compat=True) - - # Determine an upload URL for user code - upload_url_request = functions_v2.GenerateUploadUrlRequest( - kms_key_name=config.kms_key_name - ) - upload_url_request.parent = self.get_cloud_function_fully_qualified_parent() - upload_url_response = self._cloud_functions_client.generate_upload_url( - request=upload_url_request - ) - - # Upload the code to GCS - with open(archive_path, "rb") as f: - response = requests.put( - upload_url_response.upload_url, - data=f, - headers={"content-type": "application/zip"}, - ) - if response.status_code != 200: - raise bf_formatting.create_exception_with_feedback_link( - RuntimeError, - f"Failed to upload user code. code={response.status_code}, reason={response.reason}, text={response.text}", - ) - - # Deploy Cloud Function - create_function_request = functions_v2.CreateFunctionRequest() - create_function_request.parent = ( - self.get_cloud_function_fully_qualified_parent() - ) - create_function_request.function_id = name - function = functions_v2.Function() - function.name = self.get_cloud_function_fully_qualified_name(name) - function.build_config = functions_v2.BuildConfig() - function.build_config.runtime = python_version - function.build_config.entry_point = entry_point - function.build_config.source = functions_v2.Source() - function.build_config.source.storage_source = functions_v2.StorageSource() - function.build_config.source.storage_source.bucket = ( - upload_url_response.storage_source.bucket - ) - function.build_config.source.storage_source.object_ = ( - upload_url_response.storage_source.object_ - ) - if config.docker_repository is not None: - function.build_config.docker_repository = config.docker_repository - - if config.cloud_build_service_account is not None: - canonical_cloud_build_service_account = ( - config.cloud_build_service_account - if "/" in config.cloud_build_service_account - else f"projects/{self._gcp_project_id}/serviceAccounts/{config.cloud_build_service_account}" - ) - function.build_config.service_account = ( - canonical_cloud_build_service_account - ) - - function.service_config = functions_v2.ServiceConfig() - if config.memory_mib is not None: - function.service_config.available_memory = f"{config.memory_mib}Mi" - if config.cpus is not None: - function.service_config.available_cpu = str(config.cpus) - if config.timeout_seconds is not None: - if config.timeout_seconds > 1200: - raise bf_formatting.create_exception_with_feedback_link( - ValueError, - "BigQuery remote function can wait only up to 20 minutes" - ", see for more details " - "https://cloud.google.com/bigquery/quotas#remote_function_limits.", - ) - function.service_config.timeout_seconds = config.timeout_seconds - if config.max_instance_count is not None: - function.service_config.max_instance_count = config.max_instance_count - if config.vpc_connector is not None: - function.service_config.vpc_connector = config.vpc_connector - vpc_connector_egress_settings = config.vpc_connector_egress_settings - if config.vpc_connector_egress_settings is None: - msg = bfe.format_message( - "The 'vpc_connector_egress_settings' was not specified. Defaulting to 'private-ranges-only'.", - ) - warnings.warn(msg, category=UserWarning) - vpc_connector_egress_settings = "private-ranges-only" - if config.vpc_connector_egress_settings not in _VPC_EGRESS_SETTINGS_MAP: - raise bf_formatting.create_exception_with_feedback_link( - ValueError, - f"'{config.vpc_connector_egress_settings}' is not one of the supported vpc egress settings values: {list(_VPC_EGRESS_SETTINGS_MAP)}", - ) - function.service_config.vpc_connector_egress_settings = cast( - functions_v2.ServiceConfig.VpcConnectorEgressSettings, - _VPC_EGRESS_SETTINGS_MAP[vpc_connector_egress_settings], - ) - if config.cloud_run_service_account: - function.service_config.service_account_email = ( - config.cloud_run_service_account - ) - if config.concurrency: - function.service_config.max_instance_request_concurrency = ( - config.concurrency - ) - - # Functions framework use environment variables to pass config to gunicorn - # See https://github.com/GoogleCloudPlatform/functions-framework-python/issues/241 - # Code: https://github.com/GoogleCloudPlatform/functions-framework-python/blob/v3.10.1/src/functions_framework/_http/gunicorn.py#L37-L43 - env_vars = {} - if config.workers: - env_vars["WORKERS"] = str(config.workers) - if config.threads: - env_vars["THREADS"] = str(config.threads) - if env_vars: - function.service_config.environment_variables = env_vars - - if config.ingress_settings not in _INGRESS_SETTINGS_MAP: - raise bf_formatting.create_exception_with_feedback_link( - ValueError, - f"'{config.ingress_settings}' not one of the supported ingress settings values: {list(_INGRESS_SETTINGS_MAP)}", - ) - function.service_config.ingress_settings = cast( - functions_v2.ServiceConfig.IngressSettings, - _INGRESS_SETTINGS_MAP[config.ingress_settings], - ) - if config.kms_key_name: - function.kms_key_name = config.kms_key_name - create_function_request.function = function - - # Create the cloud function and wait for it to be ready to use - endpoint = None - try: - operation = self._cloud_functions_client.create_function( - request=create_function_request - ) - # operation.result() returns the Function object upon completion - function_obj = operation.result() - endpoint = function_obj.service_config.uri - - # Cleanup - os.remove(archive_path) - except google.api_core.exceptions.AlreadyExists: - # b/437124912: The most likely scenario is that - # `create_function` had a retry due to a network issue. The - # retried request then fails because the first call actually - # succeeded, but we didn't get the successful response back. - # - # Since the function name was randomly chosen to avoid - # conflicts, we know the AlreadyExist can only happen because - # we created it. This error is safe to ignore. - pass - - # Fetch the endpoint with retries if it wasn't returned by the operation - if not endpoint: - try: - endpoint = self._get_cloud_function_endpoint_with_retry(name) - except Exception as e: - raise bf_formatting.create_exception_with_feedback_link( - ValueError, f"Couldn't fetch the http endpoint: {e}" - ) - - logger.info(f"Successfully created cloud function {name} with uri ({endpoint})") - return endpoint - - def get_remote_function_specs( - self, remote_function_name: bigquery.RoutineReference - ) -> udf_def.RemoteFunctionConfig | None: - """Check whether a remote function already exists for the udf.""" - try: - routine = self._bq_client.get_routine(str(remote_function_name)) - if routine.reference == remote_function_name: - try: - return udf_def.RemoteFunctionConfig.from_bq_routine(routine) - except udf_def.ReturnTypeMissingError: - # The remote function exists, but it's missing a return type. - # Something is wrong with the function, so we should replace it. - return None - except google.api_core.exceptions.NotFound: - # The dataset might not exist, in which case the remote function doesn't, either. - # Note: list_routines doesn't make an API request until we iterate on the response object. - pass - return None - - def delete_routine(self, routine_name: bigquery.RoutineReference) -> None: - self._bq_client.delete_routine(str(routine_name), not_found_ok=True) - - def delete_cloud_function(self, cloud_function_name: str) -> None: - try: - self._cloud_functions_client.delete_function( - name=self.get_cloud_function_fully_qualified_name(cloud_function_name) - ) - except google.api_core.exceptions.NotFound: - # The dataset might not exist, in which case the remote function doesn't, either. - pass - - -def _validate_routine_name(name: str) -> None: - """Validate that the given name is a valid BigQuery routine name.""" - # Routine IDs can contain only letters (a-z, A-Z), numbers (0-9), or underscores (_) - # must also start with a letter or underscore only - if not re.match(r"^[a-zA-Z_][a-zA-Z0-9_]*$", name): - raise ValueError( - "Routine ID can contain only letters (a-z, A-Z), numbers (0-9), or underscores (_)" - ) diff --git a/bigframes/functions/_function_session.py b/bigframes/functions/_function_session.py deleted file mode 100644 index 2bc2b597372..00000000000 --- a/bigframes/functions/_function_session.py +++ /dev/null @@ -1,912 +0,0 @@ -# Copyright 2023 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -from __future__ import annotations - -import functools -import logging -import random -import string -import threading -import time -import warnings -from typing import ( - TYPE_CHECKING, - Literal, - Optional, - Sequence, - Union, -) - -from google.cloud import ( - bigquery, -) - -import bigframes.exceptions as bfe -import bigframes.formatting_helpers as bf_formatting -from bigframes import clients -from bigframes.functions import _function_client, _utils, udf_def -from bigframes.functions import function as bq_functions -from bigframes.functions._utils import ( - _BIGFRAMES_FUNCTION_PREFIX, - _BQ_FUNCTION_NAME_SEPERATOR, - _GCF_FUNCTION_NAME_SEPERATOR, -) - -if TYPE_CHECKING: - from bigframes.session import anonymous_dataset - - -_DEFAULT_FUNCTION_MEMORY_MIB = 1024 - - -logger = logging.getLogger(__name__) - - -class FunctionSession: - """Session to manage bigframes functions.""" - - def __init__( - self, - functions_client: _function_client.FunctionClient, - dataset_manager: anonymous_dataset.AnonymousDatasetManager, - default_connection: str, - location: str, - session_id: str, - manage_connections: bool, - ): - self._temp_cloud_functions: set[str] = set() - self._temp_remote_functions: set[bigquery.RoutineReference] = set() - - # Lock to synchronize the update of the session artifacts - self._artifacts_lock = threading.Lock() - - self._deployed_routines: set[bytes] = set() - self._deploying_routines: set[bytes] = set() - - self._function_client: _function_client.FunctionClient = functions_client - self._dataset_manager: anonymous_dataset.AnonymousDatasetManager = ( - dataset_manager - ) - self._default_connection: str = default_connection - self._location: str = location - self._session_id: str = session_id - self._manage_connections: bool = manage_connections - - @property - def session_id(self) -> str: - return self._session_id - - @property - def default_dataset(self) -> bigquery.DatasetReference: - # We defer this as a property since this can actually take a query to determine - # which dataset it is. - return self._dataset_manager.dataset - - def _resolve_dataset_reference( - self, - dataset: Optional[str], - ) -> bigquery.DatasetReference: - """ - Resolves the dataset reference for the bigframes function. - """ - return ( - bigquery.DatasetReference.from_string( - dataset, default_project=self.default_dataset.project - ) - if dataset - else self.default_dataset - ) - - def _resolve_routine_reference( - self, - function_name: str, - dataset: Optional[bigquery.DatasetReference] = None, - ) -> bigquery.RoutineReference: - """Resolves the routine reference for a BQ routine.""" - dataset_ref = dataset if dataset else self.default_dataset - return dataset_ref.routine(function_name) - - def _resolve_bigquery_connection_id( - self, - dataset_ref: bigquery.DatasetReference, - bigquery_connection: Optional[str] = None, - ) -> str: - """Resolves BigQuery connection id.""" - if not bigquery_connection: - bigquery_connection = self._default_connection - - bigquery_connection = clients.get_canonical_bq_connection_id( - bigquery_connection, - default_project=dataset_ref.project, - default_location=self._location, - ) - # Guaranteed to be the form of .. - ( - gcp_project_id, - bq_connection_location, - bq_connection_id, - ) = bigquery_connection.split(".") - if gcp_project_id.casefold() != dataset_ref.project.casefold(): - raise bf_formatting.create_exception_with_feedback_link( - ValueError, - "The project_id does not match BigQuery connection " - f"gcp_project_id: {dataset_ref.project}.", - ) - if bq_connection_location.casefold() != self._location.casefold(): - raise bf_formatting.create_exception_with_feedback_link( - ValueError, - "The location does not match BigQuery connection location: " - f"{self._location}.", - ) - return bq_connection_id - - def _add_temp_cloud_function(self, gcf_path: str): - with self._artifacts_lock: - self._temp_cloud_functions.add(gcf_path) - - def _add_temp_remote_function(self, bqrf_routine: bigquery.RoutineReference): - with self._artifacts_lock: - self._temp_remote_functions.add(bqrf_routine) - - def _deploy_managed_function( - self, - config: udf_def.ManagedFunctionConfig, - name: str, - temp: bool, - dataset: Optional[bigquery.DatasetReference] = None, - ) -> udf_def.BigqueryUdf: - routine_ref = self._resolve_routine_reference(name, dataset=dataset) - if temp: - self._add_temp_remote_function(routine_ref) - self._function_client.provision_bq_managed_function( - routine_ref=routine_ref, config=config - ) - return udf_def.BigqueryUdf( - routine_ref=routine_ref, - signature=config.signature, - ) - - def _deploy_udf( - self, - bq_udf: udf_def.PythonUdf, - ) -> udf_def.BigqueryUdf: - """Deploys a UDF to BigQuery if not already deployed.""" - udf_hash = bq_udf.stable_hash() - - config = bq_udf.to_managed_function_config() - bq_function_name = get_managed_function_name(config, self.session_id) - routine_ref = self._resolve_routine_reference(bq_function_name) - while True: - with self._artifacts_lock: - if udf_hash in self._deployed_routines: - return udf_def.BigqueryUdf( - routine_ref=routine_ref, - signature=bq_udf.signature, - ) - - if udf_hash not in self._deploying_routines: - self._deploying_routines.add(udf_hash) - break - - time.sleep(0.1) - try: - self._function_client.provision_bq_managed_function( - routine_ref=routine_ref, config=config - ) - except Exception: - with self._artifacts_lock: - self._deploying_routines.discard(udf_hash) - raise - self._add_temp_remote_function(routine_ref) - with self._artifacts_lock: - self._deploying_routines.discard(udf_hash) - self._deployed_routines.add(udf_hash) - return udf_def.BigqueryUdf( - routine_ref=routine_ref, - signature=bq_udf.signature, - ) - - def clean_up(self): - """Delete function artifacts in the current session.""" - with self._artifacts_lock: - for bqrf_routine in self._temp_remote_functions: - self._function_client.delete_routine(bqrf_routine) - for gcf_name in self._temp_cloud_functions: - self._function_client.delete_cloud_function(gcf_name) - - self._temp_remote_functions.clear() - self._temp_cloud_functions.clear() - - # Inspired by @udf decorator implemented in ibis-bigquery package - # https://github.com/ibis-project/ibis-bigquery/blob/main/ibis_bigquery/udf/__init__.py - # which has moved as @js to the ibis package - # https://github.com/ibis-project/ibis/blob/master/ibis/backends/bigquery/udf/__init__.py - def remote_function( - self, - *, - input_types: Union[None, type, Sequence[type]] = None, - output_type: Optional[type] = None, - dataset: Optional[str] = None, - bigquery_connection: Optional[str] = None, - reuse: bool = True, - name: Optional[str] = None, - packages: Optional[Sequence[str]] = None, - cloud_function_service_account: str, - cloud_function_kms_key_name: Optional[str] = None, - cloud_function_docker_repository: Optional[str] = None, - max_batching_rows: Optional[int] = None, - cloud_function_timeout: Optional[int] = 600, - cloud_function_max_instances: Optional[int] = None, - cloud_function_vpc_connector: Optional[str] = None, - cloud_function_vpc_connector_egress_settings: Optional[ - Literal["all", "private-ranges-only", "unspecified"] - ] = None, - cloud_function_memory_mib: Optional[int] = None, - cloud_function_cpus: Optional[float] = None, - cloud_function_ingress_settings: Literal[ - "all", "internal-only", "internal-and-gclb" - ] = "internal-only", - cloud_build_service_account: Optional[str] = None, - ): - """Decorator to turn a user defined function into a BigQuery remote function. - - .. deprecated:: 0.0.1 - This is an internal method. Please use :func:`bigframes.pandas.remote_function` instead. - - .. warning:: - To use remote functions with Bigframes 2.0 and onwards, please (preferred) - set an explicit user-managed ``cloud_function_service_account`` or (discouraged) - set ``cloud_function_service_account`` to use the Compute Engine service account - by setting it to `"default"`. - See, https://cloud.google.com/functions/docs/securing/function-identity. - - .. note:: - Please make sure following is setup before using this API: - - 1. Have the below APIs enabled for your project: - - * BigQuery Connection API - * Cloud Functions API - * Cloud Run API - * Cloud Build API - * Artifact Registry API - * Cloud Resource Manager API - - This can be done from the cloud console (change `PROJECT_ID` to yours): - https://console.cloud.google.com/apis/enableflow?apiid=bigqueryconnection.googleapis.com,cloudfunctions.googleapis.com,run.googleapis.com,cloudbuild.googleapis.com,artifactregistry.googleapis.com,cloudresourcemanager.googleapis.com&project=PROJECT_ID - - Or from the gcloud CLI: - - `$ gcloud services enable bigqueryconnection.googleapis.com cloudfunctions.googleapis.com run.googleapis.com cloudbuild.googleapis.com artifactregistry.googleapis.com cloudresourcemanager.googleapis.com` - - 2. Have following IAM roles enabled for you: - - * BigQuery Data Editor (roles/bigquery.dataEditor) - * BigQuery Connection Admin (roles/bigquery.connectionAdmin) - * Cloud Functions Developer (roles/cloudfunctions.developer) - * Service Account User (roles/iam.serviceAccountUser) on the service account `PROJECT_NUMBER-compute@developer.gserviceaccount.com` - * Storage Object Viewer (roles/storage.objectViewer) - * Project IAM Admin (roles/resourcemanager.projectIamAdmin) (Only required if the bigquery connection being used is not pre-created and is created dynamically with user credentials.) - - 3. Either the user has setIamPolicy privilege on the project, or a BigQuery connection is pre-created with necessary IAM role set: - - 1. To create a connection, follow https://cloud.google.com/bigquery/docs/reference/standard-sql/remote-functions#create_a_connection - 2. To set up IAM, follow https://cloud.google.com/bigquery/docs/reference/standard-sql/remote-functions#grant_permission_on_function - - Alternatively, the IAM could also be setup via the gcloud CLI: - - `$ gcloud projects add-iam-policy-binding PROJECT_ID --member="serviceAccount:CONNECTION_SERVICE_ACCOUNT_ID" --role="roles/run.invoker"`. - - Args: - input_types (type or sequence(type), Optional): - For scalar user defined function it should be the input type or - sequence of input types. The supported scalar input types are - `bool`, `bytes`, `float`, `int`, `str`. For row processing user - defined function (i.e. functions that receive a single input - representing a row in form of a Series), type `Series` should be - specified. - output_type (type, Optional): - Data type of the output in the user defined function. If the - user defined function returns an array, then `list[type]` should - be specified. The supported output types are `bool`, `bytes`, - `float`, `int`, `str`, `list[bool]`, `list[float]`, `list[int]` - and `list[str]`. - dataset (str, Optional): - Dataset in which to create a BigQuery remote function. It should be in - `.` or `` format. If this - parameter is not provided then session dataset id is used. - bigquery_connection (str, Optional): - Name of the BigQuery connection in the form of `CONNECTION_ID` or - `LOCATION.CONNECTION_ID` or `PROJECT_ID.LOCATION.CONNECTION_ID`. - If this param is not provided then the bigquery connection from the session - would be used. If it is pre created in the same location as the - `bigquery_client.location` then it would be used, otherwise it is created - dynamically using the `bigquery_connection_client` assuming the user has necessary - priviliges. The PROJECT_ID should be the same as the BigQuery connection project. - reuse (bool, Optional): - Reuse the remote function if already exists. - `True` by default, which will result in reusing an existing remote - function and corresponding cloud function that was previously - created (if any) for the same udf. - Please note that for an unnamed (i.e. created without an explicit - `name` argument) remote function, the BigQuery DataFrames - session id is attached in the cloud artifacts names. So for the - effective reuse across the sessions it is recommended to create - the remote function with an explicit `name`. - Setting it to `False` would force creating a unique remote function. - If the required remote function does not exist then it would be - created irrespective of this param. - name (str, Optional): - Explicit name of the persisted BigQuery remote function. Use it with - caution, because two users working in the same project and dataset - could overwrite each other's remote functions if they use the same - persistent name. When an explicit name is provided, any session - specific clean up (``bigframes.session.Session.close``/ - ``bigframes.pandas.close_session``/ - ``bigframes.pandas.reset_session``/ - ``bigframes.pandas.clean_up_by_session_id``) does not clean up - the function, and leaves it for the user to manage the function - and the associated cloud function directly. - packages (str[], Optional): - Explicit name of the external package dependencies. Each dependency - is added to the `requirements.txt` as is, and can be of the form - supported in https://pip.pypa.io/en/stable/reference/requirements-file-format/. - cloud_function_service_account (str): - Service account to use for the cloud functions. If "default" provided then - the default service account would be used. See - https://cloud.google.com/functions/docs/securing/function-identity - for more details. Please make sure the service account has the - necessary IAM permissions configured as described in - https://cloud.google.com/functions/docs/reference/iam/roles#additional-configuration. - cloud_function_kms_key_name (str, Optional): - Customer managed encryption key to protect cloud functions and - related data at rest. This is of the format - projects/PROJECT_ID/locations/LOCATION/keyRings/KEYRING/cryptoKeys/KEY. - Read https://cloud.google.com/functions/docs/securing/cmek for - more details including granting necessary service accounts - access to the key. - cloud_function_docker_repository (str, Optional): - Docker repository created with the same encryption key as - `cloud_function_kms_key_name` to store encrypted artifacts - created to support the cloud function. This is of the format - projects/PROJECT_ID/locations/LOCATION/repositories/REPOSITORY_NAME. - For more details see - https://cloud.google.com/functions/docs/securing/cmek#before_you_begin. - max_batching_rows (int, Optional): - The maximum number of rows to be batched for processing in the - BQ remote function. Default value is 1000. A lower number can be - passed to avoid timeouts in case the user code is too complex to - process large number of rows fast enough. A higher number can be - used to increase throughput in case the user code is fast enough. - `None` can be passed to let BQ remote functions service apply - default batching. See for more details - https://cloud.google.com/bigquery/docs/remote-functions#limiting_number_of_rows_in_a_batch_request. - cloud_function_timeout (int, Optional): - The maximum amount of time (in seconds) BigQuery should wait for - the cloud function to return a response. See for more details - https://cloud.google.com/functions/docs/configuring/timeout. - Please note that even though the cloud function (2nd gen) itself - allows seeting up to 60 minutes of timeout, BigQuery remote - function can wait only up to 20 minutes, see for more details - https://cloud.google.com/bigquery/quotas#remote_function_limits. - By default BigQuery DataFrames uses a 10 minute timeout. `None` - can be passed to let the cloud functions default timeout take effect. - cloud_function_max_instances (int, Optional): - The maximumm instance count for the cloud function created. This - can be used to control how many cloud function instances can be - active at max at any given point of time. Lower setting can help - control the spike in the billing. Higher setting can help - support processing larger scale data. When not specified, cloud - function's default setting applies. For more details see - https://cloud.google.com/functions/docs/configuring/max-instances. - cloud_function_vpc_connector (str, Optional): - The VPC connector you would like to configure for your cloud - function. This is useful if your code needs access to data or - service(s) that are on a VPC network. See for more details - https://cloud.google.com/functions/docs/networking/connecting-vpc. - cloud_function_vpc_connector_egress_settings (str, Optional): - Egress settings for the VPC connector, controlling what outbound - traffic is routed through the VPC connector. - Options are: `all`, `private-ranges-only`, or `unspecified`. - If not specified, `private-ranges-only` is used by default. - See for more details - https://cloud.google.com/run/docs/configuring/vpc-connectors#egress-job. - cloud_function_memory_mib (int, Optional): - The amounts of memory (in mebibytes) to allocate for the cloud - function (2nd gen) created. This also dictates a corresponding - amount of allocated CPU for the function. By default a memory of - 1024 MiB is set for the cloud functions created to support - BigQuery DataFrames remote function. If you want to let the - default memory of cloud functions be allocated, pass `None`. See - for more details - https://cloud.google.com/functions/docs/configuring/memory. - cloud_function_cpus (float, Optional): - The number of cpus to allocate for the cloud - function (2nd gen) created. - https://docs.cloud.google.com/run/docs/configuring/services/cpu. - cloud_function_ingress_settings (str, Optional): - Ingress settings controls dictating what traffic can reach the - function. Options are: `all`, `internal-only`, or `internal-and-gclb`. - If no setting is provided, `internal-only` will be used by default. - See for more details - https://cloud.google.com/functions/docs/networking/network-settings#ingress_settings. - cloud_build_service_account (str, Optional): - Service account in the fully qualified format - `projects/PROJECT_ID/serviceAccounts/SERVICE_ACCOUNT_EMAIL`, or - just the SERVICE_ACCOUNT_EMAIL. The latter would be interpreted - as belonging to the BigQuery DataFrames session project. This is - to be used by Cloud Build to build the function source code into - a deployable artifact. If not provided, the default Cloud Build - service account is used. See - https://cloud.google.com/build/docs/cloud-build-service-account - for more details. - """ - # If the user forces the cloud function service argument to None, throw - # an exception - if cloud_function_service_account is None: - raise ValueError( - 'You must provide a user managed cloud_function_service_account, or "default" if you would like to let the default service account be used.' - ) - - # BQ remote function must be persisted, for which we need a dataset. - # https://cloud.google.com/bigquery/docs/reference/standard-sql/remote-functions#:~:text=You%20cannot%20create%20temporary%20remote%20functions. - dataset_ref = self._resolve_dataset_reference(dataset) - # A connection is required for BQ remote function. - # https://cloud.google.com/bigquery/docs/reference/standard-sql/remote-functions#create_a_remote_function - bq_connection_id = self._resolve_bigquery_connection_id( - dataset_ref, bigquery_connection - ) - - # If any CMEK is intended then check that a docker repository is also specified. - if ( - cloud_function_kms_key_name is not None - and cloud_function_docker_repository is None - ): - raise bf_formatting.create_exception_with_feedback_link( - ValueError, - "cloud_function_docker_repository must be specified with cloud_function_kms_key_name." - " For more details see https://cloud.google.com/functions/docs/securing/cmek#before_you_begin.", - ) - - # A VPC connector is required to specify VPC egress settings. - if ( - cloud_function_vpc_connector_egress_settings is not None - and cloud_function_vpc_connector is None - ): - raise bf_formatting.create_exception_with_feedback_link( - ValueError, - "cloud_function_vpc_connector must be specified before cloud_function_vpc_connector_egress_settings.", - ) - - if cloud_function_ingress_settings is None: - cloud_function_ingress_settings = "internal-only" - msg = bfe.format_message( - "The `cloud_function_ingress_settings` is being set to 'internal-only' by default." - ) - warnings.warn(msg, category=UserWarning, stacklevel=2) - - def wrapper(func): - nonlocal input_types, output_type - - ### Step 1: Validate inputs and package into cloud run function, remote function defs. ### - if not callable(func): - raise bf_formatting.create_exception_with_feedback_link( - TypeError, f"func must be a callable, got {func}" - ) - - udf_sig = _utils.get_func_signature( - func, - input_types, - output_type, - ).to_remote_function_compatible() - - full_package_requirements = _utils.get_updated_package_requirements( - packages or [], udf_sig.is_row_processor - ) - memory_mib = cloud_function_memory_mib or _DEFAULT_FUNCTION_MEMORY_MIB - - # assumption is most bigframes functions are cpu bound, single-threaded and many won't release GIL - # therefore, want to allocate a worker for each cpu, and allow a concurrent request per worker - expected_milli_cpus = ( - int(cloud_function_cpus * 1000) - if (cloud_function_cpus is not None) - else _infer_milli_cpus_from_memory(memory_mib) - ) - workers = -( - expected_milli_cpus // -1000 - ) # ceil(cpus) without invoking floats - threads = 4 # (per worker) - # max concurrency==1 for vcpus < 1 hard limit from cloud run - concurrency = (workers * threads) if (expected_milli_cpus >= 1000) else 1 - - ### Step 1: Create resources or fetch existing matching resources. ### - cloud_func_spec = udf_def.CloudRunFunctionConfig( - code=udf_def.CodeDef.from_func(func, full_package_requirements), - signature=udf_sig, - timeout_seconds=cloud_function_timeout, - max_instance_count=cloud_function_max_instances, - vpc_connector=cloud_function_vpc_connector, - vpc_connector_egress_settings=cloud_function_vpc_connector_egress_settings - or "private-ranges-only", - memory_mib=memory_mib, - cpus=cloud_function_cpus, - ingress_settings=cloud_function_ingress_settings, - workers=workers, - threads=threads, - concurrency=concurrency, - kms_key_name=cloud_function_kms_key_name, - docker_repository=cloud_function_docker_repository, - cloud_build_service_account=cloud_build_service_account, - cloud_run_service_account=( - None - if (cloud_function_service_account == "default") - else cloud_function_service_account - ), - ) - uniq_suffix = None - if not reuse: - uniq_suffix = "".join( - random.choices(string.ascii_lowercase + string.digits, k=4) - ) - cf_name = get_cloud_function_name( - cloud_func_spec, - # only session scope a temp unnamed function - session_id=self.session_id if (name is None) else None, - uniq_suffix=uniq_suffix, - ) - if not name: - self._add_temp_cloud_function(cf_name) - - # Create remote function that points at the cloud function - cf_endpoint = None - if reuse is not None: - cf_endpoint = self._function_client.get_cloud_function_endpoint(cf_name) - - # If the endpoint is empty, the function might exist but the URL propagation is pending. - # Running create_cloud_function will handle AlreadyExists and retry endpoint fetching. - if not cf_endpoint: - cf_endpoint = self._function_client.create_cloud_function( - cf_name, cloud_func_spec - ) - else: - logger.info(f"Cloud function {cf_name} already exists.") - - remote_function_config = udf_def.RemoteFunctionConfig( - endpoint=cf_endpoint, - connection_id=bq_connection_id, - max_batching_rows=max_batching_rows or 1000, - signature=udf_sig, - bq_metadata=udf_sig.protocol_metadata, - ) - remote_function_name = name or get_bigframes_function_name( - remote_function_config, - session_id=self.session_id, - uniq_suffix=uniq_suffix, - ) - routine_ref = self._resolve_routine_reference( - remote_function_name, dataset=dataset_ref - ) - if not name: - self._add_temp_remote_function(routine_ref) - - self._function_client.create_bq_remote_function( - udf_def=remote_function_config, - routine_ref=routine_ref, - maybe_reuse=reuse, - try_create_connection=self._manage_connections, - ) - - udf_definition = udf_def.BigqueryUdf( - routine_ref=routine_ref, - signature=udf_sig, - ) - decorator = functools.wraps(func) - if udf_sig.is_row_processor: - msg = bfe.format_message("input_types=Series is in preview.") - warnings.warn(msg, stacklevel=1, category=bfe.PreviewWarning) - - cf_full_path = ( - self._function_client.get_cloud_function_fully_qualified_name(cf_name) - ) - return decorator( - bq_functions.BigqueryCallableRoutine( - udf_definition, - self._function_client._bq_client, - cloud_function_ref=cf_full_path, - local_func=func, - is_managed=False, - ) - ) - - return wrapper - - def deploy_remote_function( - self, - func, - **kwargs, - ): - """Orchestrates the creation of a BigQuery remote function that deploys immediately. - - This method ensures that the remote function is created and available for - use in BigQuery as soon as this call is made. - - Args: - kwargs: - All arguments are passed directly to - :meth:`~bigframes.session.Session.remote_function`. Please see - its docstring for parameter details. - - Returns: - A wrapped remote function, usable in - :meth:`~bigframes.series.Series.apply`. - """ - # TODO(tswast): If we update remote_function to defer deployment, update - # this method to deploy immediately. - return self.remote_function(**kwargs)(func) - - def udf( - self, - input_types: type | Sequence[type] | None = None, - output_type: type | None = None, - dataset: str | None = None, - bigquery_connection: str | None = None, - name: str | None = None, - packages: Sequence[str] | None = None, - max_batching_rows: int | None = None, - container_cpu: Optional[float] = None, - container_memory: Optional[str] = None, - *, - _force_deploy: bool = False, - ): - """Decorator to turn a Python user defined function (udf) into a - BigQuery managed function. - - .. note:: - This feature is in preview. The code in the udf must be - (1) self-contained, i.e. it must not contain any - references to an import or variable defined outside the function - body, and - (2) Python 3.11 compatible, as that is the environment - in which the code is executed in the cloud. - - .. note:: - Please have following IAM roles enabled for you: - - * BigQuery Data Editor (roles/bigquery.dataEditor) - - Args: - input_types (type or sequence(type), Optional): - For scalar user defined function it should be the input type or - sequence of input types. The supported scalar input types are - `bool`, `bytes`, `float`, `int`, `str`. - output_type (type, Optional): - Data type of the output in the user defined function. If the - user defined function returns an array, then `list[type]` should - be specified. The supported output types are `bool`, `bytes`, - `float`, `int`, `str`, `list[bool]`, `list[float]`, `list[int]` - and `list[str]`. - dataset (str, Optional): - Dataset in which to create a BigQuery managed function. It - should be in `.` or `` - format. If this parameter is not provided then session dataset - id is used. - bigquery_connection (str, Optional): - Name of the BigQuery connection. It is used to provide an - identity to the serverless instances running the user code. It - helps BigQuery manage and track the resources used by the udf. - This connection is required for internet access and for - interacting with other GCP services. To access GCP services, the - appropriate IAM permissions must also be granted to the - connection's Service Account. When it defaults to None, the udf - will be created without any connection. A udf without a - connection has no internet access and no access to other GCP - services. - name (str, Optional): - Explicit name of the persisted BigQuery managed function. Use it - with caution, because more than one users working in the same - project and dataset could overwrite each other's managed - functions if they use the same persistent name. When an explicit - name is provided, any session specific clean up ( - ``bigframes.session.Session.close``/ - ``bigframes.pandas.close_session``/ - ``bigframes.pandas.reset_session``/ - ``bigframes.pandas.clean_up_by_session_id``) does not clean up - the function, and leaves it for the user to manage the function - directly. - packages (str[], Optional): - Explicit name of the external package dependencies. Each - dependency is added to the `requirements.txt` as is, and can be - of the form supported in - https://pip.pypa.io/en/stable/reference/requirements-file-format/. - max_batching_rows (int, Optional): - The maximum number of rows in each batch. If you specify - max_batching_rows, BigQuery determines the number of rows in a - batch, up to the max_batching_rows limit. If max_batching_rows - is not specified, the number of rows to batch is determined - automatically. - container_cpu (float, Optional): - The CPU limits for containers that run Python UDFs. By default, - the CPU allocated is 0.33 vCPU. See details at - https://cloud.google.com/bigquery/docs/user-defined-functions-python#configure-container-limits. - container_memory (str, Optional): - The memory limits for containers that run Python UDFs. By - default, the memory allocated to each container instance is - 512 MiB. See details at - https://cloud.google.com/bigquery/docs/user-defined-functions-python#configure-container-limits. - """ - - warnings.warn("udf is in preview.", category=bfe.PreviewWarning, stacklevel=5) - # BQ managed function must be persisted, for which we need a dataset. - dataset_ref = self._resolve_dataset_reference(dataset) - - # A connection is optional for BQ managed function. - bq_connection_id = ( - self._resolve_bigquery_connection_id(dataset_ref, bigquery_connection) - if bigquery_connection - else None - ) - - # TODO(b/399129906): Write a method for the repeated part in the wrapper - # for both managed function and remote function. - def wrapper(func): - nonlocal input_types, output_type - - if not callable(func): - raise bf_formatting.create_exception_with_feedback_link( - TypeError, f"func must be a callable, got {func}" - ) - - udf_sig = _utils.get_func_signature( - func, - input_types, - output_type, - ) - - code_def = udf_def.CodeDef.from_func(func, package_requirements=packages) - requirements = udf_def.RuntimeRequirements( - container_cpu=container_cpu, - container_memory=container_memory, - bq_connection_id=bq_connection_id, - max_batching_rows=max_batching_rows, - packages=tuple(packages) if packages else (), - ) - if udf_sig.is_row_processor: - msg = bfe.format_message("input_types=Series is in preview.") - warnings.warn(msg, stacklevel=1, category=bfe.PreviewWarning) - - if ( - not name and not dataset and not _force_deploy - ): # session-owned resource - deferred deployment - udf_definition = udf_def.PythonUdf( - signature=udf_sig, - code=code_def, - requirements=requirements, - ) - return bq_functions.UdfRoutine(func=func, _udf_def=udf_definition) - else: # deploy immediately - config = udf_def.ManagedFunctionConfig( - code=code_def, - signature=udf_sig, - max_batching_rows=max_batching_rows, - container_cpu=container_cpu, - container_memory=container_memory, - bq_connection_id=bq_connection_id, - capture_references=False, - ) - function_name = name or get_managed_function_name( - config, self.session_id - ) - rf_def = self._deploy_managed_function( - config, - name=function_name, - temp=(name is None), - dataset=dataset_ref, - ) - return bq_functions.BigqueryCallableRoutine( - rf_def, - self._function_client._bq_client, - local_func=func, - is_managed=True, - ) - - return wrapper - - def deploy_udf( - self, - func, - **kwargs, - ): - """Orchestrates the creation of a BigQuery UDF that deploys immediately. - - This method ensures that the UDF is created and available for - use in BigQuery as soon as this call is made. - - Args: - func: - Function to deploy. - kwargs: - All arguments are passed directly to - :meth:`~bigframes.session.Session.udf`. Please see - its docstring for parameter details. - - Returns: - A wrapped Python user defined function, usable in - :meth:`~bigframes.series.Series.apply`. - """ - return self.udf(_force_deploy=True, **kwargs)(func) - - -def get_cloud_function_name( - function_def: udf_def.CloudRunFunctionConfig, session_id=None, uniq_suffix=False -): - """ - Get a name for the cloud function for the given user defined function. - - If make_unique is True, append a random suffix to the name. - """ - parts = [_BIGFRAMES_FUNCTION_PREFIX] - if session_id: - parts.append(session_id) - parts.append(function_def.stable_hash().hex()) - if uniq_suffix: - parts.append(uniq_suffix) - return _GCF_FUNCTION_NAME_SEPERATOR.join(parts) - - -def get_bigframes_function_name( - function: udf_def.RemoteFunctionConfig, session_id, uniq_suffix=None -): - """Get a name for the bigframes function for the given user defined function.""" - parts = [_BIGFRAMES_FUNCTION_PREFIX, session_id, function.stable_hash().hex()] - if uniq_suffix: - parts.append(uniq_suffix) - return _BQ_FUNCTION_NAME_SEPERATOR.join(parts) - - -def get_managed_function_name( - function_def: udf_def.ManagedFunctionConfig, - session_id: str | None = None, -): - """Get a name for the bigframes managed function for the given user defined function.""" - parts = [_BIGFRAMES_FUNCTION_PREFIX] - if session_id: - parts.append(session_id) - parts.append(function_def.stable_hash().hex()) - return _BQ_FUNCTION_NAME_SEPERATOR.join(parts) - - -def _infer_milli_cpus_from_memory(memory_mib: int) -> int: - # observed values, not formally documented by cloud run functions - if memory_mib < 128: - raise ValueError("Cloud run supports at minimum 128MiB per instance") - elif memory_mib == 128: - return 83 - elif memory_mib <= 256: - return 167 - elif memory_mib <= 512: - return 333 - elif memory_mib <= 1024: - return 583 - elif memory_mib <= 2048: - return 1000 - elif memory_mib <= 8192: - return 2000 - elif memory_mib <= 16384: - return 4000 - elif memory_mib <= 32768: - return 8000 - else: - raise ValueError("Cloud run supports at most 32768MiB per instance") diff --git a/bigframes/functions/_utils.py b/bigframes/functions/_utils.py deleted file mode 100644 index 358f20b2ab4..00000000000 --- a/bigframes/functions/_utils.py +++ /dev/null @@ -1,358 +0,0 @@ -# Copyright 2023 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -import collections -import hashlib -import inspect -import json -import sys -import typing -import warnings -from typing import Any, Mapping, Optional, Sequence, Set, cast - -import cloudpickle -import google.api_core.exceptions -import numpy -import pandas -import pyarrow -from google.cloud import bigquery, functions_v2 -from packaging.requirements import Requirement - -import bigframes.exceptions as bfe -import bigframes.formatting_helpers as bf_formatting -from bigframes.functions import function_typing, udf_def - -# Naming convention for the function artifacts -_BIGFRAMES_FUNCTION_PREFIX = "bigframes" -_BQ_FUNCTION_NAME_SEPERATOR = "_" -_GCF_FUNCTION_NAME_SEPERATOR = "-" - -# Protocol version 4 is available in python version 3.4 and above -# https://docs.python.org/3/library/pickle.html#data-stream-format -_pickle_protocol_version = 4 - - -def gcf_location_from_bq_location(bq_location: str) -> str: - """Get the cloud functions region that corresponds to a BQ location.""" - bq_location = bq_location.lower() - - # BigQuery has multi region but cloud functions does not. - # Any region in the multi region that supports cloud functions should work - # https://cloud.google.com/functions/docs/locations - if bq_location == "us": - return "us-central1" - elif bq_location == "eu": - return "europe-west1" - - return bq_location - - -def _package_existed(package_requirements: list[str], package: str) -> bool: - """Checks if a package (regardless of version) exists in a given list.""" - if not package_requirements: - return False - - return Requirement(package).name in { - Requirement(req).name for req in package_requirements - } - - -def get_updated_package_requirements( - package_requirements: Sequence[str] = (), - is_row_processor: bool = False, - capture_references: bool = True, - ignore_package_version: bool = False, -) -> Sequence[str]: - requirements: list[str] = [] - if capture_references: - requirements.append(f"cloudpickle=={cloudpickle.__version__}") - - if is_row_processor: - if ignore_package_version: - # TODO(jialuo): Add back the version after b/410924784 is resolved. - # Due to current limitations on the packages version in Python UDFs, - # we use `ignore_package_version` to optionally omit the version for - # managed functions only. - msg = bfe.format_message( - "numpy, pandas, and pyarrow versions in the function execution" - " environment may not precisely match your local environment." - ) - warnings.warn(msg, category=bfe.FunctionPackageVersionWarning) - requirements.append("pandas") - requirements.append("pyarrow") - requirements.append("numpy") - else: - # bigframes function will send an entire row of data as json, which - # would be converted to a pandas series and processed Ensure numpy - # versions match to avoid unpickling problems. See internal issue - # b/347934471. - requirements.append(f"pandas=={pandas.__version__}") - requirements.append(f"pyarrow=={pyarrow.__version__}") - requirements.append(f"numpy=={numpy.__version__}") - - if not requirements: - return list(package_requirements) - - result = list(package_requirements) - for package in requirements: - if not _package_existed(result, package): - result.append(package) - - return sorted(result) - - -def clean_up_by_session_id( - bqclient: bigquery.Client, - gcfclient: functions_v2.FunctionServiceClient, - dataset: bigquery.DatasetReference, - session_id: str, -): - """Delete remote function artifacts for a session id, where the session id - was not necessarily created in the current runtime. This is useful if the - user worked with a BigQuery DataFrames session previously and remembered the - session id, and now wants to clean up its temporary resources at a later - point in time. - """ - - # First clean up the BQ remote functions and then the underlying cloud - # functions, so that at no point we are left with a remote function that is - # pointing to a cloud function that does not exist - - endpoints_to_be_deleted: Set[str] = set() - match_prefix = "".join( - [ - _BIGFRAMES_FUNCTION_PREFIX, - _BQ_FUNCTION_NAME_SEPERATOR, - session_id, - _BQ_FUNCTION_NAME_SEPERATOR, - ] - ) - for routine in bqclient.list_routines(dataset): - routine = cast(bigquery.Routine, routine) - - # skip past the routines not belonging to the given session id, or - # non-remote-function routines - if ( - routine.type_ != bigquery.RoutineType.SCALAR_FUNCTION - or not cast(str, routine.routine_id).startswith(match_prefix) - or not routine.remote_function_options - or not routine.remote_function_options.endpoint - ): - continue - - # Let's forgive the edge case possibility that the BQ remote function - # may have been deleted at the same time directly by the user - bqclient.delete_routine(routine, not_found_ok=True) - endpoints_to_be_deleted.add(routine.remote_function_options.endpoint) - - # Now clean up the cloud functions - bq_location = bqclient.get_dataset(dataset).location - gcf_location = gcf_location_from_bq_location(bq_location) - parent_path = gcfclient.common_location_path( - project=dataset.project, location=gcf_location - ) - for gcf in gcfclient.list_functions(parent=parent_path): - # skip past the cloud functions not attached to any BQ remote function - # belonging to the given session id - if gcf.service_config.uri not in endpoints_to_be_deleted: - continue - - # Let's forgive the edge case possibility that the cloud function - # may have been deleted at the same time directly by the user - try: - gcfclient.delete_function(name=gcf.name) - except google.api_core.exceptions.NotFound: - pass - - -def routine_ref_to_string_for_query(routine_ref: bigquery.RoutineReference) -> str: - return f"`{routine_ref.project}.{routine_ref.dataset_id}`.{routine_ref.routine_id}" - - -# Deprecated: Use CodeDef.stable_hash() instead. -def get_hash(def_, package_requirements=None): - "Get hash (32 digits alphanumeric) of a function." - # There is a known cell-id sensitivity of the cloudpickle serialization in - # notebooks https://github.com/cloudpipe/cloudpickle/issues/538. Because of - # this, if a cell contains a udf decorated with @remote_function, a unique - # cloudpickle code is generated every time the cell is run, creating new - # cloud artifacts every time. This is slow and wasteful. - # A workaround of the same can be achieved by replacing the filename in the - # code object to a static value - # https://github.com/cloudpipe/cloudpickle/issues/120#issuecomment-338510661. - # - # To respect the user code/environment let's make this modification on a - # copy of the udf, not on the original udf itself. - def_copy = cloudpickle.loads(cloudpickle.dumps(def_)) - def_copy.__code__ = def_copy.__code__.replace( - co_filename="bigframes_place_holder_filename" - ) - - def_repr = cloudpickle.dumps(def_copy, protocol=_pickle_protocol_version) - if package_requirements: - for p in sorted(package_requirements): - def_repr += p.encode() - return hashlib.md5(def_repr).hexdigest() - - -def get_python_output_type_str_from_bigframes_metadata( - metadata_text: str, -) -> Optional[str]: - try: - metadata_dict = json.loads(metadata_text) - except (TypeError, json.decoder.JSONDecodeError): - return None - try: - return metadata_dict["value"]["python_array_output_type"] - except KeyError: - return None - - -def get_python_output_type_from_bigframes_metadata( - metadata_text: str, -) -> Optional[type]: - output_type_str = get_python_output_type_str_from_bigframes_metadata(metadata_text) - - for ( - python_output_array_type - ) in function_typing.RF_SUPPORTED_ARRAY_OUTPUT_PYTHON_TYPES: - if python_output_array_type.__name__ == output_type_str: - return list[python_output_array_type] # type: ignore - - return None - - -def get_bigframes_metadata(*, python_output_type: Optional[type] = None) -> str: - # Let's keep the actual metadata inside one level of nesting so that in - # future we can use a top level key "version" (parallel to "value"), based - # on which "value" can be interpreted according to the "version". The - # absence of "version" should be interpreted as default version. - inner_metadata = {} - if typing.get_origin(python_output_type) is list: - python_output_array_type = typing.get_args(python_output_type)[0] - if ( - python_output_array_type - in function_typing.RF_SUPPORTED_ARRAY_OUTPUT_PYTHON_TYPES - ): - inner_metadata["python_array_output_type"] = ( - python_output_array_type.__name__ - ) - - metadata = {"value": inner_metadata} - metadata_ser = json.dumps(metadata) - - # let's make sure the serialized value is deserializable - if ( - get_python_output_type_from_bigframes_metadata(metadata_ser) - != python_output_type - ): - raise bf_formatting.create_exception_with_feedback_link( - ValueError, f"python_output_type {python_output_type} is not serializable." - ) - - return metadata_ser - - -def get_python_version(is_compat: bool = False) -> str: - # Cloud Run functions use the 'compat' format (e.g., python311, see more - # from https://cloud.google.com/functions/docs/runtime-support#python), - # while managed functions use the standard format (e.g., python-3.11). - major = sys.version_info.major - minor = sys.version_info.minor - return f"python{major}{minor}" if is_compat else f"python-{major}.{minor}" - - -def has_conflict_input_type( - signature: inspect.Signature, - input_types: Sequence[Any], -) -> bool: - """Checks if the parameters have any conflict with the input_types.""" - params = list(signature.parameters.values()) - - if len(params) != len(input_types): - return True - - # Check for conflicts type hints. - for i, param in enumerate(params): - if param.annotation is not inspect.Parameter.empty: - if param.annotation != input_types[i]: - return True - - # No conflicts were found after checking all parameters. - return False - - -def has_conflict_output_type( - signature: inspect.Signature, - output_type: Any, -) -> bool: - """Checks if the return type annotation conflicts with the output_type.""" - return_annotation = signature.return_annotation - - if return_annotation is inspect.Parameter.empty: - return False - - return return_annotation != output_type - - -def get_func_signature( - func, - input_types: type | Sequence[type] | None = None, - output_type: type | None = None, -) -> udf_def.UdfSignature: - if sys.version_info >= (3, 10): - # Add `eval_str = True` so that deferred annotations are turned into their - # corresponding type objects. Need Python 3.10 for eval_str parameter. - # https://docs.python.org/3/library/inspect.html#inspect.signature - signature_kwargs: Mapping[str, Any] = {"eval_str": True} - else: - signature_kwargs = {} # type: ignore - - py_sig = resolve_signature( - inspect.signature(func, **signature_kwargs), - input_types, - output_type, - ) - return udf_def.UdfSignature.from_py_signature(py_sig) - - -def resolve_signature( - py_sig: inspect.Signature, - input_types: type | Sequence[type] | None = None, - output_type: type | None = None, -) -> inspect.Signature: - if input_types is not None: - if not isinstance(input_types, collections.abc.Sequence): - input_types = [input_types] - if has_conflict_input_type(py_sig, input_types): - msg = bfe.format_message( - "Conflicting input types detected, using the one from the decorator." - ) - warnings.warn(msg, category=bfe.FunctionConflictTypeHintWarning) - py_sig = py_sig.replace( - parameters=[ - par.replace(annotation=itype) - for par, itype in zip(py_sig.parameters.values(), input_types) - ] - ) - if output_type: - if has_conflict_output_type(py_sig, output_type): - msg = bfe.format_message( - "Conflicting return type detected, using the one from the decorator." - ) - warnings.warn(msg, category=bfe.FunctionConflictTypeHintWarning) - py_sig = py_sig.replace(return_annotation=output_type) - - return py_sig diff --git a/bigframes/functions/function.py b/bigframes/functions/function.py deleted file mode 100644 index b3a56dafcef..00000000000 --- a/bigframes/functions/function.py +++ /dev/null @@ -1,253 +0,0 @@ -# Copyright 2023 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from __future__ import annotations - -import dataclasses -import logging -from typing import TYPE_CHECKING, Callable, Optional, Protocol, Union, runtime_checkable - -import google.api_core.exceptions -from google.cloud import bigquery - -import bigframes.formatting_helpers as bf_formatting -from bigframes.functions import _function_session as bff_session -from bigframes.functions import function_typing, udf_def - -if TYPE_CHECKING: - from bigframes.session import Session - -logger = logging.getLogger(__name__) - - -class UnsupportedTypeError(ValueError): - def __init__(self, type_, supported_types): - self.type = type_ - self.supported_types = supported_types - - -class DatasetMissingError(ValueError): - pass - - -def get_routine_reference( - routine_ref_str: str, bigquery_client: bigquery.Client, session: Optional[Session] -) -> bigquery.RoutineReference: - try: - # Handle cases ".." and - # ".". - return bigquery.RoutineReference.from_string( - routine_ref_str, - default_project=bigquery_client.project, - ) - except ValueError: - # Handle case of "". - if not session: - raise DatasetMissingError - - dataset_ref = bigquery.DatasetReference( - bigquery_client.project, session._anonymous_dataset.dataset_id - ) - return dataset_ref.routine(routine_ref_str) - - -def remote_function(*args, **kwargs): - import bigframes - - function_session = bigframes.get_global_session()._function_session - return function_session.remote_function(*args, **kwargs) - - -remote_function.__doc__ = bff_session.FunctionSession.remote_function.__doc__ - - -def udf(*args, **kwargs): - import bigframes - - function_session = bigframes.get_global_session()._function_session - return function_session.udf(*args, **kwargs) - - -udf.__doc__ = bff_session.FunctionSession.udf.__doc__ - - -def _try_import_routine( - routine: bigquery.Routine, bq_client: bigquery.Client -) -> BigqueryCallableRoutine: - udf_def = _routine_as_udf_def(routine) - is_remote = ( - hasattr(routine, "remote_function_options") and routine.remote_function_options - ) - return BigqueryCallableRoutine(udf_def, bq_client, is_managed=not is_remote) - - -def _try_import_row_routine( - routine: bigquery.Routine, bq_client: bigquery.Client -) -> BigqueryCallableRoutine: - udf_def = _routine_as_udf_def(routine, is_row_processor=True) - - is_remote = ( - hasattr(routine, "remote_function_options") and routine.remote_function_options - ) - return BigqueryCallableRoutine(udf_def, bq_client, is_managed=not is_remote) - - -def _routine_as_udf_def( - routine: bigquery.Routine, is_row_processor: bool = False -) -> udf_def.BigqueryUdf: - try: - return udf_def.BigqueryUdf.from_routine( - routine, is_row_processor=is_row_processor - ) - except udf_def.ReturnTypeMissingError: - raise bf_formatting.create_exception_with_feedback_link( - ValueError, "Function return type must be specified." - ) - except function_typing.UnsupportedTypeError as e: - raise bf_formatting.create_exception_with_feedback_link( - ValueError, - f"Type {e.type} not supported, supported types are {e.supported_types}.", - ) - - -def read_gbq_function( - function_name: str, - *, - session: Session, - is_row_processor: bool = False, -): - """ - Read an existing BigQuery function and prepare it for use in future queries. - """ - bigquery_client = session.bqclient - - try: - routine_ref = get_routine_reference(function_name, bigquery_client, session) - except DatasetMissingError: - raise bf_formatting.create_exception_with_feedback_link( - ValueError, - "Project and dataset must be provided, either directly or via session.", - ) - - # Find the routine and get its arguments. - try: - routine = bigquery_client.get_routine(routine_ref) - except google.api_core.exceptions.NotFound: - raise bf_formatting.create_exception_with_feedback_link( - ValueError, f"Unknown function '{routine_ref}'." - ) - - # TODO(493293086): Deprecate is_row_processor. - if is_row_processor: - return _try_import_row_routine(routine, bigquery_client) - else: - return _try_import_routine(routine, bigquery_client) - - -@runtime_checkable -class Udf(Protocol): - """ - Protocol for all BigFrames user-defined functions. - - Has @runtime_checkable so functions like df.apply() can dispatch UDFs with isinstance() checks. - """ - - @property - def udf_def(self) -> Union[udf_def.BigqueryUdf, udf_def.PythonUdf]: ... - - -class BigqueryCallableRoutine: - """ - A reference to a routine in the context of a session. - - Can be used both directly as a callable, or as an input to dataframe ops that take a callable. - """ - - def __init__( - self, - udf_def: udf_def.BigqueryUdf, - bq_client: bigquery.Client, - *, - local_func: Optional[Callable] = None, - cloud_function_ref: Optional[str] = None, - is_managed: bool = False, - ): - self._udf_def = udf_def - self._bq_client = bq_client - self._local_fun = local_func - self._cloud_function = cloud_function_ref - self._is_managed = is_managed - - def __call__(self, *args, **kwargs): - if self._local_fun: - return self._local_fun(*args, **kwargs) - # avoid circular imports - import bigframes.session._io.bigquery as bf_io_bigquery - from bigframes.core.compile.sqlglot import sql as sg_sql - - args_string = ", ".join([sg_sql.to_sql(sg_sql.literal(v)) for v in args]) - sql = f"SELECT `{str(self._udf_def.routine_ref)}`({args_string})" - row_iterator = bf_io_bigquery.start_query_job_optional( - self._bq_client, - sql=sql, - job_config=bigquery.QueryJobConfig(), - ) # type: ignore - return list(row_iterator.to_arrow().to_pydict().values())[0][0] - - @property - def bigframes_bigquery_function(self) -> str: - return str(self._udf_def.routine_ref) - - @property - def bigframes_remote_function(self): - return None if self._is_managed else str(self._udf_def.routine_ref) - - @property - def is_row_processor(self) -> bool: - return self.udf_def.signature.is_row_processor - - @property - def udf_def(self) -> udf_def.BigqueryUdf: - return self._udf_def - - @property - def bigframes_cloud_function(self) -> Optional[str]: - return self._cloud_function - - @property - def input_dtypes(self): - return tuple(arg.bf_type for arg in self.udf_def.signature.inputs) - - @property - def output_dtype(self): - return self.udf_def.signature.output.bf_type - - @property - def bigframes_bigquery_function_output_dtype(self): - return self.udf_def.signature.output.emulating_type.bf_type - - -@dataclasses.dataclass(frozen=True) -class UdfRoutine: - func: Callable - # Try not to depend on this, bq managed function creation will be deferred later - # And this ref will be replaced with requirements rather to support lazy creation - _udf_def: Union[udf_def.BigqueryUdf, udf_def.PythonUdf] - - def __call__(self, *args, **kwargs): - return self.func(*args, **kwargs) - - @property - def udf_def(self) -> Union[udf_def.BigqueryUdf, udf_def.PythonUdf]: - return self._udf_def diff --git a/bigframes/functions/function_template.py b/bigframes/functions/function_template.py deleted file mode 100644 index 598de7c853d..00000000000 --- a/bigframes/functions/function_template.py +++ /dev/null @@ -1,386 +0,0 @@ -# Copyright 2023 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from __future__ import annotations - -import inspect -import logging -import os -import re -import textwrap - -from bigframes.functions import udf_def - -logger = logging.getLogger(__name__) - - -# Placeholder variables for testing. -input_types = ("STRING",) -output_type = "STRING" - - -# Convert inputs to BigQuery JSON. See: -# https://cloud.google.com/bigquery/docs/remote-functions#json_encoding_of_sql_data_type -# and -# https://cloud.google.com/bigquery/docs/reference/standard-sql/json_functions#to_json_string -def convert_call(input_types, call): - for type_, arg in zip(input_types, call): - yield convert_from_bq_json(type_, arg) - - -def convert_from_bq_json(type_, arg): - import base64 - import collections - - converters = collections.defaultdict(lambda: lambda value: value) # type: ignore - converters["BYTES"] = base64.b64decode - converter = converters[type_] - return converter(arg) if arg is not None else None - - -def convert_to_bq_json(type_, arg): - import base64 - import collections - - converters = collections.defaultdict(lambda: lambda value: value) # type: ignore - converters["BYTES"] = lambda value: base64.b64encode(value).decode("utf-8") - converter = converters[type_] - return converter(arg) if arg is not None else None - - -# get_pd_series is the inverse of Block._get_rows_as_json_values -# NOTE: Keep in sync with the list of supported types in DataFrame.apply. -def get_pd_series(row): - import ast - import base64 - import json - from typing import Callable, cast - - import pandas as pd - - row_json = json.loads(row) - col_names = row_json["names"] - col_types = row_json["types"] - col_values = row_json["values"] - index_length = row_json["indexlength"] - dtype = row_json["dtype"] - - # At this point we are assuming that col_names, col_types and col_values are - # arrays of the same length, representing column names, types and values for - # one row of data - - # column names are not necessarily strings - # they are serialized as repr(name) at source - evaluated_col_names = [] - for col_name in col_names: - try: - col_name = ast.literal_eval(col_name) - except Exception as ex: - raise NameError(f"Failed to evaluate column name from '{col_name}': {ex}") - evaluated_col_names.append(col_name) - col_names = evaluated_col_names - - # Supported converters for pandas to python types - value_converters = { - "boolean": lambda val: val == "true", - "Int64": int, - "Float64": float, - "string": str, - "binary[pyarrow]": base64.b64decode, - } - - def convert_value(value, value_type): - value_converter = cast(Callable, value_converters.get(value_type)) - if value_converter is None: - raise ValueError(f"Don't know how to handle type '{value_type}'") - if value is None: - return None - return value_converter(value) - - index_values = [ - pd.Series([convert_value(col_values[i], col_types[i])], dtype=col_types[i])[0] - for i in range(index_length) - ] - - data_col_names = col_names[index_length:] - data_col_types = col_types[index_length:] - data_col_values = col_values[index_length:] - data_col_values = [ - pd.Series([convert_value(a, data_col_types[i])], dtype=data_col_types[i])[0] - for i, a in enumerate(data_col_values) - ] - - row_index = index_values[0] if len(index_values) == 1 else tuple(index_values) - row_series = pd.Series( - data_col_values, index=data_col_names, name=row_index, dtype=dtype - ) - return row_series - - -def udf(*args): - """Dummy function to use as a placeholder for function code in templates.""" - pass - - -# We want to build a cloud function that works for BQ remote functions, -# where we receive `calls` in json which is a batch of rows from BQ SQL. -# The number and the order of values in each row is expected to exactly -# match to the number and order of arguments in the udf , e.g. if the udf is -# def foo(x: int, y: str): -# ... -# then the http request body could look like -# { -# ... -# "calls" : [ -# [123, "hello"], -# [456, "world"] -# ] -# ... -# } -# https://cloud.google.com/bigquery/docs/reference/standard-sql/remote-functions#input_format -def udf_http(request): - global input_types, output_type - import json - import traceback - - from flask import jsonify - - try: - request_json = request.get_json(silent=True) - calls = request_json["calls"] - replies = [] - for call in calls: - reply = convert_to_bq_json( - output_type, udf(*convert_call(input_types, call)) - ) - if type(reply) is list: - # Since the BQ remote function does not support array yet, - # return a json serialized version of the reply - reply = json.dumps(reply) - replies.append(reply) - return_json = json.dumps({"replies": replies}) - return return_json - except Exception: - return jsonify({"errorMessage": traceback.format_exc()}), 400 - - -def udf_http_row_processor(request): - global output_type - import json - import math - import traceback - - import pandas as pd - from flask import jsonify - - try: - request_json = request.get_json(silent=True) - calls = request_json["calls"] - replies = [] - for call in calls: - reply = convert_to_bq_json( - output_type, udf(get_pd_series(call[0]), *call[1:]) - ) - if type(reply) is list: - # Since the BQ remote function does not support array yet, - # return a json serialized version of the reply. - # Numpy types are not json serializable, so use their Python - # values instead. - reply = [val.item() if hasattr(val, "item") else val for val in reply] - reply = json.dumps(reply) - elif isinstance(reply, float) and (math.isnan(reply) or math.isinf(reply)): - # Json serialization of the special float values (nan, inf, -inf) - # is not in strict compliance of the JSON specification - # https://docs.python.org/3/library/json.html#basic-usage. - # Let's convert them to a quoted string representation ("NaN", - # "Infinity", "-Infinity" respectively) which is handled by - # BigQuery - reply = json.dumps(reply) - elif pd.isna(reply): - # Pandas N/A values are not json serializable, so use a python - # equivalent instead - reply = None - elif hasattr(reply, "item"): - # Numpy types are not json serializable, so use its Python - # value instead - reply = reply.item() - replies.append(reply) - return_json = json.dumps({"replies": replies}) - return return_json - except Exception: - return jsonify({"errorMessage": traceback.format_exc()}), 400 - - -def generate_udf_code(code_def: udf_def.CodeDef, directory: str): - """Generate serialized code using cloudpickle given a udf.""" - udf_code_file_name = "udf.py" - udf_pickle_file_name = "udf.cloudpickle" - - # original code, only for debugging purpose - if code_def.function_source: - udf_code_file_path = os.path.join(directory, udf_code_file_name) - with open(udf_code_file_path, "w") as f: - f.write(code_def.function_source) - - # serialized udf - udf_pickle_file_path = os.path.join(directory, udf_pickle_file_name) - # TODO(b/345433300): try io.BytesIO to avoid writing to the file system - with open(udf_pickle_file_path, "wb") as f: - f.write(code_def.pickled_code) - - return udf_code_file_name, udf_pickle_file_name - - -def generate_cloud_function_main_code( - code_def: udf_def.CodeDef, - directory: str, - *, - udf_signature: udf_def.UdfSignature, -): - """Get main.py code for the cloud function for the given user defined function.""" - - # Pickle the udf with all its dependencies - udf_code_file, udf_pickle_file = generate_udf_code(code_def, directory) - - input_types = tuple(arg.sql_type for arg in udf_signature.inputs) - output_type = udf_signature.output.sql_type - - code_blocks = [ - f"""\ -import cloudpickle - -# original udf code is in {udf_code_file} -# serialized udf code is in {udf_pickle_file} -with open("{udf_pickle_file}", "rb") as f: - udf = cloudpickle.load(f) - -input_types = {repr(input_types)} -output_type = {repr(output_type)} -""" - ] - - # For converting scalar outputs to the correct type. - code_blocks.append(inspect.getsource(convert_to_bq_json)) - - if udf_signature.is_row_processor: - code_blocks.append(inspect.getsource(get_pd_series)) - handler_func_name = "udf_http_row_processor" - code_blocks.append(inspect.getsource(udf_http_row_processor)) - else: - code_blocks.append(inspect.getsource(convert_call)) - code_blocks.append(inspect.getsource(convert_from_bq_json)) - handler_func_name = "udf_http" - code_blocks.append(inspect.getsource(udf_http)) - - main_py = os.path.join(directory, "main.py") - with open(main_py, "w") as f: - f.writelines(code_blocks) - logger.debug(f"Wrote {os.path.abspath(main_py)}:\n{open(main_py).read()}") - - return handler_func_name - - -def generate_managed_function_code( - code_def: udf_def.CodeDef, - signature: udf_def.UdfSignature, - capture_references: bool, -) -> str: - """Generates the Python code block for managed Python UDF.""" - - udf_name = "unpickled_udf" - if capture_references: - # This code path ensures that if the udf body contains any - # references to variables and/or imports outside the body, they are - # captured as well. - func_code = textwrap.dedent( - f""" - import cloudpickle - {udf_name} = cloudpickle.loads({code_def.pickled_code!r}) - """ - ) - else: - # This code path ensures that if the udf body is self contained, - # i.e. there are no references to variables or imports outside the - # body. - assert code_def.function_source is not None - assert code_def.entry_point is not None - func_code = code_def.function_source - udf_name = code_def.entry_point - match = re.search(r"^def ", func_code, flags=re.MULTILINE) - if match is None: - raise ValueError("The UDF is not defined correctly.") - func_code = func_code[match.start() :] - - if signature.is_row_processor: - udf_code = textwrap.dedent(inspect.getsource(get_pd_series)) - udf_code = udf_code[udf_code.index("def") :] - bigframes_handler_code = textwrap.dedent( - f""" - def bigframes_handler(str_arg): - return {udf_name}({get_pd_series.__name__}(str_arg)) - """ - ) - - params = list(arg.name for arg in signature.inputs) - additional_params = params[1:] - - # Build the parameter list for the new handler function definition. - # e.g., "str_arg, y: bool, z" - handler_def_parts = ["str_arg"] - handler_def_parts.extend(additional_params) - handler_def_str = ", ".join(handler_def_parts) - - # Build the argument list for the call to the original UDF. - # e.g., "get_pd_series(str_arg), y, z" - udf_call_parts = [f"{get_pd_series.__name__}(str_arg)"] - udf_call_parts.extend(additional_params) - udf_call_str = ", ".join(udf_call_parts) - - bigframes_handler_code = textwrap.dedent( - f""" - def bigframes_handler({handler_def_str}): - return {udf_name}({udf_call_str}) - """ - ) - - else: - udf_code = "" - bigframes_handler_code = textwrap.dedent( - f""" - def bigframes_handler(*args): - return {udf_name}(*args) - """ - ) - - udf_code_block = [] - if code_def.package_requirements: - # Include package requirements as comments to help force a new - # BigQuery UDF definition when only package requirements change. - packages_comment = "# Packages: " + ", ".join( - sorted(code_def.package_requirements) - ) - udf_code_block.append(packages_comment) - - if not capture_references and signature.is_row_processor: - # Enable postponed evaluation of type annotations. This converts all - # type hints to strings at runtime, which is necessary for correctly - # handling the type annotation of pandas.Series after the UDF code is - # serialized for remote execution. See more from b/445182819. - udf_code_block.append("from __future__ import annotations") - - udf_code_block.append(udf_code) - udf_code_block.append(func_code) - udf_code_block.append(bigframes_handler_code) - - return textwrap.dedent("\n".join(udf_code_block)) diff --git a/bigframes/functions/function_typing.py b/bigframes/functions/function_typing.py deleted file mode 100644 index a64b3992b68..00000000000 --- a/bigframes/functions/function_typing.py +++ /dev/null @@ -1,137 +0,0 @@ -# Copyright 2023 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from typing import Any, Type, get_args, get_origin - -from google.cloud import bigquery - -import bigframes.dtypes - -# Input and output types supported by BigQuery DataFrames remote functions. -# TODO(shobs): Extend the support to all types supported by BQ remote functions -# https://cloud.google.com/bigquery/docs/remote-functions#limitations -RF_SUPPORTED_IO_PYTHON_TYPES = { - bool: bigquery.StandardSqlDataType(type_kind=bigquery.StandardSqlTypeNames.BOOL), - bytes: bigquery.StandardSqlDataType(type_kind=bigquery.StandardSqlTypeNames.BYTES), - float: bigquery.StandardSqlDataType( - type_kind=bigquery.StandardSqlTypeNames.FLOAT64 - ), - int: bigquery.StandardSqlDataType(type_kind=bigquery.StandardSqlTypeNames.INT64), - str: bigquery.StandardSqlDataType(type_kind=bigquery.StandardSqlTypeNames.STRING), -} - -# Support array output types in BigQuery DataFrames remote functions even though -# it is not currently (2024-10-06) supported in BigQuery remote functions. -# https://cloud.google.com/bigquery/docs/remote-functions#limitations -# TODO(b/284515241): remove this special handling when BigQuery remote functions -# support array. -RF_SUPPORTED_ARRAY_OUTPUT_PYTHON_TYPES = {bool, float, int, str} - -DEFAULT_RF_TYPE = RF_SUPPORTED_IO_PYTHON_TYPES[float] - -RF_SUPPORTED_IO_BIGQUERY_TYPEKINDS = { - "BOOLEAN", - "BOOL", - "BYTES", - "FLOAT", - "FLOAT64", - "INT64", - "INTEGER", - "STRING", - "ARRAY", -} - - -TIMEDELTA_DESCRIPTION_TAG = "#microseconds" - - -class UnsupportedTypeError(ValueError): - def __init__(self, type_, supported_types): - self.type = type_ - self.supported_types = supported_types - - types_to_format = supported_types - if isinstance(supported_types, dict): - types_to_format = supported_types.keys() - - supported_types_str = ", ".join( - sorted( - [ - getattr(supported, "__name__", supported) - for supported in types_to_format - ] - ) - ) - - super().__init__( - f"'{getattr(type_, '__name__', type_)}' must be one of the supported types ({supported_types_str}) " - "or a list of one of those types." - ) - - -def sdk_type_from_python_type( - t: type, allow_lists: bool = True -) -> bigquery.StandardSqlDataType: - if (get_origin(t) is list) and allow_lists: - return sdk_array_output_type_from_python_type(t) - if t not in RF_SUPPORTED_IO_PYTHON_TYPES: - raise UnsupportedTypeError(t, RF_SUPPORTED_IO_PYTHON_TYPES) - return RF_SUPPORTED_IO_PYTHON_TYPES[t] - - -def sdk_array_output_type_from_python_type(t: type) -> bigquery.StandardSqlDataType: - array_of = get_args(t)[0] - if array_of not in RF_SUPPORTED_ARRAY_OUTPUT_PYTHON_TYPES: - raise UnsupportedTypeError(array_of, RF_SUPPORTED_ARRAY_OUTPUT_PYTHON_TYPES) - inner_type = RF_SUPPORTED_IO_PYTHON_TYPES[array_of] - return bigquery.StandardSqlDataType( - type_kind=bigquery.StandardSqlTypeNames.ARRAY, array_element_type=inner_type - ) - - -def sdk_type_to_bf_type( - sdk_type: bigquery.StandardSqlDataType, -) -> bigframes.dtypes.Dtype: - if sdk_type.array_element_type is not None: - return bigframes.dtypes.list_type( - sdk_type_to_bf_type(sdk_type.array_element_type) - ) - if sdk_type.struct_type is not None: - raise ValueError("Cannot handle struct types in remote function") - assert sdk_type.type_kind is not None - return bigframes.dtypes._TK_TO_BIGFRAMES[sdk_type.type_kind.name] - - -def sdk_type_to_py_type( - sdk_type: bigquery.StandardSqlDataType, -) -> Type[Any]: - if sdk_type.array_element_type is not None: - return list[sdk_type_to_py_type(sdk_type.array_element_type)] # type: ignore - if sdk_type.struct_type is not None: - raise ValueError("Cannot handle struct types in remote function") - for key, value in RF_SUPPORTED_IO_PYTHON_TYPES.items(): - if value == sdk_type: - return key - raise ValueError(f"Cannot handle {sdk_type} in remote function") - - -def sdk_type_to_sql_string( - sdk_type: bigquery.StandardSqlDataType, -) -> str: - if sdk_type.array_element_type is not None: - return f"ARRAY<{sdk_type_to_sql_string(sdk_type.array_element_type)}>" - if sdk_type.struct_type is not None: - raise ValueError("Cannot handle struct types in remote function") - assert sdk_type.type_kind is not None - return sdk_type.type_kind.name diff --git a/bigframes/functions/udf_def.py b/bigframes/functions/udf_def.py deleted file mode 100644 index 70e0406a6f6..00000000000 --- a/bigframes/functions/udf_def.py +++ /dev/null @@ -1,617 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -from __future__ import annotations - -import dataclasses -import functools -import inspect -import io -import os -import textwrap -import warnings -from typing import Any, Optional, Sequence, Type, cast, get_args, get_origin - -import cloudpickle -import google_crc32c -import pandas as pd -from google.cloud import bigquery - -import bigframes.dtypes -import bigframes.exceptions as bfe -import bigframes.formatting_helpers as bf_formatting -from bigframes.functions import function_typing - -# Protocol version 4 is available in python version 3.4 and above -# https://docs.python.org/3/library/pickle.html#data-stream-format -_pickle_protocol_version = 4 - - -class ReturnTypeMissingError(ValueError): - pass - - -@dataclasses.dataclass(frozen=True) -class UdfArg: - name: str = dataclasses.field() - dtype: DirectScalarType | RowSeriesInputFieldV1 - - def __post_init__(self): - assert isinstance(self.name, str) - assert isinstance(self.dtype, (DirectScalarType, RowSeriesInputFieldV1)) - - @classmethod - def from_py_param(cls, param: inspect.Parameter) -> UdfArg: - if param.annotation == pd.Series: - return cls(param.name, RowSeriesInputFieldV1()) - return cls(param.name, DirectScalarType(param.annotation)) - - @classmethod - def from_sdk(cls, arg: bigquery.RoutineArgument) -> UdfArg: - assert arg.name is not None - - if arg.data_type is None: - msg = bfe.format_message( - "The function has one or more missing input data types. BigQuery DataFrames " - f"will assume default data type {function_typing.DEFAULT_RF_TYPE} for them." - ) - warnings.warn(msg, category=bfe.UnknownDataTypeWarning) - sdk_type = function_typing.DEFAULT_RF_TYPE - else: - sdk_type = arg.data_type - return cls(arg.name, DirectScalarType.from_sdk_type(sdk_type)) - - @property - def py_type(self) -> type: - return self.dtype.py_type - - @property - def bf_type(self) -> bigframes.dtypes.Dtype: - return self.dtype.bf_type - - @property - def sql_type(self) -> str: - return self.dtype.sql_type - - def stable_hash(self) -> bytes: - hash_val = google_crc32c.Checksum() - hash_val.update(self.name.encode()) - hash_val.update(self.dtype.stable_hash()) - return hash_val.digest() - - -@dataclasses.dataclass(frozen=True) -class DirectScalarType: - """ - Represents a scalar value that is passed directly to the remote function. - - For these values, BigQuery handles the serialization and deserialization without any additional processing. - """ - - _py_type: type - - @property - def py_type(self) -> type: - return self._py_type - - @property - def bf_type(self) -> bigframes.dtypes.Dtype: - return function_typing.sdk_type_to_bf_type( - function_typing.sdk_type_from_python_type(self._py_type) - ) - - @property - def sql_type(self) -> str: - sdk_type = function_typing.sdk_type_from_python_type(self._py_type) - return function_typing.sdk_type_to_sql_string(sdk_type) - - def stable_hash(self) -> bytes: - hash_val = google_crc32c.Checksum() - hash_val.update(self._py_type.__name__.encode()) - return hash_val.digest() - - @classmethod - def from_sdk_type(cls, sdk_type: bigquery.StandardSqlDataType) -> DirectScalarType: - return cls(function_typing.sdk_type_to_py_type(sdk_type)) - - @property - def emulating_type(self) -> DirectScalarType: - return self - - -@dataclasses.dataclass(frozen=True) -class VirtualListTypeV1: - """ - Represents a list of scalar values that is emulated as a JSON array string in the remote function. - - Only works as output paramter right now where array -> string in function runtime, and then string -> array in SQL post-processing (defined in out_expr()). - """ - - _PROTOCOL_ID = "virtual_list_v1" - - inner_dtype: DirectScalarType - - @property - def py_type(self) -> Type[list[Any]]: - return list[self.inner_dtype.py_type] # type: ignore - - @property - def bf_type(self) -> bigframes.dtypes.Dtype: - return bigframes.dtypes.list_type(self.inner_dtype.bf_type) - - @property - def emulating_type(self) -> DirectScalarType: - # Regardless of list inner type, string is used to emulate the list in the remote function. - return DirectScalarType(str) - - def out_expr( - self, expr: bigframes.core.expression.Expression - ) -> bigframes.core.expression.Expression: - # essentially we are undoing json.dumps in sql - import bigframes.operations as ops - - as_str_list = ops.JSONValueArray(json_path="$").as_expr(expr) - if self.inner_dtype.py_type is str: - return as_str_list - elif self.inner_dtype.py_type is bool: - # hack so we don't need to make ArrayMap support general expressions yet - # with b/495513753 we can map the equality operator instead - return ops.ArrayMapOp(ops.IsInOp(values=("true",))).as_expr(as_str_list) - else: - return ops.ArrayMapOp(ops.AsTypeOp(self.inner_dtype.bf_type)).as_expr( - as_str_list - ) - - @property - def sql_type(self) -> str: - return f"ARRAY<{self.inner_dtype.sql_type}>" - - def stable_hash(self) -> bytes: - hash_val = google_crc32c.Checksum() - hash_val.update(self._PROTOCOL_ID.encode()) - hash_val.update(self.inner_dtype.stable_hash()) - return hash_val.digest() - - -@dataclasses.dataclass(frozen=True) -class RowSeriesInputFieldV1: - """ - Used to handle functions that logically take a series as an input, but handled via a string protocol in the remote function. - - For these, the serialization is dependent on index metadata, which must be provided by the caller. - """ - - _PROTOCOL_ID = "row_series_input_v1" - - @property - def py_type(self) -> type: - return pd.Series - - @property - def bf_type(self) -> bigframes.dtypes.Dtype: - # Code paths shouldn't hit this. - raise ValueError("Series does not have a corresponding BigFrames type.") - - @property - def sql_type(self) -> str: - return "STRING" - - @property - def emulating_type(self) -> DirectScalarType: - # Regardless of list inner type, string is used to emulate the list in the remote function. - return DirectScalarType(str) - - def stable_hash(self) -> bytes: - hash_val = google_crc32c.Checksum() - hash_val.update(self._PROTOCOL_ID.encode()) - return hash_val.digest() - - -@dataclasses.dataclass(frozen=True) -class UdfSignature: - """ - Represents the mapping of input types from bigframes to sql to python and back. - """ - - inputs: tuple[UdfArg, ...] = dataclasses.field() - output: DirectScalarType | VirtualListTypeV1 - - def __post_init__(self): - # Validate inputs and outputs are of the correct types. - assert all(isinstance(arg, UdfArg) for arg in self.inputs) - assert isinstance(self.output, (DirectScalarType, VirtualListTypeV1)) - - def to_sql_input_signature(self) -> str: - return ",".join( - f"{field.name} {field.sql_type}" - for field in self.with_devirtualize().inputs - ) - - @property - def protocol_metadata(self) -> str | None: - import bigframes.functions._utils - - if isinstance(self.output, VirtualListTypeV1): - return bigframes.functions._utils.get_bigframes_metadata( - python_output_type=self.output.py_type - ) - return None - - @property - def is_virtual(self) -> bool: - dtypes = (self.output,) + tuple(arg.dtype for arg in self.inputs) - return not all(isinstance(dtype, DirectScalarType) for dtype in dtypes) - - @property - def is_row_processor(self) -> bool: - return any(isinstance(arg.dtype, RowSeriesInputFieldV1) for arg in self.inputs) - - def with_devirtualize(self) -> UdfSignature: - return UdfSignature( - inputs=tuple( - UdfArg(arg.name, arg.dtype.emulating_type) for arg in self.inputs - ), - output=self.output.emulating_type, - ) - - # TODO(493293086): Deprecate is_row_processor. - @classmethod - def from_routine( - cls, routine: bigquery.Routine, is_row_processor: bool = False - ) -> UdfSignature: - import bigframes.functions._utils - - ## Handle return type - if routine.return_type is None: - raise ReturnTypeMissingError( - f"Routine {routine} has no return type. Routine properties: {routine._properties}" - ) - - bq_return_type = cast(bigquery.StandardSqlDataType, routine.return_type) - - return_type: DirectScalarType | VirtualListTypeV1 = ( - DirectScalarType.from_sdk_type(bq_return_type) - ) - if ( - python_output_type - := bigframes.functions._utils.get_python_output_type_from_bigframes_metadata( - routine.description - ) - ): - if bq_return_type.type_kind != "STRING": - raise bf_formatting.create_exception_with_feedback_link( - TypeError, - "An explicit output_type should be provided only for a BigQuery function with STRING output.", - ) - - if get_origin(python_output_type) is list: - inner_type = get_args(python_output_type)[0] - return_type = VirtualListTypeV1(DirectScalarType(inner_type)) - else: - raise bf_formatting.create_exception_with_feedback_link( - TypeError, - "Currently only list of a type is supported as python output type.", - ) - - ## Handle input types - udf_fields = [] - - for i, argument in enumerate(routine.arguments): - if is_row_processor and i == 0: - if argument.data_type.type_kind == "STRING": - udf_fields.append(UdfArg(argument.name, RowSeriesInputFieldV1())) - else: - raise ValueError( - "Row processor functions must have STRING input type as first argument." - ) - udf_fields.append(UdfArg.from_sdk(argument)) - - return cls( - inputs=tuple(udf_fields), - output=return_type, - ) - - @classmethod - def from_py_signature(cls, signature: inspect.Signature): - import bigframes.series - - input_types: list[UdfArg] = [] - for parameter in signature.parameters.values(): - if parameter.annotation is inspect.Signature.empty: - raise bf_formatting.create_exception_with_feedback_link( - ValueError, - "'input_types' was not set and parameter " - f"'{parameter.name}' is missing a type annotation. " - "Types are required to use udfs.", - ) - if parameter.annotation is bigframes.series.Series: - raise TypeError( - "Argument type hint must be Pandas Series, not BigFrames Series." - ) - - input_types.append(UdfArg.from_py_param(parameter)) - - if signature.return_annotation is inspect.Signature.empty: - raise bf_formatting.create_exception_with_feedback_link( - ValueError, - "'output_type' was not set and function is missing a " - "return type annotation. Types are required to use " - "udfs.", - ) - - output_type = DirectScalarType(signature.return_annotation) - return cls(tuple(input_types), output_type) - - def to_remote_function_compatible(self) -> UdfSignature: - # need to virtualize list outputs - if isinstance(self.output, DirectScalarType): - if get_origin(self.output.py_type) is list: - inner_py_type = get_args(self.output.py_type)[0] - return UdfSignature( - inputs=self.inputs, - output=VirtualListTypeV1(DirectScalarType(inner_py_type)), - ) - return self - - def stable_hash(self) -> bytes: - hash_val = google_crc32c.Checksum() - for input_type in self.inputs: - hash_val.update(input_type.stable_hash()) - hash_val.update(self.output.stable_hash()) - return hash_val.digest() - - -@dataclasses.dataclass(frozen=True) -class RuntimeRequirements: - container_cpu: Optional[float] = None - container_memory: Optional[str] = None - bq_connection_id: Optional[str] = None - max_batching_rows: Optional[int] = None - packages: tuple[str, ...] = () - - def stable_hash(self) -> bytes: - hash_val = google_crc32c.Checksum() - if self.container_cpu is not None: - hash_val.update(str(self.container_cpu).encode()) - if self.container_memory is not None: - hash_val.update(str(self.container_memory).encode()) - if self.bq_connection_id is not None: - hash_val.update(str(self.bq_connection_id).encode()) - if self.max_batching_rows is not None: - hash_val.update(str(self.max_batching_rows).encode()) - if self.packages: - for p in sorted(self.packages): - hash_val.update(p.encode()) - return hash_val.digest() - - -@dataclasses.dataclass(frozen=True) -class BigqueryUdf: - """ - Represents the information needed to call a BigQuery remote function - not a full spec. - """ - - routine_ref: bigquery.RoutineReference = dataclasses.field() - signature: UdfSignature - - def with_devirtualize(self) -> BigqueryUdf: - if not self.signature.is_virtual: - return self - return BigqueryUdf( - routine_ref=self.routine_ref, - signature=self.signature.with_devirtualize(), - ) - - @classmethod - def from_routine( - cls, routine: bigquery.Routine, is_row_processor: bool = False - ) -> BigqueryUdf: - signature = UdfSignature.from_routine( - routine, is_row_processor=is_row_processor - ) - return cls(routine.reference, signature=signature) - - -@dataclasses.dataclass(frozen=True) -class PythonUdf: - """ - Represents user-requested Python UDF semantics, including the code and runtime requirements. - """ - - signature: UdfSignature - code: CodeDef - requirements: RuntimeRequirements = dataclasses.field( - default_factory=RuntimeRequirements - ) - - def stable_hash(self) -> bytes: - hash_val = google_crc32c.Checksum() - hash_val.update(self.code.stable_hash()) - hash_val.update(self.signature.stable_hash()) - hash_val.update(self.requirements.stable_hash()) - return hash_val.digest() - - def to_managed_function_config(self) -> ManagedFunctionConfig: - return ManagedFunctionConfig( - code=self.code, - signature=self.signature, - max_batching_rows=self.requirements.max_batching_rows, - container_cpu=self.requirements.container_cpu, - container_memory=self.requirements.container_memory, - bq_connection_id=self.requirements.bq_connection_id, - capture_references=False, - ) - - -@dataclasses.dataclass(frozen=True) -class CodeDef: - # Produced by cloudpickle, not compatible across python versions - pickled_code: bytes - # This is just the function itself, and does not include referenced objects/functions/modules - function_source: Optional[str] - entry_point: Optional[str] - package_requirements: tuple[str, ...] - - @classmethod - def from_func(cls, func, package_requirements: Sequence[str] | None = None): - bytes_io = io.BytesIO() - cloudpickle.dump(func, bytes_io, protocol=_pickle_protocol_version) - source = None - entry_point = None - try: - # dedent is hacky, but works for some nested functions - source = textwrap.dedent(inspect.getsource(func)) - entry_point = func.__name__ - except OSError: - pass - return cls( - pickled_code=bytes_io.getvalue(), - function_source=source, - entry_point=entry_point, - package_requirements=tuple(package_requirements or []), - ) - - @functools.cache - def stable_hash(self) -> bytes: - # There is a known cell-id sensitivity of the cloudpickle serialization in - # notebooks https://github.com/cloudpipe/cloudpickle/issues/538. Because of - # this, if a cell contains a udf decorated with @remote_function, a unique - # cloudpickle code is generated every time the cell is run, creating new - # cloud artifacts every time. This is slow and wasteful. - # A workaround of the same can be achieved by replacing the filename in the - # code object to a static value - # https://github.com/cloudpipe/cloudpickle/issues/120#issuecomment-338510661. - # - # To respect the user code/environment let's make this modification on a - # copy of the udf, not on the original udf itself. - def_copy = cloudpickle.loads(self.pickled_code) - def_copy.__code__ = def_copy.__code__.replace( - co_filename="bigframes_place_holder_filename" - ) - - normalized_pickled_code = cloudpickle.dumps( - def_copy, protocol=_pickle_protocol_version - ) - - hash_val = google_crc32c.Checksum() - hash_val.update(normalized_pickled_code) - - if self.package_requirements: - for p in sorted(self.package_requirements): - hash_val.update(p.encode()) - - return hash_val.digest() - - def to_callable(self): - """ - Reconstructs the python callable from the pickled code. - - Assumption: package_requirements match local environment - """ - return cloudpickle.loads(self.pickled_code) - - -@dataclasses.dataclass(frozen=True) -class ManagedFunctionConfig: - code: CodeDef - signature: UdfSignature - max_batching_rows: Optional[int] - container_cpu: Optional[float] - container_memory: Optional[str] - bq_connection_id: Optional[str] - # capture_refernces=True -> deploy as cloudpickle - # capture_references=False -> deploy as source - capture_references: bool = False - - def stable_hash(self) -> bytes: - hash_val = google_crc32c.Checksum() - hash_val.update(self.code.stable_hash()) - hash_val.update(self.signature.stable_hash()) - hash_val.update(str(self.max_batching_rows).encode()) - hash_val.update(str(self.container_cpu).encode()) - hash_val.update(str(self.container_memory).encode()) - hash_val.update(str(self.bq_connection_id).encode()) - hash_val.update(str(self.capture_references).encode()) - return hash_val.digest() - - -@dataclasses.dataclass(frozen=True) -class CloudRunFunctionConfig: - code: CodeDef - signature: UdfSignature - timeout_seconds: int | None - max_instance_count: int | None - vpc_connector: str | None - vpc_connector_egress_settings: str - memory_mib: int | None - cpus: float | None - ingress_settings: str - workers: int | None - threads: int | None - concurrency: int | None - kms_key_name: str | None - docker_repository: str | None - cloud_build_service_account: str | None - cloud_run_service_account: str | None - - def stable_hash(self) -> bytes: - hash_val = google_crc32c.Checksum() - hash_val.update(self.code.stable_hash()) - hash_val.update(self.signature.stable_hash()) - hash_val.update(str(self.timeout_seconds).encode()) - hash_val.update(str(self.max_instance_count).encode()) - hash_val.update(str(self.vpc_connector).encode()) - hash_val.update(str(self.vpc_connector_egress_settings).encode()) - hash_val.update(str(self.memory_mib).encode()) - hash_val.update(str(self.cpus).encode()) - hash_val.update(str(self.ingress_settings).encode()) - hash_val.update(str(self.workers).encode()) - hash_val.update(str(self.threads).encode()) - hash_val.update(str(self.concurrency).encode()) - hash_val.update(str(self.kms_key_name).encode()) - hash_val.update(str(self.docker_repository).encode()) - hash_val.update(str(self.cloud_build_service_account).encode()) - hash_val.update(str(self.cloud_run_service_account).encode()) - return hash_val.digest() - - -@dataclasses.dataclass(frozen=True) -class RemoteFunctionConfig: - """ - Represents the information needed to create a BigQuery remote function. - """ - - endpoint: str - signature: UdfSignature - connection_id: str - max_batching_rows: int - bq_metadata: str | None = None - - @classmethod - def from_bq_routine(cls, routine: bigquery.Routine) -> RemoteFunctionConfig: - return cls( - endpoint=routine.remote_function_options.endpoint, - connection_id=os.path.basename(routine.remote_function_options.connection), - signature=UdfSignature.from_routine(routine), - max_batching_rows=routine.remote_function_options.max_batching_rows, - bq_metadata=routine.description, - ) - - def stable_hash(self) -> bytes: - hash_val = google_crc32c.Checksum() - hash_val.update(self.endpoint.encode()) - hash_val.update(self.signature.stable_hash()) - hash_val.update(self.connection_id.encode()) - hash_val.update(str(self.max_batching_rows).encode()) - hash_val.update(str(self.bq_metadata).encode()) - return hash_val.digest() diff --git a/bigframes/geopandas/__init__.py b/bigframes/geopandas/__init__.py deleted file mode 100644 index 08966ba9238..00000000000 --- a/bigframes/geopandas/__init__.py +++ /dev/null @@ -1,17 +0,0 @@ -# Copyright 2023 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from bigframes.geopandas.geoseries import GeoSeries - -__all__ = ["GeoSeries"] diff --git a/bigframes/geopandas/geoseries.py b/bigframes/geopandas/geoseries.py deleted file mode 100644 index dc373216b65..00000000000 --- a/bigframes/geopandas/geoseries.py +++ /dev/null @@ -1,130 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -from __future__ import annotations - -from typing import Optional - -import bigframes_vendored.constants as constants -import bigframes_vendored.geopandas.geoseries as vendored_geoseries -import geopandas.array # type: ignore - -import bigframes.operations as ops -import bigframes.series -import bigframes.session -from bigframes._tools import docs - - -@docs.inherit_docs(vendored_geoseries.GeoSeries) -class GeoSeries(bigframes.series.Series): - def __init__(self, data=None, index=None, **kwargs): - super().__init__( - data=data, index=index, dtype=geopandas.array.GeometryDtype(), **kwargs - ) - - @property - def length(self): - raise NotImplementedError( - "GeoSeries.length is not yet implemented. Please use bigframes.bigquery.st_length(geoseries) instead." - ) - - @property - def x(self) -> bigframes.series.Series: - series = self._apply_unary_op(ops.geo_x_op) - series.name = None - return series - - @property - def y(self) -> bigframes.series.Series: - series = self._apply_unary_op(ops.geo_y_op) - series.name = None - return series - - # GeoSeries.area overrides Series.area with something totally different. - # Ignore this type error, as we are trying to be as close to geopandas as - # we can. - @property - def area(self, crs=None) -> bigframes.series.Series: # type: ignore - raise NotImplementedError( - f"GeoSeries.area is not supported. Use bigframes.bigquery.st_area(series), instead. {constants.FEEDBACK_LINK}" - ) - - @property - def boundary(self) -> bigframes.series.Series: # type: ignore - series = self._apply_unary_op(ops.geo_st_boundary_op) - series.name = None - return series - - @property - def is_closed(self) -> bigframes.series.Series: - # TODO(tswast): GeoPandas doesn't treat Point as closed. Use ST_LENGTH - # when available to filter out "closed" shapes that return false in - # GeoPandas. - raise NotImplementedError( - f"GeoSeries.is_closed is not supported. Use bigframes.bigquery.st_isclosed(series), instead. {constants.FEEDBACK_LINK}" - ) - - @classmethod - def from_wkt( - cls, - data, - index=None, - *, - session: Optional[bigframes.session.Session] = None, - ) -> GeoSeries: - series = bigframes.series.Series(data, index=index, session=session) - - return cls(series._apply_unary_op(ops.geo_st_geogfromtext_op)) - - @classmethod - def from_xy(cls, x, y, index=None, session=None, **kwargs) -> GeoSeries: - # TODO: if either x or y is local and the other is remote. Use the - # session from the remote object. - series_x = bigframes.series.Series(x, index=index, session=session, **kwargs) - series_y = bigframes.series.Series(y, index=index, session=session, **kwargs) - - return cls(series_x._apply_binary_op(series_y, ops.geo_st_geogpoint_op)) - - def to_wkt(self: GeoSeries) -> bigframes.series.Series: - series = self._apply_unary_op(ops.geo_st_astext_op) - series.name = None - return series - - def buffer(self: GeoSeries, distance: float) -> bigframes.series.Series: # type: ignore - raise NotImplementedError( - f"GeoSeries.buffer is not supported. Use bigframes.bigquery.st_buffer(series, distance), instead. {constants.FEEDBACK_LINK}" - ) - - @property - def centroid(self: GeoSeries) -> bigframes.series.Series: # type: ignore - return self._apply_nary_op(ops.googlesql.ST_CENTROID, []) - - @property - def convex_hull(self: GeoSeries) -> bigframes.series.Series: # type: ignore - return self._apply_unary_op(ops.geo_st_convexhull_op) - - def difference(self: GeoSeries, other: GeoSeries) -> bigframes.series.Series: # type: ignore - return self._apply_binary_op(other, ops.geo_st_difference_op) - - def distance(self: GeoSeries, other: GeoSeries) -> bigframes.series.Series: # type: ignore - raise NotImplementedError( - f"GeoSeries.distance is not supported. Use bigframes.bigquery.st_distance(series, other), instead. {constants.FEEDBACK_LINK}" - ) - - def intersection(self: GeoSeries, other: GeoSeries) -> bigframes.series.Series: # type: ignore - return self._apply_binary_op(other, ops.geo_st_intersection_op) - - def simplify(self, tolerance, preserve_topology=True): - raise NotImplementedError( - f"GeoSeries.simplify is not supported. Use bigframes.bigquery.st_simplify(series, tolerance_meters), instead. {constants.FEEDBACK_LINK}" - ) diff --git a/bigframes/ml/__init__.py b/bigframes/ml/__init__.py index 368d272e7b4..55c8709d8d8 100644 --- a/bigframes/ml/__init__.py +++ b/bigframes/ml/__init__.py @@ -12,82 +12,18 @@ # See the License for the specific language governing permissions and # limitations under the License. -"""BigQuery DataFrames ML provides a SKLearn-like API on the BigQuery engine. - -.. code:: python - - from bigframes.ml.linear_model import LinearRegression - model = LinearRegression() - model.fit(feature_columns, label_columns) - model.predict(feature_columns_from_test_data) - -You can also save your fit parameters to BigQuery for later use. - -.. code:: python - - import bigframes.pandas as bpd - model.to_gbq( - your_model_id, # For example: "bqml_tutorial.penguins_model" - replace=True, - ) - saved_model = bpd.read_gbq_model(your_model_id) - saved_model.predict(feature_columns_from_test_data) - -See the `BigQuery ML linear regression tutorial -`_ for a -detailed example. - -See also the references for ``bigframes.ml`` sub-modules: - -* :mod:`bigframes.ml.cluster` -* :mod:`bigframes.ml.compose` -* :mod:`bigframes.ml.decomposition` -* :mod:`bigframes.ml.ensemble` -* :mod:`bigframes.ml.forecasting` -* :mod:`bigframes.ml.imported` -* :mod:`bigframes.ml.impute` -* :mod:`bigframes.ml.linear_model` -* :mod:`bigframes.ml.llm` -* :mod:`bigframes.ml.metrics` -* :mod:`bigframes.ml.model_selection` -* :mod:`bigframes.ml.pipeline` -* :mod:`bigframes.ml.preprocessing` -* :mod:`bigframes.ml.remote` - -Alternatively, check out mod:`bigframes.bigquery.ml` for an interface that is -more similar to the BigQuery ML SQL syntax. -""" - -from bigframes.ml import ( - cluster, - compose, - decomposition, - ensemble, - forecasting, - imported, - impute, - linear_model, - llm, - metrics, - model_selection, - pipeline, - preprocessing, - remote, -) +"""BigQuery DataFrames ML provides a SKLearn-like API on the BigQuery engine.""" __all__ = [ "cluster", "compose", "decomposition", - "ensemble", - "forecasting", - "imported", - "impute", "linear_model", - "llm", "metrics", "model_selection", "pipeline", "preprocessing", - "remote", + "llm", + "forecasting", + "imported", ] diff --git a/bigframes/ml/base.py b/bigframes/ml/base.py index fbfaf6b537c..f2478b1ce23 100644 --- a/bigframes/ml/base.py +++ b/bigframes/ml/base.py @@ -15,32 +15,23 @@ """ Wraps primitives for machine learning with BQML -This library is an evolving attempt to: - -* implement BigQuery DataFrames API for BQML -* follow as close as possible the API design of SKLearn +This library is an evolving attempt to +- implement BigQuery DataFrames API for BQML +- follow as close as possible the API design of SKLearn https://arxiv.org/pdf/1309.0238.pdf - """ import abc -import typing -import warnings -from typing import Optional, TypeVar, Union - -import bigframes_vendored.sklearn.base +from typing import cast, Optional, TypeVar, Union -import bigframes.exceptions as bfe -import bigframes.ml.utils as utils -import bigframes.pandas as bpd -from bigframes._tools import docs from bigframes.ml import core +import bigframes.pandas as bpd +import third_party.bigframes_vendored.sklearn.base -@docs.inherit_docs(bigframes_vendored.sklearn.base.BaseEstimator) -class BaseEstimator(abc.ABC): +class BaseEstimator(third_party.bigframes_vendored.sklearn.base.BaseEstimator, abc.ABC): """ - A BigQuery DataFrames machine learning component follows sklearn API + A BigQuery DataFrames machine learning component following the SKLearn API design Ref: https://bit.ly/3NyhKjN The estimator is the fundamental abstraction for all learning components. This includes learning @@ -51,16 +42,12 @@ class BaseEstimator(abc.ABC): assumed to be the list of hyperparameters. All descendents of this class should implement: - - .. code-block:: python - def __init__(self, hyperparameter_1=default_1, hyperparameter_2=default_2, hyperparameter3, ...): '''Set hyperparameters''' self.hyperparameter_1 = hyperparameter_1 self.hyperparameter_2 = hyperparameter_2 self.hyperparameter3 = hyperparameter3 ... - Note: the object variable names must be exactly the same with parameter names. In order to utilize __repr__. fit(X, y) method is optional. @@ -88,15 +75,12 @@ def fit_transform(self, x_train: Union[DataFrame, Series], y_train: Union[DataFr ... """ - def __init__(self): - self._bqml_model: Optional[core.BqmlModel] = None - def __repr__(self): - """Print the estimator's constructor with all non-default parameter values.""" + """Print the estimator's constructor with all non-default parameter values""" # Estimator pretty printer adapted from Sklearn's, which is in turn an adaption of # the inbuilt pretty-printer in CPython - import bigframes_vendored.cpython._pprint as adapted_pprint + import third_party.bigframes_vendored.cpython._pprint as adapted_pprint prettyprinter = adapted_pprint._EstimatorPrettyPrinter( compact=True, indent=1, indent_at_name=True, n_max_elements_to_show=30 @@ -105,10 +89,12 @@ def __repr__(self): return prettyprinter.pformat(self) -# TODO(garrettwu): refactor to reflect the actual property. Now the class contains .register() method. class Predictor(BaseEstimator): """A BigQuery DataFrames ML Model base class that can be used to predict outputs.""" + def __init__(self): + self._bqml_model: Optional[core.BqmlModel] = None + @abc.abstractmethod def predict(self, X): pass @@ -118,13 +104,13 @@ def predict(self, X): def register(self: _T, vertex_ai_model_id: Optional[str] = None) -> _T: """Register the model to Vertex AI. - After register, go to the Google Cloud console (https://console.cloud.google.com/vertex-ai/models) + After register, go to Google Cloud Console (https://console.cloud.google.com/vertex-ai/models) to manage the model registries. Refer to https://cloud.google.com/vertex-ai/docs/model-registry/introduction for more options. Args: vertex_ai_model_id (Optional[str], default None): - Optional string id as model id in Vertex. If not set, will default to 'bigframes_{bq_model_id}'. + optional string id as model id in Vertex. If not set, will by default to 'bigframes_{bq_model_id}'. Vertex Ai model id will be truncated to 63 characters due to its limitation. Returns: @@ -136,15 +122,11 @@ def register(self: _T, vertex_ai_model_id: Optional[str] = None) -> _T: self._bqml_model = self._create_bqml_model() # type: ignore except AttributeError: raise RuntimeError("A model must be trained before register.") - self._bqml_model = typing.cast(core.BqmlModel, self._bqml_model) + self._bqml_model = cast(core.BqmlModel, self._bqml_model) self._bqml_model.register(vertex_ai_model_id) return self - @abc.abstractmethod - def to_gbq(self, model_name, replace): - pass - class TrainablePredictor(Predictor): """A BigQuery DataFrames ML Model base class that can be used to fit and predict outputs. @@ -159,6 +141,11 @@ def _fit(self, X, y, transforms=None): def score(self, X, y): pass + # TODO(b/291812029): move to Predictor after implement in LLM and imported models + @abc.abstractmethod + def to_gbq(self, model_name, replace): + pass + class SupervisedTrainablePredictor(TrainablePredictor): """A BigQuery DataFrames ML Supervised Model base class that can be used to fit and predict outputs. @@ -169,181 +156,28 @@ class SupervisedTrainablePredictor(TrainablePredictor): def fit( self: _T, - X: utils.ArrayType, - y: utils.ArrayType, + X: Union[bpd.DataFrame, bpd.Series], + y: Union[bpd.DataFrame, bpd.Series], ) -> _T: return self._fit(X, y) -class SupervisedTrainableWithIdColPredictor(SupervisedTrainablePredictor): - """Inherits from SupervisedTrainablePredictor, - but adds an optional id_col parameter to fit().""" - - def __init__(self): - super().__init__() - self.id_col = None - - def _fit( - self, - X: utils.ArrayType, - y: utils.ArrayType, - transforms=None, - id_col: Optional[utils.ArrayType] = None, - ): - return self - - def fit( - self, - X: utils.ArrayType, - y: utils.ArrayType, - transforms=None, - id_col: Optional[utils.ArrayType] = None, - ): - self.id_col = id_col - return self._fit(X, y, transforms=transforms, id_col=self.id_col) - - -class TrainableWithEvaluationPredictor(TrainablePredictor): - """A BigQuery DataFrames ML Model base class that can be used to fit and predict outputs. - - Additional evaluation data can be provided to measure the model in the fit phase.""" - - @abc.abstractmethod - def _fit(self, X, y, transforms=None, X_eval=None, y_eval=None): - pass - - @abc.abstractmethod - def score(self, X, y): - pass - - -class SupervisedTrainableWithEvaluationPredictor(TrainableWithEvaluationPredictor): - """A BigQuery DataFrames ML Supervised Model base class that can be used to fit and predict outputs. - - Need to provide both X and y in supervised tasks. - - Additional X_eval and y_eval can be provided to measure the model in the fit phase. - """ - - _T = TypeVar("_T", bound="SupervisedTrainableWithEvaluationPredictor") - - def fit( - self: _T, - X: utils.ArrayType, - y: utils.ArrayType, - X_eval: Optional[utils.ArrayType] = None, - y_eval: Optional[utils.ArrayType] = None, - ) -> _T: - return self._fit(X, y, X_eval=X_eval, y_eval=y_eval) - - class UnsupervisedTrainablePredictor(TrainablePredictor): """A BigQuery DataFrames ML Unsupervised Model base class that can be used to fit and predict outputs. - Only need to provide X (y is optional and ignored) in unsupervised tasks.""" + Only need to provide both X (y is optional and ignored) in unsupervised tasks.""" _T = TypeVar("_T", bound="UnsupervisedTrainablePredictor") def fit( self: _T, - X: utils.ArrayType, - y: Optional[utils.ArrayType] = None, + X: Union[bpd.DataFrame, bpd.Series], + y: Optional[Union[bpd.DataFrame, bpd.Series]] = None, ) -> _T: return self._fit(X, y) - def fit_predict( - self: _T, - X: utils.ArrayType, - y: Optional[utils.ArrayType] = None, - ) -> _T: - return self.fit(X).predict(X) - - -class RetriableRemotePredictor(BaseEstimator): - def _predict_and_retry( - self, - bqml_model_predict_tvf: core.BqmlModel.TvfDef, - X: bpd.DataFrame, - options: dict, - max_retries: int, - ) -> bpd.DataFrame: - assert self._bqml_model is not None - - df_result: Union[bpd.DataFrame, None] = None # placeholder - df_succ = df_fail = X - for i in range(max_retries + 1): - if i > 0 and df_fail.empty: - break - if i > 0 and df_succ.empty: - msg = bfe.format_message("Can't make any progress, stop retrying.") - warnings.warn(msg, category=RuntimeWarning) - break - - df = bqml_model_predict_tvf.tvf(self._bqml_model, df_fail, options) - - success = df[bqml_model_predict_tvf.status_col].str.len() == 0 - df_succ = df[success] - df_fail = df[~success] - - df_result = ( - bpd.concat([df_result, df_succ]) if df_result is not None else df_succ - ) - - df_result = typing.cast( - bpd.DataFrame, - bpd.concat([df_result, df_fail]) if df_result is not None else df_fail, - ) - return df_result - - -class BaseTransformer(BaseEstimator): - """Transformer base class.""" - - @abc.abstractmethod - def _keys(self): - pass - - def _extract_output_names(self): - """Extract transform output column names. Save the results to self._output_names.""" - assert self._bqml_model is not None - - output_names = [] - for transform_col in self._bqml_model._model._properties["transformColumns"]: - transform_col_dict = typing.cast(dict, transform_col) - # pass the columns that are not transformed - if "transformSql" not in transform_col_dict: - continue - output_names.append(transform_col_dict["name"]) - - self._output_names = output_names - - def __eq__(self, other) -> bool: - return type(self) is type(other) and self._keys() == other._keys() - - def __hash__(self) -> int: - return hash(self._keys()) - - _T = TypeVar("_T", bound="BaseTransformer") - - def to_gbq(self: _T, model_name: str, replace: bool = False) -> _T: - """Save the transformer as a BigQuery model. - - Args: - model_name (str): - The name of the model. - replace (bool, default False): - Determine whether to replace if the model already exists. Default to False. - - Returns: - Saved transformer.""" - if not self._bqml_model: - raise RuntimeError("A transformer must be fitted before it can be saved") - - new_model = self._bqml_model.copy(model_name, replace) - return new_model.session.read_gbq_model(model_name) - -class Transformer(BaseTransformer): +class Transformer(BaseEstimator): """A BigQuery DataFrames Transformer base class that transforms data. Also the transformers can be attached to a pipeline with a predictor.""" @@ -358,13 +192,13 @@ def transform(self, X): def fit_transform( self, - X: utils.ArrayType, - y: Optional[utils.ArrayType] = None, + X: Union[bpd.DataFrame, bpd.Series], + y: Optional[Union[bpd.DataFrame, bpd.Series]] = None, ) -> bpd.DataFrame: return self.fit(X, y).transform(X) -class LabelTransformer(BaseTransformer): +class LabelTransformer(BaseEstimator): """A BigQuery DataFrames Label Transformer base class that transforms data. Also the transformers can be attached to a pipeline with a predictor.""" @@ -379,6 +213,6 @@ def transform(self, y): def fit_transform( self, - y: utils.ArrayType, + y: Union[bpd.DataFrame, bpd.Series], ) -> bpd.DataFrame: return self.fit(y).transform(y) diff --git a/bigframes/ml/cluster.py b/bigframes/ml/cluster.py index f7a84a57e97..772b90f666e 100644 --- a/bigframes/ml/cluster.py +++ b/bigframes/ml/cluster.py @@ -17,96 +17,55 @@ from __future__ import annotations -from typing import List, Literal, Optional, Union +from typing import cast, Dict, List, Optional, Union -import bigframes_vendored.sklearn.cluster._kmeans -import pandas as pd from google.cloud import bigquery import bigframes -import bigframes.pandas as bpd -from bigframes.core.logging import log_adapter from bigframes.ml import base, core, globals, utils - -_BQML_PARAMS_MAPPING = { - "n_clusters": "numClusters", - "init": "kmeansInitializationMethod", - "init_col": "kmeansInitializationColumn", - "distance_type": "distanceType", - "max_iter": "maxIterations", - "tol": "minRelativeProgress", - "warm_start": "warmStart", -} +import bigframes.pandas as bpd +import third_party.bigframes_vendored.sklearn.cluster._kmeans -@log_adapter.class_logger class KMeans( base.UnsupervisedTrainablePredictor, - bigframes_vendored.sklearn.cluster._kmeans.KMeans, + third_party.bigframes_vendored.sklearn.cluster._kmeans.KMeans, ): - __doc__ = bigframes_vendored.sklearn.cluster._kmeans.KMeans.__doc__ - def __init__( - self, - n_clusters: int = 8, - *, - init: Literal["kmeans++", "random", "custom"] = "kmeans++", - init_col: Optional[str] = None, - distance_type: Literal["euclidean", "cosine"] = "euclidean", - max_iter: int = 20, - tol: float = 0.01, - warm_start: bool = False, - ): + __doc__ = third_party.bigframes_vendored.sklearn.cluster._kmeans.KMeans.__doc__ + + def __init__(self, n_clusters: int = 8): self.n_clusters = n_clusters - # allow the alias to be compatible with sklearn - self.init = "kmeans++" if init == "k-means++" else init - self.init_col = init_col - self.distance_type = distance_type - self.max_iter = max_iter - self.tol = tol - self.warm_start = warm_start self._bqml_model: Optional[core.BqmlModel] = None self._bqml_model_factory = globals.bqml_model_factory() @classmethod - def _from_bq(cls, session: bigframes.Session, bq_model: bigquery.Model) -> KMeans: - assert bq_model.model_type == "KMEANS" + def _from_bq(cls, session: bigframes.Session, model: bigquery.Model) -> KMeans: + assert model.model_type == "KMEANS" - kwargs: dict = {} + kwargs = {} - kwargs = utils.retrieve_params_from_bq_model( - cls, bq_model, _BQML_PARAMS_MAPPING - ) + # See https://cloud.google.com/bigquery/docs/reference/rest/v2/models#trainingrun + last_fitting = model.training_runs[-1]["trainingOptions"] + if "numClusters" in last_fitting: + kwargs["n_clusters"] = int(last_fitting["numClusters"]) - model = cls(**kwargs) - model._bqml_model = core.BqmlModel(session, bq_model) - return model + new_kmeans = cls(**kwargs) + new_kmeans._bqml_model = core.BqmlModel(session, model) + return new_kmeans @property - def _bqml_options(self) -> dict: + def _bqml_options(self) -> Dict[str, str | int | float | List[str]]: """The model options as they will be set for BQML""" - options = { - "model_type": "KMEANS", - "num_clusters": self.n_clusters, - "KMEANS_INIT_METHOD": self.init, - "DISTANCE_TYPE": self.distance_type, - "MAX_ITERATIONS": self.max_iter, - "MIN_REL_PROGRESS": self.tol, - "WARM_START": self.warm_start, - } - - if self.init_col is not None: - options["KMEANS_INIT_COL"] = self.init_col - - return options + return {"model_type": "KMEANS", "num_clusters": self.n_clusters} def _fit( self, - X: utils.ArrayType, + X: Union[bpd.DataFrame, bpd.Series], y=None, # ignored transforms: Optional[List[str]] = None, ) -> KMeans: - (X,) = utils.batch_convert_to_dataframe(X) + (X,) = utils.convert_to_dataframe(X) self._bqml_model = self._bqml_model_factory.create_model( X_train=X, @@ -126,57 +85,26 @@ def cluster_centers_(self) -> bpd.DataFrame: def predict( self, - X: utils.ArrayType, + X: Union[bpd.DataFrame, bpd.Series], ) -> bpd.DataFrame: if not self._bqml_model: raise RuntimeError("A model must be fitted before predict") - (X,) = utils.batch_convert_to_dataframe(X, session=self._bqml_model.session) - - return self._bqml_model.predict(X) + (X,) = utils.convert_to_dataframe(X) - def detect_anomalies( - self, - X: Union[bpd.DataFrame, bpd.Series, pd.DataFrame, pd.Series], - *, - contamination: float = 0.1, - ) -> bpd.DataFrame: - """Detect the anomaly data points of the input. - - Args: - X (bigframes.dataframe.DataFrame or bigframes.series.Series): - Series or a DataFrame to detect anomalies. - contamination (float, default 0.1): - Identifies the proportion of anomalies in the training dataset that are used to create the model. - The value must be in the range [0, 0.5]. - - Returns: - bigframes.dataframe.DataFrame: detected DataFrame.""" - if contamination < 0.0 or contamination > 0.5: - raise ValueError( - f"contamination must be [0.0, 0.5], but is {contamination}." - ) - - if not self._bqml_model: - raise RuntimeError("A model must be fitted before detect_anomalies") - - (X,) = utils.batch_convert_to_dataframe(X, session=self._bqml_model.session) - - return self._bqml_model.detect_anomalies( - X, options={"contamination": contamination} - ) + return cast(bpd.DataFrame, self._bqml_model.predict(X)[["CENTROID_ID"]]) def to_gbq(self, model_name: str, replace: bool = False) -> KMeans: """Save the model to BigQuery. Args: model_name (str): - The name of the model. + the name of the model. replace (bool, default False): - Determine whether to replace if the model already exists. Default to False. + whether to replace if the model already exists. Default to False. Returns: - KMeans: Saved model.""" + KMeans: saved model.""" if not self._bqml_model: raise RuntimeError("A model must be fitted before it can be saved") @@ -185,12 +113,12 @@ def to_gbq(self, model_name: str, replace: bool = False) -> KMeans: def score( self, - X: utils.ArrayType, + X: Union[bpd.DataFrame, bpd.Series], y=None, # ignored ) -> bpd.DataFrame: if not self._bqml_model: raise RuntimeError("A model must be fitted before score") - (X,) = utils.batch_convert_to_dataframe(X, session=self._bqml_model.session) + (X,) = utils.convert_to_dataframe(X) return self._bqml_model.evaluate(X) diff --git a/bigframes/ml/compose.py b/bigframes/ml/compose.py index 0d6c58897ac..bf046ff6914 100644 --- a/bigframes/ml/compose.py +++ b/bigframes/ml/compose.py @@ -13,180 +13,68 @@ # limitations under the License. """Build composite transformers on heterogeneous data. This module is styled -after scikit-Learn's compose module: +after Scikit-Learn's compose module: https://scikit-learn.org/stable/modules/classes.html#module-sklearn.compose.""" from __future__ import annotations -import re -import types import typing -from typing import Iterable, List, Optional, Set, Tuple, Union +from typing import List, Optional, Tuple, Union -import bigframes_vendored.sklearn.compose._column_transformer -from bigframes_vendored import constants -from google.cloud import bigquery - -import bigframes.core.utils as core_utils +from bigframes import constants +from bigframes.ml import base, core, globals, preprocessing, utils import bigframes.pandas as bpd -from bigframes.core.compile.sqlglot import sql as sg_sql -from bigframes.core.logging import log_adapter -from bigframes.ml import base, core, globals, impute, preprocessing, utils - -_BQML_TRANSFROM_TYPE_MAPPING = types.MappingProxyType( - { - "ML.STANDARD_SCALER": preprocessing.StandardScaler, - "ML.ONE_HOT_ENCODER": preprocessing.OneHotEncoder, - "ML.MAX_ABS_SCALER": preprocessing.MaxAbsScaler, - "ML.MIN_MAX_SCALER": preprocessing.MinMaxScaler, - "ML.BUCKETIZE": preprocessing.KBinsDiscretizer, - "ML.QUANTILE_BUCKETIZE": preprocessing.KBinsDiscretizer, - "ML.LABEL_ENCODER": preprocessing.LabelEncoder, - "ML.POLYNOMIAL_EXPAND": preprocessing.PolynomialFeatures, - "ML.IMPUTER": impute.SimpleImputer, - } -) - - -class SQLScalarColumnTransformer: - r""" - Wrapper for plain SQL code contained in a ColumnTransformer. - - Create a single column transformer in plain sql. - This transformer can only be used inside ColumnTransformer. - - When creating an instance '{0}' can be used as placeholder - for the column to transform: - - SQLScalarColumnTransformer("{0}+1") - - The default target column gets the prefix 'transformed\_' - but can also be changed when creating an instance: - - SQLScalarColumnTransformer("{0}+1", "inc_{0}") - - **Examples:** - - >>> from bigframes.ml.compose import ColumnTransformer, SQLScalarColumnTransformer - >>> import bigframes.pandas as bpd - - >>> df = bpd.DataFrame({'name': ["James", None, "Mary"], 'city': ["New York", "Boston", None]}) - >>> col_trans = ColumnTransformer([ - ... ("strlen", - ... SQLScalarColumnTransformer("CASE WHEN {0} IS NULL THEN 15 ELSE LENGTH({0}) END"), - ... ['name', 'city']), - ... ]) - >>> col_trans = col_trans.fit(df) - >>> df_transformed = col_trans.transform(df) - >>> df_transformed - transformed_name transformed_city - 0 5 8 - 1 15 6 - 2 4 15 - - [3 rows x 2 columns] - - SQLScalarColumnTransformer can be combined with other transformers, like StandardScaler: - - >>> col_trans = ColumnTransformer([ - ... ("identity", SQLScalarColumnTransformer("{0}", target_column="{0}"), ["col1", "col5"]), - ... ("increment", SQLScalarColumnTransformer("{0}+1", target_column="inc_{0}"), "col2"), - ... ("stdscale", preprocessing.StandardScaler(), "col3"), - ... # ... - ... ]) - - """ - - def __init__(self, sql: str, target_column: str = "transformed_{0}"): - super().__init__() - self._sql = sql - # TODO: More robust unescaping - self._target_column = target_column.replace("`", "") - - def _compile_to_sql( - self, X: bpd.DataFrame, columns: Optional[Iterable[str]] = None - ) -> List[str]: - if columns is None: - columns = X.columns - columns, _ = core_utils.get_standardized_ids(columns) - result = [] - for column in columns: - current_sql = self._sql.format(sg_sql.to_sql(sg_sql.identifier(column))) - current_target_column = sg_sql.to_sql( - sg_sql.identifier(self._target_column.format(column)) - ) - result.append(f"{current_sql} AS {current_target_column}") - return result - - def __repr__(self): - return f"SQLScalarColumnTransformer(sql='{self._sql}', target_column='{self._target_column}')" - - def __eq__(self, other) -> bool: - return type(self) is type(other) and self._keys() == other._keys() - - def __hash__(self) -> int: - return hash(self._keys()) - - def _keys(self): - return (self._sql, self._target_column) - - -# Type hints for transformers contained in ColumnTransformer -SingleColTransformer = Union[ - preprocessing.PreprocessingType, - impute.SimpleImputer, - SQLScalarColumnTransformer, +import third_party.bigframes_vendored.sklearn.compose._column_transformer + +CompilablePreprocessorType = Union[ + preprocessing.OneHotEncoder, + preprocessing.StandardScaler, + preprocessing.MaxAbsScaler, + preprocessing.MinMaxScaler, + preprocessing.KBinsDiscretizer, + preprocessing.LabelEncoder, ] -@log_adapter.class_logger class ColumnTransformer( base.Transformer, - bigframes_vendored.sklearn.compose._column_transformer.ColumnTransformer, + third_party.bigframes_vendored.sklearn.compose._column_transformer.ColumnTransformer, ): __doc__ = ( - bigframes_vendored.sklearn.compose._column_transformer.ColumnTransformer.__doc__ + third_party.bigframes_vendored.sklearn.compose._column_transformer.ColumnTransformer.__doc__ ) def __init__( self, - transformers: Iterable[ + transformers: List[ Tuple[ str, - SingleColTransformer, - Union[str, Iterable[str]], + CompilablePreprocessorType, + Union[str, List[str]], ] ], ): # TODO: if any(transformers) has fitted raise warning - self.transformers = list(transformers) + self.transformers = transformers self._bqml_model: Optional[core.BqmlModel] = None self._bqml_model_factory = globals.bqml_model_factory() # call self.transformers_ to check chained transformers self.transformers_ - def _keys(self): - return (self.transformers, self._bqml_model) - @property def transformers_( self, - ) -> List[ - Tuple[ - str, - SingleColTransformer, - str, - ] - ]: + ) -> List[Tuple[str, CompilablePreprocessorType, str,]]: """The collection of transformers as tuples of (name, transformer, column).""" result: List[ Tuple[ str, - SingleColTransformer, + CompilablePreprocessorType, str, ] ] = [] + column_set: set[str] = set() for entry in self.transformers: name, transformer, column_or_columns = entry columns = ( @@ -196,168 +84,62 @@ def transformers_( ) for column in columns: - result.append((name, transformer, column)) - - return result - - AS_FLEXNAME_SUFFIX_RX = re.compile("^(.*)\\bAS\\s*`[^`]+`\\s*$", re.IGNORECASE) - - @classmethod - def _extract_from_bq_model( - cls, - bq_model: bigquery.Model, - ) -> ColumnTransformer: - """Extract transformers as ColumnTransformer obj from a BQ Model. Keep the _bqml_model field as None.""" - assert "transformColumns" in bq_model._properties - - transformers_set: Set[ - Tuple[ - str, - SingleColTransformer, - Union[str, List[str]], - ] - ] = set() - - def camel_to_snake(name): - name = re.sub("(.)([A-Z][a-z]+)", r"\1_\2", name) - return re.sub("([a-z0-9])([A-Z])", r"\1_\2", name).lower() - - output_names = [] - for transform_col in bq_model._properties["transformColumns"]: - transform_col_dict = typing.cast(dict, transform_col) - # pass the columns that are not transformed - if "transformSql" not in transform_col_dict: - continue - transform_sql: str = transform_col_dict["transformSql"] - - # workaround for bug in bq_model returning " AS `...`" suffix for flexible names - flex_name_match = cls.AS_FLEXNAME_SUFFIX_RX.match(transform_sql) - if flex_name_match: - transform_sql = flex_name_match.group(1) - - output_names.append(transform_col_dict["name"]) - found_transformer = False - for prefix in _BQML_TRANSFROM_TYPE_MAPPING: - if transform_sql.startswith(prefix): - transformer_cls = _BQML_TRANSFROM_TYPE_MAPPING[prefix] - transformers_set.add( - ( - camel_to_snake(transformer_cls.__name__), - # TODO: This is very fragile, use real SQL parser - *transformer_cls._parse_from_sql(transform_sql), # type: ignore - ) - ) - - found_transformer = True - break - if not found_transformer: - if transform_sql.startswith("ML."): + if column in column_set: raise NotImplementedError( - f"Unsupported transformer type. {constants.FEEDBACK_LINK}" - ) - - target_column = transform_col_dict["name"] - sql_transformer = SQLScalarColumnTransformer( - transform_sql.strip(), target_column=target_column - ) - input_column_name = f"?{target_column}" - transformers_set.add( - ( - camel_to_snake(sql_transformer.__class__.__name__), - sql_transformer, - input_column_name, + f"Chained transformers on the same column isn't supported. {constants.FEEDBACK_LINK}" ) - ) - - transformer = cls(transformers=list(transformers_set)) - transformer._output_names = output_names - - return transformer - - def _merge( - self, bq_model: bigquery.Model - ) -> Union[ - ColumnTransformer, Union[preprocessing.PreprocessingType, impute.SimpleImputer] - ]: - """Try to merge the column transformer to a simple transformer. Depends on all the columns in bq_model are transformed with the same transformer.""" - transformers = self.transformers - - assert len(transformers) > 0 - _, transformer_0, column_0 = transformers[0] - if isinstance(transformer_0, SQLScalarColumnTransformer): - return self # SQLScalarColumnTransformer only work inside ColumnTransformer - feature_columns_sorted = sorted( - [ - typing.cast(str, feature_column.name) - for feature_column in bq_model.feature_columns - ] - ) - - if ( - len(transformers) == 1 - and isinstance(transformer_0, preprocessing.PolynomialFeatures) - and sorted(column_0) == feature_columns_sorted - ): - transformer_0._output_names = self._output_names - return transformer_0 - - if not isinstance(column_0, str): - return self - columns = [column_0] - for _, transformer, column in transformers[1:]: - if not isinstance(column, str): - return self - # all transformers are the same - if transformer != transformer_0: - return self - columns.append(column) - # all feature columns are transformed - if sorted(columns) == feature_columns_sorted: - transformer_0._output_names = self._output_names - return transformer_0 + result.append((name, transformer, column)) - return self + return result def _compile_to_sql( self, + columns: List[str], X: bpd.DataFrame, - ) -> List[str]: + ) -> List[Tuple[str, str]]: """Compile this transformer to a list of SQL expressions that can be included in a BQML TRANSFORM clause Args: - X: DataFrame to transform. - - Returns: a list of sql_expr.""" - result = [] - for _, transformer, target_columns in self.transformers: - if isinstance(target_columns, str): - target_columns = [target_columns] - result += transformer._compile_to_sql(X, target_columns) - return result + columns (List[str]): + a list of column names to transform + X (bpd.DataFrame): + The Dataframe with training data. + + Returns: + a list of tuples of (sql_expression, output_name)""" + return [ + transformer._compile_to_sql([column], X=X)[0] + for column in columns + for _, transformer, target_column in self.transformers_ + if column == target_column + ] def fit( self, - X: utils.ArrayType, + X: Union[bpd.DataFrame, bpd.Series], y=None, # ignored ) -> ColumnTransformer: - (X,) = utils.batch_convert_to_dataframe(X) + (X,) = utils.convert_to_dataframe(X) + + compiled_transforms = self._compile_to_sql(X.columns.tolist(), X) + transform_sqls = [transform_sql for transform_sql, _ in compiled_transforms] - transform_sqls = self._compile_to_sql(X) self._bqml_model = self._bqml_model_factory.create_model( X, options={"model_type": "transform_only"}, transforms=transform_sqls, ) - self._extract_output_names() + # The schema of TRANSFORM output is not available in the model API, so save it during fitting + self._output_names = [name for _, name in compiled_transforms] return self - def transform(self, X: utils.ArrayType) -> bpd.DataFrame: + def transform(self, X: Union[bpd.DataFrame, bpd.Series]) -> bpd.DataFrame: if not self._bqml_model: raise RuntimeError("Must be fitted before transform") - (X,) = utils.batch_convert_to_dataframe(X, session=self._bqml_model.session) + (X,) = utils.convert_to_dataframe(X) df = self._bqml_model.transform(X) return typing.cast( diff --git a/bigframes/ml/core.py b/bigframes/ml/core.py index 5a096f305bd..4c5a48cf625 100644 --- a/bigframes/ml/core.py +++ b/bigframes/ml/core.py @@ -16,70 +16,50 @@ from __future__ import annotations -import dataclasses -import datetime -import typing +from typing import Callable, cast, Iterable, Mapping, Optional, Union import uuid -from typing import Callable, Iterable, Mapping, Optional, Union from google.cloud import bigquery -import bigframes.constants as constants -import bigframes.formatting_helpers as formatting_helpers -import bigframes.pandas as bpd -import bigframes.session +import bigframes from bigframes.ml import sql as ml_sql +import bigframes.pandas as bpd -class BaseBqml: - """Base class for BQML functionalities.""" - - def __init__(self, session: bigframes.session.Session): - self._session = session - self._sql_generator = ml_sql.BaseSqlGenerator() - - def ai_forecast( - self, - input_data: bpd.DataFrame, - options: Mapping[str, Union[str, int, float, Iterable[str]]], - ) -> bpd.DataFrame: - result_sql = self._sql_generator.ai_forecast( - source_sql=input_data.sql, options=options - ) - - # TODO(b/395912450): Once the limitations with local data are - # resolved, consider setting allow_large_results only when expected - # data size is large. - return self._session.read_gbq_query(result_sql, allow_large_results=True) - - -class BqmlModel(BaseBqml): +class BqmlModel: """Represents an existing BQML model in BigQuery. Wraps the BQML API and SQL interface to expose the functionality needed for BigQuery DataFrames ML. """ - @dataclasses.dataclass - class TvfDef: - tvf: Callable[[BqmlModel, bpd.DataFrame, dict], bpd.DataFrame] - status_col: str - def __init__(self, session: bigframes.Session, model: bigquery.Model): self._session = session self._model = model - model_ref = self._model.reference - assert model_ref is not None - self._sql_generator: ml_sql.ModelManipulationSqlGenerator = ( - ml_sql.ModelManipulationSqlGenerator(model_ref) + self._model_manipulation_sql_generator = ml_sql.ModelManipulationSqlGenerator( + self.model_name ) - def _apply_ml_tvf( + @property + def session(self) -> bigframes.Session: + """Get the BigQuery DataFrames session that this BQML model wrapper is tied to""" + return self._session + + @property + def model_name(self) -> str: + """Get the fully qualified name of the model, i.e. project_id.dataset_id.model_id""" + return f"{self._model.project}.{self._model.dataset_id}.{self._model.model_id}" + + @property + def model(self) -> bigquery.Model: + """Get the BQML model associated with this wrapper""" + return self._model + + def _apply_sql( self, input_data: bpd.DataFrame, - apply_sql_tvf: Callable[[str], str], + func: Callable[[bpd.DataFrame], str], ) -> bpd.DataFrame: - # Used for predict, transform, distance """Helper to wrap a dataframe in a SQL query, keeping the index intact. Args: @@ -90,286 +70,100 @@ def _apply_ml_tvf( the dataframe to be wrapped func (function): - Takes an input sql table value and applies a prediction tvf. The - resulting table value must include all input columns, with new - columns appended to the end. + a function that will accept a SQL string and produce a new SQL + string from which to construct the output dataframe. It must + include the index columns of the input SQL. """ - # TODO: Preserve ordering information? - input_sql, index_col_ids, index_labels = input_data._to_sql_query( - include_index=True - ) + _, index_col_ids, index_labels = input_data._to_sql_query(include_index=True) - result_sql = apply_sql_tvf(input_sql) - df = self._session.read_gbq_query( - result_sql, - index_col=index_col_ids, - # Many ML methods use nested JSON, which isn't yet compatible with - # joining local results. Also, there is a chance that the results - # are greater than 10 GB. - # TODO(b/395912450): Once the limitations with local data are - # resolved, consider setting allow_large_results only when expected - # data size is large. - allow_large_results=True, - ) - if df._has_index: - df.index.names = index_labels - # Restore column labels - df.rename( - columns={ - label: original_label - for label, original_label in zip( - df.columns.values, input_data.columns.values - ) - } - ) - return df + sql = func(input_data) + df = self._session.read_gbq(sql, index_col=index_col_ids) + df.index.names = index_labels - def _keys(self): - return (self._session, self._model) - - def __eq__(self, other): - return isinstance(other, self.__class__) and self._keys() == other._keys() - - def __hash__(self): - return hash(self._keys()) - - @property - def session(self) -> bigframes.Session: - """Get the BigQuery DataFrames session that this BQML model wrapper is tied to""" - return self._session - - @property - def model_name(self) -> str: - """Get the fully qualified name of the model, i.e. project_id.dataset_id.model_id""" - return f"{self._model.project}.{self._model.dataset_id}.{self._model.model_id}" - - @property - def model(self) -> bigquery.Model: - """Get the BQML model associated with this wrapper""" - return self._model - - def recommend(self, input_data: bpd.DataFrame) -> bpd.DataFrame: - return self._apply_ml_tvf( - input_data, - self._sql_generator.ml_recommend, - ) + return df def predict(self, input_data: bpd.DataFrame) -> bpd.DataFrame: - return self._apply_ml_tvf( + # TODO: validate input data schema + return self._apply_sql( input_data, - self._sql_generator.ml_predict, - ) - - def explain_predict( - self, input_data: bpd.DataFrame, options: Mapping[str, int | float] - ) -> bpd.DataFrame: - return self._apply_ml_tvf( - input_data, - lambda source_sql: self._sql_generator.ml_explain_predict( - source_sql=source_sql, - struct_options=options, - ), - ) - - def global_explain(self, options: Mapping[str, bool]) -> bpd.DataFrame: - sql = self._sql_generator.ml_global_explain(struct_options=options) - return ( - # TODO(b/395912450): Once the limitations with local data are - # resolved, consider setting allow_large_results only when expected - # data size is large. - self._session.read_gbq_query(sql, allow_large_results=True) - .sort_values(by="attribution", ascending=False) - .set_index("feature") + self._model_manipulation_sql_generator.ml_predict, ) def transform(self, input_data: bpd.DataFrame) -> bpd.DataFrame: - return self._apply_ml_tvf( + # TODO: validate input data schema + return self._apply_sql( input_data, - self._sql_generator.ml_transform, + self._model_manipulation_sql_generator.ml_transform, ) def generate_text( self, input_data: bpd.DataFrame, - options: dict[str, Union[int, float, bool]], + options: Mapping[str, int | float], ) -> bpd.DataFrame: - options["flatten_json_output"] = True - return self._apply_ml_tvf( + # TODO: validate input data schema + return self._apply_sql( input_data, - lambda source_sql: self._sql_generator.ml_generate_text( - source_sql=source_sql, + lambda source_df: self._model_manipulation_sql_generator.ml_generate_text( + source_df=source_df, struct_options=options, ), ) - generate_text_tvf = TvfDef(generate_text, "ml_generate_text_status") - - def generate_embedding( - self, - input_data: bpd.DataFrame, - options: dict[str, Union[int, float, bool]], - ) -> bpd.DataFrame: - options["flatten_json_output"] = True - return self._apply_ml_tvf( - input_data, - lambda source_sql: self._sql_generator.ml_generate_embedding( - source_sql=source_sql, - struct_options=options, - ), - ) - - generate_embedding_tvf = TvfDef(generate_embedding, "ml_generate_embedding_status") - - def generate_table( + def generate_text_embedding( self, input_data: bpd.DataFrame, - options: dict[str, Union[int, float, bool, Mapping]], + options: Mapping[str, int | float], ) -> bpd.DataFrame: - return self._apply_ml_tvf( + # TODO: validate input data schema + return self._apply_sql( input_data, - lambda source_sql: self._sql_generator.ai_generate_table( - source_sql=source_sql, + lambda source_df: self._model_manipulation_sql_generator.ml_generate_text_embedding( + source_df=source_df, struct_options=options, ), ) - generate_table_tvf = TvfDef(generate_table, "status") - - def detect_anomalies( - self, input_data: bpd.DataFrame, options: Mapping[str, int | float] - ) -> bpd.DataFrame: - assert self._model.model_type in ("PCA", "KMEANS", "ARIMA_PLUS") - - return self._apply_ml_tvf( - input_data, - lambda source_sql: self._sql_generator.ml_detect_anomalies( - source_sql=source_sql, - struct_options=options, - ), - ) - - def forecast(self, options: Mapping[str, int | float]) -> bpd.DataFrame: - sql = self._sql_generator.ml_forecast(struct_options=options) - timestamp_col_name = "forecast_timestamp" - index_cols = [timestamp_col_name] - # TODO(b/395912450): Once the limitations with local data are - # resolved, consider setting allow_large_results only when expected - # data size is large. - first_col_name = self._session.read_gbq_query( - sql, allow_large_results=True - ).columns.values[0] - if timestamp_col_name != first_col_name: - index_cols.append(first_col_name) - # TODO(b/395912450): Once the limitations with local data are - # resolved, consider setting allow_large_results only when expected - # data size is large. - return self._session.read_gbq_query( - sql, index_col=index_cols, allow_large_results=True - ).reset_index() - - def explain_forecast(self, options: Mapping[str, int | float]) -> bpd.DataFrame: - sql = self._sql_generator.ml_explain_forecast(struct_options=options) - timestamp_col_name = "time_series_timestamp" - index_cols = [timestamp_col_name] - # TODO(b/395912450): Once the limitations with local data are - # resolved, consider setting allow_large_results only when expected - # data size is large. - first_col_name = self._session.read_gbq_query( - sql, allow_large_results=True - ).columns.values[0] - if timestamp_col_name != first_col_name: - index_cols.append(first_col_name) - # TODO(b/395912450): Once the limitations with local data are - # resolved, consider setting allow_large_results only when expected - # data size is large. - return self._session.read_gbq_query( - sql, index_col=index_cols, allow_large_results=True - ).reset_index() + def forecast(self) -> bpd.DataFrame: + sql = self._model_manipulation_sql_generator.ml_forecast() + return self._session.read_gbq(sql) def evaluate(self, input_data: Optional[bpd.DataFrame] = None): - sql = self._sql_generator.ml_evaluate( - input_data.sql if (input_data is not None) else None - ) + # TODO: validate input data schema + sql = self._model_manipulation_sql_generator.ml_evaluate(input_data) - # TODO(b/395912450): Once the limitations with local data are - # resolved, consider setting allow_large_results only when expected - # data size is large. - return self._session.read_gbq_query(sql, allow_large_results=True) - - def llm_evaluate( - self, - input_data: bpd.DataFrame, - task_type: Optional[str] = None, - ): - sql = self._sql_generator.ml_llm_evaluate(input_data.sql, task_type) - - # TODO(b/395912450): Once the limitations with local data are - # resolved, consider setting allow_large_results only when expected - # data size is large. - return self._session.read_gbq_query(sql, allow_large_results=True) - - def arima_evaluate(self, show_all_candidate_models: bool = False): - sql = self._sql_generator.ml_arima_evaluate(show_all_candidate_models) - - # TODO(b/395912450): Once the limitations with local data are - # resolved, consider setting allow_large_results only when expected - # data size is large. - return self._session.read_gbq_query(sql, allow_large_results=True) - - def arima_coefficients(self) -> bpd.DataFrame: - sql = self._sql_generator.ml_arima_coefficients() - - # TODO(b/395912450): Once the limitations with local data are - # resolved, consider setting allow_large_results only when expected - # data size is large. - return self._session.read_gbq_query(sql, allow_large_results=True) + return self._session.read_gbq(sql) def centroids(self) -> bpd.DataFrame: assert self._model.model_type == "KMEANS" - sql = self._sql_generator.ml_centroids() + sql = self._model_manipulation_sql_generator.ml_centroids() - # TODO(b/395912450): Once the limitations with local data are - # resolved, consider setting allow_large_results only when expected - # data size is large. - return self._session.read_gbq_query( - sql, index_col=["centroid_id", "feature"], allow_large_results=True - ).reset_index() + return self._session.read_gbq(sql) def principal_components(self) -> bpd.DataFrame: assert self._model.model_type == "PCA" - sql = self._sql_generator.ml_principal_components() + sql = self._model_manipulation_sql_generator.ml_principal_components() - # TODO(b/395912450): Once the limitations with local data are - # resolved, consider setting allow_large_results only when expected - # data size is large. - return self._session.read_gbq_query( - sql, - index_col=["principal_component_id", "feature"], - allow_large_results=True, - ).reset_index() + return self._session.read_gbq(sql) def principal_component_info(self) -> bpd.DataFrame: assert self._model.model_type == "PCA" - sql = self._sql_generator.ml_principal_component_info() + sql = self._model_manipulation_sql_generator.ml_principal_component_info() - # TODO(b/395912450): Once the limitations with local data are - # resolved, consider setting allow_large_results only when expected - # data size is large. - return self._session.read_gbq_query(sql, allow_large_results=True) + return self._session.read_gbq(sql) def copy(self, new_model_name: str, replace: bool = False) -> BqmlModel: - job_config = self._session._prepare_copy_job_config() - + job_config = bigquery.job.CopyJobConfig() if replace: job_config.write_disposition = "WRITE_TRUNCATE" copy_job = self._session.bqclient.copy_table( self.model_name, new_model_name, job_config=job_config ) - _start_generic_job(copy_job) + self._session._start_generic_job(copy_job) new_model = self._session.bqclient.get_model(new_model_name) return BqmlModel(self._session, new_model) @@ -377,16 +171,16 @@ def copy(self, new_model_name: str, replace: bool = False) -> BqmlModel: def register(self, vertex_ai_model_id: Optional[str] = None) -> BqmlModel: if vertex_ai_model_id is None: # vertex id needs to start with letters. https://cloud.google.com/vertex-ai/docs/general/resource-naming - vertex_ai_model_id = "bigframes_" + typing.cast(str, self._model.model_id) + vertex_ai_model_id = "bigframes_" + cast(str, self._model.model_id) # truncate as Vertex ID only accepts 63 characters, easily exceeding the limit for temp models. # The possibility of conflicts should be low. vertex_ai_model_id = vertex_ai_model_id[:63] - sql = self._sql_generator.alter_model( + sql = self._model_manipulation_sql_generator.alter_model( options={"vertex_ai_model_id": vertex_ai_model_id} ) # Register the model and wait it to finish - self._session._start_query_ml_ddl(sql) + self._session._start_query(sql) self._model = self._session.bqclient.get_model(self.model_name) return self @@ -394,27 +188,24 @@ def register(self, vertex_ai_model_id: Optional[str] = None) -> BqmlModel: class BqmlModelFactory: def __init__(self): - self._model_creation_sql_generator = ml_sql.ModelCreationSqlGenerator() + model_id = self._create_temp_model_id() + self._model_creation_sql_generator = ml_sql.ModelCreationSqlGenerator(model_id) - def _create_model_ref( - self, dataset: bigquery.DatasetReference - ) -> bigquery.ModelReference: - return bigquery.ModelReference.from_string( - f"{dataset.project}.{dataset.dataset_id}.{uuid.uuid4().hex}" - ) + def _create_temp_model_id(self) -> str: + return uuid.uuid4().hex + + def _reset_model_id(self): + self._model_creation_sql_generator._model_id = self._create_temp_model_id() def _create_model_with_sql(self, session: bigframes.Session, sql: str) -> BqmlModel: # fit the model, synchronously - _, job = session._start_query_ml_ddl(sql) + _, job = session._start_query(sql) # real model path in the session specific hidden dataset and table prefix - model_name_full = f"{job.destination.project}.{job.destination.dataset_id}.{job.destination.table_id}" - model = bigquery.Model(model_name_full) - model.expires = ( - datetime.datetime.now(datetime.timezone.utc) + constants.DEFAULT_EXPIRATION - ) - model = session.bqclient.update_model(model, ["expires"]) + model_name_full = f"{job.destination.dataset_id}.{job.destination.table_id}" + model = session.bqclient.get_model(model_name_full) + self._reset_model_id() return BqmlModel(session, model) def create_model( @@ -424,7 +215,7 @@ def create_model( transforms: Optional[Iterable[str]] = None, options: Mapping[str, Union[str, int, float, Iterable[str]]] = {}, ) -> BqmlModel: - """Create a session-temporary BQML model with the CREATE OR REPLACE MODEL statement + """Create a session-temporary BQML model with the CREATE MODEL statement Args: X_train: features columns for training @@ -437,106 +228,45 @@ def create_model( Returns: a BqmlModel, wrapping a trained model in BigQuery """ options = dict(options) - # Cache dataframes to make sure base table is not a snapshot. - # Cached dataframe creates a full copy, never uses snapshot. - # This is a workaround for internal issue b/310266666. if y_train is None: - input_data = X_train.reset_index(drop=True).cache() + input_data = X_train else: - input_data = ( - X_train.join(y_train, how="outer").reset_index(drop=True).cache() - ) + input_data = X_train.join(y_train, how="outer") options.update({"INPUT_LABEL_COLS": y_train.columns.tolist()}) session = X_train._session - if session._bq_kms_key_name: - options.update({"kms_key_name": session._bq_kms_key_name}) - - model_ref = self._create_model_ref(session._anonymous_dataset) sql = self._model_creation_sql_generator.create_model( - source_sql=input_data.sql, - model_ref=model_ref, + source_df=input_data, transforms=transforms, options=options, ) return self._create_model_with_sql(session=session, sql=sql) - def create_llm_remote_model( - self, - X_train: bpd.DataFrame, - y_train: bpd.DataFrame, - connection_name: str, - options: Mapping[str, Union[str, int, float, Iterable[str]]] = {}, - ) -> BqmlModel: - """Create a session-temporary BQML model with the CREATE OR REPLACE MODEL statement - - Args: - X_train: features columns for training - y_train: labels columns for training - options: a dict of options to configure the model. Generates a BQML OPTIONS - clause - connection_name: - a BQ connection to talk with Vertex AI, of the format ... https://cloud.google.com/bigquery/docs/create-cloud-resource-connection - - Returns: a BqmlModel, wrapping a trained model in BigQuery - """ - options = dict(options) - # Cache dataframes to make sure base table is not a snapshot - # cached dataframe creates a full copy, never uses snapshot - input_data = X_train.join(y_train, how="outer").cache() - options.update({"INPUT_LABEL_COLS": y_train.columns.tolist()}) - - session = X_train._session - - model_ref = self._create_model_ref(session._anonymous_dataset) - - sql = self._model_creation_sql_generator.create_llm_remote_model( - source_sql=input_data.sql, - model_ref=model_ref, - options=options, - connection_name=connection_name, - ) - - return self._create_model_with_sql(session=session, sql=sql) - def create_time_series_model( self, X_train: bpd.DataFrame, y_train: bpd.DataFrame, - id_col: Optional[bpd.DataFrame] = None, transforms: Optional[Iterable[str]] = None, options: Mapping[str, Union[str, int, float, Iterable[str]]] = {}, ) -> BqmlModel: - assert X_train.columns.size == 1, ( - "Time series timestamp input must only contain 1 column." - ) - assert y_train.columns.size == 1, ( - "Time stamp data input must only contain 1 column." - ) - assert id_col is None or (id_col is not None and id_col.columns.size == 1), ( - "Time series id input is either None or must only contain 1 column." - ) + assert ( + X_train.columns.size == 1 + ), "Time series timestamp input must only contain 1 column." + assert ( + y_train.columns.size == 1 + ), "Time stamp data input must only contain 1 column." options = dict(options) - # Cache dataframes to make sure base table is not a snapshot - # cached dataframe creates a full copy, never uses snapshot input_data = X_train.join(y_train, how="outer") - if id_col is not None: - input_data = input_data.join(id_col, how="outer") - input_data = input_data.cache() options.update({"TIME_SERIES_TIMESTAMP_COL": X_train.columns.tolist()[0]}) options.update({"TIME_SERIES_DATA_COL": y_train.columns.tolist()[0]}) - if id_col is not None: - options.update({"TIME_SERIES_ID_COL": id_col.columns.tolist()[0]}) session = X_train._session - model_ref = self._create_model_ref(session._anonymous_dataset) sql = self._model_creation_sql_generator.create_model( - source_sql=input_data.sql, - model_ref=model_ref, + source_df=input_data, transforms=transforms, options=options, ) @@ -547,31 +277,21 @@ def create_remote_model( self, session: bigframes.Session, connection_name: str, - input: Mapping[str, str] = {}, - output: Mapping[str, str] = {}, options: Mapping[str, Union[str, int, float, Iterable[str]]] = {}, ) -> BqmlModel: - """Create a session-temporary BQML remote model with the CREATE OR REPLACE MODEL statement + """Create a session-temporary BQML remote model with the CREATE MODEL statement Args: connection_name: a BQ connection to talk with Vertex AI, of the format ... https://cloud.google.com/bigquery/docs/create-cloud-resource-connection - input: - input schema for general remote models - output: - output schema for general remote models options: a dict of options to configure the model. Generates a BQML OPTIONS clause Returns: BqmlModel: a BqmlModel wrapping a trained model in BigQuery """ - model_ref = self._create_model_ref(session._anonymous_dataset) sql = self._model_creation_sql_generator.create_remote_model( connection_name=connection_name, - model_ref=model_ref, - input=input, - output=output, options=options, ) @@ -582,7 +302,7 @@ def create_imported_model( session: bigframes.Session, options: Mapping[str, Union[str, int, float, Iterable[str]]] = {}, ) -> BqmlModel: - """Create a session-temporary BQML imported model with the CREATE OR REPLACE MODEL statement + """Create a session-temporary BQML imported model with the CREATE MODEL statement Args: options: a dict of options to configure the model. Generates a BQML OPTIONS @@ -590,49 +310,8 @@ def create_imported_model( Returns: a BqmlModel, wrapping a trained model in BigQuery """ - model_ref = self._create_model_ref(session._anonymous_dataset) sql = self._model_creation_sql_generator.create_imported_model( - model_ref=model_ref, options=options, ) return self._create_model_with_sql(session=session, sql=sql) - - def create_xgboost_imported_model( - self, - session: bigframes.Session, - input: Mapping[str, str] = {}, - output: Mapping[str, str] = {}, - options: Mapping[str, Union[str, int, float, Iterable[str]]] = {}, - ) -> BqmlModel: - """Create a session-temporary BQML imported model with the CREATE OR REPLACE MODEL statement - - Args: - input: - input schema for imported xgboost models - output: - output schema for imported xgboost models - options: a dict of options to configure the model. Generates a BQML OPTIONS - clause - - Returns: a BqmlModel, wrapping a trained model in BigQuery - """ - model_ref = self._create_model_ref(session._anonymous_dataset) - - sql = self._model_creation_sql_generator.create_xgboost_imported_model( - model_ref=model_ref, - input=input, - output=output, - options=options, - ) - - return self._create_model_with_sql(session=session, sql=sql) - - -def _start_generic_job(job: formatting_helpers.GenericJob): - if bigframes.options.display.progress_bar is not None: - formatting_helpers.wait_for_job( - job, bigframes.options.display.progress_bar - ) # Wait for the job to complete - else: - job.result() diff --git a/bigframes/ml/decomposition.py b/bigframes/ml/decomposition.py index eedf6c09170..8e6be6d28c3 100644 --- a/bigframes/ml/decomposition.py +++ b/bigframes/ml/decomposition.py @@ -17,97 +17,57 @@ from __future__ import annotations -from typing import List, Literal, Optional, Union +from typing import cast, List, Optional, Union -import bigframes_vendored.sklearn.decomposition._mf -import bigframes_vendored.sklearn.decomposition._pca from google.cloud import bigquery -import bigframes.pandas as bpd -import bigframes.session -from bigframes.core.logging import log_adapter +import bigframes from bigframes.ml import base, core, globals, utils - -_BQML_PARAMS_MAPPING = { - "svd_solver": "pcaSolver", - "feedback_type": "feedbackType", - "num_factors": "numFactors", - "user_col": "userColumn", - "item_col": "itemColumn", - "_input_label_columns": "inputLabelColumns", - "l2_reg": "l2Regularization", -} +import bigframes.pandas as bpd +import third_party.bigframes_vendored.sklearn.decomposition._pca -@log_adapter.class_logger class PCA( base.UnsupervisedTrainablePredictor, - bigframes_vendored.sklearn.decomposition._pca.PCA, + third_party.bigframes_vendored.sklearn.decomposition._pca.PCA, ): - __doc__ = bigframes_vendored.sklearn.decomposition._pca.PCA.__doc__ + __doc__ = third_party.bigframes_vendored.sklearn.decomposition._pca.PCA.__doc__ - def __init__( - self, - n_components: Optional[Union[int, float]] = None, - *, - svd_solver: Literal["full", "randomized", "auto"] = "auto", - ): + def __init__(self, n_components: int = 3): self.n_components = n_components - self.svd_solver = svd_solver self._bqml_model: Optional[core.BqmlModel] = None self._bqml_model_factory = globals.bqml_model_factory() @classmethod - def _from_bq( - cls, session: bigframes.session.Session, bq_model: bigquery.Model - ) -> PCA: - assert bq_model.model_type == "PCA" + def _from_bq(cls, session: bigframes.Session, model: bigquery.Model) -> PCA: + assert model.model_type == "PCA" - kwargs = utils.retrieve_params_from_bq_model( - cls, bq_model, _BQML_PARAMS_MAPPING - ) + kwargs = {} - last_fitting = bq_model.training_runs[-1]["trainingOptions"] + # See https://cloud.google.com/bigquery/docs/reference/rest/v2/models#trainingrun + last_fitting = model.training_runs[-1]["trainingOptions"] if "numPrincipalComponents" in last_fitting: kwargs["n_components"] = int(last_fitting["numPrincipalComponents"]) - elif "pcaExplainedVarianceRatio" in last_fitting: - kwargs["n_components"] = float(last_fitting["pcaExplainedVarianceRatio"]) - model = cls(**kwargs) - model._bqml_model = core.BqmlModel(session, bq_model) - return model - - @property - def _bqml_options(self) -> dict: - """The model options as they will be set for BQML""" - options: dict = { - "model_type": "PCA", - "pca_solver": self.svd_solver, - } - - assert self.n_components is not None - if 0 < self.n_components < 1: - options["pca_explained_variance_ratio"] = float(self.n_components) - elif self.n_components >= 1: - options["num_principal_components"] = int(self.n_components) - - return options + new_pca = cls(**kwargs) + new_pca._bqml_model = core.BqmlModel(session, model) + return new_pca def _fit( self, - X: utils.ArrayType, + X: Union[bpd.DataFrame, bpd.Series], y=None, transforms: Optional[List[str]] = None, ) -> PCA: - (X,) = utils.batch_convert_to_dataframe(X) + (X,) = utils.convert_to_dataframe(X) - # To mimic sklearn's behavior - if self.n_components is None: - self.n_components = min(X.shape) self._bqml_model = self._bqml_model_factory.create_model( X_train=X, transforms=transforms, - options=self._bqml_options, + options={ + "model_type": "PCA", + "num_principal_components": self.n_components, + }, ) return self @@ -140,43 +100,17 @@ def explained_variance_ratio_(self) -> bpd.DataFrame: ["principal_component_id", "explained_variance_ratio"] ] - def predict(self, X: utils.ArrayType) -> bpd.DataFrame: + def predict(self, X: Union[bpd.DataFrame, bpd.Series]) -> bpd.DataFrame: if not self._bqml_model: raise RuntimeError("A model must be fitted before predict") - (X,) = utils.batch_convert_to_dataframe(X, session=self._bqml_model.session) + (X,) = utils.convert_to_dataframe(X) - return self._bqml_model.predict(X) - - def detect_anomalies( - self, - X: utils.ArrayType, - *, - contamination: float = 0.1, - ) -> bpd.DataFrame: - """Detect the anomaly data points of the input. - - Args: - X (bigframes.dataframe.DataFrame or bigframes.series.Series): - Series or a DataFrame to detect anomalies. - contamination (float, default 0.1): - Identifies the proportion of anomalies in the training dataset that are used to create the model. - The value must be in the range [0, 0.5]. - - Returns: - bigframes.dataframe.DataFrame: detected DataFrame.""" - if contamination < 0.0 or contamination > 0.5: - raise ValueError( - f"contamination must be [0.0, 0.5], but is {contamination}." - ) - - if not self._bqml_model: - raise RuntimeError("A model must be fitted before detect_anomalies") - - (X,) = utils.batch_convert_to_dataframe(X, session=self._bqml_model.session) - - return self._bqml_model.detect_anomalies( - X, options={"contamination": contamination} + return cast( + bpd.DataFrame, + self._bqml_model.predict(X)[ + ["principal_component_" + str(i + 1) for i in range(self.n_components)] + ], ) def to_gbq(self, model_name: str, replace: bool = False) -> PCA: @@ -184,12 +118,12 @@ def to_gbq(self, model_name: str, replace: bool = False) -> PCA: Args: model_name (str): - The name of the model. + the name of the model. replace (bool, default False): - Determine whether to replace if the model already exists. Default to False. + whether to replace if the model already exists. Default to False. Returns: - PCA: Saved model.""" + PCA: saved model.""" if not self._bqml_model: raise RuntimeError("A model must be fitted before it can be saved") @@ -204,167 +138,5 @@ def score( if not self._bqml_model: raise RuntimeError("A model must be fitted before score") - # TODO(b/291973741): X param is ignored. Update BQML supports input in ML.EVALUATE. + # TODO(b/291973741): X param is ignored. Update BQML supports input in ML.EVALUTE. return self._bqml_model.evaluate() - - -@log_adapter.class_logger -class MatrixFactorization( - base.UnsupervisedTrainablePredictor, - bigframes_vendored.sklearn.decomposition._mf.MatrixFactorization, -): - __doc__ = bigframes_vendored.sklearn.decomposition._mf.MatrixFactorization.__doc__ - - def __init__( - self, - *, - feedback_type: Literal["explicit", "implicit"] = "explicit", - num_factors: int, - user_col: str, - item_col: str, - rating_col: str = "rating", - # TODO: Add support for hyperparameter tuning. - l2_reg: float = 1.0, - ): - feedback_type = feedback_type.lower() # type: ignore - if feedback_type not in ("explicit", "implicit"): - raise ValueError("Expected feedback_type to be `explicit` or `implicit`.") - - self.feedback_type = feedback_type - - if not isinstance(num_factors, int): - raise TypeError( - f"Expected num_factors to be an int, but got {type(num_factors)}." - ) - - if num_factors < 0: - raise ValueError( - f"Expected num_factors to be a positive integer, but got {num_factors}." - ) - - self.num_factors = num_factors - - if not isinstance(user_col, str): - raise TypeError(f"Expected user_col to be a str, but got {type(user_col)}.") - - self.user_col = user_col - - if not isinstance(item_col, str): - raise TypeError(f"Expected item_col to be STR, but got {type(item_col)}.") - - self.item_col = item_col - - if not isinstance(rating_col, str): - raise TypeError( - f"Expected rating_col to be a str, but got {type(rating_col)}." - ) - - self._input_label_columns = [rating_col] - - if not isinstance(l2_reg, (float, int)): - raise TypeError( - f"Expected l2_reg to be a float or int, but got {type(l2_reg)}." - ) - - self.l2_reg = l2_reg - self._bqml_model: Optional[core.BqmlModel] = None - self._bqml_model_factory = globals.bqml_model_factory() - - @property - def rating_col(self) -> str: - """str: The rating column name. Defaults to 'rating'.""" - return self._input_label_columns[0] - - @classmethod - def _from_bq( - cls, session: bigframes.session.Session, bq_model: bigquery.Model - ) -> MatrixFactorization: - assert bq_model.model_type == "MATRIX_FACTORIZATION" - - kwargs = utils.retrieve_params_from_bq_model( - cls, bq_model, _BQML_PARAMS_MAPPING - ) - - model = cls(**kwargs) - model._bqml_model = core.BqmlModel(session, bq_model) - return model - - @property - def _bqml_options(self) -> dict: - """The model options as they will be set for BQML""" - options: dict = { - "model_type": "matrix_factorization", - "feedback_type": self.feedback_type, - "user_col": self.user_col, - "item_col": self.item_col, - "rating_col": self.rating_col, - "l2_reg": self.l2_reg, - } - - if self.num_factors is not None: - options["num_factors"] = self.num_factors - - return options - - def _fit( - self, - X: utils.ArrayType, - y=None, - transforms: Optional[List[str]] = None, - ) -> MatrixFactorization: - if y is not None: - raise ValueError( - "Label column not supported for Matrix Factorization model but y was not `None`" - ) - - (X,) = utils.batch_convert_to_dataframe(X) - - self._bqml_model = self._bqml_model_factory.create_model( - X_train=X, - transforms=transforms, - options=self._bqml_options, - ) - return self - - def predict(self, X: utils.ArrayType) -> bpd.DataFrame: - if not self._bqml_model: - raise RuntimeError("A model must be fitted before recommend") - - (X,) = utils.batch_convert_to_dataframe(X, session=self._bqml_model.session) - - return self._bqml_model.recommend(X) - - def to_gbq(self, model_name: str, replace: bool = False) -> MatrixFactorization: - """Save the model to BigQuery. - - Args: - model_name (str): - The name of the model. - replace (bool, default False): - Determine whether to replace if the model already exists. Default to False. - - Returns: - MatrixFactorization: Saved model.""" - if not self._bqml_model: - raise RuntimeError("A model must be fitted before it can be saved") - - new_model = self._bqml_model.copy(model_name, replace) - return new_model.session.read_gbq_model(model_name) - - def score( - self, - X=None, - y=None, - ) -> bpd.DataFrame: - if not self._bqml_model: - raise RuntimeError("A model must be fitted before score") - - if X is not None and y is not None: - X, y = utils.batch_convert_to_dataframe( - X, y, session=self._bqml_model.session - ) - input_data = X.join(y, how="outer") - else: - input_data = X - - return self._bqml_model.evaluate(input_data) diff --git a/bigframes/ml/ensemble.py b/bigframes/ml/ensemble.py index 5d2b130d7ae..19ca8608ffb 100644 --- a/bigframes/ml/ensemble.py +++ b/bigframes/ml/ensemble.py @@ -12,55 +12,50 @@ # See the License for the specific language governing permissions and # limitations under the License. -"""Ensemble models. This module is styled after scikit-learn's ensemble module: +"""Ensemble models. This module is styled after Scikit-Learn's ensemble module: https://scikit-learn.org/stable/modules/ensemble.html""" from __future__ import annotations -from typing import Dict, List, Literal, Optional +from typing import cast, Dict, List, Literal, Optional, Union -import bigframes_vendored.sklearn.ensemble._forest -import bigframes_vendored.xgboost.sklearn from google.cloud import bigquery -import bigframes.dataframe -import bigframes.session -from bigframes.core.logging import log_adapter +import bigframes from bigframes.ml import base, core, globals, utils +import bigframes.pandas as bpd +import third_party.bigframes_vendored.sklearn.ensemble._forest +import third_party.bigframes_vendored.xgboost.sklearn _BQML_PARAMS_MAPPING = { "booster": "boosterType", - "dart_normalized_type": "dartNormalizeType", "tree_method": "treeMethod", - "colsample_bytree": "colsampleBytree", - "colsample_bylevel": "colsampleBylevel", + "early_stop": "earlyStop", + "colsample_bytree": "colsampleBylevel", + "colsample_bylevel": "colsampleBytree", "colsample_bynode": "colsampleBynode", "gamma": "minSplitLoss", "subsample": "subsample", "reg_alpha": "l1Regularization", "reg_lambda": "l2Regularization", "learning_rate": "learnRate", - "tol": "minRelativeProgress", - "n_estimators": "numParallelTree", + "min_rel_progress": "minRelativeProgress", + "num_parallel_tree": "numParallelTree", "min_tree_child_weight": "minTreeChildWeight", "max_depth": "maxTreeDepth", "max_iterations": "maxIterations", - "enable_global_explain": "enableGlobalExplain", - "xgboost_version": "xgboostVersion", } -@log_adapter.class_logger class XGBRegressor( - base.SupervisedTrainableWithEvaluationPredictor, - bigframes_vendored.xgboost.sklearn.XGBRegressor, + base.SupervisedTrainablePredictor, + third_party.bigframes_vendored.xgboost.sklearn.XGBRegressor, ): - __doc__ = bigframes_vendored.xgboost.sklearn.XGBRegressor.__doc__ + __doc__ = third_party.bigframes_vendored.xgboost.sklearn.XGBRegressor.__doc__ def __init__( self, - n_estimators: int = 1, - *, + num_parallel_tree: int = 1, booster: Literal["gbtree", "dart"] = "gbtree", dart_normalized_type: Literal["tree", "forest"] = "tree", tree_method: Literal["auto", "exact", "approx", "hist"] = "auto", @@ -73,13 +68,14 @@ def __init__( subsample: float = 1.0, reg_alpha: float = 0.0, reg_lambda: float = 1.0, + early_stop: float = True, learning_rate: float = 0.3, max_iterations: int = 20, - tol: float = 0.01, + min_rel_progress: float = 0.01, enable_global_explain: bool = False, xgboost_version: Literal["0.9", "1.1"] = "0.9", ): - self.n_estimators = n_estimators + self.num_parallel_tree = num_parallel_tree self.booster = booster self.dart_normalized_type = dart_normalized_type self.tree_method = tree_method @@ -92,9 +88,10 @@ def __init__( self.subsample = subsample self.reg_alpha = reg_alpha self.reg_lambda = reg_lambda + self.early_stop = early_stop self.learning_rate = learning_rate self.max_iterations = max_iterations - self.tol = tol + self.min_rel_progress = min_rel_progress self.enable_global_explain = enable_global_explain self.xgboost_version = xgboost_version self._bqml_model: Optional[core.BqmlModel] = None @@ -102,17 +99,24 @@ def __init__( @classmethod def _from_bq( - cls, session: bigframes.session.Session, bq_model: bigquery.Model + cls, session: bigframes.Session, model: bigquery.Model ) -> XGBRegressor: - assert bq_model.model_type == "BOOSTED_TREE_REGRESSOR" + assert model.model_type == "BOOSTED_TREE_REGRESSOR" - kwargs = utils.retrieve_params_from_bq_model( - cls, bq_model, _BQML_PARAMS_MAPPING - ) + kwargs = {} + + # See https://cloud.google.com/bigquery/docs/reference/rest/v2/models#trainingrun + last_fitting = model.training_runs[-1]["trainingOptions"] + + dummy_regressor = cls() + for bf_param, bf_value in dummy_regressor.__dict__.items(): + bqml_param = _BQML_PARAMS_MAPPING.get(bf_param) + if bqml_param in last_fitting: + kwargs[bf_param] = type(bf_value)(last_fitting[bqml_param]) - model = cls(**kwargs) - model._bqml_model = core.BqmlModel(session, bq_model) - return model + new_xgb_regressor = cls(**kwargs) + new_xgb_regressor._bqml_model = core.BqmlModel(session, model) + return new_xgb_regressor @property def _bqml_options(self) -> Dict[str, str | int | bool | float | List[str]]: @@ -120,8 +124,7 @@ def _bqml_options(self) -> Dict[str, str | int | bool | float | List[str]]: return { "model_type": "BOOSTED_TREE_REGRESSOR", "data_split_method": "NO_SPLIT", - "early_stop": True, - "num_parallel_tree": self.n_estimators, + "num_parallel_tree": self.num_parallel_tree, "booster_type": self.booster, "tree_method": self.tree_method, "min_tree_child_weight": self.min_tree_child_weight, @@ -133,59 +136,59 @@ def _bqml_options(self) -> Dict[str, str | int | bool | float | List[str]]: "subsample": self.subsample, "l1_reg": self.reg_alpha, "l2_reg": self.reg_lambda, + "early_stop": self.early_stop, "learn_rate": self.learning_rate, "max_iterations": self.max_iterations, - "min_rel_progress": self.tol, + "min_rel_progress": self.min_rel_progress, "enable_global_explain": self.enable_global_explain, "xgboost_version": self.xgboost_version, } def _fit( self, - X: utils.ArrayType, - y: utils.ArrayType, + X: Union[bpd.DataFrame, bpd.Series], + y: Union[bpd.DataFrame, bpd.Series], transforms: Optional[List[str]] = None, - X_eval: Optional[utils.ArrayType] = None, - y_eval: Optional[utils.ArrayType] = None, ) -> XGBRegressor: - X, y = utils.batch_convert_to_dataframe(X, y) - - bqml_options = self._bqml_options - - if X_eval is not None and y_eval is not None: - X_eval, y_eval = utils.batch_convert_to_dataframe(X_eval, y_eval) - X, y, bqml_options = utils.combine_training_and_evaluation_data( - X, y, X_eval, y_eval, bqml_options - ) + X, y = utils.convert_to_dataframe(X, y) self._bqml_model = self._bqml_model_factory.create_model( X, y, transforms=transforms, - options=bqml_options, + options=self._bqml_options, ) return self def predict( self, - X: utils.ArrayType, - ) -> bigframes.dataframe.DataFrame: + X: Union[bpd.DataFrame, bpd.Series], + ) -> bpd.DataFrame: if not self._bqml_model: raise RuntimeError("A model must be fitted before predict") - (X,) = utils.batch_convert_to_dataframe(X, session=self._bqml_model.session) - - return self._bqml_model.predict(X) + (X,) = utils.convert_to_dataframe(X) + + df = self._bqml_model.predict(X) + return cast( + bpd.DataFrame, + df[ + [ + cast(str, field.name) + for field in self._bqml_model.model.label_columns + ] + ], + ) def score( self, - X: utils.ArrayType, - y: utils.ArrayType, + X: Union[bpd.DataFrame, bpd.Series], + y: Union[bpd.DataFrame, bpd.Series], ): + X, y = utils.convert_to_dataframe(X, y) + if not self._bqml_model: raise RuntimeError("A model must be fitted before score") - X, y = utils.batch_convert_to_dataframe(X, y, session=self._bqml_model.session) - input_data = ( X.join(y, how="outer") if (X is not None) and (y is not None) else None ) @@ -196,11 +199,11 @@ def to_gbq(self, model_name: str, replace: bool = False) -> XGBRegressor: Args: model_name (str): - The name of the model. + the name of the model. replace (bool, default False): - Determine whether to replace if the model already exists. Default to False. + whether to replace if the model already exists. Default to False. - Returns: Saved model.""" + Returns: saved model.""" if not self._bqml_model: raise RuntimeError("A model must be fitted before it can be saved") @@ -208,17 +211,16 @@ def to_gbq(self, model_name: str, replace: bool = False) -> XGBRegressor: return new_model.session.read_gbq_model(model_name) -@log_adapter.class_logger class XGBClassifier( - base.SupervisedTrainableWithEvaluationPredictor, - bigframes_vendored.xgboost.sklearn.XGBClassifier, + base.SupervisedTrainablePredictor, + third_party.bigframes_vendored.xgboost.sklearn.XGBClassifier, ): - __doc__ = bigframes_vendored.xgboost.sklearn.XGBClassifier.__doc__ + + __doc__ = third_party.bigframes_vendored.xgboost.sklearn.XGBClassifier.__doc__ def __init__( self, - n_estimators: int = 1, - *, + num_parallel_tree: int = 1, booster: Literal["gbtree", "dart"] = "gbtree", dart_normalized_type: Literal["tree", "forest"] = "tree", tree_method: Literal["auto", "exact", "approx", "hist"] = "auto", @@ -231,13 +233,14 @@ def __init__( subsample: float = 1.0, reg_alpha: float = 0.0, reg_lambda: float = 1.0, + early_stop: bool = True, learning_rate: float = 0.3, max_iterations: int = 20, - tol: float = 0.01, + min_rel_progress: float = 0.01, enable_global_explain: bool = False, xgboost_version: Literal["0.9", "1.1"] = "0.9", ): - self.n_estimators = n_estimators + self.num_parallel_tree = num_parallel_tree self.booster = booster self.dart_normalized_type = dart_normalized_type self.tree_method = tree_method @@ -250,9 +253,10 @@ def __init__( self.subsample = subsample self.reg_alpha = reg_alpha self.reg_lambda = reg_lambda + self.early_stop = early_stop self.learning_rate = learning_rate self.max_iterations = max_iterations - self.tol = tol + self.min_rel_progress = min_rel_progress self.enable_global_explain = enable_global_explain self.xgboost_version = xgboost_version self._bqml_model: Optional[core.BqmlModel] = None @@ -260,17 +264,24 @@ def __init__( @classmethod def _from_bq( - cls, session: bigframes.session.Session, bq_model: bigquery.Model + cls, session: bigframes.Session, model: bigquery.Model ) -> XGBClassifier: - assert bq_model.model_type == "BOOSTED_TREE_CLASSIFIER" + assert model.model_type == "BOOSTED_TREE_CLASSIFIER" - kwargs = utils.retrieve_params_from_bq_model( - cls, bq_model, _BQML_PARAMS_MAPPING - ) + kwargs = {} + + # See https://cloud.google.com/bigquery/docs/reference/rest/v2/models#trainingrun + last_fitting = model.training_runs[-1]["trainingOptions"] + + dummy_classifier = XGBClassifier() + for bf_param, bf_value in dummy_classifier.__dict__.items(): + bqml_param = _BQML_PARAMS_MAPPING.get(bf_param) + if bqml_param is not None: + kwargs[bf_param] = type(bf_value)(last_fitting[bqml_param]) - model = cls(**kwargs) - model._bqml_model = core.BqmlModel(session, bq_model) - return model + new_xgb_classifier = cls(**kwargs) + new_xgb_classifier._bqml_model = core.BqmlModel(session, model) + return new_xgb_classifier @property def _bqml_options(self) -> Dict[str, str | int | bool | float | List[str]]: @@ -278,8 +289,7 @@ def _bqml_options(self) -> Dict[str, str | int | bool | float | List[str]]: return { "model_type": "BOOSTED_TREE_CLASSIFIER", "data_split_method": "NO_SPLIT", - "early_stop": True, - "num_parallel_tree": self.n_estimators, + "num_parallel_tree": self.num_parallel_tree, "booster_type": self.booster, "tree_method": self.tree_method, "min_tree_child_weight": self.min_tree_child_weight, @@ -291,55 +301,56 @@ def _bqml_options(self) -> Dict[str, str | int | bool | float | List[str]]: "subsample": self.subsample, "l1_reg": self.reg_alpha, "l2_reg": self.reg_lambda, + "early_stop": self.early_stop, "learn_rate": self.learning_rate, "max_iterations": self.max_iterations, - "min_rel_progress": self.tol, + "min_rel_progress": self.min_rel_progress, "enable_global_explain": self.enable_global_explain, "xgboost_version": self.xgboost_version, } def _fit( self, - X: utils.ArrayType, - y: utils.ArrayType, + X: Union[bpd.DataFrame, bpd.Series], + y: Union[bpd.DataFrame, bpd.Series], transforms: Optional[List[str]] = None, - X_eval: Optional[utils.ArrayType] = None, - y_eval: Optional[utils.ArrayType] = None, ) -> XGBClassifier: - X, y = utils.batch_convert_to_dataframe(X, y) - - bqml_options = self._bqml_options - - if X_eval is not None and y_eval is not None: - X_eval, y_eval = utils.batch_convert_to_dataframe(X_eval, y_eval) - X, y, bqml_options = utils.combine_training_and_evaluation_data( - X, y, X_eval, y_eval, bqml_options - ) + X, y = utils.convert_to_dataframe(X, y) self._bqml_model = self._bqml_model_factory.create_model( X, y, transforms=transforms, - options=bqml_options, + options=self._bqml_options, ) return self - def predict(self, X: utils.ArrayType) -> bigframes.dataframe.DataFrame: + def predict(self, X: Union[bpd.DataFrame, bpd.Series]) -> bpd.DataFrame: if not self._bqml_model: raise RuntimeError("A model must be fitted before predict") - (X,) = utils.batch_convert_to_dataframe(X, session=self._bqml_model.session) - return self._bqml_model.predict(X) + (X,) = utils.convert_to_dataframe(X) + + df = self._bqml_model.predict(X) + return cast( + bpd.DataFrame, + df[ + [ + cast(str, field.name) + for field in self._bqml_model.model.label_columns + ] + ], + ) def score( self, - X: utils.ArrayType, - y: utils.ArrayType, + X: Union[bpd.DataFrame, bpd.Series], + y: Union[bpd.DataFrame, bpd.Series], ): if not self._bqml_model: raise RuntimeError("A model must be fitted before score") - X, y = utils.batch_convert_to_dataframe(X, y, session=self._bqml_model.session) + X, y = utils.convert_to_dataframe(X, y) input_data = ( X.join(y, how="outer") if (X is not None) and (y is not None) else None @@ -351,12 +362,12 @@ def to_gbq(self, model_name: str, replace: bool = False) -> XGBClassifier: Args: model_name (str): - The name of the model. + the name of the model. replace (bool, default False): - Determine whether to replace if the model already exists. Default to False. + whether to replace if the model already exists. Default to False. Returns: - XGBClassifier: Saved model.""" + XGBClassifier: saved model.""" if not self._bqml_model: raise RuntimeError("A model must be fitted before it can be saved") @@ -364,32 +375,34 @@ def to_gbq(self, model_name: str, replace: bool = False) -> XGBClassifier: return new_model.session.read_gbq_model(model_name) -@log_adapter.class_logger class RandomForestRegressor( - base.SupervisedTrainableWithEvaluationPredictor, - bigframes_vendored.sklearn.ensemble._forest.RandomForestRegressor, + base.SupervisedTrainablePredictor, + third_party.bigframes_vendored.sklearn.ensemble._forest.RandomForestRegressor, ): - __doc__ = bigframes_vendored.sklearn.ensemble._forest.RandomForestRegressor.__doc__ + + __doc__ = ( + third_party.bigframes_vendored.sklearn.ensemble._forest.RandomForestRegressor.__doc__ + ) def __init__( self, - n_estimators: int = 100, - *, + num_parallel_tree: int = 100, tree_method: Literal["auto", "exact", "approx", "hist"] = "auto", min_tree_child_weight: int = 1, - colsample_bytree: float = 1.0, - colsample_bylevel: float = 1.0, - colsample_bynode: float = 0.8, - gamma: float = 0.0, + colsample_bytree=1.0, + colsample_bylevel=1.0, + colsample_bynode=0.8, + gamma=0.00, max_depth: int = 15, - subsample: float = 0.8, - reg_alpha: float = 0.0, - reg_lambda: float = 1.0, - tol: float = 0.01, - enable_global_explain: bool = False, + subsample=0.8, + reg_alpha=0.0, + reg_lambda=1.0, + early_stop=True, + min_rel_progress=0.01, + enable_global_explain=False, xgboost_version: Literal["0.9", "1.1"] = "0.9", ): - self.n_estimators = n_estimators + self.num_parallel_tree = num_parallel_tree self.tree_method = tree_method self.min_tree_child_weight = min_tree_child_weight self.colsample_bytree = colsample_bytree @@ -400,7 +413,8 @@ def __init__( self.subsample = subsample self.reg_alpha = reg_alpha self.reg_lambda = reg_lambda - self.tol = tol + self.early_stop = early_stop + self.min_rel_progress = min_rel_progress self.enable_global_explain = enable_global_explain self.xgboost_version = xgboost_version self._bqml_model: Optional[core.BqmlModel] = None @@ -408,25 +422,31 @@ def __init__( @classmethod def _from_bq( - cls, session: bigframes.session.Session, bq_model: bigquery.Model + cls, session: bigframes.Session, model: bigquery.Model ) -> RandomForestRegressor: - assert bq_model.model_type == "RANDOM_FOREST_REGRESSOR" + assert model.model_type == "RANDOM_FOREST_REGRESSOR" - kwargs = utils.retrieve_params_from_bq_model( - cls, bq_model, _BQML_PARAMS_MAPPING - ) + kwargs = {} + + # See https://cloud.google.com/bigquery/docs/reference/rest/v2/models#trainingrun + last_fitting = model.training_runs[-1]["trainingOptions"] + + dummy_model = cls() + for bf_param, bf_value in dummy_model.__dict__.items(): + bqml_param = _BQML_PARAMS_MAPPING.get(bf_param) + if bqml_param in last_fitting: + kwargs[bf_param] = type(bf_value)(last_fitting[bqml_param]) - model = cls(**kwargs) - model._bqml_model = core.BqmlModel(session, bq_model) - return model + new_random_forest_regressor = cls(**kwargs) + new_random_forest_regressor._bqml_model = core.BqmlModel(session, model) + return new_random_forest_regressor @property def _bqml_options(self) -> Dict[str, str | int | bool | float | List[str]]: """The model options as they will be set for BQML""" return { "model_type": "RANDOM_FOREST_REGRESSOR", - "early_stop": True, - "num_parallel_tree": self.n_estimators, + "num_parallel_tree": self.num_parallel_tree, "tree_method": self.tree_method, "min_tree_child_weight": self.min_tree_child_weight, "colsample_bytree": self.colsample_bytree, @@ -437,7 +457,8 @@ def _bqml_options(self) -> Dict[str, str | int | bool | float | List[str]]: "subsample": self.subsample, "l1_reg": self.reg_alpha, "l2_reg": self.reg_lambda, - "min_rel_progress": self.tol, + "early_stop": self.early_stop, + "min_rel_progress": self.min_rel_progress, "data_split_method": "NO_SPLIT", "enable_global_explain": self.enable_global_explain, "xgboost_version": self.xgboost_version, @@ -445,50 +466,50 @@ def _bqml_options(self) -> Dict[str, str | int | bool | float | List[str]]: def _fit( self, - X: utils.ArrayType, - y: utils.ArrayType, + X: Union[bpd.DataFrame, bpd.Series], + y: Union[bpd.DataFrame, bpd.Series], transforms: Optional[List[str]] = None, - X_eval: Optional[utils.ArrayType] = None, - y_eval: Optional[utils.ArrayType] = None, ) -> RandomForestRegressor: - X, y = utils.batch_convert_to_dataframe(X, y) - - bqml_options = self._bqml_options - - if X_eval is not None and y_eval is not None: - X_eval, y_eval = utils.batch_convert_to_dataframe(X_eval, y_eval) - X, y, bqml_options = utils.combine_training_and_evaluation_data( - X, y, X_eval, y_eval, bqml_options - ) + X, y = utils.convert_to_dataframe(X, y) self._bqml_model = self._bqml_model_factory.create_model( X, y, transforms=transforms, - options=bqml_options, + options=self._bqml_options, ) return self def predict( self, - X: utils.ArrayType, - ) -> bigframes.dataframe.DataFrame: + X: Union[bpd.DataFrame, bpd.Series], + ) -> bpd.DataFrame: if not self._bqml_model: raise RuntimeError("A model must be fitted before predict") - (X,) = utils.batch_convert_to_dataframe(X, session=self._bqml_model.session) - return self._bqml_model.predict(X) + (X,) = utils.convert_to_dataframe(X) + + df = self._bqml_model.predict(X) + return cast( + bpd.DataFrame, + df[ + [ + cast(str, field.name) + for field in self._bqml_model.model.label_columns + ] + ], + ) def score( self, - X: utils.ArrayType, - y: utils.ArrayType, + X: Union[bpd.DataFrame, bpd.Series], + y: Union[bpd.DataFrame, bpd.Series], ): """Calculate evaluation metrics of the model. .. note:: - Output matches that of the BigQuery ML.EVALUATE function. + Output matches that of the BigQuery ML.EVALUTE function. See: https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-evaluate#regression_models for the outputs relevant to this model type. @@ -504,7 +525,7 @@ def score( if not self._bqml_model: raise RuntimeError("A model must be fitted before score") - X, y = utils.batch_convert_to_dataframe(X, y, session=self._bqml_model.session) + X, y = utils.convert_to_dataframe(X, y) input_data = ( X.join(y, how="outer") if (X is not None) and (y is not None) else None @@ -516,12 +537,12 @@ def to_gbq(self, model_name: str, replace: bool = False) -> RandomForestRegresso Args: model_name (str): - The name of the model. + the name of the model. replace (bool, default False): - Determine whether to replace if the model already exists. Default to False. + whether to replace if the model already exists. Default to False. Returns: - RandomForestRegressor: Saved model.""" + RandomForestRegressor: saved model.""" if not self._bqml_model: raise RuntimeError("A model must be fitted before it can be saved") @@ -529,17 +550,18 @@ def to_gbq(self, model_name: str, replace: bool = False) -> RandomForestRegresso return new_model.session.read_gbq_model(model_name) -@log_adapter.class_logger class RandomForestClassifier( - base.SupervisedTrainableWithEvaluationPredictor, - bigframes_vendored.sklearn.ensemble._forest.RandomForestClassifier, + base.SupervisedTrainablePredictor, + third_party.bigframes_vendored.sklearn.ensemble._forest.RandomForestClassifier, ): - __doc__ = bigframes_vendored.sklearn.ensemble._forest.RandomForestClassifier.__doc__ + + __doc__ = ( + third_party.bigframes_vendored.sklearn.ensemble._forest.RandomForestClassifier.__doc__ + ) def __init__( self, - n_estimators: int = 100, - *, + num_parallel_tree: int = 100, tree_method: Literal["auto", "exact", "approx", "hist"] = "auto", min_tree_child_weight: int = 1, colsample_bytree: float = 1.0, @@ -550,11 +572,12 @@ def __init__( subsample: float = 0.8, reg_alpha: float = 0.0, reg_lambda: float = 1.0, - tol: float = 0.01, - enable_global_explain: bool = False, + early_stop=True, + min_rel_progress: float = 0.01, + enable_global_explain=False, xgboost_version: Literal["0.9", "1.1"] = "0.9", ): - self.n_estimators = n_estimators + self.num_parallel_tree = num_parallel_tree self.tree_method = tree_method self.min_tree_child_weight = min_tree_child_weight self.colsample_bytree = colsample_bytree @@ -565,7 +588,8 @@ def __init__( self.subsample = subsample self.reg_alpha = reg_alpha self.reg_lambda = reg_lambda - self.tol = tol + self.early_stop = early_stop + self.min_rel_progress = min_rel_progress self.enable_global_explain = enable_global_explain self.xgboost_version = xgboost_version self._bqml_model: Optional[core.BqmlModel] = None @@ -573,25 +597,31 @@ def __init__( @classmethod def _from_bq( - cls, session: bigframes.session.Session, bq_model: bigquery.Model + cls, session: bigframes.Session, model: bigquery.Model ) -> RandomForestClassifier: - assert bq_model.model_type == "RANDOM_FOREST_CLASSIFIER" + assert model.model_type == "RANDOM_FOREST_CLASSIFIER" - kwargs = utils.retrieve_params_from_bq_model( - cls, bq_model, _BQML_PARAMS_MAPPING - ) + kwargs = {} + + # See https://cloud.google.com/bigquery/docs/reference/rest/v2/models#trainingrun + last_fitting = model.training_runs[-1]["trainingOptions"] - model = cls(**kwargs) - model._bqml_model = core.BqmlModel(session, bq_model) - return model + dummy_model = RandomForestClassifier() + for bf_param, bf_value in dummy_model.__dict__.items(): + bqml_param = _BQML_PARAMS_MAPPING.get(bf_param) + if bqml_param is not None: + kwargs[bf_param] = type(bf_value)(last_fitting[bqml_param]) + + new_random_forest_classifier = cls(**kwargs) + new_random_forest_classifier._bqml_model = core.BqmlModel(session, model) + return new_random_forest_classifier @property def _bqml_options(self) -> Dict[str, str | int | bool | float | List[str]]: """The model options as they will be set for BQML""" return { "model_type": "RANDOM_FOREST_CLASSIFIER", - "early_stop": True, - "num_parallel_tree": self.n_estimators, + "num_parallel_tree": self.num_parallel_tree, "tree_method": self.tree_method, "min_tree_child_weight": self.min_tree_child_weight, "colsample_bytree": self.colsample_bytree, @@ -602,7 +632,8 @@ def _bqml_options(self) -> Dict[str, str | int | bool | float | List[str]]: "subsample": self.subsample, "l1_reg": self.reg_alpha, "l2_reg": self.reg_lambda, - "min_rel_progress": self.tol, + "early_stop": self.early_stop, + "min_rel_progress": self.min_rel_progress, "data_split_method": "NO_SPLIT", "enable_global_explain": self.enable_global_explain, "xgboost_version": self.xgboost_version, @@ -610,50 +641,50 @@ def _bqml_options(self) -> Dict[str, str | int | bool | float | List[str]]: def _fit( self, - X: utils.ArrayType, - y: utils.ArrayType, + X: Union[bpd.DataFrame, bpd.Series], + y: Union[bpd.DataFrame, bpd.Series], transforms: Optional[List[str]] = None, - X_eval: Optional[utils.ArrayType] = None, - y_eval: Optional[utils.ArrayType] = None, ) -> RandomForestClassifier: - X, y = utils.batch_convert_to_dataframe(X, y) - - bqml_options = self._bqml_options - - if X_eval is not None and y_eval is not None: - X_eval, y_eval = utils.batch_convert_to_dataframe(X_eval, y_eval) - X, y, bqml_options = utils.combine_training_and_evaluation_data( - X, y, X_eval, y_eval, bqml_options - ) + X, y = utils.convert_to_dataframe(X, y) self._bqml_model = self._bqml_model_factory.create_model( X, y, transforms=transforms, - options=bqml_options, + options=self._bqml_options, ) return self def predict( self, - X: utils.ArrayType, - ) -> bigframes.dataframe.DataFrame: + X: Union[bpd.DataFrame, bpd.Series], + ) -> bpd.DataFrame: if not self._bqml_model: raise RuntimeError("A model must be fitted before predict") - (X,) = utils.batch_convert_to_dataframe(X, session=self._bqml_model.session) - return self._bqml_model.predict(X) + (X,) = utils.convert_to_dataframe(X) + + df = self._bqml_model.predict(X) + return cast( + bpd.DataFrame, + df[ + [ + cast(str, field.name) + for field in self._bqml_model.model.label_columns + ] + ], + ) def score( self, - X: utils.ArrayType, - y: utils.ArrayType, + X: Union[bpd.DataFrame, bpd.Series], + y: Union[bpd.DataFrame, bpd.Series], ): """Calculate evaluation metrics of the model. .. note:: - Output matches that of the BigQuery ML.EVALUATE function. + Output matches that of the BigQuery ML.EVALUTE function. See: https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-evaluate#classification_models for the outputs relevant to this model type. @@ -669,7 +700,7 @@ def score( if not self._bqml_model: raise RuntimeError("A model must be fitted before score") - X, y = utils.batch_convert_to_dataframe(X, y, session=self._bqml_model.session) + X, y = utils.convert_to_dataframe(X, y) input_data = ( X.join(y, how="outer") if (X is not None) and (y is not None) else None @@ -681,12 +712,12 @@ def to_gbq(self, model_name: str, replace: bool = False) -> RandomForestClassifi Args: model_name (str): - The name of the model. + the name of the model. replace (bool, default False): - Determine whether to replace if the model already exists. Default to False. + whether to replace if the model already exists. Default to False. Returns: - RandomForestClassifier: Saved model.""" + RandomForestClassifier: saved model.""" if not self._bqml_model: raise RuntimeError("A model must be fitted before it can be saved") diff --git a/bigframes/ml/forecasting.py b/bigframes/ml/forecasting.py index bfdd736f855..8e309d5e736 100644 --- a/bigframes/ml/forecasting.py +++ b/bigframes/ml/forecasting.py @@ -16,450 +16,138 @@ from __future__ import annotations -from typing import List, Optional +from typing import cast, Dict, List, Optional, Union from google.cloud import bigquery -import bigframes.pandas as bpd -import bigframes.session -from bigframes.core.logging import log_adapter +import bigframes from bigframes.ml import base, core, globals, utils +import bigframes.pandas as bpd -_BQML_PARAMS_MAPPING = { - "horizon": "horizon", - "auto_arima": "autoArima", - "auto_arima_max_order": "autoArimaMaxOrder", - "auto_arima_min_order": "autoArimaMinOrder", - "order": "nonSeasonalOrder", - "data_frequency": "dataFrequency", - "include_drift": "includeDrift", - "holiday_region": "holidayRegion", - "clean_spikes_and_dips": "cleanSpikesAndDips", - "adjust_step_changes": "adjustStepChanges", - "forecast_limit_upper_bound": "forecastLimitUpperBound", - "forecast_limit_lower_bound": "forecastLimitLowerBound", - "time_series_length_fraction": "timeSeriesLengthFraction", - "min_time_series_length": "minTimeSeriesLength", - "max_time_series_length": "maxTimeSeriesLength", - "decompose_time_series": "decomposeTimeSeries", - "trend_smoothing_window_size": "trendSmoothingWindowSize", -} - - -@log_adapter.class_logger -class ARIMAPlus(base.SupervisedTrainableWithIdColPredictor): - """Time Series ARIMA Plus model. - - Args: - horizon (int, default 1,000): - The number of time points to forecast. Default to 1,000, max value 10,000. - - auto_arima (bool, default True): - Determines whether the training process uses auto.ARIMA or not. If True, training automatically finds the best non-seasonal order (that is, the p, d, q tuple) and decides whether or not to include a linear drift term when d is 1. - - auto_arima_max_order (int or None, default None): - The maximum value for the sum of non-seasonal p and q. - - auto_arima_min_order (int or None, default None): - The minimum value for the sum of non-seasonal p and q. - - data_frequency (str, default "auto_frequency"): - The data frequency of the input time series. - Possible values are "auto_frequency", "per_minute", "hourly", "daily", "weekly", "monthly", "quarterly", "yearly" - - include_drift (bool, default False): - Determines whether the model should include a linear drift term or not. The drift term is applicable when non-seasonal d is 1. - - holiday_region (str or None, default None): - The geographical region based on which the holiday effect is applied in modeling. By default, holiday effect modeling isn't used. - Possible values see https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-create-time-series#holiday_region. - - clean_spikes_and_dips (bool, default True): - Determines whether or not to perform automatic spikes and dips detection and cleanup in the model training pipeline. The spikes and dips are replaced with local linear interpolated values when they're detected. - - adjust_step_changes (bool, default True): - Determines whether or not to perform automatic step change detection and adjustment in the model training pipeline. - - forecast_limit_upper_bound (float or None, default None): - The upper bound of the forecasting values. When you specify the ``forecast_limit_upper_bound`` option, all of the forecast values must be less than the specified value. - For example, if you set ``forecast_limit_upper_bound`` to 100, then all of the forecast values are less than 100. - Also, all values greater than or equal to the ``forecast_limit_upper_bound`` value are excluded from modelling. - The forecasting limit ensures that forecasts stay within limits. - - forecast_limit_lower_bound (float or None, default None): - The lower bound of the forecasting values where the minimum value allowed is 0. When you specify the ``forecast_limit_lower_bound`` option, all of the forecast values must be greater than the specified value. - For example, if you set ``forecast_limit_lower_bound`` to 0, then all of the forecast values are larger than 0. Also, all values less than or equal to the ``forecast_limit_lower_bound`` value are excluded from modelling. - The forecasting limit ensures that forecasts stay within limits. - - time_series_length_fraction (float or None, default None): - The fraction of the interpolated length of the time series that's used to model the time series trend component. All of the time points of the time series are used to model the non-trend component. - - min_time_series_length (int or None, default None): - The minimum number of time points that are used in modeling the trend component of the time series. - - max_time_series_length (int or None, default None): - The maximum number of time points in a time series that can be used in modeling the trend component of the time series. - - trend_smoothing_window_size (int or None, default None): - The smoothing window size for the trend component. - - decompose_time_series (bool, default True): - Determines whether the separate components of both the history and forecast parts of the time series (such as holiday effect and seasonal components) are saved in the model. - """ - - def __init__( - self, - *, - horizon: int = 1000, - auto_arima: bool = True, - auto_arima_max_order: Optional[int] = None, - auto_arima_min_order: Optional[int] = None, - data_frequency: str = "auto_frequency", - include_drift: bool = False, - holiday_region: Optional[str] = None, - clean_spikes_and_dips: bool = True, - adjust_step_changes: bool = True, - forecast_limit_lower_bound: Optional[float] = None, - forecast_limit_upper_bound: Optional[float] = None, - time_series_length_fraction: Optional[float] = None, - min_time_series_length: Optional[int] = None, - max_time_series_length: Optional[int] = None, - trend_smoothing_window_size: Optional[int] = None, - decompose_time_series: bool = True, - ): - self.horizon = horizon - self.auto_arima = auto_arima - self.auto_arima_max_order = auto_arima_max_order - self.auto_arima_min_order = auto_arima_min_order - self.data_frequency = data_frequency - self.include_drift = include_drift - self.holiday_region = holiday_region - self.clean_spikes_and_dips = clean_spikes_and_dips - self.adjust_step_changes = adjust_step_changes - self.forecast_limit_upper_bound = forecast_limit_upper_bound - self.forecast_limit_lower_bound = forecast_limit_lower_bound - self.time_series_length_fraction = time_series_length_fraction - self.min_time_series_length = min_time_series_length - self.max_time_series_length = max_time_series_length - self.trend_smoothing_window_size = trend_smoothing_window_size - self.decompose_time_series = decompose_time_series - # TODO(garrettwu) add order and seasonalities params, which need struct/array +_PREDICT_OUTPUT_COLUMNS = ["forecast_timestamp", "forecast_value"] + + +class ARIMAPlus(base.SupervisedTrainablePredictor): + """Time Series ARIMA Plus model.""" + def __init__(self): self._bqml_model: Optional[core.BqmlModel] = None self._bqml_model_factory = globals.bqml_model_factory() @classmethod - def _from_bq( - cls, session: bigframes.session.Session, bq_model: bigquery.Model - ) -> ARIMAPlus: - assert bq_model.model_type == "ARIMA_PLUS" + def _from_bq(cls, session: bigframes.Session, model: bigquery.Model) -> ARIMAPlus: + assert model.model_type == "ARIMA_PLUS" - kwargs = utils.retrieve_params_from_bq_model( - cls, bq_model, _BQML_PARAMS_MAPPING - ) + kwargs: Dict[str, str | int | bool | float | List[str]] = {} - model = cls(**kwargs) - model._bqml_model = core.BqmlModel(session, bq_model) - return model + new_arima_plus = cls(**kwargs) + new_arima_plus._bqml_model = core.BqmlModel(session, model) + return new_arima_plus @property - def _bqml_options(self) -> dict: + def _bqml_options(self) -> Dict[str, str | int | bool | float | List[str]]: """The model options as they will be set for BQML.""" - options = { - "model_type": "ARIMA_PLUS", - "horizon": self.horizon, - "auto_arima": self.auto_arima, - "data_frequency": self.data_frequency, - "clean_spikes_and_dips": self.clean_spikes_and_dips, - "adjust_step_changes": self.adjust_step_changes, - "decompose_time_series": self.decompose_time_series, - } - - if self.auto_arima_max_order is not None: - options["auto_arima_max_order"] = self.auto_arima_max_order - if self.auto_arima_min_order is not None: - options["auto_arima_min_order"] = self.auto_arima_min_order - if self.holiday_region is not None: - options["holiday_region"] = self.holiday_region - if self.time_series_length_fraction is not None: - options["time_series_length_fraction"] = self.time_series_length_fraction - if self.min_time_series_length is not None: - options["min_time_series_length"] = self.min_time_series_length - if self.max_time_series_length is not None: - options["max_time_series_length"] = self.max_time_series_length - if self.trend_smoothing_window_size is not None: - options["trend_smoothing_window_size"] = self.trend_smoothing_window_size - - if self.include_drift: - options["include_drift"] = True - if self.forecast_limit_upper_bound is not None: - options["forecast_limit_upper_bound"] = self.forecast_limit_upper_bound - if self.forecast_limit_lower_bound is not None: - options["forecast_limit_lower_bound"] = self.forecast_limit_lower_bound - - return options + return {"model_type": "ARIMA_PLUS"} def _fit( self, - X: utils.ArrayType, - y: utils.ArrayType, + X: Union[bpd.DataFrame, bpd.Series], + y: Union[bpd.DataFrame, bpd.Series], transforms: Optional[List[str]] = None, - id_col: Optional[utils.ArrayType] = None, - ) -> ARIMAPlus: + ): """Fit the model to training data. Args: - X (bigframes.dataframe.DataFrame or bigframes.series.Series, - or pandas.core.frame.DataFrame or pandas.core.series.Series): - A dataframe or series of training timestamp. - y (bigframes.dataframe.DataFrame, or bigframes.series.Series, - or pandas.core.frame.DataFrame, or pandas.core.series.Series): + X (bigframes.dataframe.DataFrame or bigframes.series.Series): + A dataframe of training timestamp. + + y (bigframes.dataframe.DataFrame or bigframes.series.Series): Target values for training. transforms (Optional[List[str]], default None): Do not use. Internal param to be deprecated. Use bigframes.ml.pipeline instead. - id_col (Optional[bigframes.dataframe.DataFrame] - or Optional[bigframes.series.Series] - or Optional[pandas.core.frame.DataFrame] - or Optional[pandas.core.frame.Series] - or None, default None): - An optional dataframe or series of training id col. Returns: ARIMAPlus: Fitted estimator. """ - X, y = utils.batch_convert_to_dataframe(X, y) - if X.columns.size != 1: - raise ValueError("Time series timestamp input X contain at least 1 column.") + raise ValueError( + "Time series timestamp input X must only contain 1 column." + ) if y.columns.size != 1: raise ValueError("Time series data input y must only contain 1 column.") - if id_col is not None: - (id_col,) = utils.batch_convert_to_dataframe(id_col) - - if id_col.columns.size != 1: - raise ValueError( - "Time series id input id_col must only contain 1 column." - ) + X, y = utils.convert_to_dataframe(X, y) self._bqml_model = self._bqml_model_factory.create_time_series_model( X, y, - id_col=id_col, transforms=transforms, options=self._bqml_options, ) - return self - - def predict( - self, X=None, *, horizon: int = 3, confidence_level: float = 0.95 - ) -> bpd.DataFrame: - """Forecast time series at future horizon. - - .. note:: - Output matches that of the BigQuery ML.FORECAST function. - See: https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-forecast + def predict(self, X=None) -> bpd.DataFrame: + """Predict the closest cluster for each sample in X. Args: X (default None): ignored, to be compatible with other APIs. - horizon (int, default: 3): - an int value that specifies the number of time points to forecast. - The default value is 3, and the maximum value is 1000. - confidence_level (float, default 0.95): - A float value that specifies percentage of the future values that fall in the prediction interval. - The valid input range is [0.0, 1.0). Returns: bigframes.dataframe.DataFrame: The predicted DataFrames. Which - contains 2 columns: "forecast_timestamp", "id" as optional, and "forecast_value". + contains 2 columns "forecast_timestamp" and "forecast_value". """ - if horizon < 1 or horizon > 1000: - raise ValueError(f"horizon must be [1, 1000], but is {horizon}.") - if confidence_level < 0.0 or confidence_level >= 1.0: - raise ValueError( - f"confidence_level must be [0.0, 1.0), but is {confidence_level}." - ) - if not self._bqml_model: raise RuntimeError("A model must be fitted before predict") - return self._bqml_model.forecast( - options={"horizon": horizon, "confidence_level": confidence_level} - ) - - def predict_explain( - self, X=None, *, horizon: int = 3, confidence_level: float = 0.95 - ) -> bpd.DataFrame: - """Explain Forecast time series at future horizon. - - .. note:: - - Output matches that of the BigQuery ML.EXPLAIN_FORECAST function. - See: https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-explain-forecast - - Args: - X (default None): - ignored, to be compatible with other APIs. - horizon (int, default: 3): - an int value that specifies the number of time points to forecast. - The default value is 3, and the maximum value is 1000. - confidence_level (float, default 0.95): - A float value that specifies percentage of the future values that fall in the prediction interval. - The valid input range is [0.0, 1.0). - - Returns: - bigframes.dataframe.DataFrame: The predicted DataFrames. - """ - if horizon < 1: - raise ValueError(f"horizon must be at least 1, but is {horizon}.") - if confidence_level < 0.0 or confidence_level >= 1.0: - raise ValueError( - f"confidence_level must be [0.0, 1.0), but is {confidence_level}." - ) - - if not self._bqml_model: - raise RuntimeError("A model must be fitted before predict") - - return self._bqml_model.explain_forecast( - options={"horizon": horizon, "confidence_level": confidence_level} - ) - - @property - def coef_( - self, - ) -> bpd.DataFrame: - """Inspect the coefficients of the model. - - ..note:: - - Output matches that of the ML.ARIMA_COEFFICIENTS function. - See: https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-arima-coefficients - for the outputs relevant to this model type. - - Returns: - bigframes.dataframe.DataFrame: - A DataFrame with the coefficients for the model. - """ - - if not self._bqml_model: - raise RuntimeError("A model must be fitted before inspect coefficients") - return self._bqml_model.arima_coefficients() - - def detect_anomalies( - self, - X: utils.ArrayType, - *, - anomaly_prob_threshold: float = 0.95, - ) -> bpd.DataFrame: - """Detect the anomaly data points of the input. - - Args: - X (bigframes.dataframe.DataFrame or bigframes.series.Series or pandas.core.frame.DataFrame or pandas.core.series.Series): - Series or a DataFrame to detect anomalies. - anomaly_prob_threshold (float, default 0.95): - Identifies the custom threshold to use for anomaly detection. The value must be in the range [0, 1), with a default value of 0.95. - - Returns: - bigframes.dataframe.DataFrame: Detected DataFrame.""" - if anomaly_prob_threshold < 0.0 or anomaly_prob_threshold >= 1.0: - raise ValueError( - f"anomaly_prob_threshold must be [0.0, 1.0), but is {anomaly_prob_threshold}." - ) - - if not self._bqml_model: - raise RuntimeError("A model must be fitted before detect_anomalies") - - (X,) = utils.batch_convert_to_dataframe(X, session=self._bqml_model.session) - - return self._bqml_model.detect_anomalies( - X, options={"anomaly_prob_threshold": anomaly_prob_threshold} + return cast( + bpd.DataFrame, + self._bqml_model.forecast()[_PREDICT_OUTPUT_COLUMNS], ) def score( self, - X: utils.ArrayType, - y: utils.ArrayType, - id_col: Optional[utils.ArrayType] = None, + X: Union[bpd.DataFrame, bpd.Series], + y: Union[bpd.DataFrame, bpd.Series], ) -> bpd.DataFrame: """Calculate evaluation metrics of the model. .. note:: - Output matches that of the BigQuery ML.EVALUATE function. + Output matches that of the BigQuery ML.EVALUTE function. See: https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-evaluate#time_series_models for the outputs relevant to this model type. Args: - X (bigframes.dataframe.DataFrame or bigframes.series.Series - or pandas.core.frame.DataFrame or pandas.core.series.Series): - A dataframe or series only contains 1 column as + X (bigframes.dataframe.DataFrame or bigframes.series.Series): + A BigQuery DataFrame only contains 1 column as evaluation timestamp. The timestamp must be within the horizon of the model, which by default is 1000 data points. - y (bigframes.dataframe.DataFrame or bigframes.series.Series - or pandas.core.frame.DataFrame or pandas.core.series.Series): - A dataframe or series only contains 1 column as + y (bigframes.dataframe.DataFrame or bigframes.series.Series): + A BigQuery DataFrame only contains 1 column as evaluation numeric values. - id_col (Optional[bigframes.dataframe.DataFrame] - or Optional[bigframes.series.Series] - or Optional[pandas.core.frame.DataFrame] - or Optional[pandas.core.series.Series] - or None, default None): - An optional dataframe or series contains at least 1 column as - evaluation id column. Returns: bigframes.dataframe.DataFrame: A DataFrame as evaluation result. """ if not self._bqml_model: raise RuntimeError("A model must be fitted before score") - X, y = utils.batch_convert_to_dataframe(X, y, session=self._bqml_model.session) + X, y = utils.convert_to_dataframe(X, y) input_data = X.join(y, how="outer") - if id_col is not None: - (id_col,) = utils.batch_convert_to_dataframe(id_col) - input_data = input_data.join(id_col, how="outer") - return self._bqml_model.evaluate(input_data) - def summary( - self, - show_all_candidate_models: bool = False, - ) -> bpd.DataFrame: - """Summary of the evaluation metrics of the time series model. - - .. note:: - - Output matches that of the BigQuery ML.ARIMA_EVALUATE function. - See: https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-arima-evaluate - for the outputs relevant to this model type. - - Args: - show_all_candidate_models (bool, default to False): - Whether to show evaluation metrics or an error message for either - all candidate models or for only the best model with the lowest - AIC. Default to False. - - Returns: - bigframes.dataframe.DataFrame: A DataFrame as evaluation result. - """ - if not self._bqml_model: - raise RuntimeError("A model must be fitted before score") - return self._bqml_model.arima_evaluate(show_all_candidate_models) - def to_gbq(self, model_name: str, replace: bool = False) -> ARIMAPlus: """Save the model to BigQuery. Args: model_name (str): - The name of the model. + the name of the model. replace (bool, default False): - Determine whether to replace if the model already exists. Default to False. + whether to replace if the model already exists. Default to False. Returns: - ARIMAPlus: Saved model.""" + ARIMAPlus: saved model.""" if not self._bqml_model: raise RuntimeError("A model must be fitted before it can be saved") diff --git a/bigframes/ml/globals.py b/bigframes/ml/globals.py index 62cfdbef72f..c139476daaa 100644 --- a/bigframes/ml/globals.py +++ b/bigframes/ml/globals.py @@ -19,17 +19,6 @@ _BASE_SQL_GENERATOR = sql.BaseSqlGenerator() _BQML_MODEL_FACTORY = core.BqmlModelFactory() -_REMOTE_MODEL_SUPPORTED_DTYPES = ( - "bool", - "string", - "int64", - "float64", - "array", - "array", - "array", - "array", -) - def base_sql_generator() -> sql.BaseSqlGenerator: """Base SQL Generator.""" diff --git a/bigframes/ml/imported.py b/bigframes/ml/imported.py index ca83b0ee568..fb8aa98befd 100644 --- a/bigframes/ml/imported.py +++ b/bigframes/ml/imported.py @@ -16,33 +16,28 @@ from __future__ import annotations -import typing -from typing import Mapping, Optional +from typing import cast, Optional, Union from google.cloud import bigquery -import bigframes.pandas as bpd -import bigframes.session -from bigframes.core.logging import log_adapter +import bigframes from bigframes.ml import base, core, globals, utils +import bigframes.pandas as bpd -@log_adapter.class_logger class TensorFlowModel(base.Predictor): """Imported TensorFlow model. Args: - model_path (str): - Cloud Storage path that holds the model files. session (BigQuery Session): - BQ session to create the model. - """ + BQ session to create the model + model_path (str): + GCS path that holds the model files.""" def __init__( self, - model_path: str, - *, - session: Optional[bigframes.session.Session] = None, + session: Optional[bigframes.Session] = None, + model_path: Optional[str] = None, ): self.session = session or bpd.get_global_session() self.model_path = model_path @@ -57,72 +52,77 @@ def _create_bqml_model(self): @classmethod def _from_bq( - cls, session: bigframes.session.Session, bq_model: bigquery.Model + cls, session: bigframes.Session, model: bigquery.Model ) -> TensorFlowModel: - assert bq_model.model_type == "TENSORFLOW" + assert model.model_type == "TENSORFLOW" - model = cls(session=session, model_path="") - model._bqml_model = core.BqmlModel(session, bq_model) - return model + tf_model = cls(session=session, model_path=None) + tf_model._bqml_model = core.BqmlModel(session, model) + return tf_model - def predict(self, X: utils.ArrayType) -> bpd.DataFrame: + def predict(self, X: Union[bpd.DataFrame, bpd.Series]) -> bpd.DataFrame: """Predict the result from input DataFrame. Args: - X (bigframes.dataframe.DataFrame or bigframes.series.Series or pandas.core.frame.DataFrame or pandas.core.series.Series): - Input DataFrame. Schema is defined by the model. + X (bigframes.dataframe.DataFrame): + Input DataFrame, schema is defined by the model. Returns: - bigframes.dataframe.DataFrame: Output DataFrame. Schema is defined by the model. - """ + bigframes.dataframe.DataFrame: Output DataFrame, schema is defined by the model.""" if not self._bqml_model: if self.model_path is None: raise ValueError("Model GCS path must be provided.") self._bqml_model = self._create_bqml_model() - self._bqml_model = typing.cast(core.BqmlModel, self._bqml_model) - - (X,) = utils.batch_convert_to_dataframe(X) - - return self._bqml_model.predict(X) + self._bqml_model = cast(core.BqmlModel, self._bqml_model) + + (X,) = utils.convert_to_dataframe(X) + + df = self._bqml_model.predict(X) + return cast( + bpd.DataFrame, + df[ + [ + cast(str, field.name) + for field in self._bqml_model.model.label_columns + ] + ], + ) def to_gbq(self, model_name: str, replace: bool = False) -> TensorFlowModel: """Save the model to BigQuery. Args: model_name (str): - The name of the model. + the name of the model. replace (bool, default False): - Default to False. + whether to replace if the model already exists. Default to False. Returns: - TensorFlowModel: Saved model.""" + TensorFlowModel: saved model.""" if not self._bqml_model: if self.model_path is None: raise ValueError("Model GCS path must be provided.") self._bqml_model = self._create_bqml_model() - self._bqml_model = typing.cast(core.BqmlModel, self._bqml_model) + self._bqml_model = cast(core.BqmlModel, self._bqml_model) new_model = self._bqml_model.copy(model_name, replace) return new_model.session.read_gbq_model(model_name) -@log_adapter.class_logger class ONNXModel(base.Predictor): """Imported Open Neural Network Exchange (ONNX) model. Args: - model_path (str): - Cloud Storage path that holds the model files. session (BigQuery Session): - BQ session to create the model. - """ + BQ session to create the model + model_path (str): + Cloud Storage path that holds the model files.""" def __init__( self, - model_path: str, - *, - session: Optional[bigframes.session.Session] = None, + session: Optional[bigframes.Session] = None, + model_path: Optional[str] = None, ): self.session = session or bpd.get_global_session() self.model_path = model_path @@ -136,172 +136,58 @@ def _create_bqml_model(self): ) @classmethod - def _from_bq( - cls, session: bigframes.session.Session, bq_model: bigquery.Model - ) -> ONNXModel: - assert bq_model.model_type == "ONNX" + def _from_bq(cls, session: bigframes.Session, model: bigquery.Model) -> ONNXModel: + assert model.model_type == "ONNX" - model = cls(session=session, model_path="") - model._bqml_model = core.BqmlModel(session, bq_model) - return model + onnx_model = cls(session=session, model_path=None) + onnx_model._bqml_model = core.BqmlModel(session, model) + return onnx_model - def predict(self, X: utils.ArrayType) -> bpd.DataFrame: + def predict(self, X: Union[bpd.DataFrame, bpd.Series]) -> bpd.DataFrame: """Predict the result from input DataFrame. Args: - X (bigframes.dataframe.DataFrame or bigframes.series.Series or pandas.core.frame.DataFrame or pandas.core.series.Series): - Input DataFrame or Series. Schema is defined by the model. + X (bigframes.dataframe.DataFrame or bigframes.series.Series): + Input DataFrame or Series, schema is defined by the model. Returns: - bigframes.dataframe.DataFrame: Output DataFrame, schema is defined by the model. - """ - - if not self._bqml_model: - if self.model_path is None: - raise ValueError("Model GCS path must be provided.") - self._bqml_model = self._create_bqml_model() - self._bqml_model = typing.cast(core.BqmlModel, self._bqml_model) - - (X,) = utils.batch_convert_to_dataframe(X, session=self._bqml_model.session) + bigframes.dataframe.DataFrame: Output DataFrame, schema is defined by the model.""" - return self._bqml_model.predict(X) - - def to_gbq(self, model_name: str, replace: bool = False) -> ONNXModel: - """Save the model to BigQuery. - - Args: - model_name (str): - The name of the model. - replace (bool, default False): - Determine whether to replace if the model already exists. Default to False. - - Returns: - ONNXModel: Saved model.""" if not self._bqml_model: if self.model_path is None: raise ValueError("Model GCS path must be provided.") self._bqml_model = self._create_bqml_model() - self._bqml_model = typing.cast(core.BqmlModel, self._bqml_model) - - new_model = self._bqml_model.copy(model_name, replace) - return new_model.session.read_gbq_model(model_name) - - -@log_adapter.class_logger -class XGBoostModel(base.Predictor): - """Imported XGBoost model. - - .. warning:: - - Imported XGBoost models have the several limitations. See: - https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-create-xgboost#limitations - - Args: - model_path (str): - Cloud Storage path that holds the model files. - input (Dict, default None): - Specify the model input schema information when you - create the XGBoost model. The input should be the format of - {field_name: field_type}. Input is optional only if feature_names - and feature_types are both specified in the model file. Supported types - are "bool", "string", "int64", "float64", "array", "array", "array", "array". - output (Dict, default None): - Specify the model output schema information when you - create the XGBoost model. The input should be the format of - {field_name: field_type}. Output is optional only if feature_names - and feature_types are both specified in the model file. Supported types - are "bool", "string", "int64", "float64", "array", "array", "array", "array". - session (BigQuery Session): - BQ session to create the model. - """ - - def __init__( - self, - model_path: str, - *, - input: Optional[Mapping[str, str]] = None, - output: Optional[Mapping[str, str]] = None, - session: Optional[bigframes.session.Session] = None, - ): - self.session = session or bpd.get_global_session() - self.model_path = model_path - self.input = input - self.output = output - self._bqml_model: Optional[core.BqmlModel] = None - self._bqml_model_factory = globals.bqml_model_factory() - - def _create_bqml_model(self): - options = {"model_type": "XGBOOST", "model_path": self.model_path} - - if not self.input and not self.output: - return self._bqml_model_factory.create_imported_model( - session=self.session, options=options - ) - if not self.input or not self.output: - raise ValueError("input and output must both or neigher be set.") - self.input = { - k: utils.standardize_type(v, globals._REMOTE_MODEL_SUPPORTED_DTYPES) - for k, v in self.input.items() - } - self.output = { - k: utils.standardize_type(v, globals._REMOTE_MODEL_SUPPORTED_DTYPES) - for k, v in self.output.items() - } - - return self._bqml_model_factory.create_xgboost_imported_model( - session=self.session, - input=self.input, - output=self.output, - options=options, + self._bqml_model = cast(core.BqmlModel, self._bqml_model) + + (X,) = utils.convert_to_dataframe(X) + + df = self._bqml_model.predict(X) + return cast( + bpd.DataFrame, + df[ + [ + cast(str, field.name) + for field in self._bqml_model.model.label_columns + ] + ], ) - @classmethod - def _from_bq( - cls, session: bigframes.session.Session, bq_model: bigquery.Model - ) -> XGBoostModel: - assert bq_model.model_type == "XGBOOST" - - model = cls(session=session, model_path="") - model._bqml_model = core.BqmlModel(session, bq_model) - return model - - def predict(self, X: utils.ArrayType) -> bpd.DataFrame: - """Predict the result from input DataFrame. - - Args: - X (bigframes.dataframe.DataFrame or bigframes.series.Series or pandas.core.frame.DataFrame or pandas.core.series.Series): - Input DataFrame or Series. Schema is defined by the model. - - Returns: - bigframes.dataframe.DataFrame: Output DataFrame. Schema is defined by the model. - """ - - if not self._bqml_model: - if self.model_path is None: - raise ValueError("Model GCS path must be provided.") - self._bqml_model = self._create_bqml_model() - self._bqml_model = typing.cast(core.BqmlModel, self._bqml_model) - - (X,) = utils.batch_convert_to_dataframe(X, session=self._bqml_model.session) - - return self._bqml_model.predict(X) - - def to_gbq(self, model_name: str, replace: bool = False) -> XGBoostModel: + def to_gbq(self, model_name: str, replace: bool = False) -> ONNXModel: """Save the model to BigQuery. Args: model_name (str): - The name of the model. + the name of the model. replace (bool, default False): - Determine whether to replace if the model already exists. Default to False. + whether to replace if the model already exists. Default to False. Returns: - XGBoostModel: Saved model.""" + ONNXModel: saved model.""" if not self._bqml_model: if self.model_path is None: raise ValueError("Model GCS path must be provided.") self._bqml_model = self._create_bqml_model() - self._bqml_model = typing.cast(core.BqmlModel, self._bqml_model) + self._bqml_model = cast(core.BqmlModel, self._bqml_model) new_model = self._bqml_model.copy(model_name, replace) return new_model.session.read_gbq_model(model_name) diff --git a/bigframes/ml/impute.py b/bigframes/ml/impute.py deleted file mode 100644 index 77314c360ad..00000000000 --- a/bigframes/ml/impute.py +++ /dev/null @@ -1,121 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -"""Transformers for missing value imputation. This module is styled after -scikit-learn's preprocessing module: https://scikit-learn.org/stable/modules/impute.html.""" - -from __future__ import annotations - -import typing -from typing import Iterable, List, Literal, Optional - -import bigframes_vendored.sklearn.impute._base - -import bigframes.core.utils as core_utils -import bigframes.pandas as bpd -from bigframes.core.logging import log_adapter -from bigframes.ml import base, core, globals, utils - - -@log_adapter.class_logger -class SimpleImputer( - base.Transformer, - bigframes_vendored.sklearn.impute._base.SimpleImputer, -): - __doc__ = bigframes_vendored.sklearn.impute._base.SimpleImputer.__doc__ - - def __init__( - self, - strategy: Literal["mean", "median", "most_frequent"] = "mean", - ): - self.strategy = strategy - self._bqml_model: Optional[core.BqmlModel] = None - self._bqml_model_factory = globals.bqml_model_factory() - self._base_sql_generator = globals.base_sql_generator() - - def _keys(self): - return (self._bqml_model, self.strategy) - - def _compile_to_sql( - self, - X: bpd.DataFrame, - columns: Optional[Iterable[str]] = None, - ) -> List[str]: - """Compile this transformer to a list of SQL expressions that can be included in - a BQML TRANSFORM clause - - Args: - X: DataFrame to transform. - columns: transform columns. If None, transform all columns in X. - - Returns: a list of tuples sql_expr.""" - if columns is None: - columns = X.columns - columns, _ = core_utils.get_standardized_ids(columns) - return [ - self._base_sql_generator.ml_imputer( - column, self.strategy, f"imputer_{column}" - ) - for column in columns - ] - - @classmethod - def _parse_from_sql(cls, sql: str) -> tuple[SimpleImputer, str]: - """Parse SQL to tuple(SimpleImputer, column_label). - - Args: - sql: SQL string of format "ML.IMPUTER({col_label}, {strategy}) OVER()" - - Returns: - tuple(SimpleImputer, column_label)""" - s = sql[sql.find("(") + 1 : sql.find(")")] - col_label, strategy = s.split(", ") - return cls(strategy[1:-1]), _unescape_id(col_label) # type: ignore[arg-type] - - def fit( - self, - X: utils.ArrayType, - y=None, # ignored - ) -> SimpleImputer: - (X,) = utils.batch_convert_to_dataframe(X) - - transform_sqls = self._compile_to_sql(X) - self._bqml_model = self._bqml_model_factory.create_model( - X, - options={"model_type": "transform_only"}, - transforms=transform_sqls, - ) - - self._extract_output_names() - return self - - def transform(self, X: utils.ArrayType) -> bpd.DataFrame: - if not self._bqml_model: - raise RuntimeError("Must be fitted before transform") - - (X,) = utils.batch_convert_to_dataframe(X, session=self._bqml_model.session) - - df = self._bqml_model.transform(X) - return typing.cast( - bpd.DataFrame, - df[self._output_names], - ) - - -def _unescape_id(id: str) -> str: - """Very simple conversion to removed ` characters from ids. - - A proper sql parser should be used instead. - """ - return id.removeprefix("`").removesuffix("`") diff --git a/bigframes/ml/linear_model.py b/bigframes/ml/linear_model.py index d35a2d45ecb..f11879500b9 100644 --- a/bigframes/ml/linear_model.py +++ b/bigframes/ml/linear_model.py @@ -17,17 +17,16 @@ from __future__ import annotations -from typing import Dict, List, Literal, Optional, Union +from typing import cast, Dict, List, Literal, Optional, Union -import bigframes_vendored.constants as constants -import bigframes_vendored.sklearn.linear_model._base -import bigframes_vendored.sklearn.linear_model._logistic from google.cloud import bigquery -import bigframes.pandas as bpd -import bigframes.session -from bigframes.core.logging import log_adapter +import bigframes +import bigframes.constants as constants from bigframes.ml import base, core, globals, utils +import bigframes.pandas as bpd +import third_party.bigframes_vendored.sklearn.linear_model._base +import third_party.bigframes_vendored.sklearn.linear_model._logistic _BQML_PARAMS_MAPPING = { "optimize_strategy": "optimizationStrategy", @@ -35,51 +34,49 @@ "l1_reg": "l1Regularization", "l2_reg": "l2Regularization", "max_iterations": "maxIterations", - "learning_rate_strategy": "learnRateStrategy", - "learning_rate": "learnRate", - "tol": "minRelativeProgress", - "ls_init_learning_rate": "initialLearnRate", + "learn_rate_strategy": "learnRateStrategy", + "learn_rate": "learnRate", + "early_stop": "earlyStop", + "min_rel_progress": "minRelativeProgress", + "ls_init_learn_rate": "initialLearnRate", "warm_start": "warmStart", "calculate_p_values": "calculatePValues", "enable_global_explain": "enableGlobalExplain", + "category_encoding_method": "categoryEncodingMethod", } -@log_adapter.class_logger class LinearRegression( - base.SupervisedTrainableWithEvaluationPredictor, - bigframes_vendored.sklearn.linear_model._base.LinearRegression, + base.SupervisedTrainablePredictor, + third_party.bigframes_vendored.sklearn.linear_model._base.LinearRegression, ): - __doc__ = bigframes_vendored.sklearn.linear_model._base.LinearRegression.__doc__ + __doc__ = ( + third_party.bigframes_vendored.sklearn.linear_model._base.LinearRegression.__doc__ + ) def __init__( self, - *, optimize_strategy: Literal[ "auto_strategy", "batch_gradient_descent", "normal_equation" - ] = "auto_strategy", + ] = "normal_equation", fit_intercept: bool = True, - l1_reg: Optional[float] = None, l2_reg: float = 0.0, max_iterations: int = 20, - warm_start: bool = False, - learning_rate: Optional[float] = None, - learning_rate_strategy: Literal["line_search", "constant"] = "line_search", - tol: float = 0.01, - ls_init_learning_rate: Optional[float] = None, + learn_rate_strategy: Literal["line_search", "constant"] = "line_search", + early_stop: bool = True, + min_rel_progress: float = 0.01, + ls_init_learn_rate: float = 0.1, calculate_p_values: bool = False, enable_global_explain: bool = False, ): self.optimize_strategy = optimize_strategy self.fit_intercept = fit_intercept - self.l1_reg = l1_reg self.l2_reg = l2_reg self.max_iterations = max_iterations - self.warm_start = warm_start - self.learning_rate = learning_rate - self.learning_rate_strategy = learning_rate_strategy - self.tol = tol - self.ls_init_learning_rate = ls_init_learning_rate + self.learn_rate_strategy = learn_rate_strategy + self.early_stop = early_stop + self.min_rel_progress = min_rel_progress + self.ls_init_learn_rate = ls_init_learn_rate self.calculate_p_values = calculate_p_values self.enable_global_explain = enable_global_explain self._bqml_model: Optional[core.BqmlModel] = None @@ -87,151 +84,87 @@ def __init__( @classmethod def _from_bq( - cls, session: bigframes.session.Session, bq_model: bigquery.Model + cls, session: bigframes.Session, model: bigquery.Model ) -> LinearRegression: - assert bq_model.model_type == "LINEAR_REGRESSION" + assert model.model_type == "LINEAR_REGRESSION" - kwargs = utils.retrieve_params_from_bq_model( - cls, bq_model, _BQML_PARAMS_MAPPING - ) + # TODO(bmil): construct a standard way to extract these properties + kwargs = {} - model = cls(**kwargs) - model._bqml_model = core.BqmlModel(session, bq_model) - return model + # See https://cloud.google.com/bigquery/docs/reference/rest/v2/models#trainingrun + last_fitting = model.training_runs[-1]["trainingOptions"] + + dummy_linear = cls() + for bf_param, bf_value in dummy_linear.__dict__.items(): + bqml_param = _BQML_PARAMS_MAPPING.get(bf_param) + if bqml_param in last_fitting: + kwargs[bf_param] = type(bf_value)(last_fitting[bqml_param]) + + new_linear_regression = cls(**kwargs) + new_linear_regression._bqml_model = core.BqmlModel(session, model) + return new_linear_regression @property - def _bqml_options(self) -> dict: + def _bqml_options(self) -> Dict[str, str | int | bool | float | List[str]]: """The model options as they will be set for BQML""" - options = { + # TODO: Support l1_reg, warm_start, and learn_rate with error catching. + return { "model_type": "LINEAR_REG", "data_split_method": "NO_SPLIT", "optimize_strategy": self.optimize_strategy, "fit_intercept": self.fit_intercept, "l2_reg": self.l2_reg, "max_iterations": self.max_iterations, - "learn_rate_strategy": self.learning_rate_strategy, - "min_rel_progress": self.tol, + "learn_rate_strategy": self.learn_rate_strategy, + "early_stop": self.early_stop, + "min_rel_progress": self.min_rel_progress, + "ls_init_learn_rate": self.ls_init_learn_rate, "calculate_p_values": self.calculate_p_values, "enable_global_explain": self.enable_global_explain, } - if self.l1_reg is not None: - options["l1_reg"] = self.l1_reg - if self.learning_rate is not None: - options["learn_rate"] = self.learning_rate - if self.ls_init_learning_rate is not None: - options["ls_init_learn_rate"] = self.ls_init_learning_rate - # Even presenting warm_start returns error for NORMAL_EQUATION optimizer - if self.warm_start: - options["warm_start"] = self.warm_start - - return options def _fit( self, - X: utils.ArrayType, - y: utils.ArrayType, + X: Union[bpd.DataFrame, bpd.Series], + y: Union[bpd.DataFrame, bpd.Series], transforms: Optional[List[str]] = None, - X_eval: Optional[utils.ArrayType] = None, - y_eval: Optional[utils.ArrayType] = None, ) -> LinearRegression: - X, y = utils.batch_convert_to_dataframe(X, y) - - bqml_options = self._bqml_options - - if X_eval is not None and y_eval is not None: - X_eval, y_eval = utils.batch_convert_to_dataframe(X_eval, y_eval) - X, y, bqml_options = utils.combine_training_and_evaluation_data( - X, y, X_eval, y_eval, bqml_options - ) + X, y = utils.convert_to_dataframe(X, y) self._bqml_model = self._bqml_model_factory.create_model( X, y, transforms=transforms, - options=bqml_options, + options=self._bqml_options, ) return self - def predict(self, X: utils.ArrayType) -> bpd.DataFrame: - if not self._bqml_model: - raise RuntimeError("A model must be fitted before predict") - (X,) = utils.batch_convert_to_dataframe(X, session=self._bqml_model.session) - - return self._bqml_model.predict(X) - - def predict_explain( - self, - X: utils.ArrayType, - *, - top_k_features: int = 5, - ) -> bpd.DataFrame: - """ - Explain predictions for a linear regression model. - - .. note:: - Output matches that of the BigQuery ML.EXPLAIN_PREDICT function. - See: https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-explain-predict - - Args: - X (bigframes.dataframe.DataFrame or bigframes.series.Series or - pandas.core.frame.DataFrame or pandas.core.series.Series): - Series or a DataFrame to explain its predictions. - top_k_features (int, default 5): - an INT64 value that specifies how many top feature attribution - pairs are generated for each row of input data. The features are - ranked by the absolute values of their attributions. - - By default, top_k_features is set to 5. If its value is greater - than the number of features in the training data, the - attributions of all features are returned. - - Returns: - bigframes.pandas.DataFrame: - The predicted DataFrames with explanation columns. - """ - if top_k_features < 1: - raise ValueError( - f"top_k_features must be at least 1, but is {top_k_features}." - ) - + def predict(self, X: Union[bpd.DataFrame, bpd.Series]) -> bpd.DataFrame: if not self._bqml_model: raise RuntimeError("A model must be fitted before predict") - (X,) = utils.batch_convert_to_dataframe(X, session=self._bqml_model.session) - - return self._bqml_model.explain_predict( - X, options={"top_k_features": top_k_features} + (X,) = utils.convert_to_dataframe(X) + + df = self._bqml_model.predict(X) + return cast( + bpd.DataFrame, + df[ + [ + cast(str, field.name) + for field in self._bqml_model.model.label_columns + ] + ], ) - def global_explain( - self, - ) -> bpd.DataFrame: - """ - Provide explanations for an entire linear regression model. - - .. note:: - Output matches that of the BigQuery ML.GLOBAL_EXPLAIN function. - See: https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-global-explain - - Returns: - bigframes.pandas.DataFrame: - Dataframes containing feature importance values and corresponding attributions, designed to provide a global explanation of feature influence. - """ - - if not self._bqml_model: - raise RuntimeError("A model must be fitted before predict") - - return self._bqml_model.global_explain({}) - def score( self, - X: utils.ArrayType, - y: utils.ArrayType, + X: Union[bpd.DataFrame, bpd.Series], + y: Union[bpd.DataFrame, bpd.Series], ) -> bpd.DataFrame: if not self._bqml_model: raise RuntimeError("A model must be fitted before score") - X, y = utils.batch_convert_to_dataframe(X, y, session=self._bqml_model.session) + X, y = utils.convert_to_dataframe(X, y) input_data = X.join(y, how="outer") return self._bqml_model.evaluate(input_data) @@ -241,12 +174,12 @@ def to_gbq(self, model_name: str, replace: bool = False) -> LinearRegression: Args: model_name (str): - The name of the model. + the name of the model. replace (bool, default False): - Determine whether to replace if the model already exists. Default to False. + whether to replace if the model already exists. Default to False. Returns: - LinearRegression: Saved model.""" + LinearRegression: saved model.""" if not self._bqml_model: raise RuntimeError("A model must be fitted before it can be saved") @@ -254,193 +187,106 @@ def to_gbq(self, model_name: str, replace: bool = False) -> LinearRegression: return new_model.session.read_gbq_model(model_name) -@log_adapter.class_logger class LogisticRegression( - base.SupervisedTrainableWithEvaluationPredictor, - bigframes_vendored.sklearn.linear_model._logistic.LogisticRegression, + base.SupervisedTrainablePredictor, + third_party.bigframes_vendored.sklearn.linear_model._logistic.LogisticRegression, ): __doc__ = ( - bigframes_vendored.sklearn.linear_model._logistic.LogisticRegression.__doc__ + third_party.bigframes_vendored.sklearn.linear_model._logistic.LogisticRegression.__doc__ ) - # TODO(ashleyxu) support class_weight in the constructor. + # TODO(ashleyxu) support class_weights in the constructor. def __init__( self, - *, - optimize_strategy: Literal[ - "auto_strategy", "batch_gradient_descent" - ] = "auto_strategy", fit_intercept: bool = True, - l1_reg: Optional[float] = None, - l2_reg: float = 0.0, - max_iterations: int = 20, - warm_start: bool = False, - learning_rate: Optional[float] = None, - learning_rate_strategy: Literal["line_search", "constant"] = "line_search", - tol: float = 0.01, - ls_init_learning_rate: Optional[float] = None, - calculate_p_values: bool = False, - enable_global_explain: bool = False, - class_weight: Optional[Union[Literal["balanced"], Dict[str, float]]] = None, + class_weights: Optional[Union[Literal["balanced"], Dict[str, float]]] = None, ): - self.optimize_strategy = optimize_strategy self.fit_intercept = fit_intercept - self.l1_reg = l1_reg - self.l2_reg = l2_reg - self.max_iterations = max_iterations - self.warm_start = warm_start - self.learning_rate = learning_rate - self.learning_rate_strategy = learning_rate_strategy - self.tol = tol - self.ls_init_learning_rate = ls_init_learning_rate - self.calculate_p_values = calculate_p_values - self.enable_global_explain = enable_global_explain - self.class_weight = class_weight - self._auto_class_weight = class_weight == "balanced" + self.class_weights = class_weights + self._auto_class_weight = class_weights == "balanced" self._bqml_model: Optional[core.BqmlModel] = None self._bqml_model_factory = globals.bqml_model_factory() @classmethod def _from_bq( - cls, session: bigframes.session.Session, bq_model: bigquery.Model + cls, session: bigframes.Session, model: bigquery.Model ) -> LogisticRegression: - assert bq_model.model_type == "LOGISTIC_REGRESSION" + assert model.model_type == "LOGISTIC_REGRESSION" - kwargs = utils.retrieve_params_from_bq_model( - cls, bq_model, _BQML_PARAMS_MAPPING - ) + kwargs = {} - last_fitting = bq_model.training_runs[-1]["trainingOptions"] + # See https://cloud.google.com/bigquery/docs/reference/rest/v2/models#trainingrun + last_fitting = model.training_runs[-1]["trainingOptions"] + if "fitIntercept" in last_fitting: + kwargs["fit_intercept"] = last_fitting["fitIntercept"] if last_fitting["autoClassWeights"]: - kwargs["class_weight"] = "balanced" - # TODO(ashleyxu) support class_weight in the constructor. + kwargs["class_weights"] = "balanced" + # TODO(ashleyxu) support class_weights in the constructor. # if "labelClassWeights" in last_fitting: - # kwargs["class_weight"] = last_fitting["labelClassWeights"] + # kwargs["class_weights"] = last_fitting["labelClassWeights"] - model = cls(**kwargs) - model._bqml_model = core.BqmlModel(session, bq_model) - return model + new_logistic_regression = cls(**kwargs) + new_logistic_regression._bqml_model = core.BqmlModel(session, model) + return new_logistic_regression @property - def _bqml_options(self) -> dict: + def _bqml_options(self) -> Dict[str, str | int | float | List[str]]: """The model options as they will be set for BQML""" - options = { + return { "model_type": "LOGISTIC_REG", "data_split_method": "NO_SPLIT", "fit_intercept": self.fit_intercept, "auto_class_weights": self._auto_class_weight, - "optimize_strategy": self.optimize_strategy, - "l2_reg": self.l2_reg, - "max_iterations": self.max_iterations, - "learn_rate_strategy": self.learning_rate_strategy, - "min_rel_progress": self.tol, - "calculate_p_values": self.calculate_p_values, - "enable_global_explain": self.enable_global_explain, - # TODO(ashleyxu): support class_weight (struct array as dict in our API) - # "class_weight": self.class_weight, + # TODO(ashleyxu): support class_weights (struct array as dict in our API) + # "class_weights": self.class_weights, } - if self.l1_reg is not None: - options["l1_reg"] = self.l1_reg - if self.learning_rate is not None: - options["learn_rate"] = self.learning_rate - if self.ls_init_learning_rate is not None: - options["ls_init_learn_rate"] = self.ls_init_learning_rate - # Even presenting warm_start returns error for NORMAL_EQUATION optimizer - if self.warm_start: - options["warm_start"] = self.warm_start - - return options def _fit( self, - X: utils.ArrayType, - y: utils.ArrayType, + X: Union[bpd.DataFrame, bpd.Series], + y: Union[bpd.DataFrame, bpd.Series], transforms: Optional[List[str]] = None, - X_eval: Optional[utils.ArrayType] = None, - y_eval: Optional[utils.ArrayType] = None, ) -> LogisticRegression: - X, y = utils.batch_convert_to_dataframe(X, y) - - bqml_options = self._bqml_options - - if X_eval is not None and y_eval is not None: - X_eval, y_eval = utils.batch_convert_to_dataframe(X_eval, y_eval) - X, y, bqml_options = utils.combine_training_and_evaluation_data( - X, y, X_eval, y_eval, bqml_options - ) + """Fit model with transforms.""" + X, y = utils.convert_to_dataframe(X, y) self._bqml_model = self._bqml_model_factory.create_model( X, y, transforms=transforms, - options=bqml_options, + options=self._bqml_options, ) return self def predict( self, - X: utils.ArrayType, + X: Union[bpd.DataFrame, bpd.Series], ) -> bpd.DataFrame: if not self._bqml_model: raise RuntimeError("A model must be fitted before predict") - (X,) = utils.batch_convert_to_dataframe(X, session=self._bqml_model.session) - - return self._bqml_model.predict(X) - - def predict_explain( - self, - X: utils.ArrayType, - *, - top_k_features: int = 5, - ) -> bpd.DataFrame: - """ - Explain predictions for a logistic regression model. - - .. note:: - Output matches that of the BigQuery ML.EXPLAIN_PREDICT function. - See: https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-explain-predict - - Args: - X (bigframes.dataframe.DataFrame or bigframes.series.Series or - pandas.core.frame.DataFrame or pandas.core.series.Series): - Series or a DataFrame to explain its predictions. - top_k_features (int, default 5): - an INT64 value that specifies how many top feature attribution - pairs are generated for each row of input data. The features are - ranked by the absolute values of their attributions. - - By default, top_k_features is set to 5. If its value is greater - than the number of features in the training data, the - attributions of all features are returned. - - Returns: - bigframes.pandas.DataFrame: - The predicted DataFrames with explanation columns. - """ - if top_k_features < 1: - raise ValueError( - f"top_k_features must be at least 1, but is {top_k_features}." - ) - - if not self._bqml_model: - raise RuntimeError("A model must be fitted before predict") - - (X,) = utils.batch_convert_to_dataframe(X, session=self._bqml_model.session) - - return self._bqml_model.explain_predict( - X, options={"top_k_features": top_k_features} + (X,) = utils.convert_to_dataframe(X) + + df = self._bqml_model.predict(X) + return cast( + bpd.DataFrame, + df[ + [ + cast(str, field.name) + for field in self._bqml_model.model.label_columns + ] + ], ) def score( self, - X: utils.ArrayType, - y: utils.ArrayType, + X: Union[bpd.DataFrame, bpd.Series], + y: Union[bpd.DataFrame, bpd.Series], ) -> bpd.DataFrame: if not self._bqml_model: raise RuntimeError("A model must be fitted before score") - X, y = utils.batch_convert_to_dataframe(X, y, session=self._bqml_model.session) + X, y = utils.convert_to_dataframe(X, y) input_data = X.join(y, how="outer") return self._bqml_model.evaluate(input_data) @@ -450,19 +296,19 @@ def to_gbq(self, model_name: str, replace: bool = False) -> LogisticRegression: Args: model_name (str): - The name of the model. + the name of the model. replace (bool, default False): - Determine whether to replace if the model already exists. Default to False. + whether to replace if the model already exists. Default to False. Returns: - LogisticRegression: Saved model.""" + LogisticRegression: saved model.""" if not self._bqml_model: raise RuntimeError("A model must be fitted before it can be saved") - # TODO(ashleyxu): support class_weight (struct array as dict in our API) - if self.class_weight not in (None, "balanced"): + # TODO(ashleyxu): support class_weights (struct array as dict in our API) + if self.class_weights not in (None, "balanced"): raise NotImplementedError( - f"class_weight is not supported yet. {constants.FEEDBACK_LINK}" + f"class_weights is not supported yet. {constants.FEEDBACK_LINK}" ) new_model = self._bqml_model.copy(model_name, replace) diff --git a/bigframes/ml/llm.py b/bigframes/ml/llm.py index e99c7a41d00..3cfc28e61f8 100644 --- a/bigframes/ml/llm.py +++ b/bigframes/ml/llm.py @@ -16,1063 +16,279 @@ from __future__ import annotations -import typing -import warnings -from typing import Iterable, Literal, Mapping, Optional, Union +from typing import cast, Literal, Optional, Union -import bigframes_vendored.constants as constants -from google.cloud import bigquery - -import bigframes.bigquery as bbq -import bigframes.dataframe -import bigframes.series -from bigframes import dtypes, exceptions -from bigframes.core import blocks, global_session -from bigframes.core.logging import log_adapter +import bigframes +from bigframes import clients, constants +from bigframes.core import blocks from bigframes.ml import base, core, globals, utils +import bigframes.pandas as bpd -_BQML_PARAMS_MAPPING = { - "max_iterations": "maxIterations", -} - -_TEXT_EMBEDDING_005_ENDPOINT = "text-embedding-005" -_TEXT_EMBEDDING_004_ENDPOINT = "text-embedding-004" -_TEXT_MULTILINGUAL_EMBEDDING_002_ENDPOINT = "text-multilingual-embedding-002" -_TEXT_EMBEDDING_ENDPOINTS = ( - _TEXT_EMBEDDING_005_ENDPOINT, - _TEXT_EMBEDDING_004_ENDPOINT, - _TEXT_MULTILINGUAL_EMBEDDING_002_ENDPOINT, -) - -_MULTIMODAL_EMBEDDING_001_ENDPOINT = "multimodalembedding@001" - -_GEMINI_1P5_PRO_PREVIEW_ENDPOINT = "gemini-1.5-pro-preview-0514" -_GEMINI_1P5_PRO_FLASH_PREVIEW_ENDPOINT = "gemini-1.5-flash-preview-0514" -_GEMINI_1P5_PRO_001_ENDPOINT = "gemini-1.5-pro-001" -_GEMINI_1P5_PRO_002_ENDPOINT = "gemini-1.5-pro-002" -_GEMINI_1P5_FLASH_001_ENDPOINT = "gemini-1.5-flash-001" -_GEMINI_1P5_FLASH_002_ENDPOINT = "gemini-1.5-flash-002" -_GEMINI_2_FLASH_EXP_ENDPOINT = "gemini-2.0-flash-exp" -_GEMINI_2_FLASH_001_ENDPOINT = "gemini-2.0-flash-001" -_GEMINI_2_FLASH_LITE_001_ENDPOINT = "gemini-2.0-flash-lite-001" -_GEMINI_2P5_PRO_PREVIEW_ENDPOINT = "gemini-2.5-pro-preview-05-06" -_GEMINI_2P5_PRO_ENDPOINT = "gemini-2.5-pro" -_GEMINI_2P5_FLASH_ENDPOINT = "gemini-2.5-flash" -_GEMINI_2P5_FLASH_LITE_ENDPOINT = "gemini-2.5-flash-lite" -_GEMINI_3P1_FLASH_LITE_ENDPOINT = "gemini-3.1-flash-lite" -_GEMINI_3P5_FLASH_ENDPOINT = "gemini-3.5-flash" - -_GEMINI_ENDPOINTS = ( - _GEMINI_1P5_PRO_PREVIEW_ENDPOINT, - _GEMINI_1P5_PRO_FLASH_PREVIEW_ENDPOINT, - _GEMINI_1P5_PRO_001_ENDPOINT, - _GEMINI_1P5_PRO_002_ENDPOINT, - _GEMINI_1P5_FLASH_001_ENDPOINT, - _GEMINI_1P5_FLASH_002_ENDPOINT, - _GEMINI_2_FLASH_EXP_ENDPOINT, - _GEMINI_2_FLASH_001_ENDPOINT, - _GEMINI_2_FLASH_LITE_001_ENDPOINT, - _GEMINI_2P5_PRO_ENDPOINT, - _GEMINI_2P5_FLASH_ENDPOINT, - _GEMINI_2P5_FLASH_LITE_ENDPOINT, - _GEMINI_3P1_FLASH_LITE_ENDPOINT, - _GEMINI_3P5_FLASH_ENDPOINT, -) -_GEMINI_PREVIEW_ENDPOINTS = ( - _GEMINI_1P5_PRO_PREVIEW_ENDPOINT, - _GEMINI_1P5_PRO_FLASH_PREVIEW_ENDPOINT, - _GEMINI_2_FLASH_EXP_ENDPOINT, -) -_GEMINI_FINE_TUNE_SCORE_ENDPOINTS = ( - _GEMINI_1P5_PRO_002_ENDPOINT, - _GEMINI_1P5_FLASH_002_ENDPOINT, - _GEMINI_2_FLASH_001_ENDPOINT, - _GEMINI_2_FLASH_LITE_001_ENDPOINT, -) -_GEMINI_MULTIMODAL_ENDPOINTS = ( - _GEMINI_1P5_PRO_001_ENDPOINT, - _GEMINI_1P5_PRO_002_ENDPOINT, - _GEMINI_1P5_FLASH_001_ENDPOINT, - _GEMINI_1P5_FLASH_002_ENDPOINT, - _GEMINI_2_FLASH_EXP_ENDPOINT, - _GEMINI_2_FLASH_001_ENDPOINT, - _GEMINI_2_FLASH_LITE_001_ENDPOINT, - _GEMINI_2P5_PRO_ENDPOINT, - _GEMINI_2P5_FLASH_ENDPOINT, - _GEMINI_2P5_FLASH_LITE_ENDPOINT, - _GEMINI_3P1_FLASH_LITE_ENDPOINT, - _GEMINI_3P5_FLASH_ENDPOINT, -) - -_CLAUDE_3_SONNET_ENDPOINT = "claude-3-sonnet" -_CLAUDE_3_HAIKU_ENDPOINT = "claude-3-haiku" -_CLAUDE_3_5_SONNET_ENDPOINT = "claude-3-5-sonnet" -_CLAUDE_3_OPUS_ENDPOINT = "claude-3-opus" -_CLAUDE_3_ENDPOINTS = ( - _CLAUDE_3_SONNET_ENDPOINT, - _CLAUDE_3_HAIKU_ENDPOINT, - _CLAUDE_3_5_SONNET_ENDPOINT, - _CLAUDE_3_OPUS_ENDPOINT, -) - -_MODEL_NOT_SUPPORTED_WARNING = ( - "Model name '{model_name}' is not supported. " - "We are currently aware of the following models: {known_models}. " - "However, model names can change, and the supported models may be outdated. " - "You should use this model name only if you are sure that it is supported in BigQuery." -) - -_REMOVE_DEFAULT_MODEL_WARNING = "Since upgrading the default model can cause unintended breakages, the default model will be removed in BigFrames 3.0. Please supply an explicit model to avoid this message." +_REMOTE_TEXT_GENERATOR_MODEL_ENDPOINT = "text-bison" +_REMOTE_TEXT_GENERATOR_32K_MODEL_ENDPOINT = "text-bison-32k" +_TEXT_GENERATE_RESULT_COLUMN = "ml_generate_text_llm_result" -_GEMINI_MULTIMODAL_MODEL_NOT_SUPPORTED_WARNING = ( - "The model '{model_name}' may not be fully supported by GeminiTextGenerator for Multimodal prompts. " - "GeminiTextGenerator is known to support the following models for Multimodal prompts: {known_models}. " - "If you proceed with '{model_name}', it might not work as expected or could lead to errors with multimodal inputs." +_REMOTE_EMBEDDING_GENERATOR_MODEL_ENDPOINT = "textembedding-gecko" +_REMOTE_EMBEDDING_GENERATOR_MUlTILINGUAL_MODEL_ENDPOINT = ( + "textembedding-gecko-multilingual" ) +_EMBED_TEXT_RESULT_COLUMN = "text_embedding" -_MODEL_DEPRECATE_WARNING = ( - "'{model_name}' is going to be deprecated. Use '{new_model_name}' ({link}) instead." -) - - -@log_adapter.class_logger -class TextEmbeddingGenerator(base.RetriableRemotePredictor): - """Text embedding generator LLM model. - .. note:: - text-embedding-004 is going to be deprecated. Use text-embedding-005(https://cloud.google.com/python/docs/reference/bigframes/latest/bigframes.ml.llm.TextEmbeddingGenerator) instead. +class PaLM2TextGenerator(base.Predictor): + """PaLM2 text generator LLM model. Args: - model_name (str, Default to "text-embedding-004"): - The model for text embedding. Possible values are "text-embedding-005", "text-embedding-004" - or "text-multilingual-embedding-002". text-embedding models returns model embeddings for text inputs. - text-multilingual-embedding models returns model embeddings for text inputs which support over 100 languages. - If no setting is provided, "text-embedding-004" will be used by - default and a warning will be issued. + model_name (str, Default to "text-bison"): + The model for natural language tasks. “text-bison” returns model fine-tuned to follow natural language instructions + and is suitable for a variety of language tasks. "text-bison-32k" supports up to 32k tokens per request. + Default to "text-bison". session (bigframes.Session or None): BQ session to create the model. If None, use the global default session. connection_name (str or None): Connection to connect with remote service. str of the format ... - If None, use default connection in session context. + if None, use default connection in session context. BigQuery DataFrame will try to create the connection and attach + permission if the connection isn't fully setup. """ def __init__( self, - *, - model_name: Optional[ - Literal[ - "text-embedding-005", - "text-embedding-004", - "text-multilingual-embedding-002", - ] - ] = None, + model_name: Literal["text-bison", "text-bison-32k"] = "text-bison", session: Optional[bigframes.Session] = None, connection_name: Optional[str] = None, ): - if model_name is None: - model_name = "text-embedding-004" - msg = exceptions.format_message(_REMOVE_DEFAULT_MODEL_WARNING) - warnings.warn(msg, category=FutureWarning, stacklevel=2) self.model_name = model_name - self.session = session or global_session.get_global_session() - self.connection_name = connection_name - - self._bqml_model_factory = globals.bqml_model_factory() - self._bqml_model: core.BqmlModel = self._create_bqml_model() - - def _create_bqml_model(self): - # Parse and create connection if needed. - self.connection_name = self.session._create_bq_connection( - connection=self.connection_name, iam_role="aiplatform.user" + self.session = session or bpd.get_global_session() + self._bq_connection_manager = clients.BqConnectionManager( + self.session.bqconnectionclient, self.session.resourcemanagerclient ) - if self.model_name not in _TEXT_EMBEDDING_ENDPOINTS: - msg = exceptions.format_message( - _MODEL_NOT_SUPPORTED_WARNING.format( - model_name=self.model_name, - known_models=", ".join(_TEXT_EMBEDDING_ENDPOINTS), - ) - ) - warnings.warn(msg) - if self.model_name == "text-embedding-004": - msg = exceptions.format_message( - _MODEL_DEPRECATE_WARNING.format( - model_name=self.model_name, - new_model_name="text-embedding-005", - link="https://cloud.google.com/python/docs/reference/bigframes/latest/bigframes.ml.llm.TextEmbeddingGenerator", - ) - ) - warnings.warn(msg) - - options = { - "endpoint": self.model_name, - } - return self._bqml_model_factory.create_remote_model( - session=self.session, connection_name=self.connection_name, options=options - ) - - @classmethod - def _from_bq( - cls, session: bigframes.Session, bq_model: bigquery.Model - ) -> TextEmbeddingGenerator: - assert bq_model.model_type == "MODEL_TYPE_UNSPECIFIED" - assert "remoteModelInfo" in bq_model._properties - assert "endpoint" in bq_model._properties["remoteModelInfo"] - assert "connection" in bq_model._properties["remoteModelInfo"] - - # Parse the remote model endpoint - bqml_endpoint = bq_model._properties["remoteModelInfo"]["endpoint"] - model_connection = bq_model._properties["remoteModelInfo"]["connection"] - model_endpoint = bqml_endpoint.split("/")[-1] - - model = cls( - session=session, - model_name=model_endpoint, # type: ignore - connection_name=model_connection, + connection_name = connection_name or self.session._bq_connection + self.connection_name = self._bq_connection_manager.resolve_full_connection_name( + connection_name, + default_project=self.session._project, + default_location=self.session._location, ) - model._bqml_model = core.BqmlModel(session, bq_model) - return model - - def predict( - self, X: utils.ArrayType, *, max_retries: int = 0 - ) -> bigframes.dataframe.DataFrame: - """Predict the result from input DataFrame. - - Args: - X (bigframes.dataframe.DataFrame or bigframes.series.Series or pandas.core.frame.DataFrame or pandas.core.series.Series): - Input DataFrame or Series, can contain one or more columns. If multiple columns are in the DataFrame, it must contain a "content" column for prediction. - - max_retries (int, default 0): - Max number of retries if the prediction for any rows failed. Each try needs to make progress (i.e. has successfully predicted rows) to continue the retry. - Each retry will append newly succeeded rows. When the max retries are reached, the remaining rows (the ones without successful predictions) will be appended to the end of the result. - - Returns: - bigframes.dataframe.DataFrame: DataFrame of shape (n_samples, n_input_columns + n_prediction_columns). Returns predicted values. - """ - if max_retries < 0: - raise ValueError( - f"max_retries must be larger than or equal to 0, but is {max_retries}." - ) - - (X,) = utils.batch_convert_to_dataframe(X, session=self._bqml_model.session) - - if len(X.columns) == 1: - # BQML identified the column by name - col_label = typing.cast(blocks.Label, X.columns[0]) - X = X.rename(columns={col_label: "content"}) - - options: dict = {} - - return self._predict_and_retry( - core.BqmlModel.generate_embedding_tvf, - X, - options=options, - max_retries=max_retries, - ) - - def to_gbq(self, model_name: str, replace: bool = False) -> TextEmbeddingGenerator: - """Save the model to BigQuery. - - Args: - model_name (str): - The name of the model. - replace (bool, default False): - Determine whether to replace if the model already exists. Default to False. - - Returns: - TextEmbeddingGenerator: Saved model.""" - - new_model = self._bqml_model.copy(model_name, replace) - return new_model.session.read_gbq_model(model_name) - - -@log_adapter.class_logger -class MultimodalEmbeddingGenerator(base.RetriableRemotePredictor): - """Multimodal embedding generator LLM model. - - .. note:: - BigFrames ObjectRef is subject to the "Pre-GA Offerings Terms" in the General Service Terms section of the - Service Specific Terms(https://cloud.google.com/terms/service-terms#1). Pre-GA products and features are available "as is" - and might have limited support. For more information, see the launch stage descriptions - (https://cloud.google.com/products#product-launch-stages). - - Args: - model_name (str, Default to "multimodalembedding@001"): - The model for multimodal embedding. Can set to "multimodalembedding@001". Multimodal-embedding models returns model embeddings for text, image and video inputs. - If no setting is provided, "multimodalembedding@001" will be used by - default and a warning will be issued. - session (bigframes.Session or None): - BQ session to create the model. If None, use the global default session. - connection_name (str or None): - Connection to connect with remote service. str of the format ... - If None, use default connection in session context. - """ - - def __init__( - self, - *, - model_name: Optional[Literal["multimodalembedding@001"]] = None, - session: Optional[bigframes.Session] = None, - connection_name: Optional[str] = None, - ): - if model_name is None: - model_name = "multimodalembedding@001" - msg = exceptions.format_message(_REMOVE_DEFAULT_MODEL_WARNING) - warnings.warn(msg, category=FutureWarning, stacklevel=2) - self.model_name = model_name - self.session = session or global_session.get_global_session() - self.connection_name = connection_name - self._bqml_model_factory = globals.bqml_model_factory() self._bqml_model: core.BqmlModel = self._create_bqml_model() def _create_bqml_model(self): # Parse and create connection if needed. - self.connection_name = self.session._create_bq_connection( - connection=self.connection_name, iam_role="aiplatform.user" - ) - - if self.model_name != _MULTIMODAL_EMBEDDING_001_ENDPOINT: - msg = exceptions.format_message( - _MODEL_NOT_SUPPORTED_WARNING.format( - model_name=self.model_name, - known_models=_MULTIMODAL_EMBEDDING_001_ENDPOINT, - ) - ) - warnings.warn(msg) - - options = { - "endpoint": self.model_name, - } - return self._bqml_model_factory.create_remote_model( - session=self.session, connection_name=self.connection_name, options=options - ) - - @classmethod - def _from_bq( - cls, session: bigframes.Session, bq_model: bigquery.Model - ) -> MultimodalEmbeddingGenerator: - assert bq_model.model_type == "MODEL_TYPE_UNSPECIFIED" - assert "remoteModelInfo" in bq_model._properties - assert "endpoint" in bq_model._properties["remoteModelInfo"] - assert "connection" in bq_model._properties["remoteModelInfo"] - - # Parse the remote model endpoint - bqml_endpoint = bq_model._properties["remoteModelInfo"]["endpoint"] - model_connection = bq_model._properties["remoteModelInfo"]["connection"] - model_endpoint = bqml_endpoint.split("/")[-1] - - model = cls( - session=session, - model_name=model_endpoint, # type: ignore - connection_name=model_connection, - ) - - model._bqml_model = core.BqmlModel(session, bq_model) - return model - - def predict( - self, X: utils.ArrayType, *, max_retries: int = 0 - ) -> bigframes.dataframe.DataFrame: - """Predict the result from input DataFrame. - - Args: - X (bigframes.dataframe.DataFrame or bigframes.series.Series or pandas.core.frame.DataFrame or pandas.core.series.Series): - Input DataFrame or Series, can contain one or more columns. If multiple columns are in the DataFrame, it must contain a "content" column for prediction. - The content column must be of string type or BigFrames `ObjectRef `_ of image or video. - - max_retries (int, default 0): - Max number of retries if the prediction for any rows failed. Each try needs to make progress (i.e. has successfully predicted rows) to continue the retry. - Each retry will append newly succeeded rows. When the max retries are reached, the remaining rows (the ones without successful predictions) will be appended to the end of the result. - - Returns: - bigframes.dataframe.DataFrame: DataFrame of shape (n_samples, n_input_columns + n_prediction_columns). Returns predicted values. - """ - if max_retries < 0: + if not self.connection_name: raise ValueError( - f"max_retries must be larger than or equal to 0, but is {max_retries}." + "Must provide connection_name, either in constructor or through session options." ) - - (X,) = utils.batch_convert_to_dataframe(X, session=self._bqml_model.session) - - if len(X.columns) == 1: - # BQML identified the column by name - col_label = typing.cast(blocks.Label, X.columns[0]) - X = X.rename(columns={col_label: "content"}) - - # TODO(garrettwu): remove transform to ObjRefRuntime when BQML supports ObjRef as input - if X["content"].dtype == dtypes.OBJ_REF_DTYPE: - X["content"] = bbq.obj.get_access_url(X["content"], mode="r") - - options: dict = {} - - return self._predict_and_retry( - core.BqmlModel.generate_embedding_tvf, - X, - options=options, - max_retries=max_retries, - ) - - def to_gbq( - self, model_name: str, replace: bool = False - ) -> MultimodalEmbeddingGenerator: - """Save the model to BigQuery. - - Args: - model_name (str): - The name of the model. - replace (bool, default False): - Determine whether to replace if the model already exists. Default to False. - - Returns: - MultimodalEmbeddingGenerator: Saved model.""" - - new_model = self._bqml_model.copy(model_name, replace) - return new_model.session.read_gbq_model(model_name) - - -@log_adapter.class_logger -class GeminiTextGenerator(base.RetriableRemotePredictor): - """Gemini text generator LLM model. - - .. note:: - gemini-1.5-X are going to be deprecated. Use gemini-2.5-X (https://cloud.google.com/python/docs/reference/bigframes/latest/bigframes.ml.llm.GeminiTextGenerator) instead. - - Args: - model_name (str, Default to "gemini-2.0-flash-001"): - The model for natural language tasks. Accepted values are - "gemini-1.5-pro-preview-0514", "gemini-1.5-flash-preview-0514", - "gemini-1.5-pro-001", "gemini-1.5-pro-002", "gemini-1.5-flash-001", - "gemini-1.5-flash-002", "gemini-2.0-flash-exp", - "gemini-2.0-flash-lite-001", "gemini-2.0-flash-001", - "gemini-2.5-pro", "gemini-2.5-flash", "gemini-2.5-flash-lite", - "gemini-3.1-flash-lite" and "gemini-3.5-flash". - If no setting is provided, "gemini-2.0-flash-001" will be used by - default and a warning will be issued. - - .. note:: - "gemini-1.5-X" is going to be deprecated. Please use gemini-2.5-X instead. For example, "gemini-2.5-flash". - "gemini-2.0-flash-exp", "gemini-1.5-pro-preview-0514" and "gemini-1.5-flash-preview-0514" is subject to the "Pre-GA Offerings Terms" in the General Service Terms section of the - Service Specific Terms(https://cloud.google.com/terms/service-terms#1). Pre-GA products and features are available "as is" - and might have limited support. For more information, see the launch stage descriptions - (https://cloud.google.com/products#product-launch-stages). - - session (bigframes.Session or None): - BQ session to create the model. If None, use the global default session. - connection_name (str or None): - Connection to connect with remote service. str of the format ... - If None, use default connection in session context. BigQuery DataFrame will try to create the connection and attach - permission if the connection isn't fully set up. - max_iterations (Optional[int], Default to 300): - The number of steps to run when performing supervised tuning. - """ - - def __init__( - self, - *, - model_name: Optional[ - Literal[ - "gemini-1.5-pro-preview-0514", - "gemini-1.5-flash-preview-0514", - "gemini-1.5-pro-001", - "gemini-1.5-pro-002", - "gemini-1.5-flash-001", - "gemini-1.5-flash-002", - "gemini-2.0-flash-exp", - "gemini-2.0-flash-001", - "gemini-2.0-flash-lite-001", - "gemini-2.5-pro", - "gemini-2.5-flash", - "gemini-2.5-flash-lite", - "gemini-3.1-flash-lite", - "gemini-3.5-flash", - ] - ] = None, - session: Optional[bigframes.Session] = None, - connection_name: Optional[str] = None, - max_iterations: int = 300, - ): - if model_name in _GEMINI_PREVIEW_ENDPOINTS: - msg = exceptions.format_message( - f'Model {model_name} is subject to the "Pre-GA Offerings Terms" in ' - "the General Service Terms section of the Service Specific Terms" - "(https://cloud.google.com/terms/service-terms#1). Pre-GA products and " - 'features are available "as is" and might have limited support. For ' - "more information, see the launch stage descriptions " - "(https://cloud.google.com/products#product-launch-stages)." + connection_name_parts = self.connection_name.split(".") + if len(connection_name_parts) != 3: + raise ValueError( + f"connection_name must be of the format .., got {self.connection_name}." ) - warnings.warn(msg, category=exceptions.PreviewWarning) - - if model_name is None: - model_name = "gemini-2.0-flash-001" - msg = exceptions.format_message(_REMOVE_DEFAULT_MODEL_WARNING) - warnings.warn(msg, category=FutureWarning, stacklevel=2) - - self.model_name = model_name - self.session = session or global_session.get_global_session() - self.max_iterations = max_iterations - self.connection_name = connection_name - - self._bqml_model_factory = globals.bqml_model_factory() - self._bqml_model: core.BqmlModel = self._create_bqml_model() - - def _create_bqml_model(self): - # Parse and create connection if needed. - self.connection_name = self.session._create_bq_connection( - connection=self.connection_name, iam_role="aiplatform.user" + self._bq_connection_manager.create_bq_connection( + project_id=connection_name_parts[0], + location=connection_name_parts[1], + connection_id=connection_name_parts[2], + iam_role="aiplatform.user", ) - - if self.model_name not in _GEMINI_ENDPOINTS: - msg = exceptions.format_message( - _MODEL_NOT_SUPPORTED_WARNING.format( - model_name=self.model_name, - known_models=", ".join(_GEMINI_ENDPOINTS), - ) - ) - warnings.warn(msg) - if self.model_name.startswith("gemini-1.5"): - msg = exceptions.format_message( - _MODEL_DEPRECATE_WARNING.format( - model_name=self.model_name, - new_model_name="gemini-2.5-X", - link="https://cloud.google.com/python/docs/reference/bigframes/latest/bigframes.ml.llm.GeminiTextGenerator", - ) + if self.model_name == _REMOTE_TEXT_GENERATOR_MODEL_ENDPOINT: + options = { + "endpoint": _REMOTE_TEXT_GENERATOR_MODEL_ENDPOINT, + } + elif self.model_name == _REMOTE_TEXT_GENERATOR_32K_MODEL_ENDPOINT: + options = { + "endpoint": _REMOTE_TEXT_GENERATOR_32K_MODEL_ENDPOINT, + } + else: + raise ValueError( + f"Model name {self.model_name} is not supported. We only support {_REMOTE_TEXT_GENERATOR_MODEL_ENDPOINT} and {_REMOTE_TEXT_GENERATOR_32K_MODEL_ENDPOINT}." ) - warnings.warn(msg) - - options = {"endpoint": self.model_name} - return self._bqml_model_factory.create_remote_model( session=self.session, connection_name=self.connection_name, options=options ) - @classmethod - def _from_bq( - cls, session: bigframes.Session, bq_model: bigquery.Model - ) -> GeminiTextGenerator: - assert bq_model.model_type == "MODEL_TYPE_UNSPECIFIED" - assert "remoteModelInfo" in bq_model._properties - assert "endpoint" in bq_model._properties["remoteModelInfo"] - assert "connection" in bq_model._properties["remoteModelInfo"] - - # Parse the remote model endpoint - bqml_endpoint = bq_model._properties["remoteModelInfo"]["endpoint"] - model_connection = bq_model._properties["remoteModelInfo"]["connection"] - model_endpoint = bqml_endpoint.split("/")[-1] - - model = cls( - model_name=model_endpoint, session=session, connection_name=model_connection - ) - model._bqml_model = core.BqmlModel(session, bq_model) - return model - - @property - def _bqml_options(self) -> dict: - """The model options as they will be set for BQML""" - options = { - "max_iterations": self.max_iterations, - "data_split_method": "NO_SPLIT", - } - return options - - def fit( - self, - X: utils.ArrayType, - y: utils.ArrayType, - ) -> GeminiTextGenerator: - """Fine tune GeminiTextGenerator model. Only support "gemini-1.5-pro-002", - "gemini-1.5-flash-002", "gemini-2.0-flash-001", - and "gemini-2.0-flash-lite-001"models for now. - - .. note:: - - This product or feature is subject to the "Pre-GA Offerings Terms" in the General Service Terms section of the - Service Specific Terms(https://cloud.google.com/terms/service-terms#1). Pre-GA products and features are available "as is" - and might have limited support. For more information, see the launch stage descriptions - (https://cloud.google.com/products#product-launch-stages). - - Args: - X (bigframes.dataframe.DataFrame or bigframes.series.Series): - DataFrame of shape (n_samples, n_features). Training data. - y (bigframes.dataframe.DataFrame or bigframes.series.Series: - Training labels. - - Returns: - GeminiTextGenerator: Fitted estimator. - """ - if self.model_name not in _GEMINI_FINE_TUNE_SCORE_ENDPOINTS: - msg = exceptions.format_message( - "fit() only supports gemini-1.5-pro-002, gemini-1.5-flash-002, gemini-2.0-flash-001, or gemini-2.0-flash-lite-001 model." - ) - warnings.warn(msg) - - X, y = utils.batch_convert_to_dataframe(X, y) - - options = self._bqml_options - options["endpoint"] = self.model_name - options["prompt_col"] = X.columns.tolist()[0] - - self._bqml_model = self._bqml_model_factory.create_llm_remote_model( - X, - y, - options=options, - connection_name=typing.cast(str, self.connection_name), - ) - return self - def predict( self, - X: utils.ArrayType, - *, - temperature: float = 0.9, - max_output_tokens: int = 8192, + X: Union[bpd.DataFrame, bpd.Series], + temperature: float = 0.0, + max_output_tokens: int = 128, top_k: int = 40, - top_p: float = 1.0, - ground_with_google_search: bool = False, - max_retries: int = 0, - prompt: Optional[Iterable[Union[str, bigframes.series.Series]]] = None, - output_schema: Optional[Mapping[str, str]] = None, - ) -> bigframes.dataframe.DataFrame: + top_p: float = 0.95, + ) -> bpd.DataFrame: """Predict the result from input DataFrame. Args: - X (bigframes.dataframe.DataFrame or bigframes.series.Series or pandas.core.frame.DataFrame or pandas.core.series.Series): - Input DataFrame or Series, can contain one or more columns. If multiple columns are in the DataFrame, the "prompt" column, or created by "prompt" parameter, is used for prediction. + X (bigframes.dataframe.DataFrame or bigframes.series.Series): + Input DataFrame or Series, which needs to contain a column with name "prompt". Only the column will be used as input. Prompts can include preamble, questions, suggestions, instructions, or examples. - temperature (float, default 0.9): - The temperature is used for sampling during the response generation, which occurs when topP and topK are applied. Temperature controls the degree of randomness in token selection. Lower temperatures are good for prompts that require a more deterministic and less open-ended or creative response, while higher temperatures can lead to more diverse or creative results. A temperature of 0 is deterministic: the highest probability response is always selected. - Default 0.9. Possible values [0.0, 1.0]. + temperature (float, default 0.0): + The temperature is used for sampling during the response generation, which occurs when topP and topK are applied. + Temperature controls the degree of randomness in token selection. Lower temperatures are good for prompts that expect a true or correct response, + while higher temperatures can lead to more diverse or unexpected results. A temperature of 0 is deterministic: + the highest probability token is always selected. For most use cases, try starting with a temperature of 0.2. + Default 0. Possible values [0.0, 1.0]. - max_output_tokens (int, default 8192): - Maximum number of tokens that can be generated in the response. A token is approximately four characters. 100 tokens correspond to roughly 60-80 words. - Specify a lower value for shorter responses and a higher value for potentially longer responses. - Default 8192. Possible values are in the range [1, 8192]. + max_output_tokens (int, default 128): + Maximum number of tokens that can be generated in the response. Specify a lower value for shorter responses and a higher value for longer responses. + A token may be smaller than a word. A token is approximately four characters. 100 tokens correspond to roughly 60-80 words. + Default 128. Possible values [1, 1024]. top_k (int, default 40): - Top-K changes how the model selects tokens for output. A top-K of 1 means the next selected token is the most probable among all tokens in the model's vocabulary (also called greedy decoding), while a top-K of 3 means that the next token is selected from among the three most probable tokens by using temperature. - For each token selection step, the top-K tokens with the highest probabilities are sampled. Then tokens are further filtered based on top-P with the final token selected using temperature sampling. + Top-k changes how the model selects tokens for output. A top-k of 1 means the selected token is the most probable among all tokens + in the model's vocabulary (also called greedy decoding), while a top-k of 3 means that the next token is selected from among the 3 most probable tokens (using temperature). + For each token selection step, the top K tokens with the highest probabilities are sampled. Then tokens are further filtered based on topP with the final token selected using temperature sampling. Specify a lower value for less random responses and a higher value for more random responses. Default 40. Possible values [1, 40]. - top_p (float, default 0.95): - Top-P changes how the model selects tokens for output. Tokens are selected from the most (see top-K) to least probable until the sum of their probabilities equals the top-P value. For example, if tokens A, B, and C have a probability of 0.3, 0.2, and 0.1 and the top-P value is 0.5, then the model will select either A or B as the next token by using temperature and excludes C as a candidate. + top_p (float, default 0.95):: + Top-p changes how the model selects tokens for output. Tokens are selected from most K (see topK parameter) probable to least until the sum of their probabilities equals the top-p value. + For example, if tokens A, B, and C have a probability of 0.3, 0.2, and 0.1 and the top-p value is 0.5, then the model will select either A or B as the next token (using temperature) + and not consider C at all. Specify a lower value for less random responses and a higher value for more random responses. - Default 1.0. Possible values [0.0, 1.0]. - - ground_with_google_search (bool, default False): - Enables Grounding with Google Search for the Vertex AI model. When set - to True, the model incorporates relevant information from Google Search - results into its responses, enhancing their accuracy and factualness. - This feature provides an additional column, `ml_generate_text_grounding_result`, - in the response output, detailing the sources used for grounding. - Note: Using this feature may impact billing costs. Refer to the pricing - page for details: https://cloud.google.com/vertex-ai/generative-ai/pricing#google_models - The default is `False`. - - max_retries (int, default 0): - Max number of retries if the prediction for any rows failed. Each try needs to make progress (i.e. has successfully predicted rows) to continue the retry. - Each retry will append newly succeeded rows. When the max retries are reached, the remaining rows (the ones without successful predictions) will be appended to the end of the result. + Default 0.95. Possible values [0.0, 1.0]. - prompt (Iterable of str or bigframes.series.Series, or None, default None): - .. note:: - BigFrames ObjectRef is subject to the "Pre-GA Offerings Terms" in the General Service Terms section of the - Service Specific Terms(https://cloud.google.com/terms/service-terms#1). Pre-GA products and features are available "as is" - and might have limited support. For more information, see the launch stage descriptions - (https://cloud.google.com/products#product-launch-stages). - Construct a prompt struct column for prediction based on the input. The input must be an Iterable that can take string literals, - such as "summarize", string column(s) of X, such as X["str_col"], or `ObjectRef column(s) `_ of X, such as X["objectref_col"]. - It creates a struct column of the items of the iterable, and use the concatenated result as the input prompt. No-op if set to None. - output_schema (Mapping[str, str] or None, default None): - The schema used to generate structured output as a bigframes DataFrame. The schema is a string key-value pair of :. - Supported types are int64, float64, bool, string, array and struct. If None, output text result. Returns: - bigframes.dataframe.DataFrame: DataFrame of shape (n_samples, n_input_columns + n_prediction_columns). Returns predicted values. - """ + bigframes.dataframe.DataFrame: Output DataFrame with only 1 column as the output text results.""" # Params reference: https://cloud.google.com/vertex-ai/docs/generative-ai/learn/models if temperature < 0.0 or temperature > 1.0: raise ValueError(f"temperature must be [0.0, 1.0], but is {temperature}.") - - if max_output_tokens not in range(1, 8193): + if max_output_tokens not in range(1, 1025): raise ValueError( - f"max_output_token must be [1, 8192] for Gemini model, but is {max_output_tokens}." + f"max_output_token must be [1, 1024], but is {max_output_tokens}." ) - if top_k not in range(1, 41): raise ValueError(f"top_k must be [1, 40], but is {top_k}.") - if top_p < 0.0 or top_p > 1.0: raise ValueError(f"top_p must be [0.0, 1.0], but is {top_p}.") - if max_retries < 0: - raise ValueError( - f"max_retries must be larger than or equal to 0, but is {max_retries}." - ) - - session = self._bqml_model.session - (X,) = utils.batch_convert_to_dataframe(X, session=session) - - if prompt: - if self.model_name not in _GEMINI_MULTIMODAL_ENDPOINTS: - msg = exceptions.format_message( - _GEMINI_MULTIMODAL_MODEL_NOT_SUPPORTED_WARNING.format( - model_name=self.model_name, - known_models=", ".join(_GEMINI_MULTIMODAL_ENDPOINTS), - ) - ) - warnings.warn(msg) + (X,) = utils.convert_to_dataframe(X) - df_prompt = X[[X.columns[0]]].rename( - columns={X.columns[0]: "bigframes_placeholder_col"} + if len(X.columns) != 1: + raise ValueError( + f"Only support one column as input. {constants.FEEDBACK_LINK}" ) - for i, item in enumerate(prompt): - # must be distinct str column labels to construct a struct - if isinstance(item, str): - label = f"input_{i}" - else: # Series - label = f"input_{i}_{item.name}" - - # TODO(garrettwu): remove transform to ObjRefRuntime when BQML supports ObjRef as input - if ( - isinstance(item, bigframes.series.Series) - and item.dtype == dtypes.OBJ_REF_DTYPE - ): - item = bbq.obj.get_access_url(item, mode="r") - - df_prompt[label] = item - df_prompt = df_prompt.drop(columns="bigframes_placeholder_col") - X["prompt"] = bbq.struct(df_prompt) - if len(X.columns) == 1: - # BQML identified the column by name - col_label = typing.cast(blocks.Label, X.columns[0]) - X = X.rename(columns={col_label: "prompt"}) + # BQML identified the column by name + col_label = cast(blocks.Label, X.columns[0]) + X = X.rename(columns={col_label: "prompt"}) - options: dict = { + options = { "temperature": temperature, "max_output_tokens": max_output_tokens, - # "top_k": top_k, # TODO(garrettwu): the option is deprecated in Gemini 1.5 forward. + "top_k": top_k, "top_p": top_p, - "ground_with_google_search": ground_with_google_search, + "flatten_json_output": True, } - if output_schema: - output_schema = { - k: utils.standardize_type(v) for k, v in output_schema.items() - } - options["output_schema"] = output_schema - return self._predict_and_retry( - core.BqmlModel.generate_table_tvf, - X, - options=options, - max_retries=max_retries, - ) - - return self._predict_and_retry( - core.BqmlModel.generate_text_tvf, - X, - options=options, - max_retries=max_retries, + df = self._bqml_model.generate_text(X, options) + return cast( + bpd.DataFrame, + df[[_TEXT_GENERATE_RESULT_COLUMN]], ) - def score( - self, - X: utils.ArrayType, - y: utils.ArrayType, - task_type: Literal[ - "text_generation", "classification", "summarization", "question_answering" - ] = "text_generation", - ) -> bigframes.dataframe.DataFrame: - """Calculate evaluation metrics of the model. Only support - "gemini-1.5-pro-002", "gemini-1.5-flash-002", - "gemini-2.0-flash-lite-001", and "gemini-2.0-flash-001". - - .. note:: - - This product or feature is subject to the "Pre-GA Offerings Terms" in the General Service Terms section of the - Service Specific Terms(https://cloud.google.com/terms/service-terms#1). Pre-GA products and features are available "as is" - and might have limited support. For more information, see the launch stage descriptions - (https://cloud.google.com/products#product-launch-stages). - - .. note:: - - Output matches that of the BigQuery ML.EVALUATE function. - See: https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-evaluate#remote-model-llm - for the outputs relevant to this model type. - - Args: - X (bigframes.dataframe.DataFrame or bigframes.series.Series or pandas.core.frame.DataFrame or pandas.core.series.Series): - A BigQuery DataFrame as evaluation data, which contains only one column of input_text - that contains the prompt text to use when evaluating the model. - y (bigframes.dataframe.DataFrame or bigframes.series.Series or pandas.core.frame.DataFrame or pandas.core.series.Series): - A BigQuery DataFrame as evaluation labels, which contains only one column of output_text - that you would expect to be returned by the model. - task_type (str): - The type of the task for LLM model. Default to "text_generation". - Possible values: "text_generation", "classification", "summarization", and "question_answering". - - Returns: - bigframes.dataframe.DataFrame: The DataFrame as evaluation result. - """ - if not self._bqml_model: - raise RuntimeError("A model must be fitted before score") - - if self.model_name not in _GEMINI_FINE_TUNE_SCORE_ENDPOINTS: - msg = exceptions.format_message( - "score() only supports gemini-1.5-pro-002, gemini-1.5-flash-2, gemini-2.0-flash-001, and gemini-2.0-flash-lite-001 model." - ) - warnings.warn(msg) - - X, y = utils.batch_convert_to_dataframe(X, y, session=self._bqml_model.session) - - if len(X.columns) != 1 or len(y.columns) != 1: - raise ValueError( - f"Only support one column as input for X and y. {constants.FEEDBACK_LINK}" - ) - - # BQML identified the column by name - X_col_label = typing.cast(blocks.Label, X.columns[0]) - y_col_label = typing.cast(blocks.Label, y.columns[0]) - X = X.rename(columns={X_col_label: "input_text"}) - y = y.rename(columns={y_col_label: "output_text"}) - - input_data = X.join(y, how="outer") - - return self._bqml_model.llm_evaluate(input_data, task_type) - - def to_gbq(self, model_name: str, replace: bool = False) -> GeminiTextGenerator: - """Save the model to BigQuery. - - Args: - model_name (str): - The name of the model. - replace (bool, default False): - Determine whether to replace if the model already exists. Default to False. - - Returns: - GeminiTextGenerator: Saved model.""" - - new_model = self._bqml_model.copy(model_name, replace) - return new_model.session.read_gbq_model(model_name) - - -@log_adapter.class_logger -class Claude3TextGenerator(base.RetriableRemotePredictor): - """Claude3 text generator LLM model. - - Go to Google Cloud Console -> Vertex AI -> Model Garden page to enable the models before use. Must have the Consumer Procurement Entitlement Manager Identity and Access Management (IAM) role to enable the models. - https://cloud.google.com/vertex-ai/generative-ai/docs/partner-models/use-partner-models#grant-permissions - .. note:: - - This product or feature is subject to the "Pre-GA Offerings Terms" in the General Service Terms section of the - Service Specific Terms(https://cloud.google.com/terms/service-terms#1). Pre-GA products and features are available "as is" - and might have limited support. For more information, see the launch stage descriptions - (https://cloud.google.com/products#product-launch-stages). - - - .. note:: - - The models only available in specific regions. Check https://cloud.google.com/vertex-ai/generative-ai/docs/partner-models/use-claude#regions for details. - - .. note:: - - claude-3-sonnet model is deprecated. Use other models instead. +class PaLM2TextEmbeddingGenerator(base.Predictor): + """PaLM2 text embedding generator LLM model. Args: - model_name (str, Default to "claude-3-sonnet"): - The model for natural language tasks. Possible values are "claude-3-sonnet", "claude-3-haiku", "claude-3-5-sonnet" and "claude-3-opus". - "claude-3-sonnet" (deprecated) is Anthropic's dependable combination of skills and speed. It is engineered to be dependable for scaled AI deployments across a variety of use cases. - "claude-3-haiku" is Anthropic's fastest, most compact vision and text model for near-instant responses to simple queries, meant for seamless AI experiences mimicking human interactions. - "claude-3-5-sonnet" (deprecated) is Anthropic's most powerful AI model and maintains the speed and cost of Claude 3 Sonnet, which is a mid-tier model. - "claude-3-opus" (deprecated) is Anthropic's second-most powerful AI model, with strong performance on highly complex tasks. - https://cloud.google.com/vertex-ai/generative-ai/docs/partner-models/use-claude#available-claude-models - If no setting is provided, "claude-3-sonnet" will be used by default - and a warning will be issued. + model_name (str, Default to "textembedding-gecko"): + The model for text embedding. “textembedding-gecko” returns model embeddings for text inputs. + "textembedding-gecko-multilingual" returns model embeddings for text inputs which support over 100 languages + Default to "textembedding-gecko". session (bigframes.Session or None): BQ session to create the model. If None, use the global default session. connection_name (str or None): - Connection to connect with remote service. str of the format ... - If None, use default connection in session context. BigQuery DataFrame will try to create the connection and attach - permission if the connection isn't fully set up. + connection to connect with remote service. str of the format ... + if None, use default connection in session context. """ def __init__( self, - *, - model_name: Optional[ - Literal[ - "claude-3-sonnet", - "claude-3-haiku", - "claude-3-5-sonnet", - "claude-3-opus", - ] - ] = None, + model_name: Literal[ + "textembedding-gecko", "textembedding-gecko-multilingual" + ] = "textembedding-gecko", session: Optional[bigframes.Session] = None, connection_name: Optional[str] = None, ): - if model_name is None: - model_name = "claude-3-sonnet" - msg = exceptions.format_message(_REMOVE_DEFAULT_MODEL_WARNING) - warnings.warn(msg, category=FutureWarning, stacklevel=2) self.model_name = model_name - self.session = session or global_session.get_global_session() - self.connection_name = connection_name + self.session = session or bpd.get_global_session() + self._bq_connection_manager = clients.BqConnectionManager( + self.session.bqconnectionclient, self.session.resourcemanagerclient + ) + + connection_name = connection_name or self.session._bq_connection + self.connection_name = self._bq_connection_manager.resolve_full_connection_name( + connection_name, + default_project=self.session._project, + default_location=self.session._location, + ) self._bqml_model_factory = globals.bqml_model_factory() self._bqml_model: core.BqmlModel = self._create_bqml_model() def _create_bqml_model(self): # Parse and create connection if needed. - self.connection_name = self.session._create_bq_connection( - connection=self.connection_name, iam_role="aiplatform.user" + if not self.connection_name: + raise ValueError( + "Must provide connection_name, either in constructor or through session options." + ) + connection_name_parts = self.connection_name.split(".") + if len(connection_name_parts) != 3: + raise ValueError( + f"connection_name must be of the format .., got {self.connection_name}." + ) + self._bq_connection_manager.create_bq_connection( + project_id=connection_name_parts[0], + location=connection_name_parts[1], + connection_id=connection_name_parts[2], + iam_role="aiplatform.user", ) - - if self.model_name not in _CLAUDE_3_ENDPOINTS: - msg = exceptions.format_message( - _MODEL_NOT_SUPPORTED_WARNING.format( - model_name=self.model_name, - known_models=", ".join(_CLAUDE_3_ENDPOINTS), - ) + if self.model_name == "textembedding-gecko": + options = { + "endpoint": _REMOTE_EMBEDDING_GENERATOR_MODEL_ENDPOINT, + } + elif self.model_name == _REMOTE_EMBEDDING_GENERATOR_MUlTILINGUAL_MODEL_ENDPOINT: + options = { + "endpoint": _REMOTE_EMBEDDING_GENERATOR_MUlTILINGUAL_MODEL_ENDPOINT, + } + else: + raise ValueError( + f"Model name {self.model_name} is not supported. We only support {_REMOTE_EMBEDDING_GENERATOR_MODEL_ENDPOINT} and {_REMOTE_EMBEDDING_GENERATOR_MUlTILINGUAL_MODEL_ENDPOINT}." ) - warnings.warn(msg) - options = { - "endpoint": self.model_name, - } return self._bqml_model_factory.create_remote_model( session=self.session, connection_name=self.connection_name, options=options ) - @classmethod - def _from_bq( - cls, session: bigframes.Session, bq_model: bigquery.Model - ) -> Claude3TextGenerator: - assert bq_model.model_type == "MODEL_TYPE_UNSPECIFIED" - assert "remoteModelInfo" in bq_model._properties - assert "endpoint" in bq_model._properties["remoteModelInfo"] - assert "connection" in bq_model._properties["remoteModelInfo"] - - # Parse the remote model endpoint - bqml_endpoint = bq_model._properties["remoteModelInfo"]["endpoint"] - model_connection = bq_model._properties["remoteModelInfo"]["connection"] - model_endpoint = bqml_endpoint.split("/")[-1] - - kwargs = utils.retrieve_params_from_bq_model( - cls, bq_model, _BQML_PARAMS_MAPPING - ) - - model = cls( - **kwargs, - session=session, - model_name=model_endpoint, - connection_name=model_connection, - ) - model._bqml_model = core.BqmlModel(session, bq_model) - return model - - @property - def _bqml_options(self) -> dict: - """The model options as they will be set for BQML""" - options = { - "data_split_method": "NO_SPLIT", - } - return options - - def predict( - self, - X: utils.ArrayType, - *, - max_output_tokens: int = 128, - top_k: int = 40, - top_p: float = 0.95, - max_retries: int = 0, - ) -> bigframes.dataframe.DataFrame: + def predict(self, X: Union[bpd.DataFrame, bpd.Series]) -> bpd.DataFrame: """Predict the result from input DataFrame. Args: - X (bigframes.dataframe.DataFrame or bigframes.series.Series or pandas.core.frame.DataFrame or pandas.core.series.Series): - Input DataFrame or Series, can contain one or more columns. If multiple columns are in the DataFrame, it must contain a "prompt" column for prediction. - Prompts can include preamble, questions, suggestions, instructions, or examples. - - max_output_tokens (int, default 128): - Maximum number of tokens that can be generated in the response. Specify a lower value for shorter responses and a higher value for longer responses. - A token may be smaller than a word. A token is approximately four characters. 100 tokens correspond to roughly 60-80 words. - Default 128. Possible values are in the range [1, 4096]. - - top_k (int, default 40): - Top-k changes how the model selects tokens for output. A top-k of 1 means the selected token is the most probable among all tokens - in the model's vocabulary (also called greedy decoding), while a top-k of 3 means that the next token is selected from among the 3 most probable tokens (using temperature). - For each token selection step, the top K tokens with the highest probabilities are sampled. Then tokens are further filtered based on topP with the final token selected using temperature sampling. - Specify a lower value for less random responses and a higher value for more random responses. - Default 40. Possible values [1, 40]. - - top_p (float, default 0.95):: - Top-p changes how the model selects tokens for output. Tokens are selected from most K (see topK parameter) probable to least until the sum of their probabilities equals the top-p value. - For example, if tokens A, B, and C have a probability of 0.3, 0.2, and 0.1 and the top-p value is 0.5, then the model will select either A or B as the next token (using temperature) - and not consider C at all. - Specify a lower value for less random responses and a higher value for more random responses. - Default 0.95. Possible values [0.0, 1.0]. - - max_retries (int, default 0): - Max number of retries if the prediction for any rows failed. Each try needs to make progress (i.e. has successfully predicted rows) to continue the retry. - Each retry will append newly succeeded rows. When the max retries are reached, the remaining rows (the ones without successful predictions) will be appended to the end of the result. - + X (bigframes.dataframe.DataFrame or bigframes.series.Series): + Input DataFrame, which needs to contain a column with name "content". Only the column will be used as input. Content can include preamble, questions, suggestions, instructions, or examples. Returns: - bigframes.dataframe.DataFrame: DataFrame of shape (n_samples, n_input_columns + n_prediction_columns). Returns predicted values. + bigframes.dataframe.DataFrame: Output DataFrame with only 1 column as the output embedding results """ # Params reference: https://cloud.google.com/vertex-ai/docs/generative-ai/learn/models - if max_output_tokens not in range(1, 4097): - raise ValueError( - f"max_output_token must be [1, 4096], but is {max_output_tokens}." - ) - - if top_k not in range(1, 41): - raise ValueError(f"top_k must be [1, 40], but is {top_k}.") - - if top_p < 0.0 or top_p > 1.0: - raise ValueError(f"top_p must be [0.0, 1.0], but is {top_p}.") + (X,) = utils.convert_to_dataframe(X) - if max_retries < 0: + if len(X.columns) != 1: raise ValueError( - f"max_retries must be larger than or equal to 0, but is {max_retries}." + f"Only support one column as input. {constants.FEEDBACK_LINK}" ) - (X,) = utils.batch_convert_to_dataframe(X, session=self._bqml_model.session) - - if len(X.columns) == 1: - # BQML identified the column by name - col_label = typing.cast(blocks.Label, X.columns[0]) - X = X.rename(columns={col_label: "prompt"}) + # BQML identified the column by name + col_label = cast(blocks.Label, X.columns[0]) + X = X.rename(columns={col_label: "content"}) options = { - "max_output_tokens": max_output_tokens, - "top_k": top_k, - "top_p": top_p, + "flatten_json_output": True, } - - return self._predict_and_retry( - core.BqmlModel.generate_text_tvf, - X, - options=options, - max_retries=max_retries, + df = self._bqml_model.generate_text_embedding(X, options) + return cast( + bpd.DataFrame, + df[[_EMBED_TEXT_RESULT_COLUMN]], ) - - def to_gbq(self, model_name: str, replace: bool = False) -> Claude3TextGenerator: - """Save the model to BigQuery. - - Args: - model_name (str): - The name of the model. - replace (bool, default False): - Determine whether to replace if the model already exists. Default to False. - - Returns: - Claude3TextGenerator: Saved model.""" - - new_model = self._bqml_model.copy(model_name, replace) - return new_model.session.read_gbq_model(model_name) diff --git a/bigframes/ml/loader.py b/bigframes/ml/loader.py index 76975752457..805747c49ba 100644 --- a/bigframes/ml/loader.py +++ b/bigframes/ml/loader.py @@ -17,24 +17,18 @@ from types import MappingProxyType from typing import Union -import bigframes_vendored.constants as constants from google.cloud import bigquery -import bigframes.session +import bigframes +import bigframes.constants as constants from bigframes.ml import ( cluster, - compose, - core, decomposition, ensemble, forecasting, imported, - impute, linear_model, - llm, pipeline, - preprocessing, - utils, ) _BQML_MODEL_TYPE_MAPPING = MappingProxyType( @@ -42,7 +36,6 @@ "LINEAR_REGRESSION": linear_model.LinearRegression, "LOGISTIC_REGRESSION": linear_model.LogisticRegression, "KMEANS": cluster.KMeans, - "MATRIX_FACTORIZATION": decomposition.MatrixFactorization, "PCA": decomposition.PCA, "BOOSTED_TREE_REGRESSOR": ensemble.XGBRegressor, "BOOSTED_TREE_CLASSIFIER": ensemble.XGBClassifier, @@ -51,43 +44,13 @@ "RANDOM_FOREST_CLASSIFIER": ensemble.RandomForestClassifier, "TENSORFLOW": imported.TensorFlowModel, "ONNX": imported.ONNXModel, - "XGBOOST": imported.XGBoostModel, - } -) - -_BQML_ENDPOINT_TYPE_MAPPING = MappingProxyType( - { - llm._GEMINI_1P5_PRO_PREVIEW_ENDPOINT: llm.GeminiTextGenerator, - llm._GEMINI_1P5_PRO_FLASH_PREVIEW_ENDPOINT: llm.GeminiTextGenerator, - llm._GEMINI_1P5_PRO_001_ENDPOINT: llm.GeminiTextGenerator, - llm._GEMINI_1P5_PRO_002_ENDPOINT: llm.GeminiTextGenerator, - llm._GEMINI_1P5_FLASH_001_ENDPOINT: llm.GeminiTextGenerator, - llm._GEMINI_1P5_FLASH_002_ENDPOINT: llm.GeminiTextGenerator, - llm._GEMINI_2_FLASH_EXP_ENDPOINT: llm.GeminiTextGenerator, - llm._GEMINI_2_FLASH_001_ENDPOINT: llm.GeminiTextGenerator, - llm._GEMINI_2_FLASH_LITE_001_ENDPOINT: llm.GeminiTextGenerator, - llm._GEMINI_2P5_PRO_PREVIEW_ENDPOINT: llm.GeminiTextGenerator, - llm._GEMINI_2P5_FLASH_ENDPOINT: llm.GeminiTextGenerator, - llm._GEMINI_2P5_FLASH_LITE_ENDPOINT: llm.GeminiTextGenerator, - llm._GEMINI_2P5_PRO_ENDPOINT: llm.GeminiTextGenerator, - llm._GEMINI_3P1_FLASH_LITE_ENDPOINT: llm.GeminiTextGenerator, - llm._GEMINI_3P5_FLASH_ENDPOINT: llm.GeminiTextGenerator, - llm._CLAUDE_3_HAIKU_ENDPOINT: llm.Claude3TextGenerator, - llm._CLAUDE_3_SONNET_ENDPOINT: llm.Claude3TextGenerator, - llm._CLAUDE_3_5_SONNET_ENDPOINT: llm.Claude3TextGenerator, - llm._CLAUDE_3_OPUS_ENDPOINT: llm.Claude3TextGenerator, - llm._TEXT_EMBEDDING_005_ENDPOINT: llm.TextEmbeddingGenerator, - llm._TEXT_EMBEDDING_004_ENDPOINT: llm.TextEmbeddingGenerator, - llm._TEXT_MULTILINGUAL_EMBEDDING_002_ENDPOINT: llm.TextEmbeddingGenerator, - llm._MULTIMODAL_EMBEDDING_001_ENDPOINT: llm.MultimodalEmbeddingGenerator, } ) def from_bq( - session: bigframes.session.Session, bq_model: bigquery.Model + session: bigframes.Session, bq_model: bigquery.Model ) -> Union[ - decomposition.MatrixFactorization, decomposition.PCA, cluster.KMeans, linear_model.LinearRegression, @@ -99,14 +62,7 @@ def from_bq( ensemble.RandomForestClassifier, imported.TensorFlowModel, imported.ONNXModel, - imported.XGBoostModel, - llm.Claude3TextGenerator, - llm.TextEmbeddingGenerator, - llm.MultimodalEmbeddingGenerator, pipeline.Pipeline, - compose.ColumnTransformer, - preprocessing.PreprocessingType, - impute.SimpleImputer, ]: """Load a BQML model to BigQuery DataFrames ML. @@ -117,37 +73,16 @@ def from_bq( Returns: A BigQuery DataFrames ML model object. """ - if bq_model.model_type == "TRANSFORM_ONLY": - return _transformer_from_bq(session, bq_model) - if _is_bq_model_pipeline(bq_model): return pipeline.Pipeline._from_bq(session, bq_model) return _model_from_bq(session, bq_model) -def _transformer_from_bq(session: bigframes.session.Session, bq_model: bigquery.Model): - transformer = compose.ColumnTransformer._extract_from_bq_model(bq_model)._merge( - bq_model - ) - transformer._bqml_model = core.BqmlModel(session, bq_model) - - return transformer - - -def _model_from_bq(session: bigframes.session.Session, bq_model: bigquery.Model): +def _model_from_bq(session: bigframes.Session, bq_model: bigquery.Model): if bq_model.model_type in _BQML_MODEL_TYPE_MAPPING: return _BQML_MODEL_TYPE_MAPPING[bq_model.model_type]._from_bq( # type: ignore - session=session, bq_model=bq_model - ) - if _is_bq_model_remote(bq_model): - # Parse the remote model endpoint - bqml_endpoint = bq_model._properties["remoteModelInfo"]["endpoint"] - model_endpoint = bqml_endpoint.split("/")[-1] - model_name, _ = utils.parse_model_endpoint(model_endpoint) - - return _BQML_ENDPOINT_TYPE_MAPPING[model_name]._from_bq( # type: ignore - session=session, bq_model=bq_model + session=session, model=bq_model ) raise NotImplementedError( @@ -157,11 +92,3 @@ def _model_from_bq(session: bigframes.session.Session, bq_model: bigquery.Model) def _is_bq_model_pipeline(bq_model: bigquery.Model) -> bool: return "transformColumns" in bq_model._properties - - -def _is_bq_model_remote(bq_model: bigquery.Model) -> bool: - return ( - bq_model.model_type == "MODEL_TYPE_UNSPECIFIED" - and "remoteModelInfo" in bq_model._properties - and "endpoint" in bq_model._properties["remoteModelInfo"] - ) diff --git a/bigframes/ml/metrics.py b/bigframes/ml/metrics.py new file mode 100644 index 00000000000..5731b946ca9 --- /dev/null +++ b/bigframes/ml/metrics.py @@ -0,0 +1,331 @@ +# Copyright 2023 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Metrics functions for evaluating models. This module is styled after +Scikit-Learn's metrics module: https://scikit-learn.org/stable/modules/metrics.html.""" + +import inspect +import typing +from typing import Tuple, Union + +import numpy as np +import pandas as pd +import sklearn.metrics as sklearn_metrics # type: ignore + +import bigframes.constants as constants +from bigframes.ml import utils +import bigframes.pandas as bpd +import third_party.bigframes_vendored.sklearn.metrics._classification as vendored_mertics_classification +import third_party.bigframes_vendored.sklearn.metrics._ranking as vendored_mertics_ranking +import third_party.bigframes_vendored.sklearn.metrics._regression as vendored_metrics_regression + + +def r2_score( + y_true: Union[bpd.DataFrame, bpd.Series], + y_pred: Union[bpd.DataFrame, bpd.Series], + force_finite=True, +) -> float: + y_true_series, y_pred_series = utils.convert_to_series(y_true, y_pred) + + # total sum of squares + # (dataframe, scalar) binops + # TODO(tbergeron): These stats are eagerly evaluated. Move to lazy representation once scalar subqueries supported. + delta_from_mean = y_true_series - y_true_series.mean() + ss_total = (delta_from_mean * delta_from_mean).sum() + + # residual sum of squares + # (scalar, scalar) binops + delta_from_pred = y_true_series - y_pred_series + ss_res = (delta_from_pred * delta_from_pred).sum() + + if force_finite and ss_total == 0: + return 0.0 if ss_res > 0 else 1.0 + + return 1 - (ss_res / ss_total) + + +r2_score.__doc__ = inspect.getdoc(vendored_metrics_regression.r2_score) + + +def accuracy_score( + y_true: Union[bpd.DataFrame, bpd.Series], + y_pred: Union[bpd.DataFrame, bpd.Series], + normalize=True, +) -> float: + # TODO(ashleyxu): support sample_weight as the parameter + y_true_series, y_pred_series = utils.convert_to_series(y_true, y_pred) + + # Compute accuracy for each possible representation + # TODO(ashleyxu): add multilabel classification support where y_type + # starts with "multilabel" + score = (y_true_series == y_pred_series).astype(pd.Int64Dtype()) + + if normalize: + return score.mean() + else: + return score.sum() + + +accuracy_score.__doc__ = inspect.getdoc(vendored_mertics_classification.accuracy_score) + + +def roc_curve( + y_true: Union[bpd.DataFrame, bpd.Series], + y_score: Union[bpd.DataFrame, bpd.Series], + drop_intermediate: bool = True, +) -> Tuple[bpd.Series, bpd.Series, bpd.Series]: + # TODO(bmil): Add multi-class support + # TODO(bmil): Add multi-label support + + # TODO(bmil): Implement drop_intermediate + if drop_intermediate: + raise NotImplementedError( + f"drop_intermediate is not yet implemented. {constants.FEEDBACK_LINK}" + ) + + y_true_series, y_score_series = utils.convert_to_series(y_true, y_score) + + session = y_true_series._block.expr.session + + # We operate on rows, so, remove the index if there is one + # TODO(bmil): check that the indexes are equivalent before removing + + y_true_series = typing.cast(bpd.Series, y_true_series.reset_index(drop=True)) + y_score_series = typing.cast(bpd.Series, y_score_series.reset_index(drop=True)) + + df = bpd.DataFrame( + { + "y_true": y_true_series, + "y_score": y_score_series, + } + ) + + total_positives = y_true_series.sum() + total_negatives = y_true_series.count() - total_positives + + df = df.sort_values(by="y_score", ascending=False) + df["cum_tp"] = df["y_true"].cumsum() + # have to astype("Int64") as not supported boolean cumsum yet. + df["cum_fp"] = ( + (~typing.cast(bpd.Series, df["y_true"].astype("boolean"))) + .astype("Int64") + .cumsum() + ) + + # produce just one data point per y_score + df = df.drop_duplicates(subset="y_score", keep="last") + df = df.sort_values(by="y_score", ascending=False) + + df["tpr"] = typing.cast(bpd.Series, df["cum_tp"]) / total_positives + df["fpr"] = typing.cast(bpd.Series, df["cum_fp"]) / total_negatives + df["thresholds"] = typing.cast(bpd.Series, df["y_score"].astype("Float64")) + + # sklearn includes an extra datapoint for the origin with threshold np.inf + # having problems with concating inline + df_origin = session.read_pandas( + pd.DataFrame({"tpr": [0.0], "fpr": [0.0], "thresholds": np.inf}) + ) + df = typing.cast(bpd.DataFrame, bpd.concat([df_origin, df], ignore_index=True)) + df = df.reset_index(drop=True) + + return ( + typing.cast(bpd.Series, df["fpr"]), + typing.cast(bpd.Series, df["tpr"]), + typing.cast(bpd.Series, df["thresholds"]), + ) + + +roc_curve.__doc__ = inspect.getdoc(vendored_mertics_ranking.roc_curve) + + +def roc_auc_score( + y_true: Union[bpd.DataFrame, bpd.Series], y_score: Union[bpd.DataFrame, bpd.Series] +) -> float: + # TODO(bmil): Add multi-class support + # TODO(bmil): Add multi-label support + y_true_series, y_score_series = utils.convert_to_series(y_true, y_score) + + fpr, tpr, _ = roc_curve(y_true_series, y_score_series, drop_intermediate=False) + + # TODO(bmil): remove this once bigframes supports the necessary operations + pd_fpr = fpr.to_pandas() + pd_tpr = tpr.to_pandas() + + # Use the trapezoid rule to compute the area under the ROC curve + width_diff = pd_fpr.diff().iloc[1:].reset_index(drop=True) + height_avg = (pd_tpr.iloc[:-1] + pd_tpr.iloc[1:].reset_index(drop=True)) / 2 + return (width_diff * height_avg).sum() + + +roc_auc_score.__doc__ = inspect.getdoc(vendored_mertics_ranking.roc_auc_score) + + +def auc( + x: Union[bpd.DataFrame, bpd.Series], + y: Union[bpd.DataFrame, bpd.Series], +) -> float: + x_series, y_series = utils.convert_to_series(x, y) + + # TODO(b/286410053) Support ML exceptions and error handling. + auc = sklearn_metrics.auc(x_series.to_pandas(), y_series.to_pandas()) + return auc + + +auc.__doc__ = inspect.getdoc(vendored_mertics_ranking.auc) + + +def confusion_matrix( + y_true: Union[bpd.DataFrame, bpd.Series], + y_pred: Union[bpd.DataFrame, bpd.Series], +) -> pd.DataFrame: + # TODO(ashleyxu): support labels and sample_weight parameters + y_true_series, y_pred_series = utils.convert_to_series(y_true, y_pred) + + y_true_series = y_true_series.rename("y_true") + confusion_df = y_true_series.to_frame().assign(y_pred=y_pred_series) + confusion_df = confusion_df.assign(dummy=0) + groupby_count = ( + confusion_df.groupby(by=["y_true", "y_pred"], as_index=False) + .count() + .to_pandas() + ) + + unique_values = sorted( + set(groupby_count["y_true"]).union(set(groupby_count["y_pred"])) + ) + + confusion_matrix = pd.DataFrame( + 0, index=pd.Index(unique_values), columns=pd.Index(unique_values), dtype=int + ) + + # Loop through the result by rows and columns + for _, row in groupby_count.iterrows(): + y_true = row["y_true"] + y_pred = row["y_pred"] + count = row["dummy"] + confusion_matrix[y_pred][y_true] = count + + return confusion_matrix + + +confusion_matrix.__doc__ = inspect.getdoc( + vendored_mertics_classification.confusion_matrix +) + + +def recall_score( + y_true: Union[bpd.DataFrame, bpd.Series], + y_pred: Union[bpd.DataFrame, bpd.Series], + average: str = "binary", +) -> pd.Series: + # TODO(ashleyxu): support more average type, default to "binary" + if average is not None: + raise NotImplementedError( + f"Only average=None is supported. {constants.FEEDBACK_LINK}" + ) + + y_true_series, y_pred_series = utils.convert_to_series(y_true, y_pred) + + is_accurate = y_true_series == y_pred_series + unique_labels = ( + bpd.concat([y_true_series, y_pred_series], join="outer") + .drop_duplicates() + .sort_values() + ) + index = unique_labels.to_list() + + recall = ( + is_accurate.groupby(y_true_series).sum() + / is_accurate.groupby(y_true_series).count() + ).to_pandas() + + recall_score = pd.Series(0, index=index) + for i in recall_score.index: + recall_score.loc[i] = recall.loc[i] + + return recall_score + + +recall_score.__doc__ = inspect.getdoc(vendored_mertics_classification.recall_score) + + +def precision_score( + y_true: Union[bpd.DataFrame, bpd.Series], + y_pred: Union[bpd.DataFrame, bpd.Series], + average: str = "binary", +) -> pd.Series: + # TODO(ashleyxu): support more average type, default to "binary" + if average is not None: + raise NotImplementedError( + f"Only average=None is supported. {constants.FEEDBACK_LINK}" + ) + + y_true_series, y_pred_series = utils.convert_to_series(y_true, y_pred) + + is_accurate = y_true_series == y_pred_series + unique_labels = ( + bpd.concat([y_true_series, y_pred_series], join="outer") + .drop_duplicates() + .sort_values() + ) + index = unique_labels.to_list() + + precision = ( + is_accurate.groupby(y_pred_series).sum() + / is_accurate.groupby(y_pred_series).count() + ).to_pandas() + + precision_score = pd.Series(0, index=index) + for i in precision.index: + precision_score.loc[i] = precision.loc[i] + + return precision_score + + +precision_score.__doc__ = inspect.getdoc( + vendored_mertics_classification.precision_score +) + + +def f1_score( + y_true: Union[bpd.DataFrame, bpd.Series], + y_pred: Union[bpd.DataFrame, bpd.Series], + average: str = "binary", +) -> pd.Series: + # TODO(ashleyxu): support more average type, default to "binary" + y_true_series, y_pred_series = utils.convert_to_series(y_true, y_pred) + + if average is not None: + raise NotImplementedError( + f"Only average=None is supported. {constants.FEEDBACK_LINK}" + ) + + recall = recall_score(y_true_series, y_pred_series, average=None) + precision = precision_score(y_true_series, y_pred_series, average=None) + + f1_score = pd.Series(0, index=recall.index) + for index in recall.index: + if precision[index] + recall[index] != 0: + f1_score[index] = ( + 2 + * (precision[index] * recall[index]) + / (precision[index] + recall[index]) + ) + else: + f1_score[index] = 0 + + return f1_score + + +f1_score.__doc__ = inspect.getdoc(vendored_mertics_classification.f1_score) diff --git a/bigframes/ml/metrics/__init__.py b/bigframes/ml/metrics/__init__.py deleted file mode 100644 index f6c7d5e52f4..00000000000 --- a/bigframes/ml/metrics/__init__.py +++ /dev/null @@ -1,43 +0,0 @@ -# Copyright 2023 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from bigframes.ml.metrics import pairwise -from bigframes.ml.metrics._metrics import ( - accuracy_score, - auc, - confusion_matrix, - f1_score, - mean_absolute_error, - mean_squared_error, - precision_score, - r2_score, - recall_score, - roc_auc_score, - roc_curve, -) - -__all__ = [ - "r2_score", - "recall_score", - "accuracy_score", - "roc_curve", - "roc_auc_score", - "auc", - "confusion_matrix", - "precision_score", - "f1_score", - "mean_absolute_error", - "mean_squared_error", - "pairwise", -] diff --git a/bigframes/ml/metrics/_metrics.py b/bigframes/ml/metrics/_metrics.py deleted file mode 100644 index 1f69d60e317..00000000000 --- a/bigframes/ml/metrics/_metrics.py +++ /dev/null @@ -1,409 +0,0 @@ -# Copyright 2023 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -"""Metrics functions for evaluating models. This module is styled after -scikit-learn's metrics module: https://scikit-learn.org/stable/modules/metrics.html.""" - -from __future__ import annotations - -import inspect -import typing -from typing import Literal, Tuple, Union, overload - -import bigframes_vendored.constants as constants -import bigframes_vendored.sklearn.metrics._classification as vendored_metrics_classification -import bigframes_vendored.sklearn.metrics._ranking as vendored_metrics_ranking -import bigframes_vendored.sklearn.metrics._regression as vendored_metrics_regression -import numpy as np -import pandas as pd - -import bigframes.pandas as bpd -from bigframes.ml import utils - - -def r2_score( - y_true: Union[bpd.DataFrame, bpd.Series], - y_pred: Union[bpd.DataFrame, bpd.Series], - *, - force_finite=True, -) -> float: - y_true_series, y_pred_series = utils.batch_convert_to_series(y_true, y_pred) - - # total sum of squares - # (dataframe, scalar) binops - # TODO(tbergeron): These stats are eagerly evaluated. Move to lazy representation once scalar subqueries supported. - delta_from_mean = y_true_series - y_true_series.mean() - ss_total = (delta_from_mean * delta_from_mean).sum() - - # residual sum of squares - # (scalar, scalar) binops - delta_from_pred = y_true_series - y_pred_series - ss_res = (delta_from_pred * delta_from_pred).sum() - - if force_finite and ss_total == 0: - return 0.0 if ss_res > 0 else 1.0 - - return 1 - (ss_res / ss_total) - - -r2_score.__doc__ = inspect.getdoc(vendored_metrics_regression.r2_score) - - -def accuracy_score( - y_true: Union[bpd.DataFrame, bpd.Series], - y_pred: Union[bpd.DataFrame, bpd.Series], - *, - normalize=True, -) -> float: - # TODO(ashleyxu): support sample_weight as the parameter - y_true_series, y_pred_series = utils.batch_convert_to_series(y_true, y_pred) - - # Compute accuracy for each possible representation - # TODO(ashleyxu): add multilabel classification support where y_type - # starts with "multilabel" - score = (y_true_series == y_pred_series).astype(pd.Int64Dtype()) - - if normalize: - return score.mean() - else: - return score.sum() - - -accuracy_score.__doc__ = inspect.getdoc(vendored_metrics_classification.accuracy_score) - - -def roc_curve( - y_true: Union[bpd.DataFrame, bpd.Series], - y_score: Union[bpd.DataFrame, bpd.Series], - *, - drop_intermediate: bool = True, -) -> Tuple[bpd.Series, bpd.Series, bpd.Series]: - # TODO(bmil): Add multi-class support - # TODO(bmil): Add multi-label support - - # TODO(bmil): Implement drop_intermediate - if drop_intermediate: - raise NotImplementedError( - f"drop_intermediate is not yet implemented. {constants.FEEDBACK_LINK}" - ) - - y_true_series, y_score_series = utils.batch_convert_to_series(y_true, y_score) - - session = y_true_series._block.expr.session - - # We operate on rows, so, remove the index if there is one - # TODO(bmil): check that the indexes are equivalent before removing - - y_true_series = typing.cast(bpd.Series, y_true_series.reset_index(drop=True)) - y_score_series = typing.cast(bpd.Series, y_score_series.reset_index(drop=True)) - - df = bpd.DataFrame( - { - "y_true": y_true_series, - "y_score": y_score_series, - } - ) - - total_positives = y_true_series.sum() - total_negatives = y_true_series.count() - total_positives - - df = df.sort_values(by="y_score", ascending=False) - df["cum_tp"] = df["y_true"].cumsum() - # have to astype("Int64") as not supported boolean cumsum yet. - df["cum_fp"] = ( - (~typing.cast(bpd.Series, df["y_true"].astype("boolean"))) - .astype("Int64") - .cumsum() - ) - - # produce just one data point per y_score - df = df.drop_duplicates(subset="y_score", keep="last") - df = df.sort_values(by="y_score", ascending=False) - - df["tpr"] = typing.cast(bpd.Series, df["cum_tp"]) / total_positives - df["fpr"] = typing.cast(bpd.Series, df["cum_fp"]) / total_negatives - df["thresholds"] = typing.cast(bpd.Series, df["y_score"].astype("Float64")) - - # sklearn includes an extra datapoint for the origin with threshold np.inf - # having problems with concating inline - df_origin = session.read_pandas( - pd.DataFrame({"tpr": [0.0], "fpr": [0.0], "thresholds": np.inf}) - ) - df = typing.cast(bpd.DataFrame, bpd.concat([df_origin, df], ignore_index=True)) - df = df.reset_index(drop=True) - - return ( - typing.cast(bpd.Series, df["fpr"]), - typing.cast(bpd.Series, df["tpr"]), - typing.cast(bpd.Series, df["thresholds"]), - ) - - -roc_curve.__doc__ = inspect.getdoc(vendored_metrics_ranking.roc_curve) - - -def roc_auc_score( - y_true: Union[bpd.DataFrame, bpd.Series], y_score: Union[bpd.DataFrame, bpd.Series] -) -> float: - # TODO(bmil): Add multi-class support - # TODO(bmil): Add multi-label support - y_true_series, y_score_series = utils.batch_convert_to_series(y_true, y_score) - - fpr, tpr, _ = roc_curve(y_true_series, y_score_series, drop_intermediate=False) - - # Use the trapezoid rule to compute the area under the ROC curve - width_diff = fpr.diff().iloc[1:].reset_index(drop=True) - height_avg = (tpr.iloc[:-1] + tpr.iloc[1:].reset_index(drop=True)) / 2 - return typing.cast(float, (width_diff * height_avg).sum()) - - -roc_auc_score.__doc__ = inspect.getdoc(vendored_metrics_ranking.roc_auc_score) - - -def auc( - x: Union[bpd.DataFrame, bpd.Series], - y: Union[bpd.DataFrame, bpd.Series], -) -> float: - x_series, y_series = utils.batch_convert_to_series(x, y) - - x_pandas = x_series.to_pandas() - y_pandas = y_series.to_pandas() - return vendored_metrics_ranking.auc(x_pandas, y_pandas) - - -auc.__doc__ = inspect.getdoc(vendored_metrics_ranking.auc) - - -def confusion_matrix( - y_true: Union[bpd.DataFrame, bpd.Series], - y_pred: Union[bpd.DataFrame, bpd.Series], -) -> pd.DataFrame: - # TODO(ashleyxu): support labels and sample_weight parameters - y_true_series, y_pred_series = utils.batch_convert_to_series(y_true, y_pred) - - y_true_series = y_true_series.rename("y_true") - confusion_df = y_true_series.to_frame().assign(y_pred=y_pred_series) - confusion_df = confusion_df.assign(dummy=0) - groupby_count = ( - confusion_df.groupby(by=["y_true", "y_pred"], as_index=False) - .count() - .to_pandas() - ) - - unique_values = sorted( - set(groupby_count["y_true"]).union(set(groupby_count["y_pred"])) - ) - - confusion_matrix = pd.DataFrame( - 0, index=pd.Index(unique_values), columns=pd.Index(unique_values), dtype=int - ) - - # Loop through the result by rows and columns - for _, row in groupby_count.iterrows(): - y_true = row["y_true"] - y_pred = row["y_pred"] - count = row["dummy"] - confusion_matrix.at[y_true, y_pred] = count - - return confusion_matrix - - -confusion_matrix.__doc__ = inspect.getdoc( - vendored_metrics_classification.confusion_matrix -) - - -def recall_score( - y_true: Union[bpd.DataFrame, bpd.Series], - y_pred: Union[bpd.DataFrame, bpd.Series], - *, - average: typing.Optional[str] = "binary", -) -> pd.Series: - # TODO(ashleyxu): support more average type, default to "binary" - if average is not None: - raise NotImplementedError( - f"Only average=None is supported. {constants.FEEDBACK_LINK}" - ) - - y_true_series, y_pred_series = utils.batch_convert_to_series(y_true, y_pred) - - is_accurate = y_true_series == y_pred_series - unique_labels = ( - bpd.concat([y_true_series, y_pred_series], join="outer") - .drop_duplicates() - .sort_values(inplace=False) - ) - index = unique_labels.to_list() - - recall = ( - is_accurate.groupby(y_true_series).sum() - / is_accurate.groupby(y_true_series).count() - ).to_pandas() - - recall_score = pd.Series(0.0, index=index) - for i in recall_score.index: - recall_score.loc[i] = recall.loc[i] - - return recall_score - - -recall_score.__doc__ = inspect.getdoc(vendored_metrics_classification.recall_score) - - -@overload -def precision_score( - y_true: bpd.DataFrame | bpd.Series, - y_pred: bpd.DataFrame | bpd.Series, - *, - pos_label: int | float | bool | str = ..., - average: Literal["binary"] = ..., -) -> float: ... - - -@overload -def precision_score( - y_true: bpd.DataFrame | bpd.Series, - y_pred: bpd.DataFrame | bpd.Series, - *, - pos_label: int | float | bool | str = ..., - average: None = ..., -) -> pd.Series: ... - - -def precision_score( - y_true: bpd.DataFrame | bpd.Series, - y_pred: bpd.DataFrame | bpd.Series, - *, - pos_label: int | float | bool | str = 1, - average: Literal["binary"] | None = "binary", -) -> pd.Series | float: - y_true_series, y_pred_series = utils.batch_convert_to_series(y_true, y_pred) - - if average is None: - return _precision_score_per_label(y_true_series, y_pred_series) - - if average == "binary": - return _precision_score_binary_pos_only(y_true_series, y_pred_series, pos_label) - - raise NotImplementedError( - f"Unsupported 'average' param value: {average}. {constants.FEEDBACK_LINK}" - ) - - -precision_score.__doc__ = inspect.getdoc( - vendored_metrics_classification.precision_score -) - - -def _precision_score_per_label(y_true: bpd.Series, y_pred: bpd.Series) -> pd.Series: - is_accurate = y_true == y_pred - unique_labels = ( - bpd.concat([y_true, y_pred], join="outer") - .drop_duplicates() - .sort_values(inplace=False) - ) - index = unique_labels.to_list() - - precision = ( - is_accurate.groupby(y_pred).sum() / is_accurate.groupby(y_pred).count() - ).to_pandas() - - precision_score = pd.Series(0.0, index=index) - for i in precision.index: - precision_score.loc[i] = precision.loc[i] - - return precision_score - - -def _precision_score_binary_pos_only( - y_true: bpd.Series, y_pred: bpd.Series, pos_label: int | float | bool | str -) -> float: - unique_labels = bpd.concat([y_true, y_pred]).unique(keep_order=False) - - if unique_labels.count() != 2: - raise ValueError( - "Target is multiclass but average='binary'. Please choose another average setting." - ) - - if not (unique_labels == pos_label).any(): - raise ValueError( - f"pos_labe={pos_label} is not a valid label. It should be one of {unique_labels.to_list()}" - ) - - target_elem_idx = y_pred == pos_label - is_accurate = y_pred[target_elem_idx] == y_true[target_elem_idx] - - return is_accurate.sum() / is_accurate.count() - - -def f1_score( - y_true: Union[bpd.DataFrame, bpd.Series], - y_pred: Union[bpd.DataFrame, bpd.Series], - *, - average: typing.Optional[str] = "binary", -) -> pd.Series: - # TODO(ashleyxu): support more average type, default to "binary" - y_true_series, y_pred_series = utils.batch_convert_to_series(y_true, y_pred) - - if average is not None: - raise NotImplementedError( - f"Only average=None is supported. {constants.FEEDBACK_LINK}" - ) - - recall = recall_score(y_true_series, y_pred_series, average=None) - precision = precision_score(y_true_series, y_pred_series, average=None) - - f1_score = pd.Series(0.0, index=recall.index) - for index in recall.index: - if precision[index] + recall[index] != 0: - f1_score[index] = ( - 2 - * (precision[index] * recall[index]) - / (precision[index] + recall[index]) - ) - else: - f1_score[index] = 0 - - return f1_score - - -f1_score.__doc__ = inspect.getdoc(vendored_metrics_classification.f1_score) - - -def mean_squared_error( - y_true: Union[bpd.DataFrame, bpd.Series], - y_pred: Union[bpd.DataFrame, bpd.Series], -) -> float: - y_true_series, y_pred_series = utils.batch_convert_to_series(y_true, y_pred) - - return (y_pred_series - y_true_series).pow(2).sum() / len(y_true_series) - - -mean_squared_error.__doc__ = inspect.getdoc( - vendored_metrics_regression.mean_squared_error -) - - -def mean_absolute_error( - y_true: Union[bpd.DataFrame, bpd.Series], - y_pred: Union[bpd.DataFrame, bpd.Series], -) -> float: - y_true_series, y_pred_series = utils.batch_convert_to_series(y_true, y_pred) - - return (y_pred_series - y_true_series).abs().sum() / len(y_true_series) - - -mean_absolute_error.__doc__ = inspect.getdoc( - vendored_metrics_regression.mean_absolute_error -) diff --git a/bigframes/ml/metrics/pairwise.py b/bigframes/ml/metrics/pairwise.py deleted file mode 100644 index 41785a8462d..00000000000 --- a/bigframes/ml/metrics/pairwise.py +++ /dev/null @@ -1,82 +0,0 @@ -# Copyright 2023 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import inspect -from typing import Union - -import bigframes_vendored.sklearn.metrics.pairwise as vendored_metrics_pairwise - -import bigframes.operations as ops -import bigframes.pandas as bpd -from bigframes.ml import utils - - -def paired_cosine_distances( - X: Union[bpd.DataFrame, bpd.Series], Y: Union[bpd.DataFrame, bpd.Series] -) -> bpd.DataFrame: - X, Y = utils.batch_convert_to_series(X, Y) - joined_block, _ = X._block.join(Y._block, how="outer") - - result_block, _ = joined_block.project_expr( - ops.cosine_distance_op.as_expr( - joined_block.value_columns[0], joined_block.value_columns[1] - ), - label="cosine_distance", - ) - return bpd.DataFrame(result_block) - - -paired_cosine_distances.__doc__ = inspect.getdoc( - vendored_metrics_pairwise.paired_cosine_distances -) - - -def paired_manhattan_distance( - X: Union[bpd.DataFrame, bpd.Series], Y: Union[bpd.DataFrame, bpd.Series] -) -> bpd.DataFrame: - X, Y = utils.batch_convert_to_series(X, Y) - joined_block, _ = X._block.join(Y._block, how="outer") - - result_block, _ = joined_block.project_expr( - ops.manhattan_distance_op.as_expr( - joined_block.value_columns[0], joined_block.value_columns[1] - ), - label="manhattan_distance", - ) - return bpd.DataFrame(result_block) - - -paired_manhattan_distance.__doc__ = inspect.getdoc( - vendored_metrics_pairwise.paired_manhattan_distance -) - - -def paired_euclidean_distances( - X: Union[bpd.DataFrame, bpd.Series], Y: Union[bpd.DataFrame, bpd.Series] -) -> bpd.DataFrame: - X, Y = utils.batch_convert_to_series(X, Y) - joined_block, _ = X._block.join(Y._block, how="outer") - - result_block, _ = joined_block.project_expr( - ops.euclidean_distance_op.as_expr( - joined_block.value_columns[0], joined_block.value_columns[1] - ), - label="euclidean_distance", - ) - return bpd.DataFrame(result_block) - - -paired_euclidean_distances.__doc__ = inspect.getdoc( - vendored_metrics_pairwise.paired_euclidean_distances -) diff --git a/bigframes/ml/model_selection.py b/bigframes/ml/model_selection.py index 57e07d89301..443b9e7be6e 100644 --- a/bigframes/ml/model_selection.py +++ b/bigframes/ml/model_selection.py @@ -13,33 +13,48 @@ # limitations under the License. """Functions for test/train split and model tuning. This module is styled after -scikit-learn's model_selection module: +Scikit-Learn's model_selection module: https://scikit-learn.org/stable/modules/classes.html#module-sklearn.model_selection.""" -import inspect -import time -import typing -from itertools import chain as _chain -from typing import Generator, List, Optional, Union -import bigframes_vendored.sklearn.model_selection._split as vendored_model_selection_split -import bigframes_vendored.sklearn.model_selection._validation as vendored_model_selection_validation -import pandas as pd +import typing +from typing import List, Union -import bigframes.pandas as bpd -from bigframes._tools import docs -from bigframes.core.logging import log_adapter from bigframes.ml import utils +import bigframes.pandas as bpd def train_test_split( - *arrays: utils.ArrayType, + *arrays: Union[bpd.DataFrame, bpd.Series], test_size: Union[float, None] = None, train_size: Union[float, None] = None, random_state: Union[int, None] = None, - stratify: Union[bpd.Series, None] = None, - shuffle: bool = True, ) -> List[Union[bpd.DataFrame, bpd.Series]]: + """Splits dataframes or series into random train and test subsets. + + Args: + *arrays (bigframes.dataframe.DataFrame or bigframes.series.Series): + A sequence of BigQuery DataFrames or Series that can be joined on + their indexes + test_size (default None): + The proportion of the dataset to include in the test split. If + None, this will default to the complement of train_size. If both + are none, it will be set to 0.25. + train_size (default None): + The proportion of the dataset to include in the train split. If + None, this will default to the complement of test_size. + random_state (default None): + A seed to use for randomly choosing the rows of the split. If not + set, a random split will be generated each time. + + Returns: + List[Union[bigframes.dataframe.DataFrame, bigframes.series.Series]]: A list of BigQuery DataFrames or Series. + """ + + # TODO(garrettwu): Scikit-Learn throws an error when the dataframes don't have the same + # number of rows. We probably want to do something similar. Now the implementation is based + # on index. We'll move to based on ordering first. + if test_size is None: if train_size is None: test_size = 0.25 @@ -59,167 +74,23 @@ def train_test_split( f"The sum of train_size and test_size exceeds 1.0. train_size: {train_size}. test_size: {test_size}" ) - if not shuffle: - if stratify is not None: - raise ValueError( - "Stratified train/test split is not implemented for shuffle=False" - ) - bf_arrays = list(utils.batch_convert_to_bf_equivalent(*arrays)) - - total_rows = len(bf_arrays[0]) - train_rows = int(total_rows * train_size) - test_rows = total_rows - train_rows - - return list( - _chain.from_iterable( - [ - [bf_array.head(train_rows), bf_array.tail(test_rows)] - for bf_array in bf_arrays - ] - ) - ) + dfs = list(utils.convert_to_dataframe(*arrays)) - dfs = list(utils.batch_convert_to_dataframe(*arrays)) - - def _stratify_split(df: bpd.DataFrame, stratify: bpd.Series) -> List[bpd.DataFrame]: - """Split a single DF according to the stratify Series.""" - stratify = stratify.rename("bigframes_stratify_col") # avoid name conflicts - merged_df = df.join(stratify.to_frame(), how="outer") - - train_dfs, test_dfs = [], [] - uniq = stratify.value_counts().index - for value in uniq: - cur = merged_df[merged_df["bigframes_stratify_col"] == value] - train, test = train_test_split( - cur, - test_size=test_size, - train_size=train_size, - random_state=random_state, - ) - train_dfs.append(train) - test_dfs.append(test) - - train_df = typing.cast( - bpd.DataFrame, bpd.concat(train_dfs).drop(columns="bigframes_stratify_col") - ) - test_df = typing.cast( - bpd.DataFrame, bpd.concat(test_dfs).drop(columns="bigframes_stratify_col") - ) - return [train_df, test_df] - - joined_df = dfs[0] - for df in dfs[1:]: - joined_df = joined_df.join(df, how="outer") - if stratify is None: - joined_df_train, joined_df_test = joined_df._split( - fracs=(train_size, test_size), random_state=random_state - ) - else: - joined_df_train, joined_df_test = _stratify_split(joined_df, stratify) - - results = [] - for array in arrays: - columns = array.name if isinstance(array, bpd.Series) else array.columns - results.append(joined_df_train[columns]) - results.append(joined_df_test[columns]) + split_dfs = dfs[0]._split(fracs=(train_size, test_size), random_state=random_state) + train_index = split_dfs[0].index + test_index = split_dfs[1].index - return results + split_dfs += typing.cast( + List[bpd.DataFrame], + [df.loc[index] for df in dfs[1:] for index in (train_index, test_index)], + ) + # convert back to Series. + results: List[Union[bpd.DataFrame, bpd.Series]] = [] + for i, array in enumerate(arrays): + if isinstance(array, bpd.Series): + results += utils.convert_to_series(split_dfs[2 * i], split_dfs[2 * i + 1]) + else: + results += (split_dfs[2 * i], split_dfs[2 * i + 1]) -train_test_split.__doc__ = inspect.getdoc( - vendored_model_selection_split.train_test_split -) - - -@log_adapter.class_logger -@docs.inherit_docs(vendored_model_selection_split.KFold) -class KFold: - def __init__(self, n_splits: int = 5, *, random_state: Union[int, None] = None): - if n_splits < 2: - raise ValueError(f"n_splits must be at least 2. Got {n_splits}") - self._n_splits = n_splits - self._random_state = random_state - - def get_n_splits(self) -> int: - return self._n_splits - - def split( - self, - X: utils.ArrayType, - y: Union[utils.ArrayType, None] = None, - ) -> Generator[tuple[Union[bpd.DataFrame, bpd.Series, None], ...], None, None]: - X_df = next(utils.batch_convert_to_dataframe(X)) - y_df_or = next(utils.batch_convert_to_dataframe(y)) if y is not None else None - joined_df = X_df.join(y_df_or, how="outer") if y_df_or is not None else X_df - - fracs = (1 / self._n_splits,) * self._n_splits - - dfs = joined_df._split(fracs=fracs, random_state=self._random_state) - - for i in range(len(dfs)): - train_df = bpd.concat(dfs[:i] + dfs[i + 1 :]) - test_df = dfs[i] - - X_train = train_df[X_df.columns] - y_train = train_df[y_df_or.columns] if y_df_or is not None else None - - X_test = test_df[X_df.columns] - y_test = test_df[y_df_or.columns] if y_df_or is not None else None - - yield ( - KFold._convert_to_bf_type(X_train, X), - KFold._convert_to_bf_type(X_test, X), - KFold._convert_to_bf_type(y_train, y), - KFold._convert_to_bf_type(y_test, y), - ) - - @staticmethod - def _convert_to_bf_type( - input, - type_instance: Union[bpd.DataFrame, bpd.Series, pd.DataFrame, pd.Series, None], - ) -> Union[bpd.DataFrame, bpd.Series, None]: - if isinstance(type_instance, pd.Series) or isinstance( - type_instance, bpd.Series - ): - return next(utils.batch_convert_to_series(input)) - - if isinstance(type_instance, pd.DataFrame) or isinstance( - type_instance, bpd.DataFrame - ): - return next(utils.batch_convert_to_dataframe(input)) - - return None - - -def cross_validate( - estimator, - X: utils.ArrayType, - y: Union[utils.ArrayType, None] = None, - *, - cv: Optional[Union[int, KFold]] = None, -) -> dict[str, list]: - if cv is None: - cv = KFold(n_splits=5) - elif isinstance(cv, int): - cv = KFold(n_splits=cv) - - result: dict[str, list] = {"test_score": [], "fit_time": [], "score_time": []} - for X_train, X_test, y_train, y_test in cv.split(X, y): # type: ignore - fit_start_time = time.perf_counter() - estimator.fit(X_train, y_train) - fit_time = time.perf_counter() - fit_start_time - - score_start_time = time.perf_counter() - score = estimator.score(X_test, y_test) - score_time = time.perf_counter() - score_start_time - - result["test_score"].append(score) - result["fit_time"].append(fit_time) - result["score_time"].append(score_time) - - return result - - -cross_validate.__doc__ = inspect.getdoc( - vendored_model_selection_validation.cross_validate -) + return results diff --git a/bigframes/ml/pipeline.py b/bigframes/ml/pipeline.py index 59057fb2faf..ad0b3fae111 100644 --- a/bigframes/ml/pipeline.py +++ b/bigframes/ml/pipeline.py @@ -12,37 +12,28 @@ # See the License for the specific language governing permissions and # limitations under the License. -"""For composing estimators together. This module is styled after scikit-learn's +"""For composing estimators together. This module is styled after Scikit-Learn's pipeline module: https://scikit-learn.org/stable/modules/pipeline.html.""" + from __future__ import annotations -from typing import List, Optional, Tuple +from typing import cast, List, Optional, Tuple, Union -import bigframes_vendored.constants as constants -import bigframes_vendored.sklearn.pipeline from google.cloud import bigquery -import bigframes.dataframe -import bigframes.session -from bigframes.core.logging import log_adapter -from bigframes.ml import ( - base, - compose, - forecasting, - impute, - loader, - preprocessing, - utils, -) - - -@log_adapter.class_logger +import bigframes +import bigframes.constants as constants +from bigframes.ml import base, compose, forecasting, loader, preprocessing, utils +import bigframes.pandas as bpd +import third_party.bigframes_vendored.sklearn.pipeline + + class Pipeline( base.BaseEstimator, - bigframes_vendored.sklearn.pipeline.Pipeline, + third_party.bigframes_vendored.sklearn.pipeline.Pipeline, ): - __doc__ = bigframes_vendored.sklearn.pipeline.Pipeline.__doc__ + __doc__ = third_party.bigframes_vendored.sklearn.pipeline.Pipeline.__doc__ def __init__(self, steps: List[Tuple[str, base.BaseEstimator]]): self.steps = steps @@ -63,8 +54,6 @@ def __init__(self, steps: List[Tuple[str, base.BaseEstimator]]): preprocessing.MinMaxScaler, preprocessing.KBinsDiscretizer, preprocessing.LabelEncoder, - preprocessing.PolynomialFeatures, - impute.SimpleImputer, ), ): self._transform = transform @@ -91,42 +80,42 @@ def __init__(self, steps: List[Tuple[str, base.BaseEstimator]]): self._estimator = estimator @classmethod - def _from_bq( - cls, session: bigframes.session.Session, bq_model: bigquery.Model - ) -> Pipeline: - col_transformer = compose.ColumnTransformer._extract_from_bq_model(bq_model) - transform = col_transformer._merge(bq_model) + def _from_bq(cls, session: bigframes.Session, bq_model: bigquery.Model) -> Pipeline: + col_transformer = _extract_as_column_transformer(bq_model) + transform = _merge_column_transformer(bq_model, col_transformer) estimator = loader._model_from_bq(session, bq_model) return cls([("transform", transform), ("estimator", estimator)]) def fit( self, - X: utils.BigFramesArrayType, - y: Optional[utils.BigFramesArrayType] = None, + X: Union[bpd.DataFrame, bpd.Series], + y: Optional[Union[bpd.DataFrame, bpd.Series]] = None, ) -> Pipeline: - (X,) = utils.batch_convert_to_dataframe(X) + (X,) = utils.convert_to_dataframe(X) + + compiled_transforms = self._transform._compile_to_sql(X.columns.tolist(), X=X) + transform_sqls = [transform_sql for transform_sql, _ in compiled_transforms] - transform_sqls = self._transform._compile_to_sql(X) if y is not None: # If labels columns are present, they should pass through un-transformed - (y,) = utils.batch_convert_to_dataframe(y) + (y,) = utils.convert_to_dataframe(y) transform_sqls.extend(y.columns.tolist()) self._estimator._fit(X=X, y=y, transforms=transform_sqls) return self - def predict(self, X: utils.ArrayType) -> bigframes.dataframe.DataFrame: + def predict(self, X: Union[bpd.DataFrame, bpd.Series]) -> bpd.DataFrame: return self._estimator.predict(X) def score( self, - X: utils.BigFramesArrayType, - y: Optional[utils.BigFramesArrayType] = None, - ) -> bigframes.dataframe.DataFrame: - (X,) = utils.batch_convert_to_dataframe(X) + X: Union[bpd.DataFrame, bpd.Series], + y: Optional[Union[bpd.DataFrame, bpd.Series]] = None, + ) -> bpd.DataFrame: + (X,) = utils.convert_to_dataframe(X) if y is not None: - (y,) = utils.batch_convert_to_dataframe(y) + (y,) = utils.convert_to_dataframe(y) return self._estimator.score(X=X, y=y) @@ -135,15 +124,122 @@ def to_gbq(self, model_name: str, replace: bool = False) -> Pipeline: Args: model_name (str): - The name of the model(pipeline). + the name of the model(pipeline). replace (bool, default False): - Whether to replace if the model(pipeline) already exists. Default to False. + whether to replace if the model(pipeline) already exists. Default to False. Returns: - Pipeline: Saved model(pipeline).""" + Pipeline: saved model(pipeline).""" if not self._estimator._bqml_model: raise RuntimeError("A model must be fitted before it can be saved") new_model = self._estimator._bqml_model.copy(model_name, replace) return new_model.session.read_gbq_model(model_name) + + +def _extract_as_column_transformer( + bq_model: bigquery.Model, +) -> compose.ColumnTransformer: + """Extract transformers as ColumnTransformer obj from a BQ Model.""" + assert "transformColumns" in bq_model._properties + + transformers: List[ + Tuple[ + str, + Union[ + preprocessing.OneHotEncoder, + preprocessing.StandardScaler, + preprocessing.MaxAbsScaler, + preprocessing.MinMaxScaler, + preprocessing.KBinsDiscretizer, + preprocessing.LabelEncoder, + ], + Union[str, List[str]], + ] + ] = [] + for transform_col in bq_model._properties["transformColumns"]: + # pass the columns that are not transformed + if "transformSql" not in transform_col: + continue + + transform_sql: str = cast(dict, transform_col)["transformSql"] + if transform_sql.startswith("ML.STANDARD_SCALER"): + transformers.append( + ( + "standard_scaler", + *preprocessing.StandardScaler._parse_from_sql(transform_sql), + ) + ) + elif transform_sql.startswith("ML.ONE_HOT_ENCODER"): + transformers.append( + ( + "ont_hot_encoder", + *preprocessing.OneHotEncoder._parse_from_sql(transform_sql), + ) + ) + elif transform_sql.startswith("ML.MAX_ABS_SCALER"): + transformers.append( + ( + "max_abs_scaler", + *preprocessing.MaxAbsScaler._parse_from_sql(transform_sql), + ) + ) + elif transform_sql.startswith("ML.MIN_MAX_SCALER"): + transformers.append( + ( + "min_max_scaler", + *preprocessing.MinMaxScaler._parse_from_sql(transform_sql), + ) + ) + elif transform_sql.startswith("ML.BUCKETIZE"): + transformers.append( + ( + "k_bins_discretizer", + *preprocessing.KBinsDiscretizer._parse_from_sql(transform_sql), + ) + ) + elif transform_sql.startswith("ML.LABEL_ENCODER"): + transformers.append( + ( + "label_encoder", + *preprocessing.LabelEncoder._parse_from_sql(transform_sql), + ) + ) + else: + raise NotImplementedError( + f"Unsupported transformer type. {constants.FEEDBACK_LINK}" + ) + + return compose.ColumnTransformer(transformers=transformers) + + +def _merge_column_transformer( + bq_model: bigquery.Model, column_transformer: compose.ColumnTransformer +) -> Union[ + compose.ColumnTransformer, + preprocessing.StandardScaler, + preprocessing.OneHotEncoder, + preprocessing.MaxAbsScaler, + preprocessing.MinMaxScaler, + preprocessing.KBinsDiscretizer, + preprocessing.LabelEncoder, +]: + """Try to merge the column transformer to a simple transformer.""" + transformers = column_transformer.transformers_ + + assert len(transformers) > 0 + _, transformer_0, column_0 = transformers[0] + columns = [column_0] + for _, transformer, column in transformers[1:]: + # all transformers are the same + if transformer != transformer_0: + return column_transformer + columns.append(column) + # all feature columns are transformed + if sorted( + [cast(str, feature_column.name) for feature_column in bq_model.feature_columns] + ) == sorted(columns): + return transformer_0 + + return column_transformer diff --git a/bigframes/ml/preprocessing.py b/bigframes/ml/preprocessing.py index 28272fd6a02..5f44d402184 100644 --- a/bigframes/ml/preprocessing.py +++ b/bigframes/ml/preprocessing.py @@ -13,57 +13,55 @@ # limitations under the License. """Transformers that prepare data for other estimators. This module is styled after -scikit-learn's preprocessing module: https://scikit-learn.org/stable/modules/preprocessing.html.""" +Scikit-Learn's preprocessing module: https://scikit-learn.org/stable/modules/preprocessing.html.""" from __future__ import annotations import typing -from typing import Iterable, List, Literal, Optional, Union +from typing import Any, cast, List, Literal, Optional, Tuple, Union -import bigframes_vendored.sklearn.preprocessing._data -import bigframes_vendored.sklearn.preprocessing._discretization -import bigframes_vendored.sklearn.preprocessing._encoder -import bigframes_vendored.sklearn.preprocessing._label -import bigframes_vendored.sklearn.preprocessing._polynomial - -import bigframes.core.utils as core_utils -import bigframes.pandas as bpd -from bigframes.core.logging import log_adapter from bigframes.ml import base, core, globals, utils +import bigframes.pandas as bpd +import third_party.bigframes_vendored.sklearn.preprocessing._data +import third_party.bigframes_vendored.sklearn.preprocessing._discretization +import third_party.bigframes_vendored.sklearn.preprocessing._encoder +import third_party.bigframes_vendored.sklearn.preprocessing._label -@log_adapter.class_logger class StandardScaler( base.Transformer, - bigframes_vendored.sklearn.preprocessing._data.StandardScaler, + third_party.bigframes_vendored.sklearn.preprocessing._data.StandardScaler, ): - __doc__ = bigframes_vendored.sklearn.preprocessing._data.StandardScaler.__doc__ + __doc__ = ( + third_party.bigframes_vendored.sklearn.preprocessing._data.StandardScaler.__doc__ + ) def __init__(self): self._bqml_model: Optional[core.BqmlModel] = None self._bqml_model_factory = globals.bqml_model_factory() self._base_sql_generator = globals.base_sql_generator() - def _keys(self): - return (self._bqml_model,) + # TODO(garrettwu): implement __hash__ + def __eq__(self, other: Any) -> bool: + return type(other) is StandardScaler and self._bqml_model == other._bqml_model - def _compile_to_sql( - self, X: bpd.DataFrame, columns: Optional[Iterable[str]] = None - ) -> List[str]: + def _compile_to_sql(self, columns: List[str], X=None) -> List[Tuple[str, str]]: """Compile this transformer to a list of SQL expressions that can be included in a BQML TRANSFORM clause Args: - X: DataFrame to transform. - columns: transform columns. If None, transform all columns in X. + columns: + a list of column names to transform. + X (default None): + Ignored. - Returns: a list of tuples sql_expr.""" - if columns is None: - columns = X.columns - columns, _ = core_utils.get_standardized_ids(columns) + Returns: a list of tuples of (sql_expression, output_name)""" return [ - self._base_sql_generator.ml_standard_scaler( - column, f"standard_scaled_{column}" + ( + self._base_sql_generator.ml_standard_scaler( + column, f"standard_scaled_{column}" + ), + f"standard_scaled_{column}", ) for column in columns ] @@ -78,30 +76,33 @@ def _parse_from_sql(cls, sql: str) -> tuple[StandardScaler, str]: Returns: tuple(StandardScaler, column_label)""" col_label = sql[sql.find("(") + 1 : sql.find(")")] - return cls(), _unescape_id(col_label) + return cls(), col_label def fit( self, - X: utils.ArrayType, + X: Union[bpd.DataFrame, bpd.Series], y=None, # ignored ) -> StandardScaler: - (X,) = utils.batch_convert_to_dataframe(X) + (X,) = utils.convert_to_dataframe(X) + + compiled_transforms = self._compile_to_sql(X.columns.tolist()) + transform_sqls = [transform_sql for transform_sql, _ in compiled_transforms] - transform_sqls = self._compile_to_sql(X) self._bqml_model = self._bqml_model_factory.create_model( X, options={"model_type": "transform_only"}, transforms=transform_sqls, ) - self._extract_output_names() + # The schema of TRANSFORM output is not available in the model API, so save it during fitting + self._output_names = [name for _, name in compiled_transforms] return self - def transform(self, X: utils.ArrayType) -> bpd.DataFrame: + def transform(self, X: Union[bpd.DataFrame, bpd.Series]) -> bpd.DataFrame: if not self._bqml_model: raise RuntimeError("Must be fitted before transform") - (X,) = utils.batch_convert_to_dataframe(X, session=self._bqml_model.session) + (X,) = utils.convert_to_dataframe(X) df = self._bqml_model.transform(X) return typing.cast( @@ -110,38 +111,40 @@ def transform(self, X: utils.ArrayType) -> bpd.DataFrame: ) -@log_adapter.class_logger class MaxAbsScaler( base.Transformer, - bigframes_vendored.sklearn.preprocessing._data.MaxAbsScaler, + third_party.bigframes_vendored.sklearn.preprocessing._data.MaxAbsScaler, ): - __doc__ = bigframes_vendored.sklearn.preprocessing._data.MaxAbsScaler.__doc__ + __doc__ = ( + third_party.bigframes_vendored.sklearn.preprocessing._data.MaxAbsScaler.__doc__ + ) def __init__(self): self._bqml_model: Optional[core.BqmlModel] = None self._bqml_model_factory = globals.bqml_model_factory() self._base_sql_generator = globals.base_sql_generator() - def _keys(self): - return (self._bqml_model,) + # TODO(garrettwu): implement __hash__ + def __eq__(self, other: Any) -> bool: + return type(other) is MaxAbsScaler and self._bqml_model == other._bqml_model - def _compile_to_sql( - self, X: bpd.DataFrame, columns: Optional[Iterable[str]] = None - ) -> List[str]: + def _compile_to_sql(self, columns: List[str], X=None) -> List[Tuple[str, str]]: """Compile this transformer to a list of SQL expressions that can be included in a BQML TRANSFORM clause Args: - X: DataFrame to transform. - columns: transform columns. If None, transform all columns in X. + columns: + a list of column names to transform. + X (default None): + Ignored. - Returns: a list of tuples sql_expr.""" - if columns is None: - columns = X.columns - columns, _ = core_utils.get_standardized_ids(columns) + Returns: a list of tuples of (sql_expression, output_name)""" return [ - self._base_sql_generator.ml_max_abs_scaler( - column, f"max_abs_scaled_{column}" + ( + self._base_sql_generator.ml_max_abs_scaler( + column, f"max_abs_scaled_{column}" + ), + f"max_abs_scaled_{column}", ) for column in columns ] @@ -155,32 +158,34 @@ def _parse_from_sql(cls, sql: str) -> tuple[MaxAbsScaler, str]: Returns: tuple(MaxAbsScaler, column_label)""" - # TODO: Use real sql parser col_label = sql[sql.find("(") + 1 : sql.find(")")] - return cls(), _unescape_id(col_label) + return cls(), col_label def fit( self, - X: utils.ArrayType, + X: Union[bpd.DataFrame, bpd.Series], y=None, # ignored ) -> MaxAbsScaler: - (X,) = utils.batch_convert_to_dataframe(X) + (X,) = utils.convert_to_dataframe(X) + + compiled_transforms = self._compile_to_sql(X.columns.tolist()) + transform_sqls = [transform_sql for transform_sql, _ in compiled_transforms] - transform_sqls = self._compile_to_sql(X) self._bqml_model = self._bqml_model_factory.create_model( X, options={"model_type": "transform_only"}, transforms=transform_sqls, ) - self._extract_output_names() + # The schema of TRANSFORM output is not available in the model API, so save it during fitting + self._output_names = [name for _, name in compiled_transforms] return self - def transform(self, X: utils.ArrayType) -> bpd.DataFrame: + def transform(self, X: Union[bpd.DataFrame, bpd.Series]) -> bpd.DataFrame: if not self._bqml_model: raise RuntimeError("Must be fitted before transform") - (X,) = utils.batch_convert_to_dataframe(X, session=self._bqml_model.session) + (X,) = utils.convert_to_dataframe(X) df = self._bqml_model.transform(X) return typing.cast( @@ -189,38 +194,40 @@ def transform(self, X: utils.ArrayType) -> bpd.DataFrame: ) -@log_adapter.class_logger class MinMaxScaler( base.Transformer, - bigframes_vendored.sklearn.preprocessing._data.MinMaxScaler, + third_party.bigframes_vendored.sklearn.preprocessing._data.MinMaxScaler, ): - __doc__ = bigframes_vendored.sklearn.preprocessing._data.MinMaxScaler.__doc__ + __doc__ = ( + third_party.bigframes_vendored.sklearn.preprocessing._data.MinMaxScaler.__doc__ + ) def __init__(self): self._bqml_model: Optional[core.BqmlModel] = None self._bqml_model_factory = globals.bqml_model_factory() self._base_sql_generator = globals.base_sql_generator() - def _keys(self): - return (self._bqml_model,) + # TODO(garrettwu): implement __hash__ + def __eq__(self, other: Any) -> bool: + return type(other) is MinMaxScaler and self._bqml_model == other._bqml_model - def _compile_to_sql( - self, X: bpd.DataFrame, columns: Optional[Iterable[str]] = None - ) -> List[str]: + def _compile_to_sql(self, columns: List[str], X=None) -> List[Tuple[str, str]]: """Compile this transformer to a list of SQL expressions that can be included in a BQML TRANSFORM clause Args: - X: DataFrame to transform. - columns: transform columns. If None, transform all columns in X. + columns: + a list of column names to transform. + X (default None): + Ignored. - Returns: a list of tuples sql_expr.""" - if columns is None: - columns = X.columns - columns, _ = core_utils.get_standardized_ids(columns) + Returns: a list of tuples of (sql_expression, output_name)""" return [ - self._base_sql_generator.ml_min_max_scaler( - column, f"min_max_scaled_{column}" + ( + self._base_sql_generator.ml_min_max_scaler( + column, f"min_max_scaled_{column}" + ), + f"min_max_scaled_{column}", ) for column in columns ] @@ -234,32 +241,34 @@ def _parse_from_sql(cls, sql: str) -> tuple[MinMaxScaler, str]: Returns: tuple(MinMaxScaler, column_label)""" - # TODO: Use real sql parser col_label = sql[sql.find("(") + 1 : sql.find(")")] - return cls(), _unescape_id(col_label) + return cls(), col_label def fit( self, - X: utils.ArrayType, + X: Union[bpd.DataFrame, bpd.Series], y=None, # ignored ) -> MinMaxScaler: - (X,) = utils.batch_convert_to_dataframe(X) + (X,) = utils.convert_to_dataframe(X) + + compiled_transforms = self._compile_to_sql(X.columns.tolist()) + transform_sqls = [transform_sql for transform_sql, _ in compiled_transforms] - transform_sqls = self._compile_to_sql(X) self._bqml_model = self._bqml_model_factory.create_model( X, options={"model_type": "transform_only"}, transforms=transform_sqls, ) - self._extract_output_names() + # The schema of TRANSFORM output is not available in the model API, so save it during fitting + self._output_names = [name for _, name in compiled_transforms] return self - def transform(self, X: utils.ArrayType) -> bpd.DataFrame: + def transform(self, X: Union[bpd.DataFrame, bpd.Series]) -> bpd.DataFrame: if not self._bqml_model: raise RuntimeError("Must be fitted before transform") - (X,) = utils.batch_convert_to_dataframe(X, session=self._bqml_model.session) + (X,) = utils.convert_to_dataframe(X) df = self._bqml_model.transform(X) return typing.cast( @@ -268,18 +277,23 @@ def transform(self, X: utils.ArrayType) -> bpd.DataFrame: ) -@log_adapter.class_logger class KBinsDiscretizer( base.Transformer, - bigframes_vendored.sklearn.preprocessing._discretization.KBinsDiscretizer, + third_party.bigframes_vendored.sklearn.preprocessing._discretization.KBinsDiscretizer, ): - __doc__ = bigframes_vendored.sklearn.preprocessing._discretization.KBinsDiscretizer.__doc__ + __doc__ = ( + third_party.bigframes_vendored.sklearn.preprocessing._discretization.KBinsDiscretizer.__doc__ + ) def __init__( self, n_bins: int = 5, strategy: Literal["uniform", "quantile"] = "quantile", ): + if strategy != "uniform": + raise NotImplementedError( + f"Only strategy = 'uniform' is supported now, input is {strategy}." + ) if n_bins < 2: raise ValueError( f"n_bins has to be larger than or equal to 2, input is {n_bins}." @@ -290,97 +304,89 @@ def __init__( self._bqml_model_factory = globals.bqml_model_factory() self._base_sql_generator = globals.base_sql_generator() - def _keys(self): - return (self._bqml_model, self.n_bins, self.strategy) + # TODO(garrettwu): implement __hash__ + def __eq__(self, other: Any) -> bool: + return ( + type(other) is KBinsDiscretizer + and self.n_bins == other.n_bins + and self._bqml_model == other._bqml_model + ) def _compile_to_sql( - self, X: bpd.DataFrame, columns: Optional[Iterable[str]] = None - ) -> List[str]: + self, + columns: List[str], + X: bpd.DataFrame, + ) -> List[Tuple[str, str]]: """Compile this transformer to a list of SQL expressions that can be included in a BQML TRANSFORM clause Args: - X: DataFrame to transform. - columns: transform columns. If None, transform all columns in X. + columns: + a list of column names to transform + X: + The Dataframe with training data. - Returns: a list of tuples sql_expr.""" - if columns is None: - columns = X.columns - columns, _ = core_utils.get_standardized_ids(columns) + Returns: a list of tuples of (sql_expression, output_name)""" array_split_points = {} if self.strategy == "uniform": for column in columns: min_value = X[column].min() max_value = X[column].max() - bin_size = (max_value - min_value) / self.n_bins array_split_points[column] = [ min_value + i * bin_size for i in range(self.n_bins - 1) ] - return [ + return [ + ( self._base_sql_generator.ml_bucketize( column, array_split_points[column], f"kbinsdiscretizer_{column}" - ) - for column in columns - ] - - elif self.strategy == "quantile": - return [ - self._base_sql_generator.ml_quantile_bucketize( - column, self.n_bins, f"kbinsdiscretizer_{column}" - ) - for column in columns - ] - - else: - raise ValueError( - f"strategy should be set 'quantile' or 'uniform', but your input is {self.strategy}." + ), + f"kbinsdiscretizer_{column}", ) + for column in columns + ] @classmethod def _parse_from_sql(cls, sql: str) -> tuple[KBinsDiscretizer, str]: """Parse SQL to tuple(KBinsDiscretizer, column_label). Args: - sql: SQL string of format "ML.BUCKETIZE({col_label}, array_split_points, FALSE)" - or ML.QUANTILE_BUCKETIZE({col_label}, num_bucket) OVER()" + sql: SQL string of format "ML.BUCKETIZE({col_label}, array_split_points, FALSE) OVER()" Returns: tuple(KBinsDiscretizer, column_label)""" s = sql[sql.find("(") + 1 : sql.find(")")] + array_split_points = s[s.find("[") + 1 : s.find("]")] col_label = s[: s.find(",")] - - if sql.startswith("ML.QUANTILE_BUCKETIZE"): - num_bins = s.split(",")[1] - return cls(int(num_bins), "quantile"), _unescape_id(col_label) - else: - array_split_points = s[s.find("[") + 1 : s.find("]")] - n_bins = array_split_points.count(",") + 2 - return cls(n_bins, "uniform"), _unescape_id(col_label) + n_bins = array_split_points.count(",") + 2 + return cls(n_bins, "uniform"), col_label def fit( self, - X: utils.ArrayType, + X: Union[bpd.DataFrame, bpd.Series], y=None, # ignored ) -> KBinsDiscretizer: - (X,) = utils.batch_convert_to_dataframe(X) + (X,) = utils.convert_to_dataframe(X) + + compiled_transforms = self._compile_to_sql(X.columns.tolist(), X) + transform_sqls = [transform_sql for transform_sql, _ in compiled_transforms] - transform_sqls = self._compile_to_sql(X) self._bqml_model = self._bqml_model_factory.create_model( X, options={"model_type": "transform_only"}, transforms=transform_sqls, ) - self._extract_output_names() + # The schema of TRANSFORM output is not available in the model API, so save it during fitting + self._output_names = [name for _, name in compiled_transforms] return self - def transform(self, X: utils.ArrayType) -> bpd.DataFrame: + def transform(self, X: Union[bpd.DataFrame, bpd.Series]) -> bpd.DataFrame: if not self._bqml_model: raise RuntimeError("Must be fitted before transform") - (X,) = utils.batch_convert_to_dataframe(X, session=self._bqml_model.session) + (X,) = utils.convert_to_dataframe(X) df = self._bqml_model.transform(X) return typing.cast( @@ -389,16 +395,17 @@ def transform(self, X: utils.ArrayType) -> bpd.DataFrame: ) -@log_adapter.class_logger class OneHotEncoder( base.Transformer, - bigframes_vendored.sklearn.preprocessing._encoder.OneHotEncoder, + third_party.bigframes_vendored.sklearn.preprocessing._encoder.OneHotEncoder, ): # BQML max value https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-one-hot-encoder#syntax TOP_K_DEFAULT = 1000000 FREQUENCY_THRESHOLD_DEFAULT = 0 - __doc__ = bigframes_vendored.sklearn.preprocessing._encoder.OneHotEncoder.__doc__ + __doc__ = ( + third_party.bigframes_vendored.sklearn.preprocessing._encoder.OneHotEncoder.__doc__ + ) # All estimators must implement __init__ to document their parameters, even # if they don't have any @@ -419,25 +426,30 @@ def __init__( self._bqml_model_factory = globals.bqml_model_factory() self._base_sql_generator = globals.base_sql_generator() - def _keys(self): - return (self._bqml_model, self.drop, self.min_frequency, self.max_categories) + # TODO(garrettwu): implement __hash__ + def __eq__(self, other: Any) -> bool: + return ( + type(other) is OneHotEncoder + and self._bqml_model == other._bqml_model + and self.drop == other.drop + and self.min_frequency == other.min_frequency + and self.max_categories == other.max_categories + ) - def _compile_to_sql( - self, X: bpd.DataFrame, columns: Optional[Iterable[str]] = None - ) -> List[str]: + def _compile_to_sql(self, columns: List[str], X=None) -> List[Tuple[str, str]]: """Compile this transformer to a list of SQL expressions that can be included in a BQML TRANSFORM clause Args: - X: DataFrame to transform. - columns: transform columns. If None, transform all columns in X. + columns: + a list of column names to transform. + X (default None): + Ignored. + + Returns: a list of tuples of (sql_expression, output_name)""" - Returns: a list of tuples sql_expr.""" - if columns is None: - columns = X.columns - columns, _ = core_utils.get_standardized_ids(columns) drop = self.drop if self.drop is not None else "none" - # minus one here since BQML's implementation always includes index 0, and top_k is on top of that. + # minus one here since BQML's inplimentation always includes index 0, and top_k is on top of that. top_k = ( (self.max_categories - 1) if self.max_categories is not None @@ -449,8 +461,11 @@ def _compile_to_sql( else OneHotEncoder.FREQUENCY_THRESHOLD_DEFAULT ) return [ - self._base_sql_generator.ml_one_hot_encoder( - column, drop, top_k, frequency_threshold, f"onehotencoded_{column}" + ( + self._base_sql_generator.ml_one_hot_encoder( + column, drop, top_k, frequency_threshold, f"onehotencoded_{column}" + ), + f"onehotencoded_{column}", ) for column in columns ] @@ -467,37 +482,40 @@ def _parse_from_sql(cls, sql: str) -> tuple[OneHotEncoder, str]: s = sql[sql.find("(") + 1 : sql.find(")")] col_label, drop_str, top_k, frequency_threshold = s.split(", ") drop = ( - typing.cast(Literal["most_frequent"], "most_frequent") + cast(Literal["most_frequent"], "most_frequent") if drop_str.lower() == "'most_frequent'" else None ) max_categories = int(top_k) + 1 min_frequency = int(frequency_threshold) - return cls(drop, min_frequency, max_categories), _unescape_id(col_label) + return cls(drop, min_frequency, max_categories), col_label def fit( self, - X: utils.ArrayType, + X: Union[bpd.DataFrame, bpd.Series], y=None, # ignored ) -> OneHotEncoder: - (X,) = utils.batch_convert_to_dataframe(X) + (X,) = utils.convert_to_dataframe(X) + + compiled_transforms = self._compile_to_sql(X.columns.tolist()) + transform_sqls = [transform_sql for transform_sql, _ in compiled_transforms] - transform_sqls = self._compile_to_sql(X) self._bqml_model = self._bqml_model_factory.create_model( X, options={"model_type": "transform_only"}, transforms=transform_sqls, ) - self._extract_output_names() + # The schema of TRANSFORM output is not available in the model API, so save it during fitting + self._output_names = [name for _, name in compiled_transforms] return self - def transform(self, X: utils.ArrayType) -> bpd.DataFrame: + def transform(self, X: Union[bpd.DataFrame, bpd.Series]) -> bpd.DataFrame: if not self._bqml_model: raise RuntimeError("Must be fitted before transform") - (X,) = utils.batch_convert_to_dataframe(X, session=self._bqml_model.session) + (X,) = utils.convert_to_dataframe(X) df = self._bqml_model.transform(X) return typing.cast( @@ -506,16 +524,17 @@ def transform(self, X: utils.ArrayType) -> bpd.DataFrame: ) -@log_adapter.class_logger class LabelEncoder( base.LabelTransformer, - bigframes_vendored.sklearn.preprocessing._label.LabelEncoder, + third_party.bigframes_vendored.sklearn.preprocessing._label.LabelEncoder, ): # BQML max value https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-one-hot-encoder#syntax TOP_K_DEFAULT = 1000000 FREQUENCY_THRESHOLD_DEFAULT = 0 - __doc__ = bigframes_vendored.sklearn.preprocessing._label.LabelEncoder.__doc__ + __doc__ = ( + third_party.bigframes_vendored.sklearn.preprocessing._label.LabelEncoder.__doc__ + ) # All estimators must implement __init__ to document their parameters, even # if they don't have any @@ -534,23 +553,26 @@ def __init__( self._bqml_model_factory = globals.bqml_model_factory() self._base_sql_generator = globals.base_sql_generator() - def _keys(self): - return (self._bqml_model, self.min_frequency, self.max_categories) + # TODO(garrettwu): implement __hash__ + def __eq__(self, other: Any) -> bool: + return ( + type(other) is LabelEncoder + and self._bqml_model == other._bqml_model + and self.min_frequency == other.min_frequency + and self.max_categories == other.max_categories + ) - def _compile_to_sql( - self, X: bpd.DataFrame, columns: Optional[Iterable[str]] = None - ) -> List[str]: + def _compile_to_sql(self, columns: List[str], X=None) -> List[Tuple[str, str]]: """Compile this transformer to a list of SQL expressions that can be included in a BQML TRANSFORM clause Args: - X: DataFrame to transform. - columns: transform columns. If None, transform all columns in X. + columns: + a list of column names to transform. + X (default None): + Ignored. - Returns: a list of tuples sql_expr.""" - if columns is None: - columns = X.columns - columns, _ = core_utils.get_standardized_ids(columns) + Returns: a list of tuples of (sql_expression, output_name)""" # minus one here since BQML's inplimentation always includes index 0, and top_k is on top of that. top_k = ( @@ -564,8 +586,11 @@ def _compile_to_sql( else LabelEncoder.FREQUENCY_THRESHOLD_DEFAULT ) return [ - self._base_sql_generator.ml_label_encoder( - column, top_k, frequency_threshold, f"labelencoded_{column}" + ( + self._base_sql_generator.ml_label_encoder( + column, top_k, frequency_threshold, f"labelencoded_{column}" + ), + f"labelencoded_{column}", ) for column in columns ] @@ -584,137 +609,35 @@ def _parse_from_sql(cls, sql: str) -> tuple[LabelEncoder, str]: max_categories = int(top_k) + 1 min_frequency = int(frequency_threshold) - return cls(min_frequency, max_categories), _unescape_id(col_label) + return cls(min_frequency, max_categories), col_label def fit( self, - y: utils.ArrayType, + y: Union[bpd.DataFrame, bpd.Series], ) -> LabelEncoder: - (y,) = utils.batch_convert_to_dataframe(y) + (y,) = utils.convert_to_dataframe(y) + + compiled_transforms = self._compile_to_sql(y.columns.tolist()) + transform_sqls = [transform_sql for transform_sql, _ in compiled_transforms] - transform_sqls = self._compile_to_sql(y) self._bqml_model = self._bqml_model_factory.create_model( y, options={"model_type": "transform_only"}, transforms=transform_sqls, ) - self._extract_output_names() + # The schema of TRANSFORM output is not available in the model API, so save it during fitting + self._output_names = [name for _, name in compiled_transforms] return self - def transform(self, y: utils.ArrayType) -> bpd.DataFrame: + def transform(self, y: Union[bpd.DataFrame, bpd.Series]) -> bpd.DataFrame: if not self._bqml_model: raise RuntimeError("Must be fitted before transform") - (y,) = utils.batch_convert_to_dataframe(y, session=self._bqml_model.session) + (y,) = utils.convert_to_dataframe(y) df = self._bqml_model.transform(y) return typing.cast( bpd.DataFrame, df[self._output_names], ) - - -@log_adapter.class_logger -class PolynomialFeatures( - base.Transformer, - bigframes_vendored.sklearn.preprocessing._polynomial.PolynomialFeatures, -): - __doc__ = ( - bigframes_vendored.sklearn.preprocessing._polynomial.PolynomialFeatures.__doc__ - ) - - def __init__(self, degree: int = 2): - if degree not in range(1, 5): - raise ValueError(f"degree has to be [1, 4], input is {degree}.") - self.degree = degree - self._bqml_model: Optional[core.BqmlModel] = None - self._bqml_model_factory = globals.bqml_model_factory() - self._base_sql_generator = globals.base_sql_generator() - - def _keys(self): - return (self._bqml_model, self.degree) - - def _compile_to_sql( - self, X: bpd.DataFrame, columns: Optional[Iterable[str]] = None - ) -> List[str]: - """Compile this transformer to a list of SQL expressions that can be included in - a BQML TRANSFORM clause - - Args: - X: DataFrame to transform. - columns: transform columns. If None, transform all columns in X. - - Returns: a list of tuples sql_expr.""" - if columns is None: - columns = X.columns - columns, _ = core_utils.get_standardized_ids(columns) - output_name = "poly_feat" - return [ - self._base_sql_generator.ml_polynomial_expand( - columns, self.degree, output_name - ) - ] - - @classmethod - def _parse_from_sql(cls, sql: str) -> tuple[PolynomialFeatures, tuple[str, ...]]: - """Parse SQL to tuple(PolynomialFeatures, column_labels). - - Args: - sql: SQL string of format "ML.POLYNOMIAL_EXPAND(STRUCT(col_label0, col_label1, ...), degree)" - - Returns: - tuple(MaxAbsScaler, column_label)""" - col_labels = sql[sql.find("STRUCT(") + 7 : sql.find(")")].split(",") - col_labels = [label.strip() for label in col_labels] - degree = int(sql[sql.rfind(",") + 1 : sql.rfind(")")]) - return cls(degree), tuple(map(_unescape_id, col_labels)) - - def fit( - self, - X: utils.ArrayType, - y=None, # ignored - ) -> PolynomialFeatures: - (X,) = utils.batch_convert_to_dataframe(X) - - transform_sqls = self._compile_to_sql(X) - self._bqml_model = self._bqml_model_factory.create_model( - X, - options={"model_type": "transform_only"}, - transforms=transform_sqls, - ) - - self._extract_output_names() - - return self - - def transform(self, X: utils.ArrayType) -> bpd.DataFrame: - if not self._bqml_model: - raise RuntimeError("Must be fitted before transform") - - (X,) = utils.batch_convert_to_dataframe(X, session=self._bqml_model.session) - - df = self._bqml_model.transform(X) - return typing.cast( - bpd.DataFrame, - df[self._output_names], - ) - - -def _unescape_id(id: str) -> str: - """Very simple conversion to removed ` characters from ids. - - A proper sql parser should be used instead. - """ - return id.removeprefix("`").removesuffix("`") - - -PreprocessingType = Union[ - OneHotEncoder, - StandardScaler, - MaxAbsScaler, - MinMaxScaler, - KBinsDiscretizer, - LabelEncoder, - PolynomialFeatures, -] diff --git a/bigframes/ml/remote.py b/bigframes/ml/remote.py deleted file mode 100644 index f53ea645e92..00000000000 --- a/bigframes/ml/remote.py +++ /dev/null @@ -1,125 +0,0 @@ -# Copyright 2023 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -"""BigFrames general remote models.""" - -from __future__ import annotations - -import warnings -from typing import Mapping, Optional - -import bigframes.dataframe -import bigframes.exceptions as bfe -import bigframes.session -from bigframes.core import global_session -from bigframes.core.logging import log_adapter -from bigframes.ml import base, core, globals, utils - -_REMOTE_MODEL_STATUS = "remote_model_status" - - -@log_adapter.class_logger -class VertexAIModel(base.BaseEstimator): - """Remote model from a Vertex AI HTTPS endpoint. User must specify HTTPS endpoint, input schema and output schema. - For more information, see Deploy model on Vertex AI: https://cloud.google.com/bigquery/docs/bigquery-ml-remote-model-tutorial#Deploy-Model-on-Vertex-AI. - - Args: - endpoint (str): - Vertex AI HTTPS endpoint. - input (Mapping): - Input schema: `{column_name: column_type}`. Supported types are "bool", "string", "int64", "float64", "array", "array", "array", "array". - output (Mapping): - Output label schema: `{column_name: column_type}`. Supported the same types as the input. - session (bigframes.Session or None): - BQ session to create the model. If None, use the global default session. - connection_name (str or None): - Connection to connect with remote service. str of the format ... - If None, use default connection in session context. BigQuery DataFrame will try to create the connection and attach - permission if the connection isn't fully set up. - """ - - def __init__( - self, - endpoint: str, - input: Mapping[str, str], - output: Mapping[str, str], - *, - session: Optional[bigframes.session.Session] = None, - connection_name: Optional[str] = None, - ): - self.endpoint = endpoint - self.input = input - self.output = output - self.session = session or global_session.get_global_session() - - self._bq_connection_manager = self.session.bqconnectionmanager - self.connection_name = connection_name - - self._bqml_model_factory = globals.bqml_model_factory() - self._bqml_model: core.BqmlModel = self._create_bqml_model() - - def _create_bqml_model(self): - # Parse and create connection if needed. - self.connection_name = self.session._create_bq_connection( - connection=self.connection_name, iam_role="aiplatform.user" - ) - - options = { - "endpoint": self.endpoint, - } - - self.input = { - k: utils.standardize_type(v, globals._REMOTE_MODEL_SUPPORTED_DTYPES) - for k, v in self.input.items() - } - self.output = { - k: utils.standardize_type(v, globals._REMOTE_MODEL_SUPPORTED_DTYPES) - for k, v in self.output.items() - } - - return self._bqml_model_factory.create_remote_model( - session=self.session, - connection_name=self.connection_name, - input=self.input, - output=self.output, - options=options, - ) - - def predict( - self, - X: utils.ArrayType, - ) -> bigframes.dataframe.DataFrame: - """Predict the result from the input DataFrame. - - Args: - X (bigframes.pandas.DataFrame or bigframes.pandas.Series or pandas.DataFrame or pandas.Series): - Input DataFrame or Series, which needs to comply with the input parameter of the model. - - Returns: - bigframes.pandas.DataFrame: DataFrame of shape (n_samples, n_input_columns + n_prediction_columns). Returns predicted values. - """ - - (X,) = utils.batch_convert_to_dataframe(X, session=self._bqml_model.session) - - df = self._bqml_model.predict(X) - - # unlike LLM models, the general remote model status is null for successful runs. - if (df[_REMOTE_MODEL_STATUS].notna()).any(): - msg = bfe.format_message( - f"Some predictions failed. Check column {_REMOTE_MODEL_STATUS} for " - "detailed status. You may want to filter the failed rows and retry." - ) - warnings.warn(msg, category=RuntimeWarning) - - return df diff --git a/bigframes/ml/sql.py b/bigframes/ml/sql.py index 894fc44b1b3..601b2710999 100644 --- a/bigframes/ml/sql.py +++ b/bigframes/ml/sql.py @@ -16,90 +16,53 @@ Generates SQL queries needed for BigQuery DataFrames ML """ -from typing import Iterable, Literal, Mapping, Optional, Union +from typing import Iterable, Mapping, Optional, Union -import bigframes_vendored.constants as constants -import google.cloud.bigquery +import bigframes.constants as constants +import bigframes.pandas as bpd -from bigframes.core.compile.sqlglot import sql as sg_sql -INDENT_STR = " " - - -# TODO: Add proper escaping logic from core/compile module class BaseSqlGenerator: """Generate base SQL strings for ML. Model name isn't needed in this class.""" # General methods def encode_value(self, v: Union[str, int, float, Iterable[str]]) -> str: """Encode a parameter value for SQL""" - if isinstance(v, (str, int, float)): - return sg_sql.to_sql(sg_sql.literal(v)) + if isinstance(v, str): + return f'"{v}"' + elif isinstance(v, int) or isinstance(v, float): + return f"{v}" elif isinstance(v, Iterable): inner = ", ".join([self.encode_value(x) for x in v]) return f"[{inner}]" else: - raise ValueError( - f"Unexpected value type {type(v)}. {constants.FEEDBACK_LINK}" - ) + raise ValueError(f"Unexpected value type. {constants.FEEDBACK_LINK}") def build_parameters(self, **kwargs: Union[str, int, float, Iterable[str]]) -> str: """Encode a dict of values into a formatted Iterable of key-value pairs for SQL""" + indent_str = " " param_strs = [f"{k}={self.encode_value(v)}" for k, v in kwargs.items()] - return "\n" + INDENT_STR + f",\n{INDENT_STR}".join(param_strs) + return "\n" + indent_str + f",\n{indent_str}".join(param_strs) - def build_named_parameters( - self, **kwargs: Union[str, int, float, Iterable[str]] - ) -> str: - param_strs = [f"{k} => {self.encode_value(v)}" for k, v in kwargs.items()] - return "\n" + INDENT_STR + f",\n{INDENT_STR}".join(param_strs) - - def build_structs(self, **kwargs: Union[int, float, str, Mapping]) -> str: + def build_structs(self, **kwargs: Union[int, float]) -> str: """Encode a dict of values into a formatted STRUCT items for SQL""" - param_strs = [] - for k, v in kwargs.items(): - v_trans = self.build_schema(**v) if isinstance(v, Mapping) else v - - param_strs.append( - f"{sg_sql.to_sql(sg_sql.literal(v_trans))} AS {sg_sql.to_sql(sg_sql.identifier(k))}" - ) - - return "\n" + INDENT_STR + f",\n{INDENT_STR}".join(param_strs) + indent_str = " " + param_strs = [f"{v} AS {k}" for k, v in kwargs.items()] + return "\n" + indent_str + f",\n{indent_str}".join(param_strs) def build_expressions(self, *expr_sqls: str) -> str: """Encode a Iterable of SQL expressions into a formatted Iterable for SQL""" - return "\n" + INDENT_STR + f",\n{INDENT_STR}".join(expr_sqls) - - def build_schema(self, **kwargs: str) -> str: - """Encode a dict of values into a formatted schema type items for SQL""" - param_strs = [ - f"{sg_sql.to_sql(sg_sql.identifier(k))} {v}" for k, v in kwargs.items() - ] - return "\n" + INDENT_STR + f",\n{INDENT_STR}".join(param_strs) + indent_str = " " + return "\n" + indent_str + f",\n{indent_str}".join(expr_sqls) def options(self, **kwargs: Union[str, int, float, Iterable[str]]) -> str: """Encode the OPTIONS clause for BQML""" return f"OPTIONS({self.build_parameters(**kwargs)})" - def struct_options(self, **kwargs: Union[int, float, Mapping]) -> str: + def struct_options(self, **kwargs: Union[int, float]) -> str: """Encode a BQ STRUCT as options.""" return f"STRUCT({self.build_structs(**kwargs)})" - def struct_columns(self, columns: Iterable[str]) -> str: - """Encode a BQ Table columns to a STRUCT.""" - columns_str = ", ".join( - map(lambda x: sg_sql.to_sql(sg_sql.identifier(x)), columns) - ) - return f"STRUCT({columns_str})" - - def input(self, **kwargs: str) -> str: - """Encode a BQML INPUT clause.""" - return f"INPUT({self.build_schema(**kwargs)})" - - def output(self, **kwargs: str) -> str: - """Encode a BQML OUTPUT clause.""" - return f"OUTPUT({self.build_schema(**kwargs)})" - # Connection def connection(self, conn_name: str) -> str: """Encode the REMOTE WITH CONNECTION clause for BQML. conn_name is of the format ...""" @@ -112,47 +75,24 @@ def transform(self, *expr_sqls: str) -> str: def ml_standard_scaler(self, numeric_expr_sql: str, name: str) -> str: """Encode ML.STANDARD_SCALER for BQML""" - return f"""ML.STANDARD_SCALER({sg_sql.to_sql(sg_sql.identifier(numeric_expr_sql))}) OVER() AS {sg_sql.to_sql(sg_sql.identifier(name))}""" + return f"""ML.STANDARD_SCALER({numeric_expr_sql}) OVER() AS {name}""" def ml_max_abs_scaler(self, numeric_expr_sql: str, name: str) -> str: """Encode ML.MAX_ABS_SCALER for BQML""" - return f"""ML.MAX_ABS_SCALER({sg_sql.to_sql(sg_sql.identifier(numeric_expr_sql))}) OVER() AS {sg_sql.to_sql(sg_sql.identifier(name))}""" + return f"""ML.MAX_ABS_SCALER({numeric_expr_sql}) OVER() AS {name}""" def ml_min_max_scaler(self, numeric_expr_sql: str, name: str) -> str: """Encode ML.MIN_MAX_SCALER for BQML""" - return f"""ML.MIN_MAX_SCALER({sg_sql.to_sql(sg_sql.identifier(numeric_expr_sql))}) OVER() AS {sg_sql.to_sql(sg_sql.identifier(name))}""" - - def ml_imputer( - self, - col_name: str, - strategy: str, - name: str, - ) -> str: - """Encode ML.IMPUTER for BQML""" - return f"""ML.IMPUTER({sg_sql.to_sql(sg_sql.identifier(col_name))}, '{strategy}') OVER() AS {sg_sql.to_sql(sg_sql.identifier(name))}""" + return f"""ML.MIN_MAX_SCALER({numeric_expr_sql}) OVER() AS {name}""" def ml_bucketize( - self, - input_id: str, - array_split_points: Iterable[Union[int, float]], - output_id: str, - ) -> str: - """Encode ML.BUCKETIZE for BQML""" - # Use Python value rather than Numpy value to serialization. - points = [ - point.item() if hasattr(point, "item") else point - for point in array_split_points - ] - return f"""ML.BUCKETIZE({sg_sql.to_sql(sg_sql.identifier(input_id))}, {points}, FALSE) AS {sg_sql.to_sql(sg_sql.identifier(output_id))}""" - - def ml_quantile_bucketize( self, numeric_expr_sql: str, - num_bucket: int, + array_split_points: Iterable[Union[int, float]], name: str, ) -> str: - """Encode ML.QUANTILE_BUCKETIZE for BQML""" - return f"""ML.QUANTILE_BUCKETIZE({sg_sql.to_sql(sg_sql.identifier(numeric_expr_sql))}, {num_bucket}) OVER() AS {sg_sql.to_sql(sg_sql.identifier(name))}""" + """Encode ML.MIN_MAX_SCALER for BQML""" + return f"""ML.BUCKETIZE({numeric_expr_sql}, {array_split_points}, FALSE) AS {name}""" def ml_one_hot_encoder( self, @@ -163,9 +103,8 @@ def ml_one_hot_encoder( name: str, ) -> str: """Encode ML.ONE_HOT_ENCODER for BQML. - https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-one-hot-encoder for params. - """ - return f"""ML.ONE_HOT_ENCODER({sg_sql.to_sql(sg_sql.identifier(numeric_expr_sql))}, '{drop}', {top_k}, {frequency_threshold}) OVER() AS {sg_sql.to_sql(sg_sql.identifier(name))}""" + https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-one-hot-encoder for params.""" + return f"""ML.ONE_HOT_ENCODER({numeric_expr_sql}, '{drop}', {top_k}, {frequency_threshold}) OVER() AS {name}""" def ml_label_encoder( self, @@ -175,143 +114,73 @@ def ml_label_encoder( name: str, ) -> str: """Encode ML.LABEL_ENCODER for BQML. - https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-label-encoder for params. - """ - return f"""ML.LABEL_ENCODER({sg_sql.to_sql(sg_sql.identifier(numeric_expr_sql))}, {top_k}, {frequency_threshold}) OVER() AS {sg_sql.to_sql(sg_sql.identifier(name))}""" - - def ml_polynomial_expand( - self, columns: Iterable[str], degree: int, name: str - ) -> str: - """Encode ML.POLYNOMIAL_EXPAND. - https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-polynomial-expand - """ - return f"""ML.POLYNOMIAL_EXPAND({self.struct_columns(columns)}, {degree}) AS {sg_sql.to_sql(sg_sql.identifier(name))}""" - - def ml_distance( - self, - col_x: str, - col_y: str, - type: Literal["EUCLIDEAN", "MANHATTAN", "COSINE"], - source_sql: str, - name: str, - ) -> str: - """Encode ML.DISTANCE for BQML. - https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-distance - """ - return f"""SELECT *, ML.DISTANCE({sg_sql.to_sql(sg_sql.identifier(col_x))}, {sg_sql.to_sql(sg_sql.identifier(col_y))}, '{type}') AS {sg_sql.to_sql(sg_sql.identifier(name))} FROM ({source_sql})""" - - def ai_forecast( - self, - source_sql: str, - options: Mapping[str, Union[int, float, bool, Iterable[str]]], - ): - """Encode AI.FORECAST. - https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-ai-forecast - """ - named_parameters_sql = self.build_named_parameters(**options) - - return f"""SELECT * FROM AI.FORECAST(({source_sql}),{named_parameters_sql})""" + https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-label-encoder for params.""" + return f"""ML.LABEL_ENCODER({numeric_expr_sql}, {top_k}, {frequency_threshold}) OVER() AS {name}""" class ModelCreationSqlGenerator(BaseSqlGenerator): """Sql generator for creating a model entity. Model id is the standalone id without project id and dataset id.""" - def _model_id_sql( - self, - model_ref: google.cloud.bigquery.ModelReference, - ): - return f"{sg_sql.to_sql(sg_sql.identifier(model_ref.project))}.{sg_sql.to_sql(sg_sql.identifier(model_ref.dataset_id))}.{sg_sql.to_sql(sg_sql.identifier(model_ref.model_id))}" + def __init__(self, model_id: str): + self._model_id = model_id # Model create and alter def create_model( self, - source_sql: str, - model_ref: google.cloud.bigquery.ModelReference, + source_df: bpd.DataFrame, options: Mapping[str, Union[str, int, float, Iterable[str]]] = {}, transforms: Optional[Iterable[str]] = None, ) -> str: - """Encode the CREATE OR REPLACE MODEL statement for BQML""" - parts = [f"CREATE OR REPLACE MODEL {self._model_id_sql(model_ref)}"] - if transforms: - parts.append(self.transform(*transforms)) - if options: - parts.append(self.options(**options)) - parts.append(f"AS {source_sql}") - return "\n".join(parts) + """Encode the CREATE TEMP MODEL statement for BQML""" + source_sql = source_df.sql + transform_sql = self.transform(*transforms) if transforms is not None else None + options_sql = self.options(**options) - def create_llm_remote_model( - self, - source_sql: str, - connection_name: str, - model_ref: google.cloud.bigquery.ModelReference, - options: Mapping[str, Union[str, int, float, Iterable[str]]] = {}, - ) -> str: - """Encode the CREATE OR REPLACE MODEL statement for BQML""" - parts = [f"CREATE OR REPLACE MODEL {self._model_id_sql(model_ref)}"] - parts.append(self.connection(connection_name)) - if options: - parts.append(self.options(**options)) + parts = [f"CREATE TEMP MODEL `{self._model_id}`"] + if transform_sql: + parts.append(transform_sql) + if options_sql: + parts.append(options_sql) parts.append(f"AS {source_sql}") return "\n".join(parts) def create_remote_model( self, connection_name: str, - model_ref: google.cloud.bigquery.ModelReference, - input: Mapping[str, str] = {}, - output: Mapping[str, str] = {}, options: Mapping[str, Union[str, int, float, Iterable[str]]] = {}, ) -> str: - """Encode the CREATE OR REPLACE MODEL statement for BQML remote model.""" - parts = [f"CREATE OR REPLACE MODEL {self._model_id_sql(model_ref)}"] - if input: - parts.append(self.input(**input)) - if output: - parts.append(self.output(**output)) + """Encode the CREATE TEMP MODEL statement for BQML remote model.""" + options_sql = self.options(**options) + + parts = [f"CREATE TEMP MODEL `{self._model_id}`"] parts.append(self.connection(connection_name)) - if options: - parts.append(self.options(**options)) + if options_sql: + parts.append(options_sql) return "\n".join(parts) def create_imported_model( self, - model_ref: google.cloud.bigquery.ModelReference, options: Mapping[str, Union[str, int, float, Iterable[str]]] = {}, ) -> str: - """Encode the CREATE OR REPLACE MODEL statement for BQML remote model.""" - - parts = [f"CREATE OR REPLACE MODEL {self._model_id_sql(model_ref)}"] - if options: - parts.append(self.options(**options)) - return "\n".join(parts) + """Encode the CREATE TEMP MODEL statement for BQML remote model.""" + options_sql = self.options(**options) - def create_xgboost_imported_model( - self, - model_ref: google.cloud.bigquery.ModelReference, - input: Mapping[str, str] = {}, - output: Mapping[str, str] = {}, - options: Mapping[str, Union[str, int, float, Iterable[str]]] = {}, - ) -> str: - """Encode the CREATE OR REPLACE MODEL statement for BQML remote model.""" - - parts = [f"CREATE OR REPLACE MODEL {self._model_id_sql(model_ref)}"] - if input: - parts.append(self.input(**input)) - if output: - parts.append(self.output(**output)) - if options: - parts.append(self.options(**options)) + parts = [f"CREATE TEMP MODEL `{self._model_id}`"] + if options_sql: + parts.append(options_sql) return "\n".join(parts) class ModelManipulationSqlGenerator(BaseSqlGenerator): """Sql generator for manipulating a model entity. Model name is the full model path of project_id.dataset_id.model_id.""" - def __init__(self, model_ref: google.cloud.bigquery.ModelReference): - self._model_ref = model_ref + def __init__(self, model_name: str): + self._model_name = model_name - def _model_ref_sql(self) -> str: - return f"{sg_sql.to_sql(sg_sql.identifier(self._model_ref.project))}.{sg_sql.to_sql(sg_sql.identifier(self._model_ref.dataset_id))}.{sg_sql.to_sql(sg_sql.identifier(self._model_ref.model_id))}" + def _source_sql(self, source_df: bpd.DataFrame) -> str: + """Return DataFrame sql with index columns.""" + _source_sql, _, _ = source_df._to_sql_query(include_index=True) + return _source_sql # Alter model def alter_model( @@ -321,125 +190,67 @@ def alter_model( """Encode the ALTER MODEL statement for BQML""" options_sql = self.options(**options) - parts = [f"ALTER MODEL {self._model_ref_sql()}"] + parts = [f"ALTER MODEL `{self._model_name}`"] parts.append(f"SET {options_sql}") return "\n".join(parts) # ML prediction TVFs - def ml_recommend(self, source_sql: str) -> str: - """Encode ML.RECOMMEND for BQML""" - return f"""SELECT * FROM ML.RECOMMEND(MODEL {self._model_ref_sql()}, - ({source_sql}))""" - - def ml_predict(self, source_sql: str) -> str: + def ml_predict(self, source_df: bpd.DataFrame) -> str: """Encode ML.PREDICT for BQML""" - return f"""SELECT * FROM ML.PREDICT(MODEL {self._model_ref_sql()}, - ({source_sql}))""" + return f"""SELECT * FROM ML.PREDICT(MODEL `{self._model_name}`, + ({self._source_sql(source_df)}))""" - def ml_explain_predict( - self, source_sql: str, struct_options: Mapping[str, Union[int, float]] - ) -> str: - """Encode ML.EXPLAIN_PREDICT for BQML""" - struct_options_sql = self.struct_options(**struct_options) - return f"""SELECT * FROM ML.EXPLAIN_PREDICT(MODEL {self._model_ref_sql()}, - ({source_sql}), {struct_options_sql})""" - - def ml_global_explain(self, struct_options) -> str: - """Encode ML.GLOBAL_EXPLAIN for BQML""" - struct_options_sql = self.struct_options(**struct_options) - return f"""SELECT * FROM ML.GLOBAL_EXPLAIN(MODEL {self._model_ref_sql()}, - {struct_options_sql})""" - - def ml_forecast(self, struct_options: Mapping[str, Union[int, float]]) -> str: + def ml_forecast(self) -> str: """Encode ML.FORECAST for BQML""" - struct_options_sql = self.struct_options(**struct_options) - return f"""SELECT * FROM ML.FORECAST(MODEL {self._model_ref_sql()}, - {struct_options_sql})""" - - def ml_explain_forecast( - self, struct_options: Mapping[str, Union[int, float]] - ) -> str: - """Encode ML.EXPLAIN_FORECAST for BQML""" - struct_options_sql = self.struct_options(**struct_options) - return f"""SELECT * FROM ML.EXPLAIN_FORECAST(MODEL {self._model_ref_sql()}, - {struct_options_sql})""" + return f"""SELECT * FROM ML.FORECAST(MODEL `{self._model_name}`)""" def ml_generate_text( - self, source_sql: str, struct_options: Mapping[str, Union[int, float]] + self, source_df: bpd.DataFrame, struct_options: Mapping[str, Union[int, float]] ) -> str: """Encode ML.GENERATE_TEXT for BQML""" struct_options_sql = self.struct_options(**struct_options) - return f"""SELECT * FROM ML.GENERATE_TEXT(MODEL {self._model_ref_sql()}, - ({source_sql}), {struct_options_sql})""" + return f"""SELECT * FROM ML.GENERATE_TEXT(MODEL `{self._model_name}`, + ({self._source_sql(source_df)}), {struct_options_sql})""" - def ml_generate_embedding( - self, source_sql: str, struct_options: Mapping[str, Union[int, float]] + def ml_generate_text_embedding( + self, source_df: bpd.DataFrame, struct_options: Mapping[str, Union[int, float]] ) -> str: - """Encode ML.GENERATE_EMBEDDING for BQML""" + """Encode ML.GENERATE_TEXT_EMBEDDING for BQML""" struct_options_sql = self.struct_options(**struct_options) - return f"""SELECT * FROM ML.GENERATE_EMBEDDING(MODEL {self._model_ref_sql()}, - ({source_sql}), {struct_options_sql})""" - - def ml_detect_anomalies( - self, source_sql: str, struct_options: Mapping[str, Union[int, float]] - ) -> str: - """Encode ML.DETECT_ANOMALIES for BQML""" - struct_options_sql = self.struct_options(**struct_options) - return f"""SELECT * FROM ML.DETECT_ANOMALIES(MODEL {self._model_ref_sql()}, - {struct_options_sql}, ({source_sql}))""" + return f"""SELECT * FROM ML.GENERATE_TEXT_EMBEDDING(MODEL `{self._model_name}`, + ({self._source_sql(source_df)}), {struct_options_sql})""" # ML evaluation TVFs - def ml_evaluate(self, source_sql: Optional[str] = None) -> str: + def ml_evaluate(self, source_df: Optional[bpd.DataFrame] = None) -> str: """Encode ML.EVALUATE for BQML""" + if source_df is None: + source_sql = None + else: + # Note: don't need index as evaluate returns a new table + source_sql, _, _ = source_df._to_sql_query(include_index=False) + if source_sql is None: - return f"""SELECT * FROM ML.EVALUATE(MODEL {self._model_ref_sql()})""" + return f"""SELECT * FROM ML.EVALUATE(MODEL `{self._model_name}`)""" else: - return f"""SELECT * FROM ML.EVALUATE(MODEL {self._model_ref_sql()}, + return f"""SELECT * FROM ML.EVALUATE(MODEL `{self._model_name}`, ({source_sql}))""" - def ml_arima_coefficients(self) -> str: - """Encode ML.ARIMA_COEFFICIENTS for BQML""" - return f"""SELECT * FROM ML.ARIMA_COEFFICIENTS(MODEL {self._model_ref_sql()})""" - - # ML evaluation TVFs - def ml_llm_evaluate(self, source_sql: str, task_type: Optional[str] = None) -> str: - """Encode ML.EVALUATE for BQML""" - # Note: don't need index as evaluate returns a new table - return f"""SELECT * FROM ML.EVALUATE(MODEL {self._model_ref_sql()}, - ({source_sql}), STRUCT("{task_type}" AS task_type))""" - - # ML evaluation TVFs - def ml_arima_evaluate(self, show_all_candidate_models: bool = False) -> str: - """Encode ML.ARMIA_EVALUATE for BQML""" - return f"""SELECT * FROM ML.ARIMA_EVALUATE(MODEL {self._model_ref_sql()}, - STRUCT({show_all_candidate_models} AS show_all_candidate_models))""" - def ml_centroids(self) -> str: """Encode ML.CENTROIDS for BQML""" - return f"""SELECT * FROM ML.CENTROIDS(MODEL {self._model_ref_sql()})""" + return f"""SELECT * FROM ML.CENTROIDS(MODEL `{self._model_name}`)""" def ml_principal_components(self) -> str: """Encode ML.PRINCIPAL_COMPONENTS for BQML""" - return ( - f"""SELECT * FROM ML.PRINCIPAL_COMPONENTS(MODEL {self._model_ref_sql()})""" - ) + return f"""SELECT * FROM ML.PRINCIPAL_COMPONENTS(MODEL `{self._model_name}`)""" def ml_principal_component_info(self) -> str: """Encode ML.PRINCIPAL_COMPONENT_INFO for BQML""" - return f"""SELECT * FROM ML.PRINCIPAL_COMPONENT_INFO(MODEL {self._model_ref_sql()})""" + return ( + f"""SELECT * FROM ML.PRINCIPAL_COMPONENT_INFO(MODEL `{self._model_name}`)""" + ) # ML transform TVF, that require a transform_only type model - def ml_transform(self, source_sql: str) -> str: + def ml_transform(self, source_df: bpd.DataFrame) -> str: """Encode ML.TRANSFORM for BQML""" - return f"""SELECT * FROM ML.TRANSFORM(MODEL {self._model_ref_sql()}, - ({source_sql}))""" - - def ai_generate_table( - self, - source_sql: str, - struct_options: Mapping[str, Union[int, float, bool, Mapping]], - ) -> str: - """Encode AI.GENERATE_TABLE for BQML""" - struct_options_sql = self.struct_options(**struct_options) - return f"""SELECT * FROM AI.GENERATE_TABLE(MODEL {self._model_ref_sql()}, - ({source_sql}), {struct_options_sql})""" + return f"""SELECT * FROM ML.TRANSFORM(MODEL `{self._model_name}`, + ({self._source_sql(source_df)}))""" diff --git a/bigframes/ml/utils.py b/bigframes/ml/utils.py index 134020b7167..299282d3337 100644 --- a/bigframes/ml/utils.py +++ b/bigframes/ml/utils.py @@ -13,233 +13,46 @@ # limitations under the License. import typing -from typing import ( - Any, - Generator, - Hashable, - Iterable, - Literal, - Mapping, - Optional, - Tuple, - Union, -) - -import bigframes_vendored.constants as constants -import pandas as pd -from google.cloud import bigquery +from typing import Iterable, Union +import bigframes.constants as constants +from bigframes.core import blocks import bigframes.pandas as bpd -from bigframes.core import convert, guid -from bigframes.session import Session # Internal type alias -ArrayType = Union[bpd.DataFrame, bpd.Series, pd.DataFrame, pd.Series] -BigFramesArrayType = Union[bpd.DataFrame, bpd.Series] - +ArrayType = Union[bpd.DataFrame, bpd.Series] -def batch_convert_to_dataframe( - *input: ArrayType, - session: Optional[Session] = None, -) -> Generator[bpd.DataFrame, None, None]: - """Converts the input to BigFrames DataFrame. - Args: - session: - The session to convert local pandas instances to BigFrames counter-parts. - It is not used if the input itself is already a BigFrame data frame or series. +def convert_to_dataframe(*input: ArrayType) -> Iterable[bpd.DataFrame]: + return (_convert_to_dataframe(frame) for frame in input) - """ - _validate_sessions(*input, session=session) - return ( - convert.to_bf_dataframe(frame, default_index=None, session=session) - for frame in input +def _convert_to_dataframe(frame: ArrayType) -> bpd.DataFrame: + if isinstance(frame, bpd.DataFrame): + return frame + if isinstance(frame, bpd.Series): + return frame.to_frame() + raise ValueError( + f"Unsupported type {type(frame)} to convert to DataFrame. {constants.FEEDBACK_LINK}" ) -def batch_convert_to_series( - *input: ArrayType, session: Optional[Session] = None -) -> Generator[bpd.Series, None, None]: - """Converts the input to BigFrames Series. - - Args: - session: - The session to convert local pandas instances to BigFrames counter-parts. - It is not used if the input itself is already a BigFrame data frame or series. - - """ - _validate_sessions(*input, session=session) - - return ( - convert.to_bf_series( - _get_only_column(frame), default_index=None, session=session - ) - for frame in input - ) - - -def batch_convert_to_bf_equivalent( - *input: ArrayType, session: Optional[Session] = None -) -> Generator[Union[bpd.DataFrame, bpd.Series], None, None]: - """Converts the input to BigFrames DataFrame or Series. - - Args: - session: - The session to convert local pandas instances to BigFrames counter-parts. - It is not used if the input itself is already a BigFrame data frame or series. - - """ - _validate_sessions(*input, session=session) - - for frame in input: - if isinstance(frame, bpd.DataFrame) or isinstance(frame, pd.DataFrame): - yield convert.to_bf_dataframe(frame, default_index=None, session=session) - elif isinstance(frame, bpd.Series) or isinstance(frame, pd.Series): - yield convert.to_bf_series( - _get_only_column(frame), default_index=None, session=session - ) - else: - raise ValueError(f"Unsupported type: {type(frame)}") - - -def _validate_sessions(*input: ArrayType, session: Optional[Session]): - session_ids = set( - i._session.session_id - for i in input - if isinstance(i, bpd.DataFrame) or isinstance(i, bpd.Series) - ) - if len(session_ids) > 1: - raise ValueError("Cannot convert data from multiple sessions") - - -def _get_only_column(input: ArrayType) -> Union[pd.Series, bpd.Series]: - if isinstance(input, pd.Series) or isinstance(input, bpd.Series): - return input - - if len(input.columns) != 1: - raise ValueError( - "To convert into Series, DataFrames can only contain one column. " - f"Try input with only one column. {constants.FEEDBACK_LINK}" - ) - - label = typing.cast(Hashable, input.columns.tolist()[0]) - if isinstance(input, pd.DataFrame): - return typing.cast(pd.Series, input[label]) - return typing.cast(bpd.Series, input[label]) # type: ignore - - -def parse_model_endpoint(model_endpoint: str) -> tuple[str, Optional[str]]: - """Parse model endpoint string to model_name and version.""" - model_name = model_endpoint - version = None - - if model_endpoint.startswith("multimodalembedding"): - return model_name, version - - at_idx = model_endpoint.find("@") - if at_idx != -1: - version = model_endpoint[at_idx + 1 :] - model_name = model_endpoint[:at_idx] - - return model_name, version - +def convert_to_series(*input: ArrayType) -> Iterable[bpd.Series]: + return (_convert_to_series(frame) for frame in input) -def _resolve_param_type(t: type) -> type: - def is_optional(t): - return typing.get_origin(t) is Union and type(None) in typing.get_args(t) - # Optional[type] to type - if is_optional(t): - union_set = set(typing.get_args(t)) - union_set.remove(type(None)) - t = Union[tuple(union_set)] # type: ignore - - # Literal[value0, value1...] to type(value0) - if typing.get_origin(t) is Literal: - return type(typing.get_args(t)[0]) - - return t - - -def retrieve_params_from_bq_model( - cls, bq_model: bigquery.Model, params_mapping: Mapping[str, str] -) -> dict[str, Any]: - """Retrieve parameters of class constructor from BQ model. params_mapping specifies the names mapping param_name -> bqml_name. Params couldn't be found will be ignored.""" - kwargs = {} - - # See https://cloud.google.com/bigquery/docs/reference/rest/v2/models#trainingrun - last_fitting = bq_model.training_runs[-1]["trainingOptions"] - - for bf_param, bf_param_type in typing.get_type_hints(cls.__init__).items(): - bqml_param = params_mapping.get(bf_param) - if bqml_param in last_fitting: - bf_param_type = _resolve_param_type(bf_param_type) - kwargs[bf_param] = bf_param_type(last_fitting[bqml_param]) - - return kwargs - - -def combine_training_and_evaluation_data( - X_train: bpd.DataFrame, - y_train: bpd.DataFrame, - X_eval: bpd.DataFrame, - y_eval: bpd.DataFrame, - bqml_options: dict, -) -> Tuple[bpd.DataFrame, bpd.DataFrame, dict]: - """ - Combine training data and labels with evlauation data and labels, and keep - them differentiated through a split column in the combined data and labels. - """ - - assert X_train.columns.equals(X_eval.columns) - assert y_train.columns.equals(y_eval.columns) - - # create a custom split column for BQML and supply the evaluation - # data along with the training data in a combined single table - # https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-create-dnn-models#data_split_col. - split_col = guid.generate_guid() - assert split_col not in X_train.columns - - # To prevent side effects on the input dataframes, we operate on copies - X_train = X_train.copy() - X_eval = X_eval.copy() - - X_train[split_col] = False - X_eval[split_col] = True - - # Rename y columns to avoid collision with X columns during join - y_mapping = {col: guid.generate_guid() + str(col) for col in y_train.columns} - y_train_renamed = y_train.rename(columns=y_mapping) - y_eval_renamed = y_eval.rename(columns=y_mapping) - - # Join X and y first to preserve row alignment - train_combined = X_train.join(y_train_renamed, how="outer") - eval_combined = X_eval.join(y_eval_renamed, how="outer") - - combined = bpd.concat([train_combined, eval_combined]) - - X = combined[X_train.columns] - y = combined[list(y_mapping.values())].rename( - columns={v: k for k, v in y_mapping.items()} - ) - - # create options copy to not mutate the incoming one - bqml_options = bqml_options.copy() - bqml_options["data_split_method"] = "CUSTOM" - bqml_options["data_split_col"] = split_col - - return X, y, bqml_options - - -def standardize_type(v: str, supported_dtypes: Optional[Iterable[str]] = None): - t = v.lower() - t = t.replace("boolean", "bool") - - if supported_dtypes: - if t not in supported_dtypes: +def _convert_to_series(frame: ArrayType) -> bpd.Series: + if isinstance(frame, bpd.DataFrame): + if len(frame.columns) != 1: raise ValueError( - f"Data type {v} is not supported. We only support {', '.join(supported_dtypes)}." + "To convert into Series, DataFrames can only contain one column. " + f"Try input with only one column. {constants.FEEDBACK_LINK}" ) - return t + label = typing.cast(blocks.Label, frame.columns.tolist()[0]) + return typing.cast(bpd.Series, frame[label]) + if isinstance(frame, bpd.Series): + return frame + raise ValueError( + f"Unsupported type {type(frame)} to convert to Series. {constants.FEEDBACK_LINK}" + ) diff --git a/bigframes/operations/__init__.py b/bigframes/operations/__init__.py index 6df8da69b11..a29dd36c72a 100644 --- a/bigframes/operations/__init__.py +++ b/bigframes/operations/__init__.py @@ -14,442 +14,1097 @@ from __future__ import annotations -from bigframes.operations.ai_ops import ( - AIClassify, - AIEmbed, - AIGenerate, - AIGenerateBool, - AIGenerateDouble, - AIGenerateInt, - AIIf, - AIScore, - AISimilarity, -) -from bigframes.operations.array_ops import ( - ArrayMapOp, - ArrayReduceOp, - ArraySliceOp, - ArrayToStringOp, - ToArrayOp, -) -from bigframes.operations.base_ops import ( - BinaryOp, - NaryOp, - RowOp, - ScalarOp, - TernaryOp, - UnaryOp, -) -from bigframes.operations.blob_ops import ( - ObjGetAccessUrl, - obj_fetch_metadata_op, - obj_make_ref_json_op, - obj_make_ref_op, -) -from bigframes.operations.bool_ops import and_op, or_op, xor_op -from bigframes.operations.comparison_ops import ( - eq_null_match_op, - eq_op, - ge_op, - gt_op, - le_op, - lt_op, - ne_op, -) -from bigframes.operations.date_ops import ( - date_diff_op, - day_op, - dayofweek_op, - dayofyear_op, - iso_day_op, - iso_week_op, - iso_year_op, - month_op, - quarter_op, - year_op, -) -from bigframes.operations.datetime_ops import ( - StrftimeOp, - ToDatetimeOp, - ToTimestampOp, - UnixMicros, - UnixMillis, - UnixSeconds, - date_op, - time_op, - timestamp_diff_op, -) -from bigframes.operations.distance_ops import ( - cosine_distance_op, - euclidean_distance_op, - manhattan_distance_op, -) -from bigframes.operations.frequency_ops import ( - DatetimeToIntegerLabelOp, - FloorDtOp, - IntegerLabelToDatetimeOp, -) -from bigframes.operations.generic_ops import ( - AsTypeOp, - CaseWhenOp, - CoerceToBoolOp, - DynamicGetItemOp, - GetItemOp, - IsInOp, - MapOp, - RowKey, - SqlScalarOp, - case_when_op, - clip_op, - coalesce_op, - coerce_to_bool_op, - fillna_op, - hash_op, - invert_op, - isnull_op, - maximum_op, - minimum_op, - notnull_op, - where_op, -) -from bigframes.operations.geo_ops import ( - GeoStBufferOp, - GeoStDistanceOp, - GeoStLengthOp, - GeoStRegionStatsOp, - GeoStSimplifyOp, - geo_st_astext_op, - geo_st_boundary_op, - geo_st_convexhull_op, - geo_st_difference_op, - geo_st_geogfromtext_op, - geo_st_geogpoint_op, - geo_st_intersection_op, - geo_st_isclosed_op, - geo_x_op, - geo_y_op, -) -from bigframes.operations.googlesql import GoogleSqlScalarOp -from bigframes.operations.json_ops import ( - JSONDecode, - JSONExtract, - JSONExtractArray, - JSONExtractStringArray, - JSONKeys, - JSONQuery, - JSONQueryArray, - JSONSet, - JSONValue, - JSONValueArray, - ParseJSON, - ToJSON, - ToJSONString, -) -from bigframes.operations.numeric_ops import ( - AddOp, - DivOp, - FloorDivOp, - MulOp, - SubOp, - abs_op, - add_op, - arccos_op, - arccosh_op, - arcsin_op, - arcsinh_op, - arctan2_op, - arctan_op, - arctanh_op, - ceil_op, - cos_op, - cosh_op, - div_op, - exp_op, - expm1_op, - floor_op, - floordiv_op, - ln_op, - log1p_op, - log10_op, - mod_op, - mul_op, - neg_op, - pos_op, - pow_op, - round_op, - sin_op, - sinh_op, - sqrt_op, - sub_op, - tan_op, - tanh_op, - unsafe_pow_op, -) -from bigframes.operations.numpy_op_maps import NUMPY_TO_BINOP, NUMPY_TO_OP -from bigframes.operations.remote_function_ops import ( - PythonUdfOp, - RemoteFunctionOp, -) -from bigframes.operations.string_ops import ( - EndsWithOp, - RegexReplaceStrOp, - ReplaceStrOp, - StartsWithOp, - StrContainsOp, - StrContainsRegexOp, - StrExtractOp, - StrFindOp, - StringSplitOp, - StrLstripOp, - StrPadOp, - StrRepeatOp, - StrRstripOp, - StrSliceOp, - StrStripOp, - ZfillOp, - capitalize_op, - isalnum_op, - isalpha_op, - isdecimal_op, - isdigit_op, - islower_op, - isnumeric_op, - isspace_op, - isupper_op, - len_op, - lower_op, - reverse_op, - strconcat_op, - upper_op, -) -from bigframes.operations.struct_ops import StructFieldOp, StructOp -from bigframes.operations.time_ops import hour_op, minute_op, normalize_op, second_op -from bigframes.operations.timedelta_ops import ( - ToTimedeltaOp, - date_add_op, - date_sub_op, - timedelta_floor_op, - timestamp_add_op, - timestamp_sub_op, -) -from bigframes.operations.to_op import func_to_expr - -__all__ = [ - # Base ops - "RowOp", - "NaryOp", - "UnaryOp", - "BinaryOp", - "TernaryOp", - "ScalarOp", - # Generic ops - "AsTypeOp", - "case_when_op", - "CaseWhenOp", - "clip_op", - "coalesce_op", - "fillna_op", - "DynamicGetItemOp", - "GetItemOp", - "hash_op", - "invert_op", - "IsInOp", - "isnull_op", - "MapOp", - "maximum_op", - "minimum_op", - "notnull_op", - "CoerceToBoolOp", - "coerce_to_bool_op", - "RowKey", - "SqlScalarOp", - "where_op", - # String ops - "capitalize_op", - "EndsWithOp", - "isalnum_op", - "isalpha_op", - "isdecimal_op", - "isdigit_op", - "islower_op", - "isnumeric_op", - "isspace_op", - "isupper_op", - "len_op", - "lower_op", - "RegexReplaceStrOp", - "ReplaceStrOp", - "reverse_op", - "StartsWithOp", - "strconcat_op", - "StrContainsOp", - "StrContainsRegexOp", - "StrExtractOp", - "StrFindOp", - "StrLstripOp", - "StringSplitOp", - "strip_op", - "StrPadOp", - "StrRepeatOp", - "StrRstripOp", - "StrSliceOp", - "StrStripOp", - "upper_op", - "ZfillOp", - # Date ops - "date_diff_op", - "day_op", - "dayofweek_op", - "dayofyear_op", - "iso_day_op", - "iso_week_op", - "iso_year_op", - "month_op", - "quarter_op", - "year_op", - # Time ops - "hour_op", - "minute_op", - "second_op", - "normalize_op", - # Timedelta ops - "date_add_op", - "date_sub_op", - "timedelta_floor_op", - "timestamp_add_op", - "timestamp_sub_op", - "ToTimedeltaOp", - # Datetime ops - "date_op", - "time_op", - "timestamp_diff_op", - "ToDatetimeOp", - "ToTimestampOp", - "StrftimeOp", - "UnixMicros", - "UnixMillis", - "UnixSeconds", - # Numeric ops - "abs_op", - "add_op", - "AddOp", - "arccos_op", - "arccosh_op", - "arcsin_op", - "arcsinh_op", - "arctan2_op", - "arctan_op", - "arctanh_op", - "ceil_op", - "cos_op", - "cosh_op", - "div_op", - "DivOp", - "exp_op", - "expm1_op", - "floor_op", - "floordiv_op", - "FloorDivOp", - "ln_op", - "log1p_op", - "log10_op", - "mod_op", - "mul_op", - "MulOp", - "neg_op", - "pos_op", - "pow_op", - "round_op", - "sin_op", - "sinh_op", - "sqrt_op", - "sub_op", - "SubOp", - "tan_op", - "tanh_op", - "unsafe_pow_op", - # Array ops - "ArraySliceOp", - "ArrayToStringOp", - # Blob ops - "ObjGetAccessUrl", - "obj_make_ref_json_op", - "obj_make_ref_op", - "obj_fetch_metadata_op", - # Struct ops - "StructFieldOp", - "StructOp", - # Remote Functions ops - "RemoteFunctionOp", - "PythonUdfOp", - # Frequency ops - "DatetimeToIntegerLabelOp", - "FloorDtOp", - "IntegerLabelToDatetimeOp", - # JSON ops - "JSONDecode", - "JSONExtract", - "JSONExtractArray", - "JSONExtractStringArray", - "JSONKeys", - "JSONQuery", - "JSONQueryArray", - "JSONSet", - "JSONValue", - "JSONValueArray", - "ParseJSON", - "ToJSON", - "ToJSONString", - # Bool ops - "and_op", - "or_op", - "xor_op", - # Comparison ops - "eq_null_match_op", - "eq_op", - "ge_op", - "gt_op", - "le_op", - "lt_op", - "ne_op", - # Distance ops - "cosine_distance_op", - "euclidean_distance_op", - "manhattan_distance_op", - # Geo ops - "geo_st_boundary_op", - "geo_st_convexhull_op", - "geo_st_difference_op", - "geo_st_astext_op", - "geo_st_geogfromtext_op", - "geo_st_geogpoint_op", - "geo_st_intersection_op", - "geo_st_isclosed_op", - "geo_x_op", - "geo_y_op", - "GeoStBufferOp", - "GeoStDistanceOp", - "GeoStLengthOp", - "GeoStRegionStatsOp", - "GeoStSimplifyOp", - # AI ops - "AIClassify", - "AIGenerate", - "AIGenerateBool", - "AIGenerateDouble", - "AIGenerateInt", - "AIEmbed", - "AIIf", - "AIScore", - "AISimilarity", - # Helper functions - "func_to_expr", - # Numpy ops mapping - "NUMPY_TO_BINOP", - "NUMPY_TO_OP", - "ToArrayOp", - "ArrayReduceOp", - "ArrayMapOp", - # GoogleSql - "GoogleSqlScalarOp", +import functools +import typing + +import ibis +import ibis.common.exceptions +import ibis.expr.datatypes as ibis_dtypes +import ibis.expr.operations.generic +import ibis.expr.types as ibis_types +import numpy as np +import pandas as pd + +import bigframes.constants as constants +import bigframes.dtypes +import bigframes.dtypes as dtypes + +_ZERO = typing.cast(ibis_types.NumericValue, ibis_types.literal(0)) +_NAN = typing.cast(ibis_types.NumericValue, ibis_types.literal(np.nan)) +_INF = typing.cast(ibis_types.NumericValue, ibis_types.literal(np.inf)) +_NEG_INF = typing.cast(ibis_types.NumericValue, ibis_types.literal(-np.inf)) + +# Approx Highest number you can pass in to EXP function and get a valid FLOAT64 result +# FLOAT64 has 11 exponent bits, so max values is about 2**(2**10) +# ln(2**(2**10)) == (2**10)*ln(2) ~= 709.78, so EXP(x) for x>709.78 will overflow. +_FLOAT64_EXP_BOUND = typing.cast(ibis_types.NumericValue, ibis_types.literal(709.78)) +_INT64_EXP_BOUND = typing.cast(ibis_types.NumericValue, ibis_types.literal(43.6)) + +BinaryOp = typing.Callable[[ibis_types.Value, ibis_types.Value], ibis_types.Value] +TernaryOp = typing.Callable[ + [ibis_types.Value, ibis_types.Value, ibis_types.Value], ibis_types.Value ] + + +### Unary Ops +class UnaryOp: + def _as_ibis(self, x): + raise NotImplementedError( + f"Base class UnaryOp has no implementation. {constants.FEEDBACK_LINK}" + ) + + @property + def is_windowed(self): + return False + + +# Trig Functions +class AbsOp(UnaryOp): + def _as_ibis(self, x: ibis_types.Value): + return typing.cast(ibis_types.NumericValue, x).abs() + + +class SinOp(UnaryOp): + def _as_ibis(self, x: ibis_types.Value): + return typing.cast(ibis_types.NumericValue, x).sin() + + +class CosOp(UnaryOp): + def _as_ibis(self, x: ibis_types.Value): + return typing.cast(ibis_types.NumericValue, x).cos() + + +class TanOp(UnaryOp): + def _as_ibis(self, x: ibis_types.Value): + return typing.cast(ibis_types.NumericValue, x).tan() + + +# Inverse trig functions +class ArcsinOp(UnaryOp): + def _as_ibis(self, x: ibis_types.Value): + numeric_value = typing.cast(ibis_types.NumericValue, x) + domain = numeric_value.abs() <= _ibis_num(1) + return (~domain).ifelse(_NAN, numeric_value.asin()) + + +class ArccosOp(UnaryOp): + def _as_ibis(self, x: ibis_types.Value): + numeric_value = typing.cast(ibis_types.NumericValue, x) + domain = numeric_value.abs() <= _ibis_num(1) + return (~domain).ifelse(_NAN, numeric_value.acos()) + + +class ArctanOp(UnaryOp): + def _as_ibis(self, x: ibis_types.Value): + return typing.cast(ibis_types.NumericValue, x).atan() + + +# Hyperbolic trig functions +# BQ has these functions, but Ibis doesn't +class SinhOp(UnaryOp): + def _as_ibis(self, x: ibis_types.Value): + numeric_value = typing.cast(ibis_types.NumericValue, x) + sinh_result = ( + numeric_value.exp() - (numeric_value.negate()).exp() + ) / _ibis_num(2) + domain = numeric_value.abs() < _FLOAT64_EXP_BOUND + return (~domain).ifelse(_INF * numeric_value.sign(), sinh_result) + + +class CoshOp(UnaryOp): + def _as_ibis(self, x: ibis_types.Value): + numeric_value = typing.cast(ibis_types.NumericValue, x) + cosh_result = ( + numeric_value.exp() + (numeric_value.negate()).exp() + ) / _ibis_num(2) + domain = numeric_value.abs() < _FLOAT64_EXP_BOUND + return (~domain).ifelse(_INF, cosh_result) + + +class TanhOp(UnaryOp): + def _as_ibis(self, x: ibis_types.Value): + numeric_value = typing.cast(ibis_types.NumericValue, x) + tanh_result = (numeric_value.exp() - (numeric_value.negate()).exp()) / ( + numeric_value.exp() + (numeric_value.negate()).exp() + ) + # Beyond +-20, is effectively just the sign function + domain = numeric_value.abs() < _ibis_num(20) + return (~domain).ifelse(numeric_value.sign(), tanh_result) + + +class ArcsinhOp(UnaryOp): + def _as_ibis(self, x: ibis_types.Value): + numeric_value = typing.cast(ibis_types.NumericValue, x) + sqrt_part = ((numeric_value * numeric_value) + _ibis_num(1)).sqrt() + return (numeric_value.abs() + sqrt_part).ln() * numeric_value.sign() + + +class ArccoshOp(UnaryOp): + def _as_ibis(self, x: ibis_types.Value): + numeric_value = typing.cast(ibis_types.NumericValue, x) + sqrt_part = ((numeric_value * numeric_value) - _ibis_num(1)).sqrt() + acosh_result = (numeric_value + sqrt_part).ln() + domain = numeric_value >= _ibis_num(1) + return (~domain).ifelse(_NAN, acosh_result) + + +class ArctanhOp(UnaryOp): + def _as_ibis(self, x: ibis_types.Value): + numeric_value = typing.cast(ibis_types.NumericValue, x) + domain = numeric_value.abs() < _ibis_num(1) + numerator = numeric_value + _ibis_num(1) + denominator = _ibis_num(1) - numeric_value + ln_input = typing.cast(ibis_types.NumericValue, numerator.div(denominator)) + atanh_result = ln_input.ln().div(2) + + out_of_domain = (numeric_value.abs() == _ibis_num(1)).ifelse( + _INF * numeric_value, _NAN + ) + + return (~domain).ifelse(out_of_domain, atanh_result) + + +class SqrtOp(UnaryOp): + def _as_ibis(self, x: ibis_types.Value): + numeric_value = typing.cast(ibis_types.NumericValue, x) + domain = numeric_value >= _ZERO + return (~domain).ifelse(_NAN, numeric_value.sqrt()) + + +class Log10Op(UnaryOp): + def _as_ibis(self, x: ibis_types.Value): + numeric_value = typing.cast(ibis_types.NumericValue, x) + domain = numeric_value > _ZERO + out_of_domain = (numeric_value == _ZERO).ifelse(_NEG_INF, _NAN) + return (~domain).ifelse(out_of_domain, numeric_value.log10()) + + +class LnOp(UnaryOp): + def _as_ibis(self, x: ibis_types.Value): + numeric_value = typing.cast(ibis_types.NumericValue, x) + domain = numeric_value > _ZERO + out_of_domain = (numeric_value == _ZERO).ifelse(_NEG_INF, _NAN) + return (~domain).ifelse(out_of_domain, numeric_value.ln()) + + +class ExpOp(UnaryOp): + def _as_ibis(self, x: ibis_types.Value): + numeric_value = typing.cast(ibis_types.NumericValue, x) + domain = numeric_value < _FLOAT64_EXP_BOUND + return (~domain).ifelse(_INF, numeric_value.exp()) + + +class InvertOp(UnaryOp): + def _as_ibis(self, x: ibis_types.Value): + return typing.cast(ibis_types.NumericValue, x).negate() + + +class IsNullOp(UnaryOp): + def _as_ibis(self, x: ibis_types.Value): + return x.isnull() + + +class LenOp(UnaryOp): + def _as_ibis(self, x: ibis_types.Value): + return typing.cast(ibis_types.StringValue, x).length().cast(ibis_dtypes.int64) + + +class NotNullOp(UnaryOp): + def _as_ibis(self, x: ibis_types.Value): + return x.notnull() + + +class HashOp(UnaryOp): + def _as_ibis(self, x: ibis_types.Value): + return typing.cast(ibis_types.IntegerValue, x).hash() + + +## String Operation +class ReverseOp(UnaryOp): + def _as_ibis(self, x: ibis_types.Value): + return typing.cast(ibis_types.StringValue, x).reverse() + + +class LowerOp(UnaryOp): + def _as_ibis(self, x: ibis_types.Value): + return typing.cast(ibis_types.StringValue, x).lower() + + +class UpperOp(UnaryOp): + def _as_ibis(self, x: ibis_types.Value): + return typing.cast(ibis_types.StringValue, x).upper() + + +class StripOp(UnaryOp): + def _as_ibis(self, x: ibis_types.Value): + return typing.cast(ibis_types.StringValue, x).strip() + + +class IsNumericOp(UnaryOp): + def _as_ibis(self, x: ibis_types.Value): + # catches all members of the Unicode number class, which matches pandas isnumeric + # see https://cloud.google.com/bigquery/docs/reference/standard-sql/string_functions#regexp_contains + # TODO: Validate correctness, my miss eg ⅕ character + return typing.cast(ibis_types.StringValue, x).re_search(r"^(\pN+)$") + + +class IsAlphaOp(UnaryOp): + def _as_ibis(self, x: ibis_types.Value): + return typing.cast(ibis_types.StringValue, x).re_search( + r"^(\p{Lm}|\p{Lt}|\p{Lu}|\p{Ll}|\p{Lo})+$" + ) + + +class IsDigitOp(UnaryOp): + def _as_ibis(self, x: ibis_types.Value): + # Based on docs, should include superscript/subscript-ed numbers + # Tests however pass only when set to Nd unicode class + return typing.cast(ibis_types.StringValue, x).re_search(r"^(\p{Nd})+$") + + +class IsDecimalOp(UnaryOp): + def _as_ibis(self, x: ibis_types.Value): + return typing.cast(ibis_types.StringValue, x).re_search(r"^(\p{Nd})+$") + + +class IsAlnumOp(UnaryOp): + def _as_ibis(self, x: ibis_types.Value): + return typing.cast(ibis_types.StringValue, x).re_search( + r"^(\p{N}|\p{Lm}|\p{Lt}|\p{Lu}|\p{Ll}|\p{Lo})+$" + ) + + +class IsSpaceOp(UnaryOp): + def _as_ibis(self, x: ibis_types.Value): + # All characters are whitespace characters, False for empty string + return typing.cast(ibis_types.StringValue, x).re_search(r"^\s+$") + + +class IsLowerOp(UnaryOp): + def _as_ibis(self, x: ibis_types.Value): + # No upper case characters, min one cased character + # See: https://docs.python.org/3/library/stdtypes.html#str + return typing.cast(ibis_types.StringValue, x).re_search( + r"\p{Ll}" + ) & ~typing.cast(ibis_types.StringValue, x).re_search(r"\p{Lu}|\p{Lt}") + + +class IsUpperOp(UnaryOp): + def _as_ibis(self, x: ibis_types.Value): + # No lower case characters, min one cased character + # See: https://docs.python.org/3/library/stdtypes.html#str + return typing.cast(ibis_types.StringValue, x).re_search( + r"\p{Lu}" + ) & ~typing.cast(ibis_types.StringValue, x).re_search(r"\p{Ll}|\p{Lt}") + + +class RstripOp(UnaryOp): + def _as_ibis(self, x: ibis_types.Value): + return typing.cast(ibis_types.StringValue, x).rstrip() + + +class LstripOp(UnaryOp): + def _as_ibis(self, x: ibis_types.Value): + return typing.cast(ibis_types.StringValue, x).lstrip() + + +class CapitalizeOp(UnaryOp): + def _as_ibis(self, x: ibis_types.Value): + return typing.cast(ibis_types.StringValue, x).capitalize() + + +class ContainsStringOp(UnaryOp): + def __init__(self, pat: str, case: bool = True): + self._pat = pat + + def _as_ibis(self, x: ibis_types.Value): + return typing.cast(ibis_types.StringValue, x).contains(self._pat) + + +class ContainsRegexOp(UnaryOp): + def __init__(self, pat: str): + self._pat = pat + + def _as_ibis(self, x: ibis_types.Value): + return typing.cast(ibis_types.StringValue, x).re_search(self._pat) + + +class StrGetOp(UnaryOp): + def __init__(self, i: int): + self._i = i + + def _as_ibis(self, x: ibis_types.Value): + substr = typing.cast( + ibis_types.StringValue, typing.cast(ibis_types.StringValue, x)[self._i] + ) + return substr.nullif(ibis_types.literal("")) + + +class StrPadOp(UnaryOp): + def __init__( + self, length: int, fillchar: str, side: typing.Literal["both", "left", "right"] + ): + self._length = length + self._fillchar = fillchar + self._side = side + + def _as_ibis(self, x: ibis_types.Value): + str_val = typing.cast(ibis_types.StringValue, x) + + # SQL pad operations will truncate, we do not want to truncate though. + pad_length = ibis.greatest(str_val.length(), self._length) + if self._side == "left": + return str_val.lpad(pad_length, self._fillchar) + elif self._side == "right": + return str_val.rpad(pad_length, self._fillchar) + else: # side == both + # Pad more on right side if can't pad both sides equally + lpad_amount = ((pad_length - str_val.length()) // 2) + str_val.length() + return str_val.lpad(lpad_amount, self._fillchar).rpad( + pad_length, self._fillchar + ) + + +class ReplaceStringOp(UnaryOp): + def __init__(self, pat: str, repl: str): + self._pat = pat + self._repl = repl + + def _as_ibis(self, x: ibis_types.Value): + pat_str_value = typing.cast( + ibis_types.StringValue, ibis_types.literal(self._pat) + ) + repl_str_value = typing.cast( + ibis_types.StringValue, ibis_types.literal(self._pat) + ) + + return typing.cast(ibis_types.StringValue, x).replace( + pat_str_value, repl_str_value + ) + + +class ReplaceRegexOp(UnaryOp): + def __init__(self, pat: str, repl: str): + self._pat = pat + self._repl = repl + + def _as_ibis(self, x: ibis_types.Value): + return typing.cast(ibis_types.StringValue, x).re_replace(self._pat, self._repl) + + +class StartsWithOp(UnaryOp): + def __init__(self, pat: typing.Sequence[str]): + self._pat = pat + + def _as_ibis(self, x: ibis_types.Value): + any_match = None + for pat in self._pat: + pat_match = typing.cast(ibis_types.StringValue, x).startswith(pat) + if any_match is not None: + any_match = any_match | pat_match + else: + any_match = pat_match + return any_match if any_match is not None else ibis_types.literal(False) + + +class EndsWithOp(UnaryOp): + def __init__(self, pat: typing.Sequence[str]): + self._pat = pat + + def _as_ibis(self, x: ibis_types.Value): + any_match = None + for pat in self._pat: + pat_match = typing.cast(ibis_types.StringValue, x).endswith(pat) + if any_match is not None: + any_match = any_match | pat_match + else: + any_match = pat_match + return any_match if any_match is not None else ibis_types.literal(False) + + +class ZfillOp(UnaryOp): + def __init__(self, width: int): + self._width = width + + def _as_ibis(self, x: ibis_types.Value): + str_value = typing.cast(ibis_types.StringValue, x) + return ( + ibis.case() + .when( + str_value[0] == "-", + "-" + + StrPadOp(self._width - 1, "0", "left")._as_ibis(str_value.substr(1)), + ) + .else_(StrPadOp(self._width, "0", "left")._as_ibis(str_value)) + .end() + ) + + +## Datetime Ops +class DayOp(UnaryOp): + def _as_ibis(self, x: ibis_types.Value): + return typing.cast(ibis_types.TimestampValue, x).day().cast(ibis_dtypes.int64) + + +class DateOp(UnaryOp): + def _as_ibis(self, x: ibis_types.Value): + return typing.cast(ibis_types.TimestampValue, x).date() + + +class DayofweekOp(UnaryOp): + def _as_ibis(self, x: ibis_types.Value): + return ( + typing.cast(ibis_types.TimestampValue, x) + .day_of_week.index() + .cast(ibis_dtypes.int64) + ) + + +class HourOp(UnaryOp): + def _as_ibis(self, x: ibis_types.Value): + return typing.cast(ibis_types.TimestampValue, x).hour().cast(ibis_dtypes.int64) + + +class MinuteOp(UnaryOp): + def _as_ibis(self, x: ibis_types.Value): + return ( + typing.cast(ibis_types.TimestampValue, x).minute().cast(ibis_dtypes.int64) + ) + + +class MonthOp(UnaryOp): + def _as_ibis(self, x: ibis_types.Value): + return typing.cast(ibis_types.TimestampValue, x).month().cast(ibis_dtypes.int64) + + +class QuarterOp(UnaryOp): + def _as_ibis(self, x: ibis_types.Value): + return ( + typing.cast(ibis_types.TimestampValue, x).quarter().cast(ibis_dtypes.int64) + ) + + +class SecondOp(UnaryOp): + def _as_ibis(self, x: ibis_types.Value): + return ( + typing.cast(ibis_types.TimestampValue, x).second().cast(ibis_dtypes.int64) + ) + + +class TimeOp(UnaryOp): + def _as_ibis(self, x: ibis_types.Value): + return typing.cast(ibis_types.TimestampValue, x).time() + + +class YearOp(UnaryOp): + def _as_ibis(self, x: ibis_types.Value): + return typing.cast(ibis_types.TimestampValue, x).year().cast(ibis_dtypes.int64) + + +# Parameterized ops +class AsTypeOp(UnaryOp): + def __init__(self, to_type: dtypes.DtypeString | dtypes.Dtype): + self.to_type = bigframes.dtypes.bigframes_dtype_to_ibis_dtype(to_type) + + def _as_ibis(self, x: ibis_types.Value): + if isinstance(x, ibis_types.NullScalar): + return ibis_types.null().cast(self.to_type) + + return bigframes.dtypes.cast_ibis_value(x, self.to_type) + + +class FindOp(UnaryOp): + def __init__(self, sub, start, end): + self._sub = sub + self._start = start + self._end = end + + def _as_ibis(self, x: ibis_types.Value): + return typing.cast(ibis_types.StringValue, x).find( + self._sub, self._start, self._end + ) + + +class ExtractOp(UnaryOp): + def __init__(self, pat: str, n: int = 1): + self._pat = pat + self._n = n + + def _as_ibis(self, x: ibis_types.Value): + return typing.cast(ibis_types.StringValue, x).re_extract(self._pat, self._n) + + +class SliceOp(UnaryOp): + def __init__(self, start, stop): + self._start = start + self._stop = stop + + def _as_ibis(self, x: ibis_types.Value): + return typing.cast(ibis_types.StringValue, x)[self._start : self._stop] + + +class IsInOp(UnaryOp): + def __init__(self, values, match_nulls: bool = True): + self._values = values + self._match_nulls = match_nulls + + def _as_ibis(self, x: ibis_types.Value): + contains_nulls = any(is_null(value) for value in self._values) + matchable_ibis_values = [] + for item in self._values: + if not is_null(item): + try: + # we want values that *could* be cast to the dtype, but we don't want + # to actually cast it, as that could be lossy (eg float -> int) + item_inferred_type = ibis.literal(item).type() + if ( + x.type() == item_inferred_type + or x.type().is_numeric() + and item_inferred_type.is_numeric() + ): + matchable_ibis_values.append(item) + except TypeError: + pass + + if self._match_nulls and contains_nulls: + return x.isnull() | x.isin(matchable_ibis_values) + else: + return x.isin(matchable_ibis_values) + + +class BinopPartialRight(UnaryOp): + def __init__(self, binop: BinaryOp, right_scalar: typing.Any): + self._binop = binop + self._right = dtypes.literal_to_ibis_scalar(right_scalar, validate=False) + + def _as_ibis(self, x): + return self._binop(x, self._right) + + +class BinopPartialLeft(UnaryOp): + def __init__(self, binop: BinaryOp, left_scalar: typing.Any): + self._binop = binop + self._left = dtypes.literal_to_ibis_scalar(left_scalar, validate=False) + + def _as_ibis(self, x): + return self._binop(self._left, x) + + +class RepeatOp(UnaryOp): + def __init__(self, repeats): + self._repeats = repeats + + def _as_ibis(self, x: ibis_types.Value): + return typing.cast(ibis_types.StringValue, x).repeat(self._repeats) + + +class RemoteFunctionOp(UnaryOp): + def __init__(self, func: typing.Callable, apply_on_null=True): + if not hasattr(func, "bigframes_remote_function"): + raise TypeError( + f"only a bigframes remote function is supported as a callable. {constants.FEEDBACK_LINK}" + ) + + self._func = func + self._apply_on_null = apply_on_null + + def _as_ibis(self, x: ibis_types.Value): + x_transformed = self._func(x) + if not self._apply_on_null: + x_transformed = where_op(x, x.isnull(), x_transformed) + return x_transformed + + +abs_op = AbsOp() +invert_op = InvertOp() +isnull_op = IsNullOp() +len_op = LenOp() +notnull_op = NotNullOp() +reverse_op = ReverseOp() +lower_op = LowerOp() +upper_op = UpperOp() +strip_op = StripOp() +isalnum_op = IsAlnumOp() +isalpha_op = IsAlphaOp() +isdecimal_op = IsDecimalOp() +isdigit_op = IsDigitOp() +isnumeric_op = IsNumericOp() +isspace_op = IsSpaceOp() +islower_op = IsLowerOp() +isupper_op = IsUpperOp() +rstrip_op = RstripOp() +lstrip_op = LstripOp() +hash_op = HashOp() +day_op = DayOp() +dayofweek_op = DayofweekOp() +date_op = DateOp() +hour_op = HourOp() +minute_op = MinuteOp() +month_op = MonthOp() +quarter_op = QuarterOp() +second_op = SecondOp() +time_op = TimeOp() +year_op = YearOp() +capitalize_op = CapitalizeOp() + +# Just parameterless unary ops for now +# TODO: Parameter mappings +NUMPY_TO_OP: typing.Final = { + np.sin: SinOp(), + np.cos: CosOp(), + np.tan: TanOp(), + np.arcsin: ArcsinOp(), + np.arccos: ArccosOp(), + np.arctan: ArctanOp(), + np.sinh: SinhOp(), + np.cosh: CoshOp(), + np.tanh: TanhOp(), + np.arcsinh: ArcsinhOp(), + np.arccosh: ArccoshOp(), + np.arctanh: ArctanhOp(), + np.exp: ExpOp(), + np.log: LnOp(), + np.log10: Log10Op(), + np.sqrt: SqrtOp(), + np.abs: AbsOp(), +} + + +### Binary Ops +def short_circuit_nulls(type_override: typing.Optional[ibis_dtypes.DataType] = None): + """Wraps a binary operator to generate nulls of the expected type if either input is a null scalar.""" + + def short_circuit_nulls_inner(binop): + @functools.wraps(binop) + def wrapped_binop(x: ibis_types.Value, y: ibis_types.Value): + if isinstance(x, ibis_types.NullScalar): + return ibis_types.null().cast(type_override or y.type()) + elif isinstance(y, ibis_types.NullScalar): + return ibis_types.null().cast(type_override or x.type()) + else: + return binop(x, y) + + return wrapped_binop + + return short_circuit_nulls_inner + + +def concat_op( + x: ibis_types.Value, + y: ibis_types.Value, +): + x_string = typing.cast(ibis_types.StringValue, x) + y_string = typing.cast(ibis_types.StringValue, y) + return x_string.concat(y_string) + + +def eq_op( + x: ibis_types.Value, + y: ibis_types.Value, +): + return x == y + + +def eq_nulls_match_op( + x: ibis_types.Value, + y: ibis_types.Value, +): + """Variant of eq_op where nulls match each other. Only use where dtypes are known to be same.""" + left = x.cast(ibis_dtypes.str).fillna(ibis_types.literal("$NULL_SENTINEL$")) + right = y.cast(ibis_dtypes.str).fillna(ibis_types.literal("$NULL_SENTINEL$")) + return left == right + + +def ne_op( + x: ibis_types.Value, + y: ibis_types.Value, +): + return x != y + + +def and_op( + x: ibis_types.Value, + y: ibis_types.Value, +): + return typing.cast(ibis_types.BooleanValue, x) & typing.cast( + ibis_types.BooleanValue, y + ) + + +def or_op( + x: ibis_types.Value, + y: ibis_types.Value, +): + return typing.cast(ibis_types.BooleanValue, x) | typing.cast( + ibis_types.BooleanValue, y + ) + + +@short_circuit_nulls() +def add_op( + x: ibis_types.Value, + y: ibis_types.Value, +): + if isinstance(x, ibis_types.NullScalar) or isinstance(x, ibis_types.NullScalar): + return + return typing.cast(ibis_types.NumericValue, x) + typing.cast( + ibis_types.NumericValue, y + ) + + +@short_circuit_nulls() +def sub_op( + x: ibis_types.Value, + y: ibis_types.Value, +): + return typing.cast(ibis_types.NumericValue, x) - typing.cast( + ibis_types.NumericValue, y + ) + + +@short_circuit_nulls() +def mul_op( + x: ibis_types.Value, + y: ibis_types.Value, +): + return typing.cast(ibis_types.NumericValue, x) * typing.cast( + ibis_types.NumericValue, y + ) + + +@short_circuit_nulls(ibis_dtypes.float) +def div_op( + x: ibis_types.Value, + y: ibis_types.Value, +): + return typing.cast(ibis_types.NumericValue, x) / typing.cast( + ibis_types.NumericValue, y + ) + + +@short_circuit_nulls(ibis_dtypes.float) +def pow_op( + x: ibis_types.Value, + y: ibis_types.Value, +): + if x.type().is_integer() and y.type().is_integer(): + return _int_pow_op(x, y) + else: + return _float_pow_op(x, y) + + +@short_circuit_nulls(ibis_dtypes.float) +def unsafe_pow_op( + x: ibis_types.Value, + y: ibis_types.Value, +): + """For internal use only - where domain and overflow checks are not needed.""" + return typing.cast(ibis_types.NumericValue, x) ** typing.cast( + ibis_types.NumericValue, y + ) + + +def _int_pow_op( + x: ibis_types.Value, + y: ibis_types.Value, +): + # Need to avoid any error cases - should produce NaN instead + # See: https://cloud.google.com/bigquery/docs/reference/standard-sql/mathematical_functions#pow + x_as_decimal = typing.cast( + ibis_types.NumericValue, + x.cast(ibis_dtypes.Decimal(precision=38, scale=9, nullable=True)), + ) + y_val = typing.cast(ibis_types.NumericValue, y) + + # BQ POW() function outputs FLOAT64, which can lose precision. + # Therefore, we do math in NUMERIC and cast back down after. + # Also, explicit bounds checks, pandas will silently overflow. + pow_result = x_as_decimal**y_val + overflow_cond = (pow_result > _ibis_num((2**63) - 1)) | ( + pow_result < _ibis_num(-(2**63)) + ) + + return ( + ibis.case() + .when((overflow_cond), ibis.null()) + .else_(pow_result.cast(ibis_dtypes.int64)) + .end() + ) + + +def _float_pow_op( + x: ibis_types.Value, + y: ibis_types.Value, +): + # Most conditions here seek to prevent calling BQ POW with inputs that would generate errors. + # See: https://cloud.google.com/bigquery/docs/reference/standard-sql/mathematical_functions#pow + x_val = typing.cast(ibis_types.NumericValue, x) + y_val = typing.cast(ibis_types.NumericValue, y) + + overflow_cond = (x_val != _ZERO) & ((y_val * x_val.abs().ln()) > _FLOAT64_EXP_BOUND) + + # Float64 lose integer precision beyond 2**53, beyond this insufficient precision to get parity + exp_too_big = y_val.abs() > _ibis_num(2**53) + # Treat very large exponents as +=INF + norm_exp = exp_too_big.ifelse(_INF * y_val.sign(), y_val) + + pow_result = x_val**norm_exp + + # This cast is dangerous, need to only excuted where y_val has been bounds-checked + # Ibis needs try_cast binding to bq safe_cast + exponent_is_whole = y_val.cast(ibis_dtypes.int64) == y_val + odd_exponent = (x_val < _ZERO) & ( + y_val.cast(ibis_dtypes.int64) % _ibis_num(2) == _ibis_num(1) + ) + infinite_base = x_val.abs() == _INF + + return ( + ibis.case() + # Might be able to do something more clever with x_val==0 case + .when(y_val == _ZERO, _ibis_num(1)) + .when( + x_val == _ibis_num(1), _ibis_num(1) + ) # Need to ignore exponent, even if it is NA + .when( + (x_val == _ZERO) & (y_val < _ZERO), _INF + ) # This case would error POW function in BQ + .when(infinite_base, pow_result) + .when( + exp_too_big, pow_result + ) # Bigquery can actually handle the +-inf cases gracefully + .when((x_val < _ZERO) & (~exponent_is_whole), _NAN) + .when( + overflow_cond, _INF * odd_exponent.ifelse(_ibis_num(-1), _ibis_num(1)) + ) # finite overflows would cause bq to error + .else_(pow_result) + .end() + ) + + +@short_circuit_nulls(ibis_dtypes.bool) +def lt_op( + x: ibis_types.Value, + y: ibis_types.Value, +): + return x < y + + +@short_circuit_nulls(ibis_dtypes.bool) +def le_op( + x: ibis_types.Value, + y: ibis_types.Value, +): + return x <= y + + +@short_circuit_nulls(ibis_dtypes.bool) +def gt_op( + x: ibis_types.Value, + y: ibis_types.Value, +): + return x > y + + +@short_circuit_nulls(ibis_dtypes.bool) +def ge_op( + x: ibis_types.Value, + y: ibis_types.Value, +): + return x >= y + + +def coalesce_op( + x: ibis_types.Value, + y: ibis_types.Value, +): + if x.name("name").equals(y.name("name")): + return x + else: + return ibis.coalesce(x, y) + + +@short_circuit_nulls(ibis_dtypes.int) +def floordiv_op( + x: ibis_types.Value, + y: ibis_types.Value, +): + x_numeric = typing.cast(ibis_types.NumericValue, x) + y_numeric = typing.cast(ibis_types.NumericValue, y) + floordiv_expr = x_numeric // y_numeric + + # DIV(N, 0) will error in bigquery, but needs to return 0 for int, and inf for float in BQ so we short-circuit in this case. + # Multiplying left by zero propogates nulls. + zero_result = _INF if (x.type().is_floating() or y.type().is_floating()) else _ZERO + return ( + ibis.case() + .when(y_numeric == _ZERO, zero_result * x_numeric) + .else_(floordiv_expr) + .end() + ) + + +def _is_float(x: ibis_types.Value): + return isinstance(x, (ibis_types.FloatingColumn, ibis_types.FloatingScalar)) + + +@short_circuit_nulls() +def mod_op( + x: ibis_types.Value, + y: ibis_types.Value, +): + is_result_float = _is_float(x) | _is_float(y) + x_numeric = typing.cast( + ibis_types.NumericValue, + x.cast(ibis_dtypes.Decimal(precision=38, scale=9, nullable=True)) + if is_result_float + else x, + ) + y_numeric = typing.cast( + ibis_types.NumericValue, + y.cast(ibis_dtypes.Decimal(precision=38, scale=9, nullable=True)) + if is_result_float + else y, + ) + # Hacky short-circuit to avoid passing zero-literal to sql backend, evaluate locally instead to null. + op = y.op() + if isinstance(op, ibis.expr.operations.generic.Literal) and op.value == 0: + return ibis_types.null().cast(x.type()) + + bq_mod = x_numeric % y_numeric # Bigquery will maintain x sign here + if is_result_float: + bq_mod = typing.cast(ibis_types.NumericValue, bq_mod.cast(ibis_dtypes.float64)) + + # In BigQuery returned value has the same sign as X. In pandas, the sign of y is used, so we need to flip the result if sign(x) != sign(y) + return ( + ibis.case() + .when( + y_numeric == _ZERO, + _NAN * x_numeric if is_result_float else _ZERO * x_numeric, + ) # Dummy op to propogate nulls and type from x arg + .when( + (y_numeric < _ZERO) & (bq_mod > _ZERO), (y_numeric + bq_mod) + ) # Convert positive result to negative + .when( + (y_numeric > _ZERO) & (bq_mod < _ZERO), (y_numeric + bq_mod) + ) # Convert negative result to positive + .else_(bq_mod) + .end() + ) + + +def fillna_op( + x: ibis_types.Value, + y: ibis_types.Value, +): + return x.fillna(typing.cast(ibis_types.Scalar, y)) + + +def round_op(x: ibis_types.Value, y: ibis_types.Value): + return typing.cast(ibis_types.NumericValue, x).round( + digits=typing.cast(ibis_types.IntegerValue, y) + ) + + +def clip_lower( + value: ibis_types.Value, + lower: ibis_types.Value, +): + return ibis.case().when(lower.isnull() | (value < lower), lower).else_(value).end() + + +def clip_upper( + value: ibis_types.Value, + upper: ibis_types.Value, +): + return ibis.case().when(upper.isnull() | (value > upper), upper).else_(value).end() + + +def reverse(op: BinaryOp) -> BinaryOp: + return lambda x, y: op(y, x) + + +def partial_left(op: BinaryOp, scalar: typing.Any) -> UnaryOp: + return BinopPartialLeft(op, scalar) + + +def partial_right(op: BinaryOp, scalar: typing.Any) -> UnaryOp: + return BinopPartialRight(op, scalar) + + +NUMPY_TO_BINOP: typing.Final = { + np.add: add_op, + np.subtract: sub_op, + np.multiply: mul_op, + np.divide: div_op, + np.power: pow_op, +} + + +# Ternary ops +def where_op( + original: ibis_types.Value, + condition: ibis_types.Value, + replacement: ibis_types.Value, +) -> ibis_types.Value: + """Returns x if y is true, otherwise returns z.""" + return ibis.case().when(condition, original).else_(replacement).end() + + +def clip_op( + original: ibis_types.Value, + lower: ibis_types.Value, + upper: ibis_types.Value, +) -> ibis_types.Value: + """Clips value to lower and upper bounds.""" + if isinstance(lower, ibis_types.NullScalar) and ( + not isinstance(upper, ibis_types.NullScalar) + ): + return ( + ibis.case() + .when(upper.isnull() | (original > upper), upper) + .else_(original) + .end() + ) + elif (not isinstance(lower, ibis_types.NullScalar)) and isinstance( + upper, ibis_types.NullScalar + ): + return ( + ibis.case() + .when(lower.isnull() | (original < lower), lower) + .else_(original) + .end() + ) + elif isinstance(lower, ibis_types.NullScalar) and ( + isinstance(upper, ibis_types.NullScalar) + ): + return original + else: + # Note: Pandas has unchanged behavior when upper bound and lower bound are flipped. This implementation requires that lower_bound < upper_bound + return ( + ibis.case() + .when(lower.isnull() | (original < lower), lower) + .when(upper.isnull() | (original > upper), upper) + .else_(original) + .end() + ) + + +def partial_arg1(op: TernaryOp, scalar: typing.Any) -> BinaryOp: + return lambda x, y: op(dtypes.literal_to_ibis_scalar(scalar, validate=False), x, y) + + +def partial_arg2(op: TernaryOp, scalar: typing.Any) -> BinaryOp: + return lambda x, y: op(x, dtypes.literal_to_ibis_scalar(scalar, validate=False), y) + + +def partial_arg3(op: TernaryOp, scalar: typing.Any) -> BinaryOp: + return lambda x, y: op(x, y, dtypes.literal_to_ibis_scalar(scalar, validate=False)) + + +def is_null(value) -> bool: + # float NaN/inf should be treated as distinct from 'true' null values + return typing.cast(bool, pd.isna(value)) and not isinstance(value, float) + + +def _ibis_num(number: float): + return typing.cast(ibis_types.NumericValue, ibis_types.literal(number)) diff --git a/bigframes/operations/_matplotlib/__init__.py b/bigframes/operations/_matplotlib/__init__.py deleted file mode 100644 index caacadf5fed..00000000000 --- a/bigframes/operations/_matplotlib/__init__.py +++ /dev/null @@ -1,40 +0,0 @@ -# Copyright 2023 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import typing - -import bigframes.operations._matplotlib.core as core -import bigframes.operations._matplotlib.hist as hist - -PLOT_TYPES = typing.Union[type[core.SamplingPlot], type[hist.HistPlot]] - -PLOT_CLASSES: dict[str, PLOT_TYPES] = { - "area": core.AreaPlot, - "bar": core.BarPlot, - "barh": core.BarhPlot, - "pie": core.PiePlot, - "line": core.LinePlot, - "scatter": core.ScatterPlot, - "hist": hist.HistPlot, -} - - -def plot(data, kind, **kwargs): - plot_obj = PLOT_CLASSES[kind](data, **kwargs) - plot_obj.generate() - plot_obj.draw() - return plot_obj.result - - -__all__ = ["plot"] diff --git a/bigframes/operations/_matplotlib/core.py b/bigframes/operations/_matplotlib/core.py deleted file mode 100644 index 06fb5235d78..00000000000 --- a/bigframes/operations/_matplotlib/core.py +++ /dev/null @@ -1,194 +0,0 @@ -# Copyright 2023 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import abc -import typing -import warnings - -import bigframes_vendored.constants as constants -import pandas as pd - -import bigframes.dtypes as dtypes -import bigframes.exceptions as bfe - -DEFAULT_SAMPLING_N = 1000 -DEFAULT_SAMPLING_STATE = 0 - - -class MPLPlot(abc.ABC): - @abc.abstractmethod - def generate(self): - pass - - def draw(self) -> None: - # This import can fail with "Matplotlib failed to acquire the - # following lock file" so import here to reduce the chance of - # our parallel test suite from triggering this. - import matplotlib.pyplot as plt - - plt.draw_if_interactive() - - @property - def result(self): - if hasattr(self, "axes"): - return self.axes - else: - raise AttributeError("Axes not defined") - - -class SamplingPlot(MPLPlot): - @property - @abc.abstractmethod - def _kind(self): - pass - - @property - def _sampling_warning_msg(self) -> typing.Optional[str]: - return ( - "To optimize plotting performance, your data has been downsampled to {sampling_n} " - "rows from the original {total_n} rows. This may result in some data points " - "not being displayed. For a more comprehensive view, consider pre-processing " - "your data by aggregating it or selecting the top categories." - ) - - def __init__(self, data, **kwargs) -> None: - self.kwargs = kwargs - self.data = data - - def generate(self) -> None: - plot_data = self._compute_plot_data() - self.axes = plot_data.plot(kind=self._kind, **self.kwargs) - - def _compute_sample_data(self, data): - # TODO: Cache the sampling data in the PlotAccessor. - sampling_n = self.kwargs.pop("sampling_n", DEFAULT_SAMPLING_N) - if self._sampling_warning_msg is not None: - total_n = data.shape[0] - if sampling_n < total_n: - msg = bfe.format_message( - self._sampling_warning_msg.format( - sampling_n=sampling_n, total_n=total_n - ) - ) - warnings.warn(msg, category=UserWarning) - - sampling_random_state = self.kwargs.pop( - "sampling_random_state", DEFAULT_SAMPLING_STATE - ) - return data.sample( - n=sampling_n, - random_state=sampling_random_state, - sort=False, - ).to_pandas() - - def _compute_plot_data(self): - return self._compute_sample_data(self.data) - - -class AreaPlot(SamplingPlot): - @property - def _sampling_warning_msg(self) -> typing.Optional[str]: - return None - - @property - def _kind(self) -> typing.Literal["area"]: - return "area" - - -class BarPlot(SamplingPlot): - @property - def _kind(self) -> typing.Literal["bar"]: - return "bar" - - -class BarhPlot(SamplingPlot): - @property - def _kind(self) -> typing.Literal["barh"]: - return "barh" - - -class PiePlot(SamplingPlot): - @property - def _kind(self) -> typing.Literal["pie"]: - return "pie" - - -class LinePlot(SamplingPlot): - @property - def _kind(self) -> typing.Literal["line"]: - return "line" - - -class ScatterPlot(SamplingPlot): - @property - def _kind(self) -> typing.Literal["scatter"]: - return "scatter" - - @property - def _sampling_warning_msg(self) -> typing.Optional[str]: - return None - - def __init__(self, data, **kwargs) -> None: - super().__init__(data, **kwargs) - - c = self.kwargs.get("c", None) - if self._is_sequence_arg(c): - raise NotImplementedError( - f"Only support a single color string or a column name/posision. {constants.FEEDBACK_LINK}" - ) - - s = self.kwargs.get("s", None) - if self._is_sequence_arg(s): - raise NotImplementedError( - f"Only support a single color string or a column name/posision. {constants.FEEDBACK_LINK}" - ) - - def _compute_plot_data(self): - sample = self._compute_sample_data(self.data) - - # Works around a pandas bug: - # https://github.com/pandas-dev/pandas/commit/45b937d64f6b7b6971856a47e379c7c87af7e00a - c = self.kwargs.get("c", None) - if pd.core.dtypes.common.is_integer(c): - c = self.data.columns[c] - if self._is_column_name(c, sample) and sample[c].dtype == dtypes.STRING_DTYPE: - sample[c] = sample[c].astype("object") - - # To avoid Matplotlib's automatic conversion of `Float64` or `Int64` columns - # to `object` types (which breaks float-like behavior), this code proactively - # converts the column to a compatible format. - s = self.kwargs.get("s", None) - if pd.core.dtypes.common.is_integer(s): - s = self.data.columns[s] - if self._is_column_name(s, sample): - if sample[s].dtype == dtypes.INT_DTYPE: - sample[s] = sample[s].astype("int64") - elif sample[s].dtype == dtypes.FLOAT_DTYPE: - sample[s] = sample[s].astype("float64") - - return sample - - def _is_sequence_arg(self, arg): - return ( - arg is not None - and not isinstance(arg, str) - and isinstance(arg, typing.Iterable) - ) - - def _is_column_name(self, arg, data): - return ( - arg is not None - and pd.core.dtypes.common.is_hashable(arg) - and arg in data.columns - ) diff --git a/bigframes/operations/_matplotlib/hist.py b/bigframes/operations/_matplotlib/hist.py deleted file mode 100644 index 213e2abd775..00000000000 --- a/bigframes/operations/_matplotlib/hist.py +++ /dev/null @@ -1,172 +0,0 @@ -# Copyright 2023 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import itertools -from typing import Literal - -import bigframes_vendored.constants as constants -import numpy as np -import pandas as pd - -import bigframes.operations._matplotlib.core as bfplt - - -class HistPlot(bfplt.MPLPlot): - @property - def _kind(self) -> Literal["hist"]: - return "hist" - - def __init__( - self, - data, - bins: int = 10, - **kwargs, - ) -> None: - self.bins = bins - self.label = kwargs.get("label", None) - self.by = kwargs.pop("by", None) - self.kwargs = kwargs - - if self.by is not None: - raise NotImplementedError( - f"Non-none `by` argument is not yet supported. {constants.FEEDBACK_LINK}" - ) - if not isinstance(self.bins, int): - raise NotImplementedError( - f"Only integer values are supported for the `bins` argument. {constants.FEEDBACK_LINK}" - ) - if kwargs.get("weight", None) is not None: - raise NotImplementedError( - f"Non-none `weight` argument is not yet supported. {constants.FEEDBACK_LINK}" - ) - - self.data = self._compute_plot_data(data) - - def generate(self) -> None: - """ - Calculates weighted histograms through BigQuery and plots them through pandas - native histogram plot. - """ - hist_bars = self._calculate_hist_bars(self.data, self.bins) - bin_edges = self._calculate_bin_edges( - hist_bars, self.bins, self.kwargs.get("range", None) - ) - - weights = { - col_name: hist_bar.values for col_name, hist_bar in hist_bars.items() - } - hist_x = { - col_name: pd.Series( - ( - hist_bar.index.get_level_values("left_exclusive") - + hist_bar.index.get_level_values("right_inclusive") - ) - / 2.0 - ) - for col_name, hist_bar in hist_bars.items() - } - - # Align DataFrames for plotting despite potential differences in column - # lengths, filling shorter columns with zeros. - hist_x_pd = pd.DataFrame( - list(itertools.zip_longest(*hist_x.values())), columns=list(hist_x.keys()) - ).sort_index(axis=1)[self.data.columns.values] - weights_pd = pd.DataFrame( - list(itertools.zip_longest(*weights.values())), columns=list(weights.keys()) - ).sort_index(axis=1)[self.data.columns.values] - - # Prevents pandas from dropping NA values and causing length mismatches by - # filling them with zeros. - hist_x_pd.fillna(0, inplace=True) - weights_pd.fillna(0, inplace=True) - - self.axes = hist_x_pd.plot.hist( - bins=bin_edges, - weights=np.array(weights_pd.values), - **self.kwargs, - ) # type: ignore - - def _compute_plot_data(self, data): - """ - Prepares data for plotting, focusing on numeric data types. - - Raises: - TypeError: If the input data contains no numeric columns. - """ - # Importing at the top of the file causes a circular import. - import bigframes.series as series - - if isinstance(data, series.Series): - label = self.label - if label is None and data.name is None: - label = "" - if label is None: - data = data.to_frame() - else: - data = data.to_frame(name=label) - - # TODO(chelsealin): Support timestamp/date types here. - include_type = ["number"] - numeric_data = data.select_dtypes(include=include_type) - try: - is_empty = numeric_data.columns.empty - except AttributeError: - is_empty = not len(numeric_data) - - if is_empty: - raise TypeError("no numeric data to plot") - - return numeric_data - - @staticmethod - def _calculate_hist_bars(data, bins): - """ - Calculates histogram bars for each column in a BigFrames DataFrame, and - returns a dictionary where keys are column names and values are pandas - Series. The series values are the histogram bins' heights with a - multi-index defining 'left_exclusive' and 'right_inclusive' bin edges. - """ - import bigframes.pandas as bpd - - # TODO: Optimize this by batching multiple jobs into one. - hist_bar = {} - for _, col in enumerate(data.columns): - cutted_data = bpd.cut(data[col], bins=bins, labels=None) - hist_bar[col] = ( - cutted_data.struct.explode() - .value_counts() - .to_pandas() - .sort_index(level="left_exclusive") - ) - return hist_bar - - @staticmethod - def _calculate_bin_edges(hist_bars, bins, range): - """ - Calculate bin edges from the histogram bars. - """ - bin_edges = None - for _, hist_bar in hist_bars.items(): - left = hist_bar.index.get_level_values("left_exclusive") - right = hist_bar.index.get_level_values("right_inclusive") - if bin_edges is None: - bin_edges = left.union(right) - else: - bin_edges = left.union(right).union(bin_edges) - - if bin_edges is None: - return None - - _, bins = np.histogram(bin_edges, bins=bins, range=range) - return bins diff --git a/bigframes/operations/_op_converters.py b/bigframes/operations/_op_converters.py deleted file mode 100644 index 14417a24f6e..00000000000 --- a/bigframes/operations/_op_converters.py +++ /dev/null @@ -1,37 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import bigframes.operations as ops - - -def convert_index(key: int) -> ops.GetItemOp: - if key < 0: - raise NotImplementedError("Negative indexing is not supported.") - return ops.GetItemOp(key=key) - - -def convert_slice(key: slice) -> ops.ArraySliceOp: - if key.step is not None and key.step != 1: - raise NotImplementedError(f"Only a step of 1 is allowed, got {key.step}") - - if (key.start is not None and key.start < 0) or ( - key.stop is not None and key.stop < 0 - ): - raise NotImplementedError("Slicing with negative numbers is not allowed.") - - return ops.ArraySliceOp( - start=key.start if key.start is not None else 0, - stop=key.stop, - step=key.step, - ) diff --git a/bigframes/operations/aggregations.py b/bigframes/operations/aggregations.py index f7b89b949a8..465d1887247 100644 --- a/bigframes/operations/aggregations.py +++ b/bigframes/operations/aggregations.py @@ -14,666 +14,477 @@ from __future__ import annotations -import abc -import dataclasses import typing -from typing import TYPE_CHECKING, Callable, ClassVar, Iterable, Optional -import numpy as np -import pandas as pd -import pyarrow as pa +import ibis +import ibis.expr.datatypes as ibis_dtypes +import ibis.expr.types as ibis_types +from pandas import Int64Dtype +import bigframes.constants as constants import bigframes.dtypes as dtypes -import bigframes.operations.type as signatures -from bigframes.core import agg_expressions +import third_party.bigframes_vendored.ibis.expr.operations as vendored_ibis_ops -if TYPE_CHECKING: - from bigframes.core import expression - -@dataclasses.dataclass(frozen=True) class WindowOp: + def _as_ibis(self, value: ibis_types.Column, window=None): + raise NotImplementedError("Base class WindowOp has no implementaiton.") + @property def skips_nulls(self): """Whether the window op skips null rows.""" return True @property - def nulls_count_for_min_values(self) -> bool: - """Whether null values count for min_values.""" - return not self.skips_nulls - - @property - def implicitly_inherits_order(self): - """ - Whether the operator implicitly inherits the underlying array order, should it exist. - - Notably, rank operations do not want to inherit ordering. Even order-independent operations - may inherit order when needed for row bounds. - """ - return True - - @property - def order_independent(self): - """ - True if the output of the operator does not depend on the ordering of input rows. - - Aggregation functions are usually order independent, except array_agg, string_agg. - - Navigation functions are a notable case that are not order independent. - """ + def handles_ties(self): + """Whether the operator can handle ties without nondeterministic output. (eg. rank operator can handle ties but not the count operator)""" return False - @abc.abstractmethod - def output_type( - self, *input_types: dtypes.ExpressionType - ) -> dtypes.ExpressionType: ... - - @property - def can_be_windowized(self): - # this is more of an engine property, but will treat feasibility in bigquery sql as source of truth - return True - - -@dataclasses.dataclass(frozen=True) -class NullaryWindowOp(WindowOp): - @property - def arguments(self) -> int: - return 0 - -@dataclasses.dataclass(frozen=True) -class UnaryWindowOp(WindowOp): - @property - def arguments(self) -> int: - return 1 - - def output_type(self, *input_types: dtypes.ExpressionType) -> dtypes.ExpressionType: - return input_types[0] - - -@dataclasses.dataclass(frozen=True) class AggregateOp(WindowOp): - """Aggregate ops can be applied with or without a window clause.""" - - @property - @abc.abstractmethod - def name(self) -> str: ... - - @property - @abc.abstractmethod - def arguments(self) -> int: ... - - @property - def order_independent(self): - return True - - @property - def uses_total_row_ordering(self): - return False - - -@dataclasses.dataclass(frozen=True) -class NullaryAggregateOp(AggregateOp, NullaryWindowOp): - @property - def arguments(self) -> int: - return 0 - - def as_expr( - self, - *exprs: typing.Union[str, expression.Expression], - ) -> agg_expressions.NullaryAggregation: - from bigframes.core import agg_expressions - - return agg_expressions.NullaryAggregation(self) - - -@dataclasses.dataclass(frozen=True) -class UnaryAggregateOp(AggregateOp, UnaryWindowOp): - @property - def arguments(self) -> int: - return 1 - - def as_expr( - self, - *exprs: typing.Union[str, expression.Expression], - ) -> agg_expressions.UnaryAggregation: - from bigframes.core import agg_expressions - from bigframes.operations.base_ops import _convert_expr_input - - # Keep this in sync with output_type and compilers - inputs: list[expression.Expression] = [] - - for expr in exprs: - inputs.append(_convert_expr_input(expr)) - return agg_expressions.UnaryAggregation( - self, - inputs[0], - ) - - -@dataclasses.dataclass(frozen=True) -class BinaryAggregateOp(AggregateOp): - @property - def arguments(self) -> int: - return 2 - - def as_expr( - self, - *exprs: typing.Union[str, expression.Expression], - ) -> agg_expressions.BinaryAggregation: - from bigframes.core import agg_expressions - from bigframes.operations.base_ops import _convert_expr_input + name = "abstract_aggregate" - # Keep this in sync with output_type and compilers - inputs: list[expression.Expression] = [] + def _as_ibis(self, value: ibis_types.Column, window=None): + raise NotImplementedError("Base class AggregateOp has no implementaiton.") - for expr in exprs: - inputs.append(_convert_expr_input(expr)) - return agg_expressions.BinaryAggregation(self, inputs[0], inputs[1]) - - -@dataclasses.dataclass(frozen=True) -class SizeOp(NullaryAggregateOp): - name: ClassVar[str] = "size" - - def output_type(self, *input_types: dtypes.ExpressionType): - return dtypes.INT_DTYPE - - -# TODO: Remove this temporary hack once nullary ops are better supported in APIs -@dataclasses.dataclass(frozen=True) -class SizeUnaryOp(UnaryAggregateOp): - name: ClassVar[str] = "size" - - @property - def skips_nulls(self): - return False - - def output_type(self, *input_types: dtypes.ExpressionType): - return dtypes.INT_DTYPE - - -@dataclasses.dataclass(frozen=True) -class SumOp(UnaryAggregateOp): - name: ClassVar[str] = "sum" - - def output_type(self, *input_types: dtypes.ExpressionType) -> dtypes.ExpressionType: - if input_types[0] == dtypes.TIMEDELTA_DTYPE: - return dtypes.TIMEDELTA_DTYPE - - if dtypes.is_numeric(input_types[0]): - if pd.api.types.is_bool_dtype(input_types[0]): # type: ignore - return dtypes.INT_DTYPE - return input_types[0] - - raise TypeError(f"Type {input_types[0]} is not numeric or timedelta") - - -@dataclasses.dataclass(frozen=True) -class MedianOp(UnaryAggregateOp): - name: ClassVar[str] = "median" - - @property - def order_independent(self) -> bool: - return True - - def output_type(self, *input_types: dtypes.ExpressionType) -> dtypes.ExpressionType: - # These will change if median is changed to exact implementation. - if not dtypes.is_orderable(input_types[0]): - raise TypeError(f"Type {input_types[0]} is not orderable") - if pd.api.types.is_bool_dtype(input_types[0]): # type: ignore - return dtypes.INT_DTYPE +def numeric_op(operation): + def constrained_op(op, column: ibis_types.Column, window=None): + if column.type().is_boolean(): + column = typing.cast( + ibis_types.NumericColumn, column.cast(ibis_dtypes.int64) + ) + if column.type().is_numeric(): + return operation(op, column, window) else: - return input_types[0] + raise ValueError( + f"Numeric operation cannot be applied to type {column.type()}. {constants.FEEDBACK_LINK}" + ) + return constrained_op -@dataclasses.dataclass(frozen=True) -class QuantileOp(UnaryAggregateOp): - q: float - should_floor_result: bool = False - @property - def name(self): - return f"{int(self.q * 100)}%" +class SumOp(AggregateOp): + name = "sum" - @property - def order_independent(self) -> bool: - return True + @numeric_op + def _as_ibis( + self, column: ibis_types.NumericColumn, window=None + ) -> ibis_types.NumericValue: + # Will be null if all inputs are null. Pandas defaults to zero sum though. + bq_sum = _apply_window_if_present(column.sum(), window) + return ( + ibis.case().when(bq_sum.isnull(), ibis_types.literal(0)).else_(bq_sum).end() + ) - def output_type(self, *input_types: dtypes.ExpressionType) -> dtypes.ExpressionType: - if input_types[0] == dtypes.TIMEDELTA_DTYPE: - return dtypes.TIMEDELTA_DTYPE - return signatures.UNARY_REAL_NUMERIC.output_type(input_types[0]) +class MedianOp(AggregateOp): + name = "median" -@dataclasses.dataclass(frozen=True) -class ApproxQuartilesOp(UnaryAggregateOp): - quartile: int + @numeric_op + def _as_ibis( + self, column: ibis_types.NumericColumn, window=None + ) -> ibis_types.NumericValue: + # PERCENTILE_CONT has very few allowed windows. For example, "window + # framing clause is not allowed for analytic function percentile_cont". + if window is not None: + raise NotImplementedError( + f"Median with windowing is not supported. {constants.FEEDBACK_LINK}" + ) - @property - def name(self): - return f"{self.quartile * 25}%" + # TODO(swast): Allow switching between exact and approximate median. + # For now, the best we can do is an approximate median when we're doing + # an aggregation, as PERCENTILE_CONT is only an analytic function. + return typing.cast(ibis_types.NumericValue, column.approx_median()) + + +class ApproxQuartilesOp(AggregateOp): + def __init__(self, quartile: int): + self.name = f"{quartile*25}%" + self._quartile = quartile + + @numeric_op + def _as_ibis( + self, column: ibis_types.NumericColumn, window=None + ) -> ibis_types.NumericValue: + # PERCENTILE_CONT has very few allowed windows. For example, "window + # framing clause is not allowed for analytic function percentile_cont". + if window is not None: + raise NotImplementedError( + f"Approx Quartiles with windowing is not supported. {constants.FEEDBACK_LINK}" + ) + value = vendored_ibis_ops.ApproximateMultiQuantile( + column, num_bins=4 # type: ignore + ).to_expr()[self._quartile] + return typing.cast(ibis_types.NumericValue, value) - def output_type(self, *input_types: dtypes.ExpressionType) -> dtypes.ExpressionType: - if not dtypes.is_orderable(input_types[0]): - raise TypeError(f"Type {input_types[0]} is not orderable") - return input_types[0] - @property - def can_be_windowized(self): - return False +class MeanOp(AggregateOp): + name = "mean" + @numeric_op + def _as_ibis( + self, column: ibis_types.NumericColumn, window=None + ) -> ibis_types.NumericValue: + return _apply_window_if_present(column.mean(), window) -@dataclasses.dataclass(frozen=True) -class ApproxTopCountOp(UnaryAggregateOp): - name: typing.ClassVar[str] = "approx_top_count" - number: int - def output_type(self, *input_types: dtypes.ExpressionType) -> dtypes.ExpressionType: - if not dtypes.is_orderable(input_types[0]): - raise TypeError(f"Type {input_types[0]} is not orderable") +class ProductOp(AggregateOp): + name = "product" - input_type = input_types[0] - fields = [ - pa.field("value", dtypes.bigframes_dtype_to_arrow_dtype(input_type)), - pa.field("count", pa.int64()), - ] - return pd.ArrowDtype(pa.list_(pa.struct(fields))) + @numeric_op + def _as_ibis( + self, column: ibis_types.NumericColumn, window=None + ) -> ibis_types.NumericValue: + # Need to short-circuit as log with zeroes is illegal sql + is_zero = typing.cast(ibis_types.BooleanColumn, (column == 0)) - @property - def can_be_windowized(self): - return False + # There is no product sql aggregate function, so must implement as a sum of logs, and then + # apply power after. Note, log and power base must be equal! This impl uses base 2. + logs = typing.cast( + ibis_types.NumericColumn, + ibis.case().when(is_zero, 0).else_(column.abs().log2()).end(), + ) + logs_sum = _apply_window_if_present(logs.sum(), window) + magnitude = typing.cast(ibis_types.NumericValue, ibis_types.literal(2)).pow( + logs_sum + ) + # Can't determine sign from logs, so have to determine parity of count of negative inputs + is_negative = typing.cast( + ibis_types.NumericColumn, + ibis.case().when(column.sign() == -1, 1).else_(0).end(), + ) + negative_count = _apply_window_if_present(is_negative.sum(), window) + negative_count_parity = negative_count % typing.cast( + ibis_types.NumericValue, ibis.literal(2) + ) # 1 if result should be negative, otherwise 0 + + any_zeroes = _apply_window_if_present(is_zero.any(), window) + float_result = ( + ibis.case() + .when(any_zeroes, ibis_types.literal(0)) + .else_(magnitude * pow(-1, negative_count_parity)) + .end() + ) + return float_result.cast(column.type()) -@dataclasses.dataclass(frozen=True) -class MeanOp(UnaryAggregateOp): - name: ClassVar[str] = "mean" - should_floor_result: bool = False +class MaxOp(AggregateOp): + name = "max" - def output_type(self, *input_types: dtypes.ExpressionType) -> dtypes.ExpressionType: - if input_types[0] == dtypes.TIMEDELTA_DTYPE: - return dtypes.TIMEDELTA_DTYPE - return signatures.UNARY_REAL_NUMERIC.output_type(input_types[0]) + def _as_ibis(self, column: ibis_types.Column, window=None) -> ibis_types.Value: + return _apply_window_if_present(column.max(), window) -@dataclasses.dataclass(frozen=True) -class ProductOp(UnaryAggregateOp): - name: ClassVar[str] = "product" +class MinOp(AggregateOp): + name = "min" - def output_type(self, *input_types: dtypes.ExpressionType) -> dtypes.ExpressionType: - return signatures.FixedOutputType( - dtypes.is_numeric, dtypes.FLOAT_DTYPE, "numeric" - ).output_type(input_types[0]) + def _as_ibis(self, column: ibis_types.Column, window=None) -> ibis_types.Value: + return _apply_window_if_present(column.min(), window) -@dataclasses.dataclass(frozen=True) -class MaxOp(UnaryAggregateOp): - name: ClassVar[str] = "max" +class StdOp(AggregateOp): + name = "std" - def output_type(self, *input_types: dtypes.ExpressionType) -> dtypes.ExpressionType: - return signatures.TypePreserving(dtypes.is_orderable, "orderable").output_type( - input_types[0] + @numeric_op + def _as_ibis(self, x: ibis_types.Column, window=None) -> ibis_types.Value: + return _apply_window_if_present( + typing.cast(ibis_types.NumericColumn, x).std(), window ) -@dataclasses.dataclass(frozen=True) -class MinOp(UnaryAggregateOp): - name: ClassVar[str] = "min" +class VarOp(AggregateOp): + name = "var" - def output_type(self, *input_types: dtypes.ExpressionType) -> dtypes.ExpressionType: - return signatures.TypePreserving(dtypes.is_orderable, "orderable").output_type( - input_types[0] + @numeric_op + def _as_ibis(self, x: ibis_types.Column, window=None) -> ibis_types.Value: + return _apply_window_if_present( + typing.cast(ibis_types.NumericColumn, x).var(), window ) -@dataclasses.dataclass(frozen=True) -class StdOp(UnaryAggregateOp): - name: ClassVar[str] = "std" - - should_floor_result: bool = False - - def output_type(self, *input_types: dtypes.ExpressionType) -> dtypes.ExpressionType: - if input_types[0] == dtypes.TIMEDELTA_DTYPE: - return dtypes.TIMEDELTA_DTYPE - - return signatures.FixedOutputType( - dtypes.is_numeric, dtypes.FLOAT_DTYPE, "numeric" - ).output_type(input_types[0]) - - -@dataclasses.dataclass(frozen=True) -class VarOp(UnaryAggregateOp): - name: ClassVar[str] = "var" +class PopVarOp(AggregateOp): + name = "popvar" - def output_type(self, *input_types: dtypes.ExpressionType) -> dtypes.ExpressionType: - return signatures.FixedOutputType( - dtypes.is_numeric, dtypes.FLOAT_DTYPE, "numeric" - ).output_type(input_types[0]) - - -@dataclasses.dataclass(frozen=True) -class PopVarOp(UnaryAggregateOp): - name: ClassVar[str] = "popvar" + @numeric_op + def _as_ibis(self, x: ibis_types.Column, window=None) -> ibis_types.Value: + return _apply_window_if_present( + typing.cast(ibis_types.NumericColumn, x).var(how="pop"), window + ) - def output_type(self, *input_types: dtypes.ExpressionType) -> dtypes.ExpressionType: - return signatures.FixedOutputType( - dtypes.is_numeric, dtypes.FLOAT_DTYPE, "numeric" - ).output_type(input_types[0]) +class CountOp(AggregateOp): + name = "count" -@dataclasses.dataclass(frozen=True) -class CountOp(UnaryAggregateOp): - name: ClassVar[str] = "count" + def _as_ibis( + self, column: ibis_types.Column, window=None + ) -> ibis_types.IntegerValue: + return _apply_window_if_present(column.count(), window) @property def skips_nulls(self): return False - def output_type(self, *input_types: dtypes.ExpressionType) -> dtypes.ExpressionType: - return signatures.FixedOutputType( - lambda x: True, dtypes.INT_DTYPE, "" - ).output_type(input_types[0]) - -@dataclasses.dataclass(frozen=True) -class ArrayAggOp(UnaryAggregateOp): - name: ClassVar[str] = "arrayagg" - - @property - def order_independent(self): - return False +class CutOp(WindowOp): + def __init__(self, bins: int): + self._bins_ibis = dtypes.literal_to_ibis_scalar(bins, force_dtype=Int64Dtype()) + self._bins_int = bins + + def _as_ibis(self, x: ibis_types.Column, window=None): + col_min = _apply_window_if_present(x.min(), window) + col_max = _apply_window_if_present(x.max(), window) + bin_width = (col_max - col_min) / self._bins_ibis + out = ibis.case() + for this_bin in range(self._bins_int - 1): + out = out.when( + x <= (col_min + (this_bin + 1) * bin_width), + dtypes.literal_to_ibis_scalar(this_bin, force_dtype=Int64Dtype()), + ) + out = out.when(x.notnull(), self._bins_ibis - 1) + return out.end() @property def skips_nulls(self): - return True - - def output_type(self, *input_types: dtypes.ExpressionType) -> dtypes.ExpressionType: - return dtypes.list_type(input_types[0]) - - -@dataclasses.dataclass(frozen=True) -class StringAggOp(UnaryAggregateOp): - name: ClassVar[str] = "string_agg" - sep: str = "," - - @property - def order_independent(self): return False @property - def skips_nulls(self): + def handles_ties(self): return True - def output_type(self, *input_types: dtypes.ExpressionType) -> dtypes.ExpressionType: - if input_types[0] != dtypes.STRING_DTYPE: - raise TypeError(f"Type {input_types[0]} is not string-like") - return dtypes.STRING_DTYPE - - -@dataclasses.dataclass(frozen=True) -class CutOp(UnaryWindowOp): - # TODO: Unintuitive, refactor into multiple ops? - bins: typing.Union[int, Iterable] - right: Optional[bool] - labels: typing.Union[bool, Iterable[str], None] - @property - def skips_nulls(self): - return False - - def output_type(self, *input_types: dtypes.ExpressionType) -> dtypes.ExpressionType: - if self.labels is False: - return dtypes.INT_DTYPE - elif isinstance(self.labels, Iterable): - return dtypes.STRING_DTYPE +class QcutOp(WindowOp): + def __init__(self, quantiles: typing.Union[int, typing.Sequence[float]]): + self.name = f"qcut-{quantiles}" + self._quantiles = quantiles + + @numeric_op + def _as_ibis( + self, column: ibis_types.Column, window=None + ) -> ibis_types.IntegerValue: + if isinstance(self._quantiles, int): + quantiles_ibis = dtypes.literal_to_ibis_scalar(self._quantiles) + percent_ranks = typing.cast( + ibis_types.FloatingColumn, + _apply_window_if_present(column.percent_rank(), window), + ) + float_bucket = typing.cast( + ibis_types.FloatingColumn, (percent_ranks * quantiles_ibis) + ) + return float_bucket.ceil().clip(lower=_ibis_num(1)) - _ibis_num(1) else: - # Assumption: buckets use same numeric type - if isinstance(self.bins, int): - interval_dtype = pa.float64() - elif len(list(self.bins)) == 0: - interval_dtype = pa.int64() - else: - interval_dtype = dtypes.infer_literal_arrow_type(list(self.bins)[0][0]) - pa_type = pa.struct( - [ - pa.field( - "left_exclusive" if self.right else "left_inclusive", - interval_dtype, - nullable=True, - ), - pa.field( - "right_inclusive" if self.right else "right_exclusive", - interval_dtype, - nullable=True, - ), - ] + percent_ranks = typing.cast( + ibis_types.FloatingColumn, + _apply_window_if_present(column.percent_rank(), window), ) - - return pd.ArrowDtype(pa_type) - - @property - def order_independent(self): - return True - - -@dataclasses.dataclass(frozen=True) -class QcutOp(UnaryWindowOp): # bucket op - quantiles: typing.Union[int, typing.Tuple[float, ...]] - - @property - def name(self): - return f"qcut-{self.quantiles}" + out = ibis.case() + first_ibis_quantile = dtypes.literal_to_ibis_scalar(self._quantiles[0]) + out = out.when(percent_ranks < first_ibis_quantile, None) + for bucket_n in range(len(self._quantiles) - 1): + ibis_quantile = dtypes.literal_to_ibis_scalar( + self._quantiles[bucket_n + 1] + ) + out = out.when( + percent_ranks <= ibis_quantile, + dtypes.literal_to_ibis_scalar(bucket_n, force_dtype=Int64Dtype()), + ) + out = out.else_(None) + return out.end() @property def skips_nulls(self): return False - def output_type(self, *input_types: dtypes.ExpressionType) -> dtypes.ExpressionType: - return signatures.FixedOutputType( - dtypes.is_orderable, dtypes.INT_DTYPE, "orderable" - ).output_type(input_types[0]) - @property - def order_independent(self): + def handles_ties(self): return True -@dataclasses.dataclass(frozen=True) -class NuniqueOp(UnaryAggregateOp): - name: ClassVar[str] = "nunique" +class NuniqueOp(AggregateOp): + name = "nunique" + + def _as_ibis( + self, column: ibis_types.Column, window=None + ) -> ibis_types.IntegerValue: + return _apply_window_if_present(column.nunique(), window) @property def skips_nulls(self): return False - def output_type(self, *input_types: dtypes.ExpressionType) -> dtypes.ExpressionType: - return dtypes.INT_DTYPE - -@dataclasses.dataclass(frozen=True) -class AnyValueOp(UnaryAggregateOp): +class AnyValueOp(AggregateOp): # Warning: only use if all values are equal. Non-deterministic otherwise. # Do not expose to users. For special cases only (e.g. pivot). - name: ClassVar[str] = "any_value" - - @property - def skips_nulls(self): - return True - - -# This should really by a NullaryWindowOp, but APIs don't support that yet. -@dataclasses.dataclass(frozen=True) -class RowNumberOp(NullaryWindowOp): - name: ClassVar[str] = "rownumber" - - @property - def skips_nulls(self): - return False - - def output_type(self, *input_types: dtypes.ExpressionType) -> dtypes.ExpressionType: - return dtypes.INT_DTYPE - + name = "any_value" -@dataclasses.dataclass(frozen=True) -class RankOp(UnaryWindowOp): - name: ClassVar[str] = "rank" + def _as_ibis( + self, column: ibis_types.Column, window=None + ) -> ibis_types.IntegerValue: + return _apply_window_if_present(column.arbitrary(), window) @property def skips_nulls(self): - return False + return True - def output_type(self, *input_types: dtypes.ExpressionType) -> dtypes.ExpressionType: - return dtypes.INT_DTYPE - - @property - def implicitly_inherits_order(self): - return False +class RankOp(WindowOp): + name = "rank" -@dataclasses.dataclass(frozen=True) -class DenseRankOp(UnaryWindowOp): - name: ClassVar[str] = "dense_rank" + def _as_ibis( + self, column: ibis_types.Column, window=None + ) -> ibis_types.IntegerValue: + # Ibis produces 0-based ranks, while pandas creates 1-based ranks + return _apply_window_if_present(column.rank(), window) + 1 @property def skips_nulls(self): return False - def output_type(self, *input_types: dtypes.ExpressionType) -> dtypes.ExpressionType: - return dtypes.INT_DTYPE - @property - def implicitly_inherits_order(self): - return False - + def handles_ties(self): + return True -@dataclasses.dataclass(frozen=True) -class FirstOp(UnaryWindowOp): - name: ClassVar[str] = "first" +class DenseRankOp(WindowOp): + def _as_ibis( + self, column: ibis_types.Column, window=None + ) -> ibis_types.IntegerValue: + # Ibis produces 0-based ranks, while pandas creates 1-based ranks + return _apply_window_if_present(column.dense_rank(), window) + 1 -@dataclasses.dataclass(frozen=True) -class FirstNonNullOp(UnaryWindowOp): @property def skips_nulls(self): return False @property - def nulls_count_for_min_values(self) -> bool: - return False + def handles_ties(self): + return True -@dataclasses.dataclass(frozen=True) -class LastOp(UnaryWindowOp): - name: ClassVar[str] = "last" +class FirstOp(WindowOp): + def _as_ibis(self, column: ibis_types.Column, window=None) -> ibis_types.Value: + return _apply_window_if_present(column.first(), window) -@dataclasses.dataclass(frozen=True) -class LastNonNullOp(UnaryWindowOp): +class FirstNonNullOp(WindowOp): @property def skips_nulls(self): return False - @property - def nulls_count_for_min_values(self) -> bool: - return False - + def _as_ibis(self, column: ibis_types.Column, window=None) -> ibis_types.Value: + return _apply_window_if_present( + vendored_ibis_ops.FirstNonNullValue(column).to_expr(), window # type: ignore + ) -@dataclasses.dataclass(frozen=True) -class ShiftOp(UnaryWindowOp): - periods: int +class LastNonNullOp(WindowOp): @property def skips_nulls(self): return False + def _as_ibis(self, column: ibis_types.Column, window=None) -> ibis_types.Value: + return _apply_window_if_present( + vendored_ibis_ops.LastNonNullValue(column).to_expr(), window # type: ignore + ) -@dataclasses.dataclass(frozen=True) -class DiffOp(UnaryWindowOp): - name: ClassVar[str] = "diff" - periods: int - - @property - def skips_nulls(self): - return False - - def output_type(self, *input_types: dtypes.ExpressionType) -> dtypes.ExpressionType: - if dtypes.is_date_like(input_types[0]): - return dtypes.TIMEDELTA_DTYPE - return super().output_type(*input_types) +class ShiftOp(WindowOp): + def __init__(self, periods: int): + self._periods = periods -@dataclasses.dataclass(frozen=True) -class TimeSeriesDiffOp(UnaryWindowOp): - periods: int + def _as_ibis(self, column: ibis_types.Column, window=None) -> ibis_types.Value: + if self._periods == 0: # No-op + return column + if self._periods > 0: + return _apply_window_if_present(column.lag(self._periods), window) + return _apply_window_if_present(column.lead(-self._periods), window) @property def skips_nulls(self): return False - def output_type(self, *input_types: dtypes.ExpressionType) -> dtypes.ExpressionType: - if dtypes.is_datetime_like(input_types[0]): - return dtypes.TIMEDELTA_DTYPE - raise TypeError(f"expect datetime-like types, but got {input_types[0]}") +class DiffOp(WindowOp): + def __init__(self, periods: int): + self._periods = periods -@dataclasses.dataclass(frozen=True) -class DateSeriesDiffOp(UnaryWindowOp): - periods: int + def _as_ibis(self, column: ibis_types.Column, window=None) -> ibis_types.Value: + shifted = ShiftOp(self._periods)._as_ibis(column, window) + if column.type().is_boolean(): + return typing.cast(ibis_types.BooleanColumn, column) != typing.cast( + ibis_types.BooleanColumn, shifted + ) + elif column.type().is_numeric(): + return typing.cast(ibis_types.NumericColumn, column) - typing.cast( + ibis_types.NumericColumn, shifted + ) + else: + raise TypeError(f"Cannot perform diff on type{column.type()}") @property def skips_nulls(self): return False - def output_type(self, *input_types: dtypes.ExpressionType) -> dtypes.ExpressionType: - if input_types[0] == dtypes.DATE_DTYPE: - return dtypes.TIMEDELTA_DTYPE - raise TypeError(f"expect date type, but got {input_types[0]}") - - -@dataclasses.dataclass(frozen=True) -class AllOp(UnaryAggregateOp): - name: ClassVar[str] = "all" - - def output_type(self, *input_types: dtypes.ExpressionType) -> dtypes.ExpressionType: - return signatures.FixedOutputType( - dtypes.is_bool_coercable, dtypes.BOOL_DTYPE, "convertible to boolean" - ).output_type(input_types[0]) +class AllOp(AggregateOp): + def _as_ibis( + self, column: ibis_types.Column, window=None + ) -> ibis_types.BooleanValue: + # BQ will return null for empty column, result would be true in pandas. + result = _is_true(column).all() + return typing.cast( + ibis_types.BooleanScalar, + _apply_window_if_present(result, window).fillna(ibis_types.literal(True)), + ) -@dataclasses.dataclass(frozen=True) -class AnyOp(UnaryAggregateOp): - name: ClassVar[str] = "any" - def output_type(self, *input_types: dtypes.ExpressionType) -> dtypes.ExpressionType: - return signatures.FixedOutputType( - dtypes.is_bool_coercable, dtypes.BOOL_DTYPE, "convertible to boolean" - ).output_type(input_types[0]) +class AnyOp(AggregateOp): + name = "any" + def _as_ibis( + self, column: ibis_types.Column, window=None + ) -> ibis_types.BooleanValue: + # BQ will return null for empty column, result would be false in pandas. + result = _is_true(column).any() + return typing.cast( + ibis_types.BooleanScalar, + _apply_window_if_present(result, window).fillna(ibis_types.literal(True)), + ) -@dataclasses.dataclass(frozen=True) -class CorrOp(BinaryAggregateOp): - name: ClassVar[str] = "corr" - def output_type(self, *input_types: dtypes.ExpressionType) -> dtypes.ExpressionType: - return signatures.BINARY_REAL_NUMERIC.output_type( - input_types[0], input_types[1] +def _is_true(column: ibis_types.Column) -> ibis_types.BooleanColumn: + if column.type().is_boolean(): + return typing.cast(ibis_types.BooleanColumn, column) + elif column.type().is_numeric(): + result = typing.cast(ibis_types.NumericColumn, column).__ne__( + ibis_types.literal(0) + ) + return typing.cast(ibis_types.BooleanColumn, result) + elif column.type().is_string(): + result = typing.cast( + ibis_types.StringValue, column + ).length() > ibis_types.literal(0) + return typing.cast(ibis_types.BooleanColumn, result) + else: + # Time and geo values don't have a 'False' value + return typing.cast( + ibis_types.BooleanColumn, _map_to_literal(column, ibis_types.literal(True)) ) -@dataclasses.dataclass(frozen=True) -class CovOp(BinaryAggregateOp): - name: ClassVar[str] = "cov" +def _apply_window_if_present(value: ibis_types.Value, window): + return value.over(window) if (window is not None) else value - def output_type(self, *input_types: dtypes.ExpressionType) -> dtypes.ExpressionType: - return signatures.BINARY_REAL_NUMERIC.output_type( - input_types[0], input_types[1] - ) + +def _map_to_literal( + original: ibis_types.Value, literal: ibis_types.Scalar +) -> ibis_types.Column: + # Hack required to perform aggregations on literals in ibis, even though bigquery will let you directly aggregate literals (eg. 'SELECT COUNT(1) from table1') + return ibis.ifelse(original.isnull(), literal, literal) -size_op = SizeOp() sum_op = SumOp() mean_op = MeanOp() median_op = MedianOp() @@ -692,9 +503,7 @@ def output_type(self, *input_types: dtypes.ExpressionType) -> dtypes.ExpressionT # TODO: Alternative names and lookup from numpy function objects -_STRING_TO_AGG_OP: typing.Dict[ - str, typing.Union[UnaryAggregateOp, NullaryAggregateOp] -] = { +_AGGREGATIONS_LOOKUP: dict[str, AggregateOp] = { op.name: op for op in [ sum_op, @@ -713,44 +522,23 @@ def output_type(self, *input_types: dtypes.ExpressionType) -> dtypes.ExpressionT ApproxQuartilesOp(2), ApproxQuartilesOp(3), ] - + [ - # Add size_op separately to avoid Mypy type inference errors. - size_op, - ] -} - -_CALLABLE_TO_AGG_OP: typing.Dict[ - Callable, typing.Union[UnaryAggregateOp, NullaryAggregateOp] -] = { - np.sum: sum_op, - np.mean: mean_op, - np.median: median_op, - np.prod: product_op, - np.max: max_op, - np.min: min_op, - np.std: std_op, - np.var: var_op, - np.all: all_op, - np.any: any_op, - np.unique: nunique_op, - np.size: size_op, - # TODO(b/443252872): Solve - list: ArrayAggOp(), - len: size_op, - sum: sum_op, - min: min_op, - max: max_op, - any: any_op, - all: all_op, } -def lookup_agg_func( - key, -) -> tuple[typing.Union[UnaryAggregateOp, NullaryAggregateOp], str]: - if key in _STRING_TO_AGG_OP: - return (_STRING_TO_AGG_OP[key], key) - if key in _CALLABLE_TO_AGG_OP: - return (_CALLABLE_TO_AGG_OP[key], key.__name__) +def lookup_agg_func(key: str) -> AggregateOp: + if callable(key): + raise NotImplementedError( + "Aggregating with callable object not supported, pass method name as string instead (eg. 'sum' instead of np.sum)." + ) + if not isinstance(key, str): + raise ValueError( + f"Cannot aggregate using object of type: {type(key)}. Use string method name (eg. 'sum')" + ) + if key in _AGGREGATIONS_LOOKUP: + return _AGGREGATIONS_LOOKUP[key] else: raise ValueError(f"Unrecognize aggregate function: {key}") + + +def _ibis_num(number: float): + return typing.cast(ibis_types.NumericValue, ibis_types.literal(number)) diff --git a/bigframes/operations/ai_ops.py b/bigframes/operations/ai_ops.py deleted file mode 100644 index ad2b9850577..00000000000 --- a/bigframes/operations/ai_ops.py +++ /dev/null @@ -1,201 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from __future__ import annotations - -import dataclasses -from typing import ClassVar, Tuple - -import pandas as pd -import pyarrow as pa - -from bigframes import dtypes -from bigframes.operations import base_ops, output_schemas - - -@dataclasses.dataclass(frozen=True) -class AIGenerate(base_ops.NaryOp): - name: ClassVar[str] = "ai_generate" - - prompt_context: Tuple[str | None, ...] - connection_id: str | None = None - endpoint: str | None = None - request_type: str | None = None - model_params: str | None = None - output_schema: str | None = None - - def output_type(self, *input_types: dtypes.ExpressionType) -> dtypes.ExpressionType: - if self.output_schema is None: - output_fields = (pa.field("result", pa.string()),) - else: - output_fields = output_schemas.parse_sql_fields(self.output_schema) - - return pd.ArrowDtype( - pa.struct( - ( - *output_fields, - pa.field("full_response", dtypes.JSON_ARROW_TYPE), - pa.field("status", pa.string()), - ) - ) - ) - - -@dataclasses.dataclass(frozen=True) -class AIGenerateBool(base_ops.NaryOp): - name: ClassVar[str] = "ai_generate_bool" - - prompt_context: Tuple[str | None, ...] - connection_id: str | None = None - endpoint: str | None = None - request_type: str | None = None - model_params: str | None = None - - def output_type(self, *input_types: dtypes.ExpressionType) -> dtypes.ExpressionType: - return pd.ArrowDtype( - pa.struct( - ( - pa.field("result", pa.bool_()), - pa.field("full_response", dtypes.JSON_ARROW_TYPE), - pa.field("status", pa.string()), - ) - ) - ) - - -@dataclasses.dataclass(frozen=True) -class AIGenerateInt(base_ops.NaryOp): - name: ClassVar[str] = "ai_generate_int" - - prompt_context: Tuple[str | None, ...] - connection_id: str | None = None - endpoint: str | None = None - request_type: str | None = None - model_params: str | None = None - - def output_type(self, *input_types: dtypes.ExpressionType) -> dtypes.ExpressionType: - return pd.ArrowDtype( - pa.struct( - ( - pa.field("result", pa.int64()), - pa.field("full_response", dtypes.JSON_ARROW_TYPE), - pa.field("status", pa.string()), - ) - ) - ) - - -@dataclasses.dataclass(frozen=True) -class AIGenerateDouble(base_ops.NaryOp): - name: ClassVar[str] = "ai_generate_double" - - prompt_context: Tuple[str | None, ...] - connection_id: str | None = None - endpoint: str | None = None - request_type: str | None = None - model_params: str | None = None - - def output_type(self, *input_types: dtypes.ExpressionType) -> dtypes.ExpressionType: - return pd.ArrowDtype( - pa.struct( - ( - pa.field("result", pa.float64()), - pa.field("full_response", dtypes.JSON_ARROW_TYPE), - pa.field("status", pa.string()), - ) - ) - ) - - -@dataclasses.dataclass(frozen=True) -class AIEmbed(base_ops.UnaryOp): - name: ClassVar[str] = "ai_embed" - - endpoint: str | None = None - model: str | None = None - task_type: str | None = None - title: str | None = None - model_params: str | None = None - connection_id: str | None = None - - def output_type(self, *input_types: dtypes.ExpressionType) -> dtypes.ExpressionType: - return pd.ArrowDtype( - pa.struct( - ( - pa.field("result", pa.list_(pa.float64())), - pa.field("status", pa.string()), - ) - ) - ) - - -@dataclasses.dataclass(frozen=True) -class AIIf(base_ops.NaryOp): - name: ClassVar[str] = "ai_if" - - prompt_context: Tuple[str | None, ...] - connection_id: str | None = None - endpoint: str | None = None - optimization_mode: str | None = None - max_error_ratio: float | None = None - - def output_type(self, *input_types: dtypes.ExpressionType) -> dtypes.ExpressionType: - return dtypes.BOOL_DTYPE - - -@dataclasses.dataclass(frozen=True) -class AIClassify(base_ops.NaryOp): - name: ClassVar[str] = "ai_classify" - - prompt_context: Tuple[str | None, ...] - categories: tuple[str, ...] - examples: ( - tuple[tuple[str, str], ...] | tuple[tuple[str, tuple[str, ...]], ...] | None - ) = None - connection_id: str | None = None - endpoint: str | None = None - output_mode: str | None = None - optimization_mode: str | None = None - max_error_ratio: float | None = None - - def output_type(self, *input_types: dtypes.ExpressionType) -> dtypes.ExpressionType: - if self.output_mode is not None: - return dtypes.list_type(dtypes.STRING_DTYPE) - return dtypes.STRING_DTYPE - - -@dataclasses.dataclass(frozen=True) -class AIScore(base_ops.NaryOp): - name: ClassVar[str] = "ai_score" - - prompt_context: Tuple[str | None, ...] - connection_id: str | None = None - endpoint: str | None = None - max_error_ratio: float | None = None - - def output_type(self, *input_types: dtypes.ExpressionType) -> dtypes.ExpressionType: - return dtypes.FLOAT_DTYPE - - -@dataclasses.dataclass(frozen=True) -class AISimilarity(base_ops.BinaryOp): - name: ClassVar[str] = "ai_similarity" - - endpoint: str | None = None - model: str | None = None - model_params: str | None = None - connection_id: str | None = None - - def output_type(self, *input_types: dtypes.ExpressionType) -> dtypes.ExpressionType: - return dtypes.FLOAT_DTYPE diff --git a/bigframes/operations/array_ops.py b/bigframes/operations/array_ops.py deleted file mode 100644 index e6f5743989b..00000000000 --- a/bigframes/operations/array_ops.py +++ /dev/null @@ -1,87 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import dataclasses -import functools -import typing - -from bigframes import dtypes -from bigframes.operations import aggregations, base_ops - - -@dataclasses.dataclass(frozen=True) -class ArrayToStringOp(base_ops.UnaryOp): - name: typing.ClassVar[str] = "array_to_string" - delimiter: str - - def output_type(self, *input_types): - input_type = input_types[0] - if not dtypes.is_array_string_like(input_type): - raise TypeError("Input type must be an array of string type.") - return dtypes.STRING_DTYPE - - -@dataclasses.dataclass(frozen=True) -class ArraySliceOp(base_ops.UnaryOp): - name: typing.ClassVar[str] = "array_slice" - start: int - stop: typing.Optional[int] = None - step: typing.Optional[int] = None - - def output_type(self, *input_types): - input_type = input_types[0] - if dtypes.is_string_like(input_type): - return dtypes.STRING_DTYPE - elif dtypes.is_array_like(input_type): - return input_type - else: - raise TypeError("Input type must be an array or string-like type.") - - -class ToArrayOp(base_ops.NaryOp): - name: typing.ClassVar[str] = "array" - - def output_type(self, *input_types: dtypes.ExpressionType) -> dtypes.ExpressionType: - # very permissive, maybe should force caller to do this? - common_type = functools.reduce( - lambda t1, t2: dtypes.coerce_to_common(t1, t2), - input_types, - ) - return dtypes.list_type(common_type) - - -@dataclasses.dataclass(frozen=True) -class ArrayReduceOp(base_ops.UnaryOp): - name: typing.ClassVar[str] = "array_reduce" - aggregation: aggregations.AggregateOp - - def output_type(self, *input_types): - input_type = input_types[0] - assert dtypes.is_array_like(input_type) - inner_type = dtypes.get_array_inner_type(input_type) - return self.aggregation.output_type(inner_type) - - -@dataclasses.dataclass(frozen=True) -class ArrayMapOp(base_ops.UnaryOp): - name: typing.ClassVar[str] = "array_map" - # TODO(b/495513753): Generalize to chained expressions - map_op: base_ops.UnaryOp - - def output_type(self, *input_types): - input_type = input_types[0] - assert dtypes.is_array_like(input_type) - inner_type = dtypes.get_array_inner_type(input_type) - out_inner_type = self.map_op.output_type(inner_type) - return dtypes.list_type(out_inner_type) diff --git a/bigframes/operations/base.py b/bigframes/operations/base.py new file mode 100644 index 00000000000..d33befe4da6 --- /dev/null +++ b/bigframes/operations/base.py @@ -0,0 +1,206 @@ +# Copyright 2023 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import annotations + +import typing + +import pandas as pd + +import bigframes.constants as constants +import bigframes.core.blocks as blocks +import bigframes.core.scalar as scalars +import bigframes.dtypes +import bigframes.operations as ops +import bigframes.series as series +import bigframes.session +import third_party.bigframes_vendored.pandas.pandas._typing as vendored_pandas_typing + +# BigQuery has 1 MB query size limit, 5000 items shouldn't take more than 10% of this depending on data type. +# TODO(tbergeron): Convert to bytes-based limit +MAX_INLINE_SERIES_SIZE = 5000 + + +class SeriesMethods: + def __init__( + self, + data=None, + index: vendored_pandas_typing.Axes | None = None, + dtype: typing.Optional[ + bigframes.dtypes.DtypeString | bigframes.dtypes.Dtype + ] = None, + name: str | None = None, + copy: typing.Optional[bool] = None, + *, + session: typing.Optional[bigframes.session.Session] = None, + ): + block = None + if copy is not None and not copy: + raise ValueError( + f"Series constructor only supports copy=True. {constants.FEEDBACK_LINK}" + ) + if isinstance(data, blocks.Block): + assert len(data.value_columns) == 1 + assert len(data.column_labels) == 1 + block = data + + elif isinstance(data, SeriesMethods): + block = data._get_block() + + if block: + if name: + if not isinstance(name, typing.Hashable): + raise ValueError( + f"BigQuery DataFrames only supports hashable series names. {constants.FEEDBACK_LINK}" + ) + block = block.with_column_labels([name]) + if index: + raise NotImplementedError( + f"Series 'index' constructor parameter not supported when passing BigQuery-backed objects. {constants.FEEDBACK_LINK}" + ) + if dtype: + block = block.multi_apply_unary_op( + block.value_columns, ops.AsTypeOp(dtype) + ) + self._block = block + + else: + import bigframes.pandas + + pd_series = pd.Series( + data=data, index=index, dtype=dtype, name=name # type:ignore + ) + pd_dataframe = pd_series.to_frame() + if pd_series.name is None: + # to_frame will set default numeric column label if unnamed, but we do not support int column label, so must rename + pd_dataframe = pd_dataframe.set_axis(["unnamed_col"], axis=1) + if ( + pd_dataframe.size < MAX_INLINE_SERIES_SIZE + # TODO(swast): Workaround data types limitation in inline data. + and not any( + dt.pyarrow_dtype + for dt in pd_dataframe.dtypes + if isinstance(dt, pd.ArrowDtype) + ) + ): + self._block = blocks.block_from_local(pd_dataframe) + elif session: + self._block = session.read_pandas(pd_dataframe)._get_block() + else: + # Uses default global session + self._block = bigframes.pandas.read_pandas(pd_dataframe)._get_block() + if pd_series.name is None: + self._block = self._block.with_column_labels([None]) + + @property + def _value_column(self) -> str: + return self._block.value_columns[0] + + @property + def _name(self) -> blocks.Label: + return self._block.column_labels[0] + + @property + def _dtype(self): + return self._block.dtypes[0] + + def _set_block(self, block: blocks.Block): + self._block = block + + def _get_block(self) -> blocks.Block: + return self._block + + def _apply_unary_op( + self, + op: ops.UnaryOp, + ) -> series.Series: + """Applies a unary operator to the series.""" + block, result_id = self._block.apply_unary_op( + self._value_column, op, result_label=self._name + ) + return series.Series(block.select_column(result_id)) + + def _apply_binary_op( + self, + other: typing.Any, + op: ops.BinaryOp, + alignment: typing.Literal["outer", "left"] = "outer", + ) -> series.Series: + """Applies a binary operator to the series and other.""" + if isinstance(other, pd.Series): + # TODO: Convert to BigQuery DataFrames series + raise NotImplementedError( + f"Pandas series not supported supported as operand. {constants.FEEDBACK_LINK}" + ) + if isinstance(other, series.Series): + (left, right, block) = self._align(other, how=alignment) + + block, result_id = block.apply_binary_op( + left, right, op, self._value_column + ) + + name = self._name + if ( + isinstance(other, series.Series) + and other.name != self._name + and alignment == "outer" + ): + name = None + + return series.Series( + block.select_column(result_id).assign_label(result_id, name) + ) + else: + partial_op = ops.BinopPartialRight(op, other) + return self._apply_unary_op(partial_op) + + def _apply_corr_aggregation(self, other: series.Series) -> float: + (left, right, block) = self._align(other, how="outer") + + return block.get_corr_stat(left, right) + + def _align(self, other: series.Series, how="outer") -> tuple[str, str, blocks.Block]: # type: ignore + """Aligns the series value with another scalar or series object. Returns new left column id, right column id and joined tabled expression.""" + values, block = self._align_n( + [ + other, + ], + how, + ) + return (values[0], values[1], block) + + def _align_n( + self, + others: typing.Sequence[typing.Union[series.Series, scalars.Scalar]], + how="outer", + ) -> tuple[typing.Sequence[str], blocks.Block]: + value_ids = [self._value_column] + block = self._block + for other in others: + if isinstance(other, series.Series): + combined_index, ( + get_column_left, + get_column_right, + ) = block.index.join(other._block.index, how=how) + value_ids = [ + *[get_column_left[value] for value in value_ids], + get_column_right[other._value_column], + ] + block = combined_index._block + else: + # Will throw if can't interpret as scalar. + dtype = typing.cast(bigframes.dtypes.Dtype, self._dtype) + block, constant_col_id = block.create_constant(other, dtype=dtype) + value_ids = [*value_ids, constant_col_id] + return (value_ids, block) diff --git a/bigframes/operations/base_ops.py b/bigframes/operations/base_ops.py deleted file mode 100644 index a3a0187d2d1..00000000000 --- a/bigframes/operations/base_ops.py +++ /dev/null @@ -1,208 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from __future__ import annotations - -import dataclasses -import typing - -import bigframes.operations.type as op_typing -from bigframes import dtypes - -if typing.TYPE_CHECKING: - # Avoids circular dependency - import bigframes.core.expression - - -class RowOp(typing.Protocol): - @property - def name(self) -> str: ... - - def output_type( - self, *input_types: dtypes.ExpressionType - ) -> dtypes.ExpressionType: ... - - @property - def is_monotonic(self) -> bool: - """Whether the row operation preserves total ordering. Can be pruned from ordering expressions.""" - ... - - @property - def is_bijective(self) -> bool: - """Whether the operation has a 1:1 mapping between inputs and outputs""" - ... - - @property - def deterministic(self) -> bool: - """Whether the operation is deterministic" (given deterministic inputs)""" - ... - - @property - def expensive(self) -> bool: - """Whether the operation is expensive to calculate. Such ops shouldn't be inlined if referenced multiple places.""" - ... - - -@dataclasses.dataclass(frozen=True) -class ScalarOp: - @property - def name(self) -> str: - raise NotImplementedError("RowOp abstract base class has no implementation") - - def output_type(self, *input_types: dtypes.ExpressionType) -> dtypes.ExpressionType: - raise NotImplementedError("Abstract operation has no output type") - - @property - def is_monotonic(self) -> bool: - """Whether the row operation preserves total ordering. Can be pruned from ordering expressions.""" - return False - - @property - def is_bijective(self) -> bool: - """Whether the operation has a 1:1 mapping between inputs and outputs""" - return False - - @property - def deterministic(self) -> bool: - """Whether the operation is deterministic" (given deterministic inputs)""" - return True - - @property - def expensive(self) -> bool: - return False - - -@dataclasses.dataclass(frozen=True) -class NaryOp(ScalarOp): - def as_expr( - self, - *exprs: typing.Union[str, bigframes.core.expression.Expression], - ) -> bigframes.core.expression.Expression: - import bigframes.core.expression - - # Keep this in sync with output_type and compilers - inputs: list[bigframes.core.expression.Expression] = [] - - for expr in exprs: - inputs.append(_convert_expr_input(expr)) - - return bigframes.core.expression.OpExpression( - self, - tuple(inputs), - ) - - -# These classes can be used to create simple ops that don't take local parameters -# All is needed is a unique name, and to register an implementation in ibis_mappings.py -@dataclasses.dataclass(frozen=True) -class UnaryOp(ScalarOp): - @property - def arguments(self) -> int: - return 1 - - def as_expr( - self, input_id: typing.Union[str, bigframes.core.expression.Expression] = "arg" - ) -> bigframes.core.expression.Expression: - import bigframes.core.expression - - return bigframes.core.expression.OpExpression( - self, (_convert_expr_input(input_id),) - ) - - -@dataclasses.dataclass(frozen=True) -class BinaryOp(ScalarOp): - @property - def arguments(self) -> int: - return 2 - - def as_expr( - self, - left_input: typing.Union[str, bigframes.core.expression.Expression] = "arg1", - right_input: typing.Union[str, bigframes.core.expression.Expression] = "arg2", - ) -> bigframes.core.expression.Expression: - import bigframes.core.expression - - return bigframes.core.expression.OpExpression( - self, - ( - _convert_expr_input(left_input), - _convert_expr_input(right_input), - ), - ) - - -@dataclasses.dataclass(frozen=True) -class TernaryOp(ScalarOp): - @property - def arguments(self) -> int: - return 3 - - def as_expr( - self, - input1: typing.Union[str, bigframes.core.expression.Expression] = "arg1", - input2: typing.Union[str, bigframes.core.expression.Expression] = "arg2", - input3: typing.Union[str, bigframes.core.expression.Expression] = "arg3", - ) -> bigframes.core.expression.Expression: - import bigframes.core.expression - - return bigframes.core.expression.OpExpression( - self, - ( - _convert_expr_input(input1), - _convert_expr_input(input2), - _convert_expr_input(input3), - ), - ) - - -def _convert_expr_input( - input: typing.Union[str, bigframes.core.expression.Expression], -) -> bigframes.core.expression.Expression: - """Allows creating column references with just a string""" - import bigframes.core.expression - - if isinstance(input, str): - return bigframes.core.expression.deref(input) - else: - return input - - -# Operation Factories -def create_unary_op( - name: str, type_signature: op_typing.UnaryTypeSignature -) -> type[UnaryOp]: - return dataclasses.make_dataclass( - name, - [ - ("name", typing.ClassVar[str], name), - ("output_type", typing.ClassVar[typing.Callable], type_signature.as_method), - ], - bases=(UnaryOp,), - frozen=True, - ) - - -def create_binary_op( - name: str, type_signature: op_typing.BinaryTypeSignature -) -> type[BinaryOp]: - return dataclasses.make_dataclass( - name, - [ - ("name", typing.ClassVar[str], name), - ("output_type", typing.ClassVar[typing.Callable], type_signature.as_method), - ], - bases=(BinaryOp,), - frozen=True, - ) diff --git a/bigframes/operations/blob.py b/bigframes/operations/blob.py deleted file mode 100644 index 3666ee66602..00000000000 --- a/bigframes/operations/blob.py +++ /dev/null @@ -1,49 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from __future__ import annotations - -import bigframes.dataframe -import bigframes.operations as ops -import bigframes.series -from bigframes.core.logging import log_adapter - -FILE_FOLDER_REGEX = r"^.*\/(.*)$" -FILE_EXT_REGEX = r"(\.[0-9a-zA-Z]+$)" - - -@log_adapter.class_logger -class _BlobAccessor: - """ - Internal blob functions for Series and Index. - """ - - def __init__(self, data: bigframes.series.Series): - self._data = data - - def _get_runtime( - self, mode: str, with_metadata: bool = False - ) -> bigframes.series.Series: - s = ( - self._data._apply_unary_op(ops.obj_fetch_metadata_op) - if with_metadata - else self._data - ) - - return s._apply_unary_op(ops.ObjGetAccessUrl(mode=mode)) - - def _read_url(self) -> bigframes.series.Series: - return self._get_runtime(mode="R")._apply_unary_op( - ops.JSONValue(json_path="$.access_urls.read_url") - ) diff --git a/bigframes/operations/blob_ops.py b/bigframes/operations/blob_ops.py deleted file mode 100644 index 21d645a2fee..00000000000 --- a/bigframes/operations/blob_ops.py +++ /dev/null @@ -1,60 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import dataclasses -import typing - -import bigframes.operations.type as op_typing -from bigframes import dtypes -from bigframes.operations import base_ops - -ObjFetchMetadataOp = base_ops.create_unary_op( - name="obj_fetch_metadata", type_signature=op_typing.BLOB_TRANSFORM -) -obj_fetch_metadata_op = ObjFetchMetadataOp() - - -@dataclasses.dataclass(frozen=True) -class ObjGetAccessUrl(base_ops.UnaryOp): - name: typing.ClassVar[str] = "obj_get_access_url" - mode: str # access mode, e.g. R read, W write, RW read & write - duration: typing.Optional[int] = None # duration in microseconds - - def output_type(self, *input_types): - return dtypes.JSON_DTYPE - - -@dataclasses.dataclass(frozen=True) -class ObjMakeRef(base_ops.BinaryOp): - name: typing.ClassVar[str] = "obj_make_ref" - - def output_type(self, *input_types): - if not all(map(dtypes.is_string_like, input_types)): - raise TypeError("obj_make_ref requires string-like arguments") - - return dtypes.OBJ_REF_DTYPE - - -obj_make_ref_op = ObjMakeRef() - - -@dataclasses.dataclass(frozen=True) -class ObjMakeRefJson(base_ops.UnaryOp): - name: typing.ClassVar[str] = "obj_make_ref_json" - - def output_type(self, *input_types): - return dtypes.OBJ_REF_DTYPE - - -obj_make_ref_json_op = ObjMakeRefJson() diff --git a/bigframes/operations/bool_ops.py b/bigframes/operations/bool_ops.py deleted file mode 100644 index ce4406d8f70..00000000000 --- a/bigframes/operations/bool_ops.py +++ /dev/null @@ -1,26 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -import bigframes.operations.type as op_typing -from bigframes.operations import base_ops - -AndOp = base_ops.create_binary_op(name="and", type_signature=op_typing.LOGICAL) -and_op = AndOp() - -OrOp = base_ops.create_binary_op(name="or", type_signature=op_typing.LOGICAL) -or_op = OrOp() - -XorOp = base_ops.create_binary_op(name="xor", type_signature=op_typing.LOGICAL) -xor_op = XorOp() diff --git a/bigframes/operations/comparison_ops.py b/bigframes/operations/comparison_ops.py deleted file mode 100644 index f3c01a3536b..00000000000 --- a/bigframes/operations/comparison_ops.py +++ /dev/null @@ -1,40 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -import bigframes.operations.type as op_typing -from bigframes.operations import base_ops - -EqOp = base_ops.create_binary_op(name="eq", type_signature=op_typing.COMPARISON) -eq_op = EqOp() - -EqNullsMatchOp = base_ops.create_binary_op( - name="eq_nulls_match", type_signature=op_typing.COMPARISON -) -eq_null_match_op = EqNullsMatchOp() - -NeOp = base_ops.create_binary_op(name="ne", type_signature=op_typing.COMPARISON) -ne_op = NeOp() - -LtOp = base_ops.create_binary_op(name="lt", type_signature=op_typing.COMPARISON) -lt_op = LtOp() - -GtOp = base_ops.create_binary_op(name="gt", type_signature=op_typing.COMPARISON) -gt_op = GtOp() - -LeOp = base_ops.create_binary_op(name="le", type_signature=op_typing.COMPARISON) -le_op = LeOp() - -GeOp = base_ops.create_binary_op(name="ge", type_signature=op_typing.COMPARISON) -ge_op = GeOp() diff --git a/bigframes/operations/date_ops.py b/bigframes/operations/date_ops.py deleted file mode 100644 index 1dbb244afbc..00000000000 --- a/bigframes/operations/date_ops.py +++ /dev/null @@ -1,92 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import dataclasses -import typing - -import bigframes.operations.type as op_typing -from bigframes import dtypes -from bigframes.operations import base_ops - -DayOp = base_ops.create_unary_op( - name="day", - type_signature=op_typing.DATELIKE_ACCESSOR, -) -day_op = DayOp() - -MonthOp = base_ops.create_unary_op( - name="month", - type_signature=op_typing.DATELIKE_ACCESSOR, -) -month_op = MonthOp() - -YearOp = base_ops.create_unary_op( - name="year", - type_signature=op_typing.DATELIKE_ACCESSOR, -) -year_op = YearOp() - -IsoDayOp = base_ops.create_unary_op( - name="iso_day", type_signature=op_typing.DATELIKE_ACCESSOR -) -iso_day_op = IsoDayOp() - -IsoWeekOp = base_ops.create_unary_op( - name="iso_weeek", - type_signature=op_typing.DATELIKE_ACCESSOR, -) -iso_week_op = IsoWeekOp() - -IsoYearOp = base_ops.create_unary_op( - name="iso_year", - type_signature=op_typing.DATELIKE_ACCESSOR, -) -iso_year_op = IsoYearOp() - -DayOfWeekOp = base_ops.create_unary_op( - name="dayofweek", - type_signature=op_typing.DATELIKE_ACCESSOR, -) -dayofweek_op = DayOfWeekOp() - -DayOfYearOp = base_ops.create_unary_op( - name="dayofyear", - type_signature=op_typing.DATELIKE_ACCESSOR, -) -dayofyear_op = DayOfYearOp() - -QuarterOp = base_ops.create_unary_op( - name="quarter", - type_signature=op_typing.DATELIKE_ACCESSOR, -) -quarter_op = QuarterOp() - - -@dataclasses.dataclass(frozen=True) -class DateDiffOp(base_ops.BinaryOp): - name: typing.ClassVar[str] = "date_diff" - - def output_type(self, *input_types: dtypes.ExpressionType) -> dtypes.ExpressionType: - if input_types[0] is not input_types[1]: - raise TypeError( - f"two inputs have different types. left: {input_types[0]}, right: {input_types[1]}" - ) - - if input_types[0] != dtypes.DATE_DTYPE: - raise TypeError("expected date input") - - return dtypes.TIMEDELTA_DTYPE - - -date_diff_op = DateDiffOp() diff --git a/bigframes/operations/datetime_ops.py b/bigframes/operations/datetime_ops.py deleted file mode 100644 index 702466c4f35..00000000000 --- a/bigframes/operations/datetime_ops.py +++ /dev/null @@ -1,159 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import dataclasses -import typing - -import pandas as pd -import pyarrow as pa - -import bigframes.operations.type as op_typing -from bigframes import dtypes -from bigframes.operations import base_ops - -DateOp = base_ops.create_unary_op( - name="date", - type_signature=op_typing.FixedOutputType( - dtypes.is_date_like, dtypes.DATE_DTYPE, description="date-like" - ), -) -date_op = DateOp() - -TimeOp = base_ops.create_unary_op( - name="time", - type_signature=op_typing.FixedOutputType( - dtypes.is_time_like, dtypes.TIME_DTYPE, description="time-like" - ), -) -time_op = TimeOp() - - -@dataclasses.dataclass(frozen=True) -class ParseDatetimeOp(base_ops.UnaryOp): - # TODO: Support strict format - name: typing.ClassVar[str] = "parse_datetime" - - def output_type(self, *input_types: dtypes.ExpressionType) -> dtypes.ExpressionType: - if input_types[0] != dtypes.STRING_DTYPE: - raise TypeError("expected string input") - return pd.ArrowDtype(pa.timestamp("us", tz=None)) - - -@dataclasses.dataclass(frozen=True) -class ParseTimestampOp(base_ops.UnaryOp): - # TODO: Support strict format - name: typing.ClassVar[str] = "parse_timestamp" - - def output_type(self, *input_types: dtypes.ExpressionType) -> dtypes.ExpressionType: - if input_types[0] != dtypes.STRING_DTYPE: - raise TypeError("expected string input") - return pd.ArrowDtype(pa.timestamp("us", tz="UTC")) - - -@dataclasses.dataclass(frozen=True) -class ToDatetimeOp(base_ops.UnaryOp): - name: typing.ClassVar[str] = "to_datetime" - format: typing.Optional[str] = None - unit: typing.Optional[str] = None - - def output_type(self, *input_types: dtypes.ExpressionType) -> dtypes.ExpressionType: - if input_types[0] not in ( - dtypes.FLOAT_DTYPE, - dtypes.INT_DTYPE, - dtypes.STRING_DTYPE, - dtypes.DATE_DTYPE, - dtypes.TIMESTAMP_DTYPE, - dtypes.DATETIME_DTYPE, - ): - raise TypeError("expected string or numeric input") - return pd.ArrowDtype(pa.timestamp("us", tz=None)) - - -@dataclasses.dataclass(frozen=True) -class ToTimestampOp(base_ops.UnaryOp): - name: typing.ClassVar[str] = "to_timestamp" - format: typing.Optional[str] = None - unit: typing.Optional[str] = None - - def output_type(self, *input_types: dtypes.ExpressionType) -> dtypes.ExpressionType: - # Must be numeric or string - if input_types[0] == dtypes.TIMESTAMP_DTYPE: - raise TypeError("Already tz-aware.") - if input_types[0] not in ( - dtypes.FLOAT_DTYPE, - dtypes.INT_DTYPE, - dtypes.STRING_DTYPE, - dtypes.DATE_DTYPE, - dtypes.DATETIME_DTYPE, - ): - raise TypeError("expected string or numeric input") - return pd.ArrowDtype(pa.timestamp("us", tz="UTC")) - - -@dataclasses.dataclass(frozen=True) -class StrftimeOp(base_ops.UnaryOp): - name: typing.ClassVar[str] = "strftime" - date_format: str - - def output_type(self, *input_types: dtypes.ExpressionType) -> dtypes.ExpressionType: - return dtypes.STRING_DTYPE - - -@dataclasses.dataclass(frozen=True) -class UnixSeconds(base_ops.UnaryOp): - name: typing.ClassVar[str] = "unix_seconds" - - def output_type(self, *input_types: dtypes.ExpressionType) -> dtypes.ExpressionType: - if input_types[0] != dtypes.TIMESTAMP_DTYPE: - raise TypeError("expected timestamp input") - return dtypes.INT_DTYPE - - -@dataclasses.dataclass(frozen=True) -class UnixMillis(base_ops.UnaryOp): - name: typing.ClassVar[str] = "unix_millis" - - def output_type(self, *input_types: dtypes.ExpressionType) -> dtypes.ExpressionType: - if input_types[0] != dtypes.TIMESTAMP_DTYPE: - raise TypeError("expected timestamp input") - return dtypes.INT_DTYPE - - -@dataclasses.dataclass(frozen=True) -class UnixMicros(base_ops.UnaryOp): - name: typing.ClassVar[str] = "unix_micros" - - def output_type(self, *input_types: dtypes.ExpressionType) -> dtypes.ExpressionType: - if input_types[0] != dtypes.TIMESTAMP_DTYPE: - raise TypeError("expected timestamp input") - return dtypes.INT_DTYPE - - -@dataclasses.dataclass(frozen=True) -class TimestampDiff(base_ops.BinaryOp): - name: typing.ClassVar[str] = "timestamp_diff" - - def output_type(self, *input_types: dtypes.ExpressionType) -> dtypes.ExpressionType: - if input_types[0] != input_types[1]: - raise TypeError( - f"two inputs have different types. left: {input_types[0]}, right: {input_types[1]}" - ) - - if not dtypes.is_datetime_like(input_types[0]): - raise TypeError("expected timestamp input") - - return dtypes.TIMEDELTA_DTYPE - - -timestamp_diff_op = TimestampDiff() diff --git a/bigframes/operations/datetimes.py b/bigframes/operations/datetimes.py index b16c596120a..1b20c2d593d 100644 --- a/bigframes/operations/datetimes.py +++ b/bigframes/operations/datetimes.py @@ -14,176 +14,53 @@ from __future__ import annotations -import datetime as dt -from typing import Generic, Literal, Optional, TypeVar - -import bigframes_vendored.pandas.core.arrays.datetimelike as vendored_pandas_datetimelike -import bigframes_vendored.pandas.core.indexes.accessor as vendordt -import pandas - -import bigframes.core.col -import bigframes.core.indexes.base as indices import bigframes.operations as ops -from bigframes import dataframe, dtypes, series -from bigframes._tools import docs -from bigframes.core.logging import log_adapter - -_ONE_DAY = pandas.Timedelta("1D") -_ONE_SECOND = pandas.Timedelta("1s") -_ONE_MICRO = pandas.Timedelta("1us") -_SUPPORTED_FREQS = ("Y", "Q", "M", "W", "D", "h", "min", "s", "ms", "us") - - -T = TypeVar("T", series.Series, indices.Index, bigframes.core.col.Expression) - - -# Simpler base class for datetime properties, excludes isocalendar, unit, tz -class DatetimeSimpleMethods(Generic[T]): - def __init__(self, data: T): - self._data: T = data - - # Date accessors - @property - def day(self) -> T: - return self._data._apply_unary_op(ops.day_op) - - @property - def dayofweek(self) -> T: - return self._data._apply_unary_op(ops.dayofweek_op) - - @property - def day_of_week(self) -> T: - return self.dayofweek - - @property - def weekday(self) -> T: - return self.dayofweek +import bigframes.operations.base +import bigframes.series as series +import third_party.bigframes_vendored.pandas.core.indexes.accessor as vendordt - @property - def dayofyear(self) -> T: - return self._data._apply_unary_op(ops.dayofyear_op) - @property - def day_of_year(self) -> T: - return self.dayofyear +class DatetimeMethods( + bigframes.operations.base.SeriesMethods, vendordt.DatetimeProperties +): + __doc__ = vendordt.DatetimeProperties.__doc__ @property - def date(self) -> T: - return self._data._apply_unary_op(ops.date_op) + def day(self) -> series.Series: + return self._apply_unary_op(ops.day_op) @property - def quarter(self) -> T: - return self._data._apply_unary_op(ops.quarter_op) + def dayofweek(self) -> series.Series: + return self._apply_unary_op(ops.dayofweek_op) @property - def year(self) -> T: - return self._data._apply_unary_op(ops.year_op) + def date(self) -> series.Series: + return self._apply_unary_op(ops.date_op) @property - def month(self) -> T: - return self._data._apply_unary_op(ops.month_op) + def hour(self) -> series.Series: + return self._apply_unary_op(ops.hour_op) - # Time accessors @property - def hour(self) -> T: - return self._data._apply_unary_op(ops.hour_op) + def minute(self) -> series.Series: + return self._apply_unary_op(ops.minute_op) @property - def minute(self) -> T: - return self._data._apply_unary_op(ops.minute_op) + def month(self) -> series.Series: + return self._apply_unary_op(ops.month_op) @property - def second(self) -> T: - return self._data._apply_unary_op(ops.second_op) + def second(self) -> series.Series: + return self._apply_unary_op(ops.second_op) @property - def time(self) -> T: - return self._data._apply_unary_op(ops.time_op) + def time(self) -> series.Series: + return self._apply_unary_op(ops.time_op) - # Timedelta accessors @property - def days(self) -> T: - self._check_dtype(dtypes.TIMEDELTA_DTYPE) - - return self._data._apply_binary_op(_ONE_DAY, ops.floordiv_op) + def quarter(self) -> series.Series: + return self._apply_unary_op(ops.quarter_op) @property - def seconds(self) -> T: - self._check_dtype(dtypes.TIMEDELTA_DTYPE) - - return self._data._apply_binary_op(_ONE_DAY, ops.mod_op) // _ONE_SECOND # type: ignore - - @property - def microseconds(self) -> T: - self._check_dtype(dtypes.TIMEDELTA_DTYPE) - - return self._data._apply_binary_op(_ONE_SECOND, ops.mod_op) // _ONE_MICRO # type: ignore - - def total_seconds(self) -> T: - self._check_dtype(dtypes.TIMEDELTA_DTYPE) - - return self._data._apply_binary_op(_ONE_SECOND, ops.div_op) - - def _check_dtype(self, target_dtype: dtypes.Dtype): - if isinstance(self._data, (indices.Index, series.Series)): - if self._data.dtype != target_dtype: - raise TypeError( - f"Expect dtype: {target_dtype}, but got {self._data.dtype}" - ) - return - - def tz_localize(self, tz: Literal["UTC"] | None) -> T: - if tz == "UTC": - return self._data._apply_unary_op(ops.ToTimestampOp()) - - if tz is None: - return self._data._apply_unary_op(ops.ToDatetimeOp()) - - raise ValueError(f"Unsupported timezone {tz}") - - def day_name(self) -> T: - return self.strftime("%A") - - def strftime(self, date_format: str) -> T: - return self._data._apply_unary_op(ops.StrftimeOp(date_format=date_format)) - - def normalize(self) -> T: - return self._data._apply_unary_op(ops.normalize_op) - - def floor(self, freq: str) -> T: - if freq not in _SUPPORTED_FREQS: - raise ValueError(f"freq must be one of {_SUPPORTED_FREQS}") - return self._data._apply_unary_op(ops.FloorDtOp(freq=freq)) # type: ignore - - -# this is the version used by series.dt, and the one that shows up in reference docs -@log_adapter.class_logger -@docs.inherit_docs(vendordt.DatetimeProperties) -@docs.inherit_docs(vendored_pandas_datetimelike.DatelikeOps) -class DatetimeMethods(DatetimeSimpleMethods[bigframes.series.Series]): - def __init__(self, data: series.Series): - super().__init__(data) - - @property - def tz(self) -> Optional[dt.timezone]: - # Assumption: pyarrow dtype - tz_string = self._data._dtype.pyarrow_dtype.tz - if tz_string == "UTC": - return dt.timezone.utc - elif tz_string is None: - return None - else: - raise ValueError(f"Unexpected timezone {tz_string}") - - @property - def unit(self) -> str: - # Assumption: pyarrow dtype - return self._data._dtype.pyarrow_dtype.unit - - def isocalendar(self) -> dataframe.DataFrame: - iso_ops = [ops.iso_year_op, ops.iso_week_op, ops.iso_day_op] - labels = pandas.Index(["year", "week", "day"]) - block = self._data._block.project_exprs( - [op.as_expr(self._data._value_column) for op in iso_ops], labels, drop=True - ) - return dataframe.DataFrame(block) + def year(self) -> series.Series: + return self._apply_unary_op(ops.year_op) diff --git a/bigframes/operations/distance_ops.py b/bigframes/operations/distance_ops.py deleted file mode 100644 index 435308f9c40..00000000000 --- a/bigframes/operations/distance_ops.py +++ /dev/null @@ -1,32 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -import bigframes.operations.type as op_typing -from bigframes.operations import base_ops - -CosineDistanceOp = base_ops.create_binary_op( - name="ml_cosine_distance", type_signature=op_typing.VECTOR_METRIC -) -cosine_distance_op = CosineDistanceOp() - -ManhattanDistanceOp = base_ops.create_binary_op( - name="ml_manhattan_distance", type_signature=op_typing.VECTOR_METRIC -) -manhattan_distance_op = ManhattanDistanceOp() - -EuclidDistanceOp = base_ops.create_binary_op( - name="ml_euclidean_distance", type_signature=op_typing.VECTOR_METRIC -) -euclidean_distance_op = EuclidDistanceOp() diff --git a/bigframes/operations/frequency_ops.py b/bigframes/operations/frequency_ops.py deleted file mode 100644 index b94afa72710..00000000000 --- a/bigframes/operations/frequency_ops.py +++ /dev/null @@ -1,74 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import dataclasses -import datetime -import typing - -import numpy as np -import pandas as pd -from pandas.tseries import offsets - -from bigframes import dtypes -from bigframes.operations import base_ops - - -@dataclasses.dataclass(frozen=True) -class FloorDtOp(base_ops.UnaryOp): - name: typing.ClassVar[str] = "floor_dt" - freq: typing.Literal[ - "Y", - "Q", - "M", - "W", - "D", - "h", - "min", - "s", - "ms", - "us", - ] - - def output_type(self, *input_types): - if not dtypes.is_datetime_like(input_types[0]): - raise TypeError("dt floor requires datetime-like arguments") - return input_types[0] - - -@dataclasses.dataclass(frozen=True) -class DatetimeToIntegerLabelOp(base_ops.BinaryOp): - name: typing.ClassVar[str] = "datetime_to_integer_label" - freq: offsets.DateOffset - closed: typing.Optional[typing.Literal["right", "left"]] - origin: typing.Union[ - typing.Union[pd.Timestamp, datetime.datetime, np.datetime64, int, float, str], - typing.Literal["epoch", "start", "start_day", "end", "end_day"], - ] - - def output_type(self, *input_types): - return dtypes.INT_DTYPE - - -@dataclasses.dataclass(frozen=True) -class IntegerLabelToDatetimeOp(base_ops.BinaryOp): - name: typing.ClassVar[str] = "integer_label_to_datetime" - freq: offsets.DateOffset - label: typing.Optional[typing.Literal["right", "left"]] - origin: typing.Union[ - typing.Union[pd.Timestamp, datetime.datetime, np.datetime64, int, float, str], - typing.Literal["epoch", "start", "start_day", "end", "end_day"], - ] - - def output_type(self, *input_types): - return input_types[1] diff --git a/bigframes/operations/generic_ops.py b/bigframes/operations/generic_ops.py deleted file mode 100644 index 9b226ad28d8..00000000000 --- a/bigframes/operations/generic_ops.py +++ /dev/null @@ -1,510 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import dataclasses -import functools -import typing - -import bigframes.operations.type as op_typing -from bigframes import dtypes -from bigframes.operations import base_ops - -InvertOp = base_ops.create_unary_op( - name="invert", - type_signature=op_typing.TypePreserving( - dtypes.is_binary_like, - description="binary-like", - ), -) -invert_op = InvertOp() - -IsNullOp = base_ops.create_unary_op( - name="isnull", - type_signature=op_typing.FixedOutputType( - lambda x: True, dtypes.BOOL_DTYPE, description="nullable" - ), -) -isnull_op = IsNullOp() - -NotNullOp = base_ops.create_unary_op( - name="notnull", - type_signature=op_typing.FixedOutputType( - lambda x: True, dtypes.BOOL_DTYPE, description="nullable" - ), -) -notnull_op = NotNullOp() - - -# Semantics match Python's truth value testing (truthy and falsey objects). -# See https://docs.python.org/3/library/stdtypes.html#truth-value-testing -CoerceToBoolOp = base_ops.create_unary_op( - name="coerce_to_bool", - type_signature=op_typing.FixedOutputType( - dtypes.is_bool_coercable, dtypes.BOOL_DTYPE, description="coercable to bool" - ), -) -CoerceToBoolOp.__doc__ = ( - "Coerce a value to a boolean, matching Python's truth value testing semantics " - "(truthy/falsey). See https://docs.python.org/3/library/stdtypes.html#truth-value-testing" -) -coerce_to_bool_op = CoerceToBoolOp() - -HashOp = base_ops.create_unary_op( - name="hash", - type_signature=op_typing.FixedOutputType( - dtypes.is_string_like, dtypes.INT_DTYPE, description="string-like" - ), -) -hash_op = HashOp() - -# source, dest type -_VALID_CASTS = set( - ( - # INT casts - ( - dtypes.BOOL_DTYPE, - dtypes.INT_DTYPE, - ), - ( - dtypes.FLOAT_DTYPE, - dtypes.INT_DTYPE, - ), - ( - dtypes.NUMERIC_DTYPE, - dtypes.INT_DTYPE, - ), - ( - dtypes.BIGNUMERIC_DTYPE, - dtypes.INT_DTYPE, - ), - ( - dtypes.TIME_DTYPE, - dtypes.INT_DTYPE, - ), - ( - dtypes.DATETIME_DTYPE, - dtypes.INT_DTYPE, - ), - ( - dtypes.TIMESTAMP_DTYPE, - dtypes.INT_DTYPE, - ), - ( - dtypes.TIMEDELTA_DTYPE, - dtypes.INT_DTYPE, - ), - ( - dtypes.STRING_DTYPE, - dtypes.INT_DTYPE, - ), - # Float casts - ( - dtypes.BOOL_DTYPE, - dtypes.FLOAT_DTYPE, - ), - ( - dtypes.NUMERIC_DTYPE, - dtypes.FLOAT_DTYPE, - ), - ( - dtypes.BIGNUMERIC_DTYPE, - dtypes.FLOAT_DTYPE, - ), - ( - dtypes.INT_DTYPE, - dtypes.FLOAT_DTYPE, - ), - ( - dtypes.STRING_DTYPE, - dtypes.FLOAT_DTYPE, - ), - # Bool casts - ( - dtypes.INT_DTYPE, - dtypes.BOOL_DTYPE, - ), - ( - dtypes.FLOAT_DTYPE, - dtypes.BOOL_DTYPE, - ), - # String casts - ( - dtypes.BYTES_DTYPE, - dtypes.STRING_DTYPE, - ), - ( - dtypes.BOOL_DTYPE, - dtypes.STRING_DTYPE, - ), - ( - dtypes.FLOAT_DTYPE, - dtypes.STRING_DTYPE, - ), - ( - dtypes.TIME_DTYPE, - dtypes.STRING_DTYPE, - ), - ( - dtypes.INT_DTYPE, - dtypes.STRING_DTYPE, - ), - ( - dtypes.DATETIME_DTYPE, - dtypes.STRING_DTYPE, - ), - ( - dtypes.TIMESTAMP_DTYPE, - dtypes.STRING_DTYPE, - ), - ( - dtypes.DATE_DTYPE, - dtypes.STRING_DTYPE, - ), - # bytes casts - ( - dtypes.STRING_DTYPE, - dtypes.BYTES_DTYPE, - ), - # decimal casts - ( - dtypes.STRING_DTYPE, - dtypes.NUMERIC_DTYPE, - ), - ( - dtypes.INT_DTYPE, - dtypes.NUMERIC_DTYPE, - ), - ( - dtypes.FLOAT_DTYPE, - dtypes.NUMERIC_DTYPE, - ), - ( - dtypes.BIGNUMERIC_DTYPE, - dtypes.NUMERIC_DTYPE, - ), - # big decimal casts - ( - dtypes.STRING_DTYPE, - dtypes.BIGNUMERIC_DTYPE, - ), - ( - dtypes.INT_DTYPE, - dtypes.BIGNUMERIC_DTYPE, - ), - ( - dtypes.FLOAT_DTYPE, - dtypes.BIGNUMERIC_DTYPE, - ), - ( - dtypes.NUMERIC_DTYPE, - dtypes.BIGNUMERIC_DTYPE, - ), - # time casts - ( - dtypes.INT_DTYPE, - dtypes.TIME_DTYPE, - ), - ( - dtypes.DATETIME_DTYPE, - dtypes.TIME_DTYPE, - ), - ( - dtypes.TIMESTAMP_DTYPE, - dtypes.TIME_DTYPE, - ), - # date casts - ( - dtypes.STRING_DTYPE, - dtypes.DATE_DTYPE, - ), - ( - dtypes.DATETIME_DTYPE, - dtypes.DATE_DTYPE, - ), - ( - dtypes.TIMESTAMP_DTYPE, - dtypes.DATE_DTYPE, - ), - # datetime casts - ( - dtypes.DATE_DTYPE, - dtypes.DATETIME_DTYPE, - ), - ( - dtypes.STRING_DTYPE, - dtypes.DATETIME_DTYPE, - ), - ( - dtypes.TIMESTAMP_DTYPE, - dtypes.DATETIME_DTYPE, - ), - ( - dtypes.INT_DTYPE, - dtypes.DATETIME_DTYPE, - ), - # timestamp casts - ( - dtypes.DATE_DTYPE, - dtypes.TIMESTAMP_DTYPE, - ), - ( - dtypes.STRING_DTYPE, - dtypes.TIMESTAMP_DTYPE, - ), - ( - dtypes.DATETIME_DTYPE, - dtypes.TIMESTAMP_DTYPE, - ), - ( - dtypes.INT_DTYPE, - dtypes.TIMESTAMP_DTYPE, - ), - # timedelta casts - ( - dtypes.INT_DTYPE, - dtypes.TIMEDELTA_DTYPE, - ), - ) -) - - -def _valid_scalar_cast(src: dtypes.Dtype, dst: dtypes.Dtype): - if src == dst: - return True - elif (src, dst) in _VALID_CASTS: - return True - return False - - -def _valid_cast(src: dtypes.Dtype, dst: dtypes.Dtype): - if src == dst: - return True - # TODO: Might need to be more strict within list/array context - if dtypes.is_array_like(src) and dtypes.is_array_like(dst): - src_inner = dtypes.get_array_inner_type(src) - dst_inner = dtypes.get_array_inner_type(dst) - return _valid_cast(src_inner, dst_inner) - if dtypes.is_struct_like(src) and dtypes.is_struct_like(dst): - src_fields = dtypes.get_struct_fields(src) - dst_fields = dtypes.get_struct_fields(dst) - if len(src_fields) != len(dst_fields): - return False - for (_, src_dtype), (_, dst_dtype) in zip( - src_fields.items(), dst_fields.items() - ): - if not _valid_cast(src_dtype, dst_dtype): - return False - return True - - return _valid_scalar_cast(src, dst) - - -@dataclasses.dataclass(frozen=True) -class AsTypeOp(base_ops.UnaryOp): - name: typing.ClassVar[str] = "astype" - # TODO: Convert strings to dtype earlier - to_type: dtypes.Dtype - safe: bool = False - - def output_type(self, *input_types): - if not _valid_cast(input_types[0], self.to_type): - raise TypeError(f"Cannot cast {input_types[0]} to {self.to_type}") - - return self.to_type - - -@dataclasses.dataclass(frozen=True) -class IsInOp(base_ops.UnaryOp): - name: typing.ClassVar[str] = "is_in" - values: typing.Tuple - match_nulls: bool = True - - def output_type(self, *input_types): - return dtypes.BOOL_DTYPE - - -@dataclasses.dataclass(frozen=True) -class MapOp(base_ops.UnaryOp): - name = "map_values" - mappings: typing.Tuple[typing.Tuple[typing.Hashable, typing.Hashable], ...] - - def output_type(self, *input_types): - return input_types[0] - - -FillNaOp = base_ops.create_binary_op(name="fillna", type_signature=op_typing.COERCE) -fillna_op = FillNaOp() - -MaximumOp = base_ops.create_binary_op(name="maximum", type_signature=op_typing.COERCE) -maximum_op = MaximumOp() - -MinimumOp = base_ops.create_binary_op(name="minimum", type_signature=op_typing.COERCE) -minimum_op = MinimumOp() - -CoalesceOp = base_ops.create_binary_op(name="coalesce", type_signature=op_typing.COERCE) -coalesce_op = CoalesceOp() - - -@dataclasses.dataclass(frozen=True) -class WhereOp(base_ops.TernaryOp): - name: typing.ClassVar[str] = "where" - - def output_type(self, *input_types: dtypes.ExpressionType) -> dtypes.ExpressionType: - if input_types[1] != dtypes.BOOL_DTYPE: - raise TypeError("where condition must be a boolean") - return dtypes.coerce_to_common(input_types[0], input_types[2]) - - -where_op = WhereOp() - - -@dataclasses.dataclass(frozen=True) -class ClipOp(base_ops.TernaryOp): - name: typing.ClassVar[str] = "clip" - - def output_type(self, *input_types: dtypes.ExpressionType) -> dtypes.ExpressionType: - return dtypes.coerce_to_common( - input_types[0], dtypes.coerce_to_common(input_types[1], input_types[2]) - ) - - -clip_op = ClipOp() - - -class CaseWhenOp(base_ops.NaryOp): - name: typing.ClassVar[str] = "switch" - - def output_type(self, *input_types: dtypes.ExpressionType) -> dtypes.ExpressionType: - assert len(input_types) % 2 == 0 - # predicate1, output1, predicate2, output2... - if not all(map(lambda x: x == dtypes.BOOL_DTYPE, input_types[::2])): - raise TypeError(f"Case inputs {input_types[::2]} must be boolean-valued") - output_expr_types = input_types[1::2] - return functools.reduce( - lambda t1, t2: dtypes.coerce_to_common(t1, t2), - output_expr_types, - ) - - -case_when_op = CaseWhenOp() - - -# Really doesn't need to be its own op, but allows us to try to get the most compact representation -@dataclasses.dataclass(frozen=True) -class RowKey(base_ops.NaryOp): - name: typing.ClassVar[str] = "rowkey" - - def output_type(self, *input_types: dtypes.ExpressionType) -> dtypes.ExpressionType: - return dtypes.STRING_DTYPE - - @property - def is_bijective(self) -> bool: - """Whether the operation has a 1:1 mapping between inputs and outputs""" - return True - - @property - def deterministic(self) -> bool: - return False - - -@dataclasses.dataclass(frozen=True) -class SqlScalarOp(base_ops.NaryOp): - """An escape to SQL, representing a single column.""" - - name: typing.ClassVar[str] = "sql_scalar" - _output_type: dtypes.ExpressionType - sql_template: str - is_deterministic: bool = True - - def output_type(self, *input_types: dtypes.ExpressionType) -> dtypes.ExpressionType: - return self._output_type - - @property - def deterministic(self) -> bool: - return self.is_deterministic - - -@dataclasses.dataclass(frozen=True) -class PyUdfOp(base_ops.NaryOp): - """Represents a local UDF.""" - - name: typing.ClassVar[str] = "py_udf" - fn: typing.Callable - _output_type: dtypes.ExpressionType - - def output_type(self, *input_types: dtypes.ExpressionType) -> dtypes.ExpressionType: - return self._output_type - - -@dataclasses.dataclass(frozen=True) -class GetItemOp(base_ops.UnaryOp): - """Represents subscripting with a statically-known key (e.g. `obj[1]` or `obj["field"]`). - - We must keep this static UnaryOp separate from DynamicGetItemOp (a BinaryOp) - primarily to support Struct field subscripting. Because the return type of a Struct - field lookup depends on the specific field being accessed, and type resolution - (output_type) only has access to input types rather than input values, we must store - the static key inside the operation instance to infer the correct output type. - """ - - name: typing.ClassVar[str] = "getitem" - key: typing.Union[str, int] - - def output_type(self, *input_types): - input_type = input_types[0] - if dtypes.is_struct_like(input_type): - pa_type = input_type.pyarrow_dtype - pa_result_type = pa_type[self.key].type - return dtypes.arrow_dtype_to_bigframes_dtype(pa_result_type) - elif dtypes.is_array_like(input_type): - if not isinstance(self.key, int): - raise TypeError("Array index must be an integer") - return dtypes.arrow_dtype_to_bigframes_dtype( - input_type.pyarrow_dtype.value_type - ) - elif dtypes.is_string_like(input_type): - if not isinstance(self.key, int): - raise TypeError("String index must be an integer") - return dtypes.STRING_DTYPE - else: - raise TypeError(f"Cannot subscript input of type {input_type}") - - -@dataclasses.dataclass(frozen=True) -class DynamicGetItemOp(base_ops.BinaryOp): - """Represents subscripting with a dynamic key expression (e.g. `obj[expr]`). - - Unlike GetItemOp, this operates on 2 dynamic inputs (the container and the key). - Because SQL/BigQuery does not support dynamic struct field access (struct paths must - be statically declared), this operation is only supported for array and string - subscripting, where output type inference does not require knowing the runtime - index value. - """ - - name: typing.ClassVar[str] = "dynamic_getitem" - - def output_type(self, *input_types): - left_type = input_types[0] - right_type = input_types[1] - if not dtypes.is_numeric(right_type): - raise TypeError(f"Subscript index must be numeric type, got {right_type}") - - if dtypes.is_array_like(left_type): - return dtypes.arrow_dtype_to_bigframes_dtype( - left_type.pyarrow_dtype.value_type - ) - elif dtypes.is_string_like(left_type): - return dtypes.STRING_DTYPE - else: - raise TypeError(f"Cannot dynamically subscript input of type {left_type}") diff --git a/bigframes/operations/geo_ops.py b/bigframes/operations/geo_ops.py deleted file mode 100644 index a965ddca2b9..00000000000 --- a/bigframes/operations/geo_ops.py +++ /dev/null @@ -1,160 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import dataclasses -from typing import Optional - -import bigframes.operations.type as op_typing -from bigframes import dtypes -from bigframes.operations import base_ops - -GeoStAstextOp = base_ops.create_unary_op( - name="geo_st_astext", - type_signature=op_typing.FixedOutputType( - dtypes.is_geo_like, dtypes.STRING_DTYPE, description="geo-like" - ), -) -geo_st_astext_op = GeoStAstextOp() - -GeoStBoundaryOp = base_ops.create_unary_op( - name="geo_st_boundary", - type_signature=op_typing.FixedOutputType( - dtypes.is_geo_like, dtypes.GEO_DTYPE, description="geo-like" - ), -) -geo_st_boundary_op = GeoStBoundaryOp() - -GeoStCentroidOp = base_ops.create_unary_op( - name="geo_st_centroid", - type_signature=op_typing.FixedOutputType( - dtypes.is_geo_like, dtypes.GEO_DTYPE, description="geo-like" - ), -) -geo_st_centroid_op = GeoStCentroidOp() - -GeoStConvexhullOp = base_ops.create_unary_op( - name="geo_st_convexhull", - type_signature=op_typing.FixedOutputType( - dtypes.is_geo_like, dtypes.GEO_DTYPE, description="geo-like" - ), -) -geo_st_convexhull_op = GeoStConvexhullOp() - -GeoStDifferenceOp = base_ops.create_binary_op( - name="geo_st_difference", type_signature=op_typing.BinaryGeo() -) -geo_st_difference_op = GeoStDifferenceOp() - -GeoStGeogfromtextOp = base_ops.create_unary_op( - name="geo_st_geogfromtext", - type_signature=op_typing.FixedOutputType( - dtypes.is_string_like, dtypes.GEO_DTYPE, description="string-like" - ), -) -geo_st_geogfromtext_op = GeoStGeogfromtextOp() - -GeoStGeogpointOp = base_ops.create_binary_op( - name="geo_st_geogpoint", type_signature=op_typing.BinaryNumericGeo() -) -geo_st_geogpoint_op = GeoStGeogpointOp() - -GeoStIsclosedOp = base_ops.create_unary_op( - name="geo_st_isclosed", - type_signature=op_typing.FixedOutputType( - dtypes.is_geo_like, dtypes.BOOL_DTYPE, description="geo-like" - ), -) -geo_st_isclosed_op = GeoStIsclosedOp() - -GeoXOp = base_ops.create_unary_op( - name="geo_x", - type_signature=op_typing.FixedOutputType( - dtypes.is_geo_like, dtypes.FLOAT_DTYPE, description="geo-like" - ), -) -geo_x_op = GeoXOp() - -GeoYOp = base_ops.create_unary_op( - name="geo_y", - type_signature=op_typing.FixedOutputType( - dtypes.is_geo_like, dtypes.FLOAT_DTYPE, description="geo-like" - ), -) -geo_y_op = GeoYOp() - -GeoStIntersectionOp = base_ops.create_binary_op( - name="geo_st_intersection", type_signature=op_typing.BinaryGeo() -) -geo_st_intersection_op = GeoStIntersectionOp() - - -@dataclasses.dataclass(frozen=True) -class GeoStBufferOp(base_ops.UnaryOp): - name = "st_buffer" - buffer_radius: float - num_seg_quarter_circle: float - use_spheroid: bool - - def output_type(self, *input_types: dtypes.ExpressionType) -> dtypes.ExpressionType: - return dtypes.GEO_DTYPE - - -@dataclasses.dataclass(frozen=True) -class GeoStDistanceOp(base_ops.BinaryOp): - name = "st_distance" - use_spheroid: bool - - def output_type(self, *input_types: dtypes.ExpressionType) -> dtypes.ExpressionType: - return dtypes.FLOAT_DTYPE - - -@dataclasses.dataclass(frozen=True) -class GeoStLengthOp(base_ops.UnaryOp): - name = "geo_st_length" - use_spheroid: bool = False - - def output_type(self, *input_types: dtypes.ExpressionType) -> dtypes.ExpressionType: - return dtypes.FLOAT_DTYPE - - -@dataclasses.dataclass(frozen=True) -class GeoStRegionStatsOp(base_ops.UnaryOp): - """See: https://cloud.google.com/bigquery/docs/reference/standard-sql/geography_functions#st_regionstats""" - - name = "geo_st_regionstats" - raster_id: str - band: Optional[str] - include: Optional[str] - options: Optional[str] - - def output_type(self, *input_types: dtypes.ExpressionType) -> dtypes.ExpressionType: - return dtypes.struct_type( - [ - ("min", dtypes.FLOAT_DTYPE), - ("max", dtypes.FLOAT_DTYPE), - ("sum", dtypes.FLOAT_DTYPE), - ("count", dtypes.INT_DTYPE), - ("mean", dtypes.FLOAT_DTYPE), - ("area", dtypes.FLOAT_DTYPE), - ] - ) - - -@dataclasses.dataclass(frozen=True) -class GeoStSimplifyOp(base_ops.UnaryOp): - name = "st_simplify" - tolerance_meters: float - - def output_type(self, *input_types: dtypes.ExpressionType) -> dtypes.ExpressionType: - return dtypes.GEO_DTYPE diff --git a/bigframes/operations/googlesql/__init__.py b/bigframes/operations/googlesql/__init__.py deleted file mode 100644 index edec5b84f8e..00000000000 --- a/bigframes/operations/googlesql/__init__.py +++ /dev/null @@ -1,106 +0,0 @@ -# Copyright 2026 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -from __future__ import annotations - -import dataclasses -import typing - -import bigframes.operations as ops -from bigframes import dtypes - - -@dataclasses.dataclass(frozen=True) -class ArgSpec: - arg_name: str | None = None - optional: bool = False - is_vararg: bool = False - const_only: bool = False - - -@dataclasses.dataclass(frozen=True) -class OpSignature: - # Detailed specs for each parameter. This is particularly relevant for ren - arg_specs: typing.Sequence[ArgSpec] - resolve_return_type: typing.Any - has_varargs: bool = False - - -# Eventually we should migrate every op over to this that can be directly emitted 1:1 as a sql op -# This will allow us to fully lower to pure SQL dialect expressions and emitting sql text is trivial. -@dataclasses.dataclass(frozen=True) -class GoogleSqlScalarOp(ops.NaryOp): - name: typing.ClassVar[str] = "googlesql_scalar" - - # syntax - sql_name: str - args: tuple[ArgSpec, ...] - # typing - signature: typing.Callable[..., dtypes.ExpressionType] - - # semantics - is_deterministic: bool = True - - @property - def deterministic(self) -> bool: - return self.is_deterministic - - def output_type(self, *input_types: dtypes.ExpressionType) -> dtypes.ExpressionType: - return self.signature(*input_types) - - -RAND = GoogleSqlScalarOp( - "RAND", args=(), is_deterministic=False, signature=lambda: dtypes.FLOAT_DTYPE -) - - -def _check_geo_input( - t: dtypes.ExpressionType, out: dtypes.ExpressionType -) -> dtypes.ExpressionType: - if t is not None and not dtypes.is_geo_like(t): - raise TypeError(f"Type {t} is not supported. Type must be geo-like") - return out - - -def _check_simplify_inputs( - geo: dtypes.ExpressionType, tol: dtypes.ExpressionType -) -> dtypes.ExpressionType: - if geo is not None and not dtypes.is_geo_like(geo): - raise TypeError(f"Type {geo} is not supported. Type must be geo-like") - if tol is not None and not dtypes.is_numeric(tol): - raise TypeError(f"Type {tol} is not supported. Type must be numeric") - return dtypes.GEO_DTYPE - - -ST_AREA = GoogleSqlScalarOp( - "ST_AREA", - args=(ArgSpec(),), - is_deterministic=True, - signature=lambda geo: _check_geo_input(geo, dtypes.FLOAT_DTYPE), -) - -ST_CENTROID = GoogleSqlScalarOp( - "ST_CENTROID", - args=(ArgSpec(),), - is_deterministic=True, - signature=lambda geo: _check_geo_input(geo, dtypes.GEO_DTYPE), -) - -ST_SIMPLIFY = GoogleSqlScalarOp( - "ST_SIMPLIFY", - args=(ArgSpec(), ArgSpec()), - is_deterministic=True, - signature=_check_simplify_inputs, -) diff --git a/bigframes/operations/googlesql/aead.py b/bigframes/operations/googlesql/aead.py deleted file mode 100644 index f719d7d6989..00000000000 --- a/bigframes/operations/googlesql/aead.py +++ /dev/null @@ -1,122 +0,0 @@ -# Copyright 2026 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# -# DO NOT MODIFY THIS FILE DIRECTLY. -# This file was generated from: scripts/data/sql-functions/aead.yaml -# by the script: scripts/generate_bigframes_bigquery.py - -from __future__ import annotations - -from typing import Literal, Union - -import bigframes.core.col -import bigframes.core.googlesql -import bigframes.core.sentinels as sentinels -import bigframes.series as series -from bigframes import dtypes -from bigframes.operations import googlesql - -_DECRYPT_BYTES_OP = googlesql.GoogleSqlScalarOp( - "AEAD.DECRYPT_BYTES", - args=(googlesql.ArgSpec(), googlesql.ArgSpec(), googlesql.ArgSpec()), - signature=lambda *args: dtypes.BYTES_DTYPE, -) -_DECRYPT_STRING_OP = googlesql.GoogleSqlScalarOp( - "AEAD.DECRYPT_STRING", - args=(googlesql.ArgSpec(), googlesql.ArgSpec(), googlesql.ArgSpec()), - signature=lambda *args: dtypes.STRING_DTYPE, -) -_ENCRYPT_OP = googlesql.GoogleSqlScalarOp( - "AEAD.ENCRYPT", - args=(googlesql.ArgSpec(), googlesql.ArgSpec(), googlesql.ArgSpec()), - signature=lambda *args: dtypes.BYTES_DTYPE, -) - - -def decrypt_bytes( - keyset: Union[ - series.Series, - bigframes.core.col.Expression, - Union[Literal[sentinels.Sentinel.ARGUMENT_DEFAULT], bytes, dict], - ], - ciphertext: Union[ - series.Series, - bigframes.core.col.Expression, - Union[Literal[sentinels.Sentinel.ARGUMENT_DEFAULT], bytes], - ], - additional_data: Union[ - series.Series, - bigframes.core.col.Expression, - Union[Literal[sentinels.Sentinel.ARGUMENT_DEFAULT], bytes], - ], -) -> Union[series.Series, bigframes.core.col.Expression]: - """Uses the matching key from keyset to decrypt ciphertext and verifies the integrity of the data using additional_data. Returns an error if decryption or verification fails.""" - return bigframes.core.googlesql.apply_googlesql_scalar_op( - _DECRYPT_BYTES_OP, - keyset, - ciphertext, - additional_data, - ) - - -def decrypt_string( - keyset: Union[ - series.Series, - bigframes.core.col.Expression, - Union[Literal[sentinels.Sentinel.ARGUMENT_DEFAULT], bytes, dict], - ], - ciphertext: Union[ - series.Series, - bigframes.core.col.Expression, - Union[Literal[sentinels.Sentinel.ARGUMENT_DEFAULT], bytes], - ], - additional_data: Union[ - series.Series, - bigframes.core.col.Expression, - Union[Literal[sentinels.Sentinel.ARGUMENT_DEFAULT], str], - ], -) -> Union[series.Series, bigframes.core.col.Expression]: - """Like AEAD.DECRYPT_BYTES, but where additional_data is of type STRING.""" - return bigframes.core.googlesql.apply_googlesql_scalar_op( - _DECRYPT_STRING_OP, - keyset, - ciphertext, - additional_data, - ) - - -def encrypt( - keyset: Union[ - series.Series, - bigframes.core.col.Expression, - Union[Literal[sentinels.Sentinel.ARGUMENT_DEFAULT], bytes, dict], - ], - plaintext: Union[ - series.Series, - bigframes.core.col.Expression, - Union[Literal[sentinels.Sentinel.ARGUMENT_DEFAULT], bytes, str], - ], - additional_data: Union[ - series.Series, - bigframes.core.col.Expression, - Union[Literal[sentinels.Sentinel.ARGUMENT_DEFAULT], bytes, str], - ], -) -> Union[series.Series, bigframes.core.col.Expression]: - """Encrypts plaintext using the primary cryptographic key in keyset. The algorithm of the primary key must be AEAD_AES_GCM_256. Binds the ciphertext to the context defined by additional_data. Returns NULL if any input is NULL.""" - return bigframes.core.googlesql.apply_googlesql_scalar_op( - _ENCRYPT_OP, - keyset, - plaintext, - additional_data, - ) diff --git a/bigframes/operations/googlesql/global_namespace/__init__.py b/bigframes/operations/googlesql/global_namespace/__init__.py deleted file mode 100644 index 58d482ea386..00000000000 --- a/bigframes/operations/googlesql/global_namespace/__init__.py +++ /dev/null @@ -1,13 +0,0 @@ -# Copyright 2026 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. diff --git a/bigframes/operations/googlesql/global_namespace/aead_encryption.py b/bigframes/operations/googlesql/global_namespace/aead_encryption.py deleted file mode 100644 index 4613ddd7e6d..00000000000 --- a/bigframes/operations/googlesql/global_namespace/aead_encryption.py +++ /dev/null @@ -1,122 +0,0 @@ -# Copyright 2026 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# -# DO NOT MODIFY THIS FILE DIRECTLY. -# This file was generated from: scripts/data/sql-functions/global_namespace/aead_encryption.yaml -# by the script: scripts/generate_bigframes_bigquery.py - -from __future__ import annotations - -from typing import Literal, Union - -import bigframes.core.col -import bigframes.core.googlesql -import bigframes.core.sentinels as sentinels -import bigframes.series as series -from bigframes import dtypes -from bigframes.operations import googlesql - -_DETERMINISTIC_DECRYPT_BYTES_OP = googlesql.GoogleSqlScalarOp( - "DETERMINISTIC_DECRYPT_BYTES", - args=(googlesql.ArgSpec(), googlesql.ArgSpec(), googlesql.ArgSpec()), - signature=lambda *args: dtypes.BYTES_DTYPE, -) -_DETERMINISTIC_DECRYPT_STRING_OP = googlesql.GoogleSqlScalarOp( - "DETERMINISTIC_DECRYPT_STRING", - args=(googlesql.ArgSpec(), googlesql.ArgSpec(), googlesql.ArgSpec()), - signature=lambda *args: dtypes.STRING_DTYPE, -) -_DETERMINISTIC_ENCRYPT_OP = googlesql.GoogleSqlScalarOp( - "DETERMINISTIC_ENCRYPT", - args=(googlesql.ArgSpec(), googlesql.ArgSpec(), googlesql.ArgSpec()), - signature=lambda *args: dtypes.BYTES_DTYPE, -) - - -def deterministic_decrypt_bytes( - keyset: Union[ - series.Series, - bigframes.core.col.Expression, - Union[Literal[sentinels.Sentinel.ARGUMENT_DEFAULT], bytes, dict], - ], - ciphertext: Union[ - series.Series, - bigframes.core.col.Expression, - Union[Literal[sentinels.Sentinel.ARGUMENT_DEFAULT], bytes], - ], - additional_data: Union[ - series.Series, - bigframes.core.col.Expression, - Union[Literal[sentinels.Sentinel.ARGUMENT_DEFAULT], bytes], - ], -) -> Union[series.Series, bigframes.core.col.Expression]: - """Uses the matching key from `keyset` to decrypt `ciphertext` and verifies the integrity of the data using `additional_data`. Returns an error if decryption fails.""" - return bigframes.core.googlesql.apply_googlesql_scalar_op( - _DETERMINISTIC_DECRYPT_BYTES_OP, - keyset, - ciphertext, - additional_data, - ) - - -def deterministic_decrypt_string( - keyset: Union[ - series.Series, - bigframes.core.col.Expression, - Union[Literal[sentinels.Sentinel.ARGUMENT_DEFAULT], bytes, dict], - ], - ciphertext: Union[ - series.Series, - bigframes.core.col.Expression, - Union[Literal[sentinels.Sentinel.ARGUMENT_DEFAULT], bytes], - ], - additional_data: Union[ - series.Series, - bigframes.core.col.Expression, - Union[Literal[sentinels.Sentinel.ARGUMENT_DEFAULT], str], - ], -) -> Union[series.Series, bigframes.core.col.Expression]: - """Like `DETERMINISTIC_DECRYPT_BYTES`, but where plaintext is of type STRING.""" - return bigframes.core.googlesql.apply_googlesql_scalar_op( - _DETERMINISTIC_DECRYPT_STRING_OP, - keyset, - ciphertext, - additional_data, - ) - - -def deterministic_encrypt( - keyset: Union[ - series.Series, - bigframes.core.col.Expression, - Union[Literal[sentinels.Sentinel.ARGUMENT_DEFAULT], bytes, dict], - ], - plaintext: Union[ - series.Series, - bigframes.core.col.Expression, - Union[Literal[sentinels.Sentinel.ARGUMENT_DEFAULT], bytes, str], - ], - additional_data: Union[ - series.Series, - bigframes.core.col.Expression, - Union[Literal[sentinels.Sentinel.ARGUMENT_DEFAULT], bytes, str], - ], -) -> Union[series.Series, bigframes.core.col.Expression]: - """Encrypts `plaintext` using the primary cryptographic key in `keyset` using deterministic AEAD. The algorithm of the primary key must be `DETERMINISTIC_AEAD_AES_SIV_CMAC_256`. Binds the ciphertext to the context defined by `additional_data`. Returns `NULL` if any input is `NULL`.""" - return bigframes.core.googlesql.apply_googlesql_scalar_op( - _DETERMINISTIC_ENCRYPT_OP, - keyset, - plaintext, - additional_data, - ) diff --git a/bigframes/operations/googlesql/global_namespace/array.py b/bigframes/operations/googlesql/global_namespace/array.py deleted file mode 100644 index 94adbad1839..00000000000 --- a/bigframes/operations/googlesql/global_namespace/array.py +++ /dev/null @@ -1,892 +0,0 @@ -# Copyright 2026 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# -# DO NOT MODIFY THIS FILE DIRECTLY. -# This file was generated from: scripts/data/sql-functions/global_namespace/array.yaml -# by the script: scripts/generate_bigframes_bigquery.py - -from __future__ import annotations - -import decimal -from typing import Any, Literal, Union - -import bigframes.core.col -import bigframes.core.googlesql -import bigframes.core.sentinels as sentinels -import bigframes.series as series -from bigframes import dtypes -from bigframes.operations import googlesql - - -def _ARRAY_CONCAT_SIG(*args): - # Pad args with None to match max expected args - args = args + (None,) * (2 - len(args)) - # Try matching impl 0 - any1_val = None - match_ok = True - if match_ok and args[0] is not None: - if not dtypes.is_array_like(args[0]): - match_ok = False - else: - inner = dtypes.get_array_inner_type(args[0]) - if any1_val is not None: - try: - any1_val = dtypes.coerce_to_common(any1_val, inner) - except TypeError: - match_ok = False - else: - any1_val = inner - if match_ok and args[1] is not None: - if not dtypes.is_array_like(args[1]): - match_ok = False - else: - inner = dtypes.get_array_inner_type(args[1]) - if any1_val is not None: - try: - any1_val = dtypes.coerce_to_common(any1_val, inner) - except TypeError: - match_ok = False - else: - any1_val = inner - if match_ok: - if any1_val is not None: - return dtypes.list_type(any1_val) - else: - return None - - raise TypeError( - f"Could not find matching signature for array_concat with argument types: {[str(t) for t in args]}" - ) - - -_ARRAY_CONCAT_OP = googlesql.GoogleSqlScalarOp( - "ARRAY_CONCAT", - args=(googlesql.ArgSpec(), googlesql.ArgSpec()), - signature=_ARRAY_CONCAT_SIG, -) - - -def _ARRAY_FIRST_SIG(*args): - # Pad args with None to match max expected args - args = args + (None,) * (1 - len(args)) - # Try matching impl 0 - any1_val = None - match_ok = True - if match_ok and args[0] is not None: - if not dtypes.is_array_like(args[0]): - match_ok = False - else: - inner = dtypes.get_array_inner_type(args[0]) - if any1_val is not None: - try: - any1_val = dtypes.coerce_to_common(any1_val, inner) - except TypeError: - match_ok = False - else: - any1_val = inner - if match_ok: - return any1_val - - raise TypeError( - f"Could not find matching signature for array_first with argument types: {[str(t) for t in args]}" - ) - - -_ARRAY_FIRST_OP = googlesql.GoogleSqlScalarOp( - "ARRAY_FIRST", - args=(googlesql.ArgSpec(),), - signature=_ARRAY_FIRST_SIG, -) - - -def _ARRAY_FIRST_N_SIG(*args): - # Pad args with None to match max expected args - args = args + (None,) * (2 - len(args)) - # Try matching impl 0 - any1_val = None - match_ok = True - if match_ok and args[0] is not None: - if not dtypes.is_array_like(args[0]): - match_ok = False - else: - inner = dtypes.get_array_inner_type(args[0]) - if any1_val is not None: - try: - any1_val = dtypes.coerce_to_common(any1_val, inner) - except TypeError: - match_ok = False - else: - any1_val = inner - if match_ok and args[1] is not None: - try: - if dtypes.coerce_to_common(args[1], dtypes.INT_DTYPE) != dtypes.INT_DTYPE: - match_ok = False - except TypeError: - match_ok = False - if match_ok: - if any1_val is not None: - return dtypes.list_type(any1_val) - else: - return None - - raise TypeError( - f"Could not find matching signature for array_first_n with argument types: {[str(t) for t in args]}" - ) - - -_ARRAY_FIRST_N_OP = googlesql.GoogleSqlScalarOp( - "ARRAY_FIRST_N", - args=(googlesql.ArgSpec(), googlesql.ArgSpec()), - signature=_ARRAY_FIRST_N_SIG, -) -_ARRAY_INCLUDES_OP = googlesql.GoogleSqlScalarOp( - "ARRAY_INCLUDES", - args=(googlesql.ArgSpec(), googlesql.ArgSpec()), - signature=lambda *args: dtypes.BOOL_DTYPE, -) -_ARRAY_INCLUDES_ALL_OP = googlesql.GoogleSqlScalarOp( - "ARRAY_INCLUDES_ALL", - args=(googlesql.ArgSpec(), googlesql.ArgSpec()), - signature=lambda *args: dtypes.BOOL_DTYPE, -) -_ARRAY_INCLUDES_ANY_OP = googlesql.GoogleSqlScalarOp( - "ARRAY_INCLUDES_ANY", - args=(googlesql.ArgSpec(), googlesql.ArgSpec()), - signature=lambda *args: dtypes.BOOL_DTYPE, -) -_ARRAY_IS_DISTINCT_OP = googlesql.GoogleSqlScalarOp( - "ARRAY_IS_DISTINCT", - args=(googlesql.ArgSpec(),), - signature=lambda *args: dtypes.BOOL_DTYPE, -) - - -def _ARRAY_LAST_SIG(*args): - # Pad args with None to match max expected args - args = args + (None,) * (1 - len(args)) - # Try matching impl 0 - any1_val = None - match_ok = True - if match_ok and args[0] is not None: - if not dtypes.is_array_like(args[0]): - match_ok = False - else: - inner = dtypes.get_array_inner_type(args[0]) - if any1_val is not None: - try: - any1_val = dtypes.coerce_to_common(any1_val, inner) - except TypeError: - match_ok = False - else: - any1_val = inner - if match_ok: - return any1_val - - raise TypeError( - f"Could not find matching signature for array_last with argument types: {[str(t) for t in args]}" - ) - - -_ARRAY_LAST_OP = googlesql.GoogleSqlScalarOp( - "ARRAY_LAST", - args=(googlesql.ArgSpec(),), - signature=_ARRAY_LAST_SIG, -) -_ARRAY_LENGTH_OP = googlesql.GoogleSqlScalarOp( - "ARRAY_LENGTH", - args=(googlesql.ArgSpec(),), - signature=lambda *args: dtypes.INT_DTYPE, -) - - -def _ARRAY_REVERSE_SIG(*args): - # Pad args with None to match max expected args - args = args + (None,) * (1 - len(args)) - # Try matching impl 0 - any1_val = None - match_ok = True - if match_ok and args[0] is not None: - if not dtypes.is_array_like(args[0]): - match_ok = False - else: - inner = dtypes.get_array_inner_type(args[0]) - if any1_val is not None: - try: - any1_val = dtypes.coerce_to_common(any1_val, inner) - except TypeError: - match_ok = False - else: - any1_val = inner - if match_ok: - if any1_val is not None: - return dtypes.list_type(any1_val) - else: - return None - - raise TypeError( - f"Could not find matching signature for array_reverse with argument types: {[str(t) for t in args]}" - ) - - -_ARRAY_REVERSE_OP = googlesql.GoogleSqlScalarOp( - "ARRAY_REVERSE", - args=(googlesql.ArgSpec(),), - signature=_ARRAY_REVERSE_SIG, -) - - -def _ARRAY_SLICE_SIG(*args): - # Pad args with None to match max expected args - args = args + (None,) * (3 - len(args)) - # Try matching impl 0 - any1_val = None - match_ok = True - if match_ok and args[0] is not None: - if not dtypes.is_array_like(args[0]): - match_ok = False - else: - inner = dtypes.get_array_inner_type(args[0]) - if any1_val is not None: - try: - any1_val = dtypes.coerce_to_common(any1_val, inner) - except TypeError: - match_ok = False - else: - any1_val = inner - if match_ok and args[1] is not None: - try: - if dtypes.coerce_to_common(args[1], dtypes.INT_DTYPE) != dtypes.INT_DTYPE: - match_ok = False - except TypeError: - match_ok = False - if match_ok and args[2] is not None: - try: - if dtypes.coerce_to_common(args[2], dtypes.INT_DTYPE) != dtypes.INT_DTYPE: - match_ok = False - except TypeError: - match_ok = False - if match_ok: - if any1_val is not None: - return dtypes.list_type(any1_val) - else: - return None - - raise TypeError( - f"Could not find matching signature for array_slice with argument types: {[str(t) for t in args]}" - ) - - -_ARRAY_SLICE_OP = googlesql.GoogleSqlScalarOp( - "ARRAY_SLICE", - args=(googlesql.ArgSpec(), googlesql.ArgSpec(), googlesql.ArgSpec()), - signature=_ARRAY_SLICE_SIG, -) - - -def _ARRAY_TO_STRING_SIG(*args): - # Pad args with None to match max expected args - args = args + (None,) * (3 - len(args)) - # Try matching impl 0 - match_ok = True - if match_ok and args[0] is not None: - if not dtypes.is_array_like(args[0]): - match_ok = False - else: - inner = dtypes.get_array_inner_type(args[0]) - try: - if ( - dtypes.coerce_to_common(inner, dtypes.STRING_DTYPE) - != dtypes.STRING_DTYPE - ): - match_ok = False - except TypeError: - match_ok = False - if match_ok and args[1] is not None: - try: - if ( - dtypes.coerce_to_common(args[1], dtypes.STRING_DTYPE) - != dtypes.STRING_DTYPE - ): - match_ok = False - except TypeError: - match_ok = False - if match_ok and args[2] is not None: - try: - if ( - dtypes.coerce_to_common(args[2], dtypes.STRING_DTYPE) - != dtypes.STRING_DTYPE - ): - match_ok = False - except TypeError: - match_ok = False - if match_ok: - return dtypes.STRING_DTYPE - - # Try matching impl 1 - match_ok = True - if match_ok and args[0] is not None: - if not dtypes.is_array_like(args[0]): - match_ok = False - else: - inner = dtypes.get_array_inner_type(args[0]) - try: - if ( - dtypes.coerce_to_common(inner, dtypes.BYTES_DTYPE) - != dtypes.BYTES_DTYPE - ): - match_ok = False - except TypeError: - match_ok = False - if match_ok and args[1] is not None: - try: - if ( - dtypes.coerce_to_common(args[1], dtypes.BYTES_DTYPE) - != dtypes.BYTES_DTYPE - ): - match_ok = False - except TypeError: - match_ok = False - if match_ok and args[2] is not None: - try: - if ( - dtypes.coerce_to_common(args[2], dtypes.BYTES_DTYPE) - != dtypes.BYTES_DTYPE - ): - match_ok = False - except TypeError: - match_ok = False - if match_ok: - return dtypes.BYTES_DTYPE - - raise TypeError( - f"Could not find matching signature for array_to_string with argument types: {[str(t) for t in args]}" - ) - - -_ARRAY_TO_STRING_OP = googlesql.GoogleSqlScalarOp( - "ARRAY_TO_STRING", - args=(googlesql.ArgSpec(), googlesql.ArgSpec(), googlesql.ArgSpec(optional=True)), - signature=_ARRAY_TO_STRING_SIG, -) - - -def _FLATTEN_SIG(*args): - # Pad args with None to match max expected args - args = args + (None,) * (2 - len(args)) - # Try matching impl 0 - any1_val = None - match_ok = True - if match_ok and args[0] is not None: - if not dtypes.is_array_like(args[0]): - match_ok = False - else: - inner = dtypes.get_array_inner_type(args[0]) - if any1_val is not None: - try: - any1_val = dtypes.coerce_to_common(any1_val, inner) - except TypeError: - match_ok = False - else: - any1_val = inner - if match_ok and args[1] is not None: - try: - if dtypes.coerce_to_common(args[1], dtypes.INT_DTYPE) != dtypes.INT_DTYPE: - match_ok = False - except TypeError: - match_ok = False - if match_ok: - if any1_val is not None: - return dtypes.list_type(any1_val) - else: - return None - - raise TypeError( - f"Could not find matching signature for flatten with argument types: {[str(t) for t in args]}" - ) - - -_FLATTEN_OP = googlesql.GoogleSqlScalarOp( - "FLATTEN", - args=(googlesql.ArgSpec(), googlesql.ArgSpec(arg_name="depth", optional=True)), - signature=_FLATTEN_SIG, -) - - -def _GENERATE_ARRAY_SIG(*args): - # Pad args with None to match max expected args - args = args + (None,) * (3 - len(args)) - # Try matching impl 0 - match_ok = True - if match_ok and args[0] is not None: - try: - if dtypes.coerce_to_common(args[0], dtypes.INT_DTYPE) != dtypes.INT_DTYPE: - match_ok = False - except TypeError: - match_ok = False - if match_ok and args[1] is not None: - try: - if dtypes.coerce_to_common(args[1], dtypes.INT_DTYPE) != dtypes.INT_DTYPE: - match_ok = False - except TypeError: - match_ok = False - if match_ok and args[2] is not None: - try: - if dtypes.coerce_to_common(args[2], dtypes.INT_DTYPE) != dtypes.INT_DTYPE: - match_ok = False - except TypeError: - match_ok = False - if match_ok: - return dtypes.list_type(dtypes.INT_DTYPE) - - # Try matching impl 1 - match_ok = True - if match_ok and args[0] is not None: - try: - if ( - dtypes.coerce_to_common(args[0], dtypes.NUMERIC_DTYPE) - != dtypes.NUMERIC_DTYPE - ): - match_ok = False - except TypeError: - match_ok = False - if match_ok and args[1] is not None: - try: - if ( - dtypes.coerce_to_common(args[1], dtypes.NUMERIC_DTYPE) - != dtypes.NUMERIC_DTYPE - ): - match_ok = False - except TypeError: - match_ok = False - if match_ok and args[2] is not None: - try: - if ( - dtypes.coerce_to_common(args[2], dtypes.NUMERIC_DTYPE) - != dtypes.NUMERIC_DTYPE - ): - match_ok = False - except TypeError: - match_ok = False - if match_ok: - return dtypes.list_type(dtypes.NUMERIC_DTYPE) - - # Try matching impl 2 - match_ok = True - if match_ok and args[0] is not None: - try: - if ( - dtypes.coerce_to_common(args[0], dtypes.FLOAT_DTYPE) - != dtypes.FLOAT_DTYPE - ): - match_ok = False - except TypeError: - match_ok = False - if match_ok and args[1] is not None: - try: - if ( - dtypes.coerce_to_common(args[1], dtypes.FLOAT_DTYPE) - != dtypes.FLOAT_DTYPE - ): - match_ok = False - except TypeError: - match_ok = False - if match_ok and args[2] is not None: - try: - if ( - dtypes.coerce_to_common(args[2], dtypes.FLOAT_DTYPE) - != dtypes.FLOAT_DTYPE - ): - match_ok = False - except TypeError: - match_ok = False - if match_ok: - return dtypes.list_type(dtypes.FLOAT_DTYPE) - - raise TypeError( - f"Could not find matching signature for generate_array with argument types: {[str(t) for t in args]}" - ) - - -_GENERATE_ARRAY_OP = googlesql.GoogleSqlScalarOp( - "GENERATE_ARRAY", - args=(googlesql.ArgSpec(), googlesql.ArgSpec(), googlesql.ArgSpec(optional=True)), - signature=_GENERATE_ARRAY_SIG, -) - - -def array_concat( - array_expression_1: Union[ - series.Series, - bigframes.core.col.Expression, - Union[Any, Literal[sentinels.Sentinel.ARGUMENT_DEFAULT]], - ], - array_expression_2: Union[ - series.Series, - bigframes.core.col.Expression, - Union[Any, Literal[sentinels.Sentinel.ARGUMENT_DEFAULT]], - ], -) -> Union[series.Series, bigframes.core.col.Expression]: - """Concatenates one or more arrays with the same element type into a single array.""" - return bigframes.core.googlesql.apply_googlesql_scalar_op( - _ARRAY_CONCAT_OP, - array_expression_1, - array_expression_2, - ) - - -def array_first( - array_expression: Union[ - series.Series, - bigframes.core.col.Expression, - Union[Any, Literal[sentinels.Sentinel.ARGUMENT_DEFAULT]], - ], -) -> Union[series.Series, bigframes.core.col.Expression]: - """Takes an array and returns the first element in the array.""" - return bigframes.core.googlesql.apply_googlesql_scalar_op( - _ARRAY_FIRST_OP, - array_expression, - ) - - -def array_first_n( - input_array: Union[ - series.Series, - bigframes.core.col.Expression, - Union[Any, Literal[sentinels.Sentinel.ARGUMENT_DEFAULT]], - ], - n: Union[ - series.Series, - bigframes.core.col.Expression, - Union[Literal[sentinels.Sentinel.ARGUMENT_DEFAULT], int], - ], -) -> Union[series.Series, bigframes.core.col.Expression]: - """Returns a prefix of `input_array` consisting of the first `n` elements.""" - return bigframes.core.googlesql.apply_googlesql_scalar_op( - _ARRAY_FIRST_N_OP, - input_array, - n, - ) - - -def array_includes( - array_to_search: Union[ - series.Series, - bigframes.core.col.Expression, - Union[Any, Literal[sentinels.Sentinel.ARGUMENT_DEFAULT]], - ], - search_value: Union[ - series.Series, - bigframes.core.col.Expression, - Union[Any, Literal[sentinels.Sentinel.ARGUMENT_DEFAULT]], - ], -) -> Union[series.Series, bigframes.core.col.Expression]: - """Takes an array and returns `TRUE` if there is an element in the array that is equal to the search_value.""" - return bigframes.core.googlesql.apply_googlesql_scalar_op( - _ARRAY_INCLUDES_OP, - array_to_search, - search_value, - ) - - -def array_includes_all( - array_to_search: Union[ - series.Series, - bigframes.core.col.Expression, - Union[Any, Literal[sentinels.Sentinel.ARGUMENT_DEFAULT]], - ], - search_values: Union[ - series.Series, - bigframes.core.col.Expression, - Union[Any, Literal[sentinels.Sentinel.ARGUMENT_DEFAULT]], - ], -) -> Union[series.Series, bigframes.core.col.Expression]: - """Takes an array to search and an array of search values. Returns `TRUE` if all search values are in the array to search, otherwise returns `FALSE`.""" - return bigframes.core.googlesql.apply_googlesql_scalar_op( - _ARRAY_INCLUDES_ALL_OP, - array_to_search, - search_values, - ) - - -def array_includes_any( - array_to_search: Union[ - series.Series, - bigframes.core.col.Expression, - Union[Any, Literal[sentinels.Sentinel.ARGUMENT_DEFAULT]], - ], - search_values: Union[ - series.Series, - bigframes.core.col.Expression, - Union[Any, Literal[sentinels.Sentinel.ARGUMENT_DEFAULT]], - ], -) -> Union[series.Series, bigframes.core.col.Expression]: - """Takes an array to search and an array of search values. Returns `TRUE` if any search values are in the array to search, otherwise returns `FALSE`.""" - return bigframes.core.googlesql.apply_googlesql_scalar_op( - _ARRAY_INCLUDES_ANY_OP, - array_to_search, - search_values, - ) - - -def array_is_distinct( - array_expression: Union[ - series.Series, - bigframes.core.col.Expression, - Union[Any, Literal[sentinels.Sentinel.ARGUMENT_DEFAULT]], - ], -) -> Union[series.Series, bigframes.core.col.Expression]: - """Returns `TRUE` if the array contains no repeated elements, using the same equality comparison logic as `SELECT DISTINCT`.""" - return bigframes.core.googlesql.apply_googlesql_scalar_op( - _ARRAY_IS_DISTINCT_OP, - array_expression, - ) - - -def array_last( - array_expression: Union[ - series.Series, - bigframes.core.col.Expression, - Union[Any, Literal[sentinels.Sentinel.ARGUMENT_DEFAULT]], - ], -) -> Union[series.Series, bigframes.core.col.Expression]: - """Takes an array and returns the last element in the array.""" - return bigframes.core.googlesql.apply_googlesql_scalar_op( - _ARRAY_LAST_OP, - array_expression, - ) - - -def array_length( - series: Union[ - series.Series, - bigframes.core.col.Expression, - Union[Any, Literal[sentinels.Sentinel.ARGUMENT_DEFAULT]], - ], -) -> Union[series.Series, bigframes.core.col.Expression]: - """Compute the length of each array element in the Series. - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> import bigframes.bigquery as bbq - - >>> s = bpd.Series([[1, 2, 8, 3], [], [3, 4]]) - >>> bbq.array_length(s) - 0 4 - 1 0 - 2 2 - dtype: Int64 - - You can call this function using the Series `bigquery` accessor. - - >>> s.bigquery.array_length() - 0 4 - 1 0 - 2 2 - dtype: Int64 - - You can also use this accessor on a pandas Series after importing bigframes. - - >>> import bigframes - >>> import pandas as pd - >>> ps = pd.Series([[1, 2, 8, 3], [], [3, 4]]) - >>> ps.bigquery.array_length() - 0 4 - 1 0 - 2 2 - dtype: Int64 - - You can also apply this function directly to Series using `apply`. - - >>> s.apply(bbq.array_length, by_row=False) - 0 4 - 1 0 - 2 2 - dtype: Int64 - - Args: - series (bigframes.series.Series): A Series with array columns. - - Returns: - bigframes.series.Series: A Series of integer values indicating - the length of each element in the Series. - """ - return bigframes.core.googlesql.apply_googlesql_scalar_op( - _ARRAY_LENGTH_OP, - series, - ) - - -def array_reverse( - value: Union[ - series.Series, - bigframes.core.col.Expression, - Union[Any, Literal[sentinels.Sentinel.ARGUMENT_DEFAULT]], - ], -) -> Union[series.Series, bigframes.core.col.Expression]: - """Returns the input `ARRAY` with elements in reverse order.""" - return bigframes.core.googlesql.apply_googlesql_scalar_op( - _ARRAY_REVERSE_OP, - value, - ) - - -def array_slice( - array_to_slice: Union[ - series.Series, - bigframes.core.col.Expression, - Union[Any, Literal[sentinels.Sentinel.ARGUMENT_DEFAULT]], - ], - start_offset: Union[ - series.Series, - bigframes.core.col.Expression, - Union[Literal[sentinels.Sentinel.ARGUMENT_DEFAULT], int], - ], - end_offset: Union[ - series.Series, - bigframes.core.col.Expression, - Union[Literal[sentinels.Sentinel.ARGUMENT_DEFAULT], int], - ], -) -> Union[series.Series, bigframes.core.col.Expression]: - """Returns an array containing zero or more consecutive elements from the input array.""" - return bigframes.core.googlesql.apply_googlesql_scalar_op( - _ARRAY_SLICE_OP, - array_to_slice, - start_offset, - end_offset, - ) - - -def array_to_string( - series: Union[ - series.Series, - bigframes.core.col.Expression, - Union[Any, Literal[sentinels.Sentinel.ARGUMENT_DEFAULT]], - ], - delimiter: Union[ - series.Series, - bigframes.core.col.Expression, - Union[Literal[sentinels.Sentinel.ARGUMENT_DEFAULT], bytes, str], - ], - null_text: Union[ - series.Series, - bigframes.core.col.Expression, - Union[Literal[sentinels.Sentinel.ARGUMENT_DEFAULT], bytes, str], - ] = sentinels.Sentinel.ARGUMENT_DEFAULT, -) -> Union[series.Series, bigframes.core.col.Expression]: - """Converts array elements within a Series into delimited strings. - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> import bigframes.bigquery as bbq - - >>> s = bpd.Series([["H", "i", "!"], ["Hello", "World"], np.nan, [], ["Hi"]]) - >>> bbq.array_to_string(s, delimiter=", ") - 0 H, i, ! - 1 Hello, World - 2 - 3 - 4 Hi - dtype: string - - You can call this function using the Series `bigquery` accessor. - - >>> s.bigquery.array_to_string(delimiter=", ") - 0 H, i, ! - 1 Hello, World - 2 - 3 - 4 Hi - dtype: string - - You can also use this accessor on a pandas Series after importing bigframes. - - >>> import bigframes - >>> import pandas as pd - >>> ps = pd.Series([["H", "i", "!"], ["Hello", "World"], None, [], ["Hi"]]) - >>> ps.bigquery.array_to_string(delimiter=", ") - 0 H, i, ! - 1 Hello, World - 2 - 3 - 4 Hi - dtype: string - - Args: - series (bigframes.series.Series): A Series containing arrays. - delimiter (str): The string used to separate array elements. - null_text (str, optional): The string to replace any NULL values in the array with. - - Returns: - bigframes.series.Series: A Series containing delimited strings. - """ - return bigframes.core.googlesql.apply_googlesql_scalar_op( - _ARRAY_TO_STRING_OP, - series, - delimiter, - null_text, - ) - - -def flatten( - array_to_flatten: Union[ - series.Series, - bigframes.core.col.Expression, - Union[Any, Literal[sentinels.Sentinel.ARGUMENT_DEFAULT]], - ], - depth: Union[ - series.Series, - bigframes.core.col.Expression, - Union[Literal[sentinels.Sentinel.ARGUMENT_DEFAULT], int], - ] = sentinels.Sentinel.ARGUMENT_DEFAULT, -) -> Union[series.Series, bigframes.core.col.Expression]: - """Takes an array of nested data and flattens a specific part of it into a single, flat array with the [array elements field access operator][array-el-field-operator]. Returns `NULL` if the input value is `NULL`.""" - return bigframes.core.googlesql.apply_googlesql_scalar_op( - _FLATTEN_OP, - array_to_flatten, - depth, - ) - - -def generate_array( - start_expression: Union[ - series.Series, - bigframes.core.col.Expression, - Union[ - Literal[sentinels.Sentinel.ARGUMENT_DEFAULT], decimal.Decimal, float, int - ], - ], - end_expression: Union[ - series.Series, - bigframes.core.col.Expression, - Union[ - Literal[sentinels.Sentinel.ARGUMENT_DEFAULT], decimal.Decimal, float, int - ], - ], - step_expression: Union[ - series.Series, - bigframes.core.col.Expression, - Union[ - Literal[sentinels.Sentinel.ARGUMENT_DEFAULT], decimal.Decimal, float, int - ], - ] = sentinels.Sentinel.ARGUMENT_DEFAULT, -) -> Union[series.Series, bigframes.core.col.Expression]: - """Returns an array of values. The `start_expression` and `end_expression` parameters determine the inclusive start and end of the array.""" - return bigframes.core.googlesql.apply_googlesql_scalar_op( - _GENERATE_ARRAY_OP, - start_expression, - end_expression, - step_expression, - ) diff --git a/bigframes/operations/googlesql/global_namespace/bit.py b/bigframes/operations/googlesql/global_namespace/bit.py deleted file mode 100644 index e0c22dfc299..00000000000 --- a/bigframes/operations/googlesql/global_namespace/bit.py +++ /dev/null @@ -1,48 +0,0 @@ -# Copyright 2026 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# -# DO NOT MODIFY THIS FILE DIRECTLY. -# This file was generated from: scripts/data/sql-functions/global_namespace/bit.yaml -# by the script: scripts/generate_bigframes_bigquery.py - -from __future__ import annotations - -from typing import Any, Literal, Union - -import bigframes.core.col -import bigframes.core.googlesql -import bigframes.core.sentinels as sentinels -import bigframes.series as series -from bigframes import dtypes -from bigframes.operations import googlesql - -_BIT_COUNT_OP = googlesql.GoogleSqlScalarOp( - "BIT_COUNT", - args=(googlesql.ArgSpec(),), - signature=lambda *args: dtypes.INT_DTYPE, -) - - -def bit_count( - expression: Union[ - series.Series, - bigframes.core.col.Expression, - Union[Any, Literal[sentinels.Sentinel.ARGUMENT_DEFAULT], bytes, int], - ], -) -> Union[series.Series, bigframes.core.col.Expression]: - """The input, `expression`, must be an integer or `BYTES`. Returns the number of bits that are set in the input expression. For signed integers, this is the number of bits in two's complement form.""" - return bigframes.core.googlesql.apply_googlesql_scalar_op( - _BIT_COUNT_OP, - expression, - ) diff --git a/bigframes/operations/googlesql/global_namespace/conversion.py b/bigframes/operations/googlesql/global_namespace/conversion.py deleted file mode 100644 index cea4e45d836..00000000000 --- a/bigframes/operations/googlesql/global_namespace/conversion.py +++ /dev/null @@ -1,193 +0,0 @@ -# Copyright 2026 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# -# DO NOT MODIFY THIS FILE DIRECTLY. -# This file was generated from: scripts/data/sql-functions/global_namespace/conversion.yaml -# by the script: scripts/generate_bigframes_bigquery.py - -from __future__ import annotations - -import datetime -from typing import Literal, Union - -import bigframes.core.col -import bigframes.core.googlesql -import bigframes.core.sentinels as sentinels -import bigframes.series as series -from bigframes import dtypes -from bigframes.operations import googlesql - -_BOOL_OP = googlesql.GoogleSqlScalarOp( - "BOOL", - args=(googlesql.ArgSpec(),), - signature=lambda *args: dtypes.BOOL_DTYPE, -) -_DOUBLE_OP = googlesql.GoogleSqlScalarOp( - "DOUBLE", - args=( - googlesql.ArgSpec(), - googlesql.ArgSpec(arg_name="wide_number_mode", optional=True), - ), - signature=lambda *args: dtypes.FLOAT_DTYPE, -) -_FLOAT64_OP = googlesql.GoogleSqlScalarOp( - "FLOAT64", - args=( - googlesql.ArgSpec(), - googlesql.ArgSpec(arg_name="wide_number_mode", optional=True), - ), - signature=lambda *args: dtypes.FLOAT_DTYPE, -) -_INT64_OP = googlesql.GoogleSqlScalarOp( - "INT64", - args=(googlesql.ArgSpec(),), - signature=lambda *args: dtypes.INT_DTYPE, -) -_PARSE_BIGNUMERIC_OP = googlesql.GoogleSqlScalarOp( - "PARSE_BIGNUMERIC", - args=(googlesql.ArgSpec(),), - signature=lambda *args: dtypes.BIGNUMERIC_DTYPE, -) -_PARSE_NUMERIC_OP = googlesql.GoogleSqlScalarOp( - "PARSE_NUMERIC", - args=(googlesql.ArgSpec(),), - signature=lambda *args: dtypes.NUMERIC_DTYPE, -) -_STRING_OP = googlesql.GoogleSqlScalarOp( - "STRING", - args=(googlesql.ArgSpec(), googlesql.ArgSpec(optional=True)), - signature=lambda *args: dtypes.STRING_DTYPE, -) - - -def bool_( - json_string_expression: Union[ - series.Series, - bigframes.core.col.Expression, - Union[Literal[sentinels.Sentinel.ARGUMENT_DEFAULT], str], - ], -) -> Union[series.Series, bigframes.core.col.Expression]: - """Converts a JSON boolean to a SQL BOOL value.""" - return bigframes.core.googlesql.apply_googlesql_scalar_op( - _BOOL_OP, - json_string_expression, - ) - - -def double( - json_string_expression: Union[ - series.Series, - bigframes.core.col.Expression, - Union[Literal[sentinels.Sentinel.ARGUMENT_DEFAULT], str], - ], - wide_number_mode: Union[ - series.Series, - bigframes.core.col.Expression, - Union[Literal[sentinels.Sentinel.ARGUMENT_DEFAULT], str], - ] = sentinels.Sentinel.ARGUMENT_DEFAULT, -) -> Union[series.Series, bigframes.core.col.Expression]: - """Converts a JSON number to a SQL FLOAT64 value.""" - return bigframes.core.googlesql.apply_googlesql_scalar_op( - _DOUBLE_OP, - json_string_expression, - wide_number_mode, - ) - - -def float64( - json_string_expression: Union[ - series.Series, - bigframes.core.col.Expression, - Union[Literal[sentinels.Sentinel.ARGUMENT_DEFAULT], str], - ], - wide_number_mode: Union[ - series.Series, - bigframes.core.col.Expression, - Union[Literal[sentinels.Sentinel.ARGUMENT_DEFAULT], str], - ] = sentinels.Sentinel.ARGUMENT_DEFAULT, -) -> Union[series.Series, bigframes.core.col.Expression]: - """Converts a JSON number to a SQL FLOAT64 value.""" - return bigframes.core.googlesql.apply_googlesql_scalar_op( - _FLOAT64_OP, - json_string_expression, - wide_number_mode, - ) - - -def int64( - json_string_expression: Union[ - series.Series, - bigframes.core.col.Expression, - Union[Literal[sentinels.Sentinel.ARGUMENT_DEFAULT], str], - ], -) -> Union[series.Series, bigframes.core.col.Expression]: - """Converts a JSON number to a SQL INT64 value.""" - return bigframes.core.googlesql.apply_googlesql_scalar_op( - _INT64_OP, - json_string_expression, - ) - - -def parse_bignumeric( - string_expression: Union[ - series.Series, - bigframes.core.col.Expression, - Union[Literal[sentinels.Sentinel.ARGUMENT_DEFAULT], str], - ], -) -> Union[series.Series, bigframes.core.col.Expression]: - """Converts a STRING to a BIGNUMERIC value.""" - return bigframes.core.googlesql.apply_googlesql_scalar_op( - _PARSE_BIGNUMERIC_OP, - string_expression, - ) - - -def parse_numeric( - string_expression: Union[ - series.Series, - bigframes.core.col.Expression, - Union[Literal[sentinels.Sentinel.ARGUMENT_DEFAULT], str], - ], -) -> Union[series.Series, bigframes.core.col.Expression]: - """Converts a STRING to a NUMERIC value.""" - return bigframes.core.googlesql.apply_googlesql_scalar_op( - _PARSE_NUMERIC_OP, - string_expression, - ) - - -def string( - expression: Union[ - series.Series, - bigframes.core.col.Expression, - Union[ - Literal[sentinels.Sentinel.ARGUMENT_DEFAULT], - datetime.date, - datetime.datetime, - datetime.time, - str, - ], - ], - timezone: Union[ - series.Series, - bigframes.core.col.Expression, - Union[Literal[sentinels.Sentinel.ARGUMENT_DEFAULT], str], - ] = sentinels.Sentinel.ARGUMENT_DEFAULT, -) -> Union[series.Series, bigframes.core.col.Expression]: - """Converts a value to a STRING value.""" - return bigframes.core.googlesql.apply_googlesql_scalar_op( - _STRING_OP, - expression, - timezone, - ) diff --git a/bigframes/operations/googlesql/global_namespace/date.py b/bigframes/operations/googlesql/global_namespace/date.py deleted file mode 100644 index b6cfc9722b5..00000000000 --- a/bigframes/operations/googlesql/global_namespace/date.py +++ /dev/null @@ -1,412 +0,0 @@ -# Copyright 2026 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# -# DO NOT MODIFY THIS FILE DIRECTLY. -# This file was generated from: scripts/data/sql-functions/global_namespace/date.yaml -# by the script: scripts/generate_bigframes_bigquery.py - -from __future__ import annotations - -import datetime -from typing import Any, Literal, Union - -import bigframes.core.col -import bigframes.core.googlesql -import bigframes.core.sentinels as sentinels -import bigframes.series as series -from bigframes import dtypes -from bigframes.operations import googlesql - -_CURRENT_DATE_OP = googlesql.GoogleSqlScalarOp( - "CURRENT_DATE", - args=(googlesql.ArgSpec(optional=True),), - signature=lambda *args: dtypes.DATE_DTYPE, -) -_DATE_OP = googlesql.GoogleSqlScalarOp( - "DATE", - args=( - googlesql.ArgSpec(optional=True), - googlesql.ArgSpec(optional=True), - googlesql.ArgSpec(optional=True), - googlesql.ArgSpec(optional=True), - googlesql.ArgSpec(optional=True), - ), - signature=lambda *args: dtypes.DATE_DTYPE, -) -_DATE_ADD_OP = googlesql.GoogleSqlScalarOp( - "DATE_ADD", - args=(googlesql.ArgSpec(), googlesql.ArgSpec(), googlesql.ArgSpec()), - signature=lambda *args: dtypes.DATE_DTYPE, -) -_DATE_DIFF_OP = googlesql.GoogleSqlScalarOp( - "DATE_DIFF", - args=(googlesql.ArgSpec(), googlesql.ArgSpec(), googlesql.ArgSpec()), - signature=lambda *args: dtypes.INT_DTYPE, -) -_DATE_FROM_UNIX_DATE_OP = googlesql.GoogleSqlScalarOp( - "DATE_FROM_UNIX_DATE", - args=(googlesql.ArgSpec(),), - signature=lambda *args: dtypes.DATE_DTYPE, -) -_DATE_SUB_OP = googlesql.GoogleSqlScalarOp( - "DATE_SUB", - args=(googlesql.ArgSpec(), googlesql.ArgSpec(), googlesql.ArgSpec()), - signature=lambda *args: dtypes.DATE_DTYPE, -) -_DATE_TRUNC_OP = googlesql.GoogleSqlScalarOp( - "DATE_TRUNC", - args=(googlesql.ArgSpec(), googlesql.ArgSpec()), - signature=lambda *args: dtypes.DATE_DTYPE, -) -_EXTRACT_OP = googlesql.GoogleSqlScalarOp( - "EXTRACT", - args=(googlesql.ArgSpec(), googlesql.ArgSpec(), googlesql.ArgSpec(optional=True)), - signature=lambda *args: dtypes.INT_DTYPE, -) -_FORMAT_DATE_OP = googlesql.GoogleSqlScalarOp( - "FORMAT_DATE", - args=(googlesql.ArgSpec(), googlesql.ArgSpec()), - signature=lambda *args: dtypes.STRING_DTYPE, -) -_GENERATE_DATE_ARRAY_OP = googlesql.GoogleSqlScalarOp( - "GENERATE_DATE_ARRAY", - args=( - googlesql.ArgSpec(), - googlesql.ArgSpec(), - googlesql.ArgSpec(optional=True), - googlesql.ArgSpec(optional=True), - ), - signature=lambda *args: dtypes.list_type(dtypes.DATE_DTYPE), -) -_LAST_DAY_OP = googlesql.GoogleSqlScalarOp( - "LAST_DAY", - args=(googlesql.ArgSpec(), googlesql.ArgSpec(optional=True)), - signature=lambda *args: dtypes.DATE_DTYPE, -) -_PARSE_DATE_OP = googlesql.GoogleSqlScalarOp( - "PARSE_DATE", - args=(googlesql.ArgSpec(), googlesql.ArgSpec()), - signature=lambda *args: dtypes.DATE_DTYPE, -) -_UNIX_DATE_OP = googlesql.GoogleSqlScalarOp( - "UNIX_DATE", - args=(googlesql.ArgSpec(),), - signature=lambda *args: dtypes.INT_DTYPE, -) - - -def current_date( - time_zone_expression: Union[ - series.Series, - bigframes.core.col.Expression, - Union[Literal[sentinels.Sentinel.ARGUMENT_DEFAULT], str], - ] = sentinels.Sentinel.ARGUMENT_DEFAULT, -) -> Union[series.Series, bigframes.core.col.Expression]: - """Returns the current date as a DATE object. Parentheses are optional when called with no arguments.""" - return bigframes.core.googlesql.apply_googlesql_scalar_op( - _CURRENT_DATE_OP, - time_zone_expression, - ) - - -def date( - expression: Union[ - series.Series, - bigframes.core.col.Expression, - Union[ - Literal[sentinels.Sentinel.ARGUMENT_DEFAULT], - datetime.date, - datetime.datetime, - str, - ], - ] = sentinels.Sentinel.ARGUMENT_DEFAULT, - time_zone_expression: Union[ - series.Series, - bigframes.core.col.Expression, - Union[Literal[sentinels.Sentinel.ARGUMENT_DEFAULT], str], - ] = sentinels.Sentinel.ARGUMENT_DEFAULT, - year: Union[ - series.Series, - bigframes.core.col.Expression, - Union[Literal[sentinels.Sentinel.ARGUMENT_DEFAULT], int], - ] = sentinels.Sentinel.ARGUMENT_DEFAULT, - month: Union[ - series.Series, - bigframes.core.col.Expression, - Union[Literal[sentinels.Sentinel.ARGUMENT_DEFAULT], int], - ] = sentinels.Sentinel.ARGUMENT_DEFAULT, - day: Union[ - series.Series, - bigframes.core.col.Expression, - Union[Literal[sentinels.Sentinel.ARGUMENT_DEFAULT], int], - ] = sentinels.Sentinel.ARGUMENT_DEFAULT, -) -> Union[series.Series, bigframes.core.col.Expression]: - """Constructs or extracts a date.""" - return bigframes.core.googlesql.apply_googlesql_scalar_op( - _DATE_OP, - expression, - time_zone_expression, - year, - month, - day, - ) - - -def date_add( - date_expression: Union[ - series.Series, - bigframes.core.col.Expression, - Union[Literal[sentinels.Sentinel.ARGUMENT_DEFAULT], datetime.date], - ], - int64_expression: Union[ - series.Series, - bigframes.core.col.Expression, - Union[Literal[sentinels.Sentinel.ARGUMENT_DEFAULT], int], - ], - date_part: Union[ - series.Series, - bigframes.core.col.Expression, - Union[Any, Literal[sentinels.Sentinel.ARGUMENT_DEFAULT]], - ], -) -> Union[series.Series, bigframes.core.col.Expression]: - """Adds a specified time interval to a DATE.""" - return bigframes.core.googlesql.apply_googlesql_scalar_op( - _DATE_ADD_OP, - date_expression, - int64_expression, - date_part, - ) - - -def date_diff( - end_date: Union[ - series.Series, - bigframes.core.col.Expression, - Union[Literal[sentinels.Sentinel.ARGUMENT_DEFAULT], datetime.date], - ], - start_date: Union[ - series.Series, - bigframes.core.col.Expression, - Union[Literal[sentinels.Sentinel.ARGUMENT_DEFAULT], datetime.date], - ], - granularity: Union[ - series.Series, - bigframes.core.col.Expression, - Union[Any, Literal[sentinels.Sentinel.ARGUMENT_DEFAULT]], - ], -) -> Union[series.Series, bigframes.core.col.Expression]: - """Gets the number of unit boundaries between two DATE values (end_date - start_date) at a particular time granularity.""" - return bigframes.core.googlesql.apply_googlesql_scalar_op( - _DATE_DIFF_OP, - end_date, - start_date, - granularity, - ) - - -def date_from_unix_date( - int64_expression: Union[ - series.Series, - bigframes.core.col.Expression, - Union[Literal[sentinels.Sentinel.ARGUMENT_DEFAULT], int], - ], -) -> Union[series.Series, bigframes.core.col.Expression]: - """Interprets an INT64 expression as the number of days since 1970-01-01.""" - return bigframes.core.googlesql.apply_googlesql_scalar_op( - _DATE_FROM_UNIX_DATE_OP, - int64_expression, - ) - - -def date_sub( - date_expression: Union[ - series.Series, - bigframes.core.col.Expression, - Union[Literal[sentinels.Sentinel.ARGUMENT_DEFAULT], datetime.date], - ], - int64_expression: Union[ - series.Series, - bigframes.core.col.Expression, - Union[Literal[sentinels.Sentinel.ARGUMENT_DEFAULT], int], - ], - date_part: Union[ - series.Series, - bigframes.core.col.Expression, - Union[Any, Literal[sentinels.Sentinel.ARGUMENT_DEFAULT]], - ], -) -> Union[series.Series, bigframes.core.col.Expression]: - """Subtracts a specified time interval from a DATE.""" - return bigframes.core.googlesql.apply_googlesql_scalar_op( - _DATE_SUB_OP, - date_expression, - int64_expression, - date_part, - ) - - -def date_trunc( - date_value: Union[ - series.Series, - bigframes.core.col.Expression, - Union[Literal[sentinels.Sentinel.ARGUMENT_DEFAULT], datetime.date], - ], - granularity: Union[ - series.Series, - bigframes.core.col.Expression, - Union[Any, Literal[sentinels.Sentinel.ARGUMENT_DEFAULT]], - ], -) -> Union[series.Series, bigframes.core.col.Expression]: - """Truncates a DATE, DATETIME, or TIMESTAMP value at a particular granularity.""" - return bigframes.core.googlesql.apply_googlesql_scalar_op( - _DATE_TRUNC_OP, - date_value, - granularity, - ) - - -def extract( - date_expression: Union[ - series.Series, - bigframes.core.col.Expression, - Union[ - Literal[sentinels.Sentinel.ARGUMENT_DEFAULT], - datetime.date, - datetime.datetime, - datetime.time, - ], - ], - part: Union[ - series.Series, - bigframes.core.col.Expression, - Union[Any, Literal[sentinels.Sentinel.ARGUMENT_DEFAULT]], - ], - time_zone: Union[ - series.Series, - bigframes.core.col.Expression, - Union[Literal[sentinels.Sentinel.ARGUMENT_DEFAULT], str], - ] = sentinels.Sentinel.ARGUMENT_DEFAULT, -) -> Union[series.Series, bigframes.core.col.Expression]: - """Returns the value corresponding to the specified date part.""" - return bigframes.core.googlesql.apply_googlesql_scalar_op( - _EXTRACT_OP, - date_expression, - part, - time_zone, - ) - - -def format_date( - format_string: Union[ - series.Series, - bigframes.core.col.Expression, - Union[Literal[sentinels.Sentinel.ARGUMENT_DEFAULT], str], - ], - date_expr: Union[ - series.Series, - bigframes.core.col.Expression, - Union[Literal[sentinels.Sentinel.ARGUMENT_DEFAULT], datetime.date], - ], -) -> Union[series.Series, bigframes.core.col.Expression]: - """Formats a DATE value according to a specified format string.""" - return bigframes.core.googlesql.apply_googlesql_scalar_op( - _FORMAT_DATE_OP, - format_string, - date_expr, - ) - - -def generate_date_array( - start_date: Union[ - series.Series, - bigframes.core.col.Expression, - Union[Literal[sentinels.Sentinel.ARGUMENT_DEFAULT], datetime.date], - ], - end_date: Union[ - series.Series, - bigframes.core.col.Expression, - Union[Literal[sentinels.Sentinel.ARGUMENT_DEFAULT], datetime.date], - ], - int64_expression: Union[ - series.Series, - bigframes.core.col.Expression, - Union[Literal[sentinels.Sentinel.ARGUMENT_DEFAULT], int], - ] = sentinels.Sentinel.ARGUMENT_DEFAULT, - date_part: Union[ - series.Series, - bigframes.core.col.Expression, - Union[Any, Literal[sentinels.Sentinel.ARGUMENT_DEFAULT]], - ] = sentinels.Sentinel.ARGUMENT_DEFAULT, -) -> Union[series.Series, bigframes.core.col.Expression]: - """Generates an array of dates in a range.""" - return bigframes.core.googlesql.apply_googlesql_scalar_op( - _GENERATE_DATE_ARRAY_OP, - start_date, - end_date, - int64_expression, - date_part, - ) - - -def last_day( - date_expression: Union[ - series.Series, - bigframes.core.col.Expression, - Union[Literal[sentinels.Sentinel.ARGUMENT_DEFAULT], datetime.date], - ], - date_part: Union[ - series.Series, - bigframes.core.col.Expression, - Union[Any, Literal[sentinels.Sentinel.ARGUMENT_DEFAULT]], - ] = sentinels.Sentinel.ARGUMENT_DEFAULT, -) -> Union[series.Series, bigframes.core.col.Expression]: - """Returns the last day from a date expression. This is commonly used to return the last day of the month.""" - return bigframes.core.googlesql.apply_googlesql_scalar_op( - _LAST_DAY_OP, - date_expression, - date_part, - ) - - -def parse_date( - format_string: Union[ - series.Series, - bigframes.core.col.Expression, - Union[Literal[sentinels.Sentinel.ARGUMENT_DEFAULT], str], - ], - date_string: Union[ - series.Series, - bigframes.core.col.Expression, - Union[Literal[sentinels.Sentinel.ARGUMENT_DEFAULT], str], - ], -) -> Union[series.Series, bigframes.core.col.Expression]: - """Converts a STRING value to a DATE value.""" - return bigframes.core.googlesql.apply_googlesql_scalar_op( - _PARSE_DATE_OP, - format_string, - date_string, - ) - - -def unix_date( - date_expression: Union[ - series.Series, - bigframes.core.col.Expression, - Union[Literal[sentinels.Sentinel.ARGUMENT_DEFAULT], datetime.date], - ], -) -> Union[series.Series, bigframes.core.col.Expression]: - """Returns the number of days since 1970-01-01.""" - return bigframes.core.googlesql.apply_googlesql_scalar_op( - _UNIX_DATE_OP, - date_expression, - ) diff --git a/bigframes/operations/json_ops.py b/bigframes/operations/json_ops.py deleted file mode 100644 index c9b5849f9ed..00000000000 --- a/bigframes/operations/json_ops.py +++ /dev/null @@ -1,240 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import dataclasses -import typing - -import pandas as pd -import pyarrow as pa - -from bigframes import dtypes -from bigframes.operations import base_ops - - -@dataclasses.dataclass(frozen=True) -class JSONExtract(base_ops.UnaryOp): - name: typing.ClassVar[str] = "json_extract" - json_path: str - - def output_type(self, *input_types): - input_type = input_types[0] - if not dtypes.is_json_like(input_type): - raise TypeError( - "Input type must be a valid JSON object or JSON-formatted string type." - + f" Received type: {input_type}" - ) - return input_type - - -@dataclasses.dataclass(frozen=True) -class JSONQueryArray(base_ops.UnaryOp): - name: typing.ClassVar[str] = "json_query_array" - json_path: str - - def output_type(self, *input_types): - input_type = input_types[0] - if not dtypes.is_json_like(input_type): - raise TypeError( - "Input type must be a valid JSON object or JSON-formatted string type." - + f" Received type: {input_type}" - ) - return pd.ArrowDtype( - pa.list_(dtypes.bigframes_dtype_to_arrow_dtype(input_type)) - ) - - -@dataclasses.dataclass(frozen=True) -class JSONExtractArray(base_ops.UnaryOp): - name: typing.ClassVar[str] = "json_extract_array" - json_path: str - - def output_type(self, *input_types): - input_type = input_types[0] - if not dtypes.is_json_like(input_type): - raise TypeError( - "Input type must be a valid JSON object or JSON-formatted string type." - + f" Received type: {input_type}" - ) - return pd.ArrowDtype( - pa.list_(dtypes.bigframes_dtype_to_arrow_dtype(input_type)) - ) - - -@dataclasses.dataclass(frozen=True) -class JSONExtractStringArray(base_ops.UnaryOp): - name: typing.ClassVar[str] = "json_extract_string_array" - json_path: str - - def output_type(self, *input_types): - input_type = input_types[0] - if not dtypes.is_json_like(input_type): - raise TypeError( - "Input type must be a valid JSON object or JSON-formatted string type." - + f" Received type: {input_type}" - ) - return pd.ArrowDtype( - pa.list_(dtypes.bigframes_dtype_to_arrow_dtype(dtypes.STRING_DTYPE)) - ) - - -@dataclasses.dataclass(frozen=True) -class ParseJSON(base_ops.UnaryOp): - name: typing.ClassVar[str] = "parse_json" - - def output_type(self, *input_types): - input_type = input_types[0] - if input_type != dtypes.STRING_DTYPE: - raise TypeError( - "Input type must be a valid JSON-formatted string type." - + f" Received type: {input_type}" - ) - return dtypes.JSON_DTYPE - - -@dataclasses.dataclass(frozen=True) -class ToJSON(base_ops.UnaryOp): - name: typing.ClassVar[str] = "to_json" - safe: bool = True - - def output_type(self, *input_types): - input_type = input_types[0] - if not dtypes.is_json_encoding_type(input_type, strict=True): - raise TypeError( - "The value to be assigned must be a type that can be encoded as JSON." - + f"Received type: {input_type}" - ) - return dtypes.JSON_DTYPE - - -@dataclasses.dataclass(frozen=True) -class ToJSONString(base_ops.UnaryOp): - name: typing.ClassVar[str] = "to_json_string" - - def output_type(self, *input_types): - input_type = input_types[0] - if not dtypes.is_json_encoding_type(input_type): - raise TypeError( - "The value to be assigned must be a type that can be encoded as JSON." - + f"Received type: {input_type}" - ) - return dtypes.STRING_DTYPE - - -@dataclasses.dataclass(frozen=True) -class JSONSet(base_ops.BinaryOp): - name: typing.ClassVar[str] = "json_set" - json_path: str - - def output_type(self, *input_types): - left_type = input_types[0] - right_type = input_types[1] - if not dtypes.is_json_like(left_type): - raise TypeError( - "Input type must be a valid JSON object or JSON-formatted string type." - + f" Received type: {left_type}" - ) - if not dtypes.is_json_encoding_type(right_type): - raise TypeError( - "The value to be assigned must be a type that can be encoded as JSON." - + f"Received type: {right_type}" - ) - - return dtypes.JSON_DTYPE - - -@dataclasses.dataclass(frozen=True) -class JSONValue(base_ops.UnaryOp): - name: typing.ClassVar[str] = "json_value" - json_path: str - - def output_type(self, *input_types): - input_type = input_types[0] - if not dtypes.is_json_like(input_type): - raise TypeError( - "Input type must be a valid JSON object or JSON-formatted string type." - + f" Received type: {input_type}" - ) - return dtypes.STRING_DTYPE - - -@dataclasses.dataclass(frozen=True) -class JSONValueArray(base_ops.UnaryOp): - name: typing.ClassVar[str] = "json_value_array" - json_path: str - - def output_type(self, *input_types): - input_type = input_types[0] - if not dtypes.is_json_like(input_type): - raise TypeError( - "Input type must be a valid JSON object or JSON-formatted string type." - + f" Received type: {input_type}" - ) - return pd.ArrowDtype( - pa.list_(dtypes.bigframes_dtype_to_arrow_dtype(dtypes.STRING_DTYPE)) - ) - - -@dataclasses.dataclass(frozen=True) -class JSONQuery(base_ops.UnaryOp): - name: typing.ClassVar[str] = "json_query" - json_path: str - - def output_type(self, *input_types): - input_type = input_types[0] - if not dtypes.is_json_like(input_type): - raise TypeError( - "Input type must be a valid JSON object or JSON-formatted string type." - + f" Received type: {input_type}" - ) - return input_type - - -@dataclasses.dataclass(frozen=True) -class JSONKeys(base_ops.UnaryOp): - name: typing.ClassVar[str] = "json_keys" - max_depth: typing.Optional[int] = None - - def output_type(self, *input_types): - input_type = input_types[0] - if input_type != dtypes.JSON_DTYPE: - raise TypeError( - "Input type must be a valid JSON object or JSON-formatted string type." - + f" Received type: {input_type}" - ) - return pd.ArrowDtype( - pa.list_(dtypes.bigframes_dtype_to_arrow_dtype(dtypes.STRING_DTYPE)) - ) - - -@dataclasses.dataclass(frozen=True) -class JSONDecode(base_ops.UnaryOp): - name: typing.ClassVar[str] = "json_decode" - to_type: dtypes.Dtype - safe: bool = True - - def output_type(self, *input_types): - input_type = input_types[0] - if not dtypes.is_json_like(input_type): - raise TypeError( - "Input type must be a valid JSON object or JSON-formatted string type." - + f" Received type: {input_type}" - ) - if self.to_type not in ( - dtypes.INT_DTYPE, - dtypes.FLOAT_DTYPE, - dtypes.BOOL_DTYPE, - dtypes.STRING_DTYPE, - ): - raise TypeError(f"Cannot cast from {dtypes.JSON_DTYPE} to {self.to_type}") - return self.to_type diff --git a/bigframes/operations/lists.py b/bigframes/operations/lists.py deleted file mode 100644 index c0ff8d51650..00000000000 --- a/bigframes/operations/lists.py +++ /dev/null @@ -1,46 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from __future__ import annotations - -import inspect -from typing import Union - -import bigframes_vendored.pandas.core.arrays.arrow.accessors as vendoracessors - -import bigframes.operations as ops -import bigframes.series as series -from bigframes._tools import docs -from bigframes.core.logging import log_adapter -from bigframes.operations._op_converters import convert_index, convert_slice - - -@log_adapter.class_logger -@docs.inherit_docs(vendoracessors.ListAccessor) -class ListAccessor: - def __init__(self, data: series.Series): - self._data = data - - def len(self): - return self._data._apply_unary_op(ops.len_op) - - def __getitem__(self, key: Union[int, slice]) -> series.Series: - if isinstance(key, int): - return self._data._apply_unary_op(convert_index(key)) - elif isinstance(key, slice): - return self._data._apply_unary_op(convert_slice(key)) - else: - raise ValueError(f"key must be an int or slice, got {type(key).__name__}") - - __getitem__.__doc__ = inspect.getdoc(vendoracessors.ListAccessor.__getitem__) diff --git a/bigframes/operations/numeric_ops.py b/bigframes/operations/numeric_ops.py deleted file mode 100644 index af1eef74527..00000000000 --- a/bigframes/operations/numeric_ops.py +++ /dev/null @@ -1,366 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import dataclasses -import typing - -import bigframes.operations.type as op_typing -from bigframes import dtypes -from bigframes.operations import base_ops - -SinOp = base_ops.create_unary_op( - name="sin", type_signature=op_typing.UNARY_REAL_NUMERIC -) -sin_op = SinOp() - -CosOp = base_ops.create_unary_op( - name="cos", type_signature=op_typing.UNARY_REAL_NUMERIC -) -cos_op = CosOp() - -TanOp = base_ops.create_unary_op( - name="tan", type_signature=op_typing.UNARY_REAL_NUMERIC -) -tan_op = TanOp() - -ArcsinOp = base_ops.create_unary_op( - name="arcsin", type_signature=op_typing.UNARY_REAL_NUMERIC -) -arcsin_op = ArcsinOp() - -ArccosOp = base_ops.create_unary_op( - name="arccos", type_signature=op_typing.UNARY_REAL_NUMERIC -) -arccos_op = ArccosOp() - -ArctanOp = base_ops.create_unary_op( - name="arctan", type_signature=op_typing.UNARY_REAL_NUMERIC -) -arctan_op = ArctanOp() - -SinhOp = base_ops.create_unary_op( - name="sinh", type_signature=op_typing.UNARY_REAL_NUMERIC -) -sinh_op = SinhOp() - -CoshOp = base_ops.create_unary_op( - name="cosh", type_signature=op_typing.UNARY_REAL_NUMERIC -) -cosh_op = CoshOp() - -TanhOp = base_ops.create_unary_op( - name="tanh", type_signature=op_typing.UNARY_REAL_NUMERIC -) -tanh_op = TanhOp() - -ArcsinhOp = base_ops.create_unary_op( - name="arcsinh", type_signature=op_typing.UNARY_REAL_NUMERIC -) -arcsinh_op = ArcsinhOp() - -ArccoshOp = base_ops.create_unary_op( - name="arccosh", type_signature=op_typing.UNARY_REAL_NUMERIC -) -arccosh_op = ArccoshOp() - -ArctanhOp = base_ops.create_unary_op( - name="arctanh", type_signature=op_typing.UNARY_REAL_NUMERIC -) -arctanh_op = ArctanhOp() - -FloorOp = base_ops.create_unary_op( - name="floor", type_signature=op_typing.UNARY_REAL_NUMERIC -) -floor_op = FloorOp() - -CeilOp = base_ops.create_unary_op( - name="ceil", type_signature=op_typing.UNARY_REAL_NUMERIC -) -ceil_op = CeilOp() - -AbsOp = base_ops.create_unary_op( - name="abs", type_signature=op_typing.UNARY_NUMERIC_AND_TIMEDELTA -) -abs_op = AbsOp() - -PosOp = base_ops.create_unary_op( - name="pos", type_signature=op_typing.UNARY_NUMERIC_AND_TIMEDELTA -) -pos_op = PosOp() - -NegOp = base_ops.create_unary_op( - name="neg", type_signature=op_typing.UNARY_NUMERIC_AND_TIMEDELTA -) -neg_op = NegOp() - -ExpOp = base_ops.create_unary_op( - name="exp", type_signature=op_typing.UNARY_REAL_NUMERIC -) -exp_op = ExpOp() - -Expm1Op = base_ops.create_unary_op( - name="expm1", type_signature=op_typing.UNARY_REAL_NUMERIC -) -expm1_op = Expm1Op() - -LnOp = base_ops.create_unary_op(name="log", type_signature=op_typing.UNARY_REAL_NUMERIC) -ln_op = LnOp() - -Log10Op = base_ops.create_unary_op( - name="log10", type_signature=op_typing.UNARY_REAL_NUMERIC -) -log10_op = Log10Op() - -Log1pOp = base_ops.create_unary_op( - name="log1p", type_signature=op_typing.UNARY_REAL_NUMERIC -) -log1p_op = Log1pOp() - -SqrtOp = base_ops.create_unary_op( - name="sqrt", type_signature=op_typing.UNARY_REAL_NUMERIC -) -sqrt_op = SqrtOp() - - -@dataclasses.dataclass(frozen=True) -class AddOp(base_ops.BinaryOp): - name: typing.ClassVar[str] = "add" - - def output_type(self, *input_types): - left_type = input_types[0] - right_type = input_types[1] - # TODO: Binary/bytes addition requires impl - if all(map(lambda t: t == dtypes.STRING_DTYPE, input_types)): - # String addition - return input_types[0] - - # Temporal addition. - if dtypes.is_datetime_like(left_type) and right_type == dtypes.TIMEDELTA_DTYPE: - return left_type - if left_type == dtypes.TIMEDELTA_DTYPE and dtypes.is_datetime_like(right_type): - return right_type - - if left_type == dtypes.DATE_DTYPE and right_type == dtypes.TIMEDELTA_DTYPE: - return dtypes.DATETIME_DTYPE - - if left_type == dtypes.TIMEDELTA_DTYPE and right_type == dtypes.DATE_DTYPE: - return dtypes.DATETIME_DTYPE - - if left_type == dtypes.TIMEDELTA_DTYPE and right_type == dtypes.TIMEDELTA_DTYPE: - return dtypes.TIMEDELTA_DTYPE - - if (left_type is None or dtypes.is_numeric(left_type)) and ( - right_type is None or dtypes.is_numeric(right_type) - ): - # Numeric addition - return dtypes.coerce_to_common(left_type, right_type) - raise TypeError(f"Cannot add dtypes {left_type} and {right_type}") - - -add_op = AddOp() - - -@dataclasses.dataclass(frozen=True) -class SubOp(base_ops.BinaryOp): - name: typing.ClassVar[str] = "sub" - - # Note: this is actualyl a vararg op, but we don't model that yet - def output_type(self, *input_types): - left_type = input_types[0] - right_type = input_types[1] - - if left_type == dtypes.DATETIME_DTYPE and right_type == dtypes.DATETIME_DTYPE: - return dtypes.TIMEDELTA_DTYPE - - if left_type == dtypes.TIMESTAMP_DTYPE and right_type == dtypes.TIMESTAMP_DTYPE: - return dtypes.TIMEDELTA_DTYPE - - if left_type == dtypes.DATE_DTYPE and right_type == dtypes.DATE_DTYPE: - return dtypes.TIMEDELTA_DTYPE - - if dtypes.is_datetime_like(left_type) and right_type == dtypes.TIMEDELTA_DTYPE: - return left_type - - if left_type == dtypes.DATE_DTYPE and right_type == dtypes.TIMEDELTA_DTYPE: - return dtypes.DATETIME_DTYPE - - if left_type == dtypes.TIMEDELTA_DTYPE and right_type == dtypes.TIMEDELTA_DTYPE: - return dtypes.TIMEDELTA_DTYPE - - if left_type == dtypes.BOOL_DTYPE and right_type == dtypes.BOOL_DTYPE: - raise TypeError(f"Cannot subtract dtypes {left_type} and {right_type}") - - if (left_type is None or dtypes.is_numeric(left_type)) and ( - right_type is None or dtypes.is_numeric(right_type) - ): - # Numeric subtraction - return dtypes.coerce_to_common(left_type, right_type) - - raise TypeError(f"Cannot subtract dtypes {left_type} and {right_type}") - - -sub_op = SubOp() - - -@dataclasses.dataclass(frozen=True) -class MulOp(base_ops.BinaryOp): - name: typing.ClassVar[str] = "mul" - - def output_type(self, *input_types: dtypes.ExpressionType) -> dtypes.ExpressionType: - left_type = input_types[0] - right_type = input_types[1] - - if left_type == dtypes.TIMEDELTA_DTYPE and right_type in ( - dtypes.INT_DTYPE, - dtypes.FLOAT_DTYPE, - ): - return dtypes.TIMEDELTA_DTYPE - if ( - left_type in (dtypes.INT_DTYPE, dtypes.FLOAT_DTYPE) - and right_type == dtypes.TIMEDELTA_DTYPE - ): - return dtypes.TIMEDELTA_DTYPE - - if (left_type is None or dtypes.is_numeric(left_type)) and ( - right_type is None or dtypes.is_numeric(right_type) - ): - return dtypes.coerce_to_common(left_type, right_type) - - raise TypeError(f"Cannot multiply dtypes {left_type} and {right_type}") - - -mul_op = MulOp() - - -@dataclasses.dataclass(frozen=True) -class DivOp(base_ops.BinaryOp): - name: typing.ClassVar[str] = "div" - - def output_type(self, *input_types: dtypes.ExpressionType) -> dtypes.ExpressionType: - left_type = input_types[0] - right_type = input_types[1] - - if left_type == dtypes.TIMEDELTA_DTYPE and dtypes.is_numeric(right_type): - # will fail outright if result undefined or otherwise can't be coerced back into an int - return dtypes.TIMEDELTA_DTYPE - - if left_type == dtypes.TIMEDELTA_DTYPE and right_type == dtypes.TIMEDELTA_DTYPE: - return dtypes.FLOAT_DTYPE - - if left_type == dtypes.BOOL_DTYPE and right_type == dtypes.BOOL_DTYPE: - raise TypeError(f"Cannot divide dtypes {left_type} and {right_type}") - - if (left_type is None or dtypes.is_numeric(left_type)) and ( - right_type is None or dtypes.is_numeric(right_type) - ): - lcd_type = dtypes.coerce_to_common(left_type, right_type) - # Real numeric ops produce floats on int input - return dtypes.FLOAT_DTYPE if lcd_type == dtypes.INT_DTYPE else lcd_type - - raise TypeError(f"Cannot divide dtypes {left_type} and {right_type}") - - -div_op = DivOp() - - -@dataclasses.dataclass(frozen=True) -class FloorDivOp(base_ops.BinaryOp): - name: typing.ClassVar[str] = "floordiv" - - def output_type(self, *input_types: dtypes.ExpressionType) -> dtypes.ExpressionType: - left_type = input_types[0] - right_type = input_types[1] - - if left_type == dtypes.TIMEDELTA_DTYPE and right_type == dtypes.TIMEDELTA_DTYPE: - return dtypes.INT_DTYPE - - if left_type == dtypes.TIMEDELTA_DTYPE and dtypes.is_numeric(right_type): - return dtypes.TIMEDELTA_DTYPE - - if left_type == dtypes.BOOL_DTYPE and right_type == dtypes.BOOL_DTYPE: - raise TypeError(f"Cannot floor divide dtypes {left_type} and {right_type}") - - if (left_type is None or dtypes.is_numeric(left_type)) and ( - right_type is None or dtypes.is_numeric(right_type) - ): - return dtypes.coerce_to_common(left_type, right_type) - - raise TypeError(f"Cannot floor divide dtypes {left_type} and {right_type}") - - -floordiv_op = FloorDivOp() - - -@dataclasses.dataclass(frozen=True) -class ModOp(base_ops.BinaryOp): - name: typing.ClassVar[str] = "mod" - - def output_type(self, *input_types: dtypes.ExpressionType) -> dtypes.ExpressionType: - left_type = input_types[0] - right_type = input_types[1] - - if left_type == dtypes.TIMEDELTA_DTYPE and right_type == dtypes.TIMEDELTA_DTYPE: - return dtypes.TIMEDELTA_DTYPE - if left_type in ( - dtypes.NUMERIC_DTYPE, - dtypes.BIGNUMERIC_DTYPE, - ) or right_type in (dtypes.NUMERIC_DTYPE, dtypes.BIGNUMERIC_DTYPE): - raise TypeError(f"Cannot mod dtypes {left_type} and {right_type}") - - if left_type == dtypes.BOOL_DTYPE and right_type == dtypes.BOOL_DTYPE: - raise TypeError(f"Cannot mod dtypes {left_type} and {right_type}") - - if (left_type is None or dtypes.is_numeric(left_type)) and ( - right_type is None or dtypes.is_numeric(right_type) - ): - return dtypes.coerce_to_common(left_type, right_type) - - raise TypeError(f"Cannot mod dtypes {left_type} and {right_type}") - - -mod_op = ModOp() - -PowOp = base_ops.create_binary_op(name="pow", type_signature=op_typing.BINARY_NUMERIC) -pow_op = PowOp() - -Arctan2Op = base_ops.create_binary_op( - name="arctan2", type_signature=op_typing.BINARY_REAL_NUMERIC -) -arctan2_op = Arctan2Op() - -RoundOp = base_ops.create_binary_op( - name="round", type_signature=op_typing.BINARY_NUMERIC -) -round_op = RoundOp() - -UnsafePowOp = base_ops.create_binary_op( - name="unsafe_pow_op", type_signature=op_typing.BINARY_REAL_NUMERIC -) -unsafe_pow_op = UnsafePowOp() - -IsNanOp = base_ops.create_unary_op( - name="isnan", - type_signature=op_typing.FixedOutputType( - dtypes.is_numeric, dtypes.BOOL_DTYPE, "numeric" - ), -) -isnan_op = IsNanOp() - -IsFiniteOp = base_ops.create_unary_op( - name="isfinite", - type_signature=op_typing.FixedOutputType( - dtypes.is_numeric, dtypes.BOOL_DTYPE, "numeric" - ), -) -isfinite_op = IsFiniteOp() diff --git a/bigframes/operations/numpy_op_maps.py b/bigframes/operations/numpy_op_maps.py deleted file mode 100644 index 791e2eb8901..00000000000 --- a/bigframes/operations/numpy_op_maps.py +++ /dev/null @@ -1,57 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import numpy as np - -from bigframes.operations import base_ops, generic_ops, numeric_ops - -# Just parameterless unary ops for now -# TODO: Parameter mappings -NUMPY_TO_OP: dict[np.ufunc, base_ops.UnaryOp] = { - np.sin: numeric_ops.sin_op, - np.cos: numeric_ops.cos_op, - np.tan: numeric_ops.tan_op, - np.arcsin: numeric_ops.arcsin_op, - np.arccos: numeric_ops.arccos_op, - np.arctan: numeric_ops.arctan_op, - np.sinh: numeric_ops.sinh_op, - np.cosh: numeric_ops.cosh_op, - np.tanh: numeric_ops.tanh_op, - np.arcsinh: numeric_ops.arcsinh_op, - np.arccosh: numeric_ops.arccosh_op, - np.arctanh: numeric_ops.arctanh_op, - np.exp: numeric_ops.exp_op, - np.log: numeric_ops.ln_op, - np.log10: numeric_ops.log10_op, - np.sqrt: numeric_ops.sqrt_op, - np.abs: numeric_ops.abs_op, - np.floor: numeric_ops.floor_op, - np.ceil: numeric_ops.ceil_op, - np.log1p: numeric_ops.log1p_op, - np.expm1: numeric_ops.expm1_op, - np.isnan: numeric_ops.isnan_op, - np.isfinite: numeric_ops.isfinite_op, -} - - -NUMPY_TO_BINOP: dict[np.ufunc, base_ops.BinaryOp] = { - np.add: numeric_ops.add_op, - np.subtract: numeric_ops.sub_op, - np.multiply: numeric_ops.mul_op, - np.divide: numeric_ops.div_op, - np.power: numeric_ops.pow_op, - np.arctan2: numeric_ops.arctan2_op, - np.maximum: generic_ops.maximum_op, - np.minimum: generic_ops.minimum_op, -} diff --git a/bigframes/operations/output_schemas.py b/bigframes/operations/output_schemas.py deleted file mode 100644 index ff9c9883dc0..00000000000 --- a/bigframes/operations/output_schemas.py +++ /dev/null @@ -1,90 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import pyarrow as pa - - -def parse_sql_type(sql: str) -> pa.DataType: - """ - Parses a SQL type string to its PyArrow equivalence: - - For example: - "STRING" -> pa.string() - "ARRAY" -> pa.list_(pa.int64()) - "STRUCT, y BOOL>" -> pa.struct( - ( - pa.field("x", pa.list_(pa.float64())), - pa.field("y", pa.bool_()), - ) - ) - """ - sql = sql.strip() - - if sql.upper() == "STRING": - return pa.string() - - if sql.upper() == "INT64": - return pa.int64() - - if sql.upper() == "FLOAT64": - return pa.float64() - - if sql.upper() == "BOOL": - return pa.bool_() - - if sql.upper().startswith("ARRAY<") and sql.endswith(">"): - inner_type = sql[len("ARRAY<") : -1] - return pa.list_(parse_sql_type(inner_type)) - - if sql.upper().startswith("STRUCT<") and sql.endswith(">"): - inner_fields = parse_sql_fields(sql[len("STRUCT<") : -1]) - return pa.struct(inner_fields) - - raise ValueError(f"Unsupported SQL type: {sql}") - - -def parse_sql_fields(sql: str) -> tuple[pa.Field]: - sql = sql.strip() - - start_idx = 0 - nested_depth = 0 - fields: list[pa.field] = [] - - for end_idx in range(len(sql)): - c = sql[end_idx] - - if c == "<": - nested_depth += 1 - elif c == ">": - nested_depth -= 1 - elif c == "," and nested_depth == 0: - field = sql[start_idx:end_idx] - fields.append(parse_sql_field(field)) - start_idx = end_idx + 1 - - # Append the last field - fields.append(parse_sql_field(sql[start_idx:])) - - return tuple(sorted(fields, key=lambda f: f.name)) - - -def parse_sql_field(sql: str) -> pa.Field: - sql = sql.strip() - - space_idx = sql.find(" ") - - if space_idx == -1: - raise ValueError(f"Invalid struct field: {sql}") - - return pa.field(sql[:space_idx].strip(), parse_sql_type(sql[space_idx:])) diff --git a/bigframes/operations/plotting.py b/bigframes/operations/plotting.py deleted file mode 100644 index ecaa28e9747..00000000000 --- a/bigframes/operations/plotting.py +++ /dev/null @@ -1,108 +0,0 @@ -# Copyright 2023 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import typing - -import bigframes_vendored.constants as constants -import bigframes_vendored.pandas.plotting._core as vendordt - -import bigframes.operations._matplotlib as bfplt -from bigframes._tools import docs -from bigframes.core.logging import log_adapter - - -@log_adapter.class_logger -@docs.inherit_docs(vendordt.PlotAccessor) -class PlotAccessor: - _common_kinds = ("line", "area", "hist", "bar", "barh", "pie") - _dataframe_kinds = ("scatter", "hexbin,") - _all_kinds = _common_kinds + _dataframe_kinds - - def __call__(self, **kwargs): - import bigframes.series as series - - if kwargs.pop("backend", None) is not None: - raise NotImplementedError( - f"Only support matplotlib backend for now. {constants.FEEDBACK_LINK}" - ) - - kind = kwargs.pop("kind", "line") - if kind not in self._all_kinds: - raise NotImplementedError( - f"{kind} is not a valid plot kind supported for now. {constants.FEEDBACK_LINK}" - ) - - data = self._parent.copy() - if kind in self._dataframe_kinds and isinstance(data, series.Series): - raise ValueError(f"plot kind {kind} can only be used for data frames") - - return bfplt.plot(data, kind=kind, **kwargs) - - def __init__(self, data) -> None: - self._parent = data - - def hist( - self, by: typing.Optional[typing.Sequence[str]] = None, bins: int = 10, **kwargs - ): - return self(kind="hist", by=by, bins=bins, **kwargs) - - def line( - self, - x: typing.Optional[typing.Hashable] = None, - y: typing.Optional[typing.Hashable] = None, - **kwargs, - ): - return self(kind="line", x=x, y=y, **kwargs) - - def area( - self, - x: typing.Optional[typing.Hashable] = None, - y: typing.Optional[typing.Hashable] = None, - stacked: bool = True, - **kwargs, - ): - return self(kind="area", x=x, y=y, stacked=stacked, **kwargs) - - def bar( - self, - x: typing.Optional[typing.Hashable] = None, - y: typing.Optional[typing.Hashable] = None, - **kwargs, - ): - return self(kind="bar", x=x, y=y, **kwargs) - - def barh( - self, - x: typing.Optional[typing.Hashable] = None, - y: typing.Optional[typing.Hashable] = None, - **kwargs, - ): - return self(kind="barh", x=x, y=y, **kwargs) - - def pie( - self, - y: typing.Optional[typing.Hashable] = None, - **kwargs, - ): - return self(kind="pie", y=y, **kwargs) - - def scatter( - self, - x: typing.Optional[typing.Hashable] = None, - y: typing.Optional[typing.Hashable] = None, - s: typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]] = None, - c: typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]] = None, - **kwargs, - ): - return self(kind="scatter", x=x, y=y, s=s, c=c, **kwargs) diff --git a/bigframes/operations/python_op_maps.py b/bigframes/operations/python_op_maps.py deleted file mode 100644 index b4c58e14c7b..00000000000 --- a/bigframes/operations/python_op_maps.py +++ /dev/null @@ -1,126 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import math -import operator -from typing import Optional - -import bigframes.operations -from bigframes.operations import ( - aggregations, - array_ops, - bool_ops, - comparison_ops, - generic_ops, - numeric_ops, - string_ops, -) - -PYTHON_TO_BIGFRAMES = { - ## operators - operator.add: numeric_ops.add_op, - operator.sub: numeric_ops.sub_op, - operator.mul: numeric_ops.mul_op, - operator.truediv: numeric_ops.div_op, - operator.floordiv: numeric_ops.floordiv_op, - operator.mod: numeric_ops.mod_op, - operator.pow: numeric_ops.pow_op, - operator.pos: numeric_ops.pos_op, - operator.neg: numeric_ops.neg_op, - operator.abs: numeric_ops.abs_op, - operator.eq: comparison_ops.eq_op, - operator.ne: comparison_ops.ne_op, - operator.gt: comparison_ops.gt_op, - operator.lt: comparison_ops.lt_op, - operator.ge: comparison_ops.ge_op, - operator.le: comparison_ops.le_op, - operator.and_: bool_ops.and_op, - operator.or_: bool_ops.or_op, - operator.xor: bool_ops.xor_op, - operator.invert: generic_ops.invert_op, - operator.not_: generic_ops.invert_op, - ## math - math.log: numeric_ops.ln_op, - math.log10: numeric_ops.log10_op, - math.log1p: numeric_ops.log1p_op, - math.expm1: numeric_ops.expm1_op, - math.sin: numeric_ops.sin_op, - math.cos: numeric_ops.cos_op, - math.tan: numeric_ops.tan_op, - math.sinh: numeric_ops.sinh_op, - math.cosh: numeric_ops.cosh_op, - math.tanh: numeric_ops.tanh_op, - math.asin: numeric_ops.arcsin_op, - math.acos: numeric_ops.arccos_op, - math.atan: numeric_ops.arctan_op, - math.floor: numeric_ops.floor_op, - math.ceil: numeric_ops.ceil_op, - ## str - str.upper: string_ops.upper_op, - str.lower: string_ops.lower_op, - str.isalnum: string_ops.isalnum_op, - str.isalpha: string_ops.isalpha_op, - str.isdecimal: string_ops.isdecimal_op, - str.isdigit: string_ops.isdigit_op, - str.isnumeric: string_ops.isnumeric_op, - str.isspace: string_ops.isspace_op, - str.islower: string_ops.islower_op, - str.isupper: string_ops.isupper_op, - str.capitalize: string_ops.capitalize_op, - ## builtins - len: string_ops.len_op, - abs: numeric_ops.abs_op, - pow: numeric_ops.pow_op, - ### builtins -- iterable - all: array_ops.ArrayReduceOp(aggregations.all_op), # type: ignore - any: array_ops.ArrayReduceOp(aggregations.any_op), # type: ignore - sum: array_ops.ArrayReduceOp(aggregations.sum_op), # type: ignore - min: array_ops.ArrayReduceOp(aggregations.min_op), # type: ignore - max: array_ops.ArrayReduceOp(aggregations.max_op), # type: ignore -} - - -def python_callable_to_op(obj) -> Optional[bigframes.operations.RowOp]: - if obj in PYTHON_TO_BIGFRAMES: - return PYTHON_TO_BIGFRAMES[obj] - return None - - -SERIES_METHOD_TO_OP = { - "abs": numeric_ops.abs_op, - "sqrt": numeric_ops.sqrt_op, - "sin": numeric_ops.sin_op, - "cos": numeric_ops.cos_op, - "tan": numeric_ops.tan_op, - "log": numeric_ops.ln_op, - "log10": numeric_ops.log10_op, - "exp": numeric_ops.exp_op, - "floor": numeric_ops.floor_op, - "ceil": numeric_ops.ceil_op, - "isnull": generic_ops.isnull_op, - "isna": generic_ops.isnull_op, - "notnull": generic_ops.notnull_op, - "notna": generic_ops.notnull_op, - "upper": string_ops.upper_op, - "lower": string_ops.lower_op, - "isalnum": string_ops.isalnum_op, - "isalpha": string_ops.isalpha_op, - "isdecimal": string_ops.isdecimal_op, - "isdigit": string_ops.isdigit_op, - "isnumeric": string_ops.isnumeric_op, - "isspace": string_ops.isspace_op, - "islower": string_ops.islower_op, - "isupper": string_ops.isupper_op, - "capitalize": string_ops.capitalize_op, -} diff --git a/bigframes/operations/remote_function_ops.py b/bigframes/operations/remote_function_ops.py deleted file mode 100644 index 3ce77d51c61..00000000000 --- a/bigframes/operations/remote_function_ops.py +++ /dev/null @@ -1,45 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import dataclasses -import typing - -from bigframes.functions import udf_def -from bigframes.operations import base_ops - - -@dataclasses.dataclass(frozen=True) -class PythonUdfOp(base_ops.NaryOp): - name: typing.ClassVar[str] = "python_udf" - function_def: udf_def.PythonUdf - - @property - def expensive(self) -> bool: - return True - - def output_type(self, *input_types): - return self.function_def.signature.output.bf_type - - -@dataclasses.dataclass(frozen=True) -class RemoteFunctionOp(base_ops.NaryOp): - name: typing.ClassVar[str] = "remote_function" - function_def: udf_def.BigqueryUdf - - @property - def expensive(self) -> bool: - return True - - def output_type(self, *input_types): - return self.function_def.signature.output.bf_type diff --git a/bigframes/operations/string_ops.py b/bigframes/operations/string_ops.py deleted file mode 100644 index 21d23416314..00000000000 --- a/bigframes/operations/string_ops.py +++ /dev/null @@ -1,283 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import dataclasses -import typing - -import pandas as pd -import pyarrow as pa - -import bigframes.operations.type as op_typing -from bigframes import dtypes -from bigframes.operations import base_ops - -LenOp = base_ops.create_unary_op( - name="len", - type_signature=op_typing.FixedOutputType( - dtypes.is_iterable, dtypes.INT_DTYPE, description="iterable" - ), -) -len_op = LenOp() - -## Specialized len ops for compile-time lowering -StrLenOp = base_ops.create_unary_op( - name="strlen", - type_signature=op_typing.FixedOutputType( - dtypes.is_string_like, dtypes.INT_DTYPE, description="string-like" - ), -) -str_len_op = StrLenOp() - -ArrayLenOp = base_ops.create_unary_op( - name="arraylen", - type_signature=op_typing.FixedOutputType( - dtypes.is_array_like, dtypes.INT_DTYPE, description="array-like" - ), -) -array_len_op = ArrayLenOp() - -ReverseOp = base_ops.create_unary_op( - name="reverse", type_signature=op_typing.STRING_TRANSFORM -) -reverse_op = ReverseOp() - -LowerOp = base_ops.create_unary_op( - name="lower", type_signature=op_typing.STRING_TRANSFORM -) -lower_op = LowerOp() - -UpperOp = base_ops.create_unary_op( - name="upper", type_signature=op_typing.STRING_TRANSFORM -) -upper_op = UpperOp() - -IsAlnumOp = base_ops.create_unary_op( - name="isalnum", type_signature=op_typing.STRING_PREDICATE -) -isalnum_op = IsAlnumOp() - -IsAlphaOp = base_ops.create_unary_op( - name="isalpha", type_signature=op_typing.STRING_PREDICATE -) -isalpha_op = IsAlphaOp() - -IsDecimalOp = base_ops.create_unary_op( - name="isdecimal", type_signature=op_typing.STRING_PREDICATE -) -isdecimal_op = IsDecimalOp() - -IsDigitOp = base_ops.create_unary_op( - name="isdigit", type_signature=op_typing.STRING_PREDICATE -) -isdigit_op = IsDigitOp() - -IsNumericOp = base_ops.create_unary_op( - name="isnumeric", type_signature=op_typing.STRING_PREDICATE -) -isnumeric_op = IsNumericOp() - -IsSpaceOp = base_ops.create_unary_op( - name="isspace", type_signature=op_typing.STRING_PREDICATE -) -isspace_op = IsSpaceOp() - -IsLowerOp = base_ops.create_unary_op( - name="islower", type_signature=op_typing.STRING_PREDICATE -) -islower_op = IsLowerOp() - -IsUpperOp = base_ops.create_unary_op( - name="isupper", type_signature=op_typing.STRING_PREDICATE -) -isupper_op = IsUpperOp() - -CapitalizeOp = base_ops.create_unary_op( - name="capitalize", type_signature=op_typing.STRING_TRANSFORM -) -capitalize_op = CapitalizeOp() - - -@dataclasses.dataclass(frozen=True) -class StrContainsOp(base_ops.UnaryOp): - name: typing.ClassVar[str] = "str_contains" - pat: str - - def output_type(self, *input_types): - return op_typing.STRING_PREDICATE.output_type(input_types[0]) - - -@dataclasses.dataclass(frozen=True) -class StrContainsRegexOp(base_ops.UnaryOp): - name: typing.ClassVar[str] = "str_contains_regex" - pat: str - - def output_type(self, *input_types): - return op_typing.STRING_PREDICATE.output_type(input_types[0]) - - -@dataclasses.dataclass(frozen=True) -class StrPadOp(base_ops.UnaryOp): - name: typing.ClassVar[str] = "str_pad" - length: int - fillchar: str - side: typing.Literal["both", "left", "right"] - - def output_type(self, *input_types): - return op_typing.STRING_TRANSFORM.output_type(input_types[0]) - - -@dataclasses.dataclass(frozen=True) -class StrStripOp(base_ops.UnaryOp): - name: typing.ClassVar[str] = "str_strip" - to_strip: str - - def output_type(self, *input_types): - return op_typing.STRING_TRANSFORM.output_type(input_types[0]) - - -@dataclasses.dataclass(frozen=True) -class StrLstripOp(base_ops.UnaryOp): - name: typing.ClassVar[str] = "str_lstrip" - to_strip: str - - def output_type(self, *input_types): - return op_typing.STRING_TRANSFORM.output_type(input_types[0]) - - -@dataclasses.dataclass(frozen=True) -class StrRstripOp(base_ops.UnaryOp): - name: typing.ClassVar[str] = "str_rstrip" - to_strip: str - - def output_type(self, *input_types): - return op_typing.STRING_TRANSFORM.output_type(input_types[0]) - - -@dataclasses.dataclass(frozen=True) -class ReplaceStrOp(base_ops.UnaryOp): - name: typing.ClassVar[str] = "str_replace" - pat: str - repl: str - - def output_type(self, *input_types): - return op_typing.STRING_TRANSFORM.output_type(input_types[0]) - - -@dataclasses.dataclass(frozen=True) -class RegexReplaceStrOp(base_ops.UnaryOp): - name: typing.ClassVar[str] = "str_rereplace" - pat: str - repl: str - - def output_type(self, *input_types): - return op_typing.STRING_TRANSFORM.output_type(input_types[0]) - - -@dataclasses.dataclass(frozen=True) -class StartsWithOp(base_ops.UnaryOp): - name: typing.ClassVar[str] = "str_startswith" - pat: typing.Sequence[str] - - def output_type(self, *input_types): - return op_typing.STRING_PREDICATE.output_type(input_types[0]) - - -@dataclasses.dataclass(frozen=True) -class StringSplitOp(base_ops.UnaryOp): - name: typing.ClassVar[str] = "str_split" - pat: typing.Sequence[str] - - def output_type(self, *input_types): - input_type = input_types[0] - if not isinstance(input_type, pd.StringDtype): - raise TypeError("field accessor input must be a string type") - arrow_type = dtypes.bigframes_dtype_to_arrow_dtype(input_type) - return pd.ArrowDtype(pa.list_(arrow_type)) - - -@dataclasses.dataclass(frozen=True) -class EndsWithOp(base_ops.UnaryOp): - name: typing.ClassVar[str] = "str_endswith" - pat: typing.Sequence[str] - - def output_type(self, *input_types): - return op_typing.STRING_PREDICATE.output_type(input_types[0]) - - -@dataclasses.dataclass(frozen=True) -class ZfillOp(base_ops.UnaryOp): - name: typing.ClassVar[str] = "str_zfill" - width: int - - def output_type(self, *input_types): - return op_typing.STRING_TRANSFORM.output_type(input_types[0]) - - -@dataclasses.dataclass(frozen=True) -class StrFindOp(base_ops.UnaryOp): - name: typing.ClassVar[str] = "str_find" - substr: str - start: typing.Optional[int] - end: typing.Optional[int] - - def output_type(self, *input_types): - signature = op_typing.FixedOutputType( - dtypes.is_string_like, dtypes.INT_DTYPE, "string-like" - ) - return signature.output_type(input_types[0]) - - -@dataclasses.dataclass(frozen=True) -class StrExtractOp(base_ops.UnaryOp): - name: typing.ClassVar[str] = "str_extract" - pat: str - n: int = 1 - - def output_type(self, *input_types): - return op_typing.STRING_TRANSFORM.output_type(input_types[0]) - - -@dataclasses.dataclass(frozen=True) -class StrSliceOp(base_ops.UnaryOp): - name: typing.ClassVar[str] = "str_slice" - start: typing.Optional[int] - end: typing.Optional[int] - - def output_type(self, *input_types): - return op_typing.STRING_TRANSFORM.output_type(input_types[0]) - - -@dataclasses.dataclass(frozen=True) -class StrRepeatOp(base_ops.UnaryOp): - name: typing.ClassVar[str] = "str_repeat" - repeats: int - - def output_type(self, *input_types): - return op_typing.STRING_TRANSFORM.output_type(input_types[0]) - - -@dataclasses.dataclass(frozen=True) -class StrConcatOp(base_ops.BinaryOp): - name: typing.ClassVar[str] = "str_concat" - - # Note: this is actualyl a vararg op, but we don't model that yet - def output_type(self, *input_types): - if not all(map(dtypes.is_string_like, input_types)): - raise TypeError("string concat requires string-like arguments") - if len(set(input_types)) != 1: - raise TypeError("string concat requires like-typed arguments") - return input_types[0] - - -strconcat_op = StrConcatOp() diff --git a/bigframes/operations/strings.py b/bigframes/operations/strings.py index ad9bfd6da40..0545ea34d6a 100644 --- a/bigframes/operations/strings.py +++ b/bigframes/operations/strings.py @@ -15,20 +15,14 @@ from __future__ import annotations import re -from typing import Generic, Hashable, Literal, Optional, TypeVar, Union +from typing import cast, Literal, Optional, Union -import bigframes_vendored.constants as constants -import bigframes_vendored.pandas.core.strings.accessor as vendorstr - -import bigframes.core.col -import bigframes.core.indexes.base as indices +import bigframes.constants as constants import bigframes.dataframe as df import bigframes.operations as ops -import bigframes.operations.aggregations as agg_ops +import bigframes.operations.base import bigframes.series as series -from bigframes._tools import docs -from bigframes.core.logging import log_adapter -from bigframes.operations._op_converters import convert_index, convert_slice +import third_party.bigframes_vendored.pandas.core.strings.accessor as vendorstr # Maps from python to re2 REGEXP_FLAGS = { @@ -37,170 +31,127 @@ re.DOTALL: "s", } -T = TypeVar("T", series.Series, indices.Index, bigframes.core.col.Expression) - - -@log_adapter.class_logger -@docs.inherit_docs(vendorstr.StringMethods) -class StringMethods(Generic[T]): - def __init__(self, data: T): - self._data: T = data - def __getitem__(self, key: Union[int, slice]) -> T: - if isinstance(key, int): - return self._data._apply_unary_op(convert_index(key)) - elif isinstance(key, slice): - return self._data._apply_unary_op(convert_slice(key)) - else: - raise ValueError(f"key must be an int or slice, got {type(key).__name__}") +class StringMethods(bigframes.operations.base.SeriesMethods, vendorstr.StringMethods): + __doc__ = vendorstr.StringMethods.__doc__ def find( self, sub: str, start: Optional[int] = None, end: Optional[int] = None, - ) -> T: - return self._data._apply_unary_op( - ops.StrFindOp(substr=sub, start=start, end=end) - ) - - def len(self) -> T: - return self._data._apply_unary_op(ops.len_op) + ) -> series.Series: + return self._apply_unary_op(ops.FindOp(sub, start, end)) - def lower(self) -> T: - return self._data._apply_unary_op(ops.lower_op) + def len(self) -> series.Series: + return self._apply_unary_op(ops.len_op) - def reverse(self) -> T: - """Reverse strings in the Series. + def lower(self) -> series.Series: + return self._apply_unary_op(ops.lower_op) - **Examples:** - - >>> import bigframes.pandas as bpd - >>> s = bpd.Series(["apple", "banana", "", pd.NA]) - >>> s.str.reverse() - 0 elppa - 1 ananab - 2 - 3 - dtype: string - - Returns: - bigframes.series.Series: A Series of booleans indicating whether the given - pattern matches the start of each string element. - """ + def reverse(self) -> series.Series: + """Reverse strings in the Series.""" # reverse method is in ibis, not pandas. - return self._data._apply_unary_op(ops.reverse_op) + return self._apply_unary_op(ops.reverse_op) def slice( self, start: Optional[int] = None, stop: Optional[int] = None, - ) -> T: - return self._data._apply_unary_op(ops.StrSliceOp(start=start, end=stop)) + ) -> series.Series: + return self._apply_unary_op(ops.SliceOp(start, stop)) - def strip(self, to_strip: Optional[str] = None) -> T: - return self._data._apply_unary_op( - ops.StrStripOp(to_strip=" \n\t" if to_strip is None else to_strip) - ) + def strip(self) -> series.Series: + return self._apply_unary_op(ops.strip_op) - def upper(self) -> T: - return self._data._apply_unary_op(ops.upper_op) + def upper(self) -> series.Series: + return self._apply_unary_op(ops.upper_op) - def isnumeric(self) -> T: - return self._data._apply_unary_op(ops.isnumeric_op) + def isnumeric(self) -> series.Series: + return self._apply_unary_op(ops.isnumeric_op) def isalpha( self, - ) -> T: - return self._data._apply_unary_op(ops.isalpha_op) + ) -> series.Series: + return self._apply_unary_op(ops.isalpha_op) def isdigit( self, - ) -> T: - return self._data._apply_unary_op(ops.isdigit_op) + ) -> series.Series: + return self._apply_unary_op(ops.isdigit_op) def isdecimal( self, - ) -> T: - return self._data._apply_unary_op(ops.isdecimal_op) + ) -> series.Series: + return self._apply_unary_op(ops.isdecimal_op) def isalnum( self, - ) -> T: - return self._data._apply_unary_op(ops.isalnum_op) + ) -> series.Series: + return self._apply_unary_op(ops.isalnum_op) def isspace( self, - ) -> T: - return self._data._apply_unary_op(ops.isspace_op) + ) -> series.Series: + return self._apply_unary_op(ops.isspace_op) def islower( self, - ) -> T: - return self._data._apply_unary_op(ops.islower_op) + ) -> series.Series: + return self._apply_unary_op(ops.islower_op) def isupper( self, - ) -> T: - return self._data._apply_unary_op(ops.isupper_op) + ) -> series.Series: + return self._apply_unary_op(ops.isupper_op) - def rstrip(self, to_strip: Optional[str] = None) -> T: - return self._data._apply_unary_op( - ops.StrRstripOp(to_strip=" \n\t" if to_strip is None else to_strip) - ) + def rstrip(self) -> series.Series: + return self._apply_unary_op(ops.rstrip_op) - def lstrip(self, to_strip: Optional[str] = None) -> T: - return self._data._apply_unary_op( - ops.StrLstripOp(to_strip=" \n\t" if to_strip is None else to_strip) - ) + def lstrip(self) -> series.Series: + return self._apply_unary_op(ops.lstrip_op) - def repeat(self, repeats: int) -> T: - return self._data._apply_unary_op(ops.StrRepeatOp(repeats=repeats)) + def repeat(self, repeats: int) -> series.Series: + return self._apply_unary_op(ops.RepeatOp(repeats)) - def capitalize(self) -> T: - return self._data._apply_unary_op(ops.capitalize_op) + def capitalize(self) -> series.Series: + return self._apply_unary_op(ops.capitalize_op) - def match(self, pat, case=True, flags=0) -> T: + def match(self, pat, case=True, flags=0) -> series.Series: # \A anchors start of entire string rather than start of any line in multiline mode adj_pat = rf"\A{pat}" - return self.contains(pat=adj_pat, case=case, flags=flags) + return self.contains(adj_pat, case=case, flags=flags) - def fullmatch(self, pat, case=True, flags=0) -> T: + def fullmatch(self, pat, case=True, flags=0) -> series.Series: # \A anchors start of entire string rather than start of any line in multiline mode # \z likewise anchors to the end of the entire multiline string adj_pat = rf"\A{pat}\z" - return self.contains(pat=adj_pat, case=case, flags=flags) + return self.contains(adj_pat, case=case, flags=flags) - def get(self, i: int) -> T: - return self._data._apply_unary_op(ops.GetItemOp(key=i)) + def get(self, i: int) -> series.Series: + return self._apply_unary_op(ops.StrGetOp(i)) - def pad(self, width, side="left", fillchar=" ") -> T: - return self._data._apply_unary_op( - ops.StrPadOp(length=width, fillchar=fillchar, side=side) - ) + def pad(self, width, side="left", fillchar=" ") -> series.Series: + return self._apply_unary_op(ops.StrPadOp(width, fillchar, side)) - def ljust(self, width, fillchar=" ") -> T: - return self._data._apply_unary_op( - ops.StrPadOp(length=width, fillchar=fillchar, side="right") - ) + def ljust(self, width, fillchar=" ") -> series.Series: + return self._apply_unary_op(ops.StrPadOp(width, fillchar, "right")) - def rjust(self, width, fillchar=" ") -> T: - return self._data._apply_unary_op( - ops.StrPadOp(length=width, fillchar=fillchar, side="left") - ) + def rjust(self, width, fillchar=" ") -> series.Series: + return self._apply_unary_op(ops.StrPadOp(width, fillchar, "left")) def contains( self, pat, case: bool = True, flags: int = 0, *, regex: bool = True - ) -> T: + ) -> series.Series: if not case: - return self.contains(pat=pat, flags=flags | re.IGNORECASE, regex=True) + return self.contains(pat, flags=flags | re.IGNORECASE, regex=True) if regex: re2flags = _parse_flags(flags) if re2flags: pat = re2flags + pat - return self._data._apply_unary_op(ops.StrContainsRegexOp(pat=pat)) + return self._apply_unary_op(ops.ContainsRegexOp(pat)) else: - return self._data._apply_unary_op(ops.StrContainsOp(pat=pat)) + return self._apply_unary_op(ops.ContainsStringOp(pat)) def extract(self, pat: str, flags: int = 0) -> df.DataFrame: re2flags = _parse_flags(flags) @@ -210,19 +161,21 @@ def extract(self, pat: str, flags: int = 0) -> df.DataFrame: if compiled.groups == 0: raise ValueError("No capture groups in 'pat'") - results: dict[Hashable, series.Series] = {} + results: list[str] = [] + block = self._block for i in range(compiled.groups): labels = [ label for label, groupn in compiled.groupindex.items() if i + 1 == groupn ] - label = labels[0] if labels else i - result = self._data._apply_unary_op( - ops.StrExtractOp(pat=pat, n=i + 1), + label = labels[0] if labels else str(i) + block, id = block.apply_unary_op( + self._value_column, ops.ExtractOp(pat, i + 1), result_label=label ) - results[label] = series.Series(result) - return df.DataFrame(results) + results.append(id) + block = block.select_columns(results) + return df.DataFrame(block) def replace( self, @@ -232,92 +185,61 @@ def replace( case: Optional[bool] = None, flags: int = 0, regex: bool = False, - ) -> T: - if isinstance(pat, re.Pattern): - assert isinstance(pat.pattern, str) - pat_str = pat.pattern - flags = pat.flags | flags - else: - pat_str = pat - + ) -> series.Series: + is_compiled = isinstance(pat, re.Pattern) + patstr = cast(str, pat.pattern if is_compiled else pat) # type: ignore if case is False: - return self.replace(pat_str, repl, flags=flags | re.IGNORECASE, regex=True) + return self.replace(pat, repl, flags=flags | re.IGNORECASE, regex=True) if regex: re2flags = _parse_flags(flags) if re2flags: - pat_str = re2flags + pat_str - return self._data._apply_unary_op( - ops.RegexReplaceStrOp(pat=pat_str, repl=repl) - ) + patstr = re2flags + patstr + return self._apply_unary_op(ops.ReplaceRegexOp(patstr, repl)) else: - if isinstance(pat, re.Pattern): + if is_compiled: raise ValueError( "Must set 'regex'=True if using compiled regex pattern." ) - return self._data._apply_unary_op(ops.ReplaceStrOp(pat=pat_str, repl=repl)) + return self._apply_unary_op(ops.ReplaceStringOp(patstr, repl)) def startswith( self, pat: Union[str, tuple[str, ...]], - ) -> T: + ) -> series.Series: if not isinstance(pat, tuple): pat = (pat,) - return self._data._apply_unary_op(ops.StartsWithOp(pat=pat)) + return self._apply_unary_op(ops.StartsWithOp(pat)) def endswith( self, pat: Union[str, tuple[str, ...]], - ) -> T: + ) -> series.Series: if not isinstance(pat, tuple): pat = (pat,) - return self._data._apply_unary_op(ops.EndsWithOp(pat=pat)) - - def split( - self, - pat: str = " ", - regex: Union[bool, None] = None, - ) -> T: - if regex is True or (regex is None and len(pat) > 1): - raise NotImplementedError( - "Regular expressions aren't currently supported. Please set " - + f"`regex=False` and try again. {constants.FEEDBACK_LINK}" - ) - return self._data._apply_unary_op(ops.StringSplitOp(pat=pat)) + return self._apply_unary_op(ops.EndsWithOp(pat)) - def zfill(self, width: int) -> T: - return self._data._apply_unary_op(ops.ZfillOp(width=width)) + def zfill(self, width: int) -> series.Series: + return self._apply_unary_op(ops.ZfillOp(width)) - def center(self, width: int, fillchar: str = " ") -> T: - return self._data._apply_unary_op( - ops.StrPadOp(length=width, fillchar=fillchar, side="both") - ) + def center(self, width: int, fillchar: str = " ") -> series.Series: + return self._apply_unary_op(ops.StrPadOp(width, fillchar, "both")) def cat( self, - others: Union[str, indices.Index, series.Series], + others: Union[str, series.Series], *, join: Literal["outer", "left"] = "left", - ) -> T: - return self._data._apply_binary_op(others, ops.strconcat_op, alignment=join) - - def join(self, sep: str) -> T: - return self._data._apply_unary_op( - ops.ArrayReduceOp(aggregation=agg_ops.StringAggOp(sep=sep)) - ) + ) -> series.Series: + return self._apply_binary_op(others, ops.concat_op, alignment=join) def _parse_flags(flags: int) -> Optional[str]: re2flags = [] for reflag, re2flag in REGEXP_FLAGS.items(): - if flags & reflag: + if flags & flags: re2flags.append(re2flag) flags = flags ^ reflag - # re2 handles unicode fine by default - # most compiled re in python will have unicode set - if re.U and flags: - flags = flags ^ re.U - # Remaining flags couldn't be mapped to re2 engine if flags: raise NotImplementedError( diff --git a/bigframes/operations/struct_ops.py b/bigframes/operations/struct_ops.py deleted file mode 100644 index de51efd8a48..00000000000 --- a/bigframes/operations/struct_ops.py +++ /dev/null @@ -1,65 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import dataclasses -import typing - -import pandas as pd -import pyarrow as pa - -from bigframes import dtypes -from bigframes.operations import base_ops - - -@dataclasses.dataclass(frozen=True) -class StructFieldOp(base_ops.UnaryOp): - name: typing.ClassVar[str] = "struct_field" - name_or_index: typing.Union[str, int] - - def output_type(self, *input_types): - input_type = input_types[0] - if not isinstance(input_type, pd.ArrowDtype): - raise TypeError("field accessor input must be a struct type") - - pa_type = input_type.pyarrow_dtype - if not isinstance(pa_type, pa.StructType): - raise TypeError("field accessor input must be a struct type") - - pa_result_type = pa_type[self.name_or_index].type - return dtypes.arrow_dtype_to_bigframes_dtype(pa_result_type) - - -@dataclasses.dataclass(frozen=True) -class StructOp(base_ops.NaryOp): - name: typing.ClassVar[str] = "struct" - column_names: tuple[str, ...] - - def output_type(self, *input_types: dtypes.ExpressionType) -> dtypes.ExpressionType: - num_input_types = len(input_types) - # value1, value2, ... - assert num_input_types == len(self.column_names) - fields = [] - - for i in range(num_input_types): - arrow_type = dtypes.bigframes_dtype_to_arrow_dtype(input_types[i]) - fields.append( - pa.field( - self.column_names[i], - arrow_type, - nullable=(not pa.types.is_list(arrow_type)), - ) - ) - return pd.ArrowDtype( - pa.struct(fields) - ) # [(name1, value1), (name2, value2), ...] diff --git a/bigframes/operations/structs.py b/bigframes/operations/structs.py index c5446510a6d..506a5577094 100644 --- a/bigframes/operations/structs.py +++ b/bigframes/operations/structs.py @@ -14,79 +14,48 @@ from __future__ import annotations -import bigframes_vendored.pandas.core.arrays.arrow.accessors as vendoracessors -import pandas as pd +import typing + +import ibis.expr.types as ibis_types import bigframes.dataframe import bigframes.operations +import bigframes.operations.base import bigframes.series -from bigframes._tools import docs -from bigframes.core import backports -from bigframes.core.logging import log_adapter +import third_party.bigframes_vendored.pandas.core.arrays.arrow.accessors as vendoracessors + + +class _StructField(bigframes.operations.UnaryOp): + def __init__(self, name_or_index: str | int): + self._name_or_index = name_or_index + + def _as_ibis(self, x: ibis_types.Value): + struct_value = typing.cast(ibis_types.StructValue, x) + if isinstance(self._name_or_index, str): + name = self._name_or_index + else: + name = struct_value.names[self._name_or_index] + return struct_value[name].name(name) -@log_adapter.class_logger -@docs.inherit_docs(vendoracessors.StructAccessor) -class StructAccessor: - def __init__(self, data: bigframes.series.Series): - self._data = data +class StructAccessor( + bigframes.operations.base.SeriesMethods, vendoracessors.StructAccessor +): + __doc__ = vendoracessors.StructAccessor.__doc__ def field(self, name_or_index: str | int) -> bigframes.series.Series: - series = self._data._apply_unary_op( - bigframes.operations.StructFieldOp(name_or_index) - ) + series = self._apply_unary_op(_StructField(name_or_index)) if isinstance(name_or_index, str): name = name_or_index else: - struct_field = self._data._dtype.pyarrow_dtype[name_or_index] + struct_field = self._dtype.pyarrow_dtype[name_or_index] name = struct_field.name return series.rename(name) def explode(self) -> bigframes.dataframe.DataFrame: import bigframes.pandas - pa_type = self._data._dtype.pyarrow_dtype + pa_type = self._dtype.pyarrow_dtype return bigframes.pandas.concat( - [ - self.field(field.name) - for field in backports.pyarrow_struct_type_fields(pa_type) - ], - axis="columns", - ) - - @property - def dtypes(self) -> pd.Series: - pa_type = self._data._dtype.pyarrow_dtype - return pd.Series( - data=[ - pd.ArrowDtype(field.type) - for field in backports.pyarrow_struct_type_fields(pa_type) - ], - index=[ - field.name for field in backports.pyarrow_struct_type_fields(pa_type) - ], + [self.field(i) for i in range(pa_type.num_fields)], axis="columns" ) - - -@log_adapter.class_logger -@docs.inherit_docs(vendoracessors.StructFrameAccessor) -class StructFrameAccessor: - __doc__ = vendoracessors.StructAccessor.__doc__ - - def __init__(self, data: bigframes.dataframe.DataFrame) -> None: - self._parent = data - - def explode(self, column, *, separator: str = ".") -> bigframes.dataframe.DataFrame: - df = self._parent - column_labels = bigframes.core.explode.check_column(column) - - for label in column_labels: - position = df.columns.to_list().index(label) - df = df.drop(columns=label) - subfields = self._parent[label].struct.explode() - for subfield in reversed(subfields.columns): - df.insert( - position, f"{label}{separator}{subfield}", subfields[subfield] - ) - - return df diff --git a/bigframes/operations/time_ops.py b/bigframes/operations/time_ops.py deleted file mode 100644 index 3b6845053a7..00000000000 --- a/bigframes/operations/time_ops.py +++ /dev/null @@ -1,44 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import bigframes.operations.type as op_typing -from bigframes import dtypes -from bigframes.operations import base_ops - -HourOp = base_ops.create_unary_op( - name="hour", - type_signature=op_typing.TIMELIKE_ACCESSOR, -) -hour_op = HourOp() - -MinuteOp = base_ops.create_unary_op( - name="minute", - type_signature=op_typing.TIMELIKE_ACCESSOR, -) -minute_op = MinuteOp() - -SecondOp = base_ops.create_unary_op( - name="second", - type_signature=op_typing.TIMELIKE_ACCESSOR, -) -second_op = SecondOp() - -NormalizeOp = base_ops.create_unary_op( - name="normalize", - type_signature=op_typing.TypePreserving( - dtypes.is_time_like, - description="time-like", - ), -) -normalize_op = NormalizeOp() diff --git a/bigframes/operations/timedelta_ops.py b/bigframes/operations/timedelta_ops.py deleted file mode 100644 index 5e9a1189e44..00000000000 --- a/bigframes/operations/timedelta_ops.py +++ /dev/null @@ -1,145 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -import dataclasses -import typing - -from bigframes import dtypes -from bigframes.operations import base_ops - - -@dataclasses.dataclass(frozen=True) -class ToTimedeltaOp(base_ops.UnaryOp): - name: typing.ClassVar[str] = "to_timedelta" - unit: typing.Literal["us", "ms", "s", "m", "h", "d", "W"] - - def output_type(self, *input_types: dtypes.ExpressionType) -> dtypes.ExpressionType: - if input_types[0] in ( - dtypes.INT_DTYPE, - dtypes.FLOAT_DTYPE, - dtypes.TIMEDELTA_DTYPE, - ): - return dtypes.TIMEDELTA_DTYPE - raise TypeError("expected integer or float input") - - -@dataclasses.dataclass(frozen=True) -class TimedeltaFloorOp(base_ops.UnaryOp): - """Floors the numeric value to the nearest integer and use it to represent a timedelta. - - This operator is only meant to be used during expression tree rewrites. Do not use it anywhere else! - """ - - name: typing.ClassVar[str] = "timedelta_floor" - - def output_type(self, *input_types: dtypes.ExpressionType) -> dtypes.ExpressionType: - input_type = input_types[0] - if dtypes.is_numeric(input_type) or input_type == dtypes.TIMEDELTA_DTYPE: - return dtypes.TIMEDELTA_DTYPE - raise TypeError(f"unsupported type: {input_type}") - - -timedelta_floor_op = TimedeltaFloorOp() - - -@dataclasses.dataclass(frozen=True) -class TimestampAddOp(base_ops.BinaryOp): - name: typing.ClassVar[str] = "timestamp_add" - - def output_type(self, *input_types: dtypes.ExpressionType) -> dtypes.ExpressionType: - # timestamp + timedelta => timestamp - if ( - dtypes.is_datetime_like(input_types[0]) - and input_types[1] == dtypes.TIMEDELTA_DTYPE - ): - return input_types[0] - # timedelta + timestamp => timestamp - if input_types[0] == dtypes.TIMEDELTA_DTYPE and dtypes.is_datetime_like( - input_types[1] - ): - return input_types[1] - - raise TypeError( - f"unsupported types for timestamp_add. left: {input_types[0]} right: {input_types[1]}" - ) - - -timestamp_add_op = TimestampAddOp() - - -@dataclasses.dataclass(frozen=True) -class TimestampSubOp(base_ops.BinaryOp): - name: typing.ClassVar[str] = "timestamp_sub" - - def output_type(self, *input_types: dtypes.ExpressionType) -> dtypes.ExpressionType: - # timestamp - timedelta => timestamp - if ( - dtypes.is_datetime_like(input_types[0]) - and input_types[1] == dtypes.TIMEDELTA_DTYPE - ): - return input_types[0] - - raise TypeError( - f"unsupported types for timestamp_sub. left: {input_types[0]} right: {input_types[1]}" - ) - - -timestamp_sub_op = TimestampSubOp() - - -@dataclasses.dataclass(frozen=True) -class DateAddOp(base_ops.BinaryOp): - name: typing.ClassVar[str] = "date_add" - - def output_type(self, *input_types: dtypes.ExpressionType) -> dtypes.ExpressionType: - # date + timedelta => timestamp without timezone - if ( - input_types[0] == dtypes.DATE_DTYPE - and input_types[1] == dtypes.TIMEDELTA_DTYPE - ): - return dtypes.DATETIME_DTYPE - # timedelta + date => timestamp without timezone - if ( - input_types[0] == dtypes.TIMEDELTA_DTYPE - and input_types[1] == dtypes.DATE_DTYPE - ): - return dtypes.DATETIME_DTYPE - - raise TypeError( - f"unsupported types for date_add. left: {input_types[0]} right: {input_types[1]}" - ) - - -date_add_op = DateAddOp() - - -@dataclasses.dataclass(frozen=True) -class DateSubOp(base_ops.BinaryOp): - name: typing.ClassVar[str] = "date_sub" - - def output_type(self, *input_types: dtypes.ExpressionType) -> dtypes.ExpressionType: - # date - timedelta => timestamp without timezone - if ( - input_types[0] == dtypes.DATE_DTYPE - and input_types[1] == dtypes.TIMEDELTA_DTYPE - ): - return dtypes.DATETIME_DTYPE - - raise TypeError( - f"unsupported types for date_sub. left: {input_types[0]} right: {input_types[1]}" - ) - - -date_sub_op = DateSubOp() diff --git a/bigframes/operations/to_op.py b/bigframes/operations/to_op.py deleted file mode 100644 index 4f97a61e3c0..00000000000 --- a/bigframes/operations/to_op.py +++ /dev/null @@ -1,201 +0,0 @@ -# Copyright 2026 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -from __future__ import annotations - -import dataclasses -import inspect -import typing - -import bigframes.core.expression as ex -from bigframes._config import options -from bigframes.exceptions import TranspilationError -from bigframes.functions import Udf -from bigframes.functions.udf_def import BigqueryUdf, PythonUdf -from bigframes.operations import base_ops, remote_function_ops - -ArgKind = typing.Literal[ - "positional_only", - "positional_or_keyword", - "keyword_only", - "var_positional", - "var_keyword", -] - -_ARGKIND_MAP: dict[inspect._ParameterKind, ArgKind] = { - inspect.Parameter.POSITIONAL_ONLY: "positional_only", - inspect.Parameter.POSITIONAL_OR_KEYWORD: "positional_or_keyword", - inspect.Parameter.VAR_POSITIONAL: "var_positional", - inspect.Parameter.KEYWORD_ONLY: "keyword_only", - inspect.Parameter.VAR_KEYWORD: "var_keyword", -} - - -@dataclasses.dataclass(frozen=True) -class ArgumentSpec: - """ - Information about a single argument to a function - """ - - name: str - default_value: typing.Any - argkind: ArgKind - - @property - def is_positional(self) -> bool: - return self.argkind in ["positional_only", "positional_or_keyword"] - - @property - def is_keyword(self) -> bool: - return self.argkind in ["keyword_only", "positional_or_keyword"] - - @property - def is_var_positional(self) -> bool: - return self.argkind == "var_positional" - - @property - def is_var_keyword(self) -> bool: - return self.argkind == "var_keyword" - - @property - def is_varargs(self) -> bool: - return self.is_var_positional - - -@dataclasses.dataclass(frozen=True) -class CallableExpression: - """ - Encodes a calling convention and an expression to bind arguments to. - """ - - expr: ex.Expression - arg_specs: typing.Sequence[ArgumentSpec] - - @classmethod - def from_callable(cls, func: typing.Callable) -> CallableExpression: - sig = inspect.signature(func) - arg_specs = [] - for name, param in sig.parameters.items(): - arg_specs.append( - ArgumentSpec( - name=name, - default_value=param.default, - argkind=_ARGKIND_MAP[param.kind], - ) - ) - - from bigframes.core.bytecode import py_to_expression - - try: - expr = py_to_expression(func) - except Exception as ex: - raise TranspilationError(f"Failed to transpile function {func}") from ex - return cls(expr=expr, arg_specs=arg_specs) - - def apply(self, *args, **kwargs) -> ex.Expression: - """ - Apply the arguments to the expression. - - All args are expected to be column references, or scalars. - """ - return self.bind_partial(*args, _offset=0, **kwargs).expr - - def bind_partial( - self, - *args, - _offset: int = 0, - **kwargs, - ) -> CallableExpression: - """ - Bind a subset of arguments and return a new CallableExpression with the remaining unbound arguments. - """ - bindings: dict[typing.Hashable, ex.Expression] = {} - pos_idx = 0 - allowed_params = self.arg_specs[_offset:] - allowed_names = {spec.name for spec in allowed_params} - - # Validate unexpected keyword arguments - for key in kwargs: - if key not in allowed_names: - raise TypeError(f"got an unexpected keyword argument '{key}'") - - def to_expr(val): - if isinstance(val, ex.Expression): - return val - return ex.const(val) - - for spec in allowed_params: - if spec.is_varargs: - raise NotImplementedError( - "varargs in compiled python functions is not supported" - ) - - if pos_idx < len(args): - if spec.name in kwargs: - raise TypeError( - f"got multiple values for keyword argument '{spec.name}'" - ) - bindings[spec.name] = to_expr(args[pos_idx]) - pos_idx += 1 - elif spec.name in kwargs: - bindings[spec.name] = to_expr(kwargs[spec.name]) - elif spec.default_value is not inspect.Parameter.empty: - bindings[spec.name] = to_expr(spec.default_value) - else: - raise TypeError(f"missing required argument: '{spec.name}'") - - if pos_idx < len(args): - raise TypeError( - f"too many positional arguments: expected {len(allowed_params)}, got {len(args)}" - ) - - new_expr = self.expr.bind_variables(bindings, allow_partial_bindings=True) - remaining_specs = list(self.arg_specs[:_offset]) - return CallableExpression(expr=new_expr, arg_specs=remaining_specs) - - -def func_to_expr(op) -> CallableExpression: - """ - Convert various bigframes, python functions into bigframes CallableExpression. - """ - if isinstance(op, Udf): - bq_op: base_ops.NaryOp - if isinstance(op.udf_def, BigqueryUdf): - bq_op = remote_function_ops.RemoteFunctionOp(function_def=op.udf_def) - elif isinstance(op.udf_def, PythonUdf): - bq_op = remote_function_ops.PythonUdfOp(function_def=op.udf_def) - else: - raise TypeError(f"Unsupported UDF definition: {op.udf_def}") - - inputs_expr = tuple( - ex.free_var(arg.name) for arg in op.udf_def.signature.inputs - ) - expr = ex.OpExpression(bq_op, inputs_expr) - - arg_specs = [ - ArgumentSpec( - name=arg.name, - default_value=inspect.Parameter.empty, - # Udf specs don't have concept of positional only or keyword only yet, - # so default to positional_or_keyword. - argkind="positional_or_keyword", - ) - for arg in op.udf_def.signature.inputs - ] - return CallableExpression(expr=expr, arg_specs=arg_specs) - - elif options.experiments.enable_python_transpiler and callable(op): - return CallableExpression.from_callable(op) - - else: - raise TypeError(f"Unsupported function type: {op}") diff --git a/bigframes/operations/type.py b/bigframes/operations/type.py deleted file mode 100644 index 0ddf3a113fc..00000000000 --- a/bigframes/operations/type.py +++ /dev/null @@ -1,258 +0,0 @@ -# Copyright 2023 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import abc -import dataclasses -from typing import Callable - -import bigframes.dtypes -from bigframes.dtypes import ExpressionType - - -@dataclasses.dataclass -class TypeSignature(abc.ABC): - """ - Type Signature represent a mapping from input types to output type. - - Type signatures should throw a TypeError if the input types cannot be handled by the operation. - """ - - @property - @abc.abstractmethod - def as_method(self): - """Convert the signature into an object method. Convenience function for constructing ops that use the signature.""" - ... - - def __call__(self, *args, **kwargs): - return self.as_method(*args, **kwargs) - - -class UnaryTypeSignature(TypeSignature): - @abc.abstractmethod - def output_type(self, input_type: ExpressionType) -> ExpressionType: ... - - @property - def as_method(self): - def meth(_, *input_types: ExpressionType) -> ExpressionType: - assert len(input_types) == 1 - return self.output_type(input_types[0]) - - return meth - - -class BinaryTypeSignature(TypeSignature): - @abc.abstractmethod - def output_type( - self, left_type: ExpressionType, right_type: ExpressionType - ) -> ExpressionType: ... - - @property - def as_method(self): - def meth(_, *input_types: ExpressionType) -> ExpressionType: - assert len(input_types) == 2 - return self.output_type(input_types[0], input_types[1]) - - return meth - - -@dataclasses.dataclass -class TypePreserving(UnaryTypeSignature): - type_predicate: Callable[[ExpressionType], bool] - description: str - - def output_type(self, input_type: ExpressionType) -> ExpressionType: - if not self.type_predicate(input_type): - raise TypeError( - f"Type {input_type} is not supported. Type must be {self.description}" - ) - return input_type - - -@dataclasses.dataclass -class FixedOutputType(UnaryTypeSignature): - type_predicate: Callable[[ExpressionType], bool] - fixed_type: ExpressionType - description: str - - def output_type(self, input_type: ExpressionType) -> ExpressionType: - if (input_type is not None) and not self.type_predicate(input_type): - raise TypeError( - f"Type {input_type} is not supported. Type must be {self.description}" - ) - return self.fixed_type - - -@dataclasses.dataclass -class UnaryRealNumeric(UnaryTypeSignature): - """Type signature for real-valued functions like exp, log, sin, tan.""" - - def output_type(self, type: ExpressionType) -> ExpressionType: - if type is None: - return bigframes.dtypes.FLOAT_DTYPE - if not bigframes.dtypes.is_numeric(type): - raise TypeError(f"Type {type} is not numeric") - if type in (bigframes.dtypes.INT_DTYPE, bigframes.dtypes.BOOL_DTYPE): - # Real numeric ops produce floats on int input - return bigframes.dtypes.FLOAT_DTYPE - return type - - -@dataclasses.dataclass -class BinaryNumeric(BinaryTypeSignature): - """Type signature for numeric functions like multiply, modulo that can map ints to ints.""" - - def output_type( - self, left_type: ExpressionType, right_type: ExpressionType - ) -> ExpressionType: - if (left_type is not None) and not bigframes.dtypes.is_numeric(left_type): - raise TypeError(f"Type {left_type} is not numeric") - if (right_type is not None) and not bigframes.dtypes.is_numeric(right_type): - raise TypeError(f"Type {right_type} is not numeric") - return bigframes.dtypes.coerce_to_common(left_type, right_type) - - -@dataclasses.dataclass -@dataclasses.dataclass -class BinaryGeo(BinaryTypeSignature): - """Type signature for geo functions like difference that can map geo to geo.""" - - def output_type( - self, left_type: ExpressionType, right_type: ExpressionType - ) -> ExpressionType: - if (left_type is not None) and not bigframes.dtypes.is_geo_like(left_type): - raise TypeError(f"Type {left_type} is not geo") - if (right_type is not None) and not bigframes.dtypes.is_geo_like(right_type): - raise TypeError(f"Type {right_type} is not numeric") - return bigframes.dtypes.GEO_DTYPE - - -class BinaryNumericGeo(BinaryTypeSignature): - """Type signature for geo functions like from_xy that can map ints to ints.""" - - def output_type( - self, left_type: ExpressionType, right_type: ExpressionType - ) -> ExpressionType: - if (left_type is not None) and not bigframes.dtypes.is_numeric(left_type): - raise TypeError(f"Type {left_type} is not numeric") - if (right_type is not None) and not bigframes.dtypes.is_numeric(right_type): - raise TypeError(f"Type {right_type} is not numeric") - return bigframes.dtypes.GEO_DTYPE - - -@dataclasses.dataclass -class BinaryRealNumeric(BinaryTypeSignature): - """Type signature for real-valued functions like divide, arctan2, pow.""" - - def output_type( - self, left_type: ExpressionType, right_type: ExpressionType - ) -> ExpressionType: - if (left_type is not None) and not bigframes.dtypes.is_numeric(left_type): - raise TypeError(f"Type {left_type} is not numeric") - if (right_type is not None) and not bigframes.dtypes.is_numeric(right_type): - raise TypeError(f"Type {right_type} is not numeric") - lcd_type = bigframes.dtypes.coerce_to_common(left_type, right_type) - if lcd_type == bigframes.dtypes.INT_DTYPE: - # Real numeric ops produce floats on int input - return bigframes.dtypes.FLOAT_DTYPE - return lcd_type - - -@dataclasses.dataclass -class CoerceCommon(BinaryTypeSignature): - """Attempt to coerce inputs to a compatible type.""" - - def output_type( - self, left_type: ExpressionType, right_type: ExpressionType - ) -> ExpressionType: - return bigframes.dtypes.coerce_to_common(left_type, right_type) - - -@dataclasses.dataclass -class Comparison(BinaryTypeSignature): - """Type signature for comparison operators.""" - - def output_type( - self, left_type: ExpressionType, right_type: ExpressionType - ) -> ExpressionType: - if not bigframes.dtypes.can_compare(left_type, right_type): - raise TypeError(f"Types {left_type} and {right_type} are not comparable") - return bigframes.dtypes.BOOL_DTYPE - - -@dataclasses.dataclass -class Logical(BinaryTypeSignature): - """Type signature for logical operators like AND, OR and NOT.""" - - def output_type( - self, left_type: ExpressionType, right_type: ExpressionType - ) -> ExpressionType: - if left_type is None or right_type is None: - return bigframes.dtypes.BOOL_DTYPE - if not bigframes.dtypes.is_binary_like(left_type): - raise TypeError(f"Type {left_type} is not binary") - if not bigframes.dtypes.is_binary_like(right_type): - raise TypeError(f"Type {right_type} is not binary") - if left_type != right_type: - raise TypeError( - f"Bitwise operands {left_type} and {right_type} do not match" - ) - return left_type - - -@dataclasses.dataclass -class VectorMetric(BinaryTypeSignature): - """Type signature for logical operators like AND, OR and NOT.""" - - def output_type( - self, left_type: ExpressionType, right_type: ExpressionType - ) -> ExpressionType: - if not bigframes.dtypes.is_array_like(left_type): - raise TypeError(f"Type {left_type} is not array-like") - if not bigframes.dtypes.is_array_like(right_type): - raise TypeError(f"Type {right_type} is not array-like") - if left_type != right_type: - raise TypeError( - f"Vector op operands {left_type} and {right_type} do not match" - ) - return bigframes.dtypes.FLOAT_DTYPE - - -# Common type signatures -UNARY_NUMERIC = TypePreserving(bigframes.dtypes.is_numeric, description="numeric") -UNARY_NUMERIC_AND_TIMEDELTA = TypePreserving( - lambda x: bigframes.dtypes.is_numeric(x) or x is bigframes.dtypes.TIMEDELTA_DTYPE, - description="numeric_and_timedelta", -) -UNARY_REAL_NUMERIC = UnaryRealNumeric() -BINARY_NUMERIC = BinaryNumeric() -BINARY_REAL_NUMERIC = BinaryRealNumeric() -BLOB_TRANSFORM = TypePreserving(bigframes.dtypes.is_struct_like, description="blob") -COMPARISON = Comparison() -COERCE = CoerceCommon() -LOGICAL = Logical() -STRING_TRANSFORM = TypePreserving( - bigframes.dtypes.is_string_like, description="numeric" -) -STRING_PREDICATE = FixedOutputType( - bigframes.dtypes.is_string_like, - bigframes.dtypes.BOOL_DTYPE, - description="string-like", -) -DATELIKE_ACCESSOR = FixedOutputType( - bigframes.dtypes.is_date_like, bigframes.dtypes.INT_DTYPE, description="date-like" -) -TIMELIKE_ACCESSOR = FixedOutputType( - bigframes.dtypes.is_time_like, bigframes.dtypes.INT_DTYPE, description="time-like" -) -VECTOR_METRIC = VectorMetric() diff --git a/bigframes/pandas/__init__.py b/bigframes/pandas/__init__.py index b88816ab5ab..1c52b103fbe 100644 --- a/bigframes/pandas/__init__.py +++ b/bigframes/pandas/__init__.py @@ -12,385 +12,629 @@ # See the License for the specific language governing permissions and # limitations under the License. -""" -The primary entry point for the BigQuery DataFrames (BigFrames) pandas-compatible API. +"""BigQuery DataFrames provides a DataFrame API backed by the BigQuery engine.""" -**BigQuery DataFrames** provides a Pythonic DataFrame and machine learning (ML) API -powered by the BigQuery engine. The ``bigframes.pandas`` module implements a large -subset of the pandas API, allowing you to perform large-scale data analysis -using familiar pandas syntax while the computations are executed in the cloud. +from __future__ import annotations -**Key Features:** +from collections import namedtuple +import inspect +import typing +from typing import ( + Any, + Callable, + Dict, + IO, + Iterable, + List, + Literal, + MutableSequence, + Optional, + Sequence, + Tuple, + Union, +) -* **Petabyte-Scale Scalability:** Handle datasets that exceed local memory by - offloading computation to the BigQuery distributed engine. -* **Pandas Compatibility:** Use common pandas methods like - :func:`~bigframes.pandas.DataFrame.groupby`, - :func:`~bigframes.pandas.DataFrame.merge`, - :func:`~bigframes.pandas.DataFrame.pivot_table`, and more on BigQuery-backed - :class:`~bigframes.pandas.DataFrame` objects. -* **Direct BigQuery Integration:** Read from and write to BigQuery tables and - queries with :func:`bigframes.pandas.read_gbq` and - :func:`bigframes.pandas.DataFrame.to_gbq`. -* **User-defined Functions (UDFs):** Effortlessly deploy Python functions - functions using the :func:`bigframes.pandas.remote_function` and - :func:`bigframes.pandas.udf` decorators. -* **Data Ingestion:** Support for various formats including CSV, Parquet, JSON, - and Arrow via :func:`bigframes.pandas.read_csv`, - :func:`bigframes.pandas.read_parquet`, etc., which are automatically uploaded - to BigQuery for processing. Convert any pandas DataFrame into a BigQuery - DataFrame using :func:`bigframes.pandas.read_pandas`. +from google.cloud import bigquery +import numpy +import pandas +from pandas._typing import ( + CompressionOptions, + FilePath, + ReadPickleBuffer, + StorageOptions, +) -**Example usage:** +import bigframes._config as config +import bigframes.constants as constants +import bigframes.core.blocks +import bigframes.core.global_session as global_session +import bigframes.core.indexes +import bigframes.core.reshape +import bigframes.dataframe +import bigframes.operations as ops +import bigframes.series +import bigframes.session +import bigframes.session.clients +import third_party.bigframes_vendored.pandas.core.reshape.concat as vendored_pandas_concat +import third_party.bigframes_vendored.pandas.core.reshape.encoding as vendored_pandas_encoding +import third_party.bigframes_vendored.pandas.core.reshape.merge as vendored_pandas_merge +import third_party.bigframes_vendored.pandas.core.reshape.tile as vendored_pandas_tile - >>> import bigframes.pandas as bpd -Initialize session and set options. +# Include method definition so that the method appears in our docs for +# bigframes.pandas general functions. +@typing.overload +def concat( + objs: Iterable[bigframes.series.Series], + *, + axis: typing.Literal["index", 0] = ..., + join=..., + ignore_index=..., +) -> bigframes.series.Series: + ... - >>> bpd.options.bigquery.project = "your-project-id" # doctest: +SKIP -Load data from a BigQuery public dataset. +@typing.overload +def concat( + objs: Iterable[bigframes.dataframe.DataFrame], + *, + axis: typing.Literal["index", 0] = ..., + join=..., + ignore_index=..., +) -> bigframes.dataframe.DataFrame: + ... + - >>> df = bpd.read_gbq("bigquery-public-data.usa_names.usa_1910_2013") # doctest: +SKIP +@typing.overload +def concat( + objs: Iterable[Union[bigframes.dataframe.DataFrame, bigframes.series.Series]], + *, + axis: typing.Literal["columns", 1], + join=..., + ignore_index=..., +) -> bigframes.dataframe.DataFrame: + ... -Perform familiar pandas operations that execute in the cloud. - >>> top_names = ( - ... df.groupby("name") - ... .agg({"number": "sum"}) - ... .sort_values("number", ascending=False) - ... .head(10) - ... ) # doctest: +SKIP +@typing.overload +def concat( + objs: Iterable[Union[bigframes.dataframe.DataFrame, bigframes.series.Series]], + *, + axis=..., + join=..., + ignore_index=..., +) -> Union[bigframes.dataframe.DataFrame, bigframes.series.Series]: + ... -Bring the final, aggregated results back to local memory if needed. - >>> local_df = top_names.to_pandas() # doctest: +SKIP +def concat( + objs: Iterable[Union[bigframes.dataframe.DataFrame, bigframes.series.Series]], + *, + axis: typing.Union[str, int] = 0, + join: Literal["inner", "outer"] = "outer", + ignore_index: bool = False, +) -> Union[bigframes.dataframe.DataFrame, bigframes.series.Series]: + return bigframes.core.reshape.concat( + objs=objs, axis=axis, join=join, ignore_index=ignore_index + ) -BigQuery DataFrames is designed for data scientists and analysts who need the -power of BigQuery with the ease of use of pandas. It eliminates the "data -movement bottleneck" by keeping your data in BigQuery for processing. -""" -from __future__ import annotations +concat.__doc__ = vendored_pandas_concat.concat.__doc__ -import collections -import datetime -import inspect -import sys -import typing -from typing import Literal, Optional, Sequence, Union -import bigframes_vendored.pandas.core.tools.datetimes as vendored_pandas_datetimes -import pandas +def cut( + x: bigframes.series.Series, + bins: int, + *, + labels: Optional[bool] = None, +) -> bigframes.series.Series: + return bigframes.core.reshape.cut( + x, + bins, + labels=labels, + ) -import bigframes._config as config -import bigframes.core.global_session as global_session -import bigframes.core.indexes -import bigframes.dataframe -import bigframes.functions._utils as bff_utils -import bigframes.series -import bigframes.session -import bigframes.session._io.bigquery -import bigframes.version -from bigframes.core.col import col -from bigframes.core.logging import log_adapter -from bigframes.core.reshape.api import concat, crosstab, cut, get_dummies, merge, qcut -from bigframes.pandas import api -from bigframes.pandas.core.api import to_timedelta -from bigframes.pandas.io.api import ( - _from_glob_path, - _read_gbq_colab, - read_arrow, - read_avro, - read_csv, - read_gbq, - read_gbq_function, - read_gbq_model, - read_gbq_query, - read_gbq_table, - read_json, - read_orc, - read_pandas, - read_parquet, - read_pickle, -) -try: - import resource -except ImportError: - # resource is only available on Unix-like systems. - # https://docs.python.org/3/library/resource.html - resource = None # type: ignore +cut.__doc__ = vendored_pandas_tile.cut.__doc__ -def remote_function( - # Make sure that the input/output types, and dataset can be used - # positionally. This avoids the worst of the breaking change from 1.x to - # 2.x while still preventing possible mixups between consecutive str - # parameters. - input_types: Union[None, type, Sequence[type]] = None, - output_type: Optional[type] = None, - dataset: Optional[str] = None, +def get_dummies( + data: Union[DataFrame, Series], + prefix: Union[List, dict, str, None] = None, + prefix_sep: Union[List, dict, str, None] = "_", + dummy_na: bool = False, + columns: Optional[List] = None, + drop_first: bool = False, + dtype: Any = None, +) -> DataFrame: + # simplify input parameters into per-input-label lists + # also raise errors for invalid parameters + column_labels, prefixes, prefix_seps = _standardize_get_dummies_params( + data, prefix, prefix_sep, columns, dtype + ) + + # combine prefixes into per-column-id list + full_columns_prefixes, columns_ids = _determine_get_dummies_columns_from_labels( + data, column_labels, prefix is not None, prefixes, prefix_seps + ) + + # run queries to compute unique values + block = data._block + max_unique_value = ( + bigframes.core.blocks._BQ_MAX_COLUMNS + - len(block.value_columns) + - len(block.index_columns) + - 1 + ) // len(column_labels) + columns_values = [ + block._get_unique_values([col_id], max_unique_value) for col_id in columns_ids + ] + + # for each dummified column, add the content of the output columns via block operations + intermediate_col_ids = [] + for i in range(len(columns_values)): + level = columns_values[i].get_level_values(0).sort_values().dropna() + if drop_first: + level = level[1:] + column_label = full_columns_prefixes[i] + column_id = columns_ids[i] + block, new_intermediate_col_ids = _perform_get_dummies_block_operations( + block, level, column_label, column_id, dummy_na + ) + intermediate_col_ids.extend(new_intermediate_col_ids) + + # drop dummified columns (and the intermediate columns we added) + block = block.drop_columns(columns_ids + intermediate_col_ids) + return DataFrame(block) + + +get_dummies.__doc__ = vendored_pandas_encoding.get_dummies.__doc__ + + +def _standardize_get_dummies_params( + data: Union[DataFrame, Series], + prefix: Union[List, dict, str, None], + prefix_sep: Union[List, dict, str, None], + columns: Optional[List], + dtype: Any, +) -> Tuple[List, List[str], List[str]]: + block = data._block + + if isinstance(data, Series): + columns = [block.column_labels[0]] + if columns is not None and not pandas.api.types.is_list_like(columns): + raise TypeError("Input must be a list-like for parameter `columns`") + if dtype is not None and dtype not in [ + pandas.BooleanDtype, + bool, + "Boolean", + "boolean", + "bool", + ]: + raise NotImplementedError( + f"Only Boolean dtype is currently supported. {constants.FEEDBACK_LINK}" + ) + + if columns is None: + default_dummy_types = [pandas.StringDtype, "string[pyarrow]"] + columns = [] + columns_set = set() + for col_id in block.value_columns: + label = block.col_id_to_label[col_id] + if ( + label not in columns_set + and block.expr.get_column_type(col_id) in default_dummy_types + ): + columns.append(label) + columns_set.add(label) + + column_labels: List = typing.cast(List, columns) + + def parse_prefix_kwarg(kwarg, kwarg_name) -> Optional[List[str]]: + if kwarg is None: + return None + if isinstance(kwarg, str): + return [kwarg] * len(column_labels) + if isinstance(kwarg, dict): + return [kwarg[column] for column in column_labels] + kwarg = typing.cast(List, kwarg) + if pandas.api.types.is_list_like(kwarg) and len(kwarg) != len(column_labels): + raise ValueError( + f"Length of '{kwarg_name}' ({len(kwarg)}) did not match " + f"the length of the columns being encoded ({len(column_labels)})." + ) + if pandas.api.types.is_list_like(kwarg): + return list(map(str, kwarg)) + raise TypeError(f"{kwarg_name} kwarg must be a string, list, or dictionary") + + prefix_seps = parse_prefix_kwarg(prefix_sep or "_", "prefix_sep") + prefix_seps = typing.cast(List, prefix_seps) + prefixes = parse_prefix_kwarg(prefix, "prefix") + if prefixes is None: + prefixes = column_labels + prefixes = typing.cast(List, prefixes) + + return column_labels, prefixes, prefix_seps + + +def _determine_get_dummies_columns_from_labels( + data: Union[DataFrame, Series], + column_labels: List, + prefix_given: bool, + prefixes: List[str], + prefix_seps: List[str], +) -> Tuple[List[str], List[str]]: + block = data._block + + columns_ids = [] + columns_prefixes = [] + for i in range(len(column_labels)): + label = column_labels[i] + empty_prefix = label is None or (isinstance(data, Series) and not prefix_given) + full_prefix = "" if empty_prefix else prefixes[i] + prefix_seps[i] + + for col_id in block.label_to_col_id[label]: + columns_ids.append(col_id) + columns_prefixes.append(full_prefix) + + return columns_prefixes, columns_ids + + +def _perform_get_dummies_block_operations( + block: bigframes.core.blocks.Block, + level: pandas.Index, + column_label: str, + column_id: str, + dummy_na: bool, +) -> Tuple[bigframes.core.blocks.Block, List[str]]: + intermediate_col_ids = [] + for value in level: + new_column_label = f"{column_label}{value}" + if column_label == "": + new_column_label = value + new_block, new_id = block.apply_unary_op( + column_id, ops.BinopPartialLeft(ops.eq_op, value) + ) + intermediate_col_ids.append(new_id) + block, _ = new_block.apply_unary_op( + new_id, + ops.BinopPartialRight(ops.fillna_op, False), + result_label=new_column_label, + ) + if dummy_na: + # dummy column name for na depends on the dtype + na_string = str(pandas.Index([None], dtype=level.dtype)[0]) + new_column_label = f"{column_label}{na_string}" + block, _ = block.apply_unary_op( + column_id, ops.isnull_op, result_label=new_column_label + ) + return block, intermediate_col_ids + + +def qcut( + x: bigframes.series.Series, + q: int, *, - bigquery_connection: Optional[str] = None, - reuse: bool = True, - name: Optional[str] = None, - packages: Optional[Sequence[str]] = None, - cloud_function_service_account: str, - cloud_function_kms_key_name: Optional[str] = None, - cloud_function_docker_repository: Optional[str] = None, - max_batching_rows: Optional[int] = 1000, - cloud_function_timeout: Optional[int] = 600, - cloud_function_max_instances: Optional[int] = None, - cloud_function_vpc_connector: Optional[str] = None, - cloud_function_vpc_connector_egress_settings: Optional[ - Literal["all", "private-ranges-only", "unspecified"] + labels: Optional[bool] = None, + duplicates: typing.Literal["drop", "error"] = "error", +) -> bigframes.series.Series: + return bigframes.core.reshape.qcut(x, q, labels=labels, duplicates=duplicates) + + +qcut.__doc__ = vendored_pandas_tile.qcut.__doc__ + + +def merge( + left: DataFrame, + right: DataFrame, + how: Literal[ + "inner", + "left", + "outer", + "right", + ] = "inner", + on: Optional[str] = None, + *, + left_on: Optional[str] = None, + right_on: Optional[str] = None, + sort: bool = False, + suffixes: tuple[str, str] = ("_x", "_y"), +) -> DataFrame: + return bigframes.core.joins.merge( + left, + right, + how=how, + on=on, + left_on=left_on, + right_on=right_on, + sort=sort, + suffixes=suffixes, + ) + + +merge.__doc__ = vendored_pandas_merge.merge.__doc__ + + +def _set_default_session_location_if_possible(query): + # Set the location as per the query if this is the first query the user is + # running and: + # (1) Default session has not started yet, and + # (2) Location is not set yet, and + # (3) Use of regional endpoints is not set. + # If query is a table name, then it would be the location of the table. + # If query is a SQL with a table, then it would be table's location. + # If query is a SQL with no table, then it would be the BQ default location. + if ( + options.bigquery._session_started + or options.bigquery.location + or options.bigquery.use_regional_endpoints + ): + return + + clients_provider = bigframes.session.clients.ClientsProvider( + project=options.bigquery.project, + location=options.bigquery.location, + use_regional_endpoints=options.bigquery.use_regional_endpoints, + credentials=options.bigquery.credentials, + application_name=options.bigquery.application_name, + ) + + bqclient = clients_provider.bqclient + + if bigframes.session._is_query(query): + job = bqclient.query(query, bigquery.QueryJobConfig(dry_run=True)) + options.bigquery.location = job.location + else: + table = bqclient.get_table(query) + options.bigquery.location = table.location + + +# Note: the following methods are duplicated from Session. This duplication +# enables the following: +# +# 1. Static type checking knows the argument and return types, which is +# difficult to do with decorators. Aside: When we require Python 3.10, we +# can use Concatenate for generic typing in decorators. See: +# https://stackoverflow.com/a/68290080/101923 +# 2. docstrings get processed by static processing tools, such as VS Code's +# autocomplete. +# 3. Positional arguments function as expected. If we were to pull in the +# methods directly from Session, a Session object would need to be the first +# argument, even if we allow a default value. +# 4. Allows to set BigQuery options for the BigFrames session based on the +# method and its arguments. + + +def read_csv( + filepath_or_buffer: str | IO["bytes"], + *, + sep: Optional[str] = ",", + header: Optional[int] = 0, + names: Optional[ + Union[MutableSequence[Any], numpy.ndarray[Any, Any], Tuple[Any, ...], range] ] = None, - cloud_function_memory_mib: Optional[int] = None, - cloud_function_cpus: Optional[float] = None, - cloud_function_ingress_settings: Literal[ - "all", "internal-only", "internal-and-gclb" - ] = "internal-only", - cloud_build_service_account: Optional[str] = None, -): + index_col: Optional[ + Union[int, str, Sequence[Union[str, int]], Literal[False]] + ] = None, + usecols: Optional[ + Union[ + MutableSequence[str], + Tuple[str, ...], + Sequence[int], + pandas.Series, + pandas.Index, + numpy.ndarray[Any, Any], + Callable[[Any], bool], + ] + ] = None, + dtype: Optional[Dict] = None, + engine: Optional[ + Literal["c", "python", "pyarrow", "python-fwf", "bigquery"] + ] = None, + encoding: Optional[str] = None, + **kwargs, +) -> bigframes.dataframe.DataFrame: return global_session.with_default_session( - bigframes.session.Session.remote_function, - input_types=input_types, - output_type=output_type, - dataset=dataset, - bigquery_connection=bigquery_connection, - reuse=reuse, - name=name, - packages=packages, - cloud_function_service_account=cloud_function_service_account, - cloud_function_kms_key_name=cloud_function_kms_key_name, - cloud_function_docker_repository=cloud_function_docker_repository, - max_batching_rows=max_batching_rows, - cloud_function_timeout=cloud_function_timeout, - cloud_function_max_instances=cloud_function_max_instances, - cloud_function_vpc_connector=cloud_function_vpc_connector, - cloud_function_vpc_connector_egress_settings=cloud_function_vpc_connector_egress_settings, - cloud_function_memory_mib=cloud_function_memory_mib, - cloud_function_cpus=cloud_function_cpus, - cloud_function_ingress_settings=cloud_function_ingress_settings, - cloud_build_service_account=cloud_build_service_account, + bigframes.session.Session.read_csv, + filepath_or_buffer=filepath_or_buffer, + sep=sep, + header=header, + names=names, + index_col=index_col, + usecols=usecols, + dtype=dtype, + engine=engine, + encoding=encoding, + **kwargs, ) -remote_function.__doc__ = inspect.getdoc(bigframes.session.Session.remote_function) +read_csv.__doc__ = inspect.getdoc(bigframes.session.Session.read_csv) -def deploy_remote_function( - func, +def read_json( + path_or_buf: str | IO["bytes"], + *, + orient: Literal[ + "split", "records", "index", "columns", "values", "table" + ] = "columns", + dtype: Optional[Dict] = None, + encoding: Optional[str] = None, + lines: bool = False, + engine: Literal["ujson", "pyarrow", "bigquery"] = "ujson", **kwargs, -): +) -> bigframes.dataframe.DataFrame: return global_session.with_default_session( - bigframes.session.Session.deploy_remote_function, - func=func, + bigframes.session.Session.read_json, + path_or_buf=path_or_buf, + orient=orient, + dtype=dtype, + encoding=encoding, + lines=lines, + engine=engine, **kwargs, ) -deploy_remote_function.__doc__ = inspect.getdoc( - bigframes.session.Session.deploy_remote_function -) +read_json.__doc__ = inspect.getdoc(bigframes.session.Session.read_json) -def udf( +def read_gbq( + query_or_table: str, *, - input_types: Union[None, type, Sequence[type]] = None, - output_type: Optional[type] = None, - dataset: Optional[str] = None, - bigquery_connection: Optional[str] = None, - name: Optional[str] = None, - packages: Optional[Sequence[str]] = None, - max_batching_rows: Optional[int] = None, - container_cpu: Optional[float] = None, - container_memory: Optional[str] = None, -): + index_col: Iterable[str] | str = (), + col_order: Iterable[str] = (), + max_results: Optional[int] = None, +) -> bigframes.dataframe.DataFrame: + _set_default_session_location_if_possible(query_or_table) return global_session.with_default_session( - bigframes.session.Session.udf, - input_types=input_types, - output_type=output_type, - dataset=dataset, - bigquery_connection=bigquery_connection, - name=name, - packages=packages, - max_batching_rows=max_batching_rows, - container_cpu=container_cpu, - container_memory=container_memory, + bigframes.session.Session.read_gbq, + query_or_table, + index_col=index_col, + col_order=col_order, + max_results=max_results, ) -udf.__doc__ = inspect.getdoc(bigframes.session.Session.udf) +read_gbq.__doc__ = inspect.getdoc(bigframes.session.Session.read_gbq) -def deploy_udf( - func, - **kwargs, -): +def read_gbq_model(model_name: str): return global_session.with_default_session( - bigframes.session.Session.deploy_udf, - func=func, - **kwargs, + bigframes.session.Session.read_gbq_model, + model_name, ) -deploy_udf.__doc__ = inspect.getdoc(bigframes.session.Session.deploy_udf) +read_gbq_model.__doc__ = inspect.getdoc(bigframes.session.Session.read_gbq_model) -@typing.overload -def to_datetime( - arg: Union[ - vendored_pandas_datetimes.local_iterables, - bigframes.series.Series, - bigframes.dataframe.DataFrame, - ], +def read_gbq_query( + query: str, *, - utc: bool = False, - format: Optional[str] = None, - unit: Optional[str] = None, -) -> bigframes.series.Series: ... + index_col: Iterable[str] | str = (), + col_order: Iterable[str] = (), + max_results: Optional[int] = None, +) -> bigframes.dataframe.DataFrame: + _set_default_session_location_if_possible(query) + return global_session.with_default_session( + bigframes.session.Session.read_gbq_query, + query, + index_col=index_col, + col_order=col_order, + max_results=max_results, + ) -@typing.overload -def to_datetime( - arg: Union[int, float, str, datetime.datetime, datetime.date], - *, - utc: bool = False, - format: Optional[str] = None, - unit: Optional[str] = None, -) -> Union[pandas.Timestamp, datetime.datetime]: ... - - -def to_datetime( - arg: Union[ - Union[int, float, str, datetime.datetime, datetime.date], - vendored_pandas_datetimes.local_iterables, - bigframes.series.Series, - bigframes.dataframe.DataFrame, - ], +read_gbq_query.__doc__ = inspect.getdoc(bigframes.session.Session.read_gbq_query) + + +def read_gbq_table( + query: str, *, - utc: bool = False, - format: Optional[str] = None, - unit: Optional[str] = None, -) -> Union[pandas.Timestamp, datetime.datetime, bigframes.series.Series]: + index_col: Iterable[str] | str = (), + col_order: Iterable[str] = (), + max_results: Optional[int] = None, +) -> bigframes.dataframe.DataFrame: + _set_default_session_location_if_possible(query) return global_session.with_default_session( - bigframes.session.Session.to_datetime, - arg, - utc=utc, - format=format, - unit=unit, + bigframes.session.Session.read_gbq_table, + query, + index_col=index_col, + col_order=col_order, + max_results=max_results, ) -to_datetime.__doc__ = vendored_pandas_datetimes.to_datetime.__doc__ +read_gbq_table.__doc__ = inspect.getdoc(bigframes.session.Session.read_gbq_table) -def get_default_session_id() -> str: - """Gets the session id that is used whenever a custom session - has not been provided. +def read_pandas(pandas_dataframe: pandas.DataFrame) -> bigframes.dataframe.DataFrame: + return global_session.with_default_session( + bigframes.session.Session.read_pandas, + pandas_dataframe, + ) + - It is the session id of the default global session. It is prefixed to - the table id of all temporary tables created in the global session. +read_pandas.__doc__ = inspect.getdoc(bigframes.session.Session.read_pandas) - Returns: - str: - The default global session id, ex. 'sessiona1b2c' - """ - return get_global_session().session_id +def read_pickle( + filepath_or_buffer: FilePath | ReadPickleBuffer, + compression: CompressionOptions = "infer", + storage_options: StorageOptions = None, +): + return global_session.with_default_session( + bigframes.session.Session.read_pickle, + filepath_or_buffer=filepath_or_buffer, + compression=compression, + storage_options=storage_options, + ) -@log_adapter.method_logger -def clean_up_by_session_id( - session_id: str, - location: Optional[str] = None, - project: Optional[str] = None, -) -> None: - """Searches through BigQuery tables and routines and deletes the ones - created during the session with the given session id. The match is - determined by having the session id present in the resource name or - metadata. The cloud functions serving the cleaned up routines are also - cleaned up. - This could be useful if the session object has been lost. - Calling `session.close()` or `bigframes.pandas.close_session()` - is preferred in most cases. +read_pickle.__doc__ = inspect.getdoc(bigframes.session.Session.read_pickle) - Args: - session_id (str): - The session id to clean up. Can be found using - session.session_id or get_default_session_id(). - location (str, default None): - The location of the session to clean up. If given, used - together with project kwarg to determine the dataset - to search through for tables to clean up. +def read_parquet(path: str | IO["bytes"]) -> bigframes.dataframe.DataFrame: + return global_session.with_default_session( + bigframes.session.Session.read_parquet, + path, + ) - project (str, default None): - The project id associated with the session to clean up. - If given, used together with location kwarg to determine - the dataset to search through for tables to clean up. - Returns: - None - """ - session = get_global_session() +read_parquet.__doc__ = inspect.getdoc(bigframes.session.Session.read_parquet) - if (location is None) != (project is None): - raise ValueError( - "Only one of project or location was given. Must specify both or neither." - ) - elif location is None and project is None: - dataset = session._anonymous_dataset - else: - dataset = bigframes.session._io.bigquery.create_bq_dataset_reference( - session.bqclient, - location=location, - project=project, - publisher=session._publisher, - ) - bigframes.session._io.bigquery.delete_tables_matching_session_id( - session.bqclient, dataset, session_id +def remote_function( + input_types: List[type], + output_type: type, + dataset: Optional[str] = None, + bigquery_connection: Optional[str] = None, + reuse: bool = True, + name: Optional[str] = None, + packages: Optional[Sequence[str]] = None, +): + return global_session.with_default_session( + bigframes.session.Session.remote_function, + input_types=input_types, + output_type=output_type, + dataset=dataset, + bigquery_connection=bigquery_connection, + reuse=reuse, + name=name, + packages=packages, ) - bff_utils.clean_up_by_session_id( - session.bqclient, session.cloudfunctionsclient, dataset, session_id + +remote_function.__doc__ = inspect.getdoc(bigframes.session.Session.remote_function) + + +def read_gbq_function(function_name: str): + return global_session.with_default_session( + bigframes.session.Session.read_gbq_function, + function_name=function_name, ) +read_gbq_function.__doc__ = inspect.getdoc(bigframes.session.Session.read_gbq_function) + # pandas dtype attributes NA = pandas.NA -"""Alias for :class:`pandas.NA`.""" - BooleanDtype = pandas.BooleanDtype -"""Alias for :class:`pandas.BooleanDtype`.""" - Float64Dtype = pandas.Float64Dtype -"""Alias for :class:`pandas.Float64Dtype`.""" - Int64Dtype = pandas.Int64Dtype -"""Alias for :class:`pandas.Int64Dtype`.""" - StringDtype = pandas.StringDtype -"""Alias for :class:`pandas.StringDtype`.""" - ArrowDtype = pandas.ArrowDtype -"""Alias for :class:`pandas.ArrowDtype`.""" # Class aliases # TODO(swast): Make these real classes so we can refer to these in type # checking and docstrings. DataFrame = bigframes.dataframe.DataFrame Index = bigframes.core.indexes.Index -MultiIndex = bigframes.core.indexes.MultiIndex -DatetimeIndex = bigframes.core.indexes.DatetimeIndex Series = bigframes.series.Series -__version__ = bigframes.version.__version__ # Other public pandas attributes -NamedAgg = collections.namedtuple("NamedAgg", ["column", "aggfunc"]) +NamedAgg = namedtuple("NamedAgg", ["column", "aggfunc"]) options = config.options """Global :class:`~bigframes._config.Options` to configure BigQuery DataFrames.""" @@ -398,124 +642,35 @@ def clean_up_by_session_id( option_context = config.option_context """Global :class:`~bigframes._config.option_context` to configure BigQuery DataFrames.""" - # Session management APIs -def get_global_session(): - return global_session.get_global_session() - - -get_global_session.__doc__ = global_session.get_global_session.__doc__ - - -def close_session(): - return global_session.close_session() - - -close_session.__doc__ = global_session.close_session.__doc__ - - -def reset_session(): - return global_session.close_session() - - -reset_session.__doc__ = global_session.close_session.__doc__ - - -# SQL Compilation uses recursive algorithms on deep trees -# 10M tree depth should be sufficient to generate any sql that is under bigquery limit -# Note: This limit does not have the desired effect on Python 3.12 in -# which the applicable limit is now hard coded. See: -# https://github.com/python/cpython/issues/112282 -sys.setrecursionlimit(max(10000000, sys.getrecursionlimit())) - -if resource is not None: - soft_limit, hard_limit = resource.getrlimit(resource.RLIMIT_STACK) - if soft_limit < hard_limit or hard_limit == resource.RLIM_INFINITY: - try: - resource.setrlimit(resource.RLIMIT_STACK, (hard_limit, hard_limit)) - except Exception: - pass - -_functions = [ - _from_glob_path, - clean_up_by_session_id, - concat, - crosstab, - cut, - deploy_remote_function, - deploy_udf, - get_default_session_id, - get_dummies, - merge, - qcut, - read_arrow, - read_avro, - read_csv, - read_gbq, - _read_gbq_colab, - read_gbq_function, - read_gbq_model, - read_gbq_query, - read_gbq_table, - read_json, - read_orc, - read_pandas, - read_parquet, - read_pickle, - remote_function, - to_datetime, - to_timedelta, -] +get_global_session = global_session.get_global_session +close_session = global_session.close_session +reset_session = global_session.close_session + # Use __all__ to let type checkers know what is part of the public API. -# Note that static analysis checkers like pylance depend on these being string -# literals, not derived at runtime. -__all__ = [ - # Function names - "clean_up_by_session_id", +__all___ = [ + # Functions "concat", - "crosstab", - "col", - "cut", - "deploy_remote_function", - "deploy_udf", - "get_default_session_id", - "get_dummies", "merge", - "qcut", - "read_arrow", - "read_avro", "read_csv", "read_gbq", - "_read_gbq_colab", "read_gbq_function", "read_gbq_model", - "read_gbq_query", - "read_gbq_table", - "read_json", - "read_orc", "read_pandas", - "read_parquet", "read_pickle", "remote_function", - "to_datetime", - "to_timedelta", - # Other names - "api", # pandas dtype attributes "NA", "BooleanDtype", "Float64Dtype", "Int64Dtype", "StringDtype", - "ArrowDtype", + "ArrowDtype" # Class aliases "DataFrame", "Index", - "MultiIndex", - "DatetimeIndex", "Series", - "__version__", # Other public pandas attributes "NamedAgg", "options", @@ -524,11 +679,4 @@ def reset_session(): "get_global_session", "close_session", "reset_session", - "udf", ] - -_module = sys.modules[__name__] - -for _function in _functions: - _decorated_object = log_adapter.method_logger(_function, custom_base_name="pandas") - setattr(_module, _function.__name__, _decorated_object) diff --git a/bigframes/pandas/api/__init__.py b/bigframes/pandas/api/__init__.py deleted file mode 100644 index 6d181f92c12..00000000000 --- a/bigframes/pandas/api/__init__.py +++ /dev/null @@ -1,21 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -"""BigQuery DataFrames public pandas APIs.""" - -from bigframes.pandas.api import typing - -__all__ = [ - "typing", -] diff --git a/bigframes/pandas/api/typing.py b/bigframes/pandas/api/typing.py deleted file mode 100644 index 8d8d65eddec..00000000000 --- a/bigframes/pandas/api/typing.py +++ /dev/null @@ -1,37 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -"""BigQuery DataFrames public pandas types that aren't exposed in bigframes.pandas. - -Note: These objects aren't intended to be constructed directly. -""" - -from bigframes.core.groupby.dataframe_group_by import DataFrameGroupBy -from bigframes.core.groupby.series_group_by import SeriesGroupBy -from bigframes.core.window import Window -from bigframes.operations.datetimes import DatetimeMethods -from bigframes.operations.plotting import PlotAccessor -from bigframes.operations.strings import StringMethods -from bigframes.operations.structs import StructAccessor, StructFrameAccessor - -__all__ = [ - "DataFrameGroupBy", - "DatetimeMethods", - "PlotAccessor", - "SeriesGroupBy", - "StringMethods", - "StructAccessor", - "StructFrameAccessor", - "Window", -] diff --git a/bigframes/pandas/core/__init__.py b/bigframes/pandas/core/__init__.py deleted file mode 100644 index 0a2669d7a25..00000000000 --- a/bigframes/pandas/core/__init__.py +++ /dev/null @@ -1,13 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. diff --git a/bigframes/pandas/core/api.py b/bigframes/pandas/core/api.py deleted file mode 100644 index 0f3161afcc2..00000000000 --- a/bigframes/pandas/core/api.py +++ /dev/null @@ -1,17 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from bigframes.pandas.core.tools.timedeltas import to_timedelta - -__all__ = ["to_timedelta"] diff --git a/bigframes/pandas/core/methods/__init__.py b/bigframes/pandas/core/methods/__init__.py deleted file mode 100644 index 0a2669d7a25..00000000000 --- a/bigframes/pandas/core/methods/__init__.py +++ /dev/null @@ -1,13 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. diff --git a/bigframes/pandas/core/methods/describe.py b/bigframes/pandas/core/methods/describe.py deleted file mode 100644 index 34c116ba27d..00000000000 --- a/bigframes/pandas/core/methods/describe.py +++ /dev/null @@ -1,127 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from __future__ import annotations - -import typing - -import pandas as pd - -from bigframes import dataframe, dtypes, series -from bigframes.core import agg_expressions, blocks -from bigframes.operations import aggregations - -_DEFAULT_DTYPES = ( - dtypes.NUMERIC_BIGFRAMES_TYPES_RESTRICTIVE + dtypes.TEMPORAL_NUMERIC_BIGFRAMES_TYPES -) - - -def describe( - input: dataframe.DataFrame | series.Series, - include: None | typing.Literal["all"], -) -> dataframe.DataFrame | series.Series: - if isinstance(input, series.Series): - # Convert the series to a dataframe, describe it, and cast the result back to a series. - return series.Series(describe(input.to_frame(), include)._block) - elif not isinstance(input, dataframe.DataFrame): - raise TypeError(f"Unsupported type: {type(input)}") - - block = input._block - - describe_block = _describe(block, columns=block.value_columns, include=include) - # we override default stack behavior, because we want very specific ordering - stack_cols = pd.Index( - [ - "count", - "nunique", - "top", - "freq", - "mean", - "std", - "min", - "25%", - "50%", - "75%", - "max", - ] - ).intersection(describe_block.column_labels.get_level_values(-1)) - if not stack_cols.empty: - describe_block = describe_block.stack(override_labels=stack_cols) - return dataframe.DataFrame(describe_block).droplevel(level=0) - return dataframe.DataFrame(describe_block) - - -def _describe( - block: blocks.Block, - columns: typing.Sequence[str], - include: None | typing.Literal["all"] = None, - *, - as_index: bool = True, - by_col_ids: typing.Sequence[str] = [], - dropna: bool = False, -) -> blocks.Block: - stats: list[agg_expressions.Aggregation] = [] - column_labels: list[typing.Hashable] = [] - - # include=None behaves like include='all' if no numeric columns present - if include is None: - if not any( - block.expr.get_column_type(col) in _DEFAULT_DTYPES for col in columns - ): - include = "all" - - for col_id in columns: - label = block.col_id_to_label[col_id] - dtype = block.expr.get_column_type(col_id) - if include != "all" and dtype not in _DEFAULT_DTYPES: - continue - agg_ops = _get_aggs_for_dtype(dtype) - stats.extend(op.as_expr(col_id) for op in agg_ops) - label_tuple = (label,) if block.column_labels.nlevels == 1 else label - column_labels.extend((*label_tuple, op.name) for op in agg_ops) # type: ignore - - agg_block = block.aggregate( - by_column_ids=by_col_ids, - aggregations=stats, - dropna=dropna, - column_labels=pd.Index(column_labels, name=(*block.column_labels.names, None)), - ) - return agg_block if as_index else agg_block.reset_index(drop=False) - - -def _get_aggs_for_dtype(dtype) -> list[aggregations.UnaryAggregateOp]: - if dtype in dtypes.NUMERIC_BIGFRAMES_TYPES_RESTRICTIVE: - return [ - aggregations.count_op, - aggregations.mean_op, - aggregations.std_op, - aggregations.min_op, - aggregations.ApproxQuartilesOp(1), - aggregations.ApproxQuartilesOp(2), - aggregations.ApproxQuartilesOp(3), - aggregations.max_op, - ] - elif dtype in dtypes.TEMPORAL_NUMERIC_BIGFRAMES_TYPES: - return [aggregations.count_op] - elif dtype in [ - dtypes.STRING_DTYPE, - dtypes.BOOL_DTYPE, - dtypes.BYTES_DTYPE, - dtypes.TIME_DTYPE, - ]: - return [aggregations.count_op, aggregations.nunique_op] - elif dtypes.is_json_like(dtype) or dtype == dtypes.OBJ_REF_DTYPE: - return [aggregations.count_op] - else: - return [] diff --git a/bigframes/pandas/core/tools/__init__.py b/bigframes/pandas/core/tools/__init__.py deleted file mode 100644 index 0a2669d7a25..00000000000 --- a/bigframes/pandas/core/tools/__init__.py +++ /dev/null @@ -1,13 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. diff --git a/bigframes/pandas/core/tools/timedeltas.py b/bigframes/pandas/core/tools/timedeltas.py deleted file mode 100644 index 5d08bec5f7c..00000000000 --- a/bigframes/pandas/core/tools/timedeltas.py +++ /dev/null @@ -1,70 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import typing - -import pandas as pd -import pandas.api.types as pdtypes -from bigframes_vendored.pandas.core.tools import ( - timedeltas as vendored_pandas_timedeltas, -) - -from bigframes import operations as ops -from bigframes import series, session - - -def to_timedelta( - arg, - unit: typing.Optional[vendored_pandas_timedeltas.UnitChoices] = None, - *, - session: typing.Optional[session.Session] = None, -): - if isinstance(arg, series.Series): - canonical_unit = "us" if unit is None else _canonicalize_unit(unit) - return arg._apply_unary_op(ops.ToTimedeltaOp(canonical_unit)) - - if pdtypes.is_list_like(arg): - return to_timedelta(series.Series(arg, session=session), unit, session=session) - - return pd.to_timedelta(arg, unit) - - -to_timedelta.__doc__ = vendored_pandas_timedeltas.to_timedelta.__doc__ - - -def _canonicalize_unit( - unit: vendored_pandas_timedeltas.UnitChoices, -) -> typing.Literal["us", "ms", "s", "m", "h", "d", "W"]: - if unit in {"w", "W"}: - return "W" - - if unit in {"D", "d", "days", "day"}: - return "d" - - if unit in {"hours", "hour", "hr", "h"}: - return "h" - - if unit in {"m", "minute", "min", "minutes"}: - return "m" - - if unit in {"s", "seconds", "sec", "second"}: - return "s" - - if unit in {"ms", "milliseconds", "millisecond", "milli", "millis"}: - return "ms" - - if unit in {"us", "microseconds", "microsecond", "µs", "micro", "micros"}: - return "us" - - raise TypeError(f"Unrecognized unit: {unit}") diff --git a/bigframes/pandas/io/__init__.py b/bigframes/pandas/io/__init__.py deleted file mode 100644 index 6d5e14bcf4a..00000000000 --- a/bigframes/pandas/io/__init__.py +++ /dev/null @@ -1,13 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. diff --git a/bigframes/pandas/io/api.py b/bigframes/pandas/io/api.py deleted file mode 100644 index fa0f503a08b..00000000000 --- a/bigframes/pandas/io/api.py +++ /dev/null @@ -1,727 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from __future__ import annotations - -import functools -import inspect -import os -import threading -import typing -import warnings -from typing import ( - IO, - Any, - Callable, - Dict, - Iterable, - Literal, - MutableSequence, - Optional, - Sequence, - Tuple, - Union, - overload, -) - -import bigframes_vendored.constants as constants -import bigframes_vendored.pandas.io.gbq as vendored_pandas_gbq -import numpy -import pandas -import pyarrow as pa -from google.cloud import bigquery -from pandas._typing import ( - CompressionOptions, - FilePath, - ReadPickleBuffer, - StorageOptions, -) - -import bigframes._config as config -import bigframes._importing -import bigframes.core.global_session as global_session -import bigframes.core.indexes -import bigframes.dataframe -import bigframes.enums -import bigframes.series -import bigframes.session -import bigframes.session._io.bigquery -import bigframes.session.clients -import bigframes.session.iceberg -import bigframes.session.metrics -from bigframes.core import bq_data -from bigframes.session import dry_runs - -# Note: the following methods are duplicated from Session. This duplication -# enables the following: -# -# 1. Static type checking knows the argument and return types, which is -# difficult to do with decorators. Aside: When we require Python 3.10, we -# can use Concatenate for generic typing in decorators. See: -# https://stackoverflow.com/a/68290080/101923 -# 2. docstrings get processed by static processing tools, such as VS Code's -# autocomplete. -# 3. Positional arguments function as expected. If we were to pull in the -# methods directly from Session, a Session object would need to be the first -# argument, even if we allow a default value. -# 4. Allows to set BigQuery options for the BigFrames session based on the -# method and its arguments. - - -def read_arrow(pa_table: pa.Table) -> bigframes.dataframe.DataFrame: - """Load a PyArrow Table to a BigQuery DataFrames DataFrame. - - Args: - pa_table (pyarrow.Table): - PyArrow table to load data from. - - Returns: - bigframes.dataframe.DataFrame: - A new DataFrame representing the data from the PyArrow table. - """ - session = global_session.get_global_session() - return session.read_arrow(pa_table=pa_table) - - -def read_avro( - path: str | IO["bytes"], - *, - engine: str = "auto", -) -> bigframes.dataframe.DataFrame: - return global_session.with_default_session( - bigframes.session.Session.read_avro, - path, - engine=engine, - ) - - -read_avro.__doc__ = inspect.getdoc(bigframes.session.Session.read_avro) - - -def read_csv( - filepath_or_buffer: str | IO["bytes"], - *, - sep: Optional[str] = ",", - header: Optional[int] = 0, - names: Optional[ - Union[MutableSequence[Any], numpy.ndarray[Any, Any], Tuple[Any, ...], range] - ] = None, - index_col: Optional[ - Union[ - int, - str, - Sequence[Union[str, int]], - bigframes.enums.DefaultIndexKind, - Literal[False], - ] - ] = None, - usecols: Optional[ - Union[ - MutableSequence[str], - Tuple[str, ...], - Sequence[int], - pandas.Series, - pandas.Index, - numpy.ndarray[Any, Any], - Callable[[Any], bool], - ] - ] = None, - dtype: Optional[Dict] = None, - engine: Optional[ - Literal["c", "python", "pyarrow", "python-fwf", "bigquery"] - ] = None, - encoding: Optional[str] = None, - write_engine: constants.WriteEngineType = "default", - **kwargs, -) -> bigframes.dataframe.DataFrame: - return global_session.with_default_session( - bigframes.session.Session.read_csv, - filepath_or_buffer=filepath_or_buffer, - sep=sep, - header=header, - names=names, - index_col=index_col, - usecols=usecols, - dtype=dtype, - engine=engine, - encoding=encoding, - write_engine=write_engine, - **kwargs, - ) - - -read_csv.__doc__ = inspect.getdoc(bigframes.session.Session.read_csv) - - -def read_json( - path_or_buf: str | IO["bytes"], - *, - orient: Literal[ - "split", "records", "index", "columns", "values", "table" - ] = "columns", - dtype: Optional[Dict] = None, - encoding: Optional[str] = None, - lines: bool = False, - engine: Literal["ujson", "pyarrow", "bigquery"] = "ujson", - write_engine: constants.WriteEngineType = "default", - **kwargs, -) -> bigframes.dataframe.DataFrame: - return global_session.with_default_session( - bigframes.session.Session.read_json, - path_or_buf=path_or_buf, - orient=orient, - dtype=dtype, - encoding=encoding, - lines=lines, - engine=engine, - write_engine=write_engine, - **kwargs, - ) - - -read_json.__doc__ = inspect.getdoc(bigframes.session.Session.read_json) - - -@overload -def read_gbq( # type: ignore[overload-overlap] - query_or_table: str, - *, - index_col: Iterable[str] | str | bigframes.enums.DefaultIndexKind = ..., - columns: Iterable[str] = ..., - configuration: Optional[Dict] = ..., - max_results: Optional[int] = ..., - filters: vendored_pandas_gbq.FiltersType = ..., - use_cache: Optional[bool] = ..., - col_order: Iterable[str] = ..., - dry_run: Literal[False] = ..., - allow_large_results: Optional[bool] = ..., -) -> bigframes.dataframe.DataFrame: ... - - -@overload -def read_gbq( - query_or_table: str, - *, - index_col: Iterable[str] | str | bigframes.enums.DefaultIndexKind = ..., - columns: Iterable[str] = ..., - configuration: Optional[Dict] = ..., - max_results: Optional[int] = ..., - filters: vendored_pandas_gbq.FiltersType = ..., - use_cache: Optional[bool] = ..., - col_order: Iterable[str] = ..., - dry_run: Literal[True] = ..., - allow_large_results: Optional[bool] = ..., -) -> pandas.Series: ... - - -def read_gbq( - query_or_table: str, - *, - index_col: Iterable[str] | str | bigframes.enums.DefaultIndexKind = (), - columns: Iterable[str] = (), - configuration: Optional[Dict] = None, - max_results: Optional[int] = None, - filters: vendored_pandas_gbq.FiltersType = (), - use_cache: Optional[bool] = None, - col_order: Iterable[str] = (), - dry_run: bool = False, - allow_large_results: Optional[bool] = None, -) -> bigframes.dataframe.DataFrame | pandas.Series: - _set_default_session_location_if_possible(query_or_table) - return global_session.with_default_session( - bigframes.session.Session.read_gbq, - query_or_table, - index_col=index_col, - columns=columns, - configuration=configuration, - max_results=max_results, - filters=filters, - use_cache=use_cache, - col_order=col_order, - dry_run=dry_run, - allow_large_results=allow_large_results, - ) - - -read_gbq.__doc__ = inspect.getdoc(bigframes.session.Session.read_gbq) - - -def _run_read_gbq_colab_sessionless_dry_run( - query: str, - *, - pyformat_args: Dict[str, Any], -) -> pandas.Series: - """Run a dry_run without a session.""" - - query_formatted = bigframes.core.pyformat.pyformat( - query, - pyformat_args=pyformat_args, - dry_run=True, - ) - bqclient, _ = _get_bqclient_and_project() - job = _dry_run(query_formatted, bqclient) - return dry_runs.get_query_stats_with_inferred_dtypes(job, (), ()) - - -def _try_read_gbq_colab_sessionless_dry_run( - query: str, - *, - pyformat_args: Dict[str, Any], -) -> Optional[pandas.Series]: - """Run a dry_run without a session, only if the session hasn't yet started.""" - - global _default_location_lock - - # Avoid creating a session just for dry run. We don't want to bind to a - # location too early. This is especially important if the query only refers - # to local data and not any BigQuery tables. - with _default_location_lock: - if not config.options.bigquery._session_started: - return _run_read_gbq_colab_sessionless_dry_run( - query, pyformat_args=pyformat_args - ) - - # Explicitly return None to indicate that we didn't run the dry run query. - return None - - -@overload -def _read_gbq_colab( # type: ignore[overload-overlap] - query_or_table: str, - *, - callback: Optional[Callable[[bigframes.core.events.EventEnvelope], None]] = None, - pyformat_args: Optional[Dict[str, Any]] = None, - dry_run: Literal[False] = False, -) -> bigframes.dataframe.DataFrame: ... - - -@overload -def _read_gbq_colab( - query_or_table: str, - *, - callback: Optional[Callable[[bigframes.core.events.EventEnvelope], None]] = None, - pyformat_args: Optional[Dict[str, Any]] = None, - dry_run: Literal[True], -) -> pandas.Series: ... - - -def _read_gbq_colab( - query_or_table: str, - *, - callback: Optional[Callable[[bigframes.core.events.EventEnvelope], None]] = None, - pyformat_args: Optional[Dict[str, Any]] = None, - dry_run: bool = False, -) -> bigframes.dataframe.DataFrame | pandas.Series: - """A Colab-specific version of read_gbq. - - Calls `_set_default_session_location_if_possible` and then delegates - to `bigframes.session.Session._read_gbq_colab`. - - Args: - query_or_table (str): - SQL query or table ID (table ID not yet supported). - callback (Optional[Callable[[bigframes.core.events.EventEnvelope], None]]): - Callback to receive query execution events. - pyformat_args (Optional[Dict[str, Any]]): - Parameters to format into the query string. - dry_run (bool): - If True, estimates the query results size without returning data. - The return will be a pandas Series with query metadata. - - Returns: - Union[bigframes.dataframe.DataFrame, pandas.Series]: - A BigQuery DataFrame if `dry_run` is False, otherwise a pandas Series. - """ - if pyformat_args is None: - pyformat_args = {} - - # Only try to set the global location if it's not a dry run. We don't want - # to bind to a location too early. This is especially important if the query - # only refers to local data and not any BigQuery tables. - if dry_run: - result = _try_read_gbq_colab_sessionless_dry_run( - query_or_table, pyformat_args=pyformat_args - ) - - if result is not None: - return result - - # If we made it this far, we must have a session that has already - # started. That means we can safely call the "real" _read_gbq_colab, - # which generates slightly nicer SQL. - else: - # Delay formatting the query with the special "session-less" logic. This - # avoids doing unnecessary work if the session already has a location or has - # already started. - create_query = functools.partial( - bigframes.core.pyformat.pyformat, - query_or_table, - pyformat_args=pyformat_args, - dry_run=True, - ) - _set_default_session_location_if_possible_deferred_query(create_query) - if not config.options.bigquery._session_started: - # Don't warning about Polars in SQL cell. - # Related to b/437090788. - try: - bigframes._importing.import_polars() - warnings.simplefilter("ignore", bigframes.exceptions.PreviewWarning) - config.options.bigquery.enable_polars_execution = True - except ImportError: - pass # don't fail if polars isn't available - - return global_session.with_default_session( - bigframes.session.Session._read_gbq_colab, - query_or_table, - callback=callback, - pyformat_args=pyformat_args, - dry_run=dry_run, - ) - - -def read_gbq_model(model_name: str): - return global_session.with_default_session( - bigframes.session.Session.read_gbq_model, - model_name, - ) - - -read_gbq_model.__doc__ = inspect.getdoc(bigframes.session.Session.read_gbq_model) - - -@overload -def read_gbq_query( # type: ignore[overload-overlap] - query: str, - *, - index_col: Iterable[str] | str | bigframes.enums.DefaultIndexKind = ..., - columns: Iterable[str] = ..., - configuration: Optional[Dict] = ..., - max_results: Optional[int] = ..., - use_cache: Optional[bool] = ..., - col_order: Iterable[str] = ..., - filters: vendored_pandas_gbq.FiltersType = ..., - dry_run: Literal[False] = ..., - allow_large_results: Optional[bool] = ..., -) -> bigframes.dataframe.DataFrame: ... - - -@overload -def read_gbq_query( - query: str, - *, - index_col: Iterable[str] | str | bigframes.enums.DefaultIndexKind = ..., - columns: Iterable[str] = ..., - configuration: Optional[Dict] = ..., - max_results: Optional[int] = ..., - use_cache: Optional[bool] = ..., - col_order: Iterable[str] = ..., - filters: vendored_pandas_gbq.FiltersType = ..., - dry_run: Literal[True] = ..., - allow_large_results: Optional[bool] = ..., -) -> pandas.Series: ... - - -def read_gbq_query( - query: str, - *, - index_col: Iterable[str] | str | bigframes.enums.DefaultIndexKind = (), - columns: Iterable[str] = (), - configuration: Optional[Dict] = None, - max_results: Optional[int] = None, - use_cache: Optional[bool] = None, - col_order: Iterable[str] = (), - filters: vendored_pandas_gbq.FiltersType = (), - dry_run: bool = False, - allow_large_results: Optional[bool] = None, -) -> bigframes.dataframe.DataFrame | pandas.Series: - _set_default_session_location_if_possible(query) - return global_session.with_default_session( - bigframes.session.Session.read_gbq_query, - query, - index_col=index_col, - columns=columns, - configuration=configuration, - max_results=max_results, - use_cache=use_cache, - col_order=col_order, - filters=filters, - dry_run=dry_run, - allow_large_results=allow_large_results, - ) - - -read_gbq_query.__doc__ = inspect.getdoc(bigframes.session.Session.read_gbq_query) - - -@overload -def read_gbq_table( # type: ignore[overload-overlap] - query: str, - *, - index_col: Iterable[str] | str | bigframes.enums.DefaultIndexKind = ..., - columns: Iterable[str] = ..., - max_results: Optional[int] = ..., - filters: vendored_pandas_gbq.FiltersType = ..., - use_cache: bool = ..., - col_order: Iterable[str] = ..., - dry_run: Literal[False] = ..., -) -> bigframes.dataframe.DataFrame: ... - - -@overload -def read_gbq_table( - query: str, - *, - index_col: Iterable[str] | str | bigframes.enums.DefaultIndexKind = ..., - columns: Iterable[str] = ..., - max_results: Optional[int] = ..., - filters: vendored_pandas_gbq.FiltersType = ..., - use_cache: bool = ..., - col_order: Iterable[str] = ..., - dry_run: Literal[True] = ..., -) -> pandas.Series: ... - - -def read_gbq_table( - query: str, - *, - index_col: Iterable[str] | str | bigframes.enums.DefaultIndexKind = (), - columns: Iterable[str] = (), - max_results: Optional[int] = None, - filters: vendored_pandas_gbq.FiltersType = (), - use_cache: bool = True, - col_order: Iterable[str] = (), - dry_run: bool = False, -) -> bigframes.dataframe.DataFrame | pandas.Series: - _set_default_session_location_if_possible(query) - return global_session.with_default_session( - bigframes.session.Session.read_gbq_table, - query, - index_col=index_col, - columns=columns, - max_results=max_results, - filters=filters, - use_cache=use_cache, - col_order=col_order, - dry_run=dry_run, - ) - - -read_gbq_table.__doc__ = inspect.getdoc(bigframes.session.Session.read_gbq_table) - - -def read_orc( - path: str | IO["bytes"], - *, - engine: str = "auto", - write_engine: constants.WriteEngineType = "default", -) -> bigframes.dataframe.DataFrame: - return global_session.with_default_session( - bigframes.session.Session.read_orc, - path, - engine=engine, - write_engine=write_engine, - ) - - -read_orc.__doc__ = inspect.getdoc(bigframes.session.Session.read_orc) - - -@typing.overload -def read_pandas( - pandas_dataframe: pandas.DataFrame, - *, - write_engine: constants.WriteEngineType = "default", -) -> bigframes.dataframe.DataFrame: ... - - -@typing.overload -def read_pandas( - pandas_dataframe: pandas.Series, - *, - write_engine: constants.WriteEngineType = "default", -) -> bigframes.series.Series: ... - - -@typing.overload -def read_pandas( - pandas_dataframe: pandas.Index, - *, - write_engine: constants.WriteEngineType = "default", -) -> bigframes.core.indexes.Index: ... - - -def read_pandas( - pandas_dataframe: Union[pandas.DataFrame, pandas.Series, pandas.Index], - *, - write_engine: constants.WriteEngineType = "default", -): - return global_session.with_default_session( - bigframes.session.Session.read_pandas, - pandas_dataframe, - write_engine=write_engine, - ) - - -read_pandas.__doc__ = inspect.getdoc(bigframes.session.Session.read_pandas) - - -def read_pickle( - filepath_or_buffer: FilePath | ReadPickleBuffer, - compression: CompressionOptions = "infer", - storage_options: StorageOptions = None, - *, - write_engine: constants.WriteEngineType = "default", -): - return global_session.with_default_session( - bigframes.session.Session.read_pickle, - filepath_or_buffer=filepath_or_buffer, - compression=compression, - storage_options=storage_options, - write_engine=write_engine, - ) - - -read_pickle.__doc__ = inspect.getdoc(bigframes.session.Session.read_pickle) - - -def read_parquet( - path: str | IO["bytes"], - *, - engine: str = "auto", - write_engine: constants.WriteEngineType = "default", -) -> bigframes.dataframe.DataFrame: - return global_session.with_default_session( - bigframes.session.Session.read_parquet, - path, - engine=engine, - write_engine=write_engine, - ) - - -read_parquet.__doc__ = inspect.getdoc(bigframes.session.Session.read_parquet) - - -def read_gbq_function( - function_name: str, - is_row_processor: bool = False, -): - return global_session.with_default_session( - bigframes.session.Session.read_gbq_function, - function_name=function_name, - is_row_processor=is_row_processor, - ) - - -read_gbq_function.__doc__ = inspect.getdoc(bigframes.session.Session.read_gbq_function) - - -def _from_glob_path( - path: str, *, connection: Optional[str] = None, name: Optional[str] = None -) -> bigframes.dataframe.DataFrame: - return global_session.with_default_session( - bigframes.session.Session._from_glob_path, - path=path, - connection=connection, - name=name, - ) - - -_from_glob_path.__doc__ = inspect.getdoc(bigframes.session.Session._from_glob_path) - -_default_location_lock = threading.Lock() - - -def _get_bqclient_and_project() -> Tuple[bigquery.Client, str]: - # Address circular imports in doctest due to bigframes/session/__init__.py - # containing a lot of logic and samples. - import bigframes._config.auth - from bigframes.session import clients - - credentials, project = bigframes._config.auth.resolve_credentials_and_project( - config.options.bigquery - ) - - clients_provider = clients.ClientsProvider( - project=project, - location=config.options.bigquery.location, - use_regional_endpoints=config.options.bigquery.use_regional_endpoints, - credentials=credentials, - application_name=config.options.bigquery.application_name, - bq_kms_key_name=config.options.bigquery.kms_key_name, - client_endpoints_override=config.options.bigquery.client_endpoints_override, - requests_transport_adapters=config.options.bigquery.requests_transport_adapters, - ) - return clients_provider.bqclient, project - - -def _dry_run(query, bqclient) -> bigquery.QueryJob: - # Address circular imports in doctest due to bigframes/session/__init__.py - # containing a lot of logic and samples. - from bigframes.session import metrics as bf_metrics - - job = bqclient.query(query, bigquery.QueryJobConfig(dry_run=True)) - - # Fix for b/435183833. Log metrics even if a Session isn't available. - if bf_metrics.LOGGING_NAME_ENV_VAR in os.environ: - metrics = bf_metrics.ExecutionMetrics() - metrics.count_job_stats(job) - return job - - -def _set_default_session_location_if_possible(query): - _set_default_session_location_if_possible_deferred_query(lambda: query) - - -def _set_default_session_location_if_possible_deferred_query(create_query): - # Address circular imports in doctest due to bigframes/session/__init__.py - # containing a lot of logic and samples. - from bigframes.session._io import bigquery - - # Set the location as per the query if this is the first query the user is - # running and: - # (1) Default session has not started yet, and - # (2) Location is not set yet, and - # (3) Use of regional endpoints is not set. - # If query is a table name, then it would be the location of the table. - # If query is a SQL with a table, then it would be table's location. - # If query is a SQL with no table, then it would be the BQ default location. - global _default_location_lock - - with _default_location_lock: - if ( - config.options.bigquery._session_started - or config.options.bigquery.location - or config.options.bigquery.use_regional_endpoints - ): - return - - query = create_query() - bqclient, default_project = _get_bqclient_and_project() - - if bigquery.is_query(query): - # Intentionally run outside of the session so that we can detect the - # location before creating the session. Since it's a dry_run, labels - # aren't necessary. - job = _dry_run(query, bqclient) - config.options.bigquery.location = job.location - elif bq_data.is_irc_table(query): - irc_table = bigframes.session.iceberg.get_table( - default_project, query, bqclient._credentials - ) - config.options.bigquery.location = bq_data.get_default_bq_region( - irc_table.metadata.location - ) - else: - table = bqclient.get_table(query) - config.options.bigquery.location = table.location diff --git a/bigframes/remote_function.py b/bigframes/remote_function.py new file mode 100644 index 00000000000..a39cd033f69 --- /dev/null +++ b/bigframes/remote_function.py @@ -0,0 +1,903 @@ +# Copyright 2023 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import annotations + +import functools +import hashlib +import inspect +import logging +import os +import random +import shutil +import string +import subprocess +import sys +import tempfile +import textwrap +from typing import List, NamedTuple, Optional, Sequence, TYPE_CHECKING + +import requests + +if TYPE_CHECKING: + from bigframes.session import Session + +import cloudpickle +import google.api_core.exceptions +import google.api_core.retry +from google.cloud import ( + bigquery, + bigquery_connection_v1, + functions_v2, + resourcemanager_v3, +) +import google.iam.v1 +from ibis.backends.bigquery.compiler import compiles +from ibis.backends.bigquery.datatypes import BigQueryType +from ibis.expr.datatypes.core import DataType as IbisDataType +from ibis.expr.datatypes.core import dtype as python_type_to_bigquery_type +import ibis.expr.operations as ops +import ibis.expr.rules as rlz + +from bigframes import clients +import bigframes.constants as constants + +logger = logging.getLogger(__name__) + +# Protocol version 4 is available in python version 3.4 and above +# https://docs.python.org/3/library/pickle.html#data-stream-format +_pickle_protocol_version = 4 + +# Input and output types supported by BigQuery DataFrames remote functions. +# TODO(shobs): Extend the support to all types supported by BQ remote functions +# https://cloud.google.com/bigquery/docs/remote-functions#limitations +SUPPORTED_IO_PYTHON_TYPES = {bool, float, int, str} +SUPPORTED_IO_BIGQUERY_TYPEKINDS = { + "BOOLEAN", + "BOOL", + "FLOAT", + "FLOAT64", + "INT64", + "INTEGER", + "STRING", +} + + +def get_remote_function_locations(bq_location): + """Get BQ location and cloud functions region given a BQ client.""" + # TODO(shobs, b/274647164): Find the best way to determine default location. + # For now let's assume that if no BQ location is set in the client then it + # defaults to US multi region + bq_location = bq_location.lower() if bq_location else "us" + + # Cloud function should be in the same region as the bigquery remote function + cloud_function_region = bq_location + + # BigQuery has multi region but cloud functions does not. + # Any region in the multi region that supports cloud functions should work + # https://cloud.google.com/functions/docs/locations + if bq_location == "us": + cloud_function_region = "us-central1" + elif bq_location == "eu": + cloud_function_region = "europe-west1" + + return bq_location, cloud_function_region + + +def _get_hash(def_, package_requirements=None): + "Get hash (32 digits alphanumeric) of a function." + def_repr = cloudpickle.dumps(def_, protocol=_pickle_protocol_version) + if package_requirements: + for p in sorted(package_requirements): + def_repr += p.encode() + return hashlib.md5(def_repr).hexdigest() + + +def _run_system_command(command): + program = subprocess.Popen( + [command], stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True + ) + stdout, stderr = program.communicate() + exit_code = program.wait() + if exit_code: + raise RuntimeError( + f"Command: {command}\nOutput: {stdout.decode()}\nError: {stderr.decode()}" + f"{constants.FEEDBACK_LINK}" + ) + + +def routine_ref_to_string_for_query(routine_ref: bigquery.RoutineReference) -> str: + return f"`{routine_ref.project}.{routine_ref.dataset_id}`.{routine_ref.routine_id}" + + +class IbisSignature(NamedTuple): + parameter_names: List[str] + input_types: List[Optional[IbisDataType]] + output_type: IbisDataType + + +def get_cloud_function_name(def_, uniq_suffix=None, package_requirements=None): + "Get a name for the cloud function for the given user defined function." + cf_name = _get_hash(def_, package_requirements) + cf_name = f"bigframes-{cf_name}" # for identification + if uniq_suffix: + cf_name = f"{cf_name}-{uniq_suffix}" + return cf_name + + +def get_remote_function_name(def_, uniq_suffix=None, package_requirements=None): + "Get a name for the BQ remote function for the given user defined function." + bq_rf_name = _get_hash(def_, package_requirements) + bq_rf_name = f"bigframes_{bq_rf_name}" # for identification + if uniq_suffix: + bq_rf_name = f"{bq_rf_name}_{uniq_suffix}" + return bq_rf_name + + +class RemoteFunctionClient: + # Wait time (in seconds) for an IAM binding to take effect after creation + _iam_wait_seconds = 120 + + def __init__( + self, + gcp_project_id, + cloud_function_region, + cloud_functions_client, + bq_location, + bq_dataset, + bq_client, + bq_connection_client, + bq_connection_id, + cloud_resource_manager_client, + ): + self._gcp_project_id = gcp_project_id + self._cloud_function_region = cloud_function_region + self._cloud_functions_client = cloud_functions_client + self._bq_location = bq_location + self._bq_dataset = bq_dataset + self._bq_client = bq_client + self._bq_connection_id = bq_connection_id + self._bq_connection_manager = clients.BqConnectionManager( + bq_connection_client, cloud_resource_manager_client + ) + + def create_bq_remote_function( + self, input_args, input_types, output_type, endpoint, bq_function_name + ): + """Create a BigQuery remote function given the artifacts of a user defined + function and the http endpoint of a corresponding cloud function.""" + self._bq_connection_manager.create_bq_connection( + self._gcp_project_id, + self._bq_location, + self._bq_connection_id, + "run.invoker", + ) + + # Create BQ function + # https://cloud.google.com/bigquery/docs/reference/standard-sql/remote-functions#create_a_remote_function_2 + bq_function_args = [] + bq_function_return_type = BigQueryType.from_ibis(output_type) + # We are expecting the input type annotations to be 1:1 with the input args + for idx, name in enumerate(input_args): + bq_function_args.append( + f"{name} {BigQueryType.from_ibis(input_types[idx])}" + ) + create_function_ddl = f""" + CREATE OR REPLACE FUNCTION `{self._gcp_project_id}.{self._bq_dataset}`.{bq_function_name}({','.join(bq_function_args)}) + RETURNS {bq_function_return_type} + REMOTE WITH CONNECTION `{self._gcp_project_id}.{self._bq_location}.{self._bq_connection_id}` + OPTIONS ( + endpoint = "{endpoint}", + max_batching_rows = 1000 + )""" + + logger.info(f"Creating BQ remote function: {create_function_ddl}") + + # Make sure the dataset exists + dataset = bigquery.Dataset( + bigquery.DatasetReference.from_string( + self._bq_dataset, default_project=self._gcp_project_id + ) + ) + dataset.location = self._bq_location + self._bq_client.create_dataset(dataset, exists_ok=True) + + # TODO: Use session._start_query() so we get progress bar + query_job = self._bq_client.query(create_function_ddl) # Make an API request. + query_job.result() # Wait for the job to complete. + + logger.info(f"Created remote function {query_job.ddl_target_routine}") + + def get_cloud_function_fully_qualified_parent(self): + "Get the fully qualilfied parent for a cloud function." + return self._cloud_functions_client.common_location_path( + self._gcp_project_id, self._cloud_function_region + ) + + def get_cloud_function_fully_qualified_name(self, name): + "Get the fully qualilfied name for a cloud function." + return self._cloud_functions_client.function_path( + self._gcp_project_id, self._cloud_function_region, name + ) + + def get_cloud_function_endpoint(self, name): + """Get the http endpoint of a cloud function if it exists.""" + fully_qualified_name = self.get_cloud_function_fully_qualified_name(name) + try: + response = self._cloud_functions_client.get_function( + name=fully_qualified_name + ) + return response.service_config.uri + except google.api_core.exceptions.NotFound: + pass + return None + + def generate_udf_code(self, def_, dir): + """Generate serialized bytecode using cloudpickle given a udf.""" + udf_code_file_name = "udf.py" + udf_bytecode_file_name = "udf.cloudpickle" + + # original code, only for debugging purpose + udf_code = textwrap.dedent(inspect.getsource(def_)) + udf_code_file_path = os.path.join(dir, udf_code_file_name) + with open(udf_code_file_path, "w") as f: + f.write(udf_code) + + # serialized bytecode + udf_bytecode_file_path = os.path.join(dir, udf_bytecode_file_name) + with open(udf_bytecode_file_path, "wb") as f: + cloudpickle.dump(def_, f, protocol=_pickle_protocol_version) + + return udf_code_file_name, udf_bytecode_file_name + + def generate_cloud_function_main_code(self, def_, dir): + """Get main.py code for the cloud function for the given user defined function.""" + + # Pickle the udf with all its dependencies + udf_code_file, udf_bytecode_file = self.generate_udf_code(def_, dir) + handler_func_name = "udf_http" + + # We want to build a cloud function that works for BQ remote functions, + # where we receive `calls` in json which is a batch of rows from BQ SQL. + # The number and the order of values in each row is expected to exactly + # match to the number and order of arguments in the udf , e.g. if the udf is + # def foo(x: int, y: str): + # ... + # then the http request body could look like + # { + # ... + # "calls" : [ + # [123, "hello"], + # [456, "world"] + # ] + # ... + # } + # https://cloud.google.com/bigquery/docs/reference/standard-sql/remote-functions#input_format + code_template = textwrap.dedent( + """\ + import cloudpickle + import json + + # original udf code is in {udf_code_file} + # serialized udf code is in {udf_bytecode_file} + with open("{udf_bytecode_file}", "rb") as f: + udf = cloudpickle.load(f) + + def {handler_func_name}(request): + request_json = request.get_json(silent=True) + calls = request_json["calls"] + replies = [] + for call in calls: + reply = udf(*call) + replies.append(reply) + return_json = json.dumps({{"replies" : replies}}) + return return_json + """ + ) + + code = code_template.format( + udf_code_file=udf_code_file, + udf_bytecode_file=udf_bytecode_file, + handler_func_name=handler_func_name, + ) + + main_py = os.path.join(dir, "main.py") + with open(main_py, "w") as f: + f.write(code) + logger.debug(f"Wrote {os.path.abspath(main_py)}:\n{open(main_py).read()}") + + return handler_func_name + + def generate_cloud_function_code(self, def_, dir, package_requirements=None): + """Generate the cloud function code for a given user defined function.""" + + # requirements.txt + requirements = ["cloudpickle >= 2.1.0"] + if package_requirements: + requirements.extend(package_requirements) + requirements = sorted(requirements) + requirements_txt = os.path.join(dir, "requirements.txt") + with open(requirements_txt, "w") as f: + f.write("\n".join(requirements)) + + # main.py + entry_point = self.generate_cloud_function_main_code(def_, dir) + return entry_point + + def create_cloud_function(self, def_, cf_name, package_requirements=None): + """Create a cloud function from the given user defined function.""" + + # Build and deploy folder structure containing cloud function + with tempfile.TemporaryDirectory() as dir: + entry_point = self.generate_cloud_function_code( + def_, dir, package_requirements + ) + archive_path = shutil.make_archive(dir, "zip", dir) + + # We are creating cloud function source code from the currently running + # python version. Use the same version to deploy. This is necessary + # because cloudpickle serialization done in one python version and + # deserialization done in another python version doesn't work. + # TODO(shobs): Figure out how to achieve version compatibility, specially + # when pickle (internally used by cloudpickle) guarantees that: + # https://docs.python.org/3/library/pickle.html#:~:text=The%20pickle%20serialization%20format%20is,unique%20breaking%20change%20language%20boundary. + python_version = "python{}{}".format( + sys.version_info.major, sys.version_info.minor + ) + + # Determine an upload URL for user code + upload_url_request = functions_v2.GenerateUploadUrlRequest() + upload_url_request.parent = self.get_cloud_function_fully_qualified_parent() + upload_url_response = self._cloud_functions_client.generate_upload_url( + request=upload_url_request + ) + + # Upload the code to GCS + with open(archive_path, "rb") as f: + response = requests.put( + upload_url_response.upload_url, + data=f, + headers={"content-type": "application/zip"}, + ) + if response.status_code != 200: + raise RuntimeError( + "Failed to upload user code. code={}, reason={}, text={}".format( + response.status_code, response.reason, response.text + ) + ) + + # Deploy Cloud Function + create_function_request = functions_v2.CreateFunctionRequest() + create_function_request.parent = ( + self.get_cloud_function_fully_qualified_parent() + ) + create_function_request.function_id = cf_name + function = functions_v2.Function() + function.name = self.get_cloud_function_fully_qualified_name(cf_name) + function.build_config = functions_v2.BuildConfig() + function.build_config.runtime = python_version + function.build_config.entry_point = entry_point + function.build_config.source = functions_v2.Source() + function.build_config.source.storage_source = functions_v2.StorageSource() + function.build_config.source.storage_source.bucket = ( + upload_url_response.storage_source.bucket + ) + function.build_config.source.storage_source.object_ = ( + upload_url_response.storage_source.object_ + ) + function.service_config = functions_v2.ServiceConfig() + function.service_config.available_memory = "1024M" + function.service_config.timeout_seconds = 600 + create_function_request.function = function + + # Create the cloud function and wait for it to be ready to use + operation = self._cloud_functions_client.create_function( + request=create_function_request + ) + operation.result() + + # Cleanup + os.remove(archive_path) + + # Fetch the endpoint of the just created function + endpoint = self.get_cloud_function_endpoint(cf_name) + if not endpoint: + raise ValueError( + f"Couldn't fetch the http endpoint. {constants.FEEDBACK_LINK}" + ) + + logger.info( + f"Successfully created cloud function {cf_name} with uri ({endpoint})" + ) + return endpoint + + def provision_bq_remote_function( + self, + def_, + input_types, + output_type, + reuse, + name, + package_requirements, + ): + """Provision a BigQuery remote function.""" + # If reuse of any existing function with the same name (indicated by the + # same hash of its source code) is not intended, then attach a unique + # suffix to the intended function name to make it unique. + uniq_suffix = None + if not reuse: + uniq_suffix = "".join( + random.choices(string.ascii_lowercase + string.digits, k=8) + ) + + # Derive the name of the cloud function underlying the intended BQ + # remote function + cloud_function_name = get_cloud_function_name( + def_, uniq_suffix, package_requirements + ) + cf_endpoint = self.get_cloud_function_endpoint(cloud_function_name) + + # Create the cloud function if it does not exist + if not cf_endpoint: + cf_endpoint = self.create_cloud_function( + def_, cloud_function_name, package_requirements + ) + else: + logger.info(f"Cloud function {cloud_function_name} already exists.") + + # Derive the name of the remote function + remote_function_name = name + if not remote_function_name: + remote_function_name = get_remote_function_name( + def_, uniq_suffix, package_requirements + ) + rf_endpoint, rf_conn = self.get_remote_function_specs(remote_function_name) + + # Create the BQ remote function in following circumstances: + # 1. It does not exist + # 2. It exists but the existing remote function has different + # configuration than intended + if not rf_endpoint or ( + rf_endpoint != cf_endpoint or rf_conn != self._bq_connection_id + ): + input_args = inspect.getargs(def_.__code__).args + if len(input_args) != len(input_types): + raise ValueError( + "Exactly one type should be provided for every input arg." + ) + self.create_bq_remote_function( + input_args, input_types, output_type, cf_endpoint, remote_function_name + ) + else: + logger.info(f"Remote function {remote_function_name} already exists.") + + return remote_function_name, cloud_function_name + + def get_remote_function_specs(self, remote_function_name): + """Check whether a remote function already exists for the udf.""" + http_endpoint = None + bq_connection = None + routines = self._bq_client.list_routines( + f"{self._gcp_project_id}.{self._bq_dataset}" + ) + try: + for routine in routines: + if routine.reference.routine_id == remote_function_name: + # TODO(shobs): Use first class properties when they are available + # https://github.com/googleapis/python-bigquery/issues/1552 + rf_options = routine._properties.get("remoteFunctionOptions") + if rf_options: + http_endpoint = rf_options.get("endpoint") + bq_connection = rf_options.get("connection") + if bq_connection: + bq_connection = os.path.basename(bq_connection) + break + except google.api_core.exceptions.NotFound: + # The dataset might not exist, in which case the http_endpoint doesn't, either. + # Note: list_routines doesn't make an API request until we iterate on the response object. + pass + return (http_endpoint, bq_connection) + + +def remote_function_node( + routine_ref: bigquery.RoutineReference, ibis_signature: IbisSignature +): + """Creates an Ibis node representing a remote function call.""" + + fields = { + name: rlz.value(type_) if type_ else rlz.any + for name, type_ in zip( + ibis_signature.parameter_names, ibis_signature.input_types + ) + } + + try: + fields["output_type"] = rlz.shape_like("args", dtype=ibis_signature.output_type) # type: ignore + except TypeError: + fields["output_dtype"] = property(lambda _: ibis_signature.output_type) + fields["output_shape"] = rlz.shape_like("args") + + node = type(routine_ref_to_string_for_query(routine_ref), (ops.ValueOp,), fields) # type: ignore + + @compiles(node) + def compile_node(t, op): + return "{}({})".format(node.__name__, ", ".join(map(t.translate, op.args))) + + def f(*args, **kwargs): + return node(*args, **kwargs).to_expr() + + f.bigframes_remote_function = str(routine_ref) # type: ignore + + return f + + +class UnsupportedTypeError(ValueError): + def __init__(self, type_, supported_types): + self.type = type_ + self.supported_types = supported_types + + +def ibis_type_from_python_type(t: type) -> IbisDataType: + if t not in SUPPORTED_IO_PYTHON_TYPES: + raise UnsupportedTypeError(t, SUPPORTED_IO_PYTHON_TYPES) + return python_type_to_bigquery_type(t) + + +def ibis_type_from_type_kind(tk: bigquery.StandardSqlTypeNames) -> IbisDataType: + if tk not in SUPPORTED_IO_BIGQUERY_TYPEKINDS: + raise UnsupportedTypeError(tk, SUPPORTED_IO_BIGQUERY_TYPEKINDS) + return BigQueryType.to_ibis(tk) + + +def ibis_signature_from_python_signature( + signature: inspect.Signature, + input_types: Sequence[type], + output_type: type, +) -> IbisSignature: + return IbisSignature( + parameter_names=list(signature.parameters.keys()), + input_types=[ibis_type_from_python_type(t) for t in input_types], + output_type=ibis_type_from_python_type(output_type), + ) + + +class ReturnTypeMissingError(ValueError): + pass + + +def ibis_signature_from_routine(routine: bigquery.Routine) -> IbisSignature: + if not routine.return_type: + raise ReturnTypeMissingError + + return IbisSignature( + parameter_names=[arg.name for arg in routine.arguments], + input_types=[ + ibis_type_from_type_kind(arg.data_type.type_kind) if arg.data_type else None + for arg in routine.arguments + ], + output_type=ibis_type_from_type_kind(routine.return_type.type_kind), + ) + + +class DatasetMissingError(ValueError): + pass + + +def get_routine_reference( + routine_ref_str: str, bigquery_client: bigquery.Client, session: Optional[Session] +) -> bigquery.RoutineReference: + try: + # Handle cases ".." and + # ".". + return bigquery.RoutineReference.from_string( + routine_ref_str, + default_project=bigquery_client.project, + ) + except ValueError: + # Handle case of "". + if not session: + raise DatasetMissingError + + dataset_ref = bigquery.DatasetReference( + bigquery_client.project, session._session_dataset_id + ) + return dataset_ref.routine(routine_ref_str) + + +# Inspired by @udf decorator implemented in ibis-bigquery package +# https://github.com/ibis-project/ibis-bigquery/blob/main/ibis_bigquery/udf/__init__.py +# which has moved as @js to the ibis package +# https://github.com/ibis-project/ibis/blob/master/ibis/backends/bigquery/udf/__init__.py +def remote_function( + input_types: Sequence[type], + output_type: type, + session: Optional[Session] = None, + bigquery_client: Optional[bigquery.Client] = None, + bigquery_connection_client: Optional[ + bigquery_connection_v1.ConnectionServiceClient + ] = None, + cloud_functions_client: Optional[functions_v2.FunctionServiceClient] = None, + resource_manager_client: Optional[resourcemanager_v3.ProjectsClient] = None, + dataset: Optional[str] = None, + bigquery_connection: Optional[str] = None, + reuse: bool = True, + name: Optional[str] = None, + packages: Optional[Sequence[str]] = None, +): + """Decorator to turn a user defined function into a BigQuery remote function. + + .. deprecated:: 0.0.1 + This is an internal method. Please use :func:`bigframes.pandas.remote_function` instead. + + .. note:: + Please make sure following is setup before using this API: + + 1. Have the below APIs enabled for your project: + + * BigQuery Connection API + * Cloud Functions API + * Cloud Run API + * Cloud Build API + * Artifact Registry API + * Cloud Resource Manager API + + This can be done from the cloud console (change `PROJECT_ID` to yours): + https://console.cloud.google.com/apis/enableflow?apiid=bigqueryconnection.googleapis.com,cloudfunctions.googleapis.com,run.googleapis.com,cloudbuild.googleapis.com,artifactregistry.googleapis.com,cloudresourcemanager.googleapis.com&project=PROJECT_ID + + Or from the gcloud CLI: + + `$ gcloud services enable bigqueryconnection.googleapis.com cloudfunctions.googleapis.com run.googleapis.com cloudbuild.googleapis.com artifactregistry.googleapis.com cloudresourcemanager.googleapis.com` + + 2. Have following IAM roles enabled for you: + + * BigQuery Data Editor (roles/bigquery.dataEditor) + * BigQuery Connection Admin (roles/bigquery.connectionAdmin) + * Cloud Functions Developer (roles/cloudfunctions.developer) + * Service Account User (roles/iam.serviceAccountUser) on the service account `PROJECT_NUMBER-compute@developer.gserviceaccount.com` + * Storage Object Viewer (roles/storage.objectViewer) + * Project IAM Admin (roles/resourcemanager.projectIamAdmin) (Only required if the bigquery connection being used is not pre-created and is created dynamically with user credentials.) + + 3. Either the user has setIamPolicy privilege on the project, or a BigQuery connection is pre-created with necessary IAM role set: + + 1. To create a connection, follow https://cloud.google.com/bigquery/docs/reference/standard-sql/remote-functions#create_a_connection + 2. To set up IAM, follow https://cloud.google.com/bigquery/docs/reference/standard-sql/remote-functions#grant_permission_on_function + + Alternatively, the IAM could also be setup via the gcloud CLI: + + `$ gcloud projects add-iam-policy-binding PROJECT_ID --member="serviceAccount:CONNECTION_SERVICE_ACCOUNT_ID" --role="roles/run.invoker"`. + + Args: + input_types list(type): + List of input data types in the user defined function. + output_type type: + Data type of the output in the user defined function. + session (bigframes.Session, Optional): + BigQuery DataFrames session to use for getting default project, + dataset and BigQuery connection. + bigquery_client (google.cloud.bigquery.Client, Optional): + Client to use for BigQuery operations. If this param is not provided + then bigquery client from the session would be used. + bigquery_connection_client (google.cloud.bigquery_connection_v1.ConnectionServiceClient, Optional): + Client to use for cloud functions operations. If this param is not + provided then functions client from the session would be used. + cloud_functions_client (google.cloud.functions_v2.FunctionServiceClient, Optional): + Client to use for BigQuery connection operations. If this param is + not provided then bigquery connection client from the session would + be used. + resource_manager_client (google.cloud.resourcemanager_v3.ProjectsClient, Optional): + Client to use for cloud resource management operations, e.g. for + getting and setting IAM roles on cloud resources. If this param is + not provided then resource manager client from the session would be + used. + dataset (str, Optional.): + Dataset in which to create a BigQuery remote function. It should be in + `.` or `` format. If this + parameter is not provided then session dataset id is used. + bigquery_connection (str, Optional): + Name of the BigQuery connection in the form of `CONNECTION_ID` or + `LOCATION.CONNECTION_ID` or `PROJECT_ID.LOCATION.CONNECTION_ID`. + If this param is not provided then the bigquery connection from the session + would be used. If it is pre created in the same location as the + `bigquery_client.location` then it would be used, otherwise it is created + dynamically using the `bigquery_connection_client` assuming the user has necessary + priviliges. The PROJECT_ID should be the same as the BigQuery connection project. + reuse (bool, Optional): + Reuse the remote function if is already exists. + `True` by default, which results in reusing an existing remote + function and corresponding cloud function (if any) that was + previously created for the same udf. + Setting it to `False` forces the creation of a unique remote function. + If the required remote function does not exist then it would be + created irrespective of this param. + name (str, Optional): + Explicit name of the persisted BigQuery remote function. Use it with + caution, because two users working in the same project and dataset + could overwrite each other's remote functions if they use the same + persistent name. + packages (str[], Optional): + Explicit name of the external package dependencies. Each dependency + is added to the `requirements.txt` as is, and can be of the form + supported in https://pip.pypa.io/en/stable/reference/requirements-file-format/. + + """ + import bigframes.pandas as bpd + + session = session or bpd.get_global_session() + + # A BigQuery client is required to perform BQ operations + if not bigquery_client: + bigquery_client = session.bqclient + if not bigquery_client: + raise ValueError( + "A bigquery client must be provided, either directly or via session. " + f"{constants.FEEDBACK_LINK}" + ) + + # A BigQuery connection client is required to perform BQ connection operations + if not bigquery_connection_client: + bigquery_connection_client = session.bqconnectionclient + if not bigquery_connection_client: + raise ValueError( + "A bigquery connection client must be provided, either directly or via session. " + f"{constants.FEEDBACK_LINK}" + ) + + # A cloud functions client is required to perform cloud functions operations + if not cloud_functions_client: + cloud_functions_client = session.cloudfunctionsclient + if not cloud_functions_client: + raise ValueError( + "A cloud functions client must be provided, either directly or via session. " + f"{constants.FEEDBACK_LINK}" + ) + + # A resource manager client is required to get/set IAM operations + if not resource_manager_client: + resource_manager_client = session.resourcemanagerclient + if not resource_manager_client: + raise ValueError( + "A resource manager client must be provided, either directly or via session. " + f"{constants.FEEDBACK_LINK}" + ) + + # BQ remote function must be persisted, for which we need a dataset + # https://cloud.google.com/bigquery/docs/reference/standard-sql/remote-functions#:~:text=You%20cannot%20create%20temporary%20remote%20functions. + if dataset: + dataset_ref = bigquery.DatasetReference.from_string( + dataset, default_project=bigquery_client.project + ) + else: + dataset_ref = bigquery.DatasetReference.from_string( + session._session_dataset_id, default_project=bigquery_client.project + ) + + bq_location, cloud_function_region = get_remote_function_locations( + bigquery_client.location + ) + + # A connection is required for BQ remote function + # https://cloud.google.com/bigquery/docs/reference/standard-sql/remote-functions#create_a_remote_function + if not bigquery_connection: + bigquery_connection = session._bq_connection # type: ignore + + bigquery_connection = clients.BqConnectionManager.resolve_full_connection_name( + bigquery_connection, + default_project=dataset_ref.project, + default_location=bq_location, + ) + # Guaranteed to be the form of .. + ( + gcp_project_id, + bq_connection_location, + bq_connection_id, + ) = bigquery_connection.split(".") + if gcp_project_id.casefold() != dataset_ref.project.casefold(): + raise ValueError( + "The project_id does not match BigQuery connection gcp_project_id: " + f"{dataset_ref.project}." + ) + if bq_connection_location.casefold() != bq_location.casefold(): + raise ValueError( + "The location does not match BigQuery connection location: " + f"{bq_location}." + ) + + def wrapper(f): + if not callable(f): + raise TypeError("f must be callable, got {}".format(f)) + + signature = inspect.signature(f) + ibis_signature = ibis_signature_from_python_signature( + signature, input_types, output_type + ) + + remote_function_client = RemoteFunctionClient( + dataset_ref.project, + cloud_function_region, + cloud_functions_client, + bq_location, + dataset_ref.dataset_id, + bigquery_client, + bigquery_connection_client, + bq_connection_id, + resource_manager_client, + ) + + rf_name, cf_name = remote_function_client.provision_bq_remote_function( + f, + ibis_signature.input_types, + ibis_signature.output_type, + reuse, + name, + packages, + ) + + node = remote_function_node(dataset_ref.routine(rf_name), ibis_signature) + + node = functools.wraps(f)(node) + node.__signature__ = signature + node.bigframes_cloud_function = ( + remote_function_client.get_cloud_function_fully_qualified_name(cf_name) + ) + + return node + + return wrapper + + +def read_gbq_function( + function_name: str, + session: Optional[Session] = None, + bigquery_client: Optional[bigquery.Client] = None, +): + """ + Read an existing BigQuery function and prepare it for use in future queries. + """ + + # A BigQuery client is required to perform BQ operations + if not bigquery_client and session: + bigquery_client = session.bqclient + if not bigquery_client: + raise ValueError( + "A bigquery client must be provided, either directly or via session. " + f"{constants.FEEDBACK_LINK}" + ) + + try: + routine_ref = get_routine_reference(function_name, bigquery_client, session) + except DatasetMissingError: + raise ValueError( + "Project and dataset must be provided, either directly or via session. " + f"{constants.FEEDBACK_LINK}" + ) + + # Find the routine and get its arguments. + try: + routine = bigquery_client.get_routine(routine_ref) + except google.api_core.exceptions.NotFound: + raise ValueError(f"Unknown function '{routine_ref}'. {constants.FEEDBACK_LINK}") + + try: + ibis_signature = ibis_signature_from_routine(routine) + except ReturnTypeMissingError: + raise ValueError( + "Function return type must be specified. {constants.FEEDBACK_LINK}" + ) + except UnsupportedTypeError as e: + raise ValueError( + f"Type {e.type} not supported, supported types are {e.supported_types}. " + f"{constants.FEEDBACK_LINK}" + ) + + return remote_function_node(routine_ref, ibis_signature) diff --git a/bigframes/series.py b/bigframes/series.py index 1a56c76b023..032bdf6c429 100644 --- a/bigframes/series.py +++ b/bigframes/series.py @@ -16,231 +16,71 @@ from __future__ import annotations -import datetime -import functools import itertools import numbers import textwrap import typing -import warnings -from typing import ( - Any, - Callable, - Iterable, - List, - Literal, - Mapping, - Optional, - Sequence, - Tuple, - TypeVar, - Union, - cast, - overload, -) +from typing import Any, Mapping, Optional, Tuple, Union -import bigframes_vendored.constants as constants -import bigframes_vendored.pandas.core.series as vendored_pandas_series -import google.cloud.bigquery.job +import google.cloud.bigquery as bigquery import numpy import pandas -import pyarrow as pa +import pandas.core.dtypes.common import typing_extensions -from pandas.api import extensions as pd_ext +import bigframes.constants as constants import bigframes.core import bigframes.core.block_transforms as block_ops import bigframes.core.blocks as blocks -import bigframes.core.col -import bigframes.core.expression as ex -import bigframes.core.identifiers as ids +import bigframes.core.groupby as groupby import bigframes.core.indexers import bigframes.core.indexes as indexes -import bigframes.core.ordering as order +from bigframes.core.ordering import ( + OrderingColumnReference, + OrderingDirection, + STABLE_SORTS, +) import bigframes.core.scalar as scalars import bigframes.core.utils as utils -import bigframes.core.validations as validations import bigframes.core.window -import bigframes.core.window_spec as windows +import bigframes.core.window_spec import bigframes.dataframe import bigframes.dtypes -import bigframes.exceptions as bfe import bigframes.formatting_helpers as formatter -import bigframes.functions import bigframes.operations as ops import bigframes.operations.aggregations as agg_ops -import bigframes.operations.lists as lists -import bigframes.operations.plotting as plotting -import bigframes.operations.python_op_maps as python_ops +import bigframes.operations.base +import bigframes.operations.datetimes as dt +import bigframes.operations.strings as strings import bigframes.operations.structs as structs -import bigframes.session -from bigframes._tools import docs -from bigframes.core import agg_expressions, groupby -from bigframes.core.logging import log_adapter -from bigframes.core.window import rolling - -if typing.TYPE_CHECKING: - import bigframes.extensions.bigframes.series_accessor as series_bigquery_accessor - import bigframes.geopandas.geoseries - import bigframes.operations.datetimes as datetimes - import bigframes.operations.strings as strings - +import third_party.bigframes_vendored.pandas.core.series as vendored_pandas_series -U = TypeVar("U") LevelType = typing.Union[str, int] LevelsType = typing.Union[LevelType, typing.Sequence[LevelType]] -_bigquery_function_recommendation_message = ( - "Your functions could not be applied directly to the Series." - " Try converting it to a BigFrames BigQuery function." -) - -_list = list # Type alias to escape Series.list property - - -@log_adapter.class_logger -@docs.inherit_docs(vendored_pandas_series.Series) -class Series: - # Must be above 5000 for pandas to delegate to bigframes for binops - __pandas_priority__ = 13000 - - # Ensure mypy can more robustly determine the type of self._block since it - # gets set in various places. - _block: blocks.Block - - def __init__( - self, - data=None, - index=None, - dtype: Optional[bigframes.dtypes.DtypeString | bigframes.dtypes.Dtype] = None, - name: str | None = None, - copy: Optional[bool] = None, - *, - session: Optional[bigframes.session.Session] = None, - ): - self._query_job: Optional[google.cloud.bigquery.job.QueryJob] = None - import bigframes.pandas - - # Ignore object dtype if provided, as it provides no additional - # information about what BigQuery type to use. - if dtype is not None and bigframes.dtypes.is_object_like(dtype): - dtype = None - - read_pandas_func = ( - session.read_pandas - if (session is not None) - else (lambda x: bigframes.pandas.read_pandas(x)) - ) - - block: typing.Optional[blocks.Block] = None - if (name is not None) and not isinstance(name, typing.Hashable): - raise ValueError( - f"BigQuery DataFrames only supports hashable series names. {constants.FEEDBACK_LINK}" - ) - if copy is not None and not copy: - raise ValueError( - f"Series constructor only supports copy=True. {constants.FEEDBACK_LINK}" - ) - - if isinstance(data, blocks.Block): - block = data - elif isinstance(data, bigframes.pandas.Series): - block = data._get_block() - # special case where data is local scalar, but index is bigframes index (maybe very big) - elif ( - not utils.is_list_like(data) and not isinstance(data, indexes.Index) - ) and isinstance(index, indexes.Index): - block = index._block - block, _ = block.create_constant(data) - block = block.with_column_labels([None]) - # prevents no-op reindex later - index = None - elif isinstance(data, indexes.Index) or isinstance(index, indexes.Index): - data = indexes.Index(data, dtype=dtype, name=name, session=session) - # set to none as it has already been applied, avoid re-cast later - if data.nlevels != 1: - raise NotImplementedError("Cannot interpret multi-index as Series.") - # Reset index to promote index columns to value columns, set default index - data_block = data._block.reset_index(drop=False).with_column_labels( - data.names - ) - if index is not None: # Align data and index by offset - bf_index = indexes.Index(index, session=session) - idx_block = bf_index._block.reset_index( - drop=False - ) # reset to align by offsets, and then reset back - idx_cols = idx_block.value_columns - data_block, (l_mapping, _) = idx_block.join(data_block, how="left") - data_block = data_block.set_index([l_mapping[col] for col in idx_cols]) - data_block = data_block.with_index_labels(bf_index.names) - # prevents no-op reindex later - index = None - block = data_block - - if block: - assert len(block.value_columns) == 1 - assert len(block.column_labels) == 1 - if index is not None: # reindexing operation - bf_index = indexes.Index(index) - idx_block = bf_index._block - idx_cols = idx_block.index_columns - block, _ = idx_block.join(block, how="left") - block = block.with_index_labels(bf_index.names) - if name: - block = block.with_column_labels([name]) - if dtype: - bf_dtype = bigframes.dtypes.bigframes_type(dtype) - block = block.multi_apply_unary_op(ops.AsTypeOp(to_type=bf_dtype)) - else: - if isinstance(dtype, str) and dtype.lower() == "json": - dtype = bigframes.dtypes.JSON_DTYPE - - pd_series = pandas.Series( - data=data, - index=index, # type:ignore - dtype=dtype, # type:ignore - name=name, - ) - block = read_pandas_func(pd_series)._get_block() # type:ignore - - assert block is not None - self._block: blocks.Block = block - - self._block.session._register_object(self) +class Series(bigframes.operations.base.SeriesMethods, vendored_pandas_series.Series): + def __init__(self, *args, **kwargs): + self._query_job: Optional[bigquery.QueryJob] = None + super().__init__(*args, **kwargs) @property - def dt(self) -> datetimes.DatetimeMethods: - import bigframes.operations.datetimes as datetimes - - return datetimes.DatetimeMethods(self) + def dt(self) -> dt.DatetimeMethods: + return dt.DatetimeMethods(self._block) @property def dtype(self): - bigframes.dtypes.warn_on_db_dtypes_json_dtype([self._dtype]) return self._dtype @property def dtypes(self): - bigframes.dtypes.warn_on_db_dtypes_json_dtype([self._dtype]) return self._dtype @property - def geo(self) -> bigframes.geopandas.geoseries.GeoSeries: - """ - Accessor object for geography properties of the Series values. - - Returns: - bigframes.geopandas.geoseries.GeoSeries: - An accessor containing geography methods. - - """ - import bigframes.geopandas.geoseries - - return bigframes.geopandas.geoseries.GeoSeries(self) + def index(self) -> indexes.Index: + return indexes.Index(self) @property - @validations.requires_index def loc(self) -> bigframes.core.indexers.LocSeriesIndexer: return bigframes.core.indexers.LocSeriesIndexer(self) @@ -249,12 +89,10 @@ def iloc(self) -> bigframes.core.indexers.IlocSeriesIndexer: return bigframes.core.indexers.IlocSeriesIndexer(self) @property - @validations.requires_ordering() def iat(self) -> bigframes.core.indexers.IatSeriesIndexer: return bigframes.core.indexers.IatSeriesIndexer(self) @property - @validations.requires_index def at(self) -> bigframes.core.indexers.AtSeriesIndexer: return bigframes.core.indexers.AtSeriesIndexer(self) @@ -262,11 +100,6 @@ def at(self) -> bigframes.core.indexers.AtSeriesIndexer: def name(self) -> blocks.Label: return self._name - @name.setter - def name(self, label: blocks.Label): - new_block = self._block.with_column_labels([label]) - self._set_block(new_block) - @property def shape(self) -> typing.Tuple[int]: return (self._block.shape[0],) @@ -283,45 +116,12 @@ def ndim(self) -> int: def empty(self) -> bool: return self.shape[0] == 0 - @property - def hasnans(self) -> bool: - # Note, hasnans is actually a null check, and NaNs don't count for nullable float - return self.isnull().any() - @property def values(self) -> numpy.ndarray: return self.to_numpy() @property - @validations.requires_index - def index(self) -> indexes.Index: - return indexes.Index.from_frame(self) - - @validations.requires_index - def keys(self) -> indexes.Index: - return self.index - - @property - def bigquery( - self, - ) -> series_bigquery_accessor.BigframesBigQuerySeriesAccessor: - """ - Accessor for BigQuery functionality. - - Returns: - bigframes.extensions.core.series_accessor.BigQuerySeriesAccessor: - Accessor that exposes BigQuery functionality on a Series, - with method names closer to SQL. - """ - # Import the accessor here to avoid circular imports. - import bigframes.extensions.bigframes.series_accessor - - return bigframes.extensions.bigframes.series_accessor.BigframesBigQuerySeriesAccessor( - self - ) - - @property - def query_job(self) -> Optional[google.cloud.bigquery.job.QueryJob]: + def query_job(self) -> Optional[bigquery.QueryJob]: """BigQuery job metadata for the most recent query. Returns: @@ -334,121 +134,59 @@ def query_job(self) -> Optional[google.cloud.bigquery.job.QueryJob]: @property def struct(self) -> structs.StructAccessor: - return structs.StructAccessor(self) - - @property - def list(self) -> lists.ListAccessor: - return lists.ListAccessor(self) + return structs.StructAccessor(self._block) @property - @validations.requires_ordering() def T(self) -> Series: return self.transpose() - @property - def _info_axis(self) -> indexes.Index: - return self.index - - @property - def _session(self) -> bigframes.Session: - return self._get_block().expr.session - - @property - def _struct_fields(self) -> List[str]: - if not bigframes.dtypes.is_struct_like(self._dtype): - return [] - - struct_type = typing.cast(pa.StructType, self._dtype.pyarrow_dtype) - return [struct_type.field(i).name for i in range(struct_type.num_fields)] - - @property - def sql(self) -> str: - """Compiles this Series's expression tree to SQL. - - Returns: - A string representing the compiled SQL. - """ - - return self.to_frame().sql - - @validations.requires_ordering() def transpose(self) -> Series: return self - def _set_internal_query_job( - self, query_job: Optional[google.cloud.bigquery.job.QueryJob] - ): + def _set_internal_query_job(self, query_job: bigquery.QueryJob): self._query_job = query_job def __len__(self): return self.shape[0] - def __bool__(self): - raise ValueError( - "Cannot convert Series into bool. Consider using .empty(), .item(), .any(), or .all() methods." - ) - def __iter__(self) -> typing.Iterator: return itertools.chain.from_iterable( - map(lambda x: x.squeeze(axis=1), self._block.to_pandas_batches()) + map(lambda x: x.index, self._block.to_pandas_batches()) ) - def __contains__(self, key) -> bool: - return key in self.index - def copy(self) -> Series: return Series(self._block) - @overload - def rename( - self, - index: Union[blocks.Label, Mapping[Any, Any]] = None, - ) -> Series: ... - - @overload - def rename( - self, - index: Union[blocks.Label, Mapping[Any, Any]] = None, - *, - inplace: Literal[False], - **kwargs, - ) -> Series: ... - - @overload def rename( - self, - index: Union[blocks.Label, Mapping[Any, Any]] = None, - *, - inplace: Literal[True], - **kwargs, - ) -> None: ... - - def rename( - self, - index: Union[blocks.Label, Mapping[Any, Any]] = None, - *, - inplace: bool = False, - **kwargs, - ) -> Optional[Series]: + self, index: Union[blocks.Label, Mapping[Any, Any]] = None, **kwargs + ) -> Series: if len(kwargs) != 0: raise NotImplementedError( f"rename does not currently support any keyword arguments. {constants.FEEDBACK_LINK}" ) + # rename the Series name + if index is None or isinstance( + index, str + ): # Python 3.9 doesn't allow isinstance of Optional + index = typing.cast(Optional[str], index) + block = self._block.with_column_labels([index]) + return Series(block) + # rename the index if isinstance(index, Mapping): index = typing.cast(Mapping[Any, Any], index) block = self._block for k, v in index.items(): new_idx_ids = [] - for idx_id, idx_dtype in zip(block.index_columns, block.index.dtypes): + for idx_id, idx_dtype in zip(block.index_columns, block.index_dtypes): # Will throw if key type isn't compatible with index type, which leads to invalid SQL. block.create_constant(k, dtype=idx_dtype) # Will throw if value type isn't compatible with index type. block, const_id = block.create_constant(v, dtype=idx_dtype) - block, cond_id = block.project_expr( - ops.ne_op.as_expr(idx_id, ex.const(k)) + block, cond_id = block.apply_unary_op( + idx_id, ops.BinopPartialRight(ops.ne_op, k) ) block, new_idx_id = block.apply_ternary_op( idx_id, cond_id, const_id, ops.where_op @@ -457,59 +195,23 @@ def rename( new_idx_ids.append(new_idx_id) block = block.drop_columns([const_id, cond_id]) - block = block.set_index(new_idx_ids, index_labels=block.index.names) + block = block.set_index(new_idx_ids, index_labels=block.index_labels) - if inplace: - self._block = block - return None - else: - return Series(block) + return Series(block) # rename the Series name if isinstance(index, typing.Hashable): index = typing.cast(Optional[str], index) block = self._block.with_column_labels([index]) - - if inplace: - self._block = block - return None - else: - return Series(block) + return Series(block) raise ValueError(f"Unsupported type of parameter index: {type(index)}") - @overload - def rename_axis( - self, - mapper: typing.Union[blocks.Label, typing.Sequence[blocks.Label]], - ) -> Series: ... - - @overload def rename_axis( self, mapper: typing.Union[blocks.Label, typing.Sequence[blocks.Label]], - *, - inplace: Literal[False], - **kwargs, - ) -> Series: ... - - @overload - def rename_axis( - self, - mapper: typing.Union[blocks.Label, typing.Sequence[blocks.Label]], - *, - inplace: Literal[True], **kwargs, - ) -> None: ... - - @validations.requires_index - def rename_axis( - self, - mapper: typing.Union[blocks.Label, typing.Sequence[blocks.Label]], - *, - inplace: bool = False, - **kwargs, - ) -> Optional[Series]: + ) -> Series: if len(kwargs) != 0: raise NotImplementedError( f"rename_axis does not currently support any keyword arguments. {constants.FEEDBACK_LINK}" @@ -519,13 +221,7 @@ def rename_axis( labels = mapper else: labels = [mapper] - - block = self._block.with_index_labels(labels) - if inplace: - self._block = block - return None - else: - return Series(block) + return Series(self._block.with_index_labels(labels)) def equals( self, other: typing.Union[Series, bigframes.dataframe.DataFrame] @@ -535,321 +231,83 @@ def equals( return False return block_ops.equals(self._block, other._block) - @overload # type: ignore[override] - def reset_index( - self, - level: blocks.LevelsType = ..., - *, - name: typing.Optional[str] = ..., - drop: Literal[False] = ..., - inplace: Literal[False] = ..., - allow_duplicates: Optional[bool] = ..., - ) -> bigframes.dataframe.DataFrame: ... - - @overload def reset_index( self, - level: blocks.LevelsType = ..., - *, - name: typing.Optional[str] = ..., - drop: Literal[True] = ..., - inplace: Literal[False] = ..., - allow_duplicates: Optional[bool] = ..., - ) -> Series: ... - - @overload - def reset_index( - self, - level: blocks.LevelsType = ..., - *, - name: typing.Optional[str] = ..., - drop: bool = ..., - inplace: Literal[True] = ..., - allow_duplicates: Optional[bool] = ..., - ) -> None: ... - - @validations.requires_ordering() - def reset_index( - self, - level: blocks.LevelsType = None, *, name: typing.Optional[str] = None, drop: bool = False, - inplace: bool = False, - allow_duplicates: Optional[bool] = None, - ) -> bigframes.dataframe.DataFrame | Series | None: - if allow_duplicates is None: - allow_duplicates = False - block = self._block.reset_index(level, drop, allow_duplicates=allow_duplicates) + ) -> bigframes.dataframe.DataFrame | Series: + block = self._block.reset_index(drop) if drop: - if inplace: - self._set_block(block) - return None return Series(block) else: - if inplace: - raise ValueError( - "Series.reset_index cannot combine inplace=True and drop=False" - ) if name: block = block.assign_label(self._value_column, name) return bigframes.dataframe.DataFrame(block) - def _prepare_display_df(self) -> bigframes.dataframe.DataFrame: - return self.to_frame()._prepare_display_df() - - def _repr_mimebundle_(self, include=None, exclude=None): - """ - Custom display method for IPython/Jupyter environments. - This is called by IPython's display system when the object is displayed. - """ - # TODO(b/467647693): Anywidget integration has been tested in Jupyter, VS Code, and - # BQ Studio, but there is a known compatibility issue with Marimo that needs to be addressed. - from bigframes.display import html - - return html.repr_mimebundle(self, include=include, exclude=exclude) - def __repr__(self) -> str: - # Protect against errors with uninitialized Series. See: - # https://github.com/googleapis/python-bigquery-dataframes/issues/728 - if not hasattr(self, "_block"): - return object.__repr__(self) - # TODO(swast): Add a timeout here? If the query is taking a long time, # maybe we just print the job metadata that we have so far? # TODO(swast): Avoid downloading the whole series by using job # metadata, like we do with DataFrame. opts = bigframes.options.display + max_results = opts.max_rows if opts.repr_mode == "deferred": - return formatter.repr_query_job(self._compute_dry_run()) + return formatter.repr_query_job(self.query_job) - self._cached() - pandas_df, row_count, query_job = self._block.retrieve_repr_request_results( - opts.max_rows - ) + pandas_df, _, query_job = self._block.retrieve_repr_request_results(max_results) self._set_internal_query_job(query_job) - from bigframes.display import plaintext - return plaintext.create_text_representation( - pandas_df, - row_count, - is_series=True, - has_index=len(self._block.index_columns) > 0, - ) + return repr(pandas_df.iloc[:, 0]) def astype( self, dtype: Union[bigframes.dtypes.DtypeString, bigframes.dtypes.Dtype], - *, - errors: Literal["raise", "null"] = "raise", ) -> Series: - if errors not in ["raise", "null"]: - raise ValueError("Argument 'errors' must be one of 'raise' or 'null'") - dtype = bigframes.dtypes.bigframes_type(dtype) - safe = errors == "null" - if dtype == bigframes.dtypes.JSON_DTYPE: - return self._apply_unary_op(bigframes.operations.ToJSON(safe=safe)) - elif self.dtype == bigframes.dtypes.JSON_DTYPE: - return self._apply_unary_op( - bigframes.operations.JSONDecode(to_type=dtype, safe=safe) - ) - else: - return self._apply_unary_op( - bigframes.operations.AsTypeOp(to_type=dtype, safe=safe) - ) + return self._apply_unary_op(bigframes.operations.AsTypeOp(dtype)) def to_pandas( self, max_download_size: Optional[int] = None, sampling_method: Optional[str] = None, random_state: Optional[int] = None, - *, - ordered: bool = True, - dry_run: bool = False, - allow_large_results: Optional[bool] = None, ) -> pandas.Series: """Writes Series to pandas Series. - **Examples:** - - >>> s = bpd.Series([4, 3, 2]) - - Download the data from BigQuery and convert it into an in-memory pandas Series. - - >>> s.to_pandas() - 0 4 - 1 3 - 2 2 - dtype: Int64 - - Estimate job statistics without processing or downloading data by using `dry_run=True`. - - >>> s.to_pandas(dry_run=True) # doctest: +SKIP - columnCount 1 - columnDtypes {None: Int64} - indexLevel 1 - indexDtypes [Int64] - projectId bigframes-dev - location US - jobType QUERY - destinationTable {'projectId': 'bigframes-dev', 'datasetId': '_... - useLegacySql False - referencedTables None - totalBytesProcessed 0 - cacheHit False - statementType SELECT - creationTime 2025-04-03 18:54:59.219000+00:00 - dtype: object - Args: max_download_size (int, default None): - .. deprecated:: 2.0.0 - ``max_download_size`` parameter is deprecated. Please use ``to_pandas_batches()`` - method instead. - - Download size threshold in MB. If ``max_download_size`` is exceeded when downloading data, - the data will be downsampled if ``bigframes.options.sampling.enable_downsampling`` is - ``True``, otherwise, an error will be raised. If set to a value other than ``None``, - this will supersede the global config. + Download size threshold in MB. If max_download_size is exceeded when downloading data + (e.g., to_pandas()), the data will be downsampled if + bigframes.options.sampling.enable_downsampling is True, otherwise, an error will be + raised. If set to a value other than None, this will supersede the global config. sampling_method (str, default None): - .. deprecated:: 2.0.0 - ``sampling_method`` parameter is deprecated. Please use ``sample()`` method instead. - Downsampling algorithms to be chosen from, the choices are: "head": This algorithm returns a portion of the data from the beginning. It is fast and requires minimal computations to perform the downsampling; "uniform": This algorithm returns uniform random samples of the data. If set to a value other than None, this will supersede the global config. random_state (int, default None): - .. deprecated:: 2.0.0 - ``random_state`` parameter is deprecated. Please use ``sample()`` method instead. - The seed for the uniform downsampling algorithm. If provided, the uniform method may take longer to execute and require more computation. If set to a value other than None, this will supersede the global config. - ordered (bool, default True): - Determines whether the resulting pandas series will be ordered. - In some cases, unordered may result in a faster-executing query. - dry_run (bool, default False): - If this argument is true, this method will not process the data. Instead, it returns - a Pandas Series containing dry run job statistics - allow_large_results (bool, default None): - If not None, overrides the global setting to allow or disallow large query results - over the default size limit of 10 GB. Returns: pandas.Series: A pandas Series with all rows of this Series if the data_sampling_threshold_mb - is not exceeded; otherwise, a pandas Series with downsampled rows of the DataFrame. If dry_run - is set to True, a pandas Series containing dry run statistics will be returned. + is not exceeded; otherwise, a pandas Series with downsampled rows of the DataFrame. """ - if max_download_size is not None: - msg = bfe.format_message( - "DEPRECATED: The `max_download_size` parameters for `Series.to_pandas()` " - "are deprecated and will be removed soon. Please use `Series.to_pandas_batches()`." - ) - warnings.warn(msg, category=FutureWarning) - if sampling_method is not None or random_state is not None: - msg = bfe.format_message( - "DEPRECATED: The `sampling_method` and `random_state` parameters for " - "`Series.to_pandas()` are deprecated and will be removed soon. " - "Please use `Series.sample().to_pandas()` instead for sampling." - ) - warnings.warn(msg, category=FutureWarning) - - if dry_run: - dry_run_stats, dry_run_job = self._block._compute_dry_run( - max_download_size=max_download_size, - sampling_method=sampling_method, - random_state=random_state, - ordered=ordered, - ) - - self._set_internal_query_job(dry_run_job) - return dry_run_stats - - # Repeat the to_pandas() call to make mypy deduce type correctly, because mypy cannot resolve - # Literal[True/False] to bool df, query_job = self._block.to_pandas( + (self._value_column,), max_download_size=max_download_size, sampling_method=sampling_method, random_state=random_state, - ordered=ordered, - allow_large_results=allow_large_results, ) - - if query_job: - self._set_internal_query_job(query_job) - - series = df.squeeze(axis=1) + self._set_internal_query_job(query_job) + series = df[self._value_column] series.name = self._name return series - def to_pandas_batches( - self, - page_size: Optional[int] = None, - max_results: Optional[int] = None, - *, - allow_large_results: Optional[bool] = None, - cell_execution_count: Optional[int] = None, - ) -> Iterable[pandas.Series]: - """Stream Series results to an iterable of pandas Series. - - page_size and max_results determine the size and number of batches, - see https://cloud.google.com/python/docs/reference/bigquery/latest/google.cloud.bigquery.job.QueryJob#google_cloud_bigquery_job_QueryJob_result - - **Examples:** - - >>> s = bpd.Series([4, 3, 2, 2, 3]) - - Iterate through the results in batches, limiting the total rows yielded - across all batches via `max_results`: - - >>> for s_batch in s.to_pandas_batches(max_results=3): - ... print(s_batch) - 0 4 - 1 3 - 2 2 - dtype: Int64 - - Alternatively, control the approximate size of each batch using `page_size` - and fetch batches manually using `next()`: - - >>> it = s.to_pandas_batches(page_size=2) - >>> next(it) - 0 4 - 1 3 - dtype: Int64 - >>> next(it) - 2 2 - 3 2 - dtype: Int64 - - Args: - page_size (int, default None): - The maximum number of rows of each batch. Non-positive values are ignored. - max_results (int, default None): - The maximum total number of rows of all batches. - allow_large_results (bool, default None): - If not None, overrides the global setting to allow or disallow large query results - over the default size limit of 10 GB. - - Returns: - Iterable[pandas.Series]: - An iterable of smaller Series which combine to - form the original Series. Results stream from bigquery, - see https://cloud.google.com/python/docs/reference/bigquery/latest/google.cloud.bigquery.table.RowIterator#google_cloud_bigquery_table_RowIterator_to_arrow_iterable - """ - batches = self._block.to_pandas_batches( - page_size=page_size, - max_results=max_results, - allow_large_results=allow_large_results, - cell_execution_count=cell_execution_count, - ) - return map(lambda df: cast(pandas.Series, df.squeeze(1)), batches) - - def _compute_dry_run(self) -> google.cloud.bigquery.job.QueryJob: - _, query_job = self._block._compute_dry_run((self._value_column,)) - return query_job + def _compute_dry_run(self) -> bigquery.QueryJob: + return self._block._compute_dry_run((self._value_column,)) def drop( self, @@ -860,36 +318,32 @@ def drop( columns: Union[blocks.Label, typing.Iterable[blocks.Label]] = None, level: typing.Optional[LevelType] = None, ) -> Series: - if (labels is None) == (index is None): - raise ValueError("Must specify exactly one of 'labels' or 'index'") - - if labels is not None: - index = labels + if labels and index: + raise ValueError("Must specify exacly one of 'labels' or 'index'") + index = labels or index # ignore axis, columns params block = self._block level_id = self._resolve_levels(level or 0)[0] if _is_list_like(index): block, inverse_condition_id = block.apply_unary_op( - level_id, ops.IsInOp(values=tuple(index), match_nulls=True) + level_id, ops.IsInOp(index, match_nulls=True) ) block, condition_id = block.apply_unary_op( inverse_condition_id, ops.invert_op ) else: - block, condition_id = block.project_expr( - ops.ne_op.as_expr(level_id, ex.const(index)) + block, condition_id = block.apply_unary_op( + level_id, ops.partial_right(ops.ne_op, index) ) - block = block.filter_by_id(condition_id, keep_null=True) + block = block.filter(condition_id, keep_null=True) block = block.drop_columns([condition_id]) return Series(block.select_column(self._value_column)) - @validations.requires_index def droplevel(self, level: LevelsType, axis: int | str = 0): resolved_level_ids = self._resolve_levels(level) return Series(self._block.drop_levels(resolved_level_ids)) - @validations.requires_index def swaplevel(self, i: int = -2, j: int = -1): level_i = self._block.index_columns[i] level_j = self._block.index_columns[j] @@ -899,13 +353,12 @@ def swaplevel(self, i: int = -2, j: int = -1): ] return Series(self._block.reorder_levels(reordering)) - @validations.requires_index def reorder_levels(self, order: LevelsType, axis: int | str = 0): resolved_level_ids = self._resolve_levels(order) return Series(self._block.reorder_levels(resolved_level_ids)) def _resolve_levels(self, level: LevelsType) -> typing.Sequence[str]: - return self._block.index.resolve_level(level) + return self._block.resolve_index_level(level) def between(self, left, right, inclusive="both"): if inclusive not in ["both", "neither", "left", "right"]: @@ -918,72 +371,55 @@ def between(self, left, right, inclusive="both"): self._apply_binary_op(right, right_op) ) - def case_when(self, caselist) -> Series: - cases = [] - - for condition, output in itertools.chain(caselist, [(True, self)]): - cases.append(condition) - cases.append(output) - # In pandas, the default value if no case matches is the original value. - # This makes it impossible to change the type of the column, but if - # the condition is always True, we know it will match and no subsequent - # conditions matter (including the fallback to `self`). This break allows - # the type to change (see: internal issue 349926559). - if condition is True: - break - - return self._apply_nary_op( - ops.case_when_op, - cases, - # Self is already included in "others". - ignore_self=True, - ).rename(self.name) - - @validations.requires_ordering() def cumsum(self) -> Series: - return self._apply_window_op(agg_ops.sum_op, windows.cumulative_rows()) + return self._apply_window_op( + agg_ops.sum_op, bigframes.core.window_spec.WindowSpec(following=0) + ) - @validations.requires_ordering() def ffill(self, *, limit: typing.Optional[int] = None) -> Series: - window = windows.rows(start=None if limit is None else -limit, end=0) + window = bigframes.core.window_spec.WindowSpec(preceding=limit, following=0) return self._apply_window_op(agg_ops.LastNonNullOp(), window) pad = ffill - @validations.requires_ordering() def bfill(self, *, limit: typing.Optional[int] = None) -> Series: - window = windows.rows(start=0, end=limit) + window = bigframes.core.window_spec.WindowSpec(preceding=0, following=limit) return self._apply_window_op(agg_ops.FirstNonNullOp(), window) - @validations.requires_ordering() def cummax(self) -> Series: - return self._apply_window_op(agg_ops.max_op, windows.cumulative_rows()) + return self._apply_window_op( + agg_ops.max_op, bigframes.core.window_spec.WindowSpec(following=0) + ) - @validations.requires_ordering() def cummin(self) -> Series: - return self._apply_window_op(agg_ops.min_op, windows.cumulative_rows()) + return self._apply_window_op( + agg_ops.min_op, bigframes.core.window_spec.WindowSpec(following=0) + ) - @validations.requires_ordering() def cumprod(self) -> Series: - return self._apply_window_op(agg_ops.product_op, windows.cumulative_rows()) + return self._apply_window_op( + agg_ops.product_op, bigframes.core.window_spec.WindowSpec(following=0) + ) - @validations.requires_ordering() def shift(self, periods: int = 1) -> Series: - window_spec = windows.rows() - return self._apply_window_op(agg_ops.ShiftOp(periods), window_spec) + window = bigframes.core.window_spec.WindowSpec( + preceding=periods if periods > 0 else None, + following=-periods if periods < 0 else None, + ) + return self._apply_window_op(agg_ops.ShiftOp(periods), window) - @validations.requires_ordering() def diff(self, periods: int = 1) -> Series: - window_spec = windows.rows() - return self._apply_window_op(agg_ops.DiffOp(periods), window_spec) + window = bigframes.core.window_spec.WindowSpec( + preceding=periods if periods > 0 else None, + following=-periods if periods < 0 else None, + ) + return self._apply_window_op(agg_ops.DiffOp(periods), window) - @validations.requires_ordering() def pct_change(self, periods: int = 1) -> Series: # Future versions of pandas will not perfrom ffill automatically series = self.ffill() return Series(block_ops.pct_change(series._block, periods=periods)) - @validations.requires_ordering() def rank( self, axis=0, @@ -991,11 +427,8 @@ def rank( numeric_only=False, na_option: str = "keep", ascending: bool = True, - pct: bool = False, ) -> Series: - return Series( - block_ops.rank(self._block, method, na_option, ascending, pct=pct) - ) + return Series(block_ops.rank(self._block, method, na_option, ascending)) def fillna(self, value=None) -> Series: return self._apply_binary_op(value, ops.fillna_op) @@ -1004,91 +437,44 @@ def replace( self, to_replace: typing.Any, value: typing.Any = None, *, regex: bool = False ): if regex: - # No-op unless to_replace and series dtype are both string type - if not isinstance(to_replace, str) or not isinstance( - self.dtype, pandas.StringDtype - ): - return self - return self._regex_replace(to_replace, value) + if not (isinstance(to_replace, str) and isinstance(value, str)): + raise NotImplementedError( + f"replace regex mode only supports strings for 'to_replace' and 'value'. {constants.FEEDBACK_LINK}" + ) + block, result_col = self._block.apply_unary_op( + self._value_column, + ops.ReplaceRegexOp(to_replace, value), + result_label=self.name, + ) + return Series(block.select_column(result_col)) elif utils.is_dict_like(to_replace): - return self._mapping_replace(to_replace) # type: ignore - elif utils.is_list_like(to_replace): - replace_list = to_replace - else: # Scalar - replace_list = [to_replace] - replace_list = [ - i for i in replace_list if bigframes.dtypes.is_compatible(i, self.dtype) - ] - return self._simple_replace(replace_list, value) if replace_list else self - - def _regex_replace(self, to_replace: str, value: str): - if not bigframes.dtypes.is_dtype(value, self.dtype): raise NotImplementedError( - f"Cannot replace {self.dtype} elements with incompatible item {value} as mixed-type columns not supported. {constants.FEEDBACK_LINK}" + f"Dict 'to_replace' not supported. {constants.FEEDBACK_LINK}" ) - block, result_col = self._block.apply_unary_op( - self._value_column, - ops.RegexReplaceStrOp(to_replace, value), - result_label=self.name, - ) - return Series(block.select_column(result_col)) - - def _simple_replace(self, to_replace_list: typing.Sequence, value): - result_type = bigframes.dtypes.is_compatible(value, self.dtype) - if not result_type: - raise NotImplementedError( - f"Cannot replace {self.dtype} elements with incompatible item {value} as mixed-type columns not supported. {constants.FEEDBACK_LINK}" + elif utils.is_list_like(to_replace): + block, cond = self._block.apply_unary_op( + self._value_column, ops.IsInOp(to_replace) ) - - if result_type != self.dtype: - return self.astype(result_type)._simple_replace(to_replace_list, value) - - block, cond = self._block.apply_unary_op( - self._value_column, ops.IsInOp(tuple(to_replace_list)) - ) - block, result_col = block.project_expr( - ops.where_op.as_expr(ex.const(value), cond, self._value_column), self.name - ) - return Series(block.select_column(result_col)) - - def _mapping_replace(self, mapping: dict[typing.Hashable, typing.Hashable]): - if not mapping: - return self.copy() - - tuples = [] - lcd_types: list[typing.Optional[bigframes.dtypes.Dtype]] = [] - for key, value in mapping.items(): - lcd_type = bigframes.dtypes.is_compatible(key, self.dtype) - if not lcd_type: - continue - if not bigframes.dtypes.is_dtype(value, self.dtype): - raise NotImplementedError( - f"Cannot replace {self.dtype} elements with incompatible item {value} as mixed-type columns not supported. {constants.FEEDBACK_LINK}" - ) - tuples.append((key, value)) - lcd_types.append(lcd_type) - - result_dtype = functools.reduce( - lambda t1, t2: bigframes.dtypes.lcd_type(t1, t2) if (t1 and t2) else None, - lcd_types, - self.dtype, - ) - if not result_dtype: - raise NotImplementedError( - f"Cannot replace {self.dtype} elements with incompatible mapping {mapping} as mixed-type columns not supported. {constants.FEEDBACK_LINK}" + block, result_col = block.apply_binary_op( + cond, + self._value_column, + ops.partial_arg1(ops.where_op, value), + result_label=self.name, ) - block, result = self._block.apply_unary_op( - self._value_column, ops.MapOp(tuple(tuples)) - ) - replaced = Series(block.select_column(result)) - replaced.name = self.name - return replaced + return Series(block.select_column(result_col)) + else: # Scalar + block, cond = self._block.apply_unary_op( + self._value_column, ops.BinopPartialLeft(ops.eq_op, to_replace) + ) + block, result_col = block.apply_binary_op( + cond, + self._value_column, + ops.partial_arg1(ops.where_op, value), + result_label=self.name, + ) + return Series(block.select_column(result_col)) - @validations.requires_ordering() - @validations.requires_index def interpolate(self, method: str = "linear") -> Series: - if method == "pad": - return self.ffill() result = block_ops.interpolate(self._block, method) return Series(result) @@ -1107,64 +493,15 @@ def dropna( result = result.reset_index() return Series(result) - @validations.requires_ordering(bigframes.constants.SUGGEST_PEEK_PREVIEW) def head(self, n: int = 5) -> Series: return typing.cast(Series, self.iloc[0:n]) - @validations.requires_ordering() def tail(self, n: int = 5) -> Series: return typing.cast(Series, self.iloc[-n:]) - def peek( - self, n: int = 5, *, force: bool = True, allow_large_results=None - ) -> pandas.Series: - """ - Preview n arbitrary elements from the series without guarantees about row selection or ordering. - - ``Series.peek(force=False)`` will always be very fast, but will not succeed if data requires - full data scanning. Using ``force=True`` will always succeed, but may be perform queries. - Query results will be cached so that future steps will benefit from these queries. - - Args: - n (int, default 5): - The number of rows to select from the series. Which N rows are returned is non-deterministic. - force (bool, default True): - If the data cannot be peeked efficiently, the series will instead be fully materialized as part - of the operation if ``force=True``. If ``force=False``, the operation will throw a ValueError. - allow_large_results (bool, default None): - If not None, overrides the global setting to allow or disallow large query results - over the default size limit of 10 GB. - Returns: - pandas.Series: A pandas Series with n rows. - - Raises: - ValueError: If force=False and data cannot be efficiently peeked. - """ - maybe_result = self._block.try_peek(n, allow_large_results=allow_large_results) - if maybe_result is None: - if force: - self._cached() - maybe_result = self._block.try_peek( - n, force=True, allow_large_results=allow_large_results - ) - assert maybe_result is not None - else: - raise ValueError( - "Cannot peek efficiently when data has aggregates, joins or window functions applied. Use force=True to fully compute dataframe." - ) - as_series = maybe_result.squeeze(axis=1) - as_series.name = self.name - return as_series - - def item(self): - # Docstring is in third_party/bigframes_vendored/pandas/core/series.py - return self.peek(2).item() - def nlargest(self, n: int = 5, keep: str = "first") -> Series: if keep not in ("first", "last", "all"): raise ValueError("'keep must be one of 'first', 'last', or 'all'") - if keep != "all": - validations.enforce_ordered(self, "nlargest(keep != 'all')") return Series( block_ops.nlargest(self._block, n, [self._value_column], keep=keep) ) @@ -1172,25 +509,20 @@ def nlargest(self, n: int = 5, keep: str = "first") -> Series: def nsmallest(self, n: int = 5, keep: str = "first") -> Series: if keep not in ("first", "last", "all"): raise ValueError("'keep must be one of 'first', 'last', or 'all'") - if keep != "all": - validations.enforce_ordered(self, "nsmallest(keep != 'all')") return Series( block_ops.nsmallest(self._block, n, [self._value_column], keep=keep) ) - def isin(self, values) -> "Series": - if isinstance(values, Series): - return Series(self._block.isin(values._block)) - if isinstance(values, indexes.Index): - return Series(self._block.isin(values.to_series()._block)) + def isin(self, values) -> "Series" | None: if not _is_list_like(values): raise TypeError( "only list-like objects are allowed to be passed to " f"isin(), you passed a [{type(values).__name__}]" ) - return self._apply_unary_op( - ops.IsInOp(values=tuple(values), match_nulls=True) - ).fillna(value=False) + + return self._apply_unary_op(ops.IsInOp(values, match_nulls=True)).fillna( + value=False + ) def isna(self) -> "Series": return self._apply_unary_op(ops.isnull_op) @@ -1212,22 +544,17 @@ def __or__(self, other: bool | int | Series) -> Series: __ror__ = __or__ - def __xor__(self, other: bool | int | Series) -> Series: - return self._apply_binary_op(other, ops.xor_op) - - __rxor__ = __xor__ - - def __add__(self, other: float | int | pandas.Timedelta | Series) -> Series: + def __add__(self, other: float | int | Series) -> Series: return self.add(other) - def __radd__(self, other: float | int | pandas.Timedelta | Series) -> Series: + def __radd__(self, other: float | int | Series) -> Series: return self.radd(other) - def add(self, other: float | int | pandas.Timedelta | Series) -> Series: + def add(self, other: float | int | Series) -> Series: return self._apply_binary_op(other, ops.add_op) - def radd(self, other: float | int | pandas.Timedelta | Series) -> Series: - return self._apply_binary_op(other, ops.add_op, reverse=True) + def radd(self, other: float | int | Series) -> Series: + return self._apply_binary_op(other, ops.reverse(ops.add_op)) def __sub__(self, other: float | int | Series) -> Series: return self.sub(other) @@ -1235,11 +562,11 @@ def __sub__(self, other: float | int | Series) -> Series: def __rsub__(self, other: float | int | Series) -> Series: return self.rsub(other) - def sub(self, other) -> Series: + def sub(self, other: float | int | Series) -> Series: return self._apply_binary_op(other, ops.sub_op) - def rsub(self, other) -> Series: - return self._apply_binary_op(other, ops.sub_op, reverse=True) + def rsub(self, other: float | int | Series) -> Series: + return self._apply_binary_op(other, ops.reverse(ops.sub_op)) subtract = sub @@ -1253,37 +580,39 @@ def mul(self, other: float | int | Series) -> Series: return self._apply_binary_op(other, ops.mul_op) def rmul(self, other: float | int | Series) -> Series: - return self._apply_binary_op(other, ops.mul_op, reverse=True) + return self._apply_binary_op(other, ops.reverse(ops.mul_op)) multiply = mul - def __truediv__(self, other: float | int | pandas.Timedelta | Series) -> Series: + def __truediv__(self, other: float | int | Series) -> Series: return self.truediv(other) - def __rtruediv__(self, other: float | int | pandas.Timedelta | Series) -> Series: + def __rtruediv__(self, other: float | int | Series) -> Series: return self.rtruediv(other) - def truediv(self, other: float | int | pandas.Timedelta | Series) -> Series: + def truediv(self, other: float | int | Series) -> Series: return self._apply_binary_op(other, ops.div_op) - def rtruediv(self, other: float | int | pandas.Timedelta | Series) -> Series: - return self._apply_binary_op(other, ops.div_op, reverse=True) + def rtruediv(self, other: float | int | Series) -> Series: + return self._apply_binary_op(other, ops.reverse(ops.div_op)) + + div = truediv - div = divide = truediv + divide = truediv rdiv = rtruediv - def __floordiv__(self, other: float | int | pandas.Timedelta | Series) -> Series: + def __floordiv__(self, other: float | int | Series) -> Series: return self.floordiv(other) - def __rfloordiv__(self, other: float | int | pandas.Timedelta | Series) -> Series: + def __rfloordiv__(self, other: float | int | Series) -> Series: return self.rfloordiv(other) - def floordiv(self, other: float | int | pandas.Timedelta | Series) -> Series: + def floordiv(self, other: float | int | Series) -> Series: return self._apply_binary_op(other, ops.floordiv_op) - def rfloordiv(self, other: float | int | pandas.Timedelta | Series) -> Series: - return self._apply_binary_op(other, ops.floordiv_op, reverse=True) + def rfloordiv(self, other: float | int | Series) -> Series: + return self._apply_binary_op(other, ops.reverse(ops.floordiv_op)) def __pow__(self, other: float | int | Series) -> Series: return self.pow(other) @@ -1295,12 +624,12 @@ def pow(self, other: float | int | Series) -> Series: return self._apply_binary_op(other, ops.pow_op) def rpow(self, other: float | int | Series) -> Series: - return self._apply_binary_op(other, ops.pow_op, reverse=True) + return self._apply_binary_op(other, ops.reverse(ops.pow_op)) - def __lt__(self, other: float | int | str | Series) -> Series: + def __lt__(self, other: float | int | Series) -> Series: # type: ignore return self.lt(other) - def __le__(self, other: float | int | str | Series) -> Series: + def __le__(self, other: float | int | Series) -> Series: # type: ignore return self.le(other) def lt(self, other) -> Series: @@ -1309,10 +638,10 @@ def lt(self, other) -> Series: def le(self, other) -> Series: return self._apply_binary_op(other, ops.le_op) - def __gt__(self, other: float | int | str | Series) -> Series: + def __gt__(self, other: float | int | Series) -> Series: # type: ignore return self.gt(other) - def __ge__(self, other: float | int | str | Series) -> Series: + def __ge__(self, other: float | int | Series) -> Series: # type: ignore return self.ge(other) def gt(self, other) -> Series: @@ -1331,7 +660,7 @@ def mod(self, other) -> Series: # type: ignore return self._apply_binary_op(other, ops.mod_op) def rmod(self, other) -> Series: # type: ignore - return self._apply_binary_op(other, ops.mod_op, reverse=True) + return self._apply_binary_op(other, ops.reverse(ops.mod_op)) def divmod(self, other) -> Tuple[Series, Series]: # type: ignore # TODO(huanc): when self and other both has dtype int and other contains zeros, @@ -1343,28 +672,10 @@ def rdivmod(self, other) -> Tuple[Series, Series]: # type: ignore # the output should be dtype float, both floordiv and mod returns dtype int in this case. return (self.rfloordiv(other), self.rmod(other)) - def dot(self, other): - return (self * other).sum() - def __matmul__(self, other): - return self.dot(other) - - def __rmatmul__(self, other): - return self.dot(other) - - def combine_first(self, other: Series) -> Series: - result = self._apply_binary_op(other, ops.coalesce_op) - result.name = self.name - return result - - def update(self, other: Union[Series, Sequence, Mapping]) -> None: - result = self._apply_binary_op( - other, ops.coalesce_op, reverse=True, alignment="left" - ) - self._set_block(result._get_block()) + return (self * other).sum() - def __abs__(self) -> Series: - return self.abs() + dot = __matmul__ def abs(self) -> Series: return self._apply_unary_op(ops.abs_op) @@ -1373,8 +684,28 @@ def round(self, decimals=0) -> "Series": return self._apply_binary_op(decimals, ops.round_op) def corr(self, other: Series, method="pearson", min_periods=None) -> float: - # TODO(tbergeron): Validate early that both are numeric - # TODO(tbergeron): Handle partially-numeric columns + """ + Compute the correlation with the other Series. Non-number values are ignored in the + computation. + + Uses the "Pearson" method of correlation. Numbers are converted to float before + calculation, so the result may be unstable. + + Args: + other (Series): + The series with which this is to be correlated. + method (string, default "pearson"): + Correlation method to use - currently only "pearson" is supported. + min_periods (int, default None): + The minimum number of observations needed to return a result. Non-default values + are not yet supported, so a result will be returned for at least two observations. + + Returns: + float; Will return NaN if there are fewer than two numeric pairs, either series has a + variance or covariance of zero, or any input value is infinite. + """ + # TODO(kemppeterson): Validate early that both are numeric + # TODO(kemppeterson): Handle partially-numeric columns if method != "pearson": raise NotImplementedError( f"Only Pearson correlation is currently supported. {constants.FEEDBACK_LINK}" @@ -1383,13 +714,7 @@ def corr(self, other: Series, method="pearson", min_periods=None) -> float: raise NotImplementedError( f"min_periods not yet supported. {constants.FEEDBACK_LINK}" ) - return self._apply_binary_aggregation(other, agg_ops.CorrOp()) - - def autocorr(self, lag: int = 1) -> float: - return self.corr(self.shift(lag)) - - def cov(self, other: Series) -> float: - return self._apply_binary_aggregation(other, agg_ops.CovOp()) + return self._apply_corr_aggregation(other) def all(self) -> bool: return typing.cast(bool, self._apply_aggregation(agg_ops.all_op)) @@ -1425,11 +750,11 @@ def _central_moment(self, n: int) -> float: def agg(self, func: str | typing.Sequence[str]) -> scalars.Scalar | Series: if _is_list_like(func): - if self.dtype not in bigframes.dtypes.NUMERIC_BIGFRAMES_TYPES_PERMISSIVE: + if self.dtype not in bigframes.dtypes.NUMERIC_BIGFRAMES_TYPES: raise NotImplementedError( f"Multiple aggregations only supported on numeric series. {constants.FEEDBACK_LINK}" ) - aggregations = [agg_ops.lookup_agg_func(f)[0] for f in func] + aggregations = [agg_ops.lookup_agg_func(f) for f in func] return Series( self._block.summarize( [self._value_column], @@ -1437,14 +762,12 @@ def agg(self, func: str | typing.Sequence[str]) -> scalars.Scalar | Series: ) ) else: - return self._apply_aggregation(agg_ops.lookup_agg_func(func)[0]) - - aggregate = agg - def describe(self) -> Series: - from bigframes.pandas.core.methods import describe + return self._apply_aggregation( + agg_ops.lookup_agg_func(typing.cast(str, func)) + ) - return cast(Series, describe.describe(self, include="all")) + aggregate = agg def skew(self): count = self.count() @@ -1483,58 +806,41 @@ def kurt(self): def mode(self) -> Series: block = self._block # Approach: Count each value, return each value for which count(x) == max(counts)) - block = block.aggregate( + block, agg_ids = block.aggregate( by_column_ids=[self._value_column], - aggregations=( - agg_expressions.UnaryAggregation( - agg_ops.count_op, ex.deref(self._value_column) - ), - ), + aggregations=((self._value_column, agg_ops.count_op),), + as_index=False, ) - value_count_col_id = block.value_columns[0] + value_count_col_id = agg_ids[0] block, max_value_count_col_id = block.apply_window_op( value_count_col_id, agg_ops.max_op, - window_spec=windows.unbound(), + window_spec=bigframes.core.window_spec.WindowSpec(), ) block, is_mode_col_id = block.apply_binary_op( value_count_col_id, max_value_count_col_id, ops.eq_op, ) - block = block.filter_by_id(is_mode_col_id) - # use temporary name for reset_index to avoid collision, restore after dropping extra columns - block = ( - block.with_index_labels(["mode_temp_internal"]) - .order_by([order.ascending_over(self._value_column)]) - .reset_index(drop=False) + block = block.filter(is_mode_col_id) + mode_values_series = Series( + block.select_column(self._value_column).assign_label( + self._value_column, self.name + ) + ) + return typing.cast( + Series, mode_values_series.sort_values().reset_index(drop=True) ) - block = block.select_column(self._value_column).with_column_labels([self.name]) - mode_values_series = Series(block.select_column(self._value_column)) - return typing.cast(Series, mode_values_series) def mean(self) -> float: return typing.cast(float, self._apply_aggregation(agg_ops.mean_op)) - def median(self, *, exact: bool = True) -> float: + def median(self, *, exact: bool = False) -> float: if exact: - return typing.cast(float, self.quantile(0.5)) - else: - return typing.cast(float, self._apply_aggregation(agg_ops.median_op)) - - def quantile(self, q: Union[float, Sequence[float]] = 0.5) -> Union[Series, float]: - qs = tuple(q) if utils.is_list_like(q) else (q,) - result = block_ops.quantile(self._block, (self._value_column,), qs=qs) - if utils.is_list_like(q): - # Drop the first level, since only one column - result = result.with_column_labels(result.column_labels.droplevel(0)) - result, index_col = result.create_constant(self.name, None) - result = result.set_index([index_col]) - return Series( - result.transpose(original_row_index=pandas.Index([self.name])) + raise NotImplementedError( + f"Only approximate median is supported. {constants.FEEDBACK_LINK}" ) - else: - return cast(float, Series(result).to_pandas().squeeze()) + return typing.cast(float, self._apply_aggregation(agg_ops.median_op)) def sum(self) -> float: return typing.cast(float, self._apply_aggregation(agg_ops.sum_op)) @@ -1553,15 +859,6 @@ def __ne__(self, other: object) -> Series: # type: ignore def __invert__(self) -> Series: return self._apply_unary_op(ops.invert_op) - def __pos__(self) -> Series: - return self._apply_unary_op(ops.pos_op) - - def __neg__(self) -> Series: - return self._apply_unary_op(ops.neg_op) - - def __dir__(self) -> List[str]: - return dir(type(self)) + self._struct_fields - def eq(self, other: object) -> Series: # TODO: enforce stricter alignment return self._apply_binary_op(other, ops.eq_op) @@ -1570,80 +867,52 @@ def ne(self, other: object) -> Series: # TODO: enforce stricter alignment return self._apply_binary_op(other, ops.ne_op) - def items(self): - for batch_df in self._block.to_pandas_batches(): - assert batch_df.shape[1] == 1, ( - f"Expected 1 column in the dataframe, but got {batch_df.shape[1]}." - ) - for item in batch_df.squeeze(axis=1).items(): - yield item - - def _apply_callable(self, condition): - """ "Executes the possible callable condition as needed.""" - if callable(condition): - # When it's a bigframes function. - if isinstance(condition, bigframes.functions.Udf): - return self.apply(condition) - # When it's a plain Python function. - else: - return self.apply(condition, by_row=False) - - # When it's not a callable. - return condition - def where(self, cond, other=None): - cond = self._apply_callable(cond) - other = self._apply_callable(other) - value_id, cond_id, other_id, block = self._align3(cond, other) - block, result_id = block.project_expr( - ops.where_op.as_expr(value_id, cond_id, other_id) + block, result_id = block.apply_ternary_op( + value_id, cond_id, other_id, ops.where_op ) return Series(block.select_column(result_id).with_column_labels([self.name])) - def clip(self, lower=None, upper=None): + def clip(self, lower, upper): if lower is None and upper is None: return self if lower is None: - return self._apply_binary_op(upper, ops.minimum_op, alignment="left") + return self._apply_binary_op(upper, ops.clip_upper, alignment="left") if upper is None: - return self._apply_binary_op(lower, ops.maximum_op, alignment="left") - # special rule to coerce scalar string args to date - value_id, lower_id, upper_id, block = self._align3( - lower, upper, cast_scalars=(bigframes.dtypes.is_date_like(self.dtype)) - ) - block, result_id = block.project_expr( - ops.clip_op.as_expr(value_id, lower_id, upper_id), + return self._apply_binary_op(lower, ops.clip_lower, alignment="left") + value_id, lower_id, upper_id, block = self._align3(lower, upper) + block, result_id = block.apply_ternary_op( + value_id, lower_id, upper_id, ops.clip_op ) return Series(block.select_column(result_id).with_column_labels([self.name])) - @validations.requires_ordering() def argmax(self) -> int: block, row_nums = self._block.promote_offsets() block = block.order_by( [ - order.descending_over(self._value_column), - order.ascending_over(row_nums), + OrderingColumnReference( + self._value_column, direction=OrderingDirection.DESC + ), + OrderingColumnReference(row_nums), ] ) return typing.cast( scalars.Scalar, Series(block.select_column(row_nums)).iloc[0] ) - @validations.requires_ordering() def argmin(self) -> int: block, row_nums = self._block.promote_offsets() block = block.order_by( [ - order.ascending_over(self._value_column), - order.ascending_over(row_nums), + OrderingColumnReference(self._value_column), + OrderingColumnReference(row_nums), ] ) return typing.cast( scalars.Scalar, Series(block.select_column(row_nums)).iloc[0] ) - @validations.requires_index def unstack(self, level: LevelsType = -1): if isinstance(level, int) or isinstance(level, str): level = [level] @@ -1667,43 +936,41 @@ def unstack(self, level: LevelsType = -1): ) return bigframes.dataframe.DataFrame(pivot_block) - @validations.requires_index def idxmax(self) -> blocks.Label: block = self._block.order_by( [ - order.descending_over(self._value_column), + OrderingColumnReference( + self._value_column, direction=OrderingDirection.DESC + ), *[ - order.ascending_over(idx_col) + OrderingColumnReference(idx_col) for idx_col in self._block.index_columns ], ] ) block = block.slice(0, 1) - return indexes.Index(block).to_pandas()[0] + return indexes.Index._from_block(block).to_pandas()[0] - @validations.requires_index def idxmin(self) -> blocks.Label: block = self._block.order_by( [ - order.ascending_over(self._value_column), + OrderingColumnReference(self._value_column), *[ - order.ascending_over(idx_col) + OrderingColumnReference(idx_col) for idx_col in self._block.index_columns ], ] ) block = block.slice(0, 1) - return indexes.Index(block).to_pandas()[0] + return indexes.Index._from_block(block).to_pandas()[0] @property - @validations.requires_ordering() def is_monotonic_increasing(self) -> bool: return typing.cast( bool, self._block.is_monotonic_increasing(self._value_column) ) @property - @validations.requires_ordering() def is_monotonic_decreasing(self) -> bool: return typing.cast( bool, self._block.is_monotonic_decreasing(self._value_column) @@ -1726,23 +993,12 @@ def __getitem__(self, indexer): if isinstance(indexer, Series): (left, right, block) = self._align(indexer, "left") block = block.filter(right) - block = block.select_column(left.id.name) + block = block.select_column(left) return Series(block) return self.loc[indexer] def __getattr__(self, key: str): - # Protect against recursion errors with uninitialized Series objects. - # We use "_block" attribute to check whether the instance is initialized. - # See: - # https://github.com/googleapis/python-bigquery-dataframes/issues/728 - # and - # https://nedbatchelder.com/blog/201010/surprising_getattr_recursion.html - if key == "_block": - raise AttributeError(key) - elif hasattr(pandas.Series, key): - log_adapter.submit_pandas_labels( - self._block.session.bqclient, self.__class__.__name__, key - ) + if hasattr(pandas.Series, key): raise AttributeError( textwrap.dedent( f""" @@ -1751,32 +1007,25 @@ def __getattr__(self, key: str): """ ) ) - elif key in self._struct_fields: - return self.struct.field(key) else: raise AttributeError(key) - def __setitem__(self, key, value) -> None: - """Set item using direct assignment, delegating to .loc indexer.""" - self.loc[key] = value + def _align3(self, other1: Series | scalars.Scalar, other2: Series | scalars.Scalar, how="left") -> tuple[str, str, str, blocks.Block]: # type: ignore + """Aligns the series value with 2 other scalars or series objects. Returns new values and joined tabled expression.""" + values, index = self._align_n([other1, other2], how) + return (values[0], values[1], values[2], index) - def _apply_aggregation( - self, op: agg_ops.UnaryAggregateOp | agg_ops.NullaryAggregateOp - ) -> Any: + def _apply_aggregation(self, op: agg_ops.AggregateOp) -> Any: return self._block.get_stat(self._value_column, op) def _apply_window_op( - self, op: agg_ops.UnaryWindowOp, window_spec: windows.WindowSpec + self, op: agg_ops.WindowOp, window_spec: bigframes.core.window_spec.WindowSpec ): block = self._block block, result_id = block.apply_window_op( self._value_column, op, window_spec=window_spec, result_label=self.name ) - result = Series(block.select_column(result_id)) - if op.skips_nulls: - return result.where(self.notna(), None) - else: - return result + return Series(block.select_column(result_id)) def value_counts( self, @@ -1791,161 +1040,59 @@ def value_counts( [self._value_column], normalize=normalize, ascending=ascending, - drop_na=dropna, + dropna=dropna, ) return Series(block) - @typing.overload # type: ignore[override] - def sort_values( - self, - *, - axis=..., - inplace: Literal[True] = ..., - ascending: bool | typing.Sequence[bool] = ..., - kind: str | None = ..., - na_position: typing.Literal["first", "last"] = ..., - ) -> None: ... - - @typing.overload - def sort_values( - self, - *, - axis=..., - inplace: Literal[False] = ..., - ascending: bool | typing.Sequence[bool] = ..., - kind: str | None = ..., - na_position: typing.Literal["first", "last"] = ..., - ) -> Series: ... - def sort_values( - self, - *, - axis=0, - inplace: bool = False, - ascending=True, - kind: str | None = None, - na_position: typing.Literal["first", "last"] = "last", - ) -> Optional[Series]: - if axis != 0 and axis != "index": - raise ValueError(f"No axis named {axis} for object type Series") + self, *, axis=0, ascending=True, kind: str = "quicksort", na_position="last" + ) -> Series: if na_position not in ["first", "last"]: raise ValueError("Param na_position must be one of 'first' or 'last'") - is_stable = (kind or constants.DEFAULT_SORT_KIND) in constants.STABLE_SORT_KINDS + direction = OrderingDirection.ASC if ascending else OrderingDirection.DESC block = self._block.order_by( [ - order.ascending_over(self._value_column, (na_position == "last")) - if ascending - else order.descending_over(self._value_column, (na_position == "last")) + OrderingColumnReference( + self._value_column, + direction=direction, + na_last=(na_position == "last"), + ) ], - stable=is_stable, + stable=kind in STABLE_SORTS, ) - if inplace: - self._set_block(block) - return None - else: - return Series(block) + return Series(block) - @typing.overload # type: ignore[override] - def sort_index( - self, - *, - axis=..., - inplace: Literal[False] = ..., - ascending=..., - kind: str | None = ..., - na_position=..., - ) -> Series: ... - - @typing.overload - def sort_index( - self, - *, - axis=0, - inplace: Literal[True] = ..., - ascending=..., - kind: str | None = ..., - na_position=..., - ) -> None: ... - - @validations.requires_index - def sort_index( - self, - *, - axis=0, - inplace: bool = False, - ascending=True, - kind: str | None = None, - na_position="last", - ) -> Optional[Series]: + def sort_index(self, *, axis=0, ascending=True, na_position="last") -> Series: # TODO(tbergeron): Support level parameter once multi-index introduced. - if axis != 0 and axis != "index": - raise ValueError(f"No axis named {axis} for object type Series") if na_position not in ["first", "last"]: raise ValueError("Param na_position must be one of 'first' or 'last'") block = self._block + direction = OrderingDirection.ASC if ascending else OrderingDirection.DESC na_last = na_position == "last" ordering = [ - order.ascending_over(column, na_last) - if ascending - else order.descending_over(column, na_last) + OrderingColumnReference(column, direction=direction, na_last=na_last) for column in block.index_columns ] - is_stable = (kind or constants.DEFAULT_SORT_KIND) in constants.STABLE_SORT_KINDS - block = block.order_by(ordering, stable=is_stable) - if inplace: - self._set_block(block) - return None - else: - return Series(block) - - @validations.requires_ordering() - def rolling( - self, - window: int | pandas.Timedelta | numpy.timedelta64 | datetime.timedelta | str, - min_periods: int | None = None, - closed: Literal["right", "left", "both", "neither"] = "right", - ) -> bigframes.core.window.Window: - if isinstance(window, int): - # Rows rolling - window_spec = windows.WindowSpec( - bounds=windows.RowsWindowBounds.from_window_size(window, closed), - min_periods=window if min_periods is None else min_periods, - ) - return bigframes.core.window.Window( - self._block, window_spec, self._block.value_columns, is_series=True - ) + block = block.order_by(ordering) + return Series(block) - return rolling.create_range_window( - block=self._block, - window=window, - min_periods=min_periods, - closed=closed, - is_series=True, + def rolling(self, window: int, min_periods=None) -> bigframes.core.window.Window: + # To get n size window, need current row and n-1 preceding rows. + window_spec = bigframes.core.window_spec.WindowSpec( + preceding=window - 1, following=0, min_periods=min_periods or window + ) + return bigframes.core.window.Window( + self._block, window_spec, self._block.value_columns, is_series=True ) - @validations.requires_ordering() def expanding(self, min_periods: int = 1) -> bigframes.core.window.Window: - window_spec = windows.cumulative_rows(min_periods=min_periods) + window_spec = bigframes.core.window_spec.WindowSpec( + following=0, min_periods=min_periods + ) return bigframes.core.window.Window( self._block, window_spec, self._block.value_columns, is_series=True ) - def pipe( - self, - func: Union[Callable[..., U], tuple[Callable[..., U], str]], - *args, - **kwargs, - ) -> U: - import bigframes_vendored.pandas.core.common as common - - return common.pipe(self, func, *args, **kwargs) - - def get(self, key, default=None): - try: - return self[key] - except (KeyError, ValueError, IndexError): - return default - def groupby( self, by: typing.Union[ @@ -1974,24 +1121,17 @@ def groupby( else: raise TypeError("You have to supply one of 'by' and 'level'") - @validations.requires_index def _groupby_level( self, level: int | str | typing.Sequence[int] | typing.Sequence[str], dropna: bool = True, ) -> bigframes.core.groupby.SeriesGroupBy: - if utils.is_list_like(level): - by_key_is_singular = False - else: - by_key_is_singular = True - return groupby.SeriesGroupBy( self._block, self._value_column, by_col_ids=self._resolve_levels(level), value_name=self.name, dropna=dropna, - by_key_is_singular=by_key_is_singular, ) def _groupby_values( @@ -2003,29 +1143,27 @@ def _groupby_values( ) -> bigframes.core.groupby.SeriesGroupBy: if not isinstance(by, Series) and _is_list_like(by): by = list(by) - by_key_is_singular = False else: by = [typing.cast(typing.Union[blocks.Label, Series], by)] - by_key_is_singular = True block = self._block grouping_cols: typing.Sequence[str] = [] value_col = self._value_column for key in by: if isinstance(key, Series): - ( - block, - ( - get_column_left, - get_column_right, - ), - ) = block.join(key._block, how="inner" if dropna else "left") - - value_col = get_column_left[value_col] + combined_index, ( + get_column_left, + get_column_right, + ) = block.index.join( + key._block.index, how="inner" if dropna else "left" + ) + + value_col = get_column_left[self._value_column] grouping_cols = [ *[get_column_left[value] for value in grouping_cols], get_column_right[key._value_column], ] + block = combined_index._block else: # Interpret as index level matches = block.index_name_to_col_id.get(key, []) @@ -2041,155 +1179,23 @@ def _groupby_values( by_col_ids=grouping_cols, value_name=self.name, dropna=dropna, - by_key_is_singular=by_key_is_singular, ) - def apply( - self, - func, - by_row: typing.Union[typing.Literal["compat"], bool] = "compat", - *, - args: typing.Tuple = (), - ) -> Series: - # Note: This signature differs from pandas.Series.apply. Specifically, - # `args` is keyword-only and `by_row` is a custom parameter here. Full - # alignment would involve breaking changes. However, given that by_row - # is not frequently used, we defer any such changes until there is a - # clear need based on user feedback. - # - # See pandas docs for reference: - # https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.apply.html - - # TODO(shobs, b/274645634): Support convert_dtype, **kwargs + def apply(self, func) -> Series: + # TODO(shobs, b/274645634): Support convert_dtype, args, **kwargs # is actually a ternary op + # Reproject as workaround to applying filter too late. This forces the filter + # to be applied before passing data to remote function, protecting from bad + # inputs causing errors. + reprojected_series = Series(self._block._force_reproject()) + return reprojected_series._apply_unary_op(ops.RemoteFunctionOp(func)) - if by_row not in ["compat", False]: - raise ValueError("Param by_row must be one of 'compat' or False") - - if not callable(func) and not isinstance(func, numpy.ufunc): - raise ValueError( - "Only a ufunc (a function that applies to the entire Series) or" - " a BigFrames BigQuery function that only works on single values" - " are supported." - ) - - # Highest priority: try to map directly to an operator, for eg numpy - # ufuncs, or simple arithmetic/logic operators. - bf_op = python_ops.python_callable_to_op(func) - if bf_op and isinstance(bf_op, ops.UnaryOp): - return self._apply_unary_op(bf_op) - - if by_row: - from bigframes._config import options - - enable_transpile = options.experiments.enable_python_transpiler - return self._apply_by_row( - func, args=args, transpile_enabled=enable_transpile - ) - try: - return func(self) # type: ignore - except Exception as ex: - # This could happen if any of the operators in func is not - # supported on a Series. Let's guide the customer to use a - # bigquery function instead - if hasattr(ex, "message"): - ex.message += f"\n{_bigquery_function_recommendation_message}" - raise - - def _apply_by_row( - self, - func: typing.Callable, - args: typing.Tuple = (), - transpile_enabled: bool = False, - ) -> Series: - """ - Apply callable or deployed udf row-wise on the series. - """ - if not callable(func): - raise ValueError( - "Expected a callable function. If you meant to use a BigQuery function, please wrap it with bigframes.pandas.udf(...)" - ) - try: - expr = ops.func_to_expr(func) - # We get this message even if transpiler could have in theory translated it. - except Exception: - raise ValueError( - "You have passed a functi1on as-is. If your intention is to " - "apply this function in a vectorized way (i.e. to the " - "entire Series as a whole, and you are sure that it " - "performs only the operations that are implemented for a " - "Series (e.g. a chain of arithmetic/logical operations, " - "such as `def foo(s): return s % 2 == 1`), please also " - "specify `by_row=False`. If your function contains " - "arbitrary code, it can only be applied to every element " - "in the Series individually, in which case you must " - "convert it to a BigFrames BigQuery function using " - "`bigframes.pandas.udf`, " - "or `bigframes.pandas.remote_function` before passing." - ) - - result_series = self._apply_callable_expr(expr, args) - # TODO(jialuo): Investigate why `_apply_nary_op` drops the series - # `name`. Manually reassigning it here as a temporary fix. - result_series.name = self.name - - return result_series - - def combine( - self, - other, - func, - ) -> Series: - if not callable(func) and not isinstance(func, numpy.ufunc): - raise ValueError( - "Only a ufunc (a function that applies to the entire Series) or" - " a BigFrames BigQuery function that only works on single values" - " are supported." - ) - - from bigframes._config import options - - if isinstance(func, bigframes.functions.Udf) or ( - options.experiments.enable_python_transpiler and callable(func) - ): - result_series = self._apply_callable_expr(ops.func_to_expr(func), (other,)) - if hasattr(other, "name") and other.name != self._name: # type: ignore - result_series.name = None - else: - result_series.name = self.name - return result_series - - bf_op = python_ops.python_callable_to_op(func) - if bf_op and isinstance(bf_op, ops.BinaryOp): - result_series = self._apply_binary_op(other, bf_op) - return result_series - - # Keep this in sync with .apply - try: - return func(self, other) - except Exception as ex: - # This could happen if any of the operators in func is not - # supported on a Series. Let's guide the customer to use a - # bigquery function instead - if hasattr(ex, "message"): - ex.message += f"\n{_bigquery_function_recommendation_message}" - raise - - @validations.requires_index def add_prefix(self, prefix: str, axis: int | str | None = None) -> Series: return Series(self._get_block().add_prefix(prefix)) - @validations.requires_index def add_suffix(self, suffix: str, axis: int | str | None = None) -> Series: return Series(self._get_block().add_suffix(suffix)) - def take( - self, indices: typing.Sequence[int], axis: int | str | None = 0, **kwargs - ) -> Series: - if not utils.is_list_like(indices): - raise ValueError("indices should be a list-like object.") - return typing.cast(Series, self.iloc[indices]) - def filter( self, items: typing.Optional[typing.Iterable] = None, @@ -2205,46 +1211,47 @@ def filter( ) if len(self._block.index_columns) > 1: raise NotImplementedError( - f"Method filter does not support rows multiindex. {constants.FEEDBACK_LINK}" + "Method filter does not support rows multiindex. {constants.FEEDBACK_LINK}" ) if (like is not None) or (regex is not None): block = self._block block, label_string_id = block.apply_unary_op( self._block.index_columns[0], - ops.AsTypeOp(to_type=pandas.StringDtype(storage="pyarrow")), + ops.AsTypeOp(pandas.StringDtype(storage="pyarrow")), ) if like is not None: block, mask_id = block.apply_unary_op( - label_string_id, ops.StrContainsOp(pat=like) + label_string_id, ops.ContainsStringOp(pat=like) ) else: # regex assert regex is not None block, mask_id = block.apply_unary_op( - label_string_id, ops.StrContainsRegexOp(pat=regex) + label_string_id, ops.ContainsRegexOp(pat=regex) ) - block = block.filter_by_id(mask_id) + block = block.filter(mask_id) block = block.select_columns([self._value_column]) return Series(block) elif items is not None: # Behavior matches pandas 2.1+, older pandas versions would reindex block = self._block block, mask_id = block.apply_unary_op( - self._block.index_columns[0], ops.IsInOp(values=tuple(items)) + self._block.index_columns[0], ops.IsInOp(values=list(items)) ) - block = block.filter_by_id(mask_id) + block = block.filter(mask_id) block = block.select_columns([self._value_column]) return Series(block) else: raise ValueError("Need to provide 'items', 'like', or 'regex'") - @validations.requires_index def reindex(self, index=None, *, validate: typing.Optional[bool] = None): if validate and not self.index.is_unique: raise ValueError("Original index must be unique to reindex") keep_original_names = False if isinstance(index, indexes.Index): - new_indexer = bigframes.dataframe.DataFrame(data=index._block)[[]] + new_indexer = bigframes.dataframe.DataFrame(data=index._data._get_block())[ + [] + ] else: if not isinstance(index, pandas.Index): keep_original_names = True @@ -2267,7 +1274,6 @@ def reindex(self, index=None, *, validate: typing.Optional[bool] = None): )._block return Series(result_block) - @validations.requires_index def reindex_like(self, other: Series, *, validate: typing.Optional[bool] = None): return self.reindex(other.index, validate=validate) @@ -2275,21 +1281,8 @@ def drop_duplicates(self, *, keep: str = "first") -> Series: block = block_ops.drop_duplicates(self._block, (self._value_column,), keep) return Series(block) - def unique(self, keep_order=True) -> Series: - if keep_order: - validations.enforce_ordered(self, "unique(keep_order != False)") - return self.drop_duplicates() - block = self._block.aggregate( - [ - agg_expressions.UnaryAggregation( - agg_ops.AnyValueOp(), ex.deref(self._value_column) - ) - ], - [self._value_column], - column_labels=self._block.column_labels, - dropna=False, - ) - return Series(block.reset_index()) + def unique(self) -> Series: + return self.drop_duplicates() def duplicated(self, keep: str = "first") -> Series: block, indicator = block_ops.indicate_duplicates( @@ -2302,8 +1295,9 @@ def duplicated(self, keep: str = "first") -> Series: ) def mask(self, cond, other=None) -> Series: - cond = self._apply_callable(cond) - other = self._apply_callable(other) + if callable(cond): + cond = self.apply(cond) + if not isinstance(cond, Series): raise TypeError( f"Only bigframes series condition is supported, received {type(cond).__name__}. " @@ -2312,106 +1306,43 @@ def mask(self, cond, other=None) -> Series: return self.where(~cond, other) def to_frame(self, name: blocks.Label = None) -> bigframes.dataframe.DataFrame: - provided_name = name if name is not None else self.name + provided_name = name if name else self.name # To be consistent with Pandas, it assigns 0 as the column name if missing. 0 is the first element of RangeIndex. - column_names: List[blocks.Label] - if provided_name is None or pandas.isna([cast(Any, provided_name)])[0]: - column_names = [0] - else: - column_names = [provided_name] - block = self._block.with_column_labels(column_names) + block = self._block.with_column_labels( + [provided_name] if provided_name else ["0"] + ) return bigframes.dataframe.DataFrame(block) - def to_csv( - self, - path_or_buf=None, - sep=",", - *, - header: bool = True, - index: bool = True, - allow_large_results: Optional[bool] = None, - ) -> Optional[str]: - if utils.is_gcs_path(path_or_buf): - return self.to_frame().to_csv( - path_or_buf, - sep=sep, - header=header, - index=index, - allow_large_results=allow_large_results, - ) - else: - pd_series = self.to_pandas(allow_large_results=allow_large_results) - return pd_series.to_csv( - path_or_buf=path_or_buf, sep=sep, header=header, index=index - ) + def to_csv(self, path_or_buf=None, **kwargs) -> typing.Optional[str]: + # TODO(b/280651142): Implement version that leverages bq export native csv support to bypass local pandas step. + return self.to_pandas().to_csv(path_or_buf, **kwargs) - def to_dict( - self, - into: type[dict] = dict, - *, - allow_large_results: Optional[bool] = None, - ) -> typing.Mapping: - return typing.cast( - dict, - self.to_pandas(allow_large_results=allow_large_results).to_dict(into=into), - ) # type: ignore + def to_dict(self, into: type[dict] = dict) -> typing.Mapping: + return typing.cast(dict, self.to_pandas().to_dict(into)) - def to_excel( - self, excel_writer, sheet_name="Sheet1", *, allow_large_results=None, **kwargs - ) -> None: - return self.to_pandas(allow_large_results=allow_large_results).to_excel( - excel_writer, sheet_name=sheet_name, **kwargs - ) + def to_excel(self, excel_writer, sheet_name="Sheet1", **kwargs) -> None: + return self.to_pandas().to_excel(excel_writer, sheet_name, **kwargs) def to_json( self, path_or_buf=None, - orient: Optional[ - typing.Literal["split", "records", "index", "columns", "values", "table"] - ] = None, - *, - lines: bool = False, - index: bool = True, - allow_large_results: Optional[bool] = None, - ) -> Optional[str]: - if utils.is_gcs_path(path_or_buf): - return self.to_frame().to_json( - path_or_buf=path_or_buf, - orient=orient, - lines=lines, - index=index, - allow_large_results=allow_large_results, - ) - else: - pd_series = self.to_pandas(allow_large_results=allow_large_results) - # Pandas Series.to_json only supports a subset of orients, but bigframes Series.to_json allows all of them. - return pd_series.to_json( - path_or_buf=path_or_buf, - orient=orient, # type: ignore[arg-type] - lines=lines, - index=index, # type: ignore - ) + orient: typing.Literal[ + "split", "records", "index", "columns", "values", "table" + ] = "columns", + **kwargs, + ) -> typing.Optional[str]: + # TODO(b/280651142): Implement version that leverages bq export native csv support to bypass local pandas step. + return self.to_pandas().to_json(path_or_buf, **kwargs) def to_latex( - self, - buf=None, - columns=None, - header=True, - index=True, - *, - allow_large_results=None, - **kwargs, + self, buf=None, columns=None, header=True, index=True, **kwargs ) -> typing.Optional[str]: - return self.to_pandas(allow_large_results=allow_large_results).to_latex( + return self.to_pandas().to_latex( buf, columns=columns, header=header, index=index, **kwargs ) - def tolist( - self, - *, - allow_large_results: Optional[bool] = None, - ) -> _list: - return self.to_pandas(allow_large_results=allow_large_results).to_list() + def tolist(self) -> list: + return self.to_pandas().to_list() to_list = tolist @@ -2420,36 +1351,19 @@ def to_markdown( buf: typing.IO[str] | None = None, mode: str = "wt", index: bool = True, - *, - allow_large_results: Optional[bool] = None, **kwargs, ) -> typing.Optional[str]: - return self.to_pandas(allow_large_results=allow_large_results).to_markdown( - buf, mode=mode, index=index, **kwargs - ) # type: ignore + return self.to_pandas().to_markdown(buf, mode=mode, index=index, **kwargs) # type: ignore def to_numpy( - self, - dtype=None, - copy=False, - na_value=pd_ext.no_default, - *, - allow_large_results=None, - **kwargs, + self, dtype=None, copy=False, na_value=None, **kwargs ) -> numpy.ndarray: - return self.to_pandas(allow_large_results=allow_large_results).to_numpy( - dtype, copy, na_value, **kwargs - ) + return self.to_pandas().to_numpy(dtype, copy, na_value, **kwargs) - def __array__(self, dtype=None, copy: Optional[bool] = None) -> numpy.ndarray: - if copy is False: - raise ValueError("Cannot convert to array without copy.") - return self.to_numpy(dtype=dtype) + __array__ = to_numpy - def to_pickle(self, path, *, allow_large_results=None, **kwargs) -> None: - return self.to_pandas(allow_large_results=allow_large_results).to_pickle( - path, **kwargs - ) + def to_pickle(self, path, **kwargs) -> None: + return self.to_pandas().to_pickle(path, **kwargs) def to_string( self, @@ -2463,10 +1377,8 @@ def to_string( name=False, max_rows=None, min_rows=None, - *, - allow_large_results=None, ) -> typing.Optional[str]: - return self.to_pandas(allow_large_results=allow_large_results).to_string( + return self.to_pandas().to_string( buf, na_rep, float_format, @@ -2479,12 +1391,8 @@ def to_string( min_rows, ) - def to_xarray( - self, - *, - allow_large_results: Optional[bool] = None, - ): - return self.to_pandas(allow_large_results=allow_large_results).to_xarray() + def to_xarray(self): + return self.to_pandas().to_xarray() def _throw_if_index_contains_duplicates( self, error_message: typing.Optional[str] = None @@ -2499,7 +1407,7 @@ def _throw_if_index_contains_duplicates( def map( self, - arg: typing.Union[Mapping, Series, Callable], + arg: typing.Union[Mapping, Series], na_action: Optional[str] = None, *, verify_integrity: bool = False, @@ -2516,78 +1424,19 @@ def map( map_df = map_df.rename(columns={arg.name: self.name}) elif isinstance(arg, Mapping): map_df = bigframes.dataframe.DataFrame( - {"keys": list(arg.keys()), self.name: list(arg.values())}, # type: ignore + {"keys": list(arg.keys()), self.name: list(arg.values())}, session=self._get_block().expr.session, ) map_df = map_df.set_index("keys") elif callable(arg): - # This is for remote function and managed funtion. - from bigframes._config import options - - enable_transpile = options.experiments.enable_python_transpiler - return self._apply_by_row(arg, transpile_enabled=enable_transpile) + return self.apply(arg) else: # Mirroring pandas, call the uncallable object arg() # throws TypeError: object is not callable self_df = self.to_frame(name="series") result_df = self_df.join(map_df, on="series") - result = cast(Series, result_df[self.name]) - result.name = self.name - return result - - @validations.requires_ordering() - def sample( - self, - n: Optional[int] = None, - frac: Optional[float] = None, - *, - random_state: Optional[int] = None, - sort: Optional[bool | Literal["random"]] = "random", - ) -> Series: - if n is not None and frac is not None: - raise ValueError("Only one of 'n' or 'frac' parameter can be specified.") - - ns = (n,) if n is not None else () - fracs = (frac,) if frac is not None else () - return Series( - self._block.split(ns=ns, fracs=fracs, random_state=random_state, sort=sort)[ - 0 - ] - ) - - def explode(self, *, ignore_index: Optional[bool] = False) -> Series: - return Series( - self._block.explode( - column_ids=[self._value_column], ignore_index=ignore_index - ) - ) - - @validations.requires_ordering() - def resample( - self, - rule: str, - *, - closed: Optional[Literal["right", "left"]] = None, - label: Optional[Literal["right", "left"]] = None, - level: Optional[LevelsType] = None, - origin: Union[ - Union[ - pandas.Timestamp, datetime.datetime, numpy.datetime64, int, float, str - ], - Literal["epoch", "start", "start_day", "end", "end_day"], - ] = "start_day", - ) -> bigframes.core.groupby.SeriesGroupBy: - block = self._block._generate_resample_label( - rule=rule, - closed=closed, - label=label, - on=None, - level=level, - origin=origin, - ) - series = Series(block) - return series.groupby(level=0) + return result_df[self.name] def __array_ufunc__( self, ufunc: numpy.ufunc, method: str, *inputs, **kwargs @@ -2606,300 +1455,30 @@ def __array_ufunc__( if inputs[0] is self: return self._apply_binary_op(inputs[1], binop) else: - return self._apply_binary_op(inputs[0], binop, reverse=True) + return self._apply_binary_op(inputs[0], ops.reverse(binop)) return NotImplemented + # Keep this at the bottom of the Series class to avoid + # confusing type checker by overriding str @property - def plot(self): - return plotting.PlotAccessor(self) - - def hist( - self, by: typing.Optional[typing.Sequence[str]] = None, bins: int = 10, **kwargs - ): - return self.plot.hist(by=by, bins=bins, **kwargs) - - def line( - self, - x: typing.Optional[typing.Hashable] = None, - y: typing.Optional[typing.Hashable] = None, - **kwargs, - ): - return self.plot.line(x=x, y=y, **kwargs) - - def area( - self, - x: typing.Optional[typing.Hashable] = None, - y: typing.Optional[typing.Hashable] = None, - stacked: bool = True, - **kwargs, - ): - return self.plot.area(x=x, y=y, stacked=stacked, **kwargs) - - def bar( - self, - x: typing.Optional[typing.Hashable] = None, - y: typing.Optional[typing.Hashable] = None, - **kwargs, - ): - return self.plot.bar(x=x, y=y, **kwargs) + def str(self) -> strings.StringMethods: + return strings.StringMethods(self._block) def _slice( self, start: typing.Optional[int] = None, stop: typing.Optional[int] = None, step: typing.Optional[int] = None, - ) -> Series: - return Series( - self._block.slice( - start=start, stop=stop, step=step if (step is not None) else 1 - ).select_column(self._value_column), - ) - - def cache(self): - """ - Materializes the Series to a temporary table. - - Useful if the series will be used multiple times, as this will avoid recomputating the shared intermediate value. - - Returns: - Series: Self - """ - # Do not use session-aware cashing if user-requested - return self._cached(force=True, session_aware=False) - - def _cached(self, *, force: bool = True, session_aware: bool = True) -> Series: - self._block.cached(force=force, session_aware=session_aware) - return self - - # Keep this at the bottom of the Series class to avoid - # confusing type checker by overriding str - @property - def str(self) -> strings.StringMethods: - import bigframes.operations.strings as strings - - return strings.StringMethods(self) - - @property - def _value_column(self) -> __builtins__.str: - return self._block.value_columns[0] - - @property - def _name(self) -> blocks.Label: - return self._block.column_labels[0] - - @property - def _dtype(self): - return self._block.dtypes[0] - - def _set_block(self, block: blocks.Block): - self._block = block - - def _get_block(self) -> blocks.Block: - return self._block - - def _apply_unary_op( - self, - op: ops.UnaryOp, - ) -> Series: - """Applies a unary operator to the series.""" - block, result_id = self._block.apply_unary_op( - self._value_column, - op, - ) - return Series(block.select_column(result_id), name=self.name) # type: ignore - - def _apply_binary_op( - self, - other: typing.Any, - op: ops.BinaryOp, - alignment: typing.Literal["outer", "left"] = "outer", - reverse: bool = False, - ) -> Series: - """Applies a binary operator to the series and other.""" - if bigframes.core.convert.can_convert_to_series(other): - self_index = indexes.Index(self._block) - other_series = bigframes.core.convert.to_bf_series( - other, self_index, self._block.session - ) - (self_col, other_col, block) = self._align(other_series, how=alignment) - - name = self._name - # Drop name if both objects have name attr, but they don't match - if ( - hasattr(other, "name") - and other_series.name != self._name - and alignment == "outer" - ): - name = None - expr = op.as_expr( - other_col if reverse else self_col, self_col if reverse else other_col - ) - block, result_id = block.project_expr(expr) - block = block.select_column(result_id).with_column_labels([name]) - return Series(block) # type: ignore - - else: # Scalar binop - name = self._name - expr = op.as_expr( - ex.const(other) if reverse else self._value_column, - self._value_column if reverse else ex.const(other), - ) - block, result_id = self._block.project_expr(expr) - block = block.select_column(result_id).with_column_labels([name]) - return Series(block) # type: ignore - - def _apply_nary_op( - self, - op: ops.NaryOp, - others: Sequence[typing.Union[Series, scalars.Scalar]], - ignore_self=False, - ): - """Applies an n-ary operator to the series and others.""" - values, block = self._align_n( - others, ignore_self=ignore_self, cast_scalars=False - ) - block, result_id = block.project_expr(op.as_expr(*values)) - return Series(block.select_column(result_id).with_column_labels([None])) - - def _apply_callable_expr( - self, - callable_expr: bigframes.operations.to_op.CallableExpression, - others: Sequence[typing.Union[Series, scalars.Scalar]], - ignore_self=False, - ): - """Applies a CallableExpression to the series and others.""" - values, block = self._align_n( - others, ignore_self=ignore_self, cast_scalars=False - ) - block, result_id = block.project_expr(callable_expr.apply(*values)) - return Series(block.select_column(result_id).with_column_labels([None])) - - def _apply_binary_aggregation( - self, other: Series, stat: agg_ops.BinaryAggregateOp - ) -> float: - (left, right, block) = self._align(other, how="outer") - assert isinstance(left, ex.DerefOp) - assert isinstance(right, ex.DerefOp) - return block.get_binary_stat(left.id.name, right.id.name, stat) - - AlignedExprT = Union[ex.ScalarConstantExpression, ex.DerefOp, ex.OmittedArg] - - @typing.overload - def _align( - self, other: Series, how="outer" - ) -> tuple[ - ex.DerefOp, - ex.DerefOp, - blocks.Block, - ]: ... - - @typing.overload - def _align( - self, other: typing.Union[Series, scalars.Scalar], how="outer" - ) -> tuple[ - ex.DerefOp, - AlignedExprT, - blocks.Block, - ]: ... - - def _align( - self, other: typing.Union[Series, scalars.Scalar], how="outer" - ) -> tuple[ - ex.DerefOp, - AlignedExprT, - blocks.Block, - ]: - """Aligns the series value with another scalar or series object. Returns new left column id, right column id and joined tabled expression.""" - values, block = self._align_n( - [ - other, - ], - how, - ) - return (typing.cast(ex.DerefOp, values[0]), values[1], block) - - def _align3( - self, - other1: Series | scalars.Scalar, - other2: Series | scalars.Scalar, - how="left", - cast_scalars: bool = True, - ) -> tuple[ex.DerefOp, AlignedExprT, AlignedExprT, blocks.Block]: # type: ignore - """Aligns the series value with 2 other scalars or series objects. Returns new values and joined tabled expression.""" - values, index = self._align_n([other1, other2], how, cast_scalars=cast_scalars) - return ( - typing.cast(ex.DerefOp, values[0]), - values[1], - values[2], - index, + ) -> bigframes.series.Series: + return bigframes.series.Series( + self._block.slice(start=start, stop=stop, step=step).select_column( + self._value_column + ), ) - def _align_n( - self, - others: typing.Sequence[ - typing.Union[Series, bigframes.core.col.Expression, scalars.Scalar] - ], - how="outer", - ignore_self=False, - cast_scalars: bool = False, - ) -> tuple[ - typing.Sequence[Union[ex.ScalarConstantExpression, ex.DerefOp, ex.OmittedArg]], - blocks.Block, - ]: - if ignore_self: - value_ids: List[ - Union[ex.ScalarConstantExpression, ex.DerefOp, ex.OmittedArg] - ] = [] - else: - value_ids = [ex.deref(self._value_column)] - - block = self._block - for other in others: - if isinstance(other, Series): - ( - block, - ( - get_column_left, - get_column_right, - ), - ) = block.join(other._block, how=how) - rebindings = { - ids.ColumnId(old): ids.ColumnId(new) - for old, new in get_column_left.items() - } - remapped_value_ids = ( - value.remap_column_refs(rebindings) for value in value_ids - ) - value_ids = [ - *remapped_value_ids, # type: ignore - ex.deref(get_column_right[other._value_column]), - ] - elif isinstance(other, bigframes.core.col.Expression): - if isinstance(other._value, ex.OmittedArg): - value_ids = [*value_ids, other._value] - continue - - label_to_col_ref = { - label: ex.deref(id) for id, label in block.col_id_to_label.items() - } - resolved_expr = other._value.bind_variables(label_to_col_ref) - block = block.project_block_exprs([resolved_expr], labels=[None]) - value_ids = [*value_ids, ex.deref(block.value_columns[-1])] - else: - # Will throw if can't interpret as scalar. - dtype = typing.cast(bigframes.dtypes.Dtype, self._dtype) - value_ids = [ - *value_ids, - ex.const(other, dtype=dtype if cast_scalars else None), - ] - return (value_ids, block) - - def _throw_if_null_index(self, opname: __builtins__.str): - if len(self._block.index_columns) == 0: - raise bigframes.exceptions.NullIndexError( - f"Series cannot perform {opname} as it has no index. Set an index using set_index." - ) + def _cached(self) -> Series: + return Series(self._block.cached()) def _is_list_like(obj: typing.Any) -> typing_extensions.TypeGuard[typing.Sequence]: diff --git a/bigframes/session/__init__.py b/bigframes/session/__init__.py index e20f61901f9..b49e2469a91 100644 --- a/bigframes/session/__init__.py +++ b/bigframes/session/__init__.py @@ -17,42 +17,45 @@ from __future__ import annotations import datetime -import fnmatch -import inspect import logging import os -import secrets -import threading +import re +import textwrap import typing -import warnings -import weakref -from collections import abc from typing import ( - IO, Any, Callable, Dict, + IO, Iterable, + List, Literal, MutableSequence, Optional, Sequence, Tuple, Union, - overload, ) +import uuid +import warnings -import bigframes_vendored.constants as constants -import bigframes_vendored.google_cloud_bigquery.retry as third_party_gcb_retry -import bigframes_vendored.ibis.backends.bigquery as ibis_bigquery # noqa -import bigframes_vendored.pandas.io.gbq as third_party_pandas_gbq -import bigframes_vendored.pandas.io.parquet as third_party_pandas_parquet -import bigframes_vendored.pandas.io.parsers.readers as third_party_pandas_readers -import bigframes_vendored.pandas.io.pickle as third_party_pandas_pickle +import google.api_core.client_info +import google.api_core.client_options +import google.api_core.exceptions +import google.api_core.gapic_v1.client_info +import google.auth.credentials import google.cloud.bigquery as bigquery +import google.cloud.bigquery_connection_v1 +import google.cloud.bigquery_storage_v1 +import google.cloud.functions_v2 +import google.cloud.resourcemanager_v3 +import google.cloud.storage as storage # type: ignore +import ibis +import ibis.backends.bigquery as ibis_bigquery +import ibis.expr.datatypes as ibis_dtypes +import ibis.expr.types as ibis_types import numpy as np import pandas -import pyarrow as pa from pandas._typing import ( CompressionOptions, FilePath, @@ -60,36 +63,34 @@ StorageOptions, ) -import bigframes._config -import bigframes._config.auth import bigframes._config.bigquery_options as bigquery_options -import bigframes.clients -import bigframes.constants -import bigframes.core -import bigframes.core.events -import bigframes.core.indexes -import bigframes.core.indexes.multi -import bigframes.core.pyformat -import bigframes.formatting_helpers -import bigframes.functions.function as bff -import bigframes.session._io.bigquery as bf_io_bigquery +import bigframes.constants as constants +import bigframes.core as core +import bigframes.core.blocks as blocks +import bigframes.core.guid as guid +from bigframes.core.ordering import IntegerEncoding, OrderingColumnReference +import bigframes.core.ordering as orderings +import bigframes.core.utils as utils +import bigframes.dataframe as dataframe +import bigframes.formatting_helpers as formatting_helpers +from bigframes.remote_function import read_gbq_function as bigframes_rgf +from bigframes.remote_function import remote_function as bigframes_rf +import bigframes.session._io.bigquery as bigframes_io import bigframes.session.clients -import bigframes.session.validation -from bigframes import exceptions as bfe -from bigframes import version -from bigframes.core import blocks, utils -from bigframes.core.logging import log_adapter -from bigframes.functions import _function_client, _function_session -from bigframes.session import bigquery_session, executor, proxy_executor - -# Avoid circular imports. -if typing.TYPE_CHECKING: - import bigframes.dataframe as dataframe - import bigframes.series - import bigframes.streaming.dataframe as streaming_dataframe +import bigframes.version + +# Even though the ibis.backends.bigquery.registry import is unused, it's needed +# to register new and replacement ops with the Ibis BigQuery backend. +import third_party.bigframes_vendored.ibis.backends.bigquery.registry # noqa +import third_party.bigframes_vendored.pandas.io.gbq as third_party_pandas_gbq +import third_party.bigframes_vendored.pandas.io.parquet as third_party_pandas_parquet +import third_party.bigframes_vendored.pandas.io.parsers.readers as third_party_pandas_readers +import third_party.bigframes_vendored.pandas.io.pickle as third_party_pandas_pickle _BIGFRAMES_DEFAULT_CONNECTION_ID = "bigframes-default-connection" +_MAX_CLUSTER_COLUMNS = 4 + # TODO(swast): Need to connect to regional endpoints when performing remote # functions operations (BQ Connection IAM, Cloud Run / Cloud Functions). # Also see if resource manager client library supports regional endpoints. @@ -103,66 +104,14 @@ "UTF-32LE", } -# BigQuery has 1 MB query size limit. Don't want to take up more than a few % of that inlining a table. -# Also must assume that text encoding as literals is much less efficient than in-memory representation. -MAX_INLINE_DF_BYTES = 5000 - logger = logging.getLogger(__name__) -class _ExecutionHistory: - def __init__(self, jobs: list[dict]): - self._df = pandas.DataFrame(jobs) - if self._df.empty: - self._df = pandas.DataFrame( - columns=[ - "job_id", - "query_id", - "job_type", - "status", - "query", - "total_bytes_processed", - "job_url", - ] - ) - - def to_dataframe(self) -> pandas.DataFrame: - """Returns the execution history as a pandas DataFrame.""" - return self._df - - def _repr_html_(self) -> str | None: - import bigframes.formatting_helpers as formatter - - if self._df.empty: - return "
No executions found.
" - - cols = ["job_type", "job_id", "status", "total_bytes_processed", "job_url"] - - # Filter columns to only those that exist in the dataframe - available_cols = [c for c in cols if c in self._df.columns] +def _is_query(query_or_table: str) -> bool: + """Determine if `query_or_table` is a table ID or a SQL string""" + return re.search(r"\s", query_or_table.strip(), re.MULTILINE) is not None - def format_url(url): - return f'Open Job' if url else "" - try: - df_display = self._df[available_cols].copy() - if "total_bytes_processed" in df_display.columns: - df_display["total_bytes_processed"] = df_display[ - "total_bytes_processed" - ].apply(formatter.get_formatted_bytes) - if "job_url" in df_display.columns: - df_display["job_url"] = df_display["job_url"].apply(format_url) - - # Rename job_id to query_id to match user expectations - if "job_id" in df_display.columns: - df_display = df_display.rename(columns={"job_id": "query_id"}) - - return df_display.to_html(escape=False, index=False) - except Exception: - return self._df.to_html() - - -@log_adapter.class_logger class Session( third_party_pandas_gbq.GBQIOMixin, third_party_pandas_parquet.ParquetIOMixin, @@ -177,7 +126,7 @@ class Session( Configuration adjusting how to connect to BigQuery and related APIs. Note that some options are ignored if ``clients_provider`` is set. - clients_provider (bigframes.session.clients.ClientsProvider): + clients_provider (bigframes.session.bigframes.session.clients.ClientsProvider): An object providing client library objects. """ @@ -186,212 +135,48 @@ def __init__( context: Optional[bigquery_options.BigQueryOptions] = None, clients_provider: Optional[bigframes.session.clients.ClientsProvider] = None, ): - # Address circular imports in doctest due to bigframes/session/__init__.py - # containing a lot of logic and samples. - from bigframes.session import anonymous_dataset, clients, loader, metrics - - _warn_if_bf_version_is_obsolete() - - # Publisher needs to be created before the other objects, especially - # the executors, because they access it. - self._publisher = bigframes.core.events.Publisher() - self._publisher.subscribe( - bigframes.formatting_helpers.create_progress_callback() - ) - if context is None: context = bigquery_options.BigQueryOptions() - self._bq_kms_key_name = context.kms_key_name + # TODO(swast): Get location from the environment. + if context is None or context.location is None: + self._location = "US" + warnings.warn( + f"No explicit location is set, so using location {self._location} for the session.", + stacklevel=2, + ) + else: + self._location = context.location # Instantiate a clients provider to help with cloud clients that will be # used in the future operations in the session if clients_provider: - # this path is only for unit testing. Not meant to be used by end users. self._clients_provider = clients_provider - self._location = context.location or "US" - project = "test_project" else: - ( - credentials, - project, - ) = bigframes._config.auth.resolve_credentials_and_project(context) - if context.location is None: - with bigquery.Client( - project=project, - credentials=credentials, - ) as temp_client: - row_iter = temp_client.query_and_wait( - "SELECT 1", - job_config=bigquery.QueryJobConfig(dry_run=True), - ) - self._location = row_iter.location or "US" - msg = bfe.format_message( - f"No explicit location is set, so using location {self._location} for the session." - ) - # User's code - # -> get_global_session() - # -> connect() - # -> Session() - # - # Note: We could also have: - # User's code - # -> read_gbq() - # -> with_default_session() - # -> get_global_session() - # -> connect() - # -> Session() - # but we currently have no way to disambiguate these - # situations. - warnings.warn( - msg, stacklevel=4, category=bfe.DefaultLocationWarning - ) - else: - self._location = context.location - - self._clients_provider = clients.ClientsProvider( - project=project, - credentials=credentials, + self._clients_provider = bigframes.session.clients.ClientsProvider( + project=context.project, location=self._location, use_regional_endpoints=context.use_regional_endpoints, + credentials=context.credentials, application_name=context.application_name, - bq_kms_key_name=self._bq_kms_key_name, - client_endpoints_override=context.client_endpoints_override, - requests_transport_adapters=context.requests_transport_adapters, ) - # TODO(shobs): Remove this logic after https://github.com/ibis-project/ibis/issues/8494 - # has been fixed. The ibis client changes the default query job config - # so we are going to remember the current config and restore it after - # the ibis client has been created - original_default_query_job_config = self.bqclient.default_query_job_config - - self.bqclient.default_query_job_config = original_default_query_job_config + self._create_and_bind_bq_session() + self.ibis_client = typing.cast( + ibis_bigquery.Backend, + ibis.bigquery.connect( + project_id=context.project, + client=self.bqclient, + storage_client=self.bqstoragereadclient, + ), + ) - # Resolve the BQ connection for remote function and Vertex AI integration self._bq_connection = context.bq_connection or _BIGFRAMES_DEFAULT_CONNECTION_ID - self._skip_bq_connection_check = context._skip_bq_connection_check # Now that we're starting the session, don't allow the options to be # changed. context._session_started = True - # unique session identifier, short enough to be human readable - # only needs to be unique among sessions created by the same user - # at the same time in the same region - self._session_id: str = "session" + secrets.token_hex(3) - # store table ids and delete them when the session is closed - - self._api_methods: list[str] = [] - self._api_methods_lock = threading.Lock() - - self._objects: list[ - weakref.ReferenceType[ - Union[ - bigframes.core.indexes.Index, - bigframes.series.Series, - dataframe.DataFrame, - ] - ] - ] = [] - # Whether this session treats objects as totally ordered. - # Will expose as feature later, only False for internal testing - self._strictly_ordered: bool = context.ordering_mode != "partial" - self._allow_ambiguity = not self._strictly_ordered - self._default_index_type = ( - bigframes.enums.DefaultIndexKind.SEQUENTIAL_INT64 - if self._strictly_ordered - else bigframes.enums.DefaultIndexKind.NULL - ) - - self._metrics = metrics.ExecutionMetrics() - self._publisher.subscribe(self._metrics.on_event) - self._anon_dataset_manager = anonymous_dataset.AnonymousDatasetManager( - self._clients_provider.bqclient, - location=self._location, - session_id=self._session_id, - kms_key=self._bq_kms_key_name, - publisher=self._publisher, - ) - self._function_session = _function_session.FunctionSession( - _function_client.FunctionClient( - gcp_project_id=project, - bq_location=self._location, - bq_client=self._clients_provider.bqclient, - bq_connection_manager=bigframes.clients.BqConnectionManager( - self._clients_provider.bqconnectionclient, - self._clients_provider.resourcemanagerclient, - ), - cloud_functions_client=self._clients_provider.cloudfunctionsclient, - publisher=self._publisher, - ), - dataset_manager=self._anon_dataset_manager, - default_connection=self._bq_connection, - location=self._location, - session_id=self._session_id, - manage_connections=not self._skip_bq_connection_check, - ) - # Session temp tables don't support specifying kms key, so use anon dataset if kms key specified - self._session_resource_manager = ( - bigquery_session.SessionResourceManager( - self.bqclient, - self._location, - publisher=self._publisher, - ) - if (self._bq_kms_key_name is None) - else None - ) - self._temp_storage_manager = ( - self._session_resource_manager or self._anon_dataset_manager - ) - self._loader = loader.GbqDataLoader( - session=self, - bqclient=self._clients_provider.bqclient, - storage_manager=self._temp_storage_manager, - write_client=self._clients_provider.bqstoragewriteclient, - default_index_type=self._default_index_type, - scan_index_uniqueness=self._strictly_ordered, - force_total_order=self._strictly_ordered, - metrics=self._metrics, - publisher=self._publisher, - ) - - labels = {} - if not self._strictly_ordered: - labels["bigframes-mode"] = "unordered" - - self._executor: executor.Executor = proxy_executor.DualCompilerProxyExecutor( - bqclient=self._clients_provider.bqclient, - bqstoragereadclient=self._clients_provider.bqstoragereadclient, - loader=self._loader, - storage_manager=self._temp_storage_manager, - metrics=self._metrics, - enable_polars_execution=context.enable_polars_execution, - publisher=self._publisher, - labels=tuple(labels.items()), - function_manager=self._function_session, - ) - - def __del__(self): - """Automatic cleanup of internal resources.""" - self.close() - - def __enter__(self): - """Enter the runtime context of the Session object. - - See [With Statement Context Managers](https://docs.python.org/3/reference/datamodel.html#with-statement-context-managers) - for more details. - """ - return self - - def __exit__(self, *_): - """Exit the runtime context of the Session object. - - See [With Statement Context Managers](https://docs.python.org/3/reference/datamodel.html#with-statement-context-managers) - for more details. - """ - self.close() - @property def bqclient(self): return self._clients_provider.bqclient @@ -412,391 +197,167 @@ def cloudfunctionsclient(self): def resourcemanagerclient(self): return self._clients_provider.resourcemanagerclient - _bq_connection_manager: Optional[bigframes.clients.BqConnectionManager] = None - @property - def bqconnectionmanager(self): - if not self._skip_bq_connection_check and not self._bq_connection_manager: - self._bq_connection_manager = bigframes.clients.BqConnectionManager( - self.bqconnectionclient, self.resourcemanagerclient - ) - return self._bq_connection_manager - - @property - def options(self) -> bigframes._config.Options: - """Options for configuring BigQuery DataFrames. - - Included for compatibility between bpd and Session. - """ - # TODO(tswast): Consider making a separate session-level options object. - return bigframes._config.options - - @property - def session_id(self): - return self._session_id - - @property - def objects( - self, - ) -> Iterable[ - Union[ - bigframes.core.indexes.Index, bigframes.series.Series, dataframe.DataFrame - ] - ]: - still_alive = [i for i in self._objects if i() is not None] - self._objects = still_alive - # Create a set with strong references, be careful not to hold onto this needlessly, as will prevent garbage collection. - return tuple(i() for i in self._objects if i() is not None) # type: ignore + def _session_dataset_id(self): + """A dataset for storing temporary objects local to the session + This is a workaround for remote functions that do not + yet support session-temporary instances.""" + return self._session_dataset.dataset_id @property def _project(self): return self.bqclient.project - @property - def bytes_processed_sum(self): - """The sum of all bytes processed by bigquery jobs using this session.""" - return self._metrics.bytes_processed - - @property - def slot_millis_sum(self): - """The sum of all slot time used by bigquery jobs in this session.""" - return self._metrics.slot_millis - - def execution_history( - self, - *, - events: Optional[Iterable[bigframes.core.events.Event]] = None, - job_ids: Optional[Iterable[str]] = None, - all_cells: bool = True, - ) -> _ExecutionHistory: - """Returns the history of executions initiated by BigFrames in the current session. - - Use `.to_dataframe()` on the result to get a pandas DataFrame. + def __hash__(self): + # Stable hash needed to use in expression tree + return hash(self._session_id) + + def _create_and_bind_bq_session(self): + """Create a BQ session and bind the session id with clients to capture BQ activities: + go/bigframes-transient-data""" + job_config = bigquery.QueryJobConfig(create_session=True) + # Make sure the session is a new one, not one associated with another query. + job_config.use_query_cache = False + query_job = self.bqclient.query( + "SELECT 1", job_config=job_config, location=self._location + ) + query_job.result() # blocks until finished + self._session_id = query_job.session_info.session_id + + # The anonymous dataset is used by BigQuery to write query results and + # session tables. BigQuery DataFrames also writes temp tables directly + # to the dataset, no BigQuery Session required. Note: there is a + # different anonymous dataset per location. See: + # https://cloud.google.com/bigquery/docs/cached-results#how_cached_results_are_stored + query_destination = query_job.destination + self._anonymous_dataset = bigquery.DatasetReference( + query_destination.project, + query_destination.dataset_id, + ) - Args: - events (Iterable[Event], optional): - Filter execution history to only include jobs associated with the given events. - job_ids (Iterable[str], optional): - Filter execution history to only include jobs matching the given job IDs. - all_cells (bool, optional): - If True, do not filter execution history by notebook cell. If False, - and running in Colab/Jupyter, automatically filter history to only include - jobs executed within the current cell. Defaults to True. - """ - jobs = [job.__dict__ for job in self._metrics.jobs] - - if events is not None: - event_job_ids = { - getattr(event, "job_id", None) - for event in events - if getattr(event, "job_id", None) is not None - } - event_query_ids = { - getattr(event, "query_id", None) - for event in events - if getattr(event, "query_id", None) is not None - } - jobs = [ - job - for job in jobs - if ( - job.get("job_id") is not None and job.get("job_id") in event_job_ids - ) - or ( - job.get("query_id") is not None - and job.get("query_id") in event_query_ids - ) + self.bqclient.default_query_job_config = bigquery.QueryJobConfig( + connection_properties=[ + bigquery.ConnectionProperty("session_id", self._session_id) ] - - elif job_ids is not None: - target_job_ids = set(job_ids) - jobs = [ - job - for job in jobs - if ( - job.get("job_id") is not None - and job.get("job_id") in target_job_ids - ) - or ( - job.get("query_id") is not None - and job.get("query_id") in target_job_ids - ) + ) + self.bqclient.default_load_job_config = bigquery.LoadJobConfig( + connection_properties=[ + bigquery.ConnectionProperty("session_id", self._session_id) ] - - elif not all_cells: - from bigframes.core.utils import get_ipython_execution_count - - current_count = get_ipython_execution_count() - if current_count is not None: - jobs = [ - job - for job in jobs - if job.get("cell_execution_count") == current_count - ] - - return _ExecutionHistory(jobs) - - @property - def _allows_ambiguity(self) -> bool: - return self._allow_ambiguity - - @property - def _anonymous_dataset(self): - return self._anon_dataset_manager.dataset - - @property - def bq_connection(self) -> str: - msg = bfe.format_message( - f"""You are using the BigFrames session default connection: {self._bq_connection}, - which can be different from the BigQuery project default connection. - This default connection may change in the future.""" ) - warnings.warn(msg, category=FutureWarning) - return self._bq_connection - def __hash__(self): - # Stable hash needed to use in expression tree - return hash(str(self._session_id)) + # Dataset for storing remote functions, which don't yet + # support proper session temporary storage yet + self._session_dataset = bigquery.Dataset( + f"{self.bqclient.project}.bigframes_temp_{self._location.lower().replace('-', '_')}" + ) + self._session_dataset.location = self._location def close(self): - """Delete resources that were created with this session's session_id. - This includes BigQuery tables, remote functions and cloud functions - serving the remote functions.""" - - # Protect against failure when the Session is a fake for testing or - # failed to initialize. - if anon_dataset_manager := getattr(self, "_anon_dataset_manager", None): - anon_dataset_manager.close() - - if session_resource_manager := getattr(self, "_session_resource_manager", None): - session_resource_manager.close() - - remote_function_session = getattr(self, "_function_session", None) - if remote_function_session: - remote_function_session.clean_up() - - publisher_session = getattr(self, "_publisher", None) - if publisher_session: - publisher_session.publish( - bigframes.core.events.SessionClosed(self.session_id) - ) + """Terminated the BQ session, otherwises the session will be terminated automatically after + 24 hours of inactivity or after 7 days.""" + if self._session_id is not None and self.bqclient is not None: + abort_session_query = "CALL BQ.ABORT_SESSION('{}')".format(self._session_id) + try: + query_job = self.bqclient.query(abort_session_query) + query_job.result() # blocks until finished + except google.api_core.exceptions.BadRequest as exc: + # Ignore the exception when the BQ session itself has expired + # https://cloud.google.com/bigquery/docs/sessions-terminating#auto-terminate_a_session + if not exc.message.startswith( + f"Session {self._session_id} has expired and is no longer available." + ): + raise + except google.auth.exceptions.RefreshError: + # The refresh token may itself have been invalidated or expired + # https://developers.google.com/identity/protocols/oauth2#expiration + # Don't raise the exception in this case while closing the + # BigFrames session, so that the end user has a path for getting + # out of a bad session due to unusable credentials. + pass + self._session_id = None - @overload - def read_gbq( # type: ignore[overload-overlap] - self, - query_or_table: str, - *, - index_col: Iterable[str] | str | bigframes.enums.DefaultIndexKind = ..., - columns: Iterable[str] = ..., - configuration: Optional[Dict] = ..., - max_results: Optional[int] = ..., - filters: third_party_pandas_gbq.FiltersType = ..., - use_cache: Optional[bool] = ..., - col_order: Iterable[str] = ..., - dry_run: Literal[False] = ..., - allow_large_results: Optional[bool] = ..., - ) -> dataframe.DataFrame: ... - - @overload def read_gbq( self, query_or_table: str, *, - index_col: Iterable[str] | str | bigframes.enums.DefaultIndexKind = ..., - columns: Iterable[str] = ..., - configuration: Optional[Dict] = ..., - max_results: Optional[int] = ..., - filters: third_party_pandas_gbq.FiltersType = ..., - use_cache: Optional[bool] = ..., - col_order: Iterable[str] = ..., - dry_run: Literal[True] = ..., - allow_large_results: Optional[bool] = ..., - ) -> pandas.Series: ... - - def read_gbq( - self, - query_or_table: str, - *, - index_col: Iterable[str] | str | bigframes.enums.DefaultIndexKind = (), - columns: Iterable[str] = (), - configuration: Optional[Dict] = None, - max_results: Optional[int] = None, - filters: third_party_pandas_gbq.FiltersType = (), - use_cache: Optional[bool] = None, + index_col: Iterable[str] | str = (), col_order: Iterable[str] = (), - dry_run: bool = False, - allow_large_results: Optional[bool] = None, - ) -> dataframe.DataFrame | pandas.Series: + max_results: Optional[int] = None, + # Add a verify index argument that fails if the index is not unique. + ) -> dataframe.DataFrame: # TODO(b/281571214): Generate prompt to show the progress of read_gbq. - if columns and col_order: - raise ValueError( - "Must specify either columns (preferred) or col_order, not both" - ) - elif col_order: - columns = col_order - - if allow_large_results is None: - allow_large_results = bigframes._config.options._allow_large_results - - if bf_io_bigquery.is_query(query_or_table): - return self._loader.read_gbq_query( # type: ignore # for dry_run overload + if _is_query(query_or_table): + return self._read_gbq_query( query_or_table, index_col=index_col, - columns=columns, - configuration=configuration, + col_order=col_order, max_results=max_results, - use_cache=use_cache, - filters=filters, - dry_run=dry_run, - allow_large_results=allow_large_results, + api_name="read_gbq", ) else: - if configuration is not None: - raise ValueError( - "The 'configuration' argument is not allowed when " - "directly reading from a table. Please remove " - "'configuration' or use a query." - ) - - return self._loader.read_gbq_table( # type: ignore # for dry_run overload + # TODO(swast): Query the snapshot table but mark it as a + # deterministic query so we can avoid serializing if we have a + # unique index. + return self._read_gbq_table( query_or_table, index_col=index_col, - columns=columns, + col_order=col_order, max_results=max_results, - use_cache=use_cache if use_cache is not None else True, - filters=filters, - dry_run=dry_run, + api_name="read_gbq", ) - def _register_object( - self, - object: Union[ - bigframes.core.indexes.Index, bigframes.series.Series, dataframe.DataFrame - ], - ): - self._objects.append(weakref.ref(object)) - - @overload - def _read_gbq_colab( + def _query_to_destination( self, query: str, - *, - callback: Optional[Callable[[bigframes.core.events.EventEnvelope], None]] = ..., - pyformat_args: Optional[Dict[str, Any]] = None, - dry_run: Literal[False] = ..., - ) -> dataframe.DataFrame: ... - - @overload - def _read_gbq_colab( - self, - query: str, - *, - callback: Optional[Callable[[bigframes.core.events.EventEnvelope], None]] = ..., - pyformat_args: Optional[Dict[str, Any]] = None, - dry_run: Literal[True] = ..., - ) -> pandas.Series: ... + index_cols: List[str], + api_name: str, + ) -> Tuple[Optional[bigquery.TableReference], Optional[bigquery.QueryJob]]: + # If a dry_run indicates this is not a query type job, then don't + # bother trying to do a CREATE TEMP TABLE ... AS SELECT ... statement. + dry_run_config = bigquery.QueryJobConfig() + dry_run_config.dry_run = True + _, dry_run_job = self._start_query(query, job_config=dry_run_config) + if dry_run_job.statement_type != "SELECT": + _, query_job = self._start_query(query) + return query_job.destination, query_job + + # Create a table to workaround BigQuery 10 GB query results limit. See: + # internal issue 303057336. + # Since we have a `statement_type == 'SELECT'`, schema should be populated. + schema = typing.cast(Iterable[bigquery.SchemaField], dry_run_job.schema) + cluster_cols = [ + item.name + for item in schema + if (item.name in index_cols) and _can_cluster_bq(item) + ][:_MAX_CLUSTER_COLUMNS] + temp_table = self._create_empty_temp_table(schema, cluster_cols) + + job_config = bigquery.QueryJobConfig() + job_config.labels["bigframes-api"] = api_name + job_config.destination = temp_table - @log_adapter.log_name_override("read_gbq_colab") - def _read_gbq_colab( - self, - query: str, - *, - callback: Optional[ - Callable[[bigframes.core.events.EventEnvelope], None] - ] = None, - pyformat_args: Optional[Dict[str, Any]] = None, - dry_run: bool = False, - ) -> Union[dataframe.DataFrame, pandas.Series]: - """A version of read_gbq that has the necessary default values for use in colab integrations. - - This includes, no ordering, no index, no progress bar, always use string - formatting for embedding local variables / dataframes. - - Args: - query (str): - A SQL query string to execute. Results (if any) are turned into - a DataFrame. - callback (Optional[Callable[[bigframes.core.events.EventEnvelope], None]]): - Callback to receive query execution events. - pyformat_args (dict): - A dictionary of potential variables to replace in ``query``. - Note: strings are _not_ escaped. Use query parameters for these, - instead. Note: unlike read_gbq / read_gbq_query, even if set to - None, this function always assumes {var} refers to a variable - that is supposed to be supplied in this dictionary. - """ - if pyformat_args is None: - pyformat_args = {} - - allow_large_results = bigframes._config.options._allow_large_results - - query = bigframes.core.pyformat.pyformat( - query, - pyformat_args=pyformat_args, - session=self, - dry_run=dry_run, - ) - - def _run_query(): - return self._loader.read_gbq_query( - query=query, - index_col=bigframes.enums.DefaultIndexKind.NULL, - force_total_order=False, - dry_run=typing.cast(Union[Literal[False], Literal[True]], dry_run), - allow_large_results=allow_large_results, - ) - - if callback is not None: - with self._publisher.subscribe(callback): - return _run_query() - return _run_query() - - @overload - def read_gbq_query( # type: ignore[overload-overlap] - self, - query: str, - *, - index_col: Iterable[str] | str | bigframes.enums.DefaultIndexKind = ..., - columns: Iterable[str] = ..., - configuration: Optional[Dict] = ..., - max_results: Optional[int] = ..., - use_cache: Optional[bool] = ..., - col_order: Iterable[str] = ..., - filters: third_party_pandas_gbq.FiltersType = ..., - dry_run: Literal[False] = ..., - allow_large_results: Optional[bool] = ..., - ) -> dataframe.DataFrame: ... - - @overload - def read_gbq_query( - self, - query: str, - *, - index_col: Iterable[str] | str | bigframes.enums.DefaultIndexKind = ..., - columns: Iterable[str] = ..., - configuration: Optional[Dict] = ..., - max_results: Optional[int] = ..., - use_cache: Optional[bool] = ..., - col_order: Iterable[str] = ..., - filters: third_party_pandas_gbq.FiltersType = ..., - dry_run: Literal[True] = ..., - allow_large_results: Optional[bool] = ..., - ) -> pandas.Series: ... + try: + # Write to temp table to workaround BigQuery 10 GB query results + # limit. See: internal issue 303057336. + _, query_job = self._start_query(query, job_config=job_config) + return query_job.destination, query_job + except google.api_core.exceptions.BadRequest: + # Some SELECT statements still aren't compatible with cluster + # tables as the destination. For example, if the query has a + # top-level ORDER BY, this conflicts with our ability to cluster + # the table by the index column(s). + _, query_job = self._start_query(query) + return query_job.destination, query_job def read_gbq_query( self, query: str, *, - index_col: Iterable[str] | str | bigframes.enums.DefaultIndexKind = (), - columns: Iterable[str] = (), - configuration: Optional[Dict] = None, - max_results: Optional[int] = None, - use_cache: Optional[bool] = None, + index_col: Iterable[str] | str = (), col_order: Iterable[str] = (), - filters: third_party_pandas_gbq.FiltersType = (), - dry_run: bool = False, - allow_large_results: Optional[bool] = None, - ) -> dataframe.DataFrame | pandas.Series: + max_results: Optional[int] = None, + ) -> dataframe.DataFrame: """Turn a SQL query into a DataFrame. Note: Because the results are written to a temporary table, ordering by @@ -806,9 +367,11 @@ def read_gbq_query( **Examples:** + >>> import bigframes.pandas as bpd + >>> bpd.options.display.progress_bar = None + Simple query input: - >>> import bigframes.pandas as bpd >>> df = bpd.read_gbq_query(''' ... SELECT ... pitcherFirstName, @@ -816,6 +379,12 @@ def read_gbq_query( ... pitchSpeed, ... FROM `bigquery-public-data.baseball.games_wide` ... ''') + >>> df.head(2) + pitcherFirstName pitcherLastName pitchSpeed + 0 0 + 1 0 + + [2 rows x 3 columns] Preserve ordering in a query input. @@ -833,240 +402,495 @@ def read_gbq_query( ... WHERE year = 2016 ... GROUP BY pitcherFirstName, pitcherLastName ... ''', index_col="rowindex") - >>> print("START_OF_OUTPUT"); df.head(2) # doctest: +ELLIPSIS,+NORMALIZE_WHITESPACE - START_OF_OUTPUT - ... + >>> df.head(2) pitcherFirstName pitcherLastName averagePitchSpeed - ... + rowindex 1 Albertin Chapman 96.514113 2 Zachary Britton 94.591039 [2 rows x 3 columns] See also: :meth:`Session.read_gbq`. - - Args: - query (str): - A SQL query to execute. - index_col (Iterable[str] or str, optional): - The column(s) to use as the index for the DataFrame. This can be - a single column name or a list of column names. If not provided, - a default index will be used. - columns (Iterable[str], optional): - The columns to read from the query result. If not - specified, all columns will be read. - configuration (dict, optional): - A dictionary of query job configuration options. See the - BigQuery REST API documentation for a list of available options: - https://cloud.google.com/bigquery/docs/reference/rest/v2/jobs#configuration.query - max_results (int, optional): - The maximum number of rows to retrieve from the query - result. If not specified, all rows will be loaded. - use_cache (bool, optional): - Whether to use cached results for the query. Defaults to ``True``. - Setting this to ``False`` will force a re-execution of the query. - col_order (Iterable[str], optional): - The desired order of columns in the resulting DataFrame. This - parameter is deprecated and will be removed in a future version. - Use ``columns`` instead. - filters (list[tuple], optional): - A list of filters to apply to the data. Filters are specified - as a list of tuples, where each tuple contains a column name, - an operator (e.g., '==', '!='), and a value. - dry_run (bool, optional): - If ``True``, the function will not actually execute the query but - will instead return statistics about the query. Defaults to - ``False``. - allow_large_results (bool, optional): - Whether to allow large query results. If ``True``, the query - results can be larger than the maximum response size. - Defaults to ``bpd.options.compute.allow_large_results``. - - Returns: - bigframes.pandas.DataFrame or pandas.Series: - A DataFrame representing the result of the query. If ``dry_run`` - is ``True``, a ``pandas.Series`` containing query statistics is - returned. - - Raises: - ValueError: - When both ``columns`` and ``col_order`` are specified. """ # NOTE: This method doesn't (yet) exist in pandas or pandas-gbq, so # these docstrings are inline. - if columns and col_order: - raise ValueError( - "Must specify either columns (preferred) or col_order, not both" - ) - elif col_order: - columns = col_order - - if allow_large_results is None: - allow_large_results = bigframes._config.options._allow_large_results - - return self._loader.read_gbq_query( # type: ignore # for dry_run overload + return self._read_gbq_query( query=query, index_col=index_col, - columns=columns, - configuration=configuration, + col_order=col_order, max_results=max_results, - use_cache=use_cache, - filters=filters, - dry_run=dry_run, - allow_large_results=allow_large_results, + api_name="read_gbq_query", ) - @overload - def read_gbq_table( # type: ignore[overload-overlap] - self, - query: str, - *, - index_col: Iterable[str] | str | bigframes.enums.DefaultIndexKind = ..., - columns: Iterable[str] = ..., - max_results: Optional[int] = ..., - filters: third_party_pandas_gbq.FiltersType = ..., - use_cache: bool = ..., - col_order: Iterable[str] = ..., - dry_run: Literal[False] = ..., - ) -> dataframe.DataFrame: ... - - @overload - def read_gbq_table( + def _read_gbq_query( self, query: str, *, - index_col: Iterable[str] | str | bigframes.enums.DefaultIndexKind = ..., - columns: Iterable[str] = ..., - max_results: Optional[int] = ..., - filters: third_party_pandas_gbq.FiltersType = ..., - use_cache: bool = ..., - col_order: Iterable[str] = ..., - dry_run: Literal[True] = ..., - ) -> pandas.Series: ... + index_col: Iterable[str] | str = (), + col_order: Iterable[str] = (), + max_results: Optional[int] = None, + api_name: str = "read_gbq_query", + ) -> dataframe.DataFrame: + if isinstance(index_col, str): + index_cols = [index_col] + else: + index_cols = list(index_col) + + destination, query_job = self._query_to_destination( + query, index_cols, api_name=api_name + ) + + # If there was no destination table, that means the query must have + # been DDL or DML. Return some job metadata, instead. + if not destination: + return dataframe.DataFrame( + data=pandas.DataFrame( + { + "statement_type": [ + query_job.statement_type if query_job else "unknown" + ], + "job_id": [query_job.job_id if query_job else "unknown"], + "location": [query_job.location if query_job else "unknown"], + } + ), + session=self, + ) + + return self.read_gbq_table( + f"{destination.project}.{destination.dataset_id}.{destination.table_id}", + index_col=index_cols, + col_order=col_order, + max_results=max_results, + ) def read_gbq_table( self, query: str, *, - index_col: Iterable[str] | str | bigframes.enums.DefaultIndexKind = (), - columns: Iterable[str] = (), - max_results: Optional[int] = None, - filters: third_party_pandas_gbq.FiltersType = (), - use_cache: bool = True, + index_col: Iterable[str] | str = (), col_order: Iterable[str] = (), - dry_run: bool = False, - ) -> dataframe.DataFrame | pandas.Series: + max_results: Optional[int] = None, + ) -> dataframe.DataFrame: """Turn a BigQuery table into a DataFrame. **Examples:** + >>> import bigframes.pandas as bpd + >>> bpd.options.display.progress_bar = None + Read a whole table, with arbitrary ordering or ordering corresponding to the primary key(s). - >>> import bigframes.pandas as bpd >>> df = bpd.read_gbq_table("bigquery-public-data.ml_datasets.penguins") + >>> df.head(2) + species island culmen_length_mm \\ + 0 Adelie Penguin (Pygoscelis adeliae) Dream 36.6 + 1 Adelie Penguin (Pygoscelis adeliae) Dream 39.8 + + culmen_depth_mm flipper_length_mm body_mass_g sex + 0 18.4 184.0 3475.0 FEMALE + 1 19.1 184.0 4650.0 MALE + + [2 rows x 7 columns] See also: :meth:`Session.read_gbq`. - - Args: - table_id (str): - The identifier of the BigQuery table to read. - index_col (Iterable[str] or str, optional): - The column(s) to use as the index for the DataFrame. This can be - a single column name or a list of column names. If not provided, - a default index will be used. - columns (Iterable[str], optional): - The columns to read from the table. If not specified, all - columns will be read. - max_results (int, optional): - The maximum number of rows to retrieve from the table. If not - specified, all rows will be loaded. - filters (list[tuple], optional): - A list of filters to apply to the data. Filters are specified - as a list of tuples, where each tuple contains a column name, - an operator (e.g., '==', '!='), and a value. - use_cache (bool, optional): - Whether to use cached results for the query. Defaults to ``True``. - Setting this to ``False`` will force a re-execution of the query. - col_order (Iterable[str], optional): - The desired order of columns in the resulting DataFrame. This - parameter is deprecated and will be removed in a future version. - Use ``columns`` instead. - dry_run (bool, optional): - If ``True``, the function will not actually execute the query but - will instead return statistics about the table. Defaults to - ``False``. - - Returns: - bigframes.pandas.DataFrame or pandas.Series: - A DataFrame representing the contents of the table. If - ``dry_run`` is ``True``, a ``pandas.Series`` containing table - statistics is returned. - - Raises: - ValueError: - When both ``columns`` and ``col_order`` are specified. """ # NOTE: This method doesn't (yet) exist in pandas or pandas-gbq, so # these docstrings are inline. - if columns and col_order: - raise ValueError( - "Must specify either columns (preferred) or col_order, not both" - ) - elif col_order: - columns = col_order - - return self._loader.read_gbq_table( # type: ignore # for dry_run overload - table_id=query, + return self._read_gbq_table( + query=query, index_col=index_col, - columns=columns, + col_order=col_order, max_results=max_results, - use_cache=use_cache, - filters=filters, - dry_run=dry_run, + api_name="read_gbq_table", ) - def read_gbq_table_streaming( - self, table: str - ) -> streaming_dataframe.StreamingDataFrame: - """Turn a BigQuery table into a StreamingDataFrame. + def _read_gbq_table_to_ibis_with_total_ordering( + self, + table_ref: bigquery.table.TableReference, + *, + api_name: str, + ) -> Tuple[ibis_types.Table, Optional[Sequence[str]]]: + """Create a read-only Ibis table expression representing a table. - .. note:: + If we can get a total ordering from the table, such as via primary key + column(s), then return those too so that ordering generation can be + avoided. + """ + if table_ref.dataset_id.upper() == "_SESSION": + # _SESSION tables aren't supported by the tables.get REST API. + return ( + self.ibis_client.sql( + f"SELECT * FROM `_SESSION`.`{table_ref.table_id}`" + ), + None, + ) - The bigframes.streaming module is a preview feature, and subject to change. + table_expression = self.ibis_client.table( + table_ref.table_id, + database=f"{table_ref.project}.{table_ref.dataset_id}", + ) - **Examples:** + # If there are primary keys defined, the query engine assumes these + # columns are unique, even if the constraint is not enforced. We make + # the same assumption and use these columns as the total ordering keys. + table = self.bqclient.get_table(table_ref) + + # TODO(b/305264153): Use public properties to fetch primary keys once + # added to google-cloud-bigquery. + primary_keys = ( + table._properties.get("tableConstraints", {}) + .get("primaryKey", {}) + .get("columns") + ) + + if not primary_keys: + return table_expression, None + else: + # Read from a snapshot since we won't have to copy the table data to create a total ordering. + job_config = bigquery.QueryJobConfig() + job_config.labels["bigframes-api"] = api_name + current_timestamp = list( + self.bqclient.query( + "SELECT CURRENT_TIMESTAMP() AS `current_timestamp`", + job_config=job_config, + ).result() + )[0][0] + table_expression = self.ibis_client.sql( + bigframes_io.create_snapshot_sql(table_ref, current_timestamp) + ) + return table_expression, primary_keys + + def _read_gbq_table( + self, + query: str, + *, + index_col: Iterable[str] | str = (), + col_order: Iterable[str] = (), + max_results: Optional[int] = None, + api_name: str, + ) -> dataframe.DataFrame: + if max_results and max_results <= 0: + raise ValueError("`max_results` should be a positive number.") + + # TODO(swast): Can we re-use the temp table from other reads in the + # session, if the original table wasn't modified? + table_ref = bigquery.table.TableReference.from_string( + query, default_project=self.bqclient.project + ) + + ( + table_expression, + total_ordering_cols, + ) = self._read_gbq_table_to_ibis_with_total_ordering( + table_ref, + api_name=api_name, + ) + + for key in col_order: + if key not in table_expression.columns: + raise ValueError( + f"Column '{key}' of `col_order` not found in this table." + ) - >>> import bigframes.streaming as bst + if isinstance(index_col, str): + index_cols: List[str] = [index_col] + else: + index_cols = list(index_col) - >>> sdf = bst.read_gbq_table("bigquery-public-data.ml_datasets.penguins") + hidden_cols: typing.Sequence[str] = () + + for key in index_cols: + if key not in table_expression.columns: + raise ValueError( + f"Column `{key}` of `index_col` not found in this table." + ) + + # If the index is unique and sortable, then we don't need to generate + # an ordering column. + ordering = None + is_total_ordering = False + + if total_ordering_cols is not None: + # Note: currently, this a table has a total ordering only when the + # primary key(s) are set on a table. The query engine assumes such + # columns are unique, even if not enforced. + is_total_ordering = True + ordering = orderings.ExpressionOrdering( + ordering_value_columns=tuple( + [ + core.OrderingColumnReference(column_id) + for column_id in total_ordering_cols + ] + ), + total_ordering_columns=frozenset(total_ordering_cols), + ) + + if len(index_cols) != 0: + index_labels = typing.cast(List[Optional[str]], index_cols) + else: + # Use the total_ordering_cols to project offsets to use as the default index. + table_expression = table_expression.order_by(index_cols) + default_index_id = guid.generate_guid("bigframes_index_") + default_index_col = ( + ibis.row_number().cast(ibis_dtypes.int64).name(default_index_id) + ) + table_expression = table_expression.mutate( + **{default_index_id: default_index_col} + ) + index_cols = [default_index_id] + index_labels = [None] + elif len(index_cols) != 0: + index_labels = typing.cast(List[Optional[str]], index_cols) + distinct_table = table_expression.select(*index_cols).distinct() + is_unique_sql = f"""WITH full_table AS ( + {self.ibis_client.compile(table_expression)} + ), + distinct_table AS ( + {self.ibis_client.compile(distinct_table)} + ) + + SELECT (SELECT COUNT(*) FROM full_table) AS `total_count`, + (SELECT COUNT(*) FROM distinct_table) AS `distinct_count` + """ + results, query_job = self._start_query(is_unique_sql) + row = next(iter(results)) + + total_count = row["total_count"] + distinct_count = row["distinct_count"] + is_total_ordering = total_count == distinct_count + + ordering = orderings.ExpressionOrdering( + ordering_value_columns=tuple( + [ + core.OrderingColumnReference(column_id) + for column_id in index_cols + ] + ), + total_ordering_columns=frozenset(index_cols), + ) + + # We have a total ordering, so query via "time travel" so that + # the underlying data doesn't mutate. + if is_total_ordering: + # Get the timestamp from the job metadata rather than the query + # text so that the query for determining uniqueness of the ID + # columns can be cached. + current_timestamp = query_job.started + + # The job finished, so we should have a start time. + assert current_timestamp is not None + table_expression = self.ibis_client.sql( + bigframes_io.create_snapshot_sql(table_ref, current_timestamp) + ) + else: + # Make sure when we generate an ordering, the row_number() + # coresponds to the index columns. + table_expression = table_expression.order_by(index_cols) + warnings.warn( + textwrap.dedent( + f""" + Got a non-unique index. A consistent ordering is not + guaranteed. DataFrame has {total_count} rows, + but only {distinct_count} distinct index values. + """, + ) + ) + + # When ordering by index columns, apply limit after ordering to + # make limit more predictable. + if max_results is not None: + table_expression = table_expression.limit(max_results) + else: + if max_results is not None: + # Apply limit before generating rownums and creating temp table + # This makes sure the offsets are valid and limits the number of + # rows for which row numbers must be generated + table_expression = table_expression.limit(max_results) + table_expression, ordering = self._create_sequential_ordering( + table=table_expression, + api_name=api_name, + ) + hidden_cols = ( + (ordering.total_order_col.column_id,) + if ordering.total_order_col + else () + ) + assert len(ordering.ordering_value_columns) > 0 + is_total_ordering = True + # Block constructor will generate default index if passed empty + index_cols = [] + index_labels = [] + + return self._read_gbq_with_ordering( + table_expression=table_expression, + col_order=col_order, + index_cols=index_cols, + index_labels=index_labels, + hidden_cols=hidden_cols, + ordering=ordering, + is_total_ordering=is_total_ordering, + api_name=api_name, + ) + + def _read_gbq_with_ordering( + self, + table_expression: ibis_types.Table, + *, + col_order: Iterable[str] = (), + col_labels: Iterable[Optional[str]] = (), + index_cols: Iterable[str] = (), + index_labels: Iterable[Optional[str]] = (), + hidden_cols: Iterable[str] = (), + ordering: orderings.ExpressionOrdering, + is_total_ordering: bool = False, + api_name: str, + ) -> dataframe.DataFrame: + """Internal helper method that loads DataFrame from Google BigQuery given an ordering column. + + Args: + table_expression: + an ibis table expression to be executed in BigQuery. + col_order: + List of BigQuery column ids in the desired order for results DataFrame. + col_labels: + List of column labels as the column names. + index_cols: + List of index ids to use as the index or multi-index. + index_labels: + List of index labels as names of index. + hidden_cols: + Columns that should be hidden. Ordering columns may (not always) be hidden + ordering: + Column name to be used for ordering. If not supplied, a default ordering is generated. + api_name: + The name of the API method. Returns: - bigframes.streaming.dataframe.StreamingDataFrame: - A StreamingDataFrame representing results of the table. + A DataFrame representing results of the query or table. """ - msg = bfe.format_message( - "The bigframes.streaming module is a preview feature, and subject to change." + index_cols, index_labels = list(index_cols), list(index_labels) + if len(index_cols) != len(index_labels): + raise ValueError( + "Needs same number of index labels are there are index columns. " + f"Got {len(index_labels)}, expected {len(index_cols)}." + ) + + # Logic: + # no total ordering, index -> create sequential order, ordered by index, use for both ordering and index + # total ordering, index -> use ordering as ordering, index as index + + # This code block ensures the existence of a total ordering. + column_keys = list(col_order) + if len(column_keys) == 0: + non_value_columns = set([*index_cols, *hidden_cols]) + column_keys = [ + key for key in table_expression.columns if key not in non_value_columns + ] + if not is_total_ordering: + # Rows are not ordered, we need to generate a default ordering and materialize it + table_expression, ordering = self._create_sequential_ordering( + table=table_expression, + index_cols=index_cols, + api_name=api_name, + ) + index_col_values = [table_expression[index_id] for index_id in index_cols] + if not col_labels: + col_labels = column_keys + return self._read_ibis( + table_expression, + index_col_values, + index_labels, + column_keys, + col_labels, + ordering=ordering, ) - warnings.warn(msg, stacklevel=1, category=bfe.PreviewWarning) - import bigframes.streaming.dataframe as streaming_dataframe + def _read_bigquery_load_job( + self, + filepath_or_buffer: str | IO["bytes"], + table: bigquery.Table, + *, + job_config: bigquery.LoadJobConfig, + index_col: Iterable[str] | str = (), + col_order: Iterable[str] = (), + ) -> dataframe.DataFrame: + if isinstance(index_col, str): + index_cols = [index_col] + else: + index_cols = list(index_col) + + if not job_config.clustering_fields and index_cols: + job_config.clustering_fields = index_cols[:_MAX_CLUSTER_COLUMNS] - df = self._loader.read_gbq_table( - table, - enable_snapshot=False, - index_col=bigframes.enums.DefaultIndexKind.NULL, + if isinstance(filepath_or_buffer, str): + if filepath_or_buffer.startswith("gs://"): + load_job = self.bqclient.load_table_from_uri( + filepath_or_buffer, table, job_config=job_config + ) + else: + with open(filepath_or_buffer, "rb") as source_file: + load_job = self.bqclient.load_table_from_file( + source_file, table, job_config=job_config + ) + else: + load_job = self.bqclient.load_table_from_file( + filepath_or_buffer, table, job_config=job_config + ) + + self._start_generic_job(load_job) + + # The BigQuery REST API for tables.get doesn't take a session ID, so we + # can't get the schema for a temp table that way. + return self.read_gbq_table( + f"{table.project}.{table.dataset_id}.{table.table_id}", + index_col=index_col, + col_order=col_order, + ) + + def _read_ibis( + self, + table_expression: ibis_types.Table, + index_cols: Iterable[ibis_types.Value], + index_labels: Iterable[blocks.Label], + column_keys: Iterable[str], + column_labels: Iterable[blocks.Label], + ordering: orderings.ExpressionOrdering, + ) -> dataframe.DataFrame: + """Turns a table expression (plus index column) into a DataFrame.""" + + columns = list(index_cols) + for key in column_keys: + if key not in table_expression.columns: + raise ValueError(f"Column '{key}' not found in this table.") + columns.append(table_expression[key]) + + non_hidden_ids = [col.get_name() for col in columns] + hidden_ordering_columns = [] + for ref in ordering.all_ordering_columns: + if ref.column_id not in non_hidden_ids: + hidden_ordering_columns.append(table_expression[ref.column_id]) + + block = blocks.Block( + core.ArrayValue.from_ibis( + self, table_expression, columns, hidden_ordering_columns, ordering + ), + index_columns=[index_col.get_name() for index_col in index_cols], + column_labels=column_labels, + index_labels=index_labels, ) - return streaming_dataframe.StreamingDataFrame._from_table_df(df) + return dataframe.DataFrame(block) def read_gbq_model(self, model_name: str): """Loads a BigQuery ML model from BigQuery. **Examples:** + >>> import bigframes.pandas as bpd + >>> bpd.options.display.progress_bar = None + Read an existing BigQuery ML model. - >>> import bigframes.pandas as bpd >>> model_name = "bigframes-dev.bqml_tutorial.penguins_model" >>> model = bpd.read_gbq_model(model_name) @@ -1077,7 +901,7 @@ def read_gbq_model(self, model_name: str): to load from the default project. Returns: - A bigframes.ml Model, Transformer or Pipeline wrapping the model. + A bigframes.ml Model wrapping the model. """ import bigframes.ml.loader @@ -1087,48 +911,17 @@ def read_gbq_model(self, model_name: str): model = self.bqclient.get_model(model_ref) return bigframes.ml.loader.from_bq(self, model) - @typing.overload - def read_pandas( - self, - pandas_dataframe: pandas.Index, - *, - write_engine: constants.WriteEngineType = "default", - ) -> bigframes.core.indexes.Index: ... - - @typing.overload - def read_pandas( - self, - pandas_dataframe: pandas.Series, - *, - write_engine: constants.WriteEngineType = "default", - ) -> bigframes.series.Series: ... - - @typing.overload - def read_pandas( - self, - pandas_dataframe: pandas.DataFrame, - *, - write_engine: constants.WriteEngineType = "default", - ) -> dataframe.DataFrame: ... - - def read_pandas( - self, - pandas_dataframe: Union[pandas.DataFrame, pandas.Series, pandas.Index], - *, - write_engine: constants.WriteEngineType = "default", - ): + def read_pandas(self, pandas_dataframe: pandas.DataFrame) -> dataframe.DataFrame: """Loads DataFrame from a pandas DataFrame. The pandas DataFrame will be persisted as a temporary BigQuery table, which can be automatically recycled after the Session is closed. - .. note:: - Data is inlined in the query SQL if it is small enough (roughly 5MB - or less in memory). Larger size data is loaded to a BigQuery table - instead. - **Examples:** + >>> import bigframes.pandas as bpd + >>> import pandas as pd + >>> bpd.options.display.progress_bar = None >>> d = {'col1': [1, 2], 'col2': [3, 4]} >>> pandas_df = pd.DataFrame(data=d) @@ -1141,125 +934,85 @@ def read_pandas( [2 rows x 2 columns] Args: - pandas_dataframe (pandas.DataFrame, pandas.Series, or pandas.Index): - a pandas DataFrame/Series/Index object to be loaded. - write_engine (str): - How data should be written to BigQuery (if at all). Supported - values: - - * "default": - (Recommended) Select an appropriate mechanism to write data - to BigQuery. Depends on data size and supported data types. - * "bigquery_inline": - Inline data in BigQuery SQL. Use this when you know the data - is small enough to fit within BigQuery's 1 MB query text size - limit. - * "bigquery_load": - Use a BigQuery load job. Use this for larger data sizes. - * "bigquery_streaming": - Use the BigQuery streaming JSON API. Use this if your - workload is such that you exhaust the BigQuery load job - quota and your data cannot be embedded in SQL due to size or - data type limitations. - * "bigquery_write": - [Preview] Use the BigQuery Storage Write API. This feature - is in public preview. - Returns: - An equivalent bigframes.pandas.(DataFrame/Series/Index) object + pandas_dataframe (pandas.DataFrame): + a pandas DataFrame object to be loaded. - Raises: - ValueError: - When the object is not a Pandas DataFrame. + Returns: + bigframes.dataframe.DataFrame: The BigQuery DataFrame. """ - import bigframes.series as series - - # Try to handle non-dataframe pandas objects as well - if isinstance(pandas_dataframe, pandas.Series): - bf_df = self._read_pandas( - pandas.DataFrame(pandas_dataframe), - write_engine=write_engine, - ) - bf_series = series.Series(bf_df._block) - # wrapping into df can set name to 0 so reset to original object name - bf_series.name = pandas_dataframe.name - return bf_series - if isinstance(pandas_dataframe, pandas.Index): - return self._read_pandas( - pandas.DataFrame(index=pandas_dataframe), - write_engine=write_engine, - ).index - if isinstance(pandas_dataframe, pandas.DataFrame): - return self._read_pandas(pandas_dataframe, write_engine=write_engine) - else: - raise ValueError( - f"read_pandas() expects a pandas.DataFrame, but got a {type(pandas_dataframe)}" - ) + return self._read_pandas(pandas_dataframe, "read_pandas") def _read_pandas( - self, - pandas_dataframe: pandas.DataFrame, - *, - write_engine: constants.WriteEngineType = "default", + self, pandas_dataframe: pandas.DataFrame, api_name: str ) -> dataframe.DataFrame: - import bigframes.dataframe as dataframe - - if isinstance(pandas_dataframe, dataframe.DataFrame): - raise ValueError( - "read_pandas() expects a pandas.DataFrame, but got a " - "bigframes.pandas.DataFrame." - ) - - mem_usage = pandas_dataframe.memory_usage(deep=True).sum() - if write_engine == "default": - write_engine = ( - "bigquery_load" - if mem_usage > bigframes.constants.MAX_INLINE_BYTES - else "bigquery_inline" - ) - - if write_engine == "bigquery_inline": - if mem_usage > bigframes.constants.MAX_INLINE_BYTES: - raise ValueError( - f"DataFrame size ({mem_usage} bytes) exceeds the maximum allowed " - f"for inline data ({bigframes.constants.MAX_INLINE_BYTES} bytes)." + col_labels, idx_labels = ( + pandas_dataframe.columns.to_list(), + pandas_dataframe.index.names, + ) + new_col_ids, new_idx_ids = utils.get_standardized_ids(col_labels, idx_labels) + + # Add order column to pandas DataFrame to preserve order in BigQuery + ordering_col = "rowid" + columns = frozenset(col_labels + idx_labels) + suffix = 2 + while ordering_col in columns: + ordering_col = f"rowid_{suffix}" + suffix += 1 + + pandas_dataframe_copy = pandas_dataframe.copy() + pandas_dataframe_copy.index.names = new_idx_ids + pandas_dataframe_copy.columns = pandas.Index(new_col_ids) + pandas_dataframe_copy[ordering_col] = np.arange(pandas_dataframe_copy.shape[0]) + + # Specify the datetime dtypes, which is auto-detected as timestamp types. + schema: list[bigquery.SchemaField] = [] + for column, dtype in zip(pandas_dataframe.columns, pandas_dataframe.dtypes): + if dtype == "timestamp[us][pyarrow]": + schema.append( + bigquery.SchemaField(column, bigquery.enums.SqlTypeNames.DATETIME) ) - return self._read_pandas_inline(pandas_dataframe) - elif write_engine == "bigquery_load": - return self._loader.read_pandas(pandas_dataframe, method="load") - elif write_engine == "bigquery_streaming": - return self._loader.read_pandas(pandas_dataframe, method="stream") - elif write_engine == "bigquery_write": - return self._loader.read_pandas(pandas_dataframe, method="write") - elif write_engine == "_deferred": - import bigframes.dataframe as dataframe - - return dataframe.DataFrame(blocks.Block.from_local(pandas_dataframe, self)) - else: - raise ValueError(f"Got unexpected write_engine '{write_engine}'") - def _read_pandas_inline( - self, pandas_dataframe: pandas.DataFrame - ) -> dataframe.DataFrame: - import bigframes.dataframe as dataframe - - local_block = blocks.Block.from_local(pandas_dataframe, self) - return dataframe.DataFrame(local_block) + # Clustering probably not needed anyways as pandas tables are small + cluster_cols = [ordering_col] - def read_arrow(self, pa_table: pa.Table) -> bigframes.dataframe.DataFrame: - """Load a PyArrow Table to a BigQuery DataFrames DataFrame. + job_config = bigquery.LoadJobConfig(schema=schema) + job_config.clustering_fields = cluster_cols + job_config.labels = {"bigframes-api": api_name} - Args: - pa_table (pyarrow.Table): - PyArrow table to load data from. + load_table_destination = self._create_session_table() + load_job = self.bqclient.load_table_from_dataframe( + pandas_dataframe_copy, + load_table_destination, + job_config=job_config, + ) + self._start_generic_job(load_job) - Returns: - bigframes.dataframe.DataFrame: - A new DataFrame representing the data from the PyArrow table. - """ - import bigframes.dataframe as dataframe + ordering = orderings.ExpressionOrdering( + ordering_value_columns=tuple([OrderingColumnReference(ordering_col)]), + total_ordering_columns=frozenset([ordering_col]), + integer_encoding=IntegerEncoding(True, is_sequential=True), + ) + table_expression = self.ibis_client.sql( + f"SELECT * FROM `{load_table_destination.table_id}`" + ) - local_block = blocks.Block.from_pyarrow(pa_table, self) - return dataframe.DataFrame(local_block) + # b/297590178 Potentially a bug in bqclient.load_table_from_dataframe(), that only when the DF is empty, the index columns disappear in table_expression. + if any( + [new_idx_id not in table_expression.columns for new_idx_id in new_idx_ids] + ): + new_idx_ids, idx_labels = [], [] + + df = self._read_gbq_with_ordering( + table_expression=table_expression, + col_labels=col_labels, + index_cols=new_idx_ids, + index_labels=idx_labels, + hidden_cols=(ordering_col,), + ordering=ordering, + is_total_ordering=True, + api_name=api_name, + ) + return df def read_csv( self, @@ -1271,13 +1024,7 @@ def read_csv( Union[MutableSequence[Any], np.ndarray[Any, Any], Tuple[Any, ...], range] ] = None, index_col: Optional[ - Union[ - int, - str, - Sequence[Union[str, int]], - bigframes.enums.DefaultIndexKind, - Literal[False], - ] + Union[int, str, Sequence[Union[str, int]], Literal[False]] ] = None, usecols: Optional[ Union[ @@ -1295,186 +1042,101 @@ def read_csv( Literal["c", "python", "pyarrow", "python-fwf", "bigquery"] ] = None, encoding: Optional[str] = None, - write_engine: constants.WriteEngineType = "default", - **kwargs, - ) -> dataframe.DataFrame: - bigframes.session.validation.validate_engine_compatibility( - engine=engine, - write_engine=write_engine, - ) - - if engine != "bigquery": - # Using pandas.read_csv by default and warning about potential issues with - # large files. - return self._read_csv_w_pandas_engines( - filepath_or_buffer, - sep=sep, - header=header, - names=names, - index_col=index_col, - usecols=usecols, # type: ignore - dtype=dtype, - engine=engine, - encoding=encoding, - write_engine=write_engine, - **kwargs, - ) - else: - return self._read_csv_w_bigquery_engine( - filepath_or_buffer, - sep=sep, - header=header, - names=names, - index_col=index_col, - usecols=usecols, # type: ignore - dtype=dtype, - encoding=encoding, - ) - - def _read_csv_w_pandas_engines( - self, - filepath_or_buffer, - *, - sep, - header, - names, - index_col, - usecols, - dtype, - engine, - encoding, - write_engine, **kwargs, ) -> dataframe.DataFrame: - """Reads a CSV file using pandas engines into a BigQuery DataFrames. + table = bigquery.Table(self._create_session_table()) - This method serves as the implementation backend for read_csv when the - specified engine is one supported directly by pandas ('c', 'python', - 'pyarrow'). - """ - if isinstance(index_col, bigframes.enums.DefaultIndexKind): - raise NotImplementedError( - f"With index_col={repr(index_col)}, only engine='bigquery' is supported. " - f"{constants.FEEDBACK_LINK}" - ) - if any(arg in kwargs for arg in ("chunksize", "iterator")): - raise NotImplementedError( - "'chunksize' and 'iterator' arguments are not supported. " - f"{constants.FEEDBACK_LINK}" - ) - if isinstance(filepath_or_buffer, str): - self._check_file_size(filepath_or_buffer) + if engine is not None and engine == "bigquery": + if any(param is not None for param in (dtype, names)): + not_supported = ("dtype", "names") + raise NotImplementedError( + f"BigQuery engine does not support these arguments: {not_supported}. " + f"{constants.FEEDBACK_LINK}" + ) - pandas_df = pandas.read_csv( - filepath_or_buffer, - sep=sep, - header=header, - names=names, - index_col=index_col, - usecols=usecols, # type: ignore - dtype=dtype, - engine=engine, - encoding=encoding, - **kwargs, - ) - return self._read_pandas(pandas_df, write_engine=write_engine) # type: ignore + if index_col is not None and ( + not index_col or not isinstance(index_col, str) + ): + raise NotImplementedError( + "BigQuery engine only supports a single column name for `index_col`. " + f"{constants.FEEDBACK_LINK}" + ) - def _read_csv_w_bigquery_engine( - self, - filepath_or_buffer, - *, - sep, - header, - names, - index_col, - usecols, - dtype, - encoding, - ) -> dataframe.DataFrame: - """Reads a CSV file using the BigQuery engine into a BigQuery DataFrames. + # None value for index_col cannot be passed to read_gbq + if index_col is None: + index_col = () + + # usecols should only be an iterable of strings (column names) for use as col_order in read_gbq. + col_order: Tuple[Any, ...] = tuple() + if usecols is not None: + if isinstance(usecols, Iterable) and all( + isinstance(col, str) for col in usecols + ): + col_order = tuple(col for col in usecols) + else: + raise NotImplementedError( + "BigQuery engine only supports an iterable of strings for `usecols`. " + f"{constants.FEEDBACK_LINK}" + ) - This method serves as the implementation backend for read_csv when the - 'bigquery' engine is specified or inferred. It leverages BigQuery's - native CSV loading capabilities, making it suitable for large datasets - that may not fit into local memory. - """ - if dtype is not None and not utils.is_dict_like(dtype): - raise ValueError("dtype should be a dict-like object.") - - if names is not None: - if len(names) != len(set(names)): - raise ValueError("Duplicated names are not allowed.") - if not ( - bigframes.core.utils.is_list_like(names, allow_sets=False) - or isinstance(names, abc.KeysView) - ): - raise ValueError("Names should be an ordered collection.") + if encoding is not None and encoding not in _VALID_ENCODINGS: + raise NotImplementedError( + f"BigQuery engine only supports the following encodings: {_VALID_ENCODINGS}. " + f"{constants.FEEDBACK_LINK}" + ) - if index_col is True: - raise ValueError("The value of index_col couldn't be 'True'") + job_config = bigquery.LoadJobConfig() + job_config.create_disposition = bigquery.CreateDisposition.CREATE_IF_NEEDED + job_config.source_format = bigquery.SourceFormat.CSV + job_config.write_disposition = bigquery.WriteDisposition.WRITE_EMPTY + job_config.autodetect = True + job_config.field_delimiter = sep + job_config.encoding = encoding + job_config.labels = {"bigframes-api": "read_csv"} - # None and False cannot be passed to read_gbq. - if index_col is None or index_col is False: - index_col = () + # We want to match pandas behavior. If header is 0, no rows should be skipped, so we + # do not need to set `skip_leading_rows`. If header is None, then there is no header. + # Setting skip_leading_rows to 0 does that. If header=N and N>0, we want to skip N rows. + if header is None: + job_config.skip_leading_rows = 0 + elif header > 0: + job_config.skip_leading_rows = header - # usecols should only be an iterable of strings (column names) for use as columns in read_gbq. - columns: Tuple[Any, ...] = tuple() - if usecols is not None: - if isinstance(usecols, Iterable) and all( - isinstance(col, str) for col in usecols - ): - columns = tuple(col for col in usecols) - else: + return self._read_bigquery_load_job( + filepath_or_buffer, + table, + job_config=job_config, + index_col=index_col, + col_order=col_order, + ) + else: + if any(arg in kwargs for arg in ("chunksize", "iterator")): raise NotImplementedError( - "BigQuery engine only supports an iterable of strings for `usecols`. " + "'chunksize' and 'iterator' arguments are not supported. " f"{constants.FEEDBACK_LINK}" ) - if encoding is not None and encoding not in _VALID_ENCODINGS: - raise NotImplementedError( - f"BigQuery engine only supports the following encodings: {_VALID_ENCODINGS}. " - f"{constants.FEEDBACK_LINK}" + if isinstance(filepath_or_buffer, str): + self._check_file_size(filepath_or_buffer) + pandas_df = pandas.read_csv( + filepath_or_buffer, + sep=sep, + header=header, + names=names, + index_col=index_col, + usecols=usecols, + dtype=dtype, + engine=engine, + encoding=encoding, + **kwargs, ) - - job_config = bigquery.LoadJobConfig() - job_config.source_format = bigquery.SourceFormat.CSV - job_config.autodetect = True - job_config.field_delimiter = sep - job_config.encoding = encoding - job_config.labels = {"bigframes-api": "read_csv"} - - # b/409070192: When header > 0, pandas and BigFrames returns different column naming. - - # We want to match pandas behavior. If header is 0, no rows should be skipped, so we - # do not need to set `skip_leading_rows`. If header is None, then there is no header. - # Setting skip_leading_rows to 0 does that. If header=N and N>0, we want to skip N rows. - if header is None: - job_config.skip_leading_rows = 0 - elif header > 0: - job_config.skip_leading_rows = header + 1 - - table_id = self._loader.load_file(filepath_or_buffer, job_config=job_config) - df = self._loader.read_gbq_table( - table_id, - index_col=index_col, - columns=columns, - names=names, - index_col_in_columns=True, - ) - - if dtype is not None: - for column, dtype in dtype.items(): - if column in df.columns: - df[column] = df[column].astype(dtype) - return df + return self.read_pandas(pandas_df) def read_pickle( self, filepath_or_buffer: FilePath | ReadPickleBuffer, compression: CompressionOptions = "infer", storage_options: StorageOptions = None, - *, - write_engine: constants.WriteEngineType = "default", ): pandas_obj = pandas.read_pickle( filepath_or_buffer, @@ -1484,137 +1146,27 @@ def read_pickle( if isinstance(pandas_obj, pandas.Series): if pandas_obj.name is None: - pandas_obj.name = 0 - bigframes_df = self._read_pandas(pandas_obj.to_frame()) + pandas_obj.name = "0" + bigframes_df = self.read_pandas(pandas_obj.to_frame()) return bigframes_df[bigframes_df.columns[0]] - return self._read_pandas(pandas_obj, write_engine=write_engine) + return self._read_pandas(pandas_obj, "read_pickle") def read_parquet( self, path: str | IO["bytes"], - *, - engine: str = "auto", - write_engine: constants.WriteEngineType = "default", - ) -> dataframe.DataFrame: - bigframes.session.validation.validate_engine_compatibility( - engine=engine, - write_engine=write_engine, - ) - if engine == "bigquery": - job_config = bigquery.LoadJobConfig() - job_config.source_format = bigquery.SourceFormat.PARQUET - - # Ensure we can load pyarrow.list_ / BQ ARRAY type. - # See internal issue 414374215. - parquet_options = bigquery.ParquetOptions() - parquet_options.enable_list_inference = True - job_config.parquet_options = parquet_options - - job_config.labels = {"bigframes-api": "read_parquet"} - table_id = self._loader.load_file(path, job_config=job_config) - return self._loader.read_gbq_table(table_id) - else: - if "*" in path: - raise ValueError( - "The provided path contains a wildcard character (*), which is not " - "supported by the current engine. To read files from wildcard paths, " - "please use the 'bigquery' engine by setting `engine='bigquery'` in " - "the function call." - ) - - read_parquet_kwargs: Dict[str, Any] = {} - if pandas.__version__.startswith("1."): - read_parquet_kwargs["use_nullable_dtypes"] = True - else: - read_parquet_kwargs["dtype_backend"] = "pyarrow" - - pandas_obj = pandas.read_parquet( - path, - engine=engine, # type: ignore - **read_parquet_kwargs, - ) - return self._read_pandas(pandas_obj, write_engine=write_engine) - - def read_orc( - self, - path: str | IO["bytes"], - *, - engine: str = "auto", - write_engine: constants.WriteEngineType = "default", - ) -> dataframe.DataFrame: - """Load an ORC file to a BigQuery DataFrames DataFrame. - - Args: - path (str or IO): - The path or buffer to the ORC file. Can be a local path or Google Cloud Storage URI. - engine (str, default "auto"): - The engine used to read the file. Supported values: `auto`, `bigquery`, `pyarrow`. - write_engine (str, default "default"): - The write engine used to persist the data to BigQuery if needed. - - Returns: - bigframes.pandas.DataFrame: - A new DataFrame representing the data from the ORC file. - """ - bigframes.session.validation.validate_engine_compatibility( - engine=engine, - write_engine=write_engine, - ) - if engine == "bigquery": - job_config = bigquery.LoadJobConfig() - job_config.source_format = bigquery.SourceFormat.ORC - job_config.labels = {"bigframes-api": "read_orc"} - table_id = self._loader.load_file(path, job_config=job_config) - return self._loader.read_gbq_table(table_id) - elif engine in ("auto", "pyarrow"): - if isinstance(path, str) and "*" in path: - raise ValueError( - "The provided path contains a wildcard character (*), which is not " - "supported by the current engine. To read files from wildcard paths, " - "please use the 'bigquery' engine by setting `engine='bigquery'` in " - "your configuration." - ) - - read_orc_kwargs: Dict[str, Any] = {} - if not pandas.__version__.startswith("1."): - read_orc_kwargs["dtype_backend"] = "pyarrow" - - pandas_obj = pandas.read_orc(path, **read_orc_kwargs) - return self._read_pandas(pandas_obj, write_engine=write_engine) - else: - raise ValueError( - f"Unsupported engine: {repr(engine)}. Supported values: 'auto', 'bigquery', 'pyarrow'." - ) - - def read_avro( - self, - path: str | IO["bytes"], - *, - engine: str = "auto", ) -> dataframe.DataFrame: - """Load an Avro file to a BigQuery DataFrames DataFrame. - - Args: - path (str or IO): - The path or buffer to the Avro file. Can be a local path or Google Cloud Storage URI. - engine (str, default "auto"): - The engine used to read the file. Only `bigquery` is supported for Avro. - - Returns: - bigframes.pandas.DataFrame: - A new DataFrame representing the data from the Avro file. - """ - if engine not in ("auto", "bigquery"): - raise ValueError( - f"Unsupported engine: {repr(engine)}. Supported values: 'auto', 'bigquery'." - ) + # Note: "engine" is omitted because it is redundant. Loading a table + # from a pandas DataFrame will just create another parquet file + load + # job anyway. + table = bigquery.Table(self._create_session_table()) job_config = bigquery.LoadJobConfig() - job_config.use_avro_logical_types = True - job_config.source_format = bigquery.SourceFormat.AVRO - job_config.labels = {"bigframes-api": "read_avro"} - table_id = self._loader.load_file(path, job_config=job_config) - return self._loader.read_gbq_table(table_id) + job_config.create_disposition = bigquery.CreateDisposition.CREATE_IF_NEEDED + job_config.source_format = bigquery.SourceFormat.PARQUET + job_config.write_disposition = bigquery.WriteDisposition.WRITE_EMPTY + job_config.labels = {"bigframes-api": "read_parquet"} + + return self._read_bigquery_load_job(path, table, job_config=job_config) def read_json( self, @@ -1627,14 +1179,12 @@ def read_json( encoding: Optional[str] = None, lines: bool = False, engine: Literal["ujson", "pyarrow", "bigquery"] = "ujson", - write_engine: constants.WriteEngineType = "default", **kwargs, ) -> dataframe.DataFrame: - bigframes.session.validation.validate_engine_compatibility( - engine=engine, - write_engine=write_engine, - ) + table = bigquery.Table(self._create_session_table()) + if engine == "bigquery": + if dtype is not None: raise NotImplementedError( "BigQuery engine does not support the dtype arguments." @@ -1656,13 +1206,18 @@ def read_json( ) job_config = bigquery.LoadJobConfig() + job_config.create_disposition = bigquery.CreateDisposition.CREATE_IF_NEEDED job_config.source_format = bigquery.SourceFormat.NEWLINE_DELIMITED_JSON + job_config.write_disposition = bigquery.WriteDisposition.WRITE_EMPTY job_config.autodetect = True job_config.encoding = encoding job_config.labels = {"bigframes-api": "read_json"} - table_id = self._loader.load_file(path_or_buf, job_config=job_config) - return self._loader.read_gbq_table(table_id) + return self._read_bigquery_load_job( + path_or_buf, + table, + job_config=job_config, + ) else: if any(arg in kwargs for arg in ("chunksize", "iterator")): raise NotImplementedError( @@ -1692,35 +1247,21 @@ def read_json( engine=engine, **kwargs, ) - return self._read_pandas(pandas_df, write_engine=write_engine) + return self.read_pandas(pandas_df) def _check_file_size(self, filepath: str): max_size = 1024 * 1024 * 1024 # 1 GB in bytes if filepath.startswith("gs://"): # GCS file path - bucket_name, blob_path = filepath.split("/", 3)[2:] - - client = self._clients_provider.storageclient + client = storage.Client() + bucket_name, blob_name = filepath.split("/", 3)[2:] bucket = client.bucket(bucket_name) - - list_blobs_params = inspect.signature(bucket.list_blobs).parameters - if "match_glob" in list_blobs_params: - # Modern, efficient method for new library versions - matching_blobs = bucket.list_blobs(match_glob=blob_path) - file_size = sum(blob.size for blob in matching_blobs) - else: - # Fallback method for older library versions - prefix = blob_path.split("*", 1)[0] - all_blobs = bucket.list_blobs(prefix=prefix) - matching_blobs = [ - blob for blob in all_blobs if fnmatch.fnmatch(blob.name, blob_path) - ] - file_size = sum(blob.size for blob in matching_blobs) - elif os.path.exists(filepath): # local file path + blob = bucket.blob(blob_name) + blob.reload() + file_size = blob.size + else: # local file path file_size = os.path.getsize(filepath) - else: - file_size = None - if file_size is not None and file_size > max_size: + if file_size > max_size: # Convert to GB file_size = round(file_size / (1024**3), 1) max_size = int(max_size / 1024**3) @@ -1730,80 +1271,90 @@ def _check_file_size(self, filepath: str): "for large files to avoid loading the file into local memory." ) - def deploy_remote_function( + def _create_session_table(self) -> bigquery.TableReference: + table_name = f"{uuid.uuid4().hex}" + dataset = bigquery.Dataset( + bigquery.DatasetReference(self.bqclient.project, "_SESSION") + ) + return dataset.table(table_name) + + def _create_empty_temp_table( self, - func, - **kwargs, - ): - """Orchestrates the creation of a BigQuery remote function that deploys immediately. + schema: Iterable[bigquery.SchemaField], + cluster_cols: List[str], + ) -> bigquery.TableReference: + # Can't set a table in _SESSION as destination via query job API, so we + # run DDL, instead. + dataset = self._anonymous_dataset + expiration = ( + datetime.datetime.now(datetime.timezone.utc) + constants.DEFAULT_EXPIRATION + ) - This method ensures that the remote function is created and available for - use in BigQuery as soon as this call is made. + table = bigframes_io.create_temp_table( + self.bqclient, + dataset, + expiration, + schema=schema, + cluster_columns=cluster_cols, + ) + return bigquery.TableReference.from_string(table) - Args: - func: - Function to deploy. - kwargs: - All arguments are passed directly to - :meth:`~bigframes.session.Session.remote_function`. Please see - its docstring for parameter details. + def _create_sequential_ordering( + self, + table: ibis_types.Table, + index_cols: Iterable[str] = (), + api_name: str = "", + ) -> Tuple[ibis_types.Table, orderings.ExpressionOrdering]: + # Since this might also be used as the index, don't use the default + # "ordering ID" name. + default_ordering_name = guid.generate_guid("bigframes_ordering_") + default_ordering_col = ( + ibis.row_number().cast(ibis_dtypes.int64).name(default_ordering_name) + ) + table = table.mutate(**{default_ordering_name: default_ordering_col}) + table_ref = self._ibis_to_session_table( + table, + cluster_cols=list(index_cols) + [default_ordering_name], + api_name=api_name, + ) + table = self.ibis_client.table( + f"{table_ref.project}.{table_ref.dataset_id}.{table_ref.table_id}" + ) + ordering_reference = core.OrderingColumnReference(default_ordering_name) + ordering = orderings.ExpressionOrdering( + ordering_value_columns=tuple([ordering_reference]), + total_ordering_columns=frozenset([default_ordering_name]), + integer_encoding=IntegerEncoding(is_encoded=True, is_sequential=True), + ) + return table, ordering - Returns: - A wrapped remote function, usable in - :meth:`~bigframes.series.Series.apply`. - """ - return self._function_session.deploy_remote_function( - func, - **kwargs, + def _ibis_to_session_table( + self, + table: ibis_types.Table, + cluster_cols: Iterable[str], + api_name: str, + ) -> bigquery.TableReference: + destination, _ = self._query_to_destination( + self.ibis_client.compile(table), + index_cols=list(cluster_cols), + api_name=api_name, ) + # There should always be a destination table for this query type. + return typing.cast(bigquery.TableReference, destination) def remote_function( self, - # Make sure that the input/output types, and dataset can be used - # positionally. This avoids the worst of the breaking change from 1.x to - # 2.x while still preventing possible mixups between consecutive str - # parameters. - input_types: Union[None, type, Sequence[type]] = None, - output_type: Optional[type] = None, + input_types: List[type], + output_type: type, dataset: Optional[str] = None, - *, bigquery_connection: Optional[str] = None, reuse: bool = True, name: Optional[str] = None, packages: Optional[Sequence[str]] = None, - cloud_function_service_account: str, - cloud_function_kms_key_name: Optional[str] = None, - cloud_function_docker_repository: Optional[str] = None, - max_batching_rows: Optional[int] = 1000, - cloud_function_timeout: Optional[int] = 600, - cloud_function_max_instances: Optional[int] = None, - cloud_function_vpc_connector: Optional[str] = None, - cloud_function_vpc_connector_egress_settings: Optional[ - Literal["all", "private-ranges-only", "unspecified"] - ] = None, - cloud_function_memory_mib: Optional[int] = None, - cloud_function_cpus: Optional[float] = None, - cloud_function_ingress_settings: Literal[ - "all", "internal-only", "internal-and-gclb" - ] = "internal-only", - cloud_build_service_account: Optional[str] = None, ): """Decorator to turn a user defined function into a BigQuery remote function. Check out the code samples at: https://cloud.google.com/bigquery/docs/remote-functions#bigquery-dataframes. - .. note:: - ``input_types=Series`` scenario is in preview. It currently only - supports dataframe with column types ``Int64``/``Float64``/``boolean``/ - ``string``/``binary[pyarrow]``. - - .. warning:: - To use remote functions with Bigframes 2.0 and onwards, please (preferred) - set an explicit user-managed ``cloud_function_service_account`` or (discouraged) - set ``cloud_function_service_account`` to use the Compute Engine service account - by setting it to `"default"`. - - See, https://cloud.google.com/functions/docs/securing/function-identity. - .. note:: Please make sure following is setup before using this API: @@ -1842,19 +1393,10 @@ def remote_function( `$ gcloud projects add-iam-policy-binding PROJECT_ID --member="serviceAccount:CONNECTION_SERVICE_ACCOUNT_ID" --role="roles/run.invoker"`. Args: - input_types (type or sequence(type), Optional): - For scalar user defined function it should be the input type or - sequence of input types. The supported scalar input types are - `bool`, `bytes`, `float`, `int`, `str`. For row processing user - defined function (i.e. functions that receive a single input - representing a row in form of a Series), type `Series` should be - specified. - output_type (type, Optional): - Data type of the output in the user defined function. If the - user defined function returns an array, then `list[type]` should - be specified. The supported output types are `bool`, `bytes`, - `float`, `int`, `str`, `list[bool]`, `list[float]`, `list[int]` - and `list[str]`. + input_types (list(type)): + List of input data types in the user defined function. + output_type (type): + Data type of the output in the user defined function. dataset (str, Optional): Dataset in which to create a BigQuery remote function. It should be in `.` or `` format. If this @@ -1868,353 +1410,43 @@ def remote_function( reuse (bool, Optional): Reuse the remote function if already exists. `True` by default, which will result in reusing an existing remote - function and corresponding cloud function that was previously - created (if any) for the same udf. - Please note that for an unnamed (i.e. created without an explicit - `name` argument) remote function, the BigQuery DataFrames - session id is attached in the cloud artifacts names. So for the - effective reuse across the sessions it is recommended to create - the remote function with an explicit `name`. + function and corresponding cloud function (if any) that was + previously created for the same udf. Setting it to `False` would force creating a unique remote function. If the required remote function does not exist then it would be created irrespective of this param. name (str, Optional): - Explicit name of the persisted BigQuery remote function. Use it - with caution, because more than one users working in the same - project and dataset could overwrite each other's remote - functions if they use the same persistent name. When an explicit - name is provided, any session specific clean up ( - ``bigframes.session.Session.close``/ - ``bigframes.pandas.close_session``/ - ``bigframes.pandas.reset_session``/ - ``bigframes.pandas.clean_up_by_session_id``) does not clean up - the function, and leaves it for the user to manage the function - and the associated cloud function directly. + Explicit name of the persisted BigQuery remote function. Use it with + caution, because two users working in the same project and dataset + could overwrite each other's remote functions if they use the same + persistent name. packages (str[], Optional): Explicit name of the external package dependencies. Each dependency is added to the `requirements.txt` as is, and can be of the form supported in https://pip.pypa.io/en/stable/reference/requirements-file-format/. - cloud_function_service_account (str): - Service account to use for the cloud functions. If "default" provided - then the default service account would be used. See - https://cloud.google.com/functions/docs/securing/function-identity - for more details. Please make sure the service account has the - necessary IAM permissions configured as described in - https://cloud.google.com/functions/docs/reference/iam/roles#additional-configuration. - cloud_function_kms_key_name (str, Optional): - Customer managed encryption key to protect cloud functions and - related data at rest. This is of the format - projects/PROJECT_ID/locations/LOCATION/keyRings/KEYRING/cryptoKeys/KEY. - Read https://cloud.google.com/functions/docs/securing/cmek for - more details including granting necessary service accounts - access to the key. - cloud_function_docker_repository (str, Optional): - Docker repository created with the same encryption key as - `cloud_function_kms_key_name` to store encrypted artifacts - created to support the cloud function. This is of the format - projects/PROJECT_ID/locations/LOCATION/repositories/REPOSITORY_NAME. - For more details see - https://cloud.google.com/functions/docs/securing/cmek#before_you_begin. - max_batching_rows (int, Optional): - The maximum number of rows to be batched for processing in the - BQ remote function. Default value is 1000. A lower number can be - passed to avoid timeouts in case the user code is too complex to - process large number of rows fast enough. A higher number can be - used to increase throughput in case the user code is fast enough. - `None` can be passed to let BQ remote functions service apply - default batching. See for more details - https://cloud.google.com/bigquery/docs/remote-functions#limiting_number_of_rows_in_a_batch_request. - cloud_function_timeout (int, Optional): - The maximum amount of time (in seconds) BigQuery should wait for - the cloud function to return a response. See for more details - https://cloud.google.com/functions/docs/configuring/timeout. - Please note that even though the cloud function (2nd gen) itself - allows seeting up to 60 minutes of timeout, BigQuery remote - function can wait only up to 20 minutes, see for more details - https://cloud.google.com/bigquery/quotas#remote_function_limits. - By default BigQuery DataFrames uses a 10 minute timeout. `None` - can be passed to let the cloud functions default timeout take effect. - cloud_function_max_instances (int, Optional): - The maximumm instance count for the cloud function created. This - can be used to control how many cloud function instances can be - active at max at any given point of time. Lower setting can help - control the spike in the billing. Higher setting can help - support processing larger scale data. When not specified, cloud - function's default setting applies. For more details see - https://cloud.google.com/functions/docs/configuring/max-instances. - cloud_function_vpc_connector (str, Optional): - The VPC connector you would like to configure for your cloud - function. This is useful if your code needs access to data or - service(s) that are on a VPC network. See for more details - https://cloud.google.com/functions/docs/networking/connecting-vpc. - cloud_function_vpc_connector_egress_settings (str, Optional): - Egress settings for the VPC connector, controlling what outbound - traffic is routed through the VPC connector. - Options are: `all`, `private-ranges-only`, or `unspecified`. - If not specified, `private-ranges-only` is used by default. - See for more details - https://cloud.google.com/run/docs/configuring/vpc-connectors#egress-job. - cloud_function_memory_mib (int, Optional): - The amounts of memory (in mebibytes) to allocate for the cloud - function (2nd gen) created. This also dictates a corresponding - amount of allocated CPU for the function. By default a memory of - 1024 MiB is set for the cloud functions created to support - BigQuery DataFrames remote function. If you want to let the - default memory of cloud functions be allocated, pass `None`. See - for more details - https://cloud.google.com/functions/docs/configuring/memory. - cloud_function_cpus (float, Optional): - The number of cpus to allocate for the cloud - function (2nd gen) created. - https://docs.cloud.google.com/run/docs/configuring/services/cpu. - cloud_function_ingress_settings (str, Optional): - Ingress settings controls dictating what traffic can reach the - function. Options are: `all`, `internal-only`, or `internal-and-gclb`. - If no setting is provided, `internal-only` will be used by default. - See for more details - https://cloud.google.com/functions/docs/networking/network-settings#ingress_settings. - cloud_build_service_account (str, Optional): - Service account in the fully qualified format - `projects/PROJECT_ID/serviceAccounts/SERVICE_ACCOUNT_EMAIL`, or - just the SERVICE_ACCOUNT_EMAIL. The latter would be interpreted - as belonging to the BigQuery DataFrames session project. This is - to be used by Cloud Build to build the function source code into - a deployable artifact. If not provided, the default Cloud Build - service account is used. See - https://cloud.google.com/build/docs/cloud-build-service-account - for more details. Returns: - collections.abc.Callable: - A remote function object pointing to the cloud assets created - in the background to support the remote execution. The cloud assets can be - located through the following properties set in the object: + callable: A remote function object pointing to the cloud assets created + in the background to support the remote execution. The cloud assets can be + located through the following properties set in the object: - `bigframes_cloud_function` - The google cloud function deployed for the user defined code. + `bigframes_cloud_function` - The google cloud function deployed for the user defined code. - `bigframes_remote_function` - The bigquery remote function capable of calling into `bigframes_cloud_function`. + `bigframes_remote_function` - The bigquery remote function capable of calling into `bigframes_cloud_function`. """ - return self._function_session.remote_function( - # User-provided arguments. - input_types=input_types, - output_type=output_type, + return bigframes_rf( + input_types, + output_type, + session=self, dataset=dataset, bigquery_connection=bigquery_connection, reuse=reuse, name=name, packages=packages, - cloud_function_service_account=cloud_function_service_account, - cloud_function_kms_key_name=cloud_function_kms_key_name, - cloud_function_docker_repository=cloud_function_docker_repository, - max_batching_rows=max_batching_rows, - cloud_function_timeout=cloud_function_timeout, - cloud_function_max_instances=cloud_function_max_instances, - cloud_function_vpc_connector=cloud_function_vpc_connector, - cloud_function_vpc_connector_egress_settings=cloud_function_vpc_connector_egress_settings, - cloud_function_memory_mib=cloud_function_memory_mib, - cloud_function_cpus=cloud_function_cpus, - cloud_function_ingress_settings=cloud_function_ingress_settings, - cloud_build_service_account=cloud_build_service_account, - ) - - def deploy_udf( - self, - func, - **kwargs, - ): - """Orchestrates the creation of a BigQuery UDF that deploys immediately. - - This method ensures that the UDF is created and available for - use in BigQuery as soon as this call is made. - - Args: - func: - Function to deploy. - kwargs: - All arguments are passed directly to - :meth:`~bigframes.session.Session.udf`. Please see - its docstring for parameter details. - - Returns: - A wrapped Python user defined function, usable in - :meth:`~bigframes.series.Series.apply`. - """ - return self._function_session.deploy_udf( - func, - **kwargs, - ) - - def udf( - self, - *, - input_types: Union[None, type, Sequence[type]] = None, - output_type: Optional[type] = None, - dataset: Optional[str] = None, - bigquery_connection: Optional[str] = None, - name: Optional[str] = None, - packages: Optional[Sequence[str]] = None, - max_batching_rows: Optional[int] = None, - container_cpu: Optional[float] = None, - container_memory: Optional[str] = None, - ): - """Decorator to turn a Python user defined function (udf) into a - [BigQuery managed user-defined function](https://cloud.google.com/bigquery/docs/user-defined-functions-python). - - .. note:: - This feature is in preview. The code in the udf must be - (1) self-contained, i.e. it must not contain any - references to an import or variable defined outside the function - body, and - (2) Python 3.11 compatible, as that is the environment - in which the code is executed in the cloud. - - .. note:: - Please have BigQuery Data Editor (roles/bigquery.dataEditor) IAM - role enabled for you. - - **Examples:** - - >>> import datetime - - Turning an arbitrary python function into a BigQuery managed python udf: - - >>> bq_name = datetime.datetime.now().strftime("bigframes_%Y%m%d%H%M%S%f") - >>> @bpd.udf(dataset="bigfranes_testing", name=bq_name) # doctest: +SKIP - ... def minutes_to_hours(x: int) -> float: - ... return x/60 - - >>> minutes = bpd.Series([0, 30, 60, 90, 120]) - >>> minutes - 0 0 - 1 30 - 2 60 - 3 90 - 4 120 - dtype: Int64 - - >>> hours = minutes.apply(minutes_to_hours) # doctest: +SKIP - >>> hours # doctest: +SKIP - 0 0.0 - 1 0.5 - 2 1.0 - 3 1.5 - 4 2.0 - dtype: Float64 - - To turn a user defined function with external package dependencies into - a BigQuery managed python udf, you would provide the names of the - packages (optionally with the package version) via `packages` param. - - >>> bq_name = datetime.datetime.now().strftime("bigframes_%Y%m%d%H%M%S%f") - >>> @bpd.udf( # doctest: +SKIP - ... dataset="bigfranes_testing", - ... name=bq_name, - ... packages=["cryptography"] - ... ) - ... def get_hash(input: str) -> str: - ... from cryptography.fernet import Fernet - ... - ... # handle missing value - ... if input is None: - ... input = "" - ... - ... key = Fernet.generate_key() - ... f = Fernet(key) - ... return f.encrypt(input.encode()).decode() - - >>> names = bpd.Series(["Alice", "Bob"]) - >>> hashes = names.apply(get_hash) # doctest: +SKIP - - You can clean-up the BigQuery functions created above using the BigQuery - client from the BigQuery DataFrames session: - - >>> session = bpd.get_global_session() # doctest: +SKIP - >>> session.bqclient.delete_routine(minutes_to_hours.bigframes_bigquery_function) # doctest: +SKIP - >>> session.bqclient.delete_routine(get_hash.bigframes_bigquery_function) # doctest: +SKIP - - Args: - input_types (type or sequence(type), Optional): - For scalar user defined function it should be the input type or - sequence of input types. The supported scalar input types are - `bool`, `bytes`, `float`, `int`, `str`. - output_type (type, Optional): - Data type of the output in the user defined function. If the - user defined function returns an array, then `list[type]` should - be specified. The supported output types are `bool`, `bytes`, - `float`, `int`, `str`, `list[bool]`, `list[float]`, `list[int]` - and `list[str]`. - dataset (str, Optional): - Dataset in which to create a BigQuery managed function. It - should be in `.` or `` - format. - bigquery_connection (str, Optional): - Name of the BigQuery connection. It is used to provide an - identity to the serverless instances running the user code. It - helps BigQuery manage and track the resources used by the udf. - This connection is required for internet access and for - interacting with other GCP services. To access GCP services, the - appropriate IAM permissions must also be granted to the - connection's Service Account. When it defaults to None, the udf - will be created without any connection. A udf without a - connection has no internet access and no access to other GCP - services. - name (str): - Explicit name of the persisted BigQuery managed function. Use it - with caution, because more than one users working in the same - project and dataset could overwrite each other's managed - functions if they use the same persistent name. Please note that - any session specific clean up ( - ``bigframes.session.Session.close``/ - ``bigframes.pandas.close_session``/ - ``bigframes.pandas.reset_session``/ - ``bigframes.pandas.clean_up_by_session_id``) does not clean up - this function, and leaves it for the user to manage the function - directly. - packages (str[], Optional): - Explicit name of the external package dependencies. Each - dependency is added to the `requirements.txt` as is, and can be - of the form supported in - https://pip.pypa.io/en/stable/reference/requirements-file-format/. - max_batching_rows (int, Optional): - The maximum number of rows in each batch. If you specify - max_batching_rows, BigQuery determines the number of rows in a - batch, up to the max_batching_rows limit. If max_batching_rows - is not specified, the number of rows to batch is determined - automatically. - container_cpu (float, Optional): - The CPU limits for containers that run Python UDFs. By default, - the CPU allocated is 0.33 vCPU. See details at - https://cloud.google.com/bigquery/docs/user-defined-functions-python#configure-container-limits. - container_memory (str, Optional): - The memory limits for containers that run Python UDFs. By - default, the memory allocated to each container instance is - 512 MiB. See details at - https://cloud.google.com/bigquery/docs/user-defined-functions-python#configure-container-limits. - Returns: - collections.abc.Callable: - A managed function object pointing to the cloud assets created - in the background to support the remote execution. The cloud - ssets can be located through the following properties set in the - object: - - `bigframes_bigquery_function` - The bigquery managed function - deployed for the user defined code. - """ - return self._function_session.udf( - input_types=input_types, - output_type=output_type, - dataset=dataset, - bigquery_connection=bigquery_connection, - name=name, - packages=packages, - max_batching_rows=max_batching_rows, - container_cpu=container_cpu, - container_memory=container_memory, ) def read_gbq_function( self, function_name: str, - is_row_processor: bool = False, ): """Loads a BigQuery function from BigQuery. @@ -2224,398 +1456,108 @@ def read_gbq_function( The return type of the function must be explicitly specified in the function's original definition even if not otherwise required. - BigQuery Utils provides many public functions under the ``bqutil`` project on Google Cloud Platform project - (See: https://github.com/GoogleCloudPlatform/bigquery-utils/tree/master/udfs#using-the-udfs). - You can checkout Community UDFs to use community-contributed functions. - (See: https://github.com/GoogleCloudPlatform/bigquery-utils/tree/master/udfs/community#community-udfs). - **Examples:** - Use the [cw_lower_case_ascii_only](https://github.com/GoogleCloudPlatform/bigquery-utils/blob/master/udfs/community/README.md#cw_lower_case_ascii_onlystr-string) - function from Community UDFs. - >>> import bigframes.pandas as bpd - >>> func = bpd.read_gbq_function("bqutil.fn.cw_lower_case_ascii_only") - - You can run it on scalar input. Usually you would do so to verify that - it works as expected before applying to all values in a Series. - - >>> func('AURÉLIE') - 'aurÉlie' - - You can apply it to a BigQuery DataFrames Series. - - >>> df = bpd.DataFrame({'id': [1, 2, 3], 'name': ['AURÉLIE', 'CÉLESTINE', 'DAPHNÉ']}) - >>> df - id name - 0 1 AURÉLIE - 1 2 CÉLESTINE - 2 3 DAPHNÉ - - [3 rows x 2 columns] - - >>> df1 = df.assign(new_name=df['name'].apply(func)) - >>> df1 - id name new_name - 0 1 AURÉLIE aurÉlie - 1 2 CÉLESTINE cÉlestine - 2 3 DAPHNÉ daphnÉ - - [3 rows x 3 columns] - - You can even use a function with multiple inputs. For example, - [cw_regexp_replace_5](https://github.com/GoogleCloudPlatform/bigquery-utils/blob/master/udfs/community/README.md#cw_regexp_replace_5haystack-string-regexp-string-replacement-string-offset-int64-occurrence-int64) - from Community UDFs. - - >>> func = bpd.read_gbq_function("bqutil.fn.cw_regexp_replace_5") - >>> func('TestStr123456', 'Str', 'Cad$', 1, 1) - 'TestCad$123456' - - >>> df = bpd.DataFrame({ - ... "haystack" : ["TestStr123456", "TestStr123456Str", "TestStr123456Str"], - ... "regexp" : ["Str", "Str", "Str"], - ... "replacement" : ["Cad$", "Cad$", "Cad$"], - ... "offset" : [1, 1, 1], - ... "occurrence" : [1, 2, 1] - ... }) - >>> df - haystack regexp replacement offset occurrence - 0 TestStr123456 Str Cad$ 1 1 - 1 TestStr123456Str Str Cad$ 1 2 - 2 TestStr123456Str Str Cad$ 1 1 - - [3 rows x 5 columns] - >>> df.apply(func, axis=1) - 0 TestCad$123456 - 1 TestStr123456Cad$ - 2 TestCad$123456Str - dtype: string - - Another use case is to define your own remote function and use it later. - For example, define the remote function: - - >>> @bpd.remote_function(cloud_function_service_account="default") # doctest: +SKIP - ... def tenfold(num: int) -> float: - ... return num * 10 - - Then, read back the deployed BQ remote function: - - >>> tenfold_ref = bpd.read_gbq_function( # doctest: +SKIP - ... tenfold.bigframes_remote_function, - ... ) - - >>> df = bpd.DataFrame({'a': [1, 2], 'b': [3, 4], 'c': [5, 6]}) - >>> df - a b c - 0 1 3 5 - 1 2 4 6 - - [2 rows x 3 columns] - - >>> df['a'].apply(tenfold_ref) # doctest: +SKIP - 0 10.0 - 1 20.0 - Name: a, dtype: Float64 - - It also supports row processing by using `is_row_processor=True`. Please - note, row processor implies that the function has only one input - parameter. + >>> bpd.options.display.progress_bar = None - >>> @bpd.remote_function(cloud_function_service_account="default") # doctest: +SKIP - ... def row_sum(s: pd.Series) -> float: - ... return s['a'] + s['b'] + s['c'] - - >>> row_sum_ref = bpd.read_gbq_function( # doctest: +SKIP - ... row_sum.bigframes_remote_function, - ... is_row_processor=True, - ... ) - - >>> df = bpd.DataFrame({'a': [1, 2], 'b': [3, 4], 'c': [5, 6]}) - >>> df - a b c - 0 1 3 5 - 1 2 4 6 - - [2 rows x 3 columns] - - >>> df.apply(row_sum_ref, axis=1) # doctest: +SKIP - 0 9.0 - 1 12.0 - dtype: Float64 + >>> function_name = "bqutil.fn.cw_lower_case_ascii_only" + >>> func = bpd.read_gbq_function(function_name=function_name) + >>> func.bigframes_remote_function + 'bqutil.fn.cw_lower_case_ascii_only' Args: function_name (str): - The function's name in BigQuery in the format + the function's name in BigQuery in the format `project_id.dataset_id.function_name`, or `dataset_id.function_name` to load from the default project, or `function_name` to load from the default project and the dataset associated with the current session. - is_row_processor (bool, default False): - Whether the function is a row processor. This is set to True - for a function which receives an entire row of a DataFrame as - a pandas Series. Returns: - collections.abc.Callable: - A function object pointing to the BigQuery function read - from BigQuery. + callable: A function object pointing to the BigQuery function read + from BigQuery. - The object is similar to the one created by the `remote_function` - decorator, including the `bigframes_remote_function` property, but - not including the `bigframes_cloud_function` property. + The object is similar to the one created by the `remote_function` + decorator, including the `bigframes_remote_function` property, but + not including the `bigframes_cloud_function` property. """ - return bff.read_gbq_function( + return bigframes_rgf( function_name=function_name, session=self, - is_row_processor=is_row_processor, ) - def _prepare_copy_job_config(self) -> bigquery.CopyJobConfig: - # Create a copy so that we don't mutate the original config passed - job_config = bigquery.CopyJobConfig() - - if self._bq_kms_key_name: - job_config.destination_encryption_configuration = ( - bigquery.EncryptionConfiguration(kms_key_name=self._bq_kms_key_name) - ) - - return job_config - - def _start_query_ml_ddl( + def _start_query( self, sql: str, + job_config: Optional[bigquery.job.QueryJobConfig] = None, + max_results: Optional[int] = None, ) -> Tuple[bigquery.table.RowIterator, bigquery.QueryJob]: """ - Starts BigQuery ML DDL query job (CREATE MODEL/ALTER MODEL/...) and - waits for results. + Starts query job and waits for results. """ - job_config = typing.cast(bigquery.QueryJobConfig, bigquery.QueryJobConfig()) + job_config = self._prepare_job_config(job_config) + query_job = self.bqclient.query(sql, job_config=job_config) + + opts = bigframes.options.display + if opts.progress_bar is not None and not query_job.configuration.dry_run: + results_iterator = formatting_helpers.wait_for_query_job( + query_job, max_results, opts.progress_bar + ) + else: + results_iterator = query_job.result(max_results=max_results) + return results_iterator, query_job + + def _get_table_size(self, destination_table): + table = self.bqclient.get_table(destination_table) + return table.num_bytes + + def _rows_to_dataframe( + self, row_iterator: bigquery.table.RowIterator, dtypes: Dict + ) -> pandas.DataFrame: + arrow_table = row_iterator.to_arrow() + return bigframes.session._io.pandas.arrow_to_pandas(arrow_table, dtypes) + + def _start_generic_job(self, job: formatting_helpers.GenericJob): + if bigframes.options.display.progress_bar is not None: + formatting_helpers.wait_for_job( + job, bigframes.options.display.progress_bar + ) # Wait for the job to complete + else: + job.result() + + def _prepare_job_config( + self, job_config: Optional[bigquery.QueryJobConfig] = None + ) -> bigquery.QueryJobConfig: + if job_config is None: + job_config = self.bqclient.default_query_job_config if bigframes.options.compute.maximum_bytes_billed is not None: job_config.maximum_bytes_billed = ( bigframes.options.compute.maximum_bytes_billed ) - - # BQML expects kms_key_name through OPTIONS and not through job config, - # so we must reset any encryption set in the job config - # https://cloud.google.com/bigquery/docs/customer-managed-encryption#encrypt-model - job_config.destination_encryption_configuration = None - iterator, query_job = bf_io_bigquery.start_query_with_job( - self.bqclient, - sql, - job_config=job_config, - metrics=self._metrics, - location=None, - project=None, - timeout=None, - job_retry=third_party_gcb_retry.DEFAULT_ML_JOB_RETRY, - publisher=self._publisher, - session=self, - ) - return iterator, query_job - - def _from_glob_path( - self, path: str, *, connection: Optional[str] = None, name: Optional[str] = None - ) -> dataframe.DataFrame: - """Create a BigFrames DataFrame that contains a BigFrames ObjectRef column from a global wildcard path.""" - import bigframes.bigquery as bq - - connection = self._create_bq_connection(connection=connection) - table = self._create_object_table(path, connection) - s = bq.obj.make_ref( - self._loader.read_gbq_table(table)["uri"], authorizer=connection - ) - return s.rename(name).to_frame() - - def _create_object_table(self, path: str, connection: str) -> str: - """Create a random id Object Table from the input path and connection.""" - table = str(self._anon_dataset_manager.generate_unique_resource_id()) - - import textwrap - - sql = textwrap.dedent( - f""" - CREATE EXTERNAL TABLE `{table}` - WITH CONNECTION `{connection}` - OPTIONS( - object_metadata = 'SIMPLE', - uris = ['{path}']); - """ - ) - bf_io_bigquery.start_query_with_job( - self.bqclient, - sql, - job_config=bigquery.QueryJobConfig(), - metrics=self._metrics, - location=None, - project=None, - timeout=None, - publisher=self._publisher, - session=self, - ) - - return table - - def _create_temp_view(self, sql: str) -> bigquery.TableReference: - """Create a random id view from the sql string.""" - return self._anon_dataset_manager.create_temp_view(sql) - - def _create_temp_table( - self, schema: Sequence[bigquery.SchemaField], cluster_cols: Sequence[str] = [] - ) -> bigquery.TableReference: - """Allocate a random temporary table with the desired schema.""" - return self._temp_storage_manager.create_temp_table( - schema=schema, cluster_cols=cluster_cols - ) - - def _create_bq_connection( - self, - *, - connection: Optional[str] = None, - iam_role: Optional[str] = None, - ) -> str: - """Create the connection with the session settings and try to attach iam role to the connection SA. - If any of project, location or connection isn't specified, use the session defaults. Returns fully-qualified connection name. - """ - connection = self.bq_connection if not connection else connection - connection = bigframes.clients.get_canonical_bq_connection_id( - connection_id=connection, - default_project=self._project, - default_location=self._location, - ) - connection_parts = connection.split(".") - assert len(connection_parts) == 3 - - self.bqconnectionmanager.create_bq_connection( - project_id=connection_parts[0], - location=connection_parts[1], - connection_id=connection_parts[2], - iam_role=iam_role, - ) - - return connection - - # ========================================================================= - # bigframes.pandas attributes - # - # These are included so that Session and bigframes.pandas can be used - # interchangeably. - # ========================================================================= - def cut(self, *args, **kwargs) -> bigframes.series.Series: - """Cuts a BigQuery DataFrames object. - - Included for compatibility between bpd and Session. - - See :func:`bigframes.pandas.cut` for full documentation. - """ - import bigframes.core.reshape.tile - - return bigframes.core.reshape.tile.cut( - *args, - session=self, - **kwargs, - ) - - def crosstab(self, *args, **kwargs) -> dataframe.DataFrame: - """Compute a simple cross tabulation of two (or more) factors. - - Included for compatibility between bpd and Session. - - See :func:`bigframes.pandas.crosstab` for full documentation. - """ - import bigframes.core.reshape.pivot - - return bigframes.core.reshape.pivot.crosstab( - *args, - session=self, - **kwargs, - ) - - def DataFrame(self, *args, **kwargs): - """Constructs a DataFrame. - - Included for compatibility between bpd and Session. - - See :class:`bigframes.pandas.DataFrame` for full documentation. - """ - import bigframes.dataframe - - return bigframes.dataframe.DataFrame(*args, session=self, **kwargs) - - @property - def MultiIndex(self) -> bigframes.core.indexes.multi.MultiIndexAccessor: - """Constructs a MultiIndex. - - Included for compatibility between bpd and Session. - - See :class:`bigframes.pandas.MulitIndex` for full documentation. - """ - import bigframes.core.indexes.multi - - return bigframes.core.indexes.multi.MultiIndexAccessor(self) - - def Index(self, *args, **kwargs): - """Constructs a Index. - - Included for compatibility between bpd and Session. - - See :class:`bigframes.pandas.Index` for full documentation. - """ - import bigframes.core.indexes - - return bigframes.core.indexes.Index(*args, session=self, **kwargs) - - def Series(self, *args, **kwargs): - """Constructs a Series. - - Included for compatibility between bpd and Session. - - See :class:`bigframes.pandas.Series` for full documentation. - """ - import bigframes.series - - return bigframes.series.Series(*args, session=self, **kwargs) - - def to_datetime( - self, *args, **kwargs - ) -> Union[pandas.Timestamp, datetime.datetime, bigframes.series.Series]: - """Converts a BigQuery DataFrames object to datetime dtype. - - Included for compatibility between bpd and Session. - - See :func:`bigframes.pandas.to_datetime` for full documentation. - """ - import bigframes.core.tools - - return bigframes.core.tools.to_datetime( - *args, - session=self, - **kwargs, - ) - - def to_timedelta(self, *args, **kwargs): - """Converts a BigQuery DataFrames object to timedelta/duration dtype. - - Included for compatibility between bpd and Session. - - See :func:`bigframes.pandas.to_timedelta` for full documentation. - """ - import bigframes.pandas.core.tools.timedeltas - - return bigframes.pandas.core.tools.timedeltas.to_timedelta( - *args, - session=self, - **kwargs, - ) + return job_config def connect(context: Optional[bigquery_options.BigQueryOptions] = None) -> Session: return Session(context) -def _warn_if_bf_version_is_obsolete(): - today = datetime.datetime.today() - release_date = datetime.datetime.strptime(version.__release_date__, "%Y-%m-%d") - if today - release_date > datetime.timedelta(days=365): - msg = f"Your BigFrames version {version.__version__} is more than 1 year old. Please update to the lastest version." - warnings.warn(msg, bfe.ObsoleteVersionWarning) +def _can_cluster_bq(field: bigquery.SchemaField): + # https://cloud.google.com/bigquery/docs/clustered-tables + # Notably, float is excluded + type_ = field.field_type + return type_ in ( + "INTEGER", + "INT64", + "STRING", + "NUMERIC", + "DECIMAL", + "BIGNUMERIC", + "BIGDECIMAL", + "DATE", + "DATETIME", + "TIMESTAMP", + "BOOL", + "BOOLEAN", + ) diff --git a/bigframes/session/_io/bigquery.py b/bigframes/session/_io/bigquery.py new file mode 100644 index 00000000000..badc91e3565 --- /dev/null +++ b/bigframes/session/_io/bigquery.py @@ -0,0 +1,184 @@ +# Copyright 2023 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Private module: Helpers for I/O operations.""" + +from __future__ import annotations + +import datetime +import textwrap +import types +from typing import Dict, Iterable, Optional, Union +import uuid + +import google.cloud.bigquery as bigquery + +IO_ORDERING_ID = "bqdf_row_nums" +TEMP_TABLE_PREFIX = "bqdf{date}_{random_id}" + + +def create_export_csv_statement( + table_id: str, uri: str, field_delimiter: str, header: bool +) -> str: + return create_export_data_statement( + table_id, + uri, + "CSV", + { + "field_delimiter": field_delimiter, + "header": header, + }, + ) + + +def create_export_data_statement( + table_id: str, uri: str, format: str, export_options: Dict[str, Union[bool, str]] +) -> str: + all_options: Dict[str, Union[bool, str]] = { + "uri": uri, + "format": format, + # TODO(swast): Does pandas have an option not to overwrite files? + "overwrite": True, + } + all_options.update(export_options) + export_options_str = ", ".join( + format_option(key, value) for key, value in all_options.items() + ) + # Manually generate ORDER BY statement since ibis will not always generate + # it in the top level statement. This causes BigQuery to then run + # non-distributed sort and run out of memory. + return textwrap.dedent( + f""" + EXPORT DATA + OPTIONS ( + {export_options_str} + ) AS + SELECT * EXCEPT ({IO_ORDERING_ID}) + FROM `{table_id}` + ORDER BY {IO_ORDERING_ID} + """ + ) + + +def random_table(dataset: bigquery.DatasetReference) -> bigquery.TableReference: + """Generate a random table ID with BigQuery DataFrames prefix. + Args: + dataset (google.cloud.bigquery.DatasetReference): + The dataset to make the table reference in. Usually the anonymous + dataset for the session. + Returns: + google.cloud.bigquery.TableReference: + Fully qualified table ID of a table that doesn't exist. + """ + now = datetime.datetime.now(datetime.timezone.utc) + random_id = uuid.uuid4().hex + table_id = TEMP_TABLE_PREFIX.format( + date=now.strftime("%Y%m%d"), random_id=random_id + ) + return dataset.table(table_id) + + +def table_ref_to_sql(table: bigquery.TableReference) -> str: + """Format a table reference as escaped SQL.""" + return f"`{table.project}`.`{table.dataset_id}`.`{table.table_id}`" + + +def create_snapshot_sql( + table_ref: bigquery.TableReference, current_timestamp: datetime.datetime +) -> str: + """Query a table via 'time travel' for consistent reads.""" + + # If we have a _SESSION table, assume that it's already a copy. Nothing to do here. + if table_ref.dataset_id.upper() == "_SESSION": + return f"SELECT * FROM `_SESSION`.`{table_ref.table_id}`" + + # If we have an anonymous query results table, it can't be modified and + # there isn't any BigQuery time travel. + if table_ref.dataset_id.startswith("_"): + return f"SELECT * FROM `{table_ref.project}`.`{table_ref.dataset_id}`.`{table_ref.table_id}`" + + return textwrap.dedent( + f""" + SELECT * + FROM `{table_ref.project}`.`{table_ref.dataset_id}`.`{table_ref.table_id}` + FOR SYSTEM_TIME AS OF TIMESTAMP({repr(current_timestamp.isoformat())}) + """ + ) + + +def create_temp_table( + bqclient: bigquery.Client, + dataset: bigquery.DatasetReference, + expiration: datetime.datetime, + *, + schema: Optional[Iterable[bigquery.SchemaField]] = None, + cluster_columns: Optional[list[str]] = None, +) -> str: + """Create an empty table with an expiration in the desired dataset.""" + table_ref = random_table(dataset) + destination = bigquery.Table(table_ref) + destination.expires = expiration + destination.schema = schema + if cluster_columns: + destination.clustering_fields = cluster_columns + bqclient.create_table(destination) + return f"{table_ref.project}.{table_ref.dataset_id}.{table_ref.table_id}" + + +# BigQuery REST API returns types in Legacy SQL format +# https://cloud.google.com/bigquery/docs/data-types but we use Standard SQL +# names +# https://cloud.google.com/bigquery/docs/reference/standard-sql/data-types +BQ_STANDARD_TYPES = types.MappingProxyType( + { + "BOOLEAN": "BOOL", + "INTEGER": "INT64", + "FLOAT": "FLOAT64", + } +) + + +def bq_field_to_type_sql(field: bigquery.SchemaField): + if field.mode == "REPEATED": + nested_type = bq_field_to_type_sql( + bigquery.SchemaField( + field.name, field.field_type, mode="NULLABLE", fields=field.fields + ) + ) + return f"ARRAY<{nested_type}>" + + if field.field_type == "RECORD": + nested_fields_sql = ", ".join( + bq_field_to_sql(child_field) for child_field in field.fields + ) + return f"STRUCT<{nested_fields_sql}>" + + type_ = field.field_type + return BQ_STANDARD_TYPES.get(type_, type_) + + +def bq_field_to_sql(field: bigquery.SchemaField): + name = field.name + type_ = bq_field_to_type_sql(field) + return f"`{name}` {type_}" + + +def bq_schema_to_sql(schema: Iterable[bigquery.SchemaField]): + return ", ".join(bq_field_to_sql(field) for field in schema) + + +def format_option(key: str, value: Union[bool, str]) -> str: + if isinstance(value, bool): + return f"{key}=true" if value else f"{key}=false" + return f"{key}={repr(value)}" diff --git a/bigframes/session/_io/bigquery/__init__.py b/bigframes/session/_io/bigquery/__init__.py deleted file mode 100644 index 3d60bcc8074..00000000000 --- a/bigframes/session/_io/bigquery/__init__.py +++ /dev/null @@ -1,705 +0,0 @@ -# Copyright 2023 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -"""Private module: Helpers for BigQuery I/O operations.""" - -from __future__ import annotations - -import datetime -import itertools -import re -import textwrap -import types -import typing -from typing import ( - Dict, - Iterable, - Mapping, - Optional, - Tuple, - Union, -) - -import bigframes_vendored.google_cloud_bigquery.retry as third_party_gcb_retry -import bigframes_vendored.pandas.io.gbq as third_party_pandas_gbq -import google.api_core.exceptions -import google.api_core.retry -import google.cloud.bigquery as bigquery -import google.cloud.bigquery._job_helpers -import google.cloud.bigquery.table - -import bigframes.core.events -import bigframes.core.sql -import bigframes.session.metrics -from bigframes.core.compile.sqlglot import sql as sg_sql -from bigframes.core.logging import log_adapter - -CHECK_DRIVE_PERMISSIONS = ( - "\nCheck https://cloud.google.com/bigquery/docs/" - "query-drive-data#Google_Drive_permissions." -) - - -IO_ORDERING_ID = "bqdf_row_nums" -_LIST_TABLES_LIMIT = 10000 # calls to bqclient.list_tables -# will be limited to this many tables - -_MAX_CLUSTER_COLUMNS = 4 - - -def create_job_configs_labels( - job_configs_labels: Optional[Dict[str, str]], - api_methods: typing.List[str], -) -> Dict[str, str]: - if job_configs_labels is None: - job_configs_labels = {} - else: - job_configs_labels = dict(job_configs_labels) - - if api_methods and "bigframes-api" not in job_configs_labels: - api_methods = list(api_methods) - job_configs_labels["bigframes-api"] = api_methods[0] - del api_methods[0] - - # Make sure we always populate bigframes-api with _something_, even if we - # have a code path which doesn't populate the list of api_methods. See - # internal issue 336521938. - job_configs_labels.setdefault("bigframes-api", "unknown") - - labels = list( - itertools.chain( - job_configs_labels.keys(), - (f"recent-bigframes-api-{i}" for i in range(len(api_methods))), - ) - ) - values = list(itertools.chain(job_configs_labels.values(), api_methods)) - return dict( - zip( - labels[: log_adapter.MAX_LABELS_COUNT], - values[: log_adapter.MAX_LABELS_COUNT], - ) - ) - - -def create_export_data_statement( - table_id: str, - uri: str, - format: str, - export_options: Dict[str, Union[bool, str]], -) -> str: - all_options: Dict[str, Union[bool, str]] = { - "uri": uri, - "format": format.upper(), - # TODO(swast): Does pandas have an option not to overwrite files? - "overwrite": True, - } - all_options.update(export_options) - export_options_str = ", ".join( - format_option(key, value) for key, value in all_options.items() - ) - # Manually generate ORDER BY statement since ibis will not always generate - # it in the top level statement. This causes BigQuery to then run - # non-distributed sort and run out of memory. - return textwrap.dedent( - f""" - EXPORT DATA - OPTIONS ( - {export_options_str} - ) AS - SELECT * EXCEPT ({IO_ORDERING_ID}) - FROM `{table_id}` - ORDER BY {IO_ORDERING_ID} - """ - ) - - -def table_ref_to_sql(table: bigquery.TableReference) -> str: - """Format a table reference as escaped SQL.""" - return f"`{table.project}`.`{table.dataset_id}`.`{table.table_id}`" - - -def create_temp_table( - bqclient: bigquery.Client, - table_ref: bigquery.TableReference, - expiration: datetime.datetime, - *, - schema: Optional[Iterable[bigquery.SchemaField]] = None, - cluster_columns: Optional[list[str]] = None, - kms_key: Optional[str] = None, - session=None, -) -> str: - """Create an empty table with an expiration in the desired session. - - The table will be deleted when the session is closed or the expiration - is reached. - """ - destination = bigquery.Table(table_ref) - destination.expires = expiration - destination.schema = schema - if cluster_columns: - destination.clustering_fields = cluster_columns - if kms_key: - enc_config = bigquery.EncryptionConfiguration(kms_key_name=kms_key) - destination.encryption_configuration = enc_config - # Ok if already exists, since this will only happen from retries - # internal to this method - # as the requested table id has a random UUID4 component. - bqclient.create_table(destination, exists_ok=True) - return f"{table_ref.project}.{table_ref.dataset_id}.{table_ref.table_id}" - - -def create_temp_view( - bqclient: bigquery.Client, - table_ref: bigquery.TableReference, - *, - expiration: datetime.datetime, - sql: str, - session=None, -) -> str: - """Create an empty table with an expiration in the desired session. - - The table will be deleted when the session is closed or the expiration - is reached. - """ - destination = bigquery.Table(table_ref) - destination.expires = expiration - destination.view_query = sql - - # Ok if already exists, since this will only happen from retries - # internal to this method - # as the requested table id has a random UUID4 component. - bqclient.create_table(destination, exists_ok=True) - return f"{table_ref.project}.{table_ref.dataset_id}.{table_ref.table_id}" - - -def set_table_expiration( - bqclient: bigquery.Client, - table_ref: bigquery.TableReference, - expiration: datetime.datetime, -) -> None: - """Set an expiration time for an existing BigQuery table.""" - table = bqclient.get_table(table_ref) - table.expires = expiration - bqclient.update_table(table, ["expires"]) - - -# BigQuery REST API returns types in Legacy SQL format -# https://cloud.google.com/bigquery/docs/data-types but we use Standard SQL -# names -# https://cloud.google.com/bigquery/docs/reference/standard-sql/data-types -BQ_STANDARD_TYPES = types.MappingProxyType( - { - "BOOLEAN": "BOOL", - "INTEGER": "INT64", - "FLOAT": "FLOAT64", - } -) - - -def bq_field_to_type_sql(field: bigquery.SchemaField): - if field.mode == "REPEATED": - nested_type = bq_field_to_type_sql( - bigquery.SchemaField( - field.name, - field.field_type, - mode="NULLABLE", - fields=field.fields, - ) - ) - return f"ARRAY<{nested_type}>" - - if field.field_type == "RECORD": - nested_fields_sql = ", ".join( - bq_field_to_sql(child_field) for child_field in field.fields - ) - return f"STRUCT<{nested_fields_sql}>" - - type_ = field.field_type - return BQ_STANDARD_TYPES.get(type_, type_) - - -def bq_field_to_sql(field: bigquery.SchemaField): - name = field.name - type_ = bq_field_to_type_sql(field) - return f"`{name}` {type_}" - - -def bq_schema_to_sql(schema: Iterable[bigquery.SchemaField]): - return ", ".join(bq_field_to_sql(field) for field in schema) - - -def format_option(key: str, value: Union[bool, str]) -> str: - if isinstance(value, bool): - return f"{key}=true" if value else f"{key}=false" - return f"{key}={repr(value)}" - - -def add_and_trim_labels( - job_config, - session=None, - extra_query_labels: Optional[Mapping[str, str]] = None, -): - """ - Add additional labels to the job configuration and trim the total - number of labels to ensure they do not exceed MAX_LABELS_COUNT labels - per job. - """ - api_methods = log_adapter.get_and_reset_api_methods( - dry_run=job_config.dry_run, session=session - ) - job_config.labels = create_job_configs_labels( - job_configs_labels=job_config.labels, - api_methods=api_methods, - ) - - -def create_bq_event_callback(publisher, cell_execution_count=None): - event_map = { - google.cloud.bigquery._job_helpers.QueryFinishedEvent: ( - bigframes.core.events.BigQueryFinishedEvent - ), - google.cloud.bigquery._job_helpers.QueryReceivedEvent: ( - bigframes.core.events.BigQueryReceivedEvent - ), - google.cloud.bigquery._job_helpers.QueryRetryEvent: ( - bigframes.core.events.BigQueryRetryEvent - ), - google.cloud.bigquery._job_helpers.QuerySentEvent: ( - bigframes.core.events.BigQuerySentEvent - ), - } - - def publish_bq_event(event): - bf_event = bigframes.core.events.BigQueryUnknownEvent(event) - for bq_type, bf_type in event_map.items(): - if isinstance(event, bq_type): - bf_event = bf_type.from_bqclient(event) # type: ignore - break - envelope = bigframes.core.events.EventEnvelope( - event=bf_event, - progress_bar=bigframes.core.events._DEFAULT, - cell_execution_count=cell_execution_count, - ) - publisher.publish(envelope) - - return publish_bq_event - - -def start_query_with_job( - bq_client: bigquery.Client, - sql: str, - *, - job_config: bigquery.QueryJobConfig, - location: Optional[str] = None, - project: Optional[str] = None, - timeout: Optional[float] = None, - metrics: Optional[bigframes.session.metrics.ExecutionMetrics] = None, - # TODO(tswast): We can stop providing our own default once we use a - # google-cloud-bigquery version with - # https://github.com/googleapis/python-bigquery/pull/2256 merged, likely - # version 3.36.0 or later. - job_retry: google.api_core.retry.Retry = (third_party_gcb_retry.DEFAULT_JOB_RETRY), # noqa: E501 - publisher: bigframes.core.events.Publisher, - session=None, - cell_execution_count: Optional[int] = None, -) -> Tuple[google.cloud.bigquery.table.RowIterator, bigquery.QueryJob]: - """ - Starts query job and waits for results. - """ - if cell_execution_count is None: - from bigframes.core.utils import get_ipython_execution_count - - cell_execution_count = get_ipython_execution_count() - - # Note: Ensure no additional labels are added to job_config after this - # point, as `add_and_trim_labels` ensures the label count does not - # exceed MAX_LABELS_COUNT. - add_and_trim_labels(job_config, session=session) - - try: - query_job = bq_client.query( - sql, - job_config=job_config, - location=location, - project=project, - timeout=timeout, - job_retry=job_retry, - ) - except google.api_core.exceptions.Forbidden as ex: - if "Drive credentials" in ex.message: - ex.message += CHECK_DRIVE_PERMISSIONS - raise - - results_iterator = query_job.result() - _publish_events( - query_job=query_job, - total_rows=results_iterator.total_rows, - sql=sql, - publisher=publisher, - metrics=metrics, - cell_execution_count=cell_execution_count, - ) - return results_iterator, query_job - - -def start_query_job_optional( - bq_client: bigquery.Client, - sql: str, - *, - job_config: bigquery.QueryJobConfig, - location: Optional[str] = None, - project: Optional[str] = None, - timeout: Optional[float] = None, - metrics: Optional[bigframes.session.metrics.ExecutionMetrics] = None, - # TODO(tswast): We can stop providing our own default once we use a - # google-cloud-bigquery version with - # https://github.com/googleapis/python-bigquery/pull/2256 merged, likely - # version 3.36.0 or later. - job_retry: google.api_core.retry.Retry = (third_party_gcb_retry.DEFAULT_JOB_RETRY), # noqa: E501 - publisher: Optional[bigframes.core.events.Publisher] = None, - session=None, - cell_execution_count: Optional[int] = None, -) -> google.cloud.bigquery.table.RowIterator: - """ - Run a bigquery query, with job optional. - - See: - https://docs.cloud.google.com/bigquery/docs/running-queries#optional-job-creation - """ - if cell_execution_count is None: - from bigframes.core.utils import get_ipython_execution_count - - cell_execution_count = get_ipython_execution_count() - - add_and_trim_labels(job_config, session=session) - try: - results_iterator = bq_client._query_and_wait_bigframes( - sql, - job_config=job_config, - location=location, - project=project, - api_timeout=timeout, - job_retry=job_retry, - callback=create_bq_event_callback( - publisher, cell_execution_count=cell_execution_count - ) - if publisher - else lambda _: None, - ) - if metrics is not None: - metrics.count_job_stats( - row_iterator=results_iterator, cell_execution_count=cell_execution_count - ) - return results_iterator - except google.api_core.exceptions.Forbidden as ex: - if "Drive credentials" in ex.message: - ex.message += CHECK_DRIVE_PERMISSIONS - raise - - -def _publish_events( - query_job: bigquery.QueryJob, - sql: str, - total_rows: Optional[int], - publisher: bigframes.core.events.Publisher, - metrics: Optional[bigframes.session.metrics.ExecutionMetrics] = None, - cell_execution_count: Optional[int] = None, -): - if not query_job.configuration.dry_run: - publisher.publish( - bigframes.core.events.EventEnvelope( - event=bigframes.core.events.BigQuerySentEvent( - sql, - billing_project=query_job.project, - location=query_job.location, - job_id=query_job.job_id, - request_id=None, - ), - cell_execution_count=cell_execution_count, - ) - ) - if not query_job.configuration.dry_run: - publisher.publish( - bigframes.core.events.EventEnvelope( - event=bigframes.core.events.BigQueryFinishedEvent( - billing_project=query_job.project, - location=query_job.location, - query_id=query_job.query_id, - job_id=query_job.job_id, - destination=query_job.destination, - total_rows=total_rows, - total_bytes_processed=query_job.total_bytes_processed, - slot_millis=query_job.slot_millis, - created=query_job.created, - started=query_job.started, - ended=query_job.ended, - ), - cell_execution_count=cell_execution_count, - ) - ) - - if metrics is not None: - metrics.count_job_stats( - query_job=query_job, cell_execution_count=cell_execution_count - ) - - -def delete_tables_matching_session_id( - client: bigquery.Client, - dataset: bigquery.DatasetReference, - session_id: str, -) -> None: - """Searches within the dataset for tables conforming to the - expected session_id form, and instructs bigquery to delete them. - - Args: - client (bigquery.Client): - The client to use to list tables - dataset (bigquery.DatasetReference): - The dataset to search in - session_id (str): - The session id to match on in the table name - - Returns: - None - """ - - tables = client.list_tables( - dataset, max_results=_LIST_TABLES_LIMIT, page_size=_LIST_TABLES_LIMIT - ) - for table in tables: - split_id = table.table_id.split("_") - if not split_id[0].startswith("bqdf") or len(split_id) < 2: - continue - found_session_id = split_id[1] - if found_session_id == session_id: - client.delete_table(table, not_found_ok=True) - print("Deleting temporary table '{}'.".format(table.table_id)) - - -def create_bq_dataset_reference( - bq_client: bigquery.Client, - location: Optional[str] = None, - project: Optional[str] = None, - *, - publisher: bigframes.core.events.Publisher, -) -> bigquery.DatasetReference: - """Create and identify dataset(s) for temporary BQ resources. - - bq_client project and location will be used unless kwargs "project" - and/or "location" are given. If given, location and project - will be passed through to - https://cloud.google.com/python/docs/reference/bigquery/latest/google.cloud.bigquery.client.Client#google_cloud_bigquery_client_Client_query - - Args: - bq_client (bigquery.Client): - The bigquery.Client to use for the http request to - create the dataset reference. - location (str, default None): - The location of the project to create the dataset in. - project (str, default None): - The project id of the project to create the dataset in. - - Returns: - bigquery.DatasetReference: The constructed reference to the - anonymous dataset. - """ - job_config = google.cloud.bigquery.QueryJobConfig() - - _, query_job = start_query_with_job( - bq_client, - "SELECT 1", - location=location, - job_config=job_config, - project=project, - timeout=None, - metrics=None, - publisher=publisher, - ) - - # The anonymous dataset is used by BigQuery to write query results and - # session tables. BigQuery DataFrames also writes temp tables directly - # to the dataset, no BigQuery Session required. Note: there is a - # different anonymous dataset per location. See: - # https://cloud.google.com/bigquery/docs/cached-results#how_cached_results_are_stored - query_destination = query_job.destination - return bigquery.DatasetReference( - query_destination.project, - query_destination.dataset_id, - ) - - -def is_query(query_or_table: str) -> bool: - """Determine if `query_or_table` is a table ID or a SQL string""" - return re.search(r"\s", query_or_table.strip(), re.MULTILINE) is not None - - -def is_table_with_wildcard_suffix(query_or_table: str) -> bool: - """Determine if `query_or_table` is a table and contains a wildcard - suffix.""" - return not is_query(query_or_table) and query_or_table.endswith("*") - - -def to_query( - query_or_table: str, - columns: Iterable[str], - sql_predicate: Optional[str], - max_results: Optional[int] = None, - time_travel_timestamp: Optional[datetime.datetime] = None, -) -> str: - """Compile query_or_table with conditions(filters, wildcards) to query.""" - if is_query(query_or_table): - from_item = f"({query_or_table})" - else: - # Table ID can have 1, 2, 3, or 4 parts. Quoting all parts to be safe. - # See: - # https://cloud.google.com/bigquery/docs/reference/standard-sql/lexical#identifiers - parts = query_or_table.split(".") - from_item = ".".join(f"`{part}`" for part in parts) - - # TODO(b/338111344): Generate an index based on DefaultIndexKind if we - # don't have index columns specified. - if columns: - # We only reduce the selection if columns is set, but we always - # want to make sure index_cols is also included. - select_clause = "SELECT " + ", ".join( - f"`_bf_source`.`{column}`" for column in columns - ) - else: - select_clause = "SELECT *" - - time_travel_clause = "" - if time_travel_timestamp is not None: - time_travel_literal = sg_sql.to_sql(sg_sql.literal(time_travel_timestamp)) # noqa: E501 - time_travel_clause = f" FOR SYSTEM_TIME AS OF {time_travel_literal}" - - limit_clause = "" - if max_results is not None: - limit_clause = f" LIMIT {sg_sql.to_sql(sg_sql.literal(max_results))}" - - where_clause = f" WHERE {sql_predicate}" if sql_predicate else "" - - return ( - f"{select_clause} " - f"FROM {from_item} AS _bf_source" - f"{time_travel_clause}{where_clause}{limit_clause}" - ) - - -def compile_filters(filters: third_party_pandas_gbq.FiltersType) -> str: - """Compiles a set of filters into a boolean sql expression""" - if not filters: - return "" - filter_string = "" - valid_operators: Mapping[third_party_pandas_gbq.FilterOps, str] = { - "in": "IN", - "not in": "NOT IN", - "LIKE": "LIKE", - "==": "=", - ">": ">", - "<": "<", - ">=": ">=", - "<=": "<=", - "!=": "!=", - } - - # If single layer filter, add another pseudo layer. So the single - # layer represents "and" logic. - filters_list: list = list(filters) - if isinstance(filters_list[0], tuple) and ( - len(filters_list[0]) == 0 or not isinstance(list(filters_list[0])[0], tuple) # noqa: E501 - ): - filter_items = [filters_list] - else: - filter_items = filters_list - - for group in filter_items: - if not isinstance(group, Iterable): - group = [group] - - and_expression = "" - for filter_item in group: - if not isinstance(filter_item, tuple) or (len(filter_item) != 3): - raise ValueError( - f"Elements of filters must be tuples of length 3, " - f"but got {repr(filter_item)}.", - ) - - column, operator, value = filter_item - - if not isinstance(column, str): - raise ValueError( - f"Column name should be a string, but received " - f"'{column}' of type {type(column).__name__}." - ) - - if operator not in valid_operators: - raise ValueError(f"Operator {operator} is not valid.") - - operator_str = valid_operators[operator] - - column_ref = sg_sql.to_sql(sg_sql.identifier(column)) - if operator_str in ["IN", "NOT IN"]: - value_literal = bigframes.core.sql.multi_literal(*value) - else: - value_literal = sg_sql.to_sql(sg_sql.literal(value)) - expression = bigframes.core.sql.infix_op( - operator_str, column_ref, value_literal - ) - if and_expression: - and_expression = bigframes.core.sql.infix_op( - "AND", and_expression, expression - ) - else: - and_expression = expression - - if filter_string: - filter_string = bigframes.core.sql.infix_op( - "OR", filter_string, and_expression - ) - else: - filter_string = and_expression - - return filter_string - - -def select_cluster_cols( - schema: typing.Sequence[bigquery.SchemaField], - cluster_candidates: typing.Sequence[str], -) -> typing.Sequence[str]: - return [ - item.name - for item in schema - if (item.name in cluster_candidates) and _can_cluster_bq(item) - ][:_MAX_CLUSTER_COLUMNS] - - -def _can_cluster_bq(field: bigquery.SchemaField): - # https://cloud.google.com/bigquery/docs/clustered-tables - # Notably, float is excluded - type_ = field.field_type - return type_ in ( - "INTEGER", - "INT64", - "STRING", - "NUMERIC", - "DECIMAL", - "BIGNUMERIC", - "BIGDECIMAL", - "DATE", - "DATETIME", - "TIMESTAMP", - "BOOL", - "BOOLEAN", - ) diff --git a/bigframes/session/_io/bigquery/read_gbq_query.py b/bigframes/session/_io/bigquery/read_gbq_query.py deleted file mode 100644 index cd6368974b6..00000000000 --- a/bigframes/session/_io/bigquery/read_gbq_query.py +++ /dev/null @@ -1,143 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -"""Private helpers for implementing read_gbq_query.""" - -from __future__ import annotations - -from typing import Iterable, Optional, Tuple, cast - -import google.cloud.bigquery.table -import pandas -from google.cloud import bigquery - -import bigframes.core as core -import bigframes.core.blocks as blocks -import bigframes.core.guid -import bigframes.core.schema as schemata -import bigframes.enums -import bigframes.session -from bigframes import dataframe -from bigframes.core import local_data, pyarrow_utils - - -def should_return_query_results(query_job: bigquery.QueryJob) -> bool: - """Returns True if query_job is the kind of query we expect results from. - - If the query was DDL or DML, return some job metadata. See - https://cloud.google.com/bigquery/docs/reference/rest/v2/Job#JobStatistics2.FIELDS.statement_type - for possible statement types. Note that destination table does exist - for some DDL operations such as CREATE VIEW, but we don't want to - read from that. See internal issue b/444282709. - """ - - if query_job.statement_type == "SELECT": - return True - - if query_job.statement_type == "SCRIPT": - # Try to determine if the last statement is a SELECT. Alternatively, we - # could do a jobs.list request using query_job as the parent job and - # try to determine the statement type of the last child job. - return query_job.destination != query_job.ddl_target_table - - return False - - -def create_dataframe_from_query_job_stats( - query_job: Optional[bigquery.QueryJob], *, session: bigframes.session.Session -) -> dataframe.DataFrame: - """Convert a QueryJob into a DataFrame with key statistics about the query. - - Any changes you make here, please try to keep in sync with pandas-gbq. - """ - return dataframe.DataFrame( - data=pandas.DataFrame( - { - "statement_type": [ - query_job.statement_type if query_job else "unknown" - ], - "job_id": [query_job.job_id if query_job else "unknown"], - "location": [query_job.location if query_job else "unknown"], - } - ), - session=session, - ) - - -def create_dataframe_from_row_iterator( - rows: google.cloud.bigquery.table.RowIterator, - *, - session: bigframes.session.Session, - index_col: Iterable[str] | str | bigframes.enums.DefaultIndexKind, - columns: Iterable[str], -) -> dataframe.DataFrame: - """Convert a RowIterator into a DataFrame wrapping a LocalNode. - - This allows us to create a DataFrame from query results, even in the - 'jobless' case where there's no destination table. - """ - pa_table = rows.to_arrow() - bq_schema = list(rows.schema) - is_default_index = not index_col or isinstance( - index_col, bigframes.enums.DefaultIndexKind - ) - - if is_default_index: - # We get a sequential index for free, so use that if no index is specified. - # TODO(tswast): Use array_value.promote_offsets() instead once that node is - # supported by the local engine. - offsets_col = bigframes.core.guid.generate_guid() - pa_table = pyarrow_utils.append_offsets(pa_table, offsets_col=offsets_col) - bq_schema += [bigquery.SchemaField(offsets_col, "INTEGER")] - index_columns: Tuple[str, ...] = (offsets_col,) - index_labels: Tuple[Optional[str], ...] = (None,) - elif isinstance(index_col, str): - index_columns = (index_col,) - index_labels = (index_col,) - else: - index_col = cast(Iterable[str], index_col) - index_columns = tuple(index_col) - index_labels = cast(Tuple[Optional[str], ...], tuple(index_col)) - - # We use the ManagedArrowTable constructor directly, because the - # results of to_arrow() should be the source of truth with regards - # to canonical formats since it comes from either the BQ Storage - # Read API or has been transformed by google-cloud-bigquery to look - # like the output of the BQ Storage Read API. - mat = local_data.ManagedArrowTable( - pa_table, - schemata.ArraySchema.from_bq_schema(bq_schema), - ) - mat.validate() - - column_labels = [ - field.name for field in rows.schema if field.name not in index_columns - ] - - array_value = core.ArrayValue.from_managed(mat, session) - block = blocks.Block( - array_value, - index_columns=index_columns, - column_labels=column_labels, - index_labels=index_labels, - ) - df = dataframe.DataFrame(block) - - if columns: - df = df[list(columns)] - - if not is_default_index: - df = df.sort_index() - - return df diff --git a/bigframes/session/_io/bigquery/read_gbq_table.py b/bigframes/session/_io/bigquery/read_gbq_table.py deleted file mode 100644 index faaf0f01912..00000000000 --- a/bigframes/session/_io/bigquery/read_gbq_table.py +++ /dev/null @@ -1,384 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -""" -Private helpers for loading a BigQuery table as a BigQuery DataFrames DataFrame. -""" - -from __future__ import annotations - -import datetime -import typing -import warnings -from typing import Dict, Iterable, Optional, Sequence, Tuple, Union - -import bigframes_vendored.constants as constants -import google.api_core.exceptions -import google.cloud.bigquery as bigquery - -import bigframes.core -import bigframes.core.events -import bigframes.exceptions as bfe -import bigframes.session._io.bigquery -from bigframes.core import bq_data - -# Avoid circular imports. -if typing.TYPE_CHECKING: - import bigframes.session - - -def _convert_information_schema_table_id_to_table_reference( - table_id: str, - default_project: Optional[str], -) -> bigquery.TableReference: - """Squeeze an INFORMATION_SCHEMA reference into a TableReference. - This is kind-of a hack. INFORMATION_SCHEMA is a view that isn't available - via the tables.get REST API. - """ - parts = table_id.split(".") - parts_casefold = [part.casefold() for part in parts] - dataset_index = parts_casefold.index("INFORMATION_SCHEMA".casefold()) - - if dataset_index == 0: - project = default_project - else: - project = ".".join(parts[:dataset_index]) - - if project is None: - message = ( - "Could not determine project ID. " - "Please provide a project or region in your INFORMATION_SCHEMA table ID, " - "For example, 'region-REGION_NAME.INFORMATION_SCHEMA.JOBS'." - ) - raise ValueError(message) - - dataset = "INFORMATION_SCHEMA" - table_id_short = ".".join(parts[dataset_index + 1 :]) - return bigquery.TableReference( - bigquery.DatasetReference(project, dataset), - table_id_short, - ) - - -def get_information_schema_metadata( - bqclient: bigquery.Client, - table_id: str, - default_project: Optional[str], -) -> bigquery.Table: - job_config = bigquery.QueryJobConfig(dry_run=True) - job = bqclient.query( - f"SELECT * FROM `{table_id}`", - job_config=job_config, - ) - table_ref = _convert_information_schema_table_id_to_table_reference( - table_id=table_id, - default_project=default_project, - ) - table = bigquery.Table.from_api_repr( - { - "tableReference": table_ref.to_api_repr(), - "location": job.location, - # Prevent ourselves from trying to read the table with the BQ - # Storage API. - "type": "VIEW", - } - ) - table.schema = job.schema - return table - - -def is_information_schema(table_id: str): - table_id_casefold = table_id.casefold() - # Include the "."s to ensure we don't have false positives for some user - # defined dataset like MY_INFORMATION_SCHEMA or tables called - # INFORMATION_SCHEMA. - return ( - ".INFORMATION_SCHEMA.".casefold() in table_id_casefold - or table_id_casefold.startswith("INFORMATION_SCHEMA.".casefold()) - ) - - -def is_time_travel_eligible( - bqclient: bigquery.Client, - table: Union[bq_data.GbqNativeTable, bq_data.BiglakeIcebergTable], - columns: Optional[Sequence[str]], - snapshot_time: datetime.datetime, - filter_str: Optional[str] = None, - *, - should_warn: bool, - should_dry_run: bool, - publisher: bigframes.core.events.Publisher, -): - """Check if a table is eligible to use time-travel. - - - Args: - table: BigQuery table to check. - should_warn: - If true, raises a warning when time travel is disabled and the - underlying table is likely mutable. - - Return: - bool: - True if there is a chance that time travel may be supported on this - table. If ``should_dry_run`` is True, then this is validated with a - ``dry_run`` query. - """ - - # user code - # -> pandas.read_gbq_table - # -> with_default_session - # -> session.read_gbq_table - # -> session._read_gbq_table - # -> loader.read_gbq_table - # -> is_time_travel_eligible - stacklevel = 7 - - if isinstance(table, bq_data.GbqNativeTable): - # Anonymous dataset, does not support snapshot ever - if table.dataset_id.startswith("_"): - return False - - # Only true tables support time travel - if table.table_id.endswith("*"): - if should_warn: - msg = bfe.format_message( - "Wildcard tables do not support FOR SYSTEM_TIME AS OF queries. " - "Attempting query without time travel. Be aware that " - "modifications to the underlying data may result in errors or " - "unexpected behavior." - ) - warnings.warn( - msg, category=bfe.TimeTravelDisabledWarning, stacklevel=stacklevel - ) - return False - elif table.metadata.type != "TABLE": - if table.metadata.type == "MATERIALIZED_VIEW": - if should_warn: - msg = bfe.format_message( - "Materialized views do not support FOR SYSTEM_TIME AS OF queries. " - "Attempting query without time travel. Be aware that as materialized views " - "are updated periodically, modifications to the underlying data in the view may " - "result in errors or unexpected behavior." - ) - warnings.warn( - msg, - category=bfe.TimeTravelDisabledWarning, - stacklevel=stacklevel, - ) - return False - elif table.metadata.type == "VIEW": - return False - - # table might support time travel, lets do a dry-run query with time travel - if should_dry_run: - snapshot_sql = bigframes.session._io.bigquery.to_query( - query_or_table=table.get_full_id( - quoted=False - ), # to_query will quote for us - columns=columns or (), - sql_predicate=filter_str, - time_travel_timestamp=snapshot_time, - ) - try: - # If this succeeds, we know that time travel will for sure work. - bigframes.session._io.bigquery.start_query_job_optional( - bq_client=bqclient, - sql=snapshot_sql, - job_config=bigquery.QueryJobConfig(dry_run=True), - location=None, - project=None, - timeout=None, - metrics=None, - publisher=publisher, - ) - return True - - except google.api_core.exceptions.NotFound: - # If system time isn't supported, it returns NotFound error? - # Note that a notfound caused by a simple typo will be - # caught above when the metadata is fetched, not here. - if should_warn: - msg = bfe.format_message( - "NotFound error when reading table with time travel." - " Attempting query without time travel. Warning: Without" - " time travel, modifications to the underlying table may" - " result in errors or unexpected behavior." - ) - warnings.warn( - msg, category=bfe.TimeTravelDisabledWarning, stacklevel=stacklevel - ) - - # If we make it to here, we know for sure that time travel won't work. - return False - else: - # We haven't validated it, but there's a chance that time travel could work. - return True - - -def infer_unique_columns( - table: Union[bq_data.GbqNativeTable, bq_data.BiglakeIcebergTable], - index_cols: Sequence[str], -) -> Tuple[str, ...]: - """Return a set of columns that can provide a unique row key or empty if none can be inferred. - - Note: primary keys are not enforced, but these are assumed to be unique - by the query engine, so we make the same assumption here. - """ - # If index_cols contain the primary_keys, the query engine assumes they are - # provide a unique index. - primary_keys = table.primary_key or () - if (len(primary_keys) > 0) and frozenset(primary_keys) <= frozenset(index_cols): - # Essentially, just reordering the primary key to match the index col order - return tuple(index_col for index_col in index_cols if index_col in primary_keys) - - if primary_keys: - return primary_keys - - return () - - -def check_if_index_columns_are_unique( - bqclient: bigquery.Client, - table: Union[bq_data.GbqNativeTable, bq_data.BiglakeIcebergTable], - index_cols: Sequence[str], - *, - publisher: bigframes.core.events.Publisher, -) -> Tuple[str, ...]: - import bigframes.core.sql - import bigframes.session._io.bigquery - - # TODO(b/337925142): Avoid a "SELECT *" subquery here by ensuring - # table_expression only selects just index_cols. - is_unique_sql = bigframes.core.sql.is_distinct_sql( - index_cols, table.get_table_ref() - ) - job_config = bigquery.QueryJobConfig() - results = bigframes.session._io.bigquery.start_query_job_optional( - bq_client=bqclient, - sql=is_unique_sql, - job_config=job_config, - timeout=None, - location=None, - project=None, - metrics=None, - publisher=publisher, - ) - row = next(iter(results)) - - if row["total_count"] == row["distinct_count"]: - return tuple(index_cols) - return () - - -def get_index_cols( - table: Union[bq_data.GbqNativeTable, bq_data.BiglakeIcebergTable], - index_col: Iterable[str] - | str - | Iterable[int] - | int - | bigframes.enums.DefaultIndexKind, - *, - rename_to_schema: Optional[Dict[str, str]] = None, - default_index_type: bigframes.enums.DefaultIndexKind = bigframes.enums.DefaultIndexKind.SEQUENTIAL_INT64, -) -> Sequence[str]: - """ - If we can get a total ordering from the table, such as via primary key - column(s), then return those too so that ordering generation can be - avoided. - """ - # Transform index_col -> index_cols so we have a variable that is - # always a list of column names (possibly empty). - schema_len = len(table.physical_schema) - - index_cols = [] - if isinstance(index_col, bigframes.enums.DefaultIndexKind): - if index_col == bigframes.enums.DefaultIndexKind.SEQUENTIAL_INT64: - # User has explicity asked for a default, sequential index. - # Use that, even if there are primary keys on the table. - return [] - if index_col == bigframes.enums.DefaultIndexKind.NULL: - return [] - else: - # Note: It's actually quite difficult to mock this out to unit - # test, as it's not possible to subclass enums in Python. See: - # https://stackoverflow.com/a/33680021/101923 - raise NotImplementedError( - f"Got unexpected index_col {repr(index_col)}. {constants.FEEDBACK_LINK}" - ) - elif isinstance(index_col, str): - if rename_to_schema is not None: - index_col = rename_to_schema.get(index_col, index_col) - index_cols = [index_col] - elif isinstance(index_col, int): - if not 0 <= index_col < schema_len: - raise ValueError( - f"Integer index {index_col} is out of bounds " - f"for table with {schema_len} columns (must be >= 0 and < {schema_len})." - ) - index_cols = [table.physical_schema[index_col].name] - elif isinstance(index_col, Iterable): - for item in index_col: - if isinstance(item, str): - if rename_to_schema is not None: - item = rename_to_schema.get(item, item) - index_cols.append(item) - elif isinstance(item, int): - if not 0 <= item < schema_len: - raise ValueError( - f"Integer index {item} is out of bounds " - f"for table with {schema_len} columns (must be >= 0 and < {schema_len})." - ) - index_cols.append(table.physical_schema[item].name) - else: - raise TypeError( - "If index_col is an iterable, it must contain either strings " - "(column names) or integers (column positions)." - ) - else: - raise TypeError( - f"Unsupported type for index_col: {type(index_col).__name__}. Expected" - "an integer, an string, an iterable of strings, or an iterable of integers." - ) - - # If the isn't an index selected, use the primary keys of the table as the - # index. If there are no primary keys, we'll return an empty list. - if len(index_cols) == 0: - primary_keys = table.primary_key or () - - # If table has clustering/partitioning, fail if we haven't been able to - # find index_cols to use. This is to avoid unexpected performance and - # resource utilization because of the default sequential index. See - # internal issue 335727141. - if ( - (table.partition_col is not None or table.cluster_cols) - and not primary_keys - and default_index_type == bigframes.enums.DefaultIndexKind.SEQUENTIAL_INT64 - ): - msg = bfe.format_message( - f"Table '{str(table.get_full_id())}' is clustered and/or " - "partitioned, but BigQuery DataFrames was not able to find a " - "suitable index. To avoid this warning, set at least one of: " - # TODO(b/338037499): Allow max_results to override this too, - # once we make it more efficient. - "`index_col` or `filters`." - ) - warnings.warn(msg, category=bfe.DefaultIndexWarning) - - # If there are primary keys defined, the query engine assumes these - # columns are unique, even if the constraint is not enforced. We make - # the same assumption and use these columns as the total ordering keys. - index_cols = list(primary_keys) - - return index_cols diff --git a/bigframes/session/_io/pandas.py b/bigframes/session/_io/pandas.py index 8d41474d8c4..1af00a2d015 100644 --- a/bigframes/session/_io/pandas.py +++ b/bigframes/session/_io/pandas.py @@ -11,77 +11,34 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. -from __future__ import annotations -import dataclasses -import typing -from typing import Collection, Union +from typing import Dict, Union -import bigframes_vendored.constants as constants import geopandas # type: ignore import pandas import pandas.arrays import pyarrow # type: ignore import pyarrow.compute # type: ignore -import pyarrow.types # type: ignore -import bigframes.core.schema -import bigframes.dtypes -import bigframes.features - - -@dataclasses.dataclass(frozen=True) -class DataFrameAndLabels: - df: pandas.DataFrame - column_labels: Collection - index_labels: Collection - ordering_col: str - col_type_overrides: typing.Dict[str, bigframes.dtypes.Dtype] - - -def _arrow_to_pandas_arrowdtype( - column: pyarrow.Array, dtype: pandas.ArrowDtype -) -> pandas.Series: - if ( - pyarrow.types.is_list(dtype.pyarrow_dtype) - and not bigframes.features.PANDAS_VERSIONS.is_arrow_list_dtype_usable - ): - # This version of pandas doesn't really support ArrowDtype - # well. See internal issue 321013333 where array type has - # several problems converting a string. - return pandas.Series( - column.to_pylist(), # type: ignore - dtype="object", - ) - - # Avoid conversion logic if we are backing the pandas Series by the - # arrow array. - return pandas.Series( - pandas.arrays.ArrowExtensionArray(column), # type: ignore - dtype=dtype, - ) +import bigframes.constants def arrow_to_pandas( - arrow_table: Union[pyarrow.Table, pyarrow.RecordBatch], - schema: bigframes.core.schema.ArraySchema, + arrow_table: Union[pyarrow.Table, pyarrow.RecordBatch], dtypes: Dict ): - if len(schema) != arrow_table.num_columns: + if len(dtypes) != arrow_table.num_columns: raise ValueError( - f"Number of types {len(schema)} doesn't match number of columns " - f"{arrow_table.num_columns}. {constants.FEEDBACK_LINK}" + f"Number of types {len(dtypes)} doesn't match number of columns " + f"{arrow_table.num_columns}. {bigframes.constants.FEEDBACK_LINK}" ) serieses = {} for field, column in zip(arrow_table.schema, arrow_table): - dtype = schema.get_type(field.name) + dtype = dtypes[field.name] if dtype == geopandas.array.GeometryDtype(): series = geopandas.GeoSeries.from_wkt( - # Use `to_pylist()` is a workaround for TypeError: object of type - # 'pyarrow.lib.StringScalar' has no len() on older pyarrow, - # geopandas, shapely combinations. - column.to_pylist(), + column, # BigQuery geography type is based on the WGS84 reference ellipsoid. crs="EPSG:4326", ) @@ -89,7 +46,7 @@ def arrow_to_pandas( # Preserve NA/NaN distinction. Note: This is currently needed, even if we use # nullable Float64Dtype in the types_mapper. See: # https://github.com/pandas-dev/pandas/issues/55668 - mask = pyarrow.compute.is_null(column) # type: ignore[attr-defined] + mask = pyarrow.compute.is_null(column) nonnull = pyarrow.compute.fill_null(column, float("nan")) # Regarding type: ignore, this class has been public at this # location since pandas 1.2.0. See: @@ -106,7 +63,7 @@ def arrow_to_pandas( elif dtype == pandas.Int64Dtype(): # Avoid out-of-bounds errors in Pandas 1.5.x, which incorrectly # casts to float64 in an intermediate step. - mask = pyarrow.compute.is_null(column) # type: ignore[attr-defined] + mask = pyarrow.compute.is_null(column) nonnull = pyarrow.compute.fill_null(column, 0) pd_array = pandas.arrays.IntegerArray( nonnull.to_numpy() @@ -117,14 +74,13 @@ def arrow_to_pandas( else mask.to_numpy(zero_copy_only=False), ) series = pandas.Series(pd_array, dtype=dtype) - elif dtype == bigframes.dtypes.STRING_DTYPE: - # Pyarrow may be large_string - # Need to manually cast, as some pandas versions break otherwise - series = column.cast(pyarrow.string()).to_pandas( - types_mapper=lambda _: dtype - ) elif isinstance(dtype, pandas.ArrowDtype): - series = _arrow_to_pandas_arrowdtype(column, dtype) + # Avoid conversion logic if we are backing the pandas Series by the + # arrow array. + series = pandas.Series( + pandas.arrays.ArrowExtensionArray(column), # type: ignore + dtype=dtype, + ) else: series = column.to_pandas(types_mapper=lambda _: dtype) diff --git a/bigframes/session/anonymous_dataset.py b/bigframes/session/anonymous_dataset.py deleted file mode 100644 index ed718ff909f..00000000000 --- a/bigframes/session/anonymous_dataset.py +++ /dev/null @@ -1,199 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import datetime -import threading -import uuid -import warnings -from concurrent.futures import ThreadPoolExecutor -from typing import List, Optional, Sequence - -import google.cloud.bigquery as bigquery -from google.api_core import retry as api_core_retry - -import bigframes.core.events -import bigframes.exceptions as bfe -import bigframes.session._io.bigquery as bf_io_bigquery -from bigframes import constants -from bigframes.session import temporary_storage - -_TEMP_TABLE_ID_FORMAT = "bqdf{date}_{session_id}_{random_id}" -# UDFs older than this many days are considered stale and will be deleted -# from the anonymous dataset before creating a new UDF. -_UDF_CLEANUP_THRESHOLD_DAYS = 3 - - -class AnonymousDatasetManager(temporary_storage.TemporaryStorageManager): - """ - Responsible for allocating and cleaning up temporary gbq tables used by a BigFrames session. - """ - - def __init__( - self, - bqclient: bigquery.Client, - location: str, - session_id: str, - *, - kms_key: Optional[str] = None, - publisher: bigframes.core.events.Publisher, - ): - self.bqclient = bqclient - self._location = location - self._publisher = publisher - - self.session_id = session_id - self._table_ids: List[bigquery.TableReference] = [] - self._kms_key = kms_key - - self._dataset_lock = threading.Lock() - self._datset_ref: Optional[bigquery.DatasetReference] = None - - @property - def location(self): - return self._location - - @property - def dataset(self) -> bigquery.DatasetReference: - if self._datset_ref is not None: - return self._datset_ref - with self._dataset_lock: - if self._datset_ref is None: - self._datset_ref = bf_io_bigquery.create_bq_dataset_reference( - self.bqclient, - location=self._location, - publisher=self._publisher, - ) - return self._datset_ref - - def _default_expiration(self): - """When should the table expire automatically?""" - return ( - datetime.datetime.now(datetime.timezone.utc) + constants.DEFAULT_EXPIRATION - ) - - def create_temp_table( - self, schema: Sequence[bigquery.SchemaField], cluster_cols: Sequence[str] = [] - ) -> bigquery.TableReference: - """ - Allocates and and creates a table in the anonymous dataset. - The table will be cleaned up by clean_up_tables. - """ - expiration = self._default_expiration() - table = bf_io_bigquery.create_temp_table( - self.bqclient, - self.allocate_temp_table(), - expiration, - schema=schema, - cluster_columns=list(cluster_cols), - kms_key=self._kms_key, - ) - return bigquery.TableReference.from_string(table) - - def create_temp_view(self, sql: str) -> bigquery.TableReference: - """ - Allocates and and creates a view in the anonymous dataset. - The view will be cleaned up by clean_up_tables. - """ - expiration = self._default_expiration() - table = bf_io_bigquery.create_temp_view( - self.bqclient, - self.allocate_temp_table(), - expiration=expiration, - sql=sql, - ) - return bigquery.TableReference.from_string(table) - - def allocate_temp_table(self) -> bigquery.TableReference: - """ - Allocates a unique table id, but does not create the table. - The table will be cleaned up by clean_up_tables. - """ - table_id = self.generate_unique_resource_id() - self._table_ids.append(table_id) - return table_id - - def generate_unique_resource_id(self) -> bigquery.TableReference: - """Generate a random table ID with BigQuery DataFrames prefix. - - This resource will not be cleaned up by this manager. - - Args: - skip_cleanup (bool, default False): - If True, do not add the generated ID to the list of tables - to clean up when the session is closed. - - Returns: - google.cloud.bigquery.TableReference: - Fully qualified table ID of a table that doesn't exist. - """ - now = datetime.datetime.now(datetime.timezone.utc) - random_id = uuid.uuid4().hex - table_id = _TEMP_TABLE_ID_FORMAT.format( - date=now.strftime("%Y%m%d"), session_id=self.session_id, random_id=random_id - ) - return self.dataset.table(table_id) - - def _cleanup_old_udfs(self): - """Clean up old UDFs in the anonymous dataset.""" - dataset = self.dataset - routines = list(self.bqclient.list_routines(dataset)) - cleanup_cutoff_time = datetime.datetime.now( - datetime.timezone.utc - ) - datetime.timedelta(days=_UDF_CLEANUP_THRESHOLD_DAYS) - - for routine in routines: - if ( - routine.created < cleanup_cutoff_time - and routine._properties["routineType"] == "SCALAR_FUNCTION" - ): - try: - self.bqclient.delete_routine( - routine.reference, - not_found_ok=True, - retry=api_core_retry.Retry(timeout=0), - ) - except Exception as e: - msg = bfe.format_message( - f"Unable to clean this old UDF '{routine.reference}': {e}" - ) - warnings.warn(msg, category=bfe.CleanupFailedWarning) - - def close(self): - """Delete tables that were created with this session's session_id.""" - if self._table_ids: - try: - with ThreadPoolExecutor() as executor: - futures = [ - executor.submit( - self.bqclient.delete_table, table_ref, not_found_ok=True - ) - for table_ref in self._table_ids - ] - for future in futures: - future.result() - finally: - self._table_ids.clear() - - try: - # Before closing the session, attempt to clean up any uncollected, - # old Python UDFs residing in the anonymous dataset. These UDFs - # accumulate over time and can eventually exceed resource limits. - # See more from b/450913424. - self._cleanup_old_udfs() - except Exception as e: - # Log a warning on the failure, do not interrupt the workflow. - msg = bfe.format_message( - f"Failed to clean up the old Python UDFs before closing the session: {e}" - ) - warnings.warn(msg, category=bfe.CleanupFailedWarning) diff --git a/bigframes/session/bigquery_session.py b/bigframes/session/bigquery_session.py deleted file mode 100644 index 18f8cdeaff4..00000000000 --- a/bigframes/session/bigquery_session.py +++ /dev/null @@ -1,214 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from __future__ import annotations - -import datetime -import logging -import threading -import uuid -from typing import Callable, Optional, Sequence - -# TODO: Non-ibis implementation -import bigframes_vendored.ibis.backends.bigquery.datatypes as ibis_bq -import google.cloud.bigquery as bigquery - -import bigframes.core.events -import bigframes.session._io.bigquery as bfbqio -from bigframes.core.compile.sqlglot import sql as sg_sql -from bigframes.session import temporary_storage - -KEEPALIVE_QUERY_TIMEOUT_SECONDS = 5.0 - -KEEPALIVE_FREQUENCY = datetime.timedelta(hours=6) - - -logger = logging.getLogger(__name__) - - -class SessionResourceManager(temporary_storage.TemporaryStorageManager): - """ - Responsible for allocating and cleaning up temporary gbq tables used by a BigFrames session. - """ - - def __init__( - self, - bqclient: bigquery.Client, - location: str, - *, - publisher: bigframes.core.events.Publisher, - ): - self.bqclient = bqclient - self._location = location - self._session_id: Optional[str] = None - self._sessiondaemon: Optional[RecurringTaskDaemon] = None - self._session_lock = threading.RLock() - self._publisher = publisher - - @property - def location(self): - return self._location - - def create_temp_table( - self, schema: Sequence[bigquery.SchemaField], cluster_cols: Sequence[str] = [] - ) -> bigquery.TableReference: - """Create a temporary session table. Session is an exclusive resource, so throughput is limited""" - # Can't set a table in _SESSION as destination via query job API, so we - # run DDL, instead. - with self._session_lock: - table_ref = bigquery.TableReference( - bigquery.DatasetReference(self.bqclient.project, "_SESSION"), - f"bqdf_{uuid.uuid4()}", - ) - job_config = bigquery.QueryJobConfig( - connection_properties=[ - bigquery.ConnectionProperty("session_id", self._get_session_id()) - ] - ) - - ibis_schema = ibis_bq.BigQuerySchema.to_ibis(list(schema)) - - fields = [ - f"{sg_sql.to_sql(sg_sql.identifier(name))} {ibis_bq.BigQueryType.from_ibis(ibis_type)}" - for name, ibis_type in ibis_schema.fields.items() - ] - fields_string = ",".join(fields) - - cluster_string = "" - if cluster_cols: - cluster_cols_sql = ", ".join( - f"{sg_sql.to_sql(sg_sql.identifier(cluster_col))}" - for cluster_col in cluster_cols - ) - cluster_string = f"\nCLUSTER BY {cluster_cols_sql}" - - ddl = f"CREATE TEMP TABLE `_SESSION`.{sg_sql.to_sql(sg_sql.identifier(table_ref.table_id))} ({fields_string}){cluster_string}" - - _, job = bfbqio.start_query_with_job( - self.bqclient, - ddl, - job_config=job_config, - location=self.location, - project=None, - timeout=None, - metrics=None, - publisher=self._publisher, - ) - job.result() - # return the fully qualified table, so it can be used outside of the session - destination = job.destination - assert destination is not None, "Failure to create temp table." - return destination - - def close(self): - if self._sessiondaemon is not None: - self._sessiondaemon.stop() - - if self._session_id is not None and self.bqclient is not None: - bfbqio.start_query_job_optional( - self.bqclient, - f"CALL BQ.ABORT_SESSION('{self._session_id}')", - # Assume this is being called in the user thread, so we can access - # this thread-local config. - job_config=bigquery.QueryJobConfig( - labels=dict(bigframes.options.compute.extra_query_labels) - ), - location=self.location, - project=None, - timeout=None, - metrics=None, - publisher=self._publisher, - ) - - def _get_session_id(self) -> str: - if self._session_id: - return self._session_id - with self._session_lock: - if self._session_id is None: - job_config = bigquery.QueryJobConfig(create_session=True) - # Make sure the session is a new one, not one associated with another query. - job_config.use_query_cache = False - _, query_job = bfbqio.start_query_with_job( - self.bqclient, - "SELECT 1", - job_config=job_config, - location=self.location, - project=None, - timeout=None, - metrics=None, - publisher=self._publisher, - ) - query_job.result() # blocks until finished - assert query_job.session_info is not None - assert query_job.session_info.session_id is not None - self._session_id = query_job.session_info.session_id - self._sessiondaemon = RecurringTaskDaemon( - task=self._keep_session_alive, frequency=KEEPALIVE_FREQUENCY - ) - self._sessiondaemon.start() - return query_job.session_info.session_id - else: - return self._session_id - - def _keep_session_alive(self): - # bq sessions will default expire after 24 hours of disuse, but if queried, this is renewed to a maximum of 7 days - with self._session_lock: - job_config = bigquery.QueryJobConfig( - connection_properties=[ - bigquery.ConnectionProperty("session_id", self._get_session_id()) - ] - ) - try: - bfbqio.start_query_job_optional( - self.bqclient, - "SELECT 1", - job_config=job_config, - location=self.location, - project=None, - timeout=KEEPALIVE_QUERY_TIMEOUT_SECONDS, - metrics=None, - publisher=self._publisher, - ) - except Exception as e: - logging.warning("BigQuery session keep-alive query errored : %s", e) - - -class RecurringTaskDaemon: - def __init__(self, task: Callable[[], None], frequency: datetime.timedelta): - self._stop_event = threading.Event() - self._frequency = frequency - self._thread = threading.Thread(target=self._run_loop, daemon=True) - self._task = task - - def start(self): - """Start the daemon. Cannot be restarted once stopped.""" - if self._stop_event.is_set(): - raise RuntimeError("Cannot restart daemon thread.") - self._thread.start() - - def _run_loop(self): - while True: - self._stop_event.wait(self._frequency.total_seconds()) - if self._stop_event.is_set(): - return - try: - self._task() - except Exception as e: - logging.warning("RecurringTaskDaemon task errorred: %s", e) - - def stop(self, timeout_seconds: Optional[float] = None): - """Stop and cleanup the daemon.""" - if self._thread.is_alive(): - self._stop_event.set() - self._thread.join(timeout=timeout_seconds) diff --git a/bigframes/session/bq_caching_executor.py b/bigframes/session/bq_caching_executor.py deleted file mode 100644 index dede318d813..00000000000 --- a/bigframes/session/bq_caching_executor.py +++ /dev/null @@ -1,804 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from __future__ import annotations - -import asyncio -import concurrent.futures -import dataclasses -import math -import threading -from typing import Literal, Optional, Sequence, Tuple - -import google.api_core.exceptions -import google.cloud.bigquery_storage_v1 -from google.cloud import bigquery - -import bigframes -import bigframes.constants -import bigframes.core -import bigframes.core.events -import bigframes.core.guid -import bigframes.core.nodes as nodes -import bigframes.core.ordering -import bigframes.core.schema as schemata -import bigframes.core.tree_properties as tree_properties -import bigframes.dtypes -import bigframes.functions._function_session as bff_session -import bigframes.operations as ops -import bigframes.session._io.bigquery as bq_io -import bigframes.session.execution_cache as execution_cache -import bigframes.session.execution_spec as ex_spec -import bigframes.session.metrics -import bigframes.session.planner -import bigframes.session.temporary_storage -from bigframes.core import ( - compile, - expression, - guid, - identifiers, - local_data, - rewrite, -) -from bigframes.core.compile.sqlglot import sql as sg_sql -from bigframes.core.compile.sqlglot import sqlglot_ir -from bigframes.functions import udf_def -from bigframes.session import ( - direct_gbq_execution, - executor, - loader, - local_scan_executor, - read_api_execution, - semi_executor, -) - -# Max complexity that should be executed as a single query -QUERY_COMPLEXITY_LIMIT = 1e7 -# Number of times to factor out subqueries before giving up. -MAX_SUBTREE_FACTORINGS = 5 -_MAX_CLUSTER_COLUMNS = 4 -MAX_SMALL_RESULT_BYTES = 10 * 1024 * 1024 * 1024 # 10G - - -_bg_loop = None -_bg_thread = None -_bg_lock = threading.Lock() - - -def _get_bg_loop(): - global _bg_loop, _bg_thread - with _bg_lock: - if _bg_loop is None: - loop = asyncio.new_event_loop() - _bg_loop = loop - - def run(): - asyncio.set_event_loop(loop) - loop.run_forever() - - _bg_thread = threading.Thread( - target=run, daemon=True, name="bigframes-bg-loop" - ) - _bg_thread.start() - return _bg_loop - - -def _run_sync(coro): - try: - loop = asyncio.get_running_loop() - except RuntimeError: - loop = None - - if loop is None: - return asyncio.run(coro) - else: - bg_loop = _get_bg_loop() - future = asyncio.run_coroutine_threadsafe(coro, bg_loop) - return future.result() - - -class BigQueryCachingExecutor(executor.Executor): - """Computes BigFrames values using BigQuery Engine. - - This executor can cache expressions. If those expressions are executed later, this session - will re-use the pre-existing results from previous executions. - - This class is not thread-safe. - """ - - def __init__( - self, - bqclient: bigquery.Client, - storage_manager: bigframes.session.temporary_storage.TemporaryStorageManager, - bqstoragereadclient: google.cloud.bigquery_storage_v1.BigQueryReadClient, - loader: loader.GbqDataLoader, - *, - metrics: Optional[bigframes.session.metrics.ExecutionMetrics] = None, - enable_polars_execution: bool = False, - publisher: bigframes.core.events.Publisher, - labels: tuple[tuple[str, str], ...] = (), - compiler_name: Literal["ibis", "sqlglot"] = "sqlglot", - cache: Optional[execution_cache.ExecutionCache] = None, - function_manager: bff_session.FunctionSession, - ): - self.bqclient = bqclient - self.storage_manager = storage_manager - self.cache: execution_cache.ExecutionCache = ( - cache or execution_cache.ExecutionCache() - ) - self.metrics = metrics - self.loader = loader - self._enable_polars_execution = enable_polars_execution - self._publisher = publisher - self._compiler_name = compiler_name - - # TODO(tswast): Send events from semi-executors, too. - self._semi_executors: Sequence[semi_executor.SemiExecutor] = ( - read_api_execution.ReadApiSemiExecutor( - bqstoragereadclient=bqstoragereadclient, - project=self.bqclient.project, - ), - local_scan_executor.LocalScanExecutor(), - ) - if enable_polars_execution: - from bigframes.session import polars_executor - - self._semi_executors = ( - *self._semi_executors, - polars_executor.PolarsExecutor(), - ) - self._gbq_executor = direct_gbq_execution.DirectGbqExecutor( - bqclient, - compiler=compiler_name, - bqstoragereadclient=bqstoragereadclient, - metrics=self.metrics, - publisher=self._publisher, - labels=dict(labels), - ) - self._function_manager = function_manager - - def to_sql( - self, - array_value: bigframes.core.ArrayValue, - offset_column: Optional[str] = None, - ordered: bool = False, - enable_cache: bool = True, - ) -> str: - if offset_column: - array_value, _ = array_value.promote_offsets() - node = ( - self._prepare_plan_simplify(array_value.node) - if enable_cache - else array_value.node - ) - node = _run_sync(self._substitute_large_local_sources(node)) - compiled = compile.compile_sql( - compile.CompileRequest(node, sort_rows=ordered), - compiler_name=self._compiler_name, - ) - return compiled.sql - - def execute( - self, - array_value: bigframes.core.ArrayValue, - execution_spec: ex_spec.ExecutionSpec, - ) -> executor.ExecuteResult: - # Need to grab thread local before starting async execution. - execution_spec = execution_spec.with_compute_options(bigframes.options.compute) - return _run_sync( - self._execute_async( - array_value, - execution_spec, - ) - ) - - async def _execute_async( - self, - array_value: bigframes.core.ArrayValue, - execution_spec: ex_spec.ExecutionSpec, - ) -> executor.ExecuteResult: - await self._publisher.publish_async(bigframes.core.events.ExecutionStarted()) - maybe_result = await self._try_execute_semi_executors( - array_value, execution_spec - ) - if maybe_result is not None: - return maybe_result - result = await self._execute_bigquery( - array_value, - execution_spec, - ) - await self._publisher.publish_async( - bigframes.core.events.EventEnvelope( - event=bigframes.core.events.ExecutionFinished(result=result), - cell_execution_count=execution_spec.cell_execution_count, - ) - ) - return result - - async def _try_execute_semi_executors( - self, - array_value: bigframes.core.ArrayValue, - execution_spec: ex_spec.ExecutionSpec, - ) -> Optional[executor.ExecuteResult]: - plan = self._prepare_plan_simplify(array_value.node) - for exec in self._semi_executors: - maybe_result = await exec.execute(plan, execution_spec) - if maybe_result: - await self._publisher.publish_async( - bigframes.core.events.EventEnvelope( - event=bigframes.core.events.ExecutionFinished( - result=maybe_result, - ), - cell_execution_count=execution_spec.cell_execution_count, - ) - ) - return maybe_result - return None - - async def _execute_bigquery( - self, - array_value: bigframes.core.ArrayValue, - execution_spec: ex_spec.ExecutionSpec, - ) -> executor.ExecuteResult: - dest_spec = execution_spec.destination_spec - # Recursive handlers for different cases, maybe extract to explicit interface. - if isinstance(dest_spec, ex_spec.GcsOutputSpec): - execution_spec = dataclasses.replace( - execution_spec, destination_spec=ex_spec.EphemeralTableSpec() - ) - results = await self._execute_bigquery( - array_value, - execution_spec, - ) - await self._export_result_gcs(results, dest_spec) - return results - elif isinstance(dest_spec, ex_spec.TableOutputSpec): - return await self._execute_gbq_table_export( - array_value, - execution_spec, - ) - # Force table creation if result might be large (and user explicitly allowed large results) - elif isinstance(dest_spec, ex_spec.EphemeralTableSpec) or (dest_spec is None): - if not execution_spec.promise_under_10gb: - table = await asyncio.to_thread( - self.storage_manager.create_temp_table, - array_value.schema.to_bigquery(), - ) - execution_spec = dataclasses.replace( - execution_spec, - destination_spec=ex_spec.TableOutputSpec( - table=table, if_exists="append" - ), - ) - # We don't use _execute_gbq_table_export, as this result is internal, not exported. - return await self._execute_gbq_query_only( - array_value, - execution_spec, - ) - # At this point, dst should be unspecified, a specific bq table, or an ephemeral temp table that fits in <10gb - return await self._execute_gbq_query_only( - array_value, - execution_spec, - ) - - async def _execute_gbq_table_export( - self, - array_value: bigframes.core.ArrayValue, - execution_spec: ex_spec.ExecutionSpec, - ) -> executor.ExecuteResult: - dest_spec = execution_spec.destination_spec - assert isinstance(dest_spec, ex_spec.TableOutputSpec) - existing_table = await self._maybe_find_existing_table(dest_spec) - if (existing_table is not None) and _is_schema_match( - existing_table.schema, array_value.schema - ): - # Special DML path - maybe this should be configurable, dml vs query destination has tradeoffs - execution_spec = dataclasses.replace( - execution_spec, destination_spec=ex_spec.EphemeralTableSpec() - ) - results = await self._execute_bigquery( - array_value, - execution_spec, - ) - assert isinstance(results, executor.BQTableExecuteResult) - await self._export_gbq_with_dml(results, dest_spec) - result: executor.ExecuteResult = results - else: - result = await self._execute_gbq_query_only( - array_value, - execution_spec, - ) - - has_special_dtype_col = any( - t in (bigframes.dtypes.TIMEDELTA_DTYPE, bigframes.dtypes.OBJ_REF_DTYPE) - for t in array_value.schema.dtypes - ) - if dest_spec.if_exists != "append" and has_special_dtype_col: - table = await asyncio.to_thread(self.bqclient.get_table, dest_spec.table) - table.schema = array_value.schema.to_bigquery() - await asyncio.to_thread(self.bqclient.update_table, table, ["schema"]) - - return result - - async def _execute_gbq_query_only( - self, - array_value: bigframes.core.ArrayValue, - execution_spec: ex_spec.ExecutionSpec, - ) -> executor.ExecuteResult: - gbq_plan = await self._prepare_plan_bq_execution( - array_value.node, execution_spec.bigquery_config - ) - result = await self._gbq_executor.execute(gbq_plan, execution_spec) - if result is None: - raise ValueError( - f"Couldn't execute plan {array_value.node} with {execution_spec}" - ) - return result - - async def _export_result_gcs( - self, result: executor.ExecuteResult, gcs_export_spec: ex_spec.GcsOutputSpec - ): - query_job = result.query_job - assert query_job is not None - result_table = query_job.destination - assert result_table is not None - export_data_statement = bq_io.create_export_data_statement( - f"{result_table.project}.{result_table.dataset_id}.{result_table.table_id}", - uri=gcs_export_spec.uri, - format=gcs_export_spec.format, - export_options=dict(gcs_export_spec.export_options), - ) - await asyncio.to_thread( - bq_io.start_query_with_job, - self.bqclient, - export_data_statement, - job_config=bigquery.QueryJobConfig(), - metrics=self.metrics, - project=None, - location=None, - timeout=None, - publisher=self._publisher, - ) - - async def _export_gbq_with_dml( - self, result: executor.BQTableExecuteResult, spec: ex_spec.TableOutputSpec - ): - """ - Export the ArrayValue to an existing BigQuery table, using DML. - """ - # b/409086472: Uses DML for table appends and replacements to avoid - # BigQuery `RATE_LIMIT_EXCEEDED` errors, as per quota limits: - # https://cloud.google.com/bigquery/quotas#standard_tables - assert result.query_job is not None - assert result.query_job.destination is not None - ir = sqlglot_ir.SQLGlotIR.from_table( - result.query_job.destination.project, - result.query_job.destination.dataset_id, - result.query_job.destination.table_id, - ) - sql = "" - if spec.if_exists == "append": - sql = sg_sql.to_sql(sg_sql.insert(ir.expr.as_select_all(), spec.table)) - else: # for "replace" - assert spec.if_exists == "replace" - sql = sg_sql.to_sql(sg_sql.replace(ir.expr.as_select_all(), spec.table)) - - await asyncio.to_thread( - bq_io.start_query_with_job, - self.bqclient, - sql, - job_config=bigquery.QueryJobConfig(), - metrics=self.metrics, - publisher=self._publisher, - ) - - def dry_run( - self, array_value: bigframes.core.ArrayValue, ordered: bool = True - ) -> bigquery.QueryJob: - sql = self.to_sql(array_value, ordered=ordered) - job_config = bigquery.QueryJobConfig(dry_run=True) - query_job = self.bqclient.query(sql, job_config=job_config) - return query_job - - def cached( - self, array_value: bigframes.core.ArrayValue, *, config: executor.CacheConfig - ) -> None: - # Get compute options before passing to async method, can be thread-local - bq_compute_options = ex_spec.BqComputeOptions.from_compute_options( - bigframes.options.compute - ) - return _run_sync( - self._cached_async( - array_value, config=config, compute_options=bq_compute_options - ) - ) - - async def _cached_async( - self, - array_value: bigframes.core.ArrayValue, - *, - config: executor.CacheConfig, - compute_options: ex_spec.BqComputeOptions, - ) -> None: - """Write the block to a session table.""" - # First, see if we can reuse the existing cache - # TODO(b/415105423): Provide feedback to user on whether new caching action was deemed necessary - # TODO(b/415105218): Make cached a deferred action - if config.if_cached == "reuse-any": - if self._is_trivially_executable(array_value): - return - elif config.if_cached == "reuse-strict": - # This path basically exists to make sure that repr in head mode is optimized for subsequent repr operations. - if config.optimize_for == "head": - if tree_properties.can_fast_head(array_value.node): - return - else: - raise NotImplementedError( - "if_cached='reuse-strict' currently only supported with optimize_for='head'" - ) - elif config.if_cached != "replace": - raise ValueError(f"Unexpected 'if_cached' arg: {config.if_cached}") - - if config.optimize_for == "auto": - await self._cache_with_session_awareness( - array_value, compute_options=compute_options - ) - elif config.optimize_for == "head": - await self._cache_with_offsets(array_value, compute_options=compute_options) - else: - assert isinstance(config.optimize_for, executor.HierarchicalKey) - await self._cache_with_cluster_cols( - array_value, - cluster_cols=config.optimize_for.columns, - compute_options=compute_options, - ) - - async def _execute_to_cached_table( - self, - plan: nodes.BigFrameNode, - cache_spec: ex_spec.CacheSpec, - compute_options: ex_spec.BqComputeOptions, - ) -> executor.ExecuteResult: - # "ephemeral" temp tables created in the course of exeuction, don't need to be allocated - # materialized ordering only really makes sense for internal temp tables used by caching - cluster_cols = cache_spec.cluster_cols - # Rewrite plan to materialize ordering as extra columns - if cache_spec.ordering == "offsets_col": - order_col_id = guid.generate_guid() - plan = nodes.PromoteOffsetsNode(plan, identifiers.ColumnId(order_col_id)) - cluster_cols = (order_col_id,) - ordering: bigframes.core.ordering.RowOrdering = ( - bigframes.core.ordering.TotalOrdering.from_offset_col(order_col_id) - ) - elif cache_spec.ordering == "order_key": - plan, ordering = rewrite.pull_out_order(plan) - destination_table = await asyncio.to_thread( - self.storage_manager.create_temp_table, - plan.schema.to_bigquery(), - cluster_cols, - ) - arr_value = bigframes.core.ArrayValue(plan) - execution_spec = ex_spec.ExecutionSpec( - destination_spec=ex_spec.TableOutputSpec( - table=destination_table, - cluster_cols=cluster_cols, - if_exists="replace", - ), - bigquery_config=compute_options, - ) - # We don't use _execute_gbq_table_export, as this result is internal, not exported. - result = await self._execute_gbq_query_only( - arr_value, - execution_spec, - ) - assert isinstance(result, executor.BQTableExecuteResult), ( - "expected result to be BQTableExecuteResult" - ) - result._data = dataclasses.replace(result._data, ordering=ordering) - return result - - # Helpers - def _is_trivially_executable(self, array_value: bigframes.core.ArrayValue): - """ - Can the block be evaluated very cheaply? - If True, the array_value probably is not worth caching. - """ - # Once rewriting is available, will want to rewrite before - # evaluating execution cost. - simplified_plan = self._prepare_plan_simplify(array_value.node) - return tree_properties.is_trivially_executable(simplified_plan) - - def _prepare_plan_simplify(self, plan: nodes.BigFrameNode) -> nodes.BigFrameNode: - """Prepare the plan by simplifying it with caches and removing unused operators.""" - plan = self.cache.subsitute_cached_subplans(plan) - plan = rewrite.column_pruning(plan) - plan = plan.top_down(rewrite.fold_row_counts) - return plan - - async def _deploy_undeployed_udfs( - self, plan: nodes.BigFrameNode - ) -> nodes.BigFrameNode: - referenced_udfs = list(set(self._collect_udf_defs(plan))) - deployed_mapping: dict[udf_def.PythonUdf, udf_def.BigqueryUdf] = {} - tasks = [ - asyncio.to_thread( - self._function_manager._deploy_udf, - udf, - ) - for udf in referenced_udfs - ] - results = await asyncio.gather(*tasks) - deployed_mapping = dict(zip(referenced_udfs, results)) - - return self._subsitute_temporary_functions(plan, deployed_mapping) - - def _collect_udf_defs(self, plan: nodes.BigFrameNode) -> list[udf_def.PythonUdf]: - udf_defs: list[udf_def.PythonUdf] = [] - exprs = [ - expr for node in plan.unique_nodes() for expr in node._node_expressions - ] - expr_nodes = [expr for expr in exprs for expr in expr.walk()] - for expr_node in expr_nodes: - if ( - isinstance(expr_node, expression.OpExpression) - and isinstance(expr_node.op, ops.PythonUdfOp) - and isinstance(expr_node.op.function_def, udf_def.PythonUdf) - ): - udf_defs.append(expr_node.op.function_def) - return udf_defs - - def _subsitute_temporary_functions( - self, - plan: nodes.BigFrameNode, - deployed_mapping: dict[udf_def.PythonUdf, udf_def.BigqueryUdf], - ) -> nodes.BigFrameNode: - def replace_udf_expr(e: expression.Expression) -> expression.Expression: - if isinstance(e, expression.OpExpression) and isinstance( - e.op, ops.PythonUdfOp - ): - func_def = e.op.function_def - # We will have already deployed the function - assert func_def in deployed_mapping - deployed_func = deployed_mapping[func_def] - rf_op = ops.RemoteFunctionOp(function_def=deployed_func) - return dataclasses.replace(e, op=rf_op) - return e - - def replace_in_expr(expr: expression.Expression) -> expression.Expression: - return expr.bottom_up(replace_udf_expr) - - def replace_in_node(node: nodes.BigFrameNode) -> nodes.BigFrameNode: - if hasattr(node, "transform_exprs"): - return node.transform_exprs(replace_in_expr) - return node - - return plan.bottom_up(replace_in_node) - - async def _prepare_plan_bq_execution( - self, - plan: nodes.BigFrameNode, - compute_options: Optional[ex_spec.BqComputeOptions] = None, - ) -> nodes.BigFrameNode: - """Prepare the plan for BigQuery execution by caching subtrees and uploading large local sources.""" - plan = await self._deploy_undeployed_udfs(plan) - if compute_options is not None and compute_options.enable_multi_query_execution: - await self._simplify_with_caching(plan, compute_options=compute_options) - plan = self._prepare_plan_simplify(plan) - plan = await self._substitute_large_local_sources(plan) - return plan - - async def _cache_with_cluster_cols( - self, - array_value: bigframes.core.ArrayValue, - cluster_cols: Sequence[str], - compute_options: ex_spec.BqComputeOptions, - ): - """Executes the query and uses the resulting table to rewrite future executions.""" - cluster_cols = [ - col - for col in cluster_cols - if bigframes.dtypes.is_clusterable(array_value.schema.get_type(col)) - ] - cluster_cols = cluster_cols[:_MAX_CLUSTER_COLUMNS] - result = await self._execute_to_cached_table( - array_value.node, - ex_spec.CacheSpec(cluster_cols=tuple(cluster_cols), ordering="order_key"), - compute_options=compute_options, - ) - assert isinstance(result, executor.BQTableExecuteResult) - assert result._data.ordering is not None - self.cache.cache_results_table(array_value.node, result._data) - - async def _cache_with_offsets( - self, - array_value: bigframes.core.ArrayValue, - compute_options: ex_spec.BqComputeOptions, - ): - """Executes the query and uses the resulting table to rewrite future executions.""" - result = await self._execute_to_cached_table( - array_value.node, - ex_spec.CacheSpec(ordering="offsets_col"), - compute_options=compute_options, - ) - assert isinstance(result, executor.BQTableExecuteResult) - assert result._data.ordering is not None - self.cache.cache_results_table(array_value.node, result._data) - - async def _cache_with_session_awareness( - self, - array_value: bigframes.core.ArrayValue, - compute_options: ex_spec.BqComputeOptions, - ) -> None: - session_forest = [obj._block._expr.node for obj in array_value.session.objects] - # These node types are cheap to re-compute - target, cluster_cols = bigframes.session.planner.session_aware_cache_plan( - array_value.node, list(session_forest) - ) - cluster_cols_sql_names = [id.sql for id in cluster_cols] - if len(cluster_cols) > 0: - await self._cache_with_cluster_cols( - bigframes.core.ArrayValue(target), - cluster_cols_sql_names, - compute_options=compute_options, - ) - elif not target.order_ambiguous: - await self._cache_with_offsets( - bigframes.core.ArrayValue(target), - compute_options=compute_options, - ) - else: - await self._cache_with_cluster_cols( - bigframes.core.ArrayValue(target), - [], - compute_options=compute_options, - ) - - async def _simplify_with_caching( - self, plan: nodes.BigFrameNode, compute_options: ex_spec.BqComputeOptions - ): - """Attempts to handle the complexity by caching duplicated subtrees and breaking the query into pieces.""" - # Apply existing caching first - for _ in range(MAX_SUBTREE_FACTORINGS): - if ( - self._prepare_plan_simplify(plan).planning_complexity - < QUERY_COMPLEXITY_LIMIT - ): - return - - did_cache = await self._cache_most_complex_subtree( - plan, compute_options=compute_options - ) - if not did_cache: - return - - async def _cache_most_complex_subtree( - self, node: nodes.BigFrameNode, compute_options: ex_spec.BqComputeOptions - ) -> bool: - # TODO: If query fails, retry with lower complexity limit - selection = tree_properties.select_cache_target( - node, - min_complexity=(QUERY_COMPLEXITY_LIMIT / 500), - max_complexity=QUERY_COMPLEXITY_LIMIT, - cache=self.cache, - # Heuristic: subtree_compleixty * (copies of subtree)^2 - heuristic=lambda complexity, count: ( - math.log(complexity) + 2 * math.log(count) - ), - ) - if selection is None: - # No good subtrees to cache, just return original tree - return False - - await self._cache_with_cluster_cols( - bigframes.core.ArrayValue(selection), - [], - compute_options=compute_options, - ) - return True - - async def _substitute_large_local_sources(self, original_root: nodes.BigFrameNode): - """ - Replace large local sources with the uploaded version of those datasources. - """ - # Step 1: Upload all previously un-uploaded data - needs_upload = [] - for leaf in original_root.unique_nodes(): - if isinstance(leaf, nodes.ReadLocalNode): - if ( - leaf.local_data_source.metadata.total_bytes - > bigframes.constants.MAX_INLINE_BYTES - ): - needs_upload.append(leaf.local_data_source) - - futures: dict[concurrent.futures.Future, local_data.ManagedArrowTable] = dict() - for local_source in needs_upload: - future = self.loader.read_data_async( - local_source, bigframes.core.guid.generate_guid() - ) - futures[future] = local_source - try: - results = await asyncio.gather( - *(asyncio.wrap_future(f) for f in futures.keys()) - ) - for future, result in zip(futures.keys(), results): - self.cache.cache_remote_replacement(futures[future], result) - except Exception as e: - # cancel all futures - for future in futures: - future.cancel() - raise e - - # Step 2: Replace local scans with remote scans - def map_local_scans(node: nodes.BigFrameNode): - if not isinstance(node, nodes.ReadLocalNode): - return node - uploaded_local_data = self.cache.get_uploaded_local_data( - node.local_data_source - ) - if uploaded_local_data is None: - return node - - scan_list = node.scan_list.remap_source_ids( - uploaded_local_data.source_mapping - ) - # offsets_col isn't part of ReadTableNode, so emulate by adding to end of scan_list - if node.offsets_col is not None: - # Offsets are always implicitly the final column of uploaded data - # See: Loader.load_data - scan_list = scan_list.append( - uploaded_local_data.bq_source.table.physical_schema[-1].name, - bigframes.dtypes.INT_DTYPE, - node.offsets_col, - ) - return nodes.ReadTableNode( - uploaded_local_data.bq_source, scan_list, node.session - ) - - return original_root.bottom_up(map_local_scans) - - async def _maybe_find_existing_table( - self, spec: ex_spec.TableOutputSpec - ) -> Optional[bigquery.Table]: - # validate destination table - try: - table = await asyncio.to_thread(self.bqclient.get_table, spec.table) - if spec.if_exists == "fail": - raise ValueError(f"Table already exists: {spec.table.__str__()}") - - if len(spec.cluster_cols) != 0: - if (table.clustering_fields is None) or ( - tuple(table.clustering_fields) != spec.cluster_cols - ): - raise ValueError( - "Table clustering fields cannot be changed after the table has " - f"been created. Requested clustering fields: {spec.cluster_cols}, existing clustering fields: {table.clustering_fields}" - ) - return table - except google.api_core.exceptions.NotFound: - return None - - -def _is_schema_match( - table_schema: Tuple[bigquery.SchemaField, ...], - schema: schemata.ArraySchema, -) -> bool: - if len(table_schema) != len(schema.items): - return False - for field, schema_item in zip(table_schema, schema.items): - if field.name != schema_item.column: - return False - _, field_dtype = bigframes.dtypes.convert_schema_field(field) - if field_dtype != schema_item.dtype: - return False - return True diff --git a/bigframes/session/clients.py b/bigframes/session/clients.py index 49822bac16b..e33413002fe 100644 --- a/bigframes/session/clients.py +++ b/bigframes/session/clients.py @@ -14,51 +14,40 @@ """Clients manages the connection to Google APIs.""" -import threading -from typing import Optional, Sequence, Tuple +import os +import typing +from typing import Optional import google.api_core.client_info import google.api_core.client_options +import google.api_core.exceptions import google.api_core.gapic_v1.client_info import google.auth.credentials -import google.auth.transport.requests import google.cloud.bigquery as bigquery import google.cloud.bigquery_connection_v1 import google.cloud.bigquery_storage_v1 import google.cloud.functions_v2 import google.cloud.resourcemanager_v3 -import google.cloud.storage # type: ignore -import requests +import ibis +import pydata_google_auth -import bigframes.constants import bigframes.version -from . import environment - -_APPLICATION_NAME = f"bigframes/{bigframes.version.__version__} ibis/9.2.0" - +_ENV_DEFAULT_PROJECT = "GOOGLE_CLOUD_PROJECT" +_APPLICATION_NAME = f"bigframes/{bigframes.version.__version__} ibis/{ibis.__version__}" +_SCOPES = ["https://www.googleapis.com/auth/cloud-platform"] # BigQuery is a REST API, which requires the protocol as part of the URL. -_BIGQUERY_REGIONAL_ENDPOINT = "https://bigquery.{location}.rep.googleapis.com" +_BIGQUERY_REGIONAL_ENDPOINT = "https://{location}-bigquery.googleapis.com" # BigQuery Connection and Storage are gRPC APIs, which don't support the # https:// protocol in the API endpoint URL. -_BIGQUERYSTORAGE_REGIONAL_ENDPOINT = "bigquerystorage.{location}.rep.googleapis.com" - +_BIGQUERYCONNECTION_REGIONAL_ENDPOINT = "{location}-bigqueryconnection.googleapis.com" +_BIGQUERYSTORAGE_REGIONAL_ENDPOINT = "{location}-bigquerystorage.googleapis.com" -def _get_application_names(): - apps = [_APPLICATION_NAME] - if environment.is_vscode(): - apps.append("vscode") - if environment.is_vscode_google_cloud_code_extension_installed(): - apps.append(environment.GOOGLE_CLOUD_CODE_EXTENSION_NAME) - elif environment.is_jupyter(): - apps.append("jupyter") - if environment.is_jupyter_bigquery_plugin_installed(): - apps.append(environment.BIGQUERY_JUPYTER_PLUGIN_NAME) - - return " ".join(apps) +def _get_default_credentials_with_project(): + return pydata_google_auth.default(scopes=_SCOPES, use_local_webserver=False) class ClientsProvider: @@ -66,271 +55,143 @@ class ClientsProvider: def __init__( self, - project: str, - credentials: google.auth.credentials.Credentials, - location: Optional[str] = None, - use_regional_endpoints: Optional[bool] = None, - application_name: Optional[str] = None, - bq_kms_key_name: Optional[str] = None, - client_endpoints_override: dict = {}, - *, - requests_transport_adapters: Sequence[ - Tuple[str, requests.adapters.BaseAdapter] - ] = (), + project: Optional[str], + location: Optional[str], + use_regional_endpoints: Optional[bool], + credentials: Optional[google.auth.credentials.Credentials], + application_name: Optional[str], ): + credentials_project = None + if credentials is None: + credentials, credentials_project = _get_default_credentials_with_project() + + # Prefer the project in this order: + # 1. Project explicitly specified by the user + # 2. Project set in the environment + # 3. Project associated with the default credentials + project = ( + project + or os.getenv(_ENV_DEFAULT_PROJECT) + or typing.cast(Optional[str], credentials_project) + ) + + if not project: + raise ValueError( + "Project must be set to initialize BigQuery client. " + "Try setting `bigframes.options.bigquery.project` first." + ) + self._application_name = ( - f"{_get_application_names()} {application_name}" + f"{_APPLICATION_NAME} {application_name}" if application_name - else _get_application_names() + else _APPLICATION_NAME ) self._project = project - - if use_regional_endpoints: - if location is None: - raise ValueError(bigframes.constants.LOCATION_NEEDED_FOR_REP_MESSAGE) - elif ( - location.lower() - not in bigframes.constants.REP_ENABLED_BIGQUERY_LOCATIONS - ): - raise ValueError( - bigframes.constants.REP_NOT_SUPPORTED_MESSAGE.format( - location=location - ) - ) self._location = location self._use_regional_endpoints = use_regional_endpoints - self._requests_transport_adapters = requests_transport_adapters - self._credentials = credentials - self._bq_kms_key_name = bq_kms_key_name - self._client_endpoints_override = client_endpoints_override # cloud clients initialized for lazy load - self._bqclient_lock = threading.Lock() self._bqclient = None - - self._bqconnectionclient_lock = threading.Lock() - self._bqconnectionclient: Optional[ - google.cloud.bigquery_connection_v1.ConnectionServiceClient - ] = None - - self._bqstoragereadclient_lock = threading.Lock() - self._bqstoragereadclient: Optional[ - google.cloud.bigquery_storage_v1.BigQueryReadClient - ] = None - - self._bqstoragewriteclient_lock = threading.Lock() - self._bqstoragewriteclient: Optional[ - google.cloud.bigquery_storage_v1.BigQueryWriteClient - ] = None - - self._cloudfunctionsclient_lock = threading.Lock() - self._cloudfunctionsclient: Optional[ - google.cloud.functions_v2.FunctionServiceClient - ] = None - - self._resourcemanagerclient_lock = threading.Lock() - self._resourcemanagerclient: Optional[ - google.cloud.resourcemanager_v3.ProjectsClient - ] = None - - self._storageclient_lock = threading.Lock() - self._storageclient: Optional[google.cloud.storage.Client] = None - - def _create_bigquery_client(self): - bq_options = None - if "bqclient" in self._client_endpoints_override: - bq_options = google.api_core.client_options.ClientOptions( - api_endpoint=self._client_endpoints_override["bqclient"] - ) - elif self._use_regional_endpoints: - bq_options = google.api_core.client_options.ClientOptions( - api_endpoint=_BIGQUERY_REGIONAL_ENDPOINT.format(location=self._location) - ) - - bq_info = google.api_core.client_info.ClientInfo( - user_agent=self._application_name - ) - - requests_session = google.auth.transport.requests.AuthorizedSession( - self._credentials - ) - for prefix, adapter in self._requests_transport_adapters: - requests_session.mount(prefix, adapter) - - bq_client = bigquery.Client( - client_info=bq_info, - client_options=bq_options, - project=self._project, - location=self._location, - # Use _http so that users can override - # requests options with transport adapters. See internal issue - # b/419106112. - _http=requests_session, - credentials=self._credentials, - ) - - # If a new enough client library is available, we opt-in to the faster - # backend behavior. This only affects code paths where query_and_wait is - # used, which doesn't expose a query job directly. See internal issue - # b/417985981. - if hasattr(bq_client, "default_job_creation_mode"): - bq_client.default_job_creation_mode = "JOB_CREATION_OPTIONAL" - - if self._bq_kms_key_name: - # Note: Key configuration only applies automatically to load and query jobs, not copy jobs. - encryption_config = bigquery.EncryptionConfiguration( - kms_key_name=self._bq_kms_key_name - ) - default_load_job_config = bigquery.LoadJobConfig() - default_query_job_config = bigquery.QueryJobConfig() - default_load_job_config.destination_encryption_configuration = ( - encryption_config - ) - default_query_job_config.destination_encryption_configuration = ( - encryption_config - ) - bq_client.default_load_job_config = default_load_job_config - bq_client.default_query_job_config = default_query_job_config - - return bq_client + self._bqconnectionclient = None + self._bqstoragereadclient = None + self._cloudfunctionsclient = None + self._resourcemanagerclient = None @property def bqclient(self): - with self._bqclient_lock: - if not self._bqclient: - self._bqclient = self._create_bigquery_client() + if not self._bqclient: + bq_options = None + if self._use_regional_endpoints: + bq_options = google.api_core.client_options.ClientOptions( + api_endpoint=_BIGQUERY_REGIONAL_ENDPOINT.format( + location=self._location + ), + ) + bq_info = google.api_core.client_info.ClientInfo( + user_agent=self._application_name + ) + self._bqclient = bigquery.Client( + client_info=bq_info, + client_options=bq_options, + credentials=self._credentials, + project=self._project, + location=self._location, + ) return self._bqclient @property def bqconnectionclient(self): - with self._bqconnectionclient_lock: - if not self._bqconnectionclient: - bqconnection_options = None - if "bqconnectionclient" in self._client_endpoints_override: - bqconnection_options = google.api_core.client_options.ClientOptions( - api_endpoint=self._client_endpoints_override[ - "bqconnectionclient" - ] + if not self._bqconnectionclient: + bqconnection_options = None + if self._use_regional_endpoints: + bqconnection_options = google.api_core.client_options.ClientOptions( + api_endpoint=_BIGQUERYCONNECTION_REGIONAL_ENDPOINT.format( + location=self._location ) - - bqconnection_info = google.api_core.gapic_v1.client_info.ClientInfo( - user_agent=self._application_name ) - self._bqconnectionclient = ( - google.cloud.bigquery_connection_v1.ConnectionServiceClient( - client_info=bqconnection_info, - client_options=bqconnection_options, - credentials=self._credentials, - ) + bqconnection_info = google.api_core.gapic_v1.client_info.ClientInfo( + user_agent=self._application_name + ) + self._bqconnectionclient = ( + google.cloud.bigquery_connection_v1.ConnectionServiceClient( + client_info=bqconnection_info, + client_options=bqconnection_options, + credentials=self._credentials, ) + ) return self._bqconnectionclient @property def bqstoragereadclient(self): - with self._bqstoragereadclient_lock: - if not self._bqstoragereadclient: - bqstorage_options = None - if "bqstoragereadclient" in self._client_endpoints_override: - bqstorage_options = google.api_core.client_options.ClientOptions( - api_endpoint=self._client_endpoints_override[ - "bqstoragereadclient" - ] + if not self._bqstoragereadclient: + bqstorage_options = None + if self._use_regional_endpoints: + bqstorage_options = google.api_core.client_options.ClientOptions( + api_endpoint=_BIGQUERYSTORAGE_REGIONAL_ENDPOINT.format( + location=self._location ) - elif self._use_regional_endpoints: - bqstorage_options = google.api_core.client_options.ClientOptions( - api_endpoint=_BIGQUERYSTORAGE_REGIONAL_ENDPOINT.format( - location=self._location - ) - ) - - bqstorage_info = google.api_core.gapic_v1.client_info.ClientInfo( - user_agent=self._application_name ) - self._bqstoragereadclient = ( - google.cloud.bigquery_storage_v1.BigQueryReadClient( - client_info=bqstorage_info, - client_options=bqstorage_options, - credentials=self._credentials, - ) + bqstorage_info = google.api_core.gapic_v1.client_info.ClientInfo( + user_agent=self._application_name + ) + self._bqstoragereadclient = ( + google.cloud.bigquery_storage_v1.BigQueryReadClient( + client_info=bqstorage_info, + client_options=bqstorage_options, + credentials=self._credentials, ) + ) return self._bqstoragereadclient - @property - def bqstoragewriteclient(self): - with self._bqstoragewriteclient_lock: - if not self._bqstoragewriteclient: - bqstorage_options = None - if "bqstoragewriteclient" in self._client_endpoints_override: - bqstorage_options = google.api_core.client_options.ClientOptions( - api_endpoint=self._client_endpoints_override[ - "bqstoragewriteclient" - ] - ) - elif self._use_regional_endpoints: - bqstorage_options = google.api_core.client_options.ClientOptions( - api_endpoint=_BIGQUERYSTORAGE_REGIONAL_ENDPOINT.format( - location=self._location - ) - ) - - bqstorage_info = google.api_core.gapic_v1.client_info.ClientInfo( - user_agent=self._application_name - ) - self._bqstoragewriteclient = ( - google.cloud.bigquery_storage_v1.BigQueryWriteClient( - client_info=bqstorage_info, - client_options=bqstorage_options, - credentials=self._credentials, - ) - ) - - return self._bqstoragewriteclient - @property def cloudfunctionsclient(self): - with self._cloudfunctionsclient_lock: - if not self._cloudfunctionsclient: - functions_info = google.api_core.gapic_v1.client_info.ClientInfo( - user_agent=self._application_name - ) - self._cloudfunctionsclient = ( - google.cloud.functions_v2.FunctionServiceClient( - client_info=functions_info, - credentials=self._credentials, - ) + if not self._cloudfunctionsclient: + functions_info = google.api_core.gapic_v1.client_info.ClientInfo( + user_agent=self._application_name + ) + self._cloudfunctionsclient = ( + google.cloud.functions_v2.FunctionServiceClient( + client_info=functions_info, + credentials=self._credentials, ) + ) return self._cloudfunctionsclient @property def resourcemanagerclient(self): - with self._resourcemanagerclient_lock: - if not self._resourcemanagerclient: - resourcemanager_info = google.api_core.gapic_v1.client_info.ClientInfo( - user_agent=self._application_name - ) - self._resourcemanagerclient = ( - google.cloud.resourcemanager_v3.ProjectsClient( - credentials=self._credentials, client_info=resourcemanager_info - ) + if not self._resourcemanagerclient: + resourcemanager_info = google.api_core.gapic_v1.client_info.ClientInfo( + user_agent=self._application_name + ) + self._resourcemanagerclient = ( + google.cloud.resourcemanager_v3.ProjectsClient( + credentials=self._credentials, client_info=resourcemanager_info ) + ) return self._resourcemanagerclient - - @property - def storageclient(self): - with self._storageclient_lock: - if not self._storageclient: - storage_info = google.api_core.client_info.ClientInfo( - user_agent=self._application_name - ) - self._storageclient = google.cloud.storage.Client( - client_info=storage_info, - credentials=self._credentials, - ) - - return self._storageclient diff --git a/bigframes/session/deferred.py b/bigframes/session/deferred.py deleted file mode 100644 index 75906e2a124..00000000000 --- a/bigframes/session/deferred.py +++ /dev/null @@ -1,76 +0,0 @@ -# Copyright 2026 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from __future__ import annotations - -from typing import Any, Callable, Optional, Union - -import pandas as pd - -import bigframes.dataframe -import bigframes.series - - -class DeferredBigQueryDataFrame: - """A proxy object that defers the execution of a BigQuery job until requested.""" - - def __init__( - self, - execution_func: Callable[ - [], - Union[ - bigframes.dataframe.DataFrame, - bigframes.series.Series, - pd.Series, - pd.DataFrame, - ], - ], - ): - self._execution_func = execution_func - self._result: Optional[ - Union[ - bigframes.dataframe.DataFrame, - bigframes.series.Series, - pd.Series, - pd.DataFrame, - ] - ] = None - - @property - def executed(self) -> bool: - return self._result is not None - - def execute( - self, - ) -> Union[ - bigframes.dataframe.DataFrame, - bigframes.series.Series, - pd.Series, - pd.DataFrame, - ]: - """Executes the deferred operation and returns the resulting DataFrame.""" - if self._result is None: - self._result = self._execution_func() - return self._result - - def _repr_mimebundle_(self, include=None, exclude=None): - from bigframes.display.anywidget import TableWidget - - return TableWidget(self)._repr_mimebundle_(include=include, exclude=exclude) # type: ignore - - def __getattr__(self, name: str) -> Any: - raise AttributeError( - f"'{type(self).__name__}' object has no attribute '{name}'. " - "This is a deferred object. Display it to run the query interactively." - ) diff --git a/bigframes/session/direct_gbq_execution.py b/bigframes/session/direct_gbq_execution.py deleted file mode 100644 index bcfc29ba971..00000000000 --- a/bigframes/session/direct_gbq_execution.py +++ /dev/null @@ -1,194 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -from __future__ import annotations - -import asyncio -from typing import Literal, Mapping, Optional, Tuple - -import google.api_core.exceptions -import google.cloud.bigquery.job as bq_job -import google.cloud.bigquery.table as bq_table -import google.cloud.bigquery_storage_v1 -from google.cloud import bigquery - -import bigframes -import bigframes.core.compile -import bigframes.core.events -import bigframes.session._io.bigquery as bq_io -import bigframes.session.metrics -from bigframes import exceptions as bfe -from bigframes.core import bq_data, compile, nodes -from bigframes.core.compile.configs import CompileRequest -from bigframes.session import execution_spec, executor, semi_executor - -_WRITE_DISPOSITIONS = { - "fail": bigquery.WriteDisposition.WRITE_EMPTY, - "replace": bigquery.WriteDisposition.WRITE_TRUNCATE, - "append": bigquery.WriteDisposition.WRITE_APPEND, -} - - -class DirectGbqExecutor(semi_executor.SemiExecutor): - def __init__( - self, - bqclient: bigquery.Client, - bqstoragereadclient: google.cloud.bigquery_storage_v1.BigQueryReadClient, - *, - publisher: bigframes.core.events.Publisher, - compiler: Literal["ibis", "sqlglot"] = "sqlglot", - metrics: Optional[bigframes.session.metrics.ExecutionMetrics] = None, - labels: Mapping[str, str] = {}, - ): - self.bqclient = bqclient - self._compiler_name = compiler - self._bqstoragereadclient = bqstoragereadclient - self._publisher = publisher - self._metrics = metrics - self._labels = labels - - async def execute( - self, - plan: nodes.BigFrameNode, - spec: execution_spec.ExecutionSpec, - ) -> executor.ExecuteResult: - """Just execute whatever plan as is, without further caching or decomposition.""" - compiled = compile.compile_sql( - CompileRequest( - plan, - sort_rows=spec.ordered, - peek_count=spec.peek, - ), - compiler_name=self._compiler_name, - ) - job_config = bigquery.QueryJobConfig() - dest_spec = spec.destination_spec - cluster_cols = None - can_skip_job = True - if isinstance(dest_spec, execution_spec.TableOutputSpec): - job_config.destination = dest_spec.table - job_config.write_disposition = _WRITE_DISPOSITIONS[dest_spec.if_exists] - cluster_cols = dest_spec.cluster_cols if dest_spec.cluster_cols else None - job_config.clustering_fields = cluster_cols - can_skip_job = False - elif isinstance(dest_spec, execution_spec.EphemeralTableSpec): - # Need destination table, but jobless execution might not create a destination table - can_skip_job = False - elif dest_spec is not None: - raise ValueError( - f"Direct GBQ Executor does not support destination: {dest_spec}" - ) - - job_config.labels["bigframes-dtypes"] = compiled.encoded_type_refs - if self._labels: - job_config.labels.update(self._labels) - if spec.bigquery_config is not None: - if spec.bigquery_config.extra_query_labels: - job_config.labels.update(spec.bigquery_config.extra_query_labels) - if spec.bigquery_config.maximum_bytes_billed is not None: - job_config.maximum_bytes_billed = ( - spec.bigquery_config.maximum_bytes_billed - ) - - iterator, query_job = await asyncio.to_thread( - self._run_execute_query, - sql=compiled.sql, - job_config=job_config, - query_with_job=(not can_skip_job), - session=plan.session, - cell_execution_count=spec.cell_execution_count, - ) - result_bq_data = None - if query_job and query_job.destination: - dst = query_job.destination - result_bq_data = bq_data.BigqueryDataSource( - table=bq_data.GbqNativeTable.from_ref_and_schema( - dst, - tuple(compiled.sql_schema), - cluster_cols=cluster_cols or (), - location=iterator.location or self.bqclient.location, - table_type="TABLE", - ), - schema=plan.schema, - ordering=compiled.row_order, - n_rows=iterator.total_rows, - ) - - execution_metadata = executor.ExecutionMetadata.from_iterator_and_job( - iterator, query_job - ) - result_mostly_cached = ( - hasattr(iterator, "_is_almost_completely_cached") - and iterator._is_almost_completely_cached() - ) - - if (isinstance(dest_spec, execution_spec.EphemeralTableSpec)) or ( - (result_bq_data is not None) and not result_mostly_cached - ): - assert result_bq_data is not None, "expected result table but none exists" - return executor.BQTableExecuteResult( - data=result_bq_data, - project_id=self.bqclient.project, - storage_client=self._bqstoragereadclient, - execution_metadata=execution_metadata, - selected_fields=tuple((col, col) for col in plan.schema.names), - ) - else: - return executor.LocalExecuteResult( - data=iterator.to_arrow().select(plan.schema.names), - bf_schema=plan.schema, - execution_metadata=execution_metadata, - ) - - def _run_execute_query( - self, - sql: str, - job_config: bq_job.QueryJobConfig, - query_with_job: bool, - session, - cell_execution_count: Optional[int] = None, - ) -> Tuple[bq_table.RowIterator, Optional[bigquery.QueryJob]]: - """ - Starts BigQuery query job and waits for results. - """ - try: - if query_with_job: - return bq_io.start_query_with_job( - self.bqclient, - sql, - job_config=job_config, - metrics=self._metrics, - publisher=self._publisher, - session=session, - cell_execution_count=cell_execution_count, - ) - else: - return ( - bq_io.start_query_job_optional( - self.bqclient, - sql, - job_config=job_config, - metrics=self._metrics, - publisher=self._publisher, - session=session, - cell_execution_count=cell_execution_count, - ), - None, - ) - except google.api_core.exceptions.BadRequest as e: - # Unfortunately, this error type does not have a separate error code or exception type - if "Resources exceeded during query execution" in e.message: - new_message = "Computation is too complex to execute as a single query. Try using DataFrame.cache() on intermediate results, or setting bigframes.options.compute.enable_multi_query_execution." - raise bfe.QueryComplexityError(new_message) from e - else: - raise diff --git a/bigframes/session/dry_runs.py b/bigframes/session/dry_runs.py deleted file mode 100644 index 03688b38cd3..00000000000 --- a/bigframes/session/dry_runs.py +++ /dev/null @@ -1,189 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -from __future__ import annotations - -import copy -from typing import Any, Dict, List, Sequence, Union - -import pandas -from google.cloud import bigquery - -from bigframes import dtypes -from bigframes.core import bigframe_node, bq_data, nodes - - -def get_table_stats( - table: Union[bq_data.GbqNativeTable, bq_data.BiglakeIcebergTable], -) -> pandas.Series: - values: List[Any] = [] - index: List[Any] = [] - - # Indicate that no query is executed. - index.append("isQuery") - values.append(False) - - # Populate column and index types - col_dtypes = dtypes.bf_type_from_type_kind(table.physical_schema) - index.append("columnCount") - values.append(len(col_dtypes)) - index.append("columnDtypes") - values.append(col_dtypes) - - # Add raw BQ schema - index.append("bigquerySchema") - values.append(table.physical_schema) - - index.append("numBytes") - values.append(table.metadata.numBytes) - index.append("numRows") - values.append(table.metadata.numRows) - index.append("location") - values.append(table.metadata.location) - index.append("type") - values.append(table.metadata.type) - - index.append("creationTime") - values.append(table.metadata.created_time) - - index.append("lastModifiedTime") - values.append(table.metadata.modified_time) - - return pandas.Series(values, index=index) - - -def get_query_stats_with_inferred_dtypes( - query_job: bigquery.QueryJob, - value_cols: Sequence[str], - index_cols: Sequence[str], -) -> pandas.Series: - if query_job.schema is None: - # If the schema is not available, don't bother inferring dtypes. - return get_query_stats(query_job) - - col_dtypes = dtypes.bf_type_from_type_kind(query_job.schema) - - if value_cols: - value_col_dtypes = { - col: col_dtypes[col] for col in value_cols if col in col_dtypes - } - else: - # Use every column that is not mentioned as an index column - value_col_dtypes = { - col: dtype - for col, dtype in col_dtypes.items() - if col not in set(index_cols) - } - - index_dtypes = [col_dtypes[col] for col in index_cols] - - return get_query_stats_with_dtypes(query_job, value_col_dtypes, index_dtypes) - - -def get_query_stats_with_dtypes( - query_job: bigquery.QueryJob, - column_dtypes: Dict[str, dtypes.Dtype], - index_dtypes: Sequence[dtypes.Dtype], - expr_root: bigframe_node.BigFrameNode | None = None, -) -> pandas.Series: - """ - Returns important stats from the query job as a Pandas Series. The dtypes information is added too. - - Args: - expr_root (Optional): - The root of the expression tree that may contain local data, whose size is added to the - total bytes count if available. - - """ - index = ["columnCount", "columnDtypes", "indexLevel", "indexDtypes"] - values = [len(column_dtypes), column_dtypes, len(index_dtypes), index_dtypes] - - s = pandas.Series(values, index=index) - - result = pandas.concat([s, get_query_stats(query_job)]) - if expr_root is not None: - result["totalBytesProcessed"] += get_local_bytes(expr_root) - return result - - -def get_query_stats( - query_job: bigquery.QueryJob, -) -> pandas.Series: - """Returns important stats from the query job as a Pandas Series.""" - - index: List[Any] = [] - values: List[Any] = [] - - # Add raw BQ schema - index.append("bigquerySchema") - values.append(query_job.schema) - - job_api_repr = copy.deepcopy(query_job._properties) - - # jobReference might not be populated for "job optional" queries. - job_ref = job_api_repr.get("jobReference", {}) - for key, val in job_ref.items(): - index.append(key) - values.append(val) - - configuration = job_api_repr.get("configuration", {}) - index.append("jobType") - values.append(configuration.get("jobType", None)) - index.append("dispatchedSql") - values.append(configuration.get("query", {}).get("query", None)) - - query_config = configuration.get("query", {}) - for key in ("destinationTable", "useLegacySql"): - index.append(key) - values.append(query_config.get(key, None)) - - statistics = job_api_repr.get("statistics", {}) - query_stats = statistics.get("query", {}) - for key in ( - "referencedTables", - "totalBytesProcessed", - "cacheHit", - "statementType", - ): - index.append(key) - values.append(query_stats.get(key, None)) - - creation_time = statistics.get("creationTime", None) - index.append("creationTime") - values.append( - pandas.Timestamp(creation_time, unit="ms", tz="UTC") - if creation_time is not None - else None - ) - - result = pandas.Series(values, index=index) - if result["totalBytesProcessed"] is None: - result["totalBytesProcessed"] = 0 - else: - result["totalBytesProcessed"] = int(result["totalBytesProcessed"]) - - return result - - -def get_local_bytes(root: bigframe_node.BigFrameNode) -> int: - def get_total_bytes( - root: bigframe_node.BigFrameNode, child_results: tuple[int, ...] - ) -> int: - child_bytes = sum(child_results) - - if isinstance(root, nodes.ReadLocalNode): - return child_bytes + root.local_data_source.data.get_total_buffer_size() - - return child_bytes - - return root.reduce_up(get_total_bytes) diff --git a/bigframes/session/environment.py b/bigframes/session/environment.py deleted file mode 100644 index 940f8deed4a..00000000000 --- a/bigframes/session/environment.py +++ /dev/null @@ -1,101 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -import importlib -import json -import os -import pathlib - -Path = pathlib.Path - - -# The identifier for GCP VS Code extension -# https://cloud.google.com/code/docs/vscode/install -GOOGLE_CLOUD_CODE_EXTENSION_NAME = "googlecloudtools.cloudcode" - - -# The identifier for BigQuery Jupyter notebook plugin -# https://cloud.google.com/bigquery/docs/jupyterlab-plugin -BIGQUERY_JUPYTER_PLUGIN_NAME = "bigquery_jupyter_plugin" - - -def _is_vscode_extension_installed(extension_id: str) -> bool: - """ - Checks if a given Visual Studio Code extension is installed. - Args: - extension_id: The ID of the extension (e.g., "ms-python.python"). - Returns: - True if the extension is installed, False otherwise. - """ - try: - # Determine the user's VS Code extensions directory. - user_home = Path.home() - vscode_extensions_dir = user_home / ".vscode" / "extensions" - - # Check if the extensions directory exists. - if not vscode_extensions_dir.exists(): - return False - - # Iterate through the subdirectories in the extensions directory. - extension_dirs = filter( - lambda p: p.is_dir() and p.name.startswith(extension_id + "-"), - vscode_extensions_dir.iterdir(), - ) - for extension_dir in extension_dirs: - # As a more robust check, the manifest file must exist. - manifest_path = extension_dir / "package.json" - if not manifest_path.exists() or not manifest_path.is_file(): - continue - - # Finally, the manifest file must be a valid json - with open(manifest_path, "r", encoding="utf-8") as f: - json.load(f) - - return True - except Exception: - pass - - return False - - -def _is_package_installed(package_name: str) -> bool: - """ - Checks if a Python package is installed. - Args: - package_name: The name of the package to check (e.g., "requests", "numpy"). - Returns: - True if the package is installed, False otherwise. - """ - try: - importlib.import_module(package_name) - return True - except Exception: - return False - - -def is_vscode() -> bool: - return os.getenv("VSCODE_PID") is not None - - -def is_jupyter() -> bool: - return os.getenv("JPY_PARENT_PID") is not None - - -def is_vscode_google_cloud_code_extension_installed() -> bool: - return _is_vscode_extension_installed(GOOGLE_CLOUD_CODE_EXTENSION_NAME) - - -def is_jupyter_bigquery_plugin_installed() -> bool: - return _is_package_installed(BIGQUERY_JUPYTER_PLUGIN_NAME) diff --git a/bigframes/session/execution_cache.py b/bigframes/session/execution_cache.py deleted file mode 100644 index ef4f324afce..00000000000 --- a/bigframes/session/execution_cache.py +++ /dev/null @@ -1,88 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from __future__ import annotations - -import dataclasses -import weakref -from typing import Mapping, Optional - -from bigframes.core import bq_data, local_data, nodes - -SourceIdMapping = Mapping[str, str] - - -@dataclasses.dataclass(frozen=True) -class UploadedLocalData: - bq_source: bq_data.BigqueryDataSource - source_mapping: SourceIdMapping - - -class ExecutionCache: - def __init__(self): - # effectively two separate caches that don't interact - self._cached_executions: weakref.WeakKeyDictionary[ - nodes.BigFrameNode, bq_data.BigqueryDataSource - ] = weakref.WeakKeyDictionary() - # This upload cache is entirely independent of the plan cache. - self._uploaded_local_data: weakref.WeakKeyDictionary[ - local_data.ManagedArrowTable, - UploadedLocalData, - ] = weakref.WeakKeyDictionary() - - def subsitute_cached_subplans(self, root: nodes.BigFrameNode) -> nodes.BigFrameNode: - def replace_if_cached(node: nodes.BigFrameNode) -> nodes.BigFrameNode: - if node not in self._cached_executions: - return node - # Assumption: GBQ cached table uses field name as bq column name - scan_list = nodes.ScanList( - tuple(nodes.ScanItem(field.id, field.id.sql) for field in node.fields) - ) - bq_data = self._cached_executions[node] - cached_replacement = nodes.CachedTableNode( - source=bq_data, - scan_list=scan_list, - table_session=node.session, - original_node=node, - ) - assert node.schema == cached_replacement.schema - return cached_replacement - - return nodes.top_down(root, replace_if_cached) - - def cache_results_table( - self, - original_root: nodes.BigFrameNode, - data: bq_data.BigqueryDataSource, - ): - self._cached_executions[original_root] = data - - ## Local data upload caching - def cache_remote_replacement( - self, - local_data: local_data.ManagedArrowTable, - bq_data: bq_data.BigqueryDataSource, - ): - # bq table has one extra column for offsets, those are implicit for local data - assert len(local_data.schema.items) + 1 == len(bq_data.table.physical_schema) - mapping = { - local_data.schema.items[i].column: bq_data.table.physical_schema[i].name - for i in range(len(local_data.schema)) - } - self._uploaded_local_data[local_data] = UploadedLocalData(bq_data, mapping) - - def get_uploaded_local_data( - self, local_data: local_data.ManagedArrowTable - ) -> Optional[UploadedLocalData]: - return self._uploaded_local_data.get(local_data) diff --git a/bigframes/session/execution_spec.py b/bigframes/session/execution_spec.py deleted file mode 100644 index 89de6eec902..00000000000 --- a/bigframes/session/execution_spec.py +++ /dev/null @@ -1,146 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from __future__ import annotations - -import dataclasses -from typing import Literal, Mapping, Optional, Union - -from google.cloud import bigquery - -from bigframes._config import ComputeOptions - - -@dataclasses.dataclass(frozen=True) -class BqComputeOptions: - enable_multi_query_execution: bool = True - maximum_bytes_billed: Optional[int] = None - extra_query_labels: tuple[tuple[str, str], ...] = () - - @classmethod - def from_compute_options(cls, compute_options: ComputeOptions) -> BqComputeOptions: - return cls( - enable_multi_query_execution=compute_options.enable_multi_query_execution, - maximum_bytes_billed=compute_options.maximum_bytes_billed, - extra_query_labels=tuple(compute_options.extra_query_labels.items()), - ) - - def push_labels(self, labels: Mapping[str, str]) -> BqComputeOptions: - return dataclasses.replace( - self, - extra_query_labels=tuple(labels.items()) + self.extra_query_labels, - ) - - -@dataclasses.dataclass(frozen=True) -class ExecutionSpec: - # destination for the result of the operation. Executor may also incidentally create other temporary tables for its own purposes. - destination_spec: Union[ - TableOutputSpec, GcsOutputSpec, EphemeralTableSpec, None - ] = None - # If set, the result will be truncated to the given number of rows. Which N rows is - # implementation dependent and not stable. - peek: Optional[int] = None - # Controls whether output iterator is ordered. Cannot be true if destination is not - # guaranteed to be ordered. - ordered: bool = False - # This is an optimization flag for gbq execution, it doesn't change semantics, but if promise is falsely made, errors may occur - promise_under_10gb: bool = False - - # BigQuery specific options - bigquery_config: Optional[BqComputeOptions] = None - cell_execution_count: Optional[int] = None - - def with_bq_labels(self, labels: Mapping[str, str]) -> ExecutionSpec: - bq_config = self.bigquery_config or BqComputeOptions() - return dataclasses.replace(self, bigquery_config=bq_config.push_labels(labels)) - - def with_compute_options(self, compute_options: ComputeOptions) -> ExecutionSpec: - """ - Grabs the current global or thread-local config and binds it to the execution spec. - - Returns a new ExecutionSpec with the current configuration applied. - """ - new_bq_config = BqComputeOptions.from_compute_options(compute_options) - if self.bigquery_config: - # merge labels, new ComputeOptions takes priority for everything else - new_bq_config = new_bq_config.push_labels( - dict(self.bigquery_config.extra_query_labels) - ) - - cell_execution_count = self.cell_execution_count - if cell_execution_count is None: - from bigframes.core.utils import get_ipython_execution_count - - cell_execution_count = get_ipython_execution_count() - - return dataclasses.replace( - self, - bigquery_config=new_bq_config, - cell_execution_count=cell_execution_count, - ) - - -# Used internally by execution -@dataclasses.dataclass(frozen=True) -class EphemeralTableSpec: - """ - Specifies that the result of an operation should be a temporary table of some sort. - - No guarantees on lifetime, may be a session temp table, or a bq-created temp table with <24hr life. - - Used internally when results need temporary staging, because they are large (>10GB), or needed in subsequent operations. - """ - - pass - - -@dataclasses.dataclass(frozen=True) -class CacheSpec: - """ - Specifies that the result of an operation should be a session temp table. - The table will be automatically deleted after the session ends. - """ - - cluster_cols: tuple[ - str, ... - ] = () # if empty, will cluster using order key if ordering_key is set - # Controls ordering and whether extra columns are materialized to preserve ordering - # Any extra columns will be appended to the end of the schema. - # None: ordering may be discarded entirely (ordering metadata will still be provided if ordering is derivable from materialized columns) - # order_rows: the result iterator itself will be ordered. For gbq execution, result cannot exceed 10GB. - # order_key: the result set ordered by a key, may materialize extra columns. - # offsets_col: order the result set by an offsets column, materializes one extra column. - ordering: Literal["order_rows", "offsets_col", "order_key"] | None = None - - -@dataclasses.dataclass(frozen=True) -class TableOutputSpec: - """ - Specifies that the result of an operation should be exported to a specific named table. - - The executor is not responsible for managing lifecycle of the table. - """ - - table: bigquery.TableReference - cluster_cols: tuple[str, ...] = () - if_exists: Literal["fail", "replace", "append"] = "fail" - - -@dataclasses.dataclass(frozen=True) -class GcsOutputSpec: - uri: str - format: Literal["json", "csv", "parquet"] - # sequence of (option, value) pairs - export_options: tuple[tuple[str, Union[bool, str]], ...] diff --git a/bigframes/session/executor.py b/bigframes/session/executor.py deleted file mode 100644 index ba5ac60d74f..00000000000 --- a/bigframes/session/executor.py +++ /dev/null @@ -1,350 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from __future__ import annotations - -import abc -import dataclasses -import functools -import itertools -from typing import Iterator, Literal, Optional, Sequence, Union - -import google.cloud.bigquery.table as bq_table -import pandas as pd -import pyarrow -import pyarrow as pa -from google.cloud import bigquery, bigquery_storage_v1 - -import bigframes -import bigframes.core -import bigframes.core.schema -import bigframes.dtypes -import bigframes.session._io.pandas as io_pandas -import bigframes.session.execution_spec as ex_spec -from bigframes.core import bq_data, local_data, pyarrow_utils - -_ROW_LIMIT_EXCEEDED_TEMPLATE = ( - "Execution has downloaded {result_rows} rows so far, which exceeds the " - "limit of {maximum_result_rows}. You can adjust this limit by setting " - "`bpd.options.compute.maximum_result_rows`." -) - - -class ResultsIterator(Iterator[pa.RecordBatch]): - """ - Iterator for query results, with some extra metadata attached. - """ - - def __init__( - self, - batches: Iterator[pa.RecordBatch], - schema: bigframes.core.schema.ArraySchema, - total_rows: Optional[int] = 0, - total_bytes: Optional[int] = 0, - ): - self._batches = batches - self._schema = schema - self._total_rows = total_rows - self._total_bytes = total_bytes - - @property - def approx_total_rows(self) -> Optional[int]: - return self._total_rows - - @property - def approx_total_bytes(self) -> Optional[int]: - return self._total_bytes - - def __next__(self) -> pa.RecordBatch: - return next(self._batches) - - @property - def arrow_batches(self) -> Iterator[pyarrow.RecordBatch]: - result_rows = 0 - - for batch in self._batches: - result_rows += batch.num_rows - - maximum_result_rows = bigframes.options.compute.maximum_result_rows - if maximum_result_rows is not None and result_rows > maximum_result_rows: - message = bigframes.exceptions.format_message( - _ROW_LIMIT_EXCEEDED_TEMPLATE.format( - result_rows=result_rows, - maximum_result_rows=maximum_result_rows, - ) - ) - raise bigframes.exceptions.MaximumResultRowsExceeded(message) - - yield batch - - def to_arrow_table(self, limit: Optional[int] = None) -> pyarrow.Table: - # Need to provide schema if no result rows, as arrow can't infer - # If ther are rows, it is safest to infer schema from batches. - # Any discrepencies between predicted schema and actual schema will produce errors. - batches = iter(self.arrow_batches) - peek_it = itertools.islice(batches, 0, 1) - peek_value = list(peek_it) - # TODO: Enforce our internal schema on the table for consistency - if len(peek_value) > 0: - batches = itertools.chain(peek_value, batches) # reconstruct - if limit: - batches = pyarrow_utils.truncate_pyarrow_iterable( - batches, max_results=limit - ) - return pyarrow.Table.from_batches(batches) - else: - try: - return self._schema.to_pyarrow().empty_table() - except pa.ArrowNotImplementedError: - # Bug with some pyarrow versions, empty_table only supports base storage types, not extension types. - return self._schema.to_pyarrow(use_storage_types=True).empty_table() - - def to_pandas(self, limit: Optional[int] = None) -> pd.DataFrame: - return io_pandas.arrow_to_pandas(self.to_arrow_table(limit=limit), self._schema) - - def to_pandas_batches( - self, page_size: Optional[int] = None, max_results: Optional[int] = None - ) -> Iterator[pd.DataFrame]: - assert (page_size is None) or (page_size > 0) - assert (max_results is None) or (max_results > 0) - batch_iter: Iterator[Union[pyarrow.Table, pyarrow.RecordBatch]] = ( - self.arrow_batches - ) - if max_results is not None: - batch_iter = pyarrow_utils.truncate_pyarrow_iterable( - batch_iter, max_results - ) - - if page_size is not None: - batches_iter = pyarrow_utils.chunk_by_row_count(batch_iter, page_size) - batch_iter = map( - lambda batches: pyarrow.Table.from_batches(batches), batches_iter - ) - - yield from map( - functools.partial(io_pandas.arrow_to_pandas, schema=self._schema), - batch_iter, - ) - - def to_py_scalar(self): - columns = list(self.to_arrow_table().to_pydict().values()) - if len(columns) != 1: - raise ValueError( - f"Expected single column result, got {len(columns)} columns." - ) - column = columns[0] - if len(column) != 1: - raise ValueError(f"Expected single row result, got {len(column)} rows.") - return column[0] - - -class ExecuteResult(abc.ABC): - @property - @abc.abstractmethod - def execution_metadata(self) -> ExecutionMetadata: ... - - @property - @abc.abstractmethod - def schema(self) -> bigframes.core.schema.ArraySchema: ... - - @abc.abstractmethod - def batches(self, sample_rate: Optional[float] = None) -> ResultsIterator: ... - - @property - def query_job(self) -> Optional[bigquery.QueryJob]: - return self.execution_metadata.query_job - - @property - def total_bytes_processed(self) -> Optional[int]: - return self.execution_metadata.bytes_processed - - -@dataclasses.dataclass(frozen=True) -class ExecutionMetadata: - query_job: Optional[bigquery.QueryJob] = None - bytes_processed: Optional[int] = None - - @classmethod - def from_iterator_and_job( - cls, iterator: bq_table.RowIterator, job: Optional[bigquery.QueryJob] - ) -> ExecutionMetadata: - return cls(query_job=job, bytes_processed=iterator.total_bytes_processed) - - -class LocalExecuteResult(ExecuteResult): - def __init__( - self, - data: pa.Table, - bf_schema: bigframes.core.schema.ArraySchema, - execution_metadata: ExecutionMetadata = ExecutionMetadata(), - ): - self._data = local_data.ManagedArrowTable.from_pyarrow(data, bf_schema) - self._execution_metadata = execution_metadata - - @property - def execution_metadata(self) -> ExecutionMetadata: - return self._execution_metadata - - @property - def schema(self) -> bigframes.core.schema.ArraySchema: - return self._data.schema - - def batches(self, sample_rate: Optional[float] = None) -> ResultsIterator: - return ResultsIterator( - iter(self._data.to_arrow(sample_rate=sample_rate)[1]), - self.schema, - self._data.metadata.row_count, - self._data.metadata.total_bytes, - ) - - -class EmptyExecuteResult(ExecuteResult): - def __init__( - self, - bf_schema: bigframes.core.schema.ArraySchema, - execution_metadata: ExecutionMetadata = ExecutionMetadata(), - ): - self._schema = bf_schema - self._execution_metadata = execution_metadata - - @property - def execution_metadata(self) -> ExecutionMetadata: - return self._execution_metadata - - @property - def schema(self) -> bigframes.core.schema.ArraySchema: - return self._schema - - def batches(self, sample_rate: Optional[float] = None) -> ResultsIterator: - return ResultsIterator(iter([]), self.schema, 0, 0) - - -class BQTableExecuteResult(ExecuteResult): - def __init__( - self, - data: bq_data.BigqueryDataSource, - storage_client: bigquery_storage_v1.BigQueryReadClient, - project_id: str, - *, - execution_metadata: ExecutionMetadata = ExecutionMetadata(), - limit: Optional[int] = None, - selected_fields: Optional[Sequence[tuple[str, str]]] = None, - ): - self._data = data - self._project_id = project_id - self._execution_metadata = execution_metadata - self._storage_client = storage_client - self._limit = limit - self._selected_fields = selected_fields or [ - (name, name) for name in data.schema.names - ] - - @property - def execution_metadata(self) -> ExecutionMetadata: - return self._execution_metadata - - @property - @functools.cache - def schema(self) -> bigframes.core.schema.ArraySchema: - source_ids = [selection[0] for selection in self._selected_fields] - return self._data.schema.select(source_ids).rename(dict(self._selected_fields)) - - def batches(self, sample_rate: Optional[float] = None) -> ResultsIterator: - read_batches = bq_data.get_arrow_batches( - self._data, - [x[0] for x in self._selected_fields], - self._storage_client, - self._project_id, - sample_rate=sample_rate, - ) - arrow_batches: Iterator[pa.RecordBatch] = map( - functools.partial( - pyarrow_utils.rename_batch, names=list(self.schema.names) - ), - read_batches.iter, - ) - approx_bytes: Optional[int] = read_batches.approx_bytes - approx_rows: Optional[int] = self._data.n_rows or read_batches.approx_rows - - if self._limit is not None: - if approx_rows is not None: - approx_rows = min(approx_rows, self._limit) - arrow_batches = pyarrow_utils.truncate_pyarrow_iterable( - arrow_batches, self._limit - ) - - if self._data.sql_predicate: - approx_bytes = None - approx_rows = None - - return ResultsIterator(arrow_batches, self.schema, approx_rows, approx_bytes) - - -@dataclasses.dataclass(frozen=True) -class HierarchicalKey: - columns: tuple[str, ...] - - -@dataclasses.dataclass(frozen=True) -class CacheConfig(abc.ABC): - optimize_for: Union[Literal["auto", "head"], HierarchicalKey] = "auto" - if_cached: Literal["reuse-strict", "reuse-any", "replace"] = "reuse-any" - enable_multi_query_execution: Optional[bool] = None - - -class Executor(abc.ABC): - """ - Interface for an executor, which compiles and executes ArrayValue objects. - """ - - def to_sql( - self, - array_value: bigframes.core.ArrayValue, - offset_column: Optional[str] = None, - ordered: bool = False, - enable_cache: bool = True, - ) -> str: - """ - Convert an ArrayValue to a sql query that will yield its value. - """ - raise NotImplementedError("to_sql not implemented for this executor") - - @abc.abstractmethod - def execute( - self, - array_value: bigframes.core.ArrayValue, - execution_spec: ex_spec.ExecutionSpec, - ) -> ExecuteResult: - """ - Execute the ArrayValue. - """ - ... - - def dry_run( - self, array_value: bigframes.core.ArrayValue, ordered: bool = True - ) -> bigquery.QueryJob: - """ - Dry run executing the ArrayValue. - - Does not actually execute the data but will get stats and indicate any invalid query errors. - """ - raise NotImplementedError("dry_run not implemented for this executor") - - def cached( - self, - array_value: bigframes.core.ArrayValue, - *, - config: CacheConfig, - ) -> None: - raise NotImplementedError("cached not implemented for this executor") diff --git a/bigframes/session/iceberg.py b/bigframes/session/iceberg.py deleted file mode 100644 index 0d2539f5554..00000000000 --- a/bigframes/session/iceberg.py +++ /dev/null @@ -1,207 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from __future__ import annotations - -import datetime -import json -import urllib.parse -from typing import List - -import google.auth.transport.requests -import google.cloud.bigquery as bq -import pyiceberg -import pyiceberg.schema -import pyiceberg.types -import requests -from pyiceberg.catalog import load_catalog - -from bigframes.core import bq_data - - -def get_table( - user_project_id: str, full_table_id: str, credentials -) -> bq_data.BiglakeIcebergTable: - table_parts = full_table_id.split(".") - if len(table_parts) != 4: - raise ValueError("Iceberg catalog table must contain exactly 4 parts") - - catalog_project_id, catalog_id, namespace, table = table_parts - - credentials.refresh(google.auth.transport.requests.Request()) - token = credentials.token - - base_uri = "https://biglake.googleapis.com/iceberg/v1/restcatalog" - - # Maybe can drop the pyiceberg dependency at some point, but parsing through raw schema json seems a bit painful - catalog = load_catalog( - f"{catalog_project_id}.{catalog_id}", - **{ - "uri": base_uri, - "header.x-goog-user-project": user_project_id, - "oauth2-server-uri": "https://oauth2.googleapis.com/token", - "token": token, - "warehouse": f"gs://{catalog_id}", - }, - ) - - response = requests.get( - f"{base_uri}/extensions/projects/{urllib.parse.quote(catalog_project_id, safe='')}/catalogs/{urllib.parse.quote(catalog_id, safe='')}", - headers={ - "Authorization": f"Bearer {credentials.token}", - "Content-Type": "application/json", - "header.x-goog-user-project": user_project_id, - }, - ) - response.raise_for_status() - location = _extract_location_from_catalog_extension_data(response) - - iceberg_table = catalog.load_table(f"{namespace}.{table}") - bq_schema = pyiceberg.schema.visit(iceberg_table.schema(), SchemaVisitor()) - # TODO: Handle physical layout to help optimize - # TODO: Use snapshot metadata to get row, byte counts - return bq_data.BiglakeIcebergTable( - catalog_project_id, - catalog_id, - namespace, - table, - physical_schema=bq_schema, # type: ignore - cluster_cols=(), - metadata=bq_data.TableMetadata( - location=location, - type="TABLE", - modified_time=datetime.datetime.fromtimestamp( - iceberg_table.metadata.last_updated_ms / 1000.0 - ), - ), - ) - - -def _extract_location_from_catalog_extension_data(data): - catalog_extension_metadata = json.loads(data.text) - storage_region = catalog_extension_metadata["storage-regions"][ - 0 - ] # assumption: exactly 1 region - replicas = tuple(item["region"] for item in catalog_extension_metadata["replicas"]) - return bq_data.GcsRegion(storage_region, replicas) - - -class SchemaVisitor(pyiceberg.schema.SchemaVisitorPerPrimitiveType[bq.SchemaField]): - # Override returns a tuple of fields instead of a single field, violating supertype signature but intentional for this visitor. - def schema( # type: ignore[override] - self, schema: pyiceberg.schema.Schema, struct_result: bq.SchemaField - ) -> tuple[bq.SchemaField, ...]: - return tuple(f for f in struct_result.fields) - - def struct( - self, struct: pyiceberg.types.StructType, field_results: List[bq.SchemaField] - ) -> bq.SchemaField: - return bq.SchemaField("", "RECORD", fields=field_results) - - def field( - self, field: pyiceberg.types.NestedField, field_result: bq.SchemaField - ) -> bq.SchemaField: - return bq.SchemaField( - field.name, - field_result.field_type, - mode=field_result.mode or "NULLABLE", - fields=field_result.fields, - ) - - def map( - self, - map_type: pyiceberg.types.MapType, - key_result: bq.SchemaField, - value_result: bq.SchemaField, - ) -> bq.SchemaField: - return bq.SchemaField("", "UNKNOWN") - - def list( - self, list_type: pyiceberg.types.ListType, element_result: bq.SchemaField - ) -> bq.SchemaField: - return bq.SchemaField( - "", element_result.field_type, mode="REPEATED", fields=element_result.fields - ) - - def visit_fixed(self, fixed_type: pyiceberg.types.FixedType) -> bq.SchemaField: - return bq.SchemaField("", "UNKNOWN") - - def visit_decimal( - self, decimal_type: pyiceberg.types.DecimalType - ) -> bq.SchemaField: - # BIGNUMERIC not supported in iceberg tables yet, so just assume numeric - return bq.SchemaField("", "NUMERIC") - - def visit_boolean( - self, boolean_type: pyiceberg.types.BooleanType - ) -> bq.SchemaField: - return bq.SchemaField("", "NUMERIC") - - def visit_integer( - self, integer_type: pyiceberg.types.IntegerType - ) -> bq.SchemaField: - return bq.SchemaField("", "INTEGER") - - def visit_long(self, long_type: pyiceberg.types.LongType) -> bq.SchemaField: - return bq.SchemaField("", "INTEGER") - - def visit_float(self, float_type: pyiceberg.types.FloatType) -> bq.SchemaField: - # 32-bit IEEE 754 floating point - return bq.SchemaField("", "FLOAT") - - def visit_double(self, double_type: pyiceberg.types.DoubleType) -> bq.SchemaField: - # 64-bit IEEE 754 floating point - return bq.SchemaField("", "FLOAT") - - def visit_date(self, date_type: pyiceberg.types.DateType) -> bq.SchemaField: - # Date encoded as an int - return bq.SchemaField("", "DATE") - - def visit_time(self, time_type: pyiceberg.types.TimeType) -> bq.SchemaField: - return bq.SchemaField("", "TIME") - - def visit_timestamp( - self, timestamp_type: pyiceberg.types.TimestampType - ) -> bq.SchemaField: - return bq.SchemaField("", "DATETIME") - - def visit_timestamp_ns( - self, timestamp_type: pyiceberg.types.TimestampNanoType - ) -> bq.SchemaField: - return bq.SchemaField("", "UNKNOWN") - - def visit_timestamptz( - self, timestamptz_type: pyiceberg.types.TimestamptzType - ) -> bq.SchemaField: - return bq.SchemaField("", "TIMESTAMP") - - def visit_timestamptz_ns( - self, timestamptz_ns_type: pyiceberg.types.TimestamptzNanoType - ) -> bq.SchemaField: - return bq.SchemaField("", "UNKNOWN") - - def visit_string(self, string_type: pyiceberg.types.StringType) -> bq.SchemaField: - return bq.SchemaField("", "STRING") - - def visit_uuid(self, uuid_type: pyiceberg.types.UUIDType) -> bq.SchemaField: - return bq.SchemaField("", "UNKNOWN") - - def visit_unknown( - self, unknown_type: pyiceberg.types.UnknownType - ) -> bq.SchemaField: - """Type `UnknownType` can be promoted to any primitive type in V3+ tables per the Iceberg spec.""" - return bq.SchemaField("", "UNKNOWN") - - def visit_binary(self, binary_type: pyiceberg.types.BinaryType) -> bq.SchemaField: - return bq.SchemaField("", "BINARY") diff --git a/bigframes/session/loader.py b/bigframes/session/loader.py deleted file mode 100644 index 43f45a500f0..00000000000 --- a/bigframes/session/loader.py +++ /dev/null @@ -1,1572 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from __future__ import annotations - -import concurrent -import concurrent.futures -import copy -import dataclasses -import datetime -import io -import itertools -import math -import os -import threading -import typing -import warnings -from typing import ( - IO, - Dict, - Hashable, - Iterable, - Iterator, - List, - Literal, - Optional, - Sequence, - Tuple, - TypeVar, - Union, - cast, - overload, -) - -import bigframes_vendored.constants as constants -import bigframes_vendored.pandas.io.gbq as third_party_pandas_gbq -import google.api_core.exceptions -import google.cloud.bigquery -import google.cloud.bigquery as bigquery -import google.cloud.bigquery.table -import pandas -import pyarrow as pa -from google.cloud import bigquery_storage_v1 -from google.cloud.bigquery.job.load import LoadJob -from google.cloud.bigquery.job.query import QueryJob -from google.cloud.bigquery_storage_v1 import ( - types as bq_storage_types, -) -from google.cloud.bigquery_storage_v1 import ( - writer as bq_storage_writer, -) - -import bigframes._tools -import bigframes._tools.strings -import bigframes.core as core -import bigframes.core.blocks as blocks -import bigframes.core.events -import bigframes.core.schema as schemata -import bigframes.dtypes -import bigframes.exceptions as bfe -import bigframes.formatting_helpers as formatting_helpers -import bigframes.session._io.bigquery as bf_io_bigquery -import bigframes.session._io.bigquery.read_gbq_query as bf_read_gbq_query -import bigframes.session._io.bigquery.read_gbq_table as bf_read_gbq_table -import bigframes.session.iceberg -import bigframes.session.metrics -import bigframes.session.temporary_storage -import bigframes.session.time as session_time -from bigframes.core import ( - bq_data, - guid, - identifiers, - local_data, - nodes, - ordering, - utils, -) -from bigframes.session import dry_runs - -# Avoid circular imports. -if typing.TYPE_CHECKING: - import bigframes.dataframe as dataframe - import bigframes.session - -_PLACEHOLDER_SCHEMA = ( - google.cloud.bigquery.SchemaField("bf_loader_placeholder", "INTEGER"), -) - -_LOAD_JOB_TYPE_OVERRIDES = { - # Json load jobs not supported yet: b/271321143 - bigframes.dtypes.JSON_DTYPE: "STRING", - # Timedelta is emulated using integer in bq type system - bigframes.dtypes.TIMEDELTA_DTYPE: "INTEGER", -} - -_STREAM_JOB_TYPE_OVERRIDES = { - # Timedelta is emulated using integer in bq type system - bigframes.dtypes.TIMEDELTA_DTYPE: "INTEGER", -} - -TABLE_TYPE = Union[bq_data.GbqNativeTable, bq_data.BiglakeIcebergTable] - - -def _to_index_cols( - index_col: Iterable[str] | str | bigframes.enums.DefaultIndexKind = (), -) -> List[str]: - """Convert index_col into a list of column names.""" - if isinstance(index_col, bigframes.enums.DefaultIndexKind): - index_cols: List[str] = [] - elif isinstance(index_col, str): - index_cols = [index_col] - else: - index_cols = list(index_col) - - return index_cols - - -def _check_duplicates(name: str, columns: Optional[Iterable[str]] = None): - """Check for duplicate column names in the provided iterable.""" - if columns is None: - return - columns_list = list(columns) - set_columns = set(columns_list) - if len(columns_list) > len(set_columns): - raise ValueError( - f"The '{name}' argument contains duplicate names. " - f"All column names specified in '{name}' must be unique." - ) - - -def _check_index_col_param( - index_cols: Iterable[str], - columns: Iterable[str], - *, - table_columns: Optional[Iterable[str]] = None, - index_col_in_columns: Optional[bool] = False, -): - """Checks for duplicates in `index_cols` and resolves overlap with `columns`. - - Args: - index_cols (Iterable[str]): - Column names designated as the index columns. - columns (Iterable[str]): - Used column names from table_columns. - table_columns (Iterable[str]): - A full list of column names in the table schema. - index_col_in_columns (bool): - A flag indicating how to handle overlap between `index_cols` and - `columns`. - - If `False`, the two lists must be disjoint (contain no common - elements). An error is raised if any overlap is found. - - If `True`, `index_cols` is expected to be a subset of - `columns`. An error is raised if an index column is not found - in the `columns` list. - """ - _check_duplicates("index_col", index_cols) - - if columns is not None and len(list(columns)) > 0: - set_index = set(list(index_cols) if index_cols is not None else []) - set_columns = set(list(columns) if columns is not None else []) - - if index_col_in_columns: - if not set_index.issubset(set_columns): - raise ValueError( - f"The specified index column(s) were not found: {set_index - set_columns}. " - f"Available columns are: {set_columns}" - ) - else: - if not set_index.isdisjoint(set_columns): - raise ValueError( - "Found column names that exist in both 'index_col' and 'columns' arguments. " - "These arguments must specify distinct sets of columns." - ) - - if not index_col_in_columns and table_columns is not None: - for key in index_cols: - if key not in table_columns: - possibility = min( - table_columns, - key=lambda item: bigframes._tools.strings.levenshtein_distance( - key, item - ), - ) - raise ValueError( - f"Column '{key}' of `index_col` not found in this table. Did you mean '{possibility}'?" - ) - - -def _check_columns_param(columns: Iterable[str], table_columns: Iterable[str]): - """Validates that the specified columns are present in the table columns. - - Args: - columns (Iterable[str]): - Used column names from table_columns. - table_columns (Iterable[str]): - A full list of column names in the table schema. - Raises: - ValueError: If any column in `columns` is not found in the table columns. - """ - for column_name in columns: - if column_name not in table_columns: - possibility = min( - table_columns, - key=lambda item: bigframes._tools.strings.levenshtein_distance( - column_name, item - ), - ) - raise ValueError( - f"Column '{column_name}' is not found. Did you mean '{possibility}'?" - ) - - -def _check_names_param( - names: Iterable[str], - index_col: Iterable[str] - | str - | Iterable[int] - | int - | bigframes.enums.DefaultIndexKind, - columns: Iterable[str], - table_columns: Iterable[str], -): - len_names = len(list(names)) - len_table_columns = len(list(table_columns)) - len_columns = len(list(columns)) - if len_names > len_table_columns: - raise ValueError( - f"Too many columns specified: expected {len_table_columns}" - f" and found {len_names}" - ) - elif len_names < len_table_columns: - if isinstance(index_col, bigframes.enums.DefaultIndexKind) or index_col != (): - raise KeyError( - "When providing both `index_col` and `names`, ensure the " - "number of `names` matches the number of columns in your " - "data." - ) - if len_columns != 0: - # The 'columns' must be identical to the 'names'. If not, raise an error. - if len_columns != len_names: - raise ValueError( - "Number of passed names did not match number of header " - "fields in the file" - ) - if set(list(names)) != set(list(columns)): - raise ValueError("Usecols do not match columns") - - -@dataclasses.dataclass -class GbqDataLoader: - """ - Responsible for loading data into BigFrames using temporary bigquery tables. - - This loader is constrained to loading local data and queries against data sources in the same region as the storage manager. - - - Args: - session (bigframes.session.Session): - The session the data will be loaded into. Objects will not be compatible with other sessions. - bqclient (bigquery.Client): - An object providing client library objects. - storage_manager (bigframes.session.temp_storage.TemporaryGbqStorageManager): - Manages temporary storage used by the loader. - default_index_type (bigframes.enums.DefaultIndexKind): - Determines the index type created for data loaded from gcs or gbq. - scan_index_uniqueness (bool): - Whether the loader will scan index columns to determine whether the values are unique. - This behavior is useful in total ordering mode to use index column as order key. - metrics (bigframes.session.metrics.ExecutionMetrics or None): - Used to record query execution statistics. - """ - - def __init__( - self, - session: bigframes.session.Session, - bqclient: bigquery.Client, - write_client: bigquery_storage_v1.BigQueryWriteClient, - storage_manager: bigframes.session.temporary_storage.TemporaryStorageManager, - default_index_type: bigframes.enums.DefaultIndexKind, - scan_index_uniqueness: bool, - force_total_order: bool, - metrics: Optional[bigframes.session.metrics.ExecutionMetrics] = None, - *, - publisher: bigframes.core.events.Publisher, - ): - self._bqclient = bqclient - self._write_client = write_client - self._storage_manager = storage_manager - self._default_index_type = default_index_type - self._scan_index_uniqueness = scan_index_uniqueness - self._force_total_order = force_total_order - self._df_snapshot: Dict[str, Tuple[datetime.datetime, TABLE_TYPE]] = {} - self._metrics = metrics - self._publisher = publisher - # Unfortunate circular reference, but need to pass reference when constructing objects - self._session = session - self._clock = session_time.BigQuerySyncedClock(bqclient) - self._clock.sync() - self._threadpool = concurrent.futures.ThreadPoolExecutor( - max_workers=1, thread_name_prefix="bigframes-loader" - ) - - def read_data_async( - self, local_data: local_data.ManagedArrowTable, offsets_col: str - ) -> concurrent.futures.Future[bq_data.BigqueryDataSource]: - future = self._threadpool.submit( - self._load_data_or_write_data, local_data, offsets_col - ) - return future - - def read_pandas( - self, - pandas_dataframe: pandas.DataFrame, - method: Literal["load", "stream", "write"], - ) -> dataframe.DataFrame: - # TODO: Push this into from_pandas, along with index flag - from bigframes import dataframe - - val_cols, idx_cols = utils.get_standardized_ids( - pandas_dataframe.columns, pandas_dataframe.index.names, strict=True - ) - prepared_df = pandas_dataframe.reset_index(drop=False).set_axis( - [*idx_cols, *val_cols], axis="columns" - ) - managed_data = local_data.ManagedArrowTable.from_pandas(prepared_df) - block = blocks.Block( - self.read_managed_data(managed_data, method=method), - index_columns=idx_cols, - column_labels=pandas_dataframe.columns, - index_labels=pandas_dataframe.index.names, - ) - return dataframe.DataFrame(block) - - def read_managed_data( - self, - data: local_data.ManagedArrowTable, - method: Literal["load", "stream", "write"], - ) -> core.ArrayValue: - offsets_col = guid.generate_guid("upload_offsets_") - if method == "load": - gbq_source = self.load_data(data, offsets_col=offsets_col) - elif method == "stream": - gbq_source = self.stream_data(data, offsets_col=offsets_col) - elif method == "write": - gbq_source = self.write_data(data, offsets_col=offsets_col) - else: - raise ValueError(f"Unsupported read method {method}") - - return core.ArrayValue.from_bq_data_source( - source=gbq_source, - scan_list=nodes.ScanList( - tuple( - nodes.ScanItem(identifiers.ColumnId(item.column), item.column) - for item in data.schema.items - ) - ), - session=self._session, - ) - - def _load_data_or_write_data( - self, - data: local_data.ManagedArrowTable, - offsets_col: str, - ) -> bq_data.BigqueryDataSource: - """Write local data into BigQuery using the local API if possible, - otherwise use the write API.""" - can_load = all( - _is_dtype_can_load(item.column, item.dtype) for item in data.schema.items - ) - if can_load: - return self.load_data(data, offsets_col=offsets_col) - else: - return self.write_data(data, offsets_col=offsets_col) - - def load_data( - self, - data: local_data.ManagedArrowTable, - offsets_col: str, - ) -> bq_data.BigqueryDataSource: - """Load managed data into bigquery""" - cannot_load_columns = { - item.column: item.dtype - for item in data.schema.items - if not _is_dtype_can_load(item.column, item.dtype) - } - - if cannot_load_columns: - raise NotImplementedError( - f"Nested JSON types are currently unsupported for BigQuery Load API. " - f"Unsupported columns: {cannot_load_columns}. {constants.FEEDBACK_LINK}" - ) - - schema_w_offsets = data.schema.append( - schemata.SchemaItem(offsets_col, bigframes.dtypes.INT_DTYPE) - ) - bq_schema = schema_w_offsets.to_bigquery(_LOAD_JOB_TYPE_OVERRIDES) - - job_config = bigquery.LoadJobConfig() - job_config.source_format = bigquery.SourceFormat.PARQUET - - # Ensure we can load pyarrow.list_ / BQ ARRAY type. - # See internal issue 414374215. - parquet_options = bigquery.ParquetOptions() - parquet_options.enable_list_inference = True - job_config.parquet_options = parquet_options - - job_config.schema = bq_schema - - load_table_destination = self._storage_manager.create_temp_table( - bq_schema, [offsets_col] - ) - - buffer = io.BytesIO() - data.to_parquet( - buffer, - offsets_col=offsets_col, - geo_format="wkt", - duration_type="duration", - json_type="string", - ) - buffer.seek(0) - load_job = self._bqclient.load_table_from_file( - buffer, destination=load_table_destination, job_config=job_config - ) - self._start_generic_job(load_job) - # must get table metadata after load job for accurate metadata - destination_table = self._bqclient.get_table(load_table_destination) - return bq_data.BigqueryDataSource( - bq_data.GbqNativeTable.from_table(destination_table), - schema=schema_w_offsets, - ordering=ordering.TotalOrdering.from_offset_col(offsets_col), - n_rows=data.metadata.row_count, - ) - - def stream_data( - self, - data: local_data.ManagedArrowTable, - offsets_col: str, - ) -> bq_data.BigqueryDataSource: - """Load managed data into bigquery""" - MAX_BYTES = 10000000 # streaming api has 10MB limit - SAFETY_MARGIN = ( - 40 # Perf seems bad for large chunks, so do 40x smaller than max - ) - batch_count = math.ceil( - data.metadata.total_bytes / (MAX_BYTES // SAFETY_MARGIN) - ) - rows_per_batch = math.ceil(data.metadata.row_count / batch_count) - - schema_w_offsets = data.schema.append( - schemata.SchemaItem(offsets_col, bigframes.dtypes.INT_DTYPE) - ) - bq_schema = schema_w_offsets.to_bigquery(_STREAM_JOB_TYPE_OVERRIDES) - load_table_destination = self._storage_manager.create_temp_table( - bq_schema, [offsets_col] - ) - - rows = data.itertuples( - geo_format="wkt", duration_type="int", json_type="object" - ) - rows_w_offsets = ((*row, offset) for offset, row in enumerate(rows)) - - # TODO: don't use batched - batches = _batched(rows_w_offsets, rows_per_batch) - ids_iter = map(str, itertools.count()) - - for batch in batches: - batch_rows = list(batch) - row_ids = itertools.islice(ids_iter, len(batch_rows)) - - for errors in self._bqclient.insert_rows( - load_table_destination, - batch_rows, - selected_fields=bq_schema, - row_ids=row_ids, # used to ensure only-once insertion - ): - if errors: - raise ValueError( - f"Problem loading at least one row from DataFrame: {errors}. {constants.FEEDBACK_LINK}" - ) - destination_table = self._bqclient.get_table(load_table_destination) - return bq_data.BigqueryDataSource( - bq_data.GbqNativeTable.from_table(destination_table), - schema=schema_w_offsets, - ordering=ordering.TotalOrdering.from_offset_col(offsets_col), - n_rows=data.metadata.row_count, - ) - - def write_data( - self, - data: local_data.ManagedArrowTable, - offsets_col: str, - ) -> bq_data.BigqueryDataSource: - """Load managed data into BigQuery using multiple concurrent streams.""" - schema_w_offsets = data.schema.append( - schemata.SchemaItem(offsets_col, bigframes.dtypes.INT_DTYPE) - ) - bq_schema = schema_w_offsets.to_bigquery(_STREAM_JOB_TYPE_OVERRIDES) - bq_table_ref = self._storage_manager.create_temp_table(bq_schema, [offsets_col]) - parent = bq_table_ref.to_bqstorage() - - # Some light benchmarking went into the constants here, not definitive - TARGET_BATCH_BYTES = ( - 5_000_000 # Must stay under the hard 10MB limit per request - ) - rows_per_batch = math.ceil( - data.metadata.row_count * TARGET_BATCH_BYTES / data.metadata.total_bytes - ) - min_batches = math.ceil(data.metadata.row_count / rows_per_batch) - num_streams = min((os.cpu_count() or 4) * 4, min_batches) - - schema, all_batches = data.to_arrow( - offsets_col=offsets_col, - duration_type="int", - max_chunksize=rows_per_batch, - ) - serialized_schema = schema.serialize().to_pybytes() - - def stream_worker( - work: Iterator[pa.RecordBatch], max_outstanding: int = 5 - ) -> str: - requested_stream = bq_storage_types.WriteStream( - type_=bq_storage_types.WriteStream.Type.PENDING - ) - stream = self._write_client.create_write_stream( - parent=parent, write_stream=requested_stream - ) - base_request = bq_storage_types.AppendRowsRequest( - write_stream=stream.name, - ) - base_request.arrow_rows.writer_schema.serialized_schema = serialized_schema - - stream_manager = bq_storage_writer.AppendRowsStream( - client=self._write_client, initial_request_template=base_request - ) - stream_name = stream.name - current_offset = 0 - futures: list[bq_storage_writer.AppendRowsFuture] = [] - - for batch in work: - if len(futures) >= max_outstanding: - row_errors = futures.pop(0).result().row_errors - if row_errors: - raise ValueError( - f"Problem loading rows: {row_errors}. {constants.FEEDBACK_LINK}" - ) - - request = bq_storage_types.AppendRowsRequest(offset=current_offset) - request.arrow_rows.rows.serialized_record_batch = ( - batch.serialize().to_pybytes() - ) - - futures.append(stream_manager.send(request)) - current_offset += batch.num_rows - - for future in futures: - row_errors = future.result().row_errors - if row_errors: - raise ValueError( - f"Problem loading rows: {row_errors}. {constants.FEEDBACK_LINK}" - ) - - stream_manager.close() - self._write_client.finalize_write_stream(name=stream_name) - return stream_name - - shared_batches = ThreadSafeIterator(all_batches) - - stream_names = [] - with concurrent.futures.ThreadPoolExecutor(max_workers=num_streams) as executor: - futures = [] - for _ in range(num_streams): - try: - work = next(shared_batches) - except StopIteration: - break # existing workers have consume all work, don't create more workers - # Guarantee at least a single piece of work for each worker - future = executor.submit( - stream_worker, itertools.chain((work,), shared_batches) - ) - futures.append(future) - - for future in concurrent.futures.as_completed(futures): - stream_name = future.result() - stream_names.append(stream_name) - - # This makes all data from all streams visible in the table at once - commit_request = bq_storage_types.BatchCommitWriteStreamsRequest( - parent=parent, write_streams=stream_names - ) - response = self._write_client.batch_commit_write_streams(commit_request) - for error in response.stream_errors: - raise ValueError(f"Errors commiting stream {error}") - - result_table = bq_data.GbqNativeTable.from_ref_and_schema( - bq_table_ref, - schema=bq_schema, - cluster_cols=[offsets_col], - location=self._storage_manager.location, - table_type="TABLE", - ) - return bq_data.BigqueryDataSource( - result_table, - schema=schema_w_offsets, - ordering=ordering.TotalOrdering.from_offset_col(offsets_col), - n_rows=data.metadata.row_count, - ) - - def _start_generic_job(self, job: formatting_helpers.GenericJob): - if bigframes.options.display.progress_bar is not None: - formatting_helpers.wait_for_job( - job, bigframes.options.display.progress_bar - ) # Wait for the job to complete - else: - job.result() - - if self._metrics is not None and isinstance(job, (QueryJob, LoadJob)): - self._metrics.count_job_stats(query_job=job) - - @overload - def read_gbq_table( # type: ignore[overload-overlap] - self, - table_id: str, - *, - index_col: Iterable[str] - | str - | Iterable[int] - | int - | bigframes.enums.DefaultIndexKind = ..., - columns: Iterable[str] = ..., - names: Optional[Iterable[str]] = ..., - max_results: Optional[int] = ..., - use_cache: bool = ..., - filters: third_party_pandas_gbq.FiltersType = ..., - enable_snapshot: bool = ..., - dry_run: Literal[False] = ..., - force_total_order: Optional[bool] = ..., - n_rows: Optional[int] = None, - index_col_in_columns: bool = False, - publish_execution: bool = True, - ) -> dataframe.DataFrame: ... - - @overload - def read_gbq_table( - self, - table_id: str, - *, - index_col: Iterable[str] - | str - | Iterable[int] - | int - | bigframes.enums.DefaultIndexKind = ..., - columns: Iterable[str] = ..., - names: Optional[Iterable[str]] = ..., - max_results: Optional[int] = ..., - use_cache: bool = ..., - filters: third_party_pandas_gbq.FiltersType = ..., - enable_snapshot: bool = ..., - dry_run: Literal[True] = ..., - force_total_order: Optional[bool] = ..., - n_rows: Optional[int] = None, - index_col_in_columns: bool = False, - publish_execution: bool = True, - ) -> pandas.Series: ... - - def read_gbq_table( - self, - table_id: str, - *, - index_col: Iterable[str] - | str - | Iterable[int] - | int - | bigframes.enums.DefaultIndexKind = (), - columns: Iterable[str] = (), - names: Optional[Iterable[str]] = None, - max_results: Optional[int] = None, - use_cache: bool = True, - filters: third_party_pandas_gbq.FiltersType = (), - enable_snapshot: bool = True, - dry_run: bool = False, - force_total_order: Optional[bool] = None, - n_rows: Optional[int] = None, - index_col_in_columns: bool = False, - publish_execution: bool = True, - ) -> dataframe.DataFrame | pandas.Series: - """Read a BigQuery table into a BigQuery DataFrames DataFrame. - - This method allows you to create a DataFrame from a BigQuery table. - You can specify the columns to load, an index column, and apply - filters. - - Args: - table_id (str): - The identifier of the BigQuery table to read. - index_col (Iterable[str] | str | Iterable[int] | int | bigframes.enums.DefaultIndexKind, optional): - The column(s) to use as the index for the DataFrame. This can be - a single column name or a list of column names. If not provided, - a default index will be used based on the session's - ``default_index_type``. - columns (Iterable[str], optional): - The columns to read from the table. If not specified, all - columns will be read. - names (Optional[Iterable[str]], optional): - A list of column names to use for the resulting DataFrame. This - is useful if you want to rename the columns as you read the - data. - max_results (Optional[int], optional): - The maximum number of rows to retrieve from the table. If not - specified, all rows will be loaded. - use_cache (bool, optional): - Whether to use cached results for the query. Defaults to True. - Setting this to False will force a re-execution of the query. - filters (third_party_pandas_gbq.FiltersType, optional): - A list of filters to apply to the data. Filters are specified - as a list of tuples, where each tuple contains a column name, - an operator (e.g., '==', '!='), and a value. - enable_snapshot (bool, optional): - If True, a snapshot of the table is used to ensure that the - DataFrame is deterministic, even if the underlying table - changes. Defaults to True. - dry_run (bool, optional): - If True, the function will not actually execute the query but - will instead return statistics about the table. Defaults to False. - force_total_order (Optional[bool], optional): - If True, a total ordering is enforced on the DataFrame, which - can be useful for operations that require a stable row order. - If None, the session's default behavior is used. - n_rows (Optional[int], optional): - The number of rows to consider for type inference and other - metadata operations. This does not limit the number of rows - in the final DataFrame. - index_col_in_columns (bool, optional): - Specifies if the ``index_col`` is also present in the ``columns`` - list. Defaults to ``False``. - - * If ``False``, ``index_col`` and ``columns`` must specify - distinct sets of columns. An error will be raised if any - column is found in both. - * If ``True``, the column(s) in ``index_col`` are expected to - also be present in the ``columns`` list. This is useful - when the index is selected from the data columns (e.g., in a - ``read_csv`` scenario). The column will be used as the - DataFrame's index and removed from the list of value columns. - publish_execution (bool, optional): - If True, sends an execution started and stopped event if this - causes a query. Set to False if using read_gbq_table from - another function that is reporting execution. - """ - import bigframes.core.events - import bigframes.dataframe as dataframe - - # --------------------------------- - # Validate and transform parameters - # --------------------------------- - - if max_results and max_results <= 0: - raise ValueError( - f"`max_results` should be a positive number, got {max_results}." - ) - - _check_duplicates("columns", columns) - - columns = list(columns) - include_all_columns = columns is None or len(columns) == 0 - filters = typing.cast(list, list(filters)) - - # --------------------------------- - # Fetch table metadata and validate - # --------------------------------- - - time_travel_timestamp, table = self._get_table_metadata( - table_id=table_id, - default_project=self._bqclient.project, - bq_time=self._clock.get_time(), - use_cache=use_cache, - ) - - if not bq_data.is_compatible( - table.metadata.location, self._storage_manager.location - ): - raise ValueError( - f"Current session is in {self._storage_manager.location} but table '{table.get_full_id()}' is located in {table.metadata.location}" - ) - - table_column_names = [field.name for field in table.physical_schema] - rename_to_schema: Optional[Dict[str, str]] = None - if names is not None: - _check_names_param(names, index_col, columns, table_column_names) - - # Additional unnamed columns is going to set as index columns - len_names = len(list(names)) - len_schema = len(table.physical_schema) - if len(columns) == 0 and len_names < len_schema: - index_col = range(len_schema - len_names) - names = [ - field.name - for field in table.physical_schema[: len_schema - len_names] - ] + list(names) - - assert len_schema >= len_names - assert len_names >= len(columns) - - table_column_names = table_column_names[: len(list(names))] - rename_to_schema = dict(zip(list(names), table_column_names)) - - if len(columns) != 0: - if names is None: - _check_columns_param(columns, table_column_names) - else: - _check_columns_param(columns, names) - names = columns - assert rename_to_schema is not None - columns = [rename_to_schema[renamed_name] for renamed_name in columns] - - # Converting index_col into a list of column names requires - # the table metadata because we might use the primary keys - # when constructing the index. - index_cols = bf_read_gbq_table.get_index_cols( - table=table, - index_col=index_col, - rename_to_schema=rename_to_schema, - default_index_type=self._default_index_type, - ) - _check_index_col_param( - index_cols, - columns, - table_columns=table_column_names, - index_col_in_columns=index_col_in_columns, - ) - if index_col_in_columns and not include_all_columns: - set_index = set(list(index_cols) if index_cols is not None else []) - columns = [col for col in columns if col not in set_index] - - # ----------------------------- - # Optionally, execute the query - # ----------------------------- - - if ( - # max_results introduces non-determinism and limits the cost on - # clustered tables, so fallback to a query. We do this here so that - # the index is consistent with tables that have primary keys, even - # when max_results is set. - max_results is not None - # Views such as INFORMATION_SCHEMA can introduce non-determinism. - # They can update frequently and don't support time travel. - or bf_read_gbq_table.is_information_schema(table_id) - ): - # TODO(b/338111344): If we are running a query anyway, we might as - # well generate ROW_NUMBER() at the same time. - all_columns: Iterable[str] = ( - itertools.chain(index_cols, columns) if columns else () - ) - query = bf_io_bigquery.to_query( - table.get_full_id(quoted=False), - columns=all_columns, - sql_predicate=bf_io_bigquery.compile_filters(filters) - if filters - else None, - max_results=max_results, - # We're executing the query, so we don't need time travel for - # determinism. - time_travel_timestamp=None, - ) - - df = self.read_gbq_query( # type: ignore # for dry_run overload - query, - index_col=index_cols, - columns=columns, - use_cache=use_cache, - dry_run=dry_run, - # If max_results has been set, we almost certainly have < 10 GB - # of results. - allow_large_results=False, - ) - return df - - if dry_run: - return dry_runs.get_table_stats(table) - - # ----------------------------------------- - # Validate table access and features - # ----------------------------------------- - - # Use a time travel to make sure the DataFrame is deterministic, even - # if the underlying table changes. - - # If a dry run query fails with time travel but - # succeeds without it, omit the time travel clause and raise a warning - # about potential non-determinism if the underlying tables are modified. - filter_str = bf_io_bigquery.compile_filters(filters) if filters else None - all_columns = ( - () - if len(columns) == 0 - else (*columns, *[col for col in index_cols if col not in columns]) - ) - - enable_snapshot = enable_snapshot and bf_read_gbq_table.is_time_travel_eligible( - self._bqclient, - table, - all_columns, - time_travel_timestamp, - filter_str, - should_warn=True, - should_dry_run=True, - publisher=self._publisher, - ) - - # ---------------------------- - # Create ordering and validate - # ---------------------------- - - # TODO(b/337925142): Generate a new subquery with just the index_cols - # in the Ibis table expression so we don't have a "SELECT *" subquery - # in the query that checks for index uniqueness. - # TODO(b/338065601): Provide a way to assume uniqueness and avoid this - # check. - primary_key = bf_read_gbq_table.infer_unique_columns( - table=table, - index_cols=index_cols, - ) - - # If non in strict ordering mode, don't go through overhead of scanning index column(s) to determine if unique - if not primary_key and self._scan_index_uniqueness and index_cols: - if publish_execution: - self._publisher.publish( - bigframes.core.events.ExecutionStarted(), - ) - primary_key = bf_read_gbq_table.check_if_index_columns_are_unique( - self._bqclient, - table=table, - index_cols=index_cols, - publisher=self._publisher, - ) - if publish_execution: - self._publisher.publish( - bigframes.core.events.ExecutionFinished(), - ) - - selected_cols = None if include_all_columns else (*index_cols, *columns) - array_value = core.ArrayValue.from_table( - table, - columns=selected_cols, - predicate=filter_str, - at_time=time_travel_timestamp if enable_snapshot else None, - primary_key=primary_key, - session=self._session, - n_rows=n_rows, - ) - # if we don't have a unique index, we order by row hash if we are in strict mode - if ( - # If the user has explicitly selected or disabled total ordering for - # this API call, respect that choice. - (force_total_order is not None and force_total_order) - # If the user has not explicitly selected or disabled total ordering - # for this API call, respect the default choice for the session. - or (force_total_order is None and self._force_total_order) - ): - if not primary_key: - array_value = array_value.order_by( - [ - bigframes.core.ordering.OrderingExpression( - bigframes.operations.RowKey().as_expr( - *(id for id in array_value.column_ids) - ), - # More concise SQL this way. - na_last=False, - ) - ], - is_total_order=True, - ) - - # ---------------------------------------------------- - # Create Default Sequential Index if still have no index - # ---------------------------------------------------- - - # If no index columns provided or found, fall back to session default - if (index_col != bigframes.enums.DefaultIndexKind.NULL) and len( - index_cols - ) == 0: - index_col = self._default_index_type - - index_names: Sequence[Hashable] = index_cols - if index_col == bigframes.enums.DefaultIndexKind.SEQUENTIAL_INT64: - array_value, sequential_index_col = array_value.promote_offsets() - index_cols = [sequential_index_col] - index_names = [None] - - value_columns = [col for col in array_value.column_ids if col not in index_cols] - if names is not None: - assert rename_to_schema is not None - schema_to_rename = {value: key for key, value in rename_to_schema.items()} - if index_col != bigframes.enums.DefaultIndexKind.SEQUENTIAL_INT64: - index_names = [ - schema_to_rename.get(index_col, index_col) - for index_col in index_cols - ] - value_columns = [schema_to_rename.get(col, col) for col in value_columns] - - block = blocks.Block( - array_value, - index_columns=index_cols, - column_labels=value_columns, - index_labels=index_names, - ) - if max_results: - block = block.slice(stop=max_results) - df = dataframe.DataFrame(block) - - # If user provided index columns, should sort over it - if len(index_cols) > 0: - df.sort_index() - return df - - def _get_table_metadata( - self, - *, - table_id: str, - default_project: Optional[str], - bq_time: datetime.datetime, - use_cache: bool = True, - ) -> Tuple[ - datetime.datetime, Union[bq_data.GbqNativeTable, bq_data.BiglakeIcebergTable] - ]: - """Get the table metadata, either from cache or via REST API.""" - - cached_table = self._df_snapshot.get(table_id) - if use_cache and cached_table is not None: - snapshot_timestamp, table = cached_table - - if bf_read_gbq_table.is_time_travel_eligible( - bqclient=self._bqclient, - table=table, - columns=None, - snapshot_time=snapshot_timestamp, - filter_str=None, - # Don't warn, because that will already have been taken care of. - should_warn=False, - should_dry_run=False, - publisher=self._publisher, - ): - # This warning should only happen if the cached snapshot_time will - # have any effect on bigframes (b/437090788). For example, with - # cached query results, such as after re-running a query, time - # travel won't be applied and thus this check is irrelevent. - # - # In other cases, such as an explicit read_gbq_table(), Cache hit - # could be unexpected. See internal issue 329545805. Raise a - # warning with more information about how to avoid the problems - # with the cache. - msg = bfe.format_message( - f"Reading cached table from {snapshot_timestamp} to avoid " - "incompatibilies with previous reads of this table. To read " - "the latest version, set `use_cache=False` or close the " - "current session with Session.close() or " - "bigframes.pandas.close_session()." - ) - # There are many layers before we get to (possibly) the user's code: - # pandas.read_gbq_table - # -> with_default_session - # -> Session.read_gbq_table - # -> _read_gbq_table - # -> _get_snapshot_sql_and_primary_key - # -> get_snapshot_datetime_and_table_metadata - warnings.warn(msg, category=bfe.TimeTravelCacheWarning, stacklevel=7) - - return cached_table - - if bf_read_gbq_table.is_information_schema(table_id): - client_table = bf_read_gbq_table.get_information_schema_metadata( - bqclient=self._bqclient, - table_id=table_id, - default_project=default_project, - ) - table = bq_data.GbqNativeTable.from_table(client_table) - elif bq_data.is_irc_table(table_id): - table = bigframes.session.iceberg.get_table( - self._bqclient.project, table_id, self._bqclient._credentials - ) - else: - table_ref = google.cloud.bigquery.table.TableReference.from_string( - table_id, default_project=default_project - ) - client_table = self._bqclient.get_table(table_ref) - table = bq_data.GbqNativeTable.from_table(client_table) - - # local time will lag a little bit do to network latency - # make sure it is at least table creation time. - # This is relevant if the table was created immediately before loading it here. - if (table.metadata.created_time is not None) and ( - table.metadata.created_time > bq_time - ): - bq_time = table.metadata.created_time - - cached_table = (bq_time, table) - self._df_snapshot[table_id] = cached_table - return cached_table - - def load_file( - self, - filepath_or_buffer: str | IO["bytes"], - *, - job_config: bigquery.LoadJobConfig, - ) -> str: - # Need to create session table beforehand - table = self._storage_manager.create_temp_table(_PLACEHOLDER_SCHEMA) - # but, we just overwrite the placeholder schema immediately with the load job - job_config.write_disposition = bigquery.WriteDisposition.WRITE_TRUNCATE - if isinstance(filepath_or_buffer, str): - filepath_or_buffer = os.path.expanduser(filepath_or_buffer) - if filepath_or_buffer.startswith("gs://"): - load_job = self._bqclient.load_table_from_uri( - filepath_or_buffer, destination=table, job_config=job_config - ) - elif os.path.exists(filepath_or_buffer): # local file path - with open(filepath_or_buffer, "rb") as source_file: - load_job = self._bqclient.load_table_from_file( - source_file, destination=table, job_config=job_config - ) - else: - raise NotImplementedError( - f"BigQuery engine only supports a local file path or GCS path. " - f"{constants.FEEDBACK_LINK}" - ) - else: - load_job = self._bqclient.load_table_from_file( - filepath_or_buffer, destination=table, job_config=job_config - ) - - self._start_generic_job(load_job) - table_id = f"{table.project}.{table.dataset_id}.{table.table_id}" - return table_id - - @overload - def read_gbq_query( # type: ignore[overload-overlap] - self, - query: str, - *, - index_col: Iterable[str] | str | bigframes.enums.DefaultIndexKind = ..., - columns: Iterable[str] = ..., - configuration: Optional[Dict] = ..., - max_results: Optional[int] = ..., - use_cache: Optional[bool] = ..., - filters: third_party_pandas_gbq.FiltersType = ..., - dry_run: Literal[False] = ..., - force_total_order: Optional[bool] = ..., - allow_large_results: bool, - ) -> dataframe.DataFrame: ... - - @overload - def read_gbq_query( - self, - query: str, - *, - index_col: Iterable[str] | str | bigframes.enums.DefaultIndexKind = ..., - columns: Iterable[str] = ..., - configuration: Optional[Dict] = ..., - max_results: Optional[int] = ..., - use_cache: Optional[bool] = ..., - filters: third_party_pandas_gbq.FiltersType = ..., - dry_run: Literal[True] = ..., - force_total_order: Optional[bool] = ..., - allow_large_results: bool, - ) -> pandas.Series: ... - - def read_gbq_query( - self, - query: str, - *, - index_col: Iterable[str] | str | bigframes.enums.DefaultIndexKind = (), - columns: Iterable[str] = (), - configuration: Optional[Dict] = None, - max_results: Optional[int] = None, - use_cache: Optional[bool] = None, - filters: third_party_pandas_gbq.FiltersType = (), - dry_run: bool = False, - force_total_order: Optional[bool] = None, - allow_large_results: bool, - ) -> dataframe.DataFrame | pandas.Series: - configuration = _transform_read_gbq_configuration(configuration) - - if "query" not in configuration: - configuration["query"] = {} - - if "query" in configuration["query"]: - raise ValueError( - "The query statement must not be included in the ", - "'configuration' because it is already provided as", - " a separate parameter.", - ) - - if "useQueryCache" in configuration["query"]: - if use_cache is not None: - raise ValueError( - "'useQueryCache' in 'configuration' conflicts with" - " 'use_cache' parameter. Please specify only one." - ) - else: - configuration["query"]["useQueryCache"] = ( - True if use_cache is None else use_cache - ) - - _check_duplicates("columns", columns) - index_cols = _to_index_cols(index_col) - _check_index_col_param(index_cols, columns) - - filters_copy1, filters_copy2 = itertools.tee(filters) - has_filters = len(list(filters_copy1)) != 0 - filters = typing.cast(third_party_pandas_gbq.FiltersType, filters_copy2) - if has_filters or max_results is not None: - # TODO(b/338111344): If we are running a query anyway, we might as - # well generate ROW_NUMBER() at the same time. - all_columns = itertools.chain(index_cols, columns) if columns else () - query = bf_io_bigquery.to_query( - query, - all_columns, - bf_io_bigquery.compile_filters(filters) if has_filters else None, - max_results=max_results, - # We're executing the query, so we don't need time travel for - # determinism. - time_travel_timestamp=None, - ) - - if dry_run: - job_config = typing.cast( - bigquery.QueryJobConfig, - bigquery.QueryJobConfig.from_api_repr(configuration), - ) - job_config.dry_run = True - query_job = self._bqclient.query(query, job_config=job_config) - if self._metrics is not None: - self._metrics.count_job_stats(query_job=query_job) - return dry_runs.get_query_stats_with_inferred_dtypes( - query_job, list(columns), index_cols - ) - - # We want to make sure we show progress when we actually do execute a - # query. Since we have got this far, we know it's not a dry run. - self._publisher.publish( - bigframes.core.events.ExecutionStarted(), - ) - - query_job_for_metrics: Optional[bigquery.QueryJob] = None - destination: Optional[bigquery.TableReference] = None - - # TODO(b/421161077): If an explicit destination table is set in - # configuration, should we respect that setting? - if allow_large_results: - destination, query_job = self._query_to_destination( - query, - # No cluster candidates as user query might not be clusterable - # (eg because of ORDER BY clause) - cluster_candidates=[], - configuration=configuration, - ) - query_job_for_metrics = query_job - rows: Optional[google.cloud.bigquery.table.RowIterator] = None - else: - job_config = typing.cast( - bigquery.QueryJobConfig, - bigquery.QueryJobConfig.from_api_repr(configuration), - ) - - # TODO(b/420984164): We may want to set a page_size here to limit - # the number of results in the first jobs.query response. - rows = self._start_query_with_job_optional( - query, - job_config=job_config, - ) - - # If there is a query job, fetch it so that we can get the - # statistics and destination table, if needed. - if rows.job_id and rows.location and rows.project: - query_job = cast( - bigquery.QueryJob, - self._bqclient.get_job( - rows.job_id, project=rows.project, location=rows.location - ), - ) - destination = query_job.destination - query_job_for_metrics = query_job - - # We split query execution from results fetching so that we can log - # metrics from either the query job, row iterator, or both. - if self._metrics is not None: - self._metrics.count_job_stats( - query_job=query_job_for_metrics, row_iterator=rows - ) - - # It's possible that there's no job and therefore no corresponding - # destination table. In this case, we must create a local node. - # - # TODO(b/420984164): Tune the threshold for which we download to - # local node. Likely there are a wide range of sizes in which it - # makes sense to download the results beyond the first page, even if - # there is a job and destination table available. - if query_job_for_metrics is None and rows is not None: - df = bf_read_gbq_query.create_dataframe_from_row_iterator( - rows, - session=self._session, - index_col=index_col, - columns=columns, - ) - self._publisher.publish( - bigframes.core.events.ExecutionFinished(), - ) - return df - - # We already checked rows, so if there's no destination table, then - # there are no results to return. - if destination is None: - df = bf_read_gbq_query.create_dataframe_from_query_job_stats( - query_job_for_metrics, - session=self._session, - ) - self._publisher.publish( - bigframes.core.events.ExecutionFinished(), - ) - return df - - # If the query was DDL or DML, return some job metadata. See - # https://cloud.google.com/bigquery/docs/reference/rest/v2/Job#JobStatistics2.FIELDS.statement_type - # for possible statement types. Note that destination table does exist - # for some DDL operations such as CREATE VIEW, but we don't want to - # read from that. See internal issue b/444282709. - if ( - query_job_for_metrics is not None - and not bf_read_gbq_query.should_return_query_results(query_job_for_metrics) - ): - df = bf_read_gbq_query.create_dataframe_from_query_job_stats( - query_job_for_metrics, - session=self._session, - ) - self._publisher.publish( - bigframes.core.events.ExecutionFinished(), - ) - return df - - # Speed up counts by getting counts from result metadata. - if rows is not None: - n_rows = rows.total_rows - elif query_job_for_metrics is not None: - n_rows = query_job_for_metrics.result().total_rows - else: - n_rows = None - - df = self.read_gbq_table( - f"{destination.project}.{destination.dataset_id}.{destination.table_id}", - index_col=index_col, - columns=columns, - use_cache=configuration["query"]["useQueryCache"], - force_total_order=force_total_order, - n_rows=n_rows, - publish_execution=False, - # max_results and filters are omitted because they are already - # handled by to_query(), above. - ) - self._publisher.publish( - bigframes.core.events.ExecutionFinished(), - ) - return df - - def _query_to_destination( - self, - query: str, - cluster_candidates: List[str], - configuration: dict = {"query": {"useQueryCache": True}}, - do_clustering=True, - ) -> Tuple[Optional[bigquery.TableReference], bigquery.QueryJob]: - # If a dry_run indicates this is not a query type job, then don't - # bother trying to do a CREATE TEMP TABLE ... AS SELECT ... statement. - dry_run_config = bigquery.QueryJobConfig() - dry_run_config.dry_run = True - dry_run_job = self._start_query_with_job( - query, - job_config=dry_run_config, - ) - if dry_run_job.statement_type != "SELECT": - query_job = self._start_query_with_job(query) - return query_job.destination, query_job - - # Create a table to workaround BigQuery 10 GB query results limit. See: - # internal issue 303057336. - # Since we have a `statement_type == 'SELECT'`, schema should be populated. - schema = dry_run_job.schema - assert schema is not None - if do_clustering: - cluster_cols = bf_io_bigquery.select_cluster_cols( - schema, cluster_candidates=cluster_candidates - ) - else: - cluster_cols = [] - temp_table = self._storage_manager.create_temp_table(schema, cluster_cols) - - timeout_ms = configuration.get("jobTimeoutMs") or configuration["query"].get( - "timeoutMs" - ) - - # Convert timeout_ms to seconds, ensuring a minimum of 0.1 seconds to avoid - # the program getting stuck on too-short timeouts. - timeout = max(int(timeout_ms) * 1e-3, 0.1) if timeout_ms else None - - job_config = typing.cast( - bigquery.QueryJobConfig, - bigquery.QueryJobConfig.from_api_repr(configuration), - ) - job_config.destination = temp_table - - try: - # Write to temp table to workaround BigQuery 10 GB query results - # limit. See: internal issue 303057336. - job_config.labels["error_caught"] = "true" - query_job = self._start_query_with_job( - query, - job_config=job_config, - timeout=timeout, - ) - return query_job.destination, query_job - except google.api_core.exceptions.BadRequest: - # Some SELECT statements still aren't compatible with cluster - # tables as the destination. For example, if the query has a - # top-level ORDER BY, this conflicts with our ability to cluster - # the table by the index column(s). - query_job = self._start_query_with_job(query, timeout=timeout) - return query_job.destination, query_job - - def _prepare_job_config( - self, - job_config: Optional[google.cloud.bigquery.QueryJobConfig] = None, - ) -> google.cloud.bigquery.QueryJobConfig: - job_config = bigquery.QueryJobConfig() if job_config is None else job_config - - if bigframes.options.compute.maximum_bytes_billed is not None: - # Maybe this should be pushed down into start_query_with_job - job_config.maximum_bytes_billed = ( - bigframes.options.compute.maximum_bytes_billed - ) - - return job_config - - def _start_query_with_job_optional( - self, - sql: str, - *, - job_config: Optional[google.cloud.bigquery.QueryJobConfig] = None, - timeout: Optional[float] = None, - ) -> google.cloud.bigquery.table.RowIterator: - """ - Starts BigQuery query with job optional and waits for results. - - Do not execute dataframe through this API, instead use the executor. - """ - job_config = self._prepare_job_config(job_config) - rows = bf_io_bigquery.start_query_job_optional( - self._bqclient, - sql, - job_config=job_config, - timeout=timeout, - location=None, - project=None, - metrics=None, - publisher=self._publisher, - session=self._session, - ) - return rows - - def _start_query_with_job( - self, - sql: str, - *, - job_config: Optional[google.cloud.bigquery.QueryJobConfig] = None, - timeout: Optional[float] = None, - ) -> bigquery.QueryJob: - """ - Starts BigQuery query job and waits for results. - - Do not execute dataframe through this API, instead use the executor. - """ - job_config = self._prepare_job_config(job_config) - _, query_job = bf_io_bigquery.start_query_with_job( - self._bqclient, - sql, - job_config=job_config, - timeout=timeout, - location=None, - project=None, - metrics=None, - publisher=self._publisher, - session=self._session, - ) - return query_job - - -def _transform_read_gbq_configuration(configuration: Optional[dict]) -> dict: - """ - For backwards-compatibility, convert any previously client-side only - parameters such as timeoutMs to the property name expected by the REST API. - - Makes a copy of configuration if changes are needed. - """ - - if configuration is None: - return {} - - timeout_ms = configuration.get("query", {}).get("timeoutMs") - if timeout_ms is not None: - # Transform timeoutMs to an actual server-side configuration. - # https://github.com/googleapis/python-bigquery-pandas/issues/479 - configuration = copy.deepcopy(configuration) - del configuration["query"]["timeoutMs"] - configuration["jobTimeoutMs"] = timeout_ms - - return configuration - - -def _is_dtype_can_load(name: str, column_type: bigframes.dtypes.Dtype) -> bool: - """ - Determines whether a datatype is supported by bq load jobs. - - Due to a BigQuery IO limitation with loading JSON from Parquet files (b/374784249), - we're using a workaround: storing JSON as strings and then parsing them into JSON - objects. - TODO(b/395912450): Remove workaround solution once b/374784249 got resolved. - """ - # we can handle top-level json, but not nested yet through string conversion - if column_type == bigframes.dtypes.JSON_DTYPE: - return True - - if isinstance( - column_type, pandas.ArrowDtype - ) and bigframes.dtypes.contains_db_dtypes_json_arrow_type( - column_type.pyarrow_dtype - ): - return False - - return True - - -# itertools.batched not available in python <3.12, so we use this instead -def _batched(iterator: Iterable, n: int) -> Iterable: - assert n > 0 - while batch := tuple(itertools.islice(iterator, n)): - yield batch - - -T = TypeVar("T") - - -class ThreadSafeIterator(Iterator[T]): - """A wrapper to make an iterator thread-safe.""" - - def __init__(self, it: Iterable[T]): - self.it = iter(it) - self.lock = threading.Lock() - - def __next__(self): - with self.lock: - return next(self.it) - - def __iter__(self): - return self diff --git a/bigframes/session/local_scan_executor.py b/bigframes/session/local_scan_executor.py deleted file mode 100644 index 22007ec5eb7..00000000000 --- a/bigframes/session/local_scan_executor.py +++ /dev/null @@ -1,65 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -from __future__ import annotations - -from typing import Optional - -from bigframes.core import bigframe_node, rewrite -from bigframes.session import execution_spec, executor, semi_executor - - -class LocalScanExecutor(semi_executor.SemiExecutor): - """ - Executes plans reducible to a arrow table scan. - """ - - async def execute( - self, - plan: bigframe_node.BigFrameNode, - execution_spec: execution_spec.ExecutionSpec, - ) -> Optional[executor.ExecuteResult]: - if execution_spec.destination_spec is not None: - return None - - reduced_result = rewrite.try_reduce_to_local_scan(plan) - if not reduced_result: - return None - - node, limit = reduced_result - peek = execution_spec.peek - if limit is not None: - if peek is None or limit < peek: - peek = limit - - # TODO: Can support some sorting - offsets_col = node.offsets_col.sql if (node.offsets_col is not None) else None - arrow_table = node.local_data_source.to_pyarrow_table(offsets_col=offsets_col) - if peek: - arrow_table = arrow_table.slice(0, peek) - - needed_cols = [item.source_id for item in node.scan_list.items] - if offsets_col is not None: - needed_cols.append(offsets_col) - - arrow_table = arrow_table.select(needed_cols) - arrow_table = arrow_table.rename_columns([id.sql for id in node.ids]) - total_rows = node.row_count - - if (peek is not None) and (total_rows is not None): - total_rows = min(peek, total_rows) - - return executor.LocalExecuteResult( - data=arrow_table, - bf_schema=plan.schema, - ) diff --git a/bigframes/session/metrics.py b/bigframes/session/metrics.py deleted file mode 100644 index a9a444ecb38..00000000000 --- a/bigframes/session/metrics.py +++ /dev/null @@ -1,408 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from __future__ import annotations - -import dataclasses -import datetime -import os -from typing import Any, Mapping, Optional, Tuple, Union - -import google.cloud.bigquery as bigquery -import google.cloud.bigquery.table as bq_table -from google.cloud.bigquery.job.load import LoadJob -from google.cloud.bigquery.job.query import QueryJob - -LOGGING_NAME_ENV_VAR = "BIGFRAMES_PERFORMANCE_LOG_NAME" - - -@dataclasses.dataclass -class JobMetadata: - job_id: Optional[str] = None - query_id: Optional[str] = None - location: Optional[str] = None - project: Optional[str] = None - creation_time: Optional[datetime.datetime] = None - start_time: Optional[datetime.datetime] = None - end_time: Optional[datetime.datetime] = None - duration_seconds: Optional[float] = None - status: Optional[str] = None - total_bytes_processed: Optional[int] = None - total_slot_ms: Optional[int] = None - job_type: Optional[str] = None - error_result: Optional[Mapping[str, Any]] = None - cached: Optional[bool] = None - job_url: Optional[str] = None - query: Optional[str] = None - destination_table: Optional[str] = None - source_uris: Optional[list[str]] = None - input_files: Optional[int] = None - input_bytes: Optional[int] = None - output_rows: Optional[int] = None - source_format: Optional[str] = None - cell_execution_count: Optional[int] = None - - @classmethod - def from_job( - cls, - query_job: Union[QueryJob, LoadJob], - exec_seconds: Optional[float] = None, - cell_execution_count: Optional[int] = None, - ) -> "JobMetadata": - query_text = getattr(query_job, "query", None) - if query_text and len(query_text) > 1024: - query_text = query_text[:1021] + "..." - - job_id = getattr(query_job, "job_id", None) - job_url = None - if job_id: - job_url = ( - f"https://console.cloud.google.com/bigquery?" - f"project={query_job.project}&j=bq:{query_job.location}:" - f"{job_id}&page=queryresults" - ) - - if cell_execution_count is None: - from bigframes.core.utils import get_ipython_execution_count - - cell_execution_count = get_ipython_execution_count() - - metadata = cls( - job_id=query_job.job_id, - location=query_job.location, - project=query_job.project, - creation_time=query_job.created, - start_time=query_job.started, - end_time=query_job.ended, - duration_seconds=exec_seconds, - status=query_job.state, - job_type=query_job.job_type, - error_result=query_job.error_result, - query=query_text, - job_url=job_url, - cell_execution_count=cell_execution_count, - ) - if isinstance(query_job, QueryJob): - metadata.cached = getattr(query_job, "cache_hit", None) - metadata.destination_table = ( - str(query_job.destination) if query_job.destination else None - ) - metadata.total_bytes_processed = getattr( - query_job, "total_bytes_processed", None - ) - metadata.total_slot_ms = getattr(query_job, "slot_millis", None) - elif isinstance(query_job, LoadJob): - metadata.output_rows = getattr(query_job, "output_rows", None) - metadata.input_files = getattr(query_job, "input_files", None) - metadata.input_bytes = getattr(query_job, "input_bytes", None) - metadata.destination_table = ( - str(query_job.destination) - if getattr(query_job, "destination", None) - else None - ) - if getattr(query_job, "source_uris", None): - metadata.source_uris = list(query_job.source_uris) - if query_job.configuration and hasattr( - query_job.configuration, "source_format" - ): - metadata.source_format = query_job.configuration.source_format - - return metadata - - @classmethod - def from_row_iterator( - cls, - row_iterator: bq_table.RowIterator, - exec_seconds: Optional[float] = None, - cell_execution_count: Optional[int] = None, - ) -> "JobMetadata": - query_text = getattr(row_iterator, "query", None) - if query_text and len(query_text) > 1024: - query_text = query_text[:1021] + "..." - - job_id = getattr(row_iterator, "job_id", None) - job_url = None - if job_id: - project = getattr(row_iterator, "project", "") - location = getattr(row_iterator, "location", "") - job_url = ( - f"https://console.cloud.google.com/bigquery?" - f"project={project}&j=bq:{location}:{job_id}&page=queryresults" - ) - - if cell_execution_count is None: - from bigframes.core.utils import get_ipython_execution_count - - cell_execution_count = get_ipython_execution_count() - - # fmt: off - return cls( - job_id=job_id, - query_id=getattr(row_iterator, "query_id", None), - location=getattr(row_iterator, "location", None), - project=getattr(row_iterator, "project", None), - creation_time=getattr(row_iterator, "created", None), - start_time=getattr(row_iterator, "started", None), - end_time=getattr(row_iterator, "ended", None), - duration_seconds=exec_seconds, - status="DONE", - total_bytes_processed=getattr( - row_iterator, "total_bytes_processed", None - ), - total_slot_ms=getattr(row_iterator, "slot_millis", None), - job_type="query", - cached=getattr(row_iterator, "cache_hit", None), - query=query_text, - job_url=job_url, - cell_execution_count=cell_execution_count, - ) - # fmt: on - - -@dataclasses.dataclass -class ExecutionMetrics: - execution_count: int = 0 - slot_millis: int = 0 - bytes_processed: int = 0 - execution_secs: float = 0 - query_char_count: int = 0 - jobs: list[JobMetadata] = dataclasses.field(default_factory=list) - - # fmt: off - def count_job_stats( - self, - query_job: Optional[Union[QueryJob, LoadJob]] = None, - row_iterator: Optional[bq_table.RowIterator] = None, - *, - cell_execution_count: Optional[int] = None, - ): - if query_job is None: - assert row_iterator is not None - - # TODO(tswast): Pass None after making benchmark publishing robust - # to missing data. - bytes_processed = ( - getattr(row_iterator, "total_bytes_processed", 0) or 0 - ) - query_char_count = len(getattr(row_iterator, "query", "") or "") - slot_millis = getattr(row_iterator, "slot_millis", 0) or 0 - created = getattr(row_iterator, "created", None) - ended = getattr(row_iterator, "ended", None) - exec_seconds = ( - (ended - created).total_seconds() if created and ended else 0.0 - ) - - self.execution_count += 1 - self.query_char_count += query_char_count - self.bytes_processed += bytes_processed - self.slot_millis += slot_millis - self.execution_secs += exec_seconds - - self.jobs.append( - JobMetadata.from_row_iterator( - row_iterator, - exec_seconds=exec_seconds, - cell_execution_count=cell_execution_count, - ) - ) - - elif ( - isinstance(query_job, QueryJob) - and query_job.configuration.dry_run - ): - query_char_count = len(getattr(query_job, "query", "")) - - # TODO(tswast): Pass None after making benchmark publishing robust - # to missing data. - bytes_processed = 0 - slot_millis = 0 - exec_seconds = 0.0 - - elif isinstance(query_job, bigquery.QueryJob): - if (stats := get_performance_stats(query_job)) is not None: - ( - query_char_count, - bytes_processed, - slot_millis, - exec_seconds, - ) = stats - self.execution_count += 1 - self.query_char_count += query_char_count or 0 - self.bytes_processed += bytes_processed or 0 - self.slot_millis += slot_millis or 0 - self.execution_secs += exec_seconds or 0 - - metadata = JobMetadata.from_job( - query_job, - exec_seconds=exec_seconds, - cell_execution_count=cell_execution_count, - ) - self.jobs.append(metadata) - - else: - self.execution_count += 1 - duration = ( - (query_job.ended - query_job.created).total_seconds() - if query_job.ended and query_job.created - else None - ) - self.jobs.append( - JobMetadata.from_job( - query_job, - exec_seconds=duration, - cell_execution_count=cell_execution_count, - ) - ) - - # For pytest runs only, log information about the query job - # to a file in order to create a performance report. - if ( - isinstance(query_job, bigquery.QueryJob) - and not query_job.configuration.dry_run - ): - stats = get_performance_stats(query_job) - if stats: - write_stats_to_disk( - query_char_count=stats[0], - bytes_processed=stats[1], - slot_millis=stats[2], - exec_seconds=stats[3], - ) - elif row_iterator is not None: - bytes_processed = ( - getattr(row_iterator, "total_bytes_processed", 0) or 0 - ) - query_char_count = len(getattr(row_iterator, "query", "") or "") - slot_millis = getattr(row_iterator, "slot_millis", 0) or 0 - created = getattr(row_iterator, "created", None) - ended = getattr(row_iterator, "ended", None) - exec_seconds = ( - (ended - created).total_seconds() if created and ended else 0.0 - ) - write_stats_to_disk( - query_char_count=query_char_count, - bytes_processed=bytes_processed, - slot_millis=slot_millis, - exec_seconds=exec_seconds, - ) - # fmt: on - - def on_event(self, envelope: Any): - try: - import bigframes.core.events - from bigframes.session.executor import LocalExecuteResult - except ImportError: - return - - # Publisher.publish automatically wraps raw Event objects in an - # EventEnvelope, ensuring subscribers receive a consistent contract. - assert isinstance(envelope, bigframes.core.events.EventEnvelope) - event = envelope.event - cell_execution_count = envelope.cell_execution_count - - if isinstance(event, bigframes.core.events.ExecutionFinished): - if event.result and isinstance(event.result, LocalExecuteResult): - self.execution_count += 1 - bytes_processed = event.result.total_bytes_processed or 0 - self.bytes_processed += bytes_processed - - if cell_execution_count is None: - from bigframes.core.utils import get_ipython_execution_count - - cell_execution_count = get_ipython_execution_count() - - metadata = JobMetadata( - job_type="polars", - status="DONE", - total_bytes_processed=bytes_processed, - cell_execution_count=cell_execution_count, - ) - self.jobs.append(metadata) - - -def get_performance_stats( - query_job: bigquery.QueryJob, -) -> Optional[Tuple[int, int, int, float]]: - """Parse the query job for performance stats. - - Return None if the stats do not reflect real work done in bigquery. - """ - if ( - query_job.configuration.dry_run - or query_job.created is None - or query_job.ended is None - ): - return None - - bytes_processed = query_job.total_bytes_processed - if bytes_processed and not isinstance(bytes_processed, int): - return None # filter out mocks - - slot_millis = query_job.slot_millis - if slot_millis and not isinstance(slot_millis, int): - return None # filter out mocks - - execution_secs = (query_job.ended - query_job.created).total_seconds() - query_char_count = len(query_job.query) - - return ( - query_char_count, - # Not every job populates these. For example, slot_millis is missing - # from queries that came from cached results. - bytes_processed if bytes_processed else 0, - slot_millis if slot_millis else 0, - execution_secs, - ) - - -def write_stats_to_disk( - *, - query_char_count: int, - bytes_processed: int, - slot_millis: int, - exec_seconds: float, -): - """For pytest runs only, log information about the query job - to a file in order to create a performance report. - """ - if LOGGING_NAME_ENV_VAR not in os.environ: - return - - # when running notebooks via pytest nbmake and running benchmarks - test_name = os.environ[LOGGING_NAME_ENV_VAR] - current_directory = os.getcwd() - - # store slot milliseconds - slot_file = os.path.join(current_directory, test_name + ".slotmillis") - with open(slot_file, "a") as f: - f.write(str(slot_millis) + "\n") - - # store execution time seconds - exec_time_file = os.path.join( - current_directory, test_name + ".bq_exec_time_seconds" - ) - with open(exec_time_file, "a") as f: - f.write(str(exec_seconds) + "\n") - - # store length of query - query_char_count_file = os.path.join( - current_directory, test_name + ".query_char_count" - ) - with open(query_char_count_file, "a") as f: - f.write(str(query_char_count) + "\n") - - # store bytes processed - bytes_file = os.path.join(current_directory, test_name + ".bytesprocessed") - with open(bytes_file, "a") as f: - f.write(str(bytes_processed) + "\n") diff --git a/bigframes/session/planner.py b/bigframes/session/planner.py deleted file mode 100644 index 2a562abadf1..00000000000 --- a/bigframes/session/planner.py +++ /dev/null @@ -1,85 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from __future__ import annotations - -import itertools -from typing import Sequence, Tuple - -import bigframes.core.expression as ex -import bigframes.core.identifiers as ids -import bigframes.core.nodes as nodes -import bigframes.core.pruning as predicate_pruning -import bigframes.core.tree_properties as traversals -import bigframes.dtypes - - -def session_aware_cache_plan( - root: nodes.BigFrameNode, session_forest: Sequence[nodes.BigFrameNode] -) -> Tuple[nodes.BigFrameNode, list[ids.ColumnId]]: - """ - Determines the best node to cache given a target and a list of object roots for objects in a session. - - Returns the node to cache, and optionally a clustering column. - """ - node_counts = traversals.count_nodes(session_forest) - # These node types are cheap to re-compute, so it makes more sense to cache their children. - de_cachable_types = (nodes.FilterNode, nodes.ProjectionNode, nodes.SelectionNode) - caching_target = cur_node = root - caching_target_refs = node_counts.get(caching_target, 0) - - filters: list[ - ex.Expression - ] = [] # accumulate filters into this as traverse downwards - clusterable_cols: set[ids.ColumnId] = set() - while isinstance(cur_node, de_cachable_types): - if isinstance(cur_node, nodes.FilterNode): - # Filter node doesn't define any variables, so no need to chain expressions - filters.append(cur_node.predicate) - elif isinstance(cur_node, nodes.ProjectionNode): - # Projection defines the variables that are used in the filter expressions, need to substitute variables with their scalar expressions - # that instead reference variables in the child node. - bindings = {name: expr for expr, name in cur_node.assignments} - filters = [ - i.bind_refs(bindings, allow_partial_bindings=True) for i in filters - ] - elif isinstance(cur_node, nodes.SelectionNode): - bindings = {output: input for input, output in cur_node.input_output_pairs} - filters = [i.bind_refs(bindings) for i in filters] - else: - raise ValueError(f"Unexpected de-cached node: {cur_node}") - - cur_node = cur_node.child - cur_node_refs = node_counts.get(cur_node, 0) - if cur_node_refs > caching_target_refs: - caching_target, caching_target_refs = cur_node, cur_node_refs - cluster_compatible_cols = { - field.id - for field in cur_node.fields - if bigframes.dtypes.is_clusterable(field.dtype) - } - # Cluster cols only consider the target object and not other sesssion objects - clusterable_cols = set( - itertools.chain.from_iterable( - map( - lambda f: predicate_pruning.cluster_cols_for_predicate( - f, cluster_compatible_cols - ), - filters, - ) - ) - ) - # BQ supports up to 4 cluster columns, just prioritize by alphabetical ordering - # TODO: Prioritize caching columns by estimated filter selectivity - return caching_target, sorted(list(clusterable_cols))[:4] diff --git a/bigframes/session/polars_executor.py b/bigframes/session/polars_executor.py deleted file mode 100644 index f757de130ce..00000000000 --- a/bigframes/session/polars_executor.py +++ /dev/null @@ -1,165 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -from __future__ import annotations - -import itertools -from typing import TYPE_CHECKING, Optional - -import bigframes.operations -from bigframes.core import ( - agg_expressions, - array_value, - bigframe_node, - expression, - nodes, -) -from bigframes.operations import aggregations as agg_ops -from bigframes.operations import ( - bool_ops, - comparison_ops, - date_ops, - frequency_ops, - generic_ops, - numeric_ops, - string_ops, -) -from bigframes.session import execution_spec, executor, semi_executor - -if TYPE_CHECKING: - import polars as pl - -# Polars executor can execute more node types, but these are the validated ones -_COMPATIBLE_NODES = ( - nodes.ReadLocalNode, - nodes.OrderByNode, - nodes.ReversedNode, - nodes.SelectionNode, - nodes.ProjectionNode, - nodes.SliceNode, - nodes.AggregateNode, - nodes.FilterNode, - nodes.ConcatNode, - nodes.JoinNode, - nodes.InNode, - nodes.PromoteOffsetsNode, -) - -_COMPATIBLE_SCALAR_OPS = ( - bool_ops.AndOp, - bool_ops.OrOp, - bool_ops.XorOp, - comparison_ops.EqOp, - comparison_ops.EqNullsMatchOp, - comparison_ops.NeOp, - comparison_ops.LtOp, - comparison_ops.GtOp, - comparison_ops.LeOp, - comparison_ops.GeOp, - date_ops.YearOp, - date_ops.QuarterOp, - date_ops.MonthOp, - date_ops.DayOfWeekOp, - date_ops.DayOp, - date_ops.IsoYearOp, - date_ops.IsoWeekOp, - date_ops.IsoDayOp, - frequency_ops.FloorDtOp, - numeric_ops.AddOp, - numeric_ops.SubOp, - numeric_ops.MulOp, - numeric_ops.DivOp, - numeric_ops.CeilOp, - numeric_ops.FloorOp, - numeric_ops.FloorDivOp, - numeric_ops.ModOp, - generic_ops.AsTypeOp, - generic_ops.WhereOp, - generic_ops.CoalesceOp, - generic_ops.FillNaOp, - generic_ops.CaseWhenOp, - generic_ops.InvertOp, - generic_ops.IsInOp, - generic_ops.IsNullOp, - generic_ops.NotNullOp, - string_ops.StartsWithOp, - string_ops.EndsWithOp, - string_ops.StrContainsOp, - string_ops.StrContainsRegexOp, -) -_COMPATIBLE_AGG_OPS = ( - agg_ops.SizeOp, - agg_ops.SizeUnaryOp, - agg_ops.MinOp, - agg_ops.MaxOp, - agg_ops.SumOp, - agg_ops.MeanOp, - agg_ops.CountOp, - agg_ops.VarOp, - agg_ops.PopVarOp, - agg_ops.StdOp, -) - - -def _get_expr_ops(expr: expression.Expression) -> set[bigframes.operations.ScalarOp]: - if isinstance(expr, expression.OpExpression): - return set(itertools.chain.from_iterable(map(_get_expr_ops, expr.children))) - return set() - - -def _is_node_polars_executable(node: nodes.BigFrameNode): - if not isinstance(node, _COMPATIBLE_NODES): - return False - for expr in node._node_expressions: - if isinstance(expr, agg_expressions.Aggregation): - if type(expr.op) not in _COMPATIBLE_AGG_OPS: - return False - if isinstance(expr, expression.Expression): - if not set(map(type, _get_expr_ops(expr))).issubset(_COMPATIBLE_SCALAR_OPS): - return False - return True - - -class PolarsExecutor(semi_executor.SemiExecutor): - def __init__(self): - # This will error out if polars is not installed - from bigframes.core.compile.polars import PolarsCompiler - - self._compiler = PolarsCompiler() - - async def execute( - self, - plan: bigframe_node.BigFrameNode, - execution_spec: execution_spec.ExecutionSpec, - ) -> Optional[executor.ExecuteResult]: - if not self._can_execute(plan): - return None - if execution_spec.destination_spec is not None: - return None - try: - lazy_frame: pl.LazyFrame = self._compiler.compile( - array_value.ArrayValue(plan).node - ) - except Exception: - return None - if execution_spec.peek is not None: - lazy_frame = lazy_frame.limit(execution_spec.peek) - pl_df = await lazy_frame.collect_async() - pa_table = pl_df.to_arrow() - return executor.LocalExecuteResult( - data=pa_table, - bf_schema=plan.schema, - ) - - def _can_execute(self, plan: bigframe_node.BigFrameNode): - return all(_is_node_polars_executable(node) for node in plan.unique_nodes()) diff --git a/bigframes/session/proxy_executor.py b/bigframes/session/proxy_executor.py deleted file mode 100644 index f6c914790cb..00000000000 --- a/bigframes/session/proxy_executor.py +++ /dev/null @@ -1,188 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from __future__ import annotations - -import uuid -import warnings -from typing import Optional - -import google.cloud.bigquery as bigquery -import google.cloud.exceptions - -import bigframes.core -import bigframes.functions._function_session as bff_session -from bigframes import exceptions as bfe -from bigframes.session import ( - bq_caching_executor, - execution_cache, - execution_spec, - executor, - loader, - temporary_storage, -) - -_COMPILER_LABEL_KEY = "bigframes-compiler" - - -class DualCompilerProxyExecutor(executor.Executor): - """ - Used to rollout new compiler implementation. - """ - - def __init__( - self, - bqclient: bigquery.Client, - storage_manager: temporary_storage.TemporaryStorageManager, - bqstoragereadclient: google.cloud.bigquery_storage_v1.BigQueryReadClient, - loader: loader.GbqDataLoader, - *, - metrics: Optional[bigframes.session.metrics.ExecutionMetrics] = None, - enable_polars_execution: bool = False, - publisher: bigframes.core.events.Publisher, - function_manager: bff_session.FunctionSession, - labels: tuple[tuple[str, str], ...] = (), - ): - self._enable_polars_execution = enable_polars_execution - shared_cache = execution_cache.ExecutionCache() - self._ibis_executor = bq_caching_executor.BigQueryCachingExecutor( - bqclient, - storage_manager, - bqstoragereadclient, - loader, - metrics=metrics, - enable_polars_execution=self._enable_polars_execution, - publisher=publisher, - labels=labels, - cache=shared_cache, - compiler_name="ibis", - function_manager=function_manager, - ) - self._sqlglot_executor = bq_caching_executor.BigQueryCachingExecutor( - bqclient, - storage_manager, - bqstoragereadclient, - loader, - metrics=metrics, - enable_polars_execution=self._enable_polars_execution, - publisher=publisher, - labels=labels, - cache=shared_cache, - compiler_name="sqlglot", - function_manager=function_manager, - ) - - def to_sql( - self, - array_value: bigframes.core.ArrayValue, - offset_column: Optional[str] = None, - ordered: bool = False, - enable_cache: bool = True, - ) -> str: - """ - Convert an ArrayValue to a sql query that will yield its value. - """ - compiler_option = bigframes.options.experiments.sql_compiler - # Use ibis unless sqlglot explicitly selected, since we can't handle errors resulting - # from use of the sql produced by this method. - if compiler_option == "experimental": - return self._sqlglot_executor.to_sql( - array_value, - offset_column=offset_column, - ordered=ordered, - enable_cache=enable_cache, - ) - # stable or legacy use ibis - # TODO(b/510408650): Use sqlglot by default. - return self._ibis_executor.to_sql( - array_value, - offset_column=offset_column, - ordered=ordered, - enable_cache=enable_cache, - ) - - def execute( - self, - array_value: bigframes.core.ArrayValue, - execution_spec: execution_spec.ExecutionSpec, - ) -> executor.ExecuteResult: - compiler_option = bigframes.options.experiments.sql_compiler - if compiler_option == "legacy": - return self._ibis_executor.execute( - array_value, - execution_spec.with_bq_labels({_COMPILER_LABEL_KEY: "ibis"}), - ) - elif compiler_option == "experimental": - return self._sqlglot_executor.execute( - array_value, - execution_spec.with_bq_labels({_COMPILER_LABEL_KEY: "sqlglot"}), - ) - else: # stable - correlation_id = f"{uuid.uuid1().hex[:12]}" - try: - return self._sqlglot_executor.execute( - array_value, - execution_spec.with_bq_labels( - {_COMPILER_LABEL_KEY: f"sqlglot-{correlation_id}"} - ), - ) - except Exception as e: - msg = bfe.format_message( - f"Compiler ID {correlation_id}: Exception on sqlglot. " - f"Falling back to ibis. Details: {e}" - ) - warnings.warn(msg, category=UserWarning) - return self._ibis_executor.execute( - array_value, - execution_spec.with_bq_labels( - {_COMPILER_LABEL_KEY: f"ibis-{correlation_id}"} - ), - ) - - def dry_run( - self, array_value: bigframes.core.ArrayValue, ordered: bool = True - ) -> bigquery.QueryJob: - """ - Dry run executing the ArrayValue. - - Does not actually execute the data but will get stats and indicate any invalid query errors. - """ - # TODO(b/510408650): Use sqlglot for dry runs when sqlglot has been validated. - return self._ibis_executor.dry_run(array_value, ordered=ordered) - - def cached( - self, - array_value: bigframes.core.ArrayValue, - *, - config: executor.CacheConfig, - ) -> None: - compiler_option = bigframes.options.experiments.sql_compiler - if compiler_option == "legacy": - return self._ibis_executor.cached(array_value, config=config) - elif compiler_option == "experimental": - return self._sqlglot_executor.cached(array_value, config=config) - else: # stable - correlation_id = f"{uuid.uuid1().hex[:12]}" - try: - return self._sqlglot_executor.cached(array_value, config=config) - except Exception as e: - msg = bfe.format_message( - f"Compiler ID {correlation_id}: Exception on sqlglot. " - f"Falling back to ibis. Details: {e}" - ) - warnings.warn(msg, category=UserWarning) - return self._ibis_executor.cached( - array_value, - config=config, - ) diff --git a/bigframes/session/read_api_execution.py b/bigframes/session/read_api_execution.py deleted file mode 100644 index fff8022e40a..00000000000 --- a/bigframes/session/read_api_execution.py +++ /dev/null @@ -1,93 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -from __future__ import annotations - -from typing import Optional - -from google.cloud import bigquery_storage_v1 - -from bigframes.core import bigframe_node, bq_data, nodes, rewrite -from bigframes.session import execution_spec, executor, semi_executor - - -class ReadApiSemiExecutor(semi_executor.SemiExecutor): - """ - Executes plans reducible to a bq table scan by directly reading the table with the read api. - """ - - def __init__( - self, - bqstoragereadclient: bigquery_storage_v1.BigQueryReadClient, - project: str, - ): - self.bqstoragereadclient = bqstoragereadclient - self.project = project - - async def execute( - self, - plan: bigframe_node.BigFrameNode, - execution_spec: execution_spec.ExecutionSpec, - ) -> Optional[executor.ExecuteResult]: - if execution_spec.destination_spec is not None: - return None - - adapt_result = self._try_adapt_plan(plan, execution_spec.ordered) - if not adapt_result: - return None - node, limit = adapt_result - if node.explicitly_ordered and execution_spec.ordered: - return None - - if not isinstance(node.source.table, bq_data.GbqNativeTable): - return None - - if not node.source.table.is_physically_stored: - return None - - peek = execution_spec.peek - if limit is not None: - if peek is None or limit < peek: - peek = limit - - return executor.BQTableExecuteResult( - data=node.source, - project_id=self.project, - storage_client=self.bqstoragereadclient, - limit=peek, - selected_fields=[ - (item.source_id, item.id.sql) for item in node.scan_list.items - ], - ) - - def _try_adapt_plan( - self, - plan: bigframe_node.BigFrameNode, - ordered: bool, - ) -> Optional[tuple[nodes.ReadTableNode, Optional[int]]]: - """ - Tries to simplify the plan to an equivalent single ReadTableNode and a limit. Otherwise, returns None. - """ - plan, limit = rewrite.pull_out_limit(plan) - # bake_order does not allow slice ops - plan = plan.bottom_up(rewrite.rewrite_slice) - if not ordered: - # gets rid of order_by ops - plan = rewrite.bake_order(plan) - read_table_node = rewrite.try_reduce_to_table_scan(plan) - if read_table_node is None: - return None - if (limit is not None) and (read_table_node.source.ordering is not None): - # read api can only use physical ordering to limit, not a logical ordering - return None - return (read_table_node, limit) diff --git a/bigframes/session/semi_executor.py b/bigframes/session/semi_executor.py deleted file mode 100644 index 1f827ce9d93..00000000000 --- a/bigframes/session/semi_executor.py +++ /dev/null @@ -1,32 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -import abc -from typing import Optional - -from bigframes.core import bigframe_node -from bigframes.session import execution_spec, executor - - -# Unstable interface, in development -class SemiExecutor(abc.ABC): - """ - A semi executor executes a subset of possible plans, returns None for unsupported plans. - """ - - async def execute( - self, - plan: bigframe_node.BigFrameNode, - execution_spec: execution_spec.ExecutionSpec, - ) -> Optional[executor.ExecuteResult]: - raise NotImplementedError("execute not implemented for this executor") diff --git a/bigframes/session/temporary_storage.py b/bigframes/session/temporary_storage.py deleted file mode 100644 index 42617c8f6c1..00000000000 --- a/bigframes/session/temporary_storage.py +++ /dev/null @@ -1,29 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from typing import Protocol, Sequence - -from google.cloud import bigquery - - -class TemporaryStorageManager(Protocol): - @property - def location(self) -> str: ... - - def create_temp_table( - self, schema: Sequence[bigquery.SchemaField], cluster_cols: Sequence[str] = [] - ) -> bigquery.TableReference: ... - - # implementations should be robust to repeatedly closing - def close(self) -> None: ... diff --git a/bigframes/session/time.py b/bigframes/session/time.py deleted file mode 100644 index 1452b2952dc..00000000000 --- a/bigframes/session/time.py +++ /dev/null @@ -1,59 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import datetime -import threading -import time -from typing import Optional, cast - -import google.cloud.bigquery as bigquery - -MIN_RESYNC_SECONDS = 100 - - -class BigQuerySyncedClock: - """ - Local clock that attempts to synchronize its time with the bigquery service. - """ - - def __init__(self, bqclient: bigquery.Client): - self._bqclient = bqclient - self._sync_lock = threading.Lock() - self._sync_remote_time: Optional[datetime.datetime] = None - self._sync_monotonic_time: Optional[float] = None - - def get_time(self): - if (self._sync_monotonic_time is None) or (self._sync_remote_time is None): - self.sync() - assert self._sync_remote_time is not None - assert self._sync_monotonic_time is not None - return self._sync_remote_time + datetime.timedelta( - seconds=time.monotonic() - self._sync_monotonic_time - ) - - def sync(self): - with self._sync_lock: - if (self._sync_monotonic_time is not None) and ( - time.monotonic() - self._sync_monotonic_time - ) < MIN_RESYNC_SECONDS: - return - current_bq_time = list( - next( - self._bqclient.query_and_wait( - "SELECT CURRENT_TIMESTAMP() AS `current_timestamp`", - ) - ) - )[0] - self._sync_remote_time = cast(datetime.datetime, current_bq_time) - self._sync_monotonic_time = time.monotonic() diff --git a/bigframes/session/validation.py b/bigframes/session/validation.py deleted file mode 100644 index f2e68818442..00000000000 --- a/bigframes/session/validation.py +++ /dev/null @@ -1,38 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import bigframes_vendored.constants - - -def validate_engine_compatibility(engine, write_engine): - """Raises NotImplementedError if engine is not compatible with write_engine.""" - - if engine == "bigquery" and write_engine in ( - "bigquery_inline", - "bigquery_streaming", - ): - raise NotImplementedError( - bigframes_vendored.constants.WRITE_ENGINE_REQUIRES_LOCAL_ENGINE_TEMPLATE.format( - engine=repr(engine), - write_engine=repr(write_engine), - ) - ) - - if engine != "bigquery" and write_engine in ("bigquery_external_table",): - raise NotImplementedError( - bigframes_vendored.constants.WRITE_ENGINE_REQUIRES_BIGQUERY_ENGINE_TEMPLATE.format( - engine=repr(engine), - write_engine=repr(write_engine), - ) - ) diff --git a/bigframes/streaming/__init__.py b/bigframes/streaming/__init__.py deleted file mode 100644 index 49687090fe6..00000000000 --- a/bigframes/streaming/__init__.py +++ /dev/null @@ -1,47 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from __future__ import annotations - -import inspect -import sys - -import bigframes.core.global_session as global_session -import bigframes.session -import bigframes.streaming.dataframe as streaming_dataframe -from bigframes.core.logging import log_adapter -from bigframes.pandas.io.api import _set_default_session_location_if_possible - - -def read_gbq_table(table: str) -> streaming_dataframe.StreamingDataFrame: - _set_default_session_location_if_possible(table) - return global_session.with_default_session( - bigframes.session.Session.read_gbq_table_streaming, table - ) - - -read_gbq_table.__doc__ = inspect.getdoc( - bigframes.session.Session.read_gbq_table_streaming -) - -StreamingDataFrame = streaming_dataframe.StreamingDataFrame - -_module = sys.modules[__name__] -_functions = [read_gbq_table] - -for _function in _functions: - _decorated_object = log_adapter.method_logger(_function, custom_base_name="pandas") - setattr(_module, _function.__name__, _decorated_object) - -__all__ = ["read_gbq_table", "StreamingDataFrame"] diff --git a/bigframes/streaming/dataframe.py b/bigframes/streaming/dataframe.py deleted file mode 100644 index 98d6da45399..00000000000 --- a/bigframes/streaming/dataframe.py +++ /dev/null @@ -1,574 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -"""Module for bigquery continuous queries""" - -from __future__ import annotations - -import functools -import inspect -import json -import warnings -from abc import abstractmethod -from datetime import date, datetime -from typing import Optional, Union - -import pandas as pd -from google.cloud import bigquery - -import bigframes.exceptions as bfe -import bigframes.session -from bigframes import dataframe -from bigframes.core import nodes -from bigframes.core.logging import log_adapter - - -def _return_type_wrapper(method, cls): - @functools.wraps(method) - def wrapper(*args, **kwargs): - return_value = method(*args, **kwargs) - if isinstance(return_value, dataframe.DataFrame): - return cls._from_table_df(return_value) - return return_value - - return wrapper - - -def _curate_df_doc(doc: Optional[str]): - if not doc: - return doc - - # Remove examples, some are not applicable to StreamingDataFrame - doc = doc[: doc.find("**Examples:**")] + doc[doc.find("Args:") :] - - doc = doc.replace("dataframe.DataFrame", "streaming.StreamingDataFrame") - doc = doc.replace(" DataFrame", " StreamingDataFrame") - - return doc - - -class StreamingBase: - _session: bigframes.session.Session - - @abstractmethod - def _appends_sql( - self, start_timestamp: Optional[Union[int, float, str, datetime, date]] - ) -> str: - pass - - def to_bigtable( - self, - *, - instance: str, - table: str, - service_account_email: Optional[str] = None, - app_profile: Optional[str] = None, - truncate: bool = False, - overwrite: bool = False, - auto_create_column_families: bool = False, - bigtable_options: Optional[dict] = None, - job_id: Optional[str] = None, - job_id_prefix: Optional[str] = None, - start_timestamp: Optional[Union[int, float, str, datetime, date]] = None, - end_timestamp: Optional[Union[int, float, str, datetime, date]] = None, - ) -> bigquery.QueryJob: - """ - Export the StreamingDataFrame as a continue job and returns a - QueryJob object for some management functionality. - - This method requires an existing bigtable preconfigured to - accept the continuous query export statement. For instructions - on export to bigtable, see - https://cloud.google.com/bigquery/docs/export-to-bigtable. - - Args: - instance (str): - The name of the bigtable instance to export to. - table (str): - The name of the bigtable table to export to. - service_account_email (str): - Full name of the service account to run the continuous query. - Example: accountname@projectname.gserviceaccounts.com - If not provided, the user account will be used, but this - limits the lifetime of the continuous query. - app_profile (str, default None): - The bigtable app profile to export to. If None, no app - profile will be used. - truncate (bool, default False): - The export truncate option, see - https://cloud.google.com/bigquery/docs/reference/standard-sql/other-statements#bigtable_export_option - overwrite (bool, default False): - The export overwrite option, see - https://cloud.google.com/bigquery/docs/reference/standard-sql/other-statements#bigtable_export_option - auto_create_column_families (bool, default False): - The auto_create_column_families option, see - https://cloud.google.com/bigquery/docs/reference/standard-sql/other-statements#bigtable_export_option - bigtable_options (dict, default None): - The bigtable options dict, which will be converted to JSON - using json.dumps, see - https://cloud.google.com/bigquery/docs/reference/standard-sql/other-statements#bigtable_export_option - If None, no bigtable_options parameter will be passed. - job_id (str, default None): - If specified, replace the default job id for the query, - see job_id parameter of - https://cloud.google.com/python/docs/reference/bigquery/latest/google.cloud.bigquery.client.Client#google_cloud_bigquery_client_Client_query - job_id_prefix (str, default None): - If specified, a job id prefix for the query, see - job_id_prefix parameter of - https://cloud.google.com/python/docs/reference/bigquery/latest/google.cloud.bigquery.client.Client#google_cloud_bigquery_client_Client_query - start_timestamp (int, float, str, datetime, date, default None): - The starting timestamp for the query. Possible values are to 7 days in the past. If don't specify a timestamp (None), the query will default to the earliest possible time, 7 days ago. If provide a time-zone-naive timestamp, it will be treated as UTC. - Returns: - google.cloud.bigquery.QueryJob: - See https://cloud.google.com/python/docs/reference/bigquery/latest/google.cloud.bigquery.job.QueryJob - The ongoing query job can be managed using this object. - For example, the job can be cancelled or its error status - can be examined. - """ - if not isinstance( - start_timestamp, (int, float, str, datetime, date, type(None)) - ): - raise ValueError( - f"Unsupported start_timestamp type {type(start_timestamp)}" - ) - - return _to_bigtable( - self._appends_sql(start_timestamp), - instance=instance, - table=table, - service_account_email=service_account_email, - session=self._session, - app_profile=app_profile, - truncate=truncate, - overwrite=overwrite, - auto_create_column_families=auto_create_column_families, - bigtable_options=bigtable_options, - job_id=job_id, - job_id_prefix=job_id_prefix, - ) - - def to_pubsub( - self, - *, - topic: str, - service_account_email: str, - job_id: Optional[str] = None, - job_id_prefix: Optional[str] = None, - start_timestamp: Optional[Union[int, float, str, datetime, date]] = None, - ) -> bigquery.QueryJob: - """ - Export the StreamingDataFrame as a continue job and returns a - QueryJob object for some management functionality. - - This method requires an existing pubsub topic. For instructions - on creating a pubsub topic, see - https://cloud.google.com/pubsub/docs/samples/pubsub-quickstart-create-topic?hl=en - - Note that a service account is a requirement for continuous queries - exporting to pubsub. - - Args: - topic (str): - The name of the pubsub topic to export to. - For example: "taxi-rides" - service_account_email (str): - Full name of the service account to run the continuous query. - Example: accountname@projectname.gserviceaccounts.com - job_id (str, default None): - If specified, replace the default job id for the query, - see job_id parameter of - https://cloud.google.com/python/docs/reference/bigquery/latest/google.cloud.bigquery.client.Client#google_cloud_bigquery_client_Client_query - job_id_prefix (str, default None): - If specified, a job id prefix for the query, see - job_id_prefix parameter of - https://cloud.google.com/python/docs/reference/bigquery/latest/google.cloud.bigquery.client.Client#google_cloud_bigquery_client_Client_query - start_timestamp (int, float, str, datetime, date, default None): - The starting timestamp for the query. Possible values are to 7 days in the past. If don't specify a timestamp (None), the query will default to the earliest possible time, 7 days ago. If provide a time-zone-naive timestamp, it will be treated as UTC. - - Returns: - google.cloud.bigquery.QueryJob: - See https://cloud.google.com/python/docs/reference/bigquery/latest/google.cloud.bigquery.job.QueryJob - The ongoing query job can be managed using this object. - For example, the job can be cancelled or its error status - can be examined. - """ - if not isinstance( - start_timestamp, (int, float, str, datetime, date, type(None)) - ): - raise ValueError( - f"Unsupported start_timestamp type {type(start_timestamp)}" - ) - - return _to_pubsub( - self._appends_sql(start_timestamp), - topic=topic, - service_account_email=service_account_email, - session=self._session, - job_id=job_id, - job_id_prefix=job_id_prefix, - ) - - -@log_adapter.class_logger -class StreamingDataFrame(StreamingBase): - __doc__ = ( - _curate_df_doc(dataframe.DataFrame.__doc__) - + """ - .. note:: - - The bigframes.streaming module is a preview feature, and subject to change. - - Currently only supports basic projection, filtering and preview operations. - """ - ) - - # Private constructor - _create_key = object() - - def __init__(self, df: dataframe.DataFrame, *, create_key=0): - if create_key is not StreamingDataFrame._create_key: - raise ValueError( - "StreamingDataFrame class shouldn't be created through constructor. Call bigframes.pandas.read_gbq_table_streaming method to create." - ) - self._df = df - self._df._disable_cache_override = True - - @classmethod - def _from_table_df(cls, df: dataframe.DataFrame) -> StreamingDataFrame: - return cls(df, create_key=cls._create_key) - - @property - def _original_table(self): - def traverse(node: nodes.BigFrameNode): - if isinstance(node, nodes.ReadTableNode): - return node.source.table.get_full_id(quoted=False) - for child in node.child_nodes: - original_table = traverse(child) - if original_table: - return original_table - return None - - return traverse(self._df._block._expr.node) - - def __getitem__(self, *args, **kwargs): - return _return_type_wrapper(self._df.__getitem__, StreamingDataFrame)( - *args, **kwargs - ) - - __getitem__.__doc__ = _curate_df_doc( - inspect.getdoc(dataframe.DataFrame.__getitem__) - ) - - def __setitem__(self, *args, **kwargs): - return _return_type_wrapper(self._df.__setitem__, StreamingDataFrame)( - *args, **kwargs - ) - - __setitem__.__doc__ = _curate_df_doc( - inspect.getdoc(dataframe.DataFrame.__setitem__) - ) - - def rename(self, *args, **kwargs): - return _return_type_wrapper(self._df.rename, StreamingDataFrame)( - *args, **kwargs - ) - - rename.__doc__ = _curate_df_doc(inspect.getdoc(dataframe.DataFrame.rename)) - - def __repr__(self, *args, **kwargs): - return _return_type_wrapper(self._df.__repr__, StreamingDataFrame)( - *args, **kwargs - ) - - __repr__.__doc__ = _curate_df_doc(inspect.getdoc(dataframe.DataFrame.__repr__)) - - def _repr_mimebundle_(self, *args, **kwargs): - return _return_type_wrapper(self._df._repr_mimebundle_, StreamingDataFrame)( - *args, **kwargs - ) - - _repr_mimebundle_.__doc__ = _curate_df_doc( - inspect.getdoc(dataframe.DataFrame._repr_mimebundle_) - ) - - @property - def sql(self): - sql_str, _, _ = self._df._to_sql_query(include_index=False, enable_cache=False) - return sql_str - - sql.__doc__ = _curate_df_doc(inspect.getdoc(dataframe.DataFrame.sql)) - - # Patch for the required APPENDS clause - def _appends_sql( - self, start_timestamp: Optional[Union[int, float, str, datetime, date]] - ) -> str: - sql_str = self.sql - original_table = self._original_table - assert original_table is not None - - # TODO(b/405691193): set start time back to NULL. Now set it slightly after 7 days max interval to avoid the bug. - start_ts_str = ( - str(f"TIMESTAMP('{pd.to_datetime(start_timestamp)}')") - if start_timestamp - else "CURRENT_TIMESTAMP() - (INTERVAL 7 DAY - INTERVAL 5 MINUTE)" - ) - - appends_clause = f"APPENDS(TABLE `{original_table}`, {start_ts_str})" - sql_str = sql_str.replace(f"`{original_table}`", appends_clause) - return sql_str - - @property - def _session(self): - return self._df._session - - _session.__doc__ = _curate_df_doc(inspect.getdoc(dataframe.DataFrame._session)) - - -def _to_bigtable( - query: str, - *, - instance: str, - table: str, - service_account_email: Optional[str] = None, - session: Optional[bigframes.session.Session] = None, - app_profile: Optional[str] = None, - truncate: bool = False, - overwrite: bool = False, - auto_create_column_families: bool = False, - bigtable_options: Optional[dict] = None, - job_id: Optional[str] = None, - job_id_prefix: Optional[str] = None, -) -> bigquery.QueryJob: - """Launches a BigQuery continuous query and returns a - QueryJob object for some management functionality. - - This method requires an existing bigtable preconfigured to - accept the continuous query export statement. For instructions - on export to bigtable, see - https://cloud.google.com/bigquery/docs/export-to-bigtable. - - Args: - query (str): - The sql statement to execute as a continuous function. - For example: "SELECT * FROM dataset.table" - This will be wrapped in an EXPORT DATA statement to - launch a continuous query writing to bigtable. - instance (str): - The name of the bigtable instance to export to. - table (str): - The name of the bigtable table to export to. - service_account_email (str): - Full name of the service account to run the continuous query. - Example: accountname@projectname.gserviceaccounts.com - If not provided, the user account will be used, but this - limits the lifetime of the continuous query. - session (bigframes.session.Session, default None): - The session object to use for the query. This determines - the project id and location of the query. If None, will - default to the bigframes global session. - app_profile (str, default None): - The bigtable app profile to export to. If None, no app - profile will be used. - truncate (bool, default False): - The export truncate option, see - https://cloud.google.com/bigquery/docs/reference/standard-sql/other-statements#bigtable_export_option - overwrite (bool, default False): - The export overwrite option, see - https://cloud.google.com/bigquery/docs/reference/standard-sql/other-statements#bigtable_export_option - auto_create_column_families (bool, default False): - The auto_create_column_families option, see - https://cloud.google.com/bigquery/docs/reference/standard-sql/other-statements#bigtable_export_option - bigtable_options (dict, default None): - The bigtable options dict, which will be converted to JSON - using json.dumps, see - https://cloud.google.com/bigquery/docs/reference/standard-sql/other-statements#bigtable_export_option - If None, no bigtable_options parameter will be passed. - job_id (str, default None): - If specified, replace the default job id for the query, - see job_id parameter of - https://cloud.google.com/python/docs/reference/bigquery/latest/google.cloud.bigquery.client.Client#google_cloud_bigquery_client_Client_query - job_id_prefix (str, default None): - If specified, a job id prefix for the query, see - job_id_prefix parameter of - https://cloud.google.com/python/docs/reference/bigquery/latest/google.cloud.bigquery.client.Client#google_cloud_bigquery_client_Client_query - - Returns: - google.cloud.bigquery.QueryJob: - See https://cloud.google.com/python/docs/reference/bigquery/latest/google.cloud.bigquery.job.QueryJob - The ongoing query job can be managed using this object. - For example, the job can be cancelled or its error status - can be examined. - """ - msg = bfe.format_message( - "The bigframes.streaming module is a preview feature, and subject to change." - ) - warnings.warn(msg, stacklevel=1, category=bfe.PreviewWarning) - - # get default client if not passed - if session is None: - session = bigframes.get_global_session() - bq_client = session.bqclient - - # build export string from parameters - project = bq_client.project - - app_profile_url_string = "" - if app_profile is not None: - app_profile_url_string = f"appProfiles/{app_profile}/" - - bigtable_options_parameter_string = "" - if bigtable_options is not None: - bigtable_options_parameter_string = ( - 'bigtable_options = """' + json.dumps(bigtable_options) + '""",\n' - ) - - sql = ( - "EXPORT DATA\n" - "OPTIONS (\n" - "format = 'CLOUD_BIGTABLE',\n" - f"{bigtable_options_parameter_string}" - f"truncate = {str(truncate)},\n" - f"overwrite = {str(overwrite)},\n" - f"auto_create_column_families = {str(auto_create_column_families)},\n" - f'uri = "https://bigtable.googleapis.com/projects/{project}/instances/{instance}/{app_profile_url_string}tables/{table}"\n' - ")\n" - "AS (\n" - f"{query});" - ) - - # override continuous http parameter - job_config = bigquery.job.QueryJobConfig() - - job_config_dict: dict = {"query": {"continuous": True}} - if service_account_email is not None: - job_config_dict["query"]["connectionProperties"] = { - "key": "service_account", - "value": service_account_email, - } - job_config_filled = job_config.from_api_repr(job_config_dict) - job_config_filled.labels = {"bigframes-api": "streaming_to_bigtable"} - - # begin the query job - query_job = bq_client.query( - sql, - job_config=job_config_filled, # type:ignore - # typing error above is in bq client library - # (should accept abstract job_config, only takes concrete) - job_id=job_id, - job_id_prefix=job_id_prefix, - ) - - # return the query job to the user for lifetime management - return query_job - - -def _to_pubsub( - query: str, - *, - topic: str, - service_account_email: str, - session: Optional[bigframes.session.Session] = None, - job_id: Optional[str] = None, - job_id_prefix: Optional[str] = None, -) -> bigquery.QueryJob: - """Launches a BigQuery continuous query and returns a - QueryJob object for some management functionality. - - This method requires an existing pubsub topic. For instructions - on creating a pubsub topic, see - https://cloud.google.com/pubsub/docs/samples/pubsub-quickstart-create-topic?hl=en - - Note that a service account is a requirement for continuous queries - exporting to pubsub. - - Args: - query (str): - The sql statement to execute as a continuous function. - For example: "SELECT * FROM dataset.table" - This will be wrapped in an EXPORT DATA statement to - launch a continuous query writing to pubsub. - topic (str): - The name of the pubsub topic to export to. - For example: "taxi-rides" - service_account_email (str): - Full name of the service account to run the continuous query. - Example: accountname@projectname.gserviceaccounts.com - session (bigframes.session.Session, default None): - The session object to use for the query. This determines - the project id and location of the query. If None, will - default to the bigframes global session. - job_id (str, default None): - If specified, replace the default job id for the query, - see job_id parameter of - https://cloud.google.com/python/docs/reference/bigquery/latest/google.cloud.bigquery.client.Client#google_cloud_bigquery_client_Client_query - job_id_prefix (str, default None): - If specified, a job id prefix for the query, see - job_id_prefix parameter of - https://cloud.google.com/python/docs/reference/bigquery/latest/google.cloud.bigquery.client.Client#google_cloud_bigquery_client_Client_query - - Returns: - google.cloud.bigquery.QueryJob: - See https://cloud.google.com/python/docs/reference/bigquery/latest/google.cloud.bigquery.job.QueryJob - The ongoing query job can be managed using this object. - For example, the job can be cancelled or its error status - can be examined. - """ - msg = bfe.format_message( - "The bigframes.streaming module is a preview feature, and subject to change." - ) - warnings.warn(msg, stacklevel=1, category=bfe.PreviewWarning) - - # get default client if not passed - if session is None: - session = bigframes.get_global_session() - bq_client = session.bqclient - - # build export string from parameters - sql = ( - "EXPORT DATA\n" - "OPTIONS (\n" - "format = 'CLOUD_PUBSUB',\n" - f'uri = "https://pubsub.googleapis.com/projects/{bq_client.project}/topics/{topic}"\n' - ")\n" - "AS (\n" - f"{query});" - ) - - # override continuous http parameter - job_config = bigquery.job.QueryJobConfig() - job_config_filled = job_config.from_api_repr( - { - "query": { - "continuous": True, - "connectionProperties": { - "key": "service_account", - "value": service_account_email, - }, - } - } - ) - job_config_filled.labels = {"bigframes-api": "streaming_to_pubsub"} - - # begin the query job - query_job = bq_client.query( - sql, - job_config=job_config_filled, # type:ignore - # typing error above is in bq client library - # (should accept abstract job_config, only takes concrete) - job_id=job_id, - job_id_prefix=job_id_prefix, - ) - - # return the query job to the user for lifetime management - return query_job diff --git a/bigframes/testing/__init__.py b/bigframes/testing/__init__.py deleted file mode 100644 index 098a67bddf3..00000000000 --- a/bigframes/testing/__init__.py +++ /dev/null @@ -1,21 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -"""[Experimental] Utilities for testing BigQuery DataFrames. - -These modules are provided for testing the BigQuery DataFrames package. The -interface is not considered stable. -""" - -# Do not import modules contains pytest. (b/490160312) diff --git a/bigframes/testing/compiler_session.py b/bigframes/testing/compiler_session.py deleted file mode 100644 index b248f37cfc8..00000000000 --- a/bigframes/testing/compiler_session.py +++ /dev/null @@ -1,50 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import dataclasses -import typing - -import bigframes.core -import bigframes.core.compile as compile -import bigframes.session.executor - - -@dataclasses.dataclass -class SQLCompilerExecutor(bigframes.session.executor.Executor): - """Executor for SQL compilation using sqlglot.""" - - compiler = compile.sqlglot - - def to_sql( - self, - array_value: bigframes.core.ArrayValue, - offset_column: typing.Optional[str] = None, - ordered: bool = True, - enable_cache: bool = False, - ) -> str: - if offset_column: - array_value, _ = array_value.promote_offsets() - - # Compared with BigQueryCachingExecutor, SQLCompilerExecutor skips - # caching the subtree. - return self.compiler.compile_sql( - compile.CompileRequest(array_value.node, sort_rows=ordered) - ).sql - - def execute( - self, - array_value, - execution_spec, - ): - raise NotImplementedError("SQLCompilerExecutor.execute not implemented") diff --git a/bigframes/testing/engine_utils.py b/bigframes/testing/engine_utils.py deleted file mode 100644 index 385ca7e45cc..00000000000 --- a/bigframes/testing/engine_utils.py +++ /dev/null @@ -1,40 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import asyncio - -import pandas.testing - -from bigframes.core import nodes -from bigframes.session import execution_spec, semi_executor - -SPEC = execution_spec.ExecutionSpec( - ordered=True, -) - - -def assert_equivalence_execution( - node: nodes.BigFrameNode, - engine1: semi_executor.SemiExecutor, - engine2: semi_executor.SemiExecutor, -): - e1_result = asyncio.run(engine1.execute(node, SPEC)) - e2_result = asyncio.run(engine2.execute(node, SPEC)) - assert e1_result is not None - assert e2_result is not None - # Convert to pandas, as pandas has better comparison utils than arrow - assert e1_result.schema == e2_result.schema - e1_table = e1_result.batches().to_pandas() - e2_table = e2_result.batches().to_pandas() - pandas.testing.assert_frame_equal(e1_table, e2_table, rtol=1e-5) diff --git a/bigframes/testing/mocks.py b/bigframes/testing/mocks.py deleted file mode 100644 index f8ad43dd664..00000000000 --- a/bigframes/testing/mocks.py +++ /dev/null @@ -1,187 +0,0 @@ -# Copyright 2023 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import copy -import datetime -import unittest.mock as mock -from typing import Any, Dict, Literal, Optional, Sequence - -import google.auth.credentials -import google.cloud.bigquery -import google.cloud.bigquery.table -import pyarrow -import pytest -from bigframes_vendored.google_cloud_bigquery import _pandas_helpers - -import bigframes -import bigframes.clients -import bigframes.core.global_session -import bigframes.dataframe -import bigframes.session.clients - -"""Utilities for creating test resources.""" - - -TEST_SCHEMA = (google.cloud.bigquery.SchemaField("col", "INTEGER"),) - - -def create_bigquery_session( - *, - bqclient: Optional[mock.Mock] = None, - session_id: str = "abcxyz", - table_schema: Sequence[google.cloud.bigquery.SchemaField] = TEST_SCHEMA, - table_name: str = "test_table", - anonymous_dataset: Optional[google.cloud.bigquery.DatasetReference] = None, - location: str = "test-region", - ordering_mode: Literal["strict", "partial"] = "partial", -) -> bigframes.Session: - """[Experimental] Create a mock BigQuery DataFrames session that avoids making Google Cloud API calls. - - Intended for unit test environments that don't have access to the network. - """ - credentials = mock.create_autospec( - google.auth.credentials.Credentials, instance=True - ) - - bq_time = datetime.datetime.now() - table_time = bq_time + datetime.timedelta(minutes=1) - - if anonymous_dataset is None: - anonymous_dataset = google.cloud.bigquery.DatasetReference( - "test-project", - "test_dataset", - ) - - if bqclient is None: - bqclient = mock.create_autospec(google.cloud.bigquery.Client, instance=True) - bqclient.project = anonymous_dataset.project - bqclient.location = location - - # Mock the location. - table = mock.create_autospec(google.cloud.bigquery.Table, instance=True) - table._properties = {} - # TODO(tswast): support tables created before and after the session started. - type(table).created = mock.PropertyMock(return_value=table_time) - type(table).location = mock.PropertyMock(return_value=location) - type(table).schema = mock.PropertyMock(return_value=table_schema) - type(table).project = anonymous_dataset.project - type(table).dataset_id = anonymous_dataset.dataset_id - type(table).table_id = table_name - type(table).num_rows = mock.PropertyMock(return_value=1000000000) - bqclient.get_table.return_value = table - - queries = [] - job_configs = [] - - def query_mock( - query, - *args, - job_config: Optional[google.cloud.bigquery.QueryJobConfig] = None, - **kwargs, - ): - queries.append(query) - job_configs.append(copy.deepcopy(job_config)) - query_job = mock.create_autospec(google.cloud.bigquery.QueryJob, instance=True) - query_job._properties = {} - type(query_job).destination = mock.PropertyMock( - return_value=anonymous_dataset.table(table_name), - ) - type(query_job).statement_type = mock.PropertyMock(return_value="SELECT") - - if job_config is not None and job_config.create_session: - type(query_job).session_info = google.cloud.bigquery.SessionInfo( - {"sessionId": session_id}, - ) - - if query.startswith("SELECT CURRENT_TIMESTAMP()"): - query_job.result = mock.MagicMock(return_value=[[bq_time]]) - elif "CREATE TEMP TABLE".casefold() in query.casefold(): - type(query_job).destination = mock.PropertyMock( - return_value=anonymous_dataset.table("temp_table_from_session"), - ) - else: - type(query_job).schema = mock.PropertyMock(return_value=table_schema) - - return query_job - - def query_and_wait_mock(query, *args, job_config=None, **kwargs): - queries.append(query) - job_configs.append(copy.deepcopy(job_config)) - - if query.startswith("SELECT CURRENT_TIMESTAMP()"): - return iter([[datetime.datetime.now()]]) - - rows = mock.create_autospec( - google.cloud.bigquery.table.RowIterator, instance=True - ) - row = mock.create_autospec(google.cloud.bigquery.table.Row, instance=True) - rows.__iter__.return_value = [row] - type(rows).schema = mock.PropertyMock(return_value=table_schema) - rows.to_arrow.return_value = pyarrow.Table.from_pydict( - {field.name: [None] for field in table_schema}, - schema=pyarrow.schema( - _pandas_helpers.bq_to_arrow_field(field) for field in table_schema - ), - ) - - if job_config is not None and job_config.destination is None: - # Assume that the query finishes fast enough for jobless mode. - type(rows).job_id = mock.PropertyMock(return_value=None) - - return rows - - bqclient.query.side_effect = query_mock - bqclient.query_and_wait.side_effect = query_and_wait_mock - bqclient._query_and_wait_bigframes.side_effect = query_and_wait_mock - - clients_provider = mock.create_autospec(bigframes.session.clients.ClientsProvider) - type(clients_provider).bqclient = mock.PropertyMock(return_value=bqclient) - clients_provider._credentials = credentials - clients_provider.project = anonymous_dataset.project - - bqoptions = bigframes.BigQueryOptions( - credentials=credentials, - location=location, - ordering_mode=ordering_mode, - ) - session = bigframes.Session(context=bqoptions, clients_provider=clients_provider) - session._bq_connection_manager = mock.create_autospec( - bigframes.clients.BqConnectionManager, instance=True - ) - session._queries = queries # type: ignore - session._job_configs = job_configs # type: ignore - return session - - -def create_dataframe( - monkeypatch: pytest.MonkeyPatch, - *, - session: Optional[bigframes.Session] = None, - data: Optional[Dict[str, Sequence[Any]]] = None, -) -> bigframes.dataframe.DataFrame: - """[Experimental] Create a mock DataFrame that avoids making Google Cloud API calls. - - Intended for unit test environments that don't have access to the network. - """ - if session is None: - session = create_bigquery_session() - - if data is None: - data = {"col": []} - - # Since this may create a ReadLocalNode, the session we explicitly pass in - # might not actually be used. Mock out the global session, too. - monkeypatch.setattr(bigframes.core.global_session, "_global_session", session) - bigframes.options.bigquery._session_started = True - return bigframes.dataframe.DataFrame(data, session=session) diff --git a/bigframes/testing/polars_session.py b/bigframes/testing/polars_session.py deleted file mode 100644 index 2806dab53f9..00000000000 --- a/bigframes/testing/polars_session.py +++ /dev/null @@ -1,141 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import dataclasses -import weakref -from typing import Union - -import pandas -import polars - -import bigframes -import bigframes.core.blocks -import bigframes.core.compile.polars -import bigframes.dataframe -import bigframes.session.execution_spec -import bigframes.session.executor -import bigframes.session.metrics -from bigframes.functions import _utils, function, udf_def - - -# Does not support to_sql, dry_run, peek, cached -@dataclasses.dataclass -class TestExecutor(bigframes.session.executor.Executor): - compiler = bigframes.core.compile.polars.PolarsCompiler() - - def execute( - self, - array_value: bigframes.core.ArrayValue, - execution_spec: bigframes.session.execution_spec.ExecutionSpec, - ): - """ - Execute the ArrayValue, storing the result to a temporary session-owned table. - """ - if execution_spec.destination_spec is not None: - raise ValueError( - f"TestExecutor does not support destination spec: {execution_spec.destination_spec}" - ) - lazy_frame: polars.LazyFrame = self.compiler.compile(array_value.node) - if execution_spec.peek is not None: - lazy_frame = lazy_frame.limit(execution_spec.peek) - pa_table = lazy_frame.collect().to_arrow() - # Currently, pyarrow types might not quite be exactly the ones in the bigframes schema. - # Nullability may be different, and might use large versions of list, string datatypes. - return bigframes.session.executor.LocalExecuteResult( - data=pa_table, - bf_schema=array_value.schema, - ) - - def cached( - self, - array_value: bigframes.core.ArrayValue, - *, - config, - ) -> None: - return - - -class TestSession(bigframes.session.Session): - def __init__(self): - self._location = None # type: ignore - self._bq_kms_key_name = None # type: ignore - self._clients_provider = None # type: ignore - self._bq_connection = None # type: ignore - self._skip_bq_connection_check = True - self._session_id: str = "test_session" - self._objects: list[ - weakref.ReferenceType[ - Union[ - bigframes.core.indexes.Index, - bigframes.series.Series, - bigframes.dataframe.DataFrame, - ] - ] - ] = [] - self._strictly_ordered: bool = True - self._allow_ambiguity = False # type: ignore - self._default_index_type = bigframes.enums.DefaultIndexKind.SEQUENTIAL_INT64 - self._metrics = bigframes.session.metrics.ExecutionMetrics() - self._function_session = None # type: ignore - self._temp_storage_manager = None # type: ignore - self._executor = TestExecutor() - self._loader = None # type: ignore - - def read_pandas(self, pandas_dataframe, write_engine="default"): - original_input = pandas_dataframe - - # override read_pandas to always keep data local-only - if isinstance(pandas_dataframe, (pandas.Series, pandas.Index)): - pandas_dataframe = pandas_dataframe.to_frame() - - local_block = bigframes.core.blocks.Block.from_local(pandas_dataframe, self) - bf_df = bigframes.dataframe.DataFrame(local_block) - - if isinstance(original_input, pandas.Series): - series = bf_df[bf_df.columns[0]] - series.name = original_input.name - return series - - if isinstance(original_input, pandas.Index): - return bf_df.index - - return bf_df - - def udf( - self, - *, - input_types=None, - output_type=None, - **kwargs, - ): - def wrapper(func): - udf_sig = _utils.get_func_signature( - func, - input_types, - output_type, - ) - - code_def = udf_def.CodeDef.from_func(func) - udf_definition = udf_def.PythonUdf( - signature=udf_sig, - code=code_def, - ) - return function.UdfRoutine(func=func, _udf_def=udf_definition) - - return wrapper - - @property - def bqclient(self): - # prevents logger from trying to call bq upon any errors - return None diff --git a/bigframes/testing/utils.py b/bigframes/testing/utils.py deleted file mode 100644 index 79e99968f58..00000000000 --- a/bigframes/testing/utils.py +++ /dev/null @@ -1,558 +0,0 @@ -# Copyright 2023 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import base64 -import decimal -import re -from typing import Iterable, Optional, Sequence, Set, TypeVar, Union - -import geopandas as gpd # type: ignore -import google.api_core.operation -import numpy as np -import pandas as pd -import pandas.api.types as pd_types -import pyarrow as pa # type: ignore -import pytest -from google.cloud import bigquery, functions_v2 -from google.cloud.functions_v2.types import functions - -import bigframes.functions._utils as bff_utils -import bigframes.pandas as bpd -from bigframes import operations as ops -from bigframes.core import expression as ex - -ML_REGRESSION_METRICS = [ - "mean_absolute_error", - "mean_squared_error", - "mean_squared_log_error", - "median_absolute_error", - "r2_score", - "explained_variance", -] -ML_CLASSFICATION_METRICS = [ - "precision", - "recall", - "accuracy", - "f1_score", - "log_loss", - "roc_auc", -] -ML_GENERATE_TEXT_OUTPUT = [ - "ml_generate_text_llm_result", - "ml_generate_text_status", - "prompt", -] -ML_GENERATE_EMBEDDING_OUTPUT = [ - "ml_generate_embedding_result", - "ml_generate_embedding_statistics", - "ml_generate_embedding_status", - "content", -] -ML_MULTIMODAL_GENERATE_EMBEDDING_OUTPUT = [ - "ml_generate_embedding_result", - "ml_generate_embedding_status", - # start and end sec depend on input format. Images and videos input will contain these 2. - "ml_generate_embedding_start_sec", - "ml_generate_embedding_end_sec", - "content", -] - -SeriesOrIndexT = TypeVar("SeriesOrIndexT", pd.Series, pd.Index) - - -def pandas_major_version() -> int: - match = re.search(r"^v?(\d+)", pd.__version__.strip()) - assert match is not None - return int(match.group(1)) - - -# Prefer this function for tests that run in both ordered and unordered mode -def assert_dfs_equivalent(pd_df: pd.DataFrame, bf_df: bpd.DataFrame, **kwargs): - bf_df_local = bf_df.to_pandas() - ignore_order = not bf_df._session._strictly_ordered - assert_frame_equal(bf_df_local, pd_df, ignore_order=ignore_order, **kwargs) - - -def assert_series_equivalent(pd_series: pd.Series, bf_series: bpd.Series, **kwargs): - bf_df_local = bf_series.to_pandas() - ignore_order = not bf_series._session._strictly_ordered - assert_series_equal(bf_df_local, pd_series, ignore_order=ignore_order, **kwargs) - - -def _normalize_all_nulls(col: pd.Series) -> pd.Series: - if pd_types.is_float_dtype(col.dtype): - col = col.astype("float64").astype("Float64") - elif col.dtype == "object": - if any(isinstance(x, decimal.Decimal) for x in col): - pass - else: - try: - col = col.astype("Float64") - except (TypeError, ValueError, SystemError): - pass - return col - - -def _normalize_index_nulls(idx: pd.Index) -> pd.Index: - if isinstance(idx, pd.MultiIndex): - new_levels = [ - _normalize_index_nulls(idx.get_level_values(i)) for i in range(idx.nlevels) - ] - return pd.MultiIndex.from_arrays(new_levels, names=idx.names) - if idx.hasnans: - if pd_types.is_float_dtype(idx.dtype): - idx = idx.astype("float64").astype("Float64") - return idx - - -def assert_frame_equal( - left: pd.DataFrame, - right: pd.DataFrame, - *, - ignore_order: bool = False, - nulls_are_nan: bool = True, - downcast_object: bool = True, - **kwargs, -): - if ignore_order: - # Sort by a column to get consistent results. - if left.index.name != "rowindex": - left = left.sort_values( - list(left.columns.drop("geography_col", errors="ignore")) - ).reset_index(drop=True) - right = right.sort_values( - list(right.columns.drop("geography_col", errors="ignore")) - ).reset_index(drop=True) - else: - left = left.sort_index() - right = right.sort_index() - - # Pandas sometimes likes to produce object dtype columns - # However, nan/None/Null inconsistency makes comparison futile, convert to typed column - if downcast_object: - left = left.apply(lambda x: x.infer_objects()) - right = right.apply(lambda x: x.infer_objects()) - - if nulls_are_nan: - left = left.apply(_normalize_all_nulls) - right = right.apply(_normalize_all_nulls) - left.index = _normalize_index_nulls(left.index) - right.index = _normalize_index_nulls(right.index) - - pd.testing.assert_frame_equal(left, right, **kwargs) - - -def assert_series_equal( - left: pd.Series, - right: pd.Series, - *, - ignore_order: bool = False, - nulls_are_nan: bool = True, - **kwargs, -): - if ignore_order: - if left.index.name is None: - left = left.sort_values().reset_index(drop=True) - right = right.sort_values().reset_index(drop=True) - else: - left = left.sort_index() - right = right.sort_index() - - if isinstance(left.index, pd.RangeIndex) or pd_types.is_integer_dtype( - left.index.dtype, - ): - left.index = left.index.astype("Int64") - if isinstance(right.index, pd.RangeIndex) or pd_types.is_integer_dtype( - right.index.dtype, - ): - right.index = right.index.astype("Int64") - - if nulls_are_nan: - left = _normalize_all_nulls(left.infer_objects()) - right = _normalize_all_nulls(right.infer_objects()) - left.index = _normalize_index_nulls(left.index) - right.index = _normalize_index_nulls(right.index) - left.name = pd.NA if pd.isna(left.name) else left.name # type: ignore - right.name = pd.NA if pd.isna(right.name) else right.name # type: ignore - - pd.testing.assert_series_equal(left, right, **kwargs) - - -def assert_index_equal(left, right, **kwargs): - pd.testing.assert_index_equal(left, right, **kwargs) - - -def _standardize_index(idx): - return pd.Index(list(idx), name=idx.name) - - -def assert_pandas_index_equal_ignore_index_type(idx0, idx1): - idx0 = _standardize_index(idx0) - idx1 = _standardize_index(idx1) - - pd.testing.assert_index_equal(idx0, idx1) - - -def convert_pandas_dtypes(df: pd.DataFrame, bytes_col: bool): - """Convert pandas dataframe dtypes compatible with bigframes dataframe.""" - - # TODO(chelsealin): updates the function to accept dtypes as input rather than - # hard-code the column names here. - - # Convert basic types columns - df["bool_col"] = df["bool_col"].astype(pd.BooleanDtype()) - df["int64_col"] = df["int64_col"].astype(pd.Int64Dtype()) - df["int64_too"] = df["int64_too"].astype(pd.Int64Dtype()) - df["float64_col"] = df["float64_col"].astype(pd.Float64Dtype()) - df["string_col"] = df["string_col"].astype(pd.StringDtype(storage="pyarrow")) - - if "rowindex" in df.columns: - df["rowindex"] = df["rowindex"].astype(pd.Int64Dtype()) - if "rowindex_2" in df.columns: - df["rowindex_2"] = df["rowindex_2"].astype(pd.Int64Dtype()) - - # Convert time types columns. The `astype` works for Pandas 2.0 but hits an assert - # error at Pandas 1.5. Hence, we have to convert to arrow table and convert back - # to pandas dataframe. - if not isinstance(df["date_col"].dtype, pd.ArrowDtype): - df["date_col"] = pd.to_datetime(df["date_col"], format="%Y-%m-%d") - arrow_table = pa.Table.from_pandas( - pd.DataFrame(df, columns=["date_col"]), - schema=pa.schema([("date_col", pa.date32())]), - ) - df["date_col"] = arrow_table.to_pandas(types_mapper=pd.ArrowDtype)["date_col"] - - if not isinstance(df["datetime_col"].dtype, pd.ArrowDtype): - df["datetime_col"] = pd.to_datetime( - df["datetime_col"], format="%Y-%m-%d %H:%M:%S" - ) - arrow_table = pa.Table.from_pandas( - pd.DataFrame(df, columns=["datetime_col"]), - schema=pa.schema([("datetime_col", pa.timestamp("us"))]), - ) - df["datetime_col"] = arrow_table.to_pandas(types_mapper=pd.ArrowDtype)[ - "datetime_col" - ] - - if not isinstance(df["time_col"].dtype, pd.ArrowDtype): - df["time_col"] = pd.to_datetime(df["time_col"], format="%H:%M:%S.%f") - arrow_table = pa.Table.from_pandas( - pd.DataFrame(df, columns=["time_col"]), - schema=pa.schema([("time_col", pa.time64("us"))]), - ) - df["time_col"] = arrow_table.to_pandas(types_mapper=pd.ArrowDtype)["time_col"] - - if not isinstance(df["timestamp_col"].dtype, pd.ArrowDtype): - df["timestamp_col"] = pd.to_datetime( - df["timestamp_col"], format="%Y-%m-%d %H:%M:%S.%f%Z" - ) - arrow_table = pa.Table.from_pandas( - pd.DataFrame(df, columns=["timestamp_col"]), - schema=pa.schema([("timestamp_col", pa.timestamp("us", tz="UTC"))]), - ) - df["timestamp_col"] = arrow_table.to_pandas(types_mapper=pd.ArrowDtype)[ - "timestamp_col" - ] - - if not isinstance(df["duration_col"].dtype, pd.ArrowDtype): - df["duration_col"] = df["duration_col"].astype(pd.Int64Dtype()) - arrow_table = pa.Table.from_pandas( - pd.DataFrame(df, columns=["duration_col"]), - schema=pa.schema([("duration_col", pa.duration("us"))]), - ) - df["duration_col"] = arrow_table.to_pandas(types_mapper=pd.ArrowDtype)[ - "duration_col" - ] - - # Convert geography types columns. - if "geography_col" in df.columns: - df["geography_col"] = df["geography_col"].astype( - pd.StringDtype(storage="pyarrow") - ) - df["geography_col"] = gpd.GeoSeries.from_wkt( - df["geography_col"].replace({np.nan: None}) - ) - - if bytes_col and not isinstance(df["bytes_col"].dtype, pd.ArrowDtype): - df["bytes_col"] = df["bytes_col"].apply( - lambda value: base64.b64decode(value) if not pd.isnull(value) else value - ) - arrow_table = pa.Table.from_pandas( - pd.DataFrame(df, columns=["bytes_col"]), - schema=pa.schema([("bytes_col", pa.binary())]), - ) - df["bytes_col"] = arrow_table.to_pandas(types_mapper=pd.ArrowDtype)["bytes_col"] - - if not isinstance(df["numeric_col"].dtype, pd.ArrowDtype): - # Convert numeric types column. - df["numeric_col"] = df["numeric_col"].apply( - lambda value: decimal.Decimal(str(value)) if value else None # type: ignore - ) - arrow_table = pa.Table.from_pandas( - pd.DataFrame(df, columns=["numeric_col"]), - schema=pa.schema([("numeric_col", pa.decimal128(38, 9))]), - ) - df["numeric_col"] = arrow_table.to_pandas(types_mapper=pd.ArrowDtype)[ - "numeric_col" - ] - - -def assert_pandas_df_equal_pca_components(actual, expected, **kwargs): - """Compare two pandas dataframes representing PCA components. The columns - required to be present in the dataframes are: - numerical_value: numeric, - categorical_value: List[object(category, value)] - - The index types of `actual` and `expected` are ignored in the comparison. - - Args: - actual: Actual Pandas DataFrame - - expected: Expected Pandas DataFrame - - kwargs: kwargs to use in `pandas.testing.assert_series_equal` per column - """ - # Compare the index, columns and values separately, as the polarity of the - # PCA vectors can be arbitrary - pd.testing.assert_index_equal( - actual.index, expected.index.astype(actual.index.dtype) - ) # dtype agnostic index comparison - pd.testing.assert_index_equal(actual.columns, expected.columns) - for column in expected.columns: - try: - pd.testing.assert_series_equal(actual[column], expected[column], **kwargs) - except AssertionError: - if column not in {"numerical_value", "categorical_value"}: - raise - - # Allow for sign difference per numeric/categorical column - if column == "numerical_value": - actual_ = -actual[column] - expected_ = expected[column] - else: - # In this column each element is an array of objects, where the - # object has attributes "category" and "value". For the sake of - # comparison let's normalize by flipping the polarity of "value". - def normalize_array_of_objects(arr, reverse_polarity=False): - newarr = [] - for element in arr: - newelement = dict(element) - if reverse_polarity: - newelement["value"] = -newelement["value"] - newarr.append(newelement) - return sorted(newarr, key=lambda d: d["category"]) - - actual_ = actual[column].apply(normalize_array_of_objects, args=(True,)) - expected_ = expected[column].apply(normalize_array_of_objects) - - pd.testing.assert_series_equal(actual_, expected_, **kwargs) - - -def assert_pandas_df_equal_pca(actual, expected, **kwargs): - """Compare two pandas dataframes representing PCA predictions. The columns - in the dataframes are expected to be numeric. - - Args: - actual: Actual Pandas DataFrame - - expected: Expected Pandas DataFrame - - kwargs: kwargs to use in `pandas.testing.assert_series_equal` per column - """ - # Compare the index, columns and values separately, as the polarity of the - # PCA vector can be arbitrary - pd.testing.assert_index_equal(actual.index, expected.index) - pd.testing.assert_index_equal(actual.columns, expected.columns) - for column in expected.columns: - try: - pd.testing.assert_series_equal(actual[column], expected[column], **kwargs) - except AssertionError: - # Allow for sign difference per column - pd.testing.assert_series_equal(-actual[column], expected[column], **kwargs) - - -def check_pandas_df_schema_and_index( - pd_df: pd.DataFrame, - columns: Iterable, - index: Optional[Union[int, Iterable]] = None, - col_exact: bool = True, -): - """Check pandas df schema and index. But not the values. - - Args: - pd_df: the input pandas df - columns: target columns to check with - index: int or Iterable or None, default None. If int, only check the length (index size) of the df. If Iterable, check index values match. If None, skip checking index. - col_exact: If True, check the columns param are exact match. Otherwise only check the df contains all of those columns - """ - if col_exact: - assert list(pd_df.columns) == list(columns) - else: - assert set(columns) <= set(pd_df.columns) - - if index is None: - pass - elif isinstance(index, int): - assert len(pd_df) == index - elif isinstance(index, Iterable): - assert list(pd_df.index) == list(index) - else: - raise ValueError("Unsupported index type.") - - -def get_remote_function_endpoints( - bigquery_client: bigquery.Client, dataset_id: str -) -> Set[str]: - """Get endpoints used by the remote functions in a datset""" - endpoints = set() - routines = bigquery_client.list_routines(dataset=dataset_id) - for routine in routines: - rf_options = routine._properties.get("remoteFunctionOptions") - if not rf_options: - continue - rf_endpoint = rf_options.get("endpoint") - if rf_endpoint: - endpoints.add(rf_endpoint) - return endpoints - - -def get_cloud_functions( - functions_client: functions_v2.FunctionServiceClient, - project: str, - location: str, - name: Optional[str] = None, - name_prefix: Optional[str] = None, -) -> Iterable[functions.ListFunctionsResponse]: - """Get the cloud functions in the given project and location.""" - - assert not name or not name_prefix, ( - "Either 'name' or 'name_prefix' can be passed but not both." - ) - - location = bff_utils.gcf_location_from_bq_location(location) - parent = f"projects/{project}/locations/{location}" - request = functions_v2.ListFunctionsRequest(parent=parent) - page_result = functions_client.list_functions(request=request) - for response in page_result: - # If name is provided and it does not match then skip - if bool(name): - full_name = parent + f"/functions/{name}" - if response.name != full_name: - continue - # If name prefix is provided and it does not match then skip - elif bool(name_prefix): - full_name_prefix = parent + f"/functions/{name_prefix}" - if not response.name.startswith(full_name_prefix): - continue - - yield response - - -def delete_cloud_function( - functions_client: functions_v2.FunctionServiceClient, full_name: str -) -> google.api_core.operation.Operation: - """Delete a cloud function with the given fully qualified name.""" - request = functions_v2.DeleteFunctionRequest(name=full_name) - operation = functions_client.delete_function(request=request) - return operation - - -def get_first_file_from_wildcard(path): - return path.replace("*", "000000000000") - - -def cleanup_function_assets( - bigframes_func, - bigquery_client, - cloudfunctions_client=None, - ignore_failures=True, -) -> None: - """Clean up the GCP assets behind a bigframess function.""" - - # Clean up bigframes bigquery function. - try: - bigquery_client.delete_routine(bigframes_func.bigframes_bigquery_function) - except Exception: - # By default don't raise exception in cleanup. - if not ignore_failures: - raise - - if not ignore_failures: - # Make sure that the BQ routins is actually deleted - with pytest.raises(google.api_core.exceptions.NotFound): - bigquery_client.get_routine(bigframes_func.bigframes_bigquery_function) - - # Clean up bigframes cloud run function - if cloudfunctions_client: - # Clean up cloud function - try: - delete_cloud_function( - cloudfunctions_client, bigframes_func.bigframes_cloud_function - ) - except Exception: - # By default don't raise exception in cleanup. - if not ignore_failures: - raise - - if not ignore_failures: - # Make sure the cloud run function is actually deleted - try: - gcf = cloudfunctions_client.get_function( - name=bigframes_func.bigframes_cloud_function - ) - assert gcf.state is functions_v2.Function.State.DELETING - except google.cloud.exceptions.NotFound: - pass - - -def _apply_ops_to_sql( - obj: bpd.DataFrame, - ops_list: Sequence[ex.Expression], - new_names: Sequence[str], -) -> str: - """Applies a list of ops to the given DataFrame and returns the SQL - representing the resulting DataFrame.""" - array_value = obj._block.expr - result, old_names = array_value.compute_values(ops_list) - - # Rename columns for deterministic golden SQL results. - assert len(old_names) == len(new_names) - col_ids = {old_name: new_name for old_name, new_name in zip(old_names, new_names)} - result = result.rename_columns(col_ids).select_columns(new_names) - - sql = result.session._executor.to_sql(result, enable_cache=False) - return sql - - -def _apply_binary_op( - obj: bpd.DataFrame, - op: ops.BinaryOp, - l_arg: str, - r_arg: Union[str, ex.Expression], -) -> str: - """Applies a binary op to the given DataFrame and return the SQL representing - the resulting DataFrame.""" - return _apply_nary_op(obj, op, l_arg, r_arg) - - -def _apply_nary_op( - obj: bpd.DataFrame, - op: Union[ops.BinaryOp, ops.NaryOp], - *args: Union[str, ex.Expression], -) -> str: - """Applies a nary op to the given DataFrame and return the SQL representing - the resulting DataFrame.""" - op_expr = op.as_expr(*args) - sql = _apply_ops_to_sql(obj, [op_expr], [args[0]]) # type: ignore - return sql diff --git a/bigframes/version.py b/bigframes/version.py index db3e62ea976..0a5df274799 100644 --- a/bigframes/version.py +++ b/bigframes/version.py @@ -12,8 +12,4 @@ # See the License for the specific language governing permissions and # limitations under the License. -__version__ = "2.47.0" - -# {x-release-please-start-date} -__release_date__ = "2026-06-12" -# {x-release-please-end} +__version__ = "0.13.0" diff --git a/biome.json b/biome.json deleted file mode 100644 index d30c8687a4c..00000000000 --- a/biome.json +++ /dev/null @@ -1,16 +0,0 @@ -{ - "formatter": { - "indentStyle": "space", - "indentWidth": 2 - }, - "javascript": { - "formatter": { - "quoteStyle": "single" - } - }, - "css": { - "formatter": { - "quoteStyle": "single" - } - } -} diff --git a/conftest.py b/conftest.py deleted file mode 100644 index 5d3f116b521..00000000000 --- a/conftest.py +++ /dev/null @@ -1,62 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from __future__ import annotations - -import warnings - -import numpy as np -import pandas as pd -import pyarrow as pa -import pytest - -import bigframes._config - -# Make sure SettingWithCopyWarning is ignored if it exists. -# It was removed in pandas 3.0. -if hasattr(pd.errors, "SettingWithCopyWarning"): - warnings.simplefilter("ignore", pd.errors.SettingWithCopyWarning) - - -@pytest.fixture() -def polars_session_or_bpd(): - # Since the doctest imports fixture is autouse=True, don't skip if polars - # isn't available. - try: - from bigframes.testing import polars_session - - return polars_session.TestSession() - except ImportError: - import bigframes.pandas as bpd - - return bpd - - -@pytest.fixture(autouse=True) -def default_doctest_imports(doctest_namespace, polars_session_or_bpd): - """ - Avoid some boilerplate in pandas-inspired tests. - - See: https://docs.pytest.org/en/stable/how-to/doctest.html#doctest-namespace-fixture - """ - doctest_namespace["np"] = np - doctest_namespace["pd"] = pd - doctest_namespace["pa"] = pa - doctest_namespace["bpd"] = polars_session_or_bpd - bigframes._config.options.display.progress_bar = None - - # TODO(tswast): Consider setting the numpy printoptions here for better - # compatibility across numpy versions. - # https://numpy.org/doc/stable/release/2.0.0-notes.html#representation-of-numpy-scalars-changed - # https://numpy.org/doc/stable/reference/generated/numpy.set_printoptions.html#numpy-set-printoptions diff --git a/docs/README.rst b/docs/README.rst deleted file mode 100644 index a3aef5380bb..00000000000 --- a/docs/README.rst +++ /dev/null @@ -1,94 +0,0 @@ -BigQuery DataFrames (BigFrames) -=============================== - - -|GA| |pypi| |versions| - -BigQuery DataFrames (also known as BigFrames) provides a Pythonic DataFrame -and machine learning (ML) API powered by the BigQuery engine. It provides modules -for many use cases, including: - -* `bigframes.pandas `_ - is a pandas API for analytics. Many workloads can be - migrated from pandas to bigframes by just changing a few imports. -* `bigframes.ml `_ - is a scikit-learn-like API for ML. -* `bigframes.bigquery.ai `_ - are a collection of powerful AI methods, powered by Gemini. - -BigQuery DataFrames is an `open-source package `_. - -.. |GA| image:: https://img.shields.io/badge/support-GA-gold.svg - :target: https://github.com/googleapis/google-cloud-python/blob/main/README.rst#general-availability -.. |pypi| image:: https://img.shields.io/pypi/v/bigframes.svg - :target: https://pypi.org/project/bigframes/ -.. |versions| image:: https://img.shields.io/pypi/pyversions/bigframes.svg - :target: https://pypi.org/project/bigframes/ - -Getting started with BigQuery DataFrames ----------------------------------------- - -The easiest way to get started is to try the -`BigFrames quickstart `_ -in a `notebook in BigQuery Studio `_. - -To use BigFrames in your local development environment, - -1. Run ``pip install --upgrade bigframes`` to install the latest version. - -2. Setup `Application default credentials `_ - for your local development environment enviroment. - -3. Create a `GCP project with the BigQuery API enabled `_. - -4. Use the ``bigframes`` package to query data. - -.. code-block:: python - - import bigframes.pandas as bpd - - bpd.options.bigquery.project = your_gcp_project_id # Optional in BQ Studio. - bpd.options.bigquery.ordering_mode = "partial" # Recommended for performance. - df = bpd.read_gbq("bigquery-public-data.usa_names.usa_1910_2013") - print( - df.groupby("name") - .agg({"number": "sum"}) - .sort_values("number", ascending=False) - .head(10) - .to_pandas() - ) - -Documentation -------------- - -To learn more about BigQuery DataFrames, visit these pages - -* `Introduction to BigQuery DataFrames (BigFrames) `_ -* `Sample notebooks `_ -* `API reference `_ -* `Source code (GitHub) `_ - -License -------- - -BigQuery DataFrames is distributed with the `Apache-2.0 license -`_. - -It also contains code derived from the following third-party packages: - -* `Ibis `_ -* `pandas `_ -* `Python `_ -* `scikit-learn `_ -* `XGBoost `_ -* `SQLGlot `_ - -For details, see the `third_party -`_ -directory. - - -Contact Us ----------- - -For further help and provide feedback, you can email us at `bigframes-feedback@google.com `_. diff --git a/docs/README.rst b/docs/README.rst new file mode 120000 index 00000000000..89a0106941f --- /dev/null +++ b/docs/README.rst @@ -0,0 +1 @@ +../README.rst \ No newline at end of file diff --git a/docs/_templates/autosummary/class.rst b/docs/_templates/autosummary/class.rst deleted file mode 120000 index bd84850996f..00000000000 --- a/docs/_templates/autosummary/class.rst +++ /dev/null @@ -1 +0,0 @@ -../../../third_party/sphinx/ext/autosummary/templates/autosummary/class.rst \ No newline at end of file diff --git a/docs/_templates/autosummary/module.rst b/docs/_templates/autosummary/module.rst deleted file mode 120000 index f330261ac5c..00000000000 --- a/docs/_templates/autosummary/module.rst +++ /dev/null @@ -1 +0,0 @@ -../../../third_party/sphinx/ext/autosummary/templates/autosummary/module.rst \ No newline at end of file diff --git a/docs/CHANGELOG.md b/docs/changelog.md similarity index 100% rename from docs/CHANGELOG.md rename to docs/changelog.md diff --git a/docs/conf.py b/docs/conf.py index 2cc3ffa130d..af8c5efda89 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -1,5 +1,5 @@ # -*- coding: utf-8 -*- -# Copyright 2024 Google LLC +# Copyright 2023 Google LLC # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -24,11 +24,9 @@ # All configuration values have a default; values that are commented out # serve to show the default. -from __future__ import annotations - import os +import shlex import sys -from typing import Any # If extensions (or modules to document with autodoc) are in another directory, # add these directories to sys.path here. If the directory is relative to the @@ -58,19 +56,14 @@ "sphinx.ext.napoleon", "sphinx.ext.todo", "sphinx.ext.viewcode", - "sphinx_sitemap", - "myst_nb", + "recommonmark", ] -# myst-nb configuration -nb_execution_mode = "off" - # autodoc/autosummary flags autoclass_content = "both" autodoc_default_options = {"members": True} autosummary_generate = True -autosummary_imported_members = True -autosummary_ignore_module_all = True + # Add any paths that contain templates here, relative to this directory. templates_path = ["_templates"] @@ -105,7 +98,7 @@ # # This is also used if you do content translation via gettext catalogs. # Usually you set "language" from the command line for these cases. -language = "en-US" +language = None # There are two options for replacing |today|: either, you set today to some # non-false value, then it is used: @@ -121,7 +114,6 @@ "samples/AUTHORING_GUIDE.md", "samples/CONTRIBUTING.md", "samples/snippets/README.rst", - "README.rst", # used for include in overview.rst only ] # The reST default role (used for this markup: `text`) to use for all @@ -156,20 +148,19 @@ # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. -html_theme = "pydata_sphinx_theme" +html_theme = "alabaster" # Theme options are theme-specific and customize the look and feel of a theme # further. For a list of options available for each theme, see the # documentation. -# https://pydata-sphinx-theme.readthedocs.io/en/stable/user_guide/layout.html#references html_theme_options = { - "github_url": "https://github.com/googleapis/google-cloud-python", - "logo": { - "text": "BigQuery DataFrames (BigFrames)", - }, - "analytics": { - "google_analytics_id": "G-XVSRMCJ37X", - }, + "description": "BigQuery DataFrames provides DataFrame APIs on the BigQuery engine", + "github_user": "googleapis", + "github_repo": "python-bigquery-dataframes", + "github_banner": True, + "font_family": "'Roboto', Georgia, sans", + "head_font_family": "'Roboto', Georgia, serif", + "code_font_family": "'Roboto Mono', 'Consolas', monospace", } # Add any paths that contain custom themes here, relative to this directory. @@ -259,34 +250,21 @@ # Output file base name for HTML help builder. htmlhelp_basename = "bigframes-doc" -# https://sphinx-sitemap.readthedocs.io/en/latest/getting-started.html#usage -html_baseurl = "https://dataframes.bigquery.dev/" -sitemap_locales = [None] - -# We don't have any immediate plans to translate the API reference, so omit the -# language from the URLs. -# https://sphinx-sitemap.readthedocs.io/en/latest/advanced-configuration.html#configuration-customizing-url-scheme -sitemap_url_scheme = "{link}" - # -- Options for warnings ------------------------------------------------------ suppress_warnings = [ - # Allow unknown mimetype so we can use widgets in tutorial notebooks. - "mystnb.unknown_mime_type", # Temporarily suppress this to avoid "more than one target found for # cross-reference" warning, which are intractable for us to avoid while in # a mono-repo. # See https://github.com/sphinx-doc/sphinx/blob # /2a65ffeef5c107c19084fabdd706cdff3f52d93c/sphinx/domains/python.py#L843 - "ref.python", - # Allow external websites to be down occasionally. - "intersphinx.external", + "ref.python" ] # -- Options for LaTeX output --------------------------------------------- -latex_elements: dict[str, Any] = { +latex_elements = { # The paper size ('letterpaper' or 'a4paper'). #'papersize': 'letterpaper', # The font size ('10pt', '11pt' or '12pt'). @@ -304,7 +282,7 @@ ( root_doc, "bigframes.tex", - "BigQuery DataFrames (BigFrames)", + "bigframes Documentation", author, "manual", ) @@ -339,7 +317,7 @@ ( root_doc, "bigframes", - "BigQuery DataFrames (BigFrames)", + "bigframes Documentation", [author], 1, ) @@ -358,7 +336,7 @@ ( root_doc, "bigframes", - "BigQuery DataFrames (BigFrames)", + "bigframes Documentation", author, "bigframes", "bigframes Library", @@ -381,7 +359,7 @@ # Example configuration for intersphinx: refer to the Python standard library. intersphinx_mapping = { - "python": ("https://docs.python.org/3/", None), + "python": ("https://python.readthedocs.org/en/latest/", None), "google-auth": ("https://googleapis.dev/python/google-auth/latest/", None), "google.api_core": ( "https://googleapis.dev/python/google-api-core/latest/", @@ -390,8 +368,7 @@ "grpc": ("https://grpc.github.io/grpc/python/", None), "proto-plus": ("https://proto-plus-python.readthedocs.io/en/latest/", None), "protobuf": ("https://googleapis.dev/python/protobuf/latest/", None), - # TODO(tswast): re-enable if we can get temporary failures to be ignored. - # "pandas": ("https://pandas.pydata.org/pandas-docs/stable/", None), + "pandas": ("https://pandas.pydata.org/pandas-docs/stable/", None), "pydata-google-auth": ( "https://pydata-google-auth.readthedocs.io/en/latest/", None, diff --git a/docs/index.rst b/docs/index.rst index 51d05e7d368..d239ea3a785 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -1,85 +1,19 @@ -.. BigQuery DataFrames documentation main file +.. include:: README.rst -Scalable Python Data Analysis with BigQuery DataFrames (BigFrames) -================================================================== - -.. meta:: - :description: BigQuery DataFrames (BigFrames) provides a scalable, pandas-compatible Python API for data analysis and machine learning on petabyte-scale datasets using the BigQuery engine. - -**BigQuery DataFrames** (``bigframes``) is an open-source Python library that brings the power of **distributed computing** to your data science workflow. By providing a familiar **pandas** and **scikit-learn** compatible API, BigFrames allows you to analyze and model massive datasets where they live—directly in **BigQuery**. - -Why Choose BigQuery DataFrames? -------------------------------- - -BigFrames eliminates the "data movement bottleneck." Instead of downloading large datasets to a local environment, BigFrames translates your Python code into optimized SQL, executing complex transformations across the BigQuery fleet. - -* **Petabyte-Scale Scalability:** Effortlessly process datasets that far exceed local memory limits. -* **Familiar Python Ecosystem:** Use the same ``read_gbq``, ``groupby``, ``merge``, and ``pivot_table`` functions you already know from pandas. -* **Integrated Machine Learning:** Access BigQuery ML's powerful algorithms via a scikit-learn-like interface (``bigframes.ml``), including seamless **Gemini AI** integration. -* **Enterprise-Grade Security:** Maintain data governance and security by keeping your data within the BigQuery perimeter. -* **Hybrid Flexibility:** Easily move between distributed BigQuery processing and local pandas analysis with ``to_pandas()``. - -Core Components of BigFrames ----------------------------- - -BigQuery DataFrames is organized into specialized modules designed for the modern data stack: - -1. :mod:`bigframes.pandas`: A high-performance, pandas-compatible API for scalable data exploration, cleaning, and transformation. -2. :mod:`bigframes.bigquery`: Specialized utilities for direct BigQuery resource management, including integrations with Gemini and other AI models in the :mod:`bigframes.bigquery.ai` submodule. - - -Quickstart: Scalable Data Analysis in Seconds ---------------------------------------------- - -Install BigQuery DataFrames via pip: - -.. code-block:: bash - - pip install --upgrade bigframes - -The following example demonstrates how to perform a distributed aggregation on a public dataset with millions of rows using just a few lines of Python: - -.. code-block:: python - - import bigframes.pandas as bpd - - # If running in your local environment or Colab, uncomment these lines and add your GCP project ID - # PROJECT_ID = "bigframes-dev" - # bpd.options.bigquery.project = PROJECT_ID - - # Initialize BigFrames and load a public dataset - df = bpd.read_gbq("bigquery-public-data.usa_names.usa_1910_2013") - - # Perform familiar pandas operations that execute in the cloud - top_names = ( - df.groupby("name") - .agg({"number": "sum"}) - .sort_values("number", ascending=False) - .head(10) - ) - - # Bring the final, aggregated results back to local memory if needed - print(top_names.to_pandas()) - - -Explore the Documentation -------------------------- +API reference +------------- .. toctree:: - :maxdepth: 2 - :caption: User Documentation + :maxdepth: 3 - user_guide/index + reference/index -.. toctree:: - :maxdepth: 2 - :caption: API Reference +Changelog +--------- - reference/index - supported_pandas_apis +For a list of all BigQuery DataFrames releases: .. toctree:: - :maxdepth: 1 - :caption: Community & Updates + :maxdepth: 2 - CHANGELOG + changelog diff --git a/docs/notebooks b/docs/notebooks deleted file mode 120000 index 8f9a5b2e6d2..00000000000 --- a/docs/notebooks +++ /dev/null @@ -1 +0,0 @@ -../notebooks \ No newline at end of file diff --git a/docs/reference/.gitignore b/docs/reference/.gitignore deleted file mode 100644 index 3f127954839..00000000000 --- a/docs/reference/.gitignore +++ /dev/null @@ -1 +0,0 @@ -api/* diff --git a/docs/reference/bigframes.ml/README.rst b/docs/reference/bigframes.ml/README.rst new file mode 100644 index 00000000000..80a1fe97b73 --- /dev/null +++ b/docs/reference/bigframes.ml/README.rst @@ -0,0 +1,125 @@ +BigQuery DataFrames ML +====================== + +As BigQuery DataFrames implements the Pandas API over top of BigQuery, BigQuery +DataFrame ML implements the SKLearn API over top of BigQuery Machine Learning. + +Tutorial +-------- + +Start a session and initialize a dataframe for a BigQuery table + +.. code-block:: python + + import bigframes.pandas + + df = bigframes.pandas.read_gbq("bigquery-public-data.ml_datasets.penguins") + df + +Clean and prepare the data + +.. code-block:: python + + # filter down to the data we want to analyze + adelie_data = df[df.species == "Adelie Penguin (Pygoscelis adeliae)"] + + # drop the columns we don't care about + adelie_data = adelie_data.drop(columns=["species"]) + + # drop rows with nulls to get our training data + training_data = adelie_data.dropna() + + # take a peek at the training data + training_data + +.. code-block:: python + + # pick feature columns and label column + X = training_data[['island', 'culmen_length_mm', 'culmen_depth_mm', 'flipper_length_mm', 'sex']] + y = training_data[['body_mass_g']] + +Use train_test_split to create train and test datasets + +.. code-block:: python + + from bigframes.ml.model_selection import train_test_split + + X_train, X_test, y_train, y_test = train_test_split( + X, y, test_size=0.2) + +Define the model training pipeline + +.. code-block:: python + + from bigframes.ml.linear_model import LinearRegression + from bigframes.ml.pipeline import Pipeline + from bigframes.ml.compose import ColumnTransformer + from bigframes.ml.preprocessing import StandardScaler, OneHotEncoder + + preprocessing = ColumnTransformer([ + ("onehot", OneHotEncoder(), ["island", "species", "sex"]), + ("scaler", StandardScaler(), ["culmen_depth_mm", "culmen_length_mm", "flipper_length_mm"]), + ]) + + model = LinearRegression(fit_intercept=False) + + pipeline = Pipeline([ + ('preproc', preprocessing), + ('linreg', model) + ]) + + # view the pipeline + pipeline + +Train the pipeline + +.. code-block:: python + + pipeline.fit(X_train, y_train) + +Evaluate the model's performance on the test data + +.. code-block:: python + + from bigframes.ml.metrics import r2_score + + y_pred = pipeline.predict(X_test) + + r2_score(y_test, y_pred) + +Make predictions on new data + +.. code-block:: python + + import pandas + + new_penguins = bigframes.pandas.read_pandas( + pandas.DataFrame( + { + "tag_number": [1633, 1672, 1690], + "species": [ + "Adelie Penguin (Pygoscelis adeliae)", + "Adelie Penguin (Pygoscelis adeliae)", + "Adelie Penguin (Pygoscelis adeliae)", + ], + "island": ["Torgersen", "Torgersen", "Dream"], + "culmen_length_mm": [39.5, 38.5, 37.9], + "culmen_depth_mm": [18.8, 17.2, 18.1], + "flipper_length_mm": [196.0, 181.0, 188.0], + "sex": ["MALE", "FEMALE", "FEMALE"], + } + ).set_index("tag_number") + ) + + # view the new data + new_penguins + +.. code-block:: python + + pipeline.predict(new_penguins) + +Save the trained model to BigQuery, so we can load it later + +.. code-block:: python + + pipeline.to_gbq("bqml_tutorial.penguins_model", replace=True) diff --git a/docs/reference/bigframes.ml/cluster.rst b/docs/reference/bigframes.ml/cluster.rst new file mode 100644 index 00000000000..e91a28c0511 --- /dev/null +++ b/docs/reference/bigframes.ml/cluster.rst @@ -0,0 +1,7 @@ +bigframes.ml.cluster +==================== + +.. automodule:: bigframes.ml.cluster + :members: + :inherited-members: + :undoc-members: diff --git a/docs/reference/bigframes.ml/compose.rst b/docs/reference/bigframes.ml/compose.rst new file mode 100644 index 00000000000..9992728362f --- /dev/null +++ b/docs/reference/bigframes.ml/compose.rst @@ -0,0 +1,7 @@ +bigframes.ml.compose +==================== + +.. automodule:: bigframes.ml.compose + :members: + :inherited-members: + :undoc-members: diff --git a/docs/reference/bigframes.ml/decomposition.rst b/docs/reference/bigframes.ml/decomposition.rst new file mode 100644 index 00000000000..ec804ac8cdc --- /dev/null +++ b/docs/reference/bigframes.ml/decomposition.rst @@ -0,0 +1,7 @@ +bigframes.ml.decomposition +========================== + +.. automodule:: bigframes.ml.decomposition + :members: + :inherited-members: + :undoc-members: diff --git a/docs/reference/bigframes.ml/ensemble.rst b/docs/reference/bigframes.ml/ensemble.rst new file mode 100644 index 00000000000..2652ab5aa4d --- /dev/null +++ b/docs/reference/bigframes.ml/ensemble.rst @@ -0,0 +1,7 @@ +bigframes.ml.ensemble +===================== + +.. automodule:: bigframes.ml.ensemble + :members: + :inherited-members: + :undoc-members: diff --git a/docs/reference/bigframes.ml/forecasting.rst b/docs/reference/bigframes.ml/forecasting.rst new file mode 100644 index 00000000000..04015c99117 --- /dev/null +++ b/docs/reference/bigframes.ml/forecasting.rst @@ -0,0 +1,7 @@ +bigframes.ml.forecasting +======================== + +.. automodule:: bigframes.ml.forecasting + :members: + :inherited-members: + :undoc-members: diff --git a/docs/reference/bigframes.ml/imported.rst b/docs/reference/bigframes.ml/imported.rst new file mode 100644 index 00000000000..c151cbda6f1 --- /dev/null +++ b/docs/reference/bigframes.ml/imported.rst @@ -0,0 +1,7 @@ +bigframes.ml.imported +===================== + +.. automodule:: bigframes.ml.imported + :members: + :inherited-members: + :undoc-members: diff --git a/docs/reference/bigframes.ml/index.rst b/docs/reference/bigframes.ml/index.rst new file mode 100644 index 00000000000..f3cbe1174a7 --- /dev/null +++ b/docs/reference/bigframes.ml/index.rst @@ -0,0 +1,32 @@ +.. _bigframes_ml: +.. include:: README.rst + +API Reference +------------- + +.. toctree:: + :maxdepth: 3 + + cluster + + compose + + decomposition + + ensemble + + forecasting + + imported + + linear_model + + llm + + metrics + + model_selection + + pipeline + + preprocessing diff --git a/docs/reference/bigframes.ml/linear_model.rst b/docs/reference/bigframes.ml/linear_model.rst new file mode 100644 index 00000000000..8c6c2765b12 --- /dev/null +++ b/docs/reference/bigframes.ml/linear_model.rst @@ -0,0 +1,7 @@ +bigframes.ml.linear_model +========================= + +.. automodule:: bigframes.ml.linear_model + :members: + :inherited-members: + :undoc-members: diff --git a/docs/reference/bigframes.ml/llm.rst b/docs/reference/bigframes.ml/llm.rst new file mode 100644 index 00000000000..20ae7793e73 --- /dev/null +++ b/docs/reference/bigframes.ml/llm.rst @@ -0,0 +1,7 @@ +bigframes.ml.llm +================ + +.. automodule:: bigframes.ml.llm + :members: + :inherited-members: + :undoc-members: diff --git a/docs/reference/bigframes.ml/metrics.rst b/docs/reference/bigframes.ml/metrics.rst new file mode 100644 index 00000000000..aca11f7e9fc --- /dev/null +++ b/docs/reference/bigframes.ml/metrics.rst @@ -0,0 +1,7 @@ +bigframes.ml.metrics +==================== + +.. automodule:: bigframes.ml.metrics + :members: + :inherited-members: + :undoc-members: diff --git a/docs/reference/bigframes.ml/model_selection.rst b/docs/reference/bigframes.ml/model_selection.rst new file mode 100644 index 00000000000..d662285f990 --- /dev/null +++ b/docs/reference/bigframes.ml/model_selection.rst @@ -0,0 +1,7 @@ +bigframes.ml.model_selection +============================ + +.. automodule:: bigframes.ml.model_selection + :members: + :inherited-members: + :undoc-members: diff --git a/docs/reference/bigframes.ml/pipeline.rst b/docs/reference/bigframes.ml/pipeline.rst new file mode 100644 index 00000000000..22e877dc5b3 --- /dev/null +++ b/docs/reference/bigframes.ml/pipeline.rst @@ -0,0 +1,7 @@ +bigframes.ml.pipeline +===================== + +.. automodule:: bigframes.ml.pipeline + :members: + :inherited-members: + :undoc-members: diff --git a/docs/reference/bigframes.ml/preprocessing.rst b/docs/reference/bigframes.ml/preprocessing.rst new file mode 100644 index 00000000000..eac72da1730 --- /dev/null +++ b/docs/reference/bigframes.ml/preprocessing.rst @@ -0,0 +1,7 @@ +bigframes.ml.preprocessing +========================== + +.. automodule:: bigframes.ml.preprocessing + :members: + :inherited-members: + :undoc-members: diff --git a/docs/reference/bigframes.pandas/frame.rst b/docs/reference/bigframes.pandas/frame.rst new file mode 100644 index 00000000000..a49bcc8f7cb --- /dev/null +++ b/docs/reference/bigframes.pandas/frame.rst @@ -0,0 +1,9 @@ + +========= +DataFrame +========= + +.. autoclass:: bigframes.dataframe.DataFrame + :members: + :inherited-members: + :undoc-members: diff --git a/docs/reference/bigframes.pandas/general_functions.rst b/docs/reference/bigframes.pandas/general_functions.rst new file mode 100644 index 00000000000..4fff9aabf83 --- /dev/null +++ b/docs/reference/bigframes.pandas/general_functions.rst @@ -0,0 +1,8 @@ + +================= +General functions +================= + +.. automodule:: bigframes.pandas + :members: + :undoc-members: diff --git a/docs/reference/bigframes.pandas/groupby.rst b/docs/reference/bigframes.pandas/groupby.rst new file mode 100644 index 00000000000..483340f3487 --- /dev/null +++ b/docs/reference/bigframes.pandas/groupby.rst @@ -0,0 +1,20 @@ + +======= +GroupBy +======= + +DataFrameGroupBy +---------------- + +.. autoclass:: bigframes.core.groupby.DataFrameGroupBy + :members: + :inherited-members: + :undoc-members: + +SeriesGroupBy +------------- + +.. autoclass:: bigframes.core.groupby.SeriesGroupBy + :members: + :inherited-members: + :undoc-members: diff --git a/docs/reference/bigframes.pandas/index.rst b/docs/reference/bigframes.pandas/index.rst new file mode 100644 index 00000000000..c7ff586884e --- /dev/null +++ b/docs/reference/bigframes.pandas/index.rst @@ -0,0 +1,15 @@ + +============================ +BigQuery DataFrames (pandas) +============================ + +.. toctree:: + :maxdepth: 2 + + general_functions + series + frame + indexing + window + groupby + options diff --git a/docs/reference/bigframes.pandas/indexing.rst b/docs/reference/bigframes.pandas/indexing.rst new file mode 100644 index 00000000000..8f7f1947401 --- /dev/null +++ b/docs/reference/bigframes.pandas/indexing.rst @@ -0,0 +1,9 @@ + +============= +Index objects +============= + +.. autoclass:: bigframes.core.indexes.index.Index + :members: + :inherited-members: + :undoc-members: diff --git a/docs/reference/bigframes.pandas/options.rst b/docs/reference/bigframes.pandas/options.rst new file mode 100644 index 00000000000..60af8c826a4 --- /dev/null +++ b/docs/reference/bigframes.pandas/options.rst @@ -0,0 +1,6 @@ + +==================== +Options and settings +==================== + +``bigframes.pandas.options`` is an alias for :data:`bigframes.options`. diff --git a/docs/reference/bigframes.pandas/series.rst b/docs/reference/bigframes.pandas/series.rst new file mode 100644 index 00000000000..e212904f3ff --- /dev/null +++ b/docs/reference/bigframes.pandas/series.rst @@ -0,0 +1,44 @@ + +====== +Series +====== + +.. contents:: Table of Contents + :depth: 2 + :local: + :backlinks: none + +Series +------ + +.. autoclass:: bigframes.series.Series + :members: + :inherited-members: + :undoc-members: + +Accessors +--------- + +Datetime properties +^^^^^^^^^^^^^^^^^^^ + +.. automodule:: bigframes.operations.datetimes + :members: + :inherited-members: + :undoc-members: + +String handling +^^^^^^^^^^^^^^^ + +.. automodule:: bigframes.operations.strings + :members: + :inherited-members: + :undoc-members: + +Struct handling +^^^^^^^^^^^^^^^ + +.. automodule:: bigframes.operations.structs + :members: + :inherited-members: + :undoc-members: diff --git a/docs/reference/bigframes.pandas/window.rst b/docs/reference/bigframes.pandas/window.rst new file mode 100644 index 00000000000..55d911ecf4f --- /dev/null +++ b/docs/reference/bigframes.pandas/window.rst @@ -0,0 +1,9 @@ + +====== +Window +====== + +.. autoclass:: bigframes.core.window.Window + :members: + :inherited-members: + :undoc-members: diff --git a/docs/reference/bigframes/index.rst b/docs/reference/bigframes/index.rst new file mode 100644 index 00000000000..76d64444faf --- /dev/null +++ b/docs/reference/bigframes/index.rst @@ -0,0 +1,19 @@ + +Core objects +============ + +.. toctree:: + :maxdepth: 2 + + options + + +Session +------- + +.. autofunction:: bigframes.connect + +.. autoclass:: bigframes.session.Session + :members: + :inherited-members: + :undoc-members: diff --git a/docs/reference/bigframes/options.rst b/docs/reference/bigframes/options.rst new file mode 100644 index 00000000000..991399eb886 --- /dev/null +++ b/docs/reference/bigframes/options.rst @@ -0,0 +1,16 @@ +Options and settings +==================== + +.. currentmodule:: bigframes + +.. autodata:: options + +.. autoclass:: bigframes._config.Options + +.. autoclass:: bigframes._config.bigquery_options.BigQueryOptions + +.. autoclass:: bigframes._config.display_options.DisplayOptions + +.. autoclass:: bigframes._config.sampling_options.SamplingOptions + +.. autoclass:: bigframes._config.compute_options.ComputeOptions diff --git a/docs/reference/index.rst b/docs/reference/index.rst index 99228010b24..c790831db18 100644 --- a/docs/reference/index.rst +++ b/docs/reference/index.rst @@ -4,57 +4,9 @@ API Reference Refer to these pages for details about the public objects in the ``bigframes`` packages. -.. autosummary:: - :toctree: api +.. toctree:: + :maxdepth: 2 - bigframes._config - bigframes.bigquery - bigframes.bigquery.aead - bigframes.bigquery.ai - bigframes.bigquery.ml - bigframes.bigquery.obj - bigframes.enums - bigframes.exceptions - bigframes.geopandas - bigframes.pandas - bigframes.pandas.api.typing - bigframes.streaming - -Pandas Extensions -~~~~~~~~~~~~~~~~~ - -BigQuery DataFrames provides extensions to pandas DataFrame and Series objects. - -.. autosummary:: - :toctree: api - - bigframes.extensions.core.dataframe_accessor.BigQueryDataFrameAccessor - bigframes.extensions.core.dataframe_accessor.AIAccessor - bigframes.extensions.core.series_accessor.BigQuerySeriesAccessor - bigframes.extensions.core.series_accessor.AeadSeriesAccessor - -ML APIs -~~~~~~~ - -BigQuery DataFrames provides many machine learning modules, inspired by -scikit-learn. - - -.. autosummary:: - :toctree: api - - bigframes.ml - bigframes.ml.cluster - bigframes.ml.compose - bigframes.ml.decomposition - bigframes.ml.ensemble - bigframes.ml.forecasting - bigframes.ml.imported - bigframes.ml.impute - bigframes.ml.linear_model - bigframes.ml.llm - bigframes.ml.metrics - bigframes.ml.model_selection - bigframes.ml.pipeline - bigframes.ml.preprocessing - bigframes.ml.remote + bigframes/index + bigframes.pandas/index + bigframes.ml/index diff --git a/docs/samples b/docs/samples new file mode 120000 index 00000000000..e804737ed3a --- /dev/null +++ b/docs/samples @@ -0,0 +1 @@ +../samples \ No newline at end of file diff --git a/docs/supported_pandas_apis.rst b/docs/supported_pandas_apis.rst deleted file mode 100644 index f4b57f05d10..00000000000 --- a/docs/supported_pandas_apis.rst +++ /dev/null @@ -1,62 +0,0 @@ -Supported pandas APIs -===================== - -The following tables show the pandas APIs that have been implemented (or not) -in BigQuery DataFrames. - -* 'Y' means it implements all parameters. -* 'P' means it implements only some parameters. - -DataFrame ---------- - -.. raw:: html - :file: supported_pandas_apis/bf_dataframe.html - -DataFrameGroupBy ----------------- - -.. raw:: html - :file: supported_pandas_apis/bf_dataframegroupby.html - -Index ------ - -.. raw:: html - :file: supported_pandas_apis/bf_index.html - -pandas module -------------- - -.. raw:: html - :file: supported_pandas_apis/bf_pandas.html - -Series ------- - -.. raw:: html - :file: supported_pandas_apis/bf_series.html - -Series.dt methods ------------------ - -.. raw:: html - :file: supported_pandas_apis/bf_datetimemethods.html - -Series.str methods ------------------- - -.. raw:: html - :file: supported_pandas_apis/bf_stringmethods.html - -SeriesGroupBy -------------- - -.. raw:: html - :file: supported_pandas_apis/bf_seriesgroupby.html - -Window ------- - -.. raw:: html - :file: supported_pandas_apis/bf_window.html diff --git a/docs/supported_pandas_apis/.gitignore b/docs/supported_pandas_apis/.gitignore deleted file mode 100644 index 2d19fc766d9..00000000000 --- a/docs/supported_pandas_apis/.gitignore +++ /dev/null @@ -1 +0,0 @@ -*.html diff --git a/docs/templates/toc.yml b/docs/templates/toc.yml index 394f2a7d3cc..9879721d286 100644 --- a/docs/templates/toc.yml +++ b/docs/templates/toc.yml @@ -3,18 +3,6 @@ name: Overview - href: changelog.md name: Changelog - - items: - - href: summary_overview.html - name: Overview - - href: summary_class.html - name: Classes - - href: summary_method.html - name: Methods - - href: summary_property.html - name: Properties and Attributes - - href: supported_pandas_apis.html - name: Supported pandas APIs - name: BigQuery DataFrames API - items: - items: - name: Options @@ -32,46 +20,18 @@ - name: Session uid: bigframes.session.Session name: Session - - name: Enumerations - uid: bigframes.enums - - name: Exceptions and warnings - uid: bigframes.exceptions name: Core Objects - items: - - items: - - name: DataFrame - uid: bigframes.dataframe.DataFrame - - name: PlotAccessor - uid: bigframes.pandas.api.typing.PlotAccessor - - name: StructAccessor - uid: bigframes.operations.structs.StructFrameAccessor - name: DataFrame + - name: DataFrame + uid: bigframes.dataframe.DataFrame - items: - name: DataFrameGroupBy uid: bigframes.core.groupby.DataFrameGroupBy - name: SeriesGroupBy uid: bigframes.core.groupby.SeriesGroupBy name: Groupby - - name: Index - uid: bigframes.core.indexes.base.Index - - items: - - name: AtDataFrameIndexer - uid: bigframes.core.indexers.AtDataFrameIndexer - - name: AtSeriesIndexer - uid: bigframes.core.indexers.AtSeriesIndexer - - name: IatDataFrameIndexer - uid: bigframes.core.indexers.IatDataFrameIndexer - - name: IatSeriesIndexer - uid: bigframes.core.indexers.IatSeriesIndexer - - name: ILocDataFrameIndexer - uid: bigframes.core.indexers.ILocDataFrameIndexer - - name: IlocSeriesIndexer - uid: bigframes.core.indexers.IlocSeriesIndexer - - name: LocDataFrameIndexer - uid: bigframes.core.indexers.LocDataFrameIndexer - - name: LocSeriesIndexer - uid: bigframes.core.indexers.LocSeriesIndexer - name: Indexers + - name: Indexes + uid: bigframes.core.indexes.index.Index - name: pandas uid: bigframes.pandas - items: @@ -83,10 +43,6 @@ uid: bigframes.operations.strings.StringMethods - name: StructAccessor uid: bigframes.operations.structs.StructAccessor - - name: ListAccessor - uid: bigframes.operations.lists.ListAccessor - - name: PlotAccessor - uid: bigframes.pandas.api.typing.PlotAccessor name: Series - name: Window uid: bigframes.core.window.Window @@ -109,8 +65,6 @@ uid: bigframes.ml.decomposition - name: PCA uid: bigframes.ml.decomposition.PCA - - name: MatrixFactorization - uid: bigframes.ml.decomposition.MatrixFactorization name: decomposition - items: - name: Overview @@ -137,15 +91,7 @@ uid: bigframes.ml.imported.ONNXModel - name: TensorFlowModel uid: bigframes.ml.imported.TensorFlowModel - - name: XGBoostModel - uid: bigframes.ml.imported.XGBoostModel name: imported - - items: - - name: Overview - uid: bigframes.ml.impute - - name: SimpleImputer - uid: bigframes.ml.impute.SimpleImputer - name: impute - items: - name: Overview uid: bigframes.ml.linear_model @@ -157,25 +103,15 @@ - items: - name: Overview uid: bigframes.ml.llm - - name: GeminiTextGenerator - uid: bigframes.ml.llm.GeminiTextGenerator - name: PaLM2TextGenerator uid: bigframes.ml.llm.PaLM2TextGenerator - name: PaLM2TextEmbeddingGenerator uid: bigframes.ml.llm.PaLM2TextEmbeddingGenerator - - name: TextEmbeddingGenerator - uid: bigframes.ml.llm.TextEmbeddingGenerator - - name: Claude3TextGenerator - uid: bigframes.ml.llm.Claude3TextGenerator name: llm - items: - name: metrics uid: bigframes.ml.metrics name: metrics - - items: - - name: metrics.pairwise - uid: bigframes.ml.metrics.pairwise - name: metrics.pairwise - items: - name: model_selection uid: bigframes.ml.model_selection @@ -202,29 +138,6 @@ - name: OneHotEncoder uid: bigframes.ml.preprocessing.OneHotEncoder name: preprocessing - - items: - - name: Overview - uid: bigframes.ml.remote - - name: VertexAIModel - uid: bigframes.ml.remote.VertexAIModel - name: remote name: bigframes.ml - - items: - - name: BigQuery built-in functions - uid: bigframes.bigquery - - name: BigQuery AI Functions - uid: bigframes.bigquery._operations.ai - status: beta - name: bigframes.bigquery - - items: - - name: GeoSeries - uid: bigframes.geopandas.GeoSeries - name: bigframes.geopandas - - items: - - name: Overview - uid: bigframes.streaming - - name: StreamingDataFrame - uid: bigframes.streaming.dataframe.StreamingDataFrame - name: bigframes.streaming - status: beta name: BigQuery DataFrames + status: beta diff --git a/docs/user_guide/index.rst b/docs/user_guide/index.rst deleted file mode 100644 index 0c0935ac40a..00000000000 --- a/docs/user_guide/index.rst +++ /dev/null @@ -1,127 +0,0 @@ -User Guide -********** - -.. include:: ../README.rst - -.. toctree:: - :caption: Guides - :maxdepth: 1 - - Getting Started - Cloud Docs User Guides - -.. toctree:: - :caption: Getting Started - :maxdepth: 1 - - Quickstart Template <../notebooks/getting_started/bq_dataframes_template.ipynb> - Getting Started <../notebooks/getting_started/getting_started_bq_dataframes.ipynb> - Magics <../notebooks/getting_started/magics.ipynb> - ML Fundamentals <../notebooks/getting_started/ml_fundamentals_bq_dataframes.ipynb> - Pandas Extensions <../notebooks/getting_started/pandas_extensions.ipynb> - -.. toctree:: - :caption: DataFrames - :maxdepth: 1 - - Anywidget Mode <../notebooks/dataframes/anywidget_mode.ipynb> - Dataframe <../notebooks/dataframes/dataframe.ipynb> - Index Col Null <../notebooks/dataframes/index_col_null.ipynb> - Integrations <../notebooks/dataframes/integrations.ipynb> - Magics for Python and SQL Interoperability <../notebooks/dataframes/magics_with_local_data.ipynb> - Pypi <../notebooks/dataframes/pypi.ipynb> - -.. toctree:: - :caption: Data Types - :maxdepth: 1 - - Array <../notebooks/data_types/array.ipynb> - Json <../notebooks/data_types/json.ipynb> - Struct <../notebooks/data_types/struct.ipynb> - Timedelta <../notebooks/data_types/timedelta.ipynb> - -.. toctree:: - :caption: Generative AI - :maxdepth: 1 - - AI Functions <../notebooks/generative_ai/ai_functions.ipynb> - AI Functions for Poster Analysis <../notebooks/generative_ai/ai_movie_poster.ipynb> - AI Forecast <../notebooks/generative_ai/bq_dataframes_ai_forecast.ipynb> - LLM Code Generation <../notebooks/generative_ai/bq_dataframes_llm_code_generation.ipynb> - LLM KMeans <../notebooks/generative_ai/bq_dataframes_llm_kmeans.ipynb> - LLM Output Schema <../notebooks/generative_ai/bq_dataframes_llm_output_schema.ipynb> - LLM Vector Search <../notebooks/generative_ai/bq_dataframes_llm_vector_search.ipynb> - Drug Name Generation <../notebooks/generative_ai/bq_dataframes_ml_drug_name_generation.ipynb> - Large Language Models <../notebooks/generative_ai/large_language_models.ipynb> - -.. toctree:: - :caption: Machine Learning - :maxdepth: 1 - - ML Cross Validation <../notebooks/ml/bq_dataframes_ml_cross_validation.ipynb> - Linear Regression <../notebooks/ml/bq_dataframes_ml_linear_regression.ipynb> - Linear Regression BBQ <../notebooks/ml/bq_dataframes_ml_linear_regression_bbq.ipynb> - Linear Regression Big <../notebooks/ml/bq_dataframes_ml_linear_regression_big.ipynb> - Easy Linear Regression <../notebooks/ml/easy_linear_regression.ipynb> - Sklearn Linear Regression <../notebooks/ml/sklearn_linear_regression.ipynb> - Timeseries Analysis <../notebooks/ml/timeseries_analysis.ipynb> - -.. toctree:: - :caption: Visualization - :maxdepth: 1 - - COVID Line Graphs <../notebooks/visualization/bq_dataframes_covid_line_graphs.ipynb> - Tutorial <../notebooks/visualization/tutorial.ipynb> - -.. toctree:: - :caption: Geospatial Data - :maxdepth: 1 - - Geoseries <../notebooks/geo/geoseries.ipynb> - -.. toctree:: - :caption: Regionalized BigQuery - :maxdepth: 1 - - Regionalized <../notebooks/location/regionalized.ipynb> - -.. toctree:: - :caption: Multimodal - :maxdepth: 1 - - Multimodal Dataframe <../notebooks/multimodal/multimodal_dataframe.ipynb> - -.. toctree:: - :caption: Remote Functions - :maxdepth: 1 - - Remote Function <../notebooks/remote_functions/remote_function.ipynb> - Remote Function Usecases <../notebooks/remote_functions/remote_function_usecases.ipynb> - Remote Function Vertex Claude Model <../notebooks/remote_functions/remote_function_vertex_claude_model.ipynb> - -.. toctree:: - :caption: Streaming - :maxdepth: 1 - - Streaming Dataframe <../notebooks/streaming/streaming_dataframe.ipynb> - -.. toctree:: - :caption: Experimental - :maxdepth: 1 - - AI Operators <../notebooks/experimental/ai_operators.ipynb> - Semantic Operators <../notebooks/experimental/semantic_operators.ipynb> - -.. toctree:: - :caption: Apps - :maxdepth: 1 - - Synthetic Data Generation <../notebooks/apps/synthetic_data_generation.ipynb> - -.. toctree:: - :caption: Kaggle - :maxdepth: 1 - - AI Forecast <../notebooks/kaggle/bq_dataframes_ai_forecast.ipynb> - Describe Product Images <../notebooks/kaggle/describe-product-images-with-bigframes-multimodal.ipynb> - Vector Search Over National Jukebox <../notebooks/kaggle/vector-search-with-bigframes-over-national-jukebox.ipynb> diff --git a/mypy.ini b/mypy.ini index e3f44c262ac..901394813aa 100644 --- a/mypy.ini +++ b/mypy.ini @@ -9,9 +9,6 @@ ignore_missing_imports = True [mypy-cloudpickle.*] ignore_missing_imports = True -[mypy-flask] -ignore_missing_imports = True - [mypy-pydata_google_auth] ignore_missing_imports = True @@ -27,23 +24,5 @@ ignore_missing_imports = True [mypy-pyarrow] ignore_missing_imports = True -[mypy-ibis.*] -ignore_missing_imports = True - [mypy-ipywidgets] ignore_missing_imports = True - -[mypy-pyarrow.feather] -ignore_missing_imports = True - -[mypy-google.cloud.pubsub] -ignore_missing_imports = True - -[mypy-google.cloud.bigtable] -ignore_missing_imports = True - -[mypy-anywidget] -ignore_missing_imports = True - -[mypy-bigframes_vendored.*] -ignore_errors = True diff --git a/notebooks/.gitignore b/notebooks/.gitignore deleted file mode 100644 index d9acee9f51d..00000000000 --- a/notebooks/.gitignore +++ /dev/null @@ -1,5 +0,0 @@ -.ipynb_checkpoints/ -*.bq_exec_time_seconds -*.bytesprocessed -*.query_char_count -*.slotmillis diff --git a/notebooks/apps/synthetic_data_generation.ipynb b/notebooks/apps/synthetic_data_generation.ipynb deleted file mode 100644 index 00d30fc8a8a..00000000000 --- a/notebooks/apps/synthetic_data_generation.ipynb +++ /dev/null @@ -1,370 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# Copyright 2023 Google LLC\n", - "#\n", - "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", - "# you may not use this file except in compliance with the License.\n", - "# You may obtain a copy of the License at\n", - "#\n", - "# https://www.apache.org/licenses/LICENSE-2.0\n", - "#\n", - "# Unless required by applicable law or agreed to in writing, software\n", - "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", - "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", - "# See the License for the specific language governing permissions and\n", - "# limitations under the License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# BigQuery DataFrames: Synthetic Data Generation" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In addition to BigQuery DataFrames (installing which also installs `pandas` as a dependency) we will use\n", - "`faker` library as a building block for synthetic data generation." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "suoG7eWDZARj", - "outputId": "b5c620a9-8f5b-413f-dd38-93448f941846" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Collecting faker\n", - " Downloading faker-37.1.0-py3-none-any.whl.metadata (15 kB)\n", - "Requirement already satisfied: tzdata in /usr/local/google/home/shuowei/src/python-bigquery-dataframes/venv/lib/python3.10/site-packages (from faker) (2024.2)\n", - "Downloading faker-37.1.0-py3-none-any.whl (1.9 MB)\n", - "\u001b[2K \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.9/1.9 MB\u001b[0m \u001b[31m55.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[?25hInstalling collected packages: faker\n", - "Successfully installed faker-37.1.0\n" - ] - } - ], - "source": [ - "!pip install faker" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "id": "m3q1oeJALhsG" - }, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'PROJECT_ID' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[3], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mbigframes\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpandas\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mbpd\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m bpd\u001b[38;5;241m.\u001b[39moptions\u001b[38;5;241m.\u001b[39mbigquery\u001b[38;5;241m.\u001b[39mproject \u001b[38;5;241m=\u001b[39m \u001b[43mPROJECT_ID\u001b[49m\n", - "\u001b[0;31mNameError\u001b[0m: name 'PROJECT_ID' is not defined" - ] - } - ], - "source": [ - "import bigframes.pandas as bpd\n", - "bpd.options.bigquery.project = PROJECT_ID" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's use `GeminiTextGenerator` for our purpose, which is BigQuery DataFrame's state-of-the-art LLM integration at the time of writing this notebook (Apr 16 2024)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 69 - }, - "id": "lIYdn1woOS1n", - "outputId": "be474338-44c2-4ce0-955e-d525b8b9c84b" - }, - "outputs": [], - "source": [ - "from bigframes.ml.llm import GeminiTextGenerator\n", - "\n", - "model = GeminiTextGenerator(model_name=\"gemini-2.5-flash\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Craft a prompt for the LLM to indicate the schema of the desired data and hints for the code that could generate such data. " - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 162 - }, - "id": "SSR-lLScLa95", - "outputId": "cbaec34e-6fa6-45b4-e54a-f11ca06b61e1" - }, - "outputs": [], - "source": [ - "prompt = \"\"\"\\\n", - "Write python code to generate a pandas dataframe based on the requirements:\n", - " Column name: Name, type: string, Description: Latin American Names\n", - " Column name: Age, type: int\n", - " Column name: Gender, type: string, Description: Inclusive\n", - "\n", - "Note:\n", - " - Return the code only, no additional texts or comments\n", - " - Use faker library\n", - " - Generate 100 rows\n", - " - The final dataframe should be named 'result_df'.\n", - "\"\"\"\n", - "\n", - "df_prompt = bpd.DataFrame({\"prompt\" : [prompt]})\n", - "df_prompt" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Be accommodating that LLM may not produce a runnable code in the first go and may need some nudging. We will retry by adding the failing code and the exception it throws as additional context in the prompt." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 277 - }, - "id": "miDe3K4GNvOo", - "outputId": "f2039e80-5ad7-4551-f8b2-7ef714a89d63" - }, - "outputs": [], - "source": [ - "max_tries = 5\n", - "for i in range(max_tries):\n", - " # Get LLM generated code\n", - " df_result = model.predict(df_prompt)\n", - " llm_result = df_result['ml_generate_text_llm_result'].iloc[0]\n", - "\n", - " # Python code comes back as a markdown code block,\n", - " # remove the prefix \"```python\" and suffix \"```\"\n", - " code = llm_result[9:-3]\n", - " print(code)\n", - "\n", - " # Check if the generated code is runnable\n", - " try:\n", - " exec(code)\n", - " break\n", - " except Exception as ex:\n", - " print(ex)\n", - " error_context = f\"\"\"\n", - "Previous code:\n", - "{code}\n", - "\n", - "Had this exception:\n", - "{ex}\"\"\"\n", - "\n", - " # Update the prompt to help LLM correct error\n", - " df_prompt[\"prompt\"] += error_context\n", - "\n", - " # If we have exhausted max tries then stop trying\n", - " if i+1 == max_tries:\n", - " raise Exception(\"Failed to generate runnable code\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Run the generated code and verify that it produced the desired data." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 424 - }, - "id": "GODcPwX2PBEu", - "outputId": "dec4c872-c464-49e4-cd7f-9442fc977d18" - }, - "outputs": [], - "source": [ - "execution_context = {}\n", - "exec(code, execution_context)\n", - "execution_context.get(\"result_df\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We want to run this code at scale to generate since we want to generate large amount of data. Let's deploy a `remote_function` for this purpose." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 36 - }, - "id": "n-BsGciNqSwU", - "outputId": "996e5639-a49c-4542-a0dc-ede450e0eb6d" - }, - "outputs": [], - "source": [ - "@bpd.remote_function(packages=['faker', 'pandas'], cloud_function_service_account=\"default\")\n", - "def data_generator(id: int) -> str:\n", - " context = {}\n", - " exec(code, context)\n", - " result_df = context.get(\"result_df\")\n", - " return result_df.to_json(orient=\"records\")\n", - "\n", - "data_generator.bigframes_cloud_function" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let’s say we want to generate 1 million rows of synthetic data. Since our generated code produces 100 rows in one run, we can initialize an indicator dataframe with 1M/100 = 10K indicator rows. Then we can apply the remote function to produce 100 synthetic data rows for each indicator row." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 34 - }, - "id": "Odkmev9nsYqA", - "outputId": "4aa7a1fd-0c0d-4412-f326-a20e19f583b5" - }, - "outputs": [], - "source": [ - "desired_num_rows = 1_000_000 # 1 million rows\n", - "batch_size = 100 # used in the prompt\n", - "num_batches = int(desired_num_rows/batch_size)\n", - "\n", - "df = bpd.DataFrame({\"row_id\": range(num_batches)})" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 34 - }, - "id": "UyBhlJFVsmQC", - "outputId": "29748df5-673b-4320-bb1f-53abaace3b81" - }, - "outputs": [], - "source": [ - "df[\"json_data\"] = df[\"row_id\"].apply(data_generator)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "At this point each item in `df[\"json_data\"]` is a json serialized array of 100 records. Let’s flatten that into 1 record per row using a direct SQL." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 932 - }, - "id": "6p3eM21qvRvy", - "outputId": "333f4e49-a555-4d2f-b527-02142782b3a7" - }, - "outputs": [], - "source": [ - "sql = f\"\"\"\n", - "WITH T0 AS ({df.sql}),\n", - "T1 AS (\n", - " SELECT PARSE_JSON(json_row) AS json_row\n", - " FROM T0, UNNEST(JSON_EXTRACT_ARRAY(json_data)) AS json_row\n", - ")\n", - "SELECT STRING(json_row.Name) AS Name,\n", - " INT64(json_row.Age) AS Age,\n", - " STRING(json_row.Gender) AS Gender\n", - "FROM T1\n", - "\"\"\"\n", - "df_result = bpd.read_gbq(sql)\n", - "df_result" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "There you have it, 1 million synthetic data rows ready to use, or save them in a BigQuery table for future use." - ] - } - ], - "metadata": { - "colab": { - "provenance": [] - }, - "kernelspec": { - "display_name": "Python 3", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.15" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/notebooks/data_types/array.ipynb b/notebooks/data_types/array.ipynb deleted file mode 100644 index 96c5da5ac6c..00000000000 --- a/notebooks/data_types/array.ipynb +++ /dev/null @@ -1,503 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# Copyright 2025 Google LLC\n", - "#\n", - "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", - "# you may not use this file except in compliance with the License.\n", - "# You may obtain a copy of the License at\n", - "#\n", - "# https://www.apache.org/licenses/LICENSE-2.0\n", - "#\n", - "# Unless required by applicable law or agreed to in writing, software\n", - "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", - "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", - "# See the License for the specific language governing permissions and\n", - "# limitations under the License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Array Data Types\n", - "\n", - "In BigQuery, an [ARRAY](https://cloud.google.com/bigquery/docs/reference/standard-sql/data-types#array_type) (also called a `repeated` column) is an ordered list of zero or more elements of the same, non-`NULL` data type. It's important to note that BigQuery `ARRAY`s cannot contain nested `ARRAY`s. BigQuery DataFrames represents BigQuery `ARRAY` types to `pandas.ArrowDtype(pa.list_(T))`, where `T` is the underlying Arrow type of the array elements.\n", - "\n", - "This notebook illustrates how to work with `ARRAY` columns in BigQuery DataFrames. First, let's import the required packages and perform the necessary setup below." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import bigframes.pandas as bpd\n", - "import bigframes.bigquery as bbq\n", - "import pandas as pd\n", - "import pyarrow as pa" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "REGION = \"US\" # @param {type: \"string\"}\n", - "\n", - "bpd.options.display.progress_bar = None\n", - "bpd.options.bigquery.location = REGION" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create DataFrames with an array column\n", - "\n", - "**Example 1: Creating from a list of lists/tuples**" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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[3 rows x 2 columns in total]" - ], - "text/plain": [ - " Name Scores\n", - "0 Alice [95 88 92]\n", - "1 Bob [78 81]\n", - "2 Charlie [ 82 89 94 100]\n", - "\n", - "[3 rows x 2 columns]" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "names = [\"Alice\", \"Bob\", \"Charlie\"]\n", - "scores = [\n", - " [95, 88, 92],\n", - " [78, 81],\n", - " [82, 89, 94, 100]\n", - "]\n", - "df = bpd.DataFrame({\"Name\": names, \"Scores\": scores})\n", - "df" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Name string[pyarrow]\n", - "Scores list[pyarrow]\n", - "dtype: object" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.dtypes" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Example 2: Defining schema explicitly**" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 [95. 88. 92.]\n", - "1 [78. 81.]\n", - "2 [ 82. 89. 94. 100.]\n", - "dtype: list[pyarrow]" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bpd.Series(data=scores, dtype=pd.ArrowDtype(pa.list_(pa.float64())))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Example 3: Reading from a source**" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 [{'tables': {'score': 0.9349926710128784, 'val...\n", - "1 [{'tables': {'score': 0.9690881371498108, 'val...\n", - "2 [{'tables': {'score': 0.8667634129524231, 'val...\n", - "3 [{'tables': {'score': 0.9351968765258789, 'val...\n", - "4 [{'tables': {'score': 0.8572560548782349, 'val...\n", - "Name: predicted_default_payment_next_month, dtype: list>>[pyarrow]" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bpd.read_gbq(\"bigquery-public-data.ml_datasets.credit_card_default\", max_results=5)[\"predicted_default_payment_next_month\"]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Operate on `ARRAY` data\n", - "\n", - "BigQuery DataFrames provides two main approaches for operating on list (`ARRAY`) data:\n", - "\n", - "1. **The `Series.list` accessor**: Provides Pandas-like methods for array column manipulation.\n", - "2. **[BigQuery built-in functions](https://cloud.google.com/bigquery/docs/reference/standard-sql/array_functions)**: Allows you to use functions mirroring BigQuery SQL operations, available through the `bigframes.bigquery` module (abbreviated as `bbq` below), such as [`array_agg`](https://cloud.google.com/python/docs/reference/bigframes/latest/bigframes.bigquery#bigframes_bigquery_array_agg) and [`array_length`](https://cloud.google.com/python/docs/reference/bigframes/latest/bigframes.bigquery#bigframes_bigquery_array_length)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Get the Length of Each Arrray\n", - "\n", - "**Example 1: Using list accessor to get array length**" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 3\n", - "1 2\n", - "2 4\n", - "Name: Scores, dtype: Int64" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df['Scores'].list.len()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Example 2: Using BigQuery build-in functions to get array length**" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 3\n", - "1 2\n", - "2 4\n", - "Name: Scores, dtype: Int64" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bbq.array_length(df['Scores'])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Access Element at a Specific Index (e.g., First Element) " - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 95\n", - "1 78\n", - "2 82\n", - "Name: Scores, dtype: Int64" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df['Scores'].list[0]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Explode/Unnest Array elements into Seperate Rows\n", - "\n", - "The exploded rows preserving original order when in ordering mode. If an array has multiple elements, exploded rows are ordered by the element's index\n", - "within its original array. " - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 95\n", - "0 88\n", - "0 92\n", - "1 78\n", - "1 81\n", - "2 82\n", - "2 89\n", - "2 94\n", - "2 100\n", - "Name: Scores, dtype: Int64" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "scores = df['Scores'].explode()\n", - "scores" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Aggregate elements back into an array" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 [100. 93. 97.]\n", - "1 [83. 86.]\n", - "2 [ 87. 94. 99. 105.]\n", - "Name: Scores, dtype: list[pyarrow]" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "new_scores = scores + 5.0\n", - "new_scores_arr = bbq.array_agg(new_scores.groupby(level=0))\n", - "new_scores_arr" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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NameScoresNewScores
0Alice[95 88 92][100. 93. 97.]
1Bob[78 81][83. 86.]
2Charlie[ 82 89 94 100][ 87. 94. 99. 105.]
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[3 rows x 3 columns in total]" - ], - "text/plain": [ - " Name Scores NewScores\n", - "0 Alice [95 88 92] [100. 93. 97.]\n", - "1 Bob [78 81] [83. 86.]\n", - "2 Charlie [ 82 89 94 100] [ 87. 94. 99. 105.]\n", - "\n", - "[3 rows x 3 columns]" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Add adjusted scores into the DataFrame. This operation requires an implicit join \n", - "# between the two tables, necessitating a unique index in the DataFrame (guaranteed \n", - "# in the default ordering and index mode).\n", - "df['NewScores'] = new_scores_arr\n", - "df" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.1" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/notebooks/data_types/json.ipynb b/notebooks/data_types/json.ipynb deleted file mode 100644 index f0a8ed4ffee..00000000000 --- a/notebooks/data_types/json.ipynb +++ /dev/null @@ -1,451 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# Copyright 2025 Google LLC\n", - "#\n", - "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", - "# you may not use this file except in compliance with the License.\n", - "# You may obtain a copy of the License at\n", - "#\n", - "# https://www.apache.org/licenses/LICENSE-2.0\n", - "#\n", - "# Unless required by applicable law or agreed to in writing, software\n", - "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", - "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", - "# See the License for the specific language governing permissions and\n", - "# limitations under the License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# JSON Data Types\n", - "\n", - "When using BigQuery DataFrames, columns containing data in BigQuery's [JSON](https://cloud.google.com/bigquery/docs/reference/standard-sql/data-types#json_type) format (a lightweight standard) are represented as `pandas.ArrowDtype`. The exact underlying Arrow type depends on your library versions. Older environments typically use `db_dtypes.JSONArrowType()` for compatibility, which is an Arrow extension type acting as a light wrapper around `pa.string()`. In contrast, newer setups (pandas 3.0+ and pyarrow 19.0+) utilize the more recent `pa.json_(pa.string())` representation." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import bigframes.pandas as bpd\n", - "import bigframes.bigquery as bbq\n", - "import db_dtypes\n", - "import pandas as pd\n", - "import pyarrow as pa" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "REGION = \"US\" # @param {type: \"string\"}\n", - "\n", - "bpd.options.display.progress_bar = None\n", - "bpd.options.bigquery.location = REGION" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create Series with JSON columns\n", - "\n", - "**Example 1: Create a Series with a JSON dtype from local data**\n", - "\n", - "This example demonstrates creating a JSON Series from a list of JSON strings. Note that BigQuery standardizes these strings, for instance, by removing extra spaces and ordering dictionary keys. Specifying the `dtype` is essential; if omitted, a string-type Series will be generated." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 1\n", - "1 \"str\"\n", - "2 false\n", - "3 [\"a\",{\"b\":1},null]\n", - "4 {\"a\":{\"b\":[1,2,3],\"c\":true}}\n", - "5 \n", - "dtype: extension>[pyarrow]" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "json_data = [\n", - " \"1\",\n", - " '\"str\"',\n", - " \"false\",\n", - " '[\"a\", {\"b\": 1}, null]',\n", - " '{\"a\": {\"b\": [1, 2, 3], \"c\": true}}',\n", - " None,\n", - "]\n", - "bpd.Series(json_data, dtype=pd.ArrowDtype(db_dtypes.JSONArrowType()))\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Example 2: Create a Series with a Nested JSON dtype from local data**\n", - "\n", - "To create a BigQuery DataFrame Series containing `JSON` data nested within a `STRUCT` or `LIST` type, you must represent the `JSON` data in a `pa.array` defined with the `pa.string` type. This workaround is necessary because Pyarrow lacks support for creating structs or lists that directly contain extension types (see [issue](https://github.com/apache/arrow/issues/45262))." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 [{'key': '1'}]\n", - "1 [{'key': None}]\n", - "2 [{'key': '[\"1\",\"3\",\"5\"]'}]\n", - "3 [{'key': '{\"a\":1,\"b\":[\"x\",\"y\"],\"c\":{\"x\":[],\"z\"...\n", - "dtype: list>>>[pyarrow]" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "list_data = [\n", - " [{\"key\": \"1\"}],\n", - " [{\"key\": None}],\n", - " [{\"key\": '[\"1\",\"3\",\"5\"]'}],\n", - " [{\"key\": '{\"a\":1,\"b\":[\"x\",\"y\"],\"c\":{\"x\":[],\"z\":false}}'}],\n", - "]\n", - "pa_array = pa.array(list_data, type=pa.list_(pa.struct([(\"key\", pa.string())])))\n", - "bpd.Series(\n", - " pd.arrays.ArrowExtensionArray(pa_array),\n", - " dtype=pd.ArrowDtype(\n", - " pa.list_(pa.struct([(\"key\", db_dtypes.JSONArrowType())])),\n", - " ),\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Example 3: Create a Series with a Nested JSON dtype using BigQuery SQLs**" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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idstruct_col
01{'data': '{\"b\":100}', 'number': 2}
10{'data': '{\"a\":true}', 'number': 1}
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[2 rows x 2 columns in total]" - ], - "text/plain": [ - " id struct_col\n", - "0 1 {'data': '{\"b\":100}', 'number': 2}\n", - "1 0 {'data': '{\"a\":true}', 'number': 1}\n", - "\n", - "[2 rows x 2 columns]" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sql = \"\"\"\n", - "SELECT 0 AS id, STRUCT(JSON_OBJECT('a', True) AS data, 1 AS number) AS struct_col\n", - "UNION ALL\n", - "SELECT 1, STRUCT(JSON_OBJECT('b', 100), 2),\n", - "\"\"\"\n", - "df = bpd.read_gbq(sql)\n", - "df" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "id Int64\n", - "struct_col struct>,...\n", - "dtype: object" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.dtypes" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Operate on `JSON` data\n", - "\n", - "The `bigframes.bigquery` module (often abbreviated as `bbq`) provides access within BigQuery DataFrames to various **[BigQuery built-in functions](https://cloud.google.com/bigquery/docs/reference/standard-sql/json_functions)**. Examples relevant for JSON data include [`json_extract`](https://cloud.google.com/python/docs/reference/bigframes/latest/bigframes.bigquery#bigframes_bigquery_json_extract) and [`parse_json`](https://cloud.google.com/python/docs/reference/bigframes/latest/bigframes.bigquery#bigframes_bigquery_parse_json)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Extract JSON data via specific JSON path" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Example 1: When JSON data is represented as strings**" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "fruits = [\n", - " '{\"fruits\": [{\"name\": \"apple\"}, {\"name\": \"cherry\"}]}',\n", - " '{\"fruits\": [{\"name\": \"guava\"}, {\"name\": \"grapes\"}]}',\n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 {\"fruits\": [{\"name\": \"apple\"}, {\"name\": \"cherr...\n", - "1 {\"fruits\": [{\"name\": \"guava\"}, {\"name\": \"grape...\n", - "dtype: string" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "str_s = bpd.Series(fruits, dtype=\"string\")\n", - "str_s" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 {\"name\":\"apple\"}\n", - "1 {\"name\":\"guava\"}\n", - "dtype: string" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bbq.json_extract(str_s, \"$.fruits[0]\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Example 2: When JSON data is stored as JSON type**" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 {\"fruits\":[{\"name\":\"apple\"},{\"name\":\"cherry\"}]}\n", - "1 {\"fruits\":[{\"name\":\"guava\"},{\"name\":\"grapes\"}]}\n", - "dtype: extension>[pyarrow]" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "json_s = bpd.Series(fruits, dtype=pd.ArrowDtype(db_dtypes.JSONArrowType()))\n", - "json_s" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 {\"name\":\"apple\"}\n", - "1 {\"name\":\"guava\"}\n", - "dtype: extension>[pyarrow]" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bbq.json_extract(json_s, \"$.fruits[0]\")\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Extract an array from JSON data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 ['{\"name\":\"apple\"}' '{\"name\":\"cherry\"}']\n", - "1 ['{\"name\":\"guava\"}' '{\"name\":\"grapes\"}']\n", - "dtype: list>>[pyarrow]" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bbq.json_extract_array(json_s, \"$.fruits\")" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 ['{\"name\":\"apple\"}' '{\"name\":\"cherry\"}']\n", - "1 ['{\"name\":\"guava\"}' '{\"name\":\"grapes\"}']\n", - "dtype: list[pyarrow]" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bbq.json_extract_array(str_s, \"$.fruits\")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.1" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/notebooks/data_types/struct.ipynb b/notebooks/data_types/struct.ipynb deleted file mode 100644 index 9df0780e307..00000000000 --- a/notebooks/data_types/struct.ipynb +++ /dev/null @@ -1,483 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# Copyright 2025 Google LLC\n", - "#\n", - "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", - "# you may not use this file except in compliance with the License.\n", - "# You may obtain a copy of the License at\n", - "#\n", - "# https://www.apache.org/licenses/LICENSE-2.0\n", - "#\n", - "# Unless required by applicable law or agreed to in writing, software\n", - "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", - "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", - "# See the License for the specific language governing permissions and\n", - "# limitations under the License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Struct Data Types\n", - "\n", - "In BigQuery, a [STRUCT](https://cloud.google.com/bigquery/docs/reference/standard-sql/data-types#struct_type) (also known as a `record`) is a collection of ordered fields, each with a defined data type (required) and an optional field name. BigQuery DataFrames maps BigQuery `STRUCT` types to the pandas equivalent, `pandas.ArrowDtype(pa.struct())`. \n", - "\n", - "This notebook illustrates how to work with `STRUCT` columns in BigQuery DataFrames. First, let's import the required packages and perform the necessary setup below." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import bigframes.pandas as bpd\n", - "import bigframes.bigquery as bbq\n", - "import pandas as pd\n", - "import pyarrow as pa" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "REGION = \"US\" # @param {type: \"string\"}\n", - "\n", - "bpd.options.display.progress_bar = None\n", - "bpd.options.bigquery.location = REGION" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create DataFrames with struct columns\n", - "\n", - "**Example 1: Creating from a list of objects**" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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NameAddress
0Alice{'City': 'New York', 'State': 'NY'}
1Bob{'City': 'San Francisco', 'State': 'CA'}
2Charlie{'City': 'Seattle', 'State': 'WA'}
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[3 rows x 2 columns in total]" - ], - "text/plain": [ - " Name Address\n", - "0 Alice {'City': 'New York', 'State': 'NY'}\n", - "1 Bob {'City': 'San Francisco', 'State': 'CA'}\n", - "2 Charlie {'City': 'Seattle', 'State': 'WA'}\n", - "\n", - "[3 rows x 2 columns]" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "names = [\"Alice\", \"Bob\", \"Charlie\"]\n", - "addresses = [\n", - " {'City': 'New York', 'State': 'NY'},\n", - " {'City': 'San Francisco', 'State': 'CA'},\n", - " {'City': 'Seattle', 'State': 'WA'}\n", - "]\n", - "df = bpd.DataFrame({'Name': names, 'Address': addresses})\n", - "df" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Name string[pyarrow]\n", - "Address struct[pyarrow]\n", - "dtype: object" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.dtypes" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Example 2: Defining schema explicitly**" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 {'City': 'New York', 'State': 'NY'}\n", - "1 {'City': 'San Francisco', 'State': 'CA'}\n", - "2 {'City': 'Seattle', 'State': 'WA'}\n", - "dtype: struct[pyarrow]" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bpd.Series(\n", - " data=addresses, \n", - " dtype=bpd.ArrowDtype(pa.struct([('City', pa.string()), ('State', pa.string())]))\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Example 3: Reading from a source**" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 [{'tables': {'score': 0.8667634129524231, 'val...\n", - "1 [{'tables': {'score': 0.9351968765258789, 'val...\n", - "2 [{'tables': {'score': 0.8572560548782349, 'val...\n", - "3 [{'tables': {'score': 0.9690881371498108, 'val...\n", - "4 [{'tables': {'score': 0.9349926710128784, 'val...\n", - "Name: predicted_default_payment_next_month, dtype: list>>[pyarrow]" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bpd.read_gbq(\"bigquery-public-data.ml_datasets.credit_card_default\", max_results=5)[\"predicted_default_payment_next_month\"]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Operate on `STRUCT` data\n", - "\n", - "BigQuery DataFrames provides two main approaches for operating on `STRUCT` data:\n", - "\n", - "1. **[The `Series.struct` accessor](https://cloud.google.com/python/docs/reference/bigframes/latest/bigframes.operations.structs.StructAccessor)**: Provides Pandas-like methods for STRUCT column manipulation.\n", - "2. **The `DataFrame.struct` accessor**: Provides Pandas-like methods for all child STRUCT columns manipulation.\n", - "3. **[BigQuery built-in functions](https://cloud.google.com/bigquery/docs/reference/standard-sql/array_functions)**: Allows you to use functions mirroring BigQuery SQL operations, available through the `bigframes.bigquery` module (abbreviated as `bbq` below), such as [`struct`](https://cloud.google.com/python/docs/reference/bigframes/latest/bigframes.bigquery#bigframes_bigquery_struct)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### View Data Types of Struct Fields" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "City string[pyarrow]\n", - "State string[pyarrow]\n", - "dtype: object" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df['Address'].struct.dtypes" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Access a Struct Field by Name" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 New York\n", - "1 San Francisco\n", - "2 Seattle\n", - "Name: City, dtype: string" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df['Address'].struct.field(\"City\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Extract Struct Fields into a DataFrame\n", - "\n", - "**Example 1: Using Series `.struct` accessor**" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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[3 rows x 3 columns in total]" - ], - "text/plain": [ - " Name Address.City Address.State\n", - "0 Alice New York NY\n", - "1 Bob San Francisco CA\n", - "2 Charlie Seattle WA\n", - "\n", - "[3 rows x 3 columns]" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.struct.explode(\"Address\")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.9" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/notebooks/data_types/timedelta.ipynb b/notebooks/data_types/timedelta.ipynb deleted file mode 100644 index d65c812d83e..00000000000 --- a/notebooks/data_types/timedelta.ipynb +++ /dev/null @@ -1,571 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "8ebb6e6a", - "metadata": {}, - "outputs": [], - "source": [ - "# Copyright 2025 Google LLC\n", - "#\n", - "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", - "# you may not use this file except in compliance with the License.\n", - "# You may obtain a copy of the License at\n", - "#\n", - "# https://www.apache.org/licenses/LICENSE-2.0\n", - "#\n", - "# Unless required by applicable law or agreed to in writing, software\n", - "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", - "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", - "# See the License for the specific language governing permissions and\n", - "# limitations under the License." - ] - }, - { - "cell_type": "markdown", - "id": "c4f3bbfa", - "metadata": {}, - "source": [ - "# BigFrames Timedelta\n", - "\n", - "\n", - "\n", - " \n", - " \n", - " \n", - "
\n", - " \n", - " \"Colab Run in Colab\n", - " \n", - " \n", - " \n", - " \"GitHub\n", - " View on GitHub\n", - " \n", - " \n", - " \n", - " \"BQ\n", - " Open in BQ Studio\n", - " \n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "f74e2573", - "metadata": {}, - "source": [ - "In this notebook, you will use timedeltas to analyze the taxi trips in NYC. " - ] - }, - { - "cell_type": "markdown", - "id": "8f74dec4", - "metadata": {}, - "source": [ - "# Setup" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "51173665", - "metadata": {}, - "outputs": [], - "source": [ - "import bigframes.pandas as bpd\n", - "\n", - "PROJECT = \"bigframes-dev\" # replace this with your project\n", - "LOCATION = \"us\" # replace this with your location\n", - "\n", - "bpd.options.bigquery.project = PROJECT\n", - "bpd.options.bigquery.location = LOCATION\n", - "bpd.options.display.progress_bar = None\n", - "\n", - "bpd.options.bigquery.ordering_mode = \"partial\"" - ] - }, - { - "cell_type": "markdown", - "id": "d64fd3e3", - "metadata": {}, - "source": [ - "# Timedelta arithmetics and comparisons" - ] - }, - { - "cell_type": "markdown", - "id": "e10bd798", - "metadata": {}, - "source": [ - "First, you load the taxi data from the BigQuery public dataset `bigquery-public-data.new_york_taxi_trips.tlc_yellow_trips_2021`. The size of this table is about 6.3 GB." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "f1b11138", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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vendor_idpickup_datetimedropoff_datetimepassenger_counttrip_distancerate_codestore_and_fwd_flagpayment_typefare_amountextramta_taxtip_amounttolls_amountimp_surchargeairport_feetotal_amountpickup_location_iddropoff_location_iddata_file_yeardata_file_month
012021-06-09 07:44:46+00:002021-06-09 07:45:24+00:0012.2000000001.0N40E-90E-90E-90E-90E-90E-90E-90E-926326320216
122021-06-07 11:59:46+00:002021-06-07 12:00:00+00:0020.0100000003.0N20E-90E-90E-90E-90E-90E-90E-90E-926326320216
222021-06-23 15:03:58+00:002021-06-23 15:04:34+00:0010E-91.0N10E-90E-90E-90E-90E-90E-90E-90E-919319320216
312021-06-12 14:26:55+00:002021-06-12 14:27:08+00:0001.0000000001.0N30E-90E-90E-90E-90E-90E-90E-90E-914314320216
422021-06-15 08:39:01+00:002021-06-15 08:40:36+00:0010E-91.0N10E-90E-90E-90E-90E-90E-90E-90E-919319320216
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" - ], - "text/plain": [ - " vendor_id pickup_datetime dropoff_datetime \\\n", - "0 1 2021-06-09 07:44:46+00:00 2021-06-09 07:45:24+00:00 \n", - "1 2 2021-06-07 11:59:46+00:00 2021-06-07 12:00:00+00:00 \n", - "2 2 2021-06-23 15:03:58+00:00 2021-06-23 15:04:34+00:00 \n", - "3 1 2021-06-12 14:26:55+00:00 2021-06-12 14:27:08+00:00 \n", - "4 2 2021-06-15 08:39:01+00:00 2021-06-15 08:40:36+00:00 \n", - "\n", - " passenger_count trip_distance rate_code store_and_fwd_flag payment_type \\\n", - "0 1 2.200000000 1.0 N 4 \n", - "1 2 0.010000000 3.0 N 2 \n", - "2 1 0E-9 1.0 N 1 \n", - "3 0 1.000000000 1.0 N 3 \n", - "4 1 0E-9 1.0 N 1 \n", - "\n", - " fare_amount extra mta_tax tip_amount tolls_amount imp_surcharge \\\n", - "0 0E-9 0E-9 0E-9 0E-9 0E-9 0E-9 \n", - "1 0E-9 0E-9 0E-9 0E-9 0E-9 0E-9 \n", - "2 0E-9 0E-9 0E-9 0E-9 0E-9 0E-9 \n", - "3 0E-9 0E-9 0E-9 0E-9 0E-9 0E-9 \n", - "4 0E-9 0E-9 0E-9 0E-9 0E-9 0E-9 \n", - "\n", - " airport_fee total_amount pickup_location_id dropoff_location_id \\\n", - "0 0E-9 0E-9 263 263 \n", - "1 0E-9 0E-9 263 263 \n", - "2 0E-9 0E-9 193 193 \n", - "3 0E-9 0E-9 143 143 \n", - "4 0E-9 0E-9 193 193 \n", - "\n", - " data_file_year data_file_month \n", - "0 2021 6 \n", - "1 2021 6 \n", - "2 2021 6 \n", - "3 2021 6 \n", - "4 2021 6 " - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "taxi_trips = bpd.read_gbq(\"bigquery-public-data.new_york_taxi_trips.tlc_yellow_trips_2021\").dropna()\n", - "taxi_trips = taxi_trips[taxi_trips['pickup_datetime'].dt.year == 2021]\n", - "taxi_trips.peek(5)" - ] - }, - { - "cell_type": "markdown", - "id": "f5b13623", - "metadata": {}, - "source": [ - "Based on the dataframe content, you calculate the trip durations and store them under the column “trip_duration”. You can see that the values under \"trip_duartion\" are timedeltas." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "12fc1a5a", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "duration[us][pyarrow]" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "taxi_trips['trip_duration'] = taxi_trips['dropoff_datetime'] - taxi_trips['pickup_datetime']\n", - "taxi_trips['trip_duration'].dtype" - ] - }, - { - "cell_type": "markdown", - "id": "4b18b8d9", - "metadata": {}, - "source": [ - "To remove data outliers, you filter the taxi_trips to keep only the trips that were less than 2 hours." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "62b2d42e", - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "\n", - "taxi_trips = taxi_trips[taxi_trips['trip_duration'] <= pd.Timedelta(\"2h\")]" - ] - }, - { - "cell_type": "markdown", - "id": "665fb8a7", - "metadata": {}, - "source": [ - "Finally, you calculate the average speed of each trip, and find the median speed of all trips." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "e79e23c3", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The median speed of an average taxi trip is: 10.58 mph.\n" - ] - } - ], - "source": [ - "average_speed = taxi_trips[\"trip_distance\"] / (taxi_trips['trip_duration'] / pd.Timedelta(\"1h\"))\n", - "print(f\"The median speed of an average taxi trip is: {average_speed.median():.2f} mph.\")" - ] - }, - { - "cell_type": "markdown", - "id": "261dbdf1", - "metadata": {}, - "source": [ - "Given how packed NYC is, a median taxi speed of 10.58 mph totally makes sense." - ] - }, - { - "cell_type": "markdown", - "id": "6122c32e", - "metadata": {}, - "source": [ - "# Use timedelta for rolling aggregation" - ] - }, - { - "cell_type": "markdown", - "id": "05ae6fbb", - "metadata": {}, - "source": [ - "Using your existing dataset, you can now calculate the taxi trip count over a period of two days, and find out when NYC is at its busiest and when it is fast asleep.\n", - "\n", - "First, you pick two workdays (a Thursday and a Friday) as your target dates:" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "7dc50b1c", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Number of records: 255434\n" - ] - } - ], - "source": [ - "import datetime\n", - "\n", - "target_dates = [\n", - " datetime.date(2021, 12, 2), \n", - " datetime.date(2021, 12, 3)\n", - "]\n", - "\n", - "two_day_taxi_trips = taxi_trips[taxi_trips['pickup_datetime'].dt.date.isin(target_dates)]\n", - "print(f\"Number of records: {len(two_day_taxi_trips)}\")\n", - "# Number of records: 255434\n" - ] - }, - { - "cell_type": "markdown", - "id": "a5f39bd8", - "metadata": {}, - "source": [ - "Your next step involves aggregating the number of records associated with each unique \"pickup_datetime\" value. Additionally, the data undergo upsampling to account for any absent timestamps, which are populated with a count of 0." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "f25b34f5", - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "\n", - "two_day_trip_count = two_day_taxi_trips['pickup_datetime'].value_counts()\n", - "\n", - "full_index = pd.date_range(\n", - " start='2021-12-02 00:00:00',\n", - " end='2021-12-04 00:00:00',\n", - " freq='s',\n", - " tz='UTC'\n", - ")\n", - "two_day_trip_count = two_day_trip_count.reindex(full_index).fillna(0)" - ] - }, - { - "cell_type": "markdown", - "id": "3394b2ba", - "metadata": {}, - "source": [ - "You'll then calculate the sum of trip counts within a 5-minute rolling window. This involves using the `rolling()` method, which can accept a time window in the form of either a string or a timedelta object." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "4d5987d8", - "metadata": {}, - "outputs": [], - "source": [ - "two_day_trip_rolling_count = two_day_trip_count.sort_index().rolling(window=\"5m\").sum()" - ] - }, - { - "cell_type": "markdown", - "id": "3811b1fb", - "metadata": {}, - "source": [ - "Finally, you visualize the trip counts throughout the target dates." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "871c32c5", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from matplotlib import pyplot as plt\n", - "import matplotlib.dates as mdates\n", - "\n", - "plt.figure(figsize=(16,7))\n", - "ax = plt.gca()\n", - "formatter = mdates.DateFormatter(\"%D %H:%M:%S\")\n", - "ax.xaxis.set_major_formatter(formatter)\n", - "\n", - "ax.tick_params(axis='both', labelsize=10)\n", - "\n", - "two_day_trip_rolling_count.plot(ax=ax, legend=False)\n", - "plt.xlabel(\"Pickup Time\", fontsize=12)\n", - "plt.ylabel(\"Trip count in last 5 minutes\", fontsize=12)\n", - "plt.grid()\n", - "plt.show()\n" - ] - }, - { - "cell_type": "markdown", - "id": "336b78ce", - "metadata": {}, - "source": [ - "The taxi ride count reached its lowest point around 5:00 a.m., and peaked around 7:00 p.m. on a workday. Such is the rhythm of NYC." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.16" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks/dataframes/anywidget_mode.ipynb b/notebooks/dataframes/anywidget_mode.ipynb deleted file mode 100644 index 9cae55b26dc..00000000000 --- a/notebooks/dataframes/anywidget_mode.ipynb +++ /dev/null @@ -1,836 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "d10bfca4", - "metadata": {}, - "outputs": [], - "source": [ - "# Copyright 2025 Google LLC\n", - "#\n", - "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", - "# you may not use this file except in compliance with the License.\n", - "# You may obtain a copy of the License at\n", - "#\n", - "# https://www.apache.org/licenses/LICENSE-2.0\n", - "#\n", - "# Unless required by applicable law or agreed to in writing, software\n", - "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", - "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", - "# See the License for the specific language governing permissions and\n", - "# limitations under the License." - ] - }, - { - "cell_type": "markdown", - "id": "acca43ae", - "metadata": {}, - "source": [ - "# Demo to Show Anywidget mode" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "ca22f059", - "metadata": {}, - "outputs": [], - "source": [ - "import bigframes.pandas as bpd" - ] - }, - { - "cell_type": "markdown", - "id": "04406a4d", - "metadata": {}, - "source": [ - "This notebook demonstrates the **anywidget** display mode for BigQuery DataFrames. This mode provides an interactive table experience for exploring your data directly within the notebook.\n", - "\n", - "**Key features:**\n", - "- **Rich DataFrames & Series:** Both DataFrames and Series are displayed as interactive widgets.\n", - "- **Pagination:** Navigate through large datasets page by page without overwhelming the output.\n", - "- **Column Sorting:** Click column headers to toggle between ascending, descending, and unsorted views. Use **Shift + Click** to sort by multiple columns.\n", - "- **Column Resizing:** Drag the dividers between column headers to adjust their width.\n", - "- **Max Columns Control:** Limit the number of displayed columns to improve performance and readability for wide datasets." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "1bc5aaf3", - "metadata": {}, - "outputs": [], - "source": [ - "bpd.options.bigquery.ordering_mode = \"partial\"\n", - "bpd.options.display.render_mode = \"anywidget\"" - ] - }, - { - "cell_type": "markdown", - "id": "0a354c69", - "metadata": {}, - "source": [ - "Load Sample Data" - ] - }, - { - "cell_type": "markdown", - "id": "interactive-df-header", - "metadata": {}, - "source": [ - "## 1. Interactive DataFrame Display\n", - "Loading a dataset from BigQuery automatically renders the interactive widget." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "f289d250", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " Query processed 171.4 MB in 19 seconds of slot time. [Job bigframes-dev:US.50efe672-74c6-4292-98d9-520cba9ca516 details]\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "state gender year name number\n", - " AL F 1910 Annie 482\n", - " AL F 1910 Myrtle 104\n", - " AR F 1910 Lillian 56\n", - " CT F 1910 Anne 38\n", - " CT F 1910 Frances 45\n", - " FL F 1910 Margaret 53\n", - " GA F 1910 Mae 73\n", - " GA F 1910 Beatrice 96\n", - " GA F 1910 Lola 47\n", - " IA F 1910 Viola 49\n", - "...\n", - "\n", - "[5552452 rows x 5 columns]\n" - ] - } - ], - "source": [ - "df = bpd.read_gbq(\"bigquery-public-data.usa_names.usa_1910_2013\")\n", - "print(df)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "220340b0", - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "b1080dddbe4140d2b88ef85566e52955", - "version_major": 2, - "version_minor": 1 - }, - "text/html": [ - "
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[5552452 rows x 5 columns in total]" - ], - "text/plain": [ - "state gender year name number\n", - " AL F 1910 Lillian 99\n", - " AL F 1910 Ruby 204\n", - " AL F 1910 Helen 76\n", - " AL F 1910 Eunice 41\n", - " AR F 1910 Dora 42\n", - " CA F 1910 Edna 62\n", - " CA F 1910 Helen 239\n", - " CO F 1910 Alice 46\n", - " FL F 1910 Willie 71\n", - " FL F 1910 Thelma 65\n", - "...\n", - "\n", - "[5552452 rows x 5 columns]" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df" - ] - }, - { - "cell_type": "markdown", - "id": "3a73e472", - "metadata": {}, - "source": [ - "## 2. Interactive Series Display\n", - "BigQuery DataFrames `Series` objects now also support the full interactive widget experience, including pagination and formatting." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "42bb02ab", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " Query processed 44.4 MB in a moment of slot time.\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1967\n", - "1981\n", - "2009\n", - "1956\n", - "1960\n", - "2001\n", - "2009\n", - "2003\n", - "1985\n", - "1993\n", - "Name: year, dtype: Int64\n", - "...\n", - "\n", - "[5552452 rows]\n" - ] - } - ], - "source": [ - "test_series = df[\"year\"]\n", - "# Displaying the series triggers the interactive widget\n", - "print(test_series)" - ] - }, - { - "cell_type": "markdown", - "id": "7bcf1bb7", - "metadata": {}, - "source": [ - "Display with Pagination" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "da23e0f3", - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "46e836f10d9e47afb4d82b5c7da69660", - "version_major": 2, - "version_minor": 1 - }, - "text/html": [ - "
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Click again to sort in descending order.\n", - "- **Descending (▼):** The data is sorted from largest to smallest. Click again to return to the unsorted state.\n", - "\n", - "#### Multi-Column Sorting\n", - "You can sort by multiple columns to further refine your view:\n", - "- **Shift + Click:** Hold the `Shift` key while clicking additional column headers to add them to the sort order. \n", - "- Each column in a multi-sort also cycles through the three states (Ascending, Descending, Unsorted).\n", - "- **Indicator visibility:** Sorting indicators (▲, ▼) are always visible for all columns currently included in the sort. The unsorted indicator (●) is only visible when you hover over an unsorted column header." - ] - }, - { - "cell_type": "markdown", - "id": "adjustable-width-intro", - "metadata": {}, - "source": [ - "### Adjustable Column Widths\n", - "You can easily adjust the width of any column in the table. Simply hover your mouse over the vertical dividers between column headers. When the cursor changes to a resize icon, click and drag to expand or shrink the column to your desired width. This allows for better readability and customization of your table view.\n", - "\n", - "### Control Maximum Columns\n", - "You can control the number of columns displayed in the widget using the **Max columns** dropdown in the footer. This is useful for wide DataFrames where you want to focus on a subset of columns or improve rendering performance. Options include 3, 5, 7, 10, 20, or All." - ] - }, - { - "cell_type": "markdown", - "id": "bb15bab6", - "metadata": {}, - "source": [ - "Programmatic Navigation Demo" - ] - }, - { - "cell_type": "markdown", - "id": "programmatic-header", - "metadata": {}, - "source": [ - "## 3. Programmatic Widget Control\n", - "You can also instantiate the `TableWidget` directly for more control, such as checking page counts or driving navigation programmatically." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "6920d49b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Total pages: 555246\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "6e5f603b56fb408bb1ea41519ea8702e", - "version_major": 2, - "version_minor": 1 - }, - "text/plain": [ - "" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import math\n", - "\n", - "from bigframes.display.anywidget import TableWidget\n", - "\n", - "# Create widget programmatically \n", - "widget = TableWidget(df)\n", - "print(f\"Total pages: {math.ceil(widget.row_count / widget.page_size)}\")\n", - " \n", - "# Display the widget\n", - "widget" - ] - }, - { - "cell_type": "markdown", - "id": "02cbd1be", - "metadata": {}, - "source": [ - "Test Navigation Programmatically" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "12b68f15", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current page: 0\n", - "After next: 1\n", - "After prev: 0\n" - ] - } - ], - "source": [ - "# Simulate button clicks programmatically\n", - "print(\"Current page:\", widget.page)\n", - "\n", - "# Go to next page\n", - "widget.page = 1\n", - "print(\"After next:\", widget.page)\n", - "\n", - "# Go to previous page\n", - "widget.page = 0\n", - "print(\"After prev:\", widget.page)" - ] - }, - { - "cell_type": "markdown", - "id": "9d310138", - "metadata": {}, - "source": [ - "## 4. Edge Cases\n", - "The widget handles small datasets gracefully, disabling unnecessary pagination controls." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "a9d5d13a", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Small dataset pages: 1\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "20c94621c4ae4eb5a94fd3596ae8c236", - "version_major": 2, - "version_minor": 1 - }, - "text/plain": [ - "" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Test with very small dataset\n", - "small_df = df.sort_values([\"name\", \"year\", \"state\"]).head(5)\n", - "small_widget = TableWidget(small_df)\n", - "print(f\"Small dataset pages: {math.ceil(small_widget.row_count / small_widget.page_size)}\")\n", - "small_widget" - ] - }, - { - "cell_type": "markdown", - "id": "added-cell-2", - "metadata": {}, - "source": [ - "### Displaying Generative AI results containing JSON\n", - "The `AI.GENERATE` function in BigQuery returns results in a JSON column. While BigQuery's JSON type is not natively supported by the underlying Arrow `to_pandas_batches()` method used in anywidget mode ([Apache Arrow issue #45262](https://github.com/apache/arrow/issues/45262)), BigQuery Dataframes automatically converts JSON columns to strings for display. This allows you to view the results of generative AI functions seamlessly." - ] - }, - { - "cell_type": "markdown", - "id": "ai-header", - "metadata": {}, - "source": [ - "## 5. Advanced Data Types (JSON/Structs)\n", - "The `AI.GENERATE` function in BigQuery returns results in a JSON column. BigQuery Dataframes automatically handles complex types like JSON strings for display, allowing you to view generative AI results seamlessly." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "75000341", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " Query processed 0 Bytes in a moment of slot time. 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Thallner GmbH \n", - " 05.02.2014 Stork Bamberger Patentanwälte Linco Food Systems A/S \n", - "\n", - " inventor_line_1 title_line_1 \\\n", - " VOLGER, Alexander STEUERUNGSSYSTEM FÜR AUTOMATISCHE PARKHÄUSER \n", - "Acero Acero, Jesus VORRICHTUNG ZUR INDUKTIVEN ENERGIEÜBERTRAGUNG \n", - " Kukihara, Kensuke METHOD EXECUTED BY A COMPUTER, INFORMATION PROC... \n", - " Kurz, Florian VORRICHTUNG ZUM BONDEN VON SUBSTRATEN \n", - " Thrane, Uffe MASTHÄHNCHENCONTAINER ALS BESTANDTEIL EINER EI... \n", - "\n", - " number \n", - "EP 3 366 869 A1 \n", - "EP 3 383 141 A2 \n", - "EP 3 382 553 A1 \n", - "EP 3 382 744 A1 \n", - "EP 3 381 276 A1 \n", - "\n", - "[5 rows x 15 columns]" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bpd.read_gbq(\"\"\"\n", - " SELECT\n", - " AI.GENERATE(\n", - " prompt=>(\"Extract the values.\", OBJ.GET_ACCESS_URL(OBJ.FETCH_METADATA(OBJ.MAKE_REF(gcs_path, \"us.bigframes-default-connection\")), \"r\")),\n", - " connection_id=>\"us.bigframes-default-connection\",\n", - " output_schema=>\"publication_date string, class_international string, application_number string, filing_date string\") AS result,\n", - " *\n", - " FROM `bigquery-public-data.labeled_patents.extracted_data`\n", - " LIMIT 5;\n", - "\"\"\")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": ".venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.13.0" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks/dataframes/dataframe.ipynb b/notebooks/dataframes/dataframe.ipynb index f26b4ff1cf1..c6b276af877 100644 --- a/notebooks/dataframes/dataframe.ipynb +++ b/notebooks/dataframes/dataframe.ipynb @@ -1,27 +1,5 @@ { "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "eeec3428", - "metadata": {}, - "outputs": [], - "source": [ - "# Copyright 2023 Google LLC\n", - "#\n", - "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", - "# you may not use this file except in compliance with the License.\n", - "# You may obtain a copy of the License at\n", - "#\n", - "# https://www.apache.org/licenses/LICENSE-2.0\n", - "#\n", - "# Unless required by applicable law or agreed to in writing, software\n", - "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", - "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", - "# See the License for the specific language governing permissions and\n", - "# limitations under the License." - ] - }, { "attachments": {}, "cell_type": "markdown", @@ -34,12 +12,56 @@ { "cell_type": "code", "execution_count": 1, - "id": "96757c59-fc22-420e-a42f-c6cb956110ec", + "id": "72ebb083-f06b-4408-b24d-f349bd0851e3", "metadata": {}, "outputs": [], "source": [ + "# On the instance where you are running jupyter,\n", + "# authenticate with gcloud first:\n", + "#\n", + "# gcloud auth application-default login\n", + "\n", "import bigframes.pandas as bpd\n", "\n", + "bpd.options.bigquery.location = \"US\"" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "96757c59-fc22-420e-a42f-c6cb956110ec", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "11c27813da5c4d2e8108bf4bd9e7e55d", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HTML(value='Query job ccb31707-38d2-4d93-8502-e39352f322a3 is RUNNING. 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" Diamondbacks\n", - " Giants\n", - " 175\n", + " Brewers\n", + " Cubs\n", + " 248\n", " \n", " \n", " 24\n", - " 95d548b6-2da8-4644-812e-b277fec5b91f\n", + " dc0c9218-505c-4725-8c0c-40b72cca0956\n", " 2016\n", - " Braves\n", - " Mets\n", - " 201\n", + " Astros\n", + " Cubs\n", + " 174\n", " \n", " \n", "\n", @@ -328,64 +354,64 @@ ], "text/plain": [ " gameId year homeTeamName awayTeamName \\\n", - "0 d60c6036-0ce1-4c90-8dd9-de3b403c92a8 2016 Nationals Brewers \n", - "1 af72a0b9-65f7-49fb-9b30-d505068bdf6d 2016 Reds Brewers \n", - "2 f57e1271-d217-400a-aea6-2e2d7d6a59a0 2016 Orioles Rays \n", - "3 198f4eed-a29f-41e2-8623-cb261e5ab370 2016 Rockies Giants \n", - "4 cb3ef033-dd57-41fd-b206-cdd3bc12c74f 2016 Twins Indians \n", - "5 4be9f735-a98e-4689-87ce-852cc3a1e79d 2016 Blue Jays Orioles \n", - "6 0b2de8c3-11d9-4f0f-a186-25b59f34a5d8 2016 Yankees Mets \n", - "7 60d80663-6ced-44aa-aad9-0f4bf8d3b4d2 2016 Red Sox Rays \n", - "8 7e1c2095-4fea-454c-8773-096ceb6fb05c 2016 Cardinals Pirates \n", - "9 f7f24ce3-7f9d-4e8a-986e-095db847c4c1 2016 Rays Twins \n", - "10 5c26e7fc-c99f-48b4-92c1-4a7208c8cfe9 2016 Rays Twins \n", - "11 6d2cab13-dd85-477a-8769-669069f85836 2016 Royals Rays \n", - "12 bca90342-7ddc-468e-b189-d43fad7528ec 2016 Astros Rays \n", - "13 630f4f78-03cc-43c1-9e57-ababb9c11418 2016 Dodgers Giants \n", - "14 c0cf1376-1115-4a2f-b457-3f82bbc41a89 2016 Tigers White Sox \n", - "15 46463c50-0f5c-4dca-a661-dd194464e791 2016 Cardinals Cubs \n", - "16 392ad56d-972e-4f77-98e2-5f8577931cf8 2016 Giants Cardinals \n", - "17 307730fa-bbed-4221-b4e6-a2492f546fd5 2016 Red Sox Twins \n", - "18 1cbc558f-7615-4fa9-bf97-7ccd62040d6f 2016 Mets Braves \n", - "19 723348ba-1645-43fc-9e22-92994f7a63bd 2016 Athletics Twins \n", - "20 ffbd6ecc-82e1-4e5d-9bd1-4ea210be5992 2016 Twins Marlins \n", - "21 f2747230-7df5-4535-a475-a1c823d0d654 2016 Twins Yankees \n", - "22 db3b6f35-a7a4-430a-8703-2b2f25103e17 2016 White Sox Orioles \n", - "23 5fc8c6f0-a70e-4d1b-877f-eb1ec8e6f636 2016 Diamondbacks Giants \n", - "24 95d548b6-2da8-4644-812e-b277fec5b91f 2016 Braves Mets \n", + "0 e14b6493-9e7f-404f-840a-8a680cc364bf 2016 Marlins Cubs \n", + "1 1f32b347-cbcb-4c31-a145-0e685306d168 2016 Marlins Cubs \n", + "2 0c2292d1-7398-48be-bf8e-b41dad5e1a43 2016 Braves Cubs \n", + "3 8fbec734-a15a-42ab-8d51-60790de7750b 2016 Braves Cubs \n", + "4 89e514d5-fbf5-4b9d-bdac-6ca45bfd18dd 2016 Phillies Cubs \n", + "5 6a83e76c-dc0d-4924-9d3d-a2e7e0ab5b52 2016 Diamondbacks Cubs \n", + "6 76ea8662-c7e6-4c38-8f2a-efe373e428ce 2016 Athletics Cubs \n", + "7 66fad23d-6e89-4f99-be29-d49b6e94f95d 2016 Athletics Cubs \n", + "8 d977367c-cf0c-4687-95a0-eb4542efcb01 2016 Rockies Cubs \n", + "9 a87070ff-1084-43ca-a7ba-69278f93ecba 2016 Cardinals Cubs \n", + "10 ea6b350d-3c1d-4737-878d-4465f66999f6 2016 Cardinals Cubs \n", + "11 46463c50-0f5c-4dca-a661-dd194464e791 2016 Cardinals Cubs \n", + "12 59134e6d-9d13-49aa-978e-c3c2300eb90f 2016 Pirates Cubs \n", + "13 387630a3-a894-4327-baa1-b24ec1a654d9 2016 Pirates Cubs \n", + "14 5d084e13-94fd-4995-b95a-4801ea3ed556 2016 Giants Cubs \n", + "15 34444c94-03ec-4d12-96af-68b8f399a22f 2016 Reds Cubs \n", + "16 9580bffe-22e1-4975-978b-1b13e7505193 2016 Reds Cubs \n", + "17 645e6a08-afd6-4677-a5c9-01ef446b0cf3 2016 Reds Cubs \n", + "18 08981bd8-d1d7-48e1-8668-9098b8f7fe90 2016 Reds Cubs \n", + "19 303703bb-b55f-476d-8faf-bf582169fb1d 2016 Padres Cubs \n", + "20 71ab82a4-6e07-430a-b695-1af3bc42ea61 2016 Nationals Cubs \n", + "21 d1a110c2-f6c8-4029-bcd8-2f8a01e1561c 2016 Brewers Cubs \n", + "22 6d111b57-fa0b-4f24-82df-ff33a26f0252 2016 Brewers Cubs \n", + "23 a97e9539-bbbd-4e03-bf15-f25ea2c1d923 2016 Brewers Cubs \n", + "24 dc0c9218-505c-4725-8c0c-40b72cca0956 2016 Astros Cubs \n", "\n", " duration_minutes \n", - "0 167 \n", - "1 172 \n", - "2 166 \n", - "3 182 \n", - "4 204 \n", - "5 184 \n", - "6 182 \n", - "7 191 \n", - "8 201 \n", - "9 189 \n", - "10 177 \n", - "11 183 \n", - "12 194 \n", - "13 178 \n", - "14 193 \n", - "15 160 \n", - "16 169 \n", - "17 251 \n", - "18 151 \n", - "19 153 \n", - "20 185 \n", - "21 180 \n", - "22 199 \n", - "23 175 \n", - "24 201 \n", + "0 187 \n", + "1 189 \n", + "2 165 \n", + "3 222 \n", + "4 164 \n", + "5 201 \n", + "6 173 \n", + "7 176 \n", + "8 180 \n", + "9 157 \n", + "10 218 \n", + "11 160 \n", + "12 178 \n", + "13 205 \n", + "14 197 \n", + "15 198 \n", + "16 188 \n", + "17 188 \n", + "18 194 \n", + "19 175 \n", + "20 257 \n", + "21 178 \n", + "22 171 \n", + "23 248 \n", + "24 174 \n", "...\n", "\n", "[2431 rows x 5 columns]" ] }, - "execution_count": 3, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -397,7 +423,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "id": "a6b8b3ac-1df8-46ff-ac4f-d6e7657fc80c", "metadata": {}, "outputs": [ @@ -407,7 +433,7 @@ "(2431, 5)" ] }, - "execution_count": 4, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -427,7 +453,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "id": "34457cc7-e734-4e3f-9f2b-34cdd4e2aba4", "metadata": { "tags": [] @@ -444,7 +470,7 @@ "dtype: object" ] }, - "execution_count": 5, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -455,7 +481,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "id": "b4f4383f-f596-41d8-aad2-2fd68d261cfd", "metadata": {}, "outputs": [ @@ -465,7 +491,7 @@ "Index(['gameId', 'year', 'homeTeamName', 'awayTeamName', 'duration_minutes'], dtype='object')" ] }, - "execution_count": 6, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -485,7 +511,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "id": "c7017f3d-869d-42e3-bbd8-b3fbc408c2d0", "metadata": { "tags": [] @@ -493,23 +519,13 @@ "outputs": [ { "data": { - 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" Diamondbacks\n", - " Giants\n", - " 175\n", - " Diamondbacks vs Giants\n", + " Brewers\n", + " Cubs\n", + " 248\n", + " Brewers vs Cubs\n", " \n", " \n", " 24\n", - " 95d548b6-2da8-4644-812e-b277fec5b91f\n", + " dc0c9218-505c-4725-8c0c-40b72cca0956\n", " 2016\n", - " Braves\n", - " Mets\n", - " 201\n", - " Braves vs Mets\n", + " Astros\n", + " Cubs\n", + " 174\n", + " Astros vs Cubs\n", " \n", " \n", "\n", @@ -789,64 +807,64 @@ ], "text/plain": [ " gameId year homeTeamName awayTeamName \\\n", - "0 d60c6036-0ce1-4c90-8dd9-de3b403c92a8 2016 Nationals Brewers \n", - "1 af72a0b9-65f7-49fb-9b30-d505068bdf6d 2016 Reds Brewers \n", - "2 f57e1271-d217-400a-aea6-2e2d7d6a59a0 2016 Orioles Rays \n", - "3 198f4eed-a29f-41e2-8623-cb261e5ab370 2016 Rockies Giants \n", - "4 cb3ef033-dd57-41fd-b206-cdd3bc12c74f 2016 Twins Indians \n", - "5 4be9f735-a98e-4689-87ce-852cc3a1e79d 2016 Blue Jays Orioles \n", - "6 0b2de8c3-11d9-4f0f-a186-25b59f34a5d8 2016 Yankees Mets \n", - "7 60d80663-6ced-44aa-aad9-0f4bf8d3b4d2 2016 Red Sox Rays \n", - "8 7e1c2095-4fea-454c-8773-096ceb6fb05c 2016 Cardinals Pirates \n", - "9 f7f24ce3-7f9d-4e8a-986e-095db847c4c1 2016 Rays Twins \n", - "10 5c26e7fc-c99f-48b4-92c1-4a7208c8cfe9 2016 Rays Twins \n", - "11 6d2cab13-dd85-477a-8769-669069f85836 2016 Royals Rays \n", - "12 bca90342-7ddc-468e-b189-d43fad7528ec 2016 Astros Rays \n", - "13 630f4f78-03cc-43c1-9e57-ababb9c11418 2016 Dodgers Giants \n", - "14 c0cf1376-1115-4a2f-b457-3f82bbc41a89 2016 Tigers White Sox \n", - "15 46463c50-0f5c-4dca-a661-dd194464e791 2016 Cardinals Cubs \n", - "16 392ad56d-972e-4f77-98e2-5f8577931cf8 2016 Giants Cardinals \n", - "17 307730fa-bbed-4221-b4e6-a2492f546fd5 2016 Red Sox Twins \n", - "18 1cbc558f-7615-4fa9-bf97-7ccd62040d6f 2016 Mets Braves \n", - "19 723348ba-1645-43fc-9e22-92994f7a63bd 2016 Athletics Twins \n", - "20 ffbd6ecc-82e1-4e5d-9bd1-4ea210be5992 2016 Twins Marlins \n", - "21 f2747230-7df5-4535-a475-a1c823d0d654 2016 Twins Yankees \n", - "22 db3b6f35-a7a4-430a-8703-2b2f25103e17 2016 White Sox Orioles \n", - "23 5fc8c6f0-a70e-4d1b-877f-eb1ec8e6f636 2016 Diamondbacks Giants \n", - "24 95d548b6-2da8-4644-812e-b277fec5b91f 2016 Braves Mets \n", + "0 e14b6493-9e7f-404f-840a-8a680cc364bf 2016 Marlins Cubs \n", + "1 1f32b347-cbcb-4c31-a145-0e685306d168 2016 Marlins Cubs \n", + "2 0c2292d1-7398-48be-bf8e-b41dad5e1a43 2016 Braves Cubs \n", + "3 8fbec734-a15a-42ab-8d51-60790de7750b 2016 Braves Cubs \n", + "4 89e514d5-fbf5-4b9d-bdac-6ca45bfd18dd 2016 Phillies Cubs \n", + "5 6a83e76c-dc0d-4924-9d3d-a2e7e0ab5b52 2016 Diamondbacks Cubs \n", + "6 76ea8662-c7e6-4c38-8f2a-efe373e428ce 2016 Athletics Cubs \n", + "7 66fad23d-6e89-4f99-be29-d49b6e94f95d 2016 Athletics Cubs \n", + "8 d977367c-cf0c-4687-95a0-eb4542efcb01 2016 Rockies Cubs \n", + "9 a87070ff-1084-43ca-a7ba-69278f93ecba 2016 Cardinals Cubs \n", + "10 ea6b350d-3c1d-4737-878d-4465f66999f6 2016 Cardinals Cubs \n", + "11 46463c50-0f5c-4dca-a661-dd194464e791 2016 Cardinals Cubs \n", + "12 59134e6d-9d13-49aa-978e-c3c2300eb90f 2016 Pirates Cubs \n", + "13 387630a3-a894-4327-baa1-b24ec1a654d9 2016 Pirates Cubs \n", + "14 5d084e13-94fd-4995-b95a-4801ea3ed556 2016 Giants Cubs \n", + "15 34444c94-03ec-4d12-96af-68b8f399a22f 2016 Reds Cubs \n", + "16 9580bffe-22e1-4975-978b-1b13e7505193 2016 Reds Cubs \n", + "17 645e6a08-afd6-4677-a5c9-01ef446b0cf3 2016 Reds Cubs \n", + "18 08981bd8-d1d7-48e1-8668-9098b8f7fe90 2016 Reds Cubs \n", + "19 303703bb-b55f-476d-8faf-bf582169fb1d 2016 Padres Cubs \n", + "20 71ab82a4-6e07-430a-b695-1af3bc42ea61 2016 Nationals Cubs \n", + "21 d1a110c2-f6c8-4029-bcd8-2f8a01e1561c 2016 Brewers Cubs \n", + "22 6d111b57-fa0b-4f24-82df-ff33a26f0252 2016 Brewers Cubs \n", + "23 a97e9539-bbbd-4e03-bf15-f25ea2c1d923 2016 Brewers Cubs \n", + "24 dc0c9218-505c-4725-8c0c-40b72cca0956 2016 Astros Cubs \n", "\n", - " duration_minutes title \n", - "0 167 Nationals vs Brewers \n", - "1 172 Reds vs Brewers \n", - "2 166 Orioles vs Rays \n", - "3 182 Rockies vs Giants \n", - "4 204 Twins vs Indians \n", - "5 184 Blue Jays vs Orioles \n", - "6 182 Yankees vs Mets \n", - "7 191 Red Sox vs Rays \n", - "8 201 Cardinals vs Pirates \n", - "9 189 Rays vs Twins \n", - "10 177 Rays vs Twins \n", - "11 183 Royals vs Rays \n", - "12 194 Astros vs Rays \n", - "13 178 Dodgers vs Giants \n", - "14 193 Tigers vs White Sox \n", - "15 160 Cardinals vs Cubs \n", - "16 169 Giants vs Cardinals \n", - "17 251 Red Sox vs Twins \n", - "18 151 Mets vs Braves \n", - "19 153 Athletics vs Twins \n", - "20 185 Twins vs Marlins \n", - "21 180 Twins vs Yankees \n", - "22 199 White Sox vs Orioles \n", - "23 175 Diamondbacks vs Giants \n", - "24 201 Braves vs Mets \n", + " duration_minutes title \n", + "0 187 Marlins vs Cubs \n", + "1 189 Marlins vs Cubs \n", + "2 165 Braves vs Cubs \n", + "3 222 Braves vs Cubs \n", + "4 164 Phillies vs Cubs \n", + "5 201 Diamondbacks vs Cubs \n", + "6 173 Athletics vs Cubs \n", + "7 176 Athletics vs Cubs \n", + "8 180 Rockies vs Cubs \n", + "9 157 Cardinals vs Cubs \n", + "10 218 Cardinals vs Cubs \n", + "11 160 Cardinals vs Cubs \n", + "12 178 Pirates vs Cubs \n", + "13 205 Pirates vs Cubs \n", + "14 197 Giants vs Cubs \n", + "15 198 Reds vs Cubs \n", + "16 188 Reds vs Cubs \n", + "17 188 Reds vs Cubs \n", + "18 194 Reds vs Cubs \n", + "19 175 Padres vs Cubs \n", + "20 257 Nationals vs Cubs \n", + "21 178 Brewers vs Cubs \n", + "22 171 Brewers vs Cubs \n", + "23 248 Brewers vs Cubs \n", + "24 174 Astros vs Cubs \n", "...\n", "\n", "[2431 rows x 6 columns]" ] }, - "execution_count": 7, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -867,17 +885,19 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "id": "8bbe000a-36f0-4b6f-b403-b9ec28dd608b", "metadata": {}, "outputs": [ { "data": { - "text/html": [ - "Query job ef76c434-c4bc-4b4c-bb06-61521fc85b15 is DONE. 0 Bytes processed. 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" Diamondbacks\n", - " Giants\n", - " 175\n", - " Diamondbacks vs Giants\n", + " Brewers\n", + " Cubs\n", + " 248\n", + " Brewers vs Cubs\n", " \n", " \n", " 24\n", - " 95d548b6-2da8-4644-812e-b277fec5b91f\n", + " dc0c9218-505c-4725-8c0c-40b72cca0956\n", " 2016\n", - " Braves\n", - " Mets\n", - " 201\n", - " Braves vs Mets\n", + " Astros\n", + " Cubs\n", + " 174\n", + " Astros vs Cubs\n", " \n", " \n", "\n", @@ -1157,64 +1179,64 @@ ], "text/plain": [ " gameId year homeTeamName awayTeamName \\\n", - "0 d60c6036-0ce1-4c90-8dd9-de3b403c92a8 2016 Nationals Brewers \n", - "1 af72a0b9-65f7-49fb-9b30-d505068bdf6d 2016 Reds Brewers \n", - "2 f57e1271-d217-400a-aea6-2e2d7d6a59a0 2016 Orioles Rays \n", - "3 198f4eed-a29f-41e2-8623-cb261e5ab370 2016 Rockies Giants \n", - "4 cb3ef033-dd57-41fd-b206-cdd3bc12c74f 2016 Twins Indians \n", - "5 4be9f735-a98e-4689-87ce-852cc3a1e79d 2016 Blue Jays Orioles \n", - "6 0b2de8c3-11d9-4f0f-a186-25b59f34a5d8 2016 Yankees Mets \n", - "7 60d80663-6ced-44aa-aad9-0f4bf8d3b4d2 2016 Red Sox Rays \n", - "8 7e1c2095-4fea-454c-8773-096ceb6fb05c 2016 Cardinals Pirates \n", - "9 f7f24ce3-7f9d-4e8a-986e-095db847c4c1 2016 Rays Twins \n", - "10 5c26e7fc-c99f-48b4-92c1-4a7208c8cfe9 2016 Rays Twins \n", - "11 6d2cab13-dd85-477a-8769-669069f85836 2016 Royals Rays \n", - "12 bca90342-7ddc-468e-b189-d43fad7528ec 2016 Astros Rays \n", - "13 630f4f78-03cc-43c1-9e57-ababb9c11418 2016 Dodgers Giants \n", - "14 c0cf1376-1115-4a2f-b457-3f82bbc41a89 2016 Tigers White Sox \n", - "15 46463c50-0f5c-4dca-a661-dd194464e791 2016 Cardinals Cubs \n", - "16 392ad56d-972e-4f77-98e2-5f8577931cf8 2016 Giants Cardinals \n", - "17 307730fa-bbed-4221-b4e6-a2492f546fd5 2016 Red Sox Twins \n", - "18 1cbc558f-7615-4fa9-bf97-7ccd62040d6f 2016 Mets Braves \n", - "19 723348ba-1645-43fc-9e22-92994f7a63bd 2016 Athletics Twins \n", - "20 ffbd6ecc-82e1-4e5d-9bd1-4ea210be5992 2016 Twins Marlins \n", - "21 f2747230-7df5-4535-a475-a1c823d0d654 2016 Twins Yankees \n", - "22 db3b6f35-a7a4-430a-8703-2b2f25103e17 2016 White Sox Orioles \n", - "23 5fc8c6f0-a70e-4d1b-877f-eb1ec8e6f636 2016 Diamondbacks Giants \n", - "24 95d548b6-2da8-4644-812e-b277fec5b91f 2016 Braves Mets \n", + "0 e14b6493-9e7f-404f-840a-8a680cc364bf 2016 Marlins Cubs \n", + "1 1f32b347-cbcb-4c31-a145-0e685306d168 2016 Marlins Cubs \n", + "2 0c2292d1-7398-48be-bf8e-b41dad5e1a43 2016 Braves Cubs \n", + "3 8fbec734-a15a-42ab-8d51-60790de7750b 2016 Braves Cubs \n", + "4 89e514d5-fbf5-4b9d-bdac-6ca45bfd18dd 2016 Phillies Cubs \n", + "5 6a83e76c-dc0d-4924-9d3d-a2e7e0ab5b52 2016 Diamondbacks Cubs \n", + "6 76ea8662-c7e6-4c38-8f2a-efe373e428ce 2016 Athletics Cubs \n", + "7 66fad23d-6e89-4f99-be29-d49b6e94f95d 2016 Athletics Cubs \n", + "8 d977367c-cf0c-4687-95a0-eb4542efcb01 2016 Rockies Cubs \n", + "9 a87070ff-1084-43ca-a7ba-69278f93ecba 2016 Cardinals Cubs \n", + "10 ea6b350d-3c1d-4737-878d-4465f66999f6 2016 Cardinals Cubs \n", + "11 46463c50-0f5c-4dca-a661-dd194464e791 2016 Cardinals Cubs \n", + "12 59134e6d-9d13-49aa-978e-c3c2300eb90f 2016 Pirates Cubs \n", + "13 387630a3-a894-4327-baa1-b24ec1a654d9 2016 Pirates Cubs \n", + "14 5d084e13-94fd-4995-b95a-4801ea3ed556 2016 Giants Cubs \n", + "15 34444c94-03ec-4d12-96af-68b8f399a22f 2016 Reds Cubs \n", + "16 9580bffe-22e1-4975-978b-1b13e7505193 2016 Reds Cubs \n", + "17 645e6a08-afd6-4677-a5c9-01ef446b0cf3 2016 Reds Cubs \n", + "18 08981bd8-d1d7-48e1-8668-9098b8f7fe90 2016 Reds Cubs \n", + "19 303703bb-b55f-476d-8faf-bf582169fb1d 2016 Padres Cubs \n", + "20 71ab82a4-6e07-430a-b695-1af3bc42ea61 2016 Nationals Cubs \n", + "21 d1a110c2-f6c8-4029-bcd8-2f8a01e1561c 2016 Brewers Cubs \n", + "22 6d111b57-fa0b-4f24-82df-ff33a26f0252 2016 Brewers Cubs \n", + "23 a97e9539-bbbd-4e03-bf15-f25ea2c1d923 2016 Brewers Cubs \n", + "24 dc0c9218-505c-4725-8c0c-40b72cca0956 2016 Astros Cubs \n", "\n", - " duration_minutes headline \n", - "0 167 Nationals vs Brewers \n", - "1 172 Reds vs Brewers \n", - "2 166 Orioles vs Rays \n", - "3 182 Rockies vs Giants \n", - "4 204 Twins vs Indians \n", - "5 184 Blue Jays vs Orioles \n", - "6 182 Yankees vs Mets \n", - "7 191 Red Sox vs Rays \n", - "8 201 Cardinals vs Pirates \n", - "9 189 Rays vs Twins \n", - "10 177 Rays vs Twins \n", - "11 183 Royals vs Rays \n", - "12 194 Astros vs Rays \n", - "13 178 Dodgers vs Giants \n", - "14 193 Tigers vs White Sox \n", - "15 160 Cardinals vs Cubs \n", - "16 169 Giants vs Cardinals \n", - "17 251 Red Sox vs Twins \n", - "18 151 Mets vs Braves \n", - "19 153 Athletics vs Twins \n", - "20 185 Twins vs Marlins \n", - "21 180 Twins vs Yankees \n", - "22 199 White Sox vs Orioles \n", - "23 175 Diamondbacks vs Giants \n", - "24 201 Braves vs Mets \n", + " duration_minutes headline \n", + "0 187 Marlins vs Cubs \n", + "1 189 Marlins vs Cubs \n", + "2 165 Braves vs Cubs \n", + "3 222 Braves vs Cubs \n", + "4 164 Phillies vs Cubs \n", + "5 201 Diamondbacks vs Cubs \n", + "6 173 Athletics vs Cubs \n", + "7 176 Athletics vs Cubs \n", + "8 180 Rockies vs Cubs \n", + "9 157 Cardinals vs Cubs \n", + "10 218 Cardinals vs Cubs \n", + "11 160 Cardinals vs Cubs \n", + "12 178 Pirates vs Cubs \n", + "13 205 Pirates vs Cubs \n", + "14 197 Giants vs Cubs \n", + "15 198 Reds vs Cubs \n", + "16 188 Reds vs Cubs \n", + "17 188 Reds vs Cubs \n", + "18 194 Reds vs Cubs \n", + "19 175 Padres vs Cubs \n", + "20 257 Nationals vs Cubs \n", + "21 178 Brewers vs Cubs \n", + "22 171 Brewers vs Cubs \n", + "23 248 Brewers vs Cubs \n", + "24 174 Astros vs Cubs \n", "...\n", "\n", "[2431 rows x 6 columns]" ] }, - "execution_count": 8, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -1226,7 +1248,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "id": "87eee643-28ac-4f4b-ac61-1f3de9c08a9d", "metadata": {}, "outputs": [], @@ -1236,17 +1258,19 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "id": "fad6d3da-1f40-4c5f-94ec-0bdfe21ca5b6", "metadata": {}, "outputs": [ { "data": { - "text/html": [ - "Query job 051b3d23-5ab2-4022-adfc-f6553eb8532d is DONE. 0 Bytes processed. 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" Diamondbacks\n", - " Giants\n", - " 175\n", + " Brewers\n", + " Cubs\n", + " 248\n", " \n", " \n", " 24\n", - " 95d548b6-2da8-4644-812e-b277fec5b91f\n", + " dc0c9218-505c-4725-8c0c-40b72cca0956\n", " 2016\n", - " Braves\n", - " Mets\n", - " 201\n", + " Astros\n", + " Cubs\n", + " 174\n", " \n", " \n", "\n", @@ -1500,64 +1526,64 @@ ], "text/plain": [ " gameId year homeTeamName awayTeamName \\\n", - "0 d60c6036-0ce1-4c90-8dd9-de3b403c92a8 2016 Nationals Brewers \n", - "1 af72a0b9-65f7-49fb-9b30-d505068bdf6d 2016 Reds Brewers \n", - "2 f57e1271-d217-400a-aea6-2e2d7d6a59a0 2016 Orioles Rays \n", - "3 198f4eed-a29f-41e2-8623-cb261e5ab370 2016 Rockies Giants \n", - "4 cb3ef033-dd57-41fd-b206-cdd3bc12c74f 2016 Twins Indians \n", - "5 4be9f735-a98e-4689-87ce-852cc3a1e79d 2016 Blue Jays Orioles \n", - "6 0b2de8c3-11d9-4f0f-a186-25b59f34a5d8 2016 Yankees Mets \n", - "7 60d80663-6ced-44aa-aad9-0f4bf8d3b4d2 2016 Red Sox Rays \n", - "8 7e1c2095-4fea-454c-8773-096ceb6fb05c 2016 Cardinals Pirates \n", - "9 f7f24ce3-7f9d-4e8a-986e-095db847c4c1 2016 Rays Twins \n", - "10 5c26e7fc-c99f-48b4-92c1-4a7208c8cfe9 2016 Rays Twins \n", - "11 6d2cab13-dd85-477a-8769-669069f85836 2016 Royals Rays \n", - "12 bca90342-7ddc-468e-b189-d43fad7528ec 2016 Astros Rays \n", - "13 630f4f78-03cc-43c1-9e57-ababb9c11418 2016 Dodgers Giants \n", - "14 c0cf1376-1115-4a2f-b457-3f82bbc41a89 2016 Tigers White Sox \n", - "15 46463c50-0f5c-4dca-a661-dd194464e791 2016 Cardinals Cubs \n", - "16 392ad56d-972e-4f77-98e2-5f8577931cf8 2016 Giants Cardinals \n", - "17 307730fa-bbed-4221-b4e6-a2492f546fd5 2016 Red Sox Twins \n", - "18 1cbc558f-7615-4fa9-bf97-7ccd62040d6f 2016 Mets Braves \n", - "19 723348ba-1645-43fc-9e22-92994f7a63bd 2016 Athletics Twins \n", - "20 ffbd6ecc-82e1-4e5d-9bd1-4ea210be5992 2016 Twins Marlins \n", - "21 f2747230-7df5-4535-a475-a1c823d0d654 2016 Twins Yankees \n", - "22 db3b6f35-a7a4-430a-8703-2b2f25103e17 2016 White Sox Orioles \n", - "23 5fc8c6f0-a70e-4d1b-877f-eb1ec8e6f636 2016 Diamondbacks Giants \n", - "24 95d548b6-2da8-4644-812e-b277fec5b91f 2016 Braves Mets \n", + "0 e14b6493-9e7f-404f-840a-8a680cc364bf 2016 Marlins Cubs \n", + "1 1f32b347-cbcb-4c31-a145-0e685306d168 2016 Marlins Cubs \n", + "2 0c2292d1-7398-48be-bf8e-b41dad5e1a43 2016 Braves Cubs \n", + "3 8fbec734-a15a-42ab-8d51-60790de7750b 2016 Braves Cubs \n", + "4 89e514d5-fbf5-4b9d-bdac-6ca45bfd18dd 2016 Phillies Cubs \n", + "5 6a83e76c-dc0d-4924-9d3d-a2e7e0ab5b52 2016 Diamondbacks Cubs \n", + "6 76ea8662-c7e6-4c38-8f2a-efe373e428ce 2016 Athletics Cubs \n", + "7 66fad23d-6e89-4f99-be29-d49b6e94f95d 2016 Athletics Cubs \n", + "8 d977367c-cf0c-4687-95a0-eb4542efcb01 2016 Rockies Cubs \n", + "9 a87070ff-1084-43ca-a7ba-69278f93ecba 2016 Cardinals Cubs \n", + "10 ea6b350d-3c1d-4737-878d-4465f66999f6 2016 Cardinals Cubs \n", + "11 46463c50-0f5c-4dca-a661-dd194464e791 2016 Cardinals Cubs \n", + "12 59134e6d-9d13-49aa-978e-c3c2300eb90f 2016 Pirates Cubs \n", + "13 387630a3-a894-4327-baa1-b24ec1a654d9 2016 Pirates Cubs \n", + "14 5d084e13-94fd-4995-b95a-4801ea3ed556 2016 Giants Cubs \n", + "15 34444c94-03ec-4d12-96af-68b8f399a22f 2016 Reds Cubs \n", + "16 9580bffe-22e1-4975-978b-1b13e7505193 2016 Reds Cubs \n", + "17 645e6a08-afd6-4677-a5c9-01ef446b0cf3 2016 Reds Cubs \n", + "18 08981bd8-d1d7-48e1-8668-9098b8f7fe90 2016 Reds Cubs \n", + "19 303703bb-b55f-476d-8faf-bf582169fb1d 2016 Padres Cubs \n", + "20 71ab82a4-6e07-430a-b695-1af3bc42ea61 2016 Nationals Cubs \n", + "21 d1a110c2-f6c8-4029-bcd8-2f8a01e1561c 2016 Brewers Cubs \n", + "22 6d111b57-fa0b-4f24-82df-ff33a26f0252 2016 Brewers Cubs \n", + "23 a97e9539-bbbd-4e03-bf15-f25ea2c1d923 2016 Brewers Cubs \n", + "24 dc0c9218-505c-4725-8c0c-40b72cca0956 2016 Astros Cubs \n", "\n", " duration_minutes \n", - 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" Diamondbacks\n", - " Giants\n", - " 175\n", + " Brewers\n", + " Cubs\n", + " 248\n", " \n", " \n", " 24\n", - " 95d548b6-2da8-4644-812e-b277fec5b91f\n", + " dc0c9218-505c-4725-8c0c-40b72cca0956\n", " 2016\n", - " Braves\n", - " Mets\n", - " 201\n", + " Astros\n", + " Cubs\n", + " 174\n", " \n", " \n", "\n", @@ -1853,64 +1871,64 @@ ], "text/plain": [ " gameId year homeTeamName awayTeamName \\\n", - "0 d60c6036-0ce1-4c90-8dd9-de3b403c92a8 2016 Nationals Brewers \n", - "1 af72a0b9-65f7-49fb-9b30-d505068bdf6d 2016 Reds Brewers \n", - "2 f57e1271-d217-400a-aea6-2e2d7d6a59a0 2016 Orioles Rays \n", - "3 198f4eed-a29f-41e2-8623-cb261e5ab370 2016 Rockies Giants \n", - "4 cb3ef033-dd57-41fd-b206-cdd3bc12c74f 2016 Twins Indians \n", - "5 4be9f735-a98e-4689-87ce-852cc3a1e79d 2016 Blue Jays Orioles \n", - "6 0b2de8c3-11d9-4f0f-a186-25b59f34a5d8 2016 Yankees Mets \n", - "7 60d80663-6ced-44aa-aad9-0f4bf8d3b4d2 2016 Red Sox Rays \n", - "8 7e1c2095-4fea-454c-8773-096ceb6fb05c 2016 Cardinals Pirates \n", - "9 f7f24ce3-7f9d-4e8a-986e-095db847c4c1 2016 Rays Twins \n", - "10 5c26e7fc-c99f-48b4-92c1-4a7208c8cfe9 2016 Rays Twins \n", - "11 6d2cab13-dd85-477a-8769-669069f85836 2016 Royals Rays \n", - "12 bca90342-7ddc-468e-b189-d43fad7528ec 2016 Astros Rays \n", - "13 630f4f78-03cc-43c1-9e57-ababb9c11418 2016 Dodgers Giants \n", - "14 c0cf1376-1115-4a2f-b457-3f82bbc41a89 2016 Tigers White Sox \n", - "15 46463c50-0f5c-4dca-a661-dd194464e791 2016 Cardinals Cubs \n", - "16 392ad56d-972e-4f77-98e2-5f8577931cf8 2016 Giants Cardinals \n", - "17 307730fa-bbed-4221-b4e6-a2492f546fd5 2016 Red Sox Twins \n", - "18 1cbc558f-7615-4fa9-bf97-7ccd62040d6f 2016 Mets Braves \n", - "19 723348ba-1645-43fc-9e22-92994f7a63bd 2016 Athletics Twins \n", - "20 ffbd6ecc-82e1-4e5d-9bd1-4ea210be5992 2016 Twins Marlins \n", - "21 f2747230-7df5-4535-a475-a1c823d0d654 2016 Twins Yankees \n", - "22 db3b6f35-a7a4-430a-8703-2b2f25103e17 2016 White Sox Orioles \n", - "23 5fc8c6f0-a70e-4d1b-877f-eb1ec8e6f636 2016 Diamondbacks Giants \n", - "24 95d548b6-2da8-4644-812e-b277fec5b91f 2016 Braves Mets \n", + "0 e14b6493-9e7f-404f-840a-8a680cc364bf 2016 Marlins Cubs \n", + "1 1f32b347-cbcb-4c31-a145-0e685306d168 2016 Marlins Cubs \n", + "2 0c2292d1-7398-48be-bf8e-b41dad5e1a43 2016 Braves Cubs \n", + "3 8fbec734-a15a-42ab-8d51-60790de7750b 2016 Braves Cubs \n", + "4 89e514d5-fbf5-4b9d-bdac-6ca45bfd18dd 2016 Phillies Cubs \n", + "5 6a83e76c-dc0d-4924-9d3d-a2e7e0ab5b52 2016 Diamondbacks Cubs \n", + "6 76ea8662-c7e6-4c38-8f2a-efe373e428ce 2016 Athletics Cubs \n", + "7 66fad23d-6e89-4f99-be29-d49b6e94f95d 2016 Athletics Cubs \n", + "8 d977367c-cf0c-4687-95a0-eb4542efcb01 2016 Rockies Cubs \n", + "9 a87070ff-1084-43ca-a7ba-69278f93ecba 2016 Cardinals Cubs \n", + "10 ea6b350d-3c1d-4737-878d-4465f66999f6 2016 Cardinals Cubs \n", + "11 46463c50-0f5c-4dca-a661-dd194464e791 2016 Cardinals Cubs \n", + "12 59134e6d-9d13-49aa-978e-c3c2300eb90f 2016 Pirates Cubs \n", + "13 387630a3-a894-4327-baa1-b24ec1a654d9 2016 Pirates Cubs \n", + "14 5d084e13-94fd-4995-b95a-4801ea3ed556 2016 Giants Cubs \n", + "15 34444c94-03ec-4d12-96af-68b8f399a22f 2016 Reds Cubs \n", + "16 9580bffe-22e1-4975-978b-1b13e7505193 2016 Reds Cubs \n", + "17 645e6a08-afd6-4677-a5c9-01ef446b0cf3 2016 Reds Cubs \n", + "18 08981bd8-d1d7-48e1-8668-9098b8f7fe90 2016 Reds Cubs \n", + "19 303703bb-b55f-476d-8faf-bf582169fb1d 2016 Padres Cubs \n", + "20 71ab82a4-6e07-430a-b695-1af3bc42ea61 2016 Nationals Cubs \n", + "21 d1a110c2-f6c8-4029-bcd8-2f8a01e1561c 2016 Brewers Cubs \n", + "22 6d111b57-fa0b-4f24-82df-ff33a26f0252 2016 Brewers Cubs \n", + "23 a97e9539-bbbd-4e03-bf15-f25ea2c1d923 2016 Brewers Cubs \n", + "24 dc0c9218-505c-4725-8c0c-40b72cca0956 2016 Astros Cubs \n", "\n", " duration_minutes \n", - 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"execution_count": 15, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -2628,29 +2626,19 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 17, "id": "7ee87a01-2ff5-4021-855d-44b71cf2a225", "metadata": {}, "outputs": [ { "data": { - "text/html": [ - "Query job 9ae1e55b-36d0-4aef-ae39-67a3ad5fdb4d is DONE. 133.5 kB processed. 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" 723348ba-1645-43fc-9e22-92994f7a63bd\n", - " Athletics\n", + " 303703bb-b55f-476d-8faf-bf582169fb1d\n", + " Padres\n", " <NA>\n", " \n", " \n", " 20\n", - " ffbd6ecc-82e1-4e5d-9bd1-4ea210be5992\n", - " Twins\n", + " 71ab82a4-6e07-430a-b695-1af3bc42ea61\n", + " Nationals\n", " <NA>\n", " \n", " \n", " 21\n", - " f2747230-7df5-4535-a475-a1c823d0d654\n", - " Twins\n", + " d1a110c2-f6c8-4029-bcd8-2f8a01e1561c\n", + " Brewers\n", " <NA>\n", " \n", " \n", " 22\n", - " db3b6f35-a7a4-430a-8703-2b2f25103e17\n", - " White Sox\n", + " 6d111b57-fa0b-4f24-82df-ff33a26f0252\n", + " Brewers\n", " <NA>\n", " \n", " \n", " 23\n", - " 5fc8c6f0-a70e-4d1b-877f-eb1ec8e6f636\n", - " Diamondbacks\n", + " a97e9539-bbbd-4e03-bf15-f25ea2c1d923\n", + " Brewers\n", " <NA>\n", " \n", " \n", " 24\n", - " 95d548b6-2da8-4644-812e-b277fec5b91f\n", - " Braves\n", + " dc0c9218-505c-4725-8c0c-40b72cca0956\n", + " Astros\n", " <NA>\n", " \n", " \n", @@ -2852,37 +2842,37 @@ ], "text/plain": [ " gameId homeTeamName awayTeamName\n", - "0 d60c6036-0ce1-4c90-8dd9-de3b403c92a8 Nationals Brewers\n", - "1 af72a0b9-65f7-49fb-9b30-d505068bdf6d Reds Brewers\n", - "2 f57e1271-d217-400a-aea6-2e2d7d6a59a0 Orioles \n", - "3 198f4eed-a29f-41e2-8623-cb261e5ab370 Rockies \n", - "4 cb3ef033-dd57-41fd-b206-cdd3bc12c74f Twins \n", - "5 4be9f735-a98e-4689-87ce-852cc3a1e79d Blue Jays \n", - "6 0b2de8c3-11d9-4f0f-a186-25b59f34a5d8 Yankees \n", - "7 60d80663-6ced-44aa-aad9-0f4bf8d3b4d2 Red Sox \n", - "8 7e1c2095-4fea-454c-8773-096ceb6fb05c Cardinals \n", - "9 f7f24ce3-7f9d-4e8a-986e-095db847c4c1 Rays \n", - "10 5c26e7fc-c99f-48b4-92c1-4a7208c8cfe9 Rays \n", - "11 6d2cab13-dd85-477a-8769-669069f85836 Royals \n", - "12 bca90342-7ddc-468e-b189-d43fad7528ec Astros \n", - "13 630f4f78-03cc-43c1-9e57-ababb9c11418 Dodgers \n", - "14 c0cf1376-1115-4a2f-b457-3f82bbc41a89 Tigers \n", - "15 46463c50-0f5c-4dca-a661-dd194464e791 Cardinals \n", - "16 392ad56d-972e-4f77-98e2-5f8577931cf8 Giants \n", - "17 307730fa-bbed-4221-b4e6-a2492f546fd5 Red Sox \n", - 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"execution_count": 16, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -2893,29 +2883,19 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 18, "id": "330ed69c-f122-4af9-bf5e-96e309d3fa0c", "metadata": {}, "outputs": [ { "data": { - "text/html": [ - "Query job ec1c442e-6ea1-461c-ada7-e3dd0454b0ca is DONE. 133.5 kB processed. 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" Diamondbacks\n", - " Giants\n", - " 175\n", + " Brewers\n", + " Cubs\n", + " 248\n", " \n", " \n", " 24\n", - " 95d548b6-2da8-4644-812e-b277fec5b91f\n", + " dc0c9218-505c-4725-8c0c-40b72cca0956\n", " 2016\n", - " Braves\n", - " Mets\n", - " 201\n", + " Astros\n", + " Cubs\n", + " 174\n", " \n", " \n", "\n", @@ -3269,64 +3255,64 @@ ], "text/plain": [ " gameId year homeTeamName awayTeamName \\\n", - "0 d60c6036-0ce1-4c90-8dd9-de3b403c92a8 2016 Nationals Brewers \n", - "1 af72a0b9-65f7-49fb-9b30-d505068bdf6d 2016 Reds Brewers \n", - "2 f57e1271-d217-400a-aea6-2e2d7d6a59a0 2016 Orioles Rays \n", - "3 198f4eed-a29f-41e2-8623-cb261e5ab370 2016 Rockies Giants \n", - "4 cb3ef033-dd57-41fd-b206-cdd3bc12c74f 2016 Twins Indians \n", - "5 4be9f735-a98e-4689-87ce-852cc3a1e79d 2016 Blue Jays Orioles \n", - "6 0b2de8c3-11d9-4f0f-a186-25b59f34a5d8 2016 Yankees Mets \n", - "7 60d80663-6ced-44aa-aad9-0f4bf8d3b4d2 2016 Red Sox Rays \n", - "8 7e1c2095-4fea-454c-8773-096ceb6fb05c 2016 Cardinals Pirates \n", - "9 f7f24ce3-7f9d-4e8a-986e-095db847c4c1 2016 Rays Twins \n", - "10 5c26e7fc-c99f-48b4-92c1-4a7208c8cfe9 2016 Rays Twins \n", - "11 6d2cab13-dd85-477a-8769-669069f85836 2016 Royals Rays \n", - "12 bca90342-7ddc-468e-b189-d43fad7528ec 2016 Astros Rays \n", - "13 630f4f78-03cc-43c1-9e57-ababb9c11418 2016 Dodgers Giants \n", - "14 c0cf1376-1115-4a2f-b457-3f82bbc41a89 2016 Tigers White Sox \n", - "15 46463c50-0f5c-4dca-a661-dd194464e791 2016 Cardinals Cubs \n", - "16 392ad56d-972e-4f77-98e2-5f8577931cf8 2016 Giants Cardinals \n", - "17 307730fa-bbed-4221-b4e6-a2492f546fd5 2016 Red Sox Twins \n", - "18 1cbc558f-7615-4fa9-bf97-7ccd62040d6f 2016 Mets Braves \n", - "19 723348ba-1645-43fc-9e22-92994f7a63bd 2016 Athletics Twins \n", - "20 ffbd6ecc-82e1-4e5d-9bd1-4ea210be5992 2016 Twins Marlins \n", - "21 f2747230-7df5-4535-a475-a1c823d0d654 2016 Twins Yankees \n", - "22 db3b6f35-a7a4-430a-8703-2b2f25103e17 2016 White Sox Orioles \n", - "23 5fc8c6f0-a70e-4d1b-877f-eb1ec8e6f636 2016 Diamondbacks Giants \n", - "24 95d548b6-2da8-4644-812e-b277fec5b91f 2016 Braves Mets \n", + "0 e14b6493-9e7f-404f-840a-8a680cc364bf 2016 Marlins Cubs \n", + "1 1f32b347-cbcb-4c31-a145-0e685306d168 2016 Marlins Cubs \n", + "2 0c2292d1-7398-48be-bf8e-b41dad5e1a43 2016 Braves Cubs \n", + "3 8fbec734-a15a-42ab-8d51-60790de7750b 2016 Braves Cubs \n", + "4 89e514d5-fbf5-4b9d-bdac-6ca45bfd18dd 2016 Phillies Cubs \n", + "5 6a83e76c-dc0d-4924-9d3d-a2e7e0ab5b52 2016 Diamondbacks Cubs \n", + "6 76ea8662-c7e6-4c38-8f2a-efe373e428ce 2016 Athletics Cubs \n", + "7 66fad23d-6e89-4f99-be29-d49b6e94f95d 2016 Athletics Cubs \n", + "8 d977367c-cf0c-4687-95a0-eb4542efcb01 2016 Rockies Cubs \n", + "9 a87070ff-1084-43ca-a7ba-69278f93ecba 2016 Cardinals Cubs \n", + "10 ea6b350d-3c1d-4737-878d-4465f66999f6 2016 Cardinals Cubs \n", + "11 46463c50-0f5c-4dca-a661-dd194464e791 2016 Cardinals Cubs \n", + "12 59134e6d-9d13-49aa-978e-c3c2300eb90f 2016 Pirates Cubs \n", + "13 387630a3-a894-4327-baa1-b24ec1a654d9 2016 Pirates Cubs \n", + "14 5d084e13-94fd-4995-b95a-4801ea3ed556 2016 Giants Cubs \n", + "15 34444c94-03ec-4d12-96af-68b8f399a22f 2016 Reds Cubs \n", + "16 9580bffe-22e1-4975-978b-1b13e7505193 2016 Reds Cubs \n", + "17 645e6a08-afd6-4677-a5c9-01ef446b0cf3 2016 Reds Cubs \n", + "18 08981bd8-d1d7-48e1-8668-9098b8f7fe90 2016 Reds Cubs \n", + "19 303703bb-b55f-476d-8faf-bf582169fb1d 2016 Padres Cubs \n", + "20 71ab82a4-6e07-430a-b695-1af3bc42ea61 2016 Nationals Cubs \n", + "21 d1a110c2-f6c8-4029-bcd8-2f8a01e1561c 2016 Brewers Cubs \n", + "22 6d111b57-fa0b-4f24-82df-ff33a26f0252 2016 Brewers Cubs \n", + "23 a97e9539-bbbd-4e03-bf15-f25ea2c1d923 2016 Brewers Cubs \n", + "24 dc0c9218-505c-4725-8c0c-40b72cca0956 2016 Astros Cubs \n", "\n", " duration_minutes \n", - 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" Diamondbacks\n", - " Giants\n", - " 175\n", + " Brewers\n", + " Cubs\n", + " 248\n", " \n", " \n", " 24\n", - " 95d548b6-2da8-4644-812e-b277fec5b91f\n", + " dc0c9218-505c-4725-8c0c-40b72cca0956\n", " 2016\n", - " Braves\n", - " Mets\n", - " 201\n", + " Astros\n", + " Cubs\n", + " 174\n", " \n", " \n", "\n", @@ -4134,64 +4046,64 @@ ], "text/plain": [ " gameId year homeTeam!@#$%col awayTeamName \\\n", - "0 d60c6036-0ce1-4c90-8dd9-de3b403c92a8 2016 Nationals Brewers \n", - "1 af72a0b9-65f7-49fb-9b30-d505068bdf6d 2016 Reds Brewers \n", - "2 f57e1271-d217-400a-aea6-2e2d7d6a59a0 2016 Orioles Rays \n", - "3 198f4eed-a29f-41e2-8623-cb261e5ab370 2016 Rockies Giants \n", - "4 cb3ef033-dd57-41fd-b206-cdd3bc12c74f 2016 Twins Indians \n", - "5 4be9f735-a98e-4689-87ce-852cc3a1e79d 2016 Blue Jays Orioles \n", - "6 0b2de8c3-11d9-4f0f-a186-25b59f34a5d8 2016 Yankees Mets \n", - "7 60d80663-6ced-44aa-aad9-0f4bf8d3b4d2 2016 Red Sox Rays \n", - "8 7e1c2095-4fea-454c-8773-096ceb6fb05c 2016 Cardinals Pirates \n", - "9 f7f24ce3-7f9d-4e8a-986e-095db847c4c1 2016 Rays Twins \n", - "10 5c26e7fc-c99f-48b4-92c1-4a7208c8cfe9 2016 Rays Twins \n", - "11 6d2cab13-dd85-477a-8769-669069f85836 2016 Royals Rays \n", - "12 bca90342-7ddc-468e-b189-d43fad7528ec 2016 Astros Rays \n", - "13 630f4f78-03cc-43c1-9e57-ababb9c11418 2016 Dodgers Giants \n", - "14 c0cf1376-1115-4a2f-b457-3f82bbc41a89 2016 Tigers White Sox \n", - "15 46463c50-0f5c-4dca-a661-dd194464e791 2016 Cardinals Cubs \n", - "16 392ad56d-972e-4f77-98e2-5f8577931cf8 2016 Giants Cardinals \n", - "17 307730fa-bbed-4221-b4e6-a2492f546fd5 2016 Red Sox Twins \n", - "18 1cbc558f-7615-4fa9-bf97-7ccd62040d6f 2016 Mets Braves \n", - "19 723348ba-1645-43fc-9e22-92994f7a63bd 2016 Athletics Twins \n", - "20 ffbd6ecc-82e1-4e5d-9bd1-4ea210be5992 2016 Twins Marlins \n", - "21 f2747230-7df5-4535-a475-a1c823d0d654 2016 Twins Yankees \n", - "22 db3b6f35-a7a4-430a-8703-2b2f25103e17 2016 White Sox Orioles \n", - "23 5fc8c6f0-a70e-4d1b-877f-eb1ec8e6f636 2016 Diamondbacks Giants \n", - "24 95d548b6-2da8-4644-812e-b277fec5b91f 2016 Braves Mets \n", + "0 e14b6493-9e7f-404f-840a-8a680cc364bf 2016 Marlins Cubs \n", + "1 1f32b347-cbcb-4c31-a145-0e685306d168 2016 Marlins Cubs \n", + "2 0c2292d1-7398-48be-bf8e-b41dad5e1a43 2016 Braves Cubs \n", + "3 8fbec734-a15a-42ab-8d51-60790de7750b 2016 Braves Cubs \n", + "4 89e514d5-fbf5-4b9d-bdac-6ca45bfd18dd 2016 Phillies Cubs \n", + "5 6a83e76c-dc0d-4924-9d3d-a2e7e0ab5b52 2016 Diamondbacks Cubs \n", + "6 76ea8662-c7e6-4c38-8f2a-efe373e428ce 2016 Athletics Cubs \n", + "7 66fad23d-6e89-4f99-be29-d49b6e94f95d 2016 Athletics Cubs \n", + "8 d977367c-cf0c-4687-95a0-eb4542efcb01 2016 Rockies Cubs \n", + "9 a87070ff-1084-43ca-a7ba-69278f93ecba 2016 Cardinals Cubs \n", + "10 ea6b350d-3c1d-4737-878d-4465f66999f6 2016 Cardinals Cubs \n", + "11 46463c50-0f5c-4dca-a661-dd194464e791 2016 Cardinals Cubs \n", + "12 59134e6d-9d13-49aa-978e-c3c2300eb90f 2016 Pirates Cubs \n", + "13 387630a3-a894-4327-baa1-b24ec1a654d9 2016 Pirates Cubs \n", + "14 5d084e13-94fd-4995-b95a-4801ea3ed556 2016 Giants Cubs \n", + "15 34444c94-03ec-4d12-96af-68b8f399a22f 2016 Reds Cubs \n", + "16 9580bffe-22e1-4975-978b-1b13e7505193 2016 Reds Cubs \n", + "17 645e6a08-afd6-4677-a5c9-01ef446b0cf3 2016 Reds Cubs \n", + "18 08981bd8-d1d7-48e1-8668-9098b8f7fe90 2016 Reds Cubs \n", + "19 303703bb-b55f-476d-8faf-bf582169fb1d 2016 Padres Cubs \n", + "20 71ab82a4-6e07-430a-b695-1af3bc42ea61 2016 Nationals Cubs \n", + "21 d1a110c2-f6c8-4029-bcd8-2f8a01e1561c 2016 Brewers Cubs \n", + "22 6d111b57-fa0b-4f24-82df-ff33a26f0252 2016 Brewers Cubs \n", + "23 a97e9539-bbbd-4e03-bf15-f25ea2c1d923 2016 Brewers Cubs \n", + "24 dc0c9218-505c-4725-8c0c-40b72cca0956 2016 Astros Cubs \n", "\n", " duration_minutes \n", - 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" Diamondbacks\n", - " Giants\n", - " 175\n", + " Brewers\n", + " Cubs\n", + " 248\n", " \n", " \n", " 24\n", - " 95d548b6-2da8-4644-812e-b277fec5b91f\n", + " dc0c9218-505c-4725-8c0c-40b72cca0956\n", " 2016\n", - " Braves\n", - " Mets\n", - " 201\n", + " Astros\n", + " Cubs\n", + " 174\n", " \n", " \n", "\n", @@ -4465,65 +4381,65 @@ "[2431 rows x 5 columns in total]" ], "text/plain": [ - " gameId year team team \\\n", - "0 d60c6036-0ce1-4c90-8dd9-de3b403c92a8 2016 Nationals Brewers \n", - "1 af72a0b9-65f7-49fb-9b30-d505068bdf6d 2016 Reds Brewers \n", - "2 f57e1271-d217-400a-aea6-2e2d7d6a59a0 2016 Orioles Rays \n", - "3 198f4eed-a29f-41e2-8623-cb261e5ab370 2016 Rockies Giants \n", - "4 cb3ef033-dd57-41fd-b206-cdd3bc12c74f 2016 Twins Indians \n", - "5 4be9f735-a98e-4689-87ce-852cc3a1e79d 2016 Blue Jays Orioles \n", - "6 0b2de8c3-11d9-4f0f-a186-25b59f34a5d8 2016 Yankees Mets \n", - "7 60d80663-6ced-44aa-aad9-0f4bf8d3b4d2 2016 Red Sox Rays \n", - "8 7e1c2095-4fea-454c-8773-096ceb6fb05c 2016 Cardinals Pirates \n", - "9 f7f24ce3-7f9d-4e8a-986e-095db847c4c1 2016 Rays Twins \n", - "10 5c26e7fc-c99f-48b4-92c1-4a7208c8cfe9 2016 Rays Twins \n", - "11 6d2cab13-dd85-477a-8769-669069f85836 2016 Royals Rays \n", - "12 bca90342-7ddc-468e-b189-d43fad7528ec 2016 Astros Rays \n", - "13 630f4f78-03cc-43c1-9e57-ababb9c11418 2016 Dodgers Giants \n", - "14 c0cf1376-1115-4a2f-b457-3f82bbc41a89 2016 Tigers White Sox \n", - "15 46463c50-0f5c-4dca-a661-dd194464e791 2016 Cardinals Cubs \n", - "16 392ad56d-972e-4f77-98e2-5f8577931cf8 2016 Giants Cardinals \n", - "17 307730fa-bbed-4221-b4e6-a2492f546fd5 2016 Red Sox Twins \n", - "18 1cbc558f-7615-4fa9-bf97-7ccd62040d6f 2016 Mets Braves \n", - "19 723348ba-1645-43fc-9e22-92994f7a63bd 2016 Athletics Twins \n", - "20 ffbd6ecc-82e1-4e5d-9bd1-4ea210be5992 2016 Twins Marlins \n", - "21 f2747230-7df5-4535-a475-a1c823d0d654 2016 Twins Yankees \n", - "22 db3b6f35-a7a4-430a-8703-2b2f25103e17 2016 White Sox Orioles \n", - "23 5fc8c6f0-a70e-4d1b-877f-eb1ec8e6f636 2016 Diamondbacks Giants \n", - "24 95d548b6-2da8-4644-812e-b277fec5b91f 2016 Braves Mets \n", + " gameId year team team \\\n", + "0 e14b6493-9e7f-404f-840a-8a680cc364bf 2016 Marlins Cubs \n", + "1 1f32b347-cbcb-4c31-a145-0e685306d168 2016 Marlins Cubs \n", + "2 0c2292d1-7398-48be-bf8e-b41dad5e1a43 2016 Braves Cubs \n", + "3 8fbec734-a15a-42ab-8d51-60790de7750b 2016 Braves Cubs \n", + "4 89e514d5-fbf5-4b9d-bdac-6ca45bfd18dd 2016 Phillies Cubs \n", + "5 6a83e76c-dc0d-4924-9d3d-a2e7e0ab5b52 2016 Diamondbacks Cubs \n", + "6 76ea8662-c7e6-4c38-8f2a-efe373e428ce 2016 Athletics Cubs \n", + "7 66fad23d-6e89-4f99-be29-d49b6e94f95d 2016 Athletics Cubs \n", + "8 d977367c-cf0c-4687-95a0-eb4542efcb01 2016 Rockies Cubs \n", + "9 a87070ff-1084-43ca-a7ba-69278f93ecba 2016 Cardinals Cubs \n", + "10 ea6b350d-3c1d-4737-878d-4465f66999f6 2016 Cardinals Cubs \n", + "11 46463c50-0f5c-4dca-a661-dd194464e791 2016 Cardinals Cubs \n", + "12 59134e6d-9d13-49aa-978e-c3c2300eb90f 2016 Pirates Cubs \n", + "13 387630a3-a894-4327-baa1-b24ec1a654d9 2016 Pirates Cubs \n", + "14 5d084e13-94fd-4995-b95a-4801ea3ed556 2016 Giants Cubs \n", + "15 34444c94-03ec-4d12-96af-68b8f399a22f 2016 Reds Cubs \n", + "16 9580bffe-22e1-4975-978b-1b13e7505193 2016 Reds Cubs \n", + "17 645e6a08-afd6-4677-a5c9-01ef446b0cf3 2016 Reds Cubs \n", + "18 08981bd8-d1d7-48e1-8668-9098b8f7fe90 2016 Reds Cubs \n", + "19 303703bb-b55f-476d-8faf-bf582169fb1d 2016 Padres Cubs \n", + "20 71ab82a4-6e07-430a-b695-1af3bc42ea61 2016 Nationals Cubs \n", + "21 d1a110c2-f6c8-4029-bcd8-2f8a01e1561c 2016 Brewers Cubs \n", + "22 6d111b57-fa0b-4f24-82df-ff33a26f0252 2016 Brewers Cubs \n", + "23 a97e9539-bbbd-4e03-bf15-f25ea2c1d923 2016 Brewers Cubs \n", + "24 dc0c9218-505c-4725-8c0c-40b72cca0956 2016 Astros Cubs \n", "\n", " duration_minutes \n", - 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Open Job" - ], + "application/vnd.jupyter.widget-view+json": { + "model_id": "39eda5e2b7984f8f90a441c31b62a675", + "version_major": 2, + "version_minor": 0 + }, "text/plain": [ - "" + "HTML(value='Query job f3b7ff8a-ffdc-4f4c-91d3-6c2c702c373d is DONE. 193.8 kB processed. \n", " 0\n", " 2016\n", - " 167\n", + " 187\n", " \n", " \n", " 1\n", " 2016\n", - " 172\n", + " 189\n", " \n", " \n", " 2\n", " 2016\n", - " 166\n", + " 165\n", " \n", " \n", " 3\n", " 2016\n", - " 182\n", + " 222\n", " \n", " \n", " 4\n", " 2016\n", - " 204\n", + " 164\n", " \n", " \n", " ...\n", @@ -5309,27 +5239,27 @@ " \n", " 2426\n", " 2016\n", - " 199\n", + " 156\n", " \n", " \n", " 2427\n", " 2016\n", - " 181\n", + " 185\n", " \n", " \n", " 2428\n", " 2016\n", - " 205\n", + " 243\n", " \n", " \n", " 2429\n", " 2016\n", - " 203\n", + " 184\n", " \n", " \n", " 2430\n", " 2016\n", - " 182\n", + " 185\n", " \n", " \n", "\n", @@ -5338,22 +5268,22 @@ ], "text/plain": [ " year duration_minutes\n", - "0 2016 167\n", - "1 2016 172\n", - "2 2016 166\n", - "3 2016 182\n", - "4 2016 204\n", + "0 2016 187\n", + "1 2016 189\n", + "2 2016 165\n", + "3 2016 222\n", + "4 2016 164\n", "... ... ...\n", - "2426 2016 199\n", - "2427 2016 181\n", - "2428 2016 205\n", - "2429 2016 203\n", - "2430 2016 182\n", + "2426 2016 156\n", + "2427 2016 185\n", + "2428 2016 243\n", + "2429 2016 184\n", + "2430 2016 185\n", "\n", "[2431 rows x 2 columns]" ] }, - "execution_count": 27, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } @@ -5380,7 +5310,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.1" + "version": "3.10.12" } }, "nbformat": 4, diff --git a/notebooks/dataframes/index_col_null.ipynb b/notebooks/dataframes/index_col_null.ipynb deleted file mode 100644 index f77051e553b..00000000000 --- a/notebooks/dataframes/index_col_null.ipynb +++ /dev/null @@ -1,1401 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "eeec3428", - "metadata": {}, - "outputs": [], - "source": [ - "# Copyright 2023 Google LLC\n", - "#\n", - "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", - "# you may not use this file except in compliance with the License.\n", - "# You may obtain a copy of the License at\n", - "#\n", - "# https://www.apache.org/licenses/LICENSE-2.0\n", - "#\n", - "# Unless required by applicable law or agreed to in writing, software\n", - "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", - "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", - "# See the License for the specific language governing permissions and\n", - "# limitations under the License." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "47439dbd-4e54-4954-8b16-edc4bcd4f855", - "metadata": {}, - "source": [ - "# Operations with an \"NULL index\" DataFrame\n", - "\n", - "**Note**: This notebook describes a feature that is currently in [preview](https://cloud.google.com/blog/products/gcp/google-cloud-gets-simplified-product-launch-stages). There may be breaking changes to the functionality when using \"NULL index\" objects.\n", - "\n", - "Use the \"NULL\" index for more efficient query generation, but\n", - "some pandas-compatible methods may not be possible without an index." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "96757c59-fc22-420e-a42f-c6cb956110ec", - "metadata": {}, - "outputs": [], - "source": [ - "import bigframes.enums\n", - "import bigframes.exceptions\n", - "import bigframes.pandas as bpd\n", - "\n", - "df = bpd.read_gbq(\n", - " \"bigquery-public-data.baseball.schedules\",\n", - " index_col=bigframes.enums.DefaultIndexKind.NULL,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "d15688e1", - "metadata": {}, - "source": [ - "Use `peek()` to view an arbitrary selection of rows from the DataFrame. This is much more efficient than `head()`, which requires a total ordering for determinism." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "c93949fb", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "Query job 1b8726ce-c4ea-47fe-a47c-d6fae50d8fb0 is DONE. 582.8 kB processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " gameId homeTeamName awayTeamName\n", - "0 d60c6036-0ce1-4c90-8dd9-de3b403c92a8 Nationals Brewers\n", - "1 af72a0b9-65f7-49fb-9b30-d505068bdf6d Reds Brewers" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dfp = merged.to_pandas(ordered=False)\n", - "dfp" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "896212ab", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.1" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks/dataframes/integrations.ipynb b/notebooks/dataframes/integrations.ipynb deleted file mode 100644 index 8c7790b1ea0..00000000000 --- a/notebooks/dataframes/integrations.ipynb +++ /dev/null @@ -1,693 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# Copyright 2024 Google LLC\n", - "#\n", - "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", - "# you may not use this file except in compliance with the License.\n", - "# You may obtain a copy of the License at\n", - "#\n", - "# https://www.apache.org/licenses/LICENSE-2.0\n", - "#\n", - "# Unless required by applicable law or agreed to in writing, software\n", - "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", - "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", - "# See the License for the specific language governing permissions and\n", - "# limitations under the License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Integrating with BigQuery DataFrames\n", - "\n", - "This notebook demonstrates operations for building applications that integrate with BigQuery DataFrames. Follow these samples to build an integration that accepts a BigQuery DataFrames object or returns one." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Attributing requests initiated by BigQuery DataFrames\n", - "\n", - "Partners are required to attribute API calls to BigQuery and other Google APIs. Where possible, this should be done via the User-Agent string, but can also be done via job labels if your integration doesn't initialize the BigQuery DataFrames session.\n", - "\n", - "### Setting the User-Agent\n", - "\n", - "Set [`bpd.options.bigquery.application_name`](https://cloud.google.com/python/docs/reference/bigframes/latest/bigframes._config.bigquery_options.BigQueryOptions#bigframes__config_bigquery_options_BigQueryOptions_application_name) to a compliant string. Reach out to your Google Partner Engineering team contact for further instructions." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import bigframes.pandas as bpd\n", - "\n", - "# Set this to the string informed by your Google Partner Engineering team contact.\n", - "# Note: This can only be set once per session, so is most appropriate for partners\n", - "# who provide a Python + BigQuery DataFrames environment to their customers.\n", - "bpd.options.bigquery.application_name = \"notebook-samples/1.0.0 (GPN:notebook-samples)\"" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/swast/src/github.com/googleapis/python-bigquery-dataframes/bigframes/core/global_session.py:103: DefaultLocationWarning: No explicit location is set, so using location US for the session.\n", - " _global_session = bigframes.session.connect(\n" - ] - }, - { - "data": { - "text/html": [ - "Query job 1772ca28-2ef5-425c-87fe-8227aeb9318c is DONE. 0 Bytes processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import bigframes.pandas as bpd\n", - "\n", - "# Sample data\n", - "df = bpd.DataFrame({\n", - " \"index\": [0, 1, 2, 3, 4],\n", - " \"int_col\": [1, 2, 3, 4, 5],\n", - " \"float_col\": [1.0, -0.5, 0.25, -0.125, 0.0625],\n", - " \"string_col\": [\"a\", \"b\", \"c\", \"d\", \"e\"],\n", - "}).set_index(\"index\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Setting the job label\n", - "\n", - "If your application works with customer-created BigQuery DataFrames objects, you might not be able to set the user-agent header because the session has already started (watch https://github.com/googleapis/python-bigquery-dataframes/issues/833 for updates on this limitation). Instead, attach a label to the jobs your application initiates, such as if you are performing `to_gbq()`on an existing DataFrame, as described below.\n", - "\n", - "Use `bpd.option_context()` so that the labels are only set during the operations your application performs." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "Query job 33bd5814-b594-4ec4-baba-8f6b6e285e48 is DONE. 0 Bytes processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "with bpd.option_context(\"compute.extra_query_labels\", {\"application-name\": \"notebook-samples\"}):\n", - " table_id = df.to_gbq()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Accepting a BigQuery DataFrames (bigframes) DataFrame\n", - "\n", - "The recommended serialization format for a BigQuery DataFrames (bigframes) DataFrame is a BigQuery table. To write a DataFrame to a BigQuery table, use the `DataFrame.to_gbq()` method. With no `destination_table`, BigQuery DataFrames creates a table in the anonymous dataset corresponding to the BigQuery user & location and returns the corresponding table ID." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "Query job 1594d97a-1203-4c28-8730-caffb3ac4e9e is DONE. 0 Bytes processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "'bigframes-dev._63cfa399614a54153cc386c27d6c0c6fdb249f9e.bqdf20250530_session9fdc39_7578d5bd9949422599ccb9e4fe6451be'" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "table_id = df.to_gbq()\n", - "table_id" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Sharing the table with your application's backend\n", - "\n", - "Tables created in the user's anonymous dataset are only queryable by the user who created them. Many applications authenticate with a [service account](https://cloud.google.com/iam/docs/service-account-overview), which may be different from the end-user running BigQuery DataFrames (bigframes).\n", - "\n", - "Grant your application access to this table by granting your application's service account associated with the customer the `roles/bigquery.dataViewer` role on the [BigQuery table with an IAM policy](https://cloud.google.com/bigquery/docs/control-access-to-resources-iam#grant_access_to_a_table_or_view)." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "Query job 8afc1538-9779-487a-a063-def5f438ee11 is DONE. 0 Bytes processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " index int_col float_col string_col\n", - "0 1 2 -0.5000 b\n", - "1 2 3 0.2500 c\n", - "2 0 1 1.0000 a\n", - "3 3 4 -0.1250 d\n", - "4 4 5 0.0625 e\n" - ] - } - ], - "source": [ - "# This sample assumes the client code knows which service account to share with.\n", - "your_service_account_email = \"your-service-account@bigframes-samples.iam.gserviceaccount.com\"\n", - "\n", - "\n", - "def df_to_gbq_plus_workoad(df):\n", - " table_id = df.to_gbq()\n", - "\n", - " bqclient = df.bqclient\n", - " policy = bqclient.get_iam_policy(table_id)\n", - " binding = {\n", - " \"role\": \"roles/bigquery.dataViewer\",\n", - " \"members\": {f\"serviceAccount:{your_service_account_email}\"},\n", - " }\n", - " policy.bindings.append(binding)\n", - " bqclient.set_iam_policy(table_id, policy)\n", - "\n", - " # TODO(developer): Pass table_id to your application and start your workload.\n", - " example_workload(table_id)\n", - "\n", - "\n", - "def example_workload(table_id):\n", - " # For example, for one node workloads, use the client library to read the table\n", - " # as a pandas DataFrame.\n", - " from google.cloud import bigquery\n", - "\n", - " # This sample assumes this client is authenticated as the user\n", - " # your_service_account_email.\n", - " client = bigquery.Client()\n", - " pandas_df = client.list_rows(table_id).to_dataframe()\n", - " print(pandas_df)\n", - "\n", - "\n", - "df_to_gbq_plus_workoad(df)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "Query job b6f68a49-5129-448d-bca3-62a23dced10d is DONE. 0 Bytes processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " index int_col float_col string_col\n", - "0 3 4 -0.1250 d\n", - "1 1 2 -0.5000 b\n", - "2 4 5 0.0625 e\n", - "3 2 3 0.2500 c\n", - "4 0 1 1.0000 a\n" - ] - } - ], - "source": [ - "# This sample assumes the client code doesn't know which service account to share with.\n", - "\n", - "\n", - "def df_to_gbq_plus_workoad(df):\n", - " table_id = df.to_gbq()\n", - "\n", - " bqclient = df.bqclient\n", - " token = bqclient._http.credentials.token\n", - " project_id = bqclient.project\n", - "\n", - " share_table_and_start_workload(table_id, token, project_id)\n", - "\n", - "\n", - "def share_table_and_start_workload(table_id, token, project_id):\n", - " # This code runs in the backend for your application.\n", - " from google.cloud import bigquery\n", - " import google.oauth2.credentials\n", - "\n", - " # Note: these credentials don't have any way to be refreshed,\n", - " # so only use them long enough to share the table with the\n", - " # service account.\n", - " credentials = google.oauth2.credentials.Credentials(token)\n", - " bqclient = bigquery.Client(\n", - " project=project_id,\n", - " credentials=credentials,\n", - " )\n", - "\n", - " # This is assumed to only be available on the backend.\n", - " your_service_account_email = \"your-service-account@bigframes-samples.iam.gserviceaccount.com\"\n", - " policy = bqclient.get_iam_policy(table_id)\n", - " binding = {\n", - " \"role\": \"roles/bigquery.dataViewer\",\n", - " \"members\": {f\"serviceAccount:{your_service_account_email}\"},\n", - " }\n", - " policy.bindings.append(binding)\n", - " bqclient.set_iam_policy(table_id, policy)\n", - "\n", - " # Now that the table has been shared, bqclient with the temporary token\n", - " # is no longer needed.\n", - " example_workload(table_id)\n", - "\n", - "\n", - "def example_workload(table_id):\n", - " # For example, for one node workloads, use the client library to read the table\n", - " # as a pandas DataFrame.\n", - " from google.cloud import bigquery\n", - "\n", - " # This sample assumes this client is authenticated as the user\n", - " # your_service_account_email.\n", - " client = bigquery.Client()\n", - " pandas_df = client.list_rows(table_id).to_dataframe()\n", - " print(pandas_df)\n", - "\n", - "\n", - "df_to_gbq_plus_workoad(df)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Preserving order\n", - "\n", - "Depending on your use case, you may want to include the ordering so that it can be restored withing your application." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "Query job 0f205180-cf26-46e5-950d-109947b7f5a1 is DONE. 0 Bytes processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "'bigframes-dev._63cfa399614a54153cc386c27d6c0c6fdb249f9e.bqdf20250530_session9fdc39_240520e0723548f18fd3bd5d24cbbf82'" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ordering_column = \"ordering_id_maybe_with_some_random_text_to_avoid_collisions\"\n", - "table_id = df.to_gbq(ordering_id=ordering_column)\n", - "table_id" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Creating clustered tables\n", - "\n", - "Large tables can be optimized by passing in `clustering_columns` to create a [clustered table](https://cloud.google.com/bigquery/docs/clustered-tables)." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "Query job 80177f9a-4f6e-4a4e-97db-f119ea686c62 is DONE. 0 Bytes processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "'bigframes-dev._63cfa399614a54153cc386c27d6c0c6fdb249f9e.bqdf20250530_session9fdc39_4ca41d2f28f84feca1bbafe9304fd89f'" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "table_id = df.to_gbq(clustering_columns=(\"index\", \"int_col\"))\n", - "table_id" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Returning a BigQuery DataFrames (bigframes) DataFrame\n", - "\n", - "The recommended way to construct a DataFrame is from a BigQuery table which has a unique primary key. By default a primary key is used as the index, which allows for more efficient queries than the default index generation.\n", - "\n", - "This sample assumes there is a shared dataset that\n", - "\n", - "1. The application can write to and\n", - "2. the bigframes user can read from.\n", - "\n", - "There are many ways an application can [write to a BigQuery table](https://cloud.google.com/bigquery/docs/loading-data), including BigQuery load jobs, DML, streaming REST API, and the BigQuery Write API. Each has different costs, performance, and limitations. Choose the one that best suits your application's needs." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Dataset(DatasetReference('bigframes-dev', 'my_dataset'))" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# The assumption is that there is a shared dataset to work with.\n", - "from google.cloud import bigquery\n", - "\n", - "bqclient = bigquery.Client()\n", - "bqclient.create_dataset(\"my_dataset\", exists_ok=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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statepostal_codepop
unique_index
2MI48105669
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[10 rows x 3 columns in total]" - ], - "text/plain": [ - " state postal_code pop\n", - "unique_index \n", - "2 MI 48105 669\n", - "3 GA 30309 2581\n", - "5 TX 78701 5373\n", - "7 CO 80301 2087\n", - "11 MA 02142 2592\n", - "13 IL 60607 2630\n", - "17 MI 48201 2\n", - "19 NC 27701 801\n", - "23 CA 92612 1115\n", - "29 WA 98033 4952\n", - "\n", - "[10 rows x 3 columns]" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# For simplicity, this sample assumes your application uses\n", - "# a load job with the CSV file format.\n", - "# See: https://cloud.google.com/bigquery/docs/loading-data-cloud-storage-csv#python\n", - "import datetime\n", - "import io\n", - "import random\n", - "\n", - "\n", - "def create_table_for_bigframes():\n", - " # This code is assumed to run on the application's backend.\n", - " from google.cloud import bigquery\n", - "\n", - " client = bigquery.Client()\n", - "\n", - " # The end-user is expected to have read access to this table.\n", - " table_suffix = f\"{datetime.datetime.now().strftime('%Y%m%d_%H%M%S_%f')}_{random.randrange(1_000_000)}\"\n", - " table_id = f\"{client.project}.my_dataset.integrations_ipynb_{table_suffix}\"\n", - "\n", - " # Best practice: set the primary key to a unique column to use as the\n", - " # index and default ordering in a BigQuery DataFrames (bigframes) DataFrame.\n", - " # Having a unique identity column allows the DataFrame to be constructed\n", - " # more efficiently.\n", - " #\n", - " # Note 1: Even a random UUID would be helpful for efficiency.\n", - " #\n", - " # Note 2: Don't do this if you can't guarantee uniqueness, as the BigQuery\n", - " # query engine uses this property to optimize queries. Non-unique primary\n", - " # keys result in undefined behavior.\n", - " #\n", - " # Note 3: client.create_table doesn't support primary key, so instead\n", - " # use DDL to create the table.\n", - " create_table_ddl = f\"\"\"\n", - " CREATE OR REPLACE TABLE `{table_id}`\n", - " (\n", - " unique_index INT64,\n", - " state STRING,\n", - " postal_code STRING,\n", - " pop INT64,\n", - " PRIMARY KEY (unique_index) NOT ENFORCED\n", - " )\n", - " -- Clustering by the index column can make joins and loc operations more efficient.\n", - " -- Also cluster by columns which are expected to be used as common filters.\n", - " CLUSTER BY unique_index, state\n", - " \"\"\"\n", - " client.query_and_wait(create_table_ddl)\n", - "\n", - " csv_file = io.BytesIO(\n", - "b\"\"\"unique_index,state,postal_code,pop\n", - "2,MI,48105,669\n", - "3,GA,30309,2581\n", - "5,TX,78701,5373\n", - "7,CO,80301,2087\n", - "11,MA,02142,2592\n", - "13,IL,60607,2630\n", - "17,MI,48201,2\n", - "19,NC,27701,801\n", - "23,CA,92612,1115\n", - "29,WA,98033,4952\n", - "\"\"\"\n", - " )\n", - " job_config = bigquery.LoadJobConfig(\n", - " skip_leading_rows=1,\n", - " source_format=bigquery.SourceFormat.CSV,\n", - " )\n", - " load_job = client.load_table_from_file(\n", - " csv_file, table_id, job_config=job_config\n", - " )\n", - " load_job.result() # Waits for the job to complete.\n", - "\n", - " return table_id\n", - "\n", - "\n", - "table_id = create_table_for_bigframes()\n", - "\n", - "\n", - "# This is assumed to run on the client.\n", - "import bigframes.pandas as bpd\n", - "df = bpd.read_gbq_table(table_id, index_col=[\"unique_index\"])\n", - "df" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.10" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/notebooks/dataframes/magics_with_local_data.ipynb b/notebooks/dataframes/magics_with_local_data.ipynb deleted file mode 100644 index 675ac83988b..00000000000 --- a/notebooks/dataframes/magics_with_local_data.ipynb +++ /dev/null @@ -1,2488 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "id": "c5f9e86e", - "metadata": {}, - "outputs": [], - "source": [ - "# Copyright 2026 Google LLC\n", - "#\n", - "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", - "# you may not use this file except in compliance with the License.\n", - "# You may obtain a copy of the License at\n", - "#\n", - "# https://www.apache.org/licenses/LICENSE-2.0\n", - "#\n", - "# Unless required by applicable law or agreed to in writing, software\n", - "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", - "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", - "# See the License for the specific language governing permissions and\n", - "# limitations under the License." - ] - }, - { - "cell_type": "markdown", - "id": "71383fa0", - "metadata": {}, - "source": [ - "# Unlock SQL and Python interoperability for BigQuery with %%bqsql magic\n", - "\n", - "In this tutorial, you will learn how to seamlessly chain data processing across\n", - "SQL and Python code cells using `%%bqsql` IPython magic and BigQuery DataFrames\n", - "(BigFrames). This interoperability is now available to all Jupyter users,\n", - "whether you're in Colab, JupyterLab, or VS Code. \n", - "\n", - "While we begin by loading a local Excel dataset into a local Pandas DataFrame,\n", - "the main focus is on how you can transition between Pandas' Python-centric API\n", - "and BigQuery's SQL-centric engine. This hybrid workflow combines the best of\n", - "both worlds: the expressive power of SQL for complex transformations and the\n", - "versatile Python ecosystem for visualization and further analysis.\n", - "\n", - "Thanks to open-source packages like Jupyter, Pandas, BigFrames, and the\n", - "[BigQuery sandbox](https://docs.cloud.google.com/bigquery/docs/sandbox), you can\n", - "follow all steps in this guide for free\\* and without a credit card.\n", - "\n", - "_\\*See the [BigQuery sandbox](https://docs.cloud.google.com/bigquery/docs/sandbox) documentation for limitations._\n", - "\n", - "## The %%bqsql Magic\n", - "\n", - "Last year, Google introduced [SQL cells in Colab Enterprise\n", - "notebooks](https://docs.cloud.google.com/colab/docs/sql-cells). Now, with the\n", - "[%%bqsql cell\n", - "magics](https://dataframes.bigquery.dev/notebooks/getting_started/magics.html)\n", - "in BigQuery DataFrames, this same powerful interoperability is available to all\n", - "Jupyter users, whether you're in Colab, JupyterLab, or VS Code. These magics\n", - "allow you to write SQL queries that run directly on local pandas DataFrames,\n", - "BigFrames DataFrames, or BigQuery tables.\n", - "\n", - "\n", - "## Getting Started\n", - "\n", - "To get started,\n", - "\n", - "1. Enable the [BigQuery\n", - " sandbox](https://docs.cloud.google.com/bigquery/docs/sandbox). Make note of your\n", - " Google Cloud project ID.\n", - "\n", - "2. Set up a local Python development environment (see: [Setting up a Python\n", - " development environment](https://docs.cloud.google.com/python/docs/setup)) for\n", - " Google Cloud.\n", - "\n", - "3. Create and activate a venv to isolate Python dependencies.\n", - " On Linux or macOS, use these commands (update to your preferred Python\n", - " version):\n", - "\n", - " ```\n", - " python3.12 -m venv ~/venv\n", - " . ~/venv/bin/activate\n", - " ```\n", - "\n", - "4. Install the Jupyter, bigframes, and python-calamine packages:\n", - "\n", - " ```\n", - " pip install --upgrade jupyterlab bigframes python-calamine\n", - " ```\n", - "\n", - "5. Start Jupyter Lab.\n", - "\n", - " ```\n", - " jupyter lab\n", - " ```\n", - "\n", - "6. Open a web browser to the URL listed in the output. It will be something like\n", - " `http://localhost:8888/lab?token=somesupersecretvaluehere`.\n", - "\n", - "7. Create a new notebook using the Jupyter Lab UI.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d00aeb28", - "metadata": {}, - "outputs": [], - "source": [ - "%pip install python-calamine pandas bigframes" - ] - }, - { - "cell_type": "markdown", - "id": "5ba39d0d", - "metadata": {}, - "source": [ - "## Accessing the Dataset\n", - "\n", - "In this tutorial, you'll analyze the [USDA wheat\n", - "data](https://www.ers.usda.gov/data-products/wheat-data). Use the standard\n", - "`requests` package to download the data to a temporary file, mimicking a typical\n", - "local data analysis workflow.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "fb1dfdc2", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "\n", - "import tempfile\n", - "\n", - "import requests\n", - "\n", - "url = \"https://www.ers.usda.gov/media/5706/wheat-data-all-years.xlsx?v=52690\"\n", - "\n", - "tmp = tempfile.NamedTemporaryFile(delete=True)\n", - "\n", - "with requests.get(url, stream=True) as r:\n", - " r.raise_for_status()\n", - " for chunk in r.iter_content(chunk_size=8192):\n", - " tmp.write(chunk)\n", - "\n", - "tmp.flush()\n", - "tmp.seek(0)" - ] - }, - { - "cell_type": "markdown", - "id": "50f896bb", - "metadata": {}, - "source": [ - "Use the `pyarrow` `dtype_backend` when preparing local Pandas data for SQL\n", - "processing. This ensures more consistent handling of NULL values and seamless\n", - "schema mapping when you hand off the data to the BigQuery SQL engine. For this\n", - "example, read the 'Table05' sheet, which contains annual wheat supply and\n", - "disappearance data:\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "8a8a137b", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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Marketing year 1/Time periodBeginning stocksProductionImports 2/Total supply 3/Food useSeed useFeed and residual useTotal domestic use 3/Exports 2/Total disappearance 3/Ending stocks
01950/51MY Jun-May496.01019.011.01526.0580.0--109.0689.0345.01034.0492.0
11951/52MY Jun-May492.0988.030.01510.0585.0--110.0695.0485.01180.0330.0
21952/53MY Jun-May330.01306.024.01660.0578.0--78.0656.0332.0988.0672.0
31953/54MY Jun-May672.01173.06.01851.0556.0--87.0643.0214.0857.0994.0
41954/55MY Jun-May994.0984.03.01981.0552.0--53.0605.0267.0872.01109.0
..........................................
2811/ June–May. Latest data may be preliminary or...<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
2822/ Includes flour and selected other products ...<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
2833/ Totals may not add due to rounding.<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
284Source: USDA, Economic Research Service, based...<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
285Updated: May 12, 2026<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
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286 rows × 13 columns

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" - ], - "text/plain": [ - " Marketing year 1/ Time period \\\n", - "0 1950/51 MY Jun-May \n", - "1 1951/52 MY Jun-May \n", - "2 1952/53 MY Jun-May \n", - "3 1953/54 MY Jun-May \n", - "4 1954/55 MY Jun-May \n", - ".. ... ... \n", - "281 1/ June–May. Latest data may be preliminary or... \n", - "282 2/ Includes flour and selected other products ... \n", - "283 3/ Totals may not add due to rounding. \n", - "284 Source: USDA, Economic Research Service, based... \n", - "285 Updated: May 12, 2026 \n", - "\n", - " Beginning stocks Production Imports 2/ Total supply 3/ Food use \\\n", - "0 496.0 1019.0 11.0 1526.0 580.0 \n", - "1 492.0 988.0 30.0 1510.0 585.0 \n", - "2 330.0 1306.0 24.0 1660.0 578.0 \n", - "3 672.0 1173.0 6.0 1851.0 556.0 \n", - "4 994.0 984.0 3.0 1981.0 552.0 \n", - ".. ... ... ... ... ... \n", - "281 \n", - "282 \n", - "283 \n", - "284 \n", - "285 \n", - "\n", - " Seed use Feed and residual use Total domestic use 3/ Exports 2/ \\\n", - "0 -- 109.0 689.0 345.0 \n", - "1 -- 110.0 695.0 485.0 \n", - "2 -- 78.0 656.0 332.0 \n", - "3 -- 87.0 643.0 214.0 \n", - "4 -- 53.0 605.0 267.0 \n", - ".. ... ... ... ... \n", - "281 \n", - "282 \n", - "283 \n", - "284 \n", - "285 \n", - "\n", - " Total disappearance 3/ Ending stocks \n", - "0 1034.0 492.0 \n", - "1 1180.0 330.0 \n", - "2 988.0 672.0 \n", - "3 857.0 994.0 \n", - "4 872.0 1109.0 \n", - ".. ... ... \n", - "281 \n", - "282 \n", - "283 \n", - "284 \n", - "285 \n", - "\n", - "[286 rows x 13 columns]" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "\n", - "import pandas as pd\n", - "\n", - "df = pd.read_excel(\n", - " tmp,\n", - " sheet_name=\"Table05\",\n", - " dtype_backend=\"pyarrow\",\n", - " engine=\"calamine\",\n", - " header=1, # Skip the first row.\n", - ")\n", - "tmp.close()\n", - "df" - ] - }, - { - "cell_type": "markdown", - "id": "1a7ec573", - "metadata": {}, - "source": [ - "## Preparing the data\n", - "\n", - "Before querying the local DataFrame with SQL, ensure that the column names are\n", - "SQL-friendly. BigQuery supports [flexible column\n", - "names](https://docs.cloud.google.com/bigquery/docs/schemas#flexible-column-names),\n", - "allowing most unicode characters, but special characters like \"/\" and \"\\\" must\n", - "be removed or replaced.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "d5674020", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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Marketing year 1Time periodBeginning stocksProductionImports 2Total supply 3Food useSeed useFeed and residual useTotal domestic use 3Exports 2Total disappearance 3Ending stocks
01950/51MY Jun-May496.01019.011.01526.0580.0--109.0689.0345.01034.0492.0
11951/52MY Jun-May492.0988.030.01510.0585.0--110.0695.0485.01180.0330.0
21952/53MY Jun-May330.01306.024.01660.0578.0--78.0656.0332.0988.0672.0
31953/54MY Jun-May672.01173.06.01851.0556.0--87.0643.0214.0857.0994.0
41954/55MY Jun-May994.0984.03.01981.0552.0--53.0605.0267.0872.01109.0
..........................................
2811/ June–May. Latest data may be preliminary or...<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
2822/ Includes flour and selected other products ...<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
2833/ Totals may not add due to rounding.<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
284Source: USDA, Economic Research Service, based...<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
285Updated: May 12, 2026<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
\n", - "

286 rows × 13 columns

\n", - "
" - ], - "text/plain": [ - " Marketing year 1 Time period \\\n", - "0 1950/51 MY Jun-May \n", - "1 1951/52 MY Jun-May \n", - "2 1952/53 MY Jun-May \n", - "3 1953/54 MY Jun-May \n", - "4 1954/55 MY Jun-May \n", - ".. ... ... \n", - "281 1/ June–May. Latest data may be preliminary or... \n", - "282 2/ Includes flour and selected other products ... \n", - "283 3/ Totals may not add due to rounding. \n", - "284 Source: USDA, Economic Research Service, based... \n", - "285 Updated: May 12, 2026 \n", - "\n", - " Beginning stocks Production Imports 2 Total supply 3 Food use \\\n", - "0 496.0 1019.0 11.0 1526.0 580.0 \n", - "1 492.0 988.0 30.0 1510.0 585.0 \n", - "2 330.0 1306.0 24.0 1660.0 578.0 \n", - "3 672.0 1173.0 6.0 1851.0 556.0 \n", - "4 994.0 984.0 3.0 1981.0 552.0 \n", - ".. ... ... ... ... ... \n", - "281 \n", - "282 \n", - "283 \n", - "284 \n", - "285 \n", - "\n", - " Seed use Feed and residual use Total domestic use 3 Exports 2 \\\n", - "0 -- 109.0 689.0 345.0 \n", - "1 -- 110.0 695.0 485.0 \n", - "2 -- 78.0 656.0 332.0 \n", - "3 -- 87.0 643.0 214.0 \n", - "4 -- 53.0 605.0 267.0 \n", - ".. ... ... ... ... \n", - "281 \n", - "282 \n", - "283 \n", - "284 \n", - "285 \n", - "\n", - " Total disappearance 3 Ending stocks \n", - "0 1034.0 492.0 \n", - "1 1180.0 330.0 \n", - "2 988.0 672.0 \n", - "3 857.0 994.0 \n", - "4 872.0 1109.0 \n", - ".. ... ... \n", - "281 \n", - "282 \n", - "283 \n", - "284 \n", - "285 \n", - "\n", - "[286 rows x 13 columns]" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.columns = [name.replace(\"/\", \"\") for name in df.columns]\n", - "df" - ] - }, - { - "cell_type": "markdown", - "id": "b50c5798", - "metadata": {}, - "source": [ - "## Filtering with Pandas\n", - "\n", - "Perform a basic filter using standard Python/Pandas syntax to remove rows with missing data. This represents the initial Python-only stage of a processing chain.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "1dbad481", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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Marketing year 1Time periodBeginning stocksProductionImports 2Total supply 3Food useSeed useFeed and residual useTotal domestic use 3Exports 2Total disappearance 3Ending stocks
01950/51MY Jun-May496.01019.011.01526.0580.0--109.0689.0345.01034.0492.0
11951/52MY Jun-May492.0988.030.01510.0585.0--110.0695.0485.01180.0330.0
21952/53MY Jun-May330.01306.024.01660.0578.0--78.0656.0332.0988.0672.0
31953/54MY Jun-May672.01173.06.01851.0556.0--87.0643.0214.0857.0994.0
41954/55MY Jun-May994.0984.03.01981.0552.0--53.0605.0267.0872.01109.0
..........................................
2752025/26MY Jun-May854.7341984.537125.02964.271960.059.7100.01119.7910.02029.7934.571
2762025/26Q1 Jun-Aug854.7341984.53730.5932869.864241.0912.653239.522483.266252.58735.8462134.018
2772025/26Q2 Sep-Nov2134.0180.030.0782164.096245.5839.658-54.047231.191255.802486.9931677.103
2782025/26Q3 Dec-Feb1677.1030.032.3631709.466230.9751.75-24.747207.978201.291409.2691300.197
2792026/27MY Jun-May934.5711561.322140.02635.893960.05980.01099.0775.01874.0761.893
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280 rows × 13 columns

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" - ], - "text/plain": [ - " Marketing year 1 Time period Beginning stocks Production Imports 2 \\\n", - "0 1950/51 MY Jun-May 496.0 1019.0 11.0 \n", - "1 1951/52 MY Jun-May 492.0 988.0 30.0 \n", - "2 1952/53 MY Jun-May 330.0 1306.0 24.0 \n", - "3 1953/54 MY Jun-May 672.0 1173.0 6.0 \n", - "4 1954/55 MY Jun-May 994.0 984.0 3.0 \n", - ".. ... ... ... ... ... \n", - "275 2025/26 MY Jun-May 854.734 1984.537 125.0 \n", - "276 2025/26 Q1 Jun-Aug 854.734 1984.537 30.593 \n", - "277 2025/26 Q2 Sep-Nov 2134.018 0.0 30.078 \n", - "278 2025/26 Q3 Dec-Feb 1677.103 0.0 32.363 \n", - "279 2026/27 MY Jun-May 934.571 1561.322 140.0 \n", - "\n", - " Total supply 3 Food use Seed use Feed and residual use \\\n", - "0 1526.0 580.0 -- 109.0 \n", - "1 1510.0 585.0 -- 110.0 \n", - "2 1660.0 578.0 -- 78.0 \n", - "3 1851.0 556.0 -- 87.0 \n", - "4 1981.0 552.0 -- 53.0 \n", - ".. ... ... ... ... \n", - "275 2964.271 960.0 59.7 100.0 \n", - "276 2869.864 241.091 2.653 239.522 \n", - "277 2164.096 245.58 39.658 -54.047 \n", - "278 1709.466 230.975 1.75 -24.747 \n", - "279 2635.893 960.0 59 80.0 \n", - "\n", - " Total domestic use 3 Exports 2 Total disappearance 3 Ending stocks \n", - "0 689.0 345.0 1034.0 492.0 \n", - "1 695.0 485.0 1180.0 330.0 \n", - "2 656.0 332.0 988.0 672.0 \n", - "3 643.0 214.0 857.0 994.0 \n", - "4 605.0 267.0 872.0 1109.0 \n", - ".. ... ... ... ... \n", - "275 1119.7 910.0 2029.7 934.571 \n", - "276 483.266 252.58 735.846 2134.018 \n", - "277 231.191 255.802 486.993 1677.103 \n", - "278 207.978 201.291 409.269 1300.197 \n", - "279 1099.0 775.0 1874.0 761.893 \n", - "\n", - "[280 rows x 13 columns]" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "full_rows = df[~df['Beginning stocks'].isna()]\n", - "full_rows" - ] - }, - { - "cell_type": "markdown", - "id": "e914ce69", - "metadata": {}, - "source": [ - "## Interoperate with SQL using the BigQuery SQL magics (%%bqsql)\n", - "\n", - "The BigQuery DataFrames library provides the `%%bqsql` magic, which acts as the bridge between your Python and SQL environments. It allows the BigQuery query engine to directly reference and query your local Pandas DataFrames (by implicitly uploading them as temporary tables) as well as actual BigQuery tables and external tables in GCS (Parquet, Iceberg, CSV).\n", - "\n", - "To enable this integration in your notebook, load the `bigframes` extension. This is already completed in BigQuery Studio, Colab Enterprise, and Colab notebooks. For other environments, such as VS Code and Jupyter Lab, run the following cell:\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "3d837a5e", - "metadata": {}, - "outputs": [], - "source": [ - "%load_ext bigframes\n" - ] - }, - { - "cell_type": "markdown", - "id": "315a53b5", - "metadata": {}, - "source": [ - "To ensure the correct Google Cloud project is billed for query usage, including free tier usage, configure the project ID used by the magics. Even in the free sandbox tier, a project ID is required to allocate query resources. If you don't set it explicitly, BigFrames will try to discover it from your environment (e.g., your Application Default Credentials).\n" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "ffe5757c", - "metadata": {}, - "outputs": [], - "source": [ - "import bigframes.pandas as bpd\n", - "\n", - "PROJECT_ID = \"\" # @param {type:\"string\"}\n", - "bpd.options.bigquery.project = PROJECT_ID\n" - ] - }, - { - "cell_type": "markdown", - "id": "fe174ed2", - "metadata": {}, - "source": [ - "### Querying Local Pandas DataFrames with SQL\n", - "\n", - "With the project configured, you can now run SQL queries directly against your local Pandas DataFrame (`full_rows`) as if it were a table in BigQuery. Simply reference the variable name inside braces `{full_rows}` in your SQL query.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "fbbf52d6", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " Query processed 0 Bytes. [Job bigframes-dev:US.c3c67902-6a45-492a-9491-a91daddaada1 details]\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Load job c22ec1ce-09da-4ea1-b0a0-f28eee65aa20 is DONE. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - " Query processed 30.0 kB in a moment of slot time.\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
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Marketing year 1Time periodBeginning stocksProductionImports 2Total supply 3Food useSeed useFeed and residual useTotal domestic use 3Exports 2Total disappearance 3Ending stocks
01980/81Q2 Sep-Nov2714.00.00.62714.6162.1764.865242.965379.335622.32092.3
11987/88Q2 Sep-Nov2976.4620.04.5252980.987193.04858-79.082171.966308.453480.4192500.568
22014/15Q2 Sep-Nov1907.220.034.5511941.771248.18748.802-92.585204.404207.737412.1411529.63
31976/77Q2 Sep-Nov2385.20.00.52385.7153.064-2.795214.205277.295491.51894.2
41994/95Q2 Sep-Nov2069.4940.021.4232090.917229.29760.954-28.64261.611338.202599.8131491.104
52002/03Q2 Sep-Nov1748.9870.023.0871772.074237.75454.599-74.678217.675234.53452.2051319.869
62007/08Q2 Sep-Nov1716.9270.021.4861738.413245.02659.915-119.882185.059421.416606.4751131.938
72025/26Q2 Sep-Nov2134.0180.030.0782164.096245.5839.658-54.047231.191255.802486.9931677.103
81995/96Q2 Sep-Nov1881.0990.016.2521897.351232.15164.356-98.182198.325360.759559.0841338.267
92001/02Q2 Sep-Nov2155.8140.029.042184.854245.08851.601-23.073273.616287.783561.3991623.455
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[280 rows x 13 columns in total]" - ], - "text/plain": [ - " Marketing year 1 Time period Beginning stocks Production Imports 2 \\\n", - "0 1980/81 Q2 Sep-Nov 2714.0 0.0 0.6 \n", - "1 1987/88 Q2 Sep-Nov 2976.462 0.0 4.525 \n", - "2 2014/15 Q2 Sep-Nov 1907.22 0.0 34.551 \n", - "3 1976/77 Q2 Sep-Nov 2385.2 0.0 0.5 \n", - "4 1994/95 Q2 Sep-Nov 2069.494 0.0 21.423 \n", - "5 2002/03 Q2 Sep-Nov 1748.987 0.0 23.087 \n", - "6 2007/08 Q2 Sep-Nov 1716.927 0.0 21.486 \n", - "7 2025/26 Q2 Sep-Nov 2134.018 0.0 30.078 \n", - "8 1995/96 Q2 Sep-Nov 1881.099 0.0 16.252 \n", - "9 2001/02 Q2 Sep-Nov 2155.814 0.0 29.04 \n", - "\n", - " Total supply 3 Food use Seed use Feed and residual use \\\n", - "0 2714.6 162.1 76 4.865 \n", - "1 2980.987 193.048 58 -79.082 \n", - "2 1941.771 248.187 48.802 -92.585 \n", - "3 2385.7 153.0 64 -2.795 \n", - "4 2090.917 229.297 60.954 -28.64 \n", - "5 1772.074 237.754 54.599 -74.678 \n", - "6 1738.413 245.026 59.915 -119.882 \n", - "7 2164.096 245.58 39.658 -54.047 \n", - "8 1897.351 232.151 64.356 -98.182 \n", - "9 2184.854 245.088 51.601 -23.073 \n", - "\n", - " Total domestic use 3 Exports 2 Total disappearance 3 Ending stocks \n", - "0 242.965 379.335 622.3 2092.3 \n", - "1 171.966 308.453 480.419 2500.568 \n", - "2 204.404 207.737 412.141 1529.63 \n", - "3 214.205 277.295 491.5 1894.2 \n", - "4 261.611 338.202 599.813 1491.104 \n", - "5 217.675 234.53 452.205 1319.869 \n", - "6 185.059 421.416 606.475 1131.938 \n", - "7 231.191 255.802 486.993 1677.103 \n", - "8 198.325 360.759 559.084 1338.267 \n", - "9 273.616 287.783 561.399 1623.455 \n", - "...\n", - "\n", - "[280 rows x 13 columns]" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "%%bqsql\n", - "SELECT * FROM {full_rows}\n" - ] - }, - { - "cell_type": "markdown", - "id": "2fcd5284", - "metadata": {}, - "source": [ - "You should see the results from full_rows.\n", - "\n", - "\n", - "## Chaining SQL and Python: Saving SQL Results\n", - "\n", - "The true power of the `%%bqsql` magic lies in chaining. By providing a destination variable name as an argument to `%%bqsql` (e.g., `%%bqsql destination_var`), the query result is saved as a BigQuery DataFrame (a.k.a. BigFrames DataFrame) to that variable. \n", - "\n", - "This DataFrame lives on the BigQuery engine but behaves like a Pandas DataFrame in Python. You can immediately use it in subsequent Python cells, or reference it again in another SQL cell. This allows you to build a multi-step, hybrid processing pipeline.\n", - "\n", - "Filter the data to only yearly entries using SQL, and save the result into a new BigFrames DataFrame named `yearly`:\n" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "75fe0e10", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " Query processed 0 Bytes in a moment of slot time. [Job bigframes-dev:US.71850fc1-147f-44f7-b4c0-b94592f55639 details]\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Load job aaa74c26-b3ff-422f-a670-93222188fe9f is DONE. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - " Query processed 30.0 kB in a moment of slot time.\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
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Marketing year 1Time periodBeginning stocksProductionImports 2Total supply 3Food useSeed useFeed and residual useTotal domestic use 3Exports 2Total disappearance 3Ending stocks
01955/56MY Jun-May1109.0937.010.02056.0553.0--51.0604.0322.0926.01130.0
11957/58MY Jun-May1004.0956.010.01970.0547.0--43.0590.0418.01008.0962.0
21954/55MY Jun-May994.0984.03.01981.0552.0--53.0605.0267.0872.01109.0
31951/52MY Jun-May492.0988.030.01510.0585.0--110.0695.0485.01180.0330.0
41956/57MY Jun-May1130.01005.08.02143.0541.0--57.0598.0541.01139.01004.0
51950/51MY Jun-May496.01019.011.01526.0580.0--109.0689.0345.01034.0492.0
61962/63MY Jun-May1420.61092.05.32517.9502.761.434.7598.8649.41248.21269.7
71959/60MY Jun-May1368.01118.07.02493.0558.0--49.0607.0502.01109.01384.0
81963/64MY Jun-May1269.71146.84.02420.5487.964.928.6581.4845.61427.0993.5
91953/54MY Jun-May672.01173.06.01851.0556.0--87.0643.0214.0857.0994.0
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[77 rows x 13 columns in total]" - ], - "text/plain": [ - " Marketing year 1 Time period Beginning stocks Production Imports 2 \\\n", - "0 1955/56 MY Jun-May 1109.0 937.0 10.0 \n", - "1 1957/58 MY Jun-May 1004.0 956.0 10.0 \n", - "2 1954/55 MY Jun-May 994.0 984.0 3.0 \n", - "3 1951/52 MY Jun-May 492.0 988.0 30.0 \n", - "4 1956/57 MY Jun-May 1130.0 1005.0 8.0 \n", - "5 1950/51 MY Jun-May 496.0 1019.0 11.0 \n", - "6 1962/63 MY Jun-May 1420.6 1092.0 5.3 \n", - "7 1959/60 MY Jun-May 1368.0 1118.0 7.0 \n", - "8 1963/64 MY Jun-May 1269.7 1146.8 4.0 \n", - "9 1953/54 MY Jun-May 672.0 1173.0 6.0 \n", - "\n", - " Total supply 3 Food use Seed use Feed and residual use \\\n", - "0 2056.0 553.0 -- 51.0 \n", - "1 1970.0 547.0 -- 43.0 \n", - "2 1981.0 552.0 -- 53.0 \n", - "3 1510.0 585.0 -- 110.0 \n", - "4 2143.0 541.0 -- 57.0 \n", - "5 1526.0 580.0 -- 109.0 \n", - "6 2517.9 502.7 61.4 34.7 \n", - "7 2493.0 558.0 -- 49.0 \n", - "8 2420.5 487.9 64.9 28.6 \n", - "9 1851.0 556.0 -- 87.0 \n", - "\n", - " Total domestic use 3 Exports 2 Total disappearance 3 Ending stocks \n", - "0 604.0 322.0 926.0 1130.0 \n", - "1 590.0 418.0 1008.0 962.0 \n", - "2 605.0 267.0 872.0 1109.0 \n", - "3 695.0 485.0 1180.0 330.0 \n", - "4 598.0 541.0 1139.0 1004.0 \n", - "5 689.0 345.0 1034.0 492.0 \n", - "6 598.8 649.4 1248.2 1269.7 \n", - "7 607.0 502.0 1109.0 1384.0 \n", - "8 581.4 845.6 1427.0 993.5 \n", - "9 643.0 214.0 857.0 994.0 \n", - "...\n", - "\n", - "[77 rows x 13 columns]" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "%%bqsql yearly\n", - "SELECT *\n", - "FROM {full_rows}\n", - "WHERE STARTS_WITH(`Time period`, 'MY')\n" - ] - }, - { - "cell_type": "markdown", - "id": "19a70e9e", - "metadata": {}, - "source": [ - "### Chaining Step 2: Complex SQL Transformation on the BigFrames DataFrame\n", - "\n", - "Now, you can chain another SQL operation. Reference the `yearly` BigFrames DataFrame that you just created, extract the year using SQL regular expressions, cast it to a timestamp, and save the results into a new BigFrames DataFrame named `timeseries`.\n", - "\n", - "Notice how you are building a chain: Local Pandas -> [SQL filter] -> BigFrames `yearly` -> [SQL transform] -> BigFrames `timeseries`.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "8fbb5224", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " Query processed 0 Bytes in a moment of slot time. [Job bigframes-dev:US.fdcdabc9-e1a3-47e6-ab27-9e19d9aaa106 details]\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Load job ec6be16a-722d-4151-b7a6-5e410557577d is DONE. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - " Query processed 8.3 kB in a moment of slot time.\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
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Time periodBeginning stocksProductionImports 2Total supply 3Food useSeed useFeed and residual useTotal domestic use 3Exports 2Total disappearance 3Ending stocksyear
0MY Jun-May1004.0956.010.01970.0547.0--43.0590.0418.01008.0962.01957-01-01 00:00:00+00:00
1MY Jun-May672.01173.06.01851.0556.0--87.0643.0214.0857.0994.01953-01-01 00:00:00+00:00
2MY Jun-May496.01019.011.01526.0580.0--109.0689.0345.01034.0492.01950-01-01 00:00:00+00:00
3MY Jun-May330.01306.024.01660.0578.0--78.0656.0332.0988.0672.01952-01-01 00:00:00+00:00
4MY Jun-May962.01457.08.02427.0561.0--48.0609.0450.01059.01368.01958-01-01 00:00:00+00:00
5MY Jun-May994.0984.03.01981.0552.0--53.0605.0267.0872.01109.01954-01-01 00:00:00+00:00
6MY Jun-May492.0988.030.01510.0585.0--110.0695.0485.01180.0330.01951-01-01 00:00:00+00:00
7MY Jun-May1130.01005.08.02143.0541.0--57.0598.0541.01139.01004.01956-01-01 00:00:00+00:00
8MY Jun-May1109.0937.010.02056.0553.0--51.0604.0322.0926.01130.01955-01-01 00:00:00+00:00
9MY Jun-May1368.01118.07.02493.0558.0--49.0607.0502.01109.01384.01959-01-01 00:00:00+00:00
\n", - "

10 rows × 13 columns

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[77 rows x 13 columns in total]" - ], - "text/plain": [ - " Time period Beginning stocks Production Imports 2 Total supply 3 \\\n", - "0 MY Jun-May 1004.0 956.0 10.0 1970.0 \n", - "1 MY Jun-May 672.0 1173.0 6.0 1851.0 \n", - "2 MY Jun-May 496.0 1019.0 11.0 1526.0 \n", - "3 MY Jun-May 330.0 1306.0 24.0 1660.0 \n", - "4 MY Jun-May 962.0 1457.0 8.0 2427.0 \n", - "5 MY Jun-May 994.0 984.0 3.0 1981.0 \n", - "6 MY Jun-May 492.0 988.0 30.0 1510.0 \n", - "7 MY Jun-May 1130.0 1005.0 8.0 2143.0 \n", - "8 MY Jun-May 1109.0 937.0 10.0 2056.0 \n", - "9 MY Jun-May 1368.0 1118.0 7.0 2493.0 \n", - "\n", - " Food use Seed use Feed and residual use Total domestic use 3 Exports 2 \\\n", - "0 547.0 -- 43.0 590.0 418.0 \n", - "1 556.0 -- 87.0 643.0 214.0 \n", - "2 580.0 -- 109.0 689.0 345.0 \n", - "3 578.0 -- 78.0 656.0 332.0 \n", - "4 561.0 -- 48.0 609.0 450.0 \n", - "5 552.0 -- 53.0 605.0 267.0 \n", - "6 585.0 -- 110.0 695.0 485.0 \n", - "7 541.0 -- 57.0 598.0 541.0 \n", - "8 553.0 -- 51.0 604.0 322.0 \n", - "9 558.0 -- 49.0 607.0 502.0 \n", - "\n", - " Total disappearance 3 Ending stocks year \n", - "0 1008.0 962.0 1957-01-01 00:00:00+00:00 \n", - "1 857.0 994.0 1953-01-01 00:00:00+00:00 \n", - "2 1034.0 492.0 1950-01-01 00:00:00+00:00 \n", - "3 988.0 672.0 1952-01-01 00:00:00+00:00 \n", - "4 1059.0 1368.0 1958-01-01 00:00:00+00:00 \n", - "5 872.0 1109.0 1954-01-01 00:00:00+00:00 \n", - "6 1180.0 330.0 1951-01-01 00:00:00+00:00 \n", - "7 1139.0 1004.0 1956-01-01 00:00:00+00:00 \n", - "8 926.0 1130.0 1955-01-01 00:00:00+00:00 \n", - "9 1109.0 1384.0 1959-01-01 00:00:00+00:00 \n", - "...\n", - "\n", - "[77 rows x 13 columns]" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "%%bqsql timeseries\n", - "SELECT\n", - " * EXCEPT (`Marketing year 1`),\n", - " TIMESTAMP(CONCAT(\n", - " REGEXP_EXTRACT(`Marketing year 1`, r'([0-9]+)\\/'),\n", - " '-01-01')) AS `year`\n", - "FROM {yearly}\n" - ] - }, - { - "cell_type": "markdown", - "id": "76ba8a7d", - "metadata": {}, - "source": [ - "## Chaining Back to Python: Visualizing BigFrames Data\n", - "\n", - "Now that you've completed some SQL transformations, you can chain back to Python for visualization. Because BigFrames DataFrames implement the Pandas API, you can call standard visualization methods (like `.plot.line()`) directly on the `timeseries` DataFrame without downloading the full dataset first. The computations happen in BigQuery, and only the summarized chart data is sent back to the notebook.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "d3ff4eec", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " Query processed 8.8 kB in a moment of slot time.\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Load job c6b4d65a-4555-4efc-9f8b-7156f4c62835 is DONE. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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Time periodBeginning stocksProductionImports 2Total supply 3Food useSeed useFeed and residual useTotal domestic use 3Exports 2Total disappearance 3Ending stocks
year
1950-01-01 00:00:00+00:00MY Jun-May496.01019.011.01526.0580.0--109.0689.0345.01034.0492.0
1951-01-01 00:00:00+00:00MY Jun-May492.0988.030.01510.0585.0--110.0695.0485.01180.0330.0
1952-01-01 00:00:00+00:00MY Jun-May330.01306.024.01660.0578.0--78.0656.0332.0988.0672.0
1953-01-01 00:00:00+00:00MY Jun-May672.01173.06.01851.0556.0--87.0643.0214.0857.0994.0
1954-01-01 00:00:00+00:00MY Jun-May994.0984.03.01981.0552.0--53.0605.0267.0872.01109.0
.......................................
2022-01-01 00:00:00+00:00MY Jun-May674.4311649.713121.5852445.729971.67768.36975.5031115.549760.6121876.161569.568
2023-01-01 00:00:00+00:00MY Jun-May569.5681803.942137.7982511.308961.30362.04685.6171108.966705.9081814.874696.434
2024-01-01 00:00:00+00:00MY Jun-May696.4341978.697148.9542824.085969.49361.1112.8631143.456825.8951969.351854.734
2025-01-01 00:00:00+00:00MY Jun-May854.7341984.537125.02964.271960.059.7100.01119.7910.02029.7934.571
2026-01-01 00:00:00+00:00MY Jun-May934.5711561.322140.02635.893960.05980.01099.0775.01874.0761.893
\n", - "

77 rows × 12 columns

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" - ], - "text/plain": [ - " Time period Beginning stocks Production \\\n", - "year \n", - "1950-01-01 00:00:00+00:00 MY Jun-May 496.0 1019.0 \n", - "1951-01-01 00:00:00+00:00 MY Jun-May 492.0 988.0 \n", - "1952-01-01 00:00:00+00:00 MY Jun-May 330.0 1306.0 \n", - "1953-01-01 00:00:00+00:00 MY Jun-May 672.0 1173.0 \n", - "1954-01-01 00:00:00+00:00 MY Jun-May 994.0 984.0 \n", - "... ... ... ... \n", - "2022-01-01 00:00:00+00:00 MY Jun-May 674.431 1649.713 \n", - "2023-01-01 00:00:00+00:00 MY Jun-May 569.568 1803.942 \n", - "2024-01-01 00:00:00+00:00 MY Jun-May 696.434 1978.697 \n", - "2025-01-01 00:00:00+00:00 MY Jun-May 854.734 1984.537 \n", - "2026-01-01 00:00:00+00:00 MY Jun-May 934.571 1561.322 \n", - "\n", - " Imports 2 Total supply 3 Food use Seed use \\\n", - "year \n", - "1950-01-01 00:00:00+00:00 11.0 1526.0 580.0 -- \n", - "1951-01-01 00:00:00+00:00 30.0 1510.0 585.0 -- \n", - "1952-01-01 00:00:00+00:00 24.0 1660.0 578.0 -- \n", - "1953-01-01 00:00:00+00:00 6.0 1851.0 556.0 -- \n", - "1954-01-01 00:00:00+00:00 3.0 1981.0 552.0 -- \n", - "... ... ... ... ... \n", - "2022-01-01 00:00:00+00:00 121.585 2445.729 971.677 68.369 \n", - "2023-01-01 00:00:00+00:00 137.798 2511.308 961.303 62.046 \n", - "2024-01-01 00:00:00+00:00 148.954 2824.085 969.493 61.1 \n", - "2025-01-01 00:00:00+00:00 125.0 2964.271 960.0 59.7 \n", - "2026-01-01 00:00:00+00:00 140.0 2635.893 960.0 59 \n", - "\n", - " Feed and residual use Total domestic use 3 \\\n", - "year \n", - "1950-01-01 00:00:00+00:00 109.0 689.0 \n", - "1951-01-01 00:00:00+00:00 110.0 695.0 \n", - "1952-01-01 00:00:00+00:00 78.0 656.0 \n", - "1953-01-01 00:00:00+00:00 87.0 643.0 \n", - "1954-01-01 00:00:00+00:00 53.0 605.0 \n", - "... ... ... \n", - "2022-01-01 00:00:00+00:00 75.503 1115.549 \n", - "2023-01-01 00:00:00+00:00 85.617 1108.966 \n", - "2024-01-01 00:00:00+00:00 112.863 1143.456 \n", - "2025-01-01 00:00:00+00:00 100.0 1119.7 \n", - "2026-01-01 00:00:00+00:00 80.0 1099.0 \n", - "\n", - " Exports 2 Total disappearance 3 Ending stocks \n", - "year \n", - "1950-01-01 00:00:00+00:00 345.0 1034.0 492.0 \n", - "1951-01-01 00:00:00+00:00 485.0 1180.0 330.0 \n", - "1952-01-01 00:00:00+00:00 332.0 988.0 672.0 \n", - "1953-01-01 00:00:00+00:00 214.0 857.0 994.0 \n", - "1954-01-01 00:00:00+00:00 267.0 872.0 1109.0 \n", - "... ... ... ... \n", - "2022-01-01 00:00:00+00:00 760.612 1876.161 569.568 \n", - "2023-01-01 00:00:00+00:00 705.908 1814.874 696.434 \n", - "2024-01-01 00:00:00+00:00 825.895 1969.351 854.734 \n", - "2025-01-01 00:00:00+00:00 910.0 2029.7 934.571 \n", - "2026-01-01 00:00:00+00:00 775.0 1874.0 761.893 \n", - "\n", - "[77 rows x 12 columns]" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pddf = timeseries.set_index('year').sort_index().to_pandas()\n", - "pddf\n" - ] - }, - { - "cell_type": "markdown", - "id": "9c1242ab", - "metadata": {}, - "source": [ - "## Conclusion: The Power of Hybrid Chaining\n", - "\n", - "By leveraging BigQuery DataFrames and the `%%bqsql` magic, you have built a powerful, interoperable pipeline that seamlessly transitions between SQL and Python.\n", - "\n", - "This hybrid approach offers several key benefits:\n", - "- **Optimal Tool Selection**: Use SQL for what it does best (complex queries, window functions, regex extractions on large sets) and Python for what it does best (visualization, statistical analysis, ML, orchestrating workflow).\n", - "- **Improved Readability**: Instead of massive, unreadable SQL queries with dozens of CTEs, or long, complex Pandas method chains, you can split your pipeline into logical steps, alternating between SQL and Python.\n", - "- **Seamless Scaling**: The exact same `%%bqsql` code can scale from a tiny local Pandas DataFrame to billions of rows in a production BigQuery table. You only need to swap the initial local Pandas DataFrame with a BigQuery DataFrame reference.\n", - "\n", - "\n", - "## Next Steps\n", - "\n", - "In addition to the `%%bqsql` cell magic, BigFrames also registers a **BigQuery Accessor** on standard Pandas DataFrames, allowing you to run SQL scalar functions directly on local pandas data. \n", - "\n", - "For example, you can call powerful Google Cloud community UDFs from [BigQuery Utils](https://github.com/GoogleCloudPlatform/bigquery-utils/tree/master/udfs#bigquery-udfs), [BigFunctions](https://unytics.io/bigfunctions/bigfunctions/#function-categories), or [CARTO Analytics Toolbox for BigQuery](https://docs.carto.com/data-and-analysis/analytics-toolbox-for-bigquery) using `df.bigquery.sql_scalar(...)`:\n" - ] - }, - { - "cell_type": "markdown", - "id": "6a7928bd", - "metadata": {}, - "source": [ - "### Scaling Up: Advanced BigQuery Features\n", - "\n", - "While the BigQuery sandbox offers a powerful environment to test these hybrid Python-SQL workflows for free, some advanced features like BigQuery Machine Learning (BQML) are restricted. By connecting a billing account to your Google Cloud project, you can unlock advanced capabilities such as `ML.FORECAST` (or the `AI.FORECAST` function) to predict time-series data using Google's state-of-the-art foundational models directly from your SQL/Python chain.\n", - "\n", - "### Feedback & Community\n", - "\n", - "The BigFrames team would love to hear your feedback on the hybrid Python-SQL experience:\n", - "* **Email**: [bigframes-feedback@google.com](mailto:bigframes-feedback@google.com)\n", - "* **Issues**: File bug reports or feature requests on the [open-source BigFrames repository](https://github.com/googleapis/google-cloud-python/issues).\n", - "* **Updates**: To receive news and updates, subscribe to the [BigFrames email list](https://docs.google.com/forms/d/10EnDyYdYUW9HvelHYuBRC8L3GdGVl3rX0aroinbRZyc/edit?resourcekey=0-QUsnpzF91gm9hsp04rSA6Q).\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "bc1a6dbe-170e-4380-83da-779f37e1c00a", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.14.3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks/dataframes/pypi.ipynb b/notebooks/dataframes/pypi.ipynb deleted file mode 100644 index b1196cca8bf..00000000000 --- a/notebooks/dataframes/pypi.ipynb +++ /dev/null @@ -1,806 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# Copyright 2024 Google LLC\n", - "#\n", - "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", - "# you may not use this file except in compliance with the License.\n", - "# You may obtain a copy of the License at\n", - "#\n", - "# https://www.apache.org/licenses/LICENSE-2.0\n", - "#\n", - "# Unless required by applicable law or agreed to in writing, software\n", - "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", - "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", - "# See the License for the specific language governing permissions and\n", - "# limitations under the License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Analyzing package downloads from PyPI with BigQuery DataFrames\n", - "\n", - "In this notebook, you'll use the [PyPI public dataset](https://console.cloud.google.com/marketplace/product/gcp-public-data-pypi/pypi) and the [deps.dev public dataset](https://deps.dev/) to visualize Python package downloads for a package and its dependencies." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# Choose a package which you want to visualize.\n", - "package_name = \"pandas\"" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "import bigframes.pandas as bpd\n", - "\n", - "# Use `ordering_mode=\"partial\"` for more efficient query generation, but\n", - "# some pandas-compatible methods may not be possible without a total ordering.\n", - "bpd.options.bigquery.ordering_mode = \"partial\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Counting downloads and tracking dependencies\n", - "\n", - "The [PyPI `file_downloads`](https://console.cloud.google.com/bigquery?ws=!1m5!1m4!4m3!1sbigquery-public-data!2spypi!3sfile_downloads) table contains a row for each time there is a download request for a package. The [deps.dev Dependencies](https://console.cloud.google.com/bigquery?ws=!1m5!1m4!4m3!1sbigquery-public-data!2sdeps_dev_v1!3sDependencies) table contains a row for each dependency of each package.\n", - "\n", - "When `ordering_mode = \"partial\"`, `read_gbq_table` creates a DataFrame representing the table, but the DataFrame has no native ordering or index." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "import bigframes.enums\n", - "\n", - "# Without ordering_mode = \"partial\" it is recommended that you set\n", - "# the \"filters\" parameter to limit the number of rows subsequent queries\n", - "# have to read.\n", - "pypi = bpd.read_gbq_table(\n", - " \"bigquery-public-data.pypi.file_downloads\",\n", - "\n", - " # Using ordering_mode = \"partial\" changes the default index to a \"NULL\"\n", - " # index, meaning no index is available for implicit joins.\n", - " #\n", - " # Setting this explicitly avoids a DefaultIndexWarning.\n", - " index_col=bigframes.enums.DefaultIndexKind.NULL,\n", - ")\n", - "deps = bpd.read_gbq_table(\n", - " \"bigquery-public-data.deps_dev_v1.Dependencies\",\n", - " index_col=bigframes.enums.DefaultIndexKind.NULL,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Limit to the most recent 7 days of data\n", - "\n", - "The PyPI and deps.dev tables are partitioned by date. Query only the most recent 7 days of data to reduce the number of bytes scanned.\n", - "\n", - "Just as with the default ordering mode, filters can be describe in a pandas-compatible way by passing a Boolean Series to the DataFrame's `__getitem__` accessor." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "import datetime\n", - "\n", - "now = datetime.datetime.now(datetime.timezone.utc)\n", - "last_7_days = now - datetime.timedelta(days=7)\n", - "last_30_days = now - datetime.timedelta(days=30)\n", - "pypi = pypi[pypi[\"timestamp\"] > last_7_days]\n", - "deps = deps[deps[\"SnapshotAt\"] > last_30_days] # deps are refreshed less frequently\n", - "deps = deps[deps[\"System\"] == \"PYPI\"]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "**⚠ Warning**\n", - "\n", - "Without `ordering_mode = \"partial\"`, these filters do not change the number of bytes scanned. Instead, add column and row filters at \"read\" time. For example,\n", - "\n", - "```\n", - "import datetime\n", - "\n", - "last_7_days = datetime.datetime.now(datetime.timezone.utc) - datetime.timedelta(days=7)\n", - "\n", - "# Without ordering_mode = \"partial\", one must limit the data at \"read\" time to reduce bytes scanned.\n", - "pypi = bpd.read_gbq_table(\n", - " \"bigquery-public-data.pypi.file_downloads\",\n", - " columns=[\"timestamp\", \"project\"],\n", - " filters=[(\"timestamp\", \">\", last_7_days)],\n", - ")\n", - "```\n", - "\n", - "`head()` is not available when no ordering has been established. It fails with `OrderRequiredError`. Use `peek()` instead to download a sample of the data. This will be much more efficient, as the query doesn't need to order all rows to determine which are first." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "Query job 423d9d93-1495-4c76-b8c2-e830a6e19ff4 is DONE. 110.3 MB processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " Name DependencyName size\n", - "0 pandas pytz 168\n", - "1 pandas numpy 168\n", - "2 pandas python-dateutil 168\n", - "3 pandas six 168\n", - "4 pandas tzdata 56" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "package_deps = deps[deps[\"Name\"] == package_name].groupby([\"Name\", \"DependencyName\"], as_index=False).size()\n", - "package_deps.peek()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Count downloads for pandas and its dependencies\n", - "\n", - "The previous step created `pandas_deps` with all the dependencies of `pandas` but not pandas itself.\n", - "\n", - "Combine two DataFrames with the same column names with the `bigframes.pandas.concat` function." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "pandas_and_deps = bpd.concat(\n", - " [\n", - " package_deps.drop(columns=[\"Name\", \"size\"]).rename(columns={\"DependencyName\": \"Name\"}),\n", - " bpd.DataFrame({\"Name\": [package_name]}),\n", - " ],\n", - "\n", - " # To join DataFrames that have a NULL index, set ignore_index = True.\n", - " ignore_index=True,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Since there is no index to implicitly join on, use the `merge` method to join two DataFrames by column name." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "pandas_pypi = pandas_and_deps.merge(pypi, how=\"inner\", left_on=\"Name\", right_on=\"project\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Create a time series to visualize by grouping by the date, extracted from the `timestamp` column.\n", - "\n", - "**Note:** If you don't `peek()` at your data and only do grouped aggregations, BigQuery DataFrames can eliminate unnecessary ordering from the compilation even without `ordering_mode = \"partial\"`.\n", - "\n", - "When BigQuery DataFrames aggregates over columns, those columns provide a\n", - "unique key post-aggregation that is used for ordering. Any ordering applied before is overridden. By aggregating over\n", - "a time series, the line plots will render in the expected order." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "Query job 57037a4c-5b8b-4f30-a5c6-bfeb9731a38f is DONE. 270.4 GB processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Query job a8ea0b8e-2260-4175-b80d-668a2411c6ad is DONE. 2.0 kB processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "date project \n", - "2024-09-17 numpy 2572982\n", - " pandas 2195692\n", - " python-dateutil 3608119\n", - " pytz 1863133\n", - " six 3064640\n", - " tzdata 991989\n", - "2024-09-18 numpy 13282573\n", - " pandas 10856758\n", - " python-dateutil 17877058\n", - " pytz 9450103\n", - " six 15225000\n", - " tzdata 5230039\n", - "2024-09-19 numpy 13637868\n", - " pandas 11077817\n", - " python-dateutil 18449777\n", - " pytz 9690329\n", - " six 15706263\n", - " tzdata 5473910\n", - "2024-09-20 numpy 12609524\n", - " pandas 10758593\n", - " python-dateutil 17257536\n", - " pytz 9082050\n", - " six 14489456\n", - " tzdata 5206738\n", - "2024-09-21 numpy 8316481\n", - " pandas 7483241\n", - " python-dateutil 11604691\n", - " pytz 5494178\n", - " six 8814983\n", - " tzdata 3141578\n", - "2024-09-22 numpy 7768078\n", - " pandas 6566272\n", - " python-dateutil 10835755\n", - " pytz 5130018\n", - " six 8297507\n", - " tzdata 2811247\n", - "2024-09-23 numpy 12389164\n", - " pandas 10758931\n", - " python-dateutil 17153013\n", - " pytz 9045824\n", - " six 14512209\n", - " tzdata 5214048\n", - "2024-09-24 numpy 10385658\n", - " pandas 8830996\n", - " python-dateutil 14066307\n", - " pytz 7425446\n", - " six 11917222\n", - " tzdata 4550626\n", - "dtype: Int64" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pandas_pypi = pandas_pypi.assign(date=pandas_pypi[\"timestamp\"].dt.date)\n", - "downloads_per_day = pandas_pypi.groupby([\"date\", \"project\"]).size()\n", - "\n", - "# Cache after the aggregation so that the aggregation only runs once.\n", - "downloads_per_day.cache()\n", - "downloads_per_day.to_pandas()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "BigQuery DataFrames has several built-in visualization methods. Alternatively, download the time series with the `to_pandas()` method for further analysis and visualization." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "Query job 319558aa-e092-4fd0-a8aa-447fca216a57 is DONE. 1.6 kB processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Query job e64eb861-bd12-4748-9c29-d97990aa1241 is DONE. 1.2 kB processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "downloads_per_day.unstack().plot.line(rot=45, ylabel=\"daily downloads\", ylim=(0, 2e7))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.6" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/notebooks/experimental/ai_operators.ipynb b/notebooks/experimental/ai_operators.ipynb deleted file mode 100644 index e054484a0bf..00000000000 --- a/notebooks/experimental/ai_operators.ipynb +++ /dev/null @@ -1,73 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "title-cell", - "metadata": {}, - "source": [ - "# AI Operators (Experimental)" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "id": "UYeZd_I8iouP" - }, - "outputs": [], - "source": [ - "# Copyright 2025 Google LLC\n", - "#\n", - "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", - "# you may not use this file except in compliance with the License.\n", - "# You may obtain a copy of the License at\n", - "#\n", - "# https://www.apache.org/licenses/LICENSE-2.0\n", - "#\n", - "# Unless required by applicable law or agreed to in writing, software\n", - "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", - "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", - "# See the License for the specific language governing permissions and\n", - "# limitations under the License." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "rWJnGj2ViouP" - }, - "source": [ - "All AI functions have moved to the `bigframes.bigquery.ai` module.\n", - "\n", - "The tutorial notebook for AI functions is located at https://github.com/googleapis/python-bigquery-dataframes/blob/main/notebooks/generative_ai/ai_functions.ipynb\n", - "\n", - "For `ai.forecast`, see https://github.com/googleapis/python-bigquery-dataframes/blob/main/notebooks/generative_ai/bq_dataframes_ai_forecast.ipynb" - ] - } - ], - "metadata": { - "colab": { - "include_colab_link": true, - "provenance": [] - }, - "kernelspec": { - "display_name": "venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.17" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/notebooks/experimental/longer_ml_demo.ipynb b/notebooks/experimental/longer_ml_demo.ipynb new file mode 100644 index 00000000000..793ff58ecdf --- /dev/null +++ b/notebooks/experimental/longer_ml_demo.ipynb @@ -0,0 +1,1925 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "id": "71fbfc47", + "metadata": {}, + "source": [ + "**Note: this notebook requires changes not yet checked in**\n", + "\n", + "# Introduction\n", + "\n", + "This is a prototype for how a minimal SKLearn-like wrapper for BQML might work in BigQuery DataFrames.\n", + "\n", + "Disclaimer - this is not a polished design or a robust implementation, this is a quick prototype to workshop some ideas. Design will be next.\n", + "\n", + "What is BigQuery DataFrame?\n", + "- Pandas API for BigQuery\n", + "- Lets data scientists quickly iterate and prepare their data as they do in Pandas, but executed by BigQuery\n", + "\n", + "What is meant by SKLearn-like?\n", + "- Follow the API design practices from the SKLearn project\n", + " - [API design for machine learning software: experiences from the scikit-learn project](https://arxiv.org/pdf/1309.0238.pdf)\n", + "- Not a copy of, or compatible with, SKLearn\n", + "\n", + "Briefly, patterns taken from SKLearn are:\n", + "- Models and transforms are 'Estimators'\n", + " - A bundle of parameters with a consistent way to initialize/get/set\n", + " - And a .fit(..) method to fit to training data\n", + "- Models additionally have a .predict(..)\n", + "- By default, these objects are transient, making them easy to play around with. No need to give them names or decide how to persist them.\n", + "\n", + "\n", + "Design goals:\n", + "- Zero friction ML capabilities for BigQuery DataFrames users (no extra auth, configuration, etc)\n", + "- Offers first class integration with the Pandas-like BigQuery DataFrames API\n", + "- Uses SKLearn-like design patterns that feel familiar to data scientists\n", + "- Also a first class BigQuery experience\n", + " - Offers BigQuery's scalability and storage / compute management\n", + " - Works naturally with BigQuery's other interfaces, e.g. GUI and SQL\n", + " - BQML features" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "345c2163", + "metadata": {}, + "source": [ + "# Linear regression tutorial\n", + "\n", + "Adapted from the \"Penguin weight\" Linear Regression tutorial for BQML: https://cloud.google.com/bigquery-ml/docs/linear-regression-tutorial\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "03c9e168", + "metadata": {}, + "source": [ + "## Setting the scene\n", + "\n", + "Our conservationists have sent us some measurements of penguins found in the Antarctic islands. They say that some of the body mass measurements for the Adelie penguins are missing, and ask if we can use some data science magic to estimate them. Sounds like a job for a linear regression!\n", + "\n", + "Lets take a look at the data..." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "d7a03de2-c0ef-4f80-9cd5-f96e87cf2d54", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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tag_numberspeciesislandculmen_length_mmculmen_depth_mmflipper_length_mmbody_mass_gsex
01225Gentoo penguin (Pygoscelis papua)Biscoe<NA><NA><NA><NA><NA>
11278Gentoo penguin (Pygoscelis papua)Biscoe42.013.5210.04150.0FEMALE
21275Gentoo penguin (Pygoscelis papua)Biscoe46.513.5210.04550.0FEMALE
31233Gentoo penguin (Pygoscelis papua)Biscoe43.314.0208.04575.0FEMALE
41311Gentoo penguin (Pygoscelis papua)Biscoe47.514.0212.04875.0FEMALE
51316Gentoo penguin (Pygoscelis papua)Biscoe49.114.5212.04625.0FEMALE
61313Gentoo penguin (Pygoscelis papua)Biscoe45.514.5212.04750.0FEMALE
71381Gentoo penguin (Pygoscelis papua)Biscoe47.614.5215.05400.0MALE
81377Gentoo penguin (Pygoscelis papua)Biscoe45.114.5207.05050.0FEMALE
91380Gentoo penguin (Pygoscelis papua)Biscoe45.114.5215.05000.0FEMALE
101257Gentoo penguin (Pygoscelis papua)Biscoe46.214.5209.04800.0FEMALE
111336Gentoo penguin (Pygoscelis papua)Biscoe46.514.5213.04400.0FEMALE
121237Gentoo penguin (Pygoscelis papua)Biscoe43.214.5208.04450.0FEMALE
131302Gentoo penguin (Pygoscelis papua)Biscoe48.515.0219.04850.0FEMALE
141325Gentoo penguin (Pygoscelis papua)Biscoe49.115.0228.05500.0MALE
151285Gentoo penguin (Pygoscelis papua)Biscoe47.515.0218.04950.0FEMALE
161242Gentoo penguin (Pygoscelis papua)Biscoe49.615.0216.04750.0MALE
171246Gentoo penguin (Pygoscelis papua)Biscoe47.715.0216.04750.0FEMALE
181320Gentoo penguin (Pygoscelis papua)Biscoe45.515.0220.05000.0MALE
191244Gentoo penguin (Pygoscelis papua)Biscoe46.415.0216.04700.0FEMALE
\n", + "
[347 rows x 8 columns in total]" + ], + "text/plain": [ + " tag_number species island culmen_length_mm \\\n", + "0 1225 Gentoo penguin (Pygoscelis papua) Biscoe \n", + "1 1278 Gentoo penguin (Pygoscelis papua) Biscoe 42.0 \n", + "2 1275 Gentoo penguin (Pygoscelis papua) Biscoe 46.5 \n", + "3 1233 Gentoo penguin (Pygoscelis papua) Biscoe 43.3 \n", + "4 1311 Gentoo penguin (Pygoscelis papua) Biscoe 47.5 \n", + "5 1316 Gentoo penguin (Pygoscelis papua) Biscoe 49.1 \n", + "6 1313 Gentoo penguin (Pygoscelis papua) Biscoe 45.5 \n", + "7 1381 Gentoo penguin (Pygoscelis papua) Biscoe 47.6 \n", + "8 1377 Gentoo penguin (Pygoscelis papua) Biscoe 45.1 \n", + "9 1380 Gentoo penguin (Pygoscelis papua) Biscoe 45.1 \n", + "10 1257 Gentoo penguin (Pygoscelis papua) Biscoe 46.2 \n", + "11 1336 Gentoo penguin (Pygoscelis papua) Biscoe 46.5 \n", + "12 1237 Gentoo penguin (Pygoscelis papua) Biscoe 43.2 \n", + "13 1302 Gentoo penguin (Pygoscelis papua) Biscoe 48.5 \n", + "14 1325 Gentoo penguin (Pygoscelis papua) Biscoe 49.1 \n", + "15 1285 Gentoo penguin (Pygoscelis papua) Biscoe 47.5 \n", + "16 1242 Gentoo penguin (Pygoscelis papua) Biscoe 49.6 \n", + "17 1246 Gentoo penguin (Pygoscelis papua) Biscoe 47.7 \n", + "18 1320 Gentoo penguin (Pygoscelis papua) Biscoe 45.5 \n", + "19 1244 Gentoo penguin (Pygoscelis papua) Biscoe 46.4 \n", + "20 1390 Gentoo penguin (Pygoscelis papua) Biscoe 50.7 \n", + "21 1379 Gentoo penguin (Pygoscelis papua) Biscoe 47.8 \n", + "22 1267 Gentoo penguin (Pygoscelis papua) Biscoe 50.1 \n", + "23 1389 Gentoo penguin (Pygoscelis papua) Biscoe 47.2 \n", + "24 1269 Gentoo penguin (Pygoscelis papua) Biscoe 49.6 \n", + "\n", + " culmen_depth_mm flipper_length_mm body_mass_g sex \n", + "0 \n", + "1 13.5 210.0 4150.0 FEMALE \n", + "2 13.5 210.0 4550.0 FEMALE \n", + "3 14.0 208.0 4575.0 FEMALE \n", + "4 14.0 212.0 4875.0 FEMALE \n", + "5 14.5 212.0 4625.0 FEMALE \n", + "6 14.5 212.0 4750.0 FEMALE \n", + "7 14.5 215.0 5400.0 MALE \n", + "8 14.5 207.0 5050.0 FEMALE \n", + "9 14.5 215.0 5000.0 FEMALE \n", + "10 14.5 209.0 4800.0 FEMALE \n", + "11 14.5 213.0 4400.0 FEMALE \n", + "12 14.5 208.0 4450.0 FEMALE \n", + "13 15.0 219.0 4850.0 FEMALE \n", + "14 15.0 228.0 5500.0 MALE \n", + "15 15.0 218.0 4950.0 FEMALE \n", + "16 15.0 216.0 4750.0 MALE \n", + "17 15.0 216.0 4750.0 FEMALE \n", + "18 15.0 220.0 5000.0 MALE \n", + "19 15.0 216.0 4700.0 FEMALE \n", + "20 15.0 223.0 5550.0 MALE \n", + "21 15.0 215.0 5650.0 MALE \n", + "22 15.0 225.0 5000.0 MALE \n", + "23 15.5 215.0 4975.0 FEMALE \n", + "24 16.0 225.0 5700.0 MALE \n", + "...\n", + "\n", + "[347 rows x 8 columns]" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import bigframes.pandas\n", + "\n", + "df = bigframes.pandas.read_gbq(\"bigframes-dev.bqml_tutorial.penguins\")\n", + "df" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "359524c4", + "metadata": {}, + "source": [ + "First we note that while we have a default numbered index generated by BigQuery, actually the penguins are uniquely identified by their tags.\n", + "\n", + "Lets make the data a bit friendlier to work with by setting the tag number column as the index." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "93d01411", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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speciesislandculmen_length_mmculmen_depth_mmflipper_length_mmbody_mass_gsex
tag_number
1225Gentoo penguin (Pygoscelis papua)Biscoe<NA><NA><NA><NA><NA>
1278Gentoo penguin (Pygoscelis papua)Biscoe42.013.5210.04150.0FEMALE
1275Gentoo penguin (Pygoscelis papua)Biscoe46.513.5210.04550.0FEMALE
1233Gentoo penguin (Pygoscelis papua)Biscoe43.314.0208.04575.0FEMALE
1311Gentoo penguin (Pygoscelis papua)Biscoe47.514.0212.04875.0FEMALE
1316Gentoo penguin (Pygoscelis papua)Biscoe49.114.5212.04625.0FEMALE
1313Gentoo penguin (Pygoscelis papua)Biscoe45.514.5212.04750.0FEMALE
1381Gentoo penguin (Pygoscelis papua)Biscoe47.614.5215.05400.0MALE
1377Gentoo penguin (Pygoscelis papua)Biscoe45.114.5207.05050.0FEMALE
1380Gentoo penguin (Pygoscelis papua)Biscoe45.114.5215.05000.0FEMALE
1257Gentoo penguin (Pygoscelis papua)Biscoe46.214.5209.04800.0FEMALE
1336Gentoo penguin (Pygoscelis papua)Biscoe46.514.5213.04400.0FEMALE
1237Gentoo penguin (Pygoscelis papua)Biscoe43.214.5208.04450.0FEMALE
1302Gentoo penguin (Pygoscelis papua)Biscoe48.515.0219.04850.0FEMALE
1325Gentoo penguin (Pygoscelis papua)Biscoe49.115.0228.05500.0MALE
1285Gentoo penguin (Pygoscelis papua)Biscoe47.515.0218.04950.0FEMALE
1242Gentoo penguin (Pygoscelis papua)Biscoe49.615.0216.04750.0MALE
1246Gentoo penguin (Pygoscelis papua)Biscoe47.715.0216.04750.0FEMALE
1320Gentoo penguin (Pygoscelis papua)Biscoe45.515.0220.05000.0MALE
1244Gentoo penguin (Pygoscelis papua)Biscoe46.415.0216.04700.0FEMALE
\n", + "
[347 rows x 7 columns in total]" + ], + "text/plain": [ + " species island culmen_length_mm \\\n", + "tag_number \n", + "1225 Gentoo penguin (Pygoscelis papua) Biscoe \n", + "1278 Gentoo penguin (Pygoscelis papua) Biscoe 42.0 \n", + "1275 Gentoo penguin (Pygoscelis papua) Biscoe 46.5 \n", + "1233 Gentoo penguin (Pygoscelis papua) Biscoe 43.3 \n", + "1311 Gentoo penguin (Pygoscelis papua) Biscoe 47.5 \n", + "1316 Gentoo penguin (Pygoscelis papua) Biscoe 49.1 \n", + "1313 Gentoo penguin (Pygoscelis papua) Biscoe 45.5 \n", + "1381 Gentoo penguin (Pygoscelis papua) Biscoe 47.6 \n", + "1377 Gentoo penguin (Pygoscelis papua) Biscoe 45.1 \n", + "1380 Gentoo penguin (Pygoscelis papua) Biscoe 45.1 \n", + "1257 Gentoo penguin (Pygoscelis papua) Biscoe 46.2 \n", + "1336 Gentoo penguin (Pygoscelis papua) Biscoe 46.5 \n", + "1237 Gentoo penguin (Pygoscelis papua) Biscoe 43.2 \n", + "1302 Gentoo penguin (Pygoscelis papua) Biscoe 48.5 \n", + "1325 Gentoo penguin (Pygoscelis papua) Biscoe 49.1 \n", + "1285 Gentoo penguin (Pygoscelis papua) Biscoe 47.5 \n", + "1242 Gentoo penguin (Pygoscelis papua) Biscoe 49.6 \n", + "1246 Gentoo penguin (Pygoscelis papua) Biscoe 47.7 \n", + "1320 Gentoo penguin (Pygoscelis papua) Biscoe 45.5 \n", + "1244 Gentoo penguin (Pygoscelis papua) Biscoe 46.4 \n", + "1390 Gentoo penguin (Pygoscelis papua) Biscoe 50.7 \n", + "1379 Gentoo penguin (Pygoscelis papua) Biscoe 47.8 \n", + "1267 Gentoo penguin (Pygoscelis papua) Biscoe 50.1 \n", + "1389 Gentoo penguin (Pygoscelis papua) Biscoe 47.2 \n", + "1269 Gentoo penguin (Pygoscelis papua) Biscoe 49.6 \n", + "\n", + " culmen_depth_mm flipper_length_mm body_mass_g sex \n", + "tag_number \n", + "1225 \n", + "1278 13.5 210.0 4150.0 FEMALE \n", + "1275 13.5 210.0 4550.0 FEMALE \n", + "1233 14.0 208.0 4575.0 FEMALE \n", + "1311 14.0 212.0 4875.0 FEMALE \n", + "1316 14.5 212.0 4625.0 FEMALE \n", + "1313 14.5 212.0 4750.0 FEMALE \n", + "1381 14.5 215.0 5400.0 MALE \n", + "1377 14.5 207.0 5050.0 FEMALE \n", + "1380 14.5 215.0 5000.0 FEMALE \n", + "1257 14.5 209.0 4800.0 FEMALE \n", + "1336 14.5 213.0 4400.0 FEMALE \n", + "1237 14.5 208.0 4450.0 FEMALE \n", + "1302 15.0 219.0 4850.0 FEMALE \n", + "1325 15.0 228.0 5500.0 MALE \n", + "1285 15.0 218.0 4950.0 FEMALE \n", + "1242 15.0 216.0 4750.0 MALE \n", + "1246 15.0 216.0 4750.0 FEMALE \n", + "1320 15.0 220.0 5000.0 MALE \n", + "1244 15.0 216.0 4700.0 FEMALE \n", + "1390 15.0 223.0 5550.0 MALE \n", + "1379 15.0 215.0 5650.0 MALE \n", + "1267 15.0 225.0 5000.0 MALE \n", + "1389 15.5 215.0 4975.0 FEMALE \n", + "1269 16.0 225.0 5700.0 MALE \n", + "...\n", + "\n", + "[347 rows x 7 columns]" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = df.set_index(\"tag_number\")\n", + "df" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "f95fda6a", + "metadata": {}, + "source": [ + "We saw in the first view that there were some missing values. We're especially interested in observations that are missing just the body_mass_g, so lets look at those:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "941cb6c3-8c54-42ce-a945-4fa604176b2e", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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speciesislandculmen_length_mmculmen_depth_mmflipper_length_mmbody_mass_gsex
tag_number
1225Gentoo penguin (Pygoscelis papua)Biscoe<NA><NA><NA><NA><NA>
1393Adelie Penguin (Pygoscelis adeliae)Torgersen<NA><NA><NA><NA><NA>
1524Adelie Penguin (Pygoscelis adeliae)Dream41.620.0204.0<NA>MALE
1523Adelie Penguin (Pygoscelis adeliae)Dream38.017.5194.0<NA>FEMALE
1525Adelie Penguin (Pygoscelis adeliae)Dream36.318.5194.0<NA>MALE
\n", + "
[5 rows x 7 columns in total]" + ], + "text/plain": [ + " species island culmen_length_mm \\\n", + "tag_number \n", + "1225 Gentoo penguin (Pygoscelis papua) Biscoe \n", + "1393 Adelie Penguin (Pygoscelis adeliae) Torgersen \n", + "1524 Adelie Penguin (Pygoscelis adeliae) Dream 41.6 \n", + "1523 Adelie Penguin (Pygoscelis adeliae) Dream 38.0 \n", + "1525 Adelie Penguin (Pygoscelis adeliae) Dream 36.3 \n", + "\n", + " culmen_depth_mm flipper_length_mm body_mass_g sex \n", + "tag_number \n", + "1225 \n", + "1393 \n", + "1524 20.0 204.0 MALE \n", + "1523 17.5 194.0 FEMALE \n", + "1525 18.5 194.0 MALE \n", + "\n", + "[5 rows x 7 columns]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df[df.body_mass_g.isnull()]" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "a70c2027", + "metadata": {}, + "source": [ + "Here we see three Adelie penguins with tag numbers 1523, 1524, 1525 are missing their body_mass_g but have the other measurements. These are the ones we need to guess. We can do this by training a statistical model on the measurements that we do have, and then using it to predict the missing values.\n", + "\n", + "Our conservationists warned us that trying to generalize across species is a bad idea, so for now lets just try building a model for Adelie penguins. We can revisit it later and see if including the other observations improves the model performance." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "93ff013a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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speciesislandculmen_length_mmculmen_depth_mmflipper_length_mmbody_mass_gsex
tag_number
1172Adelie Penguin (Pygoscelis adeliae)Dream32.115.5188.03050.0FEMALE
1371Adelie Penguin (Pygoscelis adeliae)Biscoe37.716.0183.03075.0FEMALE
1417Adelie Penguin (Pygoscelis adeliae)Torgersen38.617.0188.02900.0FEMALE
1204Adelie Penguin (Pygoscelis adeliae)Dream40.717.0190.03725.0MALE
1251Adelie Penguin (Pygoscelis adeliae)Biscoe37.617.0185.03600.0FEMALE
1422Adelie Penguin (Pygoscelis adeliae)Torgersen35.717.0189.03350.0FEMALE
1394Adelie Penguin (Pygoscelis adeliae)Torgersen40.217.0176.03450.0FEMALE
1163Adelie Penguin (Pygoscelis adeliae)Dream36.417.0195.03325.0FEMALE
1329Adelie Penguin (Pygoscelis adeliae)Biscoe38.117.0181.03175.0FEMALE
1406Adelie Penguin (Pygoscelis adeliae)Torgersen44.118.0210.04000.0MALE
1196Adelie Penguin (Pygoscelis adeliae)Dream36.518.0182.03150.0FEMALE
1228Adelie Penguin (Pygoscelis adeliae)Biscoe41.618.0192.03950.0MALE
1412Adelie Penguin (Pygoscelis adeliae)Torgersen40.318.0195.03250.0FEMALE
1142Adelie Penguin (Pygoscelis adeliae)Dream35.718.0202.03550.0FEMALE
1430Adelie Penguin (Pygoscelis adeliae)Torgersen33.519.0190.03600.0FEMALE
1333Adelie Penguin (Pygoscelis adeliae)Biscoe43.219.0197.04775.0MALE
1414Adelie Penguin (Pygoscelis adeliae)Torgersen38.719.0195.03450.0FEMALE
1197Adelie Penguin (Pygoscelis adeliae)Dream41.119.0182.03425.0MALE
1443Adelie Penguin (Pygoscelis adeliae)Torgersen40.619.0199.04000.0MALE
1295Adelie Penguin (Pygoscelis adeliae)Biscoe41.020.0203.04725.0MALE
\n", + "
[146 rows x 7 columns in total]" + ], + "text/plain": [ + " species island culmen_length_mm \\\n", + "tag_number \n", + "1172 Adelie Penguin (Pygoscelis adeliae) Dream 32.1 \n", + "1371 Adelie Penguin (Pygoscelis adeliae) Biscoe 37.7 \n", + "1417 Adelie Penguin (Pygoscelis adeliae) Torgersen 38.6 \n", + "1204 Adelie Penguin (Pygoscelis adeliae) Dream 40.7 \n", + "1251 Adelie Penguin (Pygoscelis adeliae) Biscoe 37.6 \n", + "1422 Adelie Penguin (Pygoscelis adeliae) Torgersen 35.7 \n", + "1394 Adelie Penguin (Pygoscelis adeliae) Torgersen 40.2 \n", + "1163 Adelie Penguin (Pygoscelis adeliae) Dream 36.4 \n", + "1329 Adelie Penguin (Pygoscelis adeliae) Biscoe 38.1 \n", + "1406 Adelie Penguin (Pygoscelis adeliae) Torgersen 44.1 \n", + "1196 Adelie Penguin (Pygoscelis adeliae) Dream 36.5 \n", + "1228 Adelie Penguin (Pygoscelis adeliae) Biscoe 41.6 \n", + "1412 Adelie Penguin (Pygoscelis adeliae) Torgersen 40.3 \n", + "1142 Adelie Penguin (Pygoscelis adeliae) Dream 35.7 \n", + "1430 Adelie Penguin (Pygoscelis adeliae) Torgersen 33.5 \n", + "1333 Adelie Penguin (Pygoscelis adeliae) Biscoe 43.2 \n", + "1414 Adelie Penguin (Pygoscelis adeliae) Torgersen 38.7 \n", + "1197 Adelie Penguin (Pygoscelis adeliae) Dream 41.1 \n", + "1443 Adelie Penguin (Pygoscelis adeliae) Torgersen 40.6 \n", + "1295 Adelie Penguin (Pygoscelis adeliae) Biscoe 41.0 \n", + "1207 Adelie Penguin (Pygoscelis adeliae) Dream 38.8 \n", + "1349 Adelie Penguin (Pygoscelis adeliae) Biscoe 38.2 \n", + "1350 Adelie Penguin (Pygoscelis adeliae) Biscoe 37.8 \n", + "1351 Adelie Penguin (Pygoscelis adeliae) Biscoe 38.1 \n", + "1116 Adelie Penguin (Pygoscelis adeliae) Dream 37.0 \n", + "\n", + " culmen_depth_mm flipper_length_mm body_mass_g sex \n", + "tag_number \n", + "1172 15.5 188.0 3050.0 FEMALE \n", + "1371 16.0 183.0 3075.0 FEMALE \n", + "1417 17.0 188.0 2900.0 FEMALE \n", + "1204 17.0 190.0 3725.0 MALE \n", + "1251 17.0 185.0 3600.0 FEMALE \n", + "1422 17.0 189.0 3350.0 FEMALE \n", + "1394 17.0 176.0 3450.0 FEMALE \n", + "1163 17.0 195.0 3325.0 FEMALE \n", + "1329 17.0 181.0 3175.0 FEMALE \n", + "1406 18.0 210.0 4000.0 MALE \n", + "1196 18.0 182.0 3150.0 FEMALE \n", + "1228 18.0 192.0 3950.0 MALE \n", + "1412 18.0 195.0 3250.0 FEMALE \n", + "1142 18.0 202.0 3550.0 FEMALE \n", + "1430 19.0 190.0 3600.0 FEMALE \n", + "1333 19.0 197.0 4775.0 MALE \n", + "1414 19.0 195.0 3450.0 FEMALE \n", + "1197 19.0 182.0 3425.0 MALE \n", + "1443 19.0 199.0 4000.0 MALE \n", + "1295 20.0 203.0 4725.0 MALE \n", + "1207 20.0 190.0 3950.0 MALE \n", + "1349 20.0 190.0 3900.0 MALE \n", + "1350 20.0 190.0 4250.0 MALE \n", + "1351 16.5 198.0 3825.0 FEMALE \n", + "1116 16.5 185.0 3400.0 FEMALE \n", + "...\n", + "\n", + "[146 rows x 7 columns]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# get all the rows with adelie penguins\n", + "adelie_data = df[df.species == \"Adelie Penguin (Pygoscelis adeliae)\"]\n", + "\n", + "# separate out the rows that have a body mass measurement\n", + "training_data = adelie_data[adelie_data.body_mass_g.notnull()]\n", + "\n", + "# we noticed there were also some rows that were missing other values,\n", + "# lets remove these so they don't affect our results\n", + "training_data = training_data.dropna()\n", + "\n", + "# lets take a quick peek and make sure things look right:\n", + "training_data" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "d55a39f9", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "species string[pyarrow]\n", + "island string[pyarrow]\n", + "culmen_length_mm Float64\n", + "culmen_depth_mm Float64\n", + "flipper_length_mm Float64\n", + "body_mass_g Float64\n", + "sex string[pyarrow]\n", + "dtype: object" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# we'll look at the schema too:\n", + "training_data.dtypes" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "59d374b5", + "metadata": {}, + "source": [ + "Great! Now lets configure a linear regression model to predict body mass from the other columns" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "18c4cecf", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "LinearRegression()" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import bigframes.ml.linear_model as ml\n", + "\n", + "model = ml.LinearRegression()\n", + "model" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "6e54a1a2", + "metadata": {}, + "source": [ + "As in SKLearn, an unfitted model object is just a bundle of parameters." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "a2060cf1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'fit_intercept': True}" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# lets view the parameters\n", + "model.get_params()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "8e25fe41", + "metadata": {}, + "source": [ + "For this task, really all the default options are fine. But just so we can see how configuration works, lets specify that we want to use gradient descent to find the solution:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "327e2232", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "LinearRegression()" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.optimize_strategy = \"BATCH_GRADIENT_DESCENT\"\n", + "model" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "2c2e0835", + "metadata": {}, + "source": [ + "BigQuery models provide a couple of extra conveniences:\n", + "\n", + "1. By default, they will automatically perform feature engineering on the inputs - encoding our string columns and scaling our numeric columns.\n", + "2. By default, they will also automatically manage the test/training data split for us.\n", + "\n", + "So all we need to do is hook our chosen feature and label columns into the model and call .fit()!" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "085c9a99", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "LinearRegression()" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X_train = training_data[['island', 'culmen_length_mm', 'culmen_depth_mm', 'flipper_length_mm', 'sex']]\n", + "y_train = training_data[['body_mass_g']]\n", + "model.fit(X_train, y_train)\n", + "model" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "9e76e10c", + "metadata": {}, + "source": [ + "...and there, we've successfully trained a linear regressor model. Lets see how it performs, using the automatic data split:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "c9458c02", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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mean_absolute_errormean_squared_errormean_squared_log_errormedian_absolute_errorr2_scoreexplained_variance
0223.87876378553.6016340.005614181.3309110.6239510.623951
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[1 rows x 6 columns in total]" + ], + "text/plain": [ + " mean_absolute_error mean_squared_error mean_squared_log_error \\\n", + "0 223.878763 78553.601634 0.005614 \n", + "\n", + " median_absolute_error r2_score explained_variance \n", + "0 181.330911 0.623951 0.623951 \n", + "\n", + "[1 rows x 6 columns]" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.score(X_train, y_train)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "f0b39603", + "metadata": {}, + "source": [ + "Great! The model seems useful, predicting 62% of the variance.\n", + "\n", + "We realize we made a mistake though - we're trying to predict mass using a linear model, mass will increase with the cube of the penguin's size, whereas our inputs are linear with size. Can we improve our model by cubing them?" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "b94eddc7", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'\\ndef cubify(penguin_df):\\n penguin_df.culmen_length_mm = train_x.culmen_length_mm.pow(3)\\n penguin_df.culmen_depth_mm = train_x.culmen_depth_mm.pow(3)\\n penguin_df.flipper_length_mm = train_x.flipper_length_mm.pow(3)\\n\\ncubify(train_x)\\ntrain_x\\n'" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# SKIP THIS STEP (not yet work working in BigQuery DataFrame)\n", + "\n", + "# lets define a preprocessing step that adjust the linear measurements to use the cube\n", + "'''\n", + "def cubify(penguin_df):\n", + " penguin_df.culmen_length_mm = X_train.culmen_length_mm.pow(3)\n", + " penguin_df.culmen_depth_mm = X_train.culmen_depth_mm.pow(3)\n", + " penguin_df.flipper_length_mm = X_train.flipper_length_mm.pow(3)\n", + "\n", + "cubify(X_train)\n", + "X_train\n", + "'''" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "1b0e3f02", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'\\nmodel.fit(train_x, train_y)\\nmodel.evaluate()\\n'" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# AS ABOVE, SKIP FOR NOW\n", + "'''\n", + "model.fit(X_train, y_train)\n", + "model.evaluate()\n", + "'''" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "45c5e755", + "metadata": {}, + "source": [ + "Now that we're satisfied with our model, lets see what it predicts for those Adelie penguins with no body mass measurement:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "f21ebc1f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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predicted_body_mass_g
tag_number
13933459.735118
15244304.175638
15233471.668379
15253947.881639
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[4 rows x 1 columns in total]" + ], + "text/plain": [ + " predicted_body_mass_g\n", + "tag_number \n", + "1393 3459.735118\n", + "1524 4304.175638\n", + "1523 3471.668379\n", + "1525 3947.881639\n", + "\n", + "[4 rows x 1 columns]" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Lets predict the missing observations\n", + "missing_body_mass = adelie_data[adelie_data.body_mass_g.isnull()]\n", + "\n", + "model.predict(missing_body_mass)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "e66bd0b0", + "metadata": {}, + "source": [ + "Because we created it without a name, it was just a temporary model that will disappear after 24 hours. \n", + "\n", + "We decide that this approach is promising, so lets tell BigQuery to save it." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "c508691b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "LinearRegression()" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.to_gbq(\"bqml_tutorial.penguins_model\", replace=True)\n", + "model" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "46abef08", + "metadata": {}, + "source": [ + "We can now use this model anywhere in BigQuery with this name. We can also load\n", + "it again in our BigQuery DataFrames session and evaluate or inference it without\n", + "needing to retrain it:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "0c87e972", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "LinearRegression()" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model = bigframes.pandas.read_gbq_model(\"bqml_tutorial.penguins_model\")\n", + "model" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "d6ab8def", + "metadata": {}, + "source": [ + "And of course we can retrain it if we like. Lets make another version that is based on all the penguins, so we can test that assumption we made at the beginning that it would be best to separate them:" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "f4960452", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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mean_absolute_errormean_squared_errormean_squared_log_errormedian_absolute_errorr2_scoreexplained_variance
0224.71743379527.8796230.005693169.2358690.6192870.619287
\n", + "
[1 rows x 6 columns in total]" + ], + "text/plain": [ + " mean_absolute_error mean_squared_error mean_squared_log_error \\\n", + "0 224.717433 79527.879623 0.005693 \n", + "\n", + " median_absolute_error r2_score explained_variance \n", + "0 169.235869 0.619287 0.619287 \n", + "\n", + "[1 rows x 6 columns]" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# This time we'll take all the training data, for all species\n", + "training_data = df[df.body_mass_g.notnull()]\n", + "training_data = training_data.dropna()\n", + "\n", + "# And we'll include species in our features\n", + "X_train = training_data[['species', 'island', 'culmen_length_mm', 'culmen_depth_mm', 'flipper_length_mm', 'sex']]\n", + "y_train = training_data[['body_mass_g']]\n", + "model.fit(X_train, y_train)\n", + "\n", + "# And we'll evaluate it on the Adelie penguins only\n", + "adelie_data = training_data[training_data.species == \"Adelie Penguin (Pygoscelis adeliae)\"]\n", + "X_test = adelie_data[['species', 'island', 'culmen_length_mm', 'culmen_depth_mm', 'flipper_length_mm', 'sex']]\n", + "y_test = adelie_data[['body_mass_g']]\n", + "model.score(X_test, y_test)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "7d101140", + "metadata": {}, + "source": [ + "It looks like the conservationists were right! Including other species, even though it gave us more training data, worsened prediction on the Adelie penguins." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "7f3fe50d", + "metadata": {}, + "source": [ + "===============================================\n", + "\n", + "**Everything below this line not yet implemented**" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "62577c72", + "metadata": {}, + "source": [ + "We want to productionalize this model, so lets start publishing it to the vertex model registry ([prerequisites](https://cloud.google.com/bigquery-ml/docs/managing-models-vertex#prerequisites))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b82e79ee", + "metadata": {}, + "outputs": [], + "source": [ + "model.publish(\n", + " registry=\"vertex_ai\",\n", + " vertex_ai_model_version_aliases=[\"experimental\"])" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "69d2482c", + "metadata": {}, + "source": [ + "Now when we fit the model, we can see it published here: https://console.cloud.google.com/vertex-ai/models" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "b97d9b64", + "metadata": {}, + "source": [ + "# Custom feature engineering" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "c837ace9", + "metadata": {}, + "source": [ + "So far, we've relied on BigQuery to do our feature engineering for us. What if we want to do it manually?\n", + "\n", + "BigQuery DataFrames provides a way to do this using Pipelines." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "480cb12f", + "metadata": {}, + "outputs": [], + "source": [ + "from bigframes.ml.pipeline import Pipeline\n", + "from bigframes.ml.preprocessing import StandardScaler\n", + "\n", + "pipe = Pipeline([\n", + " ('scaler', StandardScaler()),\n", + " ('linreg', LinearRegression())\n", + "])\n", + "\n", + "pipe.fit(X_train, y_train)\n", + "pipe.evaluate()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "9a0e7d19", + "metadata": {}, + "source": [ + "We then can then save the entire pipeline to BigQuery, BigQuery will save this as a single model, with the pre-processing steps embedded in the TRANSFORM property:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0d1831ed", + "metadata": {}, + "outputs": [], + "source": [ + "pipe.to_gbq(\"bqml_tutorial.penguins_pipeline\")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "f6b60898", + "metadata": {}, + "source": [ + "# Custom data split" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "60ac0174", + "metadata": {}, + "source": [ + "BigQuery has also managed splitting out our training data. What if we want to do this manually?\n", + "\n", + "*TODO: Write this section*" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.9" + }, + "vscode": { + "interpreter": { + "hash": "a850322d07d9bdc9ec5f301d307e048bcab2390ae395e1cbce9335f4e081e5e2" + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/experimental/semantic_operators.ipynb b/notebooks/experimental/semantic_operators.ipynb deleted file mode 100644 index 22927e6ef94..00000000000 --- a/notebooks/experimental/semantic_operators.ipynb +++ /dev/null @@ -1,69 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "title-cell", - "metadata": {}, - "source": [ - "# Semantic Operators (Experimental)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "UYeZd_I8iouP" - }, - "outputs": [], - "source": [ - "# Copyright 2024 Google LLC\n", - "#\n", - "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", - "# you may not use this file except in compliance with the License.\n", - "# You may obtain a copy of the License at\n", - "#\n", - "# https://www.apache.org/licenses/LICENSE-2.0\n", - "#\n", - "# Unless required by applicable law or agreed to in writing, software\n", - "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", - "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", - "# See the License for the specific language governing permissions and\n", - "# limitations under the License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Semantic Operators have been deprecated since version 1.42.0. Please use AI functions instead.\n", - "\n", - "The tutorial notebook for AI functions is located at https://github.com/googleapis/python-bigquery-dataframes/blob/main/notebooks/generative_ai/ai_functions.ipynb" - ] - } - ], - "metadata": { - "colab": { - "include_colab_link": true, - "provenance": [] - }, - "kernelspec": { - "display_name": "venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.9" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/notebooks/generative_ai/ai_functions.ipynb b/notebooks/generative_ai/ai_functions.ipynb deleted file mode 100644 index 0831ea0412b..00000000000 --- a/notebooks/generative_ai/ai_functions.ipynb +++ /dev/null @@ -1,567 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "id": "acd53f9d", - "metadata": {}, - "outputs": [], - "source": [ - "# Copyright 2025 Google LLC\n", - "#\n", - "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", - "# you may not use this file except in compliance with the License.\n", - "# You may obtain a copy of the License at\n", - "#\n", - "# https://www.apache.org/licenses/LICENSE-2.0\n", - "#\n", - "# Unless required by applicable law or agreed to in writing, software\n", - "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", - "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", - "# See the License for the specific language governing permissions and\n", - "# limitations under the License." - ] - }, - { - "cell_type": "markdown", - "id": "e75ce682", - "metadata": {}, - "source": [ - "# BigQuery DataFrames (BigFrames) AI Functions\n", - "\n", - "\n", - "\n", - " \n", - " \n", - " \n", - "
\n", - " \n", - " \"Colab Run in Colab\n", - " \n", - " \n", - " \n", - " \"GitHub\n", - " View on GitHub\n", - " \n", - " \n", - " \n", - " \"BQ\n", - " Open in BQ Studio\n", - " \n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "aee05821", - "metadata": {}, - "source": [ - "This notebook provides a brief introduction to AI functions in BigQuery Dataframes." - ] - }, - { - "cell_type": "markdown", - "id": "1232f400", - "metadata": {}, - "source": [ - "## Preparation\n", - "\n", - "First, set up your BigFrames environment:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c9f924aa", - "metadata": {}, - "outputs": [], - "source": [ - "import bigframes.pandas as bpd\n", - "\n", - "PROJECT_ID = \"\" # @param {type:\"string\"}\n", - "\n", - "bpd.options.bigquery.project = PROJECT_ID\n", - "bpd.options.bigquery.ordering_mode = \"partial\"\n", - "bpd.options.display.progress_bar = None" - ] - }, - { - "cell_type": "markdown", - "id": "e2188773", - "metadata": {}, - "source": [ - "## ai.generate\n", - "\n", - "The `ai.generate` function lets you analyze any combination of text and unstructured data from BigQuery. You can mix BigFrames or Pandas series with string literals as your prompt in the form of a tuple. You are also allowed to provide only a series. Here is an example:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "471a47fe", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/google/home/swast/src/github.com/googleapis/google-cloud-python/packages/bigframes/bigframes/core/global_session.py:113: DefaultLocationWarning: No explicit location is set, so using location US for the session.\n", - " _global_session = bigframes.session.connect(\n", - "/usr/local/google/home/swast/src/github.com/googleapis/google-cloud-python/packages/bigframes/bigframes/dtypes.py:1044: JSONDtypeWarning: JSON columns will be represented as pandas.ArrowDtype(pyarrow.json_())\n", - "instead of using `db_dtypes` in the future when available in pandas\n", - "(https://github.com/pandas-dev/pandas/issues/60958) and pyarrow.\n", - " warnings.warn(msg, bigframes.exceptions.JSONDtypeWarning)\n" - ] - }, - { - "data": { - "text/html": [ - "
0    {\"result\":\"Salad\",\"full_response\":{\"candidates...\n",
-       "1    {\"result\":\"Hotdog\",\"full_response\":{\"candidate...
" - ], - "text/plain": [ - "0 {\"result\":\"Salad\",\"full_response\":{\"candidates...\n", - "1 {\"result\":\"Hotdog\",\"full_response\":{\"candidate...\n", - "Name: 0, dtype: string" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import bigframes.bigquery as bbq\n", - "\n", - "ingredients1 = bpd.Series([\"Lettuce\", \"Sausage\"])\n", - "ingredients2 = bpd.Series([\"Cucumber\", \"Long Bread\"])\n", - "\n", - "prompt = (\"What's the food made from \", ingredients1, \" and \", ingredients2, \" One word only\")\n", - "bbq.ai.generate(prompt)" - ] - }, - { - "cell_type": "markdown", - "id": "03953835", - "metadata": {}, - "source": [ - "The function returns a series of structs. The `'result'` field holds the answer, while more metadata can be found in the `'full_response'` field. The `'status'` field tells you whether LLM made a successful response for that specific row. " - ] - }, - { - "cell_type": "markdown", - "id": "b606c51f", - "metadata": {}, - "source": [ - "You can also include additional model parameters into your function call, as long as they conform to the structure of `generateContent` [request body format](https://cloud.google.com/vertex-ai/docs/reference/rest/v1/projects.locations.endpoints/generateContent#request-body). In the next example, you use `maxOutputTokens` to limit the length of the generated content." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4a3229a8", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
0    \n",
-       "1
" - ], - "text/plain": [ - "0 \n", - "1 \n", - "Name: result, dtype: string" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model_params = {\n", - " \"generationConfig\": {\"maxOutputTokens\": 2}\n", - "}\n", - "\n", - "ingredients1 = bpd.Series([\"Lettuce\", \"Sausage\"])\n", - "ingredients2 = bpd.Series([\"Cucumber\", \"Long Bread\"])\n", - "\n", - "prompt = (\"What's the food made from \", ingredients1, \" and \", ingredients2)\n", - "bbq.ai.generate(prompt, model_params=model_params).struct.field(\"result\")" - ] - }, - { - "cell_type": "markdown", - "id": "3acba92d", - "metadata": {}, - "source": [ - "The answers are cut short as expected.\n", - "\n", - "In addition to `ai.generate`, you can use `ai.generate_bool`, `ai.generate_int`, and `ai.generate_double` for other output types." - ] - }, - { - "cell_type": "markdown", - "id": "0bf9f1de", - "metadata": {}, - "source": [ - "## ai.if_\n", - "\n", - "`ai.if_` generates a series of booleans. It's a handy tool for joining and filtering your data, not only because it directly returns boolean values, but also because it provides more optimization during data processing. Here is an example of using `ai.if_`:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "718c6622", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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[2 rows x 2 columns in total]" - ], - "text/plain": [ - "creature category\n", - " Cat mammal\n", - " Salmon fish\n", - "\n", - "[2 rows x 2 columns]" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "creatures = bpd.DataFrame({\"creature\": [\"Cat\", \"Salmon\"]})\n", - "categories = bpd.DataFrame({\"category\": [\"mammal\", \"fish\"]})\n", - "\n", - "joined_df = creatures.merge(categories, how=\"cross\")\n", - "condition = bbq.ai.if_((joined_df[\"creature\"], \" is a \", joined_df[\"category\"]))\n", - "\n", - "# Filter our dataframe\n", - "joined_df = joined_df[condition]\n", - "joined_df" - ] - }, - { - "cell_type": "markdown", - "id": "bb0999df", - "metadata": {}, - "source": [ - "## ai.score" - ] - }, - { - "cell_type": "markdown", - "id": "63b5a59f", - "metadata": {}, - "source": [ - "`ai.score` ranks your input based on the prompt and assigns a double value (i.e. a score) to each item. You can then sort your data based on their scores. For example:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6875fe36", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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1spider1.0
0tiger7.0
2blue whale10.0
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[3 rows x 2 columns in total]" - ], - "text/plain": [ - " animals relative_weight\n", - "1 spider 1.0\n", - "0 tiger 7.0\n", - "2 blue whale 10.0\n", - "\n", - "[3 rows x 2 columns]" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df = bpd.DataFrame({'animals': ['tiger', 'spider', 'blue whale']})\n", - "\n", - "df['relative_weight'] = bbq.ai.score((\"Rank the relative weight of \", df['animals'], \" on the scale from 1 to 10\"))\n", - "df.sort_values(by='relative_weight')" - ] - }, - { - "cell_type": "markdown", - "id": "1ed0dff1", - "metadata": {}, - "source": [ - "## ai.classify" - ] - }, - { - "cell_type": "markdown", - "id": "c56b91cf", - "metadata": {}, - "source": [ - "`ai.classify` categories your inputs into the specified categories. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8cfb844b", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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0tigermammal
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[4 rows x 2 columns in total]" - ], - "text/plain": [ - " animal category\n", - "0 tiger mammal\n", - "1 spider anthropod\n", - "2 blue whale mammal\n", - "3 salmon fish\n", - "\n", - "[4 rows x 2 columns]" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df = bpd.DataFrame({'animal': ['tiger', 'spider', 'blue whale', 'salmon']})\n", - "\n", - "df['category'] = bbq.ai.classify(df['animal'], categories=['mammal', 'fish', 'anthropod'])\n", - "df" - ] - }, - { - "cell_type": "markdown", - "id": "9e4037bc", - "metadata": {}, - "source": [ - "Note that this function can only return the values that are provided in the `categories` argument. If your categories do not cover all cases, your may get wrong answers:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2e66110a", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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0tigermammal
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[2 rows x 2 columns in total]" - ], - "text/plain": [ - " animal category\n", - "0 tiger mammal\n", - "1 spider mammal\n", - "\n", - "[2 rows x 2 columns]" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df = bpd.DataFrame({'animal': ['tiger', 'spider']})\n", - "\n", - "df['category'] = bbq.ai.classify(df['animal'], categories=['mammal', 'fish']) # Spider belongs to neither category\n", - "df" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.14.3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks/generative_ai/ai_movie_poster.ipynb b/notebooks/generative_ai/ai_movie_poster.ipynb deleted file mode 100644 index 8f309fa7c49..00000000000 --- a/notebooks/generative_ai/ai_movie_poster.ipynb +++ /dev/null @@ -1,762 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "7add2e44", - "metadata": { - "id": "XZpKUoHjXw3_" - }, - "outputs": [], - "source": [ - "# Copyright 2026 Google LLC\n", - "#\n", - "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", - "# you may not use this file except in compliance with the License.\n", - "# You may obtain a copy of the License at\n", - "#\n", - "# https://www.apache.org/licenses/LICENSE-2.0\n", - "#\n", - "# Unless required by applicable law or agreed to in writing, software\n", - "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", - "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", - "# See the License for the specific language governing permissions and\n", - "# limitations under the License." - ] - }, - { - "cell_type": "markdown", - "id": "ee509844", - "metadata": { - "id": "SEKzWP6jW9Oj" - }, - "source": [ - "# Analyzing movie posters with BigQuery Dataframe AI functions" - ] - }, - { - "cell_type": "markdown", - "id": "81b8de8d", - "metadata": {}, - "source": [ - "\n", - "\n", - " \n", - " \n", - " \n", - "
\n", - " \n", - " \"Colab Run in Colab\n", - " \n", - " \n", - " \n", - " \"GitHub\n", - " View on GitHub\n", - " \n", - " \n", - " \n", - " \"BQ\n", - " Open in BQ Studio\n", - " \n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "256b6c02", - "metadata": { - "id": "c9CCKXG5XTb-" - }, - "source": [ - "BigQuery Dataframe provides a Pythonic way to use AI functions directly with your dataframes. In this notebook, you will use these functions to analyze old\n", - "movie posters. These posters are images stored in a public Google Cloud Storage bucket: `gs://cloud-samples-data/vertex-ai/dataset-management/datasets/classic-movie-posters`" - ] - }, - { - "cell_type": "markdown", - "id": "3f71d3cb", - "metadata": { - "id": "CUJDa_7MPbL9" - }, - "source": [ - "## Set up" - ] - }, - { - "cell_type": "markdown", - "id": "547145f5", - "metadata": { - "id": "D3iYtBSkYpCK" - }, - "source": [ - "Before you begin, you need to\n", - "\n", - "* Set up your permissions for generative AI functions with [these instructions](https://docs.cloud.google.com/bigquery/docs/permissions-for-ai-functions)\n", - "* Set up your Cloud Resource connection by following [these instructions](https://docs.cloud.google.com/bigquery/docs/create-cloud-resource-connection)\n", - "\n", - "Once you have the permissions set up, import the `bigframes.pandas` package, and\n", - "set your cloud project ID." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "d9cd6da8", - "metadata": { - "id": "6nqoRHYbPAx3" - }, - "outputs": [], - "source": [ - "import bigframes.pandas as bpd\n", - "\n", - "MY_PROJECT_ID = \"bigframes-dev\" # @param {type:\"string\"}\n", - "LOCATION = \"us\" # @param {type:\"string\"}\n", - "\n", - "bpd.options.bigquery.project = MY_PROJECT_ID\n", - "bpd.options.bigquery.location = LOCATION" - ] - }, - { - "cell_type": "markdown", - "id": "015a63c1", - "metadata": { - "id": "2XHcNHtvPhNW" - }, - "source": [ - "## Load data" - ] - }, - { - "cell_type": "markdown", - "id": "254561e0", - "metadata": { - "id": "eS-9A7DijfoQ" - }, - "source": [ - "First, you load the data from the GCS bucket to a BigQuery Dataframe:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "47acbbfe", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - }, - "id": "ZNPzFjCyPap0", - "outputId": "346d20b2-d615-4094-d24e-2d40e5c90ee2" - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " Query processed 0 Bytes in a moment of slot time.\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/google/home/shuowei/src/google-cloud-python/google-cloud-python/packages/bigframes/bigframes/dtypes.py:1044: JSONDtypeWarning: JSON columns will be represented as pandas.ArrowDtype(pyarrow.json_())\n", - "instead of using `db_dtypes` in the future when available in pandas\n", - "(https://github.com/pandas-dev/pandas/issues/60958) and pyarrow.\n", - " warnings.warn(msg, bigframes.exceptions.JSONDtypeWarning)\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " Query processed 0 Bytes in 18 seconds of slot time.\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - " Query processed 0 Bytes in 8 seconds of slot time.\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
poster
0
" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Replace with your own connection name.\n", - "MY_CONNECTION = 'bigframes-default-connection' # @param {type:\"string\"}\n", - "FULL_CONNECTION_ID = f\"{MY_PROJECT_ID}.{LOCATION}.{MY_CONNECTION}\"\n", - "\n", - "import gcsfs\n", - "import bigframes\n", - "import bigframes.pandas as bpd\n", - "import bigframes.bigquery as bbq\n", - "import json\n", - "from IPython.display import HTML, display\n", - "\n", - "session = bpd.get_global_session()\n", - "\n", - "# Configure global display parameters \n", - "bigframes.options.display.blob_display_width = 200\n", - "\n", - "def get_runtime_json_str(series, mode=\"R\", with_metadata=False):\n", - " s = bbq.obj.fetch_metadata(series) if with_metadata else series\n", - " runtime = bbq.obj.get_access_url(s, mode=mode)\n", - " return bbq.to_json_string(runtime)\n", - "\n", - "def get_read_url(series):\n", - " runtime = bbq.obj.get_access_url(series, mode=\"R\")\n", - " return bbq.json_value(runtime, \"$.access_urls.read_url\")\n", - "\n", - "def render_images(df):\n", - " \"\"\"Helper to display BigFrames DataFrame with rendered image previews.\"\"\"\n", - " from bigframes import dtypes\n", - " if isinstance(df, bpd.Series):\n", - " df = df.to_frame()\n", - " \n", - " object_cols = [col for col, dtype in zip(df.columns, df.dtypes) if dtype == dtypes.OBJ_REF_DTYPE]\n", - " if not object_cols:\n", - " display(df)\n", - " return\n", - "\n", - " limit = bigframes.options.display.max_rows or 10\n", - " view_df = df.head(limit)\n", - " runtime_cols = {\n", - " col: get_runtime_json_str(view_df[col], mode=\"R\", with_metadata=False) \n", - " for col in object_cols\n", - " }\n", - " \n", - " pandas_json_df = bpd.DataFrame(runtime_cols).to_pandas()\n", - " final_pd = view_df.to_pandas()\n", - " width = bigframes.options.display.blob_display_width or 200\n", - " \n", - " def format_cell_html(raw_json):\n", - " if not raw_json: return \"\"\n", - " try:\n", - " obj_rt = json.loads(raw_json)\n", - " if \"access_urls\" not in obj_rt: return \"Error fetching URL\"\n", - " uri = obj_rt.get(\"objectref\", {}).get(\"uri\", \"\")\n", - " url = obj_rt[\"access_urls\"][\"read_url\"]\n", - " if str(uri).lower().endswith((\".png\", \".jpg\", \".jpeg\", \".webp\")):\n", - " return f''\n", - " return f'{uri}'\n", - " except: return \"Format Error\"\n", - "\n", - " for col in object_cols:\n", - " final_pd[col] = pandas_json_df[col].map(format_cell_html)\n", - " display(HTML(final_pd.to_html(escape=False)))\n", - "\n", - "# List files using gcsfs\n", - "fs = gcsfs.GCSFileSystem(anon=True)\n", - "uris = fs.glob(\"gs://cloud-samples-data/vertex-ai/dataset-management/datasets/classic-movie-posters/*\")\n", - "\n", - "# Ensure URIs have gs:// prefix\n", - "uris = [u if u.startswith(\"gs://\") else f\"gs://{u}\" for u in uris]\n", - "\n", - "# Read the URIs into a BigQuery DataFrame\n", - "movies = bpd.read_gbq(f\"SELECT uri FROM UNNEST({uris[:5]}) as uri\")\n", - "\n", - "# Create the object reference column using the fully qualified connection ID\n", - "movies['poster'] = bbq.obj.make_ref(movies['uri'], authorizer=FULL_CONNECTION_ID)\n", - "movies = movies[['poster']]\n", - "render_images(movies.head(1))" - ] - }, - { - "cell_type": "markdown", - "id": "f1096d2f", - "metadata": { - "id": "EfkdDH08QnYw" - }, - "source": [ - "## Extract titles from posters" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "bb30d47c", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - }, - "id": "6CoZZ5tSQm1r", - "outputId": "1b3915ce-eb83-4be9-b1c1-d9a326dc9408" - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/google/home/shuowei/src/google-cloud-python/google-cloud-python/packages/bigframes/bigframes/dtypes.py:1044: JSONDtypeWarning: JSON columns will be represented as pandas.ArrowDtype(pyarrow.json_())\n", - "instead of using `db_dtypes` in the future when available in pandas\n", - "(https://github.com/pandas-dev/pandas/issues/60958) and pyarrow.\n", - " warnings.warn(msg, bigframes.exceptions.JSONDtypeWarning)\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " Query processed 0 Bytes in 23 seconds of slot time. [Job bigframes-dev:US.job_ZKfuxLQE1U49whg7fgakYFYfiz34 details]\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - " Query processed 0 Bytes in 40 seconds of slot time. [Job bigframes-dev:US.job_VwLv_BxDFdE4adNx1bpnvvM5vfZd details]\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
postertitle
0The movie title for this poster image is **Au Secours!** (Help!).
" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import bigframes.bigquery as bbq\n", - "\n", - "movies['title'] = bbq.ai.generate(\n", - " (\"What is the movie title for this poster image?\", get_read_url(movies['poster']))\n", - ").struct.field(\"result\")\n", - "render_images(movies.head(1))" - ] - }, - { - "cell_type": "markdown", - "id": "eb9eb261", - "metadata": { - "id": "cFQHQ9S2lr6t" - }, - "source": [ - "Notice that `ai.generate()` has a `struct` return type, which holds not only the LLM response, but also the status. If you do not provide a field name for your answer, `\"result\"` will be the default name. You can access LLM response content with the struct accessor (e.g. `my_response.struct.filed(\"result\")`);." - ] - }, - { - "cell_type": "markdown", - "id": "ea29eb21", - "metadata": { - "id": "R8kkUhgoS5Xz" - }, - "source": [ - "## Get movie release year\n", - "\n", - "In the example below, you will use `ai.generate_int()` to find the release year for each movie poster:" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "bf426247", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 976 - }, - "id": "cKZdHq0XS1iW", - "outputId": "72cbad57-4518-4e1e-97bb-333d424dba73" - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/google/home/shuowei/src/google-cloud-python/google-cloud-python/packages/bigframes/bigframes/dtypes.py:1044: JSONDtypeWarning: JSON columns will be represented as pandas.ArrowDtype(pyarrow.json_())\n", - "instead of using `db_dtypes` in the future when available in pandas\n", - "(https://github.com/pandas-dev/pandas/issues/60958) and pyarrow.\n", - " warnings.warn(msg, bigframes.exceptions.JSONDtypeWarning)\n", - "/usr/local/google/home/shuowei/src/google-cloud-python/google-cloud-python/packages/bigframes/bigframes/core/logging/log_adapter.py:229: ApiDeprecationWarning: The blob accessor is deprecated and will be removed in a future release. Use bigframes.bigquery.obj functions instead.\n", - " return prop(*args, **kwargs)\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " Query processed 0 Bytes in 51 seconds of slot time. [Job bigframes-dev:US.3cf4ab5b-c360-4b7c-9def-4cd03135a547 details]\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - " Query processed 1.2 kB in a moment of slot time.\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
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postertitleyear
0The movie title is **Au Secours!**1924
\n", - "

1 rows × 3 columns

\n", - "
[1 rows x 3 columns in total]" - ], - "text/plain": [ - " poster \\\n", - "0 {\"access_urls\":{\"expiry_time\":\"2026-05-09T03:1... \n", - "\n", - " title year \n", - "0 The movie title is **Au Secours!** 1924 \n", - "\n", - "[1 rows x 3 columns]" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "movies['year'] = bbq.ai.generate_int(\n", - " (\"What is the release year for this movie?\", movies['title']),\n", - " endpoint='gemini-2.5-pro'\n", - ").struct.field(\"result\")\n", - "\n", - "movies.head(1)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "8bf12352", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 250 - }, - "id": "yqRiNRY8_8fs", - "outputId": "efa60107-6883-4f5c-8e40-43c7287ea7fb" - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/google/home/shuowei/src/google-cloud-python/google-cloud-python/packages/bigframes/bigframes/dtypes.py:1044: JSONDtypeWarning: JSON columns will be represented as pandas.ArrowDtype(pyarrow.json_())\n", - "instead of using `db_dtypes` in the future when available in pandas\n", - "(https://github.com/pandas-dev/pandas/issues/60958) and pyarrow.\n", - " warnings.warn(msg, bigframes.exceptions.JSONDtypeWarning)\n" - ] - }, - { - "data": { - "text/plain": [ - "poster structSQL
WITH `bfcte_0` AS (\n",
-       "  SELECT\n",
-       "    *\n",
-       "  FROM UNNEST(ARRAY<STRUCT<`bfcol_0` STRING, `bfcol_1` INT64, `bfcol_2` INT64>>[STRUCT(\n",
-       "    'gs://cloud-samples-data/vertex-ai/dataset-management/datasets/classic-movie-posters/au_secours.jpeg',\n",
-       "    0,\n",
-       "    0\n",
-       "  ), STRUCT(\n",
-       "    'gs://cloud-samples-data/vertex-ai/dataset-management/datasets/classic-movie-posters/barque_sortant_du_port.jpeg',\n",
-       "    1,\n",
-       "    1\n",
-       "  ), STRUCT(\n",
-       "    'gs://cloud-samples-data/vertex-ai/dataset-management/datasets/classic-movie-posters/battling_butler.jpg',\n",
-       "    2,\n",
-       "    2\n",
-       "  ), STRUCT(\n",
-       "    'gs://cloud-samples-data/vertex-ai/dataset-management/datasets/classic-movie-posters/brown_of_harvard.jpeg',\n",
-       "    3,\n",
-       "    3\n",
-       "  ), STRUCT(\n",
-       "    'gs://cloud-samples-data/vertex-ai/dataset-management/datasets/classic-movie-posters/der_student_von_prag.jpg',\n",
-       "    4,\n",
-       "    4\n",
-       "  )])\n",
-       ")\n",
-       "SELECT\n",
-       "  `bfcol_1` AS `bfuid_col_60`,\n",
-       "  TO_JSON_STRING(\n",
-       "    OBJ.GET_ACCESS_URL(OBJ.MAKE_REF(`bfcol_0`, 'bigframes-dev.us.bigframes-default-connection'), 'R')\n",
-       "  ) AS `bfuid_col_66`\n",
-       "FROM `bfcte_0`\n",
-       "WHERE\n",
-       "  AI.IF(\n",
-       "    prompt => (\n",
-       "      'The movie ',\n",
-       "      AI.GENERATE(\n",
-       "        prompt => (\n",
-       "          'What is the movie title for this poster image?',\n",
-       "          JSON_VALUE(\n",
-       "            OBJ.GET_ACCESS_URL(OBJ.MAKE_REF(`bfcol_0`, 'bigframes-dev.us.bigframes-default-connection'), 'R'),\n",
-       "            '$.access_urls.read_url'\n",
-       "          )\n",
-       "        ),\n",
-       "        request_type => 'UNSPECIFIED'\n",
-       "      ).`result`,\n",
-       "      ' was made in US'\n",
-       "    ),\n",
-       "    optimization_mode => 'MINIMIZE_COST'\n",
-       "  )\n",
-       "ORDER BY\n",
-       "  `bfcol_2` ASC NULLS LAST\n",
-       "LIMIT 1
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postertitleyear
2The movie title for the poster image is **Battling Butler**.1926
" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "us_movies = movies[bbq.ai.if_(\n", - " (\"The movie \", movies['title'], \" was made in US\")\n", - ")]\n", - "render_images(us_movies.head(1))" - ] - } - ], - "metadata": { - "colab": { - "provenance": [] - }, - "kernelspec": { - "display_name": ".venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.13.0" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/notebooks/generative_ai/bq_dataframes_ai_forecast.ipynb b/notebooks/generative_ai/bq_dataframes_ai_forecast.ipynb deleted file mode 100644 index 6f8c95d3a48..00000000000 --- a/notebooks/generative_ai/bq_dataframes_ai_forecast.ipynb +++ /dev/null @@ -1,901 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# Copyright 2025 Google LLC\n", - "#\n", - "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", - "# you may not use this file except in compliance with the License.\n", - "# You may obtain a copy of the License at\n", - "#\n", - "# https://www.apache.org/licenses/LICENSE-2.0\n", - "#\n", - "# Unless required by applicable law or agreed to in writing, software\n", - "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", - "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", - "# See the License for the specific language governing permissions and\n", - "# limitations under the License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# BigFrames AI Forecast\n", - "\n", - "\n", - "\n", - " \n", - " \n", - " \n", - "
\n", - " \n", - " \"Colab Run in Colab\n", - " \n", - " \n", - " \n", - " \"GitHub\n", - " View on GitHub\n", - " \n", - " \n", - " \n", - " \"BQ\n", - " Open in BQ Studio\n", - " \n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This Notebook introduces forecasting with GenAI Fundation Model with BigFrames AI." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Setup" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "PROJECT = \"bigframes-dev\" # replace with your project\n", - "\n", - "import bigframes.pandas as bpd\n", - "bpd.options.bigquery.project = PROJECT\n", - "bpd.options.display.progress_bar = None\n", - "\n", - "# Optional, but recommended: partial ordering mode can accelerate executions and save costs.\n", - "bpd.options.bigquery.ordering_mode = \"partial\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 1. Create a BigFrames DataFrames from BigQuery public data." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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trip_idduration_secstart_datestart_station_namestart_station_idend_dateend_station_nameend_station_idbike_numberzip_code...c_subscription_typestart_station_latitudestart_station_longitudeend_station_latitudeend_station_longitudemember_birth_yearmember_genderbike_share_for_all_tripstart_station_geomend_station_geom
0201712151647221445012017-12-15 16:47:22+00:0010th St at Fallon St2012017-12-15 16:55:44+00:0010th Ave at E 15th St222144<NA>...<NA>37.797673-122.26299737.792714-122.248781984Male<NA>POINT (-122.263 37.79767)POINT (-122.24878 37.79271)
12017080523460515857122017-08-05 23:46:05+00:0010th St at Fallon St2012017-08-05 23:57:57+00:0010th Ave at E 15th St2221585<NA>...<NA>37.797673-122.26299737.792714-122.24878<NA><NA><NA>POINT (-122.263 37.79767)POINT (-122.24878 37.79271)
22017111114472028802722017-11-11 14:47:20+00:0012th St at 4th Ave2332017-11-11 14:51:53+00:0010th Ave at E 15th St2222880<NA>...<NA>37.795812-122.25555537.792714-122.248781965Female<NA>POINT (-122.25555 37.79581)POINT (-122.24878 37.79271)
32018042517262737557572018-04-25 17:26:27+00:0013th St at Franklin St3382018-04-25 17:39:05+00:0010th Ave at E 15th St2223755<NA>...<NA>37.803189-122.27057937.792714-122.248781982OtherNoPOINT (-122.27058 37.80319)POINT (-122.24878 37.79271)
42018040815560118311052018-04-08 15:56:01+00:0013th St at Franklin St3382018-04-08 16:14:26+00:0010th Ave at E 15th St222183<NA>...<NA>37.803189-122.27057937.792714-122.248781987FemaleNoPOINT (-122.27058 37.80319)POINT (-122.24878 37.79271)
52018041916485015608572018-04-19 16:48:50+00:0013th St at Franklin St3382018-04-19 17:03:08+00:0010th Ave at E 15th St2221560<NA>...<NA>37.803189-122.27057937.792714-122.248781982OtherNoPOINT (-122.27058 37.80319)POINT (-122.24878 37.79271)
62017081020445483912562017-08-10 20:44:54+00:002nd Ave at E 18th St2002017-08-10 21:05:50+00:0010th Ave at E 15th St222839<NA>...<NA>37.800214-122.2538137.792714-122.24878<NA><NA><NA>POINT (-122.25381 37.80021)POINT (-122.24878 37.79271)
7201710122044386666302017-10-12 20:44:38+00:002nd Ave at E 18th St2002017-10-12 20:55:09+00:0010th Ave at E 15th St222666<NA>...<NA>37.800214-122.2538137.792714-122.24878<NA><NA><NA>POINT (-122.25381 37.80021)POINT (-122.24878 37.79271)
82017111818232819603532017-11-18 18:23:28+00:002nd Ave at E 18th St2002017-11-18 18:29:22+00:0010th Ave at E 15th St2221960<NA>...<NA>37.800214-122.2538137.792714-122.248781988Male<NA>POINT (-122.25381 37.80021)POINT (-122.24878 37.79271)
9201708061839175102982017-08-06 18:39:17+00:002nd Ave at E 18th St2002017-08-06 18:44:15+00:0010th Ave at E 15th St222510<NA>...<NA>37.800214-122.2538137.792714-122.248781969Male<NA>POINT (-122.25381 37.80021)POINT (-122.24878 37.79271)
\n", - "

10 rows × 21 columns

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[1947417 rows x 21 columns in total]" - ], - "text/plain": [ - " trip_id duration_sec start_date \\\n", - " 20171215164722144 501 2017-12-15 16:47:22+00:00 \n", - "201708052346051585 712 2017-08-05 23:46:05+00:00 \n", - "201711111447202880 272 2017-11-11 14:47:20+00:00 \n", - "201804251726273755 757 2018-04-25 17:26:27+00:00 \n", - " 20180408155601183 1105 2018-04-08 15:56:01+00:00 \n", - "201804191648501560 857 2018-04-19 16:48:50+00:00 \n", - " 20170810204454839 1256 2017-08-10 20:44:54+00:00 \n", - " 20171012204438666 630 2017-10-12 20:44:38+00:00 \n", - "201711181823281960 353 2017-11-18 18:23:28+00:00 \n", - " 20170806183917510 298 2017-08-06 18:39:17+00:00 \n", - "\n", - " start_station_name start_station_id end_date \\\n", - " 10th St at Fallon St 201 2017-12-15 16:55:44+00:00 \n", - " 10th St at Fallon St 201 2017-08-05 23:57:57+00:00 \n", - " 12th St at 4th Ave 233 2017-11-11 14:51:53+00:00 \n", - "13th St at Franklin St 338 2018-04-25 17:39:05+00:00 \n", - "13th St at Franklin St 338 2018-04-08 16:14:26+00:00 \n", - "13th St at Franklin St 338 2018-04-19 17:03:08+00:00 \n", - " 2nd Ave at E 18th St 200 2017-08-10 21:05:50+00:00 \n", - " 2nd Ave at E 18th St 200 2017-10-12 20:55:09+00:00 \n", - " 2nd Ave at E 18th St 200 2017-11-18 18:29:22+00:00 \n", - " 2nd Ave at E 18th St 200 2017-08-06 18:44:15+00:00 \n", - "\n", - " end_station_name end_station_id bike_number zip_code ... \\\n", - "10th Ave at E 15th St 222 144 ... \n", - "10th Ave at E 15th St 222 1585 ... \n", - "10th Ave at E 15th St 222 2880 ... \n", - "10th Ave at E 15th St 222 3755 ... \n", - "10th Ave at E 15th St 222 183 ... \n", - "10th Ave at E 15th St 222 1560 ... \n", - "10th Ave at E 15th St 222 839 ... \n", - "10th Ave at E 15th St 222 666 ... \n", - "10th Ave at E 15th St 222 1960 ... \n", - "10th Ave at E 15th St 222 510 ... \n", - "\n", - "c_subscription_type start_station_latitude start_station_longitude \\\n", - " 37.797673 -122.262997 \n", - " 37.797673 -122.262997 \n", - " 37.795812 -122.255555 \n", - " 37.803189 -122.270579 \n", - " 37.803189 -122.270579 \n", - " 37.803189 -122.270579 \n", - " 37.800214 -122.25381 \n", - " 37.800214 -122.25381 \n", - " 37.800214 -122.25381 \n", - " 37.800214 -122.25381 \n", - "\n", - " end_station_latitude end_station_longitude member_birth_year \\\n", - " 37.792714 -122.24878 1984 \n", - " 37.792714 -122.24878 \n", - " 37.792714 -122.24878 1965 \n", - " 37.792714 -122.24878 1982 \n", - " 37.792714 -122.24878 1987 \n", - " 37.792714 -122.24878 1982 \n", - " 37.792714 -122.24878 \n", - " 37.792714 -122.24878 \n", - " 37.792714 -122.24878 1988 \n", - " 37.792714 -122.24878 1969 \n", - "\n", - " member_gender bike_share_for_all_trip start_station_geom \\\n", - " Male POINT (-122.263 37.79767) \n", - " POINT (-122.263 37.79767) \n", - " Female POINT (-122.25555 37.79581) \n", - " Other No POINT (-122.27058 37.80319) \n", - " Female No POINT (-122.27058 37.80319) \n", - " Other No POINT (-122.27058 37.80319) \n", - " POINT (-122.25381 37.80021) \n", - " POINT (-122.25381 37.80021) \n", - " Male POINT (-122.25381 37.80021) \n", - " Male POINT (-122.25381 37.80021) \n", - "\n", - " end_station_geom \n", - "POINT (-122.24878 37.79271) \n", - "POINT (-122.24878 37.79271) \n", - "POINT (-122.24878 37.79271) \n", - "POINT (-122.24878 37.79271) \n", - "POINT (-122.24878 37.79271) \n", - "POINT (-122.24878 37.79271) \n", - "POINT (-122.24878 37.79271) \n", - "POINT (-122.24878 37.79271) \n", - "POINT (-122.24878 37.79271) \n", - "POINT (-122.24878 37.79271) \n", - "...\n", - "\n", - "[1947417 rows x 21 columns]" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df = bpd.read_gbq(\"bigquery-public-data.san_francisco_bikeshare.bikeshare_trips\")\n", - "df" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2. Preprocess Data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Only take the start_date after 2018 and the \"Subscriber\" category as input. start_date are truncated to each hour." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "df = df[df[\"start_date\"] >= \"2018-01-01\"]\n", - "df = df[df[\"subscriber_type\"] == \"Subscriber\"]\n", - "df[\"trip_hour\"] = df[\"start_date\"].dt.floor(\"h\")\n", - "df = df[[\"trip_hour\", \"trip_id\"]]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Group and count each hour's num of trips." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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trip_hournum_trips
02018-01-01 00:00:00+00:0020
12018-01-01 01:00:00+00:0025
22018-01-01 02:00:00+00:0013
32018-01-01 03:00:00+00:0011
42018-01-01 05:00:00+00:004
52018-01-01 06:00:00+00:008
62018-01-01 07:00:00+00:008
72018-01-01 08:00:00+00:0020
82018-01-01 09:00:00+00:0030
92018-01-01 10:00:00+00:0041
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10 rows × 2 columns

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[2842 rows x 2 columns in total]" - ], - "text/plain": [ - " trip_hour num_trips\n", - "2018-01-01 00:00:00+00:00 20\n", - "2018-01-01 01:00:00+00:00 25\n", - "2018-01-01 02:00:00+00:00 13\n", - "2018-01-01 03:00:00+00:00 11\n", - "2018-01-01 05:00:00+00:00 4\n", - "2018-01-01 06:00:00+00:00 8\n", - "2018-01-01 07:00:00+00:00 8\n", - "2018-01-01 08:00:00+00:00 20\n", - "2018-01-01 09:00:00+00:00 30\n", - "2018-01-01 10:00:00+00:00 41\n", - "...\n", - "\n", - "[2842 rows x 2 columns]" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_grouped = df.groupby(\"trip_hour\").count()\n", - "df_grouped = df_grouped.reset_index().rename(columns={\"trip_id\": \"num_trips\"})\n", - "df_grouped" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 3. Make forecastings for next 1 week with DataFrames.ai.forecast API" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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forecast_timestampforecast_valueconfidence_levelprediction_interval_lower_boundprediction_interval_upper_boundai_forecast_status
02018-04-24 12:00:00+00:00147.0237430.9598.736624195.310862
12018-04-25 00:00:00+00:006.9550320.95-6.09423220.004297
22018-04-26 05:00:00+00:00-37.1965330.95-88.75956614.366499
32018-04-26 14:00:00+00:00115.6351320.9530.120832201.149432
42018-04-27 02:00:00+00:002.5160060.95-69.09559174.127604
52018-04-29 03:00:00+00:0022.5033260.95-38.71437883.721031
62018-04-24 04:00:00+00:00-12.2590790.95-45.37726220.859104
72018-04-24 14:00:00+00:00126.5192110.9596.837778156.200644
82018-04-26 11:00:00+00:00120.905670.9535.781735206.029606
92018-04-27 13:00:00+00:00162.0230260.95103.946307220.099744
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10 rows × 6 columns

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[168 rows x 6 columns in total]" - ], - "text/plain": [ - " forecast_timestamp forecast_value confidence_level \\\n", - "2018-04-24 12:00:00+00:00 147.023743 0.95 \n", - "2018-04-25 00:00:00+00:00 6.955032 0.95 \n", - "2018-04-26 05:00:00+00:00 -37.196533 0.95 \n", - "2018-04-26 14:00:00+00:00 115.635132 0.95 \n", - "2018-04-27 02:00:00+00:00 2.516006 0.95 \n", - "2018-04-29 03:00:00+00:00 22.503326 0.95 \n", - "2018-04-24 04:00:00+00:00 -12.259079 0.95 \n", - "2018-04-24 14:00:00+00:00 126.519211 0.95 \n", - "2018-04-26 11:00:00+00:00 120.90567 0.95 \n", - "2018-04-27 13:00:00+00:00 162.023026 0.95 \n", - "\n", - " prediction_interval_lower_bound prediction_interval_upper_bound \\\n", - " 98.736624 195.310862 \n", - " -6.094232 20.004297 \n", - " -88.759566 14.366499 \n", - " 30.120832 201.149432 \n", - " -69.095591 74.127604 \n", - " -38.714378 83.721031 \n", - " -45.377262 20.859104 \n", - " 96.837778 156.200644 \n", - " 35.781735 206.029606 \n", - " 103.946307 220.099744 \n", - "\n", - "ai_forecast_status \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "...\n", - "\n", - "[168 rows x 6 columns]" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import bigframes.bigquery as bbq\n", - "\n", - "# Using all the data except the last week (2842-168) for training. And predict the last week (168).\n", - "result = bbq.ai.forecast(df_grouped.head(2842-168), timestamp_col=\"trip_hour\", data_col=\"num_trips\", horizon=168) \n", - "result" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 4. Process the raw result and draw a line plot along with the training data" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "result = result.sort_values(\"forecast_timestamp\")\n", - "result = result[[\"forecast_timestamp\", \"forecast_value\"]]\n", - "result = result.rename(columns={\"forecast_timestamp\": \"trip_hour\", \"forecast_value\": \"num_trips_forecast\"})\n", - "df_all = bpd.concat([df_grouped, result])\n", - "df_all = df_all.tail(672) # 4 weeks" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Plot a line chart and compare with the actual result." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "df_all = df_all.set_index(\"trip_hour\")\n", - "df_all.plot.line(figsize=(16, 8))" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "venv (3.10.17)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.17" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/notebooks/generative_ai/bq_dataframes_llm_code_generation.ipynb b/notebooks/generative_ai/bq_dataframes_llm_code_generation.ipynb index 527d3c4aaac..0f113b84c6d 100644 --- a/notebooks/generative_ai/bq_dataframes_llm_code_generation.ipynb +++ b/notebooks/generative_ai/bq_dataframes_llm_code_generation.ipynb @@ -1,1305 +1,891 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "id": "ur8xi4C7S06n" - }, - "outputs": [], - "source": [ - "# Copyright 2022 Google LLC\n", - "#\n", - "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", - "# you may not use this file except in compliance with the License.\n", - "# You may obtain a copy of the License at\n", - "#\n", - "# https://www.apache.org/licenses/LICENSE-2.0\n", - "#\n", - "# Unless required by applicable law or agreed to in writing, software\n", - "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", - "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", - "# See the License for the specific language governing permissions and\n", - "# limitations under the License." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "JAPoU8Sm5E6e" - }, - "source": [ - "# Use BigQuery DataFrames with Generative AI for code generation\n", - "\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - "
\n", - " \n", - " \"Colab Run in Colab\n", - " \n", - " \n", - " \n", - " \"GitHub\n", - " View on GitHub\n", - " \n", - " \n", - " \n", - " \"Vertex\n", - " Open in Vertex AI Workbench\n", - " \n", - " \n", - " \n", - " \"BQ\n", - " Open in BQ Studio\n", - " \n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "24743cf4a1e1" - }, - "source": [ - "**_NOTE_**: This notebook has been tested in the following environment:\n", - "\n", - "* Python version = 3.10" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "tvgnzT1CKxrO" - }, - "source": [ - "## Overview\n", - "\n", - "Use this notebook to walk through an example use case of generating sample code by using BigQuery DataFrames and its integration with Generative AI support on Vertex AI.\n", - "\n", - "Learn more about [BigQuery DataFrames](https://cloud.google.com/python/docs/reference/bigframes/latest)." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "d975e698c9a4" - }, - "source": [ - "### Objective\n", - "\n", - "In this tutorial, you create a CSV file containing sample code for calling a given set of APIs.\n", - "\n", - "The steps include:\n", - "\n", - "- Defining an LLM model in BigQuery DataFrames, specifically the [Gemini Model](https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#gemini-models), using `bigframes.ml.llm`.\n", - "- Creating a DataFrame by reading in data from Cloud Storage.\n", - "- Manipulating data in the DataFrame to build LLM prompts.\n", - "- Sending DataFrame prompts to the LLM model using the `predict` method.\n", - "- Creating and using a custom function to transform the output provided by the LLM model response.\n", - "- Exporting the resulting transformed DataFrame as a CSV file." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "08d289fa873f" - }, - "source": [ - "### Dataset\n", - "\n", - "This tutorial uses a dataset listing the names of various pandas DataFrame and Series APIs." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "aed92deeb4a0" - }, - "source": [ - "### Costs\n", - "\n", - "This tutorial uses billable components of Google Cloud:\n", - "\n", - "* BigQuery\n", - "* Generative AI support on Vertex AI\n", - "* Cloud Functions\n", - "\n", - "Learn about [BigQuery compute pricing](https://cloud.google.com/bigquery/pricing#analysis_pricing_models),\n", - "[Generative AI support on Vertex AI pricing](https://cloud.google.com/vertex-ai/pricing#generative_ai_models), and [Cloud Functions pricing](https://cloud.google.com/functions/pricing), and use the [Pricing Calculator](https://cloud.google.com/products/calculator/)\n", - "to generate a cost estimate based on your projected usage." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "i7EUnXsZhAGF" - }, - "source": [ - "## Installation\n", - "\n", - "Install the following packages, which are required to run this notebook:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "id": "2b4ef9b72d43" - }, - "outputs": [], - "source": [ - "!pip install bigframes --upgrade --quiet" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "BF1j6f9HApxa" - }, - "source": [ - "## Before you begin\n", - "\n", - "Complete the tasks in this section to set up your environment." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Wbr2aVtFQBcg" - }, - "source": [ - "### Set up your Google Cloud project\n", - "\n", - "**The following steps are required, regardless of your notebook environment.**\n", - "\n", - "1. [Select or create a Google Cloud project](https://console.cloud.google.com/cloud-resource-manager). When you first create an account, you get a $300 credit towards your compute/storage costs.\n", - "\n", - "2. [Make sure that billing is enabled for your project](https://cloud.google.com/billing/docs/how-to/modify-project).\n", - "\n", - "3. [Click here](https://console.cloud.google.com/flows/enableapi?apiid=bigquery.googleapis.com,bigqueryconnection.googleapis.com,cloudfunctions.googleapis.com,run.googleapis.com,artifactregistry.googleapis.com,cloudbuild.googleapis.com,cloudresourcemanager.googleapis.com) to enable the following APIs:\n", - "\n", - " * BigQuery API\n", - " * BigQuery Connection API\n", - " * Cloud Functions API\n", - " * Cloud Run API\n", - " * Artifact Registry API\n", - " * Cloud Build API\n", - " * Cloud Resource Manager API\n", - " * Vertex AI API\n", - "\n", - "4. If you are running this notebook locally, install the [Cloud SDK](https://cloud.google.com/sdk)." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "WReHDGG5g0XY" - }, - "source": [ - "#### Set your project ID\n", - "\n", - "If you don't know your project ID, try the following:\n", - "* Run `gcloud config list`.\n", - "* Run `gcloud projects list`.\n", - "* See the support page: [Locate the project ID](https://support.google.com/googleapi/answer/7014113)." - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": { - "id": "oM1iC_MfAts1" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[1;31mERROR:\u001b[0m (gcloud.config.set) argument VALUE: Must be specified.\n", - "Usage: gcloud config set SECTION/PROPERTY VALUE [optional flags]\n", - " optional flags may be --help | --installation\n", - "\n", - "For detailed information on this command and its flags, run:\n", - " gcloud config set --help\n" - ] - } - ], - "source": [ - "PROJECT_ID = \"\" # @param {type:\"string\"}\n", - "\n", - "# Set the project id\n", - "! gcloud config set project {PROJECT_ID}" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "region" - }, - "source": [ - "#### Set the region\n", - "\n", - "You can also change the `REGION` variable used by BigQuery. Learn more about [BigQuery regions](https://cloud.google.com/bigquery/docs/locations#supported_locations)." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "id": "eF-Twtc4XGem" - }, - "outputs": [], - "source": [ - "REGION = \"US\" # @param {type: \"string\"}" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "sBCra4QMA2wR" - }, - "source": [ - "### Authenticate your Google Cloud account\n", - "\n", - "Depending on your Jupyter environment, you might have to manually authenticate. Follow the relevant instructions below." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "74ccc9e52986" - }, - "source": [ - "**Vertex AI Workbench**\n", - "\n", - "Do nothing, you are already authenticated." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "de775a3773ba" - }, - "source": [ - "**Local JupyterLab instance**\n", - "\n", - "Uncomment and run the following cell:" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "id": "254614fa0c46" - }, - "outputs": [], - "source": [ - "# ! gcloud auth login" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "ef21552ccea8" - }, - "source": [ - "**Colab**\n", - "\n", - "Uncomment and run the following cell:" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "id": "603adbbf0532" - }, - "outputs": [], - "source": [ - "# from google.colab import auth\n", - "# auth.authenticate_user()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "960505627ddf" - }, - "source": [ - "### Import libraries" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "id": "PyQmSRbKA8r-" - }, - "outputs": [], - "source": [ - "import bigframes.pandas as bf\n", - "from google.cloud import bigquery\n", - "from google.cloud import bigquery_connection_v1 as bq_connection" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "init_aip:mbsdk,all" - }, - "source": [ - "### Set BigQuery DataFrames options" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "id": "NPPMuw2PXGeo" - }, - "outputs": [], - "source": [ - "# Note: The project option is not required in all environments.\n", - "# On BigQuery Studio, the project ID is automatically detected.\n", - "bf.options.bigquery.project = PROJECT_ID\n", - "\n", - "# Note: The location option is not required.\n", - "# It defaults to the location of the first table or query\n", - "# passed to read_gbq(). For APIs where a location can't be\n", - "# auto-detected, the location defaults to the \"US\" location.\n", - "bf.options.bigquery.location = REGION" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "DTVtFlqeFbrU" - }, - "source": [ - "If you want to reset the location of the created DataFrame or Series objects, reset the session by executing `bf.close_session()`. After that, you can reuse `bf.options.bigquery.location` to specify another location." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "6eytf4xQHzcF" - }, - "source": [ - "# Define the LLM model\n", - "\n", - "BigQuery DataFrames provides integration with [Gemini Models](https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#gemini-models) via Vertex AI.\n", - "\n", - "This section walks through a few steps required in order to use the model in your notebook." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "qUjT8nw-jIXp" - }, - "source": [ - "## Define the model\n", - "\n", - "Use `bigframes.ml.llm` to define the model:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "sdjeXFwcHfl7" - }, - "outputs": [ - { - "data": { - "text/html": [ - "Query job 0ee1a08e-788e-4fc7-b061-52c23ab25d5a is DONE. 0 Bytes processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from bigframes.ml.llm import GeminiTextGenerator\n", - "\n", - "model = GeminiTextGenerator(model_name=\"gemini-2.5-flash\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "GbW0oCnU1s1N" - }, - "source": [ - "# Read data from Cloud Storage into BigQuery DataFrames\n", - "\n", - "You can create a BigQuery DataFrames DataFrame by reading data from any of the following locations:\n", - "\n", - "* A local data file\n", - "* Data stored in a BigQuery table\n", - "* A data file stored in Cloud Storage\n", - "* An in-memory pandas DataFrame\n", - "\n", - "In this tutorial, you create BigQuery DataFrames DataFrames by reading two CSV files stored in Cloud Storage, one containing a list of DataFrame API names and one containing a list of Series API names." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "id": "SchiTkQGIJog" - }, - "outputs": [], - "source": [ - "df_api = bf.read_csv(\"gs://cloud-samples-data/vertex-ai/bigframe/df.csv\")\n", - "series_api = bf.read_csv(\"gs://cloud-samples-data/vertex-ai/bigframe/series.csv\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "7OBjw2nmQY3-" - }, - "source": [ - "Take a peek at a few rows of data for each file:" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "id": "QCqgVCIsGGuv" - }, - "outputs": [ - { - "data": { - "text/html": [ - "Query job 48be241c-ee93-4dfa-a9e3-66b64c4b5150 is DONE. 0 Bytes processed. 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The output is the new DataFrames `df_prompt` and `series_prompt`, which contain the full prompt text." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "id": "EDAaIwHpQCDZ" - }, - "outputs": [ - { - "data": { - "text/html": [ - "Query job 17f50c10-aa81-4023-b206-4ba59ddf2269 is DONE. 0 Bytes processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Query job d6d217aa-a623-4ea4-83fb-8f1b8bfb8e68 is DONE. 0 Bytes processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Query job a275a107-752e-46f8-be9f-9cb35eb6b0b9 is DONE. 132 Bytes processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "0 Generate Pandas sample code for DataFrame.values\n", - "1 Generate Pandas sample code for DataFrame.dtypes\n", - "Name: API, dtype: string" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_prompt_prefix = \"Generate Pandas sample code for DataFrame.\"\n", - "series_prompt_prefix = \"Generate Pandas sample code for Series.\"\n", - "\n", - "df_prompt = (df_prompt_prefix + df_api['API'])\n", - "series_prompt = (series_prompt_prefix + series_api['API'])\n", - "\n", - "df_prompt.head(2)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "rwPLjqW2Ajzh" - }, - "source": [ - "## Make predictions using the LLM model\n", - "\n", - "Use the BigQuery DataFrames DataFrame containing the full prompt text as the input to the `predict` method. The `predict` method calls the LLM model and returns its generated text output back to two new BigQuery DataFrames DataFrames, `df_pred` and `series_pred`.\n", - "\n", - "Note: The predictions might take a few minutes to run." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "id": "6i6HkFJZa8na" - }, - "outputs": [ - { - "data": { - "text/html": [ - "Query job 01f95d2d-901d-4edf-bd3a-245d17c31ef6 is DONE. 0 Bytes processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Query job 55927a6f-b023-479a-b9bf-826abde77111 is DONE. 584 Bytes processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Query job 445eb0af-f643-40c5-9c1e-25aa3db8374a is DONE. 146 Bytes processed. 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Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "df_pred = model.predict(df_prompt.to_frame(), max_output_tokens=1024)\n", - "series_pred = model.predict(series_prompt.to_frame(), max_output_tokens=1024)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "89cB8MW4UIdV" - }, - "source": [ - "Once the predictions are processed, take a look at the sample output from the LLM, which provides code samples for the API names listed in the DataFrames dataset." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "id": "9A2gw6hP_2nX" - }, - "outputs": [ - { - "data": { - "text/html": [ - "Query job 65599c98-72ad-4088-8b09-f29bf05c164b is DONE. 21.8 kB processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "```python\n", - "import pandas as pd\n", - "\n", - "# Create a DataFrame\n", - "df = pd.DataFrame([[1, 2, 3], [4, 5, 6], [7, 8, 9]])\n", - "\n", - "# Get the values as a NumPy array\n", - "values = df.values\n", - "\n", - "# Print the values\n", - "print(values)\n", - "```\n" - ] - } - ], - "source": [ - "print(df_pred['ml_generate_text_llm_result'].iloc[0])" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Fx4lsNqMorJ-" - }, - "source": [ - "# Manipulate LLM output using a remote function\n", - "\n", - "The output that the LLM provides often contains additional text beyond the code sample itself. Using BigQuery DataFrames, you can deploy custom Python functions that process and transform this output.\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "d8L7SN03VByG" - }, - "source": [ - "Running the cell below creates a custom function that you can use to process the LLM output data in two ways:\n", - "1. Strip the LLM text output to include only the code block.\n", - "2. Substitute `import pandas as pd` with `import bigframes.pandas as bf` so that the resulting code block works with BigQuery DataFrames." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "id": "GskyyUQPowBT" - }, - "outputs": [], - "source": [ - "@bf.remote_function(cloud_function_service_account=\"default\")\n", - "def extract_code(text: str) -> str:\n", - " try:\n", - " res = text[text.find('\\n')+1:text.find('```', 3)]\n", - " res = res.replace(\"import pandas as pd\", \"import bigframes.pandas as bf\")\n", - " if \"import bigframes.pandas as bf\" not in res:\n", - " res = \"import bigframes.pandas as bf\\n\" + res\n", - " return res\n", - " except:\n", - " return \"\"" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "hVQAoqBUOJQf" - }, - "source": [ - "The custom function is deployed as a Cloud Function, and then integrated with BigQuery as a [remote function](https://cloud.google.com/bigquery/docs/remote-functions). Save both of the function names so that you can clean them up at the end of this notebook." - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "id": "PBlp-C-DOHRO" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Cloud Function Name projects/swast-scratch/locations/us-central1/functions/bigframes-6e7606963c3f06b8181b3cb9449a4363\n", - "Remote Function Name swast-scratch._63cfa399614a54153cc386c27d6c0c6fdb249f9e.bigframes_6e7606963c3f06b8181b3cb9449a4363\n" - ] - } - ], - "source": [ - "CLOUD_FUNCTION_NAME = format(extract_code.bigframes_cloud_function)\n", - "print(\"Cloud Function Name \" + CLOUD_FUNCTION_NAME)\n", - "REMOTE_FUNCTION_NAME = format(extract_code.bigframes_remote_function)\n", - "print(\"Remote Function Name \" + REMOTE_FUNCTION_NAME)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "4FEucaiqVs3H" - }, - "source": [ - "Apply the custom function to each LLM output DataFrame to get the processed results:" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "id": "bsQ9cmoWo0Ps" - }, - "outputs": [ - { - "data": { - "text/html": [ - "Query job 047903f8-ea67-430a-8281-8fb5a119b779 is DONE. 21.8 kB processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Query job 793df956-0b1a-46ba-bb5e-e428171f3bd0 is DONE. 26.3 kB processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "df_code = df_pred.assign(code=df_pred['ml_generate_text_llm_result'].apply(extract_code))\n", - "series_code = series_pred.assign(code=series_pred['ml_generate_text_llm_result'].apply(extract_code))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "ujQVVuhfWA3y" - }, - "source": [ - "You can see the differences by inspecting the first row of data:" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "id": "7yWzjhGy_zcy" - }, - "outputs": [ - { - "data": { - "text/html": [ - "Query job 6974c2b7-2ed9-4564-a80b-57aef6959e19 is DONE. 22.8 kB processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "import bigframes.pandas as bf\n", - "\n", - "# Create a DataFrame\n", - "df = pd.DataFrame([[1, 2, 3], [4, 5, 6], [7, 8, 9]])\n", - "\n", - "# Get the values as a NumPy array\n", - "values = df.values\n", - "\n", - "# Print the values\n", - "print(values)\n", - "\n" - ] - } - ], - "source": [ - "print(df_code['code'].iloc[0])" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "GTRdUw-Ro5R1" - }, - "source": [ - "# Save the results to Cloud Storage\n", - "\n", - "BigQuery DataFrames lets you save a BigQuery DataFrames DataFrame as a CSV file in Cloud Storage for further use. Try that now with your processed LLM output data." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "9DQ7eiQxPTi3" - }, - "source": [ - "Create a new Cloud Storage bucket with a unique name:" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": { - "id": "-J5LHgS6LLZ0" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Creating gs://code-samples-773ee0f2-e302-11ee-8298-4201c0a8181f/...\n" - ] - } - ], - "source": [ - "import uuid\n", - "BUCKET_ID = \"code-samples-\" + str(uuid.uuid1())\n", - "\n", - "!gcloud storage buckets create gs://{BUCKET_ID}" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "tyxZXj0UPYUv" - }, - "source": [ - "Use `to_csv` to write each BigQuery DataFrames DataFrame as a CSV file in the Cloud Storage bucket:" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": { - "id": "Zs_b5L-4IvER" - }, - "outputs": [ - { - "data": { - "text/html": [ - "Query job 81277037-032f-4557-a46e-1d39702f33d5 is DONE. 22.8 kB processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Query job 8dc5a38c-ac16-44e7-83dd-4187380f780f is DONE. 0 Bytes processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Query job 9087a758-b1f9-4be7-889b-7761ef0ad966 is DONE. 27.7 kB processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Query job 6126ea72-c6f7-43f0-8888-e1c2a464a8a4 is DONE. 0 Bytes processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "ur8xi4C7S06n" + }, + "outputs": [], + "source": [ + "# Copyright 2022 Google LLC\n", + "#\n", + "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", + "# you may not use this file except in compliance with the License.\n", + "# You may obtain a copy of the License at\n", + "#\n", + "# https://www.apache.org/licenses/LICENSE-2.0\n", + "#\n", + "# Unless required by applicable law or agreed to in writing, software\n", + "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", + "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", + "# See the License for the specific language governing permissions and\n", + "# limitations under the License." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "JAPoU8Sm5E6e" + }, + "source": [ + "## Use BigQuery DataFrames with Generative AI for code generation\n", + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + "
\n", + " \n", + " \"Colab Run in Colab\n", + " \n", + " \n", + " \n", + " \"GitHub\n", + " View on GitHub\n", + " \n", + " \n", + " \n", + " \"Vertex\n", + " Open in Vertex AI Workbench\n", + " \n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "24743cf4a1e1" + }, + "source": [ + "**_NOTE_**: This notebook has been tested in the following environment:\n", + "\n", + "* Python version = 3.10" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "tvgnzT1CKxrO" + }, + "source": [ + "## Overview\n", + "\n", + "Use this notebook to walk through an example use case of generating sample code by using BigQuery DataFrames and its integration with Generative AI support on Vertex AI.\n", + "\n", + "Learn more about [BigQuery DataFrames](https://cloud.google.com/python/docs/reference/bigframes/latest)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "d975e698c9a4" + }, + "source": [ + "### Objective\n", + "\n", + "In this tutorial, you create a CSV file containing sample code for calling a given set of APIs.\n", + "\n", + "The steps include:\n", + "\n", + "- Defining an LLM model in BigQuery DataFrames, specifically the [`text-bison` model of the PaLM API](https://cloud.google.com/vertex-ai/docs/generative-ai/model-reference/text), using `bigframes.ml.llm`.\n", + "- Creating a DataFrame by reading in data from Cloud Storage.\n", + "- Manipulating data in the DataFrame to build LLM prompts.\n", + "- Sending DataFrame prompts to the LLM model using the `predict` method.\n", + "- Creating and using a custom function to transform the output provided by the LLM model response.\n", + "- Exporting the resulting transformed DataFrame as a CSV file." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "08d289fa873f" + }, + "source": [ + "### Dataset\n", + "\n", + "This tutorial uses a dataset listing the names of various pandas DataFrame and Series APIs." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "aed92deeb4a0" + }, + "source": [ + "### Costs\n", + "\n", + "This tutorial uses billable components of Google Cloud:\n", + "\n", + "* BigQuery\n", + "* Generative AI support on Vertex AI\n", + "* Cloud Functions\n", + "\n", + "Learn about [BigQuery compute pricing](https://cloud.google.com/bigquery/pricing#analysis_pricing_models),\n", + "[Generative AI support on Vertex AI pricing](https://cloud.google.com/vertex-ai/pricing#generative_ai_models), and [Cloud Functions pricing](https://cloud.google.com/functions/pricing), and use the [Pricing Calculator](https://cloud.google.com/products/calculator/)\n", + "to generate a cost estimate based on your projected usage." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "i7EUnXsZhAGF" + }, + "source": [ + "## Installation\n", + "\n", + "Install the following packages, which are required to run this notebook:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "2b4ef9b72d43" + }, + "outputs": [], + "source": [ + "!pip install bigframes --upgrade --quiet" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "BF1j6f9HApxa" + }, + "source": [ + "## Before you begin\n", + "\n", + "Complete the tasks in this section to set up your environment." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Wbr2aVtFQBcg" + }, + "source": [ + "### Set up your Google Cloud project\n", + "\n", + "**The following steps are required, regardless of your notebook environment.**\n", + "\n", + "1. [Select or create a Google Cloud project](https://console.cloud.google.com/cloud-resource-manager). When you first create an account, you get a $300 credit towards your compute/storage costs.\n", + "\n", + "2. [Make sure that billing is enabled for your project](https://cloud.google.com/billing/docs/how-to/modify-project).\n", + "\n", + "3. [Click here](https://console.cloud.google.com/flows/enableapi?apiid=bigquery.googleapis.com,bigqueryconnection.googleapis.com,cloudfunctions.googleapis.com,run.googleapis.com,artifactregistry.googleapis.com,cloudbuild.googleapis.com,cloudresourcemanager.googleapis.com) to enable the following APIs:\n", + "\n", + " * BigQuery API\n", + " * BigQuery Connection API\n", + " * Cloud Functions API\n", + " * Cloud Run API\n", + " * Artifact Registry API\n", + " * Cloud Build API\n", + " * Cloud Resource Manager API\n", + " * Vertex AI API\n", + "\n", + "4. If you are running this notebook locally, install the [Cloud SDK](https://cloud.google.com/sdk)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "WReHDGG5g0XY" + }, + "source": [ + "#### Set your project ID\n", + "\n", + "If you don't know your project ID, try the following:\n", + "* Run `gcloud config list`.\n", + "* Run `gcloud projects list`.\n", + "* See the support page: [Locate the project ID](https://support.google.com/googleapi/answer/7014113)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "oM1iC_MfAts1" + }, + "outputs": [], + "source": [ + "PROJECT_ID = \"\" # @param {type:\"string\"}\n", + "\n", + "# Set the project id\n", + "! gcloud config set project {PROJECT_ID}" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "region" + }, + "source": [ + "#### Set the region\n", + "\n", + "You can also change the `REGION` variable used by BigQuery. Learn more about [BigQuery regions](https://cloud.google.com/bigquery/docs/locations#supported_locations)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "eF-Twtc4XGem" + }, + "outputs": [], + "source": [ + "REGION = \"US\" # @param {type: \"string\"}" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "sBCra4QMA2wR" + }, + "source": [ + "### Authenticate your Google Cloud account\n", + "\n", + "Depending on your Jupyter environment, you might have to manually authenticate. Follow the relevant instructions below." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "74ccc9e52986" + }, + "source": [ + "**Vertex AI Workbench**\n", + "\n", + "Do nothing, you are already authenticated." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "de775a3773ba" + }, + "source": [ + "**Local JupyterLab instance**\n", + "\n", + "Uncomment and run the following cell:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "254614fa0c46" + }, + "outputs": [], + "source": [ + "# ! gcloud auth login" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ef21552ccea8" + }, + "source": [ + "**Colab**\n", + "\n", + "Uncomment and run the following cell:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "603adbbf0532" + }, + "outputs": [], + "source": [ + "# from google.colab import auth\n", + "# auth.authenticate_user()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "960505627ddf" + }, + "source": [ + "### Import libraries" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "PyQmSRbKA8r-" + }, + "outputs": [], + "source": [ + "import bigframes.pandas as bf\n", + "from google.cloud import bigquery\n", + "from google.cloud import bigquery_connection_v1 as bq_connection" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "init_aip:mbsdk,all" + }, + "source": [ + "### Set BigQuery DataFrames options" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "NPPMuw2PXGeo" + }, + "outputs": [], + "source": [ + "bf.options.bigquery.project = PROJECT_ID\n", + "bf.options.bigquery.location = REGION" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "DTVtFlqeFbrU" + }, + "source": [ + "If you want to reset the location of the created DataFrame or Series objects, reset the session by executing `bf.close_session()`. After that, you can reuse `bf.options.bigquery.location` to specify another location." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "6eytf4xQHzcF" + }, + "source": [ + "# Define the LLM model\n", + "\n", + "BigQuery DataFrames provides integration with [`text-bison` model of the PaLM API](https://cloud.google.com/vertex-ai/docs/generative-ai/model-reference/text) via Vertex AI.\n", + "\n", + "This section walks through a few steps required in order to use the model in your notebook." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "rS4VO1TGiO4G" + }, + "source": [ + "## Create a BigQuery Cloud resource connection\n", + "\n", + "You need to create a [Cloud resource connection](https://cloud.google.com/bigquery/docs/create-cloud-resource-connection) to enable BigQuery DataFrames to interact with Vertex AI services." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "KFPjDM4LVh96" + }, + "outputs": [], + "source": [ + "CONN_NAME = \"bqdf-llm\"\n", + "\n", + "client = bq_connection.ConnectionServiceClient()\n", + "new_conn_parent = f\"projects/{PROJECT_ID}/locations/{REGION}\"\n", + "exists_conn_parent = f\"projects/{PROJECT_ID}/locations/{REGION}/connections/{CONN_NAME}\"\n", + "cloud_resource_properties = bq_connection.CloudResourceProperties({})\n", + "\n", + "try:\n", + " request = client.get_connection(\n", + " request=bq_connection.GetConnectionRequest(name=exists_conn_parent)\n", + " )\n", + " CONN_SERVICE_ACCOUNT = f\"serviceAccount:{request.cloud_resource.service_account_id}\"\n", + "except Exception:\n", + " connection = bq_connection.types.Connection(\n", + " {\"friendly_name\": CONN_NAME, \"cloud_resource\": cloud_resource_properties}\n", + " )\n", + " request = bq_connection.CreateConnectionRequest(\n", + " {\n", + " \"parent\": new_conn_parent,\n", + " \"connection_id\": CONN_NAME,\n", + " \"connection\": connection,\n", + " }\n", + " )\n", + " response = client.create_connection(request)\n", + " CONN_SERVICE_ACCOUNT = (\n", + " f\"serviceAccount:{response.cloud_resource.service_account_id}\"\n", + " )\n", + "print(CONN_SERVICE_ACCOUNT)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "W6l6Ol2biU9h" + }, + "source": [ + "## Set permissions for the service account\n", + "\n", + "The resource connection service account requires certain project-level permissions:\n", + " - `roles/aiplatform.user` and `roles/bigquery.connectionUser`: These roles are required for the connection to create a model definition using the LLM model in Vertex AI ([documentation](https://cloud.google.com/bigquery/docs/generate-text#give_the_service_account_access)).\n", + " - `roles/run.invoker`: This role is required for the connection to have read-only access to Cloud Run services that back custom/remote functions ([documentation](https://cloud.google.com/bigquery/docs/remote-functions#grant_permission_on_function)).\n", + "\n", + "Set these permissions by running the following `gcloud` commands:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "d8wja24SVq6s" + }, + "outputs": [], + "source": [ + "!gcloud projects add-iam-policy-binding {PROJECT_ID} --condition=None --no-user-output-enabled --member={CONN_SERVICE_ACCOUNT} --role='roles/bigquery.connectionUser'\n", + "!gcloud projects add-iam-policy-binding {PROJECT_ID} --condition=None --no-user-output-enabled --member={CONN_SERVICE_ACCOUNT} --role='roles/aiplatform.user'\n", + "!gcloud projects add-iam-policy-binding {PROJECT_ID} --condition=None --no-user-output-enabled --member={CONN_SERVICE_ACCOUNT} --role='roles/run.invoker'" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "qUjT8nw-jIXp" + }, + "source": [ + "## Define the model\n", + "\n", + "Use `bigframes.ml.llm` to define the model:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "sdjeXFwcHfl7" + }, + "outputs": [], + "source": [ + "from bigframes.ml.llm import PaLM2TextGenerator\n", + "\n", + "session = bf.get_global_session()\n", + "connection = f\"{PROJECT_ID}.{REGION}.{CONN_NAME}\"\n", + "model = PaLM2TextGenerator(session=session, connection_name=connection)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "GbW0oCnU1s1N" + }, + "source": [ + "# Read data from Cloud Storage into BigQuery DataFrames\n", + "\n", + "You can create a BigQuery DataFrames DataFrame by reading data from any of the following locations:\n", + "\n", + "* A local data file\n", + "* Data stored in a BigQuery table\n", + "* A data file stored in Cloud Storage\n", + "* An in-memory pandas DataFrame\n", + "\n", + "In this tutorial, you create BigQuery DataFrames DataFrames by reading two CSV files stored in Cloud Storage, one containing a list of DataFrame API names and one containing a list of Series API names." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "SchiTkQGIJog" + }, + "outputs": [], + "source": [ + "df_api = bf.read_csv(\"gs://cloud-samples-data/vertex-ai/bigframe/df.csv\")\n", + "series_api = bf.read_csv(\"gs://cloud-samples-data/vertex-ai/bigframe/series.csv\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "7OBjw2nmQY3-" + }, + "source": [ + "Take a peek at a few rows of data for each file:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "QCqgVCIsGGuv" + }, + "outputs": [], + "source": [ + "df_api.head(2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "BGJnZbgEGS5-" + }, + "outputs": [], + "source": [ + "series_api.head(2)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "m3ZJEsi7SUKV" + }, + "source": [ + "# Generate code using the LLM model\n", + "\n", + "Prepare the prompts and send them to the LLM model for prediction." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "9EMAqR37AfLS" + }, + "source": [ + "## Prompt design in BigQuery DataFrames\n", + "\n", + "Designing prompts for LLMs is a fast growing area and you can read more in [this documentation](https://cloud.google.com/vertex-ai/docs/generative-ai/learn/introduction-prompt-design).\n", + "\n", + "For this tutorial, you use a simple prompt to ask the LLM model for sample code for each of the API methods (or rows) from the last step's DataFrames. The output is the new DataFrames `df_prompt` and `series_prompt`, which contain the full prompt text." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "EDAaIwHpQCDZ" + }, + "outputs": [], + "source": [ + "df_prompt_prefix = \"Generate Pandas sample code for DataFrame.\"\n", + "series_prompt_prefix = \"Generate Pandas sample code for Series.\"\n", + "\n", + "df_prompt = (df_prompt_prefix + df_api['API'])\n", + "series_prompt = (series_prompt_prefix + series_api['API'])\n", + "\n", + "df_prompt.head(2)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "rwPLjqW2Ajzh" + }, + "source": [ + "## Make predictions using the LLM model\n", + "\n", + "Use the BigQuery DataFrames DataFrame containing the full prompt text as the input to the `predict` method. The `predict` method calls the LLM model and returns its generated text output back to two new BigQuery DataFrames DataFrames, `df_pred` and `series_pred`.\n", + "\n", + "Note: The predictions might take a few minutes to run." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "6i6HkFJZa8na" + }, + "outputs": [], + "source": [ + "df_pred = model.predict(df_prompt.to_frame(), max_output_tokens=1024)\n", + "series_pred = model.predict(series_prompt.to_frame(), max_output_tokens=1024)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "89cB8MW4UIdV" + }, + "source": [ + "Once the predictions are processed, take a look at the sample output from the LLM, which provides code samples for the API names listed in the DataFrames dataset." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "9A2gw6hP_2nX" + }, + "outputs": [], + "source": [ + "print(df_pred['ml_generate_text_llm_result'].iloc[0])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Fx4lsNqMorJ-" + }, + "source": [ + "# Manipulate LLM output using a remote function\n", + "\n", + "The output that the LLM provides often contains additional text beyond the code sample itself. Using BigQuery DataFrames, you can deploy custom Python functions that process and transform this output.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "d8L7SN03VByG" + }, + "source": [ + "Running the cell below creates a custom function that you can use to process the LLM output data in two ways:\n", + "1. Strip the LLM text output to include only the code block.\n", + "2. Substitute `import pandas as pd` with `import bigframes.pandas as bf` so that the resulting code block works with BigQuery DataFrames." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "GskyyUQPowBT" + }, + "outputs": [], + "source": [ + "@bf.remote_function([str], str, bigquery_connection=CONN_NAME)\n", + "def extract_code(text: str):\n", + " try:\n", + " res = text[text.find('\\n')+1:text.find('```', 3)]\n", + " res = res.replace(\"import pandas as pd\", \"import bigframes.pandas as bf\")\n", + " if \"import bigframes.pandas as bf\" not in res:\n", + " res = \"import bigframes.pandas as bf\\n\" + res\n", + " return res\n", + " except:\n", + " return \"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "hVQAoqBUOJQf" + }, + "source": [ + "The custom function is deployed as a Cloud Function, and then integrated with BigQuery as a [remote function](https://cloud.google.com/bigquery/docs/remote-functions). Save both of the function names so that you can clean them up at the end of this notebook." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "PBlp-C-DOHRO" + }, + "outputs": [], + "source": [ + "CLOUD_FUNCTION_NAME = format(extract_code.bigframes_cloud_function)\n", + "print(\"Cloud Function Name \" + CLOUD_FUNCTION_NAME)\n", + "REMOTE_FUNCTION_NAME = format(extract_code.bigframes_remote_function)\n", + "print(\"Remote Function Name \" + REMOTE_FUNCTION_NAME)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "4FEucaiqVs3H" + }, + "source": [ + "Apply the custom function to each LLM output DataFrame to get the processed results:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "bsQ9cmoWo0Ps" + }, + "outputs": [], + "source": [ + "df_code = df_pred.assign(code=df_pred['ml_generate_text_llm_result'].apply(extract_code))\n", + "series_code = series_pred.assign(code=series_pred['ml_generate_text_llm_result'].apply(extract_code))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ujQVVuhfWA3y" + }, + "source": [ + "You can see the differences by inspecting the first row of data:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "7yWzjhGy_zcy" + }, + "outputs": [], + "source": [ + "print(df_code['code'].iloc[0])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "GTRdUw-Ro5R1" + }, + "source": [ + "# Save the results to Cloud Storage\n", + "\n", + "BigQuery DataFrames lets you save a BigQuery DataFrames DataFrame as a CSV file in Cloud Storage for further use. Try that now with your processed LLM output data." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "9DQ7eiQxPTi3" + }, + "source": [ + "Create a new Cloud Storage bucket with a unique name:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "-J5LHgS6LLZ0" + }, + "outputs": [], + "source": [ + "import uuid\n", + "BUCKET_ID = \"code-samples-\" + str(uuid.uuid1())\n", + "\n", + "!gsutil mb gs://{BUCKET_ID}" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "tyxZXj0UPYUv" + }, + "source": [ + "Use `to_csv` to write each BigQuery DataFrames DataFrame as a CSV file in the Cloud Storage bucket:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Zs_b5L-4IvER" + }, + "outputs": [], + "source": [ + "df_code[[\"code\"]].to_csv(f\"gs://{BUCKET_ID}/df_code*.csv\")\n", + "series_code[[\"code\"]].to_csv(f\"gs://{BUCKET_ID}/series_code*.csv\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "UDBtDlrTuuh8" + }, + "source": [ + "You can navigate to the Cloud Storage bucket browser to download the two files and view them.\n", + "\n", + "Run the following cell, and then follow the link to your Cloud Storage bucket browser:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "PspCXu-qu_ND" + }, + "outputs": [], + "source": [ + "print(f'https://console.developers.google.com/storage/browser/{BUCKET_ID}/')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "RGSvUk48RK20" + }, + "source": [ + "# Summary and next steps\n", + "\n", + "You've used BigQuery DataFrames' integration with LLM models (`bigframes.ml.llm`) to generate code samples, and have tranformed LLM output by creating and using a custom function in BigQuery DataFrames.\n", + "\n", + "Learn more about BigQuery DataFrames in the [documentation](https://cloud.google.com/python/docs/reference/bigframes/latest) and find more sample notebooks in the [GitHub repo](https://github.com/googleapis/python-bigquery-dataframes/tree/main/notebooks)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "TpV-iwP9qw9c" + }, + "source": [ + "## Cleaning up\n", + "\n", + "To clean up all Google Cloud resources used in this project, you can [delete the Google Cloud\n", + "project](https://cloud.google.com/resource-manager/docs/creating-managing-projects#shutting_down_projects) you used for the tutorial.\n", + "\n", + "Otherwise, you can uncomment the remaining cells and run them to delete the individual resources you created in this tutorial:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "yw7A461XLjvW" + }, + "outputs": [], + "source": [ + "# # Delete the BigQuery Connection\n", + "# from google.cloud import bigquery_connection_v1 as bq_connection\n", + "# client = bq_connection.ConnectionServiceClient()\n", + "# CONNECTION_ID = f\"projects/{PROJECT_ID}/locations/{REGION}/connections/{CONN_NAME}\"\n", + "# client.delete_connection(name=CONNECTION_ID)\n", + "# print(f\"Deleted connection '{CONNECTION_ID}'.\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "sx_vKniMq9ZX" + }, + "outputs": [], + "source": [ + "# # Delete the Cloud Function\n", + "# ! gcloud functions delete {CLOUD_FUNCTION_NAME} --quiet\n", + "# # Delete the Remote Function\n", + "# REMOTE_FUNCTION_NAME = REMOTE_FUNCTION_NAME.replace(PROJECT_ID + \".\", \"\")\n", + "# ! bq rm --routine --force=true {REMOTE_FUNCTION_NAME}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "iQFo6OUBLmi3" + }, + "outputs": [], + "source": [ + "# # Delete the Google Cloud Storage bucket and files\n", + "# ! gsutil rm -r gs://{BUCKET_ID}\n", + "# print(f\"Deleted bucket '{BUCKET_ID}'.\")" + ] } - ], - "source": [ - "df_code[[\"code\"]].to_csv(f\"gs://{BUCKET_ID}/df_code*.csv\")\n", - "series_code[[\"code\"]].to_csv(f\"gs://{BUCKET_ID}/series_code*.csv\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "UDBtDlrTuuh8" - }, - "source": [ - "You can navigate to the Cloud Storage bucket browser to download the two files and view them.\n", - "\n", - "Run the following cell, and then follow the link to your Cloud Storage bucket browser:" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": { - "id": "PspCXu-qu_ND" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "https://console.developers.google.com/storage/browser/code-samples-773ee0f2-e302-11ee-8298-4201c0a8181f/\n" - ] + ], + "metadata": { + "colab": { + "provenance": [], + "toc_visible": true + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" } - ], - "source": [ - "print(f'https://console.developers.google.com/storage/browser/{BUCKET_ID}/')" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "RGSvUk48RK20" - }, - "source": [ - "# Summary and next steps\n", - "\n", - "You've used BigQuery DataFrames' integration with LLM models (`bigframes.ml.llm`) to generate code samples, and have tranformed LLM output by creating and using a custom function in BigQuery DataFrames.\n", - "\n", - "Learn more about BigQuery DataFrames in the [documentation](https://cloud.google.com/python/docs/reference/bigframes/latest) and find more sample notebooks in the [GitHub repo](https://github.com/googleapis/python-bigquery-dataframes/tree/main/notebooks)." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "TpV-iwP9qw9c" - }, - "source": [ - "## Cleaning up\n", - "\n", - "To clean up all Google Cloud resources used in this project, you can [delete the Google Cloud\n", - "project](https://cloud.google.com/resource-manager/docs/creating-managing-projects#shutting_down_projects) you used for the tutorial.\n", - "\n", - "Otherwise, you can uncomment the remaining cells and run them to delete the individual resources you created in this tutorial:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "bf.close_session()" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": { - "id": "yw7A461XLjvW" - }, - "outputs": [], - "source": [ - "# # Delete the BigQuery Connection\n", - "# from google.cloud import bigquery_connection_v1 as bq_connection\n", - "# client = bq_connection.ConnectionServiceClient()\n", - "# CONNECTION_ID = f\"projects/{PROJECT_ID}/locations/{REGION}/connections/{CONN_NAME}\"\n", - "# client.delete_connection(name=CONNECTION_ID)\n", - "# print(f\"Deleted connection '{CONNECTION_ID}'.\")" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": { - "id": "sx_vKniMq9ZX" - }, - "outputs": [], - "source": [ - "# # Delete the Cloud Function\n", - "# ! gcloud functions delete {CLOUD_FUNCTION_NAME} --quiet\n", - "# # Delete the Remote Function\n", - "# REMOTE_FUNCTION_NAME = REMOTE_FUNCTION_NAME.replace(PROJECT_ID + \".\", \"\")\n", - "# ! bq rm --routine --force=true {REMOTE_FUNCTION_NAME}" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": { - "id": "iQFo6OUBLmi3" - }, - "outputs": [], - "source": [ - "# # Delete the Google Cloud Storage bucket and files\n", - "# ! gcloud storage rm gs://{BUCKET_ID} --recursive\n", - "# print(f\"Deleted bucket '{BUCKET_ID}'.\")" - ] - } - ], - "metadata": { - "colab": { - "provenance": [], - "toc_visible": true - }, - "kernelspec": { - "display_name": "venv (3.10.14)", - "language": "python", - "name": "python3" }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.14" - } - }, - "nbformat": 4, - "nbformat_minor": 0 + "nbformat": 4, + "nbformat_minor": 0 } diff --git a/notebooks/generative_ai/bq_dataframes_llm_kmeans.ipynb b/notebooks/generative_ai/bq_dataframes_llm_kmeans.ipynb deleted file mode 100644 index 2d5bb46d95e..00000000000 --- a/notebooks/generative_ai/bq_dataframes_llm_kmeans.ipynb +++ /dev/null @@ -1,1758 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# Copyright 2023 Google LLC\n", - "#\n", - "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", - "# you may not use this file except in compliance with the License.\n", - "# You may obtain a copy of the License at\n", - "#\n", - "# https://www.apache.org/licenses/LICENSE-2.0\n", - "#\n", - "# Unless required by applicable law or agreed to in writing, software\n", - "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", - "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", - "# See the License for the specific language governing permissions and\n", - "# limitations under the License." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Use BigQuery DataFrames to cluster and characterize complaints\n", - "\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - "
\n", - " \n", - " \"Colab Run in Colab\n", - " \n", - " \n", - " \n", - " \"GitHub\n", - " View on GitHub\n", - " \n", - " \n", - " \n", - " \"Vertex\n", - " Open in Vertex AI Workbench\n", - " \n", - " \n", - " \n", - " \"BQ\n", - " Open in BQ Studio\n", - " \n", - "
" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Overview\n", - "\n", - "The goal of this notebook is to demonstrate a comment characterization algorithm for an online business. We will accomplish this using [Google's Embedding Models](https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#models) and [KMeans clustering](https://en.wikipedia.org/wiki/K-means_clustering) in three steps:\n", - "\n", - "1. Use TextEmbeddingGenerator to [generate text embeddings](https://cloud.google.com/vertex-ai/docs/generative-ai/embeddings/get-text-embeddings) for each of 10000 complaints sent to an online bank. If you're not familiar with what a text embedding is, it's a list of numbers that are like coordinates in an imaginary \"meaning space\" for sentences. (It's like [word embeddings](https://en.wikipedia.org/wiki/Word_embedding), but for more general text.) The important point for our purposes is that similar sentences are close to each other in this imaginary space.\n", - "2. Use KMeans clustering to group together complaints whose text embeddings are near to eachother. This will give us sets of similar complaints, but we don't yet know _why_ these complaints are similar.\n", - "3. Prompt GeminiTextGenerator in English asking what the difference is between the groups of complaints that we got. Thanks to the power of modern LLMs, the response might give us a very good idea of what these complaints are all about, but remember to [\"understand the limits of your dataset and model.\"](https://ai.google/responsibility/responsible-ai-practices/#:~:text=Understand%20the%20limitations%20of%20your%20dataset%20and%20model)\n", - "\n", - "We will tie these pieces together in Python using BigQuery DataFrames. [Click here](https://cloud.google.com/bigquery/docs/dataframes-quickstart) to learn more about BigQuery DataFrames!" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Dataset\n", - "\n", - "This notebook uses the [CFPB Consumer Complaint Database](https://console.cloud.google.com/marketplace/product/cfpb/complaint-database)." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Costs\n", - "\n", - "This tutorial uses billable components of Google Cloud:\n", - "\n", - "* BigQuery (compute)\n", - "* BigQuery ML\n", - "* Generative AI support on Vertex AI\n", - "\n", - "Learn about [BigQuery compute pricing](https://cloud.google.com/bigquery/pricing#analysis_pricing_models), [Generative AI support on Vertex AI pricing](https://cloud.google.com/vertex-ai/pricing#generative_ai_models),\n", - "and [BigQuery ML pricing](https://cloud.google.com/bigquery/pricing#bqml),\n", - "and use the [Pricing Calculator](https://cloud.google.com/products/calculator/)\n", - "to generate a cost estimate based on your projected usage." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Before you begin\n", - "\n", - "Complete the tasks in this section to set up your environment." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Set up your Google Cloud project\n", - "\n", - "**The following steps are required, regardless of your notebook environment.**\n", - "\n", - "1. [Select or create a Google Cloud project](https://console.cloud.google.com/cloud-resource-manager). When you first create an account, you get a $300 credit towards your compute/storage costs.\n", - "\n", - "2. [Make sure that billing is enabled for your project](https://cloud.google.com/billing/docs/how-to/modify-project).\n", - "\n", - "3. [Click here](https://console.cloud.google.com/flows/enableapi?apiid=bigquery.googleapis.com,bigqueryconnection.googleapis.com,aiplatform.googleapis.com) to enable the following APIs:\n", - "\n", - " * BigQuery API\n", - " * BigQuery Connection API\n", - " * Vertex AI API\n", - "\n", - "4. If you are running this notebook locally, install the [Cloud SDK](https://cloud.google.com/sdk)." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Set your project ID\n", - "\n", - "**If you don't know your project ID**, see the support page: [Locate the project ID](https://support.google.com/googleapi/answer/7014113)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# set your project ID below\n", - "PROJECT_ID = \"\" # @param {type:\"string\"}\n", - "\n", - "# Set the project id in gcloud\n", - "#! gcloud config set project {PROJECT_ID}" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Authenticate your Google Cloud account\n", - "\n", - "Depending on your Jupyter environment, you might have to manually authenticate. Follow the relevant instructions below." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Vertex AI Workbench**\n", - "\n", - "Do nothing, you are already authenticated." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Local JupyterLab instance**\n", - "\n", - "Uncomment and run the following cell:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# ! gcloud auth login" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Colab**\n", - "\n", - "Uncomment and run the following cell:" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# from google.colab import auth\n", - "# auth.authenticate_user()" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we are ready to use BigQuery DataFrames!" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "xckgWno6ouHY" - }, - "source": [ - "## Step 1: Text embedding " - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "BigQuery DataFrames setup" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "id": "R7STCS8xB5d2" - }, - "outputs": [], - "source": [ - "import bigframes.pandas as bf\n", - "\n", - "# Note: The project option is not required in all environments.\n", - "# On BigQuery Studio, the project ID is automatically detected.\n", - "bf.options.bigquery.project = PROJECT_ID" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If you want to reset the location of the created DataFrame or Series objects, reset the session by executing `bf.close_session()`. After that, you can reuse `bf.options.bigquery.location` to specify another location." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "v6FGschEowht" - }, - "source": [ - "Data Input - read the data from a publicly available BigQuery dataset" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "id": "zDSwoBo1CU3G" - }, - "outputs": [], - "source": [ - "input_df = bf.read_gbq(\"bigquery-public-data.cfpb_complaints.complaint_database\")" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "id": "tYDoaKgJChiq" - }, - "outputs": [ - { - "data": { - "text/html": [ - "Query job 960f637d-89eb-4bbf-a34c-36ed624e8e9a is DONE. 2.3 GB processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Query job 59bb207c-98e1-4dab-8686-320f276b09df is DONE. 63.7 MB processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
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consumer_complaint_narrative
2557016I've been disputing fraud accounts on my credi...
2557686American Express Platinum totally messed up my...
2558170I recently looked at my credit report and noti...
2558545Select Portfolio Servicing contacted my insura...
2558652I checked my credit report and I am upset on w...
\n", - "
" - ], - "text/plain": [ - " consumer_complaint_narrative\n", - "2557016 I've been disputing fraud accounts on my credi...\n", - "2557686 American Express Platinum totally messed up my...\n", - "2558170 I recently looked at my credit report and noti...\n", - "2558545 Select Portfolio Servicing contacted my insura...\n", - "2558652 I checked my credit report and I am upset on w..." - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "issues_df = input_df[[\"consumer_complaint_narrative\"]].dropna()\n", - "issues_df.peek(n=5) # View an arbitrary five complaints" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Downsample DataFrame to 10,000 records for model training." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "id": "OltYSUEcsSOW" - }, - "outputs": [], - "source": [ - "# Choose 10,000 complaints randomly and store them in a column in a DataFrame\n", - "downsampled_issues_df = issues_df.sample(n=10000)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "Wl2o-NYMoygb" - }, - "source": [ - "Generate the text embeddings" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "id": "li38q8FzDDMu" - }, - "outputs": [ - { - "data": { - "text/html": [ - "Query job e4616b5e-b4c0-490c-a249-484f373f89d9 is DONE. 0 Bytes processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from bigframes.ml.llm import TextEmbeddingGenerator\n", - "\n", - "model = TextEmbeddingGenerator() # No connection id needed" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "id": "cOuSOQ5FDewD" - }, - "outputs": [ - { - "data": { - "text/html": [ - "Query job 89f96e88-2dd5-4326-8912-925b237e2877 is DONE. 1.3 GB processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/google/home/garrettwu/src/bigframes/bigframes/core/__init__.py:108: PreviewWarning: Interpreting JSON column(s) as StringDtype. This behavior may change in future versions.\n", - " warnings.warn(\n" - ] - }, - { - "data": { - "text/html": [ - "Query job bcdbfe96-2cce-4269-81f4-0334033b458b is DONE. 20.0 kB processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Query job 3b89850f-4491-4343-912a-7a2fd3137790 is DONE. 0 Bytes processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Query job a2999e90-8d14-4f4a-99dc-4e769df01837 is DONE. 72.0 MB processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
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ml_generate_embedding_resultml_generate_embedding_statisticsml_generate_embedding_statuscontent
415[ 2.56774724e-02 -1.06168222e-02 3.06945704e-...{\"token_count\":171,\"truncated\":false}DEPT OF EDUCATION/XXXX is stating I was late ...
596[ 5.90653270e-02 -9.31344274e-03 -7.12460047e-...{\"token_count\":668,\"truncated\":false}I alerted my credit card company XX/XX/2017 th...
706[ 0.01298233 0.00130001 0.01800315 0.037078...{\"token_count\":252,\"truncated\":false}Sallie mae is corrupt. \n", - "I have tried to talk t...
804[-1.39777679e-02 1.68943349e-02 5.53999236e-...{\"token_count\":412,\"truncated\":false}In accordance with the Fair Credit Reporting a...
861[ 2.33309343e-02 -2.36528926e-03 3.37129943e-...{\"token_count\":160,\"truncated\":false}Hello, My name is XXXX XXXX XXXX. I have a pro...
1030[ 0.06060313 -0.06495965 -0.03605044 -0.028016...{\"token_count\":298,\"truncated\":false}Hello, I would like to complain about PayPal H...
1582[ 0.01255985 -0.01652482 -0.02638046 0.036858...{\"token_count\":814,\"truncated\":false}Transunion is listing personal information ( n...
1600[ 5.13355099e-02 4.01246967e-03 5.72342947e-...{\"token_count\":653,\"truncated\":false}On XX/XX/XXXX, I called Citizen Bank at XXXX t...
2060[ 6.44792162e-04 4.95899878e-02 4.67925966e-...{\"token_count\":136,\"truncated\":false}Theses names are the known liars that I have s...
2283[ 4.71848622e-02 -8.68239347e-03 5.80501892e-...{\"token_count\":478,\"truncated\":false}My house was hit by a tree XX/XX/2018. My insu...
2421[-2.90394691e-03 -1.81679502e-02 -7.99657404e-...{\"token_count\":389,\"truncated\":false}I became aware of a credit inquiry on my XXXX...
2422[-6.70500053e-03 1.51133696e-02 4.94448021e-...{\"token_count\":124,\"truncated\":false}I have sent numerous letters, police reports a...
2658[ 6.70989677e-02 -3.53626162e-02 1.08648362e-...{\"token_count\":762,\"truncated\":false}This letter concerns two disputes ( chargeback...
2883[-1.28255319e-02 -1.89735275e-02 5.68657108e-...{\"token_count\":71,\"truncated\":false}It is very frustrating that this has been goin...
2951[ 3.23301251e-03 -2.61142217e-02 1.31891826e-...{\"token_count\":95,\"truncated\":false}I, the consumer, in fact, have a right to priv...
2992[-2.22910382e-03 -1.07050659e-02 4.74211425e-...{\"token_count\":407,\"truncated\":false}XXXX XXXX XXXX should not be reporting to Expe...
3969[ 1.58297736e-02 3.01055871e-02 5.60088176e-...{\"token_count\":287,\"truncated\":false}DEAR CFPB ; XXXX ; XXXX ; AND TRANSUNION ; SEE...
4087[ 1.99207035e-03 -7.62321474e-03 7.92114343e-...{\"token_count\":88,\"truncated\":false}This debt was from my identity being stolen I ...
4326[ 3.44273262e-02 -3.36350128e-02 1.91939529e-...{\"token_count\":52,\"truncated\":false}The items that are reflected on my credit repo...
4682[ 2.47727744e-02 -1.77769139e-02 4.63737026e-...{\"token_count\":284,\"truncated\":false}I filed for chapter XXXX bankruptcy on XXXX...
5005[ 2.51834448e-02 -4.92606424e-02 -1.37688573e-...{\"token_count\":17,\"truncated\":false}There are 2 Inquires on my credit report that ...
5144[ 3.26358266e-02 -3.67171178e-03 3.65621522e-...{\"token_count\":105,\"truncated\":false}My mortgage was sold from XXXX XXXX to freed...
6090[ 2.47520711e-02 1.09149124e-02 1.35175223e-...{\"token_count\":545,\"truncated\":false}On XX/XX/XXXX this company received certified...
6449[ 1.86854266e-02 1.31238240e-03 -4.96791191e-...{\"token_count\":104,\"truncated\":false}After hours on the phone with multiple agents,...
6486[ 1.56347770e-02 2.23377198e-02 -1.32683543e-...{\"token_count\":211,\"truncated\":false}On XX/XX/2019 two charges one for XXXX and one...
\n", - "

25 rows × 4 columns

\n", - "
[10000 rows x 4 columns in total]" - ], - "text/plain": [ - " ml_generate_embedding_result \\\n", - "415 [ 2.56774724e-02 -1.06168222e-02 3.06945704e-... \n", - "596 [ 5.90653270e-02 -9.31344274e-03 -7.12460047e-... \n", - "706 [ 0.01298233 0.00130001 0.01800315 0.037078... \n", - "804 [-1.39777679e-02 1.68943349e-02 5.53999236e-... \n", - "861 [ 2.33309343e-02 -2.36528926e-03 3.37129943e-... \n", - "1030 [ 0.06060313 -0.06495965 -0.03605044 -0.028016... \n", - "1582 [ 0.01255985 -0.01652482 -0.02638046 0.036858... \n", - "1600 [ 5.13355099e-02 4.01246967e-03 5.72342947e-... \n", - "2060 [ 6.44792162e-04 4.95899878e-02 4.67925966e-... \n", - "2283 [ 4.71848622e-02 -8.68239347e-03 5.80501892e-... \n", - "2421 [-2.90394691e-03 -1.81679502e-02 -7.99657404e-... \n", - "2422 [-6.70500053e-03 1.51133696e-02 4.94448021e-... \n", - "2658 [ 6.70989677e-02 -3.53626162e-02 1.08648362e-... \n", - "2883 [-1.28255319e-02 -1.89735275e-02 5.68657108e-... \n", - "2951 [ 3.23301251e-03 -2.61142217e-02 1.31891826e-... \n", - "2992 [-2.22910382e-03 -1.07050659e-02 4.74211425e-... \n", - "3969 [ 1.58297736e-02 3.01055871e-02 5.60088176e-... \n", - "4087 [ 1.99207035e-03 -7.62321474e-03 7.92114343e-... \n", - "4326 [ 3.44273262e-02 -3.36350128e-02 1.91939529e-... \n", - "4682 [ 2.47727744e-02 -1.77769139e-02 4.63737026e-... \n", - "5005 [ 2.51834448e-02 -4.92606424e-02 -1.37688573e-... \n", - "5144 [ 3.26358266e-02 -3.67171178e-03 3.65621522e-... \n", - "6090 [ 2.47520711e-02 1.09149124e-02 1.35175223e-... \n", - "6449 [ 1.86854266e-02 1.31238240e-03 -4.96791191e-... \n", - "6486 [ 1.56347770e-02 2.23377198e-02 -1.32683543e-... \n", - "\n", - " ml_generate_embedding_statistics ml_generate_embedding_status \\\n", - "415 {\"token_count\":171,\"truncated\":false} \n", - "596 {\"token_count\":668,\"truncated\":false} \n", - "706 {\"token_count\":252,\"truncated\":false} \n", - "804 {\"token_count\":412,\"truncated\":false} \n", - "861 {\"token_count\":160,\"truncated\":false} \n", - "1030 {\"token_count\":298,\"truncated\":false} \n", - "1582 {\"token_count\":814,\"truncated\":false} \n", - "1600 {\"token_count\":653,\"truncated\":false} \n", - "2060 {\"token_count\":136,\"truncated\":false} \n", - "2283 {\"token_count\":478,\"truncated\":false} \n", - "2421 {\"token_count\":389,\"truncated\":false} \n", - "2422 {\"token_count\":124,\"truncated\":false} \n", - "2658 {\"token_count\":762,\"truncated\":false} \n", - "2883 {\"token_count\":71,\"truncated\":false} \n", - "2951 {\"token_count\":95,\"truncated\":false} \n", - "2992 {\"token_count\":407,\"truncated\":false} \n", - "3969 {\"token_count\":287,\"truncated\":false} \n", - "4087 {\"token_count\":88,\"truncated\":false} \n", - "4326 {\"token_count\":52,\"truncated\":false} \n", - "4682 {\"token_count\":284,\"truncated\":false} \n", - "5005 {\"token_count\":17,\"truncated\":false} \n", - "5144 {\"token_count\":105,\"truncated\":false} \n", - "6090 {\"token_count\":545,\"truncated\":false} \n", - "6449 {\"token_count\":104,\"truncated\":false} \n", - "6486 {\"token_count\":211,\"truncated\":false} \n", - "\n", - " content \n", - "415 DEPT OF EDUCATION/XXXX is stating I was late ... \n", - "596 I alerted my credit card company XX/XX/2017 th... \n", - "706 Sallie mae is corrupt. \n", - "I have tried to talk t... \n", - "804 In accordance with the Fair Credit Reporting a... \n", - "861 Hello, My name is XXXX XXXX XXXX. I have a pro... \n", - "1030 Hello, I would like to complain about PayPal H... \n", - "1582 Transunion is listing personal information ( n... \n", - "1600 On XX/XX/XXXX, I called Citizen Bank at XXXX t... \n", - "2060 Theses names are the known liars that I have s... \n", - "2283 My house was hit by a tree XX/XX/2018. My insu... \n", - "2421 I became aware of a credit inquiry on my XXXX... \n", - "2422 I have sent numerous letters, police reports a... \n", - "2658 This letter concerns two disputes ( chargeback... \n", - "2883 It is very frustrating that this has been goin... \n", - "2951 I, the consumer, in fact, have a right to priv... \n", - "2992 XXXX XXXX XXXX should not be reporting to Expe... \n", - "3969 DEAR CFPB ; XXXX ; XXXX ; AND TRANSUNION ; SEE... \n", - "4087 This debt was from my identity being stolen I ... \n", - "4326 The items that are reflected on my credit repo... \n", - "4682 I filed for chapter XXXX bankruptcy on XXXX... \n", - "5005 There are 2 Inquires on my credit report that ... \n", - "5144 My mortgage was sold from XXXX XXXX to freed... \n", - "6090 On XX/XX/XXXX this company received certified... \n", - "6449 After hours on the phone with multiple agents,... \n", - "6486 On XX/XX/2019 two charges one for XXXX and one... \n", - "...\n", - "\n", - "[10000 rows x 4 columns]" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Will take ~3 minutes to compute the embeddings\n", - "predicted_embeddings = model.predict(downsampled_issues_df)\n", - "# Notice the lists of numbers that are our text embeddings for each complaint\n", - "predicted_embeddings" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The model may have encountered errors while calculating embeddings for some rows. 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ml_generate_embedding_resultml_generate_embedding_statisticsml_generate_embedding_statuscontent
415[ 2.56774724e-02 -1.06168222e-02 3.06945704e-...{\"token_count\":171,\"truncated\":false}DEPT OF EDUCATION/XXXX is stating I was late ...
596[ 5.90653270e-02 -9.31344274e-03 -7.12460047e-...{\"token_count\":668,\"truncated\":false}I alerted my credit card company XX/XX/2017 th...
706[ 0.01298233 0.00130001 0.01800315 0.037078...{\"token_count\":252,\"truncated\":false}Sallie mae is corrupt. \n", - "I have tried to talk t...
804[-1.39777679e-02 1.68943349e-02 5.53999236e-...{\"token_count\":412,\"truncated\":false}In accordance with the Fair Credit Reporting a...
861[ 2.33309343e-02 -2.36528926e-03 3.37129943e-...{\"token_count\":160,\"truncated\":false}Hello, My name is XXXX XXXX XXXX. I have a pro...
1030[ 0.06060313 -0.06495965 -0.03605044 -0.028016...{\"token_count\":298,\"truncated\":false}Hello, I would like to complain about PayPal H...
1582[ 0.01255985 -0.01652482 -0.02638046 0.036858...{\"token_count\":814,\"truncated\":false}Transunion is listing personal information ( n...
1600[ 5.13355099e-02 4.01246967e-03 5.72342947e-...{\"token_count\":653,\"truncated\":false}On XX/XX/XXXX, I called Citizen Bank at XXXX t...
2060[ 6.44792162e-04 4.95899878e-02 4.67925966e-...{\"token_count\":136,\"truncated\":false}Theses names are the known liars that I have s...
2283[ 4.71848622e-02 -8.68239347e-03 5.80501892e-...{\"token_count\":478,\"truncated\":false}My house was hit by a tree XX/XX/2018. My insu...
2421[-2.90394691e-03 -1.81679502e-02 -7.99657404e-...{\"token_count\":389,\"truncated\":false}I became aware of a credit inquiry on my XXXX...
2422[-6.70500053e-03 1.51133696e-02 4.94448021e-...{\"token_count\":124,\"truncated\":false}I have sent numerous letters, police reports a...
2658[ 6.70989677e-02 -3.53626162e-02 1.08648362e-...{\"token_count\":762,\"truncated\":false}This letter concerns two disputes ( chargeback...
2883[-1.28255319e-02 -1.89735275e-02 5.68657108e-...{\"token_count\":71,\"truncated\":false}It is very frustrating that this has been goin...
2951[ 3.23301251e-03 -2.61142217e-02 1.31891826e-...{\"token_count\":95,\"truncated\":false}I, the consumer, in fact, have a right to priv...
2992[-2.22910382e-03 -1.07050659e-02 4.74211425e-...{\"token_count\":407,\"truncated\":false}XXXX XXXX XXXX should not be reporting to Expe...
3969[ 1.58297736e-02 3.01055871e-02 5.60088176e-...{\"token_count\":287,\"truncated\":false}DEAR CFPB ; XXXX ; XXXX ; AND TRANSUNION ; SEE...
4087[ 1.99207035e-03 -7.62321474e-03 7.92114343e-...{\"token_count\":88,\"truncated\":false}This debt was from my identity being stolen I ...
4326[ 3.44273262e-02 -3.36350128e-02 1.91939529e-...{\"token_count\":52,\"truncated\":false}The items that are reflected on my credit repo...
4682[ 2.47727744e-02 -1.77769139e-02 4.63737026e-...{\"token_count\":284,\"truncated\":false}I filed for chapter XXXX bankruptcy on XXXX...
5005[ 2.51834448e-02 -4.92606424e-02 -1.37688573e-...{\"token_count\":17,\"truncated\":false}There are 2 Inquires on my credit report that ...
5144[ 3.26358266e-02 -3.67171178e-03 3.65621522e-...{\"token_count\":105,\"truncated\":false}My mortgage was sold from XXXX XXXX to freed...
6090[ 2.47520711e-02 1.09149124e-02 1.35175223e-...{\"token_count\":545,\"truncated\":false}On XX/XX/XXXX this company received certified...
6449[ 1.86854266e-02 1.31238240e-03 -4.96791191e-...{\"token_count\":104,\"truncated\":false}After hours on the phone with multiple agents,...
6486[ 1.56347770e-02 2.23377198e-02 -1.32683543e-...{\"token_count\":211,\"truncated\":false}On XX/XX/2019 two charges one for XXXX and one...
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I have a pro... \n", - "1030 Hello, I would like to complain about PayPal H... \n", - "1582 Transunion is listing personal information ( n... \n", - "1600 On XX/XX/XXXX, I called Citizen Bank at XXXX t... \n", - "2060 Theses names are the known liars that I have s... \n", - "2283 My house was hit by a tree XX/XX/2018. My insu... \n", - "2421 I became aware of a credit inquiry on my XXXX... \n", - "2422 I have sent numerous letters, police reports a... \n", - "2658 This letter concerns two disputes ( chargeback... \n", - "2883 It is very frustrating that this has been goin... \n", - "2951 I, the consumer, in fact, have a right to priv... \n", - "2992 XXXX XXXX XXXX should not be reporting to Expe... \n", - "3969 DEAR CFPB ; XXXX ; XXXX ; AND TRANSUNION ; SEE... \n", - "4087 This debt was from my identity being stolen I ... \n", - "4326 The items that are reflected on my credit repo... \n", - "4682 I filed for chapter XXXX bankruptcy on XXXX... \n", - "5005 There are 2 Inquires on my credit report that ... \n", - "5144 My mortgage was sold from XXXX XXXX to freed... \n", - "6090 On XX/XX/XXXX this company received certified... \n", - "6449 After hours on the phone with multiple agents,... \n", - "6486 On XX/XX/2019 two charges one for XXXX and one... \n", - "...\n", - "\n", - "[10000 rows x 4 columns]" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "successful_rows = (\n", - " (predicted_embeddings[\"ml_generate_embedding_status\"] == \"\")\n", - " # Series.str.len() gives the length of an array.\n", - " # See: https://stackoverflow.com/a/41340543/101923\n", - " & (predicted_embeddings[\"ml_generate_embedding_result\"].str.len() != 0)\n", - ")\n", - "predicted_embeddings = predicted_embeddings[successful_rows]\n", - "predicted_embeddings\n" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We now have the complaints and their text embeddings as two columns in our predicted_embeddings DataFrame." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "OUZ3NNbzo1Tb" - }, - "source": [ - "## Step 2: Create k-means model and predict clusters" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "id": "AhNTnEC5FRz2" - }, - "outputs": [], - "source": [ - "from bigframes.ml.cluster import KMeans\n", - "\n", - "cluster_model = KMeans(n_clusters=10) # We will divide our complaints into 10 groups" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Perform KMeans clustering" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "id": "6poSxh-fGJF7" - }, - "outputs": [ - { - "data": { - "text/html": [ - "Query job 3e01544b-9bc2-4298-8f7d-1e9f186ac72f is DONE. 61.6 MB processed. 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CENTROID_IDNEAREST_CENTROIDS_DISTANCEml_generate_embedding_resultml_generate_embedding_statisticsml_generate_embedding_statuscontent
31721211[{'CENTROID_ID': 1, 'DISTANCE': 0.756634267893...[ 3.18095312e-02 -3.54472063e-02 -7.13569671e-...{\"token_count\":10,\"truncated\":false}Company did not provide verification and detai...
21374201[{'CENTROID_ID': 1, 'DISTANCE': 0.606628249825...[ 1.91578846e-02 5.55988774e-02 8.88887007e-...{\"token_count\":100,\"truncated\":false}I have already filed a dispute with Consumer A...
23507751[{'CENTROID_ID': 1, 'DISTANCE': 0.606676295233...[ 2.25369893e-02 2.29400061e-02 -6.42273854e-...{\"token_count\":100,\"truncated\":false}I informed Central Financial Control & provide...
29041461[{'CENTROID_ID': 1, 'DISTANCE': 0.596729348974...[ 9.35115516e-02 4.27814946e-03 4.62085977e-...{\"token_count\":100,\"truncated\":false}I received a letter from a collections agency ...
10755711[{'CENTROID_ID': 1, 'DISTANCE': 0.453806107968...[-1.93953840e-03 -5.80236455e-03 8.49655271e-...{\"token_count\":100,\"truncated\":false}I have not done business with this company, i ...
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" - ], - "text/plain": [ - " CENTROID_ID NEAREST_CENTROIDS_DISTANCE \\\n", - "3172121 1 [{'CENTROID_ID': 1, 'DISTANCE': 0.756634267893... \n", - "2137420 1 [{'CENTROID_ID': 1, 'DISTANCE': 0.606628249825... \n", - "2350775 1 [{'CENTROID_ID': 1, 'DISTANCE': 0.606676295233... \n", - "2904146 1 [{'CENTROID_ID': 1, 'DISTANCE': 0.596729348974... \n", - "1075571 1 [{'CENTROID_ID': 1, 'DISTANCE': 0.453806107968... \n", - "\n", - " ml_generate_embedding_result \\\n", - "3172121 [ 3.18095312e-02 -3.54472063e-02 -7.13569671e-... \n", - "2137420 [ 1.91578846e-02 5.55988774e-02 8.88887007e-... \n", - "2350775 [ 2.25369893e-02 2.29400061e-02 -6.42273854e-... \n", - "2904146 [ 9.35115516e-02 4.27814946e-03 4.62085977e-... \n", - "1075571 [-1.93953840e-03 -5.80236455e-03 8.49655271e-... \n", - "\n", - " ml_generate_embedding_statistics ml_generate_embedding_status \\\n", - "3172121 {\"token_count\":10,\"truncated\":false} \n", - "2137420 {\"token_count\":100,\"truncated\":false} \n", - "2350775 {\"token_count\":100,\"truncated\":false} \n", - "2904146 {\"token_count\":100,\"truncated\":false} \n", - "1075571 {\"token_count\":100,\"truncated\":false} \n", - "\n", - " content \n", - "3172121 Company did not provide verification and detai... \n", - "2137420 I have already filed a dispute with Consumer A... \n", - "2350775 I informed Central Financial Control & provide... \n", - "2904146 I received a letter from a collections agency ... \n", - "1075571 I have not done business with this company, i ... " - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Use KMeans clustering to calculate our groups. Will take ~3 minutes.\n", - "cluster_model.fit(predicted_embeddings[[\"ml_generate_embedding_result\"]])\n", - "clustered_result = cluster_model.predict(predicted_embeddings)\n", - "# Notice the CENTROID_ID column, which is the ID number of the group that\n", - "# each complaint belongs to.\n", - "clustered_result.peek(n=5)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Our DataFrame clustered_result now has an additional column that includes an ID from 1-10 (inclusive) indicating which semantically similar group they belong to." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "21rNsFMHo8hO" - }, - "source": [ - "## Step 3: Use Gemini to summarize complaint clusters" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Build prompts - we will choose just two of our categories and prompt GeminiTextGenerator to identify their salient characteristics. The prompt is natural language in a python string." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "id": "2E7wXM_jGqo6" - }, - "outputs": [ - { - "data": { - "text/html": [ - "Query job d6c61334-255f-43fe-9a8f-9fbf6cdcb2be is DONE. 10.5 MB processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Query job 03a12383-6752-45ca-9b01-36eecc74fb8a is DONE. 10.5 MB processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Using bigframes, with syntax identical to pandas,\n", - "# filter out the first and second groups\n", - "cluster_1_result = clustered_result[\n", - " clustered_result[\"CENTROID_ID\"] == 1\n", - "][[\"content\"]]\n", - "cluster_1_result_pandas = cluster_1_result.head(5).to_pandas()\n", - "\n", - "cluster_2_result = clustered_result[\n", - " clustered_result[\"CENTROID_ID\"] == 2\n", - "][[\"content\"]]\n", - "cluster_2_result_pandas = cluster_2_result.head(5).to_pandas()" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "id": "ZNDiueI9IP5e" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "comment list 1:\n", - "1. This debt was from my identity being stolen I didnt open any account that resulted in this collection i have completed a police report which can be verified with the XXXX police @ XXXX report # XXXX and i have a notarized identity theft affidavit from ftc please remove this off of my credit and close my file ASAP\n", - "2. On XX/XX/XXXX this company received certified mail asking for validation of debt. On XX/XX/XXXX the company still did not validate debt owed and they did not mark the debt disputed by XX/XX/XXXX through the major credit reporting bureaus. This is a violation of the FDCPA and FCRA. I did send a second letter which the company received on XX/XX/XXXX . A lady from the company called and talked to me about the debt on XX/XX/XXXX but again did not have the credit bureaus mark the item as disputed. The company still violated the laws. Section [ 15 U.S.C. 1681s-2 ] ( 3 ) duty to provide notice of dispute. If the completeness or accuracy of any information furnished by any person to any consumer reporting agency is disputed to such person by a consumer, the person may not furnish the information to any consumer reporting agency without notice that such information is disputed. ( B ) ti me of notice! The notice required under sub paragraph ( A ) shall be provided to the customer prior to, or no later than 30 days after, furnishing the negative information to a consumer reporting agency described in section 603 ( p ). This company violated the state laws. I received no information until XX/XX/XXXX . Therefore by law the company should have the item removed from the credit agencies such as transunion and XXXX . I tried to call the company back about the laws that was broken and left my name no return call. The copy of my credit reports are below and as you can see the items was n't marked disputed. XXXX is marked disputed because on XX/XX/XXXX I myself disputed the information with the credit bureau. The lady stated they did n't receive my dispute letter until XX/XX/XXXX . Included is certified mail reciepts with date, time stamp, and signature of the person who signed for the certified mail on XX/XX/XXXX and XX/XX/XXXX . So again the company violated the laws and I have all the proof. If I have a contract with this company please send to me by mail a contract bearing my signature of the contract.\n", - "3. On XX/XX/2022, Pioneer Credit Recovery of XXXX, NY identified an alleged debt, which I do not owe. \n", - "\n", - "On XX/XX/2022, I wrote a dispute letter to Pioneer, requesting that they stop communication with me, record my dispute, and provide verification of the debt if they believe otherwise. \n", - "\n", - "Pioneer has not responded with verification, but has attempted to collect the debt since then by phone ( XX/XX/2022 ) and mail ( XX/XX/2022 ).\n", - "4. Disputed with the company on several occasions and they still havent provided proof in a timely manner. The FCRA gives the company 30 days to respond. I have not gotten a response.\n", - "5. I am not aware of this XXXX XXXX XXXX XXXX XXXX , XXXX balance. I have never seen anything dealing with this lender. Also, I have been threated that in 30 days they will seek to make a judgement on debt that does not belong to me. I understand that they are looking to offer me a settlement. However, I do not believe the validity of such debt accusation. Furthermore, I will not be limited to the action of court threats when I did not receive any notice of debt based on communication. The amount is {$880.00} from MBNA which was acquired by Bank of America in 2006. I do not claim debt.\n", - "\n", - "comment list 2:\n", - "1. My name is XXXX XXXX XXXX. This issue with a Loan Till Payday account was previously reported to you for collection practices, etc. I had a pay day loan in 2013. At the time, I banked with XXXX XXXX, who advised me that pay day loans are not good, and in the end XXXX closed my bank account, it was involuntary. In the interim, I made payments to the agency. XXXX and XXXX were the primary contacts. On the last payment, due to the fact that I told him I was coming in to pay cash, and they withdrew the funds, electronically, my account was affected. XXXX advised me that the payment made was the last payment and the other ( which was primarily interest remaining ) would be charged off. XXXX later called me and advised that XXXX was not authorized to make that decision and demanded the payment. I do n't understand how one person can cancel the arrangements made by someone else. \n", - "\n", - "In the end, they sold my account. It was reported to you, and that creditor then stated no further collection activity would occur. \n", - "\n", - "Last week I began receiving calls from a collection agency, XXXX XXXX stating I would called for a civil deposition on this account. I do n't even know this agency. Later, I then received another call stating that I needed to hold, and after several clicks was connected to someone at a Mediaction service. I denied the owing the loan and stated it was paid. \n", - "\n", - "Today, I received a call from an outsource service courier about a missed appointment or hearing??? What?? I have no idea who these people are. I called Loan Till Payday and was advised the loan was sold and I needed to settle with the new company. So, does this mean they are continuing to attempt to collect {$200.00}. \n", - "\n", - "I attempted to call the numbers, and now no one picks up just a voicemail. I called the supposed service courier and advised that their number was showing up as a spam/fraud number and that if they were a legitimate company then they should leave their name, location, a number ( not a voicemail ), and the case they are calling me about. I have not been served with any collection documents - why am I being threatened with a deposition??? \n", - "\n", - "Telephone number recently calling me : ( XXXX ) XXXX. \n", - "\n", - "Please help.\n", - "2. I receive 2 or 3 phone calls every day since early XXXX, my references receive calls. I will gladly satisfy this debt however even after 1st telling them the calls haven't stopped as though they are going to intimidate me. If the calls stopped for just 3 or 4 days I would satisfy my obligation but not because they keep calling me as well as my references.\n", - "3. Last month I received a phone call for my husband from XXXX XXXX XXXX saying he owed money and if I did not pay today it would be sent to litigation. The debt was Wachovia/wells Fargo, and account that we have never had. I had my husband call to get more information and they became very nasty with him. I called back asking for documentation on the debt because i did not think it was our debt and they became aggressive. They did email my husband something saying how much he owed, and I called back and asked to be emailed a copy, and the dollar amounts did not match. I called Wells Fargo and went over the above and verified that we have never had an account with them and I sent them the emails the XXXX sent to us and they started a fraud investigation. Yesterday I received another collections letter in the mail from the. Still trying to collect this debt. These people have my husbands full social security number ( we did not give it to them )\n", - "4. A company call XXXX XXXX XXXX came onto my private property on XX/XX/2018 and stole my automobile. I did receive any type of notice saying they collecting on a debt. If they take or threaten to take any nonjudicial action ( i.e, without a court order ) to repossess property when there is no present right to possession of the property they is in violation. l did not receive any type of notice asking if they can enter onto my private property and steal my private automobile.\n", - "5. Navient financial continues to send me erroneous debt collection emails. I have repeatedly asked them to remove my email address and to cease all communication with me. \n", - "I have no relationship with Navient and their continued threatening email is very unsettling. \n", - "\n", - "I just want their erroneous threats to stop. \n", - "\n", - "Below is the latest email I have received from them : Last Day to call this office XXXX by XXXX Regards, XXXX XXXX Team Lead Specialist Charge off Unit XXXX XXXX\n", - "\n" - ] - } - ], - "source": [ - "# Build plain-text prompts to send to Gemini. Use only 5 complaints from each group.\n", - "prompt1 = 'comment list 1:\\n'\n", - "for i in range(5):\n", - " prompt1 += str(i + 1) + '. ' + \\\n", - " cluster_1_result_pandas[\"content\"].iloc[i] + '\\n'\n", - "\n", - "prompt2 = 'comment list 2:\\n'\n", - "for i in range(5):\n", - " prompt2 += str(i + 1) + '. ' + \\\n", - " cluster_2_result_pandas[\"content\"].iloc[i] + '\\n'\n", - "\n", - "print(prompt1)\n", - "print(prompt2)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "id": "BfHGJLirzSvH" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Please highlight the most obvious difference between the two lists of comments:\n", - "comment list 1:\n", - "1. This debt was from my identity being stolen I didnt open any account that resulted in this collection i have completed a police report which can be verified with the XXXX police @ XXXX report # XXXX and i have a notarized identity theft affidavit from ftc please remove this off of my credit and close my file ASAP\n", - "2. On XX/XX/XXXX this company received certified mail asking for validation of debt. On XX/XX/XXXX the company still did not validate debt owed and they did not mark the debt disputed by XX/XX/XXXX through the major credit reporting bureaus. This is a violation of the FDCPA and FCRA. I did send a second letter which the company received on XX/XX/XXXX . A lady from the company called and talked to me about the debt on XX/XX/XXXX but again did not have the credit bureaus mark the item as disputed. The company still violated the laws. Section [ 15 U.S.C. 1681s-2 ] ( 3 ) duty to provide notice of dispute. If the completeness or accuracy of any information furnished by any person to any consumer reporting agency is disputed to such person by a consumer, the person may not furnish the information to any consumer reporting agency without notice that such information is disputed. ( B ) ti me of notice! The notice required under sub paragraph ( A ) shall be provided to the customer prior to, or no later than 30 days after, furnishing the negative information to a consumer reporting agency described in section 603 ( p ). This company violated the state laws. I received no information until XX/XX/XXXX . Therefore by law the company should have the item removed from the credit agencies such as transunion and XXXX . I tried to call the company back about the laws that was broken and left my name no return call. The copy of my credit reports are below and as you can see the items was n't marked disputed. XXXX is marked disputed because on XX/XX/XXXX I myself disputed the information with the credit bureau. The lady stated they did n't receive my dispute letter until XX/XX/XXXX . Included is certified mail reciepts with date, time stamp, and signature of the person who signed for the certified mail on XX/XX/XXXX and XX/XX/XXXX . So again the company violated the laws and I have all the proof. If I have a contract with this company please send to me by mail a contract bearing my signature of the contract.\n", - "3. On XX/XX/2022, Pioneer Credit Recovery of XXXX, NY identified an alleged debt, which I do not owe. \n", - "\n", - "On XX/XX/2022, I wrote a dispute letter to Pioneer, requesting that they stop communication with me, record my dispute, and provide verification of the debt if they believe otherwise. \n", - "\n", - "Pioneer has not responded with verification, but has attempted to collect the debt since then by phone ( XX/XX/2022 ) and mail ( XX/XX/2022 ).\n", - "4. Disputed with the company on several occasions and they still havent provided proof in a timely manner. The FCRA gives the company 30 days to respond. I have not gotten a response.\n", - "5. I am not aware of this XXXX XXXX XXXX XXXX XXXX , XXXX balance. I have never seen anything dealing with this lender. Also, I have been threated that in 30 days they will seek to make a judgement on debt that does not belong to me. I understand that they are looking to offer me a settlement. However, I do not believe the validity of such debt accusation. Furthermore, I will not be limited to the action of court threats when I did not receive any notice of debt based on communication. The amount is {$880.00} from MBNA which was acquired by Bank of America in 2006. I do not claim debt.\n", - "comment list 2:\n", - "1. My name is XXXX XXXX XXXX. This issue with a Loan Till Payday account was previously reported to you for collection practices, etc. I had a pay day loan in 2013. At the time, I banked with XXXX XXXX, who advised me that pay day loans are not good, and in the end XXXX closed my bank account, it was involuntary. In the interim, I made payments to the agency. XXXX and XXXX were the primary contacts. On the last payment, due to the fact that I told him I was coming in to pay cash, and they withdrew the funds, electronically, my account was affected. XXXX advised me that the payment made was the last payment and the other ( which was primarily interest remaining ) would be charged off. XXXX later called me and advised that XXXX was not authorized to make that decision and demanded the payment. I do n't understand how one person can cancel the arrangements made by someone else. \n", - "\n", - "In the end, they sold my account. It was reported to you, and that creditor then stated no further collection activity would occur. \n", - "\n", - "Last week I began receiving calls from a collection agency, XXXX XXXX stating I would called for a civil deposition on this account. I do n't even know this agency. Later, I then received another call stating that I needed to hold, and after several clicks was connected to someone at a Mediaction service. I denied the owing the loan and stated it was paid. \n", - "\n", - "Today, I received a call from an outsource service courier about a missed appointment or hearing??? What?? I have no idea who these people are. I called Loan Till Payday and was advised the loan was sold and I needed to settle with the new company. So, does this mean they are continuing to attempt to collect {$200.00}. \n", - "\n", - "I attempted to call the numbers, and now no one picks up just a voicemail. I called the supposed service courier and advised that their number was showing up as a spam/fraud number and that if they were a legitimate company then they should leave their name, location, a number ( not a voicemail ), and the case they are calling me about. I have not been served with any collection documents - why am I being threatened with a deposition??? \n", - "\n", - "Telephone number recently calling me : ( XXXX ) XXXX. \n", - "\n", - "Please help.\n", - "2. I receive 2 or 3 phone calls every day since early XXXX, my references receive calls. I will gladly satisfy this debt however even after 1st telling them the calls haven't stopped as though they are going to intimidate me. If the calls stopped for just 3 or 4 days I would satisfy my obligation but not because they keep calling me as well as my references.\n", - "3. Last month I received a phone call for my husband from XXXX XXXX XXXX saying he owed money and if I did not pay today it would be sent to litigation. The debt was Wachovia/wells Fargo, and account that we have never had. I had my husband call to get more information and they became very nasty with him. I called back asking for documentation on the debt because i did not think it was our debt and they became aggressive. They did email my husband something saying how much he owed, and I called back and asked to be emailed a copy, and the dollar amounts did not match. I called Wells Fargo and went over the above and verified that we have never had an account with them and I sent them the emails the XXXX sent to us and they started a fraud investigation. Yesterday I received another collections letter in the mail from the. Still trying to collect this debt. These people have my husbands full social security number ( we did not give it to them )\n", - "4. A company call XXXX XXXX XXXX came onto my private property on XX/XX/2018 and stole my automobile. I did receive any type of notice saying they collecting on a debt. If they take or threaten to take any nonjudicial action ( i.e, without a court order ) to repossess property when there is no present right to possession of the property they is in violation. l did not receive any type of notice asking if they can enter onto my private property and steal my private automobile.\n", - "5. Navient financial continues to send me erroneous debt collection emails. I have repeatedly asked them to remove my email address and to cease all communication with me. \n", - "I have no relationship with Navient and their continued threatening email is very unsettling. \n", - "\n", - "I just want their erroneous threats to stop. \n", - "\n", - "Below is the latest email I have received from them : Last Day to call this office XXXX by XXXX Regards, XXXX XXXX Team Lead Specialist Charge off Unit XXXX XXXX\n", - "\n" - ] - } - ], - "source": [ - "# The plain English request we will make of Gemini\n", - "prompt = (\n", - " \"Please highlight the most obvious difference between \"\n", - " \"the two lists of comments:\\n\" + prompt1 + prompt2\n", - ")\n", - "print(prompt)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Get a response from Gemini by making a call to Vertex AI using our connection." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "mL5P0_3X04dE" - }, - "outputs": [ - { - "data": { - "text/html": [ - "Query job 3a46cad4-14e5-4137-a042-14380733b467 is DONE. 0 Bytes processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from bigframes.ml.llm import GeminiTextGenerator\n", - "\n", - "q_a_model = GeminiTextGenerator(model_name=\"gemini-2.5-flash\")" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "id": "ICWHsqAW1FNk" - }, - "outputs": [ - { - "data": { - "text/html": [ - "Load job 939037f0-66df-42a4-b301-0b3ba26bae7c is DONE. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Make a DataFrame containing only a single row with our prompt for Gemini\n", - "df = bf.DataFrame({\"prompt\": [prompt]})" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": { - "id": "gB7e1LXU1pst" - }, - "outputs": [ - { - "data": { - "text/html": [ - "Query job c662b2c7-7185-4681-b7c6-60c81e9c8cd4 is DONE. 8.2 kB processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/google/home/garrettwu/src/bigframes/bigframes/core/__init__.py:108: PreviewWarning: Interpreting JSON column(s) as StringDtype. This behavior may change in future versions.\n", - " warnings.warn(\n" - ] - }, - { - "data": { - "text/html": [ - "Query job 9a4d6735-c307-4a60-96f9-d81330925e6c is DONE. 2 Bytes processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Query job 17bde6e6-8b26-48a7-9c57-b7b9752c1f54 is DONE. 1.8 kB processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "\"## Key Differences between Comment Lists 1 and 2:\\n\\n**Comment List 1:**\\n\\n* **Focuses on Legal Violations:** The comments in List 1 primarily focus on how the debt collectors violated specific laws, such as the FDCPA and FCRA, by not validating debt, not marking accounts as disputed, and using illegal collection tactics.\\n* **Detailed Evidence:** Commenters provide detailed evidence of their claims, including dates, reference numbers, police reports, and copies of communications.\\n* **Formal Tone:** The language in List 1 is more formal and uses legal terminology, suggesting the commenters may have a deeper understanding of their rights.\\n* **Emphasis on Debt Accuracy:** Many comments explicitly deny owing the debt and question its validity, requesting proof and demanding removal from credit reports. \\n\\n**Comment List 2:**\\n\\n* **Focus on Harassment and Intimidation:** The comments in List 2 highlight the harassing and intimidating behavior of the debt collectors, such as making multiple calls, contacting references, and threatening legal action.\\n* **Emotional Language:** Commenters express frustration, fear, and anger towards the debt collectors' behavior.\\n* **Less Legal Detail:** While some commenters mention specific laws, they provide less detailed evidence than List 1.\\n* **Uncertainty About Debt:** Several commenters are unsure whether they actually owe the debt, questioning its origin and validity. \\n\\n**Overall:**\\n\\n* List 1 focuses on legal arguments and violations, while List 2 emphasizes emotional distress and improper collection tactics.\\n* List 1 provides more concrete evidence of wrongdoing, while List 2 relies more on personal experiences and descriptions.\\n* Both lists highlight the negative impacts of debt collection practices on individuals.\\n\"" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Send the request for Gemini to generate a response to our prompt\n", - "major_difference = q_a_model.predict(df)\n", - "# Gemini's response is the only row in the dataframe result \n", - "major_difference[\"ml_generate_text_llm_result\"].iloc[0]" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We now see GeminiTextGenerator's characterization of the different comment groups. Thanks for using BigQuery DataFrames!" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Summary and next steps\n", - "\n", - "You've used the ML and LLM capabilities of BigQuery DataFrames to help analyze and understand a large dataset of unstructured feedback.\n", - "\n", - "Learn more about BigQuery DataFrames in the [documentation](https://cloud.google.com/python/docs/reference/bigframes/latest) and find more sample notebooks in the [GitHub repo](https://github.com/googleapis/python-bigquery-dataframes/tree/main/notebooks)." - ] - } - ], - "metadata": { - "colab": { - "provenance": [] - }, - "kernelspec": { - "display_name": "venv (3.10.14)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.14" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/notebooks/generative_ai/bq_dataframes_llm_output_schema.ipynb b/notebooks/generative_ai/bq_dataframes_llm_output_schema.ipynb deleted file mode 100644 index b3e2e4ebc84..00000000000 --- a/notebooks/generative_ai/bq_dataframes_llm_output_schema.ipynb +++ /dev/null @@ -1,905 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# Copyright 2025 Google LLC\n", - "#\n", - "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", - "# you may not use this file except in compliance with the License.\n", - "# You may obtain a copy of the License at\n", - "#\n", - "# https://www.apache.org/licenses/LICENSE-2.0\n", - "#\n", - "# Unless required by applicable law or agreed to in writing, software\n", - "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", - "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", - "# See the License for the specific language governing permissions and\n", - "# limitations under the License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Format LLM output using an output schema\n", - "\n", - "\n", - "\n", - " \n", - " \n", - " \n", - "
\n", - " \n", - " \"Colab Run in Colab\n", - " \n", - " \n", - " \n", - " \"GitHub\n", - " View on GitHub\n", - " \n", - " \n", - " \n", - " \"BQ\n", - " Open in BigQuery Studio\n", - " \n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This notebook shows you how to create structured LLM output by specifying an output schema when generating predictions with a Gemini model." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Costs\n", - "\n", - "This tutorial uses billable components of Google Cloud:\n", - "\n", - "* BigQuery (compute)\n", - "* BigQuery ML\n", - "* Generative AI support on Vertex AI\n", - "\n", - "Learn about [BigQuery compute pricing](https://cloud.google.com/bigquery/pricing#analysis_pricing_models), [Generative AI support on Vertex AI pricing](https://cloud.google.com/vertex-ai/generative-ai/pricing),\n", - "and [BigQuery ML pricing](https://cloud.google.com/bigquery/pricing#section-11),\n", - "and use the [Pricing Calculator](https://cloud.google.com/products/calculator/)\n", - "to generate a cost estimate based on your projected usage." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Before you begin\n", - "\n", - "Complete the tasks in this section to set up your environment." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Set up your Google Cloud project\n", - "\n", - "**The following steps are required, regardless of your notebook environment.**\n", - "\n", - "1. [Select or create a Google Cloud project](https://console.cloud.google.com/cloud-resource-manager). When you first create an account, you get a $300 credit towards your compute/storage costs.\n", - "\n", - "2. [Make sure that billing is enabled for your project](https://cloud.google.com/billing/docs/how-to/modify-project).\n", - "\n", - "3. [Click here](https://console.cloud.google.com/flows/enableapi?apiid=bigquery.googleapis.com,bigqueryconnection.googleapis.com,aiplatform.googleapis.com) to enable the following APIs:\n", - "\n", - " * BigQuery API\n", - " * BigQuery Connection API\n", - " * Vertex AI API\n", - "\n", - "4. If you are running this notebook locally, install the [Cloud SDK](https://cloud.google.com/sdk)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Authenticate your Google Cloud account\n", - "\n", - "Depending on your Jupyter environment, you might have to manually authenticate. Follow the relevant instructions below.\n", - "\n", - "**BigQuery Studio** or **Vertex AI Workbench**\n", - "\n", - "Do nothing, you are already authenticated.\n", - "\n", - "**Local JupyterLab instance**\n", - "\n", - "Uncomment and run the following cell:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# ! gcloud auth login" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Colab**\n", - "\n", - "Uncomment and run the following cell:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# from google.colab import auth\n", - "# auth.authenticate_user()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Set up your project" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Set your project and import necessary modules. If you don't know your project ID, see [Locate the project ID](https://support.google.com/googleapi/answer/7014113)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "PROJECT_ID = \"\" # @param {type:\"string\"}\n", - "import bigframes\n", - "bigframes.options.bigquery.project = PROJECT_ID\n", - "bigframes.options.display.progress_bar = None\n", - "\n", - "import bigframes.pandas as bpd\n", - "from bigframes.ml import llm" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create a DataFrame and a Gemini model\n", - "Create a simple [DataFrame](https://cloud.google.com/python/docs/reference/bigframes/latest/bigframes.dataframe.DataFrame) of several cities:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/google/home/garrettwu/src/bigframes/bigframes/core/global_session.py:103: DefaultLocationWarning: No explicit location is set, so using location US for the session.\n", - " _global_session = bigframes.session.connect(\n" - ] - }, - { - "data": { - "text/html": [ - "
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city
0Seattle
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2Shanghai
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[3 rows x 1 columns in total]" - ], - "text/plain": [ - " city\n", - "0 Seattle\n", - "1 New York\n", - "2 Shanghai\n", - "\n", - "[3 rows x 1 columns]" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df = bpd.DataFrame({\"city\": [\"Seattle\", \"New York\", \"Shanghai\"]})\n", - "df" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Connect to a Gemini model using the [`GeminiTextGenerator` class](https://cloud.google.com/python/docs/reference/bigframes/latest/bigframes.ml.llm.GeminiTextGenerator):" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/google/home/garrettwu/src/bigframes/bigframes/core/log_adapter.py:175: FutureWarning: Since upgrading the default model can cause unintended breakages, the\n", - "default model will be removed in BigFrames 3.0. Please supply an\n", - "explicit model to avoid this message.\n", - " return method(*args, **kwargs)\n" - ] - } - ], - "source": [ - "gemini = llm.GeminiTextGenerator()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Generate structured output data\n", - "Previously, LLMs could only generate text output. For example, you could generate output that identifies whether a given city is a US city:" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/google/home/garrettwu/src/bigframes/bigframes/core/array_value.py:109: PreviewWarning: JSON column interpretation as a custom PyArrow extention in\n", - "`db_dtypes` is a preview feature and subject to change.\n", - " warnings.warn(msg, bfe.PreviewWarning)\n" - ] - }, - { - "data": { - "text/html": [ - "
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cityml_generate_text_llm_result
0SeattleYes, Seattle is a city in the United States. I...
1New YorkYes, New York City is a city in the United Sta...
2ShanghaiNo, Shanghai is not a US city. It is a major c...
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[3 rows x 2 columns in total]" - ], - "text/plain": [ - " city ml_generate_text_llm_result\n", - "0 Seattle Yes, Seattle is a city in the United States. I...\n", - "1 New York Yes, New York City is a city in the United Sta...\n", - "2 Shanghai No, Shanghai is not a US city. It is a major c...\n", - "\n", - "[3 rows x 2 columns]" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "result = gemini.predict(df, prompt=[df[\"city\"], \"is a US city?\"])\n", - "result[[\"city\", \"ml_generate_text_llm_result\"]]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The output is text that a human can read. However, if you want the output to be more useful for analysis, it is better to format the output as structured data. This is especially true when you want to have Boolean, integer, or float values to work with instead of string values. Previously, formatting the output in this way wasn't easy.\n", - "\n", - "Now, you can get structured output out-of-the-box by specifying the `output_schema` parameter when calling the Gemini model's [`predict` method](https://cloud.google.com/python/docs/reference/bigframes/latest/bigframes.ml.llm.GeminiTextGenerator#bigframes_ml_llm_GeminiTextGenerator_predict). In the following example, the model output is formatted as Boolean values:" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/google/home/garrettwu/src/bigframes/bigframes/core/array_value.py:109: PreviewWarning: JSON column interpretation as a custom PyArrow extention in\n", - "`db_dtypes` is a preview feature and subject to change.\n", - " warnings.warn(msg, bfe.PreviewWarning)\n" - ] - }, - { - "data": { - "text/html": [ - "
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cityis_us_city
0SeattleTrue
1New YorkTrue
2ShanghaiFalse
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[3 rows x 2 columns in total]" - ], - "text/plain": [ - " city is_us_city\n", - "0 Seattle True\n", - "1 New York True\n", - "2 Shanghai False\n", - "\n", - "[3 rows x 2 columns]" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "result = gemini.predict(df, prompt=[df[\"city\"], \"is a US city?\"], output_schema={\"is_us_city\": \"bool\"})\n", - "result[[\"city\", \"is_us_city\"]]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can also format model output as float or integer values. In the following example, the model output is formatted as float values to show the city's population in millions:" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/google/home/garrettwu/src/bigframes/bigframes/core/array_value.py:109: PreviewWarning: JSON column interpretation as a custom PyArrow extention in\n", - "`db_dtypes` is a preview feature and subject to change.\n", - " warnings.warn(msg, bfe.PreviewWarning)\n" - ] - }, - { - "data": { - "text/html": [ - "
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citypopulation_in_millions
0Seattle0.75
1New York19.68
2Shanghai26.32
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[3 rows x 2 columns in total]" - ], - "text/plain": [ - " city population_in_millions\n", - "0 Seattle 0.75\n", - "1 New York 19.68\n", - "2 Shanghai 26.32\n", - "\n", - "[3 rows x 2 columns]" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "result = gemini.predict(df, prompt=[\"what is the population in millions of\", df[\"city\"]], output_schema={\"population_in_millions\": \"float64\"})\n", - "result[[\"city\", \"population_in_millions\"]]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In the following example, the model output is formatted as integer values to show the count of the city's rainy days:" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/google/home/garrettwu/src/bigframes/bigframes/core/array_value.py:109: PreviewWarning: JSON column interpretation as a custom PyArrow extention in\n", - "`db_dtypes` is a preview feature and subject to change.\n", - " warnings.warn(msg, bfe.PreviewWarning)\n" - ] - }, - { - "data": { - "text/html": [ - "
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cityrainy_days
0Seattle152
1New York123
2Shanghai123
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3 rows × 2 columns

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[3 rows x 2 columns in total]" - ], - "text/plain": [ - " city rainy_days\n", - "0 Seattle 152\n", - "1 New York 123\n", - "2 Shanghai 123\n", - "\n", - "[3 rows x 2 columns]" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "result = gemini.predict(df, prompt=[\"how many rainy days per year in\", df[\"city\"]], output_schema={\"rainy_days\": \"int64\"})\n", - "result[[\"city\", \"rainy_days\"]]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Format output as multiple data types in one prediction\n", - "Within a single prediction, you can generate multiple columns of output that use different data types. \n", - "\n", - "The input doesn't have to be dedicated prompts as long as the output column names are informative to the model." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/google/home/garrettwu/src/bigframes/bigframes/core/array_value.py:109: PreviewWarning: JSON column interpretation as a custom PyArrow extention in\n", - "`db_dtypes` is a preview feature and subject to change.\n", - " warnings.warn(msg, bfe.PreviewWarning)\n" - ] - }, - { - "data": { - "text/html": [ - "
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cityis_US_citypopulation_in_millionsrainy_days_per_year
0SeattleTrue0.75152
1New YorkTrue8.8121
2ShanghaiFalse26.32115
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[3 rows x 4 columns in total]" - ], - "text/plain": [ - " city is_US_city population_in_millions rainy_days_per_year\n", - "0 Seattle True 0.75 152\n", - "1 New York True 8.8 121\n", - "2 Shanghai False 26.32 115\n", - "\n", - "[3 rows x 4 columns]" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "result = gemini.predict(df, prompt=[df[\"city\"]], output_schema={\"is_US_city\": \"bool\", \"population_in_millions\": \"float64\", \"rainy_days_per_year\": \"int64\"})\n", - "result[[\"city\", \"is_US_city\", \"population_in_millions\", \"rainy_days_per_year\"]]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Format output as a composite data type" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can generate composite data types like arrays and structs. The following example generates a `places_to_visit` column as an array of strings and a `gps_coordinates` column as a struct of floats:" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/google/home/garrettwu/src/bigframes/bigframes/core/array_value.py:109: PreviewWarning: JSON column interpretation as a custom PyArrow extention in\n", - "`db_dtypes` is a preview feature and subject to change.\n", - " warnings.warn(msg, bfe.PreviewWarning)\n" - ] - }, - { - "data": { - "text/html": [ - "
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cityis_US_citypopulation_in_millionsrainy_days_per_yearplaces_to_visitgps_coordinates
0SeattleTrue0.74150['Space Needle' 'Pike Place Market' 'Museum of...{'latitude': 47.6062, 'longitude': -122.3321}
1New YorkTrue8.4121['Times Square' 'Central Park' 'Statue of Libe...{'latitude': 40.7128, 'longitude': -74.006}
2ShanghaiFalse26.32115['The Bund' 'Yu Garden' 'Shanghai Museum' 'Ori...{'latitude': 31.2304, 'longitude': 121.4737}
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[3 rows x 6 columns in total]" - ], - "text/plain": [ - " city is_US_city population_in_millions rainy_days_per_year \\\n", - "0 Seattle True 0.74 150 \n", - "1 New York True 8.4 121 \n", - "2 Shanghai False 26.32 115 \n", - "\n", - " places_to_visit \\\n", - "0 ['Space Needle' 'Pike Place Market' 'Museum of... \n", - "1 ['Times Square' 'Central Park' 'Statue of Libe... \n", - "2 ['The Bund' 'Yu Garden' 'Shanghai Museum' 'Ori... \n", - "\n", - " gps_coordinates \n", - "0 {'latitude': 47.6062, 'longitude': -122.3321} \n", - "1 {'latitude': 40.7128, 'longitude': -74.006} \n", - "2 {'latitude': 31.2304, 'longitude': 121.4737} \n", - "\n", - "[3 rows x 6 columns]" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "result = gemini.predict(df, prompt=[df[\"city\"]], output_schema={\"is_US_city\": \"bool\", \"population_in_millions\": \"float64\", \"rainy_days_per_year\": \"int64\", \"places_to_visit\": \"array\", \"gps_coordinates\": \"struct\"})\n", - "result[[\"city\", \"is_US_city\", \"population_in_millions\", \"rainy_days_per_year\", \"places_to_visit\", \"gps_coordinates\"]]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Clean up\n", - "\n", - "To clean up all Google Cloud resources used in this project, you can [delete the Google Cloud\n", - "project](https://cloud.google.com/resource-manager/docs/creating-managing-projects#shutting_down_projects) you used for the tutorial.\n", - "\n", - "Otherwise, run the following cell to delete the temporary cloud artifacts created during the BigFrames session:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "bpd.close_session()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Next steps\n", - "\n", - "Learn more about BigQuery DataFrames in the [documentation](https://cloud.google.com/python/docs/reference/bigframes/latest) and find more sample notebooks in the [GitHub repo](https://github.com/googleapis/python-bigquery-dataframes/tree/main/notebooks)." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.14" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/notebooks/generative_ai/bq_dataframes_llm_vector_search.ipynb b/notebooks/generative_ai/bq_dataframes_llm_vector_search.ipynb deleted file mode 100644 index c9fa39926a9..00000000000 --- a/notebooks/generative_ai/bq_dataframes_llm_vector_search.ipynb +++ /dev/null @@ -1,1790 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "id": "TpJu6BBeooES" - }, - "outputs": [], - "source": [ - "# Copyright 2023 Google LLC\n", - "#\n", - "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", - "# you may not use this file except in compliance with the License.\n", - "# You may obtain a copy of the License at\n", - "#\n", - "# https://www.apache.org/licenses/LICENSE-2.0\n", - "#\n", - "# Unless required by applicable law or agreed to in writing, software\n", - "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", - "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", - "# See the License for the specific language governing permissions and\n", - "# limitations under the License." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "EQbZKS7_ooET" - }, - "source": [ - "# Build a Vector Search application using BigQuery DataFrames (aka BigFrames)\n", - "\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - "
\n", - " \n", - " \"Colab Run in Colab\n", - " \n", - " \n", - " \n", - " \"GitHub\n", - " View on GitHub\n", - " \n", - " \n", - " \n", - " \"Vertex\n", - " Open in Vertex AI Workbench\n", - " \n", - " \n", - " \n", - " \"BQ\n", - " Open in BQ Studio\n", - " \n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "vFMjpPBo9aVv" - }, - "source": [ - "**Author:** Sudipto Guha (Google)\n", - "\n", - "**Last updated:** March 16th 2025" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "SHQ3Gx-oooEU" - }, - "source": [ - "## Overview\n", - "\n", - "This notebook will guide you through a practical example of using [BigFrames](https://github.com/googleapis/python-bigquery-dataframes/issues) to perform [vector search](https://cloud.google.com/bigquery/docs/vector-search-intro) and analysis on a patent dataset within BigQuery. We will leverage Python and BigFrames to efficiently process, analyze, and gain insights from a large-scale dataset without moving data from BigQuery.\n", - "\n", - "Here's a breakdown of what we'll cover:\n", - "\n", - "1. **Data Ingestion and Embedding Generation:**\n", - "We will start by reading a public patent dataset directly from BigQuery into a BigFrames DataFrame.\n", - "We'll demonstrate how to use BigFrames' `TextEmbeddingGenerator` to create text embeddings for the patent abstracts. This process converts the textual data into numerical vectors that capture the semantic meaning of each abstract.\n", - "We'll show how BigFrames efficiently performs this embedding generation within BigQuery, avoiding data transfer to the client-side.\n", - "Finally, we'll store the generated embeddings back into a new BigQuery table for subsequent analysis.\n", - "\n", - "2. **Indexing and Similarity Search:**\n", - "Here we'll create a vector index using BigFrames to enable fast and scalable similarity searches.\n", - "We'll demonstrate how to create an IVF index for efficient approximate nearest neighbor searches.\n", - "We'll then perform a vector search using a sample query string to find patents that are semantically similar to the query. This showcases how vector search goes beyond keyword matching to find relevant results based on meaning.\n", - "\n", - "3. **AI-Powered Summarization with Retrieval Augmented Generation (RAG):**\n", - "To further enhance the analysis, we'll implement a RAG pipeline.\n", - "We'll retrieve the top most similar patents based on the vector search results from step 2.\n", - "We'll use BigFrames' `GeminiTextGenerator` to create a prompt for an LLM to generate a concise summary of the retrieved patents.\n", - "This demonstrates how to combine vector search with generative AI to extract and synthesize meaningful insights from complex patent data.\n", - "\n", - "\n", - "We will tie these pieces together in Python using BigQuery DataFrames. [Click here](https://cloud.google.com/bigquery/docs/dataframes-quickstart) to learn more about BigQuery DataFrames!" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "EHjmqb-0ooEU" - }, - "source": [ - "### Dataset\n", - "\n", - "This notebook uses the [BQ Patents Public Dataset](https://bigquery.cloud.google.com/dataset/patents-public-data:patentsview)." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "AqdihIDJooEU" - }, - "source": [ - "### Costs\n", - "\n", - "This tutorial uses billable components of Google Cloud:\n", - "\n", - "* BigQuery (compute)\n", - "* BigQuery ML\n", - "* Generative AI support on Vertex AI\n", - "\n", - "Learn about [BigQuery compute pricing](https://cloud.google.com/bigquery/pricing#analysis_pricing_models), [Generative AI support on Vertex AI pricing](https://cloud.google.com/vertex-ai/pricing#generative_ai_models),\n", - "and [BigQuery ML pricing](https://cloud.google.com/bigquery/pricing#bqml),\n", - "and use the [Pricing Calculator](https://cloud.google.com/products/calculator/)\n", - "to generate a cost estimate based on your projected usage." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "GqLjnm1hsKGU" - }, - "source": [ - "## Setup & initialization\n", - "\n", - "Make sure you have the required roles and permissions listed below:\n", - "\n", - "For [Vector embedding generation](https://cloud.google.com/bigquery/docs/generate-text-embedding#required_roles)\n", - "\n", - "For [Vector Index creation](https://cloud.google.com/bigquery/docs/vector-index#roles_and_permissions)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Z-mvYJUCooEV" - }, - "source": [ - "## Before you begin\n", - "\n", - "Complete the tasks in this section to set up your environment." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "xn-v3mSvooEV" - }, - "source": [ - "### Set up your Google Cloud project\n", - "\n", - "**The following steps are required, regardless of your notebook environment.**\n", - "\n", - "1. [Select or create a Google Cloud project](https://console.cloud.google.com/cloud-resource-manager). When you first create an account, you get a $300 credit towards your compute/storage costs.\n", - "\n", - "2. [Make sure that billing is enabled for your project](https://cloud.google.com/billing/docs/how-to/modify-project).\n", - "\n", - "3. [Click here](https://console.cloud.google.com/flows/enableapi?apiid=bigquery.googleapis.com,bigqueryconnection.googleapis.com,aiplatform.googleapis.com) to enable the following APIs:\n", - "\n", - " * BigQuery API\n", - " * BigQuery Connection API\n", - " * Vertex AI API\n", - "\n", - "4. If you are running this notebook locally, install the [Cloud SDK](https://cloud.google.com/sdk)." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Ioydzb_8ooEV" - }, - "source": [ - "#### Set your project ID\n", - "\n", - "**If you don't know your project ID**, see the support page: [Locate the project ID](https://support.google.com/googleapi/answer/7014113)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "executionInfo": { - "elapsed": 2, - "status": "ok", - "timestamp": 1742191597773, - "user": { - "displayName": "", - "userId": "" - }, - "user_tz": -480 - }, - "id": "b8bKCfIiooEV" - }, - "outputs": [], - "source": [ - "# set your project ID below\n", - "PROJECT_ID = \"bigframes-dev\" # @param {type:\"string\"}\n", - "\n", - "# set your region\n", - "REGION = \"US\" # @param {type: \"string\"}\n", - "\n", - "# Set the project id in gcloud\n", - "#! gcloud config set project {PROJECT_ID}" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "GbUgWr6LooEV" - }, - "source": [ - "#### Authenticate your Google Cloud account\n", - "\n", - "Depending on your Jupyter environment, you might have to manually authenticate. Follow the relevant instructions below." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "U7ChP8jUooEV" - }, - "source": [ - "**Vertex AI Workbench**\n", - "\n", - "Do nothing, you are already authenticated." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "VfHOYcZZooEW" - }, - "source": [ - "**Local JupyterLab instance**\n", - "\n", - "Uncomment and run the following cell:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "id": "3cGhUVM0ooEW" - }, - "outputs": [], - "source": [ - "# ! gcloud auth login" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "AoHnXlg-ooEW" - }, - "source": [ - "**Colab**\n", - "\n", - "Uncomment and run the following cell:" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "executionInfo": { - "elapsed": 2, - "status": "ok", - "timestamp": 1742191608487, - "user": { - "displayName": "", - "userId": "" - }, - "user_tz": -480 - }, - "id": "j3lmnsh7ooEW", - "outputId": "eb68daf5-5558-487a-91d2-4b4f9e476da0" - }, - "outputs": [], - "source": [ - "# from google.colab import auth\n", - "# auth.authenticate_user()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "a9gsyttuooEW" - }, - "source": [ - "Now we are ready to use BigQuery DataFrames!" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "xckgWno6ouHY" - }, - "source": [ - "## Step 1: Data Ingestion and Embedding Generation" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Hjg9jDN-ooEW" - }, - "source": [ - "Install libraries" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "executionInfo": { - "elapsed": 947, - "status": "ok", - "timestamp": 1742195413800, - "user": { - "displayName": "", - "userId": "" - }, - "user_tz": -480 - }, - "id": "R7STCS8xB5d2" - }, - "outputs": [], - "source": [ - "import bigframes.pandas as bf\n", - "import bigframes.ml as bf_ml\n", - "import bigframes.bigquery as bf_bq\n", - "import bigframes.ml.llm as bf_llm\n", - "\n", - "\n", - "from google.cloud import bigquery\n", - "from google.cloud import storage\n", - "\n", - "# Construct a BigQuery client object.\n", - "client = bigquery.Client()\n", - "\n", - "import pandas as pd\n", - "from IPython.display import Image, display\n", - "from PIL import Image as PILImage\n", - "import io\n", - "\n", - "import json\n", - "from IPython.display import Markdown\n", - "\n", - "# Note: The project option is not required in all environments.\n", - "# On BigQuery Studio, the project ID is automatically detected.\n", - "bf.options.bigquery.project = PROJECT_ID\n", - "bf.options.bigquery.location = REGION\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "iOFF9hrvs5WE" - }, - "source": [ - "Partial ordering mode allows BigQuery DataFrames to push down many more row and column filters. On large clustered and partitioned tables, this can greatly reduce the number of bytes scanned and computation slots used. This [blog post](https://medium.com/google-cloud/introducing-partial-ordering-mode-for-bigquery-dataframes-bigframes-ec35841d95c0) goes over it in more detail." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "executionInfo": { - "elapsed": 2, - "status": "ok", - "timestamp": 1742191620533, - "user": { - "displayName": "", - "userId": "" - }, - "user_tz": -480 - }, - "id": "9Gil1Oaas7KA" - }, - "outputs": [], - "source": [ - "bf.options.bigquery.ordering_mode = \"partial\"" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "XGaGyyZsooEW" - }, - "source": [ - "If you want to reset the location of the created DataFrame or Series objects, reset the session by executing `bf.close_session()`. After that, you can reuse `bf.options.bigquery.location` to specify another location." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "v6FGschEowht" - }, - "source": [ - "Data Input - read the data from a publicly available BigQuery dataset" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "executionInfo": { - "elapsed": 468, - "status": "ok", - "timestamp": 1742192516923, - "user": { - "displayName": "", - "userId": "" - }, - "user_tz": -480 - }, - "id": "zDSwoBo1CU3G", - "outputId": "83edbc2f-5a23-407b-8890-f968eb31be44" - }, - "outputs": [], - "source": [ - "publications = bf.read_gbq('patents-public-data.google_patents_research.publications')" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 34 - }, - "executionInfo": { - "elapsed": 6697, - "status": "ok", - "timestamp": 1742192524632, - "user": { - "displayName": "", - "userId": "" - }, - "user_tz": -480 - }, - "id": "tYDoaKgJChiq", - "outputId": "9174da29-a051-4a99-e38f-6a2b09cfe4e9" - }, - "outputs": [], - "source": [ - "## create patents base table (subset of 10k out of ~110M records)\n", - "\n", - "keep = (publications.embedding_v1.str.len() > 0) & (publications.title.str.len() > 0) & (publications.abstract.str.len() > 30)\n", - "\n", - "## Choose 10000 random rows to analyze\n", - "publications = publications[keep].peek(10000)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 556 - }, - "executionInfo": { - "elapsed": 6, - "status": "ok", - "timestamp": 1742191801044, - "user": { - "displayName": "", - "userId": "" - }, - "user_tz": -480 - }, - "id": "XmqdJInztzPl", - "outputId": "ae05f3a6-edeb-423a-c061-c416717e1ec5" - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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publication_numbertitletitle_translatedabstractabstract_translatedcpccpc_lowcpc_inventive_lowtop_termssimilarurlcountrypublication_descriptioncited_byembedding_v1
0WO-2007022924-B1Pharmaceutical compositions with melting point...FalseThe invention relates to the use of chemical f...False[{'code': 'A61K47/32', 'inventive': True, 'fir...['A61K47/32' 'A61K47/30' 'A61K47/00' 'A61K' 'A...['A61K47/32' 'A61K47/30' 'A61K47/00' 'A61K' 'A...['composition' 'mucosa' 'melting point' 'agent...[{'publication_number': 'WO-2007022924-B1', 'a...https://patents.google.com/patent/WO2007022924B1WIPO (PCT)Amended claims[][ 5.3550040e-02 -9.3632710e-02 1.4337189e-02 ...
1WO-03043855-B1Convenience lighting for interior and exterior...FalseA lighting apparatus for a vehicle(21) include...False[{'code': 'B60Q1/247', 'inventive': True, 'fir...['B60Q1/247' 'B60Q1/24' 'B60Q1/02' 'B60Q1/00' ...['B60Q1/247' 'B60Q1/24' 'B60Q1/02' 'B60Q1/00' ...['vehicle' 'light' 'apparatus defined' 'pillar...[{'publication_number': 'WO-03043855-B1', 'app...https://patents.google.com/patent/WO2003043855B1WIPO (PCT)Amended claims[][ 0.00484032 -0.02695554 -0.20798226 -0.207528...
2AU-2020396918-A2Shot detection and verification systemFalseA shot detection system for a projectile weapo...False[{'code': 'F41A19/01', 'inventive': True, 'fir...['F41A19/01' 'F41A19/00' 'F41A' 'F41' 'F' 'H04...['F41A19/01' 'F41A19/00' 'F41A' 'F41' 'F' 'H04...['interest' 'region' 'property' 'shot' 'test' ...[{'publication_number': 'US-2023228510-A1', 'a...https://patents.google.com/patent/AU2020396918A2AustraliaAmended post open to public inspection[][-1.49729420e-02 -2.27105440e-01 -2.68012730e-...
3PL-347539-A1Concrete mix of increased fire resistanceFalseThe burning resistance of concrete containing ...False[{'code': 'Y02W30/91', 'inventive': False, 'fi...['Y02W30/91' 'Y02W30/50' 'Y02W30/00' 'Y02W' 'Y...['Y02W30/91' 'Y02W30/50' 'Y02W30/00' 'Y02W' 'Y...['fire resistance' 'concrete mix' 'increased f...[{'publication_number': 'DK-1564194-T3', 'appl...https://patents.google.com/patent/PL347539A1PolandApplication[][ 0.01849568 -0.05340371 -0.19257502 -0.174919...
4AU-PS049302-A0Methods and systems (ap53)FalseA charging stand for charging a mobile phone, ...False[{'code': 'H02J7/00', 'inventive': True, 'firs...['H02J7/00' 'H02J' 'H02' 'H' 'H04B1/40' 'H04B1...['H02J7/00' 'H02J' 'H02' 'H' 'H04B1/40' 'H04B1...['connection pin' 'mobile phone' 'cartridge' '...[{'publication_number': 'AU-PS049302-A0', 'app...https://patents.google.com/patent/AUPS049302A0AustraliaApplication filed, as announced in the Gazette...[][ 0.00064732 -0.2136009 0.0040593 -0.024562...
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[] \n", - "\n", - " embedding_v1 \n", - "0 [ 5.3550040e-02 -9.3632710e-02 1.4337189e-02 ... \n", - "1 [ 0.00484032 -0.02695554 -0.20798226 -0.207528... \n", - "2 [-1.49729420e-02 -2.27105440e-01 -2.68012730e-... \n", - "3 [ 0.01849568 -0.05340371 -0.19257502 -0.174919... \n", - "4 [ 0.00064732 -0.2136009 0.0040593 -0.024562... " - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "## take a look at the sample dataset\n", - "\n", - "publications.head(5)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Wl2o-NYMoygb" - }, - "source": [ - "Generate the text embeddings" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 34 - }, - "executionInfo": { - "elapsed": 4528, - "status": "ok", - "timestamp": 1742192047236, - "user": { - "displayName": "", - "userId": "" - }, - "user_tz": -480 - }, - "id": "li38q8FzDDMu", - "outputId": "b8c1bd38-b484-4f71-bd38-927c8677d0c5" - }, - "outputs": [ - { - "data": { - "text/html": [ - "Query job 0e9d9117-4981-4f5c-b785-ed831c08e7aa is DONE. 0 Bytes processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Query job fa4f1a54-85d4-4030-992e-fddda5edf3e3 is DONE. 0 Bytes processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from bigframes.ml.llm import TextEmbeddingGenerator\n", - "\n", - "text_model = TextEmbeddingGenerator(\n", - " model_name=\"text-embedding-005\",\n", - " # No connection id needed\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 139 - }, - "executionInfo": { - "elapsed": 126632, - "status": "ok", - "timestamp": 1742192656608, - "user": { - "displayName": "", - "userId": "" - }, - "user_tz": -480 - }, - "id": "b5HHZob_u61B", - "outputId": "c9ecc5fd-5d11-4fd8-f59b-9dce4e12e371" - }, - "outputs": [ - { - "data": { - "text/html": [ - "Load job 70377d71-bb13-46af-80c1-71ef16bf2949 is DONE. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Query job cc3b609d-b6b7-404f-9447-c76d3a52698b is DONE. 9.5 MB processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/google/home/swast/src/github.com/googleapis/python-bigquery-dataframes-2/bigframes/core/array_value.py:109: PreviewWarning: JSON column interpretation as a custom PyArrow extention in\n", - "`db_dtypes` is a preview feature and subject to change.\n", - " warnings.warn(msg, bfe.PreviewWarning)\n" - ] - } - ], - "source": [ - "## rename abstract column to content as the desired column on which embedding will be generated\n", - "publications = publications[[\"publication_number\", \"title\", \"abstract\"]].rename(columns={'abstract': 'content'})\n", - "\n", - "## generate the embeddings\n", - "## takes ~2-3 mins to run\n", - "embedding = text_model.predict(publications)[[\"publication_number\", \"title\", \"content\", \"ml_generate_embedding_result\",\"ml_generate_embedding_status\"]]\n", - "\n", - "## filter out rows where the embedding generation failed. the embedding status value is empty if the embedding generation was successful\n", - "embedding = embedding[~embedding[\"ml_generate_embedding_status\"].isnull()]\n" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 464 - }, - "executionInfo": { - "elapsed": 6715, - "status": "ok", - "timestamp": 1742192727525, - "user": { - "displayName": "", - "userId": "" - }, - "user_tz": -480 - }, - "id": "OIT5FbqAwqG5", - "outputId": "d04c994a-a0c8-44b0-e897-d871036eeb1f" - }, - "outputs": [ - { - "data": { - "text/html": [ - "Query job 5b15fc4a-fa9a-4608-825f-be5af9953a38 is DONE. 71.0 MB processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
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publication_numbertitlecontentml_generate_embedding_resultml_generate_embedding_status
5611WO-2014005277-A1Resource management in a cloud computing envir...Technologies and implementations for managing ...[-2.92946529e-02 -1.24640828e-02 1.27173709e-...
6895AU-2011325479-B27-([1,2,3]triazol-4-yl)-pyrrolo[2,3-b]pyrazine...Compounds of formula I, in which R[-6.45397678e-02 1.19616119e-02 -9.85191786e-...
6IL-45347-A7h-indolizino(5,6,7-ij)isoquinoline derivative...Compounds of the formula:\\n[US3946019A][-3.82784344e-02 -2.31682733e-02 -4.35006060e-...
5923WO-2005111625-A3Method to predict prostate cancerA method for predicting the probability or ris...[ 0.02480386 -0.01648765 0.03873815 -0.025998...
6370US-7868678-B2Configurable differential linesEmbodiments related to configurable differenti...[ 2.71715336e-02 -1.93733890e-02 2.82729534e-...
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[5 rows x 5 columns in total]" - ], - "text/plain": [ - " publication_number title \\\n", - "5611 WO-2014005277-A1 Resource management in a cloud computing envir... \n", - "6895 AU-2011325479-B2 7-([1,2,3]triazol-4-yl)-pyrrolo[2,3-b]pyrazine... \n", - "6 IL-45347-A 7h-indolizino(5,6,7-ij)isoquinoline derivative... \n", - "5923 WO-2005111625-A3 Method to predict prostate cancer \n", - "6370 US-7868678-B2 Configurable differential lines \n", - "\n", - " content \\\n", - "5611 Technologies and implementations for managing ... \n", - "6895 Compounds of formula I, in which R \n", - "6 Compounds of the formula:\\n[US3946019A] \n", - "5923 A method for predicting the probability or ris... \n", - "6370 Embodiments related to configurable differenti... \n", - "\n", - " ml_generate_embedding_result \\\n", - "5611 [-2.92946529e-02 -1.24640828e-02 1.27173709e-... \n", - "6895 [-6.45397678e-02 1.19616119e-02 -9.85191786e-... \n", - "6 [-3.82784344e-02 -2.31682733e-02 -4.35006060e-... \n", - "5923 [ 0.02480386 -0.01648765 0.03873815 -0.025998... \n", - "6370 [ 2.71715336e-02 -1.93733890e-02 2.82729534e-... \n", - "\n", - " ml_generate_embedding_status \n", - "5611 \n", - "6895 \n", - "6 \n", - "5923 \n", - "6370 \n", - "\n", - "[5 rows x 5 columns]" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "embedding.head(5)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 53 - }, - "executionInfo": { - "elapsed": 6590, - "status": "ok", - "timestamp": 1742192833667, - "user": { - "displayName": "", - "userId": "" - }, - "user_tz": -480 - }, - "id": "GP3ZqX_bxLGq", - "outputId": "fb823ea2-e47c-415f-84d4-543dd3291e15" - }, - "outputs": [ - { - "data": { - "text/html": [ - "Query job 06ce090b-e3f9-4252-b847-45c2a296ca61 is DONE. 70.9 MB processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "'my_dataset.my_embeddings_table'" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# store embeddings in a BQ table\n", - "DATASET_ID = \"my_dataset\" # @param {type:\"string\"}\n", - "TEXT_EMBEDDING_TABLE_ID = \"my_embeddings_table\" # @param {type:\"string\"}\n", - "embedding.to_gbq(f\"{DATASET_ID}.{TEXT_EMBEDDING_TABLE_ID}\", if_exists='replace')" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "OUZ3NNbzo1Tb" - }, - "source": [ - "## Step 2: Indexing and Similarity Search" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "mvJH2FCmynMm" - }, - "source": [ - "### [Create a Vector Index](https://cloud.google.com/python/docs/reference/bigframes/latest/bigframes.bigquery#bigframes_bigquery_create_vector_index) using BigFrames\n", - "\n", - "\n", - "**Index Type**\n", - "\n", - "The algorithm to use to build the vector index.\n", - "The supported values are IVF and TREE_AH." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 34 - }, - "executionInfo": { - "elapsed": 3882, - "status": "ok", - "timestamp": 1742193028877, - "user": { - "displayName": "", - "userId": "" - }, - "user_tz": -480 - }, - "id": "6SBVdv6gyU5A", - "outputId": "6583e113-de27-4b44-972d-c1cc061e3c76" - }, - "outputs": [], - "source": [ - "## create vector index (note only works of tables >5000 rows)\n", - "\n", - "bf_bq.create_vector_index(\n", - " table_id = f\"{DATASET_ID}.{TEXT_EMBEDDING_TABLE_ID}\",\n", - " column_name = \"ml_generate_embedding_result\",\n", - " replace= True,\n", - " index_name = \"bf_python_index\",\n", - " distance_type=\"cosine\",\n", - " index_type= \"ivf\"\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "bo8mBbRLzCOA" - }, - "source": [ - "### Vector Search (semantic search) using Vector Index\n", - "\n", - "ANN (approx nearest neighbor) search using the created vector index" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "executionInfo": { - "elapsed": 639, - "status": "ok", - "timestamp": 1742194606771, - "user": { - "displayName": "", - "userId": "" - }, - "user_tz": -480 - }, - "id": "v19BJm_wzPdZ" - }, - "outputs": [], - "source": [ - "## Set variable for vector search\n", - "\n", - "TEXT_SEARCH_STRING = \"Chip assemblies employing solder bonds to back-side lands including an electrolytic nickel layer\" ## replace with whatever search string you want to use for the vector search\n", - "FRACTION_LISTS_TO_SEARCH = 0.01" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 121 - }, - "executionInfo": { - "elapsed": 6927, - "status": "ok", - "timestamp": 1742194625774, - "user": { - "displayName": "", - "userId": "" - }, - "user_tz": -480 - }, - "id": "pAQY1ejpzPap", - "outputId": "485698ad-ac6e-4c93-844e-5d0f30aff13a" - }, - "outputs": [ - { - "data": { - "text/html": [ - "Query job 016ad678-9609-4c78-8f07-3f9887ce67ac is DONE. 0 Bytes processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/google/home/swast/src/github.com/googleapis/python-bigquery-dataframes-2/bigframes/core/array_value.py:109: PreviewWarning: JSON column interpretation as a custom PyArrow extention in\n", - "`db_dtypes` is a preview feature and subject to change.\n", - " warnings.warn(msg, bfe.PreviewWarning)\n" - ] - } - ], - "source": [ - "# convert search string to dataframe\n", - "TEXT_SEARCH_DF = bf.DataFrame([TEXT_SEARCH_STRING], columns=['search_string'])\n", - "\n", - "#generate embedding of search query\n", - "search_query = bf.DataFrame(text_model.predict(TEXT_SEARCH_DF))" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 104 - }, - "executionInfo": { - "elapsed": 5110, - "status": "ok", - "timestamp": 1742194670801, - "user": { - "displayName": "", - "userId": "" - }, - "user_tz": -480 - }, - "id": "sx0AGAdn5FYX", - "outputId": "551ebac3-594f-4303-ca97-5301dfee72bb" - }, - "outputs": [], - "source": [ - "## search the base table for the user's query\n", - "\n", - "vector_search_results = bf_bq.vector_search(\n", - " base_table=f\"{DATASET_ID}.{TEXT_EMBEDDING_TABLE_ID}\",\n", - " column_to_search=\"ml_generate_embedding_result\",\n", - " query=search_query,\n", - " distance_type=\"cosine\",\n", - " query_column_to_search=\"ml_generate_embedding_result\",\n", - " top_k=5,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 270 - }, - "executionInfo": { - "elapsed": 3511, - "status": "ok", - "timestamp": 1742195090670, - "user": { - "displayName": "", - "userId": "" - }, - "user_tz": -480 - }, - "id": "px1v4iJM5L0c", - "outputId": "d107b6e3-a362-42db-c0c2-084d02acd244" - }, - "outputs": [ - { - "data": { - "text/html": [ - "Load job b6b88844-9ed7-4c92-8984-556414592f0b is DONE. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Query job aa95f59c-7229-4e76-bd2c-3a63deea3285 is DONE. 4.7 kB processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
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querypublication_numbertitle (relevant match)abstract (relevant match)distance
0Chip assemblies employing solder bonds to back...CN-103515336-AChip package, chip arrangement, circuit board ...A chip package is provided, the chip package i...0.287274
0Chip assemblies employing solder bonds to back...US-9548145-B2Microelectronic assembly with multi-layer supp...A method of forming a microelectronic assembly...0.290519
0Chip assemblies employing solder bonds to back...JP-2012074505-ASemiconductor mounting device substrate, semic...To provide a substrate for a semiconductor mou...0.294241
0Chip assemblies employing solder bonds to back...US-2015380164-A1Ceramic electronic componentA ceramic electronic component includes an ele...0.295716
0Chip assemblies employing solder bonds to back...US-2012153447-A1Microelectronic flip chip packages with solder...Processes of assembling microelectronic packag...0.300337
\n", - "

5 rows × 5 columns

\n", - "
[5 rows x 5 columns in total]" - ], - "text/plain": [ - " query publication_number \\\n", - "0 Chip assemblies employing solder bonds to back... CN-103515336-A \n", - "0 Chip assemblies employing solder bonds to back... US-9548145-B2 \n", - "0 Chip assemblies employing solder bonds to back... JP-2012074505-A \n", - "0 Chip assemblies employing solder bonds to back... US-2015380164-A1 \n", - "0 Chip assemblies employing solder bonds to back... US-2012153447-A1 \n", - "\n", - " title (relevant match) \\\n", - "0 Chip package, chip arrangement, circuit board ... \n", - "0 Microelectronic assembly with multi-layer supp... \n", - "0 Semiconductor mounting device substrate, semic... \n", - "0 Ceramic electronic component \n", - "0 Microelectronic flip chip packages with solder... \n", - "\n", - " abstract (relevant match) distance \n", - "0 A chip package is provided, the chip package i... 0.287274 \n", - "0 A method of forming a microelectronic assembly... 0.290519 \n", - "0 To provide a substrate for a semiconductor mou... 0.294241 \n", - "0 A ceramic electronic component includes an ele... 0.295716 \n", - "0 Processes of assembling microelectronic packag... 0.300337 \n", - "\n", - "[5 rows x 5 columns]" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "## View the returned results based on simalirity with the user's query\n", - "\n", - "vector_search_results[\n", - " [\n", - " 'content',\n", - " 'publication_number',\n", - " 'title',\n", - " 'content_1',\n", - " 'distance',\n", - " ]\n", - "].rename(columns={\n", - " 'content': 'query',\n", - " 'content_1':'abstract (relevant match)' ,\n", - " 'title':'title (relevant match)',\n", - "})" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "executionInfo": { - "elapsed": 1622, - "status": "ok", - "timestamp": 1742195139318, - "user": { - "displayName": "", - "userId": "" - }, - "user_tz": -480 - }, - "id": "5fb_O-ne5cvH" - }, - "outputs": [], - "source": [ - "## Brute force result (for comparison)\n", - "\n", - "\n", - "brute_force_result = bf_bq.vector_search(\n", - " base_table=f\"{DATASET_ID}.{TEXT_EMBEDDING_TABLE_ID}\",\n", - " column_to_search=\"ml_generate_embedding_result\",\n", - " query=search_query,\n", - " top_k=5,\n", - " distance_type=\"cosine\",\n", - " use_brute_force=True,\n", - ")\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "21rNsFMHo8hO" - }, - "source": [ - "## Step 3: AI-Powered Summarization with Retrieval Augmented Generation (RAG)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "K3pIQrzB7T_G" - }, - "source": [ - "Patent documents can be dense and time-consuming to digest. AI-Powered Patent Summarization utilizes Retrieval Augmented Generation (RAG) to streamline this process. By retrieving relevant patent information through vector search and then synthesizing it with a large language model, we can generate concise, human-readable summaries, saving valuable time and effort. The code sample below walks through how to set this up continuing with the same user query as the previous use case." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 34 - }, - "executionInfo": { - "elapsed": 4827, - "status": "ok", - "timestamp": 1742195565658, - "user": { - "displayName": "", - "userId": "" - }, - "user_tz": -480 - }, - "id": "jb5rueqU7T5J", - "outputId": "43732836-ebae-4fb3-b28e-bfea51146c72" - }, - "outputs": [ - { - "data": { - "text/html": [ - "Query job 3fabe659-f95b-49cb-b0c7-9d32b09177bf is DONE. 0 Bytes processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "## gemini model\n", - "\n", - "llm_model = bf_llm.GeminiTextGenerator(model_name = \"gemini-2.5-flash\") ## replace with other model as needed" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "41e12JTf70sr" - }, - "source": [ - "We will use the same user query from Section 2, and pass the list of abstracts returned by the vector search into the prompt for the RAG application" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": { - "executionInfo": { - "elapsed": 1474, - "status": "ok", - "timestamp": 1742195536109, - "user": { - "displayName": "", - "userId": "" - }, - "user_tz": -480 - }, - "id": "EyP-ZFJK8h-2" - }, - "outputs": [], - "source": [ - "TEMPERATURE = 0.4" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 72 - }, - "executionInfo": { - "elapsed": 3371, - "status": "ok", - "timestamp": 1742195421813, - "user": { - "displayName": "", - "userId": "" - }, - "user_tz": -480 - }, - "id": "eP99R6SV7Tug", - "outputId": "c34bc931-5be8-410e-ac1f-604df31ef533" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['{\"abstract\": \"A chip package is provided, the chip package including: a chip carrier; a chip disposed over and electrically connected to a chip carrier top side; an electrically insulating material disposed over and at least partially surrounding the chip; one or more electrically conductive contact regions formed over the electrically insulating material and in electrical connection with the chip; and another electrically insulating material disposed over a chip carrier bottom side. An electrically conductive contact region on the chip carrier bottom side is released from the further electrically insulating material.\"}', '{\"abstract\": \"A method of forming a microelectronic assembly includes positioning a support structure adjacent to an active region of a device but not extending onto the active region. The support structure has planar sections. Each planar section has a substantially uniform composition. The composition of at least one of the planar sections differs from the composition of at least one of the other planar sections. A lid is positioned in contact with the support structure and extends over the active region. The support structure is bonded to the device and to the lid.\"}', '{\"abstract\": \"To provide a substrate for a semiconductor mounting device capable of obtaining high reliability. In a semiconductor mounting device substrate of the present invention, a semiconductor chip can be surface-mounted by a flip chip connection method on a semiconductor chip mounting region of a first main surface of a multilayer wiring substrate. A plurality of second main surface side solder bumps 52 forming a plate-like component mounting region 53 are formed at a location immediately below the semiconductor chip 21 on the second main surface 13 of the multilayer wiring board 11. A plate-like component 101 mainly composed of an inorganic material is surface-mounted on the multilayer wiring board 11 by a flip chip connection method via a plurality of second main surface side solder bumps 52. A plurality of second main surface side solder bumps 52 are sealed by a second main surface side underfill 107 provided in the gap S <b> 2 between the second main surface 13 and the plate-like component 101. [Selection] Figure 1\"}', '{\"abstract\": \"A ceramic electronic component includes an electronic component body, an inner electrode, and an outer electrode. The outer electrode includes a fired electrode layer and first and second plated layers. The fired electrode layer is disposed on the electronic component body. The first plated layer is disposed on the fired electrode layer. The thickness of the first plated layer is about 3 \\\\u03bcm to about 8 \\\\u03bcm, for example. The first plated layer contains nickel. The second plated layer is disposed on the first plated layer. The thickness of the second plated layer is about 0.025 \\\\u03bcm to about 1 \\\\u03bcm, for example. The second plated layer contains lead.\"}', '{\"abstract\": \"Processes of assembling microelectronic packages with lead frames and/or other suitable substrates are described herein. In one embodiment, a method for fabricating a semiconductor assembly includes forming an attachment area and a non-attachment area on a lead finger of a lead frame. The attachment area is more wettable to the solder ball than the non-attachment area during reflow. The method also includes contacting a solder ball carried by a semiconductor die with the attachment area of the lead finger, reflowing the solder ball while the solder ball is in contact with the attachment area of the lead finger, and controllably collapsing the solder ball to establish an electrical connection between the semiconductor die and the lead finger of the lead frame.\"}']\n" - ] - } - ], - "source": [ - "# Extract strings into a list of JSON strings\n", - "json_strings = [json.dumps({'abstract': s}) for s in vector_search_results['content_1']]\n", - "ALL_ABSTRACTS = json_strings\n", - "\n", - "# Print the result (optional)\n", - "print(ALL_ABSTRACTS)" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "collapsed": true, - "executionInfo": { - "elapsed": 1620, - "status": "ok", - "timestamp": 1742195587180, - "user": { - "displayName": "", - "userId": "" - }, - "user_tz": -480 - }, - "id": "kSNSi1GV8OAD", - "outputId": "37fbc822-1160-4fbd-c7d6-ecb4a16db394" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "You are an expert patent analyst. I will provide you the abstracts of the top 5 patents in json format retrieved by a vector search based on a user's query.\n", - "Your task is to analyze these abstracts and generate a concise, coherent summary that encapsulates the core innovations and concepts shared among them.\n", - "\n", - "In your output, share the original user query.\n", - "Then output the concise, coherent summary that encapsulates the core innovations and concepts shared among the top 5 abstracts. The heading for this section should\n", - "be : Summary of the top 5 abstracts that are semantically closest to the user query.\n", - "\n", - "User Query: Chip assemblies employing solder bonds to back-side lands including an electrolytic nickel layer\n", - "Top 5 abstracts: ['{\"abstract\": \"A chip package is provided, the chip package including: a chip carrier; a chip disposed over and electrically connected to a chip carrier top side; an electrically insulating material disposed over and at least partially surrounding the chip; one or more electrically conductive contact regions formed over the electrically insulating material and in electrical connection with the chip; and another electrically insulating material disposed over a chip carrier bottom side. An electrically conductive contact region on the chip carrier bottom side is released from the further electrically insulating material.\"}', '{\"abstract\": \"A method of forming a microelectronic assembly includes positioning a support structure adjacent to an active region of a device but not extending onto the active region. The support structure has planar sections. Each planar section has a substantially uniform composition. The composition of at least one of the planar sections differs from the composition of at least one of the other planar sections. A lid is positioned in contact with the support structure and extends over the active region. The support structure is bonded to the device and to the lid.\"}', '{\"abstract\": \"To provide a substrate for a semiconductor mounting device capable of obtaining high reliability. In a semiconductor mounting device substrate of the present invention, a semiconductor chip can be surface-mounted by a flip chip connection method on a semiconductor chip mounting region of a first main surface of a multilayer wiring substrate. A plurality of second main surface side solder bumps 52 forming a plate-like component mounting region 53 are formed at a location immediately below the semiconductor chip 21 on the second main surface 13 of the multilayer wiring board 11. A plate-like component 101 mainly composed of an inorganic material is surface-mounted on the multilayer wiring board 11 by a flip chip connection method via a plurality of second main surface side solder bumps 52. A plurality of second main surface side solder bumps 52 are sealed by a second main surface side underfill 107 provided in the gap S <b> 2 between the second main surface 13 and the plate-like component 101. [Selection] Figure 1\"}', '{\"abstract\": \"A ceramic electronic component includes an electronic component body, an inner electrode, and an outer electrode. The outer electrode includes a fired electrode layer and first and second plated layers. The fired electrode layer is disposed on the electronic component body. The first plated layer is disposed on the fired electrode layer. The thickness of the first plated layer is about 3 \\\\u03bcm to about 8 \\\\u03bcm, for example. The first plated layer contains nickel. The second plated layer is disposed on the first plated layer. The thickness of the second plated layer is about 0.025 \\\\u03bcm to about 1 \\\\u03bcm, for example. The second plated layer contains lead.\"}', '{\"abstract\": \"Processes of assembling microelectronic packages with lead frames and/or other suitable substrates are described herein. In one embodiment, a method for fabricating a semiconductor assembly includes forming an attachment area and a non-attachment area on a lead finger of a lead frame. The attachment area is more wettable to the solder ball than the non-attachment area during reflow. The method also includes contacting a solder ball carried by a semiconductor die with the attachment area of the lead finger, reflowing the solder ball while the solder ball is in contact with the attachment area of the lead finger, and controllably collapsing the solder ball to establish an electrical connection between the semiconductor die and the lead finger of the lead frame.\"}']\n", - "\n", - "Instructions:\n", - "\n", - "Focus on identifying the common themes and key technological advancements described in the abstracts.\n", - "Synthesize the information into a clear and concise summary, approximately 150-200 words.\n", - "Avoid simply copying phrases from the abstracts. Instead, aim to provide a cohesive overview of the shared concepts.\n", - "Highlight the potential applications and benefits of the described inventions.\n", - "Maintain a professional and objective tone.\n", - "Do not mention the individual patents by number, focus on summarizing the shared concepts.\n", - "\n" - ] - } - ], - "source": [ - "## Setup the LLM prompt\n", - "\n", - "prompt = f\"\"\"\n", - "You are an expert patent analyst. I will provide you the abstracts of the top 5 patents in json format retrieved by a vector search based on a user's query.\n", - "Your task is to analyze these abstracts and generate a concise, coherent summary that encapsulates the core innovations and concepts shared among them.\n", - "\n", - "In your output, share the original user query.\n", - "Then output the concise, coherent summary that encapsulates the core innovations and concepts shared among the top 5 abstracts. The heading for this section should\n", - "be : Summary of the top 5 abstracts that are semantically closest to the user query.\n", - "\n", - "User Query: {TEXT_SEARCH_STRING}\n", - "Top 5 abstracts: {ALL_ABSTRACTS}\n", - "\n", - "Instructions:\n", - "\n", - "Focus on identifying the common themes and key technological advancements described in the abstracts.\n", - "Synthesize the information into a clear and concise summary, approximately 150-200 words.\n", - "Avoid simply copying phrases from the abstracts. Instead, aim to provide a cohesive overview of the shared concepts.\n", - "Highlight the potential applications and benefits of the described inventions.\n", - "Maintain a professional and objective tone.\n", - "Do not mention the individual patents by number, focus on summarizing the shared concepts.\n", - "\"\"\"\n", - "\n", - "print(prompt)" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": { - "executionInfo": { - "elapsed": 1, - "status": "ok", - "timestamp": 1742195567707, - "user": { - "displayName": "", - "userId": "" - }, - "user_tz": -480 - }, - "id": "njiQdfkT8Y7V" - }, - "outputs": [], - "source": [ - "## Define a function that will take the input propmpt and run the LLM\n", - "\n", - "def predict(prompt: str, temperature: float = TEMPERATURE) -> str:\n", - " # Create dataframe\n", - " input = bf.DataFrame(\n", - " {\n", - " \"prompt\": [prompt],\n", - " }\n", - " )\n", - "\n", - " # Return response\n", - " return llm_model.predict(input, temperature=temperature).ml_generate_text_llm_result.iloc[0]" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 426 - }, - "executionInfo": { - "elapsed": 14425, - "status": "ok", - "timestamp": 1742195608280, - "user": { - "displayName": "", - "userId": "" - }, - "user_tz": -480 - }, - "id": "OYYkVYbs8Y0P", - "outputId": "def839e3-3dee-4320-9cb5-cac855ddea6b" - }, - "outputs": [ - { - "data": { - "text/html": [ - "Load job 34f3b649-6e45-46db-a6e5-405ae0a8bf69 is DONE. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Query job a574725f-64ae-4a19-aac0-959bec0bffeb is DONE. 5.0 kB processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/google/home/swast/src/github.com/googleapis/python-bigquery-dataframes-2/bigframes/core/array_value.py:109: PreviewWarning: JSON column interpretation as a custom PyArrow extention in\n", - "`db_dtypes` is a preview feature and subject to change.\n", - " warnings.warn(msg, bfe.PreviewWarning)\n" - ] - }, - { - "data": { - "text/markdown": [ - "User Query: Chip assemblies employing solder bonds to back-side lands including an electrolytic nickel layer\n", - "\n", - "Summary of the top 5 abstracts that are semantically closest to the user query:\n", - "\n", - "The abstracts describe various aspects of microelectronic assembly and packaging, with a focus on enhancing reliability and electrical connectivity. A common theme is the use of solder bumps or balls for creating electrical connections between different components, such as semiconductor chips and substrates or lead frames. Several abstracts highlight methods for improving the solderability and wettability of contact regions, often involving the use of multiple layers with differing compositions. The use of electrically insulating materials to provide support and protection to the chip and electrical connections is also described. One abstract specifically mentions a nickel-containing plated layer as part of an outer electrode, suggesting its role in improving the electrical or mechanical properties of the connection. The innovations aim to improve the reliability and performance of microelectronic devices through optimized material selection, assembly processes, and structural designs.\n" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Invoke LLM with prompt\n", - "response = predict(prompt, temperature = TEMPERATURE)\n", - "\n", - "# Print results as Markdown\n", - "Markdown(response)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "sy82XLDfooEb" - }, - "source": [ - "# Summary and next steps\n", - "\n", - "Ready to dive deeper and explore the endless possibilities? Start building your own vector search applications with BigFrames and BigQuery today! Check out our [documentation](https://cloud.google.com/python/docs/reference/bigframes/latest/bigframes.bigquery#bigframes_bigquery_vector_search), explore our sample [notebooks](https://github.com/googleapis/python-bigquery-dataframes/tree/main/notebooks), and unleash the power of vector analytics on your data.\n", - "The BigFrames team would also love to hear from you. If you would like to reach out, please send an email to: bigframes-feedback@google.com or by filing an issue at the [open source BigFrames repository](https://github.com/googleapis/python-bigquery-dataframes/issues). To receive updates about BigFrames, subscribe to the BigFrames email list." - ] - } - ], - "metadata": { - "colab": { - "name": "bq_dataframes_llm_kmeans", - "provenance": [], - "toc_visible": true - }, - "kernelspec": { - "display_name": "venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.16" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/notebooks/generative_ai/bq_dataframes_ml_drug_name_generation.ipynb b/notebooks/generative_ai/bq_dataframes_ml_drug_name_generation.ipynb index 93ac3f31c14..56d7bd13558 100644 --- a/notebooks/generative_ai/bq_dataframes_ml_drug_name_generation.ipynb +++ b/notebooks/generative_ai/bq_dataframes_ml_drug_name_generation.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { "id": "ur8xi4C7S06n" }, @@ -34,12 +34,12 @@ "\n", " \n", " \n", @@ -48,16 +48,21 @@ " \"Vertex\n", " Open in Vertex AI Workbench\n", " \n", - " \n", - " \n", + " \n", "
\n", " \n", - " \"Colab Run in Colab\n", + " \"Colab Run in Colab\n", " \n", " \n", " \n", - " \"GitHub\n", + " \"GitHub\n", " View on GitHub\n", " \n", " \n", - " \n", - " \"BQ\n", - " Open in BQ Studio\n", - " \n", - "
" ] }, + { + "cell_type": "markdown", + "metadata": { + "id": "24743cf4a1e1" + }, + "source": [ + "**_NOTE_**: This notebook has been tested in the following environment:\n", + "\n", + "* Python version = 3.9" + ] + }, { "cell_type": "markdown", "metadata": { @@ -87,7 +92,7 @@ "1. Use `bigframes` to query the FDA dataset of over 100,000 drugs, filtered on the brand name, generic name, and indications & usage columns.\n", "1. Filter this dataset to find prototypical brand names that can be used as examples in prompt tuning.\n", "1. Create a prompt with the user input, general instructions, examples and counter-examples for the desired brand name.\n", - "1. Use the `bigframes.ml.llm.GeminiTextGenerator` to generate choices of brand names." + "1. Use the `bigframes.ml.llm.PaLM2TextGenerator` to generate choices of brand names." ] }, { @@ -133,13 +138,13 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": { "id": "2b4ef9b72d43" }, "outputs": [], "source": [ - "# !pip install -U --quiet bigframes" + "!pip install -U --quiet bigframes" ] }, { @@ -153,7 +158,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": { "id": "f200f10a1da3" }, @@ -177,14 +182,15 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": { "id": "PyQmSRbKA8r-" }, "outputs": [], "source": [ "import bigframes.pandas as bpd\n", - "from bigframes.ml.llm import GeminiTextGenerator\n", + "from google.cloud import bigquery_connection_v1 as bq_connection\n", + "from bigframes.ml.llm import PaLM2TextGenerator\n", "from IPython.display import Markdown" ] }, @@ -220,7 +226,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": { "id": "254614fa0c46" }, @@ -240,7 +246,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": { "id": "603adbbf0532" }, @@ -288,27 +294,13 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": { "id": "oM1iC_MfAts1" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[1;31mERROR:\u001b[0m (gcloud.config.set) argument VALUE: Must be specified.\n", - "Usage: gcloud config set SECTION/PROPERTY VALUE [optional flags]\n", - " optional flags may be --help | --installation\n", - "\n", - "For detailed information on this command and its flags, run:\n", - " gcloud config set --help\n" - ] - } - ], + "outputs": [], "source": [ - "# Please fill in these values.\n", - "PROJECT_ID = \"\" # @param {type:\"string\"}\n", + "PROJECT_ID = \"\" # @param {type:\"string\"}\n", "\n", "# Set the project id\n", "! gcloud config set project {PROJECT_ID}" @@ -328,14 +320,17 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": { "id": "G1vVsPiMsL2X" }, "outputs": [], "source": [ "# Please fill in these values.\n", - "LOCATION = \"us\" # @param {type:\"string\"}" + "LOCATION = \"us\" # @param {type:\"string\"}\n", + "CONNECTION = \"\" # @param {type:\"string\"}\n", + "\n", + "connection_name = f\"{PROJECT_ID}.{LOCATION}.{CONNECTION}\"" ] }, { @@ -347,6 +342,50 @@ "We will now try to use the provided connection, and if it doesn't exist, create a new one. We will also print the service account used." ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "56Hw42m6kFrj" + }, + "outputs": [], + "source": [ + "# Initialize client and set request parameters\n", + "client = bq_connection.ConnectionServiceClient()\n", + "new_conn_parent = f\"projects/{PROJECT_ID}/locations/{LOCATION}\"\n", + "exists_conn_parent = f\"projects/{PROJECT_ID}/locations/{LOCATION}/connections/{CONNECTION}\"\n", + "cloud_resource_properties = bq_connection.CloudResourceProperties({})\n", + "\n", + "# Try to connect using provided connection\n", + "try:\n", + " request = client.get_connection(\n", + " request=bq_connection.GetConnectionRequest(name=exists_conn_parent)\n", + " )\n", + " CONN_SERVICE_ACCOUNT = f\"serviceAccount:{request.cloud_resource.service_account_id}\"\n", + "# Create a new connection on error\n", + "except Exception:\n", + " connection = bq_connection.types.Connection(\n", + " {\"friendly_name\": CONNECTION, \"cloud_resource\": cloud_resource_properties}\n", + " )\n", + " request = bq_connection.CreateConnectionRequest(\n", + " {\n", + " \"parent\": new_conn_parent,\n", + " \"connection_id\": CONNECTION,\n", + " \"connection\": connection,\n", + " }\n", + " )\n", + " response = client.create_connection(request)\n", + " CONN_SERVICE_ACCOUNT = (\n", + " f\"serviceAccount:{response.cloud_resource.service_account_id}\"\n", + " )\n", + "# Set service account permissions\n", + "!gcloud projects add-iam-policy-binding {PROJECT_ID} --condition=None --no-user-output-enabled --member={CONN_SERVICE_ACCOUNT} --role='roles/bigquery.connectionUser'\n", + "!gcloud projects add-iam-policy-binding {PROJECT_ID} --condition=None --no-user-output-enabled --member={CONN_SERVICE_ACCOUNT} --role='roles/aiplatform.user'\n", + "!gcloud projects add-iam-policy-binding {PROJECT_ID} --condition=None --no-user-output-enabled --member={CONN_SERVICE_ACCOUNT} --role='roles/run.invoker'\n", + "\n", + "print(CONN_SERVICE_ACCOUNT)" + ] + }, { "cell_type": "markdown", "metadata": { @@ -360,20 +399,13 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": { "id": "OCccLirpkSRz" }, "outputs": [], "source": [ - "# Note: The project option is not required in all environments.\n", - "# On BigQuery Studio, the project ID is automatically detected.\n", "bpd.options.bigquery.project = PROJECT_ID\n", - "\n", - "# Note: The location option is not required.\n", - "# It defaults to the location of the first table or query\n", - "# passed to read_gbq(). For APIs where a location can't be\n", - "# auto-detected, the location defaults to the \"US\" location.\n", "bpd.options.bigquery.location = LOCATION" ] }, @@ -390,7 +422,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": { "id": "oxphj2gnuKou" }, @@ -413,25 +445,11 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": { "id": "0knz5ZWMzed-" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Provide 10 unique and modern brand names in Markdown bullet point format. Do not provide any additional explanation.\n", - "\n", - "Be creative with the brand names. Don't use English words directly; use variants or invented words.\n", - "\n", - "The generic name is: Entropofloxacin\n", - "\n", - "The indications and usage are: Entropofloxacin is a fluoroquinolone antibiotic that is used to treat a variety of bacterial infections, including: pneumonia, streptococcus infections, salmonella infections, escherichia coli infections, and pseudomonas aeruginosa infections It is taken by mouth or by injection. The dosage and frequency of administration will vary depending on the type of infection being treated. It should be taken for the full course of treatment, even if symptoms improve after a few days. Stopping the medication early may increase the risk of the infection coming back..\n" - ] - } - ], + "outputs": [], "source": [ "zero_shot_prompt = f\"\"\"Provide {NUM_NAMES} unique and modern brand names in Markdown bullet point format. Do not provide any additional explanation.\n", "\n", @@ -446,15 +464,19 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "id": "LCRE2L720f5y" + }, "source": [ - "Next, let's create a helper function to predict with our model. It will take a string input, and add it to a temporary BigFrames DataFrame. It will also return the string extracted from the response DataFrame." + "Next, let's create a helper function to predict with our model. It will take a string input, and add it to a temporary BigFrames `DataFrame`. It will also return the string extracted from the response `DataFrame`." ] }, { "cell_type": "code", - "execution_count": 12, - "metadata": {}, + "execution_count": null, + "metadata": { + "id": "LB3xgDroIxlx" + }, "outputs": [], "source": [ "def predict(prompt: str, temperature: float = TEMPERATURE) -> str:\n", @@ -466,7 +488,7 @@ " )\n", "\n", " # Return response\n", - " return model.predict(input, temperature=temperature).ml_generate_text_llm_result.iloc[0]" + " return model.predict(input, temperature).ml_generate_text_llm_result.iloc[0]" ] }, { @@ -484,96 +506,16 @@ "metadata": { "id": "UW2fQ2k5Hsic" }, - "outputs": [ - { - "data": { - "text/html": [ - "Query job 25b47284-2b28-4cd9-ac9a-90379f818c84 is DONE. 0 Bytes processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Query job 0efa6f42-6569-4274-ac21-667c7eecefc7 is DONE. 0 Bytes processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Query job c5e98170-7d58-4aa2-a3a3-6680cd9a54c0 is DONE. 8 Bytes processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Query job 5fd9d5bf-c731-4b21-b7c9-9b6244ffb412 is DONE. 2 Bytes processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Query job 36f7e8ec-ee42-4f94-8e38-bdf18b371517 is DONE. 118 Bytes processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/markdown": [ - "- Etherealox\n", - "- Zenithrox\n", - "- Aureox\n", - "- Lucentrox\n", - "- Aethrox\n", - "- Luminex\n", - "- Elysirox\n", - "- Quasarox\n", - "- Novaflux\n", - "- Arcanox" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ + "# Get BigFrames session\n", + "session = bpd.get_global_session()\n", + "\n", "# Define the model\n", - "model = GeminiTextGenerator(model_name=\"gemini-2.5-flash\")\n", + "model = PaLM2TextGenerator(session=session, connection_name=connection_name)\n", "\n", "# Invoke LLM with prompt\n", - "response = predict(zero_shot_prompt, temperature = TEMPERATURE)\n", + "response = predict(zero_shot_prompt)\n", "\n", "# Print results as Markdown\n", "Markdown(response)" @@ -610,7 +552,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "metadata": { "id": "MXdI78SOElyt" }, @@ -632,26 +574,11 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "metadata": { "id": "aQ2iscnhF2cx" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Provide 10 unique and modern brand names in Markdown bullet point format, related to the drug at the bottom of this prompt.\n", - "\n", - "Be creative with the brand names. Don't use English words directly; use variants or invented words.\n", - "\n", - "First, we will provide 3 examples to help with your thought process.\n", - "\n", - "Then, we will provide the generic name and usage for the drug we'd like you to generate brand names for.\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "prefix_prompt = f\"\"\"Provide {NUM_NAMES} unique and modern brand names in Markdown bullet point format, related to the drug at the bottom of this prompt.\n", "\n", @@ -678,143 +605,15 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "metadata": { "id": "IoO_Bp8wA07N" }, - "outputs": [ - { - "data": { - "text/html": [ - "Query job 542b0ce1-9d56-456f-bcd3-d24a6f0c825a is DONE. 84.4 MB processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Query job 2405ba41-b263-46d3-a0e5-3b5e7ecef6ab is DONE. 0 Bytes processed. 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openfda_generic_nameopenfda_brand_nameindications_and_usage
0BENZALKONIUM CHLORIDEmeijer kidsUse - hand washing to decrease bacteria on skin
3OCTINOXATE, TITANIUM DIOXIDECD DIORSKIN STAR Studio Makeup Spectacular Bri...Uses Helps prevent sunburn. If used as directe...
4TRIAMCINOLONE ACETONIDETriamcinolone AcetonideINDICATIONS AND USAGE Triamcinolone Acetonide ...
5BACITRACIN ZINC, NEOMYCIN SULFATE, POLYMYXIN B...Triple AntibioticFirst aid to help prevent infection in minor c...
6RISPERIDONERisperidone1. INDICATIONS AND USAGE Risperidone is an aty...
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[5 rows x 3 columns in total]" - ], - "text/plain": [ - " openfda_generic_name \\\n", - "0 BENZALKONIUM CHLORIDE \n", - "3 OCTINOXATE, TITANIUM DIOXIDE \n", - "4 TRIAMCINOLONE ACETONIDE \n", - "5 BACITRACIN ZINC, NEOMYCIN SULFATE, POLYMYXIN B... \n", - "6 RISPERIDONE \n", - "\n", - " openfda_brand_name \\\n", - "0 meijer kids \n", - "3 CD DIORSKIN STAR Studio Makeup Spectacular Bri... \n", - "4 Triamcinolone Acetonide \n", - "5 Triple Antibiotic \n", - "6 Risperidone \n", - "\n", - " indications_and_usage \n", - "0 Use - hand washing to decrease bacteria on skin \n", - "3 Uses Helps prevent sunburn. If used as directe... \n", - "4 INDICATIONS AND USAGE Triamcinolone Acetonide ... \n", - "5 First aid to help prevent infection in minor c... \n", - "6 1. INDICATIONS AND USAGE Risperidone is an aty... \n", - "\n", - "[5 rows x 3 columns]" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Query 3 columns of interest from drug label dataset\n", "df = bpd.read_gbq(\"bigquery-public-data.fda_drug.drug_label\",\n", - " columns=[\"openfda_generic_name\", \"openfda_brand_name\", \"indications_and_usage\"])\n", + " col_order=[\"openfda_generic_name\", \"openfda_brand_name\", \"indications_and_usage\"])\n", "\n", "# Exclude any rows with missing data\n", "df = df.dropna()\n", @@ -837,7 +636,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "metadata": { "id": "95WDe2eCCeLx" }, @@ -864,89 +663,11 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": null, "metadata": { "id": "2ohZYg7QEyJV" }, - "outputs": [ - { - "data": { - "text/html": [ - "Query job 293c90e0-7fdf-4769-9d8e-f222f35d368e is DONE. 84.4 MB processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
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openfda_generic_nameopenfda_brand_nameindications_and_usage
81748AMPICILLIN SODIUMAmpicillinINDICATIONS AND USAGE Ampicillin for Injection...
730AZTREONAMCayston1 INDICATIONS AND USAGE CAYSTON® is indicated ...
71763TERAZOSIN HYDROCHLORIDETerazosinINDICATIONS AND USAGE Terazosin capsules are i...
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" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Take a sample and convert to a Pandas dataframe for local usage.\n", "df_examples = df.sample(NUM_EXAMPLES, random_state=3).to_pandas()\n", @@ -965,19 +686,11 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "metadata": { "id": "PcJdSaw0EGcW" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[{'brand_name': 'Ampicillin', 'generic_name': 'AMPICILLIN SODIUM', 'usage': 'INDICATIONS AND USAGE Ampicillin for Injection, USP is indicated in the treatment of infections caused by susceptible strains of the designated organisms in the following conditions: Respiratory Tract Infections caused by Streptococcus pneumoniae. Staphylococcus aureus (penicillinase and nonpenicillinase-producing), H. influenzae, and Group A beta-hemolytic streptococci. Bacterial Meningitis caused by E. coli, Group B streptococci, and other Gram-negative bacteria (Listeria monocytogenes, N. meningitidis). The addition of an aminoglycoside with ampicillin may increase its effectiveness against Gram-negative bacteria. Septicemia and Endocarditis caused by susceptible Gram-positive organisms including Streptococcus spp., penicillin G-susceptible staphylococci, and enterococci. Gram-negative sepsis caused by E. coli, Proteus mirabilis and Salmonella spp. responds to ampicillin. Endocarditis due to enterococcal strains usually respond to intravenous therapy. The addition of an aminoglycoside may enhance the effectiveness of ampicillin when treating streptococcal endocarditis. Urinary Tract Infections caused by sensitive strains of E. coli and Proteus mirabilis. Gastrointestinal Infections caused by Salmonella typhi (typhoid fever), other Salmonella spp., and Shigella spp. (dysentery) usually respond to oral or intravenous therapy. Bacteriology studies to determine the causative organisms and their susceptibility to ampicillin should be performed. Therapy may be instituted prior to obtaining results of susceptibility testing. It is advisable to reserve the parenteral form of this drug for moderately severe and severe infections and for patients who are unable to take the oral forms. A change to oral ampicillin may be made as soon as appropriate. To reduce the development of drug-resistant bacteria and maintain the effectiveness of Ampicillin for Injection, USP and other antibacterial drugs, Ampicillin for Injection, USP should be used only to treat or prevent infections that are proven or strongly suspected to be caused by susceptible bacteria. When culture and susceptibility information are available, they should be considered in selecting or modifying antibacterial therapy. In the absence of such data, local epidemiology and susceptibility patterns may contribute to the empiric selection of therapy. Indicated surgical procedures should be performed.'}, {'brand_name': 'Cayston', 'generic_name': 'AZTREONAM', 'usage': '1 INDICATIONS AND USAGE CAYSTON® is indicated to improve respiratory symptoms in cystic fibrosis (CF) patients with Pseudomonas aeruginosa. Safety and effectiveness have not been established in pediatric patients below the age of 7 years, patients with FEV1 <25% or >75% predicted, or patients colonized with Burkholderia cepacia [see Clinical Studies (14) ]. To reduce the development of drug-resistant bacteria and maintain the effectiveness of CAYSTON and other antibacterial drugs, CAYSTON should be used only to treat patients with CF known to have Pseudomonas aeruginosa in the lungs. CAYSTON is a monobactam antibacterial indicated to improve respiratory symptoms in cystic fibrosis (CF) patients with Pseudomonas aeruginosa. Safety and effectiveness have not been established in pediatric patients below the age of 7 years, patients with FEV1 <25% or >75% predicted, or patients colonized with Burkholderia cepacia. (1)'}, {'brand_name': 'Terazosin', 'generic_name': 'TERAZOSIN HYDROCHLORIDE', 'usage': 'INDICATIONS AND USAGE Terazosin capsules are indicated for the treatment of symptomatic benign prostatic hyperplasia (BPH). There is a rapid response, with approximately 70% of patients experiencing an increase in urinary flow and improvement in symptoms of BPH when treated with terazosin capsules. The long-term effects of terazosin capsules on the incidence of surgery, acute urinary obstruction or other complications of BPH are yet to be determined. Terazosin capsules are also indicated for the treatment of hypertension. Terazosin capsules can be used alone or in combination with other antihypertensive agents such as diuretics or beta-adrenergic blocking agents.'}]\n" - ] - } - ], + "outputs": [], "source": [ "examples = [\n", " {\n", @@ -1006,22 +719,11 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": null, "metadata": { "id": "kzAVsF6wJ93S" }, - "outputs": [ - { - "data": { - "text/plain": [ - "'Generic name: AMPICILLIN SODIUM\\nUsage: INDICATIONS AND USAGE Ampicillin for Injection, USP is indicated in the treatment of infections caused by susceptible strains of the designated organisms in the following conditions: Respiratory Tract Infections caused by Streptococcus pneumoniae. Staphylococcus aureus (penicillinase and nonpenicillinase-producing), H. influenzae, and Group A beta-hemolytic streptococci. Bacterial Meningitis caused by E. coli, Group B streptococci, and other Gram-negative bacteria (Listeria monocytogenes, N. meningitidis). The addition of an aminoglycoside with ampicillin may increase its effectiveness against Gram-negative bacteria. Septicemia and Endocarditis caused by susceptible Gram-positive organisms including Streptococcus spp., penicillin G-susceptible staphylococci, and enterococci. Gram-negative sepsis caused by E. coli, Proteus mirabilis and Salmonella spp. responds to ampicillin. Endocarditis due to enterococcal strains usually respond to intravenous therapy. The addition of an aminoglycoside may enhance the effectiveness of ampicillin when treating streptococcal endocarditis. Urinary Tract Infections caused by sensitive strains of E. coli and Proteus mirabilis. Gastrointestinal Infections caused by Salmonella typhi (typhoid fever), other Salmonella spp., and Shigella spp. (dysentery) usually respond to oral or intravenous therapy. Bacteriology studies to determine the causative organisms and their susceptibility to ampicillin should be performed. Therapy may be instituted prior to obtaining results of susceptibility testing. It is advisable to reserve the parenteral form of this drug for moderately severe and severe infections and for patients who are unable to take the oral forms. A change to oral ampicillin may be made as soon as appropriate. To reduce the development of drug-resistant bacteria and maintain the effectiveness of Ampicillin for Injection, USP and other antibacterial drugs, Ampicillin for Injection, USP should be used only to treat or prevent infections that are proven or strongly suspected to be caused by susceptible bacteria. When culture and susceptibility information are available, they should be considered in selecting or modifying antibacterial therapy. In the absence of such data, local epidemiology and susceptibility patterns may contribute to the empiric selection of therapy. Indicated surgical procedures should be performed.\\nBrand name: Ampicillin\\n\\nGeneric name: AZTREONAM\\nUsage: 1 INDICATIONS AND USAGE CAYSTON® is indicated to improve respiratory symptoms in cystic fibrosis (CF) patients with Pseudomonas aeruginosa. Safety and effectiveness have not been established in pediatric patients below the age of 7 years, patients with FEV1 <25% or >75% predicted, or patients colonized with Burkholderia cepacia [see Clinical Studies (14) ]. To reduce the development of drug-resistant bacteria and maintain the effectiveness of CAYSTON and other antibacterial drugs, CAYSTON should be used only to treat patients with CF known to have Pseudomonas aeruginosa in the lungs. CAYSTON is a monobactam antibacterial indicated to improve respiratory symptoms in cystic fibrosis (CF) patients with Pseudomonas aeruginosa. Safety and effectiveness have not been established in pediatric patients below the age of 7 years, patients with FEV1 <25% or >75% predicted, or patients colonized with Burkholderia cepacia. (1)\\nBrand name: Cayston\\n\\nGeneric name: TERAZOSIN HYDROCHLORIDE\\nUsage: INDICATIONS AND USAGE Terazosin capsules are indicated for the treatment of symptomatic benign prostatic hyperplasia (BPH). There is a rapid response, with approximately 70% of patients experiencing an increase in urinary flow and improvement in symptoms of BPH when treated with terazosin capsules. The long-term effects of terazosin capsules on the incidence of surgery, acute urinary obstruction or other complications of BPH are yet to be determined. Terazosin capsules are also indicated for the treatment of hypertension. Terazosin capsules can be used alone or in combination with other antihypertensive agents such as diuretics or beta-adrenergic blocking agents.\\nBrand name: Terazosin\\n\\n'" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "example_prompt = \"\"\n", "for example in examples:\n", @@ -1041,21 +743,11 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": null, "metadata": { "id": "OYp6W_XfHTlo" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Generic name: Entropofloxacin\n", - "Usage: Entropofloxacin is a fluoroquinolone antibiotic that is used to treat a variety of bacterial infections, including: pneumonia, streptococcus infections, salmonella infections, escherichia coli infections, and pseudomonas aeruginosa infections It is taken by mouth or by injection. The dosage and frequency of administration will vary depending on the type of infection being treated. It should be taken for the full course of treatment, even if symptoms improve after a few days. Stopping the medication early may increase the risk of the infection coming back.\n", - "Brand names:\n" - ] - } - ], + "outputs": [], "source": [ "suffix_prompt = f\"\"\"Generic name: {GENERIC_NAME}\n", "Usage: {USAGE}\n", @@ -1075,40 +767,11 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": null, "metadata": { "id": "99xdU7l8C1h8" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Provide 10 unique and modern brand names in Markdown bullet point format, related to the drug at the bottom of this prompt.\n", - "\n", - "Be creative with the brand names. Don't use English words directly; use variants or invented words.\n", - "\n", - "First, we will provide 3 examples to help with your thought process.\n", - "\n", - "Then, we will provide the generic name and usage for the drug we'd like you to generate brand names for.\n", - "Generic name: AMPICILLIN SODIUM\n", - "Usage: INDICATIONS AND USAGE Ampicillin for Injection, USP is indicated in the treatment of infections caused by susceptible strains of the designated organisms in the following conditions: Respiratory Tract Infections caused by Streptococcus pneumoniae. Staphylococcus aureus (penicillinase and nonpenicillinase-producing), H. influenzae, and Group A beta-hemolytic streptococci. Bacterial Meningitis caused by E. coli, Group B streptococci, and other Gram-negative bacteria (Listeria monocytogenes, N. meningitidis). The addition of an aminoglycoside with ampicillin may increase its effectiveness against Gram-negative bacteria. Septicemia and Endocarditis caused by susceptible Gram-positive organisms including Streptococcus spp., penicillin G-susceptible staphylococci, and enterococci. Gram-negative sepsis caused by E. coli, Proteus mirabilis and Salmonella spp. responds to ampicillin. Endocarditis due to enterococcal strains usually respond to intravenous therapy. The addition of an aminoglycoside may enhance the effectiveness of ampicillin when treating streptococcal endocarditis. Urinary Tract Infections caused by sensitive strains of E. coli and Proteus mirabilis. Gastrointestinal Infections caused by Salmonella typhi (typhoid fever), other Salmonella spp., and Shigella spp. (dysentery) usually respond to oral or intravenous therapy. Bacteriology studies to determine the causative organisms and their susceptibility to ampicillin should be performed. Therapy may be instituted prior to obtaining results of susceptibility testing. It is advisable to reserve the parenteral form of this drug for moderately severe and severe infections and for patients who are unable to take the oral forms. A change to oral ampicillin may be made as soon as appropriate. To reduce the development of drug-resistant bacteria and maintain the effectiveness of Ampicillin for Injection, USP and other antibacterial drugs, Ampicillin for Injection, USP should be used only to treat or prevent infections that are proven or strongly suspected to be caused by susceptible bacteria. When culture and susceptibility information are available, they should be considered in selecting or modifying antibacterial therapy. In the absence of such data, local epidemiology and susceptibility patterns may contribute to the empiric selection of therapy. Indicated surgical procedures should be performed.\n", - "Brand name: Ampicillin\n", - "\n", - "Generic name: AZTREONAM\n", - "Usage: 1 INDICATIONS AND USAGE CAYSTON® is indicated to improve respiratory symptoms in cystic fibrosis (CF) patients with Pseudomonas aeruginosa. Safety and effectiveness have not been established in pediatric patients below the age of 7 years, patients with FEV1 <25% or >75% predicted, or patients colonized with Burkholderia cepacia [see Clinical Studies (14) ]. To reduce the development of drug-resistant bacteria and maintain the effectiveness of CAYSTON and other antibacterial drugs, CAYSTON should be used only to treat patients with CF known to have Pseudomonas aeruginosa in the lungs. CAYSTON is a monobactam antibacterial indicated to improve respiratory symptoms in cystic fibrosis (CF) patients with Pseudomonas aeruginosa. Safety and effectiveness have not been established in pediatric patients below the age of 7 years, patients with FEV1 <25% or >75% predicted, or patients colonized with Burkholderia cepacia. (1)\n", - "Brand name: Cayston\n", - "\n", - "Generic name: TERAZOSIN HYDROCHLORIDE\n", - "Usage: INDICATIONS AND USAGE Terazosin capsules are indicated for the treatment of symptomatic benign prostatic hyperplasia (BPH). There is a rapid response, with approximately 70% of patients experiencing an increase in urinary flow and improvement in symptoms of BPH when treated with terazosin capsules. The long-term effects of terazosin capsules on the incidence of surgery, acute urinary obstruction or other complications of BPH are yet to be determined. Terazosin capsules are also indicated for the treatment of hypertension. Terazosin capsules can be used alone or in combination with other antihypertensive agents such as diuretics or beta-adrenergic blocking agents.\n", - "Brand name: Terazosin\n", - "\n", - "Generic name: Entropofloxacin\n", - "Usage: Entropofloxacin is a fluoroquinolone antibiotic that is used to treat a variety of bacterial infections, including: pneumonia, streptococcus infections, salmonella infections, escherichia coli infections, and pseudomonas aeruginosa infections It is taken by mouth or by injection. The dosage and frequency of administration will vary depending on the type of infection being treated. It should be taken for the full course of treatment, even if symptoms improve after a few days. Stopping the medication early may increase the risk of the infection coming back.\n", - "Brand names:\n" - ] - } - ], + "outputs": [], "source": [ "# Define the prompt\n", "few_shot_prompt = prefix_prompt + example_prompt + suffix_prompt\n", @@ -1128,82 +791,11 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": null, "metadata": { "id": "d4ODRJdvLhlQ" }, - "outputs": [ - { - "data": { - "text/html": [ - "Query job 5c6c3b79-812c-4a6e-876e-ca1ff6230a6e is DONE. 0 Bytes processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Query job 168d5859-5edb-4702-8192-838ac2c7bc17 is DONE. 8 Bytes processed. 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Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/markdown": [ - "- **Aerion:** (Derived from \"aer\" meaning air)\n", - "- **Aquazone:** (Combining \"aqua\" for water and \"zone\" for area)\n", - "- **Biosphere:** (Inspired by the concept of a self-contained ecosystem)\n", - "- **Celestial:** (Evoking the vastness and healing power of the universe)\n", - "- **Ethereal:** (Conveying a sense of lightness and transcendence)\n", - "- **Luminary:** (From \"lumen\" meaning light, symbolizing hope and healing)\n", - "- **Quasar:** (Inspired by the powerful and distant cosmic objects)\n", - "- **Sanctuary:** (Creating a sense of safety and refuge)\n", - "- **Zenith:** (Reaching the highest point or peak)\n", - "- **Zephyr:** (Named after the gentle west wind, representing a calming and soothing effect)" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 42, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "response = predict(few_shot_prompt)\n", "\n", @@ -1225,143 +817,15 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": null, "metadata": { "id": "8eAutS41mx6U" }, - "outputs": [ - { - "data": { - "text/html": [ - "Query job b73f92bb-0e58-4fe4-adfb-b948fc5f4647 is DONE. 84.4 MB processed. 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openfda_generic_nameopenfda_brand_nameindications_and_usage
89MEPHITIS MEPHITICAMEPHITIS MEPHITICAINDICATIONS Condition listed above or as direc...
105ONDANSETRONONDANSETRON1 INDICATIONS AND USAGE Ondansetron Injection,...
124CLOFARABINECLOFARABINE1 INDICATIONS AND USAGE Clofarabine injection ...
273ACETAMINOPHEN AND DIPHENHYDRAMINE HYDROCHLORIDEACETAMINOPHEN AND DIPHENHYDRAMINE HYDROCHLORIDEUses Temporary relief of occasional headaches ...
284OFLOXACINOFLOXACININDICATIONS AND USAGE To reduce the developmen...
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5 rows × 3 columns

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[5 rows x 3 columns in total]" - ], - "text/plain": [ - " openfda_generic_name \\\n", - "89 MEPHITIS MEPHITICA \n", - "105 ONDANSETRON \n", - "124 CLOFARABINE \n", - "273 ACETAMINOPHEN AND DIPHENHYDRAMINE HYDROCHLORIDE \n", - "284 OFLOXACIN \n", - "\n", - " openfda_brand_name \\\n", - "89 MEPHITIS MEPHITICA \n", - "105 ONDANSETRON \n", - "124 CLOFARABINE \n", - "273 ACETAMINOPHEN AND DIPHENHYDRAMINE HYDROCHLORIDE \n", - "284 OFLOXACIN \n", - "\n", - " indications_and_usage \n", - "89 INDICATIONS Condition listed above or as direc... \n", - "105 1 INDICATIONS AND USAGE Ondansetron Injection,... \n", - "124 1 INDICATIONS AND USAGE Clofarabine injection ... \n", - "273 Uses Temporary relief of occasional headaches ... \n", - "284 INDICATIONS AND USAGE To reduce the developmen... \n", - "\n", - "[5 rows x 3 columns]" - ] - }, - "execution_count": 43, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Query 3 columns of interest from drug label dataset\n", "df_missing = bpd.read_gbq(\"bigquery-public-data.fda_drug.drug_label\",\n", - " columns=[\"openfda_generic_name\", \"openfda_brand_name\", \"indications_and_usage\"])\n", + " col_order=[\"openfda_generic_name\", \"openfda_brand_name\", \"indications_and_usage\"])\n", "\n", "# Exclude any rows with missing data\n", "df_missing = df_missing.dropna()\n", @@ -1387,7 +851,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": null, "metadata": { "id": "19TvGN1PVmVX" }, @@ -1414,53 +878,16 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": null, "metadata": { "id": "tiSHa5B4aFhw" }, - "outputs": [ - { - "data": { - "text/html": [ - "Query job d216bea6-9b9c-4918-9194-40de2745beca is DONE. 84.4 MB processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Query job 37d88636-b1fb-44da-9504-44144af9624d is DONE. 800 Bytes processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Query job 0b35db83-5bac-47b4-8a2c-b46a816c0e3e is DONE. 200 Bytes processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "def batch_predict(\n", " input: bpd.DataFrame, temperature: float = TEMPERATURE\n", ") -> bpd.DataFrame:\n", - " return model.predict(input, temperature=temperature).ml_generate_text_llm_result\n", + " return model.predict(input, temperature).ml_generate_text_llm_result\n", "\n", "\n", "response = batch_predict(df_missing[\"prompt\"])" @@ -1477,73 +904,19 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": null, "metadata": { "id": "TnizdeqBdbZj" }, - "outputs": [ - { - "data": { - "text/html": [ - "Query job 4397b5f3-5058-409c-a361-c9fa715e46ee is DONE. 84.4 MB processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Query job 147ea301-e249-49fb-8280-d61948d5df7f is DONE. 84.4 MB processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Query job 067a2a73-0f36-42a6-973e-074ab8be631a is DONE. 56.7 kB processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Generic name: MEPHITIS MEPHITICA\n", - "Brand name: INDICATIONS Condition listed above or as directed by the physician\n", - "Response: **Ephemeral** (Latin root: \"ephemerus,\" meaning \"lasting for a day\")\n", - "\n", - "**Aetheria** (Greek root: \"aither,\" meaning \"upper air, sky\")\n", - "\n", - "**Zenithar** (Combination of \"zenith\" and \"pharma\")\n", - "\n", - "**Celestian** (Latin root: \"celestial,\" meaning \"heavenly\")\n", - "\n", - "**Astralux** (Combination of \"astral\" and \"lux,\" meaning \"light\")\n" - ] - } - ], + "outputs": [], "source": [ "# Pick a sample\n", "k = 0\n", "\n", "# Gather the prompt and response details\n", - "prompt_generic = df_missing[\"openfda_generic_name\"].iloc[k]\n", - "prompt_usage = df_missing[\"indications_and_usage\"].iloc[k]\n", - "response_str = response.iloc[k]\n", + "prompt_generic = df_missing[\"openfda_generic_name\"][k].iloc[0]\n", + "prompt_usage = df_missing[\"indications_and_usage\"][k].iloc[0]\n", + "response_str = response[k].iloc[0]\n", "\n", "# Print details\n", "print(f\"Generic name: {prompt_generic}\")\n", @@ -1561,6 +934,36 @@ "\n", "You've also seen how BigFrames can manage each step of the process, including gathering data, data manipulation, and querying the LLM." ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Bys6--dVmq7R" + }, + "source": [ + "## Cleaning up\n", + "\n", + "To clean up all Google Cloud resources used in this project, you can [delete the Google Cloud\n", + "project](https://cloud.google.com/resource-manager/docs/creating-managing-projects#shutting_down_projects) you used for the tutorial.\n", + "\n", + "Otherwise, you can uncomment the remaining cells and run them to delete the individual resources you created in this tutorial:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "cIODjOLump_-" + }, + "outputs": [], + "source": [ + "# Delete the BigQuery Connection\n", + "from google.cloud import bigquery_connection_v1 as bq_connection\n", + "client = bq_connection.ConnectionServiceClient()\n", + "CONNECTION_ID = f\"projects/{PROJECT_ID}/locations/{LOCATION}/connections/{CONNECTION}\"\n", + "client.delete_connection(name=CONNECTION_ID)\n", + "print(f\"Deleted connection {CONNECTION_ID}.\")" + ] } ], "metadata": { @@ -1568,21 +971,8 @@ "provenance": [] }, "kernelspec": { - "display_name": "venv", - "language": "python", + "display_name": "Python 3", "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.1" } }, "nbformat": 4, diff --git a/notebooks/generative_ai/large_language_models.ipynb b/notebooks/generative_ai/large_language_models.ipynb index 4ff9a9d3d23..45a46c44af9 100644 --- a/notebooks/generative_ai/large_language_models.ipynb +++ b/notebooks/generative_ai/large_language_models.ipynb @@ -2,13 +2,13 @@ "cells": [ { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import bigframes.pandas\n", "import pandas as pd\n", - "from bigframes.ml.llm import GeminiTextGenerator" + "from bigframes.ml.llm import PaLM2TextGenerator" ] }, { @@ -16,51 +16,35 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Define the model" + "## Prerequisites\n", + "Create session and define a BQ connection which we already created and allowlisted. " ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_176683/987800245.py:1: ApiDeprecationWarning: gemini-1.5-X are going to be deprecated. Use gemini-2.0-X (https://cloud.google.com/python/docs/reference/bigframes/latest/bigframes.ml.llm.GeminiTextGenerator) instead. \n", - " model = GeminiTextGenerator(model_name=\"gemini-2.0-flash-001\")\n", - "/usr/local/google/home/shuowei/src/python-bigquery-dataframes/bigframes/ml/llm.py:486: DefaultLocationWarning: No explicit location is set, so using location US for the session.\n", - " self.session = session or global_session.get_global_session()\n" - ] - }, - { - "data": { - "text/html": [ - "Query job 6fa5121a-6da4-4c75-92ec-936799da4513 is DONE. 0 Bytes processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Query job 74460ae9-3e89-49e7-93ad-bafbb6197a86 is DONE. 0 Bytes processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "model = GeminiTextGenerator(model_name=\"gemini-2.5-flash\")" + "session = bigframes.pandas.get_global_session()\n", + "connection = \"bigframes-dev.us.bigframes-ml\"" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Define the model" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "model = PaLM2TextGenerator(session=session, connection_name=connection)" ] }, { @@ -99,39 +83,6 @@ "execution_count": 5, "metadata": {}, "outputs": [ - { - "data": { - "text/html": [ - "Query job 562ca203-3b53-4409-9a23-0a80d3840fcc is DONE. 0 Bytes processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/google/home/shuowei/src/python-bigquery-dataframes/bigframes/core/array_value.py:114: PreviewWarning: JSON column interpretation as a custom PyArrow extention in\n", - "`db_dtypes` is a preview feature and subject to change.\n", - " warnings.warn(msg, bfe.PreviewWarning)\n" - ] - }, - { - "data": { - "text/html": [ - "Query job 5a6ceff2-53b5-4a4a-83ff-31bffab1b8b8 is DONE. 14.0 kB processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, { "data": { "text/html": [ @@ -154,52 +105,30 @@ " \n", " \n", " ml_generate_text_llm_result\n", - " ml_generate_text_rai_result\n", - " ml_generate_text_status\n", - " prompt\n", " \n", " \n", " \n", " \n", " 0\n", - " BigQuery is a serverless, highly scalable, and...\n", - " <NA>\n", - " \n", - " What is BigQuery?\n", + " BigQuery is a fully managed, petabyte-scale an...\n", " \n", " \n", " 1\n", - " BQML stands for **BigQuery Machine Learning**....\n", - " <NA>\n", - " \n", - " What is BQML?\n", + " BQML stands for BigQuery Machine Learning. It ...\n", " \n", " \n", " 2\n", - " BigQuery DataFrames is a Python client library...\n", - " <NA>\n", - " \n", - " What is BigQuery DataFrame?\n", + " A BigQuery DataFrames is a distributed collecti...\n", " \n", " \n", "\n", "" ], "text/plain": [ - " ml_generate_text_llm_result \\\n", - "0 BigQuery is a serverless, highly scalable, and... \n", - "1 BQML stands for **BigQuery Machine Learning**.... \n", - "2 BigQuery DataFrames is a Python client library... \n", - "\n", - " ml_generate_text_rai_result ml_generate_text_status \\\n", - "0 \n", - "1 \n", - "2 \n", - "\n", - " prompt \n", - "0 What is BigQuery? \n", - "1 What is BQML? \n", - "2 What is BigQuery DataFrame? " + " ml_generate_text_llm_result\n", + "0 BigQuery is a fully managed, petabyte-scale an...\n", + "1 BQML stands for BigQuery Machine Learning. It ...\n", + "2 A BigQuery DataFrames is a distributed collecti..." ] }, "execution_count": 5, @@ -222,16 +151,16 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "\"## BigQuery: A serverless data warehouse for large-scale data analysis\\n\\nBigQuery is a serverless, highly-scalable data warehouse designed for analyzing large datasets. It's a cloud-based service offered by Google Cloud Platform (GCP), allowing users to store, manage, and analyze massive amounts of data without managing infrastructure. \\n\\nHere are some key features of BigQuery:\\n\\n**Serverless:** You don't need to worry about provisioning, managing, or scaling servers. BigQuery handles all of this automatically, letting you focus on analyzing your data.\\n\\n**Highly-scalable:** BigQuery can handle datasets of any size, from gigabytes to petabytes. It can also scale up and down automatically to meet your processing needs.\\n\\n**Cost-effective:** You only pay for the resources you use, and there are no upfront costs. Additionally, BigQuery offers several pricing models to fit your needs, including on-demand, flat-rate, and flexible slots.\\n\\n**Easy to use:** BigQuery uses SQL, a standard query language, making it easy to analyze your data. No need to learn a new programming language.\\n\\n**Integrated with GCP:** BigQuery integrates seamlessly with other GCP services, such as Google Cloud Storage, Dataflow, and Kubernetes. This allows you to build powerful data pipelines and workflows.\\n\\n**Secure:** BigQuery uses industry-standard security practices to protect your data.\\n\\nHere are some use cases for BigQuery:\\n\\n* **Data warehousing and analytics:** Store and analyze large datasets for business intelligence and reporting.\\n* **Machine learning:** Train and deploy machine learning models on your data.\\n* **Data integration:** Combine data from multiple sources for analysis.\\n* **Real-time analytics:** Analyze data in real-time for insights and decision-making.\\n\\n**Here are some additional resources that you may find helpful:**\\n\\n* **BigQuery website:** https://cloud.google.com/bigquery\\n* **BigQuery documentation:** https://cloud.google.com/bigquery/docs\\n* **BigQuery tutorial:** https://cloud.google.com/bigquery/docs/tutorials\\n* **BigQuery pricing:** https://cloud.google.com/bigquery/pricing\\n\\nI hope this gives you a good overview of BigQuery. Please let me know if you have any other questions.\"" + "'BigQuery is a fully managed, petabyte-scale analytics data warehouse that enables businesses to analyze all their data very quickly. It is a cloud-based service that offers a pay-as-you-go pricing model. BigQuery is designed to handle large amounts of data and provide fast performance. It is a good choice for businesses that need to analyze large amounts of data quickly and easily.'" ] }, - "execution_count": 5, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -239,11 +168,18 @@ "source": [ "pred.iloc[0, 0]" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "venv", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -257,7 +193,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.15" + "version": "3.10.9" } }, "nbformat": 4, diff --git a/notebooks/generative_ai/museum_art.csv b/notebooks/generative_ai/museum_art.csv deleted file mode 100644 index 37acae65d26..00000000000 --- a/notebooks/generative_ai/museum_art.csv +++ /dev/null @@ -1,14930 +0,0 @@ -object_number,is_highlight,is_public_domain,object_id,department,object_name,title,culture,period,dynasty,reign,portfolio,artist_role,artist_prefix,artist_display_name,artist_display_bio,artist_suffix,artist_alpha_sort,artist_nationality,artist_begin_date,artist_end_date,object_date,object_begin_date,object_end_date,medium,dimensions,credit_line,geography_type,city,state,county,country,region,subregion,locale,locus,excavation,river,classification,rights_and_reproduction,link_resource,metadata_date,repository -2014.247,false,true,646996,Asian Art,Screen,전(傳) 오원 장승업 (1843–1897) 청동기와 화초가 있는 정물화 조선|傳 吾園 張承業 器皿折枝圖 朝鮮|Still life with bronze vessels and flowering plants,Korea,Joseon dynasty (1392–1910),,,,Artist,Attributed to,Jang Seung-eop (pen name: Owon),"Korean, 1843–1897",,Jang Seung-eop,Korean,1843,1897,1894,1894,1894,Ten-panel folding screen; ink on paper,Overall: 77 in. × 14 ft. 2 in. (195.6 × 431.8 cm),"Gift of Mrs. Anita H. Berger, in memory of Ambassador Samuel D. Berger, 2014",,,,,,,,,,,,Screens,,http://www.metmuseum.org/art/collection/search/646996,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2012.329,false,true,77916,Asian Art,Hanging scroll,석지 채용신 학자 초상|石芝 蔡龍臣 學者肖像|Portrait of a scholar,Korea,,,,,Artist,,Chae Yongsin (artist name: Seokji) (ASA),"Korean, 1850–1941",,Chae Yongsin,Korean,1850,1941,dated by inscription to 1924,1924,1924,Hanging scroll; ink and color on silk,Image: 38 1/8 × 21 1/8 in. (96.8 × 53.7 cm) Overall with mounting: 48 × 24 7/16 in. (121.9 × 62.1 cm) Overall with knobs: 48 × 24 3/4 in. (121.9 × 62.9 cm),"Purchase, Friends of Asian Art Gifts, 2012",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/77916,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1994.377,false,true,38009,Asian Art,Painting,,"India (Himachal Pradesh, Jasrota)",,,,,Artist,Attributed to,Nainsukh,active ca. 1735–78,,Nainsukh,Indian,1725,1778,ca. 1745–50,1735,1760,"Ink, opaque watercolor, and gold on paper",Overall: 7 3/4 x 6 1/8 in. (19.7 x 15.6 cm),"Rogers Fund, 1994",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/38009,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1987.424.13,false,true,37988,Asian Art,Folio,,"India (Punjab Hills, Guler)",,,,,Artist,Workshop active in the generation after,Nainsukh,active ca. 1735–78,,Nainsukh,Indian,1725,1778,ca. 1790,1780,1800,Ink and opaque watercolor on paper,9 15/16 x 13 15/16 in. (25.2 x 35.4 cm),"Gift of Cynthia Hazen Polsky, 1987",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/37988,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2008.359.13,false,true,74660,Asian Art,Painting,,"India (Himachal Pradesh, Guler)",,,,,Artist,Attributed to a first-generation master after,Nainsukh,active ca. 1735–78,,Nainsukh,Indian,1725,1778,ca. 1780,1770,1790,Charcoal and opaque watercolor on paper,Image (sight): 5 3/8 x 7 5/8 in. (13.7 x 19.4 cm),"Gift of Subhash Kapoor, in memory of his parents, Smt Shashi Kanta and Shree Parshotam Ram Kapoor, 2008",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/74660,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2008.359.22,false,true,74674,Asian Art,Painting,,"India (Himachal Pradesh, Guler)",,,,,Artist,Attributed to a first-generation master after,Nainsukh,active ca. 1735–78,,Nainsukh,Indian,1725,1778,ca. 1780,1770,1790,"Ink, ocher and underdrawing",Image (sight): 8 3/4 x 11 3/4 in. (22.2 x 29.8 cm),"Gift of Subhash Kapoor, in memory of his parents, Smt Shashi Kanta and Shree Parshotam Ram Kapoor, 2008",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/74674,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2008.359.23,false,true,74675,Asian Art,Drawing,,"India (Pahari Hills, Guler or Kangra)",,,,,Artist,Attributed to a first-generation master after,Nainsukh,active ca. 1735–78,,Nainsukh,Indian,1725,1778,ca. 1775–80,1775,1780,Red ochre and wash on paper,8 3/8 x 11 3/8 in. (21.3 x 28.9 cm),"Gift of Subhash Kapoor, in memory of his parents, Smt Shashi Kanta and Shree Parshotam Ram Kapoor, 2008",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/74675,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2008.359.24,false,true,74677,Asian Art,Painting,,"India (Pahari Hills, Guler or Kangra)",,,,,Artist,Attributed to a follower of,Nainsukh,active ca. 1735–78,,Nainsukh,Indian,1725,1778,ca. 1780,1770,1790,Ink and wash on paper,Image (sight): 10 3/8 x 9 in. (26.4 x 22.9 cm),"Gift of Subhash Kapoor, in memory of his parents, Smt Shashi Kanta and Shree Parshotam Ram Kapoor, 2008",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/74677,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2008.359.30,false,true,74685,Asian Art,Painting,,"India (Pahari Hills, Kangra)",,,,,Artist,Attributed to,Nainsukh,active ca. 1735–78,,Nainsukh,Indian,1725,1778,ca. 1775–80,1765,1790,Ink and transparent watercolor on paper,Image (sight): 6 5/8 x 10 1/4 in. (16.8 x 26 cm) Framed: 16 x 20 in. (40.6 x 50.8 cm),"Gift of Subhash Kapoor, in memory of his parents, Smt Shashi Kanta and Shree Parshotam Ram Kapoor, 2008",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/74685,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2003.375,false,true,65595,Asian Art,Painting,,"India (Rajasthan, Mewar)",,,,,Artist,,Chokha,"Indian, active 1799–ca. 1826",,Chokha,Indian,1799,1826,ca. 1820,1810,1830,"Ink, opaque watercolor, silver, and gold on paper",8 1/4 x 13 1/4 in. (21 x 33.7 cm),"Cynthia Hazen Polsky and Leon B. Polsky Fund, 2003",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/65595,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2006.451,false,true,73261,Asian Art,Painting,,"India (Rajasthan, Mewar)",,,,,Artist,Attributed to,Chokha,"Indian, active 1799–ca. 1826",,Chokha,Indian,1799,1826,ca. 1800–10,1800,1810,"Opaque watercolor, ink and gold on paper",Image: 11 1/2 x 14 7/8 in. (29.2 x 37.8 cm) Page: 12 3/16 x 15 15/16 in. (31 x 40.5 cm),"Purchase, Friends of Asian Art Gifts, 2006",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/73261,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1996.100.5,false,true,38042,Asian Art,Painting,,"Western India, Rajasthan, Udaipur or Devgarh",,,,,Artist,Attributed to,Chokha,"Indian, active 1799–ca. 1826",,Chokha,Indian,1799,1826,ca. 1805–10,1795,1820,"Ink, opaque watercolor, and gold on paper",15 x 16 1/8 in. (38.1 x 41 cm),"Fletcher Fund, 1996",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/38042,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1998.161,false,true,39894,Asian Art,Painting,,"India (Udaipur, Rajasthan)",,,,,Artist,Attributed to,Stipple Master,"Indian, active ca. 1690–1715",,Stipple Master,Indian,1680,1725,ca. 1707–8,1697,1718,"Opaque watercolor, ink and gold on paper",Page: 18 7/8 x 14 7/8 in. (47.9 x 37.8 cm) Image: 9 1/2 x 7 5/16 in. (24.1 x 18.6 cm),"Friends of Asian Art, Purchase, Mrs. Vincent Astor Gift, 1998",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/39894,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2002.177,false,true,64891,Asian Art,Painting,,"Western India, Rajasthan, Udaipur",,,,,Artist,Attributed to,Stipple Master,"Indian, active ca. 1690–1715",,Stipple Master,Indian,1680,1725,ca. 1700–1710,1690,1720,Opaque watercolor and ink on paper,Page: 14 11/16 x 12 1/8 in. (37.3 x 30.8 cm) Image: 13 3/16 x 10 3/4 in. (33.5 x 27.3 cm),"Cynthia Hazen Polsky and Leon B. Polsky Fund, 2002",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/64891,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2003.238,false,true,65590,Asian Art,Painting,,"India (Rajasthan, Mewar)",,,,,Artist,,Stipple Master,"Indian, active ca. 1690–1715",,Stipple Master,Indian,1680,1725,ca. 1705,1695,1715,"Ink, opaque watercolor, silver, and gold on paper",18 x 13 in. (45.7 x 33 cm),"Cynthia Hazen Polsky and Leon B. Polsky Fund, 2003",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/65590,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2003.178a, b",false,true,65594,Asian Art,Painting,,"India (Rajasthan, Kota)",,,,,Artist,,The Kota Master,"Indian, active early 18th century",,Kota Master,Indian,1700,1733,ca. 1720 (recto); ca. 1750–75 (verso),1710,1785,"Ink, opaque watercolor, and gold on paper",recto: 7 1/2 x 4 3/8 in. (19.1 x 11.1 cm) verso: 9 x 5 7/8 in. (22.9 x 14.9 cm),"Cynthia Hazen Polsky and Leon B. Polsky Fund, 2003",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/65594,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1990.230,false,true,40445,Asian Art,Hanging scroll,"이유원, 매화도 조선|李裕元 梅花圖 朝鮮|Plum Branch",Korea,Joseon dynasty (1392–1910),,,,Artist,,Yi Yuwon,"Korean, 1814–1888",,Yi Yuwon,Korean,1814,1888,dated 1888,1888,1888,Hanging scroll; ink on paper,58 1/16 x 37 3/16 in. (147.5 x 94.5 cm),"Purchase, Seymour and Rogers Funds and Bequest of Dorothy Graham Bennett, 1990",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/40445,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1977.448,false,true,45063,Asian Art,Screen panel,,Korea,Joseon dynasty (1392–1910),,,,Artist,,Nam Kye-u,"Korean, 1811–1888",,Nam Kye-u,Korean,1811,1888,,1392,1910,Panel from a six-panel folding screen; ink and color on paper,36 1/4 x 12 3/16 in. (92.1 x 31 cm),"Anonymous Gift, 1977",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45063,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -12.123.1,false,true,40069,Asian Art,Hanging scroll,,China or Korea (?),Joseon dynasty (1392–1910),,,,Artist,In the style of,Muqi,"Chinese, ca. 1210–after 1269",,Muqi,Chinese,1210,1269,,1392,1910,Hanging scroll; ink and color on silk,51 1/4 x 27 1/2 in. (130.2 x 69.9 cm),"Rogers Fund, 1912",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/40069,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -12.123.2,false,true,40070,Asian Art,Hanging scroll,,China or Korea (?),Joseon dynasty (1392–1910),,,,Artist,In the style of,Muqi,"Chinese, ca. 1210–after 1269",,Muqi,Chinese,1210,1269,,1392,1910,Hanging scroll; ink and color on silk,51 1/2 x 27 1/2 in. (130.8 x 69.9 cm),"Rogers Fund, 1912",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/40070,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -57.51.38.1,false,true,37964,Asian Art,Folio,,India (Gujarat),,,,,Artist,,Bhadrabahu,"Indian, died ca. 356 B.C.",,Bhadrabahu,Indian,-0356,-0356,15th century,1400,1499,"Ink, opaque watercolor, and gold on paper",(Average size .1–.71): 4 1/2 x 11 3/8 in. (11.4 x 28.9 cm),"Bequest of Cora Timken Burnett, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/37964,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -57.51.38.2,false,true,75084,Asian Art,Folio,,India (Gujarat),,,,,Artist,,Bhadrabahu,"Indian, died ca. 356 B.C.",,Bhadrabahu,Indian,-0356,-0356,15th century,1400,1499,"Ink, opaque watercolor, and gold on paper",(Average size .1–.71): 4 1/2 x 11 3/8 in. (11.4 x 28.9 cm),"Bequest of Cora Timken Burnett, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/75084,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -57.51.38.3,false,true,75085,Asian Art,Folio,,India (Gujarat),,,,,Artist,,Bhadrabahu,"Indian, died ca. 356 B.C.",,Bhadrabahu,Indian,-0356,-0356,15th century,1400,1499,"Ink, opaque watercolor, and gold on paper",(Average size .1–.71): 4 1/2 x 11 3/8 in. (11.4 x 28.9 cm),"Bequest of Cora Timken Burnett, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/75085,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -57.51.38.4,false,true,75086,Asian Art,Folio,,India (Gujarat),,,,,Artist,,Bhadrabahu,"Indian, died ca. 356 B.C.",,Bhadrabahu,Indian,-0356,-0356,15th century,1400,1499,"Ink, opaque watercolor, and gold on paper",(Average size .1–.71): 4 1/2 x 11 3/8 in. (11.4 x 28.9 cm),"Bequest of Cora Timken Burnett, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/75086,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -57.51.38.5,false,true,75087,Asian Art,Folio,,India (Gujarat),,,,,Artist,,Bhadrabahu,"Indian, died ca. 356 B.C.",,Bhadrabahu,Indian,-0356,-0356,15th century,1400,1499,"Ink, opaque watercolor, and gold on paper",(Average size .1–.71): 4 1/2 x 11 3/8 in. (11.4 x 28.9 cm),"Bequest of Cora Timken Burnett, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/75087,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -57.51.38.6,false,true,75088,Asian Art,Folio,,India (Gujarat),,,,,Artist,,Bhadrabahu,"Indian, died ca. 356 B.C.",,Bhadrabahu,Indian,-0356,-0356,15th century,1400,1499,"Ink, opaque watercolor, and gold on paper",(Average size .1–.71): 4 1/2 x 11 3/8 in. (11.4 x 28.9 cm),"Bequest of Cora Timken Burnett, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/75088,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -57.51.38.7,false,true,75089,Asian Art,Folio,,India (Gujarat),,,,,Artist,,Bhadrabahu,"Indian, died ca. 356 B.C.",,Bhadrabahu,Indian,-0356,-0356,15th century,1400,1499,"Ink, opaque watercolor, and gold on paper",(Average size .1–.71): 4 1/2 x 11 3/8 in. (11.4 x 28.9 cm),"Bequest of Cora Timken Burnett, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/75089,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -57.51.38.8,false,true,75090,Asian Art,Folio,,India (Gujarat),,,,,Artist,,Bhadrabahu,"Indian, died ca. 356 B.C.",,Bhadrabahu,Indian,-0356,-0356,15th century,1400,1499,"Ink, opaque watercolor, and gold on paper",(Average size .1–.71): 4 1/2 x 11 3/8 in. (11.4 x 28.9 cm),"Bequest of Cora Timken Burnett, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/75090,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -57.51.38.9,false,true,75091,Asian Art,Folio,,India (Gujarat),,,,,Artist,,Bhadrabahu,"Indian, died ca. 356 B.C.",,Bhadrabahu,Indian,-0356,-0356,15th century,1400,1499,"Ink, opaque watercolor, and gold on paper",(Average size .1–.71): 4 1/2 x 11 3/8 in. (11.4 x 28.9 cm),"Bequest of Cora Timken Burnett, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/75091,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -57.51.38.10,false,true,75092,Asian Art,Folio,,India (Gujarat),,,,,Artist,,Bhadrabahu,"Indian, died ca. 356 B.C.",,Bhadrabahu,Indian,-0356,-0356,15th century,1400,1499,"Ink, opaque watercolor, and gold on paper",(Average size .1–.71): 4 1/2 x 11 3/8 in. (11.4 x 28.9 cm),"Bequest of Cora Timken Burnett, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/75092,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -57.51.38.11,false,true,75093,Asian Art,Folio,,India (Gujarat),,,,,Artist,,Bhadrabahu,"Indian, died ca. 356 B.C.",,Bhadrabahu,Indian,-0356,-0356,15th century,1400,1499,"Ink, opaque watercolor, and gold on paper",(Average size .1–.71): 4 1/2 x 11 3/8 in. (11.4 x 28.9 cm),"Bequest of Cora Timken Burnett, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/75093,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -57.51.38.12,false,true,75094,Asian Art,Folio,,India (Gujarat),,,,,Artist,,Bhadrabahu,"Indian, died ca. 356 B.C.",,Bhadrabahu,Indian,-0356,-0356,15th century,1400,1499,"Ink, opaque watercolor, and gold on paper",(Average size .1–.71): 4 1/2 x 11 3/8 in. (11.4 x 28.9 cm),"Bequest of Cora Timken Burnett, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/75094,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -57.51.38.13,false,true,75095,Asian Art,Folio,,India (Gujarat),,,,,Artist,,Bhadrabahu,"Indian, died ca. 356 B.C.",,Bhadrabahu,Indian,-0356,-0356,15th century,1400,1499,"Ink, opaque watercolor, and gold on paper",(Average size .1–.71): 4 1/2 x 11 3/8 in. (11.4 x 28.9 cm),"Bequest of Cora Timken Burnett, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/75095,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -57.51.38.14,false,true,75096,Asian Art,Folio,,India (Gujarat),,,,,Artist,,Bhadrabahu,"Indian, died ca. 356 B.C.",,Bhadrabahu,Indian,-0356,-0356,15th century,1400,1499,"Ink, opaque watercolor, and gold on paper",(Average size .1–.71): 4 1/2 x 11 3/8 in. (11.4 x 28.9 cm),"Bequest of Cora Timken Burnett, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/75096,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -57.51.38.15,false,true,75097,Asian Art,Folio,,India (Gujarat),,,,,Artist,,Bhadrabahu,"Indian, died ca. 356 B.C.",,Bhadrabahu,Indian,-0356,-0356,15th century,1400,1499,"Ink, opaque watercolor, and gold on paper",(Average size .1–.71): 4 1/2 x 11 3/8 in. (11.4 x 28.9 cm),"Bequest of Cora Timken Burnett, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/75097,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -57.51.38.16,false,true,75098,Asian Art,Folio,,India (Gujarat),,,,,Artist,,Bhadrabahu,"Indian, died ca. 356 B.C.",,Bhadrabahu,Indian,-0356,-0356,15th century,1400,1499,"Ink, opaque watercolor, and gold on paper",(Average size .1–.71): 4 1/2 x 11 3/8 in. (11.4 x 28.9 cm),"Bequest of Cora Timken Burnett, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/75098,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -57.51.38.17,false,true,75099,Asian Art,Folio,,India (Gujarat),,,,,Artist,,Bhadrabahu,"Indian, died ca. 356 B.C.",,Bhadrabahu,Indian,-0356,-0356,15th century,1400,1499,"Ink, opaque watercolor, and gold on paper",(Average size .1–.71): 4 1/2 x 11 3/8 in. (11.4 x 28.9 cm),"Bequest of Cora Timken Burnett, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/75099,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -57.51.38.18,false,true,75100,Asian Art,Folio,,India (Gujarat),,,,,Artist,,Bhadrabahu,"Indian, died ca. 356 B.C.",,Bhadrabahu,Indian,-0356,-0356,15th century,1400,1499,"Ink, opaque watercolor, and gold on paper",(Average size .1–.71): 4 1/2 x 11 3/8 in. (11.4 x 28.9 cm),"Bequest of Cora Timken Burnett, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/75100,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -57.51.38.19,false,true,75101,Asian Art,Folio,,India (Gujarat),,,,,Artist,,Bhadrabahu,"Indian, died ca. 356 B.C.",,Bhadrabahu,Indian,-0356,-0356,15th century,1400,1499,"Ink, opaque watercolor, and gold on paper",(Average size .1–.71): 4 1/2 x 11 3/8 in. (11.4 x 28.9 cm),"Bequest of Cora Timken Burnett, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/75101,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -57.51.38.20,false,true,75102,Asian Art,Folio,,India (Gujarat),,,,,Artist,,Bhadrabahu,"Indian, died ca. 356 B.C.",,Bhadrabahu,Indian,-0356,-0356,15th century,1400,1499,"Ink, opaque watercolor, and gold on paper",(Average size .1–.71): 4 1/2 x 11 3/8 in. (11.4 x 28.9 cm),"Bequest of Cora Timken Burnett, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/75102,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -57.51.38.21,false,true,75103,Asian Art,Folio,,India (Gujarat),,,,,Artist,,Bhadrabahu,"Indian, died ca. 356 B.C.",,Bhadrabahu,Indian,-0356,-0356,15th century,1400,1499,"Ink, opaque watercolor, and gold on paper",(Average size .1–.71): 4 1/2 x 11 3/8 in. (11.4 x 28.9 cm),"Bequest of Cora Timken Burnett, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/75103,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -57.51.38.22,false,true,75104,Asian Art,Folio,,India (Gujarat),,,,,Artist,,Bhadrabahu,"Indian, died ca. 356 B.C.",,Bhadrabahu,Indian,-0356,-0356,15th century,1400,1499,"Ink, opaque watercolor, and gold on paper",(Average size .1–.71): 4 1/2 x 11 3/8 in. (11.4 x 28.9 cm),"Bequest of Cora Timken Burnett, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/75104,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -57.51.38.23,false,true,75105,Asian Art,Folio,,India (Gujarat),,,,,Artist,,Bhadrabahu,"Indian, died ca. 356 B.C.",,Bhadrabahu,Indian,-0356,-0356,15th century,1400,1499,"Ink, opaque watercolor, and gold on paper",(Average size .1–.71): 4 1/2 x 11 3/8 in. (11.4 x 28.9 cm),"Bequest of Cora Timken Burnett, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/75105,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -57.51.38.24,false,true,75106,Asian Art,Folio,,India (Gujarat),,,,,Artist,,Bhadrabahu,"Indian, died ca. 356 B.C.",,Bhadrabahu,Indian,-0356,-0356,15th century,1400,1499,"Ink, opaque watercolor, and gold on paper",(Average size .1–.71): 4 1/2 x 11 3/8 in. (11.4 x 28.9 cm),"Bequest of Cora Timken Burnett, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/75106,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -57.51.38.25,false,true,75107,Asian Art,Folio,,India (Gujarat),,,,,Artist,,Bhadrabahu,"Indian, died ca. 356 B.C.",,Bhadrabahu,Indian,-0356,-0356,15th century,1400,1499,"Ink, opaque watercolor, and gold on paper",(Average size .1–.71): 4 1/2 x 11 3/8 in. (11.4 x 28.9 cm),"Bequest of Cora Timken Burnett, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/75107,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -57.51.38.26,false,true,75108,Asian Art,Folio,,India (Gujarat),,,,,Artist,,Bhadrabahu,"Indian, died ca. 356 B.C.",,Bhadrabahu,Indian,-0356,-0356,15th century,1400,1499,"Ink, opaque watercolor, and gold on paper",(Average size .1–.71): 4 1/2 x 11 3/8 in. (11.4 x 28.9 cm),"Bequest of Cora Timken Burnett, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/75108,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -57.51.38.27,false,true,75109,Asian Art,Folio,,India (Gujarat),,,,,Artist,,Bhadrabahu,"Indian, died ca. 356 B.C.",,Bhadrabahu,Indian,-0356,-0356,15th century,1400,1499,"Ink, opaque watercolor, and gold on paper",(Average size .1–.71): 4 1/2 x 11 3/8 in. (11.4 x 28.9 cm),"Bequest of Cora Timken Burnett, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/75109,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -57.51.38.28,false,true,75110,Asian Art,Folio,,India (Gujarat),,,,,Artist,,Bhadrabahu,"Indian, died ca. 356 B.C.",,Bhadrabahu,Indian,-0356,-0356,15th century,1400,1499,"Ink, opaque watercolor, and gold on paper",(Average size .1–.71): 4 1/2 x 11 3/8 in. (11.4 x 28.9 cm),"Bequest of Cora Timken Burnett, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/75110,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -57.51.38.29,false,true,75111,Asian Art,Folio,,India (Gujarat),,,,,Artist,,Bhadrabahu,"Indian, died ca. 356 B.C.",,Bhadrabahu,Indian,-0356,-0356,15th century,1400,1499,"Ink, opaque watercolor, and gold on paper",(Average size .1–.71): 4 1/2 x 11 3/8 in. (11.4 x 28.9 cm),"Bequest of Cora Timken Burnett, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/75111,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -57.51.38.30,false,true,75112,Asian Art,Folio,,India (Gujarat),,,,,Artist,,Bhadrabahu,"Indian, died ca. 356 B.C.",,Bhadrabahu,Indian,-0356,-0356,15th century,1400,1499,"Ink, opaque watercolor, and gold on paper",(Average size .1–.71): 4 1/2 x 11 3/8 in. (11.4 x 28.9 cm),"Bequest of Cora Timken Burnett, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/75112,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -57.51.38.31,false,true,75113,Asian Art,Folio,,India (Gujarat),,,,,Artist,,Bhadrabahu,"Indian, died ca. 356 B.C.",,Bhadrabahu,Indian,-0356,-0356,15th century,1400,1499,"Ink, opaque watercolor, and gold on paper",(Average size .1–.71): 4 1/2 x 11 3/8 in. (11.4 x 28.9 cm),"Bequest of Cora Timken Burnett, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/75113,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -57.51.38.32,false,true,75114,Asian Art,Folio,,India (Gujarat),,,,,Artist,,Bhadrabahu,"Indian, died ca. 356 B.C.",,Bhadrabahu,Indian,-0356,-0356,15th century,1400,1499,"Ink, opaque watercolor, and gold on paper",(Average size .1–.71): 4 1/2 x 11 3/8 in. (11.4 x 28.9 cm),"Bequest of Cora Timken Burnett, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/75114,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -57.51.38.33,false,true,75115,Asian Art,Folio,,India (Gujarat),,,,,Artist,,Bhadrabahu,"Indian, died ca. 356 B.C.",,Bhadrabahu,Indian,-0356,-0356,15th century,1400,1499,"Ink, opaque watercolor, and gold on paper",(Average size .1–.71): 4 1/2 x 11 3/8 in. (11.4 x 28.9 cm),"Bequest of Cora Timken Burnett, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/75115,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -57.51.38.34,false,true,75116,Asian Art,Folio,,India (Gujarat),,,,,Artist,,Bhadrabahu,"Indian, died ca. 356 B.C.",,Bhadrabahu,Indian,-0356,-0356,15th century,1400,1499,"Ink, opaque watercolor, and gold on paper",(Average size .1–.71): 4 1/2 x 11 3/8 in. (11.4 x 28.9 cm),"Bequest of Cora Timken Burnett, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/75116,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -57.51.38.35,false,true,75117,Asian Art,Folio,,India (Gujarat),,,,,Artist,,Bhadrabahu,"Indian, died ca. 356 B.C.",,Bhadrabahu,Indian,-0356,-0356,15th century,1400,1499,"Ink, opaque watercolor, and gold on paper",(Average size .1–.71): 4 1/2 x 11 3/8 in. (11.4 x 28.9 cm),"Bequest of Cora Timken Burnett, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/75117,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -57.51.38.36,false,true,75118,Asian Art,Folio,,India (Gujarat),,,,,Artist,,Bhadrabahu,"Indian, died ca. 356 B.C.",,Bhadrabahu,Indian,-0356,-0356,15th century,1400,1499,"Ink, opaque watercolor, and gold on paper",(Average size .1–.71): 4 1/2 x 11 3/8 in. (11.4 x 28.9 cm),"Bequest of Cora Timken Burnett, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/75118,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -57.51.38.37,false,true,75119,Asian Art,Folio,,India (Gujarat),,,,,Artist,,Bhadrabahu,"Indian, died ca. 356 B.C.",,Bhadrabahu,Indian,-0356,-0356,15th century,1400,1499,"Ink, opaque watercolor, and gold on paper",(Average size .1–.71): 4 1/2 x 11 3/8 in. (11.4 x 28.9 cm),"Bequest of Cora Timken Burnett, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/75119,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -57.51.38.38,false,true,75120,Asian Art,Folio,,India (Gujarat),,,,,Artist,,Bhadrabahu,"Indian, died ca. 356 B.C.",,Bhadrabahu,Indian,-0356,-0356,15th century,1400,1499,"Ink, opaque watercolor, and gold on paper",(Average size .1–.71): 4 1/2 x 11 3/8 in. (11.4 x 28.9 cm),"Bequest of Cora Timken Burnett, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/75120,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -57.51.38.39,false,true,75121,Asian Art,Folio,,India (Gujarat),,,,,Artist,,Bhadrabahu,"Indian, died ca. 356 B.C.",,Bhadrabahu,Indian,-0356,-0356,15th century,1400,1499,"Ink, opaque watercolor, and gold on paper",(Average size .1–.71): 4 1/2 x 11 3/8 in. (11.4 x 28.9 cm),"Bequest of Cora Timken Burnett, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/75121,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -57.51.38.40,false,true,75122,Asian Art,Folio,,India (Gujarat),,,,,Artist,,Bhadrabahu,"Indian, died ca. 356 B.C.",,Bhadrabahu,Indian,-0356,-0356,15th century,1400,1499,"Ink, opaque watercolor, and gold on paper",(Average size .1–.71): 4 1/2 x 11 3/8 in. (11.4 x 28.9 cm),"Bequest of Cora Timken Burnett, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/75122,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -57.51.38.41,false,true,75123,Asian Art,Folio,,India (Gujarat),,,,,Artist,,Bhadrabahu,"Indian, died ca. 356 B.C.",,Bhadrabahu,Indian,-0356,-0356,15th century,1400,1499,"Ink, opaque watercolor, and gold on paper",(Average size .1–.71): 4 1/2 x 11 3/8 in. (11.4 x 28.9 cm),"Bequest of Cora Timken Burnett, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/75123,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -57.51.38.42,false,true,75124,Asian Art,Folio,,India (Gujarat),,,,,Artist,,Bhadrabahu,"Indian, died ca. 356 B.C.",,Bhadrabahu,Indian,-0356,-0356,15th century,1400,1499,"Ink, opaque watercolor, and gold on paper",(Average size .1–.71): 4 1/2 x 11 3/8 in. (11.4 x 28.9 cm),"Bequest of Cora Timken Burnett, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/75124,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -57.51.38.43,false,true,75125,Asian Art,Folio,,India (Gujarat),,,,,Artist,,Bhadrabahu,"Indian, died ca. 356 B.C.",,Bhadrabahu,Indian,-0356,-0356,15th century,1400,1499,"Ink, opaque watercolor, and gold on paper",(Average size .1–.71): 4 1/2 x 11 3/8 in. (11.4 x 28.9 cm),"Bequest of Cora Timken Burnett, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/75125,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -57.51.38.44,false,true,75126,Asian Art,Folio,,India (Gujarat),,,,,Artist,,Bhadrabahu,"Indian, died ca. 356 B.C.",,Bhadrabahu,Indian,-0356,-0356,15th century,1400,1499,"Ink, opaque watercolor, and gold on paper",(Average size .1–.71): 4 1/2 x 11 3/8 in. (11.4 x 28.9 cm),"Bequest of Cora Timken Burnett, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/75126,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -57.51.38.45,false,true,75127,Asian Art,Folio,,India (Gujarat),,,,,Artist,,Bhadrabahu,"Indian, died ca. 356 B.C.",,Bhadrabahu,Indian,-0356,-0356,15th century,1400,1499,"Ink, opaque watercolor, and gold on paper",(Average size .1–.71): 4 1/2 x 11 3/8 in. (11.4 x 28.9 cm),"Bequest of Cora Timken Burnett, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/75127,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -57.51.38.46,false,true,75128,Asian Art,Folio,,India (Gujarat),,,,,Artist,,Bhadrabahu,"Indian, died ca. 356 B.C.",,Bhadrabahu,Indian,-0356,-0356,15th century,1400,1499,"Ink, opaque watercolor, and gold on paper",(Average size .1–.71): 4 1/2 x 11 3/8 in. (11.4 x 28.9 cm),"Bequest of Cora Timken Burnett, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/75128,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -57.51.38.47,false,true,75129,Asian Art,Folio,,India (Gujarat),,,,,Artist,,Bhadrabahu,"Indian, died ca. 356 B.C.",,Bhadrabahu,Indian,-0356,-0356,15th century,1400,1499,"Ink, opaque watercolor, and gold on paper",(Average size .1–.71): 4 1/2 x 11 3/8 in. (11.4 x 28.9 cm),"Bequest of Cora Timken Burnett, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/75129,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -57.51.38.48,false,true,75130,Asian Art,Folio,,India (Gujarat),,,,,Artist,,Bhadrabahu,"Indian, died ca. 356 B.C.",,Bhadrabahu,Indian,-0356,-0356,15th century,1400,1499,"Ink, opaque watercolor, and gold on paper",(Average size .1–.71): 4 1/2 x 11 3/8 in. (11.4 x 28.9 cm),"Bequest of Cora Timken Burnett, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/75130,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -57.51.38.49,false,true,75131,Asian Art,Folio,,India (Gujarat),,,,,Artist,,Bhadrabahu,"Indian, died ca. 356 B.C.",,Bhadrabahu,Indian,-0356,-0356,15th century,1400,1499,"Ink, opaque watercolor, and gold on paper",(Average size .1–.71): 4 1/2 x 11 3/8 in. (11.4 x 28.9 cm),"Bequest of Cora Timken Burnett, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/75131,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -57.51.38.50,false,true,75132,Asian Art,Folio,,India (Gujarat),,,,,Artist,,Bhadrabahu,"Indian, died ca. 356 B.C.",,Bhadrabahu,Indian,-0356,-0356,15th century,1400,1499,"Ink, opaque watercolor, and gold on paper",(Average size .1–.71): 4 1/2 x 11 3/8 in. (11.4 x 28.9 cm),"Bequest of Cora Timken Burnett, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/75132,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -57.51.38.51,false,true,75133,Asian Art,Folio,,India (Gujarat),,,,,Artist,,Bhadrabahu,"Indian, died ca. 356 B.C.",,Bhadrabahu,Indian,-0356,-0356,15th century,1400,1499,"Ink, opaque watercolor, and gold on paper",(Average size .1–.71): 4 1/2 x 11 3/8 in. (11.4 x 28.9 cm),"Bequest of Cora Timken Burnett, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/75133,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -57.51.38.52,false,true,75134,Asian Art,Folio,,India (Gujarat),,,,,Artist,,Bhadrabahu,"Indian, died ca. 356 B.C.",,Bhadrabahu,Indian,-0356,-0356,15th century,1400,1499,"Ink, opaque watercolor, and gold on paper",(Average size .1–.71): 4 1/2 x 11 3/8 in. (11.4 x 28.9 cm),"Bequest of Cora Timken Burnett, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/75134,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -57.51.38.53,false,true,75135,Asian Art,Folio,,India (Gujarat),,,,,Artist,,Bhadrabahu,"Indian, died ca. 356 B.C.",,Bhadrabahu,Indian,-0356,-0356,15th century,1400,1499,"Ink, opaque watercolor, and gold on paper",(Average size .1–.71): 4 1/2 x 11 3/8 in. (11.4 x 28.9 cm),"Bequest of Cora Timken Burnett, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/75135,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -57.51.38.54,false,true,75136,Asian Art,Folio,,India (Gujarat),,,,,Artist,,Bhadrabahu,"Indian, died ca. 356 B.C.",,Bhadrabahu,Indian,-0356,-0356,15th century,1400,1499,"Ink, opaque watercolor, and gold on paper",(Average size .1–.71): 4 1/2 x 11 3/8 in. (11.4 x 28.9 cm),"Bequest of Cora Timken Burnett, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/75136,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -57.51.38.55,false,true,75137,Asian Art,Folio,,India (Gujarat),,,,,Artist,,Bhadrabahu,"Indian, died ca. 356 B.C.",,Bhadrabahu,Indian,-0356,-0356,15th century,1400,1499,"Ink, opaque watercolor, and gold on paper",(Average size .1–.71): 4 1/2 x 11 3/8 in. (11.4 x 28.9 cm),"Bequest of Cora Timken Burnett, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/75137,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -57.51.38.56,false,true,75138,Asian Art,Folio,,India (Gujarat),,,,,Artist,,Bhadrabahu,"Indian, died ca. 356 B.C.",,Bhadrabahu,Indian,-0356,-0356,15th century,1400,1499,"Ink, opaque watercolor, and gold on paper",(Average size .1–.71): 4 1/2 x 11 3/8 in. (11.4 x 28.9 cm),"Bequest of Cora Timken Burnett, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/75138,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -57.51.38.57,false,true,75139,Asian Art,Folio,,India (Gujarat),,,,,Artist,,Bhadrabahu,"Indian, died ca. 356 B.C.",,Bhadrabahu,Indian,-0356,-0356,15th century,1400,1499,"Ink, opaque watercolor, and gold on paper",(Average size .1–.71): 4 1/2 x 11 3/8 in. (11.4 x 28.9 cm),"Bequest of Cora Timken Burnett, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/75139,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -57.51.38.58,false,true,75140,Asian Art,Folio,,India (Gujarat),,,,,Artist,,Bhadrabahu,"Indian, died ca. 356 B.C.",,Bhadrabahu,Indian,-0356,-0356,15th century,1400,1499,"Ink, opaque watercolor, and gold on paper",(Average size .1–.71): 4 1/2 x 11 3/8 in. (11.4 x 28.9 cm),"Bequest of Cora Timken Burnett, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/75140,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -57.51.38.59,false,true,75141,Asian Art,Folio,,India (Gujarat),,,,,Artist,,Bhadrabahu,"Indian, died ca. 356 B.C.",,Bhadrabahu,Indian,-0356,-0356,15th century,1400,1499,"Ink, opaque watercolor, and gold on paper",(Average size .1–.71): 4 1/2 x 11 3/8 in. (11.4 x 28.9 cm),"Bequest of Cora Timken Burnett, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/75141,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -57.51.38.60,false,true,75142,Asian Art,Folio,,India (Gujarat),,,,,Artist,,Bhadrabahu,"Indian, died ca. 356 B.C.",,Bhadrabahu,Indian,-0356,-0356,15th century,1400,1499,"Ink, opaque watercolor, and gold on paper",(Average size .1–.71): 4 1/2 x 11 3/8 in. (11.4 x 28.9 cm),"Bequest of Cora Timken Burnett, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/75142,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -57.51.38.61,false,true,75143,Asian Art,Folio,,India (Gujarat),,,,,Artist,,Bhadrabahu,"Indian, died ca. 356 B.C.",,Bhadrabahu,Indian,-0356,-0356,15th century,1400,1499,"Ink, opaque watercolor, and gold on paper",(Average size .1–.71): 4 1/2 x 11 3/8 in. (11.4 x 28.9 cm),"Bequest of Cora Timken Burnett, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/75143,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -57.51.38.63,false,true,75145,Asian Art,Folio,,India (Gujarat),,,,,Artist,,Bhadrabahu,"Indian, died ca. 356 B.C.",,Bhadrabahu,Indian,-0356,-0356,15th century,1400,1499,"Ink, opaque watercolor, and gold on paper",(Average size .1–.71): 4 1/2 x 11 3/8 in. (11.4 x 28.9 cm),"Bequest of Cora Timken Burnett, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/75145,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -57.51.38.64,false,true,75146,Asian Art,Folio,,India (Gujarat),,,,,Artist,,Bhadrabahu,"Indian, died ca. 356 B.C.",,Bhadrabahu,Indian,-0356,-0356,15th century,1400,1499,"Ink, opaque watercolor, and gold on paper",(Average size .1–.71): 4 1/2 x 11 3/8 in. (11.4 x 28.9 cm),"Bequest of Cora Timken Burnett, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/75146,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -57.51.38.65,false,true,75147,Asian Art,Folio,,India (Gujarat),,,,,Artist,,Bhadrabahu,"Indian, died ca. 356 B.C.",,Bhadrabahu,Indian,-0356,-0356,15th century,1400,1499,"Ink, opaque watercolor, and gold on paper",(Average size .1–.71): 4 1/2 x 11 3/8 in. (11.4 x 28.9 cm),"Bequest of Cora Timken Burnett, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/75147,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -57.51.38.66,false,true,75148,Asian Art,Folio,,India (Gujarat),,,,,Artist,,Bhadrabahu,"Indian, died ca. 356 B.C.",,Bhadrabahu,Indian,-0356,-0356,15th century,1400,1499,"Ink, opaque watercolor, and gold on paper",(Average size .1–.71): 4 1/2 x 11 3/8 in. (11.4 x 28.9 cm),"Bequest of Cora Timken Burnett, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/75148,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -57.51.38.67,false,true,75149,Asian Art,Folio,,India (Gujarat),,,,,Artist,,Bhadrabahu,"Indian, died ca. 356 B.C.",,Bhadrabahu,Indian,-0356,-0356,15th century,1400,1499,"Ink, opaque watercolor, and gold on paper",(Average size .1–.71): 4 1/2 x 11 3/8 in. (11.4 x 28.9 cm),"Bequest of Cora Timken Burnett, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/75149,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -57.51.38.68,false,true,75150,Asian Art,Folio,,India (Gujarat),,,,,Artist,,Bhadrabahu,"Indian, died ca. 356 B.C.",,Bhadrabahu,Indian,-0356,-0356,15th century,1400,1499,"Ink, opaque watercolor, and gold on paper",(Average size .1–.71): 4 1/2 x 11 3/8 in. (11.4 x 28.9 cm),"Bequest of Cora Timken Burnett, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/75150,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -57.51.38.69,false,true,75151,Asian Art,Folio,,India (Gujarat),,,,,Artist,,Bhadrabahu,"Indian, died ca. 356 B.C.",,Bhadrabahu,Indian,-0356,-0356,15th century,1400,1499,"Ink, opaque watercolor, and gold on paper",(Average size .1–.71): 4 1/2 x 11 3/8 in. (11.4 x 28.9 cm),"Bequest of Cora Timken Burnett, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/75151,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -57.51.38.70,false,true,75152,Asian Art,Folio,,India (Gujarat),,,,,Artist,,Bhadrabahu,"Indian, died ca. 356 B.C.",,Bhadrabahu,Indian,-0356,-0356,15th century,1400,1499,"Ink, opaque watercolor, and gold on paper",(Average size .1–.71): 4 1/2 x 11 3/8 in. (11.4 x 28.9 cm),"Bequest of Cora Timken Burnett, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/75152,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -57.51.38.71,false,true,75153,Asian Art,Folio,,India (Gujarat),,,,,Artist,,Bhadrabahu,"Indian, died ca. 356 B.C.",,Bhadrabahu,Indian,-0356,-0356,15th century,1400,1499,"Ink, opaque watercolor, and gold on paper",Overall (each): 4 1/2 × 11 3/8 in. (11.4 × 28.9 cm),"Bequest of Cora Timken Burnett, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/75153,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -15.96.4,false,true,54912,Asian Art,Painting,,Korea,Joseon dynasty (1392–1910),,,,Artist,,Zhao Songxue,"Korean, 17th century",,Zhao Songxue,Korean,1600,1699,17th century,1600,1699,Framed painting; ink on silk,23 5/8 x 14 3/8 in. (60 x 36.5 cm),"Rogers Fund, 1915",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54912,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.123,false,true,38448,Asian Art,Figure,,Tibet,,,,,Artist,Attributed to,Chosying Dorje (the Tenth Karmapa),1604–1674,,Chosying Dorje,Tibetan,1604,1674,17th century,1600,1699,Ivory,H. 6 3/4 in. (17.1 cm); W. 3 3/8 in. (8.6 cm); D. 3 1/2 in. (8.9 cm); Wt. 1 lb (.5 kg),"Louis V. Bell and Dodge Funds, 1972",,,,,,,,,,,,Sculpture,,http://www.metmuseum.org/art/collection/search/38448,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -15.96.2,false,true,40429,Asian Art,Painting,,Korea,Joseon dynasty (1392–1910),,,,Artist,,Samoje,active late 18th century,,Samoje,Korean,1771,1779,late 18th century,1767,1799,Framed painting; ink and color on silk,17 7/8 x 10 7/8 in. (45.4 x 27.6 cm),"Rogers Fund, 1915",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/40429,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -15.96.6,false,true,40432,Asian Art,Painting,,Korea,Joseon dynasty (1392–1910),,,,Artist,,Samoje,active late 18th century,,Samoje,Korean,1771,1779,late 18th century,1767,1799,Framed painting; ink and color on silk,17 7/8 x 10 7/8 in. (45.4 x 27.6 cm),"Rogers Fund, 1915",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/40432,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2006.104,false,true,73188,Asian Art,Hanging scroll,,Korea,Joseon dynasty (1392–1910),,,,Artist,,Kim Sugyu,"Korean, active late 18th–early 19th century",,Kim Sugyu,Korean,1767,1833,late 18th century,1767,1799,Hanging scroll; ink and color on cotton,Image: 10 x 13 3/4 in. (25.4 x 34.9 cm) Overall with mounting: 43 1/2 x 18 7/8 in. (110.5 x 47.9 cm) Overall with knobs: 43 1/2 x 21 1/8 in. (110.5 x 53.7 cm),"Purchase, Friends of Asian Art Gifts, 2006",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/73188,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2008.359.29,false,true,74683,Asian Art,Painting,,"India (Pahari Hills, Kangra)",,,,,Artist|Artist,First generation after|Possibly,Nainsukh|Fattu,active ca. 1735–78,,Nainsukh|Fattu,Indian,1725,1778,ca. 1785–90,1785,1790,Ochre on paper,Image (sight): 5 1/2 x 8 1/8 in. (14 x 20.6 cm) Framed: 16 x 20 in. (40.6 x 50.8 cm),"Gift of Subhash Kapoor, in memory of his parents, Smt Shashi Kanta and Shree Parshotam Ram Kapoor, 2008",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/74683,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2004.403,false,true,72589,Asian Art,Painting,,"India (Rajasthan, Mewar)",,,,,Artist|Artist,,Stipple Master|Jai Ram,"Indian, active ca. 1690–1715",(?),Stipple Master|Ram Jai,Indian,1680,1725,ca. 1712,1712,1712,"Ink, opaque watercolor, gold, and Basra pearls on paper",8 1/4 x 7 1/2 in. (21 x 19 cm),"Cynthia Hazen Polsky and Leon B. Polsky Fund, 2004",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/72589,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2008.359.9,false,true,74656,Asian Art,Drawing,,"India (Pahari Hills, Guler)",,,,,Artist|Artist,Attributed to|or attributed to,Pandit Seu|Manaku,Indian|active ca. 1725–60,,Seu Pandit|Manaku,Indian,1715,1770,late 18th century,1767,1799,Ink on paper,Image (sight): 6 1/4 x 5 5/8 in. (15.9 x 14.3 cm),"Gift of Subhash Kapoor, in memory of his parents, Smt Shashi Kanta and Shree Parshotam Ram Kapoor, 2008",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/74656,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"1977.375.31a, b",false,true,64046,Asian Art,Rubbing,張旭 「肚痛帖」; 懷素 「寄邊衣」詩|Ji bianyi|Letter about a Stomachache (Du tong tie),China,,,,,Calligrapher,,Zhang Xu,"Chinese, ca. 675–759",,Zhang Xu,Chinese,0665,0769,19th century rubbing of a 10th century stone carving,1800,1899,Ink on paper,"a (sheet, pre-conservation): 13 × 20 1/2 in. (33 × 52 cm) b (sheet, pre-conservation): 12 3/16 × 20 1/2 in. (31 × 52 cm)","Seymour and Rogers Funds, 1977",,,,,,,,,,,,Rubbing,,http://www.metmuseum.org/art/collection/search/64046,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1991.380,false,true,39899,Asian Art,Rubbing,東晉 王羲之 十七日帖 十三世紀拓本|On the Seventeenth Day,China,,,,,Calligrapher,,Wang Xizhi,"Chinese, ca. 303–ca. 361",,WANG XIZHI,Chinese,0303,0361,13th century rubbing of a 4th century text,1200,1299,Album of thirty leaves; ink on paper,Each leaf: 9 5/8 x 5 in. (24.4 x 12.7 cm),"Gift of Mr. and Mrs. Wan-go H. C. Weng, 1991",,,,,,,,,,,,Rubbing,,http://www.metmuseum.org/art/collection/search/39899,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.22,false,true,36145,Asian Art,Handscroll,,China,Qing dynasty (1644–1911),,,,Calligrapher|Calligrapher,,Wang Shu|Yan Zhenqing,"Chinese, 1688–1743|Chinese, 709–785",,Wang Shu|Yan Zhenqing,Chinese|Chinese,1688 |0709,1743 |0785,dated 1729,1729,1729,Handscroll in six sections; ink on paper,Image: 13 7/8 x 282 5/8 in. (35.2 x 717.9 cm) Overall with mounting: 14 1/2 x 457 1/8 in. (36.8 x 1161.1 cm),"Purchase, Bequest of Dorothy Graham Bennett, 1986",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/36145,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.267.255,false,true,49665,Asian Art,Folding fan mounted as an album leaf,近代 丁輔之 荔枝 扇面|Lychees,China,,,,,Artist|Calligrapher,,Ding Fuzhi|Shou Xi,"Chinese, 1879–1946|Chinese",,Ding Fuzhi|Shou Xi,Chinese|Chinese,1879,1946,dated 1941,1941,1941,Folding fan mounted as an album leaf; ink and color on alum paper,7 3/4 x 22 in. (19.7 x 55.9 cm),"Gift of Robert Hatfield Ellsworth, in memory of La Ferne Hatfield Ellsworth, 1986",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49665,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1980.426.1a–l,false,true,36130,Asian Art,Album,清 倣龔賢 十二月令山水圖 冊|Landscapes of the Twelve Months,China,Qing dynasty (1644–1911),,,,Artist|Calligrapher,,Gong Xian|Zhu Xia,"Chinese, 1619–1689|Chinese",,GONG XIAN|Zhu Xia,Chinese|Chinese,1619,1689,ca. 1685,1675,1695,Album of twelve painting leaves; ink on paper,11 7/8 x 24 3/4 in. (30.2 x 62.9 cm),"Gift of Douglas Dillon, 1980",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36130,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1987.408.1a–j,false,true,36435,Asian Art,Album,,China,Qing dynasty (1644–1911),,,,Artist|Calligrapher,,Zheng Min|Wang Quan,"Chinese, 1633–1683|Chinese",,Zheng Min|Wang Quan,Chinese|Chinese,1633,1683,dated 1688,1688,1688,Album of ten paintings; ink on paper,Each leaf: 10 1/4 x 7 1/2 in. (26 x 19.1 cm),"Edward Elliott Family Collection; Gift of Douglas Dillon, 1987",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36435,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -22.92,false,true,36036,Asian Art,Hanging scroll,,China,Qing dynasty (1644–1911),,,,Artist|Calligrapher,Attributed to,Jiang Tingxi|Jiang Wuyang,"Chinese, 1669–1732|Chinese",,Jiang Tingxi|Jiang Wuyang,Chinese|Chinese,1669,1732,dated 1724,1724,1724,Hanging scroll; ink and color on silk,44 1/2 x 23 3/4 in. (113 x 60.3 cm),"Gift of Mrs. Anna Woerishoffer, 1922",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36036,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -55.212,false,true,36072,Asian Art,Handscroll,清 程正揆 江山臥遊圖 卷|Dream Landscape,China,Qing dynasty (1644–1911),,,,Artist|Calligrapher,,Cheng Zhengkui|Wu Dacheng,"Chinese, 1604–1676|Chinese, 1835–1902",(Frontispiece),Cheng Zhengkui|Wu Dacheng,Chinese|Chinese,1604 |1835,1676 |1902,dated 1674,1600,1750,Handscroll; ink and color on silk,9 1/8 x 69 3/4 in. (23.2 x 177.2 cm),"Gift of Harry Lenart, 1955",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36072,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1983.609,false,true,49133,Asian Art,Hanging scroll,清 倣龔賢 雲山隱居圖 軸|Dwelling among Mountains and Clouds,China,Qing dynasty (1644–1911),,,,Artist|Calligrapher,Inscribed by,Gong Xian|Gong Xian,"Chinese, 1619–1689|Chinese, 1619–1689",,GONG XIAN|GONG XIAN,Chinese|Chinese,1619 |1619,1689 |1689,dated 1685,1685,1685,Hanging scroll; ink on paper,Image: 128 x 44 1/4 in. (325.1 x 112.4 cm) Overall with mounting: 163 x 55 1/4 in. (414 x 140.3 cm) Overall with knobs: 163 x 59 3/4 in. (414 x 151.8 cm),"Purchase, The Dillon Fund Gift, 1983",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49133,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -69.242.16–.21,false,true,65620,Asian Art,Album,清 倣龔賢 山水圖 冊|Landscapes,China,Qing dynasty (1644–1911),,,,Calligrapher|Artist,Inscribed by,Gong Xian|Gong Xian,"Chinese, 1619–1689|Chinese, 1619–1689",,GONG XIAN|GONG XIAN,Chinese|Chinese,1619 |1619,1689 |1689,ca. 1688,1678,1698,Album of six paintings; ink on paper,Each: 8 3/4 x 17 3/8 in. (22.2 x 44.1 cm),"The Sackler Fund, 1969",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/65620,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1978.13,false,true,40021,Asian Art,Hanging scroll,清 王翬 倣李成雪霽圖 軸 紙本|Snow Clearing: Landscape after Li Cheng,China,Qing dynasty (1644–1911),,,,Artist|Calligrapher,Inscribed by,Wang Hui|Wang Hui,"Chinese, 1632–1717|Chinese, 1632–1717",,Wang Hui|Wang Hui,Chinese|Chinese,1632 |1632,1717 |1717,dated 1669,1669,1669,Hanging scroll; ink and color on paper,Image: 44 3/8 x 14 1/8 in. (112.7 x 35.9 cm) Overall with mounting: 89 1/4 x 20 7/8 in. (226.7 x 53 cm) Overall with knobs: 89 1/4 x 24 1/8 in. (226.7 x 61.3 cm),"Ex coll.: C. C. Wang Family, Gift of Mr. and Mrs. Earl Morse, in honor of Professor Wen Fong, 1978",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/40021,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.267.30,false,true,49453,Asian Art,Hanging scroll,清 王緣 趙之謙肖像 軸|Portrait of Zhao Zhiqian,China,Qing dynasty (1644–1911),,,,Artist|Calligrapher,,Wang Yuan|Zhao Zhiqian,"Chinese, active ca. 1862–1908|Chinese, 1829–1884",,Wang Yuan|Zhao Zhiqian,Chinese|Chinese,1862 |1829,1908 |1884,dated 1871,1871,1871,Hanging scroll; ink and color on paper,41 3/4 x 13 1/4 in. (106 x 33.7 cm),"Gift of Robert Hatfield Ellsworth, in memory of La Ferne Hatfield Ellsworth, 1986",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49453,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.134,false,true,49134,Asian Art,Handscroll,清 柳堉 溪山行旅圖 卷|Traveling Amid Streams and Mountains,China,Qing dynasty (1644–1911),,,,Artist|Calligrapher,Inscribed by,Liu Yu|Liu Yu,"Chinese, 1620–after 1689|Chinese, 1620–after 1689",,Liu Yu|Liu Yu,Chinese|Chinese,1640 |1640,1700 |1700,dated 1680,1680,1680,Handscroll; ink on paper,10 3/8 x 200 1/4 in. (26.4 x 508.6 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49134,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2000.665.2a–p,false,true,65075,Asian Art,Album,王翬、楊晉、顧昉、王雲、徐玫 仿古山水圖 冊 紙本|Landscapes after old masters,China,Qing dynasty (1644–1911),,,,Artist|Artist|Artist|Artist|Artist,"leaves a, o, p by|leaves e,f,g,h,i,j by|leaves k,l,m,n by|leaf b by|leaves c, d by",Wang Hui|Yang Jin|Gu Fang|Xu Mei|Wang Yun,"Chinese, 1632–1717|Chinese, 1644–1728|Chinese, active ca. 1690–1720|Chinese, active ca. 1690–1722|Chinese, 1652–after 1735",,Wang Hui|Yang Jin|Gu Fang|Xu Mei|Wang Yun,Chinese,1632 |1644 |1690 |1690 |1652,1717 |1728 |1720 |1722 |1735,dated 1692,1692,1692,Album of sixteen leaves; ink and color on paper,Image (each leaf): 11 x 12 1/8 in. (27.9 x 30.8 cm),"Gift of Marie-Hélène and Guy A. Weill, 2000",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/65075,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.33,false,true,41468,Asian Art,Handscroll,元 吳鎮 蘆灘釣艇圖 卷|Fisherman,China,Yuan dynasty (1271–1368),,,,Artist|Calligrapher,,Wu Zhen|Wu Zhen,"Chinese, 1280–1354|Chinese, 1280–1354",,Wu Zhen|Wu Zhen,Chinese|Chinese,1280 |1280,1354 |1354,ca. 1350,1340,1354,Handscroll; ink on paper,Image: 12 1/4 x 21 3/16 in. (31.1 x 53.8 cm) Overall with mounting: 13 1/4 x 136 1/2 in. (33.7 x 346.7 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/41468,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1988.135,false,true,40507,Asian Art,Handscroll,元 趙孟頫 趙雍 趙麟 吳興趙氏三世人馬圖 卷|Grooms and Horses,China,Yuan dynasty (1271–1368),,,,Artist|Artist|Artist,,Zhao Mengfu|Zhao Yong|Zhao Lin,"Chinese, 1254–1322|Chinese, 1289–after 1360|Chinese, active second half of the 14th century",,Zhao Mengfu|Zhao Yong|ZHAO LIN,Chinese|Chinese|Chinese,1254 |1289 |1350,1322 |1360 |1399,date 1296 and 1359,1296,1359,Handscroll; ink and color on paper,Image: 11 7/8 x 70 1/8 in. (30.2 x 178.1 cm) Overall with mounting: 12 1/4 in. x 29 ft. 2 1/2 in. (31.1 x 890.3 cm),"Gift of John M. Crawford Jr., 1988",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/40507,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1984.475.1,false,true,40052,Asian Art,Handscroll,"南宋 馬和之 詩經小雅鴻雁之什六篇圖 卷|Courtly Odes, Beginning with ""Wild Geese""",China,Southern Song dynasty (1127–1279),,,,Artist|Calligrapher,,Ma Hezhi|Emperor Gaozong,"Chinese, ca. 1130–ca. 1170|Chinese, 1107–1187, r. 1127–1162",and Assistants,MA HEZHI|Gaozong Emperor,Chinese|Chinese,1130 |1107,1170 |1187,12th century,1130,1170,Handscroll in six sections; ink and color on silk,Overall with mounting: 12 3/4 in. × 42 ft. 9 3/4 in. (32.4 × 1304.9 cm),"Edward Elliott Family Collection, Gift of Douglas Dillon, 1984",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/40052,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.100.84,false,true,35995,Asian Art,Folding fan mounted as an album leaf,,China,Ming dynasty (1368–1644),,,,Calligrapher,,Wen Congjian,"Chinese, 1574–1648",,Wen Congjian,Chinese,1574,1648,,1575,1644,Folding fan mounted as an album leaf; ink on gold paper,6 1/2 x 20 31/32 in. (16.5 x 53.3 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/35995,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.100.88,false,true,35999,Asian Art,Folding fan mounted as an album leaf,,China,Ming dynasty (1368–1644),,,,Calligrapher,,Yang Wencong,"Chinese, 1597–1645/46",,Yang Wencong,Chinese,1597,1646,,1597,1644,Folding fan mounted as an album leaf; ink on paper,6 3/5 x 20 31/32 in. (16.8 x 53.3 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/35999,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.100.94,false,true,36003,Asian Art,Folding fan mounted as an album leaf,,China,Ming dynasty (1368–1644),,,,Calligrapher,,Rüan Dacheng,"Chinese, active early 17th century",,Rüan Dacheng,Chinese,1550,1700,,1600,1633,Folding fan mounted as an album leaf; ink on gold paper,7 x 19 3/4 in. (17.8 x 50.2 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/36003,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.62,false,true,45786,Asian Art,Folding fan mounted as an album leaf,,China,Ming dynasty (1368–1644),,,,Artist|Artist|Artist,,Wen Zhengming|Wang Shou|Wang Guxiang,"Chinese, 1470–1559|Chinese, active early 16th century|Chinese, 1501–1568",,Wen Zhengming|Wang Shou|Wang Guxiang,Chinese|Chinese|Chinese,1470 |1500 |1501,1559 |1600 |1568,,1470,1559,Folding fan mounted as an album leaf; ink on gold-flecked paper,6 3/8 x 20 1/4 in. (16.2 x 51.4 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/45786,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.55,false,true,73669,Asian Art,Hanging scroll,清 查昇 行書七律詩 軸|Poem,China,Qing dynasty (1644–1911),,,,Calligrapher,,Zha Sheng,"Chinese, 1650–1707",,Zha Sheng,Chinese,1650,1707,,1650,1707,Hanging scroll; ink on silk,H. 63 1/4 in. (160.6 cm); W. 18 11/16 in. (47.5 cm),"Purchase, C. C. Wang Gift, 2007",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/73669,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.154a–h,false,true,49179,Asian Art,Album,清 石濤(朱若極) 山水圖 冊|Searching for Immortals,China,Qing dynasty (1644–1911),,,,Artist|Calligrapher,Inscribed by,Shitao (Zhu Ruoji)|Shitao (Zhu Ruoji),"Chinese, 1642–1707|Chinese, 1642–1707",,Shitao|Shitao,Chinese|Chinese,1642 |1642,1707 |1707,,1644,1707,Album of eight leaves; ink and color on paper,5 7/8 x 10 3/4 in. (14.9 x 27.3 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49179,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.220.11,false,true,49226,Asian Art,Hanging scroll,,China,Qing dynasty (1644–1911),,,,Artist|Artist|Artist,In the Style of|Formerly Attributed to,Li Yin|Unidentified Artist|Ma Kui,"Chinese, active second half of the 17th–early 18th century|Chinese, active ca. 1194–1225",,Li Yin|Unidentified Artist|Ma Kui,Chinese|Chinese,1650 |1194,1825 |1225,,1644,1911,Hanging scroll; ink and color on silk,Image: 65 7/8 x 20 7/8 in. (167.3 x 53 cm); Overall with mounting: 107 3/4 x 28 5/8 in. (273.7 x 72.7 cm); Overall with knobs: 107 3/4 x 32 5/8 in. (273.7 x 82.9 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49226,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -47.18.120,false,true,51727,Asian Art,Handscroll,,China,Yuan dynasty (1271–1368) (?),,,,Artist|Calligrapher|Artist,Copy after,Unidentified Artist|Guo Zongjiang|Li Gonglin,"Chinese|Chinese, ca. 1041–1106",Chinese|(Frontispiece),Unidentified Artist|Guo Zongjiang|Li Gonglin,Chinese|Chinese,1041,1106,,1271,1368,Handscroll; ink and color on silk,12 5/8 x 184 in. (32.1 x 467.4 cm),"From the Collection of A. W. Bahr, Purchase, Fletcher Fund, 1947",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51727,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -47.18.93,false,true,51705,Asian Art,Handscroll,,China,Northern Song dynasty (960–1127),,,,Artist|Calligrapher|Artist,Copy after,Unidentified Artist|Emperor Huizong|Han Gan,"Chinese, 1082–1135; r. 1100–25|Chinese, active ca. 742–756",,Unidentified Artist|Huizong Emperor|HAN GAN,Chinese|Chinese,1082 |0742,1082 |0756,,960,1127,Handscroll; ink and color on silk,Image: 12 in. × 20 1/8 in. (30.5 × 51.1 cm),"From the Collection of A. W. Bahr, Purchase, Fletcher Fund, 1947",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51705,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -18.124.6,false,true,51528,Asian Art,Handscroll,,China,Ming (1368–1644) or Qing (1644–1911) dynasty,,,,Artist|Artist|Calligrapher|Calligrapher|Calligrapher|Calligrapher|Calligrapher|Calligrapher|Calligrapher|Calligrapher|Calligrapher,Formerly Attributed to,Unidentified Artist|Ni Zan|Li Dongyang|Wang Da|Jin Xüan|Wen Peng|Wen Jia|Zhou Tianqiu|Huang Jishui|Dong Qichang|Wang Zhideng,"Chinese, 1306–1374|Chinese, 1447–1503|Chinese, 14th century|Chinese|Chinese, 1498–1573|Chinese, 1501–1583|Chinese, 1514–1595|Chinese|Chinese, 1555–1636|Chinese, 1535–1612",,Unidentified Artist|NI ZAN|Li Dongyang|Wang Da|Jin Xüan|Wen Peng|Wen Jia|Zhou Tianqiu|Huang Jishui|Dong Qichang|Wang Zhideng,Chinese|Chinese|Chinese|Chinese|Chinese|Chinese|Chinese|Chinese|Chinese|Chinese,1306 |1447 |1300 |1498 |1501 |1514 |1555 |1535,1374 |1503 |1399 |1573 |1583 |1595 |1636 |1612,,1368,1911,Handscroll; color on paper,11 x 57 1/2 in. (27.9 x 146.1 cm),"Rogers Fund, 1918",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51528,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -47.18.33,false,true,51656,Asian Art,Album leaf,,China,Ming (1368–1644) or Qing dynasty (1644–1911),,,,Artist|Artist|Artist,In the Style of|Formerly Attributed to,Unidentified Artist|Li Zhaodao|Qiu Ying,"act 670–730|Chinese, ca. 1495–1552",,Unidentified Artist|Li Zhaodao|Qiu Ying,Chinese|Chinese,0670 |1485,0730 |1562,,1368,1911,Album leaf; ink and color on silk,14 1/8 x 10 5/8 in. (35.9 x 27.0 cm),"From the Collection of A. W. Bahr, Purchase, Fletcher Fund, 1947",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51656,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"47.18.13a, b",false,true,51644,Asian Art,Handscroll,,China,Ming (1368–1644) or Qing dynasty (1644–1911),,,,Artist|Artist|Artist,b) Formerly Attributed to|a) Formerly Attributed to,Unidentified Artist|Fang Congyi|Xia Gui,"Chinese, ca. 1301–after 1378|Chinese, active ca. 1195–1230",,Unidentified Artist|Fang Congyi|Xia Gui,Chinese|Chinese,1301 |1195,1399 |1230,,1368,1911,Two handscrolls; ink on paper,Image (a): 3 5/8 in. × 46 in. (9.2 × 116.8 cm) Image (b): 3 7/8 in. × 35 in. (9.8 × 88.9 cm),"From the Collection of A. W. Bahr, Purchase, Fletcher Fund, 1947",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51644,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1979.500.1,false,true,36129,Asian Art,Album,,China,Qing dynasty (1644–1911),,,,Artist|Artist|Artist|Artist|Artist|Artist|Artist|Artist|Calligrapher|Calligrapher,,Wang Jian|Wu Li|Tong Bin|Gao Jian|Zhu Zun|Zhang Shi|Jin Kan|Shen Lang|Xü Fu|Shen Ho,"Chinese, 1609–1677 or 1688|Chinese, 1632–1718|Chinese, 17th century|Chinese, 1634–after 1715|Chinese, 17th century|Chinese, 17th century|Chinese, died 1703|Chinese, 17th century|Chinese, 19th century|Chinese, 17th century",(title page),Wang Jian|WU LI|Tong Bin|Gao Jian|Zhu Zun|Zhang Shi|Jin Kan|Shen Lang|Xü Fu|Shen Ho,Chinese|Chinese|Chinese|Chinese|Chinese|Chinese|Chinese|Chinese|Chinese|Chinese,1609 |1632 |1600 |1634 |1600 |1600 |1600 |1800 |1600,1677 |1718 |1699 |1725 |1699 |1699 |1703 |1699 |1899 |1699,17th century,1644,1699,Album of eight painted leaves; ink and color on paper,Each leaf: 9 x 12 1/4 in. (22.9 x 31.1 cm),"Gift of Douglas Dillon, 1979",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36129,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -51.14,false,true,51770,Asian Art,Folding fan,,China,,,,,Artist|Calligrapher,,Giuseppe Castiglione|Wang Yudun,"Italian, Milan 1688–1766 Beijing|Chinese, 1692–1758",,"Castiglione, Giuseppe|Wang Yudun",Italian|Chinese,1688 |1692,1766 |1758,18th century,1700,1766,Folding fan; ink and color on paper,H. (sticks) 12 1/4 in. (31.1 cm); W. (open) 20 in. (50.8 cm),"Rogers Fund, 1951",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51770,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1977.375.14a–d,false,true,64029,Asian Art,Rubbing,唐 颜真卿 顏家廟碑 現代拓片 紙本|Yan Family Temple Stele,China,,,,,Artist,,Yan Zhenqing,"Chinese, 709–785",,Yan Zhenqing,Chinese,0709,0785,20th century,1900,1999,20th-century rubbing of a stele dated 780; ink on paper,98 x 52 x 1 3/4 in. (248.9 x 132.1 x 4.4 cm),"Seymour and Rogers Funds, 1977",,,,,,,,,,,,Rubbing,,http://www.metmuseum.org/art/collection/search/64029,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -CIB8,false,true,60764,Asian Art,Illustrated book,太平山水圖|Landscapes of Taiping Prefecture (Taiping shanshui tu),China,,,,,Artist,,Xiao Yuncong,"Chinese, 1596–1673",,Xiao Yuncong,Chinese,1596,1673,ca. 1650,1640,1660,Woodblock-printed books; ink on paper,H. 8 7/8 in. (22.5 cm); W. 6 in. (15.2 cm); D. 1 in. (2.5 cm),"Rogers Fund, 1924",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/60764,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"1989.363.205a, b",false,true,51902,Asian Art,Hanging scrolls,,China,,,,,Artist,,Wu Changshuo,"Chinese, 1844–1927",,Wu Changshuo,Chinese,1844,1927,20th century,1900,1927,Pair of hanging scrolls; ink on paper,Image (each scroll): 57 13/16 x 10 1/8 in. (146.8 x 25.7 cm) Overall (each scroll): 68 1/2 x 12 11/16 in. (174 x 32.2 cm) Overall with knobs (each scroll): 68 1/2 x 15 in. (174 x 38.1 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/51902,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1977.375.30a–c,false,true,64045,Asian Art,Rubbing,,China,,,,,Artist,,Wang Xizhi,"Chinese, ca. 303–ca. 361",,WANG XIZHI,Chinese,0303,0361,20th century,1900,1999,Ink on paper,,"Seymour and Rogers Funds, 1977",,,,,,,,,,,,Rubbing,,http://www.metmuseum.org/art/collection/search/64045,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.141.1a–i,false,true,36439,Asian Art,Album,唐 傳鍾紹京 楷書靈飛經 冊|Spiritual Flight Sutra,China,Tang dynasty (618–907),,,,Artist,Attributed to,Zhong Shaojing,"Chinese, active ca. 713–41",,Zhong Shaojing,Chinese,0713,0741,ca. 738,728,741,Album of nine leaves; ink on paper,Each leaf: 8 3/16 x 3 1/2 in. (20.8 x 8.9 cm),"Purchase, The Dillon Fund Gift, 1989",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/36439,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.28,false,true,40285,Asian Art,Handscroll,南宋 趙孟堅 行書梅竹詩譜 卷|Poems on Painting Plum Blossoms and Bamboo,China,Song dynasty (960–1279),,,,Artist,,Zhao Mengjian,"Chinese, 1199–before 1267",,Zhao Mengjian,Chinese,1199,1267,dated 1260,1260,1260,Handscroll; ink on paper,Image: 13 3/8 in. × 11 ft. 7 in. (34 × 353.1 cm) Overall with mounting: 13 5/8 in. × 40 ft. 5 11/16 in. (34.6 × 1233.6 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/40285,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.18,false,true,40107,Asian Art,Fan mounted as an album leaf,南宋 理宗趙昀 行書北宋梅堯臣 依韻和資政侍郎雪後登看山亭詩 團扇|Quatrain on Snow-covered West Lake,China,Song dynasty (960–1279),,,,Artist,,Emperor Lizong,"Chinese, 1205–64, r. 1224–64",,LIZONG EMPEROR,Chinese,1205,1264,ca. 1250–60,1250,1260,Round fan mounted as an album leaf; ink on silk,Image: 9 7/8 × 9 7/8 in. (25.1 × 25.1 cm) Mat: 15 1/2 × 14 1/2 in. (39.4 × 36.8 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/40107,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.19,false,true,40180,Asian Art,Fan mounted as an album leaf,,China,Song dynasty (960–1279),,,,Artist,,Emperor Lizong,"Chinese, 1205–64, r. 1224–64",,LIZONG EMPEROR,Chinese,1205,1264,ca. 1260–64,1260,1264,Fan mounted as an album leaf; ink on silk,Image: 9 × 9 5/8 in. (22.9 × 24.4 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/40180,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.23a–c,false,true,40194,Asian Art,Fan mounted as an album leaf,南宋 理宗 趙昀 楷書韓翊 《 潮聲山翠》 聯句 團扇|Couplet from a Poem by Han Hong,China,Song dynasty (960–1279),,,,Artist,,Emperor Lizong,"Chinese, 1205–64, r. 1224–64",,LIZONG EMPEROR,Chinese,1205,1264,1261,1261,1261,"Fan mounted as an album leaf; a) ink on silk, b) ink on paper, c) ink on paper",8 3/16 x 8 11/16 in. (20.8 x 22.1 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/40194,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.9,false,true,40060,Asian Art,Fan mounted as an album leaf,南宋 孝宗 行楷書池上水邊聯句 團扇|Couplet on pond scenery,China,Song dynasty (960–1279),,,,Artist,Attributed to,Emperor Xiaozong,"Chinese, 1127–1194; r. 1163–89",,Xiaozong Emperor,Chinese,1127,1194,ca. 12th century,1127,1194,Fan mounted as an album leaf; ink on silk,9 x 9 5/8 in. (22.9 x 24.4 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/40060,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.6,false,true,40058,Asian Art,Fan mounted as an album leaf,南宋 高宗 草書天山陰雨七絕詩 團扇|Quatrain on Heavenly Mountain,China,Song dynasty (960–1279),,,,Artist,,Emperor Gaozong,"Chinese, 1107–1187, r. 1127–1162",,Gaozong Emperor,Chinese,1107,1187,after 1162,1162,1187,Fan mounted as album leaf; ink on silk,9 1/4 x 9 5/8 in. (23.5 x 24.4 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/40058,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.47,false,true,45684,Asian Art,Folding fan mounted as an album leaf,,China,Ming dynasty (1368–1644),,,,Artist,,Shen Zhou,"Chinese, 1427–1509",,Shen Zhou,Chinese,1427,1509,dated 1493,1493,1493,Folding fan mounted as an album leaf; ink on gold-flecked paper,6 1/2 x 18 1/2 in. (16.5 x 47 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/45684,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.48,false,true,45746,Asian Art,Folding fan mounted as an album leaf,,China,Ming dynasty (1368–1644),,,,Artist,,Wu Kuan,"Chinese, 1435–1504",,Wu Kuan,Chinese,1435,1504,ca. 1498,1488,1508,Folding fan mounted as an album leaf; ink on gold-flecked paper,6 3/4 x 19 in. (17.1 x 49.5 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/45746,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1997.438.2,false,true,39766,Asian Art,Hanging scroll,明 王鏊 贈別詩 軸|Farewell Poem,China,Ming dynasty (1368–1644),,,,Artist,,Wang Ao,"Chinese, 1450–1524",,Wang Ao,Chinese,1450,1524,dated 1498,1498,1498,Hanging scroll; ink on paper,Image: 81 1/2 x 25 in. (207 x 63.5 cm) Overall: 130 x 34 1/2 in. (330.2 x 87.6 cm) Overall with rollers: 130 x 36 1/2 in. (330.2 x 92.7 cm),"The C. C. Wang Family Collection, Gift of C. C. Wang, 1997",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/39766,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.51,false,true,45749,Asian Art,Handscroll,,China,Ming dynasty (1368–1644),,,,Artist,,Zhu Yunming,"Chinese, 1461–1527",,Zhu Yunming,Chinese,1461,1527,dated 1507,1507,1507,Handscroll; ink on gold-flecked paper,Image: 12 7/8 x 371 1/2 in. (32.7 x 943.6 cm) Overall with mounting: 14 5/8 x 463 1/8 in. (37.1 x 1176.3 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/45749,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1988.394.2,false,true,44574,Asian Art,Folding fan mounted as an album leaf,,China,Ming dynasty (1368–1644),,,,Artist,,Tang Yin,"Chinese, 1470–1524",,Tang Yin,Chinese,1470,1524,dated 1522,1522,1522,Folding fan mounted as an album leaf; ink on gold-flecked paper,Image: 6 13/16 x 19 5/8 in. (17.3 x 49.8 cm),"Edward Elliott Family Collection, Douglas Dillon Gift, 1988",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/44574,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.59,false,true,45775,Asian Art,Album leaf,"明 文徵明 致丈人吳愈書 冊頁|Letter to the Artist's Father-in-law, Wu Yu",China,Ming dynasty (1368–1644),,,,Artist,,Wen Zhengming,"Chinese, 1470–1559",,Wen Zhengming,Chinese,1470,1559,ca. 1506–1510,1506,1510,Album leaf; ink on patterned paper,9 x 12 1/8 in. (22.9 x 30.8 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/45775,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.63,false,true,45780,Asian Art,Handscroll,,China,Ming dynasty (1368–1644),,,,Artist,,Wen Zhengming,"Chinese, 1470–1559",,Wen Zhengming,Chinese,1470,1559,dated 1544 and 1547,1544,1547,Handscroll; ink on paper,Image: 9 1/16 x 46 3/8 in. (23 x 117.8 cm) Overall with mounting: 9 5/16 x 288 1/16 in. (23.7 x 731.7 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/45780,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1982.229a–ff,false,true,51865,Asian Art,Album,,China,Ming dynasty (1368–1644),,,,Artist,,Wen Zhengming,"Chinese, 1470–1559",,Wen Zhengming,Chinese,1470,1559,dated 1543,1543,1543,Album of thirty-two pages; ink on paper,H. 13 1/2 in. (34.3 cm); W. 10 3/9 in. (26.2 cm) Album: H. 15 7/8 in. (40.3 cm); W. 10 7/8 in. (27.6 cm); D. 1 in. (2.5 cm),"Gift of John M. Crawford Jr., 1982",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/51865,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2012.166,false,true,77914,Asian Art,Hanging scroll mounted as panel,文彭 行草七律 水墨絹本 鏡框|Poem on Promulgating the Almanac at New Year’s,China,Ming dynasty (1368–1644),,,,Artist,,Wen Peng,"Chinese, 1498–1573",,Wen Peng,Chinese,1498,1573,undated,1498,1573,Hanging scroll remounted as a panel; ink on paper,Image: 63 1/4 x 30 1/2 in. (160.7 x 77.5 cm) Overall with mounting: 77 3/4 x 40 1/4 in. (197.5 x 102.2 cm),"Purchase, Friends of Asian Art Gifts, 2012",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/77914,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.70,false,true,48875,Asian Art,Folding fan mounted as an album leaf,,China,Ming dynasty (1368–1644),,,,Artist,,Wen Peng,"Chinese, 1498–1573",,Wen Peng,Chinese,1498,1573,dated 1567,1567,1567,Folding fan mounted as an album leaf; ink on gold paper,6 x 19 in. (15.2 x 48.3 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/48875,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1979.500.3,false,true,48955,Asian Art,Hanging scroll,,China,Ming dynasty (1368–1644),,,,Artist,,Dong Qichang,"Chinese, 1555–1636",,Dong Qichang,Chinese,1555,1636,ca. 1622–25,1612,1635,Hanging scroll; ink on silk,Image: 64 7/8 x 19 5/8 in. (164.8 x 49.8 cm) Overall with mounting: 98 1/2 x 25 5/8 in. (250.2 x 65.1 cm) Overall with knobs: 98 1/2 x 29 5/8 in. (250.2 x 75.2 cm),"Gift of Douglas Dillon, 1979",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/48955,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.100,false,true,48954,Asian Art,Hanging scroll,,China,Ming dynasty (1368–1644),,,,Artist,,Dong Qichang,"Chinese, 1555–1636",,Dong Qichang,Chinese,1555,1636,after 1632,1632,1636,Hanging scroll; ink on paper,Image: 74 3/8 x 29 1/4 in. (188.9 x 74.3 cm) Overall: 103 3/4 x 34 1/4 in. (263.5 x 87 cm) Overall with knobs: 103 3/4 x 38 in. (263.5 x 96.5 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/48954,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.115,false,true,49025,Asian Art,Folding fan mounted as an album leaf,,China,Ming dynasty (1368–1644),,,,Artist,,Wang Duo,"Chinese, 1592–1652",,Wang Duo,Chinese,1592,1652,dated 1637,1637,1637,Folding fan mounted as an album leaf; ink on gold-flecked paper,6 5/8 x 20 3/4 in. (16.8 x 52.7 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/49025,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.116,false,true,49029,Asian Art,Hanging scroll,明/清 王鐸 行草書日暮孤亭五律詩 軸|Poem on a Riverside Pavilion,China,Ming dynasty (1368–1644),,,,Artist,,Wang Duo,"Chinese, 1592–1652",,Wang Duo,Chinese,1592,1652,dated 1641,1641,1641,Hanging scroll; ink on paper,Image: 138 1/4 x 29 1/4 in. (351.2 x 74.3 cm) Overall: 169 1/2 x 39 in. (430.5 x 99.1 cm) Overall with knobs: 169 1/2 x 43 1/2 in. (430.5 x 110.5 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/49029,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1999.93,false,true,56553,Asian Art,Qin,,China,Ming dynasty (1368–1644),,,,Artist,,Prince Lu,"Chinese, 1628–1644",,Prince Lu,Chinese,1628,1644,1634,1634,1634,"Wood, lacquer, jade, silk strings",W. 10 1/2 in. (26.6 cm); D. 4 1/4 in. (11 cm); L. 46 5/8 in. (118.5 cm),"Purchase, Clara Mertens Bequest, in memory of André Mertens, Seymour Fund, The Boston Foundation Gift, Gift of Elizabeth M. Riley, by exchange, and funds from various donors, 1999",,,,,,,,,,,,Musical instruments,,http://www.metmuseum.org/art/collection/search/56553,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -30.76.201,false,true,41780,Asian Art,Ink tablet,,China,Ming dynasty (1368–1644),,,,Artist,Workshop of,Fang Yulu,active ca. 1570–1619,,Fang Yulu,Chinese,1570,1619,dated 1576,1576,1576,Pine soot and binding medium,Diam. 3 1/2 in. (8.9 cm),"Rogers Fund, 1929",,,,,,,,,,,,Ink,,http://www.metmuseum.org/art/collection/search/41780,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1994.208,false,true,39625,Asian Art,Brush holder,晚明 張希黃 留青竹刻 《醉翁亭記》 詩意圖筆筒|Brush holder with “Ode to the Pavilion of the Inebriated Old Man”,China,Ming dynasty (1368–1644),,,,Artist,,Zhang Xihuang,active early 17th century,,Zhang Xihuang,Chinese,1600,1633,early 17th century,1600,1633,Bamboo,H. 5 1/4 in. (13.4 cm),"Purchase, Mr. and Mrs. John A. Wiley Gift, Seymour Fund, Bequest of Dorothy Graham Bennett and Erich O. Grunebaum Bequest, 1994",,,,,,,,,,,,Bamboo,,http://www.metmuseum.org/art/collection/search/39625,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.87,false,true,48931,Asian Art,Hanging scroll,,China,Ming dynasty (1368–1644),,,,Artist,,Liu Xiang,"Chinese, active mid-17th century",,Liu Xiang,Chinese,1636,1670,16th–17th century,1500,1699,Hanging scroll; ink on paper,Image: 38 9/16 x 10 3/16 in. (97.9 x 25.9 cm) Overall: 72 1/4 x 18 3/16 in. (183.5 x 46.2 cm) Overall with knobs: 72 1/4 x 21 1/4 in. (183.5 x 54 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/48931,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.81,false,true,48928,Asian Art,Folding fan mounted as an album leaf,,China,Ming dynasty (1368–1644),,,,Artist,,Xue Mingyi,"Chinese, active ca. 1538–after 1597",,Xue Mingyi,Chinese,1538,1598,dated 1597,1597,1597,Folding fan mounted as an album leaf; ink on gold-flecked paper,5 3/4 x 18 in. (14.6 x 45.7 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/48928,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.100.72,false,true,48959,Asian Art,Folding fan mounted as an album leaf,,China,Qing dynasty (1644–1911),,,,Artist,After,Dong Qichang,"Chinese, 1555–1636",,Dong Qichang,Chinese,1555,1636,17th century or later,1600,1911,Folding fan mounted as an album leaf; ink on gold paper,6 11/16 x 20 1/2 in. (17.0 x 52.1 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/48959,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.130,false,true,49129,Asian Art,Handscroll,清 法若真 行草書畫說 卷|Discourse on Painting,China,Qing dynasty (1644–1911),,,,Artist,,Fa Ruozhen,"Chinese, 1613–1696",,Fa Ruozhen,Chinese,1613,1696,dated 1667,1667,1667,Handscroll; ink on paper,12 1/4 x 144 1/2 in. (31.1 x 367 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/49129,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.139,false,true,49146,Asian Art,Folding fan mounted as an album leaf,清 朱耷 (八大山人) 李治書 扇頁|Letter by Li Zhi,China,Qing dynasty (1644–1911),,,,Artist,,Bada Shanren (Zhu Da),"Chinese, 1626–1705",,Bada Shanren (Zhu Da),Chinese,1626,1705,dated 1702,1702,1702,Folding fan mounted as an album leaf; ink on paper,6 3/4 x 19 1/2 in. (17.1 x 49.5 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/49146,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1982.458a–e,false,true,49147,Asian Art,Album,明/清 朱耷（八大山人） 致方士琯書 冊 紙本|Letters to Fang Shiguan,China,Qing dynasty (1644–1911),,,,Artist,,Bada Shanren (Zhu Da),"Chinese, 1626–1705",,Bada Shanren (Zhu Da),Chinese,1626,1705,datable to ca. 1688–1705,1688,1705,Album of ten leaves; ink on patterned and plain paper,"Image (leaf a, right letter): 7 3/4 x 5 7/16 in. (19.7 x 13.8 cm) Image (leaf a, left letter): 7 1/4 x 4 9/16 in. (18.4 x 11.6 cm) Image (leaf b, right letter): 7 1/2 x 5 3/16 in. (19.1 x 13.2 cm) Image (leaf b, left letter): 7 x 4 9/16 in. (17.8 x 11.6 cm) Image (leaf c, right letter): 7 1/2 x 5 3/16 in. (19.1 x 13.2 cm) Image (leaf c, left letter): 7 1/4 x 4 9/16 in. (18.4 x 11.6 cm) Image (leaf d, right letter): 7 1/2 x 5 3/16 in. (19.1 x 13.2 cm) Image (leaf d, left letter): 7 1/4 x 4 9/16 in. (18.4 x 11.6 cm) Image (leaf e, right letter): 7 5/8 x 5 3/16 in. (19.4 x 13.2 cm) Image (leaf e, left letter): 7 1/4 x 4 3/4 in. (18.4 x 12.1 cm)","Gift of John M. Crawford Jr., 1982",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/49147,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.147,false,true,49163,Asian Art,Folding fan mounted as an album leaf,,China,Qing dynasty (1644–1911),,,,Artist,,Yun Shouping,"Chinese, 1633–1690",,YUN SHOUPING,Chinese,1633,1690,dated 1680,1680,1680,Folding fan mounted as an album leaf; ink on paper,6 11/16 x 20 in. (17 x 50.8 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/49163,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.196,false,true,51894,Asian Art,Album,"清 金農 論畫雜詩 冊|Poems on Paintings, Written for Ma Yueguan",China,Qing dynasty (1644–1911),,,,Artist,,Jin Nong,"Chinese, 1687–1773",,Jin Nong,Chinese,1687,1773,dated 1754,1754,1754,Album of eleven double leaves; ink on paper,Image: 6 5/8 x 10 3/8 in. (16.8 x 26.4 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/51894,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1980.531,false,true,36132,Asian Art,Hand scroll,,China,Qing dynasty (1644–1911),,,,Artist,,Liu Yong,"Chinese, 1719–1805",,Liu Yong,Chinese,1719,1805,dated 1803,1803,1803,Hand scroll; ink on paper,30 3/4 x 95 1/2 in. (78.1 x 242.6 cm),"Gift of Dr. and Mrs. George Fan, 1980",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/36132,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.203,false,true,51900,Asian Art,Hanging scroll,,China,Qing dynasty (1644–1911),,,,Artist,,Wang Wenzhi,"Chinese, 1730–1802",,Wang Wenzhi,Chinese,1730,1802,dated 1801,1801,1801,Hanging scroll; ink on paper,Image: 65 3/4 x 18 1/2 in. (167 x 47 cm) Overall: 85 x 23 3/4 in. (215.9 x 60.3 cm) Overall with knobs: 85 x 26 1/2 in. (215.9 x 67.3 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/51900,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2010.327a–d,false,true,75814,Asian Art,Hanging scrolls,吳煕載 篆書詩經南山有臺|Nutgrass Grows on the Southern Hills,China,Qing dynasty (1644–1911),,,,Artist,,Wu Xizai,"Chinese, 1799–1870",,Wu Xizai,Chinese,1799,1870,before 1862,1799,1862,Set of four hanging scrolls; ink on paper,each: 12 3/16 x 51 9/16 in. (31 x 131 cm) Overall with mounting (each): 16 1/8 x 64 15/16 in. (41 x 165 cm),"Gift of Judith G. and F Randall Smith, in honor of Wen C. Fong, 2010",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/75814,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2000.514.50,false,true,54308,Asian Art,Panel,清 趙之謙 隸書引首 卷|The Lingshouhua Studio,China,Qing dynasty (1644–1911),,,,Artist,,Zhao Zhiqian,"Chinese, 1829–1884",,Zhao Zhiqian,Chinese,1829,1884,1863,1863,1863,Horizontal panel; ink on paper,13 x 40 3/4 in. (33 x 103.5 cm),"Gift of Shou-cheng Zhang and Xiu-ping Loh Zhang, 2000",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/54308,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2000.345.1, .2",false,true,55495,Asian Art,Hanging scrolls,清 趙之謙 篆書五言 對聯|Couplet,China,Qing dynasty (1644–1911),,,,Artist,,Zhao Zhiqian,"Chinese, 1829–1884",,Zhao Zhiqian,Chinese,1829,1884,dated 1867,1867,1867,Pair of hanging scrolls; ink on paper,Image (each): 71 5/8 x 18 15/16 in. (181.9 x 48.1 cm) Overall with mounting (each): 92 1/2 x 23 1/2 in. (235 x 59.7 cm),"Gift of Judith G. and F Randall Smith, in honor of Maxwell K. Hearn, 2000",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/55495,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.165,false,true,49473,Asian Art,Hanging scroll,清 翁同龢 軸|Tiger Calligraphy,China,Qing dynasty (1644–1911),,,,Artist,,Weng Tonghe,"Chinese, 1830–1904",,Weng Tonghe,Chinese,1830,1904,dated 1890,1890,1890,Hanging scroll; ink on silver-flecked red paper,Image: 51 x 26 1/2 in. (129.5 x 67.3 cm) Overall: 83 1/2 x 31 3/4 in. (212.1 x 80.6 cm) Overall with knobs: 83 1/2 x 35 1/8 in. (212.1 x 89.2 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/49473,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.100.69,false,true,35987,Asian Art,Folding fan mounted as an album leaf,,China,Qing dynasty (1644–1911),,,,Artist,,Yu Dongru,"Chinese, active Ming dynasty",,Yu Dongru,Chinese,1350,1650,late 17th century,1667,1699,Folding fan mounted as an album leaf; ink on paper,6 5/8 x 19 5/8 in. (16.8 x 49.8 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/35987,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1988.324.6,false,true,44570,Asian Art,Album leaf,,China,Qing dynasty (1644–1911),,,,Artist,,Pan Zenggui,"Chinese, active late 19th century",,Pan Zenggui,Chinese,1800,1899,ca. late 19th century,1867,1899,Album leaf of calligraphy in running script; ink and color on paper,9 3/4 x 13 1/2 in. (24.8 x 34.3 cm),"Gift of Robert Hatfield Ellsworth, in memory of La Ferne Hatfield Ellsworth, 1988",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/44570,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.17,false,true,40105,Asian Art,Handscroll,元 耶律楚材 行書贈別劉滿詩 卷|Poem of Farewell to Liu Man,China,Yuan dynasty (1271–1368),,,,Artist,,Yelü Chucai,"Khitan, 1190–1244",,YELÜ CHUCAI,Khitan,1190,1244,dated 1240,1230,1250,Handscroll; ink on paper,Image: 14 1/2 x 111 3/4 in. (36.8 x 283.8 cm) Overall with mounting: 15 in. x 37 ft. 7 11/16 in. (38.1 x 1147.3 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/40105,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.30,false,true,40509,Asian Art,Handscroll,元 趙孟頫 行書右軍四事 卷|Four anecdotes from the life of Wang Xizhi,China,Yuan dynasty (1271–1368),,,,Artist,,Zhao Mengfu,"Chinese, 1254–1322",,Zhao Mengfu,Chinese,1254,1322,1310s,1310,1319,Handscroll; ink on paper,Image: 9 5/8 x 46 1/16 in. (24.4 x 117 cm) Overall with mounting: 10 7/16 in. x 27 ft. 1 1/8 in. (26.5 x 851.2 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/40509,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1994.308,false,true,42329,Asian Art,Handscroll,元 鄭元祐 楷書師孺齋記 卷|Record of the Following One's Ancestor Studio,China,Yuan dynasty (1271–1368),,,,Artist,,Zheng Yuanyou,"Chinese, 1292–1364",,ZHENG YUANYOU,Chinese,1292,1364,dated 1345,1345,1345,Handscroll; ink on paper,10 3/4 x 37 3/4 in. (27.3 x 96 cm),"Purchase, Friends of Asian Art Gifts, 1994",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/42329,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.4,true,true,39918,Asian Art,Handscroll,北宋 黃庭堅 草書廉頗藺相如傳 卷|Biographies of Lian Po and Lin Xiangru,China,Northern Song dynasty (960–1127),,,,Artist,,Huang Tingjian,"Chinese, 1045–1105",,HUANG TINGJIAN,Chinese,1045,1105,ca. 1095,1085,1105,Handscroll; ink on paper,Image: 13 1/4 in. × 60 ft. 4 1/2 in. (33.7 × 1840.2 cm) Overall with mounting: 13 1/2 in. × 71 ft. 5 5/8 in. (34.3 × 2178.4 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/39918,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1984.174,false,true,39919,Asian Art,Handscroll,北宋 米芾 草書吳江舟中詩 卷|Poem Written in a Boat on the Wu River,China,Northern Song dynasty (960–1127),,,,Artist,,Mi Fu,"Chinese, 1052–1107",,MI FU,Chinese,1052,1107,ca. 1095,1085,1105,Handscroll; ink on paper,12 1/4 in. × 18 ft. 3 1/4 in. (31.1 × 556.9 cm),"Gift of John M. Crawford Jr., in honor of Professor Wen Fong, 1984",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/39919,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.12,false,true,40103,Asian Art,Fan mounted as an album leaf,南宋 楊皇后 楷書薄薄殘妝七絕 團扇冊頁 絹本|Quatrain on spring’s radiance,China,Southern Song dynasty (1127–1279),,,,Artist,,Empress Yang Meizi,"Chinese, 1162–1232",,YANG MEIZI,Chinese,1162,1232,early 13th century,1200,1215,Round fan mounted as an album leaf; ink on silk,Image: 9 1/8 x 9 5/8 in. (23.2 x 24.4 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/40103,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.13,false,true,40104,Asian Art,Fan mounted as an album leaf,南宋 楊皇后 楷書瀹雪凝酥七絕 團扇|Quatrain on yellow roses,China,Southern Song dynasty (1127–1279),,,,Artist,,Empress Yang Meizi,"Chinese, 1162–1232",", r. 1202–24;",YANG MEIZI,Chinese,1162,1232,early 13th century,1200,1232,Round fan mounted as an album leaf; ink on silk,9 1/4 x 9 5/8 in. (23.5 x 24.5 cm); with mat: 14 1/2 x 15 1/2 in. (36.8 x 39.4 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/40104,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -30.76.198,false,true,41777,Asian Art,Ink tablet,,China,"late Qing dynasty (1644–1911, early Republic period",,,,Artist,Workshop of,Fang Yulu,active ca. 1570–1619,,Fang Yulu,Chinese,1570,1619,late 19th–early 20th century,1871,1933,Black ink,H. 4 3/8 in. (11.1 cm); W. 4 1/4 in. (10.8 cm),"Rogers Fund, 1929",,,,,,,,,,,,Ink,,http://www.metmuseum.org/art/collection/search/41777,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -30.76.280,false,true,41860,Asian Art,Ink tablet,,China,"late Qing dynasty (1644–1911, early Republic period",,,,Artist,,Fang Yulu,active ca. 1570–1619,,Fang Yulu,Chinese,1570,1619,late 19th–early 20th century,1871,1933,Ink,Diam. 4 7/8 in. (12.4 cm),"Rogers Fund, 1929",,,,,,,,,,,,Ink,,http://www.metmuseum.org/art/collection/search/41860,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -30.76.196,false,true,40018,Asian Art,Ink tablet,,China,"late Qing dynasty (1644–1911, early Republic period",,,,Artist,Workshop of,Cheng Junfang,"Chinese, 1541–ca. 1620",,Cheng Junfang,Chinese,1541,1620,late 19th–early 20th century,1871,1933,Pine soot and binding medium,Diam. 4 1/2 in. (11.4 cm),"Rogers Fund, 1929",,,,,,,,,,,,Ink,,http://www.metmuseum.org/art/collection/search/40018,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -30.76.199,false,true,41778,Asian Art,Ink tablet,,China,"late Qing dynasty (1644–1911, early Republic period",,,,Artist,,Cheng Junfang,"Chinese, 1541–ca. 1620",,Cheng Junfang,Chinese,1541,1620,late 19th–early 20th century,1875,1933,Ink,Diam. 3 1/8 in. (7.9 cm.),"Rogers Fund, 1929",,,,,,,,,,,,Ink,,http://www.metmuseum.org/art/collection/search/41778,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -30.76.203,false,true,41782,Asian Art,Ink tablet,,China,"late Qing dynasty (1644–1911, early Republic period",,,,Artist,,Cheng Junfang,"Chinese, 1541–ca. 1620",,Cheng Junfang,Chinese,1541,1620,late 19th–early 20th century,1871,1933,Ink,Diam. 5 5/8 in. (14.3 cm),"Rogers Fund, 1929",,,,,,,,,,,,Ink,,http://www.metmuseum.org/art/collection/search/41782,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -CP7,false,true,63196,Asian Art,Print,,China,,,,,Artist,Original painted by,Li Gonglin,"Chinese, ca. 1041–1106",,Li Gonglin,Chinese,1041,1106,1677,1677,1677,Polychrome woodblock print; ink and color on paper,9 5/8 x 11 13/16 in. (24.4 x 30 cm),"Rogers Fund, 1924",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/63196,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -CP6,false,true,63185,Asian Art,Print,芥子園畫傳|Mountainside View: Page from The Mustard Seed Garden Manual of Painting,China,,,,,Artist,Original painted by,Juran,"Chinese, active 10th century",,Juran,Chinese,0010,0010,probably 1878 edition,1878,1878,Polychrome woodblock print; ink and color on paper,9 5/8 x 11 13/16 in. (24.4 x 30 cm),"Rogers Fund, 1924",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/63185,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.125,false,true,49047,Asian Art,Hanging scroll,,China,Ming dynasty (1368–1644),,,,Artist,,Zhao Wenchu,"Chinese, 1595–1654",,Zhao Wenchu,Chinese,1595,1654,dated 1627,1627,1627,Hanging scroll; ink and color on paper,Image: 49 13/16 x 20 3/16 in. (126.5 x 51.3 cm) Overall: 98 1/2 x 26 1/2 in. (250.2 x 67.3 cm) Overall with knobs: 98 1/2 x 30 in. (250.2 x 76.2 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/49047,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1971.163,false,true,50549,Asian Art,Figure,,China,Ming dynasty (1368–1644),,,,Artist,,Qiao Bin,"Chinese, active 1481–1507",,Qiao Bin,Chinese,1481,1507,dated 1482,1482,1482,Glazed stoneware,H. 23 3/4 in. (60.3 cm); W. 15 in. (38.1 cm); D. 8 3/8 in. (21.3 cm),"Bequest of Harrison Cady, 1970",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/50549,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -25.227.1,false,true,53924,Asian Art,Group,,China,Ming dynasty (1368–1644),,,,Artist,,Qiao Bin,"Chinese, active 1481–1507",,Qiao Bin,Chinese,1481,1507,dated 1503,1503,1503,Glazed stoneware,H. 14 in. (35.6 cm); W. 17 1/8 in. (43.5 cm); D. 9 in. (22.9 cm),"Fletcher Fund, 1925",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/53924,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -25.227.2,false,true,53925,Asian Art,Figure,,China,Ming dynasty (1368–1644),,,,Artist,,Qiao Bin,"Chinese, active 1481–1507",,Qiao Bin,Chinese,1481,1507,dated 1503,1503,1503,Glazed pottery,H. 11 in. (27.9 cm); W. 4 1/2 in. (11.4 cm); D. 3 5/8 in. (9.2 cm),"Fletcher Fund, 1925",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/53925,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -25.227.3,false,true,53926,Asian Art,Figure,,China,Ming dynasty (1368–1644),,,,Artist,,Qiao Bin,"Chinese, active 1481–1507",,Qiao Bin,Chinese,1481,1507,dated 1503,1503,1503,Glazed pottery,H. 11 in. (27.9 cm); W. 4 5/8 in. (11.7 cm); D. 4 1/2 in. (11.4 cm),"Fletcher Fund, 1925",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/53926,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -25.227.4,false,true,53927,Asian Art,Figure,,China,Ming dynasty (1368–1644),,,,Artist,,Qiao Bin,"Chinese, active 1481–1507",,Qiao Bin,Chinese,1481,1507,dated 1503,1503,1503,Glazed pottery,H. 11 1/2 in. (29.2 cm); W. 4 5/8 in. (11.7 cm); D. 4 1/2 in. (11.4 cm),"Fletcher Fund, 1925",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/53927,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -25.227.5,false,true,53928,Asian Art,Figure,,China,Ming dynasty (1368–1644),,,,Artist,,Qiao Bin,"Chinese, active 1481–1507",,Qiao Bin,Chinese,1481,1507,dated 1503,1503,1503,Glazed pottery,H. 11 1/2 in. (29.2 cm); W. 4 5/8 in. (11.7 cm); D. 4 3/4 in. (12.1 cm),"Fletcher Fund, 1925",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/53928,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -25.227.6,false,true,53929,Asian Art,Figure,,China,Ming dynasty (1368–1644),,,,Artist,,Qiao Bin,"Chinese, active 1481–1507",,Qiao Bin,Chinese,1481,1507,dated 1503,1503,1503,Glazed pottery,H. 7 in. (17.8 cm); W. 4 1/4 in. (10.8 cm); D. 6 1/2 in. (16.5 cm),"Fletcher Fund, 1925",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/53929,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"1982.362a, b",false,true,42323,Asian Art,Teapot,,China,Qing dynasty (1644–1911),,,,Artist,,Shi Dabin,"Chinese, active 1620–40",,Shi Dabin,Chinese,1620,1640,early 17th century,1600,1633,Stoneware (Yixing ware),H. 3 3/4 in. (9.5 cm),"Purchase, Ann Eden Woodward Foundation Gift, 1982",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/42323,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.267.165,false,true,36236,Asian Art,Folding fan mounted as an album leaf,近代 張善子 黃山奇松圖 扇頁|Strange Pine in the Yellow Mountain,China,,,,,Artist,,Zhang Shanzi,(1882–1940),,ZHANG SHANZI,Chinese,1882,1940,dated 1935,1935,1935,Folding fan mounted as an album leaf; ink and color on alum paper,7 5/16 x 20 3/16 in. (18.6 x 51.3 cm),"Gift of Robert Hatfield Ellsworth, in memory of La Ferne Hatfield Ellsworth, 1986",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36236,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.163,false,true,36098,Asian Art,Hanging scroll,,China,,,,,Artist,After,Chen Hongshou,"Chinese, 1599–1652",,Chen Hongshou,Chinese,1599,1652,20th century,1912,1971,Hanging scroll; ink and color on silk,Image: 84 3/8 x 30 3/4 in. (214.3 x 78.1 cm) Overall with mounting: 127 5/8 x 32 in. (324.2 x 81.3 cm) Overall with rollers: 127 5/8 x 35 in. (324.2 x 88.9 cm),"Gift of Zhang Daqian, 1972",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36098,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2004.557.2a–d,false,true,72711,Asian Art,Album leaves,清 湯貽汾 墨梅 四冊頁|Blossoming Plum,China,,,,,Artist,,Tang Yifen,"Chinese, 1778–1853",,Tang Yifen,Chinese,1778,1853,Dated 1840,1840,1840,Four album leaves; ink on gold-flecked paper,Image: 9 1/4 x 8 1/2 in. (23.5 x 21.6 cm) Overall with mounting: 17 1/2 x 14 1/2 in. (44.5 x 36.8 cm),"Gift of Mark Shrum Pratt, 2004",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/72711,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2004.557.3a–d,false,true,72712,Asian Art,Hanging scrolls,清 胡遠 山水 四軸|Landscapes of the Four Seasons,China,,,,,Artist,,Hu Yuan,"Chinese, 1823–1886",,Hu Yuan,Chinese,1823,1886,Dated 1875,1875,1875,Set of four hanging scrolls; ink and color on paper,Image: 104 1/2 x 22 1/8 in. (265.4 x 56.2 cm) Overall with mounting (a): 143 x 29 in. (363.2 x 73.7 cm) Overall with mounting (b): 143 3/8 x 29 1/16 in. (364.2 x 73.8 cm) Overall with mounting (c): 143 1/8 x 29 1/16 in. (363.5 x 73.8 cm) Overall with mounting (d): 143 1/2 x 29 in. (364.5 x 73.7 cm) Overall with rollers (each): 33 1/4 in. (84.5 cm),"Gift of Mark Shrum Pratt, 2004",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/72712,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.267.103,false,true,36201,Asian Art,Folding fan mounted as an album leaf,近代 王振聲 友梅圖 扇面|Still-life with Plum,China,,,,,Artist,,Wang Zhensheng,"Chinese, 1842–1922",,Wang Zhensheng,Chinese,1842,1922,early 20th century,1900,1922,Folding fan mounted as an album leaf; ink and color on alum paper,6 1/8 x 19 in. (15.6 x 48.3 cm),"Gift of Robert Hatfield Ellsworth, in memory of La Ferne Hatfield Ellsworth, 1986",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36201,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1988.324.2,false,true,44567,Asian Art,Hanging scroll,近代 吳昌碩 仙芝天竹圖 軸|Spring Offerings,China,,,,,Artist,,Wu Changshuo,"Chinese, 1844–1927",,Wu Changshuo,Chinese,1844,1927,dated 1919,1919,1919,Hanging scroll; ink and color on paper,Image: 58 x 31 1/2 in. (147.3 x 80 cm) Overall with mounting: 100 3/8 x 39 3/8 in. (255 x 100 cm) Overall with knobs: 100 3/8 x 43 in. (255 x 109.2 cm),"Gift of Robert Hatfield Ellsworth, in memory of La Ferne Hatfield Ellsworth, 1988",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/44567,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.267.124,false,true,49617,Asian Art,Painting,近代 吳昌碩 烹茶圖 橫軸|Brewing Tea,China,,,,,Artist,,Wu Changshuo,"Chinese, 1844–1927",,Wu Changshuo,Chinese,1844,1927,dated 1918,1918,1918,Horizontal painting; ink on paper,15 9/16 x 54 in. (39.5 x 137.2 cm),"Gift of Robert Hatfield Ellsworth, in memory of La Ferne Hatfield Ellsworth, 1986",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49617,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.267.125,false,true,36208,Asian Art,Folding fan mounted as an album leaf,近代 吳昌碩 蘭花 扇面|Orchid,China,,,,,Artist,,Wu Changshuo,"Chinese, 1844–1927",,Wu Changshuo,Chinese,1844,1927,early 20th century,1900,1927,Folding fan mounted as an album leaf; ink and color on alum paper,7 3/8 x 20 3/8 in. (18.7 x 51.8 cm),"Gift of Robert Hatfield Ellsworth, in memory of La Ferne Hatfield Ellsworth, 1986",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36208,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.267.117,false,true,36205,Asian Art,Folding fan mounted as an album leaf,近代 林紓 合掌峰 扇面|Hezhang Peak,China,,,,,Artist,,Lin Shu,"Chinese, 1852–1924",,Lin Shu,Chinese,1852,1924,dated 1921,1921,1921,Folding fan mounted as an album leaf; ink and color on alum paper,7 11/16 x 21 1/4 in. (19.5 x 54 cm),"Gift of Robert Hatfield Ellsworth, in memory of La Ferne Hatfield Ellsworth, 1986",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36205,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.267.118,false,true,36206,Asian Art,Handscroll,近代 林紓 詩廬圖 卷|The Poetry Cottage,China,,,,,Artist,,Lin Shu,"Chinese, 1852–1924",,Lin Shu,Chinese,1852,1924,dated 1914,1914,1914,Handscroll; ink on paper,Image: 8 7/8 x 39 in. (22.5 x 99.1 cm) Overall with mounting: 11 x 320 3/8 in. (27.9 x 813.8 cm),"Gift of Robert Hatfield Ellsworth, in memory of La Ferne Hatfield Ellsworth, 1986",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36206,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.267.128,false,true,36211,Asian Art,Folding fan mounted as an album leaf,近代 吳觀岱 山水人物 扇面|Landscape and Figure,China,,,,,Artist,,Wu Guandai,"Chinese, 1862–1929",,Wu Guandai,Chinese,1862,1929,early 20th century,1900,1929,Folding fan mounted as an album leaf; ink and color on alum paper,7 5/16 x 20 3/4 in. (18.6 x 52.7 cm),"Gift of Robert Hatfield Ellsworth, in memory of La Ferne Hatfield Ellsworth, 1986",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36211,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.267.127,false,true,36210,Asian Art,Folding fan mounted as an album leaf,近代 楊逸 青松 扇面|Pine,China,,,,,Artist,,Yang Yi,"Chinese, 1864–1929",,Yang Yi,Chinese,1864,1929,dated 1923,1923,1923,Folding fan mounted as an album leaf; ink and color on alum paper,8 x 21 1/2 in. (20.3 x 54.6 cm),"Gift of Robert Hatfield Ellsworth, in memory of La Ferne Hatfield Ellsworth, 1986",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36210,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.267.136,false,true,36216,Asian Art,Folding fan mounted as an album leaf,近代 顧麟士 臨溪亭子圖 扇面|Pavilion Beside a Rock Garden and Stream,China,,,,,Artist,,Gu Linshi,"Chinese, 1865–1930",,Gu Linshi,Chinese,1865,1930,dated 1921,1921,1921,Folding fan mounted as an album leaf; ink and color on alum paper,7 x 19 5/8 in. (17.8 x 49.8 cm),"Gift of Robert Hatfield Ellsworth, in memory of La Ferne Hatfield Ellsworth, 1986",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36216,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.267.154,false,true,49629,Asian Art,Hanging scroll,近代 王震 山羊 軸|Two Goats,China,,,,,Artist,,Wang Zhen,"Chinese, 1867–1938",,WANG ZHEN,Chinese,1867,1938,dated 1914,1914,1914,Hanging scroll; ink and color on paper,57 1/2 x 15 5/8 in. (146.1 x 39.7 cm),"Gift of Robert Hatfield Ellsworth, in memory of La Ferne Hatfield Ellsworth, 1986",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49629,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.267.155,false,true,49630,Asian Art,Hanging scroll,近代 王震 漁翁圖 軸|Returning Fisherman,China,,,,,Artist,,Wang Zhen,"Chinese, 1867–1938",,WANG ZHEN,Chinese,1867,1938,dated 1917,1917,1917,Hanging scroll; ink and color on paper,Image: 70 1/8 x 37 1/8 in. (178.1 x 94.3 cm) Overall with mounting: 109 x 43 in. (276.9 x 109.2 cm) Overall with knobs: 109 x 46 3/4 in. (276.9 x 118.7 cm),"Gift of Robert Hatfield Ellsworth, in memory of La Ferne Hatfield Ellsworth, 1986",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49630,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.267.156,false,true,41504,Asian Art,Hanging scroll,近代 王震 佛祖圖 軸|Buddhist Sage,China,,,,,Artist,,Wang Zhen,"Chinese, 1867–1938",,WANG ZHEN,Chinese,1867,1938,dated 1928,1928,1928,Hanging scroll; ink and color on paper,78 1/2 x 36 7/8 in. (199.4 x 93.7 cm),"Gift of Robert Hatfield Ellsworth, in memory of La Ferne Hatfield Ellsworth, 1986",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/41504,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.267.104,false,true,49603,Asian Art,Album leaf,近代 陳衡恪 臨水書閣 冊頁|Studio by the Water,China,,,,,Artist,,Chen Hengke,"Chinese, 1876–1923",,Chen Hengke,Chinese,1876,1923,dated 1921,1921,1921,Album leaf; ink and color on paper,13 1/4 x 18 3/4 in. (33.7 x 47.6 cm),"Gift of Robert Hatfield Ellsworth, in memory of La Ferne Hatfield Ellsworth, 1986",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49603,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.267.105,false,true,49604,Asian Art,Folding fan mounted as an album leaf,近代 陳衡恪 水仙蘭花 扇面|Narcissus and Orchid,China,,,,,Artist,,Chen Hengke,"Chinese, 1876–1923",,Chen Hengke,Chinese,1876,1923,dated 1920,1920,1920,Folding fan mounted as an album leaf; ink and color on alum paper,7 7/8 x 21 2/3 in. (20.0 x 55.0 cm),"Gift of Robert Hatfield Ellsworth, in memory of La Ferne Hatfield Ellsworth, 1986",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49604,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.267.106,false,true,49605,Asian Art,Folding fan mounted as an album leaf,近代 陳衡恪 秋山蕭寺 扇面|Remote Temple on the Autumn Mountain,China,,,,,Artist,,Chen Hengke,"Chinese, 1876–1923",,Chen Hengke,Chinese,1876,1923,dated 1920,1920,1920,Folding fan mounted as an album leaf; ink and color on alum paper,7 15/16 x 22 in. (20.2 x 55.9 cm),"Gift of Robert Hatfield Ellsworth, in memory of La Ferne Hatfield Ellsworth, 1986",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49605,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.267.108,false,true,49607,Asian Art,Album leaf,近代 陳衡恪 玉簪花 冊頁|Plantain Lily,China,,,,,Artist,,Chen Hengke,"Chinese, 1876–1923",,Chen Hengke,Chinese,1876,1923,early 20th century,1900,1923,Album leaf; ink and color on paper,11 x 17 1/4 in. (27.9 x 43.8 cm),"Gift of Robert Hatfield Ellsworth, in memory of La Ferne Hatfield Ellsworth, 1986",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49607,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.267.109,false,true,36202,Asian Art,Album leaf,近代 陳衡恪 梅花 冊頁|Plum,China,,,,,Artist,,Chen Hengke,"Chinese, 1876–1923",,Chen Hengke,Chinese,1876,1923,early 20th century,1900,1923,Album leaf; ink and color on paper,11 x 17 1/4 in. (27.9 x 43.8 cm),"Gift of Robert Hatfield Ellsworth, in memory of La Ferne Hatfield Ellsworth, 1986",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36202,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.267.110,false,true,49608,Asian Art,Album leaf,近代 陳衡恪 梨花 軸|Pear-blossoms,China,,,,,Artist,,Chen Hengke,"Chinese, 1876–1923",,Chen Hengke,Chinese,1876,1923,early 20th century,1900,1923,Album leaf; ink and color on paper,11 x 17 1/4 in. (27.9 x 43.8 cm),"Gift of Robert Hatfield Ellsworth, in memory of La Ferne Hatfield Ellsworth, 1986",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49608,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"1986.267.107a, b",false,true,49606,Asian Art,Folding fan mounted as album leaves,近代 陳衡恪 山水 扇面兩幀|Landscapes,China,,,,,Artist,,Chen Hengke,"Chinese, 1876–1923",,Chen Hengke,Chinese,1876,1923,early 20th century,1900,1923,Two sides of a folding fan mounted as two album leaves; ink and color on alum paper,5 1/8 x 17 1/8 in. (13.0 x 43.5 cm),"Gift of Robert Hatfield Ellsworth, in memory of La Ferne Hatfield Ellsworth, 1986",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49606,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.267.121,false,true,49614,Asian Art,Folding fan mounted as an album leaf,近代 金城 山水 扇面|Landscape,China,,,,,Artist,,Jin Cheng,"Chinese, 1878–1926",,Jin Cheng,Chinese,1878,1926,early 20th century,1900,1926,Folding fan mounted as an album leaf; ink and color on alum paper,8 3/8 x 26 7/8 in. (21.3 x 68.3 cm),"Gift of Robert Hatfield Ellsworth, in memory of La Ferne Hatfield Ellsworth, 1986",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49614,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.267.122,false,true,49615,Asian Art,Folding fan mounted as an album leaf,近代 金城 牡丹 扇面|Peony,China,,,,,Artist,,Jin Cheng,"Chinese, 1878–1926",,Jin Cheng,Chinese,1878,1926,early 20th century,1900,1926,Folding fan mounted as an album leaf; ink and color on alum paper,8 3/8 x 26 7/8 in. (21.3 x 68.3 cm),"Gift of Robert Hatfield Ellsworth, in memory of La Ferne Hatfield Ellsworth, 1986",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49615,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.267.253,false,true,49663,Asian Art,Album leaf,近代 丁輔之 雜果圖 冊頁|Fruit,China,,,,,Artist,,Ding Fuzhi,"Chinese, 1879–1946",,Ding Fuzhi,Chinese,1879,1946,dated 1945,1945,1945,Album leaf; ink and color on paper,12 1/8 x 25 1/8 in. (30.8 x 63.8 cm),"Gift of Robert Hatfield Ellsworth, in memory of La Ferne Hatfield Ellsworth, 1986",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49663,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.267.254,false,true,49664,Asian Art,Album leaf,近代 丁輔之 冰盤進夏圖 冊頁|Dish of Iced Summer Fruit,China,,,,,Artist,,Ding Fuzhi,"Chinese, 1879–1946",,Ding Fuzhi,Chinese,1879,1946,dated 1945,1945,1945,Album leaf; ink and color on paper,12 1/8 x 25 1/8 in. (30.8 x 63.8 cm),"Gift of Robert Hatfield Ellsworth, in memory of La Ferne Hatfield Ellsworth, 1986",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49664,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.267.145,false,true,49626,Asian Art,Folding fan mounted as an album leaf,近代 俞明 桐窗仕女 扇面|A Beauty at the Window by a Wutong Tree,China,,,,,Artist,,Yu Ming,"Chinese, 1884–1935",,Yu Ming,Chinese,1884,1935,dated 1923,1923,1923,Folding fan mounted as an album leaf; ink and color on alum paper,7 1/2 x 21 7/16 in. (19.1 x 54.5 cm),"Gift of Robert Hatfield Ellsworth, in memory of La Ferne Hatfield Ellsworth, 1986",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49626,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.267.146,false,true,49627,Asian Art,Folding fan mounted as an album leaf,近代 俞明 絲路行旅圖 扇面|Travel on the Silk Road,China,,,,,Artist,,Yu Ming,"Chinese, 1884–1935",,Yu Ming,Chinese,1884,1935,early 20th century,1900,1933,Folding fan mounted as an album leaf; ink and color on alum paper,8 x 21 3/4 in. (20.3 x 55.2 cm),"Gift of Robert Hatfield Ellsworth, in memory of La Ferne Hatfield Ellsworth, 1986",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49627,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.267.147,false,true,36222,Asian Art,Album leaf,近代 俞明 堆雪獅子圖 冊頁|Making a Snow-lion,China,,,,,Artist,,Yu Ming,"Chinese, 1884–1935",,Yu Ming,Chinese,1884,1935,dated 1921,1921,1921,Album leaf; ink and color on paper,14 7/8 x 9 1/4 in. (37.8 x 23.5 cm),"Gift of Robert Hatfield Ellsworth, in memory of La Ferne Hatfield Ellsworth, 1986",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36222,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.267.149,false,true,36224,Asian Art,Hanging scroll,近代 俞明 米芾拜石圖 軸|Mi Fu at Stone Worship,China,,,,,Artist,,Yu Ming,"Chinese, 1884–1935",,Yu Ming,Chinese,1884,1935,early 20th century,1900,1933,Hanging scroll; ink and color on paper,Overall with mounting: 69 3/4 × 18 1/8 in. (177.2 × 46 cm),"Gift of Robert Hatfield Ellsworth, in memory of La Ferne Hatfield Ellsworth, 1986",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36224,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.267.141,false,true,49623,Asian Art,Hanging scroll,近代 高奇峰 啄木鳥 軸|Woodpecker,China,,,,,Artist,,Gao Qifeng,"Chinese, 1889–1933",,Gao Qifeng,Chinese,1889,1933,dated 1927,1927,1927,Hanging scroll; ink and color on alum paper,Image: 32 5/8 × 13 3/8 in. (82.9 × 34 cm) Overall with mounting: 75 1/4 × 20 5/16 in. (191.1 × 51.6 cm) Overall with knobs: 75 1/4 × 24 1/8 in. (191.1 × 61.3 cm),"Gift of Robert Hatfield Ellsworth, in memory of La Ferne Hatfield Ellsworth, 1986",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49623,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.220.124,false,true,40311,Asian Art,Handscroll,元/明 倣錢選 鮮于樞 歸去來辭 卷|Ode on Returning Home,China,,,,,Artist,After,Qian Xuan,"Chinese, ca. 1235–before 1307",,QIAN XUAN,Chinese,1235,1307,14th–15th century,1300,1499,"Handscroll; ink, color, and gold on paper",Image: 42 x 10 1/4 in. (106.7 x 26 cm) Overall with mounting: 12 1/4 in. x 13 ft. 6 in. (31.1 x 411.5 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/40311,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1973.121.4,false,true,45652,Asian Art,Handscroll,元 方從義 雲山圖 卷|Cloudy Mountains,China,Yuan (1271–1368),,,,Artist,,Fang Congyi,"Chinese, ca. 1301–after 1378",,Fang Congyi,Chinese,1301,1399,ca. 1360–70,1360,1370,Handscroll; ink and color on paper,Image: 10 3/8 x 57 in. (26.4 x 144.8 cm) Overall with mounting: 10 5/8 x 336 1/4 in. (27 x 854.1 cm),"Ex coll.: C. C. Wang Family, Purchase, Gift of J. Pierpont Morgan, by exchange, 1973",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45652,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1977.78,true,true,39901,Asian Art,Handscroll,唐 韓幹 照夜白圖 卷|Night-Shining White,China,Tang dynasty (618–907),,,,Artist,,Han Gan,"Chinese, active ca. 742–756",,HAN GAN,Chinese,0742,0756,ca. 750,740,760,Handscroll; ink on paper,Image: 12 1/8 x 13 3/8 in. (30.8 x 34 cm) Overall with mounting: 14 in. x 37 ft. 5 1/8 in. (35.4 cm x 11.4 m),"Purchase, The Dillon Fund Gift, 1977",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/39901,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1982.1.1,false,true,39959,Asian Art,Handscroll,金 傳楊邦基 聘金圖 巻|A Diplomatic Mission to the Jin,China,Jin dynasty (1115–1234),,,,Artist,Attributed to,Yang Bangji,"Chinese, ca. 1110–1181",,Yang Bangji,Chinese,1100,1191,ca. late 1150s,1156,1159,Handscroll; ink and color on silk,Image: 10 1/2 in. × 56 in. (26.7 × 142.2 cm) Overall with mounting: 11 5/8 in. × 26 ft. 11 3/4 in. (29.5 × 822.3 cm),"Edward Elliott Family Collection, Purchase, The Dillon Fund Gift, 1982",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/39959,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -49.6.12,false,true,51762,Asian Art,Album leaf,,China,Song dynasty (960–1279),,,,Artist,Attributed to,Xu Daoning,active ca. 1030–67,,Xu Daoning,Chinese,1020,1077,ca. 1000,1030,1067,Album leaf; painted silk,14 x 12 1/4 in. (35.6 x 31.1 cm),"Bequest of Ellis Gray Seymour, 1948",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51762,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"1973.121.12a, b",false,true,40062,Asian Art,Fan mounted as an album leaf,南宋 傳閻次于 松壑隱棲圖 團扇|Hermitage by a Pine-covered Bluff,China,Song dynasty (960–1279),,,,Artist,Attributed to,Yan Ciyu,"Chinese, act. ca. 1164–81",,YAN CIYU,Chinese,1164,1181,second half of the 12th century,1164,1181,Fan mounted as an album leaf; ink and color on silk,"(a): 8 7/16 x 9 1/16 in. (21.4 x 23 cm), b): 8 1/2 x 9 1/16 in. (21.6 x 23 cm)","Ex coll.: C. C. Wang Family, Purchase, Gift of Mr. and Mrs. Jeremiah Milbank and Gift of Mary Phelps Smith, in memory of Howard Caswell Smith, by exchange, 1973",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/40062,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"1989.363.10a, b",false,true,40057,Asian Art,Handscroll,南宋 李結 西塞漁社圖 卷 |Fisherman's Lodge At Mount Xisai,China,Song dynasty (960–1279),,,,Artist,,Li Jie,"Chinese, 1124– before 1197",,LI JIE,Chinese,1124,1197,ca. 1170,1160,1180,Handscroll; ink and color on silk,Image (a): 16 in. × 53 3/4 in. (40.6 × 136.5 cm) Overall with mounting (a): 16 3/8 in. × 26 ft. 1/8 in. (41.6 × 792.8 cm) Overall with mounting (b): 16 3/8 in. × 27 ft. 9 5/16 in. (41.6 × 846.6 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/40057,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -47.18.32,false,true,40094,Asian Art,Fan mounted as an album leaf,南宋 傳陳居中 胡騎春獵圖 團扇|Nomads hunting with falcons,China,Song dynasty (960–1279),,,,Artist,Attributed to,Chen Juzhong,"Chinese, active ca. 1200–30",,Chen Juzhong,Chinese,1200,1230,early 13th century,1200,1233,Fan mounted as an album leaf; ink and color on silk,9 1/2 x 10 3/4 in. (24.1 x 27.3 cm),"From the Collection of A. W. Bahr, Purchase, Fletcher Fund, 1947",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/40094,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -47.18.30,false,true,36052,Asian Art,Fan mounted as an album leaf,南宋 佚名 倣馬遠 洞天論道圖 團扇|Conversation in a Cave,China,Song dynasty (960–1279),,,,Artist,After,Ma Yuan,"Chinese, active ca. 1190–1225",,MA YUAN,Chinese,1190,1225,13th century,1200,1225,Fan mounted as an album leaf; ink and color on silk,Image: 9 3/4 × 9 15/16 in. (24.8 × 25.2 cm) Mat: 15 1/2 × 15 1/2 in. (39.4 × 39.4 cm),"From the Collection of A. W. Bahr, Purchase, Fletcher Fund, 1947",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36052,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -49.6.11,false,true,40053,Asian Art,Album leaf,,China,Song dynasty (960–1279),,,,Artist,In the style of,Xia Gui,"Chinese, active ca. 1195–1230",,Xia Gui,Chinese,1195,1230,ca. 1200,1190,1210,Album leaf; ink and color silk,9 1/8 x 9 3/8 in. (23.2 x 23.8 cm),"Bequest of Ellis Gray Seymour, 1948",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/40053,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -47.18.63,false,true,36055,Asian Art,Album leaf,南宋 馬麟 松下高士圖 冊頁 絹本|Landscape with great pine,China,Song dynasty (960–1279),,,,Artist,,Ma Lin,"Chinese, ca. 1180– after 1256",,MA LIN,Chinese,1180,1256,second quarter of the 13th century,1226,1250,Album leaf; ink and color on silk,9 15/16 x 10 1/4 in. (25.2 x 26 cm),"From the Collection of A. W. Bahr, Purchase, Fletcher Fund, 1947",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36055,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -30.76.290,false,true,44506,Asian Art,Hanging scroll,南宋 金處士 十王圖 軸|Ten Kings of Hell,China,Song dynasty (960–1279),,,,Artist,,Jin Chushi,"Chinese, active late 12th century",,JIN CHUSHI,Chinese,1167,1199,before 1195,1167,1194,One of five of a set of ten hanging scrolls; ink and color on silk,Image: 51 x 19 1/2 in. (129.5 x 49.5 cm) Overall with mounting: 80 × 25 3/8 in. (203.2 × 64.5 cm) Overall with knobs: 80 x 27 1/2 in. (203.2 x 69.9 cm),"Rogers Fund, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/44506,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -30.76.291,false,true,44507,Asian Art,Hanging scroll,南宋 金處士 十王圖 軸|Ten Kings of Hell,China,Song dynasty (960–1279),,,,Artist,,Jin Chushi,"Chinese, active late 12th century",,JIN CHUSHI,Chinese,1167,1199,before 1195,1167,1194,One of five of a set of ten hanging scrolls; ink and color on silk,Image: 51 x 19 1/2 in. (129.5 x 49.5 cm) Overall with knobs: 80 x 27 1/2 in. (203.2 x 69.9 cm),"Rogers Fund, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/44507,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -30.76.292,false,true,44508,Asian Art,Hanging scroll,南宋 金處士 十王圖 軸（之三）|Ten Kings of Hell,China,Song dynasty (960–1279),,,,Artist,,Jin Chushi,"Chinese, active late 12th century",,JIN CHUSHI,Chinese,1167,1199,before 1195,1167,1194,One of five of a set of ten hanging scrolls; ink and color on silk,Image: 51 x 19 1/2 in. (129.5 x 49.5 cm) Overall with knobs: 80 x 27 1/2 in. (203.2 x 69.9 cm),"Rogers Fund, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/44508,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -30.76.293,false,true,44509,Asian Art,Hanging scroll,南宋 金處士 十王圖 軸|Ten Kings of Hell,China,Song dynasty (960–1279),,,,Artist,,Jin Chushi,"Chinese, active late 12th century",,JIN CHUSHI,Chinese,1167,1199,before 1195,1167,1194,One of five of a set of ten hanging scrolls; ink and color on silk,Image: 51 x 19 1/2 in. (129.5 x 49.5 cm) Overall with knobs: 80 x 27 1/2 in. (203.2 x 69.9 cm),"Rogers Fund, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/44509,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -30.76.294,false,true,44510,Asian Art,Hanging scroll,南宋 金處士 十王圖 軸|Ten Kings of Hell,China,Song dynasty (960–1279),,,,Artist,,Jin Chushi,"Chinese, active late 12th century",,JIN CHUSHI,Chinese,1167,1199,before 1195,1167,1194,One of five of a set of ten hanging scrolls; ink and color on silk,Image: 51 x 19 1/2 in. (129.5 x 49.5 cm) Overall with knobs: 80 x 27 1/2 in. (203.2 x 69.9 cm),"Rogers Fund, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/44510,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1982.2.1,false,true,40278,Asian Art,Hanging scroll,南宋 傳直翁 藥山李翱問道圖 軸|Meeting between Yaoshan and Li Ao,China,Song dynasty (960–1279),,,,Artist,Attributed to,Zhiweng,"Chinese, active first half of the 13th century",,ZHIWENG,Chinese,1200,1250,before 1256,1200,1255,Horizontal painting mounted as a hanging scroll; ink on paper,Image: 12 1/2 x 33 1/4 in. (31.8 x 84.5 cm) Overall with mounting: 49 1/8 x 34 in. (124.8 x 86.4 cm) Overall with knobs: 49 1/8 x 36 1/4 in. (124.8 x 92.1 cm),"Edward Elliott Family Collection, Purchase, The Dillon Fund Gift, 1982",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/40278,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1990.318,false,true,41479,Asian Art,Handscroll,明 董其昌 荊谿招隱圖 卷|Invitation to Reclusion at Jingxi,China,Ming Dinasty (1368–1644),,,,Artist,,Dong Qichang,"Chinese, 1555–1636",,Dong Qichang,Chinese,1555,1636,dated 1611,1611,1611,Handscroll; ink on paper,Image: 10 1/4 × 36 7/16 in. (26 × 92.6 cm),"Gift of Mr. and Mrs. Wan-go H. C. Weng, 1990",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/41479,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1982.2.3,false,true,45661,Asian Art,Handscroll,明 王紱 江山漁樂圖 卷|Joys of the Fisherman,China,Ming dynasty (1368–1644),,,,Artist,,Wang Fu,"Chinese, 1362–1416",,Wang Fu,Chinese,1362,1416,ca. 1410,1400,1420,Handscroll; ink on paper,Image: 10 5/8 x 22 ft. 7 1/8 in. (27 x 688.7 cm) Overall with mounting: 10 15/16 in. x 38 ft. 3 11/16 in. (27.8 x 1167.6 cm),"Ex coll.: C. C. Wang Family, Edward Elliott Family Collection, Purchase, The Dillon Fund Gift, 1982",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45661,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.141.3,false,true,41478,Asian Art,Handscroll,明 傳謝環 杏園雅集圖 卷|Elegant Gathering in the Apricot Garden,China,Ming dynasty (1368–1644),,,,Artist,After,Xie Huan,"Chinese, 1377–1452",,Xie Huan,Chinese,1377,1452,ca. 1437,1427,1447,Handscroll; ink and color on silk,Image: 14 5/8 x 95 3/4 in. (37.1 x 243.2 cm) Overall with mounting: 14 3/4 in. x 41 ft. 11 1/4 in. (37.5 x 1278.3 cm),"Purchase, The Dillon Fund Gift, 1989",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/41478,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1992.132,false,true,44699,Asian Art,Hanging scroll,明 戴進 雪歸圖 軸|Returning home through the snow,China,Ming dynasty (1368–1644),,,,Artist,,Dai Jin,"Chinese, 1388–1462",,Dai Jin,Chinese,1388,1462,ca. 1455,1445,1465,Hanging scroll; ink and color on silk,Image: 66 x 32 1/2 in. (167.6 x 82.6 cm) Overall with mounting: 118 3/4 x 38 1/2 in. (301.6 x 97.8 cm) Overall with knobs: 118 3/4 x 42 3/4 in. (301.6 x 108.6 cm),"Purchase, John M. Crawford Jr. Bequest, 1992",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/44699,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.235.1,false,true,44590,Asian Art,Hanging scroll,明 夏昶 清風高節圖 軸|Bamboo in Wind,China,Ming dynasty (1368–1644),,,,Artist,,Xia Chang,"Chinese, 1388–1470",,Xia Chang,Chinese,1388,1470,ca. 1460,1450,1470,Hanging scroll; ink on paper,Image: 80 1/16 x 23 1/2 in. (203.4 x 59.7 cm) Overall with mounting: 118 x 29 3/8 in. (299.7 x 74.6 cm) Overall with knobs: 118 x 32 13/16 in. (299.7 x 83.3 cm),"Edward Elliott Family Collection, Gift of Douglas Dillon, 1989",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/44590,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.44,false,true,45675,Asian Art,Handscroll,明 姚綬 文飲圖 卷|Drinking and Composing Poetry,China,Ming dynasty (1368–1644),,,,Artist,,Yao Shou,"Chinese, 1423–1495",,Yao Shou,Chinese,1423,1495,1485,1485,1485,Handscroll; ink on paper,Image: 9 3/16 x 30 3/8 in. (23.3 x 77.2 cm) Colophon: 9 3/16 x 30 in. (23.3 x 76.2 cm) Overall with mounting: 9 11/16 x 299 3/16 in. (24.6 x 759.9 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45675,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1978.5,false,true,36123,Asian Art,Handscroll,,China,Ming dynasty (1368–1644),,,,Artist,After,Zhang Bi,"Chinese, 1425–1487",,Zhang Bi,Chinese,1425,1487,dated 1478,1478,1478,Handscroll; ink on paper,Image: 12 1/4 x 297 1/2 in. (31.1 x 755.7 cm) Overall with mounting: 12 3/4 x 341 in. (32.4 x 866.1 cm),"Purchase, Bequest of Dorothy Graham Bennett, 1978",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36123,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1979.75.1,false,true,45683,Asian Art,Handscroll,明 沈周 溪山秋色圖 卷|明 沈周 溪山秋色圖 卷明 楷書溪山秋色圖 引首|Autumn Colors among Streams and Mountains,China,Ming dynasty (1368–1644),,,,Artist,,Shen Zhou,"Chinese, 1427–1509",,Shen Zhou,Chinese,1427,1509,ca. 1490–1500,1480,1510,Handscroll; ink on paper,Image: 8 1/8 in. x 21 ft. 1/4 in. (20.6 x 640.7 cm) Overall with mounting: 10 1/4 in. x 36 ft. 4 1/4 in. (26 x 1108.1 cm),"Gift of Douglas Dillon, 1979",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45683,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.46,false,true,45682,Asian Art,Hanging scroll,明 沈周 秋林閒釣圖 軸|Silent Angler in an Autumn Wood,China,Ming dynasty (1368–1644),,,,Artist,,Shen Zhou,"Chinese, 1427–1509",,Shen Zhou,Chinese,1427,1509,dated 1475,1475,1475,Hanging scroll; ink and color on paper,Image: 60 x 24 3/4 in. (152.4 x 62.9 cm) Overall with mounting: 107 x 32 1/8 in. (271.8 x 81.6 cm) Overall with knobs: 107 x 35 1/4 in. (271.8 x 89.5 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45682,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1997.166,false,true,39483,Asian Art,Hanging scroll,明 郭詡 稱書圖 軸|Weighing Books,China,Ming dynasty (1368–1644),,,,Artist,,Guo Xu,"Chinese, 1456–1532",,Guo Xu,Chinese,1456,1532,early 16th century,1500,1532,Hanging scroll; ink and color on silk,Image: 48 3/8 x 28 in. (122.9 x 71.1 cm) Overall with mounting: 103 x 34 1/8 in. (261.6 x 86.7 cm) Overall with knobs: 103 x 38 3/8 in. (261.6 x 97.5 cm),"Friends of Asian Art, Purchase, The B. Y. Lam Foundation Gift, 1997",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/39483,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -67.6.1,false,true,36093,Asian Art,Hanging scroll,,China,Ming dynasty (1368–1644),,,,Artist,After,Tang Yin,"Chinese, 1470–1524",,Tang Yin,Chinese,1470,1524,late 15th–early 16th century,1470,1524,Hanging scroll; ink and color on silk,Image: 28 x 55 in. (71.1 x 139.7 cm),"Purchase, Bequest of Dorothy Graham Bennett, 1967",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36093,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.53,false,true,45752,Asian Art,Handscroll,,China,Ming dynasty (1368–1644),,,,Artist,After,Tang Yin,"Chinese, 1470–1524",,Tang Yin,Chinese,1470,1524,ca. 1508,1498,1518,Handscroll; ink on paper,Image: 11 11/16 x 42 3/8 in. (29.7 x 107.6 cm) Overall with mounting: 12 x 375 1/4 in. (30.5 x 953.1 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45752,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1982.7.1,false,true,45779,Asian Art,Folding fan mounted as an album leaf,,China,Ming dynasty (1368–1644),,,,Artist,,Wen Zhengming,"Chinese, 1470–1559",,Wen Zhengming,Chinese,1470,1559,dated 1543,1543,1543,Folding fan mounted as an album leaf; ink and color on gold-flecked paper,9 3/4 x 20 3/4 in. (24.8 x 52.7 cm),"Purchase, The Dillon Fund Gift, 1982",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45779,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.303,false,true,44601,Asian Art,Handscroll,明 文徵明 叢桂齋圖 卷|The Cassia Grove Studio,China,Ming dynasty (1368–1644),,,,Artist,,Wen Zhengming,"Chinese, 1470–1559",,Wen Zhengming,Chinese,1470,1559,ca. 1532,1522,1542,Handscroll; ink and color on paper,Image: 12 7/16 x 22 1/8 in. (31.6 x 56.2 cm) Overall with mounting: 14 x 322 11/16 in. (35.6 x 819.6 cm),"Gift of Douglas Dillon, 1989",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/44601,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.60,false,true,45776,Asian Art,Handscroll,明 文徵明 東林避暑圖 卷 |Summer Retreat in the Eastern Grove,China,Ming dynasty (1368–1644),,,,Artist,,Wen Zhengming,"Chinese, 1470–1559",,Wen Zhengming,Chinese,1470,1559,datable to before 1515,1470,1514,Handscroll; ink on paper,Image (painting): 12 1/2 x 42 1/2 in. (31.8 x 108 cm) Image (colophon): 12 1/2 x 38 3/8 in. (31.8 x 97.5 cm) Overall with mounting: 13 x 117 1/2 in. (33 x 298.5 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45776,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1979.458.1a–ii,false,true,39654,Asian Art,Album,"明 文徵明 拙政園圖詩 冊|Garden of the Inept Administrator",China,Ming dynasty (1368–1644),,,,Artist,,Wen Zhengming,"Chinese, 1470–1559",,Wen Zhengming,Chinese,1470,1559,dated 1551,1551,1551,Album of eight painted leaves with facing leaves inscribed with poems; ink on paper,Image: 10 3/8 × 10 3/4 in. (26.4 × 27.3 cm) Image with mounting: 15 3/8 × 16 3/4 in. (39.1 × 42.5 cm) Double leaf unfolded: 15 3/8 × 33 1/2 in. (39.1 × 85.1 cm) Mat: 18 5/16 × 34 in. (46.5 × 86.4 cm),"Gift of Douglas Dillon, 1979",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/39654,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.266.2,false,true,45802,Asian Art,Hanging scroll,明 陳淳 暑園圖 軸|Summer Garden,China,Ming dynasty (1368–1644),,,,Artist,,Chen Chun,"Chinese, 1483–1544",,Chen Chun,Chinese,1483,1544,ca. 1530,1520,1540,Hanging scroll; ink and color on paper,Image: 126 1/8 x 39 1/4 in. (320.4 x 99.7 cm) Overall with mounting: 171 x 48 3/8 in. (434.3 x 122.9 cm) Overall with knobs: 171 x 52 7/8 in. (434.3 x 134.3 cm),"Gift of Douglas Dillon, 1986",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45802,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.266.1a–u,false,true,45797,Asian Art,Album,明 仿陳淳 雜花圖 冊|Garden Flowers,China,Ming dynasty (1368–1644),,,,Artist,After,Chen Chun,"Chinese, 1483–1544",,Chen Chun,Chinese,1483,1544,dated 1540,1540,1540,Album of sixteen paintings and one leaf of calligraphy; ink and color on paper,Image (six leaves): 12 13/16 x 22 9/16 in. (32.5 x 57.3 cm) Image (ten leaves): 13 1/8 x 22 3/4 in. (33.3 x 57.8 cm),"Gift of Douglas Dillon, 1986",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45797,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.371,false,true,44606,Asian Art,Hanging scroll,,China,Ming dynasty (1368–1644),,,,Artist,,Lu Zhi,"Chinese, 1495–1576",,Lu Zhi,Chinese,1495,1576,dated 1556,1556,1556,Hanging scroll; ink and color on paper,Image: 52 3/8 x 24 3/4 in. (133 x 62.9 cm) Overall with mounting: 90 1/2 x 31 in. (229.9 x 78.7 cm) Overall with knobs: 90 1/2 x 35 in. (229.9 x 88.9 cm),"Gift of Michael B. Weisbrod, 1989",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/44606,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.266.3,false,true,45815,Asian Art,Hanging scroll,明 陸治 種菊圖 軸|Planting Chrysanthemums,China,Ming dynasty (1368–1644),,,,Artist,,Lu Zhi,"Chinese, 1495–1576",,Lu Zhi,Chinese,1495,1576,mid-16th century,1534,1566,Hanging scroll; ink and pale color on paper,Image: 42 x 10 3/4 in. (106.7 x 27.3 cm) Overall with mounting: 107 1/4 x 19 in. (272.4 x 48.3 cm) Overall with knobs: 107 1/4 x 22 1/2 in. (272.4 x 57.2 cm),"Edward Elliott Family Collection, Gift of Douglas Dillon, 1986",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45815,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1981.285.9,false,true,36135,Asian Art,Hanging scroll,"明 文嘉 為項元汴畫山水圖 軸|Landscape Dedicated to Xiang Yuanbian",China,Ming dynasty (1368–1644),,,,Artist,,Wen Jia,"Chinese, 1501–1583",,Wen Jia,Chinese,1501,1583,dated 1578,1578,1578,Hanging scroll; ink and color on paper,Image: 46 1/4 x 15 11/16 in. (117.5 x 39.8 cm) Overall with mounting: 90 3/4 x 23 3/8 in. (230.5 x 59.4 cm) Overall with knobs: 90 3/4 x 26 in. (230.5 x 66 cm),"Edward Elliott Family Collection, Purchase, The Dillon Fund Gift, 1981",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36135,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.83,false,true,45032,Asian Art,Handscroll,明 傳項元汴 秋江圖 卷|River Landscape,China,Ming dynasty (1368–1644),,,,Artist,,Xiang Yuanbian,"Chinese, 1525–1590",,Xiang Yuanbian,Chinese,1525,1590,dated 1578,1578,1578,Handscroll; ink on paper,Image: 11 7/8 x 36 1/2 in. (30.2 x 92.7 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45032,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1985.214.148,false,true,48934,Asian Art,Hanging scroll,明 莫是龍 倣黃公望山水圖 軸|Landscape in the Style of Huang Gongwang,China,Ming dynasty (1368–1644),,,,Artist,,Mo Shilong,"Chinese, 1537–1587",,Mo Shilong,Chinese,1537,1587,dated 1581,1581,1581,Hanging scroll; ink and color on paper,Image: 46 7/8 x 16 1/8 in. (119.1 x 41 cm) Overall with mounting: 90 x 23 1/4 in. (228.6 x 59.1 cm) Overall with knobs: 90 x 26 3/4 in. (228.6 x 67.9 cm),"Gift of Ernest Erickson Foundation, 1985",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/48934,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1982.1.7,false,true,48932,Asian Art,Hanging scroll,明 傳馬守真 蘭石圖 軸|Orchid and Rock,China,Ming dynasty (1368–1644),,,,Artist,Attributed to,Ma Shouzhen,"Chinese, 1548–1604",,Ma Shouzhen,Chinese,1548,1604,dated 1572,1572,1572,Hanging scroll; ink on paper,Image: 20 3/4 x 11 1/2 in. (52.7 x 29.2 cm) Image (with inscription): 27 5/8 x 11 1/2 in. (70.2 x 29.2 cm) Overall with mounting: 81 1/2 x 18 3/4 in. (207 x 47.6 cm) Overall with knobs: 81 1/2 x 23 in. (207 x 58.4 cm),"Edward Elliott Family Collection, Purchase, The Dillon Fund Gift, 1982",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/48932,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -55.211.1,false,true,36070,Asian Art,Handscroll,,China,Ming dynasty (1368–1644),,,,Artist,Attributed to,Dong Qichang,"Chinese, 1555–1636",,Dong Qichang,Chinese,1555,1636,dated 1611 and 1612,1350,1650,Handscroll; ink on satin,10 1/5 x 86 1/4 in. (25.9 x 219.1 cm),"Gift of Wan-go H. C. Weng, 1955",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36070,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1979.75.2,false,true,48949,Asian Art,Hanging scroll,明 董其昌 溪山樾館圖 軸 紙本|Shaded Dwellings among Streams and Mountains,China,Ming dynasty (1368–1644),,,,Artist,,Dong Qichang,"Chinese, 1555–1636",,Dong Qichang,Chinese,1555,1636,ca. 1622–25,1612,1635,Hanging scroll; ink on paper,Image: 62 3/8 x 28 3/8 in. (158.4 x 72.1 cm) Overall with mounting: 99 x 33 1/8 in. (251.5 x 84.1 cm) Overall with knobs: 99 x 37 in. (251.5 x 94 cm),"Gift of Douglas Dillon, 1979",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/48949,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1979.500.2,false,true,48952,Asian Art,Hanging scroll,,China,Ming dynasty (1368–1644),,,,Artist,,Dong Qichang,"Chinese, 1555–1636",,Dong Qichang,Chinese,1555,1636,dated 1632,1632,1632,Hanging scroll; ink on paper,Image: 60 7/8 x 21 1/2 in. (154.6 x 54.6 cm) Overall with mounting: 105 3/4 x 28 1/4 in. (268.6 x 71.8 cm) Overall with knobs: 105 3/4 x 32 1/4 in. (268.6 x 81.9 cm),"Gift of Douglas Dillon, 1979",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/48952,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.99,false,true,48953,Asian Art,Hanging scroll,明 董其昌 倣倪瓚山水圖 軸|Landscape with Trees in the Manner of Ni Zan (1301–1374),China,Ming dynasty (1368–1644),,,,Artist,,Dong Qichang,"Chinese, 1555–1636",,Dong Qichang,Chinese,1555,1636,ca. 1622–25,1612,1635,Hanging scroll; ink on paper,Image: 45 1/2 x 18 in. (115.6 x 45.7 cm) Overall with mounting: 94 7/8 x 27 1/2 in. (241 x 69.9 cm) Overall with knobs: 94 7/8 x 31 1/2 in. (241 x 80 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/48953,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.102,false,true,48951,Asian Art,Hanging scroll,,China,Ming dynasty (1368–1644),,,,Artist,,Dong Qichang,"Chinese, 1555–1636",(and assistants),Dong Qichang,Chinese,1555,1636,dated 1630,1630,1630,Hanging scroll; ink and color on silk,Overall with mounting: 130 7/8 x 46 1/8 in. (332.4 x 117.2 cm) Image: 91 x 37 in. (231.1 x 94 cm) Overall with knobs: 130 7/8 x 50 1/2 in. (332.4 x 128.3 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/48951,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.372a–h,false,true,41480,Asian Art,Album,明 董其昌 山水圖詩 冊|Landscapes and Poems,China,Ming dynasty (1368–1644),,,,Artist,,Dong Qichang,"Chinese, 1555–1636",,Dong Qichang,Chinese,1555,1636,"17th century, probably after 1625",1625,1636,"Album of eight paintings and eight calligraphies; ink, gold and color on gold-flecked paper",12 5/8 x 9 1/8 in. (32.1 x 23.2 cm),"Gift of Mr. and Mrs. Wan-go H. C. Weng, 1989",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/41480,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.266.5a–k,false,true,48950,Asian Art,Album,明 董其昌 山水圖 冊 紙本|Landscapes after old masters,China,Ming dynasty (1368–1644),,,,Artist,,Dong Qichang,"Chinese, 1555–1636",,Dong Qichang,Chinese,1555,1636,dated 1630,1630,1630,Album of eight leaves; ink on paper,Image (each): 9 5/8 x 6 5/16 in. (24.4 x 16 cm),"Edward Elliott Family Collection, Gift of Douglas Dillon, 1986",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/48950,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.108,false,true,48969,Asian Art,Handscroll,明 張瑞圖 後赤壁賦圖 卷|Second Ode on the Red Cliff,China,Ming dynasty (1368–1644),,,,Artist,,Zhang Ruitu,"Chinese, 1570–1641",,Zhang Ruitu,Chinese,1570,1641,dated 1628,1628,1628,Handscroll; ink on satin,Image: 11 in. x 10 ft. 6 in. (27.9 x 320 cm) Overall with mounting: 12 1/4 in. x 34 ft. 2 3/4 in. (31.1 x 1043.3 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/48969,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.100.70,false,true,35988,Asian Art,Folding fan mounted as an album leaf,,China,Ming dynasty (1368–1644),,,,Artist,,Li Liufang,"Chinese, 1575–1629",,Li Liufang,Chinese,1575,1629,dated 1613,1613,1613,Folding fan mounted as an album leaf; ink on gold paper,7 7/32 x 21 7/8 in. (18.4 x 55.6 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/35988,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1979.26,false,true,39713,Asian Art,Hanging scroll,明 藍瑛 紅友圖 軸|Red Friend,China,Ming dynasty (1368–1644),,,,Artist,,Lan Ying,"Chinese, 1585–1664",,Lan Ying,Chinese,1585,1664,16th– mid-17th century,1500,1650,Hanging scroll; ink and color on paper,Image: 58 5/8 x 18 5/8 in. (148.9 x 47.3 cm) Overall with mounting: 85 3/4 x 25 5/8 in. (217.8 x 65.1 cm) Overall with knobs: 85 3/4 x 29 in. (217.8 x 73.7 cm),"Gift of Mr. and Mrs. Earl Morse, in honor of Douglas Dillon, 1979",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/39713,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.114,false,true,49019,Asian Art,Hanging scroll,明 藍瑛 春江漁隱圖 軸|Hermit-Fisherman on a Spring River,China,Ming dynasty (1368–1644),,,,Artist,,Lan Ying,"Chinese, 1585–1664",,Lan Ying,Chinese,1585,1664,dated 1632,1632,1632,Hanging scroll; ink and color on silk,Image: 72 3/4 x 35 3/4 in. (184.8 x 90.8 cm) Overall with mounting: 98 3/4 x 43 1/8 in. (250.8 x 109.5 cm) Overall with knobs: 98 3/4 x 46 3/8 in. (250.8 x 117.8 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49019,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.2.2a–l,false,true,49021,Asian Art,Album,明 藍瑛 仿宋元山水圖 冊 紙本|Landscapes after Song and Yuan masters,China,Ming dynasty (1368–1644),,,,Artist,,Lan Ying,"Chinese, 1585–1664",,Lan Ying,Chinese,1585,1664,dated 1642,1642,1642,Album of twelve leaves; ink and color on paper,12 7/16 x 9 3/4 in. (31.6 x 24.8 cm),"The Sackler Fund, 1970",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49021,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.120,false,true,39717,Asian Art,Hanging scroll,,China,Ming dynasty (1368–1644),,,,Artist,,Ni Yuanlu,"Chinese, 1593–1644",,Ni Yuanlu,Chinese,1593,1644,16th–mid-17th century,1593,1644,Hanging scroll; ink on silk,Image: 51 1/2 x 17 7/8 in. (130.8 x 45.4 cm) Overall with mounting: 86 3/4 x 25 3/8 in. (220.3 x 64.5 cm) Overall with knobs: 86 3/4 x 28 3/8 in. (220.3 x 72.1 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/39717,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1981.285.3a–h,false,true,49074,Asian Art,Album,"明/清 項聖謨 山水花鳥圖 冊|Landscapes, Flowers and Birds",China,Ming dynasty (1368–1644),,,,Artist,,Xiang Shengmo,"Chinese, 1597–1658",,Xiang Shengmo,Chinese,1597,1658,dated 1639,1639,1639,Album of eight paintings; ink and color on paper,11 1/8 x 8 7/8 in. (28.3 x 22.5 cm),"Edward Elliott Family Collection, Purchase, The Dillon Fund Gift, 1981",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49074,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1992.197,false,true,41470,Asian Art,Hanging scroll,明/清 陳洪綬 準提佛母法像圖 軸|Bodhisattva Guanyin in the Form of the Buddha Mother,China,Ming dynasty (1368–1644),,,,Artist,,Chen Hongshou,"Chinese, 1599–1652",,Chen Hongshou,Chinese,1599,1652,dated 1620,1620,1620,Hanging scroll; ink on paper,Image: 49 1/4 x 19 1/16 in. (125.1 x 48.4 cm) Overall with mounting: 96 3/4 x 24 9/16 in. (245.7 x 62.4 cm) Overall with knobs: 96 3/4 x 28 1/2 in. (245.7 x 72.4 cm),"Purchase, Friends of Asian Art Gifts, 1992",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/41470,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.278.1,false,true,39558,Asian Art,Hanging scroll,明/清 傳陳洪綬 青綠山水圖 軸|Landscape in the Blue-and-Green Manner,China,Ming dynasty (1368–1644),,,,Artist,,Chen Hongshou,"Chinese, 1599–1652",,Chen Hongshou,Chinese,1599,1652,dated 1633,1633,1633,Hanging scroll; ink and color on silk,Image: 92 3/4 x 30 5/8 in. (235.6 x 77.8 cm) Overall with mounting: 126 5/8 x 34 5/8 in. (321.6 x 87.9 cm) Overall with rollers: 126 5/8 x 37 5/8 in. (321.6 x 95.6 cm),"Gift of Mr. and Mrs. Earl Morse, 1972",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/39558,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1985.121a–l,false,true,44759,Asian Art,Album,"明/清 陳洪綬 山水人物花卉圖 冊|Landscapes, Figures, and Flowers",China,Ming dynasty (1368–1644),,,,Artist,,Chen Hongshou,"Chinese, 1599–1652",,Chen Hongshou,Chinese,1599,1652,dated 1618–1622,1618,1622,Album of twelve paintings; ink and color on paper,Each leaf: 8 3/4 x 3 5/8 in. (22.2 x 9.2 cm),"Purchase, Friends of Far Eastern Art Gifts, 1985",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/44759,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.112a–l,false,true,37395,Asian Art,Album,明/清 陳洪綬 橅古圖 冊|Miscellaneous Studies,China,Ming dynasty (1368–1644),,,,Artist,,Chen Hongshou,"Chinese, 1599–1652",,Chen Hongshou,Chinese,1599,1652,one leaf dated 1619,1619,1619,Album of twelve paintings; ink on paper,Image (each leaf): 7 x 7 in. (17.8 x 17.8 cm),"Gift of Mr. and Mrs. Wan-go H. C. Weng, 2005",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/37395,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.100.87,false,true,35998,Asian Art,Folding fan mounted as an album leaf,,China,Ming dynasty (1368–1644),,,,Artist,,Shao Mi,active ca 1620–1660,,Shao Mi,Chinese,1620,1660,dated 1640,1640,1640,Folding fan mounted as an album leaf; ink and color on gold-flecked paper,6 1/2 x 18 23/32 in. (16.5 x 47.6 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/35998,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.100.77,false,true,35990,Asian Art,Folding fan mounted as an album leaf,,China,Ming dynasty (1368–1644),,,,Artist,,Wu Bin,active ca. 1583–1626,,Wu Bin,Chinese,1583,1626,dated 1603,1603,1603,Folding fan mounted as an album leaf; ink and color on paper,6 3/8 x 18 23/32 in. (16.2 x 47.6 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/35990,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.266.4,true,true,48948,Asian Art,Handscroll,明 吳彬 十六羅漢圖 卷|The Sixteen Luohans,China,Ming dynasty (1368–1644),,,,Artist,,Wu Bin,active ca. 1583–1626,,Wu Bin,Chinese,1583,1626,dated 1591,1591,1591,Handscroll; ink and color on paper,Image: 12 5/8 x 163 9/16 in. (32.1 x 415.4 cm) Overall with mounting: 13 1/4 x 398 1/16 in. (33.7 x 1011.1 cm),"Edward Elliott Family Collection, Gift of Douglas Dillon, 1986",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/48948,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.220.111,false,true,36022,Asian Art,Hanging scroll,明 顧懿德 倣王蒙玩月圖 軸|Enjoying the Moon: Landscape in the Manner of Wang Meng,China,Ming dynasty (1368–1644),,,,Artist,,Gu Yide,active ca. 1620–1630,,Gu Yide,Chinese,1620,1630,dated 1628,1368,1644,Hanging scroll; ink and color on paper,Image: 61 x 18 1/4 in. (154.9 x 46.4 cm) Overall with mounting: 123 3/4 x 26 3/4 in. (314.3 x 67.9 cm) Overall with knobs: 123 3/4 x 31 in. (314.3 x 78.7 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36022,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.220.104,false,true,36021,Asian Art,Hanging scroll,,China,Ming dynasty (1368–1644),,,,Artist,Formerly attributed to,Guanxiu,"Chinese, active ca. 940",,Guanxiu,Chinese,0930,0950,15th century?,1400,1499,Hanging scroll; ink and color on silk,Image: 46 3/8 × 18 1/2 in. (117.8 × 47 cm) Overall with mounting: 83 × 24 1/2 in. (210.8 × 62.2 cm) Overall with knobs: 83 × 28 1/4 in. (210.8 × 71.8 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36021,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -52.177.20,false,true,36067,Asian Art,Hanging scroll,,China,Ming dynasty (1368–1644),,,,Artist,,Xie Shichen,"Chinese, 1487–ca. 1567",,Xie Shichen,Chinese,1487,1567,dated 1548,1548,1548,Hanging scroll; ink and color on silk,Image: 71 7/8 x 41 7/8 in. (182.6 x 106.4 cm),"Bequest of Katherine S. Dreier, 1952",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36067,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2008.590a–d,false,true,75250,Asian Art,Hanging scrolls,明 謝時臣 四時佳興圖 軸 四幅|Landscapes of the Four Seasons,China,Ming dynasty (1368–1644),,,,Artist,,Xie Shichen,"Chinese, 1487–ca. 1567",,Xie Shichen,Chinese,1487,1567,dated 1560,1560,1560,Set of four hanging scrolls; ink and color on paper,Image (a): 126 3/8 x 37 in. (321 x 94 cm) Image (b): 127 1/4 x 36 7/8 in. (323.2 x 93.7 cm) Image (c): 126 3/4 x 36 7/8 in. (321.9 x 93.7 cm) Image (d): 126 7/8 x 36 7/8 in. (322.3 x 93.7 cm) Overall with mounting (b): 141 3/4 x 42 3/8 in. (360 x 107.6 cm),"Purchase, The Vincent Astor Foundation Gift, 2008",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/75250,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1980.80,false,true,48901,Asian Art,Handscroll,明 錢榖 蘭亭修禊圖 卷|Gathering at the Orchid Pavilion,China,Ming dynasty (1368–1644),,,,Artist,,Qian Gu,"Chinese, 1508–ca. 1578",,Qian Gu,Chinese,1508,1578,datable to 1560,1560,1560,Handscroll; ink and color on paper,Image: 9 1/2 x 171 1/2 in. (24.1 x 435.6 cm) Overall with mounting: 9 3/4 x 491 3/8 in. (24.8 x 1248.1 cm),"Ex coll.: C. C. Wang Family, Gift of Douglas Dillon, 1980",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/48901,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1993.385,false,true,45676,Asian Art,Hanging scroll,明 林良 二鷹圖 軸|Two hawks in a thicket,China,Ming dynasty (1368–1644),,,,Artist,,Lin Liang,"Chinese, ca. 1416–1480",,Lin Liang,Chinese,1416,1480,mid- 15th century,1416,1480,Hanging scroll; ink and color on silk,Image: 58 5/8 x 32 3/4 in. (148.9 x 83.2 cm) Overall with mounting: 108 1/2 x 39 in. (275.6 x 99.1 cm) Overall with knobs: 108 1/2 x 43 7/8 in. (275.6 x 111.4 cm),"Gift of Bei Shan Tang Foundation, 1993",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45676,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.100.51,false,true,51375,Asian Art,Folding fan mounted as an album leaf,,China,Ming dynasty (1368–1644),,,,Artist,,Song Xu,"Chinese, 1525–after 1606",,Song Xu,Chinese,1525,1610,dated 1587,1587,1587,Fan mounted as an album leaf; ink and color on gold paper,7 1/2 x 21 3/4 in. (19.1 x 55.2 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51375,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1996.240,false,true,39554,Asian Art,Handscroll,明 丁雲鵬 十八羅漢圖 卷|Eighteen Luohans,China,Ming dynasty (1368–1644),,,,Artist,,Ding Yunpeng,"Chinese, 1547–after 1621",,Ding Yunpeng,Chinese,1547,1621,dated 1609,1609,1609,Handscroll; ink on paper,8 1/4 x 92 in. (21 x 233.7 cm),"Purchase, Friends of Asian Art Gifts, 1996",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/39554,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -67.6.2,false,true,36094,Asian Art,Handscroll,,China,Ming dynasty (1368–1644),,,,Artist,,Zhang Hong,"Chinese, 1577–after 1652",,Zhang Hong,Chinese,1577,1652,dated 1639,1639,1639,Handscroll; ink on paper,10 3/4 x 173 23/32 in. (27.3 x 441.3 cm),"Purchase, Bequest of Dorothy Graham Bennett, 1967",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36094,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -58.148.1,false,true,36080,Asian Art,Hanging scroll,,China,Ming dynasty (1368–1644),,,,Artist,After,Zhu Duan,"Chinese, act. ca. 1500–21",,Zhu Duan,Chinese,1490,1531,dated 1518,1518,1518,Hanging scroll; ink and color on silk,Image: 35 1/4 x 76 in. (89.5 x 193 cm) Overall with mounting: 116 5/8 x 41 3/16 in. (296.2 x 104.6 cm) Overall with rollers: 116 5/8 x 46 1/8 in. (296.2 x 117.2 cm),"Gift of Alan Priest, 1958",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36080,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1985.335,false,true,48947,Asian Art,Handscroll,明 王上宮 忠孝圖 卷|Paragons of Loyalty and Filial Piety,China,Ming dynasty (1368–1644),,,,Artist,,Wang Shanggong,"Chinese, active 16th century",,Wang Shanggong,Chinese,1500,1599,dated 1593,1593,1593,Handscroll; ink on paper,Image: 11 1/8 in. x 14 ft. 8 3/4 in. (28.3 x 448.9 cm) Overall with mounting: 12 1/4 in. x 38 ft. 1 15/16 in. (31.1 x 1163.2 cm),"Purchase, Bequest of Dorothy Graham Bennett, 1985",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/48947,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.100.83,false,true,35994,Asian Art,Folding fan mounted as an album leaf,,China,Ming dynasty (1368–1644),,,,Artist,,Qi Zhijia,"Chinese, ca. 1595–ca. 1670",,Qi Zhijia,Chinese,1585,1680,dated Spring 1643,1643,1643,Folding fan mounted as an album leaf; ink on paper,6 1/2 x 19 1/2 in. (16.5 x 49.5 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/35994,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.100.95,false,true,48902,Asian Art,Folding fan mounted as an album leaf,,China,Ming dynasty (1368–1644),,,,Artist,,Chen Guan,"Chinese, active ca. 1610–40",,Chen Guan,Chinese,1600,1650,dated 1629,1368,1644,Folding fan mounted as an album leaf; ink and color on paper,6 1/2 x 19 5/8 in. (16.5 x 49.8 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/48902,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.127,false,true,49095,Asian Art,Handscroll,,China,Ming dynasty (1368–1644),,,,Artist,,Bian Wenyu,"Chinese, active ca. 1611–71",,Bian Wenyu,Chinese,1611,1671,dated 1634,1634,1634,Handscroll; ink on paper,10 1/4 x 42 1/2 in. (26 x 108 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49095,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.219,false,true,48966,Asian Art,Handscroll,明 趙左 谿山無盡圖 卷|Streams and Mountains without End,China,Ming dynasty (1368–1644),,,,Artist,,Zhao Zuo,"Chinese, ca. 1570–after 1630",,Zhao Zuo,Chinese,1560,1630,Dated 1611–12,1611,1612,Handscroll; ink and color on paper,Image: 9 5/8 x 248 5/8 in. (24.4 x 631.5 cm) Overall with mounting: 11 3/4 x 465 3/4 in. (29.8 x 1183 cm),"Purchase, The Dillon Fund Gift, 1976",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/48966,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.37,false,true,51871,Asian Art,Handscroll,,China,Ming dynasty (1368–1644),,,,Artist,Formerly Attributed to,Wang Zhenpeng,"Chinese, active ca. 1275–1330",,WANG ZHENPENG,Chinese,1265,1340,early 15th century,1400,1433,Handscroll; ink on paper,Image: 12 1/4 x 269 in. (31.1 x 683.3 cm) Overall with mounting: 13 3/8 x 443 in. (34 x 1125.2 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51871,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1991.117.2,false,true,39667,Asian Art,Hanging scroll,明 杜堇 伏生授經圖 軸|The Scholar Fu Sheng Transmitting the Book of Documents,China,Ming dynasty (1368–1644),,,,Artist,,Du Jin,"Chinese, active ca. 1465–1509",,Du Jin,Chinese,1465,1509,15th–mid-16th century,1465,1509,Hanging scroll; ink and color on silk,Image: 57 7/8 × 41 1/8 in. (147 × 104.5 cm) Overall with mounting: 9 ft. 10 3/4 in. × 50 1/4 in. (301.6 × 127.6 cm) Overall with knobs: 9 ft. 10 3/4 in. × 53 in. (301.6 × 134.6 cm),"Gift of Douglas Dillon, 1991",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/39667,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1991.438.4,false,true,44703,Asian Art,Hanging scroll,明 鍾禮 觀瀑圖 軸|Scholar looking at a waterfall,China,Ming dynasty (1368–1644),,,,Artist,,Zhong Li,"Chinese, active ca. 1480–1500",,Zhong Li,Chinese,1480,1500,late 15th century,1480,1499,Hanging scroll; ink and color on silk,Image: 70 x 40 5/8 in. (177.8 x 103.2 cm) Overall with mounting: 10 ft. 5 1/2 in. × 49 5/8 in. (318.8 × 126 cm) Overall with knobs: 10 ft. 5 1/2 in. × 53 1/4 in. (318.8 × 135.3 cm),"From the P. Y. and Kinmay W. Tang Family Collection, Gift of Oscar L. Tang, 1991",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/44703,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.220.116,false,true,36024,Asian Art,Hanging scroll,明 居節 石泉圖 軸|The Waterfall,China,Ming dynasty (1368–1644),,,,Artist,,Ju Jie,"Chinese, active ca. 1531–1585",,Ju Jie,Chinese,1531,1585,dated 1559,1368,1644,Hanging scroll; ink on silk,43 1/2 x 9 3/4 in. (110.5 x 24.8 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36024,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.82,false,true,48910,Asian Art,Hanging scroll,"明 侯懋功 高山圖 軸 |High Mountains",China,Ming dynasty (1368–1644),,,,Artist,,Hou Maogong,"Chinese, active ca. 1540–1580",,Hou Maogong,Chinese,1540,1580,dated 1569,1569,1569,Hanging scroll; ink and color on paper,Image: 46 5/8 x 11 in. (118.4 x 27.9 cm) Overall with mounting: 78 3/4 x 17 in. (200 x 43.2 cm) Overall with knobs: 78 3/4 x 20 1/2 in. (200 x 52.1 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/48910,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1987.408.2a–n,false,true,36436,Asian Art,Album,清 張風 山水圖 冊 紙本|Landscapes,China,Ming dynasty (1368–1644),,,,Artist,,Zhang Feng,"Chinese, active ca. 1628–1662",,Zhang Feng,Chinese,1618,1662,dated 1644,1644,1644,Album of twelve leaves; ink and color on paper,Each leaf: 6 1/16 x 9 in. (15.4 x 22.9 cm),"Edward Elliott Family Collection, Gift of Douglas Dillon, 1987",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36436,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2002.328,false,true,65013,Asian Art,Handscroll,清 那爾敦布 順治皇帝進京之隊伍賽馬全圖 卷|Horsemanship Competition for the Shunzhi Emperor,China,Ming dynasty (1368–1644),,,,Artist,,Nardunbu,"Manchu, active mid-17th century",,Nardunbu,Manchu,1600,1699,dated 1662,1662,1662,Handscroll; ink and color on paper,8 x 655 in. (20.3 x 1663.7 cm) Height of painting with paper mounting: 9 3/8 in. (23.8 cm); height of painting with brocade mounting: 12 in. (30.5 cm); frontispiece: 8 x 12 3/8 in. (20.3 x 31.4 cm),"Purchase, The Dillon Fund Gift, 2002",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/65013,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -47.18.130,false,true,45670,Asian Art,Hanging scroll,"一鱖禾蟹圖|Flowers, fish, and crabs",China,Ming dynasty (1368–1644),,,,Artist,,Liu Jie,"Chinese, active mid-16th century",,Liu Jie,Chinese,1534,1566,mid-16th century,1534,1566,Hanging scroll; ink and color on silk,Image: 69 1/4 × 49 1/2 in. (175.9 × 125.7 cm) Overall with knobs: 8 ft. 6 7/8 in. × 54 in. (261.3 × 137.2 cm),"From the Collection of A. W. Bahr, Purchase, Fletcher Fund, 1947",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45670,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.100.75,false,true,35989,Asian Art,Folding fan mounted as an album leaf,,China,Ming dynasty (1368–1644),,,,Artist,,Chen Jichun,"Chinese, active mid-17th century",,Chen Jichun,Chinese,1633,1667,1635,1635,1635,Folding fan mounted as an album leaf; ink and color on gold paper,7 11/32 x 21 23/32 in. (18.7 x 55.2 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/35989,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1983.85,false,true,41191,Asian Art,Hanging scroll,,China,Ming dynasty (1368–1644),,,,Artist,,Ni Jing,"Chinese, active late 14th century",,Ni Jing,Chinese,1367,1399,14th century,1367,1399,Hanging scroll; ink and pale color on paper,Image: 41 15/16 x 9 3/4 in. (106.5 x 24.8 cm) Overall with mounting: 75 3/4 x 14 1/8 in. (192.4 x 35.9 cm) Overall with knobs: 75 3/4 x 15 15/16 in. (192.4 x 40.5 cm),"Purchase, Bequest of Dorothy Graham Bennett, 1983",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/41191,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -12.37.134,false,true,45655,Asian Art,Hanging scroll,"元/明 呂敬甫 花蝶圖 軸 |Flowers",China,Ming dynasty (1368–1644),,,,Artist,Attributed to,Lü Jingfu,"Chinese, active late 14th century",,Lü Jingfu,Chinese,1367,1399,14th century,1368,1399,Hanging scroll; ink and color on silk,40 x 20 7/8 in. (101.6 x 53 cm),"Rogers Fund, 1912",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45655,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.494.4,false,true,39553,Asian Art,Hanging scroll,明 陳子和 古木酒仙圖 軸|Drunken Immortal beneath an old tree,China,Ming dynasty (1368–1644),,,,Artist,,Chen Zihe,"Chinese, active early 16th century",,Chen Zihe,Chinese,1500,1533,early 16th century,1500,1533,Hanging scroll; ink on silk,Image: 69 in. × 40 1/4 in. (175.3 × 102.2 cm) Overall with mounting: 9 ft. 9 in. × 47 3/8 in. (297.2 × 120.3 cm) Overall with knobs: 9 ft. 9 in. × 52 in. (297.2 × 132.1 cm),"Ex coll.: C. C. Wang Family, Gift of Oscar L. Tang Family, 2005",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/39553,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -59.49.1,false,true,36081,Asian Art,Hanging scroll,明 阮祖德 抑齋曾叔祖八十五齡壽像 軸|Portrait of the Artist's Great-Granduncle Yizhai at the Age of Eighty-Five,China,Ming dynasty (1368–1644),,,,Artist,,Ruan Zude,"Chinese, 16th or early 17th century",,Ruan Zude,Chinese,1500,1699,"dated ""xinyou"" (1561 or 1621?)",1561,1621,Hanging scroll; ink and color on silk,Image: 61 3/4 x 37 7/8 in. (156.8 x 96.2 cm),"Seymour Fund, 1959",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36081,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -59.49.2,false,true,36082,Asian Art,Hanging scroll,明 阮祖德 老婦像 軸|Portrait of an Old Lady,China,Ming dynasty (1368–1644),,,,Artist,,Ruan Zude,"Chinese, 16th or early 17th century",,Ruan Zude,Chinese,1500,1699,"dated ""xinyou"" (1561 or 1621?)",1561,1621,Hanging scroll; ink and color on silk,Image: 61 3/4 x 37 7/8 in. (156.8 x 96.2 cm),"Seymour Fund, 1959",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36082,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.494.3,false,true,39552,Asian Art,Hanging scroll,明 劉俊 納諫圖 軸|Remonstrating with the emperor,China,Ming dynasty (1368–1644),,,,Artist,,Liu Jun,"Chinese, active ca. 1475–ca. 1505",,Liu Jun,Chinese,1475,1505,late 15th–early 16th century,1475,1505,Hanging scroll; ink and color on silk,Image: 65 1/2 x 41 3/4 in. (166.4 x 106 cm) Overall with mounting: 116 x 50 in. (294.6 x 127 cm) Overall with knobs: 116 x 54 in. (294.6 x 137.2 cm),"Ex coll.: C. C. Wang Family, Gift of Oscar L. Tang Family, 2005",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/39552,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -55.124,false,true,45680,Asian Art,Handscroll,明 傳蔣嵩 冬景山水圖 卷|Winter Landscape,China,Ming dynasty (1368–1644),,,,Artist,Attributed to,Jiang Song,"Chinese, first half of 16th century",,Jiang Song,Chinese,1500,1550,first half of 16th century,1500,1549,Handscroll; ink and color on paper,Image: 12 1/4 x 277 in. (31.1 x 703.6 cm),"Seymour Fund, 1955",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45680,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -47.18.111,false,true,36058,Asian Art,Hanging scroll,,China,Ming dynasty (1368–1644),,,,Artist,Attributed to,Zhou Zonglian,probably Ming dynasty (1368–1644),,Zhou Zonglian,Chinese,1368,1644,dated 1532 or 1592,1532,1592,Hanging scroll; ink on silk,Image: 67 3/4 x 37 1/8 in. (172.1 x 94.3 cm),"From the Collection of A. W. Bahr, Purchase, Fletcher Fund, 1947",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36058,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -68.195,false,true,36095,Asian Art,Handscroll,明 魏之克(魏克) 金陵四時圖 卷 紙本|Views of Nanjing in the Four Seasons,China,Ming dynasty (1368–1644),,,,Artist,,Wei Zhike,"Chinese, active ca. 1600–after 1636",,Wei Zhike,Chinese,1600,1636,dated 1635,1635,1635,Handscroll; ink and color on paper,Overall with mounting: 12 5/8 in. × 38 ft. 10 in. (32.1 × 1183.6 cm),"Gift of J. T. Tai, 1968",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36095,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.100.79,false,true,35992,Asian Art,Folding fan mounted as an album leaf,,China,Ming dynasty (1368–1644),,,,Artist,,Pan Yunyu,"Chinese, active ca. 15th–16th century",,Pan Yunyu,Chinese,1400,1599,dated 1604?1664?,1604,1604,Folding fan mounted as an album leaf; ink and color on gold paper,6 1/4 x 19 in. (15.9 x 48.3 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/35992,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1982.1.5,false,true,45671,Asian Art,Hanging scroll,明 徐敬 歲寒清白圖 軸|The Pure Whiteness of Winter,China,Ming dynasty (1368–1644),,,,Artist,,Xu Jing,"Chinese, active first half 15th century",,Xu Jing,Chinese,1400,1450,dated 1441,1441,1441,Hanging scroll; ink on silk,Image: 58 7/8 x 30 in. (149.5 x 76.2 cm) Overall with mounting: 107 3/4 x 37 1/4 in. (273.7 x 94.6 cm) Overall with knobs: 107 3/4 x 41 1/4 in. (273.7 x 104.8 cm),"Ex coll.: C. C. Wang Family, Edward Elliott Family Collection, Purchase, The Dillon Fund Gift, 1982",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45671,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1984.475.3,false,true,45659,Asian Art,Hanging scroll,"元/明 吳伯理 龍松圖 軸|Dragon Pine",China,Ming dynasty (1368–1644),,,,Artist,,Wu Boli,"Chinese, active late 14th–early 15th century",,Wu Boli,Chinese,1350,1450,ca. 1400,1390,1410,Hanging scroll; ink on paper,Image: 48 x 13 1/4 in. (121.9 x 33.7 cm) Overall with mounting: 100 x 18 5/8 in. (254 x 47.3 cm) Overall with knobs: 100 x 21 in. (254 x 53.3 cm),"Edward Elliott Family Collection, Gift of Douglas Dillon, 1984",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45659,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -45.97.9,false,true,51621,Asian Art,Album,清 陸漢 山水八開 冊|Eight Landscapes,China,Qing dynasty (1644–1911),,,,Artist,,Lu Han,"Chinese, died 1722",,Lu Han,Chinese,,1722,1699,1699,1699,Album of eight leaves; ink and color on paper,Image (album): 14 × 10 in. (35.6 × 25.4 cm) Image (double leaf): 14 × 20 in. (35.6 × 50.8 cm) Image: 12 × 9 in. (30.5 × 22.9 cm),"Gift of Mrs. Henry J. Bernheim, 1945",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51621,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1985.214.149,false,true,49030,Asian Art,Hanging scroll,明/清 王鐸 山水圖 軸|Mountain Landscape,China,Qing dynasty (1644–1911),,,,Artist,,Wang Duo,"Chinese, 1592–1652",,Wang Duo,Chinese,1592,1652,dated 1651,1651,1651,Hanging scroll; ink on paper,46 1/8 x 21 5/8 in. (117.2 x 54.9 cm),"Gift of Ernest Erickson Foundation, 1985",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49030,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.117,false,true,44626,Asian Art,Hanging scroll,明/清 王鐸 山水圖 軸|Landscape,China,Qing dynasty (1644–1911),,,,Artist,,Wang Duo,"Chinese, 1592–1652",,Wang Duo,Chinese,1592,1652,dated 1649,1649,1649,Hanging scroll; ink on satin,Image: 22 x 10 5/8 in. (55.9 x 27 cm) Overall with mounting: 67 x 16 3/4 in. (170.2 x 42.5 cm) Overall with knobs: 67 x 20 3/8 in. (170.2 x 51.8 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/44626,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1980.426.2,false,true,40019,Asian Art,Hanging scroll,清　王時敏　仿黃公望山水圖　軸 絹本|Landscape in the style of Huang Gongwang,China,Qing dynasty (1644–1911),,,,Artist,,Wang Shimin,"Chinese, 1592–1680",,WANG SHIMIN,Chinese,1592,1680,dated 1666,1666,1666,Hanging scroll; ink on paper,Image: 53 x 22 1/4 in. (134.6 x 56.5 cm) Overall with mounting: 87 3/4 x 28 1/4 in. (222.9 x 71.8 cm) Overall with knobs: 87 3/4 x 31 in. (222.9 x 78.7 cm),"Ex coll.: C. C. Wang Family, Gift of Douglas Dillon, 1980",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/40019,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.119,false,true,49106,Asian Art,Folding fan mounted as an album leaf,,China,Qing dynasty (1644–1911),,,,Artist,,Wang Shimin,"Chinese, 1592–1680",,WANG SHIMIN,Chinese,1592,1680,dated 1677,1677,1677,Folding fan mounted as an album leaf; ink and color on white paper,6 3/16 x 19 1/2 in. (15.7 x 49.5 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49106,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2003.394a–i,false,true,65555,Asian Art,Album,清 蕭雲從 山水圖 冊|Landscapes,China,Qing dynasty (1644–1911),,,,Artist,,Xiao Yuncong,"Chinese, 1596–1673",,Xiao Yuncong,Chinese,1596,1673,dated 1668,1668,1668,Album of eight paintings; ink and color on paper,Each leaf: 9 1/8 x 6 3/4 in. (23.2 x 17.1 cm),"Gift of Florence and Herbert Irving Collection, in memory of Douglas Dillon, 2003",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/65555,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -64.268.1,false,true,36089,Asian Art,Album leaf,明/清 項聖謨 秋景圖 冊頁|Autumn Landscape,China,Qing dynasty (1644–1911),,,,Artist,,Xiang Shengmo,"Chinese, 1597–1658",,Xiang Shengmo,Chinese,1597,1658,datable to 1654–55,1654,1655,Leaf from a collective album of many leaves; ink and color on paper,9 3/4 x 13 in. (24.8 x 33 cm),"Seymour Fund, 1964",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36089,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1981.285.4,false,true,49080,Asian Art,Hanging scroll,明/清 項聖謨 白菊圖 軸|White Chrysanthemums,China,Qing dynasty (1644–1911),,,,Artist,,Xiang Shengmo,"Chinese, 1597–1658",,Xiang Shengmo,Chinese,1597,1658,dated 1654,1654,1654,Hanging scroll; color on paper,Image: 30 7/16 x 15 1/2 in. (77.3 x 39.4 cm) Overall with mounting: 60 x 21 in. (152.4 x 53.3 cm) Overall with knobs: 60 x 24 in. (152.4 x 61 cm),"Edward Elliott Family Collection, Purchase, The Dillon Fund Gift, 1981",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49080,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.133,false,true,40020,Asian Art,Hanging scroll,清 倣弘仁 幽谷泉聲圖 軸|The Sound of Spring in a Lonely Valley,China,Qing dynasty (1644–1911),,,,Artist,After,Hongren,"Chinese, 1610–1664",,HONGREN,Chinese,1610,1664,dated 1661,1661,1661,Hanging scroll; ink on paper,Image: 40 1/2 x 16 1/8 in. (102.9 x 41 cm) Overall with mounting: 76 1/2 x 22 in. (194.3 x 55.9 cm) Overall with knobs: 76 1/2 x 24 1/2 in. (194.3 x 62.2 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/40020,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.129,false,true,39557,Asian Art,Hanging scroll,清 倣髡殘 蒸嵐昏巒圖 軸|Wooded Mountains at Dusk,China,Qing dynasty (1644–1911),,,,Artist,,Kuncan,"Chinese, 1612–1673",,KUNCAN,Chinese,1612,1673,dated 1666,1666,1666,Hanging scroll; ink and color on paper,Image: 49 1/2 x 24 in. (125.7 x 61 cm) Overall with mounting: 101 1/4 x 31 5/8 in. (257.2 x 80.3 cm) Overall with knobs: 101 1/4 x 34 3/4 in. (257.2 x 88.3 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/39557,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2010.54,false,true,75745,Asian Art,Hanging scroll,清 法若真 雲山圖軸|Cloudy Mountains,China,Qing dynasty (1644–1911),,,,Artist,,Fa Ruozhen,"Chinese, 1613–1696",,Fa Ruozhen,Chinese,1613,1696,1684,1684,1684,"Hanging scroll, ink and color on silk",54 1/8 x 27 3/8 in. (137.5 x 69.5 cm),"Purchase, The Vincent Astor Foundation Gift, 2010",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/75745,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.132,false,true,49132,Asian Art,Hanging scroll,清 龔賢 冬景山水圖 軸 紙本|Wintry Mountains,China,Qing dynasty (1644–1911),,,,Artist,,Gong Xian,"Chinese, 1619–1689",,GONG XIAN,Chinese,1619,1689,datable ca. 1679–89,1679,1689,Hanging scroll; ink on paper,Image: 65 1/4 x 19 1/4 in. (165.7 x 48.9 cm) Overall with mounting: 114 1/4 x 26 1/2 in. (290.2 x 67.3 cm) Overall with knobs: 114 1/4 x 30 1/4 in. (290.2 x 76.8 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49132,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1979.499a–l,false,true,41483,Asian Art,Album,清 龔賢 山水圖 冊 紙本|Landscapes and trees,China,Qing dynasty (1644–1911),,,,Artist,,Gong Xian,"Chinese, 1619–1689",,GONG XIAN,Chinese,1619,1689,ca. 1679,1669,1689,Album of twelve leaves; ink on paper,Image (each leaf): 6 1/4 x 7 1/2 in. (15.9 x 19.1 cm),"From the P. Y. and Kinmay W. Tang Family Collection, Gift of Wen and Constance Fong, in honor of Mr. and Mrs. Douglas Dillon, 1979",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/41483,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1981.4.1a–o,true,true,36131,Asian Art,Album,清 龔賢 自題 山水十六開 冊|Ink Landscapes with Poems,China,Qing dynasty (1644–1911),,,,Artist,,Gong Xian,"Chinese, 1619–1689",,GONG XIAN,Chinese,1619,1689,dated 1688,1688,1688,Album of sixteen paintings; ink on paper,10 3/4 x 16 1/8 in. (27.3 x 41 cm),"Gift of Douglas Dillon, 1981",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36131,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1980.516.2a–c,false,true,51864,Asian Art,Album,清 龔賢 自題山水十六開 冊|Ink Landscapes with Poems,China,Qing dynasty (1644–1911),,,,Artist,,Gong Xian,"Chinese, 1619–1689",,GONG XIAN,Chinese,1619,1689,dated 1688,1688,1688,Album of sixteen paintings; ink on paper,10 13/16 x 14 1/16in. (27.5 x 35.7cm) Overall with mounting: 16 1/8 x 22 7/8in. (41 x 58.1cm) painting/calligraphy: 13 15/16 x 20 9/16in. (35.4 x 52.2cm),"Gift of Douglas Dillon, 1980",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51864,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1991.256,false,true,41488,Asian Art,Hanging scroll,清 戴本孝 天台異松圖 軸 紙本|The Strange Pines of Mount Tiantai,China,Qing dynasty (1644–1911),,,,Artist,,Dai Benxiao,"Chinese, 1621–1693",,DAI BENXIAO,Chinese,1621,1693,dated 1687,1687,1687,Hanging scroll; ink on paper,Image: 66 7/8 x 30 in. (169.9 x 76.2 cm) Overall with mounting: 122 3/4 x 37 1/16 in. (311.8 x 94.1 cm) Overall with knobs: 122 3/4 x 41 in. (311.8 x 104.1 cm),"Gift of Marie-Hélène and Guy Weill, in honor of Douglas Dillon, 1991",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/41488,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -51.13,false,true,51769,Asian Art,Handscroll,,China,Qing dynasty (1644–1911),,,,Artist,Attributed to,Luo Mu,"Chinese, 1622–1706",,Luo Mu,Chinese,1622,1706,dated 1661,1661,1661,Handscroll; ink on paper,12 7/8 x 262 in. (32.7 x 665.5 cm),"Rogers Fund, 1951",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51769,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1994.284,false,true,36453,Asian Art,Hanging scroll,清 梅清 雲谷曳杖圖 軸|Strolling in a Misty Valley,China,Qing dynasty (1644–1911),,,,Artist,,Mei Qing,"Chinese, 1623–1697",,MEI QING,Chinese,1623,1697,dated 1649,1649,1649,Hanging scroll; ink on satin,Image: 61 3/8 x 20 1/2 in. (155.9 x 52.1 cm) Overall with mounting: 100 x 26 3/4 in. (254 x 67.9 cm) Overall with knobs: 100 x 30 1/4 in. (254 x 76.8 cm),"Purchase, Soong Family Gift, in memory of Dr. T.V. Soong, 1994",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36453,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2002.208.1,false,true,37394,Asian Art,Hanging scroll,清 倣梅清 響山泛舟圖 軸|Boating beneath Echo Hill,China,Qing dynasty (1644–1911),,,,Artist,After,Mei Qing,"Chinese, 1623–1697",,MEI QING,Chinese,1623,1697,datable to 1673,1673,1673,Hanging scroll; ink on paper,Image: 53 x 23 1/4 in. (134.6 x 59.1 cm) Overall with mounting: 113 1/2 x 32 1/4 in. (288.3 x 81.9 cm) Overall with knobs: 113 1/2 x 36 1/4 in. (288.3 x 92.1 cm),"Gift of Cécile and Sandy Mactaggart, 2002",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/37394,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2014.721,false,true,39540,Asian Art,Hanging scroll,清 朱耷 (八大山人) 二鷹圖 軸|Two eagles,China,Qing dynasty (1644–1911),,,,Artist,,Bada Shanren (Zhu Da),"Chinese, 1626–1705",,Bada Shanren (Zhu Da),Chinese,1626,1705,dated 1702,1702,1702,Hanging scroll; ink on paper,Image: 73 3/4 x 35 1/2 in. (187.3 x 90.2 cm) Overall with mounting: 122 3/4 x 42 1/2 in. (311.8 x 108 cm) Overall with knobs: 122 3/4 x 46 1/2 in. (311.8 x 118.1 cm),"Gift of Oscar L. Tang Family, 2014",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/39540,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.135,false,true,49143,Asian Art,Handscroll,清 八大山人 （朱耷） 蓮塘戲禽圖 卷|Birds in a lotus pond,China,Qing dynasty (1644–1911),,,,Artist,,Bada Shanren (Zhu Da),"Chinese, 1626–1705",,Bada Shanren (Zhu Da),Chinese,1626,1705,ca. 1690,1680,1700,Handscroll; ink on satin,10 3/4 x 80 3/4 in. (27.3 x 205.1 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49143,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.136,false,true,49144,Asian Art,Hanging scroll,"清 朱耷 (八大山人) 倣王羲之 蘭亭序 軸|After Wang Xizhi's (303?-361?) ""Preface to the Orchid Pavilion Gathering""",China,Qing dynasty (1644–1911),,,,Artist,,Bada Shanren (Zhu Da),"Chinese, 1626–1705",,Bada Shanren (Zhu Da),Chinese,1626,1705,ca. 1694–96,1684,1705,Hanging scroll; ink on paper,Image: 18 3/8 x 10 in. (46.7 x 25.4 cm) Overall with mounting: 66 x 14 1/4 in. (167.6 x 36.2 cm) Overall with knobs: 66 x 17 1/2 in. (167.6 x 44.5 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49144,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.137,false,true,41491,Asian Art,Hanging scroll,清 八大山人（朱耷） 魚石圖 軸|Fish and rocks,China,Qing dynasty (1644–1911),,,,Artist,,Bada Shanren (Zhu Da),"Chinese, 1626–1705",,Bada Shanren (Zhu Da),Chinese,1626,1705,dated 1699,1699,1699,Hanging scroll; ink on paper,Image: 53 1/4 x 24 in. (135.3 x 61 cm) Overall with mounting: 92 1/2 x 31 1/2 in. (235 x 80 cm) Overall with knobs: 92 1/2 x 34 1/4 in. (235 x 87 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/41491,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.138a–l,false,true,49145,Asian Art,Album,清 朱耷 (八大山人) 山水圖 冊|Landscape album,China,Qing dynasty (1644–1911),,,,Artist,,Bada Shanren (Zhu Da),"Chinese, 1626–1705",,Bada Shanren (Zhu Da),Chinese,1626,1705,dated 1699,1699,1699,Album of twelve leaves; ink and color on paper,Image (each): 7 3/8 x 9 1/8 in. (18.7 x 23.2 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49145,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.190,false,true,49149,Asian Art,Hanging scroll,"倣趙伯駒山水圖 軸|Landscape in the Style of Zhao Boju (Fang Zhao Boju shanshui)",China,Qing dynasty (1644–1911),,,,Artist,After,Wang Hui,"Chinese, 1632–1717",,Wang Hui,Chinese,1632,1717,dated 1654,1654,1654,Hanging scroll; ink and color on paper,Image: 23 1/2 x 14 7/8 in. (59.7 x 37.8 cm) Overall with mounting: 76 5/8 x 19 1/2 in. (194.6 x 49.5 cm) Overall with knobs: 76 5/8 x 23 in. (194.6 x 58.4 cm),"Purchase, The Dillon Fund Gift, 1976",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49149,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1978.423,false,true,49151,Asian Art,Handscroll,清 王翬 太行山色圖 卷 絹本|The Colors of Mount Taihang,China,Qing dynasty (1644–1911),,,,Artist,,Wang Hui,"Chinese, 1632–1717",,Wang Hui,Chinese,1632,1717,Dated 1669,1669,1669,Handscroll; ink and color on silk,Image: 10 x 82 1/2 in. (25.3 x 209.4 cm) Overall with mounting: 11 3/4 x 348 in. (29.8 x 883.9 cm),"Ex coll.: C. C. Wang Family, Gift of Douglas Dillon, 1978",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49151,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.220.33,false,true,51483,Asian Art,Tapestry,,China,Qing dynasty (1644–1911),,,,Artist,In the Style of,Wang Hui,"Chinese, 1632–1717",,Wang Hui,Chinese,1632,1717,dated 1702,1702,1702,Hanging scroll; ink and color on silk,Image: 46 3/4 x 22 1/4 in. (118.7 x 56.5 cm) Overall with mounting: 100 1/2 x 29 5/8 in. (255.3 x 75.2 cm) Overall with knobs: 100 1/2 x 33 3/4 in. (255.3 x 85.7 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51483,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1979.458.2,false,true,49157,Asian Art,Handscroll,清 王翬 倣巨然燕文貴山水圖 卷|Landscape in the Style of Juran and Yan Wengui,China,Qing dynasty (1644–1911),,,,Artist,,Wang Hui,"Chinese, 1632–1717",,Wang Hui,Chinese,1632,1717,Dated 1713,1713,1713,Handscroll; ink and color on paper,Image: 12 1/4 x 158 3/8 in. (31.1 x 402.3 cm) Overall with mounting: 12 7/8 x 359 1/8 in. (32.7 x 912.2 cm),"Ex coll.: C. C. Wang Family, Gift of Douglas Dillon, 1979",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49157,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1979.5a–d,true,true,49156,Asian Art,Handscroll,"清 王翬 等 康熙南巡圖 （卷三: 濟南至泰山） 卷|The Kangxi Emperor's Southern Inspection Tour, Scroll Three: Ji'nan to Mount Tai",China,Qing dynasty (1644–1911),,,,Artist,,Wang Hui,"Chinese, 1632–1717",and assistants,Wang Hui,Chinese,1632,1717,datable to 1698,1698,1698,Handscroll; ink and color on silk,Image: 26 3/4 in. x 45 ft. 8 3/4 in. (67.9 x 1393.8 cm),"Purchase, The Dillon Fund Gift, 1979",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49156,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.141,false,true,49150,Asian Art,Hanging scroll,清 王翬 溪山雨霽圖 軸|Clearing after Rain over Streams and Mountains,China,Qing dynasty (1644–1911),,,,Artist,,Wang Hui,"Chinese, 1632–1717",,Wang Hui,Chinese,1632,1717,dated 1662,1662,1662,Hanging scroll; ink on paper,Image: 44 7/8 x 17 7/8 in. (114 x 45.4 cm) Overall with mounting: 103 1/2 x 24 1/4 in. (262.9 x 61.6 cm) Overall with knobs: 103 1/2 x 27 1/2 in. (262.9 x 69.9 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49150,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.144,false,true,49154,Asian Art,Folding fan mounted as an album leaf,,China,Qing dynasty (1644–1911),,,,Artist,,Wang Hui,"Chinese, 1632–1717",,Wang Hui,Chinese,1632,1717,dated 1695,1695,1695,Folding fan mounted as an album leaf; ink and color on paper,6 7/16 x 19 3/8 in. (16.4 x 49.2 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49154,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1977.81,false,true,49158,Asian Art,Handscroll,清 吳歷 墨井草堂消夏圖 卷|Whiling Away the Summer,China,Qing dynasty (1644–1911),,,,Artist,,Wu Li,"Chinese, 1632–1718",,WU LI,Chinese,1632,1718,dated 1679,1679,1679,Handscroll; ink on paper,Image: 14 5/16 x 105 3/4 in. (36.4 x 268.6 cm) Overall with mounting: 14 11/16 x 393 1/16 in. (37.3 x 998.4 cm),"Ex coll.: C. C. Wang Family, Purchase, Douglas Dillon Gift, 1977",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49158,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.220.117,false,true,36025,Asian Art,Hanging scroll,,China,Qing dynasty (1644–1911),,,,Artist,In the Style of,Wu Li,"Chinese, 1632–1718",,WU LI,Chinese,1632,1718,spuriously dated 1703,1703,1703,Hanging scroll; ink on paper,39 5/8 x 19 in. (100.6 x 48.3 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36025,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2012.167a–l,false,true,77870,Asian Art,Album,鄭旼 黃山八景 水墨紙本 九開冊|Eight views of the Yellow Mountains,China,Qing dynasty (1644–1911),,,,Artist,,Zheng Min,"Chinese, 1633–1683",,Zheng Min,Chinese,1633,1683,1681,1681,1681,Album of nine leaves of painting and calligraphy; ink on paper,Image (each leaf): 9 1/2 x 5 1/2 in. (24.1 x 14 cm) Overall with mounting (each double leave): 12 1/8 x 14 3/8 in. (30.8 x 36.5 cm),"Purchase, The Vincent Astor Foundation Gift and Susan Dillon Gift, in honor of James C. Y. Watt, 2012",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/77870,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1982.470,false,true,41486,Asian Art,Hanging scroll,清 傳惲壽平 夏夜清荷圖 軸|Lotuses on a Summer Evening,China,Qing dynasty (1644–1911),,,,Artist,,Yun Shouping,"Chinese, 1633–1690",,YUN SHOUPING,Chinese,1633,1690,dated 1684,1684,1684,Hanging scroll; ink and color on paper,Image: 82 5/16 x 38 11/16 in. (209.1 x 98.3 cm) Overall with mounting: 129 x 40 3/8 in. (327.7 x 102.6 cm) Overall with knobs: 129 x 44 3/4 in. (327.7 x 113.7 cm),"Gift of Marie-Hélène and Guy Weill, in honor of Professor Wen Fong, 1982",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/41486,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1981.285.12a–m,false,true,49162,Asian Art,Album,清 惲壽平 倣宋元山水圖 冊|Landscapes in the Manner of Song and Yuan Masters,China,Qing dynasty (1644–1911),,,,Artist,,Yun Shouping,"Chinese, 1633–1690",,YUN SHOUPING,Chinese,1633,1690,dated 1667,1667,1667,Album of ten paintings; ink and color on paper,10 5/8 x 15 5/8 in. (27 x 39.7 cm),"Edward Elliott Family Collection, Purchase, The Dillon Fund Gift, 1981",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49162,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.1.1,false,true,49181,Asian Art,Hanging scroll,清 石濤(朱若極) 黃山三十六峰意圖 軸|Thirty-six Peaks of Mount Huang Recollected,China,Qing dynasty (1644–1911),,,,Artist,,Shitao (Zhu Ruoji),"Chinese, 1642–1707",,Shitao,Chinese,1642,1707,ca. 1705,1695,1707,Hanging scroll; ink on paper,Image: 81 1/16 x 31 in. (205.9 x 78.7 cm) Overall with mounting: 126 x 38 in. (320 x 96.5 cm) Overall with knobs: 126 x 41 1/2 in. (320 x 105.4 cm),"Gift of Douglas Dillon, 1976",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49181,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1978.254,false,true,41492,Asian Art,Hanging scroll,"清 石濤 (朱若極) 花石圖 軸|Hibiscus, Lotus, and Rock",China,Qing dynasty (1644–1911),,,,Artist,,Shitao (Zhu Ruoji),"Chinese, 1642–1707",,Shitao,Chinese,1642,1707,ca. 1705–7,1695,1717,Hanging scroll; ink on paper,Image: 45 9/16 x 22 in. (115.7 x 55.9 cm) Overall with mounting: 88 x 28 1/2 in. (223.5 x 72.4 cm) Overall with knobs: 88 x 31 3/4 in. (223.5 x 80.6 cm),"Gift of Mr. and Mrs. David M. Levitt, by exchange, 1978",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/41492,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1982.126,false,true,49177,Asian Art,Handscroll,清 石濤(朱若極) 遊張公洞圖 卷|Outing to Zhang Gong's Grotto,China,Qing dynasty (1644–1911),,,,Artist,,Shitao (Zhu Ruoji),"Chinese, 1642–1707",,Shitao,Chinese,1642,1707,ca. 1700,1690,1707,Handscroll; ink and color on paper,Image: 18 1/16 x 112 3/4 in. (45.9 x 286.4 cm) Overall with mounting: 18 7/16 x 363 11/16 in. (46.8 x 923.8 cm),"Purchase, The Dillon Fund Gift, 1982",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49177,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1987.202,false,true,49178,Asian Art,Hanging scroll,清 石濤 (朱若極) 秋林人醉圖 軸 紙本|Drunk in Autumn Woods,China,Qing dynasty (1644–1911),,,,Artist,,Shitao (Zhu Ruoji),"Chinese, 1642–1707",,Shitao,Chinese,1642,1707,ca. 1702,1692,1712,Hanging scroll; ink and color on paper,Image: 63 3/8 x 27 3/4 in. (161 x 70.5 cm) Overall with mounting: 107 1/2 x 33 3/8 in. (273.1 x 84.8 cm) Overall with knobs: 107 1/2 x 36 7/8 in. (273.1 x 93.7 cm),"Gift of John M. Crawford Jr., 1987",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49178,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1984.475.2,false,true,49172,Asian Art,Hanging scroll,清 石濤(朱若極) 風雨竹圖 軸|Bamboo in Wind and Rain,China,Qing dynasty (1644–1911),,,,Artist,,Shitao (Zhu Ruoji),"Chinese, 1642–1707",,Shitao,Chinese,1642,1707,ca. 1694,1684,1704,Hanging scroll; ink on paper,Image: 87 3/4 x 30 in. (222.9 x 76.2 cm) Overall with mounting: 132 1/4 x 37 3/8 in. (335.9 x 94.9 cm) Overall with knobs: 132 1/4 x 41 in. (335.9 x 104.1 cm),"Edward Elliott Family Collection, Gift of Douglas Dillon, 1984",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49172,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1985.227.1,false,true,49170,Asian Art,Handscroll,清 石濤 (朱若極) 十六羅漢圖 卷|The Sixteen Luohans,China,Qing dynasty (1644–1911),,,,Artist,,Shitao (Zhu Ruoji),"Chinese, 1642–1707",,Shitao,Chinese,1642,1707,dated 1667,1667,1667,Handscroll; ink on paper,Image: 18 1/4 x 235 3/4 in. (46.4 x 598.8 cm) Overall with mounting: 22 5/16 x 895 in. (56.7 x 2273.3 cm),"Gift of Douglas Dillon, 1985",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49170,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1981.285.13,false,true,49183,Asian Art,Hanging scroll,清 石濤(朱若極) 重陽山水圖 軸|Landscape Painted on the Double Ninth Festival,China,Qing dynasty (1644–1911),,,,Artist,,Shitao (Zhu Ruoji),"Chinese, 1642–1707",,Shitao,Chinese,1642,1707,dated 1705,1705,1705,Hanging scroll; ink and color on paper,Image: 28 3/16 x 16 5/8 in. (71.6 x 42.2 cm) Overall with mounting: 86 1/8 x 23 in. (218.8 x 58.4 cm) Overall with knobs: 86 1/8 x 26 3/4 in. (218.8 x 67.9 cm),"Ex coll.: C. C. Wang Family, Edward Elliott Family Collection, Purchase, The Dillon Fund Gift, 1981",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49183,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.151,false,true,49171,Asian Art,Album leaf,清 石濤(朱若極) 山水人物圖 冊頁|Landscape with Figure,China,Qing dynasty (1644–1911),,,,Artist,,Shitao (Zhu Ruoji),"Chinese, 1642–1707",,Shitao,Chinese,1642,1707,ca. 1678,1668,1688,Album leaf; ink on paper,8 1/2 x 11 1/4 in. (21.6 x 28.6 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49171,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.153,false,true,49175,Asian Art,Folding fan mounted as an album leaf,清 石濤 (朱若極) 山水圖 扇頁|Landscape,China,Qing dynasty (1644–1911),,,,Artist,,Shitao (Zhu Ruoji),"Chinese, 1642–1707",,Shitao,Chinese,1642,1707,dated 1699,1699,1699,Folding fan mounted as an album leaf; ink and color on gold-flecked paper,6 7/8 x 17 1/2 in. (17.5 x 44.5 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49175,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"13.220.37a, b",false,true,36018,Asian Art,Handscroll,,China,Qing dynasty (1644–1911),,,,Artist,In the Style of,Shitao (Zhu Ruoji),"Chinese, 1642–1707",,Shitao,Chinese,1642,1707,19th–early 20th century,1800,1913,Handscroll; ink on paper,19 5/8 in. × 20 ft. 9 3/4 in. (49.8 × 634.4 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36018,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.122a–l,false,true,49176,Asian Art,Album,清 石濤 (朱若極) 野色圖 冊|Wilderness Colors,China,Qing dynasty (1644–1911),,,,Artist,,Shitao (Zhu Ruoji),"Chinese, 1642–1707",,Shitao,Chinese,1642,1707,ca. 1700,1690,1707,Album of twelve paintings: ink and color on paper,Image (each leaf): 10 7/8 x 9 1/2 in. (27.6 x 24.1 cm),"The Sackler Fund, 1972",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49176,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.280a–n,false,true,49173,Asian Art,Album,清 石濤 (朱若極) 歸棹 冊 紙本|Returning Home,China,Qing dynasty (1644–1911),,,,Artist,,Shitao (Zhu Ruoji),"Chinese, 1642–1707",,Shitao,Chinese,1642,1707,ca. 1695,1685,1715,Album of twelve leaves; ink and color on paper,Image (each): 6 1/2 × 4 1/8 in. (16.5 × 10.5 cm) Each leaf with painting: 8 5/16 × 5 5/16 in. (21.1 × 13.5 cm) Each double leaf unfolded: 8 5/16 × 10 5/8 in. (21.1 × 27 cm),"From the P. Y. and Kinmay W. Tang Family, Gift of Wen and Constance Fong, in honor of Mr. and Mrs. Douglas Dillon, 1976",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49173,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1977.80,false,true,49187,Asian Art,Handscroll,清 王原祁 輞川圖 卷|Wangchuan Villa,China,Qing dynasty (1644–1911),,,,Artist,,Wang Yuanqi,"Chinese, 1642–1715",,WANG YUANQI,Chinese,1642,1715,dated 1711,1711,1711,Handscroll; ink and color on paper,Image: 14 in. x 17 ft. 10 3/4 in. (35.6 x 545.5 cm) Overall with mounting: 14 3/8 in. x 34 ft. 7 1/2 in. (36.5 x 1055.4 cm),"Ex coll.: C. C. Wang Family, Purchase, Douglas Dillon Gift, 1977",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49187,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2011.574,false,true,62743,Asian Art,Hanging scroll,清 王原祁 為瞻亭畫七發妙劑圖 軸|Landscape for Zhanting,China,Qing dynasty (1644–1911),,,,Artist,,Wang Yuanqi,"Chinese, 1642–1715",,WANG YUANQI,Chinese,1642,1715,dated 1710,1710,1710,Hanging scroll; ink and color on paper,Image: 37 1/2 × 18 1/2 in. (95.3 × 47 cm) Overall with mounting: 92 5/8 × 25 1/4 in. (235.3 × 64.1 cm) Overall with knobs: 92 5/8 × 29 in. (235.3 × 73.7 cm),"Gift of Marie-Hélène Weill and Guy A. Weill, 2011",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/62743,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.278.6,false,true,41487,Asian Art,Hanging scroll,清 王原祁 倣黃公望高克恭山水圖 軸|Landscape in the Styles of Huang Gongwang and Gao Kegong,China,Qing dynasty (1644–1911),,,,Artist,,Wang Yuanqi,"Chinese, 1642–1715",,WANG YUANQI,Chinese,1642,1715,dated 1705,1705,1705,Hanging scroll; ink on paper,Image: 45 1/8 x 21 1/4 in. (114.6 x 54 cm) Overall with mounting: 102 1/4 x 29 in. (259.7 x 73.7 cm) Overall with knobs: 102 1/4 x 32 3/4 in. (259.7 x 83.2 cm),"Ex coll.: C. C. Wang Family, Gift of Mr. and Mrs. Earl Morse, 1972",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/41487,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.156,false,true,49185,Asian Art,Hanging scroll,清 王原祁 倣吳鎮山水圖 軸|Landscape after Wu Zhen,China,Qing dynasty (1644–1911),,,,Artist,,Wang Yuanqi,"Chinese, 1642–1715",,WANG YUANQI,Chinese,1642,1715,dated 1695,1695,1695,Hanging scroll; ink on paper,Image: 42 3/4 x 20 1/4 in. (108.6 x 51.4 cm) Overall with mounting: 84 x 27 1/2 in. (213.4 x 69.9 cm) Overall with knobs: 84 x 31 3/4 in. (213.4 x 80.6 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49185,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.157,false,true,49186,Asian Art,Handscroll,清 王原祁 江國垂綸圖 卷|Fishing in River Country at Blossom Time,China,Qing dynasty (1644–1911),,,,Artist,,Wang Yuanqi,"Chinese, 1642–1715",,WANG YUANQI,Chinese,1642,1715,dated 1709,1709,1709,Handscroll; ink and color on paper,10 1/4 x 57 1/2 in. (26 x 146.1 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49186,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.220.31,false,true,36015,Asian Art,Hanging scroll,清 陳書 白鸚鵡圖 軸|Cockatoo,China,Qing dynasty (1644–1911),,,,Artist,,Chen Shu,"Chinese, 1660–1736",,Chen Shu,Chinese,1660,1736,dated 1721,1721,1721,Hanging scroll; ink and color on paper,37 1/16 x 17 3/16 in. (94.1 x 43.7 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36015,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.220.119,false,true,49239,Asian Art,Hanging scroll,清 華喦 白芍藥圖 軸|White Peony and Rocks,China,Qing dynasty (1644–1911),,,,Artist,,Hua Yan,"Chinese, 1682–1756",,Hua Yan,Chinese,1682,1756,dated 1752,1752,1752,Hanging scroll; ink and color on paper,Image: 50 1/4 x 22 1/2 in. (127.6 x 57.2 cm) Overall with mounting: 115 × 30 3/8 in. (292.1 × 77.2 cm) Overall with knobs: 115 × 34 3/4 in. (292.1 × 88.3 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49239,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1981.285.8a–f,false,true,49237,Asian Art,Album,清 傳高鳳翰 書畫合璧 冊|Landscapes and Calligraphy,China,Qing dynasty (1644–1911),,,,Artist,,Gao Fenghan,"Chinese, 1683–1749",,Gao Fenghan,Chinese,1683,1749,dated 1736,1736,1736,Album of six paintings; ink and color on paper,Each leaf: 12 1/2 x 9 3/4 in. (31.8 x 24.8 cm),"Edward Elliott Family Collection, Purchase, The Dillon Fund Gift, 1981",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49237,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.220.123,false,true,36027,Asian Art,Hanging scroll,清 張庚 倣王蒙山水圖 軸|Landscape After Wang Meng,China,Qing dynasty (1644–1911),,,,Artist,,Zhang Geng,"Chinese, 1685–1760",,Zhang Geng,Chinese,1685,1760,dated 1759,1759,1759,Hanging scroll; ink and color on paper,49 x 15 31/32 in. (124.5 x 40.6 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36027,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -42.74.1a–h,false,true,36047,Asian Art,Album,清 張宗蒼 山水 冊 紙本|Miniature landscapes,China,Qing dynasty (1644–1911),,,,Artist,,Zhang Zongcang,"Chinese, 1686–1756",,Zhang Zongcang,Chinese,1686,1756,datable to 1751–54,1741,1764,Album of eight leaves; ink and color on paper,1 7/16 x 1 3/4 in. (3.7 x 4.4 cm),"Fletcher Fund, 1942",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36047,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1980.426.5a–h,false,true,49240,Asian Art,Album,清 汪士慎 山水花卉圖 冊|Landscapes and Flowers,China,Qing dynasty (1644–1911),,,,Artist,,Wang Shishen,"Chinese, 1686–1759",,Wang Shishen,Chinese,1686,1759,dated 1745,1745,1745,Album of eight paintings; ink and color on paper,Image: 8 x 9 7/8 in. (20.3 x 25.1 cm),"Gift of Douglas Dillon, 1980",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49240,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1997.438.1,false,true,39767,Asian Art,Hanging scroll,清 傳金農 番馬圖 軸|Grooms and Foreign Horses,China,Qing dynasty (1644–1911),,,,Artist,,Jin Nong,"Chinese, 1687–1773",,Jin Nong,Chinese,1687,1773,17th–18th century,1687,1763,Hanging scroll; ink and color on silk,Image: 27 1/2 x 21 3/4 in. (69.9 x 55.2 cm) Overall with mounting: 92 1/2 x 22 7/8 in. (235 x 58.1 cm) Overall with knobs: 92 1/2 x 26 1/4 in. (235 x 66.7 cm),"The C. C. Wang Family Collection, Gift of C. C. Wang, 1997",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/39767,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.160,false,true,49243,Asian Art,Hanging scroll,清 金農 墨梅圖 軸|Blossoming Prunus,China,Qing dynasty (1644–1911),,,,Artist,,Jin Nong,"Chinese, 1687–1773",,Jin Nong,Chinese,1687,1773,dated 1759,1759,1759,Hanging scroll; ink on paper,Image: 49 3/8 x 17 in. (125.4 x 43.2 cm) Overall with mounting: 85 1/2 x 21 in. (217.2 x 53.3 cm) Overall with knobs: 85 1/2 x 23 3/4 in. (217.2 x 60.3 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49243,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.495a–l,false,true,36432,Asian Art,Album,清 金農 梅花圖 冊|Plum Blossoms,China,Qing dynasty (1644–1911),,,,Artist,,Jin Nong,"Chinese, 1687–1773",,Jin Nong,Chinese,1687,1773,dated 1757,1757,1757,Album of twelve leaves; ink on paper,Image: 10 x 11 3/4 in. (25.4 x 29.8 cm),"Edward Elliott Family Collection, Gift of Douglas Dillon, 1986",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36432,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1997.439a–m,false,true,39764,Asian Art,Album,,China,Qing dynasty (1644–1911),,,,Artist,,Jin Nong,"Chinese, 1687–1773",,Jin Nong,Chinese,1687,1773,dated 1754,1754,1754,Album of twelve paintings; ink and color on paper,Each 11 1/4 x 9 3/8 in. (28.6 x 23.8 cm),"Gift of Mr. and Mrs. Wan-go H. C. Weng, 1997",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/39764,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1981.285.7,false,true,49244,Asian Art,Handscroll,清 鄭燮 蘭竹圖 卷|Orchids and Bamboo,China,Qing dynasty (1644–1911),,,,Artist,,Zheng Xie,"Chinese, 1693–1765",,Zheng Xie,Chinese,1693,1765,dated 1742,1742,1742,Handscroll; ink on paper,Image: 13 3/4 x 147 1/2 in. (34.9 x 374.7 cm) Overall with mounting: 15 1/8 x 295 1/4 in. (38.4 x 749.9 cm),"Edward Elliott Family Collection, Purchase, Dillon Fund Gift, 1981",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49244,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1990.322a–d,false,true,44620,Asian Art,Hanging scrolls,清 鄭燮 遠山煙竹圖 軸|Misty Bamboo on a Distant Mountain,China,Qing dynasty (1644–1911),,,,Artist,,Zheng Xie,"Chinese, 1693–1765",,Zheng Xie,Chinese,1693,1765,dated 1753,1753,1753,Set of four hanging scrolls; ink on paper,Overall with mounting (each): 107 1/4 × 27 in. (272.4 × 68.6 cm),"From the P. Y. and Kinmay W. Tang Family Collection, Gift of Oscar L. Tang, 1990",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/44620,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1971.256,false,true,49247,Asian Art,Hanging scroll,,China,Qing dynasty (1644–1911),,,,Artist,,Li Fangying,"Chinese, 1696–1754",,Li Fangying,Chinese,1696,1754,Dated 1743,1743,1743,Hanging scroll; ink and color on paper,Image: 44 7/8 x 23 3/8 in. (114 x 59.4 cm) Overall with mounting: 99 1/2 x 29 5/8 in. (252.7 x 75.2 cm) Overall with rollers: 99 1/2 x 33 1/4 in. (252.7 x 84.5 cm),"From the P. Y. and Kinmay W. Tang Family Collection, Gift of Professor Wen Fong, 1971",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49247,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.163,false,true,49253,Asian Art,Folding fan mounted as an album leaf,,China,Qing dynasty (1644–1911),,,,Artist,,Wang Chen,"Chinese, 1720–1797",,Wang Chen,Chinese,1720,1797,dated 1788,1788,1788,Folding fan mounted as an album leaf; ink and color on paper,7 1/8 x 20 1/4 in. (18.1 x 51.4 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49253,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.510.1,false,true,73161,Asian Art,Hanging scroll,清 浸月圖 軸|Plum Blossoms in Moonlight,China,Qing dynasty (1644–1911),,,,Artist,,Tong Yu,"Chinese, 1721–1782",,Tong Yu,Chinese,1721,1782,second half of the 18th century,1751,1799,Hanging scroll; ink on paper,Image: 49 5/8 x 11 3/4 in. (126 x 29.8 cm),"The Lin Yutang Family Collection, Gift of Hsiang Ju Lin, in memory of Taiyi Lin Lai, 2005",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/73161,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.220.34,false,true,49254,Asian Art,Hanging scroll,清 羅聘 篠園飲酒圖 軸|Drinking in the Bamboo Garden,China,Qing dynasty (1644–1911),,,,Artist,,Luo Ping,"Chinese, 1733–1799",,Luo Ping,Chinese,1733,1799,dated 1773,1773,1773,Hanging scroll; ink and color on paper,Image: 31 1/2 x 21 1/2 in. (80 x 54.6 cm) Overall with mounting: 116 x 27 7/16 in. (294.6 x 69.7 cm) Overall with knobs: 116 x 32 5/8 in. (294.6 x 82.9 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49254,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1997.438.3,false,true,39765,Asian Art,Hanging scroll,清 雙駿圖 軸|Two Horses,China,Qing dynasty (1644–1911),,,,Artist,,Qian Feng,"Chinese, 1740–1795",,Qian Feng,Chinese,1740,1795,dated 1793,1793,1793,Hanging scroll; ink and color on paper,Image: 48 5/8 x 20 1/4 in. (123.5 x 51.4 cm) Overall with mounting: 87 1/2 x 25 3/4 in. (222.3 x 65.4 cm) Overall with knobs: 87 1/2 x 29 15/16 in. (222.3 x 76 cm),"The C. C. Wang Family Collection, Gift of C. C. Wang, 1997",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/39765,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.267.2a–h,false,true,41494,Asian Art,Album,清 伊秉綬 山水 冊頁八開|Landscapes,China,Qing dynasty (1644–1911),,,,Artist,,Yi Bingshou,"Chinese, 1754–1815",,YI BINGSHOU,Chinese,1754,1815,dated 1814,1814,1814,Album of eight leaves; ink on paper,9 3/4 x 11 5/8 in. (24.8 x 29.5 cm),"Gift of Robert Hatfield Ellsworth, in memory of La Ferne Hatfield Ellsworth, 1986",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/41494,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.220.36,false,true,36017,Asian Art,Hanging scroll,清 湯貽汾 山水 軸|Landscape,China,Qing dynasty (1644–1911),,,,Artist,,Tang Yifen,"Chinese, 1778–1853",,Tang Yifen,Chinese,1778,1853,dated 1845,1845,1845,Hanging scroll; ink on paper,Image: 46 x 10 3/4 in. (116.8 x 27.3 cm) Overall with mounting: 97 1/2 x 17 1/2 in. (247.7 x 44.5 cm) Overall with knobs: 97 1/2 x 20 1/8 in. (247.7 x 51.1 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36017,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.267.26,false,true,36160,Asian Art,Hanging scroll,清 王素 東山報捷圖 軸|Bringing the Message of Victory to Dongshan,China,Qing dynasty (1644–1911),,,,Artist,,Wang Su,"Chinese, 1794–1877",,Wang Su,Chinese,1794,1877,dated 1862,1862,1862,Hanging scroll; ink and color on paper,56 1/2 x 31 3/4 in. (143.5 x 80.6 cm),"Gift of Robert Hatfield Ellsworth, in memory of La Ferne Hatfield Ellsworth, 1986",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36160,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.267.16,false,true,36155,Asian Art,Album leaf,清 程庭鷺 山水 冊頁|Landscape,China,Qing dynasty (1644–1911),,,,Artist,,Cheng Tinglu,"Chinese, 1796–1858",,Cheng Tinglu,Chinese,1796,1858,dated 1827,1827,1827,Album leaf; ink and color on paper,9 3/4 x 13 1/2 in. (24.8 x 34.3 cm),"Gift of Robert Hatfield Ellsworth, in memory of La Ferne Hatfield Ellsworth, 1986",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36155,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.267.21,false,true,49449,Asian Art,Folding fan mounted as an album leaf,清 吳熙載 蟬柳 扇面|Cicada,China,Qing dynasty (1644–1911),,,,Artist,,Wu Xizai,"Chinese, 1799–1870",,Wu Xizai,Chinese,1799,1870,dated 1852,1852,1852,Folding fan mounted as an album leaf; ink and color on alum paper,7 1/8 x 20 3/8 in. (18.1 x 51.8 cm),"Gift of Robert Hatfield Ellsworth, in memory of La Ferne Hatfield Ellsworth, 1986",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49449,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.267.24,false,true,49451,Asian Art,Hanging scroll,清 吳熙載 山水 軸|Landscape,China,Qing dynasty (1644–1911),,,,Artist,,Wu Xizai,"Chinese, 1799–1870",,Wu Xizai,Chinese,1799,1870,dated 1858,1858,1858,Hanging scroll; ink and color on paper,61 1/4 x 17 3/16 in. (155.6 x 43.7 cm),"Gift of Robert Hatfield Ellsworth, in memory of La Ferne Hatfield Ellsworth, 1986",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49451,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1984.215a–m,false,true,49434,Asian Art,Album,清 戴熙 山水 冊頁八開|Landscapes,China,Qing dynasty (1644–1911),,,,Artist,,Dai Xi,"Chinese, 1801–1860",,Dai Xi,Chinese,1801,1860,dated 1848,1848,1848,Album of eight paintings; ink and color on paper,12 1/4 x 7 5/8 in. (31.1 x 19.4 cm),"Purchase, Bequest of Dorothy Graham Bennett, 1984",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49434,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.267.40,false,true,49458,Asian Art,Album leaf,清 張熊 山水 冊頁|Landscape,China,Qing dynasty (1644–1911),,,,Artist,,Zhang Xiong,"Chinese, 1803–1886",,Zhang Xiong,Chinese,1803,1886,dated 1827,1827,1827,Album leaf; ink and color on paper,9 3/4 x 13 1/2 in. (24.8 x 34.3 cm),"Gift of Robert Hatfield Ellsworth, in memory of La Ferne Hatfield Ellsworth, 1986",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49458,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.267.19,false,true,36157,Asian Art,Folding fan mounted as an album leaf,清 劉德六 松鼠葡萄 扇面|Squirrel and Grape,China,Qing dynasty (1644–1911),,,,Artist,,Liu Deliu,"Chinese, 1806–1875",,Liu Deliu,Chinese,1806,1875,dated 1868,1868,1868,Folding fan mounted as an album leaf; ink and color on alum paper,6 15/16 x 20 1/16 in. (17.6 x 51 cm),"Gift of Robert Hatfield Ellsworth, in memory of La Ferne Hatfield Ellsworth, 1986",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36157,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.267.31,false,true,49454,Asian Art,Folding fan mounted as an album leaf,清 胡遠 山水 扇面|Landscape,China,Qing dynasty (1644–1911),,,,Artist,,Hu Yuan,"Chinese, 1823–1886",,Hu Yuan,Chinese,1823,1886,dated 1885,1885,1885,Folding fan mounted as an album leaf; ink and color on alum paper,6 15/16 x 20 1/4 in. (17.6 x 51.4 cm),"Gift of Robert Hatfield Ellsworth, in memory of La Ferne Hatfield Ellsworth, 1986",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49454,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.267.53,false,true,49468,Asian Art,Album leaf,清 虛谷 秋帆圖 冊頁|Sailing in Autumn,China,Qing dynasty (1644–1911),,,,Artist,,Xu Gu,"Chinese, 1823–1896",,Xu Gu,Chinese,1823,1896,dated 1893,1893,1893,Album leaf; ink and color on paper,14 1/8 x 35 7/8 in. (35.9 x 91.1 cm),"Gift of Robert Hatfield Ellsworth, in memory of La Ferne Hatfield Ellsworth, 1986",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49468,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.267.54,false,true,49469,Asian Art,Folding fan mounted as an album leaf,清 虛谷 松鼠 扇面|Squirrel on an Autumn Branch,China,Qing dynasty (1644–1911),,,,Artist,,Xu Gu,"Chinese, 1823–1896",,Xu Gu,Chinese,1823,1896,ca. 1880s,1870,1890,Folding fan mounted as an album leaf; ink and color on alum paper,7 1/4 x 19 in. (18.4 x 48.3 cm),"Gift of Robert Hatfield Ellsworth, in memory of La Ferne Hatfield Ellsworth, 1986",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49469,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.267.27,false,true,36161,Asian Art,Folding fan mounted as an album leaf,清 趙之謙 芍藥 扇面|Peony,China,Qing dynasty (1644–1911),,,,Artist,,Zhao Zhiqian,"Chinese, 1829–1884",,Zhao Zhiqian,Chinese,1829,1884,dated 1862,1862,1862,Folding fan mounted as an album leaf; ink and color on gold-flecked paper,7 x 20 3/4 in. (17.8 x 52.7 cm),"Gift of Robert Hatfield Ellsworth, in memory of La Ferne Hatfield Ellsworth, 1986",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36161,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.267.28,false,true,49452,Asian Art,Folding fan mounted as an album leaf,清 趙之謙 芍藥 桃花 扇面|Peach Blossoms and Peony,China,Qing dynasty (1644–1911),,,,Artist,,Zhao Zhiqian,"Chinese, 1829–1884",,Zhao Zhiqian,Chinese,1829,1884,ca. 1860,1850,1870,Folding fan mounted as an album leaf; ink and color on gold-flecked paper,7 1/2 x 21 1/2 in. (19.1 x 54.6 cm),"Gift of Robert Hatfield Ellsworth, in memory of La Ferne Hatfield Ellsworth, 1986",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49452,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.267.77,false,true,36187,Asian Art,Folding fan mounted as an album leaf,清 沙馥 松鼠葡萄 扇面|Squirrel and Grapes,China,Qing dynasty (1644–1911),,,,Artist,,Sha Fu,"Chinese, 1831–1906",,Sha Fu,Chinese,1831,1906,dated 1894,1894,1894,Folding fan mounted as an album leaf; ink and color on alum paper,6 5/8 x 19 3/4 in. (16.8 x 50.2 cm),"Gift of Robert Hatfield Ellsworth, in memory of La Ferne Hatfield Ellsworth, 1986",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36187,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.267.78,false,true,49474,Asian Art,Folding fan mounted as an album leaf,清 沙馥 人物 扇面|The Peach and Plum Garden,China,Qing dynasty (1644–1911),,,,Artist,,Sha Fu,"Chinese, 1831–1906",,Sha Fu,Chinese,1831,1906,dated 1879,1879,1879,Folding fan mounted as an album leaf; ink and color on alum paper,7 x 21 3/8 in. (17.8 x 54.3 cm),"Gift of Robert Hatfield Ellsworth, in memory of La Ferne Hatfield Ellsworth, 1986",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49474,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.267.80,false,true,49476,Asian Art,Folding fan mounted as an album leaf,清 錢慧安 仕女 扇面|A Beauty,China,Qing dynasty (1644–1911),,,,Artist,,Qian Huian,"Chinese, 1833–1911",,Qian Huian,Chinese,1833,1911,dated 1876,1876,1876,Folding fan mounted as an album leaf; ink and color on alum paper,7 1/4 x 21 in. (18.4 x 53.3 cm),"Gift of Robert Hatfield Ellsworth, in memory of La Ferne Hatfield Ellsworth, 1986",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49476,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.267.45,false,true,49460,Asian Art,Folding fan mounted as an album leaf,清 任薰 高士臨風 扇面|Scholar in the Wind,China,Qing dynasty (1644–1911),,,,Artist,,Ren Xun,"Chinese, 1835–1893",,Ren Xun,Chinese,1835,1893,ca. 1880,1870,1890,Folding fan mounted as an album leaf; ink and color on alum paper,6 9/16 x 9 3/8 in. (16.7 x 23.8 cm),"Gift of Robert Hatfield Ellsworth, in memory of La Ferne Hatfield Ellsworth, 1986",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49460,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.267.46,false,true,49461,Asian Art,Folding fan mounted as an album leaf,清 任薰 海棠小鳥 扇面|Bird on a Rock by a Flowering Branch,China,Qing dynasty (1644–1911),,,,Artist,,Ren Xun,"Chinese, 1835–1893",,Ren Xun,Chinese,1835,1893,dated 1879,1879,1879,Folding fan mounted as an album leaf; ink and color on alum paper,7 x 20 7/8 in. (17.8 x 53.0 cm),"Gift of Robert Hatfield Ellsworth, in memory of La Ferne Hatfield Ellsworth, 1986",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49461,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1985.131.2,false,true,49464,Asian Art,Album leaf,清 任頤 冊頁|Two Birds Perched on a Flowering Rose Bush,China,Qing dynasty (1644–1911),,,,Artist,,Ren Yi (Ren Bonian),"Chinese, 1840–1896",,Ren Yi (Ren Bonian),Chinese,1840,1896,late 19th century,19,19,Album leaf; ink and color on paper,10 7/8 x 10 5/8 in. (27.6 x 27.0 cm),"Bequest of Louisa L. McNeary, 1985",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49464,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1988.324.1,false,true,44566,Asian Art,Hanging scroll,"清 任頤 松鶴圖 軸|Cranes, Pine Tree, and Lichen",China,Qing dynasty (1644–1911),,,,Artist,,Ren Yi (Ren Bonian),"Chinese, 1840–1896",,Ren Yi (Ren Bonian),Chinese,1840,1896,dated 1885,1800,1940,Hanging scroll; ink and color on paper,Image: 57 3/4 x 14 3/4 in. (146.7 x 37.5 cm) Overall with mounting: 95 1/4 x 21 5/8 in. (241.9 x 54.9 cm) Overall with knobs: 95 1/4 x 23 3/4 in. (241.9 x 60.3 cm),"Gift of Robert Hatfield Ellsworth, in memory of La Ferne Hatfield Ellsworth, 1988",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/44566,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2002.208.2,false,true,37393,Asian Art,Hanging scroll,清 任頤 鍾馗 軸|Zhong Kui,China,Qing dynasty (1644–1911),,,,Artist,,Ren Yi (Ren Bonian),"Chinese, 1840–1896",,Ren Yi (Ren Bonian),Chinese,1840,1896,dated 1883,1883,1883,Hanging scroll; ink and color on paper,Image: 67 5/8 x 36 3/4 in. (171.8 x 93.3 cm) Overall with mounting: 120 x 40 5/8 in. (304.8 x 103.2 cm) Overall with knobs: 120 x 44 1/2 in. (304.8 x 113 cm),"Gift of Cécile and Sandy Mactaggart, 2002",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/37393,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.267.48,false,true,49463,Asian Art,Folding fan mounted as an album leaf,清 任頤 童子水牛 扇面|Herdboy and Buffalo,China,Qing dynasty (1644–1911),,,,Artist,,Ren Yi (Ren Bonian),"Chinese, 1840–1896",,Ren Yi (Ren Bonian),Chinese,1840,1896,dated 1890,1890,1890,Folding fan mounted as an album leaf; ink and color on alum paper,7 1/2 x 21 3/8 in. (19.1 x 54.3 cm),"Gift of Robert Hatfield Ellsworth, in memory of La Ferne Hatfield Ellsworth, 1986",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49463,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.267.49,false,true,39715,Asian Art,Folding fan mounted as an album leaf,清 任頤 石上讀書 扇面|Scholar on a Rock,China,Qing dynasty (1644–1911),,,,Artist,,Ren Yi (Ren Bonian),"Chinese, 1840–1896",,Ren Yi (Ren Bonian),Chinese,1840,1896,ca. 1880,1870,1890,Folding fan mounted as an album leaf; ink and color on paper,Image: 7 1/2 x 21 3/16 in. (19.1 x 53.8 cm),"Gift of Robert Hatfield Ellsworth, in memory of La Ferne Hatfield Ellsworth, 1986",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/39715,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.267.50,false,true,49462,Asian Art,Hanging scroll,清 任頤 軸|Man on a Bridge,China,Qing dynasty (1644–1911),,,,Artist,,Ren Yi (Ren Bonian),"Chinese, 1840–1896",,Ren Yi (Ren Bonian),Chinese,1840,1896,dated 1889,1889,1889,Hanging scroll; ink on bark paper,36 7/8 x 24 1/4 in. (93.7 x 61.6 cm),"Gift of Robert Hatfield Ellsworth, in memory of La Ferne Hatfield Ellsworth, 1986",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49462,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.267.79,false,true,49475,Asian Art,Folding fan mounted as an album leaf,清 金? 梅花 扇面|Plum,China,Qing dynasty (1644–1911),,,,Artist,,Jin Lan,"Chinese, 1841–1910",,Jin Lan,Chinese,1841,1910,dated 1886,1886,1886,Folding fan mounted as an album leaf; ink and color on alum paper,7 x 21 in. (17.8 x 53.3 cm),"Gift of Robert Hatfield Ellsworth, in memory of La Ferne Hatfield Ellsworth, 1986",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49475,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.267.68,false,true,36181,Asian Art,Folding fan mounted as an album leaf,清 吳榖祥 山水 扇面|Landscape,China,Qing dynasty (1644–1911),,,,Artist,,Wu Guxiang,"Chinese, 1848–1903",,Wu Guxiang,Chinese,1848,1903,dated 1894,1894,1894,Folding fan mounted as an album leaf; ink on gold paper,7 1/4 x 20 5/16 in. (18.4 x 51.6 cm),"Gift of Robert Hatfield Ellsworth, in memory of La Ferne Hatfield Ellsworth, 1986",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36181,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.267.66,false,true,36179,Asian Art,Folding fan mounted as an album leaf,清 徐祥 小鳥水仙 扇面|Birds and Narcissus,China,Qing dynasty (1644–1911),,,,Artist,,Xu Xiang,"Chinese, 1850–1899",,Xu Xiang,Chinese,1850,1899,dated 1883,1883,1883,Folding fan mounted as an album leaf; ink and color on alum paper,7 1/4 x 21 in. (18.4 x 53.3 cm),"Gift of Robert Hatfield Ellsworth, in memory of La Ferne Hatfield Ellsworth, 1986",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36179,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.267.101,false,true,49602,Asian Art,Folding fan mounted as an album leaf,近代 高邕 楊柳 扇面|Willow,China,Qing dynasty (1644–1911),,,,Artist,,Gao Yong,"Chinese, 1850–1921",,Gao Yong,Chinese,1850,1921,dated 1895,1895,1895,Folding fan mounted as an album leaf; ink and color on alum paper,7 1/4 x 19 3/8 in. (18.4 x 49.2 cm),"Gift of Robert Hatfield Ellsworth, in memory of La Ferne Hatfield Ellsworth, 1986",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49602,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.267.143,false,true,41905,Asian Art,Hanging scroll,近代 吳淑娟 仙姑圖 軸|Female Immortals,China,Qing dynasty (1644–1911),,,,Artist,,Wu Shujuan,"Chinese, 1853–1930",,Wu Shujuan,Chinese,1853,1930,dated 1909,1909,1909,Hanging scroll; ink and color on paper,47 3/4 x 20 5/8 in. (121.3 x 52.4 cm),"Gift of Robert Hatfield Ellsworth, in memory of La Ferne Hatfield Ellsworth, 1986",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/41905,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.267.89,false,true,36194,Asian Art,Folding fan mounted as an album leaf,近代 倪田 橋頭隱士 扇面|Scholar on the Bridge,China,Qing dynasty (1644–1911),,,,Artist,,Ni Tian,"Chinese, 1855–1919",,Ni Tian,Chinese,1855,1919,dated 1901,1901,1901,Folding fan mounted as an album leaf; ink and color on alum paper,6 3/8 x 19 5/8 in. (16.2 x 49.8 cm),"Gift of Robert Hatfield Ellsworth, in memory of La Ferne Hatfield Ellsworth, 1986",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36194,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.267.91,false,true,49594,Asian Art,Hanging scroll,近代 倪田 雙馬圖 軸|Two Horses,China,Qing dynasty (1644–1911),,,,Artist,,Ni Tian,"Chinese, 1855–1919",,Ni Tian,Chinese,1855,1919,dated 1904,1904,1904,Hanging scroll; ink and color on paper,42 7/8 x 19 9/16 in. (108.9 x 49.7 cm),"Gift of Robert Hatfield Ellsworth, in memory of La Ferne Hatfield Ellsworth, 1986",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49594,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.267.17,false,true,36156,Asian Art,Album leaf,清 沈焯 山水 冊頁|Landscape,China,Qing dynasty (1644–1911),,,,Artist,,Shen Zhuo,(active 19th century),,Shen Zhuo,Chinese,1800,1899,dated 1827,1827,1827,Album leaf; ink and color on paper,9 3/4 x 13 1/2 in. (24.8 x 34.3 cm),"Gift of Robert Hatfield Ellsworth, in memory of La Ferne Hatfield Ellsworth, 1986",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36156,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.267.55a–l,false,true,36171,Asian Art,Album,清 張之萬 山水 冊頁十二開|Landscapes,China,Qing dynasty (1644–1911),,,,Artist,,Zhang Zhiwan,"Chinese, 1811–1897",,Zhang Zhiwan,Chinese,1811,1897,dated 1875,1875,1875,Album of twelve leaves; ink and color on paper,9 5/16 x 9 13/16 in. (23.7 x 24.9 cm),"Gift of Robert Hatfield Ellsworth, in memory of La Ferne Hatfield Ellsworth, 1986",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36171,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.161,false,true,49241,Asian Art,Hanging scroll,清 傳李鱓 墨竹圖 軸|Ink Bamboo,China,Qing dynasty (1644–1911),,,,Artist,,Li Shan,"Chinese, 1686–ca. 1756",,Li Shan,Chinese,1686,1756,dated 1749,1749,1749,Hanging scroll; ink on paper,Image: 52 x 29 1/8 in. (132.1 x 74 cm) Overall with mounting: 99 1/2 x 32 1/8 in. (252.7 x 81.6 cm) Overall with knobs: 99 1/2 x 35 1/2 in. (252.7 x 90.2 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49241,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.267.41,false,true,36167,Asian Art,Folding fan mounted as an album leaf,清 居巢 蠶蛾 扇面|Silkworm,China,Qing dynasty (1644–1911),,,,Artist,,Zhü Chao,"Chinese, ca. 1823–1889",,Zhü Chao,Chinese,1823,1889,dated 1859,1859,1859,Folding fan mounted as an album leaf; ink and color on alum paper,7 1/4 x 21 in. (18.4 x 53.3 cm),"Gift of Robert Hatfield Ellsworth, in memory of La Ferne Hatfield Ellsworth, 1986",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36167,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1982.461,false,true,39714,Asian Art,Handscroll,清 佚名 臨袁江瞻園圖 卷|View of a Garden Villa,China,Qing dynasty (1644–1911),,,,Artist,After,Yuan Jiang,active ca.1680–ca.1730,,Yuan Jiang,Chinese,1680,1730,18th century (?),1700,1740,Handscroll; ink and color on silk,Image: 20 1/2 x 116 1/8 in. (52.1 x 295 cm) Overall with mounting: 256 5/8 x 24 7/8 in. (651.8 x 63.2 cm),"From the P. Y. and Kinmay W. Tang Family Collection, Gift of Constance Tang Fong, in honor of her mother, Mrs. P. Y. Tang, 1982",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/39714,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1982.125a–l,false,true,49227,Asian Art,Hanging scrolls,清 袁江 九成宮圖 屏|The Palace of Nine Perfections,China,Qing dynasty (1644–1911),,,,Artist,,Yuan Jiang,active ca.1680–ca.1730,,Yuan Jiang,Chinese,1680,1730,dated 1691,1691,1691,Set of twelve hanging scrolls; ink and color on silk,Image: 81 1/2 x 18 ft. 5 3/4 in. (207 cm x 563.2 cm) Overall with mounting: 94 1/4 x 19 ft. (239.4 x 579.1 cm),"Purchase, The Dillon Fund Gift, 1982",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49227,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.149,false,true,44581,Asian Art,Hanging scroll,,China,Qing dynasty (1644–1911),,,,Artist,,Feng Qiyong,"Chinese, active ca. 1730s",,Feng Qiyong,Chinese,1730,1739,dated 1733,1733,1733,Hanging scroll; ink on silk,Image: 45 x 25 5/16 in. (114.3 x 64.3 cm) Overall with mounting: 98 x 32 1/2 in. (248.9 x 82.6 cm) Overall with knobs: 98 x 36 1/4 in. (248.9 x 92.1 cm),"Purchase, Bequest of Dorothy Graham Bennett, 1989",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/44581,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -69.242.8–.15,false,true,72263,Asian Art,Album,清 樊圻 山水圖 冊 紙本|Landscapes,China,Qing dynasty (1644–1911),,,,Artist,,Fan Qi,"Chinese, 1616–after 1694",,Fan Qi,Chinese,1616,1694,dated 1646,1646,1646,Album of eight leaves; ink and color on paper,Each leaf: 6 5/8 x 8 in. (16.8 x 20.3 cm),"The Sackler Fund, 1969",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/72263,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.131a–h,false,true,49131,Asian Art,Album,清 樊圻 為玉翁作山水圖 冊|Landscapes Painted for Yuweng,China,Qing dynasty (1644–1911),,,,Artist,,Fan Qi,"Chinese, 1616–after 1694",,Fan Qi,Chinese,1616,1694,dated 1673,1673,1673,Album of eight leaves; ink and color on paper,6 x 7 7/16 in. (15.2 x 18.9 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49131,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2008.270,false,true,74818,Asian Art,Handscroll,"清 高簡 說詩圖 卷|Discourse on Poetry",China,Qing dynasty (1644–1911),,,,Artist,,Gao Jian,"Chinese, 1634–after 1715",,Gao Jian,Chinese,1634,1725,dated 1698,1698,1698,Handscroll; ink on paper,Image: 13 11/16 x 35 7/16 in. (34.8 x 90 cm),"Purchase, The Vincent Astor Foundation Gift and The Dillon Fund Gift, 2008",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/74818,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.7,false,true,49234,Asian Art,Hanging scroll,清 傳沈銓 高堂雙壽圖 軸|Flowering Crabapple and Pair of Birds,China,Qing dynasty (1644–1911),,,,Artist,,Shen Nanpin (Japanese: Shin Nanpin),"Chinese, 1682–after 1762",,Shen Nanpin,Chinese,1682,1762,dated 1744,1744,1744,Hanging scroll; ink and color on silk,37 x 42 1/4 in. (94.0 x 107.3 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49234,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.268.81,false,true,36105,Asian Art,Hanging scroll,"桃と月季花（長春花）に鶴図|Cranes, Peach Tree, and Chinese Roses",China,Qing dynasty (1644–1911),,,,Artist,After,Shen Nanpin (Japanese: Shin Nanpin),"Chinese, 1682–after 1762",,Shen Nanpin,Chinese,1682,1762,early 18th century,1700,1733,Hanging scroll; ink and color on silk,Image: 78 1/4 x 39 3/4 in. (198.8 x 101 cm) Overall with mounting: 97 3/4 x 48 1/8 in. (248.3 x 122.2 cm) Overall with knobs: 97 3/4 x 51 1/8 in. (248.3 x 129.9 cm),"The Harry G. C. Packard Collection of Asian Art, Gift of Harry G. C. Packard, and Purchase, Fletcher, Rogers, Harris Brisbane Dick, and Louis V. Bell Funds, Joseph Pulitzer Bequest, and The Annenberg Fund Inc. Gift, 1975",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36105,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.268.82,false,true,36106,Asian Art,Hanging scroll,,China,Qing dynasty (1644–1911),,,,Artist,,Shen Nanpin (Japanese: Shin Nanpin),"Chinese, 1682–after 1762",,Shen Nanpin,Chinese,1682,1762,1750,1750,1750,Hanging scroll; ink and color on silk,Image: 45 13/16 x 19 11/16 in. (116.4 x 50 cm) Overall with mounting: 82 x 26 1/4 in. (208.3 x 66.7 cm) Overall with knobs: 82 x 28 5/16 in. (208.3 x 71.9 cm),"The Harry G. C. Packard Collection of Asian Art, Gift of Harry G. C. Packard, and Purchase, Fletcher, Rogers, Harris Brisbane Dick, and Louis V. Bell Funds, Joseph Pulitzer Bequest, and The Annenberg Fund Inc. Gift, 1975",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36106,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.510.2,false,true,73159,Asian Art,Hanging scroll,清 李香君小影 軸|Portrait of Li Xiangjun,China,Qing dynasty (1644–1911),,,,Artist,,Cui He,"Chinese, active 1800–1850",,Cui He,Chinese,1800,1850,dated 1817,1817,1817,Hanging scroll; ink and color on paper,Image: 20 5/8 x 49 in. (52.4 x 124.5 cm) Overall with knobs: 104 1/16 x 30 1/4 in. (264.3 x 76.8 cm),"The Lin Yutang Family Collection, Gift of Hsiang Ju Lin, in memory of Taiyi Lin Lai, 2005",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/73159,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.278.4,false,true,36100,Asian Art,Hanging scroll,清 王鑑 倣黃公望秋山圖 軸 紙本|Landscape in the style of Huang Gongwang,China,Qing dynasty (1644–1911),,,,Artist,,Wang Jian,"Chinese, 1609–1677 or 1688",,Wang Jian,Chinese,1609,1677,dated 1657,1657,1657,Hanging scroll; ink and color on paper,Image: 45 5/8 x 22 1/8 in. (115.9 x 56.2 cm) Overall with mounting: 103 1/4 x 29 in. (262.3 x 73.7 cm) Overall with rollers: 103 1/4 x 32 1/2 in. (262.3 x 82.6 cm),"Gift of Mr. and Mrs. Earl Morse, 1972",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36100,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.267.76,false,true,36186,Asian Art,Handscroll,清 松年 山水 手卷|Landscape,China,Qing dynasty (1644–1911),,,,Artist,,Song Nian,"Chinese, active 19th century",,Song Nian,Chinese,1800,1899,dated 1898,1898,1898,Handscroll in six sections; ink and color on paper,Image: 13 3/8 x 107 7/8 in. (34 x 274 cm) Overall with mounting: 13 5/8 x 298 3/4 in. (34.6 x 758.8 cm),"Gift of Robert Hatfield Ellsworth, in memory of La Ferne Hatfield Ellsworth, 1986",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36186,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2000.665.1,false,true,59025,Asian Art,Hanging scroll,清 祁豸佳 倣倪瓚冬林亭子圖 軸|Pavilion amongst Wintry Trees after Ni Zan,China,Qing dynasty (1644–1911),,,,Artist,,Qi Zhijia,"Chinese, ca. 1595–ca. 1670",,Qi Zhijia,Chinese,1585,1680,dated 1661,1661,1661,Hanging scroll; ink on paper,45 5/8 x 20 in. (116 x 50.8 cm),"Gift of Marie-Hélène and Guy A. Weill, 2000",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/59025,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -64.268.3,false,true,36091,Asian Art,Album leaf,清 施霖 山莊圖 冊頁|Mountain Retreat,China,Qing dynasty (1644–1911),,,,Artist,,Shi Lin,"Chinese, active ca. 1630–60",,Shi Lin,Chinese,1610,1670,datable to 1654–55,1654,1655,Leaf from a collective album of many leaves; ink and color on paper,9 3/4 x 12 3/4 in. (24.8 x 32.4 cm),"Seymour Fund, 1964",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36091,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1993.202,false,true,44720,Asian Art,Hanging scroll,清 倣張風 石橋圖 軸|The Stone Bridge,China,Qing dynasty (1644–1911),,,,Artist,,Zhang Feng,"Chinese, active ca. 1628–1662",,Zhang Feng,Chinese,1618,1662,dated 1661,1661,1661,Hanging scroll; ink on paper,Image: 60 3/4 x 18 5/16 in. (154.3 x 46.5 cm) Overall with mounting: 86 3/4 x 25 3/4 in. (220.3 x 65.4 cm) Overall with knobs: 86 3/4 x 29 1/2 in. (220.3 x 74.9 cm),"Purchase, Friends of Asian Art Gifts, The Dillon Fund Gift and Anonymous Gift, 1993",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/44720,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2001.746.1,false,true,48946,Asian Art,Hanging scroll,清 倣張風 覓菊圖 軸|Plucking Chrysanthemums,China,Qing dynasty (1644–1911),,,,Artist,After,Zhang Feng,"Chinese, active ca. 1628–1662",,Zhang Feng,Chinese,1618,1662,dated 1658,1658,1658,Hanging scroll; ink on paper,Image: 32 1/2 x 12 in. (82.6 x 30.5 cm) Overall with mounting: 70 1/2 x 17 3/8 in. (179.1 x 44.1 cm) Overall with knobs: 70 1/2 x 21 in. (179.1 x 53.3 cm),"The C. C. Wang Family Collection, Gift of C. C. Wang, 2001",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/48946,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -64.268.2,false,true,36090,Asian Art,Album leaf,清 葉欣 白鶴嶺圖 冊頁|White Crane Mountain,China,Qing dynasty (1644–1911),,,,Artist,,Ye Xin,"Chinese, active ca. 1640–1673",,Ye Xin,Chinese,1640,1640,datable to 1654–55,1654,1655,Leaf from a collective album of many leaves; ink and color on paper,9 3/4 x 13 in. (24.8 x 33 cm),"Seymour Fund, 1964",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36090,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1987.223a–d,false,true,44521,Asian Art,Album,清 葉欣 山水圖 冊 絹本|Landscapes,China,Qing dynasty (1644–1911),,,,Artist,,Ye Xin,"Chinese, active ca. 1640–1673",,Ye Xin,Chinese,1640,1640,dated 1652,1652,1652,Album of four leaves; ink and color on silk,4 5/8 x 5 1/2in. (11.7 x 14cm),"Purchase, Mrs. C. Y. Chen Gift, 1987",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/44521,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.100.91,false,true,36001,Asian Art,Folding fan mounted as an album leaf,,China,Qing dynasty (1644–1911),,,,Artist,,Wen Zhi,"Chinese, active mid-17th century",,Wen Zhi,Chinese,1634,1699,dated 1670,1670,1670,Folding fan mounted as an album leaf; ink and color on gold paper,6 1/2 x 19 3/4 in. (16.5 x 50.2 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36001,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.50,false,true,73646,Asian Art,Album,清 高岑 擬古山水圖 冊 絹本|Landscapes in the styles of old masters,China,Qing dynasty (1644–1911),,,,Artist,,Gao Cen,"Chinese, active 1643–after 1682",,Gao Cen,Chinese,1643,1682,dated 1667,1667,1667,Album of ten leaves; ink and color on silk,Each leaf: 8 1/8 x 8 3/8 in. (20.5 x 21.3 cm),"Purchase, C. C. Wang Gift, 2007",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/73646,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2003.246,false,true,65640,Asian Art,Hanging scroll,清 袁耀 雪棧行旅圖 軸|Hostelry and Travelers in Snowy Mountains,China,Qing dynasty (1644–1911),,,,Artist,,Yuan Yao,"Chinese, active 1730–after 1778",,Yuan Yao,Chinese,1730,1778,dated 1745,1745,1745,Hanging scroll; ink and color on silk,Image: 67 1/4 x 48 3/4 in. (170.8 x 123.8 cm) Overall with mounting: 121 3/4 x 50 1/2 in. (309.2 x 128.3 cm) Overall with knobs: 121 3/4 x 54 1/2 in. (309.2 x 138.4 cm),"Gift of Mr. and Mrs. C. C. Wang and Family, in memory of Douglas Dillon, 2003",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/65640,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"13.220.127a, b",false,true,49250,Asian Art,Handscrolls,清 弘曆(乾隆皇帝) 鹿角雙幅 卷|Two Paintings of Deer Antlers,China,Qing dynasty (1644–1911),,,,Artist,,Qianlong Emperor,"Chinese, (1711–1799; r. 1736–95)",,Qianlong Emperor,Chinese,1711,1799,dated 1762 and 1767,1762,1767,Two handscrolls; ink and color on paper,a: 9 3/4 × 81 1/4 in. (24.8 × 206.4 cm) b: 9 7/8 × 81 1/4 in. (25.1 × 206.4 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49250,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1984.16a–c,false,true,49251,Asian Art,Handscroll,"清 徐揚等 乾隆南巡圖, 第四卷﹕黃淮交流|The Qianlong Emperor's Southern Inspection Tour, Scroll Four: The Confluence of the Huai and Yellow Rivers (Qianlong nanxun, juan si: Huang Huai jiaoliu)",China,Qing dynasty (1644–1911),,,,Artist,,Xu Yang,"Chinese, active ca. 1750–after 1776",and assistants,XU YANG,Chinese,1750,1776,dated 1770,1770,1770,"Handscroll; ink and color on silk, lacquer box",27 1/8 x 431 1/4 in. (68.8 x 1096.17 cm),"Purchase, The Dillon Fund Gift, 1984",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49251,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1988.350a–d,false,true,41493,Asian Art,Handscroll,"清 徐揚 等 乾隆南巡圖 （第六卷﹕大運河至蘇州） 卷|The Qianlong Emperor's Southern Inspection Tour, Scroll Six: Entering Suzhou along the Grand Canal",China,Qing dynasty (1644–1911),,,,Artist,,Xu Yang,"Chinese, active ca. 1750–after 1776",and assistants,XU YANG,Chinese,1750,1776,dated 1770,1770,1770,Handscroll; ink and color on silk,Image: 27 1/8 in. x 784 1/2 in. (68.8 x 1994 cm),"Purchase, The Dillon Fund Gift, 1988",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/41493,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1981.122,false,true,36133,Asian Art,Hanging scroll,清 李寅 高閣觀瀑圖 軸|View From a Mountain Pavilion,China,Qing dynasty (1644–1911),,,,Artist,,Li Yin,"Chinese, active second half of the 17th–early 18th century",,Li Yin,Chinese,1650,1825,dated 1700,1700,1700,Hanging scroll; ink and color on silk,Image: 87 3/4 x 44 3/4 in. (222.9 x 113.7 cm) Overall with mounting: 123 1/2 x 47 3/4 in. (313.7 x 121.3 cm) Overall with knobs: 123 1/2 x 52 in. (313.7 x 132.1 cm),"Anonymous Gift, in memory of Maitland F. Griggs, 1981",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36133,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1985.227.2,false,true,40454,Asian Art,Handscroll,元 張羽材 霖雨圖 卷|Beneficent Rain,China,Yuan dynasty (1271–1368),,,,Artist,,Zhang Yucai,"Chinese, died 1316",,Zhang Yucai,Chinese,,1316,late 13th–early 14th century,1295,1316,Handscroll; ink on silk,Image: 10 9/16 x 107 in. (26.8 x 271.8 cm) Overall with mounting: 11 in. x 24 ft. 11 13/16 in. (27.9 x 753.9 cm),"Gift of Douglas Dillon, 1985",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/40454,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1995.186,false,true,36459,Asian Art,Hanging scroll,元 邊魯 孔雀芙蓉圖 軸|Peacock and Hollyhocks,China,Yuan dynasty (1271–1368),,,,Artist,,Bian Lu,"Chinese, died 1356",,Bian Lu,Chinese,,1356,mid-14th century,1334,1366,Hanging scroll; ink and color on silk,Image: 66 7/8 x 40 1/4 in. (169.9 x 102.2 cm) Overall with mounting: 103 x 41 1/4 in. (261.6 x 104.8 cm) Overall with knobs: 103 x 45 7/8 in. (261.6 x 116.5 cm),"Purchase, The Dillon Fund and The B. Y. Lam Foundation Gifts, 1995",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36459,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"1973.120.7a, b",false,true,40456,Asian Art,Hanging scrolls,元 李衎 竹石圖 對軸|Bamboo and rocks,China,Yuan dynasty (1271–1368),,,,Artist,,Li Kan,"Chinese, 1245–1320",,LI KAN,Chinese,1245,1320,dated 1318,1318,1318,Pair of hanging scrolls; ink and color on silk,Image (each): 74 3/4 x 21 3/4 in. (189.9 x 55.2 cm) Overall with mounting (each): 106 3/16 x 22 3/8 in. (269.7 x 56.8 cm) Overall with knobs (each): 106 3/16 x 23 in. (269.7 x 58.4 cm),"Ex coll.: C. C. Wang Family, Gift of The Dillon Fund, 1973",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/40456,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1973.120.5,false,true,40508,Asian Art,Handscroll,"元 趙孟頫 雙松平遠圖 卷|Twin Pines, Level Distance",China,Yuan dynasty (1271–1368),,,,Artist,,Zhao Mengfu,"Chinese, 1254–1322",,Zhao Mengfu,Chinese,1254,1322,ca. 1310,1310,1310,Handscroll; ink on paper,Image: 10 9/16 x 42 5/16 in. (26.8 x 107.5 cm) Overall with mounting: 10 15/16 x 25 ft. 7 11/16 in. (27.8 x 781.5 cm),"Ex coll.: C. C. Wang Family, Gift of The Dillon Fund, 1973",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/40508,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1973.121.15a–p,false,true,40511,Asian Art,Album,元 佚名 倣趙孟頫 九歌圖 冊|Nine Songs,China,Yuan dynasty (1271–1368),,,,Artist,After,Zhao Mengfu,"Chinese, 1254–1322",,Zhao Mengfu,Chinese,1254,1322,14th century (?),1300,1368,Album of eleven paintings; ink on paper,10 3/8 x 6 1/4 in. (26.4 x 15.9 cm),"Ex coll.: C. C. Wang Family, Fletcher Fund, 1973",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/40511,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1985.120.1,false,true,41462,Asian Art,Hanging scroll,元 吳鎮 老松圖 軸|Crooked Pine,China,Yuan dynasty (1271–1368),,,,Artist,,Wu Zhen,"Chinese, 1280–1354",,Wu Zhen,Chinese,1280,1354,dated 1335,1335,1335,Hanging scroll; ink on silk,Image: 65 3/8 x 32 1/2 in. (166.1 x 82.6 cm) Overall with mounting: 100 1/2 x 37 1/8 in. (255.3 x 94.3 cm) Overall with knobs: 100 1/2 x 40 1/2 in. (255.3 x 102.9 cm),"Purchase, The Dillon Fund Gift, 1985",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/41462,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2012.526.1,false,true,39547,Asian Art,Hanging scroll,"元 吳鎮 高節凌雲圖 軸|Bamboo, old tree, and rock",China,Yuan dynasty (1271–1368),,,,Artist,,Wu Zhen,"Chinese, 1280–1354",,Wu Zhen,Chinese,1280,1354,dated 1338,1338,1338,Hanging scroll; ink on silk,Image: 65 5/8 x 38 1/2 in. (166.7 x 97.8 cm) Overall with mounting: 117 1/2 x 39 1/2 in. (298.5 x 100.3 cm) Overall with knobs: 117 1/2 x 43 7/8 in. (298.5 x 111.4 cm),"Ex coll.: C. C. Wang Family, Gift of Oscar L. Tang Family, 2012",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/39547,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2006.571,false,true,39546,Asian Art,Hanging scroll,元 柯九思 臨文同墨竹圖 軸|Bamboo after Wen Tong,China,Yuan dynasty (1271–1368),,,,Artist,,Ke Jiusi,"Chinese, 1290–1343",,Ke Jiusi,Chinese,1290,1343,dated 1343,1343,1343,Hanging scroll; ink on silk,Image: 42 3/8 x 18 3/4 in. (107.6 x 47.6 cm) Overall with mounting: 98 1/2 x 26 5/8 in. (250.2 x 67.6 cm) Overall with knobs: 98 1/2 x 30 in. (250.2 x 76.2 cm),"Ex coll.: C. C. Wang Family, Gift of Oscar L. Tang Family, 2006",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/39546,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1973.120.8,false,true,45636,Asian Art,Hanging scroll,元 倪瓚 虞山林壑圖 軸|Woods and Valleys of Mount Yu,China,Yuan dynasty (1271–1368),,,,Artist,,Ni Zan,"Chinese, 1306–1374",,NI ZAN,Chinese,1306,1374,dated 1372,1372,1372,Hanging scroll; ink on paper,Image: 37 1/4 x 14 1/8 in. (94.6 x 35.9 cm) Overall with mounting: 82 x 20 5/8 in. (208.3 x 52.4 cm) Overall with knobs: 82 x 24 5/8 in. (208.3 x 62.5 cm),"Ex coll.: C. C. Wang Family, Gift of The Dillon Fund, 1973",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45636,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.38,false,true,45635,Asian Art,Hanging scroll,元 倪瓚 秋林野興圖 軸|Enjoying the Wilderness in an Autumn Grove,China,Yuan dynasty (1271–1368),,,,Artist,,Ni Zan,"Chinese, 1306–1374",,NI ZAN,Chinese,1306,1374,dated 1339,1339,1339,Hanging scroll; ink on paper,Image: 38 5/8 x 27 1/8 in. (98.1 x 68.9 cm) Overall with mounting: 106 7/8 x 35 7/8 in. (271.5 x 91.1 cm) Overall with knobs: 106 7/8 x 40 in. (271.5 x 101.6 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45635,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.39,false,true,41154,Asian Art,Hanging scroll,元 倪瓚 江渚風林圖 軸|Wind among the Trees on the Riverbank,China,Yuan dynasty (1271–1368),,,,Artist,,Ni Zan,"Chinese, 1306–1374",,NI ZAN,Chinese,1306,1374,dated 1363,1363,1363,Hanging scroll; ink on paper,Image: 23 1/4 x 12 1/4 in. (59.1 x 31.1 cm) Overall with mounting: 102 1/4 x 22 1/4 in. (259.7 x 56.5 cm) Overall with knobs: 102 1/4 x 27 1/2 in. (259.7 x 69.9 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/41154,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1980.426.3,false,true,41185,Asian Art,Hanging scroll,元 張羽 松軒春靄圖 軸|Spring Clouds at the Pine Studio,China,Yuan dynasty (1271–1368),,,,Artist,,Zhang Yu,"Chinese, 1333–1385",,Zhang Yu,Chinese,1333,1333,dated 1366,1366,1366,Hanging scroll; ink and color on paper,Image: 36 1/4 x 12 1/2 in. (92.1 x 31.8 cm) Overall with mounting: 79 1/2 x 18 1/2 in. (201.9 x 47 cm) Overall with knobs: 79 1/2 x 20 7/8 in. (201.9 x 53 cm),"Gift of Douglas Dillon, 1980",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/41185,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1982.1.2,false,true,40515,Asian Art,Hanging scroll,元 李堯夫 蘆葉達摩圖 軸|Bodhidharma crossing the Yangzi River on a reed,China,Yuan dynasty (1271–1368),,,,Artist,,Li Yaofu,"Chinese, active ca. 1300",,LI YAOFU,Chinese,1300,1300,before 1317,1271,1316,Hanging scroll; ink on paper,Image: 33 3/4 × 13 5/16 in. (85.7 × 33.8 cm) Overall with mounting: 61 1/4 × 14 in. (155.6 × 35.6 cm) Overall with knobs: 61 1/4 × 15 7/8 in. (155.6 × 40.3 cm),"Edward Elliott Family Collection, Purchase, The Dillon Fund Gift, 1982",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/40515,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1973.121.5,false,true,41195,Asian Art,Hanging scroll,元 唐棣 松溪歸漁圖 軸|Returning Fishermen,China,Yuan dynasty (1271–1368),,,,Artist,,Tang Di,"Chinese, ca. 1287–1355",,TANG DI,Chinese,1277,1365,dated 1342,1342,1342,Hanging scroll; ink and color on silk,Image: 52 7/8 x 33 7/8 in. (134.3 x 86 cm) Overall with mounting: 98 3/4 x 38 1/2 in. (250.8 x 97.8 cm) Overall with knobs: 98 3/4 x 42 3/4 in. (250.8 x 108.6 cm),"Ex coll.: C. C. Wang Family, Purchase, Bequest of Joseph H. Durkee, by exchange, 1973",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/41195,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.36,false,true,40134,Asian Art,Handscroll,元 唐棣 滕王閣圖 卷|The Pavilion of Prince Teng,China,Yuan dynasty (1271–1368),,,,Artist,,Tang Di,"Chinese, ca. 1287–1355",,TANG DI,Chinese,1277,1365,dated 1352,1352,1352,Handscroll; ink on paper,Image: 10 13/16 x 33 1/4 in. (27.5 x 84.5 cm) Overall with mounting: 11 1/8 x 310 11/16 in. (28.3 x 789.1 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/40134,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1985.214.147,false,true,41194,Asian Art,Hanging scroll,元 唐棣 摩詰詩意圖 軸|Landscape after a poem by Wang Wei,China,Yuan dynasty (1271–1368),,,,Artist,,Tang Di,"Chinese, ca. 1287–1355",,TANG DI,Chinese,1277,1365,dated 1323,1323,1323,Hanging scroll; ink and color on silk,Image: 50 3/4 x 27 1/16 in. (128.9 x 68.7 cm) Overall with mounting: 8 ft. 8 1/8 in. x 33 3/4 in. (264.5 x 85.7 cm) Overall with knobs: 8 ft. 8 1/8 in. x 37 in. (264.5 x 94 cm),"Gift of Ernest Erickson Foundation, 1985",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/41194,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2012.526.2,false,true,39550,Asian Art,Hanging scroll,元 王蒙 素庵圖 軸|The Simple Retreat,China,Yuan dynasty (1271–1368),,,,Artist,,Wang Meng,"Chinese, ca. 1308–1385",,Wang Meng,Chinese,1308,1385,ca. 1370,1360,1380,Hanging scroll; ink and color on paper,Image: 53 3/4 x 17 5/8 in. (136.5 x 44.8 cm) Overall with mounting: 101 1/4 x 24 3/4 in. (257.2 x 62.9 cm) Overall with knobs: 101 1/4 x 28 11/16 in. (257.2 x 72.9 cm),"Ex coll.: C. C. Wang Family, Gift of Oscar L. Tang Family, 2012",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/39550,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.32,false,true,41451,Asian Art,Hanging scroll,,China,Yuan dynasty (1271–1368),,,,Artist,After,Zhao Yong,"Chinese, 1289–after 1360",,Zhao Yong,Chinese,1289,1360,dated 1349,1349,1349,Hanging scroll; ink on paper,Image: 45 x 14 7/8 in. (114.3 x 37.8 cm) Overall with mounting: 83 1/2 x 20 3/8 in. (212.1 x 51.8 cm) Overall with knobs: 83 1/2 x 23 in. (212.1 x 58.4 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/41451,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1973.121.6,false,true,41193,Asian Art,Hanging scroll,元 羅稚川 古木寒鴉圖 軸|Crows in Old Trees,China,Yuan dynasty (1271–1368),,,,Artist,,Luo Zhichuan,"Chinese, active ca. 1300–30",,Luo Zhichuan,Chinese,1290,1340,early 14th century,1300,1330,Hanging scroll; ink and color on silk,Image: 52 x 31 5/8 in. (132.1 x 80.3 cm) Overall with mounting: 9 ft. 6 1/4 in. x 38 3/4 in. (290.2 x 98.4 cm) Overall with knobs: 9 ft. 6 1/4 in. x 42 1/2 in. (290.2 x 108 cm),"Ex coll.: C. C. Wang Family, Purchase, Gift of J. Pierpont Morgan, by exchange, 1973",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/41193,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.345.1,false,true,36430,Asian Art,Hanging scroll,元 佚名 龍虎圖 軸|Tiger,China,Yuan dynasty (1271–1368),,,,Artist,In the style of,Muqi,"Chinese, ca. 1210–after 1269",,Muqi,Chinese,1210,1269,late 13th–14th century,1271,1368,Hanging scroll; ink on silk,31 11/16 x 15 7/8 in. (80.5 x 40.3 cm),"Gift of Mr. and Mrs. Kwan S. Wong, 1986",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36430,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.345.2,false,true,36431,Asian Art,Hanging scroll,元 龍虎圖 軸|Dragon,China,Yuan dynasty (1271–1368),,,,Artist,In the style of,Muqi,"Chinese, ca. 1210–after 1269",,Muqi,Chinese,1210,1269,late 13th–14th century,1271,1368,Hanging scroll; ink on silk,31 11/16 x 15 7/8 in. (80.5 x 40.3 cm),"Gift of Mr. and Mrs. Kwan S. Wong, 1986",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36431,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1991.438.1,false,true,44701,Asian Art,Hanging scroll,元 鄧宇 竹石圖 軸|Bamboo and rock,China,Yuan dynasty (1271–1368),,,,Artist,,Deng Yu,"Chinese, ca. 1300–after 1378",,Teng Yu,Chinese,1300,1378,ca. 1360–67,1350,1377,Hanging scroll; ink on paper,Image: 53 3/16 x 16 5/8 in. (135.1 x 42.2 cm) Overall with mounting: 104 1/4 x 23 7/8 in. (264.8 x 60.6 cm) Overall with knobs: 104 1/4 x 27 1/8 in. (264.8 x 68.9 cm),"From the P. Y. and Kinmay W. Tang Family Collection, Gift of Oscar L. Tang, 1991",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/44701,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -66.174a,false,true,40514,Asian Art,Handscroll,元 佚名 臨王振鵬 金明池圖 卷|Dragon Boat Regatta on Jinming Lake,China,Yuan dynasty (1271–1368),,,,Artist,After,Wang Zhenpeng,"Chinese, active ca. 1275–1330",,WANG ZHENPENG,Chinese,1265,1340,14th century (?),1300,1368,Handscroll; ink on silk,Image: 13 1/2 x 17 ft 6 in. (34.3 cm x 53.8 m),"Purchase, Bequest of Dorothy Graham Bennett, 1966",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/40514,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1980.276,false,true,40513,Asian Art,Handscroll,元 王振鵬 維摩不二圖 卷|Vimalakirti and the Doctrine of Nonduality,China,Yuan dynasty (1271–1368),,,,Artist,,Wang Zhenpeng,"Chinese, active ca. 1275–1330",,WANG ZHENPENG,Chinese,1265,1340,dated 1308,1308,1308,Handscroll; ink on silk,Image: 15 7/16 x 85 15/16 in. (39.2 x 218.3 cm) Overall with mounting: 15 13/16 in. x 29 ft. 4 5/8 in. (40.2 x 895.7 cm),"Purchase, The Dillon Fund Gift, 1980",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/40513,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1973.121.13,false,true,42260,Asian Art,Fan mounted as an album leaf,"元 盛懋 秋林漁隱圖 團扇|Recluse Fisherman, Autumn Trees",China,Yuan dynasty (1271–1368),,,,Artist,,Sheng Mou,"Chinese, active ca. 1310–1360",,Sheng Mou,Chinese,1310,1360,dated 1349,1349,1349,Fan mounted as an album leaf; ink and color on silk,Image: 10 1/2 × 13 1/4 in. (26.7 × 33.7 cm),"Ex coll.: C. C. Wang Family, Purchase, Florance Waterbury Bequest and Gift of Mr. and Mrs. Nathan Cummings, by exchange, 1973",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/42260,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.35,false,true,41146,Asian Art,Hanging scroll,元 盛懋 秋林漁隱圖 軸|Recluse Fishing by Autumn Trees,China,Yuan dynasty (1271–1368),,,,Artist,,Sheng Mou,"Chinese, active ca. 1310–1360",,Sheng Mou,Chinese,1310,1360,dated 1350,1350,1350,Hanging scroll; ink on paper,Image: 40 3/8 x 13 1/8 in. (102.6 x 33.3 cm) Overall with mounting: 90 1/2 x 19 3/4 in. (229.9 x 50.2 cm) Overall with knobs: 90 1/2 x 23 in. (229.9 x 58.4 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/41146,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1977.79,false,true,40309,Asian Art,Handscroll,元 錢選 梨花圖 卷|Pear Blossoms,China,Yuan dynasty (1271–1368),,,,Artist,,Qian Xuan,"Chinese, ca. 1235–before 1307",,QIAN XUAN,Chinese,1235,1307,ca. 1280,1270,1290,Handscroll; ink and color on paper,Image: 12 5/16 x 37 7/8 in. (31.3 x 96.2 cm) Overall with mounting: 12 5/8 x 34 ft. 9 1/8 in. (32.1 x 1059.5 cm),"Purchase, The Dillon Fund Gift, 1977",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/40309,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1973.120.6,true,true,40081,Asian Art,Handscroll,元 錢選 王羲之觀鵝圖 卷|Wang Xizhi watching geese,China,Yuan dynasty (1271–1368),,,,Artist,,Qian Xuan,"Chinese, ca. 1235–before 1307",,QIAN XUAN,Chinese,1235,1307,ca. 1295,1285,1305,"Handscroll; ink, color, and gold on paper",Image: 9 1/8 x 36 1/2 in. (23.2 x 92.7 cm) Overall with mounting: 11 x 418 13/16 in. (27.9 x 1063.8 cm),"Ex coll.: C. C. Wang Family, Gift of The Dillon Fund, 1973",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/40081,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1991.438.3,false,true,41476,Asian Art,Album leaf,元 夏永 黃樓圖 冊頁|The Yellow Pavilion,China,Yuan dynasty (1271–1368),,,,Artist,,Xia Yong,"Chinese, active mid-14th century",,XIA YONG,Chinese,1336,1370,ca. 1350,1340,1360,Album leaf; ink on silk,Image: 8 1/8 x 10 1/2 in. (20.6 x 26.7 cm),"Ex coll.: C. C. Wang Family, From the P. Y. and Kinmay W. Tang Family Collection, Gift of Oscar L. Tang, 1991",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/41476,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -47.18.10,false,true,40393,Asian Art,Handscroll,元 周東卿 魚樂圖 卷|The Pleasures of Fishes,China,Yuan dynasty (1271–1368),,,,Artist,,Zhou Dongqing,"Chinese, active late 13th century",,ZHOU DONGQING,Chinese,0013,0013,dated 1291,1291,1291,Handscroll; ink and color on paper,Image: 12 1/8 x 19 ft 4 in. (30.8 cm x 593.7 cm) Overall with mounting: 12 5/8 x 441 3/4 in. (32.1 x 1122 cm),"From the Collection of A. W. Bahr, Purchase, Fletcher Fund, 1947",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/40393,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1973.121.14,false,true,42261,Asian Art,Fan mounted as an album leaf,元 盛著 秋江垂釣圖 團扇|Angling in the Autumn River,China,Yuan dynasty (1271–1368),,,,Artist,,Sheng Zhu,"Chinese, active late 14th century",,Sheng Zhu,Chinese,1300,1400,ca. 1370,1360,1380,Fan mounted as an album leaf; ink and color on silk,Image: 10 3/16 × 10 13/16 in. (25.9 × 27.5 cm),"Ex coll.: C. C. Wang Family, Purchase, Bequest of Martha T. Fiske Collord, in memory of Josiah M. Fiske, Bequest of Mrs. Beekman Hoppin, and Gift of Herman Cooper, by exchange, 1973",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/42261,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1973.121.8,false,true,45647,Asian Art,Hanging scroll,元 趙原 (元) 晴川送客圖 軸|Farewell by a Stream on a Clear Day,China,Yuan dynasty (1271–1368),,,,Artist,,Zhao Yuan,"Chinese, active ca. 1350–75",,Zhao Yuan,Chinese,1350,1350,second half of the 14th century,1350,1399,Hanging scroll; ink on paper,Image: 37 1/2 x 13 7/8 in. (95.3 x 35.2 cm) Overall with mounting: 77 1/4 x 20 1/2 in. (196.2 x 52.1 cm) Overall with knobs: 77 1/4 x 23 3/8 in. (196.2 x 59.4 cm),"Ex coll.: C. C. Wang Family, Purchase, Gift of J. Pierpont Morgan, by exchange, 1973",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45647,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.100.22,false,true,35970,Asian Art,Hanging scroll,明 丁雲鵬 山水圖 軸|The Lute-song: Farewell at Xunyang,China,late Ming dynasty (1368–1644),,,,Artist,,Ding Yunpeng,"Chinese, 1547–after 1621",,Ding Yunpeng,Chinese,1547,1621,dated 1585,1585,1585,Hanging scroll; ink and color on paper,Image: 55 5/8 x 18 1/8 in. (141.3 x 46 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/35970,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1991.438.2,false,true,44702,Asian Art,Fan mounted as an album leaf,元 王蒙 蕭林寂亭圖 團扇|Sparse Trees and Pavilion,China,late Yuan dynasty (1271–1368),,,,Artist,,Wang Meng,"Chinese, ca. 1308–1385",,Wang Meng,Chinese,1308,1385,ca. 1361,1351,1371,Fan mounted as an album leaf; ink on silk,Image: 9 7/8 x 11 1/8 in. (25.1 x 28.3 cm),"Ex coll.: C. C. Wang Family, From the P. Y. and Kinmay W. Tang Family Collection, Gift of Oscar L. Tang, 1991",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/44702,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1996.479a–c,false,true,39895,Asian Art,Handscrolls,北宋 李公麟 孝經圖 卷|The Classic of Filial Piety,China,Northern Song dynasty (960–1127),,,,Artist,,Li Gonglin,"Chinese, ca. 1041–1106",,Li Gonglin,Chinese,1041,1106,ca. 1085,1075,1095,Handscroll; ink and color on silk,"Overall (a, painting): 8 5/8 x 187 1/4 in. (21.9 x 475.6 cm) Overall (b, colophons): 10 3/8 x 208 5/8 in. (26.4 x 529.9 cm) Overall (c, modern copy preserving seventeenth century silk restorations): 9 1/8 x 196 in. (23.2 x 497.8 cm)","Ex coll.: C. C. Wang Family, From the P. Y. and Kinmay W. Tang Family Collection, Gift of Oscar L. Tang Family, 1996",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/39895,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1981.276,true,true,39668,Asian Art,Handscroll,"北宋 郭熙 樹色平遠圖 卷 |Old Trees, Level Distance",China,Northern Song dynasty (960–1127),,,,Artist,,Guo Xi,"Chinese, ca. 1000–ca. 1090",,Guo Xi,Chinese,1000,1090,ca. 1080,1070,1090,Handscroll; ink and color on silk,Image: 14 in. × 41 1/8 in. (35.6 × 104.4 cm) Overall with mounting: 14 3/4 in. × 28 ft. 1/8 in. (37.5 × 853.8 cm),"Gift of John M. Crawford Jr., in honor of Douglas Dillon, 1981",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/39668,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1981.278,true,true,39936,Asian Art,Handscroll,北宋 徽宗 竹禽圖 卷|Finches and bamboo,China,Northern Song dynasty (960–1127),,,,Artist,,Emperor Huizong,"Chinese, 1082–1135; r. 1100–25",,Huizong Emperor,Chinese,1082,1082,early 12th century,1100,1127,Handscroll; ink and color on silk,Image: 13 1/4 × 21 13/16 in. (33.7 × 55.4 cm) Overall with mounting: 13 3/4 in. × 27 ft. 6 5/16 in. (34.9 × 839 cm),"John M. Crawford Jr. Collection, Purchase, Douglas Dillon Gift, 1981",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/39936,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1973.120.1,true,true,39915,Asian Art,Handscroll,北宋 傳屈鼎 夏山圖 卷|Summer Mountains,China,Northern Song dynasty (960–1127),,,,Artist,Attributed to,Qu Ding,"Chinese, active ca. 1023–ca. 1056",,QU DING,Chinese,0950,1150,ca. 1050,1040,1060,Handscroll; ink and color on silk,Image: 17 7/8 × 45 3/8 in. (45.4 × 115.3 cm) Overall with mounting: 18 1/4 in. × 23 ft. 2 in. (46.4 × 706.1 cm),"Ex coll.: C. C. Wang Family, Gift of The Dillon Fund, 1973",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/39915,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1973.121.1,false,true,40007,Asian Art,Handscroll,南宋 米友仁 雲山圖 卷|Cloudy Mountains,China,Southern Song dynasty (1127–1279),,,,Artist,,Mi Youren,"Chinese, 1074–1151",,MI YOUREN,Chinese,1074,1151,before 1200,1127,1199,Handscroll; ink on paper,Image: 10 7/8 × 22 7/16 in. (27.6 × 57 cm) Overall with mounting: 11 3/16 in. × 24 ft. 6 3/16 in. (28.4 × 747.2 cm),"Ex coll.: C. C. Wang Family, Purchase, Gift of J. Pierpont Morgan, by exchange, 1973",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/40007,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1973.120.4,false,true,40284,Asian Art,Handscroll,南宋 趙孟堅 水仙圖 卷|Narcissus,China,Southern Song dynasty (1127–1279),,,,Artist,,Zhao Mengjian,"Chinese, 1199–before 1267",,Zhao Mengjian,Chinese,1199,1267,mid-13th century,1234,1266,Handscroll; ink on paper,Image: 13 1/16 in. × 12 ft. 3 1/4 in. (33.2 × 374 cm) Overall with mounting: 13 7/16 in. × 32 ft. 7 3/16 in. (34.1 × 993.6 cm),"Ex coll.: C. C. Wang Family, Gift of The Dillon Fund, 1973",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/40284,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1973.121.3,false,true,40054,Asian Art,Handscroll,南宋 馬和之 詩經豳風圖 卷|Odes of the State of Bin,China,Southern Song dynasty (1127–1279),,,,Artist,,Ma Hezhi,"Chinese, ca. 1130–ca. 1170",", and Assistants",MA HEZHI,Chinese,1130,1170,mid-12th century,1134,1166,"Handscroll; ink, color, gold and silver on silk",Image: 10 15/16 in. × 21 ft. 9 1/4 in. (27.8 × 663.6 cm) Overall with mounting: 13 13/16 in. × 45 ft. 10 1/2 in. (35.1 × 1398.3 cm),"Ex coll.: C. C. Wang Family, Purchase, Gift of J. Pierpont Morgan, by exchange, 1973",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/40054,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1973.120.2,false,true,40051,Asian Art,Handscroll,南宋 傳李唐 晉文公復國圖 卷|Duke Wen of Jin Recovering His State,China,Southern Song dynasty (1127–1279),,,,Artist,Attributed to,Li Tang,"Chinese, ca. 1070s–ca. 1150s",,LI TANG,Chinese,1070,1159,mid-12th century,1134,1166,Handscroll; ink and color on silk,Image: 11 9/16 in. × 27 ft. 2 in. (29.4 × 828 cm) Overall with mounting: 11 7/8 in. × 40 ft. 9 1/16 in. (30.2 × 1242.2 cm),"Ex coll.: C. C. Wang Family, Gift of The Dillon Fund, 1973",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/40051,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -23.33.5,false,true,36037,Asian Art,Album leaf,南宋 佚名 倣馬遠 松陰玩月圖 冊頁|Viewing the Moon under a Pine Tree,China,Southern Song dynasty (1127–1279),,,,Artist,After,Ma Yuan,"Chinese, active ca. 1190–1225",,MA YUAN,Chinese,1190,1225,early 13th century,1200,1233,Album leaf; ink and color on silk,10 x 10 in. (25.4 x 25.4 cm),"Rogers Fund, 1923",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36037,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1973.120.9,false,true,40086,Asian Art,Album leaf,南宋 馬遠 高士觀瀑圖 冊頁 絹本|Scholar viewing a waterfall,China,Southern Song dynasty (1127–1279),,,,Artist,,Ma Yuan,"Chinese, active ca. 1190–1225",,MA YUAN,Chinese,1190,1225,late 12th–early 13th century,1190,1225,Album leaf; ink and color on silk,Image: 9 7/8 x 10 1/4 in. (25.1 x 26 cm),"Ex coll.: C. C. Wang Family, Gift of The Dillon Fund, 1973",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/40086,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.493.2,false,true,44638,Asian Art,Folding fan mounted as an album leaf,南宋 馬遠 月下賞梅圖 團扇|Viewing plum blossoms by moonlight,China,Southern Song dynasty (1127–1279),,,,Artist,,Ma Yuan,"Chinese, active ca. 1190–1225",,MA YUAN,Chinese,1190,1225,early 13th century,1200,1233,Fan mounted as an album leaf; ink and color on silk,Image: 9 7/8 x 10 1/2 in. (25.1 x 26.7 cm),"Gift of John M. Crawford Jr., in honor of Alfreda Murck, 1986",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/44638,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.100.102,false,true,36005,Asian Art,Album leaf,"南宋 夏珪 山市晴嵐圖 冊頁|Mountain Market, Clearing Mist",China,Southern Song dynasty (1127–1279),,,,Artist,,Xia Gui,"Chinese, active ca. 1195–1230",,Xia Gui,Chinese,1195,1230,early 13th century,1200,1230,Album leaf; ink on silk,9 3/4 x 8 3/8 in. (24.8 x 21.3 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36005,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1973.120.10,false,true,40133,Asian Art,Album leaf,南宋 馬麟 蘭花圖 冊頁 絹本|Orchids,China,Southern Song dynasty (1127–1279),,,,Artist,,Ma Lin,"Chinese, ca. 1180– after 1256",,MA LIN,Chinese,1180,1256,second quarter of the 13th century,1226,1250,Album leaf; ink and color on silk,Image: 10 7/16 x 8 7/8 in. (26.5 x 22.5 cm),"Ex coll.: C. C. Wang Family, Gift of The Dillon Fund, 1973",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/40133,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.100.110,false,true,39937,Asian Art,Album leaf,宋 傳趙克敻 藻魚圖 冊頁|Fish at play,China,Southern Song dynasty (1127–1279),,,,Artist,Attributed to,Zhao Kexiong,"Chinese, active early 12th century",,Zhao Kexiong,Chinese,0012,0012,12th–late 13th century,1127,1279,Album leaf; ink and color on silk,Image: 8 7/8 x 9 7/8 in. (22.5 x 25.1 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/39937,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.14,false,true,40090,Asian Art,Fan mounted as an album leaf,南宋 梁楷 澤畔行吟圖 團扇|Poet strolling by a marshy bank,China,Southern Song dynasty (1127–1279),,,,Artist,,Liang Kai,"Chinese, active early 13th century",,Liang Kai,Chinese,1200,1225,early 13th century,1200,1225,Fan mounted as an album leaf; ink on silk,Image: 9 x 9 9/16 in. (22.9 x 24.3 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/40090,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.220.98,false,true,36019,Asian Art,Handscroll,,China,Ming (1368–1644)–Qing (1644–1911) dynasty,,,,Artist,In the Style of,Wen Boren,"Chinese, 1502–ca.1575",,Wen Boren,Chinese,1502,1575,dated 1558,1558,1558,Handscroll; ink and color on paper,Image: 7 1/16 × 48 1/2 in. (17.9 × 123.2 cm) Overall with mounting: 9 9/16 in. × 10 ft. 11 1/2 in. (24.3 × 334 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36019,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1977.171a–j,false,true,49017,Asian Art,Album,明/清 惲向 仿古山水圖 冊 紙本|Landscapes after old masters,China,Ming (1368–1644) or Qing (1644–1911) dynasty,,,,Artist,,Yun Xiang,"Chinese, 1586–1655",,Yun Xiang,Chinese,1586,1655,datable to 1638 or 1650,1638,1650,Album of ten leaves; ink and color on paper,10 1/4 x 6 in. (26 x 15.2 cm),"Purchase, Douglas Dillon Gift, 1977",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49017,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1981.285.16,false,true,45658,Asian Art,Handscroll,元/明 沈巽 竹石圖 卷|Bamboo grove,China,Yuan (1271–1368) or Ming (1368–1644) dynasty,,,,Artist,,Shen Xun,(active ca. 1370–1400),,Shen Xun,Chinese,1370,1400,late 14th century,1367,1399,Handscroll; ink and color on paper,Image: 9 13/16 x 25 in. (24.9 x 63.5 cm) Overall with mounting: 10 1/8 x 347 5/16 in. (25.7 x 882.2 cm),"Edward Elliott Family Collection, Purchase, The Dillon Fund Gift, 1981",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45658,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1981.285.15,false,true,45650,Asian Art,Handscroll,元 趙原 (元) 倣燕文貴范寬山水圖 卷|Landscape in the Style of Yan Wengui and Fan Kuan,China,Yuan (1271–1368) or Ming (1368–1644) dynasty,,,,Artist,,Zhao Yuan,"Chinese, active ca. 1350–75",,Zhao Yuan,Chinese,1350,1350,late 14th century,1367,1399,Handscroll; ink on paper,Image: 9 13/16 x 30 1/2 in. (24.9 x 77.5 cm) Overall with mounting: 10 1/8 x 347 5/16 in. (25.7 x 882.2 cm),"Edward Elliott Family Collection, Purchase, The Dillon Fund Gift, 1981",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45650,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2003.132,false,true,65385,Asian Art,Folding fan mounted as an album leaf,"清 徐揚 仙山樓閣圖 扇面|Palaces of the Immortals",China,"Qing dynasty (1644–1911), Qianlong period (1736–95)",,,,Artist,,Xu Yang,"Chinese, active ca. 1750–after 1776",,XU YANG,Chinese,1750,1776,dated 1753,1753,1753,"Folding fan mounted as an album leaf; ink, color, and gold on paper",Image: 6 1/4 x 18 1/2 in. (15.9 x 47 cm) Sheet: 15 × 24 1/2 in. (38.1 × 62.2 cm),"Purchase, The B. D. G. Leviton Foundation Gift, in honor of Marie-Hélène and Guy Weill, 2003",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/65385,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.364a–l,false,true,44873,Asian Art,Folding fans mounted as album leaves,明/清 陳洪綬 竹石蛺蝶圖 扇面|Landscapes and Flowers,China,late Ming (1368–1644)–early Qing (1644–1911) dynasty,,,,Artist,,Chen Hongshou,"Chinese, 1599–1652",,Chen Hongshou,Chinese,1599,1652,first half of the 17th century,1600,1649,Twelve folding fans mounted as album leaves; ink and color on gold paper,9 1/2 x 20 1/2 in. (24.1 x 52.1 cm),"Gift of Douglas Dillon, 1989",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/44873,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1973.121.7,false,true,45637,Asian Art,Hanging scroll,元 王蒙 丹崖翠壑圖 軸|Red Cliffs and Green Valleys,China,late Yuan (1271–1368)–early Ming (1368–1644) dynasty,,,,Artist,,Wang Meng,"Chinese, ca. 1308–1385",,Wang Meng,Chinese,1308,1385,ca. 1367,1357,1377,Hanging scroll; ink on paper,Image: 26 3/4 x 13 1/2 in. (67.9 x 34.3 cm) Overall with mounting: 91 1/4 x 21 3/8 in. (231.8 x 54.3 cm) Overall with knobs: 91 1/4 x 25 1/8 in. (231.8 x 63.8 cm),"Ex coll.: C. C. Wang Family, Purchase, Gift of Darius Ogden Mills and Gift of Mrs. Robert Young, by exchange, 1973",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45637,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1997.139,false,true,39488,Asian Art,Figure,,China,Ming dynasty (1368–1644),,,,Artist,,Chen Yanqing,active 15th century,,Chen Yanqing,Chinese,1400,1499,dated 1438,1438,1438,Gilt brass; lost-wax cast,H. 7 1/2 in. (19 cm); W. 4 3/4 in. (12 cm); D. 2 3/4 in. (7 cm),"Purchase, Friends of Asian Art Gifts, 1997",,,,,,,,,,,,Sculpture,,http://www.metmuseum.org/art/collection/search/39488,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -10.221.1,false,true,48837,Asian Art,Figure,,China,Tang dynasty (618–907),,,,Artist,,Jin Renrui,"Chinese, 1608–1661",,Jin Renrui,Chinese,1608,1661,,618,907,Whitish earthenware with brown glaze,H. 15 7/8 in. (40.3 cm),"Rogers Fund, 1910",,,,,,,,,,,,Tomb Pottery,,http://www.metmuseum.org/art/collection/search/48837,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.20,false,true,40185,Asian Art,Fan mounted as an album leaf,南宋 理宗 行書秋深雨過聯句 團扇|Couplet on an Autumn Sky,China,Song dynasty (960–1279),,,,Artist,,Emperor Lizong,"Chinese, 1205–64, r. 1224–64",,LIZONG EMPEROR,Chinese,1205,1264,,1205,1264,Fan mounted as an album leaf; ink on silk,Image: 9 5/16 x 7 in. (23.7 x 17.8 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/40185,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.21,false,true,40187,Asian Art,Fan mounted as an album leaf,南宋 理宗 趙昀 行書錄光宗趙惇題楊補之 《紅梅圖》 賜貴妃詩 團扇|Quatrain on a Spring Garden,China,Song dynasty (960–1279),,,,Artist,,Emperor Lizong,"Chinese, 1205–64, r. 1224–64",,LIZONG EMPEROR,Chinese,1205,1264,,1205,1264,Fan mounted as an album leaf; ink on silk,11 x 9 1/2 in. (27.9 x 24.1 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/40187,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.22,false,true,40188,Asian Art,Fan mounted as an album leaf,南宋 理宗 趙昀 行書 《長苦春來》 七絕詩 團扇|Quatrain on Late Spring,China,Song dynasty (960–1279),,,,Artist,,Emperor Lizong,"Chinese, 1205–64, r. 1224–64",,LIZONG EMPEROR,Chinese,1205,1264,,1205,1264,Fan mounted as an album leaf; ink on silk,Image: 9 5/8 × 9 1/4 in. (24.4 × 23.5 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/40188,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.11,false,true,40061,Asian Art,Fan mounted as an album leaf,南宋 傳光宗 行楷書高標貞色聯句 團扇|Couplet by Han Yu,China,Song dynasty (960–1279),,,,Artist,Attributed to,Emperor Guangzong,"Chinese, 1147–1200, r. 1190–94",,Emperor Guangzong,Chinese,1147,1200,,1147,1200,Fan mounted as an album leaf; ink on silk,8 3/8 x 8 1/4 in. (21.3 x 21 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/40061,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.110,false,true,49006,Asian Art,Album leaf,,China,Ming dynasty (1368–1644),,,,Artist,,Song Jue,(1576–1632),,Song Jue,Chinese,1576,1632,,1575,1632,Album leaf; ink on paper,11 1/2 x 18 in. (29.2 x 45.7 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/49006,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.111,false,true,49009,Asian Art,Album leaf,,China,Ming dynasty (1368–1644),,,,Artist,,Song Jue,(1576–1632),,Song Jue,Chinese,1576,1632,,1576,1632,Album leaf; ink on paper,8 13/16 x 9 5/8 in. (22.4 x 24.4 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/49009,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.94,false,true,48944,Asian Art,Folding fan mounted as an album leaf,,China,Ming dynasty (1368–1644),,,,Artist,,Xing Tong,"Chines, 1551–1612",,Xing Tong,Chinese,1551,1612,,1551,1612,Folding fan mounted as an album leaf; ink on gold paper,6 x 19 in. (15.2 x 48.3 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/48944,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.41,false,true,45660,Asian Art,Album leaf,明 沈度 致鏞翁書 冊頁|Letter to Liang Zhongren,China,Ming dynasty (1368–1644),,,,Artist,,Shen Du,"Chinese, 1357–1434",,Shen Du,Chinese,1357,1434,,1357,1434,Album leaf; ink on paper,Image: 10 1/4 x 13 1/4 in. (26 x 33.7 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/45660,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.49,false,true,45747,Asian Art,Folding fan mounted as an album leaf,,China,Ming dynasty (1368–1644),,,,Artist,,Wang Ao,"Chinese, 1450–1524",,Wang Ao,Chinese,1450,1524,,1450,1524,Folding fan mounted as an album leaf; ink on gold-patterned paper,6 3/4 x 19 3/4 in. (17.1 x 50.2 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/45747,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.50,false,true,45748,Asian Art,Album leaf,,China,Ming dynasty (1368–1644),,,,Artist,,Wang Ao,"Chinese, 1450–1524",,Wang Ao,Chinese,1450,1524,,1450,1524,Album leaf; ink on patterned paper,9 1/2 x 11 1/2 in. (24.1 x 29.2 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/45748,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.52,false,true,42683,Asian Art,Folding fan mounted as an album leaf,,China,Ming dynasty (1368–1644),,,,Artist,,Zhu Yunming,"Chinese, 1461–1527",,Zhu Yunming,Chinese,1461,1527,,1461,1527,Folding fan mounted as an album leaf; ink on gold paper,7 1/2 x 19 1/2 in. (19.1 x 49.5 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/42683,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.66,false,true,45795,Asian Art,Folding fan mounted as an album leaf,,China,Ming dynasty (1368–1644),,,,Artist,,Xu Lin,"Chinese, 1462–1548",,Xu Lin,Chinese,1462,1548,,1462,1548,Folding fan mounted as an album leaf; ink on gold-flecked paper,6 3/8 x 19 in. (16.2 x 48.3 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/45795,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.56,false,true,45763,Asian Art,Album leaf,明 唐寅 致若容書 冊頁|Letter to Xu Shangde,China,Ming dynasty (1368–1644),,,,Artist,,Tang Yin,"Chinese, 1470–1524",,Tang Yin,Chinese,1470,1524,,1470,1524,Album leaf; ink on paper,Image: 10 3/4 × 25 1/4 in. (27.3 × 64.1 cm) Image (with title strip): 10 3/4 × 25 7/8 in. (27.3 × 65.7 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/45763,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.57,false,true,45764,Asian Art,Album leaf,,China,Ming dynasty (1368–1644),,,,Artist,,Tang Yin,"Chinese, 1470–1524",,Tang Yin,Chinese,1470,1524,,1470,1524,Album leaf; ink on gold-flecked paper,10 5/8 x 9 7/8 in. (27 x 25.1cm),"Bequest Of John M. Crawford Jr., 1988",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/45764,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.58,false,true,53796,Asian Art,Album leaf,,China,Ming dynasty (1368–1644),,,,Artist,,Tang Yin,"Chinese, 1470–1524",,Tang Yin,Chinese,1470,1524,,1470,1524,Album leaf; ink on gold-flecked paper,H. 12 1/8 in. (30.8 cm); W. 12 1/8 in. (30.8 cm),"Bequest Of John M. Crawford Jr., 1988",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/53796,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -50.230,false,true,45777,Asian Art,Hanging scroll,明 恭候大駕還自南郊詩 軸|Awaiting the Emperor's Return from the Southern Suburbs,China,Ming dynasty (1368–1644),,,,Artist,,Wen Zhengming,"Chinese, 1470–1559",,Wen Zhengming,Chinese,1470,1559,,1470,1559,Hanging scroll; ink on paper,Overall: 136 x 39 1/4 in. (345.4 x 99.7 cm) Overall with rollers: 40 3/4 in. (103.5 cm),"Anonymous Gift, 1950",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/45777,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.61,false,true,45783,Asian Art,Folding fan mounted as an album leaf,,China,Ming dynasty (1368–1644),,,,Artist,,Wen Zhengming,"Chinese, 1470–1559",,Wen Zhengming,Chinese,1470,1559,,1470,1559,Folding fan mounted as an album leaf; ink on gold-flecked paper,6 3/8 x 19 1/2 in. (16.2 x 49.5 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/45783,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.100.50,false,true,45804,Asian Art,Folding fan mounted as an album leaf,明 陳淳 行草重九詩 扇面|Poem on the Double Ninth Festival,China,Ming dynasty (1368–1644),,,,Artist,,Chen Chun,"Chinese, 1483–1544",,Chen Chun,Chinese,1483,1544,,1483,1544,Folding fan mounted as an album leaf; ink on gold paper,Image: 6 3/4 x 20 1/4 in. (17.1 x 51.4 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/45804,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.67,false,true,45812,Asian Art,Album leaf,,China,Ming dynasty (1368–1644),,,,Artist,,Wang Chong,"Chinese, 1494–1533",,Wang Chong,Chinese,1494,1533,,1494,1533,Album leaf; ink on paper,10 1/8 x 13 3/8 in. (25.7 x 34.0 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/45812,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.100.56,false,true,48876,Asian Art,Folding fan mounted as an album leaf,,China,Ming dynasty (1368–1644),,,,Artist,After,Wen Peng,"Chinese, 1498–1573",,Wen Peng,Chinese,1498,1573,,1498,1573,Folding fan mounted as an album leaf; ink on gold-flecked paper,7 1/2 x 21 in. (19.1 x 53.3 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/48876,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.69,false,true,48874,Asian Art,Album leaf,,China,Ming dynasty (1368–1644),,,,Artist,,Wen Peng,"Chinese, 1498–1573",,Wen Peng,Chinese,1498,1573,,1498,1573,Album leaf; ink on paper,11 1/2 x 15 5/8 in. (29.2 x 39.7 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/48874,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.71,false,true,48877,Asian Art,Folding fan mounted as an album leaf,,China,Ming dynasty (1368–1644),,,,Artist,,Wang Guxiang,"Chinese, 1501–1568",,Wang Guxiang,Chinese,1501,1568,,1501,1568,Folding fan mounted as an album leaf; ink on gold-flecked paper,6 3/8 x 18 5/8 in. (16.2 x 47.3 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/48877,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.72,false,true,48879,Asian Art,Album leaf,,China,Ming dynasty (1368–1644),,,,Artist,,Wen Jia,"Chinese, 1501–1583",,Wen Jia,Chinese,1501,1583,,1501,1583,Album leaf; ink on paper,8 1/2 x 15 1/4 in. (21.6 x 38.7 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/48879,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.100.82,false,true,35993,Asian Art,Folding fan mounted as an album leaf,,China,Ming dynasty (1368–1644),,,,Artist,,Yuan Zhi,"Chinese, 1502–1547",,Yuan Zhi,Chinese,1502,1547,,1502,1547,Folding fan mounted as an album leaf; ink on paper,7 3/8 x 21 in. (18.7 x 53.3 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/35993,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.79,false,true,48904,Asian Art,Album leaf,,China,Ming dynasty (1368–1644),,,,Artist,,Zhou Tianqiu,"Chinese, 1514–1595",,Zhou Tianqiu,Chinese,1514,1595,,1514,1595,Album leaf; ink on paper,7 3/8 x 12 in. (18.7 x 30.5 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/48904,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.80,false,true,48905,Asian Art,Folding fan mounted as an album leaf,,China,Ming dynasty (1368–1644),,,,Artist,,Zhou Tianqiu,"Chinese, 1514–1595",,Zhou Tianqiu,Chinese,1514,1595,,1514,1595,Folding fan mounted as an album leaf; ink on gold-flecked paper,7 3/16 x 21 1/8 in. (18.3 x 53.7 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/48905,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.88,false,true,48915,Asian Art,Folding fan mounted as an album leaf,,China,Ming dynasty (1368–1644),,,,Artist,,Zhang Fengyi,"Chinese, 1527–1613",,Zhang Fengyi,Chinese,1527,1613,,1527,1613,Folding fan mounted as an album leaf; ink on gold paper,6 1/4 x 19 1/2 in. (15.9 x 49.5 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/48915,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.84,false,true,48917,Asian Art,Folding fan mounted as an album leaf,,China,Ming dynasty (1368–1644),,,,Artist,,Wang Zhideng,"Chinese, 1535–1612",,Wang Zhideng,Chinese,1535,1612,,1535,1612,Folding fan mounted as an album leaf; ink on gold paper,7 1/8 x 20 1/4 in. (18.1 x 51.4 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/48917,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.85,false,true,48926,Asian Art,Manuscript fragment,明 王穉登 行草書山水窟殘稿 冊頁|Fragment of a Manuscript,China,Ming dynasty (1368–1644),,,,Artist,,Wang Zhideng,"Chinese, 1535–1612",,Wang Zhideng,Chinese,1535,1612,,1535,1612,Album leaf; ink on ruled paper,Image: 9 5/8 x 15 13/16 in. (24.4 x 40.2 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/48926,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.86,false,true,48927,Asian Art,Album leaf,明 王穉登 行草書札 冊頁|Letter,China,Ming dynasty (1368–1644),,,,Artist,,Wang Zhideng,"Chinese, 1535–1612",,Wang Zhideng,Chinese,1535,1612,,1535,1612,Album leaf; ink on paper,Image: 9 11/16 x 5 5/16 in. (24.6 x 13.5 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/48927,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.90,false,true,48935,Asian Art,Folding fan mounted as an album leaf,,China,Ming dynasty (1368–1644),,,,Artist,,Mo Shilong,"Chinese, 1537–1587",,Mo Shilong,Chinese,1537,1587,,1537,1587,Folding fan mounted as an album leaf; ink on gold-flecked paper,6 x 19 1/16 in. (15.2 x 48.4 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/48935,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.92,false,true,48943,Asian Art,Album leaf,明 焦竤 行草書札 冊頁|Letter,China,Ming dynasty (1368–1644),,,,Artist,,Jiao Hong,"Chinese, 1541–1620",,Jiao Hong,Chinese,1541,1620,,1541,1620,Album leaf; ink on paper,10 5/8 x 6 5/8 in. (27 x 16.8 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/48943,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.93,false,true,45384,Asian Art,Hanging scroll,,China,Ming dynasty (1368–1644),,,,Artist,,Tu Long,"Chinese, 1542–1605",,Tu Long,Chinese,1542,1605,,1542,1605,Hanging scroll; ink on paper,Image: 49 3/4 x 10 3/4 in. (126.4 x 27.3 cm) Overall: 80 1/2 x 18 in. (204.5 x 45.7 cm) Overall with knobs: 80 1/2 x 19 3/4 in. (204.5 x 50.2 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/45384,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.96,false,true,48956,Asian Art,Album leaf,"明 董其昌 行草致陳繼儒書 冊頁 |Letter to Chen Jiru (1558-1635)",China,Ming dynasty (1368–1644),,,,Artist,,Dong Qichang,"Chinese, 1555–1636",,Dong Qichang,Chinese,1555,1636,,1555,1636,Album leaf; ink on ruled paper,9 1/16 x 9 1/16 in. (23 x 23 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/48956,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.97,false,true,48957,Asian Art,Folding fan mounted as an album leaf,,China,Ming dynasty (1368–1644),,,,Artist,,Dong Qichang,"Chinese, 1555–1636",,Dong Qichang,Chinese,1555,1636,,1555,1636,Folding fan mounted as an album leaf; ink on gold paper,6 7/8 x 19 7/8 in. (17.5 x 50.5 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/48957,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.98,false,true,48958,Asian Art,Handscroll,,China,Ming dynasty (1368–1644),,,,Artist,,Dong Qichang,"Chinese, 1555–1636",,Dong Qichang,Chinese,1555,1636,,1555,1636,Handscroll; ink on satin,10 1/2 x 72 5/16 in. (26.7 x 183.7 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/48958,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.100.76,false,true,48963,Asian Art,Folding fan mounted as an album leaf,,China,Ming dynasty (1368–1644),,,,Artist,,Chen Jiru,"Chinese, 1558–1635",,Chen Jiru,Chinese,1558,1635,,1368,1644,Folding fan mounted as an album leaf; ink on gold-flecked paper,6 1/2 x 19 1/2 in. (16.5 x 49.5 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/48963,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.103,false,true,48960,Asian Art,Album,,China,Ming dynasty (1368–1644),,,,Artist,,Chen Jiru,"Chinese, 1558–1635",,Chen Jiru,Chinese,1558,1635,,1558,1635,Album of eight leaves; ink on gold-flecked paper,11 13/16 x 6 1/8 in. (30 x 15.6 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/48960,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.104,false,true,48961,Asian Art,Album,,China,Ming dynasty (1368–1644),,,,Artist,,Chen Jiru,"Chinese, 1558–1635",,Chen Jiru,Chinese,1558,1635,,1558,1635,Album of eight double leaves; ink on paper,Overall (double leaf): 8 5/8 × 11 3/4 in. (21.9 × 29.8 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/48961,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.105,false,true,48962,Asian Art,Folding fan mounted as an album leaf,,China,Ming dynasty (1368–1644),,,,Artist,,Chen Jiru,"Chinese, 1558–1635",,Chen Jiru,Chinese,1558,1635,,1558,1635,Folding fan mounted as an album leaf; ink on gold-flecked paper,7 1/4 x 20 1/4 in. (18.4 x 51.4 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/48962,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.106,false,true,48964,Asian Art,Folding fan mounted as an album leaf,,China,Ming dynasty (1368–1644),,,,Artist,,Wang Heng,"Chinese, 1561–1609",,Wang Heng,Chinese,1561,1609,,1561,1609,Folding fan mounted as an album leaf; ink on gold-flecked paper,7 1/2 x 22 1/4 in. (19.1 x 56.5 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/48964,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"1989.363.109a, b",false,true,48970,Asian Art,Hanging scrolls,,China,Ming dynasty (1368–1644),,,,Artist,,Zhang Ruitu,"Chinese, 1570–1641",,Zhang Ruitu,Chinese,1570,1641,,1570,1641,Pair of hanging scrolls; ink on paper,Image (Each): 111 x 18 in. (281.9 x 45.7 cm) Overall (Each): 127 x 21 in. (322.6 x 53.3 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/48970,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.121,false,true,49043,Asian Art,Folding fan mounted as an album leaf,,China,Ming dynasty (1368–1644),,,,Artist,,Ni Yuanlu,"Chinese, 1593–1644",,Ni Yuanlu,Chinese,1593,1644,,1593,1644,Folding fan mounted as an album leaf; ink on gold paper,6 5/8 x 18 7/8 in. (16.8 x 47.9 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/49043,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.122,false,true,49044,Asian Art,Hanging scroll,明 倪元璐 行草書七絕詩 軸|Calligraphy,China,Ming dynasty (1368–1644),,,,Artist,,Ni Yuanlu,"Chinese, 1593–1644",,Ni Yuanlu,Chinese,1593,1644,,1593,1644,Hanging scroll; ink on paper,Image: 54 7/8 x 24 1/4 in. (139.4 x 61.6 cm) Overall with mounting: 111 x 31 1/2 in. (281.9 x 80 cm) Overall with knobs: 111 x 34 1/2 in. (281.9 x 87.6 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/49044,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.100.52,false,true,49097,Asian Art,Folding fan mounted as an album leaf,,China,Ming dynasty (1368–1644),,,,Artist,,Chen Mingxia,"Chinese, 1601–1654",,Chen Mingxia,Chinese,1601,1654,,1601,1654,Folding fan mounted as an album leaf; ink on paper,6 1/2 x 20 1/2 in. (16.5 x 52.1 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/49097,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.113,false,true,49015,Asian Art,Folding fan mounted as an album leaf,,China,Ming dynasty (1368–1644),,,,Artist,,Zou Zhilin,"Chinese, 1574–ca. 1654",,Zou Zhilin,Chinese,1574,1664,,1574,1644,Folding fan mounted as an album leaf; ink on mica-flecked paper,6 5/16 x 20 5/8 in. (16 x 52.4 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/49015,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.100.86,false,true,35997,Asian Art,Folding fan mounted as an album leaf,,China,Ming dynasty (1368–1644),,,,Artist,,Li Qiao,"Chinese, active Ming dynasty",,Li Qiao,Chinese,1350,1650,,1368,1644,Folding fan mounted as an album leaf; ink on paper,6 x 20 1/4 in. (16.5 x 51.4 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/35997,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.75,false,true,48909,Asian Art,Folding fan mounted as an album leaf,,China,Ming dynasty (1368–1644),,,,Artist,,Ju Jie,"Chinese, active ca. 1531–1585",,Ju Jie,Chinese,1531,1585,,1531,1585,Folding fan mounted as an album leaf; ink on gold paper,6 1/4 x 18 13/16 in. (15.9 x 47.8 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/48909,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.77,false,true,48930,Asian Art,Folding fan mounted as an album leaf,,China,Ming dynasty (1368–1644),,,,Artist,,Chen Yuansu,"Chinese, active late 16th century",,Chen Yuansu,Chinese,1500,1599,,1567,1599,Folding fan mounted as an album leaf; ink on gold paper,6 7/8 x 20 7/8 in. (17.5 x 53 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/48930,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.73,false,true,48897,Asian Art,Folding fan mounted as an album leaf,,China,Ming dynasty (1368–1644),,,,Artist,,Shen Shi,"Chinese, active early 16th century",,Shen Shi,Chinese,1500,1533,,1500,1533,Folding fan mounted as an album leaf; ink on gold-flecked paper,7 1/4 x 19 1/2 in. (18.4 x 49.5 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/48897,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.107,false,true,48965,Asian Art,Folding fan mounted as an album leaf,,China,Ming dynasty (1368–1644),,,,Artist,,Lu Yingyang,"Chinese, active early 17th century",,Lu Yingyang,Chinese,1600,1650,,1600,1633,Folding fan mounted as an album leaf; ink on gold-flecked paper,5 15/16 x 18 3/8 in. (15.1 x 46.7 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/48965,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.150,false,true,49169,Asian Art,Hanging scroll,,China,Qing dynasty (1644–1911),,,,Artist,,Sun Yueban,"Chinese, 1639–1708",,Sun Yueban,Chinese,1639,1708,,1644,1708,Hanging scroll; ink on paper,Image: 52 x 17 in. (132.1 x 43.2 cm) Overall: 77 x 22 3/4 in. (195.6 x 57.8 cm) Overall with knobs: 77 x 26 5/8 in. (195.6 x 67.6 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/49169,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.152,false,true,49174,Asian Art,Folding fan mounted as an album leaf,清 石濤 (朱若極) 五詩 扇頁|Five Poems,China,Qing dynasty (1644–1911),,,,Artist,,Shitao (Zhu Ruoji),"Chinese, 1642–1707",,Shitao,Chinese,1642,1707,,1644,1707,Folding fan mounted as an album leaf; ink on paper,6 7/8 x 17 1/2 in. (17.5 x 44.5 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/49174,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.158,false,true,49219,Asian Art,Folding fan mounted as an album leaf,,China,Qing dynasty (1644–1911),,,,Artist,,Wang Shihong,"Chinese, 1658–1723",,Wang Shihong,Chinese,1658,1723,,1658,1723,Folding fan mounted as an album leaf; ink on paper,6 3/8 x 19 1/4 in. (16.2 x 48.9 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/49219,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.204,false,true,51901,Asian Art,Hanging scroll,,China,Qing dynasty (1644–1911),,,,Artist,,Chen Hongshou,"Chinese, 1768–1822",,Chen Hongshou,Chinese,1768,1822,,1786,1822,Hanging scroll; ink on green paper,Image: 64 7/8 x 15 in. (164.8 x 38.1 cm) Overall with mounting: 88 1/4 x 21 3/4 in. (224.2 x 55.2 cm) Overall with knobs: 88 1/4 x 26 in. (224.2 x 66 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/51901,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"1976.384.2a, b",false,true,36122,Asian Art,Hanging scrolls,清 翁同龢 對軸|Regular-script Calligraphic Couplet,China,Qing dynasty (1644–1911),,,,Artist,,Weng Tonghe,"Chinese, 1830–1904",,Weng Tonghe,Chinese,1830,1904,,1830,1904,Pair of hanging scrolls; ink on red printed paper,Image (each): 92 5/8 x 17 7/8 in. (235.3 x 45.4 cm) Overall with mounting (each): 114 1/2 x 22 in. (290.8 x 55.9 cm),"Seymour Fund, 1976",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/36122,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -54.134.3,false,true,51807,Asian Art,Handscroll fragment,,China,Ming dynasty (?) (1368–1644),,,,Artist,,Wang Zhideng,"Chinese, 1535–1612",,Wang Zhideng,Chinese,1535,1612,,1535,1612,Fragment of scroll; ink on paper,71 1/4 x 14 1/4 in. (181.0 x 36.2 cm),"Gift of the Pierpont Morgan Library, 1954",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/51807,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.100.74,false,true,51379,Asian Art,Folding fan mounted as an album leaf,,China,late Ming dynasty (1368–1644),,,,Artist,,Fan Yunlin,"Chinese, 1558–1641",,Fan Yunlin,Chinese,1558,1641,,1558,1641,Folding fan mounted as an album leaf; ink and color on gold paper,7 x 21 in. (17.8 x 53.3 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/51379,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2000.325,false,true,39534,Asian Art,Handscroll mounted as hanging scroll,南宋 張即之 行書杜甫樂遊原詩殘卷 軸|Excerpt from “Song of Leyou Park”,China,Southern Song dynasty (1127–1279),,,,Artist,,Zhang Jizhi,"Chinese, 1186–1266",,Zhang Jizhi,Chinese,1186,1266,,1186,1266,Section of a handscroll mounted as a hanging scroll; ink on paper,Image: 12 3/4 x 30 1/4 in. (32.4 x 76.8 cm) Overall with mounting: 48 1/2 x 35 1/2 in. (123.2 x 90.2 cm) Overall with knobs: 48 1/2 x 37 1/2 in. (123.2 x 95.3 cm),"Gift of Sylvan Barnet and William Burto, in honor of Tajima Mitsuru, 2000",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/39534,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1994.382,false,true,42158,Asian Art,Brush Holder,清康熙 顧玨 竹雕筆筒|Brush Holder,China,"Qing dynasty (1644–1911), Kangxi period (1662–1722)",,,,Artist,,Gu Jue,active late 17th century,,Gu Jue,Chinese,1675,1700,,1667,1699,Bamboo with hardwood rim and base,H. 7 in. (17.8 cm); D. 6 1/2 in. (16.5 cm),"Purchase, Eileen W. Bamberger Bequest, in memory of her husband, Max Bamberger, 1994",,,,,,,,,,,,Bamboo,,http://www.metmuseum.org/art/collection/search/42158,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.118,false,true,49033,Asian Art,Album leaf,明/清 王鐸 書札 冊頁|Letter,China,late Ming (1368–1644)–early Qing (1644–1911) dynasty,,,,Artist,,Wang Duo,"Chinese, 1592–1652",,Wang Duo,Chinese,1592,1652,,1592,1652,Album leaf; ink on gold-flecked paper,10 3/4 x 6 in. (27.3 x 15.2 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/49033,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.40,false,true,45657,Asian Art,Handscroll,元/明 宋克 草書負郭堂成七律詩 卷|Poem on Retirement,China,late Yuan (1271–1368)–early Ming (1368–1644) dynasty,,,,Artist,,Song Ke,"Chinese, 1327–1387",,Song Ke,Chinese,1327,1387,,1327,1387,Handscroll; ink on gold-flecked paper,Image: 10 1/2 x 27 1/4 in. (26.7 x 69.2 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/45657,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.231,false,true,43294,Asian Art,Book,,China,Qing dynasty (1644–1911),,,,Artist,,Dong Gao,"Chinese, 1740–1818",,Dong Gao,Chinese,1740,1818,,1644,1911,"Jade, rosewood",H. 7 5/8 in. (19.4 cm); W. 5 1/2 in. (14 cm); D. 1 5/8 in. (4.1 cm),"Gift of Edward R. Finch, Jr., 1976",,,,,,,,,,,,Jade,,http://www.metmuseum.org/art/collection/search/43294,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -CP37,false,true,63331,Asian Art,Print,,China,,,,,Artist,Original painted by,Wang Youcheng,"Chinese, 698–759",,Wang Youcheng,Chinese,0698,0759,,0,0,Polychrome woodblock print; ink and color on paper,9 5/8 x 11 13/16 in. (24.4 x 30 cm),"Rogers Fund, 1924",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/63331,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -CP22,false,true,63216,Asian Art,Print,,China,,,,,Artist,Original painted by,Li Cheng,"Chinese, 919–967",,LI CHENG,Chinese,0919,0967,,0,0,Circular fan-shaped woodblock print; ink and color on paper,9 5/8 x 11 13/16 in. (24.4 x 30 cm),"Rogers Fund, 1924",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/63216,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -CP48,false,true,63351,Asian Art,Print,芥子園畫傳|Study Pavilion and Plum Trees: Page from The Mustard Seed Garden Manual of Painting,China,,,,,Artist,Original painted by,Li Cheng,"Chinese, 919–967",,LI CHENG,Chinese,0919,0967,,0,0,Polychrome woodblock print; ink and color on paper,9 5/8 x 11 13/16 in. (24.4 x 30 cm),"Rogers Fund, 1924",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/63351,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -CP33,false,true,63230,Asian Art,Print,,China,,,,,Artist,Original painted by,Wen Youke,"Chinese, died 1079",,Wen Youke,Chinese,1079,1079,,0,0,Polychrome woodblock print; ink and color on paper,9 5/8 x 11 13/16 in. (24.4 x 30 cm),"Rogers Fund, 1924",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/63230,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -CP24,false,true,63218,Asian Art,Print,,China,,,,,Artist,Original painted by,Mi Youren,"Chinese, 1074–1151",,MI YOUREN,Chinese,1074,1151,,0,0,Circular fan-shaped woodblock print; ink and color on paper,9 5/8 x 11 13/16 in. (24.4 x 30 cm),"Rogers Fund, 1924",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/63218,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -CP40,false,true,63336,Asian Art,Print,,China,,,,,Artist,In the Style of,Huang Zujiu,"Chinese, 1269–1354",,Huang Zujiu,Chinese,1269,1354,,0,0,Polychrome woodblock print; ink and color on paper,9 5/8 x 5 13/16 in. (24.4 x 14.8 cm),"Rogers Fund, 1924",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/63336,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -CP34,false,true,63325,Asian Art,Print,,China,,,,,Artist,Original painted by,Ke Jiusi,"Chinese, 1290–1343",,Ke Jiusi,Chinese,1290,1343,,0,0,Polychrome woodblock print; ink and color on paper,9 5/8 x 11 13/16 in. (24.4 x 30 cm),"Rogers Fund, 1924",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/63325,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -CP10,false,true,63204,Asian Art,Print,,China,,,,,Artist,Original painted by,Yunlin,"Chinese, 1301–1374",,Yunlin,Chinese,1301,1374,,1301,1374,Polychrome woodblock print; ink and color on paper,9 5/8 x 11 13/16 in. (24.4 x 30 cm),"Rogers Fund, 1924",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/63204,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -CP25,false,true,63219,Asian Art,Print,,China,,,,,Artist,Original painted by,Ni Zan,"Chinese, 1306–1374",,NI ZAN,Chinese,1306,1374,,0,0,Circular fan-shaped woodblock print; ink and color on paper,9 5/8 x 11 13/16 in. (24.4 x 30 cm),"Rogers Fund, 1924",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/63219,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -CP42,false,true,63338,Asian Art,Print,,China,,,,,Artist,Original painted by,Shen Zhou,"Chinese, 1427–1509",,Shen Zhou,Chinese,1427,1509,,0,0,Polychrome woodblock print; ink and color on paper,9 5/8 x 5 7/8 in. (24.4 x 14.9 cm),"Rogers Fund, 1924",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/63338,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -CP8,false,true,63201,Asian Art,Print,,China,,,,,Artist,Original by,Tang Yin,"Chinese, 1470–1524",,Tang Yin,Chinese,1470,1524,,1470,1524,Polychrome woodblock print; ink and color on paper,9 5/8 x 11 13/16 in. (24.4 x 30 cm),"Rogers Fund, 1924",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/63201,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -CP19,false,true,63213,Asian Art,Print,,China,,,,,Artist,Original painted by,Tang Yin,"Chinese, 1470–1524",,Tang Yin,Chinese,1470,1524,,0,0,Circular fan-shaped woodblock print; ink and color on paper,9 5/8 x 11 13/16 in. (24.4 x 30 cm),"Rogers Fund, 1924",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/63213,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -CP46,false,true,63345,Asian Art,Print,,China,,,,,Artist,Original painted by,Xu Wei,"Chinese, 1521–1593",,Xu Wei,Chinese,1521,1593,,0,0,Polychrome woodblock print; ink and color on paper,9 5/8 x 11 13/16 in. (24.4 x 30 cm),"Rogers Fund, 1924",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/63345,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -CP41,false,true,63337,Asian Art,Print,,China,,,,,Artist,Original painted by,Li Liufang,"Chinese, 1575–1629",,Li Liufang,Chinese,1575,1629,,0,0,Polychrome woodblock print; ink and color on paper,9 5/8 x 5 7/8 in. (24.4 x 14.9 cm),"Rogers Fund, 1924",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/63337,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -CP21,false,true,63215,Asian Art,Print,,China,,,,,Artist,Original painted by,Lan Ying,"Chinese, 1585–1664",,Lan Ying,Chinese,1585,1664,,0,0,Circular fan-shaped woodblock print; ink and color on paper,9 5/8 x 11 13/16 in. (24.4 x 30 cm),"Rogers Fund, 1924",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/63215,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -CP43,false,true,63339,Asian Art,Print,,China,,,,,Artist,Original painted by,Hongren,"Chinese, 1610–1664",,HONGREN,Chinese,1610,1664,,0,0,Polychrome woodblock print; ink and color on paper,9 5/8 x 5 7/8 in. (24.4 x 14.9 cm),"Rogers Fund, 1924",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/63339,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -CP44,false,true,63341,Asian Art,Print,,China,,,,,Artist,Original painted by,Yang Wencong,"Chinese, 1597–1645/46",,Yang Wencong,Chinese,1597,1646,,0,0,Polychrome woodblock print; ink and color on paper,9 5/8 x 5 7/8 in. (24.4 x 14.9 cm),"Rogers Fund, 1924",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/63341,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -CP18,false,true,63212,Asian Art,Print,,China,,,,,Artist,Original painted by,Hu Changbo,"Chinese, active ca. 1601",,Hu Changbo,Chinese,1601,1601,,0,0,Circular fan-shaped woodblock print; ink and color on paper,9 5/8 x 11 13/16 in. (24.4 x 30 cm),"Rogers Fund, 1924",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/63212,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -CP20,false,true,63214,Asian Art,Print,,China,,,,,Artist,Original painted by,Li Gonglin,"Chinese, ca. 1041–1106",,Li Gonglin,Chinese,1041,1106,,0,0,Circular fan-shaped woodblock print; ink and color on paper,9 5/8 x 11 13/16 in. (24.4 x 30 cm),"Rogers Fund, 1924",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/63214,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -CP23,false,true,63217,Asian Art,Print,,China,,,,,Artist,Original painted by,Wang Meng,"Chinese, ca. 1308–1385",,Wang Meng,Chinese,1308,1385,,0,0,Circular fan-shaped woodblock print; ink and color on paper,9 5/8 x 11 13/16 in. (24.4 x 30 cm),"Rogers Fund, 1924",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/63217,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -CP35,false,true,63327,Asian Art,Print,,China,,,,,Artist,Original painted by,Wang Meng,"Chinese, ca. 1308–1385",,Wang Meng,Chinese,1308,1385,,0,0,Polychrome woodblock print; ink and color on paper,9 5/8 x 11 13/16 in. (24.4 x 30 cm),"Rogers Fund, 1924",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/63327,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -CP47,false,true,63348,Asian Art,Print,,China,,,,,Artist,Original painted by,Qing Ji,"Chinese, fl. 1630–1650",,Qing Ji,Chinese,1630,1650,,0,0,Polychrome woodblock print; ink and color on paper,9 5/8 x 11 13/16 in. (24.4 x 30 cm),"Rogers Fund, 1924",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/63348,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -CP15,false,true,63209,Asian Art,Print,,China,,,,,Artist,Original painted by,Huang Guzu,"Chinese, active ca. 900–960",,Huang Guzu,Chinese,0900,0960,,0,0,Polychrome woodblock print; ink and color on paper,9 5/8 x 11 13/16 in. (24.4 x 30 cm),"Rogers Fund, 1924",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/63209,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -CP29,false,true,63226,Asian Art,Print,,China,,,,,Artist,Original painted by,Fan Kuan,"Chinese, active ca. 990–1030",,Fan Kuan,Chinese,0990,1030,,0,0,Circular fan-shaped woodblock print; ink and color on paper,9 5/8 x 11 13/16 in. (24.4 x 30 cm),"Rogers Fund, 1924",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/63226,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -CP9,false,true,63203,Asian Art,Print,,China,,,,,Artist,Original painted by,Xia Gui,"Chinese, active ca. 1195–1230",,Xia Gui,Chinese,1195,1230,,1195,1230,Polychrome woodblock print; ink and color on paper,9 5/8 x 11 13/16 in. (24.4 x 30 cm),"Rogers Fund, 1924",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/63203,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -CP3,false,true,63078,Asian Art,Print,,China,"Qing dynasty (1644–1911), Kangxi period (1662–1722)",,,,Artist,Workshop of,Ding Liangxian,active first half of the 18th century,,Ding Liang-xian,Chinese,1700,1749,,1662,1722,Polychrome woodblock print; ink and color on paper,Image: 13 1/4 × 10 7/8 in. (33.7 × 27.6 cm),"Rogers Fund, 1923",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/63078,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -24.44,false,true,51548,Asian Art,Handscroll,壺人樂聚|Merry Gatherings in the Magic Jar,China,Song dynasty (960–1279),,,,Artist,,Gong Kai,"Chinese, 1222–after 1304",,Gong Kai,Chinese,1222,1310,,1222,1279,Handscroll; ink on paper,11 3/4 in. × 14 ft. 2 in. (29.8 × 431.8 cm),"Rogers Fund, 1924",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51548,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1973.121.2,false,true,39920,Asian Art,Handscroll,北宋 傳趙令穰 江村秋曉圖 卷|River Village in Autumn Dawn,China,Song dynasty (960–1279),,,,Artist,Attributed to,Zhao Lingrang,"Chinese, active ca. 1070– after 1100",,ZHAO LINGRANG,Chinese,1070,1100,,1070,1170,Handscroll; ink and color on silk,Image: 9 5/16 x 41 in. (23.7 x 104.1 cm),"Ex coll.: C. C. Wang Family, Purchase, Gift of J. Pierpont Morgan, by exchange, 1973",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/39920,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1982.205,false,true,45685,Asian Art,Handscroll,,China,Ming dynasty (1368–1644),,,,Artist,,Shen Zhou,"Chinese, 1427–1509",,Shen Zhou,Chinese,1427,1509,,1427,1509,Handscroll; ink and color on paper,10 13/16 x 198 3/4 in. (27.5 x 504.8 cm),"Purchase, The Dillon Fund Gift, in memory of Phyllis E. Dillon, 1982",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45685,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.65,false,true,45793,Asian Art,Handscroll,明 佚名 怡松圖 卷|Enjoying the Pines,China,Ming dynasty (1368–1644),,,,Artist,Unidentified Artist after,Shen Zhou,"Chinese, 1427–1509",,Shen Zhou,Chinese,1427,1509,,1368,1644,Handscroll frontispiece; ink on gold-flecked paper,Image: 12 3/4 x 57 in. (32.4 x 144.8 cm) Overall with mounting: 13 3/16 x 252 15/16 in. (33.5 x 642.5 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45793,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1980.81,false,true,45773,Asian Art,Album leaves mounted as a handscroll,,China,Ming dynasty (1368–1644),,,,Artist,,Tang Yin,"Chinese, 1470–1524",,Tang Yin,Chinese,1470,1524,,1470,1524,Eight album leaves mounted as a handscroll; ink and color on silk,Image (All leaves): 12 3/4 x 162 7/8 in. (32.4 x 413.7 cm) Overall with mounting: 13 1/16 x 306 5/16 in. (33.2 x 778 cm) Image (Leaf 1): 12 3/4 x 17 in. (32.4 x 43.2 cm) Image (Leaf 2): 12 3/4 x 17 in. (32.4 x 43.2 cm) Image (Leaf 3): 12 3/4 x 16 7/8 in. (32.4 x 42.9 cm) Image (Leaf 4): 12 3/4 x 16 3/4 in. (32.4 x 42.5 cm) Image (Leaf 5): 12 3/4 x 16 7/8 in. (32.4 x 42.9 cm) Image (Leaf 6): 12 3/4 x 16 13/16 in. (32.4 x 42.7 cm) Image (Leaf 7): 12 3/4 x 16 7/8 in. (32.4 x 42.9 cm) Image (Leaf 8): 12 3/4 x 16 7/8 in. (32.4 x 42.9 cm),"Ex coll.: C. C. Wang Family, Gift of Douglas Dillon, 1980",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45773,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1982.7.2,false,true,45772,Asian Art,Folding fan mounted as an album leaf,,China,Ming dynasty (1368–1644),,,,Artist,,Tang Yin,"Chinese, 1470–1524",,Tang Yin,Chinese,1470,1524,,1470,1524,Folding fan mounted as an album leaf; ink and color on gold-flecked paper,20 3/4 x 9 3/4 in. (52.7 x 24.8 cm),"Purchase, The Dillon Fund Gift, 1982",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45772,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -47.18.129,false,true,36062,Asian Art,Handscroll,,China,Ming dynasty (1368–1644),,,,Artist,Attributed to,Tang Yin,"Chinese, 1470–1524",,Tang Yin,Chinese,1470,1524,,1470,1524,Handscroll; ink on silk,Image: 8 × 24 1/4 in. (20.3 × 61.6 cm),"From the Collection of A. W. Bahr, Purchase, Fletcher Fund, 1947",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36062,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1988.394.1,false,true,44573,Asian Art,Folding fan mounted as an album leaf,,China,Ming dynasty (1368–1644),,,,Artist,,Tang Yin,"Chinese, 1470–1524",,Tang Yin,Chinese,1470,1524,,1470,1524,Folding fan mounted as an album leaf; ink on gold-flecked paper,6 13/16 x 19 5/8 in. (17.3 x 49.8 cm),"Edward Elliott Family Collection, Douglas Dillon Gift, 1988",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/44573,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.54,false,true,45756,Asian Art,Handscroll,,China,Ming dynasty (1368–1644),,,,Artist,After,Tang Yin,"Chinese, 1470–1524",,Tang Yin,Chinese,1470,1524,,1470,1524,Handscroll; ink on paper,Image: 11 7/16 x 59 1/8 in. (29.1 x 150.2 cm) Overall with mounting: 11 7/8 x 289 9/16 in. (30.2 x 735.5 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45756,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.55,false,true,45760,Asian Art,Hanging scroll,,China,Ming dynasty (1368–1644),,,,Artist,,Tang Yin,"Chinese, 1470–1524",,Tang Yin,Chinese,1470,1524,,1470,1524,Hanging scroll; ink on paper,Image: 28 7/16 x 14 9/16 in. (72.2 x 37 cm) Overall with mounting: 64 3/4 x 21 in. (164.5 x 53.3 cm) Overall with knobs: 64 3/4 x 23 3/4 in. (164.5 x 60.3 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45760,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.278.9a–t,false,true,36103,Asian Art,Album,,China,Ming dynasty (1368–1644),,,,Artist,,Wen Zhengming,"Chinese, 1470–1559",,Wen Zhengming,Chinese,1470,1559,,1470,1559,"Album of eight painting leaves with facing sheets inscribed with poems, preceded by a four-sheet title piece; ink on paper",8 1/4 x 7 3/4 in. (21.0 x 19.7 cm) (each album leaf); 8 1/4 x 16 7/8 in. (21.0 x 42.9 cm) (two double calligraphy leaves),"Gift of Mr. and Mrs. Earl Morse, 1972",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36103,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1991.273,false,true,45821,Asian Art,Hanging scroll,明 陸治 枚乘獨坐圖 軸|Mei Cheng Sitting Alone,China,Ming dynasty (1368–1644),,,,Artist,,Lu Zhi,"Chinese, 1495–1576",,Lu Zhi,Chinese,1495,1576,,1495,1576,Hanging scroll; ink and color on paper,Image: 48 7/8 x 15 1/8 in. (124.1 x 38.4 cm) Overall with mounting: 87 3/8 x 30 1/8 in. (221.9 x 76.5 cm) Overall with knobs: 87 3/8 x 34 1/2 in. (221.9 x 87.6 cm),"Gift of Herbert and Jeanine Coyne, 1991",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45821,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.68,false,true,48873,Asian Art,Folding fan mounted as an album leaf,,China,Ming dynasty (1368–1644),,,,Artist,Attributed to,Lu Zhi,"Chinese, 1495–1576",,Lu Zhi,Chinese,1495,1576,,1495,1576,Folding fan mounted as an album leaf; ink and color on gold paper,6 1/2 x 18 3/8 in. (16.5 x 46.7 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/48873,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.95,false,true,49256,Asian Art,Folding fan mounted as an album leaf,,China,Ming dynasty (1368–1644),,,,Artist,,Zhu Lu,"Chinese, 1553–1632",,Zhu Lu,Chinese,1553,1632,,1552,1632,Folding fan mounted as an album leaf; ink on gold-flecked paper,6 5/8 x 20 5/8 in. (16.8 x 52.4 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49256,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.101,false,true,51872,Asian Art,Hanging scroll,,China,Ming dynasty (1368–1644),,,,Artist,,Dong Qichang,"Chinese, 1555–1636",,Dong Qichang,Chinese,1555,1636,,1555,1636,Hanging scroll; ink on paper,Image: 37 5/8 x 16 3/4 in. (95.6 x 42.5 cm) Overall with mounting: 84 5/8 x 23 in. (214.9 x 58.4 cm) Overall with knobs: 84 5/8 x 25 3/4 in. (214.9 x 65.4 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51872,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1981.285.2,false,true,48968,Asian Art,Hanging scroll,明 張瑞圖 山水圖 軸|Mountains Along Riverbanks,China,Ming dynasty (1368–1644),,,,Artist,,Zhang Ruitu,"Chinese, 1570–1641",,Zhang Ruitu,Chinese,1570,1641,,1570,1641,Hanging scroll; ink on satin,Image: 65 3/4 x 20 1/4 in. (167 x 51.4 cm) Overall with mounting: 105 x 27 1/2 in. (266.7 x 69.9 cm) Overall with knobs: 105 x 31 1/2 in. (266.7 x 80 cm),"Edward Elliott Family Collection, Purchase, The Dillon Fund Gift, 1981",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/48968,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.100.55,false,true,49077,Asian Art,Folding fan mounted as an album leaf,,China,Ming dynasty (1368–1644),,,,Artist,,Xiang Shengmo,"Chinese, 1597–1658",,Xiang Shengmo,Chinese,1597,1658,,1597,1644,Folding fan mounted as an album leaf; ink on gold paper,6 1/2 x 20 in. (16.5 x 50.8 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49077,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.278.2,false,true,36099,Asian Art,Hanging scroll,,China,Ming dynasty (1368–1644),,,,Artist,After,Chen Hongshou,"Chinese, 1599–1652",,Chen Hongshou,Chinese,1599,1652,,1350,1750,Hanging scroll; ink and color on silk,Image: 63 7/8 x 23 5/8 in. (162.2 x 60 cm) Overall with mounting: 93 1/2 x 27 1/2 in. (237.5 x 69.9 cm) Overall with rollers: 93 1/2 x 31 1/4 in. (237.5 x 79.4 cm),"Gift of Mr. and Mrs. Earl Morse, 1972",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36099,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1996.308,false,true,39628,Asian Art,Hanging scroll,明 藍孟 溪山雪泛圖 軸 紙本|Boating amid Snowy Streams and Mountains,China,Ming dynasty (1368–1644),,,,Artist,,Lan Meng,ca. 1614–after 1671,,Lan Meng,Chinese,1614,1671,,1614,1644,Hanging scroll; ink and color on silk,Image: 90 x 37 1/4 in. (228.6 x 94.6 cm),"Gift of John and Lili Bussel Family,1996",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/39628,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2000.664,false,true,50168,Asian Art,Hanging scroll,明 史忠 雪景山水圖 軸|Winter Landscape with Fisherman,China,Ming dynasty (1368–1644),,,,Artist,,Shi Zhong,"Chinese, 1438–ca. 1517",,Shi Zhong,Chinese,1438,1517,,1438,1517,Hanging scroll; ink on paper,Image: 56 x 12 5/8 in. (142.2 x 32.1 cm) Overall with mounting: 83 1/2 x 19 1/8 in. (212.1 x 48.6 cm) Overall with knobs: 83 1/2 x 22 1/2 in. (212.1 x 57.2 cm),"Ex coll.: C. C. Wang Family, Gift of C. C. Wang, in honor of Wen C. Fong, 2000",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/50168,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1991.342,false,true,45810,Asian Art,Hanging scroll,明 謝時臣 谿山春曉圖 軸|Spring Morning in the Mountains,China,Ming dynasty (1368–1644),,,,Artist,,Xie Shichen,"Chinese, 1487–ca. 1567",,Xie Shichen,Chinese,1487,1567,,1487,1567,Hanging scroll; ink and color on silk,Image: 76 1/8 x 41 1/4 in. (193.4 x 104.8 cm) Overall with mounting: 112 3/4 x 42 9/16 in. (286.4 x 108.1 cm) Overall with knobs: 112 3/4 x 46 3/8 in. (286.4 x 117.8 cm),"Gift of John N. Loomis, 1991",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45810,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.100.81,false,true,45807,Asian Art,Folding fan mounted as an album leaf,明 謝時臣 隱居圖 扇面|Landscape with Figure,China,Ming dynasty (1368–1644),,,,Artist,,Xie Shichen,"Chinese, 1487–ca. 1567",,Xie Shichen,Chinese,1487,1567,,1487,1577,Folding fan mounted as an album leaf; ink on gold paper,7 x 19 3/4 in. (17.8 x 50.2 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45807,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"1982.1.9a, b",false,true,36138,Asian Art,Hanging scroll,明 傳謝時臣 松溪琴客圖 軸|Listening to the Zither Among Streams and Pines,China,Ming dynasty (1368–1644),,,,Artist,,Xie Shichen,"Chinese, 1487–ca. 1567",,Xie Shichen,Chinese,1487,1567,,1487,1567,Hanging scroll; ink and color on silk,Image: 55 1/2 x 28 in. (141 x 71.1 cm) Overall with mounting: 124 x 36 in. (315 x 91.4 cm) Overall with knobs: 124 x 38 3/4 in. (315 x 98.4 cm),"Edward Elliott Family Collection, Purchase, The Dillon Fund Gift, 1982",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36138,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.112,false,true,49013,Asian Art,Folding fan mounted as an album leaf,"明 鄒之麟 山水圖 扇面 |Landscape",China,Ming dynasty (1368–1644),,,,Artist,,Zou Zhilin,"Chinese, 1574–ca. 1654",,Zou Zhilin,Chinese,1574,1664,,1574,1644,Folding fan mounted as an album leaf; ink on gold paper,Image: 9 1/2 × 20 1/8 in. (24.1 × 51.1 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49013,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2002.296,false,true,64972,Asian Art,Hanging scroll,明 仿林良 喜上梅梢圖 軸|Birds Amidst Blossoming Plum and Bamboo,China,Ming dynasty (1368–1644),,,,Artist,After,Lin Liang,"Chinese, ca. 1416–1480",,Lin Liang,Chinese,1416,1480,,1416,1480,Hanging scroll; ink and color on silk,40 15/16 x 18 11/16 in. (104 x 47.4 cm),"Purchase, C. C. Wang Gift, 2002",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/64972,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -47.18.19,false,true,36050,Asian Art,Hanging scroll,明 倣林良 蘆鴨圖 軸|Ducks and Reeds,China,Ming dynasty (1368–1644),,,,Artist,After,Lin Liang,"Chinese, ca. 1416–1480",,Lin Liang,Chinese,1416,1480,,1416,1644,Hanging scroll; ink and color on silk,Image: 61 5/8 × 34 5/8 in. (156.5 × 87.9 cm) Overall with mounting: 10 ft. 3/8 in. × 40 3/8 in. (305.8 × 102.6 cm) Overall with knobs: 10 ft. 3/8 in. × 44 1/2 in. (305.8 × 113 cm),"From the Collection of A. W. Bahr, Purchase, Fletcher Fund, 1947",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36050,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -12.134.8,false,true,51285,Asian Art,Hanging scroll,,China,Ming dynasty (1368–1644),,,,Artist,Attributed to,Qiu Ying,"Chinese, ca. 1495–1552",,Qiu Ying,Chinese,1485,1562,,1495,1552,Hanging scroll; ink and color on silk,Image: 33 in. × 30 3/4 in. (83.8 × 78.1 cm) Overall with mounting: 73 3/4 × 35 5/8 in. (187.3 × 90.5 cm) Overall with knobs: 73 3/4 × 39 in. (187.3 × 99.1 cm),"Rogers Fund, 1912",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51285,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.220.15,false,true,36013,Asian Art,Hanging scroll,明 傳仇英 文玉圖 軸|Lady in a Bamboo Grove,China,Ming dynasty (1368–1644),,,,Artist,Attributed to,Qiu Ying,"Chinese, ca. 1495–1552",,Qiu Ying,Chinese,1485,1562,,1495,1552,Hanging scroll; ink and color on silk,Image: 49 in. × 17 1/2 in. (124.5 × 44.5 cm) Overall with mounting: 9 ft. 5 1/8 in. × 25 3/8 in. (287.3 × 64.5 cm) Overall with knobs: 9 ft. 5 1/8 in. × 29 1/2 in. (287.3 × 74.9 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36013,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -47.18.22,false,true,36051,Asian Art,Hanging scroll,,China,Ming dynasty (1368–1644),,,,Artist,,Ma Shida,"Chinese, 15th century (?)",,Ma Shida,Chinese,1400,1500,,1400,1499,Hanging scroll; ink on paper,Overall: 43 3/8 x 18 3/8 in. (110.2 x 46.7 cm) Overall with mounting: 24 1/2 in. (62.2 cm),"From the Collection of A. W. Bahr, Purchase, Fletcher Fund, 1947",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36051,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1982.1.8,false,true,48942,Asian Art,Hanging scroll,明 劉世儒 雪梅圖 軸|Plum in Snow,China,Ming dynasty (1368–1644),,,,Artist,,Liu Shiru,"Chinese, active 1550–1600",,Liu Shiru,Chinese,1550,1600,,1550,1600,Hanging scroll; ink on silk,Image: 68 3/4 x 24 3/8 in. (174.6 x 61.9 cm) Overall with mounting: 106 1/4 x 30 7/8 in. (269.9 x 78.4 cm) Overall with knobs: 106 1/4 x 33 9/16 in. (269.9 x 85.2 cm),"Edward Elliott Family Collection, Purchase, The Dillon Fund Gift, 1982",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/48942,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.220.115,false,true,36023,Asian Art,Hanging scroll,明 傳張路 溪畔漁家圖 軸|Fisherman and Family,China,Ming dynasty (1368–1644),,,,Artist,Attributed to,Zhang Lu,"Chinese, ca. 1490–ca. 1563",,Zhang Lu,Chinese,1490,1563,,1490,1563,Hanging scroll; ink and color on silk,43 3/8 x 31 7/8 in. (110.2 x 81.0 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36023,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2001.746.2,false,true,50167,Asian Art,Hanging scrolls,明 杜堇 陶淵明菊花圖 軸|Tao Yuanming Enjoying Chrysanthemums,China,Ming dynasty (1368–1644),,,,Artist,,Du Jin,"Chinese, active ca. 1465–1509",,Du Jin,Chinese,1465,1509,,1465,1509,Hanging scrolls; ink and color on paper,Image: 58 1/4 x 14 3/16 in. (148 x 36 cm) Overall with mounting: 105 1/4 x 21 3/16 in. (267.3 x 53.8 cm) Overall with knobs: 105 1/4 x 24 1/2 in. (267.3 x 62.2 cm),"The C. C. Wang Family Collection, Gift of C. C. Wang, 2001",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/50167,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.100.85,false,true,35996,Asian Art,Folding fan mounted as an album leaf,,China,Ming dynasty (1368–1644),,,,Artist,,Zhang Chong,"Chinese, active ca. 1570–1610",,Zhang Chong,Chinese,1570,1610,,1570,1610,Folding fan mounted as an album leaf; ink on gold paper,6 1/2 x 19 1/4 in. (16.5 x 48.9 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/35996,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1981.285.11,false,true,36136,Asian Art,Hanging scroll,明 張祐 萬古春風圖 軸|Spring Breeze of Myriad Pasts,China,Ming dynasty (1368–1644),,,,Artist,,Zhang You,"Chinese, active mid 15th century",,Zhang You,Chinese,1400,1499,,1434,1466,Hanging scroll; ink on silk,Image: 61 3/4 x 27 1/2 in. (156.8 x 69.9 cm) Overall with mounting: 102 1/4 x 34 3/4 in. (259.7 x 88.3 cm) Overall with knobs: 102 1/4 x 38 in. (259.7 x 96.5 cm),"Edward Elliott Family Collection, Purchase, The Dillon Fund Gift, 1981",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36136,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1980.414,false,true,41471,Asian Art,Hanging scroll,明 呂紀 秋景花鳥圖 軸|Autumn Landscape with Herons and Ducks,China,Ming dynasty (1368–1644),,,,Artist,,Lü Ji,"Chinese, active late 15th century",,Lü Ji,Chinese,1430,1504,,1467,1499,Hanging scroll; ink and color on silk,Image: 58 1/8 x 21 1/2 in. (147.6 x 54.6 cm) Overall with mounting: 106 1/4 x 27 1/2 in. (269.9 x 69.9 cm) Overall with knobs: 106 1/4 x 30 in. (269.9 x 76.2 cm),"Purchase, Bequest of Dorothy Graham Bennett, 1980",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/41471,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.494.2,false,true,39551,Asian Art,Hanging scroll,明 呂紀 鴛鴦芙蓉圖 軸|Mandarin ducks and cotton rose hibiscus,China,Ming dynasty (1368–1644),,,,Artist,,Lü Ji,"Chinese, active late 15th century",,Lü Ji,Chinese,1430,1504,,1467,1499,Hanging scroll; ink and color on silk,Image: 68 x 39 in. (172.7 x 99.1 cm) Overall with mounting: 116 1/4 x 40 1/4 in. (295.3 x 102.2 cm) Overall with knobs: 116 1/4 x 44 1/2 in. (295.3 x 113 cm),"Ex coll.: C. C. Wang Family, Gift of Oscar L. Tang Family, 2005",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/39551,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -16.151,false,true,36030,Asian Art,Hanging scroll,明 沈碩 仿仇英文玉圖 軸|Lady in a Bamboo Grove after Qiu Ying,China,Ming dynasty (1368–1644),,,,Artist,,Shen Shuo,"Chinese, active 16th–17th century",,Shen Shuo,Chinese,1500,1600,,1544,1644,Hanging scroll; ink and color on silk,Image: 46 1/2 × 19 1/2 in. (118.1 × 49.5 cm) Overall with mounting: 9 ft. 4 in. × 27 5/8 in. (284.5 × 70.2 cm) Overall with knobs: 9 ft. 4 in. × 31 3/4 in. (284.5 × 80.6 cm),"Gift of John C. Ferguson, 1916",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36030,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.100.93,false,true,36002,Asian Art,Folding fan mounted as an album leaf,,China,Qing dynasty (1644–1911),,,,Artist,,Wu Shantao,1609–1690,,Wu Shantao,Chinese,1609,1690,,1644,1690,Folding fan mounted as an album leaf; ink on gold paper,6 1/4 x 18 5/8 in. (15.9 x 47.3 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36002,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.100.89,false,true,36000,Asian Art,Folding fan mounted as an album leaf,,China,Qing dynasty (1644–1911),,,,Artist,,Zhuang Jiongsheng,"Chinese, born 1626",,Zhuang Jiongsheng,Chinese,1626,1726,,1644,1676,Folding fan mounted as an album leaf; ink on gold paper,6 3/8 x 19 3/4 in. (16.2 x 50.2 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36000,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.267.82,false,true,36188,Asian Art,Folding fan mounted as an album leaf,近代 吳石僊 春江煙雨 扇面|Misty Rain on the River in Spring,China,Qing dynasty (1644–1911),,,,Artist,,Wu Shixian,"Chinese, died 1916",,Wu Shixian,Chinese,,1916,,1816,1916,Folding fan mounted as an album leaf; ink on gold-flecked paper,7 1/8 x 20 1/4 in. (18.1 x 51.4 cm),"Gift of Robert Hatfield Ellsworth, in memory of La Ferne Hatfield Ellsworth, 1986",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36188,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -69.242.7,false,true,49130,Asian Art,Hanging scroll,清 傳查士標 泛棹圖 軸|Old Man Boating on a River,China,Qing dynasty (1644–1911),,,,Artist,,Zha Shibiao,"Chinese, 1615–1698",,Zha Shibiao,Chinese,1615,1698,,1644,1698,Hanging scroll; ink on paper,69 x 26 7/8 in. (175.3 x 68.3 cm),"The Sackler Fund, 1969",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49130,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.142a–l,false,true,39931,Asian Art,Album,清 戴本孝 山水圖 冊 紙本|Landscapes,China,Qing dynasty (1644–1911),,,,Artist,,Dai Benxiao,"Chinese, 1621–1693",,DAI BENXIAO,Chinese,1621,1693,,1621,1691,Album of twelve leaves; ink on paper,Each: 8 7/16 × 6 9/16 in. (21.4 × 16.7 cm),"Purchase, The Dillon Fund Gift, 1989",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/39931,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1981.285.5,false,true,36134,Asian Art,Folding fan mounted as an album leaf,,China,Qing dynasty (1644–1911),,,,Artist,,Mei Qing,"Chinese, 1623–1697",,MEI QING,Chinese,1623,1697,,1623,1697,Folding fan mounted as an album leaf; ink and color on gold paper,Image (each leaf): 7 1/4 x 20 1/16 in. (18.4 x 51 cm),"Edward Elliott Family Collection, Purchase, The Dillon Fund Gift, 1981",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36134,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -54.34a–l,false,true,51803,Asian Art,Album,,China,Qing dynasty (1644–1911),,,,Artist,Attributed to,Bada Shanren (Zhu Da),"Chinese, 1626–1705",,Bada Shanren (Zhu Da),Chinese,1626,1705,,1644,1705,Album of twelve paintings; ink wash on paper,Image: 7 3/4 in. × 6 in. (19.7 × 15.2 cm) Sheet: 11 in. × 7 1/4 in. (27.9 × 18.4 cm) double leaf: 11 × 14 5/8 in. (27.9 × 37.1 cm),"Seymour Fund, 1954",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51803,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.140,false,true,49148,Asian Art,Folding fan mounted as an album leaf,,China,Qing dynasty (1644–1911),,,,Artist,,Wang Wu,"Chinese, 1632–1690",,Wang Wu,Chinese,1632,1690,,1632,1690,Folding fan mounted as an album leaf; ink and color on paper,6 3/4 x 20 1/8 in. (17.1 x 51.1 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49148,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1981.285.6,false,true,41485,Asian Art,Hanging scroll,清 吳歷 倣王蒙溪山行旅圖 軸|Travelers Among Streams and Mountains,China,Qing dynasty (1644–1911),,,,Artist,,Wu Li,"Chinese, 1632–1718",,WU LI,Chinese,1632,1718,,1644,1718,Hanging scroll; ink on paper,Image: 23 1/4 x 10 5/8 in. (59.1 x 27 cm) Overall with mounting: 73 1/2 x 18 in. (186.7 x 45.7 cm) Overall with knobs: 73 1/2 x 21 1/4 in. (186.7 x 54 cm),"Ex coll.: C. C. Wang Family, Edward Elliott Family Collection, Purchase, The Dillon Fund Gift, 1981",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/41485,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.16,false,true,49165,Asian Art,Hanging scroll,,China,Qing dynasty (1644–1911),,,,Artist,After,Yun Shouping,"Chinese, 1633–1690",,YUN SHOUPING,Chinese,1633,1690,,1644,1690,Hanging scroll; ink and color on silk,Image: 69 5/8 x 35 in. (176.8 x 88.9 cm) Overall with mounting: 121 1/2 x 43 in. (308.6 x 109.2 cm) Overall with knobs: 121 1/2 x 46 1/2 in. (308.6 x 118.1 cm),"Gift of Mr. and Mrs. Earl Morse, 1972",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49165,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.220.35,false,true,36016,Asian Art,Hanging scroll,,China,Qing dynasty (1644–1911),,,,Artist,Attributed to,Yun Shouping,"Chinese, 1633–1690",,YUN SHOUPING,Chinese,1633,1690,,1644,1690,Hanging scroll; ink on paper,38 7/8 x 13 1/8 in. (98.7 x 33.3 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36016,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.278.5,false,true,52225,Asian Art,Hanging scroll,,China,Qing dynasty (1644–1911),,,,Artist,,Yun Shouping,"Chinese, 1633–1690",,YUN SHOUPING,Chinese,1633,1690,,1644,1690,Hanging scroll; ink and color on silk,Image: 52 3/4 x 25 1/8 in. (134 x 63.8 cm) Overall with mounting: 88 3/8 x 31 1/4 in. (224.5 x 79.4 cm) Overall with rollers: 88 3/8 x 34 1/2 in. (224.5 x 87.6 cm),"Gift of Mr. and Mrs. Earl Morse, 1972",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/52225,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.148,false,true,49164,Asian Art,Folding fan mounted as an album leaf,,China,Qing dynasty (1644–1911),,,,Artist,,Yun Shouping,"Chinese, 1633–1690",,YUN SHOUPING,Chinese,1633,1690,,1644,1690,Folding fan mounted as an album leaf; color on paper,6 11/16 x 20 in. (17 x 50.8 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49164,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.191,false,true,36120,Asian Art,Hanging scroll,清 陳字 掃象圖 軸|Washing the White Elephant,China,Qing dynasty (1644–1911),,,,Artist,,Chen Zi,"Chinese, 1634–1711",,Chen Zi,Chinese,1634,1711,,1644,1711,Hanging scroll; ink on paper,Image: 34 5/16 x 15 1/16 in. (87.2 x 38.3 cm) Overall with mounting: 86 3/4 x 23 in. (220.3 x 58.4 cm) Overall with knobs: 86 3/4 x 27 1/4 in. (220.3 x 69.2 cm),"Purchase, The Dillon Fund Gift, 1976",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36120,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1980.426.4,false,true,49182,Asian Art,Hanging scroll,清 石濤(朱若極) 廬山草堂圖 軸|Hermitage in Mount Lu,China,Qing dynasty (1644–1911),,,,Artist,,Shitao (Zhu Ruoji),"Chinese, 1642–1707",,Shitao,Chinese,1642,1707,,1644,1707,Hanging scroll; ink on paper,Image: 37 5/16 x 19 11/16 in. (94.8 x 50 cm) Overall with mounting: 76 1/4 x 26 in. (193.7 x 66 cm) Overall with knobs: 76 1/4 x 29 1/2 in. (193.7 x 74.9 cm),"Gift of Douglas Dillon, 1980",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49182,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.155a–h,false,true,49180,Asian Art,Album,清 石濤(朱若極) 四季山水圖 冊|Landscapes of the Four Seasons,China,Qing dynasty (1644–1911),,,,Artist,,Shitao (Zhu Ruoji),"Chinese, 1642–1707",,Shitao,Chinese,1642,1707,,1644,1707,Album of eight leaves; ink and color on paper,8 1/4 x 12 3/8 in. (21 x 31.4 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49180,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.159,false,true,49238,Asian Art,Hanging scroll,清 高鳳翰 菊石圖 軸|Chrysanthemums by a Rock,China,Qing dynasty (1644–1911),,,,Artist,,Gao Fenghan,"Chinese, 1683–1749",,Gao Fenghan,Chinese,1683,1749,,1683,1749,Hanging scroll; ink and color on paper,Image: 45 1/4 x 21 1/2 in. (114.9 x 54.6 cm) Overall with mounting: 76 x 27 1/2 in. (193 x 69.9 cm) Overall with knobs: 76 x 31 3/4 in. (193 x 80.6 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49238,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.278.8,false,true,36102,Asian Art,Hanging scroll,清 方士庶 山水圖 軸|Landscape,China,Qing dynasty (1644–1911),,,,Artist,,Fang Shishu,"Chinese, 1692–1751",,Fang Shishu,Chinese,1692,1751,,1692,1751,Hanging scroll; ink and color on paper,Image: 26 7/8 x 18 1/4 in. (68.3 x 46.4 cm) Overall with mounting: 95 7/8 x 24 7/8 in. (243.5 x 63.2 cm) Overall with knobs: 95 7/8 x 29 in. (243.5 x 73.7 cm),"Gift of Mr. and Mrs. Earl Morse, 1972",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36102,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -68.212.2,false,true,36096,Asian Art,Hanging scroll,,China,Qing dynasty (1644–1911),,,,Artist,After,Zheng Xie,"Chinese, 1693–1765",,Zheng Xie,Chinese,1693,1765,,1693,1911,Hanging scroll; ink on paper,67 1/8 x 17 15/16 in. (170.5 x 45.6 cm),"The C. C. Wang Family Collection, Gift of C. C. Wang, 1968",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36096,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1985.214.150,false,true,52946,Asian Art,Hanging scroll,,China,Qing dynasty (1644–1911),,,,Artist,,Zheng Xie,"Chinese, 1693–1765",,Zheng Xie,Chinese,1693,1765,,1693,1765,Hanging scroll; ink on paper,Image: 55 1/8 x 15 1/2 in. (140 x 39.4 cm) Overall with mounting: 86 x 21 in. (218.4 x 53.3 cm) Overall with knobs: 85 x 24 in. (215.9 x 61 cm),"Gift of Ernest Erickson Foundation, 1985",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/52946,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -52.177.15,false,true,51790,Asian Art,Hanging scroll,,China,Qing dynasty (1644–1911),,,,Artist,,Qian Weicheng,"Chinese, 1720–1772",,Qian Weicheng,Chinese,1720,1772,,1720,1772,Hanging scroll; ink and color on paper,Image: 16 3/4 × 12 1/2 in. (42.5 × 31.8 cm) Overall with mounting: 77 1/4 × 18 3/4 in. (196.2 × 47.6 cm) Overall with knobs: 77 1/4 × 18 7/8 in. (196.2 × 47.9 cm),"Bequest of Katherine S. Dreier, 1952",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51790,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1988.153.1a–l,false,true,44527,Asian Art,Album,清 錢維城 景數四氣，冬景圖 冊|Winter Landscapes and Flowers,China,Qing dynasty (1644–1911),,,,Artist,,Qian Weicheng,"Chinese, 1720–1772",,Qian Weicheng,Chinese,1720,1772,,1720,1772,Album of twelve paintings; ink and color on paper,Image (each): 8 5/8 × 11 7/8 in. (21.9 × 30.2 cm),"Purchase, The Dillon Fund Gift, 1988",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/44527,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.220.118,false,true,36026,Asian Art,Hanging scroll,,China,Qing dynasty (1644–1911),,,,Artist,After,Wang Wenzhi,"Chinese, 1730–1802",,Wang Wenzhi,Chinese,1730,1802,,1644,1911,Hanging scroll; ink and color on silk,17 1/4 x 19 1/2 in. (43.8 x 49.5 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36026,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -50.131.1–.9,false,true,42559,Asian Art,Album,,China,Qing dynasty (1644–1911),,,,Artist,,Prince Yongxing,"Chinese, 1752–1823",,Yongxing Prince,Chinese,1752,1823,,1752,1823,Album with nine paintings and nine leaves of calligraphy; ink and color on heavy paper,Each leaf: 4 7/8 x 3 7/8 in. (12.4 x 9.8 cm),"Anonymous Gift, 1950",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/42559,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.267.4,false,true,36147,Asian Art,Hanging scroll,清 改琦 插花圖 軸|Girl Arranging Flowers,China,Qing dynasty (1644–1911),,,,Artist,,Gai Qi,"Chinese, 1773–1828",,Gai Qi,Chinese,1773,1828,,1774,1829,Hanging scroll; ink and color on silk,40 7/8 x 13 1/8 in. (103.8 x 33.3 cm),"Gift of Robert Hatfield Ellsworth, in memory of La Ferne Hatfield Ellsworth, 1986",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36147,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.267.39,false,true,49457,Asian Art,Fan mounted as an album leaf,清 張熊 蟾蜍 團扇|Flower and Toad,China,Qing dynasty (1644–1911),,,,Artist,,Zhang Xiong,"Chinese, 1803–1886",,Zhang Xiong,Chinese,1803,1886,,1803,1886,Circular fan-shaped album leaf; ink and color on silk,Diam. 10 1/2 in. (26.7 cm),"Gift of Robert Hatfield Ellsworth, in memory of La Ferne Hatfield Ellsworth, 1986",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49457,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.267.9,false,true,49433,Asian Art,Hanging scroll,清 蘇仁山 李凝陽像 軸|The Immortal Li Tieguai,China,Qing dynasty (1644–1911),,,,Artist,,Su Renshan,"Chinese, 1814–1849",,Su Renshan,Chinese,1814,1850,,1814,1849,Hanging scroll; ink on paper,45 1/2 x 15 3/4 in. (115.6 x 40 cm),"Gift of Robert Hatfield Ellsworth, in memory of La Ferne Hatfield Ellsworth, 1986",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49433,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.267.32,false,true,49455,Asian Art,Folding fan mounted as an album leaf,清 胡遠 山水 扇面|Landscape,China,Qing dynasty (1644–1911),,,,Artist,,Hu Yuan,"Chinese, 1823–1886",,Hu Yuan,Chinese,1823,1886,,1823,1886,Folding fan mounted as an album leaf; ink and color on gold-flecked paper,7 1/8 x 20 1/4 in. (18.1 x 51.4 cm),"Gift of Robert Hatfield Ellsworth, in memory of La Ferne Hatfield Ellsworth, 1986",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49455,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.267.33,false,true,49456,Asian Art,Folding fan mounted as an album leaf,清 胡遠 芍藥 扇面|Herbaceous Peony,China,Qing dynasty (1644–1911),,,,Artist,,Hu Yuan,"Chinese, 1823–1886",,Hu Yuan,Chinese,1823,1886,,1823,1886,Folding fan mounted as an album leaf; ink and color on gold paper,7 1/8 x 20 3/4 in. (18.1 x 52.7 cm),"Gift of Robert Hatfield Ellsworth, in memory of La Ferne Hatfield Ellsworth, 1986",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49456,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.267.73a–h,false,true,36184,Asian Art,Album,清 居廉 花卉蟲草 冊頁 八開|Insects and Flowers,China,Qing dynasty (1644–1911),,,,Artist,,Ju Lian,"Chinese, 1828–1904",,Ju Lian,Chinese,1828,1904,,1828,1904,Album of eight leaves; ink and color on paper,12 3/8 x 14 1/4 in. (31.4 x 36.2 cm),"Gift of Robert Hatfield Ellsworth, in memory of La Ferne Hatfield Ellsworth, 1986",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36184,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.267.74,false,true,49472,Asian Art,Folding fan mounted as an album leaf,清 吳大澂 山水 扇面|Fragrant Mountains,China,Qing dynasty (1644–1911),,,,Artist,,Wu Dacheng,"Chinese, 1835–1902",,Wu Dacheng,Chinese,1835,1902,,1835,1902,Folding fan mounted as an album leaf; ink on alum paper,6 7/8 x 20 7/8 in. (17.5 x 53 cm),"Gift of Robert Hatfield Ellsworth, in memory of La Ferne Hatfield Ellsworth, 1986",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49472,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.267.56,false,true,36172,Asian Art,Fan mounted as an album leaf,清 舒浩 周敦頤愛蓮圖 團扇|Admiring Lotus,China,Qing dynasty (1644–1911),,,,Artist,,Shu Hao,"Chinese, 1850–1899",,Shu Hao,Chinese,1850,1899,,1867,1899,Circular fan-shaped album leaf; ink and color on silk,Diam. 10 1/4 in. (26 cm),"Gift of Robert Hatfield Ellsworth, in memory of La Ferne Hatfield Ellsworth, 1986",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36172,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.267.71,false,true,49471,Asian Art,Hanging scroll,清 任預 石室參禪圖 軸|Meditation in a Cave,China,Qing dynasty (1644–1911),,,,Artist,,Ren Yu,"Chinese, 1853–1901",,REN YU,Chinese,1853,1901,,1853,1901,Hanging scroll; ink and color on paper,44 7/8 x 15 3/8 in. (114.0 x 39.1 cm),"Gift of Robert Hatfield Ellsworth, in memory of La Ferne Hatfield Ellsworth, 1986",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49471,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.162,false,true,49242,Asian Art,Hanging scroll,,China,Qing dynasty (1644–1911),,,,Artist,,Li Shan,"Chinese, 1686–ca. 1756",,Li Shan,Chinese,1686,1756,,1686,1756,Hanging scroll; ink and color on paper,Image: 53 1/8 x 13 in. (134.9 x 33 cm) Overall with mounting: 75 1/2 x 18 7/8 in. (191.8 x 47.9 cm) Overall with knobs: 75 1/2 x 22 3/8 in. (191.8 x 56.8 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49242,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.267.42,false,true,49459,Asian Art,Folding fan mounted as an album leaf,清 居巢 鳥圖 扇面|Bird,China,Qing dynasty (1644–1911),,,,Artist,,Zhü Chao,"Chinese, ca. 1823–1889",,Zhü Chao,Chinese,1823,1889,,1823,1889,Folding fan mounted as an album leaf; ink and color on gold-flecked paper,7 x 20 7/8 in. (17.8 x 53 cm),"Gift of Robert Hatfield Ellsworth, in memory of La Ferne Hatfield Ellsworth, 1986",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49459,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1991.117.1,false,true,41484,Asian Art,Hanging scroll,清 王鑑 倣巨然《溪山高士圖》 軸 紙本|Lofty Scholar among Streams and Mountains after Juran,China,Qing dynasty (1644–1911),,,,Artist,,Wang Jian,"Chinese, 1609–1677 or 1688",,Wang Jian,Chinese,1609,1677,,1644,1688,Hanging scroll; ink on paper,Image: 72 x 33 in. (182.9 x 83.8 cm) Overall with mounting: 107 1/2 x 38 1/4 in. (273.1 x 97.2 cm) Overall with knobs: 107 1/2 x 42 1/2 in. (273.1 x 108 cm),"Gift of Douglas Dillon, 1991",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/41484,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.164,false,true,49236,Asian Art,Album leaf,,China,Qing dynasty (1644–1911),,,,Artist,,Bian Shoumin,"Chinese, active ca. 1729–50",,Bian Shoumin,Chinese,1729,1750,,1729,1750,Album leaf; ink on paper,8 5/8 x 18 11/16 in. (21.9 x 47.5 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49236,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.220.30,false,true,36014,Asian Art,Hanging scroll,清 山水圖 軸|Landscape,China,Qing dynasty (1644–1911),,,,Artist,Attributed to,Zhao Zuo,"Chinese, ca. 1570–after 1630",,Zhao Zuo,Chinese,1560,1630,,1630,1644,Hanging scroll; ink on silk,58 1/4 x 26 1/8 in. (148.0 x 66.4 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36014,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1982.1.10,false,true,36139,Asian Art,Hanging scroll,,China,Qing dynasty (1644–1911),,,,Artist,,Gao Xiang,"Chinese, active ca. 1700–1730",,Gao Xiang,Chinese,1700,1730,,1700,1730,Hanging scroll; ink on paper,Image: 34 1/2 x 15 5/16 in. (87.6 x 38.9 cm) Overall with mounting: 80 7/8 x 20 3/4 in. (205.4 x 52.7 cm) Overall with knobs: 80 7/8 x 23 3/8 in. (205.4 x 59.4 cm),"Edward Elliott Family Collection, Purchase, The Dillon Fund Gift, 1982",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36139,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.100.57,false,true,35981,Asian Art,Folding fan mounted as an album leaf,,China,Qing dynasty (1644–1911),,,,Artist,,Shen Hao,"Chinese, active late 17th century",,Shen Hao,Chinese,1671,1699,,1667,1699,Folding fan mounted as an album leaf; ink on gold paper,6 5/8 x 20 1/4 in. (16.8 x 51.4 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/35981,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.220.38a–l,false,true,51484,Asian Art,Album,,China,Qing dynasty (1644–1911),,,,Artist,,Cao Jian,"Chinese, active early 18th century",,Cao Jian,Chinese,1700,1733,,1700,1733,Album of twelve leaves; ink and color on paper,Overall (b–l): 10 1/2 x 11 1/2 in. (26.7 x 29.2 cm) Overall (a): 10 3/8 x 11 5/8 in. (26.4 x 29.5 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51484,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1973.121.9,false,true,40080,Asian Art,Hanging scroll,元 王冕 墨梅圖 軸|Fragrant Snow at Broken Bridge,China,Yuan dynasty (1271–1368),,,,Artist,,Wang Mian,"Chinese, 1287–1359",,WANG MIAN,Chinese,1287,1359,,1287,1359,Hanging scroll; ink on silk,Image: 44 1/2 x 19 3/4 in. (113 x 50.2 cm) Overall with colophons: 68 x 19 3/4 in. (172.7 x 50.2 cm) Overall with mounting: 106 1/8 x 25 1/4 in. (269.6 x 64.1 cm) Overall with knobs: 106 1/8 x 29 1/2 in. (269.6 x 74.9 cm),"Ex coll.: C. C. Wang Family, Purchase, Gift of J. Pierpont Morgan, by exchange, 1973",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/40080,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2011.573,false,true,39549,Asian Art,Hanging scroll,"元 姚彥卿 (廷美) 雪山行旅圖 軸|요언경, 눈 덮인 산 속 나그네 중국 원|Traveling through Snow-Covered Mountains",China,Yuan dynasty (1271–1368),,,,Artist,,Yao Yanqing (Tingmei),"Chinese, ca. 1300–after 1360",,YAO YANQING (TINGMEI),Chinese,1300,1370,,1300,1368,Hanging scroll; ink on silk,Image: 38 5/8 x 21 1/4 in. (98.1 x 54 cm),"Ex coll.: C. C. Wang Family, Gift of Oscar L. Tang Family, 2011",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/39549,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.494.1,false,true,39545,Asian Art,Handscroll,元 趙蒼雲 劉晨阮肇入天台山圖 卷|Liu Chen and Ruan Zhao Entering the Tiantai Mountains,China,Yuan dynasty (1271–1368),,,,Artist,,Zhao Cangyun,"Chinese, active late 13th–early 14th century",,ZHAO CANGYUN,Chinese,1267,1333,,1271,1333,Handscroll; ink on paper,Image: 8 7/8 in. x 18 ft. 5 in. (22.5 cm x 564 cm),"Ex coll.: C. C. Wang Family, Gift of Oscar L. Tang Family, 2005",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/39545,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.100.47,false,true,35977,Asian Art,Folding fan mounted as an album leaf,,China,Ming dynasty (1368–1644) (?),,,,Artist,,Lu Kezheng (Ming dynasty?),,,Lu Kezheng,Chinese,1350,1650,,1368,1644,Folding fan mounted as an album leaf; ink on gold paper,6 1/2 x 20 in. (16.5 x 50.8 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/35977,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -49.6.13,false,true,51763,Asian Art,Album leaf,,China,Qing dynasty (?) (1644–1911),,,,Artist,Attributed to,Xü Daoguang,"Chinese, 13th century",,Xü Daoguang,Chinese,1200,1299,,1200,1299,Album leaf; black and grey wash on paper,7 x 11 5/8 in. (17.8 x 29.5 cm),"Bequest of Ellis Gray Seymour, 1948",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51763,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1973.121.11,false,true,40087,Asian Art,Fan mounted as an album leaf,南宋 傳夏珪 澤畔疾風圖 團扇|Windswept Lakeshore,China,Southern Song dynasty (1127–1279),,,,Artist,Attributed to,Xia Gui,"Chinese, active ca. 1195–1230",,Xia Gui,Chinese,1195,1230,,1195,1230,Fan mounted as an album leaf; ink on silk,10 1/4 x 10 5/8 in. (26 x 27 cm),"Ex coll.: C. C. Wang Family, Purchase, Theodore M. Davis Collection, Bequest of Theodore M. Davis, by exchange, 1973",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/40087,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.100.65,false,true,35986,Asian Art,Folding fan mounted as an album leaf,明 （傳）程嘉燧 金箋水墨公鷄圖扇頁|A Rooster near Trees,China,Ming (1368–1644)–Qing (1644–1911) dynasty,,,,Artist,Attributed to,Cheng Jiasui,"Chinese, 1565–1644",,Cheng Jiasui,Chinese,1565,1644,,1368,1911,Folding fan mounted as an album leaf; ink on gold paper,6 1/2 x 20 3/8 in. (16.5 x 51.8 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/35986,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -47.18.7,false,true,51639,Asian Art,Handscroll,,China,Ming (1368–1644) or Qing (1644–1911) dynasty,,,,Artist,Attributed to,Shen Zhou,"Chinese, 1427–1509",,Shen Zhou,Chinese,1427,1509,,1427,1509,Handscroll; ink on paper,Image: 11 1/2 in. × 14 ft. 10 3/8 in. (29.2 × 453.1 cm),"From the Collection of A. W. Bahr, Purchase, Fletcher Fund, 1947",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51639,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -68.212.1,false,true,51856,Asian Art,Album leaf mounted as a hanging scroll,,China,Ming (1368–1644) or Qing (1644–1911) dynasty,,,,Artist,In the style of,Shen Zhou,"Chinese, 1427–1509",,Shen Zhou,Chinese,1427,1509,,1644,1911,Album leaf mounted as a hanging scroll; ink and color on paper,60 5/8 x 23 3/4 in. (154.0 x 60.3 cm),"The C. C. Wang Family Collection, Gift of C. C. Wang, 1968",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51856,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -69.131.9,false,true,51858,Asian Art,Album leaf,,China,Ming (1368–1644) or Qing (1644–1911) dynasty,,,,Artist,Attributed to,Shen Zhou,"Chinese, 1427–1509",,Shen Zhou,Chinese,1427,1509,,1427,1509,Album leaf; ink and color on paper,9 5/8 x 14 7/8 in. (24.4 x 37.8 cm),"Bequest of Walter Carlebach, 1969",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51858,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -69.131.10,false,true,53601,Asian Art,Album leaf,,China,Ming (1368–1644) or Qing (1644–1911) dynasty,,,,Artist,Attributed to,Shen Zhou,"Chinese, 1427–1509",,Shen Zhou,Chinese,1427,1509,,1427,1509,Album leaf; ink and color on paper,9 9/16 x 14 3/4 in. (24.3 x 37.5 cm),"Bequest of Walter Carlebach, 1969",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/53601,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -69.131.11,false,true,53602,Asian Art,Album leaf,,China,Ming (1368–1644) or Qing (1644–1911) dynasty,,,,Artist,Attributed to,Shen Zhou,"Chinese, 1427–1509",,Shen Zhou,Chinese,1427,1509,,1427,1509,Album leaf; ink and color on paper,9 5/8 x 14 13/16 in. (24.4 x 37.6 cm),"Bequest of Walter Carlebach, 1969",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/53602,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -69.131.12,false,true,53603,Asian Art,Album leaf,,China,Ming (1368–1644) or Qing (1644–1911) dynasty,,,,Artist,Attributed to,Shen Zhou,"Chinese, 1427–1509",,Shen Zhou,Chinese,1427,1509,,1427,1509,Album leaf; ink and color on paper,9 5/8 x 14 13/16 in. (24.4 x 37.6 cm),"Bequest of Walter Carlebach, 1969",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/53603,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -46.146,false,true,42187,Asian Art,Handscroll,,China,Ming (1368–1644) or Qing (1644–1911) dynasty,,,,Artist,Attributed to,Chen Chun,"Chinese, 1483–1544",,Chen Chun,Chinese,1483,1544,,1483,1544,Handscroll; ink and color on paper,12 7/8 in. × 24 ft. 7 1/2 in. (32.7 × 750.6 cm),"Rogers Fund, 1946",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/42187,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -62.102,false,true,51850,Asian Art,Hanging scroll,,China,Ming (1368–1644) or Qing (1644–1911) dynasty,,,,Artist,Attributed to,Dong Qichang,"Chinese, 1555–1636",,Dong Qichang,Chinese,1555,1636,,1555,1636,Hanging scroll; ink on paper,Image: 45 x 19 1/2 in. (114.3 x 49.5 cm),"Gift of Mary Griggs Burke, 1962",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51850,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -47.18.8,false,true,51640,Asian Art,Handscroll,,China,Ming (1368–1644) or Qing (1644–1911) dynasty,,,,Artist,Attributed to,Zha Shibiao,"Chinese, 1615–1698",,Zha Shibiao,Chinese,1615,1698,,1615,1698,Handscroll; ink and color on paper,Image: 11 3/8 in. × 10 ft. 10 1/4 in. (28.9 × 330.8 cm),"From the Collection of A. W. Bahr, Purchase, Fletcher Fund, 1947",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51640,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -50.157,false,true,51768,Asian Art,Handscroll,,China,Ming (1368–1644) or Qing (1644–1911) dynasty,,,,Artist,Attributed to,Wang Yuan,"Chinese, ca. 1280–after 1349",,Wang Yuan,Chinese,1270,1359,,1280,1380,Handscroll; ink and color on silk,97 1/2 x 12 5/8 in. (247.7 x 32.1 cm),"Gift of A. W. Bahr, in memory of Dr. Arnold Genthe, 1950",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51768,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -23.142.1,false,true,51546,Asian Art,Hanging scroll,,China,Song (960–1279) or Qing (1644–1911) dynasty (?),,,,Artist,In the style of,Chen Rong,active 1235–62,,Chen Rong,Chinese,1235,1262,,960,1911,"Hanging scroll, framed; ink on silk",63 x 38 1/2 in. (160 x 97.8 cm),"Fletcher Fund, 1923",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51546,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.126,false,true,49055,Asian Art,Folding fan mounted as an album leaf,,China,late Ming (1368–1644)– early Qing (1644–1911) dynasty,,,,Artist,,Chen Hongshou,"Chinese, 1599–1652",,Chen Hongshou,Chinese,1599,1652,,1599,1652,Folding fan mounted as an album leaf; ink and mineral color on gold paper,8 x 22 in. (20.3 x 55.9 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49055,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -42.200.5,false,true,51604,Asian Art,Album leaf,,China,Ming dynasty (?) (1368–1644),,,,Artist,,Wen Liang,"Chinese, active 15th century",,Wen Liang,Chinese,1400,1499,15th century,1400,1499,Miniature from album of eleven paintings; ink and color on silk,Image: 5 x 6 3/4 in. (12.7 x 17.1 cm),"Rogers Fund, 1942",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51604,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.100.48,false,true,35978,Asian Art,Folding fan mounted as an album leaf,,China,Ming dynasty (1368–1644),,,,Artist,,Wen Zhongyi,"Chinese, active mid–16th century",,Wen Zhongyi,Chinese,1500,1599,16th century,1534,1566,Folding fan mounted as an album leaf; ink on paper,6 3/5 x 19 3/8 in. (16.8 x 49.2 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/35978,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -19.165,false,true,36035,Asian Art,Handscroll,,China,Ming dynasty (1368–1644),,,,Artist,Attributed to,Wen Tong,"Chinese, 1019–1079",,Wen Tong,Chinese,1019,1079,16th century,1500,1599,Handscroll; ink and color on silk,Image: 22 x 101 in. (55.9 x 256.5 cm),"Rogers Fund, 1919",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36035,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.167,false,true,51584,Asian Art,Handscroll,明 佚名 蘇蕙璇璣圖 卷|Lady Su Hui and Her Verse Puzzle,China,Ming dynasty (1368–1644),,,,Artist,In the style of,Qiu Ying,"Chinese, ca. 1495–1552",,Qiu Ying,Chinese,1485,1562,16th century,1500,1599,Handscroll; ink and color on silk,Image: 10 1/16 in. x 10 ft. 2 3/4 in. (25.6 x 311.8 cm),"Gift of George D. Pratt, 1933",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51584,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.235.3,false,true,44592,Asian Art,Handscroll,明 傳仇英 十六羅漢圖 卷|The Sixteen Luohans,China,Ming dynasty (1368–1644),,,,Artist,Attributed to,Qiu Ying,"Chinese, ca. 1495–1552",,Qiu Ying,Chinese,1485,1562,16th century,1500,1599,Handscroll; ink on paper,Image: 13 9/16 x 206 3/8 in. (34.4 x 524.2 cm) Overall with mounting: 13 7/8 x 468 3/4 in. (35.2 x 1190.6 cm),"Gift of Douglas Dillon, 1989",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/44592,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.235.4,false,true,44593,Asian Art,Handscroll,明 傳仇英 五星二十八宿神形圖 卷|Divinities of the Planets and Constellations,China,Ming dynasty (1368–1644),,,,Artist,Attributed to,Qiu Ying,"Chinese, ca. 1495–1552",,Qiu Ying,Chinese,1485,1562,16th century,1500,1599,Handscroll; ink and color on paper,Image: 7 9/16 x 158 1/4 in. (19.2 x 402 cm) Overall with mounting: 9 3/4 x 376 15/16 in. (24.8 x 957.4 cm),"Gift of Douglas Dillon, 1989",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/44593,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -59.117,false,true,36084,Asian Art,Hanging scroll,明 劉世儒 月下雪梅圖 軸|Flowering Plum in Moonlight and Snow,China,Ming dynasty (1368–1644),,,,Artist,,Liu Shiru,"Chinese, active 1550–1600",,Liu Shiru,Chinese,1550,1600,16th century,1550,1599,Hanging scroll; ink on silk,Image: 60 x 35 5/16 in. (152.4 x 89.7 cm),"Gift of Alan Priest, 1959",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36084,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1990.6,false,true,44612,Asian Art,Hanging scroll,明 張路 觀畫圖 軸|Studying a Painting,China,Ming dynasty (1368–1644),,,,Artist,,Zhang Lu,"Chinese, ca. 1490–ca. 1563",,Zhang Lu,Chinese,1490,1563,16th century,1500,1563,Hanging scroll; ink and color on silk,Image: 58 5/8 x 38 7/8 in. (148.9 x 98.7 cm) Overall with mounting: 103 x 42 1/8 in. (261.6 x 107 cm) Overall with knobs: 103 x 46 1/4 in. (261.6 x 117.5 cm),"Ex coll.: C. C. Wang Family, Purchase, Bequest of Dorothy Graham Bennett, 1990",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/44612,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -56.115,false,true,51816,Asian Art,Album,,China,late Ming (1368–1644)–early Qing (1644–1911) dynasty,,,,Artist,,Zhang Ruitu,"Chinese, 1570–1641",,Zhang Ruitu,Chinese,1570,1641,17th century,1600,1641,Album of sixteen calligraphies; ink on tan-coated paper,Overall (leaves): 11 3/8 x 7 1/2 in. (28.9 x 19.1 cm) Overall (wood covers): 14 1/2 x 9 1/4 in. (36.8 x 23.5 cm),"Gift of George G. Cobean, 1956",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/51816,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.235.2a–u,false,true,44591,Asian Art,Album,清 王鑑 仿古山水圖 冊 紙本|Landscapes in the styles of ancient masters,China,Qing dynasty (1644–1911),,,,Artist,,Wang Jian,"Chinese, 1609–1677 or 1688",,Wang Jian,Chinese,1609,1677,17th century,1600,1699,Album of eighteen leaves; ink and color on paper,11 3/4 x 12 3/8 in. (29.8 x 31.4 cm),"Edward Elliott Family Collection, Gift of Douglas Dillon Gift, 1989",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/44591,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1983.412.1,false,true,49167,Asian Art,Album leaf,,China,Qing dynasty (1644–1911),,,,Artist,,Yun Bing,"Chinese, active late 17th– early 18th century",,Yun Bing,Chinese,1650,1750,17th century,1644,1699,Album leaf; ink and color on paper,12 1/4 x 13 1/2 in. (31.1 x 34.3 cm),"Purchase, Bequest of Dorothy Graham Bennett, 1983",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49167,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1983.412.2,false,true,49168,Asian Art,Album leaf,,China,Qing dynasty (1644–1911),,,,Artist,,Yun Bing,"Chinese, active late 17th– early 18th century",,Yun Bing,Chinese,1650,1750,17th century,1667,1699,Album leaf; ink and color on paper,12 1/4 x 13 1/2 in. (31.1 x 34.3 cm),"Purchase, Bequest of Dorothy Graham Bennett, 1983",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49168,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"1978.23a, b",false,true,36124,Asian Art,Hanging scrolls,,China,Qing dynasty (1644–1911),,,,Artist,,Liang Tongshu,"Chinese, 1723–1815",,Liang Tongshu,Chinese,1723,1815,18th century,1723,1799,Pair of hanging scrolls; ink on paper,Image (Each): 51 1/4 x 10 1/8 in. (130.2 x 25.7 cm) Overall with mounting (Each): 65 1/4 x 13 in. (165.7 x 33 cm),"Gift of Chan-hua Mao, 1978",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/36124,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.99.1a–d,false,true,40017,Asian Art,Inkstone,清 十八世紀顧二娘款鳳紋硯|Inkstone with phoenix design,China,Qing dynasty (1644–1911),,,,Artist,Attributed to,Gu Erniang,"Chinese, active early 18th century",,Gu Erniang,Chinese,1700,1733,18th century,1700,1799,Limestone,L. 5 1/16 in. (12.9 cm); W. 3 3/4 in. (9.5 cm),"Gift of Lily and Baird Hastings, 1989",,,,,,,,,,,,Inkstone,,http://www.metmuseum.org/art/collection/search/40017,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1984.472,false,true,42324,Asian Art,Vase,,China,Qing dynasty (1644–1911),,,,Artist,,Chen Jinhou,"Chinese, active 18th century",,Chen Jinhou,Chinese,1700,1799,18th century,1700,1799,Stoneware with relief decoration (Yixing ware),H. 10 1/8 in. (25.7 cm),"Gift of Michael Abraham, 1984",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/42324,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.149,false,true,49166,Asian Art,Handscroll,,China,Qing dynasty (1644–1911),,,,Artist,After,Yun Shouping,"Chinese, 1633–1690",,YUN SHOUPING,Chinese,1633,1690,18th century,1700,1799,Handscroll; ink and color on silk,16 1/2 x 255 1/2 in. (41.9 x 649 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49166,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1995.421,false,true,44294,Asian Art,Table screen,"大理石插屏|Table Screen, now converted to a wall panel",China,Qing dynasty (1644–1911),,,,Artist,,Ruan Yuan,"Chinese, 1764–1849",,Ruan Yuan,Chinese,1764,1849,19th century,1800,1899,Marble mounted in wooden frame,11 3/8 x 15 3/4 in. (28.9 x 40 cm),"Purchase, Judith G. and F Randall Smith Gift, 1995",,,,,,,,,,,,Furniture,,http://www.metmuseum.org/art/collection/search/44294,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.267.7a–h,false,true,49432,Asian Art,Album,清 潘思牧 山水 冊頁八開|Landscapes,China,Qing dynasty (1644–1911),,,,Artist,,Pan Simu,"Chinese, 1756–1842",,Pan Simu,Chinese,1756,1842,19th century,1800,1842,Album of eight leaves; ink and color on paper,10 7/8 x 13 3/4 in. (27.6 x 34.9 cm),"Gift of Robert Hatfield Ellsworth, in memory of La Ferne Hatfield Ellsworth, 1986",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49432,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.267.20,false,true,49448,Asian Art,Folding fan mounted as an album leaf,清 吳熙載 杏花 扇面|Apricot,China,Qing dynasty (1644–1911),,,,Artist,,Wu Xizai,"Chinese, 1799–1870",,Wu Xizai,Chinese,1799,1870,19th century,1800,1870,Folding fan mounted as an album leaf; ink and color on alum paper,7 3/8 x 21 1/2 in. (18.7 x 54.6 cm),"Gift of Robert Hatfield Ellsworth, in memory of La Ferne Hatfield Ellsworth, 1986",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49448,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.267.15,false,true,36154,Asian Art,Folding fan mounted as an album leaf,清 汪廷儒 山水 扇頁|Landscape,China,Qing dynasty (1644–1911),,,,Artist,,Wang Tingru,"Chinese, 1804–1852",,Wang Tingru,Chinese,1804,1852,19th century,1804,1852,Folding fan mounted as an album leaf; ink and color on gold-flecked paper,6 1/8 x 20 5/8 in. (15.6 x 52.4 cm),"Gift of Robert Hatfield Ellsworth, in memory of La Ferne Hatfield Ellsworth, 1986",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36154,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.267.18,false,true,49447,Asian Art,Folding fan mounted as an album leaf,清 任熊 牡丹 扇面|Peony,China,Qing dynasty (1644–1911),,,,Artist,,Ren Xiong,"Chinese, 1823–1857",,Ren Xiong,Chinese,1823,1857,19th century,1823,1857,Folding fan mounted as an album leaf; ink and color on gold-flecked paper,6 15/16 x 21 in. (17.6 x 53.3 cm),"Gift of Robert Hatfield Ellsworth, in memory of La Ferne Hatfield Ellsworth, 1986",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49447,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.267.52,false,true,49467,Asian Art,Hanging scroll,清 虛谷 蝶貓圖 軸|Cat and Butterfly,China,Qing dynasty (1644–1911),,,,Artist,,Xu Gu,"Chinese, 1823–1896",,Xu Gu,Chinese,1823,1896,19th century,1823,1896,Hanging scroll; ink and color on paper,52 1/2 x 25 3/4 in. (133.4 x 65.4 cm),"Gift of Robert Hatfield Ellsworth, in memory of La Ferne Hatfield Ellsworth, 1986",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49467,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.267.47a–h,false,true,36170,Asian Art,Album,"清 任頤 動物花鳥 冊頁八開|Animals, Flowers and Birds",China,Qing dynasty (1644–1911),,,,Artist,,Ren Yi (Ren Bonian),"Chinese, 1840–1896",,Ren Yi (Ren Bonian),Chinese,1840,1896,19th century,1840,1896,Album of eight leaves; ink and color on paper,10 3/16 x 12 3/4 in. (25.9 x 32.4 cm),"Gift of Robert Hatfield Ellsworth, in memory of La Ferne Hatfield Ellsworth, 1986",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36170,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -56.129.3,false,true,36075,Asian Art,Hanging scroll,,China,Qing dynasty (1644–1911),,,,Artist,,Zhu Ling,"Chinese, active ca. 1820–1850",,Zhu Ling,Chinese,1820,1850,19th century,1820,1850,Hanging scroll; ink and color on paper,36 7/8 x 14 1/2 in. (93.7 x 36.8 cm),"Rogers Fund, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36075,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.267.43a–g,false,true,36168,Asian Art,Album,清 杜湘 山水 冊頁七開|Landscapes,China,Qing dynasty (1644–1911),,,,Artist,,Du Xiang,"Chinese, active late 19th century",,Du Xiang,Chinese,1850,1899,19th century,1867,1899,Album of seven leaves; ink and color on paper,10 1/8 x 6 3/8 in. (25.7 x 16.2 cm),"Gift of Robert Hatfield Ellsworth, in memory of La Ferne Hatfield Ellsworth, 1986",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36168,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.201,false,true,51898,Asian Art,Hanging scroll,,China,,,,,Artist|Artist,After,Zheng Xie|Unidentified Artist,"Chinese, 1693–1765",,Zheng Xie|Unidentified Artist,Chinese,1693,1765,19th–20th century,1800,1999,Hanging scroll; ink on paper,Image: 53 7/16 x 26 in. (135.7 x 66 cm) Overall: 53 3/8 x 26in. (135.6 x 66cm) Overall with knobs: 84 1/4 x 34 5/8 in. (214 x 87.9 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/51898,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.7,false,true,40059,Asian Art,Fan mounted as an album leaf,南宋 傳高宗/孝宗 行楷書輕舠依岸七絕詩 團扇|Quatrain on fishermen,China,Song dynasty (960–1279),,,,Artist|Artist,Attributed to,Emperor Gaozong|Emperor Xiaozong,"Chinese, 1107–1187, r. 1127–1162|Chinese, 1127–1194; r. 1163–89",or,Gaozong Emperor|Xiaozong Emperor,Chinese|Chinese,1107 |1127,1187 |1194,12th century,1107,1187,Fan mounted as album leaf; ink on silk,9 1/4 x 10 in. (23.5 x 25.4 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/40059,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.91,false,true,48912,Asian Art,Hanging scroll,明 傳徐渭 校靜菴文有感詩 軸|Poem Composed after Editing Jingan's Literary Works,China,Ming dynasty (1368–1644),,,,Artist|Artist,After,Xu Wei|Unidentified Artist,"Chinese, 1521–1593",,Xu Wei|Unidentified Artist,Chinese,1521,1593,dated 1576,1576,1576,Hanging scroll; ink on silk,Image: 54 3/8 x 19 in. (138.1 x 48.3 cm) Overall: 87 1/4 x 26 1/4 in. (221.6 x 66.7 cm) Overall with knobs: 87 1/4 x 28 1/2 in. (221.6 x 72.4 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/48912,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.100.58,false,true,35982,Asian Art,Folding fan mounted as an album leaf,,China,Ming dynasty (1368–1644),,,,Artist|Artist,After,Wang Zhideng|Unidentified Artist,"Chinese, 1535–1612",,Wang Zhideng|Unidentified Artist,Chinese,1535,1612,spurious date of 1569,1569,1644,Folding fan mounted as an album leaf; ink on paper,6 5/8 x 19 15/32 in. (16.8 x 49.5 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/35982,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.100.64,false,true,35985,Asian Art,Folding fan mounted as an album leaf,,China,Qing dynasty (1644–1911),,,,Artist|Artist,After,Wu Kuan|Unidentified Artist,"Chinese, 1435–1504",,Wu Kuan|Unidentified Artist,Chinese,1435,1504,"18th century or later, spurious date of 1492",1700,1911,Folding fan mounted as an album leaf; ink on gold paper,6 7/8 x 19 15/32 in. (17.5 x 49.5 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/35985,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.100.66,false,true,45784,Asian Art,Folding fan mounted as an album leaf,,China,Qing dynasty (1644–1911),,,,Artist|Artist,After,Wen Zhengming|Unidentified Artist,"Chinese, 1470–1559",,Wen Zhengming|Unidentified Artist,Chinese,1470,1559,17th century or later,1644,1911,Folding fan mounted as an album leaf; ink on gold-flecked paper,7 1/8 x 20 7/8 in. (18.1 x 53 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/45784,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.100.54,false,true,35980,Asian Art,Folding fan mounted as an album leaf,明/清 傳王鐸 行草書 扇頁|Calligraphy,China,Qing dynasty (1644–1911),,,,Artist|Artist,After,Wang Duo|Unidentified Artist,"Chinese, 1592–1652",,Wang Duo|Unidentified Artist,Chinese,1592,1652,"17th century or later, spurious date of 1649",1649,1911,Folding fan mounted as an album leaf; ink on paper,6 3/8 x 20 1/4 in. (16.2 x 51.4 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/35980,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.100.46,false,true,35976,Asian Art,Folding fan mounted as an album leaf,,China,Qing dynasty (1644–1911),,,,Artist|Artist,After,Zha Shibiao|Unidentified Artist,"Chinese, 1615–1698",,Zha Shibiao|Unidentified Artist,Chinese,1615,1698,18th century or later,1700,1911,Folding fan mounted as an album leaf; ink on paper,6 1/4 x 20 7/32 in. (15.9 x 51.4 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/35976,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1971.124,false,true,51859,Asian Art,Hanging scroll,,China,,,,,Artist|Artist,After,Hongren|Unidentified Artist,"Chinese, 1610–1664",,HONGREN|Unidentified Artist,Chinese,1610,1664,20th century,1900,1999,Hanging scroll; ink and color on paper,Image: 55 x 30 3/8 in. (139.7 x 77.2 cm) Overall with mounting: 111 x 36 7/8 in. (281.9 x 93.7 cm) Overall with rollers: 111 x 41 in. (281.9 x 104.1 cm),"Purchase, Bequest of Dorothy Graham Bennett, 1971",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51859,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -47.81.1,false,true,36066,Asian Art,Album,,China,,,,,Artist|Artist,Formerly Attributed to,Yao Wenhan|Unidentified Artist,"Chinese, active ca. 1760–1790","Chinese, early 20th century (?)",Yao Wenhan|Unidentified Artist,Chinese,1760,1790,early 20th century (?),1900,1933,Album of twenty-four leaves; ink and color on silk,11 1/8 x 8 3/4 in. (28.3 x 22.2 cm),"Gift of Mrs. Edward S. Harkness, 1947",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36066,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -47.81.2,false,true,49252,Asian Art,Album,,China,,,,,Artist|Artist,Formerly Attributed to,Unidentified Artist|Yao Wenhan,"Chinese, active ca. 1760–1790","Chinese, early 20th century (?)",Unidentified Artist|Yao Wenhan,Chinese,1760,1790,early 20th century (?),1900,1933,Album of twenty leaves; ink and color on silk,11 1/8 x 8 3/4 in. (28.3 x 22.2 cm),"Gift of Mrs. Edward S. Harkness, 1947",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49252,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -47.18.34,false,true,51657,Asian Art,Album leaf,,China,20th century,,,,Artist|Artist,In the style of,Unidentified Artist|Cui Bai,"Chinese, active ca. 1040–70",,Unidentified Artist|Cui Bai,Chinese,1030,1080,20th century,1900,1947,Album leaf; ink and color on silk,12 3/8 x 10 1/8 in. (31.4 x 25.7 cm),"From the Collection of A. W. Bahr, Purchase, Fletcher Fund, 1947",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51657,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.121,false,true,40003,Asian Art,Hanging scroll,宋 佚名 倣李成 寒林策驢圖 軸|Travelers in a Wintry Forest,China,Song dynasty (960–1279),,,,Artist|Artist,Traditionally attributed to,Li Cheng|Unidentified Artist,"Chinese, 919–967","Chinese, active early 12th century",LI CHENG|Unidentified Artist,Chinese,0919,0967,early 12th century,1100,1133,Hanging scroll; ink and color on silk,Image: 63 3/4 × 39 1/2 in. (161.9 × 100.3 cm) Overall with mounting: 10 ft. 3 1/2 in. × 49 in. (313.7 × 124.5 cm) Overall with knobs: 10 ft. 3 1/2 in. × 53 3/8 in. (313.7 × 135.6 cm),"Purchase, Fletcher Fund and Bequest of Dorothy Graham Bennett, 1972",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/40003,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1983.12,false,true,40106,Asian Art,Handscroll,南宋 佚名 倣燕文貴 秋山蕭寺圖 卷|Buddhist Temples amid Autumn Mountains,China,Song dynasty (960–1279),,,,Artist|Artist,After,Yan Wengui|Unidentified Artist,"Chinese, 970–1030",,YAN WENGUI|Unidentified Artist,Chinese,0970,1030,late 12th–mid-13th century,1167,1266,Handscroll; ink and pale color on silk,Image: 12 7/8 in. × 10 ft. 6 1/2 in. (32.7 × 321.3 cm) Overall with mounting: 13 1/8 in. × 37 ft. 2 1/8 in. (33.3 × 1133.2 cm),"Purchase, The Dillon Fund Gift, 1983",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/40106,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -18.124.4,false,true,36033,Asian Art,Handscroll,南宋 佚名 百牛圖 卷|One Hundred Buffaloes,China,Song dynasty (960–1279),,,,Artist|Artist,Formerly Attributed to,Unidentified Artist|Jiangcan,"Chinese, active ca. 1200","Chinese, 13th century",Unidentified Artist|Jiangcan,Chinese,1190,1210,13th century,1200,1299,Handscroll; ink on paper,12 1/2 x 87 5/16 in. (31.8 x 221.8 cm),"Rogers Fund, 1918",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36033,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1978.4,false,true,40004,Asian Art,Handscroll,宋 佚名 倣周文矩 宮中圖 卷|In the Palace,China,Song dynasty (960–1279),,,,Artist|Artist,After,Zhou Wenju|Unidentified Artist,"Chinese, active 940–975","Chinese, active early 12th century",ZHOU WENJU|Unidentified Artist,Chinese,0940,0975,before 1140,960,1139,Handscroll; ink and touches of color on silk,Image: 10 1/4 × 57 3/4 in. (26 × 146.7 cm) Overall with mounting: 10 11/16 in. × 29 ft. 11 7/8 in. (27.1 × 914.1 cm),"Purchase, Douglas Dillon Gift, 1978",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/40004,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -56.151,false,true,40002,Asian Art,Hanging scroll,北宋 佚名 倣范寬山水圖 軸|Landscape in the style of Fan Kuan,China,Song dynasty (960–1279),,,,Artist|Artist,after,Fan Kuan|Unidentified Artist,"Chinese, active ca. 990–1030","Chinese, active 12th century",Fan Kuan|Unidentified Artist,Chinese,0990,1030,early 12th century,1100,1133,Hanging scroll; ink and color on silk,Image: 65 3/8 × 41 1/8 in. (166.1 × 104.5 cm) Overall with mounting: 9 ft. 8 1/2 in. × 49 3/4 in. (295.9 × 126.4 cm) Overall with knobs: 9 ft. 8 1/2 in. × 53 3/4 in. (295.9 × 136.5 cm),"Gift of Irene and Earl Morse, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/40002,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.16,false,true,40196,Asian Art,Fan mounted as an album leaf,,China,Song dynasty (960–1279),,,,Artist|Artist,Formerly Attributed to,Li Tang|Unidentified Artist,"Chinese, ca. 1070s–ca. 1150s",,LI TANG|Unidentified Artist,Chinese,1070,1159,13th century,1200,1299,Fan mounted as an album leaf; ink and color on silk,9 7/8 x 10 1/8 in. (25.1 x 25.7 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/40196,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.15,false,true,40078,Asian Art,Fan mounted as an album leaf,,China,Song dynasty (960–1279),,,,Artist|Artist,Formerly Attributed to,Lidi|Unidentified Artist,"Chinese, ca. 1110– after 1197",,LIDI|Unidentified Artist,Chinese,1110,1197,13th century,1200,1279,Fan mounted as an album leaf; ink and color on silk,9 3/8 x 9 1/2 in. (23.8 x 24.1 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/40078,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -40.148,false,true,39935,Asian Art,Handscroll,宋 佚名 倣周昉 戲嬰圖 卷|Palace Ladies Bathing Children,China,Song dynasty (960–1279),,,,Artist|Artist,After,Zhou Fang|Unidentified Artist,"Chinese, active ca. 780– ca.810",,ZHOU FANG|Unidentified Artist,Chinese,0780,0810,11th century,1000,1099,Handscroll; ink and color on silk,Image: 12 in. × 19 1/8 in. (30.5 × 48.6 cm) Overall with mounting: 12 7/16 × 44 in. (31.6 × 111.8 cm),"Fletcher Fund, 1940",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/39935,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.64,false,true,45781,Asian Art,Handscroll,,China,Ming dynasty (1368–1644),,,,Artist|Artist,After,Wen Zhengming|Unidentified Artist,"Chinese, 1470–1559",,Wen Zhengming|Unidentified Artist,Chinese,1470,1559,dated 1549,1549,1549,Handscroll; color on paper,Image: 11 x 52 3/8 in. (27.9 x 133 cm) Overall with mounting: 11 5/16 x 240 3/4 in. (28.7 x 611.5 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45781,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.89,false,true,48933,Asian Art,Handscroll,,China,Ming dynasty (1368–1644),,,,Artist|Artist,After,Mo Shilong|Unidentified Artist,"Chinese, 1537–1587",,Mo Shilong|Unidentified Artist,Chinese,1537,1587,dated 1577,1577,1577,Handscroll; ink and color on paper,Image: 8 5/16 x 31 7/8 in. (21.1 x 81 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/48933,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.100.63,false,true,49020,Asian Art,Folding fan mounted as an album leaf,,China,Ming dynasty (1368–1644),,,,Artist|Artist,After,Lan Ying|Unidentified Artist,"Chinese, 1585–1664",,Lan Ying|Unidentified Artist,Chinese,1585,1664,"17th century or later, spurious date of 1633",1633,1911,Folding fan mounted as an album leaf; ink and color on gold paper,6 5/8 x 20 1/2 in. (16.8 x 52.1 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49020,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.123,false,true,51873,Asian Art,Hanging scroll,,China,Ming dynasty (1368–1644),,,,Artist|Artist,After,Ni Yuanlu|Unidentified Artist,"Chinese, 1593–1644",,Ni Yuanlu|Unidentified Artist,Chinese,1593,1644,dated 1638,1638,1638,Hanging scroll; ink and color on paper,Image: 67 x 28 3/4 in. (170.2 x 73 cm) Overall with mounting: 100 1/2 x 34 5/8 in. (255.3 x 87.9 cm) Overall with knobs: 100 1/2 x 38 1/2 in. (255.3 x 97.8 cm),"Bequest of John M. Crawford, Jr., 1988",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51873,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.74,false,true,48888,Asian Art,Hanging scroll,明 文伯仁 溪山僊館圖 軸|Dwellings of the Immortals Amid Streams and Mountains,China,Ming dynasty (1368–1644),,,,Artist|Artist,After,Wen Boren|Unidentified Artist,"Chinese, 1502–ca.1575",,Wen Boren|Unidentified Artist,Chinese,1502,1575,dated 1531,1531,1531,Hanging scroll; ink and color on paper,Image: 71 x 24 7/8 in. (180.3 x 63.2 cm) Overall with mounting: 105 7/8 x 31 3/16 in. (268.9 x 79.2 cm) Overall with knobs: 105 7/8 x 35 3/4 in. (268.9 x 90.8 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/48888,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.100.97,false,true,44451,Asian Art,Fan mounted as an album leaf,"元/明 佚名 舊傳趙雍 倣盛懋 蘇軾後赤壁賦圖 扇|Illustration of Su Shi's ""Second Ode on the Red Cliff""",China,Ming dynasty (1368–1644),,,,Artist|Artist,In the style of,Sheng Mou|Unidentified Artist,"Chinese, active ca. 1310–1360",,Sheng Mou|Unidentified Artist,Chinese,1310,1360,late 14th–early 15th century,1367,1433,Fan mounted as an album leaf; ink and color on silk,Image: 12 3/8 x 12 3/8 in. (31.4 x 31.4 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/44451,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1990.54,false,true,39556,Asian Art,Handscroll,"明 沈周 ， 文徵明 合璧山水圖 卷|Joint Landscape",China,Ming dynasty (1368–1644),,,,Artist|Artist,,Shen Zhou|Wen Zhengming,"Chinese, 1427–1509|Chinese, 1470–1559",,Shen Zhou|Wen Zhengming,Chinese|Chinese,1427 |1470,1509 |1559,ca. 1509 and 1546,1509,1546,Handscroll; ink on paper,"Image: 14 1/2 x 56 ft., 8 5/6 in. (36.8 x 1729.3 cm)","Purchase, The Dillon Fund Gift, 1990",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/39556,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.100.67,false,true,48884,Asian Art,Folding fan mounted as an album leaf,,China,Qing dynasty (1644–1911),,,,Artist|Artist,After,Wen Jia|Unidentified Artist,"Chinese, 1501–1583",,Wen Jia|Unidentified Artist,Chinese,1501,1583,17th century or later,1600,1911,Folding fan mounted as an album leaf; ink on gold paper,6 7/8 x 19 1/2 in. (17.5 x 49.5 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/48884,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.100.43,false,true,35974,Asian Art,Folding fan mounted as an album leaf,,China,Qing dynasty (1644–1911),,,,Artist|Artist,After,Wang Shimin|Unidentified Artist,"Chinese, 1592–1680",,WANG SHIMIN|Unidentified Artist,Chinese,1592,1680,"18th century or later, spurious date of 1648",1700,1911,Folding fan mounted as an album leaf; ink on gold paper,6 1/2 x 20 1/2 in. (16.5 x 52.1 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/35974,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.2.1a–q,false,true,42560,Asian Art,Album,清 倣黃向堅 尋親紀行圖 冊|A Journey in Search of the Artist's Parents,China,Qing dynasty (1644–1911),,,,Artist|Artist,After,Huang Xiangjian|Unidentified Artist,"Chinese, 1609–1673",,Huang Xiangjian|Unidentified Artist,Chinese,1609,1673,Dated 1656,1656,1656,Album of fourteen leaves; ink on paper,15 1/2 x 11 3/8 in. (39.4 x 28.9 cm),"The Sackler Fund, 1970",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/42560,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.1.2,false,true,49127,Asian Art,Hanging scroll,清 倣弘仁 黃山蟠龍松圖 軸|Dragon Pine on Mount Huang,China,Qing dynasty (1644–1911),,,,Artist|Artist,After,Hongren|Unidentified Artist,"Chinese, 1610–1664",,HONGREN|Unidentified Artist,Chinese,1610,1664,ca. 1660,1650,1670,Hanging scroll; ink and pale color on paper,Image: 76 1/4 x 31 in. (193.7 x 78.7 cm) Overall with mounting: 120 1/4 x 38 1/2 in. (305.4 x 97.8 cm) Overall with knobs: 120 1/4 x 41 3/4 in. (305.4 x 106 cm),"Gift of Douglas Dillon, 1976",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49127,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.185,false,true,51887,Asian Art,Handscroll,,China,Qing dynasty (1644–1911),,,,Artist|Artist,After,Hongren|Unidentified Artist,"Chinese, 1610–1664",,HONGREN|Unidentified Artist,Chinese,1610,1664,dated 1661,1661,1661,Handscroll; ink on paper,11 1/4 x 152 1/4 in. (28.6 x 386.7cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51887,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.100.25,false,true,35971,Asian Art,Hanging scroll,,China,Qing dynasty (1644–1911),,,,Artist|Artist,After,Wang Hui|Unidentified Artist,"Chinese, 1632–1717",,Wang Hui|Unidentified Artist,Chinese,1632,1717,"18th century or later, spurious date of 1680",1700,1911,Hanging scroll; ink on silk,14 1/2 x 11 1/8 in. (36.8 x 28.3 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/35971,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.100.40,false,true,35972,Asian Art,Folding fan mounted as an album leaf,,China,Qing dynasty (1644–1911),,,,Artist|Artist,After,Wang Hui|Unidentified Artist,"Chinese, 1632–1717",,Wang Hui|Unidentified Artist,Chinese,1632,1717,"18th century or later, spurious date of 1706",1700,1911,Folding fan mounted as an album leaf; ink and color on paper,6 3/4 x 20 in. (17.1 x 50.8 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/35972,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.100.45,false,true,35975,Asian Art,Folding fan mounted as an album leaf,,China,Qing dynasty (1644–1911),,,,Artist|Artist,After,Wang Hui|Unidentified Artist,"Chinese, 1632–1717",,Wang Hui|Unidentified Artist,Chinese,1632,1717,"18th century or later, spurious date of 1707",1707,1911,Folding fan mounted as an album leaf; ink and color on paper,6 1/2 x 19 1/4 in. (16.5 x 48.9 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/35975,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.142,false,true,49152,Asian Art,Hanging scroll,,China,Qing dynasty (1644–1911),,,,Artist|Artist,After,Wang Hui|Unidentified Artist,"Chinese, 1632–1717",,Wang Hui|Unidentified Artist,Chinese,1632,1717,dated 1682,1682,1682,Hanging scroll; ink and color on paper,Image: 52 7/8 x 21 1/8 in. (134.3 x 53.7 cm) Overall with mounting: 86 1/2 x 25 1/2 in. (219.7 x 64.8 cm) Overall with knobs: 86 1/2 x 29 3/8 in. (219.7 x 74.6 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49152,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.143,false,true,49153,Asian Art,Hanging scroll,,China,Qing dynasty (1644–1911),,,,Artist|Artist,After,Wang Hui|Unidentified Artist,"Chinese, 1632–1717",,Wang Hui|Unidentified Artist,Chinese,1632,1717,dated 1686,1686,1686,Hanging scroll; ink on paper,Image: 22 1/2 x 16 in. (57.2 x 40.6 cm) Overall with mounting: 72 x 21 5/8 in. (182.9 x 54.9 cm) Overall with knobs: 72 x 25 1/4 in. (182.9 x 64.1 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49153,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -44.138,false,true,53552,Asian Art,Handscroll,,China,Qing dynasty (1644–1911),,,,Artist|Artist,In the style of,Unidentified Artist|Yun Shouping,"Chinese, 1633–1690",,Unidentified Artist|YUN SHOUPING,Chinese,1633,1690,probably 18th–19th century,1700,1899,Handscroll; ink and color on silk,11 15/16 x 102 3/8 in. (30.3 x 260 cm),"Anonymous Gift, 1944",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/53552,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.100.41,false,true,51369,Asian Art,Folding fan mounted as an album leaf,,China,Qing dynasty (1644–1911),,,,Artist|Artist,After,Yun Shouping|Unidentified Artist,"Chinese, 1633–1690",,YUN SHOUPING|Unidentified Artist,Chinese,1633,1690,18th century or later,1700,1911,Folding fan mounted as an album leaf; ink on paper,6 7/8 x 20 1/2 in. (17.5 x 52.1 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51369,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.100.44,false,true,49188,Asian Art,Folding fan mounted as an album leaf,,China,Qing dynasty (1644–1911),,,,Artist|Artist,After,Wang Yuanqi|Unidentified Artist,"Chinese, 1642–1715",,WANG YUANQI|Unidentified Artist,Chinese,1642,1715,"18th century or later, spurious date of 1715",1715,1911,Folding fan mounted as an album leaf; ink on paper,6 1/2 x 19 1/2 in. (16.5 x 49.5 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49188,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -27.24,false,true,39913,Asian Art,Handscroll,,China,Qing dynasty (1644–1911),,,,Artist|Artist,Formerly Attributed to,Unidentified Artist|Zhao Boju,"Chinese, 11th century",Chinese|in the style of Li Longmian,Unidentified Artist|Zhao Boju,Chinese,1000,1099,18th century or later,1700,1800,Handscroll; ink and color on golden paper,10 3/4 x 41 3/4 in. (27.3 x 106 cm),"Rogers Fund, 1927",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/39913,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.198a–h,false,true,51896,Asian Art,Album,,China,Qing dynasty (1644–1911),,,,Artist|Artist,After,Unidentified Artist|Li Shan,"Chinese, 1686–ca. 1756",,Unidentified Artist|Li Shan,Chinese,1686,1756,dated 1740,1740,1740,Album of eight double leaves; ink and color on paper,Image: 10 5/8 × 13 3/16 in. (27 × 33.5 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51896,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.100.42,false,true,35973,Asian Art,Folding fan mounted as an album leaf,,China,Qing dynasty (1644–1911),,,,Artist|Artist,After,Wang Jian|Unidentified Artist,"Chinese, 1609–1677 or 1688",,Wang Jian|Unidentified Artist,Chinese,1609,1677,"18th century or later, spurious date of 1676",1700,1911,Folding fan mounted as an album leaf; ink and color on paper,6 3/8 x 20 1/2 in. (16.2 x 52.1 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/35973,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.141.4a–rr,false,true,41481,Asian Art,Album,清 王翬 王時敏 仿古山水圖 冊 紙本|Landscapes after old masters,China,Qing dynasty (1644–1911),,,,Artist|Artist,"leaves k, l by",Wang Hui|Wang Shimin,"Chinese, 1632–1717|Chinese, 1592–1680",,Wang Hui|WANG SHIMIN,Chinese|Chinese,1632 |1592,1717 |1680,dated 1674 and 1677,1674,1677,Album of twelve leaves; ink and color on paper,"Ten paintings by Wang Hui (a–j): 8 5/8 x 13 1/4 in. (22 x 33.8 cm); two paintings by Wang Shimin (k, l): 10 x 13 in. (25.4 x 33 cm)","Purchase, The Dillon Fund Gift, 1989",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/41481,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1979.439a–o,false,true,49113,Asian Art,Album,清 王鑑 仿古山水圖 冊 紙本|Landscapes in the styles of old masters,China,Qing dynasty (1644–1911),,,,Artist|Artist,Title piece by,Wang Jian|Wang Shimin,"Chinese, 1609–1677 or 1688|Chinese, 1592–1680",,Wang Jian|WANG SHIMIN,Chinese|Chinese,1609 |1592,1677 |1680,dated 1668,1668,1668,Album of ten paintings; ink and color on paper,Each leaf: 10 1/8 x 6 1/2 in. (25.7 x 16.5 cm),"Purchase, The Dillon Fund Gift, 1979",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49113,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1987.149,false,true,36433,Asian Art,Handscroll,"清 石濤(朱若極), 張子為 忍菴居士像 卷|Portrait of Ren'an in a Landscape",China,Qing dynasty (1644–1911),,,,Artist|Artist,,Shitao (Zhu Ruoji)|Zhang Ziwei,"Chinese, 1642–1707|Chinese, active late 17th century",,Shitao|Zhang Ziwei,Chinese|Chinese,1642 |1600,1707 |1700,dated 1684,1684,1684,Handscroll; ink and color on paper,22 15/18 x 53 7/8 in. (58 x 136.8 cm),"Purchase, The Dillon Fund Gift, 1987",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36433,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.481,false,true,51567,Asian Art,Handscroll,,China,Yuan dynasty (1271–1368),,,,Artist|Artist,In the style of,Unidentified Artist|Zhao Mengfu,"Chinese, 1254–1322",,Unidentified Artist|Zhao Mengfu,Chinese,1254,1322,dated 1309,1309,1309,Handscroll; ink and color on silk,15 3/8 in. × 13 ft. 2 1/2 in. (39.1 × 402.6 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51567,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.476,false,true,36041,Asian Art,Hanging scroll,元 佚名 仕女像 軸|Portrait of a Lady,China,Yuan dynasty (1271–1368),,,,Artist|Artist,In the style of,Unidentified Artist|Li Gonglin,"Chinese, ca. 1041–1106",Chinese,Unidentified Artist|Li Gonglin,Chinese,1041,1106,14th century,1300,1368,Hanging scroll; ink on silk,Image: 31 × 15 in. (78.7 × 38.1 cm) Overall with mounting: 64 × 20 7/8 in. (162.6 × 53 cm) Overall with knobs: 64 × 22 3/16 in. (162.6 × 56.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36041,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -47.18.49,false,true,51668,Asian Art,Handscroll,,China,Song dynasty (?) (960–1279),,,,Artist|Artist,After,Unidentified Artist|Wang Qihan,"Chinese, 10th century",,Unidentified Artist|Wang Qihan,Chinese,0900,0999,10th century,900,999,Handscroll; ink and color on silk,Image: 9 7/8 × 18 3/4 in. (25.1 × 47.6 cm),"From the Collection of A. W. Bahr, Purchase, Fletcher Fund, 1947",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51668,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.26,false,true,40088,Asian Art,Fan mounted as an album leaf,南宋 佚名 舊傳閻次于 風雨維舟圖 團扇|Boats Moored in Wind and Rain,China,Southern Song dynasty (1127–1279),,,,Artist|Artist,Formerly Attributed to,Yan Ciyu|Unidentified Artist,"Chinese, act. ca. 1164–81","Chinese, 13th century",YAN CIYU|Unidentified Artist,Chinese,1164,1181,13th century,1200,1299,Fan mounted as an album leaf; ink and color on silk,9 3/4 x 10 3/4 in. (24.8 x 27.3 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/40088,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1982.7.3,false,true,36141,Asian Art,Fan mounted as an album leaf,南宋 佚名 倣夏珪 冒雨尋莊圖 團扇|Returning Home in a Driving Rain,China,Southern Song dynasty (1127–1279),,,,Artist|Artist,After,Xia Gui|Unidentified,"Chinese, active ca. 1195–1230",,Xia Gui|Unidentified,Chinese,1195,1230,early 13th century,1200,1233,Fan mounted as an album leaf; ink and color on silk,10 1/16 x 10 3/8 in. (25.6 x 26.4 cm),"Purchase, The Dillon Fund Gift, 1982",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36141,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1984.274,false,true,40987,Asian Art,Handscroll,南宋 傳劉松年 倣高克明溪山雪意圖 卷|Streams and Mountains Under Fresh Snow,China,Southern Song dynasty (1127–1279),,,,Artist|Artist,Attributed to|traditionally attributed to,Liu Songnian|Gao Keming,"Chinese, active ca 1175–after 1195|ca 1000–1053",,LIU SONGNIAN|GAO KEMING,Chinese,1175 |1000,1195 |1053,ca. late 12th century,1175,1199,Handscroll; ink and color on silk,Image: 16 3/8 in. × 95 in. (41.6 × 241.3 cm) Overall with mounting: 16 3/8 in. × 42 ft. 11 5/16 in. (41.6 × 1308.9 cm),"Gift of John M. Crawford Jr., 1984",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/40987,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.100.73,false,true,51378,Asian Art,Folding fan mounted as an album leaf,,China,Ming (1368–1644) or Qing (1644–1911) dynasty,,,,Artist|Artist,After,Shen Zhou|Unidentified Artist,"Chinese, 1427–1509",,Shen Zhou|Unidentified Artist,Chinese,1427,1509,16th century or later,1500,1911,Folding fan mounted as an album leaf; ink on gold paper,6 7/8 x 20 3/4 in. (17.5 x 52.7 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51378,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.220.93,false,true,51490,Asian Art,Hanging scroll,,China,Ming (1368–1644) or Qing dynasty (1644–1911),,,,Artist|Artist,Formerly Attributed to,Ni Zan|Unidentified Artist,"Chinese, 1306–1374",,NI ZAN|Unidentified Artist,Chinese,1306,1374,dated 1374,1374,1374,Hanging scroll; ink on paper,33 7/8 x 12 7/8 in. (86.0 x 32.7 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51490,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.220.28,false,true,40422,Asian Art,Hanging scroll,,China,Ming (1368–1644) or Qing dynasty (1644–1911),,,,Artist|Artist,After,Unidentified Artist|Tang Yin,"Chinese, 1470–1524",,Unidentified Artist|Tang Yin,Chinese,1470,1524,ca. 1525,1515,1535,Hanging scroll; ink and color on paper,Image: 89 x 40 1/4 in. (226.1 x 102.2 cm) Overall with mounting: 137 3/4 x 44 in. (349.9 x 111.8 cm) Overall with knobs: 137 3/4 x 48 5/8 in. (349.9 x 123.5 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/40422,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -19.166,false,true,51535,Asian Art,Handscroll,,China,Ming (1368–1644) or Qing dynasty (1644–1911),,,,Artist|Artist,Formerly Attributed to,Unidentified Artist|Qiu Ying,"Chinese, ca. 1495–1552",,Unidentified Artist|Qiu Ying,Chinese,1485,1562,ca. 1530,1520,1540,Handscroll; ink and color on silk,H. 12 3/8 in. (31.4 cm),"Gift of William Hu, 1919",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51535,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.100.27,false,true,51293,Asian Art,Handscroll,,China,Ming (1368–1644) or Qing dynasty (1644–1911),,,,Artist|Artist,After,Guo Xi|Unidentified Artist,"Chinese, ca. 1000–ca. 1090",,Guo Xi|Unidentified Artist,Chinese,1000,1090,possibly 17th century,1600,1699,Handscroll; ink and color on silk,19 3/8 in. × 19 ft. 1 1/2 in. (49.2 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51293,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1977.87,false,true,48945,Asian Art,Handscroll,,China,late Ming (1368–1644)–early Qing (1644–1911) dynasty,,,,Artist|Artist,Formerly Attributed to,Li Zai|Unidentified Artist,"Chinese, active 15th century",,Li Zai|Unidentified Artist,Chinese,1400,1500,"Dated ""yiwei"" (1595? 1655?)",1595,1655,Handscroll; ink and color on paper,Image: 11 x 126 in. (27.9 x 320 cm) Overall with mounting: 13 1/2 x 365 5/8 in. (34.3 x 928.7 cm),"Purchase, John M. Crawford Jr. Gift, 1977",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/48945,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.20.1,false,true,48941,Asian Art,Hanging scroll,清 佚名 肖像 軸|Portrait of a Scholar,China,late Ming (1368–1644)–early Qing (1644–1911) dynasty,,,,Artist|Artist,Formerly Attributed to,Jin Chushi|Unidentified Artist,"Chinese, active late 12th century",,JIN CHUSHI|Unidentified Artist,Chinese,1167,1199,17th–18th century,1600,1799,Hanging scroll; ink and color on paper,Image: 35 3/4 in. × 15 in. (90.8 × 38.1 cm) Overall with mounting: 68 1/2 × 20 3/8 in. (174 × 51.8 cm) Overall with knobs: 68 1/2 × 22 1/16 in. (174 × 56 cm),"Gift of George D. Pratt, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/48941,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1999.521a–k,false,true,37396,Asian Art,Album,"明/清 陳洪綬、陳字 雜畫 冊 絹本|Figures, flowers, and landscapes",China,late Ming (1368–1644)–early Qing (1644–1911) dynasty,,,,Artist|Artist,leaves a–d by|leaves e–k by,Chen Hongshou|Chen Zi,"Chinese, 1599–1652|Chinese, 1634–1711",,Chen Hongshou|Chen Zi,Chinese|Chinese,1599 |1634,1652 |1711,one leaf dated 1627,1600,1711,Album of eleven leaves; ink and color on silk,Image: 8 3/4 x 8 9/16 in. (22.2 x 21.7 cm),"Gift of Mr. and Mrs. Wan-go H. C. Weng, 1999",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/37396,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.100.62,false,true,45814,Asian Art,Folding fan mounted as an album leaf,,China,Ming dynasty (1368–1644),,,,Artist|Artist,After,Wang Chong|Unidentified Artist,"Chinese, 1494–1533",,Wang Chong|Unidentified Artist,Chinese,1494,1533,,1368,1644,Folding fan mounted as an album leaf; ink on paper,6 3/4 x 20 in. (17.1 x 50.8 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/45814,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.100.60,false,true,35983,Asian Art,Folding fan mounted as an album leaf,,China,Ming dynasty (1368–1644),,,,Artist|Artist,After,Peng Nian|Unidentified Artist,"Chinese, 1505–1566",,Peng Nian|Unidentified Artist,Chinese,1505,1566,,1368,1644,Folding fan mounted as an album leaf; ink on paper,6 1/2 x 18 1/2 in. (16.5 x 47.0 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/35983,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.78,false,true,48903,Asian Art,Hanging scroll,,China,Ming dynasty (1368–1644),,,,Artist|Artist,After,Hai Rui|Unidentified Artist,"Chinese, 1514–1587",,Hai Rui|Unidentified Artist,Chinese,1514,1587,,1514,1587,Hanging scroll; ink on paper,Overall with mounting: 108 3/4 x 28 in. (276.2 x 71.1 cm) Image: 82 3/8 x 20 1/8 in. (209.2 x 51.1 cm) Overall with knobs: 108 3/4 x 31 3/4 in. (276.2 x 80.6 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/48903,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.31,false,true,40510,Asian Art,Hanging scroll,明 佚名 倣趙孟頫 草書湘簾疏織七絕詩 軸|A Summer Idyll,China,Yuan dynasty (1271–1368),,,,Artist|Artist,After,Zhao Mengfu|Unidentified Artist,"Chinese, 1254–1322",Chinese,Zhao Mengfu|Unidentified Artist,Chinese,1254,1322,,1319,1368,"Hanging scroll; ink on silk, 3 columns in large running-cursive script",Image: 52 1/2 x 20 7/8 in. (133.4 x 53 cm) Overall with mounting: 111 1/2 x 29 in. (283.2 x 73.7 cm) Overall with knobs: 111 1/2 x 32 1/4 in. (283.2 x 81.9 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/40510,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.34,false,true,51870,Asian Art,Handscroll,,China,Yuan dynasty (1271–1368),,,,Artist|Artist,Copy after,Zhang Yu|Unidentified Artist,"Chinese, 1283–1350",Chinese,ZHANG YU|Unidentified Artist,Chinese,1283,1350,,1271,1368,Handscroll; ink on paper,Image: 11 3/4 x 63 in. (29.8 x 160 cm) Overall with mounting: 12 1/16 x 192 9/16 in. (30.6 x 489.1 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/51870,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.1,false,true,51869,Asian Art,Handscroll,,China,Ming (1368–1644) or Qing (1644–1911) dynasty,,,,Artist|Artist,Formerly attributed to,Huaisu|Unidentified Artist,"Chinese, 725–785","Chinese, 16th century",Huaisu|Unidentified Artist,Chinese,0725,0785,,1368,1911,Handscroll; ink on silk,Image (1): 10 5/8 x 6 1/8 in. (27 x 15.6 cm) Image (2): 10 3/8 x 7 1/4 in. (26.4 x 18.4 cm) Image (3): 10 5/8 x 13 3/8 in. (27 x 34 cm) Overall with mounting: 11 15/16 x 229 1/8 in. (30.3 x 582 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/51869,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.100.80,false,true,48906,Asian Art,Folding fan mounted as an album leaf,,China,late Ming (1368–1644)–early Qing (1644–1911) dynasty,,,,Artist|Artist,After,Zhou Tianqiu|Unidentified Artist,"Chinese, 1514–1595",,Zhou Tianqiu|Unidentified Artist,Chinese,1514,1595,,1368,1644,Folding fan mounted as an album leaf; ink on gold-flecked paper,6 7/8 x 21 1/4 in. (17.5 x 54.0 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/48906,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.100.78,false,true,35991,Asian Art,Folding fan mounted as an album leaf,,China,late Ming (1368–1644)–early Qing (1644–1911) dynasty,,,,Artist|Artist,After,Chen Hongshou|Unidentified Artist,"Chinese, 1599–1652",,Chen Hongshou|Unidentified Artist,Chinese,1599,1652,,1368,1911,Folding fan mounted as an album leaf; ink on paper,6 3/8 x 19 7/8 in. (16.2 x 50.5 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/35991,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -CP26,false,true,63220,Asian Art,Print,,China,,,,,Artist|Artist,In the Style of|Original painted by,Guo Songzheng|Xiao Yuncong,"Chinese|Chinese, 1596–1673",,Guo Songzheng|Xiao Yuncong,Chinese|Chinese,1596,1673,,0,0,Circular fan-shaped woodblock print; ink and color on paper,9 5/8 x 11 13/16 in. (24.4 x 30 cm),"Rogers Fund, 1924",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/63220,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -CP28,false,true,63225,Asian Art,Print,,China,,,,,Artist|Artist,Original painted by|In the Style of,Shi Daoren|Li Cheng,"Chinese, Qing dynasty|Chinese, 919–967",,Shi Daoren|LI CHENG,Chinese|Chinese,0919,0967,,0,0,Fan-shaped woodblock print; ink and color on paper,9 5/8 x 11 13/16 in. (24.4 x 30 cm),"Rogers Fund, 1924",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/63225,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -CP11,false,true,63205,Asian Art,Print,,China,,,,,Artist|Artist,In the Style of|Original painted by,Hwang Yifeng|Xiao Yuncong,"Chinese, 1269–1354|Chinese, 1596–1673",,Hwang Yifeng|Xiao Yuncong,Chinese|Chinese,1269 |1596,1354 |1673,,0,0,Polychrome woodblock print; ink and color on paper,9 5/8 x 11 13/16 in. (24.4 x 30 cm),"Rogers Fund, 1924",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/63205,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -CP12,false,true,63206,Asian Art,Print,,China,,,,,Artist|Artist,Original painted by|In the style of,Hwa Dang|Wu Zhen,"Chinese, Ming dynasty|Chinese, 1280–1354",,Hwa Dang|Wu Zhen,Chinese|Chinese,1280,1354,,0,0,Polychrome woodblock print; ink and color on paper,9 5/8 x 11 13/16 in. (24.4 x 30 cm),"Rogers Fund, 1924",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/63206,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -CP14,false,true,63208,Asian Art,Print,,China,,,,,Artist|Artist,Original by|In the Style of,Gao Fangshan|Mi Yuanzhang,"Chinese, Yuan dynasty|Chinese, 1051–1107",,Gao Fangshan|Mi Yuanzhang,Chinese|Chinese,1051,1107,,0,0,Polychrome woodblock print; ink and color on paper,9 5/8 x 11 13/16 in. (24.4 x 30 cm),"Rogers Fund, 1924",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/63208,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -CP31,false,true,63228,Asian Art,Print,,China,,,,,Artist|Artist,Original painted by|In the Style of,Gao Fangshan|Mi Yuanzhang,"Chinese, Yuan dynasty|Chinese, 1051–1107",,Gao Fangshan|Mi Yuanzhang,Chinese|Chinese,1051,1107,,0,0,Polychrome woodblock print; ink and color on paper,9 5/8 x 11 13/16 in. (24.4 x 30 cm),"Rogers Fund, 1924",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/63228,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1973.121.10,false,true,40102,Asian Art,Fan mounted as an album leaf,,China,,,,,Artist|Artist,After,Li Song|Unidentified Artist,"Chinese, ca. 1190–1260","Chinese, 13th–15th century?",Li Song|Unidentified Artist,Chinese,1190,1260,,1200,1499,Fan mounted as an album leaf; ink and color on silk,10 3/8 x 10 1/2 in. (26.4 x 26.7 cm),"Ex coll.: C. C. Wang Family, Purchase, Gift of J. Pierpont Morgan, by exchange, 1973",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/40102,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.3,false,true,39917,Asian Art,Hanging scroll mounted as handscroll,,China,Song dynasty (960–1279),,,,Artist|Artist,After,Su Shi|Unidentified Artist,"Chinese, 1037–1101",,Su Shi|Unidentified Artist,Chinese,1037,1101,,1037,1101,Hanging scroll mounted as a handscroll; ink on paper,Image: 21 3/8 in. × 13 in. (54.3 × 33 cm) Overall with mounting: 14 3/4 in. × 34 ft. 10 3/16 in. (37.5 × 1062.2 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/39917,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.100.113,false,true,51396,Asian Art,Fan mounted as an album leaf,,China,Song dynasty (960–1279),,,,Artist|Artist,After,Emperor Huizong|Unidentified Artist,"Chinese, 1082–1135; r. 1100–25",,Huizong Emperor|Unidentified Artist,Chinese,1082,1082,,1082,1135,Fan mounted as an album leaf; ink and color on silk,9 x 9 1/4 in. (22.9 x 23.5 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51396,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -23.33.1,false,true,51540,Asian Art,Album leaf,,China,Ming dynasty (1368–1644),,,,Artist|Artist,Formerly Attributed to,Unidentified Artist|Gu Kaizhi,344–405,,Unidentified Artist|Gu Kaizhi,Chinese,0344,0405,,1368,1644,Album leaf; ink and color on silk,7 1/8 x 8 7/8 in. (18.1 x 22.5 cm),"Rogers Fund, 1923",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51540,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -47.18.148,false,true,51746,Asian Art,Fan mounted as an album leaf,,China,Ming dynasty (1368–1644),,,,Artist|Artist,Copy after,Unidentified Artist|Li Anzhong,active 12th century,,Unidentified Artist|Li Anzhong,Chinese,0012,0012,,1368,1644,Fan mounted as an album leaf; ink and color on silk,9 3/4 x 10 in. (24.8 x 25.4 cm),"From the Collection of A. W. Bahr, Purchase, Fletcher Fund, 1947",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51746,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1981.4.2,false,true,45754,Asian Art,Hanging scroll,,China,Ming dynasty (1368–1644),,,,Artist|Artist,After,Tang Yin|Unidentified Artist,"Chinese, 1470–1524",,Tang Yin|Unidentified Artist,Chinese,1470,1524,,1368,1644,Hanging scroll; ink and color on paper,Image: 53 5/8 x 23 1/8 in. (136.2 x 58.7 cm) Overall with mounting: 109 1/4 x 30 1/4 in. (277.5 x 76.8 cm) Overall with knobs: 109 1/4 x 34 in. (277.5 x 86.4 cm),"Gift of Douglas Dillon, 1981",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45754,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.100.53,false,true,45774,Asian Art,Folding fan mounted as an album leaf,,China,Ming dynasty (1368–1644),,,,Artist|Artist,After,Tang Yin|Unidentified Artist,"Chinese, 1470–1524",,Tang Yin|Unidentified Artist,Chinese,1470,1524,,1368,1644,Folding fan mounted as an album leaf; ink and color on gold-flecked paper,7 1/4 x 20 1/8 in. (18.4 x 51.1 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45774,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.100.59,false,true,48872,Asian Art,Folding fan mounted as an album leaf,,China,Ming dynasty (1368–1644),,,,Artist|Artist,After,Lu Zhi|Unidentified Artist,"Chinese, 1495–1576",,Lu Zhi|Unidentified Artist,Chinese,1495,1576,,1368,1644,Folding fan mounted as an album leaf; ink on gold paper,7 1/4 x 19 1/4 in. (18.4 x 48.9 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/48872,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -47.18.45,false,true,51664,Asian Art,Fan mounted as an album leaf,,China,Ming dynasty (1368–1644),,,,Artist|Artist,In the style of,Unidentified Artist|Yanxiu,"Chinese, early 10th century",,Unidentified Artist|Yanxiu,Chinese,0900,0999,,1368,1644,Fan mounted as an album leaf; ink and color on silk,8 x 8 3/16 in. (20.3 x 20.8 cm),"From the Collection of A. W. Bahr, Purchase, Fletcher Fund, 1947",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51664,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -47.18.64,false,true,51679,Asian Art,Fan mounted as an album leaf,,China,Ming dynasty (1368–1644),,,,Artist|Artist,Formerly Attributed to,Unidentified Artist|Hao Cheng,"Chinese, early 11th century",,Unidentified Artist|Hao Cheng,Chinese,1000,1035,,1000,1035,Fan mounted as an album leaf; ink and color on silk,8 5/8 x 9 3/16 in. (21.9 x 23.3 cm),"From the Collection of A. W. Bahr, Purchase, Fletcher Fund, 1947",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51679,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -47.18.62,false,true,51678,Asian Art,Fan mounted as an album leaf,,China,Ming dynasty (1368–1644),,,,Artist|Artist,After,Unidentified Artist|Ma Lin,"Chinese, ca. 1180– after 1256",,Unidentified Artist|MA LIN,Chinese,1180,1256,,1368,1644,Fan mounted as an album leaf; ink and color on silk,9 3/4 x 10 1/16 in. (24.8 x 25.6 cm),"From the Collection of A. W. Bahr, Purchase, Fletcher Fund, 1947",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51678,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -47.18.41,false,true,45677,Asian Art,Album leaf,元 佚名 倣夏永 呂洞賓過岳陽樓 冊頁|The Immortal Lü Dongbin Appearing over the Yueyang Pavilion,China,Ming dynasty (1368–1644),,,,Artist|Artist,After,Unidentified Artist|Xia Yong,"Chinese, active mid-14th century","Chinese, 15th–16th century?",Unidentified Artist|XIA YONG,Chinese,1336,1370,,1400,1599,Album leaf; ink on silk,Image: 8 5/8 x 7 3/8 in. (21.9 x 18.7 cm),"From the Collection of A. W. Bahr, Purchase, Fletcher Fund, 1947",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45677,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.100.61,false,true,35984,Asian Art,Folding fan mounted as an album leaf,,China,Ming dynasty (1368–1644),,,,Artist|Artist,After,Song Maojin|Unidentified Artist,"Chinese, active late 16th century–early 17th century",,Song Maojin|Unidentified Artist,Chinese,1571,1635,,1368,1644,Folding fan mounted as an album leaf; ink and color on gold paper,6 1/4 x 18 1/4 in. (15.9 x 46.4 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/35984,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.220.99e,false,true,51498,Asian Art,Album leaf,,China,Qing dynasty (1644–1911),,,,Artist|Artist,After,Cao Zhibai|Unidentified Artist,"Chinese, died 1355",,Cao Zhibai|Unidentified Artist,Chinese,,1355,,1644,1911,Album leaf; ink and color on silk,11 7/16 x 14 5/8 in. (29.1 x 37.1 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51498,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.220.20,false,true,51428,Asian Art,Hanging scroll,,China,Qing dynasty (1644–1911),,,,Artist|Artist,After,Unidentified Artist|Zhao Mengfu,"Chinese, 1254–1322",,Unidentified Artist|Zhao Mengfu,Chinese,1254,1322,,1644,1911,Hanging scroll; ink and color on silk,21 1/2 x 13 3/4 in. (54.6 x 34.9 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51428,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.220.99g,false,true,51500,Asian Art,Album leaf,,China,Qing dynasty (1644–1911),,,,Artist|Artist,After,Zhao Mengfu|Unidentified Artist,"Chinese, 1254–1322",,Zhao Mengfu|Unidentified Artist,Chinese,1254,1322,,1644,1911,Album leaf; ink and color on silk,10 1/16 x 12 in. (25.6 x 30.5 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51500,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.220.126,false,true,51515,Asian Art,Handscroll,清 倣米芾雲山圖 卷|Mountain Scenery,China,Qing dynasty (1644–1911),,,,Artist|Artist,After,Dong Qichang|Unidentified Artist,"Chinese, 1555–1636",,Dong Qichang|Unidentified Artist,Chinese,1555,1636,,1644,1911,Handscroll; ink on silk,10 1/2 x 86 in. (26.7 x 218.4 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51515,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.145,false,true,49159,Asian Art,Album leaf,,China,Qing dynasty (1644–1911),,,,Artist|Artist,After,Yun Shouping|Unidentified Artist,"Chinese, 1633–1690",,YUN SHOUPING|Unidentified Artist,Chinese,1633,1690,,1644,1911,Album leaf; ink and color on paper,8 1/8 x 12 in. (20.6 x 30.5 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49159,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.146,false,true,49160,Asian Art,Album leaf,,China,Qing dynasty (1644–1911),,,,Artist|Artist,After,Yun Shouping|Unidentified Artist,"Chinese, 1633–1690",,YUN SHOUPING|Unidentified Artist,Chinese,1633,1690,,1644,1911,Album leaf; ink and color on paper,11 7/8 x 8 1/8 in. (30.2 x 20.6 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49160,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.220.19,false,true,51427,Asian Art,Hanging scroll,,China,Qing dynasty (1644–1911),,,,Artist|Artist,Formerly Attributed to,Unidentified Artist|Fang-hu,"Chinese, active ca. 1380",,Unidentified Artist|Fang-hu,Chinese,1380,1380,,1644,1911,Hanging scroll; ink on paper,33 1/8 x 18 5/8 in. (84.1 x 47.3 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51427,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -11.170,false,true,51284,Asian Art,Handscroll,清 佚名 清明上河圖|Going Upriver on the Qingming Festival,China,Qing dynasty (1644–1911),,,,Artist|Artist,After,Unidentified Artist|Qiu Ying,"Chinese, ca. 1495–1552","Chinese, 18th century?",Unidentified Artist|Qiu Ying,Chinese,1485,1562,,1644,1911,Handscroll; ink and color on silk,11 1/2 in. × 21 ft. 2 in. (29.2 × 645.2 cm),"Rogers Fund, 1911",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51284,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -18.124.5,false,true,51527,Asian Art,Handscroll,,China,Qing dynasty (1644–1911),,,,Artist|Artist,Formerly Attributed to,Unidentified Artist|Gong Kai,"Chinese, 1222–after 1304",,Unidentified Artist|Gong Kai,Chinese,1222,1310,,1644,1911,Handscroll; black and white on paper,H. 12 1/2 in. (31.8 cm),"Rogers Fund, 1918",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51527,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.220.131a–j,false,true,51519,Asian Art,Album,,China,Qing dynasty (1644–1911),,,,Artist|Artist,After,Song Xu|Unidentified Artist,"Chinese, 1525–after 1606",,Song Xu|Unidentified Artist,Chinese,1525,1610,,1644,1911,Album of ten leaves; ink and color on silk,Each painting: 11 1/4 x 11 1/2 in. (28.6 x 29.2 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51519,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.160.28,false,true,51574,Asian Art,Hanging scroll,,China,Qing dynasty (1644–1911),,,,Artist|Artist,Formerly attributed to,Gong Ji|Unidentified Artist,"Chinese, Northern Song dynasty",,Gong Ji|Unidentified Artist,Chinese,0960,1127,,1644,1911,Hanging scroll; ink and color on silk,26 1/2 x 16 in. (67.3 x 40.6 cm),"H. O. Havemeyer Collection, Gift of Horace Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51574,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.220.99f,false,true,51499,Asian Art,Album leaf,,China,Qing dynasty (1644–1911),,,,Artist|Artist,After,Wang Yuan|Unidentified Artist,"Chinese, ca. 1280–after 1349",,Wang Yuan|Unidentified Artist,Chinese,1270,1359,,1644,1911,Album leaf; ink and color on silk,19 3/4 x 12 3/8 in. (50.2 x 31.4 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51499,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -47.18.89,false,true,51701,Asian Art,Handscroll,,China,Qing dynasty (1644–1911),,,,Artist|Artist,After,Unidentified Artist|Xia Gui,"Chinese, active ca. 1195–1230",,Unidentified Artist|Xia Gui,Chinese,1195,1230,,1644,1911,Handscroll; ink on silk,14 3/8 x 69 3/4 in. (36.5 x 177.2 cm),"From the Collection of A. W. Bahr, Purchase, Fletcher Fund, 1947",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51701,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -47.18.5,false,true,51637,Asian Art,Handscroll,,China,Qing dynasty (1644–1911),,,,Artist|Artist,Formerly Attributed to,Unidentified Artist|Zhao Chang,"Chinese, active 10th–11th century",,Unidentified Artist|Zhao Chang,Chinese,0925,1025,,1644,1911,Handscroll; ink and color on silk,Image: 9 15/16 × 79 5/8 in. (25.2 × 202.2 cm),"From the Collection of A. W. Bahr, Purchase, Fletcher Fund, 1947",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51637,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.220.16,false,true,51425,Asian Art,Hanging scroll,,China,Qing dynasty (1644–1911),,,,Artist|Artist,Formerly attributed to,Unidentified Artist|Zhao Lingrang,"Chinese, active ca. 1070– after 1100",,Unidentified Artist|ZHAO LINGRANG,Chinese,1070,1100,,1644,1911,Hanging scroll; ink and color on silk,Image: 34 x 14 1/2 in. (86.4 x 36.8 cm) Overall with mounting: 94 1/2 x 20 5/8 in. (240 x 52.4 cm) Overall with knobs: 94 1/2 x 24 3/4 in. (240 x 62.9 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51425,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.461,false,true,50360,Asian Art,Hanging scroll,,China,Yuan dynasty (1271–1368),,,,Artist|Artist,In the style of,Unidentified Artist|Wu Daozi,"Chinese, 689–after 755",,Unidentified Artist|Wu Daozi,Chinese,0689,0760,,1271,1368,Hanging scroll; ink and color on silk,Image: 40 1/2 × 20 3/8 in. (102.9 × 51.8 cm) Overall with mounting: 72 × 27 3/8 in. (182.9 × 69.5 cm) Overall with knobs: 72 × 29 3/8 in. (182.9 × 74.6 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/50360,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -47.18.72,false,true,51687,Asian Art,Album leaf,,China,Song dynasty (?) (960–1279),,,,Artist|Artist,Formerly Attributed to,Unidentified Artist|Mao I,active 12th century,,Unidentified Artist|Mao I,Chinese,0012,0012,,960,1279,Album leaf; ink and color on silk,9 3/4 x 9 15/16 in. (24.8 x 25.2 cm),"From the Collection of A. W. Bahr, Purchase, Fletcher Fund, 1947",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51687,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -47.18.28,false,true,51654,Asian Art,Album leaf,,China,Song dynasty (?) (960–1279),,,,Artist|Artist,Formerly Attributed to,Unidentified Artist|Ma Lin,"Chinese, ca. 1180– after 1256",,Unidentified Artist|MA LIN,Chinese,1180,1256,,960,1279,Album leaf; ink on silk,9 9/16 x 10 in. (24.3 x 25.4 cm),"From the Collection of A. W. Bahr, Purchase, Fletcher Fund, 1947",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51654,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.100.114,false,true,51397,Asian Art,Album leaf,,China,Song dynasty (?) (960–1279),,,,Artist|Artist,in the style of,Unidentified Artist|Zhao Chang,"Chinese, active 10th–11th century",,Unidentified Artist|Zhao Chang,Chinese,0925,1025,,960,1279,Album leaf; ink and color on silk,8 1/8 x 9 3/8 in. (20.6 x 23.8 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51397,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -47.18.95,false,true,51707,Asian Art,Handscroll,,China,Song dynasty (?) (960–1279),,,,Artist|Artist,In the style of,Unidentified Artist|Emperor Gaozong,"Chinese, 1107–1187, r. 1127–1162",,Unidentified Artist|Gaozong Emperor,Chinese,1107,1187,,960,1279,Handscroll; ink and color on silk,10 7/8 x 83 1/8 in. (27.6 x 211.1 cm),"From the Collection of A. W. Bahr, Purchase, Fletcher Fund, 1947",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51707,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.100.34,false,true,51307,Asian Art,Handscroll,,China,Ming dynasty (1368–1644) (?),,,,Artist|Artist,After,Wu Zhen|Unidentified Artist,"Chinese, 1280–1354",,Wu Zhen|Unidentified Artist,Chinese,1280,1354,,1368,1644,Handscroll; ink on silk,13 1/2 in. × 17 ft. 3 3/4 in. (34.3 × 527.7 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51307,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -18.124.2,false,true,36032,Asian Art,Handscroll,,China,Ming dynasty (1368–1644) (?),,,,Artist|Artist,Formerly Attributed to,Unidentified Artist|Li Gonglin,"Chinese, ca. 1041–1106",,Unidentified Artist|Li Gonglin,Chinese,1041,1106,,1368,1644,Handscroll; ink on silk,13 3/8 × 85 in. (34 × 215.9 cm),"Rogers Fund, 1918",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36032,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -47.18.86,false,true,51698,Asian Art,Handscroll,,China,Ming dynasty (?) (1368–1644),,,,Artist|Artist,Copy after,Unidentified Artist|Chen Rong,active 1235–62,,Unidentified Artist|Chen Rong,Chinese,1235,1262,,1368,1644,Handscroll; ink on silk,Overall: 15 5/8 x 115 1/4 in. (39.7 x 292.7 cm),"From the Collection of A. W. Bahr, Purchase, Fletcher Fund, 1947",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51698,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -47.18.145,false,true,51744,Asian Art,Album leaf,,China,Ming dynasty (?) (1368–1644),,,,Artist|Artist,Copy after,Unidentified Artist|Yan Liben,"Chinese, 640–680",,Unidentified Artist|Yan Liben,Chinese,0640,0680,,1368,1644,Album leaf; ink and color on silk,10 x 5 15/16 in. (25.4 x 15.1 cm),"From the Collection of A. W. Bahr, Purchase, Fletcher Fund, 1947",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51744,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -18.124.1,false,true,51526,Asian Art,Handscroll,,China,Ming dynasty (?) (1368–1644),,,,Artist|Artist,In the Style of,Unidentified Artist|Wu Zongyuan,"Chinese, died 1050",,Unidentified Artist|Wu Zongyuan,Chinese,0950,1050,,1368,1644,Handscroll; ink and color on silk,20 1/2 in. × 16 ft. 3 15/16 in. (52.1 × 497.7 cm),"Rogers Fund, 1918",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51526,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.5,false,true,51522,Asian Art,Hanging scroll,,China,Ming dynasty (?) (1368–1644),,,,Artist|Artist,Formerly Attributed to,Unidentified Artist|Li Anzhong,active 12th century,,Unidentified Artist|Li Anzhong,Chinese,0012,0012,,1368,1644,Hanging scroll; color on silk,Image: 44 1/4 × 18 1/4 in. (112.4 × 46.4 cm) Overall with mounting: 79 1/2 × 23 3/4 in. (201.9 × 60.3 cm) Overall with knobs: 79 1/2 × 25 1/2 in. (201.9 × 64.8 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51522,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -47.18.117,false,true,51724,Asian Art,Handscroll,,China,Ming dynasty (?) (1368–1644),,,,Artist|Artist,In the style of,Unidentified Artist|Wu Zhen,"Chinese, 1280–1354",,Unidentified Artist|Wu Zhen,Chinese,1280,1354,,1368,1644,Handscroll; ink on silk,12 1/2 x 262 in. (31.8 x 665.5 cm),"From the Collection of A. W. Bahr, Purchase, Fletcher Fund, 1947",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51724,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -47.18.143,false,true,51742,Asian Art,Album leaf,,China,Ming dynasty (?) (1368–1644),,,,Artist|Artist,Copy after (?),Unidentified Artist|Huang Qüan,"Chinese, active ca. 950",,Unidentified Artist|Huang Qüan,Chinese,0940,0960,,1368,1644,Album leaf; ink and color on silk,8 3/4 x 9 1/2 in. (22.2 x 24.1 cm),"From the Collection of A. W. Bahr, Purchase, Fletcher Fund, 1947",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51742,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"54.134.2a, b",false,true,51806,Asian Art,Handscroll,,China,Ming dynasty (?) (1368–1644),,,,Artist|Artist,In the style of,Unidentified Artist|Qiu Ying,"Chinese, ca. 1495–1552",,Unidentified Artist|Qiu Ying,Chinese,1485,1562,,1368,1644,Handscroll; ink and color on silk,(a.) 14 15/16 x 94 1/2 in. (37.9 x 240 cm); (b.) 51 x 14 3/8 in. (129.5 x 36.5 cm),"Gift of the Pierpont Morgan Library, 1954",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51806,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -47.18.38,false,true,51660,Asian Art,Fan mounted as an album leaf,,China,Ming dynasty (?) (1368–1644),,,,Artist|Artist,In the style of,Unidentified Artist|Luo Zonggui,"Chinese, active 1228–1234",,Unidentified Artist|Luo Zonggui,Chinese,1228,1234,,1368,1644,Fan mounted as an album leaf; ink and color on silk,9 1/8 x 7 1/8 in. (23.2 x 18.1 cm),"From the Collection of A. W. Bahr, Purchase, Fletcher Fund, 1947",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51660,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -47.18.40,false,true,51662,Asian Art,Fan mounted as an album leaf,,China,Ming dynasty (?) (1368–1644),,,,Artist|Artist,In the style of,Unidentified Artist|Xia Gui,"Chinese, active ca. 1195–1230",,Unidentified Artist|Xia Gui,Chinese,1195,1230,,1368,1644,Fan mounted as an album leaf; ink on silk,9 1/8 x 9 1/2 in. (23.2 x 24.1 cm),"From the Collection of A. W. Bahr, Purchase, Fletcher Fund, 1947",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51662,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -47.18.146,false,true,51745,Asian Art,Album leaf,,China,Ming dynasty (?) (1368–1644),,,,Artist|Artist,Copy after (?),Unidentified Artist|Zhou Fang,"Chinese, active ca. 780– ca.810",,Unidentified Artist|ZHOU FANG,Chinese,0780,0810,,1368,1644,Album leaf; ink and color on silk,7 1/2 x 8 1/2 in. (19.1 x 21.6 cm),"From the Collection of A. W. Bahr, Purchase, Fletcher Fund, 1947",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51745,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -47.18.105,false,true,51715,Asian Art,Hanging scroll,,China,Ming dynasty (?) (1368–1644),,,,Artist|Artist,In the style of,Unidentified Artist|Lü Ji,"Chinese, active late 15th century",,Unidentified Artist|Lü Ji,Chinese,1430,1504,,1368,1644,Hanging scroll; ink and color on silk,75 x 38 7/8 in. (190.5 x 98.7 cm),"From the Collection of A. W. Bahr, Purchase, Fletcher Fund, 1947",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51715,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.100.107,false,true,51393,Asian Art,Fan mounted as an album leaf,,China,Ming dynasty (?) (1368–1644),,,,Artist|Artist,After,Emperor Huizong|Unidentified Artist,"Chinese, 1082–1135; r. 1100–25",,Huizong Emperor|Unidentified Artist,Chinese,1082,1082,,1368,1644,Fan mounted as an album leaf; ink and color on silk,9 1/4 x 9 5/8 in. (23.5 x 24.4 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51393,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -47.18.48,false,true,51667,Asian Art,Handscroll,,China,Qing dynasty (?) (1644–1911),,,,Artist|Artist,In the style of,Unidentified Artist|Mi Fu,"Chinese, 1052–1107",,Unidentified Artist|MI FU,Chinese,1052,1107,,1644,1911,Handscroll; ink on silk,13 1/4 x 110 3/16 in. (33.7 x 279.9 cm),"From the Collection of A. W. Bahr, Purchase, Fletcher Fund, 1947",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51667,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -47.18.139,false,true,51738,Asian Art,Album leaf,,China,Yuan dynasty (?) (1271–1368),,,,Artist|Artist,Copy after,Unidentified Artist|Li Gonglin,"Chinese, ca. 1041–1106",,Unidentified Artist|Li Gonglin,Chinese,1041,1106,,1271,1368,Album leaf; ink and color on silk,12 x 12 5/16 in. (30.5 x 31.3 cm),"From the Collection of A. W. Bahr, Purchase, Fletcher Fund, 1947",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51738,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.220.17,false,true,51426,Asian Art,Hanging scroll,,China,late Qing dynasty (1644–1911),,,,Artist|Artist,In the Style of,Wen Zhengming|Unidentified Artist,"Chinese, 1470–1559",,Wen Zhengming|Unidentified Artist,Chinese,1470,1559,,1500,1599,Hanging scroll; ink and color on paper,26 1/8 x 10 in. (66.4 x 25.4 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51426,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -47.18.98,false,true,51710,Asian Art,Handscroll,,China,early Ming dynasty (1368–1644),,,,Artist|Artist,Copy after,Unidentified Artist|Huang Qüan,"Chinese, active ca. 950",,Unidentified Artist|Huang Qüan,Chinese,0940,0960,,1368,1644,Handscroll; ink and color on silk,14 x 87 1/2 in. (35.6 x 222.3 cm),"From the Collection of A. W. Bahr, Purchase, Fletcher Fund, 1947",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51710,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.100.103,false,true,51389,Asian Art,Fan mounted as an album leaf,,China,Song dynasty (960–1279) or later,,,,Artist|Artist,Formerly attributed to,Unidentified Artist|Wu Bing,"Chinese, active 1190–1194",,Unidentified Artist|Wu Bing,Chinese,1190,1194,,960,1300,Fan mounted as an album leaf; Ink and color on silk,12.25 x 12.25 in. (31.1 x 31.1 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51389,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.100.49,false,true,35979,Asian Art,Folding fan mounted as an album leaf,,China,Ming dynasty (1368–1644) or later,,,,Artist|Artist,After,Wen Zhengming|Unidentified Artist,"Chinese, 1470–1559",,Wen Zhengming|Unidentified Artist,Chinese,1470,1559,,1368,1644,Folding fan mounted as an album leaf; ink and color on gold-flecked paper,7 1/8 x 20 1/2 in. (18.1 x 52.1 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/35979,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -38.31.2,false,true,51586,Asian Art,Handscroll,,China,Ming dynasty (1368–1644) or earlier,,,,Artist|Artist,In the style of,Unidentified Artist|Zhao Boju,"Chinese, 11th century",,Unidentified Artist|Zhao Boju,Chinese,1000,1099,,1300,1644,Handscroll; ink and color on silk,11 5/8 x 95 1/2 in. (29.5 x 242.6 cm),"Fletcher Fund, 1938",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51586,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.100.98,false,true,51384,Asian Art,Fan mounted as an album leaf,,China,Song (960–1279)–Ming (1368–1644) dynasty,,,,Artist|Artist,After,Emperor Huizong|Unidentified Artist,"Chinese, 1082–1135; r. 1100–25",,Huizong Emperor|Unidentified Artist,Chinese,1082,1082,,960,1644,Fan mounted as an album leaf; ink and color on silk,9 1/8 x 9 1/2 in. (23.2 x 24.1 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51384,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.220.110,false,true,45782,Asian Art,Hanging scroll,,China,Ming (1368–1644)–Qing (1644–1911) dynasty,,,,Artist|Artist,After,Wen Zhengming|Unidentified Artist,"Chinese, 1470–1559",,Wen Zhengming|Unidentified Artist,Chinese,1470,1559,,1368,1911,Hanging scroll; ink and color on paper,25 x 11 1/2 in. (63.5 x 29.2 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45782,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.220.24,false,true,52080,Asian Art,Handscroll,,China,Ming (1368–1644)–Qing (1644–1911) dynasty,,,,Artist|Artist,In the Style of,Li Gonglin|Unidentified Artist,"Chinese, ca. 1041–1106",,Li Gonglin|Unidentified Artist,Chinese,1041,1106,,1368,1911,Handscroll; ink on paper,11 7/8 in. × 19 ft. 5 1/2 in. (30.2 × 593.1 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/52080,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.220.99a,false,true,51495,Asian Art,Album leaf,,China,Ming (1368–1644)–Qing (1644–1911) dynasty,,,,Artist|Artist,After,Lu Qing|Unidentified Artist,"Chinese, active 1190–1195",,Lu Qing|Unidentified Artist,Chinese,1190,1195,,1368,1911,Album leaf; ink on silk,11 13/16 x 16 in. (30 x 40.6 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51495,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.220.99b,false,true,36020,Asian Art,Album leaf,,China,Ming (1368–1644)–Qing (1644–1911) dynasty,,,,Artist|Artist,In the Style of,Ma Lin|Unidentified Artist,"Chinese, ca. 1180– after 1256",,MA LIN|Unidentified Artist,Chinese,1180,1256,,1368,1911,Album leaf; ink and color on silk,10 3/4 x 10 3/4 in. (27.3 x 27.3 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36020,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -21.18,false,true,51537,Asian Art,Handscroll,,China,Ming (1368–1644) or Qing (1644–1911) dynasty,,,,Artist|Artist,In the style of,Unidentified Artist|Wu Zongyuan,"Chinese, died 1050",,Unidentified Artist|Wu Zongyuan,Chinese,0950,1050,,1368,1911,Handscroll; ink and color on silk,20 1/2 x 75 3/4 in. (52.1 x 192.4 cm),"Rogers Fund, 1921",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51537,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.220.100a–l,false,true,51505,Asian Art,Album leaves,,China,Ming (1368–1644) or Qing (1644–1911) dynasty,,,,Artist|Artist,Border by|Formerly attributed to,Unidentified Artist|Yi Yuanji,"Chinese, died 1066",,Unidentified Artist|Yi Yuanji,Chinese,0966,1066,,1368,1911,Set of twelve album leaves; ink and color on silk,Each: 11 1/2 x 10 5/8 in. (29.2 x 27 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51505,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -23.53,false,true,51544,Asian Art,Handscroll,,China,Ming (1368–1644) or Qing (1644–1911) dynasty,,,,Artist|Artist,In the Style of,Unidentified Artist|Zhao Mengfu,"Chinese, 1254–1322",,Unidentified Artist|Zhao Mengfu,Chinese,1254,1322,,1368,1911,Handscroll; ink and color on silk,10 5/8 × 34 1/2 in. (27 × 87.6 cm),"Gift of Lewis Cass Ledyard, 1923",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51544,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.220.95,false,true,51492,Asian Art,Hanging scroll,,China,Ming (1368–1644) or Qing (1644–1911) dynasty,,,,Artist|Artist,In the style of,Unidentified Artist|Zhao Mengfu,"Chinese, 1254–1322",,Unidentified Artist|Zhao Mengfu,Chinese,1254,1322,,1368,1911,Hanging scroll; ink and color on silk,56 x 23 1/2 in. (142.2 x 59.7 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51492,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.480,false,true,51566,Asian Art,Handscroll,,China,Ming (1368–1644) or Qing (1644–1911) dynasty,,,,Artist|Artist,In the style of,Unidentified Artist|Zhao Mengfu,"Chinese, 1254–1322",,Unidentified Artist|Zhao Mengfu,Chinese,1254,1322,,1368,1911,Handscroll; ink on silk,13 1/8 in. × 11 ft. 5 in. (33.3 × 348 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51566,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.220.29,false,true,51481,Asian Art,Hanging scroll,,China,Ming (1368–1644) or Qing (1644–1911) dynasty,,,,Artist|Artist,After,Unidentified Artist|Wen Zhengming,"Chinese, 1470–1559",,Unidentified Artist|Wen Zhengming,Chinese,1470,1559,,1368,1911,Hanging scroll; ink on silk,69 x 30 1/2 in. (175.3 x 77.5 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51481,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.220.113,false,true,51510,Asian Art,Hanging scroll,,China,Ming (1368–1644) or Qing (1644–1911) dynasty,,,,Artist|Artist,Formerly Attributed to,Unidentified Artist|Lu Zhi,"Chinese, 1495–1576",,Unidentified Artist|Lu Zhi,Chinese,1495,1576,,1368,1911,Hanging scroll; ink and color on paper,25 3/8 x 16 1/2 in. (64.5 x 41.9 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51510,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.530,false,true,51572,Asian Art,Handscroll,,China,Ming (1368–1644) or Qing (1644–1911) dynasty,,,,Artist|Artist,In the style of,Unidentified Artist|Jing Hao,active ca. 900–960,,Unidentified Artist|Jing Hao,Chinese,0900,0960,,1368,1911,Handscroll; ink and color on silk,16 3/8 x 52 in. (41.6 x 132.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51572,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -47.18.4,false,true,53586,Asian Art,Handscroll,,China,Ming (1368–1644) or Qing (1644–1911) dynasty,,,,Artist|Artist,Formerly Attributed to,Unidentified Artist|Zhao Boju,"Chinese, 11th century",,Unidentified Artist|Zhao Boju,Chinese,1000,1099,,1368,1911,Handscroll; ink and color on silk,Image: 6 3/8 × 34 1/2 in. (16.2 × 87.6 cm),"From the Collection of A. W. Bahr, Purchase, Fletcher Fund, 1947",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/53586,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.220.106,false,true,51507,Asian Art,Hanging scroll,,China,Ming (1368–1644) or Qing (1644–1911) dynasty,,,,Artist|Artist,Formerly Attributed to,Unidentified Artist|Zhao Boju,"Chinese, 11th century",,Unidentified Artist|Zhao Boju,Chinese,1000,1099,,1368,1911,Hanging scroll; ink and color on silk,Image: 15 3/4 x 12 1/8 in. (40 x 30.8 cm) Overall (with colophons): 35 1/4 x 12 1/8 in. (89.5 x 30.8 cm) Overall with mounting: 57 1/4 x 14 7/8 in. (145.4 x 37.8 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51507,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.220.88,false,true,40424,Asian Art,Hanging scroll,,China,Ming (1368–1644) or Qing (1644–1911) dynasty,,,,Artist|Artist,Formerly Attributed to,Unidentified Artist|Huang Qüan,"Chinese, active ca. 950",,Unidentified Artist|Huang Qüan,Chinese,0940,0960,,1368,1911,Hanging scroll,Image: 77 x 37 in. (195.6 x 94 cm) Overall with mounting: 123 x 40 7/8 in. (312.4 x 103.8 cm) Overall with knobs: 123 x 45 1/4 in. (312.4 x 114.9 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/40424,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -18.25.1,false,true,51524,Asian Art,Handscroll,,China,Ming (1368–1644) or Qing (1644–1911) dynasty,,,,Artist|Artist,Formerly Attributed to,Unidentified Artist|Li Gonglin,"Chinese, ca. 1041–1106",,Unidentified Artist|Li Gonglin,Chinese,1041,1106,,1368,1911,Handscroll; ink on silk,11 3/8 × 58 in. (28.9 × 147.3 cm),"Rogers Fund, 1918",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51524,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -18.25.2,false,true,51525,Asian Art,Handscroll,,China,Ming (1368–1644) or Qing (1644–1911) dynasty,,,,Artist|Artist,In the style of,Unidentified Artist|Li Gonglin,"Chinese, ca. 1041–1106",,Unidentified Artist|Li Gonglin,Chinese,1041,1106,,1368,1911,Handscroll; ink on silk,13 3/8 x 85 in. (34 x 215.9 cm),"Rogers Fund, 1918",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51525,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.443,false,true,51559,Asian Art,Hanging scroll,,China,Ming (1368–1644) or Qing (1644–1911) dynasty,,,,Artist|Artist,In the style of,Unidentified Artist|Li Gonglin,"Chinese, ca. 1041–1106",,Unidentified Artist|Li Gonglin,Chinese,1041,1106,,1368,1911,Hanging scroll; color on silk,Image: 47 3/4 × 18 1/4 in. (121.3 × 46.4 cm) Overall with mounting: 84 × 24 1/4 in. (213.4 × 61.6 cm) Overall with knobs: 84 × 26 1/2 in. (213.4 × 67.3 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51559,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.61.62,false,true,51523,Asian Art,Album leaf,,China,Ming (1368–1644) or Qing (1644–1911) dynasty,,,,Artist|Artist,Formerly Attributed to,Li Gonglin|Unidentified Artist,"Chinese, ca. 1041–1106",,Li Gonglin|Unidentified Artist,Chinese,1041,1106,,1368,1911,Albun leaf; ink and color on silk,Image: 10 1/4 × 10 1/4 in. (26 × 26 cm) Sheet: 12 1/4 × 15 3/4 in. (31.1 × 40 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51523,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -30.76.296,false,true,51580,Asian Art,Hanging scroll,,China,Ming (1368–1644) or Qing (1644–1911) dynasty,,,,Artist|Artist,In the style of,Unidentified Artist|Lin Liang,"Chinese, ca. 1416–1480",,Unidentified Artist|Lin Liang,Chinese,1416,1480,,1368,1911,Hanging scroll; ink on silk,Image: 37 1/2 × 18 7/8 in. (95.3 × 47.9 cm) Overall with mounting: 68 1/8 × 23 5/8 in. (173 × 60 cm) Overall with knobs: 68 1/8 × 25 5/8 in. (173 × 65.1 cm),"Rogers Fund, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51580,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -30.76.297,false,true,53515,Asian Art,Hanging scroll,,China,Ming (1368–1644) or Qing (1644–1911) dynasty,,,,Artist|Artist,In the style of,Unidentified Artist|Lin Liang,"Chinese, ca. 1416–1480",,Unidentified Artist|Lin Liang,Chinese,1416,1480,,1368,1911,Hanging scroll; ink on silk,37 1/2 x 18 7/8 in. (95.3 x 47.9 cm),"Rogers Fund, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/53515,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.220.112,false,true,40420,Asian Art,Hanging scroll,,China,Ming (1368–1644) or Qing (1644–1911) dynasty,,,,Artist|Artist,Signature of,Unidentified Artist|Cui Zizhong,"Chinese, ca. 1595–1644",,Unidentified Artist|Cui Zizhong,Chinese,1595,1644,,1368,1911,Hanging scroll; ink and color on silk,57 x 33 3/4 in. (144.8 x 85.7 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/40420,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.220.96,false,true,51493,Asian Art,Handscroll,,China,Ming (1368–1644) or Qing (1644–1911) dynasty,,,,Artist|Artist,Formerly Attributed to,Guo Xi|Unidentified Artist,"Chinese, ca. 1000–ca. 1090",,Guo Xi|Unidentified Artist,Chinese,1000,1090,,1368,1911,Handscroll; ink on silk,19 in. × 22 ft. 7 in. (48.3 × 688.3 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51493,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.220.107,false,true,51508,Asian Art,Hanging scroll,,China,Ming (1368–1644) or Qing (1644–1911) dynasty,,,,Artist|Artist,Formerly Attributed to,Unidentified Artist|Fang Congyi,"Chinese, ca. 1301–after 1378",,Unidentified Artist|Fang Congyi,Chinese,1301,1399,,1368,1911,Hanging scroll; ink on paper,Image: 45 × 18 1/2 in. (114.3 × 47 cm) Overall with mounting: 109 × 25 1/4 in. (276.9 × 64.1 cm) Overall with knobs (only one knob): 109 × 29 1/2 in. (276.9 × 74.9 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51508,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.220.125,false,true,51514,Asian Art,Panels mounted as a handscroll,,China,Ming (1368–1644) or Qing (1644–1911) dynasty,,,,Artist|Artist,Spurious signature of,Unidentified Artist|Wang Zhenpeng,"Chinese, active ca. 1275–1330",,Unidentified Artist|WANG ZHENPENG,Chinese,1265,1340,,1600,1913,Ten panels mounted as a handscroll; ink and color on silk,"Image (each panel, approx.): 13 3/8 × 16 1/4 in. (34 × 41.3 cm)","John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51514,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.521,false,true,51569,Asian Art,Hanging scroll,,China,Ming (1368–1644) or Qing (1644–1911) dynasty,,,,Artist|Artist,Formerly Attributed to,Unidentified Artist|Qian Xuan,"Chinese, ca. 1235–before 1307",,Unidentified Artist|QIAN XUAN,Chinese,1235,1307,,1368,1911,Hanging scroll; ink and color on silk,23 1/8 x 30 in. (58.7 x 76.2 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51569,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -19.159,false,true,51534,Asian Art,Hanging scroll,,China,Ming (1368–1644) or Qing (1644–1911) dynasty,,,,Artist|Artist,Formerly Attributed to,Unidentified Artist|Emperor Huizong,"Chinese, 1082–1135; r. 1100–25",,Unidentified Artist|Huizong Emperor,Chinese,1082,1082,,1368,1911,Hanging scroll; on silk,46 x 21 in. (116.8 x 53.3 cm),"Rogers Fund, 1918",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51534,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.220.89,false,true,51487,Asian Art,Handscroll,,China,Ming (1368–1644) or Qing (1644–1911) dynasty,,,,Artist|Artist,Formerly Attributed to,Unidentified Artist|Emperor Huizong,"Chinese, 1082–1135; r. 1100–25",,Unidentified Artist|Huizong Emperor,Chinese,1082,1082,,1368,1911,Handscroll; ink and color on silk,12 3/8 x 42 3/4 in. (31.4 x 108.6 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51487,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.220.22,false,true,51477,Asian Art,Handscroll,,China,Ming (1368–1644) or Qing (1644–1911) dynasty,,,,Artist|Artist,After,Unidentified Artist|Liu Songnian,"Chinese, active ca 1175–after 1195",,Unidentified Artist|LIU SONGNIAN,Chinese,1175,1195,,1368,1911,Handscroll; Ink and color on silk,8 1/2 x 56 1/8 in. (21.6 x 142.6 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51477,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.160.30,false,true,51575,Asian Art,Handscroll,,China,Ming (1368–1644) or Qing (1644–1911) dynasty,,,,Artist|Artist,In the style of,Unidentified Artist|Liu Songnian,"Chinese, active ca 1175–after 1195",,Unidentified Artist|LIU SONGNIAN,Chinese,1175,1195,,1368,1911,Handscroll; ink and color on silk,14 3/8 in. × 11 ft. 1 in. (36.5 × 337.8 cm),"H. O. Havemeyer Collection, Gift of Horace Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51575,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -47.18.97,false,true,51709,Asian Art,Handscroll,,China,Ming (1368–1644) or Qing (1644–1911) dynasty,,,,Artist|Artist,Attributed to|Copy after,Zhao Mengfu|Shi Daoshi,"Chinese, 1254–1322|Chinese, active 4th century",,Zhao Mengfu|Shi Daoshi,Chinese|Chinese,1254 |0300,1322 |0399,,1368,1911,Handscroll; ink and color on silk,11 5/8 x 104 1/4 in. (29.5 x 264.8 cm),"From the Collection of A. W. Bahr, Purchase, Fletcher Fund, 1947",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51709,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -45.170.2,false,true,40409,Asian Art,Handscroll,,China,Ming (1368–1644) or Qing dynasty (1644–1911),,,,Artist|Artist,In the Style of,Unidentified Artist|Wang Wei,"Chinese, 699–759",,Unidentified Artist|Wang Wei,Chinese,0699,0759,,1368,1911,Handscroll; ink and color on silk,10 1/2 x 80 3/16 in. (26.7 x 203.7 cm),"Gift of Robert Lehman, 1945",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/40409,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -47.18.73,false,true,51688,Asian Art,Fan mounted as an album leaf,,China,Ming (1368–1644) or Qing dynasty (1644–1911),,,,Artist|Artist,After,Li Cheng|Unidentified Artist,"Chinese, 919–967",,LI CHENG|Unidentified Artist,Chinese,0919,0967,,1368,1911,Fan mounted as an album leaf; ink and color on silk,8 7/8 x 9 3/4 in. (22.5 x 24.8 cm),"From the Collection of A. W. Bahr, Purchase, Fletcher Fund, 1947",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51688,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.100.118,false,true,51400,Asian Art,Fan mounted as an album leaf,,China,Ming (1368–1644) or Qing dynasty (1644–1911),,,,Artist|Artist,Formerly Attributed to,Yan Wengui|Unidentified Artist,"Chinese, 970–1030",,YAN WENGUI|Unidentified Artist,Chinese,0970,1030,,1368,1911,Fan mounted as an album leaf; ink and color on silk,9 1/4 x 9 3/8 in. (23.5 x 23.8 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51400,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -47.18.11,false,true,50663,Asian Art,Handscroll,,China,Ming (1368–1644) or Qing dynasty (1644–1911),,,,Artist|Artist,In the style of,Unidentified Artist|Su Shi,"Chinese, 1037–1101",,Unidentified Artist|Su Shi,Chinese,1037,1101,,1368,1911,Handscroll; ink on paper,Image: 11 1/16 in. × 9 ft. 1 7/8 in. (28.1 × 279.1 cm),"From the Collection of A. W. Bahr, Purchase, Fletcher Fund, 1947",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/50663,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.100.100,false,true,51387,Asian Art,Fan mounted as an album leaf,,China,Ming (1368–1644) or Qing dynasty (1644–1911),,,,Artist|Artist,Formerly Attributed to,Su Shi|Unidentified Artist,"Chinese, 1037–1101",,Su Shi|Unidentified Artist,Chinese,1037,1101,,1368,1911,Fan mounted as an album leaf; ink on silk,8 x 8 3/8 in. (20.3 x 21.3 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51387,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.220.4,false,true,51418,Asian Art,Handscroll,,China,Ming (1368–1644) or Qing dynasty (1644–1911),,,,Artist|Artist,In the Style of,Zhao Mengfu|Unidentified Artist,"Chinese, 1254–1322",,Zhao Mengfu|Unidentified Artist,Chinese,1254,1322,,1368,1911,Handscroll; ink and color on silk,12 x 40 1/2 in. (30.5 x 102.9 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51418,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -47.18.115,false,true,51723,Asian Art,Handscroll,,China,Ming (1368–1644) or Qing dynasty (1644–1911),,,,Artist|Artist,Copy after,Unidentified Artist|Zhao Mengfu,"Chinese, 1254–1322",,Unidentified Artist|Zhao Mengfu,Chinese,1254,1322,,1368,1644,Handscroll; ink and color on silk,9 x 35 1/4 in. (22.9 x 89.5 cm),"From the Collection of A. W. Bahr, Purchase, Fletcher Fund, 1947",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51723,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.220.26,false,true,51479,Asian Art,Hanging scroll,,China,Ming (1368–1644) or Qing dynasty (1644–1911),,,,Artist|Artist,Formerly Attributed to,Unidentified Artist|Wang Fu,"Chinese, 1362–1416",,Unidentified Artist|Wang Fu,Chinese,1362,1416,,1368,1911,Hanging scroll; ink on paper,Image: 43 7/8 x 14 5/8 in. (111.4 x 37.1 cm) Overall with mounting: 78 1/2 x 20 7/8 in. (199.4 x 53 cm) Overall with knobs: 78 1/2 x 25 3/4 in. (199.4 x 65.4 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51479,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.100.23,false,true,51291,Asian Art,Hanging scroll,,China,Ming (1368–1644) or Qing dynasty (1644–1911),,,,Artist|Artist,After,Wu Wei|Unidentified Artist,"Chinese, 1459–1508",,Wu Wei|Unidentified Artist,Chinese,1459,1508,,1368,1911,Hanging scroll; ink and color on paper,45 x 17 3/4 in. (114.3 x 45.1 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51291,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -47.18.68,false,true,51683,Asian Art,Album leaf,,China,Ming (1368–1644) or Qing dynasty (1644–1911),,,,Artist|Artist,Copy after,Unidentified Artist|Cui Que,"Chinese, 11th century",,Unidentified Artist|Cui Que,Chinese,1000,1099,,1368,1911,Album leaf; ink and color on silk,9 3/4 x 8 3/4 in. (24.8 x 22.2 cm),"From the Collection of A. W. Bahr, Purchase, Fletcher Fund, 1947",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51683,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -47.136.2,false,true,51751,Asian Art,Handscroll,,China,Ming (1368–1644) or Qing dynasty (1644–1911),,,,Artist|Artist,In the style of,Unidentified Artist|Li Gonglin,"Chinese, ca. 1041–1106",,Unidentified Artist|Li Gonglin,Chinese,1041,1106,,1368,1911,Handscroll; gold on black paper,10 1/2 x 151 1/4 in. (26.7 x 384.2 cm),"Anonymous Gift, 1947",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51751,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -54.193,false,true,51808,Asian Art,Hanging scroll,,China,Ming (1368–1644) or Qing dynasty (1644–1911),,,,Artist|Artist,In the style of,Unidentified Artist|Gao Kegong,"Chinese (Hui), 1248–1310",,Unidentified Artist|Gao Kegong,Chinese (Hui),1248,1310,,1368,1911,Hanging scroll; ink on silk,47 x 109 1/4 in. (119.4 x 277.5 cm),"Gift of Edgar Worch, in memory of his uncle, Adolphe Worch, of Paris, 1954–1955",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51808,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -47.18.12,false,true,51643,Asian Art,Handscroll,壺天聚樂圖|Merry Gatherings in the Magic Jar,China,Ming (1368–1644) or Qing dynasty (1644–1911),,,,Artist|Artist,In the Style of,Gong Kai|Unidentified Artist,"Chinese, 1222–after 1304",,Gong Kai|Unidentified Artist,Chinese,1222,1310,,1368,1911,Handscroll; ink on paper,Image: 11 1/2 in. × 12 ft. 3 3/8 in. (29.2 × 374.4 cm),"From the Collection of A. W. Bahr, Purchase, Fletcher Fund, 1947",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51643,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -47.18.56,false,true,51674,Asian Art,Hanging scroll,,China,Ming (1368–1644) or Qing dynasty (1644–1911),,,,Artist|Artist,Formerly Attributed to,Unidentified Artist|Dong Yuan,"Chinese, active 930s–960s",,Unidentified Artist|DONG YUAN,Chinese,0930,0960,,1368,1911,Hanging scroll; ink on silk,63 1/8 x 19 15/16 in. (160.3 x 50.6 cm),"From the Collection of A. W. Bahr, Purchase, Fletcher Fund, 1947",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51674,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -47.18.51,false,true,51670,Asian Art,Handscroll,,China,Ming (1368–1644) or Qing dynasty (1644–1911),,,,Artist|Artist,Copy after,Unidentified Artist|Lin Jun,"Chinese, active ca. 1174–90",,Unidentified Artist|Lin Jun,Chinese,1164,1200,,1368,1911,Handscroll; ink and color on silk,11 3/4 x 83 5/8 in. (29.8 x 212.4 cm),"From the Collection of A. W. Bahr, Purchase, Fletcher Fund, 1947",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51670,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -47.18.14,false,true,51645,Asian Art,Handscroll,,China,Ming (1368–1644) or Qing dynasty (1644–1911),,,,Artist|Artist,Formerly Attributed to,Unidentified Artist|Fang Congyi,"Chinese, ca. 1301–after 1378",,Unidentified Artist|Fang Congyi,Chinese,1301,1399,,1368,1911,Handscroll; ink and color on silk,Overall: 10 1/2 x 106 in. (26.7 x 269.2 cm) Overall with mounting: 11 1/8 in. (28.3 cm),"From the Collection of A. W. Bahr, Purchase, Fletcher Fund, 1947",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51645,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -47.18.35,false,true,51658,Asian Art,Fan mounted as an album leaf,,China,Ming (1368–1644) or Qing dynasty (1644–1911),,,,Artist|Artist,In the style of,Unidentified Artist|Ma Yuan,"Chinese, active ca. 1190–1225",,Unidentified Artist|MA YUAN,Chinese,1190,1225,,1368,1911,Fan mounted as an album leaf; ink and white pigment on silk,9 5/8 x 10 in. (24.4 x 25.4 cm),"From the Collection of A. W. Bahr, Purchase, Fletcher Fund, 1947",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51658,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -47.18.138,false,true,51737,Asian Art,Fan mounted as an album leaf,,China,Ming (1368–1644) or Qing dynasty (1644–1911),,,,Artist|Artist,Copy after (?),Unidentified Artist|Xia Gui,"Chinese, active ca. 1195–1230",,Unidentified Artist|Xia Gui,Chinese,1195,1230,,1368,1911,Fan mounted as an album leaf; ink on silk,8 7/16 x 9 7/8 in. (21.4 x 25.1 cm),"From the Collection of A. W. Bahr, Purchase, Fletcher Fund, 1947",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51737,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -47.18.96,false,true,51708,Asian Art,Handscroll,,China,Ming (1368–1644) or Qing dynasty (1644–1911),,,,Artist|Artist,Copy after,Unidentified Artist|Qian Xuan,"Chinese, ca. 1235–before 1307",,Unidentified Artist|QIAN XUAN,Chinese,1235,1307,,1368,1911,Handscroll; ink and color on silk,13 3/8 x 22 1/2 in. (34.0 x 57.2 cm),"From the Collection of A. W. Bahr, Purchase, Fletcher Fund, 1947",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51708,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -47.18.82,false,true,51695,Asian Art,Fan mounted as an album leaf,,China,Ming (1368–1644) or Qing dynasty (1644–1911),,,,Artist|Artist,After,Unidentified Artist|Emperor Huizong,"Chinese, 1082–1135; r. 1100–25",,Unidentified Artist|Huizong Emperor,Chinese,1082,1082,,1368,1911,Fan mounted as an album leaf; ink and color on silk,8 5/16 x 9 1/2 in. (21.1 x 24.1 cm),"From the Collection of A. W. Bahr, Purchase, Fletcher Fund, 1947",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51695,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -47.18.144,false,true,51743,Asian Art,Fan mounted as an album leaf,,China,Ming (1368–1644) or Qing dynasty (1644–1911),,,,Artist|Artist,Copy after (?),Unidentified Artist|Liang Kai,"Chinese, active early 13th century",,Unidentified Artist|Liang Kai,Chinese,1200,1225,,1368,1911,Album leaf; ink and color on silk,9 1/4 x 9 1/3 in. (23.5 x 23.7 cm),"From the Collection of A. W. Bahr, Purchase, Fletcher Fund, 1947",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51743,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -47.135.1,false,true,51749,Asian Art,Handscroll,,China,Yuan (1271–1368) or Ming (1368–1644) dynasty,,,,Artist|Artist,In the style of,Unidentified Artist|Zhao Yong,"Chinese, 1289–after 1360",Chinese,Unidentified Artist|Zhao Yong,Chinese,1289,1360,,1500,1599,Handscroll; ink and color on silk,10 1/2 x 68 in. (26.7 x 172.7 cm),"Gift of A. W. Bahr, 1947",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51749,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.100.99,false,true,40195,Asian Art,Fan mounted as an album leaf,明 佚名 （舊傳）吳炳 枇杷 扇|Bird on a Loquat Tree,China,probably mid- to late Ming dynasty (1368–1644),,,,Artist|Artist,Formerly Attributed to,Unidentified Artist|Wu Bing,"Chinese, active 1190–1194",,Unidentified Artist|Wu Bing,Chinese,1190,1194,,1368,1644,Fan mounted as an album leaf; ink and color on silk,10 1/2 x 10 3/4 in. (26.7 x 27.3 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/40195,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.522,false,true,51570,Asian Art,Hanging scroll,,China,Song (960–1279) or Yuan (1271–1368) dynasty (?),,,,Artist|Artist,Formerly Attributed to,Unidentified Artist|Wu Daozi,"Chinese, 689–after 755",,Unidentified Artist|Wu Daozi,Chinese,0689,0760,,960,1368,"Hanging scroll; ink, color, and gold on silk",39 1/2 x 19 5/8 in. (100.3 x 49.8 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51570,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1982.3.1,false,true,39938,Asian Art,Handscroll,北宋/金 傳黃宗道 舊傳李贊華 獵鹿圖 卷|Stag Hunt,China,Northern Song (960–1127) or Jin (1115–1234) dynasty,,,,Artist|Artist,Attributed to|Formerly Attributed to,Huang Zongdao|Li Zanhua,"Chinese, active ca. 1120|Chinese, 899–936",,HUANG ZONGDAO|LI ZANHUA,Chinese,1120 |0899,1120 |0936,,960,1234,Handscroll; ink and color on paper,Image: 9 11/16 × 31 1/16 in. (24.6 × 78.9 cm) Overall with mounting: 10 1/8 in. × 22 ft. 7 1/4 in. (25.7 × 689 cm),"Edward Elliott Family Collection, Purchase, The Dillon Fund Gift, 1982",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/39938,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -47.18.43a,false,true,36053,Asian Art,Fan mounted as an album leaf,,China,late Yuan (1271–1368)–early Ming (1368–1644) dynasty,,,,Artist|Artist,In the style of,Unidentified Artist|Wang Shen,"Chinese, active 1060–1080",,Unidentified Artist|Wang Shen,Chinese,1060,1080,,1271,1644,Fan mounted as an album leaf; ink and color on silk,9 3/4 x 10 in. (24.8 x 25.4 cm),"From the Collection of A. W. Bahr, Purchase, Fletcher Fund, 1947",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36053,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -47.18.119,false,true,51726,Asian Art,Handscroll,,China,Five Dynasties (907–960)–early Song (906–1279) dynasty,,,,Artist|Artist,Formerly attributed to,Unidentified Artist|Xiao Zhao,"Chinese, active ca. 1150","Chinese, 10th century (?)",Unidentified Artist|Xiao Zhao,Chinese,1140,1160,,906,1279,Handscroll; ink and color on silk,12 3/4 x 51 3/4 in. (32.4 x 131.4 cm),"From the Collection of A. W. Bahr, Purchase, Fletcher Fund, 1947",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51726,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.220.1,false,true,36011,Asian Art,Handscroll,明 佚名 (舊傳)夏珪 《長江萬里圖》 (前半卷)|River Landscape After Xia Gui,China,Ming dynasty (1368–1644),,,,Artist|Artist,Formerly Attributed to,Xia Gui|Unidentified Artist,"Chinese, active ca. 1195–1230",,Xia Gui|Unidentified Artist,Chinese,1195,1230,15th century,1400,1499,Handscroll; ink and color on silk,Image: 23 3/8 in. × 17 ft. 1 3/8 in. (59.4 × 521.7 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36011,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.220.18,false,true,45674,Asian Art,Handscroll,明? 佚名 (舊傳)夏珪 長江萬里圖 (後半卷)|River Landscape after Xia Gui,China,Ming dynasty (1368–1644),,,,Artist|Artist,Formerly Attributed to,Xia Gui|Unidentified Artist,"Chinese, active ca. 1195–1230",,Xia Gui|Unidentified Artist,Chinese,1195,1230,15th century,1400,1499,Handscroll; ink and color on silk,Image: 23 3/8 in. × 16 ft. (59.4 × 487.7 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45674,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -47.18.3,false,true,36049,Asian Art,Handscroll,清 佚名 倣郭熙 溪山無盡圖 卷|Streams and Mountains Without End,China,Qing dynasty (1644–1911),,,,Artist|Artist,Formerly Attributed to,Unidentified Artist|Guo Xi,"Chinese, ca. 1000–ca. 1090",,Unidentified Artist|Guo Xi,Chinese,1000,1090,17th century,1600,1699,Handscroll; ink and color on silk,11 3/16 x 76 3/8 in. (28.4 x 194 cm),"From the Collection of A. W. Bahr, Purchase, Fletcher Fund, 1947",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36049,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.267.3,false,true,36146,Asian Art,Hanging scroll,清 汪恭 摹趙孟頫肖像 軸|Copy of a Portrait of Zhao Mengfu,China,Qing dynasty (1644–1911),,,,Artist|Artist,Formerly attributed to,Wang Gong|Unidentified Artist,"Chinese, active early 19th century",,Wang Gong|Unidentified Artist,Chinese,1800,1899,19th century,1800,1899,Hanging scroll; ink and color on silk,25 1/8 x 12 1/8 in. (63.8 x 30.8 cm),"Gift of Robert Hatfield Ellsworth, in memory of La Ferne Hatfield Ellsworth, 1986",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36146,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.100.28,false,true,51305,Asian Art,Handscroll,,China,Qing dynasty (1644–1911),,,,Artist|Artist,After,Dong Yuan|Unidentified Artist,"Chinese, active 930s–960s",,DONG YUAN|Unidentified Artist,Chinese,0930,0960,18th–19th century,1700,1899,Handscroll; ink on silk,15 3/8 in. × 23 ft. 6 1/2 in. (39.1 × 717.6 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51305,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2001.423,false,true,61941,Asian Art,Folding screen,伝近衛信尋書・伝長谷川宗也絵 葛下絵色紙貼付『和漢朗詠集』屏風|Anthology of Japanese and Chinese Poems (Wakan rōeishū) with Underpainting of Arrowroot Vines,Japan,Edo period (1615–1868),,,,Calligrapher|Artist,Underpainting attributed to,Konoe Nobuhiro|Hasegawa Sōya,"Japanese, 1599–1649|Japanese, born 1590",,Konoe Nobuhiro|Hasegawa Sōya,Japanese|Japanese,1599 |1590,1649 |1690,early 17th century,1600,1633,Six-panel folding screen; ink and color on gilt paper,Image: 65 3/4 x 148 in. (167 x 375.9 cm),"Purchase, several members of The Chairman's Council Gifts, 2001",,,,,,,,,,,,Screens,,http://www.metmuseum.org/art/collection/search/61941,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"1975.268.72, .73",false,true,45234,Asian Art,Screen,"花鳥山水人物図屏風|Calligraphy with Landscapes, Figures, Flowers, and Birds",Japan,Edo period (1615–1868),,,,Artist|Calligrapher,,Nagasawa Rosetsu|Shishin Sōgin,"Japanese, 1754–1799|Japanese, 1726–1786",,Nagasawa Rosetsu|Shishin Sōgin,Japanese|Japanese,1754 |1726,1799 |1786,"second month, 1785",1785,1785,Sheets with calligraphy and painting attached to a pair of six-panel folding screens; ink on paper,Image (each screen): 62 3/8 in. x 11 ft. 7 5/8 in. (158.4 x 354.6 cm),"The Harry G. C. Packard Collection of Asian Art, Gift of Harry G. C. Packard, and Purchase, Fletcher, Rogers, Harris Brisbane Dick, and Louis V. Bell Funds, Joseph Pulitzer Bequest, and The Annenberg Fund Inc. Gift, 1975",,,,,,,,,,,,Screens,,http://www.metmuseum.org/art/collection/search/45234,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.827,false,true,78717,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist|Calligrapher,,Yamaguchi Soken|Minagawa Kien,"Japanese, 1759–1818|Japanese, 1734–1807",,Yamaguchi Soken|Minagawa Kien,Japanese|Japanese,1759 |1734,1818 |1807,1804,1804,1804,Woodblock printed book; ink on paper,10 7/16 × 7 5/16 in. (26.5 × 18.5 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78717,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.826a–c,false,true,78716,Asian Art,Illustrated books,,Japan,Edo period (1615–1868),,,,Artist|Calligrapher,,Yamaguchi Soken|Minagawa Kien,"Japanese, 1759–1818|Japanese, 1734–1807",,Yamaguchi Soken|Minagawa Kien,Japanese|Japanese,1759 |1734,1818 |1807,1800,1800,1800,Set of three woodblock printed books; ink on paper,each: 10 7/16 × 7 3/16 in. (26.5 × 18.3 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78716,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.891,false,true,78781,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist|Calligrapher,,Nagayama Koin (Hirotora)|Shokusanjin (Ōta Nanpo),"Japanese, 1765–1849|Japanese, 1749–1823",", and author",Nagayama Koin (Hirotora),Japanese|Japanese,1765 |1749,1849 |1823,1824,1824,1824,Woodblock printed book; ink and color on paper,10 1/16 × 7 3/16 in. (25.5 × 18.2 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78781,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.787a–c,false,true,78689,Asian Art,Illustrated books,,Japan,Edo period (1615–1868),,,,Calligrapher|Calligrapher,After|After,Hasegawa Myōtei|Sankinshi,"Japanese, active late 17th century|Japanese",,Hasegawa Myōtei|Sankinshi,Japanese|Japanese,1667,1699,1838,1838,1838,Set of three woodblock printed books; ink on paper,each: 10 7/16 × 7 3/8 in. (26.5 × 18.7 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78689,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.794,false,true,78810,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist|Artist|Artist,,Rankō|Watanabe Nangaku|Tetsuzan Sōdon,"Japanese|Japanese, 1763–1813|Japanese, 1532–1617",,Rankō|Watanabe Nangaku|Tetsuzan Sōdon,Japanese|Japanese|Japanese,1763 |1532,1813 |1617,1806,1806,1806,"Woodblock printed book (orihon, accordion-style); ink and color on paper with metallic pigments (?)",9 13/16 × 7 1/16 in. (25 × 18 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78810,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.743,false,true,78645,Asian Art,Illustrated book,『花月帖』|Album of Flowers and the Moon (Kagetsu jō),Japan,Edo period (1615–1868),,,,Artist|Artist|Artist,,Mori Tetsuzan|Maruyama Ōshin|Kawamura Kihō,"Japanese, 1775–1841|Japanese, 1790–1838|Japanese, 1778–1852",,Mori Tetsuzan|Maruyama Ōshin|Kawamura Kihō,Japanese|Japanese|Japanese,1775 |1790 |1778,1841 |1838 |1852,1836,1836,1836,"Woodblock-printed book (orihon, accordion-style); ink and color on paper",9 1/16 × 6 5/16 in. (23 × 16 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78645,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.876,false,true,78766,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist|Artist|Artist|Artist|Artist,,Genki (Komai Ki)|Matsumura Goshun|Watanabe Nangaku|Miguma Shiko|Fusetsu Yujo,"Japanese, 1747–1797|Japanese, 1752–1811|Japanese, 1763–1813|Japanese|Japanese",,Genki|Matsumura Goshun|Watanabe Nangaku|Miguma Shiko|Fusetsu Yujo,Japanese|Japanese|Japanese|Japanese|Japanese,1747 |1752 |1763,1797 |1811 |1813,1793,1793,1793,"Woodblock printed book (orihon, accordion-style); ink and color on paper",11 × 8 3/8 in. (28 × 21.2 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78766,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2013.781a, b",false,true,78683,Asian Art,Illustrated books,,Japan,Edo period (1615–1868),,,,Artist|Artist|Artist|Artist|Artist,,Yosa Buson|Sō Shiseki|Nankei|Rantei|Ōishi Matora,"Japanese, 1716–1783|Japanese, 1715–1786|Japanese|Japanese,|Japanese, 1793–1833",et al,Yosa Buson|Sō Shiseki|Nankei|Rantei|Ōishi Matora,Japanese|Japanese|Japanese|Japanese|Japanese,1716 |1715 |1793,1783 |1786 |1833,"1812, 1814",1812,1814,Set of two woodblock printed books; ink and color on paper,each: 11 × 7 1/2 in. (28 × 19 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78683,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.815,false,true,78705,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist|Artist|Artist|Artist|Artist,,Tsukioka Settei|Ōnishi Chinnen|Teisai Hokuba|Ikeda Koson|Nankō,"Japanese, 1710–1786|1792–1851|Japanese, 1771–1844|Japanese, 1803–1868|Japanese",,Tsukioka Settei|Ōnishi Chinnen|Teisai Hokuba|Ikeda Koson|Nankō,Japanese|Japanese|Japanese|Japanese,1710 |1792 |1771 |1803,1786 |1851 |1844 |1868,1830,1830,1830,"Woodblock printed book (orihon, accordion-style); ink and color on paper",10 1/16 × 6 11/16 in. (25.5 × 17 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78705,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2013.843a, b",false,true,78733,Asian Art,Illustrated books,,Japan,Edo period (1615–1868),,,,Artist|Artist|Artist|Artist,,Nukina Kaioku|Shiokawa Bunrin|Kawabata Gyokushō|Musō Soseki,"Japanese, 1778–1863|Japanese, 1808–1877|Japanese, 1842–1913|Japanese, 1275–1351",", et al",Nukina Kaioku|Shiokawa Bunrin|Kawabata Gyokushō|Musō Soseki,Japanese|Japanese|Japanese|Japanese,1778 |1808 |1842 |1275,1863 |1877 |1913 |1351,postscript dated 1861,1861,1861,Set of two woodblock printed books; ink and color on paper,each: 10 3/16 × 6 11/16 in. (25.8 × 17 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78733,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.873,false,true,78763,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist|Artist|Artist|Artist|Artist|Artist|Artist|Artist|Artist|Artist|Artist|Artist|Artist|Artist|Artist|Artist|Artist|Artist|Artist|Artist|Artist,,Matsumura Goshun|Matsumura Keibun|Suzuki Harushige|Aikawa Minwa|Nagasawa Roshu|Yamaguchi Soken|Okamoto Toyohiko|Oku Bunmei|Ki Chikudō|Gan Tai|Sō Geppō|Shibata Gitō|Fukuchi Hakuei|Kawamura Kihō|Shiba Kōkan|Yoshimura Kōkei|Hatta Koshū|Watanabe Nangaku|Kinoshita Ōju|Maruyama Ōzui|Hara Zaimei,"Japanese, 1752–1811|Japanese, 1779–1843|1747–1818|Japanese, active 1806–1821|Japanese, 1767–1847|Japanese, 1759–1818|1773–1845|Japanese|Japanese, died 1825|Japanese, 1760–1839|Japanese|Japanese, active early 19th century|Japanese, 1778–1852|Japanese, 1747–1818|Japanese|Japanese|Japanese, 1763–1813|Japanese|Japanese|Japanese",", et al",Matsumura Goshun|Matsumura Keibun|Suzuki Harushige|Aikawa Minwa|Nagasawa Roshu|Yamaguchi Soken|Okamoto Toyohiko|Oku Bunmei|Ki Chikudō|Gan Tai|Sō Geppō|Shibata Gitō|Fukuchi Hakuei|Kawamura Kihō|Shiba Kōkan|Yoshimura Kōkei|Hatta Koshū|Watanabe Nangaku|Kinoshita Ōju|Maruyama Ōzui|Hara Zaimei,Japanese|Japanese|Japanese|Japanese|Japanese|Japanese|Japanese|Japanese|Japanese|Japanese|Japanese|Japanese|Japanese|Japanese|Japanese|Japanese|Japanese|Japanese,1752 |1779 |1747 |1806 |1767 |1759 |1773 |1760 |1800 |1778 |1747 |1763,1811 |1843 |1818 |1821 |1847 |1818 |1845 |1825 |1839 |1833 |1852 |1818 |1813,1814,1814,1814,Woodblock printed book; ink and color on paper,10 3/8 × 7 1/2 in. (26.3 × 19 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78763,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.793,false,true,78809,Asian Art,Illustrated book,『男踏歌』|Men’s Stomping Dances (Otoko dōka),Japan,Edo period (1615–1868),,,,Artist|Artist|Artist|Artist,,Kitagawa Utamaro|Rekisentei Eiri|Katsushika Hokusai|Chōbunsai Eishi,"Japanese, 1753?–1806|Japanese, active ca. 1789–1801|Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)|Japanese, 1756–1829",,Kitagawa Utamaro|Rekisentei Eiri|Katsushika Hokusai|Chōbunsai Eishi,Japanese|Japanese|Japanese|Japanese,1753 |1789 |1760 |1756,1806 |1801 |1849 |1829,1798,1798,1798,Woodblock printed book; ink and color on paper,10 1/16 × 7 1/2 in. (25.5 × 19 cm),"Purchase, Mary and James G. Wallach Family Foundation Gift, in honor of John T. Carpenter, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78809,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.870,false,true,78760,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist|Artist|Artist|Artist|Artist,,Kuwagata Keisai|Sakai Hōitsu|Tani Bunchō|Katsushika Hokusai|Ōnishi Chinnen,"Japanese, 1764–1824|Japanese, 1761–1828|Japanese, 1763–1840|Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)|1792–1851",", et al",Kuwagata Keisai|Sakai Hōitsu|Tani Bunchō|Katsushika Hokusai|Ōnishi Chinnen,Japanese|Japanese|Japanese|Japanese|Japanese,1764 |1761 |1763 |1760 |1792,1824 |1828 |1840 |1849 |1851,ca. 1822–34,1822,1834,Woodblock printed book; ink on paper,7 3/8 × 5 1/8 in. (18.7 × 13 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78760,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.744,false,true,78646,Asian Art,Illustrated book,,Japan,Meiji period (1868–1912),,,,Artist|Artist|Artist,,Mori Tetsuzan|Maruyama Ōshin|Kawamura Kihō,"Japanese, 1775–1841|Japanese, 1790–1838|Japanese, 1778–1852",and others,Mori Tetsuzan|Maruyama Ōshin|Kawamura Kihō,Japanese|Japanese|Japanese,1775 |1790 |1778,1841 |1838 |1852,late 19th century reprint of original edition of 1836,1875,1899,"Woodblock printed book (orihon, accordion-style); ink and color on paper",10 1/8 × 6 1/2 in. (25.7 × 16.5 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78646,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2832,false,true,53719,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Calligrapher|Artist|Artist,or,Yokogawa Horitake|Utagawa Kunisada|Utagawa Toyokuni II,"Japanese, 1786–1865|Japanese, 1777–1835",,Yokogawa Horitake|Utagawa Kunisada|Utagawa Toyokuni II,Japanese|Japanese,1786 |1777,1865 |1835,1786–1864,1786,1864,Polychrome woodblock print; ink and color on paper,14 1/4 x 9 5/8 in. (36.2 x 24.4 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/53719,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1856,false,true,45029,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Calligrapher|Artist,after,Senseki|Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Senseki|Katsushika Hokusai,Japanese|Japanese,1760,1849,ca. 1816–20,1816,1820,Polychrome woodblock print; ink and color on paper,H. 14 3/16 in. (36 cm); W. 9 5/8 in. (24.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45029,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3116,false,true,37370,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist|Calligrapher,,Tōshūsai Sharaku|Ueda Shikibuchi,"Japanese, active 1794–95|Japanese, 1819–1879",,Tōshūsai Sharaku|Ueda Shikibuchi,Japanese|Japanese,1794 |1819,1795 |1879,1794,1794,1794,"Polychrome woodblock print; ink, color, white mica on paper",UMPN BV,"Harris Brisbane Dick Fund and Rogers Fund, 1949",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37370,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1855,false,true,45028,Asian Art,Print,古今書画鑑　熊谷蓮生坊真跡|Bird-and-Flower Paintings,Japan,Edo period (1615–1868),,,,Artist|Calligrapher,after,Katsushika Hokusai|Kumagai Naozane,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)|Japanese, 1141–1208",,Katsushika Hokusai|Kumagai Naozane,Japanese|Japanese,1760 |1141,1849 |1208,ca. 1816–20,1816,1820,Polychrome woodblock print; ink and color on paper,H. 14 7/16 in. (36.7 cm); W. 10 1/16 in. (25.6 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45028,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP206,false,true,36683,Asian Art,Print,小倉擬百人一首|Album of Eighty-eight Prints from the series Ogura Imitations of One Hundred Poems by One Hundred Poets (Ogura nazorae hyakunin isshu),Japan,Edo period (1615–1868),,,,Artist|Artist|Artist,,Utagawa Kuniyoshi|Utagawa Hiroshige|Utagawa Kunisada,"Japanese, 1797–1861|Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)|Japanese, 1786–1865",,"Utagawa Kuniyoshi|Utagawa, Hiroshige|Utagawa Kunisada",Japanese|Japanese|Japanese,1797 |1797 |1786,1861 |1858 |1865,about 1845–48,1845,1848,Album of 88 polychrome woodblock prints; ink and color on paper,14 × 9 1/4 × 1 in. (35.6 × 23.5 × 2.5 cm),"Gift of Mary L. Cassilly, 1894",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36683,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2011.129a–e,false,true,76559,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist|Artist|Artist|Artist|Artist,,Jukakudō Masakuni|Jugyōdō Umekuni|Juyōdō Minekuni|Jushōdō Fujikuni|Hōgadō Kishikuni,"Japanese, active 1820s|Japanese, active 1820s|Japanese, active 1820s|Japanese, active 1820s|Japanese, active 1820s",,Jukakudō Masakuni|Jugyōdō Umekuni|Juyōdō Minekuni|Jushōdō Fujikuni|Hōgadō Kishikuni,Japanese|Japanese|Japanese|Japanese|Japanese,1820 |1820 |1820 |1820 |1820,1829 |1829 |1829 |1829 |1829,1824,1824,1824,Pentaptych of polychrome woodblock prints,Each sheet (ôban tat-e pentaptych): 14 3/4 x 10 5/8 in. (37.5 x 27 cm),"Purchase, Friends of Asian Art Gifts, in honor of James C. Y. Watt, 2011",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/76559,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1999.457.2,false,true,53711,Asian Art,Prints,,Japan,Meiji period (1868–1912),,,,Artist|Artist|Artist|Artist|Artist,"prints 13,17,18,20,26,29 by|print 2 by|print 28 by|prints 25,27,31,32 by",Yōshū (Hashimoto) Chikanobu|Toyohara Kunichika|Morikawa Chikashige|Hiroaki|Kunimasa,"Japanese, 1838–1912|Japanese, 1835–1900|Japanese, second half of 19th century|Japanese, 1871–1945|Japanese",,Yōshū (Hashimoto) Chikanobu|Toyohara Kunichika|Morikawa Chikashige|Hiroaki|Kunimasa,Japanese|Japanese|Japanese,1838 |1835 |1850 |1871,1912 |1900 |1899 |1945,1883–86,1883,1886,Album of thirty-two triptychs of polychrome woodblock prints; ink and color on paper,Each H. 14 in. (35.6 cm); W. 9 1/4 in. (23.5 cm),"Gift of Eliot C. Nolen, 1999",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/53711,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.3,false,true,663886,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Calligrapher|Artist,,Mokuan Shōtō|Unidentified Artist,"Chinese, 1611–1684","Japanese, active late 17th century",Mokuan Shōtō|Unidentified Artist,Chinese,1611,1684,1676,1676,1676,Hanging scroll; ink and color on paper,Image: 47 in. × 22 3/4 in. (119.4 × 57.8 cm) Overall with mounting: 76 1/4 × 27 15/16 in. (193.7 × 71 cm) Overall with knobs: 76 1/4 × 30 1/4 in. (193.7 × 76.8 cm),"Purchase, Brooke Russell Astor Bequest, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/663886,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.268.131,false,true,45790,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist|Calligrapher,,Katsukawa Shunshō|Tegara no Okamochi,"Japanese, 1726–1792|1734–1812",,Katsukawa Shunshō|Tegara no Okamochi,Japanese|Japanese,1726 |1734,1792 |1812,1798,1798,1798,Hanging scroll; ink and color on paper,33 3/4 x 11 5/16 in. (85.7 x 28.8 cm),"The Harry G. C. Packard Collection of Asian Art, Gift of Harry G. C. Packard, and Purchase, Fletcher, Rogers, Harris Brisbane Dick, and Louis V. Bell Funds, Joseph Pulitzer Bequest, and The Annenberg Fund Inc. Gift, 1975",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45790,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2006.115,false,true,73192,Asian Art,Hanging scroll,布袋図　拄杖擊破三千界。彌勒撫掌笑呵呵，明月清風無。」|Hotei,Japan,Edo period (1615–1868),,,,Artist|Calligrapher,,Kano Takanobu|Tetsuzan Sōdon,"Japanese, 1571–1618|Japanese, 1532–1617",,Kano Takanobu|Tetsuzan Sōdon,Japanese|Japanese,1571 |1532,1618 |1617,dated 1616,1616,1616,Hanging scroll; ink and color on paper,Image: 27 1/2 x 15 in. (69.9 x 38.1 cm) Overall with mounting: 59 1/2 x 18 3/4 in. (151.1 x 47.6 cm) Overall with rollers: 59 1/2 x 20 1/2 in. (151.1 x 52.1 cm),"Funds from various donors, 2006",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/73192,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1979.407.1,false,true,45335,Asian Art,Hanging scroll,松花堂昭乗書・伝俵屋宗達下絵　立葵下絵和歌色紙　藤原興風|Poem by Onakatomi Yoshinobu with Underpainting of Hollyhocks,Japan,Edo period (1615–1868),,,,Calligrapher|Artist,Calligraphy by|Underpainting attributed to,Shōkadō Shōjō|Tawaraya Sōtatsu,"Japanese, 1584?–1639|Japanese, died ca. 1640",,Shōkadō Shōjō|Tawaraya Sōtatsu,Japanese|Japanese,1584 |1540,1639 |1640,early 17th century,1600,1633,"Poem card (shikishi) mounted as a hanging scroll; ink, gold, and silver on colored paper",Image: 7 15/16 x 6 15/16 in. (20.2 x 17.6 cm) Overall with mounting: 53 1/4 x 20 5/8 in. (135.3 x 52.4 cm) Overall with knobs: 53 1/4 x 23 in. (135.3 x 58.4 cm),"Purchase, Mrs. Jackson Burke Gift, 1979",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45335,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.268.133,false,true,45183,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist|Artist|Artist|Artist,,Tani Bunchō|Watanabe Kazan|Sakai Hōitsu|sixty-six others,"Japanese, 1763–1840|Japanese, 1793–1841|Japanese, 1761–1828|Japanese",,Tani Bunchō|Watanabe Kazan|Sakai Hōitsu|sixty-six others,Japanese|Japanese|Japanese|Japanese,1763 |1793 |1761,1840 |1841 |1828,1820,1820,1820,Hanging scroll; ink and color on paper,Image: 31 3/8 x 23 3/8 in. (79.7 x 59.4 cm) Overall with mounting: 64 5/8 x 25 3/4 in. (164.1 x 65.4 cm) Overall with knobs: 64 5/8 x 28 1/8 in. (164.1 x 71.4 cm),"The Harry G. C. Packard Collection of Asian Art, Gift of Harry G. C. Packard, and Purchase, Fletcher, Rogers, Harris Brisbane Dick, and Louis V. Bell Funds, Joseph Pulitzer Bequest, and The Annenberg Fund Inc. Gift, 1975",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45183,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.268.39,false,true,39659,Asian Art,Hanging scroll,『東坡笠屐図』|Su Shi (Dongpo) in a Bamboo Hat and Clogs,Japan,Muromachi period (1392–1573),,,,Artist|Artist|Artist|Artist|Artist,Inscribed by|Inscribed by|Inscribed by|Inscribed by|Inscribed by,Kyūen Ryūchin|Kōshi Ehō|Nankō Sōgen|Zuigan Ryūsei|Chikkō Zengo,"Japanese, died 1498|Japanese, 1414–ca.1465|Japanese, 1378–1463|Japanese, 1384–1460|Japanese, died after 1464",,Kyūen Ryūchin|Kōshi Ehō|Nankō Sōgen|Zuigan Ryūsei|Chikkō Zengo,Japanese|Japanese|Japanese|Japanese|Japanese,1414 |1378 |1384,1498 |1465 |1463 |1460 |1464,before 1460,1392,1459,Hanging scroll; ink on paper,Image: 42 3/4 x 13 1/8 in. (108.6 x 33.3 cm) Overall with mounting: 74 1/2 x 17 5/8 in. (189.2 x 44.8 cm) Overall with rollers: 74 1/2 x 17 5/8 x 19 3/4 in. (189.2 x 44.8 x 50.2 cm),"The Harry G. C. Packard Collection of Asian Art, Gift of Harry G. C. Packard, and Purchase, Fletcher, Rogers, Harris Brisbane Dick, and Louis V. Bell Funds, Joseph Pulitzer Bequest, and The Annenberg Fund Inc. Gift, 1975",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/39659,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JIB50,false,true,57650,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist|Artist|Artist,3 unknown artists:,Aoigaoka Keisei|Utagawa Kuniyoshi|Keisai Eisen,"Japanese, active 1820s–1830s|Japanese, 1797–1861|Japanese, 1790–1848",", Keishin, and Yanagawa",Aoigaoka Keisei|Utagawa Kuniyoshi|Keisai Eisen,Japanese|Japanese|Japanese,1810 |1797 |1790,1840 |1861 |1848,,1615,1868,Ink on paper,8 5/8 × 6 × 3/4 in. (21.9 × 15.2 × 1.9 cm),"Rogers Fund, 1918",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/57650,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -56.116,false,true,44899,Asian Art,Screen,,Japan,Edo period (1615–1868),,,,Calligrapher|Artist,,Shōkadō Shōjō|Maruyama Ōkyo,"Japanese, 1584?–1639|Japanese, 1733–1795",,Shōkadō Shōjō|Maruyama Ōkyo,Japanese|Japanese,1584 |1733,1639 |1795,17th century,1600,1699,Eight-panel folding screen; ink and gold,43 x 129 in. (109.2 x 327.7 cm),"Gift of Mrs. H. F. Stone and Mrs. Leon Durand Bonnet, 1956",,,,,,,,,,,,Screens,,http://www.metmuseum.org/art/collection/search/44899,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.62a–f,false,true,45701,Asian Art,Screen,狩野探幽・狩野尚信・清原雪信　花鳥図屏風|Birds and Flowers,Japan,Edo period (1615–1868),,,,Artist|Artist|Artist,,Kano Tan'yū|Kiyohara Yukinobu|Kano Naonobu,"Japanese, 1602–1674|Japanese, 1643–1682|Japanese, 1607–1650",,Kano Tan'yū|Kiyohara Yukinobu|Kano Naonobu,Japanese|Japanese|Japanese,1602 |1643 |1607,1674 |1682 |1650,17th century,1600,1699,Six-panel folding screen; ink and color on silk,70 in. x 12 ft. 11 1/2 in. (177.8 x 395 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Screens,,http://www.metmuseum.org/art/collection/search/45701,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.213.1–.30,false,true,74463,Asian Art,Album,,Japan,Edo period (1615–1868),,,,Artist|Artist|Artist|Artist|Artist|Artist|Artist|Artist|Artist|Artist|Artist|Artist|Artist|Artist|Artist|Artist|Artist|Artist|Artist|Artist|Artist|Artist|Artist|Artist|Artist|Artist|Artist|Artist,.16: After|.2: After|.3: After|.4: After|.6: After|.7: After|.8: After|.9: After|.10: After|.14: After|.15: After|.17: After|.18: After|.19: After|.20: After|.21: After|.22: After|.23: After|.24: After|.25: After|.26: After|.27: After|.28: After|.29: After|.30: After|.1. After|.13 After,Yan Ciping|Kano Tsunenobu|Yintuoluo|Wang Lipen|Po Citing|Puming|Sheng Mou|Danzhirui|Jin Dashou|Wang Yuan|Muqi|Guo Xi|Zhang Yüehu|Luochuang|Ren Renfa|Luo Xinzhong|Wen Tong|Xia Yong|Fan Anren|Cinshan|Kongshan|Su Xianzu|Li Anzhong|Xuejian|Wang Moji|Daisong|Zhao Mengfu|Chen Rong,"Chibese, active 12th century|Japanese, 1636–1713|Chinese, active 13th century|Chinese, born late Yuan, active early Ming dynasty|Chinese, died 1337(?); active first half of the 14th century|Chinese, active 14th century|Chinese, active ca. 1310–1360|active early 14th century|active 13th century|Chinese, ca. 1280–after 1349|Chinese, ca. 1210–after 1269|Chinese, ca. 1000–ca. 1090|active 13th century|active 13th century|Chinese, 1255–1328|active 13th century|Chinese, 1019–1079|Chinese, active mid-14th century|active 13th century|active 12th century|active 14th century|699–759|Chinese, 727–779|Chinese, 1254–1322|active 1235–62",,Yan Ciping|Kano Tsunenobu|Yintuoluo|Wang Lipen|Po Citing|Puming|Sheng Mou|Danzhirui|Jin Dashou|Wang Yuan|Muqi|Guo Xi|Zhang Yüehu|Luochuang|Ren Renfa|Luo Xinzhong|Wen Tong|XIA YONG|Fan Anren|Cinshan|Kongshan|Su Xianzu|Li Anzhong|Xuejian|Wang Moji|Daisong|Zhao Mengfu|Chen Rong,Chinese|Japanese|Chinese|Chinese|Chinese|Chinese|Chinese|Chinese|Chinese|Chinese|Chinese|Chinese|Chinese|Chinese|Chinese|Chinese|Chinese|Chinese|Chinese|Chinese|Chinese|Chinese|Chinese|Chinese|Chinese,1100 |1636 |0013 |1237 |1300 |1310 |0014 |0013 |1270 |1210 |1000 |0013 |0013 |1255 |0013 |1019 |1336 |0013 |0012 |0014 |0699 |0727 |1254 |1235,1199 |1713 |0013 |1337 |1399 |1360 |0014 |0013 |1359 |1269 |1090 |0013 |0013 |1328 |0013 |1079 |1370 |0013 |0012 |0014 |0759 |0779 |1322 |1262,17th century,1636,1699,Album of thirty paintings; ink and color on silk,11 7/16 x 16 3/4 in. (29 x 42.5 cm),"Gift of Mr. and Mrs. Harry Rubin, 1972",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/74463,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1985.97,false,true,40462,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Calligrapher|Artist,Inscribed by,Tangai Jōgi|Itō Jakuchū,"Japanese, 1693–1764|Japanese, 1716–1800",,Tangai Jōgi|Itō Jakuchū,Japanese|Japanese,1693 |1716,1764 |1800,18th century,1716,1764,Hanging scroll; ink on paper,Image: 49 1/2 x 18 7/8 in. (125.7 x 47.9 cm) Overall with mounting: 75 5/8 x 24 1/4 in. (192.1 x 61.6 cm) Overall with knobs: 75 5/8 x 26 1/2 in. (192.1 x 67.3 cm),"Purchase, Lita Annenberg Hazen Charitable Trust Gift, in honor of Cynthia and Leon Polsky, 1985",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/40462,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JIB94,false,true,45442,Asian Art,Illustrated book,Kyoka Kijin Gazo-shu|Poems on Portraits of the Famous and the Infamous,Japan,Edo period (1615–1868),,,,Artist|Artist|Artist,Attributed to,Utagawa Kunisada|Utagawa Hiroshige|Ryūsen,"Japanese, 1786–1865|Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)|Japanese, active mid–19th century",,"Utagawa Kunisada|Utagawa, Hiroshige|Ryūsen",Japanese|Japanese|Japanese,1786 |1797 |1834,1865 |1858 |1866,19th century,1700,1868,Polychrome Woodblock printed book; gold lacquer on red lacquer ground,8 1/4 × 5 3/4 × 1/2 in. (21 × 14.6 × 1.3 cm),"The Howard Mansfield Collection, Gift of Howard Mansfield, 1936",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/45442,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2342,false,true,54127,Asian Art,Woodblock print,,Japan,,,,,Artist|Calligrapher,,Totoya Hokkei|Shibayamadō,"Japanese, 1780–1850|Japanese",,Totoya Hokkei|Shibayamadō,Japanese|Japanese,1780,1850,19th century,1800,1899,Part of an album of woodblock prints (surimono); ink and color on paper,8 5/16 x 7 1/4 in. (21.1 x 18.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54127,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2260,false,true,54030,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Calligrapher|Artist,,Yuyu Hanko|Teisai Hokuba,"Japanese|Japanese, 1771–1844",,Yuyu Hanko|Teisai Hokuba,Japanese|Japanese,1771,1844,19th century,1800,1899,Part of an album of woodblock prints (surimono); ink and color on paper,5 7/16 x 7 3/8 in. (13.8 x 18.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54030,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2261,false,true,54031,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Calligrapher|Artist,,Yuyu Hanko|Teisai Hokuba,"Japanese|Japanese, 1771–1844",,Yuyu Hanko|Teisai Hokuba,Japanese|Japanese,1771,1844,19th century,1800,1899,Part of an album of woodblock prints (surimono); ink and color on paper,8 1/4 x 7 3/8 in. (21 x 18.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54031,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP205,false,true,36682,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist|Artist|Artist,Nineteen prints by|Two prints by|Three prints by,Kikugawa Eizan|Utagawa Kunisada|Utagawa Kunimaru,"Japanese, 1787–1867|Japanese, 1786–1865|Japanese, 1793–1829",,Kikugawa Eizan|Utagawa Kunisada|Kunimaru,Japanese|Japanese|Japanese,1787 |1786 |1793,1867 |1865 |1829,19th century,1800,1868,Album of 24 polychrome woodblock prints; ink and color on paper,14 5/8 × 9 3/4 × 1 1/4 in. (37.1 × 24.8 × 3.2 cm),"Gift of Mary L. Cassilly, 1894",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36682,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP211,false,true,36688,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist|Artist|Artist,,Utagawa Kuniyoshi|Utagawa Kunisada|Utagawa Hiroshige,"Japanese, 1797–1861|Japanese, 1786–1865|Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa Kuniyoshi|Utagawa Kunisada|Utagawa, Hiroshige",Japanese|Japanese|Japanese,1797 |1786 |1797,1861 |1865 |1858,19th century,1800,1865,Album of 98 polychrome woodblock prints; ink and color on paper,14 1/2 × 9 1/2 × 1 3/4 in. (36.8 × 24.1 × 4.4 cm),"Gift of the Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36688,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP208,false,true,36685,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist|Artist|Artist|Artist|Artist,Twenty-eight prints by|Twenty-six prints by|Three prints by|Three prints by|Three prints by,Utagawa Kuniyoshi|Utagawa Kunisada|Utagawa Kunimaro|Utagawa Hiroshige|Utagawa Kuniteru,"Japanese, 1797–1861|Japanese, 1786–1865|Japanese, active ca. 1840–70|Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)|Japanese, 1830–1874",,"Utagawa Kuniyoshi|Utagawa Kunisada|Utagawa, Kunimaro|Utagawa, Hiroshige|Utagawa Kuniteru",Japanese|Japanese|Japanese|Japanese|Japanese,1797 |1786 |1840 |1797 |1830,1861 |1865 |1870 |1858 |1874,19th century,1800,1868,Album of 58 polychrome woodblock prints; ink and color on paper,14 × 10 × 1 1/2 in. (35.6 × 25.4 × 3.8 cm),"Gift of the Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36685,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1115,false,true,55037,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist|Artist|Artist|Artist|Artist|Artist|Artist|Artist|Artist,,Utagawa Kuniyoshi|Utagawa Kunisada|Utagawa Yoshikazu|Utagawa Yoshitsuna|Utagawa Yoshitora|Utagawa Yoshitsuru|Utagawa Yoshihide|Utagawa (Gountei) Sadahide|Utagawa Yoshitsuya,"Japanese, 1797–1861|Japanese, 1786–1865|Japanese, active ca. 1850–1870|Japanese, active ca. 1850–1860|Japanese, active ca. 1850–80|Japanese, active ca. 1840–1850|Japanese, 1832–1902|Japanese, 1807–1878/79|Japanese, 1822–1866",,"Utagawa Kuniyoshi|Utagawa Kunisada|Utagawa Yoshikazu|Utagawa Yoshitsuna|Utagawa Yoshitora|Utagawa, Yoshitsuru|Utagawa, Yoshihide|Utagawa (Goutei) Sadahide|Utagawa Yoshitsuya",Japanese|Japanese|Japanese|Japanese|Japanese|Japanese|Japanese|Japanese|Japanese,1797 |1786 |1845 |1850 |1845 |1840 |1832 |1807 |1822,1861 |1865 |1870 |1860 |1880 |1850 |1902 |1879 |1866,19th century,1800,1899,Album of 15 triptychs of polychrome woodblock prints; ink and color on paper,13 15/16 × 9 7/8 × 3/4 in. (35.4 × 25.1 × 1.9 cm),"Gift of Harold de Raasloff, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55037,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1984.347,false,true,49001,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist|Calligrapher|Calligrapher,Painted and inscribed by,Rai San'yō|Rai Kyohei (Shunsō)|Rai Baishi,"1780–1832|Japanese, 1756–1834|Japanese, 1759–1843",,Rai San'yō|Rai Kyohei|Rai Baishi,Japanese|Japanese|Japanese,1780 |1756 |1759,1832 |1834 |1843,19th century,1800,1832,Hanging scroll; ink on paper,18 1/2 x 11 5/16 in. (47 x 28.7 cm),"Purchase, Bequest of John L Cadwaldader, Gift of Mrs. Russell Sage, and Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, by exchange, 1984",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49001,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.268.74,false,true,49035,Asian Art,Hanging scroll,"長沢蘆筆・皆川淇園賛　白鶏図|Rooster, Hen and Chicks",Japan,Edo period (1615–1868),,,,Artist|Calligrapher,Attributed to,Nagasawa Rosetsu|Minagawa Kien,"Japanese, 1754–1799|Japanese, 1734–1807",,Nagasawa Rosetsu|Minagawa Kien,Japanese|Japanese,1754 |1734,1799 |1807,late 18th century,1767,1799,Hanging scroll; ink on paper,Image: 49 1/8 x 10 3/4 in. (124.8 x 27.3 cm) Overall with mounting: 81 1/4 x 11 7/8 in. (206.4 x 30.2 cm) Overall with knobs: 81 1/4 x 13 7/8 in. (206.4 x 35.2 cm),"The Harry G. C. Packard Collection of Asian Art, Gift of Harry G. C. Packard, and Purchase, Fletcher, Rogers, Harris Brisbane Dick, and Louis V. Bell Funds, Joseph Pulitzer Bequest, and The Annenberg Fund Inc. Gift, 1975",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49035,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -17.118.72,false,true,62891,Asian Art,Teabowl,,Japan,,,,,Artist,,Widow of Ameya,active early 16th century,,Ameya,Korean,0016,0016,ca. 1550,1540,1560,Clay covered with glaze (Amayaki Raku),H. 3 in. (7.6 cm); Diam. 4 1/2 in. (11.4 cm),"Rogers Fund, 1917",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/62891,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -25.215.5,false,true,63092,Asian Art,Cake dish,,Japan,Edo period (1615–1868),,,,Artist,,Gempin,"Chinese, died 1771",,Gempin,Chinese,1671,1771,1645,1645,1645,Porcelaneous clay covered with a transparent glaze over blue decoration,H. 2 1/2 in. (6.4 cm); W. 6 3/4 in. (17.1 cm); L. 9 in. (22.9 cm),"Fletcher Fund, 1925",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/63092,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.268.83,false,true,49071,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Hi Kangen,active 18th century,,Hi Kangen,Chinese,0018,0018,dated 1756,1756,1756,Hanging scroll; ink and color on paper,35 5/16 x 13 7/16 in. (89.7 x 34.2 cm),"The Harry G. C. Packard Collection of Asian Art, Gift of Harry G. C. Packard, and Purchase, Fletcher, Rogers, Harris Brisbane Dick, and Louis V. Bell Funds, Joseph Pulitzer Bequest, and The Annenberg Fund Inc. Gift, 1975",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49071,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.439,false,true,54893,Asian Art,Hanging scroll,,Japan,Meiji period (1868–1912),,,,Artist,in the tradition of,Wu Daozi,"Chinese, 689–after 755",,Wu Daozi,Chinese,0689,0760,late 19th century,1871,1899,Hanging scroll; color on silk,48 1/2 x 23 1/8 in. (123.2 x 58.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54893,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.268.86,false,true,49069,Asian Art,Hanging scroll,西園方済筆　梧桐下錦鶏図|Pheasant beneath Paulownia Tree,Japan,Edo period (1615–1868),,,,Artist,,Saien Hōsai (Xiyua Fangqi),1736?–?1795,,Hō Sai (Fang Qi),Chinese,1736,1795,18th century,1736,1795,Hanging scroll; ink on paper,38 1/2 x 12 3/16 in. (97.8 x 30.9 cm),"The Harry G. C. Packard Collection of Asian Art, Gift of Harry G. C. Packard, and Purchase, Fletcher, Rogers, Harris Brisbane Dick, and Louis V. Bell Funds, Joseph Pulitzer Bequest, and The Annenberg Fund Inc. Gift, 1975",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49069,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.268.87,false,true,49070,Asian Art,Hanging scroll,西園方済筆　梅に叭叭鳥図|Mynah Bird on Plum Branch,Japan,Edo period (1615–1868),,,,Artist,,Saien Hōsai (Xiyua Fangqi),1736?–?1795,,Hō Sai (Fang Qi),Chinese,1736,1795,18th century,1736,1795,Hanging scroll; ink on paper,36 3/16 x 10 1/2 in. (91.9 x 26.7 cm),"The Harry G. C. Packard Collection of Asian Art, Gift of Harry G. C. Packard, and Purchase, Fletcher, Rogers, Harris Brisbane Dick, and Louis V. Bell Funds, Joseph Pulitzer Bequest, and The Annenberg Fund Inc. Gift, 1975",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49070,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -42.61,false,true,49101,Asian Art,Handscroll,,Japan,Edo period (1615–1868),,,,Artist,In the Style of,Zhou Wenju,"Chinese, active 940–975",,ZHOU WENJU,Chinese,0940,0975,19th century,1800,1868,Handscroll; ink and color on paper,10 3/16 in. × 18 ft. 15/16 in. (25.8 × 551 cm),"Fletcher Fund, 1942",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49101,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.651,false,true,78572,Asian Art,Illustrated book,新玉帖|Album of the New Year (Aratama jō),Japan,Edo period (1615–1868),,,,Artist|Artist,,Ōnishi Chinnen|Tani Bunchō,"1792–1851|Japanese, 1763–1840",", various artists",Ōnishi Chinnen|Tani Bunchō,Japanese|Japanese,1792 |1763,1851 |1840,"early 19th century, before 1829",1800,1825,"Woodblock printed book (orihon, accordion-style; bound); ink, color, and metallic pigments on paper",9 15/16 × 7 5/16 in. (25.3 × 18.5 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78572,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.674,false,true,78595,Asian Art,Illustrated book,艶本 婦慈のゆき|Early to Dawn (Akeyasuki),Japan,Edo period (1615–1868),,,,Artist|Artist,,Ōhara Donshū|Tanaka Nikka,"Japanese, died 1857|Japanese, died 1845",,Ōhara Donshū|Tanaka Nikka,Japanese|Japanese,1757,1857 |1845,ca. 1837,1832,1842,Woodblock printed book; ink and color on paper,9 3/4 × 7 5/16 in. (24.7 × 18.5 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78595,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.67.93,false,true,45440,Asian Art,Inrō,冨士形蒔絵印籠|Inrō in the Shape of Mount Fuji with a Crossing Ferry and Procession,Japan,Edo period (1615–1868),,,,Artist|Artist,After|Maki-e by,Hanabusa Itchō|Kajikawa,"Japanese, 1652–1724|Japanese, 1652–1724",,Hanabusa Itchō|Kajikawa,Japanese|Japanese,1652 |1652,1724 |1724,late 18th–early 19th century,1767,1833,"Two cases; lacquered wood with gold and silver takamaki-e, hiramaki-e, togidashimaki-e, cut-out gold foil on nashiji lacquer ground Netsuke: ivory; Ryūgūjō (The Dragon King's undersea palace) in a clam Ojime: agate bead",4 1/8 x 4 11/16 x 1 1/4 in. (10.5 x 11.9 x 3.2 cm),"Rogers Fund, 1913",,,,,,,,,,,,Inrō,,http://www.metmuseum.org/art/collection/search/45440,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JIB32,false,true,57563,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist|Artist,,Katsukawa Shunshō|Kitao Shigemasa,"Japanese, 1726–1792|Japanese, 1739–1820",,Katsukawa Shunshō|Kitao Shigemasa,Japanese|Japanese,1726 |1739,1792 |1820,1776,1776,1776,Ink and color on paper,11 1/8 × 7 3/8 × 3/8 in. (28.3 × 18.7 × 1 cm),"Rogers Fund, 1918",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/57563,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JIB88,false,true,57754,Asian Art,Illustrated book,画本宝能縷|Picture Book of Brocades with Precious Threads (Ehon takara no itosuji),Japan,Edo period (1615–1868),,,,Artist|Artist,,Katsukawa Shunshō|Kitao Shigemasa,"Japanese, 1726–1792|Japanese, 1739–1820","(nos. 1, 3, 6, 8, 11, 12)|(nos. 2, 4, 5, 7, 9, 10)",Katsukawa Shunshō|Kitao Shigemasa,Japanese|Japanese,1726 |1739,1792 |1820,"1786, first month",1786,1786,Polychrome woodblock printed book; ink and color on paper,Overall: 11 5/16 × 8 1/8 in. (28.8 × 20.6 cm),"The Howard Mansfield Collection, Gift of Howard Mansfield, 1936",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/57754,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.822,false,true,78712,Asian Art,Illustrated book,『青楼美人合 姿鏡』|Mirror of Yoshiwara Beauties (Seirō bijin awase sugata kagami),Japan,Edo period (1615–1868),,,,Artist|Artist,,Katsukawa Shunshō|Kitao Shigemasa,"Japanese, 1726–1792|Japanese, 1739–1820",,Katsukawa Shunshō|Kitao Shigemasa,Japanese|Japanese,1726 |1739,1792 |1820,1776,1776,1776,"Woodblock printed book; ink, color, and mica on paper",11 × 7 3/8 in. (28 × 18.8 cm),"Purchase, Mary and James G. Wallach Family Foundation Gift, in honor of John T. Carpenter, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78712,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JIB31,false,true,45021,Asian Art,Illustrated book,Seiro Bijin Awase Sugata Kagami|Mirror of the Beautiful Women of the Yoshiwara Brothels,Japan,Edo period (1615–1868),,,,Artist|Artist,,Kitao Shigemasa|Katsukawa Shunshō,"Japanese, 1739–1820|Japanese, 1726–1792",,Kitao Shigemasa|Katsukawa Shunshō,Japanese|Japanese,1739 |1726,1820 |1792,1776,1776,1776,Polychrome woodblock printed book; ink and color on paper,11 × 7 1/4 × 5/8 in. (27.9 × 18.4 × 1.6 cm),"Rogers Fund, 1918",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/45021,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.334,false,true,73587,Asian Art,Illustrated book,紅毛雜話|Chats on Novelties of Foreign Lands (Kōmōzatsuwa),Japan,Edo period (1615–1868),,,,Artist|Artist,,Shiba Kōkan|Kuwagata Keisai,"Japanese, 1747–1818|Japanese, 1764–1824",,Shiba Kōkan|Kuwagata Keisai,Japanese|Japanese,1747 |1764,1818 |1824,1797,1797,1797,"Five volumes of woodblock printed books bound as one; ink on paper,",Image (a): 8 7/8 x 6 1/4 x 7/8 in. (22.5 x 15.9 x 2.2 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/73587,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.757a–d,false,true,78659,Asian Art,Illustrated books,『光琳百圖』|One Hundred Paintings by Kōrin (Kōrin hyakuzu),Japan,Edo period (1615–1868),,,,Artist|Artist,After,Sakai Hōitsu|Ogata Kōrin,"Japanese, 1761–1828|Japanese, 1658–1716",,Sakai Hōitsu|Kōrin,Japanese|Japanese,1761 |1658,1828 |1716,1815,1815,1815,Set of four woodblock printed books; ink on paper,each: 10 1/4 × 7 3/16 in. (26 × 18.2 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78659,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.819,false,true,78709,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist|Artist,,Katsukawa Shun'ei|Katsukawa Shunshō,"Japanese, 1762–1819|Japanese, 1726–1792",,Katsukawa Shun'ei|Katsukawa Shunshō,Japanese|Japanese,1762 |1726,1819 |1792,1790,1790,1790,Woodblock printed book; ink and color on paper,8 13/16 × 6 1/4 in. (22.4 × 15.8 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78709,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.806,false,true,78696,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist|Artist,or,Hasegawa Settan|Tsukioka Settei,"Japanese, 1778–1843|Japanese, 1710–1786",,Hasegawa Settan|Tsukioka Settei,Japanese|Japanese,1778 |1710,1843 |1786,1829,1829,1829,Woodblock printed book; ink on paper,9 × 6 5/16 in. (22.8 × 16 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78696,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.659,false,true,78580,Asian Art,Illustrated book,南岳文鳳街道雙畫|Nangaku- Bunpō Highway Pictures (Nangaku Bunpō kaidō sōga),Japan,Edo period (1615–1868),,,,Artist|Artist,,Kawamura Bunpō|Watanabe Nangaku,"Japanese, 1779–1821|Japanese, 1763–1813",,Kawamura Bunpō|Watanabe Nangaku,Japanese|Japanese,1779 |1763,1821 |1813,ca. 1811,1806,1816,Woodblock printed book; ink and color on paper,10 1/4 × 6 7/8 in. (26 × 17.5 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78580,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.761,false,true,78663,Asian Art,Illustrated book,『役者三十六歌仙』|The Thirty-Six Immortals of Poetry as Kabuki Actors (Yakusha sanjūrokkasen),Japan,Edo period (1615–1868),,,,Artist|Artist,,Utagawa Kunisada|Totoya Hokkei,"Japanese, 1786–1865|Japanese, 1780–1850",,Utagawa Kunisada|Totoya Hokkei,Japanese|Japanese,1786 |1780,1865 |1850,1835,1835,1835,Woodblock printed book; ink and color on paper,10 1/16 × 7 5/16 in. (25.6 × 18.5 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78663,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"36.100.171a, b",false,true,58349,Asian Art,Box,,Japan,Edo period (1615–1868),,,,Artist|Artist,,Shibata Zeshin|Ikeda Taishin,"Japanese, 1807–1891|Japanese, 1825–1903",,Shibata Zeshin|Ikeda Taishin,Japanese|Japanese,1807 |1825,1891 |1903,1862,1862,1862,"Mokume-nuri, gold, silver, red, black lacquer, takamaki-e, hiramaki-e",H. 2 1/2 in. (6.4 cm); W. 6 in. (15.2 cm); D. 4 1/2 in. (11.4 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Lacquer,,http://www.metmuseum.org/art/collection/search/58349,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JIB5,false,true,57540,Asian Art,Illustrated book,"Nishikizuri onna sanjūrokkasen|Courtiers and Urchins, frontispiece for the album Brocade Prints of the Thirty-six Poetesses",Japan,Edo period (1615–1868),,,,Artist|Artist,,Chōbunsai Eishi|Katsushika Hokusai,"Japanese, 1756–1829|Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Chōbunsai Eishi|Katsushika Hokusai,Japanese|Japanese,1756 |1760,1829 |1849,1801,1801,1801,Ink and color on paper,9 7/8 × 7 3/8 × 3/4 in. (25.1 × 18.7 × 1.9 cm),"Rogers Fund, 1918",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/57540,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.707,false,true,78628,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist|Artist,,Totoya Hokkei|Utagawa Hiroshige,"Japanese, 1780–1850|Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Totoya Hokkei|Utagawa, Hiroshige",Japanese|Japanese,1780 |1797,1850 |1858,1840,1840,1840,Woodblock printed book; ink and color on paper,8 15/16 × 6 5/16 in. (22.7 × 16.1 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78628,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.240,false,true,58926,Asian Art,Inrō,五代目市川団十郎肖像蒔絵印籠|Inrō with Kabuki Actor Ichikawa Danjūrō V,Japan,Edo period (1615–1868),,,,Artist|Artist,Maki-e by|Design by,Jōsensai|Katsukawa Shunshō,"Japanese, active late 18th–early 19th century|Japanese, 1726–1792",,Jōsensai|Katsukawa Shunshō,Japanese|Japanese,1767 |1726,1833 |1792,late 18th–early 19th century,1767,1833,"Five cases; lacquered wood with gold, silver, black, and red togidashimaki-e on black lacquer ground Netsuke: ivory; Nō mask Ojime: lacquer bead",3 1/2 x 1 7/8 x 1 1/4 in. (8.9 x 4.8 x 3.1 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Inrō,,http://www.metmuseum.org/art/collection/search/58926,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JIB51,false,true,57651,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist|Artist,,Utagawa Hiroshige|Uoya Eikichi,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)|Japanese, ca. 1855–1866",,"Utagawa, Hiroshige|Uoya Eikichi",Japanese|Japanese,1797 |1855,1858 |1855,1856–58,1856,1858,Polychrome woodblock print; ink and color on paper,14 1/2 × 10 × 1 1/4 in. (36.8 × 25.4 × 3.2 cm),"Rogers Fund, 1918",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/57651,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JIB118a–d,false,true,57810,Asian Art,Illustrated book,酒井抱一画『光琳百圖』|One Hundred Paintings by Kōrin (Kōrin hyakuzu),Japan,Edo period (1615–1868),,,,Artist|Artist,After,Ogata Kōrin|Sakai Hōitsu,"Japanese, 1658–1716|Japanese, 1761–1828",,Kōrin|Sakai Hōitsu,Japanese|Japanese,1658 |1761,1716 |1828,1815 (first two volumes) and–1826 (two sequel volumes),1815,1826,Four volumes of woodblock printed books; ink on paper,Overall (each volume): H. 10 3/8 in. (26.4 cm); W. 7 3/16 in. (18.3 cm); D. 1 in. (2.5 cm),"The Howard Mansfield Collection, Gift of Howard Mansfield, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57810,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2795,false,true,57094,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist|Artist,or,Kitao Masanobu (Santō Kyōden)|Kitao Shigemasa,"Japanese, 1761–1816|Japanese, 1739–1820",,Kitao Masanobu (Santō Kyōden)|Kitao Shigemasa,Japanese|Japanese,1761 |1739,1816 |1820,1739–1820,1739,1820,Polychrome woodblock print; ink and color on paper,14 1/8 x 9 7/8 in. (35.9 x 25.1 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57094,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.146,false,true,73432,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist|Artist,,Utagawa Hiroshige II|Utagawa Kunisada,"Japanese, 1829–1869|Japanese, 1786–1865",,Utagawa Hiroshige II|Utagawa Kunisada,Japanese|Japanese,1829 |1786,1869 |1865,"3rd month, 1861",1861,1861,Triptych of polychrome woodblock prints; ink and color on paper,Image (a): 14 1/2 x 10 in. (36.8 x 25.4 cm) Image (b): 14 5/8 x 10 1/8 in. (37.1 x 25.7 cm) Image (c): 14 3/4 x 10 in. (37.5 x 25.4 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73432,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP186,false,true,36664,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist|Artist,,Chōkōsai Eishō|Yamaguchiya Chūsuke,"Japanese, 1793–99|Japanese, ca. 1793–1809",,Chōkōsai Eishō|Yamaguchiya Chūsuke,Japanese|Japanese,1793 |1783,1799 |1819,probably 1798,1796,1800,Polychrome woodblock print; ink and color on paper,15 5/8 x 10 in. (39.7 x 25.4 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36664,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP202,false,true,36679,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist|Artist,,Utagawa Toyokuni I|Wakasaya Yoichi,"Japanese, 1769–1825|Japanese, ca. 1794–1897",,Utagawa Toyokuni I|Wakasaya Yoichi,Japanese|Japanese,1769 |1784,1825 |1907,ca. 1800,1790,1810,Triptych of polychrome woodblock prints; ink and color on paper,14 x 30 1/8 in. (35.6 x 76.5 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36679,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.276a–c,false,true,73559,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist|Artist,,Utagawa Kunisada|Utagawa Kunitoki,"Japanese, 1786–1865|Japanese, active ca. 1860",,Utagawa Kunisada|Utagawa Kunitoki,Japanese|Japanese,1786 |1850,1865 |1870,"5th month, 1860",1860,1860,Triptych of polychrome woodblock prints; ink and color on paper,Image (a): 10 1/4 x 14 3/4 in. (26 x 37.5 cm) Image (b): 10 1/8 x 14 3/4 in. (25.7 x 37.5 cm) Image (c): 10 1/4 x 14 3/4 in. (26 x 37.5 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73559,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3332,false,true,55480,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist|Artist,,Utagawa Kuniyoshi|Wakasaya Yoichi,"Japanese, 1797–1861|Japanese, ca. 1794–1897",,Utagawa Kuniyoshi|Wakasaya Yoichi,Japanese|Japanese,1797 |1784,1861 |1907,ca. 1840,1830,1850,Polychrome woodblock print; ink and color on paper,Oban yoko-e; 8 7/8 x 14 in. (22.5 x 35.6 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55480,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2751,false,true,57017,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist|Artist,,Utagawa Kunisada|Ichiransai Kunitsuna,"Japanese, 1786–1865|Japanese, mid-nineteenth century",,Utagawa Kunisada|Ichiransai Kunitsuna,Japanese|Japanese,1786 |1800,1865 |1899,after 1844,1845,1868,Triptych of polychrome woodblock prints; ink and color on thin paper,14 1/4 x 30 1/2 in. (36.2 x 77.5 cm),"Fletcher Fund, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57017,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2753,false,true,57019,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist|Artist,,Utagawa Kunisada|Ichiransai Kunitsuna,"Japanese, 1786–1865|Japanese, mid-nineteenth century",,Utagawa Kunisada|Ichiransai Kunitsuna,Japanese|Japanese,1786 |1800,1865 |1899,after 1844,1844,1868,Drawings intended as design for woodblock prints (triptych); ink and color on paper,14 1/4 x 30 1/2 in. (36.2 x 77.5 cm),"Fletcher Fund, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57019,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1418,false,true,55446,Asian Art,Print,木曽海道六拾九次之内　軽井沢|Karuizawa,Japan,Edo period (1615–1868),,,,Artist|Artist,,Utagawa Hiroshige|Keisai Eisen,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)|Japanese, 1790–1848",,"Utagawa, Hiroshige|Keisai Eisen",Japanese|Japanese,1797 |1790,1858 |1848,ca. 1835,1825,1845,Polychrome woodblock print; ink and color on paper,H. 8 9/16 in. (21.7 cm); W. 13 1/2 in. (34.3 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55446,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1023,false,true,37282,Asian Art,Woodblock print,"歌川広重画　「名所江戸百景　駒形堂吾嬬橋」|“Azuma Bridge from Komagatadō Temple,” from the series One Hundred Famous Views of Edo (Meisho Edo hyakkei, Komagatadō Azumabashi)",Japan,Edo period (1615–1868),,,,Artist|Artist,,Utagawa Hiroshige|Uoya Eikichi,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)|Japanese, ca. 1855–1866",,"Utagawa, Hiroshige|Uoya Eikichi",Japanese|Japanese,1797 |1855,1858 |1855,1857,1857,1857,Polychrome woodblock print; ink and color on paper,H. 13 3/8 in. (34 cm); W. 8 3/4 in. (22.2 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37282,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"93.3.181a, b",false,true,47419,Asian Art,Teapot,,Japan,Edo period (1615–1868),,,,Artist|Artist,,Kentei|Shuhei,"Japanese, 16th–17th century|Japanese, 1788–1839",,Kentei|Shuhei,Japanese|Japanese,1500 |1788,1800 |1839,1790,1700,1799,"Clay, fine and thin, decorated in polychrome enamels (Kyoto ware)",H. 4 1/4 in. (10.8 cm),"Gift of Charles Stewart Smith, 1893",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/47419,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -57.84,false,true,57249,Asian Art,Hanging scroll,,Japan,,,,,Artist|Artist,Attributed to|Formerly Attributed to,Keison|Keishoki,"Japanese, active late 15th– early 16th century|Japanese, active ca. 1500",,Keison|Keishoki,Japanese|Japanese,1467 |1450,1533 |1550,15th–16th century,1467,1533,Hanging scroll; ink on paper,36 x 14 3/8 in. (91.4 x 36.5 cm),"Rogers Fund, 1957",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57249,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"1975.268.80a, b",false,true,49068,Asian Art,Hanging scrolls,雪竹図|Bamboo in Snow,Japan,Edo period (1615–1868),,,,Artist|Artist,Calligrapher:,Hakujun Shōkō|Taihō Shōkon,1695–1776|1691–1774,,Hakujun Shōkō|Taihō Shōkon,Chinese|Chinese,1695 |1691,1776 |1774,1774,1771,1774,Pair of hanging scrolls; ink on silk,Image (each): 47 1/2 in. × 20 in. (120.7 × 50.8 cm) Overall with mounting (each): 73 3/8 × 25 3/8 in. (186.4 × 64.5 cm) Overall with knobs (each): 73 3/8 × 27 5/8 in. (186.4 × 70.2 cm),"The Harry G. C. Packard Collection of Asian Art, Gift of Harry G. C. Packard, and Purchase, Fletcher, Rogers, Harris Brisbane Dick, and Louis V. Bell Funds, Joseph Pulitzer Bequest, and The Annenberg Fund Inc. Gift, 1975",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49068,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.268.132,false,true,49093,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist|Artist,,Shokusanjin (Ōta Nanpo)|Utamaro II,"Japanese, 1749–1823|Japanese (died 1831?)",", and others",Utamaro II,Japanese|Japanese,1749 |1750,1823 |1850,ca. 1801–6,1791,1806,Hanging scroll; ink and color on paper,Image: 35 5/8 x 12 in. (90.5 x 30.5 cm) Overall: 67 3/4 x 15 5/8 in. (172.1 x 39.7 cm),"The Harry G. C. Packard Collection of Asian Art, Gift of Harry G. C. Packard, and Purchase, Fletcher, Rogers, Harris Brisbane Dick, and Louis V. Bell Funds, Joseph Pulitzer Bequest, and The Annenberg Fund Inc. Gift, 1975",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49093,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.268.112,false,true,49004,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist|Artist,,Tani Bunchō|Shokusanjin (Ōta Nanpo),"Japanese, 1763–1840|Japanese, 1749–1823",,Tani Bunchō,Japanese|Japanese,1763 |1749,1840 |1823,1814,1814,1814,Hanging scroll; ink and color on paper,Image: 47 1/2 × 11 3/16 in. (120.7 × 28.4 cm) Overall with mounting: 76 × 12 1/16 in. (193 × 30.7 cm) Overall with knobs: 76 × 13 13/16 in. (193 × 35.1 cm),"The Harry G. C. Packard Collection of Asian Art, Gift of Harry G. C. Packard, and Purchase, Fletcher, Rogers, Harris Brisbane Dick, and Louis V. Bell Funds, Joseph Pulitzer Bequest, and The Annenberg Fund Inc. Gift, 1975",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49004,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.88,false,true,52988,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist|Artist,Painting by|Inscribed by,Tawaraya Sōtatsu|Takeuchi Toshiharu,"Japanese, died ca. 1640|Japanese, 1611–1647",,Tawaraya Sōtatsu|Takeuchi Toshiharu,Japanese|Japanese,1540 |1611,1640 |1647,ca. 1634,1624,1644,"Poem card (shikishi) mounted as a hanging scroll; ink, color, and gold on paper",Image: 9 11/16 × 8 3/16 in. (24.6 × 20.8 cm) Overall with mounting: 49 5/16 × 16 11/16 in. (125.3 × 42.4 cm) Overall with knobs: 49 5/16 × 18 3/8 in. (125.3 × 46.7 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/52988,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1979.407.2,false,true,45334,Asian Art,Hanging scroll,松花堂昭乗書・伝俵屋宗達下絵　鉄線下絵和歌色紙　藤原興風|Poem by Fujiwara no Okikaze with Underpainting of Clematis,Japan,Edo period (1615–1868),,,,Artist|Artist,Calligraphy by|Underpainting attributed to,Shōkadō Shōjō|Tawaraya Sōtatsu,"Japanese, 1584?–1639|Japanese, died ca. 1640",,Shōkadō Shōjō|Tawaraya Sōtatsu,Japanese|Japanese,1584 |1540,1639 |1640,early 17th century,1600,1633,"Hanging scroll; ink, gold, and silver on colored paper",Image: 7 15/16 x 6 15/16 in. (20.2 x 17.6 cm) Overall with mounting: 53 x 17 15/16 in. (134.6 x 45.6 cm) Overall with knobs: 53 x 20 5/8 in. (134.6 x 52.4 cm),"Purchase, Gift of Mrs. Russell Sage, by exchange, 1979",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45334,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.86,false,true,53246,Asian Art,Hanging scroll,小倉百人一首和歌巻断簡|Two Poems from One Hundred Poems by One Hundred Poets (Ogura hyakunin isshu),Japan,Momoyama period (1573–1615),,,,Artist|Artist,Painting by,Hon'ami Kōetsu|Tawaraya Sōtatsu,"Japanese, 1558–1637|Japanese, died ca. 1640",,Hon'ami Kōetsu|Tawaraya Sōtatsu,Japanese|Japanese,1558 |1540,1637 |1640,ca. 1615–20,1615,1620,"Section of scroll; ink, silver, and gold on paper",Image: 13 in. × 23 3/4 in. (33 × 60.4 cm) Overall with mounting: 49 5/8 × 29 1/8 in. (126 × 73.9 cm) Overall with knobs: 49 5/8 × 31 5/16 in. (126 × 79.5 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/53246,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.268.59,false,true,44861,Asian Art,Hanging scroll,本阿見光悦書・俵屋宗達下絵　桜下絵和歌色紙　鴨長明|Poem by Kamo no Chōmei with Underpainting of Cherry Blossoms,Japan,Momoyama period (1573–1615),,,,Artist|Artist,Underpainting attributed to|Calligraphy by,Tawaraya Sōtatsu|Hon'ami Kōetsu,"Japanese, died ca. 1640|Japanese, 1558–1637",,Tawaraya Sōtatsu|Hon'ami Kōetsu,Japanese|Japanese,1540 |1558,1640 |1637,dated 1606,1606,1606,"Poem card (shikishi) mounted as a hanging scroll; ink, gold, and silver on paper",Overall: 7 15/16 x 7in. (20.2 x 17.8cm) Overall with mounting: 53 x 14 3/4 in. (134.6 x 37.5 cm) Overall with knobs: 53 x 16 1/2 in. (134.6 x 41.9 cm),"The Harry G. C. Packard Collection of Asian Art, Gift of Harry G. C. Packard, and Purchase, Fletcher, Rogers, Harris Brisbane Dick, and Louis V. Bell Funds, Joseph Pulitzer Bequest, and The Annenberg Fund Inc. Gift, 1975",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/44861,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3497a–uu,false,true,55727,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist|Artist,,Toyohara Kunichika|Utagawa Kunisada,"Japanese, 1835–1900|Japanese, 1786–1865",(tt and uu),Toyohara Kunichika|Utagawa Kunisada,Japanese|Japanese,1835 |1786,1900 |1865,,1769,1900,Polychrome woodblock print; ink and color on paper,a–t: 14 x 9 1/4 in. (35.6 x 23.5 cm) (each) tt –uu: 6 3/4 x 9 1/4 in. (17.1 x 23.5 cm) (each),"Gift of Dr. and Mrs. Harold B. Bilsky, 1975",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55727,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2752,false,true,57018,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist|Artist,,Utagawa Kunisada|Ichiransai Kunitsuna,"Japanese, 1786–1865|Japanese, mid-nineteenth century",,Utagawa Kunisada|Ichiransai Kunitsuna,Japanese|Japanese,1786 |1800,1865 |1899,,1786,1864,Preliminary sketch for drawing intended as design for woodblock print; ink on thin paper,14 1/4 x 30 1/2 in. (36.2 x 77.5 cm),"Fletcher Fund, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57018,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2754,false,true,57020,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist|Artist,,Utagawa Kunisada|Ichiransai Kunitsuna,"Japanese, 1786–1865|Japanese, mid-nineteenth century",,Utagawa Kunisada|Ichiransai Kunitsuna,Japanese|Japanese,1786 |1800,1865 |1899,,1786,1864,Ink on thin paper,14 1/4 x 30 1/2 in. (36.2 x 77.5 cm),"Fletcher Fund, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57020,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3439,false,true,55643,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist|Artist,or,Utagawa Hiroshige II|Utagawa Hiroshige,"Japanese, 1829–1869|Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa Hiroshige II|Utagawa, Hiroshige",Japanese|Japanese,1829 |1797,1869 |1858,,1615,1868,Polychrome woodblock print; ink and color on paper,9 x 14 in. (22.9 x 35.6 cm),"Gift of Cole J. Younger, 1975",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55643,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.36,false,true,55354,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist|Artist,Attributed to,Matsumura Keibun|Matsumura Keibun,"Japanese, 1779–1843|Japanese, 1779–1843",,Matsumura Keibun|Matsumura Keibun,Japanese|Japanese,1779 |1779,1843 |1843,,1779,1843,Hanging scroll; ink and color on paper,40 1/8 x 11 7/8 in. (101.9 x 30.2 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/55354,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.37,false,true,55364,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist|Artist,Attributed to,Matsumura Keibun|Matsumura Keibun,"Japanese, 1779–1843|Japanese, 1779–1843",,Matsumura Keibun|Matsumura Keibun,Japanese|Japanese,1779 |1779,1843 |1843,,1779,1843,Hanging scroll; color on paper,31 5/8 x 12 5/8 in. (80.3 x 32.1 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/55364,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.477,false,true,54974,Asian Art,Hanging scroll,,Japan,Meiji period (1868–1912),,,,Artist|Artist,Attributed to|In the Style of,Kano Tambi|Li Gonglin,"Japanese, 1840–1893|Chinese, ca. 1041–1106",,Kano Tambi|Li Gonglin,Japanese|Chinese,1840 |1041,1893 |1106,,1840,1893,Hanging scroll; ink on paper,31 x 15 in. (78.7 x 38.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54974,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.905,false,true,58829,Asian Art,Inrō,,Japan,Edo period (1615–1868),,,,Artist|Artist,,Hosoya Hansai|Tomioka Tessai,"Japanese|Japanese, 1836–1924",,Hosoya Hansai|Tomioka Tessai,Japanese|Japanese,1836,1924,19th century,1800,1899,"Lacquer, roiro, black hiramakie, incised; Interior: roiro and hirame",2 9/16 x 1 7/8 x 7/8 in. (6.5 x 4.7 x 2.3 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Inrō,,http://www.metmuseum.org/art/collection/search/58829,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.67.44,false,true,58585,Asian Art,Inrō,原羊遊斎作・狩野晴川院筆　満月に鵞鳥図印籠|Inrō with Goose Flying across the Full Moon,Japan,Edo period (1615–1868),,,,Artist|Artist,After,Kano Seisen’in|Hara Yōyūsai,"1775–1828|Japanese, 1772–1845",,Kano Seisen’in|Hara Yōyūsai,Japanese|Japanese,1775 |1772,1828 |1845,19th century,1800,1899,"Lacquer, kinji, gold, silver, and black hiramaki-e, takamaki-e, and togidashi",Overall (inro): H. 3 11/16 in. (9.4 cm); W. 1 7/8 in. (4.8 cm); D. 1 1/16 in. (2.7 cm),"Rogers Fund, 1913",,,,,,,,,,,,Inrō,,http://www.metmuseum.org/art/collection/search/58585,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.245,false,true,58931,Asian Art,Inrō,原羊遊斎作・酒井抱一下絵　梅木蒔絵印籠|Inrō with Design of Blossoming Plum Tree,Japan,Edo period (1615–1868),,,,Artist|Artist,After a design by,Hara Yōyūsai|Sakai Hōitsu,"Japanese, 1772–1845|Japanese, 1761–1828",,Hara Yōyūsai|Sakai Hōitsu,Japanese|Japanese,1772 |1761,1845 |1828,19th century,1800,1899,"Sprinkled gold lacquer with gold, silver, and red makie, takamakie, and coral inlay Ojime: bead; tortoiseshell Netsuke: box with decoration of violets; gold makie lacquer with gold and silver makie",Overall (inro): H. 3 11/16 in. (9.3 cm); W. 2 1/4 in. (5.7 cm); D. 7/8 in. (2.2 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Inrō,,http://www.metmuseum.org/art/collection/search/58931,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.249,false,true,45456,Asian Art,Inrō,柴田是真作　波文印籠|Inrō with Stylized Waves,Japan,Edo period (1615–1868),,,,Artist|Artist,After a design by,Shibata Zeshin|Ogata Kōrin,"Japanese, 1807–1891|Japanese, 1658–1716",,Shibata Zeshin|Kōrin,Japanese|Japanese,1807 |1658,1891 |1716,19th century,1790,1899,Gold lacquer with pewter inlay; Ojime: bronze and gold jar; Netsuke: carved tortoiseshell turtle,Overall (inro): H. 1 15/16 in. (5 cm); W. 1 3/4 in. (4.4 cm); D. 11/16 in. (1.7 cm) Overall (netsuke): H. 11/16 in. (1.7 cm); W. 1 3/16 in. (3 cm); L. 1 9/16 in. (4 cm) Overall (ojime): H. 3/8 in. (1 cm); W. 3/16 in. (0.5 cm); D. 3/16 in. (0.5 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Inrō,,http://www.metmuseum.org/art/collection/search/45456,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.251,false,true,45446,Asian Art,Inrō,,Japan,Meiji period (1868–1912),,,,Artist|Artist,Attributed to|After an inro by,Shibata Zeshin|Ogawa Haritsu (Ritsuō),"Japanese, 1807–1891|Japanese, 1663–1747",,Shibata Zeshin|Ritsuō,Japanese|Japanese,1807 |1663,1891 |1747,19th century,1790,1899,Roiro ('waxen') lacquer with black hiramakie sprinkled and polished lacquer and takamakie sprinkled and polished lacquer relief; Interior: roiro; Netsuke: nut carved with Chinese sages and attendants; Ojime: carved peach stone with landscape and figures,2 7/16 x 2 7/16 x 9/16 in. (6.2 x 6.2 x 1.5 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Inrō,,http://www.metmuseum.org/art/collection/search/45446,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP210,false,true,36687,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist|Artist,Fourteen prints by|Sixteen prints by,Utagawa Kuniyoshi|Utagawa Kunisada,"Japanese, 1797–1861|Japanese, 1786–1865",,Utagawa Kuniyoshi|Utagawa Kunisada,Japanese|Japanese,1797 |1786,1861 |1865,19th century,1800,1865,Album of 30 polychrome woodblock prints; ink and color on paper,14 1/4 × 9 5/8 × 1/2 in. (36.2 × 24.4 × 1.3 cm),"Gift of the Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36687,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.63,false,true,49100,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist|Artist,In the Style of,Kano Osanobu|Yoden,"1796–1846|Chinese, active early Yuan period (1279–1368)",,Kano Osanobu|Yoden,Japanese|Chinese,1796 |1279,1846 |1368,19th century,1800,1846,Hanging scroll; ink on silk,37 1/2 x 17 1/4 in. (95.3 x 43.8 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49100,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.67.33,false,true,58575,Asian Art,Inrō,原羊遊斎作・酒井抱一下絵　南天に雀印籠|Inrō with Sparrows in Snow-covered Nandina,Japan,Edo period (1615–1868),,,,Artist|Artist,After a design by,Hara Yōyūsai|Sakai Hōitsu,"Japanese, 1772–1845|Japanese, 1761–1828",,Hara Yōyūsai|Sakai Hōitsu,Japanese|Japanese,1772 |1761,1845 |1828,early 19th century,1800,1833,"Lacquer with gold and silver makie, coral, and applied stained ivory Ojime: bead with stylized medallions; green stone with gold makie lacquer Netsuke: round box with iris design",Overall (inro): H. 3 11/16 in. (9.3 cm); W. 2 3/16 in. (5.6 cm); D. 13/16 in. (2.1 cm),"Rogers Fund, 1913",,,,,,,,,,,,Inrō,,http://www.metmuseum.org/art/collection/search/58575,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.300,false,true,72471,Asian Art,Hanging scroll,해강 김규진 난초|海岡 金圭鎭 墨蘭圖|Orchids in hanging basket,Korea,,,,,Artist,,Kim Gyujin,"Korean, 1868–1933",,Kim Gyujin,,1868,1933,early 20th century,1900,1933,Hanging scroll; ink on paper,Image: 55 1/4 × 15 3/4 in. (140.3 × 40 cm) Overall with mounting: 81 1/8 × 21 5/8 in. (206 × 55 cm) Overall with knobs: 81 1/8 × 23 7/8 in. (206 × 60.7 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/72471,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1988.103,false,true,38027,Asian Art,Folio,,"India (Rajasthan, Mewar)",,,,,Artist,,Sahibdin,active ca. 1628–55,,Sahibdin,,1618,1665,ca. 1665,1655,1675,Ink and opaque watercolor on paper,H. 9 5/8 in (24.4 cm); W. 7 3/4 in. (19.7 cm),"Gift of Ernest Erickson Foundation, 1988",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/38027,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -19.24.1,false,true,37947,Asian Art,Folio,,"India (Guler, Himachal Pradesh)",,,,,Artist,Attributed to,Manaku,active ca. 1725–60,,Manaku,,1715,1770,ca. 1725,1715,1735,"Opaque watercolor, ink and gold on paper",Page: 23 1/2 x 32 3/4 in. (59.7 x 83.2 cm) Image: 22 1/4 x 31 1/4 in. (56.5 x 79.4 cm),"Rogers Fund, 1919",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/37947,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -19.24.2,false,true,37948,Asian Art,Folio,,"India (Punjab Hills, Guler)",,,,,Artist,Attributed to,Manaku,active ca. 1725–60,,Manaku,,1715,1770,ca. 1725,1715,1735,Ink and opaque watercolor on paper,22 5/16 x 33 in. (56.7 x 83.8 cm),"Rogers Fund, 1919",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/37948,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -19.24.3,false,true,37949,Asian Art,Drawing,,"India (Himachal Pradesh, Guler)",,,,,Artist,Attributed to,Manaku,active ca. 1725–60,,Manaku,,1715,1770,ca. 1725,1715,1735,Ink on paper,24 1/2 x 32 5/8 in. (62.2 x 82.9 cm),"Rogers Fund, 1919",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/37949,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -19.24.4,false,true,37950,Asian Art,Drawing,,"India (Guler, Himachal Pradesh)",,,,,Artist,Attributed to,Manaku,active ca. 1725–60,,Manaku,,1715,1770,ca. 1725,1715,1735,Ink on paper,Page: 23 1/2 x 33 in. (59.7 x 83.8 cm) Image: 22 3/8 x 31 1/2 in. (56.8 x 80 cm),"Rogers Fund, 1919",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/37950,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1985.398.12,false,true,37906,Asian Art,Folio,,"India (Rajasthan, Mewar)",,,,,Artist,Style of,Manohar,active ca. 1582–1624,,Manohar,,1577,1624,ca. 1655–60,1655,1660,Ink and opaque watercolor on paper,Image: 9 1/8 x 7 1/4 in. (23.2 x 18.4 cm); Page: 10 1/4 x 8 3/8 in. (26 x 21.3 cm),"Gift of Cythian Hazen Polsky, 1985",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/37906,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1996.100.6,false,true,38043,Asian Art,Painting,,"India (Jaipur, Rajasthan)",,,,,Artist,Generation of,Bagta,active ca. 1761–1814,,Bagta,,1751,1824,ca. 1810–18,1800,1818,Opaque watercolor and ink on paper,Overall: 29 x 40 5/8 in. (73.7 x 103.2 cm) Framed: 39 3/4 x 50 3/4 in. (101 x 128.9 cm),"Fletcher Fund, 1996",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/38043,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1978.540.2,false,true,37863,Asian Art,Folio,,"India (Bikaner, Rajasthan)",,,,,Artist,,Ruknuddin,active late 17th century,,Ruknuddin,,1667,1695,ca. 1690–95,1680,1705,Opaque watercolor and ink on paper,Image: 6 in. × 4 11/16 in. (15.2 × 11.9 cm) Sheet: 10 1/8 × 7 1/4 in. (25.7 × 18.4 cm),"Gift of Mr. and Mrs. Peter Findlay, 1978",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/37863,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1980.526.2,false,true,37870,Asian Art,Folio,,"India (Rajasthan, Bikaner)",,,,,Artist,,Mohamed,active early 18th century,,Mohamed,,1700,1733,1714,1714,1714,Ink and opaque watercolor on paper,6 x 4 3/4 in. (15.2 x 12.1 cm),"Gift of John and Evelyn Kossak, The Kronos Collections, 1980",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/37870,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2001.344,true,true,61429,Asian Art,Painting,,"Western India, Rajasthan, Mewar",,,,,Artist,,Tara,"Indian, active 1836–1870",,Tara,,1836,1870,1845–46,1845,1846,"Opaque watercolor, ink, and gold on paper",Image (painting): 16 3/4 x 22 3/4 in. (42.5 x 57.8 cm) Sheet: 19 x 24 7/8 in. (48.3 x 63.2 cm),"Cynthia Hazen Polsky and Leon B. Polsky Fund, 2001",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/61429,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1994.310,false,true,38010,Asian Art,Folio,,"India (Bahu, Jammu)",,,,,Artist,,Bahu Masters,active ca. 1680–ca. 1720,,Bahu Masters,,1670,1730,ca. 1690–1710,1690,1710,Opaque watercolor and ink on paper,Page: 8 5/8 x 12 1/2 in. (21.9 x 31.8 cm) Image: 7 3/4 x 11 5/8 in. (19.7 x 29.5 cm),"Purchase, The Dillon Fund, Evelyn Kranes Kossak, and Anonymous Gifts, 1994",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/38010,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -57.185.2,true,true,37942,Asian Art,Folio,,"India (Basohli, Jammu)",,,,,Artist,,Devidasa of Nurpur,active ca. 1680–ca. 1720,,Devidasa of Nurpur,,1670,1730,dated 1694–95,1694,1695,"Opaque watercolor, ink, silver, and gold on paper",Image: 6 1/2 x 10 7/8 in. (16.5 x 27.6 cm) Sheet: 8 x 12 1/4 in. (20.3 x 31.1 cm) Framed: 15 5/8 x 20 1/2 in. (39.7 x 52.1 cm),"Gift of Dr. J. C. Burnett, 1957",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/37942,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -57.51.14,false,true,37941,Asian Art,Folio,,"India (Punjab Hills, Basohli)",,,,,Artist,,Devidasa of Nurpur,active ca. 1680–ca. 1720,,Devidasa of Nurpur,,1670,1730,dated 1694–95,1694,1695,"Opaque watercolor, ink, silver, and gold on paper",Overall: 8 5/8 x 12 3/4in. (21.9 x 32.4cm) Painting within rules: 6 3/4 x 11 1/4 in. (17.2 x 28.6 cm),"Bequest of Cora Timken Burnett, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/37941,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -18.85.2,false,true,37877,Asian Art,Painting,,"India (Rajasthan, Jaipur)",,,,,Artist,Attributed to,Sahib Ram,"active reign of Maharaja Sawai Pratap Singh, 1778–1803",,SAHIB RAM,,1778,1803,ca. 1800,1790,1810,Ink and opaque watercolor on paper,H. 27 1/4 in. (69.2 cm); W. 18 1/2 in. (47 cm),"Rogers Fund, 1918",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/37877,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -18.85.4,false,true,38465,Asian Art,Drawing,,"India (Rajasthan, Jaipur)",,,,,Artist,Attributed to,Sahib Ram,"active reign of Maharaja Sawai Pratap Singh, 1778–1803",,SAHIB RAM,,1778,1803,ca. 1800,1790,1810,Ink on paper,26 x 18 1/4 in. (66 x 46.4 cm),"Rogers Fund, 1918",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/38465,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.299,false,true,50486,Asian Art,Hanging scroll,"탄은 이정 대나무 조선|灘隱 李霆 墨竹圖 朝鮮|Bamboo in the wind",Korea,Joseon dynasty (1392–1910),,,,Artist,,Yi Jeong,"Korean, 1541–1626",,Yi Jeong,,1541,1626,early 17th century,1600,1633,Hanging scroll; ink on silk with gold on colophon,Image: 45 1/2 x 21 in. (115.6 x 53.3 cm) Overall with mounting: 87 13/16 × 26 5/16 in. (223 × 66.8 cm) Overall with knobs: 87 13/16 × 28 3/4 in. (223 × 73 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/50486,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1995.419,true,true,38648,Asian Art,Torso,,"Pakistan (ancient region of Gandhara, mondern Peshawar region)",,,,,Artist,Probably,Sahri-Bahlol Workshop,,,Sahri-Bahlol Workshop,,0350,0550,ca. 5th century,350,550,Schist,H. 64 1/2 in. (163.8 cm),"Purchase, Lila Acheson Wallace Gift, 1995",,,,,,,,,,,,Sculpture,,http://www.metmuseum.org/art/collection/search/38648,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2008.359.4,false,true,74649,Asian Art,Drawing,,"India (Pahari Hills, Guler)",,,,,Artist|Artist,Attributed to the|possibly,Seu Family|Manaku,active ca. 1725–60,,Seu Family|Manaku,,1715,1770,mid-18th century,1736,1770,Ink and wash on paper,Image (sight): 8 1/2 x 12 3/8 in. (21.6 x 31.4 cm),"Gift of Subhash Kapoor, in memory of his parents, Smt Shashi Kanta and Shree Parshotam Ram Kapoor, 2008",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/74649,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2008.359.5,false,true,74650,Asian Art,Painting,,"India (Pahari Hills, Guler)",,,,,Artist|Artist,Attributed to the|possibly,Seu Family|Manaku,active ca. 1725–60,,Seu Family|Manaku,,1715,1770,mid-18th century,1736,1770,Ink and wash on paper,Image (sight): 8 1/2 x 12 1/8 in. (21.6 x 30.8 cm),"Gift of Subhash Kapoor, in memory of his parents, Smt Shashi Kanta and Shree Parshotam Ram Kapoor, 2008",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/74650,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2008.359.6,false,true,74651,Asian Art,Painting,,"India (Pahari Hills, Guler)",,,,,Artist|Artist,Attributed to the|possibly,Seu Family|Manaku,active ca. 1725–60,,Seu Family|Manaku,,1715,1770,mid-18th century,1736,1770,Ink and wash on paper,Image (sight): 8 3/4 x 13 in. (22.2 x 33 cm),"Gift of Subhash Kapoor, in memory of his parents, Smt Shashi Kanta and Shree Parshotam Ram Kapoor, 2008",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/74651,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.100.90,false,true,51383,Asian Art,Folding fan mounted as an album leaf,,China,Ming dynasty (1368–1644),,,,Calligrapher,,Gong Dingzi,"Chinese, 1615–1673",,Gong Dingzi,,1615,1673,,1368,1644,Folding fan mounted as an album leaf; ink on gold paper,6 1/2 x 20 1/2 in. (16.5 x 52.1 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/51383,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1995.271,false,true,39626,Asian Art,Incense holder,,China,Ming dynasty (1368–1644),,,,Artist,,Zhu Sansong,active ca. 1573–1619,,Zhu Sansong,,1573,1619,late 16th–early 17th century,1567,1633,Bamboo,Overall (including new wooden ends) H. 7 in. (17.8 cm),"Purchase, Friends of Asian Art Gifts, 1995",,,,,,,,,,,,Bamboo,,http://www.metmuseum.org/art/collection/search/39626,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.784.9,false,true,65630,Asian Art,Hanging scroll,清 鄭簠 隷書軸|Poetic Maxim,China,Qing dynasty (1644–1911),,,,Artist,,Zheng Fu,"Chinese, 1622–1693",,Zheng Fu,,1622,1693,dated 1691,1691,1691,Hanging scroll; ink on paper,Image: 45 1/2 × 17 3/4 in. (115.6 × 45.1 cm) Overall with mounting: 97 3/4 × 25 1/2 in. (248.3 × 64.8 cm) Overall with knobs: 97 3/4 × 29 1/4 in. (248.3 × 74.3 cm),"Gift of Julia and John Curtis, in memory of Marie-Hélène and Guy Weill, 2015",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/65630,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1971.74a–h,false,true,64086,Asian Art,Screen,清晚期 盧葵生款 剔紅郭子儀賀壽圖屏風|Screen with birthday celebration for General Guo Ziyi,China,Qing dynasty (1644–1911),,,,Artist,,Lu Guisheng,"Chinese, active 1821–50",,"Lu, Guisheng",,1821,1850,mid-19th century,1834,1850,Carved red and black lacquer,Open flat: 84 1/8 in. × 12 ft. 4 in. (213.7 × 375.9 cm) Open curved: 84 1/8 in. × 11 ft. 3 13/16 in. × 35 7/16 in. (213.7 × 345 × 90 cm),"Gift of Mrs. Henry-George J. McNeary, 1971",,,,,,,,,,,,Lacquer,,http://www.metmuseum.org/art/collection/search/64086,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.29,false,true,40512,Asian Art,Handscroll,元 鮮于樞 草書石鼓歌 卷|Song of the Stone Drums,China,Yuan dynasty (1271–1368),,,,Artist,,Xianyu Shu,"Chinese, 1246–1302",,Xianyu Shu,,1246,1302,dated 1301,1301,1301,Handscroll; ink on paper,Image: 17 11/16 in. x 15 ft. 1 1/16 in. (44.9 x 459.9 cm) Overall with mounting: 18 in. x 38 ft. 11 3/16 in. (45.7 x 1186.7 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/40512,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -34.13,false,true,51042,Asian Art,Figure,,China,Qing dynasty (1644–1911),,,,Artist,,Su Xuejin,1869–1919,,Su Xuejin,,1869,1919,late 19th–early 20th century,1867,1933,"Porcelain with clear glaze (Dehua ware, Fujian Province)",H. 15 1/2 in. (39.4 cm); W. 5 in. (12.7 cm); D. 4 in. (10.2 cm),"Purchase, Anita M. Linzee Bequest, 1934",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/51042,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1995.181,false,true,36458,Asian Art,Table screen,"大理石插屏|Table Screen Inscribed with the Poem ""Lisao,"" now converted to a wall panel",China,Ming dynasty (1368–1644),,,,Artist,,Li Mi,active early 17th century,,Li Mi,,1600,1699,dated 1624,1624,1624,Veined marble with wood frame,H. 16 5/8 (42.2 cm); W. 14 3/8in. (36.5cm),"Purchase, Eileen W. Bamberger Bequest, in memory of her husband, Max Bamberger, 1995",,,,,,,,,,,,Furniture,,http://www.metmuseum.org/art/collection/search/36458,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.500.6.22,false,true,39879,Asian Art,Incense burner,,China,Ming dynasty (1368–1644),,,,Artist,Attributed to,Hu Wenming,"Chinese, active late 16th–early 17th century",,Hu Wenming,,1550,1650,late 16th–17th century,1567,1699,Copper with gilding,H. 6 1/4 in. (15.9 cm); W. 2 7/8 in. (7.3 cm),"Gift of Florence and Herbert Irving, 2015",,,,,,,,,,,,Metalwork,,http://www.metmuseum.org/art/collection/search/39879,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.500.6.24,false,true,39878,Asian Art,Vase,,China,Ming dynasty (1368–1644),,,,Artist,Attributed to,Hu Wenming,"Chinese, active late 16th–early 17th century",,Hu Wenming,,1550,1650,16th–17th century,1500,1799,Bronze with gilding,H. 3 7/8 in. (9.8 cm),"Gift of Florence and Herbert Irving, 2015",,,,,,,,,,,,Metalwork,,http://www.metmuseum.org/art/collection/search/39878,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.500.6.23a–c,false,true,39877,Asian Art,Incense box,,China,Ming dynasty (1368–1644),,,,Artist,Attributed to,Hu Wenming,"Chinese, active late 16th–early 17th century",,Hu Wenming,,1550,1650,16th–17th century,1500,1799,Bronze with gilding,H. 1 1/4 in.; Diam. 2 7/8 in.,"Gift of Florence and Herbert Irving, 2015",,,,,,,,,,,,Metalwork,,http://www.metmuseum.org/art/collection/search/39877,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.273,false,true,684605,Asian Art,Vase,十七/十八世紀 銅錯銀絲題竇常詩文饕餮耳方壺|Vase,China,Qing dynasty (1644–1911),,,,Artist,Attributed to,Shisou,"Chinese, active first half the 17th century",,Shisou,,1600,1649,17th–18th century,1600,1799,Bronze,H. 5 1/4 in. (13.3 cm); W. 2 1/2 in. (6.4 cm); D. 2 5/16 in. (5.9 cm),"Purchase, Seymour Fund, Barbara and Sorrell Mathes Gift, and various donors, 2015",,,,,,,,,,,,Metalwork,,http://www.metmuseum.org/art/collection/search/684605,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.784.5,false,true,65624,Asian Art,Album leaf,"明/清 惲向 欲雪圖 冊頁|Snowscape, from Album for Zhou Lianggong",China,,,,,Artist,,Yun Xiang,"Chinese, 1586–1655",,Yun Xiang,,1586,1655,Undated,1586,1655,Double album leaf from a collective album of twelve paintings and facing pages of calligraphy; ink and color on paper,9 3/4 x 13 in. (24.8 x 33 cm),"Gift of Julia and John Curtis, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/65624,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.784.3,false,true,73650,Asian Art,Handscroll,明/清 張宏 桃源勝概圖 卷|Peach Blossom Spring,China,Ming dynasty (1368–1644),,,,Artist,,Zhang Hong,"Chinese, 1577–after 1652",,Zhang Hong,,1577,1652,dated 1638,1638,1638,Handscroll; ink and color on paper,Image: 9 1/2 × 74 5/8 in. (24.1 × 189.5 cm) Overall with mounting: 11 in. × 21 ft. 1 5/8 in. (28 × 644.2 cm),"Gift of Julia and John Curtis, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/73650,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -69.242.1–.6,false,true,72262,Asian Art,Album,明 盛茂曄 唐詩意山水圖 冊|Landscapes after Tang Poems,China,Ming dynasty (1368–1644),,,,Artist,,Sheng Maoye,"Chinese, active ca. 1615–ca. 1640",,Sheng Maoye,,1615,1640,mid 17th century,1634,1666,Album of six paintings; ink and color on silk,.1: 11 1/4 x 12 in. (28.6 x 30.5 cm) .2: 11 1/4 x 12 in. (28.6 x 30.5 cm) .3: 11 5/8 x 12 in. (29.5 x 30.5 cm) .4: 11 1/4 x 12 in. (28.6 x 30.5 cm) .5: 11 7/8 x 12 in. (30.2 x 30.5 cm) .6: 11 5/8 x 12 in. (29.5 x 30.5 cm),"The Sackler Fund, 1969",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/72262,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.363.128,false,true,49125,Asian Art,Hanging scroll,,China,Qing dynasty (1644–1911),,,,Artist,,Li Yu,1611–1680,,Li Yu,,1611,1680,dated 1648,1648,1648,Hanging scroll; ink on satin,Image: 33 5/8 x 11 1/4 in. (85.4 x 28.6 cm) Overall with mounting: 85 x 17 1/8 in. (215.9 x 43.5 cm) Overall with knobs: 85 x 20 5/8 in. (215.9 x 52.4 cm),"Bequest of John M. Crawford Jr., 1988",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49125,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1988.324.5,false,true,44569,Asian Art,Album leaf,清 顧澐 冊頁|Clouds and Spring Trees at Dusk,China,Qing dynasty (1644–1911),,,,Artist,,Gu Yun,1835–1896,,Gu Yun,,1835,1896,dated 1888,1888,1888,Album leaf; ink on paper,9 3/4 x 13 1/2 in. (24.8 x 34.3 cm),"Gift of Robert Hatfield Ellsworth, in memory of La Ferne Hatfield Ellsworth, 1988",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/44569,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.267.57,false,true,36173,Asian Art,Folding fan mounted as an album leaf,清 顧澐 懷素蕉林午睡圖 扇面|Huai Su in the Banana Grove,China,Qing dynasty (1644–1911),,,,Artist,,Gu Yun,1835–1896,,Gu Yun,,1835,1896,dated 1869,1869,1869,Folding fan mounted as an album leaf; ink and color on alum paper,6 3/4 x 20 1/8 in. (17.1 x 51.1 cm),"Gift of Robert Hatfield Ellsworth, in memory of La Ferne Hatfield Ellsworth, 1986",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36173,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.784.4,false,true,73652,Asian Art,Handscroll,明/清 黃向堅 萬里尋親圖 卷|Searching for My Parents,China,Qing dynasty (1644–1911),,,,Artist,,Huang Xiangjian,"Chinese, 1609–1673",,Huang Xiangjian,,1609,1673,dated 1656,1656,1656,Handscroll; ink and color on silk,Image: 14 3/8 in. × 18 ft. 2 in. (36.5 × 553.7 cm) Overall with mounting: 14 3/4 in. × 27 ft. 1 in. (37.5 × 825.5 cm),"Gift of Julia and John Curtis, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/73652,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.278.7,false,true,36101,Asian Art,Hanging scroll,清 楊晉 滄洲牧牛圖 軸|Landscape with Figures,China,Qing dynasty (1644–1911),,,,Artist,,Yang Jin,"Chinese, 1644–1728",,Yang Jin,,1644,1728,Dated 1726,1726,1726,Hanging scroll; ink and color on paper,Image: 47 x 21 1/2 in. (119.4 x 54.6 cm) Overall with mounting: 92 1/8 x 28 1/8 in. (234 x 71.4 cm) Overall with knobs: 92 1/8 x 31 7/8 in. (234 x 81 cm),"Gift of Mr. and Mrs. Earl Morse, 1972",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36101,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.784.10,false,true,646992,Asian Art,Album,清 汪鋆 阮元遺事十景圖 冊 紙本|Ten Sites Associated with Ruan Yuan,China,Qing dynasty (1644–1911),,,,Artist,,Wang Jun,"Chinese, 1816–after 1883",,Wang Jun,,1816,1883,dated 1883,1883,1883,Album of ten paintings; ink and color on paper,Image (each): 11 × 13 1/4 in. (27.9 × 33.7 cm),"Gift of Julia and John Curtis, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/646992,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.784.6,false,true,65625,Asian Art,Album leaf,"清 葉欣 聼雪圖 冊頁|Snowscape, from Album for Zhou Lianggong",China,Qing dynasty (1644–1911),,,,Artist,,Ye Xin,"Chinese, active ca. 1640–1673",,Ye Xin,,1640,1640,undated,1640,1673,Double album leaf from a collective album of twelve paintings and facing pages of calligraphy; ink and color on paper,9 3/4 x 13 in. (24.8 x 33 cm),"Gift of Julia and John Curtis, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/65625,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -02.18.623e,false,true,51234,Asian Art,Handscroll,敬芝軒第貳圖|Second View of the Studio for Respecting the Fungus of Longevity,China,Qing dynasty (1644–1911),,,,Artist,,Yang Tianbi,"Chinese, active early 19th century",,Yang Tianbi,,1800,1833,1825,1825,1825,Handscroll; ink and color on silk,8 3/8 x 31 in. (21.3 x 78.7 cm),"Gift of Heber R. Bishop, 1902",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51234,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2008.673,false,true,39548,Asian Art,Hanging scroll,元 張遜 石上松花圖 軸|Rocky Landscape with Pines,China,Yuan dynasty (1271–1368),,,,Artist,,Zhang Xun,"Chinese, ca. 1295–after 1349",,ZHANG XUNG,,1295,1349,before 1346,1295,1345,Hanging scroll; ink on silk,Image: 35 3/4 × 16 3/4 in. (90.8 × 42.5 cm) Overall with mounting: 76 5/8 × 22 3/4 in. (194.6 × 57.8 cm) Overall with knobs: 76 5/8 × 26 1/8 in. (194.6 × 66.4 cm),"Ex coll.: C. C. Wang Family, Gift of Oscar L. Tang Family, 2008",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/39548,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1982.2.2,false,true,42328,Asian Art,Hanging scroll,元 陸廣 丹臺春曉圖 軸|Spring Dawn Over the Elixir Terrace,China,early Ming dynasty (1368–1644),,,,Artist,,Lu Guang,"Chinese, ca. 1300–after 1371",,Lu Guang,,1300,1371,ca. 1369,1359,1379,Hanging scroll; ink on paper,Image: 24 1/4 x 10 1/4 in. (61.6 x 26 cm) Overall with mounting: 87 1/2 x 17 5/8 in. (222.3 x 44.8 cm) Overall with knobs: 87 1/2 x 20 5/8 in. (222.3 x 52.4 cm),"Ex coll.: C. C. Wang Family, Edward Elliott Family Collection, Purchase, The Dillon Fund Gift, 1982",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/42328,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.500.8.3,false,true,78125,Asian Art,Handscroll,,China,Ming dynasty (1368–1644),,,,Artist,,Zhan Jingfeng,"Chinese, 1520–1602",,Zhan Jingfeng,,1520,1602,,1520,1602,Handscroll; ink on paper,Image: 12 1/4 x 22 in. (31.1 x 55.9 cm),"Gift of Florence and Herbert Irving, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/78125,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2015.500.8.2a, b",false,true,78085,Asian Art,Two leaves from an album,,China,Ming dynasty (1368–1644),,,,Artist,,Cheng Jiasui,"Chinese, 1565–1644",,Cheng Jiasui,,1565,1644,,1368,1644,Two leaves from an album; ink and color on paper,Each: 14 x 14 in. (35.6 x 35.6 cm),"Gift of Florence and Herbert Irving, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/78085,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1981.285.10,false,true,45669,Asian Art,Hanging scroll,明 周文靖 漁隱圖 軸|Rustic Retreat among Fishermen,China,Ming dynasty (1368–1644),,,,Artist,,Zhou Wenjing,"Chinese, active ca. 1430–after 1463",,Zhou Wenjing,,1430,1463,,1430,1530,Hanging scroll; ink and color on silk,Image: 35 15/16 x 16 1/2 in. (91.3 x 41.9 cm) Overall with mounting: 79 1/2 x 21 5/16 in. (201.9 x 54.1 cm) Overall with knobs: 79 1/2 x 23 1/8 in. (201.9 x 58.7 cm),"Edward Elliott Family Collection, Purchase, The Dillon Fund Gift, 1981",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45669,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.220.94,false,true,51491,Asian Art,Hanging scroll,,China,Qing dynasty (1644–1911),,,,Artist,,Zheng Zhong,"Chinese, active ca. 1612–48",,Zheng Zhong,,1612,1648,,1612,1648,Hanging scroll; ink and color on silk,Image: 34 × 12 3/8 in. (86.4 × 31.4 cm) Overall with mounting: 49 3/4 × 17 3/4 in. (126.4 × 45.1 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51491,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -47.18.6,false,true,51638,Asian Art,Handscroll,,China,Ming dynasty (?) (1368–1644),,,,Artist,Attributed to,Tai Wan,"Chinese, active 1111–25",,Tai Wan,,1111,1125,,1368,1644,Handscroll; ink and color on silk,Image: 11 in. × 21 ft. 1 1/8 in. (27.9 × 642.9 cm),"From the Collection of A. W. Bahr, Purchase, Fletcher Fund, 1947",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51638,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1991.14,false,true,44630,Asian Art,Handscroll,明 鄭重 搜山圖 卷|Searching the Mountains for Demons,China,late Ming dynasty (1368–1644),,,,Artist,,Zheng Zhong,"Chinese, active ca. 1612–48",,Zheng Zhong,,1612,1648,,1612,1644,Handscroll; ink and color on paper,10 5/8 in. x 27 ft. 9 1/2 in. (27 x 847.1 cm),"Purchase, Bequest of Dorothy Graham Bennett, 1991",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/44630,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -52.177.9,false,true,51784,Asian Art,Hanging scroll,,China,Qing dynasty (1644–1911),,,,Artist,,Empress Dowager Cixi,"Chinese, 1835–1908",,Cixi Empress Dowager,,1835,1908,18th–19th century,1700,1899,Hanging scroll; ink on gold-flecked paper,45 x 24 1/2 in. (114.3 x 62.2 cm),"Bequest of Katherine S. Dreier, 1952",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/51784,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.784.8,false,true,73330,Asian Art,Handscroll,明 二家法書合卷|Joint Calligraphy,China,Ming dynasty (1368–1644),,,,Artist|Artist,,Ni Yuanlu|Huang Daozhou,"Chinese, 1593–1644|Chinese, 1585–1646",,Ni Yuanlu|Huang Daozhou,,1593 |1585,1644 |1646,dated 1632,1632,1632,Handscroll; ink on satin,Image: 10 11/16 x 57 3/4 in. (27.1 x 146.7 cm) Overall with mounting: 10 13/16 x 357 7/16 in. (27.5 x 907.9 cm),"Gift of Julia and John Curtis, 2015",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/73330,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.100.115,false,true,51398,Asian Art,Fan mounted as an album leaf,,China,Song dynasty (?) (960–1279),,,,Artist|Artist,Formerly Attributed to,Huang Jucai|Unidentified Artist,"Chinese, Song dynasty, 933–after 993",,Huang Jucai|Unidentified Artist,,0933,1000,ca. 1000,990,1010,Fan mounted as an album leaf; ink and color on silk,7 7/8 x 8 7/8 in. (20.0 x 22.5 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51398,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.100.104,false,true,40071,Asian Art,Fan mounted as an album leaf,南宋 佚名 “三元得祿”圖扇頁|Gibbons Raiding an Egret's Nest,China,Southern Song dynasty (1127–1279),,,,Artist|Artist,Formerly Attributed to,Yi Yuanji|Unidentified Artist,"Chinese, died 1066",Chinese,Yi Yuanji|Unidentified Artist,,0966,1066,late 12th century,1167,1199,Fan mounted as an album leaf; ink and color on silk,Image: 9 1/2 in. × 9 in. (24.1 × 22.9 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/40071,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1981.285.1,false,true,48967,Asian Art,Hanging scroll,明 倣米萬鍾 行書開襟揮手聯句 軸|Poem,China,Ming dynasty (1368–1644),,,,Artist|Artist,After,Unidentified Artist|Mi Wanzhong,"Chinese, 1570–1628",,Unidentified Artist|Mi Wanzhong,,1570,1628,,1368,1644,Hanging scroll; ink on paper,Image: 134 3/4 x 39 1/8 in. (342.3 x 99.4 cm) Overall: 186 3/4 x 48 in. (474.3 x 121.9 cm) Overall with knobs: 186 3/4 x 53 3/4 in. (474.3 x 136.5 cm),"Edward Elliott Family Collection, Purchase, The Dillon Fund Gift, 1981",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/48967,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -47.18.92,false,true,51704,Asian Art,Handscroll,明 佚名（仿）吳育 新安汪氏譜牒 卷|Portrait of a Member and Record of the Wang Family,China,Ming dynasty (1368-1644),,,,Artist|Artist,after,Unidentified Artist|Wu Yu,"Chinese, 1004–1058",,"Unidentified Artist|Wu, Yu",,1004,1058,,1368,1644,"Handscroll; calligraphy, ink on paper; portrait, ink and color on paper",13 1/8 x 9 3/4 in. (33.3 x 24.8 cm),"From the Collection of A. W. Bahr, Purchase, Fletcher Fund, 1947",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51704,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.220.99d,false,true,51497,Asian Art,Album leaf,,China,Ming (1368–1644)–Qing (1644–1911) dynasty,,,,Artist|Artist,After,Guan Daosheng|Unidentified Artist,1262–1319,,Guan Daosheng|Unidentified Artist,,1262,1319,,1368,1911,Album leaf; ink on silk,10 3/4 x 10 3/4 in. (27.3 x 27.3 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51497,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.220.5,false,true,51419,Asian Art,Handscroll,,China,Ming (1368–1644) or Qing dynasty (1644–1911),,,,Artist|Artist,In the Style of,Guo Zhongshu|Unidentified Artist,"Chinese, died 977",,Guo Zhongshu|Unidentified Artist,,0877,0977,,1571,1599,Handscroll; ink and color on silk,12 1/2 in. × 16 ft. 1 in. (31.8 × 490.2 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51419,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2015.300.81.1, .2",false,true,671030,Asian Art,Screens,『伊勢物語』|Forty-nine scenes from the Tales of Ise (Ise monogatari),Japan,Edo period (1615–1868),,,,Calligrapher,Attributed to,Satomura Genchin,"Japanese, 1591–1665",,Satomura Genchin,,1591,1665,mid-17th century,1634,1666,"Pair of six-panel folding screens, with ninety-eight paintings and poem cards (shikishi) applied to gold leaf on paper; paintings: ink and red ink on paper, text: ink on paper",Image (each): 42 1/4 in. × 8 ft. 9 7/8 in. (107.3 × 268.9 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Screens,,http://www.metmuseum.org/art/collection/search/671030,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.247,false,true,671039,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Calligrapher,,Ike Taiga,"Japanese, 1723–1776",,Ike Taiga,,1723,1776,1759,1759,1759,Hanging scroll; ink on paper,Image: 9 5/16 × 13 7/16 in. (23.7 × 34.1 cm) Overall with mounting: 41 1/4 × 18 3/8 in. (104.7 × 46.7 cm) Overall with knobs: 41 1/4 × 20 5/16 in. (104.7 × 51.6 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/671039,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.248,false,true,671043,Asian Art,Album,「大雅堂画弁題詩」|“Paintings by Taigadō with Colophons”,Japan,Edo period (1615–1868),,,,Calligrapher,,Ike Taiga,"Japanese, 1723–1776",", colophons by eight calligraphers",Ike Taiga,,1723,1776,late 18th–early 19th century,1776,1833,Album,Image: 11 7/8 × 16 1/4 in. (30.2 × 41.2 cm) Album: 11 7/8 × 15 7/8 × 1 3/16 in. (30.2 × 40.4 × 3 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/671043,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.237,false,true,687621,Asian Art,Hanging scroll,『古今和歌集』断簡|Three poems from the Collection of Poems Ancient and Modern (Kokin wakashū),Japan,Kamakura period (1185–1333),,,,Calligrapher,Traditionally attributed to,Fujiwara no Tameyori,"Japanese, 939?–998",,Fujiwara no Tameyori,,0939,0998,13th century,1200,1299,Page from a book mounted as a hanging scroll; ink on paper,Image: 9 3/16 × 5 9/16 in. (23.4 × 14.1 cm) Overall with mounting: 54 5/16 × 11 in. (138 × 28 cm) Overall with knobs: 54 5/16 × 13 3/8 in. (138 × 34 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/687621,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.240,false,true,670905,Asian Art,Book,"詠歌大概|Manuscript Version of “Fundamentals of Poetic Composition” (Eiga taigai), compiled by Fujiwara no Teika (1162–1241)",Japan,Muromachi period (1392–1573),,,,Calligrapher,,Konoe Taneie,"Japanese, 1503–1566",,Konoe Taneie,,1503,1566,1531,1531,1531,Book of 102 waka by various poets; ink on paper,Image (closed): 10 1/4 × 7 1/16 in. (26 × 18 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/670905,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2015.300.164a, b",false,true,53455,Asian Art,Album leaves mounted as hanging scrolls,,Japan,Edo period (1615–1868),,,,Artist|Calligrapher,,Ike Taiga|Minagawa Kien,"Japanese, 1723–1776|Japanese, 1734–1807",,Ike Taiga|Minagawa Kien,,1723 |1734,1776 |1807,late 18th–early 19th century,1767,1807,Album leaves mounted as hanging scrolls; ink on paper,Image (a): 9 in. × 14 7/16 in. (22.8 × 36.6 cm) Overall with mounting (a): 39 3/8 × 19 7/16 in. (100 × 49.4 cm) Overall with knobs (a): 39 3/8 × 21 7/16 in. (100 × 54.5 cm) Image (b): 9 5/16 × 14 15/16 in. (23.6 × 38 cm) Overall with mounting (b): 39 3/8 × 19 1/2 in. (100 × 49.6 cm) Overall with knobs (b): 39 3/8 × 21 9/16 in. (100 × 54.7 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/53455,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2015.300.167a, b",false,true,670542,Asian Art,Album leaves mounted as hanging scrolls,,Japan,Edo period (1615–1868),,,,Calligrapher|Artist,,Rokunyo|Ike Taiga,"Japanese, 1737–1801|Japanese, 1723–1776",,Rokunyo|Ike Taiga,,1737 |1723,1801 |1776,late 18th–early 19th century,1767,1833,Album leaves mounted as hanging scrolls; ink on paper,Image (each): 9 × 14 5/8 in. (22.9 × 37.1 cm) Overall with mounting (each): 39 1/4 × 21 1/2 in. (99.7 × 54.6 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/670542,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.500.9.19,false,true,78056,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Calligrapher|Artist,,Kameda Bōsai|Sakai Hōitsu,"Japanese, 1752–1826|Japanese, 1761–1828",,Kameda Bōsai|Sakai Hōitsu,,1752 |1761,1826 |1828,ca. 1821,1821,1821,Hanging scroll; ink on paper,Image: 44 3/8 × 14 3/4 in. (112.7 × 37.5 cm) Overall with mounting: 68 1/4 × 14 3/4 in. (173.4 × 37.5 cm) Overall with knobs: 16 7/8 in. (42.9 cm),"Gift of Florence and Herbert Irving, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/78056,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.80,false,true,670935,Asian Art,Handscroll,,Japan,Edo period (1615–1868),,,,Artist|Artist|Artist,Calligraphy by|Calligraphy by,Kano Tōun|Ichiki Konzan|Saskai Genryō,"Japanese, 1625–1694|Japanese|Japanese, 1650–1723",,Kano Tōun|Ichiki Konzan|Saskai Genryō,,1625 |1650,1694 |1723,1675,1675,1675,Handscroll; ink and color on paper,Image: 10 5/8 in. × 16 ft. 10 1/16 in. (27 × 513.3 cm) Overall with knobs: 11 7/16 in. × 16 ft. 10 1/16 in. (29 × 513.3 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/670935,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.141,false,true,53447,Asian Art,Hanging scroll,柳下美人図|Courtesan and her Attendants under a Willow Tree,Japan,Edo period (1615–1868),,,,Artist|Artist|Artist,Inscribed by|Inscribed by,Unchō|Kyokutei Bakin|Kitao Masanobu (Santō Kyōden),"Japanese, active late 18th century|1767–1848|Japanese, 1761–1816",,Unchō|Kyokutei Bakin|Kitao Masanobu (Santō Kyōden),,1740 |1767 |1761,1820 |1848 |1816,1796,1600,1870,"Hanging scroll; ink, color, and gold on silk",36 1/2 x 13 3/8 in. (92.7 x 34 cm) Overall with mounting: 72 13/16 × 18 7/8 in. (185 × 48 cm) Overall with knobs: 72 13/16 × 20 5/8 in. (185 × 52.4 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/53447,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.500.9.8a–c,false,true,78167,Asian Art,Set of three hanging scrolls,,Japan,Edo period (1615–1868),,,,Calligrapher|Calligrapher|Calligrapher,a)|c),Hiin Tsūyō|Sokuhi Nyoitsu (Jifei Ruyi)|Mokuan Shōtō,"Chinese/ Japanese, 1593–1661|Chinese/ Japanese, 1616–1671|Chinese, 1611–1684",,Hiin Tsūyō|Sokuhi Nyoitsu|Mokuan Shōtō,,1593 |1616 |1611,1661 |1671 |1684,17th century,1600,1699,Set of three hanging scrolls; ink on paper,Image (a (right)): 48 7/8 in. × 11 in. (124.1 × 27.9 cm) Overall with mounting (a (right)): 79 3/4 × 15 3/8 in. (202.6 × 39.1 cm) Overall with knobs (a (right)): 79 3/4 × 17 1/4 in. (202.6 × 43.8 cm) Image (b (center)): 48 7/8 × 14 1/2 in. (124.1 × 36.8 cm) Overall with mounting (b (center)): 79 5/8 × 15 1/2 in. (202.2 × 39.4 cm) Overall with knobs (b (center)): 79 5/8 × 17 3/8 in. (202.2 × 44.1 cm) Image (c (left)): 48 7/8 in. × 11 in. (124.1 × 27.9 cm) Overall with mounting (c (left)): 79 3/4 × 15 3/8 in. (202.6 × 39.1 cm) Overall with knobs (c (left)): 79 3/4 × 17 1/4 in. (202.6 × 43.8 cm),"Gift of Florence and Herbert Irving, 2015",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/78167,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.244,false,true,671036,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Calligrapher,,Ike Taiga,"Japanese, 1723–1776",,Ike Taiga,,1723,1776,18th century,1723,1776,Hanging scroll; ink on paper,Image: 10 1/16 × 9 1/8 in. (25.5 × 23.2 cm) Overall with mounting: 41 9/16 × 14 3/4 in. (105.5 × 37.4 cm) Overall with knobs: 41 9/16 × 16 5/8 in. (105.5 × 42.3 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/671036,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.245,false,true,671037,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Calligrapher,,Ike Taiga,"Japanese, 1723–1776",,Ike Taiga,,1723,1776,18th century,1723,1776,Hanging scroll; ink on paper,Image: 9 5/16 × 8 1/8 in. (23.6 × 20.6 cm) Overall with mounting: 42 15/16 × 13 11/16 in. (109 × 34.8 cm) Overall with knobs: 42 15/16 × 15 3/4 in. (109 × 40 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/671037,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.246,false,true,671038,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Calligrapher,,Ike Taiga,"Japanese, 1723–1776",,Ike Taiga,,1723,1776,18th century,1723,1776,Hanging scroll; ink on paper,Image: 9 5/16 × 13 1/8 in. (23.7 × 33.4 cm) Overall with mounting: 40 1/4 × 17 13/16 in. (102.3 × 45.3 cm) Overall with knobs: 40 1/4 × 19 3/4 in. (102.3 × 50.2 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/671038,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.195,false,true,670944,Asian Art,Album of fifty-four sketches,,Japan,Edo (1615–1868)–Meiji period (1868–1912),,,,Artist|Artist|Artist,,Tsubaki Chinzan|Watanabe Kazan|Takagi Goan,"Japanese, 1801–1854|Japanese, 1793–1841|Japanese, active 19th century",,Tsubaki Chinzan|Watanabe Kazan|Takagi Goan,,1801 |1793 |1800,1854 |1841 |1899,19th century,1800,1899,Album of fifty-four sketches; ink and color on paper,Album: 11 7/16 × 12 3/8 × 3 13/16 in. (29 × 31.4 × 9.7 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/670944,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2015.300.166a, b",false,true,670541,Asian Art,Two album leaves mounted as two hanging scrolls,,Japan,Edo period (1615–1868),,,,Calligrapher|Artist,,Kameda Bōsai|Ike Taiga,"Japanese, 1752–1826|Japanese, 1723–1776",,Kameda Bōsai|Ike Taiga,,1752 |1723,1826 |1776,18th–19th century,1723,1826,Album leaves mounted as hanging scrolls; ink on paper,Image (a): 8 7/8 × 14 3/4 in. (22.6 × 37.5 cm) Overall with mounting (a): 39 3/8 × 19 1/2 in. (100 × 49.5 cm) Overall with knobs (a): 39 3/8 × 21 1/2 in. (100 × 54.6 cm) Image (b): 9 3/16 × 14 1/2 in. (23.4 × 36.9 cm) Overall with mounting (b): 39 3/8 × 19 9/16 in. (100 × 49.7 cm) Overall with knobs (b): 39 3/8 × 21 7/8 in. (100 × 55.5 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/670541,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.165a–c,false,true,670540,Asian Art,Three album leaves mounted as three hanging scrolls,,Japan,Edo period (1615–1868),,,,Calligrapher|Artist|Calligrapher,,Shinozaki Shōchiku|Ike Taiga|Ōkubo Shibutsu,"Japanese, 1781–1851|Japanese, 1723–1776|Japanese, 1766–1837",,Shinozaki Shōchiku|Ike Taiga|Ōkubo Shibutsu,,1781 |1723 |1766,1851 |1776 |1837,18th–19th century,1723,1851,Album leaves mounted as hanging scrolls; ink on paper,Image (a): 8 3/4 × 14 7/16 in. (22.2 × 36.7 cm) Overall with mounting (a): 39 3/16 × 19 1/2 in. (99.5 × 49.5 cm) Overall with knobs (a): 39 3/16 × 21 7/16 in. (99.5 × 54.5 cm) Image (b): 9 1/8 × 14 7/16 in. (23.1 × 36.7 cm) Overall with mounting (b): 39 5/16 × 19 7/16 in. (99.8 × 49.3 cm) Overall with knobs (b): 39 5/16 × 21 7/16 in. (99.8 × 54.5 cm) Image (c): 8 11/16 × 14 7/16 in. (22 × 36.7 cm) Overall with mounting (c): 39 3/8 × 19 7/16 in. (100 × 49.4 cm) Overall with knobs (c): 39 3/8 × 21 1/2 in. (100 × 54.6 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/670540,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.67.73,false,true,58906,Asian Art,Inrō,,Japan,Edo period (1615–1868),,,,Artist,In the Style of,Fang Shi Mopu,ca. 1588,,Fang Shi Mopu,,1588,1588,1721,1721,1721,"Lacquer, roiro, black hiramakie, takamakie; Interior: roiro and gold leaf",3 x 2 15/16 x 13/16 in. (7.6 x 7.5 x 2 cm),"Rogers Fund, 1913",,,,,,,,,,,,Inrō,,http://www.metmuseum.org/art/collection/search/58906,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.903,false,true,58827,Asian Art,Inrō,古墨形印籠 (文章司命)|Inrō with Chinese Scholars and Characters,Japan,Edo period (1615–1868),,,,Artist,based on design by,Kitajima Setsuzan,1636–1697,,Kitajima Setsuzan,,1636,1697,early to mid-18th century,1733,1766,"Three cases; lacquered wood with black and brown takamaki-e, togidashimaki-e Netsuke: manjū type, ivory; writing implements and books Ojime: malachite bead",3 9/16 x 1 11/16 x 5/8 in. (9.1 x 4.3 x 1.6 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Inrō,,http://www.metmuseum.org/art/collection/search/58827,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2015.300.226a, b",false,true,671019,Asian Art,Illustrated books,,Japan,Edo period (1615–1868),,,,Artist,,Nakagawa Tenju,"Japanese, died 1795",,Nakagawa Tenju,,,1795,1803,1803,1803,Two woodblock printed books; ink on paper,Each book 10 1/2 × 7 1/4 in. (26.7 × 18.4 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/671019,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2013.745a, b",false,true,78647,Asian Art,Illustrated books,,Japan,Edo period (1615–1868),,,,Artist,,Wang Gai,"Chinese, 1645–1710",,Wang Gai,,1645,1710,ca. 1748,1743,1753,Set of two woodblock printed books; ink and color on paper,11 1/8 × 7 1/16 in. (28.3 × 18 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78647,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.746a–e,false,true,78648,Asian Art,Illustrated books,,Japan,Edo period (1615–1868),,,,Artist,,Wang Gai,"Chinese, 1645–1710",,Wang Gai,,1645,1710,1753,1753,1753,Set of five woodblock printed books; ink and color on paper,each: 11 1/8 × 7 1/8 in. (28.3 × 18.1 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78648,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2015.300.74.1, .2",false,true,671028,Asian Art,Screens,金山西湖図屏風|Jinshan Island and West Lake,Japan,Edo period (1615–1868),,,,Artist,,Kano Sanraku,"Japanese, 1559–1635",,Kano Sanraku,,1559,1635,1630,1630,1630,"Pair of six-panel folding screens; ink, color, and gold on paper",Image: 60 1/16 in. × 11 ft. 9 in. (152.5 × 358.2 cm) Overall with mounting: 67 5/16 in. × 12 ft. 4 1/4 in. (170.9 × 376.6 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Screens,,http://www.metmuseum.org/art/collection/search/671028,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.500.9.2,false,true,78058,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Ōbaku Ingen,"Japanese, 1594–1673",,Ōbaku Ingen,,1594,1673,1615–1868,1615,1868,Hanging scroll; ink on paper,Image: 47 × 11 in. (119.4 × 27.9 cm) Overall with mounting: 80 1/2 × 15 1/2 in. (204.5 × 39.4 cm) Overall with knobs: 80 1/2 × 17 5/8 in. (204.5 × 44.8 cm),"Gift of Florence and Herbert Irving, 2015",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/78058,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2015.300.77.1, .2",false,true,53009,Asian Art,Screens,四季山水図屏風|Landscapes of the Four Seasons,Japan,Edo period (1615–1868),,,,Artist,,Kano Tan'yū,"Japanese, 1602–1674",,Kano Tan'yū,,1602,1674,1630s,1600,1700,Pair of six-panel folding screens; ink and color on paper,Image (each): 60 3/8 in. × 11 ft. 6 7/8 in. (153.4 × 352.7 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Screens,,http://www.metmuseum.org/art/collection/search/53009,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.242a,false,true,54779,Asian Art,Hanging scroll,桜下絵和歌色紙　紀 貫之|Poem by Ki no Tsurayuki (ca. 872–945) on Decorated Paper with Cherry Blossoms,Japan,Edo period (1615–1868),,,,Artist,,Ogata Sōken,"Japanese, 1621–1687",,Ogata Sōken,,1621,1687,mid- to late 17th century,1621,1687,Poem card (shikishi) mounted as a hanging scroll; ink and gold on paper,Image: 8 3/8 × 7 11/16 in. (21.2 × 19.5 cm) Overall with mounting (a): 41 1/4 × 12 1/2 in. (104.8 × 31.7 cm) Overall with knobs (a): 41 1/4 × 14 1/4 in. (104.8 × 36.2 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/54779,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.242b,false,true,727181,Asian Art,Hanging scroll,萩下絵和歌色紙　藤原家隆|Poem by Fujiwara no Ietaka (1158–1237) on Decorated Paper with Bush Clover,Japan,Edo period (1615–1868),,,,Artist,,Ogata Sōken,"Japanese, 1621–1687",,Ogata Sōken,,1621,1687,mid- to late 17th century,1621,1687,Poem card (shikishi) mounted as a hanging scroll; ink and gold on paper,Image: 8 3/8 × 7 5/8 in. (21.2 × 19.4 cm) Overall with mounting (b): 38 1/4 × 11 15/16 in. (97.2 × 30.4 cm) Overall with knobs (b): 38 1/4 × 13 7/8 in. (97.2 × 35.3 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/727181,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.83,false,true,53401,Asian Art,Screen,,Japan,Edo period (1615–1868),,,,Artist,,Hanabusa Itchō,"Japanese, 1652–1724",,Hanabusa Itchō,,1652,1724,after 1709,1700,1850,Six-panel folding screen; ink and color on paper,47 3/4 x 124 1/2 in. (121.3 x 316.2 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Screens,,http://www.metmuseum.org/art/collection/search/53401,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2004.366,false,true,72416,Asian Art,Screen,,Japan,Edo period (1615–1868),,,,Artist,,Watanabe Shikō,"Japanese, 1683–1755",,Watanabe Shikō,,1683,1755,first half of the 18th century,1700,1750,Two-panel folding screen; ink on paper,Image: 59 7/16 x 66 9/16 in. (151 x 169.1 cm),"Purchase, Friends of Asian Art Gifts, 2004",,,,,,,,,,,,Screens,,http://www.metmuseum.org/art/collection/search/72416,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.224a–c,false,true,670927,Asian Art,Illustrated books,,Japan,Edo period (1615–1868),,,,Artist,,Hanabusa Ippō,"Japanese, 1691–1760",,Hanabusa Ippō,,1691,1760,May 1751,1751,1751,Three woodblock printed books; ink on paper,Each book: 10 1/8 × 7 5/16 in. (25.7 × 18.5 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/670927,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.116,false,true,671018,Asian Art,Screen,白拍子・遊女図|Shirabyōshi Dancer and Female Servant; Courtesan and Girl Attendant,Japan,Edo period (1615–1868),,,,Artist,,Tsukioka Settei,"Japanese, 1710–1786",,Tsukioka Settei,,1710,1786,mid-18th century,1734,1766,"Hanging scroll paintings, remounted as a two‑panel folding screen; ink, color, and gold on silk",Image (each): 45 in. × 16 1/2 in. (114.3 × 41.9 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Screens,,http://www.metmuseum.org/art/collection/search/671018,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2015.300.157.1, .2",false,true,671023,Asian Art,Screens,山野行旅図屏風|Travels through Mountains and Fields,Japan,Edo period (1615–1868),,,,Artist,,Yosa Buson,"Japanese, 1716–1783",,Yosa Buson,,1716,1783,ca. 1765,1755,1775,Pair of six-panel folding screens; ink and color on silk,Image (each): 62 1/2 in. × 11 ft. 9 3/4 in. (158.8 × 360 cm) Overall (each): 63 7/8 in. × 11 ft. 11 1/16 in. (162.2 × 363.4 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Screens,,http://www.metmuseum.org/art/collection/search/671023,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.601,false,true,690313,Asian Art,Hanging scroll,至誠心|Profound Sincerity,Japan,Edo period (1615–1868),,,,Artist,,Jiun Sonja,"Japanese, 1718–1804",,Sonja Jiun,,1718,1804,ca.1780–90,1775,1800,Hanging scroll; ink and color on paper,Image: 44 × 17 7/8 in. (111.8 × 45.4 cm) Overall with mounting: 72 1/2 × 23 1/2 in. (184.2 × 59.7 cm) Overall with knobs: 26 1/2 in. (67.3 cm),"Gift of Joan B. Mirviss, in memory of T. Richard Fishbein, 2015",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/690313,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2008.66,false,true,74363,Asian Art,Screen,"楓橋夜泊|Calligraphy of a Tang-dynasty poem, ""Maple Bridge Night Mooring""",Japan,Edo period (1615–1868),,,,Artist,,Ike Taiga,"Japanese, 1723–1776",,Ike Taiga,,1723,1776,ca. 1770,1760,1780,Two-panel folding screen; ink on paper,Image (each panel): 53 1/4 x 22 1/8 in. (135.3 x 56.2 cm) Overall: 68 3/4 x 72 3/4 in. (174.6 x 184.8 cm),"Purchase, Friends of Asian Art Gifts, 2008",,,,,,,,,,,,Screens,,http://www.metmuseum.org/art/collection/search/74363,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"1987.81a, b",false,true,44865,Asian Art,Screen,,Japan,Edo period (1615–1868),,,,Artist,After,Ike Taiga,"Japanese, 1723–1776",,Ike Taiga,,1723,1776,probably 19th century,1800,1899,"Two-panel screen, originally sliding-door panels; ink on paper",65 1/2 x 70 1/2 in. (166.4 x 179 cm),"Purchase, The Charles Engelhard Foundation Gift, 1987",,,,,,,,,,,,Screens,,http://www.metmuseum.org/art/collection/search/44865,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2015.300.163.1, .2",false,true,53456,Asian Art,Folding screens,蘭亭曲水図屏風. 秋社図屏風|Orchid Pavilion Gathering; Autumn Landscape,Japan,Edo period (1615–1868),,,,Artist,,Ike Taiga,"Japanese, 1723–1776",,Ike Taiga,,1723,1776,ca. 1763,1600,1870,Pair of six-panel folding screens; ink and color on paper,Image (each): 63 1/4 in. × 11 ft. 8 3/16 in. (160.7 × 356 cm) Overall with mounting: 69 9/16 in. × 12 ft. 2 7/16 in. (176.7 × 372 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Screens,,http://www.metmuseum.org/art/collection/search/53456,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.672,false,true,78593,Asian Art,Illustrated book,絵本松のしらべ|Picture Book on the Music of the Pine Trees (Ehon matsu no shirabe),Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,,1726,1792,1795,1795,1795,Woodblock printed book; ink and color on paper,9 5/16 × 6 11/16 in. (23.7 × 17 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78593,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2015.300.197.1, .2",false,true,671024,Asian Art,Screens,芦雁図屏風; 柳に水上月図屏風|Goose and Reeds; Willows and Moon,Japan,Edo period (1615–1868),,,,Artist,,Maruyama Ōkyo,"Japanese, 1733–1795",,Maruyama Ōkyo,,1733,1795,right screen: 1774; left screen: 1793,1774,1793,"Pair of six-panel folding screens; ink, color and gold on paper",Each: Image: 60 9/16 in. × 11 ft. 7 1/2 in. (153.9 × 354.4 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Screens,,http://www.metmuseum.org/art/collection/search/671024,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2015.300.206.1, .2",false,true,53406,Asian Art,Screens,山樵漁夫図屏風|Woodcutters and Fishermen,Japan,Edo period (1615–1868),,,,Artist,,Matsumura Goshun,"Japanese, 1752–1811",,Matsumura Goshun,,1752,1811,ca. 1790–95,1600,1800,Pair of six-panel folding screens; ink and color on paper,Image (each): 65 15/16 in. × 12 ft. 2 7/16 in. (167.5 × 372 cm) Overall with mounting: 67 1/2 in. × 12 ft. 4 in. (171.5 × 376 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Screens,,http://www.metmuseum.org/art/collection/search/53406,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.889,false,true,78779,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Kitao Masanobu (Santō Kyōden),"Japanese, 1761–1816",,Kitao Masanobu (Santō Kyōden),,1761,1816,1786,1786,1786,Woodblock printed book; ink and color on paper,10 7/16 × 7 1/16 in. (26.5 × 18 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78779,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2015.300.93.1, .2",false,true,53423,Asian Art,Screens,,Japan,Edo period (1615–1868),,,,Artist,,Sakai Hōitsu,"Japanese, 1761–1828",,Sakai Hōitsu,,1761,1828,ca. 1805,1720,1920,"Pair of six-panel folding screens; ink, color, and gold on gilded paper",Image: 38 x 82 3/16 in. (96.5 x 208.8 cm) Overall with mounting: 39 9/16 × 83 3/4 in. (100.5 × 212.8 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Screens,,http://www.metmuseum.org/art/collection/search/53423,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.225a–h,false,true,670895,Asian Art,Illustrated books,,Japan,Edo period (1615–1868),,,,Artist,,Tani Bunchō,"Japanese, 1763–1840",,Tani Bunchō,,1763,1840,1809,1809,1809,Eight woodblock printed books; ink and color on paper,Each book: 9 1/4 × 6 1/4 in. (23.5 × 15.9 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/670895,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.667,false,true,78588,Asian Art,Illustrated book,融齋畫譜|Yūsai Picture Album (Yūsai gafu),Japan,Edo period (1615–1868),,,,Artist,,Nakabayashi Chikutō,"Japanese, 1776–1853",,Nakabayashi Chikutō,,1776,1853,1831,1831,1831,"Woodblock printed book (orihon, accordion-style); ink and color on paper",10 15/16 × 6 5/16 in. (27.8 × 16 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78588,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2016.254.1, .2",false,true,640049,Asian Art,Folding screens,四季琵琶湖図屏風|Lake Biwa in Four Seasons,Japan,Edo period (1615–1868),,,,Artist,,Nukina Kaioku,"Japanese, 1778–1863",,Nukina Kaioku,,1778,1863,1834,1834,1834,Pair of six-panel folding screens; ink and color on paper,Image: 24 9/16 × 60 5/8 in. (62.4 × 154 cm) Overall with mounting: 67 1/2 × 25 3/8 in. (171.4 × 64.5 cm),"Gift of Robert and Betsy Feinberg, 2016",,,,,,,,,,,,Screens,,http://www.metmuseum.org/art/collection/search/640049,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.892a–c,false,true,78782,Asian Art,Illustrated book,天の浮橋|Floating Bridge of Heaven (Ama no ukihashi),Japan,Edo period (1615–1868),,,,Artist,,Yanagawa Shigenobu,"Japanese, 1787–1832",,Yanagawa Shigenobu,,1787,1832,ca. 1830s,1830,1839,Set of three woodblock printed books; ink and color on paper,each: 9 13/16 × 7 1/16 in. (25 × 18 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78782,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2013.763a, b",false,true,78665,Asian Art,Illustrated books,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kuniyoshi,"Japanese, 1797–1861",,Utagawa Kuniyoshi,,1797,1861,ca. 1850,1845,1855,Set of two woodblock printed books; ink and color on paper,each: 7 1/16 × 4 3/4 in. (18 × 12 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78665,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -91.1.717,false,true,58488,Asian Art,Inrō,,Japan,Edo period (1615–1868),,,,Artist,Attributed to,Matsuda Sukenaga,"Japanese, 1800–1871",,"Sukenaga, Matsuda",,1800,1871,early–mid 19th century,1823,1866,"Lacquer, carved wood imitating leather, gold metal clasp; Interior: plain",H. 3 1/16 in. (7.7 cm); W. 1 15/16 in. (5 cm); D. 1 1/8 in. (2.8 cm),"Edward C. Moore Collection, Bequest of Edward C. Moore, 1891",,,,,,,,,,,,Inrō,,http://www.metmuseum.org/art/collection/search/58488,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"JIB98a, b",false,true,57785,Asian Art,Illustrated book,池田孤邨画　『抱一上人真蹟鏡』|Mirror of Genuine Work of Monk Hōitsu (Hōitsu shōnin shinseki kagami),Japan,Edo period (1615–1868),,,,Artist,,Ikeda Koson,"Japanese, 1803–1868",,Ikeda Koson,,1803,1868,1817,1817,1817,Set of two woodblock printed books; ink and color on paper,Image (a): 10 1/16 x 7 3/16 x 5/16 in. (25.5 x 18.2 x 0.8 cm) Image (b): 10 1/16 x 7 3/16 x 5/16 in. (25.6 x 18.3 x 0.8 cm) Overall: 13 3/4 in. (35 cm) (open for both volumes),"The Howard Mansfield Collection, Gift of Howard Mansfield, 1936",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/57785,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JIB71,false,true,57669,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Ōishi Shuga,"Japanese, born 1752 (?)",,Ōishi Shuga,,1752,1852,Spring 1822,1822,1822,Ink and color on paper,6 7/8 × 9 1/4 × 5/8 in. (17.5 × 23.5 × 1.6 cm),"Rogers Fund, 1919",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/57669,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JIB61,false,true,57661,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Kokan,late 17th–early 18th century,,Kokan,,1650,1750,1724,1724,1724,Ink on paper,10 5/8 × 7 1/8 × 3/8 in. (27 × 18.1 × 1 cm),"Rogers Fund, 1918",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/57661,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.222,false,true,53008,Asian Art,Paintings mounted on folding screen,,Japan,Edo period (1615–1868),,,,Artist,,Soga Nichokuan,"Japanese, active mid-17th century",,Soga Nichokuan,,1600,1700,mid-17th century,1600,1700,Pair of fan-shaped paintings mounted on two-panel folding screen; ink on paper,73 1/2 x 67 3/4 in. (186.7 x 172.1 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Screens,,http://www.metmuseum.org/art/collection/search/53008,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"1999.204.1, .2",false,true,50843,Asian Art,Screen,,Japan,Edo period (1615–1868),,,,Artist,,Kano Sanboku,"Japanese, active late 17th–early 18th century",,Kano Sanboku,,1671,1725,late 17th century,1667,1699,"Pair of six-panel folding screens; ink, color, and gold on paper",Image: 59 in. x 12 ft. 1/2 in. (149.9 x 367 cm),"Purchase, Friends of Asian Art Gifts, 1999",,,,,,,,,,,,Screens,,http://www.metmuseum.org/art/collection/search/50843,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2016.745,false,true,78458,Asian Art,Handscroll,"白居易作「醉吟先生傳」断簡|Excerpts from Bai Juyi's ""Biography of a Master of Drunken Poetry"" (Suigin sensei den)",Japan,Heian period (794–1185),,,,Artist,,Fujiwara no Yukinari (Kōzei),"Japanese, 972–1027",,Fujiwara no Yukinari,,0972,1027,early 11th century,1000,1027,Detached section of a handscroll mounted as a hanging scroll; ink on paper,Image: 10 13/16 × 3 3/8 in. (27.5 × 8.6 cm) Overall with mounting: 53 1/8 × 11 1/16 in. (134.9 × 28.1 cm) Overall with knobs: 53 1/8 × 12 7/8 in. (134.9 × 32.7 cm),"Gift of Raymond and Priscilla Vickers, 2016",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/78458,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.232,false,true,670938,Asian Art,"Page from book, mounted as hanging scroll",,Japan,Heian period (794–1185),,,,Artist,Traditionally attributed to,Fujiwara no Sadayori,"Japanese, 995–1045",,Fujiwara no Sadayori,,0995,1045,early 12th century,1100,1133,"Page from book, mounted as hanging scroll; ink on paper",Image: 8 1/8 in. × 5 in. (20.6 × 12.7 cm) Overall with mounting: 48 7/16 × 14 3/16 in. (123 × 36 cm) Overall with knobs: 48 7/16 × 15 11/16 in. (123 × 39.8 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/670938,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.235,false,true,671049,Asian Art,"Page from book, mounted as hanging scroll","『三宝絵詞』断簡　（東大寺切）|Page from Illustrations and Explanations of the Three Jewels (Sanbō e-kotoba), one of the “Tōdaiji Fragments” (Tōdaiji-gire)",Japan,Heian period (794–1185),,,,Artist,Calligraphy attributed to,Minamoto no Toshiyori,"Japanese, 1055–1129",,Minamoto no Toshiyori,,1055,1129,1120,1120,1120,Page from a book; ink on decorated paper,Image: 9 1/4 × 5 7/8 in. (23.5 × 15 cm) Overall with mounting: 49 3/16 × 13 3/8 in. (125 × 34 cm) Overall with knobs: 49 3/16 × 15 3/8 in. (125 × 39 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/671049,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.500.9.6,false,true,78166,Asian Art,Hanging scroll,"伝源俊頼　『三宝絵詞』 （東大寺切）|Page from the Illustrations and Explanations of the Three Jewels (Sanbō ekotoba), known as the Tōdaiji Fragment (Tōdaiji-gire)",Japan,Heian period (794–1185),,,,Artist,Calligraphy attributed to,Minamoto no Toshiyori,"Japanese, 1055–1129",,Minamoto no Toshiyori,,1055,1129,1120,1120,1120,Detached page from a book mounted as a hanging scroll; ink on mica paper,Image: 9 1/2 x 5 3/8 in. (24.1 x 13.7 cm),"Gift of Florence and Herbert Irving, 2015",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/78166,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.233,false,true,671014,Asian Art,"Page from book, mounted as hanging scroll",,Japan,Heian period (794–1185),,,,Artist,Traditionally attributed to,Monk Saigyō,"Japanese, 1118–1190",,,,1118,1190,late 12th century,1167,1185,"Page from book, mounted as hanging scroll; ink on paper",Image: 6 7/8 × 5 11/16 in. (17.4 × 14.5 cm) Overall with mounting: 51 5/16 × 14 1/2 in. (130.3 × 36.8 cm) Overall with knobs: 51 5/16 × 16 1/4 in. (130.3 × 41.2 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/671014,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.500.2.53a–g,false,true,40491,Asian Art,Box,,Japan,Meiji period (1868–1912),,,,Artist,,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,,1807,1891,second half of the 19th century,1850,1899,"Gold maki-e on black and brown lacquer, with mother-of-pearl inlay and pewter",L. 9 5/8 in. (24.4 cm); W. 9 3/8 in. (23.8 cm); H. 16 1/2 in. (41.9 cm),"Gift of Florence and Herbert Irving, 2015",,,,,,,,,,,,Lacquer,,http://www.metmuseum.org/art/collection/search/40491,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.500.2.54a–o,false,true,40498,Asian Art,Writing box; table,2代由木尾雪雄作|Writing Box (Suzuribako) and Writing Table (Bundai) with Pines at Takasago and Sumiyoshi,Japan,Meiji period (1868–1912),,,,Artist,,Yukio Yukio II,"Japanese, 1860–1929",,Yukio Yukio II,,1860,1929,early 20th century,1900,1912,"Lacquered wood with gold, silver takamaki-e, hiramaki-e, cut-out gold foil on nashiji ground, silver inlay, silver fittings",Writing box: H. 2 in.; W. 9 in.; L. 9 3/4 in. Table: H. 4 7/8 in.; W.14 1/2 in.; L. 24 in.,"Gift of Florence and Herbert Irving, 2015",,,,,,,,,,,,Lacquer,,http://www.metmuseum.org/art/collection/search/40498,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.228,false,true,670953,Asian Art,illustrated book,,Japan,Meiji period (1868–1912),,,,Artist,,Utagawa (Baidō) Kokunimasa,"Japanese, 1874–1944",,Utagawa (Baidō) Kokunimasa,,1874,1944,1879,1879,1879,Woodblock printed book; ink and color on paper,Image: 8 1/8 × 5 1/2 in. (20.6 × 14 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/670953,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.585,false,true,688515,Asian Art,Folding screen,,Japan,Taishō period (1912–26),,,,Artist,,Imazu Tatsuyuki,"Japanese, active early 20th century",,Imazu Tatsuyuki,,1900,1926,ca. 1925,1915,1926,Two-panel folding screen; mineral colors and metallic powders on paper,Image: 80 1/8 × 72 13/16 in. (203.5 × 185 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2015",,,,,,,,,,,,Screens,,http://www.metmuseum.org/art/collection/search/688515,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.236,false,true,670885,Asian Art,Hanging scroll,"『続古今和歌集』断簡|Two Poems from the Collection of Poems Ancient and Modern, Continued (Zoku kokin wakashū)",Japan,Kamakura period (1185–1333),,,,Artist,,Nun Abutsu,"Japanese, died 1283",,Abutsu,,,1283,13th century,1200,1299,Page from a book; ink on paper,Image: 9 1/4 × 5 1/2 in. (23.5 × 14 cm) Overall with mounting: 54 5/16 × 11 in. (138 × 27.9 cm) Overall with knobs: 54 5/16 × 13 1/2 in. (138 × 34.3 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/670885,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2014.719.3,false,true,60437,Asian Art,Hanging scroll,明恵房高弁筆 『夢の記』|Section of the Dream Diary (Yume no ki) with a Sketch of Mountains,Japan,Kamakura period (1185–1333),,,,Artist,,Myōe Kōben,"Japanese, 1173–1232",,Myōe Kōben,,1173,1232,ca. 1203–10,1203,1210,Hanging scroll; ink on paper,Image: 12 x 19 in. (30.5 x 48.3 cm) Overall with mounting: 44 5/8 x 19 5/8 in. (113.3 x 49.8 cm) Overall with knobs: 44 5/8 x 21 1/2 in. (113.3 x 54.6 cm),"Gift of Sylvan Barnet and William Burto, in honor of Saretta and Howard Barnet, 2014",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/60437,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2014.719.4,false,true,60435,Asian Art,Hanging scroll,明恵房高弁筆　『夢の記』 断簡|Section of the Dream Diary (Yume no ki),Japan,Kamakura period (1185–1333),,,,Artist,,Myōe Kōben,"Japanese, 1173–1232",,Myōe Kōben,,1173,1232,dated 1225,1225,1225,Hanging scroll; ink on paper,Image: 13 1/4 x 21 5/8 in. (33.7 x 54.9 cm) Overall with mounting: 48 x 27 in. (121.9 x 68.6 cm) Overall with knobs: 48 x 29 1/8 in. (121.9 x 74 cm),"Gift of Sylvan Barnet and William Burto, in honor of Saretta and Howard Barnet, 2014",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/60435,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2014.719.5,false,true,60451,Asian Art,Hanging scroll,,Japan,Kamakura period (1185–1333),,,,Artist,,Myōe Kōben,"Japanese, 1173–1232",,Myōe Kōben,,1173,1232,ca. 1221,1221,1221,Hanging scroll; ink on paper,Image: 8 1/4 x 17 in. (21 x 43.2 cm) Overall with mounting: 39 7/8 x 21 1/16 in. (101.3 x 53.5 cm) Overall with knobs: 39 7/8 x 22 7/8 in. (101.3 x 58.1 cm),"Gift of Sylvan Barnet and William Burto, in honor of Saretta and Howard Barnet, 2014",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/60451,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2015.300.73.1, .2",false,true,53240,Asian Art,Screens,,Japan,Momoyama period (1573–1615),,,,Artist,,Unkoku Tōgan,"Japanese, 1547–1618",,Unkoku Tōgan,,1547,1618,late 16th–early 17th century,1573,1615,"Pair of six-panel folding screens; ink, color, and gold dust on paper",Image (each): 61 15/16 in. × 11 ft. 8 1/4 in. (157.3 × 356.2 cm) Overall with mounting: 68 15/16 in. × 12 ft. 3 7/8 in. (175.1 × 375.6 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Screens,,http://www.metmuseum.org/art/collection/search/53240,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2004.261,false,true,72569,Asian Art,Calligraphy,,Japan,Momoyama period (1573–1615),,,,Artist,,Konoe Nobutada,"Japanese, 1565–1614",,Konoe Nobutada,,1565,1614,early 17th century,1600,1633,Hanging scroll; ink on paper,Image: 16 x 12 in. (40.6 x 30.5 cm),"Gift of Tomohiko and Kyoko Horie, 2004",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/72569,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2015.300.70.1, .2",false,true,72585,Asian Art,Screens,,Japan,Momoyama period (1573–1615),,,,Artist,Circle of,Kano Mitsunobu,"Japanese, ca. 1561–1608",,Kano Mitsunobu,,1561,1608,late 16th century,1567,1599,"Pair of six-panel folding screens; ink, color, and gold leaf on paper",Each: 59 15/16 in. × 11 ft. 7 7/16 in. (152.3 × 354.2 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Screens,,http://www.metmuseum.org/art/collection/search/72585,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2014.718,false,true,60472,Asian Art,Hanging scroll,,Japan,Muromachi period (1392–1573),,,,Artist,,Zekkai Chūshin,"Japanese, 1336–1405",,Zekkai Chūshin,,1336,1405,14th century,1392,1399,Hanging scroll; ink on paper,Image: 12 1/4 × 16 5/16 in. (31.1 × 41.4 cm) Overall with mounting: 45 3/8 × 21 1/2 in. (115.3 × 54.6 cm) Overall with knobs: 45 3/8 × 23 3/4 in. (115.3 × 60.3 cm),"Gift of Sylvan Barnet and William Burto, 2014",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/60472,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2014.719.10,false,true,60469,Asian Art,Hanging scroll,策彦周良筆 墨跡|Account of the Three Springs of Jiangsu Province in China,Japan,Muromachi period (1392–1573),,,,Artist,,Sakugen Shūryō,"Japanese, 1501–1579",,Sakugen Shūryō,,1501,1579,late 16th century,1567,1573,Hanging scroll; ink on paper,Image: 11 1/2 x 16 3/4 in. (29.2 x 42.5 cm) Overall with mounting: 44 1/4 x 21 7/8 in. (112.4 x 55.6 cm) Overall with knobs: 44 1/4 x 23 3/4 in. (112.4 x 60.3 cm),"Gift of Sylvan Barnet and William Burto, 2014",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/60469,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.241,false,true,671054,Asian Art,Book mounted as handscroll,"柳江書　連歌集『老葉』より「旅」|Manuscript Version of the “Travel” Section of the Linked Verse (Renga) Collection “Aged Leaves” (Wakuraba), compiled by Sōgi (1421–1502)",Japan,Muromachi period (1392–1573),,,,Artist,Calligraphy by,Ryūkō,"Japanese, active 16th century",,Ryūkō,,1500,1573,1533,1533,1533,Book mounted as handscroll; ink on paper,Overall: 9 1/16 in. × 10 ft. 15/16 in. (23 × 307.2 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/671054,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2015.300.51.1, .2",false,true,53223,Asian Art,Screens,山水図屏風|Landscape after Xia Gui,Japan,Muromachi period (1392–1573),,,,Artist,Traditionally attributed to,Tenshō Shūbun,"Japanese, active 1414–before 1463",,Tenshō Shūbun,,1414,1463,early–mid-15th century,1414,1463,Two six-panel folding screens; ink and color on paper,60 5/8 in. × 9 ft. 6 3/16 in. (154 × 290 cm) Overall with mounting: 68 in. × 9 ft. 11 11/16 in. (172.7 × 304 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Screens,,http://www.metmuseum.org/art/collection/search/53223,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2014.719.7,false,true,60470,Asian Art,Hanging scroll,墨蹟|Poem on the Theme of a Monk’s Life,Japan,Nanbokuchō period (1336–92),,,,Artist,,Sesson Yūbai,"Japanese, 1290–1346",,Sesson Yūbai,,1290,1346,14th century,1336,1392,Hanging scroll; ink on paper,Image: 16 x 23 3/8 in. (40.6 x 59.4 cm) Overall with mounting: 51 x 29 1/8 in. (129.5 x 74 cm) Overall with knobs: 51 x 31 3/16 in. (129.5 x 79.2 cm),"Gift of Sylvan Barnet and William Burto, in honor of Miyeko Murase, 2014",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/60470,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2014.719.8,false,true,60473,Asian Art,Hanging scroll,絶海中津筆 「山空松子落」|“The Mountain is Empty; A Pinecone Falls”,Japan,Nanbokuchō period (1336–92),,,,Artist,,Zekkai Chūshin,"Japanese, 1336–1405",,Zekkai Chūshin,,1336,1405,late 14th century,1367,1405,Hanging scroll; ink on paper,Image: 34 1/2 x 8 9/16 in. (87.6 x 21.7 cm) Overall with mounting: 65 7/8 x 9 7/16 in. (167.3 x 24 cm) Overall with knobs: 65 7/8 x 11 3/8 in. (167.3 x 28.9 cm),"Gift of Sylvan Barnet and William Burto, in honor of Elizabeth ten Grotenhuis and Merton Flemings, 2014",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/60473,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.289a–g,false,true,53416,Asian Art,Box,里芋菊蒔絵重箱|Stacked Food Box (Jūbako) with Taro Plants and Chrysanthemums,Japan,late Edo (1615–1868)–early Meiji (1868–1912) period,,,,Artist,,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,,1807,1891,mid-19th century,1834,1866,"Lacquered wood, gold and silver hiramaki-e, takamaki-e, and colored togidashimaki-e",H. 16 1/2 in. (41.9 cm); W. 9 in. (22.9 cm); D. 9 5/8 in. (24.4 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Lacquer,,http://www.metmuseum.org/art/collection/search/53416,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3284,false,true,53345,Asian Art,Woodblock print,Hakurai taizo no zu|View of the Large Imported Elephant,Japan,Edo period (1615–1868),,,,Artist,,Taguchi (Utagawa) Yoshimori,1830–1884,,Taguchi (Utagawa) Yoshimori,,1830,1884,"1863 (Bunkyo 3, 4th month)",1863,1863,Polychrome woodblock print; ink and color on paper,Image: 14 3/4 × 10 3/16 in. (37.5 × 25.9 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/53345,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1036,false,true,54321,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shuntei,"Japanese, 1770–1820",,Katsukawa Shuntei,,1770,1820,ca. 1815,1795,1825,Polychrome woodblock print (surimono); ink and color on paper,7 5/8 x 8 7/8 in. (19.4 x 22.5 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54321,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1933,false,true,54473,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shuntei,"Japanese, 1770–1820",,Katsukawa Shuntei,,1770,1820,ca. 1816,1806,1826,Polychrome woodblock print (surimono); ink and color on paper,7 3/4 x 7 in. (19.7 x 17.8 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54473,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2060,false,true,54840,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shuntei,"Japanese, 1770–1820",,Katsukawa Shuntei,,1770,1820,ca. 1815,1805,1825,Polychrome woodblock print (surimono); ink and color on paper,7 3/4 x 7 1/4 in. (19.7 x 18.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54840,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2336,false,true,54120,Asian Art,Print,花魁と梅盆栽|Courtesan and her Child Attendant with a Potted Plum Tree,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shuntei,"Japanese, 1770–1820",,Katsukawa Shuntei,,1770,1820,ca. 1815,1805,1825,Part of an album of woodblock prints (surimono); ink and color on paper,8 x 7 1/4 in. (20.3 x 18.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54120,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2910,false,true,56025,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shuntei,"Japanese, 1770–1820",,Katsukawa Shuntei,,1770,1820,1770–1820,1770,1820,Polychrome woodblock print; ink and color on paper,14 7/8 x 9 7/8 in. (37.8 x 25.1 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56025,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2167,false,true,55076,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Taisosai Hokushu,"Japanese, 18th–19th century",,Taisosai Hokushu,,1700,1899,probably 1819,1819,1819,Polychrome woodblock print (surimono); ink and color on paper,8 1/8 x 7 1/4 in. (20.6 x 18.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55076,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3324,false,true,53702,Asian Art,Print,外国人之図|Views of Foreigners (Gaikokujin no zu),Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshitomi,"Japanese, active mid-19th century",,Utagawa Yoshitomi,,1836,1870,1861,1861,1861,Polychrome woodblock print; ink and color on paper,H. 14 1/2 in. (36.8 cm); W. 10 in. (25.4 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/53702,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3329,false,true,55477,Asian Art,Print,Ikiutsushi Americajin no zu|生写亜墨利加人之図|An American Drawn from Life,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshitomi,"Japanese, active mid-19th century",,Utagawa Yoshitomi,,1836,1870,1861 (2nd month),1861,1861,Polychrome woodblock print; ink and color on paper,14 1/4 x 10 in. (36.2 x 25.4 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55477,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.235,false,true,73526,Asian Art,Woodblock print,Oroshia|Russian Horseman,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshitomi,"Japanese, active mid-19th century",,Utagawa Yoshitomi,,1836,1870,"10th month, 1860",1860,1860,Polychrome woodblock print; ink and color on paper,Image: 14 3/4 x 10 1/2 in. (37.5 x 26.7 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73526,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.236,false,true,73527,Asian Art,Print,Orandasen|Dutch Ship,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshitomi,"Japanese, active mid-19th century",,Utagawa Yoshitomi,,1836,1870,"2nd month, 1861",1861,1861,Polychrome woodblock print; ink and color on paper,Image: 14 x 9 1/2 in. (35.6 x 24.1 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73527,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.227a–f,false,true,53449,Asian Art,Prints,『諸國六玉川』|Six Tamagawa Rivers from Various Provinces (Shokoku Mu Tamagawa),Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",,1797,1858,1857,1750,1900,Six polychrome woodblock prints; ink and color on paper,Image (each): 14 1/4 × 9 5/8 in. (36.2 × 24.4 cm) Mat (each): 22 13/16 × 15 9/16 in. (58 × 39.6 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/53449,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -93.3.197,false,true,45917,Asian Art,Vase,,Japan,,,,,Artist,,Eiraku Hozen,1795–1854,(Zengoro Hozen),Eiraku Hozen,,1795,1854,1820,1820,1820,White porcelain decorated with gold on an iron red ground,H. 3 5/8 in. (9.2 cm),"Gift of Charles Stewart Smith, 1893",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/45917,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -93.3.297,false,true,47986,Asian Art,Bowl,,Japan,,,,,Artist,,Eiraku Hozen,1795–1854,,Eiraku Hozen,,1795,1854,1830,1830,1830,"White porcelain; reproduction of Chinese Song, Dingyao bowl (Kairakuen ware)",H. 1 3/8 in. (3.5 cm); Diam. of rim 3 7/8 in. (9.8 cm); Diam. of base 1 1/4 in. (3.2 cm),"Gift of Charles Stewart Smith, 1893",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/47986,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -93.3.427,false,true,46697,Asian Art,Washer,,Japan,,,,,Artist,,Eiraku Hozen,1795–1854,,Eiraku Hozen,,1795,1854,1800,1800,1800,Clay covered with colored and transparent glazes over relief (Kyoto ware),H. 5 7/8 in. (14.9 cm); Diam. of rim 7 1/4 in. (18.4 cm); Diam. of base 3 7/8 in. (9.8 cm),"Gift of Charles Stewart Smith, 1893",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/46697,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"93.3.191a, b",false,true,44450,Asian Art,Jar with lid,,Japan,,,,,Artist,,Eiraku Hozen,1795–1854,,Eiraku Hozen,,1795,1854,1820,1820,1820,White porcelain decorated with gold and iron red (Kyoto ware),H. 2 3/4 in. (7 cm); Diam. 2 3/4 in. (7 cm); Diam. of rim 1 1/8 in. (2.9 cm); Diam. of base 1 1/4 in. (3.2 cm),"Gift of Charles Stewart Smith, 1893",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/44450,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"93.3.203a, b",false,true,46506,Asian Art,Wine kettle,,Japan,,,,,Artist,,Eiraku Hozen,1795–1854,(Zengoro Hozen),Eiraku Hozen,,1795,1854,1810,1810,1810,White porcelain decorated with iron red and gold (Kyoto ware),H. 5 1/4 in. (13.3 cm); W. at spout 6 3/8 in. (16.2 cm); Diam. 5 in. (12.7 cm); Diam. of base 3 in. (7.6 cm),"Gift of Charles Stewart Smith, 1893",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/46506,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"93.3.381a, b",false,true,46497,Asian Art,Incense box,,Japan,,,,,Artist,,Eiraku Hozen,1795–1854,,Eiraku Hozen,,1795,1854,1800,1800,1800,Clay covered inside with a transparent crackled glaze and outside with polychrome glazes (Kyoto ware),H. 2 1/2 in. (6.4 cm); Diam. 1 3/4 in. (4.4 cm),"Gift of Charles Stewart Smith, 1893",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/46497,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -17.118.63,false,true,62883,Asian Art,Teabowl,,Japan,Edo period (1615–1868),,,,Artist,,Ichigen,died 1722,,Ichigen,,1622,1722,ca. 1720,1710,1730,"Clay covered with a pitted, black glaze (Raku ware)",H. 3 3/8 in. (8.6 cm); Diam. 4 1/4 in. (10.8 cm),"Rogers Fund, 1917",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/62883,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -93.3.216,false,true,62794,Asian Art,Teabowl,,Japan,Edo period (1615–1868),,,,Artist,,Eiraku Hozen,1795–1854,,Eiraku Hozen,,1795,1854,1790,1790,1790,"Clay covered with a fine crackled glaze; decorated with white slip, colored enamels and gold and silver (Kyoto ware)",H. 3 1/4 in. (8.3 cm); Diam. of rim 5 in. (12.7 cm); Diam. of base 1 7/8 in. (4.8 cm),"Gift of Charles Stewart Smith, 1893",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/62794,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -23.225.251,false,true,63023,Asian Art,Bowl,,Japan,Edo period (1615–1868),,,,Artist,,Eiraku Hozen,1795–1854,,Eiraku Hozen,,1795,1854,1800,1800,1800,Porcelain decorated with enamel and gold (Kyoto ware),H. 2 in. (5.1 cm); W. 6 3/8 in. (16.2 cm),"Gift of Mrs. V. Everit Macy, 1923",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/63023,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -23.225.253,false,true,63025,Asian Art,Bowl,,Japan,Edo period (1615–1868),,,,Artist,,Eiraku Hozen,1795–1854,,Eiraku Hozen,,1795,1854,1800,1800,1800,Porcelain decorated with enamel and gold (Kyoto ware),H. 3 1/4 in. (8.3 cm); W. sq. 5 3/4 in. (14.6 cm),"Gift of Mrs. V. Everit Macy, 1923",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/63025,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -23.225.259,false,true,63027,Asian Art,Bowl,,Japan,Edo period (1615–1868),,,,Artist,,Eiraku Hozen,1795–1854,,Eiraku Hozen,,1795,1854,1800,1800,1800,Porcelain glazed with recessed design in enamels (Kyoto ware),H. 3 5/8 in. (9.2 cm); Diam. 6 3/4 in. (17.1 cm),"Gift of Mrs. V. Everit Macy, 1923",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/63027,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -23.225.261,false,true,63028,Asian Art,Bowl,,Japan,Edo period (1615–1868),,,,Artist,,Eiraku Hozen,1795–1854,,Eiraku Hozen,,1795,1854,1800,1800,1800,Porcelain decorated in enamels and blue under the glaze,H. 3 1/8 in. (7.9 cm); Diam. 5 1/8 in. (13 cm),"Gift of Mrs. V. Everit Macy, 1923",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/63028,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -23.225.265,false,true,63029,Asian Art,Bowl,,Japan,Edo period (1615–1868),,,,Artist,,Eiraku Hozen,1795–1854,,Eiraku Hozen,,1795,1854,1800,1800,1800,Porcelain decorated with glaze and gold (Kyoto ware),H. 4 1/8 in. (10.5 cm); Diam. 7 in. (17.8 cm),"Gift of Mrs. V. Everit Macy, 1923",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/63029,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -23.225.266,false,true,63030,Asian Art,Cup,,Japan,Edo period (1615–1868),,,,Artist,,Eiraku Hozen,1795–1854,,Eiraku Hozen,,1795,1854,1800,1800,1800,Stoneware with lustrous glaze; the decoration in enamels (Kyoto ware),H. 3 1/2 in. (8.9 cm); Diam. 3 1/2 in. (8.9 cm),"Gift of Mrs. V. Everit Macy, 1923",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/63030,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -23.225.270,false,true,47332,Asian Art,Vase,,Japan,Edo period (1615–1868),,,,Artist,,Eiraku Hozen,1795–1854,,Eiraku Hozen,,1795,1854,1800,1800,1800,Porcelain; white glaze covered with design in high relief (Kyoto ware),H. 3 3/16 in. (8.1 cm),"Gift of Mrs. V. Everit Macy, 1923",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/47332,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -23.225.271,false,true,63031,Asian Art,Bowl,,Japan,Edo period (1615–1868),,,,Artist,,Eiraku Hozen,1795–1854,,Eiraku Hozen,,1795,1854,1800,1800,1800,Porcelain decorated with red under the glaze; bottom unglazed (Kyoto ware),H. 2 3/8 in. (6 cm); Diam. 4 1/4 in. (10.8 cm),"Gift of Mrs. V. Everit Macy, 1923",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/63031,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -23.225.272,false,true,48577,Asian Art,Vase,,Japan,Edo period (1615–1868),,,,Artist,,Eiraku Hozen,1795–1854,,Eiraku Hozen,,1795,1854,1800,1800,1800,"Faience with glaze, the design incised and gilt (Kyoto ware)",H. 14 1/4 in. (36.2 cm),"Ex coll.: V. Everit Macy, Gift of Mrs. Everit Macy",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/48577,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -23.225.380,false,true,47195,Asian Art,Bowl,,Japan,Edo period (1615–1868),,,,Artist,,Eiraku Hozen,1795–1854,,Eiraku Hozen,,1795,1854,1830,1830,1830,"Faience(?) covered with rich green glaze, craquelé (Kyoto ware)",H. 3 1/16 in. (7.8 cm); Diam. 5 3/4 in. (14.6 cm),"Gift of Mrs. V. Everit Macy, 1923",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/47195,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.635,false,true,45354,Asian Art,Teabowl,,Japan,Edo period (1615–1868),,,,Artist,,Eiraku Hozen,1795–1854,,Eiraku Hozen,,1795,1854,ca. 1825,1815,1835,Clay decorated with crackled glaze and enamels and gold (Kyo ware),H. 2 3/4 in. (7 cm); Diam. of rim 4 7/8 in. (12.4 cm); Diam. of base 1 7/8 in. (4.8 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/45354,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.120.503,false,true,63157,Asian Art,Teabowl,,Japan,Edo period (1615–1868),,,,Artist,,Eiraku Hozen,1795–1854,,Eiraku Hozen,,1795,1854,ca. 1830,1820,1840,Clay with creamy white glaze (Eiraku pottery),H. 3 3/8 in. (8.6 cm); Diam. 4 7/8 in. (12.4 cm),"The Howard Mansfield Collection, Gift of Howard Mansfield, 1936",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/63157,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"25.60.31a, b",false,true,63087,Asian Art,Tea jar,,Japan,Edo period (1615–1868),,,,Artist,,Eiraku Hozen,1795–1854,,Eiraku Hozen,,1795,1854,ca. 1820,1810,1830,Pottery with design in relief and colored enamels; pewter cover (Kyoto ware),H. 6 in. (15.2 cm); Diam. 5 in. (12.7 cm),"Rogers Fund, 1925",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/63087,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"93.3.159a, b",false,true,47074,Asian Art,Bottle,,Japan,Edo period (1615–1868),,,,Artist,,Eiraku Hozen,1795–1854,,Eiraku Hozen,,1795,1854,1790,1790,1790,Paste decorated with polychrome and transparent enamels (Kyoto ware),H. 5 3/4 in. (14.6 cm); Diam. 4 in. (10.2 cm); Diam. of rim 1 1/4 in. (3.2 cm); Diam. of base 2 1/2 in. (6.4 cm),"Gift of Charles Stewart Smith, 1893",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/47074,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"93.3.199a, b",false,true,47421,Asian Art,Bowl,,Japan,Edo period (1615–1868),,,,Artist,,Eiraku Hozen,1795–1854,(Zenichiro),Eiraku Hozen,,1795,1854,1840,1840,1840,"White porcelain decorated with blue under the glaze, polychrome enamels (Kyoto ware)",H. 6 1/2 in. (16.5 cm),"Gift of Charles Stewart Smith, 1893",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/47421,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"23.225.252a, b",false,true,63024,Asian Art,Covered box,,Japan,Edo period (1615–1868),,,,Artist,,Eiraku Hozen,1795–1854,,Eiraku Hozen,,1795,1854,1800,1800,1800,Porcelain decorated with enamel and gold (Kyoto ware),H. 2 3/8 in. (6 cm),"Gift of Mrs. V. Everit Macy, 1923",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/63024,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"23.225.275a, b",false,true,47362,Asian Art,Censer,,Japan,Edo period (1615–1868),,,,Artist,,Eiraku Hozen,1795–1854,,Eiraku Hozen,,1795,1854,1820,1820,1820,Faience with enamels (Kyoto ware),H. 5 1/8 in. (13 cm); Diam. 4 1/4 in. (10.8 cm),"Gift of Mrs. V. Everit Macy, 1923",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/47362,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -17.118.78,false,true,62902,Asian Art,Teabowl,,Japan,Edo period (1615–1868),,,,Artist,,Ichinyu,"Japanese, died 1682",,Ichinyu,,1582,1682,ca. 1675,1665,1685,Clay covered with a black glaze dappled with red (Raku ware),H. 3 in. (7.6 cm); Diam. 4 1/2 in. (11.4 cm),"Rogers Fund, 1893",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/62902,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -17.118.79,false,true,62903,Asian Art,Teabowl,,Japan,Edo period (1615–1868),,,,Artist,,Ichinyu,"Japanese, died 1682",,Ichinyu,,1582,1682,ca. 1675,1665,1685,Clay covered with a dull black glaze (Raku ware),H. 3 1/4 in. (8.3 cm); Diam. 4 1/4 in. (10.8 cm),"Rogers Fund, 1917",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/62903,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.269,false,true,77863,Asian Art,Dish,,Japan,Edo period (1615–1868),,,,Artist,,Ogata Kenzan,"Japanese, 1663–1743",,Ogata Kenzan,,1663,1743,early 18th century,1700,1733,Glazed stoneware with enamels,H. 2 1/16 in. (5.2 cm); W. 14 7/16 in. (36.7 cm); L. 12 7/16 in. (31.6 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/77863,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2015.500.9.28a, b",false,true,667248,Asian Art,Incense container,,Japan,Edo period (1615–1868),,,,Artist,,Miyagawa Chozo,"Japanese, 1797–1860",,Miyagawa Chozo,,1797,1860,1797–1860,1797,1860,Stoneware with polychrome enamels,H. 1 13/16 in. (4.6 cm); Diam. 2 13/16 in. (7.2 cm),"Gift of Florence and Herbert Irving, 2015",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/667248,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.500.9.9,false,true,78156,Asian Art,Hanging scroll,,Japan,Momoyama (1573–1615),,,,Artist,,Unkoku Tōgan,"Japanese, 1547–1618",,Unkoku Tōgan,,1547,1618,early 17th century,1600,1618,Hanging scroll; ink on paper,Image: 39 1/2 x 14 1/8 in. (100.3 x 35.9 cm),"Gift of Florence and Herbert Irving, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/78156,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2015.300.68a, b",false,true,53237,Asian Art,Hanging scrolls,四季花鳥図|Pheasants among Trees: Flowers of the Four Seasons,Japan,Muromachi (1392–1573),,,,Artist,,Kano Shōei,"Japanese, 1519–1592",,Kano Shōei,,1519,1592,probably 1560s,1560,1569,Pair of hanging scrolls; ink and color on paper,Image (a): 37 3/8 × 18 3/8 in. (95 × 46.6 cm) Overall with mounting (a): 84 3/4 × 26 5/8 in. (215.2 × 67.6 cm) Overall with knobs (a): 84 3/4 × 28 15/16 in. (215.2 × 73.5 cm) Image (b): 37 3/8 × 18 3/8 in. (95 × 46.6 cm) Overall with mounting (b): 84 15/16 × 26 5/8 in. (215.8 × 67.7 cm) Overall with knobs (b): 84 15/16 × 28 15/16 in. (215.8 × 73.5 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/53237,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.268.92,false,true,48988,Asian Art,Hanging scroll,雪竹図|Bamboo and Rock in Snow,Japan,Edo period (1615–1868),,,,Artist,,Sakaki Hyakusen,1697–1752,,Sakaki Hyakusen,,1697,1752,spring 1750,1750,1750,Hanging scroll; ink on paper,Image: 53 1/8 x 16in. (134.9 x 40.6cm) Overall with mounting: 80 7/8 x 19 5/8 in. (205.4 x 49.8 cm) Overall with rollers: 80 7/8 x 21 7/8 in. (205.4 x 55.6 cm),"The Harry G. C. Packard Collection of Asian Art, Gift of Harry G. C. Packard, and Purchase, Fletcher, Rogers, Harris Brisbane Dick, and Louis V. Bell Funds, Joseph Pulitzer Bequest, and The Annenberg Fund Inc. Gift, 1975",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/48988,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.155,false,true,53451,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Sakaki Hyakusen,1697–1752,,Sakaki Hyakusen,,1697,1752,1744,1620,1880,Hanging scroll; ink and color on paper,47 3/4 × 19 3/4 in. (121.3 × 50.2 cm) 76 9/16 × 27 3/16 in. (194.5 × 69 cm) Overall with knobs: 76 9/16 × 27 3/8 in. (194.5 × 69.5 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/53451,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.180,false,true,53461,Asian Art,Folding fan mounted,,Japan,Edo period (1615–1868),,,,Artist,,Aoki Mokubei,1767–1833,,Aoki Mokubei,,1767,1833,1825,1700,1899,Folding fan mounted as a hanging scroll; ink and color on paper,Image: 8 1/4 × 19 7/16 in. (21 × 49.4 cm) Overall with mounting: 39 3/8 × 25 1/16 in. (100 × 63.7 cm) Overall with knobs: 39 3/8 × 27 7/16 in. (100 × 69.7 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/53461,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.95,false,true,45229,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Eiraku Hozen,1795–1854,,Eiraku Hozen,,1795,1854,after 1848,1849,1854,Hanging scroll; ink on paper,Image: 38 1/2 x 11 1/4 in. (97.8 x 28.6 cm) Overall with mounting: 68 1/2 x 11 3/4 in. (174 x 29.8 cm) Overall with knobs: 68 1/2 x 14 in. (174 x 35.6 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45229,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.524,false,true,48974,Asian Art,Hanging scroll,芥子図|Poppies,Japan,Edo period (1615–1868),,,,Artist,Attributed to,Kitagawa Sōsetsu,active 1639–50,,Kitagawa Sōsetsu,,1639,1650,mid-17th century,1634,1666,Hanging scroll; color and gold on paper,34 1/4 x 14 5/8 in. (87 x 37.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/48974,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2015.300.34a, b",false,true,53892,Asian Art,Albums,,Japan,Edo period (1615–1868),,,,Artist,,Tosa Mitsunori,"Japanese, 1583–1638",,Tosa Mitsunori,,1583,1638,early 17th century,1600,1700,"Two albums, thirty leaves in each; ink, red pigment, and gold on paper",Image (each leaf): 5 5/16 × 5 1/8 in. (13.5 × 13 cm) Album: 7 1/8 × 6 1/8 × 1 3/16 in. (18.1 × 15.6 × 3 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/53892,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.221,false,true,671056,Asian Art,Hanging scroll,周茂叔愛蓮図|Zhou Maoshu Admiring Lotuses,Japan,Edo period (1615–1868),,,,Artist,,Kaihō Yūsetsu,"Japanese, 1598–1677",,Kaihō Yūsetsu,,1598,1677,mid-17th century,1634,1666,Hanging scroll; ink on silk,Image: 12 3/4 × 19 5/8 in. (32.4 × 49.8 cm) Overall with mounting: 47 3/16 × 24 7/16 in. (119.8 × 62 cm) Overall with knobs: 47 3/16 × 26 1/4 in. (119.8 × 66.7 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/671056,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.78,false,true,53010,Asian Art,Hanging scroll,笛吹地蔵図|Jizō Bosatsu Playing a Flute,Japan,Edo period (1615–1868),,,,Artist,,Kano Tan'yū,"Japanese, 1602–1674",,Kano Tan'yū,,1602,1674,mid- 17th century,1634,1666,Hanging scroll; ink and color on paper,Image: 38 3/4 in. × 15 in. (98.5 × 38.1 cm) Overall with mounting: 80 1/2 × 21 7/16 in. (204.5 × 54.5 cm) Overall with knobs: 80 1/2 × 23 5/8 in. (204.5 × 60 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/53010,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.24,false,true,670915,Asian Art,Album of thirty-six paintings and thirty-six poems,,Japan,Edo period (1615–1868),,,,Artist,,Sumiyoshi Gukei,"Japanese, 1631–1705",,Sumiyoshi Gukei,,1631,1705,1674–92,1674,1692,"Album of thirty-six paintings and thirty-six poems; ink, color and gold on silk and paper",Image (each leaf): 6 7/8 × 6 5/16 in. (17.4 × 16 cm) Album: 8 3/4 × 7 11/16 × 2 5/8 in. (22.3 × 19.5 × 6.7 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/670915,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.82a–c,false,true,54774,Asian Art,Hanging scrolls,"滝見業平図|Nunobiki Waterfall, Mount Yoshino, and Tatsuta River",Japan,Edo period (1615–1868),,,,Artist,,Kano Tsunenobu,"Japanese, 1636–1713",,Kano Tsunenobu,,1636,1713,after 1709,1709,1709,Triptych of hanging scrolls; ink and color on silk,Image (a): 58 9/16 × 31 1/2 in. (148.7 × 80 cm) Overall with mounting (a): 9 ft. 8 9/16 in. × 40 9/16 in. (296 × 103 cm) Overall with knobs (a): 9 ft. 8 9/16 in. × 43 1/2 in. (296 × 110.5 cm) Image (b): 58 9/16 × 31 1/2 in. (148.7 × 80 cm) Overall with mounting (b): 9 ft. 7 15/16 in. × 40 9/16 in. (294.5 × 103 cm) Overall with knobs (b): 9 ft. 7 15/16 in. × 43 9/16 in. (294.5 × 110.7 cm) Image (c): 58 3/4 × 31 1/2 in. (149.3 × 80 cm) Overall with mounting (c): 9 ft. 8 9/16 in. × 40 9/16 in. (296 × 103 cm) Overall with knobs (c): 9 ft. 8 9/16 in. × 43 5/8 in. (296 × 110.8 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54774,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.89,false,true,53422,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Ogata Kōrin,"Japanese, 1658–1716",,Kōrin,,1658,1716,after 1704,1650,1850,Hanging scroll; ink on paper,Image: 11 1/4 × 14 1/2 in. (28.5 × 36.8 cm) Overall with mounting: 42 1/2 × 26 9/16 in. (108 × 67.4 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/53422,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2015.300.90a, b",false,true,53421,Asian Art,Panels,,Japan,Edo period (1615–1868),,,,Artist,,Ogata Kōrin,"Japanese, 1658–1716",,Kōrin,,1658,1716,shortly after 1701,1650,1850,Pair of panels; ink and color on cryptomeria wood,Image: 54 in. × 7 7/8 in. (137.2 × 20 cm) Overall with mounting: 81 × 13 in. (205.7 × 33 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/53421,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.212,false,true,670916,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Ogawa Haritsu (Ritsuō),"Japanese, 1663–1747",,Ritsuō,,1663,1747,1741,1741,1741,Hanging scroll; ink and color on silk,Image: 15 1/4 × 21 5/16 in. (38.7 × 54.2 cm) Overall with mounting: 51 11/16 × 26 7/8 in. (131.3 × 68.3 cm) Overall with knobs: 51 11/16 × 29 1/16 in. (131.3 × 73.8 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/670916,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.500.9.49,false,true,667348,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Nishikawa Sukenobu,"Japanese, 1671–1750",,Nishikawa Sukenobu,,1671,1750,ca. 1716–1736,1716,1736,Hanging scroll; ink and color on silk,Image: 23 in. × 32 3/4 in. (58.4 × 83.2 cm) Overall with mounting: 60 1/2 × 38 1/2 in. (153.7 × 97.8 cm) Overall with knobs: 60 1/2 × 41 5/8 in. (153.7 × 105.7 cm),,,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/667348,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.154,false,true,53450,Asian Art,Hanging scroll,「竹窗雨日」図|“Window onto Bamboo on a Rainy Day”,Japan,Edo period (1615–1868),,,,Artist,,Gion Nankai,"Japanese, 1677–1751",,Gion Nankai,,1677,1751,first half of the 18th century,1700,1749,Hanging scroll; ink on paper,Image: 52 5/8 × 22 13/16 in. (133.7 × 58 cm) Overall with mounting: 84 1/16 × 29 7/16 in. (213.5 × 74.8 cm) Overall with knobs: 84 1/16 × 31 13/16 in. (213.5 × 80.8 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/53450,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.500.9.3,false,true,78145,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Hakuin Ekaku,"Japanese, 1685–1768",,Hakuin Ekaku,,1685,1768,1685–1769,1685,1769,Hanging scroll; ink on paper,Image: 46 1/4 × 21 1/4 in. (117.5 × 54 cm) Overall with mounting: 74 × 27 1/2 in. (188 × 69.9 cm) Overall with knobs: 74 × 30 in. (188 × 76.2 cm),"Gift of Florence and Herbert Irving, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/78145,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.132,false,true,670926,Asian Art,Hanging scroll,島原の節分図|Shimabara Courtesans Exorcizing Demons,Japan,Edo period (1615–1868),,,,Artist,,Miyagawa Isshō,"Japanese, 1689–1779",,Miyagawa Isshō,,1689,1779,second half of the 18th century,1750,1799,Hanging scroll; ink and color on paper,Image: 34 1/4 × 10 1/2 in. (87 × 26.6 cm) Overall with mounting: 72 5/8 × 15 13/16 in. (184.5 × 40.2 cm) Overall with knobs: 72 5/8 × 18 1/8 in. (184.5 × 46 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/670926,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.500.9.20,false,true,78060,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Miyagawa Isshō,"Japanese, 1689–1779",,Miyagawa Isshō,,1689,1779,late 1730s–early 1740s,1736,1743,"Hanging scroll; ink, color and gold on silk",Image: 35 7/16 x 14 9/16 in. (90 x 37 cm),"Gift of Florence and Herbert Irving, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/78060,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.91,false,true,77799,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Fukae Roshū,"Japanese, 1699–1757",,Fukae Roshū,,1699,1757,early 18th century,1700,1733,Fan mounted as a hanging scroll,Image: 8 13/16 × 18 1/8 in. (22.4 × 46 cm) Overall with mounting: 43 11/16 × 25 9/16 in. (111 × 65 cm) Overall with knobs: 43 11/16 × 27 5/8 in. (111 × 70.2 cm),Property of Mary Griggs Burke,,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/77799,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.156,false,true,670942,Asian Art,Hanging scroll,山水図|Landscape,Japan,Edo period (1615–1868),,,,Artist,,Yanagisawa Kien,"Japanese, 1704–1758",,Yanagisawa Kien,,1704,1758,first half of the 18th century,1700,1749,Hanging scroll; ink and color on paper,Image: 53 7/8 × 12 1/2 in. (136.8 × 31.8 cm) Overall with mounting: 82 × 18 1/4 in. (208.3 × 46.4 cm) Overall with knobs: 82 × 21 5/16 in. (208.3 × 54.2 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/670942,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.134,false,true,671034,Asian Art,Hanging scroll,文を読む遊女図|Courtesan Reading a Letter,Japan,Edo period (1615–1868),,,,Artist,,Ishikawa Toyonobu,"Japanese, 1711–1785",,Ishikawa Toyonobu,,1711,1785,mid-18th century,1734,1766,Hanging scroll; ink and color on paper,Image: 32 7/8 × 8 9/16 in. (83.5 × 21.7 cm) Overall with mounting: 67 1/2 × 12 11/16 in. (171.5 × 32.2 cm) Overall with knobs: 67 1/2 × 14 5/16 in. (171.5 × 36.4 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/671034,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.135a–c,false,true,53445,Asian Art,Hanging scrolls,"「翁」図|The Auspicious Noh Dance ""Okina""",Japan,Edo period (1615–1868),,,,Artist,,Toriyama Sekien,"Japanese, 1712–1788",,Toriyama Sekien,,1712,1788,ca. 1790–95,1790,1795,"Triptych of hanging scrolls; ink, color, and gold on paper",Image (a): 34 5/16 × 10 3/4 in. (87.2 × 27.3 cm) Overall with mounting (a): 67 5/16 × 15 in. (171 × 38.1 cm) Overall with knobs (a): 67 5/16 × 16 3/4 in. (171 × 42.5 cm) Image (b): 34 1/2 × 10 15/16 in. (87.6 × 27.8 cm) Overall with mounting (b): 67 1/16 × 15 1/16 in. (170.3 × 38.3 cm) Overall with knobs (b): 67 1/16 × 16 5/8 in. (170.3 × 42.2 cm) Image (c): 34 1/2 × 10 11/16 in. (87.6 × 27.2 cm) Overall with mounting (c): 67 5/16 × 15 in. (171 × 38.1 cm) Overall with knobs (c): 67 5/16 × 16 5/8 in. (171 × 42.3 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/53445,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.159,false,true,53453,Asian Art,Folding fan mounted as a hanging scroll,奥の細道図扇面|Scene from The Narrow Road to the Deep North (Oku no hosomichi),Japan,Edo period (1615–1868),,,,Artist,,Yosa Buson,"Japanese, 1716–1783",,Yosa Buson,,1716,1783,ca. 1780,1600,1870,Folding fan mounted as a hanging scroll; ink and color on paper,Image: 8 7/8 × 11 3/16 in. (22.6 × 28.4 cm) Overall with mounting: 40 in. × 20 15/16 in. (101.6 × 53.2 cm) Overall with knobs: 40 × 23 3/8 in. (101.6 × 59.3 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/53453,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.500.9.18,false,true,78134,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Yosa Buson,"Japanese, 1716–1783",,Yosa Buson,,1716,1783,1760s,1760,1769,Hanging scroll; ink and color on paper,Overall with mounting: 73 3/4 × 15 1/2 in. (187.3 × 39.4 cm) Image: 84 1/4 in. × 11 in. (214 × 27.9 cm) Overall with knobs: 17 5/8 in. (44.8 cm),"Gift of Florence and Herbert Irving, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/78134,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.168,false,true,671035,Asian Art,Hanging scroll,蘇鉄図|Cycad,Japan,Edo period (1615–1868),,,,Artist,,Ike Taiga,"Japanese, 1723–1776",,Ike Taiga,,1723,1776,mid-18th century,1734,1766,Hanging scroll; ink on paper,Image: 11 in. × 12 5/8 in. (28 × 32.1 cm) Overall with mounting: 41 9/16 × 18 1/16 in. (105.5 × 45.8 cm) Overall with knobs: 41 9/16 × 19 15/16 in. (105.5 × 50.7 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/671035,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.243,false,true,53454,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Ike Taiga,"Japanese, 1723–1776",,Ike Taiga,,1723,1776,1734,1600,1870,Hanging scroll; ink on paper,Image: 10 1/2 × 13 1/8 in. (26.7 × 33.3 cm) Overall with mounting: 42 15/16 × 17 7/8 in. (109 × 45.4 cm) Overall with knobs: 42 15/16 × 20 1/2 in. (109 × 52 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/53454,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.139,false,true,53446,Asian Art,Hanging scroll,立姿美人図|Woman in a Black Kimono,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,,1726,1792,1783–89,1600,1870,"Hanging scroll; ink, color, and gold on silk",Image: 33 1/2 × 11 1/4 in. (85.1 × 28.6 cm) Overall with mounting: 67 11/16 × 15 7/8 in. (172 × 40.4 cm) Overall with knobs: 67 11/16 × 17 13/16 in. (172 × 45.2 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/53446,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.500.9.4,false,true,78146,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Soga Shōhaku,"Japanese, 1730–1781",,Soga Shōhaku,,1730,1781,ca. 1770s,1770,1779,Hanging scroll; ink on paper,Image: 52 1/2 x 21 in. (133.4 x 53.3 cm),"Gift of Florence and Herbert Irving, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/78146,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2015.300.198a, b",false,true,53404,Asian Art,Pair of hanging scrolls,鮎図|Sweetfish in Summer and Autumn,Japan,Edo period (1615–1868),,,,Artist,,Maruyama Ōkyo,"Japanese, 1733–1795",,Maruyama Ōkyo,,1733,1795,1785,1600,1850,"Pair of hanging scrolls; ink, gold, and color on silk",Image (a): 40 15/16 × 14 9/16 in. (104 × 37 cm) Overall with mounting (a): 75 3/8 × 20 1/4 in. (191.5 × 51.5 cm) Overall with knobs (a): 75 3/8 × 22 5/8 in. (191.5 × 57.4 cm) Image (b): 40 15/16 × 14 1/2 in. (104 × 36.8 cm) Overall with mounting (b): 75 3/16 × 20 1/4 in. (191 × 51.5 cm) Overall with knobs (b): 75 3/16 × 22 9/16 in. (191 × 57.3 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/53404,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.217,false,true,73359,Asian Art,Hanging scroll,羅漢図|Ten Rakan Examining a Painting of White-Robed Kannon,Japan,Edo period (1615–1868),,,,Artist,,Katō Nobukiyo,"Japanese, 1734–1810",,Katō Nobukiyo,,1734,1810,1792,1792,1792,Hanging scroll; ink and color on paper,Image: 55 1/4 × 22 3/4 in. (140.3 × 57.8 cm) Overall with mounting: 91 1/8 × 32 5/8 in. (231.5 × 82.9 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/73359,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.176,false,true,53458,Asian Art,Folding fan mounted,「残雨半村」図|“Lingering Rain over Half the Village”,Japan,Edo period (1615–1868),,,,Artist,,Uragami Gyokudō,"Japanese, 1745–1820",,Uragami Gyokudō,,1745,1820,ca. 1815–20,1700,1899,Folding fan mounted as a hanging scroll; ink on paper,Image: 8 9/16 × 18 7/8 in. (21.7 × 48 cm) Overall with mounting: 41 3/4 × 23 1/2 in. (106 × 59.7 cm) Overall with knobs: 41 3/4 × 25 11/16 in. (106 × 65.2 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/53458,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.177,false,true,53460,Asian Art,Hanging scroll,"「野橋抱琴図」|“On an Earthen Bridge, Carrying a Zither” (Yakyō hōkin zu)",Japan,Edo period (1615–1868),,,,Artist,,Uragami Gyokudō,"Japanese, 1745–1820",,Uragami Gyokudō,,1745,1820,1814,1600,1880,Hanging scroll; ink on paper,Image: 50 1/4 × 21 5/16 in. (127.7 × 54.2 cm) Overall with mounting: 73 1/4 × 27 7/16 in. (186 × 69.7 cm) Overall with knobs: 73 1/4 × 32 5/16 in. (186 × 82 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/53460,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.201,false,true,64872,Asian Art,Handscroll,華洛四季遊楽図巻|Scenes of the Four Seasons in Kyoto,Japan,Edo period (1615–1868),,,,Artist,,Genki (Komai Ki),"Japanese, 1747–1797",,Genki,,1747,1797,1778,1778,1778,Handscroll; ink and color on silk,Image: 12 5/16 in. × 16 ft. 7 15/16 in. (31.3 × 507.8 cm) Overall with mounting: 12 13/16 in. × 16 ft. 7 15/16 in. (32.5 × 507.8 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/64872,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2015.300.202a, b",false,true,53405,Asian Art,Pair of hanging scrolls,燕姞・楊貴妃図|Yanji with Orchids and Yang Guifei with Peonies,Japan,Edo period (1615–1868),,,,Artist,,Genki (Komai Ki),"Japanese, 1747–1797",,Genki,,1747,1797,1785,1600,1800,Pair of hanging scrolls; ink and color on silk,Image (a): 43 3/16 × 21 15/16 in. (109.7 × 55.8 cm) Overall with mounting (a): 78 7/8 × 27 1/16 in. (200.3 × 68.8 cm) Overall with knobs (a): 78 7/8 × 29 5/16 in. (200.3 × 74.4 cm) Image (b): 43 3/16 × 21 15/16 in. (109.7 × 55.7 cm) Overall with mounting (b): 78 7/8 × 27 1/8 in. (200.3 × 68.9 cm) Overall with knobs (b): 78 7/8 × 29 5/16 in. (200.3 × 74.5 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/53405,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.173,false,true,670940,Asian Art,Handscroll section,,Japan,Edo period (1615–1868),,,,Artist,,Noro Kaiseki,"Japanese, 1747–1828",,Noro Kaiseki,,1747,1828,1826,1826,1826,Handscroll section mounted as a hanging scroll; ink and color on silk,Image: 13 7/8 × 36 7/16 in. (35.2 × 92.6 cm) Overall with mounting: 48 13/16 × 42 1/4 in. (124 × 107.3 cm) Overall with knobs: 48 13/16 × 46 5/8 in. (124 × 118.5 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/670940,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2015.300.175a, b",false,true,670892,Asian Art,Albums,,Japan,Edo period (1615–1868),,,,Artist,,Totoki Baigai,"Japanese, 1749–1804",,Totoki Baigai,,1749,1804,1800,1800,1800,"Two albums, each with ten leaves; ink and color on paper",Image (each leaf): 7 3/8 × 7 5/8 in. (18.8 × 19.4 cm) Each album: 9 5/16 × 8 11/16 × 15/16 in. (23.6 × 22 × 2.4 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/670892,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.191,false,true,53465,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Kameda Bōsai,"Japanese, 1752–1826",,Kameda Bōsai,,1752,1826,ca. 1817,1600,1899,Hanging scroll; ink and color on silk,Image: 41 7/8 × 19 1/8 in. (106.4 × 48.6 cm) Overall with mounting: 71 5/8 × 23 3/4 in. (182 × 60.4 cm) Overall with knobs: 71 5/8 × 26 in. (182 × 66 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/53465,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.500.9.16,false,true,78143,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Chōbunsai Eishi,"Japanese, 1756–1829",,Chōbunsai Eishi,,1756,1829,1756–1815,1756,1815,Hanging scroll; ink and color on silk,Image: 31 5/8 x 13 1/16 in. (80.4 x 33.2 cm),"Gift of Florence and Herbert Irving, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/78143,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.94,false,true,670923,Asian Art,Handscroll,三十六歌仙図|The Thirty-Six Poetic Immortals (Sanjūrokkasen),Japan,Edo period (1615–1868),,,,Artist,,Sakai Hōitsu,"Japanese, 1761–1828",,Sakai Hōitsu,,1761,1828,1824,1824,1824,Handscroll; ink and color on paper,Image: 11 5/8 in. × 27 ft. 1 13/16 in. (29.5 × 827.6 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/670923,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.500.9.13,false,true,78135,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Sakai Hōitsu,"Japanese, 1761–1828",,Sakai Hōitsu,,1761,1828,1761–1828,1761,1828,Hanging scroll; ink and color on silk,Image: 46 3/4 x 21 1/2 in. (118.7 x 54.6 cm),"Gift of Florence and Herbert Irving, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/78135,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.500.9.15,false,true,78139,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Sakai Hōitsu,"Japanese, 1761–1828",,Sakai Hōitsu,,1761,1828,1761–1828,1761,1828,"Hanging scroll; ink, color and gold on silk",Image: 72 1/4 x 18 5/16 in. (183.5 x 46.5 cm),"Gift of Florence and Herbert Irving, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/78139,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.149,false,true,671045,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Toyohiro,"Japanese, 1763–1828",,Utagawa Toyohiro,,1763,1828,late 18th–early 19th century,1763,1828,Hanging scroll; ink and color on silk,Image: 34 5/8 in. × 11 in. (88 × 28 cm) Overall with mounting: 66 3/4 × 15 7/8 in. (169.5 × 40.3 cm) Overall with knobs: 66 3/4 × 18 1/16 in. (169.5 × 45.9 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/671045,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.192,false,true,53466,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Tani Bunchō,"Japanese, 1763–1840",,Tani Bunchō,,1763,1840,1828,1600,1899,Hanging scroll; ink on silk,Image: 49 13/16 × 23 1/4 in. (126.5 × 59.1 cm) Overall with mounting: 88 3/16 × 29 1/8 in. (224 × 74 cm) Overall with knobs: 88 3/16 × 31 13/16 in. (224 × 80.8 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/53466,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.193,false,true,670897,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Tani Bunchō,"Japanese, 1763–1840",,Tani Bunchō,,1763,1840,1828,1828,1828,Hanging scroll; ink and color on silk,Image: 52 3/8 × 27 13/16 in. (133 × 70.7 cm) Overall with mounting: 81 1/8 × 32 15/16 in. (206 × 83.7 cm) Overall with knobs: 81 1/8 × 35 7/8 in. (206 × 91.2 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/670897,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.147,false,true,671044,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Toyokuni I,"Japanese, 1769–1825",,Utagawa Toyokuni I,,1769,1825,late 18th–early 19th century,1769,1825,Hanging scroll; ink and color on paper,Image: 23 9/16 × 10 3/8 in. (59.8 × 26.4 cm) Overall with mounting: 55 11/16 × 14 13/16 in. (141.5 × 37.7 cm) Overall with knobs: 55 11/16 × 17 1/8 in. (141.5 × 43.5 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/671044,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.71,false,true,45792,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shuntei,"Japanese, 1770–1820",,Katsukawa Shuntei,,1770,1820,dated 1795,1795,1795,Hanging scroll; ink and color on silk,35 5/8 x 10 3/4 in. (90.5 x 27.3 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45792,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.140,false,true,671006,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shuntei,"Japanese, 1770–1820",,Katsukawa Shuntei,,1770,1820,late 18th–early 19th century,1770,1820,"Hanging scroll; ink, color and gold on paper",Image: 50 9/16 × 23 7/16 in. (128.4 × 59.6 cm) Overall with mounting: 82 7/8 × 28 1/16 in. (210.5 × 71.3 cm) Overall with knobs: 82 7/8 × 30 5/8 in. (210.5 × 77.8 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/671006,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.152,false,true,670924,Asian Art,Hanging scroll,雨宿り図|Taking Shelter from the Rain,Japan,Edo period (1615–1868),,,,Artist,,Teisai Hokuba,"Japanese, 1771–1844",,Teisai Hokuba,,1771,1844,early to mid-19th century,1800,1844,Hanging scroll; ink and color on silk,Image: 15 1/2 × 27 5/16 in. (39.4 × 69.3 cm) Overall with mounting: 51 15/16 × 32 5/16 in. (132 × 82 cm) Overall with knobs: 51 15/16 × 34 3/4 in. (132 × 88.3 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/670924,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.500.9.12,false,true,667347,Asian Art,Hanging scroll,五美人図|Five Beauties,Japan,Edo period (1615–1868),,,,Artist,,Teisai Hokuba,"Japanese, 1771–1844",,Teisai Hokuba,,1771,1844,1840,1840,1840,Hanging scroll; ink and color on silk,Image: 15 3/8 × 20 3/4 in. (39.1 × 52.7 cm) Overall with mounting: 47 3/4 × 25 3/4 in. (121.3 × 65.4 cm) Overall with knobs: 47 3/4 × 28 in. (121.3 × 71.1 cm),"Gift of Florence and Herbert Irving, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/667347,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.185,false,true,670900,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Nakabayashi Chikutō,"Japanese, 1776–1853",,Nakabayashi Chikutō,,1776,1853,1840,1840,1840,Hanging scroll; ink and color on paper,Image: 42 13/16 × 17 3/8 in. (108.8 × 44.1 cm) Overall with mounting: 71 13/16 × 18 5/16 in. (182.4 × 46.5 cm) Overall with knobs: 71 13/16 × 22 5/8 in. (182.4 × 57.5 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/670900,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.181,false,true,53462,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Tanomura Chikuden,"Japanese, 1777–1835",,Tanomura Chikuden,,1777,1835,late 18th–early 19th century,1777,1835,Hanging scroll; ink and color on paper,Image: 52 1/8 x 16 5/8 in. (132.4 x 42.2 cm) Overall with mounting: 85 13/16 × 23 3/8 in. (218 × 59.3 cm) Overall with knobs: 85 13/16 × 25 7/8 in. (218 × 65.8 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/53462,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.186,false,true,670943,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Nukina Kaioku,"Japanese, 1778–1863",,Nukina Kaioku,,1778,1863,1833,1833,1833,Hanging scroll; ink and color on silk,Image: 11 7/8 in. × 7 in. (30.2 × 17.8 cm) Overall with mounting: 39 7/8 × 12 5/8 in. (101.3 × 32 cm) Overall with knobs: 39 7/8 × 14 7/8 in. (101.3 × 37.8 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/670943,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.187,false,true,670947,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Nukina Kaioku,"Japanese, 1778–1863",,Nukina Kaioku,,1778,1863,1844,1844,1844,Hanging scroll; ink and color on paper,Image: 53 in. × 20 5/8 in. (134.6 × 52.4 cm) Overall with mounting: 79 1/8 × 27 1/16 in. (201 × 68.8 cm) Overall with knobs: 79 1/8 × 29 5/16 in. (201 × 74.4 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/670947,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.84,false,true,670939,Asian Art,Handscroll,,Japan,Edo period (1615–1868),,,,Artist,,Nonoyama Kōzan,"Japanese, 1780–1847",,Nonoyama Kōzan,,1780,1847,1822,1822,1822,"Handscroll; ink, color and gold on paper",Image: 13 5/8 × 54 15/16 in. (34.6 × 139.5 cm) Overall with mounting: 14 15/16 × 93 1/2 in. (38 × 237.5 cm) Overall with knobs: 16 5/16 × 93 1/2 in. (41.5 × 237.5 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/670939,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.178,false,true,73358,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Okada Hankō,"Japanese, 1782–1846",,Okada Hankō,,1782,1846,1843,1843,1843,Hanging scroll; ink and color on paper,Image: 50 3/8 × 23 1/8 in. (128 × 58.8 cm) Overall with mounting: 87 3/16 × 29 5/16 in. (221.5 × 74.5 cm) Overall with knobs: 87 3/16 × 31 13/16 in. (221.5 × 80.8 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/73358,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.179,false,true,670921,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Okada Hankō,"Japanese, 1782–1846",,Okada Hankō,,1782,1846,1833,1833,1833,Hanging scroll; ink and color on paper,Image: 11 13/16 × 25 1/16 in. (30 × 63.7 cm) Overall with mounting: 50 3/16 × 31 3/4 in. (127.5 × 80.6 cm) Overall with knobs: 50 3/16 × 34 3/16 in. (127.5 × 86.8 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/670921,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.760,false,true,77196,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Yamamoto Baiitsu,"Japanese, 1783–1856",,Yamamoto Baiitsu,,1783,1783,dated 10th month of 1852,1852,1852,Hanging scroll; ink and color on silk,Image: 50 × 21 13/16 in. (127 × 55.4 cm) Overall with mounting: 84 × 27 9/16 in. (213.4 × 70 cm) Overall with knobs: 84 × 30 in. (213.4 × 76.2 cm),"Fishbein-Bender Collection, Gift of T. Richard Fishbein and Estelle P. Bender, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/77196,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.188,false,true,670891,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Yamamoto Baiitsu,"Japanese, 1783–1856",,Yamamoto Baiitsu,,1783,1783,1843,1843,1843,Hanging scroll; ink and color on silk,Image: 45 9/16 × 16 1/8 in. (115.8 × 40.9 cm) Overall with mounting: 78 1/16 × 22 5/8 in. (198.2 × 57.5 cm) Overall with knobs: 78 1/16 × 25 1/16 in. (198.2 × 63.7 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/670891,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.189,false,true,670893,Asian Art,Folding fan,,Japan,Edo period (1615–1868),,,,Artist,,Yamamoto Baiitsu,"Japanese, 1783–1856",,Yamamoto Baiitsu,,1783,1783,1832,1832,1832,Folding fan; ink and color on paper,Image: 6 11/16 × 18 11/16 in. (17 × 47.5 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/670893,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.500.9.47,false,true,78149,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Yamamoto Baiitsu,"Japanese, 1783–1856",,Yamamoto Baiitsu,,1783,1783,dated 1851,1851,1851,Hanging scroll; ink on silk,Image: 45 1/4 x 15 3/4 in. (114.9 x 40 cm),"Gift of Florence and Herbert Irving, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/78149,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.190a–d,false,true,53463,Asian Art,Four hanging scrolls,四季山水図|Landscapes of the Four Seasons,Japan,Edo period (1615–1868),,,,Artist,,Yamamoto Baiitsu,"Japanese, 1783–1856",,Yamamoto Baiitsu,,1783,1783,1848,1700,1899,Set of four hanging scrolls; ink and color on silk,Image (a): 40 3/8 × 13 7/8 in. (102.6 × 35.2 cm) Overall with mounting (a): 75 1/16 × 19 3/4 in. (190.7 × 50.1 cm) Overall with knobs (a): 75 1/16 × 21 5/8 in. (190.7 × 55 cm) Image (b): 40 3/8 × 13 7/8 in. (102.6 × 35.3 cm) Overall with mounting (b): 75 1/4 × 19 11/16 in. (191.2 × 50 cm) Overall with knobs (b): 75 1/4 × 21 11/16 in. (191.2 × 55.1 cm) Image (c): 40 7/16 × 13 7/8 in. (102.7 × 35.3 cm) Overall with mounting (c): 75 1/16 × 19 3/4 in. (190.6 × 50.1 cm) Overall with knobs (c): 75 1/16 × 21 3/4 in. (190.6 × 55.3 cm) Image (d): 40 1/2 × 13 15/16 in. (102.8 × 35.4 cm) Overall with mounting (d): 75 3/8 × 19 11/16 in. (191.5 × 50 cm) Overall with knobs (d): 75 3/8 × 21 11/16 in. (191.5 × 55.1 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/53463,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.194,false,true,670882,Asian Art,Album,,Japan,Edo period (1615–1868),,,,Artist,,Takaku Aigai,"Japanese, 1796–1843",,Takaku Aigai,,1796,1843,1833,1833,1833,Album with twelve leaves; ink and color on paper,Album: 13 × 7 1/2 × 1 in. (33 × 19 × 2.5 cm) Image (each leaf): 11 7/16 × 13 1/8 in. (29 × 33.3 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/670882,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.95,false,true,53424,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Kiitsu,"Japanese, 1796–1858",,Suzuki Kiitsu,,1796,1858,ca. 1805,1720,1920,Hanging scroll; ink and color on silk,Image: 39 7/8 × 12 15/16 in. (101.3 × 32.8 cm) Overall with mounting: 77 3/16 × 18 3/8 in. (196 × 46.6 cm) Overall with knobs: 77 3/16 × 20 9/16 in. (196 × 52.2 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/53424,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.98,false,true,670946,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Kiitsu,"Japanese, 1796–1858",,Suzuki Kiitsu,,1796,1858,1857,1857,1857,Hanging scroll; ink and color on paper,Image: 39 1/16 × 11 5/16 in. (99.2 × 28.7 cm) Overall with mounting: 74 5/16 × 12 1/2 in. (188.8 × 31.8 cm) Overall with knobs: 74 5/16 × 14 5/8 in. (188.8 × 37.2 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/670946,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.100a–f,false,true,53427,Asian Art,Handscrolls,,Japan,Edo period (1615–1868),,,,Artist,,Sakai Ōho,"Japanese, 1808–1841",,Sakai Ōho,,1808,1841,ca. 1839,1800,1920,"Six handscrolls; ink, color, and gold on silk",Overall (a): 3 9/16 x 46 7/16 in. (9 x 118 cm) Overall (b): 3 9/16 x 46 15/16 in. (9 x 119.2 cm) Overall (c): 3 1/2 x 46 7/16 in. (8.9 x 118 cm) Overall (d): 3 9/16 x 48 13/16 in. (9 x 124 cm) Overall (e): 3 5/8 x 46 1/2 in. (9.2 x 118.1 cm) Overall (f): 3 1/2 x 46 7/8 in. (8.9 x 119.1 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/53427,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.196,false,true,671048,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Hine Taizan,"Japanese, 1813–1869",,Hine Taizan,,1813,1869,1859,1859,1859,Hanging scroll; ink and color on silk,Image: 53 15/16 × 20 3/16 in. (137 × 51.3 cm) Overall with mounting: 88 × 26 11/16 in. (223.5 × 67.8 cm) Overall with knobs: 88 × 30 1/2 in. (223.5 × 77.5 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/671048,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.126,false,true,671003,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Ryūkadō,"Japanese, active 1740s",,Ryūkadō,,1740,1749,1740s,1740,1749,Hanging scroll; ink and color on paper,Image: 28 11/16 in. × 13 in. (72.8 × 33 cm) Overall with mounting: 60 13/16 × 14 3/4 in. (154.5 × 37.4 cm) Overall with knobs: 60 13/16 × 17 1/16 in. (154.5 × 43.3 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/671003,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.182,false,true,671012,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Takahashi Sōhei,"Japanese, 1804?–?1835",,Takahashi Sōhei,,1804,1835,1824,1824,1824,Hanging scroll; ink and color on paper,Image: 53 15/16 × 19 5/16 in. (137 × 49 cm) Overall with mounting: 77 1/16 × 21 5/8 in. (195.8 × 55 cm) Overall with knobs: 77 1/16 × 23 11/16 in. (195.8 × 60.2 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/671012,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.183,false,true,671017,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Takahashi Sōhei,"Japanese, 1804?–?1835",,Takahashi Sōhei,,1804,1835,1831,1831,1831,Hanging scroll; ink and color on paper,Image: 39 1/2 × 11 13/16 in. (100.3 × 30 cm) Overall with mounting: 63 3/4 × 16 7/16 in. (162 × 41.7 cm) Overall with knobs: 63 3/4 × 18 11/16 in. (162 × 47.5 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/671017,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.184,false,true,671011,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Takahashi Sōhei,"Japanese, 1804?–?1835",,Takahashi Sōhei,,1804,1835,1832,1832,1832,Hanging scroll; ink and color on paper,Image: 40 1/16 × 16 1/2 in. (101.8 × 41.9 cm) Overall with mounting: 69 11/16 × 22 1/16 in. (177 × 56 cm) Overall with knobs: 69 11/16 × 24 7/16 in. (177 × 62 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/671011,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.169,false,true,53457,Asian Art,Hanging scroll,牡丹に竹図|Peony and Bamboo by a Rock,Japan,Edo period (1615–1868),,,,Artist,,Tokuyama Gyokuran,"Japanse, ca. 1728–1784",,Tokuyama Gyokuran,,1728,1784,ca. 1768,1600,1870,Hanging scroll; ink and color on paper,Image: 36 5/8 × 16 7/16 in. (93 × 41.7 cm) Overall with mounting: 68 7/8 × 22 3/16 in. (175 × 56.4 cm) Overall with knobs: 68 7/8 × 24 7/16 in. (175 × 62 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/53457,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.75,false,true,671042,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Ono Otsū,"Japanese, 1568–ca. 1631",,Ono Otsū,,1568,1631,1624,1624,1624,Hanging scroll; ink on paper,Image: 24 5/8 × 16 1/16 in. (62.6 × 40.8 cm) Overall with mounting: 68 1/2 × 21 9/16 in. (174 × 54.7 cm) Overall with knobs: 68 1/2 × 23 11/16 in. (174 × 60.2 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/671042,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.136,false,true,670933,Asian Art,Hanging scroll,花魁と禿の初詣図|Courtesan and Two Attendants on New Year's Day,Japan,Edo period (1615–1868),,,,Artist,,Isoda Koryūsai,"Japanese, 1735–ca. 1790",,Isoda Koryūsai,,1735,1790,ca. 1780s,1780,1789,"Hanging scroll; ink, color and gold on paper",Image: 33 11/16 × 13 11/16 in. (85.6 × 34.7 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/670933,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.137,false,true,670932,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Isoda Koryūsai,"Japanese, 1735–ca. 1790",,Isoda Koryūsai,,1735,1790,1764–88,1764,1788,Hanging scroll; ink and color on silk,Image: 32 in. × 13 1/2 in. (81.3 × 34.3 cm) Overall with mounting: 66 15/16 × 18 13/16 in. (170 × 47.8 cm) Overall with knobs: 66 15/16 × 20 11/16 in. (170 × 52.6 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/670932,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.120,false,true,670997,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Tōsendō Rifū,"Japanese, active ca. 1730",,Tōsendō Rifū,,1720,1740,ca. 1730,1720,1740,Hanging scroll; ink and color on silk,Image: 26 7/8 × 12 1/8 in. (68.3 × 30.8 cm) Overall with mounting: 60 1/4 × 16 15/16 in. (153 × 43 cm) Overall with knobs: 60 1/4 × 18 7/8 in. (153 × 48 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/670997,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.118,false,true,53443,Asian Art,Hanging scroll mounted as panel,立姿遊女図|Standing Courtesan,Japan,Edo period (1615–1868),,,,Artist,,Kaigetsudō Ando,"Japanese, ca. 1671–1743",,Kaigetsudō Ando,,1671,1743,early 18th century,1700,1733,"Hanging scroll, mounted as panel; ink and color on paper",Image: 40 15/16 × 16 5/8 in. (104 × 42.2 cm) Overall with mounting: 44 7/8 × 20 1/2 in. (114 × 52 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/53443,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.125,false,true,670954,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Kakondō,"Japanese, active 1716–36",,Kakondō,,1716,1736,early 18th century,1716,1736,"Hanging scroll; ink, color and gold on paper",Image: 31 1/8 × 15 13/16 in. (79 × 40.2 cm) Overall with mounting: 51 3/16 × 19 5/16 in. (130 × 49 cm) Overall with knobs: 51 3/16 × 21 1/4 in. (130 × 54 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/670954,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.127,false,true,671021,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Takizawa Shigenobu,"Japanese, active 1720–40",,Takizawa Shigenobu,,1700,1799,ca. 1730,1720,1740,Hanging scroll; ink and color on silk,Image: 39 15/16 × 18 3/8 in. (101.5 × 46.6 cm) Overall with mounting: 71 7/16 × 22 11/16 in. (181.5 × 57.6 cm) Overall with knobs: 71 7/16 × 24 7/8 in. (181.5 × 63.2 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/671021,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.133,false,true,671020,Asian Art,Hanging scroll,見立松風図|The Brine Maiden Matsukaze,Japan,Edo period (1615–1868),,,,Artist,,Nishimura Shigenobu,"Japanese, active 1729–39",,Nishimura Shigenobu,,1729,1739,early 18th century,1700,1733,Hanging scroll; ink and color on paper,Image: 31 9/16 × 11 3/16 in. (80.2 × 28.4 cm) Overall with mounting: 63 3/8 × 15 5/8 in. (161 × 39.7 cm) Overall with knobs: 63 3/8 × 17 15/16 in. (161 × 45.5 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/671020,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.138,false,true,671005,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Miyagawa (Katsukawa) Shunsui,"Japanese, active 1744–64",,Miyagawa (Katsukawa) Shunsui,,1744,1764,mid-18th century,1734,1766,"Hanging scroll; ink, color and gold on silk",Image: 30 7/8 × 13 7/8 in. (78.5 × 35.2 cm) Overall with mounting: 67 5/16 × 15 1/16 in. (171 × 38.3 cm) Overall with knobs: 67 5/16 × 17 1/4 in. (171 × 43.8 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/671005,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.119,false,true,53444,Asian Art,Hanging scroll,文を書く遊女図|Courtesan Writing a Letter,Japan,Edo period (1615–1868),,,,Artist,,Kaigetsudō Doshin,"Japanese, active 1711–1736",,Kaigetsudō Doshin,,1711,1736,ca. 1715,1705,1725,Hanging scroll; ink and color on paper,Image: 19 1/2 × 23 5/8 in. (49.5 × 60 cm) Overall with mounting: 53 × 28 13/16 in. (134.6 × 73.2 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/53444,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.131,false,true,670931,Asian Art,Hanging scroll,朝比奈義秀図|The Warrior Asahina Yoshihide Lifting a Puppet of a Courtesan on a Go Board,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyoshige,"Japanese, active ca. 1716–1759",,Torii Kiyoshige,,1716,1759,mid-18th century,1734,1766,"Hanging scroll; ink, color and gold on paper",Image: 30 in. × 7 15/16 in. (76.2 × 20.1 cm) Overall with mounting: 65 3/16 × 12 11/16 in. (165.5 × 32.2 cm) Overall with knobs: 65 3/16 × 14 7/8 in. (165.5 × 37.8 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/670931,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.500.9.7,false,true,78155,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Kita Genki,"Japanese, active late 17th century",,Kita Genki,,1667,1699,dated 1674,1674,1674,Hanging scroll; ink and color on silk,Image: 43 1/8 × 16 1/2 in. (109.5 × 41.9 cm) Overall with mounting: 79 5/8 × 23 3/8 in. (202.2 × 59.4 cm),"Gift of Florence and Herbert Irving, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/78155,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.43,false,true,671053,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Hishikawa Waō,"Japanese, active early 18th century",,Hishikawa Waō,,1700,1733,early 18th century,1700,1733,"Hanging scroll; ink, color and gold on silk",Image: 14 5/8 × 17 15/16 in. (37.1 × 45.6 cm) Overall with mounting: 49 1/2 × 22 7/8 in. (125.8 × 58.1 cm) Overall with knobs: 49 1/2 × 24 15/16 in. (125.8 × 63.3 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/671053,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.123,false,true,670907,Asian Art,Hanging scroll,立姿遊女図|Standing Courtesan,Japan,Edo period (1615–1868),,,,Artist,,Baiōken Eishun,"Japanese, active early 18th century",,Baiōken Eishun,,1710,1730,probably 1720s,1720,1729,Hanging scroll; ink and color on silk,Image: 39 7/16 × 16 1/8 in. (100.2 × 41 cm) Overall with mounting: 77 9/16 × 22 3/8 in. (197 × 56.8 cm) Overall with knobs: 77 9/16 × 24 5/16 in. (197 × 61.7 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/670907,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.143,false,true,671050,Asian Art,Hanging scroll,見立寒山拾得図|Courtesans Parodying Kanzan and Jittoku,Japan,Edo period (1615–1868),,,,Artist,,Kinpūsha Toyomaro,"Japanese, active early 19th century",,Kinpūsha Toyomaro,,1800,1833,late 18th–early 19th century,1767,1833,"Hanging scroll; ink, color and gold on paper",Image: 48 1/4 × 22 1/4 in. (122.5 × 56.5 cm) Overall with mounting: 85 1/4 × 27 13/16 in. (216.5 × 70.7 cm) Overall with knobs: 85 1/4 × 30 5/16 in. (216.5 × 77 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/671050,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.122,false,true,671031,Asian Art,Hanging scroll,蚊帳美人図|Woman Reading under a Mosquito Net,Japan,Edo period (1615–1868),,,,Artist,,Fuhiken Tokikaze,active first half of the 18th century,,Fuhiken Tokikaze,,1700,1749,ca. 1720,1710,1730,Hanging scroll; ink and color on silk,Image: 27 1/2 × 14 5/8 in. (69.8 × 37.1 cm) Overall with mounting: 60 3/16 × 22 1/8 in. (152.8 × 56.2 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/671031,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2015.300.150a, b",false,true,670920,Asian Art,Diptych of hanging scrolls,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",,1797,1858,1848–54,1848,1854,Diptych of hanging scrolls; ink and color on silk,Image (a): 36 7/16 in. × 13 in. (92.5 × 33 cm) Overall with mounting (a): 67 11/16 × 17 11/16 in. (172 × 44.9 cm) Overall with knobs (a): 67 11/16 × 19 1/2 in. (172 × 49.6 cm) Image (b): 36 7/16 × 12 15/16 in. (92.5 × 32.9 cm) Overall with mounting (b): 67 5/16 × 17 11/16 in. (171 × 44.9 cm) Overall with knobs: 67 5/16 × 19 5/8 in. (171 × 49.8 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/670920,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2015.300.172a, b",false,true,670883,Asian Art,Hanging scrolls,,Japan,Edo period (1615–1868),,,,Artist,,Aiseki,"Japanese, active first half of the 19th century",,Aiseki,,1800,1849,first half of the 19th century,1800,1849,Pair of hanging scrolls; ink and color on paper,Image (a): 51 3/4 × 17 15/16 in. (131.5 × 45.5 cm) Overall with mounting (a): 81 1/2 × 22 15/16 in. (207 × 58.2 cm) Overall with knobs (a): 81 1/2 × 25 1/16 in. (207 × 63.6 cm) Image (b): 51 13/16 × 17 7/8 in. (131.6 × 45.4 cm) Overall with mounting (b): 81 1/2 × 22 15/16 in. (207 × 58.2 cm) Overall with knobs (b): 81 1/2 × 25 1/16 in. (207 × 63.7 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/670883,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.113,false,true,670973,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Furuyama Moroshige,"Japanese, active second half of the 17th century",,Furuyama Moroshige,,1650,1699,second half of the 17th century,1650,1699,Hanging scroll; ink and color on silk,Image: 11 7/16 × 18 3/16 in. (29 × 46.2 cm) Overall with mounting: 38 × 20 1/4 in. (96.5 × 51.5 cm) Overall with knobs: 38 × 22 5/8 in. (96.5 × 57.5 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/670973,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.268.8,false,true,45618,Asian Art,Hanging scroll,"月光菩薩像 「金胎仏画帖」断簡|Gakkō Bosatsu, from “Album of Buddhist Deities from the Diamond World and Womb World Mandalas” (“Kontai butsugajō”)",Japan,Heian period (794–1185),,,,Artist,Attributed to,Takuma Tametō,"Japanese, active ca. 1132–74",,Takuma Tametō,,1132,1174,mid-12th century,1134,1166,"Page from a book mounted as a hanging scroll; ink, color, and gold on paper",10 in. × 5 3/8 in. (25.4 × 13.7 cm) Overall with mounting: 49 1/4 × 15 1/2 in. (125.1 × 39.4 cm) Overall with knobs: 49 1/4 × 17 3/4 in. (125.1 × 45.1 cm),"The Harry G. C. Packard Collection of Asian Art, Gift of Harry G. C. Packard, and Purchase, Fletcher, Rogers, Harris Brisbane Dick, and Louis V. Bell Funds, Joseph Pulitzer Bequest, and The Annenberg Fund Inc. Gift, 1975",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45618,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.4,false,true,53164,Asian Art,Hanging scroll,"大精進菩薩　 「金胎仏画帖」断簡|Daishōjin Bosatsu, from “Album of Buddhist Deities from the Diamond World and Womb World Mandalas” (“Kontai butsugajō”)",Japan,Heian period (794–1185),,,,Artist,Attributed to,Takuma Tametō,"Japanese, active ca. 1132–74",,Takuma Tametō,,1132,1174,12th century,1100,1185,"Hanging scroll; ink, color, and gold on paper",Image: 9 3/4 in. × 5 in. (24.7 × 12.7 cm) Overall with mounting: 46 1/16 × 14 3/16 in. (117 × 36 cm) Overall with knobs: 46 1/16 × 15 7/8 in. (117 × 40.3 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/53164,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.31,false,true,670972,Asian Art,Album leaf remounted as a hanging scroll,"源氏物語図色紙　「柏木」|Scene from “The Oak Tree” (“Kashiwagi”), from The Tale of Genji (Genji monogatari)",Japan,Momoyama period (1573–1615),,,,Artist,,Tosa Mitsuyoshi,"Japanese, 1539–1613",,TOSA MITSUYOSHI,,1539,1613,late 16th–early 17th century,1567,1613,"Album leaf remounted as a hanging scroll; ink, color and gold on paper",Image: 9 3/4 × 8 3/16 in. (24.7 × 20.8 cm) Overall with mounting: 54 5/16 × 15 1/2 in. (138 × 39.4 cm) Overall with knobs: 54 5/16 × 17 1/4 in. (138 × 43.8 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/670972,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2015.300.33a, b",false,true,53245,Asian Art,Album leaves mounted as a pair of hanging scrolls,"玉鬘図 （『源氏物語』画帖の内）|“The Jeweled Chaplet” (“Tamakazura”), from The Tale of Genji (Genji monogatari)",Japan,Momoyama period (1573–1615),,,,Artist,Circle of,Tosa Mitsuyoshi,"Japanese, 1539–1613",,TOSA MITSUYOSHI,,1539,1613,early 17th century,1600,1615,"Album leaves mounted as a pair of hanging scrolls; ink, gold, silver, and color on paper",Image (a): 9 5/8 × 8 3/8 in. (24.4 × 21.3 cm) Overall with mounting (a): 53 1/4 × 15 11/16 in. (135.3 × 39.8 cm) Overall with knobs (a): 53 1/4 × 17 3/8 in. (135.3 × 44.2 cm) Image (b): 9 7/16 × 8 3/8 in. (24 × 21.2 cm) Overall with mounting (b): 53 1/8 × 15 5/8 in. (135 × 39.7 cm) Overall with knobs (b): 53 1/8 × 17 3/8 in. (135 × 44.1 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/53245,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.72,false,true,670979,Asian Art,Hanging scroll,,Japan,Momoyama period (1573–1615),,,,Artist,,Konoe Nobutada,"Japanese, 1565–1614",,Konoe Nobutada,,1565,1614,late 16th century,1573,1599,Hanging scroll; ink on paper,Image: 38 7/16 × 16 13/16 in. (97.7 × 42.7 cm) Overall with mounting: 72 1/16 × 17 13/16 in. (183 × 45.3 cm) Overall with knobs: 72 1/16 × 19 13/16 in. (183 × 50.4 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/670979,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.130,false,true,670930,Asian Art,Hanging scroll,羽根突き美人図|Woman with Battledore and Shuttlecock,Japan,Momoyama period (1573–1615),,,,Artist,,Torii Kiyotomo,"Japanese, active early 19th century",,Torii Kiyotomo,,1815,1820,1815–20,1815,1820,"Hanging scroll; ink, color and gold on paper",Image: 45 3/16 × 20 11/16 in. (114.8 × 52.5 cm) Overall with mounting: 71 1/4 × 24 1/8 in. (181 × 61.2 cm) Overall with knobs: 71 1/4 × 26 3/8 in. (181 × 67 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/670930,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.50,false,true,53231,Asian Art,Hanging scroll,竹林七聖図|Seven Sages of the Bamboo Grove,Japan,Muromachi period (1392–1573),,,,Artist,,Sesson Shūkei,ca. 1504–ca. 1589,,Sesson Shūkei,,1504,1589,1550s,1550,1559,Hanging scroll; ink and color on paper,Image: 40 5/16 × 20 3/8 in. (102.4 × 51.7 cm) Overall with mounting: 79 3/4 × 26 7/16 in. (202.5 × 67.2 cm) Overall with knobs: 79 3/4 × 28 9/16 in. (202.5 × 72.6 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/53231,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.49,false,true,53212,Asian Art,Hanging scroll,蘇軾騎驢図|Su Shi Riding a Donkey,Japan,Muromachi period (1392–1573),,,,Artist,,Bokudō Sojun,"Japanese, 1373–1459",,Bokudō Sojun,,1373,1459,early 15th century,1400,1433,Hanging scroll; ink and gold on paper,Image: 22 1/2 × 10 1/4 in. (57.2 × 26 cm) Overall with mounting: 57 11/16 × 14 7/8 in. (146.5 × 37.8 cm) Overall with knobs: 57 11/16 × 16 11/16 in. (146.5 × 42.4 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/53212,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.67,false,true,53233,Asian Art,Hanging scroll,伯牙鍾子期図|Bo Ya Plays the Qin as Zhong Ziqi Listens,Japan,Muromachi period (1392–1573),,,,Artist,Circle of,Kano Motonobu,"Japan, ca. 1476–1559",,Kano Motonobu,,1476,1559,1530s,1530,1539,Hanging scroll; ink and color on paper,Image: 65 1/16 × 34 1/4 in. (165.2 × 87 cm) Overall with mounting: 8 ft. 10 7/8 in. × 40 13/16 in. (271.5 × 103.7 cm) Overall with knobs: 8 ft. 10 7/8 in. × 43 3/16 in. (271.5 × 109.7 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/53233,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.268.40,false,true,45638,Asian Art,Hanging scroll,粟に燕図|Millet and Sparrows,Japan,Muromachi period (1392–1573),,,,Artist,,Geiai,active mid-16th century,,Geiai,,1534,1566,mid-16th century,1534,1566,Hanging scroll; ink on paper,Image: 39 9/16 × 17 5/8 in. (100.5 × 44.8 cm) Overall with knobs: 75 3/4 × 24 7/8 in. (192.4 × 63.2 cm),"The Harry G. C. Packard Collection of Asian Art, Gift of Harry G. C. Packard, and Purchase, Fletcher, Rogers, Harris Brisbane Dick, and Louis V. Bell Funds, Joseph Pulitzer Bequest, and The Annenberg Fund Inc. Gift, 1975",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45638,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2015.300.46a, b",false,true,65394,Asian Art,Hanging scrolls,"豊干寒山拾得図|Fenggan, Hanshan, and Shide",Japan,Muromachi period (1392–1573),,,,Artist,,Reisai,"Japanese, active ca. 1430–50",,Reisai,,1430,1450,first half of the 15th century,1400,1449,Pair of hanging scrolls; ink and color on paper,Image (a): 37 7/8 × 13 5/8 in. (96.2 × 34.6 cm) Overall with mounting (a): 72 15/16 × 18 11/16 in. (185.3 × 47.5 cm) Overall with knobs (a): 72 15/16 × 20 5/16 in. (185.3 × 51.6 cm) Image (b): 37 15/16 × 13 9/16 in. (96.3 × 34.5 cm) Overall with mounting (b): 72 5/8 × 18 11/16 in. (184.5 × 47.4 cm) Overall with knobs (b): 72 5/8 × 20 1/4 in. (184.5 × 51.5 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/65394,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.58,false,true,53219,Asian Art,Hanging scroll,葡萄蝉図|Cicada on a Grapevine,Japan,Muromachi period (1392–1573),,,,Artist,,Bokurin Guan,"Japanese, active late 14th century",,Bokurin Guan,,1367,1399,late 14th century,1392,1399,Hanging scroll; ink on paper,Image: 25 1/4 × 12 1/8 in. (64.2 × 30.8 cm) Overall with mounting: 57 5/16 × 15 1/2 in. (145.5 × 39.3 cm) Overall with knobs: 57 5/16 × 17 5/16 in. (145.5 × 44 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/53219,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.45,false,true,670971,Asian Art,Hanging scroll,騎獅文殊図|Monju on a Lion,Japan,Muromachi period (1392–1573),,,,Artist,,Shūsei,"Japanese, active late 15th century",(reading unsure),Shūsei,,1467,1499,late 15th century,1467,1499,Hanging scroll; ink on paper,Image: 32 1/16 in. × 13 in. (81.5 × 33 cm) Overall with mounting: 65 9/16 × 19 1/8 in. (166.5 × 48.6 cm) Overall with knobs: 65 9/16 × 20 7/8 in. (166.5 × 53 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/670971,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2015.300.64a, b",false,true,53221,Asian Art,Hanging scrolls,夏秋花鳥図|Birds and Flowers of Summer and Autumn,Japan,Muromachi period (1392–1573),,,,Artist,,Shikibu Terutada,"Japanese, active mid–16th century",,Shikibu Terutada,,1534,1566,mid-16th century,1534,1566,Pair of hanging scrolls; ink and color on paper,Image (a): 37 11/16 × 17 5/8 in. (95.8 × 44.8 cm) Overall with mounting (a): 74 7/16 × 23 3/8 in. (189 × 59.3 cm) Overall with knobs (a): 74 7/16 × 25 3/16 in. (189 × 64 cm) Image (b): 37 11/16 × 17 5/8 in. (95.8 × 44.8 cm) Overall with mounting (b): 74 5/8 × 23 3/8 in. (189.5 × 59.3 cm) Overall with knobs (b): 74 5/8 × 25 3/16 in. (189.5 × 64 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/53221,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -53.194.2,false,true,57338,Asian Art,Painted panel,,Japan,Muromachi period (1392–1573),,,,Artist,After,Kenkō Shokei,"Japanese, active ca. 1470–after 1523",,Kenkō Shokei,,1460,1523,late 15th century,1467,1499,Paint on paper,33 x 12 1/8 in. (83.8 x 30.8 cm),"Gift of Nathan V. Hammer, 1953",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57338,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -53.194.3,false,true,57350,Asian Art,Painted panel,,Japan,Muromachi period (1392–1573),,,,Artist,After,Kenkō Shokei,"Japanese, active ca. 1470–after 1523",,Kenkō Shokei,,1460,1523,late 15th century,1467,1499,Paint on paper,33 x 12 in. (83.3 x 30.5 cm),"Gift of Nathan V. Hammer, 1953",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57350,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.63,false,true,670904,Asian Art,Hanging scroll,瓜図|Melons,Japan,Muromachi period (1392–1573),,,,Artist,,Yamada Dōan,"Japanese, second half of the 16th century",,Yamada Dōan,,1550,1599,late 16th century,1567,1599,Hanging scroll; ink on paper,Image: 13 1/4 × 18 1/8 in. (33.6 × 46 cm) Overall with mounting: 47 1/2 × 23 7/8 in. (120.7 × 60.6 cm) Overall with knobs: 47 1/2 × 25 13/16 in. (120.7 × 65.5 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/670904,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2015.300.52a, b",false,true,53226,Asian Art,Hanging scrolls,,Japan,Muromachi period (1392–1573),,,,Artist,,Kantei,"Japanese, active second half of 15th century",,Kantei,,1400,1499,early 16th century,1500,1533,Pair of hanging scrolls; ink and color on paper,Image (a): 18 1/16 × 11 3/4 in. (45.8 × 29.9 cm) Overall with mounting (a): 51 15/16 × 16 3/8 in. (132 × 41.6 cm) Overall with knobs (a): 51 15/16 × 18 1/8 in. (132 × 46 cm) Image (b): 18 1/8 × 11 3/4 in. (46 × 29.9 cm) Overall with mounting (b): 52 1/16 × 16 5/16 in. (132.2 × 41.5 cm) Overall with knobs (b): 52 1/16 × 18 1/8 in. (132.2 × 46 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/53226,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.66,false,true,53222,Asian Art,Hanging scroll,麝香猫図|Musk Cat,Japan,Muromachi period (1392–1573),,,,Artist,,Uto Gyoshi,"Japanese, active second half of 16th century",,Uto Gyoshi,,1550,1599,second half of the 16th century,1550,1599,Hanging scroll; ink and color on paper,Image: 29 15/16 × 18 5/16 in. (76 × 46.5 cm) Overall with mounting: 66 1/8 × 24 1/8 in. (168 × 61.3 cm) Overall with knobs: 66 1/8 × 25 7/8 in. (168 × 65.8 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/53222,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.47,false,true,670981,Asian Art,Hanging scroll,牧牛図|Oxherding,Japan,Muromachi period (1392–1573),,,,Artist,,Sekkyakushi,"Japanese, active first half of the 15th century",,Sekkyakushi,,1400,1499,first half of the 15th century,1400,1449,Hanging scroll; ink on paper,Image: 21 1/16 × 11 9/16 in. (53.5 × 29.4 cm) Overall with mounting: 54 3/4 × 15 5/8 in. (139 × 39.7 cm) Overall with knobs: 54 3/4 × 17 5/8 in. (139 × 44.7 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/670981,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.55,false,true,53228,Asian Art,Hanging scroll,破墨山水図|Splashed-Ink Landscape,Japan,Muromachi period (1392–1573),,,,Artist,,Bokushō Shūshō,"Japanese, active late 15th–early 16th century",,Bokushō Shūshō,,1450,1600,early 16th century,1500,1533,Hanging scroll; ink on paper,Image: 31 1/2 × 13 3/8 in. (80 × 33.9 cm) Overall with mounting: 59 13/16 × 14 3/16 in. (152 × 36 cm) Overall with knobs: 59 13/16 × 16 1/16 in. (152 × 40.8 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/53228,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2015.300.53a, b",false,true,65392,Asian Art,Hanging scrolls,四季山水図|Landscapes of the Four Seasons,Japan,Muromachi period (1392–1573),,,,Artist,,Keison,"Japanese, active late 15th– early 16th century",,Keison,,1467,1533,late 15th–early 16th century,1467,1533,Pair of hanging scrolls; ink on paper,Image (a): 38 5/16 × 19 9/16 in. (97.3 × 49.7 cm) Overall with mounting (a): 75 13/16 × 25 11/16 in. (192.5 × 65.3 cm) Overall with knobs (a): 75 13/16 × 27 1/2 in. (192.5 × 69.8 cm) Image (b): 38 3/8 × 19 5/8 in. (97.4 × 49.8 cm) Overall with mounting (b): 75 9/16 × 25 11/16 in. (192 × 65.3 cm) Overall with knobs (b): 75 9/16 × 28 1/8 in. (192 × 71.4 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/65392,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.61,false,true,53199,Asian Art,Hanging scroll,"蘭竹図|Orchids, Bamboo, Briars, and Rocks",Japan,Nanbokuchō period (1336–92),,,,Artist,,Tesshū Tokusai,"Japanese, died 1366",,Tesshū Tokusai,,1342,1366,mid-14th century,1300,1400,Hanging scroll; ink on paper,Image: 28 3/8 × 14 1/2 in. (72 × 36.8 cm) Overall with mounting: 60 1/16 × 19 5/16 in. (152.5 × 49 cm) Overall with knobs: 60 1/16 × 21 in. (152.5 × 53.4 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/53199,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.56,false,true,53198,Asian Art,Hanging scroll,岩に鶺鴒図|Wagtail on a Rock,Japan,Nanbokuchō period (1336–92),,,,Artist,Attributed to,Taikyo Genju,"Japanese, active mid-14th century",,Taikyo Genju,,1300,1366,mid-14th century,1334,1366,Hanging scroll; ink on silk,Image: 32 3/4 × 13 3/4 in. (83.2 × 34.9 cm) Overall with mounting: 63 3/4 × 18 7/16 in. (161.9 × 46.8 cm) Overall with knobs: 63 3/4 × 20 1/4 in. (162 × 51.5 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/53198,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.234,false,true,670928,Asian Art,"Page from book, mounted as a hanging scroll",,Japan,late Heian period (794–1185),,,,Artist,Calligraphy by,Fujiwara no Norinaga,"Japanese, 1109–1180",,Fujiwara no Norinaga,,1109,1180,mid-to late 12th century,1134,1199,"Page from book, mounted as hanging scroll; ink on paper",Image: 9 15/16 × 6 1/4 in. (25.3 × 15.9 cm) Overall with mounting: 51 3/16 × 14 3/16 in. (130 × 36 cm) Overall with knobs: 51 3/16 × 16 1/8 in. (130 × 41 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/670928,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.230,false,true,53172,Asian Art,Album leaf mounted as a hanging scroll,,Japan,late Heian period (ca. 900–1185),,,,Artist,Calligraphy traditionally attributed to,Fujiwara no Yukinari (Kōzei),"Japanese, 972–1027",,Fujiwara no Yukinari,,0972,1027,2nd half of the 11th century,900,1200,Album leaf mounted as a hanging scroll; ink on paper,Image: 8 in. × 5 3/8 in. (20.3 × 13.7 cm) Overall with mounting: 49 3/4 × 14 3/16 in. (126.3 × 36 cm) Overall with knobs: 49 3/4 × 15 7/8 in. (126.3 × 40.3 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/53172,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.500.9.5,false,true,78147,Asian Art,Hanging scroll,,Japan,Momoyama (1573–1615)–Edo (1615–1868) period,,,,Artist,,Fūgai Ekun,"Japanese, 1568–1654",,Fūgai Ekun,,1568,1654,1568–1654,1568,1654,Hanging scroll; ink on paper,Image: 30 1/2 x 12 1/8 in. (77.5 x 30.8 cm),"Gift of Florence and Herbert Irving, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/78147,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2016.363.1,false,true,711870,Asian Art,Sculpture,不動明王像|Fudō Myōō,Japan,Edo period (1615–1868),,,,Artist,,Mokujiki Shōnin 木喰上人,"Japanese, 1718–1810",,Mokujiki Shōnin 木喰上人,,1718,1810,1805,1805,1805,Chisel-carved (natabori) wood,H. 35 7/16 in. (90 cm); W. 14 9/16 in. (37 cm); D. 9 13/16 in. (25 cm),"Purchase, Friends of Asian Art Gifts, 2016",,,,,,,,,,,,Sculpture,,http://www.metmuseum.org/art/collection/search/711870,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2015.300.250a, b",false,true,53175,Asian Art,Sculpture,地蔵菩薩立像|Jizō Bosatsu,Japan,Kamakura period (1185–1333),,,,Artist,,Kaikei,"Japanese, active 1183–1223",,Kaikei,,1183,1223,ca. 1202,1050,1400,"Lacquered Japanese cypress, color, gold, cut gold leaf, and inlaid crystal eyes",H. of figure incl. base 22 in. (55.9 cm); H. to top of spear 22 7/8 in. (58.1 cm); W. 6 3/4 in. (17.1 cm); D. 6 3/4 in. (17.1 cm); Diam. of base 6 3/4 in. (17.1 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Sculpture,,http://www.metmuseum.org/art/collection/search/53175,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2015.300.252a, b",false,true,53176,Asian Art,Figure,不動明王坐像|Fudō Myōō,Japan,Kamakura period (1185–1333),,,,Artist,,Kaikei,"Japanese, active 1183–1223",,Kaikei,,1183,1223,early 13th century,1100,1400,"Lacquered Japanese cypress, color, gold, cut gold (kirikane), and inlaid crystal eyes",H. 21 in. (53.3 cm); H. to top of sword 21 1/2 in. (54.6 cm); W. 16 3/4 in. (42.5 cm); D. 15 in. (38.1 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Sculpture,,http://www.metmuseum.org/art/collection/search/53176,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.500.2.46,false,true,39671,Asian Art,Tray,,Japan,,,,,Artist,Attributed to,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,,1807,1891,,1807,1891,"Wood, maki-e",L. 10 11/16 in. (27.1 cm); W. 10 5/8 in. (27 cm); H. 1 3/4 in. (4.4 cm),"Gift of Florence and Herbert Irving, 2015",,,,,,,,,,,,Lacquer,,http://www.metmuseum.org/art/collection/search/39671,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.97,false,true,53426,Asian Art,Screens,,Japan,Edo period (1615–1868),,,,Artist,,Ikeda Koson,"Japanese, 1803–1868",,Ikeda Koson,,1803,1868,,1720,1920,Two-panel folding screen; ink on paper,Image: 59 5/16 x 63 1/16 in. (150.6 x 160.2 cm) Overall with mounting: 67 1/2 x 70 1/2 in. (171.5 x 179.1 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Screens,,http://www.metmuseum.org/art/collection/search/53426,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.96,false,true,45648,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,Attributed to,Wagyoku Yogetsu,active 1521–1530,,Wagyoku Yogetsu,,1521,1530,,1521,1530,Hanging scroll; ink on paper,36 1/2 x 14 1/4 in. (92.7 x 36.2 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45648,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.44,false,true,53012,Asian Art,Handscroll,,Japan,Edo period (1615–1868),,,,Artist,,Ishiyama Moroka,"Japanese, 1669–1734",,Ishiyama Moroka,,1669,1734,,1600,1800,"Handscroll; ink, color, and gold on silk",Image: 13 in. × 25 ft. (33 × 762 cm) Overall with mounting: 14 in. (35.6 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/53012,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.129,false,true,670901,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Miyagawa Chōshun,"Japanese, 1683–1753",,Miyagawa Chōshun,,1683,1753,,1615,1868,Hanging scroll; ink and color on paper,Image: 49 3/4 × 20 7/8 in. (126.4 × 53 cm) Overall with mounting: 86 1/4 × 26 1/8 in. (219 × 66.4 cm) Overall with knobs: 86 1/4 × 28 5/8 in. (219 × 72.7 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/670901,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.158,false,true,670888,Asian Art,Hanging scrol,,Japan,Edo period (1615–1868),,,,Artist,,Yosa Buson,"Japanese, 1716–1783",,Yosa Buson,,1716,1783,,1615,1868,Hanging scroll; ink and color on paper,Image: 12 3/16 × 18 3/4 in. (31 × 47.7 cm) Overall with mounting: 44 5/16 × 21 3/16 in. (112.5 × 53.8 cm) Overall with knobs: 44 5/16 × 24 13/16 in. (112.5 × 63 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/670888,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.233,false,true,75347,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Ike Taiga,"Japanese, 1723–1776",,Ike Taiga,,1723,1776,,1723,1776,Hanging scroll; ink on paper,Image: 42 3/8 x 10 7/8 in. (107.6 x 27.6 cm),"Purchase, Friends of Asian Art Gifts, 2009",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/75347,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.96,false,true,670950,Asian Art,Folding fan mounted as an album leaf,,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Kiitsu,"Japanese, 1796–1858",,Suzuki Kiitsu,,1796,1858,,1615,1868,"Folding fan mounted as an album leaf; ink and color on paper, framed",Image: 9 7/16 × 20 5/8 in. (24 × 52.4 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/670950,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.208,false,true,670894,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Shiokawa Bunrin,"Japanese, 1808–1877",,Shiokawa Bunrin,,1808,1877,,1615,1868,"Hanging scroll; ink, color and gold on silk",Image: 37 3/16 × 13 11/16 in. (94.4 × 34.8 cm) Overall with mounting: 74 13/16 × 19 7/16 in. (190 × 49.3 cm) Overall with knobs: 74 13/16 × 21 5/16 in. (190 × 54.2 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/670894,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.144,false,true,73355,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Kaseki,"Japanese, active 18th century",,Kaseki,,1700,1799,,1615,1868,Hanging scroll; ink and color on silk,Image: 14 1/2 in. × 21 in. (36.8 × 53.3 cm) Overall with mounting: 50 × 24 5/8 in. (127 × 62.5 cm) Overall with knobs: 50 × 27 3/16 in. (127 × 69 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/73355,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.124,false,true,670956,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Kengetsudō,"Japanese, active 18th century",,Kengetsudō,,1700,1799,,1615,1868,Hanging scroll; ink and color on paper,Image: 41 7/8 × 18 3/8 in. (106.4 × 46.7 cm) Overall with mounting: 65 7/8 × 23 3/16 in. (167.3 × 58.9 cm) Overall with knobs: 65 7/8 × 25 3/8 in. (167.3 × 64.5 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/670956,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.209,false,true,671057,Asian Art,Folding fan,,Japan,Meiji period (1868–1912),,,,Artist,,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,,1807,1891,,1868,1912,Folding fan; lacquer on paper,Image: 12 3/16 × 19 7/16 in. (31 × 49.3 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/671057,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.30,false,true,670968,Asian Art,Album leaf mounted as a hanging scroll,"源氏物語図色紙　「藤袴」|Scene from “Purple Trousers” (“Fujibakama”), from The Tale of Genji (Genji monogatari)",Japan,Momoyama period (1573–1615),,,,Artist,,Tosa Mitsuyoshi,"Japanese, 1539–1613",,TOSA MITSUYOSHI,,1539,1613,,1573,1615,"Album leaf mounted as a hanging scroll; ink, color and gold on paper",Image: 10 1/8 × 8 3/8 in. (25.7 × 21.2 cm) Overall with mounting: 54 5/8 × 15 1/4 in. (138.7 × 38.7 cm) Overall with knobs: 54 5/8 × 17 3/16 in. (138.7 × 43.6 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/670968,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -53.194.4,false,true,57351,Asian Art,Painted panel,,Japan,Muromachi period (1392–1573),,,,Artist,After,Kenkō Shokei,"Japanese, active ca. 1470–after 1523",,Kenkō Shokei,,1460,1523,,1467,1533,Paint on paper,33 1/8 x 12 1/4 in. (84.1 x 31.1 cm),"Gift of Nathan V. Hammer, 1953",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57351,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.239,false,true,670887,Asian Art,Hanging scroll,「離離原上草一歳一枯榮」 （白居易『草』より）|Couplet from the Chinese Poem “Grasses” by Bai Juyi,Japan,Muromachi period (1392–1573),,,,Artist,,Motsurin Jōtō (Bokusai),"Japanese, died 1491",,Motsurin Jōtō,,,1491,15th century,1400,1499,Hanging scroll; ink on paper,Image: 46 3/4 × 10 3/8 in. (118.8 × 26.4 cm) Overall with mounting: 74 11/16 × 10 15/16 in. (189.7 × 27.8 cm) Overall with knobs: 74 11/16 × 12 1/2 in. (189.7 × 31.8 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/670887,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2014.719.9,false,true,60465,Asian Art,Hanging scroll,愚極礼才書 「極重悪人無他方便・唯稱弥陀得生極楽」|Buddhist Maxim on the Saving Power of Amida,Japan,Nanbokuchō period (1336–92),,,,Artist,,Gukyoku Reisai,"Japanese, 1369–1452",,Gukyoku Reisai,,1369,1452,15th century,1400,1452,Pair of hanging scrolls; ink on paper,Image (each scroll): 36 3/4 x 8 3/4 in. (93.4 x 22.3 cm) Overall with mounting (a): 67 1/8 x 9 1/2 in. (170.5 x 24.1 cm) Overall with knobs (a): 67 1/8 x 11 1/4 in. (170.5 x 28.6 cm) Overall with mounting (b): 67 x 9 1/2 in. (170.2 x 24.1 cm) Overall with knobs (b): 67 x 11 3/16 in. (170.2 x 28.4 cm),"Gift of Sylvan Barnet and William Burto, in memory of John M. Rosenfield, 2014",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/60465,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.56,false,true,45245,Asian Art,Hanging scroll,,Japan,Muromachi period (1392–1573),,,,Artist,Attributed to,Shinno Noami,1397–1471,,Shinno Noami,,1397,1471,15th century,1400,1471,Hanging scroll; ink on paper,31 1/4 x 12 1/2 in. (79.4 x 31.8 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45245,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.62,false,true,670896,Asian Art,Hanging scroll,,Japan,Muromachi period (1392–1573),,,,Artist,,Motsurin Jōtō (Bokusai),"Japanese, died 1491",,Motsurin Jōtō,,,1491,15th century,1400,1491,Hanging scroll; ink on paper,Image: 10 3/4 × 16 11/16 in. (27.3 × 42.4 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/670896,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.2,false,true,670899,Asian Art,Hanging scroll,"不動明王四童子種字像|Fudō Myōō with Four Attendants, Outlined in Seed Syllables",Japan,Muromachi period (1392–1573),,,,Artist,,Chikai,"Japanese, ca. 1422–ca. 1503",,Chikai,,1422,1503,15th century,1400,1499,Hanging scroll; ink and color on paper,Image: 39 15/16 × 17 1/16 in. (101.5 × 43.4 cm) Overall with mounting: 67 11/16 × 22 15/16 in. (172 × 58.3 cm) Overall with knobs: 67 11/16 × 25 3/16 in. (172 × 64 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/670899,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.54,false,true,53230,Asian Art,Hanging scroll,,Japan,Muromachi period (1392–1573),,,,Artist,,Sesson Shūkei,ca. 1504–ca. 1589,,Sesson Shūkei,,1504,1589,16th century,1504,1589,Hanging scroll; ink and color on paper,Image: 11 7/8 × 18 3/8 in. (30.2 × 46.7 cm) Overall with mounting: 44 in. × 22 5/8 in. (111.8 × 57.4 cm) Overall with knobs: 44 × 24 1/2 in. (111.8 × 62.3 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/53230,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.39,false,true,42343,Asian Art,Hanging scroll,禅機（鳥窠道林・白居昜）|Zen Encounter (Niaoke Daolin and Bai Juyi),Japan,Muromachi period (1392–1573),,,,Artist,Attributed to,Kenkō Shokei,"Japanese, active ca. 1470–after 1523",,Kenkō Shokei,,1460,1523,16th century,1467,1533,Hanging scroll; ink on paper,12 x 18 3/4 in. (30.5 x 47.6 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/42343,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.211a–g,false,true,670969,Asian Art,Fourteen figures on seven folded sheets,,Japan,Edo period (1615–1868),,,,Artist,Attributed to,Iwasa Matabei,"Japanese, 1578–1650",,Iwasa Matabei,,1578,1650,17th century,1615,1650,Fourteen figures on seven folded sheets; ink on paper,Image (a): 8 3/4 × 15 11/16 in. (22.3 × 39.9 cm) Image (b): 8 3/4 × 11 1/2 in. (22.3 × 29.2 cm) Image (c): 8 3/4 × 11 9/16 in. (22.3 × 29.3 cm) Image (d): 8 3/4 × 11 3/8 in. (22.3 × 28.9 cm) Image (e): 8 3/4 × 11 9/16 in. (22.3 × 29.4 cm) Image (f): 8 3/4 × 11 3/16 in. (22.3 × 28.4 cm) Image (g): 8 3/4 × 12 3/16 in. (22.3 × 30.9 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/670969,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.39a–x,false,true,670914,Asian Art,Twenty-four volumes of printed text and illustrations,,Japan,Edo period (1615–1868),,,,Artist,,Yamamoto Shunshō,"Japanese, 1610–1682",,Yamamoto Shunshō,,1610,1682,17th century,1610,1682,Twenty-four volumes of printed text and illustrations; black ink on paper,Each book: 10 1/2 × 7 3/8 in. (26.7 × 18.7 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/670914,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.79,false,true,670911,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Kano Kōya,"Japanese, died 1673",,Kano Kōya,,,1673,17th century,1615,1673,Hanging scroll; ink on paper,Image: 35 15/16 × 16 1/16 in. (91.3 × 40.8 cm) Overall with mounting: 67 13/16 × 19 13/16 in. (172.2 × 50.4 cm) Overall with knobs: 67 13/16 × 22 3/16 in. (172.2 × 56.3 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/670911,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2015.300.38a, b",false,true,76463,Asian Art,Handscrolls,,Japan,Edo period (1615–1868),,,,Artist,Attributed to,Kaihō Yūsetsu,"Japanese, 1598–1677",,Kaihō Yūsetsu,,1598,1677,17th century,1600,1699,Set of two handscrolls; ink and color on paper,Image (each scroll): 9 7/16 in. × 63 ft. 8 9/16 in. (24 × 1942 cm) Overall with knobs: 11 7/16 in. × 63 ft. 8 9/16 in. (29 × 1942 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/76463,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.220,false,true,671033,Asian Art,Handscroll,,Japan,Edo period (1615–1868),,,,Artist,,Unkoku Tōban,"Japanese, 1633–1724",,Unkoku Tōban,,1633,1724,17th century,1615,1699,Handscroll; ink on silk,Image: 12 3/4 × 19 5/8 in. (32.4 × 49.8 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/671033,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -91.1.679,false,true,58458,Asian Art,Inrō,,Japan,Edo period (1615–1868),,,,Artist,,Gion Nankai,"Japanese, 1677–1751",,Gion Nankai,,1677,1751,18th century,1700,1799,"Lacquer sprinkled with gold and silver makie, and foil; Ojima: floral scrolls in openwork; silver and silver wire; Netsuke: chrysanthemum medallion; ivory",H. 3 1/8 in. (7.9 cm); W. 2 5/8 in. (6.7 cm); D. 13/16 in. (2.1 cm),"Edward C. Moore Collection, Bequest of Edward C. Moore, 1891",,,,,,,,,,,,Inrō,,http://www.metmuseum.org/art/collection/search/58458,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -10.211.2098,false,true,59281,Asian Art,Netsuke,,Japan,Edo period (1615–1868),,,,Artist,,Matsuda Sukenaga,"Japanese, 1800–1871",,"Sukenaga, Matsuda",,1800,1871,18th century,1700,1799,Wood,H. 1 3/4 in. (4.4 cm),"Gift of Mrs. Russell Sage, 1910",,,,,,,,,,,,Netsuke,,http://www.metmuseum.org/art/collection/search/59281,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.27,false,true,48979,Asian Art,Folding screen,俵屋宗理筆　朝顔図屏風|Morning Glories,Japan,Edo period (1615–1868),,,,Artist,,Tawaraya Sōri,active late 18th century,,Tawaraya Sori,,1764,1780,18th century,1700,1799,Two-panel folding screen; ink and color on paper,19 15/16 x 65 3/16 in. (50.6 x 165.6 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Screens,,http://www.metmuseum.org/art/collection/search/48979,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.42,false,true,671032,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Kawamata Tsunemasa,active 1716–48,,Kawamata Tsunemasa,,1706,1758,18th century,1700,1799,Hanging scroll; ink and color on paper,Image: 13 1/8 × 21 9/16 in. (33.4 × 54.7 cm) Overall with mounting: 46 7/16 × 25 3/16 in. (118 × 64 cm) Overall with knobs: 46 7/16 × 27 5/16 in. (118 × 69.3 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/671032,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.115,false,true,671008,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Nishikawa Sukenobu,"Japanese, 1671–1750",,Nishikawa Sukenobu,,1671,1750,18th century,1700,1750,Hanging scroll; ink and color on paper,Image: 15 1/16 × 22 7/16 in. (38.3 × 57 cm) Overall with mounting: 49 13/16 × 27 5/16 in. (126.5 × 69.4 cm) Overall with knobs: 49 13/16 × 29 7/16 in. (126.5 × 74.7 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/671008,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.8,false,true,45767,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,In the Style of,Miyagawa Chōshun,"Japanese, 1683–1753",,Miyagawa Chōshun,,1683,1753,18th century,1700,1753,Hanging scroll; ink and color on silk,24 7/8 x 10 11/16 in. (63.2 x 27.1 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45767,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.128,false,true,670967,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Okumura Masanobu,"Japanese, 1686–1764",,Masanobu Okumura,,1686,1764,18th century,1700,1799,Hanging scroll; ink and color on silk,Image: 12 5/16 × 19 5/8 in. (31.3 × 49.9 cm) Overall with mounting: 49 1/8 × 25 1/8 in. (124.8 × 63.8 cm) Overall with knobs: 49 1/8 × 27 5/16 in. (124.8 × 69.3 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/670967,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.219,false,true,670917,Asian Art,Handscroll,,Japan,Edo period (1615–1868),,,,Artist,,Sumiyoshi Hiromori,"Japanese, 1705–1777",,Sumiyoshi Hiromori,,1705,1777,18th century,1705,1777,"Handscroll; ink, color and gold on paper",Image: 13 3/8 in. × 11 ft. 9 7/16 in. (33.9 × 359.3 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/670917,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.40,false,true,671015,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Tsukioka Settei,"Japanese, 1710–1786",,Tsukioka Settei,,1710,1786,18th century,1700,1799,Hanging scroll; ink and color on silk,Image: 34 13/16 × 12 7/16 in. (88.5 × 31.6 cm) Overall with mounting: 70 1/4 × 17 13/16 in. (178.5 × 45.2 cm) Overall with knobs: 70 1/4 × 19 3/4 in. (178.5 × 50.2 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/671015,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -64.55.1,false,true,48990,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Ike Taiga,"Japanese, 1723–1776",,Ike Taiga,,1723,1776,18th century,1723,1776,Hanging scroll; ink on silk,38 3/4 x 18 3/16 in. (98.4 x 46.2 cm),"Fletcher Fund, 1964",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/48990,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -64.55.2,false,true,48991,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Ike Taiga,"Japanese, 1723–1776",,Ike Taiga,,1723,1776,18th century,1723,1776,Hanging scroll; ink on silk,38 3/4 x 18 in. (98.4 x 45.7 cm),"Fletcher Fund, 1964",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/48991,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.268.93,false,true,48989,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Ike Taiga,"Japanese, 1723–1776",,Ike Taiga,,1723,1776,18th century,1723,1776,Hanging scroll; ink and color on paper,Image: 37 13/16 x 10 11/16 in. (96 x 27.2 cm) Overall with mounting: 67 3/8 x 15 3/4 in. (171.1 x 40 cm) Overall with knobs: 67 3/8 x 17 7/8 in. (171.1 x 45.4 cm),"The Harry G. C. Packard Collection of Asian Art, Gift of Harry G. C. Packard, and Purchase, Fletcher, Rogers, Harris Brisbane Dick, and Louis V. Bell Funds, Joseph Pulitzer Bequest, and The Annenberg Fund Inc. Gift, 1975",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/48989,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.268.94,false,true,48993,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Ike Taiga,"Japanese, 1723–1776",,Ike Taiga,,1723,1776,18th century,1723,1776,Hanging scroll; ink on paper,38 1/16 x 9 15/16 in. (96.7 x 25.2 cm),"The Harry G. C. Packard Collection of Asian Art, Gift of Harry G. C. Packard, and Purchase, Fletcher, Rogers, Harris Brisbane Dick, and Louis V. Bell Funds, Joseph Pulitzer Bequest, and The Annenberg Fund Inc. Gift, 1975",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/48993,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.162,false,true,671046,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Ike Taiga,"Japanese, 1723–1776",,Ike Taiga,,1723,1776,18th century,1723,1776,Hanging scroll; ink and color on paper,Image: 39 in. × 12 1/2 in. (99 × 31.7 cm) Overall with mounting: 75 × 17 15/16 in. (190.5 × 45.5 cm) Overall with knobs: 75 × 20 in. (190.5 × 50.8 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/671046,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.199,false,true,670983,Asian Art,Handscroll,,Japan,Edo period (1615–1868),,,,Artist,,Maruyama Ōkyo,"Japanese, 1733–1795",,Maruyama Ōkyo,,1733,1795,18th century,1733,1799,Handscroll; ink on paper,Image: 15 1/2 in. × 18 ft. 2 7/16 in. (39.4 × 554.8 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/670983,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.174,false,true,670890,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Totoki Baigai,"Japanese, 1749–1804",,Totoki Baigai,,1749,1804,18th century,1700,1799,Hanging scroll; ink and color on paper,Image: 10 11/16 × 22 5/16 in. (27.1 × 56.7 cm) Overall with mounting: 46 1/4 × 23 15/16 in. (117.5 × 60.8 cm) Overall with knobs: 46 1/4 × 26 in. (117.5 × 66 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/670890,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.92,false,true,670922,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Sakai Hōitsu,"Japanese, 1761–1828",,Sakai Hōitsu,,1761,1828,18th century,1700,1799,Fan mounted as a hanging scroll; ink and color on paper,Image: 8 11/16 × 17 11/16 in. (22 × 45 cm) Overall with mounting: 46 1/8 × 20 13/16 in. (117.2 × 52.8 cm) Overall with knobs: 46 1/8 × 22 11/16 in. (117.2 × 57.6 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/670922,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.117,false,true,671013,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Tsukioka Sessai,"Japanese, 1761–1839",,Tsukioka Sessai,,1761,1839,18th century,1761,1799,Hanging scroll; ink on silk,Image: 32 × 12 in. (81.3 × 30.5 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/671013,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.223,false,true,670902,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Chōgō,"Japanese, active 18th century",,Chōgō,,1700,1799,18th century,1700,1799,Hanging scroll; ink and color on paper,Image: 18 7/8 × 21 5/8 in. (48 × 55 cm) Overall with mounting: 54 1/2 × 24 3/4 in. (138.5 × 62.8 cm) Overall with knobs: 54 1/2 × 26 15/16 in. (138.5 × 68.5 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/670902,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.41,false,true,670898,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,Attributed to,Matsuno Chikanobu,"Japanese, active early 18th century",,Matsuno Chikanobu,,1700,1733,18th century,1700,1799,Hanging scroll; ink and color on paper,Image: 31 7/16 × 11 5/8 in. (79.9 × 29.6 cm) Overall with mounting: 64 9/16 × 13 7/16 in. (164 × 34.2 cm) Overall with knobs: 64 9/16 × 15 3/4 in. (164 × 40 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/670898,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.751,false,true,58663,Asian Art,Inrō,,Japan,,,,,Artist,,Kano Terunobu,1717–63,", Yusei",Kano Terunobu,,1717,1763,19th century,1800,1899,"Lacquer, roiro, white lacquer, gold and coloured hiramakie; Interior: nashiji and fundame",3 1/4 x 1 5/8 x 13/16 in. (8.3 x 4.1 x 2.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Inrō,,http://www.metmuseum.org/art/collection/search/58663,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.894,false,true,78784,Asian Art,Illustrated book,小磯前雪窓先生画帖　完|Album [of works] by the master Koiso Zensetsusō (complete) (Koiso Zensentsusō sensei gajō–kan),Japan,,,,,Artist,,Koiso Zensetsusō,"Japanese, 1832–1902",,"Koiso, Zensetsusō",,1832,1902,19th century,1832,1902,Accordion album; ink and color on paper,11 × 6 1/2 in. (28 × 16.5 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78784,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.736,false,true,58649,Asian Art,Inrō,,Japan,Edo period (1615–1868),,,,Artist,,Louisine W. Havemeyer,,,"Havemeyer, Louisine W.",,1855,1929,19th century,1800,1899,Aogai shell and gold foil inlay on black lacquer,3 1/2 x 2 7/16 x 1 in. (8.9 x 6.2 x 2.5 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Inrō,,http://www.metmuseum.org/art/collection/search/58649,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.762,false,true,58673,Asian Art,Inrō,,Japan,Edo period (1615–1868),,,,Artist,,Louisine W. Havemeyer,,,"Havemeyer, Louisine W.",,1855,1929,19th century,1800,1899,"Lacquer, gold, hirame, gold and coloured hiramakie, takamakie, nashiji; Interior: nashiji and fundame",3 5/16 x 1 15/16 x 1 in. (8.4 x 5 x 2.6 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Inrō,,http://www.metmuseum.org/art/collection/search/58673,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.771,false,true,58681,Asian Art,Inrō,,Japan,Edo period (1615–1868),,,,Artist,,Louisine W. Havemeyer,,,"Havemeyer, Louisine W.",,1855,1929,19th century,1800,1899,"Lacquer, roiro, hirame, gold and coloured hiramakie, nashiji, various inlay; Interior: fundame",3 3/16 x 2 x 7/8 in. (8.1 x 5.1 x 2.2 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Inrō,,http://www.metmuseum.org/art/collection/search/58681,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.840,false,true,58767,Asian Art,Inrō,,Japan,Edo period (1615–1868),,,,Artist,,Hogen Dohaku,died 1851,,Hogen Dohaku,,1851,1851,19th century,1800,1899,"Lacquer, fundame, sumie togidashi, applied metals; Interior: nashiji and fundame",3 3/16 x 2 5/16 x 3/4 in. (8.1 x 5.8 x 1.9 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Inrō,,http://www.metmuseum.org/art/collection/search/58767,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JIB76,false,true,57674,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Reizen Saburo Tametaka,died 1864,,Reizen Saburo Tametaka,,1864,1864,19th century,1800,1900,Ink and color on paper,11 × 7 3/4 × 1 3/8 in. (27.9 × 19.7 × 3.5 cm),"Gift of Yamanaka Co., 1926",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/57674,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.246,false,true,58932,Asian Art,Inrō,,Japan,Edo period (1615–1868),,,,Artist,In the Style of,Tanzan,1655–1729?,,Tanzan,,1655,1729,19th century,1800,1899,"Sprinkled gold and silver lacquer, makie, and takamakie Ojime: bead; tortoiseshell Netuske: box with decoration of violets; gold makie lacquer with gold and silver makie",3 1/2 x 2 3/16 x 1 in. (8.9 x 5.5 x 2.5 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Inrō,,http://www.metmuseum.org/art/collection/search/58932,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.778,false,true,58688,Asian Art,Inrō,,Japan,Meiji period (1868–1912),,,,Artist,,Fang Shi Mopu,ca. 1588,,Fang Shi Mopu,,1588,1588,19th century,1800,1899,"Black hiramaki-e, takamaki-e, ceramic and mother-of-pearl inlay; Interior: Roiro and fundame",3 1/16 x 1 7/8 x 3/4 in. (7.8 x 4.8 x 1.9 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Inrō,,http://www.metmuseum.org/art/collection/search/58688,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.500.2.56a–g,false,true,40489,Asian Art,Writing box,,Japan,Meiji period (1868–1912),,,,Artist,Style of,Ogawa Haritsu (Ritsuō),"Japanese, 1663–1747",,Ritsuō,,1663,1747,19th century,1700,1900,Gold maki-e on black lacquer,L. 8 3/4 in. (22.2 cm); W. 9 in. (22.9 cm); H. 2 in. (5.1 cm),"Gift of Florence and Herbert Irving, 2015",,,,,,,,,,,,Lacquer,,http://www.metmuseum.org/art/collection/search/40489,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -79.2.954,false,true,47053,Asian Art,Vase,,Japan,,,,,Artist,,Eiraku Hozen,1795–1854,,Eiraku Hozen,,1795,1854,19th century,1800,1899,Clay covered with polychrome glazes on ornaments outlined in relief (Kairakuen ware),H. 6 7/8 in. (17.5 cm); Diam. 4 3/8 in. (11.1 cm); Diam. of rim 3 1/8 in. (7.9 cm); Diam. of base 2 1/2 in. (6.4 cm),"Purchase by subscription, 1879",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/47053,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -79.2.978,false,true,46687,Asian Art,Vase,,Japan,,,,,Artist,,Eiraku Hozen,1795–1854,,Eiraku Hozen,,1795,1854,19th century,1800,1899,Clay covered with glazes (Kairakuen ware),H. 5 3/4 in. (14.6 cm); Diam. 3 3/8 in. (8.6 cm); Diam. of rim 2 1/8 in. (5.4 cm); Diam. of base 2 1/4 in. (5.7 cm),"Purchase by subscription, 1879",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/46687,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -93.1.168,false,true,47519,Asian Art,Flower pot,,Japan,,,,,Artist,,Eiraku Hozen,1795–1854,,Eiraku Hozen,,1795,1854,19th century,1800,1899,Paste covered with a transparent crackled glaze (Kyoto ware),H. 7 1/8 in. (18.1 cm); Diam. 7 5/8 in. (19.4 cm),"Gift of Mr. and Mrs. Samuel Colman, 1893",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/47519,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"79.2.1338a, b",false,true,62536,Asian Art,Covered bowl,,Japan,,,,,Artist,in the style of,Eiraku Hozen,1795–1854,,Eiraku Hozen,,1795,1854,19th century,1800,1899,Porcelain with polychrome glaze (Kairakuen ware),H. 3/1/4 in. (8.3 cm); W. 5 in. (12.7 cm),"Purchase by subscription, 1879",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/62536,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -93.3.490,false,true,45869,Asian Art,Vase,,Japan,,,,,Artist,,Tanzan,1655–1729?,,Tanzan,,1655,1729,19th century,1800,1899,Clay decorated with slip under a transparent glaze andcolored enamels (Nabeshima ware),H. 18 1/8 in. (46 cm),"Gift of Charles Stewart Smith, 1893",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/45869,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -93.3.491,false,true,45870,Asian Art,Vase,,Japan,,,,,Artist,,Tanzan,1655–1729?,,Tanzan,,1655,1729,19th century,1800,1899,Clay decorated with slip under a transparent glaze andcolored enamels (Nabeshima ware),H. 18 1/8 in. (46 cm),"Gift of Charles Stewart Smith, 1893",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/45870,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.500.9.43,false,true,667265,Asian Art,Bowl,,Japan,Edo (1615–1868),,,,Artist,,Nin'ami Dōhachi (Dōhachi II),"Japanese, 1783–1855",,Dōhachi II,,1783,1855,19th century,1800,1868,Stoneware with polychrome enamels,H. 4 in. (10.2 cm); Diam. 7 3/16 in. (18.2 cm),"Gift of Florence and Herbert Irving, 2015",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/667265,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.500.9.41,false,true,667263,Asian Art,Bowl,,Japan,Edo period (1615–1868),,,,Artist,,Nin'ami Dōhachi (Dōhachi II),"Japanese, 1783–1855",,Dōhachi II,,1783,1855,19th century,1800,1855,Stoneware with light blue glaze (Kyoto ware),W. 8 1/4 in. (21 cm),"Gift of Florence and Herbert Irving, 2015",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/667263,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.500.9.44,false,true,667266,Asian Art,Bowl,,Japan,Edo period (1615–1868),,,,Artist,,Nin'ami Dōhachi (Dōhachi II),"Japanese, 1783–1855",,Dōhachi II,,1783,1855,19th century,1800,1899,Stoneware with polychrome enamels,Diam. 6 1/2 in. (16.5 cm),"Gift of Florence and Herbert Irving, 2015",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/667266,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.84,false,true,49052,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,Attributed to,Okamoto Toyohiko,1773–1845,,Okamoto Toyohiko,,1773,1845,19th century,1800,1845,Hanging scroll; ink and color on silk,15 3/4 x 27 3/4 in. (40 x 70.5 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49052,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.85,false,true,40010,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Okamoto Toyohiko,1773–1845,,Okamoto Toyohiko,,1773,1845,19th century,1800,1845,Hanging scroll; ink and color on silk,43 x 16 1/2 in. (109.2 x 41.9 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/40010,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -18.77.2,false,true,49086,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Eiraku Hozen,1795–1854,,Eiraku Hozen,,1795,1854,19th century,1800,1854,Hanging scroll; ink and color on paper,Overall: 47 1/2 x 16 7/8 in. (120.7 x 42.9 cm) Overall with mounting: 67 3/8 x 21 3/4 in. (171.1 x 55.2 cm) Overall with knobs: 67 3/8 x 23 3/4 in. (171.1 x 60.3 cm),"Rogers Fund, 1918",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49086,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -18.77.3,false,true,49087,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Eiraku Hozen,1795–1854,,Eiraku Hozen,,1795,1854,19th century,1800,1854,Hanging scroll; ink and color on paper,Overall: 47 1/4 x 16 7/8 in. (120 x 42.9 cm) Overall with mounting: 67 x 21 3/4 in. (170.2 x 55.2 cm) Overall with knobs: 67 x 23 3/4 in. (170.2 x 60.3 cm),"Rogers Fund, 1918",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49087,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -18.77.4,false,true,49088,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Eiraku Hozen,1795–1854,,Eiraku Hozen,,1795,1854,19th century,1800,1854,Hanging scroll; ink and color on paper,Overall: 47 1/2 x 16 7/8 in. (120.7 x 42.9 cm) Overall with mounting: 67 x 21 3/4 in. (170.2 x 55.2 cm) Overall with knobs: 67 x 23 3/4 in. (170.2 x 60.3 cm),"Rogers Fund, 1918",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49088,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -18.77.5,false,true,49089,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Eiraku Hozen,1795–1854,,Eiraku Hozen,,1795,1854,19th century,1800,1854,Hanging scroll; ink and color on paper,Overall: 47 1/2 x 16 7/8 in. (120.7 x 42.9 cm) Overall with mounting: 67 1/4 x 21 7/8 in. (170.8 x 55.6 cm) Overall with knobs: 67 1/4 x 23 3/4 in. (170.8 x 60.3 cm),"Rogers Fund, 1918",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49089,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -18.77.6,false,true,49090,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Eiraku Hozen,1795–1854,,Eiraku Hozen,,1795,1854,19th century,1800,1854,Hanging scroll; ink and color on paper,Overall: 47 1/2 x 16 7/8 in. (120.7 x 42.9 cm) Overall with mounting: 67 1/4 x 21 7/8 in. (170.8 x 55.6 cm) Overall with knobs: 67 1/4 x 23 3/4 in. (170.8 x 60.3 cm),"Rogers Fund, 1918",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49090,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -18.77.7,false,true,49091,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Eiraku Hozen,1795–1854,,Eiraku Hozen,,1795,1854,19th century,1800,1854,Hanging scroll; ink and color on paper,Overall: 47 1/2 x 16 3/4 in. (120.7 x 42.5 cm) Overall with mounting: 67 1/4 x 21 7/8 in. (170.8 x 55.6 cm) Overall with knobs: 67 1/4 x 23 3/4 in. (170.8 x 60.3 cm),"Rogers Fund, 1918",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49091,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.83,false,true,48999,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,Attributed to,Senkaku Toshu,1804–1871,,Senkaku Toshu,,1804,1871,19th century,1804,1871,Hanging scroll; ink and color on silk,37 x 14 in. (94.0 x 35.6 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/48999,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2015.300.218a, b",false,true,671051,Asian Art,Two folding fans mounted on panels,,Japan,Edo period (1615–1868),,,,Artist,Attributed to,Okada Tamechika,1823–1864,,Okada Tamechika,,1823,1864,19th century,1823,1868,"Two folding fans mounted on panels; ink, color and gold on paper",Image (a): 9 5/16 × 19 1/8 in. (23.7 × 48.6 cm) Frame (a): 16 1/8 × 29 1/8 in. (41 × 74 cm) Image (b): 9 1/4 × 19 3/16 in. (23.5 × 48.7 cm) Frame (b): 16 1/8 × 29 1/8 in. (41 × 74 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/671051,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.160,false,true,670934,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Yokoi Kinkoku,"Japanese, 1761–1832",,Yokoi Kinkoku,,1761,1832,19th century,1800,1899,Hanging scroll; ink and color on paper,Image: 31 1/2 × 59 1/8 in. (80 × 150.2 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/670934,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.161,false,true,670945,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Yokoi Kinkoku,"Japanese, 1761–1832",,Yokoi Kinkoku,,1761,1832,19th century,1800,1868,Hanging scroll; ink and light color on paper,Image: 42 3/4 × 17 5/8 in. (108.6 × 44.8 cm) Overall with mounting: 72 5/8 × 24 7/16 in. (184.5 × 62 cm) Overall with knobs: 72 5/8 × 26 7/8 in. (184.5 × 68.3 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/670945,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.151,false,true,670925,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Teisai Hokuba,"Japanese, 1771–1844",,Teisai Hokuba,,1771,1844,19th century,1800,1844,Hanging scroll; ink and color on paper,Image: 41 1/8 in. × 11 in. (104.5 × 28 cm) Overall with mounting: 76 × 11 1/4 in. (193 × 28.5 cm) Overall with knobs: 76 × 17 1/4 in. (193 × 43.8 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/670925,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.148,false,true,671047,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Toyokuni II,"Japanese, 1777–1835",,Utagawa Toyokuni II,,1777,1835,19th century,1800,1868,Hanging scroll; ink and color on silk,Image: 15 7/8 × 21 7/16 in. (40.3 × 54.4 cm) Overall with mounting: 44 11/16 × 25 5/16 in. (113.5 × 64.3 cm) Overall with knobs: 44 11/16 × 27 3/8 in. (113.5 × 69.5 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/671047,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2015.300.99a, b",false,true,54773,Asian Art,Hanging scrolls,,Japan,Edo period (1615–1868),,,,Artist,,Ikeda Koson,"Japanese, 1803–1868",,Ikeda Koson,,1803,1868,19th century,1801,1866,"Pair of hanging scrolls; ink, color, and gold on silk",Image (a): 42 3/8 × 14 3/16 in. (107.7 × 36 cm) Overall with mounting (a): 76 3/16 × 18 11/16 in. (193.5 × 47.5 cm) Overall with knobs: 76 3/16 × 20 3/4 in. (193.5 × 52.7 cm) Image (b): 42 5/8 × 14 1/4 in. (108.3 × 36.2 cm) Overall with mounting (b): 76 3/8 × 18 3/4 in. (194 × 47.6 cm) Overall with knobs (b): 76 3/8 × 20 13/16 in. (194 × 52.8 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54773,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.101,false,true,670980,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Sakai Ōho,"Japanese, 1808–1841",,Sakai Ōho,,1808,1841,19th century,1808,1841,Hanging scroll; ink and color on silk,Image: 40 1/2 in. × 14 in. (102.9 × 35.6 cm) Overall with mounting: 76 1/8 × 19 7/16 in. (193.3 × 49.3 cm) Overall with knobs: 76 1/8 × 21 5/16 in. (193.3 × 54.1 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/670980,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.207,false,true,670951,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Nishiyama Kan'ei,"Japanese, 1834–1897",,Nishiyama Kan'ei,,1834,1897,19th century,1834,1897,Hanging scroll; ink and color on silk,Image: 13 1/8 × 32 1/8 in. (33.4 × 81.6 cm) Overall with mounting: 51 × 33 1/8 in. (129.5 × 84.2 cm) Overall with knobs: 51 × 35 5/16 in. (129.5 × 89.7 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/670951,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.85,false,true,670919,Asian Art,Hanging scroll,,Japan,Edo (1615–1868)–Meiji period (1868–1912),,,,Artist,,Kano Hōgai,"Japanese, 1828–1888",,Kano Hōgai,,1828,1888,19th century,1868,1899,Hanging scroll; ink on paper,Image: 50 7/8 × 12 11/16 in. (129.2 × 32.2 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/670919,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2015.300.203.1, .2",false,true,671026,Asian Art,Sliding panels,雪狗子図襖|Puppies in the Snow,Japan,Edo period (1615–1868),,,,Artist,,Nagasawa Rosetsu,"Japanese, 1754–1799",,Nagasawa Rosetsu,,1754,1799,late 18th century,1767,1799,Set of four sliding panels hinged together as a pair of two-panel screens; ink and color on paper,Image: 66 7/16 × 72 1/16 in. (168.7 × 183 cm) Overall with mounting: 68 × 74 5/8 in. (172.7 × 189.6 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Screens,,http://www.metmuseum.org/art/collection/search/671026,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1508,false,true,53342,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harushige,1747–1818,,Suzuki Harushige,,1747,1818,late 18th century,1767,1799,Polychrome woodblock print; ink and color on paper,H. 11 1/4 in. (28.6 cm); 8 1/4 in. (21 cm),"Fletcher Fund, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/53342,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.114,false,true,671007,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Nishikawa Sukenobu,"Japanese, 1671–1750",,Nishikawa Sukenobu,,1671,1750,late 18th century,1767,1799,Hanging scroll; ink and color on paper,Image: 33 3/4 in. × 13 in. (85.8 × 33 cm) Overall with mounting: 65 9/16 × 14 7/16 in. (166.5 × 36.7 cm) Overall with knobs: 65 9/16 × 17 1/16 in. (166.5 × 43.3 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/671007,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.602,false,true,679649,Asian Art,Hanging scroll,百年無事人|For a hundred years [I have been] a person with no attachments,Japan,Edo period (1615–1868),,,,Artist,,Jiun Sonja,"Japanese, 1718–1804",,Sonja Jiun,,1718,1804,late 18th century,1766,1799,Hanging scroll; ink on paper,Image: 46 1/2 × 10 7/8 in. (118.1 × 27.6 cm) Overall with mounting: 74 × 11 5/8 in. (188 × 29.5 cm) Overall with knobs: 13 5/8 in. (34.6 cm),"Gift of Morton Berman, in honor of Sylvan Barnet and William Burto, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/679649,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.170,false,true,670910,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Kuwayama Gyokushū,"Japanese, 1746–1799",,Kuwayama Gyokushū,,1746,1799,late 18th century,1767,1799,Hanging scroll; ink on silk,Image: 39 11/16 × 14 9/16 in. (100.8 × 37 cm) Overall with mounting: 79 5/16 × 22 3/16 in. (201.5 × 56.3 cm) Overall with knobs: 79 5/16 × 24 7/16 in. (201.5 × 62 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/670910,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.204,false,true,670999,Asian Art,Hanging scroll,飲中八仙図|Drinking Festival of the Eight Immortals,Japan,Edo period (1615–1868),,,,Artist,,Nagasawa Rosetsu,"Japanese, 1754–1799",,Nagasawa Rosetsu,,1754,1799,late 18th century,1767,1799,Hanging scroll; ink and color on paper,Image: 51 5/8 × 23 11/16 in. (131.1 × 60.1 cm) Overall with mounting: 88 3/4 × 29 11/16 in. (225.5 × 75.4 cm) Overall with knobs: 88 3/4 × 32 1/16 in. (225.5 × 81.5 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/670999,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.205,false,true,671000,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Nagasawa Rosetsu,"Japanese, 1754–1799",,Nagasawa Rosetsu,,1754,1799,late 18th century,1767,1799,Hanging scroll; ink and color on silk,Image: 39 3/4 × 13 15/16 in. (101 × 35.4 cm) Overall with mounting: 74 3/16 × 19 5/16 in. (188.5 × 49 cm) Overall with knobs: 74 3/16 × 21 1/4 in. (188.5 × 54 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/671000,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.500.9.14,false,true,78136,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Miyagawa (Katsukawa) Shunsui,"Japanese, active 1744–64",,Miyagawa (Katsukawa) Shunsui,,1744,1764,late 18th century,1744,1764,Hanging scroll; ink and color on silk,Image: 50 3/8 x 24 7/16 in. (128 x 62 cm),"Gift of Florence and Herbert Irving, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/78136,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2015.500.2.43a, b",false,true,40493,Asian Art,Smoking set,刻み煙草入れ|Portable Smoking Set,Japan,Edo period (1615–1868),,,,Artist,,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,,1807,1891,early 19th century,1800,1849,"Pipe: iron, gold, silver on wood; Pipe case: gold, silver hiramaki-e on black; Tobacco case: dyed cotton with metal fitting of a snail; Netsuke: carved staghorn with paulownia pattern",Pipe case: W. 3/4 in.; D. 5/8 in.; L. 11 1/4 in.; Pipe: L.7 3/4 in.; Tobacco case: H. 2 3/4 in.; W. 4 1/8 in.; D. 1 1/2 in.; Netsuke: Diam. 1 1/2 in.,"Gift of Florence and Herbert Irving, 2015",,,,,,,,,,,,Lacquer,,http://www.metmuseum.org/art/collection/search/40493,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.146,false,true,671041,Asian Art,Hanging scroll,桜下遊女と禿|Courtesan and her Attendant under a Cherry Tree,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Toyoharu,"Japanese, 1735–1814",,Utagawa Toyoharu,,1735,1814,early 19th century,1800,1833,Hanging scroll; ink and color on silk,Image: 35 5/16 in. × 14 in. (89.7 × 35.5 cm) Overall with mounting: 67 1/2 × 18 3/8 in. (171.5 × 46.7 cm) Overall with knobs: 67 1/2 × 20 1/2 in. (171.5 × 52.1 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/671041,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.500.9.17,false,true,78055,Asian Art,Hanging scroll,"「天満宮」　渡唐天神図|“Tenmangū,” Sugawara no Michizane as Tenjin Traveling to China",Japan,Edo period (1615–1868),,,,Artist,,Sengai Gibon,"Japanese, 1750–1837",,Sengai Gibon,,1750,1837,early 19th century,1800,1833,Hanging scroll; ink on paper,Image: 150 13/16 x 17 11/16 in. (383 x 45 cm),"Gift of Florence and Herbert Irving, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/78055,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.145,false,true,670908,Asian Art,Handscroll,三幅神吉原通い図巻 「全盛季春遊戯」|Three Gods of Good Fortune Visit the Yoshiwara; or “Scenes of Pleasure at the Height of Spring”,Japan,Edo period (1615–1868),,,,Artist,,Chōbunsai Eishi,"Japanese, 1756–1829",,Chōbunsai Eishi,,1756,1829,early 19th century,1800,1833,Handscroll; ink and color on silk,Image: 13 1/8 in. × 29 ft. 2 9/16 in. (33.3 × 890.4 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/670908,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.142a–c,false,true,53448,Asian Art,Hanging scrols,"蜀山人（大田 南畝）賛　雪・月・花図　|Snow, Moon, and Cherry Blossoms (Yoshiwara in Three Seasons)",Japan,Edo period (1615–1868),,,,Artist,,Chōbunsai Eishi,"Japanese, 1756–1829",,Chōbunsai Eishi,,1756,1829,early 19th century,1804,1815,"Triptych of hanging scrolls; ink, color, and gold on silk",Image (a): 32 3/8 × 11 13/16 in. (82.3 × 30 cm) Overall with mounting (a): 65 1/2 × 16 3/8 in. (166.3 × 41.6 cm) Overall with knobs (a): 65 1/2 × 18 9/16 in. (166.3 × 47.2 cm) Image (b): 32 3/8 × 11 13/16 in. (82.2 × 30 cm) Overall with mounting (b): 65 1/2 × 16 7/16 in. (166.3 × 41.7 cm) Overall with knobs (b): 65 1/2 × 18 9/16 in. (166.3 × 47.2 cm) Image (c): 32 3/8 × 11 3/4 in. (82.2 × 29.9 cm) Overall with mounting (c): 65 3/8 × 16 7/16 in. (166 × 41.7 cm) Overall with knobs (c): 65 3/8 × 18 9/16 in. (166 × 47.2 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/53448,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.500.9.45,false,true,78065,Asian Art,Hanging Scroll,,Japan,Edo period (1615–1868),,,,Artist,Attributed to,Kawahara Keiga,"Japanese, 1786–1860",,"Keiga, Kawahara",,1786,1860,early 19th century,1800,1833,"Hanging scroll; ink and color on silk, negoro lacquer roller knobs",Image: 19 3/4 x 9 7/8 in. (50.2 x 25.1 cm),"Gift of Florence and Herbert Irving, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/78065,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.200,false,true,670982,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Maruyama Ōshin,"Japanese, 1790–1838",,Maruyama Ōshin,,1790,1838,early 19th century,1800,1838,Hanging scroll; ink and color on silk,Image: 39 1/4 × 14 5/16 in. (99.7 × 36.3 cm) Overall with mounting: 77 3/8 × 19 13/16 in. (196.5 × 50.4 cm) Overall with knobs: 77 3/8 × 22 3/16 in. (196.5 × 56.3 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/670982,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.755,false,true,58666,Asian Art,Inrō,,Japan,Edo period (1615–1868),,,,Artist,,Fang Shi Mopu,ca. 1588,,Fang Shi Mopu,,1588,1588,18th–19th century,1700,1899,"Lacquer, roiro, black, gold, red hiramakie, takamakie, ceramic inlay; Interior: roiro and fundame",2 15/16 x 1 3/4 x 11/16 in. (7.5 x 4.4 x 1.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Inrō,,http://www.metmuseum.org/art/collection/search/58666,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.46,false,true,48898,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Suga Mitsusada,1738–1806,,Suga Mitsusada,,1738,1806,18th–19th century,1738,1806,Hanging scroll; ink and color on silk,40 x 12 3/16 in. (101.6 x 30.9 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/48898,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.47,false,true,48899,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Suga Mitsusada,1738–1806,,Suga Mitsusada,,1738,1806,18th–19th century,1738,1806,Hanging scroll; ink and color on silk,40 x 12 3/16 in. (101.6 x 30.9 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/48899,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.48,false,true,48900,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Suga Mitsusada,1738–1806,,Suga Mitsusada,,1738,1806,18th–19th century,1738,1806,Hanging scroll; ink and color on silk,40 x 12 3/16 in. (101.6 x 30.9 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/48900,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.171,false,true,670949,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Minagawa Kien,"Japanese, 1734–1807",,Minagawa Kien,,1734,1807,18th–19th century,1734,1807,Hanging scroll; ink on paper,Image: 41 1/4 × 12 1/2 in. (104.7 × 31.7 cm) Overall with mounting: 68 7/16 × 18 1/4 in. (173.8 × 46.4 cm) Overall with knobs: 68 7/16 × 20 3/8 in. (173.8 × 51.8 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/670949,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.268.135,false,true,49022,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Unpō,"Japanese, 1765–1848",,Unpō,,1765,1848,18th–19th century,1749,1848,Hanging scroll; ink on paper,49 1/2 x 11 9/16 in. (125.7 x 29.3 cm),"The Harry G. C. Packard Collection of Asian Art, Gift of Harry G. C. Packard, and Purchase, Fletcher, Rogers, Harris Brisbane Dick, and Louis V. Bell Funds, Joseph Pulitzer Bequest, and The Annenberg Fund Inc. Gift, 1975",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49022,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.500.9.46,false,true,78165,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist|Artist,Calligraphy by|Underpainting attributed to,Hon'ami Kōetsu|Tawaraya Sōtatsu,"Japanese, 1558–1637|Japanese, died ca. 1640",,Hon'ami Kōetsu|Tawaraya Sōtatsu,,1558 |1540,1637 |1640,mid-1620s,1624,1626,"Section of a handscroll, mounted as a hanging scroll",Image: 12 5/8 x 20 3/4 in. (32.1 x 52.7 cm),"Gift of Florence and Herbert Irving, 2015",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/78165,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.216,false,true,53410,Asian Art,Hanging scroll,天台山石橋図|Lions at the Stone Bridge of Mount Tiantai,Japan,Edo period (1615–1868),,,,Artist|Artist,Inscribed by,Soga Shōhaku|Gazan Yō Nansō,"Japanese, 1730–1781|Japanese, 1727–1797",,Soga Shōhaku|Gazan Yō Nansō,,1730 |1727,1781 |1797,1779,1600,1850,Hanging scroll; ink on silk,Image: 44 7/8 in. × 20 in. (114 × 50.8 cm) Overall with mounting: 79 1/8 × 25 3/16 in. (201 × 64 cm) Overall with knobs: 79 1/8 × 27 1/2 in. (201 × 69.8 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/53410,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.87,false,true,52987,Asian Art,Handscroll,木版下絵和歌巻断簡|Twelve Poems from the New Collection of Poems Ancient and Modern (Shin kokin wakashū),Japan,Edo period (1615–1868),,,,Artist|Artist,Calligraphy by|Printed designs by a follower of,Hon'ami Kōetsu|Tawaraya Sōtatsu,"Japanese, 1558–1637|Japanese, died ca. 1640",,Hon'ami Kōetsu|Tawaraya Sōtatsu,,1558 |1540,1637 |1640,ca. 1620,1550,1700,Handscroll; ink and gold on silk,Image: 13 3/8 in. × 16 ft. 1/8 in. (34 × 488 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/52987,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.121,false,true,671001,Asian Art,Hanging scroll,立美人図|Standing Courtesan,Japan,Edo period (1615–1868),,,,Artist|Artist,Inscribed by,Kamo no Suketame|Tōsendō Rifū,"Japanese, 1740–1801|Japanese, active ca. 1730",,Kamo no Suketame|Tōsendō Rifū,,1740 |1720,1801 |1740,ca. 1720,1710,1730,Hanging scroll; ink and color on silk,Image: 28 1/8 × 13 1/8 in. (71.4 × 33.4 cm) Overall with mounting: 59 1/16 in. (150 cm) Overall with knobs: 59 1/16 × 17 7/16 in. (150 × 44.3 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/671001,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2015.300.69a, b",false,true,53238,Asian Art,Hanging scrolls,政黄牛・郁山主図|Zheng Huangniu and Yushanzhu,Japan,Momoyama period (1573–1615),,,,Artist|Artist,Inscribed by,Takuan Sōhō|Kano Naizen,"1573–1645|Japanese, 1570–1616",,Takuan Sōhō|Kano Naizen,,1573 |1570,1645 |1616,early 17th century,1600,1633,Pair of hanging scrolls; ink on paper,Image (a): 44 in. × 18 11/16 in. (111.7 × 47.4 cm) Overall with mounting (a): 78 11/16 × 24 5/8 in. (199.8 × 62.6 cm) Overall with knobs (a): 78 11/16 × 26 9/16 in. (199.8 × 67.5 cm) Image (b): 44 1/16 × 18 11/16 in. (111.9 × 47.4 cm) Overall with mounting (b): 78 15/16 × 24 1/2 in. (200.5 × 62.3 cm) Overall with knobs (b): 78 15/16 × 26 9/16 in. (200.5 × 67.4 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/53238,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.71,false,true,671040,Asian Art,Hanging scroll,達磨図|Bodhidharma,Japan,Momoyama period (1573–1615),,,,Artist|Artist,Inscribed by,Unkoku Tōgan|Gyokuho Jōsō,"Japanese, 1547–1618|Japanese, 1546–1613",,Unkoku Tōgan|Gyokuho Jōsō,,1547 |1546,1618 |1613,late 16th–early 17th century,1567,1633,Hanging scroll; ink on paper,Image: 35 1/4 × 13 1/8 in. (89.6 × 33.4 cm) Overall with mounting: 70 7/8 × 18 9/16 in. (180 × 47.2 cm) Overall with knobs: 70 7/8 × 20 9/16 in. (180 × 52.3 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/671040,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.59,false,true,671052,Asian Art,Hanging scroll,枝に小禽図|Bird on a Branch,Japan,Muromachi period (1392–1573),,,,Artist|Artist,Inscribed by,Daiko Shōkaku|Unkei Eii,"Japanese, died 1535|Japanese, active first half of the 16th century",,Daiko Shōkaku|Unkei Eii,,1500,1535 |1549,early 16th century,1500,1535,Hanging scroll; ink on paper,Image: 9 5/16 × 10 13/16 in. (23.7 × 27.5 cm) Overall with mounting: 45 7/16 × 16 1/8 in. (115.4 × 41 cm) Overall with knobs: 45 7/16 × 17 15/16 in. (115.4 × 45.5 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/671052,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.60,false,true,671055,Asian Art,Hanging scroll,藻鯉図|Carp and Waterweeds,Japan,Muromachi period (1392–1573),,,,Artist|Artist,Inscribed by,Yōgetsu|Mokumoku Dōjin,"Japanese, active late 15th century|Japanese, active late 15th century",,Yōgetsu|Mokumoku Dōjin,,1467 |1467,1499 |1499,late 15th century,1467,1499,Hanging scroll; ink on silk,Image: 33 7/16 × 13 7/8 in. (85 × 35.2 cm) Overall with mounting: 65 3/4 × 19 1/16 in. (167 × 48.4 cm) Overall with knobs: 65 3/4 × 20 7/8 in. (167 × 53 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/671055,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.500.2.44,false,true,40494,Asian Art,Inrō,,Japan,Meiji period (1868–1912),,,,Artist|Artist,copied from a design by,Hon'ami Kōetsu|Shibata Zeshin,"Japanese, 1558–1637|Japanese, 1807–1891",,Hon'ami Kōetsu|Shibata Zeshin,,1558 |1807,1637 |1891,19th century,1800,1899,Gold maki-e with mother-of-pearl inlay on black lacquer; netsuke of hardwood with cloisonne,H.1 7/8 in. (4.8 cm); W. 1 1/2 in. (3.8 cm),"Gift of Florence and Herbert Irving, 2015",,,,,,,,,,,,Lacquer,,http://www.metmuseum.org/art/collection/search/40494,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.300.215,false,true,670929,Asian Art,Hanging scroll,伊藤若冲筆 伝池大雅賛 寒山拾得図|Hanshan and Shide (Japanese: Kanzan and Jittoku),Japan,Edo period (1615–1868),,,,Artist|Artist,Calligraphy attributed to,Itō Jakuchū|Ike Taiga,"Japanese, 1716–1800|Japanese, 1723–1776",,Itō Jakuchū|Ike Taiga,,1716 |1723,1800 |1776,late 18th century,1767,1799,Hanging scroll; ink on paper,Image: 39 1/2 × 11 15/16 in. (100.4 × 30.3 cm) Overall with mounting: 69 11/16 × 15 3/8 in. (177 × 39 cm) Overall with knobs: 69 11/16 × 17 5/16 in. (177 × 44 cm),"Mary Griggs Burke Collection, Gift of the Mary and Jackson Burke Foundation, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/670929,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2002.447.120a, b",false,true,49420,Asian Art,Teapot,,Japan,Edo period (1615–1868),,,,Artist,Design attributed to,Olfert Dapper,"Dutch, 1635–1689",,"Dapper, Olfert",Dutch,1639,1689,early 18th century,1700,1733,Porcelain painted with cobalt blue under transparent glaze (Jingdezhen ware),H. 6 1/2 in. (16.5 cm); W. 9 3/4 in. (24.8 cm),"Dr. and Mrs. Roger G. Gerry Collection, Bequest of Dr. and Mrs. Roger G. Gerry, 2000",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/49420,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2002.447.121,false,true,49421,Asian Art,Plate,,Japan,Edo period (1615–1868),,,,Artist,Design attributed to,Cornelis Pronk,"Dutch, Amsterdam 1691–1759 Amsterdam",,"Pronk, Cornelis",Dutch,1691,1759,ca. 1734–37,1734,1737,Porcelain painted with cobalt blue under and colored enamels over transparent glaze (Hizen ware; Imari type),H. 1 1/4 in. (3.2 cm); Diam. 10 1/2 in. (26.7 cm),"Dr. and Mrs. Roger G. Gerry Collection, Bequest of Dr. and Mrs. Roger G. Gerry, 2000",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/49421,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2002.447.122,false,true,49422,Asian Art,Dish,,Japan,Edo period (1615–1868),,,,Artist,Design attributed to,Cornelis Pronk,"Dutch, Amsterdam 1691–1759 Amsterdam",,"Pronk, Cornelis",Dutch,1691,1759,ca. 1734–37,1734,1737,Porcelain painted with cobalt blue under and colored enamels over transparent glaze (Hizen ware; Imari type),H. 1 in. (2.5 cm); Diam. 9 3/8 in. (23.9 cm),"Dr. and Mrs. Roger G. Gerry Collection, Bequest of Dr. and Mrs. Roger G. Gerry, 2000",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/49422,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2002.447.123,false,true,49423,Asian Art,Plate,,Japan,Edo period (1615–1868),,,,Artist,Design attributed to,Cornelis Pronk,"Dutch, Amsterdam 1691–1759 Amsterdam",,"Pronk, Cornelis",Dutch,1691,1759,ca. 1734–37,1734,1737,Porcelain painted with cobalt blue under and colored enamels over transparent glaze (Hizen ware; Imari type),Diam. 10 1/2 in. (26.7 cm),"Dr. and Mrs. Roger G. Gerry Collection, Bequest of Dr. and Mrs. Roger G. Gerry, 2000",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/49423,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2002.447.72,false,true,49324,Asian Art,Barber's bowl,,Japan,Edo period (1615–1868),,,,Artist,Design by,Cornelis Pronk,"Dutch, Amsterdam 1691–1759 Amsterdam",,"Pronk, Cornelis",Dutch,1691,1759,18th century,1700,1799,"Porcelain with underglaze blue (Hizen ware, Ko Imari type)",H. 3 7/16 in. (8.7 cm); Diam. 12 1/16 in. (30.6 cm),"Dr. and Mrs. Roger G. Gerry Collection, Bequest of Dr. and Mrs. Roger G. Gerry, 2000",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/49324,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1991.134,false,true,44632,Asian Art,Handscroll,"清 郎世寧 百駿圖白描稿 卷|One Hundred Horses",China,Qing dynasty (1644–1911),,,,Artist,,Giuseppe Castiglione,"Italian, Milan 1688–1766 Beijing",,"Castiglione, Giuseppe",Italian,1688,1766,datable to 1723–25,1723,1725,Handscroll; ink on paper,Image: 37 in. x 25 ft. 10 3/4 in. (94 x 789.3 cm) Overall with mounting: H. 38 1/2 in. (97.8 cm),"Purchase, Friends of Asian Art Gifts, 1991",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/44632,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -30.84,false,true,51582,Asian Art,Album leaf,,China,Qing dynasty (1644–1911),,,,Artist|Artist,In the style of,Unidentified Artist|Giuseppe Castiglione,"Italian, Milan 1688–1766 Beijing",,"Unidentified Artist|Castiglione, Giuseppe",Italian,1688,1766,,1644,1911,Album leaf; color on silk,12 3/4 x 11 1/4 in. (32.4 x 28.6 cm),"Rogers Fund, 1930",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51582,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.220.121,false,true,51513,Asian Art,Hanging scroll,,China,Qing dynasty (1644–1911),,,,Artist|Artist,Formerly Attributed to,Unidentified Artist|Giuseppe Castiglione,"Italian, Milan 1688–1766 Beijing",,"Unidentified Artist|Castiglione, Giuseppe",Italian,1688,1766,,1644,1911,Hanging scroll,Overall: 24 3/4 x 54 in. (62.9 x 137.2 cm),"John Stewart Kennedy Fund, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51513,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.513,false,true,48907,Asian Art,Painting,,Japan,Edo period (1615–1868),,,,Artist|Artist,Attributed to|In the Style of,Tsukioka Sessai|Giuseppe Castiglione,"Japanese, 1761–1839|Italian, Milan 1688–1766 Beijing",,"Tsukioka Sessai|Castiglione, Giuseppe",Italian,1761 |1688,1839 |1766,19th century,1800,1839,Framed painting; ink and color on silk,35 3/4 x 27 in. (90.8 x 68.6 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/48907,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.468,false,true,51563,Asian Art,Painting,,China,Ming dynasty (?) (1368–1644),,,,Artist|Artist,Formerly Attributed to,Unidentified Artist|Gessen,1721–1809,,Unidentified Artist|Gessen,Japanese,1721,1809,,1368,1644,Painting; ink and color on silk,Image: 10 × 23 in. (25.4 × 58.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/51563,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JIB164,false,true,57855,Asian Art,Illustrated book,Hyakunin joro shinasadame|百人女郎品定|One Hundred Women Classified According to Their Rank (Hyakunin joro shinasadame),Japan,Edo period (1615–1868),,,,Artist|Calligrapher,Preface by,Nishikawa Sukenobu|Jisho,"Japanese, 1671–1750",,Nishikawa Sukenobu|Jisho,Japanese,1671,1750,1723,1723,1723,"Vol. I: 9 double and 2 single page illustrations; vol. II: 15 double, 1 single page illustrations; ink on paper",Overall: 10 x 7 1/2in. (25.4 x 19.1cm),"Gift of Mrs. Henry J. Bernheim, 1945",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/57855,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.797,false,true,78813,Asian Art,Illustrated book,,Japan,Momoyama period (1573–1615),,,,Artist|Calligrapher|Calligrapher,in the style of,Unidentified Artist|Unidentified Artist|Hon'ami Kōetsu,"Japanese, 1558–1637",Japanese|Japanese,Unidentified Artist|Unidentified Artist|Hon'ami Kōetsu,Japanese,1558,1637,possibly ca. 1610,1600,1620,Woodblock printed book; ink on paper,13 3/4 × 10 1/8 in. (35 × 25.7 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78813,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1550,false,true,55714,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Calligrapher|Artist,Engraved by,Yokogawa Horitake|Utagawa Kuniyoshi,"Japanese, 1797–1861",,Yokogawa Horitake|Utagawa Kuniyoshi,Japanese,1797,1861,1852,1852,1852,Polychrome woodblock print; ink and color on paper,H. 14 5/8 in. (37.1 cm); W. 9 7/8 in. (25.1 cm),"Fletcher Fund, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55714,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1551,false,true,55715,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Calligrapher|Artist,Engraved by,Yokogawa Horitake|Utagawa Kuniyoshi,"Japanese, 1797–1861",,Yokogawa Horitake|Utagawa Kuniyoshi,Japanese,1797,1861,1852,1852,1852,Polychrome woodblock print; ink and color on paper,H. 14 5/8 in. (37.1 cm); W. 9 7/8 in. (25.1 cm),"Fletcher Fund, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55715,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1553,false,true,55716,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Calligrapher|Artist,Engraved by,Yokogawa Horitake|Utagawa Kuniyoshi,"Japanese, 1797–1861",,Yokogawa Horitake|Utagawa Kuniyoshi,Japanese,1797,1861,1852,1852,1852,Polychrome woodblock print; ink and color on paper,H. 14 5/8 in. (37.1 cm); W. 9 7/8 in. (25.1 cm),"Fletcher Fund, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55716,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1554,false,true,55717,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Calligrapher|Artist,Engraved by,Yokogawa Horitake|Utagawa Kuniyoshi,"Japanese, 1797–1861",,Yokogawa Horitake|Utagawa Kuniyoshi,Japanese,1797,1861,1852,1852,1852,Polychrome woodblock print; ink and color on paper,H. 14 5/8 in. (37.1 cm); W. 9 7/8 in. (25.1 cm),"Fletcher Fund, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55717,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1555,false,true,55718,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Calligrapher|Artist,Engraved by,Yokogawa Horitake|Utagawa Kuniyoshi,"Japanese, 1797–1861",,Yokogawa Horitake|Utagawa Kuniyoshi,Japanese,1797,1861,1852,1852,1852,Polychrome woodblock print; ink and color on paper,H. 14 5/8 in. (37.1 cm); W. 9 7/8 in. (25.1 cm),"Fletcher Fund, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55718,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1556,false,true,55719,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Calligrapher|Artist,Engraved by,Yokogawa Horitake|Utagawa Kuniyoshi,"Japanese, 1797–1861",,Yokogawa Horitake|Utagawa Kuniyoshi,Japanese,1797,1861,1852,1852,1852,Polychrome woodblock print; ink and color on paper,H. 14 5/8 in. (37.1 cm); W. 9 7/8 in. (25.1 cm),"Fletcher Fund, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55719,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1557,false,true,44999,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Calligrapher|Artist,Engraved by,Yokogawa Horitake|Utagawa Kuniyoshi,"Japanese, 1797–1861",,Yokogawa Horitake|Utagawa Kuniyoshi,Japanese,1797,1861,1852–53,1852,1853,Polychrome woodblock print; ink and color on paper,H. 14 5/8 in. (37.1 cm); W. 9 7/8 in. (25.1 cm),"Fletcher Fund, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/44999,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1558,false,true,55722,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Calligrapher|Artist,Engraved by,Yokogawa Horitake|Utagawa Kuniyoshi,"Japanese, 1797–1861",,Yokogawa Horitake|Utagawa Kuniyoshi,Japanese,1797,1861,1852,1852,1852,Polychrome woodblock print; ink and color on paper,H. 14 5/8 in. (37.1 cm); W. 9 7/8 in. (25.1 cm),"Fletcher Fund, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55722,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1559,false,true,55724,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Calligrapher|Artist,Engraved by,Yokogawa Horitake|Utagawa Kuniyoshi,"Japanese, 1797–1861",,Yokogawa Horitake|Utagawa Kuniyoshi,Japanese,1797,1861,1852,1852,1852,Polychrome woodblock print; ink and color on paper,H. 14 5/8 in. (37.1 cm); W. 9 7/8 in. (25.1 cm),"Fletcher Fund, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55724,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1560,false,true,55725,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Calligrapher|Artist,Engraved by,Yokogawa Horitake|Utagawa Kuniyoshi,"Japanese, 1797–1861",,Yokogawa Horitake|Utagawa Kuniyoshi,Japanese,1797,1861,1852,1852,1852,Polychrome woodblock print; ink and color on paper,H. 14 5/8 in. (37.1 cm); W. 9 5/8 in. (24.4 cm),"Fletcher Fund, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55725,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1561,false,true,55728,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Calligrapher|Artist,Engraved by,Yokogawa Horitake|Utagawa Kuniyoshi,"Japanese, 1797–1861",,Yokogawa Horitake|Utagawa Kuniyoshi,Japanese,1797,1861,1852,1852,1852,Polychrome woodblock print; ink and color on paper,H. 14 5/8 in. (37.1 cm); W. 9 7/8 in. (25.1 cm),"Fletcher Fund, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55728,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.268.34,false,true,44908,Asian Art,Hanging scroll,,Japan,Kamakura period (1185–1333),,,,Calligrapher,Calligraphy attributed to,Fujiwara no Nobuzane,"Japanese, 1176–1265",,Fujiwara no Nobuzane,Japanese,1176,1265,13th century,1200,1265,Section of a handscroll mounted as a hanging scroll; ink on paper,Image: 11 3/4 x 7 1/2 in. (29.8 x 19.1 cm) Overall: 57 1/2 x 17 7/8in. (146.1 x 45.4 cm),"The Harry G. C. Packard Collection of Asian Art, Gift of Harry G. C. Packard, and Purchase, Fletcher, Rogers, Harris Brisbane Dick, and Louis V. Bell Funds, Joseph Pulitzer Bequest, and The Annenberg Fund Inc. Gift, 1975",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/44908,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.162,false,true,49081,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Calligrapher,,Kobori Enshū,1579–1647,,Kobori Enshū,Japanese,1579,1579,17th century,1600,1647,Hanging scroll; ink on paper,8 3/4 x 11 3/4 in. (22.3 x 29.9 cm),"Gift of Mr. and Mrs. H. Jack Lang, 1975",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49081,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -94.18.1a–xx,false,true,45427,Asian Art,Album,源氏物語画帖|The Tale of Genji (Genji Monogatari),Japan,Edo period (1615–1868),,,,Calligrapher,,Shōren'in Sonjun Shinnō,"Japanese, 1581–1653",,Shōren'in Sonjun Shinnō,Japanese,1581,1653,17th century,1738,1806,"Set of twenty-four album leaves; ink, gold and color on paper",10 3/8 x 9 1/4 in. (26.3 x 23.5 cm),"Gift of Mary L. Cassilly, 1894",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45427,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2075,false,true,54906,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist|Calligrapher,,Teisai Hokuba|Chou,"Japanese, 1771–1844",,Teisai Hokuba|Chou,Japanese,1771,1844,early 19th century,1800,1833,Polychrome woodblock print (surimono); ink and color on paper,5 9/16 x 7 1/2 in. (14.1 x 19.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54906,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -10.211.2319,false,true,59380,Asian Art,Netsuke,,Japan,,,,,Artist,,Sanshō,1871–1936,,Sanshō,Japanese,1871,1936,early 20th century,1900,1933,Wood,H. 4 1/4 in. (10.8 cm); W. 1 1/4 in. (3.2 cm); D. 1 in. (2.5 cm),"Gift of Mrs. Russell Sage, 1910",,,,,,,,,,,,Netsuke,,http://www.metmuseum.org/art/collection/search/59380,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -10.211.2353,false,true,59404,Asian Art,Netsuke,,Japan,,,,,Artist,,Sanshō,1871–1936,,Sanshō,Japanese,1871,1936,late 19th century,1867,1899,Wood,H. 3 1/4 in. (8.3 cm),"Gift of Mrs. Russell Sage, 1910",,,,,,,,,,,,Netsuke,,http://www.metmuseum.org/art/collection/search/59404,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.120.687,false,true,60230,Asian Art,Kyogen mask,,Japan,,,,,Artist,,Tenkaichi Taiko,"Japanese, died 1616",,Tenkaichi Taiko,Japanese,1516,1616,early 17th century,1600,1633,Lacquered wood,H. 7 5/8 in. (19.4 cm); W. 5 3/4 in. (14.6 cm),"The Howard Mansfield Collection, Gift of Howard Mansfield, 1936",,,,,,,,,,,,Masks,,http://www.metmuseum.org/art/collection/search/60230,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -38.25.107a–e,false,true,58280,Asian Art,Writing box,,Japan,,,,,Artist,In the Style of,Ogawa Haritsu (Ritsuō),"Japanese, 1663–1747",,Ritsuō,Japanese,1663,1747,late 19th century,1871,1899,"Colored lacquer, gold maki-e, and inlaid pewter",H. 2 3/8 in. (6 cm); W. 10 1/2 in. (26.7 cm); L. 12 in. (30.5 cm),"Gift of Mrs. George A. Crocker (Elizabeth Masten), 1937",,,,,,,,,,,,Lacquer,,http://www.metmuseum.org/art/collection/search/58280,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JIB182,false,true,57912,Asian Art,Illustrated book,,Japan,,,,,Artist,,Utagawa Toyohiro,"Japanese, 1763–1828",,Utagawa Toyohiro,Japanese,1763,1828,1793,1793,1793,Ink on paper,13 13/16 x 9 1/16 in. (35.1 x 23 cm),"The Harry G. G. Packard Collection of Asian Art, Gift of Harry G. G. Packard and Purchase, Fletcher, Rogers, Harris Brisbane Dick and Louis V. Bell Funds, Joseph Pulitzer Bequest and The Annenberg Fund, Inc. Gift, 1975",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/57912,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1985.399.24,false,true,59676,Asian Art,Netsuke,,Japan,,,,,Artist,,Minkō,"Japanese, ca. 1735–1816",,Minkō,Japanese,1735,1816,late 18th–early 19th century,1767,1833,"Wood, horn",H. 1 1/16 in. (2.7 cm); W. 1 7/16 in. (3.7 cm),"Gift of Alvin H. Schechter, 1985",,,,,,,,,,,,Netsuke,,http://www.metmuseum.org/art/collection/search/59676,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.67.83,false,true,45585,Asian Art,Inrō,,Japan,Edo period (1615–1868),,,,Artist,,Yamada Jōkasai (1681–1704),,,Yamada Jōkasai,Japanese,1681,1704,second half of the 19th century,1850,1899,Gold and colored lacquer with inlaid mother-of-pearl on ro-iro black lacquer; ojime: metal bead inlaid with silver; netsuke: carved ebony inlaid with ivory design of Daruma,3 7/8 x 1 15/16 x 1 1/4 in. (9.8 x 4.9 x 3.2 cm),"Rogers Fund, 1913",,,,,,,,,,,,Inrō,,http://www.metmuseum.org/art/collection/search/45585,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JIB97,false,true,57784,Asian Art,Illustrated book,職人盡歌合|Poetry Contest by Various Artisans (Shokunin zukushi uta-awase),Japan,Edo period (1615–1868),,,,Artist,After,Tosa Mitsunobu,1434–1525,,Tosa Mitsunobu,Japanese,1434,1525,ca. 1744,1734,1754,Woodblock printed book; ink on paper,Overall: 10 1/8 × 7 3/16 × 5/8 in. (25.7 × 18.3 × 1.6 cm),"The Howard Mansfield Collection, Gift of Howard Mansfield, 1936",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/57784,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.784,false,true,78686,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,Attributed to,Tosa Mitsunari,1648–1710,,Tosa Mitsunari,Japanese,1648,1710,late 17th century,1675,1699,Accordion album; ink and color on silk,9 9/16 × 8 7/8 in. (24.3 × 22.5 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78686,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.817a–g,false,true,78707,Asian Art,Illustrated books,,Japan,Edo period (1615–1868),,,,Artist,,Ōoka Shunboku,1680–1763,,Ōoka Shunboku,Japanese,1680,1763,1750,1750,1750,Set of six woodblock printed books bound as one with additional volume; ink on paper,10 3/16 × 7 3/16 in. (25.8 × 18.2 cm) 10 3/16 × 7 1/16 in. (25.8 × 18 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78707,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.818a–c,false,true,78708,Asian Art,Illustrated books,,Japan,Edo period (1615–1868),,,,Artist,,Ōoka Shunboku,1680–1763,,Ōoka Shunboku,Japanese,1680,1763,ca. 1812,1807,1817,Set of three woodblock printed books; ink and color on paper,each: 10 13/16 × 7 in. (27.5 × 17.8 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78708,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.268.89,false,true,57224,Asian Art,Handscroll,,Japan,Edo period (1615–1868),,,,Artist,,Rai San'yō,1780–1832,,Rai San'yō,Japanese,1780,1832,dated 1824,1824,1824,Handscroll; ink on paper,11 7/8 x 116 3/4 in. (30.2 x 296.5 cm),"The Harry G. C. Packard Collection of Asian Art, Gift of Harry G. C. Packard, and Purchase, Fletcher, Rogers, Harris Brisbane Dick, and Louis V. Bell Funds, Joseph Pulitzer Bequest, and The Annenberg Fund Inc. Gift, 1975",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/57224,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JIB113,false,true,57806,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Ōnishi Chinnen,1792–1851,,Ōnishi Chinnen,Japanese,1792,1851,1832,1832,1832,Ink on paper,10 1/2 × 7 3/16 × 3/8 in. (26.7 × 18.3 × 1 cm),"The Howard Mansfield Collection, Gift of Howard Mansfield, 1936",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/57806,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.669,false,true,78590,Asian Art,Illustrated book,楚南画譜|Sōnan (Chinnen) Picture Album (Sōnan gafu),Japan,Edo period (1615–1868),,,,Artist,,Ōnishi Chinnen,1792–1851,,Ōnishi Chinnen,Japanese,1792,1851,1834,1834,1834,Woodblock printed book; ink and color on paper,10 13/16 × 7 5/16 in. (27.5 × 18.5 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78590,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.670,false,true,78591,Asian Art,Illustrated book,あづまの手ぶり|Customs of the Eastern Capital (Edo) (Azuma no teburi),Japan,Edo period (1615–1868),,,,Artist,,Ōnishi Chinnen,1792–1851,,Ōnishi Chinnen,Japanese,1792,1851,1829,1829,1829,Woodblock printed book; ink and color with hand-coloring (?) on paper,10 15/16 × 7 3/8 in. (27.8 × 18.8 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78591,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JIB64,false,true,57664,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Hishikawa Moronobu,"Japanese, died 1694",,Hishikawa Moronobu,Japanese,,1694,"1695, fourth month",1695,1695,Woodblock printed book; ink on paper,8 1/4 × 6 1/8 × 3/8 in. (21 × 15.6 × 1 cm),"Rogers Fund, 1918",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/57664,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JIB65,false,true,45061,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Hishikawa Moronobu,"Japanese, died 1694",,Hishikawa Moronobu,Japanese,,1694,ca. 1694,1684,1704,Woodblock printed book; ink on paper,10 5/8 × 7 1/4 × 1/8 in. (27 × 18.4 × 0.3 cm),"Rogers Fund, 1918",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/45061,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JIB66,false,true,57665,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Hishikawa Moronobu,"Japanese, died 1694",,Hishikawa Moronobu,Japanese,,1694,"1683, fifth month",1683,1683,Woodblock printed book; ink and color on paper,10 1/4 × 7 × 3/8 in. (26 × 17.8 × 1 cm),"Rogers Fund, 1918",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/57665,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JIB74,false,true,57672,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Hishikawa Moronobu,"Japanese, died 1694",,Hishikawa Moronobu,Japanese,,1694,"1685, second month",1685,1685,Woodblock printed book; ink and color on paper,10 3/8 × 7 3/8 × 3/16 in. (26.4 × 18.7 × 0.5 cm),"Rogers Fund, 1923",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/57672,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JIB75,false,true,57673,Asian Art,Illustrated book,美人絵づくし|Illustrations of Beautiful Women (Bijin e-zukushi),Japan,Edo period (1615–1868),,,,Artist,,Hishikawa Moronobu,"Japanese, died 1694",,Hishikawa Moronobu,Japanese,,1694,"1683, fifth month",1683,1683,Woodblock printed book; ink and color on paper,10 5/8 × 7 1/4 × 3/8 in. (27 × 18.4 × 1 cm),"Rogers Fund, 1923",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/57673,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JIB84,false,true,57743,Asian Art,Illustrated book,大和絵づくし|Compendium of Yamato-e Painting Themes (Yamato-e zukushi),Japan,Edo period (1615–1868),,,,Artist,,Hishikawa Moronobu,"Japanese, died 1694",,Hishikawa Moronobu,Japanese,,1694,"1686, ninth month",1686,1686,Woodblock printed book; ink on paper,10 3/4 x 7 3/8 in. (27.3 x 18.7 cm),"The Howard Mansfield Collection, Gift of Howard Mansfield, 1936",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/57743,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JIB86,false,true,57745,Asian Art,Illustrated book,築山図庭画畫　余慶作り庭の図|A Compendium of Model Gardens (Tsukiyama no zu niwa zukushi; Yokei tsukuri niwa no zu),Japan,Edo period (1615–1868),,,,Artist,,Hishikawa Moronobu,"Japanese, died 1694",,Hishikawa Moronobu,Japanese,,1694,"1691, fifth month",1691,1691,Woodblock printed book; ink on paper,10 1/2 x 7 1/2 in. (26.7 x 19.1 cm),"The Howard Mansfield Collection, Gift of Howard Mansfield, 1936",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/57745,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.887,false,true,78777,Asian Art,Illustrated book,『當世雛形』|Contemporary Kimono Patterns (Tōsei hiinagata),Japan,Edo period (1615–1868),,,,Artist,,Hishikawa Moronobu,"Japanese, died 1694",,Hishikawa Moronobu,Japanese,,1694,1677,1677,1677,Woodblock printed book; ink on paper,10 11/16 × 7 1/2 in. (27.2 × 19 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78777,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"JIB122a, b",false,true,57814,Asian Art,Illustrated book,ぶんしやう物語|The Tale of Bunshō (Bunshō monogatari),Japan,Edo period (1615–1868),,,,Artist,,Hishikawa Moronobu,"Japanese, died 1694",,Hishikawa Moronobu,Japanese,,1694,ca. 1685,1675,1695,Set of two woodblock printed books; ink on paper,Each: 8 3/4 × 6 1/8 × 1/8 in. (22.2 × 15.6 × 0.3 cm),"The Howard Mansfield Collection, Gift of Howard Mansfield, 1936",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/57814,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JIB26a–c,false,true,57559,Asian Art,Illustrated book,姿絵百人一首|Portraits for One Hundred Poems about One Hundred Poets (Sugata-e hyakunin isshu),Japan,Edo period (1615–1868),,,,Artist,,Hishikawa Moronobu,"Japanese, died 1694",,Hishikawa Moronobu,Japanese,,1694,"1695, fourth month",1695,1695,Set of three woodblock printed books; ink on paper,Each: 8 3/4 × 6 3/8 × 1/4 in. (22.2 × 16.2 × 0.6 cm),"Rogers Fund, 1918",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/57559,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JIB67a–c,false,true,57666,Asian Art,Illustrated book,美人絵づくし|Illustrations of Beautiful Women (Bijin e-zukushi),Japan,Edo period (1615–1868),,,,Artist,,Hishikawa Moronobu,"Japanese, died 1694",,Hishikawa Moronobu,Japanese,,1694,"1683, fifth month",1683,1683,Set of three woodblock printed books; ink and color on paper,Each: 10 3/8 × 7 5/16 × 1/4 in. (26.4 × 18.6 × 0.6 cm),"Rogers Fund, 1918",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/57666,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JIB85a–c,false,true,57744,Asian Art,Illustrated book,伊勢物語頭書抄|Tales of Ise with Annotations (Ise Monogatari tōsho shō),Japan,Edo period (1615–1868),,,,Artist,,Hishikawa Moronobu,"Japanese, died 1694",,Hishikawa Moronobu,Japanese,,1694,"1679, third month",1679,1679,Set of 3 woodblock printed books; ink on paper,Each: 10 1/2 × 7 3/8 × 3/8 in. (26.7 × 18.7 × 1 cm),"The Howard Mansfield Collection, Gift of Howard Mansfield, 1936",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/57744,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.786a–c,false,true,78688,Asian Art,Illustrated books,『和国諸職絵尽　諸織 絵本鏡』|A Picture Book Mirror of Various Occupations (Wakoku shoshoku ezukushi),Japan,Edo period (1615–1868),,,,Artist,,Hishikawa Moronobu,"Japanese, died 1694",,Hishikawa Moronobu,Japanese,,1694,1685,1685,1685,Set of three woodblock printed books; ink on paper,each: 10 5/8 × 7 5/16 in. (27 × 18.5 cm),"Purchase, Mary and James G. Wallach Family Foundation Gift, in honor of John T. Carpenter, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78688,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.82.3,false,true,45574,Asian Art,Inrō,,Japan,Edo period (1615–1868),,,,Artist,,Koma Kōryū,"Japanese, died 1796",,Koma Kōryū,Japanese,,1796,late 18th–19th century,1771,1899,"Roiro (waxen) lacquer with decoration in togisashi sprinkled and polished lacquer; Ojime: gold lacquer bead; Netsuke: ivory ""Dream of a Clam""",H. 3 3/16 in. (8.1 cm); W. 1 7/8 in. (4.8 cm); D. 15/16 in. ( 2.4 cm),"Gift of Wilton Lloyd-Smith and his wife, Marjorie Fleming Lloyd-Smith, 1929",,,,,,,,,,,,Inrō,,http://www.metmuseum.org/art/collection/search/45574,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.824,false,true,78714,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,Attributed to,Shimokōbe Shūsui,"Japanese, died 1797",,Shimokōbe Shūsui,Japanese,,1797,1797,1797,1797,Woodblock printed book; ink on paper,6 1/4 × 4 5/16 in. (15.8 × 11 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78714,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2013.697a, b",false,true,78618,Asian Art,Album,『光琳画譜』|Kōrin Picture Album (Kōrin gafu),Japan,Edo period (1615–1868),,,,Artist,,Nakamura Hōchū,"Japanese, died 1819",,Nakamura Hōchū,Japanese,,1819,after 1826 (reprinted posthumously),1827,1868,"Woodblock-printed book in two volumes (orihon, accordion-style); ink and color on paper",each: 10 5/8 × 7 11/16 in. (27 × 19.5 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78618,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2013.890a, b",false,true,78780,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Ki Chikudō,"Japanese, died 1825",,Ki Chikudō,Japanese,,1825,1815,1815,1815,Set of two woodblock printed books; ink and color on paper,10 13/16 × 6 15/16 in. (27.5 × 17.7 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78780,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.689,false,true,45562,Asian Art,Writing box,伝本阿弥光悦 橅夫蒔絵硯箱|Writing Box (Suzuribako) with Woodcutter,Japan,Edo period (1615–1868),,,,Artist,In the style of,Hon'ami Kōetsu,"Japanese, 1558–1637",,Hon'ami Kōetsu,Japanese,1558,1637,18th or early 19th century,1700,1833,Gold maki-e on black lacquer with mother-of-pearl inlay,H. 3 3/4 in. (9.5 cm); W. 9 in. (22.9 cm); L. 9 3/8 in. (23.8 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Lacquer,,http://www.metmuseum.org/art/collection/search/45562,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"1975.268.177a, b",false,true,45481,Asian Art,Basket,,Japan,Edo period (1615–1868),,,,Artist,Style of,Hon'ami Kōetsu,"Japanese, 1558–1637",,Hon'ami Kōetsu,Japanese,1558,1637,late 17th century,1667,1699,"Rattan body, gold, silver hiramaki-e",H. 4 1/8 in. (10.5 cm); W. 8 7/8 in. (22.5 cm); L. 9 7/8 in. (25.1 cm),"The Harry G. C. Packard Collection of Asian Art, Gift of Harry G. C. Packard, and Purchase, Fletcher, Rogers, Harris Brisbane Dick, and Louis V. Bell Funds, Joseph Pulitzer Bequest, and The Annenberg Fund Inc. Gift, 1975",,,,,,,,,,,,Lacquer,,http://www.metmuseum.org/art/collection/search/45481,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.753,false,true,58664,Asian Art,Inrō,馬蒔絵印籠　自適斎筆|Inrō with Two Standing Horses; Seal and Inscription (reverse),Japan,Edo period (1615–1868),,,,Artist,After,Kano Naonobu,"Japanese, 1607–1650",,Kano Naonobu,Japanese,1607,1650,mid- 18th century,1734,1766,"Four cases; lacquered wood with gold hiramaki-e, gold foil application, and mother-of-pearl inlay on black ground Netsuke: horse; ivory Ojime: iron bead with cricket and flower in gold overlay",3 1/8 x 1 7/16 x 1 in. (7.9 x 3.7 x 2.5 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Inrō,,http://www.metmuseum.org/art/collection/search/58664,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"1975.268.57, .58",false,true,45264,Asian Art,Folding screens,舞楽図屏風 ・唐獅子図屏風|Bugaku Dances (front); Chinese Lions (reverse),Japan,Edo period (1615–1868),,,,Artist,,Hanabusa Itchō,"Japanese, 1652–1724",,Hanabusa Itchō,Japanese,1652,1724,early 18th century,1700,1733,"Pair of six-panel screens; ink, color, and gold leaf on paper",Image (each screen): 72 1/8 in. x 14 ft. 9 5/8 in. (183.2 x 451.2 cm),"The Harry G. C. Packard Collection of Asian Art, Gift of Harry G. C. Packard, and Purchase, Fletcher, Rogers, Harris Brisbane Dick, and Louis V. Bell Funds, Joseph Pulitzer Bequest, and The Annenberg Fund Inc. Gift, 1975",,,,,,,,,,,,Screens,,http://www.metmuseum.org/art/collection/search/45264,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JIB100a–c,false,true,36460,Asian Art,Illustrated book,一蝶画譜|Itchō Picture Album (Itchō gafu),Japan,Edo period (1615–1868),,,,Artist,,Hanabusa Itchō,"Japanese, 1652–1724",,Hanabusa Itchō,Japanese,1652,1724,"1770, first month",1770,1770,Set of three woodblock printed books; ink on paper,Overall (vols. 1–3 each): 10 1/16 × 6 7/8 in. (25.6 × 17.5 cm),"The Howard Mansfield Collection, Gift of Howard Mansfield, 1936",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/36460,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.812,false,true,58718,Asian Art,Inrō,寒山拾得蒔絵印籠|Inrō with Rinpa Style Kanzan and Jittoku,Japan,Edo period (1615–1868),,,,Artist,School of,Ogata Kōrin,"Japanese, 1658–1716",,Kōrin,Japanese,1658,1716,first half of the 19th century,1800,1849,Three cases; lacquered wood with mother-of-pearl and pewter inlay on gold lacquer ground; Pouch: printed cotton with sarasa pattern; Ojime: metal bead,H. 2 3/16 in. (5.5 cm); W. 1 15/16 in. (4.9 cm); D. 3/4 in. (1.9 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Inrō,,http://www.metmuseum.org/art/collection/search/58718,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -26.117,true,true,44918,Asian Art,Folding screen,波濤図屏風|Rough Waves,Japan,Edo period (1615–1868),,,,Artist,,Ogata Kōrin,"Japanese, 1658–1716",,Kōrin,Japanese,1658,1716,ca. 1704–9,1694,1719,"Two-panel folding screen; ink, color, and gold leaf on paper",Image: 57 11/16 x 65 1/8 in. (146.5 x 165.4 cm) Overall: 59 1/4 x 66 1/2 in. (150.5 x 168.9 cm),"Fletcher Fund, 1926",,,,,,,,,,,,Screens,,http://www.metmuseum.org/art/collection/search/44918,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"53.7.1, .2",false,true,39664,Asian Art,Folding screen,八橋図屏風 |Irises at Yatsuhashi (Eight Bridges),Japan,Edo period (1615–1868),,,,Artist,,Ogata Kōrin,"Japanese, 1658–1716",,Kōrin,Japanese,1658,1716,after 1709,1710,1716,Pair of six-panel folding screens; ink and color on gold leaf on paper,Image (each screen): 64 7/16 in. x 11ft. 6 3/4 in. (163.7 x 352.4 cm) Overall (each screen): 70 1/2 in. x 12 ft. 2 1/4 in. (179.1 x 371.5 cm),"Purchase, Louisa Eldridge McBurney Gift, 1953",,,,,,,,,,,,Screens,,http://www.metmuseum.org/art/collection/search/39664,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"1975.268.62, .63",false,true,44896,Asian Art,Folding screen,"尾形光琳筆　松竹に鶴図屏風|Cranes, Pines, and Bamboo",Japan,Edo period (1615–1868),,,,Artist,,Ogata Kōrin,"Japanese, 1658–1716",,Kōrin,Japanese,1658,1716,late 17th century,1671,1699,Pair of folding screens; ink and light color on paper,Right screen (4-panel): 65 3/4 x 101 1/4 in. (167 x 257.2 cm) Left screen (6-panel): 65 3/4 x 151 1/4 in. (167 x 384.2 cm),"The Harry G. C. Packard Collection of Asian Art, Gift of Harry G. C. Packard, and Purchase, Fletcher, Rogers, Harris Brisbane Dick, and Louis V. Bell Funds, Joseph Pulitzer Bequest, and The Annenberg Fund Inc. Gift, 1975",,,,,,,,,,,,Screens,,http://www.metmuseum.org/art/collection/search/44896,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"JIB124a, b",false,true,57816,Asian Art,Illustrated books,池田孤邨画　『光琳新選百図』|One Hundred Newly Selected Designs by Kōrin (Kōrin shinsen hyakuzu),Japan,Edo period (1615–1868),,,,Artist,,Ogata Kōrin,"Japanese, 1658–1716",,Kōrin,Japanese,1658,1716,1864,1864,1864,Two volumes of Woodblock printed books; ink on paper,Overall (each): 10 1/16 x 7 3/8 x 3/8 in. (25.6 x 18.7 x 1 cm),"The Howard Mansfield Collection, Gift of Howard Mansfield, 1936",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/57816,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -66.149,false,true,45729,Asian Art,Screen,,Japan,Edo period (1615–1868),,,,Artist,,Kano Chikanobu,"Japanese, 1660–1728",,Kano Chikanobu,Japanese,1660,1728,17th–18th century,1600,1799,Six-panel folding screen; ink and color on silk,Image: 61 1/8 x 139 3/4 in. (155.3 x 355 cm),"Gift of Richard W. Courts, in memory of Mr. and Mrs. Thomas J. Watson, 1966",,,,,,,,,,,,Screens,,http://www.metmuseum.org/art/collection/search/45729,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.498,false,true,45217,Asian Art,Screen,,Japan,Edo period (1615–1868),,,,Artist,,Kano Chikanobu,"Japanese, 1660–1728",,Kano Chikanobu,Japanese,1660,1728,17th–18th century,1600,1799,"One of a pair of six-panel folding screens; ink, color, and gilt on paper; Reverse side: ink, color, and gold on paper",69 1/4 x 153 in. (175.9 x 388.6 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Screens,,http://www.metmuseum.org/art/collection/search/45217,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.702,false,true,58238,Asian Art,Writing box,,Japan,Edo period (1615–1868),,,,Artist,,Ogawa Haritsu (Ritsuō),"Japanese, 1663–1747",,Ritsuō,Japanese,1663,1747,17th–18th century,1600,1799,Lacquer with design in pottery and pewter,H. 1 5/8 in. (4.1 cm); W. 8 3/4 in. (22.2 cm); L. 9 1/4 in. (23.5 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Lacquer,,http://www.metmuseum.org/art/collection/search/58238,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JIB34,false,true,45356,Asian Art,Illustrated book,Onna Ichidai Fūzoku Ehon Masukagami|絵本十寸鑑|True Reflections on the Life and Manners of a Woman,Japan,Edo period (1615–1868),,,,Artist,,Nishikawa Sukenobu,"Japanese, 1671–1750",,Nishikawa Sukenobu,Japanese,1671,1750,January 1748,1748,1748,Monochrome woodblock printed book; ink on paper,Overall: 8 3/4 × 6 1/4 in. (22.2 × 15.9 cm),"Rogers Fund, 1918",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/45356,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JIB35,false,true,53815,Asian Art,Illustrated book,Ehon Asakayama|絵本浅香山|Picture Book: Mount Asaka (Ehon Asakayama),Japan,Edo period (1615–1868),,,,Artist,,Nishikawa Sukenobu,"Japanese, 1671–1750",,Nishikawa Sukenobu,Japanese,1671,1750,January 1739,1739,1739,Bound book of monochrome woodblock prints; ink on paper,Overall: 10 3/8 × 7 in. (26.4 × 17.8 cm),"Rogers Fund, 1918",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/53815,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JIB90,false,true,57756,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Nishikawa Sukenobu,"Japanese, 1671–1750",,Nishikawa Sukenobu,Japanese,1671,1750,1740,1740,1740,Woodblock print; ink on paper,Overall: 9 x 6 1/4 in. (22.9 x 15.9 cm),"The Howard Mansfield Collection, Gift of Howard Mansfield, 1936",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/57756,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JIB99,false,true,45062,Asian Art,Illustrated book,"絵本末摘花|Picture Book: Flowers Yet to be Picked, Vol. 1",Japan,Edo period (1615–1868),,,,Artist,,Nishikawa Sukenobu,"Japanese, 1671–1750",,Nishikawa Sukenobu,Japanese,1671,1750,1756,1615,1868,Woodblock printed book; ink and color on paper,Overall: 9 × 6 in. (22.9 × 15.2 cm),"The Howard Mansfield Collection, Gift of Howard Mansfield, 1936",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/45062,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JIB151,false,true,57842,Asian Art,Illustrated book,Ehon Ogurayama|絵本小倉山|Picture Book: Ogura Hill (Ehon Ogurayama),Japan,Edo period (1615–1868),,,,Artist,,Nishikawa Sukenobu,"Japanese, 1671–1750",,Nishikawa Sukenobu,Japanese,1671,1750,1749,1749,1749,Thirteen double-page monochrome illustrations; ink on paper,Overall: 8 7/8 × 6 1/4 in. (22.5 × 15.9 cm),"Bequest of W. Gedney Beatty, 1941",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/57842,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JIB152,false,true,57843,Asian Art,Illustrated book,Ehon Kai kasen|絵本貝歌仙|Illustrated Poems,Japan,Edo period (1615–1868),,,,Artist,,Nishikawa Sukenobu,"Japanese, 1671–1750",,Nishikawa Sukenobu,Japanese,1671,1750,1747,1747,1747,Nineteen pages of illustrations and poems; ink on paper,Overall: 8 5/8 × 6 3/8 in. (21.9 × 16.2 cm),"Bequest of W. Gedney Beatty, 1941",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/57843,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JIB153,false,true,57844,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Nishikawa Sukenobu,"Japanese, 1671–1750",,Nishikawa Sukenobu,Japanese,1671,1750,1741,1741,1741,Ink on paper,Overall: 10 3/4 x 7 1/2in. (27.3 x 19.1cm),"Bequest of W. Gedney Beatty, 1941",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/57844,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JIB163,false,true,57854,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Nishikawa Sukenobu,"Japanese, 1671–1750",,Nishikawa Sukenobu,Japanese,1671,1750,1731,1731,1731,"One volume, black and white, twenty-five double-page sheets; ink on paper",Overall: 10 1/4 x 6 1/2in. (26 x 16.5cm),"Gift of Mrs. Henry J. Bernheim, 1945",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/57854,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JIB167,false,true,57858,Asian Art,Illustrated book,Ehon Himetsubaki|繪本女貞木|Picture Book: Camellia (Ehon Himetsubaki),Japan,Edo period (1615–1868),,,,Artist,,Nishikawa Sukenobu,"Japanese, 1671–1750",,Nishikawa Sukenobu,Japanese,1671,1750,1745(?),1745,1745,Bound book of monochrome woodblock prints; ink on paper,Overall: 10 1/2 x 7in. (26.7 x 17.8cm),"Gift of Mrs. Morris Manges, in memory of her husband, Dr. Morris Manges, 1947",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/57858,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.833,false,true,78723,Asian Art,Illustrated book,『繪本常盤草』|Picture Book of the Evergreens (Ehon tokiwagusa),Japan,Edo period (1615–1868),,,,Artist,,Nishikawa Sukenobu,"Japanese, 1671–1750",,Nishikawa Sukenobu,Japanese,1671,1750,1731,1731,1731,Woodblock printed book; ink and hand-coloring on paper,10 7/8 × 7 9/16 in. (27.7 × 19.2 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78723,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.834,false,true,78724,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Nishikawa Sukenobu,"Japanese, 1671–1750",,Nishikawa Sukenobu,Japanese,1671,1750,1739,1739,1739,Woodblock printed book; ink on paper,10 1/2 × 7 3/16 in. (26.7 × 18.3 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78724,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.835,false,true,78725,Asian Art,Illustrated books,,Japan,Edo period (1615–1868),,,,Artist,,Nishikawa Sukenobu,"Japanese, 1671–1750",,Nishikawa Sukenobu,Japanese,1671,1750,1740,1740,1740,Set of three woodblock printed books bound as one volume; ink on paper,10 5/8 × 7 1/2 in. (27 × 19 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78725,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.836,false,true,78726,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Nishikawa Sukenobu,"Japanese, 1671–1750",,Nishikawa Sukenobu,Japanese,1671,1750,1746,1746,1746,Woodblock printed book; ink on paper,9 1/8 × 6 7/16 in. (23.2 × 16.3 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78726,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.838,false,true,78728,Asian Art,Illustrated books,『繪本小倉山』|Picture Book of Ogura Hill (Ehon ogurayama),Japan,Edo period (1615–1868),,,,Artist,,Nishikawa Sukenobu,"Japanese, 1671–1750",,Nishikawa Sukenobu,Japanese,1671,1750,1749,1749,1749,Set of three woodblock-printed books bound as one volume; ink on paper,each: 9 1/16 × 6 1/2 in. (23 × 16.5 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78728,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.839,false,true,78729,Asian Art,Illustrated book,『繪本小松原』|Picture Book of Komatsubara (Ehon komatsubara),Japan,Edo period (1615–1868),,,,Artist,,Nishikawa Sukenobu,"Japanese, 1671–1750",,Nishikawa Sukenobu,Japanese,1671,1750,1761,1761,1761,Woodblock-printed book; ink on paper,10 5/8 × 7 1/2 in. (27 × 19 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78729,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.832a–c,false,true,78722,Asian Art,Illustrated book,絵本常盤草　上・中・下|Picture Book of the Evergreens (Ehon tokiwagusa),Japan,Edo period (1615–1868),,,,Artist,,Nishikawa Sukenobu,"Japanese, 1671–1750",,Nishikawa Sukenobu,Japanese,1671,1750,1731,1731,1731,Woodblock-printed book; ink and hand-coloring on paper,10 3/4 × 7 1/2 in. (27.3 × 19 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78722,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.837a–c,false,true,78727,Asian Art,Illustrated books,,Japan,Edo period (1615–1868),,,,Artist,,Nishikawa Sukenobu,"Japanese, 1671–1750",,Nishikawa Sukenobu,Japanese,1671,1750,1748,1748,1748,Set of three woodblock printed books; ink on paper,each: 8 7/8 × 6 5/16 in. (22.5 × 16 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78727,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.268.88,false,true,44864,Asian Art,Overrobe,白繻子地墨竹図打掛　祇園南海筆|Overrobe (Uchikake) with Bamboo,Japan,Edo period (1615–1868),,,,Artist,,Gion Nankai,"Japanese, 1677–1751",,Gion Nankai,Japanese,1677,1751,first half of the 18th century,1700,1749,Ink and gold powder on silk satin,Overall: 64 3/4 x 48 7/8 in. (164.5 x 124.2 cm) Sleeve length: 37 3/4 in. (95.9 cm); sleeve width: 12 3/4 in. (32.4 cm),"The Harry G. C. Packard Collection of Asian Art, Gift of Harry G. C. Packard, and Purchase, Fletcher, Rogers, Harris Brisbane Dick, and Louis V. Bell Funds, Joseph Pulitzer Bequest, and The Annenberg Fund Inc. Gift, 1975",,,,,,,,,,,,Costumes,,http://www.metmuseum.org/art/collection/search/44864,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"JIB121a, b",false,true,57813,Asian Art,Illustrated book,唐土訓蒙図彙|Illustrated Encyclopedia of China (Morokoshi kinmō zui),Japan,Edo period (1615–1868),,,,Artist,,Tachibana Morikuni,"Japanese, 1679–1748",,Tachibana Morikuni,Japanese,1679,1748,1719; preface dated 1718,1718,1719,Set of two woodblock printed books; ink on paper,Overall (vol. 1): 11 1/16 × 10 3/16 × 9/16 in. (28.1 × 25.8 × 1.5 cm) Overall (vol. 2): 11 1/16 × 10 1/8 × 13/16 in. (28.1 × 25.7 × 2 cm),"The Howard Mansfield Collection, Gift of Howard Mansfield, 1936",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/57813,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.785a–c,false,true,78687,Asian Art,Illustrated books,,Japan,Edo period (1615–1868),,,,Artist,,Tachibana Morikuni,"Japanese, 1679–1748",,Tachibana Morikuni,Japanese,1679,1748,1749,1749,1749,Set of three woodblock printed books; ink on paper,each: 10 1/16 × 7 1/16 in. (25.5 × 18 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78687,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2014.768.2,false,true,77197,Asian Art,Hanging scroll,大字「悳」|Virtue,Japan,Edo period (1615–1868),,,,Artist,,Hakuin Ekaku,"Japanese, 1685–1768",,Hakuin Ekaku,Japanese,1685,1768,mid-18th century,1734,1766,Hanging scroll; ink on paper,Image: 41 3/4 × 20 1/2 in. (106 × 52 cm) Overall with mounting: 73 1/8 × 25 3/16 in. (185.8 × 64 cm) Overall with knobs: 73 1/8 × 27 3/8 in. (185.8 × 69.5 cm),"Fishbein-Bender Collection, Gift of T. Richard Fishbein and Estelle P. Bender, 2014",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/77197,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.883a–d,false,true,78773,Asian Art,Illustrated books,,Japan,Edo period (1615–1868),,,,Artist,,Tsukioka Settei,"Japanese, 1710–1786",,Tsukioka Settei,Japanese,1710,1786,1764,1764,1764,Set of four woodblock printed books; ink and color (vol. 5 only) on paper,10 5/8 × 7 1/2 in. (27 × 19 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78773,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.803,false,true,78693,Asian Art,Illustrated books,,Japan,Edo period (1615–1868),,,,Artist,,Toriyama Sekien,"Japanese, 1712–1788",,Toriyama Sekien,Japanese,1712,1788,1781,1781,1781,Set of three woodblock printed books bound as one volume; ink on paper,8 7/8 × 6 5/16 in. (22.5 × 16 cm),"Purchase, Mary and James G. Wallach Family Foundation Gift, in honor of John T. Carpenter, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78693,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.814a–c,false,true,78704,Asian Art,Illustrated books,,Japan,Edo period (1615–1868),,,,Artist,,Sō Shiseki,"Japanese, 1715–1786",,Sō Shiseki,Japanese,1715,1786,1764,1764,1764,"Set of three woodblock printed books; ink, color, and hand-coloring (vol. 2) on paper",each: 10 1/2 × 6 3/4 in. (26.7 × 17.2 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78704,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.665,false,true,78586,Asian Art,Illustrated book,俳諧三十六歌僊|The Thirty-six Immortals of Haikai Verse (Haikai sanjūrokkasen),Japan,Edo period (1615–1868),,,,Artist,,Yosa Buson,"Japanese, 1716–1783",,Yosa Buson,Japanese,1716,1783,1799,1799,1799,Woodblock printed book; ink with hand-coloring on paper,10 5/8 × 7 1/2 in. (27 × 19 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78586,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.666,false,true,78587,Asian Art,Illustrated book,三十六歌仙|The Thirty-six Immortals of Poetry (Sanjūrokkasen),Japan,Edo period (1615–1868),,,,Artist,,Yosa Buson,"Japanese, 1716–1783",,Yosa Buson,Japanese,1716,1783,1799,1799,1799,Woodblock printed book; ink on paper,10 15/16 × 7 1/2 in. (27.8 × 19 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78587,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.751,false,true,78653,Asian Art,Illustrated books,,Japan,Edo period (1615–1868),,,,Artist,,Kanyōsai,"Japanese, 1719–1774",,Kanyōsai,Japanese,1719,1774,1762,1762,1762,Set of three woodblock printed books; ink on paper,each: 10 13/16 × 6 15/16 in. (27.5 × 17.7 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78653,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.752a–e,false,true,78654,Asian Art,Illustrated books,,Japan,Edo period (1615–1868),,,,Artist,,Kanyōsai,"Japanese, 1719–1774",,Kanyōsai,Japanese,1719,1774,1762,1762,1762,Set of five woodblock printed books; ink on paper,each: 10 3/8 × 7 1/16 in. (26.3 × 18 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78654,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.884,false,true,78774,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,ca. 1767,1761,1771,Woodblock printed book; ink and color on paper,6 1/4 × 8 5/16 in. (15.9 × 21.1 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78774,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JIB7a–c,false,true,57542,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,1788,1788,1788,Three volumes; ink on paper,Each: 9 × 6 1/4 × 1/16 in. (22.9 × 15.9 × 0.2 cm),"Rogers Fund, 1918",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/57542,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.685a–c,false,true,78606,Asian Art,Illustrated books,,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,1763,1763,1763,Set of three woodblock printed books; ink on paper,each: 8 7/8 × 6 5/16 in. (22.5 × 16 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78606,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.687a–c,false,true,78608,Asian Art,Illustrated books,,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,ca. 1767,1761,1771,Set of three woodblock printed books; ink on paper,each: 12 3/16 × 8 1/4 in. (31 × 21 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78608,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.821,false,true,78711,Asian Art,Illustrated book,『錦百人一首東織』|Eastern Brocade of One Hundred Poems by One Hundred Poets (Nishiki hyakunin isshu azuma-ori),Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,1775,1775,1775,Woodblock printed book; ink and color on paper,11 5/8 × 7 11/16 in. (29.5 × 19.5 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78711,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.823,false,true,78713,Asian Art,illustrated book,"『役者夏の富士』|Actors [Out of Costume] Like Mount Fuji [Without Snow] in Summer (Yakusha natsu no Fuji), by Ichiba Tsūshō",Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,ca. 1830,1825,1835,Woodblock printed book; ink on paper,8 9/16 × 6 1/8 in. (21.8 × 15.5 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78713,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1994.566,false,true,40022,Asian Art,Screen,近江八景|Eight Views of Ōmi (Ōmi hakkei),Japan,Edo period (1615–1868),,,,Artist,After,Soga Shōhaku,"Japanese, 1730–1781",,Soga Shōhaku,Japanese,1730,1781,Probably late 18th or early 19th century,1700,1833,Six-panel folding screen; ink and paper,Image: 46 in. x 13 ft. (116.8 x 396.2 cm),"Gift of William and Marjorie Normand, 1994",,,,,,,,,,,,Screens,,http://www.metmuseum.org/art/collection/search/40022,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -57.156.1,false,true,45422,Asian Art,Screen,,Japan,Edo period (1615–1868),,,,Artist,,Maruyama Ōkyo,"Japanese, 1733–1795",,Maruyama Ōkyo,Japanese,1733,1795,dated 1773,1773,1773,Two-panel folding screen; ink and color on paper,Image: 22 x 53 3/8 in. (55.9 x 135.6 cm) Overall: 29 1/4 x 74 in. (74.3 x 188 cm),"Seymour Fund, 1957",,,,,,,,,,,,Screens,,http://www.metmuseum.org/art/collection/search/45422,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.792,false,true,78808,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,After,Maruyama Ōkyo,"Japanese, 1733–1795",,Maruyama Ōkyo,Japanese,1733,1795,1850,1850,1850,"Woodblock printed book (orihon, accordion-style); ink and color on paper",11 3/16 × 7 9/16 in. (28.4 × 19.2 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78808,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2013.791a, b",false,true,78807,Asian Art,Illustrated books,,Japan,Edo period (1615–1868),,,,Artist,After,Maruyama Ōkyo,"Japanese, 1733–1795",,Maruyama Ōkyo,Japanese,1733,1795,1837,1837,1837,Set of two woodblock printed books; ink and color on paper,each: 10 1/2 × 7 5/16 in. (26.7 × 18.5 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78807,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.654,false,true,78575,Asian Art,Illustrated book,九老画譜|Kyūrō (Baitei) Picture Album (Kyūrō gafu),Japan,Edo period (1615–1868),,,,Artist,,Ki Baitei,"Japanese, 1734–1810",,Ki Baitei,Japanese,1734,1810,preface and postscript dated 1797,1797,1797,Woodblock printed book; ink on paper,10 3/16 × 7 1/16 in. (25.8 × 18 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78575,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.807,false,true,78697,Asian Art,Illustrated book,閨暦大雑書玉門大成|Erotica; Compendium Guide to the Brothels of Osaka (Keiryaku ōzassho gyokumon taisei),Japan,Edo period (1615–1868),,,,Artist,,Kitao Shigemasa,"Japanese, 1739–1820",,Kitao Shigemasa,Japanese,1739,1820,ca. 1770,1765,1775,Woodblock printed book; ink and color on paper,10 9/16 × 7 5/16 in. (26.8 × 18.5 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78697,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.808,false,true,78698,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Kitao Shigemasa,"Japanese, 1739–1820",,Kitao Shigemasa,Japanese,1739,1820,1787,1787,1787,Woodblock printed book; ink and color on paper,10 7/16 × 7 1/16 in. (26.5 × 18 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78698,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.809a–c,false,true,78699,Asian Art,Illustrated books,,Japan,Edo period (1615–1868),,,,Artist,,Kitao Shigemasa,"Japanese, 1739–1820",,Kitao Shigemasa,Japanese,1739,1820,1802,1802,1802,Set of three woodblock printed books; ink and color on paper,each: 8 7/8 × 6 1/8 in. (22.5 × 15.5 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78699,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.810a–c,false,true,78700,Asian Art,Illustrated books,,Japan,Edo period (1615–1868),,,,Artist,,Kitao Shigemasa,"Japanese, 1739–1820",", et al",Kitao Shigemasa,Japanese,1739,1820,1805,1805,1805,Set of three woodblock printed books; ink on paper,each: 8 7/8 × 6 1/4 in. (22.5 × 15.8 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78700,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.820a–d,false,true,78710,Asian Art,Illustrated books,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunkō,"Japanese, 1743–1812",,Katsukawa Shunkō,Japanese,1743,1812,1847,1847,1847,Set of four woodblock printed books; ink and color on paper,9 1/16 × 6 5/16 in. (23 × 16 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78710,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.340a–f,false,true,73593,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Shiba Kōkan,"Japanese, 1747–1818",,Shiba Kōkan,Japanese,1747,1818,1803,1803,1803,Five volumes of woodblock printed books; ink on paper,Overall (each volume): 10 1/8 x 7 1/8 x 1/4 in. (25.7 x 18.1 x 0.6 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/73593,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.679,false,true,78600,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Fuyō,"Japanese, 1749–1816",,Suzuki Fuyō,Japanese,1749,1816,1809,1809,1809,Set of three woodblock printed books bound as a single volume; ink on paper,10 13/16 × 7 5/16 in. (27.5 × 18.5 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78600,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JIB6,false,true,57541,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,ca. 1790,1780,1800,Ink and color on paper,10 × 7 1/2 × 3/8 in. (25.4 × 19.1 × 1 cm),"Rogers Fund, 1918",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/57541,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"JIB22a, b",false,true,45271,Asian Art,Illustrated book,Ehon monomi ga oka|Watchtower Hill,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,1785,1600,1868,Two volumes; polychrome woodblock printed book; ink on paper,Each: 8 3/4 × 6 3/8 × 1/4 in. (22.2 × 16.2 × 0.6 cm),"Rogers Fund, 1918",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/45271,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.656,false,true,78577,Asian Art,Illustrated book,胸中山|Mountains of the Heart (Kyōchūzan),Japan,Edo period (1615–1868),,,,Artist,,Kameda Bōsai,"Japanese, 1752–1826",,Kameda Bōsai,Japanese,1752,1826,1816,1816,1816,Woodblock printed book; ink and color on paper,10 15/16 × 7 3/8 in. (27.8 × 18.7 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78577,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.657,false,true,78578,Asian Art,Illustrated book,胸中山|Mountains of the Heart (Kyōchūzan),Japan,Edo period (1615–1868),,,,Artist,,Kameda Bōsai,"Japanese, 1752–1826",,Kameda Bōsai,Japanese,1752,1826,1816,1816,1816,Woodblock printed book; ink and color on paper,10 1/4 × 6 13/16 in. (26 × 17.3 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78578,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"1975.268.75, .76",false,true,39658,Asian Art,Screen,山水唐人物図屏風|Landscapes with the Chinese Literati Su Shi and Tao Qian,Japan,Edo period (1615–1868),,,,Artist,,Nagasawa Rosetsu,"Japanese, 1754–1799",,Nagasawa Rosetsu,Japanese,1754,1799,1795–99,1795,1799,Pair of six-panel folding screens; ink on gold leaf on paper,Image (each screen): 67 3/8 in. x 12 ft. 2 3/4 in. (171.1 x 372.7 cm),"The Harry G. C. Packard Collection of Asian Art, Gift of Harry G. C. Packard, and Purchase, Fletcher, Rogers, Harris Brisbane Dick, and Louis V. Bell Funds, Joseph Pulitzer Bequest, and The Annenberg Fund Inc. Gift, 1975",,,,,,,,,,,,Screens,,http://www.metmuseum.org/art/collection/search/39658,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.828,false,true,78718,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Yamaguchi Soken,"Japanese, 1759–1818",,Yamaguchi Soken,Japanese,1759,1818,1807,1807,1807,Woodblock printed book; ink on paper,Other: 10 3/8 × 7 3/8 in. (26.3 × 18.8 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78718,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2013.829a, b",false,true,78719,Asian Art,Illustrated books,,Japan,Edo period (1615–1868),,,,Artist,,Yamaguchi Soken,"Japanese, 1759–1818",,Yamaguchi Soken,Japanese,1759,1818,1818,1818,1818,Set of two woodblock printed books; ink and hand-coloring (vol. 2) on paper,each: 10 1/16 × 7 3/16 in. (25.5 × 18.2 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78719,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JIB91,false,true,45278,Asian Art,Illustrated book,「吉原傾城」新美人合自筆鏡|Yoshiwara Courtesans: A New Mirror Comparing the Calligraphy of Beauties (Yoshiwara keisei: Shin bijin awase jihitsu kagami),Japan,Edo period (1615–1868),,,,Artist,,Kitao Masanobu (Santō Kyōden),"Japanese, 1761–1816",,Kitao Masanobu (Santō Kyōden),Japanese,1761,1816,"1784, first month",1784,1784,Polychrome woodblock printed book; ink and color on paper,Overall: 14 7/8 × 10 3/16 in. (37.8 × 25.8 cm),"The Howard Mansfield Collection, Gift of Howard Mansfield, 1936",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/45278,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JIB101,false,true,57786,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Kitao Masanobu (Santō Kyōden),"Japanese, 1761–1816",,Kitao Masanobu (Santō Kyōden),Japanese,1761,1816,1844,1844,1844,Two volumes; ink on paper,Each: 11 1/8 × 7 3/4 × 3/8 in. (28.3 × 19.7 × 1 cm),"The Howard Mansfield Collection, Gift of Howard Mansfield, 1936",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/57786,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.768,false,true,78670,Asian Art,Illustrated book,『吉原傾城新美人自筆鏡』|New Mirror Comparing the Handwriting of the Courtesans of the Yoshiwara (Yoshiwara keisei shin bijin jihitsu kagami),Japan,Edo period (1615–1868),,,,Artist,,Kitao Masanobu (Santō Kyōden),"Japanese, 1761–1816",,Kitao Masanobu (Santō Kyōden),Japanese,1761,1816,1784,1784,1784,Woodblock printed book; ink and color on paper,14 15/16 × 10 1/4 in. (38 × 26 cm),"Purchase, Mary and James G. Wallach Family Foundation Gift, in honor of John T. Carpenter, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78670,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -57.156.3,false,true,45392,Asian Art,Folding screen,酒井抱一筆　柿図屏風|The Persimmon Tree,Japan,Edo period (1615–1868),,,,Artist,,Sakai Hōitsu,"Japanese, 1761–1828",,Sakai Hōitsu,Japanese,1761,1828,1816,1816,1816,Two-panel folding screen; ink and color on paper,Image: 56 9/16 x 56 5/8 in. (143.7 x 143.8 cm) Overall: 65 1/4 x 64 in. (165.7 x 162.6 cm),"Rogers Fund, 1957",,,,,,,,,,,,Screens,,http://www.metmuseum.org/art/collection/search/45392,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JIB57,false,true,57657,Asian Art,Illustrated book,酒井抱一画　『乾山遺墨』|Ink Traces of Kenzan (Kenzan iboku),Japan,Edo period (1615–1868),,,,Artist,,Sakai Hōitsu,"Japanese, 1761–1828",,Sakai Hōitsu,Japanese,1761,1828,1823,1823,1823,Woodblock printed book; ink and color on paper,Image: 9 3/4 x 6 15/16 x 1/4 in. (24.8 x 17.7 x 0.7 cm) Overall (open): 9 3/4 x 13 in. (24.8 x 33 cm),"Rogers Fund, 1918",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/57657,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JIB58,false,true,57658,Asian Art,Illustrated book,酒井抱一 画 『鶯邨画譜』|Ōson (Hōitsu) Picture Album (Ōson gafu),Japan,Edo period (1615–1868),,,,Artist,,Sakai Hōitsu,"Japanese, 1761–1828",,Sakai Hōitsu,Japanese,1761,1828,1817,1817,1817,Woodblock printed book; ink and color on paper,Image: 10 7/8 x 7 3/8 x 1/2 in. (27.7 x 18.8 x 1.2 cm) Overall: 14 in. (35.5 cm) (open),"Rogers Fund, 1918",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/57658,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JIB95,false,true,57776,Asian Art,Illustrated book,乾山遺墨|Ink Traces of Kenzan (Kenzan iboku),Japan,Edo period (1615–1868),,,,Artist,,Sakai Hōitsu,"Japanese, 1761–1828",,Sakai Hōitsu,Japanese,1761,1828,1823,1823,1823,Woodblock printed book; ink and color on paper,Image: 9 5/8 x 6 7/8 x 1/4 in. (24.4 x 17.5 x 0.7 cm) Overall (Open): 9 5/8 x 13 in. (24.4 x 33 cm),"The Howard Mansfield Collection, Gift of Howard Mansfield, 1936",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/57776,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JIB116,false,true,57809,Asian Art,Illustrated book,池田孤邨画　『抱一上人真蹟鏡』|Ōson (Hōitsu) Picture Album (Ōson gafu),Japan,Edo period (1615–1868),,,,Artist,,Sakai Hōitsu,"Japanese, 1761–1828",,Sakai Hōitsu,Japanese,1761,1828,1817,1817,1817,Woodblock printed book; ink and color on paper,Image: 10 15/16 x 7 3/8 x 1/16 in. (27.8 x 18.8 x 0.1 cm) Overall (Open): 10 15/16 x 13 7/8 in. (27.8 x 35.3 cm),"The Howard Mansfield Collection, Gift of Howard Mansfield, 1936",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/57809,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JIB37,false,true,45272,Asian Art,Illustrated book,Edo hakkei|Eight Views of Edo,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Toyohiro,"Japanese, 1763–1828",,Utagawa Toyohiro,Japanese,1763,1828,ca. 1771,1761,1781,Bound book of polychrome woodblock prints; ink and color on paper,Overall: 9 3/4 x 14 7/8 in. (24.8 x 37.8 cm),"Rogers Fund, 1918",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/45272,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.664,false,true,78585,Asian Art,Illustrated book,『寫山樓畫本』|Shazanrō (Bunchō) Picture Book (Shazanrō ehon),Japan,Edo period (1615–1868),,,,Artist,,Tani Bunchō,"Japanese, 1763–1840",,Tani Bunchō,Japanese,1763,1840,1817,1817,1817,Woodblock-printed book; ink and color on paper,10 13/16 × 7 3/8 in. (27.5 × 18.8 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78585,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.776a–d,false,true,78678,Asian Art,Illustrated books,,Japan,Edo period (1615–1868),,,,Artist,,Kuwagata Keisai,"Japanese, 1764–1824",,Kuwagata Keisai,Japanese,1764,1824,ca. 1800,1790,1810,Four woodblocks for printed books,framed each: 13 3/4 × 9 1/16 in. (35 × 23 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Woodblocks,,http://www.metmuseum.org/art/collection/search/78678,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JIB21,false,true,57554,Asian Art,Illustrated book,略画式 (人物)|Abbreviated Drawing Styles (Ryakuga shiki)(Figures),Japan,Edo period (1615–1868),,,,Artist,,Kuwagata Keisai,"Japanese, 1764–1824",,Kuwagata Keisai,Japanese,1764,1824,"1795, twelfth month",1795,1795,Woodblock printed book; ink and color on paper,Overall: 9 3/4 × 6 15/16 in. (24.7 × 17.6 cm),"Rogers Fund, 1923",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/57554,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JIB55,false,true,57655,Asian Art,Illustrated book,鳥獣略画式|Abbreviated Drawing Styles for Birds and Animals (Chōjū ryakuga shiki),Japan,Edo period (1615–1868),,,,Artist,,Kuwagata Keisai,"Japanese, 1764–1824",,Kuwagata Keisai,Japanese,1764,1824,1797,1797,1797,Woodblock printed book; ink and color on paper,Overall: 10 1/8 × 7 in. (25.7 × 17.8 cm),"Rogers Fund, 1918",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/57655,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JIB56,false,true,57656,Asian Art,Illustrated book,人物略画式|Abbreviated Drawing Styles for Figures (Jinbutsu ryakuga shiki),Japan,Edo period (1615–1868),,,,Artist,,Kuwagata Keisai,"Japanese, 1764–1824",,Kuwagata Keisai,Japanese,1764,1824,1795,1795,1795,Woodblock printed book; ink and color on paper,Overall: 11 × 7 3/8 in. (28 × 18.8 cm),"Rogers Fund, 1918",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/57656,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.770,false,true,78672,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Kuwagata Keisai,"Japanese, 1764–1824",,Kuwagata Keisai,Japanese,1764,1824,1795,1795,1795,Woodblock printed book; ink and color on paper,10 9/16 × 7 5/16 in. (26.8 × 18.5 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78672,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.771,false,true,78673,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Kuwagata Keisai,"Japanese, 1764–1824",,Kuwagata Keisai,Japanese,1764,1824,1797,1797,1797,Woodblock printed book; ink and color on paper,10 7/16 × 7 3/16 in. (26.5 × 18.2 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78673,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.772,false,true,78674,Asian Art,Illustrated books,,Japan,Edo period (1615–1868),,,,Artist,,Kuwagata Keisai,"Japanese, 1764–1824",,Kuwagata Keisai,Japanese,1764,1824,1799,1799,1799,Set of three woodblock printed books; ink and color on paper,10 1/2 × 7 3/16 in. (26.7 × 18.3 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78674,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.773,false,true,78675,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Kuwagata Keisai,"Japanese, 1764–1824",,Kuwagata Keisai,Japanese,1764,1824,dated 1812,1812,1812,Woodblock printed book; ink and color on paper,9 3/16 × 6 9/16 in. (23.3 × 16.6 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78675,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.774,false,true,78676,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Kuwagata Keisai,"Japanese, 1764–1824",,Kuwagata Keisai,Japanese,1764,1824,1800,1800,1800,Woodblock printed book; ink and color on paper,10 3/8 × 7 5/16 in. (26.4 × 18.5 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78676,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.775,false,true,78677,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Kuwagata Keisai,"Japanese, 1764–1824",,Kuwagata Keisai,Japanese,1764,1824,1813,1813,1813,Woodblock printed book; ink and color on paper,10 5/8 × 7 1/16 in. (27 × 18 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78677,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.777,false,true,78679,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Kuwagata Keisai,"Japanese, 1764–1824",,Kuwagata Keisai,Japanese,1764,1824,1802,1802,1802,"Woodblock printed book; ink, color and mica on paper",10 7/16 × 7 1/16 in. (26.5 × 18 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78679,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.779,false,true,78681,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Kuwagata Keisai,"Japanese, 1764–1824",,Kuwagata Keisai,Japanese,1764,1824,1823,1823,1823,Woodblock printed book; ink and color on paper,10 11/16 × 7 3/16 in. (27.2 × 18.3 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78681,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.880,false,true,78770,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Kuwagata Keisai,"Japanese, 1764–1824",,Kuwagata Keisai,Japanese,1764,1824,ca. 1815–42,1810,1847,Woodblock printed book; ink and color on paper,9 × 6 5/16 in. (22.9 × 16 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78770,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.812a–c,false,true,78702,Asian Art,Illustrated books,,Japan,Edo period (1615–1868),,,,Artist,,Shakuyakutei Nagane,"Japanese, 1767–1845",,Shakuyakutei Nagane,Japanese,1767,1845,1834,1834,1834,Set of three woodblock printed books; ink and color on paper,each: 9 × 6 3/8 in. (22.8 × 16.2 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78702,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JIB38,false,true,45228,Asian Art,Illustrated book,"『俳優三階興』|Amusements of Kabuki Actors of the “Third Floor” [Dressing Room] (Yakusha sangaikyō), by Shikitei Sanba",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Toyokuni I,"Japanese, 1769–1825",,Utagawa Toyokuni I,Japanese,1769,1825,1801,1801,1801,Set of polychrome woodblock-printed books; ink and color on paper,8 1/2 × 6 in. (21.6 × 15.2 cm),"Rogers Fund, 1918",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/45228,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.848,false,true,78738,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,Attributed to,Utagawa Toyokuni I,"Japanese, 1769–1825",,Utagawa Toyokuni I,Japanese,1769,1825,1798,1798,1798,Woodblock printed book; ink and color on paper,5 × 6 7/8 in. (12.7 × 17.5 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78738,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.877,false,true,78767,Asian Art,Illustrated book,"『俳優三階興』|Amusements of Kabuki Actors of the “Third Floor” [Dressing Room] (Yakusha sangaikyō), by Shikitei Sanba",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Toyokuni I,"Japanese, 1769–1825",,Utagawa Toyokuni I,Japanese,1769,1825,1801,1801,1801,First volume of a two-volume set of woodblock-printed books; ink and color on paper,8 7/16 × 6 1/8 in. (21.5 × 15.5 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78767,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"JIB39a, b",false,true,57566,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Toyokuni I,"Japanese, 1769–1825",,Utagawa Toyokuni I,Japanese,1769,1825,1802,1802,1802,Two volumes; polychrome woodblock printed book; ink and color on paper,Each: 8 1/2 × 6 1/16 × 1/2 in. (21.6 × 15.4 × 1.3 cm),"Rogers Fund, 1918",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/57566,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2013.849a, b",false,true,78739,Asian Art,Illustrated books,『絵本時世粧』|Picture Book of Modern Figures of Fashion (Ehon imayō sugata),Japan,Edo period (1615–1868),,,,Artist,,Utagawa Toyokuni I,"Japanese, 1769–1825",,Utagawa Toyokuni I,Japanese,1769,1825,1802,1802,1802,Set of two woodblock-printed books with hand-written names in volume two; ink and color on paper,each: 8 9/16 × 6 1/8 in. (21.8 × 15.5 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78739,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2013.850a, b",false,true,78740,Asian Art,illustrated books,『役者相貌鏡』|Mirror Images of Kabuki Actors (Yakusha awase kagami),Japan,Edo period (1615–1868),,,,Artist,,Utagawa Toyokuni I,"Japanese, 1769–1825",,Utagawa Toyokuni I,Japanese,1769,1825,1804,1804,1804,Set of two woodblock printed books; ink and color on paper,each: 10 7/16 × 7 1/16 in. (26.5 × 18 cm),"Purchase, Mary and James G. Wallach Family Foundation Gift, in honor of John T. Carpenter, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78740,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.709,false,true,78630,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Teisai Hokuba,"Japanese, 1771–1844",,Teisai Hokuba,Japanese,1771,1844,1815,1815,1815,Woodblock printed book; ink on paper,8 11/16 × 6 1/8 in. (22 × 15.6 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78630,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.682,false,true,78603,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Chō Gesshō,"Japanese, 1772–1832",,Chō Gesshō,Japanese,1772,1832,1817,1817,1817,Woodblock printed book; ink and color on paper,10 5/16 × 7 5/16 in. (26.2 × 18.5 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78603,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.788a–d,false,true,78690,Asian Art,Illustrated books,,Japan,Edo period (1615–1868),,,,Artist,,Nishimura Nantei,"Japanese, 1775–1834",,Nishimura Nantei,Japanese,1775,1834,1823,1823,1823,"Set of three woodblock printed books; ink on paper and ink and color on paper (vol. ""kan"")","vol. ""plum"" each: 10 1/16 × 7 5/16 in. (25.5 × 18.5 cm) vol. ""kan"": 10 5/16 × 7 3/16 in. (26.2 × 18.2 cm)","Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78690,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2013.750a, b",false,true,78652,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Okada Kanrin,"Japanese, 1775–1849",,Okada Kanrin,Japanese,1775,1849,1845,1845,1845,Set of two woodblock printed books; ink and color on paper,each: 10 7/8 × 7 3/8 in. (27.7 × 18.7 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78652,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.668,false,true,78589,Asian Art,Illustrated books,"竹洞四君子|The Four Worthies (Plum, Bamboo, Orchid, Chrysanthemum) (Shikunshi)",Japan,Edo period (1615–1868),,,,Artist,,Nakabayashi Chikutō,"Japanese, 1776–1853",,Nakabayashi Chikutō,Japanese,1776,1853,1853,1853,1853,Set of two woodblock printed books bound as one; ink on paper,10 9/16 × 6 7/8 in. (26.8 × 17.5 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78589,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.755,false,true,78657,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Kawamura Kihō,"Japanese, 1778–1852",,Kawamura Kihō,Japanese,1778,1852,after 1825,1825,1850,Woodblock printed book; ink and color on paper,10 3/16 × 7 1/8 in. (25.8 × 18.1 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78657,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.756,false,true,78658,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Kawamura Kihō,"Japanese, 1778–1852",,Kawamura Kihō,Japanese,1778,1852,1827,1827,1827,Woodblock printed book; ink and color on paper,10 3/8 × 7 1/16 in. (26.3 × 18 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78658,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.660,false,true,78581,Asian Art,Illustrated book,金波園画譜|Kinpaen (Bunpō) Picture Album (Kinpaen gafu),Japan,Edo period (1615–1868),,,,Artist,,Kawamura Bunpō,"Japanese, 1779–1821",,Kawamura Bunpō,Japanese,1779,1821,1820,1820,1820,Woodblock printed book; ink and color on paper,10 7/16 × 4 1/2 in. (26.5 × 11.5 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78581,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.661,false,true,78582,Asian Art,Illustrated book,金波園画譜|Kinpaen (Bunpō) Picture Album (Kinpaen gafu),Japan,Edo period (1615–1868),,,,Artist,,Kawamura Bunpō,"Japanese, 1779–1821",,Kawamura Bunpō,Japanese,1779,1821,1820,1820,1820,Woodblock printed book; ink and color on paper,10 7/16 × 6 11/16 in. (26.5 × 17 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78582,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.662,false,true,78583,Asian Art,Illustrated book,文鳳山水画譜|Bunpō Landscape Picture Album (Bunpō sansui gafu),Japan,Edo period (1615–1868),,,,Artist,,Kawamura Bunpō,"Japanese, 1779–1821",,Kawamura Bunpō,Japanese,1779,1821,1824,1824,1824,Woodblock printed book; ink and color on paper,10 3/16 × 6 13/16 in. (25.8 × 17.3 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78583,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.658a–c,false,true,78579,Asian Art,Illustrated books,"文鳳画譜|Bunpō Picture Album (Bunpō gafu), First Series",Japan,Edo period (1615–1868),,,,Artist,,Kawamura Bunpō,"Japanese, 1779–1821",,Kawamura Bunpō,Japanese,1779,1821,1807,1807,1807,Set of three woodblock printed books; ink and color on paper,each: 10 5/16 × 7 1/16 in. (26.2 × 18 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78579,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.663a–c,false,true,78584,Asian Art,Illustrated book,"漢画指南二編|Guide to Chinese Painting (Kanga shinan nihen), Second Series",Japan,Edo period (1615–1868),,,,Artist,,Kawamura Bunpō,"Japanese, 1779–1821",,Kawamura Bunpō,Japanese,1779,1821,1811,1811,1811,Set of three woodblock printed books; ink and color on paper,each: 10 7/16 × 7 in. (26.5 × 17.8 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78584,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.830,false,true,78720,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Komatsubara Suikei,"Japanese, 1780–1833",,Komatsubara Suikei,Japanese,1780,1833,ca. 1831,1826,1836,"Woodblock printed book; ink, color, and metallic pigments on paper",8 7/8 × 6 5/16 in. (22.5 × 16 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78720,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JIB9,false,true,57543,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Totoya Hokkei,"Japanese, 1780–1850",,Totoya Hokkei,Japanese,1780,1850,1833,1833,1833,Ink and color on paper,10 × 6 7/8 × 1/2 in. (25.4 × 17.5 × 1.3 cm),"Rogers Fund, 1918",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/57543,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.702,false,true,78623,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Totoya Hokkei,"Japanese, 1780–1850",,Totoya Hokkei,Japanese,1780,1850,1824,1824,1824,Woodblock printed book; ink and color on paper,8 15/16 × 6 5/16 in. (22.7 × 16 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78623,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.703,false,true,78624,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Totoya Hokkei,"Japanese, 1780–1850",(et al),Totoya Hokkei,Japanese,1780,1850,1826,1826,1826,Woodblock printed book; ink and color on paper,9 × 6 5/8 in. (22.8 × 16.8 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78624,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.704,false,true,78625,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Totoya Hokkei,"Japanese, 1780–1850",,Totoya Hokkei,Japanese,1780,1850,1829,1829,1829,Woodblock printed book; ink and color on paper,9 1/16 × 6 13/16 in. (23 × 17.3 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78625,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.705,false,true,78626,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Totoya Hokkei,"Japanese, 1780–1850",,Totoya Hokkei,Japanese,1780,1850,1830,1830,1830,Woodblock printed book; ink and color on paper,9 × 6 5/16 in. (22.8 × 16 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78626,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.706,false,true,78627,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Totoya Hokkei,"Japanese, 1780–1850",,Totoya Hokkei,Japanese,1780,1850,1830s,1830,1839,Woodblock printed book; ink and color on paper,9 × 6 1/2 in. (22.8 × 16.5 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78627,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.708,false,true,78629,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Totoya Hokkei,"Japanese, 1780–1850",,Totoya Hokkei,Japanese,1780,1850,1845,1845,1845,Woodblock printed book; ink and color on paper,8 1/4 × 6 in. (21 × 15.3 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78629,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JIB8a–c,false,true,58813,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Totoya Hokkei,"Japanese, 1780–1850",,Totoya Hokkei,Japanese,1780,1850,1828–30,1828,1830,Three volumes; ink and color on paper,Each: 8 7/8 × 6 1/4 × 3/8 in. (22.5 × 15.9 × 1 cm),"Rogers Fund, 1918",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/58813,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JIB1,false,true,57536,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,1830,1830,1830,Ink and color on paper,9 × 12 1/4 × 1/2 in. (22.9 × 31.1 × 1.3 cm),"Rogers Fund, 1918",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/57536,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.759,false,true,78661,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,1837,1837,1837,Woodblock printed book; ink and color on paper,9 15/16 × 6 7/8 in. (25.2 × 17.5 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78661,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.760,false,true,78662,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,1840s,1840,1849,Woodblock printed book; ink and color on paper,9 13/16 × 6 3/4 in. (25 × 17.2 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78662,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.758a–c,false,true,78660,Asian Art,Illustrated books,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,1830s,1830,1839,Set of three woodblock printed books; ink and color on paper,each: 10 1/16 × 7 5/16 in. (25.5 × 18.5 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78660,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.762a–c,false,true,78664,Asian Art,Illustrated books,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,ca. 1850,1845,1855,Set of three woodblock printed books; ink and color on paper,each: 9 1/8 × 6 5/16 in. (23.2 × 16 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78664,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.813,false,true,78703,Asian Art,Illustrated books,,Japan,Edo period (1615–1868),,,,Artist,,Yanagawa Shigenobu,"Japanese, 1787–1832",,Yanagawa Shigenobu,Japanese,1787,1832,1839,1839,1839,Set of three woodblock printed books; ink and color on paper,each: 9 1/16 × 6 5/16 in. (23 × 16 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78703,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2013.811a, b",false,true,78701,Asian Art,Illustrated books,,Japan,Edo period (1615–1868),,,,Artist,,Yanagawa Shigenobu,"Japanese, 1787–1832",,Yanagawa Shigenobu,Japanese,1787,1832,1823,1823,1823,Set of two woodblock printed books; ink and color on paper,each: 8 7/8 × 6 1/4 in. (22.6 × 15.8 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78701,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.802,false,true,78818,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Katsura Seiyō,"Japanese, 1787–1860",,Katsura Seiyō,Japanese,1787,1860,1831,1831,1831,Woodblock printed book; ink and color on paper,Other: 9 × 6 1/2 in. (22.8 × 16.5 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78818,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.676,false,true,78597,Asian Art,Illustrated book,艶本 婦慈のゆき|An Erotic Picture Book of Snow on Fuji (Enpon fuji no yuki),Japan,Edo period (1615–1868),,,,Artist,,Keisai Eisen,"Japanese, 1790–1848",,Keisai Eisen,Japanese,1790,1848,1824,1824,1824,Woodblock printed book; ink and color on paper,8 3/4 × 6 1/4 in. (22.2 × 15.9 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78597,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.675a–c,false,true,78596,Asian Art,Illustrated books,浮世画譜|Picture Album of the Floating World (Ukiyo efu),Japan,Edo period (1615–1868),,,,Artist,,Keisai Eisen,"Japanese, 1790–1848",,Keisai Eisen,Japanese,1790,1848,ca. 1820s,1820,1829,Set of three woodblock printed books; ink and color on paper,each: 9 1/16 × 6 1/4 in. (23 × 15.9 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78596,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.677a–c,false,true,78598,Asian Art,Illustrated books,,Japan,Edo period (1615–1868),,,,Artist,,Keisai Eisen,"Japanese, 1790–1848",,Keisai Eisen,Japanese,1790,1848,1822,1822,1822,Set of three woodblock printed books; ink and color on paper,each: 8 7/8 × 6 5/16 in. (22.5 × 16.1 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78598,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.780,false,true,78682,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Ōishi Matora,"Japanese, 1793–1833",,Ōishi Matora,Japanese,1793,1833,1829,1829,1829,Woodblock printed book; ink and color on paper,9 × 6 3/16 in. (22.8 × 15.7 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78682,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2013.650a, b",false,true,78571,Asian Art,Illustrated books,持子鼠花山姥|Stories of a Fortunate Rat (Komochi nezumi hana no yamauba),Japan,Edo period (1615–1868),,,,Artist,,Akatsuki no Kanenari,"Japanese, 1793–1861",,Akatsuki no Kanenari,Japanese,1793,1861,1827,1827,1827,Set of two woodblock printed books; ink and color on paper,each: 8 3/4 × 6 in. (22.2 × 15.2 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78571,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.671,false,true,78592,Asian Art,Illustrated book,椿山翁畫譜|Chinzan Picture Album (Chinzan-ō gafu),Japan,Edo period (1615–1868),,,,Artist,,Tsubaki Chinzan,"Japanese, 1801–1854",,Tsubaki Chinzan,Japanese,1801,1854,1851,1851,1851,Accordion album; ink and color on paper,11 1/4 × 7 1/16 in. (28.5 × 18 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78592,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JIB117,false,true,45227,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Shibata Zeshin,"Japanese, 1807–1891",", and others",Shibata Zeshin,Japanese,1807,1891,1867,1615,1868,Polychrome Woodblock printed book,,"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/45227,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.866,false,true,78756,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Shibata Zeshin,"Japanese, 1807–1891",", et al",Shibata Zeshin,Japanese,1807,1891,1867,1867,1867,Woodblock printed book; ink and color on paper,9 3/4 × 7 3/16 in. (24.8 × 18.2 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78756,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -91.1.975,false,true,59689,Asian Art,Netsuke,,Japan,Edo period (1615–1868),,,,Artist,,Mitsuhiro Ōhara,"Japanese, 1810–1875",,Mitsuhiro Ōhara,Japanese,1810,1875,mid-19th century,1825,1875,Ivory,H. 1 in. (2.5 cm); W. 1 in. (2.5 cm); D. 1 3/4 in. (4.4 cm),"Edward C. Moore Collection, Bequest of Edward C. Moore, 1891",,,,,,,,,,,,Netsuke,,http://www.metmuseum.org/art/collection/search/59689,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.845,false,true,78735,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Hine Taizan,"Japanese, 1813–1869",,Hine Taizan,Japanese,1813,1869,ca. 1850,1845,1855,Accordion album; ink on paper,8 11/16 × 6 11/16 in. (22 × 17 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78735,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.862a–c,false,true,78752,Asian Art,Illustrated books,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshimune,"Japanese, 1817–1880",,Utagawa Yoshimune,Japanese,1817,1880,ca. 1860,1855,1865,Three books (in hanshita-e form); ink and light colors on paper,each: 6 3/4 × 4 3/4 in. (17.2 × 12 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78752,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.696,false,true,78617,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige II,"Japanese, 1829–1869",,Utagawa Hiroshige II,Japanese,1829,1869,1862,1862,1862,Woodblock printed book; ink and color on paper,6 15/16 × 4 5/16 in. (17.7 × 11 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78617,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JIB143,false,true,57834,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Kawanabe Kyōsai,"Japanese, 1831–1889",,Kawanabe Kyōsai,Japanese,1831,1889,1881,1881,1881,Woodblock printed book; ink and color on paper,8 1/2 x 5 3/4 in. (21.6 x 14.6 cm),"Bequest of W. Gedney Beatty, 1941",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/57834,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"JIB104a, b",false,true,57789,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Kawanabe Kyōsai,"Japanese, 1831–1889",and many others,Kawanabe Kyōsai,Japanese,1831,1889,1814,1814,1814,Ink and color on paper,Overall: 11 x 7 1/2 x 1/4 in. (27.9 x 19.1 x 0.7 cm) Overall (open): 11 x 13 3/4 in. (27.9 x 35 cm),"The Howard Mansfield Collection, Gift of Howard Mansfield, 1936",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/57789,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JIB41,false,true,45273,Asian Art,Illustrated book,絵本吾妻遊|Ehon Azuma asobi|Picture Book of Amusements of the Eastern Capital (Ehon Azuma asobi),Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,"1790, first month",1790,1790,Woodblock printed book; ink on paper,Overall: 8 11/16 × 6 1/4 in. (22 × 15.8 cm),"Rogers Fund, 1918",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/45273,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JIB43,false,true,57568,Asian Art,Illustrated book,Ehon Momochidori Kyōka-awase|百千鳥狂歌合|Myriad Birds: Picture Book of Playful Verse (Momo chidori kyōka-awase),Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,1791,1791,1791,Woodblock printed books (vols. 1 and 2); ink and color on paper,Overall: 10 x 14 7/8 x (25.4 x 37.8 cm); L. (open) 29 3/4 in. (75.6 cm),"Rogers Fund, 1918",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/57568,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JIB45,false,true,57605,Asian Art,Illustrated book,絵本和歌夷 「龢謌夷」|Picture Book with Playful Poems for the Young God Ebisu,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,1792,1792,1792,Polychrome woodblock printed book; ink and color on paper,Overall: 10 1/16 × 7 1/2 in. (25.5 × 19 cm),"Rogers Fund, 1918",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/57605,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JIB46,false,true,45323,Asian Art,Illustrated book,"狂月坊|The Moon-Mad Monk, or Crazy Gazing at the Moon (Kyōgetsubō)",Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,"1789, eighth month",1789,1789,Polychrome woodblock printed book; ink and color on paper,Overall: 9 15/16 × 7 1/2 in. (25.2 × 19 cm),"Rogers Fund, 1918",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/45323,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JIB47,false,true,57648,Asian Art,Illustrated book,潮干のつと|Gifts from the Ebb Tide (The Shell Book) (Shiohi no tsuto),Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,1789,1789,1789,Polychrome woodblock printed book; ink and color on paper,10 1/4 x 7 1/2 in. (26 x 19.1 cm),"Rogers Fund, 1918",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/57648,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JIB48,false,true,45324,Asian Art,Illustrated book,銀世界|The Silver World (Gin sekai),Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,"1790, first month",1790,1790,Polychrome woodblock printed book; ink and color on paper,Overall: 10 1/8 × 7 1/2 in. (25.7 × 19 cm),"Rogers Fund, 1918",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/45324,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JIB49,false,true,57649,Asian Art,Illustrated book,普賢像|Statue of the Bodhisattva Fugen (Fugenzō),Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,"1790, third month",1790,1790,Polychrome woodblock printed book; ink and color on paper,Overall: 10 1/16 × 7 5/16 in. (25.6 × 18.5 cm),"Rogers Fund, 1918",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/57649,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JIB89,false,true,57755,Asian Art,Illustrated book,銀世界|The Silver World (Gin sekai),Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,preface dated 1790,1790,1790,Polychrome woodblock printed book; ink and color on paper,10 1/16 x 7 1/2 in. (25.5 x 19 cm),"The Howard Mansfield Collection, Gift of Howard Mansfield, 1936",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/57755,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JIB44a,false,true,57569,Asian Art,Illustrated book,画本虫撰|Picture Book of Crawling Creatures (The Insect Book) (Ehon mushi erami),Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,probably 1823 (later edition),1823,1823,One from a set of two polychrome woodblock printed books; ink and color on paper,Overall: 9 15/16 × 7 3/16 in. (25.2 × 18.3 cm),"Rogers Fund, 1918",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/57569,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JIB44b,false,true,57570,Asian Art,Illustrated book,画本虫撰|Picture Book of Crawling Creatures (The Insect Book) (Ehon mushi erami),Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,probably 1823 (later edition),1823,1823,One from a set of two polychrome woodblock printed books; ink and color on paper,Overall: 9 5/8 × 7 1/16 in. (24.5 × 18 cm),"Rogers Fund, 1918",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/57570,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.853,false,true,78743,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,1789,1789,1789,"Woodblock printed book; ink, color, and brass dust on paper",11 5/16 × 7 3/8 in. (28.7 × 18.7 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78743,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.858,false,true,78748,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,preface dated 1794,1794,1795,"Woodblock printed book; ink, color, and metallic pigments on paper",10 3/16 × 7 1/2 in. (25.8 × 19 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78748,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.859,false,true,78749,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,1789,1789,1789,"Woodblock printed book; ink, color, and brass dust on paper",10 1/16 × 7 1/2 in. (25.5 × 19 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78749,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.897,false,true,78787,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,probably 1789,1789,1789,"Woodblock printed book (orihon, accordion-style); ink, color, mica, and gold-leaf on paper",10 11/16 × 7 9/16 in. (27.2 × 19.2 cm),"Purchase, Mary and James G. Wallach Family Foundation Gift, in honor of John T. Carpenter, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78787,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"JIB42a, b",false,true,45322,Asian Art,Illustrated book,繪本四季花|Picture Book of Flowers of the Four Seasons (Ehon shiki no hana),Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,"1801, first month",1801,1801,Set of two polychrome woodblock printed books; ink and color on paper,Overall (each volume): 8 1/4 × 5 13/16 in. (20.9 × 14.8 cm),"Rogers Fund, 1918",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/45322,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"JIB77a, b",false,true,57675,Asian Art,Illustrated book,"『百千鳥狂歌合』|Myriad Birds:A Playful Poetry Contest (Momo chidori kyōka-awase), 2 vols.",Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,1790,1790,1790,Two volumes; woodblock printed books; ink and color on paper,Each: 10 × 7 1/2 × 1/4 in. (25.4 × 19.1 × 0.6 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/57675,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2013.852a, b",false,true,78742,Asian Art,Illustrated books,,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,1788,1788,1788,"Set of two woodblock printed books; ink, color, and mica (vol. 2) on paper",each: 9 13/16 × 7 3/16 in. (25 × 18.3 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78742,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2013.854a, b",false,true,78744,Asian Art,Illustrated books,,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,1801,1801,1801,Set of two woodblock printed books; ink and color on paper,each: 8 1/2 × 6 1/16 in. (21.6 × 15.4 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78744,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2013.856a, b",false,true,78746,Asian Art,Illustrated books,『青楼繪本年中行事』|Yoshiwara Picture Book of New Year’s Festivities (Seirō ehon nenjū gyōji),Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,1804,1804,1804,Set of two woodblock printed books; ink and color on paper,each: 9 × 6 5/16 in. (22.8 × 16 cm),"Purchase, Mary and James G. Wallach Family Foundation Gift, in honor of John T. Carpenter, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78746,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2013.857a, b",false,true,78747,Asian Art,Illustrated books,,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,1804,1804,1804,Set of two woodblock printed books; ink on paper,each: 9 × 6 5/16 in. (22.8 × 16 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78747,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.855a–c,false,true,78745,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,ca. 1803,1798,1808,Set of three woodblock printed books; ink on paper,each: 8 7/16 × 6 in. (21.5 × 15.3 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78745,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JIB3,false,true,57538,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Yashima Gakutei,"Japanese, 1786?–1868",,Yashima Gakutei,Japanese,1786,1868,1823,1823,1823,Ink and color on paper,9 × 6 1/8 × 3/8 in. (22.9 × 15.6 × 1 cm),"Rogers Fund, 1918",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/57538,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.680,false,true,78601,Asian Art,Illustrated book,一老画譜|Ichirō Picture Album (Ichirō gafu),Japan,Edo period (1615–1868),,,,Artist,,Yashima Gakutei,"Japanese, 1786?–1868",,Yashima Gakutei,Japanese,1786,1868,1823,1823,1823,Woodblock printed book; ink and color on paper,9 × 6 5/16 in. (22.8 × 16 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78601,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.686,false,true,78607,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Yashima Gakutei,"Japanese, 1786?–1868",,Yashima Gakutei,Japanese,1786,1868,ca. 1820,1815,1825,Woodblock printed book; ink and color on paper,9 × 6 3/8 in. (22.8 × 16.2 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78607,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.681a–c,false,true,78602,Asian Art,Illustrated books,,Japan,Edo period (1615–1868),,,,Artist,,Yashima Gakutei,"Japanese, 1786?–1868",,Yashima Gakutei,Japanese,1786,1868,ca. 1830,1825,1835,Set of three woodblock printed books; ink and color on paper,8 11/16 × 6 3/16 in. (22 × 15.7 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78602,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2013.742a, b",false,true,78644,Asian Art,Illustrated books,,Japan,Edo period (1615–1868),,,,Artist,,Nichōsai,"Japanese, active 1780s",,Nichōsai,Japanese,1780,1789,1803,1803,1803,"Set of two woodblock printed books; one volume ink and color on paper, other volume ink on paper",10 3/16 × 7 1/16 in. (25.8 × 17.9 cm) 9 13/16 × 7 in. (25 × 17.8 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78644,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2013.863a, b",false,true,78753,Asian Art,Illustrated books,,Japan,Edo period (1615–1868),,,,Artist,,Yoshishige,"Japanese, active 1840s",,Yoshishige,Japanese,1840,1849,1848,1848,1848,Set of two woodblock printed books; ink and color on paper,each: 9 13/16 × 6 13/16 in. (25 × 17.3 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78753,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -49.35.3,false,true,48921,Asian Art,Folding screen,波に舟図屏風|Boats upon Waves,Japan,Edo period (1615–1868),,,,Artist,Studio of,Tawaraya Sōtatsu,"Japanese, died ca. 1640",,Tawaraya Sōtatsu,Japanese,1540,1640,mid- to late 17th century,1634,1699,"Six-panel folding screen; ink, color, and gold on paper",61 1/8 x 141 3/4 in. (155.2 x 360 cm),"H. O. Havemeyer Collection, Gift of Horace Havemeyer, 1949",,,,,,,,,,,,Screens,,http://www.metmuseum.org/art/collection/search/48921,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"55.94.3, .4",false,true,45388,Asian Art,Folding screen,"俵屋宗達工房 大原御幸図屏風|Royal Visit to Ōhara, from The Tale of the Heike",Japan,Edo period (1615–1868),,,,Artist,Studio of,Tawaraya Sōtatsu,"Japanese, died ca. 1640",,Tawaraya Sōtatsu,Japanese,1540,1640,first half of the 17th century,1600,1650,Pair of six-panel folding screens; ink and color on paper,Image (each screen): 63 7/16 x 143 5/16 in. (161.1 x 364 cm),"Fletcher Fund, 1955",,,,,,,,,,,,Screens,,http://www.metmuseum.org/art/collection/search/45388,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"1975.268.60, .61",false,true,48923,Asian Art,Folding screen,月に秋草図屏風|Moon and Autumn Grasses,Japan,Edo period (1615–1868),,,,Artist,Studio of,Tawaraya Sōtatsu,"Japanese, died ca. 1640",,Tawaraya Sōtatsu,Japanese,1540,1640,mid- to late 17th century,1634,1699,"Pair of six-panel folding screens; ink, color, silver, and gold flecks on paper",Overall (each screen): 59 7/16 in. x 11 ft. 10 3/8 in. (151 x 361.6 cm),"The Harry G. C. Packard Collection of Asian Art, Gift of Harry G. C. Packard, and Purchase, Fletcher, Rogers, Harris Brisbane Dick, and Louis V. Bell Funds, Joseph Pulitzer Bequest, and The Annenberg Fund Inc. Gift, 1975",,,,,,,,,,,,Screens,,http://www.metmuseum.org/art/collection/search/48923,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.335,false,true,73588,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa (Gountei) Sadahide,"Japanese, 1807–1878/79",,Utagawa (Goutei) Sadahide,Japanese,1807,1879,"February–March, 1860",1860,1860,Polychrome woodblock prints; ink and color on paper,Image: 9 1/4 x 6 1/2 x 3/8 in. (23.5 x 16.5 x 1 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/73588,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.341a–f,false,true,73594,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,Illustrations attributed to,Utagawa (Gountei) Sadahide,"Japanese, 1807–1878/79",,Utagawa (Goutei) Sadahide,Japanese,1807,1879,1853,1853,1853,Five volumes of woodblock printed books; ink on paper,Overall (each volume): 10 x 7 1/8 x 1/4 in. (25.4 x 18.1 x 0.6 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/73594,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.82.8,false,true,58638,Asian Art,Inrō,,Japan,Edo period (1615–1868),,,,Artist,,Shiomi Masanari,"Japanese, 1647–ca. 1722",,Shiomi Masanari,Japanese,1647,1732,early 18th century,1700,1733,"Case: gold on black lacquer with mother-of-pearl inlay; Fastener (ojime): gold with design of crabs, reed, and rock; Toggle (netsuke): ivory carved in the shape of a reclining bull (signed: Ran’ichi)",H. 2 13/16 in. (7.2 cm); W. 2 3/8 in. (6 cm); D. 7/8 in. (2.2 cm),"Gift of Wilton Lloyd-Smith and his wife, Marjorie Fleming Lloyd-Smith, 1929",,,,,,,,,,,,Inrō,,http://www.metmuseum.org/art/collection/search/58638,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.67.99,false,true,58622,Asian Art,Inrō,,Japan,Edo period (1615–1868),,,,Artist,,Shiomi Masanari,"Japanese, 1647–ca. 1722",,Shiomi Masanari,Japanese,1647,1732,early 18th century,1700,1733,"Case: gold, silver, and blue lacquer on black lacquer; Fastener (ojime): pierced gold with floral design; Toggle (netsuke): crystal carved in the shape of a bucket",H. 3 1/16 in. (7.8 cm); W. 2 5/16 in. (5.9 cm); D. 1 1/16 in. (2.7 cm),"Rogers Fund, 1913",,,,,,,,,,,,Inrō,,http://www.metmuseum.org/art/collection/search/58622,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JIB92,false,true,57766,Asian Art,Illustrated book,Kotori Ruishō|A Compendium of Small Birds,Japan,Edo period (1615–1868),,,,Artist,,Nantō,active early 19th century,,Nantō,Japanese,1800,1835,1836,1836,1836,Polychrome woodblock printed book,10 1/2 × 7 3/4 × 1/4 in. (26.7 × 19.7 × 0.6 cm),"The Howard Mansfield Collection, Gift of Howard Mansfield, 1936",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/57766,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.796,false,true,78812,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Sadatoshi,"Japanese, active 1716–36",,Sadatoshi,Japanese,1716,1736,1751,1751,1751,Woodblock printed book; ink and hand-coloring (tanroku bon) on paper,10 1/4 × 7 1/16 in. (26 × 18 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78812,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.874,false,true,78764,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Mori Shunkei,"Japanese, active 1800–20",,Mori Shunkei,Japanese,1800,1820,1820,1820,1820,"Woodblock printed book (orihon, accordion-style); ink and color on paper",8 1/4 × 5 11/16 in. (21 × 14.5 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78764,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JIB60,false,true,57660,Asian Art,Illustrated book,水石画譜|Suiseki Picture Album (Suiseki gafu),Japan,Edo period (1615–1868),,,,Artist,,Satō Suiseki,"Japanese, active 1806–40",,Satō Suiseki,Japanese,1806,1840,"1814, sixth month; preface dated 1814, fourth month",1814,1814,Woodblock printed book; ink and color on paper,Overall: 10 × 6 7/8 in. (25.4 × 17.4 cm),"Rogers Fund, 1918",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/57660,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.831,false,true,78721,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Satō Suiseki,"Japanese, active 1806–40",,Satō Suiseki,Japanese,1806,1840,1820,1820,1820,Woodblock printed book; ink and color on paper,10 3/16 × 7 in. (25.8 × 17.8 cm),"Purchase, Mary and James G. Wallach Family Foundation Gift, in honor of John T. Carpenter, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78721,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.865,false,true,78755,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Yamada Yoshitsuna,"Japanese, active 1848–68",,Yamada Yoshitsuna,Japanese,1848,1868,1828,1828,1828,"Woodblock printed book (orihon, accordion-style but bound); ink and color on paper",7 3/16 × 9 1/4 in. (18.3 × 23.5 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78755,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.67.31,false,true,58573,Asian Art,Inrō,,Japan,Edo period (1615–1868),,,,Artist,,Kyūho,"Japanese, active 1789–1801",,Kyūho,Japanese,1789,1801,ca. 1800,1790,1810,Gold maki-e with black lacquer Ojime: metal or lacquered bead Netsuke: ivory carved with design of chrysanthemum and waves,H. 3 3/16 in. (8.1 cm); W. 2 1/16 in. (5.3 cm); D. 3/4 in. (1.9 cm),"Rogers Fund, 1913",,,,,,,,,,,,Inrō,,http://www.metmuseum.org/art/collection/search/58573,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.782,false,true,78684,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Aikawa Minwa,"Japanese, active 1806–1821",,Aikawa Minwa,Japanese,1806,1821,1814,1814,1814,Woodblock printed book; ink and color on paper,10 3/8 × 7 1/16 in. (26.3 × 18 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78684,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.783,false,true,78685,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Aikawa Minwa,"Japanese, active 1806–1821",,Aikawa Minwa,Japanese,1806,1821,1818,1818,1818,Woodblock printed book; ink and color on paper,10 1/4 × 7 5/16 in. (26 × 18.5 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78685,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"JIB59a, b",false,true,57659,Asian Art,Illustrated book,合川珉和画『光琳画式』|Kōrin's Painting Style (Kōrin gashiki),Japan,Edo period (1615–1868),,,,Artist,,Aikawa Minwa,"Japanese, active 1806–1821",,Aikawa Minwa,Japanese,1806,1821,1818,1818,1818,Set of two Woodblock printed books; ink and color on paper,Image: 10 1/16 x 7 1/16 x 3/16 in. (25.5 x 18 x 0.5 cm) Overall: 13 1/8 in. (33.3 cm),"Rogers Fund, 1918",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/57659,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.653,false,true,78574,Asian Art,Illustrated book,"勝景眺望山水画譜|Prospects and Views, Picture Album of Landscapes (Shōkei chōbō, Sansui gafu)",Japan,Edo period (1615–1868),,,,Artist,,Kōkunsai Bairin,"Japanese, early 19th century",,Kōkunsai Bairin,Japanese,1800,1833,preface dated 1826,1826,1826,Woodblock printed book; ink and color on paper,7 3/16 × 4 13/16 in. (18.3 × 12.2 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78574,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JIB27,false,true,45447,Asian Art,Illustrated book,Kyoka Hyaku Monogatari|Poems on One Hundred Ghost Stories,Japan,Edo period (1615–1868),,,,Artist,,Masazumi Ryusai,"Japanese, active 19th century",,Masazumi Ryusai,Japanese,1800,1899,1853,1853,1853,Polychrome woodblock printed book; ink and color on paper,9 × 6 3/8 × 1 1/2 in. (22.9 × 16.2 × 3.8 cm),"Rogers Fund, 1918",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/45447,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JIB30,false,true,57562,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunchō,"Japanese, active ca. 1783–95",,Katsukawa Shunchō,Japanese,1775,1795,ca. 1790,1780,1800,Ink on paper,8 1/2 × 6 × 3/8 in. (21.6 × 15.2 × 1 cm),"Rogers Fund, 1918",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/57562,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"JIB29a, b",false,true,57560,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunchō,"Japanese, active ca. 1783–95",,Katsukawa Shunchō,Japanese,1775,1795,1790,1790,1790,Two volumes; ink and color on paper,Each: 8 1/2 × 6 × 1/4 in. (21.6 × 15.2 × 0.6 cm),"Rogers Fund, 1918",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/57560,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.864,false,true,78754,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshitora,"Japanese, active ca. 1850–80",,Utagawa Yoshitora,Japanese,1845,1880,1858,1858,1858,Woodblock printed book; ink and color on paper,8 5/8 × 6 1/8 in. (21.9 × 15.5 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78754,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JIB23,false,true,57555,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyotsune,"Japanese, active ca. 1757–1779",,Torii Kiyotsune,Japanese,1757,1779,1774,1774,1774,Ink and color on paper,8 3/4 × 6 × 3/8 in. (22.2 × 15.2 × 1 cm),"Rogers Fund, 1918",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/57555,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.893,false,true,78783,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Hasegawa Myōtei,"Japanese, active late 17th century",,Hasegawa Myōtei,Japanese,1667,1699,1714,1714,1714,Woodblock printed book; ink on paper,10 11/16 × 7 1/2 in. (27.2 × 19 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78783,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.861,false,true,78751,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Umemaru Yūzen,"Japanese, active late 18th century",,Umemaru Yūzen,Japanese,1767,1799,ca. 1820s,1820,1829,"Woodblock printed book; ink, color, and mica on paper",4 15/16 × 7 5/16 in. (12.5 × 18.5 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78751,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.868,false,true,78758,Asian Art,Illustrated book,（榎木新右衛門）画 『雛形伊勢乃海』|Book of Kosode Patterns (Hiinagata Ise no umi),Japan,Edo period (1615–1868),,,,Artist,,Enoki Hironobu,"Japanese, active mid- 18th century",,Enoki Hironobu,Japanese,1734,1766,1751,1751,1751,Three woodblock-printed books bound as one; ink on paper,10 3/4 × 7 3/8 in. (27.3 × 18.7 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78758,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.795a–c,false,true,78811,Asian Art,Illustrated books,,Japan,Edo period (1615–1868),,,,Artist,Attributed to,Takagi Sadatake,"Japanese, active early 18th century",,Takagi Sadatake,Japanese,1700,1733,1734,1734,1734,Set of two woodblock printed books; ink on paper,each: 10 5/8 × 7 5/8 in. (27 × 19.3 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78811,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.683,false,true,78604,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Fukuchi Hakuei,"Japanese, active early 19th century",,Fukuchi Hakuei,Japanese,1800,1833,ca. 1814,1804,1824,Woodblock printed book; ink and color on paper,9 15/16 × 6 1/2 in. (25.3 × 16.5 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78604,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -10.211.107,false,true,59824,Asian Art,Netsuke,文房具牙彫根付|Writing Utensils,Japan,Edo period (1615–1868),,,,Artist,,Ryūsen,"Japanese, active mid–19th century",,Ryūsen,Japanese,1834,1866,mid-19th century,1825,1875,Ivory,H. 1 1/2 in. (3.8 cm); W. 1 3/8 in. (3.5 cm),"Gift of Mrs. Russell Sage, 1910",,,,,,,,,,,,Netsuke,,http://www.metmuseum.org/art/collection/search/59824,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JIB12,false,true,57546,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,1802,1802,1802,Ink and color on paper,7 1/2 × 5 1/4 × 3/8 in. (19.1 × 13.3 × 1 cm),"Rogers Fund, 1918",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/57546,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JIB13,false,true,57547,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1845,1835,1855,Polychrome woodblock printed book; ink and color on paper,4 7/8 × 7 3/4 × 5/8 in. (12.4 × 19.7 × 1.6 cm),"Rogers Fund, 1918",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/57547,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JIB14,false,true,57548,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1840,1830,1850,Two volumes; ink and color on paper,Each: 9 × 6 1/4 × 3/8 in. (22.9 × 15.9 × 1 cm),"Rogers Fund, 1918",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/57548,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JIB16,false,true,45445,Asian Art,Illustrated book,Onna Imagawa|Precepts for Women,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1820s,1820,1829,Book of polychrome woodblock prints; ink and color on paper,9 × 6 1/4 × 1/2 in. (22.9 × 15.9 × 1.3 cm),"Rogers Fund, 1918",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/45445,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JIB17,false,true,57550,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1814,1804,1824,Polychrome woodblock prints in a book; ink and color on paper,10 × 13 1/4 × 1/4 in. (25.4 × 33.7 × 0.6 cm),"Rogers Fund, 1918",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/57550,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JIB18,false,true,57551,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,1834,1834,1834,Woodblock print; ink on paper,9 × 6 1/4 × 3/8 in. (22.9 × 15.9 × 1 cm),"Rogers Fund, 1918",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/57551,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JIB20,false,true,57553,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1847,1837,1857,Woodblock print; ink on paper,9 × 6 1/4 × 3/8 in. (22.9 × 15.9 × 1 cm),"Rogers Fund, 1918",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/57553,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JIB69,false,true,57667,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,1811,1811,1811,Woodblock print; ink on paper,8 3/4 × 6 1/16 × 1 in. (22.2 × 15.4 × 2.5 cm),"Gift of Julius Mahn, 1919",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/57667,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JIB80,false,true,57677,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1804,1794,1814,Pentaptych of polychrome woodblock prints,14 1/2 x 48 1/2 in. (36.8 x 123.2 cm),"Rogers Fund, 1918",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/57677,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JIB82,false,true,57678,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,1816,1816,1816,Woodblock printed book; ink and color on paper,Overall: 8 7/8 × 6 1/4 × 1/2 in. (22.5 × 15.9 × 1.3 cm),"Rogers Fund, 1931",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/57678,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JIB106,false,true,57791,Asian Art,Illustrated books,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,1833,1833,1833,Five volumes; ink on paper,Each: 9 × 6 3/16 × 1/4 in. (22.9 × 15.7 × 0.6 cm),"The Howard Mansfield Collection, Gift of Howard Mansfield, 1936",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/57791,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JIB107,false,true,45444,Asian Art,Illustrated book,Ehon Musashi no Abumi|A Picture Book of Japanese Warriors,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,1836,1700,1868,Polychrome Woodblock printed book,8 7/8 × 6 1/8 × 3/8 in. (22.5 × 15.6 × 1 cm),"The Howard Mansfield Collection, Gift of Howard Mansfield, 1936",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/45444,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JIB108,false,true,57793,Asian Art,Illustrated book,富岳百景|Mount Fuji of the Mists (Vol. 1); Mount Fuji of the Ascending Dragon (Vol. 2),Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,1834–35,1834,1835,Woodblock print (first and second volumes with 100 pages of illustrations); ink and color on paper,9 x 6 1/4 in. (22.9 x 15.9 cm),"The Howard Mansfield Collection, Gift of Howard Mansfield, 1936",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/57793,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JIB109,false,true,57794,Asian Art,Illustrated book,富岳百景|Fugaku Hyakkei,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,1834–35,1834,1835,Woodblock print; ink and color on paper,9 × 6 1/4 × 3/8 in. (22.9 × 15.9 × 1 cm),"The Howard Mansfield Collection, Gift of Howard Mansfield, 1936",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/57794,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JIB110,false,true,57796,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,1850,1850,1850,Ink on paper,8 7/8 × 6 1/4 × 3/8 in. (22.5 × 15.9 × 1 cm),"The Howard Mansfield Collection, Gift of Howard Mansfield, 1936",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/57796,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JIB112,false,true,57805,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,1834,1834,1834,Woodblock printed book; ink and color on paper,Overall: 9 × 6 1/4 × 1/2 in. (22.9 × 15.9 × 1.3 cm),"The Howard Mansfield Collection, Gift of Howard Mansfield, 1936",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/57805,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JIB120,false,true,57812,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,1818,1818,1818,Ink on paper,10 1/2 × 7 1/16 × 3/8 in. (26.7 × 17.9 × 1 cm),"The Howard Mansfield Collection, Gift of Howard Mansfield, 1936",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/57812,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JIB81.1,false,true,57679,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,after 1828,1828,1868,Woodblock printed book; ink and color on paper,Overall: 9 × 6 1/4 × 3/8 in. (22.9 × 15.9 × 1 cm),"Rogers Fund, 1931",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/57679,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JIB81.2,false,true,57680,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,after 1814,1815,1868,Woodblock printed book; ink and color on paper,9 x 6 1/4 x 1/2 in. (22.9 x 15.9 x 1.3 cm),"Rogers Fund, 1931",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/57680,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JIB81.4,false,true,57682,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,1819,1819,1819,Woodblock printed book; ink and color on paper,Overall: 9 × 6 × 3/8 in. (22.9 × 15.2 × 1 cm),"Rogers Fund, 1931",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/57682,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JIB81.7,false,true,57684,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,1819,1819,1819,Woodblock printed book; ink and color on paper,Overall: 9 × 6 × 3/8 in. (22.9 × 15.2 × 1 cm),"Rogers Fund, 1931",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/57684,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JIB81.8,false,true,57685,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,1817,1817,1817,Woodblock printed book; ink and color on paper,Overall: 9 × 6 × 3/8 in. (22.9 × 15.2 × 1 cm),"Rogers Fund, 1931",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/57685,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JIB81.9,false,true,57686,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,1819,1819,1819,Woodblock printed book; ink and color on paper,Overall: 9 × 6 1/4 × 3/8 in. (22.9 × 15.9 × 1 cm),"Rogers Fund, 1931",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/57686,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.711,false,true,78632,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,1799,1799,1799,Woodblock printed book; ink on paper,10 7/16 × 7 1/16 in. (26.5 × 18 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78632,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.713,false,true,78634,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,1802,1802,1802,Woodblock printed book; ink and color on paper,7 5/8 × 5 1/4 in. (19.3 × 13.4 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78634,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.714,false,true,78635,Asian Art,Illustrated book,『画本狂歌山満多山』|Picture Book of Kyōka Poems: Mountains upon Mountains (Ehon kyōka yama mata yama),Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,1804,1804,1804,Woodblock printed book; ink and color on paper,10 7/16 × 6 7/8 in. (26.5 × 17.5 cm),"Purchase, Mary and James G. Wallach Family Foundation Gift, in honor of John T. Carpenter, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78635,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.719,false,true,78790,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,1814,1814,1814,"Woodblock printed book (orihon, accordion-style); ink and color on paper",10 1/4 × 6 13/16 in. (26 × 17.3 cm),"Purchase, Mary and James G. Wallach Family Foundation Gift, in honor of John T. Carpenter, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78790,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.721,false,true,78792,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,1815,1815,1815,Woodblock printed book; ink and color on paper,10 1/4 × 6 7/8 in. (26 × 17.5 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78792,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.726,false,true,78797,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,1820,1820,1820,Woodblock printed book; ink and color on paper,10 3/8 × 6 3/4 in. (26.4 × 17.2 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78797,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.728,false,true,78799,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,1823,1823,1823,Woodblock printed book; ink and color on paper,9 × 6 1/4 in. (22.8 × 15.8 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78799,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.730,false,true,78801,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,1829,1829,1829,Woodblock printed book; ink and color on paper,8 15/16 × 6 1/4 in. (22.7 × 15.8 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78801,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.733,false,true,78804,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,1836,1836,1836,Woodblock printed book; ink on paper,8 13/16 × 6 1/8 in. (22.4 × 15.6 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78804,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.734,false,true,78805,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1840s,1840,1849,Woodblock printed book; ink on paper,8 7/8 × 6 5/16 in. (22.5 × 16 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78805,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.737,false,true,78639,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,1843,1843,1843,Woodblock printed book; ink and color on paper,8 3/4 × 6 1/8 in. (22.3 × 15.6 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78639,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.875,false,true,78765,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,1843,1843,1843,Woodblock printed book; ink on paper,8 15/16 × 6 1/8 in. (22.7 × 15.6 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78765,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.878,false,true,78768,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,1815,1815,1815,Woodblock printed book; ink on paper,8 7/8 × 6 3/8 in. (22.5 × 16.2 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78768,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.881,false,true,78771,Asian Art,Illustrated book,"繪本彩色通　初編|Picture Book on the Use of Coloring, first volume (Ehon saishikitsū shohen)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,1848,1848,1848,Woodblock printed book; ink on paper and color scribbles,7 3/16 × 5 1/16 in. (18.3 × 12.9 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78771,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.882,false,true,78772,Asian Art,Illustrated book,『絵本和漢誉』|Picture Book on Heroes of China and Japan (Ehon wakan no homare),Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,1850 (designed ca. 1836; published posthumously),1836,1850,Woodblock printed book; ink on paper,9 1/16 × 6 3/8 in. (23 × 16.2 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78772,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JIB81.10,false,true,57687,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,1819,1819,1819,Woodblock printed book; ink and color on paper,Overall: 9 × 6 × 3/8 in. (22.9 × 15.2 × 1 cm),"Rogers Fund, 1931",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/57687,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JIB81.12,false,true,57689,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,1834,1834,1834,Woodblock printed book; ink and color on paper,Overall: 9 × 6 1/4 × 3/8 in. (22.9 × 15.9 × 1 cm),"Rogers Fund, 1931",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/57689,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JIB81.13,false,true,57741,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1849,1839,1859,Woodblock printed book; ink and color on paper,Overall (JIB81.13 and .14 combined): 9 × 6 1/4 × 3/4 in. (22.9 × 15.9 × 1.9 cm),"Rogers Fund, 1931",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/57741,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JIB81.14,false,true,57742,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1875–78,1875,1878,Woodblock printed book; ink and color on paper,See JIB81.13,"Rogers Fund, 1931",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/57742,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"JIB19a, b",false,true,57552,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,1835,1835,1835,Two volumes; woodblock print; ink on paper,Each: 9 × 6 1/4 × 1/2 in. (22.9 × 15.9 × 1.3 cm),"Rogers Fund, 1918",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/57552,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"JIB79a, b",false,true,45274,Asian Art,Illustrated book,Edo meisho|Famous Sites of Edo,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,1800,1700,1868,Two volumes; polychrome woodblock print; ink and color on paper,Each: 10 1/16 × 6 5/8 × 1/4 in. (25.6 × 16.8 × 0.6 cm),"Gift of Mary L. Cassilly, 1894",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/45274,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JIB11a–c,false,true,57545,Asian Art,Illustrated books,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1828,1818,1838,Three volumes; ink on paper,9 × 6 1/4 × 1/2 in. (22.9 × 15.9 × 1.3 cm),"Rogers Fund, 1918",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/57545,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JIB15a–c,false,true,57549,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1803,1793,1813,Three volumes; ink and color on paper,Each: 10 × 6 3/4 × 1/4 in. (25.4 × 17.1 × 0.6 cm),"Rogers Fund, 1918",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/57549,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2013.710a, b",false,true,78631,Asian Art,Illustrated books,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1802,1797,1807,Set of two woodblock printed books; ink and color on paper,each: 10 1/2 × 6 7/8 in. (26.6 × 17.5 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78631,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2013.712a, b",false,true,78633,Asian Art,Illustrated books,『東都名所一覧』|Fine Views of the Eastern Capital at a Glance (Tōto meisho ichiran),Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,1800,1800,1800,Set of two woodblock printed books; ink and color on paper,each: 10 3/16 × 6 7/8 in. (25.8 × 17.5 cm),"Purchase, Mary and James G. Wallach Family Foundation Gift, in honor of John T. Carpenter, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78633,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2013.722a, b",false,true,78793,Asian Art,Illustrated books,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,"part I, 1817 and II, 1819",1817,1819,Woodblock printed books; ink on paper,"vol. ""kan"": 7 3/16 × 4 15/16 in. (18.2 × 12.6 cm) vol. ""zen"": 8 3/8 × 5 7/8 in. (21.2 × 14.9 cm)","Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78793,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2013.738a, b",false,true,78640,Asian Art,Illustrated books,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,1850,1850,1850,Set of two woodblock printed books; ink on paper,each: 9 × 6 3/16 in. (22.8 × 15.7 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78640,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.715a–d,false,true,78636,Asian Art,Illustrated books,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,1805–38,1805,1838,Set of four woodblock printed books; ink on paper,each: 9 13/16 × 7 1/16 in. (25 × 18 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78636,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.716a–c,false,true,78637,Asian Art,Illustrated books,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1806,1801,1811,Set of three woodblock printed books; ink and color on paper,each: 9 1/4 × 6 5/8 in. (23.5 × 16.9 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78637,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.717a–e,false,true,78788,Asian Art,Illustrated books,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,1809,1809,1809,Set of five woodblock printed books; ink on paper,each: 8 15/16 × 6 5/16 in. (22.7 × 16 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78788,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.718a–f,false,true,78789,Asian Art,Illustrated books,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,1810,1810,1810,Set of six woodblock printed books; ink on paper,Other (each): 9 × 6 1/4 in. (22.8 × 15.8 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78789,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.720a–r,false,true,78791,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,1814–78,1814,1878,Set of nineteen woodblock printed books; ink and color on paper,each approximately: 9 × 6 1/4 in. (22.8 × 15.8 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78791,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.729a–c,false,true,78800,Asian Art,Illustrated books,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,"part I, 1828, part II, 1830s, and part III, ca. 1848",1828,1848,Set of three woodblock printed books; ink on paper,each approximately: 8 7/8 × 6 1/16 in. (22.5 × 15.4 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78800,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.732a–c,false,true,78803,Asian Art,Illustrated books,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,1834; 1835; ca. 1849,1834,1849,Set of three woodblock printed books; ink on paper,each: 8 15/16 × 6 1/4 in. (22.7 × 15.8 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78803,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.735a–e,false,true,78806,Asian Art,Illustrated books,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,1833,1833,1833,Set of five woodblock printed books; ink on paper,each: 9 × 6 1/8 in. (22.8 × 15.6 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78806,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.689,false,true,78610,Asian Art,Illustrated books,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1839,1839,1839,"Woodblock printed book; ink, color, and white paint on paper",8 15/16 × 6 5/16 in. (22.7 × 16 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78610,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.690,false,true,78611,Asian Art,Illustrated books,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1847,1847,1847,Woodblock printed book; ink and color on paper,7 1/16 × 4 13/16 in. (18 × 12.2 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78611,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.691,false,true,78612,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1849,1849,1849,Woodblock printed book; ink and color on paper,each: 7 1/16 × 4 13/16 in. (18 × 12.2 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78612,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.693,false,true,78614,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1857,1857,1857,Woodblock printed book; ink and color on paper,10 3/8 × 7 1/16 in. (26.3 × 18 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78614,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.694,false,true,78615,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1855,1855,1855,Woodblock printed book; ink and color on paper,9 1/16 × 6 5/16 in. (23 × 16 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78615,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.695,false,true,78616,Asian Art,Illustrated books,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1859,1859,1859,Woodblock printed book; ink and color on paper,8 9/16 × 6 1/8 in. (21.7 × 15.5 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78616,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.692a–j,false,true,78613,Asian Art,Illustrated books,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1850–67,1850,1867,Set of ten woodblock printed books; ink and color on paper,each: 7 1/16 × 4 13/16 in. (18 × 12.2 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78613,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -38.25.128,false,true,58950,Asian Art,Inrō,兎月秋草蒔絵鞘印籠|Inrō with Rabbit in the Moon and Autumn Grasses,Japan,Edo period (1615–1868),,,,Artist,,Kyūkoku,"Japanese, active first half of the 19th century",,Kyūkoku,Japanese,1800,1849,first half of the 19th century,1800,1849,"Two-part (sheath type); lacquered wood with gold, silver, and pewter hiramaki-e, togidashimaki-e, on red lacquer ground Netsuke: ivory; rabbit Ojime: pierced metal with floral design",3 3/8 x 1 7/8 x 1 1/8 in. (8.5 x 4.7 x 2.8 cm),"Gift of Mrs. George A. Crocker (Elizabeth Masten), 1937",,,,,,,,,,,,Inrō,,http://www.metmuseum.org/art/collection/search/58950,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"14.40.878a, b",false,true,58628,Asian Art,Inrō,七夕蒔絵印籠|Inrō with Tanabata Story of the Weaver and the Herdboy,Japan,Edo period (1615–1868),,,,Artist,,Nomura Kyūkoku,"Japanese, active first half of the 19th century",,Nomura Kyūkoku,Japanese,1800,1849,active first half of the19th century,1800,1849,Three cases; lacquered wood with gold hiramaki-e and ivory inlay on mother-of-pearl ground; Netsuke: carved ivory; flowers and grasses with silver butterflies; Ojime: silver and gold quail in autumn grasses,Overall (inro): H. 3 7/8 in. (9.8 cm); W. 2 11/16 in. (6.9 cm); D. 13/16 in. (2 cm) Overall (netsuke): H. 11/16 in. (1.7 cm); Diam. 1 11/16 in. (4.3 cm) Overall (ojime): H. 9/16 in. (1.5 cm); W. 9/16 in. (1.4 cm),"Bequest of Benjamin Altman, 1913",,,,,,,,,,,,Inrō,,http://www.metmuseum.org/art/collection/search/58628,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -81.1.582,false,true,45437,Asian Art,Sake cup,,Japan,Edo period (1615–1868),,,,Artist,,Shomosai,"Japanese, active late 18th–early 19th century",,Shomosai,Japanese,0018,0019,mid-19th century,1834,1866,Gold lacquer on red lacquer ground,H. 1 1/16 in. (2.7 cm); W. 4 3/4 in. (12.1 cm),"Bequest of Stephen Whitney Phoenix, 1881",,,,,,,,,,,,Lacquer,,http://www.metmuseum.org/art/collection/search/45437,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -81.1.587,false,true,45439,Asian Art,Sake cup,,Japan,Edo period (1615–1868),,,,Artist,,Shomosai,"Japanese, active late 18th–early 19th century",,Shomosai,Japanese,0018,0019,mid-19th century,1834,1866,Gold lacquer on red lacquer ground,H. 1 in. (2.5 cm); W. 4 1/8 in. (10.5 cm),"Bequest of Stephen Whitney Phoenix, 1881",,,,,,,,,,,,Lacquer,,http://www.metmuseum.org/art/collection/search/45439,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -81.1.40,false,true,59162,Asian Art,Netsuke,牛牙彫根付|Ox,Japan,Edo period (1615–1868),,,,Artist,,Tomotada,"Japanese, active late 18th–early 19th century",,Tomotada,Japanese,1777,1833,late 18th–early 19th century,1777,1833,Ivory,H. 1 in. (2.5 cm); W. 2 1/4 in. (5.7 cm); D. 1 1/8 in. (2.9 cm),"Bequest of Stephen Whitney Phoenix, 1881",,,,,,,,,,,,Netsuke,,http://www.metmuseum.org/art/collection/search/59162,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -81.1.91,false,true,59180,Asian Art,Netsuke,猪牙彫根付|Boar,Japan,Edo period (1615–1868),,,,Artist,,Tomotada,"Japanese, active late 18th–early 19th century",,Tomotada,Japanese,1777,1833,late 18th–early 19th century,1767,1833,Ivory,H. 1 in. (2.5 cm); W. 2 1/8 in. (5.4 cm); D. 1 1/4 in. (3.2 cm),"Bequest of Stephen Whitney Phoenix, 1881",,,,,,,,,,,,Netsuke,,http://www.metmuseum.org/art/collection/search/59180,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.918,false,true,59612,Asian Art,Netsuke,狼牙彫根付|Wolf,Japan,Edo period (1615–1868),,,,Artist,,Tomotada,"Japanese, active late 18th–early 19th century",,Tomotada,Japanese,1777,1833,late 18th–early 19th century,1767,1833,Carved ivory,H. 1 1/4 in. (3.2 cm); W. 7/8 in. (2.2 cm); D. 2 1/8 in. (5.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Netsuke,,http://www.metmuseum.org/art/collection/search/59612,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -10.211.1106,false,true,60290,Asian Art,Netsuke,犬牙彫根付|Dog,Japan,Edo period (1615–1868),,,,Artist,,Tomotada,"Japanese, active late 18th–early 19th century",,Tomotada,Japanese,1777,1833,late 18th century–early 19th century,1771,1835,Ivory,H. 2 1/2 in. (6.4 cm); W. 1 1/4 in. (3.2 cm); D. 1/2 in. (1.3 cm),"Gift of Mrs. Russell Sage, 1910",,,,,,,,,,,,Netsuke,,http://www.metmuseum.org/art/collection/search/60290,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -10.211.1154,false,true,60305,Asian Art,Netsuke,枝豆牙彫根付|Edamame (Soy Beans),Japan,Edo period (1615–1868),,,,Artist,,Okatomo,"Japanese, active late 18th–early 19th century",,Okatomo,Japanese,1767,1833,late 18th–early 19th century,1771,1835,Ivory,H. 3/4 in. (1.9 cm); W. 2 1/4 in. (5.7 cm); D. 1 1/2 in. (3.8 cm),"Gift of Mrs. Russell Sage, 1910",,,,,,,,,,,,Netsuke,,http://www.metmuseum.org/art/collection/search/60305,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -10.211.1441,false,true,60386,Asian Art,Netsuke,東方朔仙人牙彫根付|Daoist Immortal Tōbōsaku Sennin,Japan,Edo period (1615–1868),,,,Artist,,Ryūminsai,"Japanese, active late 18th–early 19th century",,Ryūminsai,Japanese,1767,1833,late 18th–early 19th century,1767,1833,Ivory,H. 3 1/4 in. (8.3 cm); W. 1 1/2 in. (3.8 cm); D. 1 in. (2.5 cm),"Gift of Mrs. Russell Sage, 1910",,,,,,,,,,,,Netsuke,,http://www.metmuseum.org/art/collection/search/60386,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -10.211.2362,false,true,59409,Asian Art,Netsuke,鉄拐仙人木彫根付|Daoist Immortal Tekkai,Japan,Edo period (1615–1868),,,,Artist,,Chikusai,"Japanese, active late 18th–early 19th century",,Chikusai,Japanese,1767,1833,late 18th–early 19th century,1767,1833,Wood,H. 2 3/4 in. (7 cm); W. 1 in. (2.5 cm); D. 1 1/4 in. (3.2 cm),"Gift of Mrs. Russell Sage, 1910",,,,,,,,,,,,Netsuke,,http://www.metmuseum.org/art/collection/search/59409,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.67.23,false,true,58569,Asian Art,Inrō,蜻蛉蒔絵印籠|Inrō with Dragonflies,Japan,Edo period (1615–1868),,,,Artist,,Kōami Nagataka,"Japanese, active second half of the 18th century",,Kōami Nagataka,Japanese,1750,1799,second half of the 18th century,1750,1799,"Three cases; lacquered wood with gold, red lacquer takamaki-e, hiramaki-e with mother-of-pearl inlay on black lacquer ground; Netsuke: carved ivory; dog; Ojime: carved ivory; persimmon",H. 2 11/16 in. (6.8 cm); W. 2 3/4 in. (7 cm); D. 7/8 in. (2.2 cm),"Rogers Fund, 1913",,,,,,,,,,,,Inrō,,http://www.metmuseum.org/art/collection/search/58569,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.67.92,false,true,58617,Asian Art,Inrō,掛軸文字散蒔絵印籠|Inrō with Carp Hanging Scroll and Characters,Japan,Edo period (1615–1868),,,,Artist,,Kōami Chōkō,"Japanese, active second half of the 18th century",,Kōami Chōkō,Japanese,1750,1799,second half of the 18th century,1750,1799,"Three cases; lacquered wood with gold and silver takamaki-e, hiramaki-e, togidashimaki-e, gold foil cut-outs, and mother-of-pearl inlay on black ground Netsuke: kagamibuta with Shōjō design Ojime: agate bead",3 7/8 x 3 5/16 x 15/16 in. (9.8 x 8.4 x 2.4 cm),"Rogers Fund, 1913",,,,,,,,,,,,Inrō,,http://www.metmuseum.org/art/collection/search/58617,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.40.877,false,true,45573,Asian Art,Inrō,祭蒔絵印籠|Inrō with Street Festival (obverse); People Watching a Puppet Show (reverse),Japan,Edo period (1615–1868),,,,Artist,,Tatsuke Takamasu,"Japanese, active second half of the 18th century",,Tatsuke Takamasu,Japanese,1750,1799,second half of the 18th century,1750,1799,"Three cases; lacquered wood with gold, silver, and color (iroko) togidashimaki-e on black lacquer ground Netsuke: carved teakwood; peach with a monkey inside (signed: Kagetoshi) Ojime: carved teakwood with floral design",2 13/16 x 2 15/16 x 1 1/8 in. (7.1 x 7.5 x 2.9 cm),"Bequest of Benjamin Altman, 1913",,,,,,,,,,,,Inrō,,http://www.metmuseum.org/art/collection/search/45573,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.883,false,true,58803,Asian Art,Inrō,葡萄蒔絵印籠　銘「樗平」|Inrō with Grapevine,Japan,Edo period (1615–1868),,,,Artist,,Nomura Choheisai,"Japanese, active second half of the 18th century",,Nomura Choheisai,Japanese,1750,1799,second half of the 18th century,1750,1799,"One case; lacquered wood with gold hiramaki-e, gold foil application with green stained ivory, mother-of-pearl, amber, and horn inlays on black lacquer ground Netsuke: ivory; kagamibuta with inlaid design of gourd and vine Ojime: ivory bead with inlaid design of branch with fruits",H. 3 1/16 in. (7.7 cm); W. 3 13/16 in. (9.7 cm); D. 1 3/16 in. (3 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Inrō,,http://www.metmuseum.org/art/collection/search/58803,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -10.211.1436,false,true,60382,Asian Art,Netsuke,狸牙彫根付|Tanuki (Japanese Racoon Dog) with a Sake Bottle and Bills for Sake,Japan,Edo period (1615–1868),,,,Artist,,Garaku,"Japanese, active second half of the 18th century",,Garaku,Japanese,1750,1799,second half of the 18th century,1750,1799,Ivory,H. 2 3/4 in. (7 cm); W. 1 5/8 in. (4.1 cm); D. 1 in. (2.5 cm),"Gift of Mrs. Russell Sage, 1910",,,,,,,,,,,,Netsuke,,http://www.metmuseum.org/art/collection/search/60382,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.846,false,true,78736,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Ogura Tōkei,"Japanese, active second half of the 18th century",,Ogura Tōkei,Japanese,1750,1799,1809,1809,1809,Woodblock printed book; ink on paper,9 13/16 × 6 7/8 in. (25 × 17.5 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78736,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.847,false,true,78737,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Shōfusai Tōsen,"Japanese, active second half of the 19th century",,Shōfusai Tōsen,Japanese,1850,1899,mid-19th century,1825,1875,Woodblock printed book; ink and color on paper,11 1/4 × 7 7/8 in. (28.5 × 20 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78737,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"JIB105a, b",false,true,57790,Asian Art,Illustrated book,中野期明画　『尾形流百図』|One Hundred Paintings of the Ogata Lineage (Ogata ryu hyakuzu),Japan,Meiji period (1868-1912),,,,Artist,,Nakano Kimei,"Japanese, 1834–1892",,Nakano Kimei,Japanese,1834,1892,1892,1892,1892,Set of two Woodblock printed books; ink on paper,Image (a): 10 x 7 3/8 x 1/2 in. (25.4 x 18.8 x 1.2 cm) Overall: 13 7/16 in. (34.2 cm) (open) Image (b): 10 x 7 3/8 x 7/16 in. (25.4 x 18.8 x 1.1 cm) Overall: 13 9/16 in. (34.5 cm) (open),"The Howard Mansfield Collection, Gift of Howard Mansfield, 1936",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/57790,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.343a–c,false,true,73596,Asian Art,Illustrated book,橫濱開港見聞誌|Observations on the Opening of Yokohama (Yokohama kaiko kanbunshi),Japan,Shōwa period (1926–89),,,,Artist,,Utagawa (Gountei) Sadahide,"Japanese, 1807–1878/79",,Utagawa (Goutei) Sadahide,Japanese,1807,1879,1967,1967,1967,Book; ink on paper,Image (a): 8 1/2 x 6 1/8 x 7/8 in. (21.6 x 15.6 x 2.2 cm) Image (b): 8 1/4 x 5 7/8 x 1/16 in. (21 x 14.9 x 0.2 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/73596,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -91.1.642,false,true,57779,Asian Art,Box,,Japan,Meiji period (1868–1912),,,,Artist,Style of,Ogata Kōrin,"Japanese, 1658–1716",,Kōrin,Japanese,1658,1716,second half of the 19th century,1850,1899,"Gold inlaid with mother-of-pearl and tin"" to ""Gold hiramaki-e, takamaki-e, tin and mother-of-pearl inlay on gold ground",H. 1 7/8 in. (4.8 cm); W. 3 9/16 in. (9 cm); D. 2 1/16 in. (5.2 cm),"Edward C. Moore Collection, Bequest of Edward C. Moore, 1891",,,,,,,,,,,,Lacquer,,http://www.metmuseum.org/art/collection/search/57779,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"36.100.162a, b",false,true,53643,Asian Art,Box,,Japan,Meiji period (1868–1912),,,,Artist,Style of,Ogawa Haritsu (Ritsuō),"Japanese, 1663–1747",,Ritsuō,Japanese,1663,1747,second half of 19th century,1850,1899,"Bottle gourd; gold, red takamaki-e, mother-of-pearl, tin, ceramic inlay",H. 4 in. (10.2 cm); Diam. at top 3 in. (7.6 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Lacquer,,http://www.metmuseum.org/art/collection/search/53643,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.754,false,true,78656,Asian Art,Illustrated book,,Japan,Meiji period (1868–1912),,,,Artist,After,Seizei Kigyoku,"Japanese, 1732–1756",,Seizei Kigyoku,Japanese,1732,1756,1901,1901,1901,Woodblock printed book; ink and color on paper,11 1/8 × 7 1/2 in. (28.2 × 19 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78656,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.805,false,true,78695,Asian Art,Illustrated book,,Japan,Meiji period (1868–1912),,,,Artist,,Sengai Gibon,"Japanese, 1750–1837",,Sengai Gibon,Japanese,1750,1837,1894,1894,1894,Woodblock printed book; ink on paper,Other: 9 1/16 × 5 7/8 in. (23 × 15 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78695,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.268.181,false,true,53644,Asian Art,Chest,,Japan,Meiji period (1868–1912),,,,Artist,Attributed to,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,Japanese,1807,1891,ca. 1878,1868,1888,Wood with black and gold lacquer,H. 25 1/2 in. (64.8 cm); W. 28 3/4 in. (73 cm); D. 10 1/8 in. (25.7 cm),"The Harry G. C. Packard Collection of Asian Art, Gift of Harry G. C. Packard, and Purchase, Fletcher, Rogers, Harris Brisbane Dick, and Louis V. Bell Funds, Joseph Pulitzer Bequest, and The Annenberg Fund Inc. Gift, 1975",,,,,,,,,,,,Lacquer,,http://www.metmuseum.org/art/collection/search/53644,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2010.143a–g,false,true,75746,Asian Art,Tiered box,明治時代　柴田是真　果蔬蒔絵重箱|Tiered Food Box with Summer and Autumn Fruits,Japan,Meiji period (1868–1912),,,,Artist,,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,Japanese,1807,1891,ca. 1868–90,1850,1900,"Brown lacquer with gold, silver, and colored lacquer maki-e",H. 16 1/8 in. (41 cm); W. 9 in. (22.9 cm); D. 9 5/8 in. (24.4 cm),"Purchase, The Vincent Astor Foundation Gift and Parnassus Foundation/Jane and Raphael Bernstein Gift, 2010",,,,,,,,,,,,Lacquer,,http://www.metmuseum.org/art/collection/search/75746,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.142a–j,false,true,44868,Asian Art,Writing box,明治時代　柴田是真派　蒲公英酒瓢蒔絵 硯箱|Writing Box with Gourd,Japan,Meiji period (1868–1912),,,,Artist,School of,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,Japanese,1807,1891,1886,1886,1886,"Black lacquer with gold and silver hiramaki-e, colored lacquer application",H. 1 1/2 in. (3.8 cm); W. 7 5/8 in. (19.4 cm); L. 8 7/8 in. (22.5 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Lacquer,,http://www.metmuseum.org/art/collection/search/44868,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -54.56,false,true,57347,Asian Art,Screen,花鳥図風炉先屏風|Folding Screen for Tea Ceremony with Six Bird-and-Flower Paintings,Japan,Meiji period (1868–1912),,,,Artist,In the style of,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,Japanese,1807,1891,late 19th century,1867,1899,"Two-panel folding screen; lacquer, color, and silver on paper",Overall (each panel): 14 1/2 x 31 in. (36.8 x 78.7 cm),"Gift of Mr. and Mrs. Nathan V. Hammer, 1954",,,,,,,,,,,,Screens,,http://www.metmuseum.org/art/collection/search/57347,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -53.119,false,true,57345,Asian Art,Folding screen,,Japan,Meiji period (1868–1912),,,,Artist,Style of,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,Japanese,1807,1891,1868–1912,1868,1912,Two-panel folding screen; color on silk,26 3/4 x 69 in. (67.9 x 175.3 cm),"Rogers Fund, 1953",,,,,,,,,,,,Screens,,http://www.metmuseum.org/art/collection/search/57345,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2011.572.3,false,true,77169,Asian Art,Screen,柴田是真筆　烏鷺図屏風|Three Crows in Flight and Two Egrets at Rest,Japan,Meiji period (1868–1912),,,,Artist,,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,Japanese,1807,1891,late 19th century,1867,1899,Two-panel folding screen; colored lacquer and white pigment on gilt paper,Image (each panel): 53 1/2 x 36 in. (135.9 x 91.4 cm) Overall (each panel): 60 7/8 x 39 1/4 in. (154.6 x 99.7 cm),"Fishbein-Bender Collection, Gift of T. Richard Fishbein and Estelle P. Bender, 2011",,,,,,,,,,,,Screens,,http://www.metmuseum.org/art/collection/search/77169,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.268.137,false,true,45080,Asian Art,Folding screen,月に秋草図屏風|Autumn Grasses in Moonlight,Japan,Meiji period (1868–1912),,,,Artist,,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,Japanese,1807,1891,second half of the 19th century,1850,1899,"Two-panel folding screen; ink, lacquer, silver, and silver leaf on paper","Image (each panel): 18 in. × 33 1/4 in. (45.7 × 84.5 cm) Each panel, with frame: 26 1/8 × 34 3/8 in. (66.4 × 87.3 cm) Overall with frame (both panels): 26 1/8 in. × 69 in. (66.4 × 175.3 cm)","The Harry G. C. Packard Collection of Asian Art, Gift of Harry G. C. Packard, and Purchase, Fletcher, Rogers, Harris Brisbane Dick, and Louis V. Bell Funds, Joseph Pulitzer Bequest, and The Annenberg Fund Inc. Gift, 1975",,,,,,,,,,,,Screens,,http://www.metmuseum.org/art/collection/search/45080,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.186,false,true,45443,Asian Art,Inrō,,Japan,Meiji period (1868–1912),,,,Artist,,Nakayama Komin,"Japanese, 1808–1870",,Nakayama Komin,Japanese,1808,1870,late 19th century,1871,1899,"Gold lacquer with gold hiramkie sprinkled and polished lacquer, nashiji (pear skin) lacquer, and mother-of-pearl, ivory, and wood inlay; Interior: nashiji and fundame; Netsuke: wood-framed ivory plaque with bird and flower inlay; Ojime: lacquer Daikoku's hammer",3 9/16 x 2 13/16 x 1 1/8 in. (9.1 x 7.1 x 2.9 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Inrō,,http://www.metmuseum.org/art/collection/search/45443,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"1998.189.1, .2",false,true,40006,Asian Art,Folding screens,,Japan,Meiji period (1868–1912),,,,Artist,,Shiokawa Bunrin,"Japanese, 1808–1877",,Shiokawa Bunrin,Japanese,1808,1877,1875,1875,1875,Pair of six-panel folding screens; ink and gold on paper,Image (each): 59 5/8 in. x 11 ft. 6 5/8 in. (151.4 x 352.1 cm),"Purchase, Friends of Asian Art Gifts, 1998",,,,,,,,,,,,Screens,,http://www.metmuseum.org/art/collection/search/40006,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.148,false,true,45581,Asian Art,Box,果蔬蒔絵菓子箱|Two-Tiered Box with Design of Autumn Fruits,Japan,Meiji period (1868–1912),,,,Artist,,Ikeda Taishin,"Japanese, 1825–1903",,Ikeda Taishin,Japanese,1825,1903,second half of the 19th century,1850,1899,"Lacquered wood with gold, silver, black, and red takamaki-e, hiramaki-e, and e-nashiji on black lacquer ground",H. 4 1/4 in. (10.8 cm); W. 4 1/4 in. (10.8 cm); L. 6 1/8 in. (15.6 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Lacquer,,http://www.metmuseum.org/art/collection/search/45581,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.767,false,true,78669,Asian Art,Illustrated book,『暁斎百鬼画談』|Kyōsai’s Pictures of One Hundred Demons (Kyōsai hyakki gadan),Japan,Meiji period (1868–1912),,,,Artist,,Kawanabe Kyōsai,"Japanese, 1831–1889",,Kawanabe Kyōsai,Japanese,1831,1889,1890 (first edition published posthumously in 1889),1890,1890,"Woodblock printed book (orihon, accordion-style); ink and color on paper",7 13/16 × 4 3/4 in. (19.8 × 12 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78669,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.764a–d,false,true,78666,Asian Art,illustrated books,『暁斎画談』|Kyōsai’s Treatise on Painting (Kyōsai gadan),Japan,Meiji period (1868–1912),,,,Artist,,Kawanabe Kyōsai,"Japanese, 1831–1889",,Kawanabe Kyōsai,Japanese,1831,1889,1887,1887,1887,Set of four woodblock printed books; ink and color on paper,each: 9 15/16 × 6 7/8 in. (25.3 × 17.5 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78666,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.748,false,true,78650,Asian Art,Illustrated book,,Japan,Meiji period (1868–1912),,,,Artist,,Nishiyama Ken,"Japanese, 1833–1897",,Nishiyama Ken,Japanese,1833,1897,1886,1886,1886,"Woodblock printed book (orihon, accordion-style); ink and color on paper",10 5/8 × 5 1/2 in. (27 × 14 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78650,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JIB4a–c,false,true,57539,Asian Art,Illustrated book,梅嶺百鳥畫譜|Bairei Picture Album of One Hundred Birds (Bairei hyakuchō gafu),Japan,Meiji period (1868–1912),,,,Artist,,Kōno Bairei,"Japanese, 1844–1895",,Kōno Bairei,Japanese,1844,1895,1881–84,1881,1884,Set of three polychrome woodblock printed books; ink and color on paper,Overall (vol. 1): 9 5/8 × 6 3/8 in. (24.5 × 16.2 cm) Overall (vol. 2): 9 3/4 × 6 9/16 in. (24.8 × 16.7 cm) Overall (vol. 3): 9 3/4 × 6 7/16 in. (24.8 × 16.3 cm),"Rogers Fund, 1918",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/57539,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.652a–c,false,true,78573,Asian Art,Illustrated books,楳嶺百鳥畫譜|Bairei Picture Album of One Hundred Birds (Bairei hyakuchō gafu),Japan,Meiji period (1868–1912),,,,Artist,,Kōno Bairei,"Japanese, 1844–1895",,Kōno Bairei,Japanese,1844,1895,1881–84,1881,1884,Set of three woodblock printed books; ink and color on paper,each: 9 1/2 × 6 13/16 in. (24.2 × 17.3 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78573,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2003.317.1, .2",false,true,65603,Asian Art,Screen,,Japan,Meiji period (1868–1912),,,,Artist,,Suzuki Shōnen,"Japanese, 1849–1918",,Suzuki Shōnen,Japanese,1849,1918,late 19th century,1867,1899,Pair of six-panel folding screens; ink on gold-leaf,Image (each): 68 1/16 in. x 12 ft. 1 3/16 in. (172.9 x 368.8 cm),"Purchase, The B. D. G. Leviton Foundation Gift, 2003",,,,,,,,,,,,Screens,,http://www.metmuseum.org/art/collection/search/65603,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.800,false,true,78816,Asian Art,Illustrated book,,Japan,Meiji period (1868–1912),,,,Artist,,Watanabe Seitei,"Japanese, 1851–1918",,Watanabe Seitei,Japanese,1851,1918,1891,1891,1891,Woodblock printed book; ink and color on paper,9 13/16 × 6 3/4 in. (25 × 17.2 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78816,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.320.2,false,true,60501,Asian Art,Vase,菊紋楓枝文七宝瓶　（一対）|Imperial Presentation Vase with Maple Branches and Imperial Chrysanthemum Crest (one of a pair),Japan,Meiji period (1868–1912),,,,Artist,,Kawade Shibatarō,"Japanese, 1861–1921",,Kawade Shibatarō,Japanese,1861,1921,ca. 1906,1896,1916,Standard and repoussé cloisonné enamel; silver wires and rims; signed: combined marks of Andō Cloisonné Company and Kawade Shibatarō,H. 17 1/8 in. (43.5 cm); W. 6 3/4 in. (17.1 cm); D. 4 1/2 in. (11.4 cm),"Gift of Barbara S. McKenna, 1976",,,,,,,,,,,,Cloisonné,,http://www.metmuseum.org/art/collection/search/60501,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.799,false,true,78815,Asian Art,Illustrated book,,Japan,Meiji period (1868–1912),,,,Artist,,Takeuchi Seihō,"Japanese, 1864–1942",,Takeuchi Seihō,Japanese,1864,1942,ca. 1905–6,1905,1906,"Woodblock printed book (orihon, accordion-style); ink, color and metallic pigments on paper",14 1/4 × 9 13/16 in. (36.2 × 25 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78815,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JIB202,false,true,65710,Asian Art,Illustrated book,,Japan,Meiji period (1868–1912),,,,Artist,,Tsukioka Kōgyo,"Japanese, 1869–1927",,Tsukioka Kōgyo,Japanese,1869,1927,1898–1899,1898,1899,Polychrome woodblock prints; ink and color on paper,Overall: 9 1/2 x 14 1/4 in. (24.1 x 36.2 cm),"Gift of Lincoln Kirstein, 1985",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/65710,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2013.789a, b",false,true,78691,Asian Art,Illustrated books,,Japan,Meiji period (1868–1912),,,,Artist,,Ogino Issui,"Japanese, active 1900–10",,Ogino Issui,Japanese,1900,1910,1903,1903,1903,Set of two woodblock printed books; ink and color on paper,each: 10 1/16 × 7 1/16 in. (25.5 × 18 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78691,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JIB83,false,true,57390,Asian Art,Illustrated book,,Japan,Meiji period (1868–1912),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,1878,1878,1878,Woodblock printed book; ink and color on paper,Overall: 9 1/8 × 6 × 3/8 in. (23.2 × 15.2 × 1 cm),"Rogers Fund, 1932",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/57390,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.365,false,true,44603,Asian Art,Calligraphy,Sono gi o aege|Embrace Righteousness,Japan,Taishō period (1912–26),,,,Artist,,Tomioka Tessai,"Japanese, 1836–1924",,Tomioka Tessai,Japanese,1836,1924,20th century,1912,1926,Framed calligraphy; ink on paper,Image: 12 3/8 × 36 3/16 in. (31.4 × 91.9 cm) Framed: 17 3/4 × 49 1/2 in. (45.1 × 125.7 cm),"Gift of Dr. Yukikazu Iwasa, in honor of Mrs. Shizuko Iwasa, 1989",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/44603,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -91.1.2068,false,true,62167,Asian Art,Basket,掛花籃|Large Flower Basket,Japan,Meiji period (1868–1912),,,,Artist,,Hayakawa Shōkōsai I,"Japanese, 1815–1897",,Hayakawa Shōkōsai I,Japanese,1815,1897,second half of the 19th century,1850,1899,Bamboo (madake) with rattan accents,H. 19 3/4 in. (50.2 cm); Diam. 16 in. (40.6 cm),"Edward C. Moore Collection, Bequest of Edward C. Moore, 1891",,,,,,,,,,,,Basketry,,http://www.metmuseum.org/art/collection/search/62167,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"55.94.1, .2",false,true,39665,Asian Art,Screen,"源氏物語図屏風「御幸」・「浮船」・「関谷」|Scenes from The Tale of Genji: “The Royal Outing,” “Ukifune,” and “The Gatehouse”",Japan,Momoyama period (1573–1615),,,,Artist,,Tosa Mitsuyoshi,"Japanese, 1539–1613",,TOSA MITSUYOSHI,Japanese,1539,1613,mid-16th–early 17th century,1550,1633,"Pair of four-panel folding screens; ink, color, and gold leaf on paper",Image (each screen): 65 1/2 in. × 11 ft. 8 in. (166.4 × 355.6 cm),"Fletcher Fund, 1955",,,,,,,,,,,,Screens,,http://www.metmuseum.org/art/collection/search/39665,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2006.42.1, .2",false,true,73167,Asian Art,Folding screens,,Japan,Momoyama period (1573–1615),,,,Artist,Attributed to,Kano Takanobu,"Japanese, 1571–1618",,Kano Takanobu,Japanese,1571,1618,ca. 1600,1590,1610,"Pair of six-panel folding screens; ink, color, and gold on gilded paper",Image (each screen): 66 7/16 x 149 1/2 in. (168.8 x 379.7 cm),"Purchase, Gift of Mrs. Russell Sage, Bequest of Stephen Whitney Phoenix, and other gifts, bequests and funds from various donors, by exchange, Joseph Pulitzer Bequest, and Rogers, Fletcher, Harris Brisbane Dick, and Louis V. Bell Funds, 2006",,,,,,,,,,,,Screens,,http://www.metmuseum.org/art/collection/search/73167,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"1975.268.46, .47",false,true,45180,Asian Art,Screen,商山四皓・蘇東坡風水洞|The Return to Court of the Four Graybeards of Mount Shang (left); Su Shi’s Visit to the Wind and Water Cave (right,Japan,Momoyama period (1573–1615),,,,Artist,In the Style of,Kano Mitsunobu,"Japanese, ca. 1561–1608",,Kano Mitsunobu,Japanese,1561,1608,late 16th century,1571,1599,"Pair of six-panel folding screens; ink, color, gold, and gold leaf on paper",Image (each screen): 68 3/4 in. x 12 ft. 4 7/8 in. (174.6 x 378.1 cm),"The Harry G. C. Packard Collection of Asian Art, Gift of Harry G. C. Packard, and Purchase, Fletcher, Rogers, Harris Brisbane Dick, and Louis V. Bell Funds, Joseph Pulitzer Bequest, and The Annenberg Fund Inc. Gift, 1975",,,,,,,,,,,,Screens,,http://www.metmuseum.org/art/collection/search/45180,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.736a–f,false,true,78638,Asian Art,Illustrated books,,Japan,Edo period (1615–1868) 1845.,,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,1845,1845,1845,Set of six woodblock printed books; ink on paper,10 1/16 × 6 15/16 in. (25.6 × 17.7 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78638,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"1975.268.44, .45",false,true,45258,Asian Art,Screen,四季竹図屏風|Bamboo in the Four Seasons,Japan,Muromachi period (1392–1573),,,,Artist,Attributed to,Tosa Mitsunobu,1434–1525,,Tosa Mitsunobu,Japanese,1434,1525,late 15th–early 16th century,1480,1525,"Pair of six-panel screens; ink, color, and gold leaf on paper",Image: 61 13/16 x 9 ft. 9 3/4 in. (157 x 360 cm),"The Harry G. C. Packard Collection of Asian Art, Gift of Harry G. C. Packard, and Purchase, Fletcher, Rogers, Harris Brisbane Dick, and Louis V. Bell Funds, Joseph Pulitzer Bequest, and The Annenberg Fund Inc. Gift, 1975",,,,,,,,,,,,Screens,,http://www.metmuseum.org/art/collection/search/45258,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"41.59.1, .2",false,true,42344,Asian Art,Painting,四季山水画 （瀟湘八景）|Landscape of the Four Seasons (Eight Views of the Xiao and Xiang Rivers),Japan,Muromachi period (1392–1573),,,,Artist,,Sōami,"Japanese, died 1525",,Sōami,Japanese,,1525,early 16th century,1500,1533,Pair of six-panel folding screens; ink on paper,Each: 68 1/4 × 146 in. (173.4 × 370.8 cm),"Gift of John D. Rockefeller Jr., 1941",,,,,,,,,,,,Screens,,http://www.metmuseum.org/art/collection/search/42344,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"1992.8.1, .2",true,true,44696,Asian Art,Screen,猿猴捉月図屏風|Gibbons in a Landscape,Japan,Muromachi period (1392–1573),,,,Artist,,Sesson Shūkei,ca. 1504–ca. 1589,,Sesson Shūkei,Japanese,1504,1589,ca. 1570,1560,1580,Pair of six-panel screens; ink on paper,Image (each screen): 62 in. x 11 ft. 5 in. (157.5 x 348 cm),"Purchase, Rogers Fund and The Vincent Astor Foundation, Mary Livingston Griggs and Mary Griggs Burke Foundation, and Florence and Herbert Irving Gifts, 1992",,,,,,,,,,,,Screens,,http://www.metmuseum.org/art/collection/search/44696,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"1991.480.1, .2",false,true,44673,Asian Art,Screen,琴棋書画図屏風|The Four Accomplishments,Japan,Muromachi period (1392–1573),,,,Artist,,Kano Motonobu,"Japan, ca. 1476–1559",,Kano Motonobu,Japanese,1476,1559,mid-16th century,1534,1566,Pair of six-panel folding screens; ink and color on paper,Image (each screen): 67 x 150 in. (170.2 x 381 cm),"Dr. and Mrs. Roger G. Gerry Collection, Gift of Dr. and Mrs. Roger G. Gerry, 1991",,,,,,,,,,,,Screens,,http://www.metmuseum.org/art/collection/search/44673,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2011.534,false,true,60467,Asian Art,Hanging scroll,墨蹟　「雪」|Poem on the Theme of Snow,Japan,Nanbokuchō period (1336–92),,,,Artist,,Musō Soseki,"Japanese, 1275–1351",,Musō Soseki,Japanese,1275,1351,14th century,1336,1392,Hanging scroll; ink on paper,Image: 11 3/4 x 32 1/2 in. (29.8 x 82.6 cm) Overall with mounting: 48 7/8 x 38 in. (124.1 x 96.5 cm) Overall with knobs: 48 7/8 x 40 1/4 in. (124.1 x 102.2 cm),"Gift of Sylvan Barnet and William Burto, in honor of Maxwell K. Hearn, 2011",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/60467,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2014.719.12,false,true,60468,Asian Art,Hanging scroll,"夢窓疎石筆 消息|Letter to Suwa Daishin, Officer of the Shogun",Japan,Nanbokuchō period (1336–92),,,,Artist,,Musō Soseki,"Japanese, 1275–1351",,Musō Soseki,Japanese,1275,1351,ca. 1339–51,1329,1361,Hanging scroll; ink on paper,Image: 11 5/16 × 14 1/16 in. (28.7 × 35.7 cm) Overall with mounting: 44 1/2 × 18 1/2 in. (113 × 47 cm) Overall with knobs: 44 1/2 × 20 1/4 in. (113 × 51.4 cm),"Gift of Sylvan Barnet and William Burto, in honor of John T. Carpenter, 2014",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/60468,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2014.719.6,false,true,42693,Asian Art,Hanging scroll,墨跡「糖」|Poem in Chinese about Sugar,Japan,Nanbokuchō period (1336–92),,,,Artist,,Kokan Shiren,"Japanese, 1278–1346",,Kokan Shiren,Japanese,1278,1346,14th century,1336,1392,Hanging scroll; ink on paper,Image: 12 1/4 x 18 5/8 in. (31.1 x 47.3 cm) Overall with mounting: 47 x 24 in. (119.4 x 61 cm) Overall with knobs: 47 x 25 13/16 in. (119.4 x 65.6 cm),"Gift of Sylvan Barnet and William Burto, in honor of Elizabeth and Neil Swinton, 2014",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/42693,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.840,false,true,78730,Asian Art,Illustrated book,,Japan,Edo period (1615–1868) Kihei.,,,,Artist,,Nishikawa Sukenobu,"Japanese, 1671–1750",,Nishikawa Sukenobu,Japanese,1671,1750,1768,1768,1768,Woodblock printed book; ink and color on paper,9 1/16 × 6 5/16 in. (23 × 16 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78730,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.765,false,true,78667,Asian Art,illustrated book,『暁斎漫画』|Kyōsai Sketchbook (Kyōsai manga),Japan,Edo period (1615–1868Meiji period (1868–1912),,,,Artist,,Kawanabe Kyōsai,"Japanese, 1831–1889",,Kawanabe Kyōsai,Japanese,1831,1889,1881,1881,1881,Woodblock printed book; ink and color on paper,8 3/4 × 5 7/8 in. (22.3 × 15 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78667,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2013.766a, b",false,true,78668,Asian Art,illustrated books,『暁斎楽画』|Kyōsai’s Drawings for Pleasure (Kyōsai rakuga),Japan,Edo period (1615–1868Meiji period (1868–1912),,,,Artist,,Kawanabe Kyōsai,"Japanese, 1831–1889",,Kawanabe Kyōsai,Japanese,1831,1889,1881,1881,1881,"Set of two woodblock-printed books (one volume orihon, accordion-style); ink and color on paper",each: 8 7/8 × 5 7/8 in. (22.5 × 15 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78668,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -62.36.1,false,true,44914,Asian Art,Screen,芥子図屏風|Red and White Poppies,Japan,Momoyama (1573–1615)– Edo (1615–1868) period,,,,Artist,Traditionally attributed to,Tosa Mitsumochi,active 1525–ca. 1559,,Tosa Mitsumochi,Japanese,1525,1559,early 17th century,1600,1633,"Six-panel folding screen; ink, color, and gold leaf on paper",65 3/4 x 147 1/2 in. (167.0 x 374.7 cm),"H. O. Havemeyer Collection, Gift of Mrs. Dunbar W. Bostwick, John C. Wilmerding, J. Watson Webb Jr., Harry H. Webb, and Samuel B. Webb, 1962",,,,,,,,,,,,Screens,,http://www.metmuseum.org/art/collection/search/44914,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"51.89.1, .2",false,true,44941,Asian Art,Screen,見立琴碁書画屏風|Parody of the Four Accomplishments,Japan,late Edo (1615–1868) or Meiji (1868–1912) period,,,,Artist,,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,Japanese,1807,1891,second half of the 19th century,1850,1899,Pair of six-panel folding screens; ink and color on gold leaf on paper,Overall (each screen): 47 1/2 x 112 1/2 in. (120.7 x 285.8 cm),"Fletcher Fund, 1951",,,,,,,,,,,,Screens,,http://www.metmuseum.org/art/collection/search/44941,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.790,false,true,78692,Asian Art,Illustrated book,,Japan,Edo period (1615–1868) or Meiji period (1868–1912),,,,Artist,Attributed to,Rinsai Ōkubo,"Japanese, 19th century",,Rinsai Ōkubo,Japanese,1800,1899,1810 or 1870,1810,1870,Woodblock printed book; ink and color on paper,11 1/8 × 7 1/16 in. (28.3 × 18 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78692,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2013.684a, b",false,true,78605,Asian Art,Illustrated books,,Japan,Edo period (1615–1868) Osaka. 1837. Publishers: Eirakuya Tōshirō,,,,Artist,,Hanzan (Matsukawa),"Japanese, 1820–1882",,Hanzan,Japanese,1820,1882,1837,1837,1837,Set of two woodblock printed books; ink on paper,each: 8 11/16 × 6 1/8 in. (22 × 15.5 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78605,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -31.101.2,false,true,55007,Asian Art,DUPLICATE: this is JP207,,Japan,,,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,1769,1769,1769,Polychrome woodblock print; ink and color on paper,12 x 5 3/4 in. (30.5 x 14.6 cm),"Gift of Louis V. Ledoux, 1931",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55007,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2271,false,true,54044,Asian Art,Woodblock print,,Japan,,,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,probably 1815,1815,1815,Part of an album of woodblock prints (surimono); ink and color on paper,7 15/16 x 7 1/8 in. (20.2 x 18.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54044,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2272,false,true,54045,Asian Art,Print,,Japan,,,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",(?),Kubo Shunman,Japanese,1757,1820,probably 1815,1815,1815,Part of an album of woodblock prints (surimono); ink and color on paper,7 15/16 x 7 1/4 in. (20.2 x 18.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54045,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2273,false,true,54046,Asian Art,Print,,Japan,,,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",(?),Kubo Shunman,Japanese,1757,1820,probably 1815,1815,1815,Part of an album of woodblock prints (surimono); ink and color on paper,7 15/16 x 7 in. (20.2 x 17.8 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54046,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2274,false,true,54047,Asian Art,Print,,Japan,,,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",(?),Kubo Shunman,Japanese,1757,1820,probably 1815,1815,1815,Part of an album of woodblock prints (surimono); ink and color on paper,7 15/16 x 7 1/8 in. (20.2 x 18.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54047,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2277,false,true,54050,Asian Art,Print,,Japan,,,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",(?),Kubo Shunman,Japanese,1757,1820,probably 1815,1815,1815,Part of an album of woodblock prints (surimono); ink and color on paper,7 15/16 x 7 1/16 in. (20.2 x 17.9 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54050,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2279,false,true,54052,Asian Art,Print,,Japan,,,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",(?),Kubo Shunman,Japanese,1757,1820,probably 1815,1815,1815,Part of an album of woodblock prints (surimono); ink and color on paper,8 x 7 1/8 in. (20.3 x 18.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54052,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2306,false,true,54090,Asian Art,Print,,Japan,,,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",(?),Kubo Shunman,Japanese,1757,1820,probably 1814,1814,1814,Part of an album of woodblock prints (surimono); ink and color on paper,5 1/8 x 7 in. (13 x 17.8 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54090,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3685,false,true,55916,Asian Art,Print,,Japan,,,,,Artist,,Utagawa Toyokuni I,"Japanese, 1769–1825",,Utagawa Toyokuni I,Japanese,1769,1825,ca. 1797–1800,1797,1800,Polychrome woodblock print; ink and color on paper,16 1/4 x 11 1/8 in. (41.3 x 28.3 cm),"Gift of Lincoln Kirstein, 1985",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55916,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3686,false,true,55917,Asian Art,Print,,Japan,,,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,1854,1854,1854,One sheet of a triptych of polychrome woodblock prints; ink and color on paper,14 5/8 x 10 in. (37.1 x 25.4 cm),"Gift of Lincoln Kirstein, 1985",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55917,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"JP3683a, b",false,true,55914,Asian Art,Print,,Japan,,,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,1858,1858,1858,Diptych of polychrome woodblock prints; ink and color on paper,Each 14 1/4 x 9 3/4 in. (36.2 x 24.8 cm),"Gift of Lincoln Kirstein, 1985",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55914,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3684a–c,false,true,55915,Asian Art,Print,,Japan,,,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,1858,1858,1858,Triptych of polychrome woodblock prints; ink and color on paper,Each: 14 1/4 x 9 7/8 in. (36.2 x 25.1 cm),"Gift of Lincoln Kirstein, 1985",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55915,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2636,false,true,54203,Asian Art,Woodblock print,,Japan,,,,,Artist,,Watanabe Kazan,"Japanese, 1793–1841",,Watanabe Kazan,Japanese,1793,1841,ca. 1840,1830,1850,Polychrome woodblock print (surimono); ink and color on paper,7 3/4 x 7 1/8 in. (19.7 x 18.1 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54203,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3170,false,true,56723,Asian Art,Print,,Japan,,,,,Artist,,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,Japanese,1807,1891,ca. 1860,1850,1870,Polychrome woodblock print; ink and color on paper,Image: 8 1/2 × 11 5/8 in. (21.6 × 29.5 cm) Mat: 15 1/4 × 22 3/4 in. (38.7 × 57.8 cm),"Purchase, Gifts, Bequests, and Funds from various donors, by exchange, 1955",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56723,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3171,false,true,56724,Asian Art,Print,,Japan,,,,,Artist,,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,Japanese,1807,1891,ca. 1860,1850,1870,Polychrome woodblock print; ink and color on paper,Image: 7 1/8 × 9 1/2 in. (18.1 × 24.1 cm) Mat: 15 1/4 × 22 3/4 in. (38.7 × 57.8 cm),"Purchase, Gifts, Bequests, and Funds from various donors, by exchange, 1955",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56724,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3608a–c,false,true,55913,Asian Art,Print,横浜　岩亀見込の図|The Interior of the Gankiro Tea House in Yokohama,Japan,,,,,Artist,,Suzuki Hiroshige II,"Japanese, 1826–1869",,Suzuki Hiroshige II,Japanese,1826,1869,1861 (April),1861,1861,Triptych of polychrome woodblock prints; ink and color on paper,Oban triptych: 14 /14 x 28 7/8 in. (35.6 x 73.3 cm),"The Howard Mansfield Collection, Rogers Fund, by exchange, 1982",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55913,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3308,false,true,55412,Asian Art,Print,,Japan,,,,,Artist,,Ichiryūsai Yoshitoyo,"Japanese, 1830–1866",,Ichiryūsai Yoshitoyo,Japanese,1830,1866,ca. 1863,1853,1873,Polychrome woodblock print; ink and color on paper,14 1/2 x 9 7/8 in. (36.8 x 25.1 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55412,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3315,false,true,55455,Asian Art,Print,浅草観音境内ニ於イテ興行仕候 佛蘭西曲馬|French Equestrian Circus on the grounds of Asakusa Kannon temple (Asakusa kannon keidai ni oite kōgyō tsukawashi sōrō-Furansu kyokuba),Japan,,,,,Artist,,Utagawa Kuniteru,"Japanese, 1830–1874",,Utagawa Kuniteru,Japanese,1830,1874,1871,1871,1871,Polychrome woodblock print; ink and color on paper,14 1/8 x 28 13/16 in. (35.9 x 73.2 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55455,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3316,false,true,55457,Asian Art,Print,"港崎横浜一覧|A Glance at Miyosaki, Yokohama",Japan,,,,,Artist,,Utagawa Yoshimori,"Japanese, 1830–1884",,Utagawa Yoshimori,Japanese,1830,1884,ca. 1860,1850,1870,Diptych of polychrome woodblock prints; ink and color on paper,Oban; 13 7/8 x 18 7/8 in. (35.2 x 47.9 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55457,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3200,false,true,55184,Asian Art,Print,,Japan,,,,,Artist,Attributed to,Yōshū (Hashimoto) Chikanobu,"Japanese, 1838–1912",,Yōshū (Hashimoto) Chikanobu,Japanese,1838,1912,ca. 1880,1870,1890,Polychrome woodblock print; ink and color on paper,14 1/2 x 9 3/8 in. (36.8 x 23.8 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55184,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3702,false,true,55950,Asian Art,Print,"Keinen kachō gafu 景年花鳥畫譜|Two Birds and Crysanthemums, from Keinen kachō gafu (Keinen’s Flower-and-Bird Painting Manual)",Japan,,,,,Artist,,Imao Keinen,"Japanese, 1845–1924",,Imao Keinen,Japanese,1845,1924,1891,1891,1891,Polychrome woodblock print; ink and color on paper,14 1/2 x 10 1/16 in. (36.8 x 25.6 cm),"Bequest of Grace M. Pugh, 1985",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55950,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3703,false,true,55951,Asian Art,Print,"Keinen kachō gafu 景年花鳥畫譜|Cormorant and Kerria Rose (Yamabuki), from Keinen kachō gafu (Keinen’s Flower-and-Bird Painting Manual)",Japan,,,,,Artist,,Imao Keinen,"Japanese, 1845–1924",,Imao Keinen,Japanese,1845,1924,1891,1891,1891,Polychrome woodblock print; ink and color on paper,14 1/2 x 10 1/16 in. (36.8 x 25.6 cm),"Bequest of Grace M. Pugh, 1985",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55951,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3203,false,true,55187,Asian Art,Print,,Japan,,,,,Artist,,Mizuno Toshikata,"Japanese, 1866–1908",,Mizuno Toshikata,Japanese,1866,1908,ca. 1903,1893,1913,Polychrome woodblock print; ink and color on paper,8 1/2 x 11 3/8 in. (21.6 x 28.9 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55187,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3205,false,true,55189,Asian Art,Print,Sakamoto Otasuku Kankyo ni tachite tekijo o nozomu zu|Sakamoto Otasuku,Japan,,,,,Artist,Attributed to,Mizuno Toshikata,"Japanese, 1866–1908",,Mizuno Toshikata,Japanese,1866,1908,ca. 1894,1884,1904,Polychrome woodblock print; ink and color on paper,7 x 9 1/4 in. (17.8 x 23.5 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55189,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2353,false,true,54138,Asian Art,Print,"詩人と富士山『春雨集』　摺物帖|A Poet and Mount FujiFrom the Spring Rain Collection (Harusame shū), vol. 3",Japan,,,,,Artist,,Yashima Gakutei,"Japanese, 1786?–1868",,Yashima Gakutei,Japanese,1786,1868,ca. 1820s,1820,1829,Part of an album of woodblock prints (surimono); ink and color on paper,4 7/8 x 11 1/8 in. (12.4 x 28.3 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54138,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2380,false,true,54164,Asian Art,Woodblock print,,Japan,,,,,Artist,,Yashima Gakutei,"Japanese, 1786?–1868",,Yashima Gakutei,Japanese,1786,1868,probably 1817,1817,1817,Part of an album of woodblock prints (surimono); ink and color on paper,8 1/16 x 7 1/8 in. (20.5 x 18.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54164,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3208,false,true,55192,Asian Art,Print,,Japan,,,,,Artist,,Shōsai Ikkei,"Japanese, active ca. 1870",,Shōsai Ikkei,Japanese,1870,1870,ca. 1875,1865,1885,Polychrome woodblock print; ink and color on paper,14 x 9 1/2 in. (35.6 x 24.1 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55192,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3227,false,true,55219,Asian Art,Print,,Japan,,,,,Artist,,Shōsai Ikkei,"Japanese, active ca. 1870",,Shōsai Ikkei,Japanese,1870,1870,ca. 1870,1860,1880,One sheet of a triptych of polychrome woodblock prints; ink and color on paper,Triptych 14 1/16 x 28 1/8 in. (35.7 x 71.4 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55219,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3228,false,true,55220,Asian Art,Print,,Japan,,,,,Artist,,Shōsai Ikkei,"Japanese, active ca. 1870",,Shōsai Ikkei,Japanese,1870,1870,ca. 1870,1860,1880,One sheet of a triptych of polychrome woodblock prints; ink and color on paper,Triptych 14 1/16 x 28 1/8 in. (35.7 x 71.4 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55220,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3229,false,true,55221,Asian Art,Print,,Japan,,,,,Artist,,Shōsai Ikkei,"Japanese, active ca. 1870",,Shōsai Ikkei,Japanese,1870,1870,ca. 1870,1860,1880,One sheet of a triptych of polychrome woodblock prints; ink and color on paper,Triptych 14 1/16 x 28 1/8 in. (35.7 x 71.4 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55221,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2351,false,true,54136,Asian Art,Woodblock print,"胴乱印籠と懐中時計根付『春雨集』　摺物帖|Dōran (Square Leather Box Used as an Inrō) with a Watch as a NetsukeFrom the Spring Rain Collection (Harusame shū), vol. 3",Japan,,,,,Artist,,Hokusen Taigaku,"Japanese, active 1805–1825",,Hokusen Taigaku,Japanese,1805,1825,probably 1817,1817,1817,Part of an album of woodblock prints (surimono); ink and color on paper,5 9/16 x 7 1/4 in. (14.1 x 18.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54136,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3383,false,true,55549,Asian Art,Print,"Shunshoku, Onkyoku no Shirabe|Spring Scenery; Melody of a Musical Performance",Japan,,,,,Artist,,Utagawa Fusatane,"Japanese, active ca. 1849–80",,Utagawa Fusatane,Japanese,1849,1880,1877? (ink stain renders date partly illegible),1877,1877,Triptych of polychrome woodblock prints; ink and color on paper,14 1/2 x 28 5/8 in. (36.8 x 72.7 cm),"Gift of Lincoln Kirstein, 1960",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55549,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3380,false,true,55547,Asian Art,Print,亜米利加国|American Balloon Ascension (Amerikakoku),Japan,,,,,Artist,,Utagawa Yoshitora,"Japanese, active ca. 1850–80",,Utagawa Yoshitora,Japanese,1845,1880,"6th month, 1867",1867,1867,Polychrome woodblock print; ink and color on paper,14 x 28 1/2 in. (35.6 x 72.4 cm),"Gift of Lincoln Kirstein, 1960",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55547,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3074,false,true,56554,Asian Art,Print,,Japan,,,,,Artist,Attributed to,Kondo Kiyoharu,"Japanese, active ca. 1704–1720",,Kondo Kiyoharu,Japanese,1704,1720,ca. 1715,1705,1725,Polychrome woodblock print (hand colored); ink and color on paper,12 1/4 x 5 3/4 in. (31.1 x 14.6 cm),"Harris Brisbane Dick Fund and Rogers Fund, 1949",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56554,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2315,false,true,54099,Asian Art,Print,,Japan,,,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,probably 1816,1816,1816,Part of an album of woodblock prints (surimono); ink and color on paper,5 7/16 x 3 5/16 in. (13.8 x 8.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54099,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3312,false,true,55423,Asian Art,Print,Igirisujin|英吉利人|Englishmen,Japan,,,,,Artist,,Utagawa Yoshikazu,"Japanese, active ca. 1850–1870",,Utagawa Yoshikazu,Japanese,1845,1870,ca. 1862,1852,1872,Polychrome woodblock print; ink and color on paper,14 x 9 3/4 in. (35.6 x 24.8 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55423,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3313,false,true,55437,Asian Art,Print,亜墨利加人|Amerikajin,Japan,,,,,Artist,,Utagawa Yoshikazu,"Japanese, active ca. 1850–1870",,Utagawa Yoshikazu,Japanese,1845,1870,1862,1862,1862,Polychrome woodblock print; ink and color on paper,14 1/2 x 9 3/4 in. (36.8 x 24.8 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55437,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3321,false,true,55465,Asian Art,Print,魯西亜|Russians Reading and Writing,Japan,,,,,Artist,,Utagawa Yoshikazu,"Japanese, active ca. 1850–1870",,Utagawa Yoshikazu,Japanese,1845,1870,"1861 (Bunkyu 1st year, 2nd month)",1861,1861,Polychrome woodblock print; ink and color on paper,14 3/4 x 10 1/8 in. (37.5 x 25.7 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55465,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3441,false,true,55648,Asian Art,Print,,Japan,,,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,early 20th century,1900,1933,Polychrome woodblock print; ink and color on paper,7 x 9 in. (17.8 x 22.9 cm),"Gift of Cole J. Younger, 1975",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55648,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3442,false,true,55649,Asian Art,Print,,Japan,,,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,early 20th century,1900,1933,Polychrome woodblock print; ink and color on paper,7 x 9 in. (17.8 x 22.9 cm),"Gift of Cole J. Younger, 1975",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55649,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3443,false,true,55650,Asian Art,Print,,Japan,,,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,early 20th century,1900,1933,Polychrome woodblock print; ink and color on paper,7 x 9 in. (17.8 x 22.9 cm),"Gift of Cole J. Younger, 1975",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55650,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3444,false,true,55651,Asian Art,Print,,Japan,,,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,early 20th century,1900,1933,Polychrome woodblock print; ink and color on paper,7 x 9 in. (17.8 x 22.9 cm),"Gift of Cole J. Younger, 1975",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55651,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3445,false,true,55652,Asian Art,Print,,Japan,,,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,early 20th century,1900,1933,Polychrome woodblock print; ink and color on paper,7 x 9 in. (17.8 x 22.9 cm),"Gift of Cole J. Younger, 1975",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55652,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3446,false,true,55653,Asian Art,Print,,Japan,,,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,early 20th century,1900,1933,Polychrome woodblock print; ink and color on paper,7 x 9 in. (17.8 x 22.9 cm),"Gift of Cole J. Younger, 1975",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55653,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3447,false,true,55654,Asian Art,Print,,Japan,,,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,early 20th century,1900,1933,Polychrome woodblock print; ink and color on paper,7 x 9 in. (17.8 x 22.9 cm),"Gift of Cole J. Younger, 1975",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55654,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3448,false,true,55655,Asian Art,Print,,Japan,,,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,early 20th century,1900,1933,Polychrome woodblock print; ink and color on paper,7 x 9 in. (17.8 x 22.9 cm),"Gift of Cole J. Younger, 1975",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55655,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3449,false,true,55656,Asian Art,Print,,Japan,,,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,early 20th century,1900,1933,Polychrome woodblock print; ink and color on paper,7 x 9 in. (17.8 x 22.9 cm),"Gift of Cole J. Younger, 1975",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55656,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3450,false,true,55657,Asian Art,Print,,Japan,,,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,early 20th century,1900,1933,Polychrome woodblock print; ink and color on paper,7 x 9 in. (17.8 x 22.9 cm),"Gift of Cole J. Younger, 1975",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55657,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3451,false,true,55658,Asian Art,Print,,Japan,,,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,early 20th century,1900,1933,Polychrome woodblock print; ink and color on paper,7 x 9 in. (17.8 x 22.9 cm),"Gift of Cole J. Younger, 1975",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55658,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3452,false,true,55659,Asian Art,Print,,Japan,,,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,early 20th century,1900,1933,Polychrome woodblock print; ink and color on paper,7 x 9 in. (17.8 x 22.9 cm),"Gift of Cole J. Younger, 1975",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55659,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3453,false,true,55660,Asian Art,Print,,Japan,,,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,early 20th century,1900,1933,Polychrome woodblock print; ink and color on paper,7 x 9 in. (17.8 x 22.9 cm),"Gift of Cole J. Younger, 1975",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55660,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3454,false,true,55661,Asian Art,Print,,Japan,,,,,Artist,Formerly Attributed to,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,early 20th century,1900,1933,Polychrome woodblock print; ink and color on paper,7 x 9 in. (17.8 x 22.9 cm),"Gift of Cole J. Younger, 1975",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55661,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3455,false,true,55662,Asian Art,Print,,Japan,,,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,early 20th century,1900,1933,Polychrome woodblock print; ink and color on paper,7 x 9 in. (17.8 x 22.9 cm),"Gift of Cole J. Younger, 1975",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55662,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3456,false,true,55663,Asian Art,Print,,Japan,,,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,early 20th century,1900,1933,Polychrome woodblock print; ink and color on paper,7 x 9 in. (17.8 x 22.9 cm),"Gift of Cole J. Younger, 1975",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55663,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3457,false,true,55664,Asian Art,Print,,Japan,,,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,early 20th century,1900,1933,Polychrome woodblock print; ink and color on paper,7 x 9 in. (17.8 x 22.9 cm),"Gift of Cole J. Younger, 1975",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55664,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3458,false,true,55665,Asian Art,Print,,Japan,,,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,early 20th century,1900,1933,Polychrome woodblock print; ink and color on paper,7 x 9 in. (17.8 x 22.9 cm),"Gift of Cole J. Younger, 1975",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55665,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3459,false,true,55666,Asian Art,Print,,Japan,,,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,early 20th century,1900,1933,Polychrome woodblock print; ink and color on paper,7 x 9 in. (17.8 x 22.9 cm),"Gift of Cole J. Younger, 1975",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55666,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3460,false,true,55667,Asian Art,Print,,Japan,,,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,early 20th century,1900,1933,Polychrome woodblock print; ink and color on paper,7 x 9 in. (17.8 x 22.9 cm),"Gift of Cole J. Younger, 1975",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55667,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3461,false,true,55668,Asian Art,Print,,Japan,,,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,early 20th century,1900,1933,Polychrome woodblock print; ink and color on paper,7 x 9 in. (17.8 x 22.9 cm),"Gift of Cole J. Younger, 1975",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55668,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3462,false,true,55669,Asian Art,Print,,Japan,,,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,early 20th century,1900,1933,Polychrome woodblock print; ink and color on paper,7 x 9 in. (17.8 x 22.9 cm),"Gift of Cole J. Younger, 1975",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55669,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3463,false,true,55670,Asian Art,Print,,Japan,,,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,early 20th century,1900,1933,Polychrome woodblock print; ink and color on paper,7 x 9 in. (17.8 x 22.9 cm),"Gift of Cole J. Younger, 1975",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55670,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3464,false,true,55671,Asian Art,Print,,Japan,,,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,early 20th century,1900,1933,Polychrome woodblock print; ink and color on paper,7 x 9 in. (17.8 x 22.9 cm),"Gift of Cole J. Younger, 1975",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55671,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3465,false,true,55672,Asian Art,Print,,Japan,,,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,early 20th century,1900,1933,Polychrome woodblock print; ink and color on paper,7 x 9 in. (17.8 x 22.9 cm),"Gift of Cole J. Younger, 1975",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55672,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3466,false,true,55673,Asian Art,Print,,Japan,,,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,early 20th century,1900,1933,Polychrome woodblock print; ink and color on paper,7 x 9 in. (17.8 x 22.9 cm),"Gift of Cole J. Younger, 1975",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55673,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3467,false,true,55674,Asian Art,Print,,Japan,,,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,early 20th century,1900,1933,Polychrome woodblock print; ink and color on paper,7 x 9 in. (17.8 x 22.9 cm),"Gift of Cole J. Younger, 1975",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55674,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3468,false,true,55675,Asian Art,Print,,Japan,,,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,early 20th century,1900,1933,Polychrome woodblock print; ink and color on paper,7 x 9 in. (17.8 x 22.9 cm),"Gift of Cole J. Younger, 1975",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55675,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3469,false,true,55676,Asian Art,Print,,Japan,,,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,early 20th century,1900,1933,Polychrome woodblock print; ink and color on paper,7 x 9 in. (17.8 x 22.9 cm),"Gift of Cole J. Younger, 1975",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55676,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3470,false,true,55677,Asian Art,Print,,Japan,,,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,early 20th century,1900,1933,Polychrome woodblock print; ink and color on paper,7 x 9 in. (17.8 x 22.9 cm),"Gift of Cole J. Younger, 1975",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55677,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3471,false,true,55678,Asian Art,Print,,Japan,,,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,early 20th century,1900,1933,Polychrome woodblock print; ink and color on paper,7 x 9 in. (17.8 x 22.9 cm),"Gift of Cole J. Younger, 1975",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55678,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3472,false,true,55679,Asian Art,Print,"東海道五十三次　袋井|Fukuroi, from the series The Fifty-three Stations of the Tōkaidō Road",Japan,,,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,early 20th century,1900,1933,Polychrome woodblock print; ink and color on paper,7 x 9 in. (17.8 x 22.9 cm),"Gift of Cole J. Younger, 1975",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55679,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3473,false,true,55680,Asian Art,Print,"東海道五十三次　白須賀|Shirasuka, from the series The Fifty-three Stations of the Tōkaidō Road",Japan,,,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,early 20th century,1900,1933,Polychrome woodblock print; ink and color on paper,7 x 9 in. (17.8 x 22.9 cm),"Gift of Cole J. Younger, 1975",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55680,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3474,false,true,55681,Asian Art,Print,"東海道五十三次　二川|Futakawa, from the series The Fifty-three Stations of the Tōkaidō Road",Japan,,,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,early 20th century,1900,1933,Polychrome woodblock print; ink and color on paper,7 x 9 in. (17.8 x 22.9 cm),"Gift of Cole J. Younger, 1975",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55681,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3475,false,true,55682,Asian Art,Print,"東海道五十三次　吉田|Yoshida, from the series The Fifty-three Stations of the Tōkaidō Road",Japan,,,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,early 20th century,1900,1933,Polychrome woodblock print; ink and color on paper,7 x 9 in. (17.8 x 22.9 cm),"Gift of Cole J. Younger, 1975",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55682,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3476,false,true,55683,Asian Art,Print,"東海道五十三次　御油|Goyu, from the series The Fifty-three Stations of the Tōkaidō Road",Japan,,,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,early 20th century,1900,1933,Polychrome woodblock print; ink and color on paper,7 x 9 in. (17.8 x 22.9 cm),"Gift of Cole J. Younger, 1975",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55683,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3477,false,true,55684,Asian Art,Print,"東海道五十三次　赤坂|Akasaka, from the series The Fifty-three Stations of the Tōkaidō Road",Japan,,,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,early 20th century,1900,1933,Polychrome woodblock print; ink and color on paper,7 x 9 in. (17.8 x 22.9 cm),"Gift of Cole J. Younger, 1975",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55684,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3478,false,true,55685,Asian Art,Print,"東海道五十三次　藤川|Fujikawa, from the series The Fifty-three Stations of the Tōkaidō Road",Japan,,,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,early 20th century,1900,1933,Polychrome woodblock print; ink and color on paper,7 x 9 in. (17.8 x 22.9 cm),"Gift of Cole J. Younger, 1975",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55685,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3479,false,true,55686,Asian Art,Print,"東海道五十三次　岡崎|Okazaki, from the series The Fifty-three Stations of the Tōkaidō Road",Japan,,,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,early 20th century,1900,1933,Polychrome woodblock print; ink and color on paper,7 x 9 in. (17.8 x 22.9 cm),"Gift of Cole J. Younger, 1975",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55686,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3480,false,true,55687,Asian Art,Print,"東海道五十三次　池鯉鮒|Chiryūshuku, from the series The Fifty-three Stations of the Tōkaidō Road",Japan,,,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,early 20th century,1900,1933,Polychrome woodblock print; ink and color on paper,7 x 9 in. (17.8 x 22.9 cm),"Gift of Cole J. Younger, 1975",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55687,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3481,false,true,55688,Asian Art,Print,"東海道五十三次　鳴海|Narumi, from the series The Fifty-three Stations of the Tōkaidō Road",Japan,,,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,early 20th century,1900,1933,Polychrome woodblock print; ink and color on paper,7 x 9 in. (17.8 x 22.9 cm),"Gift of Cole J. Younger, 1975",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55688,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3482,false,true,55689,Asian Art,Print,"東海道五十三次　宮|Miya, from the series The Fifty-three Stations of the Tōkaidō Road",Japan,,,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,early 20th century,1900,1933,Polychrome woodblock print; ink and color on paper,7 x 9 in. (17.8 x 22.9 cm),"Gift of Cole J. Younger, 1975",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55689,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3483,false,true,55690,Asian Art,Print,"東海道五十三次　桑名|Kuwana, from the series The Fifty-three Stations of the Tōkaidō Road",Japan,,,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,early 20th century,1900,1933,Polychrome woodblock print; ink and color on paper,7 x 9 in. (17.8 x 22.9 cm),"Gift of Cole J. Younger, 1975",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55690,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3484,false,true,55691,Asian Art,Print,"東海道五十三次　四日市|Yokkaichi, from the series The Fifty-three Stations of the Tōkaidō Road",Japan,,,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,early 20th century,1900,1933,Polychrome woodblock print; ink and color on paper,7 x 9 in. (17.8 x 22.9 cm),"Gift of Cole J. Younger, 1975",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55691,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3485,false,true,55692,Asian Art,Print,"東海道五十三次　石薬師|Ishiyakushi, from the series The Fifty-three Stations of the Tōkaidō Road",Japan,,,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,early 20th century,1900,1933,Polychrome woodblock print; ink and color on paper,7 x 9 in. (17.8 x 22.9 cm),"Gift of Cole J. Younger, 1975",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55692,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3486,false,true,55693,Asian Art,Print,"東海道五十三次　庄野|Shōno, from the series The Fifty-three Stations of the Tōkaidō Road",Japan,,,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,early 20th century,1900,1933,Polychrome woodblock print; ink and color on paper,7 x 9 in. (17.8 x 22.9 cm),"Gift of Cole J. Younger, 1975",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55693,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3487,false,true,55694,Asian Art,Print,"東海道五十三次　亀山|Kameyama, from the series The Fifty-three Stations of the Tōkaidō Road",Japan,,,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,early 20th century,1900,1933,Polychrome woodblock print; ink and color on paper,7 x 9 in. (17.8 x 22.9 cm),"Gift of Cole J. Younger, 1975",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55694,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3488,false,true,55695,Asian Art,Print,"東海道五十三次　関|Seki, from the series The Fifty-three Stations of the Tōkaidō Road",Japan,,,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,early 20th century,1900,1933,Polychrome woodblock print; ink and color on paper,7 x 9 in. (17.8 x 22.9 cm),"Gift of Cole J. Younger, 1975",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55695,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3489,false,true,55696,Asian Art,Print,"東海道五十三次　阪之下|Sakanoshita, from the series The Fifty-three Stations of the Tōkaidō Road",Japan,,,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,early 20th century,1900,1933,Polychrome woodblock print; ink and color on paper,7 x 9 in. (17.8 x 22.9 cm),"Gift of Cole J. Younger, 1975",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55696,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3490,false,true,55697,Asian Art,Print,"東海道五十三次　土山|Tsuchiyama, from the series The Fifty-three Stations of the Tōkaidō Road",Japan,,,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,early 20th century,1900,1933,Polychrome woodblock print; ink and color on paper,7 x 9 in. (17.8 x 22.9 cm),"Gift of Cole J. Younger, 1975",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55697,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3491,false,true,55698,Asian Art,Print,"東海道五十三次　水口|Mizukuchi, from the series The Fifty-three Stations of the Tōkaidō Road",Japan,,,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,early 20th century,1900,1933,Polychrome woodblock print; ink and color on paper,7 x 9 in. (17.8 x 22.9 cm),"Gift of Cole J. Younger, 1975",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55698,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3492,false,true,55699,Asian Art,Print,"東海道五十三次　石部|Ishibe, from the series The Fifty-three Stations of the Tōkaidō Road",Japan,,,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,early 20th century,1900,1933,Polychrome woodblock print; ink and color on paper,7 x 9 in. (17.8 x 22.9 cm),"Gift of Cole J. Younger, 1975",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55699,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3440a–c,false,true,55647,Asian Art,Woodblock print,木曾路之山川|Mountains and Rivers Along the Kisokaidō,Japan,,,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1930s,1930,1939,Triptych of polychrome woodblock prints; ink and color on paper,14 3/4 x 10 in. (37.5 x 25.4 cm),"Gift of Cole J. Younger, 1975",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55647,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3364,false,true,55532,Asian Art,Print,,Japan,Edo (1615–1868),,,,Artist,,Utagawa Yoshimori,"Japanese, 1830–1885",,Utagawa Yoshimori,Japanese,1830,1885,ca. 1865,1855,1875,Polychrome woodblock print; ink and color on paper,9 5/8 x 14 5/16 in. (24.4 x 36.4 cm),"Gift of Lincoln Kirstein, 1960",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55532,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP669,false,true,37116,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Shigenaga,1697–1756,,Shigenaga,Japanese,1697,1756,ca. 1765,1755,1775,Polychrome woodblock print; ink and color on paper,27 7/32 x 6 15/32 in. (69.2 x 16.4 cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37116,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP819,false,true,37263,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Shigenaga,1697–1756,,Shigenaga,Japanese,1697,1756,ca. 1722,1712,1732,Polychrome woodblock print; ink and color on paper (Urushi-e),Overall: 11 3/4 x 6in. (29.8 x 15.2cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37263,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2657,false,true,56836,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Shigenaga,1697–1756,,Shigenaga,Japanese,1697,1756,ca. 1738,1728,1748,Polychrome woodblock print; ink and color on paper,13 1/4 x 6 1/8 in. (33.7 x 15.6 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56836,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3102,false,true,45056,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kaigetsudō Dohan,active 1710–16,,Kaigetsudō Dohan,Japanese,1710,1716,ca. 1714,1704,1724,Polychrome woodblock print (sumizuri-e); ink and color on paper,22 1/4 x 12 in. (56.5 x 30.5 cm),"Harris Brisbane Dick Fund and Rogers Fund, 1949",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45056,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3103,false,true,45057,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kaigetsudō Dohan,active 1710–16,,Kaigetsudō Dohan,Japanese,1710,1716,ca. 1714,1704,1724,Polychrome woodblock print (sumizuri-e); ink and color on paper,24 1/8 x 12 1/2 in. (61.3 x 31.8 cm),"Harris Brisbane Dick Fund and Rogers Fund, 1949",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45057,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3104,false,true,45058,Asian Art,Print,短冊持立美人図|Courtesan with Poetry Card (Tanzaku) at New Year,Japan,Edo period (1615–1868),,,,Artist,,Kaigetsudō Dohan,active 1710–16,,Kaigetsudō Dohan,Japanese,1710,1716,ca. 1714,1704,1724,Polychrome woodblock print (sumizuri-e); ink and color on paper,23 1/2 x 12 1/2 in. (59.7 x 31.8 cm),"Harris Brisbane Dick Fund and Rogers Fund, 1949",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45058,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1477,false,true,52008,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Chōkōsai Eishō,"Japanese, 1793–99",,Chōkōsai Eishō,Japanese,1793,1799,ca. 1800,1790,1810,Polychrome woodblock print; ink and color on paper,14 15/16 x 10 in. (37.9 x 25.4cm),"Fletcher Fund, 1925",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/52008,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1571,false,true,55748,Asian Art,Print,「扇屋昼見世畧」|Interior of the House called Ōgiya,Japan,Edo period (1615–1868),,,,Artist,,Chōkōsai Eishō,"Japanese, 1793–99",,Chōkōsai Eishō,Japanese,1793,1799,ca. 1800,1790,1810,Triptych of polychrome woodblock prints; ink and color on paper,Each H. 15 5/16 in. (38.9 cm); W. 9 5/8 in. (24.4 cm),"Fletcher Fund, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55748,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2425a–c,false,true,56676,Asian Art,Woodblock print,「丁子屋畧見世」|The Chōjiya Pleasure House by Day (Chōjiya hiru-mise),Japan,Edo period (1615–1868),,,,Artist,,Chōkōsai Eishō,"Japanese, 1793–99",,Chōkōsai Eishō,Japanese,1793,1799,ca. 1798,1788,1808,Triptych of polychrome woodblock prints; ink and color on paper,Each H. 15 in. (38.1 cm); W. 9 3/4 in. (24.8 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56676,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP229,false,true,36701,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Hishikawa Moronobu,"Japanese, died 1694",,Hishikawa Moronobu,Japanese,,1694,late 17th century,1667,1699,Polychrome woodblock print; ink and color on paper,10 x 15 in. (25.4 x 38.1 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36701,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP647,false,true,37097,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Hishikawa Moronobu,"Japanese, died 1694",,Hishikawa Moronobu,Japanese,,1694,ca. 1675–80,1673,1682,Polychrome woodblock print; ink and color on paper (sumi-e (ink print),11 1/4 x 20 2/3 in. (28.6 x 52.5 cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37097,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP676,false,true,37123,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Hishikawa Moronobu,"Japanese, died 1694",,Hishikawa Moronobu,Japanese,,1694,ca. 1675–80,1665,1690,Polychrome woodblock print; ink and color on paper,9 x 13 1/4 in. (22.9 x 33.7 cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37123,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP677,false,true,37124,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Hishikawa Moronobu,"Japanese, died 1694",,Hishikawa Moronobu,Japanese,,1694,ca. 1690,1680,1700,Polychrome woodblock print; ink and color on paper,10 x 13 27/32 in. (25.4 x 35.2 cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37124,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP807,false,true,37251,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Hishikawa Moronobu,"Japanese, died 1694",,Hishikawa Moronobu,Japanese,,1694,ca. 1680,1670,1690,Polychrome woodblock print; ink and color on paper,10 1/8 x 15 1/5 in. (25.7 x 38.6 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37251,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP808,false,true,37252,Asian Art,Print,"よしわらの躰　揚屋町入り口|The Entrance to Ageya-machi, from the series Scenes in the Yoshiwara (Yoshiwara no tei)",Japan,Edo period (1615–1868),,,,Artist,,Hishikawa Moronobu,"Japanese, died 1694",,Hishikawa Moronobu,Japanese,,1694,1681–84,1681,1684,Woodblock print (sumizuri-e); ink on paper,10 1/8 x 15 1/5 in. (25.7 x 38.6 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37252,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP809,false,true,37253,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Hishikawa Moronobu,"Japanese, died 1694",,Hishikawa Moronobu,Japanese,,1694,ca. 1680,1670,1690,Monochrome woodblock print; ink on paper,10 1/8 x 15 1/5 in. (25.7 x 38.6 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37253,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP810,false,true,37254,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Hishikawa Moronobu,"Japanese, died 1694",,Hishikawa Moronobu,Japanese,,1694,ca. 1680,1670,1690,Polychrome woodblock print; ink and color on paper,10 1/8 x 15 1/5 in. (25.7 x 38.6 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37254,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP811,false,true,37255,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Hishikawa Moronobu,"Japanese, died 1694",,Hishikawa Moronobu,Japanese,,1694,ca. 1680,1670,1680,Monochrome woodblock print; ink and color on paper,10 1/8 x 15 1/5 in. (25.7 x 38.6 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37255,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP812,false,true,37256,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Hishikawa Moronobu,"Japanese, died 1694",,Hishikawa Moronobu,Japanese,,1694,ca. 1680,1670,1690,Woodblock print; ink on paper,10 1/8 x 15 1/5 in. (25.7 x 38.6 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37256,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP813,false,true,37257,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Hishikawa Moronobu,"Japanese, died 1694",,Hishikawa Moronobu,Japanese,,1694,ca. 1680,1670,1690,Polychrome woodblock print; ink and color on paper,11 1/4 x 17 31/32 in. (28.6 x 45.7 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37257,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP814,false,true,37258,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Hishikawa Moronobu,"Japanese, died 1694",,Hishikawa Moronobu,Japanese,,1694,ca. 1685,1675,1695,Polychrome woodblock print; ink and color on paper,8 5/8 x 12 7/8 in. (21.9 x 32.7 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37258,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1590,false,true,45045,Asian Art,Print,Wakoku Hyakujo|Leaf from One Hundred Japanese Women,Japan,Edo period (1615–1868),,,,Artist,,Hishikawa Moronobu,"Japanese, died 1694",,Hishikawa Moronobu,Japanese,,1694,ca. 1695,1685,1705,Monochrome woodblock print; ink on paper,H. 6 1/2 in. (16.5 cm); W. 6 3/4 in. (17.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45045,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1594,false,true,55758,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Hishikawa Moronobu,"Japanese, died 1694",,Hishikawa Moronobu,Japanese,,1694,late 17th century,1667,1699,Monochrome woodblock print; ink on paper,H. 10 1/16 in. (25.6 cm); W. 15 3/16 in. (38.6 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55758,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2643,false,true,56825,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Hishikawa Moronobu,"Japanese, died 1694",,Hishikawa Moronobu,Japanese,,1694,ca. 1685,1675,1695,Monochrome woodblock print (sumie); ink on paper,11 x 16 1/4 in. (27.9 x 41.3 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56825,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3069,false,true,56550,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Hishikawa Moronobu,"Japanese, died 1694",,Hishikawa Moronobu,Japanese,,1694,1680s,1680,1689,Woodblock print; ink on paper,9 1/4 × 13 1/4 in. (23.5 × 33.7 cm),"Harris Brisbane Dick Fund and Rogers Fund, 1949",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56550,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3402,false,true,55584,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Hishikawa Moronobu,"Japanese, died 1694",,Hishikawa Moronobu,Japanese,,1694,late 17th century,1667,1694,Monochrome woodblock print; ink on paper,10 3/4 x 15 1/4 in. (27.3 x 38.7 cm),"Bequest of Julia H. Manges, 1960",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55584,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP123,false,true,36602,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunjō,"Japanese, died 1787",,Katsukawa Shunjō,Japanese,1700,1787,ca. 1780,1770,1790,Polychrome woodblock print; ink and color on paper,11 3/8 x 5 1/2 in. (28.9 x 14.0 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36602,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP386,false,true,36850,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunjō,"Japanese, died 1787",,Katsukawa Shunjō,Japanese,1700,1787,"2nd month, 1782",1782,1782,Polychrome woodblock print; ink and color on paper,11 7/8 x 5 5/8 in. (30.2 x 14.3 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36850,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP388,false,true,36851,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunjō,"Japanese, died 1787",,Katsukawa Shunjō,Japanese,1700,1787,1779,1779,1779,Diptych of polychrome woodblock prints; ink and color on paper,a: H. 12 13/16 in (32.5 cm); W. 5 3/4 in. (14.6 cm) b: H. 12 3/4 in. (32.4 cm); W. 5 11/16 in. (14.4 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36851,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1303,false,true,55242,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunjō,"Japanese, died 1787",,Katsukawa Shunjō,Japanese,1700,1787,ca. 1780,1770,1790,Polychrome woodblock print; ink and color on paper,H. 12 1/4 in. (31.1 cm); W. 5 3/4 in. (14.6 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55242,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1402,false,true,55410,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunjō,"Japanese, died 1787",,Katsukawa Shunjō,Japanese,1700,1787,ca. 1780,1770,1790,Polychrome woodblock print; ink and color on paper,H. 12 5/8 in. (32.1 cm); W. 6 1/4 in. (15.9 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55410,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1541,false,true,55706,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunjō,"Japanese, died 1787",,Katsukawa Shunjō,Japanese,1700,1787,ca. 1785,1775,1795,Polychrome woodblock print; ink and color on paper,H. 13 in. (33 cm); W. 5 3/4 in. (14.6 cm),"Fletcher Fund, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55706,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2678,false,true,56862,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunjō,"Japanese, died 1787",,Katsukawa Shunjō,Japanese,1700,1787,ca. 1790,1780,1810,Polychrome woodblock print; ink and color on paper,13 x 5 1/2 in. (33 x 14 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56862,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2679,false,true,56863,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunjō,"Japanese, died 1787",,Katsukawa Shunjō,Japanese,1700,1787,ca. 1785,1775,1795,Polychrome woodblock print; ink and color on paper,12 1/2 x 5 5/8 in. (31.8 x 14.3 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56863,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2905,false,true,56020,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunjō,"Japanese, died 1787",,Katsukawa Shunjō,Japanese,1700,1787,1700–1787,1700,1787,Polychrome woodblock print; ink and color on paper,12 1/8 x 5 1/2 in. (30.8 x 14 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56020,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP191,false,true,36668,Asian Art,Woodblock print,"『浅草観音奉掛額之 図』　「扇屋内滝川　富川、粂川、玉川、津川、歌川、清川　め浪、お浪」|A Votive Picture to Be Donated to the Kannon of Asakusa (Asakusa Kannon hō kakegaku no zu), by Takigawa of the Ōgiya, Kamuro Menami and Onami, with Tomikawa, Kumegawa, Tamagawa, Tsugawa, Utagawa, and Kiyokawa",Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Kikumaro,"Japanese, died 1830",,Kitagawa Kikumaro,Japanese,,1830,ca. 1800,1790,1810,Triptych of polychrome woodblock prints; ink and color on paper,Overall: H. 14 3/4 in. (37.5 cm); W. 23 3/4 in. (60. 3 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36668,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1091,false,true,55028,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Kikumaro,"Japanese, died 1830",,Kitagawa Kikumaro,Japanese,,1830,ca. 1815,1805,1825,Polychrome woodblock print; ink and color on paper,Aiban; H. 13 3/4 in. (34.9 cm); W. 9 1/16 in. (23 cm),The Metropolitan Museum of Art,,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55028,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1968,false,true,54552,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Kikumaro,"Japanese, died 1830",,Kitagawa Kikumaro,Japanese,,1830,probably 1815,1815,1815,Polychrome woodblock print (surimono); ink and color on paper,8 1/4 x 7 1/4 in. (21 x 18.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54552,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2011.135,false,true,76565,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Shunbaisai Hokuei,"Japanese, died 1837",,Shunbaisai Hokuei,Japanese,,1837,1832,1832,1832,Polychrome woodblock print,Image (ôban tate-e): 14 7/8 x 10 1/8 in. (37.8 x 25.7 cm),"Purchase, Friends of Asian Art Gifts, in honor of James C. Y. Watt, 2011",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/76565,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2011.138,false,true,76568,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Shunbaisai Hokuei,"Japanese, died 1837",,Shunbaisai Hokuei,Japanese,,1837,1835,1835,1835,Polychrome woodblock print,Image (ôban tate-e): 14 5/8 x 10 1/8 in. (37.1 x 25.7 cm),"Purchase, Friends of Asian Art Gifts, in honor of James C. Y. Watt, 2011",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/76568,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2011.139,false,true,76569,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Shunbaisai Hokuei,"Japanese, died 1837",,Shunbaisai Hokuei,Japanese,,1837,1837,1837,1837,Polychrome woodblock print,Image (ôban tate-e): 15 1/4 x 10 in. (38.7 x 25.4 cm),"Purchase, Friends of Asian Art Gifts, in honor of James C. Y. Watt, 2011",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/76569,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2011.137a–d,false,true,76567,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Shunbaisai Hokuei,"Japanese, died 1837",,Shunbaisai Hokuei,Japanese,,1837,1835,1835,1835,Tetraptych of polychrome woodblock prints,Each sheet (ôban tate-e tetraptych): 14 5/8 x 10 1/8 in. (37.1 x 25.7 cm),"Purchase, Friends of Asian Art Gifts, in honor of James C. Y. Watt, 2011",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/76567,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1000,false,true,54877,Asian Art,Print,"風流六玉川　紀伊　高野の玉川|The Kōya no Tamagawa, Province of Kii",Japan,Edo period (1615–1868),,,,Artist,,Utamaro II,Japanese (died 1831?),,Utamaro II,Japanese,1750,1850,ca. 1806,1796,1816,Polychrome woodblock print; ink and color on paper,H. 13 7/8 in. (35.2 cm); W. 8 7/8 in. (22.5 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54877,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1001,false,true,54878,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Utamaro II,Japanese (died 1831?),,Utamaro II,Japanese,1750,1850,ca. 1806,1796,1816,Polychrome woodblock print; ink and color on paper,H. 15 in. (38.1 cm); W. 9 5/8 in. (24.4 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54878,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1002,false,true,54879,Asian Art,Print,遊君出そめ初衣裳　扇屋内花扇|The Oiran Hanaogi of Ogiya attended by Two Shinzo and Her Kamuro Yoshino,Japan,Edo period (1615–1868),,,,Artist,,Utamaro II,Japanese (died 1831?),,Utamaro II,Japanese,1750,1850,ca. 1806,1796,1816,Polychrome woodblock print; ink and color on paper,H. 14 5/8 in. (37.1 cm); W. 9 1/2 in. (24.1 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54879,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1991.341,false,true,54231,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Utamaro II,Japanese (died 1831?),,Utamaro II,Japanese,1750,1850,ca. 1807,1797,1817,Polychrome woodblock print (surimono); ink and color on paper,15 3/4 x 21 5/8 in. (40 x 54.9 cm) (unfolded),"Gift of Joan B. Mirviss and Robert J. Levine, in memory of Carolyn D. Solomon, 1991",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54231,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP648,false,true,37098,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonobu,"Japanese, 1664–1729",,Torii Kiyonobu,Japanese,1664,1729,ca. 1701–06,1699,1708,Polychrome woodblock print; ink and color on paper,20 31/32 x 11 7/8 in. (53.3 x 30.2 cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37098,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP649,false,true,37099,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonobu,"Japanese, 1664–1729",,Torii Kiyonobu,Japanese,1664,1729,ca. 1700–05,1695,1715,Polychrome woodblock print; ink and color on paper,22 x 12 3/4 in. (55.9 x 32.4 cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37099,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP650,false,true,37100,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonobu,"Japanese, 1664–1729",,Torii Kiyonobu,Japanese,1664,1729,ca. 1725,1715,1735,Polychrome woodblock print; ink and color on paper,27 15/32 x 6 1/8 in. (69.8 x 15.6 cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37100,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP680,false,true,37127,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonobu,"Japanese, 1664–1729",,Torii Kiyonobu,Japanese,1664,1729,ca. 1705,1695,1715,Polychrome woodblock print; ink and color on paper,11 7/8 x 5 3/4 in. (30.2 x 14.6 cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37127,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP681,false,true,37128,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonobu,"Japanese, 1664–1729",,Torii Kiyonobu,Japanese,1664,1729,ca. 1705,1695,1715,Polychrome woodblock print; ink and color on paper,11 3/5 x 5 31/32 in. (29.5 x 15.2 cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37128,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP828,false,true,54482,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonobu,"Japanese, 1664–1729",,Torii Kiyonobu,Japanese,1664,1729,1748,1748,1748,Polychrome woodblock print; ink and color on paper,H. 12 1/2 in. (31.8 cm); W. 5 7/8 in. (14.9 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54482,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP829,false,true,54483,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonobu,"Japanese, 1664–1729",,Torii Kiyonobu,Japanese,1664,1729,ca. 1744,1734,1754,Polychrome woodblock print; ink and color on paper,H. 12 1/8 in. (30.8 cm); 5 5/8 in. (14.3 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54483,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP830,false,true,54484,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonobu,"Japanese, 1664–1729",,Torii Kiyonobu,Japanese,1664,1729,ca. 1745,1735,1755,Polychrome woodblock print; ink and color on paper,H. 12 1/4 in. (31.1 cm); W. 6 in. (15.2 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54484,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP831,false,true,54485,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonobu,"Japanese, 1664–1729",,Torii Kiyonobu,Japanese,1664,1729,ca. 1745,1735,1755,Polychrome woodblock print; ink and color on paper,H. 12 3/8 in. (31.4 cm); W. 6 in. (15.2 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54485,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1448,false,true,55494,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonobu,"Japanese, 1664–1729",,Torii Kiyonobu,Japanese,1664,1729,ca. 1750,1740,1760,Polychrome woodblock print; ink and color on paper,H. 11 5/8 in. (29.5 cm); W. 5 3/4 in. (14.6 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55494,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1464,false,true,37326,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonobu,"Japanese, 1664–1729",,Torii Kiyonobu,Japanese,1664,1729,ca. 1742,1732,1752,Polychrome woodblock print; ink and color on paper,H. 10 7/8 in. (27.6 cm); W. 5 3/4 in. (14.6 cm),"Rogers Fund, 1925",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37326,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2623,false,true,56766,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonobu,"Japanese, 1664–1729",,Torii Kiyonobu,Japanese,1664,1729,ca. 1720–25,1720,1725,Polychrome woodblock print; ink and color on paper,13 1/4 x 6 1/4 in. (33.7 x 15.9 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56766,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3071,false,true,56552,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonobu,"Japanese, 1664–1729",,Torii Kiyonobu,Japanese,1664,1729,1703,1703,1703,Polychrome woodblock print (hand-colored); ink and color on paper,10 3/4 x 14 1/2 in. (27.3 x 36.8 cm),"Harris Brisbane Dick Fund and Rogers Fund, 1949",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56552,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3097,false,true,45060,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonobu,"Japanese, 1664–1729",,Torii Kiyonobu,Japanese,1664,1729,1698,1688,1708,Polychrome woodblock print (sumizuri-e); ink and color on paper,23 1/4 x 12 1/2 in. (59.1 x 31.8 cm),"Harris Brisbane Dick Fund and Rogers Fund, 1949",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45060,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3098,false,true,56610,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,Attributed to,Torii Kiyonobu,"Japanese, 1664–1729",,Torii Kiyonobu,Japanese,1664,1729,ca. 1708,1698,1718,Polychrome woodblock print; ink and color on paper,21 3/4 x 11 1/2 in. (55.2 x 29.2 cm),"Harris Brisbane Dick Fund and Rogers Fund, 1949",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56610,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP672,false,true,37119,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Nishikawa Sukenobu,"Japanese, 1671–1750",,Nishikawa Sukenobu,Japanese,1671,1750,1731,1731,1731,Polychrome woodblock print; ink and color on paper,10 3/4 x 13 1/10 in. (27.3 x 33.3 cm),"Gift of Mrs. Henry J. Bernheim, 1945",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37119,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2717,false,true,56985,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Nishikawa Sukenobu,"Japanese, 1671–1750",,Nishikawa Sukenobu,Japanese,1671,1750,ca. 1730,1720,1740,Monochrome woodblock print; ink on paper,10 3/8 x 16 1/2 in. (26.4 x 41.9 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56985,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2755,false,true,57021,Asian Art,Book illustration,,Japan,Edo period (1615–1868),,,,Artist,,Nishikawa Sukenobu,"Japanese, 1671–1750",,Nishikawa Sukenobu,Japanese,1671,1750,1671–1751,1671,1751,Monochrome woodblock print; ink on paper,14 1/2 x 10 1/4 in. (36.8 x 26 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57021,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP188,false,true,36658,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Okumura Masanobu,"Japanese, 1686–1764",,Masanobu Okumura,Japanese,1686,1764,ca. 1738,1728,1748,"Polychrome woodblock print; ink, color, and hand-coloring on paper",11 1/4 x 16 1/2 in. (28.6 x 41.9 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36658,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP189,false,true,36666,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Okumura Masanobu,"Japanese, 1686–1764",,Masanobu Okumura,Japanese,1686,1764,ca. 1748,1738,1758,Polychrome woodblock print; ink and color on paper,27 5/8 x 9 7/8 in. (70.2 x 25.1 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36666,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP539,false,true,36990,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Okumura Masanobu,"Japanese, 1686–1764",,Masanobu Okumura,Japanese,1686,1764,ca. 1709,1699,1719,Polychrome woodblock print; ink and color on paper,10 3/8 x 14 3/4 in. (26.4 x 37.5 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36990,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP540,false,true,36991,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Okumura Masanobu,"Japanese, 1686–1764",,Masanobu Okumura,Japanese,1686,1764,probably 1709,1707,1711,Polychrome woodblock print; ink and color on paper,10 3/8 x 14 3/4 in. (26.4 x 37.5 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36991,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP541,false,true,36992,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Okumura Masanobu,"Japanese, 1686–1764",,Masanobu Okumura,Japanese,1686,1764,probably 1709,1707,1711,Polychrome woodblock print; ink and color on paper,10 3/8 x 14 3/4 in. (26.4 x 37.5 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36992,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP542,false,true,36993,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Okumura Masanobu,"Japanese, 1686–1764",,Masanobu Okumura,Japanese,1686,1764,1710–13,1710,1713,Polychrome woodblock print; ink and color on paper,10 3/8 x 14 3/4 in. (26.4 x 37.5 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36993,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP543,false,true,36994,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Okumura Masanobu,"Japanese, 1686–1764",,Masanobu Okumura,Japanese,1686,1764,probably 1709,1707,1711,Polychrome woodblock print; ink and color on paper,10 7/32 x 14 3/4 in. (26.0 x 37.5 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36994,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP544,false,true,36995,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Okumura Masanobu,"Japanese, 1686–1764",,Masanobu Okumura,Japanese,1686,1764,probably 1709,1707,1711,Polychrome woodblock print; ink and color on paper,10 3/8 x 14 3/4 in. (26.4 x 37.5 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36995,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP545,false,true,36996,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Okumura Masanobu,"Japanese, 1686–1764",,Masanobu Okumura,Japanese,1686,1764,probably 1709,1707,1711,Polychrome woodblock print; ink and color on paper,10 3/8 x 14 3/4 in. (26.4 x 37.5 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36996,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP546,false,true,36997,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Okumura Masanobu,"Japanese, 1686–1764",,Masanobu Okumura,Japanese,1686,1764,1710–13,1710,1713,Polychrome woodblock print; ink and color on paper,10 3/8 x 14 3/4 in. (26.4 x 37.5 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36997,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP547,false,true,36998,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Okumura Masanobu,"Japanese, 1686–1764",,Masanobu Okumura,Japanese,1686,1764,ca. 1710,1690,1700,Polychrome woodblock print; ink and color on paper,10 3/8 x 14 3/4 in. (26.4 x 37.5 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36998,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP548,false,true,36999,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Okumura Masanobu,"Japanese, 1686–1764",,Masanobu Okumura,Japanese,1686,1764,probably 1709,1707,1711,Polychrome woodblock print; ink and color on paper,10 3/8 x 14 3/4 in. (26.4 x 37.5 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36999,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP549,false,true,37000,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Okumura Masanobu,"Japanese, 1686–1764",,Masanobu Okumura,Japanese,1686,1764,probably 1709,1707,1711,Polychrome woodblock print; ink and color on paper,10 3/8 x 14 3/4 in. (26.4 x 37.5 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37000,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP550,false,true,37001,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Okumura Masanobu,"Japanese, 1686–1764",,Masanobu Okumura,Japanese,1686,1764,1710–13,1710,1713,Polychrome woodblock print; ink and color on paper,10 3/8 x 13 27/32 in. (26.4 x 35.2 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37001,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP666,false,true,37113,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Okumura Masanobu,"Japanese, 1686–1764",,Masanobu Okumura,Japanese,1686,1764,ca. 1763,1753,1773,Polychrome woodblock print; ink and color on paper,28 23/32 x 5 7/32 in. (73.0 x 13.3 cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37113,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP678,false,true,37125,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Okumura Masanobu,"Japanese, 1686–1764",,Masanobu Okumura,Japanese,1686,1764,ca. 1700,1690,1710,Polychrome woodblock print; ink and color on paper,10 3/8 x 14 3/4 in. (26.4 x 37.5 cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37125,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP679,false,true,37126,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Okumura Masanobu,"Japanese, 1686–1764",,Masanobu Okumura,Japanese,1686,1764,ca. 1700–1703,1690,1713,Polychrome woodblock print; ink and color on paper,10 1/8 x 13 23/32 in. (25.7 x 34.9 cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37126,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP682,false,true,37129,Asian Art,Print,"見立紫式部図|Parody of Murasaki Shikibu, Author of The Tale of Genji",Japan,Edo period (1615–1868),,,,Artist,,Okumura Masanobu,"Japanese, 1686–1764",,Masanobu Okumura,Japanese,1686,1764,early 18th century,1700,1733,Monochrome woodblock print; ink on paper,10 3/8 x 14 1/8 in. (26.4 x 35.9 cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37129,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP683,false,true,37130,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Okumura Masanobu,"Japanese, 1686–1764",,Masanobu Okumura,Japanese,1686,1764,early 18th century,1700,1733,Monochrome woodblock print; ink and color on paper,10 3/8 x 14 in. (26.4 x 35.6 cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37130,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP684,false,true,37131,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Okumura Masanobu,"Japanese, 1686–1764",,Masanobu Okumura,Japanese,1686,1764,ca. 1705–7,1695,1717,Polychrome woodblock print; ink and color on paper,10 x 14 15/32 in. (25.4 x 36.8 cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37131,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP685,false,true,37132,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Okumura Masanobu,"Japanese, 1686–1764",,Masanobu Okumura,Japanese,1686,1764,ca. 1705–7,1695,1717,Polychrome woodblock print; ink and color on paper,9 15/32 x 13 1/4 in. (24.1 x 33.7 cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37132,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP686,false,true,37133,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Okumura Masanobu,"Japanese, 1686–1764",,Masanobu Okumura,Japanese,1686,1764,ca. 1730,1720,1740,Polychrome woodblock print; ink and color on paper,12 7/32 x 5 31/32 in. (31.1 x 15.2 cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37133,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP687,false,true,37134,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Okumura Masanobu,"Japanese, 1686–1764",,Masanobu Okumura,Japanese,1686,1764,ca. 1749,1739,1759,Polychrome woodblock print; ink and color on paper,4 1/4 x 6 7/8 in. (10.8 x 17.5 cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37134,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP816,false,true,37260,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Okumura Masanobu,"Japanese, 1686–1764",,Masanobu Okumura,Japanese,1686,1764,early 18th century,1700,1735,Monochrome woodblock print; ink and color on paper,9 7/8 x 14 3/4 in. (25.1 x 37.5 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37260,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1308,false,true,37318,Asian Art,Lacquer print,,Japan,Edo period (1615–1868),,,,Artist,,Okumura Masanobu,"Japanese, 1686–1764",,Masanobu Okumura,Japanese,1686,1764,1740,1740,1740,Polychrome lacquer print (urushi-e),12 7/8 x 6 1/4 in. (32.7 x 15.9 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37318,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1499,false,true,55597,Asian Art,Print,"『閨の雛形』　正月|Plate from the Erotic Book Mounds of Dyed Colors: A Pattern Book for the Boudoir (Someiro no yama neya no hinagata), First Month",Japan,Edo period (1615–1868),,,,Artist,,Okumura Masanobu,"Japanese, 1686–1764",,Masanobu Okumura,Japanese,1686,1764,ca. 1736–44,1736,1744,Hand-colored woodblock illustration; ink and color on paper,H. 9 9/16 in. (24.3 cm); W. 14 1/4 in. (36.2 cm),"Fletcher Fund, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55597,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2637,false,true,56780,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Okumura Masanobu,"Japanese, 1686–1764",,Masanobu Okumura,Japanese,1686,1764,ca. 1715,1705,1725,Monochrome woodblock print (sumie); ink on paper,11 3/8 x 16 1/4 in. (28.9 x 41.3 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56780,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2638,false,true,56782,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Okumura Masanobu,"Japanese, 1686–1764",,Masanobu Okumura,Japanese,1686,1764,1750,1750,1750,Polychrome woodblock print; ink and color on paper,16 x 11 1/4 in. (40.6 x 28.6 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56782,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2639,false,true,56781,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Okumura Masanobu,"Japanese, 1686–1764",,Masanobu Okumura,Japanese,1686,1764,ca. 1715,1705,1725,Monochrome woodblock print (sumie); ink on paper,10 x 14 in. (25.4 x 35.6 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56781,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2640,false,true,45050,Asian Art,Print,Sanpuku Tsui|Moon in Musashi Province,Japan,Edo period (1615–1868),,,,Artist,,Okumura Masanobu,"Japanese, 1686–1764",,Masanobu Okumura,Japanese,1686,1764,ca. 1740,1730,1750,"Center sheet of a triptych of polychrome woodblock prints; ink and applied color (""tan-e"") on paper",12 1/2 x 6 1/8 in. (31.8 x 15.6 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45050,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2641,false,true,56821,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Okumura Masanobu,"Japanese, 1686–1764",,Masanobu Okumura,Japanese,1686,1764,ca. 1744,1734,1754,Polychrome woodblock print; ink and color on paper,12 1/8 x 5 1/2 in. (30.8 x 14 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56821,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3025,false,true,56423,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Okumura Masanobu,"Japanese, 1686–1764",,Masanobu Okumura,Japanese,1686,1764,ca. 1739,1729,1749,Polychrome woodblock print; ink and color on paper (hand colored),10 3/4 x 15 5/8 in. (27.3 x 39.7 cm),"Anonymous Gift, 1945",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56423,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3026,false,true,56424,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Okumura Masanobu,"Japanese, 1686–1764",,Masanobu Okumura,Japanese,1686,1764,ca. 1739,1729,1749,Polychrome woodblock print; ink and color on paper (hand colored),10 3/4 x 15 in. (27.3 x 38.1 cm),"Anonymous Gift, 1945",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56424,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3027,false,true,56425,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Okumura Masanobu,"Japanese, 1686–1764",,Masanobu Okumura,Japanese,1686,1764,ca. 1739,1729,1749,Polychrome woodblock print; ink and color on paper (hand colored),10 3/4 x 15 1/4 in. (27.3 x 38.7 cm),"Anonymous Gift, 1945",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56425,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3028,false,true,56426,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Okumura Masanobu,"Japanese, 1686–1764",,Masanobu Okumura,Japanese,1686,1764,ca. 1739,1729,1749,Polychrome woodblock print; ink and color on paper (hand colored),10 3/4 x 15 1/8 in. (27.3 x 38.4 cm),"Anonymous Gift, 1945",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56426,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3029,false,true,56427,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Okumura Masanobu,"Japanese, 1686–1764",,Masanobu Okumura,Japanese,1686,1764,ca. 1739,1729,1749,Polychrome woodblock print; ink and color on paper (hand colored),10 3/4 x 15 1/8 in. (27.3 x 38.4 cm),"Anonymous Gift, 1945",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56427,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3030,false,true,56428,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Okumura Masanobu,"Japanese, 1686–1764",,Masanobu Okumura,Japanese,1686,1764,ca. 1739,1729,1749,Polychrome woodblock print; ink and color on paper (hand colored),10 3/4 x 15 1/8 in. (27.3 x 38.4 cm),"Anonymous Gift, 1945",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56428,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3031,false,true,56429,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Okumura Masanobu,"Japanese, 1686–1764",,Masanobu Okumura,Japanese,1686,1764,ca. 1739,1729,1749,Polychrome woodblock print; ink and color on paper (hand colored),10 3/4 x 15 1/8 in. (27.3 x 38.4 cm),"Anonymous Gift, 1945",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56429,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3032,false,true,56490,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Okumura Masanobu,"Japanese, 1686–1764",,Masanobu Okumura,Japanese,1686,1764,ca. 1739,1729,1749,Polychrome woodblock print; ink and color on paper (hand colored),10 3/4 x 15 1/8 in. (27.3 x 38.4 cm),"Anonymous Gift, 1946",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56490,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3033,false,true,56491,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Okumura Masanobu,"Japanese, 1686–1764",,Masanobu Okumura,Japanese,1686,1764,ca. 1739,1729,1749,Polychrome woodblock print; ink and color on paper (hand colored),10 3/4 x 15 1/8 in. (27.3 x 38.4 cm),"Anonymous Gift, 1945",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56491,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3034,false,true,56492,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Okumura Masanobu,"Japanese, 1686–1764",,Masanobu Okumura,Japanese,1686,1764,ca. 1739,1729,1749,Polychrome woodblock print; ink and color on paper (hand colored),10 3/4 x 15 1/8 in. (27.3 x 38.4 cm),"Anonymous Gift, 1945",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56492,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3035,false,true,56493,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Okumura Masanobu,"Japanese, 1686–1764",,Masanobu Okumura,Japanese,1686,1764,ca. 1739,1729,1749,Polychrome woodblock print; ink and color on paper (hand colored),10 3/4 x 15 3/8 in. (27.3 x 39.1 cm),"Anonymous Gift, 1945",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56493,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3036,false,true,56494,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Okumura Masanobu,"Japanese, 1686–1764",,Masanobu Okumura,Japanese,1686,1764,ca. 1739,1729,1749,Polychrome woodblock print; ink and color on paper (hand colored),10 3/4 x 15 1/8 in. (27.3 x 38.4 cm),"Anonymous Gift, 1945",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56494,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3077,false,true,45239,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Okumura Masanobu,"Japanese, 1686–1764",,Masanobu Okumura,Japanese,1686,1764,ca. 1730,1720,1740,Polychrome woodblock print (hand colored); ink and color on paper,12 3/4 x 6 1/4 in. (32.4 x 15.9 cm),"Harris Brisbane Dick Fund and Rogers Fund, 1949",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45239,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3078,false,true,56588,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Okumura Masanobu,"Japanese, 1686–1764",,Masanobu Okumura,Japanese,1686,1764,ca. 1755,1745,1765,Polychrome woodblock print; ink and color on paper,12 1/8 x 5 1/2 in. (30.8 x 14 cm),"Harris Brisbane Dick Fund and Rogers Fund, 1949",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56588,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3079,false,true,51993,Asian Art,Print,見立『平家物語』 紅葉焚図|Parody of Palace Servants Heating Sake over a Fire of Maple Leaves,Japan,Edo period (1615–1868),,,,Artist,,Okumura Masanobu,"Japanese, 1686–1764",,Masanobu Okumura,Japanese,1686,1764,ca. 1750,1740,1760,Red-colored woodblock print (benizuri-e); ink and color on paper,Image: 16 5/16 × 11 3/4 in. (41.4 × 29.8 cm),"Harris Brisbane Dick Fund and Rogers Fund, 1949",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/51993,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3107,false,true,56613,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Okumura Masanobu,"Japanese, 1686–1764",,Masanobu Okumura,Japanese,1686,1764,ca. 1745,1735,1755,Polychrome woodblock print; ink and color on paper,28 1/2 x 6 1/4 in. (72.4 x 15.9 cm),"Harris Brisbane Dick Fund and Rogers Fund, 1949",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56613,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3169,false,true,56722,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Okumura Masanobu,"Japanese, 1686–1764",,Masanobu Okumura,Japanese,1686,1764,early 18th century,1700,1733,Polychrome woodblock print; ink and color on paper (Beni-e),H. 27 in. (68.6 cm); W. 10 in. (25.4 cm),"Bequest of Katherine S. Dreier, 1952",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56722,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1307,false,true,55265,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyomasu II,"Japanese, 1706–1763",,Torii Kiyomasu II,Japanese,1706,1763,ca. 1748,1738,1758,Polychrome lacquer print (urushi-e),12 1/2 x 5 7/8 in. (31.8 x 14.9 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55265,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1447,false,true,51088,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyomasu II,"Japanese, 1706–1763",,Torii Kiyomasu II,Japanese,1706,1763,early 18th century,1700,1733,"Polychrome woodblock print; (beni-e); black, red and green on paper",12 1/2 x 6 1/8 in. (31.8 x 15.6 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/51088,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2758,false,true,54861,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,Attributed to,Torii Kiyomasu II,"Japanese, 1706–1763",,Torii Kiyomasu II,Japanese,1706,1763,ca. 1730–40,1720,1750,Tan-e (hand-colored print); ink and color on paper,H. 11 3/8 in. (28.9 cm); W. 6 in. (15.2 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54861,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3081,false,true,56594,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,Attributed to,Torii Kiyomasu II,"Japanese, 1706–1763",,Torii Kiyomasu II,Japanese,1706,1763,before 1763,1663,1763,Polychrome woodblock print; ink and color on paper,12 3/4 x 5 3/4 in. (32.4 x 14.6 cm),"Harris Brisbane Dick Fund and Rogers Fund, 1949",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56594,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP656,false,true,37106,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ishikawa Toyonobu,"Japanese, 1711–1785",,Ishikawa Toyonobu,Japanese,1711,1785,ca. 1750,1740,1760,Polychrome woodblock print; ink and color on paper,17 7/32 x 12 7/32 in. (43.8 x 31.1 cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37106,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP691,false,true,37138,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ishikawa Toyonobu,"Japanese, 1711–1785",,Ishikawa Toyonobu,Japanese,1711,1785,ca. 1748,1738,1758,Polychrome woodblock print; ink and color on paper,12 7/32 x 5 5/8 in. (31.1 x 14.3 cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37138,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP844,false,true,45240,Asian Art,Print,Go Gatsu|The Fifth Month,Japan,Edo period (1615–1868),,,,Artist,,Ishikawa Toyonobu,"Japanese, 1711–1785",,Ishikawa Toyonobu,Japanese,1711,1785,ca. 1748,1738,1758,Polychrome woodblock print; ink and color on paper,H. 12 1/4 in. (31.1 cm); W. 2 15/16 in. (7.5 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45240,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP845,false,true,45288,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ishikawa Toyonobu,"Japanese, 1711–1785",,Ishikawa Toyonobu,Japanese,1711,1785,ca. 1750,1740,1760,Polychrome woodblock print; ink and color on paper,H. 7 1/4 in. (18.4 cm); W. 11 3/8 in. (28.9 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45288,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP846,false,true,54499,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ishikawa Toyonobu,"Japanese, 1711–1785",,Ishikawa Toyonobu,Japanese,1711,1785,ca. 1750,1740,1760,Polychrome woodblock print; ink and color on paper,H. 7 1/4 in. (18.4 cm); W. 11 3/8 in. (28.9 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54499,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP847,false,true,54501,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ishikawa Toyonobu,"Japanese, 1711–1785",,Ishikawa Toyonobu,Japanese,1711,1785,ca. 1752,1742,1762,Polychrome woodblock print; ink and color on paper,H. 15 1/2 in. (39.4 cm); W. 11 3/16 in. (28.4 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54501,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP848,false,true,54505,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ishikawa Toyonobu,"Japanese, 1711–1785",,Ishikawa Toyonobu,Japanese,1711,1785,ca. 1758,1748,1768,Polychrome woodblock print; ink and color on paper,H. 11 1/2 in. (29.2 cm); W. 17 1/8 in. (43.5 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54505,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1616,false,true,55781,Asian Art,Print,初代瀬川菊之丞の傾城図|The Kabuki Actor Segawa Kikunojo in the Role of a Courtesan Reading a Letter,Japan,Edo period (1615–1868),,,,Artist,,Ishikawa Toyonobu,"Japanese, 1711–1785",,Ishikawa Toyonobu,Japanese,1711,1785,late 1740s,1745,1749,Polychrome woodblock print; ink and color on paper,H. 26 3/4 in. (67.9 cm); W. 9 5/8 in. (24.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55781,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2727,false,true,56999,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ishikawa Toyonobu,"Japanese, 1711–1785",,Ishikawa Toyonobu,Japanese,1711,1785,probably January 1749,1749,1749,Polychrome woodblock print; ink and color on paper,11 1/2 x 5 3/8 in. (29.2 x 13.7 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56999,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2728,false,true,57001,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ishikawa Toyonobu,"Japanese, 1711–1785",,Ishikawa Toyonobu,Japanese,1711,1785,1748–1750,1748,1750,Polychrome woodblock print; ink and color on paper,16 3/4 x 12 in. (42.5 x 30.5 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57001,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2729,false,true,57002,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ishikawa Toyonobu,"Japanese, 1711–1785",,Ishikawa Toyonobu,Japanese,1711,1785,1744,1744,1744,Polychrome woodblock print; ink and color on paper,15 x 11 1/4 in. (38.1 x 28.6 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57002,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2759,false,true,57023,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ishikawa Toyonobu,"Japanese, 1711–1785",,Ishikawa Toyonobu,Japanese,1711,1785,1711–1785,1711,1785,Polychrome woodblock print; ink and color on paper,15 5/8 x 6 7/8 in. (39.7 x 17.5 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57023,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2760,false,true,57024,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Ishikawa Toyonobu,"Japanese, 1711–1785",,Ishikawa Toyonobu,Japanese,1711,1785,1711–1785,1711,1785,Polychrome woodblock print; ink and color on paper,12 1/8 x 5 3/4 in. (30.8 x 14.6 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57024,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3085,false,true,56598,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,Attributed to,Ishikawa Toyonobu,"Japanese, 1711–1785",,Ishikawa Toyonobu,Japanese,1711,1785,ca. 1728,1718,1738,Polychrome woodblock print (hand-colored); ink and color on paper,10 1/2 x 15 in. (26.7 x 38.1 cm),"Harris Brisbane Dick Fund and Rogers Fund, 1949",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56598,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3086,false,true,56599,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ishikawa Toyonobu,"Japanese, 1711–1785",,Ishikawa Toyonobu,Japanese,1711,1785,1758,1758,1758,Polychrome woodblock print; ink and color on paper,15 7/8 x 6 7/8 in. (40.3 x 17.5 cm),"Harris Brisbane Dick Fund and Rogers Fund, 1949",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56599,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3087,false,true,56600,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ishikawa Toyonobu,"Japanese, 1711–1785",,Ishikawa Toyonobu,Japanese,1711,1785,1761,1761,1761,Polychrome woodblock print; ink and color on paper,15 3/8 x 6 1/2 in. (39.1 x 16.5 cm),"Harris Brisbane Dick Fund and Rogers Fund, 1949",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56600,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3088,false,true,56601,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ishikawa Toyonobu,"Japanese, 1711–1785",,Ishikawa Toyonobu,Japanese,1711,1785,1750 or 1751,1750,1751,Triptych of polychrome woodblock prints; ink and color on paper,12 x 17 1/2 in. (30.5 x 44.5 cm),"Harris Brisbane Dick Fund and Rogers Fund, 1949",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56601,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3108,false,true,56614,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ishikawa Toyonobu,"Japanese, 1711–1785",,Ishikawa Toyonobu,Japanese,1711,1785,ca. 1745,1735,1755,Polychrome woodblock print (hand colored); ink and color on paper,25 1/2 x 6 in. (64.8 x 15.2 cm),"Harris Brisbane Dick Fund and Rogers Fund, 1949",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56614,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3109,false,true,56615,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ishikawa Toyonobu,"Japanese, 1711–1785",,Ishikawa Toyonobu,Japanese,1711,1785,ca. 1748,1738,1758,Polychrome woodblock print (hand colored); ink and color on paper,19 3/4 x 9 in. (50.2 x 22.9 cm),"Harris Brisbane Dick Fund and Rogers Fund, 1949",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56615,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3110,false,true,56617,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ishikawa Toyonobu,"Japanese, 1711–1785",,Ishikawa Toyonobu,Japanese,1711,1785,ca. 1743,1733,1753,Polychrome woodblock print (hand colored); ink and color on paper,24 x 9 3/4 in. (61 x 24.8 cm),"Harris Brisbane Dick Fund and Rogers Fund, 1949",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56617,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP655,false,true,37105,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Furuyama Moromasa,"Japanese, 1712–1772",,Furuyama Moromasa,Japanese,1712,1772,ca. 1740,1730,1750,Polychrome woodblock print; ink and color on paper,Oban: 12 31/32 x 18 1/2 in. (33.0 x 47.0 cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37105,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1399,false,true,55407,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Toriyama Sekien,"Japanese, 1712–1788",,Toriyama Sekien,Japanese,1712,1788,ca. 1775,1765,1785,Polychrome woodblock print; ink and color on paper,H. 12 7/8 in. (32.7 cm); W. 9 7/16 in. (24 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55407,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP154,false,true,36633,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,ca.1766–70,1766,1770,Polychrome woodblock print; ink and color on paper,11 1/4 x 8 7/16 in. (28.6 x 21.4 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36633,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP155,false,true,36634,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,ca. 1766,1756,1776,Polychrome woodblock print; ink and color on paper,11 3/8 x 8 17/32 in. (28.9 x 21.7 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36634,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP156,false,true,36635,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,probably 1799,1797,1801,Polychrome woodblock print; ink and color on paper,11 1/4 x 8 1/2 in. (28.6 x 21.6 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36635,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP157,false,true,36636,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,ca. 1768,1758,1778,Polychrome woodblock print; ink and color on paper,25 1/2 x 4 3/4 in. (64.8 x 12.1 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36636,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP158,false,true,36637,Asian Art,Print,風俗六玉川|A Young Komuso,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,ca. 1768,1758,1778,Polychrome woodblock print; ink and color on paper,26 3/5 x 4 3/4 in. (67.6 x 12.1 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36637,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP226,false,true,45090,Asian Art,Print,"青楼美人合|The Courtesans, from the Series, ""Seiro Bijin Awase Carver End Shigoro"" (sic.)",Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,ca. 1765,1755,1775,Polychrome woodblock print; ink and color on paper,H. 8 1/2 in. (21.6 cm); W. 5 3/4 in. (14.6 cm),"Rogers Fund, 1917",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45090,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP227,false,true,48892,Asian Art,Print,"青楼美人合|The Courtesans, from the series, ""Seiro Bijin Awase Carver End Shigoro"" (sic)",Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,ca. 1765,1755,1775,Polychrome woodblock print; ink and color on paper,8 1/2 x 5 3/4 in. (21.6 x 14.6 cm),"Rogers Fund, 1917",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/48892,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP565,false,true,37016,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,ca. 1769,1759,1789,Polychrome woodblock print; ink and color on paper,10 3/4 x 7 15/16 in. (27.3 x 20.2 cm) medium-size print (chu-ban),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37016,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP566,false,true,37017,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,1769 or 1770,1769,1770,Polychrome woodblock print; ink and color on paper,10 1/2 x 5 5/8 in. (26.7 x 14.3 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37017,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP668,false,true,37115,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,ca. 1763,1753,1773,Polychrome woodblock print; ink and color on paper,27 11/32 x 4 in. (69.5 x 10.2 cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37115,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP670,false,true,37117,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,ca. 1768,1758,1778,Polychrome woodblock print; ink and color on paper,25 1/8 x 4 15/32 in. (63.8 x 11.4 cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37117,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP671,false,true,37118,Asian Art,Print,風流七小町|Visiting,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,ca. 1769,1759,1779,Polychrome woodblock print; ink and color on paper,25 31/32 x 4 1/4 in. (66.0 x 10.8 cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37118,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP697,false,true,37144,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,ca. 1764,1754,1774,Polychrome woodblock print; ink and color on paper,7 3/4 x 12 1/8 in. (19.7 x 30.8 cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37144,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP698,true,true,37145,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,ca. 1766,1756,1776,"Polychrome woodblock print (first edition); ink and colors on paper, medium-sized print (chuban)",11 1/4 x 8 in. (28.6 x 20.3 cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37145,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP699,false,true,37146,Asian Art,Print,風流七小町　しみず|Shimizu Temple,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,ca. 1765,1755,1775,Polychrome woodblock print; ink and color on paper; Benizuri-e; small print (hosoban),12 7/32 x 5 1/2 in. (31.1 x 14.0 cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37146,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP861,false,true,54540,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,ca. 1765,1755,1775,Polychrome woodblock print; ink and color on paper,H. 11 in. (27.9 cm); W. 8 1/8 in. (20.6 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54540,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP862,false,true,54541,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,ca. 1766,1756,1776,Polychrome woodblock print; ink and color on paper,H. 10 3/4 in. (27.3 cm); W. 8 1/8 in. (20.6 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54541,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP863,false,true,54542,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,probably 1765,1760,1770,Polychrome woodblock print; ink and color on paper,H. 11 1/8 in. (28.3 cm); W. 8 3/8 in. (21.3 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54542,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP864,false,true,54543,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,ca. 1766,1756,1777,Polychrome woodblock print; ink and color on paper,H. 8 3/16 in. (20.8 cm); W. 2 3/4 in. (7 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54543,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP865,false,true,54546,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,probably 1766,1761,1771,Polychrome woodblock print; ink and color on paper,H. 10 5/8 in. (27 cm); W. 8 1/8 in. (20.6 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54546,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP866,false,true,54550,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,ca. 1765,1755,1777,Polychrome woodblock print; ink and color on paper,H. 11 3/16 in. (28.4 cm); W. 8 3/8 in. (21.3 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54550,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP867,false,true,54551,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,late 1760s,1765,1769,Polychrome woodblock print; ink and color on paper,H. 7 11/16 in. (19.5 cm); W. 9 9/16 in. (24.3 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54551,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP868,false,true,37268,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,ca. 1767,1757,1777,Polychrome woodblock print; ink and color on paper,10 1/4 x 7 1/4 in. (26 x 18.4 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37268,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP869,false,true,54554,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,ca. 1767,1757,1777,Polychrome woodblock print; ink and color on paper,H. 10 11/16 in. (27.1 cm); W. 8 1/4 in. (21 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54554,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP870,false,true,45078,Asian Art,Print,百人一首　天智天皇|Sympathy,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,ca. 1768,1758,1778,Polychrome woodblock print; ink and color on paper,H. 10 in. (25.4 cm); W. 7 5/ 16 in. (18.6 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45078,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP871,false,true,45079,Asian Art,Print,"百人一首　小式の内持|Koshikibu no Naishi (999–1025), from ""Hyakunin Isshu"" (One Hundred Poems by One Hundred Poets)",Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,ca. 1768,1758,1778,Polychrome woodblock print; ink and color on paper,H. 11 in. (27.9 cm); W. 8 1/8 in. (20.6 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45079,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP872,false,true,45075,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,ca. 1768,1758,1778,Polychrome woodblock print; ink and color on paper,H. 11 in. (27.9 cm); W. 8 in. (20.3 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45075,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP873,false,true,45069,Asian Art,Print,百人一首　藤原元真|Poem by Fujiwara no Motozane (ca. 860) from the Series Thirty-Six Poets,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,ca. 1768,1758,1778,Polychrome woodblock print; ink and color on paper with embossing (karazuri),Image: 10 7/8 x 8 1/8 in. (27.6 x 20.6 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45069,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP875,false,true,51994,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,probably 1768,1768,1768,Polychrome woodblock print; ink and color on paper,H. 8 1/8 in. (20.6 cm); W. 11 1/4 in. (28.6 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/51994,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP876,false,true,54569,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,1764–72,1764,1772,Polychrome woodblock print; ink and color on paper,H. 11 1/8 in. (28.3 cm); W. 8 1/4 in. (21 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54569,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP877,false,true,54570,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,ca. 1769,1759,1779,Polychrome woodblock print; ink and color on paper,H. 20 1/8 in. (51.5 cm); W. 4 1/2 in. (11.4 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54570,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP878,false,true,54571,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,ca. 1769,1759,1779,Polychrome woodblock print; ink and color on paper,H. 25 7/8 in. (65.7 cm); W. 4 1/2 in. (11.4 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54571,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP879,false,true,54572,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,School of,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,ca. 1765,1755,1775,Polychrome woodblock print; ink and color on paper,H. 11 1/4 in. (28.6 cm); W. 8 3/8 in. (21.3 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54572,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1010,false,true,54886,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,ca. 1765,1755,1775,Polychrome woodblock print; ink and color on paper,H. 11 5/8 in. (29.5 cm); W. 8 3/8 in. (21.3 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54886,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1219,false,true,55142,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,1770,1770,1770,Polychrome woodblock print; ink and color on paper,H. 11 1/8 in. (28.3 cm); W. 8 1/8 in. (20.6 cm),"Rogers Fund, 1921",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55142,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1220,false,true,55143,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,1770,1770,1770,Polychrome woodblock print; ink and color on paper,H. 10 1/4 in. (26 cm); W. 8 1/8 in. (20.6 cm),"Rogers Fund, 1921",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55143,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1223,false,true,55145,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,1770,1770,1770,Polychrome woodblock print; ink and color on paper,H. 11 in. (27.9 cm); W. 8 5/16 in. (21.1 cm),"Rogers Fund, 1921",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55145,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1273,false,true,55194,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,ca. 1766,1756,1776,Polychrome woodblock print; ink and color on paper,10 5/8 x 8 1/16 in. (27 x 20.5 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55194,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1274,false,true,45073,Asian Art,Print,Aki|風俗四季歌仙　立秋|First Day of Autumn (Risshu),Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,ca. 1865,1855,1875,Polychrome woodblock print; ink and color on paper,Image: 11 × 8 in. (27.9 × 20.3 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45073,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1275,false,true,55204,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,ca. 1760,1750,1770,Polychrome woodblock print; ink and color on paper,H. 10 1/4 in. (26 cm); W. 8 1/8 in. (20.6 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55204,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1276,false,true,45089,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,ca. 1769,1759,1779,Polychrome woodblock print; pillar print (hashira-e); ink and color on paper,H. 25 7/8 in. (65.7 cm); W. 4 7/8 in. (12.4 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45089,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1375,false,true,55356,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,ca. 1770,1760,1780,Polychrome woodblock print; ink and color on paper,H. 27 1/2 in. (69.9 cm); W. 4 3/4 in. (12.1 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55356,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1376,false,true,55358,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,ca. 1770,1760,1780,Polychrome woodblock print (pillar print); ink and color on paper,H. 26 3/4 in. (67.9 cm); W. 5 in. (12.7 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55358,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1384,false,true,55371,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,ca. 1769,1759,1779,Polychrome woodblock print; ink and color on paper,H. 8 in. (20.3 cm); W. 10 1/4 in. (26 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55371,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1435,false,true,55468,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,ca. 1770,1760,1780,Polychrome woodblock print; ink and color on paper,H. 27 5/8 in. (70.2 cm); W. 4 3/4 in. (12.1 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55468,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1504,false,true,53895,Asian Art,Print,回文歌　京　大阪　江戸|Palindromic Poems (Kaibunka): Kyo,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,ca. 1768,1758,1778,Right-hand sheet of a triptych of polychrome woodblock prints; ink and color on paper,12 3/8 x 5 3/8 in. (31.4 x 13.7 cm),"Fletcher Fund, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/53895,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1505,false,true,55608,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,ca. 1768,1758,1778,Polychrome woodblock print; ink and color on paper,H. 11 in. (27.9 cm); W. 8 1/8 in. (20.6 cm),"Fletcher Fund, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55608,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1625,false,true,54867,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,ca. 1767,1757,1777,Polychrome woodblock print; ink and color on paper,H. 11 1/4 in. (28.6 cm); W. 8 1/8 in. (20.6 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54867,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1627,false,true,45086,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,ca. 1769,1759,1779,Polychrome woodblock print; ink and color on paper,H. 10 1/8 in. (25.7 cm); W. 7 1/2 in. (19.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45086,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1632,false,true,55796,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,ca. 1768,1758,1778,Polychrome woodblock print; ink and color on paper,H. 11 in. (27.9 cm); W. 8 1/2 in. (21.6 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55796,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1633,false,true,55797,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,1764–72,1764,1772,Polychrome woodblock print; ink and color on paper,H. 10 3/4 in. (27.3 cm); W. 8 1/4 in. (21 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55797,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1634,false,true,45068,Asian Art,Print,Uzuki|風俗四季歌仙　卯月|The Fourth Month (April),Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,ca. 1768,1758,1778,Polychrome woodblock print; ink and color on paper,H. 11 in. (27.9 cm); W. 8 3/16 in. (20.8 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45068,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1636,false,true,45083,Asian Art,Print,百人一首　僧正遍昭|Poem by Henjō Sojō,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,ca. 1766,1756,1776,Polychrome woodblock print; ink and color on paper,H. 10 9/16 in. (26.8 cm); W. 8 3/16 in. (20.8 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45083,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1637,false,true,45084,Asian Art,Print,百人一首　陽成院|Yozei no In,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,ca. 1766,1756,1776,Polychrome woodblock print; ink and color on paper,10 7/8 x 8 in. (27.6 x 20.3 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45084,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1638,false,true,45065,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,1768–70,1700,1868,Polychrome woodblock print; ink and color on paper,10 5/16 x 7 9/1 6in. (26.2 x 19.2 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45065,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1640,false,true,54863,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,1764–72,1764,1772,Polychrome woodblock print; ink and color on paper,H. 11 1/8 in. (28.3 cm); W. 8 1/8 in. (20.6 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54863,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1643,false,true,55801,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,ca. late 18th century,1767,1799,Polychrome woodblock print; ink and color on paper,H. 10 11/16 in. (27.1 cm); W. 8 in. (20.3 cm) medium-size print (chu-ban),"Fletcher Fund, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55801,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1644,false,true,55802,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,ca. 1765,1755,1775,Polychrome woodblock print; ink and color on paper,H. 11 1/4 in. (28.6 cm); W. 8 1/8 in. (20.6 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55802,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1646,false,true,55804,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,ca. 1765–70,1755,1780,Polychrome woodblock print; chuban yoko-e; ink and color on paper,H. 8 in. (20.3 cm); W. 12 5/16 in. (31.3 cm) medium-size print (chu-ban),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55804,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1649,false,true,53608,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,1764–70,1764,1770,Polychrome woodblock print; ink and color on paper,H. 10 3/4 in. (27.3 cm); W. 7 13/16 in. (19.8 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/53608,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1654,false,true,55814,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,1764–72,1764,1772,Polychrome woodblock print; ink and color on paper,H. 26 1/2 in. (67.3 cm); W. 4 7/16 in. (11.3 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55814,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1655,false,true,55815,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,ca. 1765–70,1755,1780,Polychrome woodblock print; pillar print (hashira-e); ink and color on paper,H. 27 in. (68.6 cm); W. 4 13/16 in. (12.2 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55815,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2434,false,true,42563,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,ca. 1766,1756,1776,Polychrome woodblock print; ink and color on paper,H. 11 in. (27.9 cm); W. 10 1/2 in. (26.7 cm) medium-size block (chu-ban),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/42563,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2435,false,true,45268,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,ca. 1765,1755,1775,Polychrome woodblock print; ink and color on paper,10 3/8 x 7 1/2 in. (26.4 x 19.1 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45268,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2436,false,true,44943,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,ca. 1766,1756,1776,Polychrome woodblock print; ink and color on paper,"H. 7 15/16 in. (20.2 cm); W. 11 1/8 in. (28.3 cm) Medium-size block (""chuban"")","The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/44943,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2437,false,true,54181,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,1765,1765,1765,Polychrome woodblock print; ink and color on paper,10 1/2 x 7 7/8 in. (26.7 x 20 cm) medium-size print (chu-ban),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54181,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2438,false,true,56874,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,1765,1765,1765,Polychrome woodblock print; ink and color on paper,11 1/8 x 8 1/8 in. (28.3 x 20.6 cm) medium-size print (chu-ban),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56874,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2439,false,true,56789,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,ca. 1765,1755,1775,Polychrome woodblock print; ink and color on paper,H. 11 1/8 in. (28.3 cm); W. 8 3/8 in. (21.3 cm) medium-size print (chuban),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56789,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2440,false,true,56791,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,1764–72,1764,1772,Polychrome woodblock print; ink and color on paper,H. 10 3/8 in. (26.4 cm); W. 8 1/8 in. (20.6 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56791,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2441,false,true,45066,Asian Art,Print,百人一首　素性法師|Poem by the Monk Sosei (act. 850-97),Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,ca. 1767–68,1762,1775,"Polychrome woodblock print; ink and color on paper, with embossing (karazuri)",10 7/8 x 8 in. (27.6 x 20.3 cm) medium-size print (chu-ban),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45066,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2442,false,true,51995,Asian Art,Print,萩|The Bush Clover (Hagi),Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,ca. 1766,1756,1776,Polychrome woodblock print; ink and color on paper,10 7/8 x 7 7/8 in. (27.6 x 20 cm) medium-size print (chu-ban),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/51995,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2443,false,true,56875,Asian Art,Print,"井手の玉川|The Tama River at Ide, Yamashiro Province",Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,probably 1766,1766,1766,Polychrome woodblock print; ink and color on paper,10 1/4 x 7 3/4 in. (26 x 19.7 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56875,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2444,false,true,56876,Asian Art,Print,"六玉川　「千鳥の玉川　陸奥名所」|“The Jewel River of Plovers, a Famous Place in Mutsu Province,” from the series Six Jewel Rivers (Mu Tamagawa: Chidori no Tamagawa, Mutsu meisho)",Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,probably 1766,1766,1766,Polychrome woodblock print; ink and color on paper,10 7/8 x 8 in. (27.6 x 20.3 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56876,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2445,false,true,40991,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,ca. 1770,1760,1780,Polychrome woodblock print; ink and color on paper,10 3/4 x 7 7/8 in. (27.3 x 20 cm) medium-size print (chu-ban),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/40991,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2446,false,true,56877,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,ca. 1762,1752,1772,Polychrome woodblock print; ink and color on paper,10 3/4 x 7 7/8 in. (27.3 x 20 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56877,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2447,false,true,56792,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,ca. 1766,1756,1776,Polychrome woodblock print; chuban; ink and color on paper,H. 11 1/8 in. (28.3 cm); W. 7 1/16 in. (17.9 cm) medium-size print (chu-ban),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56792,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2448,false,true,56878,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,probably 1770,1770,1770,Polychrome woodblock print; ink and color on paper,Hashirae: 25 x 5 in. (63.5 x 12.7 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56878,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2450,false,true,56880,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,ca. 1766,1756,1776,Polychrome woodblock print; ink and color on paper,11 1/4 x 8 in. (28.6 x 8 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56880,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2451,false,true,56881,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,ca. 1768,1758,1778,Polychrome woodblock print; ink and color on paper,10 3/4 x 7 7/8 in. (27.3 x 20 cm) medium-size print (chu-ban),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56881,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2452,false,true,56882,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,ca. 1768,1758,1778,Polychrome woodblock print; ink and color on paper,11 1/2 x 8 3/8 in. (29.2 x 21.3 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56882,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2453,false,true,45085,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,1764–72,1764,1772,"Polychrome woodblock print; ink and color on paper, with embossing (karazuri)",H. 11 1/4 in. (28.6 cm); W. 8 1/8 in. (20.6 cm) medium-size print (chu-ban),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45085,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2454,false,true,56883,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,probably 1770,1770,1770,Polychrome woodblock print; ink and color on paper,Hashire: 27 1/2 x 5 in. (69.9 x 12.7 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56883,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2455,false,true,56884,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,probably 1770,1770,1770,Polychrome woodblock print; ink and color on paper,Hashirae: 28 1/8 x 5 in. (71.4 x 12.7 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56884,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2767,false,true,45074,Asian Art,Print,未月|The Seventh Month (Fumizuki),Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,ca. 1865,1855,1875,Polychrome woodblock print; ink and color on paper,14 x 3 in. (35.6 x 7.6 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45074,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2768,false,true,45091,Asian Art,Print,青楼美人合|The Courtesan Kasugano Writing a Letter,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,ca. 1765,1755,1775,Polychrome woodblock print (Yonkyokuban); ink and color on paper,8 3/8 x 5 3/4 in. (21.3 x 14.6 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45091,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2769,false,true,45092,Asian Art,Print,青楼美人合|The Courtesan Itsuhata with Her Pipe,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,ca. 1765,1755,1775,Polychrome woodblock print (Yonkyokuban); ink and color on paper,8 3/8 x 5 3/4 in. (21.3 x 14.6 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45092,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2771,false,true,45067,Asian Art,Print,Sakura-gari|桜狩|Cherry Blossom Viewing,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,1764–70,1764,1770,Polychrome woodblock print; ink and color on paper,H. 11 3/8 in. (28.9 cm); W. 8 3/8 in. (21.3 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45067,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2772,false,true,45077,Asian Art,Print,百人一首　西行法師|Poem by the Monk Saigyō (1118-1190),Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,ca. 1767–68,1767,1768,Polychrome woodblock print; ink and color on paper,11 1/6 x 8 1/4 in. (28.4 x 21 cm) medium-size block (chu-ban),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45077,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2773,false,true,42564,Asian Art,Print,百人一首　中納言兼輔|Man and Woman Playing Shogi,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,mid-18th century,1734,1766,Polychrome woodblock print; ink and color on paper,11 1/8 x 8 1/8 in. (28.3 x 20.6 cm),"Henry J. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/42564,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2774,false,true,57031,Asian Art,Print,Yuki|雪月花　雪|Snow,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,ca. 1769,1759,1779,Polychrome woodblock print; ink and color on paper,11 1/8 x 8 3/8 in. (28.3 x 21.3 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57031,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2775,false,true,57032,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,1725–1770,1725,1770,Polychrome woodblock print; ink and color on paper,11 1/4 x 8 5/8 in. (28.6 x 21.9 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57032,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2776,false,true,45064,Asian Art,Print,風俗四季歌仙　立春|The First Day of Spring (Risshun),Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,ca. 1768,1778,1798,Polychrome woodblock print; ink and color on paper,11 x 8 1/4 in. (27.9 x 21 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45064,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2777,false,true,57033,Asian Art,Woodblock print,遊女と新造|Courtesan and Shinzō,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,1725–1770,1725,1770,Polychrome woodblock print; ink and color on paper,11 1/4 x 8 1/2 in. (28.6 x 21.6 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57033,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2778,false,true,57034,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,ca. 1768–69,1768,1769,Polychrome woodblock print; ink and color on paper,11 1/4 x 8 5/8 in. (28.6 x 21.9 cm) medium-size print (chu-ban),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57034,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2779,false,true,57035,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,1725–1770,1725,1770,Polychrome woodblock print; ink and color on paper,11 1/8 x 8 1/2 in. (28.3 x 21.6 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57035,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2780,false,true,57036,Asian Art,Print,Sumidagawa no rakugan|風俗江戸八景　隅田川落雁|Wild Geese Flying Down the Sumida River,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,ca. 1769,1759,1779,Polychrome woodblock print; ink and color on paper,11 1/4 x 8 3/8 in. (28.6 x 21.3 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57036,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3019,false,true,56417,Asian Art,Print,井手の玉川|Jewel River at Ide (Ide no Tamagawa),Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,ca. 1768,1758,1778,Polychrome woodblock print; ink and color on paper,10 3/4 x 7 7/16 in. (27.3 x 18.9 cm) medium-size print (chu-ban),"Harris Brisbane Dick Fund, 1946",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56417,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3021,false,true,56419,Asian Art,Woodblock print,Kōromō Uchi Tamagawa|Fulling Cloth at the Jewel River (Kinuta no Tamagawa),Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,ca. 1768,1758,1778,Polychrome woodblock print; ink and color on paper,10 3/4 x 7 7/8 in. (27.3 x 20 cm) medium-size print (chu-ban),"Harris Brisbane Dick Fund, 1946",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56419,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3022,false,true,56420,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,ca. 1768,1758,1778,Polychrome woodblock print; ink and color on paper,10 5/8 x 7 3/4 in. (27 x 19.7 cm) medium-size print (chu-ban),"Harris Brisbane Dick Fund, 1946",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56420,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3043,false,true,45041,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,ca. 1760,1750,1770,Polychrome woodblock print; ink and color on paper,13 3/4 x 5 3/4 in. (34.9 x 14.6 cm),"Gift of Mrs. Morris Manges, in memory of her husband, Dr. Morris Manges, 1947",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45041,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3052,false,true,56507,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,ca. 1768,1758,1778,Polychrome woodblock print; chuban; ink and color on paper,10 x 15 1/2 in. (25.4 x 39.4 cm) medium-size print (chu-ban),"Gift of Mrs. Morris Manges, in memory of her husband, Dr. Morris Manges, 1947",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56507,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3406,false,true,55593,Asian Art,Print,"風流江戸八景　真乳山の暮雪|Evening Snow on Matsuchi Hilll, from the series Eight Fashionable Views of Edo (Furyu Edo hakkei)",Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,ca. 1765–70,1755,1780,Polychrome woodblock print; ink and color on paper,11 x 8 1/8 in. (27.9 x 20.6 cm) medium-size print (chu-ban),"Bequest of Julia H. Manges, 1960",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55593,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"JP2770a, b",false,true,57030,Asian Art,Print,青楼美人合|Two Girls Play the Finger Game of Kitsume Ken,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,1725–1770,1725,1770,Diptych of polychrome woodblock prints; ink and color on paper,a Left sheet: 8 3/8 x 5 3/4 in. (21.3 x 14.6 cm); b Right sheet: 8 3/8 x 5 3/4 in. (21.3 x 14.6 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57030,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP111,false,true,36590,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,probably 1785,1785,1785,Polychrome woodblock print; ink and color on paper,14 1/3 x 9 7/16 in. (36.4 x 24.0 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36590,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP112,false,true,36591,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,ca. 1779,1769,1789,Polychrome woodblock print; ink and color on paper,11 7/8 x 5 5/8 in. (30.2 x 14.3 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36591,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP113,false,true,36592,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,"9th month, 1774",1774,1774,Polychrome woodblock print; ink and color on paper,12 1/8 x 5 2/3 in. (30.8 x 14.4 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36592,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP114,false,true,36593,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,1782,1782,1782,Middle sheet of a triptych of polychrome woodblock prints; ink and color on paper,12 3/4 x 5 7/8 in. (32.4 x 14.9 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36593,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP115,false,true,36594,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,dated October or November 1778,1778,1778,Polychrome woodblock print; ink and color on paper,12 5/8 x 5 5/8 in. (32.1 x 14.3 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36594,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP116,false,true,36595,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,ca. 1773,1763,1783,Polychrome woodblock print; ink and color on paper,12 x 5 3/8 in. (30.5 x 13.7 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36595,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP117,false,true,36596,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,ca. 1778,1768,1788,Polychrome woodblock print; ink and color on paper,12 5/16 x 5 13/16 in. (31.3 x 14.8 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36596,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP118,false,true,36597,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,1773 or 1774,1773,1774,Polychrome woodblock print; ink and color on paper,11 3/8 x 5 1/5 in. (28.9 x 13.2 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36597,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP119,false,true,36598,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,ca. 1782,1772,1792,Polychrome woodblock print; ink and color on paper,12 4/5 x 5 3/4 in. (32.5 x 14.6 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36598,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP120,false,true,36599,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,ca. 1770,1760,1780,Polychrome woodblock print; ink and color on paper,12 x 5 5/8 in. (30.5 x 14.3 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36599,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP121,false,true,36600,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,1773 or 1774,1773,1774,Polychrome woodblock print; ink and color on paper,12 x 5 1/2 in. (30.5 x 14.0 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36600,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP122,false,true,36601,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,1773 or 1774,1773,1774,Polychrome woodblock print; ink and color on paper,11 7/8 x 5 3/4 in. (30.2 x 14.6 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36601,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP124,false,true,36603,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,1781,1781,1781,Polychrome woodblock print; ink and color on paper,11 7/8 x 5 31/32 in. (30.2 x 15.2 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36603,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP125,false,true,36604,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,ca. 1778,1768,1788,Polychrome woodblock print; ink and color on paper,11 1/2 x 5 1/2 in. (29.2 x 14.0 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36604,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP126,false,true,36605,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,"2nd month, 1785",1785,1785,Polychrome woodblock print; ink and color on paper,10 31/32 x 5 1/2 in. (27.9 x 14 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36605,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP127,false,true,36606,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,ca. 1772,1762,1782,Polychrome woodblock print; ink and color on paper,11 1/2 x 5 1/5 in. (29.2 x 13.2 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36606,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP207,false,true,36684,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,dated 1769,1769,1769,Polychrome woodblock print; ink and color on paper,Overall: 12 1/16 x 5 13/16 in. (30.6 x 14.8 cm),"Gift of Louis V. Ledoux, 1931",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36684,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP342,false,true,36807,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,ca. 1780,1778,1782,Polychrome woodblock print; ink and color on paper,12 7/32 x 5 3/4 in. (31.1 x 14.6 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36807,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP343,false,true,36808,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,"6th month, 1783",1783,1783,Polychrome woodblock print; ink and color on paper,12 11/32 x 5 17/32 in. (31.4 x 14.1 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36808,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP344,false,true,36809,Asian Art,Woodblock print,五代目市川団十郎|Kabuki Actor Ichikawa Danjūrō V as Sakata Kintoki in the Play Raikō’s Four Intrepid Retainers in the Costume of the Night Watch (Shitennō tonoi no kisewata),Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,"11th month, 1781",1781,1781,Polychrome woodblock print; ink and color on paper,12 5/16 x 5 1/2 in. (31.3 x 14 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36809,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP345,false,true,36810,Asian Art,Woodblock print,五代目市川団十郎|Kabuki Actor Ichikawa Danjūrō V in a Shibaraku (Stop Right There!) Role,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,"11th month, 1779",1779,1779,Polychrome woodblock print; ink and color on paper,Hosoban 12 1/2 x 5 7/8 in. (31.8 x 14.9 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36810,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP346,false,true,36811,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,ca. 1785?,1783,1787,Polychrome woodblock print; ink and color on paper,12 11/32 x 5 1/2 in. (31.4 x 14.0 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36811,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP347,false,true,36812,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,"2nd month, 1785",1785,1785,Polychrome woodblock print; ink and color on paper,Hosoban 12 7/8 x 5 3/4 in. (32.7 x 14.6 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36812,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP348,false,true,36813,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,ca. 1778,1768,1788,Polychrome woodblock print; ink and color on paper,12 9/16 x 5 17/32 in. (31.9 x 14.1 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36813,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP349,false,true,36814,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,ca. 1778,1776,1780,Polychrome woodblock print; ink and color on paper,12 11/32 x 5 3/4 in. (31.4 x 14.6 cm),"Purchase, Joseph Pultizer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36814,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP350,false,true,36815,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,ca. 1783–84,1781,1785,Polychrome woodblock print; ink and color on paper,12 7/16 x 5 7/8 in. (31.6 x 14.9 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36815,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP351,false,true,36816,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,"10th month, 1777",1777,1777,Polychrome woodblock print; ink and color on paper,11 3/10 x 5 3/4 in. (28.7 x 14.6 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36816,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP352,false,true,48895,Asian Art,Print,"二代目市川八百藏・二代目中島三甫右衛門・三代目市川海老藏・九代目市村羽左衛門|Kabuki Actors Ichikawa Yaozō II, Nakajima Mihoemon II, Ichikawa Ebizō III, and Ichimura Uzaemon IX in the Play Sugawara’s Secrets of Calligraphy (Sugawara denju tenarai kagami)",Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,"7th month, 1776",1776,1776,One sheet of a triptych of polychrome woodblock prints; ink and color on paper,Overall: 12 5/8 x 17 7/16 in. (32.1 x 44.3 cm); Image: 12 3/4 x 5 7/8 in. (32.4 x 14.9 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/48895,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP353,false,true,36817,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,ca. 1787,1777,1797,Polychrome woodblock print; ink and color on paper,11 7/8 x 5 2/3 in. (30.2 x 14.4 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36817,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP354,false,true,36818,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,"12th month, 1780",1780,1780,Left-hand sheet of a diptych of polychrome woodblock prints; ink and color on paper,12 1/2 x 5 7/8 in. (31.8 x 14.9 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36818,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP355,false,true,36819,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,1780,1780,1780,Polychrome woodblock print; ink and color on paper,12 4/5 x 5 7/8 in. (32.5 x 14.9 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36819,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP356,false,true,36820,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,1787 or 1788,1787,1788,Polychrome woodblock print; ink and color on paper,12 4/5 x 5 13/16 in. (32.5 x 14.8 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36820,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP357,false,true,36821,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,ca. 1778,1768,1788,Polychrome woodblock print; ink and color on paper,11 23/32 x 5 3/4 in. (29.8 x 14.6 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36821,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP358,false,true,36822,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,ca. 1779–83,1777,1785,Polychrome woodblock print; ink and color on paper,12 1/2 x 5 13/16 in. (31.8 x 14.8 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36822,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP359,false,true,36823,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,1778,1778,1778,One sheet of a triptych of polychrome woodblock prints; ink and color on paper,12 9/16 x 5 7/8 in. (31.9 x 14.9 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36823,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP360,false,true,36824,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,1776,1776,1776,Polychrome woodblock print; ink and color on paper,12 7/32 x 5 5/8 in. (31.1 x 14.3 cm),"Purchase, Joseph Pultizer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36824,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP361,false,true,36825,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,ca. 1779,1769,1789,Polychrome woodblock print; ink and color on paper,12 9/16 x 5 13/16 in. (31.9 x 14.8 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36825,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP362,false,true,36826,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,ca. 1788,1778,1798,Polychrome woodblock print; ink and color on paper,12 5/8 x 5 3/4 in. (32.1 x 14.6 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36826,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP363,false,true,36827,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,probably 1782,1780,1784,Polychrome woodblock print; ink and color on paper,12 5/8 x 5 2/3 in. (32.1 x 14.4 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36827,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP364,false,true,36828,Asian Art,Print,三代目大谷廣右衛門|The Third Otani Hiroemon as an Outlaw Standing Near a Willow Tree,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,"3rd month, 1777",1777,1777,Polychrome woodblock print; ink and color on paper,11 15/16 x 5 5/8 in. (30.3 x 14.3 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36828,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP365,false,true,36829,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,ca. 1785,1775,1795,Polychrome woodblock print; ink and color on paper,12 5/16 x 5 2/3 in. (31.3 x 14.4 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36829,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP366,false,true,36830,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,ca. 1783,1773,1793,Polychrome woodblock print; ink and color on paper,12 5/8 x 5 3/4 in. (32.1 x 14.6 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36830,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP367,false,true,36831,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,ca. 1782,1772,1792,Polychrome woodblock print; ink and color on paper,12 4/5 x 5 7/8 in. (32.5 x 14.9 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36831,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP368,false,true,36832,Asian Art,Woodblock print,五代目市川団十郎|Kabuki Actor Ichikawa Danjūrō V in a Shibaraku (Stop Right There!) Role as Hannya no Gorō,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,11th month 1776,1776,1776,Polychrome woodblock print; ink and color on paper,Hosoban 12 9/16 x 5 7/8 in. (31.9 x 14.9 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36832,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP369,false,true,36833,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,ca. 1778,1768,1788,Polychrome woodblock print; ink and color on paper,12 3/4 x 5 7/8 in. (32.4 x 14.9 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36833,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP370,false,true,36834,Asian Art,Print,初代中村仲蔵|The First Nakamura Nakazō in the Role of Shimada no Hachizō,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,1783,1783,1783,Polychrome woodblock print; ink and color on paper,12 7/8 x 5 13/16 in. (32.7 x 14.8 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36834,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP371,false,true,36835,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,probably 1775,1773,1777,Polychrome woodblock print; ink and color on paper,12 7/8 x 5 7/8 in. (32.7 x 14.9 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36835,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP372,false,true,36836,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,ca. 1777,1767,1787,Polychrome woodblock print; ink and color on paper,12 1/32 x 5 3/4 in. (30.6 x 14.6 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36836,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP373,false,true,36837,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,"12th month, 1786",1786,1786,Polychrome woodblock print; ink and color on paper,12 7/32 x 5 1/2 in. (31.1 x 14.0 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36837,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP374,false,true,36838,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,ca. 1781,1771,1791,Polychrome woodblock print; ink and color on paper,12 7/16 x 5 5/8 in. (31.6 x 14.3 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36838,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP375,false,true,36839,Asian Art,Print,二代目中村助五郎|Kabuki Actor Nakamura Sukegorō II as Kaminari Shōkurō,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,"2nd month, 1780",1780,1780,One sheet of a pentaptych; polychrome woodblock print; ink and color on paper,12 11/32 x 5 5/8 in. (31.4 x 14.3 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36839,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP376,false,true,36840,Asian Art,Print,三代目瀬川菊之丞|Kabuki Actor Segawa Kikunojō III in a Female Role (Shizuka Gozen),Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,ca. 1777,1767,1787,Polychrome woodblock print; ink and color on paper,12 11/32 x 5 17/32 in. (31.4 x 14.1 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36840,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP377,false,true,36841,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,ca. 1777,1767,1787,Polychrome woodblock print; ink and color on paper,12 1/2 x 5 3/4 in. (31.8 x 14.6 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36841,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP378,false,true,36842,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,ca. 1779,1769,1789,Polychrome woodblock print; ink and color on paper,12 1/2 x 5 5/8 in. (31.8 x 14.3 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36842,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP379,false,true,36843,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,1784,1784,1784,Polychrome woodblock print; ink and color on paper,12 11/16 x 5 5/8 in. (32.2 x 14.3 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36843,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP380,false,true,36844,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,ca. 1787–90,1785,1792,Polychrome woodblock print; ink and color on paper,12 x 5 5/8 in. (30.5 x 14.3 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36844,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP381,false,true,36845,Asian Art,Print,初代中村富十郎|Kabuki Actor Nakamura Tomijūrō I in a Female Dance Role,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,ca. 1777,1767,1787,Polychrome woodblock print; ink and color on paper,Hosoban 11 7/8 x 5 17/32 in. (30.2 x 14.1 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36845,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP382,false,true,36846,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,"2nd month, 1773",1773,1773,Polychrome woodblock print; ink and color on paper,Overall: 13 x 5 3/4in. (33 x 14.6cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36846,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP383,false,true,36847,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,1780,1780,1780,Polychrome woodblock print; ink and color on paper,12 11/32 x 5 1/2 in. (31.4 x 14.0 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36847,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP384,false,true,36848,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,ca. 1786–87,1784,1789,Polychrome woodblock print; ink and color on paper,12 1/2 x 5 3/4 in. (31.8 x 14.6 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36848,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP385,false,true,36849,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,"summer, 1772",1772,1772,Polychrome woodblock print; ink and color on paper,12 1/8 x 5 3/4 in. (30.8 x 14.6 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36849,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP392,false,true,36852,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,ca. 1785,1775,1795,Polychrome woodblock print; ink and color on paper,12 7/32 x 5 3/4 in. (31.1 x 14.6 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36852,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP395,false,true,36853,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,probably 1778,1776,1780,Diptych (probably two sheets of a triptych) of polychrome woodblock prints; ink and color on paper,A: H. 12 11/16 in. (32.2 cm); W. 5 11/16 in. (14.4 cm) B: H. 12 9/16 in. (31.9 cm); W. 5 3/4 in. (14.6 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36853,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP399,false,true,36854,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,1774,1774,1774,Diptych of polychrome woodblock prints; ink and color on paper,A: H. 12 11/16 in. (32.2 cm); W. 5 5/8 in. (14.3 cm) B: H. 12 13/16 in (32.5 cm); W. 5 9/16 in. (14.1 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36854,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP402,false,true,36855,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,1773,1773,1773,Polychrome woodblock print; ink and color on paper,12 11/32 x 5 1/2 in. (31.4 x 14.0 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36855,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP403,false,true,36856,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,1773,1773,1773,Polychrome woodblock print; ink and color on paper,12 3/4 x 5 13/16 in. (32.4 x 14.8 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36856,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP404,false,true,36857,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,"12th month, 1775",1775,1775,Polychrome woodblock print; ink and color on paper,12 5/16 x 5 1/2 in. (31.3 x 14.0 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36857,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP405,false,true,36858,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,1774 or 1775,1774,1775,Polychrome woodblock print; ink and color on paper,12 11/32 x 5 3/8 in. (31.4 x 13.7 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36858,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP406,false,true,36859,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,ca. 1766,1756,1776,Polychrome woodblock print; ink and color on paper,12 3/4 x 5 13/16 in. (32.4 x 14.8 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36859,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP407,false,true,36860,Asian Art,Print,四代目市川団十郎|Kabuki Actor Ichikawa Ebizō III (Ichikawa Danjūrō IV),Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,"2nd month, 1774",1774,1774,Polychrome woodblock print; ink and color on paper,12 3/4 x 5 3/4 in. (32.4 x 14.6 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36860,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP408,false,true,36861,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,probably 1773,1771,1775,Polychrome woodblock print; ink and color on paper,11 23/32 x 5 5/8 in. (29.8 x 14.3 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36861,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP409,false,true,36862,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,ca. 1774,1764,1784,Polychrome woodblock print; ink and color on paper,12 11/16 x 5 7/8 in. (32.2 x 14.9 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36862,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP410,false,true,36863,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,1773 or 1774,1773,1774,Polychrome woodblock print; ink and color on paper,12 4/5 x 5 7/8 in. (32.5 x 14.9 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36863,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP411,false,true,36864,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,1774 or 1775,1774,1775,Polychrome woodblock print; ink and color on paper,12 1/2 x 5 3/4 in. (31.8 x 14.6 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36864,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP412,false,true,36865,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,ca. 1771,1761,1781,Polychrome woodblock print; ink and color on paper,12 5/8 x 5 13/16 in. (32.1 x 14.8 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36865,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP413,false,true,36866,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,ca. 1775,1765,1785,One sheet of a diptych or triptych of polychrome woodblock prints; ink and color on paper,12 x 5 7/8 in. (30.5 x 14.9 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36866,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP414,false,true,36867,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,1773 ?,1771,1775,Polychrome woodblock print; ink and color on paper,12 5/16 x 5 13/16 in. (31.3 x 14.8 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36867,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP415,false,true,36868,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,ca. 1771,1761,1781,Polychrome woodblock print; ink and color on paper,12 1/8 x 5 7/8 in. (30.8 x 14.9 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36868,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP416,false,true,36869,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,ca. 1775,1765,1785,Polychrome woodblock print; ink and color on paper,12 1/2 x 5 13/16 in. (31.8 x 14.8 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36869,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP417,false,true,36870,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,1773 or 1774,1773,1774,Polychrome woodblock print; ink and color on paper,12 11/16 x 5 13/16 in. (32.2 x 14.8 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36870,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP418,false,true,36871,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,"2nd month, 1771",1771,1771,Polychrome woodblock print; ink and color on paper,12 7/8 x 5 15/16 in. (32.7 x 15.1 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36871,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP419,false,true,36872,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,"12th month, 1774",1774,1774,Polychrome woodblock print; ink and color on paper,12 11/32 x 5 1/2 in. (31.4 x 14.0 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36872,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP420,false,true,36873,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,1772 or 1773,1772,1773,Polychrome woodblock print; ink and color on paper,11 11/16 x 5 2/3 in. (29.7 x 14.4 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36873,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP422,false,true,36875,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,probably 1773,1771,1775,Polychrome woodblock print; ink and color on paper,Hosoban; 12 5/16 x 5 9/16 in. (31.3 x 14.1 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36875,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP423,false,true,36876,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,1774 or 1775,1774,1775,Polychrome woodblock print; ink and color on paper,12 11/16 x 5 3/4 in. (32.2 x 14.6 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36876,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP424,false,true,36877,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,1773 or 1774,1773,1774,Polychrome woodblock print; ink and color on paper,12 5/8 x 5 3/4 in. (32.1 x 14.6 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36877,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP425,false,true,36878,Asian Art,Print,鳥高斎栄昌画　「丁子屋畧見世」|The First Nakamura Tomijuro as an Oiran Standing in a Room,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,1771,1771,1771,Polychrome woodblock print; ink and color on paper,12 1/5 x 5 13/16 in. (31.0 x 14.8 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36878,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP426,false,true,36879,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,ca. 1774,1764,1784,Polychrome woodblock print; ink and color on paper,12 5/8 x 5 7/8 in. (32.1 x 14.9 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36879,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP427,false,true,36880,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,probably 1774,1772,1776,Left-hand sheet of a triptych of polychrome woodblock prints; ink and color on paper,12 11/32 x 5 13/16 in. (31.4 x 14.8 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36880,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP428,false,true,36881,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,1774 or 1775,1774,1775,One sheet of a triptych of polychrome woodblock prints; ink and color on paper,12 11/32 x 5 13/16 in. (31.4 x 14.8 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36881,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP429,false,true,36882,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,ca. 1775,1765,1785,One sheet of a triptych of polychrome woodblock prints; ink and color on paper,11 7/8 x 5 5/8 in. (30.2 x 14.3 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36882,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP430,false,true,36883,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,ca. 1775,1765,1785,Polychrome woodblock print; ink and color on paper,12 1/2 x 5 5/8 in. (31.8 x 14.3 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36883,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP431,false,true,36884,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,probably 1774,1772,1776,Polychrome woodblock print; ink and color on paper,12 11/32 x 5 17/32 in. (31.4 x 14.1 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36884,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP432,false,true,36885,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,"2nd month, 1771",1771,1771,Polychrome woodblock print; ink and color on paper,12 x 5 5/8 in. (30.5 x 14.3 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36885,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP433,false,true,36886,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,probably 1775,1773,1777,Polychrome woodblock print; ink and color on paper,12 7/32 x 5 13/16 in. (31.1 x 14.8 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36886,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP434,false,true,36887,Asian Art,Print,四代目市川団十郎|Kabuki Actor Ichikawa Danjūrō IV,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,"12th month, 1771",1771,1771,Polychrome woodblock print; ink and color on paper,12 31/32 x 5 13/16 in. (33.0 x 14.8 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36887,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP435,false,true,36888,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,"2nd month, 1771",1771,1771,Polychrome woodblock print; ink and color on paper,12 4/5 x 5 7/8 in. (32.5 x 14.9 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36888,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP436,false,true,36889,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,"2nd month, 1770",1770,1770,Polychrome woodblock print; ink and color on paper,12 5/8 x 5 5/8 in. (32.1 x 14.3 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36889,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP437,false,true,36890,Asian Art,Woodblock print,九代目市村羽左衛門・三代目大谷廣次|Kabuki Actors Ichimura Uzaemon IX as Ko-kakeyama and Ōtani Hiroji III as Kōga Saburō,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,ca. 1771,1761,1781,Polychrome woodblock print; ink and color on paper,12 x 5 5/8 in. (30.5 x 14.3 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36890,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP438,false,true,36891,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,ca. 1771,1761,1781,Polychrome woodblock print; ink and color on paper,12 11/32 x 5 17/32 in. (31.4 x 14.1 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36891,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP439,false,true,36892,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,"12th month, 1768",1768,1768,Polychrome woodblock print; ink and color on paper,12 x 5 1/2 in. (30.5 x 14.0 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36892,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP440,false,true,36893,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,1768 or 1769,1768,1769,Polychrome woodblock print; ink and color on paper,12 3/4 x 5 2/3 in. (32.4 x 14.4 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36893,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP441,false,true,36894,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,ca. 1773,1763,1783,Polychrome woodblock print; ink and color on paper,12 5/8 x 5 7/8 in. (32.1 x 14.9 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36894,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP442,false,true,36895,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,1770,1770,1770,Polychrome woodblock print; ink and color on paper,12 9/16 x 5 7/8 in. (31.9 x 14.9 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36895,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP443,false,true,36896,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,1769 or 1770,1769,1770,Polychrome woodblock print; ink and color on paper,12 5/8 x 5 13/16 in. (32.1 x 14.8 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36896,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP444,false,true,36897,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,1769 Autumn,1769,1769,Polychrome woodblock print; ink and color on paper,12 11/32 x 5 17/32 in. (31.4 x 14.1 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36897,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP445,false,true,36898,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,probably 1770,1768,1772,Polychrome woodblock print; ink and color on paper,12 11/32 x 5 1/2 in. (31.4 x 14.0 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36898,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP446,false,true,36899,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,"12th month, 1769",1769,1769,Polychrome woodblock print; ink and color on paper,12 11/32 x 5 7/16 in. (31.4 x 13.8 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36899,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP447,false,true,36900,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,1770,1770,1770,Polychrome woodblock print; ink and color on paper,12 3/4 x 5 7/8 in. (32.4 x 14.9 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36900,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP448,false,true,36901,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,1770 or 1771,1770,1771,Polychrome woodblock print; ink and color on paper,12 1/2 x 5 13/16 in. (31.8 x 14.8 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36901,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP449,false,true,36902,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,1769 or 1770,1769,1770,Polychrome woodblock print; ink and color on paper,12 1/2 x 5 3/4 in. (31.8 x 14.6 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36902,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP450,false,true,36903,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,1769 or 1770,1769,1770,Polychrome woodblock print; ink and color on paper,12 3/4 x 5 7/8 in. (32.4 x 14.9 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36903,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP451,false,true,36904,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,"2nd month, 1770",1770,1770,Polychrome woodblock print; ink and color on paper,12 1/8 x 5 5/8 in. (30.8 x 14.3 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36904,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP452,false,true,36905,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,"summer, 1768",1768,1768,Polychrome woodblock print; ink and color on paper,12 1/8 x 5 17/32 in. (30.8 x 14.1 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36905,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP453,false,true,36906,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,1769,1769,1769,Polychrome woodblock print; ink and color on paper,12 1/2 x 5 3/4 in. (31.8 x 14.6 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36906,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP454,false,true,36907,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,1768 or 1769,1768,1769,Polychrome woodblock print; ink and color on paper,12 x 5 1/2 in. (30.5 x 14.0 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36907,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP455,false,true,36908,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,"12th month, 1770",1770,1770,Polychrome woodblock print; ink and color on paper,12 7/16 x 5 7/32 in. (31.6 x 13.3 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36908,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP456,false,true,36909,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,ca. 1770,1760,1780,Polychrome woodblock print; ink and color on paper,12 7/16 x 5 1/2 in. (31.6 x 14.0 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36909,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP458,false,true,36911,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,"12th month, 1768",1768,1768,Polychrome woodblock print; ink and color on paper,12 11/32 x 5 1/2 in. (31.4 x 14.0 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36911,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP460,false,true,36913,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,1768 or 1769,1768,1769,Polychrome woodblock print; ink and color on paper,12 7/16 x 5 1/2 in. (31.6 x 14 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36913,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP461,false,true,36914,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,1768 or 1769,1768,1769,Polychrome woodblock print; ink and color on paper,12 11/32 x 5 2/3 in. (31.4 x 14.4 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36914,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP462,false,true,36915,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,1791,1791,1791,Polychrome woodblock print; ink and color on paper,12 5/8 x 5 3/4 in. (32.1 x 14.6 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36915,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP463,false,true,36916,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,ca. 1775,1765,1785,Polychrome woodblock print; ink and color on paper,11 3/5 x 5 3/4 in. (29.5 x 14.6 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36916,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP464,false,true,48894,Asian Art,Print,"二代目市川八百藏・二代目中島三甫右衛門・三代目市川海老藏・九代目市村羽左衛門|Kabuki Actors Ichikawa Yaozō II, Nakajima Mihoemon II, Ichikawa Ebizō III, and Ichimura Uzaemon IX in the Play Sugawara’s Secrets of Calligraphy (Sugawara denju tenarai kagami)",Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,"7th month, 1776",1776,1776,One sheet of a triptych of polychrome woodblock prints; ink and color on paper,Overall: 12 5/8 x 17 7/16 in. (32.1 x 44.3 cm); Image: 12 11/16 x 5 15/16 in. (32.2 x 15.1 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/48894,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP465,false,true,36917,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,ca. 1779,1759,1779,Polychrome woodblock print; ink and color on paper,11 7/8 x 5 3/4 in. (30.2 x 14.6 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36917,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP551,false,true,37002,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,1769 spring,1769,1769,Polychrome woodblock print; ink and color on paper,12 5/8 x 5 3/4 in. (32.1 x 14.6 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37002,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP552,false,true,37003,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,ca. 1775 or 1776,1173,1778,Polychrome woodblock print; ink and color on paper,11 15/16 x 5 3/8 in. (30.3 x 13.7 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37003,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP553,false,true,37004,Asian Art,Print,"二代目市川八百藏・二代目中島三甫右衛門・三代目市川海老藏・九代目市村羽左衛門|Kabuki Actors Ichikawa Yaozō II, Nakajima Mihoemon II, Ichikawa Ebizō III, and Ichimura Uzaemon IX in the Play Sugawara’s Secrets of Calligraphy (Sugawara denju tenarai kagami)",Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,"7th month, 1776",1776,1776,One sheet of a triptych of polychrome woodblock prints; ink and color on paper,Overall: 12 5/8 x 17 7/16 in. (32.1 x 44.3 cm); Image: 12 9/16 x 5 3/4 in. (31.9 x 14.6 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37004,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP703,false,true,37150,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,ca. 1765,1755,1775,Polychrome woodblock print; ink and color on paper,12 x 5 1/2 in. (30.5 x 14.0 cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37150,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP704,false,true,37152,Asian Art,Print,"「風流六く歌仙紀友則　十」|“Two Young Women on a Verandah Watching Plovers,” from the series Stylish Six Poetic Immortals (Fūryū rokkasen: Ki no Tomonori, jū)",Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,ca. 1770,1760,1780,Polychrome woodblock print; ink and color on paper,9 7/8 x 7 1/8 in. (25.1 x 18.1 cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37152,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP705,false,true,37153,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,1772,1772,1772,Polychrome woodblock print; ink and color on paper,11 1/2 x 5 7/32 in. (29.2 x 13.3 cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37153,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP706,false,true,37154,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,ca. 1777,1767,1787,Polychrome woodblock print; ink and color on paper,12 7/32 x 5 5/8 in. (31.1 x 14.3 cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37154,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP707,false,true,37155,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,ca. 1778,1768,1788,Polychrome woodblock print; ink and color on paper,12 31/32 x 5 7/8 in. (33.0 x 14.9 cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37155,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP708,false,true,37156,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,ca. 1778,1768,1788,Polychrome woodblock print; ink and color on paper,12 7/32 x 17 1/5 in. (31.1 x 43.7 cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37156,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP709,false,true,37157,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,ca. 1778,1768,1788,Polychrome woodblock print; ink and color on paper,12 3/4 x 8 27/32 in. (32.4 x 22.5 cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37157,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP710,false,true,37158,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,ca. 1778,1768,1788,Polychrome woodblock print; ink and color on paper,12 7/32 x 8 1/10 in. (31.1 x 20.6 cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37158,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP711,false,true,37159,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,ca. 1779,1769,1789,Polychrome woodblock print; ink and color on paper,12 7/32 x 5 5/8 in. (31.1 x 14.3 cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37159,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP893,false,true,54587,Asian Art,Print,"花形見風折烏帽子瀬川富三郎の娘道成寺|Segawa Tomisaburo in the Role of Musume Dojoji in ""Hanagatami Kazaori Eboshi""",Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,February 1774,1769,1779,Polychrome woodblock print; ink and color on paper,H. 12 1/4 in. (31.1 cm); W. 5 3/4 in. (14.6 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54587,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP894,false,true,54588,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,ca. 1773,1763,1783,Polychrome woodblock print; ink and color on paper,H. 11 3/8 in. (28.9 cm); W. 5 7/8 in. (14.9 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54588,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP895,false,true,54589,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,ca. 1774,1764,1784,Polychrome woodblock print; ink and color on paper,H. 12 1/2 in. (31.8 cm); W. 5 7/8 in. (14.9 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54589,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP896,false,true,54590,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,1774,1774,1774,Polychrome woodblock print; ink and color on paper,H. 12 1/8 in. (30.8 cm); W. 5 7/8 in. (14.9 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54590,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP897,false,true,54591,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,1776,1776,1776,Polychrome woodblock print; ink and color on paper,H. 8 1/2 in. (21.6 cm); W. 11 3/4 in. (29.8 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54591,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP898,false,true,54592,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,ca. 1776,1766,1786,Polychrome woodblock print; ink and color on paper,H. 12 1/2 in. (31.8 cm); W. 8 1/4 in. (21 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54592,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP899,false,true,54595,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,ca. 1776,1766,1786,Polychrome woodblock print; ink and color on paper,H. 11 3/4 in. (29.8 cm); W. 8 1/4 in. (21 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54595,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP900,false,true,54596,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,ca. 1777,1767,1787,Polychrome woodblock print; ink and color on paper,H. 12 1/4 in. (31.1 cm); W. 5 3/4 in. (14.6 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54596,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP901,false,true,54597,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,ca. 1778,1768,1788,Polychrome woodblock print; ink and color on paper,H. 12 1/4 in. (31.1 cm); W. 5 5/8 in. (14.3 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54597,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP902,false,true,54599,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,ca. 1778,1768,1788,Polychrome woodblock print; ink and color on paper,H. 12 1/2 in. (31.8 cm); W. 5 3/4 in. (14.6 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54599,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP903,false,true,54600,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,ca. 1778,1768,1788,Diptych of polychrome woodblock prints; ink and color on paper,H. 14 1/4 in. (36.2 cm); W. 6 11/16 in. (17 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54600,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP904,false,true,54601,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,ca. 1778,1768,1788,Polychrome woodblock print; ink and color on paper,H. 12 1/4 in. (31.1 cm); W. 5 7/8 in. (14.9 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54601,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP905,false,true,54602,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,ca. 1779,1769,1789,Polychrome woodblock print; ink and color on paper,H. 12 1/4 in. (31.1 cm); W. 5 1/2 in. (14 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54602,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP906,false,true,54647,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,ca. 1779,1769,1789,Polychrome woodblock print; ink and color on paper,H. 12 3/4 in. (32.4 cm); W. 6 9/16 in. (16.7 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54647,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1270,false,true,55167,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,ca. 1778,1768,1788,Diptych of polychrome woodblock prints; ink and color on paper,H. 14 1/4 in. (36.2 cm); W. 6 11/16 in. (17 cm),"Rogers Fund, 1921",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55167,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1298,false,true,55239,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,ca. 1781,1771,1791,Polychrome woodblock print; ink and color on paper,H. 12 1/4 in. (31.1 cm); W. 5 7/8 in. (14.9 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55239,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1355,false,true,55331,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,ca. 1771,1761,1781,Polychrome woodblock print; ink and color on paper,H. 12 13/16 in. (32.5 cm); W. 5 9/16 in. (14.1 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55331,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1356,false,true,55333,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,1768,1768,1768,Polychrome woodblock print; ink and color on paper,H. 12 3/4 in. (32.4 cm); W. 5 11/16 in. (14.4 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55333,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1357,false,true,55334,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,1769,1769,1769,Polychrome woodblock print; ink and color on paper,H. 12 7/8 in. (32.7 cm); W. 5 7/8 in. (14.9 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55334,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1358,false,true,55335,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,1774,1774,1774,Polychrome woodblock print; ink and color on paper,H. 12 3/4 in. (32.4 cm); W. 5 7/8 in. (14.9 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55335,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1359,false,true,55336,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,ca. 1775,1765,1785,Polychrome woodblock print; ink and color on paper,H. 12 1/2 in. (31.8 cm); W. 5 7/8 in. (14.9 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55336,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1360,false,true,55337,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,ca. 1775,1765,1785,Polychrome woodblock print; ink and color on paper,H. 12 1/2 in. (31.8 cm); W. 5 11/16 in. (14.4 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55337,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1366,false,true,55317,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,ca. 1776,1766,1786,Polychrome woodblock print; ink and color on paper,Uncut double hosoban 12 3/4 x 11 15/16 in. (32.4 x 30.3 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55317,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1445,false,true,55489,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,1770,1770,1770,Polychrome woodblock print; ink and color on paper,H. 5 7/16 in. (13.8 cm); W. 6 3/16 in. (15.7 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55489,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1450,false,true,55497,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,ca. 1781,1771,1791,Polychrome woodblock print; ink and color on paper,H. 12 3/4 in. (32.4 cm); W. 5 3/4 in. (14.6 cm),"Rogers Fund, 1925",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55497,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1451,false,true,55498,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,ca. 1781,1771,1791,Polychrome woodblock print; ink and color on paper,H. 12 13/16 in. (32.5 cm); W. 5 11/16 in. (14.4 cm),"Rogers Fund, 1925",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55498,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1458,false,true,39719,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,ca. 1785,1775,1795,Polychrome woodblock print; ink and color on paper,14 3/8 x 19 1/4 in. (36.5 x 48.9 cm),"Rogers Fund, 1925",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/39719,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1489,false,true,39720,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,ca. 1775,1765,1785,Polychrome woodblock print; ink and color on paper,17 7/8 x W. Top 12 13/16 in. (45.4 x 32.5 cm) 17 7/8 x W. Bot 12 7/8 in (45.4 x 32.7 cm),"Rogers Fund, 1927",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/39720,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1532,false,true,45036,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,ca. 1780,1770,1790,Polychrome woodblock print; ink and color on paper,12 13/16 x 5 3/4 in. (32.5 x 14.6 cm),"Fletcher Fund, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45036,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1533,false,true,55645,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,ca. 1780,1770,1790,Polychrome woodblock print; ink and color on paper,H. 12 in. (30.5 cm); W. 6 in. (15.2 cm),"Fletcher Fund, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55645,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1534,false,true,55646,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,1771–72,1771,1772,Polychrome woodblock print; ink and color on paper,H. 12 3/4 in. (32.4 cm); W. 5 15/16 in. (15.1 cm),"Fletcher Fund, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55646,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1535,false,true,55700,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,1771–72,1771,1772,Polychrome woodblock print; ink and color on paper,H. 12 13/16 in. (32.5 cm); W. 5 13/16 in. (14.8 cm),"Fletcher Fund, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55700,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1757,false,true,56063,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,1771,1771,1771,Polychrome woodblock print; ink and color on paper,H. 12 3/8 in. (31.4 cm); W. 5 5/8 in. (14.3 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56063,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2405,false,true,56808,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,1770,1770,1770,Polychrome woodblock print; ink and color on paper,11 1/4 x 5 1/4 in. (28.6 x 13.3 cm),"Gift of Louis V. Ledoux, 1931",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56808,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2406,false,true,56809,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,ca. 1780,1770,1790,Polychrome woodblock print; ink and color on paper,12 3/8 x 6 in. (31.4 x 15.2 cm),"Gift of Louis V. Ledoux, 1931",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56809,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2680,false,true,56864,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,1768 or 1769,1768,1769,Polychrome woodblock print; ink and color on paper,12 3/8 x 5 5/8 in. (31.4 x 14.3 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56864,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2682,false,true,56866,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,ca. 1786,1776,1796,Polychrome woodblock print; ink and color on paper,12 3/4 x 5 3/4 in. (32.4 x 14.6 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56866,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2683,false,true,56867,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,ca. 1778,1768,1788,Polychrome woodblock print; ink and color on paper,12 3/4 x 5 7/8 in. (32.4 x 14.9 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56867,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2684,false,true,56868,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,"2nd month, 1772",1772,1772,Polychrome woodblock print; ink and color on paper,12 1/8 x 5 5/8 in. (30.8 x 14.3 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56868,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2685,false,true,56869,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,1786 (?),1786,1786,Polychrome woodblock print; ink and color on paper,12 7/8 x 5 3/4 in. (32.7 x 14.6 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56869,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2686,false,true,56870,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,ca. 1772,1762,1782,Polychrome woodblock print; ink and color on paper,12 3/4 x 5 3/4 in. (32.4 x 14.6 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56870,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2687,false,true,56871,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,ca. 1772,1762,1782,Polychrome woodblock print; ink and color on paper,12 3/4 x 5 3/4 in. (32.4 x 14.6 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56871,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2688,false,true,56872,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,"12th month, 1780",1780,1780,Polychrome woodblock print; ink and color on paper,12 3/4 x 6 in. (32.4 x 15.2 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56872,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2689,false,true,56947,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,ca. 1780,1770,1790,Polychrome woodblock print; ink and color on paper,12 3/4 x 5 3/4 in. (32.4 x 14.6 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56947,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2690,false,true,56948,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,ca. 1780,1770,1790,Polychrome woodblock print; ink and color on paper,12 1/4 x 5 7/8 in. (31.1 x 14.9 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56948,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2691,false,true,56952,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,ca. 1780,1770,1790,Polychrome woodblock print; ink and color on paper,12 1/4 x 5 3/4 in. (31.1 x 14.6 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56952,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2692,false,true,56953,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,1782,1782,1782,Polychrome woodblock print; ink and color on paper,12 1/4 x 5 1/2 in. (31.1 x 14 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56953,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2693,false,true,56954,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,"5th month, 1781",1781,1781,Polychrome woodblock print; ink and color on paper,12 1/8 x 8 3/4 in. (30.8 x 22.2 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56954,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2695,false,true,56957,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,ca. 1772,1762,1782,Polychrome woodblock print; ink and color on paper,12 1/2 x 5 3/4 in. (31.8 x 14.6 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56957,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2696,false,true,56959,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,1774,1774,1774,Polychrome woodblock print; ink and color on paper,12 5/8 x 5 5/8 in. (32.1 x 14.3 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56959,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2697,false,true,56960,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,ca. 1780,1770,1790,Polychrome woodblock print; ink and color on paper,11 3/4 x 5 1/2 in. (29.8 x 14 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56960,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2698,false,true,56962,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,ca. 1780,1770,1790,Polychrome woodblock print; ink and color on paper,12 1/4 x 5 5/8 in. (31.1 x 14.3 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56962,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2699,false,true,56963,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,ca. 1778,1768,1788,Polychrome woodblock print; ink and color on paper,12 1/2 x 5 3/8 in. (31.8 x 13.7 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56963,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2700,false,true,56964,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,ca. 1775,1765,1785,Polychrome woodblock print; ink and color on paper,12 3/8 x 5 5/8 in. (31.4 x 14.3 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56964,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2701,false,true,56966,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,ca. 1780,1770,1790,Polychrome woodblock print; ink and color on paper,12 x 5 3/4 in. (30.5 x 14.6 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56966,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2702,false,true,56968,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,ca. 1779,1769,1789,Polychrome woodblock print; ink and color on paper,12 x 5 in. (30.5 x 12.7 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56968,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2703,false,true,56969,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,1772,1772,1772,Polychrome woodblock print; ink and color on paper,12 1/2 x 6 in. (31.8 x 15.2 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56969,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2704,false,true,44995,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,ca. 1785,1775,1795,Polychrome woodblock print; ink and color on paper,12 1/8 x 5 3/4 in. (30.8 x 14.6 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/44995,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2705,false,true,56970,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,ca. 1772 (?),1762,1782,Polychrome woodblock print; ink and color on paper,12 x 5 3/4 in. (30.5 x 14.6 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56970,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2706,false,true,56971,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",", New York, NY (1936; sold to MMA).",Katsukawa Shunshō,Japanese,1726,1792,ca. 1790,1780,1810,Polychrome woodblock print; ink and color on paper,13 x 6 in. (33 x 15.2 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56971,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2783,false,true,57037,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,1726–1792,1726,1792,Polychrome woodblock print; ink and color on paper,12 1/2 x 5 5/8 in. (31.8 x 14.3 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57037,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2784,false,true,57105,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,1726–1792,1726,1792,Polychrome woodblock print; ink and color on paper,14 5/8 x 6 5/8 in. (37.1 x 16.8 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57105,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2886,false,true,56001,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,1726–1792,1726,1792,Polychrome woodblock print; ink and color on paper,11 1/2 x 5 1/2 in. (29.2 x 14 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56001,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2887,false,true,56002,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,1726–1792,1726,1792,Polychrome woodblock print; ink and color on paper,11 3/4 x 5 1/2 in. (29.8 x 14 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56002,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2888,false,true,56003,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,1726–1792,1726,1792,Polychrome woodblock print; ink and color on paper,12 3/4 x 5 3/4 in. (32.4 x 14.6 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56003,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2890,false,true,56005,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,1726–1792,1726,1792,Polychrome woodblock print; ink and color on paper,12 1/2 x 5 3/4 in. (31.8 x 14.6 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56005,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2891,false,true,56006,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,1726–1792,1726,1792,Polychrome woodblock print; ink and color on paper,11 7/8 x 5 3/4 in. (30.2 x 14.6 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56006,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2892,false,true,56007,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,1726–1792,1726,1792,Polychrome woodblock print; ink and color on paper,12 1/8 x 5 7/8 in. (30.8 x 14.9 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56007,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2893,false,true,56008,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,1726–1792,1726,1792,Polychrome woodblock print; ink and color on paper,12 3/4 x 6 in. (32.4 x 15.2 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56008,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP703.5,false,true,37151,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,1771,1771,1771,Polychrome woodblock print; ink and color on paper,12 7/8 x 6 1/8 in. (32.7 x 15.6 cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37151,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"JP387a, b",false,true,36473,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,ca. 1778,1768,1788,Diptych of polychrome woodblock prints; ink and color on paper,H. 12 1/8 in. (30.8 cm); W. 11 3/8 in. (28.9 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36473,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"JP390a, b",false,true,36471,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,1776,1776,1776,Diptych of polychrome woodblock prints; ink and color on paper,H. 12 1/4 in. (31.1 cm); W. 11 1/2 in. (29.2 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36471,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"JP391a, b",false,true,36470,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,ca. 1780,1770,1790,Diptych of polychrome woodblock prints; ink and color on paper,A. H. 12 5/16 in. (31.3 cm); W. 5 1/2 in. (14 cm) B. H. 12 1/16 in. (30.6 cm); W. 5 11/16 in. (14.4 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36470,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"JP393a, b",false,true,36469,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,ca. 1791,1781,1801,Polychrome woodblock print; ink and color on paper,Hosoban diptych 12 1/4 x 11 1/4 in. (31.1 x 28.6 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36469,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"JP394a, b",false,true,36468,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,ca. 1775,1765,1785,Diptych of polychrome woodblock prints; ink and color on paper,H. 12 5/16 in. (31.3 cm); W. 5 1/2 in. (14 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36468,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"JP400a, b",false,true,36464,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,ca. 1778,1768,1788,Diptych of polychrome woodblock prints; ink and color on paper,A: H. 12 in. (30 cm); W. 5 5/8 in. (14.3 cm) B: H. 11 15/16 in. (30.3 cm); W. 5 5/8 in. (14.3 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36464,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"JP401a, b",false,true,36463,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,ca. 1773,1763,1783,Diptych of polychrome woodblock prints; ink and color on paper,Overall: H. 12 5/16 in. (31.3 cm); W. 11 in. (27.9 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36463,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"JP466a, b",false,true,36462,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,1780,1780,1780,Diptych of polychrome woodblock prints; ink and color on paper,A: 12 5/8 x 5 3/4 in. (32.1 x 14.6 cm) B: 12 5/8 x 5 3/4 in. (32.1 x 14.6 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36462,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"JP2681a, b",false,true,56865,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,ca. 1780,1770,1790,Diptych of polychrome woodblock prints; ink and color on paper,Image (left): 10 7/8 × 5 1/2 in. (27.6 × 14 cm) Image (right): 10 7/8 × 5 3/4 in. (27.6 × 14.6 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56865,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP389a–c,false,true,36472,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,1775,1778,1782,Triptych of polychrome woodblock prints; ink and color on paper,Overall: 12 5/8 x 5 1/2 in. (32.1 x 14 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36472,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP396a–e,false,true,36467,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,"2nd month, 1780",1780,1780,Pentaptych of polychrome woodblock prints; ink and color on paper,Overall: H. 12 5/16 (31.3 cm); W. 27 1/2 in. (69.9 cm),"Purchase, Joseph Pulitzer Bequest, 1918 (JP396a-d) Rogers Fund, 1922 (JP396e)",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36467,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP398a–d,false,true,36465,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,1780,1780,1780,Tetraptych of polychrome woodblock prints; ink and color on paper,Overall: H. 13 in. (33 cm); W. 22 3/4 in. (57.8 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36465,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP459a–d,false,true,36912,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,Designed by,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,1768,1768,1768,Polychrome woodblock prints; ink and color on paper,Four of a set of five hosoban 12 1/2 x 22 in. (31.8 x 55.9 cm) B.12 5/16 x 5 7/16 in.(32.4 x 14 cm) C.12 1/8 x 5 1/2 in. (30.8 x 14 cm) D.12 5/6 x 5 7/16 in. (32.4 x 14 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36912,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1342a–c,false,true,55307,Asian Art,Print,"三代目市川八百藏・初代目尾上松助・三代目澤村宗十郎|Kabuki Actors Ichikawa Yaozō III, Onoe Matsusuke I, and Sawamura Sōjūrō III",Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,"11th month, 1786",1786,1786,Triptych of polychrome woodblock prints; ink and color on paper,a: 12 5/8 in. (32.1 cm); W. 5 3/4 in. (14.6 cm) b: 12 5/8 in. (32.1 cm); W. 5 13/16 in. (14.8 cm) c: 12 11/16 in. (32.2 cm); W. 5 13/16 in. (14.8 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55307,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2889a–c,false,true,56004,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,third month of 1780,1780,1780,Triptych of polychrome woodblock prints; ink and color on paper,A: 12 7/8 x 5 5/8 in. (32.7 x 14.3 cm) B: 12 3/4 x 5 3/4 in. (32.4 x 14.6 cm) C: 12 3/4 x 5 7/8 in. (32.4 x 14.9 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56004,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP199,false,true,36676,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyomitsu,"Japanese, 1735–1785",,Torii Kiyomitsu,Japanese,1735,1785,probably 1767,1765,1769,Polychrome woodblock print; ink and color on paper,12 7/32 x 5 7/8 in. (31.1 x 14.9 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36676,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP468,false,true,36919,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyomitsu,"Japanese, 1735–1785",,Torii Kiyomitsu,Japanese,1735,1785,ca. 1760?,1750,1760,Polychrome woodblock print; ink and color on paper,12 1/4 x 5 7/16 in. (31.1 x 13.8 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36919,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP469,false,true,36920,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyomitsu,"Japanese, 1735–1785",,Torii Kiyomitsu,Japanese,1735,1785,"2nd month, 1771",1771,1771,Polychrome woodblock print; ink and color on paper,12 7/32 x 5 1/8 in. (31.1 x 13.0 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36920,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP667,false,true,37114,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyomitsu,"Japanese, 1735–1785",,Torii Kiyomitsu,Japanese,1735,1785,ca. 1764,1754,1774,Polychrome woodblock print; ink and color on paper,27 11/32 x 4 in. (69.5 x 10.2 cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37114,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP692,false,true,37139,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyomitsu,"Japanese, 1735–1785",,Torii Kiyomitsu,Japanese,1735,1785,ca. 1755,1745,1765,Polychrome woodblock print; ink and color on paper,12 7/32 x 5 5/8 in. (31.1 x 14.3 cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37139,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP693,false,true,37140,Asian Art,Print,「岩井半四郎図」|The Actor Iwai Hanshiro as a Courtesan Reading a Love Letter while Mounted on a Black Ox,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyomitsu,"Japanese, 1735–1785",,Torii Kiyomitsu,Japanese,1735,1785,ca. 1763,1753,1773,Polychrome woodblock print; ink and color on paper,11 1/2 x 5 1/2 in. (29.2 x 14.0 cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37140,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP694,false,true,37141,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyomitsu,"Japanese, 1735–1785",,Torii Kiyomitsu,Japanese,1735,1785,ca. 1763,1753,1773,Polychrome woodblock print; ink and color on paper,12 7/32 x 17 in. (31.1 x 43.2 cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37141,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP695,false,true,37142,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyomitsu,"Japanese, 1735–1785",,Torii Kiyomitsu,Japanese,1735,1785,ca. 1763,1753,1773,Polychrome woodblock print; ink and color on paper,12 1/8 x 16 1/2 in. (30.8 x 41.9 cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37142,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP835,false,true,54490,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyomitsu,"Japanese, 1735–1785",,Torii Kiyomitsu,Japanese,1735,1785,ca. 1757,1747,1767,Polychrome woodblock print; ink and color on paper,H. 12 in. (30.5 cm); W. 5 1/2 in. (14 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54490,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP836,false,true,54491,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyomitsu,"Japanese, 1735–1785",,Torii Kiyomitsu,Japanese,1735,1785,ca. 1761,1751,1771,Polychrome woodblock print; ink and color on paper,H. 11 7/8 in. (30.2 cm); W. 5 5/8 in. (14.3 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54491,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP837,false,true,54492,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyomitsu,"Japanese, 1735–1785",,Torii Kiyomitsu,Japanese,1735,1785,ca. 1761,1751,1771,Polychrome woodblock print; ink and color on paper,H. 12 1/4 in. (31.1 cm); 5 7/8 in. (14.9 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54492,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP838,false,true,54493,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyomitsu,"Japanese, 1735–1785",,Torii Kiyomitsu,Japanese,1735,1785,ca. 1762,1752,1772,Polychrome woodblock print; ink and color on paper,H. 23 1/2 in. (59.7 cm); W. 4 1/8 in. (10.5 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54493,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP839,false,true,54494,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyomitsu,"Japanese, 1735–1785",,Torii Kiyomitsu,Japanese,1735,1785,probably 1765,1760,1770,Polychrome woodblock print; ink and color on paper,H. 11 7/8 in. (30.2 cm); W. 5 1/2 in. (14 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54494,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1378,false,true,55362,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyomitsu,"Japanese, 1735–1785",,Torii Kiyomitsu,Japanese,1735,1785,ca. 1760,1750,1770,Polychrome woodblock print; ink and color on paper,H. 11 15/16 in. (30.3 cm); W. 5 1/2 in. (14 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55362,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1385,false,true,55383,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyomitsu,"Japanese, 1735–1785",,Torii Kiyomitsu,Japanese,1735,1785,ca. 1760,1750,1770,Polychrome woodblock print; ink and color on paper,H. 12 7/16 in. (31.6 cm); W. 5 3/4 in. (14.6 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55383,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1500,false,true,55602,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyomitsu,"Japanese, 1735–1785",,Torii Kiyomitsu,Japanese,1735,1785,ca. 1767,1757,1777,Polychrome woodblock print; ink and color on paper,H. 11 7/8 in. (30.2 cm); W. 5 3/8 in. (13.7 cm),"Rogers Fund, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55602,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1501,false,true,55606,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyomitsu,"Japanese, 1735–1785",,Torii Kiyomitsu,Japanese,1735,1785,ca. 1767,1767,1777,Polychrome woodblock print; ink and color on paper,H. 12 1/4 in. (31.1 cm); W. 5 1/2 in. (14 cm),"Fletcher Fund, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55606,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1502,false,true,55607,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyomitsu,"Japanese, 1735–1785",,Torii Kiyomitsu,Japanese,1735,1785,ca. 1768,1758,1778,One sheet of a triptych of polychrome woodblock prints; ink and color on paper,H. 12 3/8 in. (31.4 cm); W. 5 3/8 in. (13.7 cm),"Fletcher Fund, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55607,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1507,false,true,51090,Asian Art,Print,Amagoi Komachi|Komachi Praying for Rain,Japan,Edo period (1615–1868),,,,Artist,Attributed to,Torii Kiyomitsu,"Japanese, 1735–1785",,Torii Kiyomitsu,Japanese,1735,1785,ca. 1765,1755,1775,Polychrome woodblock print; ink and color on paper,H. 11 5/16 in. (28.7 cm); W. 8 1/2 in. (21.6 cm),"Fletcher Fund, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/51090,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1568,false,true,55745,Asian Art,Print,「二代目瀬川菊之丞図」|The Kabuki Actor Segawa Kikunojō II as a Woman Reading a Letter,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyomitsu,"Japanese, 1735–1785",,Torii Kiyomitsu,Japanese,1735,1785,ca. 1760s,1750,1770,Polychrome woodblock print; ink and color on paper,H. 28 1/8 in. (71.4 cm); W. 4 3/8 in. (11.1 cm),"Fletcher Fund, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55745,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2588,false,true,56734,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyomitsu,"Japanese, 1735–1785",,Torii Kiyomitsu,Japanese,1735,1785,1757 or 1758,1757,1758,Polychrome woodblock print; ink and color on paper,12 5/8 x 5 3/4 in. (32.1 x 14.6 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56734,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2590,false,true,56736,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyomitsu,"Japanese, 1735–1785",,Torii Kiyomitsu,Japanese,1735,1785,ca. 1759,1749,1769,Polychrome woodblock print; ink and color on paper,14 3/8 x 6 5/8 in. (36.5 x 16.8 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56736,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2591,false,true,56737,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyomitsu,"Japanese, 1735–1785",,Torii Kiyomitsu,Japanese,1735,1785,ca. 1764,1754,1774,Polychrome woodblock print; ink and color on paper,21 3/8 x 4 1/8 in. (54.3 x 10.5 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56737,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2592,false,true,56738,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyomitsu,"Japanese, 1735–1785",,Torii Kiyomitsu,Japanese,1735,1785,ca. 1763,1753,1773,Polychrome woodblock print; ink and color on paper,27 7/8 x 4 in. (70.8 x 10.2 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56738,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2763,false,true,57027,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyomitsu,"Japanese, 1735–1785",,Torii Kiyomitsu,Japanese,1735,1785,1735–1785,1735,1785,Polychrome woodblock print; ink and color on paper,12 1/8 x 5 1/2 in. (30.8 x 14 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57027,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2764,false,true,57028,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyomitsu,"Japanese, 1735–1785",,Torii Kiyomitsu,Japanese,1735,1785,1735–1785,1735,1785,Polychrome woodblock print; ink and color on paper,12 1/8 x 5 3/8 in. (30.8 x 13.7 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57028,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2765,false,true,57029,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyomitsu,"Japanese, 1735–1785",,Torii Kiyomitsu,Japanese,1735,1785,1761,1761,1761,Polychrome woodblock print; ink and color on paper,11 7/8 x 5 1/2 in. (30.2 x 14 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57029,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3042,false,true,56498,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyomitsu,"Japanese, 1735–1785",,Torii Kiyomitsu,Japanese,1735,1785,ca. 1756,1746,1766,Polychrome woodblock print; ink and color on paper,12 x 5 3/4 in. (30.5 x 14.6 cm),"Gift of Mrs. Morris Manges, in memory of her husband, Dr. Morris Manges, 1947",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56498,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3093,false,true,56606,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyomitsu,"Japanese, 1735–1785",,Torii Kiyomitsu,Japanese,1735,1785,1768,1768,1768,Polychrome woodblock print; ink and color on paper,12 1/4 x 5 1/2 in. (31.1 x 14 cm),"Harris Brisbane Dick Fund and Rogers Fund, 1949",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56606,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3094,false,true,56607,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyomitsu,"Japanese, 1735–1785",,Torii Kiyomitsu,Japanese,1735,1785,1756,1756,1756,Polychrome woodblock print; ink and color on paper,12 1/4 x 5 5/8 in. (31.1 x 14.3 cm),"Harris Brisbane Dick Fund and Rogers Fund, 1949",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56607,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3095,false,true,56608,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyomitsu,"Japanese, 1735–1785",,Torii Kiyomitsu,Japanese,1735,1785,1766,1766,1766,Polychrome woodblock print; ink and color on paper,15 x 7 in. (38.1 x 17.8 cm),"Harris Brisbane Dick Fund and Rogers Fund, 1949",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56608,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP192,false,true,36669,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Toyoharu,"Japanese, 1735–1814",,Utagawa Toyoharu,Japanese,1735,1814,ca. 1780,1770,1790,Polychrome woodblock print; ink and color on paper,10 x 14 3/4 in. (25.4 x 37.5 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36669,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP224,false,true,36698,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Toyoharu,"Japanese, 1735–1814",,Utagawa Toyoharu,Japanese,1735,1814,ca. 1780,1770,1790,Polychrome woodblock print; ink and color on paper,9 7/16 x 14 3/4 in. (24.0 x 37.5 cm),"Rogers Fund, 1917",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36698,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP675,false,true,37122,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Toyoharu,"Japanese, 1735–1814",,Utagawa Toyoharu,Japanese,1735,1814,ca. 1771,1761,1781,Polychrome woodblock print (pillar print); ink and color on paper,26 1/4 x 4 1/4 in. (66.7 x 10.8 cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37122,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP740,false,true,37188,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Toyoharu,"Japanese, 1735–1814",,Utagawa Toyoharu,Japanese,1735,1814,ca. 1770,1760,1780,Polychrome woodblock print; ink and color on paper,15 1/8 x 5 1/8 in. (38.4 x 13.0 cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37188,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1417,false,true,55445,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Toyoharu,"Japanese, 1735–1814",,Utagawa Toyoharu,Japanese,1735,1814,ca. 1805,1795,1815,Polychrome woodblock print; ink and color on paper,H. 9 3/8 in. (23.8 cm); W. 14 1/6 in. (36 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55445,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1420,false,true,55448,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Toyoharu,"Japanese, 1735–1814",,Utagawa Toyoharu,Japanese,1735,1814,ca. 1780,1770,1790,Polychrome woodblock print; ink and color on paper,H. 9 13/16 in. (24.9 cm); W. 14 3/4 in. (37.5 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55448,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1566,false,true,55744,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Toyoharu,"Japanese, 1735–1814",,Utagawa Toyoharu,Japanese,1735,1814,ca. 1790,1780,1800,Triptych of polychrome woodblock prints (surimono enriched with gold); ink and color on paper,Aiban; H. 13 7/8 in. (35.2 cm); W. 9 7/8 in. (25.1 cm),"Fletcher Fund, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55744,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1605,false,true,55769,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Toyoharu,"Japanese, 1735–1814",,Utagawa Toyoharu,Japanese,1735,1814,ca. 1780,1770,1790,Polychrome woodblock print; ink and color on paper,H. 8 3/4 in. (22.2 cm); W. 14 3/4 in. (37.5 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55769,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2855,false,true,45259,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Toyoharu,"Japanese, 1735–1814",,Utagawa Toyoharu,Japanese,1735,1814,ca. 1780,1770,1790,Polychrome woodblock print; ink and color on paper,10 1/2 x 15 1/8 in. (26.7 x 38.4 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45259,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP194,false,true,36671,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kitao Shigemasa,"Japanese, 1739–1820",,Kitao Shigemasa,Japanese,1739,1820,ca. 1782,1772,1792,Polychrome woodblock print; ink and color on paper,14 11/16 x 10 in. (37.3 x 25.4 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36671,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP195,false,true,36672,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kitao Shigemasa,"Japanese, 1739–1820",,Kitao Shigemasa,Japanese,1739,1820,ca. 1782,1772,1792,Polychrome woodblock print; ink and color on paper,14 3/5 x 9 7/8 in. (37.1 x 25.1 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36672,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP662,false,true,37109,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kitao Shigemasa,"Japanese, 1739–1820",,Kitao Shigemasa,Japanese,1739,1820,ca. 1800,1790,1810,Polychrome woodblock print (surimono); ink and color on paper,15 x 20 in. (38.1 x 50.8 cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37109,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP696,false,true,37143,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Kitao Shigemasa,"Japanese, 1739–1820",,Kitao Shigemasa,Japanese,1739,1820,1767,1757,1757,Polychrome woodblock print; ink and color on paper,11 1/8 x 5 1/2 in. (28.3 x 14.0 cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37143,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP932,false,true,54802,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kitao Shigemasa,"Japanese, 1739–1820",,Kitao Shigemasa,Japanese,1739,1820,ca. 1759,1749,1769,Polychrome woodblock print; ink and color on paper,H. 11 5/8 in. (29.5 cm); W. 5 1/8 in. (13 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54802,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP933,false,true,54803,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kitao Shigemasa,"Japanese, 1739–1820",,Kitao Shigemasa,Japanese,1739,1820,ca. 1772,1762,1782,Polychrome woodblock print; ink and color on paper,H. 9 7/16 in. (24 cm); W. 13 7/8 in. (35.2 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54803,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP934,false,true,45237,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kitao Shigemasa,"Japanese, 1739–1820",,Kitao Shigemasa,Japanese,1739,1820,ca. 1780,1770,1790,Polychrome woodblock print; ink and color on paper,H. 14 9/16 in. (37 cm); W. 9 13 /16 in. (24.9 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45237,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP936,false,true,54806,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kitao Shigemasa,"Japanese, 1739–1820",,Kitao Shigemasa,Japanese,1739,1820,ca. 1790,1780,1800,Polychrome woodblock print; ink and color on paper,H. 8 2/4 in. (21.6 cm); W. 12 3/8 in. (31.4 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54806,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1269,false,true,55166,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kitao Shigemasa,"Japanese, 1739–1820",,Kitao Shigemasa,Japanese,1739,1820,ca. 1764,1754,1774,Polychrome woodblock print; ink and color on paper,H. 11 1/8 in. (28.3 cm); W. 5 1/8 in. (13 cm),"Rogers Fund, 1921",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55166,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2123,false,true,54977,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kitao Shigemasa,"Japanese, 1739–1820",,Kitao Shigemasa,Japanese,1739,1820,1813,1813,1813,Polychrome woodblock print (surimono); ink and color on paper,8 5/16 x 3 5/16 in. (21.1 x 8.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54977,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2126,false,true,54981,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kitao Shigemasa,"Japanese, 1739–1820",,Kitao Shigemasa,Japanese,1739,1820,1813,1813,1813,Polychrome woodblock print (surimono); ink and color on paper,8 3/16 x 3 7/16 in. (20.8 x 8.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54981,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2338,false,true,54123,Asian Art,Print,判子と赤肉箱|Seals and a Carved Lacquer Container for Seal Ink,Japan,Edo period (1615–1868),,,,Artist,,Kitao Shigemasa,"Japanese, 1739–1820",,Kitao Shigemasa,Japanese,1739,1820,1817,1817,1817,Part of an album of woodblock prints (surimono); ink and color on paper,5 9/16 x 7 5/16 in. (14.1 x 18.6 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54123,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2651,false,true,56829,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kitao Shigemasa,"Japanese, 1739–1820",,Kitao Shigemasa,Japanese,1739,1820,ca. 1766,1756,1776,Polychrome woodblock print; ink and color on paper,12 1/4 x 5 1/2 in. (31.1 x 14 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56829,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2652,false,true,56831,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kitao Shigemasa,"Japanese, 1739–1820",,Kitao Shigemasa,Japanese,1739,1820,1769 (early),1769,1769,Polychrome woodblock print; ink and color on paper,12 1/8 x 5 1/4 in. (30.8 x 13.3 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56831,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2653,false,true,45248,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kitao Shigemasa,"Japanese, 1739–1820",,Kitao Shigemasa,Japanese,1739,1820,ca. 1770,1760,1780,Polychrome woodblock print; ink and color on paper,10 5/8 x 7 3/4 in. (27 x 19.7 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45248,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2654,false,true,56833,Asian Art,Woodblock print,Daibutsu no bansho|Vesper Bell of the Temple of Great Buddha,Japan,Edo period (1615–1868),,,,Artist,,Kitao Shigemasa,"Japanese, 1739–1820",,Kitao Shigemasa,Japanese,1739,1820,ca. 1778,1768,1788,Polychrome woodblock print; ink and color on paper,15 x 9 7/8 in. (38.1 x 25.1 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56833,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2655,false,true,56834,Asian Art,Woodblock print,Tesage andon|The Hand Lantern,Japan,Edo period (1615–1868),,,,Artist,,Kitao Shigemasa,"Japanese, 1739–1820",,Kitao Shigemasa,Japanese,1739,1820,ca. 1790,1780,1810,Polychrome woodblock print; ink and color on paper,14 5/8 x 9 3/4 in. (37.1 x 24.8 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56834,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2656,false,true,56835,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kitao Shigemasa,"Japanese, 1739–1820",,Kitao Shigemasa,Japanese,1739,1820,ca. 1790,1780,1810,Polychrome woodblock print; ink and color on paper,15 x 10 1/8 in. (38.1 x 25.7 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56835,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2794,false,true,57095,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kitao Shigemasa,"Japanese, 1739–1820",,Kitao Shigemasa,Japanese,1739,1820,1739–1820,1739,1820,Polychrome woodblock print; ink and color on paper,14 3/4 x 10 1/8 in. (37.5 x 25.7 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57095,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP159,false,true,36638,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunkō,"Japanese, 1743–1812",,Katsukawa Shunkō,Japanese,1743,1812,ca. 1789,1779,1799,Polychrome woodblock print; ink and color on paper,12 3/4 x 5 3/4 in. (32.4 x 14.6 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36638,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP160,false,true,36639,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunkō,"Japanese, 1743–1812",,Katsukawa Shunkō,Japanese,1743,1812,probably 1777,1767,1787,Polychrome woodblock print; ink and color on paper,12 31/32 x 5 15/16 in. (33.0 x 15.1 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36639,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP161,false,true,36640,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunkō,"Japanese, 1743–1812",,Katsukawa Shunkō,Japanese,1743,1812,ca. 1789,1779,1799,Polychrome woodblock print; ink and color on paper,12 1/2 x 5 2/3 in. (31.8 x 14.4 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36640,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP162,false,true,36641,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunkō,"Japanese, 1743–1812",,Katsukawa Shunkō,Japanese,1743,1812,ca. 1784–88,1784,1788,Polychrome woodblock print; ink and color on paper,12 7/32 x 5 3/8 in. (31.1 x 13.7 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36641,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP163,false,true,36642,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunkō,"Japanese, 1743–1812",,Katsukawa Shunkō,Japanese,1743,1812,ca. 1780,1770,1790,Polychrome woodblock print; ink and color on paper,11 3/5 x 5 3/8 in. (29.5 x 13.7 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36642,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP164,false,true,36643,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunkō,"Japanese, 1743–1812",,Katsukawa Shunkō,Japanese,1743,1812,ca. 1780,1770,1790,Polychrome woodblock print; ink and color on paper,12 x 5 3/8 in. (30.5 x 13.7 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36643,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP165,false,true,36644,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunkō,"Japanese, 1743–1812",,Katsukawa Shunkō,Japanese,1743,1812,June 1786,1786,1786,Polychrome woodblock print; ink and color on paper,11 15/32 x 5 in. (29.1 x 12.7 cm) (trimmed),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36644,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP166,false,true,36645,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunkō,"Japanese, 1743–1812",,Katsukawa Shunkō,Japanese,1743,1812,April 1783,1783,1783,Polychrome woodblock print; ink and color on paper,12 4/5 x 5 15/16 in. (32.5 x 15.1 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36645,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP167,false,true,36646,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunkō,"Japanese, 1743–1812",,Katsukawa Shunkō,Japanese,1743,1812,dated 1788,1788,1788,Polychrome woodblock print; ink and color on paper,12 x 5 3/4 in. (30.5 x 14.6 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36646,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP168,false,true,36647,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunkō,"Japanese, 1743–1812",,Katsukawa Shunkō,Japanese,1743,1812,"ca. 12th month, 1779",1769,1789,Polychrome woodblock print; ink and color on paper,11 1/2 x 5 7/32 in. (29.2 x 13.3 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36647,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP169,false,true,36648,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunkō,"Japanese, 1743–1812",,Katsukawa Shunkō,Japanese,1743,1812,dated 1787,1787,1787,Middle sheet of a triptych of polychrome woodblock prints; ink and color on paper,12 3/4 x 5 3/4 in. (32.4 x 14.6 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36648,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP170,false,true,36649,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunkō,"Japanese, 1743–1812",,Katsukawa Shunkō,Japanese,1743,1812,ca. 1790,1780,1800,Polychrome woodblock print; ink and color on paper,11 1/2 x 5 1/2 in. (29.2 x 14 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36649,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP171,false,true,36650,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunkō,"Japanese, 1743–1812",,Katsukawa Shunkō,Japanese,1743,1812,December 1785,1785,1785,Polychrome woodblock print; ink and color on paper,14 9/16 x 9 11/16 in. (37.0 x 24.6 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36650,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP274,false,true,36746,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunkō,"Japanese, 1743–1812",,Katsukawa Shunkō,Japanese,1743,1812,1784,1784,1784,Polychrome woodblock print; ink and color on paper,11 7/8 x 5 3/4 in. (30.2 x 14.6 cm) (trimmed),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36746,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP275,false,true,36747,Asian Art,Print,六代目中山小十郎|Kabuki Actor Nakayama Kojūrō VI,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunkō,"Japanese, 1743–1812",,Katsukawa Shunkō,Japanese,1743,1812,1786,1786,1786,Polychrome woodblock print; ink and color on paper,12 5/8 x 5 3/4 in. (32.1 x 14.6 cm),"Purchase, Joseph Pulitzer Beqeust, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36747,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP276,false,true,36748,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunkō,"Japanese, 1743–1812",,Katsukawa Shunkō,Japanese,1743,1812,ca. 1784–88,1782,1790,Polychrome woodblock print; ink and color on paper,12 3/4 x 5 3/4 in. (32.4 x 14.6 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36748,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP277,false,true,36749,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunkō,"Japanese, 1743–1812",,Katsukawa Shunkō,Japanese,1743,1812,"2nd month, 1788",1788,1788,Polychrome woodblock print; ink and color on paper,12 1/32 x 5 3/8 in. (30.6 x 13.7 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36749,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP278,false,true,36750,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunkō,"Japanese, 1743–1812",,Katsukawa Shunkō,Japanese,1743,1812,ca. 1790,1780,1800,Polychrome woodblock print; ink and color on paper,12 7/32 x 5 2/3 in. (31.1 x 14.4 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36750,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP279,false,true,36751,Asian Art,Woodblock print,五代目市川団十郎|Scene from the Play Yoshitsune and the Thousand Cherry Trees (Yoshitsune senbon zakura),Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunkō,"Japanese, 1743–1812",,Katsukawa Shunkō,Japanese,1743,1812,"8th or 9th month, 1784",1784,1784,Polychrome woodblock print; ink and color on paper,12 7/16 x 5 3/4 in. (31.6 x 14.6 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36751,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP280,false,true,36752,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunkō,"Japanese, 1743–1812",,Katsukawa Shunkō,Japanese,1743,1812,ca. 1788?,1778,1798,Polychrome woodblock print; ink and color on paper,11 13/16 x 5 5/8 in. (30.0 x 14.3 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36752,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP281,false,true,36753,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunkō,"Japanese, 1743–1812",,Katsukawa Shunkō,Japanese,1743,1812,ca. 1790,1780,1800,Polychrome woodblock print; ink and color on paper,12 3/4 x 5 1/2 in. (32.4 x 14.0 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36753,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP282,false,true,36754,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunkō,"Japanese, 1743–1812",,Katsukawa Shunkō,Japanese,1743,1812,1787,1787,1787,Polychrome woodblock print; ink and color on paper,12 1/2 x 5 3/4 in. (31.8 x 14.6 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36754,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP283,false,true,36755,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunkō,"Japanese, 1743–1812",,Katsukawa Shunkō,Japanese,1743,1812,ca. 1779?,1769,1789,Polychrome woodblock print; ink and color on paper,12 7/8 x 5 7/8 in. (32.7 x 14.9 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36755,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP284,false,true,36756,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunkō,"Japanese, 1743–1812",,Katsukawa Shunkō,Japanese,1743,1812,Spring of 1785,1785,1785,Polychrome woodblock print; ink and color on paper,12 11/32 x 5 7/8 in. (31.4 x 14.9 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36756,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP285,false,true,36757,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunkō,"Japanese, 1743–1812",,Katsukawa Shunkō,Japanese,1743,1812,ca. 1790,1780,1800,Polychrome woodblock print; ink and color on paper,11 3/5 x 5 5/8 in. (29.5 x 14.3 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36757,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP286,false,true,36758,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunkō,"Japanese, 1743–1812",,Katsukawa Shunkō,Japanese,1743,1812,ca. 1780,1780,1790,Polychrome woodblock print; ink and color on paper,Hosoban 12 3/8 x 5 15/16 in. (31.4 x 15.1 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36758,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP287,false,true,36759,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunkō,"Japanese, 1743–1812",,Katsukawa Shunkō,Japanese,1743,1812,ca. 1785–90,1775,1800,Polychrome woodblock print; ink and color on paper,Overall: 12 1/4 x 5 9/16in. (31.1 x 14.1cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36759,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP288,false,true,36760,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunkō,"Japanese, 1743–1812",,Katsukawa Shunkō,Japanese,1743,1812,ca. 1774,1764,1784,Polychrome woodblock print; ink and color on paper,12 x 5 3/4 in. (30.5 x 14.6 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36760,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP289,false,true,36761,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunkō,"Japanese, 1743–1812",,Katsukawa Shunkō,Japanese,1743,1812,ca. 1790?,1780,1800,Polychrome woodblock print; ink and color on paper,12 7/32 x 5 5/8 in. (31.1 x 14.3 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36761,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP290,false,true,36762,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunkō,"Japanese, 1743–1812",,Katsukawa Shunkō,Japanese,1743,1812,late 1777 or early in 1778,1777,1778,Polychrome woodblock print; ink and color on paper,11 23/32 x 5 3/4 in. (29.8 x 14.6 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36762,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP295,false,true,36763,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunkō,"Japanese, 1743–1812",,Katsukawa Shunkō,Japanese,1743,1812,ca. 1778,1768,1788,Polychrome woodblock print; ink and color on paper,11 23/32 x 5 1/2 in. (29.8 x 14.0 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36763,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP296,false,true,36764,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunkō,"Japanese, 1743–1812",,Katsukawa Shunkō,Japanese,1743,1812,"4th month, 1783",1783,1783,Polychrome woodblock print; ink and color on paper,12 11/16 x 5 3/4 in. (32.2 x 14.6 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36764,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP297,false,true,36765,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunkō,"Japanese, 1743–1812",,Katsukawa Shunkō,Japanese,1743,1812,ca. 1780–85,1770,1795,Polychrome woodblock print; ink and color on paper,12 7/8 x 5 3/4 in. (32.7 x 14.6 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36765,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP298,false,true,36766,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunkō,"Japanese, 1743–1812",,Katsukawa Shunkō,Japanese,1743,1812,ca. 1785?,1775,1795,Polychrome woodblock print; ink and color on paper,12 3/4 x 5 3/4 in. (32.4 x 14.6 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36766,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP299,false,true,36767,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunkō,"Japanese, 1743–1812",,Katsukawa Shunkō,Japanese,1743,1812,ca. 1788,1778,1798,Polychrome woodblock print; ink and color on paper,12 5/8 x 5 5/8 in. (32.1 x 14.3 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36767,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP341,false,true,36806,Asian Art,Print,二代目嵐龍藏・三代目瀬川菊之丞|Kabuki Actors Arashi Ryūzō II and Segawa Kikunojō III,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunkō,"Japanese, 1743–1812",,Katsukawa Shunkō,Japanese,1743,1812,ca. 1790,1788,1792,Polychrome woodblock print; ink and color on paper,11 3/8 x 5 5/16 in. (28.9 x 13.5 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36806,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP712,false,true,37160,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunkō,"Japanese, 1743–1812",,Katsukawa Shunkō,Japanese,1743,1812,ca. 1791,1781,1801,Polychrome woodblock print; ink and color on paper,12 7/32 x 5 3/4 in. (31.1 x 14.6 cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37160,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP713,false,true,37161,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunkō,"Japanese, 1743–1812",,Katsukawa Shunkō,Japanese,1743,1812,ca. 1778,1768,1788,Polychrome woodblock print; ink and color on paper,12 7/32 x 5 5/8 in. (31.1 x 14.3 cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37161,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP714,false,true,37162,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunkō,"Japanese, 1743–1812",,Katsukawa Shunkō,Japanese,1743,1812,ca. 1786,1776,1796,Polychrome woodblock print; ink and color on paper,14 1/3 x 10 in. (36.4 x 25.4 cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37162,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP908,false,true,54648,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunkō,"Japanese, 1743–1812",,Katsukawa Shunkō,Japanese,1743,1812,1776,1776,1776,Polychrome woodblock print; ink and color on paper,H. 12 1/2 in. (31.8 cm); W. 5 3/4 in. (14.6 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54648,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP909,false,true,54649,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunkō,"Japanese, 1743–1812",,Katsukawa Shunkō,Japanese,1743,1812,ca. 1772,1762,1782,Polychrome woodblock print; ink and color on paper,H. 12 7/8 in. (32.7 cm); W. 6 in. (15.2 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54649,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP910,false,true,54650,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunkō,"Japanese, 1743–1812",,Katsukawa Shunkō,Japanese,1743,1812,ca. 1773,1763,1783,Polychrome woodblock print; ink and color on paper,H. 12 1/2 in. (31.8 cm); W. 5 7/8 in. (14.9 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54650,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP911,false,true,54651,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunkō,"Japanese, 1743–1812",,Katsukawa Shunkō,Japanese,1743,1812,ca. 1777,1767,1787,Polychrome woodblock print; ink and color on paper,H. 12 7/8 in. (32.7 cm); W. 5 7/8 in. (14.9 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54651,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1304,false,true,55243,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunkō,"Japanese, 1743–1812",,Katsukawa Shunkō,Japanese,1743,1812,ca. 1779,1769,1789,Polychrome woodblock print; ink and color on paper,H. 11 15/16 (30.3 cm); W. 5 3/8 in. (13.7 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55243,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1343,false,true,55308,Asian Art,Print,二代目市川門之助|Kabuki Actor Ichikawa Monnosuke II as Shinozuka in a Shibaraku (Stop Right There!) Scene,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunkō,"Japanese, 1743–1812",,Katsukawa Shunkō,Japanese,1743,1812,"11th month, 1790",1790,1790,Polychrome woodblock print; ink and color on paper,Hosoban 12 7/8 x 5 3/4 in. (32.7 x 14.6 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55308,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1344,false,true,55309,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunkō,"Japanese, 1743–1812",,Katsukawa Shunkō,Japanese,1743,1812,1786,1786,1786,Polychrome woodblock print; ink and color on paper,H. 12 5/8 in. (32.1 cm); W. 5 3/4 in. (14.6 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55309,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1345,false,true,55311,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunkō,"Japanese, 1743–1812",,Katsukawa Shunkō,Japanese,1743,1812,1795,1795,1795,Polychrome woodblock print; ink and color on paper,H. 12 13/16 in. (32.5 cm); W. 5 5/8 in. (14.3 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55311,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1346,false,true,55312,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunkō,"Japanese, 1743–1812",,Katsukawa Shunkō,Japanese,1743,1812,ca. 1790,1780,1800,Polychrome woodblock print; ink and color on paper,H. 12 9/16 in. (31.9 cm); W. 5 13/16 in. (14.8 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55312,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1352,false,true,55328,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunkō,"Japanese, 1743–1812",,Katsukawa Shunkō,Japanese,1743,1812,ca. 1790,1780,1800,Polychrome woodblock print; ink and color on paper,H. 13 in. (33 cm); W. 5 15/16 in. (15.1 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55328,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1364,false,true,55344,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunkō,"Japanese, 1743–1812",,Katsukawa Shunkō,Japanese,1743,1812,ca. 1790,1780,1800,Polychrome woodblock print; ink and color on paper,H. 12 9/16 in. (31.9 cm); W. 5 7/8 in. (14.9 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55344,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1479,false,true,45221,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunkō,"Japanese, 1743–1812",,Katsukawa Shunkō,Japanese,1743,1812,ca. 1789,1779,1899,Polychrome woodblock print; ink and color on paper,H. 12 11/16 in. (32.2 cm); W. 8 3/4 in. (22.2 cm),"Fletcher Fund, 1925",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45221,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1480,false,true,55566,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunkō,"Japanese, 1743–1812",,Katsukawa Shunkō,Japanese,1743,1812,ca. 1789,1779,1799,Polychrome woodblock print; ink and color on paper,H. 12 7/8 in. (32.7 cm); W. 8 5/8 in. (21.9 cm),"Fletcher Fund, 1925",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55566,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1494,false,true,55589,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunkō,"Japanese, 1743–1812",,Katsukawa Shunkō,Japanese,1743,1812,1795,1795,1795,Polychrome woodblock print; ink and color on paper,H. 12 7/8 in. (32.7 cm); W. 5 5/8 in. (14.3 cm),"Fletcher Fund, 1928",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55589,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1542,false,true,55707,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunkō,"Japanese, 1743–1812",,Katsukawa Shunkō,Japanese,1743,1812,ca. 1780,1770,1790,Polychrome woodblock print; ink and color on paper,H. 12 7/8 in. (32.7 cm); W. 5 3/4 in. (14.6 cm),"Fletcher Fund, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55707,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1543,false,true,55708,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunkō,"Japanese, 1743–1812",,Katsukawa Shunkō,Japanese,1743,1812,ca. 1780,1770,1790,Polychrome woodblock print; ink and color on paper,H. 12 1/4 in. (31.1 cm); W. 5 3/4 in. (14.6 cm),"Fletcher Fund, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55708,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2666,false,true,56844,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunkō,"Japanese, 1743–1812",,Katsukawa Shunkō,Japanese,1743,1812,ca. 1775,1765,1785,Polychrome woodblock print; ink and color on paper,12 1/4 x 5 3/4 in. (31.1 x 14.6 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56844,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2667,false,true,56845,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunkō,"Japanese, 1743–1812",,Katsukawa Shunkō,Japanese,1743,1812,ca. 1790,1780,1810,Polychrome woodblock print; ink and color on paper,13 x 6 in. (33 x 15.2 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56845,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2668,false,true,56846,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunkō,"Japanese, 1743–1812",,Katsukawa Shunkō,Japanese,1743,1812,ca. 1790,1780,1810,Polychrome woodblock print; ink and color on paper,13 x 5 3/4 in. (33 x 14.6 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56846,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2669,false,true,56847,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunkō,"Japanese, 1743–1812",,Katsukawa Shunkō,Japanese,1743,1812,ca. 1790,1780,1810,Polychrome woodblock print; ink and color on paper,12 3/4 x 5 3/4 in. (32.4 x 14.6 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56847,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2671,false,true,56849,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunkō,"Japanese, 1743–1812",,Katsukawa Shunkō,Japanese,1743,1812,ca. 1785,1775,1795,Polychrome woodblock print; ink and color on paper,12 3/4 x 5 3/4 in. (32.4 x 14.6 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56849,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2672,false,true,56850,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunkō,"Japanese, 1743–1812",,Katsukawa Shunkō,Japanese,1743,1812,ca. 1780,1770,1790,Polychrome woodblock print; ink and color on paper,11 7/8 x 5 7/8 in. (30.2 x 14.9 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56850,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2673,false,true,56851,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunkō,"Japanese, 1743–1812",,Katsukawa Shunkō,Japanese,1743,1812,1784,1784,1784,Polychrome woodblock print; ink and color on paper,12 1/2 x 5 5/8 in. (31.8 x 14.3 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56851,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2674,false,true,56852,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunkō,"Japanese, 1743–1812",,Katsukawa Shunkō,Japanese,1743,1812,ca. 1786,1776,1796,Polychrome woodblock print; ink and color on paper,12 3/8 x 5 5/8 in. (31.4 x 14.3 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56852,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2675,false,true,56853,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunkō,"Japanese, 1743–1812",,Katsukawa Shunkō,Japanese,1743,1812,ca. 1780,1770,1790,Polychrome woodblock print; ink and color on paper,12 1/4 x 5 1/2 in. (31.1 x 14 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56853,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2830,false,true,57070,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunkō,"Japanese, 1743–1812",,Katsukawa Shunkō,Japanese,1743,1812,1743–1812,1743,1812,Polychrome woodblock print; ink and color on paper,15 3/16 x 10 1/4 in. (38.6 x 26 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57070,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2907,false,true,56022,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunkō,"Japanese, 1743–1812",,Katsukawa Shunkō,Japanese,1743,1812,1743–1812,1743,1812,Polychrome woodblock print; ink and color on paper,13 7/8 x 6 in. (35.2 x 15.2 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56022,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2908,false,true,56023,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunkō,"Japanese, 1743–1812",,Katsukawa Shunkō,Japanese,1743,1812,1743–1812,1743,1812,Polychrome woodblock print; ink and color on paper,12 1/4 x 5 1/2 in. (31.1 x 14 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56023,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2909,false,true,56024,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunkō,"Japanese, 1743–1812",,Katsukawa Shunkō,Japanese,1743,1812,1743–1812,1743,1812,Polychrome woodblock print; ink and color on paper,12 3/4 x 8 5/8 in. (32.4 x 21.9 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56024,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"JP293a, b",false,true,36478,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunkō,"Japanese, 1743–1812",,Katsukawa Shunkō,Japanese,1743,1812,ca. 1787,1777,1797,Diptych of polychrome woodblock prints; ink and color on paper,H. 11 7/8 in. (30.2 cm); W. 11 3/8 in. (28.9 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36478,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"JP294a, b",false,true,36477,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunkō,"Japanese, 1743–1812",,Katsukawa Shunkō,Japanese,1743,1812,ca. 1789,1779,1799,Diptych of polychrome woodblock prints; ink and color on paper,H. 12 9/16 in. (31.9 cm); W. 11 11/16 in. (29.7 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36477,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"JP2670a, b",false,true,56848,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunkō,"Japanese, 1743–1812",,Katsukawa Shunkō,Japanese,1743,1812,ca. 1780,1770,1790,Diptych of polychrome woodblock prints; ink and color on paper,L. 12 3/8 x 5 3/4 in. (31.4 x 14.6 cm) R. 12 1/4 x 5 7/8 in. (31.1 x 14.9 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56848,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP291a–c,false,true,36480,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunkō,"Japanese, 1743–1812",,Katsukawa Shunkō,Japanese,1743,1812,ca. 1788,1778,1798,Triptych of polychrome woodblock prints; ink and color on paper,Mat measurements: H. 22 3/4 in. (57.8 cm); W. 27 1/2 in. (69.9 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36480,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP292a–c,false,true,36479,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunkō,"Japanese, 1743–1812",,Katsukawa Shunkō,Japanese,1743,1812,ca. 1785,1775,1795,Triptych of polychrome woodblock prints; ink and color on paper,H. 12 1/4 (31.1 cm); W. 17 3/16 in. (43.7 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36479,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP128,false,true,36607,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,ca. 1783,1773,1793,Diptych of polychrome woodblock prints; ink and color on paper,15 x 20 3/8 in. (38.1 x 51.8 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36607,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP129,false,true,36608,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,ca. 1784,1774,1794,Polychrome woodblock print; ink and color on paper,14 11/16 x 10 1/8 in. (37.3 x 25.7 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36608,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP130,false,true,36609,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,ca. 1779,1769,1789,Polychrome woodblock print; ink and color on paper,9 3/5 x 7 1/5 in. (24.4 x 18.3 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36609,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP131,false,true,36610,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,ca. 1780,1770,1790,Polychrome woodblock print; ink and color on paper,10 x 7 1/8 in. (25.4 x 18.1 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36610,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP132,false,true,36611,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,ca. 1785,1775,1795,Polychrome woodblock print; ink and color on paper,25 31/32 x 4 15/32 in. (66.0 x 11.4 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36611,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP133,false,true,36612,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,ca. 1785,1775,1795,Polychrome woodblock print; ink and color on paper,26 7/8 x 4 3/4 in. (68.3 x 12.1 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36612,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP134,false,true,36613,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,ca. 1785,1775,1795,Polychrome woodblock print; ink and color on paper,26 3/5 x 4 3/5 in. (67.6 x 11.7 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36613,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP135,false,true,36614,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,ca. 1788–90,1786,1792,Triptych of polychrome woodblock prints; ink and color on paper,14 3/4 x 10 in. (37.5 x 25.4 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36614,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP564,false,true,37015,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,ca. 1790,1780,1800,Polychrome woodblock print; ink and color on paper,14 3/5 x 9 15/32 in. (37.1 x 24.1 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37015,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP717,false,true,37165,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,ca. 1763,1753,1775,Polychrome woodblock print; ink and color on paper,12 x 5 7/32 in. (30.5 x 13.3 cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37165,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP718,false,true,37166,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,1773,1773,1773,Polychrome woodblock print; ink and color on paper,11 3/8 x 5 3/8 in. (28.9 x 13.7 cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37166,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP719,false,true,37167,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,ca. 1770,1760,1780,Polychrome woodblock print; ink and color on paper,12 11/32 x 5 17/32 in. (31.4 x 14.1 cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37167,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP720,false,true,37168,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,ca. 1779,1769,1789,Polychrome woodblock print; ink and color on paper,10 x 7 11/32 in. (25.4 x 18.7 cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37168,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP721,false,true,37169,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,ca. 1782,1772,1792,Polychrome woodblock print; ink and color on paper,15 x 9 1/16 in. (38.1 x 23.0 cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37169,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP722,false,true,37170,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,ca. 1782,1772,1792,Right-hand sheet of a diptych of polychrome woodblock prints; ink and color on paper,14 15/32 x 9 3/4 in. (36.8 x 24.8 cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37170,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP723,false,true,37171,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,ca. 1783,1773,1793,Polychrome woodblock print; ink and color on paper,15 3/8 x 10 7/32 in. (39.1 x 26 cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37171,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP724,false,true,37172,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,ca. 1781–89,1781,1789,Polychrome woodblock print; ink and color on paper,Aiban; 13 1/4 x 9 7/8 in. (33.7 x 25.1 cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37172,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP725,false,true,37173,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,ca. 1788,1778,1798,Polychrome woodblock print; ink and color on paper,15 1/8 x 10 1/8 in. (38.4 x 25.7 cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37173,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP726,false,true,37174,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,ca. 1788,1778,1798,Polychrome woodblock print; ink and color on paper,14 15/32 x 10 1/8 in. (36.8 x 25.7 cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37174,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP727,false,true,37175,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,ca. 1784,1774,1794,Polychrome woodblock print; ink and color on paper,12 31/32 x 5 3/4 in. (33.0 x 14.6 cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37175,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP728,false,true,37176,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,ca. 1790,1780,1800,Polychrome woodblock print; ink and color on paper,15 1/8 x 10 1/8 in. (38.4 x 25.7 cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37176,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP729,false,true,37177,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,ca. 1790,1780,1800,One sheet of a triptych of polychrome woodblock prints; ink and color on paper,15 x 10 1/8 in. (38.1 x 25.7 cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37177,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP730,false,true,37178,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,ca. 1790,1780,1800,Middle sheet of a triptych of polychrome woodblock prints; ink and color on paper,14 1/3 x 10 in. (36.4 x 25.4 cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37178,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP806,false,true,37250,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,ca. 1789,1779,1799,Polychrome woodblock print; ink and color on paper,9 3/4 x 7 1/2 in. (24.8 x 19.1 cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37250,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP918,false,true,54658,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,ca. 1779,1769,1789,Polychrome woodblock print; ink and color on paper,H. 8 1/16 in. (20.5 cm); W. 8 1/16 in. (20.5 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54658,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP919,false,true,54659,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,ca. 1779,1769,1789,Polychrome woodblock print; ink and color on paper,H. 10 1/2 in. (26.7 cm); W. 7 3/4 in. (19.7 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54659,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP920,false,true,54660,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,ca. 1781,1771,1791,Polychrome woodblock print; ink and color on paper,H. 10 1/4 in. (26 cm); W. 7 1/2 in. (19.1 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54660,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP921,false,true,54661,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,ca. 1781,1776,1786,Polychrome woodblock print; ink and color on paper,H. 9 13/16 in. (24.9 cm); W. 7 1/4 in. (18.4 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54661,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP922,false,true,54662,Asian Art,Print,玉花子栄茂図|Gyoku-kashi Eimo Preparing Calligraphy Offerings,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,ca. 1782,1772,1792,Polychrome woodblock print; ink and color on paper,H. 15 1/8 in. (38.4 cm); W. 10 1/4 in. (26 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54662,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP923,false,true,54663,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,ca. 1784,1774,1794,Polychrome woodblock print; ink and color on paper,H. 14 1/4 in. (36.2 cm); W. 9 5/16 in. (23.7 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54663,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP924,false,true,54664,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,ca. 1782,1772,1792,Polychrome woodblock print; ink and color on paper,14 1/8 x 9 9/16in. (35.9 x 24.3cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54664,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP925,false,true,54665,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,ca. 1784,1774,1794,Polychrome woodblock print; ink and color on paper,H. 13 1/8 in. (33.3 cm); W. 10 1/8 in. (25.7 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54665,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP926,false,true,54666,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,ca. 1785,1775,1795,Polychrome woodblock print; ink and color on paper,H. 14 1/8 in. (35.9 cm); W. 9 5/8 in. (24.4 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54666,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP927,false,true,45241,Asian Art,Print,"Kodomo Mando, Kojimachi 1,2, 3-chome, Sanno Go-sairei|Childrens' Lantern Float, Kojimachi 1,2, 3-chome Block Association, Sanno Festival",Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,ca. 1780,1771,1790,Polychrome woodblock print; ink and color on paper,H. 10 1/4 in. (26 cm); W. 7 1/2 in. (19.1 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45241,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP928,false,true,54667,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,ca. 1790,1780,1800,Polychrome woodblock print; ink and color on paper,H. 14 7/8 in. (37.8 cm); W. 9 1/2 in. (24.1 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54667,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP929,false,true,54668,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,ca. 1791,1781,1801,Polychrome woodblock print; ink and color on paper,H. 10 3/8 in. (26.4 cm); W. 7 1/2 in. (19.1 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54668,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP930,false,true,54669,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,ca. 1793,1783,1803,Polychrome woodblock print; ink and color on paper,H. 9 5/8 in. (24.4 cm); W. 7 1/8 in. (18.1 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54669,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP931,false,true,54670,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,ca. 1792,1782,1802,Polychrome woodblock print; ink and color on paper,H. 14 7/8 in. (37.8 cm); W. 10 in. (25.4 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54670,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1211,false,true,55134,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,ca. 1780,1770,1790,Polychrome woodblock print; ink and color on paper,H. 27 7/16 in. (69.7 cm); W. 4 7/8 in. (12.4 cm),"Rogers Fund, 1920",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55134,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1372,false,true,55352,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,ca. 1788,1778,1798,Polychrome woodblock print; ink and color on paper,H. 15 1/4 in. (38.7 cm); W. 9 3/4 in. (24.8 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55352,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1373,false,true,45038,Asian Art,Print,Shiokumi|The Dance of the Beach Maidens from the series Brocade of the East,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,ca. 1770,1760,1780,Polychrome woodblock print; ink and color on paper,Image: 15 3/16 x 9 13/16 in. (38.6 x 24.9 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45038,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1377,false,true,55360,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,1780,1780,1780,Polychrome woodblock print; ink and color on paper,H. 27 3/8 in. (69.5 cm); W. 4 5/8 in. (11.7 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55360,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1463,false,true,55510,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,ca. 1779,1769,1789,Polychrome woodblock print; ink and color on paper,H. 10 3/8 in. (26.4 cm); W. 7 1/2 in. (19.1 cm),"Rogers Fund, 1925",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55510,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1509,false,true,45031,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,ca. 1800,1790,1810,Polychrome woodblock print; ink and color on paper,H. 14 1/8 in. (35.9 cm); W. 19 1/4 in. (48.9 cm),"Fletcher Fund, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45031,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1510,false,true,55610,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,ca. 1785,1775,1795,Polychrome woodblock print; ink and color on paper,H. 15 1/8 in. (38.4 cm); W. 10 in. (25.4 cm),"Fletcher Fund, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55610,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1511,false,true,55613,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,ca. 1785,1775,1795,Polychrome woodblock print; ink and color on paper,H. 15 5/16 in. (38.9 cm); W. 10 3/8 in. (26.4 cm),"Fletcher Fund, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55613,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1512,false,true,51998,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,ca. 1790–91,1660,1900,Polychrome woodblock print; ink and color on paper,Image: 14 3/4 x 9 15/16 in. (37.5 x 25.2 cm),"Fletcher Fund, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/51998,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1513,false,true,55615,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,ca. 1784,1774,1794,Polychrome woodblock print; ink and color on paper,H. 14 1/2 in. (36.8 cm); W. 9 1/2 in. (24.1 cm),"Fletcher Fund, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55615,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1514,false,true,55616,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,ca. 1788,1778,1798,Polychrome woodblock print; ink and color on paper,H. 14 15/16 in. (37.9 cm); W. 9 3/4 in. (24.8 cm),"Fletcher Fund, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55616,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1713,false,true,55966,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,1782,1779,1789,Polychrome woodblock print; ink and color on paper,14 5/8 x 10 in. (37.1 x 25.4cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55966,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1724,false,true,56035,Asian Art,Print,"「美南見十二候」　五月 |The Fifth Month, from the series Twelve Months in the Southern Pleasure District (Minami jūni kō)",Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,1784,1784,1784,Left sheet of a diptych of polychrome woodblock prints; ink and color on paper,14 7/8 x 9 15/16in. (37.8 x 25.2cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56035,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1734,false,true,45256,Asian Art,Print,Okawabata yu-suzumi|Enjoying the Evening Cool on the Banks of the Sumida River,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,ca. 1784,1774,1794,Diptych of polychrome woodblock prints; ink and color on paper,A: H. 15 in. (38.1 cm); W. 10 in. (25.4 cm) B: H. 14 5/8 in. (37.1 cm); W. 9 15/16 in. (25.2 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45256,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2593,false,true,56739,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,ca. 1787,1777,1797,Polychrome woodblock print; ink and color on paper,10 1/4 x 7 5/8 in. (26 x 19.4 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56739,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2594,false,true,56740,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,ca. 1779,1769,1789,Polychrome woodblock print; ink and color on paper,10 1/2 x 7 3/4 in. (26.7 x 19.7 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56740,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2595,false,true,56741,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,ca. 1778,1768,1788,Polychrome woodblock print; ink and color on paper,10 5/16 x 7 3/4 in. (26.2 x 19.7 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56741,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2596,false,true,56742,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,ca. 1778,1768,1788,Polychrome woodblock print; ink and color on paper,10 1/4 x 7 3/8 in. (26 x 18.7 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56742,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2597,false,true,56743,Asian Art,Print,Shiba: Atago|Atago Hill at Shiba,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,ca. 1783,1773,1793,Polychrome woodblock print; ink and color on paper,10 7/16 x 7 5/8 in. (26.5 x 19.4 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56743,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2598,false,true,56744,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,ca. 1788,1778,1798,Polychrome woodblock print; ink and color on paper,12 5/8 x 8 3/4 in. (32.1 x 22.2 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56744,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2599,false,true,56745,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,ca. 1783,1773,1793,Polychrome woodblock print; ink and color on paper,12 3/4 x 8 3/4 in. (32.4 x 22.2 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56745,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2600,false,true,56746,Asian Art,Print,二階座敷に三人の女|Three Women on a Balcony,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,ca. 1786,1776,1796,Upper sheet of a vertical diptych of polychrome woodblock prints; ink and color on paper,14 7/8 x 10 1/16 in. (37.8 x 25.6 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56746,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2602,false,true,56748,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,ca. 1785,1775,1795,Polychrome woodblock print; ink and color on paper,11 3/4 x 5 7/8 in. (29.8 x 14.9 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56748,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2603,false,true,56749,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,1784,1784,1784,Polychrome woodblock print; ink and color on paper,15 1/4 x 10 1/4 in. (38.7 x 26 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56749,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2604,false,true,45246,Asian Art,Print,"「風俗東之錦」 姫君と侍女四人|High-Ranking Samurai Girl with Four Attendants, from the series A Brocade of Eastern Manners (Fūzoku Azuma no nishiki)",Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,ca. 1784,1774,1794,Polychrome woodblock print; ink and color on paper,15 x 10 in. (38.1 x 25.4 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45246,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2605,false,true,56750,Asian Art,Print,Kitchugi|Geisha of the Tachibana Street,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,ca. 1786,1776,1796,Polychrome woodblock print; ink and color on paper,14 7/8 x 10 1/4 in. (37.8 x 26 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56750,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2607,false,true,56751,Asian Art,Print,"「風俗東之錦」　 武家の息女と侍女と若党 |A Lady from a Samurai Household with Three Attendants, from the series A Brocade of Eastern Manners (Fūzoku Azuma no nishiki)",Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,ca. 1820,1810,1830,Polychrome woodblock print; ink and color on paper,15 1/4 x 10 3/8 in. (38.7 x 26.4 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56751,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2608,false,true,56752,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,1782,1782,1782,Polychrome woodblock print; ink and color on paper,15 1/4 x 10 in. (38.7 x 25.4 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56752,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2609,false,true,56753,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,1784–85,1784,1785,Polychrome woodblock print; ink and color on paper,15 1/8 x 10 1/4 in. (38.4 x 26 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56753,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2610,false,true,56754,Asian Art,Print,四代目松本幸四郎とその家庭|The Kabuki Actor Matsumoto Kōshirō IV,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,ca. 1783,1773,1793,Polychrome woodblock print; ink and color on paper,15 1/4 x 10 1/2 in. (38.7 x 26.7 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56754,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2611,false,true,56755,Asian Art,Woodblock print,三代目沢村宗十郎と遊女|The Kabuki Actor Sawamura Sōjūrō III and Courtesans,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,ca. 1783–84,1783,1784,Polychrome woodblock print; ink and color on paper,14 7/8 x 10 in. (37.8 x 25.4 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56755,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2612,false,true,56756,Asian Art,Print,Nakasu no suzumi|Enjoying the Evening Cool at Nakasu,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,ca. 1782–83,1782,1783,Polychrome woodblock print; ink and color on paper,14 3/4 x 10 1/2 in. (37.5 x 26.7 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56756,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2613,false,true,56757,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,ca. 1781,1771,1791,Polychrome woodblock print; ink and color on paper,15 x 10 1/8 in. (38.1 x 25.7 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56757,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2614,false,true,56758,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,ca. 1786,1776,1796,Polychrome woodblock print; ink and color on paper,15 1/8 x 10 1/8 in. (38.4 x 25.7 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56758,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2615,false,true,52003,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,ca. 1790,1790,1810,Polychrome woodblock print; ink and color on paper,15 x 10 in. (38.1 x 25.4 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/52003,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2616,false,true,56760,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,ca. 1788,1778,1798,Polychrome woodblock print; ink and color on paper,15 x 10 1/8 in. (38.1 x 25.7 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56760,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2617,false,true,56761,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,ca. 1784,1774,1794,Diptych of polychrome woodblock prints; ink and color on paper,Each sheet: 15 x 10 in. (38.1 x 25.4 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56761,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2618,false,true,56762,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,ca. 1785,1775,1795,Polychrome woodblock print; ink and color on paper,26 1/4 x 4 3/4 in. (66.7 x 12.1 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56762,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2797,false,true,57092,Asian Art,Print,Kitchugi|Dancers of Tachibana Street,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,1742–1815,1742,1815,Polychrome woodblock print; ink and color on paper,15 1/8 x 10 1/8 in. (38.4 x 25.7 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57092,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2798,false,true,57091,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,1783,1783,1783,Polychrome woodblock print; ink and color on paper,15 x 10 in. (38.1 x 25.4 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57091,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2799,false,true,53904,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,1783,1750,1868,Polychrome woodblock print; ink and color on paper,15 1/4 x 10 1/8 in. (38.7 x 25.7 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/53904,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2800,false,true,57090,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,1784,1784,1784,Polychrome woodblock print; ink and color on paper,14 15/16 x 9 3/4 in. (37.9 x 24.8 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57090,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2801,false,true,57089,Asian Art,Print,Sako no Suzumi|Cooling Off at Nakazu,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,1782,1782,1782,Left-hand sheet of a diptych of polychrome woodblock prints; ink and color on paper,14 1/2 x 9 3/4 in. (36.8 x 24.8 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57089,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2802,false,true,57088,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,1785,1785,1785,Polychrome woodblock print; ink and color on paper,15 3/8 x 10 1/8 in. (39.1 x 25.7 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57088,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"JP1268a, b",false,true,55165,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,ca. 1783,1773,1793,Diptych of polychrome woodblock prints; ink with color reprinted and revamped on paper,Oban diptych 15 1/2 x 20 in. (39.4 x 50.8 cm),"Gift of Frank L. Wright, 1921",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55165,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"JP2622a, b",false,true,56765,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,ca. 1785,1785,1795,Diptych of polychrome woodblock prints; ink and color on paper,Each sheet: 15 1/4 x 10 1/4 in. (38.7 x 26 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56765,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2619a–c,false,true,56763,Asian Art,Print,仲の町の牡丹|The Peony Show,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,ca. 1790,1780,1810,Triptych of polychrome woodblock prints; ink and color on paper,Each sheet: 15 x 10 in. (38.1 x 25.4 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56763,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2620a–c,false,true,45257,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,ca. 1785,1775,1795,Triptych of polychrome woodblock prints; ink and color on paper,Each sheet: 15 1/4 x 10 1/4 in. (38.7 x 26 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45257,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2621a–c,false,true,56764,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,ca. 1785,1775,1795,Triptych of polychrome woodblock prints; ink and color on paper,Each sheet: 15 x 10 in. (38.1 x 25.4 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56764,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3393a–c,false,true,56670,Asian Art,Print,三俳優隅田川船遊び|Sumida River Holiday,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,1788–90,1788,1790,Triptych of polychrome woodblock prints; ink and color on paper,Triptych; each H. 14 1/2 in. (36.8 cm); W. 10 1/8 in. (25.7 cm),"Gift of Mr. and Mrs. Arthur J. Steel, 1960",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56670,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP178,false,true,36656,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Chōbunsai Eishi,"Japanese, 1756–1829",,Chōbunsai Eishi,Japanese,1756,1829,ca. 1780,1770,1790,Polychrome woodblock print; ink and color on paper,25 7/8 x 4 7/8 in. (65.7 x 12.4 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36656,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP179,false,true,36657,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Chōbunsai Eishi,"Japanese, 1756–1829",,Chōbunsai Eishi,Japanese,1756,1829,ca. 1780,1770,1790,Polychrome woodblock print; ink and color on paper,25 7/32 x 4 3/5 in. (64.1 x 11.7 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36657,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP180,false,true,36481,Asian Art,Print,新大橋橋下の涼み船|Pleasure Boats on the Sumida River beneath Shin-Ōhashi Bridge,Japan,Edo period (1615–1868),,,,Artist,,Chōbunsai Eishi,"Japanese, 1756–1829",,Chōbunsai Eishi,Japanese,1756,1829,ca. 1792,1782,1802,Pentaptych of polychrome woodblock prints; ink and color on paper,15 3/8 x 49 7/8 in. (39.1 x 126.7 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36481,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP181,false,true,36659,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Chōbunsai Eishi,"Japanese, 1756–1829",,Chōbunsai Eishi,Japanese,1756,1829,ca. 1790,1780,1800,Two sheets from a triptych of polychrome woodblock prints; ink and color on paper,15 1/8 x 20 1/2 in. (38.4 x 52.1 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36659,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP182,false,true,36660,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Chōbunsai Eishi,"Japanese, 1756–1829",,Chōbunsai Eishi,Japanese,1756,1829,ca. 1794,1784,1804,Triptych of polychrome woodblock prints; ink and color on paper,15 7/32 x 29 15/32 in. (38.7 x 74.9 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36660,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP183,false,true,36661,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Chōbunsai Eishi,"Japanese, 1756–1829",,Chōbunsai Eishi,Japanese,1756,1829,ca. 1793,1783,1803,Triptych of polychrome woodblock prints; ink and color on paper,14 15/32 x 29 1/4 in. (36.8 x 74.3 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36661,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP184,false,true,36662,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Chōbunsai Eishi,"Japanese, 1756–1829",,Chōbunsai Eishi,Japanese,1756,1829,ca. 1794,1784,1804,Triptych of polychrome woodblock prints (trimmed); ink and color on paper,14 3/8 x 28 23/32 in. (36.5 x 73.0 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36662,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP185,false,true,36663,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Chōbunsai Eishi,"Japanese, 1756–1829",,Chōbunsai Eishi,Japanese,1756,1829,ca. 1797,1787,1807,Triptych of polychrome woodblock prints; ink and color on paper,15 7/32 x 30 in. (38.7 x 76.2 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36663,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP944,false,true,54830,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Chōbunsai Eishi,"Japanese, 1756–1829",,Chōbunsai Eishi,Japanese,1756,1829,ca. 1788,1778,1798,Polychrome woodblock print; ink and color on paper,H. 9 5/8 in. (24.4 cm); W. 7 1/16 in. (17.9 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54830,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP945,false,true,54833,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Chōbunsai Eishi,"Japanese, 1756–1829",,Chōbunsai Eishi,Japanese,1756,1829,ca. 1788,1778,1798,Polychrome woodblock print; ink and color on paper,H. 15 in. (38.1 cm); W. 9 7/8 in. (25.1 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54833,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP946,false,true,54834,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Chōbunsai Eishi,"Japanese, 1756–1829",,Chōbunsai Eishi,Japanese,1756,1829,ca. 1788,1778,1798,Polychrome woodblock print; ink and color on paper,H. 10 1/16 in. (25.6 cm); W. 7 3/16 in. (18.3 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54834,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP947,false,true,54837,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Hosoda Eishi,"Japanese, 1756–1829",,Hosoda Eishi,Japanese,1756,1829,ca. 1788,1778,1798,Polychrome woodblock print; ink and color on paper,15 1/2 x 10 1/8 in. (39.4 x 25.7 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54837,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP948,false,true,54839,Asian Art,Print,和歌三神図|Honoring the Three Gods of Poetry: Women Composing Poems,Japan,Edo period (1615–1868),,,,Artist,,Chōbunsai Eishi,"Japanese, 1756–1829",,Chōbunsai Eishi,Japanese,1756,1829,ca. 1792,1782,1802,Triptych of polychrome woodblock prints; ink and color on paper,H. 14 1/2 in. (36.8 cm); W. 30 in. (76.2 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54839,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP949,false,true,54841,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Chōbunsai Eishi,"Japanese, 1756–1829",,Chōbunsai Eishi,Japanese,1756,1829,ca. 1794,1784,1804,Polychrome woodblock print; ink and color on paper,H. 14 5/8 in. (37.1 cm); 9 7/8 in. (25.1 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54841,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP950,false,true,54844,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Chōbunsai Eishi,"Japanese, 1756–1829",,Chōbunsai Eishi,Japanese,1756,1829,ca. 1794,1789,1799,Polychrome woodblock print; ink and color on paper,H. 15 1/2 in. (39.4 cm); W. 10 1/4 in. (26 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54844,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP951,false,true,54845,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Chōbunsai Eishi,"Japanese, 1756–1829",,Chōbunsai Eishi,Japanese,1756,1829,ca. 1795,1785,1805,Triptych of polychrome woodblock prints; ink and color on paper,H. 14 3/8 in. (36.5 cm); W. 30 in. (76.2 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54845,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP952,false,true,54846,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Chōbunsai Eishi,"Japanese, 1756–1829",,Chōbunsai Eishi,Japanese,1756,1829,ca. 1797,1787,1807,Triptych of polychrome woodblock prints; ink and color on paper,14 3/8 x 30 in. (36.5 x 76.2cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54846,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1124,false,true,55044,Asian Art,Print,"『青楼美人六花仙』「扇屋花扇」|“Hanaōgi of the Ōgiya,” from the series Beauties of the Yoshiwara as Six Floral Immortals (Seirō bijin rokkasen)",Japan,Edo period (1615–1868),,,,Artist,,Chōbunsai Eishi,"Japanese, 1756–1829",,Chōbunsai Eishi,Japanese,1756,1829,ca. 1794,1784,1804,Polychrome woodblock print; ink and color on paper,H. 14 9/16 in. (37 cm); W. 9 7/8 in. (25.1 cm),"Rogers Fund, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55044,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1442,false,true,55486,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Chōbunsai Eishi,"Japanese, 1756–1829",,Chōbunsai Eishi,Japanese,1756,1829,ca. 1790,1780,1800,Polychrome woodblock print; ink and color on paper,H. 14 5/8 in. (37.1 cm); W. 9 13/16 in. (24.9 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55486,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1524,false,true,53657,Asian Art,Print,"「青楼美撰合　初売座敷之図　扇屋　滝川」|Takigawa of the Ōgiya House, from the series A Comparison of Selected Beauties of the Pleasure Quarters (Seirō bisen awase)",Japan,Edo period (1615–1868),,,,Artist,,Chōbunsai Eishi,"Japanese, 1756–1829",,Chōbunsai Eishi,Japanese,1756,1829,ca. 1794–95,1794,1795,Polychrome woodblock print; ink and color on paper,H. 14 5/8 in. (37.1 cm); W. 10 in. (25.4 cm),"Fletcher Fund, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/53657,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1569,false,true,55746,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Chōbunsai Eishi,"Japanese, 1756–1829",,Chōbunsai Eishi,Japanese,1756,1829,ca. 1790,1780,1800,Triptych of polychrome woodblock prints; ink and color on paper,a: H. 14 7/8 in. (37.8 cm); W. 9 7/8 in. (25.1 cm); b: H. 14 7/8 in. (37.8 cm); W. 9 3/4 in. (24.8 cm); c: H. 15 in. (38.1 cm); W. 10 in. (25.4 cm),"Fletcher Fund, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55746,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1786,false,true,45215,Asian Art,Print,"「畧六花撰」|Matching Shells (Kaiawase), from the series Six Immortal Poets in Modern Guise (Yatsushi Rokkasen)",Japan,Edo period (1615–1868),,,,Artist,,Chōbunsai Eishi,"Japanese, 1756–1829",,Chōbunsai Eishi,Japanese,1756,1829,ca. 1796–98,1786,1808,Polychrome woodblock print; ink and color on paper,14 15/16 x 9 3/4 in. (37.9 x 24.8 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45215,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2420,false,true,51091,Asian Art,Print,Amagoi|Ono no Komachi Praying for Rain,Japan,Edo period (1615–1868),,,,Artist,,Chōbunsai Eishi,"Japanese, 1756–1829",,Chōbunsai Eishi,Japanese,1756,1829,ca. 1791,1781,1801,Monochrome woodblock print; ink on paper,H. 15 1/4 in. (38.7 cm); W. 10 1/4 in. (26 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/51091,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2421,false,true,56824,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Chōbunsai Eishi,"Japanese, 1756–1829",,Chōbunsai Eishi,Japanese,1756,1829,ca. 1792,1782,1802,One sheet of a triptych of polychrome woodblock prints; ink and color on paper,14 1/2 x 9 5/8 in. (36.8 x 24.4 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56824,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2422,false,true,56828,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Chōbunsai Eishi,"Japanese, 1756–1829",,Chōbunsai Eishi,Japanese,1756,1829,ca. 1790,1780,1800,Polychrome woodblock print; ink and color on paper,15 1/4 x 10 1/4 in. (38.7 x 26 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56828,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2423,false,true,52000,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Chōbunsai Eishi,"Japanese, 1756–1829",,Chōbunsai Eishi,Japanese,1756,1829,ca. 1790,1780,1800,One sheet of a triptych of polychrome woodblock prints; ink and color on paper,15 1/8 x 10 1/8 in. (38.4 x 25.7 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/52000,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2424,false,true,56827,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Chōbunsai Eishi,"Japanese, 1756–1829",,Chōbunsai Eishi,Japanese,1756,1829,ca. 1795,1785,1805,Polychrome woodblock print; ink and color on paper,15 1/4 x 10 1/4 in. (38.7 x 26 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56827,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2815,false,true,53655,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Chōbunsai Eishi,"Japanese, 1756–1829",,Chōbunsai Eishi,Japanese,1756,1829,1756–1829,1756,1829,Polychrome woodblock print; ink and color on paper,14 5/8 x 9 5/8 in. (37.1 x 24.4 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/53655,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2816,false,true,57079,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Chōbunsai Eishi,"Japanese, 1756–1829",,Chōbunsai Eishi,Japanese,1756,1829,ca. 1795,1785,1805,Polychrome woodblock print; ink and color on paper,13 7/8 x 9 5/8 in. (35.2 x 24.4 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57079,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2817,false,true,57078,Asian Art,Print,Seiryu Edo|Courtesan District of Edo,Japan,Edo period (1615–1868),,,,Artist,,Chōbunsai Eishi,"Japanese, 1756–1829",,Chōbunsai Eishi,Japanese,1756,1829,1756–1829,1756,1829,Polychrome woodblock print; ink and color on paper,15 x 10 1/8 in. (38.1 x 25.7 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57078,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2819,false,true,57076,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Chōbunsai Eishi,"Japanese, 1756–1829",,Chōbunsai Eishi,Japanese,1756,1829,1756–1829,1756,1829,Polychrome woodblock print; ink and color on paper,14 7/8 x 9 9/16 in. (37.8 x 24.3 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57076,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2820,false,true,57075,Asian Art,Print,Onono Komachi|Parrot Komachi,Japan,Edo period (1615–1868),,,,Artist,,Chōbunsai Eishi,"Japanese, 1756–1829",,Chōbunsai Eishi,Japanese,1756,1829,ca. 1788,1778,1798,Polychrome woodblock print; ink and color on paper,14 5/8 x 9 7/8 in. (37.1 x 25.1 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57075,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3132,false,true,56680,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Chōbunsai Eishi,"Japanese, 1756–1829",,Chōbunsai Eishi,Japanese,1756,1829,ca. 1781–1800,1781,1800,Triptych of polychrome woodblock prints; ink and color on paper,Triptych; Overall: 14 1/4 x 30 1/8 in. (36.2 x 76.5 cm),"Gift of Francis M. Weld, 1948",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56680,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1096c,false,true,639381,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Chōbunsai Eishi,"Japanese, 1756–1829",,Chōbunsai Eishi,Japanese,1756,1829,ca. 1796 (Kansei 8),1796,1796,Polychrome woodblock print; ink and color on paper,Image: 14 3/4 × 9 1/2 in. (37.5 × 24.1 cm),The Metropolitan Museum of Art,,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/639381,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2818a–c,false,true,57077,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Chōbunsai Eishi,"Japanese, 1756–1829",,Chōbunsai Eishi,Japanese,1756,1829,ca. 1794,1784,1804,Triptych of polychrome woodblock prints; ink and color on paper,A. (left): 14 5/8 x 9 7/8 in. (37.1 x 25.1 cm) B. (center): 14 5/8 x 10 in. (37.1 x 25.4 cm) C. (right): 14 5/8 x 10 in. (37.1 x 25.4 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57077,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP177,false,true,36655,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,ca. 1790,1770,1790,Polychrome woodblock print; ink and color on paper,15 x 9 3/5 in. (38.1 x 24.4 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36655,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP731,false,true,37179,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,ca. 1790,1780,1800,Polychrome woodblock print; ink and color on paper,14 7/8 x 9 1/8 in. (37.8 x 23.2 cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37179,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP958,false,true,54852,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,ca. 1788,1778,1798,Diptych of polychrome woodblock prints; ink and color on paper,H. 15 in. (38.1 cm); W. 20 1/2 in. (52.1 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54852,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP959,false,true,54853,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,ca. 1790,1780,1800,Polychrome woodblock print; ink and color on paper,H. 8 7/16 in. (21.4 cm); W. 14 7 /16 in. (36.7 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54853,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP960,false,true,54314,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,ca. 1800,1790,1810,Polychrome woodblock print (surimono); ink and color on paper,7 7/8 x 7 1/16 in. (20 x 17.9 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54314,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1106,false,true,54335,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,ca. 1800,1790,1810,Polychrome woodblock print (surimono); ink and color on paper,7 15/16 x 7 3/16 in. (20.2 x 18.3 cm),"Rogers Fund, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54335,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1254,false,true,54386,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,possibly,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,ca. 1800,1790,1810,Polychrome woodblock print (surimono); ink and color on paper,7 7/8 x 7 1/8 in. (20 x 18.1 cm),"Rogers Fund, 1921",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54386,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1907,false,true,54446,Asian Art,Print,"窪俊満画 煙草入れ袋と煙管『春雨集』　摺物帖|Pipe and Tobacco PouchFrom the Spring Rain Collection (Harusame shū), vol. 1",Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,ca. 1810s,1810,1819,Polychrome woodblock print (surimono); ink and color on paper,5 9/16 x 7 1/8 in. (14.1 x 18.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54446,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1912,false,true,54451,Asian Art,Print,"「松風台七番之内柄」|“Hilt of a Sword,” from the series of Seven Prints for the Shōfudai Poetry Circle",Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,1810s,1810,1819,Polychrome woodblock print (surimono); ink and color on paper,5 3/8 x 7 5/16 in. (13.7 x 18.6 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54451,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1927,false,true,54467,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,probably 1816,1816,1816,Polychrome woodblock print (surimono); ink and color on paper,5 1/2 x 7 5/8 in. (14 x 19.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54467,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1931,false,true,54471,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,1816,1816,1816,Polychrome woodblock print (surimono); ink and color on paper,5 9/16 x 5 1/2 in. (14.1 x 14 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54471,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1932,false,true,54472,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,1815,1815,1815,Polychrome woodblock print (surimono); ink and color on paper,5 1/2 x 6 5/8 in. (14 x 16.8 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54472,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1951,false,true,54519,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,probably 1820,1820,1820,Polychrome woodblock print (surimono); ink and color on paper,7 7/8 x 7 in. (20 x 17.8 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54519,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1953,false,true,54521,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,probably 1810,1810,1810,Polychrome woodblock print (surimono); ink and color on paper,8 1/4 x 7 1/4 in. (21 x 18.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54521,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1954,false,true,54523,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,1813,1813,1813,Polychrome woodblock print (surimono); ink and color on paper,7 7/8 x 7 1/8 in. (20 x 18.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54523,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1971,false,true,54556,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",(?),Kubo Shunman,Japanese,1757,1820,1814,1814,1814,Polychrome woodblock print (surimono); ink and color on paper,8 1/4 x 7 1/4 in. (21 x 18.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54556,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2035,false,true,54801,Asian Art,Print,"『春雨集』　摺物帖窪俊満画　『鎌倉志』　「影向石」|Spring Rain Collection (Harusame shū), vol. 1: “Offering Incense to the Deity of the Stone” (Yōgōishi), from the series History of Kamakura (Kamakura shi)",Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,early to mid-1810s,1810,1816,Privately published woodblock prints (surimono) mounted in an album; ink and color on paper,7 7/8 x 7 1/8 in. (20 x 18.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54801,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2038,false,true,54809,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,early to mid-1810s,1810,1816,Privately published woodblock prints (surimono) mounted in an album; ink and color on paper,7 11/16 x 7 3/16 in. (19.5 x 18.3 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54809,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2050,false,true,54823,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,1814,1814,1814,Polychrome woodblock print (surimono); ink and color on paper,8 5/16 x 11 3/16 in. (21.1 x 28.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54823,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2052,false,true,54826,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,probably 1816,1816,1816,Polychrome woodblock print (surimono); ink and color on paper,5 7/16 x 7 3/16 in. (13.8 x 18.3 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54826,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2113,false,true,54965,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,1808,1808,1808,Polychrome woodblock print (surimono); ink and color on paper,5 1/2 x 7 1/2 in. (14 x 19.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54965,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2139,false,true,54994,Asian Art,Print,"『浅草側いせ暦』　節分の悪霊ばらい『春雨集』　摺物帖 |“Beans for Tossing During Setsubun Exorcism Ceremony,” from the series Ise Calendars for the Asakusa Group (Asakusa-gawa Ise goyomi)From the Spring Rain Collection (Harusame shū), vol. 2",Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",(?),Kubo Shunman,Japanese,1757,1820,1810s,1810,1819,Polychrome woodblock print (surimono); ink and color on paper,8 5/16 x 7 3/8 in. (21.1 x 18.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54994,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2140,false,true,54995,Asian Art,Print,"『浅草側いせ暦』　文房具と梅熨斗『春雨集』　摺物帖 |“Desk with Writing Set and Plum Flowers,” from the series Ise Calendars for the Asakusa Group (Asakusa-gawa Ise goyomi)From the Spring Rain Collection (Harusame shū), vol. 2",Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",(?),Kubo Shunman,Japanese,1757,1820,probably 1814 (Year of the Dog),1814,1814,Polychrome woodblock print (surimono); ink and color on paper,8 1/4 x 7 3/8 in. (21 x 18.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54995,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2141,false,true,54996,Asian Art,Print,"『名物革仝印籠仝根付』 人形手金唐革　菖蒲革『春雨集』　摺物帖|“Gold-decorated Leather with Figure of a Chinese Boy” and “Patterned Leather,” from the series Famous Leathers, Inrō, and Netsuke Meibutsu kawa, inrō, netsuke)From the Spring Rain Collection (Harusame shū), vol. 2",Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,1810s,1810,1819,Polychrome woodblock print (surimono); ink and color on paper,5 1/2 x 7 1/8 in. (14 x 18.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54996,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2142,false,true,54997,Asian Art,Print,"『名物革仝印籠仝根付』 印籠根付『春雨集』　摺物帖|“Inrō and Netsuke,” from the series Famous Leathers, Inrō, and Netsuke (Meibutsu kawa, inrō, netsuke)From the Spring Rain Collection (Harusame shū), vol. 2",Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,1810s,1810,1819,Polychrome woodblock print (surimono); ink and color on paper,5 7/16 x 7 1/4 in. (13.8 x 18.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54997,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2143,false,true,54998,Asian Art,Print,"『浅草側いせ暦』　弓道具『春雨集』　摺物帖|“Bow, Arrows, Target, and Other Outfits for Archery,” from the series Ise Calendars for the Asakusa Group (Asakusa-gawa Ise goyomi)From the Spring Rain Collection (Harusame shū), vol. 2",Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",(?),Kubo Shunman,Japanese,1757,1820,ca. 1814,1804,1824,Polychrome woodblock print (surimono); ink and color on paper,8 1/4 x 7 5/16 in. (21 x 18.6 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54998,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2152,false,true,55056,Asian Art,Print,"『春雨集』　摺物帖窪俊満画　胡蝶舞の衣装|Spring Rain Collection (Harusame shū), vol. 2: Costume for the Butterfly Dance (Kochō no mai)",Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,1810s,1810,1819,Privately published woodblock prints (surimono) mounted in an album; ink and color on paper,8 5/16 x 7 1/4 in. (21.1 x 18.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55056,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2155,false,true,55062,Asian Art,Print,"『春雨集』　摺物帖窪俊満画　クサボタン、萩、シャ ガ、ツバキ、モチツバキ|Spring Rain Collection (Harusame shū), vol. 2: Cut Flowers: Clematis, Bush Clover, Iris, Camellia, and Azalea",Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,1815 (Year of the Ox),1815,1815,Privately published woodblock prints (surimono) mounted in an album; ink and color on paper,8 5/16 x 11 1/16 in. (21.1 x 28.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55062,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2179,false,true,55093,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,probably 1816,1816,1816,Polychrome woodblock print (surimono); ink and color on paper,8 1/4 x 5 3/8 in. (21 x 13.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55093,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2197,false,true,55114,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,1813,1813,1813,Polychrome woodblock print (surimono); ink and color on paper,7 7/8 x 10 13/16 in. (20 x 27.5 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55114,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2214,false,true,53980,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",(?),Kubo Shunman,Japanese,1757,1820,1815,1815,1815,Polychrome woodblock print (surimono); ink and color on paper,8 7/16 x 11 1/4 in. (21.4 x 28.6 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/53980,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2291,false,true,54064,Asian Art,Print,"石橋物|Two Dancers Performing a “Shakkyōmono” Kabuki Dance, from Spring Rain Surimono Album (Harusame surimono-jō), vol. 3",Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,ca. 1805–10,1795,1820,Privately published polychrome woodblock prints (surimono) mounted in an album; ink and color on paper,8 5/16 x 5 1/2 in. (21.1 x 14 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54064,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2292,false,true,54065,Asian Art,Print,"『鳥合』　桜草に雲雀|Skylarks and Primroses,” from the Series An Array of Birds (Tori awase), from Spring Rain Surimono Album (Harusame surimono-jō, vol. 3)",Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,ca. 1805–10,1800,1820,Privately published polychrome woodblock prints (surimono) mounted in an album; ink and color on paper,8 1/4 x 5 3/8 in. (21 x 13.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54065,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2293,false,true,54066,Asian Art,Woodblock print,"『鳥合』　桃花に目白|Japanese White-eyes on a Branch of Peach Tree,” from the Series An Array of Birds (Tori awase), from Spring Rain Surimono Album (Harusame surimono-jō, vol. 3)",Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,ca. 1805–10,1800,1820,Privately published polychrome woodblock prints (surimono) mounted in an album; ink and color on paper,8 3/16 x 5 3/8 in. (20.8 x 13.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54066,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2294,false,true,54067,Asian Art,Woodblock print,"梅と柳に目白|Japanese White-eyes with Plum Tree and Willow, from Spring Rain Surimono Album (Harusame surimono-jō, vol. 3)",Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",(?),Kubo Shunman,Japanese,1757,1820,ca. 1810,1800,1820,Privately published polychrome woodblock prints (surimono) mounted in an album; ink and color on paper,8 1/4 x 7 5/16 in. (21 x 18.6 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54067,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2318,false,true,54102,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,"1816, year of the rat",1816,1816,Part of an album of woodblock prints (surimono); ink and color on paper,5 1/2 x 8 1/4 in. (14 x 21 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54102,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2340,false,true,54125,Asian Art,Print,舞楽|Courtier Playing a Flute to Accompany a Bugaku Dance,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,probably 1810,1810,1810,Part of an album of woodblock prints (surimono); ink and color on paper,5 1/2 x 8 3/16 in. (14 x 20.8 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54125,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2012.248.1,false,true,77872,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,1789,1789,1789,Polychrome woodblock print (surimono); ink and color on paper,Image: 4 1/8 x 6 1/2 in. (10.5 x 16.5 cm),"Purchase, Marjorie H. Holden Gift, 2012",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/77872,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2012.248.2,false,true,77873,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,1793,1793,1793,Polychrome woodblock print (surimono); ink and color on paper,Image: 6 5/8 x 10 in. (16.8 x 25.4 cm),"Purchase, Marjorie H. Holden Gift, 2012",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/77873,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2012.248.3,false,true,77874,Asian Art,Print,書初め図|Young Woman Writing Calligraphy,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,1793 (Year of the Goat),1793,1793,Polychrome woodblock print (surimono); ink and color on paper,Image: 4 1/4 x 6 5/8 in. (10.8 x 16.8 cm),"Purchase, Marjorie H. Holden Gift, 2012",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/77874,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2012.248.4,false,true,77875,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,"1794, year of the tiger",1794,1794,Polychrome woodblock print (surimono); ink and color on paper,Image: 5 3/8 x 6 1/2 in. (13.7 x 16.5 cm),"Purchase, Marjorie H. Holden Gift, 2012",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/77875,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2012.248.5,false,true,77876,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,"1795, year of the rabbit",1795,1795,Polychrome woodblock print (surimono); ink and color on paper,Image: 5 3/8 x 11 in. (13.7 x 27.9 cm),"Purchase, Marjorie H. Holden Gift, 2012",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/77876,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2012.248.6,false,true,77877,Asian Art,Print,女官図|Court Woman at her Desk with Poem Cards,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,1795 (Year of the Rabbit),1795,1795,Polychrome woodblock print (surimono); ink and color on paper,Image: 5 3/8 x 7 1/4 in. (13.7 x 18.4 cm),"Purchase, Marjorie H. Holden Gift, 2012",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/77877,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2012.248.9,false,true,77880,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,"1795, year of the rabbit",1795,1795,Polychrome woodblock print (surimono); ink and color on paper,Image: 4 x 5 3/4 in. (10.2 x 14.6 cm),"Purchase, Marjorie H. Holden Gift, 2012",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/77880,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"JP2821a, b",false,true,57074,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,1757–1820,1757,1820,Polychrome woodblock print; ink and color on paper,A. 15 5/16 x 10 6/16 in. (38.9 x 26.4 cm); B. 15 1/2 x 10 5/16 in. (38.9 x 26.4 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57074,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2012.248.10,false,true,77881,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,"1795, year of the rabbit",1795,1795,Polychrome woodblock print (surimono); ink and color on paper,Image: 4 1/8 x 7 1/8 in. (10.5 x 18.1 cm),"Purchase, Marjorie H. Holden Gift, 2012",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/77881,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2012.248.32,false,true,77903,Asian Art,Print,遊女と詩人|Courtesan with Client before a Tokonoma Alcove,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,1798,1798,1798,Polychrome woodblock print (surimono); ink and color on paper,Image: 5 1/2 x 11 in. (14 x 27.9 cm),"Purchase, Marjorie H. Holden Gift, 2012",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/77903,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP937,false,true,54808,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Kitao Masanobu (Santō Kyōden),"Japanese, 1761–1816",,Kitao Masanobu (Santō Kyōden),Japanese,1761,1816,ca. 1783,1773,1793,Polychrome woodblock print; ink and color on paper,H. 10 1/8 in. (25.7 cm); W. 7 1/4 in. (18.4 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54808,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2796,false,true,57093,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kitao Masanobu (Santō Kyōden),"Japanese, 1761–1816",,Kitao Masanobu (Santō Kyōden),Japanese,1761,1816,1761–1816,1761,1816,Polychrome woodblock print; ink and color on paper,10 3/8 x 8 3/4 in. (26.4 x 22.2 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57093,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP174,false,true,36652,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shun'ei,"Japanese, 1762–1819",,Katsukawa Shun'ei,Japanese,1762,1819,ca. 1796,1786,1806,Polychrome woodblock print; ink and color on paper,12 11/32 x 5 3/8 in. (31.4 x 13.7 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36652,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP301,false,true,36769,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shun'ei,"Japanese, 1762–1819",,Katsukawa Shun'ei,Japanese,1762,1819,ca. 1792?,1782,1802,Polychrome woodblock print; ink and color on paper,12 3/4 x 5 5/8 in. (32.4 x 14.3 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36769,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP302,false,true,36770,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shun'ei,"Japanese, 1762–1819",,Katsukawa Shun'ei,Japanese,1762,1819,ca. 1787,1777,1797,Polychrome woodblock print; ink and color on paper,11 15/16 x 5 5/16 in. (30.3 x 13.5 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36770,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP303,false,true,36771,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shun'ei,"Japanese, 1762–1819",,Katsukawa Shun'ei,Japanese,1762,1819,"2nd month, 1792",1792,1792,Polychrome woodblock print; ink and color on paper,12 11/32 x 5 5/8 in. (31.4 x 14.3 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36771,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP304,false,true,36772,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shun'ei,"Japanese, 1762–1819",,Katsukawa Shun'ei,Japanese,1762,1819,ca. 1793?,1783,1883,Polychrome woodblock print; ink and color on paper,12 7/32 x 5 1/2 in. (31.1 x 14.0 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36772,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP305,false,true,36773,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shun'ei,"Japanese, 1762–1819",,Katsukawa Shun'ei,Japanese,1762,1819,ca. 1792–93,1782,1803,Polychrome woodblock print; ink and color on paper,12 5/8 x 5 2/3 in. (32.1 x 14.4 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36773,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP306,false,true,36774,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shun'ei,"Japanese, 1762–1819",,Katsukawa Shun'ei,Japanese,1762,1819,ca. 1793?,1783,1803,Polychrome woodblock print; ink and color on paper,11 1/2 x 5 5/16 in. (29.2 x 13.5 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36774,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP307,false,true,36775,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shun'ei,"Japanese, 1762–1819",,Katsukawa Shun'ei,Japanese,1762,1819,ca. 1791,1781,1801,Polychrome woodblock print; ink and color on paper,12 7/16 x 5 17/32 in. (31.6 x 14.1 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36775,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP308,false,true,36776,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shun'ei,"Japanese, 1762–1819",,Katsukawa Shun'ei,Japanese,1762,1819,ca. 1795,1785,1805,Polychrome woodblock print; ink and color on paper,12 3/4 x 5 5/8 in. (32.4 x 14.3 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36776,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP309,false,true,36777,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shun'ei,"Japanese, 1762–1819",,Katsukawa Shun'ei,Japanese,1762,1819,ca. 1798,1788,1808,Polychrome woodblock print; ink and color on paper,12 x 5 1/2 in. (30.5 x 14.0 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36777,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP310,false,true,36778,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shun'ei,"Japanese, 1762–1819",,Katsukawa Shun'ei,Japanese,1762,1819,1787,1787,1787,Polychrome woodblock print; ink and color on paper,12 x 5 1/2 in. (30.5 x 14.0 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36778,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP311,false,true,36779,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shun'ei,"Japanese, 1762–1819",,Katsukawa Shun'ei,Japanese,1762,1819,ca. 1792?,1782,1804,Polychrome woodblock print; ink and color on paper,12 7/32 x 5 1/2 in. (31.1 x 14.0 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36779,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP312,false,true,36780,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shun'ei,"Japanese, 1762–1819",,Katsukawa Shun'ei,Japanese,1762,1819,ca. 1794?,1784,1804,Polychrome woodblock print; ink and color on paper,12 5/16 x 5 1/2 in. (31.3 x 14.0 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36780,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP313,false,true,36781,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shun'ei,"Japanese, 1762–1819",,Katsukawa Shun'ei,Japanese,1762,1819,"2nd month, 1789",1789,1789,Polychrome woodblock print; ink and color on paper,12 5/8 x 5 7/8 in. (32.1 x 14.9 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36781,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP314,false,true,36782,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shun'ei,"Japanese, 1762–1819",,Katsukawa Shun'ei,Japanese,1762,1819,ca. 1781,1771,1791,One sheet of a triptych of polychrome woodblock prints; ink and color on paper,12 1/2 x 5 2/3 in. (31.8 x 14.4 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36782,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP315,false,true,36783,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shun'ei,"Japanese, 1762–1819",,Katsukawa Shun'ei,Japanese,1762,1819,ca. 1793,1783,1803,One sheet of a triptych of polychrome woodblock prints; ink and color on paper,12 3/4 x 5 3/4 in. (32.4 x 14.6 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36783,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP316,false,true,36784,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shun'ei,"Japanese, 1762–1819",,Katsukawa Shun'ei,Japanese,1762,1819,ca. 1795,1785,1805,Polychrome woodblock print; ink and color on paper,12 3/4 x 5 5/8 in. (32.4 x 14.3 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36784,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP317,false,true,36785,Asian Art,Print,三代目大谷鬼次|Kabuki Actor Ōtani Oniji III as a Samurai,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shun'ei,"Japanese, 1762–1819",,Katsukawa Shun'ei,Japanese,1762,1819,ca. 1792,1782,1802,Polychrome woodblock print; ink and color on paper,12 15/16 x 5 2/3 in. (32.9 x 14.4 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36785,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP318,false,true,36786,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shun'ei,"Japanese, 1762–1819",,Katsukawa Shun'ei,Japanese,1762,1819,ca. 1793,1783,1803,Polychrome woodblock print; ink and color on paper,12 1/8 x 5 5/8 in. (30.8 x 14.3 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36786,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP319,false,true,36787,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shun'ei,"Japanese, 1762–1819",,Katsukawa Shun'ei,Japanese,1762,1819,ca. 1793,1783,1803,Polychrome woodblock print; ink and color on paper,12 7/8 x 5 3/4 in. (32.7 x 14.6 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36787,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP320,false,true,36788,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shun'ei,"Japanese, 1762–1819",,Katsukawa Shun'ei,Japanese,1762,1819,ca. 1795,1785,1805,Polychrome woodblock print; ink and color on paper,12 3/4 x 5 3/4 in. (32.4 x 14.6 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36788,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP321,false,true,36789,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shun'ei,"Japanese, 1762–1819",,Katsukawa Shun'ei,Japanese,1762,1819,December 1790,1790,1790,Polychrome woodblock print; ink and color on paper,11 9/16 x 5 5/8 in. (29.4 x 14.3 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36789,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP322,false,true,36790,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shun'ei,"Japanese, 1762–1819",,Katsukawa Shun'ei,Japanese,1762,1819,"12th month, 1788",1788,1788,Polychrome woodblock print; ink and color on paper,12 7/8 x 5 17/32 in. (32.7 x 14.1 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36790,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP323,false,true,36791,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shun'ei,"Japanese, 1762–1819",,Katsukawa Shun'ei,Japanese,1762,1819,ca. 1795,1785,1805,Polychrome woodblock print; ink and color on paper,12 3/4 x 5 3/4 in. (32.4 x 14.6 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36791,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP324,false,true,36792,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shun'ei,"Japanese, 1762–1819",,Katsukawa Shun'ei,Japanese,1762,1819,ca. 1793,1783,1803,Polychrome woodblock print; ink and color on paper,12 x 5 3/8 in. (30.5 x 13.7 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36792,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP325,false,true,36793,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shun'ei,"Japanese, 1762–1819",,Katsukawa Shun'ei,Japanese,1762,1819,ca. 1793?,1783,1803,Polychrome woodblock print; ink and color on paper,12 3/4 x 5 5/8 in. (32.4 x 14.3 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36793,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP326,false,true,36794,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shun'ei,"Japanese, 1762–1819",,Katsukawa Shun'ei,Japanese,1762,1819,ca. 1788,1778,1798,One sheet of a triptych of polychrome woodblock prints; ink and color on paper,12 5/8 x 5 3/4 in. (32.1 x 14.6 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36794,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP327,false,true,36795,Asian Art,Print,三代目坂田半五郎|Kabuki Actor Sakata Hangorō III as an Outlaw,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shun'ei,"Japanese, 1762–1819",,Katsukawa Shun'ei,Japanese,1762,1819,ca. 1791,1781,1801,Polychrome woodblock print; ink and color on paper,12 4/5 x 5 2/3 in. (32.5 x 14.4 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36795,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP328,false,true,36796,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shun'ei,"Japanese, 1762–1819",,Katsukawa Shun'ei,Japanese,1762,1819,"5th month, 1795",1795,1795,Polychrome woodblock print; ink and color on paper,12 5/8 x 5 7/16 in. (32.1 x 13.8 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36796,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP329,false,true,36797,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shun'ei,"Japanese, 1762–1819",,Katsukawa Shun'ei,Japanese,1762,1819,ca. 1793,1783,1803,Polychrome woodblock print; ink and color on paper,12 3/4 x 5 3/4 in. (32.4 x 14.6 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36797,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP330,false,true,36798,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shun'ei,"Japanese, 1762–1819",,Katsukawa Shun'ei,Japanese,1762,1819,ca. 1796,1786,1806,One sheet of a triptych of polychrome woodblock prints; ink and color on paper,12 5/8 x 5 5/8 in. (32.1 x 14.3 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36798,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP331,false,true,36799,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shun'ei,"Japanese, 1762–1819",,Katsukawa Shun'ei,Japanese,1762,1819,ca. 1795?,1785,1805,Polychrome woodblock print; ink and color on paper,12 5/16 x 5 5/8 in. (31.3 x 14.3 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36799,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP333,false,true,36801,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shun'ei,"Japanese, 1762–1819",,Katsukawa Shun'ei,Japanese,1762,1819,ca. 1790?,1780,1800,Polychrome woodblock print; ink and color on paper,12 3/4 x 5 17/32 in. (32.4 x 14.1 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36801,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP334,false,true,36802,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shun'ei,"Japanese, 1762–1819",,Katsukawa Shun'ei,Japanese,1762,1819,ca. 1789,1779,1799,Polychrome woodblock print; ink and color on paper,12 11/32 x 5 17/32 in. (31.4 x 14.1 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36802,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP335,false,true,36803,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shun'ei,"Japanese, 1762–1819",,Katsukawa Shun'ei,Japanese,1762,1819,ca. 1791?,1781,1801,One sheet of a triptych of polychrome woodblock prints; ink and color on paper,12 3/4 x 5 5/8 in. (32.4 x 14.3 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36803,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP336,false,true,36804,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shun'ei,"Japanese, 1762–1819",,Katsukawa Shun'ei,Japanese,1762,1819,ca. 1797,1787,1807,One sheet of a triptych of polychrome woodblock prints; ink and color on paper,12 1/2 x 5 2/3 in. (31.8 x 14.4 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36804,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP337,false,true,36805,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shun'ei,"Japanese, 1762–1819",,Katsukawa Shun'ei,Japanese,1762,1819,"2nd month, 1794",1794,1794,Polychrome woodblock print; ink and color on paper,11 7/8 x 5 7/16 in. (30.2 x 13.8 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36805,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP715,false,true,37163,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shun'ei,"Japanese, 1762–1819",,Katsukawa Shun'ei,Japanese,1762,1819,ca. 1788,1778,1798,Polychrome woodblock print; ink and color on paper,12 1/8 x 5 3/4 in. (30.8 x 14.6 cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37163,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP716,false,true,37164,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shun'ei,"Japanese, 1762–1819",,Katsukawa Shun'ei,Japanese,1762,1819,ca. 1799,1789,1809,Polychrome woodblock print; ink and color on paper,14 1/8 x 9 7/8 in. (35.9 x 25.1 cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37164,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP912,false,true,54652,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shun'ei,"Japanese, 1762–1819",,Katsukawa Shun'ei,Japanese,1762,1819,ca. 1790,1780,1800,Polychrome woodblock print; ink and color on paper,H. 10 5/16 in. (26.2 cm); W. 7 9/16 in. (19.2 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54652,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP913,false,true,54653,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shun'ei,"Japanese, 1762–1819",,Katsukawa Shun'ei,Japanese,1762,1819,1796,1796,1796,Polychrome woodblock print; ink and color on paper,H. 12 3/4 in. (32.4 cm); W. 5 3/4 in. (14.6 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54653,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP914,false,true,54654,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shun'ei,"Japanese, 1762–1819",,Katsukawa Shun'ei,Japanese,1762,1819,ca. 1703,1693,1713,Polychrome woodblock print; ink and color on paper,H. 12 in. (30.5 cm); W. 5 1/2 in. (14 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54654,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP915,false,true,54655,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shun'ei,"Japanese, 1762–1819",,Katsukawa Shun'ei,Japanese,1762,1819,ca. 1793,1783,1803,Polychrome woodblock print; ink and color on paper,H. 12 7/8 in. (32.7 cm); W. 5 3/4 in. (14.6 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54655,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP916,false,true,54656,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shun'ei,"Japanese, 1762–1819",,Katsukawa Shun'ei,Japanese,1762,1819,ca. 1794,1784,1804,Polychrome woodblock print; ink and color on paper,H. 14 1/2 in. (36.8 cm); W. 9 3/4 in. (24.8 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54656,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1221,false,true,55144,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shun'ei,"Japanese, 1762–1819",,Katsukawa Shun'ei,Japanese,1762,1819,ca. 1795,1785,1805,Polychrome woodblock print; ink and color on paper,H. 12 1/2 in. (31.8 cm); W. 5 3/4 in. (14.6 cm),"Rogers Fund, 1921",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55144,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1302,false,true,55241,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shun'ei,"Japanese, 1762–1819",,Katsukawa Shun'ei,Japanese,1762,1819,1794 (Kansei 6),1794,1794,Polychrome woodblock print; ink and color on paper,H. 12 1/8 in. (30.8 cm); W. 5 9/16 in. (14.1 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55241,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1347,false,true,55313,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shun'ei,"Japanese, 1762–1819",,Katsukawa Shun'ei,Japanese,1762,1819,1785,1785,1785,Polychrome woodblock print; ink and color on paper,Hosoban 12 5/16 x 5 1/2 in. (31.3 x 14 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55313,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1348,false,true,55315,Asian Art,Print,三代目瀬川菊之丞|Kabuki Actor Segawa Kikunojō III in a Female Role,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shun'ei,"Japanese, 1762–1819",,Katsukawa Shun'ei,Japanese,1762,1819,1788,1788,1788,Polychrome woodblock print; ink and color on paper,Hosoban 12 5/16 x 5 1/2 in. (31.3 x 14 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55315,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1349,false,true,55321,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shun'ei,"Japanese, 1762–1819",,Katsukawa Shun'ei,Japanese,1762,1819,1794,1794,1794,Polychrome woodblock print; ink and color on paper,12 13/16 x 5 13/16 in. (32.5 x 14.8 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55321,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1350,false,true,55322,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shun'ei,"Japanese, 1762–1819",,Katsukawa Shun'ei,Japanese,1762,1819,1789,1789,1789,Polychrome woodblock print; ink and color on paper,H. 12 1/4 in. (31.1 cm); W. 5 11/16 in. (14.4 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55322,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1354,false,true,55330,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shun'ei,"Japanese, 1762–1819",,Katsukawa Shun'ei,Japanese,1762,1819,1782,1782,1782,Polychrome woodblock print; ink and color on paper,H. 12 in. (30.5 cm); W. 5 1/4 in. (13.3 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55330,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1386,false,true,55387,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shun'ei,"Japanese, 1762–1819",,Katsukawa Shun'ei,Japanese,1762,1819,1794,1794,1794,Polychrome woodblock print; ink and color on paper,H. 15 in. (38.1 cm); W. 9 7/8 in. (25.1 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55387,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1401,false,true,45242,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shun'ei,"Japanese, 1762–1819",,Katsukawa Shun'ei,Japanese,1762,1819,ca. 1788,1778,1798,Polychrome woodblock print; uchiwa fan format; ink and color on paper,W. 10 1/4 in. (26 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45242,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1493,false,true,55588,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shun'ei,"Japanese, 1762–1819",,Katsukawa Shun'ei,Japanese,1762,1819,ca. 1780,1770,1790,Polychrome woodblock print; ink and color on paper,H. 12 in. (30.5 cm); W. 5 in. (12.7 cm),"Fletcher Fund, 1928",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55588,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1536,false,true,55701,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shun'ei,"Japanese, 1762–1819",,Katsukawa Shun'ei,Japanese,1762,1819,ca. 1793,1783,1803,Polychrome woodblock print; ink and color on paper,H. 8 in. (20.3 cm); W. 9 9/16 in. (24.3 cm),"Fletcher Fund, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55701,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1537,false,true,55702,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shun'ei,"Japanese, 1762–1819",,Katsukawa Shun'ei,Japanese,1762,1819,ca. 1792,1782,1802,Polychrome woodblock print; ink and color on paper,H. 12 1/2 in. (31.8 cm); W. 5 3/4 in. (14.6 cm),"Fletcher Fund, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55702,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1538,false,true,55703,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shun'ei,"Japanese, 1762–1819",,Katsukawa Shun'ei,Japanese,1762,1819,ca. 1790,1780,1800,Polychrome woodblock print; ink and color on paper,H. 12 9/16 in. (31.9 cm); W. 5 5/8 in. (14.3 cm),"Fletcher Fund, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55703,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1539,false,true,55704,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shun'ei,"Japanese, 1762–1819",,Katsukawa Shun'ei,Japanese,1762,1819,ca. 1793,1783,1803,Polychrome woodblock print; ink and color on paper,H. 12 7/8 in. (32.7 cm); W. 6 in. (15.2 cm),"Fletcher Fund, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55704,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1540,false,true,55705,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shun'ei,"Japanese, 1762–1819",,Katsukawa Shun'ei,Japanese,1762,1819,ca. 1795,1785,1805,Polychrome woodblock print; ink and color on paper,H. 13 1/8 in. (33.3 cm); W. 5 3/4 in. (14.6 cm),"Fletcher Fund, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55705,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2404,false,true,56807,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shun'ei,"Japanese, 1762–1819",,Katsukawa Shun'ei,Japanese,1762,1819,1789,1789,1789,Polychrome woodblock print; ink and color on paper,12 3/4 x 5 3/4 in. (32.4 x 14.6 cm),"Gift of Louis V. Ledoux, 1931",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56807,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2707,false,true,56972,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shun'ei,"Japanese, 1762–1819",,Katsukawa Shun'ei,Japanese,1762,1819,ca. 1790,1780,1810,Polychrome woodblock print; ink and color on paper,12 x 6 in. (30.5 x 15.2 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56972,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2708,false,true,56974,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shun'ei,"Japanese, 1762–1819",,Katsukawa Shun'ei,Japanese,1762,1819,ca. 1791,1781,1811,Polychrome woodblock print; ink and color on paper,12 5/6 x 5 5/8 in. (32.6 x 14.3 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56974,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2709,false,true,56975,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shun'ei,"Japanese, 1762–1819",,Katsukawa Shun'ei,Japanese,1762,1819,ca. 1790–1797,1790,1797,Polychrome woodblock print; ink and color on paper,11 3/4 x 5 3/8 in. (29.8 x 13.7 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56975,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2710,false,true,56976,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shun'ei,"Japanese, 1762–1819",,Katsukawa Shun'ei,Japanese,1762,1819,ca. 1789,1779,1799,Polychrome woodblock print; ink and color on paper,12 3/8 x 5 9/16 in. (31.4 x 14.1 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56976,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2711,false,true,56978,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shun'ei,"Japanese, 1762–1819",,Katsukawa Shun'ei,Japanese,1762,1819,ca. 1790,1780,1810,Polychrome woodblock print; ink and color on paper,12 1/8 x 5 1/2 in. (30.8 x 14 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56978,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2712,false,true,56980,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shun'ei,"Japanese, 1762–1819",,Katsukawa Shun'ei,Japanese,1762,1819,ca. 1790,1780,1810,Polychrome woodblock print; ink and color on paper,12 3/4 x 5 3/4 in. (32.4 x 14.6 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56980,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2713,false,true,56981,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shun'ei,"Japanese, 1762–1819",,Katsukawa Shun'ei,Japanese,1762,1819,ca. 1790,1780,1810,Polychrome woodblock print; ink and color on paper,12 3/8 x 5 1/2 in. (31.4 x 14 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56981,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2714,false,true,56982,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shun'ei,"Japanese, 1762–1819",,Katsukawa Shun'ei,Japanese,1762,1819,ca. 1792,1782,1802,Probably the middle sheet of a triptych or the right-hand sheet of a diptych of polychrome woodblock prints; ink and color on paper,12 3/4 x 5 5/8 in. (32.4 x 14.3 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56982,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2715,false,true,56983,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shun'ei,"Japanese, 1762–1819",,Katsukawa Shun'ei,Japanese,1762,1819,1793,1793,1793,Polychrome woodblock print; ink and color on paper,12 1/2 x 5 5/8 in. (31.8 x 14.3 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56983,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2824,false,true,45037,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shun'ei,"Japanese, 1762–1819",,Katsukawa Shun'ei,Japanese,1762,1819,ca. 1800,1790,1810,Polychrome woodblock print; ink and color on paper,15 1/4 x 10 1/4 in. (38.7 x 26 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45037,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2894,false,true,56009,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shun'ei,"Japanese, 1762–1819",,Katsukawa Shun'ei,Japanese,1762,1819,1762–1819,1762,1819,Polychrome woodblock print; ink and color on paper,12 1/2 x 5 5/8 in. (31.8 x 14.3 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56009,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2895,false,true,56010,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shun'ei,"Japanese, 1762–1819",,Katsukawa Shun'ei,Japanese,1762,1819,1762–1819,1762,1819,Polychrome woodblock print; ink and color on paper,11 7/8 x 5 1/4 in. (30.2 x 13.3 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56010,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2897,false,true,56012,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shun'ei,"Japanese, 1762–1819",,Katsukawa Shun'ei,Japanese,1762,1819,1762–1819,1762,1819,Polychrome woodblock print; ink and color on paper,12 3/4 x 6 in. (32.4 x 15.2 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56012,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2898,false,true,56013,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shun'ei,"Japanese, 1762–1819",,Katsukawa Shun'ei,Japanese,1762,1819,1762–1819,1762,1819,Polychrome woodblock print; ink and color on paper,12 x 5 1/4 in. (30.5 x 13.3 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56013,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2899,false,true,56014,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shun'ei,"Japanese, 1762–1819",,Katsukawa Shun'ei,Japanese,1762,1819,1762–1819,1762,1819,Polychrome woodblock print; ink and color on paper,12 x 5 3/8 in. (30.5 x 13.7 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56014,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"JP338a, b",false,true,36476,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shun'ei,"Japanese, 1762–1819",,Katsukawa Shun'ei,Japanese,1762,1819,ca. 1792,1782,1802,Diptych of polychrome woodblock prints; ink and color on paper,A: H. 12 13/16 (32.5 cm); W. 5 11/16 in. (14.4 cm) B: H. 13 in. (33 cm); W. 5 3/4 in. (14.6 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36476,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"JP340a, b",false,true,36474,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shun'ei,"Japanese, 1762–1819",,Katsukawa Shun'ei,Japanese,1762,1819,ca. 1793,1783,1803,Diptych of polychrome woodblock prints; ink and color on paper,Overall: H. 12 9/16 in. (31.9 cm); W. 11 1/2 in. (29.2 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36474,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP339a–c,false,true,36475,Asian Art,Woodblock prints,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shun'ei,"Japanese, 1762–1819",,Katsukawa Shun'ei,Japanese,1762,1819,ca. 1793,1783,1803,Triptych of polychrome woodblock prints; ink and color on paper,A: H. 12 3/8 in. (31.4 cm); W. 5 1/2 in. (14 cm) B: H. 12 5/8 in. (32.1 cm); W. 5 5/8 in. (14.3 cm) C: H. 13 in. (33 cm); W. 5 7/8 in. (14.9 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36475,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2896a–c,false,true,56011,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shun'ei,"Japanese, 1762–1819",,Katsukawa Shun'ei,Japanese,1762,1819,1762–1819,1762,1819,Triptych of polychrome woodblock prints; ink and color on paper,A: 12 3/4 x 5 5/8 in. (32.4 x 14.3 cm) B: 12 3/4 x 5 3/4 in. (32.4 x 14.6 cm) C: 12 3/4 x 5 3/4 in. (32.4 x 14.6 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56011,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1005,false,true,54881,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Toyohiro,"Japanese, 1763–1828",,Utagawa Toyohiro,Japanese,1763,1828,ca. 1804,1794,1814,Two sheets of a pentaptych of polychrome woodblock prints; ink and color on paper,Aiban; H. 13 3/8 in. (34 cm); W. 19 1/2 in. (49.5 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54881,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1006,false,true,54882,Asian Art,Print,"Horinouchi Myōhōji Eho Mairi no Zu|Pilgrimage to Myōhōji in Horinouchi, Edo",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Toyohiro,"Japanese, 1763–1828",,Utagawa Toyohiro,Japanese,1763,1828,ca. 1804,1794,1814,Two sheets of a pentaptych of polychrome woodblock prints; ink and color on paper,H. 14 3/8 in. (36.5 cm); W. 19 in. (48.3 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54882,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1007,false,true,54883,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Toyohiro,"Japanese, 1763–1828",,Utagawa Toyohiro,Japanese,1763,1828,ca. 1805,1795,1815,One sheet of a triptych(?)of polychrome woodblock prints; ink and color on paper,H. 15 in. (38.1 cm); W. 10 1/4 in. (26 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54883,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1009,false,true,54885,Asian Art,Print,Tsurigitsune|Trapping the Fox,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Toyohiro,"Japanese, 1763–1828",,Utagawa Toyohiro,Japanese,1763,1828,1808,1808,1808,Polychrome woodblock print; ink and color on paper,H. 14 in. (35.6 cm); W. 9 1/4 in. (23.5 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54885,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2718,false,true,56986,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Toyohiro,"Japanese, 1763–1828",,Utagawa Toyohiro,Japanese,1763,1828,ca. 1802,1792,1812,Probably one sheet of a triptych of polychrome woodblock prints; ink and color on paper,12 3/8 x 8 3/8 in. (31.4 x 21.3 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56986,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP193,false,true,36670,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kuwagata Keisai,"Japanese, 1764–1824",,Kuwagata Keisai,Japanese,1764,1824,ca. 1790,1780,1800,Polychrome woodblock print; ink and color on paper,10 x 7 7/32 in. (25.4 x 18.4 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36670,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP527,false,true,36978,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kuwagata Keisai,"Japanese, 1764–1824",,Kuwagata Keisai,Japanese,1764,1824,1789,1789,1789,Polychrome woodblock print; ink and color on paper,10 x 14 7/8 in. (25.4 x 37.8 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36978,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP528,false,true,36979,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Kuwagata Keisai,"Japanese, 1764–1824",,Kuwagata Keisai,Japanese,1764,1824,1789,1789,1789,Polychrome woodblock print; ink and color on paper,10 x 14 7/8 in. (25.4 x 37.8 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36979,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP529,false,true,36980,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kuwagata Keisai,"Japanese, 1764–1824",,Kuwagata Keisai,Japanese,1764,1824,1789,1789,1789,Polychrome woodblock print; ink and color on paper,10 x 14 7/8 in. (25.4 x 37.8 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36980,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP530,false,true,36981,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kuwagata Keisai,"Japanese, 1764–1824",,Kuwagata Keisai,Japanese,1764,1824,1789,1789,1789,Polychrome woodblock print; ink and color on paper,10 x 14 7/8 in. (25.4 x 37.8 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36981,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP531,false,true,36982,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kuwagata Keisai,"Japanese, 1764–1824",,Kuwagata Keisai,Japanese,1764,1824,1789,1789,1789,Polychrome woodblock print; ink and color on paper,10 x 14 7/8 in. (25.4 x 37.8 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36982,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP532,false,true,36983,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kuwagata Keisai,"Japanese, 1764–1824",,Kuwagata Keisai,Japanese,1764,1824,1789,1789,1789,Polychrome woodblock print; ink and color on paper,10 x 14 7/8 in. (25.4 x 37.8 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36983,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP533,false,true,36984,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kuwagata Keisai,"Japanese, 1764–1824",,Kuwagata Keisai,Japanese,1764,1824,1789,1789,1789,Polychrome woodblock print; ink and color on paper,10 x 14 7/8 in. (25.4 x 37.8 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36984,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP534,false,true,36985,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kuwagata Keisai,"Japanese, 1764–1824",,Kuwagata Keisai,Japanese,1764,1824,1789,1789,1789,Polychrome woodblock print; ink and color on paper,10 x 14 7/8 in. (25.4 x 37.8 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36985,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP535,false,true,36986,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kuwagata Keisai,"Japanese, 1764–1824",,Kuwagata Keisai,Japanese,1764,1824,1789,1789,1789,Polychrome woodblock print; ink and color on paper,10 x 14 7/8 in. (25.4 x 37.8 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36986,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP536,false,true,36987,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kuwagata Keisai,"Japanese, 1764–1824",,Kuwagata Keisai,Japanese,1764,1824,1789,1789,1789,Polychrome woodblock print; ink and color on paper,10 x 14 7/8 in. (25.4 x 37.8 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36987,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP537,false,true,36988,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kuwagata Keisai,"Japanese, 1764–1824",,Kuwagata Keisai,Japanese,1764,1824,1789,1789,1789,Polychrome woodblock print; ink and color on paper,10 x 14 7/8 in. (25.4 x 37.8 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36988,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP538,false,true,36989,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kuwagata Keisai,"Japanese, 1764–1824",,Kuwagata Keisai,Japanese,1764,1824,1789,1789,1789,Polychrome woodblock print; ink and color on paper,10 x 14 7/8 in. (25.4 x 37.8 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36989,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP938,false,true,45243,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kuwagata Keisai,"Japanese, 1764–1824",,Kuwagata Keisai,Japanese,1764,1824,ca. 1790,1780,1800,Polychrome woodblock print; ink and color on paper,H. 8 5/8 in. (21.9 cm); W. 6 3/8 in. (16.2 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45243,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3053,false,true,56508,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kuwagata Keisai,"Japanese, 1764–1824",,Kuwagata Keisai,Japanese,1764,1824,ca. 1780,1770,1790,Polychrome woodblock print; ink and color on paper,11 5/8 x 16 5/8 in. (29.5 x 42.2 cm),"Gift of Mrs. Morris Manges, in memory of her husband, Dr. Morris Manges, 1947",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56508,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP200,false,true,36677,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Toyokuni I,"Japanese, 1769–1825",,Utagawa Toyokuni I,Japanese,1769,1825,ca. 1797,1787,1807,Pentaptych of polychrome woodblock prints; ink and color on paper,15 7/32 x 50 1/2 in. (38.7 x 128.3 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36677,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP201,false,true,36678,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Toyokuni I,"Japanese, 1769–1825",,Utagawa Toyokuni I,Japanese,1769,1825,ca. 1791,1781,1801,Triptych of polychrome woodblock prints; ink and color on paper,15 3/8 x 29 3/4 in. (39.1 x 75.6 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36678,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP203,false,true,36680,Asian Art,Print,雪こかし|Courtesans and Attendants Playing in the Snow,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Toyokuni I,"Japanese, 1769–1825",,Utagawa Toyokuni I,Japanese,1769,1825,ca. 1796,1786,1806,Triptych of polychrome woodblock prints; ink and color on paper,14 15/32 x 29 15/32 in. (36.8 x 74.9 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36680,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP204,false,true,36681,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Toyokuni I,"Japanese, 1769–1825",,Utagawa Toyokuni I,Japanese,1769,1825,ca. 1791,1781,1801,Triptych of polychrome woodblock prints; ink and color on paper,15 5/8 x 30 5/8 in. (39.7 x 77.8 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36681,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP235,false,true,36707,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Toyokuni I,"Japanese, 1769–1825",,Utagawa Toyokuni I,Japanese,1769,1825,ca. 1789,1779,1799,Polychrome woodblock print; ink and color on paper,10 1/8 x 7 5/8 in. (25.7 x 19.4 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36707,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP664,false,true,37111,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Toyokuni I,"Japanese, 1769–1825",,Utagawa Toyokuni I,Japanese,1769,1825,ca. 1801,1791,1821,Two sheets of a triptych of polychrome woodblock prints; ink and color on paper,14 1/8 x 19 15/32 in. (35.9 x 49.5 cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37111,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP665,false,true,37112,Asian Art,Woodblock prints,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Toyokuni I,"Japanese, 1769–1825",,Utagawa Toyokuni I,Japanese,1769,1825,ca. 1800,1790,1810,Triptych of polychrome woodblocks print; ink and color on paper,15 1/2 x 30 1/8 in. (39.4 x 76.5 cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37112,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1003,false,true,54880,Asian Art,Print,吉原仲の町花魁道中|Courtesans Promenading on the Nakanochō in Yoshiwara,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Toyokuni I,"Japanese, 1769–1825",,Utagawa Toyokuni I,Japanese,1769,1825,ca. 1795,1785,1805,Triptych of polychrome woodblock prints; ink and color on paper,H. 15 3/8 in. (39.1 cm); W. 50 in. (127 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54880,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1004,false,true,45226,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Toyokuni I,"Japanese, 1769–1825",,Utagawa Toyokuni I,Japanese,1769,1825,1796,1615,1868,Polychrome woodblock print; ink and color on paper,H. 15 in. (38.1 cm); W. 10 1/8 in. (25.7 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45226,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1119,false,true,55041,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Toyokuni I,"Japanese, 1769–1825",,Utagawa Toyokuni I,Japanese,1769,1825,1798–99,1798,1799,Polychrome woodblock print; ink and color on paper,H. 15 in. (38.1 cm); W. 9 5/8 in. (24.4 cm),"Rogers Fund, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55041,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1371,false,true,55350,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Toyokuni I,"Japanese, 1769–1825",,Utagawa Toyokuni I,Japanese,1769,1825,ca. 1798,1788,1808,Polychrome woodblock print; ink and color on paper,H. 14 5/8 in. (37.1 cm); W. 9 7/8 in. (25.1 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55350,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1387,false,true,55389,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Toyokuni I,"Japanese, 1769–1825",,Utagawa Toyokuni I,Japanese,1769,1825,ca. 1798,1788,1808,Polychrome woodblock print; ink and color on paper,H. 14 1/4 in. (36.2 cm); W. 10 1/4 in. (26 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55389,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1388,false,true,55394,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Toyokuni I,"Japanese, 1769–1825",,Utagawa Toyokuni I,Japanese,1769,1825,ca. 1798,1788,1808,Polychrome woodblock print; ink and color on paper,H. 15 1/4 in. (38.7 cm); W. 10 1/4 in. (26 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55394,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1389,false,true,55395,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Toyokuni I,"Japanese, 1769–1825",,Utagawa Toyokuni I,Japanese,1769,1825,1798,1798,1798,Polychrome woodblock print; ink and color on paper,H. 12 1/2 in. (31.8 cm); W. 5 5/8 in. (14.3 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55395,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1390,false,true,55397,Asian Art,Print,"『祇園神輿洗　ねり物姿』「いろは歌の売」|“The Geisha To’e as a Vendor of Poems,” from the series Gion Festival Costume Parade (Gion mikoshi arai nerimono sugata)",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Toyokuni I,"Japanese, 1769–1825",,Utagawa Toyokuni I,Japanese,1769,1825,ca. 1795,1785,1805,Polychrome woodblock print; ink and color on paper,H. 13 1/4 in. (33.7 cm); W. 6 1/4 in. (15.9 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55397,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1391,false,true,55398,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Toyokuni I,"Japanese, 1769–1825",,Utagawa Toyokuni I,Japanese,1769,1825,ca. 1810,1800,1820,Polychrome woodblock print; ink and color on paper,H. 14 1/4 in. (36.2 cm); W. 9 3/4 in. (24.8 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55398,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1407,false,true,55420,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Toyokuni I,"Japanese, 1769–1825",,Utagawa Toyokuni I,Japanese,1769,1825,1804,1804,1804,Polychrome woodblock print; ink and color on paper,H. 15 1/8 in. (38.4 cm); W. 10 3/16 in. (25.9 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55420,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1408,false,true,55421,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Toyokuni I,"Japanese, 1769–1825",,Utagawa Toyokuni I,Japanese,1769,1825,ca. 1804,1794,1814,Polychrome woodblock print; ink and color on paper,H. 15 1/4 in. (38.7 cm); W. 9 7/8 in. (25.1 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55421,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1492,false,true,45290,Asian Art,Print,"Tokaido Yotsuya Kaidan|Onoe Matsusuke as the Ghost of the Murdered Wife Oiwa, in ""A Tale of Horror from the Yotsuya Station on the Tokaido Road""",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Toyokuni I,"Japanese, 1769–1825",,Utagawa Toyokuni I,Japanese,1769,1825,1812,1615,1868,Polychrome woodblock print; ink and color on paper,H. 14 3/4 in. (37.5 cm); W. 10 1/8 in. (25.7 cm),"Gift of Louis V. Ledoux, 1927",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45290,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1529,false,true,55640,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Toyokuni I,"Japanese, 1769–1825",,Utagawa Toyokuni I,Japanese,1769,1825,1798 (Kansei 9),1798,1798,Polychrome woodblock print; ink and color on paper,H. 14 1/4 in. (36.2 cm); W. 9 1/2 in. (24.1 cm),"Fletcher Fund, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55640,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1530,false,true,55641,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Toyokuni I,"Japanese, 1769–1825",,Utagawa Toyokuni I,Japanese,1769,1825,ca. 1795,1785,1805,Polychrome woodblock print; ink and color on paper; mica background,14 3/4 x 10 in. (37.5 x 25.4cm),"Fletcher Fund, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55641,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1531,false,true,55644,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Toyokuni I,"Japanese, 1769–1825",,Utagawa Toyokuni I,Japanese,1769,1825,ca. 1794,1784,1804,Polychrome woodblock print; ink and color on paper,15 3/8 x 10 1/8 in. (39.1 x 25.7cm),"Fletcher Fund, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55644,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1570,false,true,55747,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Toyokuni I,"Japanese, 1769–1825",,Utagawa Toyokuni I,Japanese,1769,1825,ca. 1797,1787,1807,Triptych of polychrome woodblock prints; ink and color on paper,H. 14 1/2 in. (36.8 cm); W. 9 7/8 in. (25.1 cm),"Fletcher Fund, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55747,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1743,false,true,56050,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Toyokuni I,"Japanese, 1769–1825",,Utagawa Toyokuni I,Japanese,1769,1825,ca. 1795,1785,1805,Polychrome woodblock print; ink and color on paper,H. 15 1/4 in. (38.7 cm); W. 10 1/8 in. (25.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56050,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1747,false,true,56054,Asian Art,Print,南四季 夏景|The Four Seasons in Southern Edo: A Summer Scene (Minami shiki; Natsu [no] kei),Japan,Edo period (1615–1868),,,,Artist,,Utagawa Toyokuni I,"Japanese, 1769–1825",,Utagawa Toyokuni I,Japanese,1769,1825,late 1780s,1787,1789,Right and center sheet of a triptych of polychrome woodblock prints; ink and color on paper,Image (a): 14 3/8 x 10 1/8 in. (36.5 x 25.7 cm) Image (b): 14 1/2 x 9 15/16 in. (36.8 x 25.2 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56054,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1749,false,true,56056,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Toyokuni I,"Japanese, 1769–1825",,Utagawa Toyokuni I,Japanese,1769,1825,ca. 1800,1790,1810,Triptych of polychrome woodblock prints; ink and color on paper,A: H. 14 13/16 in. (37.6 cm); W. 10 1/16 in. (25.6 cm) B: H. 14 3/4 in. (37.5 cm); W. 10 in. (25.4 cm) C: H. 14 3/16 in. (36 cm); W. 9 15/16 in. (25.2 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56056,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1910,false,true,54449,Asian Art,Print,娘道成寺図の錦絵|Print of a Kabuki Dancer from the Maiden of the Dōjōji Temple (Musume Dōjōji),Japan,Edo period (1615–1868),,,,Artist,,Utagawa Toyokuni I,"Japanese, 1769–1825",,Utagawa Toyokuni I,Japanese,1769,1825,ca. 1810s,1810,1819,Polychrome woodblock print (surimono); ink and color on paper,5 3/8 x 7 1/8 in. (13.7 x 18.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54449,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2719,false,true,56987,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Toyokuni I,"Japanese, 1769–1825",,Utagawa Toyokuni I,Japanese,1769,1825,ca. 1796,1786,1806,Polychrome woodblock print; ink and color on paper,15 x 10 in. (38.1 x 25.4 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56987,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2720,false,true,56989,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Toyokuni I,"Japanese, 1769–1825",,Utagawa Toyokuni I,Japanese,1769,1825,ca. 1795,1785,1805,Polychrome woodblock print; ink and color on paper,Image: 14 1/8 x 9 1/2 in. (35.9 x 24.1 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56989,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2721,false,true,56991,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Toyokuni I,"Japanese, 1769–1825",,Utagawa Toyokuni I,Japanese,1769,1825,ca. 1795,1785,1805,Polychrome woodblock print; ink and color on paper,14 7/8 x 10 in. (37.8 x 25.4 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56991,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2722,false,true,56992,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Toyokuni I,"Japanese, 1769–1825",,Utagawa Toyokuni I,Japanese,1769,1825,1774,1794,1794,Polychrome woodblock print; ink and color on paper,14 7/8 x 9 5/8 in. (37.8 x 24.4 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56992,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2723,false,true,56993,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Toyokuni I,"Japanese, 1769–1825",,Utagawa Toyokuni I,Japanese,1769,1825,1796,1796,1796,Polychrome woodblock print; ink and color on paper,14 1/2 x 9 7/8 in. (36.8 x 25.1 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56993,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2725,false,true,51136,Asian Art,Print,御影堂扇屋図|The Mieidō Fan Shop,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Toyokuni I,"Japanese, 1769–1825",,Utagawa Toyokuni I,Japanese,1769,1825,ca. 1785–93,1785,1793,Triptych of polychrome woodblock prints; ink and color on paper,Each H. 15 1/8 in. (38.4 cm); W. 10 1/8 in. (25.7 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/51136,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2726,false,true,56997,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Toyokuni I,"Japanese, 1769–1825",,Utagawa Toyokuni I,Japanese,1769,1825,ca. 1800,1790,1810,Diptych of polychrome woodblock prints; ink and color on paper,Each sheet: 15 x 10 in. (38.1 x 25.4 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56997,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2825,false,true,57073,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Toyokuni I,"Japanese, 1769–1825",,Utagawa Toyokuni I,Japanese,1769,1825,ca. 1790s,1785,1805,Polychrome woodblock print; ink and color on paper,15 13/16 x 9 3/8 in. (40.2 x 23.8 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57073,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2826,false,true,57072,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Toyokuni I,"Japanese, 1769–1825",,Utagawa Toyokuni I,Japanese,1769,1825,1769–1825,1769,1825,Polychrome woodblock print; ink and color on paper,14 7/8 x 9 7/8 in. (37.8 x 25.1 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57072,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2828,false,true,44997,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Toyokuni I,"Japanese, 1769–1825",,Utagawa Toyokuni I,Japanese,1769,1825,ca. 1795,1785,1805,Polychrome woodblock print; ink and color on paper,H. 14 7/8 in. (37.8 cm); W. 9 3/4 in. (24.8 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/44997,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2829,false,true,57071,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Toyokuni I,"Japanese, 1769–1825",,Utagawa Toyokuni I,Japanese,1769,1825,1769–1825,1769,1825,Polychrome woodblock print; ink and color on paper,14 11/16 x 10 in. (37.3 x 25.4 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57071,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2900,false,true,56015,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Toyokuni I,"Japanese, 1769–1825",,Utagawa Toyokuni I,Japanese,1769,1825,1769–1825,1769,1825,Polychrome woodblock print; ink and color on paper,12 1/8 x 5 1/2 in. (30.8 x 14 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56015,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2901,false,true,56016,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Toyokuni I,"Japanese, 1769–1825",,Utagawa Toyokuni I,Japanese,1769,1825,1769–1825,1769,1825,Polychrome woodblock print; ink and color on paper,13 x 6 in. (33 x 15.2 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56016,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2902,false,true,56017,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Toyokuni I,"Japanese, 1769–1825",,Utagawa Toyokuni I,Japanese,1769,1825,1769–1825,1769,1825,Polychrome woodblock print; ink and color on paper,12 3/8 x 5 1/4 in. (31.4 x 13.3 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56017,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3391,false,true,55567,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Toyokuni I,"Japanese, 1769–1825",,Utagawa Toyokuni I,Japanese,1769,1825,ca. 1800,1790,1810,Polychrome woodblock print; ink and color on paper,15 1/8 x 10 1/4 in. (38.4 x 26 cm),"Gift of Mr. and Mrs. Arthur J. Steel, 1960",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55567,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2011.125,false,true,76555,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Toyokuni I,"Japanese, 1769–1825",,Utagawa Toyokuni I,Japanese,1769,1825,1812,1812,1812,Polychrome woodblock print with metallic pigment and lacquer details,Image (ôban tate-e): 14 7/8 x 10 1/8 in. (37.8 x 25.7 cm),"Purchase, Friends of Asian Art Gifts, in honor of James C. Y. Watt, 2011",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/76555,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2724a–c,false,true,56995,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Toyokuni I,"Japanese, 1769–1825",,Utagawa Toyokuni I,Japanese,1769,1825,ca. 1799,1789,1809,Triptych of polychrome woodblock prints; ink and color on paper,Each sheet: 15 x 10 in. (38.1 x 25.4 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56995,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2026,false,true,54786,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Teisai Hokuba,"Japanese, 1771–1844",,Teisai Hokuba,Japanese,1771,1844,probably 1808,1808,1808,Polychrome woodblock print (surimono); ink and color on paper,5 1/8 x 7 3/16 in. (13 x 18.3 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54786,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2362,false,true,54146,Asian Art,Print,"摺物帖　『春雨集』　『花鳥六番之内　下野宇都宮』　牡丹に燕|Spring Rain Collection (Harusame shū), vol. 3: Swallows and Peonies",Japan,Edo period (1615–1868),,,,Artist,,Teisai Hokuba,"Japanese, 1771–1844",,Teisai Hokuba,Japanese,1771,1844,ca. 1820,1810,1830,Privately published woodblock prints (surimono) mounted in an album; ink and color on paper,8 1/4 x 7 1/16 in. (21 x 17.9 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54146,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2363,false,true,54147,Asian Art,Print,"摺物帖　『春雨集』　『花鳥六番之内　下野宇都宮』　海棠に山雀|Spring Rain Collection (Harusame shū), vol. 3: Marsh-tits and Crab Apple Flowers",Japan,Edo period (1615–1868),,,,Artist,,Teisai Hokuba,"Japanese, 1771–1844",,Teisai Hokuba,Japanese,1771,1844,ca. 1820,1810,1830,Privately published woodblock prints (surimono) mounted in an album; ink and color on paper,8 x 7 3/8 in. (20.3 x 18.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54147,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2364,false,true,54148,Asian Art,Print,"摺物帖　『春雨集』　『花鳥六番之内　下野宇都宮』　タンポポに雀|Spring Rain Collection (Harusame shū), vol. 3: Sparrows and Dandelions",Japan,Edo period (1615–1868),,,,Artist,,Teisai Hokuba,"Japanese, 1771–1844",,Teisai Hokuba,Japanese,1771,1844,ca. 1820,1810,1830,Privately published woodblock prints (surimono) mounted in an album; ink and color on paper,8 3/8 x 7 1/2 in. (21.3 x 19.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54148,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2365,false,true,54149,Asian Art,Woodblock print,"摺物帖　『春雨集』　『花鳥六番之内　下野宇都宮』　桃に山鳩|Spring Rain Collection (Harusame shū), vol. 3: Mountain Dove and Peach Flowers",Japan,Edo period (1615–1868),,,,Artist,,Teisai Hokuba,"Japanese, 1771–1844",,Teisai Hokuba,Japanese,1771,1844,ca. 1820,1810,1830,Privately published woodblock prints (surimono) mounted in an album; ink and color on paper,8 1/4 x 7 7/16 in. (21 x 18.9 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54149,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2635,false,true,45225,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunimasa,"Japanese, 1773–1810",,Utagawa Kunimasa,Japanese,1773,1810,late 1790s,1796,1799,Polychrome woodblock print; ink and color on paper,14 1/2 x 9 3/4 in. (36.8 x 24.8 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45225,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1416,false,true,51086,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Toyokuni II,"Japanese, 1777–1835",,Utagawa Toyokuni II,Japanese,1777,1835,ca. 1830,1820,1840,Polychrome woodblock print; ink and color on paper,H. 9 1/2 in. (24.1 cm); W. 14 5/16 in. (36.4 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/51086,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1750,false,true,54396,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Toyokuni II,"Japanese, 1777–1835",,Utagawa Toyokuni II,Japanese,1777,1835,ca. 1834,1824,1844,Polychrome woodblock print (surimono); ink and color on paper,7 x 6 3/4 in. (17.8 x 17.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54396,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1153,false,true,54359,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Takashima Chiharu,"Japanese, 1777–1859",,Takashima Chiharu,Japanese,1777,1859,ca. 1820,1810,1830,Polychrome woodblock print (surimono); ink and color on paper,8 1/16 x 7 1/16 in. (20.5 x 17.9 cm),"Gift of T. Ito, Chicago, Ill, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54359,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2064,false,true,54892,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Takashima Chiharu,"Japanese, 1777–1859",,Takashima Chiharu,Japanese,1777,1859,probably 1815,1815,1815,Polychrome woodblock print (surimono); ink and color on paper,5 9/16 x 7 7/16 in. (14.1 x 18.9 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54892,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP749,false,true,54307,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Totoya Hokkei,"Japanese, 1780–1850",,Totoya Hokkei,Japanese,1780,1850,ca. 1830,1820,1840,Diptych of woodblock prints (surimono); ink and color on paper,8 1/8 x 14 7/16 in. (20.6 x 36.7 cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54307,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1032,false,true,54317,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Totoya Hokkei,"Japanese, 1780–1850",,Totoya Hokkei,Japanese,1780,1850,1814,1814,1814,Polychrome woodblock print (surimono); ink and color on paper,8 1/16 x 7 in. (20.5 x 17.8 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54317,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1033,false,true,54318,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Totoya Hokkei,"Japanese, 1780–1850",,Totoya Hokkei,Japanese,1780,1850,ca. 1825,1825,1825,Polychrome woodblock print (surimono); ink and color on paper,8 1/2 x 7 3/16 in. (21.6 x 18.3 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54318,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1035,false,true,54320,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Totoya Hokkei,"Japanese, 1780–1850",,Totoya Hokkei,Japanese,1780,1850,ca. 1825,1815,1835,Polychrome woodblock print (surimono); ink and color on paper,8 5/8 x 7 5/8 in. (21.9 x 19.4 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54320,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1098,false,true,54327,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Totoya Hokkei,"Japanese, 1780–1850",,Totoya Hokkei,Japanese,1780,1850,ca. 1820,1810,1830,Polychrome woodblock print (surimono); ink and color on paper,7 3/4 x 6 3/4 in. (19.7 x 17.1 cm),"Rogers Fund, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54327,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1100,false,true,54329,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Totoya Hokkei,"Japanese, 1780–1850",,Totoya Hokkei,Japanese,1780,1850,ca. 1820,1810,1830,Polychrome woodblock print (surimono); ink and color on paper,16 3/16 x 7 1/4 in. (41.1 x 18.4 cm),"Rogers Fund, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54329,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1144,false,true,54351,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Totoya Hokkei,"Japanese, 1780–1850",(?),Totoya Hokkei,Japanese,1780,1850,ca. 1820,1810,1830,Polychrome woodblock print (surimono); ink and color on paper,8 1/4 x 7 5/8 in. (21 x 19.4 cm),"Rogers Fund, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54351,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1145,false,true,54352,Asian Art,Print,Sumidagawa|Sumida River,Japan,Edo period (1615–1868),,,,Artist,,Totoya Hokkei,"Japanese, 1780–1850",,Totoya Hokkei,Japanese,1780,1850,"Year of the Dragon, probably 1832",1832,1832,Polychrome woodblock print (surimono); ink and color on paper,7 7/8 x 7 3/8 in. (20 x 18.7 cm),"Rogers Fund, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54352,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1146,false,true,44978,Asian Art,Print,Tosa no umi|Inland Sea near Tosa,Japan,Edo period (1615–1868),,,,Artist,,Totoya Hokkei,"Japanese, 1780–1850",,Totoya Hokkei,Japanese,1780,1850,ca. 1820,1810,1830,Polychrome woodblock print (surimono); ink and color on paper,8 1/16 x 7 1/4 in. (20.5 x 18.4 cm),"Rogers Fund, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/44978,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1150,false,true,54356,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Totoya Hokkei,"Japanese, 1780–1850",,Totoya Hokkei,Japanese,1780,1850,ca. 1800,1790,1810,Polychrome woodblock print (surimono); ink and color on paper,7 3/4 x 7 1/4 in. (19.7 x 18.4 cm),"Rogers Fund, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54356,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1154,false,true,54360,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Totoya Hokkei,"Japanese, 1780–1850",,Totoya Hokkei,Japanese,1780,1850,ca. 1820,1810,1830,Polychrome woodblock print (surimono); ink and color on paper,7 3/4 x 6 7/8 in. (19.7 x 17.5 cm),"Gift of T. Ito, Chicago, Ill, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54360,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1240,false,true,54371,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Totoya Hokkei,"Japanese, 1780–1850",,Totoya Hokkei,Japanese,1780,1850,1830,1830,1830,Polychrome woodblock print (surimono); ink and color on paper,8 1/4 x 7 1/4 in. (21 x 18.4 cm),"Rogers Fund, 1921",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54371,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1241,false,true,54372,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Totoya Hokkei,"Japanese, 1780–1850",,Totoya Hokkei,Japanese,1780,1850,1830,1830,1830,Polychrome woodblock print (surimono); ink and color on paper,8 1/16 x 7 1/4 in. (20.5 x 18.4 cm),"Rogers Fund, 1921",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54372,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1242,false,true,54373,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Totoya Hokkei,"Japanese, 1780–1850",,Totoya Hokkei,Japanese,1780,1850,1830,1830,1830,Polychrome woodblock print (surimono); ink and color on paper,8 x 7 1/8 in. (20.3 x 18.1 cm),"Rogers Fund, 1921",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54373,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1253,false,true,54385,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Totoya Hokkei,"Japanese, 1780–1850",,Totoya Hokkei,Japanese,1780,1850,ca. 1800,1790,1810,Polychrome woodblock print (surimono); ink and color on paper,8 1/16 x 11 in. (20.5 x 27.9 cm),"Rogers Fund, 1921",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54385,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1297,false,true,54387,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Totoya Hokkei,"Japanese, 1780–1850",,Totoya Hokkei,Japanese,1780,1850,ca. 1820,1810,1830,Polychrome woodblock print (surimono); ink and color on paper,8 3/8 x 7 1/8 in. (21.3 x 18.1 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54387,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1434,false,true,54392,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Totoya Hokkei,"Japanese, 1780–1850",,Totoya Hokkei,Japanese,1780,1850,ca. 1825,1815,1835,Polychrome woodblock print (surimono); ink and color on paper,8 1/8 x 7 in. (20.6 x 17.8 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54392,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1811,false,true,54399,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Totoya Hokkei,"Japanese, 1780–1850",,Totoya Hokkei,Japanese,1780,1850,ca. 1815–20,1815,1820,Polychrome woodblock print (surimono); ink and color on paper,8 1/8 x 7 1/4 in. (20.6 x 18.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54399,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1909,false,true,54448,Asian Art,Print,"松・滝に孔雀図と緋毛氈|Painting of Peacocks, Pines, a Waterfall, and a Roll of Red Fabric",Japan,Edo period (1615–1868),,,,Artist,,Totoya Hokkei,"Japanese, 1780–1850",,Totoya Hokkei,Japanese,1780,1850,ca. 1818,1808,1828,Polychrome woodblock print (surimono); ink and color on paper,8 1/2 x 7 1/4 in. (21.6 x 18.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54448,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1914,false,true,54453,Asian Art,Woodblock print,鮭頭|Head of a Salmon,Japan,Edo period (1615–1868),,,,Artist,,Totoya Hokkei,"Japanese, 1780–1850",,Totoya Hokkei,Japanese,1780,1850,ca. 1820,1810,1830,Polychrome woodblock print (surimono); ink and color on paper,8 5/16 x 7 3/8 in. (21.1 x 18.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54453,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1917,false,true,54456,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Totoya Hokkei,"Japanese, 1780–1850",(?),Totoya Hokkei,Japanese,1780,1850,probably 1814,1814,1814,Polychrome woodblock print (surimono); ink and color on paper,7 15/16 x 7 1/8 in. (20.2 x 18.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54456,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1929,false,true,54469,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Totoya Hokkei,"Japanese, 1780–1850",,Totoya Hokkei,Japanese,1780,1850,probably 1816,1816,1816,Polychrome woodblock print (surimono); ink and color on paper,8 3/8 x 7 1/4 in. (21.3 x 18.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54469,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1945,false,true,54513,Asian Art,Print,"印鑑と朱に孔雀羽根|Seal-stone and Seal-ink with Peacock Feathers, from Spring Rain Surimono Album (Harusame surimono-jō), vol. 1",Japan,Edo period (1615–1868),,,,Artist,,Totoya Hokkei,"Japanese, 1780–1850",,Totoya Hokkei,Japanese,1780,1850,probably 1817,1817,1817,Privately published polychrome woodblock prints (surimono) mounted in an album; ink and color on paper,7 9/16 x 5 1/8 in. (19.2 x 13 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54513,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1948,false,true,54516,Asian Art,Print,"元日仕度|Preparations for the New Year, from Spring Rain Surimono Album (Harusame surimono-jō, vol. 1)",Japan,Edo period (1615–1868),,,,Artist,,Totoya Hokkei,"Japanese, 1780–1850",,Totoya Hokkei,Japanese,1780,1850,1817,1817,1817,Privately published polychrome woodblock prints (surimono) mounted in an album; ink and color on paper,8 1/4 x 7 1/4 in. (21 x 18.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54516,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1977,false,true,54562,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Totoya Hokkei,"Japanese, 1780–1850",,Totoya Hokkei,Japanese,1780,1850,1815,1815,1815,Polychrome woodblock print (surimono); ink and color on paper,8 9/16 x 7 3/8 in. (21.7 x 18.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54562,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2011,false,true,54731,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Totoya Hokkei,"Japanese, 1780–1850",,Totoya Hokkei,Japanese,1780,1850,1811,1811,1811,Polychrome woodblock print (surimono); ink and color on paper,5 11/16 x 7 1/2 in. (14.4 x 19.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54731,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2033,false,true,54799,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Totoya Hokkei,"Japanese, 1780–1850",,Totoya Hokkei,Japanese,1780,1850,1816,1816,1816,Polychrome woodblock print (surimono); ink and color on paper,5 3/8 x 7 5/16 in. (13.7 x 18.6 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54799,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2046,false,true,54817,Asian Art,Print,"「文齊側五行火　花街細見合」|Courtesan by a Lantern, “Fire,” from the series Five Elements for the Bunsai Poetry Group, a Guide to the Yoshiwara Pleasure Quarters",Japan,Edo period (1615–1868),,,,Artist,,Totoya Hokkei,"Japanese, 1780–1850",,Totoya Hokkei,Japanese,1780,1850,ca. 1820,1810,1830,Polychrome woodblock print (surimono); ink and color on paper,7 7/8 x 7 3/16 in. (20 x 18.3 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54817,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2107,false,true,54959,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Totoya Hokkei,"Japanese, 1780–1850",,Totoya Hokkei,Japanese,1780,1850,1816,1816,1816,Polychrome woodblock print (surimono); ink and color on paper,5 1/4 x 7 3/16 in. (13.3 x 18.3 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54959,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2288,false,true,54061,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Totoya Hokkei,"Japanese, 1780–1850",,Totoya Hokkei,Japanese,1780,1850,1816,1816,1816,Part of an album of woodblock prints (surimono); ink and color on paper,5 11/16 x 7 1/2 in. (14.4 x 19.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54061,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2309,false,true,54093,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Totoya Hokkei,"Japanese, 1780–1850",,Totoya Hokkei,Japanese,1780,1850,ca. 1800,1790,1810,Part of an album of woodblock prints (surimono); ink and color on paper,7 1/8 x 6 5/8 in. (18.1 x 16.8 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54093,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2350,false,true,54135,Asian Art,Woodblock print,"印籠と牛根付『春雨集』　摺物帖|Lacquer Inrō with Waterbirds and Ox-shaped Netsuke in a BoxFrom the Spring Rain Collection (Harusame shū), vol. 3",Japan,Edo period (1615–1868),,,,Artist,,Totoya Hokkei,"Japanese, 1780–1850",,Totoya Hokkei,Japanese,1780,1850,probably 1817,1817,1817,Part of an album of woodblock prints (surimono); ink and color on paper,5 1/4 x 7 3/8 in. (13.3 x 18.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54135,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2371,false,true,54155,Asian Art,Print,"「三ひらの内」松・牡丹に孔雀|Peacock on Pine Tree and Peonies, from the series Three Sheets (Mihira no uchi)",Japan,Edo period (1615–1868),,,,Artist,,Totoya Hokkei,"Japanese, 1780–1850",,Totoya Hokkei,Japanese,1780,1850,mid- 1810s,1814,1816,Part of an album of woodblock prints (surimono); ink and color on paper,8 3/8 x 7 3/8 in. (21.3 x 18.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54155,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2372,false,true,54156,Asian Art,Album leaf,"「三ひらの内」汀五羽の鶴|Five Cranes by the Water’s Edge, from the series Three Sheets (Mihira no uchi)",Japan,Edo period (1615–1868),,,,Artist,,Totoya Hokkei,"Japanese, 1780–1850",,Totoya Hokkei,Japanese,1780,1850,mid- 1810s,1814,1816,Part of an album of woodblock prints (surimono); ink and color on paper,8 3/8 x 7 5/16 in. (21.3 x 18.6 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54156,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2373,false,true,54157,Asian Art,Print,"「三ひらの内」日輪に烏|Three Crows against the Rising Sun, from the series Three Sheets (Mihira no uchi)",Japan,Edo period (1615–1868),,,,Artist,,Totoya Hokkei,"Japanese, 1780–1850",,Totoya Hokkei,Japanese,1780,1850,mid- 1810s,1814,1816,Part of an album of woodblock prints (surimono); ink and color on paper,8 1/4 x 7 1/4 in. (21 x 18.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54157,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2544,false,true,45040,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Totoya Hokkei,"Japanese, 1780–1850",,Totoya Hokkei,Japanese,1780,1850,ca. 1825,1815,1835,Polychrome woodblock print (surimono); ink and color on paper,8 1/2 x 7 1/4 in. (21.6 x 18.4 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45040,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2545,false,true,54182,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Totoya Hokkei,"Japanese, 1780–1850",,Totoya Hokkei,Japanese,1780,1850,ca. 1825,1815,1835,Polychrome woodblock print (surimono); ink and color on paper,10 x 7 in. (25.4 x 17.8 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54182,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3005,false,true,54221,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Totoya Hokkei,"Japanese, 1780–1850",,Totoya Hokkei,Japanese,1780,1850,1780–1850,1780,1850,Polychrome woodblock print (surimono); ink and color on paper,8 1/4 x 7 1/4 in. (21 x 18.4 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54221,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3006,false,true,54222,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Totoya Hokkei,"Japanese, 1780–1850",,Totoya Hokkei,Japanese,1780,1850,1780–1850,1780,1850,Polychrome woodblock print (surimono); ink and color on paper,7 7/8 x 6 11/16 in. (20 x 17 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54222,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3007,false,true,54224,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Totoya Hokkei,"Japanese, 1780–1850",,Totoya Hokkei,Japanese,1780,1850,1780–1850,1780,1850,Polychrome woodblock print (surimono); ink and color on paper,8 1/8 x 7 3/16 in. (20.6 x 18.3 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54224,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3009,false,true,54226,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Totoya Hokkei,"Japanese, 1780–1850",,Totoya Hokkei,Japanese,1780,1850,1831,1831,1831,Polychrome woodblock print (surimono); ink and color on paper,16 5/8 x 7 in. (42.2 x 17.8 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54226,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.1126.3,false,true,633305,Asian Art,woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Totoya Hokkei,"Japanese, 1780–1850",,Totoya Hokkei,Japanese,1780,1850,1835,1835,1835,Polychrome woodblock print (shikishiban surimono); ink and color on paper,Image: 7 1/4 × 6 3/4 in. (18.4 × 17.1 cm),"Gift of Dorothy Tapper Goldman, 2013",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/633305,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP225,false,true,36699,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,ca. 1840,1830,1850,Polychrome woodblock print; ink and color on paper,9 15/32 x 14 3/8 in. (24.1 x 36.5 cm),"Rogers Fund, 1917",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36699,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1147,false,true,54353,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,ca. 1819,1809,1829,Polychrome woodblock print (surimono); ink and color on paper,8 3/16 x 7 1/4 in. (20.8 x 18.4 cm),"Rogers Fund, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54353,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1216,false,true,55139,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,ca. 1850,1840,1860,Polychrome woodblock print; ink and color on paper,H. 14 5/16 in. (36.4 cm); W. 10 in. (25.4 cm),"Rogers Fund, 1920",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55139,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1251,false,true,54383,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,ca. 1824,1814,1834,Polychrome woodblock print (surimono); ink and color on paper,8 7/16 x 7 3/8 in. (21.4 x 18.7 cm),"Rogers Fund, 1921",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54383,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1267,false,true,55164,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,1833–47,1833,1847,Polychrome woodblock print; ink and color on paper,14 1/8 x 10 1/8 in. (35.9 x 25.7 cm),"Gift of Albert Gallatin, 1921",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55164,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1454,false,true,54395,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,ca. 1840,1830,1850,Polychrome woodblock print (surimono); ink and color on paper,8 7/16 x 7 3/8 in. (21.4 x 18.7 cm),"Rogers Fund, 1923",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54395,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1497,false,true,44979,Asian Art,Print,"Futami-ga-ura akebono-no kuni|Futami-ga-ura Rocks at Ise, Land of Dawn",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,ca. 1835,1825,1845,Polychrome woodblock print; ink and color on paper,H. 9 1/2 in. (24.1 cm); W. 14 in. (35.6 cm),"Fletcher Fund, 1928",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/44979,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2051,false,true,54824,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,probably 1815,1815,1815,Polychrome woodblock print (surimono); ink and color on paper,7 7/8 x 5 5/16 in. (20 x 13.5 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54824,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2396,false,true,54176,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,1819,1819,1819,Polychrome woodblock print (surimono); ink and color on paper,8 3/16 x 7 7/16 in. (20.8 x 18.9 cm),"H. O. Havemeyer Collection, Gift of Mrs. J. Watson Webb, 1930",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54176,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2631,false,true,56679,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,mid-19th century,1834,1866,Polychrome woodblock print; ink and color on paper,H. 9 3/4 in. (24.8 cm); W. 14 1/2 in. (36.8 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56679,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2863,false,true,56611,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,mid-19th century,1834,1866,Polychrome woodblock print; ink and color on paper,H. 9 3/4 in. (24.8 cm); W. 14 1/2 in. (36.8 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56611,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2911,false,true,56026,Asian Art,Print,O Ateri Kyogen Uchi|Wild Words - a Play,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,1786–1864,1786,1864,Polychrome woodblock print; ink and color on paper,14 7/8 x 9 7/8 in. (37.8 x 25.1 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56026,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3358,false,true,55526,Asian Art,Print,"「湯灌場子僧吉三 市川竹之丞」（五代目） 「近世水滸伝」|Ichimura Takenojō V as Yukanba Kozō Kichiza, from A Modern Water Margin (Kinsei suikoden)",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,1862,1862,1862,Polychrome woodblock print; ink and color on paper,13 5/18 x 9 9/16 in. (33.7 x 24.3 cm),"Gift of Lincoln Kirstein, 1960",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55526,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3359,false,true,55527,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,"1854 (year of the Horse, 7th month)",1854,1854,Polychrome woodblock print; ink and color on paper,14 1/4 x 9 3/4 in. (36.2 x 24.8 cm),"Gift of Lincoln Kirstein, 1960",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55527,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3360,false,true,55528,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,"1854 (year of the Horse, 7th month)",1854,1854,Polychrome woodblock print; ink and color on paper,14 1/8 x 9 3/4 in. (35.9 x 24.8 cm),"Gift of Lincoln Kirstein, 1960",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55528,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3361,false,true,55529,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,"1854 (year of the Horse, 7th month)",1854,1854,Polychrome woodblock print; ink and color on paper,14 1/8 x 9 3/4 in. (35.9 x 24.8 cm),"Gift of Lincoln Kirstein, 1960",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55529,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3376,false,true,55543,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,1863 (5th month),1863,1863,Polychrome woodblock print; ink and color on paper,14 1/8 x 28 1/2 in. (35.9 x 72.4 cm),"Gift of Lincoln Kirstein, 1960",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55543,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP212.15,false,true,58000,Asian Art,Print,「籠細工　浪花細工人一田庄七郎」|Basketry Work: By the Craftsman Ichida Shōshichirō of Naniwa (Kagosaiku Naniwa saikujin Ichida Shōshichirō),Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,1819,1819,1819,Polychrome woodblock print; ink and color on paper,Image: 13 1/2 × 9 1/2 in. (34.3 × 24.1 cm) Mat: 15 1/2 × 22 3/4 in. (39.4 × 57.8 cm),"Gift of the Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58000,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP212.16,false,true,58001,Asian Art,Print,「籠細工　浪花細工人一田庄七郎」|Basketry Work: By the Craftsman Ichida Shōshichirō of Naniwa (Kagosaiku Naniwa saikujin Ichida Shōshichirō),Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,1819,1819,1819,Polychrome woodblock print; ink and color on paper,Image: 13 1/2 × 9 1/2 in. (34.3 × 24.1 cm) Mat: 15 1/2 × 22 3/4 in. (39.4 × 57.8 cm),"Gift of the Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58001,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP212.17,false,true,58002,Asian Art,Print,「籠細工　浪花細工人一田庄七郎」|Basketry Work: By the Craftsman Ichida Shōshichirō of Naniwa (Kagosaiku Naniwa saikujin Ichida Shōshichirō),Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,1819,1819,1819,Polychrome woodblock print; ink and color on paper,Image: 13 1/2 × 9 1/2 in. (34.3 × 24.1 cm) Mat: 15 1/2 × 22 3/4 in. (39.4 × 57.8 cm),"Gift of the Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58002,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP212.18,false,true,58003,Asian Art,Print,「籠細工　浪花細工人一田庄七郎」|Basketry Work: By the Craftsman Ichida Shōshichirō of Naniwa (Kagosaiku Naniwa saikujin Ichida Shōshichirō),Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,1819,1819,1819,Polychrome woodblock print; ink and color on paper,Image: 13 1/2 × 9 1/2 in. (34.3 × 24.1 cm) Mat: 15 1/2 × 22 3/4 in. (39.4 × 57.8 cm),"Gift of the Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58003,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP212.58,false,true,58042,Asian Art,Print,"三代目関三十郎の大寺正兵衛　小袖曽我薊色縫|Seki Sanjūrō III as Ōdera Shōbei from ""Kosode Soga azami no ironui""",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,1859,1859,1859,Right panel of a tryptich of polychrome woodblock prints; ink and color on paper,Image: 13 5/8 × 9 5/8 in. (34.6 × 24.4 cm),"Gift of the Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58042,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1093.11,false,true,58263,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,1830s (Tenpô era),1830,1839,Polychrome woodblock print; ink and color on paper,Image: 14 3/4 × 10 1/8 in. (37.5 × 25.7 cm),The Metropolitan Museum of Art,,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58263,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1093.12,false,true,58264,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,mid-19th century,1834,1866,Polychrome woodblock print; ink and color on paper,Image: 14 3/8 × 10 1/2 in. (36.5 × 26.7 cm),The Metropolitan Museum of Art,,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58264,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1999.457.1,false,true,53710,Asian Art,Print,"重陽後の月宴 十二月ノ内|Banquet of the Next Full Moon at the Chrysanthemum Festival, from the series The Twelve Months (Chōyō nochi no tsuki no en, Jūni tsuki no uchi)",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,1854,1854,1854,Triptych of polychrome woodblock prints; ink and color on paper,Overall: 14 5/8 x 30 1/8in. (37.1 x 76.5cm),"Gift of Eliot C. Nolen, 1999",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/53710,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2001.715.3,false,true,63357,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,ca. 1820,1810,1830,"Polychrome woodblock print (surimono); ink, silver, and color on paper",8 1/2 x 7 1/2 in. (21.6 x 19.1 cm),"Purchase, Jack Greene Gift, 2001",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/63357,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2001.715.13,false,true,63384,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,ca. 1840,1830,1850,Polychrome woodblock print; ink and color on paper,"Oban tate-e, 14 3/4 x 10 1/4 in. (37.5 x 26 cm)","Purchase, Arnold Weinstein Gift, 2001",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/63384,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.271,false,true,73611,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,1864,1864,1864,Polychrome woodblock print; ink and color on paper,Image: 14 3/8 x 10 in. (36.5 x 25.4 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73611,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.1126.1,false,true,633299,Asian Art,woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,probably 1829,1829,1829,Polychrome woodblock print (shikishiban surimono); ink and color on paper,Image: 8 1/4 × 7 1/4 in. (21 × 18.4 cm),"Gift of Dorothy Tapper Goldman, 2013",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/633299,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.1126.2,false,true,633302,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,ca. 1830,1820,1840,"Polychrome woodblock print (shikishiban surimono); ink, color and metallic pigments on paper",Image: 8 1/4 × 7 1/4 in. (21 × 18.4 cm),"Gift of Dorothy Tapper Goldman, 2013",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/633302,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2001.715.12a–c,false,true,63381,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,1811,1801,1821,Triptych of polychrome woodblock prints; ink and color on paper,"Oban tate-e; triptych, each: 15 1/8 x 10 1/8 in. (38.4 x 25.7 cm)","Purchase, Arnold Weinstein Gift, 2001",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/63381,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1217,false,true,55140,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Yanagawa Shigenobu,"Japanese, 1787–1832",,Yanagawa Shigenobu,Japanese,1787,1832,ca. 1825,1815,1835,Polychrome woodblock print; ink and color on paper,H. 15 in. (38.1 cm); W. 10 in. (25.4 cm),"Rogers Fund, 1921",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55140,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1218,false,true,55141,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Yanagawa Shigenobu,"Japanese, 1787–1832",,Yanagawa Shigenobu,Japanese,1787,1832,ca. 1825,1815,1835,Polychrome woodblock print; ink and color on paper,H. 14 7/8 in. (37.8 cm); W. 9 3/4 in. (24.8 cm),"Rogers Fund, 1921",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55141,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1244,false,true,54375,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Yanagawa Shigenobu,"Japanese, 1787–1832",,Yanagawa Shigenobu,Japanese,1787,1832,1828,1828,1828,Polychrome woodblock print (surimono); ink and color on paper,8 1/4 x 7 5/16 in. (21 x 18.6 cm),"Rogers Fund, 1921",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54375,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1245,false,true,54376,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Yanagawa Shigenobu,"Japanese, 1787–1832",,Yanagawa Shigenobu,Japanese,1787,1832,1830,1830,1830,Polychrome woodblock print (surimono); ink and color on paper,8 5/16 x 7 5/16 in. (21.1 x 18.6 cm),"Rogers Fund, 1921",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54376,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1281,false,true,55215,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Yanagawa Shigenobu,"Japanese, 1787–1832",,Yanagawa Shigenobu,Japanese,1787,1832,ca. 1825,1815,1835,Polychrome woodblock print; ink and color on paper,H. 14 3/4 in. (37.5 cm); W. 9 7/8 in. (25.1 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55215,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1944,false,true,54512,Asian Art,Print,"鶏図衝立を見る鶏|Cock Eyeing a Free-standing Screen Painted with Cock, Hen, and Chicks, from Spring Rain Surimono Album (Harusame surimono-jō), vol. 1",Japan,Edo period (1615–1868),,,,Artist,,Yanagawa Shigenobu,"Japanese, 1787–1832",,Yanagawa Shigenobu,Japanese,1787,1832,probably 1813,1813,1813,Privately published polychrome woodblock prints (surimono) mounted in an album; ink and color on paper,7 11/16 x 10 7/8 in. (19.5 x 27.6 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54512,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2659,false,true,56838,Asian Art,Print,Shōkei|Celebrated Waterfall,Japan,Edo period (1615–1868),,,,Artist,,Yanagawa Shigenobu,"Japanese, 1787–1832",,Yanagawa Shigenobu,Japanese,1787,1832,1820–1830,1820,1830,Polychrome woodblock print; ink and color on paper,13 x 6 3/4 in. (33 x 17.1 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56838,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1017,false,true,54926,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kikugawa Eizan,"Japanese, 1787–1867",,Kikugawa Eizan,Japanese,1787,1867,ca. 1805,1795,1815,Triptych of polychrome woodblock prints; ink and color on paper,H. 15 1/8 in. (38.4 cm); W. 30 in. (76.2 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54926,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1962,false,true,54537,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kikugawa Eizan,"Japanese, 1787–1867",,Kikugawa Eizan,Japanese,1787,1867,probably 1815,1815,1815,Polychrome woodblock print (surimono); ink and color on paper,5 x 7 1/16 in. (12.7 x 17.9 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54537,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1018,false,true,54927,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyomine,"Japanese, 1787–1868",,Torii Kiyomine,Japanese,1787,1868,ca. 1820,1810,1830,Triptych of monochrome woodblock prints; ink on paper,H. 15 in. (38.1 cm); W. 30 in. (76.2 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54927,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1019,false,true,54316,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyomine,"Japanese, 1787–1868",,Torii Kiyomine,Japanese,1787,1868,ca. 1820,1810,1830,Polychrome woodblock print (surimono); ink and color on paper,8 7/16 x 7 3/16 in. (21.4 x 18.3 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54316,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1801,false,true,56103,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyomine,"Japanese, 1787–1868",,Torii Kiyomine,Japanese,1787,1868,ca. 1808,1798,1818,Polychrome woodblock print; ink and color on paper,14 15/16 x 9 7/8 in. (37.9 x 25.1cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56103,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1802,false,true,56104,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyomine,"Japanese, 1787–1868",,Torii Kiyomine,Japanese,1787,1868,ca. 1804,1794,1814,Polychrome woodblock print; ink and color on paper,19 1/16 x 9 1/16in. (48.4 x 23cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56104,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP222,false,true,36696,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Keisai Eisen,"Japanese, 1790–1848",,Keisai Eisen,Japanese,1790,1848,ca. 1842,1832,1852,Polychrome woodblock print; ink and color on paper,9 3/4 x 14 in. (24.8 x 35.6 cm),"Rogers Fund, 1917",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36696,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1031,false,true,54951,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Keisai Eisen,"Japanese, 1790–1848",,Keisai Eisen,Japanese,1790,1848,ca. 1838,1833,1843,Polychrome woodblock print; ink and color on paper,H. 8 5/8 in. (21.9 cm); W. 13 1/2 in. (34.3 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54951,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1065,false,true,55012,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Keisai Eisen,"Japanese, 1790–1848",,Keisai Eisen,Japanese,1790,1848,1824,1824,1824,Polychrome woodblock print; ink and color on paper,H. 7 in. (17.8 cm); W. 5 in. (12.7 cm),"Gift of Estate of Samuel Isham, 1915",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55012,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1066,false,true,55013,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Keisai Eisen,"Japanese, 1790–1848",,Keisai Eisen,Japanese,1790,1848,1824,1824,1824,Polychrome woodblock print; ink and color on paper,H. 7 in. (17.8 cm); W. 5 in. (12.7 cm),"Gift of Estate of Samuel Isham, 1915",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55013,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1067,false,true,55014,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Keisai Eisen,"Japanese, 1790–1848",,Keisai Eisen,Japanese,1790,1848,1824,1824,1824,Polychrome woodblock print; ink and color on paper,H. 7in. (17.8 cm); W. 5 in. (12.7 cm),"Gift of Estate of Samuel Isham, 1915",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55014,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1068,false,true,55015,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Keisai Eisen,"Japanese, 1790–1848",,Keisai Eisen,Japanese,1790,1848,ca. 1825,1815,1835,Polychrome woodblock print; ink and color on paper,H. 7 in. (17.8 cm); W. 5 in. (12.7 cm),"Gift of Estate of Samuel Isham, 1915",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55015,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1069,false,true,55016,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Keisai Eisen,"Japanese, 1790–1848",,Keisai Eisen,Japanese,1790,1848,1824,1824,1824,Polychrome woodblock print; ink and color on paper,H. 7 in. (17.8 cm); W. 5 in. (12.7 cm),"Gift of Estate of Samuel Isham, 1915",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55016,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1070,false,true,55017,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Keisai Eisen,"Japanese, 1790–1848",,Keisai Eisen,Japanese,1790,1848,ca. 1825,1815,1835,Polychrome woodblock print; ink and color on paper,H. 7 in. (17.8 cm); W. 5 in. (12.7 cm),"Gift of Estate of Samuel Isham, 1915",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55017,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1071,false,true,55018,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Keisai Eisen,"Japanese, 1790–1848",,Keisai Eisen,Japanese,1790,1848,ca. 1825,1815,1835,Polychrome woodblock print; ink and color on paper,H. 7 in. (17.8 cm); W. 5 in. (12.7 cm),"Gift of Estate of Samuel Isham, 1915",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55018,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1148,false,true,54354,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Keisai Eisen,"Japanese, 1790–1848",,Keisai Eisen,Japanese,1790,1848,ca. 1830,1820,1840,Polychrome woodblock print (surimono); ink and color on paper,8 1/4 x 7 7/8 in. (21 x 20 cm),"Rogers Fund, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54354,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1201,false,true,55119,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Keisai Eisen,"Japanese, 1790–1848",,Keisai Eisen,Japanese,1790,1848,late 1830s,1830,1840,Polychrome woodblock print; ink and color on paper,Oban 9 5/8 x 14 7/16 in. (24.4 x 36.7 cm),"Rogers Fund, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55119,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1282,false,true,55218,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Keisai Eisen,"Japanese, 1790–1848",,Keisai Eisen,Japanese,1790,1848,ca. 1845,1835,1855,Polychrome woodblock print; ink and color on paper,H. 6 5/8 in. (16.8 cm); W. 12 1/4 in. (31.1 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55218,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1283,false,true,55222,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Keisai Eisen,"Japanese, 1790–1848",,Keisai Eisen,Japanese,1790,1848,mid-19th century,1834,1866,Polychrome woodblock print; ink and color on paper,H. 9 in. (22.9 cm); W. 14 in. (35.6 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55222,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1419,false,true,55447,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Keisai Eisen,"Japanese, 1790–1848",,Keisai Eisen,Japanese,1790,1848,ca. 1835,1825,1845,Polychrome woodblock print; ink and color on paper,H. 10 in. (25.4 cm); W. 14 3/4 in. (37.5 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55447,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2220,false,true,53986,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Keisai Eisen,"Japanese, 1790–1848",,Keisai Eisen,Japanese,1790,1848,probably 1812,1812,1812,Polychrome woodblock print (surimono); ink and color on paper,7 3/4 x 6 9/16 in. (19.7 x 16.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/53986,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2742,false,true,54204,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Keisai Eisen,"Japanese, 1790–1848",,Keisai Eisen,Japanese,1790,1848,ca. 1840,1830,1850,Polychrome woodblock print (surimono); ink and color on paper,8 3/8 x 7 1/4 in. (21.3 x 18.4 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54204,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2870,false,true,57041,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Keisai Eisen,"Japanese, 1790–1848",,Keisai Eisen,Japanese,1790,1848,1790–1848,1790,1848,Polychrome woodblock print; ink and color on paper,8 5/8 x 13 1/2 in. (21.9 x 34.3 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57041,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2874,false,true,57012,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Keisai Eisen,"Japanese, 1790–1848",,Keisai Eisen,Japanese,1790,1848,1790–1848,1790,1848,Polychrome woodblock print; ink and color on paper,9 1/2 x 14 1/2 in. (24.1 x 36.8 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57012,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2001.715.14,false,true,63397,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Keisai Eisen,"Japanese, 1790–1848",,Keisai Eisen,Japanese,1790,1848,ca. 1825,1815,1835,Polychrome woodblock print; ink and color on paper,"Oban yoko-e, 10 1/4 x 15 1/8 in. (26 x 38.4 cm)","Purchase, Arnold Weinstein Gift, 2001",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/63397,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2231,false,true,53997,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ōishi Matora,"Japanese, 1793–1833",,Ōishi Matora,Japanese,1793,1833,1827,1827,1827,Polychrome woodblock print (surimono); ink and color on paper,9 7/8 x 5 3/4 in. (25.1 x 14.6 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/53997,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1936,false,true,54498,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kuninao,"Japanese, 1793–1854",,Utagawa Kuninao,Japanese,1793,1854,1814,1814,1814,Polychrome woodblock print (surimono); ink and color on paper,8 1/4 x 3 1/4 in. (21 x 8.3 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54498,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1959,false,true,54532,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kuninao,"Japanese, 1793–1854",,Utagawa Kuninao,Japanese,1793,1854,1814,1814,1814,Polychrome woodblock print (surimono); ink and color on paper,8 5/16 x 7 1/4 in. (21.1 x 18.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54532,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2037,false,true,54807,Asian Art,Print,"『春雨集』　摺物帖歌川国直画　元禄美人|Spring Rain Collection (Harusame shū), vol. 1: Genroku-style Courtesan",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kuninao,"Japanese, 1793–1854",,Utagawa Kuninao,Japanese,1793,1854,probably 1810s,1810,1819,Privately published woodblock prints (surimono) mounted in an album; ink and color on paper,8 7/16 x 3 7/16 in. (21.4 x 8.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54807,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2001.715.4a–c,false,true,63358,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kuniyasu,"Japanese, 1794–1834",,Utagawa Kuniyasu,Japanese,1794,1834,ca. 1823,1813,1833,Triptych of polychrome woodblock prints (surimono); ink and color on paper,"Triptych, each: 7 1/4 x 8 3/8 in. (18.4 x 21.3 cm)","Purchase, Jack Greene Gift, 2001",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/63358,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP221,false,true,36695,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kuniyoshi,"Japanese, 1797–1861",,Utagawa Kuniyoshi,Japanese,1797,1861,ca. 1840,1830,1850,Polychrome woodblock print; ink and color on paper,10 1/2 x 15 1/8 in. (26.7 x 38.4 cm),"Rogers Fund, 1917",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36695,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP237,false,true,36709,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kuniyoshi,"Japanese, 1797–1861",,Utagawa Kuniyoshi,Japanese,1797,1861,ca. 1830,1820,1840,Polychrome woodblock print; ink and color on paper,8 7/10 x 13 5/8 in. (22.1 x 34.6 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36709,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP750,false,true,37194,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kuniyoshi,"Japanese, 1797–1861",,Utagawa Kuniyoshi,Japanese,1797,1861,ca. 1840,1830,1850,Polychrome woodblock print; ink and color on paper,10 1/8 x 14 15/32 in. (25.7 x 36.8 cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37194,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1118,false,true,55040,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kuniyoshi,"Japanese, 1797–1861",,Utagawa Kuniyoshi,Japanese,1797,1861,1840,1840,1840,Polychrome woodblock print; ink and color on paper,H. 8 5/8 in. (21.9 cm); W. 13 9/16 in. (34.4 cm),"Rogers Fund, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55040,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1120,false,true,55042,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kuniyoshi,"Japanese, 1797–1861",,Utagawa Kuniyoshi,Japanese,1797,1861,1845,1845,1845,Polychrome woodblock print; ink and color on paper,H. 13 1/8 in. (33.3 cm); W. 8 15/16 in. (22.7 cm),"Rogers Fund, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55042,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1149,false,true,54355,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kuniyoshi,"Japanese, 1797–1861",,Utagawa Kuniyoshi,Japanese,1797,1861,ca. 1830,1820,1840,Polychrome woodblock print (surimono); ink and color on paper,8 3/8 x 7 3/8 in. (21.3 x 18.7 cm),"Rogers Fund, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54355,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1252,false,true,54384,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kuniyoshi,"Japanese, 1797–1861",,Utagawa Kuniyoshi,Japanese,1797,1861,1840,1840,1840,Polychrome woodblock print (surimono); ink and color on paper,7 15/16 x 7 1/8 in. (20.2 x 18.1 cm),"Rogers Fund, 1921",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54384,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1421,false,true,55449,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kuniyoshi,"Japanese, 1797–1861",,Utagawa Kuniyoshi,Japanese,1797,1861,1840,1840,1840,Polychrome woodblock print; ink and color on paper,H. 10 1/8 in. (25.7 cm); W. 15 1/16 in. (38.3 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55449,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1422,false,true,55451,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kuniyoshi,"Japanese, 1797–1861",,Utagawa Kuniyoshi,Japanese,1797,1861,ca. 1830,1820,1840,Polychrome woodblock print; ink and color on paper,Oban: H. 9 3/4 in. (24.8 cm); W. 14 11/16 in. (37.3 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55451,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1423,false,true,55452,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kuniyoshi,"Japanese, 1797–1861",,Utagawa Kuniyoshi,Japanese,1797,1861,ca. 1850,1840,1860,Polychrome woodblock print; ink and color on paper,H. 9 5/8 in. (24.4 cm); W. 14 1/16 in. (35.7 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55452,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1424,false,true,55453,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kuniyoshi,"Japanese, 1797–1861",,Utagawa Kuniyoshi,Japanese,1797,1861,ca. 1850,1840,1860,Polychrome woodblock print; ink and color on paper,H. 10 1/8 in. (25.7 cm); W. 14 in. (35.6 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55453,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1465,false,true,55514,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kuniyoshi,"Japanese, 1797–1861",,Utagawa Kuniyoshi,Japanese,1797,1861,ca. 1845,1835,1855,Polychrome woodblock print; ink and color on paper,H. 14 11/16 in. (37.3 cm); W. 10 1/16 in. (25.6 cm),"Rogers Fund, 1925",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55514,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1563,false,true,45282,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kuniyoshi,"Japanese, 1797–1861",,Utagawa Kuniyoshi,Japanese,1797,1861,ca. 1850,1840,1860,Diptych of polychrome woodblock prints; ink and color on paper,A: H. 14 3/8 in. (36.5 cm); W. 10 in. (25.4 cm) B: H. 14 3/8 in. (36.5 cm); W. 10 in. (25.4 cm),"Fletcher Fund, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45282,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1564,false,true,45281,Asian Art,Print,Ryugu Tamatori Hime no su|Recovering the Stolen Jewel from the Palace of the Dragon King,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kuniyoshi,"Japanese, 1797–1861",,Utagawa Kuniyoshi,Japanese,1797,1861,1853,1615,1868,Triptych of polychrome woodblock prints; ink and color on paper,A: H. 14 3/8 in. (36.5 cm); W. 9 3/4 in. (24.8 cm) B: H. 14 3/8 in. (36.5 cm); W. 9 7/8 in. (25.1 cm) C: H. 14 3/8 in. (36.5 cm); W. 9 3/4 in. (24.8 cm),"Fletcher Fund, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45281,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1565,false,true,55743,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kuniyoshi,"Japanese, 1797–1861",,Utagawa Kuniyoshi,Japanese,1797,1861,1843–47,1843,1847,Triptych of polychrome woodblock prints; ink and color on paper,Oban triptych: Each H. 14 3/4 in. (37.5 cm); W. 9 7/8 in. (25.1 cm),"Fletcher Fund, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55743,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1805,false,true,51085,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kuniyoshi,"Japanese, 1797–1861",,Utagawa Kuniyoshi,Japanese,1797,1861,1830–44,1830,1844,Polychrome woodblock print; ink and color on paper,H. 10 1/8 in. (25.7 cm); W. 14 9/16 in. (37 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/51085,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2633,false,true,51084,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kuniyoshi,"Japanese, 1797–1861",,Utagawa Kuniyoshi,Japanese,1797,1861,mid-19th century,1834,1866,Polychrome woodblock print; ink and color on paper,H. 10 1/4 in. (26 cm); W. 13 3/4 in. (34.9 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/51084,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2634,false,true,56779,Asian Art,Print,"Sashu Tsukahara setchu|Nichiren in Snow at Tsukahara, Sodo Province",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kuniyoshi,"Japanese, 1797–1861",,Utagawa Kuniyoshi,Japanese,1797,1861,ca. 1840,1830,1850,Polychrome woodblock print; ink and color on paper,"8 7/8 x 13 3/5 in. (22.5 x 34.5 cm), excluding margins","The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56779,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2749,false,true,56790,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kuniyoshi,"Japanese, 1797–1861",,Utagawa Kuniyoshi,Japanese,1797,1861,mid-19th century,1834,1866,Polychrome woodblock print; ink on thin paper,H. 8 1/2 in. (21.6 cm); W. 13 3/4 in. (34.9 cm),"Fletcher Fund, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56790,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2856,false,true,57049,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kuniyoshi,"Japanese, 1797–1861",,Utagawa Kuniyoshi,Japanese,1797,1861,1835–36,1835,1836,Polychrome woodblock print; ink and color on paper,Oban: 9 3/4 x 14 3/4 in. (24.8 x 37.5 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57049,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2857,false,true,57048,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kuniyoshi,"Japanese, 1797–1861",,Utagawa Kuniyoshi,Japanese,1797,1861,1797–1861,1797,1861,Polychrome woodblock print; ink and color on paper,10 3/4 x 14 7/8 in. (27.3 x 37.8 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57048,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2858,false,true,57047,Asian Art,Print,Toto Mitsumata no zu|Picture of Mitsumata,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kuniyoshi,"Japanese, 1797–1861",,Utagawa Kuniyoshi,Japanese,1797,1861,1797–1861,1797,1861,Polychrome woodblock print; ink and color on paper,10 x 14 1/2 in. (25.4 x 36.8 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57047,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2859,false,true,45286,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kuniyoshi,"Japanese, 1797–1861",,Utagawa Kuniyoshi,Japanese,1797,1861,ca. 1835,1825,1845,Polychrome woodblock print; ink and color on paper,8 3/4 x 13 5/8 in. (22.2 x 34.6 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45286,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2860,false,true,45287,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kuniyoshi,"Japanese, 1797–1861",,Utagawa Kuniyoshi,Japanese,1797,1861,ca. 1835,1825,1845,Polychrome woodblock print; ink and color on paper,8 7/8 x 13 3/4 in. (22.5 x 34.9 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45287,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2861,false,true,45284,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kuniyoshi,"Japanese, 1797–1861",,Utagawa Kuniyoshi,Japanese,1797,1861,ca. 1835,1825,1845,Polychrome woodblock print; ink and color on paper,8 7/8 x 13 3/4 in. (22.5 x 34.9 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45284,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2862,false,true,57046,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kuniyoshi,"Japanese, 1797–1861",,Utagawa Kuniyoshi,Japanese,1797,1861,1797–1861,1797,1861,Polychrome woodblock print; ink and color on paper,14 3/4 x 10 in. (37.5 x 25.4 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57046,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3008,false,true,54225,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kuniyoshi,"Japanese, 1797–1861",,Utagawa Kuniyoshi,Japanese,1797,1861,1797–1861,1797,1861,Polychrome woodblock print (surimono); ink and color on paper,8 1/2 x 7 1/2 in. (21.6 x 19.1 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54225,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3136,false,true,56684,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kuniyoshi,"Japanese, 1797–1861",,Utagawa Kuniyoshi,Japanese,1797,1861,ca. 1844–46,1834,1856,"Triptych of polychrome woodblock prints; ink, silver, and color on paper",9 3/4 x 22 1/8 in. (24.8 x 56.2 cm),"Gift of Francis M. Weld, 1948",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56684,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2001.612.1,false,true,62028,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kuniyoshi,"Japanese, 1797–1861",,Utagawa Kuniyoshi,Japanese,1797,1861,ca. 1845–46,1835,1856,Polychrome woodblock print; ink and color on paper,13 1/2 x 9 in. (34.3 x 22.9 cm),"Gift of Judith Underwood Stewart, in memory of Martha Davenport Heard, 2001",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/62028,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2001.612.2,false,true,62029,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kuniyoshi,"Japanese, 1797–1861",,Utagawa Kuniyoshi,Japanese,1797,1861,ca. 1845–46,1835,1856,Polychrome woodblock print; ink and color on paper,13 1/2 x 9 in. (34.3 x 22.9 cm),"Gift of Judith Underwood Stewart, in memory of Martha Davenport Heard, 2001",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/62029,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2001.612.3,false,true,62030,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kuniyoshi,"Japanese, 1797–1861",,Utagawa Kuniyoshi,Japanese,1797,1861,ca. 1845–46,1835,1856,Polychrome woodblock print; ink and color on paper,13 1/2 x 9 in. (34.3 x 22.9 cm),"Gift of Judith Underwood Stewart, in memory of Martha Davenport Heard, 2001",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/62030,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2001.612.4,false,true,62031,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kuniyoshi,"Japanese, 1797–1861",,Utagawa Kuniyoshi,Japanese,1797,1861,ca. 1845–46,1835,1856,Polychrome woodblock print; ink and color on paper,13 1/2 x 9 in. (34.3 x 22.9 cm),"Gift of Judith Underwood Stewart, in memory of Martha Davenport Heard, 2001",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/62031,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2001.612.5,false,true,62032,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kuniyoshi,"Japanese, 1797–1861",,Utagawa Kuniyoshi,Japanese,1797,1861,ca. 1845–46,1835,1856,Polychrome woodblock print; ink and color on paper,13 1/2 x 9 in. (34.3 x 22.9 cm),"Gift of Judith Underwood Stewart, in memory of Martha Davenport Heard, 2001",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/62032,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2001.715.6,false,true,63361,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kuniyoshi,"Japanese, 1797–1861",,Utagawa Kuniyoshi,Japanese,1797,1861,ca. 1848,1838,1858,Polychrome woodblock print; ink and color on paper,"Oban tate-e, 14 7/8 x 10 in. (37.8 x 25.4 cm)","Purchase, Arnold Weinstein Gift, 2001",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/63361,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2001.715.7,false,true,63364,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kuniyoshi,"Japanese, 1797–1861",,Utagawa Kuniyoshi,Japanese,1797,1861,ca. 1848,1838,1858,Polychrome woodblock print; ink and color on paper,"Oban tate-e, 14 3/4 x 10 1/8 in. (37.5 x 25.7 cm)","Purchase, Arnold Weinstein Gift, 2001",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/63364,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2001.715.8,false,true,63371,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kuniyoshi,"Japanese, 1797–1861",,Utagawa Kuniyoshi,Japanese,1797,1861,ca. 1830,1820,1840,Polychrome woodblock print; ink and color on paper,"Oban tate-e, 17 7/8 x 10 1/8 in. (45.4 x 25.7 cm)","Purchase, Arnold Weinstein Gift, 2001",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/63371,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2001.715.9,false,true,63374,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kuniyoshi,"Japanese, 1797–1861",,Utagawa Kuniyoshi,Japanese,1797,1861,"1856, 2nd month",1856,1856,Polychrome woodblock print; ink and color on paper,"Oban tate-e, 14 5/8 x 10 in. (37.1 x 25.4 cm)","Purchase, Arnold Weinstein Gift, 2001",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/63374,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.277,false,true,73560,Asian Art,Woodblock print,"二十四孝童子鑑　 楊香|Yang Xiang (Yō Kō), from the series A Child’s Mirror of the Twenty-four Paragons of Filial Piety (Nijūshi kō dōji kagami)",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kuniyoshi,"Japanese, 1797–1861",,Utagawa Kuniyoshi,Japanese,1797,1861,ca. 1843,1843,1843,Polychrome woodblock print; ink and color on paper,Image: 8 5/8 x 13 7/8 in. (21.9 x 35.2 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73560,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1130a–c,false,true,55050,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kuniyoshi,"Japanese, 1797–1861",,Utagawa Kuniyoshi,Japanese,1797,1861,ca. 1850,1840,1860,"Triptych of polychrome woodblock prints; ink, silver, and color on paper",a) H. 14 7/8 in. (37.8 cm); W. 10 1/8 in. (25.7 cm) b): H. 14 3/4 in. (37.5 cm); W. 10 3/8 in. (26.4 cm) c): H. 14 13/16 in. (37.6 cm); W. 10 1/16 in. (25.6 cm),"Rogers Fund, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55050,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3518a–j,false,true,45005,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kuniyoshi,"Japanese, 1797–1861",,Utagawa Kuniyoshi,Japanese,1797,1861,mid-19th century,1834,1866,Polychrome woodblock print; ink and color on paper,H. 5 7/8 in. (14.9 cm); W. 4 1/8 in. (10.5 cm),"Gift of Lincoln Kirstein, 1966",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45005,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2011.133a, b",false,true,76563,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Gyokuryūtei Shigeharu,"Japanese, 1803–1853",,Gyokuryūtei Shigeharu,Japanese,1803,1853,1830,1830,1830,Diptych of polychrome woodblock prints,Each sheet ( ôban tate-e diptych): 14 1/2 x 9 3/4 in. (36.8 x 24.8 cm),"Purchase, Friends of Asian Art Gifts, in honor of James C. Y. Watt, 2011",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/76563,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2743,false,true,45265,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,Japanese,1807,1891,1860,1700,1868,Polychrome woodblock print; ink and color on paper,9 1/8 x 6 3/8 in. (23.2 x 16.2 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45265,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2746,false,true,54205,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,Japanese,1807,1891,1862 (Dog Year),1862,1862,Polychrome woodblock print (surimono); ink and color on paper,7 7/8 x 10 3/4 in. (20 x 27.3 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54205,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3172,false,true,73598,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,Japanese,1807,1891,ca. 1860,1860,1860,Polychrome woodblock print; ink and color on paper,Image: 7 3/4 × 10 7/8 in. (19.7 × 27.6 cm) Mat: 15 1/4 × 22 3/4 in. (38.7 × 57.8 cm),"Purchase, Gifts, Bequests, and Funds from various donors, by exchange, 1955",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73598,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3367,false,true,55534,Asian Art,Print,Fusen zu|写真鏡・風船図|Picture of a Balloon,Japan,Edo period (1615–1868),,,,Artist,,Miyagi Gengyo,"Japanese, 1817–1880",,Gengyo,Japanese,1817,1880,1860,1860,1860,Polychrome woodblock print; ink and color on paper,14 x 9 3/8 in. (35.6 x 23.8 cm),"Gift of Lincoln Kirstein, 1960",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55534,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1093.3,false,true,58255,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshitsuya,"Japanese, 1822–1866",,Utagawa Yoshitsuya,Japanese,1822,1866,mid-19th century,1822,1866,Polychrome woodblock print; ink and color on paper,Image: 14 1/4 × 9 1/2 in. (36.2 × 24.1 cm),The Metropolitan Museum of Art,,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58255,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.255,false,true,73546,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshitsuya,"Japanese, 1822–1866",,Utagawa Yoshitsuya,Japanese,1822,1866,"5th month, 1863",1863,1863,Polychrome woodblock print; ink and color on paper,Image: 13 7/8 x 9 3/4 in. (35.2 x 24.8 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73546,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2632,false,true,54197,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada II,"Japanese, 1823–1880",,Utagawa Kunisada II,Japanese,1823,1880,1852–64,1852,1864,Polychrome woodblock print (surimono); ink and color on paper,8 1/8 x 7 1/8 in. (20.6 x 18.1 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54197,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP212.59,false,true,58043,Asian Art,Print,"岩井紫若（二代目）の道具屋娘おかめ|Iwai Shijaku II as Okame, the Daughter of a Furniture Store",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada II,"Japanese, 1823–1880",,Utagawa Kunisada II,Japanese,1823,1880,1864,1864,1864,Right panel of a triptych of polychrome woodblock prints; ink and color on paper,Image: 13 5/8 × 9 5/8 in. (34.6 × 24.4 cm),"Gift of the Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58043,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.434.5,false,true,75302,Asian Art,Print,大坂下り早竹虎吉|Hayatake Torakichi from Osaka: Performance in Ryōgoku,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada II,"Japanese, 1823–1880",,Utagawa Kunisada II,Japanese,1823,1880,1857,1857,1857,Polychrome woodblock print; ink and color on paper,14 1/2 x 10 in. (36.8 x 25.4 cm),"Gift of Takemitsu Oba, 2009",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/75302,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3320,false,true,55464,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshifuji,"Japanese, 1828–1887",,Utagawa Yoshifuji,Japanese,1828,1887,1861,1861,1861,Polychrome woodblock print; ink and color on paper,14 x 9 in. (35.6 x 22.9 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55464,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3322,false,true,55467,Asian Art,Print,Worosiiazin yuko|魯西亜人遊行|Russians Strolling,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshifuji,"Japanese, 1828–1887",,Utagawa Yoshifuji,Japanese,1828,1887,February 1861,1861,1861,Polychrome woodblock print; ink and color on paper,14 x 9 1/2 in. (35.6 x 24.1 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55467,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3424,false,true,37390,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshifuji,"Japanese, 1828–1887",,Utagawa Yoshifuji,Japanese,1828,1887,1867,1867,1867,Triptych of polychrome woodblock prints; ink and color on paper,Oban; 14 1/8 x 28 3/4 in. (35.9 x 73 cm),"Gift of Lincoln Kirstein, 1962",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37390,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.251,false,true,73542,Asian Art,Print,Amerikajin|An American Family,Japan,Edo period (1615–1868),,,,Artist,,Ippōsai Yoshifuji,"Japanese, 1828–1887",,Ippōsai Yoshifuji,Japanese,1828,1887,"2nd, 1861",1861,1861,Polychrome woodblock print; ink and color on paper,Image: 14 1/2 x 10 in. (36.8 x 25.4 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73542,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.252,false,true,73543,Asian Art,Print,Oroshiajin|Russian Soldier with His Family,Japan,Edo period (1615–1868),,,,Artist,,Ippōsai Yoshifuji,"Japanese, 1828–1887",,Ippōsai Yoshifuji,Japanese,1828,1887,"2nd month, 1861",1861,1861,Polychrome woodblock print; ink and color on paper,Image: 14 1/8 x 9 3/4 in. (35.9 x 24.8 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73543,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.253,false,true,73544,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ippōsai Yoshifuji,"Japanese, 1828–1887",,Ippōsai Yoshifuji,Japanese,1828,1887,"2nd month, 1861",1861,1861,Polychrome woodblock print; ink and color on paper,Image: 14 1/2 x 10 in. (36.8 x 25.4 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73544,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.254,false,true,73545,Asian Art,Woodblock print,Gojin jūshin no hataraku|Five People Working Like Ten,Japan,Edo period (1615–1868),,,,Artist,,Ippōsai Yoshifuji,"Japanese, 1828–1887",,Ippōsai Yoshifuji,Japanese,1828,1887,"3rd month, 1861",1861,1861,Polychrome woodblock print; ink and color on paper,Image: 14 1/4 x 10 in. (36.2 x 25.4 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73545,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.434.1,false,true,75298,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshiharu,"Japanese, 1828–1888",,Utagawa Yoshiharu,Japanese,1828,1888,1857,1857,1857,Polychrome woodblock print; ink and color on paper,14 1/4 x 10 in. (36.2 x 25.4 cm),"Gift of Takemitsu Oba, 2009",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/75298,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.434.2,false,true,75299,Asian Art,Print,大坂下り早竹虎吉|The Plum Blossom that Flew on Lightning from Chikushino,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshiharu,"Japanese, 1828–1888",,Utagawa Yoshiharu,Japanese,1828,1888,1857,1857,1857,Polychrome woodblock print; ink and color on paper,14 1/4 x 9 5/8 in. (36.2 x 24.4 cm),"Gift of Takemitsu Oba, 2009",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/75299,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.434.3,false,true,75300,Asian Art,Print,大坂下り早竹虎吉|Hayatake Torakichi from Osaka,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshiharu,"Japanese, 1828–1888",,Utagawa Yoshiharu,Japanese,1828,1888,1857,1857,1857,Polychrome woodblock print; ink and color on paper,14 1/4 x 10 in. (36.2 x 25.4 cm),"Gift of Takemitsu Oba, 2009",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/75300,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.434.4,false,true,75301,Asian Art,Print,大坂下り早竹虎吉|Hayatake Torakichi from Osaka: Spinning Tops in Ryogoku,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshiharu,"Japanese, 1828–1888",,Utagawa Yoshiharu,Japanese,1828,1888,1857,1857,1857,Polychrome woodblock print; ink and color on paper,14 1/4 x 10 1/8 in. (36.2 x 25.7 cm),"Gift of Takemitsu Oba, 2009",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/75301,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP236,false,true,36708,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige II,"Japanese, 1829–1869",,Utagawa Hiroshige II,Japanese,1829,1869,1859,1859,1859,Polychrome woodblock print; ink and color on paper,H. 14 1/8 in. (35.9 cm); W. 9 1/2 in. (24.1 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36708,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1195,false,true,45263,Asian Art,Print,Kyoto Shijo yu-suzumi|Cooling Off at the Kamo River near Shijo in Kyoto,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige II,"Japanese, 1829–1869",,Utagawa Hiroshige II,Japanese,1829,1869,ca. 1860,1850,1870,Polychrome woodblock print; ink and color on paper,H. 14 5/8 in. (37.1 cm); W. 9 7/8 in. (25.1 cm),"Rogers Fund, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45263,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1196,false,true,55113,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige II,"Japanese, 1829–1869",,Utagawa Hiroshige II,Japanese,1829,1869,1859–61,1859,1861,Polychrome woodblock print; ink and color on paper,H. 14 7/16 in. (36.7 cm); W. 9 7/8 in. (25.1 cm),"Rogers Fund, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55113,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1485,false,true,55579,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige II,"Japanese, 1829–1869",,Utagawa Hiroshige II,Japanese,1829,1869,1862,1862,1862,Polychrome woodblock print; ink and color on paper,H. 9 13/16 in. (24.9 cm); W. 14 5/8 in. (37.1 cm),"Rogers Fund, 1926",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55579,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1549,false,true,55713,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige II,"Japanese, 1829–1869",,Utagawa Hiroshige II,Japanese,1829,1869,ca. 1859,1849,1899,Polychrome woodblock print; ink and color on paper,H. 14 1/8 in. (35.9 cm); W. 9 1/2 in. (24.1 cm),"Fletcher Fund, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55713,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2524,false,true,56937,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige II,"Japanese, 1829–1869",,Utagawa Hiroshige II,Japanese,1829,1869,1859,1859,1859,Polychrome woodblock print; ink and color on paper,14 3/4 x 10 in. (37.5 x 25.4 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56937,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1093.7,false,true,58259,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige II,"Japanese, 1829–1869",,Utagawa Hiroshige II,Japanese,1829,1869,mid-19th century,1834,1866,Polychrome woodblock print; ink and color on paper,Image: 14 3/4 × 9 7/8 in. (37.5 × 25.1 cm),The Metropolitan Museum of Art,,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58259,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.136,false,true,73422,Asian Art,Print,諸国名所百景　肥前長崎唐船の津|Dutch and Chinese Ships in the Harbor at Nagasaki in Hizen Province,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige II,"Japanese, 1829–1869",,Utagawa Hiroshige II,Japanese,1829,1869,"3rd month, 1859",1859,1859,Polychrome woodblock print; ink and color on paper,Overall: 14 1/4 x 9 3/4 in. (36.2 x 24.8 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73422,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.137,false,true,73423,Asian Art,Print,諸国名所百景　對州海岸|Dutch Ship at Anchor off the Coast of Tsushima,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige II,"Japanese, 1829–1869",,Utagawa Hiroshige II,Japanese,1829,1869,"3rd month, 1859",1859,1859,Polychrome woodblock print; ink and color on paper,Image: 14 1/4 x 9 1/2 in. (36.2 x 24.1 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73423,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.138,false,true,73424,Asian Art,Print,"Bushu Yokohama Gankirō|諸国名所百景　武州横浜岩亀楼|Entrance to the Gankirō Tea House in the Miyozaki District, Yokohama, Bushu",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige II,"Japanese, 1829–1869",,Utagawa Hiroshige II,Japanese,1829,1869,"3rd month, 1859",1859,1859,Polychrome woodblock print; ink and color on paper,Image: 14 1/4 x 9 3/4 in. (36.2 x 24.8 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73424,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.139,false,true,73425,Asian Art,Woodblock print,"横浜賣物図会の内　唐犬|Copper Plate Engraving of a Woman Riding a Horse, a Goat and a Dog",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige II,"Japanese, 1829–1869",,Utagawa Hiroshige II,Japanese,1829,1869,"3rd month, 1860",1860,1860,Polychrome woodblock print; ink and color on paper,Image: 14 3/4 x 10 in. (37.5 x 25.4 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73425,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.142,false,true,73428,Asian Art,Woodblock print,「亞墨利加」|America: A Woman on Horseback in the Snow,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige II,"Japanese, 1829–1869",,Utagawa Hiroshige II,Japanese,1829,1869,"10th month, 1860",1860,1860,Polychrome woodblock print; ink and color on paper,Image: 14 x 9 7/8 in. (35.6 x 25.1 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73428,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.143,false,true,73429,Asian Art,Print,"Furansu|ふらんす|French Woman, Her Child and Pet Dog",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige II,"Japanese, 1829–1869",,Utagawa Hiroshige II,Japanese,1829,1869,"10th month, 1860",1860,1860,Polychrome woodblock print; ink and color on paper,Image: 14 3/4 x 10 1/4 in. (37.5 x 26 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73429,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.144,false,true,73430,Asian Art,Print,南京　於魯西亜|Russians and a Chinese Inscribing a Fan,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige II,"Japanese, 1829–1869",,Utagawa Hiroshige II,Japanese,1829,1869,"12th month, 1860",1860,1860,Polychrome woodblock print; ink and color on paper,Image: 14 1/2 x 9 3/4 in. (36.8 x 24.8 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73430,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.145,false,true,73431,Asian Art,Print,"亜墨利加　横浜本村本牧道|American Woman Riding Side-Saddle on the Road at Hommoku, Motomura, Yokohama",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige II,"Japanese, 1829–1869",,Utagawa Hiroshige II,Japanese,1829,1869,"2nd month, 1861",1861,1861,Polychrome woodblock print; ink and color on paper,Image: 14 3/4 x 10 in. (37.5 x 25.4 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73431,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.147,false,true,73433,Asian Art,Print,Tōtō Takanawa Kaigen|Foreigners Riding Along the Coast at Takanawa in the Eastern Capital,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige II,"Japanese, 1829–1869",,Utagawa Hiroshige II,Japanese,1829,1869,"9th month, 1861",1861,1861,Polychrome woodblock print; ink and color on paper,Image: 14 1/4 x 10 5/8 in. (36.2 x 27 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73433,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.148,false,true,73434,Asian Art,Print,Tōtō Takanawa Kaigen|Foreigners Riding Along the Coast at Takanawa in the Eastern Capital,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige II,"Japanese, 1829–1869",,Utagawa Hiroshige II,Japanese,1829,1869,"9th month, 1861",1861,1861,Polychrome woodblock print; ink and color on paper,Image: 14 3/8 x 10 in. (36.5 x 25.4 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73434,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.140a–c,false,true,73426,Asian Art,Print,Yokohama Gankirō no zu|View of the Interior of the Gankirō Tea House in Yokohama,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige II,"Japanese, 1829–1869",,Utagawa Hiroshige II,Japanese,1829,1869,"4th month, 1860",1860,1860,Triptych of polychrome woodblock prints; ink and color on paper,Image (a): 14 3/4 x 10 in. (37.5 x 25.4 cm) Image (b): 14 5/8 x 10 in. (37.1 x 25.4 cm) Image (c): 14 1/2 x 10 in. (36.8 x 25.4 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73426,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.141a–c,false,true,73427,Asian Art,Print,Yokohama Gankirō age|横浜岩亀楼上|Upper Floor of the Gankirō Tea House in Yokohama,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige II,"Japanese, 1829–1869",,Utagawa Hiroshige II,Japanese,1829,1869,"4th month, 1860",1860,1860,Triptych of polychrome woodblock prints; ink and color on paper,Image (a): 14 3/4 x 10 in. (37.5 x 25.4 cm) Image (b): 14 3/4 x 10 in. (37.5 x 25.4 cm) Image (c): 14 3/4 x 10 in. (37.5 x 25.4 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73427,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.149a–c,false,true,73435,Asian Art,Print,Yokohama ijin kyaku no zu|Foreigner's Residence in Yokohama,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige II,"Japanese, 1829–1869",,Utagawa Hiroshige II,Japanese,1829,1869,"10th month, 1861",1861,1861,Triptych of polychrome woodblock prints; ink and color on paper,Image (a): 14 x 9 1/2 in. (35.6 x 24.1 cm) Image (b): 14 x 9 1/2 in. (35.6 x 24.1 cm) Image (c): 14 x 10 in. (35.6 x 25.4 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73435,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3305,false,true,55406,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Ichiryūsai Yoshitoyo,"Japanese, 1830–1866",,Ichiryūsai Yoshitoyo,Japanese,1830,1866,1863 (3rd month),1863,1863,Diptych of polychrome woodblock prints; ink and color on paper,,"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55406,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3334,false,true,55482,Asian Art,Print,Furansu-jin Yukyo|フランス人遊興|French Pastimes,Japan,Edo period (1615–1868),,,,Artist,,Ichiryūsai Yoshitoyo,"Japanese, 1830–1866",,Ichiryūsai Yoshitoyo,Japanese,1830,1866,"1860 (Man–en, 1st year)",1860,1860,Polychrome woodblock print; ink and color on paper,14 1/2 x 10 in. (36.8 x 25.4 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55482,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.244,false,true,73535,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Ichiryūsai Yoshitoyo,"Japanese, 1830–1866",,Ichiryūsai Yoshitoyo,Japanese,1830,1866,"7th month, 1860",1860,1860,Polychrome woodblock print; ink and color on paper,Image: 14 x 9 1/2 in. (35.6 x 24.1 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73535,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.245,false,true,73536,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ichiryūsai Yoshitoyo,"Japanese, 1830–1866",,Ichiryūsai Yoshitoyo,Japanese,1830,1866,"7th month, 1860",1860,1860,Polychrome woodblock print; ink and color on paper,Image: 14 1/2 x 10 in. (36.8 x 25.4 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73536,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.246,false,true,73537,Asian Art,Print,Igirisujin ryōkō no zu|Englishman Walking for Pleasure,Japan,Edo period (1615–1868),,,,Artist,,Ichiryūsai Yoshitoyo,"Japanese, 1830–1866",,Ichiryūsai Yoshitoyo,Japanese,1830,1866,"12th month, 1860",1860,1860,Polychrome woodblock print; ink and color on paper,Image: 13 7/8 x 10 in. (35.2 x 25.4 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73537,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.247,false,true,73538,Asian Art,Print,英吉利人之図|Illustration of English People (Igirisujin no zu),Japan,Edo period (1615–1868),,,,Artist,,Ichiryūsai Yoshitoyo,"Japanese, 1830–1866",,Ichiryūsai Yoshitoyo,Japanese,1830,1866,"1st month, 1861",1861,1861,Polychrome woodblock print; ink and color on paper,Image: 13 7/8 x 9 5/8 in. (35.2 x 24.4 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73538,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.248,false,true,73539,Asian Art,Print,Orandajin no zu|Dutchman with Black Servant,Japan,Edo period (1615–1868),,,,Artist,,Ichiryūsai Yoshitoyo,"Japanese, 1830–1866",,Ichiryūsai Yoshitoyo,Japanese,1830,1866,"1st month, 1861",1861,1861,Polychrome woodblock print; ink and color on paper,Image: 14 3/8 x 9 3/4 in. (36.5 x 24.8 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73539,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.250,false,true,73541,Asian Art,Woodblock print,Chū tenjiku Maruka koku shūsshō daizō zu|Picture of an Elephant Born in Maruka in Central India,Japan,Edo period (1615–1868),,,,Artist,,Ichiryūsai Yoshitoyo,"Japanese, 1830–1866",,Ichiryūsai Yoshitoyo,Japanese,1830,1866,"2nd month, 1863",1863,1863,Polychrome woodblock print; ink and color on paper,Image: 14 1/8 x 9 1/2 in. (35.9 x 24.1 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73541,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2007.49.249a, b",false,true,73540,Asian Art,Woodblock print,Shintō hakurai no daizō|Newly Imported Great Elephant,Japan,Edo period (1615–1868),,,,Artist,,Ichiryūsai Yoshitoyo,"Japanese, 1830–1866",,Ichiryūsai Yoshitoyo,Japanese,1830,1866,"2nd month, 1863",1863,1863,Diptych of polychrome woodblock prints; ink and color on paper,"Image (a): 14 1/4 x 9 1/2 in. (36.2 cm, 24130 g) Image (b): 14 1/4 x 9 1/2 in. (36.2 x 24.1 cm)","Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73540,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1093.8,false,true,58260,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kuniteru,"Japanese, 1830–1874",,Utagawa Kuniteru,Japanese,1830,1874,1853,1853,1853,Polychrome woodblock print; ink and color on paper,Image: 14 1/2 × 9 3/4 in. (36.8 × 24.8 cm),The Metropolitan Museum of Art,,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58260,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1093.9,false,true,58261,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kuniteru,"Japanese, 1830–1874",,Utagawa Kuniteru,Japanese,1830,1874,1853,1853,1853,Polychrome woodblock print; ink and color on paper,Image: 14 3/4 × 9 7/8 in. (37.5 × 25.1 cm),The Metropolitan Museum of Art,,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58261,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.237,false,true,73528,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshimori,"Japanese, 1830–1884",,Utagawa Yoshimori,Japanese,1830,1884,"4th month, 1860",1860,1860,Polychrome woodblock print; ink and color on paper,Image: 14 3/4 x 9 3/4 in. (37.5 x 24.8 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73528,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.238,false,true,73529,Asian Art,Woodblock print,"歌川芳盛画　「鳥獣図會」豹と阿蘭陀婦人|“Dutchwoman with Leopard,” from the series Pictures of Birds and Animals (Chōjū zue)",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshimori,"Japanese, 1830–1884",,Utagawa Yoshimori,Japanese,1830,1884,"7th month, 1860",1860,1860,Polychrome woodblock print; ink and color on paper,Image: 14 1/4 x 9 1/2 in. (36.2 x 24.1 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73529,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.239,false,true,73530,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshimori,"Japanese, 1830–1884",,Utagawa Yoshimori,Japanese,1830,1884,"11th month, 1860",1860,1860,Polychrome woodblock print; ink and color on paper,Image: 14 3/4 x 9 7/8 in. (37.5 x 25.1 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73530,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.240,false,true,73531,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshimori,"Japanese, 1830–1884",,Utagawa Yoshimori,Japanese,1830,1884,"11th month, 1860",1860,1860,Polychrome woodblock print; ink and color on paper,Image: 14 1/2 x 9 3/4 in. (36.8 x 24.8 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73531,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.241,false,true,73532,Asian Art,Print,Amerikajin|Mounted American Woman,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshimori,"Japanese, 1830–1884",,Utagawa Yoshimori,Japanese,1830,1884,"12th month, 1860",1860,1860,Polychrome woodblock print; ink and color on paper,Image: 14 5/8 x 9 7/8 in. (37.1 x 25.1 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73532,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.262,false,true,73553,Asian Art,Woodblock print,"「今昔未見　生物猛虎之真図」|Never Seen Before: True Picture of a Live Wild Tiger (Konjaku miken, Ikimono mōko no shinzu)",Japan,Edo period (1615–1868),,,,Artist,,Kawanabe Kyōsai,"Japanese, 1831–1889",,Kawanabe Kyōsai,Japanese,1831,1889,sixth month 1860,1860,1860,Polychrome woodblock print; ink and color on paper,Image: 14 3/8 x 9 7/8 in. (36.5 x 25.1 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73553,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3335,false,true,55483,Asian Art,Print,Roshiajin|Russians,Japan,Edo period (1615–1868),,,,Artist,,Kunihisa,"Japanese, 1832–1891",,Kunihisa,Japanese,1832,1891,1861 (10th month),1861,1861,Polychrome woodblock print; ink and color on paper,14 3/4 x 10 in. (37.5 x 25.4 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55483,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3181,false,true,55129,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshiiku,"Japanese, 1833–1904",,Utagawa Yoshiiku,Japanese,1833,1904,1860,1860,1860,Polychrome woodblock print; ink and color on paper,14 1/2 x 9 3/4 in. (36.8 x 24.8 cm),"GIft of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55129,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3184,false,true,53243,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshiiku,"Japanese, 1833–1904",,Utagawa Yoshiiku,Japanese,1833,1904,1861,1861,1861,Polychrome woodblock print; ink and color on paper,Oban 13 3/4 x 9 3/4 in. (34.9 x 24.8 cm),"GIft of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/53243,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3197,false,true,55181,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshiiku,"Japanese, 1833–1904",,Utagawa Yoshiiku,Japanese,1833,1904,1865,1865,1865,Polychrome woodblock print; ink and color on paper,9 5/16 x 14 in. (23.6 x 35.5 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55181,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3682,false,true,45280,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshiiku,"Japanese, 1833–1904",,Utagawa Yoshiiku,Japanese,1833,1904,1864,1615,1868,Polychrome woodblock print; ink and color on paper,14 1/2 x 10 in. (36.8 x 25.4 cm),"Gift of Lincoln Kirstein, 1985",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45280,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3314a,false,true,55439,Asian Art,Woodblock print,亜米利加・南京|An American on Horseback and a Chinese with a Furled Umbrella,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshiiku,"Japanese, 1833–1904",,Utagawa Yoshiiku,Japanese,1833,1904,"12th month, 1860",1860,1860,Polychrome woodblock print; ink and color on paper,14 x 9 in. (35.6 x 22.9 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55439,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3314b,false,true,55440,Asian Art,Print,魯西亞 ・英吉利|An English Man and a Russian Woman,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshiiku,"Japanese, 1833–1904",,Utagawa Yoshiiku,Japanese,1833,1904,"12th month, 1860",1860,1860,Polychrome woodblock print; ink and color on paper,14 1/2 x 9 1/2 in. (36.8 x 24.1 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55440,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.225,false,true,73516,Asian Art,Print,Sumo no homane|Sumo Wrestler Tossing a Foreigner,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshiiku,"Japanese, 1833–1904",,Utagawa Yoshiiku,Japanese,1833,1904,"1st month, 1861",1861,1861,Polychrome woodblock print; ink and color on paper,Image: 14 1/4 x 9 3/8 in. (36.2 x 23.8 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73516,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.226,false,true,73517,Asian Art,Print,Bijin zu|Beauties,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshiiku,"Japanese, 1833–1904",,Utagawa Yoshiiku,Japanese,1833,1904,"1st month, 1861",1861,1861,Polychrome woodblock print; ink and color on paper,Image: 14 5/8 x 9 3/8 in. (37.1 x 23.8 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73517,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.227,false,true,73518,Asian Art,Print,Oroshia|Russian Couple,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshiiku,"Japanese, 1833–1904",,Utagawa Yoshiiku,Japanese,1833,1904,"1st month, 1861",1861,1861,Polychrome woodblock print; ink and color on paper,Image: 14 1/4 x 9 7/8 in. (36.2 x 25.1 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73518,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.228,false,true,73519,Asian Art,Print,Oroshia|Chinese Servant and Frenchman,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshiiku,"Japanese, 1833–1904",,Utagawa Yoshiiku,Japanese,1833,1904,"2nd month, 1861",1861,1861,Polychrome woodblock print; ink and color on paper,Image: 15 3/4 x 11 1/4 in. (40 x 28.6 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73519,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.231,false,true,73522,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshiiku,"Japanese, 1833–1904",,Utagawa Yoshiiku,Japanese,1833,1904,"10th month, 1861",1861,1861,Polychrome woodblock print; ink and color on paper,Image: 14 1/4 x 9 7/8 in. (36.2 x 25.1 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73522,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.232,false,true,73523,Asian Art,Print,Igirisu to korombojin|English Woman with Black Man,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshiiku,"Japanese, 1833–1904",,Utagawa Yoshiiku,Japanese,1833,1904,"11th month, 1861",1861,1861,Polychrome woodblock print; ink and color on paper,Image: 14 1/2 x 9 3/4 in. (36.8 x 24.8 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73523,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.233,false,true,73524,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshiiku,"Japanese, 1833–1904",,Utagawa Yoshiiku,Japanese,1833,1904,"7th month, 1863",1863,1863,Polychrome woodblock print; ink and color on paper,Image: 13 x 8 5/8 in. (33 x 21.9 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73524,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.224a–c,false,true,73515,Asian Art,Print,Gok'koku o Gankirō sakamori no zu|The Five Nations Enjoying a Drunken Revel at the Gankirō Tea House,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshiiku,"Japanese, 1833–1904",,Utagawa Yoshiiku,Japanese,1833,1904,"12th month, 1860",1860,1860,Triptych of polychrome woodblock prints; ink and color on paper,Image (a): 14 3/4 x 10 in. (37.5 x 25.4 cm) Image (b): 14 3/4 x 10 in. (37.5 x 25.4 cm) Image (c): 14 3/4 x 10 in. (37.5 x 25.4 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73515,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.229a–c,false,true,73520,Asian Art,Print,万国男女人物図絵|Picture of Men and Women from all Nations (Bankoku danjo jinbutsu zue),Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshiiku,"Japanese, 1833–1904",,Utagawa Yoshiiku,Japanese,1833,1904,"4th month, 1861",1861,1861,Triptych of polychrome woodblock prints; ink and color on paper,Image (a): 14 x 9 3/4 in. (35.6 x 24.8 cm) Image (b): 14 x 9 3/4 in. (35.6 x 24.8 cm) Image (c): 14 x 9 3/4 in. (35.6 x 24.8 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73520,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.230a–c,false,true,73521,Asian Art,Print,Bankoku danjō jimbutsu zue|Picture of Men and Women from all nations (Bankoku danjo jinbutsu zue),Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshiiku,"Japanese, 1833–1904",,Utagawa Yoshiiku,Japanese,1833,1904,"4th month, 1861",1861,1861,Triptych of polychrome woodblock prints; ink and color on paper,Image (a): 14 3/4 x 10 in. (37.5 x 25.4 cm) Image (b): 14 3/4 x 10 in. (37.5 x 25.4 cm) Image (c): 14 3/4 x 10 in. (37.5 x 25.4 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73521,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1094a,false,true,55029,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kuniaki II,"Japanese, 1835–1888",,Utagawa Kuniaki II,Japanese,1835,1888,"1862 (Bunkyū 2), 6th month",1862,1862,Polychrome woodblock print; ink and color on paper,Image: 14 1/4 × 9 1/4 in. (36.2 × 23.5 cm),The Metropolitan Museum of Art,,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55029,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1094b,false,true,638607,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kuniaki II,"Japanese, 1835–1888",,Utagawa Kuniaki II,Japanese,1835,1888,"1862 (Bunkyū 2), 6th month",1862,1862,Polychrome woodblock print; ink and color on paper,Image: 14 in. × 9 1/2 in. (35.6 × 24.1 cm),The Metropolitan Museum of Art,,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/638607,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1094c,false,true,638609,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kuniaki II,"Japanese, 1835–1888",,Utagawa Kuniaki II,Japanese,1835,1888,"1862 (Bunkyū 2), 6th month",1862,1862,Polychrome woodblock print; ink and color on paper,Image: 14 in. × 9 5/8 in. (35.6 × 24.4 cm),The Metropolitan Museum of Art,,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/638609,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1094d,false,true,638610,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kuniaki II,"Japanese, 1835–1888",,Utagawa Kuniaki II,Japanese,1835,1888,"1862 (Bunkyū 2), 6th month",1862,1862,Polychrome woodblock print; ink and color on paper,Image: 14 1/4 × 9 5/8 in. (36.2 × 24.4 cm),The Metropolitan Museum of Art,,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/638610,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1094e,false,true,638611,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kuniaki II,"Japanese, 1835–1888",,Utagawa Kuniaki II,Japanese,1835,1888,"1862 (Bunkyū 2), 6th month",1862,1862,Polychrome woodblock print; ink and color on paper,Image: 13 3/4 × 9 5/8 in. (34.9 × 24.4 cm),The Metropolitan Museum of Art,,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/638611,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1094f,false,true,638612,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kuniaki II,"Japanese, 1835–1888",,Utagawa Kuniaki II,Japanese,1835,1888,"1862 (Bunkyū 2), 6th month",1862,1862,Polychrome woodblock print; ink and color on paper,Image: 14 1/16 × 9 5/8 in. (35.7 × 24.4 cm),The Metropolitan Museum of Art,,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/638612,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1094g,false,true,638613,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kuniaki II,"Japanese, 1835–1888",,Utagawa Kuniaki II,Japanese,1835,1888,"1862 (Bunkyū 2), 6th month",1862,1862,Polychrome woodblock print; ink and color on paper,Image: 14 1/8 × 9 5/8 in. (35.9 × 24.4 cm),The Metropolitan Museum of Art,,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/638613,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3178,false,true,55125,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Tsukioka Yoshitoshi,"Japanese, 1839–1892",,Tsukioka Yoshitoshi,Japanese,1839,1892,ca. 1865,1855,1875,Polychrome woodblock print; ink and color on paper,9 7/16 x 13 7/16 in. (23.9 x 34.2 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55125,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.257,false,true,73548,Asian Art,Print,仏蘭西英吉利三兵大調錬之図|Maneuvers by Three Categories of French and English Soldiers (Furansu Igirisu sanhei ōchōren no zu),Japan,Edo period (1615–1868),,,,Artist,,Tsukioka Yoshitoshi,"Japanese, 1839–1892",,Tsukioka Yoshitoshi,Japanese,1839,1892,"8th month, 1867",1867,1867,Triptych of polychrome woodblock prints; ink and color on paper,Image: 14 1/2 x 28 3/16 in. (36.8 x 71.6 cm) Overall (mat): 19 x 32 in. (48.3 x 81.3 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73548,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.259,false,true,73550,Asian Art,Print,"騎兵体歩兵体大調練之図|Illustration of Cavalry, Infantry and Soldiers Retreating (Kiheitai, hoheitai, daichōren no zu)",Japan,Edo period (1615–1868),,,,Artist,,Tsukioka Yoshitoshi,"Japanese, 1839–1892",,Tsukioka Yoshitoshi,Japanese,1839,1892,"8th month, 1867",1867,1867,Triptych of polychrome woodblock prints; ink and color on paper,Image: 14 1/4 x 28 3/4 in. (36.2 x 73 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73550,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.348a–c,false,true,72826,Asian Art,Print,"武勇雪月花之内　ゑびらの梅|Plum Blossoms in the Forrest of Ikuta, from the series Bravery-Beauty of the four seasons (Buyū setsugekka no uchi-Ikuta no mori, Ebira no ume)",Japan,Edo period (1615–1868),,,,Artist,,Tsukioka Yoshitoshi,"Japanese, 1839–1892",,Tsukioka Yoshitoshi,Japanese,1839,1892,1867,1867,1867,Triptych of polychrome woodblock prints; ink and color on paper,Overall (a): 14 3/4 x 10 in. (37.5 x 25.4 cm) Overall (b): 14 9/16 x 10 in. (37 x 25.4 cm) Overall (c): 14 9/16 x 10 in. (37 x 25.4 cm),"Purchase, Friends of Asian Art Gifts, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/72826,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.351a–c,false,true,72829,Asian Art,Print,"太平記正清難戦之図|Masakiyo's Challenging Battle, from the series Taiheiki (Taiheiki, Masakiyo nansen no zu)",Japan,Edo period (1615–1868),,,,Artist,,Tsukioka Yoshitoshi,"Japanese, 1839–1892",,Tsukioka Yoshitoshi,Japanese,1839,1892,1866,1866,1866,Triptych of polychrome woodblock prints; ink and color on paper,Overall (a): 14 3/8 x 10 in. (36.5 x 25.4 cm) Overall (b): 14 3/8 x 9 7/8 in. (36.5 x 25.1 cm) Overall (c): 14 9/16 x 10 in. (37 x 25.4 cm),"Purchase, Friends of Asian Art Gifts, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/72829,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.256a–c,false,true,73547,Asian Art,Print,仏蘭西大湊諸国交易図|Illustration of a Large French Port Trading with Many Nations (Furansukoku oominato shokoku kōeki zu),Japan,Edo period (1615–1868),,,,Artist,,Tsukioka Yoshitoshi,"Japanese, 1839–1892",,Tsukioka Yoshitoshi,Japanese,1839,1892,"4th month, 1866",1866,1866,Triptych of polychrome woodblock prints; ink and color on paper,Image (a): 14 x 9 1/2 in. (35.6 x 24.1 cm) Image (b): 14 x 9 1/4 in. (35.6 x 23.5 cm) Image (c): 14 x 9 1/2 in. (35.6 x 24.1 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73547,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.258a–c,false,true,73549,Asian Art,Print,仏蘭西英吉利三兵大調錬之図|Maneuvers by Three Categories of French and English Soldiers (Furansu Igirisu sanhei ōchōren no zu),Japan,Edo period (1615–1868),,,,Artist,,Tsukioka Yoshitoshi,"Japanese, 1839–1892",,Tsukioka Yoshitoshi,Japanese,1839,1892,"8th month, 1867",1867,1867,Triptych of polychrome woodblock prints; ink and color on paper,Image (a): 14 1/2 x 9 1/4 in. (36.8 x 23.5 cm) Image (b): 14 3/8 x 9 1/4 in. (36.5 x 23.5 cm) Image (c): 14 3/8 x 9 1/2 in. (36.5 x 24.1 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73549,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3101,false,true,45055,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kaigetsudo Anchi,"Japanese, active 1714",,Kaigetsudo Anchi,Japanese,1714,1714,ca. 1714,1704,1724,Polychrome woodblock print (sumizuri-e); ink and color on paper,22 3/4 x 12 3/4 in. (57.8 x 32.4 cm),"Harris Brisbane Dick Fund and Rogers Fund, 1949",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45055,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3106,false,true,56612,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kaigetsudo Anchi,"Japanese, active 1714",,Kaigetsudo Anchi,Japanese,1714,1714,ca. 1714,1704,1724,Polychrome woodblock print; ink and color on paper,22 3/4 x 12 3/4 in. (57.8 x 32.4 cm),"Harris Brisbane Dick Fund and Rogers Fund, 1949",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56612,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP142,false,true,36621,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,ca. 1796,1786,1806,Polychrome woodblock print; ink and color on paper,H. 15 in. (38.1 cm); W. 10 in. (25.4 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36621,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP143,false,true,36622,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,ca. 1800,1790,1810,Polychrome woodblock print; ink and color on paper,20 x 8 1/2 in. (50.8 x 21.6 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36622,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP144,false,true,36623,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,1790s,1790,1799,Polychrome woodblock print; ink and color on paper,H. 14 5/8 in. (37.1 cm); W. 9 5/8 in. (24.4 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36623,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP145,false,true,36624,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,ca. 1795,1785,1805,Polychrome woodblock print; ink and color on paper,13 3/4 x 9 7/8 in. (34.9 x 25.1 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36624,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP146,false,true,36625,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,ca. 1796,1786,1806,One sheet of a hexaptych of polychrome woodblock prints; ink and color on paper,15 3/32 x 10 1/5 in. (38.3 x 25.9 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36625,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP147,false,true,36626,Asian Art,Print,歌撰恋之部　夜毎に逢恋|A Young Woman Reading A Letter,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,1790s,1790,1799,Polychrome woodblock print; ink and color on paper,14 1/2 x 10 1/8 in. (36.8 x 25.7 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36626,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP148,false,true,36627,Asian Art,Woodblock print,女織蚕手業草　十二終|Women Weaving Silk Cloth,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,ca. 1800,1790,1810,Polychrome woodblock print; ink and color on paper,14 7/8 x 9 3/4 in. (37.8 x 24.8 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36627,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP149,false,true,36628,Asian Art,Print,三囲神社の御開帳|Display of Treasures at Mimeguri Shrine (Mimeguri jinja no onkaichō),Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,1799,1799,1799,Triptych of polychrome woodblock prints; ink and color on paper,15 3/8 x 9 7/8 x 10 in. (39.1 x 25.1 x 25.4 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36628,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP150,false,true,36629,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,"Rat year, i.e. 1804",1804,1804,Triptych of polychrome woodblock prints; ink and color on paper,15 7/32 x 30 in. (38.7 x 76.2 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36629,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP151,false,true,36630,Asian Art,Print,蛍狩|Catching fireflies (Hotaru gari),Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,ca. 1796–97,1796,1797,Triptych of polychrome woodblock prints; ink and color on paper,Image (triptych): 13 7/8 x 28 5/8 in. (35.2 x 72.7 cm) Image (each sheet): 13 7/8 x 9 1/2 in. (35.2 x 24.1 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36630,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP152,false,true,36631,Asian Art,Woodblock print,"婦人手業操鏡|A Woman Weaving, Seated at a Hand Loom",Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,ca. 1796,1786,1806,Polychrome woodblock print; ink and color on paper,14 3/4 x 9 7/8 in. (37.5 x 25.1 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36631,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP153,false,true,36632,Asian Art,Print,実競色乃美名家見　三浦屋小紫　白井権八|The Lovers Miura-ya Komurasaki and Shirai Gonpachi.,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,probably 1797,1795,1799,Polychrome woodblock print; ink and color on paper,15 x 9 15/32 in. (38.1 x 24.1 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36632,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP555,false,true,37006,Asian Art,Print,江戸の花　娘浄瑠璃|A Woman Playing with a Young Boy,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,ca. 1804,1794,1814,Polychrome woodblock print; ink and color on paper,14 3/5 x 9 29/32 in. (37.1 x 25.2 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37006,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP556,false,true,37007,Asian Art,Print,名君　閨中の粧ひ|The Oiran Yoso-oi Seated at Her Toilet,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,ca. 1799,1789,1809,Polychrome woodblock print; ink and color on paper,14 3/5 x 9 29/32 in. (37.1 x 25.2 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37007,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP663,false,true,37110,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,ca. 1800,1790,1810,Two sheets of a hexaptych of polychrome woodblock prints; ink and color on paper,14 1/4 x 20 1/8 in. (36.2 x 51.1 cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37110,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP735,false,true,37183,Asian Art,Print,新吉原仮宅両国之図|Picture of the Temporary Lodgings of the New Yoshiwara Pleasure Quarter at Ryōgoku (Shin Yoshiwara Karitaku Ryogoku no zu),Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,1784,1784,1784,Polychrome woodblock print; ink and color on paper,12 1/8 x 17 in. (30.8 x 43.2 cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37183,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP736,false,true,37184,Asian Art,Print,扇屋内春日野わかな こてう|The Oiran Kasugano of Ōgiya on Parade,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,ca. 1788,1778,1798,Polychrome woodblock print; ink and color on paper,12 1/8 x 5 1/8 in. (30.8 x 13.0 cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37184,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP737,false,true,37185,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,1790s,1790,1799,Left-hand of a triptych of polychrome woodblock prints; ink and color on paper,H. 15 in. (38.1 cm); W. 10 1/4 in. (26 cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37185,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP738,false,true,37186,Asian Art,Print,丁子屋内雛鶴|The Oiran Hinazuru of Chojiya Standing upon a Pile of Futon,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,ca. 1794,1784,1804,Polychrome woodblock print; ink and color on paper,12 1/8 x 8 1/2 in. (30.8 x 21.6 cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37186,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP961,false,true,37269,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,1789,1789,1789,Polychrome woodblock print; ink and color on paper,H. 8 1/4 in. (21 cm); W. 14 1/4 in. (36.2 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37269,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP962,false,true,37270,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,1789,1789,1789,Polychrome woodblock print; ink and color on paper,H. 9 in. (22.9 cm); W. 14 5/8 in. (37.1 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37270,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP963,false,true,54854,Asian Art,Print,扇屋内春日野|The Oiran Kasugano of Ogiya on Parade under Blossoming Cherry Trees,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,ca. 1788,1778,1798,Polychrome woodblock print; ink and color on paper,H. 15 in. (38.1 cm); W. 10 in. (25.4 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54854,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP964,false,true,45250,Asian Art,Print,Mando|子供遊に和賀　万度|Lantern Float,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,early 1800s,1800,1833,Polychrome woodblock print; ink and color on paper,H. 9 1/2 in. (24.1 cm); W. 7 in. (17.8 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45250,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP966,false,true,37272,Asian Art,Print,狂月坊|A Party of Merrymakers in a House in the Yoshiwara on a Moonlight Night,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,1789,1789,1789,Polychrome woodblock print; ink and color on paper,9 1/8 x 14 3/5 in. (23.2 x 37.1 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37272,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP967,false,true,37273,Asian Art,Print,狂月坊|The Palace in the Moon,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,"8th month, 1789",1789,1789,Polychrome woodblock print; ink and color on paper,9 3/8 x 14 11/16 in. (23.8 x 37.3 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37273,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP968,false,true,37274,Asian Art,Print,狂月坊|The Full Moon at the Time of the Imo Harvest,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,"8th month, 1789",1789,1789,Polychrome woodblock print; ink and color on paper,9 1/4 x 14 3/4 in. (23.5 x 37.5 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37274,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP969,false,true,37275,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,1789,1789,1789,Polychrome woodblock print; ink and color on paper,9 1/4 x 14 3/5 in. (23.5 x 37.1 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37275,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP970,false,true,37276,Asian Art,Woodblock print,"喜多川歌麿画　『男踏歌』　鶯の餌すり|“Preparing Food for the Warbler,” from the album Men’s Stamping Dance (Otoko dōka, uguisu no esa suri)",Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,1798,1798,1798,Page from a woodblock printed book; ink and color on paper,8 1/2 x 14 in. (21.6 x 35.6 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37276,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP971,false,true,45473,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,ca. 1788,1778,1798,Polychrome woodblock print; ink and color on paper,H. 14 7/8 in. (37.8 cm); W. 9 7/8 in. (25.1 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45473,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP973,false,true,54855,Asian Art,Print,"音曲比翼の番組　小波　力弥|Rikiya and Konami, from the series A Program with Music about Loving Couples (Ongyoku hiyoku no bangumi)",Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,1798,1798,1798,Polychrome woodblock print; ink and color on paper,Aiban; H. 13 3/16 in. (33.5 cm); W. 9 in. (22.9 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54855,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP974,false,true,45009,Asian Art,Print,"『五色染六歌仙』　僧正遍昭|“The Poet Sōjō Henjō (816–890) Slipping a Letter into a Woman’s Sleeve,” from the series Five Colors of Love for the Six Poetic Immortals (Goshiki-zome rokkasen)",Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,ca. 1798,1788,1808,Polychrome woodblock print; ink and color on paper,H. 12 5/8 in. (32.1 cm); W. 8 1/2 in. (21.6 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45009,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP975,false,true,45012,Asian Art,Print,"『五色染六歌仙』 在原業平と小野小町|“The Poet Ariwara no Narihira (825–880) and Ono no Komachi,” from the series Five Colors of Love for the Six Poetic Immortals (Goshiki-zome rokkasen)",Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,ca. 1798,1788,1808,Polychrome woodblock print; ink and color on paper,H. 12 5/8 in. (32.1 cm); W. 8 1/2 in. (21.6 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45012,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP977,false,true,45013,Asian Art,Print,Ushi no Koku|青楼十二時　続　丑の刻|The Hour of the Ox (1 A.M.–3 A.M.),Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,ca. 1794,1784,1804,Polychrome woodblock print; ink and color on paper,H. 14 5/8 in. (37.1 cm); W. 9 5/8 in. (24.4 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45013,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP978,false,true,54857,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,1790s,1790,1799,Polychrome woodblock print; ink and color on paper,H. 15 1/2 in. (39.4 cm); W. 10 in. (25.4 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54857,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP979,false,true,54858,Asian Art,Print,山姥と金太郎|Yamauba Combing Her Hair and Kintoki,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,ca. 1801,1791,1811,Polychrome woodblock print; ink and color on paper,H. 14 5/8 in. (37.1 cm); W. 10 in. (25.4 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54858,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP980,false,true,45100,Asian Art,Print,山姥と金太郎|Yamauba and Kintoki,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,ca. 1795,1785,1805,Polychrome woodblock print; ink and color on paper,H. 15 1/8 in. (38.4 cm); W. 8 7/8 in. (22.5 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45100,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP981,false,true,54859,Asian Art,Print,山姥と金太郎|Yamauba Playing with the Young Kintoki,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,ca. 1795,1795,1805,Polychrome woodblock print; ink and color on paper,H. 14 1/4 in. (36.2 cm); W. 9 in. (22.9 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54859,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP982,false,true,54860,Asian Art,Print,山姥と金太郎|Yamauba and Kintarō,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,ca. 1795,1785,1805,Polychrome woodblock print; ink and color on paper,H. 15 1/8 in. (38.4 cm); W. 9 3/8 in. (23.8 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54860,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP983,false,true,54869,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,1790s,1790,1799,Polychrome woodblock print; ink and color on paper,H. 14 7/8 in. (37.8 cm); W. 9 7/8 in. (25.1 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54869,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP984,false,true,37277,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,ca. 1797,1787,1807,Triptych of polychrome woodblock prints; ink and color on paper,Overall: 15 1/8 x 30 in. (38.4 x 76.2 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37277,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP985,false,true,54870,Asian Art,Print,絵兄弟|A Woman Dressing a Girl for a Kabuki Dance (E-kyodai),Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,1790s,1790,1800,Polychrome woodblock print; ink and color on paper,H. 15 1/4 in. (38.7 cm); W. 10 1/8 in. (25.7 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54870,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP986,false,true,54871,Asian Art,Print,二葉草七小町　清水小町|Kiyomizu Komachi,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,1790s,1790,1800,Polychrome woodblock print; ink and color on paper,H. 14 1/2 in. (36.8 cm); W. 9 1/2 in. (24.1 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54871,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP987,false,true,54872,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,ca. 1798,1788,1808,Polychrome woodblock print; ink and color on paper,H. 13 in. (33 cm); W. 8 7/8 in. (22.5 cm); Diam. of circle 8 3/8 in. (21.3 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54872,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP988,false,true,45474,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,ca. 1793,1783,1803,Polychrome woodblock print; ink and color on paper,H. 14 5/8 in. (37.1 cm); W. 10 in. (25.4 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45474,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP989,false,true,54873,Asian Art,Print,吉原時計　夜の七つ|The Seventh Hour of the Night,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,ca. 1800,1790,1810,Polychrome woodblock print; ink and color on paper,H. 9 1/4 in. (23.5 cm); W. 6 3/4 in. (17.1 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54873,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP990,false,true,45292,Asian Art,Print,江戸仕入大津土産|Young Woman with an Otsue Demon Dressed as an Itinerant Priest,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,ca. 1804,1794,1814,Polychrome woodblock print; ink and color on paper,14 1/2 x 9 1/2 in. (36.8 x 24.1 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45292,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP991,false,true,45291,Asian Art,Print,"江戸仕入大津土産　槍持奴　鷹匠|Souvenir Paintings from Ōtsu, Stocked in Edo (Edo shi-ire Ōtsu miyage) Foot-soldier with a Spear and Hawk-handler (Yari mochi yakko to taka shō)",Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,ca. 1802–3,1802,1803,Polychrome woodblock print; ink and color on paper,H. 14 1/2 in. (36.8 cm); W. 9 1/2 in. (24.1 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45291,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP992,false,true,37278,Asian Art,Print,太閤五妻洛東遊観之図|A View of the Pleasures of the Taiko and His Five Wives at Rakutō,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,1804,1804,1804,Triptych of polychrome woodblock prints; ink and color on paper,15 x 30 in. (38.1 x 76.2 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37278,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP993,false,true,37279,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,ca. 1805,1795,1815,Triptych of polychrome woodblock prints; ink and color on paper,14 3/8 x 29 in. (36.5 x 73.7 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37279,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP994,false,true,37280,Asian Art,Print,柿もぎ|Picking Persimmons ((Kaki mogi)),Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,ca. 1803–4,1793,1814,Triptych of polychrome woodblock prints; ink and color on paper,15 x 30 in. (38.1 x 76.2 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37280,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP996,false,true,37281,Asian Art,Print,忠臣蔵三段目|Chushingura Act III,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,ca. 1800,1790,1810,Polychrome woodblock print; ink and color on paper,H. 15 1/8 in. (38.4 cm); W. 10 1/2 in. (26.7 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37281,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1043,false,true,37283,Asian Art,Woodblock print,"『画本虫撰』 「蜂」「毛虫」|Paper Wasp (Hachi); Hairy Caterpillar (Kemushi), from the Picture Book of Crawling Creatures (Ehon mushi erami)",Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,1788,1788,1788,Page from woodblock printed book; ink and color on paper,10 1/2 x 7 7/32 in. (26.7 x 18.4 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37283,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1044,false,true,37284,Asian Art,Woodblock print,"『画本虫撰』 「馬追虫」「むかて」|Katydid (Umaoi-mushi); Centipede, (Mukade), from the Picture Book of Crawling Creatures (Ehon mushi erami)",Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,1788,1788,1788,Page from woodblock printed book; ink and color on paper,10 1/2 x 7 7/32 in. (26.7 x 18.4 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37284,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1045,false,true,37285,Asian Art,Woodblock print,"『画本虫撰』 「けら」「はさみむし」|Mole Cricket (Kera); Earwig, (Hasami-mushi), from the Picture Book of Crawling Creatures (Ehon mushi erami)",Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,1788,1788,1788,Page from woodblock printed book; ink and color on paper,10 1/2 x 7 7/32 in. (26.7 x 18.4 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37285,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1046,false,true,37286,Asian Art,Woodblock print,"『画本虫撰』 「蝶」「蜻蛉」|Butterfly (Chō); Dragonfly (Kagerō or Tonbo), from the Picture Book of Crawling Creatures (Ehon mushi erami)",Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,1788,1788,1788,Page from woodblock printed book; ink and color on paper,10 1/2 x 7 1/4 in. (26.7 x 18.4 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37286,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1047,false,true,37287,Asian Art,Woodblock print,"『画本虫撰』 「虻」「芋虫」|Horsefly (abu); Green Caterpillar, imomushi, from the Picture Book of Crawling Creatures (Ehon mushi erami)",Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,1788,1788,1788,Page from woodblock printed book; ink and color on paper,10 1/2 x 7 7/32 in. (26.7 x 18.4 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37287,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1048,false,true,37288,Asian Art,Woodblock print,"『画本虫撰』 「松虫」「虫蛍」|Tree cricket (Matsumushi); Firefly (Hotaru), from the Picture Book of Crawling Creatures (Ehon mushi erami)",Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,1788,1788,1788,Page from woodblock printed book; ink and color on paper,10 1/2 x 7 7/32 in. (26.7 x 18.4 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37288,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1049,false,true,37289,Asian Art,Woodblock print,"画本虫撰　バッタと蟷螂|Cone-headed Grasshopper or Locust, (batta); Praying Mantis (Tōrō or Kamakiri), from the Picture Book of Crawling Creatures (Ehon mushi erami)",Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,1788,1788,1788,Page from woodblock printed book; ink and color on paper,10 1/2 x 7 7/32 in. (26.7 x 18.4 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37289,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1050,false,true,37290,Asian Art,Print,"『画本虫撰』 「ひくらし」「くも」|Evening Cicada, Higurashi; Spider, Kumo, from the Picture Book of Crawling Creatures (Ehon mushi erami)",Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,1788,1788,1788,Page from woodblock printed book; ink and color on paper,10 1/2 x 7 7/32 in. (26.7 x 18.4 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37290,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1051,false,true,37291,Asian Art,Woodblock print,"『画本虫撰』「赤蜻蛉」「いなこ」|Red Dragonfly (Akatonbo); Locust (Inago), from the Picture Book of Crawling Creatures (Ehon mushi erami)",Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,1788,1788,1788,Page from woodblock-printed book; ink and color on paper,Overall: 10 1/2 x 7 1/4 in. (26.7 x 18.4 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37291,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1052,false,true,37292,Asian Art,Woodblock print,"『画本虫撰』「虵」「とかけ」|Rat Snake (Hebi); Lizard or Skink (Tokage), from the Picture Book of Crawling Creatures (Ehon mushi erami)",Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,1788,1788,1788,Page from woodblock-printed book; ink and color on paper,Overall: 10 1/2 x 7 1/4in. (26.7 x 18.4cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37292,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1053,false,true,37293,Asian Art,Woodblock print,"『画本虫撰』「蓑虫」「兜虫」|Bagworm (Minomushi); Horned Scarab Beetle (Kabutomushi), from the Picture Book of Crawling Creatures (Ehon mushi erami)",Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,1788,1788,1788,Page from woodblock-printed book; ink and color on paper,10 1/2 x 7 7/32 in. (26.7 x 18.4 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37293,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1054,false,true,37294,Asian Art,Woodblock print,"『画本虫撰』「蝸牛」「轡虫」|Land Snail (Katatsumuri); Giant Katydid (Kutsuwamushi), from the Picture Book of Crawling Creatures (Ehon mushi erami)",Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,1788,1788,1788,Page from woodblock-printed book; ink and color on paper,10 1/2 x 7 7/32 in. (26.7 x 18.4 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37294,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1055,false,true,37295,Asian Art,Woodblock print,"『画本虫撰』「きりきりす」「蝉」|Grasshopper (Kirigirisu); Cicada (Semi), from the Picture Book of Crawling Creatures (Ehon mushi erami)",Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,1788,1788,1788,Page from woodblock-printed book; ink and color on paper,10 1/2 x 7 1/4 in. (26.7 x 18.4 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37295,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1056,false,true,37296,Asian Art,Woodblock print,"『画本虫撰』「蚓」「こうろき」|Earthworm (Mimizu); Cricket (Kōrogi), from the Picture Book of Crawling Creatures (Ehon mushi erami)",Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,1788,1788,1788,Page from woodblock-printed book; ink and color on paper,10 1/2 x 7 7/32 in. (26.7 x 18.4 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37296,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1058,false,true,37298,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,ca. 1790,1780,1800,Polychrome woodblock print; ink and color on paper,14 1/2 x 9 in. (36.8 x 22.9 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37298,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1059,false,true,37299,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,1790,1790,1790,Polychrome woodblock print; ink and color on paper,9 1/8 x 14 7/8 in. (23.2 x 37.8 cm),"Gift of Estate of Samuel Isham, 1915",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37299,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1060,false,true,37300,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,1790,1790,1790,Polychrome woodblock print; ink and color on paper,9 1/8 x 14 15/32 in. (23.2 x 36.8 cm),"Gift of Estate of Samuel Isham, 1915",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37300,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1061,false,true,37301,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,1790,1790,1790,Polychrome woodblock print; ink and color on paper,9 x 14 3/4 in. (22.9 x 37.5 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37301,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1062,false,true,37302,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,1790,1790,1790,Polychrome woodblock print; ink and color on paper,9 1/8 x 14 1/2 in. (23.2 x 36.8 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37302,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1063,false,true,37303,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,1790,1790,1790,Polychrome woodblock print; ink and color on paper,9 1/8 x 14 7/8 in. (23.2 x 37.8 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37303,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1064,false,true,37304,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,1790,1790,1790,Polychrome woodblock print; ink and color on paper,9 1/8 x 14 3/5 in. (23.2 x 37.1 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37304,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1072,false,true,37305,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,1801,1801,1801,Polychrome woodblock print; ink and color on paper,6 13/16 x 10 5/16 in. (17.3 x 26.2 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37305,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1073,false,true,37306,Asian Art,Woodblock print,"四季の花|The Coming Thunderstorm, from the illustrated book Flowers of the Four Seasons",Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,1801,1801,1801,Polychrome woodblock print; ink and color on paper,6 7/8 x 9 7/8 in. (17.5 x 25.1 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37306,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1074,false,true,37307,Asian Art,Woodblock print,"四季の花|Women on a Bridge, from the illustrated book Flowers of the Four Seasons",Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,1801,1801,1801,Polychrome woodblock print; ink and color on paper,6 3/4 × 9 7/8 in. (17.1 × 25.1 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37307,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1075,false,true,37308,Asian Art,Woodblock print,"四季の花|A Child Lighting Fireworks, from the illustrated book Flowers of the Four Seasons",Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,1801,1801,1801,Polychrome woodblock print; ink and color on paper,6 3/4 × 9 7/8 in. (17.1 × 25.1 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37308,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1076,false,true,37309,Asian Art,Woodblock print,"四季の花|Girls Getting on Board a Boat, from the illustrated book Flowers of the Four Seasons",Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,1801,1801,1801,Polychrome woodblock print; ink and color on paper,6 3/4 x 9 7/8 in. (17.1 x 25.1 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37309,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1077,false,true,37310,Asian Art,Woodblock print,"四季の花|Girls Picking Green Leaves, from the illustrated book Flowers of the Four Seasons",Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,1801,1801,1801,Polychrome woodblock print; ink and color on paper,6 13/16 x 9 7/8 in. (17.3 x 25.1 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37310,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1078,false,true,37311,Asian Art,Woodblock print,"四季の花|New Year's Games, from the printed book Flowers of the Four Seasons (Shiki no hana)",Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,1801,1801,1801,Polychrome woodblock print; ink and color on paper,6 7/8 x 9 7/8 in. (17.5 x 25.1 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37311,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1079,false,true,37312,Asian Art,Woodblock print,"四季の花|Girls Entertained by Performers, from the illustrated book Flowers of the Four Seasons",Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,1801,1801,1801,Polychrome woodblock print; ink and color on paper,6 7/8 x 9 7/8 in. (17.5 x 25.1 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37312,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1080,false,true,37313,Asian Art,Woodblock print,"四季の花|Poetry, from the illustrated book Flowers of the Four Seasons",Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,1801,1801,1801,Polychrome woodblock print; ink and color on paper,6 13/16 x 9 4/5 in. (17.3 x 24.9 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37313,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1111,false,true,37314,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,ca. 1800,1790,1810,Polychrome woodblock print; ink and color on paper,H. 14 3/4 in. (37.5 cm); W. 10 in. (25.4 cm),"Rogers Fund, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37314,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1112,false,true,52009,Asian Art,Print,吾妻美人ゑらみ|A Tea-house Waitress,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,ca. 1795,1785,1805,Album of eighty-nine prints; ink and color on paper,H. 13 in. (33 cm); W. 14 in. (35.6 cm),"Rogers Fund, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/52009,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1113,false,true,45096,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,ca. 1795,1785,1805,Polychrome woodblock print; ink and color on paper,H. 15 in. (38.1 cm); W. 9 1/2 in. (24.1 cm),"Rogers Fund, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45096,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1121,false,true,55043,Asian Art,Print,当世風俗通　女房風|Mother and Child,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,ca. 1790,1780,1810,Polychrome woodblock print; ink and color on paper,H. 14 7/8 in. (37.8 cm); W. 10 in. (25.4 cm),"Rogers Fund, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55043,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1122,false,true,37315,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,1806–20,1806,1820,Polychrome woodblock print; ink and color on paper,15 x 10 in. (38.1 x 25.4 cm),"Rogers Fund, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37315,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1123,false,true,39647,Asian Art,Print,三勝と半七|Sankatsu and Hanshichi,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,ca. 1800,1790,1810,Polychrome woodblock print; ink and color on paper,H. 24 1/2 in. (62.2 cm); W. 5 7/8 in. (14.9 cm),"Rogers Fund, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/39647,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1214,false,true,55137,Asian Art,Print,逢身八契　権八小紫の床の通気|Gonpachi ni Komurasaki no Toko no Tsuki,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,ca. 1795,1785,1805,Polychrome woodblock print; ink and color on paper,H. 15 1/8 in. (38.4 cm); W. 9 7/8 in. (25.1 cm),"Rogers Fund, 1920",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55137,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1215,false,true,55138,Asian Art,Print,"『青楼七小町』 「玉屋内花紫」|“Hanamurasaki of the Tamaya,” from the series Seven Komachi of the Pleasure Quarters (Seirō Nana Komachi)",Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,ca. 1790,1780,1800,Polychrome woodblock print; ink and color on paper,H. 14 1/2 in. (36.8 cm); W. 9 7/8 in. (25.1 cm),"Rogers Fund, 1920",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55138,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1272,false,true,55193,Asian Art,Print,青楼仁和嘉女芸者之部　扇売　団扇売　麦つき|The Niwaka Performers,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,ca. 1795,1785,1805,Polychrome woodblock print; ink and color on paper,H. 15 3/16 in. (38.6 cm); W. 10 1/8 in. (25.7 cm),"Rogers Fund, 1921",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55193,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1277,false,true,55208,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,1795,1795,1795,Polychrome woodblock print; ink and color on paper,H. 14 3/4 in. (37.5 cm); W. 10 in. (25.4 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55208,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1278,false,true,51137,Asian Art,Print,風俗美人時計　子ノ刻　妾|Midnight: Mother and Sleepy Child,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,1790,1790,1790,Polychrome woodblock print; ink and color on paper,14 3/8 x 9 5/8 in. (36.5 x 24.4cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/51137,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1367,false,true,53689,Asian Art,Print,南国美人合|Courtesan Holding a Fan,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,ca. 1793,1783,1803,Polychrome woodblock print; ink and color on paper 02_18_60---(Mica ground),14 1/2 x 9 5/16 in. (36.8 x 23.7 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/53689,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1368,false,true,55318,Asian Art,Print,"『北国五色墨』「おいらん」|“High-Ranking Courtesan” (Oiran), from the series Five Shades of Ink in the Northern Quarter (Hokkoku goshiki-zumi),",Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,1794–95,1794,1795,Polychrome woodblock print; ink and color on paper,Oban 14 3/4 x 9 3/4 in. (37.5 x 24.8 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55318,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1392,false,true,55399,Asian Art,Print,富本豊ひな|The Lady Tomimoto Toyohina Reading a Letter,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,1790,1790,1790,Polychrome woodblock print; ink and color on paper,H. 14 5/8 in. (37.1 cm); W. 9 3/8 in. (23.8 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55399,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1438,false,true,45099,Asian Art,Print,山姥と金太郎|Yamauba and Kintoki,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,ca. 1795,1785,1805,Polychrome woodblock print; ink and color on paper,H. 15 1/16 in. (38.3 cm); W. 9 7/8 in. (25.1 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45099,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1439,false,true,45015,Asian Art,Print,当世恋歌八契　お七と吉三郎|The Lovers Oshichi and Kichisaburo,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,ca. 1800,1790,1810,Polychrome woodblock print; ink and color on paper,H. 15 1/4 in. (38.7 cm); W. 9 3/4 in. (24.8 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45015,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1440,false,true,45016,Asian Art,Print,Gompachi Komurasaki no Toko no Tsuki|逢身八契　権八小紫の床の通気|Shared Feelings in the Bedchamber of Komurasaki and Gompachi,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,ca. 1795,1785,1805,Polychrome woodblock print; ink and color on paper,H. 14 15/16 in. (37.9 cm); W. 10 3/16 in. (25.9 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45016,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1441,false,true,55485,Asian Art,Print,八百屋お七　寺小姓吉三郎|O Shichi and Kichisaburo,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,ca. 1780,1770,1790,Polychrome woodblock print; ink and color on paper,H. 24 3/4 in. (62.9 cm); W. 5 5/16 in. (13.5 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55485,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1461,false,true,54862,Asian Art,Print,"名取酒六家選　兵庫屋華妻　坂上の剣菱|""Hanazuma of Hyōgoya, Kenbishi of Sakagami” from the series The Peers of Saké Likened to Select Denizens of Six Houses (Natori zake rokkasen: Hyōgoya Hanazuma, Sakagami no Kenbishi)",Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,ca. 1794,1784,1804,Polychrome woodblock print; ink and color on paper,H. 14 3/4 in. (37.5 cm); W. 9 1/2 in. (24.1 cm),"Rogers Fund, 1925",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54862,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1462,false,true,45098,Asian Art,Print,江戸の園花合　東屋の花|Azumaya no Hana,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,ca. 1790,1780,1800,Polychrome woodblock print; ink and color on paper,H. 13 1/16 in. (33.2 cm); W. 8 7/8 in. (22.5 cm),"Rogers Fund, 1925",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45098,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1478,false,true,55565,Asian Art,Print,娘日時計　申ノ刻|Seru no Koku,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,ca. 1795,1785,1805,Polychrome woodblock print; ink and color on paper,H. 14 15/16 in. (37.9 cm); W. 10 1/4 in. (26 cm),"Fletcher Fund, 1925",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55565,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1525,false,true,51093,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,1790s,1790,1800,Polychrome woodblock print; ink and color on paper,H. 14 3/4 in. (37.5 cm); W. 9 5/8 in. (24.4 cm),"Fletcher Fund, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/51093,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1526,false,true,54865,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,ca. 1793,1783,1803,Polychrome woodblock print; ink and color on paper,H. 14 7/8 in. (37.8 cm); W. 9 7/8 in. (25.1 cm),"Fletcher Fund, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54865,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1527,false,true,55636,Asian Art,Print,"「扇屋内花扇図」|The Courtesan Hanaōgi of the Ōgiya Brothel in Yoshiwara (Ōgiya uchi Hanaōgi, Yoshino, Tatsuta)",Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,ca. 1793–94,1793,1794,Polychrome woodblock print; ink and color on paper,H. 14 5/8 in. (37.1 cm); W. 10 13/16 in. (27.5 cm),"Fletcher Fund, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55636,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1528,false,true,55639,Asian Art,Print,三婦艶|Three Beauties of the Kwansei Period,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,ca. 1791,1781,1801,Polychrome woodblock print; ink and color on paper,H. 15 1/4 in. (38.7 cm); W. 10 1/8 in. (25.7 cm),"Fletcher Fund, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55639,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1657,false,true,55818,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,ca. 1805,1795,1815,Polychrome woodblock print; ink and color on paper,H. 15 in. (38.1 cm); W. 9 13/16 in. (24.9 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55818,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1658,false,true,55819,Asian Art,Print,藤棚下の遊女たち|Courtesans Beneath a Wisteria Arbor (Fuji dana shita no yūjo tachi),Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,ca. 1795,1785,1805,Polychrome woodblock print; ink and color on paper,H. 15 1/8 in. (38.4 cm); W. 10 7/8 in. (27.6 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55819,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1659,false,true,55434,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,1790s,1790,1800,Polychrome woodblock print; ink and color on paper,H. 15 in. (38.1 cm); W. 10 in. (25.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55434,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1660,false,true,44990,Asian Art,Print,実競色乃美名家見　紙屋次兵衛　紀ノ国屋小春|Jihei of Kamiya Eloping with Koharu of Kinokuniya,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,early 1800s,1800,1833,Polychrome woodblock print; ink and color on paper,Image: 13 3/4 x 10 in. (34.9 x 25.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/44990,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1661,false,true,45477,Asian Art,Print,行水|Bathtime (Gyōzui),Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,ca. 1801,1791,1811,Polychrome woodblock print; ink and color on paper,14 11/16 x 9 7/8 in. (37.3 x 25.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45477,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1662,false,true,45017,Asian Art,Print,婦女人相十品　相観歌麿考画|Woman with a Glass Noisemaker (Popen),Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,early 1790s,1790,1794,Polychrome woodblock print; ink and color on paper,15 5/16 x 10 13/16 in. (38.9 x 27.5 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45017,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1664,false,true,37335,Asian Art,Print,当世美人三遊　芸妓|“Geisha” from the series Three Amusements of Contemporary Beauties (Tōsei bijin san’yū: Geigi),Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,ca. 1800,1790,1810,Polychrome woodblock print; ink and color on paper,20 7/8 x 9 9/16 in. (53.0 x 24.3 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37335,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1665,false,true,55862,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,ca. 1797,1787,1807,Polychrome woodblock print; ink and color on paper,H. 14 1/8 in. (35.9 cm); W. 9 1/4 in. (23.5 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55862,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1666,false,true,37336,Asian Art,Print,雪の桟橋|Landing-stage in the Snow (Yuki no sanbashi),Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,1800,1800,1800,Polychrome woodblock print; ink and color on paper,H. 20 1/2 in. (52.1 cm); W. 7 7/16 in. (18.9 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37336,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1667,false,true,55863,Asian Art,Print,青楼仁和嘉女芸者之部　唐人　獅子 角力|Seiro Niwaka Onna Geisha no Bu Tojin Shishi Sumo,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,ca. 1791,1781,1801,Polychrome woodblock print; ink and color on paper,H. 10 1/16 in. (25.6 cm); W. 15 1/8 in. (38.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55863,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1668,false,true,55867,Asian Art,Print,難波屋おきた|Okita of the Naniwa-ya Tea-house,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,1790s,1790,1799,Polychrome woodblock print; ink and color on paper,H. 14 5/16 in. (36.4 cm); W. 9 1/2 in. (24.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55867,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1669,false,true,37337,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,1790,1790,1790,Polychrome woodblock print; ink and color on paper,Image: 8 7/8 in. × 14 in. (22.5 × 35.6 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37337,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1672,false,true,53653,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,ca. 1793–94,1793,1794,One sheet of a triptych of polychrome woodblock prints; ink and color on paper,H. 15 1/8 in. (38.4 cm); W. 10 3/16 in. (25.9 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/53653,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1673,false,true,54864,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,1790s,1790,1799,Polychrome woodblock print; ink and color on paper,H. 18 5/8 in. (47.3 cm); W. 7 5/8 in. (19.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54864,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1674,false,true,45479,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,ca. 1793,1783,1803,Polychrome woodblock print; ink and color on paper,20 3/4 x 7 5/8 in. (52.7 x 19.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45479,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1675,false,true,37338,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,ca. 1794–95,1794,1795,Right-hand sheet of a diptych of polychrome woodblock prints; ink and color on paper,Image: 15 × 10 in. (38.1 × 25.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37338,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1676,false,true,53654,Asian Art,Print,"美人気量競　五明楼　花扇|“Hanaōgi of the Gomeirō,” from the series Comparing the Charms of Beauties (Bijin kiryō kurabe: Gomeirō Hanaōgi)",Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,1794–95,1794,1795,Polychrome woodblock print; ink and color on paper,H. 14 15/16 in. (37.9 cm); W. 9 11/16 in. (24.6 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/53654,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1677,false,true,37339,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,1791,1791,1791,Triptych of polychrome woodblock prints; ink and color on paper,7 7/16 x 14 3/5 in. (18.9 x 37.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37339,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1679,false,true,45018,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,ca. 1795,1785,1805,Polychrome woodblock print; ink and color on paper,H. 24 7/8 in. (63.2 cm); W. 5 5/8 in. (14.3 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45018,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1685,false,true,53897,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,ca. 1800,1790,1810,Three sheets of a hexaptych of polychrome woodblock prints; ink and color on paper,15 x 29 3/4in. (38.1 x 75.6cm) Framed: 38 1/8 × 38 1/8 in. (96.8 × 96.8 cm) shares frame with JP1686,"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/53897,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1686,false,true,37344,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,ca. 1800,1790,1810,Three sheets of a hexaptych of polychrome woodblock prints; ink and color on paper,15 x 29 3/4in. (38.1 x 75.6cm) Framed: 38 1/8 × 38 1/8 in. (96.8 × 96.8 cm) shares frame with JP1685,"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37344,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1687,false,true,55922,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,1790s,1790,1800,Triptych of polychrome woodblock prints; ink and color on paper,a: H. 14 7/8 in. (37.8 cm); W. 9 7/8 in. (25.1 cm) b: H. 15 in. (38.1 cm); W. 9 3/4 in. (24.8 cm) c: H. 14 5/16 in. (36.4 cm); W. 9 15/16 in. (25.2 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55922,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2393,false,true,56798,Asian Art,Print,忠臣蔵四段目|A Woman and a Man Arranging Flowers for the Tsukimi (Moon Festival),Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,1802,1802,1802,Polychrome woodblock print; ink and color on paper,15 1/8 x 10 1/8 in. (38.4 x 25.7 cm),"H. O. Havemeyer Collection, Gift of Mrs. J. Watson Webb, 1930",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56798,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2395,false,true,56800,Asian Art,Print,玉屋内　志津賀|A Courtesan,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,1807,1807,1807,Polychrome woodblock print; ink and color on paper,14 3/16 x 9 7/8 in. (36 x 25.1 cm),"H. O. Havemeyer Collection, Gift of Mrs. J. Watson Webb, 1930",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56800,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2730,false,true,55319,Asian Art,Print,婦女人相十品　相観|“Woman Holding Up a Parasol” from the series Ten Classes of Women’s Physiognomy (Fujo ninsō juppen: Higasa o sasu onna),Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,ca. 1792–93,1792,1793,Polychrome woodblock print; ink and color on paper,15 x 10 1/8 in. (38.1 x 25.7 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55319,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2731,false,true,52001,Asian Art,Print,女織蚕手業草　十|The Making of Silk Floss,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,ca. 1790,1780,1800,Polychrome woodblock print; ink and color on paper,15 x 10 in. (38.1 x 25.4 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/52001,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2732,false,true,37354,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,1795,1795,1795,Right-hand sheet of a triptych of polychrome woodblock prints; ink and color on paper,15 x 10 in. (38.1 x 25.4 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37354,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2734,false,true,45019,Asian Art,Print,Naniwaya Okita|Teahouse Waitress,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,ca. 1793,1783,1803,Polychrome woodblock print; ink and color on paper,14 1/8 x 9 7/8 in. (35.9 x 25.1 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45019,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2735,false,true,52002,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,ca. 1790,1780,1800,Polychrome woodblock print; ink and color on paper,15 3/8 x 10 1/8 in. (39.1 x 25.7 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/52002,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2736,false,true,53656,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,ca. 1795,1785,1805,Polychrome woodblock print; ink and color on paper,15 x 9 3/4 in. (38.1 x 24.8 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/53656,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2737,false,true,37355,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,ca. 1790,1780,1800,Right-hand sheet of a triptych of polychrome woodblock prints; ink and color on paper,14 7/8 x 9 3/4 in. (37.8 x 24.8 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37355,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2738,false,true,57011,Asian Art,Print,実競色乃美名家見　紙屋次兵衛　紀ノ国屋小春|Jihei of Kamiya Eloping with the Geisha Koharu of Kinokuniya,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,ca. 1798,1788,1808,Polychrome woodblock print; ink and color on paper,15 1/8 x 9 5/8 in. (38.4 x 24.4 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57011,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2739,false,true,37356,Asian Art,Print,青楼仁和嘉女芸者之部　茶せん売　黒木売　さいもん|Three Niwaka Performers,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,ca. 1794,1784,1804,Polychrome woodblock print; ink and color on paper,15 x 9 3/4 in. (38.1 x 24.8 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37356,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2740,false,true,37357,Asian Art,Print,契情三人酔　三幅之内　腹立上戸　泣上戸　笑上戸|Three Intoxicated Courtesans,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,1790s,1790,1799,Triptych of polychrome woodblock prints; ink and color on paper,Each H. 12 7/8 in. (32.7 cm); W. 8 1/2 in. (21.6 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37357,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2741,false,true,57013,Asian Art,Print,六玉川　扇屋内　花扇|The Oiran Hanaogi of Ogiya,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,ca. 1796,1786,1806,Polychrome woodblock print; ink and color on paper,15 1/8 x 10 in. (38.4 x 25.4 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57013,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2809,false,true,57081,Asian Art,Print,Uguisu|風流小鳥合　鶯|Japanese Bush Warbler,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,1754–1806,1754,1806,Polychrome woodblock print; ink and color on paper,8 3/4 x 6 1/4 in. (22.2 x 15.9 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57081,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2810,false,true,57080,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,1615–1806,1615,1806,Polychrome woodblock print; ink and color on paper,14 5/8 x 10 1/8 in. (37.1 x 25.7 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57080,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2811,false,true,54866,Asian Art,Print,児戯意乃三笑　恵恩芳子|Mother and Child,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,ca. 1800,1790,1810,Polychrome woodblock print; ink and color on paper,H. 14 5/16 in. (36.4 cm); W. 9 9/16 in. (24.3 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54866,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2812,false,true,45020,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,ca. 1794–95,1750,1850,Polychrome woodblock print; ink and color on paper,14 7/8 x 10 in. (37.8 x 25.4 cm),"Henry L. Phillips Collection; Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45020,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2813,false,true,56793,Asian Art,Print,"喜多川歌麿画　『美人花合』　「兵庫屋内　花妻図」|“The Courtesan Hanazuma Reading a Letter,” from the series Beauties Compared to Flowers (Bijin hana awase)",Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,1790s,1790,1799,Polychrome woodblock print; ink and color on paper,H. 15 1/4 in. (38.7 cm); W. 10 1/4 in. (26 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56793,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2814,false,true,45097,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,ca. 1798,1788,1808,Polychrome woodblock print; ink and color on paper,14 7/16 x 9 3/4 in. (36.7 x 24.8 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45097,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2853,false,true,37360,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,1754–1806,1754,1806,Polychrome woodblock print; ink and color on paper,9 7/8 x 14 7/8 in. (25.1 x 37.8 cm) (including margins),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37360,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2854,false,true,37361,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,1754–1806,1754,1806,Polychrome woodblock print; ink and color on paper,9 7/8 x 14 7/8 in. (25.1 x 37.8 cm) (including margins),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37361,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3018,false,true,51135,Asian Art,Print,姿見七人化粧|Naniwa Okita Admiring Herself in a Mirror,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,ca. 1790–95,1790,1795,"Polychrome woodblock print; ink and color on paper, mica ground",14 1/2 x 9 7/8 in. (36.8 x 25.1 cm),"Harris Brisbane Dick Fund, 1946",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/51135,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3060,false,true,37364,Asian Art,Print,仮宅の後朝|Scene in the Yoshiwara,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,1790,1790,1790,Triptych of polychrome woodblock prints; ink and color on paper,Triptych; each H. 14 3/4 in. (37.5 cm); W. 11 5/8 in. (29.5 cm),"Gift of Mrs. Morris Manges, in memory of her husband, Dr. Morris Manges, 1947",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37364,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1996.85,false,true,39615,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,ca. 1800,1790,1810,Polychrome woodblock print; mineral pigments and ink on paper,14 x 9 3/4 in. (35.6 x 24.8 cm); oban size 14 3/8 x 9 1/2 in. (36.5 x 24.1 cm),"Gift of Mr. and Mrs. Horace H. Wilson, 1996",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/39615,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1996.463,false,true,40244,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,ca. 1795,1785,1805,Ink on mulberry paper,Oban: 14 3/4 x 9 3/4 in. (37.5 x 24.8 cm),"Gift of John and Lili Bussel, 1996",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/40244,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"JP1369a, b",false,true,37325,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,ca. 1794–95,1784,1805,Diptych of polychrome woodblock prints; ink and color on paper,Image (each): 14 5/8 × 9 7/8 in. (37.1 × 25.1 cm) Image (diptych): 14 5/8 × 19 3/4 in. (37.1 × 50.2 cm) Framed: 24 1/4 × 30 3/4 in. (61.6 × 78.1 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37325,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP965a–c,false,true,37271,Asian Art,Print,琴棋書画図|The Four Elegant Accomplishments (Kin ki sho ga),Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,ca. 1788,1778,1798,Triptych of polychrome woodblock prints; ink and color on paper,15 x 30 in. (38.1 x 76.2 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37271,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1057a–c,false,true,37297,Asian Art,Woodblock print,"『画本虫撰』「蛙」「こかねむし」|Frog (Kaeru); Gold Beetle (Kogane mushi), from the Picture Book of Crawling Creatures (Ehon mushi erami)",Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,1788,1788,1788,Page from woodblock-printed book; ink and color on paper,10 1/2 x 7 7/32 in. (26.7 x 18.4 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37297,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1037,false,true,54322,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Yashima Gakutei,"Japanese, 1786?–1868",,Yashima Gakutei,Japanese,1786,1868,1835,1835,1835,Polychrome woodblock print (surimono); ink and color on paper,8 1/4 x 7 1/4 in. (21 x 18.4 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54322,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1038,false,true,54323,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,Formerly attributed to,Yashima Gakutei,"Japanese, 1786?–1868",,Yashima Gakutei,Japanese,1786,1868,1835,1835,1835,Polychrome woodblock print (surimono); ink and color on paper,7 7/8 x 8 3/4 in. (20 x 22.2 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54323,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1039,false,true,54324,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Yashima Gakutei,"Japanese, 1786?–1868",,Yashima Gakutei,Japanese,1786,1868,1835?,1835,1835,Polychrome woodblock print (surimono); ink and color on paper,7 7/8 x 7 in. (20 x 17.8 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54324,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1101,false,true,54330,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Yashima Gakutei,"Japanese, 1786?–1868",,Yashima Gakutei,Japanese,1786,1868,ca. 1820,1810,1830,Diptych of Polychrome woodblock print (surimono); ink and color on paper,"8 3/16 x 7 1/8 in. (20.8 x 18.1 cm); dyptych, vertical","Rogers Fund, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54330,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1107,false,true,54336,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Yashima Gakutei,"Japanese, 1786?–1868",,Yashima Gakutei,Japanese,1786,1868,ca. 1825,1815,1835,Polychrome woodblock print (surimono); ink and color on paper,8 x 6 15/16 in. (20.3 x 17.6 cm),"Rogers Fund, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54336,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1131,false,true,54338,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Yashima Gakutei,"Japanese, 1786?–1868",,Yashima Gakutei,Japanese,1786,1868,ca. 1820,1810,1830,Polychrome woodblock print (surimono); ink and color on paper,8 3/16 x 7 3/8 in. (20.8 x 18.7 cm),"Rogers Fund, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54338,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1132,false,true,54339,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Yashima Gakutei,"Japanese, 1786?–1868",,Yashima Gakutei,Japanese,1786,1868,ca. 1820,1810,1830,Polychrome woodblock print (surimono); ink and color on paper,8 3/16 x 7 5/16 in. (20.8 x 18.6 cm),"Rogers Fund, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54339,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1133,false,true,54340,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Yashima Gakutei,"Japanese, 1786?–1868",,Yashima Gakutei,Japanese,1786,1868,ca. 1830,1820,1840,Polychrome woodblock print (surimono); ink and color on paper,8 1/16 x 7 3/16 in. (20.5 x 18.3 cm),"Rogers Fund, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54340,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1134,false,true,54341,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Yashima Gakutei,"Japanese, 1786?–1868",,Yashima Gakutei,Japanese,1786,1868,ca. 1820,1810,1830,Polychrome woodblock print (surimono); ink and color on paper,8 5/16 x 7 5/16 in. (21.1 x 18.6 cm),"Rogers Fund, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54341,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1135,false,true,54342,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Yashima Gakutei,"Japanese, 1786?–1868",,Yashima Gakutei,Japanese,1786,1868,ca. 1825,1815,1835,Polychrome woodblock print (surimono); ink and color on paper,8 5/16 x 7 5/16 in. (21.1 x 18.6 cm),"Rogers Fund, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54342,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1136,false,true,54343,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Yashima Gakutei,"Japanese, 1786?–1868",,Yashima Gakutei,Japanese,1786,1868,ca. 1820,1810,1830,Polychrome woodblock print (surimono); ink and color on paper,8 5/16 x 7 7/16 in. (21.1 x 18.9 cm),"Rogers Fund, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54343,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1137,false,true,54344,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Yashima Gakutei,"Japanese, 1786?–1868",,Yashima Gakutei,Japanese,1786,1868,ca. 1830,1820,1840,Polychrome woodblock print (surimono); ink and color on paper,8 5/16 x 7 7/16 in. (21.1 x 18.9 cm),"Rogers Fund, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54344,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1138,false,true,54345,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Yashima Gakutei,"Japanese, 1786?–1868",,Yashima Gakutei,Japanese,1786,1868,ca. 1830,1820,1840,Polychrome woodblock print (surimono); ink and color on paper,8 x 7 5/16 in. (20.3 x 18.6 cm),"Rogers Fund, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54345,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1139,false,true,54346,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Yashima Gakutei,"Japanese, 1786?–1868",,Yashima Gakutei,Japanese,1786,1868,ca. 1830,1820,1840,Polychrome woodblock print (surimono); ink and color on paper,8 5/8 x 7 1/4 in. (21.9 x 18.4 cm),"Rogers Fund, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54346,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1231,false,true,54361,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Yashima Gakutei,"Japanese, 1786?–1868",,Yashima Gakutei,Japanese,1786,1868,ca. 1830,1820,1840,Polychrome woodblock print (surimono); ink and color on paper,8 1/4 x 7 1/2 in. (21 x 19.1 cm),"Rogers Fund, 1921",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54361,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1232,false,true,54362,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Yashima Gakutei,"Japanese, 1786?–1868",,Yashima Gakutei,Japanese,1786,1868,ca. 1830,1820,1840,Polychrome woodblock print (surimono); ink and color on paper,8 1/4 x 6 3/4 in. (21 x 17.1 cm),"Rogers Fund, 1921",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54362,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1233,false,true,54363,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Yashima Gakutei,"Japanese, 1786?–1868",,Yashima Gakutei,Japanese,1786,1868,ca. 1820,1810,1830,Polychrome woodblock print (surimono); ink and color on paper,8 1/4 x 7 1/4 in. (21 x 18.4 cm),"Rogers Fund, 1921",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54363,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1234,false,true,54364,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Yashima Gakutei,"Japanese, 1786?–1868",,Yashima Gakutei,Japanese,1786,1868,ca. 1810,1800,1820,Polychrome woodblock print (surimono); ink and color on paper,8 1/4 x 7 3/16 in. (21 x 18.3 cm),"Rogers Fund, 1921",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54364,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1235,false,true,54365,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Yashima Gakutei,"Japanese, 1786?–1868",,Yashima Gakutei,Japanese,1786,1868,ca. 1820,1810,1830,Polychrome woodblock print (surimono); ink and color on paper,8 3/8 x 7 5/16 in. (21.3 x 18.6 cm),"Rogers Fund, 1921",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54365,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1236,false,true,54366,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Yashima Gakutei,"Japanese, 1786?–1868",,Yashima Gakutei,Japanese,1786,1868,ca. 1830,1820,1840,Polychrome woodblock print (surimono); ink and color on paper,7 3/4 x 7 1/8 in. (19.7 x 18.1 cm),"Rogers Fund, 1921",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54366,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1237,false,true,54367,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Yashima Gakutei,"Japanese, 1786?–1868",,Yashima Gakutei,Japanese,1786,1868,ca. 1820,1810,1830,Polychrome woodblock print (surimono); ink and color on paper,8 3/16 x 7 3/16 in. (20.8 x 18.3 cm),"Rogers Fund, 1921",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54367,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1238,false,true,54369,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Yashima Gakutei,"Japanese, 1786?–1868",,Yashima Gakutei,Japanese,1786,1868,ca. 1830,1820,1840,Polychrome woodblock print (surimono); ink and color on paper,8 1/4 x 7 3/8 in. (21 x 18.7 cm),"Rogers Fund, 1921",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54369,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1239,false,true,54370,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Yashima Gakutei,"Japanese, 1786?–1868",,Yashima Gakutei,Japanese,1786,1868,ca. 1820,1810,1830,Polychrome woodblock print (surimono); ink and color on paper,5 1/2 x 7 1/2 in. (14 x 19.1 cm),"Rogers Fund, 1921",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54370,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1250,false,true,54382,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Yashima Gakutei,"Japanese, 1786?–1868",,Yashima Gakutei,Japanese,1786,1868,1840,1840,1840,Polychrome woodblock print (surimono); ink and color on paper,7 1/8 x 6 13/16 in. (18.1 x 17.3 cm),"Rogers Fund, 1921",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54382,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1300,false,true,54388,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Yashima Gakutei,"Japanese, 1786?–1868",,Yashima Gakutei,Japanese,1786,1868,ca. 1820,1810,1830,Polychrome woodblock print (surimono); ink and color on paper,8 9/16 x 7 3/8 in. (21.7 x 18.7 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54388,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1411,false,true,55441,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Yashima Gakutei,"Japanese, 1786?–1868",,Yashima Gakutei,Japanese,1786,1868,1838,1838,1838,Polychrome woodblock print; ink and color on paper,H. 10 1/16 in. (25.6 cm); W. 14 15/16 in. (37.9 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55441,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1412,false,true,55442,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Yashima Gakutei,"Japanese, 1786?–1868",,Yashima Gakutei,Japanese,1786,1868,1838,1838,1838,Polychrome woodblock print; ink and color on paper,H. 10 1/8 in. (25.7 cm); W. 14 15/16 in. (37.9 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55442,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1413,false,true,55443,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Yashima Gakutei,"Japanese, 1786?–1868",,Yashima Gakutei,Japanese,1786,1868,1838,1838,1838,Polychrome woodblock print; ink and color on paper,H. 10 1/16 in. (25.6 cm); W. 14 13/16 in. (37.6 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55443,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1414,false,true,55444,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Yashima Gakutei,"Japanese, 1786?–1868",,Yashima Gakutei,Japanese,1786,1868,1838,1838,1838,Polychrome woodblock print; ink and color on paper,H. 10 1/16 in. (25.6 cm); W. 14 7/8 in. (37.8 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55444,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1415,false,true,45266,Asian Art,Print,Osaka Tenmangu sairei no zu|The Tenmangu Festival at Osaka,Japan,Edo period (1615–1868),,,,Artist,,Yashima Gakutei,"Japanese, 1786?–1868",,Yashima Gakutei,Japanese,1786,1868,1834,1700,1868,Polychrome woodblock print; ink and color on paper,H. 10 1/8 in. (25.7 cm); W. 15 in. (38.1 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45266,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1452,false,true,54393,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Yashima Gakutei,"Japanese, 1786?–1868",,Yashima Gakutei,Japanese,1786,1868,ca. 1830,1820,1840,Polychrome woodblock print (surimono); ink and color on paper,8 3/16 x 7 1/4 in. (20.8 x 18.4 cm),"Rogers Fund, 1923",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54393,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1903,false,true,54442,Asian Art,Print,"『和歌三神』柿本人麻呂『春雨集』　摺物帖|Kakinomoto no Hitomaro (ca. 662–710), One of the Three Gods of PoetryFrom the Spring Rain Collection (Harusame shū), vol. 1",Japan,Edo period (1615–1868),,,,Artist,,Yashima Gakutei,"Japanese, 1786?–1868",,Yashima Gakutei,Japanese,1786,1868,ca. 1820s,1820,1829,Polychrome woodblock print (surimono); ink and color on paper,8 x 5 1/4 in. (20.3 x 13.3 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54442,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1904,false,true,54443,Asian Art,Print,"『和歌三神』衣通姫『春雨集』　摺物帖|Sotoori-hime (early 5th century), One of the Three Gods of PoetryFrom the Spring Rain Collection (Harusame shū), vol. 1",Japan,Edo period (1615–1868),,,,Artist,,Yashima Gakutei,"Japanese, 1786?–1868",,Yashima Gakutei,Japanese,1786,1868,ca. 1820s,1820,1829,Polychrome woodblock print (surimono); ink and color on paper,8 x 5 3/8 in. (20.3 x 13.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54443,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1905,false,true,54444,Asian Art,Print,"『和歌三神』山部赤人『春雨集』　摺物帖|Yamabe no Akahito (active 724–736), One of the Three Gods of PoetryFrom the Spring Rain Collection (Harusame shū), vol. 1",Japan,Edo period (1615–1868),,,,Artist,,Yashima Gakutei,"Japanese, 1786?–1868",,Yashima Gakutei,Japanese,1786,1868,ca. 1820s,1820,1829,Polychrome woodblock print (surimono); ink and color on paper,8 x 5 1/4 in. (20.3 x 13.3 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54444,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1940,false,true,54504,Asian Art,Print,"「一陽連文房四友 硯 伯英」『春雨集』　摺物帖|The Chinese Calligrapher Boying (Japanese: Hakuei; also known as the “Sage of Cursive Script”); “Inkstone” (Suzuri), from Four Friends of the Writing Table for the Ichiyō Poetry Circle (Ichiyō-ren Bunbō shiyū) From the Spring Rain Collection (Harusame shū), vol. 1",Japan,Edo period (1615–1868),,,,Artist,,Yashima Gakutei,"Japanese, 1786?–1868",,Yashima Gakutei,Japanese,1786,1868,ca. 1827,1817,1837,Polychrome woodblock print (surimono); ink and color on paper,8 x 7 1/8 in. (20.3 x 18.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54504,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1941,false,true,54508,Asian Art,Print,"「一陽連文房四友　筆　道風」『春雨集』　摺物帖|The Heian Court Calligrapher Ono no Tōfū (894–966); “Calligraphy Brush” (Fude), from Four Friends of the Writing Table for the Ichiyō Poetry Circle (Ichiyō-ren Bunbō shiyū)From the Spring Rain Collection (Harusame shū), vol. 1",Japan,Edo period (1615–1868),,,,Artist,,Yashima Gakutei,"Japanese, 1786?–1868",,Yashima Gakutei,Japanese,1786,1868,ca. 1827,1817,1837,Polychrome woodblock print (surimono); ink and color on paper,8 x 7 1/16 in. (20.3 x 17.9 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54508,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1942,false,true,54509,Asian Art,Print,"「一陽連文房四友　紙　女凢」『春雨集』　摺物帖 |Nuji (Japanese: Joki; female attendant who compiled writings by Daoist sages); “Paper” (Kami), from Four Friends of the Writing Table for the Ichiyō Poetry Circle (Ichiyō-ren Bunbō shiyū)From the Spring Rain Collection (Harusame shū), vol. 1",Japan,Edo period (1615–1868),,,,Artist,,Yashima Gakutei,"Japanese, 1786?–1868",,Yashima Gakutei,Japanese,1786,1868,ca. 1827,1817,1837,Polychrome woodblock print (surimono); ink and color on paper,8 x 7 1/16 in. (20.3 x 17.9 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54509,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1943,false,true,54510,Asian Art,Print,"「一陽連文房四友　墨　玄宗皇帝」『春雨集』　摺物帖 |Emperor Xuanzong (Japanese: Gensō) and Daoist Magician Lo Gongyuan Arising from an Inkstone; “Ink” (Sumi), from Four Friends of the Writing Table for the Ichiyō Poetry Circle (Ichiyō-ren Bunbō shiyū)From the Spring Rain Collection (Harusame shū), vol. 1",Japan,Edo period (1615–1868),,,,Artist,,Yashima Gakutei,"Japanese, 1786?–1868",,Yashima Gakutei,Japanese,1786,1868,ca. 1827,1817,1837,Polychrome woodblock print (surimono); ink and color on paper,8 x 7 1/16 in. (20.3 x 17.9 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54510,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1952,false,true,54520,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Yashima Gakutei,"Japanese, 1786?–1868",,Yashima Gakutei,Japanese,1786,1868,probably 1819,1819,1819,Polychrome woodblock print (surimono); ink and color on paper,7 1/8 x 6 1/2 in. (18.1 x 16.5 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54520,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2029,false,true,54790,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Yashima Gakutei,"Japanese, 1786?–1868",,Yashima Gakutei,Japanese,1786,1868,1819,1819,1819,Polychrome woodblock print (surimono); ink and color on paper,8 1/16 x 7 1/8 in. (20.5 x 18.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54790,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2047,false,true,54818,Asian Art,Print,"孫悟空|The Monkey King Songokū, from the Chinese novel Journey to the West",Japan,Edo period (1615–1868),,,,Artist,,Yashima Gakutei,"Japanese, 1786?–1868",,Yashima Gakutei,Japanese,1786,1868,probably 1824,1824,1824,Polychrome woodblock print (surimono); ink and color on paper,8 1/8 x 7 3/16 in. (20.6 x 18.3 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54818,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2049,false,true,54822,Asian Art,Woodblock print,玉藻前と三浦介|The Warrior Miura-no-suke Confronting the Court Lady Tamamo-no-mae as She Turns into an Evil Fox with Nine Tails,Japan,Edo period (1615–1868),,,,Artist,,Yashima Gakutei,"Japanese, 1786?–1868",,Yashima Gakutei,Japanese,1786,1868,late 1820s,1826,1829,Polychrome woodblock print (surimono); ink and color on paper,8 5/16 x 7 3/8 in. (21.1 x 18.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54822,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2232,false,true,53998,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Yashima Gakutei,"Japanese, 1786?–1868",,Yashima Gakutei,Japanese,1786,1868,probably 1828,1828,1828,Polychrome woodblock print (surimono); ink and color on paper,8 1/4 x 7 5/16 in. (21 x 18.6 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/53998,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2339,false,true,54124,Asian Art,Print,三味線の調弦|Woman Tuning a Shamisen and a Cat Looking at its Own Reflection,Japan,Edo period (1615–1868),,,,Artist,,Yashima Gakutei,"Japanese, 1786?–1868",,Yashima Gakutei,Japanese,1786,1868,mid- 1820s,1824,1826,Part of an album of woodblock prints (surimono); ink and color on paper,8 3/16 x 7 5/16 in. (20.8 x 18.6 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54124,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2352,false,true,54137,Asian Art,Print,"墨をする官女『春雨集』　摺物帖|Court Lady at Her Writing TableFrom the Spring Rain Collection (Harusame shū), vol. 3",Japan,Edo period (1615–1868),,,,Artist,,Yashima Gakutei,"Japanese, 1786?–1868",,Yashima Gakutei,Japanese,1786,1868,ca. 1820s,1820,1829,Part of an album of woodblock prints (surimono); ink and color on paper,4 15/16 x 11 1/8 in. (12.5 x 28.3 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54137,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2374,false,true,54158,Asian Art,Woodblock print,鯉の滝登り|Red Carp Ascending a Waterfall,Japan,Edo period (1615–1868),,,,Artist,,Yashima Gakutei,"Japanese, 1786?–1868",,Yashima Gakutei,Japanese,1786,1868,late 1820s,1826,1829,Part of an album of woodblock prints (surimono); ink and color on paper,7 1/4 x 6 9/16 in. (18.4 x 16.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54158,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2426,false,true,54178,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Yashima Gakutei,"Japanese, 1786?–1868",,Yashima Gakutei,Japanese,1786,1868,ca. 1840,1830,1850,Polychrome woodblock print (surimono); ink and color on paper,8 1/4 x 7 1/4 in. (21 x 18.4 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54178,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2427,false,true,54179,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Yashima Gakutei,"Japanese, 1786?–1868",,Yashima Gakutei,Japanese,1786,1868,ca. 1840,1830,1850,Polychrome woodblock print (surimono); ink and color on paper,8 1/4 x 7 1/4 in. (21 x 18.4 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54179,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2428,false,true,54180,Asian Art,Woodblock print,"Kamakura no Koshi|The Filial Son at Kamakura, From the Book: Sasekishu",Japan,Edo period (1615–1868),,,,Artist,,Yashima Gakutei,"Japanese, 1786?–1868",,Yashima Gakutei,Japanese,1786,1868,ca. 1835,1825,1845,Polychrome woodblock print (surimono); ink and color on paper,8 1/4 x 7 3/8 in. (21 x 18.7 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54180,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2747,false,true,54206,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Yashima Gakutei,"Japanese, 1786?–1868",,Yashima Gakutei,Japanese,1786,1868,first half of the 19th century,1800,1849,"Triptych of polychrome woodblock prints(surimono); gold, copper and silver on paper",Each print: 8 3/8 x 7 1/2 in. (21.3 x 19.1 cm),"Gift of Louis V. Ledoux, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54206,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2999,false,true,54215,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Yashima Gakutei,"Japanese, 1786?–1868",,Yashima Gakutei,Japanese,1786,1868,1786–1868,1786,1868,Polychrome woodblock print (surimono); ink and color on paper,8 5/16 x 7 7/16 in. (21.1 x 18.9 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54215,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2054,false,true,54829,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ryūgetsusai Shinkō,"Japanese, active 1810s",,Ryūgetsusai Shinkō,Japanese,1810,1819,probably 1814,1814,1814,Polychrome woodblock print (surimono); ink and color on paper,5 3/8 x 7 7/16 in. (13.7 x 18.9 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54829,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2156,false,true,55063,Asian Art,Print,"『春雨集』　摺物帖柳月斎辰光　少女に鳥かご|Spring Rain Collection (Harusame shū), vol. 2: Young Woman with a Birdcage",Japan,Edo period (1615–1868),,,,Artist,,Ryūgetsusai Shinkō,"Japanese, active 1810s",,Ryūgetsusai Shinkō,Japanese,1810,1819,1810s,1810,1819,Privately published woodblock prints (surimono) mounted in an album; ink and color on paper,8 3/8 x 4 1/4 in. (21.3 x 10.8 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55063,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2011.132,false,true,76562,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Juyōdō Minekuni,"Japanese, active 1820s",,Juyōdō Minekuni,Japanese,1820,1829,1826,1826,1826,Polychrome woodblock print,Image (ôban tate-e): 14 5/8 x 10 1/8 in. (37.1 x 25.7 cm),"Purchase, Friends of Asian Art Gifts, in honor of James C. Y. Watt, 2011",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/76562,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.263,false,true,73554,Asian Art,Woodblock print,"「広影写生　両国の虎」|“The Tiger of Ryōkoku,” from the series True Scenes by Hirokage",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hirokage,"Japanese, active 1860s",,Utagawa Hirokage,Japanese,1860,1869,"8th month, 1860",1860,1860,Polychrome woodblock print; ink and color on paper,Image: 14 1/4 x 9 3/4 in. (36.2 x 24.8 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73554,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP817,false,true,37261,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Okumura Toshinobu,active ca. 1725–1750,,Okumura Toshinobu,Japanese,1725,1750,ca. 1730,1720,1740,Monochrome woodblock print; ink and color on paper,9 x 12 in. (22.9 x 30.5 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37261,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP818,false,true,37262,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Okumura Toshinobu,active ca. 1725–1750,,Okumura Toshinobu,Japanese,1725,1750,ca. 1728,1718,1738,Polychrome woodblock print; ink and color on paper (Urushi-e),Overall: 13 x 6in. (33 x 15.2cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37262,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1310,false,true,45044,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Okumura Toshinobu,active ca. 1725–1750,,Okumura Toshinobu,Japanese,1725,1750,ca. 1728,1718,1738,Polychrome woodblock print (urushi-e); ink and color on paper,H. 12 5/16 in. (31.3 cm); W. 5 7/8 in. (14.9 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45044,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1606,false,true,45046,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Okumura Toshinobu,active ca. 1725–1750,,Okumura Toshinobu,Japanese,1725,1750,ca. 1730,1720,1740,Polychrome woodblock print (urushi-e); ink and color on paper,H. 12 in. (30.5 cm); W. 5 1/2 in. (14 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45046,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1607,false,true,45047,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Okumura Toshinobu,active ca. 1725–1750,,Okumura Toshinobu,Japanese,1725,1750,ca. 1730,1720,1740,Polychrome woodblock print (urushi-e); ink and color on paper,H. 12 3/8 in. (31.4 cm); W. 5 3/4 in. (14.6 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45047,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1608,false,true,45048,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Okumura Toshinobu,active ca. 1725–1750,,Okumura Toshinobu,Japanese,1725,1750,ca. 1730,1720,1740,Polychrome woodblock print (urushi-e); ink and color on paper,H. 12 1/2 in. (31.8 cm); W. 6 1/16 in. (15.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45048,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1609,false,true,45049,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Okumura Toshinobu,active ca. 1725–1750,,Okumura Toshinobu,Japanese,1725,1750,ca. 1730,1720,1740,Polychrome woodblock print (urushi-e); ink and color on paper,H. 12 7/16 in. (31.6 cm); W. 6 in. (15.2 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45049,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2757,false,true,45051,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Okumura Toshinobu,active ca. 1725–1750,,Okumura Toshinobu,Japanese,1725,1750,ca. 1793,1783,1803,Tan-e (hand-colored print); ink and color on paper,H. 10 3/4 in. (27.3 cm); W. 6 in. (15.2 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45051,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3080,false,true,56593,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,Attributed to,Okumura Toshinobu,active ca. 1725–1750,,Okumura Toshinobu,Japanese,1725,1750,ca. 1730,1720,1740,Polychrome woodblock print (hand colored); ink and color on paper,13 1/4 x 6 1/2 in. (33.7 x 16.5 cm),"Harris Brisbane Dick Fund and Rogers Fund, 1949",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56593,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3002,false,true,54218,Asian Art,Print,"うし和歌春|Two Women, from the series Spring Poems on Ushiwaka for the Year of the Ox (Ushiwaka haru)",Japan,Edo period (1615–1868),,,,Artist,,Hishikawa Sōri,active ca. 1797–1813,,Hishikawa Sōri,Japanese,1797,1813,1805,1805,1805,Polychrome woodblock print (surimono); ink and color on paper,5 5/16 x 6 11/16 in. (13.5 x 17 cm) (trimmed),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54218,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP172,false,true,36651,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunsen,"Japanese, 1762–ca.1830",,Katsukawa Shunsen,Japanese,1762,1830,ca. 1785,1775,1795,Polychrome woodblock print; ink and color on paper,12 1/2 x 5 5/8 in. (31.8 x 14.3 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36651,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1351,false,true,55326,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunsen,"Japanese, 1762–ca.1830",,Katsukawa Shunsen,Japanese,1762,1830,1786,1786,1786,Polychrome woodblock print; ink and color on paper,H. 12 1/4 in. (31.1 cm); W. 5 1/2 in. (14 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55326,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP173a–c,false,true,36482,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunsen,"Japanese, 1762–ca.1830",,Katsukawa Shunsen,Japanese,1762,1830,ca. 1785,1775,1795,Triptych of polychrome woodblock prints; ink and color on paper,Overall H. 12 1/4 in. (31.1 cm); W. 16 5/8 in. (42.2 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36482,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3179,false,true,55127,Asian Art,Print,『横浜異 人商館座敷之図』|Drawing Room of a Foreign Business Establishment in Yokohama,Japan,Edo period (1615–1868),,,,Artist,,Utagawa (Gountei) Sadahide,"Japanese, 1807–1878/79",,Utagawa (Goutei) Sadahide,Japanese,1807,1879,"9th month, 1861",1861,1861,Center and right sheets of a triptych of polychrome woodblock prints; ink and color on paper,Image: 13 7/8 × 18 1/4 in. (35.2 × 46.4 cm) Mat: 18 3/4 × 23 1/4 in. (47.6 × 59.1 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55127,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3245,false,true,55257,Asian Art,Print,Yokohama Shukan Shin no zu|A True View of a Trading House of a Yokohama Merchant,Japan,Edo period (1615–1868),,,,Artist,,Utagawa (Gountei) Sadahide,"Japanese, 1807–1878/79",,Utagawa (Goutei) Sadahide,Japanese,1807,1879,"1st month, 1861",1861,1861,Triptych of polychrome woodblock prints; ink and color on paper,14 3/8 x 29 1/4 in. (36.5 x 74.3 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55257,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3246,false,true,55258,Asian Art,Print,Uchoren no zu|Great Military Drill,Japan,Edo period (1615–1868),,,,Artist,,Utagawa (Gountei) Sadahide,"Japanese, 1807–1878/79",,Utagawa (Goutei) Sadahide,Japanese,1807,1879,"1866 (Keio 2, 2nd month)",1866,1866,Triptych of polychrome woodblock prints; ink and color on paper,13 5/8 x 27 7/16 in. (34.6 x 69.7 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55258,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3247,false,true,55259,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa (Gountei) Sadahide,"Japanese, 1807–1878/79",,Utagawa (Goutei) Sadahide,Japanese,1807,1879,1861,1861,1861,Triptych of polychrome woodblock prints; ink and color on paper,Oban,"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55259,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3265,false,true,37389,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa (Gountei) Sadahide,"Japanese, 1807–1878/79",,Utagawa (Goutei) Sadahide,Japanese,1807,1879,1861,1861,1861,Triptych of polychrome woodblock prints; ink and color on paper,Oban 14 1/4 x 28 5/8 in. (36.2 x 72.7 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37389,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3268,false,true,55332,Asian Art,Print,Amerika Karuhorunia Ko shuppan no zu|Sailing from a California Port,Japan,Edo period (1615–1868),,,,Artist,,Utagawa (Gountei) Sadahide,"Japanese, 1807–1878/79",,Utagawa (Goutei) Sadahide,Japanese,1807,1879,1862,1862,1862,Triptych of polychrome woodblock prints; ink and color on paper,Image: 14 1/4 x 28 15/16 in. (36.2 x 73.5 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55332,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3298,false,true,55396,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa (Gountei) Sadahide,"Japanese, 1807–1878/79",,Utagawa (Goutei) Sadahide,Japanese,1807,1879,November 1860,1860,1860,Polychrome woodblock print; ink and color on paper,14 x 10 in. (35.6 x 25.4 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55396,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3331,false,true,55479,Asian Art,Print,Yokohama torai Amerika shonin ryoko no zu|横浜渡來亜墨利加商人旅行之図|American Merchant Strolling in Yokohama,Japan,Edo period (1615–1868),,,,Artist,,Utagawa (Gountei) Sadahide,"Japanese, 1807–1878/79",,Utagawa (Goutei) Sadahide,Japanese,1807,1879,1861 (1st month),1861,1861,Polychrome woodblock print; ink and color on paper,14 x 10 in. (35.6 x 25.4 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55479,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3419,false,true,55614,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa (Gountei) Sadahide,"Japanese, 1807–1878/79",,Utagawa (Goutei) Sadahide,Japanese,1807,1879,"1861 (Bunkyu 1, 1st month)",1861,1861,Polychrome woodblock print; ink and color on paper,14 x 9 7/8 in. (35.6 x 25.1 cm),"Gift of Lincoln Kirstein, 1962",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55614,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.101,false,true,73391,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa (Gountei) Sadahide,"Japanese, 1807–1878/79",,Utagawa (Goutei) Sadahide,Japanese,1807,1879,"2nd month, 1860",1860,1860,Polychrome woodblock print; ink and color on paper,Image: 9 1/2 x 12 3/4 in. (24.1 x 32.4 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73391,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.102,false,true,73602,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa (Gountei) Sadahide,"Japanese, 1807–1878/79",,Utagawa (Goutei) Sadahide,Japanese,1807,1879,"2nd month, 1860",1860,1860,Polychrome woodblock print; ink and color on paper,Image: 9 1/2 x 12 5/8 in. (24.1 x 32.1 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73602,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.103,false,true,73603,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa (Gountei) Sadahide,"Japanese, 1807–1878/79",,Utagawa (Goutei) Sadahide,Japanese,1807,1879,"2nd month, 1860",1860,1860,Polychrome woodblock print; ink and color on paper,Image: 9 1/2 x 12 3/4 in. (24.1 x 32.4 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73603,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.104,false,true,73604,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa (Gountei) Sadahide,"Japanese, 1807–1878/79",,Utagawa (Goutei) Sadahide,Japanese,1807,1879,"2nd month, 1860",1860,1860,Polychrome woodblock print; ink and color on paper,Image: 9 1/2 x 12 5/8 in. (24.1 x 32.1 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73604,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.105,false,true,73605,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa (Gountei) Sadahide,"Japanese, 1807–1878/79",,Utagawa (Goutei) Sadahide,Japanese,1807,1879,"2nd month, 1860",1860,1860,Polychrome woodblock print; ink and color on paper,Image: 10 x 14 5/8 in. (25.4 x 37.1 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73605,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.106,false,true,73606,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa (Gountei) Sadahide,"Japanese, 1807–1878/79",,Utagawa (Goutei) Sadahide,Japanese,1807,1879,"2nd month, 1860",1860,1860,Polychrome woodblock print; ink and color on paper,Image: 9 1/2 x 12 7/8 in. (24.1 x 32.7 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73606,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.107,false,true,73392,Asian Art,Print,「神名川横浜新開港圖」|“The Newly Opened Port of Yokohama in Kanagawa Prefecture”,Japan,Edo period (1615–1868),,,,Artist,,Utagawa (Gountei) Sadahide,"Japanese, 1807–1878/79",,Utagawa (Goutei) Sadahide,Japanese,1807,1879,"2nd month, 1860",1860,1860,Triptych of polychrome woodblock prints; ink and color on paper,Image: 14 1/2 x 29 in. (36.8 x 73.7 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73392,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.109,false,true,73394,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa (Gountei) Sadahide,"Japanese, 1807–1878/79",,Utagawa (Goutei) Sadahide,Japanese,1807,1879,"3rd month, 1860",1860,1860,Polychrome woodblock print; ink and color on paper,Image: 14 1/2 x 9 1/2 in. (36.8 x 24.1 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73394,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.112,false,true,73397,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa (Gountei) Sadahide,"Japanese, 1807–1878/79",,Utagawa (Goutei) Sadahide,Japanese,1807,1879,"3rd month, 1860",1860,1860,Polychrome woodblock print; ink and color on paper,Image: 14 7/8 x 10 in. (37.8 x 25.4 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73397,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.113,false,true,73398,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa (Gountei) Sadahide,"Japanese, 1807–1878/79",,Utagawa (Goutei) Sadahide,Japanese,1807,1879,"3rd month,1860",1860,1860,Polychrome woodblock print; ink and color on paper,Image: 10 1/4 x 14 3/4 in. (26 x 37.5 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73398,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.114,false,true,73399,Asian Art,Print,Doban-e Jōgyō shiki|Color Print of a Copperplate Picture of a Toy Shop,Japan,Edo period (1615–1868),,,,Artist,,Utagawa (Gountei) Sadahide,"Japanese, 1807–1878/79",,Utagawa (Goutei) Sadahide,Japanese,1807,1879,1860,1860,1860,Polychrome woodblock print; ink and color on paper,Image: 9 1/2 x 13 3/4 in. (24.1 x 34.9 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73399,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.116,false,true,73401,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa (Gountei) Sadahide,"Japanese, 1807–1878/79",,Utagawa (Goutei) Sadahide,Japanese,1807,1879,"4th month, 1860",1860,1860,Polychrome woodblock print; ink and color on paper,Image: 14 1/2 x 10 1/8 in. (36.8 x 25.7 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73401,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.118,false,true,73403,Asian Art,Print,"「生寫異國人物」|American Woman Playing a Concertina, from the series Life Drawings of People from Foreign Nations",Japan,Edo period (1615–1868),,,,Artist,,Utagawa (Gountei) Sadahide,"Japanese, 1807–1878/79",,Utagawa (Goutei) Sadahide,Japanese,1807,1879,1860,1860,1860,Polychrome woodblock print; ink and color on paper,Image: 14 5/8 x 10 in. (37.1 x 25.4 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73403,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.119,false,true,73405,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa (Gountei) Sadahide,"Japanese, 1807–1878/79",,Utagawa (Goutei) Sadahide,Japanese,1807,1879,"11th month, 1860",1860,1860,Polychrome woodblock print; ink and color on paper,Image: 14 3/8 x 10 in. (36.5 x 25.4 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73405,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.120,false,true,73406,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa (Gountei) Sadahide,"Japanese, 1807–1878/79",,Utagawa (Goutei) Sadahide,Japanese,1807,1879,1860,1860,1860,Polychrome woodblock print; ink and color on pape22,Image: 14 3/4 x 10 in. (37.5 x 25.4 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73406,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.121,false,true,73407,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa (Gountei) Sadahide,"Japanese, 1807–1878/79",,Utagawa (Goutei) Sadahide,Japanese,1807,1879,"11th month, 1860",1860,1860,Polychrome woodblock print; ink and color on paper,Image: 14 1/2 x 10 in. (36.8 x 25.4 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73407,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.122,false,true,73408,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa (Gountei) Sadahide,"Japanese, 1807–1878/79",,Utagawa (Goutei) Sadahide,Japanese,1807,1879,1861,1861,1861,Polychrome woodblock print; ink and color on paper,Image: 14 1/2 x 9 3/4 in. (36.8 x 24.8 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73408,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.123,false,true,73409,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa (Gountei) Sadahide,"Japanese, 1807–1878/79",,Utagawa (Goutei) Sadahide,Japanese,1807,1879,1861,1861,1861,Polychrome woodblock print; ink and color on paper,Image: 14 1/2 x 9 7/8 in. (36.8 x 25.1 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73409,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.125,false,true,73411,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa (Gountei) Sadahide,"Japanese, 1807–1878/79",,Utagawa (Goutei) Sadahide,Japanese,1807,1879,"2nd month, 1861",1861,1861,Polychrome woodblock print; ink and color on paper,Image: 14 1/4 x 9 3/4 in. (36.2 x 24.8 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73411,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.128,false,true,73413,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa (Gountei) Sadahide,"Japanese, 1807–1878/79",,Utagawa (Goutei) Sadahide,Japanese,1807,1879,"7th month, 1861",1861,1861,Polychrome woodblock print; ink and color on paper,Image: 14 1/2 x 9 3/4 in. (36.8 x 24.8 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73413,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.132,false,true,73417,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa (Gountei) Sadahide,"Japanese, 1807–1878/79",,Utagawa (Goutei) Sadahide,Japanese,1807,1879,"9th month, 1861",1861,1861,Triptych of polychrome woodblock prints; ink and color on paper,Image: 14 1/8 x 29 1/16 in. (35.9 x 73.8 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73417,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3389a–e,false,true,55564,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa (Gountei) Sadahide,"Japanese, 1807–1878/79",,Utagawa (Goutei) Sadahide,Japanese,1807,1879,April 1861,1861,1861,Oban pentaptych of polychrome woodblock prints; ink and color on paper,Image: 14 1/8 x 49 13/16 in. (35.9 x 126.5 cm),"Gift of Lincoln Kirstein, 1960",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55564,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.108a–c,false,true,73393,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa (Gountei) Sadahide,"Japanese, 1807–1878/79",,Utagawa (Goutei) Sadahide,Japanese,1807,1879,"2nd month, 1860",1860,1860,Triptych of polychrome woodblock prints; ink and color on paper,Image (a): 14 3/4 x 9 7/8 in. (37.5 x 25.1 cm) Image (b): 14 3/4 x 9 7/8 in. (37.5 x 25.1 cm) Image (c): 14 3/4 x 9 7/8 in. (37.5 x 25.1 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73393,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.110a–c,false,true,73395,Asian Art,Print,「横浜買物圖繒　唐物店之圖」|Curio Shop in Yokohama,Japan,Edo period (1615–1868),,,,Artist,,Utagawa (Gountei) Sadahide,"Japanese, 1807–1878/79",,Utagawa (Goutei) Sadahide,Japanese,1807,1879,"3rd month, 1860",1860,1860,Triptych of polychrome woodblock prints; ink and color on paper,Image (a): 14 1/4 x 9 5/8 in. (36.2 x 24.4 cm) Image (b): 14 1/4 x 9 7/8 in. (36.2 x 25.1 cm) Image (c): 14 1/4 x 9 5/8 in. (36.2 x 24.4 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73395,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.111a–c,false,true,73396,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa (Gountei) Sadahide,"Japanese, 1807–1878/79",,Utagawa (Goutei) Sadahide,Japanese,1807,1879,"3rd month, 1860",1860,1860,Triptych of polychrome woodblock prints; ink and color on paper,Image (a): 14 1/8 x 9 5/8 in. (35.9 x 24.4 cm) Image (b): 14 1/8 x 9 5/8 in. (35.9 x 24.4 cm) Image (c): 14 1/8 x 9 5/8 in. (35.9 x 24.4 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73396,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.115a–c,false,true,73400,Asian Art,Print,Yokohama Hon-chō...ni Miyozaki...kenkin zu|Detailed Print of Yokohama Hon-chō and the Miyozaki Pleasure Quarter,Japan,Edo period (1615–1868),,,,Artist,,Utagawa (Gountei) Sadahide,"Japanese, 1807–1878/79",,Utagawa (Goutei) Sadahide,Japanese,1807,1879,"4th month, 1860",1860,1860,Triptych of polychrome woodblock prints; ink and color on paper,Image (a): 14 3/8 x 9 5/8 in. (36.5 x 24.4 cm) Image (b): 14 1/4 x 9 3/4 in. (36.2 x 24.8 cm) Image (c): 14 1/4 x 9 3/8 in. (36.2 x 23.8 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73400,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.117a–c,false,true,73402,Asian Art,Print,Kanagwa Yokohama minato...zue|Pictorial Guide to Yokohama Harbor,Japan,Edo period (1615–1868),,,,Artist,,Utagawa (Gountei) Sadahide,"Japanese, 1807–1878/79",,Utagawa (Goutei) Sadahide,Japanese,1807,1879,"7th month, 1860",1860,1860,Triptych of polychrome woodblock prints; ink and color on paper,Image (a): 14 1/4 x 9 7/8 in. (36.2 x 25.1 cm) Image (b): 14 1/4 x 9 5/8 in. (36.2 x 24.4 cm) Image (c): 14 1/4 x 9 3/8 in. (36.2 x 23.8 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73402,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.124a–f,false,true,73410,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa (Gountei) Sadahide,"Japanese, 1807–1878/79",,Utagawa (Goutei) Sadahide,Japanese,1807,1879,"2nd month, 1861",1861,1861,Hexaptych of polychrome woodblock prints; ink and color on paper,Image (a): 14 1/2 x 10 in. (36.8 x 25.4 cm) Image (b): 14 1/2 x 9 7/8 in. (36.8 x 25.1 cm) Image (c): 14 5/8 x 10 in. (37.1 x 25.4 cm) Image (d): 14 1/2 x 9 7/8 in. (36.8 x 25.1 cm) Image (e): 14 5/8 x 9 7/8 in. (37.1 x 25.1 cm) Image (f): 14 1/2 x 9 7/8 in. (36.8 x 25.1 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73410,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.126a–c,false,true,73572,Asian Art,Print,Gok'koku jimbutsu gyo...no zu|Picture of a Parade of the Five Nations,Japan,Edo period (1615–1868),,,,Artist,,Utagawa (Gountei) Sadahide,"Japanese, 1807–1878/79",,Utagawa (Goutei) Sadahide,Japanese,1807,1879,"3rd month, 1861",1861,1861,Triptych of polychrome woodblock prints; ink and color on paper,Image (a): 14 1/4 x 9 3/4 in. (36.2 x 24.8 cm) Image (b): 14 1/4 x 9 3/4 in. (36.2 x 24.8 cm) Image (c): 14 1/4 x 9 3/4 in. (36.2 x 24.8 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73572,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.127a–e,false,true,73412,Asian Art,Woodblock print,「横浜交易西洋人荷物運送之圖」|“Yokohama Trade: Westerners Loading Cargo”,Japan,Edo period (1615–1868),,,,Artist,,Utagawa (Gountei) Sadahide,"Japanese, 1807–1878/79",,Utagawa (Goutei) Sadahide,Japanese,1807,1879,"4th month, 1861",1861,1861,Pentaptych of polychrome woodblock prints; ink and color on paper,Image (a): 14 1/2 x 9 1/2 in. (36.8 x 24.1 cm) Image (b): 14 1/2 x 9 7/8 in. (36.8 x 25.1 cm) Image (c): 14 1/2 x 9 3/4 in. (36.8 x 24.8 cm) Image (d): 14 1/2 x 9 7/8 in. (36.8 x 25.1 cm) Image (e): 14 1/2 x 9 7/8 in. (36.8 x 25.1 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73412,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.129a–c,false,true,73414,Asian Art,Print,『横浜異 人商館写真之図』|Foreign Business Establishment in Yokohama,Japan,Edo period (1615–1868),,,,Artist,,Utagawa (Gountei) Sadahide,"Japanese, 1807–1878/79",,Utagawa (Goutei) Sadahide,Japanese,1807,1879,1861,1861,1861,Triptych of polychrome woodblock prints; ink and color on paper,Image (a): 13 3/4 x 9 1/4 in. (34.9 x 23.5 cm) Image (b): 14 x 9 7/8 in. (35.6 x 25.1 cm) Image (c): 14 x 9 1/4 in. (35.6 x 23.5 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73414,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.130a–c,false,true,73415,Asian Art,Print,「横浜異人商館買場之圖」|Foreign Business Establishment in Yokohama,Japan,Edo period (1615–1868),,,,Artist,,Utagawa (Gountei) Sadahide,"Japanese, 1807–1878/79",,Utagawa (Goutei) Sadahide,Japanese,1807,1879,"9th month, 1861",1861,1861,Triptych of polychrome woodblock prints; ink and color on paper,Image (a): 14 1/2 x 10 in. (36.8 x 25.4 cm) Image (b): 14 1/2 x 10 in. (36.8 x 25.4 cm) Image (c): 14 1/2 x 10 in. (36.8 x 25.4 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73415,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.131a–c,false,true,73416,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa (Gountei) Sadahide,"Japanese, 1807–1878/79",,Utagawa (Goutei) Sadahide,Japanese,1807,1879,"9th month, 1861",1861,1861,Triptych of polychrome woodblock prints; ink and color on paper,Image (a): 14 x 9 3/4 in. (35.6 x 24.8 cm) Image (b): 14 x 9 3/8 in. (35.6 x 23.8 cm) Image (c): 14 x 10 7/8 in. (35.6 x 27.6 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73416,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.133a–c,false,true,73419,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa (Gountei) Sadahide,"Japanese, 1807–1878/79",,Utagawa (Goutei) Sadahide,Japanese,1807,1879,"3rd month, 1862",1862,1862,Triptych of polychrome woodblock prints; ink and color on paper,Image (a): 14 1/2 x 9 7/8 in. (36.8 x 25.1 cm) Image (b): 14 1/2 x 10 in. (36.8 x 25.4 cm) Image (c): 14 1/2 x 10 in. (36.8 x 25.4 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73419,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.290a–c,false,true,73567,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa (Gountei) Sadahide,"Japanese, 1807–1878/79",,Utagawa (Goutei) Sadahide,Japanese,1807,1879,ca. 1862–63,1862,1863,Triptych of polychrome woodblock prints; ink and color on paper,Image (a): 14 1/2 x 9 1/2 in. (36.8 x 24.1 cm) Image (b): 14 1/2 x 9 3/8 in. (36.8 x 23.8 cm) Image (c): 14 3/8 x 9 1/2 in. (36.5 x 24.1 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73567,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1490,false,true,55586,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Ryūkōsai,active late 18th century,,Ryūkōsai,Japanese,1771,1799,ca. 1790,1780,1800,Polychrome woodblock print; ink and color on paper,12 13/16 x 5 11/16 in. (32.5 x 14.4 cm),"Gift of S. C. Bosch-Reitz, 1927",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55586,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP136,false,true,36615,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Isoda Koryūsai,"Japanese, 1735–ca. 1790",,Isoda Koryūsai,Japanese,1735,1790,ca. 1770,1760,1780,Polychrome woodblock print; ink and color on paper,26 23/32 x 4 3/4 in. (67.9 x 12.1 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36615,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP137,false,true,36616,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Isoda Koryūsai,"Japanese, 1735–ca. 1790",,Isoda Koryūsai,Japanese,1735,1790,ca. 1770,1760,1780,Polychrome woodblock print; ink and color on paper,26 1/8 x 4 3/4 in. (66.4 x 12.1 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36616,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP138,false,true,36617,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Isoda Koryūsai,"Japanese, 1735–ca. 1790",,Isoda Koryūsai,Japanese,1735,1790,ca. 1770,1760,1780,Polychrome woodblock print; ink and color on paper,26 3/8 x 4 3/5 in. (67.0 x 11.7 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36617,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP139,false,true,36618,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Isoda Koryūsai,"Japanese, 1735–ca. 1790",,Isoda Koryūsai,Japanese,1735,1790,ca. 1770,1760,1780,Polychrome woodblock print; ink and color on paper,27 15/32 x 4 3/8 in. (69.8 x 11.1 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36618,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP140,false,true,36619,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Isoda Koryūsai,"Japanese, 1735–ca. 1790",,Isoda Koryūsai,Japanese,1735,1790,ca. 1775,1765,1785,Polychrome woodblock print; ink and color on paper,14 3/5 x 9 7/8 in. (37.1 x 25.1 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36619,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP141,false,true,36620,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Isoda Koryūsai,"Japanese, 1735–ca. 1790",,Isoda Koryūsai,Japanese,1735,1790,ca. 1770,1760,1780,Polychrome woodblock print; ink and color on paper,10 3/8 x 7 5/8 in. (26.4 x 19.4 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36620,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP220,false,true,36694,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Isoda Koryūsai,"Japanese, 1735–ca. 1790",,Isoda Koryūsai,Japanese,1735,1790,ca. 1770,1760,1780,Polychrome woodblock print; ink and color on paper,28 1/2 x 4 3/5 in. (72.4 x 11.7 cm),"Rogers Fund, 1917",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36694,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP558,false,true,37009,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Isoda Koryūsai,"Japanese, 1735–ca. 1790",,Isoda Koryūsai,Japanese,1735,1790,ca. 1768,1758,1778,Polychrome woodblock print; ink and color on paper,10 x 7 11/32 in. (25.4 x 18.7 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37009,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP559,false,true,37010,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Isoda Koryūsai,"Japanese, 1735–ca. 1790",,Isoda Koryūsai,Japanese,1735,1790,ca. 1768,1758,1778,Polychrome woodblock print; ink and color on paper,10 x 7 7/32 in. (25.4 x 18.4 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37010,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP560,false,true,37011,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Isoda Koryūsai,"Japanese, 1735–ca. 1790",,Isoda Koryūsai,Japanese,1735,1790,ca. 1770,1760,1780,Polychrome woodblock print; ink and color on paper,10 x 7 1/8 in. (25.4 x 18.1 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37011,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP673,false,true,37120,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Isoda Koryūsai,"Japanese, 1735–ca. 1790",,Isoda Koryūsai,Japanese,1735,1790,ca. 1769,1759,1779,Polychrome woodblock print; ink and color on paper,27 x 4 1/8 in. (68.6 x 10.5 cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37120,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP674,false,true,37121,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Isoda Koryūsai,"Japanese, 1735–ca. 1790",,Isoda Koryūsai,Japanese,1735,1790,ca. 1770,1760,1790,Polychrome woodblock print; ink and color on paper,27 3/4 x 4 5/32 in. (70.5 x 10.6 cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37121,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP700,false,true,37147,Asian Art,Print,"『雛形若菜の初模様　つるや内　すがたみ』|The Courtesan Sugatami of the Tsuruya Brothel, from the series “A Pat-tern Book of the Year’s First Designs, Fresh as Spring Herbs” (“Hinagata wakana no hatsu moyō”)",Japan,Edo period (1615–1868),,,,Artist,,Isoda Koryūsai,"Japanese, 1735–ca. 1790",,Isoda Koryūsai,Japanese,1735,1790,1777–78,1777,1778,Polychrome woodblock print; ink and color on paper,15 7/32 x 10 1/8 in. (38.7 x 25.7 cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37147,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP881,false,true,54574,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Isoda Koryūsai,"Japanese, 1735–ca. 1790",,Isoda Koryūsai,Japanese,1735,1790,ca. 1769,1759,1779,Polychrome woodblock print; ink and color on paper,H. 9 7/8 in. (25.1 cm); W. 7 1/2 in. (19.1 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54574,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP882,false,true,54575,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Isoda Koryūsai,"Japanese, 1735–ca. 1790",,Isoda Koryūsai,Japanese,1735,1790,ca. 1760,1750,1770,Polychrome woodblock print; ink and color on paper,Image: 8 5/8 × 8 1/4 in. (21.9 × 21 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54575,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP883,false,true,54577,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Isoda Koryūsai,"Japanese, 1735–ca. 1790",,Isoda Koryūsai,Japanese,1735,1790,ca. 1769,1759,1779,Polychrome woodblock print; ink and color on paper,H. 10 1/4 in. (26 cm); W. 7 5/8 in. (19.4 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54577,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP884,false,true,54578,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Isoda Koryūsai,"Japanese, 1735–ca. 1790",,Isoda Koryūsai,Japanese,1735,1790,ca. 1769,1759,1779,Polychrome woodblock print; ink and color on paper,H. 27 1/5 in. (69.1 cm); W. 3 7/8 in. (9.8 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54578,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP885,false,true,54579,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Isoda Koryūsai,"Japanese, 1735–ca. 1790",,Isoda Koryūsai,Japanese,1735,1790,ca 1773–75,1763,1785,Polychrome woodblock print; ink and color on paper,H. 10 3/8 in. (26.4 cm); W. 7 5/8 in. (19.4 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54579,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP886,false,true,54580,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Isoda Koryūsai,"Japanese, 1735–ca. 1790",,Isoda Koryūsai,Japanese,1735,1790,ca. 1770,1780,1780,Polychrome woodblock print; ink and color on paper,H. 10 in. (25.4 cm); W. 6 7/8 in. (22.5 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54580,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP887,false,true,54581,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Isoda Koryūsai,"Japanese, 1735–ca. 1790",,Isoda Koryūsai,Japanese,1735,1790,ca. 1770,1760,1780,Polychrome woodblock print; ink and color on paper,H. 10 3/8 in. (26.4 cm); W. 7 5/8 in. (19.4 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54581,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP888,false,true,54582,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Isoda Koryūsai,"Japanese, 1735–ca. 1790",,Isoda Koryūsai,Japanese,1735,1790,ca. 1771,1761,1781,Polychrome woodblock print; ink and color on paper,H. 10 5/8 in. (27 cm); W. 7 5/8 in. (19.4 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54582,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP889,false,true,54583,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Isoda Koryūsai,"Japanese, 1735–ca. 1790",,Isoda Koryūsai,Japanese,1735,1790,ca. 1773,1763,1783,Polychrome woodblock print; ink and color on paper,H. 10 1/4 in. (26 cm); W. 7 5/8 in. (19.4 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54583,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP890,false,true,54584,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Isoda Koryūsai,"Japanese, 1735–ca. 1790",,Isoda Koryūsai,Japanese,1735,1790,ca. 1773,1763,1783,Polychrome woodblock print; ink and color on paper,H. 10 1/8 in. (26.7 cm); W. 7 1/2 in. (19.1 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54584,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP892,false,true,54586,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Isoda Koryūsai,"Japanese, 1735–ca. 1790",,Isoda Koryūsai,Japanese,1735,1790,ca. 1773,1763,1783,Polychrome woodblock print; ink and color on paper,H. 8 3/8 in. (21.3 cm); W. 14 3/4 in. (37.5 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54586,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1125,false,true,55045,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Isoda Koryūsai,"Japanese, 1735–ca. 1790",,Isoda Koryūsai,Japanese,1735,1790,ca. 1770,1760,1780,Polychrome woodblock print; ink and color on paper,27 3/8 x 4 7/8in. (69.5 x 12.4cm),"Rogers Fund, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55045,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1208,false,true,55132,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Isoda Koryūsai,"Japanese, 1735–ca. 1790",,Isoda Koryūsai,Japanese,1735,1790,ca. 1770,1760,1780,Polychrome woodblock print; ink and color on paper,H. 25 9/16 in. (64.9 cm); W. 4 3/4 in. (12.1 cm),"Rogers Fund, 1920",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55132,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1209,false,true,55133,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Isoda Koryūsai,"Japanese, 1735–ca. 1790",,Isoda Koryūsai,Japanese,1735,1790,ca. 1770,1760,1780,Polychrome woodblock print; ink and color on paper,H. 27 1/4 in. (69.2 cm); W. 4 5/8 in. (11.7 cm),"Rogers Fund, 1920",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55133,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1230,false,true,55151,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Isoda Koryūsai,"Japanese, 1735–ca. 1790",,Isoda Koryūsai,Japanese,1735,1790,ca. 1765,1755,1775,Polychrome woodblock print; ink and color on paper,H. 10 3/16 in. (25.9 cm); W. 7 9/16 in. (19.2 cm),"Rogers Fund, 1921",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55151,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1271,false,true,55168,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Isoda Koryūsai,"Japanese, 1735–ca. 1790",,Isoda Koryūsai,Japanese,1735,1790,ca. 1770,1760,1780,Polychrome woodblock print; ink and color on paper,H. 28 1/8 in. (71.4 cm); W. 5 in. (12.7 cm),"Rogers Fund, 1921",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55168,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1291,false,true,55233,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Isoda Koryūsai,"Japanese, 1735–ca. 1790",,Isoda Koryūsai,Japanese,1735,1790,1770,1770,1770,Polychrome woodblock print; ink and color on paper,H. 27 3/4 in. (70.5 cm); W. 4 3/8 in. (11.1 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55233,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1459,false,true,55506,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Isoda Koryūsai,"Japanese, 1735–ca. 1790",,Isoda Koryūsai,Japanese,1735,1790,probably 18th century,1700,1799,Hand-colored print (ishizuri-e); ink and color on paper,29 15/16 x 10 1/4 in. (76 x 26 cm),"Rogers Fund, 1923",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55506,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1703,false,true,55944,Asian Art,Print,"『雛形若菜初模様　玉や内　しら玉』|The Courtesan Shiratama of the Tamaya Brothel, from the series “A Pat-tern Book of the Year’s First Designs, Fresh as Spring Herbs” (“Hinagata wakana hatsu moyō”)",Japan,Edo period (1615–1868),,,,Artist,,Isoda Koryūsai,"Japanese, 1735–ca. 1790",,Isoda Koryūsai,Japanese,1735,1790,1777–78,1777,1778,Polychrome woodblock print; ink and color on paper,H. 15 1/4 in. (38.7 cm); W. 10 1/2 in. (26.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55944,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2626,false,true,56775,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Isoda Koryūsai,"Japanese, 1735–ca. 1790",,Isoda Koryūsai,Japanese,1735,1790,ca. 1770,1760,1780,Polychrome woodblock print; ink and color on paper,10 1/2 x 7 1/4 in. (26.7 x 18.4 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56775,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2627,false,true,51996,Asian Art,Print,"『雛形若菜初模様　四ツ目屋内　にしき木』|The Courtesan Nishikigi of the Yotsumeya Brothel, from the series “A Pattern Book of the Year’s First Designs, Fresh as Spring Herbs” (“Hinagata wakana hatsu moyō”)",Japan,Edo period (1615–1868),,,,Artist,,Isoda Koryūsai,"Japanese, 1735–ca. 1790",,Isoda Koryūsai,Japanese,1735,1790,1776,1776,1776,Polychrome woodblock print; ink and color on paper,14 7/8 x 10 1/8 in. (37.8 x 25.7 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/51996,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2628,false,true,56776,Asian Art,Print,"『雛形若菜初模様　四ツ目屋内　さよぎぬ』|The Courtesan Sayoginu of the Yotsumeya Brothel, from the series “A Pattern Book of the Year’s First Designs, Fresh as Spring Herbs” (“Hinagata wakana hatsu moyō”)",Japan,Edo period (1615–1868),,,,Artist,,Isoda Koryūsai,"Japanese, 1735–ca. 1790",,Isoda Koryūsai,Japanese,1735,1790,1776,1776,1776,Polychrome woodblock print; ink and color on paper,15 1/4 x 10 3/8 in. (38.7 x 26.4 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56776,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2629,false,true,56777,Asian Art,Print,"『雛形若菜の初模様　つたや内　人まち』|The Courtesan Hitomachi of the Tsutaya Brothel, from the series “A Pat-tern Book of the Year’s First Designs, Fresh as Spring Herbs” (“Hinagata wakana no hatsu moyō”)",Japan,Edo period (1615–1868),,,,Artist,,Isoda Koryūsai,"Japanese, 1735–ca. 1790",,Isoda Koryūsai,Japanese,1735,1790,1777–78,1777,1778,Polychrome woodblock print; ink and color on paper,15 1/2 x 10 3/8 in. (39.4 x 26.4 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56777,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2630,false,true,56778,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Isoda Koryūsai,"Japanese, 1735–ca. 1790",,Isoda Koryūsai,Japanese,1735,1790,ca. 1770,1760,1780,Polychrome woodblock print; ink and color on paper,10 3/8 x 7 5/8 in. (26.4 x 19.4 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56778,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2786,false,true,57103,Asian Art,Print,Umemi Tsuki|Plum-Seeing Month: Second Month,Japan,Edo period (1615–1868),,,,Artist,,Isoda Koryūsai,"Japanese, 1735–ca. 1790",,Isoda Koryūsai,Japanese,1735,1790,1735–1790,1735,1790,Polychrome woodblock print; ink and color on paper,10 3/8 x 7 3/4 in. (26.4 x 19.7 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57103,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2787,false,true,57102,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Isoda Koryūsai,"Japanese, 1735–ca. 1790",,Isoda Koryūsai,Japanese,1735,1790,1735–1790,1735,1790,Polychrome woodblock print; ink and color on paper,10 1/4 x 7 5/8 in. (26 x 19.4 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57102,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2788,false,true,57101,Asian Art,Woodblock print,"『雛形若菜の初模様　扇屋内　七越』|The Courtesan Nanakoshi of the Ōgiya Brothel, from the series “A Pat-tern Book of the Year’s First Designs, Fresh as Spring Herbs” (“Hinagata wakana no hatsu moyō”)",Japan,Edo period (1615–1868),,,,Artist,,Isoda Koryūsai,"Japanese, 1735–ca. 1790",,Isoda Koryūsai,Japanese,1735,1790,1777–78,1777,1778,Polychrome woodblock print; ink and color on paper,15 1/4 x 10 1/2 in. (38.7 x 26.7 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57101,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3408,false,true,55596,Asian Art,Print,"『雛形若菜の初模様　扇屋内　からうた』|The Courtesan Karauta of the Ōgiya Brothel, from the series “A Pattern Book of the Year’s First Designs, Fresh as Spring Herbs” (“Hinagata wakana no hatsu moyō”)",Japan,Edo period (1615–1868),,,,Artist,,Isoda Koryūsai,"Japanese, 1735–ca. 1790",,Isoda Koryūsai,Japanese,1735,1790,1777–78,1777,1778,Polychrome woodblock print; ink and color on paper,15 x 10 1/4 in. (38.1 x 26 cm),"Bequest of Julia H. Manges, 1960",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55596,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1436,false,true,55470,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Haruji,"Japanese, active ca. 1770",,Suzuki Haruji,Japanese,1770,1770,ca. 1770,1760,1780,Polychrome woodblock print; ink and color on paper,H. 27 13/16 in. (70.6 cm); W. 4 5/8 in. (11.7 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55470,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2456,false,true,56885,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Haruji,"Japanese, active ca. 1770",,Suzuki Haruji,Japanese,1770,1770,ca. 1770,1760,1780,Polychrome woodblock print; ink and color on paper,26 3/4 x 4 5/8 in. (67.9 x 11.7 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56885,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2624,false,true,56768,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonobu II,"Japanese, ca. 1702–1752",,Torii Kiyonobu II,Japanese,1702,1752,1735 or 1736,1735,1736,Polychrome woodblock print; ink and color on paper,12 1/8 x 5 3/4 in. (30.8 x 14.6 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56768,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2625,false,true,56769,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonobu II,"Japanese, ca. 1702–1752",,Torii Kiyonobu II,Japanese,1702,1752,1747,1747,1747,Polychrome woodblock print; ink and color on paper,11 1/2 x 5 9/16 in. (29.2 x 14.1 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56769,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1486,false,true,55580,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kashosai Shunsen,"Japanese, died after 1830",,Kashosai Shunsen,Japanese,1830,1830,ca. 1810,1800,1820,Polychrome woodblock print; ink and color on paper,H. 7 13/16 in. (19.8 cm); W. 15 3/16 in. (38.6 cm),"Rogers Fund, 1926",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55580,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP972,false,true,45475,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Okimura Toshinobu,"Japanese, active 1725–50",,Okimura Toshinobu,Japanese,1725,1750,ca. 1793,1783,1803,Tan-e (hand-colored print); ink and color on paper,Aiban; H. 13 3/16 in. (33.5 cm); W. 9 in. (22.9 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45475,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2658,false,true,56837,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Nishimura Shigenobu,"Japanese, active 1729–39",,Nishimura Shigenobu,Japanese,1729,1739,ca. 1738,1728,1748,Polychrome woodblock print; ink and color on paper,13 1/4 x 6 1/4 in. (33.7 x 15.9 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56837,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2756,false,true,57022,Asian Art,Print,Odari Kafu|Style of the Dancer,Japan,Edo period (1615–1868),,,,Artist,,Nishimura Shigenobu,"Japanese, active 1729–39",,Nishimura Shigenobu,Japanese,1729,1739,first half of 18th century,1700,1749,Tan-e print (hand-colored); ink and color on paper,11 7/8 x 6 in. (30.2 x 15.2 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57022,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2766,false,true,56671,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Yamamoto Yoshinobu,"Japanese, active 1748–63",,Yamamoto Yoshinobu,Japanese,1748,1763,ca. 1750,1740,1760,Beni-e (rouge) woodblock print; ink and color on paper,H. 12 1/8 in. (30.8 cm); W. 5 1/2 in. (14 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56671,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP196,false,true,36673,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Tōshūsai Sharaku,"Japanese, active 1794–95",,Tōshūsai Sharaku,Japanese,1794,1795,1794,1792,1796,"Polychrome woodblock print; ink, color, white mica on paper",14 7/8 x 9 7/8 in. (37.8 x 25.1 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36673,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP197,false,true,36674,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Tōshūsai Sharaku,"Japanese, active 1794–95",,Tōshūsai Sharaku,Japanese,1794,1795,ca. 1794,1784,1804,Polychrome woodblock print; ink and color on paper,15 1/8 x 9 7/8 in. (38.4 x 25.1 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36674,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP732,false,true,37180,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Tōshūsai Sharaku,"Japanese, active 1794–95",,Tōshūsai Sharaku,Japanese,1794,1795,1794–75,1794,1795,Middle sheet of a triptych of polychrome woodblock prints; ink and color on paper,Image: 12 1/2 x 6 in. (31.8 x 15.2 cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37180,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP733,false,true,37181,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,After,Tōshūsai Sharaku,"Japanese, active 1794–95",,Tōshūsai Sharaku,Japanese,1794,1795,Probably late 1880s or early 1890s,1880,1899,Polychrome woodblock print; ink and color on paper,14 3/4 x 10 1/8 in. (37.5 x 25.7 cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37181,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP734,false,true,37182,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,After,Tōshūsai Sharaku,"Japanese, active 1794–95",,Tōshūsai Sharaku,Japanese,1794,1795,Probably late 1880s or early 1890s,1880,1899,Polychrome woodblock print; ink and color on paper with mica ground,15 x 10 1/5 in. (38.1 x 25.9 cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37182,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1495,false,true,37327,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Tōshūsai Sharaku,"Japanese, active 1794–95",,Tōshūsai Sharaku,Japanese,1794,1795,1794,1794,1794,"Polychrome woodblock print; ink, color, white mica on paper",H. 14 1/2 in. (36.8 cm); W. 9 1/2 in. (24.1 cm),"Fletcher Fund, 1928",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37327,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1519,false,true,37328,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Tōshūsai Sharaku,"Japanese, active 1794–95",,Tōshūsai Sharaku,Japanese,1794,1795,1794–95,1794,1795,Possibly one sheet of a triptych of polychrome woodblock prints; ink and color on paper,12 1/2 x 5 7/8 in. (31.8 x 14.9 cm),"Fletcher Fund, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37328,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1520,false,true,37329,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Tōshūsai Sharaku,"Japanese, active 1794–95",,Tōshūsai Sharaku,Japanese,1794,1795,1794–95,1794,1795,Polychrome woodblock print; ink and color on paper,Image: 12 3/4 x 6 in. (32.4 x 15.2 cm),"Fletcher Fund, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37329,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1521,false,true,37330,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Tōshūsai Sharaku,"Japanese, active 1794–95",,Tōshūsai Sharaku,Japanese,1794,1795,1794,1794,1794,"Polychrome woodblock print; ink, color, white mica on paper",H. 14 1/2 in. (36.8 cm); W. 9 11/16 in. (24.6 cm),"Fletcher Fund, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37330,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1522,false,true,37331,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Tōshūsai Sharaku,"Japanese, active 1794–95",,Tōshūsai Sharaku,Japanese,1794,1795,1794,1794,1794,Polychrome woodblock print; ink and color on paper with mica ground,15 x 10 in. (38.1 x 25.4 cm),"Fletcher Fund, 1912",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37331,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1523,false,true,37332,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Tōshūsai Sharaku,"Japanese, active 1794–95",,Tōshūsai Sharaku,Japanese,1794,1795,1794,1794,1794,"Polychrome woodblock print; ink, color, white mica on paper",14 3/5 x 9 3/5 in. (37.1 x 24.4 cm),"Fletcher Fund, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37332,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1737,false,true,37347,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Tōshūsai Sharaku,"Japanese, active 1794–95",,Tōshūsai Sharaku,Japanese,1794,1795,1794,1794,1794,"Polychrome woodblock print; ink, color, white mica on paper",14 1/8 x 9 7/16 in. (35.9 x 24.0 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37347,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2645,false,true,37348,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Tōshūsai Sharaku,"Japanese, active 1794–95",,Tōshūsai Sharaku,Japanese,1794,1795,1794–95,1794,1795,Polychrome woodblock print; ink and color on paper,12 7/8 x 5 31/32 in. (32.7 x 15.2 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37348,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2646,false,true,37349,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Tōshūsai Sharaku,"Japanese, active 1794–95",,Tōshūsai Sharaku,Japanese,1794,1795,1794–75,1794,1975,Polychrome woodblock print; ink and color on paper,12 1/2 x 5 5/8 in. (31.8 x 14.3 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37349,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2647,false,true,37350,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Tōshūsai Sharaku,"Japanese, active 1794–95",,Tōshūsai Sharaku,Japanese,1794,1795,ca. 1794,1784,1804,"Polychrome woodblock print; ink and color on paper (hoso-e, Yellow ground)",12 31/32 x 6 11/32 in. (33 x 16.1 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37350,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2648,false,true,37351,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Tōshūsai Sharaku,"Japanese, active 1794–95",,Tōshūsai Sharaku,Japanese,1794,1795,1794,1794,1794,Left-hand sheet of a triptych of polychrome woodblock prints; ink and color on paper,12 3/4 x 5 31/32 in. (32.4 x 15.2 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37351,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2649,false,true,37352,Asian Art,Print,三代目市川高麗蔵の志賀大七|Kabuki Actor Ichikawa Komazō III as Shiga Daishichi in the Play A Medley of Tales of Revenge (Katakiuchi noriaibanashi),Japan,Edo period (1615–1868),,,,Artist,,Tōshūsai Sharaku,"Japanese, active 1794–95",,Tōshūsai Sharaku,Japanese,1794,1795,"5th month, 1794",1794,1794,"Polychrome woodblock print; ink, color, white mica on paper",14 3/8 x 9 7/8 in. (36.5 x 25.1 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37352,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2650,false,true,37353,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Tōshūsai Sharaku,"Japanese, active 1794–95",,Tōshūsai Sharaku,Japanese,1794,1795,1794,1794,1794,"Polychrome woodblock print; ink, color, white mica on paper",14 7/16 x 9 1/3 in. (36.7 x 23.7 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37353,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2822,false,true,37358,Asian Art,Print,三代目大谷鬼次の奴江戸兵衛|Kabuki Actor Ōtani Oniji III as Yakko Edobei in the Play The Colored Reins of a Loving Wife (Koi nyōbō somewake tazuna),Japan,Edo period (1615–1868),,,,Artist,,Tōshūsai Sharaku,"Japanese, active 1794–95",,Tōshūsai Sharaku,Japanese,1794,1795,"6th month, 1794",1794,1794,"Polychrome woodblock print; ink, color, white mica on paper",15 x 9 7/8 in. (38.1 x 25.1 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37358,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2823,false,true,37359,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Tōshūsai Sharaku,"Japanese, active 1794–95",,Tōshūsai Sharaku,Japanese,1794,1795,1794,1794,1794,"Polychrome woodblock print; ink, color, white mica on paper",H. 15 in. (38.1 cm); W. 9 7/8 in. (25.1 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37359,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3017,false,true,37363,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Tōshūsai Sharaku,"Japanese, active 1794–95",,Tōshūsai Sharaku,Japanese,1794,1795,1794–95,1794,1795,Polychrome woodblock print; ink and color on paper; white mica ground,14 3/5 x 9 3/4 in. (37.1 x 24.8 cm) Oban,"Harris Brisbane Dick Fund, 1946",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37363,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3111,false,true,37365,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Tōshūsai Sharaku,"Japanese, active 1794–95",,Tōshūsai Sharaku,Japanese,1794,1795,1794,1794,1794,"Polychrome woodblock print; ink, color, white mica on paper",10 x 14 3/8 in. (25.4 x 36.5 cm),"Harris Brisbane Dick Fund and Rogers Fund, 1949",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37365,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3112,false,true,37366,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Tōshūsai Sharaku,"Japanese, active 1794–95",,Tōshūsai Sharaku,Japanese,1794,1795,1794,1794,1794,"Polychrome woodblock print; ink, color, white mica on paper",H. 14 5/8 in. (37.1 cm); W. 9 7/8 in. (25.1 cm),"Harris Brisbane Dick Fund and Rogers Fund, 1949",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37366,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3113,false,true,37367,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Tōshūsai Sharaku,"Japanese, active 1794–95",,Tōshūsai Sharaku,Japanese,1794,1795,1794,1794,1794,"Polychrome woodblock print; ink, color, white mica on paper",H. 15 in. (38.1 cm); W. 9 15/16 in. (25.2 cm),"Harris Brisbane Dick Fund and Rogers Fund, 1949",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37367,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3114,false,true,37368,Asian Art,Print,市川鰕蔵の竹村定之進|Kabuki Actor Ichikawa Ebizō (Ichikawa Danjūrō V) in the play The Colored Reins of a Loving Wife (Koi nyōbō somewake tazuna),Japan,Edo period (1615–1868),,,,Artist,,Tōshūsai Sharaku,"Japanese, active 1794–95",,Tōshūsai Sharaku,Japanese,1794,1795,"5th month, 1794",1794,1794,Polychrome woodblock print; ink and color on paper with mica ground,14 15/32 x 9 3/5 in. (36.8 x 24.4 cm),"Harris Brisbane Dick Fund and Rogers Fund, 1949",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37368,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3115,false,true,37369,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Tōshūsai Sharaku,"Japanese, active 1794–95",,Tōshūsai Sharaku,Japanese,1794,1795,1794,1794,1794,"Polychrome woodblock print; ink, color, white mica on paper",H. 14 (35.6 cm); W. 9 in. (22.9 cm),"Harris Brisbane Dick Fund and Rogers Fund, 1949",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37369,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3117,false,true,37371,Asian Art,Print,八代目守田勘弥の鴬の次郎作|Kabuki Actor Morita Kan’ya VIII as the Palanquin-Bearer in the Play A Medley of Tales of Revenge (Katakiuchi noriaibanashi),Japan,Edo period (1615–1868),,,,Artist,,Tōshūsai Sharaku,"Japanese, active 1794–95",,Tōshūsai Sharaku,Japanese,1794,1795,"5th month, 1794",1794,1794,"Polychrome woodblock print; ink, color, white mica on paper",15 x 10 in. (38.1 x 25.4 cm),"Harris Brisbane Dick Fund and Rogers Fund, 1949",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37371,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3118,false,true,37372,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Tōshūsai Sharaku,"Japanese, active 1794–95",,Tōshūsai Sharaku,Japanese,1794,1795,1794,1794,1794,"Polychrome woodblock print; ink, color, white mica on paper",15 1/8 x 10 in. (38.4 x 25.4 cm),"Harris Brisbane Dick Fund and Rogers Fund, 1949",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37372,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3119,false,true,37373,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Tōshūsai Sharaku,"Japanese, active 1794–95",,Tōshūsai Sharaku,Japanese,1794,1795,1794–95,1794,1795,Polychrome woodblock print; ink and color on paper; white mica ground,Image: 14 1/2 x 9 7/8 in. (36.8 x 25.1 cm),"Harris Brisbane Dick Fund and Rogers Fund, 1949",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37373,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3120,false,true,37374,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Tōshūsai Sharaku,"Japanese, active 1794–95",,Tōshūsai Sharaku,Japanese,1794,1795,1794–95,1794,1795,Polychrome woodblock print; ink and color on paper,12 3/4 x 5 31/32 in. (32.4 x 15.2 cm),"Harris Brisbane Dick Fund and Rogers Fund, 1949",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37374,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3121,false,true,37375,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Tōshūsai Sharaku,"Japanese, active 1794–95",,Tōshūsai Sharaku,Japanese,1794,1795,1794–95,1794,1795,Polychrome woodblock print; ink and color on paper,12 31/32 x 5 31/32 in. (33 x 15.2 cm),"Harris Brisbane Dick Fund and Rogers Fund, 1949",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37375,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3122,false,true,37376,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Tōshūsai Sharaku,"Japanese, active 1794–95",,Tōshūsai Sharaku,Japanese,1794,1795,1794,1794,1794,Polychrome woodblock print; white mica ground; ink and color on paper,14 3/4 x 9 3/4 in. (37.5 x 24.8 cm),"Harris Brisbane Dick Fund and Rogers Fund, 1949",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37376,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3123,false,true,37377,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Tōshūsai Sharaku,"Japanese, active 1794–95",,Tōshūsai Sharaku,Japanese,1794,1795,1794–95,1794,1795,Polychrome woodblock print; ink and color on paper,12 11/32 x 5 5/8 in. (31.4 x 14.3 cm),"Harris Brisbane Dick Fund and Rogers Fund, 1949",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37377,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3124,false,true,37378,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Tōshūsai Sharaku,"Japanese, active 1794–95",,Tōshūsai Sharaku,Japanese,1794,1795,1794–95,1794,1795,Polychrome woodblock print; ink and color on paper,Image: 12 3/4 × 5 15/16 in. (32.4 × 15.1 cm) Mat: 22 3/4 × 15 1/2 in. (57.8 × 39.4 cm),"Harris Brisbane Dick Fund and Rogers Fund, 1949",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37378,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3125,false,true,37379,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Tōshūsai Sharaku,"Japanese, active 1794–95",,Tōshūsai Sharaku,Japanese,1794,1795,1794–95,1794,1795,Polychrome woodblock print; ink and color on paper,12 7/32 x 5 3/4 in. (31.1 x 14.6 cm),"Harris Brisbane Dick Fund and Rogers Fund, 1949",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37379,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3126,false,true,37380,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Tōshūsai Sharaku,"Japanese, active 1794–95",,Tōshūsai Sharaku,Japanese,1794,1795,1794–95,1794,1795,Polychrome woodblock print; ink and color on paper,Image: 12 1/2 x 6 in. (31.8 x 15.2 cm),"Harris Brisbane Dick Fund and Rogers Fund, 1949",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37380,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3127,false,true,37381,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Tōshūsai Sharaku,"Japanese, active 1794–95",,Tōshūsai Sharaku,Japanese,1794,1795,1794–95,1794,1795,Polychrome woodblock print; ink and color on paper,14 11/16 x 5 3/4 in. (37.3 x 14.6 cm),"Harris Brisbane Dick Fund and Rogers Fund, 1949",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37381,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3129,false,true,37383,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Tōshūsai Sharaku,"Japanese, active 1794–95",,Tōshūsai Sharaku,Japanese,1794,1795,1794,1794,1794,"Polychrome woodblock print; ink, color, white mica on paper",12 x 8 1/2 in. (30.5 x 21.6 cm),"Harris Brisbane Dick Fund and Rogers Fund, 1949",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37383,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3130,false,true,37384,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Tōshūsai Sharaku,"Japanese, active 1794–95",,Tōshūsai Sharaku,Japanese,1794,1795,1794–95,1794,1795,One sheet of a pentaptych of polychrome woodblock prints; ink and color on paper,12 7/8 x 5 3/4 in. (32.7 x 14.6 cm),"Harris Brisbane Dick Fund and Rogers Fund, 1949",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37384,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3131,false,true,37385,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Tōshūsai Sharaku,"Japanese, active 1794–95",,Tōshūsai Sharaku,Japanese,1794,1795,1794–95,1794,1795,Polychrome woodblock print; ink and color on paper with mica ground,12 11/32 x 5 3/4 in. (31.4 x 14.6 cm),"Harris Brisbane Dick Fund and Rogers Fund, 1949",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37385,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"JP3128a, b",false,true,37382,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Tōshūsai Sharaku,"Japanese, active 1794–95",,Tōshūsai Sharaku,Japanese,1794,1795,1794–95,1794,1795,Diptych of polychrome woodblock prints; ink and color on paper,12 1/2 x 5 31/32 in. (31.8 x 15.2 cm),"Harris Brisbane Dick Fund and Rogers Fund, 1949",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37382,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2011.127,false,true,76557,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Shunkōsai Hokushū,"Japanese, active 1808–32",,Shunkōsai Hokushū,Japanese,1808,1832,1822,1822,1822,Polychrome woodblock print,Image (ôban tate-e): 14 3/4 x 10 1/8 in. (37.5 x 25.7 cm),"Purchase, Friends of Asian Art Gifts, in honor of James C. Y. Watt, 2011",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/76557,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2011.128,false,true,76558,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Shunkōsai Hokushū,"Japanese, active 1808–32",,Shunkōsai Hokushū,Japanese,1808,1832,1821,1821,1821,Polychrome woodblock print,Image (ôban tate-e): 15 x 10 in. (38.1 x 25.4 cm),"Purchase, Friends of Asian Art Gifts, in honor of James C. Y. Watt, 2011",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/76558,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2011.130,false,true,76560,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Shunkōsai Hokushū,"Japanese, active 1808–32",,Shunkōsai Hokushū,Japanese,1808,1832,1825,1825,1825,Polychrome woodblock print,Image (ôban tate-e): 15 x 10 1/8 in. (38.1 x 25.7 cm),"Purchase, Friends of Asian Art Gifts, in honor of James C. Y. Watt, 2011",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/76560,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2011.126a, b",false,true,76556,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Shunkōsai Hokushū,"Japanese, active 1808–32",,Shunkōsai Hokushū,Japanese,1808,1832,1822,1822,1822,Diptych of polychrome woodblock prints,Each sheet (ôban tate-e diptych): 15 x 10 in. (38.1 x 25.4 cm),"Purchase, Friends of Asian Art Gifts, in honor of James C. Y. Watt, 2011",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/76556,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2011.143,false,true,76573,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Sadahiro,"Japanese, active 1825–75",,Utagawa Sadahiro,Japanese,1825,1875,1841,1841,1841,Polychrome woodblock print,Image (chûban tate-e): 10 x 7 1/4 in. (25.4 x 18.4 cm),"Purchase, Friends of Asian Art Gifts, in honor of James C. Y. Watt, 2011",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/76573,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2011.147,false,true,76577,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hirosada,"Japanese, active 1825–75",,Utagawa Hirosada,Japanese,1825,1875,ca. 1852,1842,1862,Polychrome woodblock print,Image (chûban tate-e): 9 1/2 x 7 1/8 in. (24.1 x 18.1 cm),"Purchase, Friends of Asian Art Gifts, in honor of James C. Y. Watt, 2011",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/76577,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2011.146a–e,false,true,76576,Asian Art,Woodblock prints,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hirosada,"Japanese, active 1825–75",,Utagawa Hirosada,Japanese,1825,1875,ca. 1852,1842,1862,Nine polychrome woodblock prints,Each (chûban tate-e): 10 x 7 1/4 in. (25.4 x 18.4 cm),"Purchase, Friends of Asian Art Gifts, in honor of James C. Y. Watt, 2011",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/76576,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2011.144,false,true,76574,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunimasu,"Japanese, active 1830–52",,Utagawa Kunimasu,Japanese,1830,1852,ca. 1849,1839,1859,Polychrome woodblock print,Image (ôban tate-e): 15 1/4 x 10 in. (38.7 x 25.4 cm),"Purchase, Friends of Asian Art Gifts, in honor of James C. Y. Watt, 2011",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/76574,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2011.145,false,true,76575,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunimasu,"Japanese, active 1830–52",,Utagawa Kunimasu,Japanese,1830,1852,1850,1850,1850,Polychrome woodblock print,Image (chûban tate-e): 9 7/8 x 7 in. (25.1 x 17.8 cm),"Purchase, Friends of Asian Art Gifts, in honor of James C. Y. Watt, 2011",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/76575,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP661,false,true,37108,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyotada,"Japanese, fl. ca. 1720–50",,Torii Kiyotada,Japanese,1720,1750,ca. early 1740s,1740,1745,Polychrome woodblock print; ink and color on paper,Oban: 17 1/8 x 25 3/8 in. (43.5 x 64.5 cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37108,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP833,false,true,54487,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyotada,"Japanese, fl. ca. 1720–50",,Torii Kiyotada,Japanese,1720,1750,ca. 1735,1725,1745,Polychrome woodblock print; ink and color on paper (Urushi-e),H. 11 in. (27.9 cm); W. 4 7/8 in. (12.4 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54487,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP834,false,true,54488,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyotada,"Japanese, fl. ca. 1720–50",,Torii Kiyotada,Japanese,1720,1750,ca. 1735,1725,1745,Polychrome woodblock print; ink and color on paper (Urushi-e),H. 12 1/4 in. (31.1 cm); 6 3/8 in. (16.2 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54488,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1567,false,true,45035,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyotada,"Japanese, fl. ca. 1720–50",,Torii Kiyotada,Japanese,1720,1750,ca. 1738,1728,1748,Polychrome woodblock print; ink and color on paper,H. 16 3/4 in. (42.5 cm); W. 25 in. (63.5 cm),"Fletcher Fund, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45035,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3075,false,true,56555,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyotada,"Japanese, fl. ca. 1720–50",,Torii Kiyotada,Japanese,1720,1750,ca. 1715,1705,1725,Woodblock print; ink and hand-painted color (tan-e) on paper,11 1/4 x 6 in. (28.6 x 15.2 cm),"Harris Brisbane Dick Fund and Rogers Fund, 1949",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56555,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP198,false,true,36675,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyomasu I,"Japanese, active 1696–1716",,Torii Kiyomasu I,Japanese,1696,1716,"5th month, 1736",1736,1736,Polychrome woodblock print; ink and color on paper,11 1/4 x 5 13/16 in. (28.6 x 14.8 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36675,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP230,false,true,36702,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyomasu I,"Japanese, active 1696–1716",,Torii Kiyomasu I,Japanese,1696,1716,ca. 1748,1738,1758,Polychrome woodblock print; ink and color on paper,10 1/2 x 5 17/32 in. (26.7 x 14.1 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36702,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP231,false,true,36703,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyomasu I,"Japanese, active 1696–1716",,Torii Kiyomasu I,Japanese,1696,1716,ca. 1748,1738,1758,Polychrome woodblock print; ink and color on paper,12 1/32 x 5 5/8 in. (30.6 x 14.3 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36703,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP232,false,true,36704,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyomasu I,"Japanese, active 1696–1716",,Torii Kiyomasu I,Japanese,1696,1716,dated 1749,1749,1749,Polychrome woodblock print; ink and color on paper,10 17/32 x 5 1/2 in. (26.8 x 14.0 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36704,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP651,false,true,37101,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyomasu I,"Japanese, active 1696–1716",,Torii Kiyomasu I,Japanese,1696,1716,ca. 1705,1695,1715,Polychrome woodblock print; ink and color on paper,12 x 20 3/8 in. (30.5 x 51.8 cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37101,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP652,false,true,37102,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyomasu I,"Japanese, active 1696–1716",,Torii Kiyomasu I,Japanese,1696,1716,ca. 1705,1695,1715,Polychrome woodblock print; ink and color on paper,21 7/8 x 12 3/4 in. (55.6 x 32.4 cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37102,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP653,false,true,37103,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyomasu I,"Japanese, active 1696–1716",,Torii Kiyomasu I,Japanese,1696,1716,ca. 1713,1703,1723,Polychrome woodblock print; ink and color on paper,20 31/32 x 12 5/8 in. (53.3 x 32.1 cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37103,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP654,false,true,37104,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyomasu I,"Japanese, active 1696–1716",,Torii Kiyomasu I,Japanese,1696,1716,ca. 1710,1700,1720,Polychrome woodblock print; ink and color on paper,20 3/4 x 12 5/8 in. (52.7 x 32.1 cm),"The Francis Lathorp Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37104,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP688,false,true,37135,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyomasu I,"Japanese, active 1696–1716",,Torii Kiyomasu I,Japanese,1696,1716,ca. 1730,1720,1740,Polychrome woodblock print; ink and color on paper,12 1/8 x 5 3/4 in. (30.8 x 14.6 cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37135,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP689,false,true,37136,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyomasu I,"Japanese, active 1696–1716",,Torii Kiyomasu I,Japanese,1696,1716,ca. 1750,1740,1760,Polychrome woodblock print; ink and color on paper,12 1/2 x 5 7/8 in. (31.8 x 14.9 cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37136,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP690,false,true,37137,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyomasu I,"Japanese, active 1696–1716",,Torii Kiyomasu I,Japanese,1696,1716,ca. 1755,1745,1765,Polychrome woodblock print; ink and color on paper,11 3/8 x 5 7/32 in. (28.9 x 13.3 cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37137,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP821,false,true,37265,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyomasu I,"Japanese, active 1696–1716",,Torii Kiyomasu I,Japanese,1696,1716,ca. 1730,1720,1740,Polychrome woodblock print; ink and color on paper (Urushi-e),13 1/10 x 6 1/8 in. (33.3 x 15.6 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37265,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP822,false,true,37266,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyomasu I,"Japanese, active 1696–1716",,Torii Kiyomasu I,Japanese,1696,1716,ca. 1735,1725,1745,Polychrome woodblock print; ink and color on paper (Urushi-e),Overall: 12 1/4 x 5 7/8in. (31.1 x 14.9cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37266,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP823,false,true,37267,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyomasu I,"Japanese, active 1696–1716",,Torii Kiyomasu I,Japanese,1696,1716,ca. 1745,1735,1755,Polychrome woodblock print; ink and color on paper,12 11/32 x 5 7/8 in. (31.4 x 14.9 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37267,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP824,false,true,54478,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyomasu I,"Japanese, active 1696–1716",,Torii Kiyomasu I,Japanese,1696,1716,ca. 1745,1735,1755,Polychrome woodblock print; ink and color on paper,H. 12 3/8 in. (31.4 cm); W. 5 3/4 in. (14.6 cm),"Gift of Estate of Samuel Ishma, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54478,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP825,false,true,54479,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyomasu I,"Japanese, active 1696–1716",,Torii Kiyomasu I,Japanese,1696,1716,ca. 1748,1738,1758,Polychrome woodblock print; ink and color on paper,H. 12 3/8 in. (31.4 cm); W. 5 7/8 in. (14.9 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54479,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP826,false,true,54480,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyomasu I,"Japanese, active 1696–1716",,Torii Kiyomasu I,Japanese,1696,1716,ca. 1748,1738,1758,Polychrome woodblock print; ink and color on paper,H. 12 3/8 in. (31.4 cm); W. 5 3/4 in. (14.6 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54480,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP827,false,true,54481,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyomasu I,"Japanese, active 1696–1716",,Torii Kiyomasu I,Japanese,1696,1716,ca. 1752,1742,1762,Polychrome woodblock print; ink and color on paper,H. 12 1/2 in. (31.8 cm); W. 5 7/8 in. (14.9 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54481,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1110,false,true,55035,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyomasu I,"Japanese, active 1696–1716",,Torii Kiyomasu I,Japanese,1696,1716,ca. 1745,1735,1755,Polychrome woodblock print; ink and color on paper,H. 12 1/2 in. (31.8 cm); W. 5 1/2 in. (14 cm),"Rogers Fund, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55035,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2583,false,true,56728,Asian Art,Lacquer print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyomasu I,"Japanese, active 1696–1716",,Torii Kiyomasu I,Japanese,1696,1716,"12th month, 1743 or 1st month, 1744",1743,1744,Urushi-e (lacquer) print,12 1/2 x 5 3/4 in. (31.8 x 14.6 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56728,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2584,false,true,56729,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyomasu I,"Japanese, active 1696–1716",,Torii Kiyomasu I,Japanese,1696,1716,ca. 1734,1724,1744,Urushi-e (lacquer) print,11 1/2 x 5 3/4 in. (29.2 x 14.6 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56729,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2585,false,true,56730,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyomasu I,"Japanese, active 1696–1716",,Torii Kiyomasu I,Japanese,1696,1716,ca. 1741,1731,1751,Polychrome woodblock print; ink and color on paper,12 3/4 x 5 3/4 in. (32.4 x 14.6 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56730,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3072,false,true,45247,Asian Art,Print,"Kairaishi|The Actor Ichimura Takenojo VIII in the Role of a Puppeteer, showing Puppets to a Courtesan",Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyomasu I,"Japanese, active 1696–1716",,Torii Kiyomasu I,Japanese,1696,1716,ca. 1715,1705,1725,Monochrome woodblock print; ink on paper,11 x 16 in. (27.9 x 40.6 cm),"Harris Brisbane Dick Fund and Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45247,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3073,false,true,45052,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyomasu I,"Japanese, active 1696–1716",,Torii Kiyomasu I,Japanese,1696,1716,ca. 1712,1702,1722,"Polychrome ""tan-e"" woodblock print; ink and color on paper",12 1/4 x 6 in. (31.1 x 15.2 cm),"Harris Brisbane Dick Fund and Rogers Fund, 1949",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45052,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3099,false,true,45053,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyomasu I,"Japanese, active 1696–1716",,Torii Kiyomasu I,Japanese,1696,1716,ca. 1712,1702,1722,Monochrome woodblock (tan-e) print; ink on paper,22 1/2 x 12 1/4 in. (57.2 x 31.1 cm),"Harris Brisbane Dick Fund and Rogers Fund, 1949",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45053,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3100,false,true,45054,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyomasu I,"Japanese, active 1696–1716",,Torii Kiyomasu I,Japanese,1696,1716,ca. 1716,1706,1726,Polychrome woodblock print (tan-e); ink and color on paper,23 1/4 x 12 1/4 in. (59.1 x 31.1 cm),"Harris Brisbane Dick Fund and Rogers Fund, 1949",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45054,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3105,false,true,45059,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kaigetsudō Doshin,"Japanese, active 1711–1736",,Kaigetsudō Doshin,Japanese,1711,1736,ca. 1714,1704,1724,Polychrome woodblock print (sumizuri-e); ink and color on paper,23 1/2 x 12 1/2 in. (59.7 x 31.8 cm),"Harris Brisbane Dick Fund and Rogers Fund, 1949",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45059,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1311,false,true,55270,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kondo Katsunobu,"Japanese, active 1716–1736",,Kondo Katsunobu,Japanese,1716,1736,ca. 1730,1720,1740,Polychrome woodblock print (urushi-e); ink and color on paper,H. 11 3/4 in. (29.8 cm); W. 5 7/8 in. (14.9 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55270,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3082,false,true,56595,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kiyonobu II,"Japanese, active 1720–1750",,Kiyonobu II,Japanese,1720,1750,1739,1739,1739,Polychrome woodblock print (hand-colored); ink and color on paper,12 x 6 in. (30.5 x 15.2 cm),"Harris Brisbane Dick Fund and Rogers Fund, 1949",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56595,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP240,false,true,36712,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ippitsusai Bunchō,"Japanese, active 1760–1794",,Ippitsusai Bunchō,Japanese,1760,1794,ca. 1770,1760,1780,Polychrome woodblock print; ink and color on paper,12 1/32 x 5 5/8 in. (30.6 x 14.3 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36712,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP241,false,true,36713,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ippitsusai Bunchō,"Japanese, active 1760–1794",,Ippitsusai Bunchō,Japanese,1760,1794,ca. 1770,1760,1780,Polychrome woodblock print; ink and color on paper,12 7/32 x 5 17/32 in. (31.1 x 14.1 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36713,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP242,false,true,36714,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ippitsusai Bunchō,"Japanese, active 1760–1794",,Ippitsusai Bunchō,Japanese,1760,1794,probably 1770,1768,1772,Polychrome woodblock print; ink and color on paper,12 5/8 x 5 7/8 in. (32.1 x 14.9 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36714,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP470,false,true,36921,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ippitsusai Bunchō,"Japanese, active 1760–1794",,Ippitsusai Bunchō,Japanese,1760,1794,ca. 1769,1759,1779,Polychrome woodblock print; ink and color on paper,11 3/5 x 5 1/2 in. (29.5 x 14 cm),"Gift of Frank Lloyd Wright, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36921,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP561,false,true,37012,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ippitsusai Bunchō,"Japanese, active 1760–1794",,Ippitsusai Bunchō,Japanese,1760,1794,1770,1770,1770,Polychrome woodblock print; ink and color on paper,12 7/32 x 5 5/8 in. (31.1 x 14.3 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37012,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP562,false,true,37013,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ippitsusai Bunchō,"Japanese, active 1760–1794",,Ippitsusai Bunchō,Japanese,1760,1794,"12th month, 1768",1768,1768,Polychrome woodblock print; ink and color on paper,12 5/8 x 5 7/8 in. (32.1 x 14.9 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37013,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP563,false,true,37014,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ippitsusai Bunchō,"Japanese, active 1760–1794",,Ippitsusai Bunchō,Japanese,1760,1794,1770 or 1771,1770,1771,Polychrome woodblock print; ink and color on paper,12 5/16 x 5 13/16 in. (31.3 x 14.8 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37014,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP701,false,true,37148,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ippitsusai Bunchō,"Japanese, active 1760–1794",,Ippitsusai Bunchō,Japanese,1760,1794,ca. 1770,1760,1780,Polychrome woodblock print; ink and color on paper,12 7/32 x 5 3/4 in. (31.1 x 14.6 cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37148,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP702,false,true,37149,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ippitsusai Bunchō,"Japanese, active 1760–1794",,Ippitsusai Bunchō,Japanese,1760,1794,ca. 1771,1761,1781,Polychrome woodblock print; ink and color on paper,11 23/32 x 5 1/2 in. (29.8 x 14.0 cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37149,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP907,false,true,51997,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ippitsusai Bunchō,"Japanese, active 1760–1794",,Ippitsusai Bunchō,Japanese,1760,1794,ca. 1770,1760,1780,Polychrome woodblock print; ink and color on paper,H. 12 1/8 in. (30.8 cm); W. 5 1/2 in. (14 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/51997,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1116,false,true,55038,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ippitsusai Bunchō,"Japanese, active 1760–1794",,Ippitsusai Bunchō,Japanese,1760,1794,ca. 1770,1760,1780,Polychrome woodblock print; ink and color on paper,H. 10 1/2 in. (26.7 cm); W. 5 1/2 in. (14 cm),"Rogers Fund, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55038,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1226,false,true,55148,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ippitsusai Bunchō,"Japanese, active 1760–1794",,Ippitsusai Bunchō,Japanese,1760,1794,ca. 1770,1760,1780,Polychrome woodblock print; ink and color on paper,H. 10 1/16 in. (25.6 cm); W. 7 3/16 in. (18.3 cm),"Rogers Fund, 1921",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55148,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1227,false,true,55149,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ippitsusai Bunchō,"Japanese, active 1760–1794",,Ippitsusai Bunchō,Japanese,1760,1794,ca. 1770,1760,1780,Polychrome woodblock print; ink and color on paper,H. 11 1/4 in. (28.6 cm); W. 5 5/16 in. (13.5 cm),"Rogers Fund, 1921",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55149,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1279,false,true,55210,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Ippitsusai Bunchō,"Japanese, active 1760–1794",,Ippitsusai Bunchō,Japanese,1760,1794,1769,1769,1769,Polychrome woodblock print; ink and color on paper,H. 12 1/4 in. (31.1 cm); W. 5 5/8 in. (14.3 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55210,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1361,false,true,55339,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ippitsusai Bunchō,"Japanese, active 1760–1794",,Ippitsusai Bunchō,Japanese,1760,1794,ca. 1770,1760,1780,Polychrome woodblock print; ink and color on paper,H. 12 3/8 in. (31.4 cm); W. 5 7/8 in. (14.9 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55339,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1362,false,true,55341,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ippitsusai Bunchō,"Japanese, active 1760–1794",,Ippitsusai Bunchō,Japanese,1760,1794,ca. 1770,1760,1780,Polychrome woodblock print; ink and color on paper,H. 11 7/16 in. (29.1 cm); W. 5 3/4 in. (14.6 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55341,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1363,false,true,55342,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ippitsusai Bunchō,"Japanese, active 1760–1794",,Ippitsusai Bunchō,Japanese,1760,1794,1764–71,1764,1771,Polychrome woodblock print; ink and color on paper,H. 12 3/4 in. (32.4 cm); W. 5 7/8 in. (14.9 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55342,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1365,false,true,55346,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ippitsusai Bunchō,"Japanese, active 1760–1794",,Ippitsusai Bunchō,Japanese,1760,1794,ca. 1769,1759,1779,Polychrome woodblock print; ink and color on paper,H. 12 1/2 in. (31.8 cm); W. 5 3/4 in. (14.6 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55346,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1460,false,true,55507,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ippitsusai Bunchō,"Japanese, active 1760–1794",,Ippitsusai Bunchō,Japanese,1760,1794,ca. 1772,1762,1782,Polychrome woodblock print; ink and color on paper,H. 11 3/4 in. (29.8 cm); W. 5 1/2 in. (14 cm),"Rogers Fund, 1925",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55507,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1544,false,true,55709,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ippitsusai Bunchō,"Japanese, active 1760–1794",,Ippitsusai Bunchō,Japanese,1760,1794,ca. 1769,1759,1779,Polychrome woodblock print; ink and color on paper,H. 12 1/2 in. (31.8 cm); W. 5 5/8 in. (14.3 cm),"Fletcher Fund, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55709,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1545,false,true,55710,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ippitsusai Bunchō,"Japanese, active 1760–1794",,Ippitsusai Bunchō,Japanese,1760,1794,ca. 1770,1760,1780,Polychrome woodblock print; ink and color on paper,H. 12 1/4 in. (31.1 cm); W. 5 5/8 in. (14.3 cm),"Fletcher Fund, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55710,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2407,false,true,56810,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ippitsusai Bunchō,"Japanese, active 1760–1794",,Ippitsusai Bunchō,Japanese,1760,1794,ca. 1770,1760,1780,Polychrome woodblock print; ink and color on paper,12 1/4 x 5 3/4 in. (31.1 x 14.6 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56810,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2408,false,true,56811,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ippitsusai Bunchō,"Japanese, active 1760–1794",,Ippitsusai Bunchō,Japanese,1760,1794,ca. 1769,1759,1779,Polychrome woodblock print; ink and color on paper,Hosoe; 12 x 5 3/4 in. (30.5 x 14.6 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56811,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2409,false,true,56812,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ippitsusai Bunchō,"Japanese, active 1760–1794",,Ippitsusai Bunchō,Japanese,1760,1794,ca. 1769,1759,1779,Polychrome woodblock print; ink and color on paper,Hosoe: 12 1/8 x 5 3/4 in. (30.8 x 14.6 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56812,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2410,false,true,56813,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ippitsusai Bunchō,"Japanese, active 1760–1794",,Ippitsusai Bunchō,Japanese,1760,1794,ca. 1772,1762,1782,Polychrome woodblock print; ink and color on paper,Hosoe: 12 3/8 x 5 3/4 in. (31.4 x 14.6 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56813,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2411,false,true,56814,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ippitsusai Bunchō,"Japanese, active 1760–1794",,Ippitsusai Bunchō,Japanese,1760,1794,ca. 1771,1761,1781,Polychrome woodblock print; ink and color on paper,Hosoe: 12 3/4 x 5 7/8 in. (32.4 x 14.9 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56814,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2412,false,true,56815,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ippitsusai Bunchō,"Japanese, active 1760–1794",,Ippitsusai Bunchō,Japanese,1760,1794,ca. 1769,1759,1779,Polychrome woodblock print; ink and color on paper,Hosoe: 12 3/4 x 6 in. (32.4 x 15.2 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56815,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2413,false,true,56816,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ippitsusai Bunchō,"Japanese, active 1760–1794",,Ippitsusai Bunchō,Japanese,1760,1794,ca. 1769,1759,1779,Polychrome woodblock print; ink and color on paper,12 1/2 x 5 5/8 in. (31.8 x 14.3 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56816,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2414,false,true,56817,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ippitsusai Bunchō,"Japanese, active 1760–1794",,Ippitsusai Bunchō,Japanese,1760,1794,ca. 1790,1780,1800,Polychrome woodblock print; ink and color on paper,Hosoe: 11 3/4 x 5 5/8 in. (29.8 x 14.3 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56817,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2415,false,true,56818,Asian Art,Print,"初世尾上菊五郎の戸無瀬|Onoe Kikugorō as Tonase, from Kanadehon Chūshingura (Kanadehon Chūshingura, Shosei Onoe Kikugorō no Tonase)",Japan,Edo period (1615–1868),,,,Artist,,Ippitsusai Bunchō,"Japanese, active 1760–1794",,Ippitsusai Bunchō,Japanese,1760,1794,1773,1773,1773,Polychrome woodblock print; ink and color on paper,Hosoe: 11 3/4 x 5 3/4 in. (29.8 x 14.6 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56818,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2416,false,true,56819,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ippitsusai Bunchō,"Japanese, active 1760–1794",,Ippitsusai Bunchō,Japanese,1760,1794,ca. 1772,1762,1802,Polychrome woodblock print; ink and color on paper,Hosoe: 12 7/16 x 5 7/8 in. (31.6 x 14.9 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56819,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2789,false,true,57100,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ippitsusai Bunchō,"Japanese, active 1760–1794",,Ippitsusai Bunchō,Japanese,1760,1794,1770,1770,1770,Polychrome woodblock print; ink and color on paper,12 5/8 x 5 3/4 in. (32.1 x 14.6 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57100,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2790,false,true,57099,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ippitsusai Bunchō,"Japanese, active 1760–1794",,Ippitsusai Bunchō,Japanese,1760,1794,1723–1792,1723,1792,Polychrome woodblock print; ink and color on paper,12 1/2 x 5 7/8 in. (31.8 x 14.9 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57099,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2791,false,true,57098,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ippitsusai Bunchō,"Japanese, active 1760–1794",,Ippitsusai Bunchō,Japanese,1760,1794,1723–1792,1723,1792,Polychrome woodblock print; ink and color on paper,10 3/8 x 7 1/2 in. (26.4 x 19.1 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57098,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2792,false,true,57097,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Ippitsusai Bunchō,"Japanese, active 1760–1794",,Ippitsusai Bunchō,Japanese,1760,1794,1723–1792,1723,1792,Polychrome woodblock print; ink and color on paper,12 3/4 x 6 1/8 in. (32.4 x 15.6 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57097,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2793,false,true,57096,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Ippitsusai Bunchō,"Japanese, active 1760–1794",,Ippitsusai Bunchō,Japanese,1760,1794,1723–1792,1723,1792,Polychrome woodblock print; ink and color on paper,12 3/4 x 5 7/8 in. (32.4 x 14.9 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57096,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP849,false,true,54506,Asian Art,Print,Shō gatsu|The First Month,Japan,Edo period (1615–1868),,,,Artist,,Ishikawa Toyomasa,"Japanese, active 1770–1790",,Ishikawa Toyomasa,Japanese,1770,1790,ca. 1767,1757,1777,Polychrome woodblock print; ink and color on paper,H. 10 in. (25.4 cm); W. 7 3/8 in. (18.7 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54506,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP850,false,true,54507,Asian Art,Print,Inari-ko|The Second Month,Japan,Edo period (1615–1868),,,,Artist,,Ishikawa Toyomasa,"Japanese, active 1770–1790",,Ishikawa Toyomasa,Japanese,1770,1790,ca. 1767,1757,1777,Polychrome woodblock print; ink and color on paper,H. 10 in. (25.4 cm); W. 7 3/8 in. (18.7 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54507,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP851,false,true,54511,Asian Art,Print,Hina no Sekku|The Festival of Dolls (Third Month),Japan,Edo period (1615–1868),,,,Artist,,Ishikawa Toyomasa,"Japanese, active 1770–1790",,Ishikawa Toyomasa,Japanese,1770,1790,ca. 1767,1757,1777,Polychrome woodblock print; ink and color on paper,H. 10 in. (25.4 cm); W. 7 3/8 in. (18.7 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54511,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP852,false,true,54522,Asian Art,Print,Shi Gatsu|The Fourth Month,Japan,Edo period (1615–1868),,,,Artist,,Ishikawa Toyomasa,"Japanese, active 1770–1790",,Ishikawa Toyomasa,Japanese,1770,1790,ca. 1767,1757,1777,Polychrome woodblock print; ink and color on paper,H. 10 in. (25.4 cm); W. 7 3/8 in. (18.7 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54522,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP853,false,true,54524,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ishikawa Toyomasa,"Japanese, active 1770–1790",,Ishikawa Toyomasa,Japanese,1770,1790,ca. 1767,1757,1777,Polychrome woodblock print; ink and color on paper,H. 10 in. (25.4 cm); W. 7 3/8 in. (18.7 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54524,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP854,false,true,54526,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ishikawa Toyomasa,"Japanese, active 1770–1790",,Ishikawa Toyomasa,Japanese,1770,1790,ca. 1767,1757,1777,Polychrome woodblock print; ink and color on paper,H. 10 in. (25.4 cm); W. 7 3/8 in. (18.7 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54526,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP855,false,true,54528,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ishikawa Toyomasa,"Japanese, active 1770–1790",,Ishikawa Toyomasa,Japanese,1770,1790,ca. 1767,1757,1777,Polychrome woodblock print; ink and color on paper,H. 10 in. (25.4 cm); W. 7 3/8 in. (18.7 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54528,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP856,false,true,54531,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ishikawa Toyomasa,"Japanese, active 1770–1790",,Ishikawa Toyomasa,Japanese,1770,1790,ca. 1767,1757,1777,Polychrome woodblock print; ink and color on paper,H. 10 in. (25.4 cm); W. 7 3/8 in. (18.7 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54531,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP857,false,true,54534,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ishikawa Toyomasa,"Japanese, active 1770–1790",,Ishikawa Toyomasa,Japanese,1770,1790,ca. 1767,1757,1777,Polychrome woodblock print; ink and color on paper,H. 10 in. (25.4 cm); W. 7 3/8 in. (18.7 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54534,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP858,false,true,54536,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ishikawa Toyomasa,"Japanese, active 1770–1790",,Ishikawa Toyomasa,Japanese,1770,1790,ca. 1767,1757,1777,Polychrome woodblock print; ink and color on paper,H. 10 in. (25.4 cm); W. 7 3/8 in. (18.7 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54536,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP859,false,true,54538,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ishikawa Toyomasa,"Japanese, active 1770–1790",,Ishikawa Toyomasa,Japanese,1770,1790,ca. 1767,1757,1777,Polychrome woodblock print; ink and color on paper,H. 10 in. (25.4 cm); W. 7 3/8 in. (18.7 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54538,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP860,false,true,54539,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ishikawa Toyomasa,"Japanese, active 1770–1790",,Ishikawa Toyomasa,Japanese,1770,1790,ca. 1767,1757,1777,Polychrome woodblock print; ink and color on paper,H. 10 in. (25.4 cm); W. 7 3/8 in. (18.7 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54539,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2716,false,true,56984,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunzan,"Japanese, active 1782–1798",,Katsukawa Shunzan,Japanese,1782,1798,ca. 1789,1779,1799,Polychrome woodblock print; ink and color on paper,15 x 9 3/4 in. (38.1 x 24.8 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56984,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1292,false,true,55234,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Shotei Hokuju,"Japanese, active 1790–1820",,Shotei Hokuju,Japanese,1790,1820,ca. 1825,1815,1835,Polychrome woodblock print; ink and color on paper,Oban 9 5/8 x 14 1/4 in. (24.4 x 36.2 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55234,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1394,false,true,55400,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Shotei Hokuju,"Japanese, active 1790–1820",,Shotei Hokuju,Japanese,1790,1820,ca. 1830,1820,1830,Polychrome woodblock print; ink and color on paper,H. 10 5/16 in. (26.2 cm); W. 15 1/4 in. (38.7 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55400,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1409,false,true,55424,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Shotei Hokuju,"Japanese, active 1790–1820",,Shotei Hokuju,Japanese,1790,1820,ca. 1830,1820,1840,Polychrome woodblock print; ink and color on paper,H. 10 1/4 in. (26 cm); W. 14 1/2 in. (36.8 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55424,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2582,false,true,54196,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ishikawa Kazan,"Japanese, active 1810–1823",,Ishikawa Kazan,Japanese,1810,1823,ca. 1820,1810,1830,Polychrome woodblock print (surimono); ink and color on paper,7 1/4 x 6 5/8 in. (18.4 x 16.8 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54196,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2011.142,false,true,76572,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Hasegawa Sadamasu,"Japanese, active 1830s–40s",,Hasegawa Sadamasu,Japanese,1830,1849,1841,1841,1841,Polychrome woodblock print,Image (chûban tate-e): 10 x 7 1/4 in. (25.4 x 18.4 cm),"Purchase, Friends of Asian Art Gifts, in honor of James C. Y. Watt, 2011",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/76572,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2011.136a–d,false,true,76566,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Hasegawa Sadamasu,"Japanese, active 1830s–40s",,Hasegawa Sadamasu,Japanese,1830,1849,1834,1834,1834,Tetraptych of polychrome woodblock prints,Each sheet (ôban tate-e tetraptych): 14 1/8 x 10 in. (35.9 x 25.4 cm),"Purchase, Friends of Asian Art Gifts, in honor of James C. Y. Watt, 2011",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/76566,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2001.715.11a–c,false,true,63378,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshifusa,"Japanese, active 1837–1860",,Utagawa Yoshifusa,Japanese,1837,1860,"1856, 2nd month",1856,1856,Triptych of polychrome woodblock prints; ink and color on paper,Oban tate-e; triptych (right): 14 1/2 x 9 3/4 in. (36.8 x 24.8 cm) (middle): 10 3/8 x 9 3/4 in. (26.4 x 24.8 cm) (left): 14 1/2 x 9 3/4 in. (36.8 x 24.8 cm),"Purchase, Arnold Weinstein Gift, 2001",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/63378,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP223,false,true,36697,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Toyoshige,"Japanese, active 1802?–?1835",,Utagawa Toyoshige,Japanese,1802,1835,ca. 1830,1820,1850,Polychrome woodblock print; ink and color on paper,9 4/5 x 14 15/32 in. (24.9 x 36.8 cm),"Rogers Fund, 1917",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36697,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1370,false,true,55349,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Toyoshige,"Japanese, active 1802?–?1835",,Utagawa Toyoshige,Japanese,1802,1835,ca. 1828,1818,1838,Polychrome woodblock print; ink and color on paper,H. 10 1/4 in. (26 cm); W. 13 7/8 in. (35.2 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55349,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2001.715.5,false,true,63359,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Toyoshige,"Japanese, active 1802?–?1835",,Utagawa Toyoshige,Japanese,1802,1835,ca. 1825,1815,1835,Polychrome woodblock print; ink and color on paper,"Oban tate-e, 15 1/2 x 10 3/4 in. (39.4 x 27.3 cm)","Purchase, Jack Greene Gift, 2001",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/63359,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2048,false,true,54820,Asian Art,Print,騎龍弁財天|Benzaiten (Goddess of Music and Good Fortune) Seated on a White Dragon,Japan,Edo period (1615–1868),,,,Artist,,Aoigaoka Keisei,"Japanese, active 1820s–1830s",,Aoigaoka Keisei,Japanese,1810,1840,1832,1832,1832,Polychrome woodblock print (surimono); ink and color on paper,8 1/2 x 7 1/4 in. (21.6 x 18.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54820,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP176,false,true,36654,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunchō,"Japanese, active ca. 1783–95",,Katsukawa Shunchō,Japanese,1775,1795,ca. 1789,1779,1799,Middle sheet of a triptych of polychrome woodblock prints; ink and color on paper,14 9/16 x 10 in. (37 x 25.4 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36654,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP940,false,true,54819,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunchō,"Japanese, active ca. 1783–95",,Katsukawa Shunchō,Japanese,1775,1795,ca. 1778,1768,1788,Polychrome woodblock print; ink and color on paper,13 × 9 in. (33 × 22.9 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54819,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP941,false,true,54821,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunchō,"Japanese, active ca. 1783–95",,Katsukawa Shunchō,Japanese,1775,1795,ca. 1778,1768,1788,Polychrome woodblock print; ink and color on paper,13 × 9 in. (33 × 22.9 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54821,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP942,false,true,54825,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunchō,"Japanese, active ca. 1783–95",,Katsukawa Shunchō,Japanese,1775,1795,ca. 1788,1778,1798,Polychrome woodblock print; ink and color on paper,9 11/16 × 7 3/4 in. (24.6 × 19.7 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54825,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP943,false,true,54827,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunchō,"Japanese, active ca. 1783–95",,Katsukawa Shunchō,Japanese,1775,1795,ca. 1788,1778,1798,Polychrome woodblock print; ink and color on paper,14 11/16 × 9 7/8 in. (37.3 × 25.1 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54827,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1042,false,true,54952,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunchō,"Japanese, active ca. 1783–95",,Katsukawa Shunchō,Japanese,1775,1795,ca. 1787,1777,1797,Triptych of polychrome woodblock prints; ink and color on paper,12 3/4 × 25 1/2 in. (32.4 × 64.8 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54952,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1515,false,true,55618,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunchō,"Japanese, active ca. 1783–95",,Katsukawa Shunchō,Japanese,1775,1795,ca. 1788,1778,1798,Polychrome woodblock print; ink and color on paper,15 3/8 × 10 3/16 in. (39.1 × 25.9 cm),"Fletcher Fund, 1925",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55618,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1516,false,true,55622,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunchō,"Japanese, active ca. 1783–95",,Katsukawa Shunchō,Japanese,1775,1795,ca. 1790,1780,1800,Polychrome woodblock print; ink and color on paper,15 1/8 × 10 1/16 in. (38.4 × 25.6 cm),"Fletcher Fund, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55622,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1517,false,true,55625,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunchō,"Japanese, active ca. 1783–95",,Katsukawa Shunchō,Japanese,1775,1795,ca. 1790,1790,1800,Polychrome woodblock print; ink and color on paper,15 1/8 × 10 1/8 in. (38.4 × 25.7 cm),"Fletcher Fund, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55625,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1518,false,true,55626,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunchō,"Japanese, active ca. 1783–95",,Katsukawa Shunchō,Japanese,1775,1795,ca. 1785,1775,1795,Polychrome woodblock print; ink and color on paper,14 1/2 × 9 3/4 in. (36.8 × 24.8 cm),"Fletcher Fund, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55626,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1834,false,true,56119,Asian Art,Print,あやめ燈籠図|Three Women Enjoying Literary Pursuits,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunchō,"Japanese, active ca. 1783–95",,Katsukawa Shunchō,Japanese,1775,1795,ca. late 1780s,1785,1789,One sheet of a triptych of polychrome woodblock prints; ink and color on paper,14 3/4 × 9 5/8 in. (37.5 × 24.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56119,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2660,false,true,56839,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunchō,"Japanese, active ca. 1783–95",,Katsukawa Shunchō,Japanese,1775,1795,ca. 1789,1779,1799,Polychrome woodblock print; ink and color on paper,14 3/8 × 10 in. (36.5 × 25.4 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56839,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2661,false,true,56840,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunchō,"Japanese, active ca. 1783–95",,Katsukawa Shunchō,Japanese,1775,1795,ca. 1789,1779,1799,One sheet of a triptych of polychrome woodblock prints; ink and color on paper,15 x 10 in. (38.1 x 25.4 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56840,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2662,false,true,56841,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunchō,"Japanese, active ca. 1783–95",,Katsukawa Shunchō,Japanese,1775,1795,1787,1787,1787,Polychrome woodblock print; ink and color on paper,15 x 10 in. (38.1 x 25.4 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56841,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2665,false,true,56843,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunchō,"Japanese, active ca. 1783–95",,Katsukawa Shunchō,Japanese,1775,1795,ca. 1786,1776,1796,Polychrome woodblock print; ink and color on paper,15 x 30 in. (38.1 x 76.2 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56843,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2804,false,true,57086,Asian Art,Print,Ukiyoe Ga|The Beauty of the Floating World,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunchō,"Japanese, active ca. 1783–95",,Katsukawa Shunchō,Japanese,1775,1795,1780–1795,1780,1795,Polychrome woodblock print; ink and color on paper,15 x 10 in. (38.1 x 25.4 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57086,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2805,false,true,57085,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunchō,"Japanese, active ca. 1783–95",,Katsukawa Shunchō,Japanese,1775,1795,1780–1795,1780,1795,Polychrome woodblock print; ink and color on paper,14 3/4 x 9 7/8 in. (37.5 x 25.1 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57085,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2806,false,true,57084,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunchō,"Japanese, active ca. 1783–95",,Katsukawa Shunchō,Japanese,1775,1795,1780–1795,1780,1795,Polychrome woodblock print; ink and color on paper,10 5/16 x 7 9/16 in. (26.2 x 19.2 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57084,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2807,false,true,57083,Asian Art,Print,Junigatsu|The Twelfth Month: December,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunchō,"Japanese, active ca. 1783–95",,Katsukawa Shunchō,Japanese,1775,1795,1780–1795,1780,1795,Polychrome woodblock print; ink and color on paper,9 3/4 x 6 7/8 in. (24.8 x 17.5 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57083,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2808,false,true,57082,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunchō,"Japanese, active ca. 1783–95",,Katsukawa Shunchō,Japanese,1775,1795,1780–1795,1780,1795,Polychrome woodblock print; ink and color on paper,15 1/2 x 10 3/8 in. (39.4 x 26.4 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57082,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"JP2664a, b",false,true,56842,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunchō,"Japanese, active ca. 1783–95",,Katsukawa Shunchō,Japanese,1775,1795,ca. 1789,1779,1799,Diptych of polychrome woodblock prints; ink and color on paper,Image (each): 14 7/8 × 10 in. (37.8 × 25.4 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56842,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"JP2803a, b",false,true,57087,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunchō,"Japanese, active ca. 1783–95",,Katsukawa Shunchō,Japanese,1775,1795,ca. 1780–95,1780,1795,Diptych of polychrome woodblock prints; ink and color on paper,Image (each): 15 3/8 × 10 3/8 in. (39.1 × 26.4 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57087,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.261a–c,false,true,73552,Asian Art,Woodblock print,Daizō shasei|Big Elephants Being Attacked,Japan,Edo period (1615–1868),,,,Artist,,Isshinsai Yoshikata,"Japanese, active ca. 1841–64",,Isshinsai Yoshikata,Japanese,1841,1864,"2nd month, 1863",1863,1863,Triptych of polychrome woodblock prints; ink and color on paper,Image (a): 14 1/2 x 10 in. (36.8 x 25.4 cm) Image (b): 14 1/2 x 9 5/8 in. (36.8 x 24.4 cm) Image (c): 14 5/8 x 9 7/8 in. (37.1 x 25.1 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73552,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3183,false,true,55169,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshitora,"Japanese, active ca. 1850–80",,Utagawa Yoshitora,Japanese,1845,1880,ca. 1861,1851,1871,Polychrome woodblock print; ink and color on paper,Oban 14 x 9 1/2 in. (35.6 x 24.1 cm),"GIft of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55169,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3185,false,true,55170,Asian Art,Print,Amerika jin Yuko Sakamori|An American Carousing,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshitora,"Japanese, active ca. 1850–80",,Utagawa Yoshitora,Japanese,1845,1880,"1861 (Bunkyu 1, first month)",1861,1861,Polychrome woodblock print; ink and color on paper,14 1/4 x 9 3/4 in. (36.2 x 24.8 cm),"GIft of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55170,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3186,false,true,55171,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshitora,"Japanese, active ca. 1850–80",,Utagawa Yoshitora,Japanese,1845,1880,1860,1860,1860,Polychrome woodblock print; ink and color on paper,Image: 14 x 9 1/2 in. (35.6 x 24.1 cm),"GIft of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55171,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3243,false,true,55256,Asian Art,Print,"東都芝浦之風景|View of Shibaura, from the series Eastern Capital (Tōto, Shibaura no fūkei)",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshitora,"Japanese, active ca. 1850–80",,Utagawa Yoshitora,Japanese,1845,1880,"6th month, 1863",1863,1863,Sheet from a triptych of polychrome woodblock prints; ink and color on paper,13 x 28 1/8 in. (33 x 71.4 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55256,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3248,false,true,55260,Asian Art,Print,Goko Kokujinmotsu-Don Taku no zu|View of Eating and Drinking by People of Five Countries,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshitora,"Japanese, active ca. 1850–80",,Utagawa Yoshitora,Japanese,1845,1880,"1861, 12th month (?)",1861,1861,Triptych of polychrome woodblock prints; ink and color on paper,14 x 28 1/2 in. (35.6 x 72.4 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55260,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3311,false,true,55422,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshitora,"Japanese, active ca. 1850–80",,Utagawa Yoshitora,Japanese,1845,1880,January 1861,1861,1861,Polychrome woodblock print; ink and color on paper,14 x 9 1/2 in. (35.6 x 24.1 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55422,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3330,false,true,55478,Asian Art,Print,五箇国人物呑託之図|People of the Five Nations,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshitora,"Japanese, active ca. 1850–80",,Utagawa Yoshitora,Japanese,1845,1880,1861,1861,1861,Triptych of polychrome woodblock prints; ink and color on paper,Image: 14 1/4 x 29 1/2 in. (36.2 x 74.9 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55478,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3339,false,true,55493,Asian Art,Print,英吉利国倫敦図|Illustration of London in England (Igirisukoku rondon zu),Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshitora,"Japanese, active ca. 1850–80",,Utagawa Yoshitora,Japanese,1845,1880,1866,1866,1866,Triptych of polychrome woodblock prints; ink and color on paper,Oban; 29 5/8 x 14 9/16 in. (75.2 x 37 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55493,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3362,false,true,55530,Asian Art,Print,Orandajin|Dutchmen,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshitora,"Japanese, active ca. 1850–80",,Utagawa Yoshitora,Japanese,1845,1880,1860,1860,1860,Polychrome woodblock print; ink and color on paper,14 3/4 x 9 3/4 in. (37.5 x 24.8 cm),"Gift of Lincoln Kirstein, 1960",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55530,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3368,false,true,55535,Asian Art,Print,Orandajin|A Dutch Couple,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshitora,"Japanese, active ca. 1850–80",,Utagawa Yoshitora,Japanese,1845,1880,1863 (Bunkyu 3),1863,1863,Polychrome woodblock print; ink and color on paper,14 3/8 x 9 15/16 in. (36.5 x 25.2 cm),"Gift of Lincoln Kirstein, 1960",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55535,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3369,false,true,55536,Asian Art,Print,英吉利人|Ingirisu-jin,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshitora,"Japanese, active ca. 1850–80",,Utagawa Yoshitora,Japanese,1845,1880,"1863, 7th month (Bunkyu 3)",1863,1863,Polychrome woodblock print; ink and color on paper,14 1/8 x 10 in. (35.9 x 25.4 cm),"Gift of Lincoln Kirstein, 1960",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55536,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3378,false,true,55545,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshitora,"Japanese, active ca. 1850–80",,Utagawa Yoshitora,Japanese,1845,1880,1870,1870,1870,Triptych of polychrome woodblock prints; ink and color on paper,13 3/4 x 27 1/2 in. (34.9 x 69.9 cm),"Gift of Lincoln Kirstein, 1960",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55545,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP212.25,false,true,57985,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshitora,"Japanese, active ca. 1850–80",,Utagawa Yoshitora,Japanese,1845,1880,ca. 1858,1848,1868,Polychrome woodblock print; ink and color on paper,Image: 13 1/2 × 9 1/4 in. (34.3 × 23.5 cm) Mat: 15 1/2 × 22 3/4 in. (39.4 × 57.8 cm),"Gift of the Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57985,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2001.715.10,false,true,63376,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshitora,"Japanese, active ca. 1850–80",,Utagawa Yoshitora,Japanese,1845,1880,"1859, 2nd month",1859,1859,Polychrome woodblock print; ink and color on paper,"Obant tate-e, 14 5/8 x 10 1/4 in. (37.1 x 26 cm)","Purchase, Arnold Weinstein Gift, 2001",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/63376,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.180,false,true,73467,Asian Art,Print,Porosiajin|A Prussian Couple,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshitora,"Japanese, active ca. 1850–80",,Utagawa Yoshitora,Japanese,1845,1880,"12th month, 1860",1860,1860,Polychrome woodblock print; ink and color on paper,Image: 14 1/4 x 9 1/2 in. (36.2 x 24.1 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73467,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.181,false,true,73468,Asian Art,Print,Karajin|Two Chinese Women,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshitora,"Japanese, active ca. 1850–80",,Utagawa Yoshitora,Japanese,1845,1880,"12th month, 1860",1860,1860,Polychrome woodblock print; ink and color on paper,Image: 14 1/2 x 10 in. (36.8 x 25.4 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73468,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.182,false,true,73469,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshitora,"Japanese, active ca. 1850–80",,Utagawa Yoshitora,Japanese,1845,1880,"12th month, 1860",1860,1860,Polychrome woodblock print; ink and color on paper,Image: 14 1/4 x 9 3/4 in. (36.2 x 24.8 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73469,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.183,false,true,73470,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshitora,"Japanese, active ca. 1850–80",,Utagawa Yoshitora,Japanese,1845,1880,"12th month, 1860",1860,1860,Polychrome woodblock print; ink and color on paper,Image: 14 3/8 x 9 7/8 in. (36.5 x 25.1 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73470,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.184,false,true,73471,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshitora,"Japanese, active ca. 1850–80",,Utagawa Yoshitora,Japanese,1845,1880,"1st month, 1861",1861,1861,Polychrome woodblock print; ink and color on paper,Image: 14 1/4 x 9 7/8 in. (36.2 x 25.1 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73471,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.185,false,true,73472,Asian Art,Print,Nōgei no seiran|Returning Sails at Nōgei [American couple riding over the Nōgei Bridge],Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshitora,"Japanese, active ca. 1850–80",,Utagawa Yoshitora,Japanese,1845,1880,"1st month, 1861",1861,1861,Polychrome woodblock print; ink and color on paper,Image: 14 1/8 x 9 3/4 in. (35.9 x 24.8 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73472,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.186,false,true,73473,Asian Art,Woodblock print,Michiyuki no embō|Evening Glow on a Traveling Drama [Chinese watching a Kabuki play],Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshitora,"Japanese, active ca. 1850–80",,Utagawa Yoshitora,Japanese,1845,1880,"1st month, 1861",1861,1861,Polychrome woodblock print; ink and color on paper,Image: 14 1/2 x 9 7/8 in. (36.8 x 25.1 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73473,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.187,false,true,73474,Asian Art,Print,Asa ichi no yuki|Snow at an Early Morning Market [Chinese shopping for vegetables],Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshitora,"Japanese, active ca. 1850–80",,Utagawa Yoshitora,Japanese,1845,1880,"1st month, 1861",1861,1861,Polychrome woodblock print; ink and color on paper,Image: 14 5/8 x 9 3/8 in. (37.1 x 23.8 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73474,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.188,false,true,73475,Asian Art,Print,Motomura no yūdachi|Evening Glow at Motomura [Two Englishmen looking at the sunset],Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshitora,"Japanese, active ca. 1850–80",,Utagawa Yoshitora,Japanese,1845,1880,"1st month, 1861",1861,1861,Polychrome woodblock print; ink and color on paper,Image: 14 1/8 x 9 3/4 in. (35.9 x 24.8 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73475,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.189,false,true,73476,Asian Art,Print,Miyozaki no shūgetsu|Autumn Moon at Miyozaki,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshitora,"Japanese, active ca. 1850–80",,Utagawa Yoshitora,Japanese,1845,1880,"1st month, 1861",1861,1861,Polychrome woodblock print; ink and color on paper,Image: 14 3/4 x 10 in. (37.5 x 25.4 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73476,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.190,false,true,73477,Asian Art,Print,Gankirō yoru no ame|Night Rain at Gankirō,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshitora,"Japanese, active ca. 1850–80",,Utagawa Yoshitora,Japanese,1845,1880,"1st month, 1861",1861,1861,Polychrome woodblock print; ink and color on paper,Image: 14 3/8 x 10 in. (36.5 x 25.4 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73477,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.191,false,true,73478,Asian Art,Print,Hatoba no kihan|Returning Sails at the Wharves [American couple],Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshitora,"Japanese, active ca. 1850–80",,Utagawa Yoshitora,Japanese,1845,1880,"1st month, 1861",1861,1861,Polychrome woodblock print; ink and color on paper,Image: 14 7/8 x 9 15/16 in. (37.8 x 25.2 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73478,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.192,false,true,73480,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshitora,"Japanese, active ca. 1850–80",,Utagawa Yoshitora,Japanese,1845,1880,"1st month, 1861",1861,1861,Polychrome woodblock print; ink and color on paper,Image: 10 1/16 x 14 7/16 in. (25.6 x 36.7 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73480,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.193,false,true,73481,Asian Art,Woodblock print,Amerika|American Horseman,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshitora,"Japanese, active ca. 1850–80",,Utagawa Yoshitora,Japanese,1845,1880,"1st month, 1861",1861,1861,Polychrome woodblock print; ink and color on paper,Image: 14 5/8 x 9 7/8 in. (37.1 x 25.1 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73481,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.194,false,true,73482,Asian Art,Woodblock print,Amerika nyōjin|American Horsewoman,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshitora,"Japanese, active ca. 1850–80",,Utagawa Yoshitora,Japanese,1845,1880,"1st month, 1861",1861,1861,Polychrome woodblock print; ink and color on paper,Image: 14 3/8 x 9 7/8 in. (36.5 x 25.1 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73482,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.195,false,true,73483,Asian Art,Print,Oranda|Dutch Couple,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshitora,"Japanese, active ca. 1850–80",,Utagawa Yoshitora,Japanese,1845,1880,"1st month, 1861",1861,1861,Polychrome woodblock print; ink and color on paper,Image: 14 3/8 x 9 5/8 in. (36.5 x 24.4 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73483,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.196,false,true,73484,Asian Art,Print,Igirisu|Englishmen Dining,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshitora,"Japanese, active ca. 1850–80",,Utagawa Yoshitora,Japanese,1845,1880,"1st month, 1861",1861,1861,Polychrome woodblock print; ink and color on paper,Image: 14 1/2 x 9 7/8 in. (36.8 x 25.1 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73484,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.197,false,true,73485,Asian Art,Woodblock print,Igirisu nyōjin|Englishmen Woman on Horseback,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshitora,"Japanese, active ca. 1850–80",,Utagawa Yoshitora,Japanese,1845,1880,"1st month, 1861",1861,1861,Polychrome woodblock print; ink and color on paper,Image: 14 1/2 x 10 1/16 in. (36.8 x 25.6 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73485,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.198,false,true,73486,Asian Art,Print,Amerika nyōjin|American Woman with Her Child on Stilts,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshitora,"Japanese, active ca. 1850–80",,Utagawa Yoshitora,Japanese,1845,1880,"1st month, 1861",1861,1861,Polychrome woodblock print; ink and color on paper,Image: 14 5/8 x 9 7/8 in. (37.1 x 25.1 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73486,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.199,false,true,73487,Asian Art,Print,Furansu nyōjin|French Housewife and Her Husband,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshitora,"Japanese, active ca. 1850–80",,Utagawa Yoshitora,Japanese,1845,1880,"1st month, 1861",1861,1861,Polychrome woodblock print; ink and color on paper,Image: 14 3/4 x 10 in. (37.5 x 25.4 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73487,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.203,false,true,73491,Asian Art,Print,Oranda|Dutch Printers,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshitora,"Japanese, active ca. 1850–80",,Utagawa Yoshitora,Japanese,1845,1880,"2nd month, 1861",1861,1861,Polychrome woodblock print; ink and color on paper,Image: 14 3/4 x 10 1/8 in. (37.5 x 25.7 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73491,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.205,false,true,73493,Asian Art,Print,Orosia|Mounted Russian,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshitora,"Japanese, active ca. 1850–80",,Utagawa Yoshitora,Japanese,1845,1880,"5th month, 1861",1861,1861,Polychrome woodblock print; ink and color on paper,Image: 14 × 10 in. (35.6 × 25.4 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73493,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.209,false,true,73499,Asian Art,Print,Tōsen no zu|Chinese Junk,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshitora,"Japanese, active ca. 1850–80",,Utagawa Yoshitora,Japanese,1845,1880,"2nd month, 1862",1862,1862,Polychrome woodblock print; ink and color on paper,Image: 14 5/8 x 10 1/4 in. (37.1 x 26 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73499,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.210,false,true,73500,Asian Art,Print,Igirisu fune|English Ship,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshitora,"Japanese, active ca. 1850–80",,Utagawa Yoshitora,Japanese,1845,1880,"2nd month, 1862",1862,1862,Polychrome woodblock print; ink and color on paper,Image: 14 x 9 3/4 in. (35.6 x 24.8 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73500,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.211,false,true,73501,Asian Art,Print,Kita Amerka fune no zu|North American Ship,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshitora,"Japanese, active ca. 1850–80",,Utagawa Yoshitora,Japanese,1845,1880,"4th month, 1862",1862,1862,Polychrome woodblock print; ink and color on paper,Image: 14 3/4 x 10 1/8 in. (37.5 x 25.7 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73501,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.214,false,true,73504,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshitora,"Japanese, active ca. 1850–80",,Utagawa Yoshitora,Japanese,1845,1880,"3rd month, 1864",1864,1864,Polychrome woodblock print; ink and color on paper,Image: 14 1/4 x 9 3/8 in. (36.2 x 23.8 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73504,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.215,false,true,73505,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshitora,"Japanese, active ca. 1850–80",,Utagawa Yoshitora,Japanese,1845,1880,"3rd month, 1864",1864,1864,Polychrome woodblock print; ink and color on paper,Image: 14 1/4 x 9 5/8 in. (36.2 x 24.4 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73505,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.216,false,true,73506,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshitora,"Japanese, active ca. 1850–80",,Utagawa Yoshitora,Japanese,1845,1880,"3rd month, 1864",1864,1864,Polychrome woodblock print; ink and color on paper,Image: 14 1/4 x 9 1/2 in. (36.2 x 24.1 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73506,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.217,false,true,73507,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshitora,"Japanese, active ca. 1850–80",,Utagawa Yoshitora,Japanese,1845,1880,"3rd month, 1864",1864,1864,Polychrome woodblock print; ink and color on paper,Image: 14 1/4 x 9 5/8 in. (36.2 x 24.4 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73507,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.222,false,true,73512,Asian Art,Print,「亜墨利加國」|“America”: Enjoying Hot Air Balloons,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshitora,"Japanese, active ca. 1850–80",,Utagawa Yoshitora,Japanese,1845,1880,1867,1867,1867,Triptych of polychrome woodblock prints; ink and color on paper,Image: 14 3/4 x 28 3/4 in. (37.5 x 73 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73512,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3275a–c,false,true,55357,Asian Art,Print,亜米利加国|American Balloon Ascension (Amerikakoku),Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshitora,"Japanese, active ca. 1850–80",,Utagawa Yoshitora,Japanese,1845,1880,1867,1867,1867,Triptych of polychrome woodblock prints; ink and color on paper,Image (a): 14 5/8 × 9 5/8 in. (37.1 × 24.4 cm) Image (b): 14 3/4 in. × 10 in. (37.5 × 25.4 cm) Image (c): 14 3/4 in. × 10 in. (37.5 × 25.4 cm) Mat: 22 7/8 in. × 37 in. (58.1 × 94 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55357,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2007.49.200a, b",false,true,73488,Asian Art,Print,Gaikokujin yūkyō no zu|Foreigners Enjoying a Party,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshitora,"Japanese, active ca. 1850–80",,Utagawa Yoshitora,Japanese,1845,1880,"1st month, 1861",1861,1861,Polychrome woodblock print; ink and color on paper,Image (a): 14 3/8 x 10 in. (36.5 x 25.4 cm) Image (b): 14 1/4 x 19 1/4 in. (36.2 x 48.9 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73488,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2007.49.204a, b",false,true,73492,Asian Art,Print,Yokohama hatoba keshiki|A View of the Wharves in Yokohama,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshitora,"Japanese, active ca. 1850–80",,Utagawa Yoshitora,Japanese,1845,1880,"2nd month, 1861",1861,1861,Diptych of polychrome woodblock prints; ink and color on paper,Image (a): 14 3/8 x 9 3/4 in. (36.5 x 24.8 cm) Image (b): 14 5/8 x 9 3/4 in. (37.1 x 24.8 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73492,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.177a–c,false,true,73465,Asian Art,Print,"神奈川横浜港崎町遊女屋光景|View of the Miyozaki Brothel District in Yokohama, Kanagawa (Kanagwa Yokohama Miyozaki machi yūjoya kōkei)",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshitora,"Japanese, active ca. 1850–80",,Utagawa Yoshitora,Japanese,1845,1880,"4th month, 1864",1864,1864,Triptych of polychrome woodblock prints; ink and color on paper,Image (a): 14 3/8 x 9 3/4 in. (36.5 x 24.8 cm) Image (b): 14 1/2 x 9 7/8 in. (36.8 x 25.1 cm) Image (c): 14 1/2 x 9 3/4 in. (36.8 x 24.8 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73465,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.178a–c,false,true,73466,Asian Art,Print,Kanagawa yōri Yokohama...ken no zu|A Distant View of Yokohama from Kanagawa,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshitora,"Japanese, active ca. 1850–80",,Utagawa Yoshitora,Japanese,1845,1880,"5th month, 1860",1860,1860,Triptych of polychrome woodblock prints; ink and color on paper,Image (a): 14 3/8 x 9 5/8 in. (36.5 x 24.4 cm) Image (b): 14 5/8 x 9 3/4 in. (37.1 x 24.8 cm) Image (c): 14 1/4 x 10 1/8 in. (36.2 x 25.7 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73466,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.179a–c,false,true,73691,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshitora,"Japanese, active ca. 1850–80",,Utagawa Yoshitora,Japanese,1845,1880,"5th month, 1860",1860,1860,Triptych of polychrome woodblock prints; ink and color on paper,Image (d): 14 1/4 x 9 1/2 in. (36.2 x 24.1 cm) Image (e): 14 1/4 x 9 3/4 in. (36.2 x 24.8 cm) Image (f): 14 1/8 x 9 7/8 in. (35.9 x 25.1 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73691,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.201a–c,false,true,73489,Asian Art,Print,"Bushu Yokohama gaikokujin yūkyō no zu|A View of the Amusements of the Foreigners in Yokohama, Bushu",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshitora,"Japanese, active ca. 1850–80",,Utagawa Yoshitora,Japanese,1845,1880,"1st month, 1861",1861,1861,Triptych of polychrome woodblock prints; ink and color on paper,Image (a): 14 1/2 x 9 3/4 in. (36.8 x 24.8 cm) Image (b): 14 1/2 x 9 3/4 in. (36.8 x 24.8 cm) Image (c): 14 1/2 x 9 3/4 in. (36.8 x 24.8 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73489,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.202a–c,false,true,73490,Asian Art,Print,"Bushu Yokohama gaikokujin yūkyō no zu|A View of the Amusements of the Foreigners in Yokohama, Bushu",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshitora,"Japanese, active ca. 1850–80",,Utagawa Yoshitora,Japanese,1845,1880,"1st month, 1861",1861,1861,Triptych of polychrome woodblock prints; ink and color on paper,Image (a): 14 1/2 x 9 3/4 in. (36.8 x 24.8 cm) Image (b): 14 1/2 x 9 3/4 in. (36.8 x 24.8 cm) Image (c): 14 1/2 x 9 3/4 in. (36.8 x 24.8 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73490,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.206a–c,false,true,73494,Asian Art,Print,Amerika Washinton fu|City of Washington in America,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshitora,"Japanese, active ca. 1850–80",,Utagawa Yoshitora,Japanese,1845,1880,"1st month, 1862",1862,1862,Triptych of polychrome woodblock prints; ink and color on paper,Image (a): 14 1/4 x 9 5/8 in. (36.2 x 24.4 cm) Image (b): 14 1/4 x 9 5/8 in. (36.2 x 24.4 cm) Image (c): 14 1/4 x 9 5/8 in. (36.2 x 24.4 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73494,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.207a–c,false,true,73495,Asian Art,Print,Igirisu Rondon no kaiko|The Port of London England,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshitora,"Japanese, active ca. 1850–80",,Utagawa Yoshitora,Japanese,1845,1880,"2nd month, 1862",1862,1862,Triptych of polychrome woodblock prints; ink and color on paper,Image (a): 14 1/2 x 9 3/4 in. (36.8 x 24.8 cm) Image (b): 14 1/2 x 9 3/4 in. (36.8 x 24.8 cm) Image (c): 14 1/2 x 9 3/4 in. (36.8 x 24.8 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73495,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.208a–c,false,true,73497,Asian Art,Print,Daishin Nankin fu no shiō|Nankin in China,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshitora,"Japanese, active ca. 1850–80",,Utagawa Yoshitora,Japanese,1845,1880,"2nd month, 1862",1862,1862,Triptych of polychrome woodblock prints; ink and color on paper,Image (a): 14 5/8 x 10 in. (37.1 x 25.4 cm) Image (b): 14 5/8 x 10 in. (37.1 x 25.4 cm) Image (c): 14 3/4 x 9 5/8 in. (37.5 x 24.4 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73497,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.212a–c,false,true,73502,Asian Art,Print,"Furansu Paris no fu|Paris, France",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshitora,"Japanese, active ca. 1850–80",,Utagawa Yoshitora,Japanese,1845,1880,"6th month, 1862",1862,1862,Triptych of polychrome woodblock prints; ink and color on paper,Image (a): 14 1/4 x 10 in. (36.2 x 25.4 cm) Image (b): 14 1/4 x 10 in. (36.2 x 25.4 cm) Image (c): 14 1/4 x 10 in. (36.2 x 25.4 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73502,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.213a–c,false,true,73503,Asian Art,Woodblock print,Tenjiku maru no zu|A View of Indian Elephants,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshitora,"Japanese, active ca. 1850–80",,Utagawa Yoshitora,Japanese,1845,1880,"6th month, 1863",1863,1863,Triptych of polychrome woodblock prints; ink and color on paper,Image (a): 14 1/4 x 9 5/8 in. (36.2 x 24.4 cm) Image (b): 14 3/8 x 9 5/8 in. (36.5 x 24.4 cm) Image (c): 14 1/4 x 9 5/8 in. (36.2 x 24.4 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73503,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.218a–c,false,true,73508,Asian Art,Print,"万国名勝尽競之内魯西亜本都伯徳|Heidoru (St. Petersberg), Capital of Russia, from the series Famous Places from All Nations (Bankoku meisho zukushi - Oroshiya miyako heidoru)",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshitora,"Japanese, active ca. 1850–80",,Utagawa Yoshitora,Japanese,1845,1880,"2nd month, 1865",1865,1865,Triptych of polychrome woodblock prints; ink and color on paper,Image (a): 14 5/8 x 10 in. (37.1 x 25.4 cm) Image (b): 14 5/8 x 9 7/8 in. (37.1 x 25.1 cm) Image (c): 14 1/2 x 10 in. (36.8 x 25.4 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73508,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.219a–c,false,true,73509,Asian Art,Print,仏蘭西国|France (furansukoku),Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshitora,"Japanese, active ca. 1850–80",,Utagawa Yoshitora,Japanese,1845,1880,"2nd month, 1865",1865,1865,Triptych of polychrome woodblock prints; ink and color on paper,Image (a): 14 3/8 x 9 5/8 in. (36.5 x 24.4 cm) Image (b): 14 1/2 x 9 1/2 in. (36.8 x 24.1 cm) Image (c): 14 3/8 x 9 7/8 in. (36.5 x 25.1 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73509,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.220a–c,false,true,73510,Asian Art,Print,英吉利国|England (Igirisukoku),Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshitora,"Japanese, active ca. 1850–80",,Utagawa Yoshitora,Japanese,1845,1880,"2nd month, 1865",1865,1865,Triptych of polychrome woodblock prints; ink and color on paper,Image (a): 13 3/4 x 9 3/4 in. (34.9 x 24.8 cm) Image (b): 13 1/2 x 9 1/2 in. (34.3 x 24.1 cm) Image (c): 13 3/4 x 9 3/4 in. (34.9 x 24.8 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73510,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.221a–c,false,true,73511,Asian Art,Print,"英吉利国倫敦図|Illustration of London, England (Igirisukoku rondon zu)",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshitora,"Japanese, active ca. 1850–80",,Utagawa Yoshitora,Japanese,1845,1880,"3rd month, 1866",1866,1866,Triptych of polychrome woodblock prints; ink and color on paper,Image (a): 14 1/4 x 9 3/4 in. (36.2 x 24.8 cm) Image (b): 14 1/4 x 9 3/4 in. (36.2 x 24.8 cm) Image (c): 14 1/4 x 9 3/4 in. (36.2 x 24.8 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73511,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3070,false,true,56551,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Sugimura Jihei Masataka,"Japanese, active ca. 1680–1698",,Sugimura Jihei Masataka,Japanese,1680,1698,ca. 1685,1675,1695,Monochrome woodblock print; ink on paper,10 3/4 x 15 1/2 in. (27.3 x 39.4 cm),"Harris Brisbane Dick Fund and Rogers Fund, 1949",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56551,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2642,false,true,56823,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Hishikawa Morofusa,"Japanese, active ca. 1685–1715",(?),Hishikawa Morofusa,Japanese,1685,1715,early 18th century,1700,1733,Monochrome woodblock print (sumie); ink on paper,9 1/2 x 13 3/4 in. (24.1 x 34.9 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56823,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP467,false,true,36918,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyoshige,"Japanese, active ca. 1716–1759",,Torii Kiyoshige,Japanese,1716,1759,"2nd month, 1763",1763,1763,Polychrome woodblock print; ink and color on paper,11 7/8 x 5 7/16 in. (30.2 x 13.8 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36918,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2761,false,true,57025,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyoshige,"Japanese, active ca. 1716–1759",,Torii Kiyoshige,Japanese,1716,1759,1716–1759,1716,1759,Polychrome woodblock print; ink and color on paper,14 7/8 x 6 7/8 in. (37.8 x 17.5 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57025,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3083,false,true,56596,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Hasegawa Mitsunobu,"Japanese, active ca. 1724–1754",,Hasegawa Mitsunobu,Japanese,1724,1754,ca. 1730,1720,1740,Monochrome woodblock print; ink on paper,10 1/2 x 15 in. (26.7 x 38.1 cm),"Harris Brisbane Dick Fund and Rogers Fund, 1949",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56596,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3084,false,true,56597,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Hasegawa Mitsunobu,"Japanese, active ca. 1724–1754",,Hasegawa Mitsunobu,Japanese,1724,1754,ca. 1730,1720,1740,Monochrome woodblock print; ink on paper,10 1/2 x 15 in. (26.7 x 38.1 cm),"Harris Brisbane Dick Fund and Rogers Fund, 1949",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56597,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1309,false,true,45043,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Tamura Sadanobu,"Japanese, active ca. 1725–1740",,Tamura Sadanobu,Japanese,1725,1740,ca. 1730,1720,1740,Polychrome lacquer print (urushi-e),H. 12 1/16 in. (30.6 cm); W. 6 in. (15.2 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45043,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP233,false,true,36705,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyohiro,"Japanese, active ca. 1737–1766",,Torii Kiyohiro,Japanese,1737,1766,"2nd month, 1746",1746,1746,Polychrome woodblock print; ink and color on paper,11 15/32 x 5 7/16 in. (29.1 x 13.8 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36705,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP840,false,true,54495,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyohiro,"Japanese, active ca. 1737–1766",,Torii Kiyohiro,Japanese,1737,1766,ca. 1757,1747,1767,Polychrome woodblock print; ink and color on paper,H. 11 7/8 in. (30.2 cm); W. 5 9/16 in. (14.1 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54495,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP841,false,true,54496,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyohiro,"Japanese, active ca. 1737–1766",,Torii Kiyohiro,Japanese,1737,1766,ca. 1756,1746,1766,Polychrome woodblock print; ink and color on paper,H. 12 1/8 in. (30.8 cm); W. 5 1/2 in. (14 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54496,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP842,false,true,51089,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyohiro,"Japanese, active ca. 1737–1766",,Torii Kiyohiro,Japanese,1737,1766,ca. 1756,1746,1766,Polychrome woodblock print (beni-e); ink and color on paper,H. 12 1/8 in. (30.8 cm); W. 5 3/4 in. (14.6 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/51089,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP843,false,true,54497,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyohiro,"Japanese, active ca. 1737–1766",,Torii Kiyohiro,Japanese,1737,1766,ca. 1756,1746,1766,Polychrome woodblock print; ink and color on paper,H. 12 1/8 in. (30.8 cm); W. 5 3/4 in. (14.6 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54497,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1109,false,true,55033,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyohiro,"Japanese, active ca. 1737–1766",,Torii Kiyohiro,Japanese,1737,1766,ca. 1754,1744,1764,Polychrome woodblock print; ink and color on paper,H. 12 in. (30.5 cm); W. 5 3/4 in. (14.6 cm),"Rogers Fund, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55033,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1449,false,true,55496,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyohiro,"Japanese, active ca. 1737–1766",,Torii Kiyohiro,Japanese,1737,1766,ca. 1755,1745,1765,Polychrome woodblock print; ink and color on paper,H. 16 1/4 in. (41.3 cm); W. 11 1/4 in. (28.6 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55496,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2586,false,true,56732,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyohiro,"Japanese, active ca. 1737–1766",,Torii Kiyohiro,Japanese,1737,1766,"12th month, 1754 or 1st month, 1755",1754,1755,Polychrome woodblock print; ink and color on paper,12 1/2 x 5 3/4 in. (31.8 x 14.6 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56732,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2587,false,true,56733,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyohiro,"Japanese, active ca. 1737–1766",,Torii Kiyohiro,Japanese,1737,1766,ca. 1754,1734,1754,Polychrome woodblock print; ink and color on paper,Overall: 11 7/8 x 5 5/8 in. (30.2 x 14.3 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56733,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2762,false,true,57026,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyohiro,"Japanese, active ca. 1737–1766",,Torii Kiyohiro,Japanese,1737,1766,1737–1766,1737,1766,Polychrome woodblock print; ink and color on paper,15 1/8 x 6 3/4 in. (38.4 x 17.1 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57026,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3089,false,true,56602,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyohiro,"Japanese, active ca. 1737–1766",,Torii Kiyohiro,Japanese,1737,1766,ca. 1755,1745,1765,Polychrome woodblock print; ink and color on paper,17 x 11 in. (43.2 x 27.9 cm),"Harris Brisbane Dick Fund and Rogers Fund, 1949",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56602,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3090,false,true,56603,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyohiro,"Japanese, active ca. 1737–1766",,Torii Kiyohiro,Japanese,1737,1766,1756,1756,1756,Polychrome woodblock print; ink and color on paper,12 1/4 x 5 3/4 in. (31.1 x 14.6 cm),"Harris Brisbane Dick Fund and Rogers Fund, 1949",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56603,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3091,false,true,56604,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyohiro,"Japanese, active ca. 1737–1766",,Torii Kiyohiro,Japanese,1737,1766,1754,1754,1754,Polychrome woodblock print; ink and color on paper,12 x 5 1/2 in. (30.5 x 14 cm),"Harris Brisbane Dick Fund and Rogers Fund, 1949",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56604,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3092,false,true,56605,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyohiro,"Japanese, active ca. 1737–1766",,Torii Kiyohiro,Japanese,1737,1766,1757,1757,1757,Polychrome woodblock print; ink and color on paper,15 x 7 in. (38.1 x 17.8 cm),"Harris Brisbane Dick Fund and Rogers Fund, 1949",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56605,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1562,false,true,55729,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Masafusa,"Japanese, active ca. 1750–1770",,Masafusa,Japanese,1750,1770,ca. 1750–70,1750,1770,Polychrome woodblock print; ink and color on paper,H. 11 1/2 in. (29.2 cm); W. 6 in. (15.2 cm),"Fletcher Fund, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55729,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1503,false,true,53894,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyotsune,"Japanese, active ca. 1757–1779",,Torii Kiyotsune,Japanese,1757,1779,ca. 1768,1758,1778,Left sheet of a triptych of polychrome woodblock prints; ink and color on paper,12 3/8 x 5 3/8 in. (31.4 x 13.7 cm),"Fletcher Fund, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/53894,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3096,false,true,56609,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyotsune,"Japanese, active ca. 1757–1779",,Torii Kiyotsune,Japanese,1757,1779,1767,1767,1767,Polychrome woodblock print; ink and color on paper,12 x 5 1/2 in. (30.5 x 14 cm),"Harris Brisbane Dick Fund and Rogers Fund, 1949",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56609,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2785,false,true,57104,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Haruhiro,"Japanese, active ca. 1765–1784",,Haruhiro,Japanese,1765,1784,1765–1784,1765,1784,Polychrome woodblock print; ink and color on paper,11 3/8 x 8 3/16 in. (28.9 x 20.8 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57104,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP187,false,true,36665,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Rekisentei Eiri,"Japanese, active ca. 1789–1801",,Rekisentei Eiri,Japanese,1789,1801,ca. 1797,1787,1807,Pentaptych of polychrome woodblock prints; ink and color on paper,15 5/8 x 9 5/8 in. (39.7 x 24.46 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36665,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP739,false,true,37187,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Rekisentei Eiri,"Japanese, active ca. 1789–1801",,Rekisentei Eiri,Japanese,1789,1801,ca. 1791,1781,1801,Polychrome woodblock print; ink and color on paper,12 3/4 x 8 5/8 in. (32.4 x 21.9 cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37187,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP953,false,true,54847,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Rekisentei Eiri,"Japanese, active ca. 1789–1801",,Rekisentei Eiri,Japanese,1789,1801,ca. 1791,1781,1801,Polychrome woodblock print; ink and color on paper,H. 9 3/8 in. (23.8 cm); W. 7 1/4 in. (18.4 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54847,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP954,false,true,54848,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Rekisentei Eiri,"Japanese, active ca. 1789–1801",,Rekisentei Eiri,Japanese,1789,1801,ca. 1791,1781,1801,Polychrome woodblock print; ink and color on paper,H. 9 3/16 in. (23.3 cm); W. 7 1/16 in. (17.9 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54848,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP955,false,true,54849,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Rekisentei Eiri,"Japanese, active ca. 1789–1801",,Rekisentei Eiri,Japanese,1789,1801,ca. 1795,1785,1805,Polychrome woodblock print; ink and color on paper,H. 12 3/4 in. (32.4 cm); W. 8 3/8 in. (21.3 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54849,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1212,false,true,55135,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Rekisentei Eiri,"Japanese, active ca. 1789–1801",,Rekisentei Eiri,Japanese,1789,1801,ca. 1795,1785,1805,Polychrome woodblock print; ink and color on paper,H. 12 5/8 in. (32.1 cm); W. 8 5/8 in. (21.9 cm),"Rogers Fund, 1920",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55135,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1305,false,true,55244,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunkaku,"Japanese, active ca. 1789–1801",,Katsukawa,Japanese,1789,1801,ca. 1795,1785,1805,Polychrome woodblock print; ink and color on paper,H. 12 3/8 in. (31.4 cm); W. 5 9/16 in. (14.1 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55244,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1496,false,true,45224,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Rekisentei Eiri,"Japanese, active ca. 1789–1801",,Rekisentei Eiri,Japanese,1789,1801,ca. 1795,1785,1805,Polychrome woodblock print; ink and color on paper,H. 15 1/2 in. (39.4 cm); W. 10 1/4 in. (26 cm),"Fletcher Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45224,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1788,false,true,45216,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Rekisentei Eiri,"Japanese, active ca. 1789–1801",,Rekisentei Eiri,Japanese,1789,1801,ca. 1795,1785,1805,Polychrome woodblock print; ink and color on paper,H. 14 3/4 in. (37.5 cm); W. 9 1/2 in. (24.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45216,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2419,false,true,45223,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Rekisentei Eiri,"Japanese, active ca. 1789–1801",,Rekisentei Eiri,Japanese,1789,1801,ca. 1795,1785,1805,Polychrome woodblock print; ink and color on paper,15 3/8 x 10 1/4 in. (39.1 x 26 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45223,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP557,false,true,37008,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Ichirakutei Eisui,"Japanese, active ca. 1793–1801",,Ichirakutei Eisui,Japanese,1793,1801,ca. 1797,1787,1807,Polychrome woodblock print; ink and color on paper,14 1/4 x 9 1/8 in. (36.2 x 23.2 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37008,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1794,false,true,56096,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ichirakutei Eisui,"Japanese, active ca. 1793–1801",,Ichirakutei Eisui,Japanese,1793,1801,1790s,1790,1790,Polychrome woodblock print; ink and color on paper,H. 14 5/8 in. (37.1 cm); W. 9 15/16 in. (25.2 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56096,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1280,false,true,55212,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Momokawa Shiko II,"Japanese, active ca. 1797–1810",,Momokawa Shiko II,Japanese,1797,1810,ca. 1800,1790,1810,Polychrome woodblock print; ink and color on paper,H. 14 7/8 in. (37.8 cm); W. 9 7/8 in. (25.1 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55212,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1481,false,true,55568,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Momokawa Shiko II,"Japanese, active ca. 1797–1810",,Momokawa Shiko II,Japanese,1797,1810,ca. 1800,1790,1810,Polychrome woodblock print; ink and color on paper,H. 5 9/16 in. (14.1 cm); W. 12 3/8 in. (31.4 cm),"Rogers Fund, 1926",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55568,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1140,false,true,54347,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,ca. 1820,1810,1830,Polychrome woodblock print (surimono); ink and color on paper,8 1/2 x 7 1/2 in. (21.6 x 19.1 cm),"Rogers Fund, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54347,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1141,false,true,54348,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,ca. 1800,1790,1810,Polychrome woodblock print (surimono); ink and color on paper,5 9/16 x 7 1/8 in. (14.1 x 18.1 cm),"Rogers Fund, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54348,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1248,false,true,54379,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,ca. 1830,1820,1840,Polychrome woodblock print (surimono); ink and color on paper,8 5/8 x 7 5/8 in. (21.9 x 19.4 cm),"Rogers Fund, 1921",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54379,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1410,false,true,55427,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,ca. 1820,1810,1830,Polychrome woodblock print; ink and color on paper,9 15/16 x 14 1/2 in. (25.2 x 36.8 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55427,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1906,false,true,54445,Asian Art,Print,"舞楽衣装『春雨集』　摺物帖|Costume for Bugaku Court DanceFrom the Spring Rain Collection (Harusame shū), vol. 1",Japan,Edo period (1615–1868),,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,probably 1818 (Year of the Tiger),1818,1818,Polychrome woodblock print (surimono); ink and color on paper,7 5/8 x 6 7/16 in. (19.4 x 16.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54445,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1908,false,true,54447,Asian Art,Print,"柳々居辰斎画 子春　嫁入の具『春雨集』　摺物帖|Accoutrements for a BrideFrom the Spring Rain Collection (Harusame shū), vol. 1",Japan,Edo period (1615–1868),,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,probably 1816 (Year of the Rat),1816,1816,Polychrome woodblock print (surimono); ink and color on paper,5 1/2 x 7 7/16 in. (14 x 18.9 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54447,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1913,false,true,54452,Asian Art,Print,"「楽器其三」|Biwa with Brocade Cover, from the series Musical Instruments",Japan,Edo period (1615–1868),,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,probably 1808,1808,1808,Polychrome woodblock print (surimono); ink and color on paper,5 1/2 x 7 3/8 in. (14 x 18.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54452,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1946,false,true,54514,Asian Art,Print,"文具一式|Writing Set and Poem Card Box (Shikishi-bako), from Spring Rain Surimono Album (Harusame surimono-jō), vol. 1",Japan,Edo period (1615–1868),,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,ca. 1805–10,1800,1820,Privately published polychrome woodblock prints (surimono) mounted in an album; ink and color on paper,4 15/16 x 6 13/16 in. (12.5 x 17.3 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54514,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1947,false,true,54515,Asian Art,Print,"衝立、虎図、文具一式|Desk Screen, Writing Set, Painting of Tiger, and Mounting Paraphernalia, from Spring Rain Surimono Album (Harusame surimono-jō, vol. 1)",Japan,Edo period (1615–1868),,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,probably 1818,1818,1818,Privately published polychrome woodblock prints (surimono) mounted in an album; ink and color on paper,5 1/2 x 7 3/8 in. (14 x 18.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54515,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1956,false,true,54527,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,probably 1813,1813,1813,Polychrome woodblock print (surimono); ink and color on paper,5 3/8 x 7 1/4 in. (13.7 x 18.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54527,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2023,false,true,54772,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,probably 1816,1816,1816,Polychrome woodblock print (surimono); ink and color on paper,5 1/2 x 7 3/8 in. (14 x 18.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54772,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2024,false,true,54784,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,1808,1808,1808,Polychrome woodblock print (surimono); ink and color on paper,5 9/16 x 7 7/16 in. (14.1 x 18.9 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54784,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2036,false,true,54804,Asian Art,Print,"『春雨集』　摺物帖柳々居辰斎画　紅梅|Spring Rain Collection (Harusame shū), vol. 1: Plum Tree in Bloom",Japan,Edo period (1615–1868),,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,ca. 1805–10,1805,1810,Privately published woodblock prints (surimono) mounted in an album; ink and color on paper,8 1/4 x 5 1/2 in. (21 x 14 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54804,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2039,false,true,54810,Asian Art,Print,"『春雨集』　摺物帖柳々居辰斎画　蟹と蓮華|Spring Rain Collection (Harusame shū), vol. 1: Crabs and Lotus Blossoms",Japan,Edo period (1615–1868),,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,ca. 1805–10,1805,1810,Privately published woodblock prints (surimono) mounted in an album; ink and color on paper,5 1/2 x 7 7/16 in. (14 x 18.9 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54810,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2040,false,true,54811,Asian Art,Woodblock print,"摺物帖　『春雨集』　鴨と葱|Spring Rain Collection (Harusame shū), vol. 1: Duck and Scallions",Japan,Edo period (1615–1868),,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,1810s,1810,1819,Privately published woodblock prints (surimono) mounted in an album; ink and color on paper,5 5/8 x 7 9/16 in. (14.3 x 19.2 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54811,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2043,false,true,54814,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",(?),Ryūryūkyo Shinsai,Japanese,1799,1823,probably 1816,1816,1816,Polychrome woodblock print (surimono); ink and color on paper,5 1/2 x 7 3/8 in. (14 x 18.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54814,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2068,false,true,54898,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,probably 1819,1819,1819,Polychrome woodblock print (surimono); ink and color on paper,8 1/4 x 7 5/16 in. (21 x 18.6 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54898,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2108,false,true,54960,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,probably 1819,1819,1819,Polychrome woodblock print (surimono); ink and color on paper,7 15/16 x 7 1/16 in. (20.2 x 17.9 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54960,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2110,false,true,54962,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,probably 1815,1815,1815,Polychrome woodblock print (surimono); ink and color on paper,5 5/8 x 7 5/16 in. (14.3 x 18.6 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54962,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2135,false,true,54990,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,probably 1816,1816,1816,Polychrome woodblock print (surimono); ink and color on paper,5 5/8 x 7 7/16 in. (14.3 x 18.9 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54990,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2136,false,true,54991,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,probably 1816,1816,1816,Polychrome woodblock print (surimono); ink and color on paper,5 5/8 x 7 1/2 in. (14.3 x 19.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54991,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2153,false,true,55058,Asian Art,Print,"『春雨集』　摺物帖柳々居辰斎画　鎌倉の鶴岡八幡宮に鶴|Spring Rain Collection (Harusame shū), vol. 2: Cranes at Tsurugaoka Hachimangō Shrine in Kamakura",Japan,Edo period (1615–1868),,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,1810s,1810,1819,Privately published woodblock prints (surimono) mounted in an album; ink and color on paper,5 1/2 x 7 1/4 in. (14 x 18.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55058,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2196,false,true,55112,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,1810,1810,1810,Polychrome woodblock print (surimono); ink and color on paper,4 1/8 x 5 11/16 in. (10.5 x 14.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55112,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2256,false,true,54025,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,probably 1816,1800,1900,Part of an album of woodblock prints (surimono); ink and color on paper,5 5/8 x 7 9/16 in. (14.3 x 19.2 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54025,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2257,false,true,54026,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,probably 1815,1815,1815,Part of an album of woodblock prints (surimono); ink and color on paper,5 5/8 x 7 9/16 in. (14.3 x 19.2 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54026,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2295,false,true,54068,Asian Art,Print,"鉢梅、懐中日時計、羅針盤|Bonsai Plum, Compass, and Pocket Sundial with Design of Calendar, from Spring Rain Surimono Album (Harusame surimono-jō, vol. 3)",Japan,Edo period (1615–1868),,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,1806,1806,1806,Privately published polychrome woodblock prints (surimono) mounted in an album; ink and color on paper,5 1/2 x 7 1/4 in. (14 x 18.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54068,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2296,false,true,54069,Asian Art,Woodblock print,"『蝶揃・寵愛』帯と簪|Roll of Cloth for an Obi and Tortoise-shell Hair Ornaments (“Presents for One’s Beloved”), from the Butterfly Series, from Spring Rain Surimono Album (Harusame surimono-jō, vol. 3)",Japan,Edo period (1615–1868),,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,ca. 1805–10,1800,1820,Privately published polychrome woodblock prints (surimono) mounted in an album; ink and color on paper,5 7/8 x 7 9/16 in. (14.9 x 19.2 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54069,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2337,false,true,54122,Asian Art,Woodblock print,「牛和歌十二段矢矧長者」|Dance Robe and Koto (Zither) Representing the Wealthy Man of Yahagi from the Jōruri Play Ushiwaka (Minamoto no Yoshitsune),Japan,Edo period (1615–1868),,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,probably 1810,1810,1810,Part of an album of woodblock prints (surimono); ink and color on paper,5 9/16 x 7 3/8 in. (14.1 x 18.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54122,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2341,false,true,54126,Asian Art,Print,"「職人合香聞 」|Utensils for the Incense Ceremony, “Incense Master” (Kōgiki), from the series An Array of Artisans",Japan,Edo period (1615–1868),,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,ca. 1810s,1805,1820,Part of an album of woodblock prints (surimono); ink and color on paper,5 11/16 x 7 9/16 in. (14.4 x 19.2 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54126,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2344,false,true,54129,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,1811,1811,1811,Part of an album of woodblock prints (surimono); ink and color on paper,4 1/6 x 7 7/16 in. (10.6 x 18.9 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54129,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3000,false,true,54216,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,1750–1835,1750,1835,Polychrome woodblock print (surimono); ink and color on paper,5 1/2 x 7 7/16 in. (14 x 18.9 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54216,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3004,false,true,54220,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,1750–1835,1750,1835,Polychrome woodblock print (surimono); ink and color on paper,8 3/16 x 7 7/16 in. (20.8 x 18.9 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54220,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1093.5,false,true,58257,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Toyohara Sadatora,"Japanese, active ca. 1818–1844",,Toyohara Sadatora,Japanese,1818,1844,first half of the 19th century,1818,1844,Polychrome woodblock print; ink and color on paper,Image: 15 in. × 10 1/8 in. (38.1 × 25.7 cm),The Metropolitan Museum of Art,,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58257,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3264,false,true,37388,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshikazu,"Japanese, active ca. 1850–1870",,Utagawa Yoshikazu,Japanese,1845,1870,ca. 1861,1851,1871,Triptych of polychrome woodblock prints; ink and color on paper,Image: 14 1/2 in. × 30 in. (36.8 × 76.2 cm) Mat: 20 3/4 in. × 37 in. (52.7 × 94 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37388,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3303,false,true,53701,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshikazu,"Japanese, active ca. 1850–1870",,Utagawa Yoshikazu,Japanese,1845,1870,1861,1861,1861,Polychrome woodblock print; ink and color on paper,H. 14 in. 35.6 cm); W. 9 1/2 in. ( 24.1 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/53701,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3327,false,true,55475,Asian Art,Print,Furansukoku|仏蘭西国|France,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshikazu,"Japanese, active ca. 1850–1870",,Utagawa Yoshikazu,Japanese,1845,1870,1861 (10th month),1861,1861,Polychrome woodblock print; ink and color on paper,13 3/4 x 9 5/8 in. (34.9 x 24.4 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55475,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3333,false,true,55481,Asian Art,Print,Gaikokujin kodomo choai no zu|A Foreigner Enjoying Her Children,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshikazu,"Japanese, active ca. 1850–1870",,Utagawa Yoshikazu,Japanese,1845,1870,1860 (10th month),1860,1860,Polychrome woodblock print; ink and color on paper,13 1/2 x 9 in. (34.3 x 22.9 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55481,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3344,false,true,55508,Asian Art,Print,Amerika Koku Jokisen naka no zu|View Inside an American Steamship,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshikazu,"Japanese, active ca. 1850–1870",,Utagawa Yoshikazu,Japanese,1845,1870,"1861 (Bunkyu, 1st year, 4th month)",1861,1861,Triptych of polychrome woodblock prints; ink and color on paper,Oban; 10 1/4 x 27 1/4 in. (26 x 69.2 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55508,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3363,false,true,55531,Asian Art,Woodblock print,Gaikokujin ifuku shitate no zu|Picture of a Foreigner Making Clothes,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshikazu,"Japanese, active ca. 1850–1870",,Utagawa Yoshikazu,Japanese,1845,1870,1860,1860,1860,Polychrome woodblock print; ink and color on paper,Image: 13 1/2 x 9 in. (34.3 x 22.9 cm),"Gift of Lincoln Kirstein, 1970",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55531,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3365,false,true,53596,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshikazu,"Japanese, active ca. 1850–1870",,Utagawa Yoshikazu,Japanese,1845,1870,dated 1861,1800,1899,Polychrome woodblock print; ink and color on paper,14 x 9 1/4 in. (35.6 x 23.5 cm),"Gift of Lincoln Kirstein, 1970",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/53596,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3366,false,true,55533,Asian Art,Print,Igirisujin|Englishmen,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshikazu,"Japanese, active ca. 1850–1870",,Utagawa Yoshikazu,Japanese,1845,1870,1861,1861,1861,Polychrome woodblock print; ink and color on paper,14 3/8 x 9 7/8 in. (36.5 x 25.1 cm),"Gift of Lincoln Kirstein, 1960",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55533,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3323a,false,true,55469,Asian Art,Print,亜墨利加|American Family with a Dancing Daughter,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshikazu,"Japanese, active ca. 1850–1870",,Utagawa Yoshikazu,Japanese,1845,1870,"1861 (Bunkyu 1, 2nd month)",1861,1861,Polychrome woodblock print; ink and color on paper,Oban tate-e,"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55469,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3323b,false,true,55471,Asian Art,Print,仏蘭西|French Photographer with His Wife,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshikazu,"Japanese, active ca. 1850–1870",,Utagawa Yoshikazu,Japanese,1845,1870,"1861 (Bunkyu 1, 2nd month)",1861,1861,Polychrome woodblock print; ink and color on paper,Oban tate-e,"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55471,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.152,false,true,73438,Asian Art,Print,"Gaikokujin Sake no zu|A Foreigner's Wine Party (Gaikokujin shuen no zu), from an untitled series of foreigners at home",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshikazu,"Japanese, active ca. 1850–1870",,Utagawa Yoshikazu,Japanese,1845,1870,"10th month, 1860",1860,1860,Polychrome woodblock print; ink and color on paper,Image: 13 3/8 x 9 in. (34 x 22.9 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73438,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.153,false,true,73439,Asian Art,Print,Ijiin Yashiki ryōri no zu|Inside a Foreign Restaurant,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshikazu,"Japanese, active ca. 1850–1870",,Utagawa Yoshikazu,Japanese,1845,1870,"10th month, 1860",1860,1860,Polychrome woodblock print; ink and color on paper,Image: 13 1/2 x 9 in. (34.3 x 22.9 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73439,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.154,false,true,73440,Asian Art,Print,Gaikokujin yoru benkyo no zu|Foreigners Studying at Night,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshikazu,"Japanese, active ca. 1850–1870",,Utagawa Yoshikazu,Japanese,1845,1870,1861,1861,1861,Polychrome woodblock print; ink and color on paper,Image: 13 1/2 x 9 in. (34.3 x 22.9 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73440,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.155,false,true,73441,Asian Art,Print,Gaikoku shashin kagami no zu|Foreigners Employing a Camera,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshikazu,"Japanese, active ca. 1850–1870",,Utagawa Yoshikazu,Japanese,1845,1870,"11th month, 1860",1860,1860,Polychrome woodblock print; ink and color on paper,Image: 13 1/4 x 8 7/8 in. (33.7 x 22.5 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73441,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.156,false,true,73442,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshikazu,"Japanese, active ca. 1850–1870",,Utagawa Yoshikazu,Japanese,1845,1870,1860,1860,1860,Polychrome woodblock print; ink and color on paper,Image: 13 1/4 x 8 7/8 in. (33.7 x 22.5 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73442,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.157,false,true,73443,Asian Art,Print,"Yokohama kenbutsu zue|Picture of Sights in Yokohama: Woman with a Ringer, Lamp Post, a Steamboat at Full Sail and a Woman with a Sewing Machine",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshikazu,"Japanese, active ca. 1850–1870",,Utagawa Yoshikazu,Japanese,1845,1870,"11th month, 1860",1860,1860,Polychrome woodblock print; ink and color on paper,Image: 14 3/4 x 10 in. (37.5 x 25.4 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73443,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.159,false,true,73445,Asian Art,Print,「横浜異人屋敷之圖」|A Foreign Residence in Yokohama,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshikazu,"Japanese, active ca. 1850–1870",,Utagawa Yoshikazu,Japanese,1845,1870,"1st month, 1861",1861,1861,Triptych of polychrome woodblock prints; ink and color on paper,Image: 29 1/2 x 14 3/4 in. (74.9 x 37.5 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73445,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.161,false,true,73447,Asian Art,Print,Yokohama Gankirō gaikokujin gyōretsu no zu|Picture of a Procession of Foreigners at Yokohama,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshikazu,"Japanese, active ca. 1850–1870",,Utagawa Yoshikazu,Japanese,1845,1870,"2nd month, 1861",1861,1861,Triptych of polychrome woodblock prints; ink and color on paper,Image: 14 1/8 x 29 1/16 in. (35.9 x 73.8 cm) Overall (Mat): 20 x 35 in. (50.8 x 88.9 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73447,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.165,false,true,73451,Asian Art,Print,Furansu|French Photographer,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshikazu,"Japanese, active ca. 1850–1870",,Utagawa Yoshikazu,Japanese,1845,1870,1861,1861,1861,Polychrome woodblock print; ink and color on paper,Image: 13 7/8 x 9 1/2 in. (35.2 x 24.1 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73451,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.166,false,true,73453,Asian Art,Print,Orosia|Russian Printers,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshikazu,"Japanese, active ca. 1850–1870",,Utagawa Yoshikazu,Japanese,1845,1870,"2nd month, 1861",1861,1861,Polychrome woodblock print; ink and color on paper,Image: 14 1/8 x 9 7/8 in. (35.9 x 25.1 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73453,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.167,false,true,73454,Asian Art,Woodblock print,Oranda|A Dutch Group,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshikazu,"Japanese, active ca. 1850–1870",,Utagawa Yoshikazu,Japanese,1845,1870,"2nd month, 1861",1861,1861,Polychrome woodblock print; ink and color on paper,Image: 14 1/4 x 9 3/4 in. (36.2 x 24.8 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73454,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.169,false,true,73457,Asian Art,Print,Amerika koku jōkisen naka no zu|Interior of an American Steamship,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshikazu,"Japanese, active ca. 1850–1870",,Utagawa Yoshikazu,Japanese,1845,1870,"4th month, 1861",1861,1861,Triptych of polychrome woodblock prints; ink and color on paper,Overall: 14 x 28 3/4 in. (35.6 x 73 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73457,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.171,false,true,73459,Asian Art,Print,Furansujin|French Couple,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshikazu,"Japanese, active ca. 1850–1870",,Utagawa Yoshikazu,Japanese,1845,1870,"4th month, 1861",1861,1861,Polychrome woodblock print; ink and color on paper,Image: 14 1/2 x 10 in. (36.8 x 25.4 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73459,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.172,false,true,73460,Asian Art,Print,"Igirisujin|An English Woman with a Chinese Servant in the Foreign District, from the series Famous Places in Yokohama",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshikazu,"Japanese, active ca. 1850–1870",,Utagawa Yoshikazu,Japanese,1845,1870,1861,1861,1861,Polychrome woodblock print; ink and color on paper,Image: 14 3/8 x 9 3/4 in. (36.5 x 24.8 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73460,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.175,false,true,73463,Asian Art,Print,Orandajin|Dutch Couple,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshikazu,"Japanese, active ca. 1850–1870",,Utagawa Yoshikazu,Japanese,1845,1870,"10th month, 1862",1862,1862,Polychrome woodblock print; ink and color on paper,Image: 14 x 9 1/2 in. (35.6 x 24.1 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73463,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.158a–c,false,true,73444,Asian Art,Print,Yokohama Miyozaki-kaku Gankirō ijin yūkyō no zu|Foreigners Enjoying a Party at the Gankirō Tea House,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshikazu,"Japanese, active ca. 1850–1870",,Utagawa Yoshikazu,Japanese,1845,1870,"1st month, 1861",1861,1861,Triptych of polychrome woodblock prints; ink and color on paper,Image (a): 14 3/8 x 9 7/8 in. (36.5 x 25.1 cm) Image (b): 14 1/4 x 10 in. (36.2 x 25.4 cm) Image (c): 14 1/8 x 9 1/2 in. (35.9 x 24.1 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73444,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.160a–c,false,true,73446,Asian Art,Print,Yokohama Gankirō kodomo te odori no zu|Foreigners Enjoying Children's Kabuki at the Gankirō Tea House,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshikazu,"Japanese, active ca. 1850–1870",,Utagawa Yoshikazu,Japanese,1845,1870,"1st month, 1861",1861,1861,Triptych of polychrome woodblock prints; ink and color on paper,Image (a): 14 3/8 x 9 7/8 in. (36.5 x 25.1 cm) Image (b): 14 3/8 x 9 7/8 in. (36.5 x 25.1 cm) Image (c): 14 3/8 x 9 7/8 in. (36.5 x 25.1 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73446,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.162a–c,false,true,73448,Asian Art,Print,Yokohama Gankirō gaikokujin gyōretsu no zu|Picture of a Procession of Foreigners at Yokohama,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshikazu,"Japanese, active ca. 1850–1870",,Utagawa Yoshikazu,Japanese,1845,1870,"2nd month, 1861",1861,1861,Triptych of polychrome woodblock prints; ink and color on paper,Image (a): 14 1/4 x 9 3/4 in. (36.2 x 24.8 cm) Image (b): 14 1/4 x 9 3/4 in. (36.2 x 24.8 cm) Image (c): 14 1/4 x 9 3/4 in. (36.2 x 24.8 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73448,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.163a–c,false,true,73449,Asian Art,Print,Kanagawa Gongenyama Gaikokujin yūran|Foreigners Visiting the Famous Site of Mt. Gongen in Kanagawa,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshikazu,"Japanese, active ca. 1850–1870",,Utagawa Yoshikazu,Japanese,1845,1870,"2nd month, 1861",1861,1861,Triptych of polychrome woodblock prints; ink and color on paper,Image (a): 14 1/4 x 9 3/4 in. (36.2 x 24.8 cm) Image (b): 14 1/4 x 9 7/8 in. (36.2 x 25.1 cm) Image (c): 14 1/4 x 9 5/8 in. (36.2 x 24.4 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73449,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.164a–c,false,true,73450,Asian Art,Print,Kanagawa Gongenyama Gaikokujin yūran|Foreigners Visiting the Famous Site of Mt. Gongen in Kanagawa,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshikazu,"Japanese, active ca. 1850–1870",,Utagawa Yoshikazu,Japanese,1845,1870,"2nd month, 1861",1861,1861,Triptych of polychrome woodblock prints; ink and color on paper,Image (a): 14 1/4 x 9 3/4 in. (36.2 x 24.8 cm) Image (b): 14 1/4 x 9 3/4 in. (36.2 x 24.8 cm) Image (c): 14 1/4 x 9 5/8 in. (36.2 x 24.4 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73450,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.168a–c,false,true,73455,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshikazu,"Japanese, active ca. 1850–1870",,Utagawa Yoshikazu,Japanese,1845,1870,"3rd month, 1861",1861,1861,Triptych of polychrome woodblock prints; ink and color on paper,Image (a): H. 14 1/4 in. (36.2 cm); W. 9 5/8 in. (24.4 cm) Image (b): H. 14 1/4 in. (36.2 cm); W. 9 5/8 in. (24.4 cm) Image (c): 14 1/4 x 9 1/2 in. (36.2 x 24.1 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73455,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.170a–c,false,true,73458,Asian Art,Print,Amerika koku jōkisen naka no zu|Interior of an American Steamship,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshikazu,"Japanese, active ca. 1850–1870",,Utagawa Yoshikazu,Japanese,1845,1870,"4th month, 1861",1861,1861,Triptych of polychrome woodblock prints; ink and color on paper,Image (a): 14 1/4 x 9 7/8 in. (36.2 x 25.1 cm) Image (b): 14 1/4 x 9 5/8 in. (36.2 x 24.4 cm) Image (c): 14 1/2 x 9 7/8 in. (36.8 x 25.1 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73458,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.173a–c,false,true,73461,Asian Art,Print,「亞墨利加國蒸氣車往来」|“America”: A Steamship in Transit,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshikazu,"Japanese, active ca. 1850–1870",,Utagawa Yoshikazu,Japanese,1845,1870,1861,1861,1861,Triptych of polychrome woodblock prints; ink and color on paper,Image (a): 14 1/2 x 9 3/4 in. (36.8 x 24.8 cm) Image (b): 14 3/8 x 9 3/4 in. (36.5 x 24.8 cm) Image (c): 14 1/4 x 9 3/4 in. (36.2 x 24.8 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73461,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.174a–c,false,true,73462,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshikazu,"Japanese, active ca. 1850–1870",,Utagawa Yoshikazu,Japanese,1845,1870,"2nd month, 1862",1862,1862,Triptych of polychrome woodblock prints; ink and color on paper,Image (a): 14 x 9 5/8 in. (35.6 x 24.4 cm) Image (b): 17 5/8 x 9 3/4 in. (44.8 x 24.8 cm) Image (c): 13 7/8 x 9 5/8 in. (35.2 x 24.4 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73462,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.176a–c,false,true,73464,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshikazu,"Japanese, active ca. 1850–1870",,Utagawa Yoshikazu,Japanese,1845,1870,"4th month, 1864",1864,1864,Triptych of polychrome woodblock prints; ink and color on paper,Image (a): 14 x 9 7/8 in. (35.6 x 25.1 cm) Image (b): 14 x 9 7/8 in. (35.6 x 25.1 cm) Image (c): 14 x 9 3/4 in. (35.6 x 24.8 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73464,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1911,false,true,54450,Asian Art,Woodblock print,蒔絵櫛|Box with a Lacquer Comb,Japan,Edo period (1615–1868),,,,Artist,,Uematsu Tōshū,"Japanese, active late 1810s–20s",,Uematsu Tōshū,Japanese,1810,1830,1812,1812,1812,Polychrome woodblock print (surimono); ink and color on paper,5 3/8 x 6 7/8 in. (13.7 x 17.5 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54450,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1432,false,true,54423,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Sadakage,"Japanese, active mid-19th century",,Utagawa Sadakage,Japanese,1800,1899,ca. 1840,1830,1850,Polychrome woodblock print (surimono); ink and color on paper,8 1/16 x 7 1/8 in. (20.5 x 18.1 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54423,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1103,false,true,54332,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Kurokawa Michita,"Japanese, active early 19th century",,Kurokawa Michita,Japanese,1800,1899,probably 1820,1820,1820,Polychrome woodblock print (surimono); ink and color on paper,8 1/16 x 7 1/16 in. (20.5 x 17.9 cm),"Rogers Fund, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54332,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1151,false,true,54357,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kosetsu,"Japanese, active early 19th century",(?),Kosetsu,Japanese,0019,0019,ca. 1820,1810,1830,Polychrome woodblock print (surimono); ink and color on paper,8 1/4 x 7 3/16 in. (21 x 18.3 cm),"Gift of T. Ito, Chicago, Ill, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54357,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1152,false,true,54358,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kosetsu,"Japanese, active early 19th century",,Kosetsu,Japanese,0019,0019,ca. 1820,1810,1830,Polychrome woodblock print (surimono); ink and color on paper,8 3/16 x 7 1/16 in. (20.8 x 17.9 cm),"Gift of T. Ito, Chicago, Ill, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54358,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1824,false,true,56112,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyotomo,"Japanese, active early 19th century",,Torii Kiyotomo,Japanese,1815,1820,ca. 1721,1711,1731,Polychrome woodblock print; ink and color on paper,H. 12 7/16 in. (31.6 cm); W. 6 1/2 in. (16.5 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56112,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3076,false,true,56556,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,Attributed to,Torii Kiyotomo,"Japanese, active early 19th century",,Torii Kiyotomo,Japanese,1815,1820,ca. 1720,1710,1730,Polychrome woodblock print (hand colored); ink and color on paper,13 1/4 x 6 1/4 in. (33.7 x 15.9 cm),"Harris Brisbane Dick Fund and Rogers Fund, 1949",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56556,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1243,false,true,54374,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Yanagawa Shigenobu II,"Japanese, active ca. 1820s–late 1850s",,Yanagawa Shigenobu II,Japanese,1820,1859,1830,1830,1830,Polychrome woodblock print (surimono); ink and color on paper,8 5/16 x 7 1/4 in. (21.1 x 18.4 cm),"Rogers Fund, 1921",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54374,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2011.131,false,true,76561,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Gigadō Ashiyuki,"Japanese, active first half of 19th century",,Gigadō Ashiyuki,Japanese,1800,1849,1826,1826,1826,Polychrome woodblock print,Image (ôban tate-e): 14 5/8 x 10 1/4 in. (37.1 x 26 cm),"Purchase, Friends of Asian Art Gifts, in honor of James C. Y. Watt, 2011",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/76561,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2011.134,false,true,76564,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Gigadō Ashiyuki,"Japanese, active first half of 19th century",,Gigadō Ashiyuki,Japanese,1800,1849,1832,1832,1832,Polychrome woodblock print,Image (ôban tate-e): 14 3/4 x 10 1/4 in. (37.5 x 26 cm),"Purchase, Friends of Asian Art Gifts, in honor of James C. Y. Watt, 2011",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/76564,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1,false,true,37391,Asian Art,Print,鞠子|Mariko,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1805,1795,1815,Polychrome woodblock print; ink and color on paper,4 15/16 x 14 3/16 in. (12.5 x 36 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37391,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2,false,true,36484,Asian Art,Woodblock print,"諸國瀧廻リ　相州大山ろうべんの瀧|Rōben Waterfall at Ōyama in Sagami Province (Sōshū Ōyama Rōben no taki), from the series A Tour of Waterfalls in Various Provinces (Shokoku taki meguri)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1827,1817,1837,Polychrome woodblock print; ink and color on paper,14 3/4 x 10 1/4 in. (37.5 x 26 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36484,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3,false,true,36485,Asian Art,Woodblock print,"百人一首　うはかゑとき　持統天皇|Poem by Jitō Tenno (Empress Jitō), from the series One Hundred Poems Explained by the Nurse (Hyakunin isshu uba ga etoki)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,1839,1839,1839,Polychrome woodblock print; ink and color on paper,10 1/8 x 14 3/4 in. (25.7 x 37.5 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36485,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP4,false,true,36486,Asian Art,Woodblock print,"百人一首　乳母かゑとき　柿本人麿|Poem by Kakinomoto Hitomaro, from the series One Hundred Poems Explained by the Nurse (Hyakunin isshu uba ga etoki)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,1839,1839,1839,Polychrome woodblock print; ink and color on paper,9 7/8 x 14 in. (25.1 x 35.6 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36486,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP5,false,true,36487,Asian Art,Woodblock print,"百人一首　うはかゑとき　菅家|Poem by Kanke (Sugawara Michizane), from the series One Hundred Poems Explained by the Nurse (Hyakunin isshu uba ga etoki)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1839,1829,1849,Polychrome woodblock print; ink and color on paper,10 x 14 3/5 in. (25.4 x 37.1 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36487,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP6,false,true,53920,Asian Art,Woodblock print,"百人一首　うはかゑとき　源宗于朝臣|Poem by Minamoto no Muneyuki Ason, from the series One Hundred Poems Explained by the Nurse (Hyakunin isshu uba ga etoki)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,1839,1839,1839,Polychrome woodblock print; ink and color on paper,H. 9 7/8 in. (25.1 cm); W. 14 5/8 in. (37.1 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/53920,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP7,false,true,36488,Asian Art,Woodblock print,"百人一首　姥か恵と起　大中臣能宣朝臣|Poem by Ōnakatomi no Yoshinobu Ason, from the series One Hundred Poems Explained by the Nurse (Hyakunin isshu uba ga etoki)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,1839,1839,1839,Polychrome woodblock print; ink and color on paper,10 x 14 1/8 in. (25.4 x 35.9 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36488,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP8,false,true,36489,Asian Art,Print,"諸國名橋奇覧　すほうの国きんたいはし|Kintai Bridge in Suō Province (Suō no kuni Kintaibashi), from the series Remarkable Views of Bridges in Various Provinces (Shokoku meikyō kiran)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,1827–30,1827,1830,Polychrome woodblock print; ink and color on paper,10 7/32 x 15 7/32 in. (26.0 x 38.7 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36489,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP9,false,true,36490,Asian Art,Woodblock print,"「富嶽三十六景　凱風快晴」|South Wind, Clear Sky (Gaifū kaisei), also known as Red Fuji, from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1820,1842,Polychrome woodblock print; ink and color on paper,9 5/8 x 14 in. (24.4 x 35.6 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36490,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP10,false,true,36491,Asian Art,Woodblock print,"冨嶽三十六景　神奈川沖浪裏|Under the Wave off Kanagawa (Kanagawa oki nami ura), also known as The Great Wave, from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1820,1842,Polychrome woodblock print; ink and color on paper,9 5/8 x 14 1/16 in. (24.4 x 35.7 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36491,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP11,false,true,36492,Asian Art,Woodblock print,"冨嶽三十六景　山下白雨|Storm below Mount Fuji (Sanka no haku u), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1820,1842,Polychrome woodblock print; ink and color on paper,10 1/8 x 15 1/8 in. (25.7 x 38.4 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36492,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP12,false,true,36493,Asian Art,Woodblock print,"冨嶽三十六景　駿州江尻|Ejiri in Suruga Province (Sunshū Ejiri), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1820,1842,Polychrome woodblock print; ink and color on paper,10 x 14 3/5 in. (25.4 x 37.1 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36493,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP13,false,true,36494,Asian Art,Woodblock print,"冨嶽三十六景　身延川裏不二|View from the Other Side of Fuji from the Minobu River (Minobugawa ura Fuji), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1820,1842,Polychrome woodblock print; ink and color on paper,9 7/8 x 14 3/4 in. (25.1 x 37.5 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36494,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP14,false,true,36495,Asian Art,Woodblock print,"冨嶽三十六景　甲州三坂水面|Reflection in Lake at Misaka in Kai Province (Kōshū Misaka suimen), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1820,1842,Polychrome woodblock print; ink and color on paper,9 4/5 x 14 9/16 in. (24.9 x 37.0 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36495,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP15,false,true,36496,Asian Art,Woodblock print,"冨嶽三十六景　相州七里浜|Shichirigahama in Sagami Province (Sōshū Shichirigahama), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1820,1842,Polychrome woodblock print; ink and color on paper,10 1/8 x 15 in. (25.7 x 38.1 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36496,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP16,false,true,36497,Asian Art,Woodblock print,"冨嶽三十六景　武州玉川|Tama River in Musashi Province (Bushū Tamagawa), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1820,1842,Polychrome woodblock print; ink and color on paper,10 1/8 x 14 3/4 in. (25.7 x 37.5 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36497,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP17,false,true,36498,Asian Art,Woodblock print,"冨嶽三十六景　相州箱根湖水|The Lake at Hakone in Sagami Province (Sōshū Hakone kosui), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1820,1842,Polychrome woodblock print; ink and color on paper,10 1/8 x 15 in. (25.7 x 38.1 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36498,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP18,false,true,36499,Asian Art,Woodblock print,"冨嶽三十六景　甲州三島越|Mishima Pass in Kai Province (Kōshū Mishima goe), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1820,1832,Polychrome woodblock print; ink and color on paper,9 3/5 x 14 3/8 in. (24.4 x 36.5 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36499,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP19,false,true,36500,Asian Art,Woodblock print,"冨嶽三十六景　尾州不二見原|Fujimigahara in Owari Province (Bishū Fujimigahara), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1820,1842,Polychrome woodblock print; ink and color on paper,9 15/32 x 15 5/16 in. (24.1 x 38.9 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36500,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP20,false,true,36501,Asian Art,Woodblock print,"「冨嶽三十六景　相州梅沢左」|“Umezawa Manor in Sagami Province,” from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei, Sōshū Umezawa zai)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1820,1842,Polychrome woodblock print; ink and color on paper,10 1/8 x 15 1/8 in. (25.7 x 38.4 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36501,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP21,false,true,36502,Asian Art,Woodblock print,"冨嶽三十六景　上総の海路|At Sea off Kazusa (Kazusa no kairo), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1820,1842,Polychrome woodblock print; ink and color on paper,10 3/32 x 14 3/4 in. (25.6 x 37.5 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36502,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP22,false,true,36503,Asian Art,Woodblock print,"冨嶽三十六景　相州江の島|Enoshima in Sagami Province (Sōshū Enoshima), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1820,1842,Polychrome woodblock print; ink and color on paper,10 1/8 x 14 7/8 in. (25.7 x 37.8 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36503,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP23,false,true,36504,Asian Art,Woodblock print,"冨嶽三十六景　武陽佃島|Tsukudajima in Musashi Province (Buyō Tsukudajima), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1820,1842,Polychrome woodblock print; ink and color on paper,10 1/5 x 15 1/8 in. (25.9 x 38.4 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36504,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP24,false,true,36505,Asian Art,Woodblock print,"冨嶽三十六景　遠江山中|In the Mountains of Tōtomi Province (Tōtomi sanchū), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1820,1842,Polychrome woodblock print; ink and color on paper,9 15/32 x 14 3/5 in. (24.1 x 37.1 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36505,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP25,false,true,36506,Asian Art,Woodblock print,"冨嶽三十六景　遠江山中|In the Mountains of Tōtomi Province (Tōtomi sanchū), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1830,1832,Polychrome woodblock print; ink and color on paper,9 3/5 x 14 29/32 in. (24.4 x 37.9 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36506,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP26,false,true,36507,Asian Art,Woodblock print,"冨嶽三十六景　武陽佃島|Tsukudajima in Musashi Province (Buyō Tsukudajima), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1820,1842,Polychrome woodblock print; ink and color on paper,9 15/32 x 14 3/8 in. (24.1 x 36.5 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36507,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP27,false,true,36508,Asian Art,Woodblock print,"冨嶽三十六景　常州牛掘|Ushibori in Hitachi Province (Jōshū Ushibori), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1820,1842,Polychrome woodblock print; ink and color on paper,9 11/16 x 14 7/16 in. (24.6 x 36.7 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36508,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP28,false,true,36509,Asian Art,Woodblock print,"冨嶽三十六景　青山円座松|Cushion Pine at Aoyama (Aoyama enza no matsu), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1820,1842,Polychrome woodblock print; ink and color on paper,10 x 15 in. (25.4 x 38.1 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36509,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP657,false,true,54301,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1801,1791,1811,Polychrome woodblock print (surimono); ink and color on paper,7 1/8 x 20 1/4 in. (18.1 x 51.4 cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54301,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP658,false,true,54302,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1801,1791,1811,Polychrome woodblock print (surimono); ink and color on paper,8 1/4 x 22 1/8 in. (21 x 56.2 cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54302,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP659,false,true,54303,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1799–1810,1799,1810,Polychrome woodblock print (surimono); ink and color on paper,8 1/4 x 22 1/8 in. (21 x 56.2 cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54303,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP660,false,true,37107,Asian Art,Woodblock print,雪松に鶴|Cranes on Branch of Snow-covered Pine,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,late 1820s,1825,1829,Polychrome woodblock print; ink and color on paper,20 3/8 x 9 1/8 in. (51.8 x 23.2 cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911 Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37107,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP741,false,true,37189,Asian Art,Print,高根山与一右ェ門　千田川吉五郎|The Sumo Wrestlers Takaneyama Yoichiemon and Sendagawa Kichigorō,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1790–93,1790,1793,Polychrome woodblock print; ink and color on paper,H. 12 1/3 ( 30.6 cm; W. 5 3/32 in. (12.9 cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37189,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP742,false,true,37190,Asian Art,Print,和田原甚四郎　花項山五郎吉|The Sumo Wrestlers Wadagahara Jinshirō and Kachōzan Gorokichi,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1783,1773,1793,Polychrome woodblock print; ink and color on paper,12 1/32 x 5 3/32 in. (30.6 x 12.9 cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37190,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP743,false,true,37191,Asian Art,Print,唐子書画図|Chinese Boys Learning to Write and Paint,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1785,1775,1795,Polychrome woodblock print; ink and color on paper,15 3/8 x 10 1/8 in. (39.1 x 25.7 cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37191,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP744,false,true,54304,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1800,1790,1810,Polychrome woodblock print (surimono); ink and color on paper,7 3/8 x 13 1/8 in. (18.7 x 33.3 cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54304,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP745,false,true,54305,Asian Art,Print,"露草に鶏と雛|Rooster, Hen and Chicken with Spiderwort",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–33,1830,1833,Polychrome woodblock print; ink and color on paper,Image: 9 x 11 1/2 in. (22.9 x 29.2 cm) Overall with paper mount: 12 7/8 x 18 1/2 in. (32.7 x 47 cm) Overall with matt: 15 1/2 x 22 3/4 in. (39.4 x 57.8 cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54305,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP746,false,true,54306,Asian Art,Woodblock print,葛飾北斎画　桔梗に蜻蛉|Dragonfly and Bellflower,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,late 1820s,1820,1830,Polychrome woodblock print (surimono); ink and color on paper,9 3/4 x 14 3/16 in. (24.8 x 36 cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54306,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP747,false,true,37192,Asian Art,Print,葛飾北斎画　燕子花|Grasshopper and Iris,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,late 1820s,1824,1829,Polychrome woodblock print; ink and color on paper,Overall: 9 3/4 x 14 3/16in. (24.8 x 36 cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37192,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP748,false,true,37193,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1835,1825,1845,Polychrome woodblock print; ink and color on paper,7 31/32 x 12 1/2 in. (20.3 x 31.8 cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37193,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1011,false,true,54315,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1800,1790,1810,Polychrome woodblock print (surimono); ink and color on paper,7 11/16 x 20 3/8 in. (19.5 x 51.8 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54315,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1012,false,true,54887,Asian Art,Print,Fuji Tohō|The Top of Mount Fuji,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1800,1790,1810,Polychrome woodblock print; ink and color on paper,H. 10 1/8 in. (25.7 cm); W. 15 1/4 in. (38.7 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54887,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1013,false,true,54888,Asian Art,Print,今戸川|Imadogawa,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1801–4,1801,1804,Polychrome woodblock print; ink and color on paper,H. 10 3/16 in. (25.9 cm); W. 15 1/4 in. (38.3 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54888,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1014,false,true,54889,Asian Art,Woodblock print,"百人一首　乳母かえ説　元良親王|Poem by Motoyoshi Shinnō, from the series One Hundred Poems Explained by the Nurse (Hyakunin isshu uba ga etoki)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1839,1829,1849,Polychrome woodblock print; ink and color on paper,H. 10 1/8 in. (25.7 cm); W. 14 1/2 in. (36.8 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54889,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1016,false,true,54925,Asian Art,Print,葛飾北斎画　菊に雀|Sparrows and Chrysanthemums,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1825,1815,1835,Polychrome woodblock print; ink and color on paper,H. 8 1/2 in. (21.6 cm); W. 10 13/16 in. (27.5 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54925,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1081,false,true,55019,Asian Art,Woodblock print,"諸國瀧廻リ　下野黒髪山 きりふりの滝|Kirifuri Waterfall at Kurokami Mountain in Shimotsuke (Shimotsuke Kurokamiyama Kirifuri no taki), from the series A Tour of Waterfalls in Various Provinces (Shokoku taki meguri)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1832,1822,1842,Polychrome woodblock print; ink and color on paper,Image: 14 3/4 × 10 1/4 in. (37.5 × 26 cm),The Metropolitan Museum of Art,,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55019,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1082,false,true,55020,Asian Art,Woodblock print,"諸國瀧廻リ　木曾海道小野ノ瀑布|Ono Waterfall on the Kisokaidō (Kisokaidō Ono no bakufu), from the series A Tour of Waterfalls in Various Provinces (Shokoku taki meguri)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1832,1822,1842,Polychrome woodblock print; ink and color on paper,14 7/8 x 10 5/16 in. (37.8 x 26.2 cm),The Metropolitan Museum of Art,,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55020,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1083,false,true,55021,Asian Art,Woodblock print,"諸國瀧廻リ　東海道坂ノ下 清瀧くわんおん|Kiyotaki Kannon Waterfall at Sakanoshita on the Tōkaidō (Tōkaidō Sakanoshita Kiyotaki kannon), from the series A Tour of Waterfalls in Various Provinces (Shokoku taki meguri)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1832,1822,1842,Polychrome woodblock print; ink and color on paper,H. 14 5/8 in. (37.1 cm); W. 10 3/16 in. (25.9 cm),The Metropolitan Museum of Art,,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55021,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1084,false,true,45027,Asian Art,Woodblock print,"諸國瀧廻リ　和州吉野義経馬洗滝|The Waterfall Where Yoshitsune Washed His Horse at Yoshino in Yamato Province (Washū Yoshino Yoshitsune uma arai no taki), from the series A Tour of Waterfalls in Various Provinces (Shokoku taki meguri)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1832,1822,1842,Polychrome woodblock print; ink and color on paper,H. 14 5/8 in. (37.1 cm); W. 10 in. (25.4 cm),"Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45027,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1085,false,true,55022,Asian Art,Woodblock print,"諸國瀧廻リ　木曽路ノ奥 阿彌陀ヶ瀧|The Amida Falls in the Far Reaches of the Kisokaidō Road (Kisoji no oku Amida-ga-taki), from the series A Tour of Waterfalls in Various Provinces (Shokoku taki meguri)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1827,1817,1837,Polychrome woodblock print; ink and color on paper,Image: 14 1/2 x 10 in. (36.8 x 25.4 cm),The Metropolitan Museum of Art,,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55022,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1086,false,true,55023,Asian Art,Print,"諸國瀧廻　東都葵ヶ岡の瀧|Fall of Aoiga Oka, Yedo",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1827,1817,1837,Polychrome woodblock print; ink and color on paper,H. 14 3/4 in. (37.5 cm); W. 10 3/16 in. (25.9 cm),The Metropolitan Museum of Art,,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55023,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1087,false,true,55024,Asian Art,Woodblock print,"諸國瀧廻リ　相州大山ろうべんの瀧|Rōben Waterfall at Ōyama in Sagami Province (Sōshū Ōyama Rōben no taki), from the series A Tour of Waterfalls in Various Provinces (Shokoku taki meguri)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1827,1817,1837,Polychrome woodblock print; ink and color on paper,H. 14 3/8 in. (36.5 cm); W. 10 1/8 in. (25.7 cm),The Metropolitan Museum of Art,,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55024,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1088,false,true,55025,Asian Art,Woodblock print,"諸國瀧廻リ　美濃ノ国養老の滝|Yōrō Waterfall in Mino Province (Mino no Yōrō no taki), from the series A Tour of Waterfalls in Various Provinces (Shokoku taki meguri)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1827,1817,1837,Polychrome woodblock print; ink and color on paper,H. 14 7/16 in. (36.7 cm); W. 10 3/16 in. (25.9 cm),The Metropolitan Museum of Art,,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55025,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1117,false,true,55039,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1825,1815,1835,Polychrome woodblock print; ink and color on paper,H. 13 7/8 in. (35.2 cm); W. 4 9/16 in. (11.6 cm),"Rogers Fund, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55039,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1142,false,true,54349,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1810,1800,1820,Polychrome woodblock print (surimono); ink and color on paper,8 3/8 x 7 1/4 in. (21.3 x 18.4 cm),"Rogers Fund, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54349,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1246,false,true,54377,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1814,1804,1824,Polychrome woodblock print (surimono); ink and color on paper,8 5/16 x 5 9/16 in. (21.1 x 14.1 cm),"Rogers Fund, 1921",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54377,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1247,false,true,54378,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,1820,1820,1820,Polychrome woodblock print (surimono); ink and color on paper,8 x 10 3/8 in. (20.3 x 26.4 cm),"Rogers Fund, 1921",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54378,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1284,false,true,55223,Asian Art,Woodblock print,"冨嶽三十六景　東海道品川御殿山の不二|Fuji from Gotenyama on the Tōkaidō at Shinagawa (Tōkaidō Shinagawa Gotenyama no Fuji), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1820,1842,Polychrome woodblock print; ink and color on paper,H. 9 3/4 in. (24.8 cm); W. 14 7/16 in. (36.7 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55223,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1285,false,true,55225,Asian Art,Woodblock print,"冨嶽三十六景　本所立川|Tatekawa in Honjō (Honjō Tatekawa), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1820,1842,Polychrome woodblock print; ink and color on paper,9 3/4 x 14 1/8 in. (24.8 x 35.9cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55225,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1286,false,true,55226,Asian Art,Woodblock print,"冨嶽三十六景　下目黒|Lower Meguro (Shimo Meguro), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1820,1842,Polychrome woodblock print; ink and color on paper,H. 9 11/16 in. (24.6 cm); W. 14 3/8 in. (36.5 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55226,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1287,false,true,55227,Asian Art,Woodblock print,"冨嶽三十六景　東都駿台|Surugadai in Edo (Tōto Sundai), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1820,1842,Polychrome woodblock print; ink and color on paper,H. 9 3/4 in. (24.8 cm); W. 14 3/8 in. (36.5 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55227,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1288,false,true,55228,Asian Art,Woodblock print,"冨嶽三十六景　東海道江尻田子の浦略図|Tago Bay near Ejiri on the Tōkaidō (Tōkaidō Ejiri Tago no ura ryaku zu), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1820,1842,Polychrome woodblock print; ink and color on paper,9 3/4 x 14 3/8 in. (24.8 x 36.5 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55228,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1289,false,true,55231,Asian Art,Woodblock print,"冨嶽三十六景　武州千住|Senju in Musashi Province (Bushū Senju), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1820,1842,Polychrome woodblock print; ink and color on paper,H. 9 11/16 in. (24.6 cm); W. 14 3/8 in. (36.5 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55231,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1290,false,true,45218,Asian Art,Print,"風流無くてななくせ|Squeaking a Ground Cherry, from the series Seven Fashionable Useless Habits (Furyu nakute nana kuse)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1798,1788,1808,Polychrome woodblock print; ink and color on paper,14 5/16 x 9 3/4 in. (36.4 x 24.8cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45218,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1294,false,true,55236,Asian Art,Woodblock print,"冨嶽三十六景　東海道金谷の不二|Fuji Seen from Kanaya on the Tōkaidō (Tōkaidō Kanaya no Fuji), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1820,1842,Polychrome woodblock print; ink and color on paper,H. 10 in. (25.4 cm); W. 14 1/2 in. (36.8 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55236,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1295,false,true,55237,Asian Art,Woodblock print,"冨嶽三十六景　江戸日本橋|Nihonbashi in Edo (Edo Nihonbashi), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1820,1842,Polychrome woodblock print; ink and color on paper,H. 10 in. (25.4 cm); W. 14 1/2 in. (36.8 cm),"Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55237,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1296,false,true,55238,Asian Art,Woodblock print,"冨嶽三十六景　江都駿河町三井見世略図|Mitsui Shop at Surugachō in Edo (Edo Surugachō Mitsui mise ryaku zu), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1820,1842,Polychrome woodblock print; ink and color on paper,H. 10 1/4 in. (26 cm); W. 15 1/8 in. (38.4 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55238,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1322,false,true,55281,Asian Art,Woodblock print,"冨嶽三十六景　諸人登山|Groups of Mountain Climbers (Shojin tozan), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1820,1842,Polychrome woodblock print; ink and color on paper,H. 10 in. (25.4 cm); W. 14 3/4 in. (37.5 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55281,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1323,false,true,55282,Asian Art,Woodblock print,"冨嶽三十六景　東都浅草本願寺|Honganji at Asakusa in Edo (Tōto Asakusa Honganji), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1820,1842,Polychrome woodblock print; ink and color on paper,H. 10 1/8 in. (25.7 cm); W. 15 1/8 in. (38.4 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55282,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1324,false,true,55283,Asian Art,Woodblock print,"冨嶽三十六景　東海道吉田|Yoshida on the Tōkaidō (Tōkaidō Yoshida), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1830,1832,Polychrome woodblock print; ink and color on paper,H. 10 1/8 in. (25.7 cm); W. 15 1/4 in. (38.7 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55283,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1325,false,true,55284,Asian Art,Woodblock print,"冨嶽三十六景　相州仲原|Nakahara in Sagami Province (Sōshū Nakahara), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1820,1842,Polychrome woodblock print; ink and color on paper,H. 10 in. (25.4 cm); W. 14 3/4 in. (37.5 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55284,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1326,false,true,55285,Asian Art,Woodblock print,"冨嶽三十六景　従千住花街眺望の不二|Fuji Seen in the Distance from Senju Pleasure Quarter (Senju kagai yori chōbō no Fuji), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1820,1842,Polychrome woodblock print; ink and color on paper,H. 9 7/8 in. (25.1 cm); W. 14 3/4 in. (37.5 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55285,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1327,false,true,39656,Asian Art,Woodblock print,"冨嶽三十六景　甲州石班沢|Kajikazawa in Kai Province (Kōshū Kajikazawa), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1820,1842,Polychrome woodblock print; ink and color on paper,10 x 15 in. (25.4 x 38.1 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/39656,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1328,false,true,55286,Asian Art,Woodblock print,"冨嶽三十六景　甲州伊沢暁|Dawn at Isawa in Kai Province (Kōshū Isawa no akatsuki), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1820,1842,Polychrome woodblock print; ink and color on paper,H. 10 5/16 in. (26.2 cm); W. 15 in. (38.1 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55286,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1329,false,true,55287,Asian Art,Woodblock print,"冨嶽三十六景　江都駿河町三井見世略図|Mitsui Shop at Surugachō in Edo (Edo Surugachō Mitsui mise ryaku zu), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1820,1842,Polychrome woodblock print; ink and color on paper,Oban 10 x 15 in. (25.4 x 38.1 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55287,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1330,false,true,55288,Asian Art,Woodblock print,"冨嶽三十六景　礫川雪の旦|Morning after the Snow at Koishikawa in Edo (Koishikawa yuki no ashita), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1820,1842,Polychrome woodblock print; ink and color on paper,H. 10 1/8 in. (25.7 cm); W. 15 1/16 in. (38.3 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55288,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1331,false,true,37319,Asian Art,Woodblock print,"冨嶽三十六景　御厩川岸より両国橋夕陽見|Viewing the Sunset over Ryōgoku Bridge from the Onmaya Embankment (Onmayagashi yori Ryōgokubashi sekiyō o miru), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1823,1843,Polychrome woodblock print; ink and color on paper,9 15/16 x 14 11/16 in. (25.2 x 37.3 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37319,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1332,false,true,55289,Asian Art,Woodblock print,"冨嶽三十六景　深川万年橋下|Under the Mannen Bridge at Fukagawa (Fukagawa Mannenbashi shita), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1820,1842,Polychrome woodblock print; ink and color on paper,10 1/8 x 15 3/16 in. (25.7 x 38.6 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55289,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1333,false,true,55290,Asian Art,Woodblock print,"冨嶽三十六景　駿州片倉茶園の不二|Fuji from the Katakura Tea Fields in Suruga (Sunshū Katakura chaen no Fuji), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1820,1842,Polychrome woodblock print; ink and color on paper,H. 10 1/4 in. (26 cm); W. 15 in. (38.1 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55290,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1334,false,true,37320,Asian Art,Woodblock print,"冨嶽三十六景　駿州片倉茶園の不二|Fuji from the Katakura Tea Fields in Suruga (Sunshū Katakura chaen no Fuji), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1820,1842,Polychrome woodblock print; ink and color on paper,H. 9 13/16 in. (24.9 cm); W. 14 5/8 in. (37.1 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37320,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1335,false,true,37321,Asian Art,Woodblock print,"冨嶽三十六景　五百らかん寺さざゐどう|Sazai Hall at the Temple of the Five Hundred Arhats (Gohyaku Rakanji Sazaidō), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1820,1842,Polychrome woodblock print; ink and color on paper,H. 10 1/16 in. (25.6 cm); W. 14 5/8 in. (37.1 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37321,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1336,false,true,37322,Asian Art,Woodblock print,"冨嶽三十六景　隠田の水車|The Waterwheel at Onden (Onden no suisha), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1820,1842,Polychrome woodblock print; ink and color on paper,H. 10 in. (25.4 cm); W. 14 7/8 in. (37.8 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37322,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1337,false,true,39655,Asian Art,Woodblock print,"冨嶽三十六景　登戸浦|Noboto Bay (Noboto no ura), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1820,1842,Polychrome woodblock print; ink and color on paper,10 1/4 x 15 3/16 in. (26.0 x 38.6 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/39655,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1338,false,true,55291,Asian Art,Woodblock print,"冨嶽三十六景　隅田川関屋の里|Sekiya Village on the Sumida River (Sumidagawa Sekiya no sato), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1820,1842,Polychrome woodblock print; ink and color on paper,H. 10 in. (25.4 cm); W. 15 1/4 in. (38.7 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55291,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1339,false,true,37323,Asian Art,Woodblock print,"百人一首　宇波か縁説　藤原道信朝臣|Poem by Fujiwara no Michinobu Ason, from the series One Hundred Poems Explained by the Nurse (Hyakunin isshu uba ga etoki)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,1839,1839,1839,Polychrome woodblock print; ink and color on paper,H. 9 15/16 in. (25.2 cm); W. 14 3/8 in. (36.5 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37323,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1340,false,true,37324,Asian Art,Woodblock print,"百人一首　うばがゑとき　伊勢|Poem by Ise, from the series One Hundred Poems Explained by the Nurse (Hyakunin isshu uba ga etoki)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,1839,1839,1839,Polychrome woodblock print; ink and color on paper,H. 9 15/16 in. (25.2 cm); W. 14 3/8 in. (36.5 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37324,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1341,false,true,55306,Asian Art,Woodblock print,"百人一首　うはかゑとき　天智天皇|Poem by Tenchi Tennō, from the series One Hundred Poems Explained by the Nurse (Hyakunin isshu uba ga etoki)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,1839,1839,1839,Polychrome woodblock print; ink and color on paper,H. 9 15/16 in. (25.2 cm); W. 14 5/16 in. (36.4 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55306,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1380,false,true,50916,Asian Art,Woodblock print,"諸國名橋奇覧　東海道岡崎矢はぎのはし|Yahagi Bridge at Okazaki on the Tōkaidō (Tōkaidō Okazaki Yahagi no hashi), from the series Remarkable Views of Bridges in Various Provinces (Shokoku meikyō kiran)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,1827–30,1827,1830,Polychrome woodblock print; ink and color on paper,H. 9 3/4 in. (24.8 cm); W. 14 1/2 in. (36.8 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/50916,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1381,false,true,53699,Asian Art,Woodblock print,"諸國名橋奇覧　かうつけ佐野ふなはしの古づ|Old View of the Boat-bridge at Sano in Kōzuke Province (Kōzuke Sano funabashi no kozu), from the series Remarkable Views of Bridges in Various Provinces (Shokoku meikyō kiran)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,1827–30,1827,1830,Polychrome woodblock print; ink and color on paper,H. 9 3/4 in. (24.8 cm); W. 14 9/16 in. (37 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/53699,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1382,false,true,50924,Asian Art,Woodblock print,"諸國名橋奇覧　ゑちぜんふくゐの橋|Fukui Bridge in Echizen Province (Echizen Fukui no hashi), from the series Remarkable Views of Bridges in Various Provinces (Shokoku meikyō kiran)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,1827–30,1827,1830,Polychrome woodblock print; ink and color on paper,H. 9 3/4 in. (24.8 cm); W. 14 1/2 in. ( 36.8 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/50924,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1383,false,true,55369,Asian Art,Woodblock print,"冨嶽三十六景　東海道保土ケ谷|Hodogaya on the Tōkaidō (Tōkaidō Hodogaya), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1830,1832,Polychrome woodblock print; ink and color on paper,H. 9 3/4 in. (24.8 cm); W. 14 9/16 in. (37 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55369,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1393,false,true,54389,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,1822,1822,1822,Polychrome woodblock print (surimono); ink and color on paper,8 1/4 x 7 3/8 in. (21 x 18.7 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54389,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1395,false,true,55402,Asian Art,Woodblock print,"雪月花　吉野|Cherry Blossoms at Yoshino (Yoshino), from the series Snow, Moon, and Flowers (Setsugekka)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1833,1823,1843,Polychrome woodblock print; ink and color on paper,H. 10 1/4 in. (26 cm); W. 14 15/16 in. (37.9 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55402,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1396,false,true,53788,Asian Art,Woodblock print,"諸國名橋奇覧　足利行道山くものかけはし|The Hanging-cloud Bridge at Mount Gyōdō near Ashikaga (Ashikaga Gyōdōzan kumo no kakehashi), from the series Remarkable Views of Bridges in Various Provinces (Shokoku meikyō kiran)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830,1820,1840,Polychrome woodblock print; ink and color on paper,H. 9 1/2 in. (24.1 cm); W. 14 1/16 in. (35.7 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/53788,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1397,false,true,53789,Asian Art,Woodblock print,"諸國名橋奇覧　飛越の堺つりはし|The Suspension Bridge on the Border of Hida and Etchū Provinces (Hietsu no sakai tsuribashi), from the series Remarkable Views of Bridges in Various Provinces (Shokoku meikyō kiran)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830,1820,1840,Polychrome woodblock print; ink and color on paper,H. 10 1/4 in. (26 cm); W. 15 1/8 in. (38.4 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/53789,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1398,false,true,45026,Asian Art,Woodblock print,"諸國名橋奇覧　三河の八ツ橋の古図|Ancient View of Yatsuhashi in Mikawa Province (Mikawa no Yatsuhashi no kozu), from the series Remarkable Views of Bridges in Various Provinces (Shokoku meikyō kiran)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1834,1824,1844,Polychrome woodblock print; ink and color on paper,9 1/8 x 14 5/8 in. (23.2 x 37.1 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45026,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1425,false,true,55454,Asian Art,Woodblock print,"百人一首　乳母か縁説　在原業平|Poem by Ariwara no Narihira, from the series One Hundred Poems Explained by the Nurse (Hyakunin isshu uba ga etoki)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,1839,1839,1839,Polychrome woodblock print; ink and color on paper,H. 10 1/8 in. (25.7 cm); W. 15 in. (38.1 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55454,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1426,false,true,55456,Asian Art,Woodblock print,"冨嶽三十六景　甲州犬目峠|The Inume Pass in Kai Province (Kōshū Inume tōge), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1820,1842,Polychrome woodblock print; ink and color on paper,H. 9 11/16 in. (24.6 cm); W. 14 1/4 in. (36.2 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55456,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1427,false,true,55458,Asian Art,Woodblock print,"冨嶽三十六景　東海道保土ケ谷|Hodogaya on the Tōkaidō (Tōkaidō Hodogaya), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1820,1842,Polychrome woodblock print; ink and color on paper,H. 9 15/16 in. (25.2 cm); W. 14 3/4 in. (37.5 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55458,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1428,false,true,55461,Asian Art,Woodblock print,"諸國瀧廻リ　東海道坂ノ下 清瀧くわんおん|Kiyotaki Kannon Waterfall at Sakanoshita on the Tōkaidō (Tōkaidō Sakanoshita Kiyotaki kannon), from the series A Tour of Waterfalls in Various Provinces (Shokoku taki meguri)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1832,1822,1842,Polychrome woodblock print; ink and color on paper,H. 14 3/4 in. (37.5 cm); W. 10 in. (25.4 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55461,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1429,false,true,55463,Asian Art,Woodblock print,"雪月花　淀川|Moonlight on the Yodo River (Yodogawa), from the series Snow, Moon, and Flowers (Setsugekka)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1833,1823,1843,Polychrome woodblock print; ink and color on paper,H. 9 5/8 in. (24.4 cm); W. 14 9/16 in. (37 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55463,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1430,false,true,55466,Asian Art,Woodblock print,"琉球八景　城嶽霊泉|The Sacred Spring at Jōgaku (Jōgaku reisen), from the series Eight Views of the Ryūkyū Islands (Ryūkyū hakkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,1832,1822,1842,Polychrome woodblock print; ink and color on paper,H. 10 1/16 in. (25.6 cm); 14 5/8 in. (37.1 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55466,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1456,false,true,55503,Asian Art,Woodblock print,"冨嶽三十六景　駿州大野新田|The New Fields at Ōno in Suruga Province (Sunshū Ōno shinden), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1820,1842,Polychrome woodblock print; ink and color on paper,9 7/8 x 14 7/8in. (25.1 x 37.8cm),"Rogers Fund, 1923",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55503,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1457,false,true,55505,Asian Art,Woodblock print,"諸國名橋奇覧　摂洲阿治川口天保山|Tenpōzan at the Mouth of the Aji River in Settsu Province (Sesshū Ajikawaguchi Tenpōzan), from the series Remarkable Views of Bridges in Various Provinces (Shokoku meikyō kiran)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,1827–30,1827,1830,Polychrome woodblock print; ink and color on paper,H. 10 in. (25.4 cm); W. 14 5/8 in. (37.1 cm),"Rogers Fund, 1923",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55505,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1482,false,true,55571,Asian Art,Woodblock print,"冨嶽三十六景　上総の海路|At Sea off Kazusa (Kazusa no kairo), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1830,1832,Polychrome woodblock print; ink and color on paper,H. 9 7/8 in. (25.1 cm); W. 15 1/16 in. (38.3 cm),"Rogers Fund, 1926",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55571,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1843,false,true,56128,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,1830s,1830,1839,Monochrome woodblock print; ink on paper,H. 10 1/2 in. (26.7 cm); W. 14 3/4 in. (37.5 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56128,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1846,false,true,56131,Asian Art,Woodblock print,"冨嶽三十六景　隠田の水車|The Waterwheel at Onden (Onden no suisha), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1820,1842,Polychrome woodblock print; ink and color on paper,H. 9 3/8 in. (23.8 cm); W. 14 in. (35.6 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56131,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1847,false,true,45434,Asian Art,Woodblock print,"「富嶽三十六景　神奈川沖浪裏」|Under the Wave off Kanagawa (Kanagawa oki nami ura), also known as The Great Wave, from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1820,1842,Polychrome woodblock print; ink and color on paper,10 1/8 x 14 15/16 in. (25.7 x 37.9 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45434,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1848,false,true,56132,Asian Art,Woodblock print,"冨嶽三十六景　常州牛掘|Ushibori in Hitachi Province (Jōshū Ushibori), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1820,1842,Polychrome woodblock print; ink and color on paper,H. 9 13/16 in. (24.9 cm); W. 13 7/8 in. (35.2 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56132,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1849,false,true,56133,Asian Art,Woodblock print,"冨嶽三十六景　信州諏訪湖|Lake Suwa in Shinano Province (Shinshū Suwako), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1820,1842,Polychrome woodblock print; ink and color on paper,H. 10 1/8 in. (25.7 cm); W. 14 13/16 in. (37.6 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56133,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1850,false,true,56135,Asian Art,Woodblock print,"冨嶽三十六景　東海道品川御殿山の不二|Fuji from Gotenyama at Shinagawa on the Tōkaidō (Tōkaidō Shinagawa Gotenyama no Fuji), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1830,1842,Polychrome woodblock print; ink and color on paper,H. 9 1/2 in. (24.1 cm); W. 14 1/8 in. (35.9 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56135,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1851,false,true,53700,Asian Art,Woodblock print,"「諸國名橋寄覧　東海道岡崎 矢はぎのはし」|Yahagi Bridge at Okazaki on the Tōkaidō (Tōkaidō Okazaki Yahagi no hashi), from the series Remarkable Views of Bridges in Various Provinces (Shokoku meikyō kiran)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1834,1824,1844,Polychrome woodblock print; ink and color on paper,H. 10 3/16 in. (25.9 cm); W. 15 1/16 in. (38.3 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/53700,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1852,false,true,56136,Asian Art,Woodblock print,"百人一首　乳母かゑとき　猿丸太夫|Poem by Sarumaru Dayū, from the series One Hundred Poems Explained by the Nurse (Hyakunin isshu uba ga etoki)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,1839,1839,1839,Polychrome woodblock print; ink and color on paper,H. 10 in. (25.4 cm); W. 14 3/8 in. (36.5 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56136,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1854,false,true,56172,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1800,1790,1810,Polychrome woodblock print; ink and color on paper,H. 9 9/16 in. (24.3 cm); W. 14 7/16 in. (36.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56172,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1859,false,true,56178,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1825,1815,1835,Sketch for a woodblock print; ink and color on paper,Image: 7 1/16 x 11 7/8 in. (17.9 x 30.2 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56178,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1860,false,true,56183,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,1820–34,1820,1834,Polychrome woodblock print; ink and color on paper,H. 9 3/4 in. (24.8 cm); W. 14 1/4 in. (36.2 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56183,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1865,false,true,54430,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,1800–1815,1800,1815,Polychrome woodblock print (surimono); ink and color on paper,7 5/16 x 19 7/8 in. (18.6 x 50.5 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54430,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1868,false,true,54433,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,1820–33,1820,1833,Polychrome woodblock print (surimono); ink and color on paper,8 9/16 x 7 1/16 in. (21.7 x 17.9 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54433,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1870,false,true,54435,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1804–13,1804,1813,Polychrome woodblock print (surimono); ink and color on paper,8 1/8 x 10 11/16 in. (20.6 x 27.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54435,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1873,false,true,54438,Asian Art,Print,"元禄歌仙貝合|Ashi Clam, from the series ""Genroku Kasen Kai-awase""",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,1821,1821,1821,Polychrome woodblock print; ink and color on paper,7 15/16 x 7 in. (20.2 x 17.8 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54438,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1900,false,true,54439,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,1808–27,1808,1827,Polychrome woodblock print (surimono); ink and color on paper,8 1/4 x 7 1/2 in. (21 x 19.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54439,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2019,false,true,54768,Asian Art,Print,初詣|Young Women Visiting a Shinto Shrine,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,1814,1814,1814,Polychrome woodblock print (surimono); ink and color on paper,8 3/8 x 5 7/16 in. (21.3 x 13.8 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54768,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2224,false,true,53990,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,probably 1807,1807,1807,Polychrome woodblock print (surimono); ink and color on paper,5 7/16 x 11 1/8 in. (13.8 x 28.3 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/53990,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2330,false,true,54114,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,1816,1816,1816,Part of an album of woodblock prints (surimono); ink and color on paper,8 7/16 x 7 1/2 in. (21.4 x 19.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54114,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2403,false,true,56806,Asian Art,Woodblock print,"百人一首　乳母かゑとき　猿丸太夫|Poem by Sarumaru Dayū, from the series One Hundred Poems Explained by the Nurse (Hyakunin isshu uba ga etoki)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,1839,1839,1839,Polychrome woodblock print; ink and color on paper,10 x 14 3/8 in. (25.4 x 36.5 cm),"Gift of Louis V. Ledoux, 1931",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56806,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2546,false,true,56961,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1795,1785,1805,Polychrome woodblock print; ink and color on paper,10 x 15 1/2 in. (25.4 x 39.4 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56961,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2547,false,true,53787,Asian Art,Woodblock print,"諸國名橋奇覧　かうつけ佐野ふなはしの古づ|Old View of the Boat-bridge at Sano in Kōzuke Province (Kōzuke Sano funabashi no kozu), from the series Remarkable Views of Bridges in Various Provinces (Shokoku meikyō kiran)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830,1820,1840,Polychrome woodblock print; ink and color on paper,10 x 14 7/8 in. (25.4 x 37.8 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/53787,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2548,false,true,56965,Asian Art,Woodblock print,"百人一首　うばがゑとき　伊勢|Poem by Ise, from the series One Hundred Poems Explained by the Nurse (Hyakunin isshu uba ga etoki)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1835,1825,1845,Polychrome woodblock print; ink and color on paper,10 1/2 x 14 3/4 in. (26.7 x 37.5 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56965,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2549,false,true,56973,Asian Art,Woodblock print,"百人一首　うばがゑとき　大納言経信|Poem by Dainagon Tsunenobu (Minamoto no Tsunenobu, Katsura no Dainagon), from the series One Hundred Poems Explained by the Nurse (Hyakunin isshu uba ga etoki)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,1839,1839,1839,Polychrome woodblock print; ink and color on paper,Image: 9 15/16 × 14 3/8 in. (25.2 × 36.5 cm) Mat: 22 3/4 × 15 1/2 in. (57.8 × 39.4 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56973,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2550,false,true,56967,Asian Art,Woodblock print,"百人一首　うはかゑとき　持統天皇|Poem by Jitō Tenno (Empress Jitō), from the series One Hundred Poems Explained by the Nurse (Hyakunin isshu uba ga etoki)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1839,1829,1849,Polychrome woodblock print; ink and color on paper,10 x 14 5/8 in. (25.4 x 37.1 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56967,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2552,false,true,56979,Asian Art,Woodblock print,"百人一首　うはかゑとき　源宗于朝臣|Poem by Minamoto no Muneyuki Ason, from the series One Hundred Poems Explained by the Nurse (Hyakunin isshu uba ga etoki)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1835,1825,1845,Polychrome woodblock print; ink and color on paper,Overall: 9 7/8 x 14 1/2 in. (25.1 x 36.8 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56979,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2553,false,true,56988,Asian Art,Woodblock print,"冨嶽三十六景　駿州江尻|Ejiri in Suruga Province (Sunshū Ejiri), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1820,1842,Polychrome woodblock print; ink and color on paper,9 5/8 x 14 3/4 in. (24.4 x 37.5 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56988,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2554,false,true,53845,Asian Art,Woodblock print,"冨嶽三十六景　御厩川岸より両国橋夕陽見|Viewing the Sunset over Ryōgoku Bridge from the Onmayagashi Embankment (Onmayagashi yori Ryōgokubashi sekiyō o miru), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1820,1842,Woodblock print; ink and color on paper,H. 10 1/8 in. (25.7 cm); W. 14 7/8 in. (37.8 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/53845,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2555,false,true,56990,Asian Art,Woodblock print,"冨嶽三十六景　礫川雪の旦|Morning after the Snow at Koishikawa in Edo (Koishikawa yuki no ashita), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1820,1842,Polychrome woodblock print; ink and color on paper,10 x 15 in. (25.4 x 38.1 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56990,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2556,false,true,56786,Asian Art,Woodblock print,"冨嶽三十六景　甲州三島越|Mishima Pass in Kai Province (Kōshū Mishima goe), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1820,1842,Polychrome woodblock print; ink and color on paper,9 3/4 × 14 3/4 in. (24.8 × 37.5 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56786,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2557,false,true,56787,Asian Art,Woodblock print,"冨嶽三十六景　尾州不二見原|Fujimigahara in Owari Province (Bishū Fujimigahara), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1820,1842,Polychrome woodblock print; ink and color on paper,H. 10 in. (25.4 cm); W. 15 in. (38.1 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56787,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2558,false,true,56994,Asian Art,Woodblock print,"冨嶽三十六景　甲州犬目峠|The Inume Pass in Kai Province (Kōshū Inume tōge), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1820,1842,Polychrome woodblock print; ink and color on paper,10 x 15 in. (25.4 x 38.1 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56994,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2559,false,true,56996,Asian Art,Woodblock print,"冨嶽三十六景　相州江の島|Enoshima in Sagami Province (Sōshū Enoshima), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1820,1842,Polychrome woodblock print; ink and color on paper,10 1/8 x 14 3/4 in. (25.7 x 37.5 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56996,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2560,false,true,56998,Asian Art,Woodblock print,"冨嶽三十六景　武州玉川|Tama River in Musashi Province (Bushū Tamagawa), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1820,1842,Polychrome woodblock print; ink and color on paper,10 x 15 in. (25.4 x 38.1 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56998,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2561,false,true,57000,Asian Art,Woodblock print,"冨嶽三十六景　東海道江尻田子の浦略図|Tago Bay near Ejiri on the Tōkaidō (Tōkaidō Ejiri Tago no ura ryaku zu), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1820,1842,Polychrome woodblock print; ink and color on paper,9 7/8 x 14 5/8 in. (25.1 x 37.1 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57000,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2562,false,true,57003,Asian Art,Woodblock print,"冨嶽三十六景　東海道保土ケ谷|Hodogaya on the Tōkaidō (Tōkaidō Hodogaya), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1820,1842,Polychrome woodblock print; ink and color on paper,10 1/8 x 15 in. (25.7 x 38.1 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57003,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2563,false,true,57004,Asian Art,Woodblock print,"冨嶽三十六景　武陽佃島|Tsukudajima in Musashi Province (Buyō Tsukudajima), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1820,1842,Polychrome woodblock print; ink and color on paper,10 x 15 1/4 in. (25.4 x 38.7 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57004,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2564,false,true,39798,Asian Art,Woodblock print,"冨嶽三十六景　信州諏訪湖|Lake Suwa in Shinano Province (Shinshū Suwako), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1820,1842,Polychrome woodblock print; ink and color on paper,9 3/4 x 14 7/8 in. (24.8 x 37.8 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/39798,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2565,false,true,56785,Asian Art,Woodblock print,"冨嶽三十六景　常州牛掘|Ushibori in Hitachi Province (Jōshū Ushibori), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1820,1842,Polychrome woodblock print; ink and color on paper,H. 10 in. (25.4 cm); W. 15 in. (38.1 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56785,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2566,false,true,57005,Asian Art,Woodblock print,"冨嶽三十六景　本所立川|Tatekawa in Honjō (Honjō Tatekawa), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1820,1842,Polychrome woodblock print; ink and color on paper,10 x 15 in. (25.4 x 38.1 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57005,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2567,false,true,57006,Asian Art,Woodblock print,"冨嶽三十六景　山下白雨|Storm below Mount Fuji (Sanka no haku u), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1820,1842,Polychrome woodblock print; ink and color on paper,9 3/4 x 14 3/4 in. (24.8 x 37.5 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57006,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2568,false,true,57007,Asian Art,Woodblock print,"冨嶽三十六景　凱風快晴|South Wind, Clear Sky (Gaifū kaisei), also known as Red Fuji, from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1820,1842,Polychrome woodblock print; ink and color on paper,Oban nishiki-e triptych: 10 x 15 in. (25.4 x 38.1 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57007,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2569,false,true,39799,Asian Art,Woodblock print,"「富嶽三十六景　神奈川沖浪裏」|Under the Wave off Kanagawa (Kanagawa oki nami ura), or The Great Wave, from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1820,1842,Polychrome woodblock print; ink and color on paper,10 x 15 in. (25.4 x 38.1 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/39799,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2570,false,true,45494,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",", New York, NY (1936; sold to MMA).",Katsushika Hokusai,Japanese,1760,1849,ca. 1796,1786,1806,Polychrome woodblock print; ink and color on paper,8 1/2 x 6 in. (21.6 x 15.2 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45494,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2571,false,true,57008,Asian Art,Woodblock print,松本幸四郎|Matsumoto Koshiro IV as Tsurifune no Sabu,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1796,1786,1806,Polychrome woodblock print; ink and color on paper,10 3/4 x 5 3/4 in. (27.3 x 14.6 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57008,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2572,false,true,57009,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1800,1790,1810,Polychrome woodblock print; ink and color on paper,7 7/8 x 12 7/8 in. (20 x 32.7 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57009,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2573,false,true,54188,Asian Art,Print,Horimono-shi|職人三十六番|The Metal Carver,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,1802,1802,1802,Polychrome woodblock print (surimono); ink and color on paper,5 1/2 x 7 1/2 in. (14 x 19.1 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54188,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2574,false,true,54189,Asian Art,Print,Tachi-shi|職人三十六番|The Swordsmith,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,1802,1802,1802,Polychrome woodblock print (surimono); ink and color on paper,5 3/8 x 7 3/8 in. (13.7 x 18.7 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54189,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2575,false,true,54191,Asian Art,Print,Hata-ori|職人三十六歌仙|The Weaving Factory,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1802,1792,1812,Polychrome woodblock print (surimono); ink and color on paper,5 3/8 x 7 3/8 in. (13.7 x 18.7 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54191,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2576,false,true,54192,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,1841,1841,1841,Polychrome woodblock print (surimono); ink and color on paper,5 3/8 x 7 1/4 in. (13.7 x 18.4 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54192,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2577,false,true,54193,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1800,1790,1810,Polychrome woodblock print (surimono); ink and color on paper,5 1/2 x 10 1/2 in. (14 x 26.7 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54193,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2578,false,true,54194,Asian Art,Print,"Sakura-gai|元禄歌仙貝合|Cherry Shell, from the series Genroku Poetry Shell Games",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,probably 1821,1821,1821,Polychrome woodblock print (surimono); ink and color on paper,7 3/4 x 6 7/8 in. (19.7 x 17.5 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54194,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2579,false,true,54195,Asian Art,Print,Miyako-gai|元禄歌仙貝合|Miyako Shell,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,probably 1821,1821,1821,Polychrome woodblock print (surimono); ink and color on paper,7 7/8 x 7 in. (20 x 17.8 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54195,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2580,false,true,57010,Asian Art,Print,新柳橋の白雨|Shower at the New Yanagi Bridge,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,1806,1806,1806,Woodblock print ; ink and color on paper,7 7/8 x 11 3/4 in. (20 x 29.8 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57010,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2581,false,true,56727,Asian Art,Print,"冨嶽三十六景　甲州石班沢|Kajikazawa in Kai Province (Kōshū Kajikazawa), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1830,1832,Polychrome woodblock print; ink and color on paper,10 x 15 1/8 in. (25.4 x 38.4 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56727,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2831,false,true,57069,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,1760–1849,1760,1849,Polychrome woodblock print; ink and color on paper,14 x 6 3/8 in. (35.6 x 16.2 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57069,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2903,false,true,56018,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,1781–1801,1781,1801,Polychrome woodblock print; ink and color on paper,11 3/4 x 5 1/4 in. (29.8 x 13.3 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56018,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2904,false,true,56019,Asian Art,Print,"二代中村野塩|The Actor Nakamura Noshio II, in Female Role, Holding a Shakuhachi (Bamboo Flute)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,"1796 (Kansei, 6th year)",1796,1796,Polychrome woodblock print; ink and color on paper,11 3/4 x 5 1/4 in. (29.8 x 13.3 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56019,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2912,false,true,49934,Asian Art,Woodblock print,"琉球八景　泉崎夜月|Evening Moon at Izumizaki (Izaumizaki yagetsu), from the series Eight Views of the Ryūkyū Islands (Ryūkyū hakkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1832,1822,1842,Polychrome woodblock print; ink and color on paper,9 7/8 x 14 5/8 in. (25.1 x 37.1 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/49934,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2913,false,true,49935,Asian Art,Woodblock print,"琉球八景　中島蕉園|Banana Garden at Nakashima (Nakashima shōen), from the series Eight Views of the Ryūkyū Islands (Ryūkyū hakkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1832,1822,1842,Polychrome woodblock print; ink and color on paper,9 7/8 x 14 5/8 in. (25.1 x 37.1 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/49935,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2914,false,true,49936,Asian Art,Woodblock print,"琉球八景　粂村竹籬|Bamboo Hedge at Kumemura (Kumemura chikuri), from the series Eight Views of the Ryūkyū Islands (Ryūkyū hakkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1832,1822,1842,Polychrome woodblock print; ink and color on paper,9 7/8 x 14 5/8 in. (25.1 x 37.1 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/49936,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2915,false,true,49937,Asian Art,Woodblock print,"琉球八景　城嶽霊泉|The Sacred Spring at Jōgaku (Jōgaku reisen), from the series Eight Views of the Ryūkyū Islands (Ryūkyū hakkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1832,1822,1842,Polychrome woodblock print; ink and color on paper,9 7/8 x 14 5/8 in. (25.1 x 37.1 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/49937,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2916,false,true,49938,Asian Art,Woodblock print,"琉球八景　臨海潮（湖）聲|Sound of the Lake at Rinkai (Rinkai kosei), from the series Eight Views of the Ryūkyū Islands (Ryūkyū hakkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1832,1822,1842,Polychrome woodblock print; ink and color on paper,Oban 9 3/4 x 14 1/2 in. (24.8 x 36.8 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/49938,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2917,false,true,49939,Asian Art,Woodblock print,"琉球八景　筍崖夕照|Evening Glow at Jungai (Jungai sekishō), from the series Eight Views of the Ryūkyū Islands (Ryūkyū hakkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1832,1822,1842,Polychrome woodblock print; ink and color on paper,9 7/8 x 14 1/2 in. (25.1 x 36.8 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/49939,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2918,false,true,49940,Asian Art,Woodblock print,"琉球八景　長虹秋霽|Autumn Sky at Chōkō (Chōkō shūsei), from the series Eight Views of the Ryūkyū Islands (Ryūkyū hakkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1832,1822,1842,Polychrome woodblock print; ink and color on paper,9 7/8 x 14 5/8 in. (25.1 x 37.1 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/49940,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2919,false,true,49941,Asian Art,Woodblock print,"琉球八景　龍洞松濤|Pines and Waves at Ryūtō (Ryūtō shōtō), from the series Eight Views of the Ryūkyū Islands (Ryūkyū hakkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1832,1822,1842,Polychrome woodblock print; ink and color on paper,9 7/8 x 14 5/8 in. (25.1 x 37.1 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/49941,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2920,false,true,55739,Asian Art,Print,"千絵の海　下総登戸|Noboto at Shimōsa (Shimōsa Noboto), from the series One Thousand Pictures of the Sea (Chie no umi)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,1832–33,1832,1833,Polychrome woodblock print; ink and color on paper,H. 7 1/4 in. (18.4 cm); W. 10 in. (25.4 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55739,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2921,false,true,44981,Asian Art,Woodblock print,"雪月花　隅田|Snow on the Sumida River (Sumida), from the series, Snow, Moon, and Flowers (Setsugekka)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1833,1823,1843,Polychrome woodblock print; ink and color on paper,9 3/4 x 14 1/2 in. (24.8 x 36.8 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/44981,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2922,false,true,56137,Asian Art,Woodblock print,"雪月花　淀川|Moonlight on the Yodo River (Yodogawa), from the series Snow, Moon, and Flowers (Setsugekka)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1833,1823,1843,Polychrome woodblock print; ink and color on paper,9 7/8 x 14 7/8 in. (25.1 x 37.8 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56137,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2923,false,true,56138,Asian Art,Woodblock print,"雪月花　吉野|Cherry Blossoms at Yoshino (Yoshino), from the series Snow, Moon, and Flowers (Setsugekka)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1833,1823,1843,Polychrome woodblock print; ink and color on paper,9 7/8 x 14 5/8 in. (25.1 x 37.1 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56138,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2924,false,true,56139,Asian Art,Woodblock print,"諸國瀧廻リ　下野黒髪山 きりふりの滝|Kirifuri Waterfall at Kurokami Mountain in Shimotsuke (Shimotsuke Kurokamiyama Kirifuri no taki), from the series A Tour of Waterfalls in Various Provinces (Shokoku taki meguri)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1832,1827,1837,Polychrome woodblock print; ink and color on paper,14 5/8 x 10 5/16 in. (37.1 x 26.2 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56139,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2925,false,true,56140,Asian Art,Woodblock print,"諸國瀧廻リ　木曾海道小野ノ瀑布|Ono Waterfall on the Kisokaidō (Kisokaidō Ono no bakufu), from the series A Tour of Waterfalls in Various Provinces (Shokoku taki meguri)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1833,1823,1843,Polychrome woodblock print; ink and color on paper,14 3/4 x 10 1/4 in. (37.5 x 26 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56140,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2926,false,true,56141,Asian Art,Woodblock print,"諸國瀧廻リ　東海道坂ノ下 清瀧くわんおん|Kiyotaki Kannon Waterfall at Sakanoshita on the Tōkaidō (Tōkaidō Sakanoshita Kiyotaki kannon), from the series A Tour of Waterfalls in Various Provinces (Shokoku taki meguri)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1832,1822,1842,Polychrome woodblock print; ink and color on paper,14 5/8 x 10 1/4 in. (37.1 x 26 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56141,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2927,false,true,56142,Asian Art,Woodblock print,"諸國瀧廻リ　和州吉野義経馬洗滝|The Waterfall Where Yoshitsune Washed His Horse at Yoshino in Yamato Province (Washū Yoshino Yoshitsune uma arai no taki), from the series A Tour of Waterfalls in Various Provinces (Shokoku taki meguri)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1832,1822,1842,Polychrome woodblock print; ink and color on paper,14 5/8 x 10 1/4 in. (37.1 x 26 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56142,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2928,false,true,56143,Asian Art,Woodblock print,"諸國瀧廻リ　木曽路ノ奥 阿彌陀ヶ瀧|The Amida Falls in the Far Reaches of the Kisokaidō Road (Kisoji no oku Amida-ga-taki), from the series A Tour of Waterfalls in Various Provinces (Shokoku taki meguri)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1832,1830,1833,Polychrome woodblock print; ink and color on paper,Image: 14 3/4 x 10 1/4 in. (37.5 x 26 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56143,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2930,false,true,56145,Asian Art,Woodblock print,"諸國瀧廻リ　相州大山ろうべんの瀧|Rōben Waterfall at Ōyama in Sagami Province (Sōshū Ōyama Rōben no taki), from the series A Tour of Waterfalls in Various Provinces (Shokoku taki meguri)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,1760–1849,1760,1849,Polychrome woodblock print; ink and color on paper,14 1/2 x 10 in. (36.8 x 25.4 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56145,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2931,false,true,56146,Asian Art,Woodblock print,"諸國瀧廻リ　美濃ノ国養老の滝|Yōrō Waterfall in Mino Province (Mino no Yōrō no taki), from the series A Tour of Waterfalls in Various Provinces (Shokoku taki meguri)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1832,1822,1842,Polychrome woodblock print; ink and color on paper,14 5/8 x 10 1/4 in. (37.1 x 26 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56146,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2932,false,true,56147,Asian Art,Woodblock print,"百人一首　宇波か縁説　参儀等|Poem by Sangi Hitoshi (Minamoto no Hitoshi), from the series One Hundred Poems Explained by the Nurse (Hyakunin isshu uba ga etoki)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,1760–1849,1760,1849,Polychrome woodblock print; ink and color on paper,9 7/8 x 14 1/4 in. (25.1 x 36.2 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56147,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2933,false,true,56148,Asian Art,Woodblock print,"百人一首　乳母かゑとき　柿の本人麿|Poem by Kakinomoto Hitomaro, from the series One Hundred Poems Explained by the Nurse (Hyakunin isshu uba ga etoki)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,1760–1849,1760,1849,Polychrome woodblock print; ink and color on paper,10 1/8 x 14 5/8 in. (25.7 x 37.1 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56148,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2934,false,true,56149,Asian Art,Woodblock print,"百人一首　乳母か縁説　中納言家持|Poem by Chūnagon Yakamochi (Ōtomo no Yakamochi), from the series One Hundred Poems Explained by the Nurse (Hyakunin isshu uba ga etoki)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,1760–1849,1760,1849,Polychrome woodblock print; ink and color on paper,9 3/4 x 14 3/8 in. (24.8 x 36.5 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56149,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2935,false,true,56150,Asian Art,Woodblock print,"百人一首　うはかゑとき　源宗于朝臣|Poem by Minamoto no Muneyuki Ason, from the series One Hundred poems Explained by the Nurse (Hyakunin isshu uba ga etoki)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1835,1825,1845,Polychrome woodblock print; ink and color on paper,9 5/8 x 14 3/8 in. (24.4 x 36.5 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56150,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2936,false,true,56151,Asian Art,Woodblock print,"百人一首　宇波か縁説　藤原道信朝臣|Poem by Fujiwara no Michinobu Ason, from the series One Hundred Poems Explained by the Nurse (Hyakunin isshu uba ga etoki)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,1760–1845,1760,1845,Polychrome woodblock print; ink and color on paper,10 3/8 x 14 7/8 in. (26.4 x 37.8 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56151,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2937,false,true,56157,Asian Art,Woodblock print,"百人一首　宇波か縁説　権中納言定家|Poem by Gon-Chūnagon Sadaie, from the series One Hundred Poems Explained by the Nurse (Hyakunin isshu uba ga etoki)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,1760–1849,1760,1849,Polychrome woodblock print; ink and color on paper,10 1/4 x 15 in. (26 x 38.1 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56157,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2938,false,true,56171,Asian Art,Woodblock print,"百人一首　うはかゑとき　文屋朝康|Poem by Funya no Asayasu, from the series One Hundred Poems Explained by a Nurse (Hyakunin isshu ubaga etoki)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,1760–1849,1760,1849,Polychrome woodblock print; ink and color on paper,9 3/4 x 14 3/8 in. (24.8 x 36.5 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56171,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2939,false,true,45261,Asian Art,Woodblock print,"百人一首　うはか縁説　清原深養父|Poem by Kiyohara no Fukayabu, from the series One Hundred Poems Explained by the Nurse (Hyakunin isshu uba ga etoki)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1835,1825,1845,Polychrome woodblock print; ink and color on paper,10 5/8 x 14 1/2 in. (27 x 36.8 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45261,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2940,false,true,56175,Asian Art,Woodblock print,"百人一首　宇波かゑとき　安部仲麿|Poem by Abe no Nakamaro, from the series One Hundred Poems Explained by the Nurse (Hyakunin isshu uba ga etoki)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,1760–1849,1760,1849,Polychrome woodblock print; ink and color on paper,10 3/8 x 14 7/8 in. (26.4 x 37.8 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56175,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2941,false,true,55734,Asian Art,Woodblock print,"百人一首　乳母か絵とき　参議篁|Poem by Sangi no Takamura (Ono no Takamura), from the series One Hundred Poems Explained by the Nurse (Hyakunin isshu uba ga etoki)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1835,1825,1845,Polychrome woodblock print; ink and color on paper,Oban 10 1/4 x 14 3/4 in. (26 x 37.5 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55734,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2942,false,true,53191,Asian Art,Woodblock print,"諸國名橋奇覧　足利行道山くものかけはし|The Hanging-cloud Bridge at Mount Gyōdō near Ashikaga (Ashikaga Gyōdōzan kumo no kakehashi), from the series Remarkable Views of Bridges in Various Provinces (Shokoku meikyō kiran)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,1760–1849,1760,1849,Polychrome woodblock print; ink and color on paper,H. 10 1/8 in. (25.7 cm); W. 15 1/8 in. (38.4 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/53191,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2943,false,true,53698,Asian Art,Woodblock print,"諸國名橋奇覧　かうつけ佐野ふなはしの古づ|Old View of the Boat-bridge at Sano in Kōzuke Province (Kōzuke Sano funabashi no kozu), from the series Remarkable Views of Bridges in Various Provinces (Shokoku meikyō kiran)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830,1820,1840,Polychrome woodblock print; ink and color on paper,10 1/8 x 15 1/2 in. (25.7 x 39.4 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/53698,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2944,false,true,53192,Asian Art,Woodblock print,"諸國名橋奇覧　飛越の堺つりはし|The Suspension Bridge on the Border of Hida and Etchū Provinces (Hietsu no sakai tsuribashi), from the series Remarkable Views of Bridges in Various Provinces (Shokoku meikyō kiran)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830,1820,1840,Polychrome woodblock print; ink and color on paper,H. 10 1/8 in. (25.7 cm); W. 15 1/8 in. (38.4 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/53192,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2945,false,true,53791,Asian Art,Woodblock print,"諸國名橋奇覧　東海道岡崎矢はぎのはし|Yahagi Bridge at Okazaki on the Tōkaidō (Tōkaidō Okazaki Yahagi no hashi), from the series Remarkable Views of Bridges in Various Provinces (Shokoku meikyō kiran)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830,1820,1840,Polychrome woodblock print; ink and color on paper,10 1/8 x 15 1/8 in. (25.7 x 38.4 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/53791,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2946,false,true,53790,Asian Art,Woodblock print,"諸國名橋奇覧　すほうの国きんたいはし|Kintai Bridge in Suō Province (Suō no kuni Kintaibashi), from the series Remarkable Views of Bridges in Various Provinces (Shokoku meikyō kiran)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830,1820,1840,Polychrome woodblock print; ink and color on paper,10 1/4 x 15 1/8 in. (26 x 38.4 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/53790,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2947,false,true,53792,Asian Art,Woodblock print,"諸國名橋奇覧　かめゐど天神たいこはし|The Arched Bridge at Kameido Tenjin Shrine (Kameido Tenjin Taikobashi), from the series Remarkable Views of Bridges in Various Provinces (Shokoku meikyō kiran)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830,1820,1840,Polychrome woodblock print; ink and color on paper,10 1/8 x 15 1/8 in. (25.7 x 38.4 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/53792,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2948,false,true,53786,Asian Art,Woodblock print,"諸國名橋奇覧　山城あらし山吐月橋　|Togetsu Bridge at Arashiyama in Yamashiro, from the series Remarkable Views of Bridges in Various Provinces (Shokoku meikyō kiran)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830,1820,1840,Polychrome woodblock print; ink and color on paper,10 3/8 x 15 1/4 in. (26.4 x 38.7 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/53786,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2949,false,true,53193,Asian Art,Woodblock print,"諸國名橋奇覧　摂洲天満橋|Tenman Bridge at Settsu Province (Sesshū Tenmanbashi), from the series Remarkable Views of Bridges in Various Provinces (Shokoku meikyō kiran)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,1760–1849,1760,1849,Polychrome woodblock print; ink and color on paper,H. 10 1/8 in. (25.7 cm); W. 15 1/8 in. (38.4 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/53193,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2950,false,true,56202,Asian Art,Woodblock print,"諸國名橋奇覧　摂洲阿治川口天保山|Tenpōzan at the Mouth of the Aji River in Settsu Province (Sesshū Ajikawaguchi Tenpōzan), from the series Remarkable Views of Bridges in Various Provinces (Shokoku meikyō kiran)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,1760–1849,1760,1849,Polychrome woodblock print; ink and color on paper,10 1/4 x 15 1/8 in. (26 x 38.4 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56202,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2951,false,true,53793,Asian Art,Woodblock print,"諸國名橋奇覧　三河の八ツ橋の古図|Ancient View of Yatsuhashi in Mikawa Province (Mikawa no Yatsuhashi no kozu), from the series Remarkable Views of Bridges in Various Provinces (Shokoku meikyō kiran)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830,1820,1840,Polychrome woodblock print; ink and color on paper,9 7/8 x 14 7/8 in. (25.1 x 37.8 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/53793,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2952,false,true,56210,Asian Art,Woodblock print,"諸國名橋奇覧 ゑちぜんふくゐの橋|Fukui Bridge in Echizen Province (Echizen Fukui no hashi), from the series Remarkable Views of Bridges in Various Provinces (Shokoku meikyō kiran)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830,1820,1840,Polychrome woodblock print; ink and color on paper,10 1/8 x 15 1/8 in. (25.7 x 38.4 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56210,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2953,false,true,55735,Asian Art,Woodblock print,"冨嶽三十六景　駿州江尻|Ejiri in Suruga Province (Sunshū Ejiri), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1830,1832,Polychrome woodblock print; ink and color on paper,Oban 9 7/8 x 14 3/4 in. (25.1 x 37.5 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55735,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2954,false,true,56212,Asian Art,Woodblock print,"冨嶽三十六景　駿州大野新田|The New Fields at Ōno in Suruga Province (Sunshū Ōno shinden), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1820,1842,Polychrome woodblock print; ink and color on paper,10 x 14 3/4 in. (25.4 x 37.5 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56212,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2955,false,true,56213,Asian Art,Woodblock print,"冨嶽三十六景　駿州片倉茶園の不二|Fuji from the Katakura Tea Fields in Suruga (Sunshū Katakura chaen no Fuji), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1820,1842,Polychrome woodblock print; ink and color on paper,9 5/8 x 14 3/4 in. (24.4 x 37.5 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56213,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2956,false,true,56214,Asian Art,Woodblock print,"冨嶽三十六景　尾州不二見原|Fujimigahara in Owari Province (Bishū Fujimigahara), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1830,1832,Polychrome woodblock print; ink and color on paper,10 1/16 x 14 7/8 in. (25.6 x 37.8 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56214,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2957,false,true,56215,Asian Art,Woodblock print,"冨嶽三十六景　礫川雪の旦|Morning after the Snow at Koishikawa in Edo (Koishikawa yuki no ashita), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1820,1842,Polychrome woodblock print; ink and color on paper,10 1/8 x 15 in. (25.7 x 38.1 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56215,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2958,false,true,56216,Asian Art,Woodblock print,"冨嶽三十六景　登戸浦|Noboto Bay (Noboto no ura), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1820,1842,Polychrome woodblock print; ink and color on paper,9 3/4 x 14 3/8 in. (24.8 x 36.5 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56216,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2959,false,true,56217,Asian Art,Woodblock print,"冨嶽三十六景　身延川裏不二|View from the Other Side of Fuji from the Minobu River (Minobugawa ura Fuji), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1820,1842,Polychrome woodblock print; ink and color on paper,10 x 14 5/8 in. (25.4 x 37.1 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56217,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2960,false,true,55736,Asian Art,Woodblock print,"冨嶽三十六景　凱風快晴|South Wind, Clear Sky (Gaifū kaisei), also known as Red Fuji, from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1830,1832,Polychrome woodblock print; ink and color on paper,Oban 10 x 14 7/8 in. (25.4 x 37.8 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55736,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2961,false,true,56229,Asian Art,Print,"冨嶽三十六景　山下白雨|Storm below Mount Fuji (Sanka no haku u), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1830,1832,Polychrome woodblock print; ink and color on paper,10 x 14 3/4 in. (25.4 x 37.5 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56229,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2962,false,true,56235,Asian Art,Woodblock print,"冨嶽三十六景　諸人登山|Groups of Mountain Climbers (Shojin tozan), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1830,1832,Polychrome woodblock print; ink and color on paper,9 1/2 x 14 5/8 in. (24.1 x 37.1 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56235,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2963,false,true,56238,Asian Art,Woodblock print,"冨嶽三十六景　上総の海路|At Sea off Kazusa (Kazusa no kairo), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1820,1842,Polychrome woodblock print; ink and color on paper,9 1/2 x 14 7/8 in. (24.1 x 37.8 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56238,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2964,false,true,56239,Asian Art,Woodblock print,"冨嶽三十六景　常州牛掘|Ushibori in Hitachi Province (Jōshū Ushibori), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1820,1842,Polychrome woodblock print; ink and color on paper,10 1/4 x 14 7/8 in. (26 x 37.8 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56239,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2965,false,true,56240,Asian Art,Woodblock print,"冨嶽三十六景　信州諏訪湖|Lake Suwa in Shinano Province (Shinshū Suwako), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1820,1842,Polychrome woodblock print; ink and color on paper,10 1/4 x 15 1/8 in. (26 x 38.4 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56240,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2966,false,true,56241,Asian Art,Woodblock print,"冨嶽三十六景　遠江山中|In the Mountains of Tōtomi Province (Tōtomi sanchū), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1820,1842,Polychrome woodblock print; ink and color on paper,10 3/8 x 15 1/4 in. (26.4 x 38.7 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56241,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2967,false,true,56242,Asian Art,Woodblock print,"冨嶽三十六景　隠田の水車|The Waterwheel at Onden (Onden no suisha), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1820,1842,Polychrome woodblock print; ink and color on paper,10 1/4 x 15 1/4 in. (26 x 38.7 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56242,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2968,false,true,45030,Asian Art,Woodblock print,"冨嶽三十六景　甲州犬目峠|The Inume Pass in Kai Province (Kōshū Inume tōge), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1831–32,1820,1842,Polychrome woodblock print; ink and color on paper,9 7/8 x 14 7/8 in. (25.1 x 37.8 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45030,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2969,false,true,54868,Asian Art,Woodblock print,"冨嶽三十六景　甲州三坂水面|Reflection in Lake at Misaka in Kai Province (Kōshū Misaka suimen), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1820,1842,Polychrome woodblock print; ink and color on paper,9 5/8 x 14 3/4 in. (24.4 x 37.5 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54868,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2970,false,true,56346,Asian Art,Woodblock print,"冨嶽三十六景　甲州三島越|Mishima Pass in Kai Province (Kōshū Mishima goe), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1830,1830,Polychrome woodblock print; ink and color on paper,9 5/8 x 14 7/8 in. (24.4 x 37.8 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56346,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2971,false,true,56349,Asian Art,Woodblock print,"冨嶽三十六景　甲州伊沢暁|Dawn at Isawa in Kai Province (Kōshū Isawa no akatsuki), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1820,1842,Polychrome woodblock print; ink and color on paper,9 7/8 x 14 5/8 in. (25.1 x 37.1 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56349,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2972,false,true,56353,Asian Art,Woodblock print,"冨嶽三十六景　神奈川沖浪裏|Under the Wave off Kanagawa (Kanagawa oki nami ura), also known as The Great Wave, from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1820,1842,Polychrome woodblock print; ink and color on paper,9 7/8 x 14 7/8 in. (25.1 x 37.8 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56353,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2973,false,true,56357,Asian Art,Woodblock print,"冨嶽三十六景　東海道保土ケ谷|Hodogaya on the Tōkaidō (Tōkaidō Hodogaya), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1820,1842,Polychrome woodblock print; ink and color on paper,9 5/8 x 14 7/8 in. (24.4 x 37.8 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56357,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2974,false,true,56360,Asian Art,Woodblock print,"冨嶽三十六景　東海道吉田|Yoshida on the Tōkaidō (Tōkaidō Yoshida), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1820,1842,Polychrome woodblock print; ink and color on paper,10 3/8 x 15 1/8 in. (26.4 x 38.4 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56360,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2975,false,true,56365,Asian Art,Woodblock print,"冨嶽三十六景　東海道金谷の不二|Fuji Seen from Kanaya on the Tōkaidō (Tōkaidō Kanaya no Fuji), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1830,1832,Polychrome woodblock print; ink and color on paper,10 1/4 x 15 1/4 in. (26 x 38.7 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56365,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2976,false,true,56373,Asian Art,Woodblock print,"冨嶽三十六景　東海道江尻田子の浦略図|Tago Bay near Ejiri on the Tōkaidō (Tōkaidō Ejiri Tago no ura ryaku zu), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1830,1832,Polychrome woodblock print; ink and color on paper,9 3/4 x 14 3/4 in. (24.8 x 37.5 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56373,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2977,false,true,56376,Asian Art,Woodblock print,"冨嶽三十六景　相州江の島|Enoshima in Sagami Province (Sōshū Enoshima), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1820,1842,Polychrome woodblock print; ink and color on paper,9 7/8 x 14 3/4 in. (25.1 x 37.5 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56376,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2978,false,true,56384,Asian Art,Woodblock print,"冨嶽三十六景　相州仲原|Nakahara in Sagami Province (Sōshū Nakahara), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1830,1832,Polychrome woodblock print; ink and color on paper,10 x 15 in. (25.4 x 38.1 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56384,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2979,false,true,55737,Asian Art,Woodblock print,"冨嶽三十六景　相州七里浜|Shichirigahama in Sagami Province (Sōshū Shichirigahama), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1830,1832,Polychrome woodblock print; ink and color on paper,Oban 10 x 14 3/4 in. (25.4 x 37.5 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55737,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2980,false,true,56385,Asian Art,Woodblock print,"冨嶽三十六景　相州箱根湖水|The Lake at Hakone in Sagami Province (Sōshū Hakone kosui), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1820,1842,Polychrome woodblock print; ink and color on paper,9 3/4 x 14 7/8 in. (24.8 x 37.8 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56385,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2981,false,true,55738,Asian Art,Woodblock print,"冨嶽三十六景　相州梅沢左|Umezawa Manor in Sagami Province (Sōshū Umezawa zai), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1830,1832,Polychrome woodblock print; ink and color on paper,Oban 10 1/8 x 15 in. (25.7 x 38.1 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55738,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2982,false,true,56386,Asian Art,Woodblock print,"冨嶽三十六景　本所立川|Tatekawa in Honjō (Honjō Tatekawa), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1830,1832,Polychrome woodblock print; ink and color on paper,10 1/16 x 15 in. (25.6 x 38.1 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56386,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2983,false,true,56387,Asian Art,Woodblock print,"冨嶽三十六景　深川万年橋下|Under the Mannen Bridge at Fukagawa (Fukagawa Mannenbashi shita), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1830,1832,Polychrome woodblock print; ink and color on paper,10 x 14 3/8 in. (25.4 x 36.5 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56387,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2984,false,true,55740,Asian Art,Woodblock print,"冨嶽三十六景　五百らかん寺さざゐどう|Sazai Hall at the Temple of the Five Hundred Arhats (Gohyaku Rakanji Sazaidō), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1830,1832,Polychrome woodblock print; ink and color on paper,Oban 10 1/4 x 15 1/4 in. (26 x 38.7 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55740,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2985,false,true,56388,Asian Art,Woodblock print,"冨嶽三十六景　青山円座松|Cushion Pine at Aoyama (Aoyama enza no matsu), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1820,1842,Polychrome woodblock print; ink and color on paper,9 5/8 x 14 3/4 in. (24.4 x 37.5 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56388,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2986,false,true,39800,Asian Art,Woodblock print,"冨嶽三十六景　甲州石班沢|Kajikazawa in Kai Province (Kōshū Kajikazawa), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1820,1842,Polychrome woodblock print; ink and color on paper,10 1/4 x 15 1/8 in. (26 x 38.4 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/39800,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2987,false,true,56389,Asian Art,Woodblock print,"冨嶽三十六景　下目黒|Lower Meguro (Shimo Meguro), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1820,1842,Polychrome woodblock print; ink and color on paper,10 1/4 x 15 1/4 in. (26 x 38.7 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56389,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2988,false,true,56390,Asian Art,Woodblock print,"冨嶽三十六景　武州千住|Senju in Musashi Province (Bushū Senju), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1820,1842,Polychrome woodblock print; ink and color on paper,9 5/8 x 15 in. (24.4 x 38.1 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56390,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2989,false,true,56391,Asian Art,Woodblock print,"冨嶽三十六景　従千住花街眺望の不二|Fuji Seen in the Distance from Senju Pleasure Quarter (Senju kagai yori chōbō no Fuji), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1820,1842,Polychrome woodblock print; ink and color on paper,10 1/4 x 15 1/4 in. (26 x 38.7 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56391,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2990,false,true,55741,Asian Art,Woodblock print,"冨嶽三十六景　武陽佃島|Tsukudajima in Musashi Province (Buyō Tsukudajima), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1830,1832,Polychrome woodblock print; ink and color on paper,Oban 10 1/8 x 15 in. (25.7 x 38.1 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55741,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2991,false,true,56392,Asian Art,Woodblock print,"冨嶽三十六景　武州玉川|Tama River in Musashi Province (Bushū Tamagawa), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1820,1842,Polychrome woodblock print; ink and color on paper,9 7/8 x 14 7/8 in. (25.1 x 37.8 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56392,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2992,false,true,56393,Asian Art,Woodblock print,"冨嶽三十六景　東海道品川御殿山の不二|Fuji from Gotenyama at Shinagawa on the Tōkaidō (Tōkaidō Shinagawa Gotenyama no Fuji), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1820,1842,Polychrome woodblock print; ink and color on paper,9 7/8 x 14 7/8 in. (25.1 x 37.8 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56393,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2993,false,true,56394,Asian Art,Woodblock print,"冨嶽三十六景　江戸日本橋|Nihonbashi in Edo (Edo Nihonbashi), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1820,1842,Polychrome woodblock print; ink and color on paper,9 7/8 x 14 3/4 in. (25.1 x 37.5 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56394,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2994,false,true,53692,Asian Art,Woodblock print,"冨嶽三十六景　江都駿河町三井見世略図|Mitsui Shop at Surugachō in Edo (Edo Surugachō Mitsui mise ryaku zu), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1820,1842,Polychrome woodblock print; ink and color on paper,10 1/8 x 14 7/8 in. (25.7 x 37.8 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/53692,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2995,false,true,56395,Asian Art,Woodblock print,"冨嶽三十六景　東都駿台|Surugadai in Edo (Tōto Sundai), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1820,1842,Polychrome woodblock print; ink and color on paper,9 3/4 x 14 3/4 in. (24.8 x 37.5 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56395,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2996,false,true,56396,Asian Art,Woodblock print,"冨嶽三十六景　東都浅草本願寺|Honganji at Asakusa in Edo (Tōto Asakusa Honganji), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1820,1842,Polychrome woodblock print; ink and color on paper,10 x 15 in. (25.4 x 38.1 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56396,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2997,false,true,37362,Asian Art,Woodblock print,"冨嶽三十六景　御厩川岸より両国橋夕陽見|Viewing the Sunset over Ryōgoku Bridge from the Onmaya Embankment (Onmayagashi yori Ryōgokubashi sekiyō o miru), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–31,1820,1842,Polychrome woodblock print; ink and color on paper,9 5/8 x 14 3/4 in. (24.4 x 37.5 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37362,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2998,false,true,55742,Asian Art,Woodblock print,"冨嶽三十六景　隅田川関屋の里|Sekiya Village on the Sumida River (Sumidagawa Sekiya no sato), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1830,1832,Polychrome woodblock print; ink and color on paper,Oban 10 1/4 x 15 1/8 in. (26 x 38.4 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55742,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3001,false,true,54217,Asian Art,Print,衣食住|Attire,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,1760–1849,1760,1849,Polychrome woodblock print (surimono); ink and color on paper,5 5/8 x 7 1/2 in. (14.3 x 19.1 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54217,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3003,false,true,54219,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,1760–1849,1760,1849,Polychrome woodblock print (surimono); ink and color on paper,5 1/8 x 6 13/16 in. (13 x 17.3 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54219,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3138,false,true,56686,Asian Art,Print,"冨嶽三十六景　武州玉川|Fuji—The Tama River, Musashi Province, from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1830–32,1820,1842,Polychrome woodblock print; ink and color on paper,10 1/8 x 14 3/4 in. (25.7 x 37.5 cm),"Gift of Francis M. Weld, 1948",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56686,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3516,false,true,44900,Asian Art,Print,詩歌写真鏡　李白|Ri Haku from the series Mirrors of Japanese and Chinese Poems (Shiika shashin kyō),Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1832,1822,1842,Polychrome woodblock print; ink and color on paper,20 3/8 x 9 in. (51.8 x 22.9 cm),"The Harry G. C. Packard Collection of Asian Art, Gift of Harry G. C. Packard, and Purchase, Fletcher, Rogers, Harris Brisbane Dick, and Louis V. Bell Funds, Joseph Pulitzer Bequest, and The Annenberg Fund Inc. Gift, 1975",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/44900,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP29a–e,false,true,36483,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1804,1794,1814,Pentaptych of polychrome woodblock prints; ink and color on paper,"Oban, pentaptych: 14 15/32 x 48 1/2 in. (36.8 x 123.2 cm)","Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36483,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP30,false,true,36510,Asian Art,Print,東都名所 御殿山之夕桜|Evening Cherries on Gotem Yama,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1830,1830,1830,Polychrome woodblock print; ink and color on paper,9 x 13 15/16 in. (22.9 x 35.4 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36510,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP31,false,true,36511,Asian Art,Print,金沢八景 乙艫帰帆|Otomo Kihan,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1836,1826,1846,Polychrome woodblock print; ink and color on paper,9 1/4 x 14 15/32 in. (23.5 x 36.8 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36511,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP32,false,true,36512,Asian Art,Print,江戸近郊八景之内 羽根田落雁|Haneda Rakugan,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1835,1825,1845,Polychrome woodblock print; ink and color on paper,9 15/32 x 13 27/32 in. (24.1 x 35.2 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36512,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP33,false,true,36513,Asian Art,Print,木曽海道六拾九次之内 大井|Ōi Station,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1835,1825,1845,Polychrome woodblock print; ink and color on paper,9 1/4 x 14 15/32 in. (23.5 x 36.8 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36513,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP35,false,true,36515,Asian Art,Print,"京都名所之内 あらし山満花|Cherry Blossoms at Arashiyama, from the series Famous Places of Kyōto",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1834,1824,1844,Polychrome woodblock print; ink and color on paper,9 3/8 x 14 15/32 in. (23.8 x 36.8 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36515,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP36,false,true,36516,Asian Art,Print,京都名所之内 八瀬之里|Yase no Sato,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1834,1824,1844,Polychrome woodblock print; ink and color on paper,10 7/32 x 15 1/8 in. (26.0 x 38.4 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36516,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP37,false,true,36517,Asian Art,Print,京都名所之内 淀川|Yodogawa,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1834,1824,1844,Polychrome woodblock print; ink and color on paper,9 29/32 x 14 7/8 in. (25.2 x 37.8 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36517,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP38,false,true,36518,Asian Art,Print,東海道五十三次 見附 天竜川|Mitsukei Tenryugawa,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1834,1824,1844,Polychrome woodblock print; ink and color on paper,9 3/4 x 14 29/32 in. (24.8 x 37.9 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36518,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP40,false,true,36520,Asian Art,Print,東海道五十三次 三島 朝霧|Morning Mist at Mishima,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1833,1823,1843,Polychrome woodblock print; ink and color on paper,H. 9 1/2 in. (24.1 cm); W. 14 7/8 in. (37.8 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36520,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP41,false,true,36521,Asian Art,Print,"東海道五十三次・庄野 白雨|Sudden Shower at Shōno, from the series Fifty-three Stations of the Tōkaidō",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1834–35,1834,1835,Polychrome woodblock print; ink and color on paper,Image: 9 3/4 x 14 1/4 in. (24.8 x 36.2 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36521,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP42,false,true,36522,Asian Art,Print,木曽海道六拾九次之内 和田|Wada Station,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1835,1825,1845,Polychrome woodblock print; ink and color on paper,9 15/32 x 14 1/4 in. (24.1 x 36.2 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36522,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP43,false,true,36523,Asian Art,Print,木曽海道六拾九次之内 下諏訪|Shimono Suwa Station,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1835,1825,1845,Polychrome woodblock print; ink and color on paper,9 9/16 x 14 3/8 in. (24.3 x 36.5 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36523,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP44,false,true,36524,Asian Art,Print,吾妻の森|View of Azuma Wood,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1858,1848,1868,Polychrome woodblock print (surimono); ink and color on paper,20 3/8 x 7 7/32 in. (51.8 x 18.4 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36524,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP45,false,true,45321,Asian Art,Print,Asakusa Kinryuzan shita Azumabashi uchu nozomi|東都名所 浅草金龍山下東橋雨中望|View of the Asakusa Kinryuzan Temple from the Azuma Bridge in the Rain,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1840,1830,1850,"Polychrome woodblock print; ink and color on paper, tanzaku format",14 9/16 x 5 in. (37.0 x 12.7 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45321,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP46,false,true,36525,Asian Art,Print,東都名所 佃島海辺朧月|Tsukudajima Kaihin Rōgetsu,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1836,1826,1846,Polychrome woodblock print; ink and color on paper,13 15/16 x 4 3/4 in. (35.4 x 12.1 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36525,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP47,false,true,36526,Asian Art,Print,東都名所 新吉原衣紋阪秋月|Shin Yoshiwara Emonzaka Aki no Tsuki,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1836,1826,1846,Polychrome woodblock print; ink and color on paper,14 3/4 x 4 7/8 in. (37.5 x 12.4 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36526,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP48,false,true,36527,Asian Art,Print,近江八景之内 石山秋月|The Autumn Moon at Ishiyama on Lake Biwa,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1835,1825,1845,Polychrome woodblock print; ink and color on paper,9 7/8 x 14 15/32 in. (25.1 x 36.8 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36527,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP49,false,true,36528,Asian Art,Print,"近江八景之内 瀬田夕照|Seta no Sekisho. Sunset, Seta. Lake Biwa",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1835,1825,1845,Polychrome woodblock print; ink and color on paper,8 23/32 x 13 5/8 in. (22.2 x 34.6 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36528,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP50,false,true,36529,Asian Art,Print,"近江八景之内 粟津晴嵐|Clearing Weather at Awazu, Lake Biwa",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1835,1825,1845,Polychrome woodblock print; ink and color on paper,8 23/32 x 13 3/8 in. (22.2 x 34.0 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36529,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP51,false,true,36530,Asian Art,Print,"近江八景之内 堅田落雁|Geese Alighting at Katada, Lake Biwa",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1835,1825,1845,Polychrome woodblock print; ink and color on paper,8 13/16 x 13 5/8 in. (22.4 x 34.6 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36530,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP52,false,true,36531,Asian Art,Print,"近江八景之内 唐崎夜雨|Night Rain at Karasaki, from the series Eight Views of Ō-mi",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1835,1825,1845,Polychrome woodblock print; ink and color on paper,Image: 8 3/4 × 13 5/8 in. (22.2 × 34.6 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36531,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP53,false,true,36532,Asian Art,Woodblock print,"近江八景之内 三井晩鐘|Vesper Bell at Mii Temple, Lake Biwa",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1835,1825,1845,Polychrome woodblock print; ink and color on paper,8 23/32 x 13 1/2 in. (22.2 x 34.3 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36532,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP54,false,true,36533,Asian Art,Print,"近江八景之内 比良暮雪|Evening Snow on Hira, Lake Biwa",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1835,1825,1845,Polychrome woodblock print; ink and color on paper,8 27/32 x 13 23/32 in. (22.5 x 34.9 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36533,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP55,false,true,36534,Asian Art,Woodblock print,"近江八景之内 矢橋帰帆|Sailing Boats Returning to Yabase, Lake Biwa",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1835,1825,1845,Polychrome woodblock print; ink and color on paper,9 9/16 x 14 in. (24.3 x 35.6 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36534,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP56,false,true,36535,Asian Art,Print,江戸名所 御茶之水|Ochanomizu,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,"dated 11th month, Ox year, 1853",11,1853,Polychrome woodblock print; ink and color on paper,Oban 9 1/8 x 14 in. (23.2 x 35.6 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36535,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP57,false,true,36536,Asian Art,Print,"富士三十六景 東都両ごく|Toto, Ryogoku, from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,"4th month, Horse year 1858",1858,1858,Polychrome woodblock print; ink and color on paper,14 15/32 x 9 15/32 in. (36.8 x 24.1 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36536,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP58,false,true,36537,Asian Art,Print,"富士三十六景 武蔵越かや在|View of Mount Fuji from Koshigaya, Province of Musashi (Musashi, Koshigaya Zai), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,"4th month, Horse year 1858",1858,1858,Polychrome woodblock print; ink and color on paper,13 27/32 x 9 1/4 in. (35.2 x 23.5 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36537,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP59,false,true,36538,Asian Art,Print,"富士三十六景 相模七里ケ浜|View of Mount Fuji from Seven-ri Beach, Province of Sagami (Sōshū: Shichi-ri ga hama), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,"dated 4th month, Horse year 1858",1858,1858,Polychrome woodblock print; ink and color on paper,13 27/32 x 9 1/4 in. (35.2 x 23.5 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36538,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP60,false,true,36539,Asian Art,Print,"名所江戸百景 浅草田圃酉の町詣|Revelers Returned from the Tori no Machi Festival at Asakusa, from the series One Hundred Famous Views of Edo",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1857,1857,1857,Polychrome woodblock print; ink and color on paper,13 1/16 x 8 11/16 in. (33.2 x 22.1 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36539,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP61,false,true,36540,Asian Art,Print,"Ukeji, Akiba no Keidai|名所江戸百景 請地秋葉の境内|Inside the Akiba Shrine at Ukeji",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,"8th month, Snake year 1857",1857,1857,Polychrome woodblock print; ink and color on paper,13 1/4 x 8 23/32 in. (33.7 x 22.2 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36540,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP62,false,true,36541,Asian Art,Print,"名所江戸百景 市中繁栄七夕祭|The Tanabata Festival, from the series One Hundred Famous Views of Edo",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1857,1857,1857,Polychrome woodblock print; ink and color on paper,14 x 9 3/5 in. (35.6 x 24.4 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36541,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP63,false,true,36542,Asian Art,Woodblock print,"「名所江戸百景　蓑輪　金杉　三河しま」|“Minowa, Kanasugi at Mikawashima,” from the series One Hundred Famous Views of Edo (Meisho Edo hyakkei, Minowa Kanasugi, Mikawashima)",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,snake year 1857,1857,1857,Polychrome woodblock print; ink and color on paper,13 1/4 x 8 17/32 in. (33.7 x 21.7 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36542,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP64,false,true,36543,Asian Art,Print,"名所江戸百景 真崎辺より水神の森内川関屋の里を見る|The Suijin Temple Grove, Uchikawa, and the Village of Sekiya",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,8th month 1857,1857,1857,Polychrome woodblock print; ink and color on paper,H. 13 1/8 in. (33.3 cm); W. 8 11/16 in. (22.1 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36543,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP65,false,true,36544,Asian Art,Print,名所江戸百景 廓中東雲|Kakuchu Shinonome,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,"4th month, Snake year 1857",1857,1857,Polychrome woodblock print; ink and color on paper,14 3/5 x 9 7/8 in. (37.1 x 25.1 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36544,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP66,false,true,36545,Asian Art,Woodblock print,Kisojo no San Sen|木曽路之山川|The Kiso Mountains in Snow,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,"dated 8th month of the Snake year, 1857",1857,1857,Triptych of woodblock prints; ink and color on paper,Each H. 14 1/4 in. (36.2 cm); W. 9 3/4 in. (24.8 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36545,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP67,false,true,36546,Asian Art,Print,東海道五十三次 品川|Shinagawa Station,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1838,1828,1848,Polychrome woodblock print; ink and color on paper,8 1/2 x 6 1/2 in. (21.6 x 16.5 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36546,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP68,false,true,36547,Asian Art,Print,東海道五十三次 川崎|Kawasaki,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1838,1828,1848,Polychrome woodblock print; ink and color on paper,8 1/2 x 6 1/2 in. (21.6 x 16.5 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36547,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP69,false,true,36548,Asian Art,Print,東海道五十三次 神奈川|Kanagawa,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1838,1828,1848,Polychrome woodblock print; ink and color on paper,8 1/2 x 6 1/2 in. (21.6 x 16.5 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36548,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP70,false,true,36549,Asian Art,Print,東海道五十三次 保土ヶ谷|Hodogaya,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1838,1828,1848,Polychrome woodblock print; ink and color on paper,8 1/2 x 6 1/2 in. (21.6 x 16.5 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36549,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP71,false,true,36550,Asian Art,Print,東海道五十三次 戸塚|Totsuka,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1838,1828,1848,Polychrome woodblock print; ink and color on paper,8 1/2 x 6 1/2 in. (21.6 x 16.5 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36550,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP72,false,true,36551,Asian Art,Print,東海道五十三次 藤沢|Fujisawa,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1838,1828,1848,Polychrome woodblock print; ink and color on paper,8 1/2 x 6 1/2 in. (21.6 x 16.5 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36551,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP73,false,true,36552,Asian Art,Print,東海道五十三次　平塚|Hiratsuka,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1838,1828,1848,Polychrome woodblock print; ink and color on paper,8 1/2 x 6 1/2 in. (21.6 x 16.5 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36552,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP74,false,true,36553,Asian Art,Print,東海道五十三次　大磯|Ōiso,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1838,1828,1848,Polychrome woodblock print; ink and color on paper,8 1/2 x 6 1/2 in. (21.6 x 16.5 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36553,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP75,false,true,36554,Asian Art,Print,東海道五十三次　小田原|Odawara,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1838,1828,1848,Polychrome woodblock print; ink and color on paper,8 1/2 x 6 1/2 in. (21.6 x 16.5 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36554,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP76,false,true,36555,Asian Art,Print,東海道五十三次　箱根|Hakone,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1838,1828,1848,Polychrome woodblock print; ink and color on paper,8 1/2 x 6 1/2 in. (21.6 x 16.5 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36555,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP77,false,true,36556,Asian Art,Print,"東海道五十三次　三島|Mishima, from the series Fifty-three Stations of the Tōkaidō Road (Tōkaidō gojūsan tsugi, Mishima), also known as the Kyōka (Witty Verse) Tōkaidō",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1838,1828,1848,Polychrome woodblock print; ink and color on paper,8 1/2 x 6 1/2 in. (21.6 x 16.5 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36556,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP78,false,true,36557,Asian Art,Print,東海道五十三次　沼津|Numazu,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1838,1828,1848,Polychrome woodblock print; ink and color on paper,8 1/2 x 6 1/2 in. (21.6 x 16.5 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36557,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP79,false,true,36558,Asian Art,Print,東海道五十三次　原|Hara,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1838,1828,1848,Polychrome woodblock print; ink and color on paper,8 1/2 x 6 1/2 in. (21.6 x 16.5 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36558,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP80,false,true,36559,Asian Art,Woodblock print,東海道五十三次　蒲原|Kambara,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1838,1828,1848,Polychrome woodblock print; ink and color on paper,8 1/2 x 6 1/2 in. (21.6 x 16.5 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36559,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP81,false,true,36560,Asian Art,Print,東海道五十三次　興津|Okitsu,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1838,1828,1848,Polychrome woodblock print; ink and color on paper,8 1/2 x 6 1/2 in. (21.6 x 16.5 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36560,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP82,false,true,36561,Asian Art,Print,東海道五十三次　江尻|Ejiri,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1838,1828,1848,Polychrome woodblock print; ink and color on paper,8 1/2 x 6 1/2 in. (21.6 x 16.5 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36561,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP83,false,true,36562,Asian Art,Print,東海道五十三次　府中|Fuchu,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1838,1828,1848,Polychrome woodblock print; ink and color on paper,8 1/2 x 6 1/2 in. (21.6 x 16.5 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36562,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP84,false,true,36563,Asian Art,Print,東海道五十三次　鞠子|Mariko,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1838,1828,1848,Polychrome woodblock print; ink and color on paper,8 1/2 x 6 1/2 in. (21.6 x 16.5 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36563,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP85,false,true,36564,Asian Art,Print,東海道五十三次　岡部|Okabe,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1838,1828,1848,Polychrome woodblock print; ink and color on paper,8 1/2 x 6 1/2 in. (21.6 x 16.5 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36564,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP86,false,true,36565,Asian Art,Print,東海道五十三次　藤枝|Fujieda,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1838,1828,1848,Polychrome woodblock print; ink and color on paper,8 1/2 x 6 1/2 in. (21.6 x 16.5 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36565,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP87,false,true,36566,Asian Art,Print,東海道五十三次　金谷|Kanaya,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1838,1828,1848,Polychrome woodblock print; ink and color on paper,8 1/2 x 6 1/2 in. (21.6 x 16.5 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36566,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP88,false,true,36567,Asian Art,Print,東海道五十三次　袋井|Fukuroi,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1838,1828,1848,Polychrome woodblock print; ink and color on paper,8 1/2 x 6 1/2 in. (21.6 x 16.5 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36567,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP89,false,true,36568,Asian Art,Print,東海道五十三次　見附|Mitsuki,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1838,1828,1848,Polychrome woodblock print; ink and color on paper,8 1/2 x 6 1/2 in. (21.6 x 16.5 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36568,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP90,false,true,36569,Asian Art,Print,東海道五十三次　舞阪|Maizaka Station,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1838,1828,1848,Polychrome woodblock print; ink and color on paper,8 1/2 x 6 1/2 in. (21.6 x 16.5 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36569,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP91,false,true,36570,Asian Art,Print,東海道五十三次　荒井|Arai,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1838,1828,1848,Polychrome woodblock print; ink and color on paper,8 1/2 x 6 1/2 in. (21.6 x 16.5 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36570,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP92,false,true,36571,Asian Art,Print,東海道五十三次　白須賀|Shirasuka,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1838,1828,1848,Polychrome woodblock print; ink and color on paper,8 1/2 x 6 1/2 in. (21.6 x 16.5 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36571,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP93,false,true,36572,Asian Art,Print,東海道五十三次　赤坂|Akasaka,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1838,1828,1848,Polychrome woodblock print; ink and color on paper,8 1/2 x 6 1/2 in. (21.6 x 16.5 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36572,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP94,false,true,36573,Asian Art,Print,東海道五十三次　岡崎|Okazaki,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1838,1828,1848,Polychrome woodblock print; ink and color on paper,8 1/2 x 6 1/2 in. (21.6 x 16.5 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36573,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP95,false,true,36574,Asian Art,Print,東海道五十三次　池鯉鮒|Chiryu,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1838,1828,1848,Polychrome woodblock print; ink and color on paper,8 1/2 x 6 1/2 in. (21.6 x 16.5 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36574,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP96,false,true,36575,Asian Art,Print,東海道五十三次　鳴海|Narumi,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1838,1828,1848,Polychrome woodblock print; ink and color on paper,8 1/2 x 6 1/2 in. (21.6 x 16.5 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36575,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP97,false,true,36576,Asian Art,Print,東海道五十三次　宮|Miya,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1838,1828,1848,Polychrome woodblock print; ink and color on paper,8 1/2 x 6 1/2 in. (21.6 x 16.5 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36576,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP98,false,true,36577,Asian Art,Print,東海道五十三次　桑名|Kuwana,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1838,1828,1848,Polychrome woodblock print; ink and color on paper,8 1/2 x 6 1/2 in. (21.6 x 16.5 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36577,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP99,false,true,36578,Asian Art,Print,東海道五十三次　四日市|Yokkaichi,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1838,1828,1848,Polychrome woodblock print; ink and color on paper,8 1/2 x 6 1/2 in. (21.6 x 16.5 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36578,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP100,false,true,36579,Asian Art,Print,東海道五十三次　石薬師|Ishiyakushi,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1838,1828,1848,Polychrome woodblock print; ink and color on paper,8 1/2 x 6 1/2 in. (21.6 x 16.5 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36579,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP101,false,true,36580,Asian Art,Print,東海道五十三次　庄野|Shono,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1838,1828,1848,Polychrome woodblock print; ink and color on paper,8 1/2 x 6 1/2 in. (21.6 x 16.5 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36580,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP102,false,true,36581,Asian Art,Print,東海道五十三次　亀山|Kameyama,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1838,1828,1848,Polychrome woodblock print; ink and color on paper,8 1/2 x 6 1/2 in. (21.6 x 16.5 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36581,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP103,false,true,36582,Asian Art,Print,東海道五十三次　関|Seki,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1838,1828,1848,Polychrome woodblock print; ink and color on paper,8 1/2 x 6 1/2 in. (21.6 x 16.5 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36582,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP104,false,true,36583,Asian Art,Print,東海道五十三次　坂下|Saka-no-shita,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1838,1828,1848,Polychrome woodblock print; ink and color on paper,8 1/2 x 6 1/2 in. (21.6 x 16.5 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36583,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP105,false,true,36584,Asian Art,Print,東海道五十三次　土山 鈴鹿山の図|Tsuchiyama: Suzuka-yama no zu.,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1838,1828,1848,Polychrome woodblock print; ink and color on paper,8 1/2 x 6 1/2 in. (21.6 x 16.5 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36584,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP106,false,true,36585,Asian Art,Print,東海道五十三次　水口|Mizukuchi,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1838,1828,1848,Polychrome woodblock print; ink and color on paper,8 1/2 x 6 1/2 in. (21.6 x 16.5 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36585,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP107,false,true,36586,Asian Art,Print,東海道五十三次　草津|Kusatsu,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1838,1828,1848,Polychrome woodblock print; ink and color on paper,8 1/2 x 6 1/2 in. (21.6 x 16.5 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36586,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP108,false,true,36587,Asian Art,Print,東海道五十三次　大津|Otsu,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1838,1828,1848,Polychrome woodblock print; ink and color on paper,8 1/2 x 6 1/2 in. (21.6 x 16.5 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36587,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP109,false,true,36588,Asian Art,Print,東海道五十三次　京　三条大橋図|Kyoto: Sanju Ohashi no zu.,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1838,1828,1848,Polychrome woodblock print; ink and color on paper,8 1/2 x 6 1/2 in. (21.6 x 16.5 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36588,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP110,false,true,36589,Asian Art,Print,東海道五十三　京　内裏|Kyoto: Dairi,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1838,1828,1848,Polychrome woodblock print; ink and color on paper,8 1/2 x 6 1/2 in. (21.6 x 16.5 cm),"Rogers Fund, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36589,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP217,false,true,36693,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1797–1858,1797,1858,Polychrome woodblock print; ink and color on paper,Image: 14 3/8 in. × 10 in. (36.5 × 25.4 cm) Mat: 12 5/8 × 18 1/2 in. (32.1 × 47 cm),"Gift of Mrs. Russell Sage, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36693,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP238,false,true,36710,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,early 1830s,1830,1835,Polychrome woodblock print; ink and color on paper,13 1/2 x 4 15/32 in. (34.3 x 11.4 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36710,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP239,false,true,36711,Asian Art,Print,歌川広重画　罌栗に鶉|Quails and Poppies,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1835,1825,1845,Polychrome woodblock print; ink and color on paper,14 x 5 in. (35.6 x 12.7 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36711,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP243,false,true,36715,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1836,1826,1846,Polychrome woodblock print; ink and color on paper,Overall: 14 7/8 x 5 1/16 in. (37.8 x 12.9 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36715,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP244,false,true,36716,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1833,1823,1843,Polychrome woodblock print; ink and color on paper,10 x 4 7/8 in. (25.4 x 12.4 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36716,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP245,false,true,36717,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1833,1823,1843,Polychrome woodblock print; ink and color on paper,9 7/8 x 4 11/16 in. (25.1 x 11.9 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36717,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP246,false,true,36718,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1840,1830,1850,Polychrome woodblock print; ink and color on paper,8 1/2 x 6 3/8 in. (21.6 x 16.2 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36718,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP247,false,true,36719,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1833,1823,1843,Polychrome woodblock print; ink and color on paper,8 27/32 x 6 3/8 in. (22.5 x 16.2 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36719,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP248,false,true,36720,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1837,1827,1847,Polychrome woodblock print,10 1/8 x 7 1/2 in. (25.7 x 19.1 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36720,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP249,false,true,36721,Asian Art,Woodblock print,紅蜀葵に燕と川蝉図|Swallows and Kingfisher with Rose Mallows,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1838,1828,1848,Polychrome woodblock print,10 7/16 x 7 11/32 in. (26.5 x 18.7 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36721,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP250,false,true,36722,Asian Art,Woodblock print,椿に目白と四十雀図|Japanese White-eye and Titmouse on a Camellia Branch,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1840,1830,1850,Polychrome woodblock print,10 7/32 x 7 7/32 in. (26.0 x 18.4 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36722,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP251,false,true,36723,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1833,1823,1843,Polychrome woodblock print; ink and color on paper,8 15/16 x 6 11/16 in. (22.7 x 17.0 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36723,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP252,false,true,36724,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1832,1822,1842,Polychrome woodblock print; ink and color on paper,8 15/16 x 6 3/5 in. (22.7 x 16.8 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36724,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP253,false,true,36725,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1838,1828,1848,Polychrome woodblock print; ink and color on paper,8 27/32 x 6 9/16 in. (22.5 x 16.7 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36725,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP254,false,true,36726,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,mid-1840s,1843,1846,Polychrome woodblock print; ink and color on paper,8 27/32 x 6 3/5 in. (22.5 x 16.8 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36726,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP255,false,true,36727,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1842,1832,1852,Polychrome woodblock print; ink and color on paper,8 23/32 x 6 9/16 in. (22.2 x 16.7 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36727,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP256,false,true,36728,Asian Art,Print,歌川広重画　芙蓉に高麗鶯|Black-naped Oriole Perched on a Stem of Rose Mallow,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1834,1824,1844,Polychrome woodblock print; ink and color on paper,14 11/16 x 4 29/32 in. (37.3 x 12.5 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36728,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP257,false,true,36729,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1834,1824,1844,Polychrome woodblock print,14 15/32 x 5 in. (36.8 x 12.7 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36729,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP258,false,true,36730,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1840,1830,1850,Polychrome woodblock print; ink and color on paper,12 31/32 x 4 1/4 in. (33.0 x 10.8 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36730,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP259,false,true,36731,Asian Art,Print,歌川広重画　菊に百舌鳥|Shrike and Chrysanthemums,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1830,1820,1840,Polychrome woodblock print (hosoban); ink and color on paper,14 7/8 x 5 in. (37.8 x 12.7 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36731,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP260,false,true,36732,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1833,1823,1843,Polychrome woodblock print; ink and color on paper,13 3/32 x 4 7/16 in. (33.2 x 11.3 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36732,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP261,false,true,36733,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1832,1822,1842,Polychrome woodblock print; ink and color on paper,15 x 5 in. (38.1 x 12.7 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36733,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP262,false,true,36734,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1840,1830,1850,Polychrome woodblock print; ink and color on paper,13 1/10 x 4 3/8 in. (33.3 x 11.1 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36734,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP263,false,true,36735,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1842,1832,1852,Polychrome woodblock print; ink and color on paper,13 3/32 x 4 15/32 in. (33.2 x 11.4 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36735,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP264,false,true,36736,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1830,1820,1840,Polychrome woodblock print; ink and color on paper,14 3/5 x 4 29/32 in. (37.1 x 12.5 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36736,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP265,false,true,36737,Asian Art,Print,歌川広重画|Morning Glories with Poem by Gyōkō,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1843,1833,1853,Polychrome woodblock print (hosoban); ink and color on paper,12 15/16 x 4 3/8 in. (32.9 x 11.1 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36737,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP266,false,true,36738,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1843,1841,1845,Polychrome woodblock print; ink and color on paper,13 1/4 x 4 15/32 in. (33.7 x 11.4 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36738,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP267,false,true,36739,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1833,1831,1835,Polychrome woodblock print; ink and color on paper,14 15/32 x 6 11/16 in. (36.8 x 17.0 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36739,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP268,false,true,36740,Asian Art,Print,波に丹頂鶴|Crane and Surf,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1833,1823,1843,Polychrome woodblock print; ink and color on paper,14 1/8 x 6 3/8 in. (35.9 x 16.2 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36740,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP269,false,true,36741,Asian Art,Print,歌川広重画　楓に孔雀|Peacock Perched on a Maple Tree,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1833,1823,1843,Polychrome woodblock print (hosoban); ink and color on paper,14 13/16 x 5 in. (37.6 x 12.7 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36741,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP270,false,true,36742,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1833,1823,1843,Polychrome woodblock print; ink and color on paper,15 x 4 29/32 in. (38.1 x 12.5 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36742,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP271,false,true,36743,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1835,1825,1845,Polychrome woodblock print; ink and color on paper,14 29/32 x 4 7/8 in. (37.9 x 12.4 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36743,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP272,false,true,36744,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1835,1825,1845,Polychrome woodblock print; ink and color on paper,10 x 4 3/4 in. (25.4 x 12.1 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36744,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP273,false,true,36745,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1835,1825,1845,Polychrome woodblock print; ink and color on paper,10 1/5 x 4 29/32 in. (25.9 x 12.5 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36745,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP471,false,true,36922,Asian Art,Print,東海道五十三次之内　日本橋　朝之景|Stations One: Morning View of Nihonbashi,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1833–34,1833,1834,Polychrome woodblock print; ink and color on paper,H. 9 1/2 in. (24.1 cm); W. 13 7/8 in. (35.2 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36922,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP472,false,true,36923,Asian Art,Print,東海道五十三次之内　品川　日之出|Daybreak at Shinagawa,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1834,1824,1844,Polychrome woodblock print; ink and color on paper,9 15/32 x 14 3/5 in. (24.1 x 37.1 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36923,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP473,false,true,36924,Asian Art,Print,東海道五十三次之内　川崎　六郷渡舟|Ferry Boat Crossing the Rokugo River,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1834,1824,1844,Polychrome woodblock print; ink and color on paper,9 15/32 x 14 3/4 in. (24.1 x 37.5 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36924,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP474,false,true,36925,Asian Art,Print,東海道五十三次之内　神奈川　宿台之景|View of the Kanagawa station at sunset,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1833–34,1833,1834,Polychrome woodblock print; ink and color on paper,9 15/32 x 14 in. (24.1 x 35.6 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36925,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP475,false,true,36926,Asian Art,Print,東海道五十三次之内　保土ヶ谷　新町橋|Hodogaya Station and Shinkame Bridge,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1834,1824,1844,Polychrome woodblock print; ink and color on paper,9 15/32 x 14 1/8 in. (24.1 x 35.9 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36926,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP476,false,true,36927,Asian Art,Print,東海道五十三次之内　戸塚　元町別道|Totsuka; Moto Machi Betsudo,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1834,1824,1844,Polychrome woodblock print; ink and color on paper,9 15/32 x 14 in. (24.1 x 35.6 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36927,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP477,false,true,36928,Asian Art,Print,東海道五十三次之内　藤澤　遊行寺|Fujiwara; Yugyoji,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1834,1824,1844,Polychrome woodblock print; ink and color on paper,9 15/32 x 14 in. (24.1 x 35.6 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36928,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP478,false,true,36929,Asian Art,Print,東海道五十三次之内　平塚　縄手道|Hiratsuka; Nawate Do,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1834,1824,1844,Polychrome woodblock print; ink and color on paper,9 15/32 x 14 1/8 in. (24.1 x 35.9 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36929,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP479,false,true,36930,Asian Art,Woodblock print,東海道五十三次之内　大磯　虎ケ雨|Tiger Rain at Ōiso Station,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1834,1824,1844,Polychrome woodblock print; ink and color on paper,9 15/32 x 14 in. (24.1 x 35.6 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36930,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP480,false,true,36931,Asian Art,Print,東海道五十三次之内　小田原　酒匂川|Odawara; Sakogawa,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1833–34,1833,1834,Polychrome woodblock print; ink and color on paper,9 15/32 x 14 1/8 in. (24.1 x 35.9 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36931,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP481,false,true,36932,Asian Art,Print,東海道五十三次之内　箱根　湖水図|Hakone; Kosui,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1833–34,1833,1834,Polychrome woodblock print; ink and color on paper,9 15/32 x 14 1/8 in. (24.1 x 35.9 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36932,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP482,false,true,36933,Asian Art,Print,"東海道五十三次之内　三島　朝霧|Mishima, Asa Kiri",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1833–34,1833,1834,Polychrome woodblock print; ink and color on paper,9 15/32 x 14 7/8 in. (24.1 x 37.8 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36933,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP483,false,true,36934,Asian Art,Print,東海道五十三次之内　沼津　黄昏図|Numazu Ki Kure,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1834,1824,1844,Polychrome woodblock print; ink and color on paper,9 15/32 x 14 in. (24.1 x 35.6 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36934,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP484,false,true,36935,Asian Art,Print,東海道五十三次之内　原　朝の富士|Hara; Asa no Fuji,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1833–34,1833,1834,Polychrome woodblock print; ink and color on paper,9 15/32 x 14 1/8 in. (24.1 x 35.9 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36935,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP485,false,true,36936,Asian Art,Print,"東海道五十三次之内　吉原　左富士|Yoshiwara, Hidari Fuji",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1834,1824,1844,Polychrome woodblock print; ink and color on paper,9 15/32 x 14 in. (24.1 x 35.6 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36936,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP486,false,true,36937,Asian Art,Woodblock print,東海道五十三次之内　蒲原　夜の雪|A Snowy Evening at Kambara Station,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1833–34,1833,1834,Polychrome woodblock print; ink and color on paper,Image: 9 1/2 × 14 1/8 in. (24.1 × 35.9 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36937,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP487,false,true,36938,Asian Art,Print,東海道五十三次之内　由井　薩埵嶺|Satta Peak at Yui,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1834,1824,1844,Polychrome woodblock print; ink and color on paper,9 15/32 x 14 1/8 in. (24.1 x 35.9 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36938,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP488,false,true,36939,Asian Art,Print,"東海道五十三次之内　奥津　興津川|Okitsu, Okitsugawa",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1834,1824,1844,Polychrome woodblock print; ink and color on paper,9 15/32 x 14 1/8 in. (24.1 x 35.9 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36939,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP489,false,true,36940,Asian Art,Print,東海道五十三次之内　江尻　三保遠望|Distant View of Miho Beach from Ejiri,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1834,1824,1844,Polychrome woodblock print; ink and color on paper,9 15/32 x 14 in. (24.1 x 35.6 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36940,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP490,false,true,36941,Asian Art,Print,東海道五十三次之内　府中　安部川|Travellers Fording the Abe River at Fuchu,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1833–34,1833,1834,Polychrome woodblock print; ink and color on paper,9 15/32 x 14 in. (24.1 x 35.6 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36941,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP491,false,true,36942,Asian Art,Print,東海道五十三次之内　鞠子　名物茶店|Mariko; Meibutsu Chaya,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1834,1824,1844,Polychrome woodblock print; ink and color on paper,9 15/32 x 14 1/16 in. (24.1 x 35.7 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36942,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP492,false,true,36943,Asian Art,Print,東海道五十三次之内　岡部　宇津の山|Okabe; Utsu no Yama,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1834,1824,1844,Polychrome woodblock print; ink and color on paper,9 15/32 x 14 1/8 in. (24.1 x 35.9 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36943,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP493,false,true,36944,Asian Art,Print,東海道五十三次之内　藤枝　人馬継立|Fujieda; Hito Uma Keitatsu,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1834,1824,1844,Polychrome woodblock print; ink and color on paper,9 15/32 x 14 1/16 in. (24.1 x 35.7 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36944,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP494,false,true,36945,Asian Art,Print,"東海道五十三次之内　嶋田　大井川駿岸|Shimada, Oigawa Shun Gan",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1834,1824,1844,Polychrome woodblock print; ink and color on paper,9 15/32 x 14 1/8 in. (24.1 x 35.9 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36945,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP495,false,true,36946,Asian Art,Print,"東海道五十三次之内　金谷　大井川遠岸|Kanaya, Oigawa Em Gan",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1834,1824,1844,Polychrome woodblock print; ink and color on paper,9 15/32 x 14 1/8 in. (24.1 x 35.9 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36946,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP496,false,true,36947,Asian Art,Print,"東海道五十三次之内　日坂　佐夜の中山|Nissaka, Sayo Nakayama",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1834,1824,1844,Polychrome woodblock print; ink and color on paper,9 15/32 x 14 1/8 in. (24.1 x 35.9 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36947,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP497,false,true,36948,Asian Art,Print,"東海道五十三次之内　掛川　秋葉山遠望|Kakegawa, Akihasan Empo",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1834,1824,1844,Polychrome woodblock print; ink and color on paper,9 3/5 x 14 1/8 in. (24.4 x 35.9 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36948,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP498,false,true,36949,Asian Art,Print,東海道五十三次之内　袋井　出茶屋の図|Fukuroi; De Chaya,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1834,1824,1844,Polychrome woodblock print; ink and color on paper,9 15/32 x 14 1/8 in. (24.1 x 35.9 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36949,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP500,false,true,36951,Asian Art,Print,"東海道五十三次之内　濱松　冬枯の図|Hamamatsu, Toko no Zu",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1834,1824,1844,Polychrome woodblock print; ink and color on paper,9 15/32 x 14 1/16 in. (24.1 x 35.7 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36951,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP501,false,true,36952,Asian Art,Print,東海道五十三次之内　舞坂　今切真景|View of Imaki Point from Maizaka,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1834,1824,1844,Polychrome woodblock print; ink and color on paper,9 15/32 x 14 1/16 in. (24.1 x 35.7 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36952,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP502,false,true,36953,Asian Art,Print,"東海道五十三次之内　荒井　渡舟の図|Arai, Tosen",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1834,1824,1844,Polychrome woodblock print; ink and color on paper,9 15/32 x 14 1/16 in. (24.1 x 35.7 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36953,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP503,false,true,36954,Asian Art,Print,"東海道五十三次之内　白須賀　汐見阪図|Shirasuka, Shio-mi Zaka",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1834,1824,1844,Polychrome woodblock print; ink and color on paper,9 15/32 x 14 1/16 in. (24.1 x 35.7 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36954,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP504,false,true,36955,Asian Art,Print,"東海道五十三次之内　二川　猿ヶ馬場|Futagawa, Saru ga Baba",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1834,1824,1844,Polychrome woodblock print; ink and color on paper,9 15/32 x 14 1/16 in. (24.1 x 35.7 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36955,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP505,false,true,36956,Asian Art,Print,"東海道五十三次之内　吉田　豊川橋|Yoshida, Toyokawa Hashi",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1834,1824,1844,Polychrome woodblock print; ink and color on paper,9 15/32 x 14 1/16 in. (24.1 x 35.7 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36956,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP506,false,true,36957,Asian Art,Print,"東海道五十三次之内　御油　旅人留女|Goyu, Tabibito Ryujo",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1833–34,1833,1834,Polychrome woodblock print; ink and color on paper,9 15/32 x 14 in. (24.1 x 35.6 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36957,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP507,false,true,36958,Asian Art,Print,"東海道五十三次之内　赤阪　旅舎招婦の図|Akasaka, Ryosha Sho-fu",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1834,1824,1844,Polychrome woodblock print; ink and color on paper,9 15/32 x 14 1/16 in. (24.1 x 35.7 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36958,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP508,false,true,36959,Asian Art,Print,"東海道五十三次之内　藤川　棒鼻の図|Fujikawa, Bo Bana",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1834,1824,1844,Polychrome woodblock print; ink and color on paper,9 15/32 x 14 1/16 in. (24.1 x 35.7 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36959,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP509,false,true,36960,Asian Art,Print,"東海道五十三次之内　岡崎　矢矧の橋|Okazaki, Tenshin no Hashi",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1834,1824,1844,Polychrome woodblock print; ink and color on paper,9 15/32 x 14 in. (24.1 x 35.6 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36960,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP510,false,true,36961,Asian Art,Print,"東海道五十三次之内　池鯉鮒　首夏馬市|Chiryu, Shuka Uma Ichi",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1834,1824,1844,Polychrome woodblock print; ink and color on paper,9 15/32 x 14 in. (24.1 x 35.6 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36961,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP511,false,true,36962,Asian Art,Print,"東海道五十三次之内　鳴海　名物有松絞|Narumi, Meibutsu Arimatsu Shibori",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1834,1824,1844,Polychrome woodblock print; ink and color on paper,9 15/32 x 14 in. (24.1 x 35.6 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36962,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP512,false,true,36963,Asian Art,Print,"東海道五十三次之内　宮　熱田神事|Miya, Atsuta Shin Ji",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1834,1824,1844,Polychrome woodblock print; ink and color on paper,9 15/32 x 14 1/16 in. (24.1 x 35.7 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36963,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP513,false,true,36964,Asian Art,Print,"東海道五十三次之内　桑名　七里渡口|Kuwana, Shichi-Ri Watashi Guchi",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1834,1824,1844,Polychrome woodblock print; ink and color on paper,9 15/32 x 14 1/16 in. (24.1 x 35.7 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36964,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP514,false,true,36965,Asian Art,Print,"東海道五十三次之内　四日市　三重川|Yokkaichi, Sanchokawa",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1834,1824,1844,Polychrome woodblock print; ink and color on paper,9 15/32 x 14 1/16 in. (24.1 x 35.7 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36965,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP515,false,true,36966,Asian Art,Print,"東海道五十三次之内　石薬師　石薬師寺|Ishiyakushi, Ishiyakushi Ji",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1834,1824,1844,Polychrome woodblock print; ink and color on paper,9 15/32 x 14 in. (24.1 x 35.6 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36966,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP516,false,true,36967,Asian Art,Print,東海道五十三次之内　庄野　白雨|Sudden Shower in Shōno,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1833–34,1832,1836,Polychrome woodblock print; ink and color on paper,9 15/32 x 14 in. (24.1 x 35.6 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36967,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP517,false,true,36968,Asian Art,Print,"東海道五十三次之内　亀山　雪晴|Kameyama, Yuki Hare",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1834,1824,1844,Polychrome woodblock print; ink and color on paper,9 15/32 x 14 in. (24.1 x 35.6 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36968,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP518,false,true,36969,Asian Art,Print,"東海道五十三次之内　関　本陣早立|Seki, Honjin Sotatsu",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1834,1824,1844,Polychrome woodblock print; ink and color on paper,9 15/32 x 14 1/16 in. (24.1 x 35.7 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36969,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP519,false,true,36970,Asian Art,Print,"東海道五十三次之内　阪之下　筆捨嶺|Saka-no-shita, Fude-sute Mine",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1834,1824,1844,Polychrome woodblock print; ink and color on paper,Image: 9 1/2 × 14 1/8 in. (24.1 × 35.9 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36970,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP520,false,true,36971,Asian Art,Print,"東海道五十三次　土山　春の雨|Spring Rain at Tsuchiyama, from the series Fifty-three Stations of the Tōkaidō",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1834–35,1834,1835,Polychrome woodblock print; ink and color on paper,H. 9 1/2 in. (24.1 cm); W. 14 in. (35.6 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36971,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP521,false,true,36972,Asian Art,Print,"東海道五十三次之内　水口　名物干瓢|Mizukuchi, Meibutsu Kampyo",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1834,1824,1844,Polychrome woodblock print; ink and color on paper,9 15/32 x 14 in. (24.1 x 35.6 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36972,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP522,false,true,36973,Asian Art,Print,"東海道五十三次之内　石部　目川の里|Ishibe, Megawa Sato",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1834,1824,1844,Polychrome woodblock print; ink and color on paper,9 15/32 x 14 1/16 in. (24.1 x 35.7 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36973,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP523,false,true,36974,Asian Art,Print,"東海道五十三次之内　草津　名物立場|Kusatsu: Famous Post House (Kusatsu, Meibutsu tateba), from the series Fifty-three Stations of the Tōkaidō Road (Tōkaidō gojūsan tsugi no uchi)",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1833–34,1833,1834,Polychrome woodblock print; ink and color on paper,9 15/32 x 14 in. (24.1 x 35.6 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36974,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP524,false,true,36975,Asian Art,Print,"東海道五十三次之内　大津　走井茶屋|Otsu, Soii Chaya",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1834,1824,1844,Polychrome woodblock print; ink and color on paper,9 15/32 x 14 in. (24.1 x 35.6 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36975,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP525,false,true,36976,Asian Art,Print,"東海道五十三次之内　大尾　京師　三条大橋|Kyoto, Sanjo Ohashi",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1833–34,1833,1834,Polychrome woodblock print; ink and color on paper,9 3/5 x 14 1/16 in. (24.4 x 35.7 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36976,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP526,false,true,36977,Asian Art,Print,東都名所　日本橋の白雨|Sunshower at Nihonbashi,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1833–34,1833,1834,Polychrome woodblock print; ink and color on paper,10 1/8 x 15 in. (25.7 x 38.1 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36977,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP567,false,true,37018,Asian Art,Woodblock print,忠臣蔵　十一段目　夜打　押寄|The Loyal Ronin Crossing the Long Bridge to Embark for the Night Attack upon Moronao,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1840,1830,1850,Polychrome woodblock print; ink and color on paper,9 1/8 x 14 in. (23.2 x 35.6 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37018,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP568,false,true,37019,Asian Art,Print,東海道五十三次之内　荒井　海上壹リ半舟渡之図|Arai,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1842,1832,1852,Polychrome woodblock print; ink and color on paper,9 x 13 5/8 in. (22.9 x 34.6 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37019,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP569,false,true,37020,Asian Art,Print,東海道五十三次之内　吉原|Yoshiwara,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1842,1832,1852,Polychrome woodblock print; ink and color on paper,9 x 13 5/8 in. (22.9 x 34.6 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37020,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP570,false,true,37021,Asian Art,Print,東海道五十三次之内　川崎　六郷の渡し舟|Kawasaki,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1842,1832,1852,Polychrome woodblock print; ink and color on paper,Overall: 9 x 13 3/4in. (22.9 x 34.9cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37021,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP571,false,true,37022,Asian Art,Print,東海道五十三次之内　亀山|Kameyama,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1842,1832,1852,Polychrome woodblock print; ink and color on paper,9 1/16 x 13 7/10 in. (23.0 x 34.8 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37022,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP572,false,true,37023,Asian Art,Print,東海道五十三次之内　赤阪|Akasaka Station,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1842,1832,1852,Polychrome woodblock print; ink and color on paper,8 27/32 x 13 1/2 in. (22.5 x 34.3 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37023,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP573,false,true,37024,Asian Art,Print,東海道五十三次之内　石薬師|Gyosho Tokaido: Ishikushi,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1842,1832,1852,Polychrome woodblock print; ink and color on paper,8 13/16 x 13 17/32 in. (22.4 x 34.4 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37024,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP574,false,true,37025,Asian Art,Print,東海道五十三次之内　蒲原　岩淵よりふじ川を見る圖|Kambura Station,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1842,1832,1852,Polychrome woodblock print; ink and color on paper,8 27/32 x 13 1/2 in. (22.5 x 34.3 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37025,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP575,false,true,37026,Asian Art,Print,東海道五十三次之内　品川　鮫洲朝之景|Shinagawa Samesu Asa no Kei,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1842,1832,1852,Polychrome woodblock print; ink and color on paper,8 27/32 x 13 1/2 in. (22.5 x 34.3 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37026,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP576,false,true,37027,Asian Art,Print,東海道五十三次之内　岡部　宇津の山之図|Okabe,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1842,1832,1852,Polychrome woodblock print; ink and color on paper,9 1/8 x 13 23/32 in. (23.2 x 34.9 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37027,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP577,false,true,37028,Asian Art,Print,東海道五十三次之内　平塚　馬入川舟渡しの図|Hiratsuka; Banyugawa Funa Watashi no Zu,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1842,1832,1852,Polychrome woodblock print; ink and color on paper,8 27/32 x 13 1/2 in. (22.5 x 34.3 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37028,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP578,false,true,37029,Asian Art,Print,東海道五十三次之内　嶋田　大井川駿岸|Shimada,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1842,1832,1852,Polychrome woodblock print; ink and color on paper,9 x 13 5/8 in. (22.9 x 34.6 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37029,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP579,false,true,37030,Asian Art,Print,東海道五十三次之内　草津|Kusatsu Station,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1842,1832,1852,Polychrome woodblock print; ink and color on paper,8 27/32 x 13 1/2 in. (22.5 x 34.3 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37030,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP580,false,true,37031,Asian Art,Print,東海道五十三次之内　大津|Otsu Station,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1842,1832,1852,Polychrome woodblock print; ink and color on paper,8 27/32 x 13 1/2 in. (22.5 x 34.3 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37031,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP581,false,true,37032,Asian Art,Print,東海道五十三次之内　神奈川　浅間下より台を見る図|Kanazawa,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1842,1832,1852,Polychrome woodblock print; ink and color on paper,9 1/8 x 13 23/32 in. (23.2 x 34.9 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37032,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP582,false,true,37033,Asian Art,Print,東海道五十三次之内　江尻　清水之湊遠望|Ejiri,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1842,1832,1852,Polychrome woodblock print; ink and color on paper,8 27/32 x 13 1/2 in. (22.5 x 34.3 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37033,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP583,false,true,37034,Asian Art,Print,東海道五十三次之内　袋井　出茶屋の図|Fukuroi; De Chaya,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1834,1824,1844,Polychrome woodblock print; ink and color on paper,9 3/4 x 14 1/3 in. (24.8 x 36.4 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37034,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP584,false,true,37035,Asian Art,Print,東海道五十三次之内　品川　諸侯出立|Shinagawa; Shoko Detachi,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1834,1824,1844,Polychrome woodblock print; ink and color on paper,9 1/4 x 14 1/4 in. (23.5 x 36.2 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37035,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP585,false,true,37036,Asian Art,Print,木曽海道六拾九次之内　望月|Mochizuki Station,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1835,1825,1845,Polychrome woodblock print; ink and color on paper,9 3/4 x 14 7/8 in. (24.8 x 37.8 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37036,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP586,false,true,37037,Asian Art,Print,木曽海道六拾九次之内　芦田|Ashida Station,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1835,1825,1845,Polychrome woodblock print; ink and color on paper,Oban 10 x 14 29/32 in. (25.4 x 37.9 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37037,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP587,false,true,37038,Asian Art,Print,"本朝名所　相州七里ヶ浜|Soshu, Shichi-ri ga Hama",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1830,1820,1840,Polychrome woodblock print; ink and color on paper,9 x 14 1/3 in. (22.9 x 36.4 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37038,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP588,false,true,37039,Asian Art,Print,"本朝名所　相州江ノ嶋岩屋之図|Soshu, Enoshima Iwaya no Zu",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1830,1820,1840,Polychrome woodblock print; ink and color on paper,9 7/8 x 14 3/5 in. (25.1 x 37.1 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37039,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP589,false,true,37040,Asian Art,Print,"六十余州名所図会　隠岐　焚火の社|The Takihi Shrine, Oki Province, from the series Views of Famous Places in the Sixty-Odd Provinces",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1853,1843,1863,Polychrome woodblock print; ink and color on paper,14 x 9 7/16 in. (35.6 x 24 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37040,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP590,false,true,37041,Asian Art,Print,"六十余州名所図会　大和　立田山　龍田川|Yamato, Tatsutayama, Tatsutagawa",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,7th month ox year 1853,1853,1853,Polychrome woodblock print; ink and color on paper,14 1/8 x 9 3/4 in. (35.9 x 24.8 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37041,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP591,false,true,37042,Asian Art,Print,"「六十余州名所図絵　伊予　西条」|“Iyo Province, Saijō ,” from the series Views of Famous Places in the Sixty-odd Provinces (Rokujū yoshū meisho zu-e, Iyo, Saijō)",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1853,1843,1863,Polychrome woodblock print; ink and color on paper,14 1/4 x 9 15/32 in. (36.2 x 24.1 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37042,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP592,false,true,37043,Asian Art,Woodblock print,六十余州名所図会　対馬　海岸夕晴|Tsushima Kaigan Yubare,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,3rd month dragon year 1856,1856,1856,Polychrome woodblock print; ink and color on paper,14 x 9 3/5 in. (35.6 x 24.4 cm),"Purchase, Joseph Pultizer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37043,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP593,false,true,37044,Asian Art,Print,"六十余州名所図会　因幡　か路小山|View of Kajikoyama, Inaba Province, from the series Views of Famous Places in the Sixty-Odd Provinces",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1853,1843,1863,Polychrome woodblock print; ink and color on paper,Aiban; 13 15/16 x 9 1/4 in. (35.4 x 23.5 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37044,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP594,false,true,37045,Asian Art,Print,"六十余州名所図会　武蔵　隅田川　雪の朝|Morning after a Snowfall, the Sumida River, Musashi Province, from the series Views of Famous Places in the Sixty-Odd Provinces",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1853,1843,1863,Polychrome woodblock print; ink and color on paper,14 1/8 x 9 1/3 in. (35.9 x 23.7 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37045,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP595,false,true,37046,Asian Art,Print,"六十余州名所図会　播磨　舞子の浜|Harima, Maiko no Hama",Japan,Edo period (1615–1868),,,,Artist,Designed by,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1853–56,1853,1856,Polychrome woodblock print; ink and color on paper,Oban tate-e 14 x 9 7/16 in. (35.6 x 24 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37046,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP596,false,true,37047,Asian Art,Print,"Kazusa Yazashi-ga-ura tsumei|六十余州名所図会　上総　矢さしか浦　通名九十九里|Yasashi Beach, known as Kujūkuri, Kazusa Province, from the series Views of Famous Places in the Sixty-Odd Provinces",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1853,1843,1863,Polychrome woodblock print; ink and color on paper,Oban tate-e 14 3/5 x 10 1/8 in. (37.1 x 25.7 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37047,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP597,false,true,37048,Asian Art,Print,"東都名所　二丁町芝居の図|View of the Kabuki Theaters at Sakai-cho on Opening Day of the New Season (Sakai-cho Shibai no Zu), from the series, ""Toto Meisho""",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1838,1828,1848,Polychrome woodblock print; ink and color on paper,10 1/8 x 14 7/8 in. (25.7 x 37.8 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37048,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP598,false,true,37049,Asian Art,Print,東都名所　二丁町芝居の図|Sakai Cho Shibai no Zu,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1838,1828,1848,Polychrome woodblock print; ink and color on paper,10 x 14 29/32 in. (25.4 x 37.9 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37049,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP599,false,true,37050,Asian Art,Print,江都名所　新橋の図|Shimbashi no Zu,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1837,1827,1847,Polychrome woodblock print; ink and color on paper,10 1/8 x 15 in. (25.7 x 38.1 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37050,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP600,false,true,37051,Asian Art,Print,東都名所　芝増上寺山内の図|Shiba Zōjōji Sannai no Zu,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1833–34,1831,1836,Polychrome woodblock print; ink and color on paper,10 1/8 x 14 7/8 in. (25.7 x 37.8 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37051,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP601,false,true,37052,Asian Art,Print,東都名所　駿河町の図|Suruga Street,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1836,1826,1846,Polychrome woodblock print; ink and color on paper,9 9/16 x 14 1/2 in. (24.3 x 36.8 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37052,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP602,false,true,37053,Asian Art,Print,東都名所　亀戸天満宮境内雪|Kameido Tenmangu Keidai no Yuki,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1833–34,1831,1836,Polychrome woodblock print; ink and color on paper,10 x 14 5/8 in. (25.4 x 37.1 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37053,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP603,false,true,37054,Asian Art,Print,東都名所　亀戸天満宮境内雪|Kameido Tenmangu Keidai no Yuki,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1833–34,1831,1836,Polychrome woodblock print; ink and color on paper,9 3/8 x 13 11/16 in. (23.8 x 34.8 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37054,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP604,false,true,37055,Asian Art,Print,"東都名所　芝赤羽根之雪|Shiba, Akabane no Yuki",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1837,1827,1847,Polychrome woodblock print; ink and color on paper,10 1/8 x 14 15/16 in. (25.7 x 37.9 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37055,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP605,false,true,37056,Asian Art,Print,東都名所　高輪の夕景|Takanawa no Yukei,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1833,1823,1843,Polychrome woodblock print; ink and color on paper,9 1/2 x 14 1/2 in. (24.1 x 36.8 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37056,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP606,false,true,37057,Asian Art,Print,東都名所　芝愛宕山上の図|Shiba Atago Sanjo no Zu,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1833–34,1831,1836,Polychrome woodblock print; ink and color on paper,9 13/16 x 14 11/16 in. (24.9 x 37.3 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37057,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP607,false,true,37058,Asian Art,Print,東都名所　浅草金龍山年の市|Year End Fair at Kinryuzan Temple,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1836,1826,1846,Polychrome woodblock print; ink and color on paper,9 15/16 x 14 5/8 in. (25.2 x 37.1 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37058,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP608,false,true,37059,Asian Art,Print,東都名所　吉原夜桜の図|Yoshiwara Yo Zakura no Zu,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1841,1831,1851,Polychrome woodblock print; ink and color on paper,9 13/16 x 14 3/8 in. (24.9 x 36.5 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37059,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP609,false,true,37060,Asian Art,Print,東都名所　浅草金龍山年の市|Asakusa Kinryusan Toshi no Ichi,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1838,1828,1848,Polychrome woodblock print; ink and color on paper,9 7/16 x 14 7/16 in. (24 x 36.6 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37060,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP610,false,true,37061,Asian Art,Print,江都名所　飛鳥山はな見|Asakayama Hanami,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1834,1824,1844,Polychrome woodblock print; ink and color on paper,9 1/4 x 14 3/4 in. (23.5 x 37.5 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37061,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP611,false,true,37062,Asian Art,Print,東都名所　両国花火の図|Ryogoku Hanabi no Zu,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1841,1831,1851,Polychrome woodblock print; ink and color on paper,10 3/32 x 14 3/4 in. (25.6 x 37.5 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37062,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP612,false,true,37063,Asian Art,Print,新撰江戸名所　高輪廿六夜之図|Takanawa Ni-ju-roku Ya,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1840,1838,1842,Polychrome woodblock print; ink and color on paper,8 1/2 x 13 5/8 in. (21.6 x 34.6 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37063,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP613,false,true,37064,Asian Art,Print,東都名所　高輪之図|Takanawa no Zu,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1841,1831,1851,Polychrome woodblock print; ink and color on paper,10 5/32 x 14 11/16 in. (25.8 x 37.3 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37064,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP614,false,true,37065,Asian Art,Print,東都名所　新吉原日本堤衣紋坂曙|Shin Yoshiwara Nihon Tsutsumi Emonzaka Akatsuki,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1835,1825,1845,Polychrome woodblock print; ink and color on paper,10 1/5 x 14 7/16 in. (25.9 x 36.7 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37065,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP616,false,true,37067,Asian Art,Print,東都名所　新吉原|Shin Yoshiwara,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1840,1830,1850,Polychrome woodblock print; ink and color on paper,10 5/16 x 14 7/8 in. (26.2 x 37.8 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37067,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP617,false,true,37068,Asian Art,Print,東都名所　佃島初郭公|Tsukudajima Hatsu Hototogisu,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,probably 1830,1828,1832,Polychrome woodblock print; ink and color on paper,8 23/32 x 14 1/16 in. (22.2 x 35.7 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37068,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP618,false,true,37069,Asian Art,Print,"東都名所　外桜田弁慶桜の井|Soto Sakurada, Benkei Bori, Sakura-no-i",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1842,1832,1852,Polychrome woodblock print; ink and color on paper,10 1/8 x 14 3/4 in. (25.7 x 37.5 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37069,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP620,false,true,37071,Asian Art,Print,日本湊尽　相州浦賀|Uraga Harbor,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1837,1827,1847,Polychrome woodblock print; ink and color on paper,8 5/8 x 13 5/8 in. (21.9 x 34.6 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37071,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP621,false,true,37072,Asian Art,Print,"日本湊尽　東都品川|Toto, Shinagawa",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1838,1828,1848,Polychrome woodblock print; ink and color on paper,9 3/4 x 14 15/32 in. (24.8 x 36.8 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37072,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP622,false,true,37073,Asian Art,Print,"江戸名所　上野不忍の池|Ueno, Shinobazu no Ike",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1835,1833,1837,Polychrome woodblock print; ink and color on paper,8 5/8 x 13 1/2 in. (21.9 x 34.3 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37073,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP623,false,true,37074,Asian Art,Print,"諸国六玉河　陸奥　野田の玉川|Mutsu, Noda no Tamagawa",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1833,1823,1843,Polychrome woodblock print; ink and color on paper,9 3/4 x 14 3/5 in. (24.8 x 37.1 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37074,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP624,false,true,37075,Asian Art,Print,諸国六玉河　武蔵　調布の玉川|Musashi: Chōfu no Tamagawa,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1833,1823,1843,Polychrome woodblock print; ink and color on paper,9 3/4 x 14 3/4 in. (24.8 x 37.5 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37075,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP625,false,true,37076,Asian Art,Print,"山海見立相撲　摂津安治川口|The Harbor of Ajikawa, Settsu Province",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1858,1858,1858,Polychrome woodblock print; ink and color on paper,9 11/16 x 14 1/4 in. (24.6 x 36.2 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37076,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP626,false,true,37077,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,6th month ox year 1853,1853,1853,Polychrome woodblock print; ink and color on paper,9 7/16 x 14 11/16 in. (24.0 x 37.3 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37077,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP627,false,true,37078,Asian Art,Print,東都名所　神田明神境内雪晴之図|Kanda Myojin Kyodai Yuki Hare no Zu,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1840,1830,1852,Polychrome woodblock print; ink and color on paper,9 15/32 x 14 3/5 in. (24.1 x 37.1 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37078,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP628,false,true,37079,Asian Art,Print,東都名所　永代橋佃沖漁舟|Eitai Bashi Tsukudajima Ryosen,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1835,1825,1845,Polychrome woodblock print; ink and color on paper,9 7/16 x 14 1/16 in. (24.0 x 35.7 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37079,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP629,false,true,37080,Asian Art,Print,東都名所 上野東叡山ノ圖|Ueno Toezan no Zu,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1835,1825,1845,Polychrome woodblock print; ink and color on paper,9 7/16 x 13 27/32 in. (24.0 x 35.2 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37080,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP630,false,true,37081,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1842,1832,1852,Polychrome woodblock print; ink and color on paper,9 11/16 x 14 1/4 in. (24.6 x 36.2 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37081,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP631,false,true,37082,Asian Art,Print,東都名所　飛鳥山花盛|Asukayama Hana Zakari,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1844,1834,1854,Polychrome woodblock print; ink and color on paper,9 4/5 x 14 7/8 in. (24.9 x 37.8 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37082,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP632,false,true,37083,Asian Art,Print,江戸名所　外桜田弁慶堀|Soto Sakurada Benkei Bori,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1843,1833,1853,Polychrome woodblock print; ink and color on paper,9 7/8 x 14 3/5 in. (25.1 x 37.1 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37083,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP633,false,true,37084,Asian Art,Print,江戸名所　高輪月の景|Takanawa Tsuki no Kei,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,7th month tiger year 1854,1854,1854,Polychrome woodblock print; ink and color on paper,9 3/5 x 14 3/8 in. (24.4 x 36.5 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37084,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP635,false,true,37086,Asian Art,Print,近江八景之内　石山秋月|The Autumn Moon at Ishiyama on Lake Biwa.,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1835,1825,1845,Polychrome woodblock print; ink and color on paper,10 x 14 9/16 in. (25.4 x 37.0 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37086,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP636,false,true,37087,Asian Art,Print,五十三次名所図会　藤川　山中の里別名宮路山|Fujikawa; Sanchu Yamanaka no Sato Miyajiyama,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,7th month Hare year 1855,1855,1855,Polychrome woodblock print; ink and color on paper,14 x 8 27/32 in. (35.6 x 22.5 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37087,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP637,false,true,37088,Asian Art,Print,"五十三次名所図会　鳴海　名產有松しぼり店|Narumi, Meisan Arimatsu Shibori Mise",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,7th month Hare year 1855,1855,1855,Polychrome woodblock print; ink and color on paper,Aiban; 13 23/32 x 9 1/4 in. (34.9 x 23.5 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37088,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP638,false,true,37089,Asian Art,Print,"富士三十六景　甲斐大月の原|Kai, Otsuki no Hara",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,4th month horse year 1858,1858,1858,Polychrome woodblock print; ink and color on paper,14 x 9 15/32 in. (35.6 x 24.1 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37089,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP639,false,true,37090,Asian Art,Print,"富士三十六景　武蔵越かや在|Musashi, Koshigaya Zai",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,4th month horse year 1858,1858,1858,Polychrome woodblock print; ink and color on paper,14 1/16 x 9 1/3 in. (35.7 x 23.7 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37090,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP640,false,true,37091,Asian Art,Print,東都三十六景　佃しま漁舟|Tsukudajima Gyoshoi,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1868,1868,1868,Polychrome woodblock print; ink and color on paper,14 15/32 x 9 7/8 in. (36.8 x 25.1 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37091,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP641,false,true,37092,Asian Art,Print,"東都三十六景　隅田川三囲り堤|Sumidagawa, Mimeguri",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,3rd month dragon year 1868,1868,1868,Polychrome woodblock print; ink and color on paper,14 15/32 x 9 4/5 in. (36.8 x 24.9 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37092,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP642,false,true,37093,Asian Art,Print,"名所江戶百景　両国花火|Fireworks at Ryōgoku Bridge, from the series One Hundred Famous Views of Edo",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1858,1858,1858,Polychrome woodblock print; ink and color on paper,Image: 13 1/4 × 8 3/4 in. (33.7 × 22.2 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37093,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP643,false,true,36461,Asian Art,Print,"Ōhashi Atake no yūdachi|名所江戶百景　大はしあたけの夕立|Sudden Shower over Shin-Ōhashi Bridge and Atake (Ōhashi Atake no yūdachi), from the series One Hundred Famous Views of Edo (Meisho Edo hyakkei)",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1857,1857,1857,Polychrome woodblock print; ink and color on paper,13 3/8 x 9 1/2 in. (34 x 24.1 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36461,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP644,false,true,37094,Asian Art,Woodblock print,"名所江戶百景　大はしあたけの夕立|Sudden Shower over Shin-Ōhashi Bridge and Atake (Ōhashi Atake no yūdachi), from the series One Hundred Famous Views of Edo (Meisho Edo hyakkei)",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1857,1857,1857,Polychrome woodblock print; ink and color on paper,14 3/8 x 9 9/16 in. (36.5 x 24.3 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37094,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP645,false,true,37095,Asian Art,Print,"名所江戶百景　目黒太鼓橋夕ひの岡|Taiko Bridge, Meguro, on a Snowy Evening",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,4th month snake year 1857,1857,1857,Polychrome woodblock print; ink and color on paper,14 1/4 x 9 1/4 in. (36.2 x 23.5 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37095,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP646,false,true,37096,Asian Art,Print,"名所江戸百景　真間の紅葉手古那の社継はし|Maples at Mama, from the series One Hundred Famous Views of Edo",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1857,1847,1867,Polychrome woodblock print; ink and color on paper,Oban 14 1/8 x 9 1/16 in. (35.9 x 23.0 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37096,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP751,false,true,37195,Asian Art,Print,東海道五十三次　日本橋|Nihon bashi,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1848–49,1848,1849,Polychrome woodblock print; ink and color on paper,Overall: 8 3/4 x 13 3/4in. (22.2 x 34.9cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37195,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP752,false,true,37196,Asian Art,Print,東海道五十三次　品川　鮫州の茶や|Shinagawa,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1840,1830,1850,Polychrome woodblock print; ink and color on paper,Overall: 8 3/4 x 13 3/4in. (22.2 x 34.9cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37196,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP753,false,true,37197,Asian Art,Print,東海道五十三次　川崎　六郷のわたし|Kawasaki,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1840,1830,1850,Polychrome woodblock print; ink and color on paper,8 23/32 x 13 23/32 in. (22.2 x 34.9 cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37197,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP754,false,true,37198,Asian Art,Print,東海道五十三次　神奈川　台の茶や|Kanagawa,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1840,1830,1850,Polychrome woodblock print; ink and color on paper,Overall: 8 3/4 x 13 3/4in. (22.2 x 34.9cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37198,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP755,false,true,37199,Asian Art,Print,東海道五十三次　程ヶ谷　かたびら橋　かたびら川|Hodogaya,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1840,1830,1850,Polychrome woodblock print; ink and color on paper,8 23/32 x 13 23/32 in. (22.2 x 34.9 cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37199,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP756,false,true,37200,Asian Art,Print,東海道五十三次　戸塚|Totsuka,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1840,1830,1850,Polychrome woodblock print; ink and color on paper,Overall: 8 3/4 x 13 3/4in. (22.2 x 34.9cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37200,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP757,false,true,37201,Asian Art,Print,東海道五十三次　藤澤|Fujisawa,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1840,1830,1850,Polychrome woodblock print; ink and color on paper,Overall: 8 3/4 x 13 3/4in. (22.2 x 34.9cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37201,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP758,false,true,37202,Asian Art,Print,東海道五十三次　平塚|Hiratsuka,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1840,1830,1850,Polychrome woodblock print; ink and color on paper,Overall: 8 3/4 x 13 3/4in. (22.2 x 34.9cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37202,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP759,false,true,37203,Asian Art,Print,東海道五十三次　大磯　鴫立沢西行庵|Ōiso,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1840,1830,1850,Polychrome woodblock print; ink and color on paper,8 23/32 x 13 23/32 in. (22.2 x 34.9 cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37203,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP760,false,true,37204,Asian Art,Print,東海道五十三次　小田原　酒匂川|Odawara,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1840,1830,1850,Polychrome woodblock print; ink and color on paper,Overall: 8 3/4 x 13 3/4in. (22.2 x 34.9cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37204,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP761,false,true,37205,Asian Art,Print,東海道五十三次　箱根　夜中松明とり|Hakone,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1848–49,1848,1849,Polychrome woodblock print; ink and color on paper,Overall: 8 3/4 x 13 3/4in. (22.2 x 34.9cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37205,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP762,false,true,37206,Asian Art,Print,東海道五十三次　三島|Mishima,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1840,1830,1850,Polychrome woodblock print; ink and color on paper,Overall: 8 3/4 x 13 3/4in. (22.2 x 34.9cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37206,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP763,false,true,37207,Asian Art,Print,東海道五十三次　沼津|Numazu,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1840,1830,1850,Polychrome woodblock print; ink and color on paper,Overall: 8 3/4 x 13 3/4in. (22.2 x 34.9cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37207,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP764,false,true,37208,Asian Art,Print,東海道五十三次　原|Hara,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1848–49,1848,1849,Polychrome woodblock print; ink and color on paper,Overall: 8 3/4 x 13 3/4in. (22.2 x 34.9cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37208,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP765,false,true,37209,Asian Art,Print,東海道五十三次　吉原　名所左り不二|Yoshiwara,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1840,1830,1850,Polychrome woodblock print; ink and color on paper,Overall: 8 3/4 x 13 3/4in. (22.2 x 34.9cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37209,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP766,false,true,37210,Asian Art,Woodblock print,東海道五十三次　蒲原　富士川渡舟|Kambara,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1840,1830,1850,Polychrome woodblock print; ink and color on paper,Overall: 8 3/4 x 13 3/4in. (22.2 x 34.9cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37210,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP767,false,true,37211,Asian Art,Print,東海道五十三次　由井|Yui,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1840,1830,1850,Polychrome woodblock print; ink and color on paper,Overall: 8 3/4 x 13 3/4in. (22.2 x 34.9cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37211,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP768,false,true,37212,Asian Art,Print,東海道五十三次　奥津　清見かせき　清見寺|Okitsu,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1840,1830,1850,Polychrome woodblock print; ink and color on paper,Overall: 8 3/4 x 13 3/4in. (22.2 x 34.9cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37212,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP769,false,true,37213,Asian Art,Print,東海道五十三次　江尻|Ejiri,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1840,1830,1850,Polychrome woodblock print; ink and color on paper,Overall: 8 3/4 x 13 3/4in. (22.2 x 34.9cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37213,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP770,false,true,37214,Asian Art,Print,東海道五十三次　府中|Fuchu,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1840,1830,1850,Polychrome woodblock print; ink and color on paper,Overall: 8 3/4 x 13 3/4in. (22.2 x 34.9cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37214,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP771,false,true,37215,Asian Art,Print,"東海道五十三次　鞠子|Mariko, from the series Tokaidō (popularly known as the Reisho Tokaidō)",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1840,1830,1850,Polychrome woodblock print; ink and color on paper,Overall: 8 3/4 x 13 3/4in. (22.2 x 34.9cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37215,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP772,false,true,37216,Asian Art,Print,東海道五十三次　岡部　宇津の山|Okabe,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1840,1830,1850,Polychrome woodblock print; ink and color on paper,Overall: 8 3/4 x 13 3/4in. (22.2 x 34.9cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37216,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP773,false,true,37217,Asian Art,Print,東海道五十三次　藤枝|Fujieda,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1840,1830,1850,Polychrome woodblock print; ink and color on paper,Overall: 8 3/4 x 13 3/4in. (22.2 x 34.9cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37217,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP774,false,true,37218,Asian Art,Print,東海道五十三次　島田　大井川|Shimada,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1840,1830,1850,Polychrome woodblock print; ink and color on paper,Overall: 8 3/4 x 13 3/4in. (22.2 x 34.9cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37218,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP775,false,true,37219,Asian Art,Print,東海道五十三次　金谷　かなや駅　大井川|Kanaya,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1840,1830,1850,Polychrome woodblock print; ink and color on paper,Overall: 8 3/4 x 13 3/4in. (22.2 x 34.9cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37219,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP776,false,true,37220,Asian Art,Print,東海道五十三次　日阪　夜啼石　無間山　小夜の中山|Nissaka,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1848–49,1848,1849,Polychrome woodblock print; ink and color on paper,Overall: 8 3/4 x 13 3/4in. (22.2 x 34.9cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37220,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP777,false,true,37221,Asian Art,Print,東海道五十三次　懸川　秋葉山別道|Kakegawa,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1840,1830,1850,Polychrome woodblock print; ink and color on paper,Overall: 8 3/4 x 13 3/4in. (22.2 x 34.9cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37221,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP778,false,true,37222,Asian Art,Print,東海道五十三次　袋井　名物遠川だこ|Fukoroi,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1840,1830,1850,Polychrome woodblock print; ink and color on paper,Overall: 8 3/4 x 13 3/4in. (22.2 x 34.9cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37222,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP779,false,true,37223,Asian Art,Print,東海道五十三次　見附　天竜川渡舟|Mitsuke,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1840,1830,1850,Polychrome woodblock print; ink and color on paper,Overall: 8 3/4 x 13 3/4in. (22.2 x 34.9cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37223,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP780,false,true,37224,Asian Art,Print,東海道五十三次　はま松|Hamamatsu,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1840,1830,1850,Polychrome woodblock print; ink and color on paper,Overall: 8 3/4 x 13 3/4in. (22.2 x 34.9cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37224,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP781,false,true,37225,Asian Art,Print,東海道五十三次　舞坂|Maizaka,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1840,1830,1850,Polychrome woodblock print; ink and color on paper,Overall: 8 3/4 x 13 3/4in. (22.2 x 34.9cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37225,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP782,false,true,37226,Asian Art,Print,東海道五十三次　荒井|Arai,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1840,1830,1850,Polychrome woodblock print; ink and color on paper,Overall: 8 3/4 x 13 3/4in. (22.2 x 34.9cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37226,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP783,false,true,37227,Asian Art,Print,東海道五十三次　白須賀　汐見坂|Shirasuke,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1840,1850,1850,Polychrome woodblock print; ink and color on paper,Overall: 8 3/4 x 13 3/4in. (22.2 x 34.9cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37227,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP784,false,true,37228,Asian Art,Print,東海道五十三次　二川　 猿ヶ馬場|Futagawa,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1840,1830,1850,Polychrome woodblock print; ink and color on paper,Overall: 8 3/4 x 13 3/4in. (22.2 x 34.9cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37228,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP785,false,true,37229,Asian Art,Print,東海道五十三次　吉田　六月十五日天王祭|Yoshida,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1840,1830,1850,Polychrome woodblock print; ink and color on paper,Overall: 8 3/4 x 13 3/4in. (22.2 x 34.9cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37229,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP786,false,true,37230,Asian Art,Print,東海道五十三次　御油　古街道本野ヶ原|Goyu,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1840,1830,1850,Polychrome woodblock print; ink and color on paper,Overall: 8 3/4 x 13 3/4 in. (22.2 x 34.9 cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37230,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP787,false,true,37231,Asian Art,Print,東海道五十三次　赤阪|Akasaka,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1840,1830,1850,Polychrome woodblock print; ink and color on paper,Overall: 8 3/4 x 13 3/4in. (22.2 x 34.9cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37231,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP788,false,true,37232,Asian Art,Print,東海道五十三次　藤川|Fujikawa,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1840,1830,1850,Polychrome woodblock print; ink and color on paper,Overall: 8 3/4 x 13 3/4in. (22.2 x 34.9cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37232,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP789,false,true,37233,Asian Art,Print,東海道五十三次　岡崎　矢はぎ川|Okazaki,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1840,1830,1850,Polychrome woodblock print; ink and color on paper,Overall: 8 3/4 x 13 3/4in. (22.2 x 34.9cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37233,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP790,false,true,37234,Asian Art,Print,東海道五十三次　池鯉鮒|Chiryu,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1840,1830,1850,Polychrome woodblock print; ink and color on paper,Overall: 8 3/4 x 13 3/4in. (22.2 x 34.9cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37234,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP791,false,true,37235,Asian Art,Print,東海道五十三次　鳴海　名産絞り店|Narumi,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1840,1830,1850,Polychrome woodblock print; ink and color on paper,Overall: 8 3/4 x 13 3/4in. (22.2 x 34.9cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37235,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP792,false,true,37236,Asian Art,Print,東海道五十三次　宮　七里の渡し　熱田鳥居　寝覚の里|Miya,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1840,1830,1850,Polychrome woodblock print; ink and color on paper,Overall: 8 3/4 x 13 3/4in. (22.2 x 34.9cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37236,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP793,false,true,37237,Asian Art,Print,東海道五十三次　桑名　七里の渡舟|Kuwana,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1840,1830,1850,Polychrome woodblock print; ink and color on paper,Overall: 8 3/4 x 13 3/4in. (22.2 x 34.9cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37237,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP794,false,true,37238,Asian Art,Print,東海道五十三次　四日市　日永村追分　参宮道|Yokkaichi,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1840,1830,1850,Polychrome woodblock print; ink and color on paper,Overall: 8 3/4 x 13 3/4in. (22.2 x 34.9cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37238,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP795,false,true,37239,Asian Art,Print,東海道五十三次　石薬師|Ishiyakushi,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1840,1830,1850,Polychrome woodblock print; ink and color on paper,Overall: 8 3/4 x 13 3/4in. (22.2 x 34.9cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37239,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP796,false,true,37240,Asian Art,Print,東海道五十三次　庄野|Shono,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1840,1830,1850,Polychrome woodblock print; ink and color on paper,Overall: 8 3/4 x 13 3/4in. (22.2 x 34.9cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37240,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP797,false,true,37241,Asian Art,Print,東海道五十三次　亀山|Kameyama,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1840,1830,1850,Polychrome woodblock print; ink and color on paper,Overall: 8 3/4 x 13 3/4in. (22.2 x 34.9cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37241,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP798,false,true,37242,Asian Art,Print,東海道五十三次　関|Seki,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1848–49,1848,1849,Polychrome woodblock print; ink and color on paper,Overall: 8 3/4 x 13 3/4in. (22.2 x 34.9cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37242,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP799,false,true,37243,Asian Art,Print,東海道五十三次　坂の下|Saka no Shita,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1840,1830,1850,Polychrome woodblock print; ink and color on paper,Overall: 8 3/4 x 13 3/4in. (22.2 x 34.9cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37243,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP800,false,true,37244,Asian Art,Print,東海道五十三次　土山|Tsuchiyama,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1840,1830,1850,Polychrome woodblock print; ink and color on paper,Overall: 8 3/4 x 13 3/4in. (22.2 x 34.9cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37244,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP801,false,true,37245,Asian Art,Print,東海道五十三次　水口　平松山美松|Minaguchi,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1840,1830,1850,Polychrome woodblock print; ink and color on paper,Overall: 8 3/4 x 13 3/4in. (22.2 x 34.9cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37245,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP802,false,true,37246,Asian Art,Print,東海道五十三次　石部|Ishibe,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1840,1830,1850,Polychrome woodblock print; ink and color on paper,Overall: 8 3/4 x 13 3/4in. (22.2 x 34.9cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37246,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP803,false,true,37247,Asian Art,Print,東海道五十三次　草津　矢ばせの渡口　琵琶湖風景|Kusatsu,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1840,1830,1850,Polychrome woodblock print; ink and color on paper,Overall: 8 3/4 x 13 3/4in. (22.2 x 34.9cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37247,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP804,false,true,37248,Asian Art,Print,東海道五十三次　大津|Otsu,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1840,1830,1850,Polychrome woodblock print; ink and color on paper,Overall: 8 3/4 x 13 3/4in. (22.2 x 34.9cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37248,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP805,false,true,37249,Asian Art,Print,東海道五十三次　京　三条大はし|Kyoto,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1848–49,1848,1849,Polychrome woodblock print; ink and color on paper,Overall: 8 3/4 x 13 3/4 in. (22.2 x 34.9 cm),"The Francis Lathrop Collection, Purchase, Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37249,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1021,false,true,53697,Asian Art,Print,六十余州名所図会　壱岐　志作|Winter View of Shimasaku in the Province of Iki,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1856,1856,1856,Polychrome woodblock print; ink and color on paper,H. 13 1/2 in. (34.3 cm); W. 8 13/16 in. (22.4 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/53697,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1022,false,true,54930,Asian Art,Woodblock print,"富士三十六景　伊豆の山中|View of Fuji san from the Mountains in the Province of Izu (Izu no Sanchu), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1858,1858,1858,Polychrome woodblock print; ink and color on paper,H. 12 5/8 in. (32.1 cm); W. 8 1/4 in. (21 cm) (trimmed),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54930,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1024,false,true,54931,Asian Art,Print,名所江戸百景・廓中東雲|The Entrance to the Yoshiwara at Dawn,Japan,Edo period (1615–1868),,,,Artist,Designed by,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1857,1857,1857,Polychrome woodblock print; ink and color on paper,H. 13 3/8 in. (34 cm); W. 8 3/4 in. (22.2 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54931,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1025,false,true,53680,Asian Art,Print,名所江戸百景　浅草　金龍山|The Kinryusan Temple at Asakusa,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1856,1856,1856,Polychrome woodblock print; ink and color on paper,H. 13 3/8 in. (34 cm); W. 8 3/4 in. (22.2 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/53680,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1026,false,true,54935,Asian Art,Print,"名所江戸百景　真崎辺より水神の森内川関屋の里を見る|Susaki Hen-yori Suijin no Mori, Uchikawa",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1857,1857,1857,Polychrome woodblock print; ink and color on paper,H. 13 3/8 in. (34 cm); W. 8 3/4 in. (22.2 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54935,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1027,false,true,54938,Asian Art,Woodblock print,道中膝栗毛|The Practical Jokers Yajirobei and Kitahachi,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1840,1830,1850,Monochrome woodblock print; ink on paper,H. 9 in. (22.9 cm); W. 13 7/8 in. (35.2 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54938,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1028,false,true,54940,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1840,1830,1850,Monochrome woodblock print; ink on paper,H. 8 3/4 in. (22.2 cm); W. 13 3/4 in. (34.9 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54940,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1029,false,true,54942,Asian Art,Woodblock print,道中膝栗毛　四日市追分|The Branch Road at Yokkaichi,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1840,1830,1850,Polychrome woodblock print; ink and color on paper,H. 9 in. (22.9 cm); W. 13 7/8 in. (35.2 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54942,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1030,false,true,54944,Asian Art,Print,道中膝栗毛　京都の町|Street in Kyoto,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1840,1830,1850,Polychrome woodblock print; ink and color on paper,H. 9 in. (22.9 cm); W. 13 7/8 in. (35.2 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54944,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1129,false,true,55049,Asian Art,Print,"東海道五十三次之内　平塚　縄手道|Hiratsuka, Nawate Do",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1834,1824,1844,Polychrome woodblock print; ink and color on paper,H. 9 15/16 in. (25.2 cm); W. 14 13/16 in. (37.6 cm),"Rogers Fund, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55049,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1155,false,true,45303,Asian Art,Print,江戸高名会亭尽　両国|The Aoyagi in Ryogoku,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1835–42,1825,1852,Polychrome woodblock print (surimono); ink and color on paper,H. 9 15/16 in. (25.2 cm); W. 14 7/16 in. (36.7 cm),"Rogers Fund, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45303,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1156,false,true,45304,Asian Art,Print,江戸高名会亭尽　浅草雷門前　かめや|Asakusa Kaminarimon Mae (Kameya),Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1835–42,1825,1852,Polychrome woodblock print; ink and color on paper,H. 9 7/8 in. (25.1 cm); W. 14 3/4 in. (37.5 cm),"Rogers Fund, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45304,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1157,false,true,45305,Asian Art,Print,江戸高名会亭尽　両国柳橋　大のし|The Ono at Ryogoku Yanagibashi,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1835–42,1825,1852,Polychrome woodblock print; ink and color on paper,H. 9 7/8 in. (25.1 cm); W. 14 9/16 in. (37 cm),"Rogers Fund, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45305,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1158,false,true,45306,Asian Art,Print,江戸高名会亭尽　柳ばし夜景　万八|The Manpachi at Evening in Yanagibashi,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1835–42,1825,1852,Polychrome woodblock print; ink and color on paper,H. 9 13/16 in. (24.9 cm); W. 14 9/16 in. (37 cm),"Rogers Fund, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45306,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1159,false,true,45307,Asian Art,Print,江戸高名会亭尽　柳島の図　橋本|Yanagishima no Zu,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1835–42,1825,1852,Polychrome woodblock print; ink and color on paper,H. 9 5/16 in. (23.7 cm); W. 14 3/16 in. (36 cm),"Rogers Fund, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45307,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1160,false,true,45308,Asian Art,Print,Ryogoku Yanagibashi (Umegawa)|江戸高名会亭尽　柳ばし|The Umegawa at Ryogoku Yanagibashi,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1835–42,1825,1852,Polychrome woodblock print; ink and color on paper,H. 9 7/16 in. (24 cm); W. 14 3/16 in. (36 cm),"Rogers Fund, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45308,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1161,false,true,45309,Asian Art,Print,Mukōjima (Daikokuya)|江戸高名会亭尽　向島　大七|The Daikokuya at Mukojima,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1835–42,1825,1852,Polychrome woodblock print; ink and color on paper,H. 9 5/16 in. (23.7 cm); W. 14 3/16 in. (36 cm),"Rogers Fund, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45309,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1162,false,true,45310,Asian Art,Print,江戸高名会亭尽　大をんし前　田川屋|Daisenji Mae (Tagawaya),Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1835–42,1825,1852,Polychrome woodblock print; ink and color on paper,H. 9 7/8 in. (25.1 cm); W. 14 9/16 in. (37 cm),"Rogers Fund, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45310,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1163,false,true,45311,Asian Art,Print,江戸高名会亭尽　隅田川橋場渡之図　柳屋|Sumidagawa Hashiba Watashi Zu (Yanagiya),Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1835–42,1825,1852,Polychrome woodblock print; ink and color on paper,H. 9 in. (22.9 cm); W. 13 7/8 in. (35.2 cm),"Rogers Fund, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45311,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1164,false,true,45312,Asian Art,Print,Ryogoku Yanagibashi (Kawachiya)|江戸高名会亭尽　両国柳橋　河内屋|Tea-house at the Willow Bridge,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1835–42,1825,1852,Polychrome woodblock print; ink and color on paper,H. 9 5/16 in. (23.7 cm); W. 14 3/16 in. (36 cm),"Rogers Fund, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45312,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1165,false,true,45313,Asian Art,Print,Fukagawa Hachiman Keidai (Niken Jyaya)|江戸高名会亭尽　深川八幡境内　二軒茶屋|Tea-house inside Hachiman Shrine,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1835–42,1825,1852,Polychrome woodblock print; ink and color on paper,H. 9 5/8 in. (24.4 cm); W. 14 5/8 in. (37.1 cm),"Rogers Fund, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45313,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1166,false,true,45314,Asian Art,Print,Shitaya Hirokōji (Oike)|江戸高名会亭尽　下谷広小路　河内楼|Teahouse at Hirokōji,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1835–42,1615,1868,Polychrome woodblock print; ink and color on paper,H. 9 7/8 in. (25.1 cm); W. 14 1/2 in. (36.8 cm),"Rogers Fund, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45314,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1167,false,true,45315,Asian Art,Print,Shinyoshiwara Emonzaka Nihonzutsumi (Harimaya)|江戸高名会亭尽　新吉原衣紋坂日本堤　播磨屋|The Harimaya at Shinyoshiwara Emonzaka Nihonzutsumi,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1835–42,1825,1852,Polychrome woodblock print; ink and color on paper,H. 9 5/8 in. (24.4 cm); W. 14 3/4 in. (37.5 cm),"Rogers Fund, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45315,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1168,false,true,45316,Asian Art,Print,Hakusan Keiseiga Kubo (Daisen)|江戸高名会亭尽　白山傾城ヶ窪|Tea house in Hakusen district,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1835–42,1825,1852,Polychrome woodblock print; ink and color on paper,H. 9 5/8 in. (24.4 cm); W. 14 3/8 in. (36.5 cm),"Rogers Fund, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45316,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1169,false,true,45317,Asian Art,Print,Mokuboji Yukimi (Uekiya)|江戸高名会亭尽　木母寺雪見　植木屋|Uekiya Restaurant at Mokuboji,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1835–42,1825,1852,Polychrome woodblock print; ink and color on paper,H. 9 3/8 in. (23.8 cm); W. 14 3/16 in. (36 cm),"Rogers Fund, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45317,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1170,false,true,45318,Asian Art,Print,江戸高名会亭尽　王子　扇屋|The Ōgiya at Ōji,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1835–42,1615,1868,Polychrome woodblock print; ink and color on paper,H. 9 3/8 in. (23.8 cm); W. 14 3/16 in. (36.1 cm),"Rogers Fund, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45318,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1171,false,true,55051,Asian Art,Print,江戸高名会亭尽　芝神明社内　車轍楼|Shiba Shinmeisha Uchi (Shatetsu-ro),Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1840,1830,1850,Polychrome woodblock print; ink and color on paper,H. 9 3/4 in. (24.8 cm); W. 14 1/2 in. (36.8 cm),"Rogers Fund, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55051,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1172,false,true,55052,Asian Art,Woodblock print,江戸高名会亭尽　亀戸裏門　玉屋|Kameido Uramon (Tama-ya),Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1840,1830,1850,Polychrome woodblock print; ink and color on paper,H. 9 3/8 in. (23.8 cm); W. 14 3/16 in. (36 cm),"Rogers Fund, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55052,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1173,false,true,55053,Asian Art,Print,江戸高名会亭尽　牛嶋　武蔵屋|Ushijima (Musashi-ya),Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1840,1830,1850,Polychrome woodblock print; ink and color on paper,H. 9 3/8 in. (23.8 cm); W. 14 3/16 in. (36 cm),"Rogers Fund, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55053,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1174,false,true,55054,Asian Art,Print,江戸高名会亭尽　向島の図　平岩|Mukojima no Zu (Hira-Iwa),Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1840,1830,1850,Polychrome woodblock print; ink and color on paper,H. 8 13/16 in. (22.4 cm); W. 13 3/4 in. (34.9 cm),"Rogers Fund, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55054,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1175,false,true,55057,Asian Art,Print,江戸高名会亭尽　本所小梅　小倉庵|Honjo Komme (Ogura-an),Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1840,1830,1850,Polychrome woodblock print; ink and color on paper,H. 9 1/2 in. (24.1 cm); W. 14 7/16 in. (36.7 cm),"Rogers Fund, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55057,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1176,false,true,55059,Asian Art,Print,江戸高名会亭尽　湯島　松琴亭|Yushima (Matsu Kane-ya),Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1840,1830,1850,Polychrome woodblock print; ink and color on paper,H. 9 15/16 in. (25.2 cm); W. 14 7/16 in. (36.7 cm),"Rogers Fund, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55059,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1177,false,true,55061,Asian Art,Print,江戸高名会亭尽　池之端　蓬莱亭　青楼花見の休み|Ikeno Mata (Horai-ya),Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1840,1830,1850,Polychrome woodblock print; ink and color on paper,H. 9 7/8 in. (25.1 cm); W. 14 5/8 in. (37.1 cm),"Rogers Fund, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55061,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1178,false,true,55075,Asian Art,Print,江戸高名会亭尽　今戸橋之図　金波楼|Imadobashi no Zu (Tama-Sho),Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1840,1830,1850,Polychrome woodblock print; ink and color on paper,H. 9 7/16 in. (24 cm); W. 14 3/16 in. (36 cm),"Rogers Fund, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55075,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1179,false,true,55077,Asian Art,Print,江戸高名会亭尽　三囲之景　出羽屋|Mimeguri no Kei (Toyoha-ya),Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1840,1830,1850,Polychrome woodblock print; ink and color on paper,H. 9 3/8 in. (23.8 cm); W. 14 3/16 in. (36 cm),"Rogers Fund, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55077,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1180,false,true,55078,Asian Art,Print,江戸高名会亭尽　雑司ヶ谷の図　茗荷屋|Zoshigaya no Zu (Myoga-ya),Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1840,1830,1850,Polychrome woodblock print; ink and color on paper,H. 9 7/16 in. (24 cm); W. 14 3/16 in. (36 cm),"Rogers Fund, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55078,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1181,false,true,55079,Asian Art,Woodblock print,江戸高名会亭尽　山谷　八百善|San-ya (Yaozen),Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1840,1830,1850,Polychrome woodblock print; ink and color on paper,H. 9 3/8 in. (23.8 cm); W. 14 3/16 in. (36 cm),"Rogers Fund, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55079,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1182,false,true,55081,Asian Art,Print,名所江戸百景　千駄木団子坂花屋敷|Sendagi Dangozaka,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1856,1856,1856,Polychrome woodblock print; ink and color on paper,Aiban; H. 13 7/8 in. (35.2 cm); W. 9 3/4 in. (24.8 cm),"Rogers Fund, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55081,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1183,false,true,55084,Asian Art,Woodblock print,"名所江戸百景　馬喰町初音の馬場|The First Race Course, Horse-Dealer's Street",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1857,1857,1857,Polychrome woodblock print; ink and color on paper,H. 14 1/16 in. (35.7 cm); W. 9 1/4 in. (23.5 cm),"Rogers Fund, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55084,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1184,false,true,37316,Asian Art,Print,"名所江戸百景　深川萬年橋|Mannen Bridge, Fukagawa",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1858,1858,1858,Polychrome woodblock print; ink and color on paper,H. 14 1/16 in. (35.7 cm); W. 9 7/16 in. (24 cm),"Rogers Fund, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37316,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1185,false,true,55095,Asian Art,Print,名所江戸百景　月の岬|Moon Viewing Point,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1857,1857,1857,Polychrome woodblock print; ink and color on paper,Oban 13 7/8 x 9 5/8 in. (35.2 x 24.4 cm),"Rogers Fund, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55095,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1186,false,true,55096,Asian Art,Print,"富士三十六景　相州三浦の海上|Fuji from Miura, Sagami (Soshu Miura no Kaijo), from the series Thirty-six Views of Mount Fuji (Fugaku sanjūrokkei)",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1858,1858,1858,Polychrome woodblock print; ink and color on paper,H. 14 1/4 in. (36.2 cm); W. 9 7/16 in. (24 cm),"Rogers Fund, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55096,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1187,false,true,49926,Asian Art,Print,近江八景　辛崎夜雨|Evening Rain at Karasaki Pine Tree,Japan,Edo period (1615–1868),,,,Artist,Designed by,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1857,1847,1867,Polychrome woodblock print; ink and color on paper,H. 6 1/2 in. (16.5 cm); W. 9 1/16 in. (23 cm),"Rogers Fund, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/49926,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1188,false,true,49927,Asian Art,Print,近江八景　石山秋月|Autumn Moon at Ishiyama,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1857,1857,1857,Polychrome woodblock print; ink and color on paper,H. 6 5/8 in. (16.8 cm); W. 9 in. (22.9 cm),"Rogers Fund, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/49927,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1189,false,true,49928,Asian Art,Print,近江八景　比良暮雪|Evening Snow at Mt. Hira,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1857,1847,1867,Polychrome woodblock print; ink and color on paper,H. 6 5/8 in. (16.8 cm); W. 8 3/4 in. (22.2 cm),"Rogers Fund, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/49928,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1190,false,true,49929,Asian Art,Print,近江八景　堅田落雁|Returning Geese at Katata,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1857,1857,1857,Polychrome woodblock print; ink and color on paper,H. 6 13/16 in. (17.3 cm); W. 9 in. (22.9 cm),"Rogers Fund, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/49929,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1191,false,true,49930,Asian Art,Print,近江八景　瀬田夕照|Sunset at Seta,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1857,1847,1867,Polychrome woodblock print; ink and color on paper,H. 6 5/8 in. (16.8 cm); W. 9 in. (22.9 cm),"Rogers Fund, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/49930,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1192,false,true,49931,Asian Art,Woodblock print,近江八景　三井晩鍾|Vesper Bells at Mii Temple,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1857,1847,1867,Polychrome woodblock print; ink and color on paper,H. 6 11/16 in. (17 cm); W. 9 1/8 in. (23.2 cm),"Rogers Fund, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/49931,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1193,false,true,49932,Asian Art,Woodblock print,近江八景　矢橋帰帆|Fishing Boats Sailing Back to Yabase,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1857,1847,1867,Polychrome woodblock print; ink and color on paper,H. 6 11/16 in. (17 cm); W. 9 1/16 in. (23 cm),"Rogers Fund, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/49932,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1194,false,true,49933,Asian Art,Print,近江八景　粟津晴嵐|Clearing Weather at Awazu,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1857,1857,1857,Polychrome woodblock print; ink and color on paper,H. 6 13/16 in. (17.3 cm); W. 9 1/8 in. (23.2 cm),"Rogers Fund, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/49933,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1197,false,true,53781,Asian Art,Print,"六十余州名所図会　肥後　五ヶの庄|Goka no Shō, Higo Province, from the series Views of Famous Places in the Sixty-Odd Provinces",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1853,1843,1863,Polychrome woodblock print; ink and color on paper,H. 13 5/8 in. (34.6 cm); W. 9 1/4 in. (23.5 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/53781,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1198,false,true,53783,Asian Art,Print,"六十余州名所図会　阿波　鳴門の風波|Naruto Whirlpool, Awa Province, from the series Views of Famous Places in the Sixty-Odd Provinces",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1853,1843,1863,Polychrome woodblock print; ink and color on paper,Oban tate-e 14 x 9 5/8 in. (35.6 x 24.4 cm),"Rogers Fund, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/53783,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1199,false,true,53784,Asian Art,Print,"六十余州名所図会　備後　阿武門観音堂|Kannondo, Abuto, Bingo Province, from the series Views of Famous Places in the Sixty-Odd Provinces",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1853,1843,1863,Polychrome woodblock print; ink and color on paper,H. 14 5/8 in. (37.1 cm); W. 10 1/15 in. (25.6 cm),"Rogers Fund, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/53784,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1200,false,true,55116,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1833–43,1833,1843,Polychrome woodblock print; ink and color on paper,H. 8 5/8 in. (21.9 cm); W. 13 1/2 in. (34.3 cm),"Rogers Fund, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55116,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1202,false,true,55120,Asian Art,Print,浪花名所図会　順慶町夜見世の図|Junkei machi Yomise no Zu,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1828,1818,1838,Polychrome woodblock print; ink and color on paper,H. 9 1/4 in. (23.5 cm); W. 14 3/16 in. (36 cm),"Rogers Fund, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55120,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1203,false,true,55122,Asian Art,Print,近江八景　石山秋月|The Autumn Moon on Ishiyama,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1857,1857,1857,Polychrome woodblock print; ink and color on paper,H. 14 5/8 in. (37.1 cm); W. 9 15/16 in. (25.2 cm),"Rogers Fund, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55122,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1204,false,true,55123,Asian Art,Print,五十三次名所図会　藤川　山中の里別名宮路山|Fujikawa; Sanchu Yamanaka no Sato Miyajiyama,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1855,1855,1855,Polychrome woodblock print; ink and color on paper,H. 14 7/16 in. (36.7 cm); W. 9 3/4 in. (24.8 cm),"Rogers Fund, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55123,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1205,false,true,55126,Asian Art,Print,五十三次名所図会　御油　本野ヶ原本坂ごへ|Goyu,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1855,1855,1855,Polychrome woodblock print; ink and color on paper,H. 14 1/16 in. (35.7 cm); W. 9 1/4 in. (23.5 cm),"Rogers Fund, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55126,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1206,false,true,55130,Asian Art,Print,五十三次名所図会　見付　天竜川舟渡し|Mitsuke,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1855,1855,1855,Polychrome woodblock print; ink and color on paper,H. 14 in. (35.6 cm); W. 9 1/4 in. (23.5 cm),"Rogers Fund, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55130,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1207,false,true,55131,Asian Art,Print,五十三次名所図会　水口|Mizukuchi,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1855,1855,1855,Polychrome woodblock print; ink and color on paper,H. 14 9/16 in. (37 cm); W. 9 13/16 in. (24.9 cm),"Rogers Fund, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55131,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1255,false,true,55152,Asian Art,Print,東海道五十三次　日本橋|Nihon Bashi,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1838,1828,1848,Polychrome woodblock print; ink and color on paper,H. 9 1/8 in. (23.2 cm); W. 6 5/8 in. (16.8 cm),"Rogers Fund, 1921",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55152,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1256,false,true,55153,Asian Art,Print,東海道五十三次　吉原　左リ富士ノ縄手|Yoshiwara,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1838,1828,1848,Polychrome woodblock print; ink and color on paper,H. 8 11/16 in. (22.1 cm); W. 6 5/16 in. (16 cm),"Rogers Fund, 1921",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55153,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1257,false,true,55154,Asian Art,Print,東海道五十三次　由井　由井川|Yui,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1838,1828,1848,Polychrome woodblock print; ink and color on paper,H. 8 1/2 in. (21.6 cm); W. 6 1/2 in. (16.5 cm),"Rogers Fund, 1921",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55154,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1258,false,true,55155,Asian Art,Print,"東海道五十三次　嶋田　大井川|Shimada; Oigawa Shun-Gan, Banks of the Oi River",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1838,1828,1848,Polychrome woodblock print; ink and color on paper,H. 8 7/8 in. (22.5 cm); W. 6 1/2 in. (16.5 cm),"Rogers Fund, 1921",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55155,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1259,false,true,55156,Asian Art,Woodblock print,"東海道五十三次　日阪　倭園琴桜|Nissaka; Sayo no Naka Yama, pass in the Bayo Mountains",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1838,1828,1848,Polychrome woodblock print; ink and color on paper,H. 8 5/8 in. (21.9 cm); W. 6 3/8 in. (16.2 cm),"Rogers Fund, 1921",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55156,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1260,false,true,55157,Asian Art,Print,東海道五十三次　掛川|Kakegawa; Akiba-san Embo,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1838,1828,1848,Polychrome woodblock print; ink and color on paper,H. 9 1/8 in. (23.2 cm); W. 6 5/8 in. (16.8 cm),"Rogers Fund, 1921",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55157,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1261,false,true,55158,Asian Art,Print,東海道五十三次　浜松|Hamamatsu,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1838,1828,1848,Polychrome woodblock print; ink and color on paper,H. 8 15/16 in. (22.7 cm); W. 6 11/16 in. (17 cm),"Rogers Fund, 1921",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55158,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1262,false,true,55159,Asian Art,Print,東海道五十三次　二川|Futagawa,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1838,1828,1848,Polychrome woodblock print; ink and color on paper,H. 8 11/16 in. (22.1 cm); W. 6 7/16 in. (16.4 cm),"Rogers Fund, 1912",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55159,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1263,false,true,55160,Asian Art,Print,"東海道五十三次　吉田|Yoshida; Toyokawa-Bashi, Toyokawa Bridge",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1838,1828,1848,Polychrome woodblock print; ink and color on paper,H. 8 1/2 in. (21.6 cm); W. 6 7/16 in. (16.4 cm),"Rogers Fund, 1921",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55160,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1264,false,true,55161,Asian Art,Print,東海道五十三次　御油|Goyu,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1838,1828,1848,Polychrome woodblock print; ink and color on paper,H. 8 7/8 in. (22.5 cm); W. 6 1/2 in. (16.5 cm),"Rogers Fund, 1921",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55161,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1265,false,true,55162,Asian Art,Print,東海道五十三次　藤川|Fujikawa,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1838,1828,1848,Polychrome woodblock print; ink and color on paper,H. 9 1/8 in. (23.2 cm); W. 6 11/16 in. (17 cm),"Rogers Fund, 1921",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55162,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1266,false,true,55163,Asian Art,Print,東海道五十三次　石部|Ishibe,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1838,1828,1848,Polychrome woodblock print; ink and color on paper,H. 8 5/8 in. (21.9 cm); W. 6 1/2 in. (16.5 cm),"Rogers Fund, 1921",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55163,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1293,false,true,55235,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1840,1830,1850,Polychrome woodblock print; ink and color on paper,H. 4 3/8 (11.1 cm); W. 6 1/2 in. (16.5 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55235,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1313,false,true,45293,Asian Art,Print,Takanawa no Meigetsu|東都名所　高輪之明月|Full Moon at Takanawa,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1831,1821,1841,Polychrome woodblock print; ink and color on paper,H. 9 5/8 in. (24.4 cm); W. 15 1/8 in. (38.4 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45293,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1317,false,true,55275,Asian Art,Print,木曽海道六拾九次之内　三渡野|Santono Station,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1835,1825,1845,Polychrome woodblock print; ink and color on paper,H. 10 in. (25.4 cm); W. 15 in. (38.1 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55275,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1319,false,true,55278,Asian Art,Print,京都名所之内　四条河原夕涼|Cooling off in the Evening at Shijogawara,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1834,1824,1844,Polychrome woodblock print; ink and color on paper,Image: 8 5/8 x 13 7/8 in. (21.9 x 35.2 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55278,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1468,false,true,55551,Asian Art,Print,忠臣蔵　五段目|Sadakuro Threatening to Kill Yoichibei,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1840,1830,1850,Polychrome woodblock print; ink and color on paper,H. 9 1/8 in. (23.2 cm); W. 13 3/4 in. (34.9 cm),"Rogers Fund, 1925",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55551,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1469,false,true,55552,Asian Art,Print,名所江戸百景　馬喰町初音の馬場|Hatsune no Baba; Bakurocho,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1857,1847,1867,Polychrome woodblock print; ink and color on paper,H. 13 1/2 in. (34.3 cm); W. 8 13/16 in. (22.4 cm),"Rogers Fund, 1925",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55552,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1470,false,true,55553,Asian Art,Print,"名所江戸百景　王子装束ゑの木大晦日の狐火|New Year's Eve Foxfires at the Changing Tree, Ōji",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1857,1847,1867,Polychrome woodblock print; ink and color on paper,Image: 12 13/16 × 8 5/8 in. (32.5 × 21.9 cm),"Rogers Fund, 1925",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55553,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1473,false,true,55557,Asian Art,Print,東海道五十三次之内　神奈川　台之景|View of Kangawa at Sunset,Japan,Edo period (1615–1868),,,,Artist,Designed by,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1834,1824,1844,Polychrome woodblock print; ink and color on paper,H. 9 3/4 in. (24.8 cm); W. 14 in. (35.6 cm),"Gift of Mrs. Henry J. Bernheim, 1945",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55557,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1474,false,true,55558,Asian Art,Print,"東海道五十三次之内　御油　旅人留女|Goyu, Tabibito Ryujo",Japan,Edo period (1615–1868),,,,Artist,Designed by,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1834,1824,1844,Polychrome woodblock print; ink and color on paper,H. 13 7/8 in. (35.2 cm); W. 10 3/4 in. (27.3 cm),"Gift of Mrs. Henry J. Bernheim, 1945",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55558,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1475,false,true,55560,Asian Art,Print,"東海道五十三次之内　鳴海　名物有松絞|Narumi, Meibutsu Arimatsu Shibori",Japan,Edo period (1615–1868),,,,Artist,Designed by,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1734,1724,1744,Polychrome woodblock print; ink and color on paper,H. 9 3/4 in. (24.8 cm); W. 14 1/2 in. (36.8 cm),"Gift of Mrs. Henry J. Bernheim, 1945",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55560,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1476,false,true,55563,Asian Art,Print,五十三次名所図会　草津|Kusatsu,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1855,1855,1855,Polychrome woodblock print; ink and color on paper,H. 14 1/2 in. (36.8 cm); W. 9 3/4 in. (24.8 cm),"Gift of Mrs. Henry J. Bernheim, 1945",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55563,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1483,false,true,55574,Asian Art,Print,江戸高名会亭尽　深川八幡境内　二軒茶屋|Fukagawa Hachiman Keidai (Niken Jya-ya),Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1840,1830,1850,Polychrome woodblock print; ink and color on paper,H. 9 5/8 in. (24.4 cm); W. 14 5/8 in. (37.1 cm),"Rogers Fund, 1926",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55574,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1484,false,true,55577,Asian Art,Print,浪花名所図会　今宮　十日恵比寿|Imamiya Toka Ebisu,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1828,1818,1838,Polychrome woodblock print; ink and color on paper,H. 9 11/16 in. (24.6 cm); W. 14 11/16 in. (37.3 cm),"Rogers Fund, 1926",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55577,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1487,false,true,55583,Asian Art,Print,歌川広重画　桜花に都鳥|Hooded Gulls and Cherry Blossoms,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,probably 1850–62,1850,1862,Polychrome woodblock print; ink and color on paper,H. 7 7/8 in. (20 cm); W. (top) 9 in. (22.9 cm); (bottom) 7 3/4 in. (19.7 cm),"Rogers Fund, 1926",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55583,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1488,false,true,55585,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1840–50,1840,1850,Polychrome woodblock print; ink and color on paper,H. 12 7/8 in. (32.7 cm); W. 4 13/16 in. (12.2 cm),"Rogers Fund, 1926",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55585,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1491,false,true,55587,Asian Art,Print,"東海道五十三次之内　桑名　七里渡口|Station Forty-Three: Kuwana, Seven-Ri Ferry at the Port, from the Fifty-Three Stations of the Tokaido",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1833–34,1833,1834,Polychrome woodblock print; ink and color on paper,H. 9 3/8 in. (23.8 cm); W. 14 7/8 in. (37.8 cm),"Gift of Louis V. Ledoux, 1927",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55587,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1498,false,true,55594,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1835,1825,1845,Polychrome woodblock print; ink and color on paper,H. 8 1/2 in. (21.6 cm); W. 11 1/2 in. (29.2 cm),"Fletcher Fund, 1928",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55594,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1546,false,true,55711,Asian Art,Print,木曽海道六拾九次之内　洗馬|Senba Station,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",(?),"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1835–48,1835,1848,Polychrome woodblock print; ink and color on paper,H. 9 5/8 in. (24.4 cm); W. 14 7/16 in. (36.7 cm),"Fletcher Fund, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55711,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1547,false,true,55712,Asian Art,Print,"名所江戸百景　御厩河岸|Ommayagashi, Sumida River",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1857,1847,1867,Polychrome woodblock print; ink and color on paper,H. 14 3/4 in. (37.5 cm); W. 10 1/8 in. (25.7 cm),"Fletcher Fund, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55712,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1548,false,true,53682,Asian Art,Print,"名所江戸百景　京橋竹がし|Full Moon Over Canal, with Bridge and Huge Stacks of Bamboo along the Bank",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1857,1847,1867,Polychrome woodblock print; ink and color on paper,Aiban; H. 13 7/8 in. (35.2 cm); W. 9 1/4 in. (23.5 cm),"Fletcher Fund, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/53682,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1876,false,true,56196,Asian Art,Print,"東海道五十三次　池鯉鮒　首夏馬市|Chiriu, Station No. 40",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1834,1824,1844,Polychrome woodblock print; ink and color on paper,H. 9 in. (22.9 cm); W. 13 7/8 in. (35.2 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56196,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1877,false,true,56258,Asian Art,Print,"東海道五十三次　関　本陣早立|Seki, Stations No. 48",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1834,1824,1844,Polychrome woodblock print; ink and color on paper,9 3/4 x 14 7/16 in. (24.8 x 36.7cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56258,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1880,false,true,44980,Asian Art,Print,Shinshu-sarashina tagoto no tsuki|本朝名所　信州更科田毎之月|Reflections of the Moon in the Rice Fields of Sarashina in Shinshu,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1832,1822,1842,Polychrome woodblock print; ink and color on paper,Image: 9 1/4 × 14 1/8 in. (23.5 × 35.9 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/44980,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1887,false,true,56519,Asian Art,Print,江戸近郊八景之内　玉川秋月|Autumn Moon on the Tama River,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1838,1828,1848,Polychrome woodblock print; ink and color on paper,H. 9 3/8 in. (23.8 cm); W. 14 5/16 in. (36.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56519,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1889,false,true,44623,Asian Art,Print,四季江都名所　冬　隅田川之雪|Snow on the Sumida River,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1835,1825,1845,Polychrome woodblock print; ink and color on paper,14 5/8 x 5 in. (37.1 x 12.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/44623,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1892,false,true,56590,Asian Art,Print,雪月花　阿波鳴門之風景|Rapids at Naruto,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1857,1857,1857,Triptych of polychrome woodblock prints; ink and color on paper,A: H. 14 1/2 in. (36.8 cm); W. 9 11/16 in. (24.6 cm) B: H. 14 1/2 in. (36.8 cm); W. 9 11/16 in. (24.6 cm) C: H. 14 1/2 in. (36.8 cm); W. 9 11/16 in. (24.6 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56590,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1893,false,true,56591,Asian Art,Woodblock print,雪月花　武陽金沢八勝夜景|Panorama of the Eight Views of Kanasawa under a Full Moon,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1857,1857,1857,Triptych of polychrome woodblock prints; ink and color on paper,H. 14 5/8 in. (37.1 cm); W. 29 7/8 in. (75.9 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56591,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1894,false,true,53688,Asian Art,Woodblock print,雪中芦に鴨|A Wild Duck near a Snow-laden Shore,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1843,1833,1853,Polychrome woodblock print; ink and color on paper,H. 14 7/8 in. (37.8 cm); W. 6 3/4 in. (17.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/53688,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1895,false,true,45023,Asian Art,Woodblock print,歌川広重画　雪中芦に鴨|Mallard Ducks and Snow-covered Reeds,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1843,1833,1853,Polychrome woodblock print; ink and color on paper,H. 14 7/8 in. (37.8 cm); W. 5 1/16 in. (12.9 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45023,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1898,false,true,56796,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1834,1824,1844,Polychrome woodblock print; ink and color on paper,14 11/16 x 4 15/16 in. (37.3 x 12.5 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56796,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1899,false,true,53687,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1840s,1840,1849,Polychrome woodblock print; ink and color on paper,H. 8 15/16 in. (22.7 cm); W. 11 3/16 in. (28.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/53687,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2349,false,true,54134,Asian Art,Woodblock print,"西洋風の懐中時計『春雨集』　摺物帖|Western Pocket WatchFrom the Spring Rain Collection (Harusame shū), vol. 3",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,dated 1823,1823,1823,Part of an album of woodblock prints (surimono); ink and color on paper,7 11/16 x 6 1/4 in. (19.5 x 15.9 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54134,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2457,false,true,45298,Asian Art,Print,Gotenyama no yu-zakura|東都名所　御殿山之夕桜|Evening Cherry Blossoms at Gotenyama,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1831,1831,1831,Polychrome woodblock print; ink and color on paper,13 3/4 x 8 1/2 in. (34.9 x 21.6 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45298,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2458,false,true,45300,Asian Art,Print,"Sumidagawa, hazakura no kei|東都名所　隅田川葉桜之景|A View of Cherry Trees in Leaf along the Sumida River",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1831,1831,1831,Polychrome woodblock print; ink and color on paper,13 3/4 x 8 1/2 in. (34.9 x 21.6 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45300,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2459,false,true,45299,Asian Art,Print,"Masaki, boshun no kei|東都名所　真崎暮春之景|A View of Late Spring at Masaki",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1831,1831,1831,Polychrome woodblock print; ink and color on paper,13 3/4 x 8 1/2 in. (34.9 x 21.6 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45299,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2461,false,true,45301,Asian Art,Print,Tsukudajima hatsuhotogizu|東都名所　佃島初郭公|The Year's First Song of the Cuckoo at Tsukudajima,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1831,1831,1831,Polychrome woodblock print; ink and color on paper,13 3/4 x 8 1/2 in. (34.9 x 21.6 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45301,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2462,false,true,56887,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1834,1824,1844,Polychrome woodblock print; ink and color on paper,13 1/2 x 4 1/2 in. (34.3 x 11.4 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56887,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2463,false,true,45295,Asian Art,Print,Susaki yuki no hatsuhi|東都名所　洲崎雪之初日|New Year's Sunrise after Snow at Susaki,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1831,1821,1841,Polychrome woodblock print; ink and color on paper,8 1/2 x 14 in. (21.6 x 35.6 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45295,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2464,false,true,56888,Asian Art,Print,京都名所之内　八瀬之里|Village of Yase,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1832,1822,1842,Polychrome woodblock print; ink and color on paper,8 3/4 x 14 in. (22.2 x 35.6 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56888,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2465,false,true,56889,Asian Art,Print,京都名所之内　祇園社雪中|The Gion Shrine in Snow,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1832,1822,1842,Polychrome woodblock print; ink and color on paper,8 3/4 x 14 in. (22.2 x 35.6 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56889,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2466,false,true,45302,Asian Art,Print,Kameido Tenmangu keidai no yuki|東都名所　亀戸天満宮境内雪|Tenmangū Shrine at Kameido in Snow,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1833–43,1615,1868,Polychrome woodblock print; ink and color on paper,8 3/4 x 13 3/4 in. (22.2 x 34.9 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45302,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2467,false,true,56890,Asian Art,Print,金沢八景　内川暮雪|Evening Snow at Uchikawa,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1836,1826,1846,Polychrome woodblock print; ink and color on paper,9 x 13 7/8 in. (22.9 x 35.2 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56890,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2468,false,true,56891,Asian Art,Woodblock print,"山海見立相撲　備前偸賀山|Mount Yuga in Bizen Province (Bizen Yugasan), from the series Wrestling Matches between Mountains and Seas (Sankai mitate zumō)",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,"8th month, 1858",1858,1858,Polychrome woodblock print; ink and color on paper,8 3/4 x 13 in. (22.2 x 33 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56891,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2469,false,true,56892,Asian Art,Woodblock print,近江八景之内　三井晩鐘|Vesper Bells at Mii Temple,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1832,1822,1842,Polychrome woodblock print; ink and color on paper,9 x 13 3/4 in. (22.9 x 34.9 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56892,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2470,false,true,56893,Asian Art,Print,近江八景之内　堅田落雁|Returning Geese at Katada,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1832,1822,1842,Polychrome woodblock print; ink and color on paper,8 7/8 x 13 3/4 in. (22.5 x 34.9 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56893,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2471,false,true,56894,Asian Art,Print,近江八景之内　粟津晴嵐|Clearing Weather at Awazu,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1832,1822,1842,Polychrome woodblock print; ink and color on paper,8 7/8 x 13 3/4 in. (22.5 x 34.9 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56894,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2472,false,true,56895,Asian Art,Woodblock print,近江八景之内　矢橋帰帆|Fishing Boats Returning to Yabase,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1832,1822,1842,Polychrome woodblock print; ink and color on paper,8 7/8 x 13 3/4 in. (22.5 x 34.9 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56895,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2473,false,true,56896,Asian Art,Print,近江八景之内　瀬田夕照|Sunset at Seta,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1832,1822,1842,Polychrome woodblock print; ink and color on paper,8 7/8 x 13 3/4 in. (22.5 x 34.9 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56896,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2474,false,true,56897,Asian Art,Print,近江八景之内　比良暮雪|Evening Snow on Mount Hira,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1832,1822,1842,Polychrome woodblock print; ink and color on paper,8 7/8 x 15 15/16 in. (22.5 x 40.5 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56897,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2475,false,true,56898,Asian Art,Print,近江八景之内　石山秋月|The Autumn Full Moon at Ishiyama,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1832,1822,1842,Polychrome woodblock print; ink and color on paper,8 7/8 x 13 11/16 in. (22.5 x 34.8 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56898,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2476,false,true,56899,Asian Art,Print,近江八景之内　唐崎夜雨|Evening Rain at Karasaki-Pine Tree,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1832,1822,1842,Polychrome woodblock print; ink and color on paper,8 7/8 x 13 3/4 in. (22.5 x 34.9 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56899,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2477,false,true,56900,Asian Art,Print,江戸近郊八景之内　芝浦晴嵐|Clearing Weather at Shibaura,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1838,1828,1848,Polychrome woodblock print; ink and color on paper,10 x 15 in. (25.4 x 38.1 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56900,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2478,false,true,56901,Asian Art,Print,江戸近郊八景之内　行徳帰帆|Boats Returning to Gyotoku,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1838,1828,1848,Polychrome woodblock print; ink and color on paper,8 3/4 x 13 3/4 in. (22.2 x 34.9 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56901,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2479,false,true,56902,Asian Art,Print,江戸近郊八景之内　飛鳥山暮雪|Asukayama in Evening Snow,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1838,1828,1848,Polychrome woodblock print; ink and color on paper,8 3/4 x 13 3/4 in. (22.2 x 34.9 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56902,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2480,false,true,56903,Asian Art,Print,江戸近郊八景之内　玉川秋月|Autumn Moon on the Tama River,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1838,1828,1848,Polychrome woodblock print; ink and color on paper,8 3/4 x 13 3/4 in. (22.2 x 34.9 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56903,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2481,false,true,56904,Asian Art,Woodblock print,東海道五十三次之内　江尻　三保遠望|Panorama of Miwo Pine Wood from Ejiri,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1834,1834,1834,Polychrome woodblock print; ink and color on paper,8 7/8 x 13 3/4 in. (22.5 x 34.9 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56904,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2482,false,true,56905,Asian Art,Print,東海道五十三次之内　石薬師　石薬師寺|The Ishiyakushi Temple at Ishiyakushi,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1834,1834,1834,Polychrome woodblock print; ink and color on paper,8 7/8 x 13 5/8 in. (22.5 x 34.6 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56905,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2483,false,true,56906,Asian Art,Print,"東海道五十三次之内　藤川　棒鼻の図|Station Thirty-Eight: Fujikawa, Scene at the Border, from the Fifty-Three Stations of the Tokaido",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1833–34,1833,1834,Polychrome woodblock print; ink and color on paper,9 x 14 in. (22.9 x 35.6 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56906,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2484,false,true,56907,Asian Art,Print,東海道五十三次之内　四日市　三重川|Mie River at Yokkaichi,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1834,1834,1834,Polychrome woodblock print; ink and color on paper,8 7/8 x 13 3/4 in. (22.5 x 34.9 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56907,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2485,false,true,56908,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1832,1822,1842,Polychrome woodblock print; ink and color on paper,14 7/8 x 5 1/8 in. (37.8 x 13 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56908,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2487,false,true,56910,Asian Art,Woodblock print,"東海道五十三次之内　関　本陣早立|Station Forty-Eight: Seki, Early Departure from the Headquarters Inn, from the Fifty-Three Stations of the Tokaido",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1833–34,1833,1834,Polychrome woodblock print; ink and color on paper,8 7/8 13 5/8 in. (22.5 x 34.6 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56910,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2489,false,true,56912,Asian Art,Print,東海道五十三次之内　岡部　宇津の山|Utsu Hill at Okabe,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1834,1834,1834,Polychrome woodblock print; ink and color on paper,9 x 14 in. (22.9 x 35.6 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56912,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2490,false,true,56913,Asian Art,Print,東海道五十三次之内　金谷　大井川遠岸|The Far Bank of the Ōi River at Kanaya,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1834,1834,1834,Polychrome woodblock print; ink and color on paper,9 x 14 in. (22.9 x 35.6 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56913,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2492,false,true,56915,Asian Art,Woodblock print,"東海道五十三次之内　蒲原　夜の雪|Evening Snow at Kanbara, from the series ""Fifty-three Stations of the Tōkaidō""",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1833–34,1823,1844,Polychrome woodblock print; ink and color on paper,8 7/8 x 13 3/4 in. (22.5 x 34.9 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56915,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2493,false,true,56916,Asian Art,Print,東海道五十三次之内　庄野　白雨|Shower at Shōno,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1834,1834,1834,Polychrome woodblock print; ink and color on paper,8 7/8 x 13 5/8 in. (22.5 x 34.6 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56916,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2494,false,true,56917,Asian Art,Print,東海道五十三次　はま松|Hamamatsu Station,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1842,1832,1852,Polychrome woodblock print; ink and color on paper,8 9/16 x 13 5/8 in. (21.7 x 34.6 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56917,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2495,false,true,56918,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1833,1823,1843,Uncut triptych of polychrome woodblock prints; ink and color on paper,9 1/4 x 14 1/2 in. (23.5 x 36.8 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56918,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2496,false,true,56919,Asian Art,Print,木曽海道六拾九次之内　あし田|Ashida Station,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1836,1826,1846,Polychrome woodblock print; ink and color on paper,8 3/4 x 13 3/4 in. (22.2 x 34.9 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56919,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2497,false,true,56920,Asian Art,Woodblock print,歌川広重画　雪中芦に鴨|Mallard Duck and Snow-covered Reeds,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1832,1822,1842,Polychrome woodblock print; ink and color on paper,15 x 6 7/8 in. (38.1 x 17.5 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56920,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2498,false,true,56921,Asian Art,Print,木曽海道六拾九次之内　長久保|Nagakubo Station,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1836,1826,1846,Polychrome woodblock print; ink and color on paper,8 3/4 x 13 3/4 in. (22.2 x 34.9 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56921,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2499,false,true,56922,Asian Art,Print,木曽海道六拾九次之内　福しま|Fukushima Station,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1837,1827,1847,Polychrome woodblock print; ink and color on paper,8 3/4 x 13 3/4 in. (22.2 x 34.9 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56922,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2500,false,true,56923,Asian Art,Print,木曽海道六拾九次之内　宮ノ越|Miyanokoshi Station,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1836,1826,1846,Polychrome woodblock print; ink and color on paper,8 7/8 x 13 3/8 in. (22.5 x 34 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56923,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2501,false,true,56924,Asian Art,Print,木曽海道六拾九次之内　大井|Ōi Station,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1837,1827,1847,Polychrome woodblock print; ink and color on paper,8 7/8 x 13 3/8 in. (22.5 x 34 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56924,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2502,false,true,56925,Asian Art,Print,"歌川広重画　朝顔に鶏と傘|Rooster, Umbrella, and Morning Glories",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1830,1820,1840,Polychrome woodblock print; ink and color on paper,14 3/4 x 6 1/2 in. (37.5 x 16.5 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56925,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2503,false,true,45320,Asian Art,Print,"Sumidagawa no yuki|Sumida River in the Snow, from the series ""Famous Places in Edo in the Four Seasons""",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1834,1834,1834,Polychrome woodblock print (tanzaku size); ink and color on paper,Tanzaku 14 3/4 x 5 in. (37.5 x 12.7 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45320,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2504,false,true,56926,Asian Art,Print,月二拾八景之内　弓張月|Bow Moon,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1832,1832,1832,Polychrome woodblock print; ink and color on paper,14 3/4 x 6 7/8 in. (37.5 x 17.5 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56926,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2506,false,true,56928,Asian Art,Print,"富士三十六景　房州保田の海岸|Seashore at Hoda, Province of Awa",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1858–59,1858,1859,Polychrome woodblock print; ink and color on paper,13 1/4 x 8 3/4 in. (33.7 x 22.2 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56928,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2507,false,true,56929,Asian Art,Woodblock print,富士三十六景　駿河三保の松原|Pine Groves of Miho in Suruga Province,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1858,1858,1858,Polychrome woodblock print; ink and color on paper,13 3/8 x 8 3/4 in. (34 x 22.2 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56929,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2508,false,true,53785,Asian Art,Woodblock print,"六十余州名所図会　 対馬　海岸　夕晴|Evening Glow, Tsushima Province, from the series Views of Famous Places in the Sixty-Odd Provinces",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1853,1843,1863,Polychrome woodblock print; ink and color on paper,13 3/8 x 9 in. (34 x 22.9 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/53785,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2509,false,true,53780,Asian Art,Print,"六十余州名所図会　播磨　舞子の浜|Maiko Beach, Harima Province, from the series Views of Famous Places in the Sixty-Odd Provinces",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1853,1843,1863,Polychrome woodblock print; ink and color on paper,Oban tate-e 12 3/8 x 9 in. (31.4 x 22.9 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/53780,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2510,false,true,53681,Asian Art,Print,Kamata no Baien|名所江戸百景　蒲田の梅園|Plum Garden at Kamata,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1857,1857,1857,Polychrome woodblock print; ink and color on paper,13 1/4 x 8 5/8 in. (33.7 x 21.9 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/53681,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2511,false,true,56930,Asian Art,Print,歌川広重画　鉄線花に鳥|Clematis and Bird,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1852,1852,1852,Polychrome woodblock print; ink and color on paper,6 7/8 x 9 in. (17.5 x 22.9 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56930,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2512,false,true,53679,Asian Art,Print,"名所江戸百景　昌平橋　聖堂　神田川|Shohei Bridge, Seido Temple and Kanda River",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1857,1857,1857,Polychrome woodblock print; ink and color on paper,13 1/4 x 8 3/4 in. (33.7 x 22.2 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/53679,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2513,false,true,56692,Asian Art,Print,"名所江戸百景　神田紺屋町|Dye House at Konya-cho, Kanda",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1857,1857,1857,Polychrome woodblock print; ink and color on paper,H. 13 in. (33 cm); W. 8 1/2 in. (21.6 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56692,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2514,false,true,56931,Asian Art,Print,"「名所江戸百景　よし原 日本堤」|“Nihon Embankment at Yoshiwara,” from the series One Hundred Famous Views of Edo (Meisho Edo hyakkei, Yoshiwara, Nihonzutsumi)",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1857,1857,1857,Polychrome woodblock print; ink and color on paper,14 1/4 x 9 1/2 in. (36.2 x 24.1 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56931,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2515,false,true,56932,Asian Art,Print,名所江戸百景　深川木場|The Lumber Yard at Fukagawa,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1856,1856,1856,Polychrome woodblock print; ink and color on paper,14 1/4 x 9 3/8 in. (36.2 x 23.8 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56932,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2516,false,true,56933,Asian Art,Print,名所江戸百景　目黒太鼓橋夕日の岡|The Taiko (Drum) Bridge and the Yuhi Mound at Meguro,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1857,1857,1857,Polychrome woodblock print; ink and color on paper,Oban: 14 1/8 x 9 1/8 in. (35.9 x 23.2 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56933,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2517,false,true,53659,Asian Art,Print,名所江戸百景　亀戸天神境内|In the Kameido Tenjin Shrine Compound,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1856,1856,1856,Polychrome woodblock print; ink and color on paper,14 1/4 x 9 3/4 in. (36.2 x 24.8 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/53659,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2518,false,true,56934,Asian Art,Print,名所江戸百景　請地秋葉の境内|Inside Akiba Shrine at Ukeji,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1857,1857,1857,Polychrome woodblock print; ink and color on paper,13 1/4 x 8 3/4 in. (33.7 x 22.2 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56934,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2519,false,true,56689,Asian Art,Print,"名所江戸百景　浅草金龍山|Kinryūsan Temple at Asakusa, from the series ""One Hundred Famous Views of Edo""",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1856,1856,1856,Polychrome woodblock print; ink and color on paper,H. 14 1/16 in. (35.7 cm); W. 9 1/2 in. (24.1 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56689,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2520,false,true,55733,Asian Art,Print,"「名所江戸百景　深川洲崎十万坪」|“Jūmantsubo Plain at Fukagawa Susaki,” from the series One Hundred Famous Views of Edo (Meisho Edo hyakkei, Fukagawa Susaki Jūmantsubo)",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1856,1856,1856,Polychrome woodblock print; ink and color on paper,Oban 14 1/16 x 9 1/2 in. (35.7 x 24.1 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55733,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2521,false,true,56935,Asian Art,Print,名所江戸百景　京橋竹がし|Bamboo Market at Capital Bridge,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1857,1857,1857,Polychrome woodblock print; ink and color on paper,14 1/16 x 9 1/2 in. (35.7 x 24.1 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56935,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2522,false,true,55433,Asian Art,Print,"Ōhashi Atake no yūdachi|名所江戸百景　大はしあたけの夕立|Sudden Shower over Shin-Ōhashi Bridge and Atake (Ōhashi Atake no yūdachi), from the series One Hundred Famous Views of Edo (Meisho Edo hyakkei)",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1857,1857,1857,Polychrome woodblock print; ink and color on paper,Oban 13 3/8 x 9 1/2 in. (34 x 24.1 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55433,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2523,false,true,56936,Asian Art,Print,名所江戸百景　王子装束ゑの木大晦日の狐火|Shozokuenoki Tree at Oji: Fox–fires on New Years Eve,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1857,1857,1857,Polychrome woodblock print; ink and color on paper,14 1/16 x 9 1/2 in. (35.7 x 24.1 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56936,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2525,false,true,56938,Asian Art,Woodblock print,"五十三次名所図会　藤川　山中の里別名宮路山|Fujikawa, a Village in the Mountains Formerly Called Miyajiyama",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1855,1855,1855,Polychrome woodblock print; ink and color on paper,13 1/2 x 8 7/8 in. (34.3 x 22.5 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56938,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2526,false,true,56939,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1854,1854,1854,Polychrome woodblock print; ink and color on paper,13 1/8 x 4 3/8 in. (33.3 x 11.1 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56939,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2527,false,true,56940,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1847,1837,1857,Polychrome woodblock print; ink and color on paper,13 5/8 x 4 1/2 in. (34.6 x 11.4 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56940,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2528,false,true,56941,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1835,1825,1845,Polychrome woodblock print; ink and color on paper,14 1/2 x 5 in. (36.8 x 12.7 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56941,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2529,false,true,56942,Asian Art,Woodblock print,歌川広重画　梅に鶯|Warbler on a Plum Branch,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1835,1825,1845,Polychrome woodblock print; ink and color on paper,14 1/2 x 5 1/8 in. (36.8 x 13 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56942,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2530,false,true,56943,Asian Art,Woodblock print,歌川広重画　燕子花に川蝉|Kingfisher and Iris,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1832–34,1832,1834,Polychrome woodblock print; ink and color on paper,15 x 5 1/8 in. (38.1 x 13 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56943,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2531,false,true,56944,Asian Art,Woodblock print,歌川広重画|Five Swallows above a Branch,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1835,1845,1855,Polychrome woodblock print; ink and color on paper,15 1/8 x 5 1/8 in. (38.4 x 13 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56944,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2532,false,true,56945,Asian Art,Print,歌川広重画　罌栗に瑠璃鳥|Bluebird and Flowering Poppies,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1832–34,1832,1834,Polychrome woodblock print; ink and color on paper,13 1/4 x 4 1/4 in. (33.7 x 10.8 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56945,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2533,false,true,56946,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,Designed by,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1835,1825,1845,Polychrome woodblock print; ink and color on paper,14 1/4 x 5 in. (36.2 x 12.7 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56946,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2534,false,true,39648,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1847,1837,1857,Polychrome woodblock print; ink and color on paper,13 x 4 3/8 in. (33 x 11.1 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/39648,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2535,false,true,56949,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1845,1835,1855,Polychrome woodblock print; ink and color on paper,13 3/4 x 4 1/2 in. (34.9 x 11.4 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56949,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2536,false,true,45024,Asian Art,Print,歌川広重画　蔦に柄長鳥|Long-tailed Tit on Autumn Ivy,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1835,1825,1845,Polychrome woodblock print (hosoban); ink and color on paper,14 3/4 x 4 3/4 in. (37.5 x 12.1 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45024,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2537,false,true,56950,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1835,1825,1845,Polychrome woodblock print; ink and color on paper,8 3/8 x 13 3/8 in. (21.3 x 34 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56950,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2538,false,true,56951,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1840,1830,1850,Polychrome woodblock print; ink and color on paper,10 3/8 x 7 1/4 in. (26.4 x 18.4 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56951,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2539,false,true,44894,Asian Art,Print,歌川広重画　雪中小松に錦雉|Golden Pheasant and Pine Shoots in Snow,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1835,1825,1845,Polychrome woodblock print; ink and color on paper,14 1/2 x 6 1/2 in. (36.8 x 16.5 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/44894,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2542,false,true,56956,Asian Art,Print,木曽海道六拾九次之内　洗馬|Senba Station,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1836,1826,1846,Polychrome woodblock print; ink and color on paper,8 3/4 x 13 3/4 in. (22.2 x 34.9 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56956,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2543,false,true,56958,Asian Art,Print,歌川広重画　芦に鷺|White Heron Standing among Reeds,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1835,1825,1845,Polychrome woodblock print; ink and color on paper,15 x 5 1/8 in. (38.1 x 13 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56958,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2836,false,true,57066,Asian Art,Print,Zumihari Zuki|月二拾八景之内　弓張月|Bow Moon,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1797–1858,1797,1858,Polychrome woodblock print; ink and color on paper,14 7/8 x 6 15/16 in. (37.8 x 17.6 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57066,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2837,false,true,57065,Asian Art,Print,Azuma Mori Yau|江戸近郊八景之内　吾嬬杜夜雨|Evening Rain in Azuma Wood,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1838,1828,1848,Polychrome woodblock print; ink and color on paper,9 5/8 x 14 3/4 in. (24.4 x 37.5 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57065,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2838,false,true,57064,Asian Art,Woodblock print,Ikegami Bansho|江戸近郊八景之内　池上晩鐘|Vesper Bells at Ikegami,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1797–1858,1797,1858,Polychrome woodblock print; ink and color on paper,10 x 14 5/8 in. (25.4 x 37.1 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57064,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2839,false,true,57063,Asian Art,Print,江戸近郊八景之内　小金井橋夕照|Evening Glow at Koganei Border,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1797–1858,1797,1858,Polychrome woodblock print; ink and color on paper,9 7/8 x 14 1/4 in. (25.1 x 36.2 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57063,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2840,false,true,57062,Asian Art,Print,Gyotoku Kihan|江戸近郊八景之内　行徳帰帆|Boats Returning to Gyotoku,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1797–1858,1797,1858,Polychrome woodblock print; ink and color on paper,8 7/8 x 13 1/8 in. (22.5 x 33.3 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57062,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2841,false,true,57061,Asian Art,Print,Asukayama Bosetsu|江戸近郊八景之内　飛鳥山暮雪|Asukayama in the Snow at Evening,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1797–1858,1797,1858,Polychrome woodblock print; ink and color on paper,9 1/4 x 14 1/4 in. (23.5 x 36.2 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57061,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2842,false,true,57060,Asian Art,Print,Shibaura Seiran|江戸近郊八景之内　芝浦晴嵐|Clearing Weather at Shibaura,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1797–1858,1797,1858,Polychrome woodblock print; ink and color on paper,9 1/4 x 14 1/4 in. (23.5 x 36.2 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57060,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2843,false,true,57059,Asian Art,Print,Haneda Rakugan|江戸近郊八景之内　羽根田落雁|Wild Geese at Haneda,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1797–1858,1797,1858,Polychrome woodblock print; ink and color on paper,14 5/8 x 10 in. (37.1 x 25.4 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57059,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2844,false,true,57058,Asian Art,Print,Tamagawa Shugetsu|江戸近郊八景之内　玉川秋月|Autumn Moon on the Tama River,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1838,1828,1848,Polychrome woodblock print; ink and color on paper,9 3/8 x 14 3/8 in. (23.8 x 36.5 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57058,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2845,false,true,57057,Asian Art,Print,Awazu no Seiran|近江八景之内　粟津晴嵐|Clearing Weather at Awazu,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1797–1858,1797,1858,Polychrome woodblock print; ink and color on paper,9 7/8 x 14 7/8 in. (25.1 x 37.8 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57057,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2846,false,true,57056,Asian Art,Woodblock print,Yabase no Kihan|近江八景之内　矢橋帰帆|Fishing Boats Returning to Yabase,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1797–1858,1797,1858,Polychrome woodblock print; ink and color on paper,9 3/4 x 14 7/8 in. (24.8 x 37.8 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57056,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2847,false,true,57055,Asian Art,Print,Ishiyama no Shūgetsu|近江八景之内　石山秋月|Autumn Full Moon at Ishiyama,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1797–1858,1797,1858,Polychrome woodblock print; ink and color on paper,Image: 9 3/4 × 14 3/4 in. (24.8 × 37.5 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57055,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2848,false,true,57054,Asian Art,Print,Hira no Bosetsu|近江八景の内　比良暮雪|Evening Snow on Mount Hira,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1797–1858,1797,1858,Polychrome woodblock print; ink and color on paper,9 3/4 x 14 3/4 in. (24.8 x 37.5 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57054,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2849,false,true,57053,Asian Art,Print,"Karasaki no Yau|近江八景の内　唐崎夜雨|Evening Rain at Karasaki, Pine Tree",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1797–1858,1797,1858,Polychrome woodblock print; ink and color on paper,9 3/4 x 14 7/8 in. (24.8 x 37.8 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57053,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2850,false,true,57052,Asian Art,Woodblock print,Mii no Bansho|近江八景の内　三井晩鍾|Vesper Bells at Mii Temple,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1797–1858,1797,1858,Polychrome woodblock print; ink and color on paper,9 3/4 x 14 7/8 in. (24.8 x 37.8 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57052,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2851,false,true,57051,Asian Art,Print,Katada no Rakugan|近江八景之内　堅田落雁|Returning Geese at Katada,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1797–1858,1797,1858,Polychrome woodblock print; ink and color on paper,9 3/4 x 15 in. (24.8 x 38.1 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57051,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2852,false,true,57050,Asian Art,Print,Seta no Yusho|近江八景の内　瀬田夕照|Sunset at Seta,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1797–1858,1797,1858,Polychrome woodblock print; ink and color on paper,9 5/8 x 14 5/8 in. (24.4 x 37.1 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57050,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2865,false,true,57045,Asian Art,Woodblock print,東海道五十三次之内　亀山　雪晴|Clear Weather after Snow,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1797–1861,1797,1861,Polychrome woodblock print; ink and color on paper,9 3/8 x 14 1/8 in. (23.8 x 35.9 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57045,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2866,false,true,57044,Asian Art,Woodblock print,東海道五十三次之内　蒲原　夜の雪|Evening Snow,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1797–1861,1797,1861,Polychrome woodblock print; ink and color on paper,9 5/8 x 14 5/8 in. (24.4 x 37.1 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57044,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2867,false,true,57043,Asian Art,Print,Shōno Hakuu|東海道五十三次之内　庄野　白雨|White Rain at Shōno,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1797–1861,1797,1861,Polychrome woodblock print; ink and color on paper,9 1/2 x 14 1/2 in. (24.1 x 36.8 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57043,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2868,false,true,56678,Asian Art,Print,"木曽海道六拾九次之内　須原|Suhara, from The Sixty-nine Stations of the Kisokaidō",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1838,1828,1848,Polychrome woodblock print; ink and color on paper,H. 9 5/8 in. (24.4 cm); W. 14 1/2 in. (36.8 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56678,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2869,false,true,57042,Asian Art,Print,木曽海道六拾九次之内　長久保|Nagakubo,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1797–1858,1797,1858,Polychrome woodblock print; ink and color on paper,9 7/8 x 14 7/8 in. (25.1 x 37.8 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57042,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2871,false,true,56677,Asian Art,Print,"木曽海道六拾九次之内　宮ノ越|Moonlit Night at Miyanokoshi, from The Sixty-nine Stations of the Kisokaidō",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1838,1828,1848,Polychrome woodblock print; ink and color on paper,H. 9 5/8 in. (24.4 cm); W. 14 3/4 in. (37.5 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56677,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2872,false,true,57040,Asian Art,Print,木曽海道六拾九次之内　大井|Ōi,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1797–1858,1797,1858,Polychrome woodblock print; ink and color on paper,10 x 14 7/8 in. (25.4 x 37.8 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57040,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2875,false,true,55989,Asian Art,Print,東海道五十三次　沼津|Numazu,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1797–1858,1797,1858,Polychrome woodblock print; ink and color on paper,9 7/8 x 14 3/4 in. (25.1 x 37.5 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55989,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2876,false,true,55991,Asian Art,Print,東海道五十三次　はま松|Hamamatsu,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1797–1858,1797,1858,Polychrome woodblock print; ink and color on paper,9 5/8 x 14 1/4 in. (24.4 x 36.2 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55991,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2877,false,true,55992,Asian Art,Print,Miya Atsuta Shinji|東海道五十三次之内　宮　熱田神事|Festival at Atsuta Temple,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1797–1858,1797,1858,Polychrome woodblock print; ink and color on paper,9 5/8 x 14 1/4 in. (24.4 x 36.2 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55992,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2878,false,true,55993,Asian Art,Woodblock print,"Nissaka-sayo no Naka Yama|東海道五十三次之内　日坂　佐夜の中山|Station Twenty-six: Nissaka, Sayo no Nakayama, from the Fifty-three Stations of the Tokaido",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1833–34,1833,1834,Polychrome woodblock print; ink and color on paper,9 3/8 x 14 3/8 in. (23.8 x 36.5 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55993,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2879,false,true,55994,Asian Art,Print,Mishima Asa-Giri|東海道五十三次之内　三島　朝霧|Morning Mist,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1797–1858,1797,1858,Polychrome woodblock print; ink and color on paper,9 1/2 x 14 1/4 in. (24.1 x 36.2 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55994,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2880,false,true,55995,Asian Art,Print,Tsuchiyama Haru no Ame|東海道五十三次之内　土山　春の雨|Spring Rain at Tsuchiyama (50th Station of the Tōkaidō),Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1797–1858,1797,1858,Polychrome woodblock print; ink and color on paper,9 3/8 x 14 1/4 in. (23.8 x 36.2 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55995,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2881,false,true,55996,Asian Art,Print,Arashiyama|京都名所之内　あらし山満花|Full Blossom at Arashiyama on the Oi River,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1797–1858,1797,1858,Polychrome woodblock print; ink and color on paper,9 7/8 x 14 5/8 in. (25.1 x 37.1 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55996,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2882,false,true,55997,Asian Art,Print,Yodogawa|京都名所之内　淀川|On the Yodo River,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1797–1858,1797,1858,Polychrome woodblock print; ink and color on paper,10 1/4 x 15 in. (26.0 x 38.1 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55997,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2883,false,true,55998,Asian Art,Print,京都名所之内　祇園社雪中|Gion Shrine in Snow,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1797–1858,1797,1858,Polychrome woodblock print; ink and color on paper,9 3/4 x 15 1/8 in. (24.8 x 38.4 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55998,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2884,false,true,55999,Asian Art,Woodblock print,京都名所之内　嶋原出口之柳|Gate of the Shimbara,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1797–1858,1797,1858,Polychrome woodblock print; ink and color on paper,10 1/4 x 15 3/8 in. (26 x 39.1 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55999,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2885,false,true,56000,Asian Art,Woodblock print,歌川広重画　雪中芦に鴨|Mallard and Snow-covered Reeds,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1843,1833,1853,Polychrome woodblock print; ink and color on paper,9 7/8 x 14 5/8 in. (25.1 x 37.1 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56000,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3010,false,true,56411,Asian Art,Print,Karasaki ya'u|近江八景　唐崎夜雨|Pine Tree at Karasaki,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1857,1857,1857,Polychrome woodblock print; ink and color on paper,14 3/4 x 10 in. (37.5 x 25.4 cm),"Gift of Mrs. Henry J. Bernheim, 1945",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56411,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3037,false,true,53782,Asian Art,Print,"六十余州名所図会　壱岐　志作|Snowfall at Shimasaku, Iki Province, from the series Views of Famous Places in the Sixty-Odd Provinces",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1853,1843,1863,Polychrome woodblock print; ink and color on paper,14 3/8 x 9 1/4 in. (36.5 x 23.5 cm),"Gift of Mrs. Morris Manges, in memory of her husband, Dr. Morris Manges, 1947",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/53782,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3038,false,true,56495,Asian Art,Print,Atagoshita Yabu-Kōji|東都名所　芝赤羽根之雪|Winter Landscape,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1846,1846,1846,Polychrome woodblock print; ink and color on paper,9 3/4 x 14 1/2 in. (24.8 x 36.8 cm),"Gift of Mrs. Morris Manges, in memory of her husband, Dr. Morris Manges, 1947",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56495,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3039,false,true,56496,Asian Art,Print,東都司馬八景　高輪帰帆|River View at Takanawa,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1856,1846,1866,Polychrome woodblock print; ink and color on paper,9 x 14 in. (22.9 x 35.6 cm),"Gift of Mrs. Morris Manges, in memory of her husband, Dr. Morris Manges, 1947",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56496,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3040,false,true,45319,Asian Art,Woodblock print,Toto meisho Sumidagawa zenzu settchukei|東都名所　隅田川全図雪中景|Celebrated Places in the Eastern Capital: Panoramic View of the Sumida River in Snow,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1840,1830,1850,Triptych of polychrome woodblock prints; ink and color on paper,14 1/2 x 10 in. (36.8 x 25.4 cm),"Gift of Mrs. Morris Manges, in memory of her husband, Dr. Morris Manges, 1947",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45319,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3148,false,true,56699,Asian Art,Print,歌川広重画　菊に雉|Pheasant with Chrysanthemums,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1835,1825,1845,Polychrome woodblock print (hosoban); ink and color on paper,14 3/4 x 6 3/4 in. (37.5 x 17.1 cm),"Bequest of Ellis G. Seymour, 1949",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56699,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3174,false,true,37386,Asian Art,Print,"Ōhashi Atake no yūdachi|名所江戸百景　大はしあたけの夕立|Sudden Shower over Shin-Ōhashi Bridge and Atake (Ōhashi Atake no yūdachi), from the series One Hundred Famous Views of Edo (Meisho Edo hyakkei)",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1857,1857,1857,Polychrome woodblock print; ink and color on paper,Image: 14 3/4 × 9 5/8 in. (37.5 × 24.4 cm) Mat: 22 3/4 × 15 1/2 in. (57.8 × 39.4 cm),"Gift of Mr. and Mrs. A. I. Sherr, 1956",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37386,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3413,false,true,55603,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1850,1850,1850,Polychrome woodblock print; ink and color on paper,9 1/4 x 7 in. (23.5 x 17.8 cm),"Bequest of Julia H. Manges, 1960",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55603,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3414,false,true,55604,Asian Art,Print,江戸名所四十八景　神田明神|Kanda Temple Snow,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1861,1861,1861,Polychrome woodblock print; ink and color on paper,7 1/4 x 9 1/4 in. (18.4 x 23.5 cm),"Bequest of Julia H. Manges, 1960",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55604,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3435,false,true,55635,Asian Art,Print,名所江戸百景　目黒太鼓橋夕ひの岡|Ochanomizu,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,probably late 19th century,1867,1899,Polychrome woodblock print; ink and color on paper,9 1/2 x 14 1/2 in. (24.1 x 36.8 cm),"Gift of Cole J. Younger, 1975",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55635,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3510,false,true,55803,Asian Art,Print,「甲陽猿橋之図」|The Monkey Bridge in Kai Province (Kōyō Saruhashi no zu),Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1841–42,1841,1842,Vertical ōban diptych mounted as a hanging scroll; ink and color on paper,Overall: 38 3/8 x 13 3/8 in. (97.5 x 34 cm); painting: 28 3/4 x 9 5/8 in. (73 x 24.4 cm),"The Harry G. C. Packard Collection of Asian Art, Gift of Harry G. C. Packard, and Purchase, Fletcher, Rogers, Harris Brisbane Dick, and Louis V. Bell Funds, Joseph Pulitzer Bequest, and The Annenberg Fund Inc. Gift, 1975",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55803,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3512,false,true,53691,Asian Art,Print,名所江戸百景　王子装束えの木大晦日の狐火|Foxes Meeting at Oji,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1857,1857,1857,Polychrome woodblock print; ink and color on paper,H. 14 5/8 in. (37.1 cm); W. 10 in. (25.4 cm),"The Harry G. C. Packard Collection of Asian Art, Gift of Harry G. C. Packard, and Purchase, Fletcher, Rogers, Harris Brisbane Dick, and Louis V. Bell Funds, Joseph Pulitzer Bequest, and The Annenberg Fund Inc. Gift, 1975",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/53691,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3592,false,true,55897,Asian Art,Woodblock print,"魚づくし　こちに茄子|Kochi Fish with Eggplant, from the series Uozukushi (Every Variety of Fish)",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1830s,1830,1839,Polychrome woodblock print; ink and color on paper,11 1/4 x 14 9/16 in. (28.6 x 37 cm),"Gift of Mr. and Mrs. Bryan Holme, 1980",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55897,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3593,false,true,55898,Asian Art,Woodblock print,"魚づくし　ぼらにうど|Bora Fish with Camellia, from the series Uozukushi (Every Variety of Fish)",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1830s,1830,1839,Polychrome woodblock print; ink and color on paper,9 7/8 x 14 9/16 in. (25.1 x 37 cm),"Gift of Mr. and Mrs. Bryan Holme, 1980",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55898,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3594,false,true,55900,Asian Art,Woodblock print,"魚づくし　鰹に桜|Katsuo Fish with Cherry Buds, from the series Uozukushi (Every Variety of Fish)",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1830s,1830,1839,Polychrome woodblock print; ink and color on paper,10 1/16 x 14 9/16 in. (25.6 x 37 cm),"Gift of Mr. and Mrs. Bryan Holme, 1980",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55900,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3595,false,true,55901,Asian Art,Woodblock print,"魚づくし　車海老　鯵にたで|Aji Fish and Kuruma-ebi, from the series Uozukushi (Every Variety of Fish)",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1830s,1830,1839,Polychrome woodblock print; ink and color on paper,10 1/8 x 14 1/2 in. (25.7 x 36.9 cm),"Gift of Mr. and Mrs. Bryan Holme, 1980",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55901,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3596,false,true,55902,Asian Art,Woodblock print,"魚づくし　黒鯛　小鯛に山椒|Kurodai and Kodai Fish with Bamboo Shoots and Berries, from the series Uozukushi (Every Variety of Fish)",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1830s,1830,1839,Polychrome woodblock print; ink and color on paper,10 1/8 x 14 1/2 in. (25.7 x 36.9 cm),"Gift of Mr. and Mrs. Bryan Holme, 1980",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55902,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3597,false,true,55903,Asian Art,Woodblock print,"魚づくし　伊勢海老　芝蝦|Ise-ebi and Shiba-ebi, from the series Uozukushi (Every Variety of Fish)",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1830s,1830,1839,Polychrome woodblock print; ink and color on paper,9 15/16 x 14 3/16 in. (25.3 x 36.1 cm),"Gift of Mr. and Mrs. Bryan Holme, 1980",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55903,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3598,false,true,55904,Asian Art,Woodblock print,"魚づくし　かれい　かながしらに笹|Kanagashira and Karei Fish, from the series Uozukushi (Every Variety of Fish)",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1830s,1830,1839,Polychrome woodblock print; ink and color on paper,10 1/8 x 14 1/2 in. (25.7 x 36.9 cm),"Gift of Mr. and Mrs. Bryan Holme, 1980",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55904,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3599,false,true,55905,Asian Art,Woodblock print,"魚づくし　かさご　いさきに生姜|Isaki and Kasago Fish, from the series Uozukushi (Every Variety of Fish)",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1830s,1830,1839,Polychrome woodblock print; ink and color on paper,10 1/4 x 14 7/8 in. (26 x 37.8 cm),"Gift of Mr. and Mrs. Bryan Holme, 1980",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55905,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3600,false,true,55906,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1840s,1840,1849,Polychrome woodblock print; ink and color on paper,10 1/8 x 14 5/8 in. (25.7 x 37.2 cm),"Gift of Mr. and Mrs. Bryan Holme, 1980",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55906,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3601,false,true,55907,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1840s,1840,1849,Polychrome woodblock print; ink and color on paper,10 1/8 x 14 9/16 in. (25.7 x 37 cm),"Gift of Mr. and Mrs. Bryan Holme, 1980",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55907,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3602,false,true,55908,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1840s,1840,1849,Polychrome woodblock print; ink and color on paper,10 1/16 x 14 15/16 in. (25.5 x 38 cm),"Gift of Mr. and Mrs. Bryan Holme, 1980",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55908,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3603,false,true,55909,Asian Art,Woodblock print,"魚づくし|Medetai Fush and Sasaki Bamboo, from the series Uozukushi (Every Variety of Fish)",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1840s,1840,1849,Polychrome woodblock print; ink and color on paper,10 x 14 5/8 in. (25.4 x 37.2 cm),"Gift of Mr. and Mrs. Bryan Holme, 1980",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55909,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3604,false,true,55911,Asian Art,Woodblock print,魚づくし　いなだ　ふぐに梅|Suzuki and Kinmedai Fish from the series Uozukushi (Every Variety of Fish),Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1840s,1840,1849,Polychrome woodblock print; ink and color on paper,10 1/8 x 14 9/16 in. (25.7 x 37 cm),"Gift of Mr. and Mrs. Bryan Holme, 1980",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55911,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3605,false,true,55912,Asian Art,Woodblock print,"魚づくし　ひらめ　めばるに桜|Hirame and Mebaru Fish with Cherry Blossoms, from the series Uozukushi (Every Variety of Fish)",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1840s,1840,1849,Polychrome woodblock print; ink and color on paper,10 1/8 x 14 9/16 in. (25.7 x 37 cm),"Gift of Mr. and Mrs. Bryan Holme, 1980",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55912,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3701,false,true,55946,Asian Art,Print,名所江戸百景　川口のわたし善光寺|Kawaguchi,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1857,1857,1857,Polychrome woodblock print; ink and color on paper,14 x 9 1/2 in. (35.6 x 24.1 cm),"Bequest of Grace M. Pugh, 1985",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55946,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1093.10,false,true,58262,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1854,1854,1854,Polychrome woodblock print; ink and color on paper,Image: 14 1/2 in. × 10 in. (36.8 × 25.4 cm),The Metropolitan Museum of Art,,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58262,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2540a–c,false,true,45283,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,ca. 1845,1835,1855,Triptych of polychrome woodblock prints; ink and color on paper,15 x 29 in. (38.1 x 73.7 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45283,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2833a–c,false,true,57068,Asian Art,Print,"Buyō Kanazawa Hasshō Yakei|雪月花　武陽金沢八勝夜景|Full Moon at Kanazawa, Province of Musashi",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1857,1857,1857,Triptych of polychrome woodblock prints; ink and color on paper,Each page: 14 7/8 x 10 1/8 in. (37.8 x 25.7 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57068,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2834a–c,false,true,45025,Asian Art,Print,Awa no Naruto|雪月花　阿波鳴門之風景|The Whirlpools of Awa,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1857,1857,1857,Triptych of polychrome woodblock prints; ink and color on paper,Image (triptych): 14 1/2 × 29 1/2 in. (36.8 × 74.9 cm) Image (each): 14 1/2 in. × 10 in. (36.8 × 25.4 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45025,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2835a–c,false,true,57067,Asian Art,Woodblock print,Kisoji no Sansen|雪月花　木曽路之山川|Kisō Mountains in Snow,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,1857,1857,1857,Triptych of polychrome woodblock prints; ink and color on paper,A and B (each): 14 5/8 x 10 in. (37.8 x 25.7 cm) C: 14 5/8 x 9 7/8 in. (37.8 x 25.1 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57067,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1040,false,true,54325,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Keiri,"Japanese, active first half of the 19th century",,Keiri,Japanese,1800,1899,1835?,1835,1835,Polychrome woodblock print (surimono); ink and color on paper,8 1/2 x 7 5/16 in. (21.6 x 18.6 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54325,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP815,false,true,37259,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Furuyama Moroshige,"Japanese, active second half of the 17th century",,Furuyama Moroshige,Japanese,1650,1699,ca. 1690,1680,1700,Polychrome woodblock print; ink and color on paper,10 1/2 x 13 1/2 in. (26.7 x 34.3 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37259,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1443,false,true,55487,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Yanagawa Shunsui,"Japanese, active last quarter of the 18th century",,Yanagawa Shunsui,Japanese,1700,1799,ca. 1780,1770,1790,Polychrome woodblock print; ink and color on paper,H. 8 1/8 in. (20.6 cm); W. 6 in. (15.2 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55487,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1961,false,true,54535,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusen,"Japanese, active first half of the nineteenth century",,Katsushika Hokusen,Japanese,1800,1850,probably 1813,1813,1813,Polychrome woodblock print (surimono); ink and color on paper,5 1/2 x 7 3/8 in. (14 x 18.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54535,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3214,false,true,55200,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Munakata,,,Munakata,Japanese,1868,1912,ca. 1906,1896,1916,Polychrome woodblock print; ink and color on paper,12 1/2 x 8 7/8 in. (31.8 x 22.5 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55200,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3218,false,true,55205,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Hashimoto Naoyoshi,,,Hashimoto Naoyoshi,Japanese,1838,1912,dated 1882,1882,1882,Two panels of a triptych of polychrome woodblock prints; ink and color on paper,14 x 9 1/8 in. (35.6 x 23.2 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55205,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3219,false,true,55206,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Hashimoto Naoyoshi,,,Hashimoto Naoyoshi,Japanese,1838,1912,dated 1882,1882,1882,Two panels of a triptych of polychrome woodblock prints; ink and color on paper,14 x 9 1/8 in. (35.6 x 23.2 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55206,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -MJP3,false,true,57114,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,After,Okumura Masanobu,"Japanese, 1686–1764",,Masanobu Okumura,Japanese,1686,1764,ca. 1910,1900,1920,Polychrome woodblock print; ink and color on paper,14 1/2 x 19 15/16 in. (36.8 x 50.6 cm),"Gift of Teiji Ito, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57114,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3717,false,true,55981,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,After,Genki (Komai Ki),"Japanese, 1747–1797",,Genki,Japanese,1747,1797,1895,1895,1895,Polychrome woodblock print; ink and color on paper,Album: 8 1/2 x 5 5/16 in. (21.6 x 13.5 cm),"Gift of Donald Keene, in honor of Julia Meech-Pekarik, 1986",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55981,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2744,false,true,57014,Asian Art,Woodblock print,,Japan,Meiji period (1868–1912),,,,Artist,,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,Japanese,1807,1891,ca. 1875,1865,1885,Polychrome woodblock print; ink and color on paper,6 7/8 x 10 1/8 in. (17.5 x 25.7 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57014,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2745,false,true,57015,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,Japanese,1807,1891,ca. 1875,1865,1885,Polychrome woodblock print; ink and color on paper,7 3/4 x 10 in. (19.7 x 25.4 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57015,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3215,false,true,55201,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Utagawa Kunisada II,"Japanese, 1823–1880",,Utagawa Kunisada II,Japanese,1823,1880,Feb. 1889 (Meiji 22),1889,1889,One sheet of a triptych of polychrome woodblock prints; ink and color on paper,Triptych 13 3/4 x 27 3/4 in. (35 x 70.5 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55201,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3270,false,true,55340,Asian Art,Print,東京高輪鉄道蒸気車走行之図|Illustration of a Steam Locomotive Running on the Takanawa Railroad in Tokyo (Tōkyō takanawa tetsudō jōkisha sōkō no zu),Japan,Meiji period (1868–1912),,,,Artist,,Utagawa Kuniteru,"Japanese, 1830–1874",,Utagawa Kuniteru,Japanese,1830,1874,ca. 1873,1863,1883,Triptych of polychrome woodblock prints; ink and color on paper,14.2 x 29.1 in. (36.1 x 73.9 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55340,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3347,false,true,55512,Asian Art,Print,東京汐留鉄道蒸気車通行図|Illustration of a Steam Locomotive Passing Shiodome in Tokyo (Tōkyō Shiodome testudō jōkisha tsūkō zu),Japan,Meiji period (1868–1912),,,,Artist,,Utagawa Kuniteru,"Japanese, 1830–1874",,Utagawa Kuniteru,Japanese,1830,1874,1872,1872,1872,Triptych of polychrome woodblock prints; ink and color on paper,Oban; 28 1/2 x 14 1/2 in. (72.4 x 36.8 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55512,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3348,false,true,55513,Asian Art,Print,東京高輪鉄道蒸気車走行之全図|Tokyo /Takanawa Steam Railway,Japan,Meiji period (1868–1912),,,,Artist,,Utagawa Kuniteru,"Japanese, 1830–1874",,Utagawa Kuniteru,Japanese,1830,1874,"1879 (Meiji 3, 2nd month)",1879,1879,Triptych of polychrome woodblock prints; ink and color on paper,Oban; 14 3/16 x 30 in. (36 x 76.2 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55513,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.243,false,true,73534,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Utagawa Yoshimori,"Japanese, 1830–1884",,Utagawa Yoshimori,Japanese,1830,1884,"October, 1872",1872,1872,Polychrome woodblock print; ink and color on paper,Image: 14 1/4 x 9 1/2 in. (36.2 x 24.1 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73534,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.242a–c,false,true,73533,Asian Art,Print,"Kak'koku han e sukushi Igirisu Rondon|""View in London,"" the Prosperity of Countries: London, England",Japan,Meiji period (1868–1912),,,,Artist,,Utagawa Yoshimori,"Japanese, 1830–1884",,Utagawa Yoshimori,Japanese,1830,1884,September 1872,1872,1872,Polychrome woodblock print; ink and color on paper,Image (a): 14 1/4 x 9 3/8 in. (36.2 x 23.8 cm) Image (b): 14 1/4 x 9 3/8 in. (36.2 x 23.8 cm) Image (c): 14 1/4 x 9 1/2 in. (36.2 x 24.1 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73533,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3249,false,true,45001,Asian Art,Print,Yokohama Igirisu Shokan hanei no zu|Picture of a Prosperous English Trading Firm in Yokohama,Japan,Meiji period (1868–1912),,,,Artist,,Utagawa Yoshiiku,"Japanese, 1833–1904",,Utagawa Yoshiiku,Japanese,1833,1904,January 1871,1871,1871,Triptych of polychrome woodblock prints; ink and color on paper,14 1/4 x 29 3/4 in. (36.2 x 75.6 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45001,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.234a–c,false,true,73525,Asian Art,Print,「横浜英吉利西商館繁栄圖」|“The Flourishing of an English Trading Firm in Yokohama”,Japan,Meiji period (1868–1912),,,,Artist,,Utagawa Yoshiiku,"Japanese, 1833–1904",,Utagawa Yoshiiku,Japanese,1833,1904,"9th month, 1870",1870,1870,Triptych of polychrome woodblock prints; ink and color on paper,Image (a): 14 1/8 x 9 5/8 in. (35.9 x 24.4 cm) Image (b): 14 x 9 5/8 in. (35.6 x 24.4 cm) Image (c): 14 1/4 x 9 5/8 in. (36.2 x 24.4 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73525,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3198,false,true,55182,Asian Art,Print,"『東風俗福づくし　大 礼ふく』|“Ceremonial Attire” from the series An Array of Auspicious Customs of Eastern Japan (Azuma fūzoku, fukuzukushi: Tairei fuku)",Japan,Meiji period (1868–1912),,,,Artist,,Yōshū (Hashimoto) Chikanobu,"Japanese, 1838–1912",,Yōshū (Hashimoto) Chikanobu,Japanese,1838,1912,ca. 1889,1879,1899,Triptych of polychrome woodblock prints; ink and color on paper,Oban 14 5/8 x 9 11/16 in. (37.1 x 24.6 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55182,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3201,false,true,55185,Asian Art,Woodblock print,"『東風俗福づくし　洋 ふく』|Western Clothing from the series An Array of Auspicious Customs of Eastern Japan (Azuma fūzoku, fukuzukushi-Yōfuku)",Japan,Meiji period (1868–1912),,,,Artist,,Yōshū (Hashimoto) Chikanobu,"Japanese, 1838–1912",,Yōshū (Hashimoto) Chikanobu,Japanese,1838,1912,1889,1889,1889,Triptych of polychrome woodblock prints; ink and color on paper,Oban 12 15/16 x 8 15/16 in. (32.9 x 22.7 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55185,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3221,false,true,55209,Asian Art,Print,"勲功之将天杯賜之図|Illustration of the Honored Commanders, Receiving the Emperor's Gift Cup (Kunkō no shō tenpai o tamau no zu)",Japan,Meiji period (1868–1912),,,,Artist,,Yōshū (Hashimoto) Chikanobu,"Japanese, 1838–1912",,Yōshū (Hashimoto) Chikanobu,Japanese,1838,1912,"September, 1877",1877,1877,One sheet of a triptych of polychrome woodblock prints; ink and color on paper,Image (each): 13 7/8 × 9 1/4 in. (35.2 × 23.5 cm) Mat: 18 3/4 × 23 1/4 in. (47.6 × 59.1 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55209,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3222,false,true,55211,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Yōshū (Hashimoto) Chikanobu,"Japanese, 1838–1912",,Yōshū (Hashimoto) Chikanobu,Japanese,1838,1912,"Sept. 5, 1877 (Meiji 10)",1877,1877,One sheet of a triptych of polychrome woodblock prints; ink and color on paper,Image (each): 13 7/8 × 9 1/4 in. (35.2 × 23.5 cm) Mat: 23 × 37 in. (58.4 × 94 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55211,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3223,false,true,55213,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Yōshū (Hashimoto) Chikanobu,"Japanese, 1838–1912",,Yōshū (Hashimoto) Chikanobu,Japanese,1838,1912,"Sept. 5, 1877 (Meiji 10)",1877,1877,One sheet of a triptych of polychrome woodblock prints; ink and color on paper,Image: 14 in. × 9 1/4 in. (35.6 × 23.5 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55213,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3236,false,true,55249,Asian Art,Print,大山綱良糾問の図|Illustration of the Inquisition of Ōyama Tsunayoshi (Ōyama Tsunayoshi kyūmon no zu),Japan,Meiji period (1868–1912),,,,Artist,,Yōshū (Hashimoto) Chikanobu,"Japanese, 1838–1912",,Yōshū (Hashimoto) Chikanobu,Japanese,1838,1912,"August, 1877",1877,1877,One sheet of a triptych of polychrome woodblock prints; ink and color on paper,Triptych 13 15/16 x 28 1/8 in. (35.4 x 71.4 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55249,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3237,false,true,55250,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Yōshū (Hashimoto) Chikanobu,"Japanese, 1838–1912",,Yōshū (Hashimoto) Chikanobu,Japanese,1838,1912,"August 27, 1877 (Meiji 10)",1877,1877,One sheet of a triptych of polychrome woodblock prints; ink and color on paper,Triptych 13 15/16 x 28 1/8 in. (35.4 x 71.4 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55250,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3238,false,true,55251,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Yōshū (Hashimoto) Chikanobu,"Japanese, 1838–1912",,Yōshū (Hashimoto) Chikanobu,Japanese,1838,1912,"August 27, 1877 (Meiji 10)",1877,1877,One sheet of a triptych of polychrome woodblock prints; ink and color on paper,Triptych 13 15/16 x 28 1/8 in. (35.4 x 71.4 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55251,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3239,false,true,55252,Asian Art,Print,チャリネ大曲馬御遊覧ノ図|Illustration of the Imperial Excursion to see the Charini's Circus (Charine daikyokuba goyūran no zu),Japan,Meiji period (1868–1912),,,,Artist,,Yōshū (Hashimoto) Chikanobu,"Japanese, 1838–1912",,Yōshū (Hashimoto) Chikanobu,Japanese,1838,1912,"November, 1886",1886,1886,Polychrome woodblock print; ink and color on paper,14 3/4 x 27 3/4 in. (37.5 x 70.5 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55252,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3240,false,true,55253,Asian Art,Print,憲法発布式之図|Illustration of the Ceremony Issuing the Constitution (Kenpō happu shiki no zu),Japan,Meiji period (1868–1912),,,,Artist,,Yōshū (Hashimoto) Chikanobu,"Japanese, 1838–1912",,Yōshū (Hashimoto) Chikanobu,Japanese,1838,1912,1889,1889,1889,Polychrome woodblock print; ink and color on paper,Oban 14 1/2 x 29 5/8 in. (36.8 x 75.2 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55253,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3250,false,true,55261,Asian Art,Print,『鹿児島賊徒鎮静依諸将 天杯頂戴之図』|Leaders of the Pacification of the Kagoshima Rebels Celebrating with Cups of Wine from the Emperor (Kagoshima zokuto chinsei ni yotte shoshō tenhai chōdai no zu),Japan,Meiji period (1868–1912),,,,Artist,,Yōshū (Hashimoto) Chikanobu,"Japanese, 1838–1912",,Yōshū (Hashimoto) Chikanobu,Japanese,1838,1912,"September 20th, 1877",1877,1877,Triptych of polychrome woodblock prints; ink and color on paper,Image (each): 14 1/2 × 9 3/4 in. (36.8 × 24.8 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55261,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3263,false,true,55323,Asian Art,Woodblock print,"西国鎮静撫諸将天杯賜之図|Illustration of the Commanders who Pacified Western Japan, Receiving the Emperor's Gift Cups (Saigoku chinbu shoshō tenpai o tamau no zu)",Japan,Meiji period (1868–1912),,,,Artist,,Yōshū (Hashimoto) Chikanobu,"Japanese, 1838–1912",,Yōshū (Hashimoto) Chikanobu,Japanese,1838,1912,"July, 1877",1877,1877,Polychrome woodblock print; ink and color on paper,Oban 14 1/4 x 28 3/4 in. (36.2 x 73 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55323,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3269,false,true,55338,Asian Art,Print,世上各国写画帝王鏡|Mirror of Portraits of All Sovereigns in the World (Sejō kakkoku shaga teiō kagami),Japan,Meiji period (1868–1912),,,,Artist,,Yōshū (Hashimoto) Chikanobu,"Japanese, 1838–1912",,Yōshū (Hashimoto) Chikanobu,Japanese,1838,1912,"April, 1879",1879,1879,Polychrome woodblock print; ink and color on paper,14 5/8 x 29 in. (37.1 x 73.7 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55338,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3273,false,true,55347,Asian Art,Woodblock print,『上野不忍競馬図』|View of the Horse Track at Shinobazu in Ueno Park (Ueno shinobazu keiba zu),Japan,Meiji period (1868–1912),,,,Artist,,Yōshū (Hashimoto) Chikanobu,"Japanese, 1838–1912",,Yōshū (Hashimoto) Chikanobu,Japanese,1838,1912,1885,1885,1885,Triptych of polychrome woodblock prints; ink and color on paper,Image: 14 1/2 × 29 5/8 in. (36.8 × 75.2 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55347,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3276,false,true,53314,Asian Art,Print,『欧州管絃楽合奏之図』|Concert of European Music (Ōshū kangengaku gassō no zu),Japan,Meiji period (1868–1912),,,,Artist,,Yōshū (Hashimoto) Chikanobu,"Japanese, 1838–1912",,Yōshū (Hashimoto) Chikanobu,Japanese,1838,1912,1889,1889,1889,Triptych of polychrome woodblock prints; ink and color on paper,Image: 14 1/2 x 29 in. (36.8 x 73.7 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/53314,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3306,false,true,55408,Asian Art,Print,『踏舞会 上野桜花観 遊ノ図』|A Dance Party: Enjoying Cherry Blossom Viewing at Ueno (Tōbukai Ueno ōka yūran no zu),Japan,Meiji period (1868–1912),,,,Artist,,Yōshū (Hashimoto) Chikanobu,"Japanese, 1838–1912",,Yōshū (Hashimoto) Chikanobu,Japanese,1838,1912,"March, 1887",1887,1887,Triptych of polychrome woodblock prints; ink and color on paper,Image: 14 in. × 28 1/4 in. (35.6 × 71.8 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55408,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3338,false,true,55492,Asian Art,Print,『高貴納涼ノ図』|Nobility in the Evening Cool (Koki nōryō no zu),Japan,Meiji period (1868–1912),,,,Artist,,Yōshū (Hashimoto) Chikanobu,"Japanese, 1838–1912",,Yōshū (Hashimoto) Chikanobu,Japanese,1838,1912,1887,1887,1887,Triptych of polychrome woodblock prints; ink and color on paper,Image: 13 3/4 x 27 1/2 in. (34.9 x 69.9 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55492,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3340,false,true,55499,Asian Art,Woodblock prints,『女官洋服裁縫之図』|Court Ladies Sewing Western Clothing (Jokan yōfuku saihō no zu),Japan,Meiji period (1868–1912),,,,Artist,,Yōshū (Hashimoto) Chikanobu,"Japanese, 1838–1912",,Yōshū (Hashimoto) Chikanobu,Japanese,1838,1912,"August 23rd, 1887",1887,1887,Triptych of polychrome woodblock prints; ink and color on paper,Image: 14 5/16 x 29 11/16 in. (36.4 x 75.4 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55499,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3341,false,true,55500,Asian Art,Woodblock print,『雪中梅荘群児遊戯 図』|Children Playing in the Snow under Plum Trees in Bloom (Secchū baisō gunji yūgi zu),Japan,Meiji period (1868–1912),,,,Artist,,Yōshū (Hashimoto) Chikanobu,"Japanese, 1838–1912",,Yōshū (Hashimoto) Chikanobu,Japanese,1838,1912,November 1887,1887,1887,Triptych of polychrome woodblock prints; ink and color on paper,Image: 14 x 28 3/4 in. (35.6 x 73 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55500,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3342,false,true,55502,Asian Art,Print,『雨過洗庭之図』|A Garden Refreshed by the Passing Rain (Ukasentei no zu),Japan,Meiji period (1868–1912),,,,Artist,,Yōshū (Hashimoto) Chikanobu,"Japanese, 1838–1912",,Yōshū (Hashimoto) Chikanobu,Japanese,1838,1912,July 1888,1888,1888,Polychrome woodblock print; ink and color on paper,Image: 14 3/4 x 28 1/4 in. (37.5 x 71.8 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55502,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3343,false,true,55504,Asian Art,Print,『開花貴婦人競』|A Contest of Elegant Ladies among the Cherry Blossoms (Kaika kifujin kisoi),Japan,Meiji period (1868–1912),,,,Artist,,Yōshū (Hashimoto) Chikanobu,"Japanese, 1838–1912",,Yōshū (Hashimoto) Chikanobu,Japanese,1838,1912,September 1887,1887,1887,Triptych of polychrome woodblock prints; ink and color on paper,Image: 14 5/8 x 29 1/8 in. (37.1 x 74 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55504,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3386,false,true,55559,Asian Art,Print,"西国諸将鎮静天杯賜ル之図|Illustration of the Commanders who Pacified Western Japan, Receiving the Emperor's Gift Cups (Saigoku chinsei shoshō tenpai o tamawaru no zu)",Japan,Meiji period (1868–1912),,,,Artist,,Yōshū (Hashimoto) Chikanobu,"Japanese, 1838–1912",,Yōshū (Hashimoto) Chikanobu,Japanese,1838,1912,"September, 1887",1887,1887,Triptych of polychrome woodblock prints; ink and color on paper,14 1/4 x 29 1/4 in. (36.2 x 74.3 cm),"Gift of Lincoln Kirstein, 1960",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55559,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3422,false,true,55623,Asian Art,Print,帝国議会貴族院之図|Illustration of The Imperial Assembly of the House of Peers (Teikoku gikai kizokuin no zu),Japan,Meiji period (1868–1912),,,,Artist,,Yōshū (Hashimoto) Chikanobu,"Japanese, 1838–1912",,Yōshū (Hashimoto) Chikanobu,Japanese,1838,1912,1890,1890,1890,Triptych of polychrome woodblock prints; ink and color on paper,Oban; 14 x 29 3/4 in. (35.6 x 75.6 cm),"Gift of Lincoln Kirstein, 1962",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55623,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3519,false,true,55820,Asian Art,Print,千代田の大奥|The Inner Palace of Chiyoda (Chiyoda no Ōoku),Japan,Meiji period (1868–1912),,,,Artist,,Yōshū (Hashimoto) Chikanobu,"Japanese, 1838–1912",,Yōshū (Hashimoto) Chikanobu,Japanese,1838,1912,1895,1895,1895,Diptych of polychrome woodblock prints; ink and color on paper,L. (page) 13 7/8 in. (35.2 cm); W. (page) 9 1/4 in. (23.4 cm),"Gift of Mrs. W. Walton Butterworth, 1979",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55820,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3520,false,true,55821,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Yōshū (Hashimoto) Chikanobu,"Japanese, 1838–1912",,Yōshū (Hashimoto) Chikanobu,Japanese,1838,1912,1895,1895,1895,Triptych of polychrome woodblock prints; ink and color on paper,L. (page) 13 7/8 in. (35.2 cm); W. (page) 9 1/4 in. (23.4 cm),"Gift of Mrs. W. Walton Butterworth, 1979",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55821,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3521,false,true,55822,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Yōshū (Hashimoto) Chikanobu,"Japanese, 1838–1912",,Yōshū (Hashimoto) Chikanobu,Japanese,1838,1912,1895,1895,1895,Triptych of polychrome woodblock prints; ink and color on paper,L. (page) 13 7/8 in. (35.2 cm); W. (page) 9 1/4 in. (23.4 cm),"Gift of Mrs. W. Walton Butterworth, 1979",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55822,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3522,false,true,55823,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Yōshū (Hashimoto) Chikanobu,"Japanese, 1838–1912",,Yōshū (Hashimoto) Chikanobu,Japanese,1838,1912,1895,1895,1895,Triptych of polychrome woodblock prints; ink and color on paper,L. (page) 13 7/8 in. (35.2 cm); W. (page) 9 1/4 in. (23.4 cm),"Gift of Mrs. W. Walton Butterworth, 1979",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55823,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3523,false,true,55824,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Yōshū (Hashimoto) Chikanobu,"Japanese, 1838–1912",,Yōshū (Hashimoto) Chikanobu,Japanese,1838,1912,1895,1895,1895,Triptych of polychrome woodblock prints; ink and color on paper,L. (page) 13 7/8 in. (35.2 cm); W. (page) 9 1/4 in. (23.4 cm),"Gift of Mrs. W. Walton Butterworth, 1979",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55824,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3524,false,true,55825,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Yōshū (Hashimoto) Chikanobu,"Japanese, 1838–1912",,Yōshū (Hashimoto) Chikanobu,Japanese,1838,1912,1895,1895,1895,Triptych of polychrome woodblock prints; ink and color on paper,L. (page) 13 7/8 in. (35.2 cm); W. (page) 9 1/4 in. (23.4 cm),"Gift of Mrs. W. Walton Butterworth, 1979",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55825,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3525,false,true,55826,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Yōshū (Hashimoto) Chikanobu,"Japanese, 1838–1912",,Yōshū (Hashimoto) Chikanobu,Japanese,1838,1912,1895,1895,1895,Triptych of polychrome woodblock prints; ink and color on paper,L. (page) 13 7/8 in. (35.2 cm); W. (page) 9 1/4 in. (23.4 cm),"Gift of Mrs. W. Walton Butterworth, 1979",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55826,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3526,false,true,55827,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Yōshū (Hashimoto) Chikanobu,"Japanese, 1838–1912",,Yōshū (Hashimoto) Chikanobu,Japanese,1838,1912,1895,1895,1895,Triptych of polychrome woodblock prints; ink and color on paper,L. (page) 13 7/8 in. (35.2 cm); W. (page) 9 1/4 in. (23.4 cm),"Gift of Mrs. W. Walton Butterworth, 1979",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55827,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3527,false,true,55828,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Yōshū (Hashimoto) Chikanobu,"Japanese, 1838–1912",,Yōshū (Hashimoto) Chikanobu,Japanese,1838,1912,1895,1895,1895,Triptych of polychrome woodblock prints; ink and color on paper,L. (page) 13 7/8 in. (35.2 cm); W. (page) 9 1/4 in. (23.4 cm),"Gift of Mrs. W. Walton Butterworth, 1979",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55828,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3528,false,true,55829,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Yōshū (Hashimoto) Chikanobu,"Japanese, 1838–1912",,Yōshū (Hashimoto) Chikanobu,Japanese,1838,1912,1895,1895,1895,Triptych of polychrome woodblock prints; ink and color on paper,L. (page) 13 7/8 in. (35.2 cm); W. (page) 9 1/4 in. (23.4 cm),"Gift of Mrs. W. Walton Butterworth, 1979",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55829,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3529,false,true,55830,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Yōshū (Hashimoto) Chikanobu,"Japanese, 1838–1912",,Yōshū (Hashimoto) Chikanobu,Japanese,1838,1912,1895,1895,1895,Triptych of polychrome woodblock prints; ink and color on paper,L. (page) 13 7/8 in. (35.2 cm); W. (page) 9 1/4 in. (23.4 cm),"Gift of Mrs. W. Walton Butterworth, 1979",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55830,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3530,false,true,55831,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Yōshū (Hashimoto) Chikanobu,"Japanese, 1838–1912",,Yōshū (Hashimoto) Chikanobu,Japanese,1838,1912,1895,1895,1895,Triptych of polychrome woodblock prints; ink and color on paper,L. (page) 13 7/8 in. (35.2 cm); W. (page) 9 1/4 in. (23.4 cm),"Gift of Mrs. W. Walton Butterworth, 1979",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55831,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3531,false,true,55832,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Yōshū (Hashimoto) Chikanobu,"Japanese, 1838–1912",,Yōshū (Hashimoto) Chikanobu,Japanese,1838,1912,1895,1895,1895,Triptych of polychrome woodblock prints; ink and color on paper,L. (page) 13 7/8 in. (35.2 cm); W. (page) 9 1/4 in. (23.4 cm),"Gift of Mrs. W. Walton Butterworth, 1979",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55832,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3532,false,true,55833,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Yōshū (Hashimoto) Chikanobu,"Japanese, 1838–1912",,Yōshū (Hashimoto) Chikanobu,Japanese,1838,1912,1895,1895,1895,Triptych of polychrome woodblock prints; ink and color on paper,L. (page) 13 7/8 in. (35.2 cm); W. (page) 9 1/4 in. (23.4 cm),"Gift of Mrs. W. Walton Butterworth, 1979",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55833,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3533,false,true,55834,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Yōshū (Hashimoto) Chikanobu,"Japanese, 1838–1912",,Yōshū (Hashimoto) Chikanobu,Japanese,1838,1912,1895,1895,1895,Triptych of polychrome woodblock prints; ink and color on paper,L. (page) 13 7/8 in. (35.2 cm); W. (page) 9 1/4 in. (23.4 cm),"Gift of Mrs. W. Walton Butterworth, 1979",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55834,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3534,false,true,55835,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Yōshū (Hashimoto) Chikanobu,"Japanese, 1838–1912",,Yōshū (Hashimoto) Chikanobu,Japanese,1838,1912,1895,1895,1895,Triptych of polychrome woodblock prints; ink and color on paper,L. (page) 13 7/8 in. (35.2 cm); W. (page) 9 1/4 in. (23.4 cm),"Gift of Mrs. W. Walton Butterworth, 1979",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55835,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3535,false,true,55836,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Yōshū (Hashimoto) Chikanobu,"Japanese, 1838–1912",,Yōshū (Hashimoto) Chikanobu,Japanese,1838,1912,1895,1895,1895,Triptych of polychrome woodblock prints; ink and color on paper,L. (page) 13 7/8 in. (35.2 cm); W. (page) 9 1/4 in. (23.4 cm),"Gift of Mrs. W. Walton Butterworth, 1979",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55836,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3536,false,true,55837,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Yōshū (Hashimoto) Chikanobu,"Japanese, 1838–1912",,Yōshū (Hashimoto) Chikanobu,Japanese,1838,1912,1895,1895,1895,Triptych of polychrome woodblock prints; ink and color on paper,L. (page) 13 7/8 in. (35.2 cm); W. (page) 9 1/4 in. (23.4 cm),"Gift of Mrs. W. Walton Butterworth, 1979",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55837,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3537,false,true,55838,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Yōshū (Hashimoto) Chikanobu,"Japanese, 1838–1912",,Yōshū (Hashimoto) Chikanobu,Japanese,1838,1912,1895,1895,1895,Triptych of polychrome woodblock prints; ink and color on paper,L. (page) 13 7/8 in. (35.2 cm); W. (page) 9 1/4 in. (23.4 cm),"Gift of Mrs. W. Walton Butterworth, 1979",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55838,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3538,false,true,55839,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Yōshū (Hashimoto) Chikanobu,"Japanese, 1838–1912",,Yōshū (Hashimoto) Chikanobu,Japanese,1838,1912,1895,1895,1895,Triptych of polychrome woodblock prints; ink and color on paper,L. (page) 13 7/8 in. (35.2 cm); W. (page) 9 1/4 in. (23.4 cm),"Gift of Mrs. W. Walton Butterworth, 1979",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55839,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3540,false,true,55841,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Yōshū (Hashimoto) Chikanobu,"Japanese, 1838–1912",,Yōshū (Hashimoto) Chikanobu,Japanese,1838,1912,1895,1895,1895,Polychrome woodblock prints; ink and color on paper,L. (page) 13 7/8 in. (35.2 cm); W. (page) 9 1/4 in. (23.4 cm),"Gift of Mrs. W. Walton Butterworth, 1979",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55841,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3541,false,true,55842,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Yōshū (Hashimoto) Chikanobu,"Japanese, 1838–1912",,Yōshū (Hashimoto) Chikanobu,Japanese,1838,1912,1895,1895,1895,Quintiptych of polychrome woodblock prints; ink and color on paper,L. (page) 13 7/8 in. (35.2 cm); W. (page) 9 1/4 in. (23.4 cm),"Gift of Mrs. W. Walton Butterworth, 1979",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55842,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3542,false,true,55843,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Yōshū (Hashimoto) Chikanobu,"Japanese, 1838–1912",,Yōshū (Hashimoto) Chikanobu,Japanese,1838,1912,1895,1895,1895,Triptych of polychrome woodblock prints; ink and color on paper,L. (page) 13 7/8 in. (35.2 cm); W. (page) 9 1/4 in. (23.4 cm),"Gift of Mrs. W. Walton Butterworth, 1979",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55843,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3543,false,true,55844,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Yōshū (Hashimoto) Chikanobu,"Japanese, 1838–1912",,Yōshū (Hashimoto) Chikanobu,Japanese,1838,1912,1895,1895,1895,Triptych of polychrome woodblock prints; ink and color on paper,L. (page) 13 7/8 in. (35.2 cm); W. (page) 9 1/4 in. (23.4 cm),"Gift of Mrs. W. Walton Butterworth, 1979",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55844,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3544,false,true,55845,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Yōshū (Hashimoto) Chikanobu,"Japanese, 1838–1912",,Yōshū (Hashimoto) Chikanobu,Japanese,1838,1912,1895,1895,1895,Triptych of polychrome woodblock prints; ink and color on paper,L. (page) 13 7/8 in. (35.2 cm); W. (page) 9 1/4 in. (23.4 cm),"Gift of Mrs. W. Walton Butterworth, 1979",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55845,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3545,false,true,55846,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Yōshū (Hashimoto) Chikanobu,"Japanese, 1838–1912",,Yōshū (Hashimoto) Chikanobu,Japanese,1838,1912,1895,1895,1895,Triptych of polychrome woodblock prints; ink and color on paper,L. (page) 13 7/8 in. (35.2 cm); W. (page) 9 1/4 in. (23.4 cm),"Gift of Mrs. W. Walton Butterworth, 1979",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55846,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3546,false,true,55847,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Yōshū (Hashimoto) Chikanobu,"Japanese, 1838–1912",,Yōshū (Hashimoto) Chikanobu,Japanese,1838,1912,1895,1895,1895,Triptych of polychrome woodblock prints; ink and color on paper,L. (page) 13 7/8 in. (35.2 cm); W. (page) 9 1/4 in. (23.4 cm),"Gift of Mrs. W. Walton Butterworth, 1979",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55847,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3547,false,true,55848,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Yōshū (Hashimoto) Chikanobu,"Japanese, 1838–1912",,Yōshū (Hashimoto) Chikanobu,Japanese,1838,1912,1895,1895,1895,Triptych of polychrome woodblock prints; ink and color on paper,L. (page) 13 7/8 in. (35.2 cm); W. (page) 9 1/4 in. (23.4 cm),"Gift of Mrs. W. Walton Butterworth, 1979",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55848,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3548,false,true,55849,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Yōshū (Hashimoto) Chikanobu,"Japanese, 1838–1912",,Yōshū (Hashimoto) Chikanobu,Japanese,1838,1912,1895,1895,1895,Triptych of polychrome woodblock prints; ink and color on paper,L. (page) 13 7/8 in. (35.2 cm); W. (page) 9 1/4 in. (23.4 cm),"Gift of Mrs. W. Walton Butterworth, 1979",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55849,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3549,false,true,55850,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Yōshū (Hashimoto) Chikanobu,"Japanese, 1838–1912",,Yōshū (Hashimoto) Chikanobu,Japanese,1838,1912,1895,1895,1895,Triptych of polychrome woodblock prints; ink and color on paper,L. (page) 13 7/8 in. (35.2 cm); W. (page) 9 1/4 in. (23.4 cm),"Gift of Mrs. W. Walton Butterworth, 1979",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55850,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3550,false,true,55851,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Yōshū (Hashimoto) Chikanobu,"Japanese, 1838–1912",,Yōshū (Hashimoto) Chikanobu,Japanese,1838,1912,1895,1895,1895,Triptych of polychrome woodblock prints; ink and color on paper,L. (page) 13 7/8 in. (35.2 cm); W. (page) 9 1/4 in. (23.4 cm),"Gift of Mrs. W. Walton Butterworth, 1979",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55851,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3551,false,true,55852,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Yōshū (Hashimoto) Chikanobu,"Japanese, 1838–1912",,Yōshū (Hashimoto) Chikanobu,Japanese,1838,1912,1895,1895,1895,Triptych of polychrome woodblock prints; ink and color on paper,L. (page) 13 7/8 in. (35.2 cm); W. (page) 9 1/4 in. (23.4 cm),"Gift of Mrs. W. Walton Butterworth, 1979",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55852,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3552,false,true,55853,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Yōshū (Hashimoto) Chikanobu,"Japanese, 1838–1912",,Yōshū (Hashimoto) Chikanobu,Japanese,1838,1912,1895,1895,1895,Triptych of polychrome woodblock prints; ink and color on paper,L. (page) 13 7/8 in. (35.2 cm); W. (page) 9 1/4 in. (23.4 cm),"Gift of Mrs. W. Walton Butterworth, 1979",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55853,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3553,false,true,55854,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Yōshū (Hashimoto) Chikanobu,"Japanese, 1838–1912",,Yōshū (Hashimoto) Chikanobu,Japanese,1838,1912,1895,1895,1895,Triptych of polychrome woodblock prints; ink and color on paper,L. (page) 13 7/8 in. (35.2 cm); W. (page) 9 1/4 in. (23.4 cm),"Gift of Mrs. W. Walton Butterworth, 1979",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55854,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3554,false,true,55855,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Yōshū (Hashimoto) Chikanobu,"Japanese, 1838–1912",,Yōshū (Hashimoto) Chikanobu,Japanese,1838,1912,1895,1895,1895,Triptych of polychrome woodblock prints; ink and color on paper,L. (page) 13 7/8 in. (35.2 cm); W. (page) 9 1/4 in. (23.4 cm),"Gift of Mrs. W. Walton Butterworth, 1979",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55855,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3555,false,true,55856,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Yōshū (Hashimoto) Chikanobu,"Japanese, 1838–1912",,Yōshū (Hashimoto) Chikanobu,Japanese,1838,1912,1895,1895,1895,Triptych from an album of polychrome woodblock prints; ink and color on paper,13 7/8 x 9 1/4 in. (35.2 x 23.5 cm),"Gift of Mrs. W. Walton Butterworth, 1979",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55856,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3556,false,true,55857,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Yōshū (Hashimoto) Chikanobu,"Japanese, 1838–1912",,Yōshū (Hashimoto) Chikanobu,Japanese,1838,1912,1895,1895,1895,Triptych from an album of polychrome woodblock prints; ink and color on paper,9 1/4 x 13 7/8 in. (23.5 x 35.2 cm),"Gift of Mrs. W. Walton Butterworth, 1979",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55857,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3557,false,true,55858,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Yōshū (Hashimoto) Chikanobu,"Japanese, 1838–1912",,Yōshū (Hashimoto) Chikanobu,Japanese,1838,1912,1895,1895,1895,Triptych of polychrome woodblock prints; ink and color on paper,L. (page) 13 7/8 in. (35.2 cm); W. (page) 9 1/4 in. (23.4 cm),"Gift of Mrs. W. Walton Butterworth, 1979",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55858,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3558,false,true,55859,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Yōshū (Hashimoto) Chikanobu,"Japanese, 1838–1912",,Yōshū (Hashimoto) Chikanobu,Japanese,1838,1912,1895,1895,1895,Triptych of polychrome woodblock prints; ink and color on paper,L. (page) 13 7/8 in. (35.2 cm); W. (page) 9 1/4 in. (23.4 cm),"Gift of Mrs. W. Walton Butterworth, 1979",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55859,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3559,false,true,55860,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Yōshū (Hashimoto) Chikanobu,"Japanese, 1838–1912",,Yōshū (Hashimoto) Chikanobu,Japanese,1838,1912,1895,1895,1895,Pentaptych of polychrome woodblock prints; ink and color on paper,L. (page) 13 7/8 in. (35.2 cm); W. (page) 9 1/4 in. (23.4 cm),"Gift of Mrs. W. Walton Butterworth, 1979",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55860,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3560,false,true,55861,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Yōshū (Hashimoto) Chikanobu,"Japanese, 1838–1912",,Yōshū (Hashimoto) Chikanobu,Japanese,1838,1912,1897,1897,1897,Diptych of polychrome woodblock prints; ink and color on paper,L. (page) 13 7/8 in. (35.2 cm); W. (page) 9 1/4 in. (23.4 cm); thickness of album 1 9/16 in. (4 cm),"Gift of Mrs. W. Walton Butterworth, 1979",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55861,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3561,false,true,55864,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Yōshū (Hashimoto) Chikanobu,"Japanese, 1838–1912",,Yōshū (Hashimoto) Chikanobu,Japanese,1838,1912,1897,1897,1897,Hexaptych of polychrome woodblock prints; ink and color on paper,L. (page) 13 7/8 in. (35.2 cm); W. (page) 9 1/4 in. (23.4 cm); thickness of album 1 9/16 in. (4 cm),"Gift of Mrs. W. Walton Butterworth, 1979",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55864,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3562,false,true,55865,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Yōshū (Hashimoto) Chikanobu,"Japanese, 1838–1912",,Yōshū (Hashimoto) Chikanobu,Japanese,1838,1912,1897,1897,1897,Triptych of polychrome woodblock prints; ink and color on paper,L. (page) 13 7/8 in. (35.2 cm); W. (page) 9 1/4 in. (23.4 cm); thickness of album 1 9/16 in. (4 cm),"Gift of Mrs. W. Walton Butterworth, 1979",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55865,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3563,false,true,55866,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Yōshū (Hashimoto) Chikanobu,"Japanese, 1838–1912",,Yōshū (Hashimoto) Chikanobu,Japanese,1838,1912,1897,1897,1897,Triptych of polychrome woodblock prints; ink and color on paper,L. (page) 13 7/8 in. (35.2 cm); W. (page) 9 1/4 in. (23.4 cm); thickness of album 1 9/16 in. (4 cm),"Gift of Mrs. W. Walton Butterworth, 1979",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55866,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3564,false,true,55868,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Yōshū (Hashimoto) Chikanobu,"Japanese, 1838–1912",,Yōshū (Hashimoto) Chikanobu,Japanese,1838,1912,1897,1897,1897,Triptych of polychrome woodblock prints; ink and color on paper,L. (page) 13 7/8 in. (35.2 cm); W. (page) 9 1/4 in. (23.4 cm); thickness of album 1 9/16 in. (4 cm),"Gift of Mrs. W. Walton Butterworth, 1979",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55868,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3565,false,true,55869,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Yōshū (Hashimoto) Chikanobu,"Japanese, 1838–1912",,Yōshū (Hashimoto) Chikanobu,Japanese,1838,1912,1897,1897,1897,Triptych of polychrome woodblock prints; ink and color on paper,L. (page) 13 7/8 in. (35.2 cm); W. (page) 9 1/4 in. (23.4 cm); thickness of album 1 9/16 in. (4 cm),"Gift of Mrs. W. Walton Butterworth, 1979",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55869,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3566,false,true,55870,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Yōshū (Hashimoto) Chikanobu,"Japanese, 1838–1912",,Yōshū (Hashimoto) Chikanobu,Japanese,1838,1912,1897,1897,1897,Triptych of polychrome woodblock prints; ink and color on paper,L. (page) 13 7/8 in. (35.2 cm); W. (page) 9 1/4 in. (23.4 cm); thickness of album 1 9/16 in. (4 cm),"Gift of Mrs. W. Walton Butterworth, 1979",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55870,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3567,false,true,55871,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Yōshū (Hashimoto) Chikanobu,"Japanese, 1838–1912",,Yōshū (Hashimoto) Chikanobu,Japanese,1838,1912,1897,1897,1897,Triptych of polychrome woodblock prints; ink and color on paper,L. (page) 13 7/8 in. (35.2 cm); W. (page) 9 1/4 in. (23.4 cm); thickness of album 1 9/16 in. (4 cm),"Gift of Mrs. W. Walton Butterworth, 1979",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55871,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3568,false,true,55872,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Yōshū (Hashimoto) Chikanobu,"Japanese, 1838–1912",,Yōshū (Hashimoto) Chikanobu,Japanese,1838,1912,1897,1897,1897,Triptych of polychrome woodblock prints; ink and color on paper,L. (page) 13 7/8 in. (35.2 cm); W. (page) 9 1/4 in. (23.4 cm); thickness of album 1 9/16 in. (4 cm),"Gift of Mrs. W. Walton Butterworth, 1979",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55872,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3569,false,true,55873,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Yōshū (Hashimoto) Chikanobu,"Japanese, 1838–1912",,Yōshū (Hashimoto) Chikanobu,Japanese,1838,1912,1897,1897,1897,Triptych of polychrome woodblock prints; ink and color on paper,L. (page) 13 7/8 in. (35.2 cm); W. (page) 9 1/4 in. (23.4 cm); thickness of album 1 9/16 in. (4 cm),"Gift of Mrs. W. Walton Butterworth, 1979",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55873,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3570,false,true,55874,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Yōshū (Hashimoto) Chikanobu,"Japanese, 1838–1912",,Yōshū (Hashimoto) Chikanobu,Japanese,1838,1912,1897,1897,1897,Triptych of polychrome woodblock prints; ink and color on paper,L. (page) 13 7/8 in. (35.2 cm); W. (page) 9 1/4 in. (23.4 cm); thickness of album 1 9/16 in. (4 cm),"Gift of Mrs. W. Walton Butterworth, 1979",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55874,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3571,false,true,55875,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Yōshū (Hashimoto) Chikanobu,"Japanese, 1838–1912",,Yōshū (Hashimoto) Chikanobu,Japanese,1838,1912,1897,1897,1897,Triptych of polychrome woodblock prints; ink and color on paper,L. (page) 13 7/8 in. (35.2 cm); W. (page) 9 1/4 in. (23.4 cm); thickness of album 1 9/16 in. (4 cm),"Gift of Mrs. W. Walton Butterworth, 1979",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55875,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3572,false,true,55876,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Yōshū (Hashimoto) Chikanobu,"Japanese, 1838–1912",,Yōshū (Hashimoto) Chikanobu,Japanese,1838,1912,1897,1897,1897,Triptych of polychrome woodblock prints; ink and color on paper,L. (page) 13 7/8 in. (35.2 cm); W. (page) 9 1/4 in. (23.4 cm); thickness of album 1 9/16 in. (4 cm),"Gift of Mrs. W. Walton Butterworth, 1979",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55876,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3573,false,true,55877,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Yōshū (Hashimoto) Chikanobu,"Japanese, 1838–1912",,Yōshū (Hashimoto) Chikanobu,Japanese,1838,1912,1897,1897,1897,Triptych of polychrome woodblock prints; ink and color on paper,L. (page) 13 7/8 in. (35.2 cm); W. (page) 9 1/4 in. (23.4 cm); thickness of album 1 9/16 in. (4 cm),"Gift of Mrs. W. Walton Butterworth, 1979",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55877,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3574,false,true,55878,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Yōshū (Hashimoto) Chikanobu,"Japanese, 1838–1912",,Yōshū (Hashimoto) Chikanobu,Japanese,1838,1912,1897,1897,1897,Triptych of polychrome woodblock prints; ink and color on paper,L. (page) 13 7/8 in. (35.2 cm); W. (page) 9 1/4 in. (23.4 cm); thickness of album 1 9/16 in. (4 cm),"Gift of Mrs. W. Walton Butterworth, 1979",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55878,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3576,false,true,55880,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Yōshū (Hashimoto) Chikanobu,"Japanese, 1838–1912",,Yōshū (Hashimoto) Chikanobu,Japanese,1838,1912,1897,1897,1897,Triptych of polychrome woodblock prints; ink and color on paper,L. (page) 13 7/8 in. (35.2 cm); W. (page) 9 1/4 in. (23.4 cm); thickness of album 1 9/16 in. (4 cm),"Gift of Mrs. W. Walton Butterworth, 1979",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55880,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3577,false,true,55881,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Yōshū (Hashimoto) Chikanobu,"Japanese, 1838–1912",,Yōshū (Hashimoto) Chikanobu,Japanese,1838,1912,1897,1897,1897,Triptych of polychrome woodblock prints; ink and color on paper,L. (page) 13 7/8 in. (35.2 cm); W. (page) 9 1/4 in. (23.4 cm); thickness of album 1 9/16 in. (4 cm),"Gift of Mrs. W. Walton Butterworth, 1979",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55881,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3578,false,true,55882,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Yōshū (Hashimoto) Chikanobu,"Japanese, 1838–1912",,Yōshū (Hashimoto) Chikanobu,Japanese,1838,1912,1897,1897,1897,Triptych of polychrome woodblock prints; ink and color on paper,L. (page) 13 7/8 in. (35.2 cm); W. (page) 9 1/4 in. (23.4 cm); thickness of album 1 9/16 in. (4 cm),"Gift of Mrs. W. Walton Butterworth, 1979",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55882,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3579,false,true,55883,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Yōshū (Hashimoto) Chikanobu,"Japanese, 1838–1912",,Yōshū (Hashimoto) Chikanobu,Japanese,1838,1912,1897,1897,1897,Triptych of polychrome woodblock prints; ink and color on paper,L. (page) 13 7/8 in. (35.2 cm); W. (page) 9 1/4 in. (23.4 cm); thickness of album 1 9/16 in. (4 cm),"Gift of Mrs. W. Walton Butterworth, 1979",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55883,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3580,false,true,55884,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Yōshū (Hashimoto) Chikanobu,"Japanese, 1838–1912",,Yōshū (Hashimoto) Chikanobu,Japanese,1838,1912,1897,1897,1897,Triptych of polychrome woodblock prints; ink and color on paper,L. (page) 13 7/8 in. (35.2 cm); W. (page) 9 1/4 in. (23.4 cm); thickness of album 1 9/16 in. (4 cm),"Gift of Mrs. W. Walton Butterworth, 1979",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55884,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3581,false,true,55885,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Yōshū (Hashimoto) Chikanobu,"Japanese, 1838–1912",,Yōshū (Hashimoto) Chikanobu,Japanese,1838,1912,1897,1897,1897,Triptych of polychrome woodblock prints; ink and color on paper,L. (page) 13 7/8 in. (35.2 cm); W. (page) 9 1/4 in. (23.4 cm); thickness of album 1 9/16 in. (4 cm),"Gift of Mrs. W. Walton Butterworth, 1979",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55885,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3582,false,true,55886,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Yōshū (Hashimoto) Chikanobu,"Japanese, 1838–1912",,Yōshū (Hashimoto) Chikanobu,Japanese,1838,1912,1897,1897,1897,Triptych of polychrome woodblock prints; ink and color on paper,L. (page) 13 7/8 in. (35.2 cm); W. (page) 9 1/4 in. (23.4 cm); thickness of album 1 9/16 in. (4 cm),"Gift of Mrs. W. Walton Butterworth, 1979",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55886,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3583,false,true,55887,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Yōshū (Hashimoto) Chikanobu,"Japanese, 1838–1912",,Yōshū (Hashimoto) Chikanobu,Japanese,1838,1912,1897,1897,1897,Triptych of polychrome woodblock prints; ink and color on paper,L. (page) 13 7/8 in. (35.2 cm); W. (page) 9 1/4 in. (23.4 cm); thickness of album 1 9/16 in. (4 cm),"Gift of Mrs. W. Walton Butterworth, 1979",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55887,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3584,false,true,55888,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Yōshū (Hashimoto) Chikanobu,"Japanese, 1838–1912",,Yōshū (Hashimoto) Chikanobu,Japanese,1838,1912,1897,1897,1897,Triptych of polychrome woodblock prints; ink and color on paper,L. (page) 13 7/8 in. (35.2 cm); W. (page) 9 1/4 in. (23.4 cm); thickness of album 1 9/16 in. (4 cm),"Gift of Mrs. W. Walton Butterworth, 1979",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55888,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3585,false,true,55889,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Yōshū (Hashimoto) Chikanobu,"Japanese, 1838–1912",,Yōshū (Hashimoto) Chikanobu,Japanese,1838,1912,1897,1897,1897,Triptych of polychrome woodblock prints; ink and color on paper,L. (page) 13 7/8 in. (35.2 cm); W. (page) 9 1/4 in. (23.4 cm); thickness of album 1 9/16 in. (4 cm),"Gift of Mrs. W. Walton Butterworth, 1979",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55889,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3586,false,true,55890,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Yōshū (Hashimoto) Chikanobu,"Japanese, 1838–1912",,Yōshū (Hashimoto) Chikanobu,Japanese,1838,1912,1897,1897,1897,Triptych of polychrome woodblock prints; ink and color on paper,L. (page) 13 7/8 in. (35.2 cm); W. (page) 9 1/4 in. (23.4 cm); thickness of album 1 9/16 in. (4 cm),"Gift of Mrs. W. Walton Butterworth, 1979",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55890,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3587,false,true,55891,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Yōshū (Hashimoto) Chikanobu,"Japanese, 1838–1912",,Yōshū (Hashimoto) Chikanobu,Japanese,1838,1912,1897,1897,1897,Triptych of polychrome woodblock prints; ink and color on paper,L. (page) 13 7/8 in. (35.2 cm); W. (page) 9 1/4 in. (23.4 cm); thickness of album 1 9/16 in. (4 cm),"Gift of Mrs. W. Walton Butterworth, 1979",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55891,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3588,false,true,55892,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Yōshū (Hashimoto) Chikanobu,"Japanese, 1838–1912",,Yōshū (Hashimoto) Chikanobu,Japanese,1838,1912,1897,1897,1897,Triptych of polychrome woodblock prints; ink and color on paper,L. (page) 13 7/8 in. (35.2 cm); W. (page) 9 1/4 in. (23.4 cm); thickness of album 1 9/16 in. (4 cm),"Gift of Mrs. W. Walton Butterworth, 1979",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55892,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3589,false,true,55893,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Yōshū (Hashimoto) Chikanobu,"Japanese, 1838–1912",,Yōshū (Hashimoto) Chikanobu,Japanese,1838,1912,1897,1897,1897,Triptych of polychrome woodblock prints; ink and color on paper,L. (page) 13 7/8 in. (35.2 cm); W. (page) 9 1/4 in. (23.4 cm); thickness of album 1 9/16 in. (4 cm),"Gift of Mrs. W. Walton Butterworth, 1979",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55893,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3590,false,true,55894,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Yōshū (Hashimoto) Chikanobu,"Japanese, 1838–1912",,Yōshū (Hashimoto) Chikanobu,Japanese,1838,1912,1897,1897,1897,Triptych of polychrome woodblock prints; ink and color on paper,L. (page) 13 7/8 in. (35.2 cm); W. (page) 9 1/4 in. (23.4 cm); thickness of album 1 9/16 in. (4 cm),"Gift of Mrs. W. Walton Butterworth, 1979",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55894,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3591,false,true,55895,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Yōshū (Hashimoto) Chikanobu,"Japanese, 1838–1912",,Yōshū (Hashimoto) Chikanobu,Japanese,1838,1912,1897,1897,1897,Triptych of polychrome woodblock prints; ink and color on paper,L. (page) 13 7/8 in. (35.2 cm); W. (page) 9 1/4 in. (23.4 cm); thickness of album 1 9/16 in. (4 cm),"Gift of Mrs. W. Walton Butterworth, 1979",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55895,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3241a–c,false,true,55254,Asian Art,Print,"『扶桑高貴鑑』|A Mirror of Japan’s Nobility: The Emperor Meiji, His Wife, and Prince Haru (Fūsō kōki kagami)",Japan,Meiji period (1868–1912),,,,Artist,,Yōshū (Hashimoto) Chikanobu,"Japanese, 1838–1912",,Yōshū (Hashimoto) Chikanobu,Japanese,1838,1912,"August 8th, 1887",1887,1887,Triptych of polychrome woodblock prints; ink and color on paper,14 3/4 x 29 5/8 in. (37.5 x 75.2 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55254,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3253a–c,false,true,55264,Asian Art,Print,『上野第三回内国勧業 博覧会御幸之図』|Visit of the Empress to the Third National Industrial Promotional Exhibition at Ueno Park (Ueno dai sankai naikoku kangyō hakuran kai gyokō no zu),Japan,Meiji period (1868–1912),,,,Artist,,Yōshū (Hashimoto) Chikanobu,"Japanese, 1838–1912",,Yōshū (Hashimoto) Chikanobu,Japanese,1838,1912,1889,1889,1889,Triptych of polychrome woodblock prints; ink and color on paper,Image (each): 14 1/2 × 9 1/2 in. (36.8 × 24.1 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55264,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3688a–c,false,true,55920,Asian Art,Print,"二品親王女三宮|The Third Princess and Kashiwagi, from Chapter 34, “New Herbs I (Wakana I)” (Nihon shinnō onna sannomiya)",Japan,Meiji period (1868–1912),,,,Artist,,Yōshū (Hashimoto) Chikanobu,"Japanese, 1838–1912",,Yōshū (Hashimoto) Chikanobu,Japanese,1838,1912,1890,1890,1890,Triptych of polychrome woodblock prints; ink and color on paper,Each 14 1/4 x 9 1/4 in. (36.2 x 23.5 cm),"Gift of Lincoln Kirstein, 1985",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55920,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"JP3539.1, .2",false,true,55840,Asian Art,Print,千代田の大奥　茶の湯辺り花|Chiyoda Inner Palace: No.20 Flower Arranging in Turn (Chiyoda no Ōoku: Chanoyu mawaribana),Japan,Meiji period (1868–1912),,,,Artist,,Hashimoto Chikanobu,"Japanese, 1838–1912",,Hashimoto Chikanobu,Japanese,1838,1912,August 1895,1895,1895,Triptych of polychrome woodblock prints; ink and color on paper,L. (page) 13 7/8 in. (35.2 cm); W. (page) 9 1/4 in. (23.4 cm),"Gift of Mrs. W. Walton Butterworth, 1979",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55840,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"JP3575.1, .2",false,true,55879,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Yōshū (Hashimoto) Chikanobu,"Japanese, 1838–1912",,Yōshū (Hashimoto) Chikanobu,Japanese,1838,1912,1897,1897,1897,Triptych of polychrome woodblock prints; ink and color on paper,L. (page) 13 7/8 in. (35.2 cm); W. (page) 9 1/4 in. (23.4 cm); thickness of album 1 9/16 in. (4 cm),"Gift of Mrs. W. Walton Butterworth, 1979",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55879,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.352a–c,false,true,72830,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Yōshū (Hashimoto) Chikanobu,"Japanese, 1838–1912",,Yōshū (Hashimoto) Chikanobu,Japanese,1838,1912,1898,1898,1898,Triptych of polychrome woodblock prints; color on paper,Overall (a): 14 5/8 x 9 15/16 in. (37.1 x 25.2 cm) Overall (b): 14 7/8 x 9 7/8 in. (37.8 x 25.1 cm) Overall (c): 14 5/8 x 9 15/16 in. (37.1 x 25.2 cm),"Purchase, Friends of Asian Art Gifts, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/72830,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2007.49.331a, b",false,true,73583,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Yōshū (Hashimoto) Chikanobu,"Japanese, 1838–1912",,Yōshū (Hashimoto) Chikanobu,Japanese,1838,1912,late 19th century,1867,1899,Polychrome woodblock prints; ink and color on paper,Image: 13 1/8 x 9 in. (33.3 x 22.9 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73583,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3277,false,true,55359,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Tsukioka Yoshitoshi,"Japanese, 1839–1892",,Tsukioka Yoshitoshi,Japanese,1839,1892,September 1877,1877,1877,Triptych of polychrome woodblock prints; ink and color on paper,14 3/16 x 29 1/4 in. (36 x 74.3 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55359,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3387,false,true,55561,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Tsukioka Yoshitoshi,"Japanese, 1839–1892",,Tsukioka Yoshitoshi,Japanese,1839,1892,September 1877,1877,1877,Polychrome woodblock print; ink and color on paper,13 1/2 x 28 1/2 in. (34.3 x 72.4 cm),"Gift of Lincoln Kirstein, 1960",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55561,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.308,false,true,73575,Asian Art,Print,"東錦浮世稿談－幡随院長兵衛|Banzuiin Chōbei, from the series Story of Brocades of the East in the Floating World (Azuma no hana ukiyo kōdan - Banzuiin Chōbei)",Japan,Meiji period (1868–1912),,,,Artist,,Tsukioka Yoshitoshi,"Japanese, 1839–1892",,Tsukioka Yoshitoshi,Japanese,1839,1892,"10th month, 1867",1867,1867,Polychrome woodblock print; ink and color on paper,Image: 14 1/2 x 9 1/2 in. (36.8 x 24.1 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73575,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.310,false,true,73623,Asian Art,Print,月百姿 - 煙中月|Moon in the Flame from the Series One Hundred Images of the Moon (Tsuki hyaku sugata-enchūgetsu),Japan,Meiji period (1868–1912),,,,Artist,,Tsukioka Yoshitoshi,"Japanese, 1839–1892",,Tsukioka Yoshitoshi,Japanese,1839,1892,"February, 1886",1886,1886,Polychrome woodblock print; ink and color on paper,Image: 14 x 9 3/8 in. (35.6 x 23.8 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73623,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.311,false,true,73624,Asian Art,Print,"郵便報知新聞 645号|Postal Hōchi Newspaper no. 645, Englishman raping a wine shopkeeper's daughter (Yūbin Hōchi shinbun, roppyaku yonjū gogō)",Japan,Meiji period (1868–1912),,,,Artist,,Tsukioka Yoshitoshi,"Japanese, 1839–1892",,Tsukioka Yoshitoshi,Japanese,1839,1892,"August, 1875",1875,1875,Polychrome woodblock print; ink and color on paper,Image: 14 1/8 x 9 3/8 in. (35.9 x 23.8 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73624,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.312,false,true,73625,Asian Art,Print,皇国一新見聞誌|Chronicle of the Imperial Restoration (Kōkoku isshin kenbunshi),Japan,Meiji period (1868–1912),,,,Artist,,Tsukioka Yoshitoshi,"Japanese, 1839–1892",,Tsukioka Yoshitoshi,Japanese,1839,1892,"June, 1876",1876,1876,Polychrome woodblock print; ink and color on paper,Image: 14 1/2 x 9 3/8 in. (36.8 x 23.8 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73625,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.313,false,true,73626,Asian Art,Print,"新柳二十四時 午前十二時|Twenty-Four Hours at Shinbashi/Yanagibashi: 12 Noon. (Shinyanagi nijūyo-ji, gozen jūni-ji)",Japan,Meiji period (1868–1912),,,,Artist,,Tsukioka Yoshitoshi,"Japanese, 1839–1892",,Tsukioka Yoshitoshi,Japanese,1839,1892,1880,1880,1880,Polychrome woodblock print; ink and color on paper,Image: 10 x 14 5/8 in. (25.4 x 37.1 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73626,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.315,false,true,73628,Asian Art,Print,"見立多以尽 - 洋行がしたい|Collection of Desires, Wish for Foreign Travel (Mitate Tai zukushi-yōkō ga shitai)",Japan,Meiji period (1868–1912),,,,Artist,,Tsukioka Yoshitoshi,"Japanese, 1839–1892",,Tsukioka Yoshitoshi,Japanese,1839,1892,"January, 1878",1878,1878,Polychrome woodblock print; ink and color on paper,Image: 14 1/2 x 9 7/8 in. (36.8 x 25.1 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73628,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.316,false,true,73622,Asian Art,Print,"新柳二十四時 午前十二時|Twenty-Four Hours at Shinbashi/Yanagibashi: 12 Noon. (Shinyanagi nijūyo-ji, gozen jūni-ji)",Japan,Meiji period (1868–1912),,,,Artist,,Tsukioka Yoshitoshi,"Japanese, 1839–1892",,Tsukioka Yoshitoshi,Japanese,1839,1892,1880,1800,1899,Polychrome woodblock print; ink and color on paper,Image: 17 x 11 in. (43.2 x 27.9 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73622,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3252a–c,false,true,55263,Asian Art,Print,官女ステーション着車図|Illustration of Ladies-in-waiting boarding at a station (Kanjo sutēshon chakusha zu),Japan,Meiji period (1868–1912),,,,Artist,,Tsukioka Yoshitoshi,"Japanese, 1839–1892",,Tsukioka Yoshitoshi,Japanese,1839,1892,1879,1879,1879,Polychrome woodblock print; ink and color on paper,13 5/8 x 27 3/4 in. (34.6 x 70.5 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55263,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.350a–c,false,true,72828,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Tsukioka Yoshitoshi,"Japanese, 1839–1892",,Tsukioka Yoshitoshi,Japanese,1839,1892,1885,1885,1885,Triptych of polychrome woodblock prints; ink and color on paper,Overall (a): 14 3/4 x 10 in. (37.5 x 25.4 cm) Overall (b): 14 5/8 x 10 in. (37.1 x 25.4 cm) Overall (c): 14 15/16 x 10 1/16 in. (37.9 x 25.6 cm),"Purchase, Friends of Asian Art Gifts, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/72828,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.260a–c,false,true,73551,Asian Art,Print,"東京名勝高輪　蒸気車鉄道之全図|Illustration of Steam Locomotive Tracks at Takanawa, from the series Famous Places in Tokyo (Tōkyō meishō Takanawa-jōki kikansha no zen zu)",Japan,Meiji period (1868–1912),,,,Artist,,Tsukioka Yoshitoshi,"Japanese, 1839–1892",,Tsukioka Yoshitoshi,Japanese,1839,1892,1872,1872,1872,Triptych of polychrome woodblock prints; ink and color on paper,Image (a): 14 x 9 1/2 in. (35.6 x 24.1 cm) Image (b): 14 1/4 x 9 5/8 in. (36.2 x 24.4 cm) Image (c): 14 1/8 x 9 3/4 in. (35.9 x 24.8 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73551,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.307a–c,false,true,73574,Asian Art,Print,清盛入道布引滝遊覧悪源太義平霊討難波次郎|Kiyomori and the History of Nunobiki Waterfall: The spirit of Akugenta Yoshihira strikes Nanba Jirō. (Kiyomori nyūdō nunobiki no taki yūran akugenta yoshihira no rei nanba jirō o utsu),Japan,Meiji period (1868–1912),,,,Artist,,Tsukioka Yoshitoshi,"Japanese, 1839–1892",,Tsukioka Yoshitoshi,Japanese,1839,1892,1868,1868,1868,Triptych of polychrome woodblock prints; ink and color on paper,Image (a): 9 1/2 x 14 in. (24.1 x 35.6 cm) Image (b): 9 1/2 x 14 3/4 in. (24.1 x 37.5 cm) Image (c): 9 1/2 x 14 in. (24.1 x 35.6 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73574,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3259,false,true,55274,Asian Art,Print,"東京名所従　上野公園不忍池中嶋弁天之景|View of Benten Shrine on Nakanoshima Island in Shinobazu Pond, Ueno Park, from the series Famous Views of Tokyo (Tōkyō Tokyo meisho yori Ueno kōen Shinobazu no ike Nakanoshima Benten no kei)",Japan,Meiji period (1868–1912),,,,Artist,,Utagawa Hiroshige III,"Japanese, 1843–1894",,Utagawa Hiroshige III,Japanese,1843,1894,"May, 1881",1881,1881,Polychrome woodblock print; ink and color on paper,Image (each): 14 3/4 × 9 1/2 in. (37.5 × 24.1 cm) 14 3/4 × 29 1/8 in. (37.5 × 74 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55274,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3267,false,true,55327,Asian Art,Print,"東京名所 上野公園内国勧業第二博覧会美術館図|Illustration of the Museum at the Second National Industrial Exhibition in Ueno, from the series Famous Places in Tokyo (Tokyo meisho-Ueno kōen naikoku kangyō daini hakurankai bijutsukan zu)",Japan,Meiji period (1868–1912),,,,Artist,,Utagawa Hiroshige III,"Japanese, 1843–1894",,Utagawa Hiroshige III,Japanese,1843,1894,1881,1881,1881,Triptych of polychrome woodblock prints; ink and color on paper,13 3/4 x 28 1/8 in. (34.9 x 71.4 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55327,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3349,false,true,55515,Asian Art,Print,横浜商館天主堂ノ図|Illustration of Foreign Residences and the Catholic Church in Yokohama (Yokohama shōkan tenshudō no zu),Japan,Meiji period (1868–1912),,,,Artist,,Utagawa Hiroshige III,"Japanese, 1843–1894",,Utagawa Hiroshige III,Japanese,1843,1894,"10th month, 1870",1870,1870,Triptych of polychrome woodblock prints; ink and color on paper,28 3/8 x 14 1/2 in. (72.1 x 36.8 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55515,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3385,false,true,55556,Asian Art,Woodblock print,"諸国名所図会内　西京東山一覧|A Glimpse of Higayashiyama the Western Capital, from the series Famous Places in the Nation (Shokoku meisho zukai no uchi-Saikyō Higashiyama ichiran)",Japan,Meiji period (1868–1912),,,,Artist,,Utagawa Hiroshige III,"Japanese, 1843–1894",,Utagawa Hiroshige III,Japanese,1843,1894,"May, 1880",1880,1880,Triptych of polychrome woodblock prints; ink and color on paper,14 1/2 x 29 1/8 in. (36.8 x 74 cm),"Gift of Lincoln Kirstein, 1960",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55556,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3388,false,true,55562,Asian Art,Woodblock print,不二詣諸品下山之図|View of the Descent from a Mountain by Many from Pilgrimage to Mt. Fuji (Fuji mōde shoshina gesan no zu),Japan,Meiji period (1868–1912),,,,Artist,,Utagawa Hiroshige III,"Japanese, 1843–1894",,Utagawa Hiroshige III,Japanese,1843,1894,"November, 1883",1883,1883,Triptych of polychrome woodblock prints; ink and color on paper,14 1/4 x 28 1/4 in. (36.2 x 71.8 cm),"Gift of Lincoln Kirstein, 1960",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55562,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.280,false,true,73563,Asian Art,Print,横浜海岸通り之風景|View of the Seafront in Yokohama (Yokohama Kagandori no fūkei),Japan,Meiji period (1868–1912),,,,Artist,,Utagawa Hiroshige III,"Japanese, 1843–1894",,Utagawa Hiroshige III,Japanese,1843,1894,"5th month, 1870",1870,1870,Triptych of polychrome woodblock prints; ink and color on paper,Image: 14 1/2 x 28 5/8 in. (36.8 x 72.7 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73563,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.150a–c,false,true,73436,Asian Art,Print,東都築地ホテル館庭前の図|Illustration of the Front Garden of the Tsukiji Hotel in the Eastern Capital (Tōto Tsukiji hoteru kan niwa mae no zu),Japan,Meiji period (1868–1912),,,,Artist,,Utagawa Hiroshige III,"Japanese, 1843–1894",,Utagawa Hiroshige III,Japanese,1843,1894,ca. 1868–72,1868,1872,Triptych of polychrome woodblock prints; ink and color on paper,Image (a): 14 3/8 x 10 in. (36.5 x 25.4 cm) Image (b): 14 1/2 x 10 in. (36.8 x 25.4 cm) Image (c): 14 1/2 x 10 in. (36.8 x 25.4 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73436,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.151a–c,false,true,73437,Asian Art,Print,東京築地ホテル館|The Tsukiji Hotel in Tokyo (Tokyo Tsukiji hoteru kan),Japan,Meiji period (1868–1912),,,,Artist,,Utagawa Hiroshige III,"Japanese, 1843–1894",,Utagawa Hiroshige III,Japanese,1843,1894,"5th month, 1870",1870,1870,Triptych of polychrome woodblock prints; ink and color on paper,Image (a): 14 1/2 x 9 5/8 in. (36.8 x 24.4 cm) Image (b): 14 1/2 x 9 5/8 in. (36.8 x 24.4 cm) Image (c): 14 1/2 x 9 5/8 in. (36.8 x 24.4 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73437,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3384,false,true,55550,Asian Art,Print,Kempo happu shiki no zu|View of the Issuance of the Constitution,Japan,Meiji period (1868–1912),,,,Artist,,Baiju Kunitoshi,"Japanese, 1847–1899",,Baiju Kunitoshi,Japanese,1847,1899,March 1889,1889,1889,Triptych of polychrome woodblock prints; ink and color on paper,Oban; 14 1/2 x 28 5/8 in. (36.8 x 72.7 cm),"Gift of Lincoln Kirstein, 1960",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55550,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP228,false,true,36700,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Kobayashi Kiyochika,"Japanese, 1847–1915",,Kobayashi Kiyochika,Japanese,1847,1915,ca. 1878,1868,1888,Polychrome woodblock print; ink and color on paper,9 5/32 x 13 5/8 in. (23.3 x 34.6 cm),"Rogers Fund, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36700,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3209,false,true,55195,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Kobayashi Kiyochika,"Japanese, 1847–1915",,Kobayashi Kiyochika,Japanese,1847,1915,September 1904,1904,1904,Polychrome woodblock print; ink and color on paper,Oban 14 1/4 x 9 7/8 in. (36.2 x 25.1 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55195,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3210,false,true,55196,Asian Art,Print,Kitai taihora|The Spiraling (Effect) of the Fundamental Law on the Fearful Party (Russians),Japan,Meiji period (1868–1912),,,,Artist,,Kobayashi Kiyochika,"Japanese, 1847–1915",,Kobayashi Kiyochika,Japanese,1847,1915,September 1904,1904,1904,Polychrome woodblock print; ink and color on paper,14 1/2 x 9 7/8 in. (36.8 x 25.1 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55196,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3211,false,true,55197,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Kobayashi Kiyochika,"Japanese, 1847–1915",,Kobayashi Kiyochika,Japanese,1847,1915,October 1904,1904,1904,Polychrome woodblock print; ink and color on paper,14 1/2 x 9 7/8 in. (36.8 x 25.1 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55197,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3212,false,true,55198,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Kobayashi Kiyochika,"Japanese, 1847–1915",,Kobayashi Kiyochika,Japanese,1847,1915,"1895 (Meiji 28, 7th month)",1895,1895,Polychrome woodblock print; ink and color on paper,14 x 9 5/16 in. (35.6 x 23.7 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55198,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3258,false,true,55272,Asian Art,Print,凱旋新橋ステーション御着之図|Illustration of the Arrival of the Emperor at Shinbashi Station Following a Victory (Gaisen Shinbashi stēshon gochaku no zu),Japan,Meiji period (1868–1912),,,,Artist,,Kobayashi Kiyochika,"Japanese, 1847–1915",,Kobayashi Kiyochika,Japanese,1847,1915,1895,1895,1895,Triptych of polychrome woodblock prints; ink and color on paper,Each panel: 14 1/4 x 9 1/2 in. (36.2 x 24.1 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55272,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3266,false,true,55325,Asian Art,Print,参謀本部行啓之図|Illustration of the Empress Visiting the General Staff Headquarters [to present a tray of bandages] (Sanbō honbu gyōkei no zu),Japan,Meiji period (1868–1912),,,,Artist,,Kobayashi Kiyochika,"Japanese, 1847–1915",,Kobayashi Kiyochika,Japanese,1847,1915,1895,1895,1895,Triptych of polychrome woodblock prints; ink and color on paper,Oban 14 x 27 3/4 in. (35.6 x 70.5 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55325,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3274,false,true,55348,Asian Art,Print,野戦病院行幸之図|Illustration of the Empress Visiting a Field Hospital [in Hiroshima] (Yasen byōin gyōkō no zu),Japan,Meiji period (1868–1912),,,,Artist,,Kobayashi Kiyochika,"Japanese, 1847–1915",,Kobayashi Kiyochika,Japanese,1847,1915,1895,1895,1895,Triptych of polychrome woodblock prints; ink and color on paper,14 3/4 x 30 in. (37.5 x 76.2cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55348,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3310,false,true,55419,Asian Art,Print,朝鮮大戦争之図|Illustration of the Great Korean War (Chōsen dai sensō no zu),Japan,Meiji period (1868–1912),,,,Artist,,Kobayashi Kiyochika,"Japanese, 1847–1915",,Kobayashi Kiyochika,Japanese,1847,1915,"August, 1882",1882,1882,Right-hand sheet of a triptych of polychrome woodblock prints; ink and color on paper,14 1/8 x 9 in. (35.9 x 22.9 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55419,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3336,false,true,55484,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Kobayashi Kiyochika,"Japanese, 1847–1915",,Kobayashi Kiyochika,Japanese,1847,1915,October 1878,1878,1878,Triptych of polychrome woodblock prints; ink and color on paper,Oban; 14 1/8 x 28 1/8 in. (35.9 x 71.4 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55484,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3420,false,true,55617,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Kobayashi Kiyochika,"Japanese, 1847–1915",,Kobayashi Kiyochika,Japanese,1847,1915,October 1885,1885,1885,Polychrome woodblock print; ink and color on paper,Oban tat-e; 14 x 9 1/4 in. (35.6 x 23.5 cm),"Gift of Lincoln Kirstein, 1962",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55617,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1993.310,false,true,55988,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Kobayashi Kiyochika,"Japanese, 1847–1915",,Kobayashi Kiyochika,Japanese,1847,1915,dated 1874,1874,1874,Polychrome woodblock print; ink and color on paper,14 x 8 5/16 in. (35.6 x 21.1 cm),"Bequest of Gustave von Groschwitz, 1993",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55988,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.328,false,true,73581,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Kobayashi Kiyochika,"Japanese, 1847–1915",,Kobayashi Kiyochika,Japanese,1847,1915,late 19th century,1867,1899,Triptych of polychrome woodblock prints; ink and color on paper,Image: 14 3/4 x 28 1/4 in. (37.5 x 71.8 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73581,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.322a–c,false,true,73632,Asian Art,Print,『万国衣装鑑』|Mirror of National Costumes of All Nations (Bankoku ishō kagami),Japan,Meiji period (1868–1912),,,,Artist,,Kobayashi Kiyochika,"Japanese, 1847–1915",,Kobayashi Kiyochika,Japanese,1847,1915,1882,1882,1882,Triptych of polychrome woodblock prints; ink and color on paper,Image (a): 14 x 9 1/4 in. (35.6 x 23.5 cm) Image (b): 13 7/8 x 9 in. (35.2 x 22.9 cm) Image (c): 14 x 9 1/2 in. (35.6 x 24.1 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73632,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3287,false,true,55375,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Watanabe Seitei,"Japanese, 1851–1918",(?),Watanabe Seitei,Japanese,1851,1918,ca. 1906,1896,1916,Polychrome woodblock print; ink and color on paper,8 7/8 x 11 9/16 in. (22.5 x 29.4 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55375,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3283,false,true,55370,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Mishima Shōsō,"Japanese, 1856–1928",,Mishima Shōsō,Japanese,1856,1928,ca. 1906,1896,1916,Polychrome woodblock print; ink and color on paper,8 3/4 x 11 1/8 in. (22.2 x 28.3 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55370,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3289,false,true,55382,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Suzuki Kason,"Japanese, 1860–1919",,Suzuki Kason,Japanese,1860,1919,ca. 1906,1896,1916,Polychrome woodblock print; ink and color on paper,8 15/16 x 10 3/4 in. (22.7 x 27.3 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55382,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3296,false,true,55393,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Suzuki Kason,"Japanese, 1860–1919",,Suzuki Kason,Japanese,1860,1919,1904,1904,1904,Frontispiece; polychrome woodblock print; ink and color on paper,Image: 8 3/4 x 12 1/4 in. (22.2 x 31.1 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55393,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3190,false,true,55175,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Takeuchi Keishū,"Japanese, 1861–1943",,Takeuchi Keishū,Japanese,1861,1943,ca. 1906,1896,1916,Polychrome woodblock print; ink and color on paper,8 3/4 x 12 1/16 in. (22.2 x 30.6 cm),"GIft of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55175,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3193,false,true,55177,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Takeuchi Keishū,"Japanese, 1861–1943",,Takeuchi Keishū,Japanese,1861,1943,ca. 1905,1895,1915,Polychrome woodblock print; ink and color on paper,12 x 8 1/4 in. (30.5 x 21 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55177,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3279,false,true,45279,Asian Art,Print,"(Frontispiece to) ""Fukuro Monogatari"", (by) Izumi Kyoka|Frontispiece to ""An Owl's Story"", by Izumi Kyoka",Japan,Meiji period (1868–1912),,,,Artist,,Takeuchi Keishū,"Japanese, 1861–1943",,Takeuchi Keishū,Japanese,1861,1943,ca. 1900,1890,1910,Polychrome woodblock print; ink and color on paper,Overall: 8 3/4 x 11 1/4 in. (22.2 x 28.6 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45279,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3286,false,true,55373,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Takeuchi Keishū,"Japanese, 1861–1943",(?),Takeuchi Keishū,Japanese,1861,1943,ca. 1906,1896,1916,Polychrome woodblock print; ink and color on paper,8 1/2 x 11 1/4 in. (21.6 x 28.6 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55373,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3319,false,true,55462,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Takeuchi Keishū,"Japanese, 1861–1943",,Takeuchi Keishū,Japanese,1861,1943,ca. 1906,1896,1916,Polychrome woodblock print; ink and color on paper,11 3/4 x 8 1/2 in. (29.8 x 21.6 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55462,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3242,false,true,55255,Asian Art,Woodblock print,青上練兵場観兵式之図|Illustration of Emperor's Military Review of a Parade Ground at Aoyama (Aoyama renpeijō kanpeishiki no zu),Japan,Meiji period (1868–1912),,,,Artist,,Inoue Yasuji,"Japanese, 1864–1889",,INOUE YASUJI,Japanese,1864,1889,"June, 1888",1888,1888,Triptych of polychrome woodblock prints; ink and color on paper,Oban 14 1/2 x 28 3/4 in. (36.8 x 73 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55255,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3244,false,true,37387,Asian Art,Print,東京名所之内吾妻橋新築之図|Illustration of the Opening of Azuma Bridge in Tokyo (Tokyo meisho no uchi azuma bashi shinchiku no zu),Japan,Meiji period (1868–1912),,,,Artist,,Inoue Yasuji,"Japanese, 1864–1889",,INOUE YASUJI,Japanese,1864,1889,1887,1887,1887,Triptych of polychrome woodblock prints; ink and color on paper,14 1/8 x 28 1/2 in. (35.9 x 72.4 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37387,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3337,false,true,55490,Asian Art,Print,共楽泰平貴顕図|Illustration of the Emperor Enjoying a Moment of Peace with his Family (Kyōraku taihei kiken zu),Japan,Meiji period (1868–1912),,,,Artist,,Inoue Yasuji,"Japanese, 1864–1889",,INOUE YASUJI,Japanese,1864,1889,1887,1887,1887,Polychrome woodblock print; ink and color on paper,Image: 14 3/8 x 29 3/8 in. (36.5 x 74.6 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55490,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3196,false,true,55180,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Tomioka Eisen,"Japanese, 1864–1905",,Tomioka Eisen,Japanese,1864,1905,ca. 1906,1896,1916,Polychrome woodblock print; ink and color on paper,8 5/8 x 12 3/8 in. (21.9 x 31.4 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55180,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3282,false,true,55368,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Tomioka Eisen,"Japanese, 1864–1905",,Tomioka Eisen,Japanese,1864,1905,early 20th century,1900,1933,Polychrome woodblock print; ink and color on paper,8 1/2 x 10 3/8 in. (21.6 x 26.4 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55368,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3294,false,true,55391,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Tomioka Eisen,"Japanese, 1864–1905",,Tomioka Eisen,Japanese,1864,1905,ca. 1906,1896,1916,Polychrome woodblock print; ink and color on paper,8 5/8 x 12 1/4 in. (21.9 x 31.1 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55391,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3202,false,true,55186,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Kobori Tomoto,"Japanese, 1864–1931",,Kobori Tomoto,Japanese,1864,1931,ca. 1906,1896,1916,Polychrome woodblock print; ink and color on paper,11 5/8 x 8 1/2 in. (29.5 x 21.6 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55186,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3206,false,true,55190,Asian Art,Print,"Ito Chujo tekidan o mite shi warau suru zu|Vice Admiral Ito Mocks, Points and Looks at the Enemy Bullets",Japan,Meiji period (1868–1912),,,,Artist,,Mizuno Toshikata,"Japanese, 1866–1908",,Mizuno Toshikata,Japanese,1866,1908,ca. 1894,1884,1904,Polychrome woodblock print; ink and color on paper,6 11/16 x 9 1/8 in. (17 x 23.2 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55190,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3207,false,true,55191,Asian Art,Print,Kabayama Chujo furu yumo shin no zu|Vice Admiral Kabayama Advancing Bravely and Heartily,Japan,Meiji period (1868–1912),,,,Artist,,Mizuno Toshikata,"Japanese, 1866–1908",,Mizuno Toshikata,Japanese,1866,1908,December 1894,1894,1894,Polychrome woodblock print; ink and color on paper,7 x 9 1/8 in. (17.8 x 23.2 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55191,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3260,false,true,55276,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Mizuno Toshikata,"Japanese, 1866–1908",,Mizuno Toshikata,Japanese,1866,1908,1894,1894,1894,Triptych of polychrome woodblock prints; ink and color on paper,Oban 13 7/8 x 27 3/4 in. (35.2 x 70.5 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55276,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3261,false,true,55316,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Mizuno Toshikata,"Japanese, 1866–1908",,Mizuno Toshikata,Japanese,1866,1908,1894 (Meiji 27),1894,1894,Triptych of polychrome woodblock prints; ink and color on paper,Oban 14 x 27 3/4 in. (35.6 x 70.5 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55316,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3281,false,true,55366,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Mizuno Toshikata,"Japanese, 1866–1908",,Mizuno Toshikata,Japanese,1866,1908,ca. 1906,1896,1916,Polychrome woodblock print; ink and color on paper,8 11/16 x 11 13/16 in. (22.1 x 30 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55366,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3288,false,true,55381,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Mizuno Toshikata,"Japanese, 1866–1908",,Mizuno Toshikata,Japanese,1866,1908,ca. 1905,1895,1915,Polychrome woodblock print; ink and color on paper,8 3/4 x 11 3/4 in. (22.2 x 29.8 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55381,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3292,false,true,55388,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Mizuno Toshikata,"Japanese, 1866–1908",,Mizuno Toshikata,Japanese,1866,1908,ca. 1906,1896,1916,Frontispiece; polychrome woodblock print; ink and color on paper,Image: 8 1/2 x 11 1/4 in. (21.6 x 28.6 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55388,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1984.210.1–.72,false,true,57107,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Mizuno Toshikata,"Japanese, 1866–1908",,Mizuno Toshikata,Japanese,1866,1908,1891–93,1891,1893,Album of 72 polychrome woodblock prints; ink and color on paper,Overall (each): 14 x 9 1/2 in. (35.6 x 24.1 cm),"Gift of Mr. and Mrs. Malcolm P. Aldrich, 1984",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57107,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3297,false,true,45252,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Terazaki,"Japanese, 1866–1919",,Terazaki,Japanese,1866,1919,1906,1868,1912,Polychrome woodblock print; ink and color on paper,12 1/2 x 17 3/4 in. (31.8 x 45.1 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45252,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -MJP2,false,true,57113,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Tsukioka Kōgyo,"Japanese, 1869–1927",,Tsukioka Kōgyo,Japanese,1869,1927,ca. 1910,1900,1920,Polychrome woodblock print; ink and color on paper,14 1/2 x 20 in. (36.8 x 50.8 cm),"Gift of Teiji Ito, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57113,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -MJP4,false,true,45495,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Tsukioka Kōgyo,"Japanese, 1869–1927",,Tsukioka Kōgyo,Japanese,1869,1927,ca. 1910,1900,1920,Polychrome woodblock print; ink and color on paper,14 1/2 x 19 7/8 in. (36.8 x 50.5 cm),"Gift of Teiji Ito, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45495,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -MJP5,false,true,57115,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Tsukioka Kōgyo,"Japanese, 1869–1927",,Tsukioka Kōgyo,Japanese,1869,1927,ca. 1910,1900,1920,Polychrome woodblock print; ink and color on paper,14 1/2 x 20 in. (36.8 x 50.8 cm),"Gift of Teiji Ito, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57115,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -MJP6,false,true,57116,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Tsukioka Kōgyo,"Japanese, 1869–1927",,Tsukioka Kōgyo,Japanese,1869,1927,ca. 1910,1900,1920,Polychrome woodblock print; ink and color on paper,14 9/16 x 19 15/16 in. (37 x 50.6 cm),"Gift of Teiji Ito, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57116,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -MJP7,false,true,53816,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Tsukioka Kōgyo,"Japanese, 1869–1927",,Tsukioka Kōgyo,Japanese,1869,1927,ca. 1910,1900,1920,Polychrome woodblock print; ink and color on paper,14 9/16 x 20 in. (37 x 50.8 cm),"Gift of Teiji Ito, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/53816,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -MJP8,false,true,45277,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Tsukioka Kōgyo,"Japanese, 1869–1927",,Tsukioka Kōgyo,Japanese,1869,1927,ca. 1910,1900,1920,Polychrome woodblock print; ink and color on paper,14 9/16 x 19 15/16 in. (37 x 50.6 cm),"Gift of Teiji Ito, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45277,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -MJP9,false,true,57117,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Tsukioka Kōgyo,"Japanese, 1869–1927",,Tsukioka Kōgyo,Japanese,1869,1927,ca. 1910,1900,1920,Polychrome woodblock print; ink and color on paper,14 5/8 x 20 in. (37.1 x 50.8 cm),"Gift of Teiji Ito, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57117,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -MJP10,false,true,57118,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Tsukioka Kōgyo,"Japanese, 1869–1927",,Tsukioka Kōgyo,Japanese,1869,1927,ca. 1910,1900,1920,Polychrome woodblock print; ink and color on paper,14 9/16 x 20 in. (37 x 50.8 cm),"Gift of Teiji Ito, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57118,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -MJP17,false,true,45496,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Tsukioka Kōgyo,"Japanese, 1869–1927",,Tsukioka Kōgyo,Japanese,1869,1927,ca. 1910,1900,1920,Polychrome woodblock print; ink and color on paper,14 1/2 x 20 in. (36.8 x 50.8 cm),"Gift of Frederick E Church, 1928",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45496,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -MJP18,false,true,57125,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Tsukioka Kōgyo,"Japanese, 1869–1927",,Tsukioka Kōgyo,Japanese,1869,1927,ca. 1910,1900,1920,Polychrome woodblock print; ink and color on paper,14 1/2 x 20 in. (36.8 x 50.8 cm),"Gift of Frederick E Church, 1928",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57125,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -MJP20,false,true,57127,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Tsukioka Kōgyo,"Japanese, 1869–1927",,Tsukioka Kōgyo,Japanese,1869,1927,ca. 1910,1900,1920,Polychrome woodblock print; ink and color on paper,14 1/2 x 20 in. (36.8 x 50.8 cm),"Gift of Frederick E Church, 1928",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57127,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -MJP21,false,true,57128,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Tsukioka Kōgyo,"Japanese, 1869–1927",,Tsukioka Kōgyo,Japanese,1869,1927,ca. 1910,1900,1920,Polychrome woodblock print; ink and color on paper,14 1/2 x 19 15/16 in. (36.8 x 50.6 cm),"Gift of Frederick E Church, 1928",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57128,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -MJP22,false,true,57129,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Tsukioka Kōgyo,"Japanese, 1869–1927",,Tsukioka Kōgyo,Japanese,1869,1927,ca. 1910,1900,1920,Polychrome woodblock print; ink and color on paper,14 5/8 x 19 13/16 in. (37.1 x 50.3 cm),"Gift of Frederick E Church, 1928",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57129,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -MJP23,false,true,57130,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Tsukioka Kōgyo,"Japanese, 1869–1927",,Tsukioka Kōgyo,Japanese,1869,1927,ca. 1910,1900,1920,Polychrome woodblock print; ink and color on paper,14 1/2 x 20 1/16 in. (36.8 x 51 cm),"Gift of Frederick E Church, 1928",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57130,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -MJP24,false,true,57131,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Tsukioka Kōgyo,"Japanese, 1869–1927",,Tsukioka Kōgyo,Japanese,1869,1927,ca. 1910,1900,1920,Polychrome woodblock print; ink and color on paper,14 5/8 x 19 13/16 in. (37.1 x 50.3 cm),"Gift of Frederick E Church, 1928",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57131,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -MJP25,false,true,57132,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Tsukioka Kōgyo,"Japanese, 1869–1927",,Tsukioka Kōgyo,Japanese,1869,1927,ca. 1910,1900,1920,Polychrome woodblock print; ink and color on paper,14 13/16 x 19 in. (37.6 x 48.3 cm),"Gift of Frederick E Church, 1928",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57132,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -MJP26,false,true,57133,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Tsukioka Kōgyo,"Japanese, 1869–1927",,Tsukioka Kōgyo,Japanese,1869,1927,ca. 1910,1900,1920,Polychrome woodblock print; ink and color on paper,14 9/16 x 20 1/16 in. (37 x 51 cm),"Gift of Frederick E Church, 1928",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57133,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -MJP27,false,true,57134,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Tsukioka Kōgyo,"Japanese, 1869–1927",,Tsukioka Kōgyo,Japanese,1869,1927,ca. 1910,1900,1920,Polychrome woodblock print; ink and color on paper,14 13/16 x 19 in. (37.6 x 48.3 cm),"Gift of Frederick E Church, 1928",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57134,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3187,false,true,55172,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Kajita Hanko,"Japanese, 1870–1917",,Kajita Hanko,Japanese,1870,1917,ca. 1900,1890,1910,Polychrome woodblock print; ink and color on paper,8 15/16 x 11 5/6 in. (22.7 x 30.1 cm),"GIft of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55172,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3204,false,true,55188,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Kajita Hanko,"Japanese, 1870–1917",,Kajita Hanko,Japanese,1870,1917,ca. 1906,1896,1916,Polychrome woodblock print; ink and color on paper,8 5/8 x 8 7/8 in. (21.9 x 22.5 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55188,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3290,false,true,55386,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Kajita Hanko,"Japanese, 1870–1917",,Kajita Hanko,Japanese,1870,1917,November 1901,1901,1901,Polychrome woodblock print; ink and color on paper,8 3/4 x 11 7/8 in. (22.2 x 30.2 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55386,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3295,false,true,55392,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Kajita Hanko,"Japanese, 1870–1917",,Kajita Hanko,Japanese,1870,1917,January 1906,1906,1906,Polychrome woodblock print; ink and color on paper,8 3/4 x 11 3/4 in. (22.2 x 29.8 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55392,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3318,false,true,55460,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Kajita Hanko,"Japanese, 1870–1917",,Kajita Hanko,Japanese,1870,1917,1902,1902,1902,Frontispiece; polychrome woodblock print; ink and color on paper,Image: 8 5/8 x 12 1/16 in. (21.9 x 30.6 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55460,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3278,false,true,55361,Asian Art,Print,御世の栄東の粧|Imperial Prosperity: Ceremony in the Eastern Capital (Miyo no sakae azuma no kewai),Japan,Meiji period (1868–1912),,,,Artist,,Yōsai Nobukazu 楊斎延一,"Japanese, 1872–1944",,Yōsai Nobukazu,Japanese,1872,1944,ca. 1900,1890,1910,Triptych of polychrome woodblock prints; ink and color on paper,Image: 14 5/8 x 29 in. (37.1 x 73.7 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55361,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3271a–c,false,true,55343,Asian Art,Print,東京名所帝国議事堂|Famous Places in Tokyo: The Imperial Diet Building (Tōkyō Meisho: Teikoku Kokkai Gijidō),Japan,Meiji period (1868–1912),,,,Artist,,Yōsai Nobukazu 楊斎延一,"Japanese, 1872–1944",,Yōsai Nobukazu,Japanese,1872,1944,1899,1899,1899,Triptych of polychrome woodblock prints; ink and color on paper,14.3 x 28.7 in. (36.3 x 72.9 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55343,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.134a–f,false,true,73420,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Utagawa (Gountei) Sadahide,"Japanese, 1807–1878/79",,Utagawa (Goutei) Sadahide,Japanese,1807,1879,ca. 1869,1859,1879,Hexaptych of polychrome woodblock prints; ink and color on paper,Image: 14 1/8 x 58 1/2 in. (35.9 x 148.6 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73420,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.135a–f,false,true,73421,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Utagawa (Gountei) Sadahide,"Japanese, 1807–1878/79",,Utagawa (Goutei) Sadahide,Japanese,1807,1879,"7th–10th month, 1870",1870,1870,Hexaptych of polychrome woodblock prints; ink and color on paper,Image (a): 9 7/8 x 14 1/2 in. (25.1 x 36.8 cm) Image (b): 9 7/8 x 14 1/2 in. (25.1 x 36.8 cm) Image (c): 9 7/8 x 14 1/2 in. (25.1 x 36.8 cm) Image (d): 9 5/8 x 14 1/8 in. (24.4 x 35.9 cm) Image (e): 9 5/8 x 14 1/8 in. (24.4 x 35.9 cm) Image (f): 9 1/2 x 14 1/8 in. (24.1 x 35.9 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73421,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3421a–c,false,true,55619,Asian Art,Print,Doitsukokukan naibu kikai|The Interior Works of an Armed Japanese Battleship,Japan,Meiji period (1868–1912),,,,Artist,,Unsen,"Japanese, active ca. 1875",,Unsen,Japanese,1875,1875,1874 (Meiji 7),1874,1874,Triptych of polychrome woodblock prints; ink and color on paper,Oban; 14 1/8 x 28 3/8 in. (35.9 x 72.1 cm),"Gift of Lincoln Kirstein, 1962",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55619,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3233,false,true,55232,Asian Art,Print,新皇居於テ正殿憲法発布式之図|Illustration of the Issuing of the State Constitution in the State Chamber of the New Imperial Palace (Shin kōkyo ni oite seiden kenpō happushiki no zu),Japan,Meiji period (1868–1912),,,,Artist,,Adachi (Shōsai) Ginkō,"Japanese, active 1874–97",,Adachi Ginkō,Japanese,1874,1897,"March 14, 1889",1889,1889,One sheet of a triptych of polychrome woodblock prints; ink and color on paper,Image: 14 5/8 in. × 10 in. (37.1 × 25.4 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55232,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3234,false,true,55246,Asian Art,Print,Shinkokyo Oite Seiden Kempo Happu no zu|View of the Issuance of the State Constitution in the State Chamber of the New Imperial Palace,Japan,Meiji period (1868–1912),,,,Artist,,Adachi (Shōsai) Ginkō,"Japanese, active 1874–97",,Adachi Ginkō,Japanese,1874,1897,"March 2, 1889 (Meiji 22)",1889,1889,One sheet of a triptych of polychrome woodblock prints; ink and color on paper,Image: 14 3/4 × 9 1/2 in. (37.5 × 24.1 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55246,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3235,false,true,55247,Asian Art,Print,Shinkokyo Oite Seiden Kempo Happu no zu|View of the Issuance of the State Constitution in the State Chamber of the New Imperial Palace,Japan,Meiji period (1868–1912),,,,Artist,,Adachi (Shōsai) Ginkō,"Japanese, active 1874–97",,Adachi Ginkō,Japanese,1874,1897,"March 2, 1889 (Meiji 22)",1889,1889,One sheet of a triptych of polychrome woodblock prints; ink and color on paper,Image: 14 3/4 × 9 1/2 in. (37.5 × 24.1 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55247,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3272,false,true,55345,Asian Art,Print,『貴女裁縫之図』|Ladies Sewing (Kijo saihō no zu),Japan,Meiji period (1868–1912),,,,Artist,,Adachi (Shōsai) Ginkō,"Japanese, active 1874–97",,Adachi Ginkō,Japanese,1874,1897,"September 3rd, 1887",1887,1887,Triptych of polychrome woodblock prints; ink and color on paper,Image: 14 in. × 28 1/2 in. (35.6 × 72.4 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55345,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.323,false,true,73585,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Adachi (Shōsai) Ginkō,"Japanese, active 1874–97",,Adachi Ginkō,Japanese,1874,1897,late 19th century,1871,1899,Polychrome woodblock print; ink and color on paper,Image: 13 7/8 x 9 in. (35.2 x 22.9 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73585,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.324,false,true,73390,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Adachi (Shōsai) Ginkō,"Japanese, active 1874–97",,Adachi Ginkō,Japanese,1874,1897,late 19th century,1867,1899,Polychrome woodblock print; ink and color on paper,14 1/4 x 9 1/2 in. (36.2 x 24.1 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73390,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3346,false,true,55511,Asian Art,Print,『東京築地舶来ぜんま い大仕かけきぬ糸をとる図』|Imported Silk Reeling Machine at Tsukiji in Tokyo,Japan,Meiji period (1868–1912),,,,Artist,,Utagawa Yoshitora,"Japanese, active ca. 1850–80",,Utagawa Yoshitora,Japanese,1845,1880,"4th month, 1872",1872,1872,Triptych of polychrome woodblock prints; ink and color on paper,Image: 14 1/2 x 28 3/4 in. (36.8 x 73 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55511,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.223a–c,false,true,73514,Asian Art,Print,風船昇遥図|Illustration of a Balloon Ascending (Fūsen shōyō no zu),Japan,Meiji period (1868–1912),,,,Artist,,Utagawa Yoshitora,"Japanese, active ca. 1850–80",,Utagawa Yoshitora,Japanese,1845,1880,"November, 1872",1872,1872,Triptych of polychrome woodblock prints; ink and color on paper,Image (a): 14 1/2 x 9 7/8 in. (36.8 x 25.1 cm) Image (b): 14 5/8 x 9 7/8 in. (37.1 x 25.1 cm) Image (c): 14 3/8 x 9 3/4 in. (36.5 x 24.8 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73514,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.330a–c,false,true,73582,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Utagawa Masanobu,"Japanese, active ca. 1882–87",,Utagawa Masanobu,Japanese,1882,1887,"September 4, 1886",1886,1886,Three single polychrome woodblock prints; ink and color on paper,Image (a): 14 1/2 x 9 3/4 in. (36.8 x 24.8 cm) Image (b): 14 1/2 x 10 in. (36.8 x 25.4 cm) Image (c): 14 1/2 x 10 in. (36.8 x 25.4 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73582,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3224,false,true,55214,Asian Art,Woodblock print,Saigo Ryusei Kubi jitsu ken|Presentation of the Head of Saigo to the Prince Arisogawa,Japan,Meiji period (1868–1912),,,,Artist,,Yamazaki Toshinobu,"Japanese, active ca. 1857–1886",,Yamazaki Toshinobu,Japanese,1857,1886,"Oct. 16, 1877 (Meiji 10)",1877,1877,One sheet of a triptych of polychrome woodblock prints; ink and color on paper,14 in. × 9 1/2 in. (35.6 × 24.1 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55214,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3225,false,true,55216,Asian Art,Woodblock print,Saigo Ryusei Kubi jitsu ken|Presentation of the Head of Saigo to the Prince Arisogawa,Japan,Meiji period (1868–1912),,,,Artist,,Yamazaki Toshinobu,"Japanese, active ca. 1857–1886",,Yamazaki Toshinobu,Japanese,1857,1886,"Oct. 16, 1877 (Meiji 10)",1877,1877,One sheet of a triptych of polychrome woodblock prints; ink and color on paper,Triptych 14 x 27 11/16 in. (35.5 x 70.4 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55216,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3226,false,true,55217,Asian Art,Woodblock print,Saigo Ryusei Kubi jitsu ken|Presentation of the Head of Saigo to the Prince Arisogawa,Japan,Meiji period (1868–1912),,,,Artist,,Yamazaki Toshinobu,"Japanese, active ca. 1857–1886",,Yamazaki Toshinobu,Japanese,1857,1886,"Oct. 16, 1877 (Meiji 10)",1877,1877,One sheet of a triptych of polychrome woodblock prints; ink and color on paper,Triptych 14 x 27 11/16 in. (35.5 x 70.4 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55217,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3251,false,true,55262,Asian Art,Woodblock prints,Taiseikan Shosho Shiten|Commanders Receiving the Emperor's Drinking Cups,Japan,Meiji period (1868–1912),,,,Artist,,Yamazaki Toshinobu,"Japanese, active ca. 1857–1886",,Yamazaki Toshinobu,Japanese,1857,1886,1886 (Meiji 19),1886,1886,Triptych of polychrome woodblock prints; ink and color on paper,13 1/8 x 29 1/8 in. (33.3 x 74 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55262,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3372,false,true,55539,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Hamada Josen,"Japanese, active turn of 20th century",,Hamada Josen,Japanese,1880,1930,ca. 1906,1896,1916,Polychrome woodblock print; ink and color on paper,8 1/16 x 11 5/8 in. (20.5 x 29.5 cm),"Gift of Lincoln Kirstein, 1960",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55539,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3230,false,true,55224,Asian Art,Print,御鳳輦之図|Illustration of the Imperial Carriage (Gohōren no zu),Japan,Meiji period (1868–1912),,,,Artist,,Utagawa Kunitoshi,"Japanese, active 2nd half of 19th century",,Utagawa Kunitoshi,Japanese,1850,1899,1889,1889,1889,One sheet of a triptych of polychrome woodblock prints; ink and color on paper,Triptych 14 9/16 x 29 in. (37 x 73.6 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55224,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3231,false,true,55229,Asian Art,Print,Go Horen no zu|View of the Imperial Carriage,Japan,Meiji period (1868–1912),,,,Artist,,Utagawa Kunitoshi,"Japanese, active 2nd half of 19th century",,Utagawa Kunitoshi,Japanese,1850,1899,1889 (Meiji 22),1889,1889,One sheet of a triptych of polychrome woodblock prints; ink and color on paper,Triptych 14 9/16 x 29 in. (37 x 73.6 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55229,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3232,false,true,55230,Asian Art,Print,Go Horen no zu|View of the Imperial Carriage,Japan,Meiji period (1868–1912),,,,Artist,,Utagawa Kunitoshi,"Japanese, active 2nd half of 19th century",,Utagawa Kunitoshi,Japanese,1850,1899,1889 (Meiji 22),1889,1889,One sheet of a triptych of polychrome woodblock prints; ink and color on paper,Triptych 14 9/16 x 29 in. (37 x 73.6 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55230,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -MJP46,false,true,57155,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Natori Shunsen,"Japanese, 1886–1960, born in Kushigata machi, Yamanishi Prefecture",,Natori Shunsen,Japanese,1886,1960,1929,1929,1929,Polychrome woodblock print; ink and color on paper,14 3/4 x 9 7/8 (37.5 x 25.1 cm),"Gift of H. J. Isaacson, 1948",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57155,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2431,false,true,44586,Asian Art,Print,長襦袢の女|Woman Dressing,Japan,Taishō period (1912–26),,,,Artist,,Hashiguchi Goyō,"Japanese, 1881–1921",,Hashiguchi Goyō,Japanese,1881,1921,1920,1920,1920,Polychrome woodblock print; ink and color on paper,Image: 17 3/4 × 5 3/4 in. (45.1 × 14.6 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/44586,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2432,false,true,44587,Asian Art,Print,夏衣の女|Woman in Summer Clothing,Japan,Taishō period (1912–26),,,,Artist,,Hashiguchi Goyō,"Japanese, 1881–1921",,Hashiguchi Goyō,Japanese,1881,1921,1920,1920,1920,Polychrome woodblock print; ink and color on paper,Image: 17 3/4 × 11 1/2 in. (45.1 × 29.2 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/44587,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2433,false,true,56873,Asian Art,Woodblock print,,Japan,Taishō period (1912–26),,,,Artist,,Hashiguchi Goyō,"Japanese, 1881–1921",,Hashiguchi Goyō,Japanese,1881,1921,1920,1920,1920,Polychrome woodblock print; ink and color on paper,9 x 15 1/2 in. (22.9 x 39.4 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56873,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3415,false,true,55605,Asian Art,Woodblock print,"百人一首　うはかゑとき　権中納言匡房|Poem by Gon-chūnagon Masafusa (Ōe no Masafusa), from the series One Hundred Poems Explained by the Nurse (Hyakunin isshu uba ga etoki)",Japan,Taishō period (1912–26),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,1921,1921,1921,Polychrome woodblock print; ink and color on paper,9 3/4 x 14 5/8 in. (24.8 x 37.1 cm),"Gift of Mrs. Carll Tucker, 1962",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55605,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3416,false,true,55609,Asian Art,Woodblock print,"百人一首　うはかゑとき　赤染衛門|Poem by Akazome Emon, from the series One Hundred Poems Explained by the Nurse (Hyakunin isshu uba ga etoki)",Japan,Taishō period (1912–26),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,1921,1921,1921,Polychrome woodblock print; ink and color on paper,9 5/8 x 14 3/8 in. (24.4 x 36.5 cm),"Gift of Mrs. Carll Tucker, 1962",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55609,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3417,false,true,55611,Asian Art,Woodblock print,"百人一首　乳かゑとき　中納言敦忠|Poem by Chūnagon Atsutada (Fujiwara no Asatada), from the series One Hundred Poems Explained by the Nurse (Hyakunin isshu uba ga etoki)",Japan,Taishō period (1912–26),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,1921,1921,1921,Polychrome woodblock print; ink and color on paper,Image: 10 1/4 × 15 1/4 in. (26 × 38.7 cm) Mat: 15 5/8 × 22 7/8 in. (39.7 × 58.1 cm),"Gift of Mrs. Carll Tucker, 1962",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55611,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3418,false,true,55612,Asian Art,Woodblock print,"百人一首　乳母か縁説　素胜法師|Poem by Sōsei Hōshi, from the series One Hundred Poems Explained by the Nurse (Hyakunin isshu uba ga etoki)",Japan,Taishō period (1912–26),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,1921,1921,1921,Polychrome woodblock print; ink and color on paper,9 3/4 x 14 3/4 in. (24.8 x 37.5 cm),"Gift of Mrs. Carll Tucker, 1962",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55612,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3494,false,true,55721,Asian Art,Woodblock print,,Japan,Meiji (1868–1912)–Taishō (1912–26) period,,,,Artist,,Kahō,"Japanese, early 20th century",,Kahō,Japanese,1900,1930,early 20th century,1900,1912,Polychrome woodblock print; ink and color on paper,7 x 9 in. (17.8 x 22.9 cm),"Gift of Cole J. Younger, 1975",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55721,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3438,false,true,55642,Asian Art,Print,江戸名所　御茶の水|Meguro,Japan,Meiji (1868–1912) or Taishō (1912–26) period,,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,early 20th century,1900,1926,Polychrome woodblock print; ink and color on paper,10 x 14 1/2 in. (25.4 x 36.8 cm),"Gift of Cole J. Younger, 1975",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55642,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -17.118.74,false,true,62898,Asian Art,Teabowl,,Japan,,,,,Artist,,Chōjirō,(1516–?1592),,Chōjirō,Japanese,1516,1592,ca. 1575,1565,1585,Clay covered with a dull black glaze (Raku ware),H. 3 5/8 in. (9.2 cm); Diam. 3 3/4 in. (9.5 cm),"Rogers Fund, 1917",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/62898,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -17.118.76,false,true,62900,Asian Art,Teabowl,,Japan,,,,,Artist,,Raku Donyu,"Japanese, died 1656",,"Raku, Donyu",Japanese,1556,1656,ca. 1650,1640,1660,Clay covered with a shiny black glaze and frothy edge of glaze (Raku ware),H. 3 in. (7.6 cm); Diam. 5 in. (12.7 cm),"Rogers Fund, 1917",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/62900,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"93.3.305a, b",false,true,47110,Asian Art,Bowl,,Japan,,,,,Artist,,Minpei,active 19th century,,Minpei,Japanese,0019,0019,1840,1840,1840,Porcelaneous ware covered with a finely crackled glaze over which is a black enameled glaze (Awaji ware),H. 3 3/4 in. (9.5 cm); Diam. 5 1/2 in. (14 cm),"Gift of Charles Stewart Smith, 1893",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/47110,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -16.13.1,false,true,62879,Asian Art,Teabowl,,Japan,,,,,Artist,,Hon'ami Kōetsu,"Japanese, 1558–1637",,Hon'ami Kōetsu,Japanese,1558,1637,ca. 1600,1590,1610,"Clay covered with glaze, except on lower part where it is left bare",H. 4 1/2 in. (11.4 cm); Diam. 4 5/8 in. (11.7 cm),"Rogers Fund, 1916",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/62879,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"93.3.217a, b",false,true,47328,Asian Art,Teapot,,Japan,,,,,Artist,,Shuhei,"Japanese, 1788–1839",,Shuhei,Japanese,1788,1839,1800,1800,1899,"Clay covered with a partly crackled glaze and decorated with enamels on a gold ground (Kyoto ware, Satsuma style)",H. 3 1/4 in. (8.3 cm),"Gift of Charles Stewart Smith, 1893",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/47328,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"93.1.143a, b",false,true,62678,Asian Art,Teabowl,,Japan,,,,,Artist,,Eiraku Wazen,"Japanese, 1821–1896",,Eiraku Wazen,Japanese,1821,1896,1850,1850,1850,"Clay with speckled glaze (Kyoto ware, Bizen type)",H. 3 1/8 in. (7.9 cm); Diam. 4 3/8 in. (11.1 cm),"Gift of Mr. and Mrs. Samuel Colman, 1893",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/62678,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -93.1.26,false,true,62620,Asian Art,Cup,,Japan,,,,,Artist,,Kenya,"Japanese, 1825–1889",,Miura Kenya,Japanese,1825,1889,ca. 1840,1830,1850,Crackled porcelain covered with glaze and decoration in enamels,H. 2 3/4 in. (7 cm),"Gift of Mr. and Mrs. Samuel Colman, 1893",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/62620,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"93.3.15a, b",false,true,47814,Asian Art,Pot,,Japan,,,,,Artist,,Makuzu Kōzan I (Miyagawa Toranosuke),"Japanese, 1842–1916",,Makuzu Kōzan,Japanese,1842,1916,1890,1890,1890,"White porcelain decorated in red under the glaze (Kyoto ware, Makuzu type)",H. inc. lid 8 1/2 in. (21.6 cm); H. w/o lid 7 3/4 in. (19.7 cm); Diam. 8 1/4 in. (21 cm),"Gift of Charles Stewart Smith, 1893",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/47814,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"93.3.424a, b",false,true,62836,Asian Art,Censer,,Japan,,,,,Artist,,Takemoto,"Japanese, 1845–1892",,Takemoto,Japanese,1845,1892,1892,1892,1892,"White porcelain covered with a blue glaze, run like Zhun",H. 3 3/4 in. (9.5 cm); Diam. 4 7/8 in. (12.4 cm),"Gift of Charles Stewart Smith, 1893",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/62836,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1984.392.2,false,true,63938,Asian Art,Teabowl,,Japan,,,,,Artist,,Ōhi Chōzaemon,"Japanese, 1850–1927",,Ōhi Chōzaemon,Japanese,1850,1927,20th century,1850,1927,Pottery (brown Raku ware),H. 3 1/4 in. (8.3 cm); Diam. 4 1/2 in. (11.4 cm),"Gift of Toshiro Ohi, 1984",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/63938,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -46.39.11,false,true,63332,Asian Art,Bowl,,Japan,,,,,Artist,,Yabu Meizan,"Japanese, 1853–1934",,Yabu Meizan,Japanese,1853,1934,late 19th century,1871,1899,"Porcelain; exterior, spiral millefleurs bands in a variety of enamels and gold; geometric bands in black and rust enamels and gold on lower part of bowl; interior, closely covered with tiny butterflies in red, black, yellow and pale blue enamels, with gold; gold lip rim (Satsuma ware)",H. 1 5/8 in. (4.1 cm); Diam. 3 in. (7.6 cm),"Gift of Philip A. Rollins, 1946",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/63332,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"93.3.221a, b",false,true,62795,Asian Art,Incense box,,Japan,,,,,Artist,,Nonomura Ninsei,"Japanese, active ca. 1646–94",,Ninsei Nonomura,Japanese,1646,1694,1670,1670,1670,"Paste covered with a transparent crackled glaze and decorated with colored enamels and gold (Kyoto ware, Satsuma type)",H. 1 1/4 in. (3.2 cm); Diam. 3 1/8 in. (7.9 cm),"Gift of Charles Stewart Smith, 1893",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/62795,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -93.1.46,false,true,62006,Asian Art,Bowl,,Japan,Edo period (1615–1868),,,,Artist,In the style of,Shunzan,,,Shunzan,Japanese,0019,0019,ca. 1770,1760,1780,"Seto ware, Oribe Revival type; glazed stoneware",H. 3 1/2 in. (8.9 cm); L. 10 3/4 in. (27.3 cm),"Gift of Mr. and Mrs. Samuel Colman, 1893",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/62006,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -17.118.70,false,true,62889,Asian Art,Teabowl,,Japan,Edo period (1615–1868),,,,Artist,,Ryōnyu,died 1835,,Ryōnyu,Japanese,1735,1835,ca. 1800,1790,1810,Clay covered with a shiny black glaze (Raku ware),H. 3 3/4 in. (9.5 cm); Diam. 4 in. (10.2 cm),"Rogers Fund, 1917",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/62889,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.120.545,false,true,63233,Asian Art,Teabowl,,Japan,Edo period (1615–1868),,,,Artist,,Ryōnyu,died 1835,,Ryōnyu,Japanese,1735,1835,ca. 1790,1780,1800,"Rounded body, well defined foot; brown clay, yellow underglaze, black, red and green overglaze (Raku ware)",H. 2 3/4 in. (7 cm); Diam. 4 1/2 in. (11.4 cm),"The Howard Mansfield Collection, Gift of Howard Mansfield, 1936",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/63233,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.120.537,false,true,63197,Asian Art,Teabowl,,Japan,Edo period (1615–1868),,,,Artist,,Keinyu,died 1893,,Keinyu,Japanese,1793,1893,ca. 1852,1842,1862,"Cylindrical with small foot; horizontal lines incised below uneven lip; brown clay with creamy glaze and green overglaze; brown latticed strokes; inside, three flying birds suggested (Raku ware)",H. 3 in. (7.6 cm); Diam. 4 1/4 in. (10.8 cm),"The Howard Mansfield Collection, Gift of Howard Mansfield, 1936",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/63197,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -23.225.248,false,true,63021,Asian Art,Bowl,,Japan,Edo period (1615–1868),,,,Artist,,Aoki Mokubei,1767–1833,,Aoki Mokubei,Japanese,1767,1833,ca. 1800,1790,1810,Porcelain decorated in enamels and gold (Kyoto ware),H. 4 in. (10.2 cm); Diam. 9 in. (22.9 cm),"Gift of Mrs. V. Everit Macy, 1923",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/63021,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -23.225.257,false,true,47204,Asian Art,Fire box,,Japan,Edo period (1615–1868),,,,Artist,,Aoki Mokubei,1767–1833,,Aoki Mokubei,Japanese,1767,1833,1800,1800,1800,Modeled and unglazed (Kyoto ware),H. 9 in. (22.9 cm),"Gift of Mrs. V. Everit Macy, 1923",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/47204,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"23.225.258a, b",false,true,47249,Asian Art,Teapot,,Japan,Edo period (1615–1868),,,,Artist,,Aoki Mokubei,1767–1833,(?),Aoki Mokubei,Japanese,1767,1833,1800,1800,1800,Faience decorated with design in relief and glaze (Kyoto ware),H. 4 5/8 in. (11.7 cm); L. 5 3/4 in. (14.6 cm),"Gift of Mrs. V. Everit Macy, 1923",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/47249,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"23.225.267a, b",false,true,47250,Asian Art,Teapot,,Japan,Edo period (1615–1868),,,,Artist,,Aoki Mokubei,1767–1833,,Aoki Mokubei,Japanese,1767,1833,1800,1800,1800,"Faience decorated in relief, celadon glaze (Kyoto ware)",H. (with cover) 4 5/8 in. (11.7 cm); L. 6 1/8 in. (15.6 cm),"Gift of Mrs. V. Everit Macy, 1923",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/47250,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"93.3.182a, b",false,true,47329,Asian Art,Teapot,,Japan,Edo period (1615–1868),,,,Artist,,Bizan,died 1838 (?),,Bizan,Japanese,1738,1838,ca. 1850,1840,1860,"Claycovered with a finely crackled, smooth glaze and decorated in enamels and gold (Kyoto ware)",H. 3 3/4 in. (9.5 cm),"Gift of Charles Stewart Smith, 1893",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/47329,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -17.118.77,false,true,62901,Asian Art,Teabowl,,Japan,Edo period (1615–1868),,,,Artist,,Raku Donyu,"Japanese, died 1656",,"Raku, Donyu",Japanese,1556,1656,ca. 1650,1640,1660,Clay covered with a dull red glaze (Raku ware),H. 3 1/4 in. (8.3 cm); Diam. 4 1/2 in. (11.4 cm),"Rogers Fund, 1917",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/62901,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"25.60.18a, b",false,true,47113,Asian Art,Covered incense box,,Japan,Edo period (1615–1868),,,,Artist,,Raku Donyu,"Japanese, died 1656",,"Raku, Donyu",Japanese,1556,1656,ca. 1650,1640,1660,"Pottery incised in scroll pattern, in imitation of Chinese carved lacquer; brownish white glaze (Raku ware)",H. 1 1/2 in. (3.8 cm); W. sq. 2 1/2 in. (6.4 cm),"Rogers Fund, 1925",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/47113,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -17.118.73,false,true,62897,Asian Art,Teabowl,,Japan,Edo period (1615–1868),,,,Artist,,Sonyu,"Japanese, died 1725",,Sonyu,Japanese,1625,1725,ca. 1710,1700,1720,Clay covered with glaze (Raku ware),H. 4 in. (10.2 cm); Diam. 4 1/8 in. (10.5 cm),"Rogers Fund, 1917",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/62897,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.120.523,false,true,63177,Asian Art,Teabowl,,Japan,Edo period (1615–1868),,,,Artist,,Sonyu,"Japanese, died 1725",,Sonyu,Japanese,1625,1725,ca. 1710,1700,1720,Clay with lustrous black glaze (Raku ware),H. 3 1/8 in. (7.9 cm); Diam. 4 3/8 in. (11.1 cm),"The Howard Mansfield Collection, Gift of Howard Mansfield, 1936",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/63177,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.120.491,false,true,63147,Asian Art,Teabowl,,Japan,Edo period (1615–1868),,,,Artist,,Minpei,active 19th century,,Minpei,Japanese,0019,0019,ca. 1830,1820,1840,White glaze which stops short of foot; elaborate decoration in enamel colors (Agano ware),H. 3 in. (7.6 cm); Diam. 3 7/8 in. (9.8 cm),"The Howard Mansfield Collection, Gift of Howard Mansfield, 1936",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/63147,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"29.100.615a, b",false,true,63109,Asian Art,Jar,,Japan,Edo period (1615–1868),,,,Artist,,Hon'ami Kōetsu,"Japanese, 1558–1637",,Hon'ami Kōetsu,Japanese,1558,1637,ca. 1620,1610,1630,"Clay, red and green glaze (Kyoto ware)",H. 5 1/2 in. (14 cm); Diam. 7 in. (17.8 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/63109,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -91.1.361,false,true,56158,Asian Art,Teabowl,,Japan,Edo period (1615–1868),,,,Artist,Style of,Ogata Kenzan,"Japanese, 1663–1743",,Ogata Kenzan,Japanese,1663,1743,mid- to late18th century,1734,1799,Pottery covered with glaze and decorated with designs in slip (Kyoto ware),H. 3 in. (7.6 cm); Diam. 4 3/4 in. (12.1 cm); Diam. of foot 2 1/2 in. (6.4 cm),"Edward C. Moore Collection, Bequest of Edward C. Moore, 1891",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/56158,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -17.118.89,false,true,44929,Asian Art,Dish,乾山様式　色絵竹文皿|Kenzan-style Dish with Bamboo Leaves,Japan,Edo period (1615–1868),,,,Artist,In the style of,Ogata Kenzan,"Japanese, 1663–1743",(?),Ogata Kenzan,Japanese,1663,1743,17th–18th century,1600,1799,Buff stoneware decorated with white and iron-brown slip and underglaze cobalt blue; gold lacquer repair,H. 1 3/8 in. (3.5 cm); Diam. 6 7/8 in. (17.5 cm),"Rogers Fund, 1917",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/44929,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -17.118.90,false,true,45514,Asian Art,Dish,乾山様式　色絵竹文皿|Kenzan-style Dish with Bamboo Leaves,Japan,Edo period (1615–1868),,,,Artist,In the style of,Ogata Kenzan,"Japanese, 1663–1743",(?),Ogata Kenzan,Japanese,1663,1743,17th–18th century,1600,1799,Buff stoneware decorated with white and iron-brown slip and underglaze cobalt blue; gold lacquer repair,H. 1 3/8 in. (3.5 cm); Diam. 6 7/8 in. (17.5 cm),"Rogers Fund, 1917",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/45514,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.120.635,false,true,63314,Asian Art,Tray,,Japan,Edo period (1615–1868),,,,Artist,,Ogata Kenzan,"Japanese, 1663–1743",,Ogata Kenzan,Japanese,1663,1743,ca. 1720,1710,1730,"Oblong with upturned rim; gray clay; crackled cream glaze; inside, chrysanthemum sprays and poem, in brown; outer rim, conventional floral diaper in blue (Tokyo ware)",H. 1 1/2 in. (3.8 cm); W. 8 7/8 in. (22.5 cm); L. 9 3/4 in. (24.8 cm),"The Howard Mansfield Collection, Gift of Howard Mansfield, 1936",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/63314,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.120.655,false,true,63318,Asian Art,Reading screen,,Japan,Edo period (1615–1868),,,,Artist,,Ogata Kenzan,"Japanese, 1663–1743",,Ogata Kenzan,Japanese,1663,1743,ca. 1740,1730,1750,"Oblong; dark brown clay; moulded frame and shaped supports glazed black; both faces of screen with ivory glaze, decorated with landscapes in black (Tokyo ware)",H. 10 1/2 in. (26.7 cm); L. 15 3/4 in. (40 cm),"The Howard Mansfield Collection, Gift of Howard Mansfield, 1936",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/63318,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"29.100.614a, b",false,true,52514,Asian Art,Jar,伝尾形乾山　松文水差|Water Jar (Mizusashi) with Pine Trees,Japan,Edo period (1615–1868),,,,Artist,Attributed to,Ogata Kenzan,"Japanese, 1663–1743",,Ogata Kenzan,Japanese,1663,1743,ca. 1720,1710,1730,Stoneware with underglaze iron oxide; lacquer cover,H. 4 1/2 in. (11.4 cm); Diam. 6 1/2 in. (16.5 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/52514,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"36.120.634a, b",false,true,63313,Asian Art,Water jar,,Japan,Edo period (1615–1868),,,,Artist,,Ogata Kenzan,"Japanese, 1663–1743",,Ogata Kenzan,Japanese,1663,1743,ca. 1705,1695,1715,"Cylindrical, the flat cover inset; hard, light clay; bluish-gray glaze, streaked showing white underglaze; snowy landscape with figures in boat modeled in white and brown slip, in low relief; meander borders in white and blue (Tokyo ware)",H. 5 3/4 in. (14.6 cm); Diam. 5 3/4 in. (14.6 cm),"The Howard Mansfield Collection, Gift of Howard Mansfield, 1936",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/63313,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.120.517,false,true,63171,Asian Art,Teabowl,,Japan,Edo period (1615–1868),,,,Artist,,Raku Sanyū,"Japanese, 1685–1739",,Sanyū,Japanese,1685,1739,ca. 1730,1720,1740,"Clay pitted; glaze on outside, mottled within; small unglazed space on foot (Raku ware)",H. 2 7/8 in. (7.3 cm); Diam. 3 3/8 in. (8.6 cm),"The Howard Mansfield Collection, Gift of Howard Mansfield, 1936",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/63171,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -17.118.68,false,true,62887,Asian Art,Teabowl,,Japan,Edo period (1615–1868),,,,Artist,,Raku Chōnyū,"Japanese, 1714–1770",,"Raku, Chōnyū",Japanese,1714,1770,ca. 1750,1740,1760,"Clay partly covered with a black, pitted glaze (Raku ware)",H. 3 1/2 in. (8.9 cm); Diam. 5 1/4 in. (13.3 cm),"Rogers Fund, 1917",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/62887,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"23.225.277a, b",false,true,45946,Asian Art,Jar with cover,,Japan,Edo period (1615–1868),,,,Artist,,Kiyomizu Rokubei I,"Japanese, 1737–1799",,Kiyomizu Rokubei I,Japanese,1737,1799,1820,1820,1820,Faience boldly craquelé with design in colored enamels (Kiyomizu ware),H. 7 7/8 in. (20 cm); Diam. 8 1/8 in. (20.6 cm),"Gift of Mrs. V. Everit Macy, 1923",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/45946,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.120.518a–e,false,true,58854,Asian Art,Teabowl,,Japan,Edo period (1615–1868),,,,Artist,,Kiyomizu Rokubei I,"Japanese, 1737–1799",,Kiyomizu Rokubei I,Japanese,1737,1799,mid- to late 18th century,1750,1799,Stoneware with inlaid design (Kyoto ware),H. 3 3/4 in. (9.5 cm); Diam. of rim 3 7/8 in. (9.8 cm); Diam. of foot 2 1/4 in. (5.7 cm),"The Howard Mansfield Collection, Gift of Howard Mansfield, 1936",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/58854,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -93.3.210,false,true,58850,Asian Art,Cup,,Japan,Edo period (1615–1868),,,,Artist,,Nin'ami Dōhachi (Dōhachi II),"Japanese, 1783–1855",,Dōhachi II,Japanese,1783,1855,1770,1700,1799,Earthenware with colored enamels and gold (Kyoto ware),H. 3 in. (7.6 cm); Diam. 4 3/4 in. (12.1 cm),"Gift of Charles Stewart Smith, 1893",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/58850,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.634,false,true,63115,Asian Art,Bowl,,Japan,Edo period (1615–1868),,,,Artist,,Nin'ami Dōhachi (Dōhachi II),"Japanese, 1783–1855",,Dōhachi II,Japanese,1783,1855,ca. 1850,1840,1860,"Clay, glaze decorated with in overglaze enamels (Kiyomizu ware)",H. 3 1/8 in. (7.9 cm); Diam. 4 3/4 in. (12.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/63115,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.120.546,false,true,63234,Asian Art,Teabowl,,Japan,Edo period (1615–1868),,,,Artist,,Nin'ami Dōhachi (Dōhachi II),"Japanese, 1783–1855",,Dōhachi II,Japanese,1783,1855,ca. 1850,1840,1860,"Distinct wheelmarks; unglazed foot; fawn-colored clay, very thin; pinkish glaze (Kiyomizu ware)",H. 3 in. (7.6 cm); Diam. 4 1/4 in. (10.8 cm),"The Howard Mansfield Collection, Gift of Howard Mansfield, 1936",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/63234,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.120.547,false,true,63235,Asian Art,Teabowl,,Japan,Edo period (1615–1868),,,,Artist,,Nin'ami Dōhachi (Dōhachi II),"Japanese, 1783–1855",,Dōhachi II,Japanese,1783,1855,ca. 1850,1840,1860,"Slightly curved outline, small unglazed foot; lightweight fawn-colored clay; thin lustrous black glaze with pine boughs and poem in white (Kiyomizu ware)",H. 3 1/8 in. (7.9 cm); Diam. 4 3/8 in. (11.1 cm),"The Howard Mansfield Collection, Gift of Howard Mansfield, 1936",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/63235,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -17.118.80,false,true,62904,Asian Art,Teabowl,,Japan,Edo period (1615–1868),,,,Artist,,Raku Tannyu,"Japanese, 1795–1854",,"Raku, Tannyu",Japanese,1795,1854,ca. 1840,1830,1850,Clay partly covered with a shiny black glaze,H. 3 in. (7.6 cm); Diam. 4 1/2 in. (11.4 cm),"Rogers Fund, 1917",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/62904,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.120.500,false,true,63154,Asian Art,Teabowl,,Japan,Edo period (1615–1868),,,,Artist,,Raku Tannyu,"Japanese, 1795–1854",,"Raku, Tannyu",Japanese,1795,1854,ca. 1810,1800,1820,"Clay completely covered with uniform glaze, over a gray glaze (Raku ware)",H. 3 3/8 in. (8.6 cm); Diam. 4 3/8 in. (11.1 cm),"The Howard Mansfield Collection, Gift of Howard Mansfield, 1936",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/63154,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.120.522,false,true,63176,Asian Art,Teabowl,,Japan,Edo period (1615–1868),,,,Artist,,Raku Tannyu,"Japanese, 1795–1854",,"Raku, Tannyu",Japanese,1795,1854,ca. 1810,1800,1820,"Clay covered, except for spots on foot, by glaze and overglaze which has turned black (Raku ware)",H. 2 3/4 in. (7 cm); Diam. 4 5/8 in. (11.7 cm),"The Howard Mansfield Collection, Gift of Howard Mansfield, 1936",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/63176,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -93.1.87,false,true,45971,Asian Art,Cup,,Japan,Edo period (1615–1868),,,,Artist,,Shuntai,"Japanese, 1799–1878",,Shuntai,Japanese,1799,1799,1825,1825,1825,"Clay covered with a transparent crackled glaze with streaks (Mino ware, Ofuke type)",H. 5 in. (12.7 cm),"Gift of Mr. and Mrs. Samuel Colman, 1893",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/45971,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.120.516,false,true,63170,Asian Art,Teabowl,,Japan,Edo period (1615–1868),,,,Artist,,Eiraku Wazen,"Japanese, 1821–1896",,Eiraku Wazen,Japanese,1821,1896,ca. 1850,1840,1860,"Clay; thick glaze, sufflé with touches of black inside and out (Eiraku ware)",H. 3 in. (7.6 cm); Diam. 4 1/4 in. (10.8 cm),"The Howard Mansfield Collection, Gift of Howard Mansfield, 1936",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/63170,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.120.531,false,true,63190,Asian Art,Teabowl,,Japan,Edo period (1615–1868),,,,Artist,,Makuzu Kōzan I (Miyagawa Toranosuke),"Japanese, 1842–1916",,Makuzu Kōzan,Japanese,1842,1916,ca. 1850,1840,1860,Clay with flecks of crackle inside and out (Awata ware),H. 3 in. (7.6 cm); Diam. 4 5/8 in. (11.7 cm),"The Howard Mansfield Collection, Gift of Howard Mansfield, 1936",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/63190,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"93.3.195a, b",false,true,47267,Asian Art,Teapot,,Japan,Edo period (1615–1868),,,,Artist,,Takahashi Sōhei,"Japanese, 1804?–?1835",,Takahashi Sōhei,Japanese,1804,1835,1840,1840,1840,"Clay covered with a finely crackled transparent glaze and decorated in colored enamels (Kyoto ware, after Ninsei)",H. 2 1/2 in. (6.4 cm),"Gift of Charles Stewart Smith, 1893",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/47267,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -25.60.34,false,true,63089,Asian Art,Teabowl,,Japan,Edo period (1615–1868),,,,Artist,,Tokunyu,"Japanese, died ca. 1775",,Tokunyu,Japanese,1675,1775,ca. 1770,1760,1780,Pottery with black glaze (Raku ware),Diam. 4 3/8 in. (11.1 cm),"Rogers Fund, 1925",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/63089,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -17.118.69,false,true,62888,Asian Art,Teabowl,,Japan,Edo period (1615–1868),,,,Artist,,Tokunyu,"Japanese, died ca. 1775",,Tokunyu,Japanese,1675,1775,ca. 1770,1760,1780,Clay partly covered with a dull black glaze (Raku ware),H. 3 1/4 in. (8.3 cm); Diam. 5 in. (12.7 cm),"Rogers Fund, 1917",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/62888,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -93.3.204,false,true,45962,Asian Art,Jar,,Japan,Edo period (1615–1868),,,,Artist,,Nonomura Ninsei,"Japanese, active ca. 1646–94",,Ninsei Nonomura,Japanese,1646,1694,1730,1730,1730,"Clay covered with a transparent crackled glaze; decorated colored enamels and gold (Kyoto ware, Takamatsu type)",H. 8 1/2 in. (21.6 cm),"Gift of Charles Stewart Smith, 1893",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/45962,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.666,false,true,63141,Asian Art,Tea jar,,Japan,Edo period (1615–1868),,,,Artist,,Nonomura Ninsei,"Japanese, active ca. 1646–94",,Ninsei Nonomura,Japanese,1646,1694,ca. 1660,1650,1670,Clay; black glaze with two bands of crackled white glaze; (Awata ware),H. 3 1/2 in. (8.9 cm); Diam. 2 1/8 in. (5.4 cm); Diam. of rim 1 in. (2.5 cm); Diam. of base 1 1/8 in. (2.9 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/63141,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.668,false,true,63143,Asian Art,Incense burner,野々村仁清工房　四季花文香炉|Ninsei-style Incense Burner with Flowers of the Four Seasons,Japan,Edo period (1615–1868),,,,Artist,Workshop of,Nonomura Ninsei,"Japanese, active ca. 1646–94",,Ninsei Nonomura,Japanese,1646,1694,mid-17th century,1634,1666,Stoneware with overglaze enamels,H. 6 3/4 in. (17.1 cm); W. 7 1/4 in. (18.4 cm); D. 7 1/4 in. (18.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/63143,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"23.225.236a, b",false,true,44928,Asian Art,Incense box,,Japan,Edo period (1615–1868),,,,Artist,Attributed to,Nonomura Ninsei,"Japanese, active ca. 1646–94",,Ninsei Nonomura,Japanese,1646,1694,ca. 1648–1649,1648,1649,"Glazed stoneware, colored enamels and gold",H. 1 1/4 in. (3.2 cm); Diam. 3 1/4 in. (8.3 cm),"Gift of Mrs. V. Everit Macy, 1923",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/44928,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.120.559a–f,false,true,53832,Asian Art,Tea caddy,,Japan,Edo period (1615–1868),,,,Artist,,Nonomura Ninsei,"Japanese, active ca. 1646–94",,Ninsei Nonomura,Japanese,1646,1694,ca. 1650,1640,1660,"Stoneware with red, brown, and black glazes",H. (with lid) 6 in. (15.2 cm); Diam. 1 7/8 in. (4.8 cm); Diam. of rim 1 1/8 in. (2.9 cm); Diam. of base 1 1/4 in. (3.2 cm),"The Howard Mansfield Collection, Gift of Howard Mansfield, 1936",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/53832,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -17.118.71,false,true,62890,Asian Art,Teabowl,,Japan,Meiji period (1868–1912),,,,Artist,,Keinyu,died 1893,,Keinyu,Japanese,1793,1893,ca. 1870,1860,1880,Clay covered with a black glaze; on the lower part with a transparent brown glaze (Raku ware),H. 4 in. (10.2 cm); Diam. 4 3/8 in. (11.1 cm),"Rogers Fund, 1917",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/62890,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.103,false,true,57186,Asian Art,Album leaf,,Japan,Meiji period (1868–1912),,,,Artist,,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,Japanese,1807,1891,1882,1882,1882,Lacquer on paper,7 1/2 x 6 1/2 in. (19.1 x 16.5 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/57186,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -93.3.111,false,true,46691,Asian Art,Flower vase,紫馬麒麟卍紗綾形紋瓶 (一対)|Vase with Horse and Kirin on Geometric Sayagata (key fret) Pattern (one of a pair),Japan,Meiji period (1868–1912),,,,Artist,Style of,Makuzu Kōzan I (Miyagawa Toranosuke),"Japanese, 1842–1916",,Makuzu Kōzan,Japanese,1842,1916,late 19th century,1867,1899,Porcelain with incised design and underglaze red,H. 4 3/4 in. (12.1 cm); Diam. of rim 1 1/4 in. (3.2 cm); Diam. 4 3/4 in. (12.1 cm); Diam. of base 2 1/4 in. (5.7 cm),"Gift of Charles Stewart Smith, 1893",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/46691,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -93.3.112,false,true,46692,Asian Art,Flower vase,紫馬麒麟卍紗綾形紋瓶 (一対)|Vase with Horse and Kirin on Geometric Sayagata (key fret) Pattern (one of a pair),Japan,Meiji period (1868–1912),,,,Artist,Style of,Makuzu Kōzan I (Miyagawa Toranosuke),"Japanese, 1842–1916",,Makuzu Kōzan,Japanese,1842,1916,late 19th century,1867,1899,Porcelain with incised design and underglaze red,H. 4 3/4 in. (12.1 cm); Diam. of rim 1 1/4 in. (3.2 cm); Diam. 4 3/4 in. (12.1 cm); Diam. of base 2 1/4 in. (5.7 cm),"Gift of Charles Stewart Smith, 1893",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/46692,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1984.408.1,false,true,63939,Asian Art,Vase,波に鯉文瓶|Vase with Carps in Waves,Japan,Meiji period (1868–1912),,,,Artist,,Makuzu Kōzan I (Miyagawa Toranosuke),"Japanese, 1842–1916",,Makuzu Kōzan,Japanese,1842,1916,late 19th–early 20th century,1867,1933,Porcelain with metal fittings,H. 10 in. (25.4 cm); Diam. 3 1/4 in. (8.3 cm) Diam. of rim: 3 3/4 in. (9.5 cm) Diam. of foot: 4 7/8 in. (12.4 cm),"Gift of Stanley J. Love, 1984",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/63939,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"91.1.367a, b",false,true,53613,Asian Art,Water jar,バッタ行列文水指|Water Jar with Procession of Grasshoppers,Japan,Meiji period (1868–1912),,,,Artist,,Makuzu Kōzan I (Miyagawa Toranosuke),"Japanese, 1842–1916",,Makuzu Kōzan,Japanese,1842,1916,late 19th century,1868,1899,Stoneware with polychrome overglaze enamels and gold with a wood lid and ivory knob (Makuzu ware),H. 5 3/4 in. (14.6 cm),"Edward C. Moore Collection, Bequest of Edward C. Moore, 1891",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/53613,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"93.3.364a, b",false,true,45997,Asian Art,Wine pot,染付龍文水注|Ewer with Dragon,Japan,Meiji period (1868–1912),,,,Artist,Style of,Makuzu Kōzan I (Miyagawa Toranosuke),"Japanese, 1842–1916",,Makuzu Kōzan,Japanese,1842,1916,late 19th century,1867,1899,Porcelain with underglaze blue,H. 9 1/4 in. (23.5 cm),"Gift of Charles Stewart Smith, 1893",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/45997,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -93.3.446,false,true,46627,Asian Art,Vase,蝶文瓶|Vase with Butterflies,Japan,Meiji period (1868–1912),,,,Artist,,Katō Tomotarō,"Japanese, 1851–1916",,Katō Tomotarō,Japanese,1851,1916,late 19th century,1867,1899,"Porcelain with underglaze blue, overglaze pink and white slip",H. 6 1/4 in. (15.9 cm); Diam. 4 in. (10.2 cm),"Gift of Charles Stewart Smith, 1893",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/46627,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"93.3.35a, b",false,true,62722,Asian Art,Covered pot,青磁月に梅樹文水指|Water Jar with Plum Tree,Japan,Meiji period (1868–1912),,,,Artist,,Katō Tomotarō,"Japanese, 1851–1916",,Katō Tomotarō,Japanese,1851,1916,ca. 1890,1880,1900,Porcelain with blue-and-white slip under celadon glaze,H. 7 3/4 in. (19.7 cm),"Gift of Charles Stewart Smith, 1893",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/62722,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"25.60.28a, b",false,true,63085,Asian Art,Tea jar,,Japan,Kamakura period (1185–1333),,,,Artist,Attributed to,Tôshiro,,,Tôshiro,Japanese,1200,1230,ca. 1245,1235,1255,Crackled brown glaze on a clay that has burned very dark; specimen of class known as Manako Tubutsu (Seto ware),H. 3 in. (7.6 cm),"Rogers Fund, 1925",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/63085,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"93.3.147a, b",false,true,62763,Asian Art,Tea jar,,Japan,Kamakura period (1185–1333),,,,Artist,,Tôshiro,,,Tôshiro,Japanese,1200,1230,ca. 1320 (?),1310,1330,Clay covered with a mottled glaze (Seto ware),H. 4 1/4 in. (10.8 cm),"Gift of Charles Stewart Smith, 1893",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/62763,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -17.118.64,false,true,53833,Asian Art,Teabowl,,Japan,Momoyama period (1573–1615),,,,Artist,,Hon'ami Kōetsu,"Japanese, 1558–1637",,Hon'ami Kōetsu,Japanese,1558,1637,early 17th century,1600,1633,Earthenware covered with light red glaze (Raku ware),H. 3 7/8 in. (9.8 cm); Diam. 5 in. (12.7 cm),"Rogers Fund, 1917",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/53833,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.120.492a–c,false,true,63148,Asian Art,Teabowl,,Japan,Momoyama period (1573–1615),,,,Artist,Attributed to,Hon'ami Kōetsu,"Japanese, 1558–1637",,Hon'ami Kōetsu,Japanese,1558,1637,ca. 1600,1590,1610,"Cclay, pitted, covered with glaze having patches; vertical incisions near lip, under glaze (Raku ware)",H. 3 3/4 in. (9.5 cm); Diam. 5 1/8 in. (13 cm),"The Howard Mansfield Collection, Gift of Howard Mansfield, 1936",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/63148,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.68a–c,false,true,60505,Asian Art,Cabinet,,Japan,,,,,Artist,,Ogawa Haritsu (Ritsuō),"Japanese, 1663–1747",,Ritsuō,Japanese,1663,1747,1663–1747,1663,1747,Lacquered wood,Cabinet and stools: H. 66 3/4 in. (169.5 cm); W. 41 1/4 in. (104.8 cm); D. 19 1/2 in. (49.5 cm) Cabinet: H. 36 1/4 in. (92.1 cm); W. 41 1/4 in. (104.8 cm); D. 9 1/2 in. (49.5 cm) Each stool: H. 30 1/2 in. (77.5 cm); W. 18 1/2 in. (47 cm); D. 8 1/2 in. (47 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Furniture,,http://www.metmuseum.org/art/collection/search/60505,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1984.408.2,false,true,60138,Asian Art,Water jug,,Japan,Meiji period (1868–1912),,,,Artist,,Tō Kai Ko,"Japanese, active late 19th century",,Tō Kai Ko,Japanese,1871,1899,late 19th century,1871,1899,Silver,H. 6 1/2 in. (16.5 cm); W. 5 1/2 in. (14 cm); W. (at base) 3 in. (7.6 cm),"Gift of Stanley J. Love, 1984",,,,,,,,,,,,Metalwork,,http://www.metmuseum.org/art/collection/search/60138,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -52.177.22,false,true,57332,Asian Art,Hanging scroll,,Japan,,,,,Artist,"Anonymous, in the style of",Mori Sosen,"Japanese, 1747–1821",,Mori Sosen,Japanese,1747,1821,style of 18th century,1700,1799,Hanging scroll; ink and color on silk,40 1/2 x 13 3/4 in. (102.9 x 34.9 cm),"Bequest of Katherine S. Dreier, 1952",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57332,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.38,false,true,55367,Asian Art,Hanging scroll,,Japan,,,,,Artist,Attributed to,Imao Keinen,"Japanese, 1845–1924",,Imao Keinen,Japanese,1845,1924,19th–20th century,1845,1924,Hanging scroll; ink and color on silk,45 3/4 x 10 1/2 in. (116.2 x 26.7 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/55367,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -52.160.4,false,true,57330,Asian Art,Painting,,Japan,,,,,Artist,,Hashimoto Kansetsu,"Japanese, 1883–1945",,Hashimoto Kansetsu,Japanese,1883,1945,20th century,1900,1945,Painting; watercolor on paper,19 x 22 in. (48.3 x 55.9 cm),"Purchase, Gifts, Bequests, and Funds from various donors, by exchange, 1952",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57330,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.19,false,true,49053,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Gyokusen,died 1852,,Gyokusen,Japanese,1752,1852,1812,1615,1868,Hanging scroll; ink on paper,34 x 15 1/2 in. (86.4 x 39.4 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49053,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.268.110,false,true,49072,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Hidaka Tetsuo,1791–1875,,Hidaka Tetsuo,Japanese,1791,1875,1862,1862,1862,Hanging scroll; ink on satin,45 1/2 x 20 5/8 in. (115.5 x 52.4 cm),"The Harry G. C. Packard Collection of Asian Art, Gift of Harry G. C. Packard, and Purchase, Fletcher, Rogers, Harris Brisbane Dick, and Louis V. Bell Funds, Joseph Pulitzer Bequest, and The Annenberg Fund Inc. Gift, 1975",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49072,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.99,false,true,45736,Asian Art,Hanging scroll,西王母図|Queen Mother of the West,Japan,Edo period (1615–1868),,,,Artist,,Kano Osanobu,1796–1846,,Kano Osanobu,Japanese,1796,1846,first half of the 19th century,1800,1846,Hanging scroll; ink and color on silk,39 3/4 x 14 7/8 in. (101 x 37.8 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45736,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.268.78,false,true,45378,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Kita Genki,active 1664–98,,Kita Genki,Japanese,1664,1698,1666,1666,1666,Hanging scroll; ink and color on silk,Image: 43 1/8 × 17 5/16 in. (109.5 × 44 cm) Overall with mounting: 79 1/4 × 22 1/4 in. (201.3 × 56.5 cm) Overall with knobs: 79 1/4 × 24 3/8 in. (201.3 × 61.9 cm),"The Harry G. C. Packard Collection of Asian Art, Gift of Harry G. C. Packard, and Purchase, Fletcher, Rogers, Harris Brisbane Dick, and Louis V. Bell Funds, Joseph Pulitzer Bequest, and The Annenberg Fund Inc. Gift, 1975",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45378,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -57.138.1,false,true,45380,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,Attributed to,Kaigetsudō Dohan,active 1710–16,,Kaigetsudō Dohan,Japanese,1710,1716,ca. 1715,1710,1716,Hanging scroll; ink and color on paper,51 x 17 3/4 in. (129.5 x 45.1 cm),"Seymour Fund, 1957",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45380,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.87,false,true,45770,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Kawamata Tsunemasa,active 1716–48,,Kawamata Tsunemasa,Japanese,1706,1758,first half of the 18th century,1716,1749,"Hanging scroll; ink, color, and gold on silk",33 3/4 x 10 5/8 in. (85.7 x 27 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45770,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -63.174.1–.3,false,true,45389,Asian Art,Triptych of hanging scrolls,,Japan,Edo period (1615–1868),,,,Artist,Attributed to,Kano Kōi,"Japanese, died 1636",,Kano Kōi,Japanese,,1636,early 17th century,1600,1636,Triptych of hanging scrolls; ink and color on paper,46 1/2 x 19 in. (118.1 x 48.3 cm),"Gift of Mr. and Mrs. Benjamin J. Levy, 1963",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45389,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.64,false,true,57175,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,After,Sesson Shūkei,ca. 1504–ca. 1589,,Sesson Shūkei,Japanese,1504,1589,probably 19th century,1800,1868,Hanging scroll; ink on paper,28 1/2 x 16 in. (72.4 x 40.6 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57175,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.453,false,true,40353,Asian Art,Painting,,Japan,Edo period (1615–1868),,,,Artist,After,Sesson Shūkei,ca. 1504–ca. 1589,,Sesson Shūkei,Japanese,1504,1589,probably 19th century,1800,1868,Matted painting; ink on paper,Image: 16 1/2 x 20 in. (41.9 x 50.8 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/40353,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.1133.2,false,true,77188,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Kano Shōei,"Japanese, 1519–1592",,Kano Shōei,Japanese,1519,1592,late 16th century,1567,1599,Hanging scroll; ink and color on paper,Image: 25 1/2 × 12 15/16 in. (64.7 × 32.9 cm) Overall with knobs: 59 1/8 × 19 3/4 in. (150.1 × 50.1 cm) Overall with mounting: 59 1/8 × 17 13/16 in. (150.1 × 45.3 cm),"Fishbein-Bender Collection, Gift of T. Richard Fishbein and Estelle P. Bender, 2013",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/77188,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.1133.1,false,true,77179,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Fūgai Ekun,"Japanese, 1568–1654",,Fūgai Ekun,Japanese,1568,1654,datable to 1650,1650,1650,Hanging scroll; ink on paper,Image: 12 15/16 × 17 3/16 in. (32.9 × 43.7 cm) Overall with knobs: 45 9/16 × 20 1/16 in. (115.8 × 50.9 cm) Overall with mounting: 45 9/16 × 18 1/16 in. (115.8 × 45.9 cm),"Fishbein-Bender Collection, Gift of T. Richard Fishbein and Estelle P. Bender, 2013",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/77179,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.268.124,false,true,45340,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Iwasa Matabei,"Japanese, 1578–1650",,Iwasa Matabei,Japanese,1578,1650,early 17th century,1600,1633,"Hanging scroll; ink, color, gold and silver on paper",Image: 11 1/4 × 12 3/4 in. (28.6 × 32.4 cm); Overall with mounting: 47 3/4 × 16 5/8 in. (121.3 × 42.2 cm); Overall with knobs: 47 3/4 × 18 3/4 in. (121.3 × 47.6 cm),"The Harry G. C. Packard Collection of Asian Art, Gift of Harry G. C. Packard, and Purchase, Fletcher, Rogers, Harris Brisbane Dick, and Louis V. Bell Funds, Joseph Pulitzer Bequest, and The Annenberg Fund Inc. Gift, 1975",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45340,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.142,false,true,44761,Asian Art,Handscroll,烏丸光広筆　十牛図歌賛図巻|Ten Oxherding Songs,Japan,Edo period (1615–1868),,,,Artist,,Karasumaru Mitsuhiro,"Japanese, 1579–1638",,Karasumaru Mitsuhiro,Japanese,1579,1638,ca. 1634,1624,1638,Handscroll; ink on dyed paper with stenciled decoration in gold and silver,11 3/4 x 107 in. (29.9 x 271.8 cm),"Purchase, Friends of Asian Art Gifts, 1986",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/44761,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.268.48a–d,true,true,44858,Asian Art,Door panels,老梅図襖|Old Plum,Japan,Edo period (1615–1868),,,,Artist,,Kano Sansetsu,"Japanese, 1590–1651",,Kano Sansetsu,Japanese,1590,1651,1646,1646,1646,"Four sliding-door panels (fusuma); ink, color, gold, and gold leaf on paper",Overall (of all four panels): 68 3/4 x 191 1/8 in. (174.6 x 485.5 cm) Overall (a): 68 3/8 x 47 5/8 in. (173.7 x 121 cm) Overall (b): 68 3/8 x 48 3/4 in. (173.7 x 123.8 cm) Overall (c): 68 1/2 x 47 3/4 in. (174 x 121.3 cm) Overall (d): 68 1/2 x 47 3/4 in. (174 x 121.3 cm),"The Harry G. C. Packard Collection of Asian Art, Gift of Harry G. C. Packard, and Purchase, Fletcher, Rogers, Harris Brisbane Dick, and Louis V. Bell Funds, Joseph Pulitzer Bequest, and The Annenberg Fund Inc. Gift, 1975",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/44858,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.26,false,true,45698,Asian Art,Hanging scroll,神農・夏冬山水図|Winter Landscape,Japan,Edo period (1615–1868),,,,Artist,,Kano Tan'yū,"Japanese, 1602–1674",,Kano Tan'yū,Japanese,1602,1674,1662,1662,1662,One from a set of three hanging scrolls; ink and color on silk,44 1/2 x 15 1/2 in. (113 x 39.4 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45698,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.27,false,true,45699,Asian Art,Hanging scroll,神農・夏冬山水図|Portrait of Emperor Shennong,Japan,Edo period (1615–1868),,,,Artist,,Kano Tan'yū,"Japanese, 1602–1674",,Kano Tan'yū,Japanese,1602,1674,1665,1665,1665,One from a set of three hanging scrolls; ink and color on silk,41 1/2 x 18 3/16 in. (105.4 x 46.2 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45699,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.28,false,true,45700,Asian Art,Hanging scroll,神農・夏冬山水図|Summer Landscape,Japan,Edo period (1615–1868),,,,Artist,,Kano Tan'yū,"Japanese, 1602–1674",,Kano Tan'yū,Japanese,1602,1674,1662,1662,1662,One from a set of three hanging scrolls; ink and color on silk,44 1/2 x 15 1/2 in. (113 x 39.4 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45700,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2006.174,false,true,73193,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Kano Tan'yū,"Japanese, 1602–1674",,Kano Tan'yū,Japanese,1602,1674,1635–45,1635,1645,Hanging scroll; ink on paper,Image: 40 x 9 1/2 in. (101.6 x 24.1 cm) Overall with mounting: 70 1/2 x 10 in. (179.1 x 25.4 cm) Overall with rollers: W. 12 in. (30.5 cm),"The Miriam and Ira D. Wallach Foundation Fund, 2006",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/73193,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1996.464.1,false,true,44297,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,Kano artist After,Kano Tan'yū,"Japanese, 1602–1674",,Kano Tan'yū,Japanese,1602,1674,dated 1666,1666,1666,Hanging scroll mounted as a panel; ink and color on paper,Image: 33 1/8 x 16 1/4 in. (84.1 x 41.3 cm) Overall with mounting: 36 1/2 x 18 7/8 in. (92.7 x 47.9 cm) Framed: 37 3/4 x 19 7/8 in. (95.9 x 50.5 cm),"Gift of John and Lili Bussel, 1996",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/44297,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1996.464.2,false,true,56098,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,Kano artist after,Kano Tan'yū,"Japanese, 1602–1674",,Kano Tan'yū,Japanese,1602,1674,dated 1666,1666,1666,Hanging scroll mounted as a panel; ink and color on paper,Image: 33 1/8 x 16 in. (84.1 x 40.6 cm) Overall with mounting: 36 1/2 x 18 5/8 in. (92.7 x 47.3 cm) Framed: 37 7/16 x 19 7/8 in. (95.1 x 50.5 cm),"Gift of John and Lili Bussel, 1996",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/56098,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.61.61,false,true,54704,Asian Art,Album leaf,,Japan,Edo period (1615–1868),,,,Artist,,Kano Tan'yū,"Japanese, 1602–1674",,Kano Tan'yū,Japanese,1602,1674,ca. 1650,1640,1650,Album leaf; ink on paper,8 3/8 x 10 3/4 in. (21.3 x 27.3 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54704,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.61.64,false,true,54707,Asian Art,Album leaf,,Japan,Edo period (1615–1868),,,,Artist,,Kano Tan'yū,"Japanese, 1602–1674",,Kano Tan'yū,Japanese,1602,1674,ca. 1650,1640,1660,Album leaf; ink and color on silk,10 x 14 in. (25.4 x 35.6 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54707,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.268.49,false,true,45418,Asian Art,Hanging scroll,月夜山水図|Landscape in Moonlight,Japan,Edo period (1615–1868),,,,Artist,,Kano Tan'yū,"Japanese, 1602–1674",,Kano Tan'yū,Japanese,1602,1674,after 1662,1663,1674,One of a triptych of hanging scrolls; ink on silk,Image: 39 5/8 x 16 3/4 in. (100.6 x 42.5 cm) Overall: 75 3/8 x 23 1/2 in. (191.5 x 59.7 cm),"The Harry G. C. Packard Collection of Asian Art, Gift of Harry G. C. Packard, and Purchase, Fletcher, Rogers, Harris Brisbane Dick, and Louis V. Bell Funds, Joseph Pulitzer Bequest, and The Annenberg Fund Inc. Gift, 1975",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45418,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.268.50,false,true,45419,Asian Art,Hanging scroll,月夜山水図|Landscape in Moonlight,Japan,Edo period (1615–1868),,,,Artist,,Kano Tan'yū,"Japanese, 1602–1674",,Kano Tan'yū,Japanese,1602,1674,after 1662,1663,1674,One of a triptych of hanging scrolls; ink on silk,Image: 39 5/8 x 16 3/4 in. (100.6 x 42.5 cm) Overall: 75 1/4 x 23 1/2 in. (191.1 x 59.7 cm),"The Harry G. C. Packard Collection of Asian Art, Gift of Harry G. C. Packard, and Purchase, Fletcher, Rogers, Harris Brisbane Dick, and Louis V. Bell Funds, Joseph Pulitzer Bequest, and The Annenberg Fund Inc. Gift, 1975",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45419,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.268.51,false,true,45420,Asian Art,Hanging scroll,月夜山水図|Landscape in Moonlight,Japan,Edo period (1615–1868),,,,Artist,,Kano Tan'yū,"Japanese, 1602–1674",,Kano Tan'yū,Japanese,1602,1674,after 1662,1663,1674,One of a triptych of hanging scrolls; ink on silk,Image: 39 5/8 x 16 3/4 in. (100.6 x 42.5 cm) Overall: 75 1/4 x 23 1/2 in. (191.1 x 59.7 cm),"The Harry G. C. Packard Collection of Asian Art, Gift of Harry G. C. Packard, and Purchase, Fletcher, Rogers, Harris Brisbane Dick, and Louis V. Bell Funds, Joseph Pulitzer Bequest, and The Annenberg Fund Inc. Gift, 1975",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45420,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.268.52,false,true,49098,Asian Art,Handscroll,狩野探幽筆　『画苑』|Famous Themes for Painting Study Known as “The Garden of Painting” (Gaen),Japan,Edo period (1615–1868),,,,Artist,,Kano Tan'yū,"Japanese, 1602–1674",,Kano Tan'yū,Japanese,1602,1674,1670,1600,1670,One of a pair of handscrolls; ink on paper,10 7/8 in. x 10 ft. 7 5/8 in. (27.6 x 324.2 cm),"The Harry G. C. Packard Collection of Asian Art, Gift of Harry G. C. Packard, and Purchase, Fletcher, Rogers, Harris Brisbane Dick, and Louis V. Bell Funds, Joseph Pulitzer Bequest, and The Annenberg Fund Inc. Gift, 1975",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49098,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.268.53,false,true,49099,Asian Art,Handscroll,狩野探幽筆　『画苑』|Famous Themes for Painting Study Known as “The Garden of Painting” (Gaen),Japan,Edo period (1615–1868),,,,Artist,,Kano Tan'yū,"Japanese, 1602–1674",,Kano Tan'yū,Japanese,1602,1674,1670,1670,1670,Handscroll; ink on paper,10 7/8 in. x 11 ft. 7 7/16 in. (27.6 x 354.2 cm),"The Harry G. C. Packard Collection of Asian Art, Gift of Harry G. C. Packard, and Purchase, Fletcher, Rogers, Harris Brisbane Dick, and Louis V. Bell Funds, Joseph Pulitzer Bequest, and The Annenberg Fund Inc. Gift, 1975",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49099,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2014.768.1,false,true,77184,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Tosa Mitsuoki,"Japanese, 1617–1691",,Tosa Mitsuoki,Japanese,1617,1691,mid- to late 17th century,1634,1699,Hanging scroll; color on silk,Image: 46 5/8 × 22 3/16 in. (118.5 × 56.3 cm) Overall with knobs: 69 × 28 1/16 in. (175.2 × 71.2 cm) Overall with mounting: 69 × 26 in. (175.2 × 66.1 cm),"Fishbein-Bender Collection, Gift of T. Richard Fishbein and Estelle P. Bender, 2014",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/77184,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -61.63,false,true,57239,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Kano Tsunenobu,"Japanese, 1636–1713",,Kano Tsunenobu,Japanese,1636,1713,late 17th–early 18th century,1667,1713,One of a triptych of hanging scrolls; ink and color on paper,31 x 11 3/8 in. (78.7 x 28.9 cm),"Purchase, Gifts, Bequests, and Funds from various donors, by exchange, 1961",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57239,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -26.146,false,true,44897,Asian Art,Handscroll,,Japan,Edo period (1615–1868),,,,Artist,,Kano Tsunenobu,"Japanese, 1636–1713",,Kano Tsunenobu,Japanese,1636,1713,17th–18th century,1636,1713,Handscroll; ink and color on paper,12 3/4 in. × 16 ft. 1 1/4 in. (32.4 × 490.9 cm),"Purchase, Fletcher Fund and J. Pierpont Morgan Gift, 1926",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/44897,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.88,false,true,45721,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,Attributed to,Kano Tsunenobu,"Japanese, 1636–1713",,Kano Tsunenobu,Japanese,1636,1713,17th–18th century,1636,1713,Hanging scroll; ink and color on silk,35 3/4 x 13 in. (90.8 x 33 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45721,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.89,false,true,45722,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,Attributed to,Kano Tsunenobu,"Japanese, 1636–1713",,Kano Tsunenobu,Japanese,1636,1713,17th–18th century,1636,1713,Hanging scroll; ink and color on silk,16 3/4 x 28 1/2 in. (42.5 x 72.4 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45722,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.268.55,false,true,45042,Asian Art,Hanging scroll,四愛図|Four Admirers,Japan,Edo period (1615–1868),,,,Artist,,Kano Tsunenobu,"Japanese, 1636–1713",,Kano Tsunenobu,Japanese,1636,1713,late 17th–early 18th century,1667,1713,Hanging scroll; ink and color on silk,Image: 21 5/16 x 46 in. (54.1 x 116.8 cm) Overall with mounting: 67 5/8 x 55 1/2 in. (171.8 x 141 cm),"The Harry G. C. Packard Collection of Asian Art, Gift of Harry G. C. Packard, and Purchase, Fletcher, Rogers, Harris Brisbane Dick, and Louis V. Bell Funds, Joseph Pulitzer Bequest, and The Annenberg Fund Inc. Gift, 1975",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45042,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.1398a–j,false,true,45723,Asian Art,Album leaves,十鷹書画冊|Album of Hawks and Calligraphy,Japan,Edo period (1615–1868),,,,Artist,,Kano Tsunenobu,"Japanese, 1636–1713",,Kano Tsunenobu,Japanese,1636,1713,17th–18th century,1636,1713,Album of ten paintings; ink and color on silk,Each leaf: 10 3/4 x 9 7/16 in. (27.3 x 23.9 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45723,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.45,false,true,45734,Asian Art,Hanging scroll,粟に鶉図|Quail and Millet,Japan,Edo period (1615–1868),,,,Artist,,Kiyohara Yukinobu,"Japanese, 1643–1682",,Kiyohara Yukinobu,Japanese,1643,1682,late 17th century,1667,1682,Hanging scroll; ink and color on silk,46 5/8 x 18 3/4 in. (118.4 x 47.6 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45734,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2012.522.2,false,true,77204,Asian Art,Hanging scroll,"小川に連雀桜竹図|Waxwings, Cherry Blossoms, and Bamboo by a Stream",Japan,Edo period (1615–1868),,,,Artist,,Kiyohara Yukinobu,"Japanese, 1643–1682",,Kiyohara Yukinobu,Japanese,1643,1682,late 17th century,1667,1682,Hanging scroll; ink and color on silk,Image: 39 1/8 x 16 3/8 in. (99.4 x 41.6 cm) Overall with knobs: 23 1/2 in. (59.7 cm) Overall with mounting: 71 7/8 x 21 9/16 in. (182.5 x 54.7 cm),"Fishbein-Bender Collection, Gift of T. Richard Fishbein and Estelle P. Bender, 2012",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/77204,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.33,false,true,45733,Asian Art,Hanging scroll,地蔵菩薩像|Jizō Bosatsu,Japan,Edo period (1615–1868),,,,Artist,,Hanabusa Itchō,"Japanese, 1652–1724",,Hanabusa Itchō,Japanese,1652,1724,1667–98,1667,1698,"Hanging scroll; ink, color, and gold on paper",24 3/4 x 10 1/2 in. (62.9 x 26.7 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45733,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.23,false,true,45724,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Tanshin Morimasa,"Japanese, 1653–1718",,Tanshin Morimasa,Japanese,1653,1718,late 17th century,1667,1699,One of a triptych of hanging scrolls; ink and color on silk,60 x 30 1/4 in. (152.4 x 76.8 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45724,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.24,false,true,45725,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Tanshin Morimasa,"Japanese, 1653–1718",,Tanshin Morimasa,Japanese,1653,1718,late 17th century,1667,1699,One of a triptych of hanging scrolls; ink and color on silk,59 1/4 x 30 1/4 in. (150.5 x 76.8 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45725,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.25,false,true,45726,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Tanshin Morimasa,"Japanese, 1653–1718",,Tanshin Morimasa,Japanese,1653,1718,17th–18th century,1653,1718,One of a triptych of hanging scrolls; ink and color on silk,58 3/4 x 30 1/4 in. (149.2 x 76.8 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45726,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.78,false,true,45727,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,Attributed to,Tanshin Morimasa,"Japanese, 1653–1718",,Tanshin Morimasa,Japanese,1653,1718,17th–18th century,1653,1718,Hanging scroll; ink and color on silk,37 13/16 x 15 13/16 in. (96 x 40.1 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45727,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.526,false,true,40347,Asian Art,Folding fan mounting as a hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,Attributed to,Ogata Kōrin,"Japanese, 1658–1716",,Kōrin,Japanese,1658,1716,17th–18th century,1658,1716,"Folding fan mounting as a hanging scroll; ink, color, and gold on paper",6 3/4 x 20 3/4 in. (17.2 x 52.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/40347,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.6,false,true,45728,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Kano Chikanobu,"Japanese, 1660–1728",,Kano Chikanobu,Japanese,1660,1728,17th–18th century,1660,1728,Hanging scroll; ink and color on silk,40 1/4 x 17 5/8 in. (102.2 x 44.8 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45728,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.7,false,true,45730,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Kano Chikanobu,"Japanese, 1660–1728",,Kano Chikanobu,Japanese,1660,1728,17th–18th century,1660,1728,Hanging scroll; ink and color on silk,Image: 50 1/4 × 21 1/4 in. (127.6 × 54 cm) Overall with mounting: 85 1/8 × 26 1/8 in. (216.2 × 66.4 cm) Overall with knobs: 85 1/8 × 28 1/2 in. (216.2 × 72.4 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45730,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -53.118.1,false,true,48908,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Kano Chikanobu,"Japanese, 1660–1728",,Kano Chikanobu,Japanese,1660,1728,17th–18th century,1660,1728,Hanging scroll; ink and color on paper Reverse side: ink and color on silk,40 x 13 9/16 in. (101.6 x 34.4 cm),"Rogers Fund, 1953",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/48908,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.512,false,true,40354,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,In the Style of,Ogata Kenzan,"Japanese, 1663–1743",,Ogata Kenzan,Japanese,1663,1743,dated 1741,1741,1741,Hanging scroll; ink and color on paper,12 x 17 3/4 in. (30.5 x 45.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/40354,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.268.65,false,true,45275,Asian Art,Hanging scroll,定家詠十二ヶ月和歌花鳥図『拾遺愚草』より四月|“Fourth Month” from Fujiwara no Teika’s “Birds and Flowers of the Twelve Months”,Japan,Edo period (1615–1868),,,,Artist,,Ogata Kenzan,"Japanese, 1663–1743",,Ogata Kenzan,Japanese,1663,1743,1743,1743,1743,Hanging scroll; ink and color on paper,Image: 6 5/16 x 8 15/16 in. (16 x 22.7 cm) Overall with mounting: 43 1/4 x 19 in. (109.9 x 48.3 cm) Overall with knobs: 43 1/4 x 20 5/8 in. (109.9 x 52.4 cm),"The Harry G. C. Packard Collection of Asian Art, Gift of Harry G. C. Packard, and Purchase, Fletcher, Rogers, Harris Brisbane Dick, and Louis V. Bell Funds, Joseph Pulitzer Bequest, and The Annenberg Fund Inc. Gift, 1975",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45275,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.268.66,false,true,45276,Asian Art,Hanging scroll,尾形乾山筆　定家詠十二ヶ月和歌花鳥図「拾遺愚草』より六月|“Sixth Month” from Fujiwara no Teika’s “Birds and Flowers of the Twelve Months”,Japan,Edo period (1615–1868),,,,Artist,,Ogata Kenzan,"Japanese, 1663–1743",,Ogata Kenzan,Japanese,1663,1743,1743,1743,1743,Hanging scroll; ink and color on paper,Image: 6 1/4 x 9 1/8 in. (15.9 x 23.2 cm) Overall with mounting: 43 1/4 x 19 in. (109.9 x 48.3 cm) Overall with knobs: 43 1/4 x 20 5/8 in. (109.9 x 52.4 cm),"The Harry G. C. Packard Collection of Asian Art, Gift of Harry G. C. Packard, and Purchase, Fletcher, Rogers, Harris Brisbane Dick, and Louis V. Bell Funds, Joseph Pulitzer Bequest, and The Annenberg Fund Inc. Gift, 1975",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45276,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.268.67,false,true,45076,Asian Art,Hanging scroll,蔦紅葉図|Autumn Ivy,Japan,Edo period (1615–1868),,,,Artist,,Ogata Kenzan,"Japanese, 1663–1743",,Ogata Kenzan,Japanese,1663,1743,after 1732,1733,1743,"Album leaf mounted as a hanging scroll; ink, color, and gold on paper",Image: 8 3/8 x 10 7/8 in. (21.3 x 27.6 cm) Overall: 44 7/8 x 22 1/4 in. (114 x 56.5 cm),"The Harry G. C. Packard Collection of Asian Art, Gift of Harry G. C. Packard, and Purchase, Fletcher, Rogers, Harris Brisbane Dick, and Louis V. Bell Funds, Joseph Pulitzer Bequest, and The Annenberg Fund Inc. Gift, 1975",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45076,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.58,false,true,49085,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Ogawa Haritsu (Ritsuō),"Japanese, 1663–1747",,Ritsuō,Japanese,1663,1747,1740,1700,1800,Hanging scroll; ink and color on silk,37 1/4 x 12 1/4 in. (94.6 x 31.1 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49085,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.73,false,true,49084,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Yokoya Sōmin,"Japanese, 1669–1733",,Yokoya Sōmin,Japanese,1669,1733,early 18th century,1700,1733,Hanging scroll; ink on paper,Image: 27 1/8 x 8 1/8 in. (68.9 x 20.6 cm) Overall with mounting: 60 1/2 x 12 15/16 in. (153.7 x 32.9 cm) Overall with knobs: 60 1/2 x 14 13/16 in. (153.7 x 37.6 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49084,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.24,false,true,45376,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Kano Furunobu,"Japanese, 1696–1731",,Kano Furunobu,Japanese,1696,1731,early 18th century,1700,1731,Hanging scroll; ink and color on silk,19 1/2 x 41 1/8 in. (49.5 x 104.5 cm),"Gift of August Belmont, 1976",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45376,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.268.97,false,true,45352,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Yosa Buson,"Japanese, 1716–1783",,Yosa Buson,Japanese,1716,1783,ca. 1780,1770,1783,"Hanging scroll, color on satin",14 3/16 x 9 15/16 in. (36.1 x 25.2 cm),"The Harry G. C. Packard Collection of Asian Art, Gift of Harry G. C. Packard, and Purchase, Fletcher, Rogers, Harris Brisbane Dick, and Louis V. Bell Funds, Joseph Pulitzer Bequest, and The Annenberg Fund Inc. Gift, 1975",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45352,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2011.572.1,false,true,73348,Asian Art,Hanging scroll,曽我蕭白筆　寿老人図|The God of Good Fortune Jurōjin,Japan,Edo period (1615–1868),,,,Artist,,Soga Shōhaku,"Japanese, 1730–1781",,Soga Shōhaku,Japanese,1730,1781,mid- to late 18th century,1734,1799,Hanging scroll; ink and color on paper,Image: 52 3/4 x 22 5/16 in. (134 x 56.7 cm) Overall with mounting: 81 1/2 x 28 1/2 in. (207 x 72.4 cm) Overall with knobs: 81 1/2 x 30 3/4 in. (207 x 78.1 cm),"Fishbein-Bender Collection, Gift of T. Richard Fishbein and Estelle P. Bender, 2011",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/73348,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.268.71,false,true,49031,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Maruyama Ōkyo,"Japanese, 1733–1795",,Maruyama Ōkyo,Japanese,1733,1795,1769,1769,1769,Hanging scroll; ink on paper,14 x 22 5/16 in. (35.5 x 56.6 cm),"The Harry G. C. Packard Collection of Asian Art, Gift of Harry G. C. Packard, and Purchase, Fletcher, Rogers, Harris Brisbane Dick, and Louis V. Bell Funds, Joseph Pulitzer Bequest, and The Annenberg Fund Inc. Gift, 1975",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49031,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2000.245,false,true,54038,Asian Art,Hanging scroll,宋紫山筆　嵐に鶏図|Rooster in a Storm,Japan,Edo period (1615–1868),,,,Artist,,Sō Shizan,"Japanese, 1733–1805",,Sō Shizan,Japanese,1733,1805,1783,1783,1783,"Hanging scroll; ink, color, and gold on silk",38 7/8 x 17 1/4 in. (98.7 x 43.8 cm),"Friends of Asian Art, Purchase, The Dillon Fund Gift, in honor of Wen C. Fong, 2000",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54038,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.268.103,false,true,45426,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Okada Beisanjin,"Japanese, 1744–1820",,Okada Beisanjin,Japanese,1744,1820,1817,1817,1817,Hanging scroll; ink and color on paper,Image: 70 1/8 x 35 5/8 in. (178.1 x 90.5 cm) Overall: 91 x 43 1/4in. (231.1 x 109.9 cm),"The Harry G. C. Packard Collection of Asian Art, Gift of Harry G. C. Packard, and Purchase, Fletcher, Rogers, Harris Brisbane Dick, and Louis V. Bell Funds, Joseph Pulitzer Bequest, and The Annenberg Fund Inc. Gift, 1975",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45426,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.268.99,false,true,45393,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Noro Kaiseki,"Japanese, 1747–1828",,Noro Kaiseki,Japanese,1747,1828,1822,1822,1822,Hanging scroll; ink and color on silk,Image: 53 3/4 x 26 7/8 in. (136.5 x 68.3 cm) Overall: 86 3/8 x 36 7/8in. (219.4 x 93.7 cm),"The Harry G. C. Packard Collection of Asian Art, Gift of Harry G. C. Packard, and Purchase, Fletcher, Rogers, Harris Brisbane Dick, and Louis V. Bell Funds, Joseph Pulitzer Bequest, and The Annenberg Fund Inc. Gift, 1975",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45393,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.268.134,false,true,49083,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Shokusanjin (Ōta Nanpo),"Japanese, 1749–1823",,,Japanese,1749,1823,1820,1820,1820,Hanging scroll; ink on silk,38 1/8 x 13 5/16 in. (96.8 x 33.8 cm),"The Harry G. C. Packard Collection of Asian Art, Gift of Harry G. C. Packard, and Purchase, Fletcher, Rogers, Harris Brisbane Dick, and Louis V. Bell Funds, Joseph Pulitzer Bequest, and The Annenberg Fund Inc. Gift, 1975",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49083,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.21,false,true,49061,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Ganku,"Japanese, 1749–1838",,Ganku,Japanese,1749,1838,dated 1790,1615,1868,Hanging scroll; ink and color on silk,38 3/4 x 13 3/4 in. (98.4 x 34.9 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49061,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2011.572.2a, b",false,true,77168,Asian Art,Hanging scrolls,鶴図|Cranes,Japan,Edo period (1615–1868),,,,Artist,,Nagasawa Rosetsu,"Japanese, 1754–1799",,Nagasawa Rosetsu,Japanese,1754,1799,1780s,1780,1789,Pair of hanging scrolls; ink and color on paper,Image (each scroll): 61 7/16 x 35 7/8 in. (156 x 91.2 cm) Overall with mounting (each scroll): 84 x 36 3/4 in. (213.4 x 93.3 cm) Overall with knobs (each scroll): 84 x 39 1/4 in. (213.4 x 99.7 cm),"Fishbein-Bender Collection, Gift of T. Richard Fishbein and Estelle P. Bender, 2011",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/77168,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.9,false,true,45398,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,Attributed to,Chōbunsai Eishi,"Japanese, 1756–1829",,Chōbunsai Eishi,Japanese,1756,1829,late 18th–19th century,1767,1829,Hanging scroll; ink and color on silk,12 5/8 x 27 5/8 in. (32.1 x 70.2 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45398,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.49,false,true,45800,Asian Art,Painting,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,dated 1780,1615,1868,Ink and color on silk,29 7/8 x 11 11/16 in. (75.9 x 29.7 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45800,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2014.727,false,true,675695,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,ca. 1810,1810,1810,Hanging scroll; ink and color on silk,36 × 12 3/4 in. (91.4 × 32.4 cm),"Gift of Sebastian Izzard and Masaharu Nagano, in memory of T. Richard Fishbein, 2014",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/675695,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.268.68,false,true,48981,Asian Art,Handscroll,,Japan,Edo period (1615–1868),,,,Artist,After,Sakai Hōitsu,"Japanese, 1761–1828",,Sakai Hōitsu,Japanese,1761,1828,1815,1815,1815,Handscroll; ink and color on paper,13 3/8 in. x 30 ft. 4 3/16 in. (33.9 x 925 cm),"The Harry G. C. Packard Collection of Asian Art, Gift of Harry G. C. Packard, and Purchase, Fletcher, Rogers, Harris Brisbane Dick, and Louis V. Bell Funds, Joseph Pulitzer Bequest, and The Annenberg Fund Inc. Gift, 1975",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/48981,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.51,false,true,49003,Asian Art,Hanging scroll,牡丹に孔雀図|Peacocks and Peonies,Japan,Edo period (1615–1868),,,,Artist,,Tani Bunchō,"Japanese, 1763–1840",,Tani Bunchō,Japanese,1763,1840,1820,1810,1830,Hanging scroll; ink and color on silk,Image: 60 1/4 × 34 3/4 in. (153 × 88.2 cm) Overall with mounting: 79 15/16 × 41 3/4 in. (203 × 106 cm) Overall with knobs: 79 15/16 × 46 3/4 in. (203 × 118.8 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49003,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.268.113,false,true,45395,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Tani Bunchō,"Japanese, 1763–1840",,Tani Bunchō,Japanese,1763,1840,1791,1791,1791,Hanging scroll; ink and color on paper,Image: 52 15/16 in. × 11 in. (134.5 × 27.9 cm) Overall with mounting: 81 7/8 × 16 3/16 in. (208 × 41.1 cm) Overall with knobs: 81 7/8 × 18 9/16 in. (208 × 47.1 cm),"The Harry G. C. Packard Collection of Asian Art, Gift of Harry G. C. Packard, and Purchase, Fletcher, Rogers, Harris Brisbane Dick, and Louis V. Bell Funds, Joseph Pulitzer Bequest, and The Annenberg Fund Inc. Gift, 1975",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45395,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.86,false,true,39629,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,Attributed to,Utagawa Toyokuni I,"Japanese, 1769–1825",,Utagawa Toyokuni I,Japanese,1769,1825,ca. 1795,1695,1895,Hanging scroll; ink and color on silk,34 3/4 x 10 1/2 in. (88.3 x 26.7 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/39629,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.268.107,false,true,49024,Asian Art,Hanging scroll,観瀑山水図|Landscape with Waterfall,Japan,Edo period (1615–1868),,,,Artist,,Nakabayashi Chikutō,"Japanese, 1776–1853",,Nakabayashi Chikutō,Japanese,1776,1853,1841,1841,1841,Hanging scroll; ink on paper,Image: 61 15/16 x 34 3/16 in. (157.3 x 86.8 cm) Overall with mounting: 111 1/4 x 41 15/16 in. (282.5 x 106.5 cm) Overall with knobs: 111 1/4 x 45 1/16 in. (282.5 x 114.5 cm),"The Harry G. C. Packard Collection of Asian Art, Gift of Harry G. C. Packard, and Purchase, Fletcher, Rogers, Harris Brisbane Dick, and Louis V. Bell Funds, Joseph Pulitzer Bequest, and The Annenberg Fund Inc. Gift, 1975",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49024,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.268.108,false,true,49026,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Nakabayashi Chikutō,"Japanese, 1776–1853",,Nakabayashi Chikutō,Japanese,1776,1853,ca. 1840,1830,1850,Hanging scroll; ink on paper,51 3/4 x 23 11/16 in. (131.5 x 60.2 cm),"The Harry G. C. Packard Collection of Asian Art, Gift of Harry G. C. Packard, and Purchase, Fletcher, Rogers, Harris Brisbane Dick, and Louis V. Bell Funds, Joseph Pulitzer Bequest, and The Annenberg Fund Inc. Gift, 1975",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49026,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.268.105,false,true,48998,Asian Art,Handscroll,,Japan,Edo period (1615–1868),,,,Artist,,Okada Hankō,"Japanese, 1782–1846",,Okada Hankō,Japanese,1782,1846,1831,1831,1831,Handscroll; ink and color on paper,6 3/4 in. × 12 ft. 7 5/16 in. (17.2 × 384.3 cm),"The Harry G. C. Packard Collection of Asian Art, Gift of Harry G. C. Packard, and Purchase, Fletcher, Rogers, Harris Brisbane Dick, and Louis V. Bell Funds, Joseph Pulitzer Bequest, and The Annenberg Fund Inc. Gift, 1975",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/48998,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1995.247,false,true,44285,Asian Art,Handscroll,,Japan,Edo period (1615–1868),,,,Artist,,Yamamoto Baiitsu,"Japanese, 1783–1856",,Yamamoto Baiitsu,Japanese,1783,1783,dated 1847,1847,1847,Handscroll; ink and color on silk,Image: 8 1/4 x 95 in. (21 x 241.3 cm),"Purchase, Friends of Asian Art Gifts, 1995",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/44285,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1984.346,false,true,45430,Asian Art,Hanging scroll,三樹坡曉色图|View of the Kamo River from Sanbogi at Dawn,Japan,Edo period (1615–1868),,,,Artist,,Oda Kaisen,"Japanese, 1785–1862",,Oda Kaisen,Japanese,1785,1862,1829,1829,1829,Hanging scroll; ink and color on paper,Image: 26 x 9 1/8 in. (66 x 23.2 cm) Overall with mounting: 43 5/8 x 28 3/4 in. (110.8 x 73 cm) Overall with rollers: 32 1/2 in. (82.6 cm),"Purchase, Bequest of John L. Cadwalader, Gift of Mrs. Russell Sage, and Charles Sewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, by exchange, 1984",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45430,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.23,false,true,75265,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,1836,1836,1836,Hanging scroll: ink and color on silk,Image: 35 1/4 x 13 5/8 in. (89.5 x 34.6 cm) Overall with mounting: 67 3/4 x 17 1/4 in. (172.1 x 43.8 cm) Overall with knobs: 19 1/4 x 67 3/4 in. (48.9 x 172.1 cm),"Purchase, Friends of Asian Art Gifts, 2009",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/75265,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.98,false,true,55295,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,Attributed to,Yosai,"Japanese, 1788–1878",,Yosai,Japanese,1788,1878,dated 1867,1867,1867,Hanging scroll; ink and color on silk,46 5/8 x 15 1/2 in. (118.4 x 39.4 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/55295,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2001.611,false,true,61278,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Ōtagaki Rengetsu,"Japanese, 1791–1871",,Ōtagaki Rengetsu,Japanese,1791,1871,1868,1868,1868,Hanging scroll; ink and color on paper,11 3/8 x 49 1/8 in. (28.9 x 124.8 cm),"Gift of Professor Donald Keene, 2001",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/61278,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -57.156.7,false,true,44854,Asian Art,Handscroll,,Japan,Edo period (1615–1868),,,,Artist,,Ukita Ikkei,"Japanese, 1795–1859",,Ukita Ikkei,Japanese,1795,1859,ca. 1858,1848,1859,Handscroll; ink and color on paper,11 3/4 in. x 25 ft. 6 in. (29.8 x 777.2 cm),"Rogers Fund, 1957",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/44854,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.268.123,false,true,45332,Asian Art,Folding fan,墨竹扇面図|Bamboo and Rocks by a Stream,Japan,Edo period (1615–1868),,,,Artist,,Takaku Aigai,"Japanese, 1796–1843",,Takaku Aigai,Japanese,1796,1843,1832,1832,1832,"Folding fan; ink on paper, wood ribs",Overall: 11 5/16 x 18 in. (28.7 x 45.7 cm),"The Harry G. C. Packard Collection of Asian Art, Gift of Harry G. C. Packard, and Purchase, Fletcher, Rogers, Harris Brisbane Dick, and Louis V. Bell Funds, Joseph Pulitzer Bequest, and The Annenberg Fund Inc. Gift, 1975",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45332,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2012.522.3,false,true,77202,Asian Art,Hanging scroll,芥子図|Poppies,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Kiitsu,"Japanese, 1796–1858",,Suzuki Kiitsu,Japanese,1796,1858,mid-19th century,1834,1866,Hanging scroll; ink and color on silk,Image: 38 9/16 x 13 1/8 in. (98 x 33.3 cm) Overall with knobs: 40 13/16 x 21 in. (103.7 x 53.3 cm) Overall with mounting: 40 13/16 x 18 11/16 in. (103.7 x 47.5 cm),"Fishbein-Bender Collection, Gift of T. Richard Fishbein and Estelle P. Bender, 2012",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/77202,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.268.117,false,true,49010,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Tsubaki Chinzan,"Japanese, 1801–1854",,Tsubaki Chinzan,Japanese,1801,1854,dated Fall 1850,1850,1850,Hanging scroll; ink and color on paper,Image: 43 3/8 x 17 1/2 in. (110.2 x 44.4 cm) Overall: 75 3/8 x 30 3/8in. (191.5 x 77.2 cm),"The Harry G. C. Packard Collection of Asian Art, Gift of Harry G. C. Packard, and Purchase, Fletcher, Rogers, Harris Brisbane Dick, and Louis V. Bell Funds, Joseph Pulitzer Bequest, and The Annenberg Fund Inc. Gift, 1975",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49010,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.268.120,false,true,49014,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Tsubaki Chinzan,"Japanese, 1801–1854",,Tsubaki Chinzan,Japanese,1801,1854,1854,1854,1854,Hanging scroll; ink and color on paper,51 1/8 x 11 5/16 in. (129.9 x 28.8 cm),"The Harry G. C. Packard Collection of Asian Art, Gift of Harry G. C. Packard, and Purchase, Fletcher, Rogers, Harris Brisbane Dick, and Louis V. Bell Funds, Joseph Pulitzer Bequest, and The Annenberg Fund Inc. Gift, 1975",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49014,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.268.121,false,true,49016,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Tsubaki Chinzan,"Japanese, 1801–1854",,Tsubaki Chinzan,Japanese,1801,1854,1854,1854,1854,Hanging scroll; ink and color on paper,51 x 11 5/16 in. (129.5 x 28.8 cm),"The Harry G. C. Packard Collection of Asian Art, Gift of Harry G. C. Packard, and Purchase, Fletcher, Rogers, Harris Brisbane Dick, and Louis V. Bell Funds, Joseph Pulitzer Bequest, and The Annenberg Fund Inc. Gift, 1975",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49016,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.112,false,true,57197,Asian Art,Album leaf,,Japan,Edo period (1615–1868),,,,Artist,,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,Japanese,1807,1891,1882,1882,1882,Album leaf; lacquer on gold paper,4 3/4 x 3 1/2 in. (12.1 x 8.9 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57197,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.113,false,true,57198,Asian Art,Album leaf,,Japan,Edo period (1615–1868),,,,Artist,,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,Japanese,1807,1891,1882,1882,1882,Album leaf; lacquer on silver paper,4 3/4 x 3 1/2 in. (12.1 x 8.9 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57198,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.70b,false,true,45803,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,ca. 1801–4,1791,1814,Hanging scroll; ink and color on silk,Image: 40 3/4 x 12 1/2 in. (103.5 x 31.8 cm) Overall with mounting: 69 11/16 x 19 1/2 in. (177 x 49.5 cm) with ivory rollers (dia. 15/16 in.),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45803,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.172,false,true,72722,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Takahashi Sōhei,"Japanese, 1804?–?1835",,Takahashi Sōhei,Japanese,1804,1835,1827,1827,1827,Hanging scroll; ink on paper,Image: 53 3/8 x 16 3/8 in. (135.5 x 41.6 cm) Overall with mounting: 83 15/16 x 21 9/16 in. (213.2 x 54.8 cm) Overall with rollers: 83 15/16 x 25 in. (213.2 x 63.5 cm),"Gift of Gitter-Yelen Collection, 2005",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/72722,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.51,false,true,49000,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,Attributed to,Takahashi Sōhei,"Japanese, 1804?–?1835",,Takahashi Sōhei,Japanese,1804,1835,1832,1700,1900,Hanging scroll; ink and color on paper,48 1/8 x 11 5/8 in. (122.2 x 29.5 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49000,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2006.570,false,true,65586,Asian Art,Hanging scroll,"伝俵屋宗達筆　源氏物語図「宿木」|Scene from ""The Ivy"" (Yadorigi), chapter 49 of the Tale of Genji",Japan,Edo period (1615–1868),,,,Artist,Studio of,Tawaraya Sōtatsu,"Japanese, died ca. 1640",,Tawaraya Sōtatsu,Japanese,1540,1640,early 17th century,1615,1633,"Hanging scroll; ink, color, and gold on paper",10 x 21 3/4 in. (25.4 x 55.2 cm),"Gift of Chizuko and Frank Korn, in honor of Miyeko Murase, 2006",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/65586,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.32,false,true,45396,Asian Art,Hanging scroll,雪兎図|Painting the Eyes on a Snow Rabbit,Japan,Edo period (1615–1868),,,,Artist,,Isoda Koryūsai,"Japanese, 1735–ca. 1790",,Isoda Koryūsai,Japanese,1735,1790,ca. 1780,1770,1790,Hanging scroll; ink and color on silk,Image: 23 3/4 in. × 16 in. (60.3 × 40.6 cm) Overall with mounting: 62 1/4 in. × 23 in. (158.1 × 58.4 cm) Overall with knobs: 62 1/4 × 24 3/4 in. (158.1 × 62.9 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45396,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2011.525,false,true,76811,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Shunkōsai Hokushū,"Japanese, active 1808–32",,Shunkōsai Hokushū,Japanese,1808,1832,ca. 1812,1802,1822,Hanging scroll; ink and color on silk,Image: 26 x 13 1/2 in. (66 x 34.3 cm) Overall with mounting: 58 1/2 x 17 1/8 in. (148.6 x 43.5 cm),"Gift of Miki and Sebastian Izzard, in honor of James C. Y. Watt, 2011",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/76811,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2012.522.1,false,true,77198,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Soga Nichokuan,"Japanese, active mid-17th century",,Soga Nichokuan,Japanese,1600,1700,early 17th century,1615,1633,Hanging scroll; ink on paper,Image: 45 3/16 x 20 3/16 in. (114.8 x 51.2 cm) Overall with mounting: 81 5/16 x 21 1/8 in. (206.5 x 53.7 cm) Overall with knobs: 22 13/16 in. (58 cm),"Fishbein-Bender Collection, Gift of T. Richard Fishbein and Estelle P. Bender, 2012",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/77198,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -57.156.2,false,true,40450,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,Attributed to,Unkoku Tōetsu,"Japanese, active second half 17th century",,Unkoku Tōetsu,Japanese,1650,1699,late 17th century,1667,1699,Hanging scroll; ink on paper,50 3/4 x 18 3/4 in. (128.9 x 47.6 cm),"Seymour Fund, 1957",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/40450,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.30,false,true,45822,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,dated 1790,1790,1790,Hanging scroll; ink and color on silk,Image: 33 3/4 × 12 5/16 in. (85.8 × 31.3 cm) Overall with mounting: 67 5/16 × 12 5/16 in. (171 × 31.3 cm) Overall with knobs: 67 5/16 × 19 1/8 in. (171 × 48.5 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45822,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.34,false,true,48878,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,dated 1780,1780,1780,Hanging scroll; ink and color on silk,Image: 49 3/16 × 19 5/8 in. (125 × 49.8 cm) Overall with mounting: 90 3/16 × 28 1/16 in. (229 cm) Overall with knobs: 90 3/16 × 30 3/16 in. (229 × 76.7 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/48878,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.36,false,true,45811,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,dated 1847,1847,1847,Hanging scroll; ink and color on silk,Image: 31 3/4 × 12 1/2 in. (80.7 × 31.7 cm) Overall with mounting: 64 7/16 × 17 9/16 in. (163.7 × 44.6 cm) Overall with knobs: 64 7/16 × 19 5/16 in. (163.7 × 49 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45811,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.37,false,true,45818,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,dated 1847,1847,1847,Hanging scroll; ink and color on silk,Image: 23 1/4 × 11 7/8 in. (59.1 × 30.2 cm) Overall with mounting: 61 × 19 1/2 in. (154.9 × 49.6 cm) Overall with knobs: 61 in. × 21 5/16 in. (154.9 × 54.1 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45818,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.44,false,true,48885,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,dated 1790,1615,1868,Hanging scroll; ink and color on silk,12 3/4 x 22 1/2 in. (32.4 x 57.2 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/48885,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.56,false,true,45816,Asian Art,Hanging scroll,軍鶏図|Gamecocks,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,dated 1838,1838,1838,Hanging scroll; ink and color on silk,Image: 21 3/4 × 33 7/16 in. (55.3 × 85 cm) Overall with mounting: 59 7/16 × 40 3/16 in. (151 × 102 cm) Overall with knobs: 59 7/16 × 63 3/8 in. (151 × 160.9 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45816,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.57,false,true,45819,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,1849,1615,1868,Hanging scroll; ink on paper,Image: 42 1/4 × 14 3/16 in. (107.3 × 36 cm) Overall with mounting: 72 5/8 × 18 1/2 in. (184.5 × 47 cm) Overall with knobs: 72 5/8 × 20 1/2 in. (184.5 × 52 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45819,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1991.151,false,true,44634,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1810,1800,1820,Fan mounted as hanging scroll; ink and color on paper,Image: 9 7/16 × 20 3/16 in. (24 × 51.3 cm) Overall with mounting: 54 3/4 × 28 3/4 in. (139 × 73 cm) Overall with knobs: 54 3/4 × 31 in. (139 × 78.7 cm),"Purchase, Friends of Asian Art Gifts, 1991",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/44634,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.1403,false,true,48880,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,1839,1839,1839,Hanging scroll; ink and color on paper,Image: 11 3/16 × 27 9/16 in. (28.4 × 70 cm) Overall with mounting: 45 3/8 × 30 7/16 in. (115.3 × 77.3 cm) Overall with knobs: 45 3/8 × 30 13/16 in. (115.3 × 78.2 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/48880,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.58.1–.25,false,true,50915,Asian Art,Album leaves,『画本葛飾振』|Picture Book in the Katsushika Style (Ehon Katsushika-buri),Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,ca. 1836,1826,1846,Album of twenty-five preparatory drawings (hanshita-e) for book illustrations; ink on paper,10 x 15 1/2 in. (25.4 x 39.4 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/50915,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.517,false,true,54978,Asian Art,Hanging scroll,地獄太夫図|The Hell Courtesan,Japan,Edo period (1615–1868),,,,Artist,,Seikei,"Japanese, active second half of the 19th century",,Seikei,Japanese,1850,1899,late 19th century,1868,1899,Hanging scroll; ink and color on silk,Image: 39 1/8 × 20 1/8 in. (99.4 × 51.1 cm) Overall with mounting: 81 1/4 × 26 1/4 in. (206.4 × 66.7 cm) Overall with knobs: 26 1/4 × 29 in. (66.7 × 73.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54978,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1990.116,false,true,44614,Asian Art,Hanging scroll,法華経断簡|Segment of the Lotus Sutra (Hokekyō),Japan,Heian period (794–1185),,,,Artist,Attributed to,Kujō Kanezane,"Japanese, 1149–1207",,Kujo Kanezane,Japanese,1149,1207,12th century,1149,1185,"Hanging scroll; ink on colored paper decorated with cut gold (kirikane), sprinkled gold (sunago), and silver leaf",10 x 3 9/16 in. (25.4 x 9.1 cm),"Purchase, Friends of Asian Art Gifts, 1990",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/44614,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.268.4,false,true,45616,Asian Art,Handscroll,宗観筆　九曜秘暦|The Secrets of the Nine Luminaries (Kuyō hiryaku),Japan,Heian period (794–1185),,,,Artist,,Sōkan,"Japanese, active late 11th–early 12th century",,Sōkan,Japanese,1050,1150,1125,1125,1125,Handscroll; ink and color on paper,11 1/4 in. x 26 ft. 6 1/4 in. (28.5 x 808.3 cm),"The Harry G. C. Packard Collection of Asian Art, Gift of Harry G. C. Packard, and Purchase, Fletcher, Rogers, Harris Brisbane Dick, and Louis V. Bell Funds, Joseph Pulitzer Bequest, and The Annenberg Fund Inc. Gift, 1975",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45616,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -52.160.6a,false,true,56616,Asian Art,Fan mounted as an album leaf,,Japan,Meiji period (1868–1912),,,,Artist,,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,Japanese,1807,1891,late 19th century,1867,1891,Fan painting mounted as album leaf; tempera on paper,Overall: 13 x 23 5/8 in. (33 x 60 cm) Image: 6 3/8 x 19 1/2 in. (16.2 x 49.5 cm),"Purchase, Gifts, Bequests, and Funds from various donors, by exchange, 1952",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/56616,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -52.160.6b,false,true,75156,Asian Art,Fan mounted as an album leaf,,Japan,Meiji period (1868–1912),,,,Artist,,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,Japanese,1807,1891,late 19th century,1867,1891,Fan painting mounted as album leaf; tempera on paper,Overall: 13 x 23 5/8 in. (33 x 60 cm) Image: 7 7/16 x 20 5/8 in. (18.9 x 52.4 cm),"Purchase, Gifts, Bequests, and Funds from various donors, by exchange, 1952",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/75156,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -52.160.6c,false,true,75157,Asian Art,Fan mounted as an album leaf,,Japan,Meiji period (1868–1912),,,,Artist,,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,Japanese,1807,1891,late 19th century,1867,1891,Fan painting mounted as album leaf; tempera on paper,Overall: 13 x 23 5/8 in. (33 x 60 cm) Image: 7 1/2 x 20 5/8 in. (19.1 x 52.4 cm),"Purchase, Gifts, Bequests, and Funds from various donors, by exchange, 1952",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/75157,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -52.160.6d,false,true,75158,Asian Art,Fan mounted as an album leaf,,Japan,Meiji period (1868–1912),,,,Artist,,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,Japanese,1807,1891,late 19th century,1867,1891,Fan painting mounted as album leaf; tempera on paper,Overall: 13 x 23 5/8 in. (33 x 60 cm) Image: 7 x 20 7/8 in. (17.8 x 53 cm),"Purchase, Gifts, Bequests, and Funds from various donors, by exchange, 1952",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/75158,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -52.160.6e,false,true,75159,Asian Art,Fan mounted as an album leaf,,Japan,Meiji period (1868–1912),,,,Artist,,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,Japanese,1807,1891,late 19th century,1867,1891,Fan painting mounted as album leaf; tempera on paper,Overall: 13 x 23 5/8 in. (33 x 60 cm) Image: 7 1/2 x 20 5/8 in. (19.1 x 52.4 cm),"Purchase, Gifts, Bequests, and Funds from various donors, by exchange, 1952",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/75159,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -52.160.6f,false,true,75160,Asian Art,Fan mounted as an album leaf,,Japan,Meiji period (1868–1912),,,,Artist,,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,Japanese,1807,1891,late 19th century,1867,1891,Fan painting mounted as album leaf; tempera on paper,Overall: 13 x 23 5/8 in. (33 x 60 cm) Image: 7 9/16 x 20 3/4 in. (19.2 x 52.7 cm),"Purchase, Gifts, Bequests, and Funds from various donors, by exchange, 1952",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/75160,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -52.160.6g,false,true,75161,Asian Art,Fan mounted as an album leaf,,Japan,Meiji period (1868–1912),,,,Artist,,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,Japanese,1807,1891,late 19th century,1867,1891,Fan painting mounted as album leaf; tempera on paper,Overall: 13 x 23 5/8 in. (33 x 60 cm) Image: 7 7/16 x 20 3/4 in. (18.9 x 52.7 cm),"Purchase, Gifts, Bequests, and Funds from various donors, by exchange, 1952",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/75161,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -52.160.6h,false,true,75162,Asian Art,Fan mounted as an album leaf,,Japan,Meiji period (1868–1912),,,,Artist,,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,Japanese,1807,1891,late 19th century,1867,1891,Fan painting mounted as album leaf; tempera on paper,Overall: 13 x 23 5/8 in. (33 x 60 cm) Image: 6 7/8 x 20 1/8 in. (17.5 x 51.1 cm),"Purchase, Gifts, Bequests, and Funds from various donors, by exchange, 1952",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/75162,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -52.160.6i,false,true,75163,Asian Art,Fan mounted as an album leaf,,Japan,Meiji period (1868–1912),,,,Artist,,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,Japanese,1807,1891,late 19th century,1867,1891,Fan painting mounted as album leaf; tempera on paper,Overall: 13 x 23 5/8 in. (33 x 60 cm) Image: 7 1/4 x 20 1/8 in. (18.4 x 51.1 cm),"Purchase, Gifts, Bequests, and Funds from various donors, by exchange, 1952",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/75163,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -52.160.6j,false,true,75164,Asian Art,Fan mounted as an album leaf,,Japan,Meiji period (1868–1912),,,,Artist,,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,Japanese,1807,1891,late 19th century,1867,1891,Fan painting mounted as album leaf; tempera on paper,Overall: 13 x 23 5/8 in. (33 x 60 cm) Image: 7 1/2 x 20 7/8 in. (19.1 x 53 cm),"Purchase, Gifts, Bequests, and Funds from various donors, by exchange, 1952",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/75164,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -52.160.6k,false,true,75165,Asian Art,Fan mounted as an album leaf,,Japan,Meiji period (1868–1912),,,,Artist,,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,Japanese,1807,1891,late 19th century,1867,1891,Fan painting mounted as album leaf; tempera on paper,Overall: 13 x 23 5/8 in. (33 x 60 cm) Image: 7 1/2 x 20 1/2 in. (19.1 x 52.1 cm),"Purchase, Gifts, Bequests, and Funds from various donors, by exchange, 1952",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/75165,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -52.160.6l,false,true,75166,Asian Art,Fan mounted as an album leaf,,Japan,Meiji period (1868–1912),,,,Artist,,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,Japanese,1807,1891,late 19th century,1867,1891,Fan painting mounted as album leaf; tempera on paper,Overall: 13 x 23 5/8 in. (33 x 60 cm) Image: 7 x 20 1/4 in. (17.8 x 51.4 cm),"Purchase, Gifts, Bequests, and Funds from various donors, by exchange, 1952",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/75166,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.100,false,true,57195,Asian Art,Hanging scroll,旭日図|Rising Sun,Japan,Meiji period (1868–1912),,,,Artist,,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,Japanese,1807,1891,second half of the 19th century,1850,1891,Hanging scroll; ink and color on silk,31 1/2 x 9 1/4 in. (80 x 23.5 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57195,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.101,false,true,57196,Asian Art,Hanging scroll,,Japan,Meiji period (1868–1912),,,,Artist,,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,Japanese,1807,1891,dated 1890,1890,1890,Mounted and hanging scroll; ink on paper,14 1/8 x 18 5/8 in. (35.9 x 57.3 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57196,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.102,false,true,57185,Asian Art,Painting,,Japan,Meiji period (1868–1912),,,,Artist,,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,Japanese,1807,1891,1882,1882,1882,Lacquer on paper,7 1/2 x 6 1/2 in. (19.1 x 16.5 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57185,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.104,false,true,57187,Asian Art,Painting,,Japan,Meiji period (1868–1912),,,,Artist,,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,Japanese,1807,1891,1882,1882,1882,Lacquer on paper,7 1/2 x 6 1/2 in. (19.1 x 16.5 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57187,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.105,false,true,57188,Asian Art,Painting,,Japan,Meiji period (1868–1912),,,,Artist,,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,Japanese,1807,1891,1882,1882,1882,Lacquer on paper,7 1/2 x 6 1/2 in. (19.1 x 16.5 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57188,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.106,false,true,57189,Asian Art,Album leaf,,Japan,Meiji period (1868–1912),,,,Artist,,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,Japanese,1807,1891,1882,1882,1882,"Album leaf; ink, color, and lacquer on paper",7 1/2 x 6 1/2 in. (19.1 x 16.5 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57189,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.107,false,true,57190,Asian Art,Painting,,Japan,Meiji period (1868–1912),,,,Artist,,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,Japanese,1807,1891,1882,1882,1882,Lacquer on paper,7 1/2 x 6 1/2 in. (19.1 x 16.5 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57190,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.108,false,true,57191,Asian Art,Painting,,Japan,Meiji period (1868–1912),,,,Artist,,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,Japanese,1807,1891,1882,1882,1882,Lacquer on paper,7 1/2 x 6 1/2 in. (19.1 x 16.5 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57191,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.109,false,true,57192,Asian Art,Painting,,Japan,Meiji period (1868–1912),,,,Artist,,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,Japanese,1807,1891,1882,1882,1882,Lacquer on paper,7 1/2 x 6 1/2 in. (19.1 x 16.5 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57192,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.110,false,true,57193,Asian Art,Painting,,Japan,Meiji period (1868–1912),,,,Artist,,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,Japanese,1807,1891,1882,1882,1882,Lacquer and gold on paper,7 1/2 x 6 1/2 in. (19.1 x 16.5 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57193,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.111,false,true,57194,Asian Art,Painting,,Japan,Meiji period (1868–1912),,,,Artist,,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,Japanese,1807,1891,dated 1881,1881,1881,Lacquer and mother-of-pearl fragments,7 1/2 x 6 1/2 in. (19.1 x 16.5 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57194,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.268.136,false,true,57161,Asian Art,Hanging scroll,,Japan,Meiji period (1868–1912),,,,Artist,,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,Japanese,1807,1891,dated 1879,1879,1879,"Hanging scroll; colored lacquer with mother-of-pearl, gold and ink on paper",11 3/8 x 16 1/8 in. (28.9 x 41 cm),"The Harry G. C. Packard Collection of Asian Art, Gift of Harry G. C. Packard, and Purchase, Fletcher, Rogers, Harris Brisbane Dick, and Louis V. Bell Funds, Joseph Pulitzer Bequest, and The Annenberg Fund Inc. Gift, 1975",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57161,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.77,false,true,57180,Asian Art,Hanging scroll,,Japan,Meiji period (1868–1912),,,,Artist,Attributed to,Tazaki Soun,"Japanese, 1815–1898",,Tazaki Soun,Japanese,1815,1898,dated 1827,1827,1827,Hanging scroll; ink and color on silk,38 3/8 x 13 in. (97.5 x 33 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57180,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1981.26,false,true,57168,Asian Art,Hanging scroll,,Japan,Meiji period (1868–1912),,,,Artist,,Taki Katei,"Japanese, 1830–1901",,Taki Katei,Japanese,1830,1901,dated January 1896,1896,1896,Hanging scroll; ink and color on silk,43 1/16 x 16 3/8 in. (109.4 x 41.6 cm),"Gift of Daniel Slott, 1981",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57168,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.138,false,true,57238,Asian Art,Painting,,Japan,Meiji period (1868–1912),,,,Artist,Attributed to,Kawanabe Kyōsai,"Japanese, 1831–1889",,Kawanabe Kyōsai,Japanese,1831,1889,late 19th century,1867,1889,Ink on paper,8 x 5 in. (20.3 x 12.7 cm),"Gift of F. Tikotin, 1937",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57238,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -57.70.4,false,true,50771,Asian Art,Hanging scroll,,Japan,Meiji period (1868–1912),,,,Artist,,Kawanabe Kyōsai,"Japanese, 1831–1889",,Kawanabe Kyōsai,Japanese,1831,1889,after 1888,1888,1912,Hanging scroll; ink and color on silk,38 7/8 x 14 1/4 in. (98.7 x 36.2 cm),"Gift of Edward M. Bratter, 1957",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/50771,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.119.2,false,true,57229,Asian Art,Handscroll,,Japan,Meiji period (1868–1912),,,,Artist,,Kawanabe Kyōsai,"Japanese, 1831–1889",,Kawanabe Kyōsai,Japanese,1831,1889,"late 19th century, before 1870",1868,1869,Handscroll; ink on paper,17 ft 3 1/2 in. x 10 3/4 in. (516.9 x 27.3 cm),"Fletcher Fund, 1937",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57229,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.119.5,false,true,57231,Asian Art,Wash drawing,,Japan,Meiji period (1868–1912),,,,Artist,,Kawanabe Kyōsai,"Japanese, 1831–1889",,Kawanabe Kyōsai,Japanese,1831,1889,late 19th century,1867,1889,On paper,9 1/8 x 13 1/4 in. (23.2 x 33.7 cm),"Fletcher Fund, 1937",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57231,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.119.6,false,true,57233,Asian Art,Wash drawing,,Japan,Meiji period (1868–1912),,,,Artist,,Kawanabe Kyōsai,"Japanese, 1831–1889",,Kawanabe Kyōsai,Japanese,1831,1889,late 19th century,1867,1889,Ink on paper,6 x 10 1/4 in. (15.2 x 26 cm),"Fletcher Fund, 1937",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57233,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.64a,false,true,54604,Asian Art,Hanging scroll,猿を襲う鷲図|Eagle Attacking a Monkey,Japan,Meiji period (1868–1912),,,,Artist,,Kawanabe Kyōsai,"Japanese, 1831–1889",,Kawanabe Kyōsai,Japanese,1831,1889,1885,1885,1885,Hanging scroll; ink and color on paper,Image: 65 1/2 x 33 in. (166.4 x 83.8 cm) Overall with mounting: 111 1/2 x 43 1/2 in. (283.2 x 110.5 cm) Overall with knobs: 111 1/2 x 47 1/2 in. (283.2 x 120.7 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54604,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.64b,false,true,54605,Asian Art,Hanging scroll,,Japan,Meiji period (1868–1912),,,,Artist,,Kawanabe Kyōsai,"Japanese, 1831–1889",,Kawanabe Kyōsai,Japanese,1831,1889,1885,1885,1885,Hanging scroll; ink and color on paper,Image: 65 1/2 x 33 in. (166.4 x 83.8 cm) Overall with mounting: 111 1/2 x 43 1/2 in. (283.2 x 110.5 cm) Overall with knobs: 111 1/2 x 47 1/2 in. (283.2 x 120.7 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54605,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.64c,false,true,54606,Asian Art,Hanging scroll,,Japan,Meiji period (1868–1912),,,,Artist,,Kawanabe Kyōsai,"Japanese, 1831–1889",,Kawanabe Kyōsai,Japanese,1831,1889,1885,1885,1885,Hanging scroll; ink and color on paper,Image: 65 1/2 x 33 in. (166.4 x 83.8 cm) Overall with mounting: 111 1/2 x 43 1/2 in. (283.2 x 110.5 cm) Overall with knobs: 111 1/2 x 43 1/2 in. (283.2 x 110.5 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54606,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.64d,false,true,54607,Asian Art,Hanging scroll,兎を追う鷲図|Eagle Pursuing Rabbit,Japan,Meiji period (1868–1912),,,,Artist,,Kawanabe Kyōsai,"Japanese, 1831–1889",,Kawanabe Kyōsai,Japanese,1831,1889,1885,1885,1885,Hanging scroll; ink and color on paper,Image: 65 1/2 x 33 in. (166.4 x 83.8 cm) Overall with mounting: 111 1/2 x 43 1/2 in. (283.2 x 110.5 cm) Overall with knobs: 111 1/2 x 47 1/2 in. (283.2 x 120.7 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54607,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.44,false,true,55378,Asian Art,Hanging scroll,,Japan,Meiji period (1868–1912),,,,Artist,,Kawanabe Kyōsai,"Japanese, 1831–1889",,Kawanabe Kyōsai,Japanese,1831,1889,late 19th century,1868,1889,Hanging scroll; ink on silk,49 3/8 x 19 1/2 in. (125.4 x 49.5 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/55378,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.61.1,false,true,54608,Asian Art,Album leaf,白衣観音図|White-Robed Kannon,Japan,Meiji period (1868–1912),,,,Artist,,Kawanabe Kyōsai,"Japanese, 1831–1889",,Kawanabe Kyōsai,Japanese,1831,1889,ca. 1887,1877,1897,Album leaf; ink and color on silk,14 3/8 x 11 in. (36.5 x 27.9 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54608,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.61.2,false,true,54609,Asian Art,Album leaf,松に鴉図|Two Crows on a Pine Branch,Japan,Meiji period (1868–1912),,,,Artist,,Kawanabe Kyōsai,"Japanese, 1831–1889",,Kawanabe Kyōsai,Japanese,1831,1889,ca. 1887,1877,1897,Album leaf; ink and color on silk,14 5/16 x 10 3/4 in. (36.4 x 27.3 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54609,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.61.3,false,true,54610,Asian Art,Album leaf,富士図|Mount Fuji,Japan,Meiji period (1868–1912),,,,Artist,,Kawanabe Kyōsai,"Japanese, 1831–1889",,Kawanabe Kyōsai,Japanese,1831,1889,ca. 1887,1877,1897,Album leaf; ink and color on silk,14 1/8 x 10 3/4 in. (35.9 x 27.3 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54610,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.61.4,false,true,54611,Asian Art,Album leaf,旭に群鴉図|Flock of Crows at Dawn,Japan,Meiji period (1868–1912),,,,Artist,,Kawanabe Kyōsai,"Japanese, 1831–1889",,Kawanabe Kyōsai,Japanese,1831,1889,ca. 1887,1877,1897,Album leaf; ink and color on silk,14 3/8 x 10 1/4 in. (36.5 x 26.0 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54611,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.61.5,false,true,54612,Asian Art,Album leaf,,Japan,Meiji period (1868–1912),,,,Artist,,Kawanabe Kyōsai,"Japanese, 1831–1889",,Kawanabe Kyōsai,Japanese,1831,1889,ca. 1887,1877,1897,Album leaf; ink and color on silk,14 1/4 x 10 3/4 in. (36.2 x 27.3 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54612,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.61.6,false,true,54613,Asian Art,Album leaf,滝に燕図|Swallows by a Waterfall,Japan,Meiji period (1868–1912),,,,Artist,,Kawanabe Kyōsai,"Japanese, 1831–1889",,Kawanabe Kyōsai,Japanese,1831,1889,ca. 1887,1877,1897,Album leaf; ink and color on silk,14 5/16 x 10 1/2 in. (36.4 x 26.7 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54613,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.61.7,false,true,54614,Asian Art,Album leaf,水辺に鴉図|Crow and Reeds by a Stream,Japan,Meiji period (1868–1912),,,,Artist,,Kawanabe Kyōsai,"Japanese, 1831–1889",,Kawanabe Kyōsai,Japanese,1831,1889,ca. 1887,1877,1897,Album leaf; ink and color on silk,14 1/4 x 10 3/4 in. (36.2 x 27.3 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54614,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.61.8,false,true,54615,Asian Art,Album leaf,,Japan,Meiji period (1868–1912),,,,Artist,,Kawanabe Kyōsai,"Japanese, 1831–1889",,Kawanabe Kyōsai,Japanese,1831,1889,ca. 1887,1877,1897,Album leaf; ink and color on silk,14 1/4 x 10 5/8 in. (36.2 x 27 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54615,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.61.9,false,true,54616,Asian Art,Album leaf,,Japan,Meiji period (1868–1912),,,,Artist,,Kawanabe Kyōsai,"Japanese, 1831–1889",,Kawanabe Kyōsai,Japanese,1831,1889,March 1888,1888,1888,Album leaf; ink and color on silk,14 1/4 x 10 7/8 in. (36.2 x 27.6 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54616,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.61.10,false,true,54617,Asian Art,Album leaf,,Japan,Meiji period (1868–1912),,,,Artist,,Kawanabe Kyōsai,"Japanese, 1831–1889",,Kawanabe Kyōsai,Japanese,1831,1889,ca. 1887,1877,1897,Album leaf; ink and color on silk,14 1/8 x 10 3/4 in. (35.9 x 27.3 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54617,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.61.11,false,true,54618,Asian Art,Album leaf,竹に鴉図|Crow on a Bamboo Branch,Japan,Meiji period (1868–1912),,,,Artist,,Kawanabe Kyōsai,"Japanese, 1831–1889",,Kawanabe Kyōsai,Japanese,1831,1889,ca. 1887,1877,1897,Album leaf; ink and color on silk,14 1/4 x 10 1/2 in. (36.2 x 26.7 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54618,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.61.12,false,true,54619,Asian Art,Album leaf,,Japan,Meiji period (1868–1912),,,,Artist,,Kawanabe Kyōsai,"Japanese, 1831–1889",,Kawanabe Kyōsai,Japanese,1831,1889,ca. 1887,1877,1897,Album leaf; ink and color on silk,14 1/4 x 10 3/4 in. (36.2 x 27.3 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54619,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.61.13,false,true,54620,Asian Art,Album leaf,,Japan,Meiji period (1868–1912),,,,Artist,,Kawanabe Kyōsai,"Japanese, 1831–1889",,Kawanabe Kyōsai,Japanese,1831,1889,ca. 1887,1877,1897,Album leaf; ink and color on silk,14 1/4 x 10 3/4 in. (36.2 x 27.3 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54620,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.61.14,false,true,54621,Asian Art,Album leaf,木に鴉図|Crow on a Branch,Japan,Meiji period (1868–1912),,,,Artist,,Kawanabe Kyōsai,"Japanese, 1831–1889",,Kawanabe Kyōsai,Japanese,1831,1889,ca. 1887,1877,1897,Album leaf; ink and color on silk,14 1/4 x 10 3/4 in. (36.2 x 27.3 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54621,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.61.15,false,true,54622,Asian Art,Album leaf,,Japan,Meiji period (1868–1912),,,,Artist,,Kawanabe Kyōsai,"Japanese, 1831–1889",,Kawanabe Kyōsai,Japanese,1831,1889,ca. 1887,1877,1897,Album leaf; ink and color on silk,14 1/4 x 11 1/4 in. (36.2 x 28.6 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54622,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.61.16,false,true,45526,Asian Art,Album leaf,,Japan,Meiji period (1868–1912),,,,Artist,,Kawanabe Kyōsai,"Japanese, 1831–1889",,Kawanabe Kyōsai,Japanese,1831,1889,ca. 1887,1877,1897,Album leaf; ink and color on silk,14 1/4 x 10 1/2 in. (36.2 x 26.7 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45526,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.61.17,false,true,54623,Asian Art,Album leaf,,Japan,Meiji period (1868–1912),,,,Artist,,Kawanabe Kyōsai,"Japanese, 1831–1889",,Kawanabe Kyōsai,Japanese,1831,1889,ca. 1887,1877,1897,Album leaf; ink and color on silk,14 1/4 x 10 7/8 in. (36.2 x 27.6 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54623,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.61.18,false,true,54624,Asian Art,Album leaf,,Japan,Meiji period (1868–1912),,,,Artist,,Kawanabe Kyōsai,"Japanese, 1831–1889",,Kawanabe Kyōsai,Japanese,1831,1889,ca. 1887,1877,1897,Album leaf; ink and color on silk,14 1/4 x 10 3/8 in. (36.2 x 26.4 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54624,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.61.19,false,true,54625,Asian Art,Album leaf,,Japan,Meiji period (1868–1912),,,,Artist,,Kawanabe Kyōsai,"Japanese, 1831–1889",,Kawanabe Kyōsai,Japanese,1831,1889,ca. 1887,1877,1897,Album leaf; ink and color on silk,14 1/4 x 10 7/8 in. (36.2 x 27.6 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54625,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.61.20,false,true,54626,Asian Art,Album leaf,,Japan,Meiji period (1868–1912),,,,Artist,,Kawanabe Kyōsai,"Japanese, 1831–1889",,Kawanabe Kyōsai,Japanese,1831,1889,ca. 1887,1877,1897,Album leaf; ink and color on silk,14 1/8 x 10 5/8 in. (35.9 x 27 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54626,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.61.21,false,true,54627,Asian Art,Album leaf,岩に鴉図|Crow on a Rock,Japan,Meiji period (1868–1912),,,,Artist,,Kawanabe Kyōsai,"Japanese, 1831–1889",,Kawanabe Kyōsai,Japanese,1831,1889,ca. 1887,1877,1897,Album leaf; ink and color on silk,14 1/4 x 10 7/8 in. (36.2 x 27.6 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54627,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.61.22,false,true,54628,Asian Art,Album leaf,,Japan,Meiji period (1868–1912),,,,Artist,,Kawanabe Kyōsai,"Japanese, 1831–1889",,Kawanabe Kyōsai,Japanese,1831,1889,ca. 1887,1877,1897,Album leaf; ink and color on silk,14 1/4 x 10 7/8 in. (36.2 x 27.6 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54628,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.61.23,false,true,54629,Asian Art,Album leaf,瓜に鼠図|Mice in a Melon,Japan,Meiji period (1868–1912),,,,Artist,,Kawanabe Kyōsai,"Japanese, 1831–1889",,Kawanabe Kyōsai,Japanese,1831,1889,ca. 1887,1877,1897,Album leaf; ink and color on silk,14 1/4 x 10 7/8 in. (36.2 x 27.6 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54629,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.61.24,false,true,54630,Asian Art,Album leaf,,Japan,Meiji period (1868–1912),,,,Artist,,Kawanabe Kyōsai,"Japanese, 1831–1889",,Kawanabe Kyōsai,Japanese,1831,1889,ca. 1887,1877,1897,Album leaf; ink and color on silk,14 1/4 x 10 1/2 in. (36.2 x 26.7 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54630,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.61.25,false,true,54631,Asian Art,Album leaf,月に鴉図|Crow and the Moon,Japan,Meiji period (1868–1912),,,,Artist,,Kawanabe Kyōsai,"Japanese, 1831–1889",,Kawanabe Kyōsai,Japanese,1831,1889,ca. 1887,1877,1897,Album leaf; ink and color on silk,14 1/4 x 10 7/8 in. (36.2 x 27.6 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54631,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.61.26,false,true,54632,Asian Art,Album leaf,木に鴉図|Crow on a Branch,Japan,Meiji period (1868–1912),,,,Artist,,Kawanabe Kyōsai,"Japanese, 1831–1889",,Kawanabe Kyōsai,Japanese,1831,1889,ca. 1887,1877,1897,Album leaf; ink and color on silk,14 1/4 x 10 3/4 in. (36.2 x 27.3 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54632,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.61.27,false,true,54633,Asian Art,Album leaf,,Japan,Meiji period (1868–1912),,,,Artist,,Kawanabe Kyōsai,"Japanese, 1831–1889",,Kawanabe Kyōsai,Japanese,1831,1889,ca. 1887,1877,1897,Album leaf; ink and gold on silk,14 1/4 x 10 3/4 in. (36.2 x 27.3 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54633,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.61.28,false,true,54634,Asian Art,Album leaf,,Japan,Meiji period (1868–1912),,,,Artist,,Kawanabe Kyōsai,"Japanese, 1831–1889",,Kawanabe Kyōsai,Japanese,1831,1889,ca. 1887,1877,1897,Album leaf; ink and red on silk,14 1/8 x 10 3/4 in. (35.9 x 27.3 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54634,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.61.29,false,true,54635,Asian Art,Album leaf,ムクドリ図|Starlings on a Branch,Japan,Meiji period (1868–1912),,,,Artist,,Kawanabe Kyōsai,"Japanese, 1831–1889",,Kawanabe Kyōsai,Japanese,1831,1889,ca. 1887,1877,1897,Album leaf; ink and color on silk,14 1/4 x 10 1/2 in. (36.2 x 26.7 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54635,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.61.30,false,true,54636,Asian Art,Album leaf,,Japan,Meiji period (1868–1912),,,,Artist,,Kawanabe Kyōsai,"Japanese, 1831–1889",,Kawanabe Kyōsai,Japanese,1831,1889,ca. 1887,1877,1897,Album leaf; ink and color on silk,14 1/4 x 10 1/2 in. (36.2 x 26.7 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54636,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.61.31,false,true,54637,Asian Art,Album leaf,,Japan,Meiji period (1868–1912),,,,Artist,,Kawanabe Kyōsai,"Japanese, 1831–1889",,Kawanabe Kyōsai,Japanese,1831,1889,ca. 1887,1877,1897,Album leaf; ink and color on silk,14 1/2 x 10 1/2 in. (36.8 x 26.7 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54637,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.61.32,false,true,54638,Asian Art,Album leaf,,Japan,Meiji period (1868–1912),,,,Artist,,Kawanabe Kyōsai,"Japanese, 1831–1889",,Kawanabe Kyōsai,Japanese,1831,1889,ca. 1887,1877,1897,Album leaf; ink and color on silk,14 1/4 x 10 3/4 in. (36.2 x 27.3 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54638,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.61.33,false,true,54639,Asian Art,Album leaf,,Japan,Meiji period (1868–1912),,,,Artist,,Kawanabe Kyōsai,"Japanese, 1831–1889",,Kawanabe Kyōsai,Japanese,1831,1889,ca. 1887,1877,1897,Album leaf; ink and color on silk,14 1/4 x 10 3/4 in. (36.2 x 27.3 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54639,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.61.34,false,true,54640,Asian Art,Album leaf,雪中鴉図|Crow Flying in the Snow,Japan,Meiji period (1868–1912),,,,Artist,,Kawanabe Kyōsai,"Japanese, 1831–1889",,Kawanabe Kyōsai,Japanese,1831,1889,ca. 1887,1877,1897,Album leaf; ink and color on silk,14 1/4 x 10 3/4 in. (36.2 x 27.3 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54640,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.61.35,false,true,54641,Asian Art,Album leaf,,Japan,Meiji period (1868–1912),,,,Artist,,Kawanabe Kyōsai,"Japanese, 1831–1889",,Kawanabe Kyōsai,Japanese,1831,1889,ca. 1887,1877,1897,Album leaf; ink and color on silk,14 1/4 x 10 1/2 in. (36.2 x 26.7 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54641,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.61.36,false,true,54642,Asian Art,Album leaf,柳に鴉図|Crow and Willow Tree,Japan,Meiji period (1868–1912),,,,Artist,,Kawanabe Kyōsai,"Japanese, 1831–1889",,Kawanabe Kyōsai,Japanese,1831,1889,November 1887,1887,1887,Album leaf; ink and color on silk,14 1/4 x 10 1/2 in. (36.2 x 26.7 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54642,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.61.40,false,true,54646,Asian Art,Album leaf,,Japan,Meiji period (1868–1912),,,,Artist,,Hashimoto Gahō,"Japanese, 1835–1908",,Hashimoto Gahō,Japanese,1835,1908,ca. 1885–89,1885,1889,Album leaf; ink and color on silk,Image: 14 1/4 × 10 1/2 in. (36.2 × 26.7 cm) Mat: 22 7/8 × 15 1/2 in. (58.1 × 39.4 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54646,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.61.47,false,true,54677,Asian Art,Album leaf,,Japan,Meiji period (1868–1912),,,,Artist,,Hashimoto Gahō,"Japanese, 1835–1908",,Hashimoto Gahō,Japanese,1835,1908,ca. 1885–89,1885,1889,Album leaf; ink and color on silk,Image: 14 1/4 × 10 3/8 in. (36.2 × 26.4 cm) Mat: 22 7/8 × 15 1/2 in. (58.1 × 39.4 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54677,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.61.55,false,true,54696,Asian Art,Album leaf,,Japan,Meiji period (1868–1912),,,,Artist,,Hashimoto Gahō,"Japanese, 1835–1908",,Hashimoto Gahō,Japanese,1835,1908,ca. 1885–89,1885,1889,Album leaf; ink and color on silk,14 x 10 in. (35.6 x 25.4 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54696,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.61.59,false,true,54701,Asian Art,Album leaf,,Japan,Meiji period (1868–1912),,,,Artist,,Hashimoto Gahō,"Japanese, 1835–1908",,Hashimoto Gahō,Japanese,1835,1908,ca. 1885–89,1885,1889,Album leaf; ink and color on silk,Image: 14 in. × 10 1/2 in. (35.6 × 26.7 cm) Mat: 22 7/8 × 15 1/2 in. (58.1 × 39.4 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54701,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.61.73,false,true,50826,Asian Art,Album leaf,猫に蜘蛛図|Cat Watching a Spider,Japan,Meiji period (1868–1912),,,,Artist,,Ōide Tōkō,"Japanese, 1841–1905",,Ōide Tōkō,Japanese,1841,1905,ca. 1888–92,1888,1892,Album leaf; ink and color on silk,14 3/4 x 11 in. (37.5 x 27.9 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/50826,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -08.148.10,false,true,54476,Asian Art,Panel,,Japan,Meiji period (1868–1912),,,,Artist,,Kawabata Gyokushō,"Japanese, 1842–1913",,Kawabata Gyokushō,Japanese,1842,1913,20th century,1900,1912,Ink and color on silk,51 7/8 x 21 5/8 in. (131.8 x 54.9 cm),"Gift of Francis Lathrop, 1908",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54476,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -08.148.11,false,true,54477,Asian Art,Panel,,Japan,Meiji period (1868–1912),,,,Artist,,Kawabata Gyokushō,"Japanese, 1842–1913",,Kawabata Gyokushō,Japanese,1842,1913,20th century,1900,1912,Ink and color on silk,50 7/16 x 19 11/16 in. (128.1 x 50 cm),"Gift of Francis Lathrop, 1908",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54477,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.61.65,false,true,54708,Asian Art,Album leaf,,Japan,Meiji period (1868–1912),,,,Artist,,Kawabata Gyokushō,"Japanese, 1842–1913",,Kawabata Gyokushō,Japanese,1842,1913,1800,1800,1800,Album leaf; ink and color on silk,14 1/2 x 11 1/8 in. (36.8 x 28.3 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54708,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.61.66,false,true,54709,Asian Art,Album leaf,,Japan,Meiji period (1868–1912),,,,Artist,,Kawabata Gyokushō,"Japanese, 1842–1913",,Kawabata Gyokushō,Japanese,1842,1913,1800,1800,1800,Album leaf; ink and color on silk,14 1/2 x 11 in. (36.8 x 27.9 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54709,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.61.67,false,true,54710,Asian Art,Album leaf,,Japan,Meiji period (1868–1912),,,,Artist,,Kawabata Gyokushō,"Japanese, 1842–1913",,Kawabata Gyokushō,Japanese,1842,1913,1800,1800,1800,Album leaf; ink and color on silk,14 1/2 x 11 1/2 in. (36.8 x 29.2 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54710,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.61.68,false,true,54711,Asian Art,Album leaf,,Japan,Meiji period (1868–1912),,,,Artist,,Kawabata Gyokushō,"Japanese, 1842–1913",,Kawabata Gyokushō,Japanese,1842,1913,1868,1868,1868,Album leaf; ink and color on silk,14 1/2 x 11 in. (36.8 x 27.9 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54711,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.61.85,false,true,54745,Asian Art,Album leaf,,Japan,Meiji period (1868–1912),,,,Artist,,Kawabata Gyokushō,"Japanese, 1842–1913",,Kawabata Gyokushō,Japanese,1842,1913,1887–92,1887,1892,Album leaf; ink on silk,13 5/8 x 10 7/8 in. (34.6 x 27.6 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54745,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.61.86,false,true,54746,Asian Art,Album leaf,,Japan,Meiji period (1868–1912),,,,Artist,,Kawabata Gyokushō,"Japanese, 1842–1913",,Kawabata Gyokushō,Japanese,1842,1913,1887–92,1887,1892,Album leaf; silk,13 1/2 x 11 in. (34.3 x 27.9 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54746,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.61.87,false,true,54747,Asian Art,Album leaf,猫図|Cat Seen from Behind,Japan,Meiji period (1868–1912),,,,Artist,,Kawabata Gyokushō,"Japanese, 1842–1913",,Kawabata Gyokushō,Japanese,1842,1913,1868,1868,1868,Album leaf; ink and color on silk,13 1/2 x 10 7/8 in. (34.3 x 27.6 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54747,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.61.88,false,true,54748,Asian Art,Album leaf,,Japan,Meiji period (1868–1912),,,,Artist,,Kawabata Gyokushō,"Japanese, 1842–1913",,Kawabata Gyokushō,Japanese,1842,1913,1887–92,1887,1892,Album leaf; silk,13 1/2 x 10 7/8 in. (34.3 x 27.6 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54748,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.61.89,false,true,54749,Asian Art,Album leaf,狗児図|A Pair of Puppies,Japan,Meiji period (1868–1912),,,,Artist,,Kawabata Gyokushō,"Japanese, 1842–1913",,Kawabata Gyokushō,Japanese,1842,1913,1868,1868,1868,Album leaf; ink on silk,13 1/2 x 10 7/8 in. (34.3 x 27.6 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54749,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.61.90,false,true,54750,Asian Art,Album leaf,,Japan,Meiji period (1868–1912),,,,Artist,,Kawabata Gyokushō,"Japanese, 1842–1913",,Kawabata Gyokushō,Japanese,1842,1913,1887–92,1887,1892,Album leaf; ink on silk,13 1/2 x 10 7/8 in. (34.3 x 27.6 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54750,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.61.91,false,true,54751,Asian Art,Album leaf,,Japan,Meiji period (1868–1912),,,,Artist,,Kawabata Gyokushō,"Japanese, 1842–1913",,Kawabata Gyokushō,Japanese,1842,1913,1887–92,1887,1892,Album leaf; silk,13 1/2 x 10 7/8 in. (34.3 x 27.6 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54751,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.61.92,false,true,54752,Asian Art,Album leaf,,Japan,Meiji period (1868–1912),,,,Artist,,Kawabata Gyokushō,"Japanese, 1842–1913",,Kawabata Gyokushō,Japanese,1842,1913,1887–92,1887,1892,Album leaf; silk,13 1/2 x 10 7/8 in. (34.3 x 27.6 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54752,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.61.37,false,true,54643,Asian Art,Album leaf,,Japan,Meiji period (1868–1912),,,,Artist,,Watanabe Seitei,"Japanese, 1851–1918",,Watanabe Seitei,Japanese,1851,1918,ca. 1887,1877,1897,Album leaf; ink and color on silk,14 1/4 x 10 7/8 in. (36.2 x 27.6 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54643,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.61.38,false,true,54644,Asian Art,Album leaf,桜に小禽図|Birds on a Flowering Branch,Japan,Meiji period (1868–1912),,,,Artist,,Watanabe Seitei,"Japanese, 1851–1918",,Watanabe Seitei,Japanese,1851,1918,ca. 1887,1877,1897,Album leaf; ink and color on silk,14 1/4 x 10 3/4 in. (36.2 x 27.3 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54644,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.61.39,false,true,54645,Asian Art,Album leaf,,Japan,Meiji period (1868–1912),,,,Artist,,Watanabe Seitei,"Japanese, 1851–1918",,Watanabe Seitei,Japanese,1851,1918,ca. 1887,1877,1897,Album leaf; ink and color on silk,14 1/4 x 10 3/4 in. (36.2 x 27.3 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54645,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.61.41,false,true,54671,Asian Art,Album leaf,,Japan,Meiji period (1868–1912),,,,Artist,,Watanabe Seitei,"Japanese, 1851–1918",,Watanabe Seitei,Japanese,1851,1918,ca. 1887,1877,1897,Album leaf; ink and color on silk,14 3/16 x 10 7/8 in. (36.0 x 27.6 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54671,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.61.42,false,true,54672,Asian Art,Album leaf,,Japan,Meiji period (1868–1912),,,,Artist,,Watanabe Seitei,"Japanese, 1851–1918",,Watanabe Seitei,Japanese,1851,1918,ca. 1887,1877,1897,Album leaf; ink and color on silk,14 1/4 x 10 3/4 in. (36.2 x 27.3 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54672,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.61.43,false,true,54673,Asian Art,Album leaf,,Japan,Meiji period (1868–1912),,,,Artist,,Watanabe Seitei,"Japanese, 1851–1918",,Watanabe Seitei,Japanese,1851,1918,ca. 1887,1877,1897,Album leaf; ink and color on silk,14 1/4 x 10 3/8 in. (36.2 x 26.4 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54673,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.61.44,false,true,54674,Asian Art,Album leaf,,Japan,Meiji period (1868–1912),,,,Artist,,Watanabe Seitei,"Japanese, 1851–1918",,Watanabe Seitei,Japanese,1851,1918,ca. 1887,1877,1897,Album leaf; ink and color on silk,14 1/8 x 10 3/4 in. (35.9 x 27.3 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54674,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.61.45,false,true,54675,Asian Art,Album leaf,,Japan,Meiji period (1868–1912),,,,Artist,,Watanabe Seitei,"Japanese, 1851–1918",,Watanabe Seitei,Japanese,1851,1918,ca. 1887,1877,1897,Album leaf; ink and color on silk,14 1/8 x 10 3/4 in. (35.9 x 27.3 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54675,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.61.46,false,true,54676,Asian Art,Album leaf,,Japan,Meiji period (1868–1912),,,,Artist,,Watanabe Seitei,"Japanese, 1851–1918",,Watanabe Seitei,Japanese,1851,1918,ca. 1887,1877,1897,Album leaf; ink and color on silk,14 1/8 x 10 3/4 in. (35.9 x 27.3 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54676,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.61.48,false,true,54684,Asian Art,Album leaf,,Japan,Meiji period (1868–1912),,,,Artist,,Watanabe Seitei,"Japanese, 1851–1918",,Watanabe Seitei,Japanese,1851,1918,ca. 1887,1877,1897,Album leaf; ink and color on silk,14 1/4 x 10 3/4 in. (36.2 x 27.3 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54684,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.61.52,false,true,54692,Asian Art,Album leaf,,Japan,Meiji period (1868–1912),,,,Artist,,Watanabe Seitei,"Japanese, 1851–1918",,Watanabe Seitei,Japanese,1851,1918,ca. 1887,1877,1897,Album leaf; ink and color on silk,14 x 10 3/4 in. (35.6 x 27.3 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54692,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.61.53,false,true,54694,Asian Art,Album leaf,,Japan,Meiji period (1868–1912),,,,Artist,,Watanabe Seitei,"Japanese, 1851–1918",,Watanabe Seitei,Japanese,1851,1918,ca. 1887,1877,1897,Album leaf; ink and color on silk,14 x 10 1/2 in. (35.6 x 26.7 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54694,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.61.54,false,true,54695,Asian Art,Album leaf,,Japan,Meiji period (1868–1912),,,,Artist,,Watanabe Seitei,"Japanese, 1851–1918",,Watanabe Seitei,Japanese,1851,1918,ca. 1887,1877,1897,Album leaf; ink and color on silk,14 x 10 1/2 in. (35.6 x 26.7 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54695,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.61.56,false,true,54698,Asian Art,Album leaf,,Japan,Meiji period (1868–1912),,,,Artist,,Watanabe Seitei,"Japanese, 1851–1918",,Watanabe Seitei,Japanese,1851,1918,ca. 1887,1877,1897,Album leaf; ink and color on silk,14 x 10 3/4 in. (35.6 x 27.3 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54698,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.61.57,false,true,54699,Asian Art,Album leaf,,Japan,Meiji period (1868–1912),,,,Artist,,Watanabe Seitei,"Japanese, 1851–1918",,Watanabe Seitei,Japanese,1851,1918,ca. 1887,1877,1897,Album leaf; ink and color on silk,14 x 10 3/4 in. (35.6 x 27.3 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54699,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.61.58,false,true,54700,Asian Art,Album leaf,,Japan,Meiji period (1868–1912),,,,Artist,,Watanabe Seitei,"Japanese, 1851–1918",,Watanabe Seitei,Japanese,1851,1918,ca. 1887,1877,1897,Album leaf; ink and color on silk,14 x 10 3/4 in. (35.6 x 27.3 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54700,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.61.60,false,true,54703,Asian Art,Album leaf,,Japan,Meiji period (1868–1912),,,,Artist,,Watanabe Seitei,"Japanese, 1851–1918",,Watanabe Seitei,Japanese,1851,1918,ca. 1887,1877,1897,Album leaf; ink and color on silk,14 x 10 3/4 in. (35.6 x 27.3 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54703,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.61.75,false,true,54735,Asian Art,Album leaf,,Japan,Meiji period (1868–1912),,,,Artist,,Watanabe Seitei,"Japanese, 1851–1918",,Watanabe Seitei,Japanese,1851,1918,ca. 1887,1877,1897,Album leaf; ink and color on silk,13 7/8 x 10 1/2 in. (35.2 x 26.7 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54735,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.61.76,false,true,54736,Asian Art,Album leaf,,Japan,Meiji period (1868–1912),,,,Artist,,Watanabe Seitei,"Japanese, 1851–1918",,Watanabe Seitei,Japanese,1851,1918,ca. 1887,1877,1897,Album leaf; ink and color on silk,13 7/8 x 10 1/2 in. (35.2 x 26.7 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54736,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.61.79,false,true,54739,Asian Art,Album leaf,,Japan,Meiji period (1868–1912),,,,Artist,,Watanabe Seitei,"Japanese, 1851–1918",,Watanabe Seitei,Japanese,1851,1918,ca. 1887,1877,1897,Album leaf; ink and color on silk,13 7/8 x 10 1/2 in. (35.2 x 26.7 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54739,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.61.80,false,true,54740,Asian Art,Album leaf,,Japan,Meiji period (1868–1912),,,,Artist,,Watanabe Seitei,"Japanese, 1851–1918",,Watanabe Seitei,Japanese,1851,1918,ca. 1887,1877,1897,Album leaf; ink and color on silk,13 7/8 x 10 3/8 in. (35.2 x 26.4 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54740,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.61.70,false,true,54714,Asian Art,Album leaf,,Japan,Meiji period (1868–1912),,,,Artist,,Seki Shūkō,"Japanese, 1858–1915",,Seki Shūkō,Japanese,1858,1915,"Summer, 1891",1891,1891,Album leaf; ink and color on silk,14 3/4 x 11 1/4 in. (37.5 x 28.6 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54714,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.61.74,false,true,54734,Asian Art,Album leaf,,Japan,Meiji period (1868–1912),,,,Artist,,Seki Shūkō,"Japanese, 1858–1915",,Seki Shūkō,Japanese,1858,1915,ca. 1890–92,1890,1892,Album leaf; ink and color on silk,14 3/4 x 11 in. (37.5 x 27.9 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54734,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.61.77,false,true,54737,Asian Art,Album leaf,,Japan,Meiji period (1868–1912),,,,Artist,,Seki Shūkō,"Japanese, 1858–1915",,Seki Shūkō,Japanese,1858,1915,1892,1892,1892,Album leaf; ink and color on silk,13 7/8 x 10 1/2 in. (35.2 x 26.7 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54737,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.61.78,false,true,54738,Asian Art,Album leaf,,Japan,Meiji period (1868–1912),,,,Artist,,Seki Shūkō,"Japanese, 1858–1915",,Seki Shūkō,Japanese,1858,1915,1892,1892,1892,Album leaf; silk,13 7/8 x 10 1/2 in. (35.2 x 26.7 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54738,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.61.81,false,true,54741,Asian Art,Album leaf,,Japan,Meiji period (1868–1912),,,,Artist,,Seki Shūkō,"Japanese, 1858–1915",,Seki Shūkō,Japanese,1858,1915,ca. 1890–92,1890,1892,Album leaf; silk,14 1/4 x 10 5/8 in. (36.2 x 27.0 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54741,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.61.82,false,true,54742,Asian Art,Album leaf,,Japan,Meiji period (1868–1912),,,,Artist,,Seki Shūkō,"Japanese, 1858–1915",,Seki Shūkō,Japanese,1858,1915,ca. 1890–92,1890,1892,Album leaf; ink and color silk,14 1/4 x 10 1/2 in. (36.2 x 26.7 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54742,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.61.83,false,true,54743,Asian Art,Album leaf,,Japan,Meiji period (1868–1912),,,,Artist,,Seki Shūkō,"Japanese, 1858–1915",,Seki Shūkō,Japanese,1858,1915,ca. 1890–92,1890,1892,Album leaf; ink and color silk,14 1/4 x 10 5/8 in. (36.2 x 27.0 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54743,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.61.84,false,true,54744,Asian Art,Album leaf,,Japan,Meiji period (1868–1912),,,,Artist,,Seki Shūkō,"Japanese, 1858–1915",,Seki Shūkō,Japanese,1858,1915,ca. 1890–92,1890,1892,Album leaf; ink and color silk,14 1/4 x 10 5/8 in. (36.2 x 27.0 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54744,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.61.93,false,true,54753,Asian Art,Album leaf,,Japan,Meiji period (1868–1912),,,,Artist,,Seki Shūkō,"Japanese, 1858–1915",,Seki Shūkō,Japanese,1858,1915,ca. 1890–92,1890,1892,Album leaf; silk,13 5/8 x 10 3/4 in. (34.6 x 27.3 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54753,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.61.94,false,true,54754,Asian Art,Album leaf,,Japan,Meiji period (1868–1912),,,,Artist,,Seki Shūkō,"Japanese, 1858–1915",,Seki Shūkō,Japanese,1858,1915,ca. 1890–92,1890,1892,Album leaf; silk,13 1/2 x 10 7/8 in. (34.3 x 27.6 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54754,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.61.95,false,true,54755,Asian Art,Album leaf,,Japan,Meiji period (1868–1912),,,,Artist,,Seki Shūkō,"Japanese, 1858–1915",,Seki Shūkō,Japanese,1858,1915,ca. 1890–92,1890,1892,Album leaf; silk,13 1/2 x 10 7/8 in. (34.3 x 27.6 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54755,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.61.96,false,true,54756,Asian Art,Album leaf,,Japan,Meiji period (1868–1912),,,,Artist,,Seki Shūkō,"Japanese, 1858–1915",,Seki Shūkō,Japanese,1858,1915,ca. 1890–92,1890,1892,Album leaf; silk,13 5/8 x 10 7/8 in. (34.6 x 27.6 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54756,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.61.97,false,true,54757,Asian Art,Album leaf,,Japan,Meiji period (1868–1912),,,,Artist,,Seki Shūkō,"Japanese, 1858–1915",,Seki Shūkō,Japanese,1858,1915,ca. 1890–92,1890,1892,Album leaf; silk,13 1/2 x 10 7/8 in. (34.3 x 27.6 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54757,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.61.98,false,true,54758,Asian Art,Album leaf,,Japan,Meiji period (1868–1912),,,,Artist,,Seki Shūkō,"Japanese, 1858–1915",,Seki Shūkō,Japanese,1858,1915,ca. 1890–92,1890,1892,Album leaf; silk,13 1/2 x 10 3/4 in. (34.3 x 27.3 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54758,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.61.99,false,true,54759,Asian Art,Album leaf,,Japan,Meiji period (1868–1912),,,,Artist,,Seki Shūkō,"Japanese, 1858–1915",,Seki Shūkō,Japanese,1858,1915,ca. 1890–92,1890,1892,Album leaf; silk,13 1/2 x 10 7/8 in. (34.3 x 27.6 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54759,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.61.100,false,true,54760,Asian Art,Album leaf,,Japan,Meiji period (1868–1912),,,,Artist,,Seki Shūkō,"Japanese, 1858–1915",,Seki Shūkō,Japanese,1858,1915,ca. 1890–92,1890,1892,Album leaf; silk,13 1/2 x 10 7/8 in. (34.3 x 27.6 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54760,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.61.69,false,true,54712,Asian Art,Album leaf,,Japan,Meiji period (1868–1912),,,,Artist,,Okada Baison,"Japanese, 1864–1913",,Okada Baison,Japanese,1864,1913,ca. 1891–92,1891,1892,Album leaf; ink and color on silk,14 3/4 x 11 1/2 in. (37.5 x 29.2 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54712,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.61.71,false,true,54732,Asian Art,Album leaf,,Japan,Meiji period (1868–1912),,,,Artist,,Okada Baison,"Japanese, 1864–1913",,Okada Baison,Japanese,1864,1913,ca. 1891–92,1891,1892,Album leaf; ink and color on silk,14 5/8 x 11 in. (37.1 x 27.9 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54732,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.61.72,false,true,54733,Asian Art,Album leaf,,Japan,Meiji period (1868–1912),,,,Artist,,Okada Baison,"Japanese, 1864–1913",,Okada Baison,Japanese,1864,1913,ca. 1891–92,1891,1892,Album leaf; ink and color on silk,14 5/8 x 11 in. (37.1 x 27.9 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54733,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.510,false,true,75612,Asian Art,Hanging scroll,,Japan,Taishō period (1912–26),,,,Artist,,Fukuda Kodōjin,"Japanese, 1865–1944",,Fukuda Kodōjin,Japanese,1865,1944,1922,1922,1922,Hanging scroll; ink on paper,Image: 57 3/4 x 12 1/4 in. (146.7 x 31.1 cm) Overall with mounting: 81 x 17 5/16 in. (205.7 x 44 cm) Overall with knobs: 81 x 19 1/4 in. (205.7 x 48.9 cm),"Gift of the Gitter-Yelen Collection, 2009",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/75612,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -53.7.3,false,true,44849,Asian Art,Handscroll,"『妙法蓮華経』「観世音菩薩普門品」|“Universal Gateway,” Chapter 25 of the Lotus Sutra",Japan,Kamakura period (1185–1333),,,,Artist,Calligrapher:,Sugawara Mitsushige,"Japanese, active mid- 13th century",,Sugawara Mitsushige,Japanese,1234,1266,dated 1257,1257,1257,"Handscroll; ink, color, and gold on paper",Overall with mounting: 9 11/16 in. × 30 ft. 8 1/16 in. (24.6 × 934.9 cm),"Purchase, Louisa Eldridge McBurney Gift, 1953",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/44849,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.160.29,false,true,45372,Asian Art,Hanging scroll,玄奘三蔵像|Portrait of Xuanzang (Genjō) with Attendant,Japan,Kamakura period (1185–1333),,,,Artist,In the Style of,Kasuga Motomitsu,"Japanese, active early 11th century",,Kasuga Motomitsu,Japanese,1000,1099,14th century,1300,1333,Hanging scroll; ink and color on silk,Image: 48 3/4 x 29 1/4 in. (123.8 x 74.3 cm) Overall with mounting: 87 1/2 x 37 1/2 in. (222.3 x 95.3 cm) Overall with knobs: 87 1/2 x 39 1/2 in. (222.3 x 100.3 cm),"H. O. Havemeyer Collection, Gift of Horace Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45372,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.29,false,true,73645,Asian Art,Hanging scroll,芦葉達磨図|Bodhidharma Crossing the Yangzi River on a Reed,Japan,Momoyama period (1573–1615),,,,Artist,,Kano Sōshū,"Japanese, 1551–1601",,Kano Sōshū,Japanese,1551,1601,late 16th century,1567,1599,Hanging scroll; ink on paper,Image: 31 3/4 x 12 3/4 in. (80.6 x 32.4 cm) Overall with mounting: 64 x 17 1/2 in. (162.6 x 44.5 cm) Overall with knobs: 64 x 19 1/4 in. (162.6 x 48.9 cm),"Purchase, Friends of Asian Art Gifts, 2007",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/73645,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.11,false,true,54576,Asian Art,Hanging scroll,,Japan,Muromachi period (1392–1573),,,,Artist,Attributed to,Sesshū Tōyō,"Japanese, 1420–1506",,Sesshū Tōyō,Japanese,1420,1506,1480,1334,1573,Hanging scroll; ink on paper,39 1/4 x 16 in. (99.7 x 40.6 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54576,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2002.3,false,true,63956,Asian Art,Hanging scroll,張騫図|The Chinese Explorer Zhang Qian on a Raft,Japan,Muromachi period (1392–1573),,,,Artist,,Maejima Sōyū,active mid-16th century,,Maejima Sōyū,Japanese,1536,1570,mid-16th century,1534,1566,Hanging scroll; ink on paper,20 5/16 x 13 11/16 in. (51.6 x 34.7cm),"Purchase, Mary Livingston Griggs and Mary Griggs Burke Foundation Gift, 2002",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/63956,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -53.123.1,false,true,45643,Asian Art,Hanging scroll,,Japan,Muromachi period (1392–1573),,,,Artist,Attributed to,Shubun,"Japanese, active ca. 1414",,Shubun,Japanese,1414,1414,first half of the 15th century,1400,1424,Hanging scroll; ink and color on paper,37 1/8 x 14 7/16 in. (94.3 x 36.7 cm),"Purchase, Gifts, Bequests, and Funds from various donors, by exchange, 1953",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45643,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -53.123.2,false,true,45644,Asian Art,Hanging scroll,,Japan,Muromachi period (1392–1573),,,,Artist,Attributed to,Shubun,"Japanese, active ca. 1414",,Shubun,Japanese,1414,1414,first half of the 15th century,1400,1424,Hanging scroll; ink and color on paper,36 3/8 x 14 7/16 in. (92.4 x 36.7 cm),"Purchase, Gifts, Bequests, and Funds from various donors, by exchange, 1953",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45644,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2001.385,false,true,60786,Asian Art,Hanging scroll,,Japan,Muromachi period (1392–1573),,,,Artist,,Kano Yukinobu,"Japanese, ca. 1513–1575",,Kano Yukinobu,Japanese,1513,1575,mid-16th century,1534,1566,Hanging scroll; ink and color on paper,18 3/8 x 15 3/4 in. (46.7 x 40 cm) Overall with mounting: 52 5/8 x 20 3/8 in. (133.7 x 51.8 cm),"Purchase, Friends of Asian Art Gifts, in honor of Douglas Dillon, 2001",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/60786,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.268.38,false,true,44857,Asian Art,Hanging scroll,蘭蕙同芳図|Orchids and Rock,Japan,Muromachi period (1392–1573),,,,Artist,,Gyokuen Bonpō,"Japanese, ca. 1348–after 1420",,Gyokuen Bonpo,Japanese,1348,1420,late 14th–early 15th century,1367,1433,Hanging scroll; ink on paper,39 9/16 x 13 1/8 in. (100.5 x 33.4 cm),"The Harry G. C. Packard Collection of Asian Art, Gift of Harry G. C. Packard, and Purchase, Fletcher, Rogers, Harris Brisbane Dick, and Louis V. Bell Funds, Joseph Pulitzer Bequest, and The Annenberg Fund Inc. Gift, 1975",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/44857,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.514,false,true,45326,Asian Art,Hanging scroll,ガマズミに山鵲図|Magpie on Viburnum Branch,Japan,Muromachi period (1392–1573),,,,Artist,,Genga,"Japanese, active early 16th century",", in Song tradition",Genga,Japanese,1500,1550,early 16th century,1500,1533,Hanging scroll; ink and color on paper,Image: 18 1/4 in. × 14 in. (46.3 × 35.5 cm) Overall with mounting: 53 1/2 × 19 1/2 in. (135.9 × 49.5 cm) Overall with knobs: 53 1/2 × 21 1/2 in. (135.9 × 54.6 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45326,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1977.172,false,true,44856,Asian Art,Hanging scroll,芦雁図|Reeds and Geese,Japan,Nanbokuchō period (1336–92),,,,Artist,,Tesshū Tokusai,"Japanese, died 1366",,Tesshū Tokusai,Japanese,1342,1366,"dated 11th month, 1343",1343,1343,One of a pair of hanging scrolls; ink on silk,Image: 43 7/16 x 17 5/16 in. (110.4 x 44 cm),"Purchase, Mrs. Jackson Burke Gift, 1977",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/44856,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.268.37,false,true,44855,Asian Art,Hanging scroll,芦雁図|Reeds and Geese,Japan,Nanbokuchō period (1336–92),,,,Artist,,Tesshū Tokusai,"Japanese, died 1366",,Tesshū Tokusai,Japanese,1342,1366,"dated 11th month, 1343",1343,1343,One of a pair of hanging scrolls; ink on silk,Image: 43 1/2 x 17 3/8 in. (110.5 x 44.1 cm) Overall: 71 3/4 x 20 1/4 in. (182.2 x 51.4 cm),"The Harry G. C. Packard Collection of Asian Art, Gift of Harry G. C. Packard, and Purchase, Fletcher, Rogers, Harris Brisbane Dick, and Louis V. Bell Funds, Joseph Pulitzer Bequest, and The Annenberg Fund Inc. Gift, 1975",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/44855,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.508,false,true,45630,Asian Art,Hanging scroll,,Japan,Nanbokuchō period (1336–92),,,,Artist,In the Style of,Toba Sōjō,"Japanese, 1053–1140",,Toba Sōjō,Japanese,1053,1140,14th century,1336,1392,"Hanging scroll; ink, color, and gold on silk",Image: 32 1/2 × 20 9/16 in. (82.6 × 52.2 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45630,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.268.25,false,true,45606,Asian Art,Hanging scroll,衿羯羅童子像　|Kongara-doji,Japan,Nanbokuchō period (1336–92),,,,Artist,,Ryūshū Shūtaku (Myōtaku),"Japanese, 1307–1388",,Ryūshū Shūtaku,Japanese,1307,1388,1387,1387,1387,Hanging scroll; ink and color on silk,43 x 15 3/4 in. (109.2 x 40 cm),"The Harry G. C. Packard Collection of Asian Art, Gift of Harry G. C. Packard, and Purchase, Fletcher, Rogers, Harris Brisbane Dick, and Louis V. Bell Funds, Joseph Pulitzer Bequest, and The Annenberg Fund Inc. Gift, 1975",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45606,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1993.476,false,true,45230,Asian Art,Hanging scroll,,Japan,Meiji (1868–1912)–Taishō (1912–26) period,,,,Artist,,Tomioka Tessai,"Japanese, 1836–1924",,Tomioka Tessai,Japanese,1836,1924,19th–20th century,1868,1924,Hanging scroll; ink on paper,20 1/4 x 13 1/4 in. (51.4 x 33.7 cm),"Gift of Dr. Yukikazu Iwasa, in honor of Shizuko Iwasa, 1993",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45230,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -52.160.1,false,true,57327,Asian Art,Hanging scroll,,Japan,Late Edo (1615–1868) or Meiji (1868–1912) period,,,,Artist,,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,Japanese,1807,1891,second half of the 19th century,1850,1891,Hanging scroll; ink and color on silk,Image: 35 1/2 x 13 in. (90.2 x 33 cm) Overall with knobs: 70 1/4 x 19 3/4 in. (178.4 x 50.2 cm),"Purchase, Gifts, Bequests, and Funds from various donors, by exchange, 1952",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57327,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -27.69,false,true,40009,Asian Art,Figure,羅漢像|Rakan,Japan,Edo period (1615–1868),,,,Artist,,Shōun Genkei,"Japanese, 1648–1710",,Shōun Genkei,Japanese,1648,1710,1688–95,1688,1695,"One of a set of five hundred; wood with lacquer, gold leaf, and paint",H. 33 1/2 in. (85.1 cm); W. 28 3/4 in. (73 cm); D. 26 1/4 in. (66.7 cm),"Fletcher Fund, 1927",,,,,,,,,,,,Sculpture,,http://www.metmuseum.org/art/collection/search/40009,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"57.157.1, .2",false,true,45693,Asian Art,Pair of folding screens,伝狩野山楽筆　粟に小禽図屏風|Autumn Millet and Small Birds,Japan,Edo period (1615–1868),,,,Artist,Attributed to,Kano Sanraku,"Japanese, 1559–1635",,Kano Sanraku,Japanese,1559,1635,,1559,1635,"Pair of eight-panel foldingscreens; ink, color, and gold on gilt paper",Image (each screen): 33 1/2 x 134 1/2 in. (85.1 x 341.6 cm),"Purchase, Joseph Pulitzer Bequest, 1957",,,,,,,,,,,,Screens,,http://www.metmuseum.org/art/collection/search/45693,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JIB149,false,true,57840,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,After,Kano Tan'yū,"Japanese, 1602–1674",,Kano Tan'yū,Japanese,1602,1674,,1615,1868,Monochrome woodblock print; ink on paper,10 x 7 1/2 in. (25.4 x 19.1 cm),"Bequest of W. Gedney Beatty, 1941",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/57840,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2000.453,false,true,55450,Asian Art,Screen,,Japan,Edo period (1615–1868),,,,Artist,,Kano Yasunobu,"Japanese, 1613–1685",,Kano Yasunobu,Japanese,1613,1685,,1615,1868,"Six-panel screen; ink, color, and gold on gilded paper",Image: 69 3/8 x 146 3/8 in. (176.2 x 371.8 cm),"Gift of James L. Greenfield, in memory of Margaret Greenfield, 2000",,,,,,,,,,,,Screens,,http://www.metmuseum.org/art/collection/search/55450,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -49.35.1,false,true,57344,Asian Art,Screen,,Japan,Edo period (1615–1868),,,,Artist,School of,Ogata Kōrin,"Japanese, 1658–1716",,Kōrin,Japanese,1658,1716,,1615,1868,Six-panel folding screen; color on paper,67 x 12 ft. 7 in. (170.2 x 383.5 cm),"H. O. Havemeyer Collection, Gift of Horace Havemeyer, 1949",,,,,,,,,,,,Screens,,http://www.metmuseum.org/art/collection/search/57344,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.784,false,true,58693,Asian Art,Inrō,,Japan,Edo period (1615–1868),,,,Artist,,Ogawa Haritsu (Ritsuō),"Japanese, 1663–1747",,Ritsuō,Japanese,1663,1747,,1615,1868,"Ceramic, mother-of-pearl, pewter on brown lacquer with sprinkled gold Ojime: bead; agate Netsuke: ivory and wood",2 15/16 x 2 1/8 x 7/8 in. (7.5 x 5.4 x 2.3 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Inrō,,http://www.metmuseum.org/art/collection/search/58693,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.671,false,true,47413,Asian Art,Figure,,Japan,Edo period (1615–1868),,,,Artist,,Ogawa Haritsu (Ritsuō),"Japanese, 1663–1747",,Ritsuō,Japanese,1663,1747,,1615,1868,Lacquered pottery,H. 8 3/4 in. (22.2 cm); W. 9 1/2 in. (24.1 cm); D. 6 1/2 in. (16.5 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Lacquer,,http://www.metmuseum.org/art/collection/search/47413,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.672,false,true,47405,Asian Art,Figure,,Japan,Edo period (1615–1868),,,,Artist,Attributed to,Ogawa Haritsu (Ritsuō),"Japanese, 1663–1747",,Ritsuō,Japanese,1663,1747,,1615,1868,"Ceramic body covered in lacquer, gold foil and covered in lacquer again",H. 9 in. (22.9 cm); W. 9 in. (22.9 cm); D. 7 in. (17.8 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Lacquer,,http://www.metmuseum.org/art/collection/search/47405,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"36.100.161a, b",false,true,56165,Asian Art,Tea caddy,,Japan,Edo period (1615–1868),,,,Artist,Attributed to,Ogawa Haritsu (Ritsuō),"Japanese, 1663–1747",,Ritsuō,Japanese,1663,1747,,1663,1747,Lacquer,H. 3 1/8 in. (7.9 cm); Diam. 2 5/8 in. (6.7 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Lacquer,,http://www.metmuseum.org/art/collection/search/56165,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1996.104,false,true,39581,Asian Art,Screen,,Japan,Edo period (1615–1868),,,,Artist,,Soga Shōhaku,"Japanese, 1730–1781",,Soga Shōhaku,Japanese,1730,1781,,1615,1868,Two-panel folding screen; ink and gold paint on paper,Image: 61 3/4 x 68 3/8 in. (156.8 x 173.7 cm),"Purchase, Barbara and William Karatz Gift and Rogers Fund, 1996",,,,,,,,,,,,Screens,,http://www.metmuseum.org/art/collection/search/39581,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.778,false,true,78680,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Kuwagata Keisai,"Japanese, 1764–1824",,Kuwagata Keisai,Japanese,1764,1824,,1764,1824,Woodblock printed book; ink and color on paper,9 3/4 × 6 7/8 in. (24.7 × 17.5 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78680,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2009.511.1, .2",false,true,75552,Asian Art,Screen,,Japan,Edo period (1615–1868),,,,Artist,,Maruyama Ōshin,"Japanese, 1790–1838",,Maruyama Ōshin,Japanese,1790,1838,,1790,1838,"Pair of six-panel folding screens; ink, color, and gold on paper",Image (each screen): 32 5/16 x 103 3/16 in. (82 x 262.1 cm),"Gift of the Gitter-Yelen Foundation, 2009",,,,,,,,,,,,Screens,,http://www.metmuseum.org/art/collection/search/75552,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JIB81.3,false,true,57681,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,,1615,1868,Woodblock printed book; ink and color on paper,Overall: 9 × 6 1/4 × 3/8 in. (22.9 × 15.9 × 1 cm),"Rogers Fund, 1931",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/57681,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JIB81.6,false,true,57683,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,,1615,1868,Woodblock printed book; ink and color on paper,Overall: 9 × 6 1/4 × 3/8 in. (22.9 × 15.9 × 1 cm),"Rogers Fund, 1931",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/57683,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JIB81.11,false,true,57688,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,,1615,1868,Woodblock printed book; ink and color on paper,Overall: 9 × 6 1/4 × 3/8 in. (22.9 × 15.9 × 1 cm),"Rogers Fund, 1931",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/57688,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JIB111a–k,false,true,57804,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,,1615,1868,Eleven volumes of Woodblock printed books; ink and color on paper,Overall (each volume): 8 15/16 x 6 1/4 x 1/2 in. (22.7 x 15.8 x 1.3 cm) Image: 7 x 4 7/8 in. (17.8 x 12.4 cm),"The Howard Mansfield Collection, Gift of Howard Mansfield, 1936",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/57804,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JIB52,false,true,57652,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,,1615,1868,Ink on paper,4 1/2 × 6 1/2 × 2 3/8 in. (11.4 × 16.5 × 6 cm),"Rogers Fund, 1918",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/57652,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.69,false,true,57975,Asian Art,Panel,,Japan,late Edo period (1615–1868)–early Meiji period (1868–1912) ?,,,,Artist,Style of,Ogawa Haritsu (Ritsuō),"Japanese, 1663–1747",,Ritsuō,Japanese,1663,1747,,1615,1912,Lacquer,L. 67 in. (170.2 cm); W. 8 1/2 in. (21.6 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Lacquer,,http://www.metmuseum.org/art/collection/search/57975,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.70,false,true,50225,Asian Art,Panel,,Japan,late Edo period (1615–1868)–early Meiji period (1868–1912) ?,,,,Artist,Style of,Ogawa Haritsu (Ritsuō),"Japanese, 1663–1747",,Ritsuō,Japanese,1663,1747,,1615,1912,Lacquer,W. 5 3/8 in. (13.7 cm); L. 64 1/2 in. (163.9 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Lacquer,,http://www.metmuseum.org/art/collection/search/50225,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3156,false,true,56709,Asian Art,Print,,Japan,,,,,Artist,,Torii Kiyonobu,"Japanese, 1664–1729",,Torii Kiyonobu,Japanese,1664,1729,,1664,1729,Polychrome woodblock print; ink and color on paper,5 3/4 x 12 1/2 in. (14.6 x 31.8 cm),"Gift of Mrs. Francis Ormond, 1949",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56709,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2346,false,true,54131,Asian Art,Print,,Japan,,,,,Artist,,Teisai Hokuba,"Japanese, 1771–1844",,Teisai Hokuba,Japanese,1771,1844,,1700,1899,Part of an album of woodblock prints (surimono); ink and color on paper,8 7/16 x 5 7/16 in. (21.4 x 13.8 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54131,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3285,false,true,55372,Asian Art,Print,,Japan,,,,,Artist,,Keisai Eisen,"Japanese, 1790–1848",,Keisai Eisen,Japanese,1790,1848,,1790,1848,Polychrome woodblock print; ink and color on paper,Overall: 8 11/16 x 11 15/16 in. (22.1 x 30.3 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55372,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3176,false,true,56726,Asian Art,Print,,Japan,,,,,Artist,,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,Japanese,1807,1891,,1807,1891,Polychrome woodblock print; ink and color on paper,Image: 7 1/2 × 10 1/8 in. (19.1 × 25.7 cm) Mat: 15 1/4 × 22 3/4 in. (38.7 × 57.8 cm),"Gift of Roland Koscherak, 1957",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56726,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3153,false,true,56706,Asian Art,Print,,Japan,,,,,Artist,,Kawanabe Kyōsai,"Japanese, 1831–1889",,Kawanabe Kyōsai,Japanese,1831,1889,,1831,1899,Polychrome woodblock print; ink and color on paper,9 7/8 x 14 1/8 in. (25.1 x 35.9 cm),"Bequest of Ellis G. Seymour, 1949",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56706,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3133,false,true,56681,Asian Art,Woodblock print,,Japan,,,,,Artist,,Tsukioka Yoshitoshi,"Japanese, 1839–1892",,Tsukioka Yoshitoshi,Japanese,1839,1892,,1831,1889,Triptych of polychrome woodblock prints; ink and color on paper,14 1/4 x 29 5/8 in. (36.2 x 75.2 cm),"Gift of Francis M. Weld, 1948",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56681,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2297,false,true,54070,Asian Art,Print,,Japan,,,,,Artist,,Ryūgetsusai Shinkō,"Japanese, active 1810s",,Ryūgetsusai Shinkō,Japanese,1810,1819,,1700,1899,Part of an album of woodblock prints (surimono); ink and color on paper,5 3/16 x 7 1/8 in. (13.2 x 18.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54070,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3397,false,true,55575,Asian Art,Print,,Japan,,,,,Artist,Attributed to,Yumiaki Toriyama,"Japanese, active ca. 1800",,Yumiaki Toriyama,Japanese,1790,1810,,1790,1810,Polychrome woodblock print; ink and color on paper,26 3/4 x 4 3/4 in. (67.9 x 12.1 cm),"Bequest of Julia H. Manges, 1960",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55575,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2367,false,true,54151,Asian Art,Print,,Japan,,,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,,1760,1849,Part of an album of woodblock prints (surimono); ink and color on paper,7 1/2 x 10 1/4 in. (19.1 x 26 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54151,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2929,false,true,56144,Asian Art,Woodblock print,"諸國瀧廻　東都葵ヶ岡の瀧|The Falls at Aoigaoka in the Eastern Capital (Tōto Aoigaoka no taki), from the series A Tour of Waterfalls in Various Provinces (Shokoku taki meguri)",Japan,,,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,,1760,1849,Polychrome woodblock print; ink and color on paper,14 5/8 x 10 1/4 in. (37.1 x 26 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56144,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3146,false,true,56697,Asian Art,Print,冨嶽三十六景　甲州犬目峠|Fuji from Inume (?) Pass,Japan,,,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,,1760,1849,Polychrome woodblock print; ink and color on paper,14 1/4 x 9 1/4 in. (36.2 x 23.5 cm),"Bequest of Ellis G. Seymour, 1949",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56697,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3175,false,true,54227,Asian Art,Print,,Japan,,,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,,1760,1849,Polychrome woodblock print (surimono); ink and color on paper,Image: 7 5/8 × 21 1/16 in. (19.4 × 53.5 cm) Mat: 12 1/2 in. × 37 in. (31.8 × 94 cm),"Gift of Mrs. Henry L. Phillips, in memory of her husband, Henry L. Phillips, 1957",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54227,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1620,false,true,55786,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Shigenaga,1697–1756,,Shigenaga,Japanese,1697,1756,,1697,1756,Polychrome woodblock print; ink and color on paper,H. 11 1/4 in. (28.6 cm); W. 5 7/8 in. (14.9 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55786,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1374,false,true,55355,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Komatsuken,1710–1792,,Komatsuken,Japanese,1710,1792,,1710,1792,Polychrome woodblock print; ink and color on paper,H. 10 9/16 in. (26.8 cm); W. 8 7/16 in. (21.4 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55355,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1790,false,true,56092,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Chōkōsai Eishō,"Japanese, 1793–99",,Chōkōsai Eishō,Japanese,1793,1799,,1792,1801,Polychrome woodblock print; ink and color on paper,H. 14 1/2 in. (36.8 cm); W. 10 1/8 in. (25.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56092,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1791,false,true,56093,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Chōkōsai Eishō,"Japanese, 1793–99",,Chōkōsai Eishō,Japanese,1793,1799,,1792,1801,Monochrome woodblock print; ink on paper,H. 10 5/8 in. (27 cm); W. 7 1/2 in. (19.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56093,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3064,false,true,56541,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Chōkōsai Eishō,"Japanese, 1793–99",,Chōkōsai Eishō,Japanese,1793,1799,,1615,1868,Polychrome woodblock print; ink and color on paper,13 x 8 3/4 in. (33 x 22.2 cm),"Gift of Mrs. Morris Manges, in memory of her husband, Dr. Morris Manges, 1947",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56541,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1591,false,true,55755,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Hishikawa Moronobu,"Japanese, died 1694",,Hishikawa Moronobu,Japanese,,1694,,1625,1694,Monochrome woodblock print; ink on paper,H. 6 1/2 in. (16.5 cm); W. 6 7/16 in. (16.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55755,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1592,false,true,55756,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Hishikawa Moronobu,"Japanese, died 1694",,Hishikawa Moronobu,Japanese,,1694,,1625,1694,Monochrome woodblock print; ink on paper,H. 6 5/16 in. (16 cm); W. 6 7/16 in. (16.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55756,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1593,false,true,55757,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Hishikawa Moronobu,"Japanese, died 1694",,Hishikawa Moronobu,Japanese,,1694,,1625,1694,Monochrome woodblock print; ink on paper,H. 5 1/8 in. (13 cm); 8 13/16 in. (22.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55757,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3048,false,true,56503,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Hishikawa Moronobu,"Japanese, died 1694",,Hishikawa Moronobu,Japanese,,1694,,1615,1868,Monochrome woodblock print; ink on paper,9 3/4 x 13 1/4 in. (24.8 x 33.7 cm),"Gift of Mrs. Morris Manges, in memory of her husband, Dr. Morris Manges, 1947",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56503,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3049,false,true,56504,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Hishikawa Moronobu,"Japanese, died 1694",,Hishikawa Moronobu,Japanese,,1694,,1615,1868,Monochrome woodblock print; ink on paper,9 5/8 x 13 in. (24.4 x 33 cm),"Gift of Mrs. Morris Manges, in memory of her husband, Dr. Morris Manges, 1947",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56504,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3066,false,true,56546,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Hishikawa Moronobu,"Japanese, died 1694",,Hishikawa Moronobu,Japanese,,1694,,1615,1868,Monochrome woodblock print; ink on paper,10 1/4 x 16 3/4 in. (26 x 42.5 cm),"Harris Brisbane Dick Fund and Rogers Fund, 1949",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56546,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3067,false,true,56548,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Hishikawa Moronobu,"Japanese, died 1694",,Hishikawa Moronobu,Japanese,,1694,,1615,1868,Monochrome woodblock print; ink on paper,10 1/2 x 16 1/4 in. (26.7 x 41.3 cm),"Harris Brisbane Dick Fund and Rogers Fund, 1949",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56548,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3068,false,true,56549,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Hishikawa Moronobu,"Japanese, died 1694",,Hishikawa Moronobu,Japanese,,1694,,1615,1868,Monochrome woodblock print; ink on paper,10 1/2 x 16 in. (26.7 x 40.6 cm),"Harris Brisbane Dick Fund and Rogers Fund, 1949",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56549,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1691,false,true,55926,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Kikumaro,"Japanese, died 1830",,Kitagawa Kikumaro,Japanese,,1830,,1789,1829,Polychrome woodblock print; ink and color on paper,H. 13 1/4 in. (33.7 cm); W. 8 3/4 in. (22.2 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55926,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1692,false,true,55927,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Kikumaro,"Japanese, died 1830",,Kitagawa Kikumaro,Japanese,,1830,,1789,1829,Polychrome woodblock print; ink and color on paper,H. 23 1/8 in. (58.7 cm); W. 4 1/4 in. (10.8 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55927,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1089,false,true,55026,Asian Art,Print,青楼美人　六花撰　岡本屋内　重岡|A Courtesan with Morning-glories on the Background,Japan,Edo period (1615–1868),,,,Artist,,Utamaro II,Japanese (died 1831?),,Utamaro II,Japanese,1750,1850,,1615,1831,Polychrome woodblock print; ink and color on paper,H. 14 1/4 in. (36.2 cm); W. 9 3/8 in. (23.8),The Metropolitan Museum of Art,,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55026,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1090,false,true,55027,Asian Art,Print,青楼美人　六花撰　扇屋内花扇|A Courtesan with Wisteria on the Background,Japan,Edo period (1615–1868),,,,Artist,,Utamaro II,Japanese (died 1831?),,Utamaro II,Japanese,1750,1850,,1615,1831,Polychrome woodblock print; ink and color on paper,H. 14 1/8 in. (35.9 cm); W. 9 1/4 in. (23.5 cm),The Metropolitan Museum of Art,,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55027,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3394,false,true,55570,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utamaro II,Japanese (died 1831?),,Utamaro II,Japanese,1750,1850,,1804,1817,Triptych of polychrome woodblock prints; ink and color on paper,15 x 30 in. (38.1 x 76.2 cm),"Bequest of Julia H. Manges, 1960",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55570,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1828,false,true,56114,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ogata Kōrin,"Japanese, 1658–1716",,Kōrin,Japanese,1658,1716,,1658,1716,Polychrome woodblock print (leaf from an album); ink and color on paper,H. 10 in. (25.4 cm); W. 14 3/16 in. (36 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56114,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1829,false,true,56115,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ogata Kōrin,"Japanese, 1658–1716",,Kōrin,Japanese,1658,1716,,1658,1716,Polychrome woodblock print (album leaf); ink and color on paper,H. 10 in. (25.4 cm); W. 14 7/16 in. (36.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56115,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1597,false,true,55761,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonobu,"Japanese, 1664–1729",,Torii Kiyonobu,Japanese,1664,1729,,1664,1729,Polychrome woodblock print; ink and color on paper,H. 12 1/2 in. (31.8 cm); W. 6 in. (15.2 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55761,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1598,false,true,55762,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonobu,"Japanese, 1664–1729",,Torii Kiyonobu,Japanese,1664,1729,,1664,1729,Polychrome woodblock print; ink and color on paper,H. 11 3/4 in. (29.8 cm); W. 5 3/4 in. (14.6 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55762,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1584,false,true,55749,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Nishikawa Sukenobu,"Japanese, 1671–1750",,Nishikawa Sukenobu,Japanese,1671,1750,,1671,1751,Monochrome woodblock print; ink on paper,H. 10 3/16 in. (25.9 cm); W. 14 1/2 in. (36.8 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55749,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1585,false,true,55750,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Nishikawa Sukenobu,"Japanese, 1671–1750",,Nishikawa Sukenobu,Japanese,1671,1750,,1671,1751,Monochrome woodblock print; ink on paper,H. 10 3/16 in. (25.9 cm); W. 14 1/2 in. (36.8 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55750,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3050,false,true,56505,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Nishikawa Sukenobu,"Japanese, 1671–1750",,Nishikawa Sukenobu,Japanese,1671,1750,,1615,1868,Monochrome woodblock print; ink on paper,9 1/4 x 12 3/4 in. (23.5 x 32.4 cm),"Gift of Mrs. Morris Manges, in memory of her husband, Dr. Morris Manges, 1947",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56505,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3051,false,true,56506,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Nishikawa Sukenobu,"Japanese, 1671–1750",,Nishikawa Sukenobu,Japanese,1671,1750,,1615,1868,Monochrome woodblock print; ink on paper,9 1/2 x 12 3/4 in. (24.1 x 32.4 cm),"Gift of Mrs. Morris Manges, in memory of her husband, Dr. Morris Manges, 1947",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56506,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3065,false,true,56544,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Nishikawa Sukenobu,"Japanese, 1671–1750",,Nishikawa Sukenobu,Japanese,1671,1750,,1615,1868,Polychrome woodblock print (hand-colored); ink and color on paper,8 3/4 x 6 1/2 in. (22.2 x 16.5 cm),"Gift of Mrs. Morris Manges, in memory of her husband, Dr. Morris Manges, 1947",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56544,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3405,false,true,55592,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Nishikawa Sukenobu,"Japanese, 1671–1750",,Nishikawa Sukenobu,Japanese,1671,1750,,1671,1751,Monochrome woodblock print; ink on paper,6 1/2 x 5 in. (16.5 x 12.7 cm),"Bequest of Julia H. Manges, 1960",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55592,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1586,false,true,55751,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Okumura Masanobu,"Japanese, 1686–1764",,Masanobu Okumura,Japanese,1686,1764,,1691,1768,Polychrome woodblock print; ink and color on paper,H. 12 1/8 in. (30.8 cm); W. 17 in. (43.2 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55751,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1587,false,true,55752,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Okumura Masanobu,"Japanese, 1686–1764",,Masanobu Okumura,Japanese,1686,1764,,1691,1768,Undivided triptych of polychrome woodblock prints; ink and color on paper,H. 11 3/4 in. (29.8 cm); W. 16 15/16 in. (43 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55752,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1588,false,true,55753,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Okumura Masanobu,"Japanese, 1686–1764",,Masanobu Okumura,Japanese,1686,1764,,1691,1768,Undivided triptych of polychrome woodblock prints; ink and color on paper,H. 11 7/8 in. (30.2 cm); W. 17 3/8 in. (44.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55753,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1595,false,true,55759,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Okumura Masanobu,"Japanese, 1686–1764",,Masanobu Okumura,Japanese,1686,1764,,1691,1768,Monochrome woodblock print; ink on paper,H. 11 1/2 in. (29.2 cm); W. 15 3/4 in. (40 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55759,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1596,false,true,55760,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Okumura Masanobu,"Japanese, 1686–1764",,Masanobu Okumura,Japanese,1686,1764,,1691,1768,Monochrome woodblock print; ink on paper,H. 11 1/2 in. (29.2 cm); W. 15 3/4 in. (40 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55760,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3059,false,true,56534,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Okumura Masanobu,"Japanese, 1686–1764",,Masanobu Okumura,Japanese,1686,1764,,1615,1868,Polychrome woodblock print; ink and color on paper,12 x 7 in. (30.5 x 17.8 cm),"Gift of Mrs. Morris Manges, in memory of her husband, Dr. Morris Manges, 1947",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56534,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3411,false,true,55600,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Okumura Masanobu,"Japanese, 1686–1764",,Masanobu Okumura,Japanese,1686,1764,,1686,1764,Monochrome woodblock print; ink on paper,11 x 12 1/4 in. (27.9 x 31.1 cm),"Bequest of Julia H. Manges, 1960",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55600,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1600,false,true,55764,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyomasu II,"Japanese, 1706–1763",,Torii Kiyomasu II,Japanese,1706,1763,,1706,1763,Polychrome woodblock print; ink and color on paper,H. 12 in. (30.5 cm); W. 5 3/4 in. (14.6 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55764,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3396,false,true,55573,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyomasu II,"Japanese, 1706–1763",,Torii Kiyomasu II,Japanese,1706,1763,,1706,1763,Monochrome woodblock print (probably hand colored); ink and color on paper,12 x 6 1/4 in. (30.5 x 15.9 cm),"Bequest of Julia H. Manges, 1960",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55573,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1225,false,true,55147,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Ishikawa Toyonobu,"Japanese, 1711–1785",,Ishikawa Toyonobu,Japanese,1711,1785,,1711,1785,Polychrome woodblock print; ink and color on paper,H. 14 7/8 in. (37.8 cm); W. 10 5/16 in. (26.2 cm),"Rogers Fund, 1921",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55147,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1611,false,true,55771,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ishikawa Toyonobu,"Japanese, 1711–1785",,Ishikawa Toyonobu,Japanese,1711,1785,,1711,1785,Polychrome woodblock print; ink and color on paper,H. 11 3/8 (28.9 cm); W. 5 5/8 in. (14.3 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55771,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1612,false,true,55773,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ishikawa Toyonobu,"Japanese, 1711–1785",,Ishikawa Toyonobu,Japanese,1711,1785,,1711,1785,Polychrome woodblock print; ink and color on paper,H. 11 3/8 in. (28.9 cm); W. 5 1/4 in. (13.3 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55773,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1613,false,true,55776,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Ishikawa Toyonobu,"Japanese, 1711–1785",,Ishikawa Toyonobu,Japanese,1711,1785,,1711,1785,Polychrome woodblock print; ink and color on paper,H. 14 1/4 in. (36.2 cm); W. 6 in. (15.2 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55776,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1614,false,true,55778,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ishikawa Toyonobu,"Japanese, 1711–1785",,Ishikawa Toyonobu,Japanese,1711,1785,,1711,1785,Polychrome woodblock print; ink and color on paper,H. 27 3/4 in. (70.5 cm); W. 4 3/16 in. (10.6 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55778,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1615,false,true,55779,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Ishikawa Toyonobu,"Japanese, 1711–1785",,Ishikawa Toyonobu,Japanese,1711,1785,,1711,1785,Polychrome woodblock print; ink and color on paper,H. 25 9/16 in. (64.9 cm); W. 4 1/16 in. (10.3 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55779,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3142,false,true,56691,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ishikawa Toyonobu,"Japanese, 1711–1785",,Ishikawa Toyonobu,Japanese,1711,1785,,1711,1785,Polychrome woodblock print; ink and color on paper,11 x 5 1/4 in. (27.9 x 13.3 cm),"Gift of Francis M. Weld, 1948",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56691,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP874,false,true,54568,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,,1725,1770,Polychrome woodblock print; ink and color on paper,H. 7 5/16 in. (18.6 cm); W. 10 3/16 in. (25.9 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54568,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1224,false,true,55146,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,,1725,1770,Polychrome woodblock print; ink and color on paper,H. 9 3/4 in. (24.8 cm); W. 7 7/16 in. (18.9 cm),"Rogers Fund, 1921",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55146,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1506,false,true,41057,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,,1725,1770,Polychrome woodblock print with embossing (karazuri); ink and color on paper,Image: 12 3/4 x 8 1/4 in. (32.4 x 21 cm),"Fletcher Fund, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/41057,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1623,false,true,55790,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,,1725,1770,Polychrome woodblock print; ink and color on paper,H. 17 7/16 in. (44.3 cm); W. 8 1/8 in. (20.6 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55790,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1624,false,true,55791,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,,1725,1770,Polychrome woodblock print; ink and color on paper,H. 10 13/16 in. (27.5 cm); W. 8 1/8 in. (20.6 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55791,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1626,false,true,55792,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,,1725,1770,Polychrome woodblock print; ink and color on paper,H. 11 in. (27.9 cm); W. 8 3/8 in. (21.3 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55792,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1629,false,true,42562,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,,1725,1770,Polychrome woodblock print; ink and color on paper,H. 11 in. (27.9 cm); W. 8 1/8 in. (20.6 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/42562,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1630,false,true,55794,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,,1725,1770,Polychrome woodblock print; ink and color on paper,H. 10 1/4 in. (26 cm); W. 7 13/16 in. (19.8 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55794,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1631,false,true,55795,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,,1725,1770,Polychrome woodblock print; ink and color on paper,H. 9 13/16 in. (24.9 cm); W. 7 5/16 in. (18.6 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55795,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1635,false,true,45071,Asian Art,Print,すだれ貝|The Curtain Clam,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,,1725,1770,Polychrome woodblock print; ink and color on paper,H. 11 1/4 in. (28.6 cm); W. 8 9/16 in. (21.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45071,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1639,false,true,55798,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,,1725,1770,Polychrome woodblock print; ink and color on paper,H. 10 in. (25.4 cm); W. 7 5/8 in. (19.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55798,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1641,false,true,55799,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,,1725,1770,Polychrome woodblock print; ink and color on paper,H. 10 in. (25.4 cm); W. 8 in. (20.3 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55799,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1642,false,true,55800,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,,1725,1770,Polychrome woodblock print; ink and color on paper,H. 10 11/16 in. (27.1 cm); W. 8 1/4 in. (21 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55800,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1645,false,true,45087,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,,1725,1770,Polychrome woodblock print; ink and color on paper,"H. 7 1/4 in. (18.4 cm); W. 12 3/8 in. (31.4 cm) Medium-size block (""chuban"")","H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45087,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1647,false,true,55805,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,,1725,1770,Polychrome woodblock print; ink and color on paper,H. 10 7/16 in. (26.5 cm); W. 7 15/16 in. (20.2 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55805,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1648,false,true,55806,Asian Art,Print,風俗江戸八景　浅草晴嵐|Asakusa Seiran,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,,1725,1770,Polychrome woodblock print; ink and color on paper,H. 10 3/4 in. (27.3 cm); W. 7 13/16 in. (19.8 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55806,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1650,false,true,55807,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,,1725,1770,Polychrome woodblock print; ink and color on paper,H. 10 3/4 in. (27.3 cm); W. 8 7/16 in. (21.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55807,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1652,false,true,55811,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,,1725,1770,Polychrome woodblock print; ink and color on paper,H. 11 in. (27.9 cm); W. 8 1/4 in. (21 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55811,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1653,false,true,55813,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,,1725,1770,Polychrome woodblock print; ink and color on paper,H. 10 15/16 in. (27.8 cm); W. 8 1/16 in. (20.5 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55813,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3020,false,true,56418,Asian Art,Print,"六玉川　「千鳥の玉川　陸奥名所」|“The Jewel River of Plovers, a Famous Place in Mutsu Province,” from the series Six Jewel Rivers (Mu Tamagawa: Chidori no Tamagawa, Mutsu meisho)",Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,,1725,1770,Polychrome woodblock print; ink and color on paper,10 13/16 x 7 13/16 in. (27.5 x 19.8 cm),"Harris Brisbane Dick Fund, 1946",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56418,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3023,false,true,56421,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,,1725,1770,Polychrome woodblock print; ink and color on paper,10 3/4 x 7 7/8 in. (27.3 x 20 cm),"Harris Brisbane Dick Fund, 1946",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56421,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3024,false,true,56422,Asian Art,Print,"Koya no Tamagawa|Boy, Girl and Viewing Glass",Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,,1725,1770,Polychrome woodblock print; ink and color on paper,10 3/4 x 7 3/4 in. (27.3 x 19.7 cm),"Harris Brisbane Dick Fund, 1946",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56422,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3139,false,true,56687,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,,1725,1770,Polychrome woodblock print; ink and color on paper,11 x 8 in. (27.9 x 20.3 cm),"Gift of Francis M. Weld, 1948",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56687,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3403,false,true,55590,Asian Art,Print,梅|Blowing Soap Bubbles Under the Plum Blossom,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,,1725,1770,Polychrome woodblock print; ink and color on paper,11 x 8 in. (27.9 x 20.3 cm),"Bequest of Julia H. Manges, 1960",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55590,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP457,false,true,36910,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,,1726,1792,Polychrome woodblock print; ink and color on paper,12 3/4 x 5 3/4 in. (32.4 x 14.6 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36910,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1353,false,true,55329,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,,1726,1792,Polychrome woodblock print; ink and color on paper,H. 12 11/16 in. (32.2 cm); W. 5 15/16 in. (15.1 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55329,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1444,false,true,55488,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,,1726,1792,Polychrome woodblock print; ink and color on paper,H. 5 1/2 in. (14 cm); W. 6 3/16 in. (15.7 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55488,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1446,false,true,55491,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,,1726,1792,Polychrome woodblock print; ink and color on paper,H. 12 1/2 in. (31.8 cm); W. 5 13/16 in. (14.8 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55491,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1455,false,true,55501,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,,1726,1792,Polychrome woodblock print; ink and color on paper,H. 12 7/16 in. (31.6 cm); 5 7/8 in. (14.9 cm),"Rogers Fund, 1923",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55501,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1751,false,true,56057,Asian Art,Design for a fan,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,,1726,1792,Monochrome woodblock print; ink on paper,H. 9 3/4 in. (24.8 cm); W. 13 1/2 in. (34.3 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56057,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1752,false,true,56058,Asian Art,Design for a fan,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,,1726,1792,Monochrome woodblock print; ink on paper,H. 9 3/4 in. (24.8 cm); W. 13 1/2 in. (34.3 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56058,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1753,false,true,56059,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,,1726,1792,Polychrome woodblock print; ink and color on paper,H. 12 15/16 in. (32.9 cm); W. 5 15/16 in. (15.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56059,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1754,false,true,56060,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,,1726,1792,Polychrome woodblock print; ink and color on paper,H. 12 3/4 in. (32.4 cm); W. 6 in. (15.2 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56060,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1755,false,true,56061,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,,1726,1792,Polychrome woodblock print; ink and color on paper,H. 11 in. (27.9 cm); W. 8 3/8 in. (21.3 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56061,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1756,false,true,56062,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,,1726,1792,Polychrome woodblock print; ink and color on paper,H. 12 3/4 in. (32.4 cm); W. 8 15/16 in. (22.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56062,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1758,false,true,56064,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,,1726,1792,Left-hand sheet of a diptych of polychrome woodblock prints; ink and color on paper,H. 8 7/8 in. (22.5 cm); W. 6 7/16 in. (16.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56064,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3057,false,true,56512,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,,1615,1868,Polychrome woodblock print; ink and color on paper,12 5/8 x 5 3/4 in. (32.1 x 14.6 cm),"Gift of Mrs. Morris Manges, in memory of her husband, Dr. Morris Manges, 1947",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56512,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3058,false,true,56513,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,,1615,1868,Polychrome woodblock print; ink and color on paper,12 x 5 3/8 in. (30.5 x 13.7 cm),"Gift of Mrs. Morris Manges, in memory of her husband, Dr. Morris Manges, 1947",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56513,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3061,false,true,56536,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,,1615,1868,Diptych of polychrome woodblock prints; ink and color on paper,10 1/8 x 7 3/8 in. (25.7 x 18.7 cm),"Gift of Mrs. Morris Manges, in memory of her husband, Dr. Morris Manges, 1947",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56536,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"JP397a, b",false,true,36466,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,,1726,1792,Diptych of polychrome woodblock prints; ink and color on paper,H. 12 1/4 in. (31.1 cm); W. 11 1/4 in. (28.6 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36466,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1601,false,true,55765,Asian Art,Print,新板浮絵忍ヶ岡之圖|Perspective Print: Shinobazu Pond,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Toyoharu,"Japanese, 1735–1814",,Utagawa Toyoharu,Japanese,1735,1814,,1734,1815,Polychrome woodblock print; ink and color on paper,H. 9 3/16 in. (23.3 cm); W. 13 1/2 in. (34.3 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55765,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1602,false,true,55766,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Toyoharu,"Japanese, 1735–1814",,Utagawa Toyoharu,Japanese,1735,1814,,1735,1814,Polychrome woodblock print; ink and color on paper,H. 4 1/2 in. (11.4 cm); W. 12 3/8 in. (31.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55766,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1603,false,true,55767,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Toyoharu,"Japanese, 1735–1814",,Utagawa Toyoharu,Japanese,1735,1814,,1735,1814,Polychrome woodblock print; ink and color on paper,H. 9 3/16 in. (23.3 cm); W. 14 7/8 in. (37.8 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55767,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1604,false,true,55768,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Toyoharu,"Japanese, 1735–1814",,Utagawa Toyoharu,Japanese,1735,1814,,1735,1814,Polychrome woodblock print; ink and color on paper,H. 4 1/2 in. (11.4 cm); W. 12 3/8 in. (31.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55768,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP935,false,true,54805,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kitao Shigemasa,"Japanese, 1739–1820",,Kitao Shigemasa,Japanese,1739,1820,,1739,1820,Polychrome woodblock print; ink and color on paper,H. 9 in. (22.9 cm); W. 14 in. (35.6 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54805,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1622,false,true,55789,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kitao Shigemasa,"Japanese, 1739–1820",,Kitao Shigemasa,Japanese,1739,1820,,1739,1820,Polychrome woodblock print; ink and color on paper,H. 8 3/4 in. (22.2 cm); W. 14 13/16 in. (37.6 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55789,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1726,false,true,56037,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kitao Shigemasa,"Japanese, 1739–1820",,Kitao Shigemasa,Japanese,1739,1820,,1739,1820,Polychrome woodblock print; ink and color on paper,H. 14 9/16 in. (37 cm); W. 9 7/8 in. (25.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56037,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1835,false,true,56120,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Kitao Shigemasa,"Japanese, 1739–1820",,Kitao Shigemasa,Japanese,1739,1820,,1739,1820,Monochrome woodblock print; ink on paper,H. 10 13/16 in. (27.5 cm); W. 7 9/16 in. (19.2 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56120,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2178,false,true,55092,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kitao Shigemasa,"Japanese, 1739–1820",,Kitao Shigemasa,Japanese,1739,1820,,1739,1820,Polychrome woodblock print (surimono); ink and color on paper,8 x 3 9/16 in. (20.3 x 9 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55092,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2505,false,true,56927,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kitao Shigemasa,"Japanese, 1739–1820",,Kitao Shigemasa,Japanese,1739,1820,,1739,1820,Polychrome woodblock print; ink and color on paper,Image: 20 1/8 × 14 1/4 in. (51.1 × 36.2 cm) Mat: 22 3/4 × 27 1/2 in. (57.8 × 69.9 cm),"The Howard Mansfield Collection, Gift of Howard Mansfield, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56927,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1127,false,true,55047,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunkō,"Japanese, 1743–1812",,Katsukawa Shunkō,Japanese,1743,1812,,1743,1812,Polychrome woodblock print; ink and color on paper,H. 12 5/8 in. (32.1 cm); W. 5 5/8 in. (14.3 cm),"Rogers Fund, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55047,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1761,false,true,56066,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunkō,"Japanese, 1743–1812",,Katsukawa Shunkō,Japanese,1743,1812,,1743,1812,Polychrome woodblock print; ink and color on paper,H. 11 7/8 in. (30.2 cm); W. 5 7/16 in. (13.8 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56066,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1762,false,true,56067,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunkō,"Japanese, 1743–1812",,Katsukawa Shunkō,Japanese,1743,1812,,1743,1812,Polychrome woodblock print; ink and color on paper,H. 11 1/2 in. (29.2 cm); W. 5 1/2 in. (14 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56067,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1763,false,true,56068,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunkō,"Japanese, 1743–1812",,Katsukawa Shunkō,Japanese,1743,1812,,1743,1812,Polychrome woodblock print; ink and color on paper,H. 12 7/8 in. (32.7 cm); W. 5 5/8 in. (14.3 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56068,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1764,false,true,56069,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunkō,"Japanese, 1743–1812",,Katsukawa Shunkō,Japanese,1743,1812,,1743,1812,Polychrome woodblock print; ink and color on paper,H. 8 in. (20.3 cm); W. 13 1/4 in. (33.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56069,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3063,false,true,56540,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunkō,"Japanese, 1743–1812",,Katsukawa Shunkō,Japanese,1743,1812,,1615,1868,Polychrome woodblock print; ink and color on paper,15 1/4 x 10 1/8 in. (38.7 x 25.7 cm),"Gift of Mrs. Morris Manges, in memory of her husband, Dr. Morris Manges, 1947",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56540,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1437,false,true,55472,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,,1742,1815,Polychrome woodblock print; ink and color on paper,H. 28 5/16 in. (71.9 cm); W. 4 7/8 in. (12.4 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55472,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1712,false,true,55964,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,,1742,1815,Polychrome woodblock print; ink and color on paper,H. 15 1/16 in. (38.3 cm); W. 9 7/8 in. (25.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55964,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1714,false,true,55968,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,,1742,1815,Polychrome woodblock print; ink and color on paper,H. 9 11/16 in. (24.6 cm); W. 7 1/4 in. (18.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55968,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1716,false,true,56027,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,,1742,1815,Polychrome woodblock print; ink and color on paper,H. 7 1/16 in. (17.9 cm); W. 9 1/2 in. (24.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56027,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1717,false,true,56028,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,,1742,1815,Polychrome woodblock print; ink and color on paper,H. 10 5/16 in. (26.2 cm); W. 7 9/16 in. (19.2 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56028,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1718,false,true,56029,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,,1742,1815,Polychrome woodblock print; ink and color on paper,H. 10 1/16 in. (25.6 cm); W. 7 9/16 in. (19.2 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56029,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1719,false,true,56030,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,,1742,1815,Polychrome woodblock print; ink and color on paper,H. 15 3/8 in. (39.1 cm); W. 10 15/16 in. (27.8 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56030,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1720,false,true,56031,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,,1742,1815,Diptych of polychrome woodblock prints; ink and color on paper,H. 15 1/8 in. (38.4 cm); W. 10 1/8 in. (25.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56031,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1721,false,true,56032,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,,1742,1815,Polychrome woodblock print; ink and color on paper,H. 14 7/8 in. (37.8 cm); w. 9 7/8 in. (25.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56032,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1722,false,true,56033,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,,1742,1815,Polychrome woodblock print; ink and color on paper,H. 14 13/16 in. (37.6 cm); W. 10 1/16 in. (25.6 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56033,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1723,false,true,56034,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,,1742,1815,Polychrome woodblock print; ink and color on paper,H. 15 1/4 in. (38.7 cm); W. 10 5/16 in. (26.2 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56034,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1725,false,true,56036,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,,1742,1815,Polychrome woodblock print; ink and color on paper,H. 14 13/16 in. (37.6 cm); W. 10 in. (25.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56036,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1727,false,true,56038,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,,1742,1815,Polychrome woodblock print; ink and color on paper,H. 27 in. (68.6 cm); W. 4 9/16 in. (11.6 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56038,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1728,false,true,56039,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,,1742,1815,Polychrome woodblock print; ink and color on paper,H. 27 1/8 in. (68.9 cm); W. 4 1/4 in. (10.8 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56039,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1730,false,true,56041,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,,1742,1815,Polychrome woodblock print; ink and color on paper,H. 27 1/8 in. (68.9 cm); W. 4 13/16 in. (12.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56041,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1731,false,true,45270,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,,1742,1815,Polychrome woodblock print (hashira-e); ink and color on paper,H. 27 3/8 in. (69.5 cm); W. 4 13/16 in. (12.2 cm),"H. O. Havemeyer Collection; Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45270,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1732,false,true,56042,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,,1742,1815,Polychrome woodblock print; ink and color on paper,Overall: 28 1/4 x 4 13/16 in. (71.8 x 12.2 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56042,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1733,false,true,56043,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,,1742,1815,Polychrome woodblock print; ink and color on paper,H. 25 5/8 in. (65.1 cm); W. 4 5/8 in. (11.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56043,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1735,false,true,56044,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,,1742,1815,Triptych of polychrome woodblock prints; ink and color on paper,a: H. 14 5/8 in. (37.1 cm); W. 9 11/16 in. (24.6 cm) b: H. 14 5/8 in. (37.1 cm); W. 9 11/16 in. (24.6 cm) c: H. 14 5/8 in. (37.1 cm); W. 9 15/16 in. (25.2 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56044,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2399,false,true,56802,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,,1742,1815,Polychrome woodblock print; ink and color on paper,15 x 9 13/16 in. (38.1 x 24.9 cm),"H. O. Havemeyer Collection, Gift of Mrs. J. Watson Webb, 1930",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56802,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3404,false,true,55591,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,,1742,1815,Polychrome woodblock print; ink and color on paper,10 1/4 x 7 5/8 in. (26 x 19.4 cm),"Bequest of Julia H. Manges, 1960",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55591,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2180,false,true,55094,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Yomo no Utagaki Magao,"Japanese, 1753–1829",,Yomo no Utagaki Magao,Japanese,1753,1829,,1753,1829,Polychrome woodblock print (surimono); ink and color on paper,7 9/16 x 2 3/8 in. (19.2 x 6 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55094,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1222,false,true,37317,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Chōbunsai Eishi,"Japanese, 1756–1829",,Chōbunsai Eishi,Japanese,1756,1829,,1756,1829,Diptych of polychrome woodblock prints; ink and color on paper,H. 14 3/8 in. (36.5 cm); W. 9 1/2 in. (24.1 cm),"Rogers Fund, 1921",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37317,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1781,false,true,56085,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Chōbunsai Eishi,"Japanese, 1756–1829",,Chōbunsai Eishi,Japanese,1756,1829,,1756,1829,Monochrome woodblock print; ink on paper,H. 10 5/8 in. (27 cm); W. 15 3/16 in. (38.6 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56085,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1782,false,true,56086,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Chōbunsai Eishi,"Japanese, 1756–1829",,Chōbunsai Eishi,Japanese,1756,1829,,1756,1829,Monochrome woodblock print; ink on paper,H. 13 5/8 in. (34.6 cm); W. 8 15/16 in. (22.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56086,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1783,false,true,56087,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Chōbunsai Eishi,"Japanese, 1756–1829",,Chōbunsai Eishi,Japanese,1756,1829,,1756,1829,Monochrome woodblock print; ink on paper,H. 14 in. (35.6 cm); W. 10 in. (25.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56087,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1784,false,true,56088,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Chōbunsai Eishi,"Japanese, 1756–1829",,Chōbunsai Eishi,Japanese,1756,1829,,1756,1829,Polychrome woodblock print; ink and color on paper,H. 14 5/8 in. (37.1 cm); W. 9 1/2 in. (24.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56088,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1785,false,true,56089,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Chōbunsai Eishi,"Japanese, 1756–1829",,Chōbunsai Eishi,Japanese,1756,1829,,1756,1829,Monochrome woodblock print; ink on paper,H. 14 3/4 in. (37.5 cm); W. 10 in. (25.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56089,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1787,false,true,56090,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Chōbunsai Eishi,"Japanese, 1756–1829",,Chōbunsai Eishi,Japanese,1756,1829,,1756,1829,Polychrome woodblock print; ink and color on paper,24 1/4 x 4 7/16 in. (61.6 x 11.3cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56090,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2186,false,true,55102,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Chōbunsai Eishi,"Japanese, 1756–1829",,Chōbunsai Eishi,Japanese,1756,1829,,1756,1829,Polychrome woodblock print (surimono); ink and color on paper,4 3/16 x 6 1/4 in. (10.6 x 15.9 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55102,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2398,false,true,56801,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Chōbunsai Eishi,"Japanese, 1756–1829",,Chōbunsai Eishi,Japanese,1756,1829,,1756,1829,Polychrome woodblock print; ink and color on paper,14 13/16 x 9 5/8 in. (37.6 x 24.4 cm),"H. O. Havemeyer Collection, Gift of Mrs. J. Watson Webb, 1930",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56801,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1096a,false,true,55031,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Chōbunsai Eishi,"Japanese, 1756–1829",,Chōbunsai Eishi,Japanese,1756,1829,,1756,1829,Polychrome woodblock print; ink and color on paper,Image: 14 3/4 × 9 7/8 in. (37.5 × 25.1 cm),The Metropolitan Museum of Art,,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55031,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1096b,false,true,639380,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Chōbunsai Eishi,"Japanese, 1756–1829",,Chōbunsai Eishi,Japanese,1756,1829,,1756,1829,Polychrome woodblock print; ink and color on paper,Image: 14 3/4 × 9 3/4 in. (37.5 × 24.8 cm),The Metropolitan Museum of Art,,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/639380,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1818,false,true,54406,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,,1757,1820,Polychrome woodblock print (surimono); ink and color on paper,7 13/16 x 7 1/16 in. (19.8 x 17.9 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54406,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1902,false,true,54441,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",(?),Kubo Shunman,Japanese,1757,1820,,1757,1820,Polychrome woodblock print (surimono); ink and color on paper,5 1/8 x 7 3/16 in. (13 x 18.3 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54441,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1922,false,true,54461,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,,1757,1820,Polychrome woodblock print (surimono); ink and color on paper,8 1/8 x 7 3/16 in. (20.6 x 18.3 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54461,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2032,false,true,54798,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",(?),Kubo Shunman,Japanese,1757,1820,,1757,1820,Polychrome woodblock print (surimono); ink and color on paper,8 1/16 x 7 1/4 in. (20.5 x 18.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54798,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2181,false,true,55097,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,,1780,1850,Polychrome woodblock print (surimono); ink and color on paper,7 5/8 x 6 3/4 in. (19.4 x 17.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55097,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2184,false,true,55100,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,,1615,1868,Polychrome woodblock print (surimono); ink and color on paper,5 9/16 x 7 1/8 in. (14.1 x 18.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55100,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2198,false,true,55115,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,,1757,1820,Polychrome woodblock print (surimono); ink and color on paper,8 5/16 x 5 1/2 in. (21.1 x 14 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55115,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1780,false,true,56084,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shun'ei,"Japanese, 1762–1819",,Katsukawa Shun'ei,Japanese,1762,1819,,1762,1819,Polychrome woodblock print; ink and color on paper,H. 14 3/4 in. (37.5 cm); W. 9 7/16 in. (24 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56084,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3041,false,true,56497,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shun'ei,"Japanese, 1762–1819",,Katsukawa Shun'ei,Japanese,1762,1819,,1762,1819,Polychrome woodblock print; ink and color on paper,14 1/2 x 91/2 in. (36.8 x 24.1 cm),"Gift of Mrs. Morris Manges, in memory of her husband, Dr. Morris Manges, 1947",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56497,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3137,false,true,56685,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shun'ei,"Japanese, 1762–1819",,Katsukawa Shun'ei,Japanese,1762,1819,,1762,1819,Polychrome woodblock print; ink and color on paper,13 3/4 x 9 in. (34.9 x 22.9 cm),"Gift of Francis M. Weld, 1948",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56685,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1008,false,true,54884,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Toyohiro,"Japanese, 1763–1828",,Utagawa Toyohiro,Japanese,1763,1828,,1763,1828,Polychrome woodblock print; ink and color on paper,H. 14 3/8 in. (36.5 cm); W. 10 in. (25.4 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54884,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1819,false,true,56109,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Toyohiro,"Japanese, 1763–1828",,Utagawa Toyohiro,Japanese,1763,1828,,1763,1828,Polychrome woodblock print; ink and color on paper,H. 14 5/8 in. (37.1 cm); W. 9 11/16 in. (24.6 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56109,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1820,false,true,56110,Asian Art,Print,Yahashi Kiho|Sails Returning to Yahashi,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Toyohiro,"Japanese, 1763–1828",,Utagawa Toyohiro,Japanese,1763,1828,,1763,1828,Polychrome woodblock print; ink and color on paper,H. 8 13/16 in. (22.4 cm); W. 5 15/16 in. (15.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56110,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1821,false,true,54407,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Toyohiro,"Japanese, 1763–1828",,Utagawa Toyohiro,Japanese,1763,1828,,1763,1828,Polychrome woodblock print (surimono); ink and color on paper,6 13/16 x 8 1/8 in. (17.3 x 20.6 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54407,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2228,false,true,53994,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Toyohiro,"Japanese, 1763–1828",,Utagawa Toyohiro,Japanese,1763,1828,,1763,1828,Polychrome woodblock print (surimono); ink and color on paper,8 9/16 x 7 9/16 in. (21.7 x 19.2 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/53994,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1741,false,true,56048,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Toyokuni I,"Japanese, 1769–1825",,Utagawa Toyokuni I,Japanese,1769,1825,,1769,1825,Middle sheet of a triptych of polychrome woodblock prints; ink and color on paper,H. 15 1/2 in. (39.4 cm); W. 10 1/8 in. (25.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56048,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1742,false,true,56049,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Toyokuni I,"Japanese, 1769–1825",,Utagawa Toyokuni I,Japanese,1769,1825,,1769,1825,Polychrome woodblock print; ink and color on paper,H. 14 1/8 in. (35.9 cm); W. 10 in. (25.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56049,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1744,false,true,56051,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Toyokuni I,"Japanese, 1769–1825",,Utagawa Toyokuni I,Japanese,1769,1825,,1769,1825,Polychrome woodblock print; ink and color on paper,H. 15 1/8 in. (38.4 cm); W. 10 1/4 in. (26 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56051,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1745,false,true,56052,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Toyokuni I,"Japanese, 1769–1825",,Utagawa Toyokuni I,Japanese,1769,1825,,1769,1825,Polychrome woodblock print; ink and color on paper,H. 14 5/8 in. (37.1 cm); W. 10 1/8 in. (25.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56052,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1748,false,true,56055,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Toyokuni I,"Japanese, 1769–1825",,Utagawa Toyokuni I,Japanese,1769,1825,,1769,1825,Triptych of polychrome woodblock prints; ink and color on paper,A: H. 15 1/16 in. (38.3 cm); W. 9 3/4 in. (24.8 cm) B: H. 15 1/16 in. (38.3 cm); W. 9 13/16 in. (24.9 cm) C: H. 15 1/16 in. (38.3 cm); W. 9 15/16 in. (25.2 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56055,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1831,false,true,56116,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Toyokuni I,"Japanese, 1769–1825",,Utagawa Toyokuni I,Japanese,1769,1825,,1769,1825,Monochrome woodblock print; ink on paper,H. 10 1/2 in. (26.7 cm); W. 7 7/16 in. (18.9 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56116,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1832,false,true,56117,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Toyokuni I,"Japanese, 1769–1825",,Utagawa Toyokuni I,Japanese,1769,1825,,1769,1825,Monochrome woodblock print; ink on paper,H. 10 1/2 in. (26.7 cm); W. 7 1/8 in. (18.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56117,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2211,false,true,53977,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Toyokuni I,"Japanese, 1769–1825",,Utagawa Toyokuni I,Japanese,1769,1825,,1769,1825,Polychrome woodblock print (surimono); ink and color on paper,5 1/2 x 7 3/8 in. (14 x 18.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/53977,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2401,false,true,56804,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Toyokuni I,"Japanese, 1769–1825",,Utagawa Toyokuni I,Japanese,1769,1825,,1769,1825,Diptych of polychrome woodblock prints; ink and color on paper,Diptych; each 15 x 10 1/16 in. (38.1 x 25.6 cm),"H. O. Havemeyer Collection, Gift of Mrs. J. Watson Webb, 1930",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56804,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3062,false,true,56538,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Toyokuni I,"Japanese, 1769–1825",,Utagawa Toyokuni I,Japanese,1769,1825,,1615,1868,Polychrome woodblock print; ink and color on paper,14 3/4 x 9 3/4 in. (37.5 x 24.8 cm),"Gift of Mrs. Morris Manges, in memory of her husband, Dr. Morris Manges, 1947",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56538,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3154,false,true,56707,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Toyokuni I,"Japanese, 1769–1825",,Utagawa Toyokuni I,Japanese,1769,1825,,1769,1825,Polychrome woodblock print; ink and color on paper,10 1/4 x 14 5/8 in. (26 x 37.1 cm),"Gift of Mrs. Francis Ormond, 1949",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56707,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3155,false,true,56708,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Toyokuni I,"Japanese, 1769–1825",,Utagawa Toyokuni I,Japanese,1769,1825,,1769,1825,Polychrome woodblock print; ink and color on paper,10 x 14 in. (25.4 x 35.6 cm),"Gift of Mrs. Francis Ormond, 1949",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56708,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3392,false,true,55569,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,Woodblock for a print designed by,Utagawa Toyokuni I,"Japanese, 1769–1825",,Utagawa Toyokuni I,Japanese,1769,1825,,1615,1868,Polychrome woodblock print; ink and color on paper,14 x 8 7/8 in. (35.6 x 22.5 cm),"Gift of Mr. and Mrs. Arthur J. Steel, 1960",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55569,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1830,false,true,54427,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Teisai Hokuba,"Japanese, 1771–1844",,Teisai Hokuba,Japanese,1771,1844,,1771,1844,Polychrome woodblock print (surimono); ink and color on paper,5 1/2 x 11 1/8 in. (14 x 28.3 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54427,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2194,false,true,55110,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Teisai Hokuba,"Japanese, 1771–1844",,Teisai Hokuba,Japanese,1771,1844,,1771,1844,Polychrome woodblock print (surimono); ink and color on paper,8 1/8 x 7 5/16 in. (20.6 x 18.6 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55110,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2216,false,true,53982,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Teisai Hokuba,"Japanese, 1771–1844",,Teisai Hokuba,Japanese,1771,1844,,1771,1844,Polychrome woodblock print (surimono); ink and color on paper,8 7/16 x 7 5/8 in. (21.4 x 19.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/53982,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2161,false,true,55068,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Harukawa Goshichi,"Japanese, 1776–1831",,Harukawa Goshichi,Japanese,1776,1831,,1776,1831,Polychrome woodblock print (surimono); ink and color on paper,8 9/16 x 7 7/16 in. (21.7 x 18.9 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55068,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1099,false,true,54328,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kita Busei,"Japanese, 1776–1856",,Kita Busei,Japanese,1776,1856,,1776,1856,Polychrome woodblock print (surimono); ink and color on paper,7 3/16 x 10 3/4 in. (18.3 x 27.3 cm),"Rogers Fund, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54328,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3054a–e,false,true,56509,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Toyokuni II,"Japanese, 1777–1835",,Utagawa Toyokuni II,Japanese,1777,1835,,1615,1868,Pentaptych of polychrome woodblock prints; ink and color on paper,a: 15 1/2 x 10 1/4 in. (39.4 x 26 cm); b: 15 1/4 x 10 in. (38.7 x 25.4 cm); c: 15 3/8 x 10 1/2 in. (39.1 x 26.7 cm); d: 15 3/8 x 10 1/8 in. (39.1 x 25.7 cm); e: 15 1/4 x 10 1/2 in. (38.7 x 26.7 cm),"Gift of Mrs. Morris Manges, in memory of her husband, Dr. Morris Manges, 1947",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56509,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1034,false,true,54319,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Totoya Hokkei,"Japanese, 1780–1850",,Totoya Hokkei,Japanese,1780,1850,,1780,1850,Polychrome woodblock print (surimono); ink and color on paper,8 1/4 x 7 1/8 in. (21 x 18.1 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54319,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1466,false,true,55516,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Totoya Hokkei,"Japanese, 1780–1850",,Totoya Hokkei,Japanese,1780,1850,,1780,1850,Polychrome woodblock print; ink and color on paper,H. 10 1/2 in. (26.7 cm); W. 7 1/2 in. (19.1 cm),"Rogers Fund, 1925",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55516,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1467,false,true,55517,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Totoya Hokkei,"Japanese, 1780–1850",,Totoya Hokkei,Japanese,1780,1850,,1780,1850,Polychrome woodblock print; ink and color on paper,H. 10 1/2 in. (26.7 cm); W. 8 in. (20.3 cm),"Rogers Fund, 1925",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55517,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1810,false,true,54398,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Totoya Hokkei,"Japanese, 1780–1850",,Totoya Hokkei,Japanese,1780,1850,,1780,1850,Diptych of polychrome woodblock prints (surimono); ink and color on paper,Each print: 8 1/4 x 7 1/8 in. (21 x 18.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54398,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1812,false,true,54400,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Totoya Hokkei,"Japanese, 1780–1850",,Totoya Hokkei,Japanese,1780,1850,,1780,1850,Polychrome woodblock print (surimono); ink and color on paper,8 5/16 x 7 3/16 in. (21.1 x 18.3 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54400,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1813,false,true,54401,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Totoya Hokkei,"Japanese, 1780–1850",,Totoya Hokkei,Japanese,1780,1850,,1780,1850,Polychrome woodblock print (surimono); ink and color on paper,8 1/8 x 6 13/16 in. (20.6 x 17.3 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54401,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1871,false,true,54436,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Totoya Hokkei,"Japanese, 1780–1850",,Totoya Hokkei,Japanese,1780,1850,,1780,1850,Polychrome woodblock print (surimono); ink and color on paper,8 1/4 x 7 1/8 in. (21 x 18.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54436,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1915,false,true,54454,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Totoya Hokkei,"Japanese, 1780–1850",,Totoya Hokkei,Japanese,1780,1850,,1780,1850,Polychrome woodblock print (surimono); ink and color on paper,5 5/8 x 7 1/2 in. (14.3 x 19.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54454,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1937,false,true,54500,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Totoya Hokkei,"Japanese, 1780–1850",,Totoya Hokkei,Japanese,1780,1850,,1780,1850,Polychrome woodblock print (surimono); ink and color on paper,8 x 7 5/16 in. (20.3 x 18.6 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54500,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2185,false,true,55101,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Totoya Hokkei,"Japanese, 1780–1850",,Totoya Hokkei,Japanese,1780,1850,,1780,1850,Polychrome woodblock print (surimono); ink and color on paper,5 5/8 x 7 11/16 in. (14.3 x 19.5 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55101,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2219,false,true,53985,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Totoya Hokkei,"Japanese, 1780–1850",,Totoya Hokkei,Japanese,1780,1850,,1780,1850,Polychrome woodblock print (surimono); ink and color on paper,8 3/16 x 7 1/4 in. (20.8 x 18.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/53985,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2222,false,true,53988,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Totoya Hokkei,"Japanese, 1780–1850",,Totoya Hokkei,Japanese,1780,1850,,1780,1850,Polychrome woodblock print (surimono); ink and color on paper,5 1/2 x 7 1/4 in. (14 x 18.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/53988,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1092.1,false,true,58198,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,,1786,1854,Polychrome woodblock print; ink and color on paper,Image: 14 5/8 × 10 1/8 in. (37.1 × 25.7 cm),The Metropolitan Museum of Art,,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58198,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1092.2,false,true,58199,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,,1786,1854,Polychrome woodblock print; ink and color on paper,Image: 14 in. × 10 1/16 in. (35.6 × 25.6 cm),The Metropolitan Museum of Art,,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58199,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1092.3,false,true,58200,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,,1786,1854,Polychrome woodblock print; ink and color on paper,Image: 14 1/8 × 10 1/8 in. (35.9 × 25.7 cm),The Metropolitan Museum of Art,,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58200,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1092.4,false,true,58201,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,,1786,1854,Polychrome woodblock print; ink and color on paper,Image: 14 5/8 × 9 7/8 in. (37.1 × 25.1 cm),The Metropolitan Museum of Art,,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58201,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1092.5,false,true,58202,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,,1786,1854,Polychrome woodblock print; ink and color on paper,Image: 14 5/8 in. × 10 in. (37.1 × 25.4 cm),The Metropolitan Museum of Art,,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58202,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1092.6,false,true,58203,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,,1786,1854,Polychrome woodblock print; ink and color on paper,Image: 14 1/8 × 9 7/8 in. (35.9 × 25.1 cm),The Metropolitan Museum of Art,,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58203,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1092.7,false,true,58204,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,,1786,1854,Polychrome woodblock print; ink and color on paper,Image: 14 1/16 × 9 7/8 in. (35.7 × 25.1 cm),The Metropolitan Museum of Art,,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58204,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1092.8,false,true,58205,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,,1786,1854,Polychrome woodblock print; ink and color on paper,Image: 14 5/8 in. × 10 in. (37.1 × 25.4 cm),The Metropolitan Museum of Art,,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58205,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1092.9,false,true,58206,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,,1786,1854,Polychrome woodblock print; ink and color on paper,Image: 14 1/8 × 9 7/8 in. (35.9 × 25.1 cm),The Metropolitan Museum of Art,,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58206,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1092.10,false,true,58207,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,,1786,1854,Polychrome woodblock print; ink and color on paper,Image: 14 3/8 × 9 7/8 in. (36.5 × 25.1 cm),The Metropolitan Museum of Art,,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58207,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1092.11,false,true,58208,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,,1786,1854,Polychrome woodblock print; ink and color on paper,Image: 14 1/4 × 9 7/8 in. (36.2 × 25.1 cm),The Metropolitan Museum of Art,,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58208,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1092.12,false,true,58209,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,,1786,1854,Polychrome woodblock print; ink and color on paper,Image: 14 5/8 × 10 1/16 in. (37.1 × 25.6 cm),The Metropolitan Museum of Art,,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58209,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1092.13,false,true,58210,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,,1786,1854,Polychrome woodblock print; ink and color on paper,Image: 14 3/8 × 9 7/8 in. (36.5 × 25.1 cm),The Metropolitan Museum of Art,,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58210,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1092.14,false,true,58211,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,,1786,1854,Polychrome woodblock print; ink and color on paper,Image: 14 1/4 in. × 10 in. (36.2 × 25.4 cm),The Metropolitan Museum of Art,,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58211,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1092.15,false,true,58212,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,,1786,1854,Polychrome woodblock print; ink and color on paper,Image: 14 1/16 × 9 3/4 in. (35.7 × 24.8 cm),The Metropolitan Museum of Art,,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58212,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1092.16,false,true,58213,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,,1786,1854,Polychrome woodblock print; ink and color on paper,Image: 14 1/2 × 9 3/4 in. (36.8 × 24.8 cm),The Metropolitan Museum of Art,,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58213,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1092.17,false,true,58214,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,,1786,1854,Polychrome woodblock print; ink and color on paper,Image: 14 7/8 in. × 10 in. (37.8 × 25.4 cm),The Metropolitan Museum of Art,,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58214,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1092.18,false,true,58215,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,,1786,1854,Polychrome woodblock print; ink and color on paper,Image: 13 15/16 × 9 3/8 in. (35.4 × 23.8 cm),The Metropolitan Museum of Art,,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58215,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1092.19,false,true,58216,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,,1786,1854,Polychrome woodblock print; ink and color on paper,Image: 14 1/4 × 9 7/8 in. (36.2 × 25.1 cm),The Metropolitan Museum of Art,,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58216,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1092.20,false,true,58217,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,,1786,1854,Polychrome woodblock print; ink and color on paper,Image: 14 1/4 × 9 7/8 in. (36.2 × 25.1 cm),The Metropolitan Museum of Art,,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58217,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1092.21,false,true,58218,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,,1786,1854,Polychrome woodblock print; ink and color on paper,Image: 14 3/16 × 9 3/4 in. (36 × 24.8 cm),The Metropolitan Museum of Art,,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58218,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1092.22,false,true,58219,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,,1786,1854,Polychrome woodblock print; ink and color on paper,Image: 14 3/16 in. × 10 in. (36 × 25.4 cm),The Metropolitan Museum of Art,,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58219,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1092.23,false,true,58220,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,,1786,1854,Polychrome woodblock print; ink and color on paper,Image: 14 1/4 × 10 1/8 in. (36.2 × 25.7 cm),The Metropolitan Museum of Art,,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58220,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1092.24,false,true,58221,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,,1786,1854,Polychrome woodblock print; ink and color on paper,Image: 14 3/4 × 10 1/8 in. (37.5 × 25.7 cm),The Metropolitan Museum of Art,,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58221,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1092.25,false,true,58222,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,,1786,1854,Polychrome woodblock print; ink and color on paper,Image: 14 3/4 × 10 1/8 in. (37.5 × 25.7 cm),The Metropolitan Museum of Art,,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58222,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1092.26,false,true,58223,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,,1786,1854,Polychrome woodblock print; ink and color on paper,Image: 14 7/8 × 10 1/8 in. (37.8 × 25.7 cm),The Metropolitan Museum of Art,,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58223,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1092.27,false,true,58224,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,,1786,1854,Polychrome woodblock print; ink and color on paper,Image: 14 1/8 × 9 7/8 in. (35.9 × 25.1 cm),The Metropolitan Museum of Art,,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58224,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1092.28,false,true,58225,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,,1786,1854,Polychrome woodblock print; ink and color on paper,Image: 14 1/4 in. × 10 in. (36.2 × 25.4 cm),The Metropolitan Museum of Art,,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58225,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1092.29,false,true,58226,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,,1786,1854,Polychrome woodblock print; ink and color on paper,Image: 14 1/4 × 9 3/4 in. (36.2 × 24.8 cm),The Metropolitan Museum of Art,,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58226,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1092.30,false,true,58227,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,,1786,1854,Polychrome woodblock print; ink and color on paper,Image: 14 1/8 × 9 7/8 in. (35.9 × 25.1 cm),The Metropolitan Museum of Art,,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58227,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1092.31,false,true,58228,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,,1786,1854,Polychrome woodblock print; ink and color on paper,Image: 14 in. × 10 1/16 in. (35.6 × 25.6 cm),The Metropolitan Museum of Art,,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58228,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1092.32,false,true,58229,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,,1786,1854,Polychrome woodblock print; ink and color on paper,Image: 14 5/8 in. × 10 in. (37.1 × 25.4 cm),The Metropolitan Museum of Art,,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58229,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1092.33,false,true,58230,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,,1786,1854,Polychrome woodblock print; ink and color on paper,Image: 14 1/16 × 9 7/8 in. (35.7 × 25.1 cm),The Metropolitan Museum of Art,,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58230,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1092.34,false,true,58231,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,,1786,1854,Polychrome woodblock print; ink and color on paper,Image: 13 5/8 × 9 7/8 in. (34.6 × 25.1 cm),The Metropolitan Museum of Art,,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58231,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1092.35,false,true,58232,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,,1786,1854,Polychrome woodblock print; ink and color on paper,Image: 14 in. × 10 1/16 in. (35.6 × 25.6 cm),The Metropolitan Museum of Art,,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58232,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1092.36,false,true,58233,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,,1786,1854,Polychrome woodblock print; ink and color on paper,Image: 14 3/4 in. × 10 in. (37.5 × 25.4 cm),The Metropolitan Museum of Art,,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58233,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1092.37,false,true,58234,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,,1786,1854,Polychrome woodblock print; ink and color on paper,Image: 14 1/4 × 9 1/2 in. (36.2 × 24.1 cm),The Metropolitan Museum of Art,,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58234,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1092.38,false,true,58235,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,,1786,1854,Polychrome woodblock print; ink and color on paper,Image: 14 3/8 × 9 7/8 in. (36.5 × 25.1 cm),The Metropolitan Museum of Art,,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58235,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1092.39,false,true,58237,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,,1786,1854,Polychrome woodblock print; ink and color on paper,Image: 14 1/8 in. × 10 in. (35.9 × 25.4 cm),The Metropolitan Museum of Art,,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58237,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1092.40,false,true,58239,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,,1786,1854,Polychrome woodblock print; ink and color on paper,Image: 14 3/8 in. × 10 in. (36.5 × 25.4 cm),The Metropolitan Museum of Art,,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58239,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1795,false,true,56097,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kikugawa Eizan,"Japanese, 1787–1867",,Kikugawa Eizan,Japanese,1787,1867,,1787,1867,Polychrome woodblock print; ink and color on paper,H. 14 15/16 in. (37.9 cm); W. 10 3/4 in. (27.3 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56097,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1796,false,true,56099,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kikugawa Eizan,"Japanese, 1787–1867",,Kikugawa Eizan,Japanese,1787,1867,,1787,1867,Polychrome woodblock print; ink and color on paper,H. 24 1/16 in. (61.1 cm); W. 4 1/4 in. (10.8 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56099,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1797,false,true,56100,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kikugawa Eizan,"Japanese, 1787–1867",,Kikugawa Eizan,Japanese,1787,1867,,1787,1867,Polychrome woodblock print; ink and color on paper,24 x 4 1/16 in. (61 x 10.3cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56100,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1798,false,true,56101,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kikugawa Eizan,"Japanese, 1787–1867",,Kikugawa Eizan,Japanese,1787,1867,,1787,1867,Polychrome woodblock print; ink and color on paper,H. 23 9/16 in. (59.8 cm); W. 3 15/16 in. 910 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56101,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1799,false,true,56102,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kikugawa Eizan,"Japanese, 1787–1867",,Kikugawa Eizan,Japanese,1787,1867,,1787,1867,Triptych of polychrome woodblock prints; ink and color on paper,H. 14 5/8 in. (37.1 cm); W. 9 7/8 in (25.1 cm) H. 14 5/8 in. (37.1 cm); W. 9 13/16 in. (24.9 cm) H. 14 5/8 in. (37.1 cm); W. 10 1/8 in. (25.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56102,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3055,false,true,56510,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kikugawa Eizan,"Japanese, 1787–1867",,Kikugawa Eizan,Japanese,1787,1867,,1615,1868,Triptych of polychrome woodblock prints; ink and color on paper,11 5/8 x 16 5/8 in. (29.5 x 42.2 cm),"Gift of Mrs. Morris Manges, in memory of her husband, Dr. Morris Manges, 1947",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56510,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP554,false,true,37005,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Keisai Eisen,"Japanese, 1790–1848",,Keisai Eisen,Japanese,1790,1848,,1790,1848,Polychrome woodblock print; ink and color on paper,10 x 15 in. (25.4 x 38.1 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37005,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JIB170,false,true,57861,Asian Art,Prints,"「傾城道中双六　見立よしはら五十三つい」|Album of prints from the series A Tōkaidō Board Game of Courtesans, Fifty-three Pairings in the Yoshiwara (Keisei dōchū sugoroku, Mitate Yoshiwara gojūsan tsui)",Japan,Edo period (1615–1868),,,,Artist,,Keisai Eisen,"Japanese, 1790–1848",,Keisai Eisen,Japanese,1790,1848,,1790,1848,Fifty-six polychrome woodblock prints mounted as an album; ink and color on paper,11 in. × 14 3/4 in. (27.9 × 37.5 cm) Image (each): 9 3/8 × 13 1/4 in. (23.8 × 33.7 cm),"Gift of Suizan Miki, 1952",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57861,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1229,false,true,55150,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Keisai Eisen,"Japanese, 1790–1848",,Keisai Eisen,Japanese,1790,1848,,1790,1848,Polychrome woodblock print; ink and color on paper,H. 13 1/2 in. (34.3 cm); W. 8 11/16 in. (22.1 cm),"Rogers Fund, 1921",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55150,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1822,false,true,56111,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Keisai Eisen,"Japanese, 1790–1848",,Keisai Eisen,Japanese,1790,1848,,1790,1848,Polychrome woodblock print; ink and color on paper,H. 14 3/16 in. (36 cm); W. 8 13/16 in. (22.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56111,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1823,false,true,54424,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Keisai Eisen,"Japanese, 1790–1848",,Keisai Eisen,Japanese,1790,1848,,1790,1848,Polychrome woodblock print (surimono); ink and color on paper,8 1/16 x 7 3/16 in. (20.5 x 18.3 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54424,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1301,false,true,42651,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Ichikawa Danjuro VII,"Japanese, 1791–1859",,Danjuro VII,Japanese,1791,1859,,1790,1860,Polychrome woodblock print (surimono); ink and color on paper,8 x 7 in. (20.3 x 17.8 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/42651,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1833,false,true,56118,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kuniyasu,"Japanese, 1794–1834",,Utagawa Kuniyasu,Japanese,1794,1834,,1794,1834,Monochrome woodblock print; ink on paper,H. 10 5/8 in. (27 cm); W. 7 7/16 in. (18.9 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56118,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1431,false,true,54390,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Kiitsu,"Japanese, 1796–1858",,Suzuki Kiitsu,Japanese,1796,1858,,1798,1810,Polychrome woodblock print (surimono); ink and color on paper,8 1/8 x 7 3/16 in. (20.6 x 18.3 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54390,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1804,false,true,54397,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kuniyoshi,"Japanese, 1797–1861",,Utagawa Kuniyoshi,Japanese,1797,1861,,1797,1861,Polychrome woodblock print (surimono); ink and color on paper,8 1/16 x 7 1/8 in. (20.5 x 18.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54397,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP634,false,true,37085,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige II,"Japanese, 1829–1869",,Utagawa Hiroshige II,Japanese,1829,1869,,1829,1869,Polychrome woodblock print; ink and color on paper,8 7/16 x 13 1/2 in. (21.4 x 34.3 cm),"Purchase, Joseph Pulitzer Bequest, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37085,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1128,false,true,55048,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige II,"Japanese, 1829–1869",,Utagawa Hiroshige II,Japanese,1829,1869,,1829,1869,Polychrome woodblock print; ink and color on paper,H. 14 3/8 in. (36.5 cm); W. 9 1/2 in. (24.1 cm),"Rogers Fund, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55048,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP976,false,true,54856,Asian Art,Print,青楼歌舞妓やつし画尽　十番続|The Oiran Yoyogiku of Matsubaya Standing under a Cherry Tree,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,,1754,1806,Polychrome woodblock print; ink and color on paper,H. 15 1/8 in. (38.4 cm); W. 9 7/8 in. (25.1 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54856,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP995,false,true,54874,Asian Art,Print,忠臣蔵二段目|A Young Man at the Side of a House,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,,1754,1806,Polychrome woodblock print; ink and color on paper,H. 15 in. (38.1 cm); W. 10 3/8 in. (26.4 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54874,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP997,false,true,54875,Asian Art,Print,忠臣蔵五段目|A Woman Snatching a Bag of Sweetmeats from Her MotHer,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,,1754,1806,Polychrome woodblock print; ink and color on paper,H. 14 1/8 in. (35.9 cm); W. 9 7/8 in. (25.1 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54875,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP998,false,true,54876,Asian Art,Print,"忠臣蔵八段目|Two Tori-oi, or Itinerant Women Musicians of the Eta Class",Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,,1754,1806,Polychrome woodblock print; ink and color on paper,H. 15 in. (38.1 cm); W. 10 1/2 in. (26.7 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54876,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP999,false,true,45476,Asian Art,Print,"忠臣蔵九段目|A Woman at Her Toilet Seated before a Mirror, Having Her Hair combed by a Kameyui (Woman Hairdresser)",Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,,1754,1806,Polychrome woodblock print; ink and color on paper,H. 15 1/8 in. (38.4 cm); W. 10 1/2 in. (26.7 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45476,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1663,false,true,40601,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,,1754,1806,Polychrome woodblock print; ink and color on paper,H. 14 3/4 in. (37.5 cm); W. 9 3/4 in. (24.8 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/40601,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1670,false,true,55896,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,,1754,1806,Polychrome woodblock print; ink and color on paper,H. 12 3/4 in. (32.4 cm); W. 7 3/4 in. (19.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55896,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1671,false,true,45478,Asian Art,Print,風流七小町　通ひ|Young MotHer Nursing Her Baby,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,,1754,1806,Polychrome woodblock print; ink and color on paper,H. 10 in. (25.4 cm); W. 14 11/16 in. (37.3 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45478,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1678,false,true,55910,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,,1754,1806,Polychrome woodblock print; ink and color on paper,H. 23 1/2 in. (59.7 cm); W. 4 3/8 in. (11.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55910,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1680,false,true,37340,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,,1754,1806,Triptych of polychrome woodblock prints; ink and color on paper,14 1/8 x 28 23/32 in. (35.9 x 73.0 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37340,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1681,false,true,37341,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,,1754,1806,Triptych of polychrome woodblock prints; ink and color on paper,14 15/32 x 29 7/16 in. (36.8 x 74.8 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37341,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1682,false,true,37342,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,,1754,1806,Triptych of polychrome woodblock prints; ink and color on paper,15 1/8 x 28 13/16 in. (38.4 x 73.2 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37342,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1688,false,true,37345,Asian Art,Print,風流六玉川　武蔵　紀伊　陸奥|Women and a Man in the Country; Some pageant(?),Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,,1754,1806,Triptych of polychrome woodblock prints; ink and color on paper,A: H. 14 5/8 in. (37.21 cm); W. 9 3/8 in. (23.8 cm) B: H. 14 11/16 in. (37.31 cm); W. 9 9/16 in. (24. 31 cm) C: H. 14 11/16 in. (37.31 cm); W. 9 7/16 in. (24 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37345,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1689,false,true,37346,Asian Art,Print,風流六玉川　山城　近江　摂津|Women and Children on the Banks of a Stream,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,,1754,1806,Triptych of polychrome woodblock prints; ink and color on paper,14 5/8 x 9 7/16 in. (37.21 x 23.9 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37346,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1690,false,true,55925,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,,1754,1806,Polychrome woodblock print; ink and color on paper,H. 29 1/2 in. (74.9 cm); W. 9 5/8 in. (24.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55925,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2394,false,true,56799,Asian Art,Print,名取酒六家選　大もんぢや内浅じふ　木綿屋七ッ梅|A Courtesan,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,,1754,1806,Polychrome woodblock print; ink and color on paper,14 3/8 x 9 11/16 in. (36.5 x 24.6 cm),"H. O. Havemeyer Collection, Gift of Mrs. J. Watson Webb, 1930",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56799,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3157,false,true,56710,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,,1753,1806,Polychrome woodblock print; ink and color on paper,Image: 10 in. × 15 1/8 in. (25.4 × 38.4 cm) Mat: 15 1/2 × 22 3/4 in. (39.4 × 57.8 cm),"Anonymous Gift, 1951",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56710,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3158,false,true,56711,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,,1753,1806,Polychrome woodblock print; ink and color on paper,Image: 10 in. × 15 1/8 in. (25.4 × 38.4 cm) Image: 15 1/2 × 22 3/4 in. (39.4 × 57.8 cm),"Anonymous Gift, 1951",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56711,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3159,false,true,56712,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,,1753,1806,Polychrome woodblock print; ink and color on paper,Image: 10 in. × 15 1/8 in. (25.4 × 38.4 cm) Mat: 15 1/2 × 22 3/4 in. (39.4 × 57.8 cm),"Anonymous Gift, 1951",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56712,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3160,false,true,56713,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,,1753,1806,Polychrome woodblock print; ink and color on paper,Image: 10 in. × 15 1/8 in. (25.4 × 38.4 cm) Mat: 15 1/2 × 22 3/4 in. (39.4 × 57.8 cm),"Anonymous Gift, 1951",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56713,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3161,false,true,56714,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,,1753,1806,Polychrome woodblock print; ink and color on paper,Image: 10 in. × 15 1/8 in. (25.4 × 38.4 cm) Mat: 15 1/2 × 22 3/4 in. (39.4 × 57.8 cm),"Anonymous Gift, 1952",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56714,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3162,false,true,56715,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,,1753,1806,Polychrome woodblock print; ink and color on paper,Image: 10 in. × 15 1/8 in. (25.4 × 38.4 cm) Mat: 15 1/2 × 22 3/4 in. (39.4 × 57.8 cm),"Anonymous Gift, 1952",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56715,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3163,false,true,56716,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,,1753,1806,Polychrome woodblock print; ink and color on paper,Image: 10 in. × 15 1/8 in. (25.4 × 38.4 cm) Mat: 15 1/2 × 22 3/4 in. (39.4 × 57.8 cm),"Anonymous Gift, 1952",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56716,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3164,false,true,56717,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,,1753,1806,Polychrome woodblock print; ink and color on paper,Image: 10 in. × 15 1/8 in. (25.4 × 38.4 cm) Mat: 15 1/2 × 22 3/4 in. (39.4 × 57.8 cm),"Anonymous Gift, 1952",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56717,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3165,false,true,56718,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,,1753,1806,Polychrome woodblock print; ink and color on paper,Image: 10 in. × 15 1/8 in. (25.4 × 38.4 cm) Mat: 15 1/2 × 22 3/4 in. (39.4 × 57.8 cm),"Anonymous Gift, 1949",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56718,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3166,false,true,56719,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,,1753,1806,Polychrome woodblock print; ink and color on paper,Image: 10 in. × 15 1/8 in. (25.4 × 38.4 cm) Mat: 15 1/2 × 22 3/4 in. (39.4 × 57.8 cm),"Anonymous Gift, 1949",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56719,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3167,false,true,56720,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,,1753,1806,Polychrome woodblock print; ink and color on paper,Image: 10 in. × 15 1/8 in. (25.4 × 38.4 cm) Mat: 15 1/2 × 22 3/4 in. (39.4 × 57.8 cm),"Anonymous Gift, 1949",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56720,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3168,false,true,56721,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,,1753,1806,Polychrome woodblock print; ink and color on paper,Image: 10 in. × 15 1/8 in. (25.4 × 38.4 cm) Mat: 15 1/2 × 22 3/4 in. (39.4 × 57.8 cm),"Anonymous Gift, 1949",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56721,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1433,false,true,54391,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Yashima Gakutei,"Japanese, 1786?–1868",,Yashima Gakutei,Japanese,1786,1868,,1786,1868,Polychrome woodblock print (surimono); ink and color on paper,8 11/16 x 7 1/2 in. (22.1 x 19.1 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54391,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1825,false,true,54425,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Yashima Gakutei,"Japanese, 1786?–1868",,Yashima Gakutei,Japanese,1786,1868,,1786,1868,Polychrome woodblock print (surimono); ink and color on paper,7 7/8 x 6 7/8 in. (20 x 17.5 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54425,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1826,false,true,54426,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Yashima Gakutei,"Japanese, 1786?–1868",,Yashima Gakutei,Japanese,1786,1868,,1786,1868,Polychrome woodblock print (surimono); ink and color on paper,8 1/8 x 7 1/4 in. (20.6 x 18.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54426,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3606,false,true,54229,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Yashima Gakutei,"Japanese, 1786?–1868",,Yashima Gakutei,Japanese,1786,1868,,1786,1868,Polychrome woodblock print (surimono); ink and color on paper,7 7/8 x 6 1/3 in. (20 x 16.1 cm),"Seymour Fund, 1981",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54229,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3607,false,true,54230,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Yashima Gakutei,"Japanese, 1786?–1868",,Yashima Gakutei,Japanese,1786,1868,,1776,1878,Polychrome woodblock print (surimono); ink and color on paper,7 7/16 x 7 3/16 in. (18.9 x 18.3 cm),"Seymour Fund, 1981",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54230,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1926,false,true,54466,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ryūgetsusai Shinkō,"Japanese, active 1810s",,Ryūgetsusai Shinkō,Japanese,1810,1819,,1810,1819,Polychrome woodblock print (surimono); ink and color on paper,8 5/16 x 10 9/16 in. (21.1 x 26.8 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54466,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1610,false,true,55770,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Okumura Toshinobu,active ca. 1725–1750,,Okumura Toshinobu,Japanese,1725,1750,,1715,1760,Polychrome woodblock print; ink and color on paper,H. 12 5/16 in. (31.3 cm); W. 5 5/8 in. (14.3 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55770,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3409,false,true,55598,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Okumura Toshinobu,active ca. 1725–1750,,Okumura Toshinobu,Japanese,1725,1750,,1725,1750,Polychrome woodblock print; ink and color on paper,12 1/4 x 5 3/4 in. (31.1 x 14.6 cm),"Bequest of Julia H. Manges, 1960",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55598,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3328,false,true,55476,Asian Art,Print,生写異国人物 清朝南京人感賞皇州扇之図|Two Chinese Men,Japan,Edo period (1615–1868),,,,Artist,,Utagawa (Gountei) Sadahide,"Japanese, 1807–1878/79",,Utagawa (Goutei) Sadahide,Japanese,1807,1879,,1807,1879,Polychrome woodblock print; ink and color on paper,14 1/4 x 10 in. (36.2 x 25.4 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55476,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1306,false,true,55245,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ryūkōsai,active late 18th century,,Ryūkōsai,Japanese,1771,1799,,1771,1799,Polychrome woodblock print; ink and color on paper,H. 12 1/16 in. (30.6 cm); W. 5 9/16 in. (14.1 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55245,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP880,false,true,54573,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Isoda Koryūsai,"Japanese, 1735–ca. 1790",,Isoda Koryūsai,Japanese,1735,1790,,1735,1790,Polychrome woodblock print; ink and color on paper,H. 9 7/8 in. (25.1 cm); W. 7 in. (17.8 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54573,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP891,false,true,54585,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Isoda Koryūsai,"Japanese, 1735–ca. 1790",,Isoda Koryūsai,Japanese,1735,1790,,1735,1790,Polychrome woodblock print; ink and color on paper,H. 10 1/8 in. (25.7 cm); W. 7 1/2 in. (19.1 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54585,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1693,false,true,55928,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Isoda Koryūsai,"Japanese, 1735–ca. 1790",,Isoda Koryūsai,Japanese,1735,1790,,1735,1790,Polychrome woodblock print; ink and color on paper,H. 10 1/8 in. (25.7 cm); W. 7 9/16 in. (19.2 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55928,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1694,false,true,55929,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Isoda Koryūsai,"Japanese, 1735–ca. 1790",,Isoda Koryūsai,Japanese,1735,1790,,1770,1790,Polychrome woodblock print; ink and color on paper,H. 10 1/8 in. (25.7 cm); W. 7 9/16 in. (19.2 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55929,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1695,false,true,55930,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Isoda Koryūsai,"Japanese, 1735–ca. 1790",,Isoda Koryūsai,Japanese,1735,1790,,1770,1790,Polychrome woodblock print; ink and color on paper,H. 10 1/4 in. (26 cm); W. 7 3/4 in. (19.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55930,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1696,false,true,55932,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Isoda Koryūsai,"Japanese, 1735–ca. 1790",,Isoda Koryūsai,Japanese,1735,1790,,1770,1790,Polychrome woodblock print; ink and color on paper,H. 8 1/8 in. (20.6 cm); W. 5 7/8 in. (14.9 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55932,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1697,false,true,55934,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Isoda Koryūsai,"Japanese, 1735–ca. 1790",,Isoda Koryūsai,Japanese,1735,1790,,1770,1790,Polychrome woodblock print; ink and color on paper,H. 9 1/2 in. (24.1 cm); W. 7 7/16 in. (18.9 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55934,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1698,false,true,55936,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Isoda Koryūsai,"Japanese, 1735–ca. 1790",,Isoda Koryūsai,Japanese,1735,1790,,1770,1790,Polychrome woodblock print; ink and color on paper,10 × 7 1/2 in. (25.4 × 19.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55936,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1699,false,true,55939,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Isoda Koryūsai,"Japanese, 1735–ca. 1790",,Isoda Koryūsai,Japanese,1735,1790,,1770,1790,Polychrome woodblock print; ink and color on paper,H. 10 in. (25.4 cm); W. 7 1/8 in. (18.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55939,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1700,false,true,55941,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Isoda Koryūsai,"Japanese, 1735–ca. 1790",,Isoda Koryūsai,Japanese,1735,1790,,1770,1790,Polychrome woodblock print; ink and color on paper,H. 10 1/4 in. (26 cm); W. 7 5/8 in. (19.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55941,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1701,false,true,55942,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Isoda Koryūsai,"Japanese, 1735–ca. 1790",,Isoda Koryūsai,Japanese,1735,1790,,1770,1790,Polychrome woodblock print; ink and color on paper,H. 10 1/4 in. (26 cm); W. 7 11/16 in. (19.5 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55942,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1702,false,true,55943,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Isoda Koryūsai,"Japanese, 1735–ca. 1790",,Isoda Koryūsai,Japanese,1735,1790,,1770,1790,Polychrome woodblock print; ink and color on paper,H. 10 5/16 in. (26.2 cm); W. 7 3/4 in. (19.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55943,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1705,false,true,55947,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Isoda Koryūsai,"Japanese, 1735–ca. 1790",,Isoda Koryūsai,Japanese,1735,1790,,1770,1790,Polychrome woodblock print; ink and color on paper,H. 27 3/4 in. (70.5 cm); W. 4 5/8 in. (11.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55947,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1706,false,true,55948,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Isoda Koryūsai,"Japanese, 1735–ca. 1790",,Isoda Koryūsai,Japanese,1735,1790,,1770,1790,Polychrome woodblock print; ink and color on paper,H. 26 3/8 in. (67 cm); W. 4 5/8 in. (11.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55948,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1707,false,true,55949,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Isoda Koryūsai,"Japanese, 1735–ca. 1790",,Isoda Koryūsai,Japanese,1735,1790,,1770,1790,Polychrome woodblock print; ink and color on paper,H. 26 1/8 in. (66.4 cm); W. 4 7/16 in. (11.3 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55949,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1708,false,true,55953,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Isoda Koryūsai,"Japanese, 1735–ca. 1790",,Isoda Koryūsai,Japanese,1735,1790,,1770,1790,Polychrome woodblock print; ink and color on paper,H. 26 1/4 in. (66.7 cm); W. 4 3/4 in. (12.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55953,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1709,false,true,55955,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Isoda Koryūsai,"Japanese, 1735–ca. 1790",,Isoda Koryūsai,Japanese,1735,1790,,1770,1790,Polychrome woodblock print; ink and color on paper,H. 27 9/16 in. (70 cm); W. 4 7/8 in. (12.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55955,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1710,false,true,55957,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Isoda Koryūsai,"Japanese, 1735–ca. 1790",,Isoda Koryūsai,Japanese,1735,1790,,1770,1790,Polychrome woodblock print; ink and color on paper,H. 27 5/8 in. (70.2 cm); W. 4 7/16 in. (11.3 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55957,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3140,false,true,56688,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Isoda Koryūsai,"Japanese, 1735–ca. 1790",,Isoda Koryūsai,Japanese,1735,1790,,1735,1790,Polychrome woodblock print; ink and color on paper,12 5/8 x 8 3/8 in. (32.1 x 21.3 cm),"Gift of Francis M. Weld, 1948",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56688,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3412,false,true,55601,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Isoda Koryūsai,"Japanese, 1735–ca. 1790",,Isoda Koryūsai,Japanese,1735,1790,,1735,1790,Polychrome woodblock print; ink and color on paper,10 1/2 x 7 1/2 in. (26.7 x 19.1 cm),"Bequest of Julia H. Manges, 1960",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55601,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2095,false,true,54941,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Shūchōdō Monoyana,"Japanese, 1761–ca. 1830",,Shūchōdō Monoyana,Japanese,1761,1830,,1761,1830,Polychrome woodblock print (surimono); ink and color on paper,5 7/8 x 6 15/16 in. (14.9 x 17.6 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54941,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1379,false,true,55365,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Suikōdō Sakei,"Japanese, active ca. 1764",,Suikōdō Sakei,Japanese,1754,1774,,1754,1774,Polychrome woodblock print; ink and color on paper,Image: 10 x 7 5/8 in. (25.4 x 19.4 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55365,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3398,false,true,55576,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Tosen,"Japanese, active ca. 1770",,Tosen,Japanese,1770,1770,,1760,1780,Polychrome woodblock print; ink and color on paper,27 x 5 1/4 in. (68.6 x 13.3 cm),"Bequest of Julia H. Manges, 1960",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55576,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3395,false,true,55572,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Hanekawa Chinchō,"Japanese, ca. 1679–1754",,Hanekawa Chinchō,Japanese,1679,1754,,1669,1754,Monochrome woodblock print; ink on paper,12 1/4 x 21 3/4 in. (31.1 x 55.2 cm),"Bequest of Julia H. Manges, 1960",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55572,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1808,false,true,56107,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Nishimura Shigenobu,"Japanese, active 1729–39",,Nishimura Shigenobu,Japanese,1729,1739,,1711,1785,Polychrome woodblock print; ink and color on paper,12 9/16 x 5 7/8 in. (31.9 x 14.9cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56107,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3056,false,true,56511,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Nishimura Shigenobu,"Japanese, active 1729–39",,Nishimura Shigenobu,Japanese,1729,1739,,1615,1868,Polychrome woodblock print; ink and color on paper,12 1/2 in. x 6 in. (31.8 x 15.2 cm),"Gift of Mrs. Morris Manges, in memory of her husband, Dr. Morris Manges, 1947",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56511,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1736,false,true,56045,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Tōshūsai Sharaku,"Japanese, active 1794–95",,Tōshūsai Sharaku,Japanese,1794,1795,,1749,1795,Monochrome woodblock print; ink on paper,H. 15 3/8 in. (39.1 cm); W. 10 7/8 in. (27.6 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56045,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1738,false,true,56046,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Tōshūsai Sharaku,"Japanese, active 1794–95",,Tōshūsai Sharaku,Japanese,1794,1795,,1749,1795,Polychrome woodblock print; ink and color on paper,H. 14 7/8 in. (37.8 cm); W. 9 3/4 in. (24.8 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56046,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1599,false,true,55763,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyomasu I,"Japanese, active 1696–1716",,Torii Kiyomasu I,Japanese,1696,1716,,1696,1716,Polychrome woodblock print; ink and color on paper,H. 12 1/8 in. (30.8 cm); W. 5 3/4 in. (14.6 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55763,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1774,false,true,56078,Asian Art,Design for a fan,,Japan,Edo period (1615–1868),,,,Artist,,Ippitsusai Bunchō,"Japanese, active 1760–1794",,Ippitsusai Bunchō,Japanese,1760,1794,,1723,1792,Monochrome woodblock print; ink on paper,H. 9 3/4 in. (24.8 cm); W. 13 7/16 in. (34. 1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56078,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1775,false,true,56079,Asian Art,Design for a fan,,Japan,Edo period (1615–1868),,,,Artist,,Ippitsusai Bunchō,"Japanese, active 1760–1794",,Ippitsusai Bunchō,Japanese,1760,1794,,1723,1792,Monochrome woodblock print; ink on paper,H. 9 3/4 in. (24.8 cm); W. 13 1/2 in. (34.3 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56079,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1776,false,true,56080,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ippitsusai Bunchō,"Japanese, active 1760–1794",,Ippitsusai Bunchō,Japanese,1760,1794,,1723,1792,Monochrome woodblock print; ink on paper,H. 9 14/16 in. (25.1 cm); W. 13 9/16 in. (34.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56080,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1777,false,true,56081,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Ippitsusai Bunchō,"Japanese, active 1760–1794",,Ippitsusai Bunchō,Japanese,1760,1794,,1723,1792,Polychrome woodblock print; ink and color on paper,H. 11 5/8 in. (29.5 cm); W. 5 5/8 in. (14.3 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56081,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1778,false,true,56082,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ippitsusai Bunchō,"Japanese, active 1760–1794",,Ippitsusai Bunchō,Japanese,1760,1794,,1723,1792,Polychrome woodblock print; ink and color on paper,H. 11 1/2 in. (29.2 cm); W. 5 3/8 in. (13.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56082,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1779,false,true,56083,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ippitsusai Bunchō,"Japanese, active 1760–1794",,Ippitsusai Bunchō,Japanese,1760,1794,,1723,1792,Polychrome woodblock print; ink and color on paper,H. 5 7/8 in. (14.9 cm); W. 11 1/2 in. (29.2 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56083,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3044,false,true,56499,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Ippitsusai Bunchō,"Japanese, active 1760–1794",,Ippitsusai Bunchō,Japanese,1760,1794,,1615,1868,Polychrome woodblock print; ink and color on paper,11 3/4 x 5 5/8 in. (29.8 x 14.3 cm),"Gift of Mrs. Morris Manges, in memory of her husband, Dr. Morris Manges, 1947",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56499,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3410,false,true,55599,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunzan,"Japanese, active 1782–1798",,Katsukawa Shunzan,Japanese,1782,1798,,1782,1798,Polychrome woodblock print; ink and color on paper,15 x 9 7/8 in. (38.1 x 25.1 cm),"Bequest of Julia H. Manges, 1960",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55599,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1809,false,true,56108,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Shotei Hokuju,"Japanese, active 1790–1820",,Shotei Hokuju,Japanese,1790,1820,,1790,1820,Polychrome woodblock print; ink and color on paper,10 x 14 15/16 in. (25.4 x 37.9 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56108,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3399,false,true,55578,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,Attributed to,Yuko,"Japanese, early 18th century",,Yuko,Japanese,1700,1750,,1700,1735,Polychrome woodblock print; ink and color on paper,12 x 5 3/8 in. (30.5 x 13.7 cm),"Bequest of Julia H. Manges, 1960",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55578,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1935,false,true,54475,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Shungensai,"Japanese, 18th–19th century",,Shungensai,Japanese,1700,1899,,1768,1868,Polychrome woodblock print (surimono); ink and color on paper,8 x 10 7/8 in. (20.3 x 27.6 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54475,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2091,false,true,54934,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Yukeisha,"Japanese, 18th–19th century",,Yukeisha,Japanese,1700,1899,,1768,1868,Polychrome woodblock print (surimono); ink and color on paper,5 3/8 x 7 1/16 in. (13.7 x 17.9 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54934,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1925,false,true,54465,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Toyoshige,"Japanese, active 1802?–?1835",,Utagawa Toyoshige,Japanese,1802,1835,,1802,1835,Polychrome woodblock print (surimono); ink and color on paper,8 1/8 x 5 1/2 in. (20.6 x 14 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54465,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1765,false,true,56070,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunchō,"Japanese, active ca. 1783–95",,Katsukawa Shunchō,Japanese,1775,1795,,1780,1795,Polychrome woodblock print; ink and color on paper,14 × 9 11/16 in. (35.6 × 24.6 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56070,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1766,false,true,56071,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunchō,"Japanese, active ca. 1783–95",,Katsukawa Shunchō,Japanese,1775,1795,,1780,1795,Middle sheet of a triptych of polychrome woodblock prints; ink and color on paper,15 1/8 × 9 7/8 in. (38.4 × 25.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56071,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1767,false,true,56072,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunchō,"Japanese, active ca. 1783–95",,Katsukawa Shunchō,Japanese,1775,1795,,1780,1795,Polychrome woodblock print; ink and color on paper,12 1/2 × 8 7/8 in. (31.8 × 22.5 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56072,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1768,false,true,56073,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunchō,"Japanese, active ca. 1783–95",,Katsukawa Shunchō,Japanese,1775,1795,,1780,1795,Polychrome woodblock print; ink and color on paper,25 3/8 × 4 1/2 in. (64.5 × 11.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56073,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1769,false,true,56074,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunchō,"Japanese, active ca. 1783–95",,Katsukawa Shunchō,Japanese,1775,1795,,1780,1795,Polychrome woodblock print; ink and color on paper,25 7/8 × 4 9/16 in. (65.7 × 11.6 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56074,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1771,false,true,56075,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunchō,"Japanese, active ca. 1783–95",,Katsukawa Shunchō,Japanese,1775,1795,,1780,1795,Polychrome woodblock print; ink and color on paper,27 3/4 × 4 13/16 in. (70.5 × 12.2 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56075,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1772,false,true,56076,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunchō,"Japanese, active ca. 1783–95",,Katsukawa Shunchō,Japanese,1775,1795,,1780,1795,Polychrome woodblock print; ink and color on paper,27 1/8 × 4 9/16 in. (68.9 × 11.6 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56076,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1773,false,true,56077,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunchō,"Japanese, active ca. 1783–95",,Katsukawa Shunchō,Japanese,1775,1795,,1780,1795,Diptych of polychrome woodblock prints; ink and color on paper,(a): 15 3/16 × 10 in. (38.6 × 25.4 cm) (b): 14 7/8 × 9 7/8 in. (37.8 × 25.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56077,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2402,false,true,56805,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunchō,"Japanese, active ca. 1783–95",,Katsukawa Shunchō,Japanese,1775,1795,,1780,1795,Diptych of polychrome woodblock prints; ink and color on paper,Image (each): 14 7/16 × 9 3/4 in. (36.7 × 24.8 cm),"H. O. Havemeyer Collection, Gift of Mrs. J. Watson Webb, 1930",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56805,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3182,false,true,53244,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshitora,"Japanese, active ca. 1850–80",,Utagawa Yoshitora,Japanese,1845,1880,,1615,1868,Polychrome woodblock print; ink and color on paper,14 3/4 x 9 3/4 in. (37.5 x 24.8 cm),"GIft of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/53244,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3325,false,true,55473,Asian Art,Print,"外国人物図畫仏蘭西|France, from the series Pictures of People from Foreign Lands (Gaikoku jinbutsu zuga)",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshitora,"Japanese, active ca. 1850–80",,Utagawa Yoshitora,Japanese,1845,1880,,1850,1880,Polychrome woodblock print; ink and color on paper,14 x 9 5/8 in. (35.6 x 24.4 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55473,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1807,false,true,56106,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Tsunegawa Shigenobu,"Japanese, active ca. 1724–1735",,Tsunegawa Shigenobu,Japanese,1724,1735,,1724,1735,Polychrome woodblock print; ink and color on paper,12 9/16 x 6 3/16 in. (31.9 x 15.7cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56106,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1213,false,true,55136,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Eiju,"Japanese, active ca. 1789–1801",,Eiju,Japanese,1789,1801,,1789,1801,Polychrome woodblock print; ink and color on paper,H. 15 7/16 in. (39.2 cm); W. 10 1/8 in. (25.7 cm),"Rogers Fund, 1920",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55136,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1792,false,true,56094,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ichirakutei Eisui,"Japanese, active ca. 1793–1801",,Ichirakutei Eisui,Japanese,1793,1801,,1793,1801,Polychrome woodblock print; ink and color on paper,H. 13 3/16 in. (33.5 cm); W. 9 3/16 in. (23.3 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56094,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1793,false,true,56095,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ichirakutei Eisui,"Japanese, active ca. 1793–1801",,Ichirakutei Eisui,Japanese,1793,1801,,1793,1801,Polychrome woodblock print; ink and color on paper,Image: 14 15/16 x 9 11/16 in. (37.9 x 24.6 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56095,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1105,false,true,54334,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,,1801,1813,Polychrome woodblock print (surimono); ink and color on paper,5 7/16 x 7 7/16 in. (13.8 x 18.9 cm),"Rogers Fund, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54334,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1249,false,true,54380,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,,1750,1835,Polychrome woodblock print (surimono); ink and color on paper,8 3/8 x 7 1/16 in. (21.3 x 17.9 cm),"Rogers Fund, 1921",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54380,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1814,false,true,54402,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,,1750,1835,Polychrome woodblock print (surimono); ink and color on paper,7 5/16 x 10 7/16 in. (18.6 x 26.5 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54402,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1815,false,true,54403,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,,1750,1835,Polychrome woodblock print (surimono); ink and color on paper,5 7/16 x 7 3/8 in. (13.8 x 18.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54403,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1816,false,true,54404,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,,1750,1835,Polychrome woodblock print (surimono) in shape of a twofold screen; ink and color on paper,8 1/4 x 7 3/8 in. (21 x 18.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54404,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1817,false,true,54405,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,,1750,1835,Polychrome woodblock print (surimono); ink and color on paper,5 3/8 x 7 3/8 in. (13.7 x 18.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54405,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1916,false,true,54455,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,,1750,1835,Polychrome woodblock print (surimono); ink and color on paper,5 1/4 x 7 1/4 in. (13.3 x 18.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54455,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1918,false,true,54457,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,,1750,1835,Polychrome woodblock print (surimono); ink and color on paper,8 1/4 x 7 5/16 in. (21 x 18.6 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54457,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1919,false,true,54458,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,,1750,1835,Polychrome woodblock print (surimono); ink and color on paper,8 3/8 x 7 5/16 in. (21.3 x 18.6 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54458,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1923,false,true,54462,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,,1799,1823,Polychrome woodblock print (surimono); ink and color on paper,8 1/4 x 3 5/8 in. (21 x 9.2 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54462,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1928,false,true,54468,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,,1799,1823,Polychrome woodblock print (surimono); ink and color on paper,6 x 7 3/8 in. (15.2 x 18.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54468,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1938,false,true,54502,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,,1750,1835,Polychrome woodblock print (surimono); ink and color on paper,5 11/16 x 7 1/2 in. (14.4 x 19.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54502,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1939,false,true,54503,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,,1750,1835,Polychrome woodblock print (surimono); ink and color on paper,5 13/16 x 7 3/4 in. (14.8 x 19.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54503,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2053,false,true,54828,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,,1750,1835,Polychrome woodblock print (surimono); ink and color on paper,5 5/8 x 3 7/8 in. (14.3 x 9.8 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54828,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2183,false,true,55099,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,,1750,1835,Polychrome woodblock print (surimono); ink and color on paper,8 3/16 x 11 in. (20.8 x 27.9 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55099,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2188,false,true,55104,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,,1750,1835,Polychrome woodblock print (surimono); ink and color on paper,5 3/8 x 7 1/4 in. (13.7 x 18.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55104,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2191,false,true,55107,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,,1750,1835,Polychrome woodblock print (surimono); ink and color on paper,8 1/16 x 7 1/4 in. (20.5 x 18.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55107,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2193,false,true,55109,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,,1750,1835,Polychrome woodblock print (surimono); ink and color on paper,5 3/4 x 7 1/2 in. (14.6 x 19.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55109,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1803,false,true,56105,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Hakusanjin Hokui,"Japanese, active ca. 1830–1840",,Hakusanjin Hokui,Japanese,1830,1840,,1830,1840,Polychrome woodblock print; ink and color on paper,9 x 12 1/2 in. (22.9 x 31.8cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56105,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3326,false,true,55474,Asian Art,Print,Furansukoku|仏蘭西国|France,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshikazu,"Japanese, active ca. 1850–1870",,Utagawa Yoshikazu,Japanese,1845,1870,,1840,1860,Polychrome woodblock print; ink and color on paper,13 x 9 1/4 in. (33 x 23.5 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55474,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2130,false,true,54985,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Uematsu Tōshū,"Japanese, active late 1810s–20s",,Uematsu Tōshū,Japanese,1810,1830,,1615,1868,Polychrome woodblock print (surimono); ink and color on paper,5 x 7 1/8 in. (12.7 x 18.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54985,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2131,false,true,54986,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Uematsu Tōshū,"Japanese, active late 1810s–20s",,Uematsu Tōshū,Japanese,1810,1830,,1615,1868,Polychrome woodblock print (surimono); ink and color on paper,5 1/8 x 7 in. (13 x 17.8 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54986,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2170,false,true,55083,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Uematsu Tōshū,"Japanese, active late 1810s–20s",,Uematsu Tōshū,Japanese,1810,1830,,1615,1868,Polychrome woodblock print (surimono); ink and color on paper,8 x 7 3/16 in. (20.3 x 18.3 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55083,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2223,false,true,53989,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Uematsu Tōshū,"Japanese, active late 1810s–20s",,Uematsu Tōshū,Japanese,1810,1830,,1760,1849,Polychrome woodblock print (surimono); ink and color on paper,5 1/8 x 10 3/16 in. (13 x 25.9 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/53989,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1020,false,true,54928,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Ippyotei Yoshikuni,"Japanese, active mid-19th century",,Ippyotei Yoshikuni,Japanese,1800,1899,,1836,1870,Polychrome woodblock print; ink and color on paper,H. 14 5/8 in. (37.1 cm); W. 10 1/4 in. (26 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54928,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1836,false,true,56121,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Ranshu,"Japanese, latter half of the 18th century",,Ranshu,Japanese,0018,0018,,1750,1799,Monochrome woodblock print; ink on paper,H. 9 3/4 in. (24.8 cm); W. 13 1/2 in. (34.3 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56121,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1015,false,true,54890,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,,1760,1849,Polychrome woodblock print; ink and color on paper,H. 9 3/4 in. (24.8 cm); W. 14 1/2 in. (36.8 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54890,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1108,false,true,54337,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,,1760,1850,Polychrome woodblock print (surimono); ink and color on paper,8 1/2 x 13 5/16 in. (21.6 x 33.8 cm),"Rogers Fund, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54337,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1844,false,true,56129,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,,1760,1849,Monochrome woodblock print; ink on paper,H. 14 15/16 in. (37.9 cm); W. 10 5/16 in. (26.2 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56129,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1845,false,true,56130,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,,1760,1849,Monochrome woodblock print; ink on paper,H. 15 in. (38.1 cm); W. 10 3/8 in. (26.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56130,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1853,false,true,54428,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,,1760,1849,Polychrome woodblock print (surimono); ink and color on paper,6 11/16 x 18 3/16 in. (17 x 46.2 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54428,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1857,false,true,45034,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,,1760,1849,Polychrome woodblock print; ink and color on paper,H. 7 7/8 in. (20 cm); W. 21 7/8 in. (55.6 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45034,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1858,false,true,56177,Asian Art,Print,市川団十郎|The Actor Ichikawa Danjuro I 1660–1704,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,,1760,1849,Polychrome woodblock print; ink and color on paper,H. 12 in. (30.5 cm); W. 5 3/4 in. (14.6 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56177,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1862,false,true,56185,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,,1760,1849,Polychrome woodblock print; ink and color on paper,H. 13 1/16 in. (33.2 cm); W. 9 3/8 in. (23.8 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56185,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1863,false,true,56186,Asian Art,Print,七日夜|Wave,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,,1760,1849,Monochrome woodblock print; ink on paper,H. 14 11/16 in. (37.3 cm); W. 10 11/16 in. (27.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56186,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1864,false,true,54429,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,,1760,1849,Polychrome woodblock print (surimono); ink and color on paper,7 3/4 x 27 7/16 in. (19.7 x 69.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54429,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1866,false,true,54431,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,,1760,1849,Polychrome woodblock print (surimono); ink and color on paper,5 1/8 x 7 5/16 in. (13 x 18.6 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54431,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1867,false,true,54432,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,,1760,1849,Polychrome woodblock print (surimono); ink and color on paper,7 7/8 x 7 1/4 in. (20 x 18.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54432,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1869,false,true,54434,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,,1760,1849,Polychrome woodblock print (surimono); ink and color on paper,6 5/16 x 12 7/16 in. (16 x 31.6 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54434,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1872,false,true,54437,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,,1760,1849,Polychrome woodblock print (surimono); ink and color on paper,8 1/16 x 7 1/4 in. (20.5 x 18.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54437,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2150,false,true,55011,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,,1760,1849,Polychrome woodblock print (surimono); ink and color on paper,7 1/2 x 10 3/8 in. (19.1 x 26.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55011,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2177,false,true,55091,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,,1760,1849,Polychrome woodblock print (surimono); ink and color on paper,7 1/2 x 21 1/4 in. (19.1 x 54 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55091,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2392,false,true,56797,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,,1760,1849,Polychrome woodblock print; ink and color on paper,9 13/16 x 13 1/16 in. (24.9 x 33.2 cm),"H. O. Havemeyer Collection, Gift of Mrs. J. Watson Webb, 1930",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56797,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2551,false,true,56977,Asian Art,Woodblock print,"百人一首　うばがゑとき　三条院|Poem by Sanjō-in, from the series One Hundred Poems Explained by the Nurse (Hyakunin isshu uba ga etoki)",Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,,1760,1849,Polychrome woodblock print; ink and color on paper,Overall: 10 1/4 x 14 3/4 in. (26 x 37.5 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56977,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1312,false,true,45294,Asian Art,Print,Ryogoku no yoizuki|東都名所　両国之宵月|Twilight Moon at Ryōgoku Bridge,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,,1797,1858,Polychrome woodblock print; ink and color on paper,H. 9 5/8 in. (24.4 cm); W. 15 3/16 in. (38.6 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45294,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1314,false,true,55271,Asian Art,Print,東都名所　新吉原朝桜之図|Morning Cherries at Yoshiwara,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,,1797,1858,Polychrome woodblock print; ink and color on paper,H. 9 7/16 in. (24 cm); W. 15 1/8 in. (38.4 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55271,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1315,false,true,45296,Asian Art,Print,"Shibaura, shiohi-gari no zu|東都名所　芝浦汐干之図|Shell Gathering at Shibaura",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,,1797,1858,Polychrome woodblock print; ink and color on paper,H. 9 7/8 in. (25.1 cm); W. 15 in. (38.1 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45296,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1316,false,true,55273,Asian Art,Print,江都名所　飛鳥山はな見|Asukayama Hanami,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,,1797,1858,Polychrome woodblock print; ink and color on paper,H. 9 13/16 in. (24.9 cm); W. 14 5/8 in. (37.1 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55273,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1318,false,true,55277,Asian Art,Print,京都名所之内　清水|Kiyomizu,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,,1797,1858,Polychrome woodblock print; ink and color on paper,H. 9 1/2 in. (24.1 cm); W. 14 11/16 in. (37.3 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55277,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1320,false,true,55279,Asian Art,Print,東都名所　目黒行人阪之図|Meguro Gionin Zaka,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,,1797,1858,Polychrome woodblock print; ink and color on paper,H. 9 1/2 in. (24.1 cm); W. 14 1/4 in. (36.2 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55279,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1321,false,true,55280,Asian Art,Print,東都名所　品川大井　八景坂鎧掛松|Shinagawa Hakkei Zaka,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,,1797,1858,Polychrome woodblock print; ink and color on paper,H. 9 7/16 in. (24 cm); W. 14 1/4 in. (36.2 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55280,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1861,false,true,56184,Asian Art,Print,東都名所　芝増上寺雪中ノ図|Zojoji Temple at Shiba in Snow,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,,1797,1858,Polychrome woodblock print; ink and color on paper,H. 10 in. (25.4 cm); W. 14 3/4 in. (37.5 cm),"Gift of Mrs. Henry J. Bernheim, 1945",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56184,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1874,false,true,56188,Asian Art,Print,近江八景之内　唐崎夜雨|Evening Rain on the Karasaki Pine,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,,1797,1858,Polychrome woodblock print; ink and color on paper,H. 10 1/4 in. (26 cm); W. 15 in. (38.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56188,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1875,false,true,56191,Asian Art,Print,近江八景之内　瀬田夕照|Sunset at Seta,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,,1797,1858,Polychrome woodblock print; ink and color on paper,H. 8 15/16 in. (22.7 cm); W. 13 7/8 in. (35.2 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56191,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1878,false,true,56259,Asian Art,Print,"東海道五十三次　見附　天竜川図|Mitsuke; Tenryugawa Ferry, Station No. 29",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,,1797,1858,Polychrome woodblock print; ink and color on paper,8 7/8 x 13 11/16 in. (22.5 x 34.8cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56259,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1879,false,true,56293,Asian Art,Print,"木曽海道六拾九次之内　長久保|Nagakubo, Station No. 28",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,,1797,1858,Polychrome woodblock print; ink and color on paper,H. 8 5/8 in. (21.9 cm); W. 13 1/8 in. (33.3 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56293,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1881,false,true,56294,Asian Art,Print,"本朝名所　相州江ノ嶋岩屋之図|Sōshū, Enoshima Iwaya no Zu",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,,1797,1858,Polychrome woodblock print; ink and color on paper,H. 8 7/8 in. (22.5 cm); W. 14 in. (35.6 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56294,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1882,false,true,56514,Asian Art,Print,"本朝名所　相州七里ヶ浜|Seven-ri Beach, Province of Soshu",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,,1797,1858,Polychrome woodblock print; ink and color on paper,H. 9 1/8 in. (23.2 cm); W. 14 1/16 in. (35.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56514,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1883,false,true,56515,Asian Art,Print,東都名所　猿若町芝居の図|Picture of the Theatres in Sakai Cho,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,,1797,1858,Polychrome woodblock print; ink and color on paper,H. 8 15/16 in. (22.7 cm); W. 13 5/8 in. (34.6 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56515,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1884,false,true,56516,Asian Art,Print,東都名所　真崎雪晴ノ図|Clearing Weather after Snow at Massaki,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,,1797,1858,Polychrome woodblock print; ink and color on paper,H. 9 7/8 in. (25.1 cm); W. 13 7/16 in. (34.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56516,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1885,false,true,56517,Asian Art,Print,京都名所之内　糺川原之夕立|Tea-houses on the Bank of the Tadasu River in a Shower,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,,1797,1858,Polychrome woodblock print; ink and color on paper,H. 8 1/2 in. (21.6 cm); W. 13 11/16 in. (34.8 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56517,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1886,false,true,56518,Asian Art,Print,京都名所之内　あらし山満花|Arashiyama Manka,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,,1797,1858,Polychrome woodblock print; ink and color on paper,H. 8 3/4 in. (22.2 cm); W. 14 in. (35.6 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56518,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1888,false,true,56520,Asian Art,Print,東都名所　佃島海辺朧月|Tsukudajima no Oborozuki,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,,1797,1858,Polychrome woodblock print; ink and color on paper,H. 14 3/8 in. (36.5 cm); W. 4 15/16 in. (12.5 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56520,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1890,false,true,56521,Asian Art,Print,江戸十二景　隅田川|Gotenyama-no Hana,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,,1797,1858,Polychrome woodblock print; ink and color on paper,H. 4 7/16 in. (11.3 cm); W. 6 5/8 in. (16.8 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56521,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1891,false,true,56589,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,,1797,1858,Polychrome woodblock print; ink and color on paper,H. 4 7/16 in. (11.3 cm); W. 6 5/8 in. (16.8 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56589,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1896,false,true,56592,Asian Art,Print,小松にきじ|Pheasant and Pine-trees on Snowy Hillside,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,,1797,1858,Polychrome woodblock print; ink and color on paper,H. 14 7/8 in. (37.8 cm); W. 6 3/4 in. (17.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56592,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1897,false,true,56795,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,,1797,1858,Polychrome woodblock print; ink and color on paper,13 1/16 x 4 5/16 in. (33.2 x 11 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56795,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2400,false,true,56803,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,,1797,1858,Polychrome woodblock print; ink and color on paper,9 1/16 x 4 1/16 in. (23 x 10.3 cm) Probably harimazé (mixed print),"H. O. Havemeyer Collection, Gift of Mrs. J. Watson Webb, 1930",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56803,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2460,false,true,56886,Asian Art,Print,東都名所　両国之宵月|Twilight Moon at Ryōgoku Bridge,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,,1797,1858,Polychrome woodblock print; ink and color on paper,13 3/4 x 8 1/2 in. (34.9 x 21.6 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56886,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2873,false,true,57039,Asian Art,Print,木曽海道六拾九次之内　望月|Mochizuki,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,,1797,1858,Polychrome woodblock print; ink and color on paper,9 1/4 x 14 3/4 in. (23.5 x 37.5 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57039,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3134,false,true,56682,Asian Art,Print,雪月花　木曽路之山川|Kiso Gorge in the Snow,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,,1797,1858,Triptych of polychrome woodblock prints; ink and color on paper,14 x 29 5/8 in. (35.6 x 75.2 cm),"Gift of Francis M. Weld, 1948",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56682,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3135,false,true,56683,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,,1615,1868,Polychrome woodblock print; ink and color on paper,28 3/4 x 9 1/2 in. (73 x 24.1 cm),"Gift of Francis M. Weld, 1948",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56683,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3141,false,true,56690,Asian Art,Print,月二拾八景之内　弓張月|Bow Moon,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,,1797,1858,Polychrome woodblock print; ink and color on paper,14 7/8 x 6 1/4 in. (37.8 x 15.9 cm),"Gift of Francis M. Weld, 1948",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56690,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3144,false,true,56695,Asian Art,Print,近江八景之内　堅田落雁|Geese Flying Down to Katada,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,,1797,1858,Polychrome woodblock print; ink and color on paper,13 3/8 x 8 5/8 in. (34 x 21.9 cm),"Bequest of Ellis G. Seymour, 1949",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56695,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3145,false,true,56696,Asian Art,Print,近江八景之内　瀬田夕照|Long Bridge of Seta,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,,1797,1858,Polychrome woodblock print; ink and color on paper,13 3/4 x 8 3/4 in. (34.9 x 22.2 cm),"Bequest of Ellis G. Seymour, 1949",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56696,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3147,false,true,56698,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,,1797,1858,Polychrome woodblock print; ink and color on paper,14 5/8 x 5 in. (37.1 x 12.7 cm),"Bequest of Ellis G. Seymour, 1949",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56698,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3149,false,true,56700,Asian Art,Print,梅に三光鳥|Long Tailed Bird,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,,1797,1858,Polychrome woodblock print; ink and color on paper,14 1/2 x 6 3/4 in. (36.8 x 17.1 cm),"Bequest of Ellis G. Seymour, 1949",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56700,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3150,false,true,56702,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,,1797,1858,Polychrome woodblock print; ink and color on paper,12 3/4 x 2 3/8 in. (32.4 x 6 cm),"Bequest of Ellis G. Seymour, 1949",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56702,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3151,false,true,56703,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,,1615,1868,Polychrome woodblock print; ink and color on paper,13 1/8 x 4 5/16 in. (33.3 x 11 cm),"Bequest of Ellis G. Seymour, 1949",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56703,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3152,false,true,56705,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,,1797,1858,Polychrome woodblock print; ink and color on paper,8 7/8 x 6 1/2 in. (22.5 x 16.5 cm),"Bequest of Ellis G. Seymour, 1949",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56705,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3426,false,true,55628,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,,1797,1858,Polychrome woodblock print; ink and color on paper,27 3/4 x 9 3/8 in. (70.5 x 23.8 cm),"Bequest of Gertrude Abbot Phillips, 1965",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55628,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3436,false,true,55637,Asian Art,Print,江戸名所　芝愛宕山|Shiba Atogayama,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,,1615,1868,Polychrome woodblock print; ink and color on paper,10 x 14 1/2 in. (25.4 x 36.8 cm),"Gift of Cole J. Younger, 1975",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55637,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3437,false,true,55638,Asian Art,Print,江都名所　飛鳥山はな見|Asukayama Hanami,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,,1615,1868,Polychrome woodblock print; ink and color on paper,9 1/2 x 14 1/2 in. (24.1 x 36.8 cm),"Gift of Cole J. Younger, 1975",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55638,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3511,false,true,45297,Asian Art,Print,"Shin-Yoshiwara, asazukura no zu|東都名所　新吉原朝桜之図|Morning Cherry Blossoms at Shin-Yoshiwara",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,,1797,1858,Polychrome woodblock print; ink and color on paper,10 1/4 x 15 1/8 in. (26 x 38.4 cm),"The Harry G. C. Packard Collection of Asian Art, Gift of Harry G. C. Packard, and Purchase, Fletcher, Rogers, Harris Brisbane Dick, and Louis V. Bell Funds, Joseph Pulitzer Bequest, and The Annenberg Fund Inc. Gift, 1975",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45297,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3513,false,true,55808,Asian Art,Woodblock print,梅に三光鳥|Bird on a Plum Branch,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,,1615,1868,Polychrome woodblock print; ink and color on paper,15 1/4 x 6 3/4 in. (38.7 x 17.1 cm),"The Harry G. C. Packard Collection of Asian Art, Gift of Harry G. C. Packard, and Purchase, Fletcher, Rogers, Harris Brisbane Dick, and Louis V. Bell Funds, Joseph Pulitzer Bequest, and The Annenberg Fund Inc. Gift, 1975",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55808,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3514,false,true,55809,Asian Art,Print,花菖蒲に白鷺|White Heron and Iris,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,,1800,1858,Polychrome woodblock print; ink and color on paper,15 3/8 x 6 1/2 in. (39.1 x 16.5 cm),"The Harry G. C. Packard Collection of Asian Art, Gift of Harry G. C. Packard, and Purchase, Fletcher, Rogers, Harris Brisbane Dick, and Louis V. Bell Funds, Joseph Pulitzer Bequest, and The Annenberg Fund Inc. Gift, 1975",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55809,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3515,false,true,55812,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,,1615,1868,Polychrome woodblock print; ink and color on paper,14 1/2 x 4 7/8 in. (36.8 x 12.4 cm),"The Harry G. C. Packard Collection of Asian Art, Gift of Harry G. C. Packard, and Purchase, Fletcher, Rogers, Harris Brisbane Dick, and Louis V. Bell Funds, Joseph Pulitzer Bequest, and The Annenberg Fund Inc. Gift, 1975",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55812,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3509a–c,false,true,55787,Asian Art,Print,"雪月花　武陽金沢八勝夜景|Full Moon at Kanazawa, Province of Musashi",Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,,1797,1858,Triptych of polychrome woodblock prints; ink and color on paper,L. sheet: 9 15/16 x 14 5/8 in. (25.2 x 37.1 cm); C. sheet: 14 21/32 x 9 29/32 in. (37.2 x 25.2 cm); R. sheet: 14 21/32 x 9 15/16 in. (37.2 x 25.2 cm),"The Harry G. C. Packard Collection of Asian Art, Gift of Harry G. C. Packard, and Purchase, Fletcher, Rogers, Harris Brisbane Dick, and Louis V. Bell Funds, Joseph Pulitzer Bequest, and The Annenberg Fund Inc. Gift, 1975",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55787,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -MJP28,false,true,57135,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,After,Okumura Masanobu,"Japanese, 1686–1764",,Masanobu Okumura,Japanese,1686,1764,,1868,1912,Monochrome woodblock print; ink on paper,10 1/4 x 14 11/16 in. (26 x 37.3 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57135,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -MJP29,false,true,57136,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,After,Okumura Masanobu,"Japanese, 1686–1764",,Masanobu Okumura,Japanese,1686,1764,,1868,1912,Monochrome woodblock print; ink on paper,10 1/4 x 14 11/16 in. (26 x 37.3 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57136,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -MJP30,false,true,57137,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,After,Okumura Masanobu,"Japanese, 1686–1764",,Masanobu Okumura,Japanese,1686,1764,,1868,1912,Monochrome woodblock print; ink on paper,10 1/4 x 14 11/16 in. (26 x 37.3 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57137,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -MJP31,false,true,57140,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,After,Okumura Masanobu,"Japanese, 1686–1764",,Masanobu Okumura,Japanese,1686,1764,,1868,1912,Monochrome woodblock print; ink on paper,10 1/4 x 14 11/16 in. (26 x 37.3 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57140,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -MJP32,false,true,57141,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,After,Okumura Masanobu,"Japanese, 1686–1764",,Masanobu Okumura,Japanese,1686,1764,,1868,1912,Monochrome woodblock print; ink on paper,10 1/4 x 14 11/16 in. (26 x 37.3 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57141,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -MJP33,false,true,57142,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,After,Okumura Masanobu,"Japanese, 1686–1764",,Masanobu Okumura,Japanese,1686,1764,,1868,1912,Monochrome woodblock print; ink on paper,10 1/4 x 14 11/16 in. (26 x 37.3 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57142,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -MJP34,false,true,57143,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,After,Okumura Masanobu,"Japanese, 1686–1764",,Masanobu Okumura,Japanese,1686,1764,,1868,1912,Monochrome woodblock print; ink on paper,10 1/4 x 14 11/16 in. (26 x 37.3 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57143,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -MJP35,false,true,57144,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,After,Okumura Masanobu,"Japanese, 1686–1764",,Masanobu Okumura,Japanese,1686,1764,,1868,1912,Monochrome woodblock print; ink on paper,10 1/4 x 14 11/16 in. (26 x 37.3 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57144,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -MJP36,false,true,57145,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,After,Okumura Masanobu,"Japanese, 1686–1764",,Masanobu Okumura,Japanese,1686,1764,,1868,1912,Monochrome woodblock print; ink on paper,10 1/4 x 14 11/16 in. (26 x 37.3 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57145,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -MJP37,false,true,57146,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,After,Okumura Masanobu,"Japanese, 1686–1764",,Masanobu Okumura,Japanese,1686,1764,,1868,1912,Monochrome woodblock print; ink on paper,10 1/4 x 14 11/16 in. (26 x 37.3 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57146,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -MJP38,false,true,57147,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,After,Okumura Masanobu,"Japanese, 1686–1764",,Masanobu Okumura,Japanese,1686,1764,,1868,1912,Monochrome woodblock print; ink on paper,10 1/4 x 14 11/16 in. (26 x 37.3 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57147,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -MJP39,false,true,57148,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,After,Okumura Masanobu,"Japanese, 1686–1764",,Masanobu Okumura,Japanese,1686,1764,,1868,1912,Monochrome woodblock print; ink on paper,10 1/4 x 14 11/16 in. (26 x 37.3 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57148,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3712,false,true,55976,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Watanabe Seitei,"Japanese, 1851–1918",,Watanabe Seitei,Japanese,1851,1918,,1851,1918,Frontispiece; polychrome woodblock print; ink and color on paper,Album: 8 1/2 x 5 5/16 in. (21.6 x 13.5 cm),"Gift of Donald Keene, in honor of Julia Meech-Pekarik, 1986",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55976,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3709,false,true,55967,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Mishima Shōsō,"Japanese, 1856–1928",,Mishima Shōsō,Japanese,1856,1928,,1856,1928,Frontispiece; polychrome woodblock print; ink and color on paper,Album: 8 1/2 x 5 5/16 in. (21.6 x 13.5 cm),"Gift of Donald Keene, in honor of Julia Meech-Pekarik, 1986",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55967,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3710,false,true,55970,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Mishima Shōsō,"Japanese, 1856–1928",,Mishima Shōsō,Japanese,1856,1928,,1868,1912,Frontispiece; polychrome woodblock print; ink and color on paper,Album: 8 1/2 x 5 5/16 in. (21.6 x 13.5 cm),"Gift of Donald Keene, in honor of Julia Meech-Pekarik, 1986",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55970,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3714,false,true,55978,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Suzuki Kason,"Japanese, 1860–1919",,Suzuki Kason,Japanese,1860,1919,,1868,1912,Polychrome woodblock print; ink and color on paper,Album: 8 1/2 x 5 5/16 in. (21.6 x 13.5 cm),"Gift of Donald Keene, in honor of Julia Meech-Pekarik, 1986",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55978,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3715,false,true,55979,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Suzuki Kason,"Japanese, 1860–1919",,Suzuki Kason,Japanese,1860,1919,,1860,1919,Frontispiece; polychrome woodblock print; ink and color on paper,Album: 8 1/2 x 5 5/16 in. (21.6 x 13.5 cm),"Gift of Donald Keene, in honor of Julia Meech-Pekarik, 1986",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55979,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3718,false,true,55982,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Takeuchi Keishū,"Japanese, 1861–1943",,Takeuchi Keishū,Japanese,1861,1943,,1868,1912,Polychrome woodblock print; ink and color on paper,Album: 8 1/2 x 5 5/16 in. (21.6 x 13.5 cm),"Gift of Donald Keene, in honor of Julia Meech-Pekarik, 1986",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55982,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3719,false,true,55983,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Takeuchi Keishū,"Japanese, 1861–1943",,Takeuchi Keishū,Japanese,1861,1943,,1868,1912,Polychrome woodblock print; ink and color on paper,Album: 8 1/2 x 5 5/16 in. (21.6 x 13.5 cm),"Gift of Donald Keene, in honor of Julia Meech-Pekarik, 1986",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55983,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3708,false,true,55962,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Tomioka Eisen,"Japanese, 1864–1905",,Tomioka Eisen,Japanese,1864,1905,,1864,1905,Frontispiece; polychrome woodblock print; ink and color on paper,Album: 8 1/2 x 5 5/16 in. (21.6 x 13.5 cm),"Gift of Donald Keene, in honor of Julia Meech-Pekarik, 1986",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55962,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3711,false,true,55975,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Tomioka Eisen,"Japanese, 1864–1905",,Tomioka Eisen,Japanese,1864,1905,,1864,1905,Polychrome woodblock print; ink and color on paper,Album: 8 1/2 x 5 5/16 in. (21.6 x 13.5 cm),"Gift of Donald Keene, in honor of Julia Meech-Pekarik, 1986",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55975,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3721,false,true,55985,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Tomioka Eisen,"Japanese, 1864–1905",,Tomioka Eisen,Japanese,1864,1905,,1864,1905,Polychrome woodblock print; ink and color on paper,Album: 8 1/2 x 5 5/16 in. (21.6 x 13.5 cm),"Gift of Donald Keene, in honor of Julia Meech-Pekarik, 1986",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55985,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3713,false,true,55977,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Mizuno Toshikata,"Japanese, 1866–1908",,Mizuno Toshikata,Japanese,1866,1908,,1866,1908,Frontispiece; polychrome woodblock print; ink and color on paper,Album: 8 1/2 x 5 5/16 in. (21.6 x 13.5 cm),"Gift of Donald Keene, in honor of Julia Meech-Pekarik, 1986",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55977,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3716,false,true,55980,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Kajita Hanko,"Japanese, 1870–1917",,Kajita Hanko,Japanese,1870,1917,,1870,1917,Polychrome woodblock print; ink and color on paper,Album: 8 1/2 x 5 5/16 in. (21.6 x 13.5 cm),"Gift of Donald Keene, in honor of Julia Meech-Pekarik, 1986",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55980,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3720,false,true,55984,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Kajita Hanko,"Japanese, 1870–1917",,Kajita Hanko,Japanese,1870,1917,,1868,1912,Polychrome woodblock print; ink and color on paper,Album: 8 1/2 x 5 5/16 in. (21.6 x 13.5 cm),"Gift of Donald Keene, in honor of Julia Meech-Pekarik, 1986",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55984,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3173,false,true,56725,Asian Art,Print,,Japan,Taishō period (1912–26),,,,Artist,,Hashiguchi Goyō,"Japanese, 1881–1921",,Hashiguchi Goyō,Japanese,1881,1921,,1880,1921,Polychrome woodblock print; ink and color on paper,Image: 16 1/4 × 20 1/2 in. (41.3 × 52.1 cm) Mat: 22 3/4 × 27 5/8 in. (57.8 × 70.2 cm),"Gift of Mr. and Mrs. A. I. Sherr, 1956",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56725,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3370,false,true,55537,Asian Art,Print,Furansukoku|France,Japan,Edo (1615–1868)–Meiji period (1868–1912),,,,Artist,,Utagawa Yoshikazu,"Japanese, active ca. 1850–1870",,Utagawa Yoshikazu,Japanese,1845,1870,,1850,1870,Polychrome woodblock print; ink and color on paper,13 1/2 x 9 1/4 in. (34.3 x 23.5 cm),"Gift of Lincoln Kirstein, 1960",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55537,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.669,false,true,45511,Asian Art,Writer's box,,Japan,Edo period (1615–1868),,,,Artist,In the style of,Ogata Kenzan,"Japanese, 1663–1743",,Ogata Kenzan,Japanese,1663,1743,,1615,1868,"Clay with crackled glaze, decorated on outside and inside (Kyoto ware, Kenzan style)",H. 3 1/2 in. (8.9 cm); W. 8 in. (20.3 cm); L. 9 in. (22.9 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/45511,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.670,false,true,45512,Asian Art,Writer's box,,Japan,Edo period (1615–1868),,,,Artist,In the style of,Ogata Kenzan,"Japanese, 1663–1743",,Ogata Kenzan,Japanese,1663,1743,,1615,1868,"Crackled glaze; design modelled in relief (Kyoto ware, Kenzan style)",H. 3 1/4 in. (8.3 cm); W. 7 1/4 in. (18.4 cm); L. 9 1/4 in. (23.5 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/45512,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.268.603,false,true,52308,Asian Art,Teabowl,,Japan,late Edo period (1615–1868),,,,Artist,,Eiraku Wazen,"Japanese, 1821–1896",,Eiraku Wazen,Japanese,1821,1896,,1615,1868,Stoneware with gilt and polychrome enamels (Kyoto ware),H. 2 3/4 in. (7 cm); Diam. 4 7/8 in. (12.4 cm),"The Harry G. C. Packard Collection of Asian Art, Gift of Harry G. C. Packard, and Purchase, Fletcher, Rogers, Harris Brisbane Dick, and Louis V. Bell Funds, Joseph Pulitzer Bequest, and The Annenberg Fund Inc. Gift, 1975",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/52308,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -67.254,false,true,57244,Asian Art,Hanging scroll,,Japan,,,,,Artist,,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,Japanese,1807,1891,,1807,1891,Hanging scroll; ink on silk,32 1/8 x 9 3/8 in. (81.6 x 23.8 cm),"Gift of Mr. and Mrs. Harold G. Henderson, 1967",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57244,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -39.92.1,false,true,57310,Asian Art,Fan,,Japan,,,,,Artist,,Kano Motonobu,"Japan, ca. 1476–1559",,Kano Motonobu,Japanese,1476,1559,,1466,1569,Color on paper,7 3/4 x 19 1/8 in. (19.7 x 48.6 cm),"Purchase, Harris Brisbane Dick Fund, 1938",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57310,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -39.92.2,false,true,57311,Asian Art,Fan,,Japan,,,,,Artist,,Kano Motonobu,"Japan, ca. 1476–1559",,Kano Motonobu,Japanese,1476,1559,,1466,1569,Color on paper,8 1/4 x 19 3/4 in. (21 x 50.2 cm),"Purchase, Harris Brisbane Dick Fund, 1938",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57311,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -39.92.3,false,true,57312,Asian Art,Fan,,Japan,,,,,Artist,,Kano Motonobu,"Japan, ca. 1476–1559",,Kano Motonobu,Japanese,1476,1559,,1466,1569,Color on paper,7 11/16 x 19 in. (19.5 x 48.3 cm),"Purchase, Harris Brisbane Dick Fund, 1938",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57312,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -39.92.4,false,true,57313,Asian Art,Fan,,Japan,,,,,Artist,,Kano Motonobu,"Japan, ca. 1476–1559",,Kano Motonobu,Japanese,1476,1559,,1466,1569,Color on paper,8 x 19 1/4 in. (20.3 x 48.9 cm),"Purchase, Dick Fund, 1939",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57313,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1991.116,false,true,44625,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Gessen,1721–1809,,Gessen,Japanese,1721,1809,,1721,1809,Hanging scroll; ink and color on silk,41 9/16 x 14 1/4 in. (105.5 x 36.2 cm),"Gift of Akiko Kobayashi Bowers, 1991",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/44625,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.446,false,true,54897,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,School of,Hishikawa Moronobu,"Japanese, died 1694",,Hishikawa Moronobu,Japanese,,1694,,1618,1694,Hanging scroll; color on paper,Painting only: 30 3/4 x 16 1/2 in. (78.1 x 41.9 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54897,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.454,false,true,40349,Asian Art,Painting,,Japan,Edo period (1615–1868),,,,Artist,,Hishikawa Moronobu,"Japanese, died 1694",,Hishikawa Moronobu,Japanese,,1694,,1618,1694,Matted painting; ink and color on paper,Overall: 25 5/8 x 12 13/16 in. (65.1 x 32.5 cm) Image: 23 7/16 x 11 3/8 in. (59.5 x 28.9 cm) Mat: 29 3/8 x 17 1/4 in. (74.6 x 43.8 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/40349,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.61,false,true,57173,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,Attributed to,Kano Sansetsu,"Japanese, 1590–1651",,Kano Sansetsu,Japanese,1590,1651,,1615,1651,Hanging scroll; ink on paper,29 1/8 x 9 3/8 in. (74 x 23.8 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57173,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.53,false,true,55380,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,Attributed to,Kano Naonobu,"Japanese, 1607–1650",,Kano Naonobu,Japanese,1607,1650,,1607,1650,Hanging scroll; ink on paper,40 3/4 x 15 1/2 in. (103.5 x 39.4 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/55380,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.54,false,true,55384,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,Attributed to,Kano Naonobu,"Japanese, 1607–1650",,Kano Naonobu,Japanese,1607,1650,,1607,1650,Hanging scroll; ink on paper,41 x 15 1/2 in. (104.1 x 39.4 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/55384,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.61.63,false,true,54706,Asian Art,Album leaf,,Japan,Edo period (1615–1868),,,,Artist,,Kano Naonobu,"Japanese, 1607–1650",,Kano Naonobu,Japanese,1607,1650,,1607,1650,Album leaf; ink and color on paper,10 x 13 5/8 in. (25.4 x 34.6 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54706,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.93,false,true,55292,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Kano Yasunobu,"Japanese, 1613–1685",,Kano Yasunobu,Japanese,1613,1685,,1613,1685,Hanging scroll; ink on silk,32 1/4 x 16 in. (81.9 x 40.6 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/55292,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.94,false,true,55293,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,Attributed to,Kano Yasunobu,"Japanese, 1613–1685",,Kano Yasunobu,Japanese,1613,1685,,1613,1685,Hanging scroll; ink and color on silk,47 1/8 x 18 3/4 in. (119.7 x 47.6 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/55293,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.52,false,true,45327,Asian Art,Hanging scroll,秋の花に鶉図|Quail Under Autumn Flowers,Japan,Edo period (1615–1868),,,,Artist,,Tosa Mitsuoki,"Japanese, 1617–1691",,Tosa Mitsuoki,Japanese,1617,1691,,1617,1691,Hanging scroll; ink and color on silk,38 1/2 x 16 3/8 in. (97.8 x 41.6 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45327,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -60.86.1,false,true,57232,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Kano Tsunenobu,"Japanese, 1636–1713",,Kano Tsunenobu,Japanese,1636,1713,,1636,1713,One of a triptych of hanging scrolls; ink and color on paper,35 1/4 x 11 3/8 in. (89.5 x 28.9 cm),"Rogers Fund, 1960",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57232,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -60.86.2,false,true,72771,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Kano Tsunenobu,"Japanese, 1636–1713",,Kano Tsunenobu,Japanese,1636,1713,,1636,1713,One of a triptych of hanging scrolls; ink and color on paper,35 1/4 x 11 3/8 in. (89.5 x 28.9 cm),"Rogers Fund, 1960",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/72771,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.48,false,true,55379,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,Attributed to,Ogata Kōrin,"Japanese, 1658–1716",,Kōrin,Japanese,1658,1716,,1658,1716,Hanging scroll; color on paper,38 3/4 x 14 in. (98.4 x 35.6 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/55379,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -17.118.117,false,true,48984,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,Attributed to,Ogata Kōrin,"Japanese, 1658–1716",,Kōrin,Japanese,1658,1716,,1658,1716,Hanging scroll; ink and color on silk,11 3/4 x 16 1/2 in. (29.8 x 41.9 cm),"Rogers Fund, 1917",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/48984,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.268.64,false,true,45329,Asian Art,Folding fan mounted as a hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,Follower of,Ogata Kōrin,"Japanese, 1658–1716",,Kōrin,Japanese,1658,1716,,1658,1716,Folding fan remounted as a hanging scroll; ink and color on paper,Image: 15 1/4 × 22 3/8 in. (38.7 × 56.8 cm) Overall with mounting: 51 1/8 × 27 3/4 in. (129.9 × 70.5 cm) Overall with knobs: 51 1/8 × 29 3/4 in. (129.9 × 75.6 cm),"The Harry G. C. Packard Collection of Asian Art, Gift of Harry G. C. Packard, and Purchase, Fletcher, Rogers, Harris Brisbane Dick, and Louis V. Bell Funds, Joseph Pulitzer Bequest, and The Annenberg Fund Inc. Gift, 1975",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45329,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.3,false,true,55297,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,Attributed to,Katō Bunrei,"Japanese, 1706–1782",,Katō Bunrei,Japanese,1706,1782,,1706,1782,Hanging scroll; ink and color on silk,38 1/4 x 14 3/8 in. (97.2 x 36.5 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/55297,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.268.84,false,true,49073,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Sō Shiseki,"Japanese, 1715–1786",,Sō Shiseki,Japanese,1715,1786,,1715,1786,Hanging scroll; ink and color on silk,Image: 39 1/16 x 11 3/16 in. (99.2 x 28.4 cm) Overall: 69 x 18 7/8in. (175.3 x 47.9 cm),"The Harry G. C. Packard Collection of Asian Art, Gift of Harry G. C. Packard, and Purchase, Fletcher, Rogers, Harris Brisbane Dick, and Louis V. Bell Funds, Joseph Pulitzer Bequest, and The Annenberg Fund Inc. Gift, 1975",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49073,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.268.96,false,true,48995,Asian Art,Hanging scroll,与謝蕪村筆　柳緑桃紅図|Birds in Willows and Blossoming Peach Tree,Japan,Edo period (1615–1868),,,,Artist,,Yosa Buson,"Japanese, 1716–1783",,Yosa Buson,Japanese,1716,1783,,1716,1783,Hanging scroll; ink and color on silk,51 1/2 x 24 9/16 in. (130.8 x 62.4 cm),"The Harry G. C. Packard Collection of Asian Art, Gift of Harry G. C. Packard, and Purchase, Fletcher, Rogers, Harris Brisbane Dick, and Louis V. Bell Funds, Joseph Pulitzer Bequest, and The Annenberg Fund Inc. Gift, 1975",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/48995,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.268.102,false,true,45394,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Okada Beisanjin,"Japanese, 1744–1820",,Okada Beisanjin,Japanese,1744,1820,,1744,1820,Hanging scroll; ink and color on paper,Image: 53 11/16 x 11 9/16 in. (136.4 x 29.4 cm) Overall: 72 3/4 x 18 3/8in. (184.8 x 46.7cm),"The Harry G. C. Packard Collection of Asian Art, Gift of Harry G. C. Packard, and Purchase, Fletcher, Rogers, Harris Brisbane Dick, and Louis V. Bell Funds, Joseph Pulitzer Bequest, and The Annenberg Fund Inc. Gift, 1975",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45394,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.74,false,true,57178,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,Attributed to,Mori Sosen,"Japanese, 1747–1821",,Mori Sosen,Japanese,1747,1821,,1747,1821,Hanging scroll; ink on silk,40 3/8 x 12 1/2 in. (102.6 x 31.8 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57178,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.75,false,true,57179,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,Attributed to,Mori Sosen,"Japanese, 1747–1821",,Mori Sosen,Japanese,1747,1821,,1747,1821,Hanging scroll; ink on silk,40 3/8 x 12 1/2 in. (102.6 x 31.8 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57179,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.18,false,true,45222,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,Attributed to,Matsumura Goshun,"Japanese, 1752–1811",,Matsumura Goshun,Japanese,1752,1811,,1752,1811,Hanging scroll ;ink on paper,14 15/16 x 21 7/16 in. (38 x 54.5 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45222,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.20,false,true,55304,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,Attributed to,Matsumura Goshun,"Japanese, 1752–1811",,Matsumura Goshun,Japanese,1752,1811,,1752,1811,Hanging scroll; ink on silk,16 1/8 x 10 3/8 in. (41.0 x 26.4 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/55304,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.59,false,true,55385,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,Attributed to,Nagasawa Rosetsu,"Japanese, 1754–1799",,Nagasawa Rosetsu,Japanese,1754,1799,,1754,1799,Hanging scroll; color on paper,37 5/8 x 11 in. (95.6 x 27.9 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/55385,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.23,false,true,55305,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,Attributed to,Sakai Hōitsu,"Japanese, 1761–1828",,Sakai Hōitsu,Japanese,1761,1828,,1761,1828,Hanging scroll; ink and color on paper,34 1/4 x 13 in. (87x 33 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/55305,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.24,false,true,55310,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,Attributed to,Sakai Hōitsu,"Japanese, 1761–1828",,Sakai Hōitsu,Japanese,1761,1828,,1761,1828,Hanging scroll; ink and color on silk,44 1/4 x 16 in. (112.4 x 40.6 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/55310,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.25,false,true,55314,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,Attributed to,Sakai Hōitsu,"Japanese, 1761–1828",,Sakai Hōitsu,Japanese,1761,1828,,1761,1828,Hanging scroll; ink and color on paper,44 5/8 x 20 1/4 in. (113.3 x 51.4 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/55314,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.1,false,true,55300,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,Attributed to,Tani Bunchō,"Japanese, 1763–1840",,Tani Bunchō,Japanese,1763,1840,,1763,1840,Hanging scroll; ink on silk,Image: 35 13/16 × 8 7/16 in. (91 × 21.5 cm) Overall with mounting: 67 11/16 × 16 7/8 in. (172 × 42.9 cm) Overall with knobs: 67 11/16 × 18 3/4 in. (172 × 47.7 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/55300,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.2,false,true,55301,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,Attributed to,Tani Bunchō,"Japanese, 1763–1840",,Tani Bunchō,Japanese,1763,1840,,1763,1840,Hanging scroll; ink on silk,Image: 35 13/16 × 8 1/2 in. (91 × 21.6 cm) Overall with mounting: 68 1/8 × 16 7/8 in. (173 × 42.9 cm) Overall with knobs: 68 1/8 × 18 3/4 in. (173 × 47.7 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/55301,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2000.214,false,true,53707,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Nagasawa Roshu,"Japanese, 1767–1847",,Nagasawa Roshu,Japanese,1767,1847,,1767,1847,Hanging scroll; ink and color on silk,34 7/8 x 14 1/8 in. (88.6 x 35.9 cm),"Anonymous Gift, 2000",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/53707,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.268.109,false,true,49027,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Yamamoto Baiitsu,"Japanese, 1783–1856",,Yamamoto Baiitsu,Japanese,1783,1783,,1783,1856,Hanging scroll; color on silk,Image: 57 1/2 x 28 3/16 in. (146.1 x 71.6 cm) Overall: 88 3/4 x 37 1/4 in. (225.4 x 94.6 cm),"The Harry G. C. Packard Collection of Asian Art, Gift of Harry G. C. Packard, and Purchase, Fletcher, Rogers, Harris Brisbane Dick, and Louis V. Bell Funds, Joseph Pulitzer Bequest, and The Annenberg Fund Inc. Gift, 1975",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49027,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.97,false,true,55294,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Yosai,"Japanese, 1788–1878",,Yosai,Japanese,1788,1878,,1788,1868,Hanging scroll; ink and color on silk,43 1/4 x 15 3/4 in. (109.9 x 40 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/55294,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2000.661.1,false,true,53824,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Ōtagaki Rengetsu,"Japanese, 1791–1871",,Ōtagaki Rengetsu,Japanese,1791,1871,,1791,1871,Hanging scroll; ink and color on paper,44 1/16 x 10 3/4 in. (111.9 x 27.3 cm),"Gift of Donald Keene, 2000",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/53824,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2000.661.2,false,true,59004,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Ōtagaki Rengetsu,"Japanese, 1791–1871",,Ōtagaki Rengetsu,Japanese,1791,1871,,1791,1871,Hanging scroll; ink and color on paper,44 1/16 x 10 3/4 in. (111.9 x 27.3 cm),"Gift of Donald Keene, 2000",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/59004,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2000.661.3,false,true,59005,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Ōtagaki Rengetsu,"Japanese, 1791–1871",,Ōtagaki Rengetsu,Japanese,1791,1871,,1791,1871,Hanging scroll; ink and color on paper,44 1/16 x 10 3/4 in. (111.9 x 27.3 cm),"Gift of Donald Keene, 2000",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/59005,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.268.115,false,true,49007,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Tsubaki Chinzan,"Japanese, 1801–1854",,Tsubaki Chinzan,Japanese,1801,1854,,1801,1854,Hanging scroll; color on paper,Image: 55 1/2 x 14 5/8 in. (141 x 37.1 cm) Overall with mounting: 85 3/4 x 24 1/4 in. (217.8 x 61.6 cm),"The Harry G. C. Packard Collection of Asian Art, Gift of Harry G. C. Packard, and Purchase, Fletcher, Rogers, Harris Brisbane Dick, and Louis V. Bell Funds, Joseph Pulitzer Bequest, and The Annenberg Fund Inc. Gift, 1975",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49007,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.268.116,false,true,49008,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Tsubaki Chinzan,"Japanese, 1801–1854",,Tsubaki Chinzan,Japanese,1801,1854,,1801,1854,Hanging scroll; color on paper,Image: 56 7/8 x 14 13/16 in. (144.5 x 37.6 cm) Overall with mounting: 85 1/4 x 24 3/16 in. (216.5 x 61.4 cm),"The Harry G. C. Packard Collection of Asian Art, Gift of Harry G. C. Packard, and Purchase, Fletcher, Rogers, Harris Brisbane Dick, and Louis V. Bell Funds, Joseph Pulitzer Bequest, and The Annenberg Fund Inc. Gift, 1975",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49008,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.114,false,true,57199,Asian Art,Album leaf,,Japan,Edo period (1615–1868),,,,Artist,,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,Japanese,1807,1891,,1807,1868,Album leaf; lacquer on gold paper,4 3/4 x 3 1/2 in. (12.1 x 8.9 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57199,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.115,false,true,57200,Asian Art,Album leaf,,Japan,Edo period (1615–1868),,,,Artist,,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,Japanese,1807,1891,,1807,1868,Album leaf; brown and gold lacquer on silver paper,3 1/2 x 4 3/4 in. (8.9 x 12.1 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57200,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.116,false,true,57201,Asian Art,Album leaf,,Japan,Edo period (1615–1868),,,,Artist,,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,Japanese,1807,1891,,1807,1868,Album leaf; lacquer on gold paper,4 3/4 x 3 1/2 in. (12.1 x 8.9 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57201,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.117,false,true,57202,Asian Art,Album leaf,,Japan,Edo period (1615–1868),,,,Artist,,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,Japanese,1807,1891,,1807,1868,Album leaf; lacquer on silver paper,4 3/4 x 3 1/2 in. (12.1 x 8.9 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57202,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.118,false,true,57203,Asian Art,Album leaf,,Japan,Edo period (1615–1868),,,,Artist,,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,Japanese,1807,1891,,1807,1868,Album leaf; lacquer on gold paper,4 3/4 x 3 1/2 in. (12.1 x 8.9 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57203,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.119,false,true,57204,Asian Art,Album leaf,,Japan,Edo period (1615–1868),,,,Artist,,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,Japanese,1807,1891,,1807,1868,Album leaf; lacquer on silver paper,4 3/4 x 3 1/2 in. (12.1 x 8.9 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57204,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.120,false,true,57205,Asian Art,Album leaf,,Japan,Edo period (1615–1868),,,,Artist,,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,Japanese,1807,1891,,1807,1868,Album leaf; lacquer on gold paper,4 3/4 x 3 1/2 in. (12.1 x 8.9 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57205,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.121,false,true,57206,Asian Art,Album leaf,,Japan,Edo period (1615–1868),,,,Artist,,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,Japanese,1807,1891,,1807,1868,Album leaf; lacquer on silver paper,4 3/4 x 3 1/2 in. (12.1 x 8.9 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57206,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.122,false,true,57207,Asian Art,Album leaf,,Japan,Edo period (1615–1868),,,,Artist,,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,Japanese,1807,1891,,1807,1868,Album leaf; lacquer on gold paper,4 3/4 x 3 1/2 in. (12.1 x 8.9 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57207,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.123,false,true,57208,Asian Art,Album leaf,,Japan,Edo period (1615–1868),,,,Artist,,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,Japanese,1807,1891,,1807,1868,Album leaf; lacquer on silver paper,4 3/4 x 3 1/2 in. (12.1 x 8.9 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57208,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.124,false,true,57209,Asian Art,Album leaf,,Japan,Edo period (1615–1868),,,,Artist,,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,Japanese,1807,1891,,1807,1868,Album leaf; lacquer on paper,4 1/2 x 3 1/2 in. (11.4 x 8.9 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57209,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.125,false,true,57210,Asian Art,Album leaf,,Japan,Edo period (1615–1868),,,,Artist,,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,Japanese,1807,1891,,1807,1868,Album leaf; lacquer on paper,4 1/2 x 3 1/2 in. (11.4 x 8.9 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57210,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.126,false,true,57211,Asian Art,Album leaf,,Japan,Edo period (1615–1868),,,,Artist,,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,Japanese,1807,1891,,1807,1868,Album leaf; lacquer on paper,4 1/2 x 3 1/2 in. (11.4 x 8.9 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57211,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.127,false,true,57212,Asian Art,Album leaf,,Japan,Edo period (1615–1868),,,,Artist,,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,Japanese,1807,1891,,1807,1868,Album leaf; lacquer on paper,4 1/2 x 3 1/2 in. (11.4 x 8.9 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57212,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.128,false,true,57213,Asian Art,Album leaf,,Japan,Edo period (1615–1868),,,,Artist,,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,Japanese,1807,1891,,1807,1868,Album leaf; lacquer on paper,4 1/2 x 3 1/2 in. (11.4 x 8.9 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57213,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.129,false,true,57214,Asian Art,Album leaf,,Japan,Edo period (1615–1868),,,,Artist,,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,Japanese,1807,1891,,1807,1868,Album leaf; lacquer on paper,4 1/2 x 3 1/2 in. (11.4 x 8.9 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57214,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.130,false,true,57215,Asian Art,Album leaf,,Japan,Edo period (1615–1868),,,,Artist,,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,Japanese,1807,1891,,1807,1868,Album leaf; lacquer on paper,4 1/2 x 3 1/2 in. (11.4 x 8.9 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57215,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.131,false,true,57216,Asian Art,Album leaf,,Japan,Edo period (1615–1868),,,,Artist,,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,Japanese,1807,1891,,1807,1868,Album leaf; lacquer on paper,4 1/2 x 3 1/2 in. (11.4 x 8.9 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57216,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.132,false,true,57217,Asian Art,Album leaf,,Japan,Edo period (1615–1868),,,,Artist,,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,Japanese,1807,1891,,1807,1868,Album leaf; lacquer on paper,4 1/2 x 3 1/2 in. (11.4 x 8.9 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57217,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.133,false,true,57218,Asian Art,Album leaf,,Japan,Edo period (1615–1868),,,,Artist,,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,Japanese,1807,1891,,1807,1868,Album leaf; lacquer on paper,4 1/2 x 3 1/2 in. (11.4 x 8.9 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57218,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.134,false,true,57219,Asian Art,Album leaf,,Japan,Edo period (1615–1868),,,,Artist,,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,Japanese,1807,1891,,1807,1868,Album leaf; lacquer on paper,4 1/2 x 3 1/2 in. (11.4 x 8.9 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57219,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.135,false,true,57220,Asian Art,Album leaf,,Japan,Edo period (1615–1868),,,,Artist,,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,Japanese,1807,1891,,1807,1868,Album leaf; lacquer on paper,4 1/2 x 3 1/2 in. (11.4 x 8.9 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57220,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.136,false,true,57221,Asian Art,Album leaf,,Japan,Edo period (1615–1868),,,,Artist,,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,Japanese,1807,1891,,1807,1868,Album leaf; lacquer on paper,4 1/2 x 3 1/2 in. (11.4 x 8.9 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57221,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.137,false,true,57222,Asian Art,Album leaf,,Japan,Edo period (1615–1868),,,,Artist,,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,Japanese,1807,1891,,1807,1868,Album leaf; lacquer on paper,4 1/2 x 3 1/2 in. (11.4 x 8.9 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57222,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.138,false,true,57223,Asian Art,Album leaf,,Japan,Edo period (1615–1868),,,,Artist,,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,Japanese,1807,1891,,1807,1868,Album leaf; lacquer on paper,4 1/2 x 3 1/2 in. (11.4 x 8.9 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57223,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.4,false,true,55298,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,Attributed to,Shiokawa Bunrin,"Japanese, 1808–1877",,Shiokawa Bunrin,Japanese,1808,1877,,1808,1877,Hanging scroll; ink and color on silk,12 x 16 1/4 in. (30.5 x 41.3 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/55298,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2000.210,false,true,54037,Asian Art,Folding fan,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Isai,"Japanese, 1821–1880",,Katsushika Isai,Japanese,1821,1880,,1821,1868,"Folding fan; ink and color on paper, with mounting in ivory",H. 16 1/8 in. (41 cm); W. 29 1/2 in. (75 cm),"Purchase, Friends of Asian Art Gifts, in honor of Wen C. Fong, 2000",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54037,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2001.561.1,false,true,61942,Asian Art,Folding fan mounted as hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Isai,"Japanese, 1821–1880",,Katsushika Isai,Japanese,1821,1880,,1821,1868,Folding fan remounted as a hanging scroll; ink and color on silk,7 3/4 x 27 in. (19.7 x 68.6 cm),"Gift of Rosemarie and Leighton Longhi, 2001",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/61942,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2001.561.2,false,true,64505,Asian Art,Folding fan mounted as hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Isai,"Japanese, 1821–1880",,Katsushika Isai,Japanese,1821,1880,,1821,1868,"Folding fan, remounted as a hanging scroll; ink and color on silk",7 3/4 x 27 in. (19.7 x 68.6 cm),"Gift of Rosemarie and Leighton Longhi, 2001",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/64505,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.268.122,false,true,45333,Asian Art,Folding fan,,Japan,Edo period (1615–1868),,,,Artist,,Tsubaki Kakoku,"Japanese, 1823–1848",,Tsubaki Kakoku,Japanese,1823,1848,,1823,1848,Folding fan; ink and color on paper,10 1/4 x 17 3/8 in. (26 x 44.1 cm),"The Harry G. C. Packard Collection of Asian Art, Gift of Harry G. C. Packard, and Purchase, Fletcher, Rogers, Harris Brisbane Dick, and Louis V. Bell Funds, Joseph Pulitzer Bequest, and The Annenberg Fund Inc. Gift, 1975",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45333,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.67,false,true,57177,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,Attributed to,Shōkadō Shōjō,"Japanese, 1584?–1639",,Shōkadō Shōjō,Japanese,1584,1639,,1615,1639,Hanging scroll; ink on paper,10 1/4 x 23 1/8 in. (26 x 58.7 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57177,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.516,false,true,54967,Asian Art,Painting,,Japan,Edo period (1615–1868),,,,Artist,Attributed to,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,,1754,1806,Painting; color on silk,34 3/4 x 14 1/4 in. (88.3 x 36.2 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54967,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.268.95,false,true,48994,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Ike Gyokuran,"Japanese, 1727/8–1784",,Ike Gyokuran,Japanese,1727,1784,,1728,1784,Fan mounted as a hanging scroll; ink and color on paper,Image: 7 1/2 x 20 9/16 in. (19.1 x 52.3 cm) Overall with mounting: 52 1/4 x 30 3/4 in. (132.7 x 78.1 cm) Overall with knobs: 52 1/4 x 33 1/8 in. (132.7 x 84.1 cm),"The Harry G. C. Packard Collection of Asian Art, Gift of Harry G. C. Packard, and Purchase, Fletcher, Rogers, Harris Brisbane Dick, and Louis V. Bell Funds, Joseph Pulitzer Bequest, and The Annenberg Fund Inc. Gift, 1975",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/48994,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.511,false,true,54953,Asian Art,Painting,,Japan,Edo period (1615–1868),,,,Artist,Attributed to,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,,1760,1849,Painting; color on paper,23 7/8 x 15 3/4 in. (60.6 x 40 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54953,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2011.526.1a–d,false,true,76806,Asian Art,Door panels,神坂雪佳筆 竹波図襖|Bamboo and Waves,Japan,Shōwa period (1926–89),,,,Artist,,Kamisaka Sekka,"Japanese, 1866–1942",,Kamisaka Sekka,Japanese,1866,1942,,1926,1942,Set of four sliding-door panels (fusuma); ink and gold on paper,Overall (for four panels): 68 1/2 x 190 3/8 in. (174 x 483.6 cm),"Gift of Gitter-Yelen Foundation, in honor of Maxwell K. Hearn, 2011",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/76806,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2011.526.2,false,true,76924,Asian Art,Hanging scroll,神坂雪佳筆　寿老人図|Jurōjin,Japan,Meiji period (1866–1912),,,,Artist,,Kamisaka Sekka,"Japanese, 1866–1942",,Kamisaka Sekka,Japanese,1866,1942,,1866,1912,Hanging scroll; ink and color on silk,Image: 48 1/2 x 16 7/16 in. (123.2 x 41.8 cm) Overall with mounting: 84 x 22 in. (213.4 x 55.9 cm) Overall with knobs: 85 1/4 x 24 1/8 in. (216.5 x 61.3 cm),"Gift of Gitter-Yelen Foundation, in honor of John T. Carpenter, 2011",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/76924,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.61.49,false,true,54686,Asian Art,Album leaf,,Japan,Meiji period (1868–1912),,,,Artist,,Hashimoto Gahō,"Japanese, 1835–1908",,Hashimoto Gahō,Japanese,1835,1908,,1868,1912,Album leaf; ink and color on silk,14 1/8 x 10 3/8 in. (35.9 x 26.4 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54686,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.61.50,false,true,54687,Asian Art,Album leaf,,Japan,Meiji period (1868–1912),,,,Artist,,Hashimoto Gahō,"Japanese, 1835–1908",,Hashimoto Gahō,Japanese,1835,1908,,1868,1912,Album leaf; ink and color on silk,14 1/8 x 10 3/8 in. (35.9 x 26.4 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54687,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1977.50,false,true,57165,Asian Art,Hanging scroll,,Japan,Meiji period (1868–1912),,,,Artist,,Tomioka Tessai,"Japanese, 1836–1924",,Tomioka Tessai,Japanese,1836,1924,,1836,1924,Hanging scroll; ink and color on paper,11 1/2 x 26 3/8 in. (29.2 x 67 cm),"Gift of Dr. and Mrs. Joseph Kurstin, 1977",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57165,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1979.72,false,true,57166,Asian Art,Hanging scroll,,Japan,Meiji period (1868–1912),,,,Artist,,Suzuki Shōnen,"Japanese, 1849–1918",,Suzuki Shōnen,Japanese,1849,1918,,1868,1912,"Hanging scroll; ink, color, and gold on silk",Image: 48 3/4 x 19 1/2 in. (123.8 x 49.5 cm) Overall with mounting: 79 1/2 x 25 3/4 in. (201.9 x 65.4 cm) Overall with knobs: 79 1/4 x 28 in. (201.3 x 71.1 cm),"Purchase, Gift of Mrs. Russell Sage, by exchange, 1979",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57166,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.62,false,true,57174,Asian Art,Hanging scroll,,Japan,Meiji period (1868–1912),,,,Artist,,Takeuchi Seihō,"Japanese, 1864–1942",,Takeuchi Seihō,Japanese,1864,1942,,1868,1912,Hanging scroll; ink and color on paper,Overall: 40 3/4 x 13 1/2in. (103.5 x 34.3cm) Overall with mounting: 71 x 14 3/4 in. (180.3 x 37.5 cm) Overall with knobs: 71 x 17 1/8 in. (180.3 x 43.5 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57174,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.460,false,true,36040,Asian Art,Hanging scroll,,Japan,Momoyama period (1573–1615),,,,Artist,In the Style of,Hasegawa Tōhaku,"Japanese, 1539–1610",,Hasegawa Tōhaku,Japanese,1539,1610,,1573,1615,Hanging scroll; ink and color on silk,28 1/2 x 22 31/32 in. (72.4 x 58.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36040,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.31,false,true,55324,Asian Art,Hanging scroll,,Japan,Muromachi period (1392–1573),,,,Artist,In the Style of,Ikkyu Sojun,"Japanese, 1394–1481",,Ikkyu Sojun,Japanese,1394,1481,,1392,1573,Hanging scroll; ink on paper,34 1/4 x 10 1/4 in. (87 x 26 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/55324,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.8,false,true,54567,Asian Art,Painting,,Japan,Muromachi period (1392–1573),,,,Artist,Attributed to,Sesshū Tōyō,"Japanese, 1420–1506",,Sesshū Tōyō,Japanese,1420,1506,,1420,1506,India ink on paper,Image: 10 1/2 × 15 3/4 in. (26.7 × 40 cm) Overall with mounting: 44 7/8 × 20 1/2 in. (114 × 52.1 cm) Overall with knobs: 44 7/8 × 22 3/8 in. (114 × 56.8 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54567,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.10,false,true,45642,Asian Art,Hanging scroll,,Japan,Muromachi period (1392–1573),,,,Artist,Attributed to,Gakuo Zokyu,"Japanese, active ca. 1500",,Gakuo Zokyu,Japanese,1500,1500,,1490,1510,Hanging scroll; ink on paper,27 1/2 x 12 1/2 in. (69.9 x 31.8 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45642,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.65,false,true,57176,Asian Art,Hanging scroll,,Japan,Muromachi period (1392–1573),,,,Artist,Attributed to,Shūkō,"Japanese, active 1504–20",,Shūkō,Japanese,1504,1520,,1504,1520,Hanging scroll; ink on paper,40 1/2 x 12 in. (102.9 x 30.5 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57176,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.450,false,true,45696,Asian Art,Painting,,Japan,Muromachi period (1392–1573),,,,Artist,,Isei,"Japanese, mid-16th century",,Isei,Japanese,1536,1570,,1534,1566,Framed painting; ink and color on paper,18 1/2 x 12 1/2 in. (47 x 31.8 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45696,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.451,false,true,45697,Asian Art,Painting,,Japan,Muromachi period (1392–1573),,,,Artist,,Isei,"Japanese, mid-16th century",,Isei,Japanese,1536,1570,,1534,1573,Framed painting; ink and color on paper,18 1/2 x 12 1/2 in. (47 x 31.8 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45697,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.268.26,false,true,45202,Asian Art,Hanging scroll,不動明王二童子像|Fudō Myōō and Two Attendants,Japan,Nanbokuchō period (1336–92),,,,Artist,After,Ryūshū Shūtaku (Myōtaku),"Japanese, 1307–1388",,Ryūshū Shūtaku,Japanese,1307,1388,,1336,1388,One of a triptych of hanging scrolls; hand-colored woodblock print on paper,Image: 40 5/16 x 14 in. (102.4 x 35.6 cm) Overall: 71 1/8 x 22 1/2 in. (180.7 x 57.2 cm),"The Harry G. C. Packard Collection of Asian Art, Gift of Harry G. C. Packard, and Purchase, Fletcher, Rogers, Harris Brisbane Dick, and Louis V. Bell Funds, Joseph Pulitzer Bequest, and The Annenberg Fund Inc. Gift, 1975",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45202,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.268.27,false,true,45203,Asian Art,Hanging scroll,不動明王二童子像|Fudō Myōō and Two Attendants,Japan,Nanbokuchō period (1336–92),,,,Artist,After,Ryūshū Shūtaku (Myōtaku),"Japanese, 1307–1388",,Ryūshū Shūtaku,Japanese,1307,1388,,1336,1388,One of a triptych of hanging scrolls; hand-colored woodblock print on paper,Image: 40 1/4 x 14 in. (102.3 x 35.6 cm) Overall: 71 x 22 1/2 in. (180.3 x 57.2 cm),"The Harry G. C. Packard Collection of Asian Art, Gift of Harry G. C. Packard, and Purchase, Fletcher, Rogers, Harris Brisbane Dick, and Louis V. Bell Funds, Joseph Pulitzer Bequest, and The Annenberg Fund Inc. Gift, 1975",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45203,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.268.28,false,true,45204,Asian Art,Hanging scroll,不動明王二童子像|Fudō Myōō and Two Attendants,Japan,Nanbokuchō period (1336–92),,,,Artist,After,Ryūshū Shūtaku (Myōtaku),"Japanese, 1307–1388",,Ryūshū Shūtaku,Japanese,1307,1388,,1336,1388,One of a triptych of hanging scrolls; hand-colored woodblock print on paper,Image: 40 3/8 x 14 in. (102.6 x 35.6 cm) Overall: 71 3/8 x 22 1/2 in. (181.3 x 57.2 cm),"The Harry G. C. Packard Collection of Asian Art, Gift of Harry G. C. Packard, and Purchase, Fletcher, Rogers, Harris Brisbane Dick, and Louis V. Bell Funds, Joseph Pulitzer Bequest, and The Annenberg Fund Inc. Gift, 1975",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45204,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.35,false,true,55353,Asian Art,Hanging scroll,,Japan,Nanbokuchō (1336–92)–Muromachi (1392–1573) period,,,,Artist,Attributed to,Kao,,(Ryozen?),Kao,Japanese,,1345,,1336,1573,Hanging scroll; ink on silk,33 x 14 1/4 in. (83.8 x 36.2 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/55353,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JIB53,false,true,57653,Asian Art,Illustrated book,,Japan,Meiji period (1868–1912),,,,Artist,,Kawanabe Kyōsai,"Japanese, 1831–1889",,Kawanabe Kyōsai,Japanese,1831,1889,1888,1888,1888,Album of 29 leaves; ink and color on paper,8 7/8 x 5 7/8 in. (22.5 x 14.9 cm),"Gift of Albert Gallatin, 1922",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/57653,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3199,false,true,55183,Asian Art,Print,『上野公園開花図』|Blossoming Cherry Trees in Ueno Park (Ueno kōen kaika zu),Japan,Meiji period (1868–1912),,,,Artist,,Yōshū (Hashimoto) Chikanobu,"Japanese, 1838–1912",,Yōshū (Hashimoto) Chikanobu,Japanese,1838,1912,1888,1888,1888,Triptych of polychrome woodblock prints; ink and color on paper,Oban 14 3/4 x 9 7/8 in. (37.5 x 25.1 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55183,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3687a,false,true,55918,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Tsukioka Yoshitoshi,"Japanese, 1839–1892",,Tsukioka Yoshitoshi,Japanese,1839,1892,1888,1888,1888,Polychrome woodblock print; ink and color on paper,14 x 9 3/8 in. (35.6 x 23.8 cm),"Gift of Lincoln Kirstein, 1985",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55918,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3687b,false,true,55919,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Tsukioka Yoshitoshi,"Japanese, 1839–1892",,Tsukioka Yoshitoshi,Japanese,1839,1892,1888,1888,1888,Polychrome woodblock print; ink and color on paper,14 9/16 x 10 in. (37 x 25.4 cm),"Gift of Lincoln Kirstein, 1985",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55919,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.349,false,true,72827,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Tsukioka Yoshitoshi,"Japanese, 1839–1892",,Tsukioka Yoshitoshi,Japanese,1839,1892,1888,1888,1888,Polychrome woodblock print; ink and color on paper,14 5/8 x 10 in. (37.1 x 25.4 cm),"Purchase, Friends of Asian Art Gifts, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/72827,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3307a–c,false,true,55411,Asian Art,Print,九段坂上靖国神社庭内真図|True View of the Courtyard of Yasukuni Shrine at Kudan Sakaue (Kudan Sakaue Yasukuni jinsha teinai shin zu),Japan,Meiji period (1868–1912),,,,Artist,,Inoue Yasuji,"Japanese, 1864–1889",,INOUE YASUJI,Japanese,1864,1889,1888,1888,1888,Triptych of polychrome woodblock prints; ink and color on paper,Image: 14 x 29 1/2 in. (35.6 x 74.9 cm),"Gift of Lincoln Kirstein, 1959",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55411,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.61.51,false,true,54688,Asian Art,Album leaf,,Japan,Meiji period (1868–1912),,,,Artist,,Kawanabe Kyōsai,"Japanese, 1831–1889",,Kawanabe Kyōsai,Japanese,1831,1889,ca. 1888,1878,1898,Album leaf mounted as a hanging scroll; ink and color on silk,14 1/2 x 11 in. (36.8 x 27.9 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54688,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.13,false,true,45692,Asian Art,Hanging scroll,,Japan,Muromachi period (1392–1573),,,,Artist,Attributed to,Kano Motonobu,"Japan, ca. 1476–1559",,Kano Motonobu,Japanese,1476,1559,15th century,1400,1499,Hanging scroll; ink on paper,25 1/8 x 12 1/2 in. (63.8 x 31.8 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45692,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1982.244.16,false,true,45072,Asian Art,Screen,,Japan,Muromachi period (1392–1573),,,,Artist,,Sesson Shūkei,ca. 1504–ca. 1589,,Sesson Shūkei,Japanese,1504,1589,16th century,1500,1599,Six-panel folding screen; ink on paper,60 1/2 in. x 11 ft. 8 1/4 in. (153.7 x 356.2 cm),"Bequest of Hope Skillman Schary, 1981",,,,,,,,,,,,Screens,,http://www.metmuseum.org/art/collection/search/45072,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"29.100.495g, h",false,true,45219,Asian Art,Hanging scroll,,Japan,Momoyama period (1573–1615),,,,Artist,In the Style of,Kano Eitoku,"Japanese, 1534–1590",,Kano Eitoku,Japanese,1543,1590,16th century,1534,1590,"Hanging scroll; ink, color, and gold on gilded paper",61 1/8 x 45 1/2in. (155.3 x 115.6cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45219,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -51.95,false,true,45070,Asian Art,Painting,,Japan,Muromachi period (1392–1573),,,,Artist,Attributed to,Kano Motonobu,"Japan, ca. 1476–1559",,Kano Motonobu,Japanese,1476,1559,16th century,1500,1573,Framed painting; ink on paper,23 1/4 x 42 in. (59.1 x 106.7 cm),"Fletcher Fund,1951",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45070,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.268.41,false,true,42342,Asian Art,Hanging scroll,山水図|Landscape,Japan,Muromachi period (1392–1573),,,,Artist,,Maejima Sōyū,active mid-16th century,,Maejima Sōyū,Japanese,1536,1570,16th century,1500,1573,Hanging scroll; ink and color on paper,Image: 20 1/4 × 13 5/8 in. (51.4 × 34.6 cm) Overall with mounting: 55 3/4 × 18 3/4 in. (141.6 × 47.6 cm) Overall with knobs: 55 3/4 × 20 3/4 in. (141.6 × 52.7 cm),"The Harry G. C. Packard Collection of Asian Art, Gift of Harry G. C. Packard, and Purchase, Fletcher, Rogers, Harris Brisbane Dick, and Louis V. Bell Funds, Joseph Pulitzer Bequest, and The Annenberg Fund Inc. Gift, 1975",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/42342,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.10,false,true,45374,Asian Art,Fan mounted as a hanging scroll,伝狩野之信　列子御風図扇面|Resshi Flying on a Cloud,Japan,Muromachi period (1392–1573),,,,Artist,,Kano Yukinobu,"Japanese, ca. 1513–1575",,Kano Yukinobu,Japanese,1513,1575,16th century,1513,1575,Fan mounted as hanging scroll; ink on paper,8 1/4 x 20 1/2 in. (21 x 52 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45374,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1997.140,false,true,39487,Asian Art,Fan mounted as hanging scroll,,Japan,Muromachi period (1392–1573),,,,Artist,Attributed to,Kano Yukinobu,"Japanese, ca. 1513–1575",,Kano Yukinobu,Japanese,1513,1575,16th century,1500,1573,"Originally a fan mounted as a hanging scroll; ink, color, and silver on gilded paper",9 3/4 x 19 1/2 in. (24.8 x 49.5 cm),"Purchase, Friends of Asian Art Gifts, 1997",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/39487,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.91,false,true,45206,Asian Art,Hanging scroll,,Japan,Muromachi period (1392–1573),,,,Artist,Attributed to,Kano Yukinobu,"Japanese, ca. 1513–1575",,Kano Yukinobu,Japanese,1513,1575,16th century,1513,1575,Hanging scroll; ink on paper,39 1/8 x 16 3/4 in. (99.4 x 42.5 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45206,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.92,false,true,45207,Asian Art,Hanging scroll,,Japan,Muromachi period (1392–1573),,,,Artist,,Kano Yukinobu,"Japanese, ca. 1513–1575",,Kano Yukinobu,Japanese,1513,1575,16th century,1513,1575,Hanging scroll; ink on paper,39 1/8 x 16 3/4 in. (99.4 x 42.5 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45207,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"53.156.1, .2",false,true,57346,Asian Art,Screen,,Japan,,,,,Artist,,Kano Tan'yū,"Japanese, 1602–1674",,Kano Tan'yū,Japanese,1602,1674,17th century,1600,1699,Pair of six-panel folding screens; paint and gilt on paper,Overall (each screen): 70 1/4 x 77 1/4 in. (178.4 x 196.2 cm),"Gift of Major General R. B. Woodruff, 1953",,,,,,,,,,,,Screens,,http://www.metmuseum.org/art/collection/search/57346,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"17.118.114, .115",false,true,45423,Asian Art,Screen,,Japan,Edo period (1615–1868),,,,Artist,,Unkoku Tōeki,1591–1644,,Unkoku Tōeki,Japanese,1591,1644,17th century,1600,1699,Pair of six-panel screens; ink and gold on paper,Overall (each screen): 67 1/2 x 147 in. (171.5 x 373.4 cm),"Rogers Fund, 1917",,,,,,,,,,,,Screens,,http://www.metmuseum.org/art/collection/search/45423,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"1972.179.1, .2",false,true,45082,Asian Art,Screen,,Japan,Edo period (1615–1868),,,,Artist,,Kusumi Morikage,ca. 1620–1690,,Kusumi Morikage,Japanese,1610,1700,17th century,1600,1699,Pair of six-panel folding screens ; ink and light color on paper,Overall (.1): 68 3/4 x 146 3/8 in. (174.6 x 371.8 cm) Overall (.2): 69 x 146 3/8 in. (175.3 x 371.8 cm),"Purchase, Joseph Pulitzer Bequest, 1972",,,,,,,,,,,,Screens,,http://www.metmuseum.org/art/collection/search/45082,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.496,false,true,45651,Asian Art,Screen,,Japan,Edo period (1615–1868),,,,Artist,Attributed to,Sesson Shūkei,ca. 1504–ca. 1589,,Sesson Shūkei,Japanese,1504,1589,17th century,1600,1699,Two-panel folding screen; ink on paper,59 1/4 x 66 7/16 in. (150.5 x 168.8 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Screens,,http://www.metmuseum.org/art/collection/search/45651,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -25.215.53,false,true,45654,Asian Art,Screen,,Japan,Edo period (1615–1868),,,,Artist,,Kano Eitoku,"Japanese, 1534–1590",,Kano Eitoku,Japanese,1543,1590,17th century,1600,1699,"Two-panel folding screen; ink, color, and gold on paper (obverse); ink and color on paper (reverse side)",22 x 73 1/2 in. (55.9 x 186.7 cm),"Fletcher Fund, 1925",,,,,,,,,,,,Screens,,http://www.metmuseum.org/art/collection/search/45654,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.495a–f,false,true,45695,Asian Art,Screen,,Japan,Edo period (1615–1868),,,,Artist,In the Style of,Kano Eitoku,"Japanese, 1534–1590",,Kano Eitoku,Japanese,1543,1590,17th century,1600,1699,Six-panel folding screen; ink and color on gilt paper,65 1/4 x 146 1/4in. (165.8 x 371.5 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Screens,,http://www.metmuseum.org/art/collection/search/45695,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"29.100.491, .492",false,true,45341,Asian Art,Folding screens,伝三谷等宿筆　松と椿に鷹・柳と椿に小禽図屏風|Pine and Camellia with Hawks and Willow and Camellia with Small Birds,Japan,Edo period (1615–1868),,,,Artist,Attributed to,Mitani Tōshuku,"Japanese, 1577–1654",,Mitani Tōshuku,Japanese,1577,1654,17th century,1600,1699,Pair of six-panel folding screens; ink and color on paper,Image (each): 58 7/16 in. x 10 ft. 8 13/16 in. (148.5 x 327.2 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Screens,,http://www.metmuseum.org/art/collection/search/45341,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2014.719.11,false,true,60485,Asian Art,Hanging scroll,清巌宗渭 「地獄」|Hell,Japan,Edo period (1615–1868),,,,Artist,,Seigan Sōi,"Japanese, 1588–1661",,Seigan Sōi,Japanese,1588,1661,17th century,1600,1699,Hanging scroll; ink on paper,Image: 12 x 35 1/2 in. (30.5 x 90.2 cm),"Gift of Sylvan Barnet and William Burto, in honor of Setsu Isao, 2014",,,,,,,,,,,,Calligraphy,,http://www.metmuseum.org/art/collection/search/60485,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2009.260.1, .2",false,true,75372,Asian Art,Screen,,Japan,Edo period (1615–1868),,,,Artist,,Kano Einō,"Japanese, 1631–1697",,Kano Einō,Japanese,1631,1697,17th century,1600,1699,"Pair of six-panel folding screens; ink, color, and gold on paper",Image (each screen): 44 7/8 x 111 in. (114 x 282 cm),"Purchase, Lila Acheson Wallace Gift, Mary and James G. Wallach Foundation Gift, Gift of Dr. Mortimer D. Sackler, Theresa Sackler and Family, and Dodge Fund, 2009",,,,,,,,,,,,Screens,,http://www.metmuseum.org/art/collection/search/75372,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.89,false,true,45373,Asian Art,Screen,,Japan,Momoyama period (1573–1615),,,,Artist,,Hasegawa Tōchō,"Japanese, active 1624–43",,Hasegawa Tōcho,Japanese,1624,1643,17th century,1600,1699,Six-panel screen; ink on paper,58 1/4 x 121 3/4 in. (147.9 x 309.2 cm),"Fletcher Fund, 1933",,,,,,,,,,,,Screens,,http://www.metmuseum.org/art/collection/search/45373,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"36.120.643a, b",false,true,63316,Asian Art,Incense burner,,Japan,,,,,Artist,,Hon'ami Kōetsu,"Japanese, 1558–1637",,Hon'ami Kōetsu,Japanese,1558,1637,17th century,1600,1699,"Square with sunken panels on all sides and bottom; four moulded feet; light brown clay; yellow glaze; decoration of flowers, grasses and butterflies in green and dark blue; perforated iron cover (Kyoto ware)",H. 4 1/4 in. (10.8 cm); W. sq. 5 in. (12.7 cm),"The Howard Mansfield Collection, Gift of Howard Mansfield, 1936",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/63316,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"66.155.6a, b",false,true,53214,Asian Art,Incense burner with lid,,Japan,Edo period (1615–1868),,,,Artist,,Jokei,"Japanese, died 1636",,Jokei,Japanese,1536,1636,17th century,1600,1700,Earthenware with glaze; lid of network brass grille (Raku ware),H. 4 1/4 in. (10.8 cm); Gr. Diam. 4 1/4 in. (10.8 cm),"Gift of Harold G. Henderson, 1966",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/53214,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.628,false,true,63111,Asian Art,Teabowl,,Japan,Edo period (1615–1868),,,,Artist,Attributed to,Hon'ami Kōetsu,"Japanese, 1558–1637",,Hon'ami Kōetsu,Japanese,1558,1637,17th century,1600,1699,Clay; crackled glaze with markings; (Kyoto ware),H. 3 3/4 in. (9.5 cm); Diam. 4 7/8 in. (12.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/63111,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -91.1.320,false,true,39576,Asian Art,Water jar,,Japan,Edo period (1615–1868),,,,Artist,,Nonomura Ninsei,"Japanese, active ca. 1646–94",,Ninsei Nonomura,Japanese,1646,1694,17th century,1600,1699,Pottery covered with glaze; handles at sides (Kyoto ware),H. 6 1/3 in. (16.1 cm),"Edward C. Moore Collection, Bequest of Edward C. Moore, 1891",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/39576,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.29,false,true,54593,Asian Art,Hanging scroll,,Japan,,,,,Artist,In the Style of,Kano Sanraku,"Japanese, 1559–1635",,Kano Sanraku,Japanese,1559,1635,17th century,1600,1699,Hanging scroll; monochrome on silk,40 1/2 x 21 in. (102.9 x 53.3 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54593,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -60.47,false,true,57228,Asian Art,Hanging scroll,,Japan,,,,,Artist,,Kano Tan'yū,"Japanese, 1602–1674",,Kano Tan'yū,Japanese,1602,1674,17th century,1602,1674,Hanging scroll; ink on paper,5 1/4 x 19 3/8 in. (13.3 x 49.2 cm),"Rogers Fund, 1960",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57228,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.35,false,true,54594,Asian Art,Hanging scroll,,Japan,,,,,Artist,Attributed to,Kano Tan'yū,"Japanese, 1602–1674",,Kano Tan'yū,Japanese,1602,1674,17th century,1600,1699,Hanging scroll; india ink on paper,48 x 21 1/8 in. (121.9 x 53.7 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/54594,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -53.118.2,false,true,57336,Asian Art,Hanging scroll,,Japan,,,,,Artist,Attributed to,Kano Naonobu,"Japanese, 1607–1650",,Kano Naonobu,Japanese,1607,1650,17th century,1607,1650,Hanging scroll; ink on paper,38 3/8 x 11 3/16 in. (97.5 x 28.4 cm),"Rogers Fund, 1953",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57336,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.140.5,false,true,45702,Asian Art,Painting,,Japan,Edo period (1615–1868),,,,Artist,,Kano Shōun,1637–1702,,Kano Shōun,Japanese,1637,1702,17th century,1600,1799,Unmounted shikisi leaf; ink and color on silk,7 1/4 × 6 1/2 in. (18.4 × 16.5 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45702,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.140.6,false,true,45703,Asian Art,Painting,,Japan,Edo period (1615–1868),,,,Artist,,Kano Shōun,1637–1702,,Kano Shōun,Japanese,1637,1702,17th century,1600,1699,Unmounted shikisi leaf; ink and color on silk,7 1/4 × 6 1/2 in. (18.4 × 16.5 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45703,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.140.7,false,true,45704,Asian Art,Painting,,Japan,Edo period (1615–1868),,,,Artist,,Kano Shōun,1637–1702,,Kano Shōun,Japanese,1637,1702,17th century,1600,1699,Unmounted shikisi leaf; ink and color on silk,7 1/4 × 6 1/2 in. (18.4 × 16.5 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45704,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.140.8,false,true,45705,Asian Art,Painting,,Japan,Edo period (1615–1868),,,,Artist,,Kano Shōun,1637–1702,,Kano Shōun,Japanese,1637,1702,17th century,1600,1699,Unmounted shikisi leaf; ink and color on silk,7 1/4 × 6 1/2 in. (18.4 × 16.5 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45705,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.140.9,false,true,45706,Asian Art,Painting,,Japan,Edo period (1615–1868),,,,Artist,,Kano Shōun,1637–1702,,Kano Shōun,Japanese,1637,1702,17th century,1600,1699,Unmounted shikisi leaf; ink and color on silk,7 1/4 × 6 1/2 in. (18.4 × 16.5 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45706,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.140.10,false,true,658681,Asian Art,Painting,,Japan,Edo period (1615–1868),,,,Artist,,Kano Shōun,1637–1702,,Kano Shōun,Japanese,1637,1702,17th century,1600,1799,Unmounted shikisi leaf; ink and color on silk,7 1/4 × 6 1/2 in. (18.4 × 16.5 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/658681,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.140.11,false,true,45708,Asian Art,Painting,,Japan,Edo period (1615–1868),,,,Artist,,Kano Shōun,1637–1702,,Kano Shōun,Japanese,1637,1702,17th century,1600,1699,Unmounted shikisi leaf; ink and color on silk,7 1/4 × 6 1/2 in. (18.4 × 16.5 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45708,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.140.12,false,true,45709,Asian Art,Painting,,Japan,Edo period (1615–1868),,,,Artist,,Kano Shōun,1637–1702,,Kano Shōun,Japanese,1637,1702,17th century,1600,1699,Unmounted shikisi leaf; ink and color on silk,7 1/4 × 6 1/2 in. (18.4 × 16.5 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45709,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.140.13,false,true,45710,Asian Art,Painting,,Japan,Edo period (1615–1868),,,,Artist,,Kano Shōun,1637–1702,,Kano Shōun,Japanese,1637,1702,17th century,1600,1699,Unmounted shikisi leaf; ink and color on silk,7 1/4 × 6 1/2 in. (18.4 × 16.5 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45710,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.140.14,false,true,45711,Asian Art,Painting,,Japan,Edo period (1615–1868),,,,Artist,,Kano Shōun,1637–1702,,Kano Shōun,Japanese,1637,1702,17th century,1600,1699,Unmounted shikisi leaf; ink and color on silk,7 1/4 × 6 1/2 in. (18.4 × 16.5 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45711,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.140.15,false,true,45712,Asian Art,Painting,,Japan,Edo period (1615–1868),,,,Artist,,Kano Shōun,1637–1702,,Kano Shōun,Japanese,1637,1702,17th century,1600,1699,Unmounted shikisi leaf; ink and color on silk,7 1/4 × 6 1/2 in. (18.4 × 16.5 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45712,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.140.16,false,true,45713,Asian Art,Painting,,Japan,Edo period (1615–1868),,,,Artist,,Kano Shōun,1637–1702,,Kano Shōun,Japanese,1637,1702,17th century,1600,1699,Unmounted shikisi leaf; ink and color on silk,7 1/4 × 6 1/2 in. (18.4 × 16.5 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45713,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.140.17,false,true,45714,Asian Art,Painting,,Japan,Edo period (1615–1868),,,,Artist,,Kano Shōun,1637–1702,,Kano Shōun,Japanese,1637,1702,17th century,1600,1699,Unmounted shikisi leaf; ink and color on silk,7 1/4 × 6 1/2 in. (18.4 × 16.5 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45714,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.140.18,false,true,45715,Asian Art,Painting,,Japan,Edo period (1615–1868),,,,Artist,,Kano Shōun,1637–1702,,Kano Shōun,Japanese,1637,1702,17th century,1600,1699,Unmounted shikisi leaf; ink and color on silk,7 1/4 × 6 1/2 in. (18.4 × 16.5 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45715,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.140.19,false,true,45716,Asian Art,Painting,,Japan,Edo period (1615–1868),,,,Artist,,Kano Shōun,1637–1702,,Kano Shōun,Japanese,1637,1702,17th century,1600,1699,Unmounted shikisi leaf; ink and color on silk,7 1/4 × 6 1/2 in. (18.4 × 16.5 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45716,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.140.20,false,true,45717,Asian Art,Painting,,Japan,Edo period (1615–1868),,,,Artist,,Kano Shōun,1637–1702,,Kano Shōun,Japanese,1637,1702,17th century,1600,1699,Unmounted shikisi leaf; ink and color on silk,7 1/4 × 6 1/2 in. (18.4 × 16.5 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45717,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.140.21,false,true,45718,Asian Art,Painting,,Japan,Edo period (1615–1868),,,,Artist,,Kano Shōun,1637–1702,,Kano Shōun,Japanese,1637,1702,17th century,1600,1699,Unmounted shikisi leaf; ink and color on silk,7 1/4 × 6 1/2 in. (18.4 × 16.5 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45718,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.140.22,false,true,45719,Asian Art,Painting,,Japan,Edo period (1615–1868),,,,Artist,,Kano Shōun,1637–1702,,Kano Shōun,Japanese,1637,1702,17th century,1600,1699,Unmounted shikisi leaf; ink and color on silk,7 1/4 × 6 1/2 in. (18.4 × 16.5 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45719,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.268.56,false,true,45731,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Kusumi Morikage,ca. 1620–1690,,Kusumi Morikage,Japanese,1610,1700,17th century,1620,1690,Hanging scroll; ink on paper,48 9/16 x 20 7/8 in. (123.4 x 53.1 cm),"The Harry G. C. Packard Collection of Asian Art, Gift of Harry G. C. Packard, and Purchase, Fletcher, Rogers, Harris Brisbane Dick, and Louis V. Bell Funds, Joseph Pulitzer Bequest, and The Annenberg Fund Inc. Gift, 1975",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45731,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -59.80,false,true,45646,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,Formerly attributed to,Sōami,"Japanese, died 1525",,Sōami,Japanese,,1525,17th century,1600,1699,Hanging scroll; ink on paper,17 3/4 x 10 1/2 in. (45.1 x 26.7 cm),"Gift of Mrs. John D. Rockefeller 3rd, 1959",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45646,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.72,false,true,45645,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,Attributed to,Sōami,"Japanese, died 1525",,Sōami,Japanese,,1525,17th century,1600,1699,Hanging scroll; ink on paper,13 1/4 x 27 3/8 in. (33.7 x 69.5 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45645,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.529,false,true,45649,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,In the Style of,Sesshū Tōyō,"Japanese, 1420–1506",,Sesshū Tōyō,Japanese,1420,1506,17th century,1615,1699,Hanging scroll; ink on paper,33 1/4 x 11 5/8 in. (84.5 x 29.5 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45649,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1974.224.1–.31,false,true,74462,Asian Art,Album leaf,,Japan,Edo period (1615–1868),,,,Artist,,Kano Tsunenobu,"Japanese, 1636–1713",,Kano Tsunenobu,Japanese,1636,1713,17th century,1636,1699,Album leaf; ink and color on silk,11 7/16 x 16 3/4 in. (29 x 42.5 cm),"Gift of Mr. and Mrs. Harry Rubin, 1974",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/74462,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -54.22.1,false,true,57339,Asian Art,Painting,,Japan,Edo period (1615–1868),,,,Artist,,Matsuo Basho,"Japanese, 1644–1694",,Matsuo Basho,Japanese,1644,1694,17th century,1644,1694,Ink wash and color on paper,43 x 11 1/2 in. (109.2 x 29.2 cm),"Gift of Dr. Robert Pollak, 1954",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57339,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.440,false,true,48971,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,In the Style of,Tawaraya Sōtatsu,"Japanese, died ca. 1640",,Tawaraya Sōtatsu,Japanese,1540,1640,17th century,1600,1640,Hanging scroll; ink and color on paper,33 7/8 x 17 1/8 in. (86 x 43.5 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/48971,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.464,false,true,48972,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,School of,Tawaraya Sōtatsu,"Japanese, died ca. 1640",,Tawaraya Sōtatsu,Japanese,1540,1640,17th century,1600,1650,Hanging scroll; ink and color on paper,49 3/4 x 18 1/4 in. (126.4 x 46.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/48972,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.68,false,true,45641,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,Attributed to,Shubun,"Japanese, active ca. 1414",,Shubun,Japanese,1414,1414,17th century,1600,1699,Hanging scroll; ink and color on paper,25 3/4 x 12 1/8 in. (65.4 x 30.8 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45641,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.268.79,false,true,49067,Asian Art,Hanging scroll,即非如一筆　墨蹟|Reading a Sutra by Moonlight,Japan,Edo period (1615–1868),,,,Artist,,Sokuhi Nyoitsu (Jifei Ruyi),"Chinese/ Japanese, 1616–1671",,Sokuhi Nyoitsu,Japanese,1616,1671,17th century,1616,1671,Hanging scroll; ink on paper,Image: 10 13/16 x 23 7/8 in. (27.5 x 60.6 cm) Overall with mounting: 44 1/4 × 24 5/8 in. (112.4 × 62.5 cm) Overall with knobs: 44 1/4 × 26 3/4 in. (112.4 × 68 cm),"The Harry G. C. Packard Collection of Asian Art, Gift of Harry G. C. Packard, and Purchase, Fletcher, Rogers, Harris Brisbane Dick, and Louis V. Bell Funds, Joseph Pulitzer Bequest, and The Annenberg Fund Inc. Gift, 1975",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49067,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"51.132.1a, b",false,true,58328,Asian Art,Coal container,,Japan,,,,,Artist,School of,Ogata Kōrin,"Japanese, 1658–1716",,Kōrin,Japanese,1658,1716,18th century,1700,1799,"Wood, metal, mother-of-pearl, lacquer",H. (with cover) 13 3/4 in. (34.9 cm); Diam. 16 1/4 in. (41.3 cm),"Gift of Mrs. David Randall-MacIver and Mrs. Natalie Tuttle Martin in memory of her husband Dr. George Montgomery Tuttle, 1951",,,,,,,,,,,,Woodwork,,http://www.metmuseum.org/art/collection/search/58328,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -51.132.2a–c,false,true,56533,Asian Art,Coal container,,Japan,,,,,Artist,,Ogawa Haritsu (Ritsuō),"Japanese, 1663–1747",,Ritsuō,Japanese,1663,1747,18th century,1700,1799,"Wood, metal, mother-of-pearl",H. (w/cover and base) 14 in. (35.6 cm); Diam. (grt.) 13 in. (33 cm),"Gift of Mrs. David Randall-MacIver and Mrs. Natalie Tuttle Martin in memory of her husband, Dr. George Montgomery Tuttle, 1951",,,,,,,,,,,,Woodwork,,http://www.metmuseum.org/art/collection/search/56533,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -27.117.1,false,true,57108,Asian Art,Printer's woodblock,,Japan,,,,,Artist,Original illustration designed by,Nishikawa Sukenobu,"Japanese, 1671–1750",,Nishikawa Sukenobu,Japanese,1671,1750,18th century,1700,1799,Wood,10 5/8 x 9 11/16 in. (27 x 24.6 cm),"Rogers Fund, 1927",,,,,,,,,,,,Woodblocks,,http://www.metmuseum.org/art/collection/search/57108,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -10.211.192,false,true,59880,Asian Art,Netsuke,,Japan,,,,,Artist,Formerly Attributed to,Garaku,"Japanese, active second half of the 18th century",,Garaku,Japanese,1750,1799,18th century,1700,1799,Ivory,H. 13/16 in. (2.1 cm); W. 1 1/2 in. (3.8 cm); L. 1 13/16 in. (4.6 cm),"Gift of Mrs. Russell Sage, 1910",,,,,,,,,,,,Netsuke,,http://www.metmuseum.org/art/collection/search/59880,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.226,false,true,58914,Asian Art,Inrō,古墨形鞘印籠 (宝露臺)|Inrō Imitating an Old Chinese Ink Cake,Japan,Edo period (1615–1868,,,,Artist,,Ogawa Haritsu (Ritsuō),"Japanese, 1663–1747",,Ritsuō,Japanese,1663,1747,18th century,1700,1799,"Two-part (sheath-type); lacquered wood, metal, leather, with black and brown togidashimaki-e, takamaki-e on black ground Netsuke: manjū type with maki-e Daruma Ojime: ceramic bead with incense and ash design",3 1/16 x 2 1/16 x 11/16 in. (7.7 x 5.3 x 1.7 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Inrō,,http://www.metmuseum.org/art/collection/search/58914,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.528,false,true,49082,Asian Art,Screen,,Japan,Edo period (1615–1868),,,,Artist,,Miwa Zaiei,died 1789,,Miwa Zaiei,Japanese,,1789,18th century,1700,1799,"Two-panel folding screen; ink, color, and gold on paper",40 x 49 1/4 in. (101.6 x 125.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Screens,,http://www.metmuseum.org/art/collection/search/49082,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -10.211.2325,false,true,59383,Asian Art,Netsuke,,Japan,Edo period (1615–1868),,,,Artist,,Sanshō,1871–1936,,Sanshō,Japanese,1871,1936,18th century,1700,1799,Wood with ivory insert,H. 2 3/4 in. (7 cm),"Gift of Mrs. Russell Sage, 1910",,,,,,,,,,,,Netsuke,,http://www.metmuseum.org/art/collection/search/59383,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.206,false,true,53936,Asian Art,Inrō,古満休伯作 群鶴蒔絵印籠|Inrō with Cranes and Pines,Japan,Edo period (1615–1868),,,,Artist,,Koma Kyūhaku V,"Japanese, died 1794",,Koma Kyūhaku V,Japanese,,1794,18th century,1701,1800,"Gold togidashi lacquer ground with gold and silver takamaki-e and hiramaki-e, and black and red lacquer Netsuke: hat and mask; lacquer on wood Ojime: jade bead",H. 3 1/16 in. (7.7 cm); W. 2 3/16 in. ( 5.5 cm); D. 7/8 in. (2.3 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Inrō,,http://www.metmuseum.org/art/collection/search/53936,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.67.72,false,true,45462,Asian Art,Inrō,,Japan,Edo period (1615–1868),,,,Artist,After,Ogata Kōrin,"Japanese, 1658–1716",,Kōrin,Japanese,1658,1716,18th century,1700,1799,Gold lacquer with mother-of-pearl and pewter inlay Ojime: carnelian bead Netsuke: carved wood deer,2 1/16 x 1 15/16 x 3/4 in. (5.3 x 4.9 x 1.9 cm),"Rogers Fund, 1913",,,,,,,,,,,,Inrō,,http://www.metmuseum.org/art/collection/search/45462,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.229,false,true,45457,Asian Art,Inrō,,Japan,Edo period (1615–1868),,,,Artist,After,Ogata Kōrin,"Japanese, 1658–1716",,Kōrin,Japanese,1658,1716,18th century,1700,1799,Gold maki-e and mother-of-pearl inlay Ojime: wood-and-gilt bead in shape of sake bottle Netsuke: ivory with lacquer design of toys,H. 2 3/8 in. (6.1 cm); W. 2 3/16 in. (5.5 cm); D. 13/16 in. (2.1 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Inrō,,http://www.metmuseum.org/art/collection/search/45457,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.139a–d,false,true,57925,Asian Art,Writing box,,Japan,Edo period (1615–1868),,,,Artist,Formerly Attributed to,Ogata Kōrin,"Japanese, 1658–1716",,Kōrin,Japanese,1658,1716,18th century,1700,1799,Lacquer with pewter and gold,H. 1 5/8 in. (4.1 cm); W. 8 1/4 in. (21 cm); L. 9 in. (22.9 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Lacquer,,http://www.metmuseum.org/art/collection/search/57925,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.703,false,true,44867,Asian Art,Writing box,鼠扇蒔絵象嵌硯箱|Writing Box (suzuribako) with Mice and Fan,Japan,Edo period (1615–1868),,,,Artist,School of,Ogawa Haritsu (Ritsuō),"Japanese, 1663–1747",,Ritsuō,Japanese,1663,1747,18th century,1700,1799,"Lacquered wood with gold, silver hiramaki-e, ceramic, mother-of-pearl, and pewter inlays on wood ground",H. 1 3/4 in. (4.4 cm); W. 8 1/8 in. (20.6 cm); L. 10 1/8 in. (25.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Lacquer,,http://www.metmuseum.org/art/collection/search/44867,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.151,false,true,58335,Asian Art,Panel,,Japan,Edo period (1615–1868),,,,Artist,Attributed to,Ogawa Haritsu (Ritsuō),"Japanese, 1663–1747",,Ritsuō,Japanese,1663,1747,18th century,1700,1799,Lacquered wood,L. 10 3/4 in. (27.3 cm); W. 14 3/4 in. (37.5 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Lacquer,,http://www.metmuseum.org/art/collection/search/58335,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"36.100.146a, b",false,true,57937,Asian Art,Box,,Japan,Edo period (1615–1868),,,,Artist,,Ogawa Haritsu (Ritsuō),"Japanese, 1663–1747",,Ritsuō,Japanese,1663,1747,18th century,1700,1799,Lacquer,H. 4 5/8 in. (11.7 cm); L. 9 3/8 in. (23.8 cm); W. 8 3/4 in. (22.2 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Lacquer,,http://www.metmuseum.org/art/collection/search/57937,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.141a–e,false,true,44940,Asian Art,Writing box,,Japan,Edo period (1615–1868),,,,Artist,Style of,Ogawa Haritsu (Ritsuō),"Japanese, 1663–1747",,Ritsuō,Japanese,1663,1747,18th century,1700,1799,Gold and glazed pottery on colored lacquer inlaid with mother-of-pearl,H. 1 1/2 in. (3.8 cm); W. 7 in. (17.8 cm); L 9 1/2 in. (24.1 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Lacquer,,http://www.metmuseum.org/art/collection/search/44940,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.769a–c,false,true,78671,Asian Art,Illustrated books,,Japan,Edo period (1615–1868),,,,Artist,,Okumura Masanobu,"Japanese, 1686–1764",,Masanobu Okumura,Japanese,1686,1764,18th century,1700,1764,Set of three woodblock printed books; ink and color on paper,each: 5 9/16 × 8 1/16 in. (14.2 × 20.5 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78671,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.188,false,true,44866,Asian Art,Inrō,,Japan,Edo period (1615–1868),,,,Artist,,Mochizuki Hanzan,"Japanese, 1743–?1790",,Mochizuki Hanzan,Japanese,1743,1790,18th century,1700,1799,"Case: powdered gold (maki-e) on lacquer with ceramic, lead, and mother-of-pearl inlays; ceramic seal on base; Fastener (ojime): alloy of silver and copper with design of mice in rice cakes; Toggle (netsuke): lacquer with design of lotus leaf and caterpillar in lead and ivory inlays",H. 3 in. (7.6 cm); W. 2 3/16 in. (5.5 cm); D. 13/16 in. (2 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Inrō,,http://www.metmuseum.org/art/collection/search/44866,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"36.100.145a, b",false,true,57929,Asian Art,Sutra chest,,Japan,Edo period (1615–1868),,,,Artist,,Mochizuki Hanzan,"Japanese, 1743–?1790",,Mochizuki Hanzan,Japanese,1743,1790,18th century,1700,1799,"Colored lacquer, gold, and ceramic on natural wood",H. 10 3/4 in. (27.3 cm); L. 17 3/4 in. (45.1 cm); W. 10 1/4 in. (26 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Lacquer,,http://www.metmuseum.org/art/collection/search/57929,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.147a–c,false,true,57939,Asian Art,Document box,,Japan,Edo period (1615–1868),,,,Artist,,Nagata Yūji,"Japanese, acive 1711–36",,Nagata Yūji,Japanese,1711,1736,18th century,1700,1799,Lacquer with gold and pewter,H. 6 in. (15.2 cm); W. 8 1/4 in. (21 cm); L. 10 3/4 in. (27.3 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Lacquer,,http://www.metmuseum.org/art/collection/search/57939,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -10.211.1981,false,true,59141,Asian Art,Netsuke,虎木彫根付|Tiger,Japan,Edo period (1615–1868),,,,Artist,,Minkō,"Japanese, ca. 1735–1816",,Minkō,Japanese,1735,1816,18th century,1700,1799,Wood,H. 1 1/8 in. (2.9 cm); W. 1 5/8 in. (4.1 cm),"Gift of Mrs. Russell Sage, 1910",,,,,,,,,,,,Netsuke,,http://www.metmuseum.org/art/collection/search/59141,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -10.211.2278,false,true,59357,Asian Art,Netsuke,獏木彫根付|Crouching Baku (Mythical Creature),Japan,Edo period (1615–1868),,,,Artist,,Sadatake,"Japanese, active 18th century",,Sadatake,Japanese,1700,1799,18th century,1700,1799,Wood,H. 2 in. (5.1 cm); W. 2 1/4 in. (5.7 cm); D. 1 1/2 in. (3.8 cm),"Gift of Mrs. Russell Sage, 1910",,,,,,,,,,,,Netsuke,,http://www.metmuseum.org/art/collection/search/59357,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -10.211.2383,false,true,59420,Asian Art,Netsuke,ガルダ木彫根付|Garuda,Japan,Edo period (1615–1868),,,,Artist,,Tori,"Japanese, active 18th century",,Tori,Japanese,1700,1799,18th century,1700,1799,Wood; dark brown,H. 1 7/8 in. (4.8 cm); W. 1 5/8 in. (4.1 cm),"Gift of Mrs. Russell Sage, 1910",,,,,,,,,,,,Netsuke,,http://www.metmuseum.org/art/collection/search/59420,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.67.64,false,true,45586,Asian Art,Inrō,,Japan,Edo period (1615–1868),,,,Artist,,Tōyō,"Japanese, active ca. 1764–71",,Tōyō,Japanese,1764,1771,18th century,1700,1799,Aventurine lacquer with gold sprinkled and polished lacquer; Netsuke: Box with flowers; Ojime: coral bead,2 3/8 x 2 5/16 x 15/16 in. (6 x 5.8 x 2.4 cm),"Rogers Fund, 1913",,,,,,,,,,,,Inrō,,http://www.metmuseum.org/art/collection/search/45586,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1993.341.2,false,true,45493,Asian Art,Noh mask,,Japan,Edo period (1615–1868),,,,Artist,,Genkyu Michinaga,"Japanese, active second half of the 17th century",,Genkyu Michinaga,Japanese,1650,1699,18th century,1700,1799,Painted wood,W. 5 1/2 in. (14 cm); L. 8 1/2 in. (21.6 cm),"Purchase, Lila Acheson Wallace Gift, 1993",,,,,,,,,,,,Masks,,http://www.metmuseum.org/art/collection/search/45493,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1617,false,true,37333,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Shigenaga,1697–1756,,Shigenaga,Japanese,1697,1756,18th century,1700,1756,Polychrome woodblock print; ink and color on paper,H. 5 3/4 in. (14.6 cm); W. 12 11/16 in. (32.2 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37333,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1618,false,true,37334,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Shigenaga,1697–1756,,Shigenaga,Japanese,1697,1756,18th century,1700,1756,Polychrome woodblock print; ink and color on paper,H. 5 1/4 in. (13.3 cm); W. 12 11/16 in. (32.2 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37334,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1589,false,true,55754,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Okumura Masanobu,"Japanese, 1686–1764",,Masanobu Okumura,Japanese,1686,1764,18th century,1700,1764,"""White–line"" woodblock print; ink and color on paper",H. 11 7/16 in. (29.1 cm); W. 5 1/2 in. (14 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55754,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1628,false,true,55793,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,18th century,1764,1772,Polychrome woodblock print; ink and color on paper,H. 11 1/2 in. (29.2 cm); W. 8 1/4 in. (21 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55793,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1651,false,true,55810,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,18th century,1764,1772,Polychrome woodblock print; ink and color on paper,H. 10 7/8 in. (27.6 cm); W. 8 1/8 in. (20.6 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55810,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1656,false,true,55817,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,18th century,1764,1772,Polychrome woodblock print (hashira-e); ink and color on paper,H. 27 1/2 in. (69.9 cm); W. 4 13/16 in. (12.2 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55817,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.265,false,true,73556,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,18th century,1725,1770,Polychrome woodblock print; ink and color on paper,Image: 7 5/8 x 11 in. (19.4 x 27.9 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73556,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2694,false,true,39722,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,18th century,1700,1799,Polychrome woodblock print; ink and color on paper,12 3/8 x 8 3/4 in. (31.4 x 22.2 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/39722,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.266,false,true,73557,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Rekisentei Eiri,"Japanese, active ca. 1789–1801",,Rekisentei Eiri,Japanese,1789,1801,18th century,1789,1801,Polychrome woodblock print; ink and color on paper,Image: 9 7/8 x 14 3/4 in. (25.1 x 37.5 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73557,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -25.60.7,false,true,63067,Asian Art,Bowl,,Japan,Edo period (1615–1868),,,,Artist,,Seifu Yohei,1803–1861,,Seifu Yohei,Japanese,1803,1861,18th century,1700,1799,"White porcelain decorated with blue under the glaze, the inside partly unglazed (Kyoto ware)",H. 2 3/4 in. (7 cm); Diam. 5 1/8 in. (13 cm),"Rogers Fund, 1925",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/63067,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.120.534,false,true,63193,Asian Art,Teabowl,,Japan,Edo period (1615–1868),,,,Artist,,Sonyu,"Japanese, died 1725",,Sonyu,Japanese,1625,1725,18th century,1700,1799,"Clay, thick black glaze, patch of reddish underglaze (Raku ware)",H. 5 1/4 in. (13.3 cm); Diam. 4 1/4 in. (10.8 cm),"The Howard Mansfield Collection, Gift of Howard Mansfield, 1936",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/63193,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -93.1.27,false,true,62621,Asian Art,Cup,,Japan,Edo period (1615–1868),,,,Artist,,Ogata Kenzan,"Japanese, 1663–1743",,Ogata Kenzan,Japanese,1663,1743,18th century,1700,1799,"Porcelain decorated in blue under the glaze, polychrome enamels over the glaze (Kenzan ware)",H. 2 1/4 in. (5.7 cm),"Gift of Mr. and Mrs. Samuel Colman, 1893",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/62621,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -93.1.180,false,true,62690,Asian Art,Water pot,,Japan,Edo period (1615–1868),,,,Artist,,Ogata Kenzan,"Japanese, 1663–1743",,Ogata Kenzan,Japanese,1663,1743,18th century,1700,1799,Clay covered with white and black glazes and decorated on the white parts under the glaze (Kenzan style),H. 3 1/2 in. (8.9 cm); Diam. 7 in. (17.8 cm),"Gift of Mr. and Mrs. Samuel Colman, 1893",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/62690,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -93.1.115,false,true,62670,Asian Art,Teabowl,,Japan,Edo period (1615–1868),,,,Artist,,Kiyomizu Rokubei I,"Japanese, 1737–1799",,Kiyomizu Rokubei I,Japanese,1737,1799,18th century,1700,1799,"Clay covered with glaze and overglaze (Kiyomizu ware, Awata type)",H. 3 5/8 in. (9.2 cm),"Gift of Mr. and Mrs. Samuel Colman, 1893",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/62670,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1979.455,false,true,63796,Asian Art,Stem cup,,Japan,Edo period (1615–1868),,,,Artist,,Okuda Eisen,"Japanese, 1753–1811",,Okuda Eisen,Japanese,1753,1811,18th century,1700,1799,Porcelain painted with red and green enamels,H. 4 5/8 in. (11.7 cm); Diam. 6 5/8 in. (16.8 cm),"Gift of Mr. and Mrs. John R. Menke, 1979",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/63796,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2002.447.115,false,true,49416,Asian Art,Octagonal bowl,,Japan,Edo period (1615–1868),,,,Artist,Attributed to,Shibuemon,"Japanese, active 17th century",,Shibuemon,Japanese,1600,1699,18th century,1700,1799,Porcelain with underglaze cobalt and enamels,Diam. 6 3/4 in. (17.2 cm),"Dr. and Mrs. Roger G. Gerry Collection, Bequest of Dr. and Mrs. Roger G. Gerry, 2000",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/49416,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -93.1.139,false,true,62674,Asian Art,Teabowl,,Japan,Edo period (1615–1868),,,,Artist,,Nonomura Ninsei,"Japanese, active ca. 1646–94",,Ninsei Nonomura,Japanese,1646,1694,18th century,1700,1799,Clay covered with a warm Seto glaze and a border of white enamel (Awata ware),H. 1 7/8 in. (4.8 cm); Diam. 5 in. (12.7 cm); Diam. of foot 2 in. (5.1 cm),"Gift of Mr. and Mrs. Samuel Colman, 1893",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/62674,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.90,false,true,44938,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Kawamata Tsuneyuki,1676 (?)–1741,,Kawamata Tsuneyuki,Japanese,1676,1741,18th century,1700,1741,Hanging scroll; ink and color on paper,14 3/8 x 18 5/8 in. (36.5 x 47.3 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/44938,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.268.125,false,true,45381,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Kaigetsudō Dohan,active 1710–16,,Kaigetsudō Dohan,Japanese,1710,1716,18th century,1710,1716,Hanging scroll; ink and color on paper,Image: 32 3/16 x 13 3/16 in. (81.8 x 33.5 cm) Overall with mounting: 64 3/8 x 20 1/8 in. (163.5 x 51.1 cm),"The Harry G. C. Packard Collection of Asian Art, Gift of Harry G. C. Packard, and Purchase, Fletcher, Rogers, Harris Brisbane Dick, and Louis V. Bell Funds, Joseph Pulitzer Bequest, and The Annenberg Fund Inc. Gift, 1975",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45381,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.34,false,true,49076,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,Attributed to,Kakutei,"Japanese, died 1785",,Kakutei,Japanese,,1785,18th century,1700,1785,Hanging scroll; ink and color on silk,29 1/4 x 14 1/2 in. (74.3 x 36.8 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49076,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.49,false,true,48975,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,Attributed to,Ogata Kōrin,"Japanese, 1658–1716",,Kōrin,Japanese,1658,1716,18th century,1700,1716,Hanging scroll; ink and color on silk,43 5/16 x 16 1/4 in. (110 x 41.3 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/48975,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.527,false,true,40351,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Ogata Kenzan,"Japanese, 1663–1743",,Ogata Kenzan,Japanese,1663,1743,18th century,1700,1743,Hanging scroll; ink and color on paper,10 1/4 x 13 3/8 in. (26 x 34 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/40351,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.444,false,true,49104,Asian Art,Painting,,Japan,Edo period (1615–1868),,,,Artist,In the Style of,Ogawa Haritsu (Ritsuō),"Japanese, 1663–1747",,Ritsuō,Japanese,1663,1747,18th century,1700,1747,"Matted painting; color on papier mache in relief, against a paper background",Diam. 10 3/4 in. (27.3 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49104,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.445,false,true,49105,Asian Art,Painting,,Japan,Edo period (1615–1868),,,,Artist,In the Style of,Ogawa Haritsu (Ritsuō),"Japanese, 1663–1747",,Ritsuō,Japanese,1663,1747,18th century,1700,1747,"Matted painting; color on papier-mâché in relief, against a paper background",Diam. 10 3/4 in. (27.3 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49105,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.945,false,true,36043,Asian Art,Painting,,Japan,Edo period (1615–1868),,,,Artist,In the Style of,Ogawa Haritsu (Ritsuō),"Japanese, 1663–1747",,Ritsuō,Japanese,1663,1747,18th century,1700,1747,Watercolor on paper,7 1/32 x 10 7/32 in. (17.9 x 26 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/36043,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.87.1,false,true,45769,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Nishikawa Sukenobu,"Japanese, 1671–1750",,Nishikawa Sukenobu,Japanese,1671,1750,18th century,1700,1750,Hanging scroll; ink and color on silk,Image: 11 1/4 × 14 3/4 in. (28.6 × 37.5 cm) Overall with mounting: 44 3/4 × 19 3/4 in. (113.7 × 50.2 cm) Overall with knobs: 44 3/4 × 21 5/8 in. (113.7 × 54.9 cm),"Fletcher Fund, 1933",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45769,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.42,false,true,45768,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Nishikawa Sukenobu,"Japanese, 1671–1750",,Nishikawa Sukenobu,Japanese,1671,1750,18th century,1700,1750,Hanging scroll; ink and color on paper,Image: 14 1/4 in. × 21 in. (36.2 × 53.3 cm) Overall with mounting: 51 1/8 × 26 7/8 in. (129.9 × 68.3 cm) Overall with knobs: 51 1/8 × 29 1/4 in. (129.9 × 74.3 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45768,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.268.126,false,true,44863,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Okumura Masanobu,"Japanese, 1686–1764",,Masanobu Okumura,Japanese,1686,1764,18th century,1700,1764,Hanging scroll; ink and color on silk,Image: 33 1/8 x 12 7/8 in. (84.1 x 32.7 cm) Overall with mounting: 66 3/4 x 17 3/4 in. (169.5 x 45.1 cm) Overall with knobs: 66 3/4 x 20 1/4 in. (169.5 x 51.4 cm),"The Harry G. C. Packard Collection of Asian Art, Gift of Harry G. C. Packard, and Purchase, Fletcher, Rogers, Harris Brisbane Dick, and Louis V. Bell Funds, Joseph Pulitzer Bequest, and The Annenberg Fund Inc. Gift, 1975",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/44863,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.31,false,true,45762,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Furuyama Moromasa,"Japanese, 1712–1772",,Furuyama Moromasa,Japanese,1712,1772,18th century,1712,1772,Hanging scroll; ink and color on silk,Image: 16 3/4 × 23 3/8 in. (42.5 × 59.4 cm) Overall with mounting: 55 × 29 3/4 in. (139.7 × 75.6 cm) Overall with knobs: 55 × 31 3/4 in. (139.7 × 80.6 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45762,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1985.243,false,true,49075,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Sō Shiseki,"Japanese, 1715–1786",,Sō Shiseki,Japanese,1715,1786,18th century,1715,1786,Hanging scroll; ink and color on silk,40 7/8 x 13 3/4 in. (103.8 x 34.9 cm),"Purchase, Bequest of Stephen Whitney Phoenix, by exchange, 1985",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49075,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -54.22.3,false,true,48996,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,In the Style of,Yosa Buson,"Japanese, 1716–1783",,Yosa Buson,Japanese,1716,1783,18th century,1716,1783,Hanging scroll; ink and color on paper,40 1/4 x 20 5/16 in. (102.2 x 51.6 cm),"Gift of Robert Pollak, 1954",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/48996,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.38,false,true,45231,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,18th century,1726,1792,Hanging scroll; ink and color on silk,28 1/2 x 12 1/8 in. (72.4 x 30.8 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45231,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.39,false,true,45232,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,18th century,1726,1792,Hanging scroll; ink and color on silk,28 1/2 x 12 1/2 in. (72.4 x 31.8 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45232,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.40,false,true,45233,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,18th century,1726,1792,Hanging scroll; ink and color on silk,28 1/2 x 12 1/8 in. (72.4 x 30.8 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45233,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.41,false,true,45788,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,18th century,1726,1792,Hanging scroll; ink and color on silk,38 3/4 x 8 1/4 in. (98.4 x 21 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45788,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.70c,false,true,45787,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,18th century,1726,1792,Hanging scroll; ink and color on silk,40 3/4 x 12 1/2 in. (103.5 x 31.8 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45787,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.70d,false,true,45789,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,18th century,1726,1792,Hanging scroll; ink and color on silk,40 3/4 x 12 1/2 in. (103.5 x 31.8 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45789,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.70,false,true,39630,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,Attributed to,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,18th century,1726,1792,Hanging scroll; ink and color on silk,Image: 44 x 12 in. (111.8 x 30.5 cm) Overall with mounting: 74 x 17 11/16 in. (188 x 44.9 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/39630,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.66,false,true,49079,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Soga Shōhaku,"Japanese, 1730–1781",,Soga Shōhaku,Japanese,1730,1781,18th century,1730,1781,Hanging scroll; ink and color on silk,Image: 18 x 25 in. (45.7 x 63.5 cm) Overall with mounting: 54 1/2 x 29 5/8 in. (138.4 x 75.2 cm) Overall with knobs: 54 1/2 x 32 in. (138.4 x 81.3 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49079,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.268.85,false,true,42634,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Sō Shizan,"Japanese, 1733–1805",,Sō Shizan,Japanese,1733,1805,18th century,1733,1799,Hanging scroll; ink and color on silk,Image: 36 1/4 x 13 9/16 in. (92.1 x 34.4 cm),"The Harry G. C. Packard Collection of Asian Art, Gift of Harry G. C. Packard, and Purchase, Fletcher, Rogers, Harris Brisbane Dick, and Louis V. Bell Funds, Joseph Pulitzer Bequest, and The Annenberg Fund Inc. Gift, 1975",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/42634,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.70f,false,true,45805,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Toyoharu,"Japanese, 1735–1814",,Utagawa Toyoharu,Japanese,1735,1814,18th century,1735,1799,Hanging scroll; ink and color on silk,40 3/4 x 12 1/2 in. (103.5 x 31.8 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45805,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1971.190,false,true,49050,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Matsumura Goshun,"Japanese, 1752–1811",,Matsumura Goshun,Japanese,1752,1811,18th century,1752,1799,Hanging scroll; ink and color on paper,39 13/16 x 11 5/8 in. (101.2 x 29.5 cm),"Rogers Fund, 1971",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49050,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.268.77,false,true,49049,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Matsumura Goshun,"Japanese, 1752–1811",,Matsumura Goshun,Japanese,1752,1811,18th century,1752,1799,Hanging scroll; ink and color on paper,69 x 36 7/8 in. (175.2 x 93.6 cm),"The Harry G. C. Packard Collection of Asian Art, Gift of Harry G. C. Packard, and Purchase, Fletcher, Rogers, Harris Brisbane Dick, and Louis V. Bell Funds, Joseph Pulitzer Bequest, and The Annenberg Fund Inc. Gift, 1975",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49049,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.70e,false,true,45796,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,18th century,1752,1799,Hanging scroll; ink and color on silk,40 3/4 x 12 1/2 in. (103.5 x 31.8 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45796,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.47,false,true,45794,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,18th century,1752,1799,Hanging scroll; ink and color on silk,39 7/8 x 14 1/2 in. (101.3 x 36.8 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45794,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.268.127,false,true,45799,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,18th century,1757,1799,Hanging scroll; ink and color on paper,Image: 32 x 11 15/16 in. (81.3 x 30.3 cm) Overall: 63 7/8 x 18 3/4in. (162.2 x 47.6 cm),"The Harry G. C. Packard Collection of Asian Art, Gift of Harry G. C. Packard, and Purchase, Fletcher, Rogers, Harris Brisbane Dick, and Louis V. Bell Funds, Joseph Pulitzer Bequest, and The Annenberg Fund Inc. Gift, 1975",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45799,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.268.101,false,true,57160,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Kushiro Unsen,"Japanese, 1759–1811",,Kushiro Unsen,Japanese,1759,1811,18th century,1759,1799,Hanging scroll; ink and color on paper,42 5/8 x 11 1/8 in. (108.3 x 28.3 cm),"The Harry G. C. Packard Collection of Asian Art, Gift of Harry G. C. Packard, and Purchase, Fletcher, Rogers, Harris Brisbane Dick, and Louis V. Bell Funds, Joseph Pulitzer Bequest, and The Annenberg Fund Inc. Gift, 1975",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57160,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.55,false,true,48891,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Teisai Hokuba,"Japanese, 1771–1844",,Teisai Hokuba,Japanese,1771,1844,18th century,1771,1799,"Hanging scroll; ink and color, and gold on silk",29 x 10 3/4 in. (73.7 x 27.3 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/48891,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.70a,false,true,45785,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Isoda Koryūsai,"Japanese, 1735–ca. 1790",,Isoda Koryūsai,Japanese,1735,1790,18th century,1735,1790,Hanging scroll; ink and color on silk,40 3/4 x 12 1/2 in. (103.5 x 31.8 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45785,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.404,false,true,45771,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Kondo Katsunobu,"Japanese, active 1716–1736",,Kondo Katsunobu,Japanese,1716,1736,18th century,1716,1736,Hanging scroll; ink and color on paper,38 3/16 x 16 5/8 in. (97 x 42.3 cm),"Gift of Francis T. Henderson Jr., in memory of Harold G. Henderson, 1976",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45771,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.69,false,true,45791,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,Attributed to,Katsukawa Shunchō,"Japanese, active ca. 1783–95",,Katsukawa Shunchō,Japanese,1775,1795,18th century,1783,1795,Hanging scroll; ink and color on silk,35 7/8 x 12 1/8 in. (91.1 x 30.8 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45791,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.67.74,false,true,58908,Asian Art,Inrō,,Japan,,,,,Artist,,Gyokuzan,1737–1812,,Gyokuzan,Japanese,1737,1812,19th century,1800,1899,"Lacquer, roiro, gold and colored hiramakie, takamakie, kirigane, nashiji; Interior: nashiji and fundame",3 3/4 x 2 x 1 1/8 in. (9.5 x 5.1 x 2.9 cm),"Rogers Fund, 1913",,,,,,,,,,,,Inrō,,http://www.metmuseum.org/art/collection/search/58908,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.214,false,true,58895,Asian Art,Inrō,,Japan,,,,,Artist,,Koma Kōryū,"Japanese, died 1796",,Koma Kōryū,Japanese,,1796,19th century,1800,1899,"Lacquer, silver ground, gold, black and red togidashi, ivory lid; Interior: nashiji and fundame",2 1/2 x 2 3/16 x 1 1/16 (6.3 x 5.6 x 2.7 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Inrō,,http://www.metmuseum.org/art/collection/search/58895,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1985.399.8,false,true,59650,Asian Art,Netsuke,,Japan,,,,,Artist,,Masakazu,"Japanese, died 1886",,Masakazu,Japanese,,1886,19th century,1800,1899,Wood,H. 1 1/8 in. (2.9 cm); W. 1 1/8 in. (2.9 cm),"Gift of Alvin H. Schechter, 1985",,,,,,,,,,,,Netsuke,,http://www.metmuseum.org/art/collection/search/59650,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.82.7,false,true,58637,Asian Art,Inrō,,Japan,,,,,Artist,In the Style of,Hanabusa Itchō,"Japanese, 1652–1724",,Hanabusa Itchō,Japanese,1652,1724,19th century,1800,1899,"Metal and lacquer, dark silver metal, incised, roiro, gold hiramakie, raden, aogai; Interior: fundame, four boxes",4 1/8 x 1 9/16 x 7/8 in. (10.4 x 4 x 2.2 cm),"Gift of Wilton Lloyd-Smith and his wife, Marjorie Fleming Lloyd-Smith, 1929",,,,,,,,,,,,Inrō,,http://www.metmuseum.org/art/collection/search/58637,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.879,false,true,78769,Asian Art,Woodblock,,Japan,,,,,Artist,After,Kuwagata Keisai,"Japanese, 1764–1824",,Kuwagata Keisai,Japanese,1764,1824,19th century,1800,1824,Woodblock,8 9/16 × 6 3/4 in. (21.7 × 17.2 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Woodblocks,,http://www.metmuseum.org/art/collection/search/78769,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -10.211.1894,false,true,59113,Asian Art,Netsuke,,Japan,,,,,Artist,,Toyomasa,"Japanese, 1773–1856",,Toyomasa,Japanese,1773,1856,19th century,1800,1899,Wood,H. 2 in. (5.1 cm),"Gift of Mrs. Russell Sage, 1910",,,,,,,,,,,,Netsuke,,http://www.metmuseum.org/art/collection/search/59113,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.830,false,true,58735,Asian Art,Inrō,,Japan,,,,,Artist,,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,Japanese,1807,1891,19th century,1800,1899,"Bamboo, black, silver and gold hiramakie, takamakie, wood lid; Interior: plain",2 1/4 x 2 5/8 x 1 5/8 in. (5.7 x 6.7 x 4.2 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Inrō,,http://www.metmuseum.org/art/collection/search/58735,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.248,false,true,58934,Asian Art,Inrō,,Japan,,,,,Artist,,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,Japanese,1807,1891,19th century,1800,1899,"Lacquer, dark brown, imitating leather, relief, light brown rim; Interior: dark brown and nashiji",2 15/16 x 2 5/16 x 11/16 in. (7.4 x 5.9 x 1.7 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Inrō,,http://www.metmuseum.org/art/collection/search/58934,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -51.137.5,false,true,58329,Asian Art,Box,,Japan,,,,,Artist,,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,Japanese,1807,1891,19th century,1800,1899,Lacquer,H. 1 3/8 in. (3.5 cm); W. 2 1/4 in. (5.7 cm); L. 3 1/4 in. (8.3 cm),"Purchase, Gifts, Bequests, and Funds from various donors, by exchange, 1951",,,,,,,,,,,,Lacquer,,http://www.metmuseum.org/art/collection/search/58329,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.153,false,true,58336,Asian Art,Tray,,Japan,,,,,Artist,Lacquered by,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,Japanese,1807,1891,19th century,1800,1899,Lacquer with gold,H. 1 1/2 in. (3.8 cm); W. 4 5/8 in. (11.7 cm); L. 8 1/2 in. (21.6 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Lacquer,,http://www.metmuseum.org/art/collection/search/58336,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.154,false,true,56529,Asian Art,Tray,,Japan,,,,,Artist,Lacquered by,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,Japanese,1807,1891,19th century,1800,1899,Lacquer,W. 10 in. (25.4 cm); L. 20 3/4 in. (52.7 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Lacquer,,http://www.metmuseum.org/art/collection/search/56529,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.170,false,true,56166,Asian Art,Tea caddy,,Japan,,,,,Artist,Lacquered by,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,Japanese,1807,1891,19th century,1800,1899,Lacquer,H. 4 1/8 in. (10.5 cm); Diam. 3 in. (7.6 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Lacquer,,http://www.metmuseum.org/art/collection/search/56166,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.175,false,true,58353,Asian Art,Box,,Japan,,,,,Artist,Attributed to,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,Japanese,1807,1891,19th century,1800,1899,Lacquer,H. 3/8 in. (1 cm); W. 1 1/2 in. (3.8 cm); L. 7 1/8 in. (18.1 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Lacquer,,http://www.metmuseum.org/art/collection/search/58353,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"36.100.168a, b",false,true,58345,Asian Art,Box,,Japan,,,,,Artist,,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,Japanese,1807,1891,19th century,1800,1899,Lacquer,H. 1 in. (2.5 cm); W. 3 7/8 in. (9.8 cm); D. 3 7/8 in. (9.8 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Lacquer,,http://www.metmuseum.org/art/collection/search/58345,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -66.155.1,false,true,57348,Asian Art,Screen,,Japan,,,,,Artist,,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,Japanese,1807,1891,19th century,1800,1899,Two-panel folding screen; ink on paper,41 1/2 x 55 in. (105.4 x 139.7 cm),"Gift of Harold G. Henderson, 1966",,,,,,,,,,,,Screens,,http://www.metmuseum.org/art/collection/search/57348,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.749,false,true,78651,Asian Art,Illustrated book,,Japan,,,,,Artist,,Nishiyama Ken,"Japanese, 1833–1897",,Nishiyama Ken,Japanese,1833,1897,19th century,1833,1897,Accordion album; ink and color on paper,10 5/8 × 5 1/2 in. (27 × 14 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78651,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -67.55.82,false,true,59630,Asian Art,Netsuke,,Japan,,,,,Artist,,Koichi,"Japanese, 19th century",,Koichi,Japanese,1800,1899,19th century,1800,1899,"Wood, horn",H. 1 3/4 in. (4.4 cm); W. 1 1/8 in. (2.9 cm); D. 1 1/4 in. (3.2 cm),"Bequest of Susan Dwight Bliss, 1966",,,,,,,,,,,,Netsuke,,http://www.metmuseum.org/art/collection/search/59630,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1979.459.1,false,true,70647,Asian Art,Pressing board,,Japan,,,,,Artist,,Tabata Sadahiko,"Japanese, 19th century",,Tabata Sadahiko,Japanese,0019,0019,19th century,1800,1899,,9 x 18 1/2 in. (22.86 x 46.99 cm),"Gift of Takami Sugiura and Sadahiko Tabata, 1979",,,,,,,,,,,,Textiles-Methods and Materials,,http://www.metmuseum.org/art/collection/search/70647,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.221,false,true,58902,Asian Art,Inrō,,Japan,,,,,Artist,,Tōyō,"Japanese, active ca. 1764–71",,Tōyō,Japanese,1764,1771,19th century,1800,1899,"Lacquer, roiro, gyobu, gold, black, silver and brown hiramakie, takamakie; Interior: nashiji and fundame",3 15/16 x 1 7/8 x 1 1/8 in. (10 x 4.8 x 2.9 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Inrō,,http://www.metmuseum.org/art/collection/search/58902,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -10.211.1001,false,true,60266,Asian Art,Netsuke,,Japan,,,,,Artist,,Ryūsa,"Japanese, active late 18th century",,Ryūsa,Japanese,1767,1799,19th century,1800,1899,Ivory,H. 1 3/16 in. (3 cm); W. 9/16 in. (1.4 cm); L. 1 3/8 in. (3.5 cm),"Gift of Mrs. Russell Sage, 1910",,,,,,,,,,,,Netsuke,,http://www.metmuseum.org/art/collection/search/60266,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -10.211.1262,false,true,60327,Asian Art,Netsuke,,Japan,,,,,Artist,,Ryūsa,"Japanese, active late 18th century",,Ryūsa,Japanese,1767,1799,19th century,1800,1899,Ivory,H. 3/4 in. (1.9 cm); Diam. 1 7/8 in. (4.8 cm),"Gift of Mrs. Russell Sage, 1910",,,,,,,,,,,,Netsuke,,http://www.metmuseum.org/art/collection/search/60327,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -55.153,false,true,57110,Asian Art,Printer's woodblock,,Japan,,,,,Artist,Original print designed by,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,19th century,1800,1899,Wood,13 3/4 x 9 1/2 in. (34.9 x 24.1 cm),"Gift of Harry E. Goldman, 1955",,,,,,,,,,,,Woodblocks,,http://www.metmuseum.org/art/collection/search/57110,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.67.84,false,true,45569,Asian Art,Inrō,,Japan,Edo period (1615–1868),,,,Artist,,Yamada Jōkasai (1681–1704),,,Yamada Jōkasai,Japanese,1681,1704,19th century,1800,1899,"Lacquer decorated with sprinkled hiramakie lacquer, sprinkled and polished takamakie lacquer relief, and nashiji (pear skin) lacquer; Ojime: ivory and gold lacquer bead; Netsuke: carved wood bird (signed Shuko)",3 5/16 x 2 5/16 x 1 1/16 in. (8.4 x 5.8 x 2.7 cm),"Rogers Fund, 1913",,,,,,,,,,,,Inrō,,http://www.metmuseum.org/art/collection/search/45569,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.889,false,true,58809,Asian Art,Inrō,,Japan,Edo period (1615–1868),,,,Artist,,Toyōsai (1772–1845),,,Toyōsai,Japanese,1772,1845,19th century,1800,1899,Black lacquer with sprinkled gold and silver makie and mother-of-pearl Ojime: bead with openwork design of waves; silver Netsuke: fish laid on bamboo branch; stained ivory,2 7/8 x 3 1/16 x 11/16 in. (7.3 x 7.7 x 1.8 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Inrō,,http://www.metmuseum.org/art/collection/search/58809,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.198,false,true,45503,Asian Art,Inrō,,Japan,Edo period (1615–1868),,,,Artist,,Yamada Jōkasai (1681–1704),,,Yamada Jōkasai,Japanese,1681,1704,19th century,1800,1899,"Sprinkled gold lacquer with mother-of-pearl, stone, and metal Ojime: ovoid gold bead decorated with grasses Netuske: black wood carved as a piece of firewood",3 3/4 x 2 11/16 x 3/4 in. (9.5 x 6.8 x 1.9 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Inrō,,http://www.metmuseum.org/art/collection/search/45503,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"36.100.157a, b",false,true,45364,Asian Art,Incense box,,Japan,Edo period (1615–1868),,,,Artist,,Yamada Jōkasai (1681–1704),,,Yamada Jōkasai,Japanese,1681,1704,19th century,1800,1899,Gold-speckled aventurine (nashiji) lacquer with sprinkled and polished design (hiramakie),H. 1 1/16 in. (2.7 cm); W. 1 1/8 in. (2.9 cm); L. 3 5/8 in. (9.2 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Lacquer,,http://www.metmuseum.org/art/collection/search/45364,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"36.100.159a, b",false,true,45497,Asian Art,Round box,,Japan,Edo period (1615–1868),,,,Artist,,Yamada Jōkasai (1681–1704),,,Yamada Jōkasai,Japanese,1681,1704,19th century,1800,1899,"Lacquer on wood with gold, mother-of-pearl inlay, and colored lacquer",H. 1 3/8 in. (3.5 cm); Diam. 3 in. (7.6 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Lacquer,,http://www.metmuseum.org/art/collection/search/45497,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.40.885a–e,false,true,57953,Asian Art,Writing box,,Japan,Edo period (1615–1868),,,,Artist,,Tatsuke Takanori,1757–1833,,Tatsuke Takanori,Japanese,1757,1833,19th century,1800,1899,Gold and silver maki-e with colored lacquer on black lacquer,H. 2 1/16 in. (5.2 cm); W. 9 in. (22.9 cm); D. 9 1/2 in. (24.2 cm),"Bequest of Benjamin Altman, 1913",,,,,,,,,,,,Lacquer,,http://www.metmuseum.org/art/collection/search/57953,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.195,false,true,58881,Asian Art,Inrō,,Japan,Edo period (1615–1868),,,,Artist,,Kano Seisen’in,1775–1828,,Kano Seisen’in,Japanese,1775,1828,19th century,1800,1899,"Lacquer with sprinkled gold, silver, and red makie and takamakie Ojime: bead; tortoiseshell Netsuke: basket; woven reeds",3 7/16 x 2 3/8 x 15/16 in. (8.8 x 6 x 2.4 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Inrō,,http://www.metmuseum.org/art/collection/search/58881,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.199,false,true,58883,Asian Art,Inrō,山田常嘉斎作・狩野晴川院筆　満月に鵞鳥図印籠|Inrō with Goose Flying across the Full Moon,Japan,Edo period (1615–1868),,,,Artist,After,Kano Seisen’in,1775–1828,,Kano Seisen’in,Japanese,1775,1828,19th century,1800,1899,"Lacquer, kinji, gold, silver, black and red hiramakie, togidashi, aogai inlay; Interior: nashiji and fundame",Overall (inro): H. 3 1/16 in. (7.7 cm); W. 2 1/16 in. (5.2 cm); D. 13/16 in. (2 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Inrō,,http://www.metmuseum.org/art/collection/search/58883,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.745,false,true,58657,Asian Art,Inrō,古満巨柳作 鷹蒔絵印籠|Inrō with Hawks on Perches,Japan,Edo period (1615–1868),,,,Artist,,Koma Kōryū,"Japanese, died 1796",,Koma Kōryū,Japanese,,1796,19th century,1800,1899,"Black lacquer ground with gold and silver togidashi, takamaki-e and hiramaki-e, red lacquer, and applied gold and silver foil Netsuke: gourd; guri lacquer, silver ring and stopper in chrysanthemum shape Ojime: butterfly and flower; cloisonné bead",2 15/16 x 2 5/8 x 3/4 in. (7.4 x 6.7 x 1.9 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Inrō,,http://www.metmuseum.org/art/collection/search/58657,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.213,false,true,45575,Asian Art,Inrō,,Japan,Edo period (1615–1868),,,,Artist,,Koma Kōryū,"Japanese, died 1796",,Koma Kōryū,Japanese,,1796,19th century,1800,1899,Roiro (waxen) lacquer with decoration in togidashi sprinkled and polished lacquer and nashiji (pear-skin) lacquer; Interior: nashiji and fundame; Ojime: metal (zogan) inlay with spider; Netsuke: carved wood with stone inlay of snail on broken roof tile,3 x 1 7/8 x 1 1/8 in. (7.6 x 4.8 x 2.8 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Inrō,,http://www.metmuseum.org/art/collection/search/45575,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.690,false,true,45563,Asian Art,Writing box,橅夫蒔絵硯箱|Writing Box (Suzuribako) with Woodcutter,Japan,Edo period (1615–1868),,,,Artist,In the Style of,Ogata Kōrin,"Japanese, 1658–1716",,Kōrin,Japanese,1658,1716,19th century,1800,1899,Black and gold lacquer on wood with mother-of-pearl and pewter inlay,H. 2 in. (5.1 cm); W. 9 7/8 in. (25.1 cm); L. 9 1/4 in. (23.5 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Lacquer,,http://www.metmuseum.org/art/collection/search/45563,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.691,false,true,45564,Asian Art,Writing box,,Japan,Edo period (1615–1868),,,,Artist,In the Style of,Ogata Kōrin,"Japanese, 1658–1716",,Kōrin,Japanese,1658,1716,19th century,1800,1899,Black and gold lacquer on wood with mother-of-pearl and pewter inlay,H. 1 7/8 in. (4.8 cm); W. 8 3/4 in. (22.2 cm); L. 9 1/2 in. (24.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Lacquer,,http://www.metmuseum.org/art/collection/search/45564,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.697,false,true,58168,Asian Art,Box,,Japan,Edo period (1615–1868),,,,Artist,Style of,Ogata Kōrin,"Japanese, 1658–1716",,Kōrin,Japanese,1658,1716,19th century,1800,1899,Gold inlaid with mother-of-pearl and tin,H. 4 3/4 in. (12.1 cm); W. 4 1/2 in. (11.4 cm); L. 7 3/4 in. (19.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Lacquer,,http://www.metmuseum.org/art/collection/search/58168,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.719,false,true,56523,Asian Art,Incense box,江戸時代 琳派　梅蒔絵螺鈿香合|Incense Box with Flowering Plum Tree,Japan,Edo period (1615–1868),,,,Artist,School of,Ogata Kōrin,"Japanese, 1658–1716",,Kōrin,Japanese,1658,1716,19th century,1800,1899,"Gold lacquer with gold hiramaki-e, black lacquer, and mother-of-pearl inlay",H. 1 1/2 in. (3.8 cm); W. 2 1/4 in. (5.7 cm); L. 2 1/4 in. (5.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Lacquer,,http://www.metmuseum.org/art/collection/search/56523,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"58.169a, b",false,true,58334,Asian Art,Writing box,橅夫蒔絵硯箱|Writing Box (Suzuribako) with Woodcutter,Japan,Edo period (1615–1868),,,,Artist,Attributed to,Ogata Kōrin,"Japanese, 1658–1716",,Kōrin,Japanese,1658,1716,19th century,1800,1899,Black and gold lacquer on wood with gold maki-e and mother-of-pearl inlay,(a): H. 8 1/4 in. (21 cm); W. 7 3/8 in. (18.7 cm); D. 1 3/8 in. (3.5 cm) (b): H. 8 9/16 in. (21.7 cm); W. 7 3/4 in. (19.7 cm); D. 15/16 in. (2.4 cm),"Gift of Nathan Hammer, 1958",,,,,,,,,,,,Lacquer,,http://www.metmuseum.org/art/collection/search/58334,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.832,false,true,58737,Asian Art,Inrō,,Japan,Edo period (1615–1868),,,,Artist,In the Style of,Ogawa Haritsu (Ritsuō),"Japanese, 1663–1747",,Ritsuō,Japanese,1663,1747,19th century,1800,1899,Colored togidashi and gold hiramaki-e on black lacquer,3 5/16 x 2 1/2 x 1 1/16 in. (8.4 x 6.4 x 2.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Inrō,,http://www.metmuseum.org/art/collection/search/58737,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.679,false,true,58096,Asian Art,Document box,,Japan,Edo period (1615–1868),,,,Artist,Style of,Ogawa Haritsu (Ritsuō),"Japanese, 1663–1747",,Ritsuō,Japanese,1663,1747,19th century,1800,1899,"Colored lacquer, gold and silver foil, mother-of-pearl, ivory, tortoiseshell, and ceramic on black lacquer",H. 2 3/4 in. (7 cm); W. 10 in. (25.4 cm); L. 11 in. (27.9 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Lacquer,,http://www.metmuseum.org/art/collection/search/58096,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.727,false,true,58642,Asian Art,Inrō,,Japan,Edo period (1615–1868),,,,Artist,,Sō Shiseki,"Japanese, 1715–1786",,Sō Shiseki,Japanese,1715,1786,19th century,1800,1899,"Lacquer, roiro, gold foil, aogai inlay; Interior: roiro",3 1/16 x 3 1/16 x 15/16 in. (7.8 x 7.8 x 2.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Inrō,,http://www.metmuseum.org/art/collection/search/58642,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.811,false,true,58717,Asian Art,Inrō,,Japan,Edo period (1615–1868),,,,Artist,,Mori Sosen,"Japanese, 1747–1821",,Mori Sosen,Japanese,1747,1821,19th century,1800,1899,"Metal, brass metal, incised, various applied metals; Interior: silver metal",2 5/8 x 1 15/16 x 13/16 in. (6.7 x 5 x 2 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Inrō,,http://www.metmuseum.org/art/collection/search/58717,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.845,false,true,58771,Asian Art,Inrō,,Japan,Edo period (1615–1868),,,,Artist,,Mori Sosen,"Japanese, 1747–1821",,Mori Sosen,Japanese,1747,1821,19th century,1800,1899,"Lacquer, gold and silver hirame, gold, red and coloured hiramakie, takamakie; Interior: red lacquer, fundame and decoration; the interior risers decorated in gold and silver togidashi with various brocade pattern, key-fret, waves, etc.",3 1/8 x 3 1/8 x 13/16 in. (7.9 x 8 . 2.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Inrō,,http://www.metmuseum.org/art/collection/search/58771,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.250,false,true,45576,Asian Art,Inrō,,Japan,Edo period (1615–1868),,,,Artist,,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,Japanese,1807,1891,19th century,1800,1899,Roiro (waxen) lacquer with sprinkled hiramkie lacquer and togidashi sprinkled and polished lacquer; Interior: nashiji and fundame; Ojime: silver shibuichi lacquer bead decorated with design of castle grounds; Netsuke: carved wooden sleeping goose,2 11/16 x 2 1/16 x 15/16 in. (6.8 x 5.3 x 2.4 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Inrō,,http://www.metmuseum.org/art/collection/search/45576,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.210,false,true,54240,Asian Art,Inrō,,Japan,Edo period (1615–1868),,,,Artist,,Nakayama Komin,"Japanese, 1808–1870",,Nakayama Komin,Japanese,1808,1870,19th century,1800,1899,"Hiramaki-e with nashiji on black lacquer, roiro, nashiji, gold and silver hiramakie, gold and silver foil; Interior: nashiji and fundame; Ojime: ivory bead with vines and grasses in gold lacquer; Netsuke: woven basket with shell and gold lacquer)",3 1/16 x 2 1/16 x 13/16 in. (7.8 x 5.2 x 2 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Inrō,,http://www.metmuseum.org/art/collection/search/54240,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.40.879a–n,false,true,44916,Asian Art,Writing box,中山胡民作　伊勢物語硯箱|Writing Box (Suzuribako) with Episodes from the Tales of Ise (Ise monogatari),Japan,Edo period (1615–1868),,,,Artist,,Nakayama Komin,"Japanese, 1808–1870",,Nakayama Komin,Japanese,1808,1870,19th century,1800,1899,Gold maki-e with inlaid silver on lacquer,H. 1 1/2 in. (3.8 cm); W. 7 in. (17.8 cm); D. 7 3/4 in. (19.7 cm),"Bequest of Benjamin Altman, 1913",,,,,,,,,,,,Lacquer,,http://www.metmuseum.org/art/collection/search/44916,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.67.34,false,true,58576,Asian Art,Inrō,山田常嘉斎作 木蓮オウム蒔絵螺鈿印籠|Inrō with Cockatoo and Magnolia,Japan,Edo period (1615–1868),,,,Artist,,Yamada Jōkasai,"Japanese, 1811–1879",,Yamada Jōkasai,Japanese,1811,1879,19th century,1800,1899,"Red lacquer ground with gold maki-e, carved red lacquer, and mother-of-pearl inlay Netsuke: poppy; ivory Ojime: roundels; red and green lacquer in wood",3 5/8 x 1 13/16 x 1 3/16 in. (9.2 x 4.6 x 3 cm),"Rogers Fund, 1913",,,,,,,,,,,,Inrō,,http://www.metmuseum.org/art/collection/search/58576,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.673,false,true,78594,Asian Art,Illustrated book,奇観幀|Album of Twelve Nanga-style Landscapes (Kikanchō),Japan,Edo period (1615–1868),,,,Artist,,Tanomura Chokunyū,"Japanese, 1814–1907",,Tanomura Chokunyū,Japanese,1814,1907,19th century,1800,1868,Accordion album; ink and color on paper,8 7/16 × 5 13/16 in. (21.5 × 14.8 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78594,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.722,false,true,58289,Asian Art,Pipe-case with pipe,,Japan,Edo period (1615–1868),,,,Artist,,Tamakaji Zōkoku,"Japanese, 1803?–1866",,Tamakaji Zōkoku,Japanese,1803,1866,19th century,1800,1899,"Lacquer, silver and shakudo",W. 1 1/2 in. (3.8 cm); L. 11 in. (27.9 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Lacquer,,http://www.metmuseum.org/art/collection/search/58289,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.338a–d,false,true,73591,Asian Art,Illustrated book,橫濱開港見聞誌|Observations on the Opening of Yokohama (Yokohama kaikō kenbunshi),Japan,Edo period (1615–1868),,,,Artist,,Utagawa (Gountei) Sadahide,"Japanese, 1807–1878/79",,Utagawa (Goutei) Sadahide,Japanese,1807,1879,19th century,1807,1879,Three volumes of woodblock printed books; ink on paper,Image (a-c): 9 1/2 x 6 3/4 x 3/4 in. (24.1 x 17.1 x 1.9 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/73591,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.339a–c,false,true,73592,Asian Art,Illustrated book,橫濱開港見聞誌|Observations on the Opening of Yokohama (Yokohama kaiko kenbunshi),Japan,Edo period (1615–1868),,,,Artist,,Utagawa (Gountei) Sadahide,"Japanese, 1807–1878/79",,Utagawa (Goutei) Sadahide,Japanese,1807,1879,19th century,1807,1879,Three volumes of woodblock printed books; ink on paper,Image (a-c): 9 5/8 x 6 7/8 x 1/2 in. (24.4 x 17.5 x 1.3 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/73592,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.183,false,true,58871,Asian Art,Inrō,梶川文竜斎作 宝船蒔絵印籠|Inrō with Treasure Boat,Japan,Edo period (1615–1868),,,,Artist,,Kajikawa Bunryūsai,"Japanese, ca. 1751–1817",,Kajikawa Bunryūsai,Japanese,1741,1827,19th century,1800,1899,"Gold and black lacquer and nashiji ground with gold and silver hiramaki-e, red lacquer, and mother-of-pearl inlay Netsuke: kingfisher; carved ivory Ojime: Chinese children; porcelain (Kutani ware",3 7/16 x 2 1/16 x 13/16 in. (8.7 x 5.3 x 2 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Inrō,,http://www.metmuseum.org/art/collection/search/58871,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"JIB141a, b",false,true,48887,Asian Art,Two booklets,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,19th century,1750,1900,Two volumes of drawings; ink and color on paper,each: 7 3/4 × 5 1/4 in. (19.7 × 13.3 cm),"Fletcher Fund, 1941",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/48887,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -81.1.569,false,true,45436,Asian Art,Sake cup,,Japan,Edo period (1615–1868),,,,Artist,,Shomosai,"Japanese, active late 18th–early 19th century",,Shomosai,Japanese,0018,0019,19th century,1800,1899,Gold lacquer on red lacquer ground,H. 1 in. (2.5 cm); W. 4 3/8 in. (11.1 cm),"Bequest of Stephen Whitney Phoenix, 1881",,,,,,,,,,,,Lacquer,,http://www.metmuseum.org/art/collection/search/45436,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.82.2,false,true,58633,Asian Art,Inrō,光琳様式　梅鶴蒔絵螺鈿印籠|Inrō with Crane and Plum Tree,Japan,Meiji period (1868–1912),,,,Artist,Style of,Ogata Kōrin,"Japanese, 1658–1716",,Kōrin,Japanese,1658,1716,19th century,1800,1899,Gold lacquer ground with mother-of-pearl and pewter inlay Netsuke: fish on wheels; carved wood Ojime: vajra (thunderbolt); metal,2 7/16 x 2 1/4 x 13/16 in. (6.2 x 5.7 x 2.1 cm),"Gift of Wilton Lloyd-Smith and his wife, Marjorie Fleming Lloyd-Smith, 1929",,,,,,,,,,,,Inrō,,http://www.metmuseum.org/art/collection/search/58633,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -57.46,false,true,36077,Asian Art,Inrō,,Japan,Meiji period (1868–1912),,,,Artist,,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,Japanese,1807,1891,19th century,1800,1899,Gold and colored lacquer on wood Signed: Zeshin Ojime: carved wooden dragon (signed: Ichiryūsai Furuta) Netsuke: ceramic figure of Kaduzōsu (signed: Eiraku),2 1/4 x 2 3/4 x 1 in. (5.72 x 6.99 x 2.54 cm),"Purchase, Gifts, Bequests, and Funds from various donors, by exchange, 1957",,,,,,,,,,,,Inrō,,http://www.metmuseum.org/art/collection/search/36077,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -57.53,false,true,36078,Asian Art,Inrō,,Japan,Meiji period (1868–1912),,,,Artist,In the style of,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,Japanese,1807,1891,19th century,1800,1899,"Colored lacquer and gold maki-e on wood Netsuke: ivory in form of fox reclining on large leaf; Ojima: gold bead; connecting cord, dark green silk Inro with netsuke and ojime",3 1/16 x 2 1/8 x 13/16 in. (7.7 x 5.4 x 2 cm),"Rogers Fund, 1957",,,,,,,,,,,,Inrō,,http://www.metmuseum.org/art/collection/search/36078,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.67.13,false,true,58563,Asian Art,Inrō,,Japan,Meiji period (1868–1912),,,,Artist,In the style of,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,Japanese,1807,1891,19th century,1800,1899,"Gold, silver, red, and black hiramaki-e, takamaki-e, mother-of-pearl inlay on bamboo; interior: plain wood, drawers",2 13/16 x 1 9/16 x 11/16 in. (7.2 x 4 x 1.8 cm),"Rogers Fund, 1913",,,,,,,,,,,,Inrō,,http://www.metmuseum.org/art/collection/search/58563,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -38.25.148,false,true,59011,Asian Art,Inrō,,Japan,Meiji period (1868–1912),,,,Artist,,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,Japanese,1807,1891,19th century,1800,1899,"Black lacquer with wood, shell, and glass inlays; Fastener (ojime): metal; Toggle (netsuke): carved ivory in the shape of a demon",H. 3 1/16 in. (9.4 cm); W. 1 7/8 in. (4.8 cm); D. 1 3/16 in. (3 cm),"Gift of Mrs. George A. Crocker (Elizabeth Masten), 1937",,,,,,,,,,,,Inrō,,http://www.metmuseum.org/art/collection/search/59011,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.247,false,true,58933,Asian Art,Inrō,,Japan,Meiji period (1868–1912),,,,Artist,,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,Japanese,1807,1891,19th century,1800,1899,"Lacquer, roiro, incised, gold, silver and brown hiramakie, takamakie, aogai; Interior: gyobu nashiji and fundame",2 7/8 x 2 x 13/16 in. (7.3 x 5.1 x 2 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Inrō,,http://www.metmuseum.org/art/collection/search/58933,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.252,false,true,58937,Asian Art,Inrō,,Japan,Meiji period (1868–1912),,,,Artist,,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,Japanese,1807,1891,19th century,1800,1899,Gold and silver maki-e with colored lacquer on black lacquer Ojime: gilded metal with fox-monk Netuske: Lacquer oval box with diamond floral design (signed Kiyoharu),3 11/16 x 2 1/16 x 1 1/8 in. (9.4 x 5.2 x 2.9 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Inrō,,http://www.metmuseum.org/art/collection/search/58937,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.253,false,true,45448,Asian Art,Inrō,,Japan,Meiji period (1868–1912),,,,Artist,,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,Japanese,1807,1891,19th century,1800,1899,Roiro (waxen) lacquer with black hiramakie sprinkled and polished lacquer and takamakie sprinkled and polished lacquer relief; Interior: roiro; Netsuke: lacquer in shape of a worn ink stick; Ojime: black lacquer bead,2 5/16 x 1 1/2 x 11/16 in. (5.9 x 3.8 x 1.7 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Inrō,,http://www.metmuseum.org/art/collection/search/45448,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.257,false,true,58940,Asian Art,Inrō,,Japan,Meiji period (1868–1912),,,,Artist,,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,Japanese,1807,1891,19th century,1800,1899,"Lacquer, roiro, gold black, brown hiramakie, aogai; Interior: nashiji and fundame",2 7/8 x 1 15/16 x 3/4 in. (7.3 x 4.9 x 1.9 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Inrō,,http://www.metmuseum.org/art/collection/search/58940,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -54.77,false,true,56577,Asian Art,Tray,,Japan,Meiji period (1868–1912),,,,Artist,Style of,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,Japanese,1807,1891,19th century,1800,1899,"Hiramaki-e, cut-out silver foil, gold, silver application, mother-of-pearl application",H. 1/2 in. (1.3 cm); W. 3 5/16 in. (8.4 cm); L. 6 7/16 in. (16.4 cm),"Gift of Joseph U. Seo, 1954",,,,,,,,,,,,Lacquer,,http://www.metmuseum.org/art/collection/search/56577,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"36.100.169a, b",false,true,58346,Asian Art,Tea caddy,,Japan,Meiji period (1868–1912),,,,Artist,In the style of,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,Japanese,1807,1891,19th century,1800,1899,Colored lacquer and gold maki-e on wood,H. 1 7/8 in. (4.8 cm); Diam. 2 3/4 in. (7 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Lacquer,,http://www.metmuseum.org/art/collection/search/58346,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.155,false,true,58337,Asian Art,Tray,,Japan,Meiji period (1868–1912),,,,Artist,,Ikeda Taishin,"Japanese, 1825–1903",,Ikeda Taishin,Japanese,1825,1903,19th century,1800,1899,"Silver, gold takamaki-e, hiramaki-e, kirikane, black lacquer",W. 5 in. (12.7 cm); L. 8 in. (20.3 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Lacquer,,http://www.metmuseum.org/art/collection/search/58337,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -91.1.684,false,true,58463,Asian Art,Inrō,,Japan,Meiji period (1868–1912),,,,Artist,Signed,Tōyō,"Japanese, active ca. 1764–71",,Tōyō,Japanese,1764,1771,19th century,1800,1899,"Gold hiramaki-e, takamaki-e, cut-out gold foil application and mother-of-pearl inlay on red lacquer ground; Inside: Nashiji and fundame",H. 2 13/16 in. (7.2 cm); W. 2 3/8 in. (6 cm); D. 13/16 in. (2.1 cm),"Edward C. Moore Collection, Bequest of Edward C. Moore, 1891",,,,,,,,,,,,Inrō,,http://www.metmuseum.org/art/collection/search/58463,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"36.100.172a, b",false,true,58350,Asian Art,Box,花熨斗蒔絵短冊箱|Poem Card (Tanzaku) Box with Flower Bouquet,Japan,Meiji period (1869–1912),,,,Artist,Style of,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,Japanese,1807,1891,19th century,1800,1899,"Lacquered wood with gold, silver, color, takamaki-e, hiramaki-e, and togidashimaki-e",H. 7/8 in. (2.2 cm); W. 2 3/8 in. (6 cm); L. 14 1/2 in. (36.8 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Lacquer,,http://www.metmuseum.org/art/collection/search/58350,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1960,false,true,54533,Asian Art,Woodblock print,,Japan,,,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,8 1/8 x 7 3/8 in. (20.6 x 18.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54533,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2148,false,true,55006,Asian Art,Print,,Japan,,,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,5 3/8 x 7 3/4 in. (13.7 x 19.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55006,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2157,false,true,55064,Asian Art,Woodblock print,,Japan,,,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,8 3/16 x 7 1/4 in. (20.8 x 18.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55064,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2210,false,true,53976,Asian Art,Print,,Japan,,,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",(?),Kubo Shunman,Japanese,1757,1820,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,8 3/8 x 7 3/8 in. (21.3 x 18.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/53976,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2225,false,true,53991,Asian Art,Woodblock print,,Japan,,,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,8 15/16 x 4 5/16 in. (22.7 x 11 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/53991,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2270,false,true,54043,Asian Art,Print,,Japan,,,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",(?),Kubo Shunman,Japanese,1757,1820,19th century,1800,1899,Part of an album of woodblock prints (surimono); ink and color on paper,5 1/2 x 5 1/4 in. (14 x 13.3 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54043,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2284,false,true,54057,Asian Art,Woodblock print,,Japan,,,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,19th century,1800,1899,Part of an album of woodblock prints (surimono); ink and color on paper,8 x 10 3/16 in. (20.3 x 25.9 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54057,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2285,false,true,54058,Asian Art,Woodblock print,,Japan,,,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,19th century,1800,1899,Part of an album of woodblock prints (surimono); ink and color on paper,8 3/8 x 5 3/8 in. (21.3 x 13.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54058,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2286,false,true,54059,Asian Art,Print,,Japan,,,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",(?),Kubo Shunman,Japanese,1757,1820,19th century,1800,1899,Part of an album of woodblock prints (surimono); ink and color on paper,8 1/4 x 7 3/8 in. (21 x 18.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54059,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2289,false,true,54062,Asian Art,Print,,Japan,,,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,19th century,1800,1899,Part of an album of woodblock prints (surimono); ink and color on paper,8 3/8 x 7 1/4 in. (21.3 x 18.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54062,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2290,false,true,54063,Asian Art,Print,,Japan,,,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,19th century,1800,1899,Part of an album of woodblock prints (surimono); ink and color on paper,8 1/4 x 7 1/4 in. (21 x 18.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54063,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2299,false,true,54072,Asian Art,Print,,Japan,,,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",(?),Kubo Shunman,Japanese,1757,1820,19th century,1800,1899,Part of an album of woodblock prints (surimono); ink and color on paper,8 1/8 x 7 3/8 in. (20.6 x 18.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54072,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2300,false,true,54073,Asian Art,Print,,Japan,,,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",(?),Kubo Shunman,Japanese,1757,1820,19th century,1800,1899,Part of an album of woodblock prints (surimono); ink and color on paper,5 1/4 x 7 1/8 in. (13.3 x 18.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54073,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2302,false,true,54086,Asian Art,Print,,Japan,,,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",(?),Kubo Shunman,Japanese,1757,1820,19th century,1800,1899,Part of an album of woodblock prints (surimono); ink and color on paper,8 x 7 1/4 in. (20.3 x 18.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54086,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2303,false,true,54087,Asian Art,Woodblock print,,Japan,,,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,19th century,1800,1899,Part of an album of woodblock prints (surimono); ink and color on paper,8 1/4 x 7 3/8 in. (21 x 18.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54087,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2304,false,true,54088,Asian Art,Woodblock print,,Japan,,,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,19th century,1800,1899,Part of an album of woodblock prints (surimono); ink and color on paper,8 3/8 x 7 3/8 in. (21.3 x 18.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54088,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2305,false,true,54089,Asian Art,Print,,Japan,,,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,19th century,1800,1899,Part of an album of woodblock prints (surimono); ink and color on paper,8 3/8 x 7 3/8 in. (21.3 x 18.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54089,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2308,false,true,54092,Asian Art,Print,,Japan,,,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,19th century,1800,1899,Part of an album of woodblock prints (surimono); ink and color on paper,8 1/4 x 7 3/8 in. (21 x 18.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54092,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2310,false,true,54094,Asian Art,Print,,Japan,,,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,19th century,1800,1899,Part of an album of woodblock prints (surimono); ink and color on paper,8 5/16 x 7 3/8 in. (21.1 x 18.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54094,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2311,false,true,54095,Asian Art,Print,,Japan,,,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,19th century,1800,1899,Part of an album of woodblock prints (surimono); ink and color on paper,8 1/4 x 7 1/4 in. (21 x 18.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54095,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2312,false,true,54096,Asian Art,Print,,Japan,,,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,19th century,1800,1899,Part of an album of woodblock prints (surimono); ink and color on paper,8 1/4 x 7 1/4 in. (21 x 18.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54096,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2313,false,true,54097,Asian Art,Print,,Japan,,,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,19th century,1800,1899,Part of an album of woodblock prints (surimono); ink and color on paper,8 1/4 x 7 1/4 in. (21 x 18.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54097,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2316,false,true,54100,Asian Art,Print,,Japan,,,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,19th century,1800,1899,Part of an album of woodblock prints (surimono); ink and color on paper,8 1/4 x 7 1/4 in. (21 x 18.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54100,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2325,false,true,54109,Asian Art,Print,,Japan,,,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,19th century,1800,1899,Part of an album of woodblock prints (surimono); ink and color on paper,8 1/4 x 7 5/16 in. (21 x 18.6 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54109,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2329,false,true,54113,Asian Art,Print,,Japan,,,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,19th century,1800,1899,Part of an album of woodblock prints (surimono); ink and color on paper,8 1/4 x 7 1/4 in. (21 x 18.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54113,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2334,false,true,54118,Asian Art,Print,,Japan,,,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,19th century,1800,1899,Part of an album of woodblock prints (surimono); ink and color on paper,8 1/8 x 6 5/8 in. (20.6 x 16.8 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54118,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2347,false,true,54132,Asian Art,Woodblock print,,Japan,,,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,19th century,1800,1899,Part of an album of woodblock prints (surimono); ink and color on paper,6 1/8 x 11 in. (15.6 x 27.9 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54132,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2348,false,true,54133,Asian Art,Print,,Japan,,,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,19th century,1800,1899,Part of an album of woodblock prints (surimono); ink and color on paper,7 1/4 x 4 7/8 in. (18.4 x 12.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54133,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2354,false,true,54139,Asian Art,Print,,Japan,,,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,19th century,1800,1899,Part of an album of woodblock prints (surimono); ink and color on paper,7 15/16 x 7 1/8 in. (20.2 x 18.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54139,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2355,false,true,53813,Asian Art,Print,,Japan,,,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,7 7/8 x 7 1/8 in. (20 x 18.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/53813,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2356,false,true,54140,Asian Art,Print,,Japan,,,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,19th century,1800,1899,Part of an album of woodblock prints (surimono); ink and color on paper,7 3/4 x 7 3/16 in. (19.7 x 18.3 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54140,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2357,false,true,54141,Asian Art,Print,,Japan,,,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,19th century,1800,1899,Part of an album of woodblock prints (surimono); ink and color on paper,8 x 7 1/4 in. (20.3 x 18.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54141,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2358,false,true,54142,Asian Art,Print,,Japan,,,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,19th century,1800,1899,Part of an album of woodblock prints (surimono); ink and color on paper,7 13/16 x 7 1/8 in. (19.8 x 18.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54142,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2359,false,true,54143,Asian Art,Print,,Japan,,,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,19th century,1800,1899,Part of an album of woodblock prints (surimono); ink and color on paper,7 13/16 x 7 13/16 in. (19.8 x 19.8 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54143,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2360,false,true,54144,Asian Art,Print,,Japan,,,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,19th century,1800,1899,Part of an album of woodblock prints (surimono); ink and color on paper,7 15/16 x 7 1/8 in. (20.2 x 18.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54144,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2332,false,true,54116,Asian Art,Print,,Japan,,,,,Artist,,Utagawa Toyokuni I,"Japanese, 1769–1825",(?),Utagawa Toyokuni I,Japanese,1769,1825,19th century,1800,1899,Part of an album of woodblock prints (surimono); ink and color on paper,8 1/4 x 7 5/16 in. (21 x 18.6 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54116,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2361,false,true,54145,Asian Art,Print,,Japan,,,,,Artist,,Teisai Hokuba,"Japanese, 1771–1844",,Teisai Hokuba,Japanese,1771,1844,19th century,1800,1899,Part of an album of woodblock prints (surimono); ink and color on paper,8 5/16 x 7 5/16 in. (21.1 x 18.6 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54145,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2227,false,true,53993,Asian Art,Print,,Japan,,,,,Artist,,Takashima Chiharu,"Japanese, 1777–1859",(?),Takashima Chiharu,Japanese,1777,1859,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,8 13/16 x 3 15/16 in. (22.4 x 10 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/53993,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2368,false,true,54152,Asian Art,Woodblock print,,Japan,,,,,Artist,,Takashima Chiharu,"Japanese, 1777–1859",,Takashima Chiharu,Japanese,1777,1859,19th century,1800,1899,Part of an album of woodblock prints (surimono); ink and color on paper,8 7/8 x 3 3/4 in. (22.5 x 9.5 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54152,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2327,false,true,54111,Asian Art,Woodblock print,,Japan,,,,,Artist,,Totoya Hokkei,"Japanese, 1780–1850",,Totoya Hokkei,Japanese,1780,1850,19th century,1800,1899,Part of an album of woodblock prints (surimono); ink and color on paper,5 9/16 x 7 3/8 in. (14.1 x 18.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54111,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2345,false,true,54130,Asian Art,Print,,Japan,,,,,Artist,,Totoya Hokkei,"Japanese, 1780–1850",,Totoya Hokkei,Japanese,1780,1850,19th century,1800,1899,Part of an album of woodblock prints (surimono); ink and color on paper,8 x 10 1/4 in. (20.3 x 26 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54130,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2369,false,true,54153,Asian Art,Print,,Japan,,,,,Artist,,Totoya Hokkei,"Japanese, 1780–1850",,Totoya Hokkei,Japanese,1780,1850,19th century,1800,1899,Part of an album of woodblock prints (surimono); ink and color on paper,4 1/2 x 10 1/2 in. (11.4 x 26.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54153,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2390,false,true,54174,Asian Art,Woodblock print,,Japan,,,,,Artist,,Totoya Hokkei,"Japanese, 1780–1850",,Totoya Hokkei,Japanese,1780,1850,19th century,1800,1899,Part of an album of woodblock prints (surimono); ink and color on paper,5 3/8 x 7 3/8 in. (13.7 x 18.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54174,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2278,false,true,54051,Asian Art,Print,,Japan,,,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,19th century,1800,1899,Part of an album of woodblock prints (surimono); ink and color on paper,8 1/4 x 7 1/6 in. (21 x 18.2 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54051,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2319,false,true,54103,Asian Art,Print,,Japan,,,,,Artist,,Keisai Eisen,"Japanese, 1790–1848",,Keisai Eisen,Japanese,1790,1848,19th century,1800,1899,Part of an album of woodblock prints (surimono); ink and color on paper,7 3/4 x 6 9/16 in. (19.7 x 16.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54103,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2333,false,true,54117,Asian Art,Woodblock print,,Japan,,,,,Artist,,Keisai Eisen,"Japanese, 1790–1848",,Keisai Eisen,Japanese,1790,1848,19th century,1800,1899,Part of an album of woodblock prints (surimono); ink and color on paper,7 11/16 x 6 5/8 in. (19.5 x 16.8 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54117,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2162,false,true,55069,Asian Art,Print,,Japan,,,,,Artist,,Yashima Gakutei,"Japanese, 1786?–1868",,Yashima Gakutei,Japanese,1786,1868,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,8 3/16 x 7 1/8 in. (20.8 x 18.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55069,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2163,false,true,55070,Asian Art,Print,,Japan,,,,,Artist,,Yashima Gakutei,"Japanese, 1786?–1868",,Yashima Gakutei,Japanese,1786,1868,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,8 3/16 x 7 1/8 in. (20.8 x 18.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55070,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2322,false,true,54106,Asian Art,Print,,Japan,,,,,Artist,,Yashima Gakutei,"Japanese, 1786?–1868",,Yashima Gakutei,Japanese,1786,1868,19th century,1800,1899,Part of an album of woodblock prints (surimono); ink and color on paper,5 3/8 x 7 3/8 in. (13.7 x 18.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54106,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2324,false,true,54108,Asian Art,Print,,Japan,,,,,Artist,,Yashima Gakutei,"Japanese, 1786?–1868",,Yashima Gakutei,Japanese,1786,1868,19th century,1800,1899,Part of an album of woodblock prints (surimono); ink and color on paper,8 3/8 x 7 3/8 in. (21.3 x 18.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54108,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2343,false,true,54128,Asian Art,Print,,Japan,,,,,Artist,,Yashima Gakutei,"Japanese, 1786?–1868",,Yashima Gakutei,Japanese,1786,1868,19th century,1800,1899,Part of an album of woodblock prints (surimono); ink and color on paper,8 5/16 x 7 1/4 in. (21.1 x 18.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54128,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2326,false,true,54110,Asian Art,Print,,Japan,,,,,Artist,,Ishikawa Kazan,"Japanese, active 1810–1823",,Ishikawa Kazan,Japanese,1810,1823,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,8 1/4 x 7 5/16 in. (21 x 18.6 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54110,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2158,false,true,55065,Asian Art,Woodblock print,,Japan,,,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,19th century,1750,1835,Polychrome woodblock print (surimono); ink and color on paper,5 5/8 x 7 1/2 in. (14.3 x 19.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55065,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2171,false,true,55085,Asian Art,Print,,Japan,,,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,8 1/4 x 7 3/8 in. (21 x 18.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55085,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2207,false,true,53973,Asian Art,Woodblock print,,Japan,,,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,5 1/2 x 7 3/8 in. (14 x 18.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/53973,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2208,false,true,53974,Asian Art,Woodblock print,,Japan,,,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,8 x 7 1/4 in. (20.3 x 18.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/53974,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2212,false,true,53978,Asian Art,Print,,Japan,,,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,5 1/2 x 7 3/8 in. (14 x 18.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/53978,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2226,false,true,53992,Asian Art,Woodblock print,,Japan,,,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,8 1/4 x 11 1/6 in. (21 x 28.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/53992,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2229,false,true,53995,Asian Art,Print,,Japan,,,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,5 x 6 13/16 in. (12.7 x 17.3 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/53995,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2230,false,true,53996,Asian Art,Print,,Japan,,,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,5 x 7 1/16 in. (12.7 x 17.9 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/53996,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2234,false,true,54002,Asian Art,Woodblock print,,Japan,,,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,19th century,1800,1899,Part of an album of woodblock prints (surimono); ink and color on paper,8 1/4 x 10 15/16 in. (21 x 27.8 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54002,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2269,false,true,54042,Asian Art,Woodblock print,,Japan,,,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,19th century,1800,1899,Part of an album of woodblock prints (surimono); ink and color on paper,6 3/4 x 8 3/4 in. (17.1 x 22.2 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54042,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2275,false,true,54048,Asian Art,Print,,Japan,,,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,19th century,1800,1899,Part of an album of woodblock prints (surimono); ink and color on paper,5 7/16 x 7 7/16 in. (13.8 x 18.9 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54048,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2276,false,true,54049,Asian Art,Print,,Japan,,,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,19th century,1800,1899,Part of an album of woodblock prints (surimono); ink and color on paper,5 7/16 x 7 1/2 in. (13.8 x 19.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54049,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2280,false,true,54053,Asian Art,Print,,Japan,,,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,19th century,1800,1899,Part of an album of woodblock prints (surimono); ink and color on paper,8 x 7 1/4 in. (20.3 x 18.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54053,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2281,false,true,54054,Asian Art,Print,,Japan,,,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,19th century,1800,1899,Part of an album of woodblock prints (surimono); ink and color on paper,5 1/2 x 7 7/16 in. (14 x 18.9 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54054,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2282,false,true,54055,Asian Art,Print,,Japan,,,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,19th century,1800,1899,Part of an album of woodblock prints (surimono); ink and color on paper,5 1/2 x 7 7/16 in. (14 x 18.9 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54055,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2283,false,true,54056,Asian Art,Woodblock print,,Japan,,,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,19th century,1800,1899,Part of an album of woodblock prints (surimono); ink and color on paper,4 1/8 x 5 11/16 in. (10.5 x 14.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54056,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2287,false,true,54060,Asian Art,Woodblock print,,Japan,,,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,19th century,1800,1899,Part of an album of woodblock prints (surimono); ink and color on paper,5 7/16 x 7 1/4 in. (13.8 x 18.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54060,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2301,false,true,54085,Asian Art,Woodblock print,,Japan,,,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,19th century,1800,1899,Part of an album of woodblock prints (surimono); ink and color on paper,5 3/8 x 7 1/4 in. (13.7 x 18.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54085,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2314,false,true,54098,Asian Art,Print,,Japan,,,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,19th century,1800,1899,Part of an album of woodblock prints (surimono); ink and color on paper,5 1/2 x 7 3/8 in. (14 x 18.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54098,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2317,false,true,54101,Asian Art,Print,,Japan,,,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,19th century,1800,1899,Part of an album of woodblock prints (surimono); ink and color on paper,5 5/16 x 7 3/16 in. (13.5 x 18.3 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54101,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2323,false,true,54107,Asian Art,Print,,Japan,,,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,19th century,1800,1899,Part of an album of woodblock prints (surimono); ink and color on paper,5 1/2 x 7 3/8 in. (14 x 18.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54107,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2328,false,true,54112,Asian Art,Woodblock print,,Japan,,,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,19th century,1800,1899,Part of an album of woodblock prints (surimono); ink and color on paper,5 5/8 x 7 9/16 in. (14.3 x 19.2 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54112,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2375,false,true,54159,Asian Art,Print,,Japan,,,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,19th century,1800,1899,Part of an album of woodblock prints (surimono); ink and color on paper,7 11/16 x 6 9/16 in. (19.5 x 16.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54159,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2388,false,true,54172,Asian Art,Print,,Japan,,,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,19th century,1800,1899,Part of an album of woodblock prints (surimono); ink and color on paper,8 1/2 x 7 1/2 in. (21.6 x 19.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54172,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2389,false,true,54173,Asian Art,Print,,Japan,,,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,19th century,1800,1899,Part of an album of woodblock prints (surimono); ink and color on paper,8 1/4 x 7 3/8 in. (21 x 18.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54173,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2391,false,true,54175,Asian Art,Print,,Japan,,,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,19th century,1800,1899,Part of an album of woodblock prints (surimono); ink and color on paper,5 7/8 x 7 1/2 in. (14.9 x 19.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54175,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2307,false,true,54091,Asian Art,Print,,Japan,,,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,19th century,1800,1899,Part of an album of woodblock prints (surimono); ink and color on paper,5 1/8 x 7 3/16 in. (13 x 18.3 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54091,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1930,false,true,54470,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,19th century,1757,1820,Polychrome woodblock print (surimono); ink and color on paper,5 3/8 x 10 11/16 in. (13.7 x 27.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54470,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1949,false,true,54517,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,5 3/8 x 7 1/4 in. (13.7 x 18.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54517,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1969,false,true,54553,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,19th century,1800,1820,Polychrome woodblock print (surimono); ink and color on paper,5 5/8 x 7 1/4 in. (14.3 x 18.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54553,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1974,false,true,54559,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",(?),Kubo Shunman,Japanese,1757,1820,19th century,1800,1820,Polychrome woodblock print (surimono); ink and color on paper,5 7/16 x 7 1/4 in. (13.8 x 18.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54559,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1980,false,true,54565,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,8 1/4 x 7 3/8 in. (21 x 18.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54565,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1981,false,true,54679,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,5 1/4 x 7 1/16 in. (13.3 x 17.9 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54679,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1982,false,true,54680,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,5 3/8 x 7 3/16 in. (13.7 x 18.3 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54680,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1983,false,true,54681,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,8 x 7 3/16 in. (20.3 x 18.3 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54681,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1984,false,true,54682,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,8 1/4 x 7 1/4 in. (21 x 18.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54682,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1985,false,true,54683,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,8 3/16 x 7 1/8 in. (20.8 x 18.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54683,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1986,false,true,54685,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,8 5/16 x 7 5/16 in. (21.1 x 18.6 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54685,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1987,false,true,54689,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,5 9/16 x 7 1/4 in. (14.1 x 18.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54689,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1988,false,true,54690,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,5 3/8 x 7 1/4 in. (13.7 x 18.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54690,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1989,false,true,54691,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,7 15/16 x 7 3/16 in. (20.2 x 18.3 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54691,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1991,false,true,54697,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,5 1/2 x 7 3/8 in. (14 x 18.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54697,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1992,false,true,54702,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,8 x 7 1/8 in. (20.3 x 18.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54702,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1993,false,true,54705,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,8 5/16 x 7 3/8 in. (21.1 x 18.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54705,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1994,false,true,54713,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,8 1/4 x 7 1/4 in. (21 x 18.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54713,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1995,false,true,54715,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,8 1/4 x 7 5/16 in. (21 x 18.6 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54715,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1996,false,true,54716,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,8 5/16 x 7 1/4 in. (21.1 x 18.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54716,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1999,false,true,54719,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,8 5/16 x 7 1/4 in. (21.1 x 18.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54719,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2000,false,true,54720,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,8 3/8 x 7 1/4 in. (21.3 x 18.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54720,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2003,false,true,54723,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,8 1/16 x 7 1/4 in. (20.5 x 18.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54723,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2004,false,true,54724,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,8 1/16 x 7 1/4 in. (20.5 x 18.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54724,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2005,false,true,54725,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,7 15/16 x 7 3/16 in. (20.2 x 18.3 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54725,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2006,false,true,54726,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,7 15/16 x 7 3/16 in. (20.2 x 18.3 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54726,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2007,false,true,54727,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,8 x 7 1/16 in. (20.3 x 17.9 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54727,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2009,false,true,54729,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,5 7/16 x 7 5/16 in. (13.8 x 18.6 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54729,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2010,false,true,54730,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,8 1/16 x 7 3/16 in. (20.5 x 18.3 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54730,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2013,false,true,54762,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,7 15/16 x 7 1/4 in. (20.2 x 18.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54762,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2014,false,true,54763,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,8 x 7 1/4 in. (20.3 x 18.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54763,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2016,false,true,54765,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,19th century,1800,1820,Polychrome woodblock print (surimono); ink and color on paper,5 1/4 x 10 13/16 in. (13.3 x 27.5 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54765,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2017,false,true,54766,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,6 x 12 5/8 in. (15.2 x 32.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54766,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2020,false,true,54769,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,8 x 7 1/4 in. (20.3 x 18.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54769,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2021,false,true,54770,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,8 1/4 x 7 1/4 in. (21 x 18.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54770,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2027,false,true,54787,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,8 1/4 x 7 1/4 in. (21 x 18.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54787,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2030,false,true,54793,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",(?),Kubo Shunman,Japanese,1757,1820,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,8 1/4 x 7 1/8 in. (21 x 18.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54793,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2031,false,true,54797,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",(?),Kubo Shunman,Japanese,1757,1820,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,8 1/4 x 7 3/16 in. (21 x 18.3 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54797,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2044,false,true,54815,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",(?),Kubo Shunman,Japanese,1757,1820,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,5 1/2 x 7 3/8 in. (14 x 18.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54815,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2063,false,true,54891,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",(?),Kubo Shunman,Japanese,1757,1820,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,7 15/16 x 7 1/8 in. (20.2 x 18.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54891,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2066,false,true,54895,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",(?),Kubo Shunman,Japanese,1757,1820,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,8 5/16 x 7 3/8 in. (21.1 x 18.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54895,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2067,false,true,54896,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",(?),Kubo Shunman,Japanese,1757,1820,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,8 1/16 x 7 3/16 in. (20.5 x 18.3 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54896,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2069,false,true,54899,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",(?),Kubo Shunman,Japanese,1757,1820,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,8 x 7 1/4 in. (20.3 x 18.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54899,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2072,false,true,54903,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",(?),Kubo Shunman,Japanese,1757,1820,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,8 1/8 x 7 1/4 in. (20.6 x 18.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54903,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2073,false,true,54904,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",(?),Kubo Shunman,Japanese,1757,1820,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,8 x 7 1/16 in. (20.3 x 17.9 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54904,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2076,false,true,54907,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",(?),Kubo Shunman,Japanese,1757,1820,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,8 5/16 x 7 1/4 in. (21.1 x 18.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54907,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2077,false,true,54908,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",(?),Kubo Shunman,Japanese,1757,1820,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,8 9/16 x 7 3/8 in. (21.7 x 18.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54908,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2078,false,true,54909,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",(?),Kubo Shunman,Japanese,1757,1820,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,8 x 7 3/16 in. (20.3 x 18.3 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54909,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2079,false,true,54910,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",(?),Kubo Shunman,Japanese,1757,1820,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,7 15/16 x 7 1/16 in. (20.2 x 17.9 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54910,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2080,false,true,54916,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",(?),Kubo Shunman,Japanese,1757,1820,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,8 x 7 1/8 in. (20.3 x 18.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54916,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2083,false,true,54920,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",(?),Kubo Shunman,Japanese,1757,1820,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,8 x 7 3/16 in. (20.3 x 18.3 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54920,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2086,false,true,54923,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",(?),Kubo Shunman,Japanese,1757,1820,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,8 x 7 3/16 in. (20.3 x 18.3 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54923,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2087,false,true,54924,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",(?),Kubo Shunman,Japanese,1757,1820,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,7 15/16 x 7 1/4 in. (20.2 x 18.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54924,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2092,false,true,54936,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,19th century,1800,1820,Polychrome woodblock print (surimono); ink and color on paper,5 1/2 x 7 7/16 in. (14 x 18.9 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54936,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2096,false,true,54943,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",(?),Kubo Shunman,Japanese,1757,1820,19th century,1800,1820,Polychrome woodblock print (surimono); ink and color on paper,7 7/8 x 7 3/16 in. (20 x 18.3 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54943,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2120,false,true,54973,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,8 1/8 x 7 1/16 in. (20.6 x 17.9 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54973,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2124,false,true,54979,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",(?),Kubo Shunman,Japanese,1757,1820,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,8 x 4 1/8 in. (20.3 x 10.5 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54979,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2125,false,true,54980,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",(?),Kubo Shunman,Japanese,1757,1820,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,8 1/8 x 4 3/16 in. (20.6 x 10.6 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54980,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2128,false,true,54983,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",(?),Kubo Shunman,Japanese,1757,1820,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,7 7/8 x 7 3/16 in. (20 x 18.3 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54983,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2129,false,true,54984,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",(?),Kubo Shunman,Japanese,1757,1820,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,8 1/4 x 7 1/4 in. (21 x 18.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54984,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2134,false,true,54989,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",(?),Kubo Shunman,Japanese,1757,1820,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,8 3/16 x 7 5/16 in. (20.8 x 18.6 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54989,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2137,false,true,54992,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",(?),Kubo Shunman,Japanese,1757,1820,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,7 7/8 x 7 1/4 in. (20 x 18.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54992,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2138,false,true,54993,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",(?),Kubo Shunman,Japanese,1757,1820,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,8 5/16 x 7 3/8 in. (21.1 x 18.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54993,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2144,false,true,54999,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",(?),Kubo Shunman,Japanese,1757,1820,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,7 7/8 x 7 in. (20 x 17.8 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54999,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2145,false,true,55003,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",(?),Kubo Shunman,Japanese,1757,1820,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,5 7/16 x 7 1/8 in. (13.8 x 18.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55003,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2149,false,true,55010,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,5 9/16 x 7 7/16 in. (14.1 x 18.9 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55010,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2166,false,true,55073,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",(?),Kubo Shunman,Japanese,1757,1820,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,8 1/4 x 7 3/8 in. (21 x 18.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55073,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2192,false,true,55108,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",(?),Kubo Shunman,Japanese,1757,1820,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,5 7/16 x 8 1/4 in. (13.8 x 21 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55108,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2200,false,true,53812,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,6 3/16 x 7 3/16 in. (15.7 x 18.3 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/53812,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2236,false,true,54004,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,19th century,1800,1899,Part of an album of woodblock prints (surimono); ink and color on paper,8 3/16 x 7 5/16 in. (20.8 x 18.6 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54004,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2238,false,true,54007,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",(?),Kubo Shunman,Japanese,1757,1820,19th century,1800,1899,Part of an album of woodblock prints (surimono); ink and color on paper,8 1/8 x 5 5/16 in. (20.6 x 13.5 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54007,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2240,false,true,54009,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,19th century,1800,1820,Part of an album of woodblock prints (surimono); ink and color on paper,8 1/8 x 7 3/8 in. (20.6 x 18.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54009,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2241,false,true,54010,Asian Art,Album,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",(?),Kubo Shunman,Japanese,1757,1820,19th century,1800,1820,Part of an album of woodblock prints (surimono); ink and color on paper,8 1/8 x 5 7/16 in. (20.6 x 13.8 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54010,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2244,false,true,54013,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",(?),Kubo Shunman,Japanese,1757,1820,19th century,1800,1820,Part of an album of woodblock prints (surimono); ink and color on paper,8 3/16 x 5 1/2 in. (20.8 x 14 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54013,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2247,false,true,54016,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",(?),Kubo Shunman,Japanese,1757,1820,19th century,1800,1820,Part of an album of woodblock prints (surimono); ink and color on paper,8 1/4 x 5 7/16 in. (21 x 13.8 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54016,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2250,false,true,54019,Asian Art,Print,Asazuma-bune|Courtesan in Ancient Costume Seated in a Boat,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",(?),Kubo Shunman,Japanese,1757,1820,19th century,1800,1899,Part of an album of woodblock prints (surimono); ink and color on paper,8 3/16 x 5 5/16 in. (20.8 x 13.5 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54019,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2251,false,true,54020,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",(?),Kubo Shunman,Japanese,1757,1820,19th century,1800,1899,Part of an album of woodblock prints (surimono); ink and color on paper,8 1/8 x 10 9/16 in. (20.6 x 26.8 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54020,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2254,false,true,54023,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",(?),Kubo Shunman,Japanese,1757,1820,19th century,1800,1899,Part of an album of woodblock prints (surimono); ink and color on paper,8 1/8 x 5 5/16 in. (20.6 x 13.5 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54023,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2255,false,true,54024,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,19th century,1800,1899,Part of an album of woodblock prints (surimono); ink and color on paper,7 5/8 x 5 3/16 in. (19.4 x 13.2 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54024,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2376,false,true,54160,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,19th century,1800,1899,Part of an album of woodblock prints (surimono); ink and color on paper,8 1/16 x 7 1/8 in. (20.5 x 18.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54160,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2377,false,true,54161,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,19th century,1800,1899,Part of an album of woodblock prints (surimono); ink and color on paper,8 1/16 x 6 15/16 in. (20.5 x 17.6 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54161,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2378,false,true,54162,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,19th century,1800,1899,Part of an album of woodblock prints (surimono); ink and color on paper,8 x 7 1/8 in. (20.3 x 18.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54162,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2379,false,true,54163,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,19th century,1800,1899,Part of an album of woodblock prints (surimono); ink and color on paper,8 1/16 x 7 3/16 in. (20.5 x 18.3 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54163,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2217,false,true,53983,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Nagayama Koin (Hirotora),"Japanese, 1765–1849",,Nagayama Koin (Hirotora),Japanese,1765,1849,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,8 1/2 x 7 3/16 in. (21.6 x 18.3 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/53983,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2215,false,true,53981,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Toyokuni I,"Japanese, 1769–1825",,Utagawa Toyokuni I,Japanese,1769,1825,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,8 1/8 x 4 1/2 in. (20.6 x 11.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/53981,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2397,false,true,54177,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Teisai Hokuba,"Japanese, 1771–1844",,Teisai Hokuba,Japanese,1771,1844,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,7 3/8 x 10 1/4 in. (18.7 x 26 cm),"H. O. Havemeyer Collection, Gift of Mrs. J. Watson Webb, 1930",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54177,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1955,false,true,54525,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Totoya Hokkei,"Japanese, 1780–1850",,Totoya Hokkei,Japanese,1780,1850,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,5 7/16 x 5 5/8 in. (13.8 x 14.3 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54525,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2012,false,true,54761,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Totoya Hokkei,"Japanese, 1780–1850",,Totoya Hokkei,Japanese,1780,1850,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,5 9/16 x 7 1/8 in. (14.1 x 18.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54761,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2022,false,true,54771,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Totoya Hokkei,"Japanese, 1780–1850",,Totoya Hokkei,Japanese,1780,1850,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,8 3/16 x 7 3/8 in. (20.8 x 18.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54771,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2041,false,true,54812,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Totoya Hokkei,"Japanese, 1780–1850",(?),Totoya Hokkei,Japanese,1780,1850,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,8 x 7 in. (20.3 x 17.8 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54812,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2045,false,true,54816,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Totoya Hokkei,"Japanese, 1780–1850",,Totoya Hokkei,Japanese,1780,1850,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,7 15/16 x 7 1/4 in. (20.2 x 18.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54816,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2081,false,true,54918,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Totoya Hokkei,"Japanese, 1780–1850",,Totoya Hokkei,Japanese,1780,1850,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,5 3/4 x 7 7/16 in. (14.6 x 18.9 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54918,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2093,false,true,54937,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Totoya Hokkei,"Japanese, 1780–1850",,Totoya Hokkei,Japanese,1780,1850,19th century,1800,1850,Polychrome woodblock print (surimono); ink and color on paper,7 1/16 x 6 5/8 in. (17.9 x 16.8 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54937,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2112,false,true,54964,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Totoya Hokkei,"Japanese, 1780–1850",,Totoya Hokkei,Japanese,1780,1850,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,7 3/4 x 6 9/16 in. (19.7 x 16.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54964,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2117,false,true,54970,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Totoya Hokkei,"Japanese, 1780–1850",,Totoya Hokkei,Japanese,1780,1850,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,8 1/8 x 7 1/8 in. (20.6 x 18.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54970,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2121,false,true,54975,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Totoya Hokkei,"Japanese, 1780–1850",,Totoya Hokkei,Japanese,1780,1850,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,8 1/8 x 7 1/16 in. (20.6 x 17.9 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54975,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2160,false,true,55067,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Totoya Hokkei,"Japanese, 1780–1850",,Totoya Hokkei,Japanese,1780,1850,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,7 7/8 x 6 7/8 in. (20 x 17.5 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55067,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2201,false,true,53967,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Totoya Hokkei,"Japanese, 1780–1850",,Totoya Hokkei,Japanese,1780,1850,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,3 1/2 x 11 in. (8.9 x 27.9 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/53967,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2213,false,true,53979,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Totoya Hokkei,"Japanese, 1780–1850",,Totoya Hokkei,Japanese,1780,1850,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,2 3/4 x 9 1/2 in. (7.0 x 24.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/53979,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2235,false,true,54003,Asian Art,Print,Ushi-no-toki mairi|Woman in the Rain at Midnight Driving a Nail into a Tree to Invoke Evil on Her Unfaithful Lover,Japan,Edo period (1615–1868),,,,Artist,,Totoya Hokkei,"Japanese, 1780–1850",,Totoya Hokkei,Japanese,1780,1850,19th century,1800,1899,Part of an album of woodblock prints (surimono); ink and color on paper,8 3/16 x 5 3/8 in. (20.8 x 13.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54003,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2248,false,true,54017,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Totoya Hokkei,"Japanese, 1780–1850",,Totoya Hokkei,Japanese,1780,1850,19th century,1800,1850,Part of an album of woodblock prints (surimono); ink and color on paper,7 15/16 x 7 1/4 in. (20.2 x 18.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54017,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2259,false,true,54029,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Totoya Hokkei,"Japanese, 1780–1850",,Totoya Hokkei,Japanese,1780,1850,19th century,1800,1899,Part of an album of woodblock prints (surimono); ink and color on paper,7 9/16 x 10 5/16 in. (19.2 x 26.2 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54029,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2381,false,true,54165,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Totoya Hokkei,"Japanese, 1780–1850",,Totoya Hokkei,Japanese,1780,1850,19th century,1800,1899,Part of an album of woodblock prints (surimono); ink and color on paper,7 1/8 x 6 11/16 in. (18.1 x 17 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54165,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2382,false,true,54166,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Hokucho Joren,"Japanese, 1780–1850",,Hokucho Joren,Japanese,1780,1850,19th century,1800,1899,Part of an album of woodblock prints (surimono); ink and color on paper,7 15/16 x 7 1/16 in. (20.2 x 17.9 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54166,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2383,false,true,54167,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Totoya Hokkei,"Japanese, 1780–1850",,Totoya Hokkei,Japanese,1780,1850,19th century,1800,1899,Part of an album of woodblock prints (surimono); ink and color on paper,8 1/8 x 7 1/4 in. (20.6 x 18.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54167,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2384,false,true,54168,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Totoya Hokkei,"Japanese, 1780–1850",,Totoya Hokkei,Japanese,1780,1850,19th century,1800,1899,Part of an album of woodblock prints (surimono); ink and color on paper,8 1/8 x 7 1/4 in. (20.6 x 18.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54168,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2385,false,true,54169,Asian Art,Album,,Japan,Edo period (1615–1868),,,,Artist,,Totoya Hokkei,"Japanese, 1780–1850",,Totoya Hokkei,Japanese,1780,1850,19th century,1800,1899,Part of an album of woodblock prints (surimono); ink and color on paper,8 1/8 x 7 1/8 in. (20.6 x 18.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54169,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2386,false,true,54170,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Totoya Hokkei,"Japanese, 1780–1850",,Totoya Hokkei,Japanese,1780,1850,19th century,1800,1899,Part of an album of woodblock prints (surimono); ink and color on paper,7 15/16 x 7 1/8 in. (20.2 x 18.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54170,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2387,false,true,54171,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Totoya Hokkei,"Japanese, 1780–1850",,Totoya Hokkei,Japanese,1780,1850,19th century,1800,1899,Part of an album of woodblock prints (surimono); ink and color on paper,8 x 7 3/16 in. (20.3 x 18.3 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54171,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP209,false,true,36686,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,19th century,1800,1865,Album of 48 polychrome woodblock prints; ink and color on paper,14 3/4 × 10 1/4 × 1/2 in. (37.5 × 26 × 1.3 cm),"Gift of the Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/36686,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1114,false,true,55036,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,19th century,1800,1865,Album of 18 polychrome woodblock prints; ink and color on paper,91 in. × 10 3/8 in. × 3/8 in. (231.1 × 26.4 × 1 cm),"Gift of Harold de Raasloff, 1918",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55036,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2864,false,true,51087,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,19th century,1800,1899,Polychrome woodblock print; ink and color on paper,H. 9 3/8 in. (23.8 cm); W. 14 3/8 in. (36.5 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/51087,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP212.2,false,true,57987,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,19th century,1800,1865,Polychrome woodblock print; ink and color on paper,Image: 13 5/8 × 9 1/2 in. (34.6 × 24.1 cm),"Gift of the Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57987,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP212.3,false,true,57988,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,19th century,1800,1865,Polychrome woodblock print; ink and color on paper,Image: 13 5/8 × 9 5/8 in. (34.6 × 24.4 cm),"Gift of the Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57988,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP212.4,false,true,57989,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,19th century,1800,1865,Polychrome woodblock print; ink and color on paper,Image: 13 5/8 × 9 5/8 in. (34.6 × 24.4 cm),"Gift of the Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57989,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP212.6,false,true,57991,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,19th century,1800,1865,Polychrome woodblock print; ink and color on paper,Image: 13 5/8 × 9 5/8 in. (34.6 × 24.4 cm),"Gift of the Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57991,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP212.7,false,true,57992,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,19th century,1800,1865,Polychrome woodblock print; ink and color on paper,Image: 13 5/8 × 9 5/8 in. (34.6 × 24.4 cm),"Gift of the Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57992,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP212.8,false,true,57993,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,19th century,1800,1865,Polychrome woodblock print; ink and color on paper,Image: 13 5/8 × 9 5/8 in. (34.6 × 24.4 cm),"Gift of the Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57993,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP212.9,false,true,57994,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,19th century,1800,1865,Polychrome woodblock print; ink and color on paper,Image: 13 5/8 × 9 5/8 in. (34.6 × 24.4 cm),"Gift of the Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57994,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP212.10,false,true,57995,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,19th century,1800,1865,Polychrome woodblock print; ink and color on paper,Image: 13 5/8 × 9 5/8 in. (34.6 × 24.4 cm),"Gift of the Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57995,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP212.11,false,true,57996,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,19th century,1800,1865,Polychrome woodblock print; ink and color on paper,Image: 13 5/8 × 9 1/2 in. (34.6 × 24.1 cm),"Gift of the Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57996,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP212.12,false,true,57997,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,19th century,1800,1865,Polychrome woodblock print; ink and color on paper,Image: 13 5/8 × 9 5/8 in. (34.6 × 24.4 cm),"Gift of the Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57997,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP212.13,false,true,57998,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,19th century,1800,1865,Polychrome woodblock print; ink and color on paper,Image: 13 5/8 × 9 1/8 in. (34.6 × 23.2 cm),"Gift of the Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57998,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP212.14,false,true,57999,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,19th century,1800,1865,Polychrome woodblock print; ink and color on paper,Image: 13 5/8 × 9 5/8 in. (34.6 × 24.4 cm),"Gift of the Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57999,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP212.19,false,true,58004,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,19th century,1800,1865,Polychrome woodblock print; ink and color on paper,Image: 13 5/8 × 9 5/8 in. (34.6 × 24.4 cm),"Gift of the Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58004,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP212.20,false,true,58005,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,19th century,1800,1865,Polychrome woodblock print; ink and color on paper,Image: 13 5/8 × 9 5/8 in. (34.6 × 24.4 cm),"Gift of the Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58005,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP212.21,false,true,58006,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,19th century,1800,1865,Polychrome woodblock print; ink and color on paper,Image: 13 5/8 × 9 5/8 in. (34.6 × 24.4 cm),"Gift of the Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58006,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP212.24,false,true,58009,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,19th century,1800,1865,Polychrome woodblock print; ink and color on paper,Image: 13 5/8 × 9 5/8 in. (34.6 × 24.4 cm),"Gift of the Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58009,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP212.26,false,true,58010,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,19th century,1800,1865,Polychrome woodblock print; ink and color on paper,Image: 13 3/8 × 8 3/4 in. (34 × 22.2 cm),"Gift of the Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58010,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP212.27,false,true,58011,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,19th century,1800,1865,Polychrome woodblock print; ink and color on paper,Image: 13 1/2 × 9 5/8 in. (34.3 × 24.4 cm),"Gift of the Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58011,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP212.28,false,true,58012,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,19th century,1800,1865,Polychrome woodblock print; ink and color on paper,Image: 13 1/2 × 9 5/8 in. (34.3 × 24.4 cm),"Gift of the Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58012,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP212.29,false,true,58013,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,19th century,1800,1865,Polychrome woodblock print; ink and color on paper,Image: 13 1/2 × 9 1/2 in. (34.3 × 24.1 cm),"Gift of the Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58013,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP212.31,false,true,58015,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,19th century,1800,1865,Polychrome woodblock print; ink and color on paper,Image: 13 5/8 × 9 5/8 in. (34.6 × 24.4 cm),"Gift of the Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58015,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP212.32,false,true,58016,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,19th century,1800,1865,Polychrome woodblock print; ink and color on paper,Image: 13 5/8 × 9 5/8 in. (34.6 × 24.4 cm),"Gift of the Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58016,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP212.33,false,true,58017,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,19th century,1800,1865,Polychrome woodblock print; ink and color on paper,Image: 13 5/8 × 9 1/2 in. (34.6 × 24.1 cm),"Gift of the Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58017,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP212.34,false,true,58018,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,19th century,1800,1865,Polychrome woodblock print; ink and color on paper,Image: 13 5/8 × 9 1/2 in. (34.6 × 24.1 cm),"Gift of the Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58018,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP212.35,false,true,58019,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,19th century,1800,1865,Polychrome woodblock print; ink and color on paper,Image: 13 5/8 × 9 1/2 in. (34.6 × 24.1 cm),"Gift of the Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58019,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP212.36,false,true,58020,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,19th century,1800,1865,Polychrome woodblock print; ink and color on paper,Image: 13 5/8 × 9 1/2 in. (34.6 × 24.1 cm),"Gift of the Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58020,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP212.37,false,true,58021,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,19th century,1800,1865,Polychrome woodblock print; ink and color on paper,Image: 13 5/8 × 9 5/8 in. (34.6 × 24.4 cm),"Gift of the Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58021,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP212.39,false,true,58023,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,19th century,1800,1865,Polychrome woodblock print; ink and color on paper,Image: 13 5/8 × 9 1/2 in. (34.6 × 24.1 cm),"Gift of the Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58023,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP212.41,false,true,58025,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,19th century,1800,1865,Polychrome woodblock print; ink and color on paper,Image: 13 5/8 × 9 5/8 in. (34.6 × 24.4 cm),"Gift of the Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58025,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP212.43,false,true,58027,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,19th century,1800,1865,Polychrome woodblock print; ink and color on paper,Image: 13 5/8 × 9 5/8 in. (34.6 × 24.4 cm),"Gift of the Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58027,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP212.44,false,true,58028,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,19th century,1800,1865,Polychrome woodblock print; ink and color on paper,Image: 13 5/8 × 9 5/8 in. (34.6 × 24.4 cm),"Gift of the Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58028,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP212.47,false,true,58031,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,19th century,1800,1865,Polychrome woodblock print; ink and color on paper,Image: 13 5/8 × 9 5/8 in. (34.6 × 24.4 cm),"Gift of the Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58031,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP212.48,false,true,58032,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,19th century,1800,1865,Polychrome woodblock print; ink and color on paper,Image: 13 5/8 × 9 5/8 in. (34.6 × 24.4 cm),"Gift of the Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58032,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP212.50,false,true,58034,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,19th century,1800,1865,Polychrome woodblock print; ink and color on paper,Image: 13 5/8 × 9 1/2 in. (34.6 × 24.1 cm),"Gift of the Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58034,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP212.55,false,true,58039,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,19th century,1800,1865,Polychrome woodblock print; ink and color on paper,Image: 13 5/8 × 9 5/8 in. (34.6 × 24.4 cm),"Gift of the Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58039,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP212.56,false,true,58040,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,19th century,1800,1865,Polychrome woodblock print; ink and color on paper,Image: 13 5/8 × 9 5/8 in. (34.6 × 24.4 cm),"Gift of the Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58040,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP212.57,false,true,58041,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,19th century,1800,1865,Polychrome woodblock print; ink and color on paper,Image: 13 5/8 × 9 5/8 in. (34.6 × 24.4 cm),"Gift of the Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58041,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP212.60,false,true,58044,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,19th century,1800,1865,Polychrome woodblock print; ink and color on paper,Image: 13 5/8 × 9 5/8 in. (34.6 × 24.4 cm),"Gift of the Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58044,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP212.61,false,true,58045,Asian Art,Print,相模屋亭主|Actor as Master of Sagamiya (Sagamiya teishu),Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,19th century,1800,1865,Panel from a triptych of polychrome woodblock prints; ink and color on paper,Image: 13 5/8 × 9 1/2 in. (34.6 × 24.1 cm),"Gift of the Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58045,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP212.62,false,true,58046,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,19th century,1800,1865,Polychrome woodblock print; ink and color on paper,Image: 13 5/8 × 9 5/8 in. (34.6 × 24.4 cm),"Gift of the Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58046,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP212.63,false,true,58047,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,19th century,1800,1865,Polychrome woodblock print; ink and color on paper,Image: 13 5/8 × 9 5/8 in. (34.6 × 24.4 cm),"Gift of the Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58047,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP212.64,false,true,58048,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,19th century,1800,1865,Polychrome woodblock print; ink and color on paper,Image: 13 5/8 × 9 5/8 in. (34.6 × 24.4 cm),"Gift of the Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58048,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP212.65,false,true,58049,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,19th century,1800,1865,Polychrome woodblock print; ink and color on paper,Image: 13 5/8 × 9 5/8 in. (34.6 × 24.4 cm),"Gift of the Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58049,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP212.66,false,true,58050,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,19th century,1800,1865,Polychrome woodblock print; ink and color on paper,Image: 13 5/8 × 9 5/8 in. (34.6 × 24.4 cm),"Gift of the Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58050,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP212.67,false,true,58051,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,19th century,1800,1865,Polychrome woodblock print; ink and color on paper,Image: 13 5/8 × 9 5/8 in. (34.6 × 24.4 cm),"Gift of the Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58051,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP212.68,false,true,58052,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,19th century,1800,1865,Polychrome woodblock print; ink and color on paper,Image: 13 5/8 × 9 5/8 in. (34.6 × 24.4 cm),"Gift of the Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58052,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP212.69,false,true,58053,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,19th century,1800,1865,Polychrome woodblock print; ink and color on paper,Image: 13 3/4 × 9 1/2 in. (34.9 × 24.1 cm),"Gift of the Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58053,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP212.70,false,true,58054,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,19th century,1800,1865,Polychrome woodblock print; ink and color on paper,Image: 13 3/4 × 9 5/8 in. (34.9 × 24.4 cm),"Gift of the Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58054,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP212.71,false,true,58058,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,19th century,1800,1865,Polychrome woodblock print; ink and color on paper,Image: 13 1/2 × 9 1/2 in. (34.3 × 24.1 cm),"Gift of the Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58058,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP212.72,false,true,58059,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,19th century,1800,1865,Polychrome woodblock print; ink and color on paper,Image: 13 5/8 × 9 1/2 in. (34.6 × 24.1 cm),"Gift of the Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58059,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP212.73,false,true,58060,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,19th century,1800,1865,Polychrome woodblock print; ink and color on paper,Image: 13 5/8 × 9 5/8 in. (34.6 × 24.4 cm),"Gift of the Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58060,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP212.74,false,true,58061,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,19th century,1800,1865,Polychrome woodblock print; ink and color on paper,Image: 13 5/8 × 9 5/8 in. (34.6 × 24.4 cm),"Gift of the Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58061,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP212.75,false,true,58062,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,19th century,1800,1865,Polychrome woodblock print; ink and color on paper,Image: 13 3/4 × 9 5/8 in. (34.9 × 24.4 cm),"Gift of the Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58062,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP212.76,false,true,58063,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,19th century,1800,1865,Polychrome woodblock print; ink and color on paper,Image: 13 3/4 × 9 3/4 in. (34.9 × 24.8 cm),"Gift of the Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58063,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP212.77,false,true,58064,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,19th century,1800,1865,Polychrome woodblock print; ink and color on paper,Image: 13 5/8 × 9 5/8 in. (34.6 × 24.4 cm),"Gift of the Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58064,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP212.78,false,true,58065,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,19th century,1800,1865,Polychrome woodblock print; ink and color on paper,Image: 13 3/4 × 9 5/8 in. (34.9 × 24.4 cm),"Gift of the Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58065,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP212.79,false,true,58066,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,19th century,1800,1865,Polychrome woodblock print; ink and color on paper,Image: 13 5/8 × 9 5/8 in. (34.6 × 24.4 cm),"Gift of the Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58066,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP212.80,false,true,58067,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,19th century,1800,1865,Polychrome woodblock print; ink and color on paper,Image: 13 5/8 × 9 5/8 in. (34.6 × 24.4 cm),"Gift of the Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58067,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP212.81,false,true,58068,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,19th century,1800,1865,Polychrome woodblock print; ink and color on paper,Image: 13 5/8 × 9 5/8 in. (34.6 × 24.4 cm),"Gift of the Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58068,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP212.82,false,true,58069,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,19th century,1800,1865,Polychrome woodblock print; ink and color on paper,Image: 13 5/8 × 9 5/8 in. (34.6 × 24.4 cm),"Gift of the Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58069,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP212.83,false,true,58070,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,19th century,1800,1865,Polychrome woodblock print; ink and color on paper,Image: 13 3/4 × 9 5/8 in. (34.9 × 24.4 cm),"Gift of the Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58070,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP212.84,false,true,58071,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,19th century,1800,1865,Polychrome woodblock print; ink and color on paper,Image: 13 3/4 × 9 5/8 in. (34.9 × 24.4 cm),"Gift of the Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58071,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP212.86,false,true,58073,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,19th century,1800,1865,Polychrome woodblock print; ink and color on paper,Image: 13 3/4 × 9 1/2 in. (34.9 × 24.1 cm),"Gift of the Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58073,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP212.88,false,true,58076,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,19th century,1800,1865,Polychrome woodblock print; ink and color on paper,Image: 13 3/4 × 9 5/8 in. (34.9 × 24.4 cm),"Gift of the Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58076,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP212.89,false,true,58077,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,19th century,1800,1865,Polychrome woodblock print; ink and color on paper,Image: 13 3/4 × 9 1/2 in. (34.9 × 24.1 cm),"Gift of the Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58077,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.267,false,true,73558,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,19th century,1786,1865,Polychrome woodblock print; ink and color on paper,Image: 11 3/8 x 8 3/4 in. (28.9 x 22.2 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73558,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.268,false,true,73608,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,19th century,1786,1865,Polychrome woodblock print; ink and color on paper,Image: 11 7/8 x 9 3/8 in. (30.2 x 23.8 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73608,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.269,false,true,73609,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,19th century,1786,1865,Polychrome woodblock print; ink and color on paper,Image: 14 x 9 3/4 in. (35.6 x 24.8 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73609,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.270,false,true,73610,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,19th century,1800,1865,Polychrome woodblock print; ink and color on paper,Image: 14 5/8 x 9 7/8 in. (37.1 x 25.1 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73610,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.272,false,true,73612,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,19th century,1800,1865,Polychrome woodblock print; ink and color on paper,Image: 14 3/16 x 9 3/4 in. (36 x 24.8 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73612,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.273,false,true,73613,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,19th century,1800,1865,Polychrome woodblock print; ink and color on paper,Image: 14 1/8 x 9 1/2 in. (35.9 x 24.1 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73613,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.274,false,true,73614,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,19th century,1800,1865,Polychrome woodblock print; ink and color on paper,Image: 14 3/8 x 9 7/8 in. (36.5 x 25.1 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73614,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.275,false,true,73615,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,19th century,1800,1865,Polychrome woodblock print; ink and color on paper,Image: 14 1/8 x 9 1/2 in. (35.9 x 24.1 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73615,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3495a–c,false,true,55723,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kunisada,"Japanese, 1786–1865",,Utagawa Kunisada,Japanese,1786,1865,19th century,1800,1865,Triptych of polychrome woodblock prints; ink and color on paper,Each 10 x 14 1/2 in. (25.4 x 36.8 cm),"Gift of Cole J. Younger, 1975",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55723,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2202,false,true,53968,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Yanagawa Shigenobu,"Japanese, 1787–1832",,Yanagawa Shigenobu,Japanese,1787,1832,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,8 1/4 x 11 1/16 in. (21 x 28.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/53968,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2085,false,true,54922,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Kikugawa Eizan,"Japanese, 1787–1867",,Kikugawa Eizan,Japanese,1787,1867,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,5 5/8 x 7 7/16 in. (14.3 x 18.9 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54922,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2133,false,true,54988,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Yosai,"Japanese, 1788–1878",,Yosai,Japanese,1788,1878,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,5 3/4 x 7 5/8 in. (14.6 x 19.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54988,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1963,false,true,54544,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Keisai Eisen,"Japanese, 1790–1848",,Keisai Eisen,Japanese,1790,1848,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,7 11/16 x 7 1/8 in. (19.5 x 18.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54544,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3434,false,true,55634,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Keisai Eisen,"Japanese, 1790–1848",,Keisai Eisen,Japanese,1790,1848,19th century,1800,1899,Polychrome woodblock print; ink and color on paper,14 x 10 in. (35.6 x 25.4 cm),"Gift of Cole J. Younger, 1975",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55634,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1041,false,true,54326,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Watanabe Kazan,"Japanese, 1793–1841",,Watanabe Kazan,Japanese,1793,1841,19th century,1800,1841,Polychrome woodblock print (surimono); ink and color on paper,8 1/4 x 7 1/8 in. (21 x 18.1 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54326,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2750,false,true,57016,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kuniyoshi,"Japanese, 1797–1861",,Utagawa Kuniyoshi,Japanese,1797,1861,19th century,1800,1899,Woodblock print; ink on thin paper (some corrections have been pasted over the drawing),13 3/4 x 8 1/2 in. (34.9 x 21.6 cm),"Fletcher Fund, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57016,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3706,false,true,55956,Asian Art,Print,木曽街道六十九次之内・下諏訪 八重垣姫|Princess Yaegaki,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kuniyoshi,"Japanese, 1797–1861",,Utagawa Kuniyoshi,Japanese,1797,1861,19th century,1800,1899,Polychrome woodblock print; ink and color on paper,14 1/2 x 10 in. (36.8 x 25.4 cm),"Bequest of Grace M. Pugh, 1985",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55956,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP212.22,false,true,58007,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kuniyoshi,"Japanese, 1797–1861",,Utagawa Kuniyoshi,Japanese,1797,1861,19th century,1800,1861,Polychrome woodblock print; ink and color on paper,Image: 13 5/8 × 9 5/8 in. (34.6 × 24.4 cm),"Gift of the Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58007,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP212.23,false,true,58008,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kuniyoshi,"Japanese, 1797–1861",,Utagawa Kuniyoshi,Japanese,1797,1861,19th century,1800,1861,Polychrome woodblock print; ink and color on paper,Image: 13 5/8 × 9 5/8 in. (34.6 × 24.4 cm),"Gift of the Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58008,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP212.30,false,true,58014,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kuniyoshi,"Japanese, 1797–1861",,Utagawa Kuniyoshi,Japanese,1797,1861,19th century,1800,1861,Polychrome woodblock print; ink and color on paper,Image: 13 1/2 × 9 1/2 in. (34.3 × 24.1 cm),"Gift of the Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58014,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP212.38,false,true,58022,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kuniyoshi,"Japanese, 1797–1861",,Utagawa Kuniyoshi,Japanese,1797,1861,19th century,1800,1861,Polychrome woodblock print; ink and color on paper,Image: 13 5/8 × 9 5/8 in. (34.6 × 24.4 cm),"Gift of the Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58022,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP212.40,false,true,58024,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kuniyoshi,"Japanese, 1797–1861",,Utagawa Kuniyoshi,Japanese,1797,1861,19th century,1800,1865,Polychrome woodblock print; ink and color on paper,Image: 13 5/8 × 9 1/2 in. (34.6 × 24.1 cm),"Gift of the Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58024,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP212.42,false,true,58026,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kuniyoshi,"Japanese, 1797–1861",,Utagawa Kuniyoshi,Japanese,1797,1861,19th century,1800,1861,Polychrome woodblock print; ink and color on paper,Image: 13 5/8 × 9 5/8 in. (34.6 × 24.4 cm),"Gift of the Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58026,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP212.51,false,true,58035,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kuniyoshi,"Japanese, 1797–1861",,Utagawa Kuniyoshi,Japanese,1797,1861,19th century,1800,1861,Polychrome woodblock print; ink and color on paper,Image: 13 5/8 × 9 1/2 in. (34.6 × 24.1 cm),"Gift of the Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58035,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP212.85,false,true,58072,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kuniyoshi,"Japanese, 1797–1861",,Utagawa Kuniyoshi,Japanese,1797,1861,19th century,1800,1861,Polychrome woodblock print; ink and color on paper,Image: 13 3/4 × 9 1/2 in. (34.9 × 24.1 cm),"Gift of the Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58072,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP212.87,false,true,58074,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kuniyoshi,"Japanese, 1797–1861",,Utagawa Kuniyoshi,Japanese,1797,1861,19th century,1800,1861,Polychrome woodblock print; ink and color on paper,Image: 13 3/4 × 9 5/8 in. (34.9 × 24.4 cm),"Gift of the Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58074,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1806a–e,false,true,45235,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kuniyoshi,"Japanese, 1797–1861",,Utagawa Kuniyoshi,Japanese,1797,1861,19th century,1800,1868,Pentaptych of polychrome woodblock prints; ink and color on paper,14 5/8 x 50 in. (37.1 x 127 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45235,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1993.309,false,true,55987,Asian Art,Print,江戸名所　芝神明|Famous Places of Edo: Shiba Shinmei,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige II,"Japanese, 1829–1869",,Utagawa Hiroshige II,Japanese,1829,1869,19th century,1829,1868,Polychrome woodblock print; ink and color on paper,8 1/2 x 13 3/8 in. (21.6 x 34 cm),"Bequest of Gustave von Groschwitz, 1993",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55987,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.279,false,true,73562,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige II,"Japanese, 1829–1869",,Utagawa Hiroshige II,Japanese,1829,1869,19th century,1829,1869,Polychrome woodblock print; ink and color on paper,Image: 8 3/8 x 11 1/4 in. (21.3 x 28.6 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73562,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.320a–c,false,true,73578,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshiiku,"Japanese, 1833–1904",,Utagawa Yoshiiku,Japanese,1833,1904,19th century,1833,1868,Triptych of polychrome woodblock prints; ink and color on paper,Image (a): 14 1/4 x 9 3/4 in. (36.2 x 24.8 cm) Image (b): 14 1/2 x 9 1/2 in. (36.8 x 24.1 cm) Image (c): 14 1/4 x 9 7/8 in. (36.2 x 25.1 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73578,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1453,false,true,54394,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Yashima Gakutei,"Japanese, 1786?–1868",,Yashima Gakutei,Japanese,1786,1868,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,8 1/4 x 7 1/2 in. (21 x 19.1 cm),"Rogers Fund, 1923",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54394,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1934,false,true,54474,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Yashima Gakutei,"Japanese, 1786?–1868",,Yashima Gakutei,Japanese,1786,1868,19th century,1800,1868,Polychrome woodblock print (surimono); ink and color on paper,8 3/16 x 7 3/8 in. (20.8 x 18.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54474,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1964,false,true,54545,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Yashima Gakutei,"Japanese, 1786?–1868",,Yashima Gakutei,Japanese,1786,1868,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,8 1/4 x 7 3/8 in. (21 x 18.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54545,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1965,false,true,54547,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Yashima Gakutei,"Japanese, 1786?–1868",,Yashima Gakutei,Japanese,1786,1868,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,8 5/16 x 7 3/8 in. (21.1 x 18.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54547,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1966,false,true,54548,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Yashima Gakutei,"Japanese, 1786?–1868",,Yashima Gakutei,Japanese,1786,1868,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,8 5/16 x 7 3/8 in. (21.1 x 18.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54548,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1967,false,true,54549,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Yashima Gakutei,"Japanese, 1786?–1868",,Yashima Gakutei,Japanese,1786,1868,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,8 5/16 x 7 3/8 in. (21.1 x 18.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54549,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1972,false,true,54557,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Yashima Gakutei,"Japanese, 1786?–1868",,Yashima Gakutei,Japanese,1786,1868,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,7 3/4 x 6 1/2 in. (19.7 x 16.5 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54557,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1978,false,true,54563,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Yashima Gakutei,"Japanese, 1786?–1868",,Yashima Gakutei,Japanese,1786,1868,19th century,1800,1868,Polychrome woodblock print (surimono); ink and color on paper,8 1/8 x 7 3/16 in. (20.6 x 18.3 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54563,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1979,false,true,54564,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Yashima Gakutei,"Japanese, 1786?–1868",,Yashima Gakutei,Japanese,1786,1868,19th century,1800,1868,Polychrome woodblock print (surimono); ink and color on paper,8 3/16 x 7 1/4 in. (20.8 x 18.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54564,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2042,false,true,54813,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Yashima Gakutei,"Japanese, 1786?–1868",,Yashima Gakutei,Japanese,1786,1868,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,7 3/16 x 6 1/2 in. (18.3 x 16.5 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54813,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2061,false,true,54842,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Yashima Gakutei,"Japanese, 1786?–1868",,Yashima Gakutei,Japanese,1786,1868,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,7 7/8 x 6 13/16 in. (20 x 17.3 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54842,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2062,false,true,54843,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Yashima Gakutei,"Japanese, 1786?–1868",,Yashima Gakutei,Japanese,1786,1868,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,7 15/16 x 6 3/4 in. (20.2 x 17.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54843,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2088,false,true,54929,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Yashima Gakutei,"Japanese, 1786?–1868",,Yashima Gakutei,Japanese,1786,1868,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,8 3/16 x 7 3/8 in. (20.8 x 18.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54929,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2089,false,true,54932,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Yashima Gakutei,"Japanese, 1786?–1868",,Yashima Gakutei,Japanese,1786,1868,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,8 3/8 x 7 7/16 in. (21.3 x 18.9 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54932,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2090,false,true,54933,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Yashima Gakutei,"Japanese, 1786?–1868",,Yashima Gakutei,Japanese,1786,1868,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,8 7/16 x 7 3/8 in. (21.4 x 18.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54933,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2098,false,true,54947,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Yashima Gakutei,"Japanese, 1786?–1868",,Yashima Gakutei,Japanese,1786,1868,19th century,1800,1868,Polychrome woodblock print (surimono); ink and color on paper,8 3/16 x 7 1/4 in. (20.8 x 18.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54947,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2099,false,true,54948,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Yashima Gakutei,"Japanese, 1786?–1868",,Yashima Gakutei,Japanese,1786,1868,19th century,1800,1868,Polychrome woodblock print (surimono); ink and color on paper,8 3/16 x 7 1/4 in. (20.8 x 18.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54948,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2100,false,true,54949,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Yashima Gakutei,"Japanese, 1786?–1868",,Yashima Gakutei,Japanese,1786,1868,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,8 1/8 x 7 5/16 in. (20.6 x 18.6 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54949,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2101,false,true,54950,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Yashima Gakutei,"Japanese, 1786?–1868",,Yashima Gakutei,Japanese,1786,1868,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,8 1/4 x 7 5/16 in. (21 x 18.6 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54950,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2102,false,true,54954,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Yashima Gakutei,"Japanese, 1786?–1868",,Yashima Gakutei,Japanese,1786,1868,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,8 3/16 x 7 5/16 in. (20.8 x 18.6 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54954,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2103,false,true,54955,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Yashima Gakutei,"Japanese, 1786?–1868",,Yashima Gakutei,Japanese,1786,1868,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,8 3/16 x 7 5/16 in. (20.8 x 18.6 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54955,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2104,false,true,54956,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Yashima Gakutei,"Japanese, 1786?–1868",,Yashima Gakutei,Japanese,1786,1868,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,8 3/16 x 7 5/16 in. (20.8 x 18.6 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54956,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2105,false,true,54957,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Yashima Gakutei,"Japanese, 1786?–1868",,Yashima Gakutei,Japanese,1786,1868,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,8 3/16 x 7 5/16 in. (20.8 x 18.6 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54957,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2115,false,true,54968,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Yashima Gakutei,"Japanese, 1786?–1868",,Yashima Gakutei,Japanese,1786,1868,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,8 1/16 x 7 3/16 in. (20.5 x 18.3 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54968,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2122,false,true,54976,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Yashima Gakutei,"Japanese, 1786?–1868",,Yashima Gakutei,Japanese,1786,1868,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,8 1/16 x 7 1/4 in. (20.5 x 18.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54976,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2127,false,true,54982,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Yashima Gakutei,"Japanese, 1786?–1868",,Yashima Gakutei,Japanese,1786,1868,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,8 1/16 x 7 5/16 in. (20.5 x 18.6 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54982,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2164,false,true,55071,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Yashima Gakutei,"Japanese, 1786?–1868",,Yashima Gakutei,Japanese,1786,1868,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,8 7/16 x 7 3/8 in. (21.4 x 18.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55071,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2165,false,true,55072,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Yashima Gakutei,"Japanese, 1786?–1868",,Yashima Gakutei,Japanese,1786,1868,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,8 1/4 x 7 5/16 in. (21 x 18.6 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55072,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2262,false,true,54032,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Yashima Gakutei,"Japanese, 1786?–1868",,Yashima Gakutei,Japanese,1786,1868,19th century,1800,1899,Part of an album of woodblock prints (surimono); ink and color on paper,7 11/16 x 6 9/16 in. (19.5 x 16.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54032,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.281,false,true,73564,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa (Gountei) Sadahide,"Japanese, 1807–1878/79",,Utagawa (Goutei) Sadahide,Japanese,1807,1879,19th century,1807,1879,Proof line-block print; ink on paper,Image: 11 3/4 x 9 1/2 in. (29.8 x 24.1 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73564,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.282,false,true,73637,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa (Gountei) Sadahide,"Japanese, 1807–1878/79",,Utagawa (Goutei) Sadahide,Japanese,1807,1879,19th century,1807,1879,Proof line-block print for fan; ink on paper,Image: 11 7/8 x 9 1/2 in. (30.2 x 24.1 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73637,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.283,false,true,73638,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa (Gountei) Sadahide,"Japanese, 1807–1878/79",,Utagawa (Goutei) Sadahide,Japanese,1807,1879,19th century,1807,1879,Proof line-block print for fan; ink on paper,Image: 12 x 9 1/2 in. (30.5 x 24.1 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73638,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.284,false,true,73639,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa (Gountei) Sadahide,"Japanese, 1807–1878/79",,Utagawa (Goutei) Sadahide,Japanese,1807,1879,19th century,1807,1879,Proof line-block print for fan; ink on paper,Image: 12 x 9 1/2 in. (30.5 x 24.1 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73639,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.285,false,true,73640,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa (Gountei) Sadahide,"Japanese, 1807–1878/79",,Utagawa (Goutei) Sadahide,Japanese,1807,1879,19th century,1807,1879,Proof line-block print for fan; ink on paper,Image: 12 x 9 1/2 in. (30.5 x 24.1 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73640,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.286,false,true,73641,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa (Gountei) Sadahide,"Japanese, 1807–1878/79",,Utagawa (Goutei) Sadahide,Japanese,1807,1879,19th century,1807,1879,Proof line-block print for fan; ink on paper,Image: 11 3/4 x 9 1/2 in. (29.8 x 24.1 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73641,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.287,false,true,73642,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa (Gountei) Sadahide,"Japanese, 1807–1878/79",,Utagawa (Goutei) Sadahide,Japanese,1807,1879,19th century,1807,1879,Proof line-block print for fan; ink on paper,Image: 12 x 9 1/2 in. (30.5 x 24.1 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73642,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.289,false,true,73566,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa (Gountei) Sadahide,"Japanese, 1807–1878/79",,Utagawa (Goutei) Sadahide,Japanese,1807,1879,19th century,1807,1879,Preparatory drawing for triptych of woodblock prints; ink on paper,Image: 14 1/2 x 32 7/8 in. (36.8 x 83.5 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73566,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.297,false,true,73643,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa (Gountei) Sadahide,"Japanese, 1807–1878/79",,Utagawa (Goutei) Sadahide,Japanese,1807,1879,19th century,1807,1879,Triptych of polychrome woodblock prints; ink and color on paper,Image: 13 7/8 x 28 5/8 in. (35.2 x 72.7 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73643,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.298,false,true,73644,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa (Gountei) Sadahide,"Japanese, 1807–1878/79",,Utagawa (Goutei) Sadahide,Japanese,1807,1879,19th century,1807,1879,Polychrome woodblock print; ink and color on paper,Image: 9 3/8 x 14 3/8 in. (23.8 x 36.5 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73644,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.291a–c,false,true,73647,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa (Gountei) Sadahide,"Japanese, 1807–1878/79",,Utagawa (Goutei) Sadahide,Japanese,1807,1879,19th century,1807,1879,Triptych of polychrome woodblock prints; ink and color on paper,Image (d): 14 3/8 x 9 3/4 in. (36.5 x 24.8 cm) Image (e): 14 1/2 x 9 5/8 in. (36.8 x 24.4 cm) Image (f): 14 3/8 x 9 1/2 in. (36.5 x 24.1 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73647,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.292a–c,false,true,73648,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa (Gountei) Sadahide,"Japanese, 1807–1878/79",,Utagawa (Goutei) Sadahide,Japanese,1807,1879,19th century,1807,1879,Triptych of polychrome woodblock prints; ink and color on paper,Image (g): 14 1/2 x 9 1/2 in. (36.8 x 24.1 cm) Image (h): 14 1/2 x 9 1/2 in. (36.8 x 24.1 cm) Image (i): 14 1/8 x 9 1/2 in. (35.9 x 24.1 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73648,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.293a–c,false,true,73649,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa (Gountei) Sadahide,"Japanese, 1807–1878/79",,Utagawa (Goutei) Sadahide,Japanese,1807,1879,19th century,1807,1879,Triptych of polychrome woodblock prints; ink and color on paper,Image (j): 14 1/8 x 9 5/8 in. (35.9 x 24.4 cm) Image (k): 14 1/8 x 9 1/2 in. (35.9 x 24.1 cm) Image (l): 14 1/4 x 9 1/2 in. (36.2 x 24.1 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73649,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.294a–c,false,true,73568,Asian Art,Woodblock print,Tōtō Ryōgokubashi natsu keshiki|Panoramic View of Ryōgoku Bridge in the Summer,Japan,Edo period (1615–1868),,,,Artist,,Utagawa (Gountei) Sadahide,"Japanese, 1807–1878/79",,Utagawa (Goutei) Sadahide,Japanese,1807,1879,19th century,1807,1879,Triptych of polychrome woodblock prints; ink and color on paper,Image (a): 13 3/4 x 9 1/2 in. (34.9 x 24.1 cm) Image (b): 13 3/4 x 9 1/2 in. (34.9 x 24.1 cm) Image (c): 14 x 9 1/2 in. (35.6 x 24.1 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73568,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.295a–c,false,true,73569,Asian Art,Woodblock prints,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa (Gountei) Sadahide,"Japanese, 1807–1878/79",,Utagawa (Goutei) Sadahide,Japanese,1807,1879,19th century,1807,1879,Triptych of polychrome woodblock prints; ink and color on paper,Image (a): 13 7/8 x 9 1/2 in. (35.2 x 24.1 cm) Image (b): 13 7/8 x 9 1/4 in. (35.2 x 23.5 cm) Image (c): 13 3/4 x 9 1/2 in. (34.9 x 24.1 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73569,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.296a–f,false,true,73570,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa (Gountei) Sadahide,"Japanese, 1807–1878/79",,Utagawa (Goutei) Sadahide,Japanese,1807,1879,19th century,1807,1879,Hexaptych of polychrome woodblock prints; ink and color on paper,Image (a): 14 1/4 x 9 3/4 in. (36.2 x 24.8 cm) Image (b): 14 1/4 x 9 3/4 in. (36.2 x 24.8 cm) Image (c): 14 1/4 x 9 5/8 in. (36.2 x 24.4 cm) Image (d): 14 1/4 x 9 3/4 in. (36.2 x 24.8 cm) Image (e): 14 1/4 x 9 7/8 in. (36.2 x 25.1 cm) Image (f): 14 1/8 x 9 7/8 in. (35.9 x 25.1 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73570,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.299a–c,false,true,73571,Asian Art,Print,Jōkisen zenzu|Complete Picture of a Steamship off Kanazawa,Japan,Edo period (1615–1868),,,,Artist,,Utagawa (Gountei) Sadahide,"Japanese, 1807–1878/79",,Utagawa (Goutei) Sadahide,Japanese,1807,1879,19th century,1807,1879,Triptych of polychrome woodblock prints; ink and color on paper,Image (a): 14 1/8 x 9 3/8 in. (35.9 x 23.8 cm) Image (b): 14 1/8 x 9 1/4 in. (35.9 x 23.5 cm) Image (c): 14 1/8 x 9 1/4 in. (35.9 x 23.5 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73571,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2097,false,true,54945,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ishikawa Kazan,"Japanese, active 1810–1823",,Ishikawa Kazan,Japanese,1810,1823,19th century,1810,1823,Polychrome woodblock print (surimono); ink and color on paper,8 1/8 x 7 1/4 in. (20.6 x 18.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54945,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.317,false,true,73576,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshitora,"Japanese, active ca. 1850–80",,Utagawa Yoshitora,Japanese,1845,1880,19th century,1850,1880,Woodblock print; ink on paper,Image: 13 1/2 x 9 7/8 in. (34.3 x 25.1 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73576,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.319,false,true,73631,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshitora,"Japanese, active ca. 1850–80",,Utagawa Yoshitora,Japanese,1845,1880,19th century,1850,1880,Polychrome woodblock print; ink and color on paper,Image: 14 1/2 x 9 3/4 in. (36.8 x 24.8 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73631,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.318a–c,false,true,73577,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshitora,"Japanese, active ca. 1850–80",,Utagawa Yoshitora,Japanese,1845,1880,19th century,1850,1880,Triptych of polychrome woodblock prints; ink and color on paper,Image (a): 14 1/4 x 9 3/4 in. (36.2 x 24.8 cm) Image (b): 14 1/4 x 9 3/4 in. (36.2 x 24.8 cm) Image (c): 14 1/4 x 9 1/2 in. (36.2 x 24.1 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73577,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1950,false,true,54518,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,5 1/2 x 7 1/8 in. (14 x 18.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54518,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1958,false,true,54530,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,19th century,1800,1823,Polychrome woodblock print (surimono); ink and color on paper,8 1/4 x 11 3/8 in. (21 x 28.9 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54530,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1970,false,true,54555,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,5 1/2 x 7 9/16 in. (14 x 19.2 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54555,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1973,false,true,54558,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,8 1/4 x 7 3/8 in. (21 x 18.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54558,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1975,false,true,54560,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,19th century,1800,1823,Polychrome woodblock print (surimono); ink and color on paper,5 3/4 x 6 7/8 in. (14.6 x 17.5 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54560,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1976,false,true,54561,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,19th century,1800,1823,Polychrome woodblock print (surimono); ink and color on paper,7 3/4 x 6 1/2 in. (19.7 x 16.5 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54561,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1990,false,true,54693,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,5 5/8 x 7 7/16 in. (14.3 x 18.9 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54693,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1997,false,true,54717,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,5 1/8 x 7 in. (13 x 17.8 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54717,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1998,false,true,54718,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,5 5/8 x 7 7/16 in. (14.3 x 18.9 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54718,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2001,false,true,54721,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,5 5/8 x 7 1/2 in. (14.3 x 19.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54721,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2002,false,true,54722,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,5 5/8 x 7 1/2 in. (14.3 x 19.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54722,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2008,false,true,54728,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,5 7/16 x 7 5/16 in. (13.8 x 18.6 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54728,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2018,false,true,54767,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,8 5/8 x 11 1/4 in. (21.9 x 28.6 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54767,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2025,false,true,54785,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,5 5/16 x 7 5/16 in. (13.5 x 18.6 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54785,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2028,false,true,54788,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,8 1/16 x 7 1/8 in. (20.5 x 18.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54788,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2034,false,true,54800,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,5 11/16 x 7 9/16 in. (14.4 x 19.2 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54800,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2056,false,true,54832,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,8 1/4 x 10 3/4 in. (21 x 27.3 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54832,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2058,false,true,54836,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,5 11/16 x 7 3/8 in. (14.4 x 18.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54836,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2059,false,true,54838,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,5 3/8 x 7 1/4 in. (13.7 x 18.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54838,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2065,false,true,54894,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,4 7/8 x 10 3/4 in. (12.4 x 27.3 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54894,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2070,false,true,54901,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,5 7/16 x 7 3/8 in. (13.8 x 18.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54901,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2071,false,true,54902,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,5 11/16 x 7 1/2 in. (14.4 x 19.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54902,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2074,false,true,54905,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,5 7/16 x 7 3/8 in. (13.8 x 18.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54905,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2082,false,true,54919,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,5 7/16 x 7 5/16 in. (13.8 x 18.6 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54919,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2084,false,true,54921,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,5 9/16 x 7 1/2 in. (14.1 x 19.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54921,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2094,false,true,54939,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,19th century,1800,1823,Polychrome woodblock print (surimono); ink and color on paper,5 11/16 x 7 9/16 in. (14.4 x 19.2 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54939,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2109,false,true,54961,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,7 1/16 x 6 11/16 in. (17.9 x 17 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54961,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2111,false,true,54963,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,5 9/16 x 7 7/16 in. (14.1 x 18.9 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54963,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2116,false,true,54969,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,7 3/16 x 6 9/16 in. (18.3 x 16.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54969,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2118,false,true,54971,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,5 1/2 x 7 7/16 in. (14 x 18.9 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54971,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2119,false,true,54972,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,5 7/16 x 7 3/8 in. (13.8 x 18.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54972,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2154,false,true,55060,Asian Art,Print,"『春雨集』　摺物帖柳々居辰斎画　若松|Spring Rain Collection (Harusame shū), vol. 2: Pine Shoots and Accoutrements for New Year’s Celebrations",Japan,Edo period (1615–1868),,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,19th century,1800,1899,Privately published woodblock prints (surimono) mounted in an album; ink and color on paper,5 7/16 x 7 3/8 in. (13.8 x 18.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55060,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2159,false,true,55066,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,5 3/16 x 7 1/16 in. (13.2 x 17.9 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55066,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2172,false,true,55086,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,7 13/16 x 7 5/16 in. (19.8 x 18.6 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55086,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2173,false,true,55087,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,19th century,1800,1899,Polychrome woodblock print (surimono); ink and color on paper,5 5/16 x 7 3/8 in. (13.5 x 18.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55087,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2237,false,true,54005,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,19th century,1800,1899,Part of an album of woodblock prints (surimono); ink and color on paper,3 1/2 x 7 1/8 in. (8.9 x 18.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54005,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2245,false,true,54014,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,19th century,1800,1823,Part of an album of woodblock prints (surimono); ink and color on paper,3 1/2 x 7 1/8 in. (8.9 x 18.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54014,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2246,false,true,54015,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,19th century,1800,1823,Part of an album of woodblock prints (surimono); ink and color on paper,7 5/8 x 10 7/8 in. (19.4 x 27.6 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54015,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2252,false,true,54021,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,19th century,1800,1899,Part of an album of woodblock prints (surimono); ink and color on paper,5 1/2 x 7 5/16 in. (14 x 18.6 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54021,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2253,false,true,54022,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,19th century,1800,1899,Part of an album of woodblock prints (surimono); ink and color on paper,5 13/16 x 7 7/16 in. (14.8 x 18.9 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54022,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2263,false,true,54033,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,19th century,1800,1899,Part of an album of woodblock prints (surimono); ink and color on paper,5 1/2 x 7 7/16 in. (14 x 18.9 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54033,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2264,false,true,54034,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,19th century,1800,1899,Part of an album of woodblock prints (surimono); ink and color on paper,5 1/2 x 7 1/2 in. (14 x 19.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54034,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2265,false,true,54035,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,19th century,1800,1899,Part of an album of woodblock prints (surimono); ink and color on paper,7 3/4 x 6 9/16 in. (19.7 x 16.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54035,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2266,false,true,54039,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,19th century,1800,1823,Part of an album of woodblock prints (surimono); ink and color on paper,5 1/2 x 7 7/16 in. (14 x 18.9 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54039,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2267,false,true,54040,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,19th century,1800,1823,Part of an album of woodblock prints (surimono); ink and color on paper,5 1/2 x 7 1/2 in. (14 x 19.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54040,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2370,false,true,54154,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Ryūryūkyo Shinsai,"Japanese, active ca. 1799–1823",,Ryūryūkyo Shinsai,Japanese,1799,1823,19th century,1800,1899,Part of an album of woodblock prints (surimono); ink and color on paper,6 3/8 x 11 5/8 in. (16.2 x 29.5 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54154,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP212.45,false,true,58029,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshikazu,"Japanese, active ca. 1850–1870",,Utagawa Yoshikazu,Japanese,1845,1870,19th century,1850,1870,Polychrome woodblock print; ink and color on paper,Image: 13 5/8 × 9 1/2 in. (34.6 × 24.1 cm),"Gift of the Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58029,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP212.46,false,true,58030,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Yoshikazu,"Japanese, active ca. 1850–1870",,Utagawa Yoshikazu,Japanese,1845,1870,19th century,1850,1870,Polychrome woodblock print; ink and color on paper,Image: 13 5/8 × 9 1/2 in. (34.6 × 24.1 cm),"Gift of the Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58030,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP212.52,false,true,58036,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Sadakage,"Japanese, active mid-19th century",,Utagawa Sadakage,Japanese,1800,1899,19th century,1834,1866,Polychrome woodblock print; ink and color on paper,Image: 13 5/8 × 9 1/2 in. (34.6 × 24.1 cm),"Gift of the Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58036,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP212.53,false,true,58037,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Sadakage,"Japanese, active mid-19th century",,Utagawa Sadakage,Japanese,1800,1899,19th century,1834,1866,Polychrome woodblock print; ink and color on paper,Image: 13 5/8 × 9 1/2 in. (34.6 × 24.1 cm),"Gift of the Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58037,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP212.54,false,true,58038,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Sadakage,"Japanese, active mid-19th century",,Utagawa Sadakage,Japanese,1800,1899,19th century,1834,1866,Polychrome woodblock print; ink and color on paper,Image: 13 5/8 × 9 1/2 in. (34.6 × 24.1 cm),"Gift of the Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58038,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP212.1,false,true,57986,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,19th century,1800,1858,Polychrome woodblock print; ink and color on paper,Image: 13 1/2 × 9 1/2 in. (34.3 × 24.1 cm),"Gift of the Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/57986,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.278,false,true,73561,Asian Art,Print,六十余州名所図会 薩摩　坊ノ浦　雙剣石|Upright Landscape,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,19th century,1800,1858,Polychrome woodblock print; ink and color on paper,Image: 14 3/16 x 9 3/4 in. (36 x 24.8 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73561,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2748.3,false,true,58267,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Utagawa Kuniteru,"Japanese, 1830–1874",,Utagawa Kuniteru,Japanese,1830,1874,19th century,1800,1899,Polychrome woodblock print; ink and color on paper,Approx. 14 1/2 x 9 1/2 in. (36.8 x 24.1 cm),"Bequest of Mary Martin, 1938",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58267,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2748.1,false,true,58265,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Utagawa Yoshiiku,"Japanese, 1833–1904",,Utagawa Yoshiiku,Japanese,1833,1904,19th century,1800,1899,Polychrome woodblock print; ink and color on paper,Approx. 14 1/2 x 9 1/2 in. (36.8 x 24.1 cm),"Bequest of Mary Martin, 1938",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58265,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2748.2,false,true,58266,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Utagawa Yoshiiku,"Japanese, 1833–1904",,Utagawa Yoshiiku,Japanese,1833,1904,19th century,1800,1899,Polychrome woodblock print; ink and color on paper,Approx. 14 1/2 x 9 1/2 in. (36.8 x 24.1 cm),"Bequest of Mary Martin, 1938",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58266,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2748.4,false,true,58268,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Utagawa Yoshiiku,"Japanese, 1833–1904",,Utagawa Yoshiiku,Japanese,1833,1904,19th century,1800,1899,Polychrome woodblock print; ink and color on paper,Approx. 14 1/2 x 9 1/2 in. (36.8 x 24.1 cm),"Bequest of Mary Martin, 1938",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58268,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2748.5,false,true,58269,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Utagawa Yoshiiku,"Japanese, 1833–1904",,Utagawa Yoshiiku,Japanese,1833,1904,19th century,1800,1899,Polychrome woodblock print; ink and color on paper,Approx. 14 1/2 x 9 1/2 in. (36.8 x 24.1 cm),"Bequest of Mary Martin, 1938",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58269,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2748.6,false,true,58270,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Utagawa Yoshiiku,"Japanese, 1833–1904",,Utagawa Yoshiiku,Japanese,1833,1904,19th century,1800,1899,Polychrome woodblock print; ink and color on paper,Approx. 14 1/2 x 9 1/2 in. (36.8 x 24.1 cm),"Bequest of Mary Martin, 1938",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/58270,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.309,false,true,73630,Asian Art,Print,真柴秀吉公名護屋陣先手諸将繰出之図|Hideyoshi and His Troops Leaving Nagoya Camp (Mashiba Hideyoshi kō nagoya jin saki te no shoshō kuridashi no zu),Japan,Meiji period (1868–1912),,,,Artist,,Tsukioka Yoshitoshi,"Japanese, 1839–1892",,Tsukioka Yoshitoshi,Japanese,1839,1892,19th century,1800,1899,Triptych of polychrome woodblock prints; ink and color on paper,Image (a): 13 7/8 x 9 3/4 in. (35.2 x 24.8 cm) Image (b): 13 7/8 x 9 7/8 in. (35.2 x 25.1 cm) Image (c): 13 7/8 x 9 7/8 in. (35.2 x 25.1 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73630,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.314,false,true,73627,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Tsukioka Yoshitoshi,"Japanese, 1839–1892",,Tsukioka Yoshitoshi,Japanese,1839,1892,19th century,1800,1899,Polychrome woodblock print; ink and color on paper,Image: 14 9/16 x 9 7/8 in. (37 x 25.1 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73627,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.321a–c,false,true,73579,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Kobayashi Kiyochika,"Japanese, 1847–1915",,Kobayashi Kiyochika,Japanese,1847,1915,19th century,1800,1899,Triptych of polychrome woodblock prints; ink and color on paper,Image (a): 14 x 9 1/2 in. (35.6 x 24.1 cm) Image (b): 9 3/8 x 14 in. (23.8 x 35.6 cm) Image (c): 14 x 9 1/2 in. (35.6 x 24.1 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73579,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.325,false,true,73580,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Unsen,"Japanese, active ca. 1875",,Unsen,Japanese,1875,1875,19th century,1800,1899,Triptych of preparatory drawings; ink on paper,Image: 14 1/8 x 27 3/4 in. (35.9 x 70.5 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73580,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.326,false,true,73633,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Unsen,"Japanese, active ca. 1875",,Unsen,Japanese,1875,1875,19th century,1800,1899,Triptych of proof prints; ink on paper,Image: 15 1/8 x 30 1/8 in. (38.4 x 76.5 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73633,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.327,false,true,73634,Asian Art,Print,,Japan,Meiji period (1868–1912),,,,Artist,,Unsen,"Japanese, active ca. 1875",,Unsen,Japanese,1875,1875,19th century,1800,1899,Triptych of polychrome woodblock prints; ink and color on paper,Image: 14 1/4 x 28 7/8 in. (36.2 x 73.3 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/73634,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -93.3.367,false,true,62816,Asian Art,Bowl,,Japan,,,,,Artist,,Seifu Yohei,1803–1861,,Seifu Yohei,Japanese,1803,1861,19th century,1800,1899,"White porcelain decorated with blue under the glaze, polychrome enamels and gold (Kyoto ware)",H. 2 1/8 in. (5.4 cm); Diam. 5 1/8 in. (13 cm),"Gift of Charles Stewart Smith, 1893",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/62816,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -93.3.368,false,true,46485,Asian Art,Bowl,,Japan,,,,,Artist,,Seifu Yohei,1803–1861,,Seifu Yohei,Japanese,1803,1861,19th century,1800,1899,"White porcelain decorated with blue under the glaze, polychrome enamels (Kyoto ware)",H. 2 3/8 in. (6 cm); Diam. 5 1/2 in. (14 cm),"Gift of Charles Stewart Smith, 1893",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/46485,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -93.1.151,false,true,62679,Asian Art,Bowl,,Japan,,,,,Artist,,Nin'ami Dōhachi (Dōhachi II),"Japanese, 1783–1855",,Dōhachi II,Japanese,1783,1855,19th century,1800,1899,"Clay partly covered with glaze; reserves forming patterns having slip dots in the center, covered with a transparent glaze (Kyoto ware)",H. 3 1/4 in. (8.3 cm); Diam. 6 3/8 in. (16.2 cm),"Gift of Mr. and Mrs. Samuel Colman, 1893",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/62679,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -25.215.55,false,true,53585,Asian Art,Tile,,Japan,,,,,Artist,,Raku Tannyu,"Japanese, 1795–1854",,"Raku, Tannyu",Japanese,1795,1854,19th century,1800,1899,Earthenware (Raku ware),H. 4 1/2 (11.4 cm); W. 2 3/4 in. (7 cm),"Fletcher Fund, 1925",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/53585,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -07.13,false,true,62871,Asian Art,Wine bottle,,Japan,,,,,Artist,,Shuntai,"Japanese, 1799–1878",,Shuntai,Japanese,1799,1799,19th century,1800,1899,Clay covered with buff crackled glaze and decoration in overglaze (Shino Oribe type),H. 9 1/8 in. (23.2 cm),"Rogers Fund, 1907",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/62871,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -93.1.3,false,true,62608,Asian Art,Deep bowl,,Japan,,,,,Artist,,Shuntai,"Japanese, 1799–1878",,Shuntai,Japanese,1799,1799,19th century,1800,1899,"Clay covered with a transparent crackled glaze over incised decoration (Mino ware, Ofuke type)",H. 4 in. (10.2 cm); Diam. 5 1/2 in. (14 cm),"Gift of Mr. and Mrs. Samuel Colman, 1893",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/62608,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1984.392.1,false,true,63937,Asian Art,Teabowl,,Japan,,,,,Artist,,Mitsutada Ohi,"Japanese, 1835–1896",,Mitsutada Ohi,Japanese,1835,1896,19th century,1800,1899,Pottery (brown raku ware),H. 4 1/4 in. (10.8 cm); Diam. 4 1/4 in. (10.8 cm),"Gift of Toshiro Ohi, 1984",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/63937,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -93.3.271,false,true,47808,Asian Art,Vase,,Japan,,,,,Artist,,Makuzu Kōzan I (Miyagawa Toranosuke),"Japanese, 1842–1916",,Makuzu Kōzan,Japanese,1842,1916,19th century,1800,1899,Clay covered with high-fired glazes,H. 6 3/4 in. (17.1 cm),"Gift of Charles Stewart Smith, 1893",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/47808,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -93.3.423,false,true,47983,Asian Art,Vase,,Japan,,,,,Artist,,Makuzu Kōzan I (Miyagawa Toranosuke),"Japanese, 1842–1916",,Makuzu Kōzan,Japanese,1842,1916,19th century,1800,1899,Porcelain covered with a dark green glaze and design in white enamel (Kyoto ware),H. 5 3/4 in. (14.6 cm); Diam. 5 1/2 in. (14 cm),"Gift of Charles Stewart Smith, 1893",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/47983,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -93.3.480,false,true,47970,Asian Art,Vase,,Japan,,,,,Artist,,Makuzu Kōzan I (Miyagawa Toranosuke),"Japanese, 1842–1916",,Makuzu Kōzan,Japanese,1842,1916,19th century,1800,1899,White porcelain covered with a mazarine blue glaze (Kyoto ware),H. 11 3/8 in. (28.9 cm); Diam. 11 in. (27.9 cm),"Gift of Charles Stewart Smith, 1893",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/47970,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -93.3.321,false,true,47982,Asian Art,Vase,,Japan,,,,,Artist,,Takemoto,"Japanese, 1845–1892",,Takemoto,Japanese,1845,1892,19th century,1800,1899,White porcelain with a dappled red and white glaze,H. 4 1/4 in. (10.8 cm); Diam. 9 5/8 in. (24.4 cm),"Gift of Charles Stewart Smith, 1893",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/47982,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -93.3.382,false,true,48015,Asian Art,Flower vase,,Japan,,,,,Artist,,Takemoto,"Japanese, 1845–1892",,Takemoto,Japanese,1845,1892,19th century,1800,1899,"White porcelain, faintly green, with a flambé collar",H. 8 1/4 in. (21 cm); Diam. 5 in. (12.7 cm),"Gift of Charles Stewart Smith, 1893",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/48015,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -93.1.91,false,true,62646,Asian Art,Teabowl,,Japan,,,,,Artist,,Kiyomizu Rokubei III,"Japanese, active 1820–1880",,Kiyomizu Rokubei III,Japanese,1820,1880,19th century,1800,1899,Clay covered with finely crackled glaze over a design (Kiyomizu ware),H. 2 1/4 in. (5.7 cm),"Gift of Mr. and Mrs. Samuel Colman, 1893",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/62646,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -93.1.135,false,true,47357,Asian Art,Bowl,,Japan,,,,,Artist,,Nonomura Ninsei,"Japanese, active ca. 1646–94",,Ninsei Nonomura,Japanese,1646,1694,19th century,1800,1899,"Clay covered with a transparent crackled glaze and decorated with polychrome enamel (Kyoto ware, Banko style)",H. 3 in. (7.6 cm); Diam. 4 3/4 in. (12.1 cm); Diam. of foot 1 7/8 in. (4.8 cm),"Gift of Mr. and Mrs. Samuel Colman, 1893",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/47357,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.268.697,false,true,52317,Asian Art,Wine cup,,Japan,Edo period (1615–1868),,,,Artist,Design attributed to,Aoki Mokubei,1767–1833,,Aoki Mokubei,Japanese,1767,1833,19th century,1800,1899,Porcelain with green and red enamels (Kyoto ware),H. 1 1/2 in. (3.8 cm); Diam. 4 1/4 in. (10.8 cm),"The Harry G. C. Packard Collection of Asian Art, Gift of Harry G. C. Packard, and Purchase, Fletcher, Rogers, Harris Brisbane Dick, and Louis V. Bell Funds, Joseph Pulitzer Bequest, and The Annenberg Fund Inc. Gift, 1975",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/52317,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.268.698,false,true,52318,Asian Art,Wine cup,,Japan,Edo period (1615–1868),,,,Artist,Design attributed to,Aoki Mokubei,1767–1833,,Aoki Mokubei,Japanese,1767,1833,19th century,1800,1899,Porcelain with green and red enamels (Kyoto ware),H. 1 1/2 in. (3.8 cm); Diam. 3 1/4 in. (8.3 cm),"The Harry G. C. Packard Collection of Asian Art, Gift of Harry G. C. Packard, and Purchase, Fletcher, Rogers, Harris Brisbane Dick, and Louis V. Bell Funds, Joseph Pulitzer Bequest, and The Annenberg Fund Inc. Gift, 1975",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/52318,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -X.127,false,true,58286,Asian Art,Dish,,Japan,Edo period (1615–1868),,,,Artist,Style of,Ogata Kenzan,"Japanese, 1663–1743",,Ogata Kenzan,Japanese,1663,1743,19th century,1800,1899,Stoneware with colored enamels (Kyoto ware),H. 1 1/2 in. (3.8 cm); W. 6 7/8 in. (17.5 cm),Museum Accession,,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/58286,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -93.1.34,false,true,62625,Asian Art,Dish,,Japan,Edo period (1615–1868),,,,Artist,,Shuntai,"Japanese, 1799–1878",,Shuntai,Japanese,1799,1799,19th century,1800,1899,"Glazed stoneware (Seto ware, Oribe Revival type)",H. 2 1/4 in. (5.7 cm); W. 9 in. square (22.9 cm square),"Gift of Mr. and Mrs. Samuel Colman, 1893",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/62625,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"23.225.5a, b",false,true,60782,Asian Art,Incense box,,Japan,Edo (1615–1868) or Meiji period (1868–1912),,,,Artist,,Minpei,active 19th century,,Minpei,Japanese,0019,0019,19th century,1800,1899,"Pottery decorated with colors, black and gold (Awaji ware)",H. 2 1/2 in. (6.4 cm),"Gift of Mrs. V. Everit Macy, 1923",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/60782,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -23.225.336,false,true,47856,Asian Art,Figure,,Japan,Edo (1615–1868) or Meiji period (1868–1912),,,,Artist,,Kawamoto Hansuke IV,"Japanese, active first half of the 19th century",,Kawamoto Hansuke,Japanese,1800,1849,19th century,1800,1899,Stoneware and white porcelain (Seto ware),H. 4 1/2 in. (11.4 cm); L. 9 3/8 in. (23.8 cm),"Gift of Mrs. V. Everit Macy, 1923",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/47856,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -23.225.337,false,true,48557,Asian Art,Figure,,Japan,Edo (1615–1868) or Meiji period (1868–1912),,,,Artist,,Kawamoto Hansuke IV,"Japanese, active first half of the 19th century",,Kawamoto Hansuke,Japanese,1800,1849,19th century,1800,1899,Stoneware and porcelain (Seto ware),H. 6 1/4 in. (15.9 cm); L. 10 1/8 in. (25.7 cm),"Gift of Mrs. V. Everit Macy, 1923",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/48557,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.13,false,true,49065,Asian Art,Hanging scroll,,Japan,,,,,Artist,Attributed to,Kishi Ganryo,1798–1852,,Kishi Ganryo,Japanese,1798,1852,19th century,1800,1899,Hanging scroll; ink and color on silk,38 1/2 x 13 1/2 in. (97.8 x 34.3 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49065,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.14.2,false,true,57227,Asian Art,Hanging scroll,,Japan,,,,,Artist,,Yusen,"Japanese, 1778–1850",,Yusen,Japanese,1778,1850,19th century,1800,1850,Hanging scroll; ink and color on silk,39 3/8 x 12 1/8 in. (100 x 30.8 cm),"Fletcher Fund, 1937",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57227,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -57.70.1,false,true,57246,Asian Art,Hanging scroll,,Japan,,,,,Artist,Attributed to,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,Japanese,1807,1891,19th century,1807,1891,Hanging scroll; ink on silk,41 1/4 x 11 1/2 in. (104.8 x 29.2 cm),"Gift of Edward M. Bratter, 1957",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57246,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -57.70.3,false,true,57248,Asian Art,Hanging scroll,,Japan,,,,,Artist,Attributed to,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,Japanese,1807,1891,19th century,1807,1891,Hanging scroll; ink on silk,5 1/16 x 4 3/4 in. (12.9 x 12.1 cm),"Gift of Edward M. Bratter, 1957",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57248,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -64.31.1,false,true,57242,Asian Art,Painting,,Japan,,,,,Artist,,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,Japanese,1807,1891,19th century,1807,1891,Painting; colored lacquers on paper,8 x 6 3/4 in. (20.3 x 17.1 cm),"Rogers Fund, 1964",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57242,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -51.137.1,false,true,57315,Asian Art,Hanging scroll,,Japan,,,,,Artist,,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,Japanese,1807,1891,19th century,1807,1891,Hanging scroll; ink on silk,42 7/8 x 16 3/8 in. (108.9 x 41.6 cm),"Purchase, Gifts, Bequests, and Funds from various donors, by exchange, 1951",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57315,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -56.124.1,false,true,57294,Asian Art,Painting,,Japan,,,,,Artist,,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,Japanese,1807,1891,19th century,1807,1891,Painting; ink and color on paper (tanzaku),14 3/4 x 2 1/4 in. (37.5 x 5.7 cm),"Funds from various donors, by exchange, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57294,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -56.124.2,false,true,57295,Asian Art,Painting,,Japan,,,,,Artist,,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,Japanese,1807,1891,19th century,1807,1891,Painting; ink and color on paper (tanzaku),14 5/8 x 2 7/16 in. (37.1 x 6.2 cm),"Funds from various donors, by exchange, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57295,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -56.124.3,false,true,57296,Asian Art,Painting,,Japan,,,,,Artist,,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,Japanese,1807,1891,19th century,1807,1891,Painting; ink and color on paper (tanzaku),14 x 2 5/8 in. (35.6 x 6.7 cm),"Funds from various donors, by exchange, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57296,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -56.124.4,false,true,57297,Asian Art,Painting,,Japan,,,,,Artist,,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,Japanese,1807,1891,19th century,1807,1891,Painting; ink and color on paper (tanzaku),14 3/4 x 2 3/8 in. (37.5 x 6 cm),"Funds from various donors, by exchange, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57297,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -56.124.5,false,true,57298,Asian Art,Painting,,Japan,,,,,Artist,,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,Japanese,1807,1891,19th century,1807,1891,Painting; ink and color on paper (tanzaku),14 x 2 1/2 in. (35.6 x 6.4 cm),"Funds from various donors, by exchange, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57298,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.5,false,true,55299,Asian Art,Hanging scroll,,Japan,,,,,Artist,Attributed to,Shiokawa Bunrin,"Japanese, 1808–1877",,Shiokawa Bunrin,Japanese,1808,1877,19th century,1800,1899,Hanging scroll; ink and color on silk,39 1/4 x 13 7/8 in. (99.7 x 35.2 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/55299,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.282.2,false,true,57164,Asian Art,Hanging scroll,,Japan,,,,,Artist,,Tsurana,"Japanese, 1809–1892",,Tsurana,Japanese,1809,1892,19th century,1809,1892,Hanging scroll; ink and color on silk,15 1/2 x 49 1/2 in. (39.4 x 125.7 cm),"Gift of Dr. and Mrs. Harold B. Bilsky, 1975",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57164,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.16,false,true,55302,Asian Art,Hanging scroll,,Japan,,,,,Artist,Attributed to,Hasegawa Gyokuho,"Japanese, 1822–1879",,Hasegawa Gyokuho,Japanese,1822,1879,19th century,1822,1879,Hanging scroll; ink and color on silk,Overall: 41 x 16 1/8in. (104.1 x 41cm) Overall with mounting: 41 x 21 3/4 in. (104.1 x 55.2 cm) Overall with knobs: 41 x 24 in. (104.1 x 61 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/55302,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -44.35.2,false,true,57314,Asian Art,Painting,,Japan,,,,,Artist,,Toyosei Kimigi,"Japanese, active 19th century",(?),Toyosei Kimigi,Japanese,1800,1899,19th century,1800,1899,Painting; color on silk,24 5/8 x 34 5/8 in. (62.5 x 87.9 cm),"Gift of Susan Dwight Bliss, 1944",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57314,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.14,false,true,45737,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Kano Tansui,active 19th century,,Kano Tansui,Japanese,1800,1899,19th century,1800,1868,Hanging scroll; ink and color on silk,37 1/2 x 13 1/2 in. (95.3 x 34.3 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45737,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.15,false,true,44876,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Kano Tansui,active 19th century,,Kano Tansui,Japanese,1800,1899,19th century,1800,1868,Hanging scroll; ink and color on silk,37 1/2 x 13 1/2 in. (95.3 x 34.3 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/44876,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.16,false,true,45738,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Kano Tansui,active 19th century,,Kano Tansui,Japanese,1800,1899,19th century,1800,1868,Hanging scroll; ink and color on silk,37 1/2 x 13 1/2 in. (95.3 x 34.3 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45738,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -08.148.1,false,true,48985,Asian Art,Painting,,Japan,Edo period (1615–1868),,,,Artist,,Ogata Kōrin,"Japanese, 1658–1716",,Kōrin,Japanese,1658,1716,19th century,1800,1899,Matted painting; ink and color on paper,39 7/16 x 14 3/16 in. (100.2 x 36 cm),"Gift of Francis Lathrop, 1908",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/48985,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -08.148.2,false,true,48986,Asian Art,Painting,,Japan,Edo period (1615–1868),,,,Artist,,Ogata Kōrin,"Japanese, 1658–1716",,Kōrin,Japanese,1658,1716,19th century,1800,1899,Matted painting; ink and color on paper,39 7/16 x 14 3/16 in. (100.2 x 36 cm),"Gift of Francis Lathrop, 1908",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/48986,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -08.148.3,false,true,48987,Asian Art,Painting,,Japan,Edo period (1615–1868),,,,Artist,,Ogata Kōrin,"Japanese, 1658–1716",,Kōrin,Japanese,1658,1716,19th century,1800,1899,Matted painting; ink and color on paper,39 7/16 x 14 3/16 in. (100.2 x 36 cm),"Gift of Francis Lathrop, 1908",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/48987,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -52.160.5,false,true,48911,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Nishikawa Sukenobu,"Japanese, 1671–1750",,Nishikawa Sukenobu,Japanese,1671,1750,19th century,1800,1899,Hanging scroll; ink and color on silk,21 1/2 x 25 in. (54.6 x 63.5 cm),"Purchase, Gifts, Bequests, and Funds from various donors, by exchange, 1952",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/48911,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.11,false,true,49063,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Ganku,"Japanese, 1749–1838",,Ganku,Japanese,1749,1838,19th century,1800,1838,Hanging scroll; ink and color on silk,42 x 16 1/8 in. (106.7 x 41 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49063,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2003.265,false,true,65574,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Teisai Hokuba,"Japanese, 1771–1844",,Teisai Hokuba,Japanese,1771,1844,19th century,1800,1899,"Hanging scroll; ink, color, and gold on silk",37 5/16 x 12 7/16 in. (94.7 x 31.6 cm),"Purchase, Friends of Asian Art Gifts, 2003",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/65574,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.268.106,false,true,49023,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Nakabayashi Chikutō,"Japanese, 1776–1853",,Nakabayashi Chikutō,Japanese,1776,1853,19th century,1800,1853,Hanging scroll; ink on paper,Image: 69 1/2 x 34 in. (176.5 x 86.4 cm) Overall: 92 1/4 x 46 1/4 in. (234.3 x 117.5 cm),"The Harry G. C. Packard Collection of Asian Art, Gift of Harry G. C. Packard, and Purchase, Fletcher, Rogers, Harris Brisbane Dick, and Louis V. Bell Funds, Joseph Pulitzer Bequest, and The Annenberg Fund Inc. Gift, 1975",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49023,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.268.104,false,true,48997,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Okada Hankō,"Japanese, 1782–1846",,Okada Hankō,Japanese,1782,1846,19th century,1800,1846,Hanging scroll; ink and color on paper,Image: 68 1/2 x 18 7/8 in. (174 x 47.9 cm) Overall: 92 1/4 x 28 3/8in. (234.3 x 72.1cm),"The Harry G. C. Packard Collection of Asian Art, Gift of Harry G. C. Packard, and Purchase, Fletcher, Rogers, Harris Brisbane Dick, and Louis V. Bell Funds, Joseph Pulitzer Bequest, and The Annenberg Fund Inc. Gift, 1975",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/48997,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.268.54,false,true,45739,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Kano Tanshin Moromichi,"Japanese, 1785–1835",,Tanshin Moromichi Kano,Japanese,1785,1835,19th century,1800,1835,Hanging scroll; ink on silk,36 11/16 x 15 in. (93.2 x 38.1 cm),"The Harry G. C. Packard Collection of Asian Art, Gift of Harry G. C. Packard, and Purchase, Fletcher, Rogers, Harris Brisbane Dick, and Louis V. Bell Funds, Joseph Pulitzer Bequest, and The Annenberg Fund Inc. Gift, 1975",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45739,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1998.483.2,false,true,49821,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Ōtagaki Rengetsu,"Japanese, 1791–1871",,Ōtagaki Rengetsu,Japanese,1791,1871,19th century,1800,1868,"Hanging scroll; ink, color, and silver on paper",38 1/4 x 11 7/8 in. (97.2 x 30.2 cm),"Gift of Donald Keene, 1998",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49821,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.268.114,false,true,45379,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Watanabe Kazan,"Japanese, 1793–1841",,Watanabe Kazan,Japanese,1793,1841,19th century,1800,1841,Hanging scroll; ink and color on paper,29 1/8 x 17 in. (74 x 43.2 cm),"The Harry G. C. Packard Collection of Asian Art, Gift of Harry G. C. Packard, and Purchase, Fletcher, Rogers, Harris Brisbane Dick, and Louis V. Bell Funds, Joseph Pulitzer Bequest, and The Annenberg Fund Inc. Gift, 1975",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45379,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -59.137.1,false,true,48983,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Kiitsu,"Japanese, 1796–1858",,Suzuki Kiitsu,Japanese,1796,1858,19th century,1800,1858,Hanging scroll; ink and color on silk,48 1/2 x 13 5/16 in. (123.2 x 33.8 cm),"Rogers Fund, 1959",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/48983,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.537,false,true,40352,Asian Art,Painting,,Japan,Edo period (1615–1868),,,,Artist,Attributed to,Utagawa Kuniyoshi,"Japanese, 1797–1861",,Utagawa Kuniyoshi,Japanese,1797,1861,19th century,1800,1861,Matted; ink and color on silk,16 1/2 x 23 1/2 in. (41.9 x 59.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/40352,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.268.118,false,true,49011,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Tsubaki Chinzan,"Japanese, 1801–1854",,Tsubaki Chinzan,Japanese,1801,1854,19th century,1800,1854,Hanging scroll; ink and color on silk,43 15/16 x 18 7/8 in. (111.6 x 48 cm),"The Harry G. C. Packard Collection of Asian Art, Gift of Harry G. C. Packard, and Purchase, Fletcher, Rogers, Harris Brisbane Dick, and Louis V. Bell Funds, Joseph Pulitzer Bequest, and The Annenberg Fund Inc. Gift, 1975",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49011,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.268.119,false,true,49012,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Tsubaki Chinzan,"Japanese, 1801–1854",,Tsubaki Chinzan,Japanese,1801,1854,19th century,1800,1854,Hanging scroll; ink and color on paper,65 7/8 x 33 1/4 in. (167.3 x 84.4 cm),"The Harry G. C. Packard Collection of Asian Art, Gift of Harry G. C. Packard, and Purchase, Fletcher, Rogers, Harris Brisbane Dick, and Louis V. Bell Funds, Joseph Pulitzer Bequest, and The Annenberg Fund Inc. Gift, 1975",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49012,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.14,false,true,49066,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,Attributed to,Gantoku,"Japanese, 1805–1859",,Gantoku,Japanese,1805,1859,19th century,1805,1859,Hanging scroll; ink on paper,38 1/2 x 12 in. (97.8 x 30.5 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49066,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.30,false,true,49054,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Nishiyama Hōen,"Japanese, 1807–1867",,Nishiyama Hōen,Japanese,1807,1867,19th century,1807,1867,Hanging scroll; ink and color on silk,39 5/16 x 16 1/8 in. (99.8 x 41 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49054,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -51.137.2,false,true,57317,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,Japanese,1807,1891,19th century,1807,1868,Hanging scroll; ink on silk,39 1/8 x 14 in. (99.4 x 35.6 cm),"Purchase, Gifts, Bequests, and Funds from various donors, by exchange, 1951",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57317,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -51.137.3,false,true,57318,Asian Art,Hanging scroll,三羽黒鳥図|Three Crows in Flight,Japan,Edo period (1615–1868),,,,Artist,,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,Japanese,1807,1891,19th century,1807,1891,Hanging scroll; ink on silk,Image: 28 7/8 × 6 3/4 in. (73.3 × 17.1 cm),"Purchase, Gifts, Bequests, and Funds from various donors, by exchange, 1951",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57318,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -51.137.4,false,true,57319,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,Japanese,1807,1891,19th century,1807,1891,Hanging scroll; ink on paper,Image: 13 5/8 x 21 3/8 in. (34.6 x 54.3 cm) Overall with knobs: 50 1/4 x 25 in. (127.6 x 63.5 cm),"Purchase, Gifts, Bequests, and Funds from various donors, by exchange, 1951",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57319,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.282.3,false,true,49028,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Nakabayashi Chikkei,"Japanese, 1816–1867",,Nakabayashi Chikkei,Japanese,1816,1867,19th century,1816,1867,Hanging scroll; ink and color on silk,47 1/2 x 16 1/2 in. (120.7 x 41.9 cm),"Gift of Dr. and Mrs. Harold B. Bilsky, 1975",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49028,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.21,false,true,45188,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,Attributed to,Hanzan (Matsukawa),"Japanese, 1820–1882",,Hanzan,Japanese,1820,1882,19th century,1800,1882,Hanging scroll; ink and color on paper,12 1/2 x 16 13/16 in. (31.8 x 42.7 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45188,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2014.513,false,true,650682,Asian Art,Hanging scroll,神宮皇后|Empress Jingū,Japan,Edo period (1615–1868),,,,Artist,,Kōsai Hokushin,"Japanese, 1824–1876",,Kōsai Hokushin,Japanese,1824,1876,19th century,1800,1899,"Hanging scroll; ink, color, and gold on silk",Image: 28 7/8 × 12 15/16 in. (73.3 × 32.9 cm) Overall with mounting: 63 1/8 × 16 5/16 in. (160.3 × 41.4 cm) Overall with knobs: 63 1/8 × 18 in. (160.3 × 45.7 cm),"Purchase, Friends of Asian Art Gifts, 2014",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/650682,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -60.129,false,true,45809,Asian Art,Painting,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Kuniteru,"Japanese, 1830–1874",,Utagawa Kuniteru,Japanese,1830,1874,19th century,1800,1868,Matted painting; ink on paper,14 3/8 x 29 5/16 in. (36.5 x 74.5 cm),"Gift of Lincoln Kirstein, 1960",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45809,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.268.111,false,true,49002,Asian Art,Folding fan,,Japan,Edo period (1615–1868),,,,Artist,,Takahashi Sōhei,"Japanese, 1804?–?1835",,Takahashi Sōhei,Japanese,1804,1835,19th century,1804,1835,Folding fan; ink on paper,10 3/16 x 16 15/16 in. (25.8 x 43.1 cm),"The Harry G. C. Packard Collection of Asian Art, Gift of Harry G. C. Packard, and Purchase, Fletcher, Rogers, Harris Brisbane Dick, and Louis V. Bell Funds, Joseph Pulitzer Bequest, and The Annenberg Fund Inc. Gift, 1975",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49002,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.49.288,false,true,73565,Asian Art,Sketch,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa (Gountei) Sadahide,"Japanese, 1807–1878/79",,Utagawa (Goutei) Sadahide,Japanese,1807,1879,19th century,1807,1879,Preparatory drawing; ink on paper,Image: 9 1/2 x 12 in. (24.1 x 30.5 cm),"Bequest of William S. Lieberman, 2005",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/73565,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.45,false,true,48881,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Hokuga,"Japanese, active early 19th century",,Hokuga,Japanese,1800,1835,19th century,1800,1868,Hanging scroll; ink and color on silk,Image: 12 13/16 × 22 5/8 in. (32.5 × 57.5 cm) Overall with mounting: 50 1/8 × 28 3/8 in. (127.3 × 72 cm) Overall with knobs: 50 1/8 × 30 9/16 in. (127.3 × 77.6 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/48881,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -59.37,false,true,45813,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,19th century,1800,1849,Hanging scroll; ink and color on silk,Image: 33 11/16 × 13 9/16 in. (85.5 × 34.5 cm) Overall with mounting: 59 1/2 × 16 15/16 in. (151.2 × 43 cm) Overall with knobs: 59 1/2 × 19 3/16 in. (151.2 × 48.7 cm),"Seymour Fund, 1959",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45813,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.119.3,false,true,48889,Asian Art,Painting,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,19th century,1800,1849,Matted painting; ink on paper (wash drawing?),10 3/8 x 14 in. (26.4 x 35.6 cm),"Fletcher Fund, 1937",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/48889,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.28,false,true,45817,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,Attributed to,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,19th century,1800,1849,Hanging scroll; ink and color on silk,Image: 27 3/8 × 10 15/16 in. (69.5 × 27.8 cm) Overall with mounting: 62 5/8 × 16 15/16 in. (159 × 43 cm) Overall with knobs: 62 5/8 × 19 in. (159 × 48.2 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45817,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.29,false,true,48883,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,Attributed to,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,19th century,1800,1849,Hanging scroll; ink and color on paper,Image: 44 5/16 × 9 3/16 in. (112.5 × 23.4 cm) Overall with mounting: 80 9/16 × 10 11/16 in. (204.7 × 27.2 cm) Overall with knobs: 80 9/16 × 12 1/2 in. (204.7 × 31.8 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/48883,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.507,false,true,45820,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,19th century,1800,1849,Hanging scroll; ink and color on paper,Image: 30 7/8 × 9 5/8 in. (78.5 × 24.5 cm) Overall with mounting: 60 3/4 × 13 3/16 in. (154.3 × 33.5 cm) Overall with knobs: 60 3/4 × 15 1/8 in. (154.3 × 38.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45820,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.59.1–.102,false,true,40011,Asian Art,Album leaves,葛飾北斎筆 鶏と木材鶏図|Album of Sketches by Katsushika Hokusai and His Disciples,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,19th century,1800,1868,Album of ninety-seven leaves; ink and color on paper,Each leaf: 15 1/2 x 10 1/2 in. (39.4 x 26.7 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/40011,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.60.1–.109,false,true,48886,Asian Art,Album leaves,,Japan,Edo period (1615–1868),,,,Artist,,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",and others,Katsushika Hokusai,Japanese,1760,1849,19th century,1800,1868,"Album of one hundred and nine leaves; ink on paper, ink and color on paper",15 3/8 x 10 11/16 in. (39.1 x 27.1 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/48886,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -08.148.4,false,true,40336,Asian Art,Painting,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,19th century,1797,1858,Matted painting; ink and color on silk (Ukiyo-e),13 15/16 x 21 7/16 in. (35.4 x 54.5 cm),"Gift of Francis Lathrop, 1908",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/40336,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -08.148.5,false,true,40337,Asian Art,Painting,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,19th century,1797,1858,Matted painting; ink and color on silk,13 15/16 x 21 7/16 in. (35.4 x 54.4 cm),"Gift of Francis Lathrop, 1908",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/40337,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -08.148.6,false,true,40338,Asian Art,Painting,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,19th century,1797,1858,Matted painting; ink and color on silk,13 15/16 x 21 7/16 in. (35.4 x 54.4 cm),"Gift of Francis Lathrop, 1908",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/40338,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -08.148.7,false,true,40339,Asian Art,Painting,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,19th century,1797,1858,Matted painting; ink and color on silk,13 15/16 x 21 7/16 in. (35.4 x 54.4 cm),"Gift of Francis Lathrop, 1908",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/40339,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -08.148.8,false,true,40340,Asian Art,Painting,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,19th century,1797,1858,Matted painting; ink and color on silk,8 1/4 x 12 3/16 in. (21.0 x 30.9 cm),"Gift of Francis Lathrop, 1908",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/40340,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -08.148.9,false,true,40341,Asian Art,Painting,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,19th century,1797,1858,Matted painting; ink and color on silk,8 1/4 x 12 3/16 in. (20.9 x 31 cm),"Gift of Francis Lathrop, 1908",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/40341,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.268.130,false,true,45425,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Hiroshige,"Japanese, Tokyo (Edo) 1797–1858 Tokyo (Edo)",,"Utagawa, Hiroshige",Japanese,1797,1858,19th century,1800,1858,Hanging scroll; ink and color on silk,16 3/8 x 22 5/8 in. (41.6 x 57.5 cm),"The Harry G. C. Packard Collection of Asian Art, Gift of Harry G. C. Packard, and Purchase, Fletcher, Rogers, Harris Brisbane Dick, and Louis V. Bell Funds, Joseph Pulitzer Bequest, and The Annenberg Fund Inc. Gift, 1975",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45425,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -52.160.2,false,true,57328,Asian Art,Hanging scroll,,Japan,Meiji period (1868–1912),,,,Artist,,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,Japanese,1807,1891,19th century,1807,1891,Hanging scroll; color on silk,13 3/4 x 5 in. (34.9 x 12.7 cm),"Purchase, Gifts, Bequests, and Funds from various donors, by exchange, 1952",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57328,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -53.194.1,false,true,57337,Asian Art,Hanging scroll,,Japan,Meiji period (1868–1912),,,,Artist,,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,Japanese,1807,1891,19th century,1807,1891,Hanging scroll; black paint on silk,37 3/8 x 11 7/8 in. (30.2 x 94.9 cm),"Gift of Nathan V. Hammer, 1953",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57337,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -55.161.2,false,true,57342,Asian Art,Hanging scroll,,Japan,Meiji period (1868–1912),,,,Artist,In the Style of,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,Japanese,1807,1891,19th century,1807,1891,Hanging scroll; ink and color on silk,14 3/8 x 10 3/4 in. (36.5 x 27.3 cm),"Purchase, Gifts, Bequests, and Funds from various donors, by exchange, 1955",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57342,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -52.160.3a–q,false,true,57329,Asian Art,Album leaves,,Japan,Meiji period (1868–1912),,,,Artist,,Shibata Zeshin,"Japanese, 1807–1891",,Shibata Zeshin,Japanese,1807,1891,19th century,1807,1891,Album of seventeen sketches; watercolor on paper; mounted on natural silk,Image: 11 x 14 in. (27.9 x 35.6 cm),"Purchase, Gifts, Bequests, and Funds from various donors, by exchange, 1952",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57329,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.282.1a–m,false,true,57163,Asian Art,Album,桃に鶴図|Flowers and Birds,Japan,Meiji period (1868–1912),,,,Artist,,Taki Katei,"Japanese, 1830–1901",,Taki Katei,Japanese,1830,1901,19th century,1800,1899,Album of twelve leaves; ink and color on silk,Each painting: 10 x 12 in. (25.4 x 30.5 cm),"Gift of Dr. and Mrs. Harold B. Bilsky, 1975",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57163,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.119.1,false,true,50829,Asian Art,Handscroll,,Japan,Meiji period (1868–1912),,,,Artist,,Kawanabe Kyōsai,"Japanese, 1831–1889",,Kawanabe Kyōsai,Japanese,1831,1889,19th century,1831,1889,Handscroll; wash drawing,20 ft. 8 in. x 10 7/8 in. (629.9 x 27.6 cm),"Fletcher Fund, 1937",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/50829,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.67.26,false,true,58571,Asian Art,Inrō,印籠扇面蒔絵印籠|Inrō with Inrō and Fan,Japan,Edo period (1615–1868),,,,Artist,,Koma Kyūhaku V,"Japanese, died 1794",,Koma Kyūhaku V,Japanese,,1794,late 18th century,1767,1799,"Four cases; lacquered wood with gold, black, red lacquer takamaki-e, hiramaki-e, tgidashimaki-e on red lacquer ground; Netsuke: carved ivory; beans; Ojime: metal bead with insects",H. 2 15/16 in. (7.5 cm); W. 2 1/2in. (6.3 cm); D. 1 in. (2.5 cm),"Rogers Fund, 1913",,,,,,,,,,,,Inrō,,http://www.metmuseum.org/art/collection/search/58571,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.67.68,false,true,45568,Asian Art,Inrō,,Japan,Edo period (1615–1868),,,,Artist,,Tōyō,"Japanese, active ca. 1764–71",,Tōyō,Japanese,1764,1771,late 18th century,1767,1799,Silver shibuichi lacquer decorated with roiro (waxen) lacquer and gold sprinkled and polished hiramakie lacquer; Ojime: coral bead; Netsuke: metal zogan inlay pine tree,3 7/16 x 2 9/16 x 1 1/8 in. (8.7 x 6.5 x 2.9 cm),"Rogers Fund, 1913",,,,,,,,,,,,Inrō,,http://www.metmuseum.org/art/collection/search/45568,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -10.211.1271,false,true,60329,Asian Art,Netsuke,草花螳螂牙彫根付|Flowers and Grasses with a Praying-Mantis,Japan,Edo period (1615–1868),,,,Artist,Attributed to,Ryūsa,"Japanese, active late 18th century",,Ryūsa,Japanese,1767,1799,late 18th century,1767,1799,Ivory,H. 7/8 in. (2.2 cm); Diam. 2 1/8 in. (5.4 cm),"Gift of Mrs. Russell Sage, 1910",,,,,,,,,,,,Netsuke,,http://www.metmuseum.org/art/collection/search/60329,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2449,false,true,56879,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Harunobu,"Japanese, 1725–1770",,Suzuki Harunobu,Japanese,1725,1770,late 18th century,1767,1799,"Polychrome woodblock print with embossing (karazuri), ink and color on paper",11 1/8 x 8 1/8 in. (28.3 x 20.6 cm) medium-size print (chu-ban),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56879,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1759,false,true,56065,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,late 18th century,1767,1799,Polychrome woodblock pillar print; ink and color on paper,Image: 28 x 5 3/16 in. (71.1 x 13.2 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56065,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1760,false,true,39721,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,late 18th century,1767,1799,Polychrome woodblock print; ink and color on paper,27 7/8 x 6 1/2 in. (70.8 x 16.5 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/39721,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1770,false,true,51092,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,late 18th century,1767,1799,Polychrome woodblock print (pillar print); ink and color on paper,H. 27 in. (68.6 cm); W. 4 5/8 in. (11.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/51092,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2781,false,true,39723,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,late 18th century,1767,1799,Polychrome woodblock print; ink and color on paper,10 1/2 x 7 1/2 in. (26.7 x 19.1 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/39723,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2782,false,true,39724,Asian Art,Print,Tatohe uta|Analogy,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunshō,"Japanese, 1726–1792",,Katsukawa Shunshō,Japanese,1726,1792,late 18th century,1767,1799,Polychrome woodblock print; ink and color on paper,10 1/8 x 7 3/4 in. (25.7 x 19.7 cm),"Henry L.Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/39724,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1621,false,true,55788,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kitao Shigemasa,"Japanese, 1739–1820",,Kitao Shigemasa,Japanese,1739,1820,late 18th century,1767,1799,Polychrome woodblock print; ink and color on paper,H. 8 7/8 in. (22.5 cm); W. 12 3/8 in. (31.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55788,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1126,false,true,55046,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,late 18th century,1767,1799,Polychrome woodblock pillar print; ink and color on paper,Image: 26 3/4 x 4 9/16 in. (67.9 x 11.6 cm),"Rogers Fund, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55046,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1210,false,true,55118,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,late 18th century,1767,1799,Polychrome woodblock print; ink and color on paper,H. 26 5/16 in. (66.8 cm); W. 4 3/4 in. (12.1 cm),"Rogers Fund, 1920",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55118,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1228,false,true,45039,Asian Art,Print,Shiokumi|Dance of the Beach Maidens,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,late 18th century,1767,1799,Polychrome woodblock print; ink and color on paper,Image: 25 3/4 x 4 9/16 in. (65.4 x 11.6 cm),"Rogers Fund, 1922",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45039,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1729,false,true,56040,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,late 18th century,1767,1799,Polychrome woodblock print; ink and color on paper,H. 27 7/16 in. (69.7 cm); W. 4 5/8 in. (11.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56040,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP939,false,true,44989,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kuwagata Keisai,"Japanese, 1764–1824",,Kuwagata Keisai,Japanese,1764,1824,late 18th century,1767,1799,Polychrome woodblock print; ink and color on paper,H. 14 1/2 in. (36.8 cm); W. 9 8/12 in. (24.6 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/44989,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1684,false,true,42697,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,late 18th century,1767,1799,Diptych of polychrome woodblock prints; ink and color on paper,H. 13 7/16 in. (34.1 cm); W. 17 5/8 in. (44.8 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/42697,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1095b,false,true,639384,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,late 18th century,1754,1806,Polychrome woodblock print; ink and color on paper,Image: 14 in. × 9 1/2 in. (35.6 × 24.1 cm),The Metropolitan Museum of Art,,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/639384,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1095c,false,true,639385,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,late 18th century,1754,1806,Polychrome woodblock print; ink and color on paper,Image: 14 1/2 × 9 3/8 in. (36.8 × 23.8 cm),The Metropolitan Museum of Art,,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/639385,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1095d,false,true,639386,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,late 18th century,1754,1806,Polychrome woodblock print; ink and color on paper,Image: 15 1/8 × 10 1/8 in. (38.4 × 25.7 cm),The Metropolitan Museum of Art,,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/639386,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1704,false,true,55945,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Isoda Koryūsai,"Japanese, 1735–ca. 1790",,Isoda Koryūsai,Japanese,1735,1790,late 18th century,1767,1799,Polychrome woodblock print; ink and color on paper,H. 27 3/16 in. (69.1 cm); W. 5 in. (12.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55945,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1711,false,true,45269,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Isoda Koryūsai,"Japanese, 1735–ca. 1790",,Isoda Koryūsai,Japanese,1735,1790,late 18th century,1767,1799,Polychrome woodblock print (hashira-e); ink and color on paper,H. 26 1/8 in. (66.4 cm); W. 4 1/2 in. (11.4 cm),"H. O. Havemeyer Collection; Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45269,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1800,false,true,45260,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunzan,"Japanese, active 1782–1798",,Katsukawa Shunzan,Japanese,1782,1798,late 18th century,1767,1799,Triptych of polychrome woodblock prints; ink and color on paper,A: H. 15 in. (38.1 cm); W. 10 1/4 in. (26 cm) B: H. 14 15/16 in. (37.9 cm); W. 10 1/4 in. (26 cm) C: H. 14 7/8 in. (37.9 cm); W. 10 1/4 in. (26 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45260,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2663,false,true,51999,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Katsukawa Shunchō,"Japanese, active ca. 1783–95",,Katsukawa Shunchō,Japanese,1775,1795,late 18th century,1767,1799,Triptych of polychrome woodblock prints; ink and color on paper,Image (each): 15 × 10 in. (38.1 × 25.4 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/51999,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1789,false,true,56091,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Rekisentei Eiri,"Japanese, active ca. 1789–1801",,Rekisentei Eiri,Japanese,1789,1801,late 18th century,1767,1799,Triptych of polychrome woodblock prints; ink and color on paper,Image (each): 14 9/16 x 9 3/4 in. (37 x 24.8 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56091,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1095a,false,true,55030,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Juka Sekijō,"Japanese, active ca. 1789–1817",,Juka Sekijō,Japanese,1789,1817,late 18th century,1754,1806,Polychrome woodblock print; ink and color on paper,Image: 13 7/8 in. × 9 in. (35.2 × 22.9 cm),The Metropolitan Museum of Art,,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55030,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.268.98a–p,false,true,45331,Asian Art,Album,,Japan,Edo period (1615–1868),,,,Artist,,Kuwayama Gyokushū,"Japanese, 1746–1799",,Kuwayama Gyokushū,Japanese,1746,1799,late 18th century,1767,1799,Album of fourteen paintings and one calligraphy; Ink and color on paper,Image (each): 8 7/8 x 5 7/16 in. (22.5 x 13.8 cm) Overall (album opened): 10 1/2 x 13 5/8 in. (26.7 x 34.6 cm),"The Harry G. C. Packard Collection of Asian Art, Gift of Harry G. C. Packard, and Purchase, Fletcher, Rogers, Harris Brisbane Dick, and Louis V. Bell Funds, Joseph Pulitzer Bequest, and The Annenberg Fund Inc. Gift, 1975",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45331,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1985.399.25,false,true,59677,Asian Art,Netsuke,,Japan,,,,,Artist,,Minkō,"Japanese, ca. 1735–1816",,Minkō,Japanese,1735,1816,early 19th century,1800,1833,Wood; brass and horn inlay,H. 1 3/16 in. (3 cm); W. 1 7/8 in. (4.8 cm),"Gift of Alvin H. Schechter, 1985",,,,,,,,,,,,Netsuke,,http://www.metmuseum.org/art/collection/search/59677,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.877,false,true,45504,Asian Art,Inrō,秋蔦蒔絵印籠|Inrō with Autumn Ivy,Japan,Edo period (1615–1868),,,,Artist,Attributed to,Koma Kyūhaku VI,"Japanese, died 1816",,Koma Kyūhaku VI,Japanese,,1816,early 19th century,1800,1833,"Four cases; lacquered wood with gold, silver, and color (iroko) togidashimaki-e on black lacquer ground Netsuke: ivory; folded letter decorated with paulownia and Genji incense symbols Ojime: oblong bead; gilt bronze with openwork design of autumn flowers",3 11/16 x 2 3/8 x 3/4 in. (9.3 x 6 x 1.9 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Inrō,,http://www.metmuseum.org/art/collection/search/45504,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2001.768.3, .4",false,true,64397,Asian Art,Folding screen,,Japan,Edo period (1615–1868),,,,Artist,,Mori Shūhō,"Japanese, 1738–1823",,Mori Shūhō,Japanese,1738,1823,early 19th century,1800,1833,"Pair of six-panel folding screens; ink, color, and gold flecks on gilded paper",Image (each screen): 5 ft. 2 3/16 in. x 11 ft. 8 15/16 in. (1.58 x 3.58 m),"Gift of Rosemarie and Leighton Longhi, 2001",,,,,,,,,,,,Screens,,http://www.metmuseum.org/art/collection/search/64397,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.67.22,false,true,53935,Asian Art,Inrō,,Japan,Edo period (1615–1868),,,,Artist,,Hara Yōyūsai,"Japanese, 1772–1845",,Hara Yōyūsai,Japanese,1772,1845,early 19th century,1800,1833,Case: powdered gold (maki-e) and colored lacquer on black lacquer with mother-of-pearl and gold inlays; Fastener (ojime): ivory carved with abstract design; Toggle (netsuke): ivory carved in the shape of a crab,H. 3 1/4 (8.3 cm); W. 2 in. ( 5.1 cm),"Rogers Fund, 1913",,,,,,,,,,,,Inrō,,http://www.metmuseum.org/art/collection/search/53935,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"54.69.1, .2",false,true,48982,Asian Art,Folding screen,朝顔図屏風|Morning Glories,Japan,Edo period (1615–1868),,,,Artist,,Suzuki Kiitsu,"Japanese, 1796–1858",,Suzuki Kiitsu,Japanese,1796,1858,early 19th century,1800,1833,"Pair of six-panel folding screens; ink, color, and gold leaf on paper",Image (each screen): 70 3/16 x 12 ft. 5 1/2 in. (178.3 x 379.7 cm),"Seymour Fund, 1954",,,,,,,,,,,,Screens,,http://www.metmuseum.org/art/collection/search/48982,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1715,false,true,45251,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Kitao Shigemasa,"Japanese, 1739–1820",,Kitao Shigemasa,Japanese,1739,1820,early 19th century,1800,1833,Polychrome woodblock print; ink and color on paper,H. 8 3/8 in. (21.3 cm); W. 14 3/4 in. (37.5 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/45251,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1746,false,true,56053,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Toyokuni I,"Japanese, 1769–1825",,Utagawa Toyokuni I,Japanese,1769,1825,early 19th century,1800,1833,Polychrome woodblock print; ink and color on paper,H. 14 11/16 in. (37.3 cm); W. 9 7/8 in. (25.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56053,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1921,false,true,54460,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Totoya Hokkei,"Japanese, 1780–1850",,Totoya Hokkei,Japanese,1780,1850,early 19th century,1800,1833,Polychrome woodblock print (surimono); ink and color on paper,7 3/4 x 6 5/8 in. (19.7 x 16.8 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54460,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1957,false,true,54529,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Totoya Hokkei,"Japanese, 1780–1850",,Totoya Hokkei,Japanese,1780,1850,early 19th century,1800,1833,Polychrome woodblock print (surimono); ink and color on paper,8 1/4 x 5 1/8 in. (21 x 13 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54529,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2055,false,true,54831,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Totoya Hokkei,"Japanese, 1780–1850",,Totoya Hokkei,Japanese,1780,1850,early 19th century,1800,1833,Polychrome woodblock print (surimono); ink and color on paper,5 7/16 x 11 1/8 in. (13.8 x 28.3 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54831,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2132,false,true,54987,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist,,Yosai,"Japanese, 1788–1878",,Yosai,Japanese,1788,1878,early 19th century,1800,1833,Polychrome woodblock print (surimono); ink and color on paper,5 9/16 x 7 7/16 in. (14.1 x 18.9 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54987,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1683,false,true,37343,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,early 19th century,1800,1833,Triptych of polychrome woodblock prints; ink and color on paper,Overall: 15 1/4 x 29 1/2 in. (38.7 x 74.9 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/37343,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2733,false,true,56794,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Kitagawa Utamaro,"Japanese, 1753?–1806",,Kitagawa Utamaro,Japanese,1753,1806,early 19th century,1800,1833,Right sheet of a triptych of polychrome woodblock prints; ink and color on paper,Image (oban triptych): 15 1/8 x 10 1/8 in. (38.4 x 25.7 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56794,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1102,false,true,54331,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Yashima Gakutei,"Japanese, 1786?–1868",,Yashima Gakutei,Japanese,1786,1868,early 19th century,1800,1833,Diptych of polychrome woodblock prints (surimono); ink and color on paper,a) 8 7/16 x 7 3/8 in. (21.4 x 18.7 cm) b): 8 5/16 x 7 3/8 in. (21.1 x 18.7 cm),"Rogers Fund, 1919",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54331,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1920,false,true,54459,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Yashima Gakutei,"Japanese, 1786?–1868",,Yashima Gakutei,Japanese,1786,1868,early 19th century,1800,1833,Polychrome woodblock print (surimono); ink and color on paper,7 11/16 x 6 1/2 in. (19.5 x 16.5 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54459,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP3493,false,true,55720,Asian Art,Print,Seishu Futami ga Ura|View of Futami Beach at Ise,Japan,Edo period (1615–1868),,,,Artist,,Shotei Hokuju,"Japanese, active 1790–1820",,Shotei Hokuju,Japanese,1790,1820,early 19th century,1800,1833,Polychrome woodblock print; ink and color on paper,Oban 10 x 15 in. (25.4 x 38.1 cm),"Gift of Cole J. Younger, 1975",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55720,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP1827,false,true,56113,Asian Art,Print,,Japan,Edo period (1615–1868),,,,Artist,,Koikawa Harumasa,"Japanese, active 1800–1820",,Koikawa Harumasa,Japanese,1800,1820,early 19th century,1800,1833,Polychrome woodblock print; ink and color on paper,H. 23 1/2 in. (59.7 cm); W. 4 9/16 in. (11.6 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/56113,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -93.1.20,false,true,62617,Asian Art,Teabowl,,Japan,,,,,Artist,,Kenzan III,"Japanese, 1767–1810",,Kenzan III,Japanese,1767,1810,early 19th century,1800,1833,"Clay, ribbed and covered a mottled glaze (Kenzan ware)",H. 3 in. (7.6 cm),"Gift of Mr. and Mrs. Samuel Colman, 1893",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/62617,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -17.118.67,false,true,62886,Asian Art,Teabowl,,Japan,Edo period (1615–1868),,,,Artist,,Raku Ryōnyū,"Japanese, 1756–1834",,Raku Ryonyu,Japanese,1756,1834,early 19th century,1800,1833,Clay covered with a black glaze (Raku ware),H. 3 3/8 in. (8.6 cm); Diam. 4 in. (10.2 cm),"Rogers Fund, 1917",,,,,,,,,,,,Ceramics,,http://www.metmuseum.org/art/collection/search/62886,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.46,false,true,39725,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,Attributed to,Torii Kiyonaga,"Japanese, 1752–1815",,Torii Kiyonaga,Japanese,1752,1815,early 19th century,1800,1815,Hanging scroll; ink and color on silk,Image: 15 3/4 × 23 1/4 in. (40 × 59.1 cm) Overall with mounting: 51 1/8 × 28 1/4 in. (129.9 × 71.8 cm) Overall with knobs: 51 1/8 × 30 3/8 in. (129.9 × 77.2 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/39725,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.43,false,true,45806,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Toyohiro,"Japanese, 1763–1828",,Utagawa Toyohiro,Japanese,1763,1828,early 19th century,1800,1828,Hanging scroll; ink and color on silk,31 5/16 x 10 1/4 in. (79.6 x 26 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45806,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.268.129,false,true,45808,Asian Art,Hanging scroll,夕顔棚下納涼図|Enjoying the Evening Cool under a Gourd Trellis,Japan,Edo period (1615–1868),,,,Artist,,Utagawa Toyohiro,"Japanese, 1763–1828",,Utagawa Toyohiro,Japanese,1763,1828,early 19th century,1800,1828,Hanging scroll; ink and color on paper,Image: 33 1/4 x 11 in. (84.5 x 27.9 cm) Overall with knobs: 65 1/8 x 16 1/2 in. (165.4 x 41.9 cm),"The Harry G. C. Packard Collection of Asian Art, Gift of Harry G. C. Packard, and Purchase, Fletcher, Rogers, Harris Brisbane Dick, and Louis V. Bell Funds, Joseph Pulitzer Bequest, and The Annenberg Fund Inc. Gift, 1975",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45808,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1977.445,false,true,49005,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Tani Bunchō,"Japanese, 1763–1840",,Tani Bunchō,Japanese,1763,1840,early 19th century,1800,1833,Hanging scroll; ink and color on silk,64 15/16 x 44 3/4 in. (165 x 113.6 cm),"Gift of Mr. and Mrs. Theodore R. Conant, 1977",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49005,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.26,false,true,42314,Asian Art,Hanging scroll,三味線持つ美人図|Female Entertainer with Shamisen,Japan,Edo period (1615–1868),,,,Artist,Attributed to,Teisai Hokuba,"Japanese, 1771–1844",,Teisai Hokuba,Japanese,1771,1844,early 19th century,1800,1828,Hanging scroll; ink and color on paper,31 1/8 x 11 1/4 in. (79.1 x 28.6 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/42314,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.43,false,true,72603,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Tōshū,"Japanese, active ca. 1800",,Tōshū,Japanese,1790,1810,early 19th century,1800,1833,Hanging scroll; ink and color on silk,Image: 39 7/8 x 11 1/8 in. (101.3 x 28.3 cm) Overall with mounting: 62 1/2 x 15 1/2 in. (158.8 x 39.4 cm) Overall with rollers: 62 1/2 x 18 1/8 in. (158.8 x 46 cm),"Gift of Jack Jacoby, 2005",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/72603,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.197,false,true,58882,Asian Art,Inrō,,Japan,,,,,Artist,,Kano Seisen’in,1775–1828,,Kano Seisen’in,Japanese,1775,1828,18th–19th century,1700,1899,"Lacquer, roiro, yamimakie, black hiramakie, takamakie; Interior: gyobu nashiji and fundame",3 11/16 x 1 7/8 x 1 1/16 in. (9.4 x 4.8 x 2.7 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Inrō,,http://www.metmuseum.org/art/collection/search/58882,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.795,false,true,58704,Asian Art,Inrō,,Japan,,,,,Artist,,Hanabusa Itchō,"Japanese, 1652–1724",,Hanabusa Itchō,Japanese,1652,1724,18th–19th century,1700,1899,"Lacquer, roiro, gold, silver, brown and red hiramakie, various inlay; Interior: nashiji and fundame",4 1/8 x 1 3/4 x 15/16 in. (10.4 x 4.4 x 2.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Inrō,,http://www.metmuseum.org/art/collection/search/58704,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.228,false,true,58916,Asian Art,Inrō,,Japan,,,,,Artist,Attributed to,Ogawa Haritsu (Ritsuō),"Japanese, 1663–1747",,Ritsuō,Japanese,1663,1747,18th–19th century,1700,1899,"Lacquer, roiro, gold and silver hiramakie, togidashi, kimpun, ivory, horn inlay; Interior: fundame",3 3/8 x 2 3/16 x 7/8 in. (8.5 x 5.5 x 2.3 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Inrō,,http://www.metmuseum.org/art/collection/search/58916,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.191,false,true,58878,Asian Art,Inrō,,Japan,,,,,Artist,,Maruyama Ōkyo,"Japanese, 1733–1795",,Maruyama Ōkyo,Japanese,1733,1795,18th–19th century,1700,1899,"Lacquer, roiro, gold and silver togidashi, mura nashiji; Interior: roiro and fundame",2 15/16 x 2 7/16 x 1 in. (7.4 x 6.2 x 2.6 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Inrō,,http://www.metmuseum.org/art/collection/search/58878,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -49.46.1,false,true,53814,Asian Art,Printer's woodblock,,Japan,,,,,Artist,Original print designed by,Utagawa Toyokuni I,"Japanese, 1769–1825",,Utagawa Toyokuni I,Japanese,1769,1825,18th–19th century,1700,1899,,15 1/2 x 10 1/2 in. (39.4 x 26.7 cm),"Gift of Mrs. Howard Mansfield, 1949",,,,,,,,,,,,Woodblocks,,http://www.metmuseum.org/art/collection/search/53814,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.222,false,true,58910,Asian Art,Inrō,,Japan,,,,,Artist,,Tōyō,"Japanese, active ca. 1764–71",,Tōyō,Japanese,1764,1771,18th–19th century,1700,1899,"Lacquer, silver brown ground, incised; Interior: gyobu nashiji and fundame",4 3/16 x 1 15/16 x 1 5/16 in. (10.7 x 4.9 x 3.4 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Inrō,,http://www.metmuseum.org/art/collection/search/58910,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -91.1.701,false,true,58478,Asian Art,Inrō,,Japan,Edo period (1615–1868),,,,Artist,In the style of,Ogata Kōrin,"Japanese, 1658–1716",,Kōrin,Japanese,1658,1716,18th–19th century,1700,1899,"Gold lacquer with dark gray ishime, gold, red, black, and silver makie, pewter, and mother-of-pearl; Ojime: bead with autumn wild flowers; Netsuke: rat eating peach; boxwood",H. 2 3/8 in. (6 cm); W. 2 1/2 in. (6.4 cm); D. 3/4 in. (1.9 cm),"Edward C. Moore Collection, Bequest of Edward C. Moore, 1891",,,,,,,,,,,,Inrō,,http://www.metmuseum.org/art/collection/search/58478,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.187,false,true,58874,Asian Art,Inrō,,Japan,Edo period (1615–1868),,,,Artist,,Mochizuki Hanzan,"Japanese, 1743–?1790",,Mochizuki Hanzan,Japanese,1743,1790,18th–19th century,1700,1899,"Wood, brushed wood ground, gold, red and green hiramakie, takamakie, raden; Interior: nashiji and fundame",3 x 2 5/8 x 7/8 in. (7.6 x 6.7 x 2.3 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Inrō,,http://www.metmuseum.org/art/collection/search/58874,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2204,false,true,53970,Asian Art,Print,,Japan,,,,,Artist,,Haikairyō Henpuku,"Japanese, 1744–1830",,Haikairyō Henpuku,Japanese,1744,1830,18th–19th century,1700,1899,Polychrome woodblock print (surimono); ink and color on paper,3 7/8 x 5 7/16 in. (9.8 x 13.8 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/53970,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2175,false,true,55089,Asian Art,Woodblock print,,Japan,,,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,18th–19th century,1700,1899,Polychrome woodblock print (surimono); ink and color on paper,8 x 7 3/16 in. (20.3 x 18.3 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55089,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2221,false,true,53987,Asian Art,Print,,Japan,,,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,18th–19th century,1700,1900,Polychrome woodblock print (surimono); ink and color on paper,5 1/4 x 7 7/8 in. (13.3 x 20 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/53987,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2205,false,true,53971,Asian Art,Print,,Japan,,,,,Artist,,Yanagawa Shigenobu,"Japanese, 1787–1832",,Yanagawa Shigenobu,Japanese,1787,1832,18th–19th century,1700,1899,Polychrome woodblock print (surimono); ink and color on paper,8 9/16 x 7 5/16 in. (21.7 x 18.6 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/53971,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2176,false,true,55090,Asian Art,Print,,Japan,,,,,Artist,,Yanagawa Shigemasa,"Japanese, 18th–19th century",,Yanagawa Shigemasa,Japanese,1700,1899,18th–19th century,1700,1899,Polychrome woodblock print (surimono); ink and color on paper,8 1/16 x 3 5/8 in. (20.5 x 9.2 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55090,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2187,false,true,55103,Asian Art,Woodblock print,,Japan,,,,,Artist,,Reisai,"Japanese, 18th–19th century",,Reisai,Japanese,1700,1899,18th–19th century,1700,1899,Polychrome woodblock print (surimono); ink and color on paper,7 x 6 1/2 in. (17.8 x 16.5 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/55103,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP2827,false,true,39584,Asian Art,Print,,Japan,Edo (1615–1868),,,,Artist,,Utagawa Toyokuni I,"Japanese, 1769–1825",,Utagawa Toyokuni I,Japanese,1769,1825,18th–19th century,1700,1899,Polychrome woodblock print; ink and color on paper,14-1/8 x 9-9/16 in. (35.9 x 24.3 cm),"Henry L. Phillips Collection, Bequest of Henry L. Phillips, 1939",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/39584,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -57.70.2,false,true,57247,Asian Art,Hanging scroll,,Japan,,,,,Artist,Attributed to,Chōbunsai Eishi,"Japanese, 1756–1829",,Chōbunsai Eishi,Japanese,1756,1829,18th–19th century,1756,1829,Hanging scroll; ink and color on paper,36 1/2 x 15 3/4 in. (92.7 x 40 cm),"Gift of Edward M. Bratter, 1957",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57247,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.32,false,true,45735,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,Attributed to,Kano Seisen’in,1775–1828,,Kano Seisen’in,Japanese,1775,1828,18th–19th century,1775,1828,Hanging scroll; ink and color on silk,38 x 14 1/4 in. (96.5 x 36.2 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45735,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.17,false,true,49058,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Mori Sosen,"Japanese, 1747–1821",,Mori Sosen,Japanese,1747,1821,18th–19th century,1747,1821,Hanging scroll; ink and color on silk,26 3/4 x 6 1/4 in. (67.9 x 15.9 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49058,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.18,false,true,49059,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Mori Sosen,"Japanese, 1747–1821",,Mori Sosen,Japanese,1747,1821,18th–19th century,1747,1821,Hanging scroll; ink and color on silk,26 3/4 x 6 1/4 in. (67.9 x 15.9 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/49059,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.268.128,false,true,45798,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Kubo Shunman,"Japanese, 1757–1820",,Kubo Shunman,Japanese,1757,1820,18th–19th century,1757,1820,Hanging scroll; ink and color on paper,36 3/8 x 13 1/16 in. (92.4 x 33.2 cm),"The Harry G. C. Packard Collection of Asian Art, Gift of Harry G. C. Packard, and Purchase, Fletcher, Rogers, Harris Brisbane Dick, and Louis V. Bell Funds, Joseph Pulitzer Bequest, and The Annenberg Fund Inc. Gift, 1975",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45798,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.268.100,false,true,44895,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Kushiro Unsen,"Japanese, 1759–1811",,Kushiro Unsen,Japanese,1759,1811,18th–19th century,1759,1811,Hanging scroll; ink and color on satin,Image: 58 7/16 x 13 1/4 in. (148.4 x 33.7 cm) Overall: 82 1/2 x 20 3/4in. (209.6 x 52.7cm),"The Harry G. C. Packard Collection of Asian Art, Gift of Harry G. C. Packard, and Purchase, Fletcher, Rogers, Harris Brisbane Dick, and Louis V. Bell Funds, Joseph Pulitzer Bequest, and The Annenberg Fund Inc. Gift, 1975",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/44895,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.76.33,false,true,45801,Asian Art,Hanging scroll,,Japan,Edo period (1615–1868),,,,Artist,,Yanagi Buncho II,active ca. 1764–1801,,Yanagi Buncho II,Japanese,1764,1801,18th–19th century,1764,1801,Hanging scroll; ink and color on silk,17 5/8 x 20 1/2 in. (44.8 x 52.1 cm),"Charles Stewart Smith Collection, Gift of Mrs. Charles Stewart Smith, Charles Stewart Smith Jr., and Howard Caswell Smith, in memory of Charles Stewart Smith, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/45801,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -56.121.1,false,true,48890,Asian Art,Painting,,Japan,Edo period (1615–1868),,,,Artist,Attributed to,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,18th–19th century,1760,1849,Unmounted painting; ink on paper,10 3/4 x 6 7/8 in. (27.3 x 17.5 cm),"Gift of Annette Young, in memory of her brother, Innis Young, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/48890,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -56.121.2,false,true,57252,Asian Art,Drawing,,Japan,Edo period (1615–1868),,,,Artist,Attributed to,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,18th–19th century,1760,1849,Ink on paper,9 7/16 x 12 3/8 in. (24 x 31.4 cm),"Gift of Annette Young, in memory of her brother, Innis Young, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57252,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -56.121.3,false,true,57253,Asian Art,Drawing,,Japan,Edo period (1615–1868),,,,Artist,Attributed to,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,18th–19th century,1760,1849,Ink on paper,6 7/8 x 9 3/8 in. (17.5 x 23.8 cm),"Gift of Annette Young, in memory of her brother, Innis Young, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57253,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -56.121.4,false,true,57254,Asian Art,Drawing,,Japan,Edo period (1615–1868),,,,Artist,Attributed to,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,18th–19th century,1760,1849,Ink on paper,9 1/2 x 12 7/8 in. (24.1 x 32.7 cm),"Gift of Annette Young, in memory of her brother, Innis Young, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57254,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -56.121.5,false,true,57255,Asian Art,Drawing,,Japan,Edo period (1615–1868),,,,Artist,Attributed to,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,18th–19th century,1760,1849,Ink and color on paper,9 3/8 x 13 15/16 in. (23.8 x 35.4 cm),"Gift of Annette Young, in memory of her brother, Innis Young, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57255,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -56.121.6,false,true,57256,Asian Art,Drawing,,Japan,Edo period (1615–1868),,,,Artist,Attributed to,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,18th–19th century,1760,1849,Ink on paper,10 7/8 x 7 9/16 in. (27.6 x 19.2 cm),"Gift of Annette Young, in memory of her brother, Innis Young, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57256,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -56.121.7,false,true,57257,Asian Art,Drawing,,Japan,Edo period (1615–1868),,,,Artist,Attributed to,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,18th–19th century,1760,1849,Ink on paper,13 1/16 x 9 3/4 in. (33.2 x 24.8 cm),"Gift of Annette Young, in memory of her brother, Innis Young, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57257,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -56.121.8,false,true,57258,Asian Art,Drawing,,Japan,Edo period (1615–1868),,,,Artist,Attributed to,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,18th–19th century,1760,1849,Ink and color on paper,9 5/8 x 12 3/8 in. (24.4 x 31.4 cm),"Gift of Annette Young, in memory of her brother, Innis Young, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57258,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -56.121.9,false,true,57259,Asian Art,Drawing,,Japan,Edo period (1615–1868),,,,Artist,Attributed to,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,18th–19th century,1760,1849,Ink and color on paper,13 x 9 1/4 in. (33 x 23.5 cm),"Gift of Annette Young, in memory of her brother, Innis Young, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57259,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -56.121.10,false,true,57260,Asian Art,Drawing,,Japan,Edo period (1615–1868),,,,Artist,Attributed to,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,18th–19th century,1760,1849,Ink on paper,18 1/2 x 12 3/16 in. (47 x 31 cm),"Gift of Annette Young, in memory of her brother, Innis Young, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57260,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -56.121.11,false,true,57261,Asian Art,Drawing,,Japan,Edo period (1615–1868),,,,Artist,Attributed to,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,18th–19th century,1760,1849,Ink on paper,9 7/8 x 7 7/16 in. (25.1 x 18.9 cm),"Gift of Annette Young, in memory of her brother, Innis Young, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57261,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -56.121.12,false,true,57262,Asian Art,Drawing,,Japan,Edo period (1615–1868),,,,Artist,Attributed to,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,18th–19th century,1760,1849,Ink on paper,11 3/8 x 8 3/8 in. (28.9 x 21.3 cm),"Gift of Annette Young, in memory of her brother, Innis Young, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57262,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -56.121.13,false,true,57263,Asian Art,Drawing,,Japan,Edo period (1615–1868),,,,Artist,Attributed to,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,18th–19th century,1760,1849,Ink on paper,8 1/4 x 10 7/8 in. (21 x 27.6 cm),"Gift of Annette Young, in memory of her brother, Innis Young, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57263,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -56.121.14,false,true,57264,Asian Art,Drawings,,Japan,Edo period (1615–1868),,,,Artist,Attributed to,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,18th–19th century,1760,1849,Ink and color on paper,Turtle: 5 x 9 5/16 in. (12.7 x 23.7 cm) Other sketch. 5 3/8 x 7 7/8 in. (13.7 x 20 cm),"Gift of Annette Young, in memory of her brother, Innis Young, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57264,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -56.121.15,false,true,57265,Asian Art,Drawing,,Japan,Edo period (1615–1868),,,,Artist,School of,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,18th–19th century,1760,1849,Ink on paper,9 5/8 x 13 1/8 in. (24.4 x 33.3 cm),"Gift of Annette Young, in memory of her brother, Innis Young, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57265,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -56.121.16,false,true,57266,Asian Art,Drawing,,Japan,Edo period (1615–1868),,,,Artist,School of,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,18th–19th century,1760,1849,Ink and red color on paper,16 13/16 x 10 1/2 in. (42.7 x 26.7 cm),"Gift of Annette Young, in memory of her brother, Innis Young, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57266,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -56.121.17,false,true,57267,Asian Art,Drawing,,Japan,Edo period (1615–1868),,,,Artist,School of,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,18th–19th century,1760,1849,Ink on paper,15 3/8 x 10 13/16 in. (39.1 x 27.5 cm),"Gift of Annette Young, in memory of her brother, Innis Young, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57267,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -56.121.18,false,true,57268,Asian Art,Drawing,,Japan,Edo period (1615–1868),,,,Artist,School of,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,18th–19th century,1760,1849,Ink and color on brown prepared paper,11 x 16 in. (27.9 x 40.6 cm),"Gift of Annette Young, in memory of her brother, Innis Young, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57268,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -56.121.19,false,true,57269,Asian Art,Drawing,,Japan,Edo period (1615–1868),,,,Artist,School of,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,18th–19th century,1760,1849,Ink on paper,10 11/16 x 7 1/8 in. (27.1 x 18.1 cm),"Gift of Annette Young, in memory of her brother, Innis Young, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57269,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -56.121.20,false,true,57270,Asian Art,Drawing,,Japan,Edo period (1615–1868),,,,Artist,School of,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,18th–19th century,1760,1849,Ink on paper,12 15/16 x 9 1/2 in. (32.9 x 24.1 cm),"Gift of Annette Young, in memory of her brother, Innis Young, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57270,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -56.121.21,false,true,57271,Asian Art,Drawing,,Japan,Edo period (1615–1868),,,,Artist,School of,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,18th–19th century,1760,1849,Ink on paper,12 7/8 x 9 1/2 in. (32.7 x 24.1 cm),"Gift of Annette Young, in memory of her brother, Innis Young, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57271,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -56.121.22,false,true,57272,Asian Art,Drawing,,Japan,Edo period (1615–1868),,,,Artist,School of,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,18th–19th century,1700,1899,Ink on paper,9 3/4 x 13 5/8 in. (24.8 x 34.6 cm),"Gift of Annette Young, in memory of her brother, Innis Young, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57272,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -56.121.23,false,true,57273,Asian Art,Drawing,,Japan,Edo period (1615–1868),,,,Artist,School of,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,18th–19th century,1700,1899,Ink and color on paper,11 x 16 1/2 in. (27.9 x 41.9 cm),"Gift of Annette Young, in memory of her brother, Innis Young, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57273,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -56.121.24,false,true,57274,Asian Art,Drawing,,Japan,Edo period (1615–1868),,,,Artist,School of,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,18th–19th century,1700,1899,Ink on paper,11 3/8 x 5 7/8 in. (28.9 x 14.9 cm),"Gift of Annette Young, in memory of her brother, Innis Young, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57274,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -56.121.25,false,true,57275,Asian Art,Drawing,,Japan,Edo period (1615–1868),,,,Artist,School of,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,18th–19th century,1700,1899,Ink and color on paper,9 7/16 x 13 1/2 in. (24 x 34.3 cm),"Gift of Annette Young, in memory of her brother, Innis Young, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57275,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -56.121.26,false,true,57276,Asian Art,Drawing,,Japan,Edo period (1615–1868),,,,Artist,School of,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,18th–19th century,1700,1899,Ink on paper,12 5/16 x 9 3/16 in. (31.3 x 23.3 cm),"Gift of Annette Young, in memory of her brother, Innis Young, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57276,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -56.121.27,false,true,57277,Asian Art,Drawing,,Japan,Edo period (1615–1868),,,,Artist,School of,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,18th–19th century,1700,1899,Ink and color on paper,11 3/16 x 8 3/4 in. (28.4 x 22.2 cm),"Gift of Annette Young, in memory of her brother, Innis Young, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57277,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -56.121.28,false,true,57278,Asian Art,Drawing,,Japan,Edo period (1615–1868),,,,Artist,School of,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,18th–19th century,1700,1899,Ink and color on paper,11 x 16 1/2 in. (27.9 x 41.9 cm),"Gift of Annette Young, in memory of her brother, Innis Young, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57278,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -56.121.29,false,true,57279,Asian Art,Drawing,,Japan,Edo period (1615–1868),,,,Artist,School of,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,18th–19th century,1700,1899,Ink on paper,16 1/4 x 10 1/4 in. (41.3 x 26 cm),"Gift of Annette Young, in memory of her brother, Innis Young, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57279,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -56.121.30,false,true,57280,Asian Art,Drawing,,Japan,Edo period (1615–1868),,,,Artist,School of,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,18th–19th century,1700,1899,Ink on paper,16 1/4 x 10 3/8 in. (41.3 x 26.4 cm),"Gift of Annette Young, in memory of her brother, Innis Young, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57280,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -56.121.31,false,true,57281,Asian Art,Drawing,,Japan,Edo period (1615–1868),,,,Artist,School of,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,18th–19th century,1700,1899,Ink on paper,9 9/16 x 5 1/2 in. (24.3 x 14 cm),"Gift of Annette Young, in memory of her brother, Innis Young, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57281,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -56.121.32,false,true,57282,Asian Art,Drawing,,Japan,Edo period (1615–1868),,,,Artist,School of,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,18th–19th century,1700,1899,Ink and color on paper,16 1/8 x 9 5/16 in. (41 x 23.7 cm),"Gift of Annette Young, in memory of her brother, Innis Young, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57282,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -56.121.33,false,true,57283,Asian Art,Drawing,,Japan,Edo period (1615–1868),,,,Artist,School of,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,18th–19th century,1700,1899,Ink and color on paper,10 11/16 x 7 in. (27.1 x 17.8 cm),"Gift of Annette Young, in memory of her brother, Innis Young, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57283,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -56.121.34,false,true,57284,Asian Art,Drawing,,Japan,Edo period (1615–1868),,,,Artist,School of,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,18th–19th century,1700,1899,Ink on paper,9 3/4 x 7 3/8 in. (24.8 x 18.7 cm),"Gift of Annette Young, in memory of her brother, Innis Young, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57284,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -56.121.35,false,true,57285,Asian Art,Drawing,,Japan,Edo period (1615–1868),,,,Artist,School of,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,18th–19th century,1700,1899,Ink on paper,12 7/8 x 9 7/8 in. (32.7 x 25.1 cm),"Gift of Annette Young, in memory of her brother, Innis Young, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57285,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -56.121.36,false,true,57286,Asian Art,Painting,,Japan,Edo period (1615–1868),,,,Artist,School of,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,18th–19th century,1700,1899,Ink and color on paper,7 9/16 x 10 1/4 in. (19.2 x 26 cm),"Gift of Annette Young, in memory of her brother, Innis Young, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57286,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -56.121.37,false,true,57287,Asian Art,Drawing,,Japan,Edo period (1615–1868),,,,Artist,School of,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,18th–19th century,1700,1899,Ink on paper,7 9/16 x 5 7/8 in. (19.2 x 14.9 cm),"Gift of Annette Young, in memory of her brother, Innis Young, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57287,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -56.121.38,false,true,57288,Asian Art,Drawing,,Japan,Edo period (1615–1868),,,,Artist,School of,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,18th–19th century,1700,1899,Ink on paper,10 3/4 x 7 1/8 in. (27.3 x 18.1 cm),"Gift of Annette Young, in memory of her brother, Innis Young, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57288,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -56.121.39,false,true,57289,Asian Art,Drawing,,Japan,Edo period (1615–1868),,,,Artist,School of,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,18th–19th century,1700,1899,Ink on paper,12 1/8 x 5 5/8 in. (30.8 x 14.3 cm),"Gift of Annette Young, in memory of her brother, Innis Young, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57289,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -56.121.40,false,true,57290,Asian Art,Painting,,Japan,Edo period (1615–1868),,,,Artist,School of,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,18th–19th century,1700,1899,Ink and color on paper,10 5/8 x 15 3/8 in. (27 x 39.1 cm),"Gift of Annette Young, in memory of her brother, Innis Young, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57290,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -56.121.41,false,true,57291,Asian Art,Painting,,Japan,Edo period (1615–1868),,,,Artist,School of,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,18th–19th century,1700,1899,Two paintings mounted together; ink on paper,Left painting: 7 11/16 x 5 1/2 in. (19.5 x 14 cm) Right painting: 7 3/4 x 5 1/2 in. (19.7 x 14 cm),"Gift of Annette Young, in memory of her brother, Innis Young, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57291,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -56.121.42,false,true,57292,Asian Art,Painting,,Japan,Edo period (1615–1868),,,,Artist,School of,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,18th–19th century,1700,1899,Ink and color on paper,Mount: 8 5/16 x 9 7/8 in. (21.1 x 25.1 cm),"Gift of Annette Young, in memory of her brother, Innis Young, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57292,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -56.121.43,false,true,57293,Asian Art,Painting,,Japan,Edo period (1615–1868),,,,Artist,School of,Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",,Katsushika Hokusai,Japanese,1760,1849,18th–19th century,1700,1899,Ink and color on paper,8 1/4 x 12 5/8 in. (21 x 32.1 cm),"Gift of Annette Young, in memory of her brother, Innis Young, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/57293,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.842,false,true,78732,Asian Art,Illustrated book,『大雅堂画譜』|Taigadō (Taiga Hall at Sōrinji Temple) Picture Album (Taigadō gafu),Japan,Edo period (1615–1868),,,,Artist|Artist,,Ike Taiga|Sō Geppō,"Japanese, 1723–1776|Japanese, 1760–1839",,Ike Taiga|Sō Geppō,Japanese,1723 |1760,1776 |1839,1804,1804,1804,"Woodblock printed book (orihon, accordion-style); ink and color on paper",11 1/4 × 7 1/2 in. (28.5 × 19 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78732,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JIB10a–c,false,true,57544,Asian Art,Illustrated book,,Japan,Edo period (1615–1868),,,,Artist|Artist,,Rokuzotei|Katsushika Hokusai,"Japanese, Tokyo (Edo) 1760–1849 Tokyo (Edo)",(assisting draftsman),Rokuzotei|Katsushika Hokusai,Japanese,1760,1849,1802,1802,1802,Three volumes; ink and color on paper,Each: 10 1/4 × 6 3/4 × 1/4 in. (26 × 17.1 × 0.6 cm),"Rogers Fund, 1918",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/57544,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.841,false,true,78731,Asian Art,Illustrated books,『山水畫譜』|Picture Album of Landscapes by Yi Fujiu and Ike no Taiga (I Fukyū Ike no Taiga sansui gafu),Japan,Edo period (1615–1868),,,,Artist|Artist,,Ike Taiga|Yi Fujiu,"Japanese, 1723–1776|Japanese, active 1726–50",,Ike Taiga|Yi Fujiu,Japanese,1723 |1726,1776 |1750,1803,1803,1803,Set of two woodblock-printed books bound as one volume; ink on paper,Other (each): 10 3/8 × 7 5/16 in. (26.3 × 18.5 cm),"Purchase, Mary and James G. Wallach Foundation Gift, 2013",,,,,,,,,,,,Illustrated Books,,http://www.metmuseum.org/art/collection/search/78731,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.67.76,false,true,45438,Asian Art,Inrō,,Japan,Meiji period (1868–1912),,,,Artist|Artist,After a painting by,Tokoku Fuzui|Hanabusa Itchō,"Japanese, 1652–1724",,Tokoku Fuzui|Hanabusa Itchō,Japanese,1652,1724,late 19th century,1867,1899,"Gold lacquer with ivory and wood inlay; Netsuke: ivory and lacquered wood figure, Ojime: gold bead with face of Daikoku, god of good fortune",3 1/4 x 2 3/8 x 13/16 in. (8.3 x 6.1 x 2 cm),"Rogers Fund, 1913",,,,,,,,,,,,Inrō,,http://www.metmuseum.org/art/collection/search/45438,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -JP832,false,true,54486,Asian Art,Woodblock print,,Japan,Edo period (1615–1868),,,,Artist|Artist,Made by,Estate of Samuel Isham|Torii Kiyonobu,"Japanese, 1664–1729",,Estate of Samuel Isham|Torii Kiyonobu,Japanese,1664,1729,ca. 1749,1739,1759,Polychrome woodblock print; ink and color on paper,H. 12 3/8 in. (31.4 cm); W. 5 7/8 in. (14.9 cm),"Gift of Estate of Samuel Isham, 1914",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/54486,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1985.7,false,true,42346,Asian Art,Hanging scroll,賢江祥啓筆山水図|Landscape with Pavilion,Japan,Muromachi period (1392–1573),,,,Artist|Artist,Inscribed by,Kenkō Shōkei|Tōgen Zuisen,"active ca. 1470–after 1523|Japanese, 1430–1480",,Kenkō Shōkei|Tōgen Zuisen,Japanese,1470 |1430,1550 |1480,1478–80,1478,1480,Hanging scroll; ink and color on paper,Image: 19 13/16 x 13 13/16 in. (50.3 x 35.1 cm) Entire scroll: 58 5/8 x 19 in. (148.9 x 48.3 cm) Width including rollers: 21 in. (53.3 cm),"Purchase, Bequest of Stephen Whitney Phoenix, by exchange, 1985",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/42346,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.67.25,false,true,45435,Asian Art,Inrō,,Japan,Edo period (1615–1868),,,,Artist|Artist,After a painting by,Tachibana Gyokuzan|Hanabusa Itchō,"Japanese, 1652–1724",,Tachibana Gyokuzan|Hanabusa Itchō,Japanese,1652,1724,19th century,1800,1899,"Fundami sprinkled lacquer, gold and colored hiramakie sprinkled and polished lacquer, takamakie sprinkled and polished lacquer relief, and foil decoration; Netsuke: lacquered wood figure of Juro-jin; Ojime: zogan metal",3 5/16 x 2 1/16 x 3/4 in. (8.4 x 5.3 x 1.9 cm),"Rogers Fund, 1913",,,,,,,,,,,,Inrō,,http://www.metmuseum.org/art/collection/search/45435,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.853,false,true,45441,Asian Art,Inrō,,Japan,Edo period (1615–1868),,,,Artist|Artist,In the Style of,Nikkōsai|Hanabusa Itchō,"Japanese, 1652–1724",,Nikkōsai|Hanabusa Itchō,Japanese,1652,1724,19th century,1800,1899,Gold lacquer with gold and colored hiramkie sprinkled and polished lacquer and ivory inlay; Netsuke: polished wood button; Ojime: red lacquer bead; Interior: nashiji and fundame,3 3/8 x 2 5/16 x 13/16 in. (8.6 x 5.9 x 2 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Inrō,,http://www.metmuseum.org/art/collection/search/45441,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.100.212,false,true,58894,Asian Art,Inrō,手長猿蒔絵印籠|Inrō with Gibbons in a Landscape,Japan,Edo period (1615–1868),,,,Artist|Artist,Maki-e by|Design by,Hasegawa Kyorinsai|Kano Sukekiyo,"Japanese, active early 19th century|1787–1840",,Hasegawa Kyorinsai|Kano Sukekiyo,Japanese,1800 |1787,1833 |1840,early 19th century,1800,1833,Four cases; lacquered wood with togidashimaki-e imitating ink painting (togikirimaki-e) on gold lacquer ground Netsuke: ivory; monkey on horseback Ojime: bronze; monkey,3 5/16 x 2 3/16 x 3/4 in. (8.4 x 5.5 x 1.9 cm),"The Howard Mansfield Collection, Purchase, Rogers Fund, 1936",,,,,,,,,,,,Inrō,,http://www.metmuseum.org/art/collection/search/58894,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.838,false,true,58765,Asian Art,Inrō,,Japan,Edo period (1615–1868),,,,Artist|Artist,,Hogen Eisen|Kano Hidenobu,"Japanese, 1588–1672",,Hogen Eisen|Kano Hidenobu,Japanese,1588,1672,18th–19th century,1700,1899,"Lacquer, red ground, gold and black togidashi; Interior: nashiji and fundame",2 13/16 x 2 13/16 x 13/16 in. (7.1 x 7.1 x 2 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Inrō,,http://www.metmuseum.org/art/collection/search/58765,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -63.210.3,false,true,456212,Islamic Art,Illustrated album leaf,Page of Calligraphy from a Mantiq al-tair (Language of the Birds),,,,,,Author,,Farid al-Din `Attar,ca. 1142–1220,,Farid al-Din `Attar,,1142,1220,dated A.H. 892/ A.D. 1486,1461,1511,Opaque watercolor and gold on paper,,"Fletcher Fund, 1963",,,,,,,,,,,,Codices,,http://www.metmuseum.org/art/collection/search/456212,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.228.23.2,false,true,446600,Islamic Art,Folio from an illustrated manuscript,"""A Religious Devotee Summoned to Pray for the King's Recovery"", Folio from a Bustan (Orchard) of Sa'di",,,,,,Author,,Sa'di,1213/19–92,,Sa'di,,1213,1292,17th century,1600,1699,"Ink, opaque watercolor, and gold on paper",,"Gift of Alexander Smith Cochran, 1913",,,,,,,,,,,,Codices,,http://www.metmuseum.org/art/collection/search/446600,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.228.23.3,false,true,454668,Islamic Art,Folio from an illustrated manuscript,"""A Fire-Worshipper Received at the Board of Abraham the Patriarch"", Folio from a Bustan (Orchard) of Sa'di",,,,,,Author,,Sa'di,1213/19–92,,Sa'di,,1213,1292,17th century,1600,1699,"Ink, opaque watercolor, and gold on paper",,"Gift of Alexander Smith Cochran, 1913",,,,,,,,,,,,Codices,,http://www.metmuseum.org/art/collection/search/454668,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.228.23.4,false,true,446601,Islamic Art,Folio from an illustrated manuscript,"""King Salih of Syria Entertaining Two Dervishes"", Folio from a Bustan (Orchard) of Sa'di",,,,,,Author,,Sa'di,1213/19–92,,Sa'di,,1213,1292,17th century,1600,1699,"Ink, opaque watercolor, and gold on paper",,"Gift of Alexander Smith Cochran, 1913",,,,,,,,,,,,Codices,,http://www.metmuseum.org/art/collection/search/446601,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.500.3 (1-44),false,true,286316,Photographs,Album,Voyage en Orient et en Espagne Vols 3 & 4 [bound together],,,,,,Artist|Lithographer|Printer,,Louis de Clercq|H. Jannin|J. Blondeau et Antonin,"French, 1837–1901|French",,"de Clercq, Louis|Jannin, H.|J. Blondeau et Antonin",French|French|French,1837,1901,1860 or later,1860,1860,Albumen silver prints from paper negatives,Album: 46.4 x 62.2 cm (18 1/4 x 24 1/2 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Albums,,http://www.metmuseum.org/art/collection/search/286316,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.18 (1-22),false,true,290386,Photographs,Photographically illustrated book,Photographic Facsimiles of the Remains of the Epistles of Clement of Rome. Made from the Unique Copy Preserved in the Codex Alexandrinus.,,,,,,Artist|Author,,Roger Fenton|Frederic Madden,"British, 1819–1869|British, 1801–1873",,"Fenton, Roger|Madden, Frederic",British|British,1819 |1801,1819 |1873,1856,1856,1856,Salted paper prints from glass negatives,Images: 34.3 x 29.8 cm (13 1/2 x 11 3/4 in.),"Purchase, Alfred Stieglitz Society Gifts, 2009",,,,,,,,,,,,Books,,http://www.metmuseum.org/art/collection/search/290386,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.37 (18),false,true,289207,Photographs,Letter; Correspondence,[Manuscript Letter from William-Fox Strangways to Antonio Bertoloni],,,,,,Correspondent|Author,,Antonio Bertoloni|William Thomas Horner Fox-Strangways,"Italian, 1775–1869|British, 1795–1865",,"Bertoloni, Antonio|Fox-Strangways, William Thomas Horner",Italian|British,1775 |1795-05-07,1869 |1865-01-10,1839,1839,1839,Ink on paper (manuscript),Sheet: 23.2 x 36.8 cm (9 1/8 x 14 1/2 in.),"Harris Brisbane Dick Fund, 1936",,,,,,,,,,,,Manuscript Materials,,http://www.metmuseum.org/art/collection/search/289207,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.505.1 (1-48),false,true,285940,Photographs,Album,[Chinese and Japanese Portraits],,,,,,Artist|Artist|Artist,,Raimund von Stillfried|Unknown|Suzuki Shin'ichi,"Austrian, 1839–1911|Japanese, 1835–1919",", et al","Stillfried, Raimond von|Unknown|Suzuki, Shin'ichi",Austrian|Japanese,1839 |1835,1911 |1919,1870s,1870,1879,Albumen silver prints from glass negatives,28 x 35.3 x 4.8 cm (11 x 13 7/8 x 1 7/8 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Albums,,http://www.metmuseum.org/art/collection/search/285940,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.502.1 (1-50),false,true,283195,Photographs,Album,"Gardner's Photographic Sketchbook of the War, Volume 1",,,,,,Artist|Artist|Artist|Artist|Artist|Artist|Artist|Artist|Printer|Author,,Timothy H. O'Sullivan|Alexander Gardner|George N. Barnard|James Gardner|John Reekie|David Knox|William R. Pywell|David B. Woodbury|Alexander Gardner|Alexander Gardner,"American, born Ireland, 1840–1882|American, Glasgow, Scotland 1821–1882 Washington, D.C.|American, 1819–1902|American, born 1832|American, active 1860s|American|American, died 1866|American, Glasgow, Scotland 1821–1882 Washington, D.C.|American, Glasgow, Scotland 1821–1882 Washington, D.C.",,"O'Sullivan, Timothy H.|Gardner, Alexander|Barnard, George N.|Gardner, James|Reekie, John|Knox, David|Pywell, William R.|Woodbury, David B.|Gardner, Alexander|Gardner, Alexander","American, born Ireland|American, Scottish|American|American|American|American|American|American, Scottish|American, Scottish",1840 |1821 |1819 |1832 |1860 |1821 |1821,1882 |1882 |1902 |1869 |1866 |1882 |1882,1863,1863,1863,Albumen silver prints from glass negatives,17.8 x 22.7 cm (7 x 8 15/16 in.),"Gilman Collection, Purchase, Ann Tenenbaum and Thomas H. Lee Gift, 2005",,,,,,,,,,,,Albums,,http://www.metmuseum.org/art/collection/search/283195,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.502.2 (1-50),false,true,286128,Photographs,Album,"Gardner's Photographic Sketchbook of the War, Volume 2",,,,,,Artist|Artist|Artist|Artist|Artist|Artist|Artist|Artist|Printer|Author,,Timothy H. O'Sullivan|Alexander Gardner|James Gardner|George N. Barnard|John Reekie|David Knox|William R. Pywell|David B. Woodbury|Alexander Gardner|Alexander Gardner,"American, born Ireland, 1840–1882|American, Glasgow, Scotland 1821–1882 Washington, D.C.|American, born 1832|American, 1819–1902|American, active 1860s|American|American, died 1866|American, Glasgow, Scotland 1821–1882 Washington, D.C.|American, Glasgow, Scotland 1821–1882 Washington, D.C.",,"O'Sullivan, Timothy H.|Gardner, Alexander|Gardner, James|Barnard, George N.|Reekie, John|Knox, David|Pywell, William R.|Woodbury, David B.|Gardner, Alexander|Gardner, Alexander","American, born Ireland|American, Scottish|American|American|American|American|American|American, Scottish|American, Scottish",1840 |1821 |1832 |1819 |1860 |1821 |1821,1882 |1882 |1902 |1869 |1866 |1882 |1882,1863,1863,1863,Albumen silver prints from glass negatives,Images approx: 17.4 × 22.5 cm (6 7/8 × 8 7/8 in.),"Gilman Collection, Purchase, Ann Tenenbaum and Thomas H. Lee Gift, 2005",,,,,,,,,,,,Albums,,http://www.metmuseum.org/art/collection/search/286128,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1987.1100.282,false,true,265335,Photographs,Photographically illustrated book,Reichsparteitag der Arbeit,,,,,,Artist|Author|Author,,Heinrich Hoffmann|Pitter Gern|Doctor Otto Dietrich,"German, 1885–1957|German|German, 1897–1952",,"Hoffmann, Heinrich|Gern, Pitter|Dietrich, Doctor Otto",German|German|German,1885 |1897,1957 |1952,1930s,1930,1939,Gelatin silver prints,,"Ford Motor Company Collection, Gift of Ford Motor Company and John C. Waddell, 1987",,,,,,,,,,,,Books,,http://www.metmuseum.org/art/collection/search/265335,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2008.669 (1-40),false,true,290484,Photographs,Photographically illustrated book,Life and Landscape on the Norfolk Broads,,,,,,Artist|Author|Printer,,Peter Henry Emerson|Thomas Frederick Goodall|Valentine of Dundee,"British, born Cuba, 1856–1936|British, 1857–1944",,"Emerson, Peter Henry|Goodall, Thomas Frederick|Valentine of Dundee","British, born Cuba|British",1856 |1857,1936 |1944,1885–86,1885,1886,Platinum prints from glass negatives,Images: 12 x 18 cm (4 3/4 x 7 1/16 in.) to 23 x 30 cm (9 1/16 x 11 13/16 in.) Binding: 30.5 x 41.9 x 5.1 cm (12 x 16 1/2 x 2 in.),"Gift of Joyce F. Menschel, 2008",,,,,,,,,,,,Books,,http://www.metmuseum.org/art/collection/search/290484,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.37 (2),false,true,289191,Photographs,Letter; Correspondence,[Manuscript Letter from W. H. Fox Talbot to Antonio Bertoloni],,,,,,Correspondent|Author,,Antonio Bertoloni|William Henry Fox Talbot,"Italian, 1775–1869|British, Dorset 1800–1877 Lacock",,"Bertoloni, Antonio|Talbot, William Henry Fox",Italian|British,1775 |1800,1869 |1800,1839,1839,1839,Ink on paper (manuscript),Sheet: 30 x 37.4 cm (11 13/16 x 14 3/4 in.),"Harris Brisbane Dick Fund, 1936",,,,,,,,,,,,Manuscript Materials,,http://www.metmuseum.org/art/collection/search/289191,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.37 (12),false,true,289201,Photographs,Letter; Correspondence,[Manuscript Letter from W. H. Fox Talbot to Antonio Bertoloni],,,,,,Correspondent|Author,,Antonio Bertoloni|William Henry Fox Talbot,"Italian, 1775–1869|British, Dorset 1800–1877 Lacock",,"Bertoloni, Antonio|Talbot, William Henry Fox",Italian|British,1775 |1800,1869 |1800,1839,1839,1839,Ink on paper (manuscript),Sheet: 22.7 x 37.2 cm (8 15/16 x 14 5/8 in.),"Harris Brisbane Dick Fund, 1936",,,,,,,,,,,,Manuscript Materials,,http://www.metmuseum.org/art/collection/search/289201,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.37 (30),false,true,289218,Photographs,Letter; Correspondence,[Manuscript Letter from W. H. Fox Talbot to Antonio Bertoloni],,,,,,Correspondent|Author,,Antonio Bertoloni|William Henry Fox Talbot,"Italian, 1775–1869|British, Dorset 1800–1877 Lacock",,"Bertoloni, Antonio|Talbot, William Henry Fox",Italian|British,1775 |1800,1869 |1800,1840,1840,1840,Ink on paper (manuscript),Sheet: 22.6 x 37.6 cm (8 7/8 x 14 13/16 in.),"Harris Brisbane Dick Fund, 1936",,,,,,,,,,,,Manuscript Materials,,http://www.metmuseum.org/art/collection/search/289218,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.410 (1-9),false,true,286154,Photographs,Album,[Duc de Morny Album],,,,,,Artist|Artist|Patron,Painted and retouched by|Commissioned by,"Pierre-Louis Pierson|Marck|Charles-Auguste-Louis-Joseph de Morny, duc de Morny","French, 1822–1913|French (born Switzerland) 1811–1865 Paris",", et al","Pierson, Pierre-Louis|Marck|Morny, Charles-Auguste-Louis-Joseph de, duc de Morny","French|French, born Switzerland",1822 |1811,1913 |1865,before 1865,1855,1865,Albumen silver prints from glass negatives,38 x 31.5 x 8.5 cm (14 15/16 x 12 3/8 x 3 3/8 in.) Album 35 CDVs approximately 8.6 x 5.1 cm 6 photographs various sizes from 10.1 x 7.1 cm to 16.9 x 11.9 cm,"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Albums,,http://www.metmuseum.org/art/collection/search/286154,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.381.1.1–.10,false,true,285625,Photographs,Album,"Photographic Views in Madura, Part I",,,,,,Artist|Author,,Linnaeus Tripe|Martin Norman,"British, Devonport (Plymouth Dock) 1822–1902 Devonport|British",,"Tripe, Linnaeus|Norman, Martin",British|British,1822,1902,1858,1858,1858,Albumen silver prints from paper negatives,Various: approx. 34.6 x 28.3,"Gilman Collection, Purchase, Cynthia Hazen Polsky Gift, 2005",,,,,,,,,,,,Albums,,http://www.metmuseum.org/art/collection/search/285625,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.381.2.1–.15,false,true,285624,Photographs,Album,"Photographic Views in Madura, Part III",,,,,,Artist|Author,,Linnaeus Tripe|Martin Norman,"British, Devonport (Plymouth Dock) 1822–1902 Devonport|British",,"Tripe, Linnaeus|Norman, Martin",British|British,1822,1902,1858,1858,1858,Albumen silver prints from paper negatives,Various: approx. 34.6 x 28.3,"Gilman Collection, Purchase, Cynthia Hazen Polsky Gift, 2005",,,,,,,,,,,,Albums,,http://www.metmuseum.org/art/collection/search/285624,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1987.1183.38,false,true,265767,Photographs,Album,[Collection of British Calotypes and Wood-engravings],,,,,,Artist|Artist|Artist|Artist,,"William John Newton|Sir Thomas Maryon Wilson, 8th Baronet|Arthur James Melhuish|Unknown","British, 1785–1869|British, 1800–1869|British, 1829–1895|British",,"Newton, William John|Wilson, Thomas Maryon, Sir, 8th Baronet|Melhuish, Arthur James|Unknown",British|British|British,1785 |1800 |1829,1869 |1869 |1895,1850s,1850,1859,Salted paper prints and engravings,20.0 x 13.8 cm (7 7/8 x 5 7/16 in.),"Gift of Paul F. Walter, in memory of Christopher Hemphill, 1987",,,,,,,,,,,,Albums,,http://www.metmuseum.org/art/collection/search/265767,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.588.2.1–.151,false,true,286628,Photographs,Album,Demi-Monde I 56,,,,,,Artist|Person in Photograph|Person in Photograph,Person in photograph|Person in photograph,André-Adolphe-Eugène Disdéri|Cora Pearl|Emily Fowler,"French, Paris 1819–1889 Paris|British, 1835?–1886|British, 1849–1896",,"Disdéri, André-Adolphe-Eugène|Pearl, Cora|Fowler, Emily",French|British|British,1819 |1835 |1849,1889 |1886 |1896,1858–68,1858,1868,Albumen silver print from glass negative,Image (.2.54-64143): 18.4 x 24.8 cm (7 1/4 x 9 3/4 in.) Album page: 26.2 x 34.8 cm (10 5/16 x 13 11/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Albums,,http://www.metmuseum.org/art/collection/search/286628,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -53.627.1,false,true,269659,Photographs,Cartes-de-visite,[Carte-de-Visite Album of Prominent Personages],,,,,,Artist|Artist|Artist|Artist|Artist|Artist|Artist|Artist|Artist|Artist|Artist|Artist|Artist|Artist|Artist|Artist,,Mayer & Pierson|Neurdein Frères|Leon and Lévy|Boulton|Franck|Pierre-Louis Pierson|H. B. Randall|Sergei Luvovich Levitsky|William Downey|Horatio Nelson King|André-Adolphe-Eugène Disdéri|George Washington Wilson|Vernon Heath|Daniel Downey|Sergei Luvovich Levitsky|Robert Jefferson Bingham,"French|French, active Paris, 1870s–1900s|French|French, 1816–1906|French, 1822–1913|Russian, 1819–1898|British, born 1828|British, 1830–1905|French, Paris 1819–1889 Paris|British, Grampian (Baffshire), Scotland 1823–1893 Abedeen, Scotland|British, 1819–1895|British|Russian, 1819–1898|British, active France, 1825–70",,"Mayer & Pierson|Neurdein Frères|Leon and Lévy|Boulton|Franck|Pierson, Pierre-Louis|Randall, H. B.|Levitsky, Sergei Luvovich|Downey, William and Daniel|King, Horatio Nelson|Disdéri, André-Adolphe-Eugène|Wilson, George Washington|Heath, Vernon|Downey, Daniel |Levitsky, Sergei Luvovich|Bingham, Robert Jefferson","French|French|French|French|French|Russian|British|British|French|British, Scottish|British|British|Russian|British, active France",1863 |1816 |1822 |1819 |1828 |1830 |1819 |1823 |1819 |1819 |1825,1910 |1906 |1913 |1898 |1828 |1905 |1889 |1893 |1895 |1898 |1870,1860s–70s,1860,1879,Albumen silver prints,,"Gift of Susanna Myers, 1953",,,,,,,,,,,,Albums,,http://www.metmuseum.org/art/collection/search/269659,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1991.1073.97 (1–17),false,true,266381,Photographs,Photographically illustrated book,Pictorial Photographs. A Record of the Photographic Salon of 1897. In Seventeen Plates Reproduced in Photogravure,,,,,,Artist|Artist|Artist|Artist|Artist|Artist|Artist|Artist|Artist|Artist|Artist|Artist|Artist|Artist|Artist|Artist|Artist|Author,,W. Thomas|Paul Martin|Heinrich Kühn|Robert Demachy|Alfred Horsley Hinton|Alfred Stieglitz|William Crooke|Ernest R. Ashton|Ralph Winwood Robinson|Paul Bergon|J. B. B. Wellington|Reginald W. Craigie|Maitland|Rev. F. C. Lambert|Frederick H. Evans|Frank M. Sutcliffe|Lionel C. Bennett|Walter L. Colls,"British|Austrian (born Germany), Dresden 1866–1944 Birgitz|French, 1859–1936|British, 1863–1908|American, Hoboken, New Jersey 1864–1946 New York|British, born Scotland|British, 1867–1952|British, 1862–1942|French, 1863–1912|British, 1858–1939|British|British, London 1853–1943 London|British, 1853–1941|British|British",,"Thomas, W.|Martin, Paul|Kühn, Heinrich|Demachy, Robert|Hinton, Afred Horsley|Stieglitz, Alfred|Crooke, William|Ashton, Ernest R.|Robinson, Ralph W.|Bergon, Paul|Wellington, J. B. B.|Craigie, Reginald W.|Maitland, Viscount|Lambert, Rev. F. C.|Evans, Frederick Henry|Sutcliffe, Frank Meadow|Bennett, Lionel C.|Colls, Walter L.","British|Austrian, born Germany|French|British|American|British, Scottish|British|British|French|British|British|British|British|British|British",1866 |1859 |1863 |1864 |1867 |1862 |1863 |1858 |1853 |1853,1944 |1936 |1908 |1946 |1952 |1942 |1912 |1939 |1943 |1941,1897,1897,1897,Photogravures,Each sheet: 14 3/4 × 10 1/2 in. (37.5 × 26.7 cm) Plate dimesions vary,"Gift of Alfred Stieglitz, 1922, transferred from the Library",,,,,,,,,,,,Books,,http://www.metmuseum.org/art/collection/search/266381,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1977.683.1–.76,false,true,262199,Photographs,Album,The Wilkinson Album,,,,,,Artist|Artist|Artist|Artist|Artist,Attributed to,Unknown|Henri de Couliboeuf de Blocqueville|Frances Carlhian|Antonio Giannuzzi|Luigi Pesce,"French, active 1850s–60s|French, 1818–1870|Italian, 1818–1876|Italian, 1818–1891",,"Unknown|Couliboeuf de Blocqueville, Henri de|Carlhian, Frances|Giannuzzi, Antonio|Pesce, Luigi",French|French|Italian|Italian,1818 |1818 |1818,1870 |1876 |1891,1840s–60s,1840,1869,Albumen silver and salted paper prints,,"Gift of Charles K. and Irma B. Wilkinson, 1977",,,,,,,,,,,,Albums,,http://www.metmuseum.org/art/collection/search/262199,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.96,false,true,283200,Photographs,Broadside; Photographs,"[Broadside for the Capture of John Wilkes Booth, John Surratt, and David Herold]",,,,,,Maker|Artist|Photography Studio|Photography Studio,,"Unknown|Alexander Gardner|Silsbee, Case & Company|Unknown","American|American, Glasgow, Scotland 1821–1882 Washington, D.C.|American, active Boston",,"Unknown|Gardner, Alexander|Silsbee, Case & Company|Unknown","American, Scottish|American",1821,1882,"April 20, 1865",1865,1865,Ink on paper with three albumen silver prints from glass negatives,Sheet: 60.5 x 31.3 cm (23 13/16 x 12 5/16 in.) Each photograph: 8.6 x 5.4 cm (3 3/8 x 2 1/8 in.),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Collages,,http://www.metmuseum.org/art/collection/search/283200,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.585 (1-35),false,true,283216,Photographs,Album,"Wheeler Survey, Season of 1872",,,,,,Artist|Patron,Commissioned by,William Bell|Lieutenant George Montague Wheeler,"American (born England) Liverpool 1831–1910 Philadelphia, Pennsylvania|American, 1842–1905",,"Bell, William|Wheeler, Lieutenant George Montague","American, born Britain|American",1831 |1842,1910 |1905,1872,1872,1872,Albumen silver print from glass negative,"28.2 x 20.2 cm (11 1/8 x 7 15/16 in.), each (approx.)","Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Albums,,http://www.metmuseum.org/art/collection/search/283216,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.495,false,true,286333,Photographs,Panorama,Panorama of Niagara Falls,,,,,,Artist|Former Attribution,,Frederick Langenheim|William Langenheim,"American, born Germany, Schöningen 1809–1879|American, born Germany, Schöningen 1807–1874",,"Langenheim, Frederick|Langenheim, William","American, born Germany",1809 |1807,1879 |1874,July 1845,1845,1845,Daguerreotype,"Each plate, visible: 31/2 x 2 3/4; Unframed: 12 x 18; Framed: 13 x 193/8","Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Parts-Stone,,http://www.metmuseum.org/art/collection/search/286333,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.586.2,false,true,261590,Photographs,Photographically illustrated book,"The Far East: A Monthly Journal, Illustrated with Photographs",,,,,,Artist|Artist|Artist|Artist|Editor|Artist|Artist,,L.P. Fisler|Christopher T. Gardner|Thomas Child|Unknown|John Reddie Black|William Thomas Saunders|Kameya Tokujirō,"British, 1865–1886|British|British|Chinese|British, 1827–1880|British, 1832–1892|Japanese, 1825–1884",,"Fisler, L.P.|Gardner, Christopher T.|Child, Thomas|Unknown|Black, John Reddie|Saunders, William Thomas|Kameya, Tokujirō",British|British|British|British|British|Japanese,1865 |1827 |1832 |1825,1886 |1880 |1892 |1884,1870s,1870,1879,Albumen silver prints,,"Gift of John J. McKendry, 1975",,,,,,,,,,,,Periodicals,,http://www.metmuseum.org/art/collection/search/261590,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -52.640,false,true,269625,Photographs,Photographically illustrated book,A Photographic Tour Among the Abbeys of Yorkshire,,,,,,Artist|Artist|Author|Author,,Joseph Cundall|Philip Henry Delamotte|John Richard Walbran|William Jones,"British, Norwich, Norfolk 1818–1895 Wallington, Surrey|British, 1821–1889|British, 1817–1869|British, 1817–1885",,"Cundall, Joseph|Delamotte, Philip Henry|Walbran, John Richard|Jones, William",British|British|British|British,1818 |1821 |1817 |1817,1895 |1889 |1869 |1885,1850s,1850,1859,Albumen silver prints,"45.3 x 31.8 x 3.4 cm (17 13/16 x 12 1/2 x 1 5/16 in.), closed","David Hunter McAlpin Fund, 1952",,,,,,,,,,,,Books,,http://www.metmuseum.org/art/collection/search/269625,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1991.1073.98,false,true,266382,Photographs,Photographically illustrated book,Architecture in Dharwar and Mysore,,,,,,Artist|Artist|Artist|Author|Author,,Thomas Biggs|Andrew Charles Brisbane Neill|Doctor William Henry Pigou|Colonel Philip Meadows Taylor|James Fergusson,"British|British, active India, 1814–1891|British, active India, 1818–1858|British, 1808–1876|British, 1808–1886",,"Biggs, Thomas|Neill, Andrew Charles Brisbane|Pigou, Doctor William Henry|Taylor, Colonel Philip Meadows|Fergusson, James","British|British, active India|British, active India|British|British",1814 |1818 |1808 |1808,1891 |1858 |1876 |1886,1860s,1860,1869,Albumen silver prints,,"Rogers Fund, 1920, transferred from the Library",,,,,,,,,,,,Books,,http://www.metmuseum.org/art/collection/search/266382,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -60.554.11,false,true,270265,Photographs,Portfolio,Productions of the Leeds Photographic Society,,,,,,Artist|Artist|Artist|Artist|Artist|Artist|Artist|Artist|Artist|Artist|Artist|Artist|Artist|Editor,,Ramsden and Birch|W. Birch|T. Dixon|Ramsden and Hope|George Fowler Jones|Pumphrey & Fowler|William A. Pumphrey|William Gardam|E. Holliday|Thomas Henry Briggs|William Fieldhouse|John William Ramsden|J. A. Hope|Leeds Photographic Society,"British|British|British|British|British|British|British, 1817–1905|British|British|British|British|British, 1834–1894|British",,"Ramsden and Birch|Birch, W.|Diseon, & Gardam|Ramsden and Hope|Jones, George Fowler|Pumphrey & Fowler|Pumphrey, William A.|Gardam, William|Holliday, E.|Briggs, Thomas Henry|Fieldhouse, William|Ramsden, John William|Hope, J. A.|Leeds Photographic Society",British|British|British|British|British|British|British|British|British|British|British|British|British|British,1817 |1834 |1852,1905 |1894,1852,1852,1852,Salted paper prints,,"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1960",,,,,,,,,,,,Books,,http://www.metmuseum.org/art/collection/search/270265,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1068,false,true,264745,Photographs,Photographically illustrated book,Paris-Théàtre,,,,,,Artist|Artist|Artist|Artist|Artist|Artist|Artist|Artist|Artist|Artist|Artist|Artist|Artist|Artist|Artist|Artist|Artist|Artist|Artist|Artist|Artist|Artist|Artist|Artist|Artist|Artist|Artist|Editor|Artist|Artist|Artist,,"Nadar|Paul, fils Bacard|Truchelut|Franck|Charles Reutlinger|E. Flamant|Frank|Eugenio Maunoury|Gaston et Mathieu|Alphonse J. Liébert|Louis Ghémar|Saglio|Emile Bondonneau|Joseph Lemercier|Étienne Carjat|Carrette|Buguet|J. M. Lopez|Ulric Grob|Alexandre Quinet|Dupont|J. Tourtin|Pierre Petit|Héribert Mayer|D. H. Mayer|Gougenheim et Forest|Alexander Courtin|Eugène Paz|Ferdinand Mulnier|Ferdinand Mulnier|Émile Tourtin","French, Paris 1820–1910 Paris|French|French, 1816–1906|German, Karlsruhe 1816–1881 Karlsruhe|Peruvian|French|French, 1827–1913|Belgian, 1819–1873|French|French, 1803–1887|French, Fareins 1828–1906 Paris|French|French|French, born 1836|French|French, Aups 1832–1909 Paris|French|German|French|French, active 1850s–70s|French, active 1850s–70s|French, active 1860s–70s",,"Nadar|Bacard, Paul|Truchelut|Franck|Reutlinger, Charles|Flamant, E.|Frank|Maunoury, Eugenio|Gaston et Mathieu|Liébert, Alphonse J.|Ghémar, Louis|Saglio|Bondonneau, Emile|Lemercier, Joseph|Carjat, Étienne|Carrette|Buguet|Lopez, J. M.|Grob, Ulric|Quinet, Alexandre|Dupont|Tourtin, J.|Petit, Pierre|Mayer, Héribert|Mayer, D. H.|Gougenheim et Forest|Courtin, Alexander|Paz, Eugène|Mulnier, Ferdinand|Mulnier, Ferdinand|Tourtin, Émile",French|French|French|German|Peruvian|French|French|Belgian|French|French|French|French|French|French|French|French|French|German|French|French|French|French,1820 |1816 |1816 |1827 |1819 |1803 |1828 |1836 |1832 |1850 |1850 |1860,1910 |1906 |1881 |1913 |1873 |1887 |1906 |1909 |1875 |1875 |1880,1870s,1870,1879,Woodburytypes,,"Purchase, Mary Martin Fund, 1986",,,,,,,,,,,,Periodicals,,http://www.metmuseum.org/art/collection/search/264745,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.490.1–.10,false,true,291692,Photographs,Books,"Galerie Contemporaine, Littéraire, Artistique, v. 1 - 5",,,,,,Artist|Artist|Artist|Artist|Artist|Artist|Artist|Artist|Artist|Artist|Artist|Artist|Artist|Artist|Artist|Publisher|Artist|Artist|Artist|Printer,,Unknown|Pierre Petit|Nadar|Lejeune|Valery|Ernest Eugène Appert|Delphin|Emile Courtin|Étienne Carjat|Charles-Albert Arnoux Bertall|Vallois|Franck|Antoine-Samuel Adam-Salomon|G. Fontaine|L. Baschet|Goupil et Cie|Émile Tourtin|Ferdinand Mulnier|Melandri|Goupil et Cie,"French|French, Aups 1832–1909 Paris|French, Paris 1820–1910 Paris|French|French, 1831–1891|French|French|French, Fareins 1828–1906 Paris|French, Paris 1820–1882 Paris|French|French, 1816–1906|French, La Ferté-sous-Jouarre 1811–1881 Paris|French|French|French, active 1850–84|French, active 1860s–70s|French, active 1850s–70s|French, active 1860s|French, active 1850–84",,"Unknown|Petit, Pierre|Nadar|Lejeune|Valery|Appert, Ernest Eugène|Delphin|Courtin, Emile|Carjat, Étienne|Bertall, Charles-Albert Arnoux|Vallois|Franck|Adam-Salomon, Antoine-Samuel|Fontaine, G.|Baschet, L.|Goupil et Cie|Tourtin, Émile|Mulnier, Ferdinand|Melandri|Goupil et Cie",French|French|French|French|French|French|French|French|French|French|French|French|French|French|French|French|French|French,1832 |1820 |1831 |1828 |1820 |1816 |1811 |1850 |1860 |1850 |1850,1909 |1910 |1891 |1906 |1882 |1906 |1881 |1884 |1880 |1875 |1884,1876–1881,1876,1881,Woodburytypes,,"Gift of Samuel P. Avery, transferred from the Library",,,,,,,,,,,,Periodicals,,http://www.metmuseum.org/art/collection/search/291692,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.382 (1-85),false,true,283076,Photographs,Album,[Emma Charlotte Dillwyn Llewelyn's Album],,,,,,Artist|Artist|Artist|Artist|Artist|Artist|Artist,,John Dillwyn Llewelyn|Thereza Dillwyn Llewelyn|Jane Martha St. John|James Knight|Miss Bush|P. W. Fry|M.D.,"British, Swansea, Wales 1810–1882 Swansea, Wales|British, 1803–1882|British|British, active 19th century|British, active 19th century",", et al","Llewelyn, John Dillwyn|Llewelyn, Thereza Dillwyn|St., John Jane Martha|Knight, James|Bush Miss|Fry, P. W.|M.D.","British, Welsh|British|British|British|British",1810 |1803,1882 |1882,1853–56,1853,1856,128 salted paper prints and albumen silver prints from paper and glass negatives,28.8 × 22.3 cm (11 5/16 × 8 3/4 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Albums,,http://www.metmuseum.org/art/collection/search/283076,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -38.170,false,true,268615,Photographs,Photographically illustrated book,"The Philadelphia Photographer, Vol. I & II, Nos. 1-24",,,,,,Artist|Artist|Artist|Artist|Artist|Artist|Artist|Editor,,"Wenderoth, Taylor & Brown|Henry P. Moore|John Coates Browne|John Carbutt|J. D. Sergeant|Leon S. Levy|Max Petsch|Edward Livingston Wilson","American, active Philadelphia, 1860s|American, 1833–1911|American, 1838–1918|American, 1832–1905|German, active 1860s|American, 1838–1903",,"Wenderoth, Taylor & Brown|Moore, Henry P.|Browne, John Coates|Carbutt, John|Sergeant, J. D.|Levy, Leon S. & Cornelius Cohen|Loescher & Petsch|Wilson, Edward Livingston",American|American|American|American|German|American,1833 |1838 |1832 |1860 |1838,1911 |1918 |1905 |1870 |1903,1864–65,1864,1865,Albumen silver prints,,"Harris Brisbane Dick Fund, 1938",,,,,,,,,,,,Periodicals,,http://www.metmuseum.org/art/collection/search/268615,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -69.604.5,false,true,271509,Photographs,Cartes-de-visite,[Carte-de-Visite Album of British and European Royalty],,,,,,Artist|Artist|Artist|Artist|Artist|Artist|Artist|Artist|Artist|Artist|Artist,,F. Joubert|Hermann Günther|Hills and Saunders|W. C. Lacy|Alfred Windsor|Cornelius Jabez Hughes|Camille Silvy|John Jabez Edwin Mayall|S. Mauer|Daniel Downey|Frères Ghemar,"British|German|British, active 1856–95|British|British, 1819–1884|French, 1835–1869|British, Oldham, Lancashire 1813–1901 West Sussex|British",,"Joubert, F.|Günther, Hermann|Hills and Saunders|Lacy, W. C.|Windsor, Alfred, Prince of England|Hughes, Jabez|Silvy, Camille|Mayall, John Jabez Edwin|Mauer, S.|Downey, Daniel |Ghemar, Frères",British|German|British|British|British|French|British|British|Belgian,1856 |1819 |1835 |1813,1895 |1884 |1869 |1901,1860s–70s,1860,1879,Albumen silver prints,,"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1969",,,,,,,,,,,,Albums,,http://www.metmuseum.org/art/collection/search/271509,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548,false,true,261267,Photographs,Album,La Colombe et Le Tigre. Ma collection de photographies de la Comtesse de Castiglione,,,,,,Artist|Artist|Artist|Artist|Artist|Artist|Artist|Person in Photograph,,Duroni et Murer|Adolphe Braun|Alphonse (Jean-Baptiste) Bernoud|Mayer & Pierson|Giovanni Morotti|Pierre-Louis Pierson|André-Adolphe-Eugène Disdéri|Countess Virginia Oldoini Verasis di Castiglione,"Italian, 1807–1870|French, Besançon 1811–1877 Dornach|French, 1820–1889|French|Italian|French, 1822–1913|French, Paris 1819–1889 Paris|1835–1899",,"Duroni et Murer|Braun, Adolphe|Bernoud, Alphonse|Mayer & Pierson|Morotti, Giovanni|Pierson, Pierre-Louis|Disdéri, André-Adolphe-Eugène|Castiglione, di, Virginia Oldoini Verasis Countess",Italian|French|French|French|Italian|French|French,1807 |1811 |1820 |1822 |1819 |1835,1870 |1877 |1889 |1913 |1889 |1899,1860s,1860,1869,Albumen silver prints,,"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Albums,,http://www.metmuseum.org/art/collection/search/261267,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -68.519 (1),false,true,675527,Photographs,Album; Artist's book,The Thames near Kelmscott Manor,,,,,,Correspondent|Artist|Artist|Artist|Artist|Author|Correspondent,Bookplate designed by|Bookplate designed by,Sir Sydney Cockerell|Frederick H. Evans|Unknown|Aubrey Vincent Beardsley|Frederick Colin Tilney|Ernest Radford|Theodore Watts-Dunton,"1910–1957|British, London 1853–1943 London|British|British, Brighton, Sussex 1872–1898 Menton|British, 1870–1951|British, active late 19th century|British, 1832–1914",,"Cockerell Sydney Sir|Evans, Frederick Henry|Unknown|Beardsley, Aubrey Vincent|Tilney, Frederick Colin|Radford, Ernest|Watts-Dunton, Theodore",British|British|British|British|British,1910 |1853 |1872 |1870 |1850 |1832,1957 |1943 |1898 |1951 |1950 |1914,1896,1896,1896,Platinum print,,"Purchase, David Hunter McAlpin Fund, 1968",,,,,,,,,,,,Albums,,http://www.metmuseum.org/art/collection/search/675527,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -68.519 (4),false,true,675530,Photographs,Album; Artist's book,"Entrance, On Right",,,,,,Correspondent|Artist|Artist|Artist|Artist|Author|Correspondent,Bookplate designed by|Bookplate designed by,Sir Sydney Cockerell|Frederick H. Evans|Unknown|Aubrey Vincent Beardsley|Frederick Colin Tilney|Ernest Radford|Theodore Watts-Dunton,"1910–1957|British, London 1853–1943 London|British|British, Brighton, Sussex 1872–1898 Menton|British, 1870–1951|British, active late 19th century|British, 1832–1914",,"Cockerell Sydney Sir|Evans, Frederick Henry|Unknown|Beardsley, Aubrey Vincent|Tilney, Frederick Colin|Radford, Ernest|Watts-Dunton, Theodore",British|British|British|British|British,1910 |1853 |1872 |1870 |1850 |1832,1957 |1943 |1898 |1951 |1950 |1914,1896,1896,1896,Platinum print,,"Purchase, David Hunter McAlpin Fund, 1968",,,,,,,,,,,,Albums,,http://www.metmuseum.org/art/collection/search/675530,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -68.519 (6),false,true,675532,Photographs,Album; Artist's book,Main Entrance,,,,,,Correspondent|Artist|Artist|Artist|Artist|Author|Correspondent,Bookplate designed by|Bookplate designed by,Sir Sydney Cockerell|Frederick H. Evans|Unknown|Aubrey Vincent Beardsley|Frederick Colin Tilney|Ernest Radford|Theodore Watts-Dunton,"1910–1957|British, London 1853–1943 London|British|British, Brighton, Sussex 1872–1898 Menton|British, 1870–1951|British, active late 19th century|British, 1832–1914",,"Cockerell Sydney Sir|Evans, Frederick Henry|Unknown|Beardsley, Aubrey Vincent|Tilney, Frederick Colin|Radford, Ernest|Watts-Dunton, Theodore",British|British|British|British|British,1910 |1853 |1872 |1870 |1850 |1832,1957 |1943 |1898 |1951 |1950 |1914,1896,1896,1896,Platinum print,,"Purchase, David Hunter McAlpin Fund, 1968",,,,,,,,,,,,Albums,,http://www.metmuseum.org/art/collection/search/675532,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -68.519 (21),false,true,675547,Photographs,Album; Artist's book,[Tapestry Room],,,,,,Correspondent|Artist|Artist|Artist|Artist|Author|Correspondent,Bookplate designed by|Bookplate designed by,Sir Sydney Cockerell|Frederick H. Evans|Unknown|Aubrey Vincent Beardsley|Frederick Colin Tilney|Ernest Radford|Theodore Watts-Dunton,"1910–1957|British, London 1853–1943 London|British|British, Brighton, Sussex 1872–1898 Menton|British, 1870–1951|British, active late 19th century|British, 1832–1914",,"Cockerell Sydney Sir|Evans, Frederick Henry|Unknown|Beardsley, Aubrey Vincent|Tilney, Frederick Colin|Radford, Ernest|Watts-Dunton, Theodore",British|British|British|British|British,1910 |1853 |1872 |1870 |1850 |1832,1957 |1943 |1898 |1951 |1950 |1914,1896,1896,1896,Platinum print,,"Purchase, David Hunter McAlpin Fund, 1968",,,,,,,,,,,,Albums,,http://www.metmuseum.org/art/collection/search/675547,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -68.519 (28),false,true,675554,Photographs,Album; Artist's book,"[Tithe Barn, Great Cokkeswell]",,,,,,Correspondent|Artist|Artist|Artist|Artist|Author|Correspondent,Bookplate designed by|Bookplate designed by,Sir Sydney Cockerell|Frederick H. Evans|Unknown|Aubrey Vincent Beardsley|Frederick Colin Tilney|Ernest Radford|Theodore Watts-Dunton,"1910–1957|British, London 1853–1943 London|British|British, Brighton, Sussex 1872–1898 Menton|British, 1870–1951|British, active late 19th century|British, 1832–1914",,"Cockerell Sydney Sir|Evans, Frederick Henry|Unknown|Beardsley, Aubrey Vincent|Tilney, Frederick Colin|Radford, Ernest|Watts-Dunton, Theodore",British|British|British|British|British,1910 |1853 |1872 |1870 |1850 |1832,1957 |1943 |1898 |1951 |1950 |1914,1896,1896,1896,Platinum print,,"Purchase, David Hunter McAlpin Fund, 1968",,,,,,,,,,,,Albums,,http://www.metmuseum.org/art/collection/search/675554,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -68.519 (27),false,true,675553,Photographs,Album; Artist's book,K. Church,,,,,,Correspondent|Artist|Artist|Artist|Author|Correspondent|Artist,Bookplate designed by|Bookplate designed by,Sir Sydney Cockerell|Frederick H. Evans|Unknown|Aubrey Vincent Beardsley|Ernest Radford|Theodore Watts-Dunton|Frederick Colin Tilney,"1910–1957|British, London 1853–1943 London|British|British, Brighton, Sussex 1872–1898 Menton|British, active late 19th century|British, 1832–1914|British, 1870–1951",,"Cockerell Sydney Sir|Evans, Frederick Henry|Unknown|Beardsley, Aubrey Vincent|Radford, Ernest|Watts-Dunton, Theodore|Tilney, Frederick Colin",British|British|British|British|British,1910 |1853 |1872 |1850 |1832 |1870,1957 |1943 |1898 |1950 |1914 |1951,1896,1896,1896,Platinum print,,"Purchase, David Hunter McAlpin Fund, 1968",,,,,,,,,,,,Albums,,http://www.metmuseum.org/art/collection/search/675553,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -68.519 (16),false,true,675542,Photographs,Album; Artist's book,Passage to Panelled Room,,,,,,Correspondent|Correspondent|Artist|Author|Artist|Artist|Artist,Bookplate designed by|Bookplate designed by,Theodore Watts-Dunton|Sir Sydney Cockerell|Frederick Colin Tilney|Ernest Radford|Frederick H. Evans|Unknown|Aubrey Vincent Beardsley,"British, 1832–1914|1910–1957|British, 1870–1951|British, active late 19th century|British, London 1853–1943 London|British|British, Brighton, Sussex 1872–1898 Menton",,"Watts-Dunton, Theodore|Cockerell Sydney Sir|Tilney, Frederick Colin|Radford, Ernest|Evans, Frederick Henry|Unknown|Beardsley, Aubrey Vincent",British|British|British|British|British,1832 |1910 |1870 |1850 |1853 |1872,1914 |1957 |1951 |1950 |1943 |1898,1896,1896,1896,Platinum print,,"Purchase, David Hunter McAlpin Fund, 1968",,,,,,,,,,,,Albums,,http://www.metmuseum.org/art/collection/search/675542,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -68.519 (24),false,true,675550,Photographs,Album; Artist's book,From the Tapestry Room,,,,,,Correspondent|Correspondent|Artist|Artist|Artist|Artist|Author,Bookplate designed by|Bookplate designed by,Theodore Watts-Dunton|Sir Sydney Cockerell|Frederick H. Evans|Unknown|Aubrey Vincent Beardsley|Frederick Colin Tilney|Ernest Radford,"British, 1832–1914|1910–1957|British, London 1853–1943 London|British|British, Brighton, Sussex 1872–1898 Menton|British, 1870–1951|British, active late 19th century",,"Watts-Dunton, Theodore|Cockerell Sydney Sir|Evans, Frederick Henry|Unknown|Beardsley, Aubrey Vincent|Tilney, Frederick Colin|Radford, Ernest",British|British|British|British|British,1832 |1910 |1853 |1872 |1870 |1850,1914 |1957 |1943 |1898 |1951 |1950,1896,1896,1896,Platinum print,,"Purchase, David Hunter McAlpin Fund, 1968",,,,,,,,,,,,Albums,,http://www.metmuseum.org/art/collection/search/675550,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -68.519 (26),false,true,675552,Photographs,Album; Artist's book,In the Attics,,,,,,Correspondent|Correspondent|Artist|Artist|Artist|Author|Artist,Bookplate designed by|Bookplate designed by,Theodore Watts-Dunton|Sir Sydney Cockerell|Unknown|Aubrey Vincent Beardsley|Frederick Colin Tilney|Ernest Radford|Frederick H. Evans,"British, 1832–1914|1910–1957|British|British, Brighton, Sussex 1872–1898 Menton|British, 1870–1951|British, active late 19th century|British, London 1853–1943 London",,"Watts-Dunton, Theodore|Cockerell Sydney Sir|Unknown|Beardsley, Aubrey Vincent|Tilney, Frederick Colin|Radford, Ernest|Evans, Frederick Henry",British|British|British|British|British,1832 |1910 |1872 |1870 |1850 |1853,1914 |1957 |1898 |1951 |1950 |1943,1896,1896,1896,Platinum print,Image: 15.4 x 21 cm (6 1/16 x 8 1/4 in.),"Purchase, David Hunter McAlpin Fund, 1968",,,,,,,,,,,,Albums,,http://www.metmuseum.org/art/collection/search/675552,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -68.519 (18),false,true,675544,Photographs,Album; Artist's book,The Green Room,,,,,,Artist|Author|Correspondent|Correspondent|Artist|Artist|Artist,Bookplate designed by|Bookplate designed by,Frederick Colin Tilney|Ernest Radford|Theodore Watts-Dunton|Sir Sydney Cockerell|Frederick H. Evans|Unknown|Aubrey Vincent Beardsley,"British, 1870–1951|British, active late 19th century|British, 1832–1914|1910–1957|British, London 1853–1943 London|British|British, Brighton, Sussex 1872–1898 Menton",,"Tilney, Frederick Colin|Radford, Ernest|Watts-Dunton, Theodore|Cockerell Sydney Sir|Evans, Frederick Henry|Unknown|Beardsley, Aubrey Vincent",British|British|British|British|British,1870 |1850 |1832 |1910 |1853 |1872,1951 |1950 |1914 |1957 |1943 |1898,1896,1896,1896,Platinum print,,"Purchase, David Hunter McAlpin Fund, 1968",,,,,,,,,,,,Albums,,http://www.metmuseum.org/art/collection/search/675544,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -68.519 (8),false,true,675534,Photographs,Album; Artist's book,[Main Entrance],,,,,,Artist|Artist|Artist|Correspondent|Artist|Author|Correspondent,Bookplate designed by|Bookplate designed by,Frederick H. Evans|Unknown|Aubrey Vincent Beardsley|Sir Sydney Cockerell|Frederick Colin Tilney|Ernest Radford|Theodore Watts-Dunton,"British, London 1853–1943 London|British|British, Brighton, Sussex 1872–1898 Menton|1910–1957|British, 1870–1951|British, active late 19th century|British, 1832–1914",,"Evans, Frederick Henry|Unknown|Beardsley, Aubrey Vincent|Cockerell Sydney Sir|Tilney, Frederick Colin|Radford, Ernest|Watts-Dunton, Theodore",British|British|British|British|British,1853 |1872 |1910 |1870 |1850 |1832,1943 |1898 |1957 |1951 |1950 |1914,1896,1896,1896,Platinum print,,"Purchase, David Hunter McAlpin Fund, 1968",,,,,,,,,,,,Albums,,http://www.metmuseum.org/art/collection/search/675534,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -68.519,false,true,271329,Photographs,Album; Artist's book,Kelmscott Manor Photographs,,,,,,Artist|Artist|Artist|Artist|Author|Correspondent|Correspondent,Bookplate designed by|Bookplate designed by,Frederick H. Evans|Unknown|Aubrey Vincent Beardsley|Frederick Colin Tilney|Ernest Radford|Theodore Watts-Dunton|Sir Sydney Cockerell,"British, London 1853–1943 London|British|British, Brighton, Sussex 1872–1898 Menton|British, 1870–1951|British, active late 19th century|British, 1832–1914|1910–1957",,"Evans, Frederick Henry|Unknown|Beardsley, Aubrey Vincent|Tilney, Frederick Colin|Radford, Ernest|Watts-Dunton, Theodore|Cockerell Sydney Sir",British|British|British|British|British,1853 |1872 |1870 |1850 |1832 |1910,1943 |1898 |1951 |1950 |1914 |1957,1896,1896,1896,Platinum prints; albumen silver print,,"Purchase, David Hunter McAlpin Fund, 1968",,,,,,,,,,,,Albums,,http://www.metmuseum.org/art/collection/search/271329,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -68.519 (2),false,true,675528,Photographs,Album; Artist's book,From the Meadows,,,,,,Artist|Artist|Artist|Artist|Author|Correspondent|Correspondent,Bookplate designed by|Bookplate designed by,Frederick H. Evans|Unknown|Aubrey Vincent Beardsley|Frederick Colin Tilney|Ernest Radford|Theodore Watts-Dunton|Sir Sydney Cockerell,"British, London 1853–1943 London|British|British, Brighton, Sussex 1872–1898 Menton|British, 1870–1951|British, active late 19th century|British, 1832–1914|1910–1957",,"Evans, Frederick Henry|Unknown|Beardsley, Aubrey Vincent|Tilney, Frederick Colin|Radford, Ernest|Watts-Dunton, Theodore|Cockerell Sydney Sir",British|British|British|British|British,1853 |1872 |1870 |1850 |1832 |1910,1943 |1898 |1951 |1950 |1914 |1957,1896,1896,1896,Platinum print,,"Purchase, David Hunter McAlpin Fund, 1968",,,,,,,,,,,,Albums,,http://www.metmuseum.org/art/collection/search/675528,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -68.519 (3),false,true,675529,Photographs,Album; Artist's book,From the Fields,,,,,,Artist|Artist|Artist|Artist|Author|Correspondent|Correspondent,Bookplate designed by|Bookplate designed by,Frederick H. Evans|Unknown|Aubrey Vincent Beardsley|Frederick Colin Tilney|Ernest Radford|Theodore Watts-Dunton|Sir Sydney Cockerell,"British, London 1853–1943 London|British|British, Brighton, Sussex 1872–1898 Menton|British, 1870–1951|British, active late 19th century|British, 1832–1914|1910–1957",,"Evans, Frederick Henry|Unknown|Beardsley, Aubrey Vincent|Tilney, Frederick Colin|Radford, Ernest|Watts-Dunton, Theodore|Cockerell Sydney Sir",British|British|British|British|British,1853 |1872 |1870 |1850 |1832 |1910,1943 |1898 |1951 |1950 |1914 |1957,1896,1896,1896,Platinum print,,"Purchase, David Hunter McAlpin Fund, 1968",,,,,,,,,,,,Albums,,http://www.metmuseum.org/art/collection/search/675529,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -68.519 (5),false,true,675531,Photographs,Album; Artist's book,Main Gate Entrance,,,,,,Artist|Artist|Artist|Artist|Author|Correspondent|Correspondent,Bookplate designed by|Bookplate designed by,Frederick H. Evans|Unknown|Aubrey Vincent Beardsley|Frederick Colin Tilney|Ernest Radford|Theodore Watts-Dunton|Sir Sydney Cockerell,"British, London 1853–1943 London|British|British, Brighton, Sussex 1872–1898 Menton|British, 1870–1951|British, active late 19th century|British, 1832–1914|1910–1957",,"Evans, Frederick Henry|Unknown|Beardsley, Aubrey Vincent|Tilney, Frederick Colin|Radford, Ernest|Watts-Dunton, Theodore|Cockerell Sydney Sir",British|British|British|British|British,1853 |1872 |1870 |1850 |1832 |1910,1943 |1898 |1951 |1950 |1914 |1957,1896,1896,1896,Platinum print,,"Purchase, David Hunter McAlpin Fund, 1968",,,,,,,,,,,,Albums,,http://www.metmuseum.org/art/collection/search/675531,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -68.519 (9),false,true,675535,Photographs,Album; Artist's book,[Main Entrance],,,,,,Artist|Artist|Artist|Artist|Author|Correspondent|Correspondent,Bookplate designed by|Bookplate designed by,Frederick H. Evans|Unknown|Aubrey Vincent Beardsley|Frederick Colin Tilney|Ernest Radford|Theodore Watts-Dunton|Sir Sydney Cockerell,"British, London 1853–1943 London|British|British, Brighton, Sussex 1872–1898 Menton|British, 1870–1951|British, active late 19th century|British, 1832–1914|1910–1957",,"Evans, Frederick Henry|Unknown|Beardsley, Aubrey Vincent|Tilney, Frederick Colin|Radford, Ernest|Watts-Dunton, Theodore|Cockerell Sydney Sir",British|British|British|British|British,1853 |1872 |1870 |1850 |1832 |1910,1943 |1898 |1951 |1950 |1914 |1957,1896,1896,1896,Platinum print,,"Purchase, David Hunter McAlpin Fund, 1968",,,,,,,,,,,,Albums,,http://www.metmuseum.org/art/collection/search/675535,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -68.519 (10),false,true,675536,Photographs,Album; Artist's book,"[Main Entrance, with Hedge]",,,,,,Artist|Artist|Artist|Artist|Author|Correspondent|Correspondent,Bookplate designed by|Bookplate designed by,Frederick H. Evans|Unknown|Aubrey Vincent Beardsley|Frederick Colin Tilney|Ernest Radford|Theodore Watts-Dunton|Sir Sydney Cockerell,"British, London 1853–1943 London|British|British, Brighton, Sussex 1872–1898 Menton|British, 1870–1951|British, active late 19th century|British, 1832–1914|1910–1957",,"Evans, Frederick Henry|Unknown|Beardsley, Aubrey Vincent|Tilney, Frederick Colin|Radford, Ernest|Watts-Dunton, Theodore|Cockerell Sydney Sir",British|British|British|British|British,1853 |1872 |1870 |1850 |1832 |1910,1943 |1898 |1951 |1950 |1914 |1957,1896,1896,1896,Platinum print,,"Purchase, David Hunter McAlpin Fund, 1968",,,,,,,,,,,,Albums,,http://www.metmuseum.org/art/collection/search/675536,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -68.519 (11),false,true,675537,Photographs,Album; Artist's book,"[Garden, Front]",,,,,,Artist|Artist|Artist|Artist|Author|Correspondent|Correspondent,Bookplate designed by|Bookplate designed by,Frederick H. Evans|Unknown|Aubrey Vincent Beardsley|Frederick Colin Tilney|Ernest Radford|Theodore Watts-Dunton|Sir Sydney Cockerell,"British, London 1853–1943 London|British|British, Brighton, Sussex 1872–1898 Menton|British, 1870–1951|British, active late 19th century|British, 1832–1914|1910–1957",,"Evans, Frederick Henry|Unknown|Beardsley, Aubrey Vincent|Tilney, Frederick Colin|Radford, Ernest|Watts-Dunton, Theodore|Cockerell Sydney Sir",British|British|British|British|British,1853 |1872 |1870 |1850 |1832 |1910,1943 |1898 |1951 |1950 |1914 |1957,1896,1896,1896,Platinum print,,"Purchase, David Hunter McAlpin Fund, 1968",,,,,,,,,,,,Albums,,http://www.metmuseum.org/art/collection/search/675537,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -68.519 (12),false,true,675538,Photographs,Album; Artist's book,"[Garden, Front]",,,,,,Artist|Artist|Artist|Artist|Author|Correspondent|Correspondent,Bookplate designed by|Bookplate designed by,Frederick H. Evans|Unknown|Aubrey Vincent Beardsley|Frederick Colin Tilney|Ernest Radford|Theodore Watts-Dunton|Sir Sydney Cockerell,"British, London 1853–1943 London|British|British, Brighton, Sussex 1872–1898 Menton|British, 1870–1951|British, active late 19th century|British, 1832–1914|1910–1957",,"Evans, Frederick Henry|Unknown|Beardsley, Aubrey Vincent|Tilney, Frederick Colin|Radford, Ernest|Watts-Dunton, Theodore|Cockerell Sydney Sir",British|British|British|British|British,1853 |1872 |1870 |1850 |1832 |1910,1943 |1898 |1951 |1950 |1914 |1957,1896,1896,1896,Platinum print,,"Purchase, David Hunter McAlpin Fund, 1968",,,,,,,,,,,,Albums,,http://www.metmuseum.org/art/collection/search/675538,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -68.519 (13),false,true,675539,Photographs,Album; Artist's book,In the Orchard,,,,,,Artist|Artist|Artist|Artist|Author|Correspondent|Correspondent,Bookplate designed by|Bookplate designed by,Frederick H. Evans|Unknown|Aubrey Vincent Beardsley|Frederick Colin Tilney|Ernest Radford|Theodore Watts-Dunton|Sir Sydney Cockerell,"British, London 1853–1943 London|British|British, Brighton, Sussex 1872–1898 Menton|British, 1870–1951|British, active late 19th century|British, 1832–1914|1910–1957",,"Evans, Frederick Henry|Unknown|Beardsley, Aubrey Vincent|Tilney, Frederick Colin|Radford, Ernest|Watts-Dunton, Theodore|Cockerell Sydney Sir",British|British|British|British|British,1853 |1872 |1870 |1850 |1832 |1910,1943 |1898 |1951 |1950 |1914 |1957,1896,1896,1896,Platinum print,,"Purchase, David Hunter McAlpin Fund, 1968",,,,,,,,,,,,Albums,,http://www.metmuseum.org/art/collection/search/675539,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -68.519 (14),false,true,675540,Photographs,Album; Artist's book,Bed Morris Was Born In,,,,,,Artist|Artist|Artist|Artist|Author|Correspondent|Correspondent,Bookplate designed by|Bookplate designed by,Frederick H. Evans|Unknown|Aubrey Vincent Beardsley|Frederick Colin Tilney|Ernest Radford|Theodore Watts-Dunton|Sir Sydney Cockerell,"British, London 1853–1943 London|British|British, Brighton, Sussex 1872–1898 Menton|British, 1870–1951|British, active late 19th century|British, 1832–1914|1910–1957",,"Evans, Frederick Henry|Unknown|Beardsley, Aubrey Vincent|Tilney, Frederick Colin|Radford, Ernest|Watts-Dunton, Theodore|Cockerell Sydney Sir",British|British|British|British|British,1853 |1872 |1870 |1850 |1832 |1910,1943 |1898 |1951 |1950 |1914 |1957,1896,1896,1896,Platinum print,,"Purchase, David Hunter McAlpin Fund, 1968",,,,,,,,,,,,Albums,,http://www.metmuseum.org/art/collection/search/675540,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -68.519 (15),false,true,675541,Photographs,Album; Artist's book,WM's Bedroom,,,,,,Artist|Artist|Artist|Artist|Author|Correspondent|Correspondent,Bookplate designed by|Bookplate designed by,Frederick H. Evans|Unknown|Aubrey Vincent Beardsley|Frederick Colin Tilney|Ernest Radford|Theodore Watts-Dunton|Sir Sydney Cockerell,"British, London 1853–1943 London|British|British, Brighton, Sussex 1872–1898 Menton|British, 1870–1951|British, active late 19th century|British, 1832–1914|1910–1957",,"Evans, Frederick Henry|Unknown|Beardsley, Aubrey Vincent|Tilney, Frederick Colin|Radford, Ernest|Watts-Dunton, Theodore|Cockerell Sydney Sir",British|British|British|British|British,1853 |1872 |1870 |1850 |1832 |1910,1943 |1898 |1951 |1950 |1914 |1957,1896,1896,1896,Platinum print,,"Purchase, David Hunter McAlpin Fund, 1968",,,,,,,,,,,,Albums,,http://www.metmuseum.org/art/collection/search/675541,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -68.519 (17),false,true,675543,Photographs,Album; Artist's book,The Panelled Room,,,,,,Artist|Artist|Artist|Artist|Author|Correspondent|Correspondent,Bookplate designed by|Bookplate designed by,Frederick H. Evans|Unknown|Aubrey Vincent Beardsley|Frederick Colin Tilney|Ernest Radford|Theodore Watts-Dunton|Sir Sydney Cockerell,"British, London 1853–1943 London|British|British, Brighton, Sussex 1872–1898 Menton|British, 1870–1951|British, active late 19th century|British, 1832–1914|1910–1957",,"Evans, Frederick Henry|Unknown|Beardsley, Aubrey Vincent|Tilney, Frederick Colin|Radford, Ernest|Watts-Dunton, Theodore|Cockerell Sydney Sir",British|British|British|British|British,1853 |1872 |1870 |1850 |1832 |1910,1943 |1898 |1951 |1950 |1914 |1957,1896,1896,1896,Platinum print,,"Purchase, David Hunter McAlpin Fund, 1968",,,,,,,,,,,,Albums,,http://www.metmuseum.org/art/collection/search/675543,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -68.519 (20),false,true,675546,Photographs,Album; Artist's book,[Tapestry Room],,,,,,Artist|Artist|Artist|Artist|Author|Correspondent|Correspondent,Bookplate designed by|Bookplate designed by,Frederick H. Evans|Unknown|Aubrey Vincent Beardsley|Frederick Colin Tilney|Ernest Radford|Theodore Watts-Dunton|Sir Sydney Cockerell,"British, London 1853–1943 London|British|British, Brighton, Sussex 1872–1898 Menton|British, 1870–1951|British, active late 19th century|British, 1832–1914|1910–1957",,"Evans, Frederick Henry|Unknown|Beardsley, Aubrey Vincent|Tilney, Frederick Colin|Radford, Ernest|Watts-Dunton, Theodore|Cockerell Sydney Sir",British|British|British|British|British,1853 |1872 |1870 |1850 |1832 |1910,1943 |1898 |1951 |1950 |1914 |1957,1896,1896,1896,Platinum print,,"Purchase, David Hunter McAlpin Fund, 1968",,,,,,,,,,,,Albums,,http://www.metmuseum.org/art/collection/search/675546,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -68.519 (22),false,true,675548,Photographs,Album; Artist's book,[Tapestry Room],,,,,,Artist|Artist|Artist|Artist|Author|Correspondent|Correspondent,Bookplate designed by|Bookplate designed by,Frederick H. Evans|Unknown|Aubrey Vincent Beardsley|Frederick Colin Tilney|Ernest Radford|Theodore Watts-Dunton|Sir Sydney Cockerell,"British, London 1853–1943 London|British|British, Brighton, Sussex 1872–1898 Menton|British, 1870–1951|British, active late 19th century|British, 1832–1914|1910–1957",,"Evans, Frederick Henry|Unknown|Beardsley, Aubrey Vincent|Tilney, Frederick Colin|Radford, Ernest|Watts-Dunton, Theodore|Cockerell Sydney Sir",British|British|British|British|British,1853 |1872 |1870 |1850 |1832 |1910,1943 |1898 |1951 |1950 |1914 |1957,1896,1896,1896,Platinum print,,"Purchase, David Hunter McAlpin Fund, 1968",,,,,,,,,,,,Albums,,http://www.metmuseum.org/art/collection/search/675548,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -68.519 (23),false,true,675549,Photographs,Album; Artist's book,The Tapestry Room,,,,,,Artist|Artist|Artist|Artist|Author|Correspondent|Correspondent,Bookplate designed by|Bookplate designed by,Frederick H. Evans|Unknown|Aubrey Vincent Beardsley|Frederick Colin Tilney|Ernest Radford|Theodore Watts-Dunton|Sir Sydney Cockerell,"British, London 1853–1943 London|British|British, Brighton, Sussex 1872–1898 Menton|British, 1870–1951|British, active late 19th century|British, 1832–1914|1910–1957",,"Evans, Frederick Henry|Unknown|Beardsley, Aubrey Vincent|Tilney, Frederick Colin|Radford, Ernest|Watts-Dunton, Theodore|Cockerell Sydney Sir",British|British|British|British|British,1853 |1872 |1870 |1850 |1832 |1910,1943 |1898 |1951 |1950 |1914 |1957,1896,1896,1896,Platinum print,,"Purchase, David Hunter McAlpin Fund, 1968",,,,,,,,,,,,Albums,,http://www.metmuseum.org/art/collection/search/675549,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -68.519 (25),false,true,675551,Photographs,Album; Artist's book,In the Attics,,,,,,Artist|Artist|Artist|Artist|Author|Correspondent|Correspondent,Bookplate designed by|Bookplate designed by,Frederick H. Evans|Unknown|Aubrey Vincent Beardsley|Frederick Colin Tilney|Ernest Radford|Theodore Watts-Dunton|Sir Sydney Cockerell,"British, London 1853–1943 London|British|British, Brighton, Sussex 1872–1898 Menton|British, 1870–1951|British, active late 19th century|British, 1832–1914|1910–1957",,"Evans, Frederick Henry|Unknown|Beardsley, Aubrey Vincent|Tilney, Frederick Colin|Radford, Ernest|Watts-Dunton, Theodore|Cockerell Sydney Sir",British|British|British|British|British,1853 |1872 |1870 |1850 |1832 |1910,1943 |1898 |1951 |1950 |1914 |1957,1896,1896,1896,Platinum print,,"Purchase, David Hunter McAlpin Fund, 1968",,,,,,,,,,,,Albums,,http://www.metmuseum.org/art/collection/search/675551,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -68.519 (29),false,true,675555,Photographs,Album; Artist's book,"[Tithe Barn, Great Cokkeswell]",,,,,,Artist|Artist|Artist|Artist|Author|Correspondent|Correspondent,Bookplate designed by|Bookplate designed by,Frederick H. Evans|Unknown|Aubrey Vincent Beardsley|Frederick Colin Tilney|Ernest Radford|Theodore Watts-Dunton|Sir Sydney Cockerell,"British, London 1853–1943 London|British|British, Brighton, Sussex 1872–1898 Menton|British, 1870–1951|British, active late 19th century|British, 1832–1914|1910–1957",,"Evans, Frederick Henry|Unknown|Beardsley, Aubrey Vincent|Tilney, Frederick Colin|Radford, Ernest|Watts-Dunton, Theodore|Cockerell Sydney Sir",British|British|British|British|British,1853 |1872 |1870 |1850 |1832 |1910,1943 |1898 |1951 |1950 |1914 |1957,1896,1896,1896,Platinum print,,"Purchase, David Hunter McAlpin Fund, 1968",,,,,,,,,,,,Albums,,http://www.metmuseum.org/art/collection/search/675555,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -68.519 (30),false,true,675556,Photographs,Album; Artist's book,"Tithe Barn, Great Cokkeswell, Interior",,,,,,Artist|Artist|Artist|Artist|Author|Correspondent|Correspondent,Bookplate designed by|Bookplate designed by,Frederick H. Evans|Unknown|Aubrey Vincent Beardsley|Frederick Colin Tilney|Ernest Radford|Theodore Watts-Dunton|Sir Sydney Cockerell,"British, London 1853–1943 London|British|British, Brighton, Sussex 1872–1898 Menton|British, 1870–1951|British, active late 19th century|British, 1832–1914|1910–1957",,"Evans, Frederick Henry|Unknown|Beardsley, Aubrey Vincent|Tilney, Frederick Colin|Radford, Ernest|Watts-Dunton, Theodore|Cockerell Sydney Sir",British|British|British|British|British,1853 |1872 |1870 |1850 |1832 |1910,1943 |1898 |1951 |1950 |1914 |1957,1896,1896,1896,Platinum print,,"Purchase, David Hunter McAlpin Fund, 1968",,,,,,,,,,,,Albums,,http://www.metmuseum.org/art/collection/search/675556,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -68.519 (31),false,true,675557,Photographs,Album; Artist's book,[Kelmscott Manor from the Garden],,,,,,Artist|Artist|Artist|Artist|Author|Correspondent|Correspondent,Bookplate designed by|Bookplate designed by,Frederick H. Evans|Unknown|Aubrey Vincent Beardsley|Frederick Colin Tilney|Ernest Radford|Theodore Watts-Dunton|Sir Sydney Cockerell,"British, London 1853–1943 London|British|British, Brighton, Sussex 1872–1898 Menton|British, 1870–1951|British, active late 19th century|British, 1832–1914|1910–1957",,"Evans, Frederick Henry|Unknown|Beardsley, Aubrey Vincent|Tilney, Frederick Colin|Radford, Ernest|Watts-Dunton, Theodore|Cockerell Sydney Sir",British|British|British|British|British,1853 |1872 |1870 |1850 |1832 |1910,1943 |1898 |1951 |1950 |1914 |1957,1896,1896,1896,Platinum print,,"Purchase, David Hunter McAlpin Fund, 1968",,,,,,,,,,,,Albums,,http://www.metmuseum.org/art/collection/search/675557,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -68.519 (7),false,true,675533,Photographs,Album; Artist's book,[Main Entrance],,,,,,Author|Correspondent|Correspondent|Artist|Artist|Artist|Artist,Bookplate designed by|Bookplate designed by,Ernest Radford|Theodore Watts-Dunton|Sir Sydney Cockerell|Frederick H. Evans|Unknown|Aubrey Vincent Beardsley|Frederick Colin Tilney,"British, active late 19th century|British, 1832–1914|1910–1957|British, London 1853–1943 London|British|British, Brighton, Sussex 1872–1898 Menton|British, 1870–1951",,"Radford, Ernest|Watts-Dunton, Theodore|Cockerell Sydney Sir|Evans, Frederick Henry|Unknown|Beardsley, Aubrey Vincent|Tilney, Frederick Colin",British|British|British|British|British,1850 |1832 |1910 |1853 |1872 |1870,1950 |1914 |1957 |1943 |1898 |1951,1896,1896,1896,Platinum print,,"Purchase, David Hunter McAlpin Fund, 1968",,,,,,,,,,,,Albums,,http://www.metmuseum.org/art/collection/search/675533,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -68.519 (19),false,true,675545,Photographs,Album; Artist's book,Tapestry Room,,,,,,Author|Correspondent|Correspondent|Artist|Artist|Artist|Artist,Bookplate designed by|Bookplate designed by,Ernest Radford|Theodore Watts-Dunton|Sir Sydney Cockerell|Frederick H. Evans|Unknown|Aubrey Vincent Beardsley|Frederick Colin Tilney,"British, active late 19th century|British, 1832–1914|1910–1957|British, London 1853–1943 London|British|British, Brighton, Sussex 1872–1898 Menton|British, 1870–1951",,"Radford, Ernest|Watts-Dunton, Theodore|Cockerell Sydney Sir|Evans, Frederick Henry|Unknown|Beardsley, Aubrey Vincent|Tilney, Frederick Colin",British|British|British|British|British,1850 |1832 |1910 |1853 |1872 |1870,1950 |1914 |1957 |1943 |1898 |1951,1896,1896,1896,Platinum print,,"Purchase, David Hunter McAlpin Fund, 1968",,,,,,,,,,,,Albums,,http://www.metmuseum.org/art/collection/search/675545,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1994.129,false,true,266993,Photographs,Album,[Album of 226 albumen silver prints of Japan],,,,,,Artist|Artist|Editor|Artist|Artist|Photography Studio|Publisher,,Raimund von Stillfried|Felice Beato|Stillfried and Andersen|Kusakabe Kimbei|Tamamura Kōzaburō|Yamamoto Studio|Stillfried and Andersen,"Austrian, 1839–1911|British (born Italy), Venice 1832–1909 Luxor, Egypt|active Japan, 1875–1885|Japanese, 1841–1934|Japanese, 1856–1923?|Japanese|active Japan, 1875–1885",,"Stillfried, Raimond von|Beato, Felice|Stillfried and Andersen|Kusakabe, Kimbei|Tamamura, Kōzaburō|Yamamoto Studio|Stillfried and Andersen","Austrian|British, born Italy|Japanese|Japanese|Japanese",1839 |1832 |1875 |1841 |1856 |1875,1911 |1909 |1885 |1934 |1923 |1885,1860s–90s,1860,1899,Albumen silver prints,,"Gift of H. de Rassloff, 1918, transferred from the Library",,,,,,,,,,,,Albums,,http://www.metmuseum.org/art/collection/search/266993,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -52.641,false,true,269626,Photographs,Photographically illustrated book,"La Photographie ses origines, ses progrès, ses transformations",,,,,,Artist|Artist|Artist|Artist|Artist|Artist|Artist|Artist|Artist|Artist|Artist|Artist|Artist,,Goupil et Cie|Édouard Baldus|B. Deloose|M. Marion|Henri Garnier|Zurcher|François-Auguste Renard|Joseph Nicéphore Niépce|Maxime Du Camp|Ernest Edwards|Louis-Désiré Blanquart-Évrard|Adolphe Braun|Frères Dujardin,"French, active 1850–84|French, born Prussia, 1813–1889|French|French|French, 1765–1833|French, 1822–1894|British, 1837–1903|French, 1802–1872|French, Besançon 1811–1877 Dornach|French",,"Goupil et Cie|Baldus, Édouard|Deloose, B.|Marion, M.|Garnier, Henri|Zurcher|Renard, François-Auguste|Niépce, Joseph Nicéphore|Du Camp, Maxime|Edwards, Ernest|Blanquart-Évrard, Louis-Désiré|Braun, Adolphe|Dujardin, Frères","French|French, born Prussia|French|French|French|French|British|French|French|French",1850 |1813 |1765 |1822 |1837 |1802 |1811,1884 |1889 |1833 |1894 |1903 |1872 |1877,1870s,1870,1879,Multiple photographic processes,,"David Hunter McAlpin Fund, 1952",,,,,,,,,,,,Books,,http://www.metmuseum.org/art/collection/search/269626,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1982.1182.1158–.1187,false,true,288304,Photographs,Stereographs,"[Group of 30 Stereograph Views of Colorado and Arizona, United States of America]",,,,,,Publisher|Artist|Publisher|Publisher|Artist|Artist|Publisher|Artist|Publisher|Publisher|Publisher|Artist|Publisher|Artist|Person in Photograph|Publisher,,Underwood & Underwood|Benneville Lloyd Singley|H. C. White Company|American Scenery|Strohmeyer & Wyman|Charles Weitfle|Barkalow Brothers|C. H. Graves|Universal Photo Art Co.|American Views|Montgomery Ward & Co.|William H. Rau|Continent Stereoscopic Company|Unknown|Grace Greenwood|Keystone View Company,"American|American, Union Township, Pennsylvania 1864–1938 Meadville, Pennsylvania|American|American|American|American, born Germany, 1836–after 1884|American|American|American|American, Chicago, Illinois|American, 1855–1920|New York|American|American, 1823–1904",,"Underwood & Underwood|Singley, Benneville Lloyd|H. C. White Company|American Scenery|Strohmeyer & Wyman|Weitfle, Charles|Barkalow Brothers|Graves, C. H.|Universal Photo Art Co.|American Views|Montgomery Ward & Co.|Rau, William H.|Continent Stereoscopic Company|Unknown|Greenwood, Grace|Keystone View Company",American|American|American|American|American|American|American|American|American|American|American|New York|American,1864 |1836 |1855 |1823,1938 |1884 |1920 |1904,1850s–1910s,1850,1919,Albumen silver prints,Mounts approximately: 8.8 x 17.3 cm (3 7/16 x 6 13/16 in.) to 10 x 17.8 cm (3 15/16 x 7 in.),"Gift of Weston J. Naef, in memory of Kathleen W. Naef and Weston J. Naef Sr., 1982",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288304,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1982.1182.256–.298,false,true,288121,Photographs,Stereographs,[Group of 43 Stereograph Views of Astronomy Related Scenes],,,,,,Patron|Publisher|Publisher|Artist|Artist|Publisher|Publisher|Publisher|Publisher|Publisher|Photography Studio|Publisher|Publisher|Publisher|Artist|Publisher|Artist|Artist|Publisher|Publisher|Artist|Publisher|Publisher|Artist|Artist|Publisher,Commissioned by,"Nautical Almanac Office|New H Series|Charles Bierstadt|Professor H. Draper|L. M. Rutherford|Popular Series|T. W. Ingersoll|Stereo Gems|Liberty Brand Stereo Views|Underwood & Underwood|Sun Sculpture Works and Studios|Strohmeyer & Wyman|Yerkes Observatory|Carnegie Intitute|Kilburn Brothers|Edward Kilburn|William N. Hobbs|G. R. Proctor|Edward Bierstadt|Whiple|Deloss Barnum|E. & H. T. Anthony|Smith, Beck & Beck|G. W. Thorne|Unknown|Keystone View Company","American|American, 1819–1903|American|American|American|American|American|American|American|American, active ca. 1865–1890|American, 1830–1884|American|American|American, born Germany, born 1824|American|American, 1825–1873 Cortland, New York|American|British|American",,"Nautical Almanac Office|New H Series|Bierstadt, Charles|Draper, H. Professor|Rutherford, L. M.|Popular Series|Ingersoll, T. W.|Stereo Gems|Liberty Brand Stereo Views|Underwood & Underwood|Sun Sculpture Works and Studios|Strohmeyer & Wyman|Yerkes Observatory|Carnegie Intitute|Kilburn Brothers|Kilburn, Edward|Hobbs, William N.|Proctor, G. R.|Bierstadt, Edward|Whiple|Barnum, Deloss|E. & H. T. Anthony|Smith, Beck & Beck|Thorne, G. W.|Unknown|Keystone View Company","American|American|American|American|American|American|American|American|American|American|American|American|American|American, born Germany|American|American|American|British|American",1819 |1863 |1830 |1824 |1825,1903 |1892 |1884 |1873,1850s–1910s,1850,1919,Albumen silver prints,Mounts approximately: 8.3 x 17.1 cm (3 1/4 x 6 3/4 in.) to 8.9 x 17.8 cm (3 1/2 x 7 in.),"Gift of Weston J. Naef, in memory of Kathleen W. Naef and Weston J. Naef Sr., 1982",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288121,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.500.3 (1),false,true,679863,Photographs,Photograph,Jérusalem,,,,,,Artist|Lithographer|Printer,,Louis de Clercq|H. Jannin|J. Blondeau et Antonin,"French, 1837–1901|French",,"de Clercq, Louis|Jannin, H.|J. Blondeau et Antonin",French|French|French,1837,1901,1860 or later,1860,1860,Albumen silver print from paper negative,Image: 8 1/8 × 31 1/2 in. (20.7 × 80 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/679863,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.500.3 (2),false,true,679864,Photographs,Photograph,"Jérusalem. État actuel du dôme, du St. Sépulcre et Minaret d'Omar",,,,,,Artist|Lithographer|Printer,,Louis de Clercq|H. Jannin|J. Blondeau et Antonin,"French, 1837–1901|French",,"de Clercq, Louis|Jannin, H.|J. Blondeau et Antonin",French|French|French,1837,1901,1860 or later,1860,1860,Albumen silver print from paper negative,Image: 8 3/16 × 10 7/8 in. (20.8 × 27.7 cm) Mount: 17 15/16 × 23 1/4 in. (45.5 × 59 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/679864,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.500.3 (3),false,true,679865,Photographs,Photograph,Jérusalem. Entrée de l'Église du St. Sépulcre,,,,,,Artist|Lithographer|Printer,,Louis de Clercq|H. Jannin|J. Blondeau et Antonin,"French, 1837–1901|French",,"de Clercq, Louis|Jannin, H.|J. Blondeau et Antonin",French|French|French,1837,1901,1860 or later,1860,1860,Albumen silver print from paper negative,Image: 10 13/16 × 8 5/16 in. (27.5 × 21.1 cm) Mount: 17 15/16 × 23 1/4 in. (45.5 × 59 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/679865,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.500.3 (4),false,true,679866,Photographs,Photograph,Jérusalem. Tour de David avec ses grandes assises salomoniennes,,,,,,Artist|Lithographer|Printer,,Louis de Clercq|H. Jannin|J. Blondeau et Antonin,"French, 1837–1901|French",,"de Clercq, Louis|Jannin, H.|J. Blondeau et Antonin",French|French|French,1837,1901,1860 or later,1860,1860,Albumen silver print from paper negative,Image: 8 1/8 × 10 7/8 in. (20.6 × 27.7 cm) Mount: 17 15/16 × 23 1/4 in. (45.5 × 59 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/679866,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.500.3 (5),false,true,679867,Photographs,Photograph,Jérusalem. Église Ste Anne appartenant à la France,,,,,,Artist|Lithographer|Printer,,Louis de Clercq|H. Jannin|J. Blondeau et Antonin,"French, 1837–1901|French",,"de Clercq, Louis|Jannin, H.|J. Blondeau et Antonin",French|French|French,1837,1901,1860 or later,1860,1860,Albumen silver print from paper negative,Image: 8 7/16 in. × 11 in. (21.4 × 28 cm) Mount: 17 15/16 × 23 1/4 in. (45.5 × 59 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/679867,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.500.3 (6),false,true,679868,Photographs,Photograph,Jérusalem. Façade de l'Église Ste. Anne.,,,,,,Artist|Lithographer|Printer,,Louis de Clercq|H. Jannin|J. Blondeau et Antonin,"French, 1837–1901|French",,"de Clercq, Louis|Jannin, H.|J. Blondeau et Antonin",French|French|French,1837,1901,1860 or later,1860,1860,Albumen silver print from paper negative,Image: 11 in. × 8 9/16 in. (28 × 21.7 cm) Mount: 17 15/16 × 23 1/4 in. (45.5 × 59 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/679868,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.500.3 (7),false,true,679869,Photographs,Photograph,Jérusalem. Intérieur de l'Église Ste Anne.,,,,,,Artist|Lithographer|Printer,,Louis de Clercq|H. Jannin|J. Blondeau et Antonin,"French, 1837–1901|French",,"de Clercq, Louis|Jannin, H.|J. Blondeau et Antonin",French|French|French,1837,1901,1860 or later,1860,1860,Albumen silver print from paper negative,Image: 10 7/8 × 8 1/16 in. (27.7 × 20.5 cm) Mount: 17 15/16 × 23 1/4 in. (45.5 × 59 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/679869,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.500.3 (8),false,true,679870,Photographs,Photograph,Jérusalem. Hospice autrichien et ancienne Église St Jean,,,,,,Artist|Lithographer|Printer,,Louis de Clercq|H. Jannin|J. Blondeau et Antonin,"French, 1837–1901|French",,"de Clercq, Louis|Jannin, H.|J. Blondeau et Antonin",French|French|French,1837,1901,1860 or later,1860,1860,Albumen silver print from paper negative,Image: 10 7/8 × 8 7/16 in. (27.7 × 21.4 cm) Mount: 17 15/16 × 23 1/4 in. (45.5 × 59 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/679870,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.500.3 (9),false,true,679871,Photographs,Photograph,Jérusalem. Tour Antonia et Environs,,,,,,Artist|Lithographer|Printer,,Louis de Clercq|H. Jannin|J. Blondeau et Antonin,"French, 1837–1901|French",,"de Clercq, Louis|Jannin, H.|J. Blondeau et Antonin",French|French|French,1837,1901,1860 or later,1860,1860,Albumen silver print from paper negative,Image: 8 1/8 × 11 1/4 in. (20.7 × 28.5 cm) Mount: 17 15/16 × 23 1/4 in. (45.5 × 59 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/679871,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.500.3 (10),false,true,679872,Photographs,Photograph,Jérusalem. Massif de la Tour Antonia,,,,,,Artist|Lithographer|Printer,,Louis de Clercq|H. Jannin|J. Blondeau et Antonin,"French, 1837–1901|French",,"de Clercq, Louis|Jannin, H.|J. Blondeau et Antonin",French|French|French,1837,1901,1860 or later,1860,1860,Albumen silver print from paper negative,Image: 10 13/16 × 8 1/16 in. (27.5 × 20.5 cm) Mount: 17 15/16 × 23 1/4 in. (45.5 × 59 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/679872,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.500.3 (11),false,true,679873,Photographs,Photograph,"Jérusalem. Mosquée d'Omar, construite sur l'emplacement su Temple de Salomon",,,,,,Artist|Lithographer|Printer,,Louis de Clercq|H. Jannin|J. Blondeau et Antonin,"French, 1837–1901|French",,"de Clercq, Louis|Jannin, H.|J. Blondeau et Antonin",French|French|French,1837,1901,1860 or later,1860,1860,Albumen silver print from paper negative,Image: 8 7/16 × 11 1/8 in. (21.5 × 28.2 cm) Mount: 17 15/16 × 23 1/4 in. (45.5 × 59 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/679873,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.500.3 (12),false,true,679874,Photographs,Photograph,Jérusalem. Mur oú pleurent les juifs. Grandes Assises du Temple de Salomon,,,,,,Artist|Lithographer|Printer,,Louis de Clercq|H. Jannin|J. Blondeau et Antonin,"French, 1837–1901|French",,"de Clercq, Louis|Jannin, H.|J. Blondeau et Antonin",French|French|French,1837,1901,1860 or later,1860,1860,Albumen silver print from paper negative,Image: 8 5/8 × 11 1/8 in. (21.9 × 28.3 cm) Mount: 17 15/16 × 23 1/4 in. (45.5 × 59 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/679874,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.500.3 (13),false,true,679875,Photographs,Photograph,Jérusalem. Une rue de Jérusalem et entrée du Grand Couvent,,,,,,Artist|Lithographer|Printer,,Louis de Clercq|H. Jannin|J. Blondeau et Antonin,"French, 1837–1901|French",,"de Clercq, Louis|Jannin, H.|J. Blondeau et Antonin",French|French|French,1837,1901,1860 or later,1860,1860,Albumen silver print from paper negative,Image: 11 1/8 × 8 3/8 in. (28.2 × 21.3 cm) Mount: 17 15/16 × 23 1/4 in. (45.5 × 59 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/679875,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.500.3 (14),false,true,679876,Photographs,Photograph,Jérusalem. Vue des Remparts.,,,,,,Artist|Lithographer|Printer,,Louis de Clercq|H. Jannin|J. Blondeau et Antonin,"French, 1837–1901|French",,"de Clercq, Louis|Jannin, H.|J. Blondeau et Antonin",French|French|French,1837,1901,1860 or later,1860,1860,Albumen silver print from paper negative,Image: 7 3/8 × 10 3/16 in. (18.8 × 25.8 cm) Mount: 17 15/16 × 23 1/4 in. (45.5 × 59 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/679876,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.500.3 (15),false,true,679877,Photographs,Photograph,Jérusalem. Chapelle protestante et environs,,,,,,Artist|Lithographer|Printer,,Louis de Clercq|H. Jannin|J. Blondeau et Antonin,"French, 1837–1901|French",,"de Clercq, Louis|Jannin, H.|J. Blondeau et Antonin",French|French|French,1837,1901,1860 or later,1860,1860,Albumen silver print from paper negative,Image: 8 in. × 10 1/4 in. (20.3 × 26 cm) Mount: 17 15/16 × 23 1/4 in. (45.5 × 59 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/679877,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.500.3 (16),false,true,679878,Photographs,Photograph,Jérusalem. Synagogue juive et environs,,,,,,Artist|Lithographer|Printer,,Louis de Clercq|H. Jannin|J. Blondeau et Antonin,"French, 1837–1901|French",,"de Clercq, Louis|Jannin, H.|J. Blondeau et Antonin",French|French|French,1837,1901,1860 or later,1860,1860,Albumen silver print from paper negative,Image: 8 in. × 10 1/4 in. (20.3 × 26 cm) Mount: 17 15/16 × 23 1/4 in. (45.5 × 59 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/679878,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.500.3 (17),false,true,679879,Photographs,Photograph,Jérusalem. Porte de Hebron et de Jaffa. (Bab-el-Khalil),,,,,,Artist|Lithographer|Printer,,Louis de Clercq|H. Jannin|J. Blondeau et Antonin,"French, 1837–1901|French",,"de Clercq, Louis|Jannin, H.|J. Blondeau et Antonin",French|French|French,1837,1901,1860 or later,1860,1860,Albumen silver print from paper negative,Image: 11 3/16 × 8 7/16 in. (28.4 × 21.4 cm) Mount: 17 15/16 × 23 1/4 in. (45.5 × 59 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/679879,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.500.3 (18),false,true,679880,Photographs,Photograph,Jérusalem. Porte de Damas ou des colonnes (Bab-el-Ahmoud),,,,,,Artist|Lithographer|Printer,,Louis de Clercq|H. Jannin|J. Blondeau et Antonin,"French, 1837–1901|French",,"de Clercq, Louis|Jannin, H.|J. Blondeau et Antonin",French|French|French,1837,1901,1860 or later,1860,1860,Albumen silver print from paper negative,Image: 8 7/16 × 11 1/8 in. (21.4 × 28.2 cm) Mount: 17 15/16 × 23 1/4 in. (45.5 × 59 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/679880,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.500.3 (19),false,true,679881,Photographs,Photograph,Jérusalem. Porte de Damas (Bab-el-Ahmoud),,,,,,Artist|Lithographer|Printer,,Louis de Clercq|H. Jannin|J. Blondeau et Antonin,"French, 1837–1901|French",,"de Clercq, Louis|Jannin, H.|J. Blondeau et Antonin",French|French|French,1837,1901,1860 or later,1860,1860,Albumen silver print from paper negative,Image: 11 in. × 8 9/16 in. (28 × 21.7 cm) Mount: 17 15/16 × 23 1/4 in. (45.5 × 59 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/679881,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.500.3 (21),false,true,679883,Photographs,Photograph,Jérusalem. (Environs) Montagne de Sion. Cenacle et Maison de Caiphe.,,,,,,Artist|Lithographer|Printer,,Louis de Clercq|H. Jannin|J. Blondeau et Antonin,"French, 1837–1901|French",,"de Clercq, Louis|Jannin, H.|J. Blondeau et Antonin",French|French|French,1837,1901,1860 or later,1860,1860,Albumen silver print from paper negative,Image: 11 in. × 8 1/2 in. (28 × 21.6 cm) Mount: 17 15/16 × 23 1/4 in. (45.5 × 59 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/679883,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.500.3 (22),false,true,679884,Photographs,Photograph,Jérusalem. (Environs) Jardin Gethsemani et Mont des Oliviers,,,,,,Artist|Lithographer|Printer,,Louis de Clercq|H. Jannin|J. Blondeau et Antonin,"French, 1837–1901|French",,"de Clercq, Louis|Jannin, H.|J. Blondeau et Antonin",French|French|French,1837,1901,1860 or later,1860,1860,Albumen silver print from paper negative,Image: 11 in. × 8 1/2 in. (28 × 21.6 cm) Mount: 17 15/16 × 23 1/4 in. (45.5 × 59 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/679884,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.500.3 (23),false,true,679885,Photographs,Photograph,Jérusalem. (Environs) Grotte de Jérémie,,,,,,Artist|Lithographer|Printer,,Louis de Clercq|H. Jannin|J. Blondeau et Antonin,"French, 1837–1901|French",,"de Clercq, Louis|Jannin, H.|J. Blondeau et Antonin",French|French|French,1837,1901,1860 or later,1860,1860,Albumen silver print from paper negative,Image: 11 in. × 8 1/4 in. (28 × 21 cm) Mount: 17 15/16 × 23 1/4 in. (45.5 × 59 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/679885,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.500.3 (24),false,true,679886,Photographs,Photograph,Jérusalem. (Environs) Tombeau des Rois,,,,,,Artist|Lithographer|Printer,,Louis de Clercq|H. Jannin|J. Blondeau et Antonin,"French, 1837–1901|French",,"de Clercq, Louis|Jannin, H.|J. Blondeau et Antonin",French|French|French,1837,1901,1860 or later,1860,1860,Albumen silver print from paper negative,Image: 10 13/16 × 8 3/8 in. (27.5 × 21.2 cm) Mount: 17 15/16 × 23 1/4 in. (45.5 × 59 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/679886,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.500.3 (25),false,true,679887,Photographs,Photograph,Jérusalem. (Environs) Tombeau de la vierge,,,,,,Artist|Lithographer|Printer,,Louis de Clercq|H. Jannin|J. Blondeau et Antonin,"French, 1837–1901|French",,"de Clercq, Louis|Jannin, H.|J. Blondeau et Antonin",French|French|French,1837,1901,1860 or later,1860,1860,Albumen silver print from paper negative,Image: 11 in. × 8 1/4 in. (28 × 21 cm) Mount: 17 15/16 × 23 1/4 in. (45.5 × 59 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/679887,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.500.3 (26),false,true,679888,Photographs,Photograph,Jérusalem. (Environs) Tombeau d'Absalon,,,,,,Artist|Lithographer|Printer,,Louis de Clercq|H. Jannin|J. Blondeau et Antonin,"French, 1837–1901|French",,"de Clercq, Louis|Jannin, H.|J. Blondeau et Antonin",French|French|French,1837,1901,1860 or later,1860,1860,Albumen silver print from paper negative,Image: 8 9/16 × 10 7/8 in. (21.8 × 27.7 cm) Mount: 17 15/16 × 23 1/4 in. (45.5 × 59 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/679888,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.500.3 (27),false,true,679889,Photographs,Photograph,Jérusalem. Tombeaux de St Jacques et de Zacharie,,,,,,Artist|Lithographer|Printer,,Louis de Clercq|H. Jannin|J. Blondeau et Antonin,"French, 1837–1901|French",,"de Clercq, Louis|Jannin, H.|J. Blondeau et Antonin",French|French|French,1837,1901,1860 or later,1860,1860,Albumen silver print from paper negative,Image: 8 11/16 × 11 1/4 in. (22 × 28.5 cm) Mount: 17 15/16 × 23 1/4 in. (45.5 × 59 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/679889,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.500.3 (28),false,true,679890,Photographs,Photograph,Jérusalem. (Environs) St Jean du Désert,,,,,,Artist|Lithographer|Printer,,Louis de Clercq|H. Jannin|J. Blondeau et Antonin,"French, 1837–1901|French",,"de Clercq, Louis|Jannin, H.|J. Blondeau et Antonin",French|French|French,1837,1901,1860 or later,1860,1860,Albumen silver print from paper negative,Image: 11 in. × 8 3/8 in. (28 × 21.3 cm) Mount: 17 15/16 × 23 1/4 in. (45.5 × 59 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/679890,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.500.3 (29),false,true,679891,Photographs,Photograph,Arcade de l'Ecce Homo. Ponce Pilate présente Jésus au Peuple,,,,,,Artist|Lithographer|Printer,,Louis de Clercq|H. Jannin|J. Blondeau et Antonin,"French, 1837–1901|French",,"de Clercq, Louis|Jannin, H.|J. Blondeau et Antonin",French|French|French,1837,1901,1860 or later,1860,1860,Albumen silver print from paper negative,Image: 10 13/16 × 8 3/16 in. (27.4 × 20.8 cm) Mount: 17 15/16 × 23 1/4 in. (45.5 × 59 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/679891,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.500.3 (30),false,true,679892,Photographs,Photograph,Entrée d'une caserne turque. C'est a cette porte que les pèlerins font les prières de la 1e Station. N'ayant pas la permission d'entrer dans la caserne,,,,,,Artist|Lithographer|Printer,,Louis de Clercq|H. Jannin|J. Blondeau et Antonin,"French, 1837–1901|French",,"de Clercq, Louis|Jannin, H.|J. Blondeau et Antonin",French|French|French,1837,1901,1860 or later,1860,1860,Albumen silver print from paper negative,Image: 10 9/16 in. × 8 in. (26.8 × 20.3 cm) Mount: 17 15/16 × 23 1/4 in. (45.5 × 59 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/679892,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.500.3 (31),false,true,679893,Photographs,Photograph,Ie Station. Jésus est condamné à mort. Une cour intérieure de la caserne turque où la tradition place l'endroit du prêtoire où Jésus fut jugé,,,,,,Artist|Lithographer|Printer,,Louis de Clercq|H. Jannin|J. Blondeau et Antonin,"French, 1837–1901|French",,"de Clercq, Louis|Jannin, H.|J. Blondeau et Antonin",French|French|French,1837,1901,1860 or later,1860,1860,Albumen silver print from paper negative,Image: 11 5/16 × 8 1/2 in. (28.7 × 21.6 cm) Mount: 17 15/16 × 23 1/4 in. (45.5 × 59 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/679893,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.500.3 (32),false,true,679894,Photographs,Photograph,"IIe Station. Jésus est chargé de sa croix. Cette Station est placée au bas de la scala santa qui a été entièrement transportée à Rome, il n'en reste que les première assises",,,,,,Artist|Lithographer|Printer,,Louis de Clercq|H. Jannin|J. Blondeau et Antonin,"French, 1837–1901|French",,"de Clercq, Louis|Jannin, H.|J. Blondeau et Antonin",French|French|French,1837,1901,1860 or later,1860,1860,Albumen silver print from paper negative,Image: 10 11/16 × 8 1/4 in. (27.1 × 20.9 cm) Mount: 17 15/16 × 23 1/4 in. (45.5 × 59 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/679894,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.500.3 (33),false,true,679895,Photographs,Photograph,IIIe Station. Jésus tombe pour la première fois. Une colonne brisée et entendue a terre indique la place de cette station au lieu où la voie douloureuse tourne brusquement a gauche,,,,,,Artist|Lithographer|Printer,,Louis de Clercq|H. Jannin|J. Blondeau et Antonin,"French, 1837–1901|French",,"de Clercq, Louis|Jannin, H.|J. Blondeau et Antonin",French|French|French,1837,1901,1860 or later,1860,1860,Albumen silver print from paper negative,Image: 11 in. × 8 1/4 in. (28 × 21 cm) Mount: 17 15/16 × 23 1/4 in. (45.5 × 59 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/679895,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.500.3 (34),false,true,679896,Photographs,Photograph,IVe Station. Jésus rencontre sa très Sainte Mère. Cette station est située a quelques pas a peine de la précédente. La tradition la place a l'arcade que l'on voit représentée ici,,,,,,Artist|Lithographer|Printer,,Louis de Clercq|H. Jannin|J. Blondeau et Antonin,"French, 1837–1901|French",,"de Clercq, Louis|Jannin, H.|J. Blondeau et Antonin",French|French|French,1837,1901,1860 or later,1860,1860,Albumen silver print from paper negative,Image: 10 11/16 × 8 1/4 in. (27.2 × 21 cm) Mount: 17 15/16 × 23 1/4 in. (45.5 × 59 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/679896,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.500.3 (35),false,true,679897,Photographs,Photograph,Ve Station. Jésus aidé par Simon de Cyrène. Une marque dans le mur indique seule cette station. La maison que l'on voit au fond est celle du mauvais riche,,,,,,Artist|Lithographer|Printer,,Louis de Clercq|H. Jannin|J. Blondeau et Antonin,"French, 1837–1901|French",,"de Clercq, Louis|Jannin, H.|J. Blondeau et Antonin",French|French|French,1837,1901,1860 or later,1860,1860,Albumen silver print from paper negative,Image: 11 3/16 × 8 1/4 in. (28.4 × 21 cm) Mount: 17 15/16 × 23 1/4 in. (45.5 × 59 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/679897,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.500.3 (36),false,true,679898,Photographs,Photograph,VIe Station. Ste Véronique essuie la face sanglante de Jésus. Aucune marque extérieure n'indique cette station. La tradition la place au pied du petit escalier,,,,,,Artist|Lithographer|Printer,,Louis de Clercq|H. Jannin|J. Blondeau et Antonin,"French, 1837–1901|French",,"de Clercq, Louis|Jannin, H.|J. Blondeau et Antonin",French|French|French,1837,1901,1860 or later,1860,1860,Albumen silver print from paper negative,Image: 11 in. × 8 7/16 in. (28 × 21.5 cm) Mount: 17 15/16 × 23 1/4 in. (45.5 × 59 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/679898,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.500.3 (37),false,true,679899,Photographs,Photograph,"VIIe Station. Jésus tombe pour la seconde fois. Ancienne porte judiciaire, il s'y trouvait une colonne ou l'on exposait les condamnés; Elle éxiste encore cachée dans l'intérieur de la maison a gauche",,,,,,Artist|Lithographer|Printer,,Louis de Clercq|H. Jannin|J. Blondeau et Antonin,"French, 1837–1901|French",,"de Clercq, Louis|Jannin, H.|J. Blondeau et Antonin",French|French|French,1837,1901,1860 or later,1860,1860,Albumen silver print from paper negative,Image: 11 in. × 8 1/8 in. (28 × 20.7 cm) Mount: 17 15/16 × 23 1/4 in. (45.5 × 59 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/679899,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.500.3 (38),false,true,679900,Photographs,Photograph,VIIIe Station. Jésus console les filles de Jérusalem. Ici encore une simple marque faite sur le fut d'une colonne encastrée dans le mur indique la station.,,,,,,Artist|Lithographer|Printer,,Louis de Clercq|H. Jannin|J. Blondeau et Antonin,"French, 1837–1901|French",,"de Clercq, Louis|Jannin, H.|J. Blondeau et Antonin",French|French|French,1837,1901,1860 or later,1860,1860,Albumen silver print from paper negative,Image: 10 13/16 × 8 7/16 in. (27.5 × 21.5 cm) Mount: 17 15/16 × 23 1/4 in. (45.5 × 59 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/679900,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.500.3 (39),false,true,679901,Photographs,Photograph,IXe Station. Jésus tombe pour la troisième fois. Le fut de colonne qui se trouve au pied du mur indique cette station.,,,,,,Artist|Lithographer|Printer,,Louis de Clercq|H. Jannin|J. Blondeau et Antonin,"French, 1837–1901|French",,"de Clercq, Louis|Jannin, H.|J. Blondeau et Antonin",French|French|French,1837,1901,1860 or later,1860,1860,Albumen silver print from paper negative,Image: 11 in. × 8 7/16 in. (28 × 21.5 cm) Mount: 17 15/16 × 23 1/4 in. (45.5 × 59 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/679901,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.500.3 (40),false,true,679902,Photographs,Photograph,Xe Station. Jésus est dépouillé de ses Vêtements. L'emplacement de cette station est indique par la mosaïque en marbre que l'on voit devant l'autel.,,,,,,Artist|Lithographer|Printer,,Louis de Clercq|H. Jannin|J. Blondeau et Antonin,"French, 1837–1901|French",,"de Clercq, Louis|Jannin, H.|J. Blondeau et Antonin",French|French|French,1837,1901,1860 or later,1860,1860,Albumen silver print from paper negative,Image: 10 1/16 × 7 13/16 in. (25.6 × 19.8 cm) Mount: 17 15/16 × 23 1/4 in. (45.5 × 59 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/679902,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.500.3 (41),false,true,679903,Photographs,Photograph,XIe Station. Jésus est cloué sur la croix. Cette station se trouve placée au pied même de l'autel.,,,,,,Artist|Lithographer|Printer,,Louis de Clercq|H. Jannin|J. Blondeau et Antonin,"French, 1837–1901|French",,"de Clercq, Louis|Jannin, H.|J. Blondeau et Antonin",French|French|French,1837,1901,1860 or later,1860,1860,Albumen silver print from paper negative,Image: 10 1/8 × 7 7/8 in. (25.7 × 20 cm) Mount: 17 15/16 × 23 1/4 in. (45.5 × 59 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/679903,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.500.3 (42),false,true,679904,Photographs,Photograph,XIIe Station. Jésus meurt sur la croix. Autel élevé sur le lieu même ou le Christ a été crucifié,,,,,,Artist|Lithographer|Printer,,Louis de Clercq|H. Jannin|J. Blondeau et Antonin,"French, 1837–1901|French",,"de Clercq, Louis|Jannin, H.|J. Blondeau et Antonin",French|French|French,1837,1901,1860 or later,1860,1860,Albumen silver print from paper negative,Image: 10 1/8 × 7 7/8 in. (25.7 × 20 cm) Mount: 17 15/16 × 23 1/4 in. (45.5 × 59 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/679904,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.500.3 (43),false,true,679905,Photographs,Photograph,XIIIe Station. Jésus est remis entre les mains de sa mère. Cet autel est construit sur le rocher où se tenait la vierge marie pendant le crucifiement de son fils.,,,,,,Artist|Lithographer|Printer,,Louis de Clercq|H. Jannin|J. Blondeau et Antonin,"French, 1837–1901|French",,"de Clercq, Louis|Jannin, H.|J. Blondeau et Antonin",French|French|French,1837,1901,1860 or later,1860,1860,Albumen silver print from paper negative,Image: 10 1/8 × 7 7/8 in. (25.7 × 20 cm) Mount: 17 15/16 × 23 1/4 in. (45.5 × 59 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/679905,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.500.3 (44),false,true,679906,Photographs,Photograph,XIVe Station. Le corps de Jésus est deposé dans le tombeau. Monument du St Sépulcre où le corps du Christ a été enseveli.,,,,,,Artist|Lithographer|Printer,,Louis de Clercq|H. Jannin|J. Blondeau et Antonin,"French, 1837–1901|French",,"de Clercq, Louis|Jannin, H.|J. Blondeau et Antonin",French|French|French,1837,1901,1860 or later,1860,1860,Albumen silver print from paper negative,Image: 9 5/8 × 7 9/16 in. (24.5 × 19.2 cm) Mount: 17 15/16 × 23 1/4 in. (45.5 × 59 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/679906,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.500.3 (20),false,true,679882,Photographs,Photograph,Jérusalem. Portes Dorées,,,,,,Lithographer|Printer|Artist,,H. Jannin|J. Blondeau et Antonin|Louis de Clercq,"French|French, 1837–1901",,"Jannin, H.|J. Blondeau et Antonin|de Clercq, Louis",French|French|French,1837,1901,1860 or later,1860,1860,Albumen silver print from paper negative,Image: 8 3/8 × 10 3/4 in. (21.3 × 27.3 cm) Mount: 17 15/16 × 23 1/4 in. (45.5 × 59 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/679882,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.491.1 (3),false,true,287593,Photographs,Photograph,"[The Earl Canning, K.G., K.S.I., G.C.B., Calcutta]",,,,,,Artist|Artist|Artist,,Bourne and Shephard|Samuel Bourne|Charles Shepherd,"British, 1834–1912|British",,"Bourne and Shepherd|Bourne, Samuel|Shepherd, Charles",British|British,1834,1912,1858–61,1858,1861,Albumen silver print from glass negative,Image: 26.5 x 21.7 cm (10 7/16 x 8 9/16 in.) Mount: 33 x 26.4 cm (13 x 10 3/8 in.),"Gilman Collection, Purchase, Cynthia Hazen Polsky Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287593,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.69,false,true,290464,Photographs,Photograph,Village de Murols,,,,,,Artist|Printer,,Édouard Baldus|Chicago Albumen Works,"French, born Prussia, 1813–1889",,"Baldus, Édouard|Chicago Albumen Works","French, born Prussia",1813,1889,"1854, printed 1979",1854,1854,Salted paper print from paper negative,Image: 34.2 x 44.3 cm (13 7/16 x 17 7/16 in.) Sheet: 35.3 x 47.2 cm (13 7/8 x 18 9/16 in.),"The Horace W. Goldsmith Foundation Fund, through Joyce and Robert Menschel, 2009",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/290464,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1988.1112.1,false,true,265967,Photographs,Photograph,[Rocks in the Auvergne],,,,,,Artist|Printer,,Édouard Baldus|Chicago Albumen Works,"French, born Prussia, 1813–1889",,"Baldus, Édouard|Chicago Albumen Works","French, born Prussia",1813,1889,"1854, printed ca. 1981",1854,1854,Salted paper print from paper negative,Image: 33.9 x 44.8 cm. (13 3/6 x 17 5/8 in.),"Museum Accession, 1988",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/265967,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1982.1182.492–.591,false,true,288149,Photographs,Stereographs,[Group of 100 Stereograph Views of California Nature and Landscapes With a Focus on Yosemite],,,,,,Author|Artist|Artist|Artist|Publisher|Publisher|Publisher|Artist|Publisher|Publisher|Artist|Publisher|Artist|Publisher|Publisher|Publisher|Artist|Publisher|Publisher|Author|Photography Studio|Publisher|Publisher|Publisher|Artist|Publisher|Publisher|Publisher|Publisher|Publisher|Publisher|Publisher|Publisher|Publisher,,"James Mason Hutchings|G. H. Aldrich & Company|Unknown|Unknown|C. W. Woodward|Strohmeyer & Wyman|Underwood & Underwood|H. C. White Company|American Scenery|Views in California|American Stereoscopic Company|Quaker Oats Company|T. W. Ingersoll|S. F. Sanderson|New H Series|S. W. Kelley|George W. Griffith|Griffith & Griffith, American|Popular Series|John Muir|Sun Sculpture Works and Studios|Dodge, Collier, & Perkins|J. Merrill & Son|Continent Stereoscopic Company|Andrew Price|American Series|H. Ropes & Co., American|Canvassers|J. F. Jarvis|Standard Series|A. Fuller|Lovejoy & Foster|American Views|Keystone View Company","American (born England), 1820–1902|American|American|American|American|American|American|American|American|American|American|American|American|American|American, born Scotland, 1838–1914|American|American|American|New York|American|American|American|American|American|American|American",,"Hutchings, James Mason|G. H. Aldrich & Company|Unknown|Unknown|Woodward, C. W.|Strohmeyer & Wyman|Underwood & Underwood|H. C. White Company|American Scenery|Views in California|American Stereoscopic Company|Quaker Oats Company|Ingersoll, T. W.|Sanderson, S. F.|New H Series|Kelley, S. W.|Griffith, George W.|Griffith & Griffith|Popular Series|Muir, John|Sun Sculpture Works and Studios|Dodge, Collier, & Perkins|J. Merrill & Son|Continent Stereoscopic Company|Price, Andrew|American Series|Ropes & Co., H.|Canvassers|Jarvis, J. F.|Standard Series|Fuller, A.|Lovejoy & Foster|American Views|Keystone View Company","American|American|American|American|American|American|American|American|American|American|American|American|American|American|American, born Scotland|American|American|American|New York|American|American|American|American|American|American|American|American",1820 |1838,1902 |1914,1860s–1910s,1860,1919,Albumen silver prints,Mounts approximately: 8.6 x 17.5 cm (3 3/8 x 6 7/8 in.) to 11.4 x 17.8 cm (4 1/2 x 7 in.),"Gift of Weston J. Naef, in memory of Kathleen W. Naef and Weston J. Naef Sr., 1982",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288149,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1977.683.22,false,true,285455,Photographs,Photograph,Naser al-Din Shah,,,,,,Artist|Person in Photograph,Person in photograph,Unknown|Naser od-Din Shah,"Iranian, Tehran 1831–1896 Tehran",,"Unknown|Din, Naser od- Shah",Iranian,1831,1896,ca. 1852–55,1852,1855,Salted paper print from glass negative,Image: 33.7 x 21.2 cm (13 1/4 x 8 3/8 in.),"Gift of Charles K. and Irma B. Wilkinson, 1977",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285455,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -38.82.1,false,true,268623,Photographs,Photograph,[Woman Opening Parasol],,,,,,Artist|Printer,,Eadweard Muybridge|The Photo-Gravure Company,"American, born Britain, 1830–1904",,"Muybridge, Eadweard|Photo-Gravure Company","American, born Britain",1830,1904,"1883–86, printed 1887",1883,1886,Collotype,,"Gift of the Philadelphia Commercial Museum, 1938",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268623,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -38.82.3,false,true,268638,Photographs,Photograph,[Boys Playing Leap Frog],,,,,,Artist|Printer,,Eadweard Muybridge|The Photo-Gravure Company,"American, born Britain, 1830–1904",,"Muybridge, Eadweard|Photo-Gravure Company","American, born Britain",1830,1904,"1883–86, printed 1887",1883,1886,Collotype,,"Gift of the Philadelphia Commercial Museum, 1938",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268638,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1991.1135.9.69,false,true,266441,Photographs,Photograph,[Horse and Rider Galloping],,,,,,Artist|Printer,,Eadweard Muybridge|The Photo-Gravure Company,"American, born Britain, 1830–1904",,"Muybridge, Eadweard|Photo-Gravure Company","American, born Britain",1830,1904,"1883–86, printed 1887",1883,1886,Collotype,,"Rogers Fund, transferred from the Library",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/266441,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -X.673,false,true,271667,Photographs,Photograph,Queen Victoria Presiding at the Reopening of the Reconstructed Crystal Palace at Sydenham,,,,,,Former Attribution|Artist,Attributed to,Philip Henry Delamotte|T. R. Williams,"British, 1821–1889|British, born 1825",,"Delamotte, Philip Henry|Williams, T. R.",British|British,1821 |1825,1889 |1825,1854,1854,1854,Albumen silver print from glass negative,,Museum Accession,,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271667,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1982.1182.1–.14,false,true,288103,Photographs,Stereographs,[Group of 14 stereographs of Africa and Actors],,,,,,Artist|Publisher|Artist|Artist|Publisher|Publisher,,"Kilburn Brothers|Underwood & Underwood|Unknown|H. Ropes & Co., American|F. G. Weller|Keystone View Company","American, active ca. 1865–1890|American",,"Kilburn Brothers|Underwood & Underwood|Unknown|Ropes & Co., H.|Weller., F. G.|Keystone View Company",American|American|American,1863,1892,1850s–1910s,1850,1919,Albumen silver prints,Mounts approximately: 8.2 x 17.1 cm (3 1/4 x 6 3/4 in.) to 8.9 x 17.8 cm (3 1/2 x 7 in.),"Gift of Weston J. Naef, in memory of Kathleen W. Naef and Weston J. Naef Sr., 1982",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288103,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1983.1163,false,true,263729,Photographs,Photograph,Copacabana,,,,,,Artist|Printer,,Marc Ferrez|Alan B. Newman,"Brazilian, 1843–1923|American, born 1946",,"Ferrez, Marc|Newman, Alan B.",Brazilian|American,1843 |1946,1923,"1880, printed 1983",1880,1880,Platinum print from glass negative,Image: 17.3 x 32.9 cm (6 13/16 x 12 15/16 in.) Mount: 28.4 x 38 cm (11 3/16 x 14 15/16 in.),"Gift of H. L. Hoffenberg, 1983",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/263729,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -46.122.16,false,true,269064,Photographs,Photograph,Étude d'arbre,,,,,,Artist|Printer,,"Edward King Tenison|Imprimerie photographique de Blanquart-Évrard, à Lille","Irish, 1805–1878|French, active 1851–55",,"Tenison, Edward King|Imprimerie photographique de Blanquart-Évrard, à Lille",Irish,1805 |1851,1878 |1855,1850–53,1850,1853,Salted paper print (Blanquart-Évrard process) from paper negative,,"Harris Brisbane Dick Fund, 1946",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269064,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.410 (1),false,true,288141,Photographs,Photograph,[Duchesse de Morny],,,,,,Artist|Artist|Person in Photograph,Painted and retouched by|Person in photograph,"Pierre-Louis Pierson|Marck|Sophie Troubetzkoi, duchesse de Morny","French, 1822–1913|Russian, Moscow 1838–1896",", et al","Pierson, Pierre-Louis|Marck|Troubetzkoi, Sophie, duchesse de Morny",French|Russian,1822 |1838,1913 |1896,before 1865,1855,1865,Albumen silver print from glass negative,Image: 6 5/16 × 4 3/4 in. (16 × 12 cm) Mount: 14 3/16 in. × 11 in. (36 × 28 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288141,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.41,false,true,676536,Photographs,Photograph,"Zouave, 2nd Division",,,,,,Artist|Publisher|Person in Photograph,Person in photograph,"Roger Fenton|Thomas Agnew & Sons, Ltd.|Roger Fenton","British, 1819–1869|London|British, 1819–1869",,"Fenton, Roger|Agnew, Thomas & Sons, Ltd.|Fenton, Roger",British|British,1819 |1819,1819 |1819,1855,1855,1855,Salted paper print from collodion glass negative,Image: 7 5/8 × 6 7/16 in. (19.4 × 16.4 cm) Mount: 23 5/8 × 17 1/4 in. (60 × 43.8 cm),"Purchase, Alfred Stieglitz Society Gifts, 2015",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/676536,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1982.1182.315–.385,false,true,288138,Photographs,Stereographs,"[Group of 71 Stereograph Views of African-Americans and Early Black American Culture, including Colloquial Black Humor]",,,,,,Publisher|Publisher|Publisher|Publisher|Artist|Artist|Publisher|Publisher|Publisher|Artist|Publisher|Photography Studio|Artist|Artist|Artist|Artist|Artist|Publisher|Publisher|Publisher|Artist|Artist|Artist|Publisher|Artist|Publisher|Publisher|Publisher|Publisher|Artist|Publisher|Artist|Publisher,,"Francis Hendricks|European and American Views|Webster & Albee|American Stereoscopic Company|J. Mullen|E. F. Smith|American Scenery|Life Groups|America Illustrated|Wilson & Havens|Underwood & Underwood|Sun Sculpture Works and Studios|Strohmeyer & Wyman|George Barker|Kilburn Brothers|H. C. White Company|T. W. Ingersoll|Canvassers|Continent Stereoscopic Company|Littleton View Company|Alfred S. Campbell|James M. Davis|C. H. Graves|Florida Novelties and Views|Griffith & Griffith, American|Metropolitan Series|Ingersoll View Company|Popular Series|Universal View Co.|William H. Rau|M. H. Zahner|Unknown|Keystone View Company","American, Syracuse, New York|American|American|American|American|American|American|American|American|American|American|American|American, born Canada, 1844–1894|American, active ca. 1865–1890|American|New York|American|American|American|American|American|American|American|American, 1855–1920|American",,"Hendricks, Francis|European and American Views|Webster & Albee|American Stereoscopic Company|Mullen, J.|Smith, E. F.|American Scenery|Life Groups|America Illustrated|Wilson & Havens|Underwood & Underwood|Sun Sculpture Works and Studios|Strohmeyer & Wyman|Barker, George|Kilburn Brothers|H. C. White Company|Ingersoll, T. W.|Canvassers|Continent Stereoscopic Company|Littleton View Company|Campbell, Alfred S.|Davis, James M.|Graves, C. H.|Florida Novelties and Views|Griffith & Griffith|Metropolitan Series|Ingersoll View Company|Popular Series|Universal View Co.|Rau, William H.|Zahner, M. H.|Unknown|Keystone View Company",American|American|American|American|American|American|American|American|American|American|American|American|American|American|American|New York|American|American|American|American|American|American|American|American|American|American,1844 |1863 |1855,1894 |1892 |1920,1850s–1910s,1850,1919,Albumen silver prints,Mounts approximately: 8.5 x 17.5 cm (3 3/8 x 6 7/8 in.) to 10.7 x 17.8 cm (4 3/16 x 7 in.),"Gift of Weston J. Naef, in memory of Kathleen W. Naef and Weston J. Naef Sr., 1982",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288138,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.196,false,true,285607,Photographs,Photograph,[Portrait in a White Dress],,,,,,Artist|Artist|Person in Photograph,Painted and retouched by,Pierre-Louis Pierson|Aquilin Schad|Countess Virginia Oldoini Verasis di Castiglione,"French, 1822–1913|Austrian, 1817–1866|1835–1899",,"Pierson, Pierre-Louis|Schad, Aquilin|Castiglione, di, Virginia Oldoini Verasis Countess",French|Austrian,1822 |1817 |1835,1913 |1866 |1899,"1856–57, printed 1861–66",1856,1866,Salted paper print from glass negative,Image: 32.5 x 27 cm (12 13/16 x 10 5/8 in.) Mount: 39.1 x 29.7 cm (15 3/8 x 11 11/16 in.) Mat: 61 x 50.8 cm (24 x 20 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285607,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -48.77,false,true,269223,Photographs,Photograph,Auguste Rodin,,,,,,Artist|Person in Photograph,,Gertrude Käsebier|Auguste Rodin,"American, 1852–1934|French, Paris 1840–1917 Meudon",,"Käsebier, Gertrude|Rodin, Auguste",American|French,1852 |1840,1934 |1917,1907,1907,1907,Platinum print,,"Gift of Adele Rollins Clifton, 1948",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269223,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.185,false,true,306170,Photographs,Photograph,View of the Arruns Pass and Peak from the Pont de Soubé,,,,,,Artist|Printer,,"John Stewart|Imprimerie photographique de Blanquart-Évrard, à Lille","British, Scotland 1814–1887|French, active 1851–55",,"Stewart, John|Imprimerie photographique de Blanquart-Évrard, à Lille","British, Scottish",1814 |1851,1887 |1855,1852,1852,1852,"Salted paper print from paper negative, Blanquart Evrard process",Image: 22.2 × 29.3 cm (8 3/4 × 11 9/16 in.) Mount: 43.6 × 61.9 cm (17 3/16 × 24 3/8 in.),"Purchase, Susan and Thomas Dunn and Peter Bunnell Gifts, 2013",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306170,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1977.683.23,false,true,652117,Photographs,Photograph,[Naser al-Din Shah],,,,,,Person in Photograph|Artist,Person in photograph|Possibly by,Naser od-Din Shah|Luigi Pesce,"Iranian, Tehran 1831–1896 Tehran|Italian, 1818–1891",,"Din, Naser od- Shah|Pesce, Luigi",Iranian|Italian,1831 |1818,1896 |1891,1840s–60s,1840,1869,Salted paper print,,"Gift of Charles K. and Irma B. Wilkinson, 1977",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/652117,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1977.683.24,false,true,285456,Photographs,Photograph,Naser al-Din Shah,,,,,,Artist|Person in Photograph,Possibly by|Person in photograph,Luigi Pesce|Naser od-Din Shah,"Italian, 1818–1891|Iranian, Tehran 1831–1896 Tehran",,"Pesce, Luigi|Din, Naser od- Shah",Italian|Iranian,1818 |1831,1891 |1896,ca. 1855–58,1855,1858,Salted paper print from paper negative,Image: 19.5 x 13.5 cm (7 11/16 x 5 5/16 in.),"Gift of Charles K. and Irma B. Wilkinson, 1977",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285456,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1973.613.1,false,true,260977,Photographs,Photograph,LeRoy Beaulieu,,,,,,Artist|Maker,Mounted by,Gertrude Käsebier|Frederick H. Evans,"American, 1852–1934|British, London 1853–1943 London",,"Käsebier, Gertrude|Evans, Frederick Henry",American|British,1852 |1853,1934 |1943,ca. 1901,1899,1903,Platinum print,Image: 7 7/16 × 5 11/16 in. (18.9 × 14.5 cm) Sheet: 14 13/16 × 9 15/16 in. (37.6 × 25.2 cm),"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1973",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260977,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1973.613.2,false,true,260980,Photographs,Photograph,Luks: Painter,,,,,,Artist|Maker,,Gertrude Käsebier|Frederick H. Evans,"American, 1852–1934|British, London 1853–1943 London",,"Käsebier, Gertrude|Evans, Frederick Henry",American|British,1852 |1853,1934 |1943,ca. 1901,1899,1903,Platinum print,Image: 8 1/16 × 6 1/8 in. (20.4 × 15.6 cm) Sheet: 14 7/8 × 9 15/16 in. (37.8 × 25.2 cm),"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1973",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260980,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1973.613.3,false,true,260981,Photographs,Photograph,Josephine (Portrait of Miss B.),,,,,,Artist|Maker,,Gertrude Käsebier|Frederick H. Evans,"American, 1852–1934|British, London 1853–1943 London",,"Käsebier, Gertrude|Evans, Frederick Henry",American|British,1852 |1853,1934 |1943,1903,1903,1903,Platinum print,Image: 8 in. × 5 15/16 in. (20.3 × 15.1 cm) Sheet: 14 15/16 in. × 10 in. (37.9 × 25.4 cm),"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1973",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260981,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1973.613.4,false,true,260982,Photographs,Photograph,"""When the Sands are Running Low""",,,,,,Artist|Maker,,Gertrude Käsebier|Frederick H. Evans,"American, 1852–1934|British, London 1853–1943 London",,"Käsebier, Gertrude|Evans, Frederick Henry",American|British,1852 |1853,1934 |1943,ca. 1901,1899,1903,Platinum print,,"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1973",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260982,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1973.613.5,false,true,260983,Photographs,Photograph,Mrs. F. H. Evans,,,,,,Artist|Maker,Mounted by,Gertrude Käsebier|Frederick H. Evans,"American, 1852–1934|British, London 1853–1943 London",,"Käsebier, Gertrude|Evans, Frederick Henry",American|British,1852 |1853,1934 |1943,ca. 1901,1899,1903,Platinum print,Image: 7 13/16 × 5 7/8 in. (19.8 × 14.9 cm) Sheet: 14 15/16 × 10 1/16 in. (38 × 25.6 cm),"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1973",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260983,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1973.613.6,false,true,260984,Photographs,Photograph,Mrs. F. H. Evans,,,,,,Artist|Maker,Mounted by,Gertrude Käsebier|Frederick H. Evans,"American, 1852–1934|British, London 1853–1943 London",,"Käsebier, Gertrude|Evans, Frederick Henry",American|British,1852 |1853,1934 |1943,ca. 1901,1899,1903,Platinum print,Image: 6 3/4 × 5 15/16 in. (17.1 × 15.1 cm) Sheet: 14 5/8 in. × 10 in. (37.1 × 25.4 cm),"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1973",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260984,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1973.613.7,false,true,260985,Photographs,Photograph,Mrs. F. H. Evans,,,,,,Artist|Maker,Mounted by,Gertrude Käsebier|Frederick H. Evans,"American, 1852–1934|British, London 1853–1943 London",,"Käsebier, Gertrude|Evans, Frederick Henry",American|British,1852 |1853,1934 |1943,ca. 1901,1899,1903,Platinum print,Image: 7 1/2 × 5 3/4 in. (19 × 14.6 cm) Sheet: 14 7/8 × 10 1/8 in. (37.8 × 25.7 cm),"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1973",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260985,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1973.613.8,false,true,260986,Photographs,Photograph,Frederick H. Evans,,,,,,Artist|Maker,Mounted by,Gertrude Käsebier|Frederick H. Evans,"American, 1852–1934|British, London 1853–1943 London",,"Käsebier, Gertrude|Evans, Frederick Henry",American|British,1852 |1853,1934 |1943,ca. 1901,1899,1903,Platinum print,Image: 9 3/4 × 5 9/16 in. (24.8 × 14.1 cm) Sheet: 14 3/4 × 7 7/16 in. (37.5 × 18.9 cm),"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1973",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260986,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1973.613.9,false,true,260987,Photographs,Photograph,F. H. Evans,,,,,,Artist|Maker,Mounted by,Gertrude Käsebier|Frederick H. Evans,"American, 1852–1934|British, London 1853–1943 London",,"Käsebier, Gertrude|Evans, Frederick Henry",American|British,1852 |1853,1934 |1943,ca. 1901,1899,1903,Platinum print,Image: 8 in. × 5 13/16 in. (20.3 × 14.7 cm) Sheet: 14 13/16 × 10 1/16 in. (37.7 × 25.6 cm),"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1973",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260987,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1973.613.10,false,true,260978,Photographs,Photograph,F. H. Evans,,,,,,Artist|Maker,Mounted by,Gertrude Käsebier|Frederick H. Evans,"American, 1852–1934|British, London 1853–1943 London",,"Käsebier, Gertrude|Evans, Frederick Henry",American|British,1852 |1853,1934 |1943,ca. 1901,1899,1903,Platinum print,Image: 8 in. × 5 7/8 in. (20.3 × 15 cm) Sheet: 14 13/16 × 10 5/16 in. (37.7 × 26.2 cm),"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1973",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260978,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1973.613.11,false,true,260979,Photographs,Photograph,F. H. Evans,,,,,,Artist|Maker,Mounted by,Gertrude Käsebier|Frederick H. Evans,"American, 1852–1934|British, London 1853–1943 London",,"Käsebier, Gertrude|Evans, Frederick Henry",American|British,1852 |1853,1934 |1943,ca. 1901,1899,1903,Platinum print,Image: 7 11/16 in. × 6 in. (19.5 × 15.3 cm) Sheet: 14 7/8 × 10 3/8 in. (37.8 × 26.3 cm),"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1973",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260979,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1982.1010.3,false,true,263303,Photographs,Photograph,"View of Transept, Looking South",,,,,,Artist|Printer,,Hugh Owen|Nicolaas Henneman,"British, 1808–1897|Dutch, Heemskerk 1813–1898 London",,"Owen, Hugh|Henneman, Nicolaas",British|Dutch,1808 |1813,1897 |1898,1851,1851,1851,Salted paper print from paper negative,Image: 21.6 x 16.3 cm (8 1/2 x 6 7/16 in.) Mount: 35 x 21.2 cm (13 3/4 x 8 3/8 in.),"Purchase, Emanuel Gerard Gift, 1982",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/263303,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1982.1010.5,false,true,263305,Photographs,Photograph,Greek Slave,,,,,,Artist|Printer,,Hugh Owen|Nicolaas Henneman,"British, 1808–1897|Dutch, Heemskerk 1813–1898 London",,"Owen, Hugh|Henneman, Nicolaas",British|Dutch,1808 |1813,1897 |1898,1851,1851,1851,Salted paper print from paper negative,Image: 8 3/8 × 6 5/16 in. (21.3 × 16.1 cm) Mount: 13 3/4 × 9 13/16 in. (35 × 25 cm),"Purchase, Emanuel Gerard Gift, 1982",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/263305,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -66.724.2,false,true,271123,Photographs,Photograph,"Mirror Lake, Valley of the Yosemite",,,,,,Artist|Printer|Printer,,Eadweard Muybridge|Bradley and Rulofson|Henry W. Bradley,"American, born Britain, 1830–1904|American, 1813–1891",,"Muybridge, Eadweard|Bradley and Rulofson|Bradley, Henry W.","American, born Britain|American",1830 |1813,1904 |1891,1872,1872,1872,Albumen silver print from glass negative,42.8 x 54.3 cm (16 7/8 x 21 3/8 in. ),"David Hunter McAlpin Fund, 1966",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271123,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -49.40.83,false,true,269270,Photographs,Photograph,"Notre Dame, Paris",,,,,,Artist|Artist|Photography Studio,,Louis-Auguste Bisson|Auguste-Rosalie Bisson|Bisson Frères,"French, 1814–1876|French, 1826–1900|French, active 1852–1863",,"Bisson, Louis-Auguste|Bisson, Auguste-Rosalie|Bisson Frères",French|French|French,1814 |1826 |1852,1876 |1900 |1863,1850s,1850,1859,Albumen silver print,,"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1949",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269270,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.410 (4),false,true,288159,Photographs,Photograph,[Empress Eugénie as an Odalisque],,,,,,Artist|Artist|Person in Photograph,Painted and retouched by|Person in photograph,Pierre-Louis Pierson|Marck|Empress Eugénie de Montijo,"French, 1822–1913|French (born Spain), Granada 1826–1920 Madrid",", et al","Pierson, Pierre-Louis|Marck|Montijo, Eugénie de, Empress","French|French, born Spain",1822 |1826,1913 |1920,1861–65,1861,1865,Albumen silver print,Image: 17 x 12 cm (6 11/16 x 4 3/4 in.) Mount: 38 x 28 cm (14 15/16 x 11 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288159,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.494.1,false,true,289273,Photographs,Photograph,Ru-Shan Monastery,,,,,,Former Attribution|Artist,Possibly by,John Thomson|Lai Fong,"British, Edinburgh, Scotland 1837–1921 London|Chinese, 1839–1890",,"Thomson, John|Lai, Fong","British, Scottish",1837 |1839,1921 |1890,ca. 1869,1869,1869,Albumen silver print from glass negative,Image: 8 15/16 × 11 1/8 in. (22.7 × 28.2 cm),"Gilman Collection, Purchase, Robert Rosenkranz Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289273,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.494.27,false,true,289299,Photographs,Photograph,"Yuen-foo River, View from the Hill",,,,,,Former Attribution|Artist,Possibly by,John Thomson|Lai Fong,"British, Edinburgh, Scotland 1837–1921 London|Chinese, 1839–1890",,"Thomson, John|Lai, Fong","British, Scottish",1837 |1839,1921 |1890,ca. 1869,1869,1869,Albumen silver print from glass negative,Image: 8 1/16 × 11 7/16 in. (20.4 × 29 cm),"Gilman Collection, Purchase, Robert Rosenkranz Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289299,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.494.30,false,true,289302,Photographs,Photograph,On the Road up to Yuen-foo Monastery,,,,,,Former Attribution|Artist,Possibly by,John Thomson|Lai Fong,"British, Edinburgh, Scotland 1837–1921 London|Chinese, 1839–1890",,"Thomson, John|Lai, Fong","British, Scottish",1837 |1839,1921 |1890,ca. 1869,1869,1869,Albumen silver print from glass negative,Image: 7 5/16 × 9 3/8 in. (18.6 × 23.8 cm),"Gilman Collection, Purchase, Robert Rosenkranz Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289302,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.494.33,false,true,289305,Photographs,Photograph,Bankers Glen Yuen foo Monastery,,,,,,Former Attribution|Artist,Possibly by,John Thomson|Lai Fong,"British, Edinburgh, Scotland 1837–1921 London|Chinese, 1839–1890",,"Thomson, John|Lai, Fong","British, Scottish",1837 |1839,1921 |1890,ca. 1869,1869,1869,Albumen silver print from glass negative,Image: 9 1/16 × 11 3/8 in. (23 × 28.9 cm),"Gilman Collection, Purchase, Robert Rosenkranz Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289305,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.494.43,false,true,289315,Photographs,Photograph,"Bowling Alley and Raquet Court, Foochow",,,,,,Former Attribution|Artist,Possibly by,John Thomson|Lai Fong,"British, Edinburgh, Scotland 1837–1921 London|Chinese, 1839–1890",,"Thomson, John|Lai, Fong","British, Scottish",1837 |1839,1921 |1890,ca. 1869,1869,1869,Albumen silver print from glass negative,Image: 8 15/16 × 11 5/16 in. (22.7 × 28.7 cm),"Gilman Collection, Purchase, Robert Rosenkranz Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289315,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1982.1182.714–.736,false,true,288229,Photographs,Stereographs,[Group of 23 Early Stereograph Views of British Cathedrals],,,,,,Artist|Artist|Publisher|Artist|Publisher|Artist|Publisher|Artist,,Unknown|Budge|Edinburgh Stereographic Company|F. G. O. Stuart|John Browning|Mrs. Charles Lawrence|H. T. Ramsay|Thomas Heaviside,"British|British|British|British|British|British|British, 1828–1886",,"Unknown|Budge|Edinburgh Stereographic Company|Stuart, F. G. O.|Browning, John|Lawrence, Charles Mrs.|Ramsay, H. T.|Heaviside, Thomas","British|British, Scottish|British|British|British|British|British",1828,1886,1860s–80s,1860,1889,Albumen silver prints,Mounts approximately: 8.4 x 16.9 cm (3 5/16 x 6 5/8 in.) to 8.5 x 17.6 cm (3 3/8 x 6 15/16 in.),"Gift of Weston J. Naef, in memory of Kathleen W. Naef and Weston J. Naef Sr., 1982",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288229,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1982.1182.97–.204,false,true,288118,Photographs,Stereographs,[Group of 107 Stereograph Views of Animals],,,,,,Publisher|Publisher|Publisher|Publisher|Publisher|Artist|Photography Studio|Artist|Publisher|Artist|Artist|Artist|Publisher|Publisher|Artist|Artist|Publisher|Artist|Publisher|Publisher|Publisher|Artist|Publisher|Publisher|Publisher|Publisher|Artist|Publisher|Publisher|Publisher|Publisher|Artist|Publisher,,"Griffith & Griffith, American|William H. Rau|C. H. Graves|Universal Photo Art Co.|Underwood & Underwood|George Barker|Sun Sculpture Works and Studios|John L. Lovell|J. A. French|Merrimac Stereoscopic Company|E. M. Van Aken|H. Rikard, American|H. C. White Company|International View Company|C. L. Wasson|Allen|Stereoscopic Gems|H. Werner|Wilder & Williamson|Strohmeyer & Wyman|Union View Company|Benneville Lloyd Singley|Standard Series|J. F. Jarvis|Canvassers|Webster & Albee|T. W. Ingersoll|Metropolitan Series|Littleton View Company|The Whiting View Company|T. C. Fletcher & Company|Unknown|Keystone View Company","American, 1855–1920|American|American|American, born Canada, 1844–1894|American|American, 1825–1903|American|American|American|American|American|American|American|American|American|American, Union Township, Pennsylvania 1864–1938 Meadville, Pennsylvania|American|American|American|American|American|American|American",,"Griffith & Griffith|Rau, William H.|Graves, C. H.|Universal Photo Art Co.|Underwood & Underwood|Barker, George|Sun Sculpture Works and Studios|Lovell, John L.|French, J. A.|Merrimac Stereoscopic Company|Van Aken, E. M.|Rikard, H.|H. C. White Company|International View Company|Wasson, C. L.|Allen|Stereoscopic Gems|Werner, H.|Wilder & Williamson|Strohmeyer & Wyman|Union View Company|Singley, Benneville Lloyd|Standard Series|Jarvis, J. F.|Canvassers|Webster & Albee|Ingersoll, T. W.|Metropolitan Series|Littleton View Company|Whiting View Company|T. C. Fletcher & Company|Unknown|Keystone View Company",American|American|American|American|American|American|American|American|American|American|American|American|American|American|American|American|American|American|American|American|American|American|American|American|American,1855 |1844 |1825 |1864,1920 |1894 |1903 |1938,1850s–1910s,1850,1919,Albumen silver prints,Mounts approximately: 8.3 x 17.2 cm (3 1/4 x 6 3/4 in.) to 11.4 x 17.7 cm (4 1/2 x 6 15/16 in.),"Gift of Weston J. Naef, in memory of Kathleen W. Naef and Weston J. Naef Sr., 1982",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288118,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1982.1182.1940–.1950,false,true,288315,Photographs,Stereographs,"[Group of 11 Stereograph Views of the 1869 and 1872 World Peace Jubilees, Boston, Massachusetts, United States of America]",,,,,,Publisher|Artist|Subject|Publisher|Artist,Cited in manuscript,Charles Pollock|William G. Preston|Joseph H. Chadwick|John P. Soule|S. Towle,"American|American, 1842–1910|American|American, 1827–1904|American",,"Pollock, Charles|Preston, William G.|Chadwick, Joseph H.|Soule, John P.|Towle, S.",American|American|American|American|American,1842 |1827,1910 |1904,1850s–1910s,1850,1919,Albumen silver prints,Mounts approximately: 8.1 x 17.3 cm (3 3/16 x 6 13/16 in.) to 8.7 x 17.7 cm (3 7/16 x 6 15/16 in.),"Gift of Weston J. Naef, in memory of Kathleen W. Naef and Weston J. Naef Sr., 1982",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288315,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2011.206,false,true,296346,Photographs,Photograph,"[Distortograph: William Hale ""Big Bill"" Thompson, Mayor of Chicago]",,,,,,Artist|Person in Photograph,,Herbert George Ponting|William Hale Thompson,"British, Salisbury, Wiltshire 1870–1935 London|American, 1869–1944",,"Ponting, Herbert George|Thompson, William Hale",British|American,1870 |1869,1935 |1944,1927,1927,1927,Gelatin silver print,"Image: 9.8 x 7.3 cm (3 7/8 x 2 7/8 in.) Sheet: 16.3 x 12 cm (6 7/16 x 4 3/4 in.) Frame: 50.8 x 40.6 cm (20 x 16 in.) (Framed with 2011.207, .208, .209)","Twentieth-Century Photography Fund, 2011",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/296346,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2011.207,false,true,296347,Photographs,Photograph,"[Distortograph: William Hale ""Big Bill"" Thompson, Mayor of Chicago]",,,,,,Artist|Person in Photograph,,Herbert George Ponting|William Hale Thompson,"British, Salisbury, Wiltshire 1870–1935 London|American, 1869–1944",,"Ponting, Herbert George|Thompson, William Hale",British|American,1870 |1869,1935 |1944,1927,1927,1927,Gelatin silver print,"Image: 9.8 x 7.3 cm (3 7/8 x 2 7/8 in.) Sheet: 16.2 x 12.1 cm (6 3/8 x 4 3/4 in.) Frame: 50.8 x 40.6 cm (20 x 16 in.) (Framed with 2011.206, .208, .209)","Twentieth-Century Photography Fund, 2011",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/296347,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2011.208,false,true,296348,Photographs,Photograph,"[Distortograph: William Hale ""Big Bill"" Thompson, Mayor of Chicago]",,,,,,Artist|Person in Photograph,,Herbert George Ponting|William Hale Thompson,"British, Salisbury, Wiltshire 1870–1935 London|American, 1869–1944",,"Ponting, Herbert George|Thompson, William Hale",British|American,1870 |1869,1935 |1944,1927,1927,1927,Gelatin silver print,"Image: 9.8 x 7.3 cm (3 7/8 x 2 7/8 in.) Sheet: 16.3 x 11.9 cm (6 7/16 x 4 11/16 in.) Frame: 50.8 x 40.6 cm (20 x 16 in.) (Framed with 2011.206, .207, .209)","Twentieth-Century Photography Fund, 2011",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/296348,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2011.209,false,true,296349,Photographs,Photograph,"[Distortograph: William Hale ""Big Bill"" Thompson, Mayor of Chicago]",,,,,,Artist|Person in Photograph,,Herbert George Ponting|William Hale Thompson,"British, Salisbury, Wiltshire 1870–1935 London|American, 1869–1944",,"Ponting, Herbert George|Thompson, William Hale",British|American,1870 |1869,1935 |1944,1927,1927,1927,Gelatin silver print,"Image: 9.8 x 7.3 cm (3 7/8 x 2 7/8 in.) Sheet: 16.3 x 12 cm (6 7/16 x 4 3/4 in.) Frame: 50.8 x 40.6 cm (20 x 16 in.) (Framed with 2011.206, .207, .208)","Twentieth-Century Photography Fund, 2011",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/296349,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.159.4,false,true,306205,Photographs,Photograph,The Last Sitting,,,,,,Artist|Person in Photograph,Person in photograph,Lewis Carroll|Alice Pleasance Liddell,"British, Daresbury, Cheshire 1832–1898 Guildford|British, 1852–1934",,"Carroll, Lewis|Liddell, Alice",British|British,1832 |1852,1898 |1934,"June 25, 1870",1870,1870,Albumen silver print from glass negative,Sheet: 6 1/4 × 5 9/16 in. (15.9 × 14.1 cm) Image: 5 7/8 × 4 15/16 in. (15 × 12.6 cm),"Bequest of Maurice B. Sendak, 2012",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306205,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.20,false,true,283092,Photographs,Photograph,"Alice Liddell as ""The Beggar Maid""",,,,,,Artist|Subject,,Lewis Carroll|Alice Pleasance Liddell,"British, Daresbury, Cheshire 1832–1898 Guildford|British, 1852–1934",,"Carroll, Lewis|Liddell, Alice",British|British,1832 |1852,1898 |1934,1858,1858,1858,Albumen silver print from glass negative,Image: 16.3 x 10.9cm (6 7/16 x 4 5/16in.) Mount: 14 1/8 in. × 10 7/8 in. (35.8 × 27.6 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283092,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.159.6,false,true,306207,Photographs,Photograph,Edith Liddell Seated Beside a Vase of Foxgloves,,,,,,Artist|Person in Photograph,Person in photograph,Lewis Carroll|Edith Mary Liddell,"British, Daresbury, Cheshire 1832–1898 Guildford|British, 1854–1876",,"Carroll, Lewis|Liddell, Edith Mary",British|British,1832 |1854,1898 |1876,1860,1860,1860,Albumen silver print from glass negative,Sheet: 6 1/4 × 5 3/4 in. (15.9 × 14.6 cm) Image: 5 1/4 × 3 3/4 in. (13.3 × 9.5 cm),"Bequest of Maurice B. Sendak, 2012",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306207,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.636,false,true,286007,Photographs,Photograph,"""The Prettiest Doll in the World""",,,,,,Artist|Subject,,"Lewis Carroll|Alexandra ""Xie"" Rhoda Kitchin","British, Daresbury, Cheshire 1832–1898 Guildford|British, 1864–1925",,"Carroll, Lewis|Kitchin, Alexandra ""Xie"" Rhoda",British|British,1832 |1864,1898 |1925,"July 5, 1870",1870,1870,Albumen silver print from glass negative,7 3/4 x 5 13/16,"Gilman Collection, Purchase, Mr. and Mrs. Henry R. Kravis Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286007,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.43.408,false,true,267824,Photographs,Photograph,Römische Villa,,,,,,Former Attribution|Artist,,Hugo Henneberg|Heinrich Kühn,"Austrian, 1863–1918|Austrian (born Germany), Dresden 1866–1944 Birgitz",,"Henneberg, Hugo|Kühn, Heinrich","Austrian|Austrian, born Germany",1863 |1866,1918 |1944,1898–1900,1898,1900,Gum bichromate print,55.8 x 74.5 cm. (22 x 29 5/16 in.),"Alfred Stieglitz Collection, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267824,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.43.353,false,true,267768,Photographs,Photograph,Robert Browning,,,,,,Artist|Printer,,Julia Margaret Cameron|The Autotype Company,"British (born India), Calcutta 1815–1879 Kalutara, Ceylon|British, London",,"Cameron, Julia Margaret|Autotype Company","British, born India|British",1815,1815,"1865, printed ca. 1902",1865,1865,Carbon print,21.3 x 22.1 cm (8 3/8 x 8 11/16 in.),"Alfred Stieglitz Collection, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267768,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -49.55.319,false,true,269428,Photographs,Photograph,"Alfred, Lord Tennyson",,,,,,Artist|Printer,,Julia Margaret Cameron|The Autotype Company,"British (born India), Calcutta 1815–1879 Kalutara, Ceylon|British, London",,"Cameron, Julia Margaret|Autotype Company","British, born India|British",1815,1815,"1869, printed 1905",1869,1869,Carbon print,35.8 x 26.2 cm. (14 1/16 x 10 5/16 in.),"Alfred Stieglitz Collection, 1949",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269428,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -49.55.320,false,true,269430,Photographs,Photograph,"Alfred, Lord Tennyson",,,,,,Artist|Printer,,Julia Margaret Cameron|The Autotype Company,"British (born India), Calcutta 1815–1879 Kalutara, Ceylon|British, London",,"Cameron, Julia Margaret|Autotype Company","British, born India|British",1815,1815,"1867, printed 1905",1867,1867,Carbon print,34.4 x 25.6 cm. (13 9/16 x 10 1/16 in.),"Alfred Stieglitz Collection, 1949",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269430,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -49.55.321,false,true,269431,Photographs,Photograph,George Frederick Watts,,,,,,Artist|Printer,,Julia Margaret Cameron|The Autotype Company,"British (born India), Calcutta 1815–1879 Kalutara, Ceylon|British, London",,"Cameron, Julia Margaret|Autotype Company","British, born India|British",1815,1815,"1864, printed ca. 1905",1864,1864,Carbon print,25.5 x 20.2 cm. (10 1/16 x 7 15/16 in.),"Alfred Stieglitz Collection, 1949",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269431,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -49.55.322,false,true,269432,Photographs,Photograph,Lord Justice James,,,,,,Artist|Printer,,Julia Margaret Cameron|The Autotype Company,"British (born India), Calcutta 1815–1879 Kalutara, Ceylon|British, London",,"Cameron, Julia Margaret|Autotype Company","British, born India|British",1815,1815,"ca. 1870, printed ca. 1905",1868,1872,Carbon print,33.3 x 26.6 cm. (13 1/8 x 10 1/2 in.),"Alfred Stieglitz Collection, 1949",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269432,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -49.55.323,false,true,269433,Photographs,Photograph,"Ellen Terry, at the age of sixteen",,,,,,Artist|Printer,,Julia Margaret Cameron|The Autotype Company,"British (born India), Calcutta 1815–1879 Kalutara, Ceylon|British, London",,"Cameron, Julia Margaret|Autotype Company","British, born India|British",1815,1815,"1864, printed ca. 1913",1864,1864,Carbon print,24 x 26.7 cm (9 7/16 x 10 1/2 in. ),"Alfred Stieglitz Collection, 1949",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269433,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -49.55.324,false,true,269434,Photographs,Photograph,Thomas Carlyle,,,,,,Artist|Printer,,Julia Margaret Cameron|The Autotype Company,"British (born India), Calcutta 1815–1879 Kalutara, Ceylon|British, London",,"Cameron, Julia Margaret|Autotype Company","British, born India|British",1815,1815,1867,1867,1867,Carbon print,35.0 x 28.1 cm. (13 3/4 x 11 1/16 in.),"Alfred Stieglitz Collection, 1949",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269434,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1982.1182.2035–.2037,false,true,288318,Photographs,Stereographs,"[Group of 3 Stereograph Views of the 1901 Pan American Exposition, Buffalo, New York]",,,,,,Subject|Artist|Person in Photograph|Person in Photograph,,James M. Davis|Kilburn Brothers|William McKinley|First Lady Ida Saxton McKinley,"American, active ca. 1865–1890|American, 1843–1901|American, 1847–1907",,"Davis, James M.|Kilburn Brothers|McKinley, William|McKinley, Ida Saxton First Lady",American|American|American,1863 |1843 |1847,1892 |1901 |1907,1850s–1910s,1850,1919,Albumen silver prints,Mounts: 8.9 x 17.8 cm (3 1/2 x 7 in.),"Gift of Weston J. Naef, in memory of Kathleen W. Naef and Weston J. Naef Sr., 1982",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288318,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.159.53,false,true,306337,Photographs,Photograph,Group of Gentlemen Conversing over a Glass of Wine,,,,,,Person in Photograph|Artist,Person in photograph,Antoine-François-Jean Claudet|William Henry Fox Talbot,"French, active Great Britain, 1797–1867|British, Dorset 1800–1877 Lacock",,"Claudet, Antione-François-Jean|Talbot, William Henry Fox","French, active Great Britain|British",1797 |1800,1867 |1800,"February 7, 1846",1846,1846,Salted paper print from paper negative,Mount: 12 3/16 × 9 11/16 in. (31 × 24.6 cm) Image: 5 1/2 × 7 13/16 in. (13.9 × 19.9 cm) Top corners trimmed,"Bequest of Maurice B. Sendak, 2012",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306337,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.245,false,true,268040,Photographs,Photograph,"The Crack Team of the 1st Division, 6th Corps near Hazel River, Virginia",,,,,,Former Attribution|Artist,Formerly attributed to,Mathew B. Brady|Timothy H. O'Sullivan,"American, born Ireland, 1823?–1896 New York|American, born Ireland, 1840–1882",,"Brady, Mathew B.|O'Sullivan, Timothy H.","American|American, born Ireland",1823 |1840,1896 |1882,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268040,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.266,false,true,268063,Photographs,Photograph,"[U.S. Gunboat]. Brady album, p. 161",,,,,,Former Attribution|Artist,Formerly attributed to,Mathew B. Brady|Timothy H. O'Sullivan,"American, born Ireland, 1823?–1896 New York|American, born Ireland, 1840–1882",,"Brady, Mathew B.|O'Sullivan, Timothy H.","American|American, born Ireland",1823 |1840,1896 |1882,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268063,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.3,false,true,268100,Photographs,Photograph,"Yellow House, Warren Station, Virginia",,,,,,Artist|Former Attribution,Formerly attributed to,Timothy H. O'Sullivan|Mathew B. Brady,"American, born Ireland, 1840–1882|American, born Ireland, 1823?–1896 New York",,"O'Sullivan, Timothy H.|Brady, Mathew B.","American, born Ireland",1840 |1823,1882 |1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268100,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.10,false,true,267880,Photographs,Photograph,"General Hospital, Point of Rocks, Appomattox River below Petersburg",,,,,,Artist|Former Attribution,Formerly attributed to,Timothy H. O'Sullivan|Mathew B. Brady,"American, born Ireland, 1840–1882|American, born Ireland, 1823?–1896 New York",,"O'Sullivan, Timothy H.|Brady, Mathew B.","American, born Ireland",1840 |1823,1882 |1896,1864,1864,1864,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267880,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.12,false,true,267902,Photographs,Photograph,"Poplar Grove Church, built by 50th New York Volunteers",,,,,,Artist|Former Attribution,Formerly attributed to,Timothy H. O'Sullivan|Mathew B. Brady,"American, born Ireland, 1840–1882|American, born Ireland, 1823?–1896 New York",,"O'Sullivan, Timothy H.|Brady, Mathew B.","American, born Ireland",1840 |1823,1882 |1896,1865,1865,1865,Albumen silver print from glass negative,16.5 x 20.2 cm (6 1/2 x 7 15/16 in. ),"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267902,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.13,false,true,267913,Photographs,Panorama,"Confederate Prisoners at Belle Plain, May 12",,,,,,Artist|Former Attribution,Formerly attributed to,Timothy H. O'Sullivan|Mathew B. Brady,"American, born Ireland, 1840–1882|American, born Ireland, 1823?–1896 New York",,"O'Sullivan, Timothy H.|Brady, Mathew B.","American, born Ireland",1840 |1823,1882 |1896,1863,1863,1863,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267913,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.19,false,true,267979,Photographs,Photograph,"White House Landing, Pamunkey River",,,,,,Artist|Former Attribution,Formerly attributed to,Timothy H. O'Sullivan|Mathew B. Brady,"American, born Ireland, 1840–1882|American, born Ireland, 1823?–1896 New York",,"O'Sullivan, Timothy H.|Brady, Mathew B.","American, born Ireland",1840 |1823,1882 |1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267979,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.26,false,true,268056,Photographs,Photograph,Bull Run. Bridge near Union Mills (destroyed seven times),,,,,,Artist|Former Attribution,Formerly attributed to,Timothy H. O'Sullivan|Mathew B. Brady,"American, born Ireland, 1840–1882|American, born Ireland, 1823?–1896 New York",,"O'Sullivan, Timothy H.|Brady, Mathew B.","American, born Ireland",1840 |1823,1882 |1896,ca. 1862,1860,1864,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268056,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.28,false,true,268078,Photographs,Photograph,"Colored Battery, Petersburg, June",,,,,,Artist|Former Attribution,Formerly attributed to,Timothy H. O'Sullivan|Mathew B. Brady,"American, born Ireland, 1840–1882|American, born Ireland, 1823?–1896 New York",,"O'Sullivan, Timothy H.|Brady, Mathew B.","American, born Ireland",1840 |1823,1882 |1896,1864,1864,1864,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268078,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.35,false,true,268156,Photographs,Photograph,"Commodore Perry, Pamunkey River",,,,,,Artist|Former Attribution,Formerly attributed to,Timothy H. O'Sullivan|Mathew B. Brady,"American, born Ireland, 1840–1882|American, born Ireland, 1823?–1896 New York",,"O'Sullivan, Timothy H.|Brady, Mathew B.","American, born Ireland",1840 |1823,1882 |1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268156,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.54,false,true,268246,Photographs,Photograph,"White House Landing, Pamunkey River",,,,,,Artist|Former Attribution,Formerly attributed to,Timothy H. O'Sullivan|Mathew B. Brady,"American, born Ireland, 1840–1882|American, born Ireland, 1823?–1896 New York",,"O'Sullivan, Timothy H.|Brady, Mathew B.","American, born Ireland",1840 |1823,1882 |1896,1861–65,1861,1865,Albumen silver print from glass negative,13.1 x 20.2 cm (5 3/16 x 7 15/16 in. ),"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268246,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.55,false,true,268247,Photographs,Photograph,"White House Landing, Pamunkey River",,,,,,Artist|Former Attribution,Formerly attributed to,Timothy H. O'Sullivan|Mathew B. Brady,"American, born Ireland, 1840–1882|American, born Ireland, 1823?–1896 New York",,"O'Sullivan, Timothy H.|Brady, Mathew B.","American, born Ireland",1840 |1823,1882 |1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268247,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.56,false,true,268248,Photographs,Photograph,"Bridge Across Pamunkey River, near White House",,,,,,Artist|Former Attribution,Formerly attributed to,Timothy H. O'Sullivan|Mathew B. Brady,"American, born Ireland, 1840–1882|American, born Ireland, 1823?–1896 New York",,"O'Sullivan, Timothy H.|Brady, Mathew B.","American, born Ireland",1840 |1823,1882 |1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268248,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.64,false,true,268257,Photographs,Photograph,"Blackburn's Ford / Rapidan River, The Wilderness",,,,,,Artist|Former Attribution,Formerly attributed to,Timothy H. O'Sullivan|Mathew B. Brady,"American, born Ireland, 1840–1882|American, born Ireland, 1823?–1896 New York",,"O'Sullivan, Timothy H.|Brady, Mathew B.","American, born Ireland",1840 |1823,1882 |1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268257,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.65,false,true,268258,Photographs,Photograph,"White House Landing, Pamunkey River",,,,,,Artist|Former Attribution,Formerly attributed to,Timothy H. O'Sullivan|Mathew B. Brady,"American, born Ireland, 1840–1882|American, born Ireland, 1823?–1896 New York",,"O'Sullivan, Timothy H.|Brady, Mathew B.","American, born Ireland",1840 |1823,1882 |1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268258,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.66,false,true,268259,Photographs,Photograph,"White House Landing, Pamunkey River",,,,,,Artist|Former Attribution,Formerly attributed to,Timothy H. O'Sullivan|Mathew B. Brady,"American, born Ireland, 1840–1882|American, born Ireland, 1823?–1896 New York",,"O'Sullivan, Timothy H.|Brady, Mathew B.","American, born Ireland",1840 |1823,1882 |1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268259,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.67,false,true,268260,Photographs,Photograph,"White House Landing, Pamunkey River",,,,,,Artist|Former Attribution,Formerly attributed to,Timothy H. O'Sullivan|Mathew B. Brady,"American, born Ireland, 1840–1882|American, born Ireland, 1823?–1896 New York",,"O'Sullivan, Timothy H.|Brady, Mathew B.","American, born Ireland",1840 |1823,1882 |1896,1861–65,1861,1865,Albumen silver print from glass negative,12.9 x 19.2 cm (5 1/16 x 7 9/16 in. ),"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268260,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.80,false,true,268275,Photographs,Photograph,"U.S. Gunboat ""Commodore Perry"" on Pamunkey River",,,,,,Artist|Former Attribution,Formerly attributed to,Timothy H. O'Sullivan|Mathew B. Brady,"American, born Ireland, 1840–1882|American, born Ireland, 1823?–1896 New York",,"O'Sullivan, Timothy H.|Brady, Mathew B.","American, born Ireland",1840 |1823,1882 |1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268275,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.105,false,true,267886,Photographs,Photograph,Bull Run. Blackburn's Ford,,,,,,Artist|Former Attribution,Formerly attributed to,Timothy H. O'Sullivan|Mathew B. Brady,"American, born Ireland, 1840–1882|American, born Ireland, 1823?–1896 New York",,"O'Sullivan, Timothy H.|Brady, Mathew B.","American, born Ireland",1840 |1823,1882 |1896,ca. 1862,1860,1864,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267886,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.111,false,true,267893,Photographs,Photograph,"Swamp near Broadway Landing, Appomattox River",,,,,,Artist|Former Attribution,Formerly attributed to,Timothy H. O'Sullivan|Mathew B. Brady,"American, born Ireland, 1840–1882|American, born Ireland, 1823?–1896 New York",,"O'Sullivan, Timothy H.|Brady, Mathew B.","American, born Ireland",1840 |1823,1882 |1896,1864,1864,1864,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267893,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.130,false,true,267914,Photographs,Photograph,"U.S. Gunboat ""Commodore Perry"" on Pamunkey River",,,,,,Artist|Former Attribution,Formerly attributed to,Timothy H. O'Sullivan|Mathew B. Brady,"American, born Ireland, 1840–1882|American, born Ireland, 1823?–1896 New York",,"O'Sullivan, Timothy H.|Brady, Mathew B.","American, born Ireland",1840 |1823,1882 |1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267914,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.140,false,true,267925,Photographs,Photograph,"Interior of Fort Steadman, front of Petersburg",,,,,,Artist|Former Attribution,Formerly attributed to,Timothy H. O'Sullivan|Mathew B. Brady,"American, born Ireland, 1840–1882|American, born Ireland, 1823?–1896 New York",,"O'Sullivan, Timothy H.|Brady, Mathew B.","American, born Ireland",1840 |1823,1882 |1896,1864,1864,1864,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267925,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.144,false,true,267929,Photographs,Photograph,"Outer Confederate Line, Petersburg, Captured June 15, 1864",,,,,,Artist|Former Attribution,Formerly attributed to,Timothy H. O'Sullivan|Mathew B. Brady,"American, born Ireland, 1840–1882|American, born Ireland, 1823?–1896 New York",,"O'Sullivan, Timothy H.|Brady, Mathew B.","American, born Ireland",1840 |1823,1882 |1896,1864,1864,1864,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267929,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.145,false,true,267930,Photographs,Photograph,"Fort Burnham, front of Petersburg",,,,,,Artist|Former Attribution,Formerly attributed to,Timothy H. O'Sullivan|Mathew B. Brady,"American, born Ireland, 1840–1882|American, born Ireland, 1823?–1896 New York",,"O'Sullivan, Timothy H.|Brady, Mathew B.","American, born Ireland",1840 |1823,1882 |1896,1864,1864,1864,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267930,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.146,false,true,267931,Photographs,Photograph,"The Mine, Petersburg",,,,,,Artist|Former Attribution,Formerly attributed to,Timothy H. O'Sullivan|Mathew B. Brady,"American, born Ireland, 1840–1882|American, born Ireland, 1823?–1896 New York",,"O'Sullivan, Timothy H.|Brady, Mathew B.","American, born Ireland",1840 |1823,1882 |1896,1864,1864,1864,Albumen silver print from glass negative,16 x 20.4 cm (6 5/16 x 8 1/16 in. ),"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267931,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.147,false,true,267932,Photographs,Photograph,"Interior of Fort Sedgwick, before Petersburg",,,,,,Artist|Former Attribution,Formerly attributed to,Timothy H. O'Sullivan|Mathew B. Brady,"American, born Ireland, 1840–1882|American, born Ireland, 1823?–1896 New York",,"O'Sullivan, Timothy H.|Brady, Mathew B.","American, born Ireland",1840 |1823,1882 |1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267932,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.149,false,true,267934,Photographs,Photograph,"The Crater, Petersburg",,,,,,Artist|Former Attribution,Formerly attributed to,Timothy H. O'Sullivan|Mathew B. Brady,"American, born Ireland, 1840–1882|American, born Ireland, 1823?–1896 New York",,"O'Sullivan, Timothy H.|Brady, Mathew B.","American, born Ireland",1840 |1823,1882 |1896,1864,1864,1864,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267934,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.154,false,true,267940,Photographs,Photograph,In front of Petersburg,,,,,,Artist|Former Attribution,Formerly attributed to,Timothy H. O'Sullivan|Mathew B. Brady,"American, born Ireland, 1840–1882|American, born Ireland, 1823?–1896 New York",,"O'Sullivan, Timothy H.|Brady, Mathew B.","American, born Ireland",1840 |1823,1882 |1896,1864,1864,1864,Albumen silver print from glass negative,16.3 x 21.3 cm (6 7/16 x 8 3/8 in. ),"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267940,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.156,false,true,267942,Photographs,Photograph,"Outer Confederate Line, Petersburg, Captured June 15, 1864",,,,,,Artist|Former Attribution,Formerly attributed to,Timothy H. O'Sullivan|Mathew B. Brady,"American, born Ireland, 1840–1882|American, born Ireland, 1823?–1896 New York",,"O'Sullivan, Timothy H.|Brady, Mathew B.","American, born Ireland",1840 |1823,1882 |1896,1864,1864,1864,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267942,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.157,false,true,267943,Photographs,Photograph,Front of Petersburg Lines,,,,,,Artist|Former Attribution,Formerly attributed to,Timothy H. O'Sullivan|Mathew B. Brady,"American, born Ireland, 1840–1882|American, born Ireland, 1823?–1896 New York",,"O'Sullivan, Timothy H.|Brady, Mathew B.","American, born Ireland",1840 |1823,1882 |1896,1864,1864,1864,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267943,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.160,false,true,267947,Photographs,Photograph,"Outer Confederate Line at Petersburg. Captured by 18th Corps, June 15, 1864",,,,,,Artist|Former Attribution,Formerly attributed to,Timothy H. O'Sullivan|Mathew B. Brady,"American, born Ireland, 1840–1882|American, born Ireland, 1823?–1896 New York",,"O'Sullivan, Timothy H.|Brady, Mathew B.","American, born Ireland",1840 |1823,1882 |1896,1864,1864,1864,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267947,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.162,false,true,267949,Photographs,Photograph,"Warren Station, near Petersburg (graves)",,,,,,Artist|Former Attribution,Formerly attributed to,Timothy H. O'Sullivan|Mathew B. Brady,"American, born Ireland, 1840–1882|American, born Ireland, 1823?–1896 New York",,"O'Sullivan, Timothy H.|Brady, Mathew B.","American, born Ireland",1840 |1823,1882 |1896,1864,1864,1864,Albumen silver print from glass negative,14.8 x 20.8 cm (5 13/16 x 8 3/16 in. ),"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267949,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.168,false,true,267955,Photographs,Photograph,Bull Run. Blackburn's Ford,,,,,,Artist|Former Attribution,Formerly attributed to,Timothy H. O'Sullivan|Mathew B. Brady,"American, born Ireland, 1840–1882|American, born Ireland, 1823?–1896 New York",,"O'Sullivan, Timothy H.|Brady, Mathew B.","American, born Ireland",1840 |1823,1882 |1896,ca. 1862,1860,1864,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267955,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.169,false,true,267956,Photographs,Photograph,Bull Run. Blackburn's Ford,,,,,,Artist|Former Attribution,Formerly attributed to,Timothy H. O'Sullivan|Mathew B. Brady,"American, born Ireland, 1840–1882|American, born Ireland, 1823?–1896 New York",,"O'Sullivan, Timothy H.|Brady, Mathew B.","American, born Ireland",1840 |1823,1882 |1896,ca. 1862,1860,1864,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267956,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.186,false,true,267975,Photographs,Photograph,Confederate Prisoners at Belle Plain,,,,,,Artist|Former Attribution,Formerly attributed to,Timothy H. O'Sullivan|Mathew B. Brady,"American, born Ireland, 1840–1882|American, born Ireland, 1823?–1896 New York",,"O'Sullivan, Timothy H.|Brady, Mathew B.","American, born Ireland",1840 |1823,1882 |1896,1863,1863,1863,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267975,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.201,false,true,267993,Photographs,Panorama,"Camp of Confederate Prisoners at Belle Plain, May 12, 1863",,,,,,Artist|Former Attribution,Formerly attributed to,Timothy H. O'Sullivan|Mathew B. Brady,"American, born Ireland, 1840–1882|American, born Ireland, 1823?–1896 New York",,"O'Sullivan, Timothy H.|Brady, Mathew B.","American, born Ireland",1840 |1823,1882 |1896,1863,1863,1863,Albumen silver print from glass negative,15.1 x 20.7 cm (5 15/16 x 8 1/8 in. ),"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267993,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.233,false,true,268027,Photographs,Photograph,"Quartermaster and Ambulance Camp, 6th Corps, Brandy Station, Virginia",,,,,,Artist|Former Attribution,Formerly attributed to,Timothy H. O'Sullivan|Mathew B. Brady,"American, born Ireland, 1840–1882|American, born Ireland, 1823?–1896 New York",,"O'Sullivan, Timothy H.|Brady, Mathew B.","American, born Ireland",1840 |1823,1882 |1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268027,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.241,false,true,268036,Photographs,Photograph,"Pennsylvania Light Artillery, Battery B, Petersburg, Virginia",,,,,,Artist|Former Attribution,Formerly attributed to,Timothy H. O'Sullivan|Mathew B. Brady,"American, born Ireland, 1840–1882|American, born Ireland, 1823?–1896 New York",,"O'Sullivan, Timothy H.|Brady, Mathew B.","American, born Ireland",1840 |1823,1882 |1896,1864,1864,1864,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268036,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.256,false,true,268052,Photographs,Photograph,"Quartermaster and Ambulance Camp, Brandy Station, Virginia",,,,,,Artist|Former Attribution,Formerly attributed to,Timothy H. O'Sullivan|Mathew B. Brady,"American, born Ireland, 1840–1882|American, born Ireland, 1823?–1896 New York",,"O'Sullivan, Timothy H.|Brady, Mathew B.","American, born Ireland",1840 |1823,1882 |1896,1861–65,1861,1865,Albumen silver print from glass negative,13.2 x 20.6 cm (5 3/16 x 8 1/8 in. ),"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268052,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.258,false,true,268054,Photographs,Photograph,"Quartermaster and Ambulance Camp, Brandy Station, Virginia",,,,,,Artist|Former Attribution,Formerly attributed to,Timothy H. O'Sullivan|Mathew B. Brady,"American, born Ireland, 1840–1882|American, born Ireland, 1823?–1896 New York",,"O'Sullivan, Timothy H.|Brady, Mathew B.","American, born Ireland",1840 |1823,1882 |1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268054,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.259,false,true,268055,Photographs,Photograph,"Gabions in Engineers' Camp, Petersburg",,,,,,Artist|Former Attribution,Formerly attributed to,Timothy H. O'Sullivan|Mathew B. Brady,"American, born Ireland, 1840–1882|American, born Ireland, 1823?–1896 New York",,"O'Sullivan, Timothy H.|Brady, Mathew B.","American, born Ireland",1840 |1823,1882 |1896,1864,1864,1864,Albumen silver print from glass negative,16.3 x 20.2 cm (6 7/16 x 7 15/16 in. ),"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268055,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.260,false,true,268057,Photographs,Photograph,Aqueduct near Petersburg,,,,,,Artist|Former Attribution,Formerly attributed to,Timothy H. O'Sullivan|Mathew B. Brady,"American, born Ireland, 1840–1882|American, born Ireland, 1823?–1896 New York",,"O'Sullivan, Timothy H.|Brady, Mathew B.","American, born Ireland",1840 |1823,1882 |1896,1864,1864,1864,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268057,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.261,false,true,268058,Photographs,Photograph,"Fort Sedgwick near Petersburg, interior",,,,,,Artist|Former Attribution,Formerly attributed to,Timothy H. O'Sullivan|Mathew B. Brady,"American, born Ireland, 1840–1882|American, born Ireland, 1823?–1896 New York",,"O'Sullivan, Timothy H.|Brady, Mathew B.","American, born Ireland",1840 |1823,1882 |1896,1864,1864,1864,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268058,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.273,false,true,268071,Photographs,Photograph,Bull Run. Bridge Near Union Mills,,,,,,Artist|Former Attribution,Formerly attributed to,Timothy H. O'Sullivan|Mathew B. Brady,"American, born Ireland, 1840–1882|American, born Ireland, 1823?–1896 New York",,"O'Sullivan, Timothy H.|Brady, Mathew B.","American, born Ireland",1840 |1823,1882 |1896,ca. 1862,1860,1864,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268071,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.292,false,true,268092,Photographs,Photograph,"Warren Station, Virginia",,,,,,Artist|Former Attribution,Formerly attributed to,Timothy H. O'Sullivan|Mathew B. Brady,"American, born Ireland, 1840–1882|American, born Ireland, 1823?–1896 New York",,"O'Sullivan, Timothy H.|Brady, Mathew B.","American, born Ireland",1840 |1823,1882 |1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268092,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.294,false,true,268094,Photographs,Photograph,"Quartermaster cargoes and transports, Pamunkey River",,,,,,Artist|Former Attribution,Formerly attributed to,Timothy H. O'Sullivan|Mathew B. Brady,"American, born Ireland, 1840–1882|American, born Ireland, 1823?–1896 New York",,"O'Sullivan, Timothy H.|Brady, Mathew B.","American, born Ireland",1840 |1823,1882 |1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268094,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.327,false,true,268131,Photographs,Photograph,Appomattox River,,,,,,Artist|Former Attribution,Formerly attributed to,Timothy H. O'Sullivan|Mathew B. Brady,"American, born Ireland, 1840–1882|American, born Ireland, 1823?–1896 New York",,"O'Sullivan, Timothy H.|Brady, Mathew B.","American, born Ireland",1840 |1823,1882 |1896,1864,1864,1864,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268131,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.329,false,true,268133,Photographs,Photograph,"Cobb's Hill, Lookout in Distance",,,,,,Artist|Former Attribution,Formerly attributed to,Timothy H. O'Sullivan|Mathew B. Brady,"American, born Ireland, 1840–1882|American, born Ireland, 1823?–1896 New York",,"O'Sullivan, Timothy H.|Brady, Mathew B.","American, born Ireland",1840 |1823,1882 |1896,1864,1864,1864,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268133,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.330,false,true,268135,Photographs,Photograph,Appomattox River,,,,,,Artist|Former Attribution,Formerly attributed to,Timothy H. O'Sullivan|Mathew B. Brady,"American, born Ireland, 1840–1882|American, born Ireland, 1823?–1896 New York",,"O'Sullivan, Timothy H.|Brady, Mathew B.","American, born Ireland",1840 |1823,1882 |1896,1864,1864,1864,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268135,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.332,false,true,268137,Photographs,Photograph,Warren Station near Petersburg,,,,,,Artist|Former Attribution,Formerly attributed to,Timothy H. O'Sullivan|Mathew B. Brady,"American, born Ireland, 1840–1882|American, born Ireland, 1823?–1896 New York",,"O'Sullivan, Timothy H.|Brady, Mathew B.","American, born Ireland",1840 |1823,1882 |1896,1864,1864,1864,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268137,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.335,false,true,268140,Photographs,Photograph,Fort Sedgwick,,,,,,Artist|Former Attribution,Formerly attributed to,Timothy H. O'Sullivan|Mathew B. Brady,"American, born Ireland, 1840–1882|American, born Ireland, 1823?–1896 New York",,"O'Sullivan, Timothy H.|Brady, Mathew B.","American, born Ireland",1840 |1823,1882 |1896,1864,1864,1864,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268140,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.338,false,true,268143,Photographs,Photograph,Fort Sedgwick in front of Petersburg,,,,,,Artist|Former Attribution,Formerly attributed to,Timothy H. O'Sullivan|Mathew B. Brady,"American, born Ireland, 1840–1882|American, born Ireland, 1823?–1896 New York",,"O'Sullivan, Timothy H.|Brady, Mathew B.","American, born Ireland",1840 |1823,1882 |1896,1864,1864,1864,Albumen silver print from glass negative,8.7 x 9.4 cm (3 7/16 x 3 11/16 in. ),"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268143,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.342,false,true,268148,Photographs,Photograph,Fort Sedgwick in front of Petersburg,,,,,,Artist|Former Attribution,Formerly attributed to,Timothy H. O'Sullivan|Mathew B. Brady,"American, born Ireland, 1840–1882|American, born Ireland, 1823?–1896 New York",,"O'Sullivan, Timothy H.|Brady, Mathew B.","American, born Ireland",1840 |1823,1882 |1896,1864,1864,1864,Albumen silver print from glass negative,8.6 x 9.9 cm (3 3/8 x 3 7/8 in. ),"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268148,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.346,false,true,268152,Photographs,Photograph,Fort Price,,,,,,Artist|Former Attribution,Formerly attributed to,Timothy H. O'Sullivan|Mathew B. Brady,"American, born Ireland, 1840–1882|American, born Ireland, 1823?–1896 New York",,"O'Sullivan, Timothy H.|Brady, Mathew B.","American, born Ireland",1840 |1823,1882 |1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268152,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.348,false,true,268154,Photographs,Photograph,"[Fort Sedgwick]/[Fort Price]. Brady album, p. 27",,,,,,Artist|Former Attribution,Formerly attributed to,Timothy H. O'Sullivan|Mathew B. Brady,"American, born Ireland, 1840–1882|American, born Ireland, 1823?–1896 New York",,"O'Sullivan, Timothy H.|Brady, Mathew B.","American, born Ireland",1840 |1823,1882 |1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268154,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.355,false,true,268162,Photographs,Photograph,"Signal Tower, Cobb's Hill, Appomattox River",,,,,,Artist|Former Attribution,Formerly attributed to,Timothy H. O'Sullivan|Mathew B. Brady,"American, born Ireland, 1840–1882|American, born Ireland, 1823?–1896 New York",,"O'Sullivan, Timothy H.|Brady, Mathew B.","American, born Ireland",1840 |1823,1882 |1896,1864,1864,1864,Albumen silver print from glass negative,10.1 x 9.9 cm (4 x 3 7/8 in. ),"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268162,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.374,false,true,268183,Photographs,Photograph,New York Herald Headquarters,,,,,,Artist|Former Attribution,Formerly attributed to,Timothy H. O'Sullivan|Mathew B. Brady,"American, born Ireland, 1840–1882|American, born Ireland, 1823?–1896 New York",,"O'Sullivan, Timothy H.|Brady, Mathew B.","American, born Ireland",1840 |1823,1882 |1896,1863,1863,1863,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268183,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.393,false,true,268204,Photographs,Panorama,Gettysburg from the West,,,,,,Artist|Former Attribution,Formerly attributed to,Timothy H. O'Sullivan|Mathew B. Brady,"American, born Ireland, 1840–1882|American, born Ireland, 1823?–1896 New York",,"O'Sullivan, Timothy H.|Brady, Mathew B.","American, born Ireland",1840 |1823,1882 |1896,1863,1863,1863,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268204,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.398,false,true,268209,Photographs,Photograph,"Signal Corps, Rappidan River/Signal Corps Reconnoitering at Fredericksburg, Virginia",,,,,,Artist|Former Attribution,Formerly attributed to,Timothy H. O'Sullivan|Mathew B. Brady,"American, born Ireland, 1840–1882|American, born Ireland, 1823?–1896 New York",,"O'Sullivan, Timothy H.|Brady, Mathew B.","American, born Ireland",1840 |1823,1882 |1896,1863,1863,1863,Albumen silver print from glass negative,13.4 x 20.2 cm (5 1/4 x 7 15/16 in. ),"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268209,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1982.1182.1131–.1151,false,true,288302,Photographs,Stereographs,[Group of 21 Stereograph Views of China],,,,,,Artist|Artist|Publisher|Publisher|Publisher|Photography Studio|Publisher|Artist|Artist|Artist|Publisher|Publisher|Editor|Artist,,"T. W. Ingersoll|Kilburn Brothers|James M. Davis|Kawin and Company|Underwood & Underwood|Sun Sculpture Works and Studios|Griffith & Griffith, American|George W. Griffith|Strohmeyer & Wyman|C. H. Graves|Universal Photo Art Co.|J. Good|B. K.|Unknown","American, active ca. 1865–1890|American|American|American|American|American|French",,"Ingersoll, T. W.|Kilburn Brothers|Davis, James M.|Kawin and Company|Underwood & Underwood|Sun Sculpture Works and Studios|Griffith & Griffith|Griffith, George W.|Strohmeyer & Wyman|Graves, C. H.|Universal Photo Art Co.|Good, J.|K., B.|Unknown",American|American|American|American|American|American|American|French,1863,1892,1850s–1910s,1850,1919,Albumen silver prints,Mounts approximately: 8.4 x 17.1 cm (3 5/16 x 6 3/4 in.) to 10 x 17.8 cm (3 15/16 x 7 in.),"Gift of Weston J. Naef, in memory of Kathleen W. Naef and Weston J. Naef Sr., 1982",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288302,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.1101,false,true,631959,Photographs,Photograph,La Tour Eiffel. – Détail du Campanile.,,,,,,Photography Studio|Artist|Artist,,Neurdein Frères|Louis-Antonin Neurdein|Etienne Neurdein,"French, active Paris, 1870s–1900s|French, 1846–after 1915|French, 1832–after 1915",,"Neurdein Frères|Neurdein, Louis-Antonin|Neurdein, Etienne",French|French|French,1863 |1846 |1832,1910 |1920 |1920,1889,1889,1889,Albumen silver print,Image: 8 5/8 × 10 11/16 in. (21.9 × 27.1 cm) Mount: 12 1/2 × 14 13/16 in. (31.7 × 37.6 cm),"Gift of Stéphane Samuel and Robert Melvin Rubin, 2013",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/631959,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1999.188.1,false,true,282759,Photographs,Photograph,"Boutique, Marché aux Halles, Paris",,,,,,Artist|Printer,,Eugène Atget|Berenice Abbott,"French, Libourne 1857–1927 Paris|American, Springfield, Ohio 1898–1991 Monson, Maine",,"Atget, Eugène|Abbott, Berenice",French|American,1857-02-02|1898,1927-08-04|1991,"1925, printed ca. 1929",1925,1925,Gelatin silver print,23.1 x 17 cm (9 1/8 x 6 11/16 in. ),"Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 1999",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/282759,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1999.188.2,false,true,282760,Photographs,Photograph,Fête du Trône,,,,,,Artist|Printer,,Eugène Atget|Berenice Abbott,"French, Libourne 1857–1927 Paris|American, Springfield, Ohio 1898–1991 Monson, Maine",,"Atget, Eugène|Abbott, Berenice",French|American,1857-02-02|1898,1927-08-04|1991,"1925, printed ca. 1929",1925,1925,,23.4 x 17 cm (9 3/16 x 6 11/16 in. ),"Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 1999",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/282760,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1982.1182.747–.763,false,true,288230,Photographs,Stereographs,[Group of 17 Early Stereograph Views of British Churches],,,,,,Publisher|Artist|Artist|Artist|Artist|Artist|Publisher|Publisher|Artist,,Sedgfield's English Scenery|E. W. Wyatt|Mrs. Charles Lawrence|Unknown|Unknown|Francis Bedford|Catherall and Prichard|London Stereoscopic Company|Taylor,"British|British|British|British|British, London 1816–1894 London|British|British|British",,"Sedgfield's English Scenery|Wyatt, E. W.|Lawrence, Charles Mrs.|Unknown|Unknown|Bedford, Francis|Catherall and Prichard|London Stereoscopic Company|Taylor, Mr.",British|British|British|British|British|British|British,1816,1894,1850s–1910s,1850,1919,Albumen silver prints,Mounts approximately: 8.5 x 16.9 cm (3 3/8 x 6 5/8 in.) to 8.7 x 17.7 cm (3 7/16 x 6 15/16 in.),"Gift of Weston J. Naef, in memory of Kathleen W. Naef and Weston J. Naef Sr., 1982",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288230,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.410 (9a-d),false,true,288168,Photographs,Cartes-de-visite,"[Duchesse d'Albe, Unknown Sitter, Duchesse de Morny, and Duc de Morny]",,,,,,Artist|Artist|Person in Photograph|Person in Photograph,Painted and retouched by|Person in photograph|Person in photograph,"Pierre-Louis Pierson|Marck|Sophie Troubetzkoi, duchesse de Morny|Charles-Auguste-Louis-Joseph de Morny, duc de Morny","French, 1822–1913|Russian, Moscow 1838–1896|French (born Switzerland) 1811–1865 Paris",", et al","Pierson, Pierre-Louis|Marck|Troubetzkoi, Sophie, duchesse de Morny|Morny, Charles-Auguste-Louis-Joseph de, duc de Morny","French|Russian|French, born Switzerland",1822 |1838 |1811,1913 |1896 |1865,before 1865,1855,1865,Albumen silver prints from glass negatives,Image: 3 3/8 in. × 2 in. (8.6 × 5.1 cm) (each),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288168,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1202,false,true,285645,Photographs,Photograph,"Chief Officer and Clerks of the Ambulance Department, 9th Army Corps, in Front of Petersburg, Virginia",,,,,,Artist|Printer,,Timothy H. O'Sullivan|Alexander Gardner,"American, born Ireland, 1840–1882|American, Glasgow, Scotland 1821–1882 Washington, D.C.",,"O'Sullivan, Timothy H.|Gardner, Alexander","American, born Ireland|American, Scottish",1840 |1821,1882 |1882,August 1864,1864,1864,Albumen silver print from glass negative,Image: 6 7/8 × 8 7/8 in. (17.5 × 22.5 cm) Mount: 10 5/16 × 15 1/16 in. (26.2 × 38.2 cm),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285645,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.502.1 (37),false,true,286882,Photographs,Photograph,"Field Where General Reynolds Fell, Gettysburg",,,,,,Artist|Printer,,Timothy H. O'Sullivan|Alexander Gardner,"American, born Ireland, 1840–1882|American, Glasgow, Scotland 1821–1882 Washington, D.C.",,"O'Sullivan, Timothy H.|Gardner, Alexander","American, born Ireland|American, Scottish",1840 |1821,1882 |1882,1863,1863,1863,Albumen silver print from glass negative,17.8 x 22.7 cm (7 x 8 15/16 in.),"Gilman Collection, Purchase, Ann Tenenbaum and Thomas H. Lee Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286882,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1982.1182.950–.952,false,true,288285,Photographs,Stereographs,"[Group of 3 Stereograph Views of Fleet Street, London, England]",,,,,,Publisher|Artist|Artist|Artist|Publisher,,Underwood & Underwood|J. F. Jarvis|Benneville Lloyd Singley|Unknown|Keystone View Company,"American|American|American, Union Township, Pennsylvania 1864–1938 Meadville, Pennsylvania",,"Underwood & Underwood|Jarvis, J. F.|Singley, Benneville Lloyd|Unknown|Keystone View Company",American|American|American,1864,1938,1850s–1910s,1850,1919,Albumen silver prints,Mounts approximately: 8.9 x 17.8 cm (3 1/2 x 7 in.),"Gift of Weston J. Naef, in memory of Kathleen W. Naef and Weston J. Naef Sr., 1982",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288285,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1133,false,true,286099,Photographs,Photograph,"Fortifications, Manassas",,,,,,Artist|Artist|Artist|Publisher,,Barnard & Gibson|George N. Barnard|James F. Gibson|Brady & Co.,"American, active 1860s|American, 1819–1902|American, born 1828|American, active 1840s–1880s",,"Barnard & Gibson|Barnard, George N.|Gibson, James F.|Brady & Co.",American|American|American|American,1860 |1819 |1828 |1840,1870 |1902 |1928 |1889,March 1862,1862,1862,Albumen silver print from glass negative,Image: 18.7 × 23.4 cm (7 3/8 × 9 3/16 in.) Mount: 27.1 × 34.6 cm (10 11/16 × 13 5/8 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286099,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.22,false,true,283094,Photographs,Photograph,"Flora Rankin, Irene MacDonald, and Mary Josephine MacDonald at Elm Lodge",,,,,,Artist|Subject|Subject|Subject,,Lewis Carroll|Flora Rankin|Irene MacDonald|Mary Josephine MacDonald,"British, Daresbury, Cheshire 1832–1898 Guildford|British|British, born 1857|British, 1853–1878",,"Carroll, Lewis|Rankin, Flora|MacDonald, Irene|MacDonald, Mary Josephine",British|British|British|British,1832 |1857 |1853,1898 |1878,July 1863,1863,1863,Albumen silver print from glass negative,22.2 x 18 cm (8 3/4 x 7 1/16 in.),"Gilman Collection, Purchase, Joyce F. Menschel Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283094,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.25,false,true,268045,Photographs,Photograph,"Belle Plain, Virginia",,,,,,Artist|Artist|Former Attribution,Formerly attributed to,James Gardner|Timothy H. O'Sullivan|Mathew B. Brady,"American, born 1832|American, born Ireland, 1840–1882|American, born Ireland, 1823?–1896 New York",,"Gardner, James|O'Sullivan, Timothy H.|Brady, Mathew B.","American|American, born Ireland",1832 |1840 |1823,1882 |1896,1864,1864,1864,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268045,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.139,false,true,267923,Photographs,Photograph,"Belle Plain, Virginia. Potomac River, Upper Wharf",,,,,,Artist|Artist|Former Attribution,Formerly attributed to,James Gardner|Timothy H. O'Sullivan|Mathew B. Brady,"American, born 1832|American, born Ireland, 1840–1882|American, born Ireland, 1823?–1896 New York",,"Gardner, James|O'Sullivan, Timothy H.|Brady, Mathew B.","American|American, born Ireland",1832 |1840 |1823,1882 |1896,1864,1864,1864,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267923,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.165,false,true,267952,Photographs,Photograph,"Belle Plain, Virginia",,,,,,Artist|Artist|Former Attribution,Formerly attributed to,James Gardner|Timothy H. O'Sullivan|Mathew B. Brady,"American, born 1832|American, born Ireland, 1840–1882|American, born Ireland, 1823?–1896 New York",,"Gardner, James|O'Sullivan, Timothy H.|Brady, Mathew B.","American|American, born Ireland",1832 |1840 |1823,1882 |1896,1864,1864,1864,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267952,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.200,false,true,267992,Photographs,Photograph,"Confederate Earthworks, Belle Plain, Virginia",,,,,,Artist|Artist|Former Attribution,Formerly attributed to,James Gardner|Timothy H. O'Sullivan|Mathew B. Brady,"American, born 1832|American, born Ireland, 1840–1882|American, born Ireland, 1823?–1896 New York",,"Gardner, James|O'Sullivan, Timothy H.|Brady, Mathew B.","American|American, born Ireland",1832 |1840 |1823,1882 |1896,1863,1863,1863,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267992,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.202,false,true,267994,Photographs,Photograph,"Belle Plain, Virginia",,,,,,Artist|Artist|Former Attribution,Formerly attributed to,James Gardner|Timothy H. O'Sullivan|Mathew B. Brady,"American, born 1832|American, born Ireland, 1840–1882|American, born Ireland, 1823?–1896 New York",,"Gardner, James|O'Sullivan, Timothy H.|Brady, Mathew B.","American|American, born Ireland",1832 |1840 |1823,1882 |1896,1864,1864,1864,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267994,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.255,false,true,268051,Photographs,Photograph,Camp near Brandy Station,,,,,,Artist|Artist|Former Attribution,Formerly attributed to,James Gardner|Timothy H. O'Sullivan|Mathew B. Brady,"American, born 1832|American, born Ireland, 1840–1882|American, born Ireland, 1823?–1896 New York",,"Gardner, James|O'Sullivan, Timothy H.|Brady, Mathew B.","American|American, born Ireland",1832 |1840 |1823,1882 |1896,1863–64,1863,1864,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268051,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.184,false,true,267973,Photographs,Photograph,"Belle Plain, Virginia",,,,,,Artist|Artist|Former Attribution,Formerly attributed to,Timothy H. O'Sullivan|James Gardner|Mathew B. Brady,"American, born Ireland, 1840–1882|American, born 1832|American, born Ireland, 1823?–1896 New York",,"O'Sullivan, Timothy H.|Gardner, James|Brady, Mathew B.","American, born Ireland|American",1840 |1832 |1823,1882 |1896,1864,1864,1864,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267973,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.203,false,true,267995,Photographs,Photograph,"Belle Plain, Virginia",,,,,,Artist|Artist|Former Attribution,Formerly attributed to,Timothy H. O'Sullivan|James Gardner|Mathew B. Brady,"American, born Ireland, 1840–1882|American, born 1832|American, born Ireland, 1823?–1896 New York",,"O'Sullivan, Timothy H.|Gardner, James|Brady, Mathew B.","American, born Ireland|American",1840 |1832 |1823,1882 |1896,1864,1864,1864,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267995,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.204,false,true,267996,Photographs,Photograph,Belle Plain. Distant View of Landing,,,,,,Artist|Artist|Former Attribution,Formerly attributed to,Timothy H. O'Sullivan|James Gardner|Mathew B. Brady,"American, born Ireland, 1840–1882|American, born 1832|American, born Ireland, 1823?–1896 New York",,"O'Sullivan, Timothy H.|Gardner, James|Brady, Mathew B.","American, born Ireland|American",1840 |1832 |1823,1882 |1896,1864,1864,1864,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267996,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.257,false,true,268053,Photographs,Photograph,"Brandy Station, Virginia",,,,,,Artist|Artist|Former Attribution,Formerly attributed to,Timothy H. O'Sullivan|James Gardner|Mathew B. Brady,"American, born Ireland, 1840–1882|American, born 1832|American, born Ireland, 1823?–1896 New York",,"O'Sullivan, Timothy H.|Gardner, James|Brady, Mathew B.","American, born Ireland|American",1840 |1832 |1823,1882 |1896,1863–64,1863,1864,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268053,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.313,false,true,268116,Photographs,Photograph,"Belle Plain, Virginia. Lower Wharf",,,,,,Artist|Artist|Former Attribution,Formerly attributed to,Timothy H. O'Sullivan|James Gardner|Mathew B. Brady,"American, born Ireland, 1840–1882|American, born 1832|American, born Ireland, 1823?–1896 New York",,"O'Sullivan, Timothy H.|Gardner, James|Brady, Mathew B.","American, born Ireland|American",1840 |1832 |1823,1882 |1896,1864,1864,1864,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268116,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1982.1182.1230–.1240,false,true,288308,Photographs,Stereographs,[Group of 11 Stereograph Views of Cowboys],,,,,,Publisher|Publisher|Artist|Photography Studio|Artist|Publisher,,European and American Views|Underwood & Underwood|Benneville Lloyd Singley|Sun Sculpture Works and Studios|Unknown|Keystone View Company,"American|American, Union Township, Pennsylvania 1864–1938 Meadville, Pennsylvania|American|American",,"European and American Views|Underwood & Underwood|Singley, Benneville Lloyd|Sun Sculpture Works and Studios|Unknown|Keystone View Company",American|American|American,1864,1938,1850s–1910s,1850,1919,Albumen silver prints,Mounts approximately: 3.9 x 17.8 cm (1 9/16 x 7 in.),"Gift of Weston J. Naef, in memory of Kathleen W. Naef and Weston J. Naef Sr., 1982",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288308,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.22,false,true,268013,Photographs,Photograph,"Ruins of Richmond & Petersburg Railroad Bridge, Richmond, Virginia",,,,,,Artist|Former Attribution,Formerly attributed to,Alexander Gardner|Mathew B. Brady,"American, Glasgow, Scotland 1821–1882 Washington, D.C.|American, born Ireland, 1823?–1896 New York",,"Gardner, Alexander|Brady, Mathew B.","American, Scottish",1821 |1823,1882 |1896,ca. 1865,1863,1867,Albumen silver print from glass negative,Image: 16.3 × 21.4 cm (6 7/16 × 8 7/16 in.),"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268013,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.57,false,true,268249,Photographs,Photograph,"St. Peter's Church near White House, Where Washington was Married. General E. V. Sumner and Staff",,,,,,Artist|Former Attribution,Formerly attributed to,Alexander Gardner|Mathew B. Brady,"American, Glasgow, Scotland 1821–1882 Washington, D.C.|American, born Ireland, 1823?–1896 New York",,"Gardner, Alexander|Brady, Mathew B.","American, Scottish",1821 |1823,1882 |1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268249,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.211,false,true,268004,Photographs,Photograph,"Custom House, Richmond, Virginia (after evacuation)",,,,,,Artist|Former Attribution,Formerly attributed to,Alexander Gardner|Mathew B. Brady,"American, Glasgow, Scotland 1821–1882 Washington, D.C.|American, born Ireland, 1823?–1896 New York",,"Gardner, Alexander|Brady, Mathew B.","American, Scottish",1821 |1823,1882 |1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268004,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.212,false,true,268005,Photographs,Photograph,"Sanitary Commission Headquarters, Richmond, Virginia",,,,,,Artist|Former Attribution,Formerly attributed to,Alexander Gardner|Mathew B. Brady,"American, Glasgow, Scotland 1821–1882 Washington, D.C.|American, born Ireland, 1823?–1896 New York",,"Gardner, Alexander|Brady, Mathew B.","American, Scottish",1821 |1823,1882 |1896,1865,1865,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268005,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.213,false,true,268006,Photographs,Photograph,"Richmond, Virginia",,,,,,Artist|Former Attribution,Formerly attributed to,Alexander Gardner|Mathew B. Brady,"American, Glasgow, Scotland 1821–1882 Washington, D.C.|American, born Ireland, 1823?–1896 New York",,"Gardner, Alexander|Brady, Mathew B.","American, Scottish",1821 |1823,1882 |1896,1865,1865,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268006,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.214,false,true,268007,Photographs,Photograph,"Penitentiary, Richmond",,,,,,Artist|Former Attribution,Formerly attributed to,Alexander Gardner|Mathew B. Brady,"American, Glasgow, Scotland 1821–1882 Washington, D.C.|American, born Ireland, 1823?–1896 New York",,"Gardner, Alexander|Brady, Mathew B.","American, Scottish",1821 |1823,1882 |1896,1865,1865,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268007,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.215,false,true,268008,Photographs,Photograph,"Castle Thunder, ex-tobacco factory, Petersburg",,,,,,Artist|Former Attribution,Formerly attributed to,Alexander Gardner|Mathew B. Brady,"American, Glasgow, Scotland 1821–1882 Washington, D.C.|American, born Ireland, 1823?–1896 New York",,"Gardner, Alexander|Brady, Mathew B.","American, Scottish",1821 |1823,1882 |1896,1864,1864,1864,Albumen silver print from glass negative,15.9 x 20.4 cm (6 1/4 x 8 1/16 in.),"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268008,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.216,false,true,268009,Photographs,Photograph,"Ruins near Canal Basin, Richmond",,,,,,Artist|Former Attribution,Formerly attributed to,Alexander Gardner|Mathew B. Brady,"American, Glasgow, Scotland 1821–1882 Washington, D.C.|American, born Ireland, 1823?–1896 New York",,"Gardner, Alexander|Brady, Mathew B.","American, Scottish",1821 |1823,1882 |1896,1865,1865,1865,Albumen silver print from glass negative,15.8 x 21 cm (6 1/4 x 8 1/4 in. ),"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268009,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.219,false,true,268012,Photographs,Photograph,"Smokestack of Confederate Ram Merrimac at Richmond/Remains of Ironclad Ram ""Virginia #2"", April, 1865",,,,,,Artist|Former Attribution,Formerly attributed to,Alexander Gardner|Mathew B. Brady,"American, Glasgow, Scotland 1821–1882 Washington, D.C.|American, born Ireland, 1823?–1896 New York",,"Gardner, Alexander|Brady, Mathew B.","American, Scottish",1821 |1823,1882 |1896,1865,1865,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268012,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.220,false,true,268014,Photographs,Photograph,Richmond after the Evacuation,,,,,,Artist|Former Attribution,Formerly attributed to,Alexander Gardner|Mathew B. Brady,"American, Glasgow, Scotland 1821–1882 Washington, D.C.|American, born Ireland, 1823?–1896 New York",,"Gardner, Alexander|Brady, Mathew B.","American, Scottish",1821 |1823,1882 |1896,1865,1865,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268014,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.221,false,true,268015,Photographs,Photograph,"Richmond, Virginia, after Evacuation",,,,,,Artist|Former Attribution,Formerly attributed to,Alexander Gardner|Mathew B. Brady,"American, Glasgow, Scotland 1821–1882 Washington, D.C.|American, born Ireland, 1823?–1896 New York",,"Gardner, Alexander|Brady, Mathew B.","American, Scottish",1821 |1823,1882 |1896,1865,1865,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268015,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.222,false,true,268016,Photographs,Photograph,"Wharves at Richmond, Virginia",,,,,,Artist|Former Attribution,Formerly attributed to,Alexander Gardner|Mathew B. Brady,"American, Glasgow, Scotland 1821–1882 Washington, D.C.|American, born Ireland, 1823?–1896 New York",,"Gardner, Alexander|Brady, Mathew B.","American, Scottish",1821 |1823,1882 |1896,1865,1865,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268016,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.223,false,true,268017,Photographs,Photograph,"Ruins of Arsenal, Richmond, Virginia",,,,,,Artist|Former Attribution,Formerly attributed to,Alexander Gardner|Mathew B. Brady,"American, Glasgow, Scotland 1821–1882 Washington, D.C.|American, born Ireland, 1823?–1896 New York",,"Gardner, Alexander|Brady, Mathew B.","American, Scottish",1821 |1823,1882 |1896,1865,1865,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268017,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.224,false,true,268018,Photographs,Photograph,"Ruins on North Bank of Canal, Richmond",,,,,,Artist|Former Attribution,Formerly attributed to,Alexander Gardner|Mathew B. Brady,"American, Glasgow, Scotland 1821–1882 Washington, D.C.|American, born Ireland, 1823?–1896 New York",,"Gardner, Alexander|Brady, Mathew B.","American, Scottish",1821 |1823,1882 |1896,1865,1865,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268018,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.225,false,true,268019,Photographs,Photograph,"Ruins of R & P Railroad Bridge, Richmond",,,,,,Artist|Former Attribution,Formerly attributed to,Alexander Gardner|Mathew B. Brady,"American, Glasgow, Scotland 1821–1882 Washington, D.C.|American, born Ireland, 1823?–1896 New York",,"Gardner, Alexander|Brady, Mathew B.","American, Scottish",1821 |1823,1882 |1896,ca. 1865,1863,1867,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268019,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.227,false,true,268020,Photographs,Photograph,"Ruins at end of Richmond and Petersburg Railroad Bridge, Richmond",,,,,,Artist|Former Attribution,Formerly attributed to,Alexander Gardner|Mathew B. Brady,"American, Glasgow, Scotland 1821–1882 Washington, D.C.|American, born Ireland, 1823?–1896 New York",,"Gardner, Alexander|Brady, Mathew B.","American, Scottish",1821 |1823,1882 |1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268020,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.229,false,true,268022,Photographs,Photograph,"Richmond, Virginia",,,,,,Artist|Former Attribution,Formerly attributed to,Alexander Gardner|Mathew B. Brady,"American, Glasgow, Scotland 1821–1882 Washington, D.C.|American, born Ireland, 1823?–1896 New York",,"Gardner, Alexander|Brady, Mathew B.","American, Scottish",1821 |1823,1882 |1896,1865,1865,1865,Albumen silver print from glass negative,17 x 21.1 cm (6 11/16 x 8 5/16 in. ),"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268022,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.230,false,true,268024,Photographs,Photograph,"Jeff. Davis House, Executive Mansion, C.S.A., Richmond",,,,,,Artist|Former Attribution,Formerly attributed to,Alexander Gardner|Mathew B. Brady,"American, Glasgow, Scotland 1821–1882 Washington, D.C.|American, born Ireland, 1823?–1896 New York",,"Gardner, Alexander|Brady, Mathew B.","American, Scottish",1821 |1823,1882 |1896,1865,1865,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268024,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.231,false,true,268025,Photographs,Photograph,"Richmond, Virginia. Looking toward Manchester",,,,,,Artist|Former Attribution,Formerly attributed to,Alexander Gardner|Mathew B. Brady,"American, Glasgow, Scotland 1821–1882 Washington, D.C.|American, born Ireland, 1823?–1896 New York",,"Gardner, Alexander|Brady, Mathew B.","American, Scottish",1821 |1823,1882 |1896,ca. 1865,1863,1867,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268025,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.287,false,true,268086,Photographs,Photograph,Headquarters of General Hooker,,,,,,Artist|Former Attribution,Formerly attributed to,Alexander Gardner|Mathew B. Brady,"American, Glasgow, Scotland 1821–1882 Washington, D.C.|American, born Ireland, 1823?–1896 New York",,"Gardner, Alexander|Brady, Mathew B.","American, Scottish",1821 |1823,1882 |1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268086,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.368,false,true,268176,Photographs,Photograph,Aquia Creek Landing,,,,,,Artist|Former Attribution,Formerly attributed to,Alexander Gardner|Mathew B. Brady,"American, Glasgow, Scotland 1821–1882 Washington, D.C.|American, born Ireland, 1823?–1896 New York",,"Gardner, Alexander|Brady, Mathew B.","American, Scottish",1821 |1823,1882 |1896,1863,1863,1863,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268176,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"33.65.11, .226",true,true,267891,Photographs,Panorama,"Ruins of Gallego Flour Mills, Richmond",,,,,,Artist|Former Attribution,Formerly attributed to,Alexander Gardner|Mathew B. Brady,"American, Glasgow, Scotland 1821–1882 Washington, D.C.|American, born Ireland, 1823?–1896 New York",,"Gardner, Alexander|Brady, Mathew B.","American, Scottish",1821 |1823,1882 |1896,1865,1865,1865,Albumen silver prints from glass negatives,16.3 x 36.9 cm (6 7/16 x 14 1/2 in.),"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267891,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.123,false,true,267906,Photographs,Photograph,Bull Run. Orange and Alexandria R.R. near Union Mills,,,,,,Artist|Artist|Former Attribution,Formerly attributed to,George N. Barnard|Timothy H. O'Sullivan|Mathew B. Brady,"American, 1819–1902|American, born Ireland, 1840–1882|American, born Ireland, 1823?–1896 New York",,"Barnard, George N.|O'Sullivan, Timothy H.|Brady, Mathew B.","American|American, born Ireland",1819 |1840 |1823,1902 |1882 |1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267906,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.272,false,true,268070,Photographs,Photograph,"Bull Run, Virginia",,,,,,Artist|Artist|Former Attribution,Formerly attributed to,George N. Barnard|Timothy H. O'Sullivan|Mathew B. Brady,"American, 1819–1902|American, born Ireland, 1840–1882|American, born Ireland, 1823?–1896 New York",,"Barnard, George N.|O'Sullivan, Timothy H.|Brady, Mathew B.","American|American, born Ireland",1819 |1840 |1823,1902 |1882 |1896,1861–62,1861,1862,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268070,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.159,false,true,267945,Photographs,Photograph,"Crow's Nest, Battery and Lookout",,,,,,Artist|Artist|Former Attribution,Formerly attributed to,Andrew Joseph Russell|Timothy H. O'Sullivan|Mathew B. Brady,"American, 1830–1902|American, born Ireland, 1840–1882|American, born Ireland, 1823?–1896 New York",,"Russell, Andrew Joseph|O'Sullivan, Timothy H.|Brady, Mathew B.","American|American, born Ireland",1830 |1840 |1823,1902 |1882 |1896,1864,1864,1864,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267945,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.107,false,true,267888,Photographs,Photograph,Bull Run. Orange and Alexandria R.R. near Union Mills,,,,,,Artist|Artist|Former Attribution,Formerly attributed to,Timothy H. O'Sullivan|George N. Barnard|Mathew B. Brady,"American, born Ireland, 1840–1882|American, 1819–1902|American, born Ireland, 1823?–1896 New York",,"O'Sullivan, Timothy H.|Barnard, George N.|Brady, Mathew B.","American, born Ireland|American",1840 |1819 |1823,1882 |1902 |1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267888,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.108,false,true,267889,Photographs,Photograph,Bull Run. The Stone Bridge,,,,,,Artist|Artist|Former Attribution,Formerly attributed to,Timothy H. O'Sullivan|George N. Barnard|Mathew B. Brady,"American, born Ireland, 1840–1882|American, 1819–1902|American, born Ireland, 1823?–1896 New York",,"O'Sullivan, Timothy H.|Barnard, George N.|Brady, Mathew B.","American, born Ireland|American",1840 |1819 |1823,1882 |1902 |1896,1861–62,1861,1862,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267889,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.134,false,true,267918,Photographs,Photograph,Bull Run. Pontoon Bridge near Blackburn's Ford,,,,,,Artist|Artist|Former Attribution,Formerly attributed to,Timothy H. O'Sullivan|George N. Barnard|Mathew B. Brady,"American, born Ireland, 1840–1882|American, 1819–1902|American, born Ireland, 1823?–1896 New York",,"O'Sullivan, Timothy H.|Barnard, George N.|Brady, Mathew B.","American, born Ireland|American",1840 |1819 |1823,1882 |1902 |1896,1862,1862,1862,Albumen silver print from glass negative,13.1 x 20.2 cm (5 3/16 x 7 15/16 in. ),"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267918,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.365,false,true,268173,Photographs,Photograph,"Confederate Fortifications, Petersburg",,,,,,Artist|Artist|Former Attribution,Formerly attributed to,Timothy H. O'Sullivan|Andrew Joseph Russell|Mathew B. Brady,"American, born Ireland, 1840–1882|American, 1830–1902|American, born Ireland, 1823?–1896 New York",,"O'Sullivan, Timothy H.|Russell, Andrew Joseph|Brady, Mathew B.","American, born Ireland|American",1840 |1830 |1823,1882 |1902 |1896,1864,1864,1864,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268173,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1982.1182.308–.311,false,true,288136,Photographs,Stereographs,[Group of 4 Stereograph Views of Berlin Beer Gardens],,,,,,Publisher|Publisher|Artist|Publisher|Artist|Artist|Publisher,,Strohmeyer & Wyman|Underwood & Underwood|Bert Underwood|Moser Senior|Benneville Lloyd Singley|Unknown|Keystone View Company,"American|American|American|German|American, Union Township, Pennsylvania 1864–1938 Meadville, Pennsylvania",,"Strohmeyer & Wyman|Underwood & Underwood|Underwood, Bert|Moser Senior|Singley, Benneville Lloyd|Unknown|Keystone View Company",American|American|American|German|American,1864,1938,1850s–1910s,1850,1919,Albumen silver prints,Mounts approximately: 8.9 x 17.8 cm (3 1/2 x 7 in.),"Gift of Weston J. Naef, in memory of Kathleen W. Naef and Weston J. Naef Sr., 1982",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288136,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1982.1182.401–.407,false,true,288140,Photographs,Stereographs,"[Group of 7 Stereograph Views of the Forth Bridge, Queensferry, Scotland]",,,,,,Publisher|Artist|Publisher|Artist|Photography Studio|Artist|Publisher,,International Stereoscopic View Company|Strohmeyer & Wyman|Underwood & Underwood|Benneville Lloyd Singley|Sun Sculpture Works and Studios|Unknown|Keystone View Company,"American|American|American|American, Union Township, Pennsylvania 1864–1938 Meadville, Pennsylvania|American",,"International Stereoscopic View Company|Strohmeyer & Wyman|Underwood & Underwood|Singley, Benneville Lloyd|Sun Sculpture Works and Studios|Unknown|Keystone View Company",American|American|American|American|American,1864,1938,1850s–1910s,1850,1919,Albumen silver prints,Mounts approximately: 8.9 x 17.8 cm (3 1/2 x 7 in.),"Gift of Weston J. Naef, in memory of Kathleen W. Naef and Weston J. Naef Sr., 1982",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288140,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1982.1182.917–.920,false,true,288272,Photographs,Stereographs,[Group of 4 Stereograph Views of London Bridges],,,,,,Publisher|Artist|Publisher|Publisher|Artist|Artist|Artist|Publisher,,"Griffith & Griffith, American|George W. Griffith|Strohmeyer & Wyman|Underwood & Underwood|Benneville Lloyd Singley|J. F. Jarvis|Unknown|Keystone View Company","American|American|American|American, Union Township, Pennsylvania 1864–1938 Meadville, Pennsylvania|American",,"Griffith & Griffith|Griffith, George W.|Strohmeyer & Wyman|Underwood & Underwood|Singley, Benneville Lloyd|Jarvis, J. F.|Unknown|Keystone View Company",American|American|American|American|American|American,1864,1938,1850s–1910s,1850,1919,Albumen silver prints,Mounts approximately: 8.9 x 17.8 cm (3 1/2 x 7 in.),"Gift of Weston J. Naef, in memory of Kathleen W. Naef and Weston J. Naef Sr., 1982",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288272,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.159.5,false,true,306206,Photographs,Photograph,"Edith, Ina and Alice Liddell on a Sofa",,,,,,Artist|Person in Photograph|Person in Photograph|Person in Photograph,Person in photograph|Person in photograph|Person in photograph,Lewis Carroll|Alice Pleasance Liddell|Edith Mary Liddell|Ina Liddell,"British, Daresbury, Cheshire 1832–1898 Guildford|British, 1852–1934|British, 1854–1876|British, 1849–1930",,"Carroll, Lewis|Liddell, Alice|Liddell, Edith Mary|Liddell, Ina",British|British|British|British,1832 |1852 |1854 |1849,1898 |1934 |1876 |1930,Summer 1858,1858,1858,Albumen silver print from glass negative,Mount: 4 3/16 in. × 6 7/16 in. (10.7 × 16.3 cm) Image: 4 1/16 × 6 1/16 in. (10.3 × 15.4 cm),"Bequest of Maurice B. Sendak, 2012",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306206,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.627.1,false,true,261925,Photographs,Photograph,Abraham Lincoln,,,,,,Artist|Printer|Person in Photograph,,Alexander Gardner|Rice|Abraham Lincoln,"American, Glasgow, Scotland 1821–1882 Washington, D.C.|American, Hardin County, Kentucky 1809–1865 Washington, D.C.",,"Gardner, Alexander|Rice|Lincoln, Abraham","American, Scottish|American",1821 |1809,1882 |1865,"1863, printed 1901",1863,1863,Gelatin silver print,Image: 45.7 x 38.1 cm (18 x 15 in.),"Warner Communications Inc. Purchase Fund, 1976",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261925,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -67.580.4.4,false,true,288741,Photographs,Carte-de-visite,[Napoleon III and Empress Eugenie],,,,,,Artist|Person in Photograph|Person in Photograph,,André-Adolphe-Eugène Disdéri|Charles-Louis-Napoleon Bonaparte|Empress Eugénie de Montijo,"French, Paris 1819–1889 Paris|French, Paris 1808–1873 Chislehurst, Kent|French (born Spain), Granada 1826–1920 Madrid",,"Disdéri, André-Adolphe-Eugène|Bonaparte, Charles-Louis-Napoleon|Montijo, Eugénie de, Empress","French|French|French, born Spain",1819 |1808 |1826,1889 |1873 |1920,ca. 1865,1865,1865,Albumen silver print,Image: 9.5 x 6 cm (3 3/4 x 2 3/8 in.),"Gift of A. Hyatt Mayor, 1967",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288741,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.228,false,true,268021,Photographs,Photograph,"Ruins in Carey Street, Richmond",,,,,,Artist|Artist|Former Attribution,Formerly attributed to,Thomas C. Roche|Alexander Gardner|Mathew B. Brady,"American, 1826–1895|American, Glasgow, Scotland 1821–1882 Washington, D.C.|American, born Ireland, 1823?–1896 New York",,"Roche, Thomas C.|Gardner, Alexander|Brady, Mathew B.","American|American, Scottish",1826 |1821 |1823,1895 |1882 |1896,1865,1865,1865,Albumen silver print from glass negative,Image: 18.6 x 23.8 cm (7 5/16 x 9 3/8 in.),"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268021,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.396,false,true,268207,Photographs,Photograph,"Falmouth, Virginia. Abandoned Camp",,,,,,Artist|Artist|Former Attribution,Formerly attributed to,Andrew Joseph Russell|Alexander Gardner|Mathew B. Brady,"American, 1830–1902|American, Glasgow, Scotland 1821–1882 Washington, D.C.|American, born Ireland, 1823?–1896 New York",,"Russell, Andrew Joseph|Gardner, Alexander|Brady, Mathew B.","American|American, Scottish",1830 |1821 |1823,1902 |1882 |1896,1862,1862,1862,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268207,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.167,false,true,267954,Photographs,Photograph,Bull Run,,,,,,Artist|Artist|Artist|Former Attribution,Formerly attributed to,George N. Barnard|Timothy H. O'Sullivan|Andrew Joseph Russell|Mathew B. Brady,"American, 1819–1902|American, born Ireland, 1840–1882|American, 1830–1902|American, born Ireland, 1823?–1896 New York",,"Barnard, George N.|O'Sullivan, Timothy H.|Russell, Andrew Joseph|Brady, Mathew B.","American|American, born Ireland|American",1819 |1840 |1830 |1823,1902 |1882 |1902 |1896,1861–62,1861,1862,Albumen silver print from glass negative,10.2 x 20 cm (4 x 7 7/8 in. ),"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267954,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.133,false,true,267917,Photographs,Photograph,Bull Run,,,,,,Artist|Artist|Artist|Former Attribution,Formerly attributed to,Andrew Joseph Russell|George N. Barnard|Timothy H. O'Sullivan|Mathew B. Brady,"American, 1830–1902|American, 1819–1902|American, born Ireland, 1840–1882|American, born Ireland, 1823?–1896 New York",,"Russell, Andrew Joseph|Barnard, George N.|O'Sullivan, Timothy H.|Brady, Mathew B.","American|American|American, born Ireland",1830 |1819 |1840 |1823,1902 |1902 |1882 |1896,1861–62,1861,1862,Albumen silver print from glass negative,16.1 x 20.7 cm (6 5/16 x 8 1/8 in. ),"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267917,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.109,false,true,267890,Photographs,Photograph,Bull Run,,,,,,Artist|Artist|Artist|Former Attribution,,Andrew Joseph Russell|Timothy H. O'Sullivan|George N. Barnard|Mathew B. Brady,"American, 1830–1902|American, born Ireland, 1840–1882|American, 1819–1902|American, born Ireland, 1823?–1896 New York",,"Russell, Andrew Joseph|O'Sullivan, Timothy H.|Barnard, George N.|Brady, Mathew B.","American|American, born Ireland|American",1830 |1840 |1819 |1823,1902 |1882 |1902 |1896,1861–62,1861,1862,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267890,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.20,false,true,267991,Photographs,Photograph,Wharf opposite Richmond,,,,,,Artist|Artist|Former Attribution,Formerly attributed to,John Reekie|Alexander Gardner|Mathew B. Brady,"American, active 1860s|American, Glasgow, Scotland 1821–1882 Washington, D.C.|American, born Ireland, 1823?–1896 New York",,"Reekie, John|Gardner, Alexander|Brady, Mathew B.","American|American, Scottish",1860 |1821 |1823,1869 |1882 |1896,ca. 1865,1863,1867,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267991,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1982.1182.15–.57,false,true,288104,Photographs,Stereographs,[Group of 42 Stereograph Views of Alaska Including the Gold Rush],,,,,,Publisher|Artist|Publisher|Artist|Artist|Publisher|Artist|Publisher|Publisher|Publisher|Publisher,,"George W. Griffith|Benneville Lloyd Singley|Berry, Kelley & Chadwick|Griffith & Griffith, American|Unknown|T. W. Ingersoll|William H. Rau|Universal View Co.|C. H. Graves|Universal Photo Art Co.|Keystone View Company","American|American, Union Township, Pennsylvania 1864–1938 Meadville, Pennsylvania|American|American, 1855–1920|American|American",,"Griffith, George W.|Singley, Benneville Lloyd|Berry, Kelley & Chadwick|Griffith & Griffith|Unknown|Ingersoll, T. W.|Rau, William H.|Universal View Co.|Graves, C. H.|Universal Photo Art Co.|Keystone View Company",American|American|American|American|American|American|American,1864 |1855,1938 |1920,1850s–1910s,1850,1919,Albumen silver prints,Mounts approximately: 8.9 x 17.8 cm (3 1/2 x 7 in.),"Gift of Weston J. Naef, in memory of Kathleen W. Naef and Weston J. Naef Sr., 1982",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288104,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1982.1182.1285–.1534,false,true,296355,Photographs,Stereographs,"[Group of 250 Stereograph Views From the London Stereoscopic Company, 1860-1870, Many Hand-Colored to Illustrate Books]",,,,,,Publisher|Publisher|Publisher|Artist|Artist|Publisher|Artist|Publisher|Publisher|Publisher|Publisher|Artist|Artist|Publisher|Publisher|Publisher|Artist|Artist|Publisher|Person in Photograph|Publisher|Publisher|Publisher|Printer|Publisher|Publisher|Publisher|Publisher|Author|Artist|Person in Photograph|Publisher|Publisher,Collaborated with,"New York Stereoscopic Company|L. H. Stockwell|London Stereoscopic Company|Unknown|Unknown|G. W. Thorne|Unknown|M. W. S. Jackson|J. L. Bates|Mrs. Charles Lawrence|F. W. & R. King|Benneville Lloyd Singley|Sir David Brewster|Littleton View Company|Underwood & Underwood|William Hall & Son|C. E. Goodman|J. Elliott|London Stereoscopic and Photographic Company|Henry IV, the Pius, Duke of Saxony|G. Hawgood|J. Eastlake|E. H. Chamberlain|Nachmann|E. Vimard|Gebhardt, Rottmann, & Co.|B. B. Savary|L. J. Cist|William Hepworth Dixon|M. Laroche|Charles-Louis-Napoleon Bonaparte|Keystone View Company|McAllister & Brother","American|American|British|British|American|American|American|American|British|American, Union Township, Pennsylvania 1864–1938 Meadville, Pennsylvania|British, Jedburgh, Scotland 1781–1868 Melrose|American|American|New York|British|German, 1473–1541|American|French|French|British|American|British, 1821–1879|French, Paris 1808–1873 Chislehurst, Kent|American, active 1860s–1870s",,"New York Stereoscopic Company|Stockwell, L. H.|London Stereoscopic Company|Unknown|Unknown|Thorne, G. W.|Unknown|Jackson, M. W. S.|Bates, J. L.|Lawrence, Charles Mrs.|F. W. & R. King|Singley, Benneville Lloyd|Brewster, David, Sir|Littleton View Company|Underwood & Underwood|William Hall & Son|Goodman, C. E.|Elliott, J.|London Stereoscopic and Photographic Company|Duke of Saxony Henry IV, the Pius,|Hawgood, G.|Eastlake, J.|Chamberlain, E. H.|Nachmann|Vimard, E.|Gebhardt, Rottmann, & Co.|Savary, B. B.|Cist, L. J.|Dixon, William Hepworth|Laroche, M.|Bonaparte, Charles-Louis-Napoleon|Keystone View Company|McAllister & Brother","American|American|British|American|American|American|British|American|British, Scottish|American|American|American|British|German|American|French|French|British|American|British|French|American",1864 |1781 |1473 |1821 |1808 |1850,1938 |1868 |1541 |1879 |1873 |1880,1860–70,1860,1870,Albumen silver prints,Mounts approximately: 8.6 x 17.5 cm (3 3/8 x 6 7/8 in.),"Gift of Weston J. Naef, in memory of Kathleen W. Naef and Weston J. Naef Sr., 1982",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/296355,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1982.1182.1242–.1283,false,true,288310,Photographs,Stereographs,"[Group of 42 Stereograph Views From the London Stereoscopic Company, 1860-1870, Many Hand-Colored to Illustrate Books]",,,,,,Publisher|Artist|Artist|Publisher|Artist|Publisher|Publisher|Publisher|Publisher|Artist|Artist|Publisher|Publisher|Publisher|Artist|Artist|Publisher|Person in Photograph|Publisher|Publisher|Publisher|Printer|Publisher|Publisher|Publisher|Publisher|Author|Artist|Publisher|Publisher|Person in Photograph|Publisher|Publisher,Collaborated with,"London Stereoscopic Company|Unknown|Unknown|G. W. Thorne|Unknown|M. W. S. Jackson|J. L. Bates|Mrs. Charles Lawrence|F. W. & R. King|Benneville Lloyd Singley|Sir David Brewster|Littleton View Company|Underwood & Underwood|William Hall & Son|C. E. Goodman|J. Elliott|London Stereoscopic and Photographic Company|Henry IV, the Pius, Duke of Saxony|G. Hawgood|J. Eastlake|E. H. Chamberlain|Nachmann|E. Vimard|Gebhardt, Rottmann, & Co.|B. B. Savary|L. J. Cist|William Hepworth Dixon|M. Laroche|New York Stereoscopic Company|L. H. Stockwell|Charles-Louis-Napoleon Bonaparte|Keystone View Company|McAllister & Brother","British|British|American|American|American|American|British|American, Union Township, Pennsylvania 1864–1938 Meadville, Pennsylvania|British, Jedburgh, Scotland 1781–1868 Melrose|American|American|New York|British|German, 1473–1541|American|French|French|British|American|British, 1821–1879|American|American|French, Paris 1808–1873 Chislehurst, Kent|American, active 1860s–1870s",,"London Stereoscopic Company|Unknown|Unknown|Thorne, G. W.|Unknown|Jackson, M. W. S.|Bates, J. L.|Lawrence, Charles Mrs.|F. W. & R. King|Singley, Benneville Lloyd|Brewster, David, Sir|Littleton View Company|Underwood & Underwood|William Hall & Son|Goodman, C. E.|Elliott, J.|London Stereoscopic and Photographic Company|Duke of Saxony Henry IV, the Pius,|Hawgood, G.|Eastlake, J.|Chamberlain, E. H.|Nachmann|Vimard, E.|Gebhardt, Rottmann, & Co.|Savary, B. B.|Cist, L. J.|Dixon, William Hepworth|Laroche, M.|New York Stereoscopic Company|Stockwell, L. H.|Bonaparte, Charles-Louis-Napoleon|Keystone View Company|McAllister & Brother","British|American|American|American|British|American|British, Scottish|American|American|American|British|German|American|French|French|British|American|British|American|American|French|American",1864 |1781 |1473 |1821 |1808 |1850,1938 |1868 |1541 |1879 |1873 |1880,1860–70,1860,1870,Albumen silver prints,Mounts approximately: 8.6 x 17.5 cm (3 3/8 x 6 7/8 in.),"Gift of Weston J. Naef, in memory of Kathleen W. Naef and Weston J. Naef Sr., 1982",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288310,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1174.4,false,true,302667,Photographs,Carte-de-visite,Hamilton's Floating Battery Moored at the End of Sullivan's Island the Night Before They Opened Fire upon Fort Sumter,,,,,,Publisher|Artist|Artist,Attributed to|Attributed to,Edward Anthony|Alma A. Pelot|Jesse H. Bolles,"American, 1818–1888|American, active Charleston, South Carolina, 1850s–1860s|American, active Charleston, South Corlina, 1850s–1860s",,"Anthony, Edward|Pelot, Alma A.|Bolles, Jesse H.",American|American|American,1818 |1850 |1850,1888 |1869 |1869,April 1861,1861,1861,Albumen silver print from glass negative,Image: 1 15/16 × 3 1/8 in. (5 × 7.9 cm) Mount: 3 3/8 in. × 4 3/4 in. (8.5 × 12 cm),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/302667,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1174.7,false,true,302670,Photographs,Carte-de-visite,"The Evacuation of Fort Sumter, April 1861",,,,,,Publisher|Artist|Artist,Attributed to|Attributed to,Edward Anthony|Alma A. Pelot|Jesse H. Bolles,"American, 1818–1888|American, active Charleston, South Carolina, 1850s–1860s|American, active Charleston, South Corlina, 1850s–1860s",,"Anthony, Edward|Pelot, Alma A.|Bolles, Jesse H.",American|American|American,1818 |1850 |1850,1888 |1869 |1869,April 1861,1861,1861,Albumen silver print from glass negative,Image: 1 15/16 × 2 11/16 in. (5 × 6.9 cm) Mount: 3 3/8 in. × 4 3/4 in. (8.5 × 12 cm),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/302670,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1174.8,false,true,302671,Photographs,Carte-de-visite,"The Evacuation of Fort Sumter, April 1861",,,,,,Publisher|Artist|Artist,Attributed to|Attrobuted to,Edward Anthony|Alma A. Pelot|Jesse H. Bolles,"American, 1818–1888|American, active Charleston, South Carolina, 1850s–1860s|American, active Charleston, South Corlina, 1850s–1860s",,"Anthony, Edward|Pelot, Alma A.|Bolles, Jesse H.",American|American|American,1818 |1850 |1850,1888 |1869 |1869,April 1861,1861,1861,Albumen silver print from glass negative,Image: 1 15/16 × 3 1/8 in. (5 × 7.9 cm) Mount: 3 3/8 in. × 4 3/4 in. (8.5 × 12 cm),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/302671,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1174.9,false,true,302672,Photographs,Carte-de-visite,"The Evacuation of Fort Sumter, April 1861",,,,,,Publisher|Artist|Artist,Attributed to|Attrobuted to,Edward Anthony|Alma A. Pelot|Jesse H. Bolles,"American, 1818–1888|American, active Charleston, South Carolina, 1850s–1860s|American, active Charleston, South Corlina, 1850s–1860s",,"Anthony, Edward|Pelot, Alma A.|Bolles, Jesse H.",American|American|American,1818 |1850 |1850,1888 |1869 |1869,April 1861,1861,1861,Albumen silver print from glass negative,Image: 1 15/16 × 3 1/8 in. (5 × 7.9 cm) Mount: 3 3/8 in. × 4 3/4 in. (8.5 × 12 cm),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/302672,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1174.11,false,true,302674,Photographs,Carte-de-visite,"The Evacuation of Fort Sumter, April 1861",,,,,,Publisher|Artist|Artist,Attributed to|Attributed to,Edward Anthony|Alma A. Pelot|Jesse H. Bolles,"American, 1818–1888|American, active Charleston, South Carolina, 1850s–1860s|American, active Charleston, South Corlina, 1850s–1860s",,"Anthony, Edward|Pelot, Alma A.|Bolles, Jesse H.",American|American|American,1818 |1850 |1850,1888 |1869 |1869,April 1861,1861,1861,Albumen silver print from glass negative,Image: 1 15/16 × 3 1/8 in. (5 × 7.9 cm) Mount: 3 3/8 in. × 4 3/4 in. (8.5 × 12 cm),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/302674,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1174.13,false,true,302676,Photographs,Carte-de-visite,"The Evacuation of Fort Sumter, April 1861",,,,,,Publisher|Artist|Artist,Attributed to|Attributed to,Edward Anthony|Alma A. Pelot|Jesse H. Bolles,"American, 1818–1888|American, active Charleston, South Carolina, 1850s–1860s|American, active Charleston, South Corlina, 1850s–1860s",,"Anthony, Edward|Pelot, Alma A.|Bolles, Jesse H.",American|American|American,1818 |1850 |1850,1888 |1869 |1869,April 1861,1861,1861,Albumen silver print from glass negative,Image: 1 15/16 × 3 1/8 in. (5 × 7.9 cm) Mount: 3 3/8 in. × 4 3/4 in. (8.5 × 12 cm),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/302676,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.151,false,true,267937,Photographs,Photograph,Petersburg,,,,,,Artist|Artist|Former Attribution,Formerly attributed to,Alexander Gardner|Timothy H. O'Sullivan|Mathew B. Brady,"American, Glasgow, Scotland 1821–1882 Washington, D.C.|American, born Ireland, 1840–1882|American, born Ireland, 1823?–1896 New York",,"Gardner, Alexander|O'Sullivan, Timothy H.|Brady, Mathew B.","American, Scottish|American, born Ireland",1821 |1840 |1823,1882 |1882 |1896,1864,1864,1864,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267937,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.150,false,true,267936,Photographs,Photograph,Petersburg,,,,,,Artist|Artist|Former Attribution,Formerly attributed to,Timothy H. O'Sullivan|Alexander Gardner|Mathew B. Brady,"American, born Ireland, 1840–1882|American, Glasgow, Scotland 1821–1882 Washington, D.C.|American, born Ireland, 1823?–1896 New York",,"O'Sullivan, Timothy H.|Gardner, Alexander|Brady, Mathew B.","American, born Ireland|American, Scottish",1840 |1821 |1823,1882 |1882 |1896,1864,1864,1864,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267936,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.152,false,true,267938,Photographs,Photograph,Petersburg,,,,,,Artist|Artist|Former Attribution,Formerly attributed to,Timothy H. O'Sullivan|Alexander Gardner|Mathew B. Brady,"American, born Ireland, 1840–1882|American, Glasgow, Scotland 1821–1882 Washington, D.C.|American, born Ireland, 1823?–1896 New York",,"O'Sullivan, Timothy H.|Gardner, Alexander|Brady, Mathew B.","American, born Ireland|American, Scottish",1840 |1821 |1823,1882 |1882 |1896,1864,1864,1864,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267938,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.153,false,true,267939,Photographs,Photograph,Petersburg,,,,,,Artist|Artist|Former Attribution,Formerly attributed to,Timothy H. O'Sullivan|Alexander Gardner|Mathew B. Brady,"American, born Ireland, 1840–1882|American, Glasgow, Scotland 1821–1882 Washington, D.C.|American, born Ireland, 1823?–1896 New York",,"O'Sullivan, Timothy H.|Gardner, Alexander|Brady, Mathew B.","American, born Ireland|American, Scottish",1840 |1821 |1823,1882 |1882 |1896,1864,1864,1864,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267939,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1982.1182.946–.949,false,true,288283,Photographs,Stereographs,"[Group of 4 Stereograph Views of the Coronation of Edward VII, London, England]",,,,,,Publisher|Artist|Publisher|Publisher|Photography Studio|Artist|Person in Photograph|Publisher,,"H. C. White Company|R. Y. Young|American Stereoscopic Company|Underwood & Underwood|Sun Sculpture Works and Studios|Benneville Lloyd Singley|Edward VII, King of Great Britain and Northern Ireland|Keystone View Company","American|American|American|American|American, Union Township, Pennsylvania 1864–1938 Meadville, Pennsylvania|British, London 1841–1910 London",,"H. C. White Company|Young, R. Y.|American Stereoscopic Company|Underwood & Underwood|Sun Sculpture Works and Studios|Singley, Benneville Lloyd|Edward VII, King of Great Britain and Northern Ireland|Keystone View Company",American|American|American|American|American|British,1864 |1841,1938 |1910,1850s–1910s,1850,1919,Albumen silver prints,Mounts: 8.9 x 17.8 cm (3 1/2 x 7 in.),"Gift of Weston J. Naef, in memory of Kathleen W. Naef and Weston J. Naef Sr., 1982",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288283,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1982.1182.1003–.1030,false,true,288296,Photographs,Stereographs,[Group of 28 Stereograph Views of Children],,,,,,Publisher|Artist|Publisher|Publisher|Publisher|Artist|Publisher|Publisher|Publisher|Publisher|Publisher|Publisher|Publisher|Artist|Artist,,Keystone View Company|Benneville Lloyd Singley|Merrimac Stereoscopic Company|A. Fuller|The Globe Photo Art Company|C. W. Woodward|Life Groups|Popular Series|International Stereoscopic View Company|F. G. Weller|Underwood & Underwood|Littleton View Company|Canvassers|Unknown|Unknown,"American, Union Township, Pennsylvania 1864–1938 Meadville, Pennsylvania|American|American|American|American|American|American|American|American",,"Keystone View Company|Singley, Benneville Lloyd|Merrimac Stereoscopic Company|Fuller, A.|Globe Photo Art Company|Woodward, C. W.|Life Groups|Popular Series|International Stereoscopic View Company|Weller., F. G.|Underwood & Underwood|Littleton View Company|Canvassers|Unknown|Unknown",American|American|American|American|American|American|American|American,1864,1938,1850s–1910s,1850,1919,Albumen silver prints,Mounts approximately: 8.9 x 17.8 cm (3 1/2 x 7 in.),"Gift of Weston J. Naef, in memory of Kathleen W. Naef and Weston J. Naef Sr., 1982",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288296,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1982.1182.1188–.1224,false,true,288305,Photographs,Stereographs,"[Group of 37 Stereograph Views of the Garden of the Gods and Other Colorado Scenery, United States of America]",,,,,,Publisher|Publisher|Photography Studio|Publisher|Publisher|Artist|Artist|Artist|Publisher|Publisher|Publisher|Artist|Publisher,,"H. C. White Company|Underwood & Underwood|Sun Sculpture Works and Studios|Berry, Kelley & Chadwick|American Colotype Company|J. F. Jarvis|Strohmeyer & Wyman|Benneville Lloyd Singley|Griffith & Griffith, American|Ingersoll View Company|Canvassers|Unknown|Keystone View Company","American|American|American|American|American|American|American, Union Township, Pennsylvania 1864–1938 Meadville, Pennsylvania|American|American",,"H. C. White Company|Underwood & Underwood|Sun Sculpture Works and Studios|Berry, Kelley & Chadwick|American Colotype Company|Jarvis, J. F.|Strohmeyer & Wyman|Singley, Benneville Lloyd|Griffith & Griffith|Ingersoll View Company|Canvassers|Unknown|Keystone View Company",American|American|American|American|American|American|American|American|American,1864,1938,1850s–1910s,1850,1919,Albumen silver prints,,"Gift of Weston J. Naef, in memory of Kathleen W. Naef and Weston J. Naef Sr., 1982",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288305,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1982.1182.465–.490,false,true,288147,Photographs,Stereographs,"[Group of 26 Stereograph Views of San Francisco, California]",,,,,,Publisher|Publisher|Publisher|Publisher|Publisher|Artist|Publisher|Publisher|Photography Studio|Publisher|Artist|Artist|Publisher,,Continent Stereoscopic Company|American Scenery|Popular Series|American Series|American Views|Benneville Lloyd Singley|Standard Series|Underwood & Underwood|Sun Sculpture Works and Studios|Stereoscopic Views|Unknown|Unknown|Keystone View Company,"New York|American|American|American|American, Union Township, Pennsylvania 1864–1938 Meadville, Pennsylvania|American|American|American|American",,"Continent Stereoscopic Company|American Scenery|Popular Series|American Series|American Views|Singley, Benneville Lloyd|Standard Series|Underwood & Underwood|Sun Sculpture Works and Studios|Stereoscopic Views|Unknown|Unknown|Keystone View Company",New York|American|American|American|American|American|American|American,1864,1938,1850s–1910s,1850,1919,Albumen silver prints,Mounts approximately: 8.9 x 17.6 cm (3 1/2 x 6 15/16 in.) to 10.5 x 17.8 cm (4 1/8 x 7 in.),"Gift of Weston J. Naef, in memory of Kathleen W. Naef and Weston J. Naef Sr., 1982",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288147,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1201,false,true,285644,Photographs,Photograph,"A Harvest of Death, Gettysburg, Pennsylvania",,,,,,Artist|Printer|Publisher,,Timothy H. O'Sullivan|Alexander Gardner|Alexander Gardner,"American, born Ireland, 1840–1882|American, Glasgow, Scotland 1821–1882 Washington, D.C.|American, Glasgow, Scotland 1821–1882 Washington, D.C.",,"O'Sullivan, Timothy H.|Gardner, Alexander|Gardner, Alexander","American, born Ireland|American, Scottish|American, Scottish",1840 |1821 |1821,1882 |1882 |1882,July 1863,1863,1863,Albumen silver print from glass negative,Image: 17 13/16 × 22 1/2 in. (45.2 × 57.2 cm) Mount: 11 15/16 × 15 5/8 in. (30.4 × 39.7 cm),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285644,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -46.1.21,false,true,268962,Photographs,Photograph,"William Etty, R.A.",,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"David Hunter McAlpin Fund, 1946",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268962,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -43.10.37,false,true,268791,Photographs,Photograph,Sir William Allan,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Gift of Mrs. Pirie MacDonald and Mr. and Mrs. Everett Tutchings, 1943",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268791,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -43.10.38,false,true,268792,Photographs,Photograph,"Ogilvie Fairly, Capt. Hamilton, and Gilmore",,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Gift of Mrs. Pirie MacDonald and Mr. and Mrs. Everett Tutchings, 1943",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268792,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -43.10.40,false,true,268795,Photographs,Photograph,Mrs. Rigby,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Gift of Mrs. Pirie MacDonald and Mr. and Mrs. Everett Tutchings, 1943",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268795,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -43.10.41,false,true,268796,Photographs,Photograph,Mr. and Mrs. John Thomson,,,,,,Artist|Printer|Artist|Photography Studio,,David Octavius Hill|Ingals|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Ingals|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Gift of Mrs. Pirie MacDonald and Mr. and Mrs. Everett Tutchings, 1943",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268796,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -43.10.42,false,true,268797,Photographs,Photograph,Henning with Parthenon Frieze,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Gift of Mrs. Pirie MacDonald and Mr. and Mrs. Everett Tutchings, 1943",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268797,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -43.10.43,false,true,268798,Photographs,Photograph,"[Man, Full-length]",,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Gift of Mrs. Pirie MacDonald and Mr. and Mrs. Everett Tutchings, 1943",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268798,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -43.10.44,false,true,268799,Photographs,Photograph,Newhaven Fishwives,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Gift of Mrs. Pirie MacDonald and Mr. and Mrs. Everett Tutchings, 1943",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268799,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -43.10.46,false,true,268801,Photographs,Photograph,Dr. Jabez Bunting,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Gift of Mrs. Pirie MacDonald and Mr. and Mrs. Everett Tutchings, 1943",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268801,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -43.10.47,false,true,268802,Photographs,Photograph,Hugh Miller,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Gift of Mrs. Pirie MacDonald and Mr. and Mrs. Everett Tutchings, 1943",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268802,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -43.10.49,false,true,268803,Photographs,Photograph,Presbytery of Dumbarton,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Gift of Mrs. Pirie MacDonald and Mr. and Mrs. Everett Tutchings, 1943",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268803,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -43.10.50,false,true,268805,Photographs,Photograph,Rev. Dr. Thomas Chalmers (?),,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Gift of Mrs. Pirie MacDonald and Mr. and Mrs. Everett Tutchings, 1943",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268805,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -43.10.51,false,true,268806,Photographs,Photograph,[Man],,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Gift of Mrs. Pirie MacDonald and Mr. and Mrs. Everett Tutchings, 1943",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268806,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -43.10.53,false,true,268808,Photographs,Photograph,[Man],,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Gift of Mrs. Pirie MacDonald and Mr. and Mrs. Everett Tutchings, 1943",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268808,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -43.10.54,false,true,268809,Photographs,Photograph,"Prof. Fraser, Rev. Welsh, Rev. Hamilton, and Three Other Men",,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Gift of Mrs. Pirie MacDonald and Mr. and Mrs. Everett Tutchings, 1943",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268809,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -46.1.141,false,true,268914,Photographs,Photograph,Kenneth Macleay,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"David Hunter McAlpin Fund, 1946",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268914,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -46.1.142,false,true,268915,Photographs,Photograph,Sobieski Stuart,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"David Hunter McAlpin Fund, 1946",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268915,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -46.1.143,false,true,268916,Photographs,Photograph,David Roberts,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"David Hunter McAlpin Fund, 1946",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268916,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -46.1.153,false,true,268927,Photographs,Photograph,"Kenneth MaCleay, R.S.A.",,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"David Hunter McAlpin Fund, 1946",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268927,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -46.1.162,false,true,268937,Photographs,Photograph,James Glencairn Burns,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"David Hunter McAlpin Fund, 1946",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268937,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -46.1.163,false,true,268938,Photographs,Photograph,Finlay - The Deerstalker,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"David Hunter McAlpin Fund, 1946",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268938,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -46.1.165,false,true,268940,Photographs,Photograph,John Ban MacKenzie,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"David Hunter McAlpin Fund, 1946",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268940,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -46.1.166,false,true,268941,Photographs,Photograph,Alexander Thompson,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"David Hunter McAlpin Fund, 1946",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268941,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -46.1.169,false,true,268944,Photographs,Photograph,Sir John McNeill,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"David Hunter McAlpin Fund, 1946",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268944,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -46.1.171,false,true,268947,Photographs,Photograph,[Man],,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"David Hunter McAlpin Fund, 1946",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268947,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -46.1.172,false,true,268948,Photographs,Photograph,George Moon,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"David Hunter McAlpin Fund, 1946",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268948,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -46.1.173,false,true,268949,Photographs,Photograph,Sobieski Stuart,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"David Hunter McAlpin Fund, 1946",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268949,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -46.1.174,false,true,268950,Photographs,Photograph,"Dr. Inglis, Halifax",,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"David Hunter McAlpin Fund, 1946",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268950,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.1.1,false,true,268366,Photographs,Photograph,St. Andrews,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268366,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.1.2,false,true,268393,Photographs,Photograph,St. Andrews,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268393,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.1.3,false,true,268404,Photographs,Photograph,St. Andrews,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268404,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.1.4,false,true,268415,Photographs,Photograph,St. Andrews,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268415,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.1.5,false,true,268426,Photographs,Photograph,St. Andrews,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268426,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.1.6,false,true,268437,Photographs,Photograph,St. Andrews,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268437,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.1.7,false,true,268448,Photographs,Photograph,St. Andrews,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268448,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.1.8,false,true,268459,Photographs,Photograph,St. Andrews,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268459,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.1.9,false,true,268470,Photographs,Photograph,St. Andrews,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268470,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.1,false,true,268482,Photographs,Photograph,Rev. Dr. Thomas Chalmers and Thomas Chalmers Hanna,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268482,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.2,false,true,268525,Photographs,Photograph,"William Borthwick Johnstone, William Leighton Leitch and David Scott as ""The Monks of Kennaquhair"" from Sir Walter Scott's ""The Abbott""",,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268525,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.3,false,true,268536,Photographs,Photograph,Dunlop Esq. of Craigton,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268536,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.4,false,true,268547,Photographs,Photograph,"William Etty, R.A.",,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268547,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.5,false,true,268558,Photographs,Photograph,William Etty,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268558,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.6,false,true,268569,Photographs,Photograph,"Sir William Allan, P.R.S.A.",,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268569,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.7,false,true,268580,Photographs,Photograph,"Thomas Duncan, R.S.A.",,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268580,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.8,false,true,268591,Photographs,Photograph,Thomas Duncan,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268591,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.9,false,true,268602,Photographs,Photograph,Thomas Duncan and His Brother,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268602,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.1.10,false,true,268367,Photographs,Photograph,St. Andrews. The Harbor,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268367,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.1.11,false,true,268378,Photographs,Photograph,St. Andrews. College Church of St. Salvator,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268378,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.1.12,false,true,268385,Photographs,Photograph,St. Andrews. The College Church of St. Salvator,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268385,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.1.13,false,true,268386,Photographs,Photograph,St. Andrews. The Pends,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268386,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.1.14,false,true,268387,Photographs,Photograph,St. Andrews. The Pends,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268387,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.1.15,false,true,268388,Photographs,Photograph,St. Andrews. The West Port,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268388,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.1.16,false,true,268389,Photographs,Photograph,St. Andrews. The Abbey Wall,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268389,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.1.17,false,true,268390,Photographs,Photograph,St. Andrews. The Fore Tower of the Castle,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268390,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.1.18,false,true,268391,Photographs,Photograph,St. Andrews. The Fore Tower of the Castle,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268391,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.1.19,false,true,268392,Photographs,Photograph,St. Andrews. The Fore Tower of the Castle,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268392,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.1.20,false,true,268394,Photographs,Photograph,St. Andrews. The Spindle Rock,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268394,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.1.21,false,true,268395,Photographs,Photograph,St. Andrews. College Church of St. Salvator,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268395,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.1.22,false,true,268396,Photographs,Photograph,St. Andrews. Blackfriars' Chapel,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268396,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.1.23,false,true,268397,Photographs,Photograph,St. Andrews Cathedral,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268397,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.1.24,false,true,268398,Photographs,Photograph,St. Andrews (?). Ships in the Harbor,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268398,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.1.25,false,true,268399,Photographs,Photograph,St. Andrews. Madras College,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268399,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.1.26,false,true,268400,Photographs,Photograph,Edinburgh. The High Street with John Knox's House,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268400,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.1.27,false,true,268401,Photographs,Photograph,Edinburgh. Greyfriars' Churchyard,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268401,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.1.28,false,true,268402,Photographs,Photograph,Edinburgh. Greyfriars' Churchyard,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268402,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.1.29,false,true,268403,Photographs,Photograph,Edinburgh. The Orphan Hospital,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268403,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.1.30,false,true,268405,Photographs,Photograph,Edinburgh. The Scott Monument,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268405,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.1.31,false,true,268406,Photographs,Photograph,Edinburgh. The Scott Monument,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268406,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.1.32,false,true,268407,Photographs,Photograph,Edinburgh. The Royal High School,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268407,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.1.33,false,true,268408,Photographs,Photograph,"[Old Royal High School, Calton Hill, Edinburgh]",,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268408,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.1.34,false,true,268409,Photographs,Photograph,Edinburgh. Greyfriars' Churchyard,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268409,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.1.35,false,true,268410,Photographs,Photograph,Edinburgh. Greyfriars' Churchyard,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268410,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.1.36,false,true,268411,Photographs,Photograph,Edinburgh. Greyfriars' Churchyard,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268411,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.1.37,false,true,268412,Photographs,Photograph,Edinburgh. Greyfriars' Churchyard,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268412,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.1.38,false,true,268413,Photographs,Photograph,Edinburgh. Greyfriars' Churchyard,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268413,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.1.39,false,true,268414,Photographs,Photograph,Edinburgh. Greyfriars' Churchyard,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268414,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.1.40,false,true,268416,Photographs,Photograph,Edinburgh. Greyfriar's Churchyard,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268416,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.1.41,false,true,268417,Photographs,Photograph,Edinburgh. Greyfriars' Churchyard,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268417,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.1.42,false,true,268418,Photographs,Photograph,Edinburgh. Greyfriars' Churchyard,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268418,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.1.43,false,true,268419,Photographs,Photograph,Bonaly Towers,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268419,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.1.44,false,true,268420,Photographs,Photograph,Bonaly Towers. Home of Lord Cockburn,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268420,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.1.45,false,true,268421,Photographs,Photograph,Bonaly Towers. Home of Lord Cockburn,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268421,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.1.46,false,true,268422,Photographs,Photograph,"John Henning as Edie Ochiltree from Sir Walter Scott's ""The Antiquary""",,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268422,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.1.47,false,true,268423,Photographs,Photograph,"Burnside, Fife / Island in the Almond River",,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268423,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.1.48,false,true,268424,Photographs,Photograph,Tree at Colinton,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268424,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.1.49,false,true,268425,Photographs,Photograph,Tree at Colinton,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268425,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.1.50,false,true,268427,Photographs,Photograph,Lindlithgow Castle,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268427,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.1.51,false,true,268428,Photographs,Photograph,Newhaven,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268428,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.1.52,false,true,268429,Photographs,Photograph,"St. Andrews. North Street, Fishergate",,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268429,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.1.53,false,true,268430,Photographs,Photograph,Newhaven Fishwives,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268430,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.1.54,false,true,268431,Photographs,Photograph,Newhaven Children,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268431,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.1.55,false,true,268432,Photographs,Photograph,Newhaven Family,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268432,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.1.56,false,true,268433,Photographs,Photograph,Newhaven Group,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268433,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.1.57,false,true,268434,Photographs,Photograph,Newhaven Boys,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268434,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.1.58,false,true,268435,Photographs,Photograph,Newhaven Fishwife,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268435,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.1.59,false,true,268436,Photographs,Photograph,Newhaven Fishwives,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268436,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.1.60,false,true,268438,Photographs,Photograph,Newhaven Fishwives,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268438,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.1.61,false,true,268439,Photographs,Photograph,Newhaven Fishwives,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268439,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.1.63,false,true,268441,Photographs,Photograph,Newhaven Fishwife,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268441,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.1.64,false,true,268442,Photographs,Photograph,Fisher Lassies,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268442,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.1.65,false,true,268443,Photographs,Photograph,Newhaven Fishwife,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268443,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.1.66,false,true,268444,Photographs,Photograph,Newhaven Fishwife,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268444,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.1.67,false,true,268445,Photographs,Photograph,Newhaven Group,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268445,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.1.68,false,true,268446,Photographs,Photograph,Newhaven Fishwives,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268446,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.1.69,false,true,268447,Photographs,Photograph,Newhaven Fishwife,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268447,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.1.70,false,true,268449,Photographs,Photograph,Newhaven Fisherman with Two Boys,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268449,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.1.71,false,true,268450,Photographs,Photograph,Newhaven Fisherman,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268450,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.1.72,false,true,268451,Photographs,Photograph,Newhaven Fisherman,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268451,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.1.73,false,true,268452,Photographs,Photograph,Newhaven Boy,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268452,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.1.74,false,true,268453,Photographs,Photograph,Newhaven Group,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268453,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.1.76,false,true,268455,Photographs,Photograph,Newhaven Fisherman,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268455,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.1.77,false,true,268456,Photographs,Photograph,Newhaven Fishermen,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268456,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.1.78,false,true,268457,Photographs,Photograph,Newhaven Fishermen,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268457,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.1.79,false,true,268458,Photographs,Photograph,"The Porthole / Sergeant and Private of the 42nd Gordon Highlanders, Edinburgh Castle",,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268458,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.1.80,false,true,268460,Photographs,Photograph,"The 42nd Gordon Highlanders, Edinburgh Castle",,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268460,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.1.81,false,true,268461,Photographs,Photograph,"The 42nd Gordon Highlanders, Edinburgh Castle",,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268461,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.1.82,false,true,268462,Photographs,Photograph,Lane and Lewis / Lane and Redding,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268462,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.1.83,false,true,268463,Photographs,Photograph,Lane,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268463,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.1.84,false,true,268464,Photographs,Photograph,Rev. Peter Jones,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268464,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.1.85,false,true,268465,Photographs,Photograph,Miss Elizabeth Logan,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268465,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.1.86,false,true,268466,Photographs,Photograph,Jimmy Miller,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268466,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.1.87,false,true,268467,Photographs,Photograph,Jimmy Miller,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268467,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.1.88,false,true,268468,Photographs,Photograph,Jimmy Miller,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268468,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.1.90,false,true,268471,Photographs,Photograph,"John Henning as Edie Ochiltree from Sir Walter Scott's ""The Antiquary""",,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,Image: 20.6 x 15.8 cm (8 1/8 x 6 1/4 in.),"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268471,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.1.91,false,true,268472,Photographs,Photograph,Patrick Byrne,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268472,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.1.92,false,true,268473,Photographs,Photograph,Patrick Byrne,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268473,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.1.93,false,true,268474,Photographs,Photograph,Patrick Byrne,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268474,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.1.94,false,true,268475,Photographs,Photograph,The Misses McCandlish,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268475,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.1.95,false,true,268476,Photographs,Photograph,The Misses McCandlish,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268476,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.1.96,false,true,268477,Photographs,Photograph,Finlay Children,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268477,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.1.98,false,true,268479,Photographs,Photograph,Master Finlay,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268479,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.1.99,false,true,268480,Photographs,Photograph,"Lady, Standing",,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268480,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.10,false,true,268483,Photographs,Photograph,William Leighton Leitch,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268483,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.11,false,true,268494,Photographs,Photograph,William Leighton Leitch,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268494,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.12,false,true,268505,Photographs,Photograph,"Henning, Handyside Ritchie, & D.O. Hill, R.S.A.",,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268505,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.13,false,true,268516,Photographs,Photograph,Henning,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268516,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.14,false,true,268519,Photographs,Photograph,Sir John Robert Steell,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268519,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.15,false,true,268520,Photographs,Photograph,"H.B. Johnston, R.S.A.",,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268520,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.16,false,true,268521,Photographs,Photograph,D.O. Hill and W.B. Johnstone,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268521,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.17,false,true,268522,Photographs,Photograph,Kenneth Macleay,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268522,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.18,false,true,268523,Photographs,Photograph,Prof. John Wilson,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268523,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.19,false,true,268524,Photographs,Photograph,"""Edinburgh Ale"" James Ballentine, Dr. George Bell, D.O. Hill",,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268524,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.20,false,true,268526,Photographs,Photograph,Moir (?) and John Wilson,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268526,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.21,false,true,268527,Photographs,Photograph,John Wilson,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268527,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.22,false,true,268528,Photographs,Photograph,John Wilson,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268528,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.23,false,true,268529,Photographs,Photograph,Thomas Chalmers,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268529,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.24,false,true,268530,Photographs,Photograph,Thomas Chalmers,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268530,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.25,false,true,268531,Photographs,Photograph,Dr. Welsh (Retiring Moderator of Gel' Assembly 1843),,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268531,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.26,false,true,268532,Photographs,Photograph,Dr. Welsh,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268532,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.27,false,true,268533,Photographs,Photograph,Dr. Welsh,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268533,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.28,false,true,268534,Photographs,Photograph,"Cunningham, Beff, John Hamilton, Guthrie",,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268534,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.29,false,true,268535,Photographs,Photograph,Dr. Arnold,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268535,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.30,false,true,268537,Photographs,Photograph,Guthrie,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268537,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.31,false,true,268538,Photographs,Photograph,Lord Robertson,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268538,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.32,false,true,268539,Photographs,Photograph,Rev. Mr. Smith of Borgue,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268539,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.33,false,true,268540,Photographs,Photograph,Swinton,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268540,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.34,false,true,268541,Photographs,Photograph,Rev. Henshaw Jones,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268541,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.35,false,true,268542,Photographs,Photograph,Dr. Jabez Bunting,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268542,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.36,false,true,268543,Photographs,Photograph,Dr. Jabez Bunting,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268543,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.37,false,true,268544,Photographs,Photograph,Dr. Monro,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268544,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.38,false,true,268545,Photographs,Photograph,Campbell of Monzie,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268545,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.39,false,true,268546,Photographs,Photograph,Campbell of Monzie,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268546,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.40,false,true,268548,Photographs,Photograph,Rev. John Julius Wood,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268548,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.42,false,true,268550,Photographs,Photograph,Robert Dundas Cay,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–44,1843,1844,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268550,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.43,false,true,268551,Photographs,Photograph,George Gilfillan and Samuel Brown,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268551,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.44,false,true,268552,Photographs,Photograph,Annan Presbytery,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268552,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.45,false,true,268553,Photographs,Photograph,Sir James Young Simpson & Wainhouse (or Muirhouse),,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268553,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.46,false,true,268554,Photographs,Photograph,Sir John Jaffray and Dhanjiobai Nauroji,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268554,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.47,false,true,268555,Photographs,Photograph,Presbytery of Dundee,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268555,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.48,false,true,268556,Photographs,Photograph,"Sir David Brewster, Earle Monteith, Dr. Welsh & Two Others",,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268556,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.49,false,true,268557,Photographs,Photograph,"Alexander of Duntocher, McMillan of Cardross and Two Others",,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268557,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.50,false,true,268559,Photographs,Photograph,[Two Unidentified Men],,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268559,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.51,false,true,268560,Photographs,Photograph,"Symington, Paisley, and Glasgow",,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268560,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.52,false,true,268561,Photographs,Photograph,Rev. Miller and His Son Rev. Samuel Miller,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268561,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.53,false,true,268562,Photographs,Photograph,"James Gordon, Dr. Hanna, and Mr. Cowan",,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268562,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.55,false,true,268564,Photographs,Photograph,Rev. Dr. Keith,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268564,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.56,false,true,268565,Photographs,Photograph,Rev. Dr. Keith,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268565,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.57,false,true,268566,Photographs,Photograph,Dr. Capadore,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268566,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.58,false,true,268567,Photographs,Photograph,Davidson of Aberdeen,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268567,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.59,false,true,268568,Photographs,Photograph,Dr. Sampson of York,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268568,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.60,false,true,268570,Photographs,Photograph,"Rev. Stephen Hislop, Missionary",,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268570,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.61,false,true,268571,Photographs,Photograph,[Man Holding Umbrella],,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268571,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.62,false,true,268572,Photographs,Photograph,Rev. Mr. Elder of Watts,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268572,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.63,false,true,268573,Photographs,Photograph,"Dr. George Cook, St. Andrews",,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268573,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.64,false,true,268574,Photographs,Photograph,[Unidentified Man],,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268574,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.65,false,true,268575,Photographs,Photograph,Rev. Dr. William Hamilton Burns,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268575,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.66,false,true,268576,Photographs,Photograph,Dr. Foulis,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268576,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.67,false,true,268577,Photographs,Photograph,"""Cookie"" Miller",,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268577,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.68,false,true,268578,Photographs,Photograph,William Scott Moncrieff,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268578,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.69,false,true,268579,Photographs,Photograph,"Thomas Kitchenham Staveley, M.P. Ripon",,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268579,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.70,false,true,268581,Photographs,Photograph,"Rev. W. W. Duncan, Peebles (Sweet William)",,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268581,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.71,false,true,268582,Photographs,Photograph,John Murray (Publisher),,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268582,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.72,false,true,268583,Photographs,Photograph,[Man],,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268583,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.73,false,true,268584,Photographs,Photograph,Cookie Miller,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268584,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.74,false,true,268585,Photographs,Photograph,Rev. Charles John Brown,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268585,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.75,false,true,268586,Photographs,Photograph,[Man],,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268586,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.76,false,true,268587,Photographs,Photograph,Frederic Monod (Paris),,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268587,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.77,false,true,268588,Photographs,Photograph,"Rev. Thomas Jollie, Bowden",,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268588,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.78,false,true,268589,Photographs,Photograph,"Rev. Robert Aitken, Dundee",,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268589,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.79,false,true,268590,Photographs,Photograph,Archibald Butler of Faskally,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268590,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.80,false,true,268592,Photographs,Photograph,[Man],,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268592,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.81,false,true,268593,Photographs,Photograph,James Dymock,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268593,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.82,false,true,268594,Photographs,Photograph,Rev. James Scott,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268594,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.83,false,true,268595,Photographs,Photograph,Rev. James Scott,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268595,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.84,false,true,268596,Photographs,Photograph,"Thomas Bell, Leswalt",,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268596,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.85,false,true,268597,Photographs,Photograph,"David Maitland Makgill Crichton, Rankeillour",,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268597,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.86,false,true,268598,Photographs,Photograph,"Dr. Inglis, Halifax",,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268598,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.87,false,true,268599,Photographs,Photograph,James Aytoun,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268599,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.88,false,true,268600,Photographs,Photograph,"Rev. George Lewis, Dundee",,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268600,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.89,false,true,268601,Photographs,Photograph,[Man],,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268601,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.90,false,true,268603,Photographs,Photograph,[Man],,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268603,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.91,false,true,268604,Photographs,Photograph,Rev. Thomas Jolly of Bowden,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268604,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.92,false,true,268605,Photographs,Photograph,[Man],,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268605,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.93,false,true,268606,Photographs,Photograph,"Robert Paul, Commercial Bank",,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268606,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.94,false,true,268607,Photographs,Photograph,[Man],,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268607,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.95,false,true,268608,Photographs,Photograph,Jacob Abbott,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268608,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.96,false,true,268609,Photographs,Photograph,"General John Munro, Teanich",,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268609,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.97,false,true,268610,Photographs,Photograph,[Man],,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268610,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.98,false,true,268611,Photographs,Photograph,[Man],,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268611,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.99,false,true,268612,Photographs,Photograph,[Man],,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268612,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1997.382.14,false,true,282017,Photographs,Photograph,Rev. Dr. Thomas Chalmers,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,ca. 1843,1841,1845,Salted paper print from paper negative,16.1 x 11.9 cm (6 5/16 x 4 11/16 in.),"The Rubel Collection, Purchase, Harris Brisbane Dick Fund and Warner Communication Inc. Purchase Fund, by exchange, 1997",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/282017,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1997.382.18,false,true,282021,Photographs,Photograph,Lady Ruthven,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,ca. 1845,1843,1847,Salted paper print from paper negative,19.9 x 15 cm (7 13/16 x 5 7/8 in.),"The Rubel Collection, Purchase, Manfred Heiting and Lila Acheson Wallace Gifts, 1997",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/282021,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1997.382.19,true,true,282022,Photographs,Photograph,[Newhaven Fishwives],,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,ca. 1845,1843,1847,Salted paper print from paper negative,29.5 x 21.7 cm (11 5/8 x 8 9/16 in.),"The Rubel Collection, Purchase, Lila Acheson Wallace, Harriette and Noel Levine, and Alexandra R. Marshall Gifts, 1997",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/282022,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1997.382.25,false,true,282028,Photographs,Photograph,"[Officer of the 92nd Gordon Highlanders Reading to the Troops, Edinburgh Castle]",,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,"April 9, 1846",1846,1846,Salted paper print from paper negative,14.5 x 19.2 cm (5 11/16 x 7 9/16 in. ),"The Rubel Collection, Purchase, Lila Acheson Wallace, Ann Tenenbaum and Thomas H. Lee, and Harriette and Noel Levine Gifts, 1997",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/282028,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1997.382.29,false,true,282032,Photographs,Photograph,The Fairy Tree at Colinton,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1846,1846,1846,Salted paper print from paper negative,20.8 x 15 cm (8 3/16 x 5 7/8 in.),"The Rubel Collection, Purchase, Ann Tenenbaum and Thomas H. Lee and Lila Acheson Wallace Gifts, 1997",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/282032,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.1.100,false,true,268368,Photographs,Photograph,Mrs. Marian Murray,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268368,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.1.101,false,true,268369,Photographs,Photograph,Mrs. Jameson,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268369,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.1.102,false,true,268370,Photographs,Photograph,Lady Elizabeth Eastlake,,,,,,Artist|Artist|Photography Studio|Person in Photograph,Person in photograph,David Octavius Hill|Robert Adamson|Hill and Adamson|Lady Elizabeth Rigby Eastlake,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson|Eastlake, Elizabeth Rigby, Lady","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843 |1809,1870 |1848 |1848 |1893,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268370,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.1.103,false,true,268371,Photographs,Photograph,Lady Elizabeth Eastlake,,,,,,Artist|Artist|Photography Studio|Person in Photograph,Person in photograph,David Octavius Hill|Robert Adamson|Hill and Adamson|Lady Elizabeth Rigby Eastlake,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson|Eastlake, Elizabeth Rigby, Lady","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843 |1809,1870 |1848 |1848 |1893,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268371,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.1.104,false,true,268372,Photographs,Photograph,John Henning with Group of Ladies,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268372,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.1.105,false,true,268373,Photographs,Photograph,Miss Binney,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268373,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.1.106,false,true,268374,Photographs,Photograph,Unidentified Woman,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268374,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.1.107,false,true,268375,Photographs,Photograph,"Couple Seated, Woman Reading",,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268375,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.1.108,false,true,268376,Photographs,Photograph,Mrs. Watson,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268376,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.1.109,false,true,268377,Photographs,Photograph,"Mrs. Marian Murray, Lady Stair",,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268377,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.1.110,false,true,268379,Photographs,Photograph,Lady Ruthven,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268379,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.1.111,false,true,268380,Photographs,Photograph,Mrs. Shanker,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268380,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.1.112,false,true,268381,Photographs,Photograph,Mrs. Grierson,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268381,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.1.113,false,true,268382,Photographs,Photograph,Mrs. Rigby,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268382,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.1.114,false,true,268383,Photographs,Photograph,Miss Munro,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268383,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.1.115,false,true,268384,Photographs,Photograph,Miss Kemp as Ophelia,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268384,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.100,false,true,268484,Photographs,Photograph,Rev. Dr. Andrew Sutherland,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268484,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.101,false,true,268485,Photographs,Photograph,[George Gordon (?)],,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268485,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.102,false,true,268486,Photographs,Photograph,Dr. Cook of St. Andrews,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268486,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.103,false,true,268487,Photographs,Photograph,Dr. Cook,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268487,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.104,false,true,268488,Photographs,Photograph,Dr. Cook,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268488,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.105,false,true,268489,Photographs,Photograph,[Man],,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268489,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.106,false,true,268490,Photographs,Photograph,[Man],,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268490,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.107,false,true,268491,Photographs,Photograph,[Man],,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268491,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.108,false,true,268492,Photographs,Photograph,Hartcourt (Brother of Archbishop of York),,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268492,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.109,false,true,268493,Photographs,Photograph,Sir Charles Lyell - Geologist,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268493,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.110,false,true,268495,Photographs,Photograph,"Rev. Dr. John Purves, Jedburgh",,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268495,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.111,false,true,268496,Photographs,Photograph,Scott (of Peel),,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268496,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.112,false,true,268497,Photographs,Photograph,Rev D.T.K. Drummond,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268497,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.113,false,true,268498,Photographs,Photograph,[Man],,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268498,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.114,false,true,268499,Photographs,Photograph,[Man],,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268499,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.115,false,true,268500,Photographs,Photograph,[Man],,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268500,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.116,false,true,268501,Photographs,Photograph,Earl of Rosemore,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268501,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.117,false,true,268502,Photographs,Photograph,Dr. Latham - Editor of Dictionary,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268502,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.118,false,true,268503,Photographs,Photograph,Dr. MacCulloch of Kelso and Greenock,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268503,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.119,false,true,268504,Photographs,Photograph,Mr. McNab - Botanical Gardens,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268504,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.120,false,true,268506,Photographs,Photograph,[Man],,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268506,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.121,false,true,268507,Photographs,Photograph,Sir John Boilleau,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268507,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.122,false,true,268508,Photographs,Photograph,MacKenzie (Tongue),,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268508,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.123,false,true,268509,Photographs,Photograph,"Principal Haldane, St. Andrews",,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268509,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.124,false,true,268510,Photographs,Photograph,Rev. R. Brewster of Craig,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268510,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.125,false,true,268511,Photographs,Photograph,Dr. Smyttan,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268511,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.126,false,true,268512,Photographs,Photograph,James Nasmyth (Steam Hammer),,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268512,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.127,false,true,268513,Photographs,Photograph,Laird of Portmoak,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268513,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.128,false,true,268514,Photographs,Photograph,"Rev. Henry Grey, D.D., St. Mary's, Edinburgh",,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268514,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.129,false,true,268515,Photographs,Photograph,James Ballantine,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268515,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.130,false,true,268517,Photographs,Photograph,Sobieski Stuart,,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268517,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.98.2.131,false,true,268518,Photographs,Photograph,"Prof. Fraser, Rev. Welsh, Rev. Hamilton, and Three Other Men",,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268518,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.354,false,true,285726,Photographs,Photograph,"The Morning After ""He Greatly Daring Dined""",,,,,,Artist|Artist|Photography Studio,,David Octavius Hill|Robert Adamson|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843,1870 |1848 |1848,1843–47,1843,1847,Salted paper print from paper negative,Image: 19.7 x 14.6 cm (7 3/4 x 5 3/4 in.) Mount: 14 3/4 in. × 10 3/8 in. (37.4 × 26.3 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285726,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.159.17,false,true,306219,Photographs,Photograph,The Artist and the Grave Digger,,,,,,Artist|Artist|Photography Studio,,Robert Adamson|David Octavius Hill|Hill and Adamson,"British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, active 1843–1848",,"Adamson, Robert|Hill, David Octavius|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1821 |1802 |1843,1848 |1870 |1848,1843–44,1843,1844,Salted paper print from paper negative,Mount: 14 7/8 in. × 10 1/2 in. (37.8 × 26.7 cm) Image: 8 3/8 × 6 5/16 in. (21.2 × 16 cm),"Bequest of Maurice B. Sendak, 2012",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306219,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.159.23,false,true,306226,Photographs,Photograph,"David Young and Unknown Man, Newhaven",,,,,,Artist|Artist|Photography Studio,,Robert Adamson|David Octavius Hill|Hill and Adamson,"British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, active 1843–1848",,"Adamson, Robert|Hill, David Octavius|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish",1821 |1802 |1843,1848 |1870 |1848,1845,1845,1845,Salted paper print from paper negative,Image: 6 1/8 × 4 7/16 in. (15.6 × 11.3 cm),"Bequest of Maurice B. Sendak, 2012",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306226,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1982.1182.2038–.2084,false,true,288319,Photographs,Stereographs,[Group of 47 Stereograph Views of the 1904 St. Louis World's Fair and Louisiana Purchase Exposition],,,,,,Artist|Publisher|Publisher|Photography Studio|Artist|Artist|Publisher|Artist|Subject|Publisher|Artist|Person in Photograph|Publisher,,C. H. Graves|Universal View Co.|Underwood & Underwood|Sun Sculpture Works and Studios|Strohmeyer & Wyman|William H. Rau|Metropolitan Series|Kilburn Brothers|James M. Davis|H. C. White Company|Unknown|Geronimo (Goyaalé)|Keystone View Company,"American|American|American|American|American|American, 1855–1920|American|American, active ca. 1865–1890|American|American|American Indian (Apache), 1829–1909",,"Graves, C. H.|Universal View Co.|Underwood & Underwood|Sun Sculpture Works and Studios|Strohmeyer & Wyman|Rau, William H.|Metropolitan Series|Kilburn Brothers|Davis, James M.|H. C. White Company|Unknown|Geronimo (Goyaalé)|Keystone View Company","American|American|American|American|American|American|American|American|American|American Indian, Apache",1855 |1863 |1829,1920 |1892 |1909,1850s–1910s,1850,1919,Albumen silver prints,Mounts approximately: 8.9 x 17.8 cm (3 1/2 x 7 in.),"Gift of Weston J. Naef, in memory of Kathleen W. Naef and Weston J. Naef Sr., 1982",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288319,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1982.1182.411–.433,false,true,288143,Photographs,Stereographs,[Group of 23 Stereograph Views of Railroad Bridges],,,,,,Publisher|Publisher|Publisher|Publisher|Publisher|Publisher|Publisher|Publisher|Artist|Publisher|Artist|Publisher|Publisher,,E. P. Libby|American Scenery|American Series|American Views|Allen & Hovey|Mrs. M. E. Allen|E. C. Barnum|New H Series|Littleton View Company|Western View Company|Benneville Lloyd Singley|H. L. & G. E. Williams|Keystone View Company,"American|American|American|American|American|American|American|American|American|American, Union Township, Pennsylvania 1864–1938 Meadville, Pennsylvania|American",,"Libby, E. P.|American Scenery|American Series|American Views|Allen & Hovey|Allen, M. E. Mrs.|Barnum, E. C.|New H Series|Littleton View Company|Western View Company|Singley, Benneville Lloyd|H. L. & G. E. Williams|Keystone View Company",American|American|American|American|American|American|American|American|American|American|American,1864,1938,1850s–1910s,1850,1919,Albumen silver prints,Mounts approximately: 8.2 x 17.1 cm (3 1/4 x 6 3/4 in.) to 10 x 17.8 cm (3 15/16 x 7 in.),"Gift of Weston J. Naef, in memory of Kathleen W. Naef and Weston J. Naef Sr., 1982",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288143,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1982.1182.1044–.1074,false,true,288300,Photographs,Stereographs,[Group of 31 Stereograph Views of Children With Animals],,,,,,Publisher|Artist|Artist|Publisher|Photography Studio|Publisher|Artist|Artist|Publisher|Publisher|Publisher|Publisher|Publisher|Publisher|Publisher|Artist|Artist|Publisher,,Life Groups|Benneville Lloyd Singley|Strohmeyer & Wyman|Underwood & Underwood|Sun Sculpture Works and Studios|Universal View Co.|William H. Rau|C. H. Graves|Universal Photo Art Co.|Canvassers|Webster & Albee|Popular Series|European and American Views|Stereoscopic Gems|Comics and Groups|Unknown|Unknown|Keystone View Company,"American|American, Union Township, Pennsylvania 1864–1938 Meadville, Pennsylvania|American|American|American|American|American, 1855–1920|American|American|American",,"Life Groups|Singley, Benneville Lloyd|Strohmeyer & Wyman|Underwood & Underwood|Sun Sculpture Works and Studios|Universal View Co.|Rau, William H.|Graves, C. H.|Universal Photo Art Co.|Canvassers|Webster & Albee|Popular Series|European and American Views|Stereoscopic Gems|Comics and Groups|Unknown|Unknown|Keystone View Company",American|American|American|American|American|American|American|American|American,1864 |1855,1938 |1920,1850s–1910s,1850,1919,Albumen silver prints,Mounts approximately: 8.9 x 17.8 cm (3 1/2 x 7 in.),"Gift of Weston J. Naef, in memory of Kathleen W. Naef and Weston J. Naef Sr., 1982",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288300,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1982.1182.1152–.1157,false,true,288303,Photographs,Stereographs,[Group of 6 Stereograph Views of Christmas Scenes],,,,,,Publisher|Artist|Publisher|Artist|Artist|Publisher|Artist|Publisher|Photography Studio|Artist|Publisher|Artist|Publisher,,"Hegger|C. H. Graves|Universal Photo Art Co.|Benneville Lloyd Singley|Kilburn Brothers|James M. Davis|Strohmeyer & Wyman|Underwood & Underwood|Sun Sculpture Works and Studios|George W. Griffith|Griffith & Griffith, American|Unknown|Keystone View Company","American|American|American, Union Township, Pennsylvania 1864–1938 Meadville, Pennsylvania|American, active ca. 1865–1890|American|American|American|American|American",,"Hegger|Graves, C. H.|Universal Photo Art Co.|Singley, Benneville Lloyd|Kilburn Brothers|Davis, James M.|Strohmeyer & Wyman|Underwood & Underwood|Sun Sculpture Works and Studios|Griffith, George W.|Griffith & Griffith|Unknown|Keystone View Company",American|American|American|American|American|American|American|American|American,1864 |1863,1938 |1892,1850s–1910s,1850,1919,Albumen silver prints,Mounts approximately: 8.9 x 17.8 cm (3 1/2 x 7 in.),"Gift of Weston J. Naef, in memory of Kathleen W. Naef and Weston J. Naef Sr., 1982",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288303,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.14.25,false,true,268323,Photographs,Photograph,Horace Mann,,,,,,Artist|Photography Studio|Artist,,Josiah Johnson Hawes|Southworth and Hawes|Albert Sands Southworth,"American, Wayland, Massachusetts 1808–1901 Crawford Notch, New Hampshire|American, active 1843–1863|American, West Fairlee, Vermont 1811–1894 Charlestown, Massachusetts",,"Hawes, Josiah Johnson|Southworth and Hawes|Southworth, Albert Sands",American|American|American,1808 |1843 |1811,1901 |1863 |1894,ca. 1850,1848,1852,Daguerreotype,21.6 x 16.5 cm (8 1/2 x 6 1/2 in.),"Gift of I. N. Phelps Stokes, Edward S. Hawes, Alice Mary Hawes, and Marion Augusta Hawes, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268323,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -38.34,true,true,268621,Photographs,Photograph,Lemuel Shaw,,,,,,Artist|Artist|Photography Studio,,Albert Sands Southworth|Josiah Johnson Hawes|Southworth and Hawes,"American, West Fairlee, Vermont 1811–1894 Charlestown, Massachusetts|American, Wayland, Massachusetts 1808–1901 Crawford Notch, New Hampshire|American, active 1843–1863",,"Southworth, Albert Sands|Hawes, Josiah Johnson|Southworth and Hawes",American|American|American,1811 |1808 |1843,1894 |1901 |1863,ca. 1850,1848,1852,Daguerreotype,21.6 x 16.5 cm (8 1/2 x 6 1/2 in.),"Gift of Edward S. Hawes, Alice Mary Hawes, and Marion Augusta Hawes, 1938",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268621,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.14.2,false,true,268317,Photographs,Photograph,Daniel Webster,,,,,,Artist|Artist|Photography Studio,,Albert Sands Southworth|Josiah Johnson Hawes|Southworth and Hawes,"American, West Fairlee, Vermont 1811–1894 Charlestown, Massachusetts|American, Wayland, Massachusetts 1808–1901 Crawford Notch, New Hampshire|American, active 1843–1863",,"Southworth, Albert Sands|Hawes, Josiah Johnson|Southworth and Hawes",American|American|American,1811 |1808 |1843,1894 |1901 |1863,ca. 1850,1848,1852,Daguerreotype,21.5 x 16.6 cm (8 7/16 x 6 9/16 in.),"Gift of I. N. Phelps Stokes, Edward S. Hawes, Alice Mary Hawes, and Marion Augusta Hawes, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268317,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.14.3,false,true,268328,Photographs,Photograph,"[View Down Brattle Street from the Southworth & Hawes Studio at 5 1/2 Tremont Row, Boston]",,,,,,Artist|Artist|Photography Studio,,Albert Sands Southworth|Josiah Johnson Hawes|Southworth and Hawes,"American, West Fairlee, Vermont 1811–1894 Charlestown, Massachusetts|American, Wayland, Massachusetts 1808–1901 Crawford Notch, New Hampshire|American, active 1843–1863",,"Southworth, Albert Sands|Hawes, Josiah Johnson|Southworth and Hawes",American|American|American,1811 |1808 |1843,1894 |1901 |1863,1855,1855,1855,Daguerreotype,21.6 x 16.5 cm (8 1/2 x 6 1/2 in.),"Gift of I. N. Phelps Stokes, Edward S. Hawes, Alice Mary Hawes, and Marion Augusta Hawes, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268328,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.14.4,false,true,268339,Photographs,Photograph,[Woman in Black Taffeta Dress and Lace Shawl],,,,,,Artist|Artist|Photography Studio,,Albert Sands Southworth|Josiah Johnson Hawes|Southworth and Hawes,"American, West Fairlee, Vermont 1811–1894 Charlestown, Massachusetts|American, Wayland, Massachusetts 1808–1901 Crawford Notch, New Hampshire|American, active 1843–1863",,"Southworth, Albert Sands|Hawes, Josiah Johnson|Southworth and Hawes",American|American|American,1811 |1808 |1843,1894 |1901 |1863,ca. 1850,1848,1852,Daguerreotype,21.6 x 16.5 cm (8 1/2 x 6 1/2 in.),"Gift of I. N. Phelps Stokes, Edward S. Hawes, Alice Mary Hawes, and Marion Augusta Hawes, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268339,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.14.5,false,true,268350,Photographs,Photograph,[Unidentified Woman],,,,,,Artist|Artist|Photography Studio,,Albert Sands Southworth|Josiah Johnson Hawes|Southworth and Hawes,"American, West Fairlee, Vermont 1811–1894 Charlestown, Massachusetts|American, Wayland, Massachusetts 1808–1901 Crawford Notch, New Hampshire|American, active 1843–1863",,"Southworth, Albert Sands|Hawes, Josiah Johnson|Southworth and Hawes",American|American|American,1811 |1808 |1843,1894 |1901 |1863,ca. 1850,1848,1852,Daguerreotype,21.6 x 16.5 cm (8 1/2 x 6 1/2 in.),"Gift of I. N. Phelps Stokes, Edward S. Hawes, Alice Mary Hawes, and Marion Augusta Hawes, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268350,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.14.6,false,true,268359,Photographs,Photograph,[Boston Lawyers or Clergymen (?)],,,,,,Artist|Artist|Photography Studio,,Albert Sands Southworth|Josiah Johnson Hawes|Southworth and Hawes,"American, West Fairlee, Vermont 1811–1894 Charlestown, Massachusetts|American, Wayland, Massachusetts 1808–1901 Crawford Notch, New Hampshire|American, active 1843–1863",,"Southworth, Albert Sands|Hawes, Josiah Johnson|Southworth and Hawes",American|American|American,1811 |1808 |1843,1894 |1901 |1863,ca. 1850,1848,1852,Daguerreotype,16.5 x 21.6 cm (6 1/2 x 8 1/2 in.),"Gift of I. N. Phelps Stokes, Edward S. Hawes, Alice Mary Hawes, and Marion Augusta Hawes, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268359,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.14.7,false,true,268360,Photographs,Photograph,Niagara Falls from the Canadian Side,,,,,,Artist|Artist|Photography Studio,,Albert Sands Southworth|Josiah Johnson Hawes|Southworth and Hawes,"American, West Fairlee, Vermont 1811–1894 Charlestown, Massachusetts|American, Wayland, Massachusetts 1808–1901 Crawford Notch, New Hampshire|American, active 1843–1863",,"Southworth, Albert Sands|Hawes, Josiah Johnson|Southworth and Hawes",American|American|American,1811 |1808 |1843,1894 |1901 |1863,ca. 1850,1848,1852,Daguerreotype,21.6 x 16.5 cm (8 1/2 x 6 1/2 in.),"Gift of I. N. Phelps Stokes, Edward S. Hawes, Alice Mary Hawes, and Marion Augusta Hawes, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268360,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.14.9,false,true,268362,Photographs,Photograph,Francis Parkman,,,,,,Artist|Artist|Photography Studio,,Albert Sands Southworth|Josiah Johnson Hawes|Southworth and Hawes,"American, West Fairlee, Vermont 1811–1894 Charlestown, Massachusetts|American, Wayland, Massachusetts 1808–1901 Crawford Notch, New Hampshire|American, active 1843–1863",,"Southworth, Albert Sands|Hawes, Josiah Johnson|Southworth and Hawes",American|American|American,1811 |1808 |1843,1894 |1901 |1863,ca. 1850,1848,1852,Daguerreotype,10.8 x 8.3 cm (4 1/4 x 3 1/4 in.),"Gift of I. N. Phelps Stokes, Edward S. Hawes, Alice Mary Hawes, and Marion Augusta Hawes, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268362,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -39.22.2,false,true,268648,Photographs,Photograph,[Man in a Sheraton Chair],,,,,,Artist|Artist|Photography Studio,,Albert Sands Southworth|Josiah Johnson Hawes|Southworth and Hawes,"American, West Fairlee, Vermont 1811–1894 Charlestown, Massachusetts|American, Wayland, Massachusetts 1808–1901 Crawford Notch, New Hampshire|American, active 1843–1863",,"Southworth, Albert Sands|Hawes, Josiah Johnson|Southworth and Hawes",American|American|American,1811 |1808 |1843,1894 |1901 |1863,ca. 1850,1848,1852,Daguerreotype,21.6 x 16.5 cm (8 1/2 x 6 1/2 in.),"Gift Edward S. Hawes and Marion Augusta Hawes, 1939",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268648,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -39.22.4,false,true,268650,Photographs,Photograph,[Unidentified Woman in Nine Oval Views],,,,,,Artist|Artist|Photography Studio,,Albert Sands Southworth|Josiah Johnson Hawes|Southworth and Hawes,"American, West Fairlee, Vermont 1811–1894 Charlestown, Massachusetts|American, Wayland, Massachusetts 1808–1901 Crawford Notch, New Hampshire|American, active 1843–1863",,"Southworth, Albert Sands|Hawes, Josiah Johnson|Southworth and Hawes",American|American|American,1811 |1808 |1843,1894 |1901 |1863,ca. 1850,1848,1852,Daguerreotype,21.6 x 16.5 cm (8 1/2 x 6 1/2 in.),"Gift of Edward S. Hawes and Marion Augusta Hawes, 1939",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268650,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.14.10,false,true,268307,Photographs,Photograph,Margaret Fuller (Marchioness Ossoli),,,,,,Artist|Artist|Photography Studio,,Albert Sands Southworth|Josiah Johnson Hawes|Southworth and Hawes,"American, West Fairlee, Vermont 1811–1894 Charlestown, Massachusetts|American, Wayland, Massachusetts 1808–1901 Crawford Notch, New Hampshire|American, active 1843–1863",,"Southworth, Albert Sands|Hawes, Josiah Johnson|Southworth and Hawes",American|American|American,1811 |1808 |1843,1894 |1901 |1863,ca. 1850,1848,1852,Daguerreotype,10.8 x 8.3 cm (4 1/4 x 3 1/4 in.),"Gift of I. N. Phelps Stokes, Edward S. Hawes, Alice Mary Hawes, and Marion Augusta Hawes, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268307,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.14.11,false,true,268308,Photographs,Photograph,[Nancy Southworth Hawes],,,,,,Artist|Artist|Photography Studio,,Albert Sands Southworth|Josiah Johnson Hawes|Southworth and Hawes,"American, West Fairlee, Vermont 1811–1894 Charlestown, Massachusetts|American, Wayland, Massachusetts 1808–1901 Crawford Notch, New Hampshire|American, active 1843–1863",,"Southworth, Albert Sands|Hawes, Josiah Johnson|Southworth and Hawes",American|American|American,1811 |1808 |1843,1894 |1901 |1863,ca. 1850,1848,1852,Daguerreotype,8.3 x 7.0 cm (3 1/4 x 2 3/4 in.),"Gift of I. N. Phelps Stokes, Edward S. Hawes, Alice Mary Hawes, and Marion Augusta Hawes, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268308,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.14.12,false,true,268309,Photographs,Photograph,[Unidentified Boy in Dark Suit],,,,,,Artist|Artist|Photography Studio,,Albert Sands Southworth|Josiah Johnson Hawes|Southworth and Hawes,"American, West Fairlee, Vermont 1811–1894 Charlestown, Massachusetts|American, Wayland, Massachusetts 1808–1901 Crawford Notch, New Hampshire|American, active 1843–1863",,"Southworth, Albert Sands|Hawes, Josiah Johnson|Southworth and Hawes",American|American|American,1811 |1808 |1843,1894 |1901 |1863,ca. 1850,1848,1852,Daguerreotype,21.6 x 16.5 cm (8 1/2 x 6 1/2 in.),"Gift of I. N. Phelps Stokes, Edward S. Hawes, Alice Mary Hawes, and Marion Augusta Hawes, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268309,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.14.13,false,true,268310,Photographs,Photograph,[Unidentified Woman],,,,,,Artist|Artist|Photography Studio,,Albert Sands Southworth|Josiah Johnson Hawes|Southworth and Hawes,"American, West Fairlee, Vermont 1811–1894 Charlestown, Massachusetts|American, Wayland, Massachusetts 1808–1901 Crawford Notch, New Hampshire|American, active 1843–1863",,"Southworth, Albert Sands|Hawes, Josiah Johnson|Southworth and Hawes",American|American|American,1811 |1808 |1843,1894 |1901 |1863,ca. 1850,1848,1852,Daguerreotype,21.6 x 16.5 cm (8 1/2 x 6 1/2 in.),"Gift of I. N. Phelps Stokes, Edward S. Hawes, Alice Mary Hawes, and Marion Augusta Hawes, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268310,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.14.14,false,true,268311,Photographs,Photograph,Millard Fillmore,,,,,,Artist|Artist|Photography Studio,,Albert Sands Southworth|Josiah Johnson Hawes|Southworth and Hawes,"American, West Fairlee, Vermont 1811–1894 Charlestown, Massachusetts|American, Wayland, Massachusetts 1808–1901 Crawford Notch, New Hampshire|American, active 1843–1863",,"Southworth, Albert Sands|Hawes, Josiah Johnson|Southworth and Hawes",American|American|American,1811 |1808 |1843,1894 |1901 |1863,ca. 1850,1848,1852,Daguerreotype,21.6 x 16.5 cm (8 1/2 x 6 1/2 in.),"Gift of I. N. Phelps Stokes, Edward S. Hawes, Alice Mary Hawes, and Marion Augusta Hawes, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268311,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.14.15,false,true,268312,Photographs,Photograph,Henry Clay,,,,,,Artist|Artist|Photography Studio,,Albert Sands Southworth|Josiah Johnson Hawes|Southworth and Hawes,"American, West Fairlee, Vermont 1811–1894 Charlestown, Massachusetts|American, Wayland, Massachusetts 1808–1901 Crawford Notch, New Hampshire|American, active 1843–1863",,"Southworth, Albert Sands|Hawes, Josiah Johnson|Southworth and Hawes",American|American|American,1811 |1808 |1843,1894 |1901 |1863,ca. 1850,1848,1852,Daguerreotype,14.0 x 10.8 cm (5 1/2 x 4 1/4 in.),"Gift of I. N. Phelps Stokes, Edward S. Hawes, Alice Mary Hawes, and Marion Augusta Hawes, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268312,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.14.16,false,true,268313,Photographs,Photograph,Albert Sands Southworth,,,,,,Artist|Artist|Photography Studio,,Albert Sands Southworth|Josiah Johnson Hawes|Southworth and Hawes,"American, West Fairlee, Vermont 1811–1894 Charlestown, Massachusetts|American, Wayland, Massachusetts 1808–1901 Crawford Notch, New Hampshire|American, active 1843–1863",,"Southworth, Albert Sands|Hawes, Josiah Johnson|Southworth and Hawes",American|American|American,1811 |1808 |1843,1894 |1901 |1863,ca. 1845–50,1845,1850,Daguerreotype,8.3 x 7.0 cm (3 1/4 x 2 3/4 in.),"Gift of I. N. Phelps Stokes, Edward S. Hawes, Alice Mary Hawes, and Marion Augusta Hawes, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268313,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.14.17,false,true,268314,Photographs,Photograph,[Elderly Woman in Black Cape and Bonnet with Mourning Crape],,,,,,Artist|Artist|Photography Studio,,Albert Sands Southworth|Josiah Johnson Hawes|Southworth and Hawes,"American, West Fairlee, Vermont 1811–1894 Charlestown, Massachusetts|American, Wayland, Massachusetts 1808–1901 Crawford Notch, New Hampshire|American, active 1843–1863",,"Southworth, Albert Sands|Hawes, Josiah Johnson|Southworth and Hawes",American|American|American,1811 |1808 |1843,1894 |1901 |1863,ca. 1850,1848,1852,Daguerreotype,21.6 x 16.5 cm (8 1/2 x 6 1/2 in.),"Gift of I. N. Phelps Stokes, Edward S. Hawes, Alice Mary Hawes, and Marion Augusta Hawes, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268314,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.14.18,false,true,268315,Photographs,Photograph,[Unidentified Woman],,,,,,Artist|Artist|Photography Studio,,Albert Sands Southworth|Josiah Johnson Hawes|Southworth and Hawes,"American, West Fairlee, Vermont 1811–1894 Charlestown, Massachusetts|American, Wayland, Massachusetts 1808–1901 Crawford Notch, New Hampshire|American, active 1843–1863",,"Southworth, Albert Sands|Hawes, Josiah Johnson|Southworth and Hawes",American|American|American,1811 |1808 |1843,1894 |1901 |1863,ca. 1850,1848,1852,Daguerreotype,21.6 x 16.5 cm (8 1/2 x 6 1/2 in.),"Gift of I. N. Phelps Stokes, Edward S. Hawes, Alice Mary Hawes, and Marion Augusta Hawes, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268315,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.14.22,false,true,268320,Photographs,Photograph,Classroom in the Emerson School for Girls,,,,,,Artist|Artist|Photography Studio,,Albert Sands Southworth|Josiah Johnson Hawes|Southworth and Hawes,"American, West Fairlee, Vermont 1811–1894 Charlestown, Massachusetts|American, Wayland, Massachusetts 1808–1901 Crawford Notch, New Hampshire|American, active 1843–1863",,"Southworth, Albert Sands|Hawes, Josiah Johnson|Southworth and Hawes",American|American|American,1811 |1808 |1843,1894 |1901 |1863,ca. 1850,1848,1852,Daguerreotype,21.6 x 16.5 cm (8 1/2 x 6 1/2 in.),"Gift of I. N. Phelps Stokes, Edward S. Hawes, Alice Mary Hawes, and Marion Augusta Hawes, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268320,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.14.23,false,true,268321,Photographs,Photograph,Commodore Charles Morris,,,,,,Artist|Artist|Photography Studio,,Albert Sands Southworth|Josiah Johnson Hawes|Southworth and Hawes,"American, West Fairlee, Vermont 1811–1894 Charlestown, Massachusetts|American, Wayland, Massachusetts 1808–1901 Crawford Notch, New Hampshire|American, active 1843–1863",,"Southworth, Albert Sands|Hawes, Josiah Johnson|Southworth and Hawes",American|American|American,1811 |1808 |1843,1894 |1901 |1863,ca. 1850,1848,1852,Daguerreotype,14.0 x 10.8 cm (5 1/2 x 4 1/4 in.),"Gift of I. N. Phelps Stokes, Edward S. Hawes, Alice Mary Hawes, and Marion Augusta Hawes, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268321,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.14.24,false,true,268322,Photographs,Photograph,William Hickling Prescott,,,,,,Artist|Artist|Photography Studio,,Albert Sands Southworth|Josiah Johnson Hawes|Southworth and Hawes,"American, West Fairlee, Vermont 1811–1894 Charlestown, Massachusetts|American, Wayland, Massachusetts 1808–1901 Crawford Notch, New Hampshire|American, active 1843–1863",,"Southworth, Albert Sands|Hawes, Josiah Johnson|Southworth and Hawes",American|American|American,1811 |1808 |1843,1894 |1901 |1863,ca. 1850,1848,1852,Daguerreotype,14.0 x 10.8 cm (5 1/2 x 4 1/4 in.),"Gift of I. N. Phelps Stokes, Edward S. Hawes, Alice Mary Hawes, and Marion Augusta Hawes, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268322,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.14.27,false,true,268325,Photographs,Photograph,Mrs. James Thomas Fields (Annie Adams),,,,,,Artist|Artist|Photography Studio,,Albert Sands Southworth|Josiah Johnson Hawes|Southworth and Hawes,"American, West Fairlee, Vermont 1811–1894 Charlestown, Massachusetts|American, Wayland, Massachusetts 1808–1901 Crawford Notch, New Hampshire|American, active 1843–1863",,"Southworth, Albert Sands|Hawes, Josiah Johnson|Southworth and Hawes",American|American|American,1811 |1808 |1843,1894 |1901 |1863,1861,1861,1861,Daguerreotype,21.6 x 16.5 cm (8 1/2 x 6 1/2 in.),"Gift of I. N. Phelps Stokes, Edward S. Hawes, Alice Mary Hawes, and Marion Augusta Hawes, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268325,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.14.29,false,true,268327,Photographs,Photograph,[Unidentified Man Wearing Turban],,,,,,Artist|Artist|Photography Studio,,Albert Sands Southworth|Josiah Johnson Hawes|Southworth and Hawes,"American, West Fairlee, Vermont 1811–1894 Charlestown, Massachusetts|American, Wayland, Massachusetts 1808–1901 Crawford Notch, New Hampshire|American, active 1843–1863",,"Southworth, Albert Sands|Hawes, Josiah Johnson|Southworth and Hawes",American|American|American,1811 |1808 |1843,1894 |1901 |1863,1851–52,1851,1852,Daguerreotype,8.3 x 7.0 cm (3 1/4 x 2 3/4 in.),"Gift of I. N. Phelps Stokes, Edward S. Hawes, Alice Mary Hawes, and Marion Augusta Hawes, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268327,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.14.30,false,true,268329,Photographs,Photograph,George Peabody,,,,,,Artist|Artist|Photography Studio,,Albert Sands Southworth|Josiah Johnson Hawes|Southworth and Hawes,"American, West Fairlee, Vermont 1811–1894 Charlestown, Massachusetts|American, Wayland, Massachusetts 1808–1901 Crawford Notch, New Hampshire|American, active 1843–1863",,"Southworth, Albert Sands|Hawes, Josiah Johnson|Southworth and Hawes",American|American|American,1811 |1808 |1843,1894 |1901 |1863,ca. 1850,1848,1852,Daguerreotype,10.8 x 8.3 cm (4 1/4 x 3 1/4 in.),"Gift of I. N. Phelps Stokes, Edward S. Hawes, Alice Mary Hawes, and Marion Augusta Hawes, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268329,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.14.31,false,true,268330,Photographs,Photograph,Henry Wadsworth Longfellow,,,,,,Artist|Artist|Photography Studio,,Albert Sands Southworth|Josiah Johnson Hawes|Southworth and Hawes,"American, West Fairlee, Vermont 1811–1894 Charlestown, Massachusetts|American, Wayland, Massachusetts 1808–1901 Crawford Notch, New Hampshire|American, active 1843–1863",,"Southworth, Albert Sands|Hawes, Josiah Johnson|Southworth and Hawes",American|American|American,1811 |1808 |1843,1894 |1901 |1863,ca. 1850,1848,1852,Daguerreotype,10.8 x 8.3 cm (4 1/4 x 3 1/4 in.),"Gift of I. N. Phelps Stokes, Edward S. Hawes, Alice Mary Hawes, and Marion Augusta Hawes, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268330,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.14.32,false,true,268331,Photographs,Photograph,Zachary Taylor,,,,,,Artist|Artist|Photography Studio,,Albert Sands Southworth|Josiah Johnson Hawes|Southworth and Hawes,"American, West Fairlee, Vermont 1811–1894 Charlestown, Massachusetts|American, Wayland, Massachusetts 1808–1901 Crawford Notch, New Hampshire|American, active 1843–1863",,"Southworth, Albert Sands|Hawes, Josiah Johnson|Southworth and Hawes",American|American|American,1811 |1808 |1843,1894 |1901 |1863,ca. 1850,1840,1859,Daguerreotype,8.3 x 7.0 cm (3 1/4 x 2 3/4 in.),"Gift of I. N. Phelps Stokes, Edward S. Hawes, Alice Mary Hawes, and Marion Augusta Hawes, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268331,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.14.33,false,true,268332,Photographs,Photograph,John Howard Payne,,,,,,Artist|Artist|Photography Studio,,Albert Sands Southworth|Josiah Johnson Hawes|Southworth and Hawes,"American, West Fairlee, Vermont 1811–1894 Charlestown, Massachusetts|American, Wayland, Massachusetts 1808–1901 Crawford Notch, New Hampshire|American, active 1843–1863",,"Southworth, Albert Sands|Hawes, Josiah Johnson|Southworth and Hawes",American|American|American,1811 |1808 |1843,1894 |1901 |1863,ca. 1850,1848,1852,Daguerreotype,14.0 x 10.8 cm (5 1/2 x 4 1/4 in.),"Gift of I. N. Phelps Stokes, Edward S. Hawes, Alice Mary Hawes, and Marion Augusta Hawes, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268332,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.14.35,false,true,268334,Photographs,Photograph,[Woman in Profile with Lace Collar and Shawl],,,,,,Artist|Artist|Photography Studio,,Albert Sands Southworth|Josiah Johnson Hawes|Southworth and Hawes,"American, West Fairlee, Vermont 1811–1894 Charlestown, Massachusetts|American, Wayland, Massachusetts 1808–1901 Crawford Notch, New Hampshire|American, active 1843–1863",,"Southworth, Albert Sands|Hawes, Josiah Johnson|Southworth and Hawes",American|American|American,1811 |1808 |1843,1894 |1901 |1863,ca. 1850,1848,1852,Daguerreotype,21.6 x 16.5 cm (8 1/2 x 6 1/2 in.),"Gift of I. N. Phelps Stokes, Edward S. Hawes, Alice Mary Hawes, and Marion Augusta Hawes, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268334,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.14.36,false,true,268335,Photographs,Photograph,James Thomas Fields,,,,,,Artist|Artist|Photography Studio,,Albert Sands Southworth|Josiah Johnson Hawes|Southworth and Hawes,"American, West Fairlee, Vermont 1811–1894 Charlestown, Massachusetts|American, Wayland, Massachusetts 1808–1901 Crawford Notch, New Hampshire|American, active 1843–1863",,"Southworth, Albert Sands|Hawes, Josiah Johnson|Southworth and Hawes",American|American|American,1811 |1808 |1843,1894 |1901 |1863,1861,1861,1861,Daguerreotype,21.6 x 16.5 cm (8 1/2 x 6 1/2 in.),"Gift of I. N. Phelps Stokes, Edward S. Hawes, Alice Mary Hawes, and Marion Augusta Hawes, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268335,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.14.37,false,true,268336,Photographs,Photograph,William Lloyd Garrison,,,,,,Artist|Artist|Photography Studio,,Albert Sands Southworth|Josiah Johnson Hawes|Southworth and Hawes,"American, West Fairlee, Vermont 1811–1894 Charlestown, Massachusetts|American, Wayland, Massachusetts 1808–1901 Crawford Notch, New Hampshire|American, active 1843–1863",,"Southworth, Albert Sands|Hawes, Josiah Johnson|Southworth and Hawes",American|American|American,1811 |1808 |1843,1894 |1901 |1863,ca. 1850,1848,1852,Daguerreotype,14.0 x 10.8 cm (5 1/2 x 4 1/4 in.),"Gift of I. N. Phelps Stokes, Edward S. Hawes, Alice Mary Hawes, and Marion Augusta Hawes, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268336,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.14.38,false,true,268337,Photographs,Photograph,Josiah Johnson Hawes,,,,,,Artist|Artist|Photography Studio,,Albert Sands Southworth|Josiah Johnson Hawes|Southworth and Hawes,"American, West Fairlee, Vermont 1811–1894 Charlestown, Massachusetts|American, Wayland, Massachusetts 1808–1901 Crawford Notch, New Hampshire|American, active 1843–1863",,"Southworth, Albert Sands|Hawes, Josiah Johnson|Southworth and Hawes",American|American|American,1811 |1808 |1843,1894 |1901 |1863,ca. 1845–50,1845,1850,Daguerreotype,10.8 x 8.3 cm (4 1/4 x 3 1/4 in.),"Gift of I. N. Phelps Stokes, Edward S. Hawes, Alice Mary Hawes, and Marion Augusta Hawes, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268337,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.14.39,false,true,268338,Photographs,Photograph,[Boston Doctors],,,,,,Artist|Artist|Photography Studio,,Albert Sands Southworth|Josiah Johnson Hawes|Southworth and Hawes,"American, West Fairlee, Vermont 1811–1894 Charlestown, Massachusetts|American, Wayland, Massachusetts 1808–1901 Crawford Notch, New Hampshire|American, active 1843–1863",,"Southworth, Albert Sands|Hawes, Josiah Johnson|Southworth and Hawes",American|American|American,1811 |1808 |1843,1894 |1901 |1863,ca. 1850,1848,1852,Daguerreotype,16.5 x 21.6 cm (6 1/2 x 8 1/2 in.),"Gift of I. N. Phelps Stokes, Edward S. Hawes, Alice Mary Hawes, and Marion Augusta Hawes, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268338,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.14.40,false,true,268340,Photographs,Photograph,Harriet Beecher Stowe,,,,,,Artist|Artist|Photography Studio,,Albert Sands Southworth|Josiah Johnson Hawes|Southworth and Hawes,"American, West Fairlee, Vermont 1811–1894 Charlestown, Massachusetts|American, Wayland, Massachusetts 1808–1901 Crawford Notch, New Hampshire|American, active 1843–1863",,"Southworth, Albert Sands|Hawes, Josiah Johnson|Southworth and Hawes",American|American|American,1811 |1808 |1843,1894 |1901 |1863,1850s,1850,1850,Daguerreotype,10.8 x 8.3 cm. (4 1/4 x 3 1/4 in.),"Gift of I. N. Phelps Stokes, Edward S. Hawes, Alice Mary Hawes, and Marion Augusta Hawes, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268340,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.14.41,false,true,268341,Photographs,Photograph,Lola Montez,,,,,,Artist|Artist|Photography Studio,,Albert Sands Southworth|Josiah Johnson Hawes|Southworth and Hawes,"American, West Fairlee, Vermont 1811–1894 Charlestown, Massachusetts|American, Wayland, Massachusetts 1808–1901 Crawford Notch, New Hampshire|American, active 1843–1863",,"Southworth, Albert Sands|Hawes, Josiah Johnson|Southworth and Hawes",American|American|American,1811 |1808 |1843,1894 |1901 |1863,ca. 1850,1848,1852,Daguerreotype,21.6 x 16.5 cm (8 1/2 x 6 1/2 in.),"Gift of I. N. Phelps Stokes, Edward S. Hawes, Alice Mary Hawes, and Marion Augusta Hawes, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268341,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.14.43,false,true,268343,Photographs,Photograph,James Freeman Clarke,,,,,,Artist|Artist|Photography Studio,,Albert Sands Southworth|Josiah Johnson Hawes|Southworth and Hawes,"American, West Fairlee, Vermont 1811–1894 Charlestown, Massachusetts|American, Wayland, Massachusetts 1808–1901 Crawford Notch, New Hampshire|American, active 1843–1863",,"Southworth, Albert Sands|Hawes, Josiah Johnson|Southworth and Hawes",American|American|American,1811 |1808 |1843,1894 |1901 |1863,ca. 1850,1848,1852,Daguerreotype,14.0 x 10.8 cm (5 1/2 x 4 1/4 in.),"Gift of I. N. Phelps Stokes, Edward S. Hawes, Alice Mary Hawes, and Marion Augusta Hawes, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268343,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.14.48,false,true,268348,Photographs,Photograph,Rufus Choate,,,,,,Artist|Artist|Photography Studio,,Albert Sands Southworth|Josiah Johnson Hawes|Southworth and Hawes,"American, West Fairlee, Vermont 1811–1894 Charlestown, Massachusetts|American, Wayland, Massachusetts 1808–1901 Crawford Notch, New Hampshire|American, active 1843–1863",,"Southworth, Albert Sands|Hawes, Josiah Johnson|Southworth and Hawes",American|American|American,1811 |1808 |1843,1894 |1901 |1863,ca. 1850,1848,1852,Daguerreotype,20.4 x 15.3 cm (8 x 6 in.),"Gift of I. N. Phelps Stokes, Edward S. Hawes, Alice Mary Hawes, and Marion Augusta Hawes, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268348,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.14.50,false,true,268351,Photographs,Photograph,Charles Sprague,,,,,,Artist|Artist|Photography Studio,,Albert Sands Southworth|Josiah Johnson Hawes|Southworth and Hawes,"American, West Fairlee, Vermont 1811–1894 Charlestown, Massachusetts|American, Wayland, Massachusetts 1808–1901 Crawford Notch, New Hampshire|American, active 1843–1863",,"Southworth, Albert Sands|Hawes, Josiah Johnson|Southworth and Hawes",American|American|American,1811 |1808 |1843,1894 |1901 |1863,ca. 1850,1848,1852,Daguerreotype,42.2 x 32.4 cm (16 5/8 x 12 3/4 in.),"Gift of I. N. Phelps Stokes, Edward S. Hawes, Alice Mary Hawes, and Marion Augusta Hawes, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268351,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.14.51,false,true,268352,Photographs,Photograph,[Young Girl with Hand Raised to Shoulder],,,,,,Artist|Artist|Photography Studio,,Albert Sands Southworth|Josiah Johnson Hawes|Southworth and Hawes,"American, West Fairlee, Vermont 1811–1894 Charlestown, Massachusetts|American, Wayland, Massachusetts 1808–1901 Crawford Notch, New Hampshire|American, active 1843–1863",,"Southworth, Albert Sands|Hawes, Josiah Johnson|Southworth and Hawes",American|American|American,1811 |1808 |1843,1894 |1901 |1863,ca. 1850,1848,1852,Daguerreotype,21.6 x 16.5 cm (8 1/2 x 6 1/2 in.),"Gift of I. N. Phelps Stokes, Edward S. Hawes, Alice Mary Hawes, and Marion Augusta Hawes, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268352,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.14.52,false,true,268353,Photographs,Photograph,[Elderly Man; Full Face],,,,,,Artist|Artist|Photography Studio,,Albert Sands Southworth|Josiah Johnson Hawes|Southworth and Hawes,"American, West Fairlee, Vermont 1811–1894 Charlestown, Massachusetts|American, Wayland, Massachusetts 1808–1901 Crawford Notch, New Hampshire|American, active 1843–1863",,"Southworth, Albert Sands|Hawes, Josiah Johnson|Southworth and Hawes",American|American|American,1811 |1808 |1843,1894 |1901 |1863,ca. 1850,1848,1852,Daguerreotype,21.6 x 16.5 cm (8 1/2 x 6 1/2 in.),"Gift of I. N. Phelps Stokes, Edward S. Hawes, Alice Mary Hawes, and Marion Augusta Hawes, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268353,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.14.53,false,true,268354,Photographs,Photograph,[Girl with Portrait of George Washingtion],,,,,,Artist|Artist|Photography Studio,,Albert Sands Southworth|Josiah Johnson Hawes|Southworth and Hawes,"American, West Fairlee, Vermont 1811–1894 Charlestown, Massachusetts|American, Wayland, Massachusetts 1808–1901 Crawford Notch, New Hampshire|American, active 1843–1863",,"Southworth, Albert Sands|Hawes, Josiah Johnson|Southworth and Hawes",American|American|American,1811 |1808 |1843,1894 |1901 |1863,ca. 1850,1848,1852,Daguerreotype,21.6 x 16.5 cm (8 1/2 x 6 1/2 in.),"Gift of I. N. Phelps Stokes, Edward S. Hawes, Alice Mary Hawes, and Marion Augusta Hawes, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268354,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.14.54,false,true,268355,Photographs,Photograph,Dr. John Collins Warren,,,,,,Artist|Artist|Photography Studio,,Albert Sands Southworth|Josiah Johnson Hawes|Southworth and Hawes,"American, West Fairlee, Vermont 1811–1894 Charlestown, Massachusetts|American, Wayland, Massachusetts 1808–1901 Crawford Notch, New Hampshire|American, active 1843–1863",,"Southworth, Albert Sands|Hawes, Josiah Johnson|Southworth and Hawes",American|American|American,1811 |1808 |1843,1894 |1901 |1863,ca. 1850,1848,1852,Daguerreotype,21.6 x 16.5 cm (8 1/2 x 6 1/2 in.),"Gift of I. N. Phelps Stokes, Edward S. Hawes, Alice Mary Hawes, and Marion Augusta Hawes, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268355,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.14.55,false,true,268356,Photographs,Photograph,[Man in Judge's Robes; Seated],,,,,,Artist|Artist|Photography Studio,,Albert Sands Southworth|Josiah Johnson Hawes|Southworth and Hawes,"American, West Fairlee, Vermont 1811–1894 Charlestown, Massachusetts|American, Wayland, Massachusetts 1808–1901 Crawford Notch, New Hampshire|American, active 1843–1863",,"Southworth, Albert Sands|Hawes, Josiah Johnson|Southworth and Hawes",American|American|American,1811 |1808 |1843,1894 |1901 |1863,ca. 1850,1848,1852,Daguerreotype,21.6 x 16.5 cm (8 1/2 x 6 1/2 in.),"Gift of I. N. Phelps Stokes, Edward S. Hawes, Alice Mary Hawes, and Marion Augusta Hawes, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268356,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.14.56,false,true,268357,Photographs,Photograph,[Students from the Emerson School for Girls],,,,,,Artist|Artist|Photography Studio,,Albert Sands Southworth|Josiah Johnson Hawes|Southworth and Hawes,"American, West Fairlee, Vermont 1811–1894 Charlestown, Massachusetts|American, Wayland, Massachusetts 1808–1901 Crawford Notch, New Hampshire|American, active 1843–1863",,"Southworth, Albert Sands|Hawes, Josiah Johnson|Southworth and Hawes",American|American|American,1811 |1808 |1843,1894 |1901 |1863,ca. 1850,1848,1852,Daguerreotype,16.5 x 21.6 cm (6 1/2 x 8 1/2 in.),"Gift of I. N. Phelps Stokes, Edward S. Hawes, Alice Mary Hawes, and Marion Augusta Hawes, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268357,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.14.57,false,true,268358,Photographs,Photograph,John L. Tucker,,,,,,Artist|Artist|Photography Studio,,Albert Sands Southworth|Josiah Johnson Hawes|Southworth and Hawes,"American, West Fairlee, Vermont 1811–1894 Charlestown, Massachusetts|American, Wayland, Massachusetts 1808–1901 Crawford Notch, New Hampshire|American, active 1843–1863",,"Southworth, Albert Sands|Hawes, Josiah Johnson|Southworth and Hawes",American|American|American,1811 |1808 |1843,1894 |1901 |1863,ca. 1850,1848,1852,Daguerreotype,33.4 x 41.3 cm (13 1/8 x 16 1/4 in.),"Gift of I. N. Phelps Stokes, Edward S. Hawes, Alice Mary Hawes, and Marion Augusta Hawes, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268358,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1997.382.40,false,true,282045,Photographs,Photograph,"Sculpture Gallery, Boston Athenaeum",,,,,,Artist|Artist|Photography Studio,,Albert Sands Southworth|Josiah Johnson Hawes|Southworth and Hawes,"American, West Fairlee, Vermont 1811–1894 Charlestown, Massachusetts|American, Wayland, Massachusetts 1808–1901 Crawford Notch, New Hampshire|American, active 1843–1863",,"Southworth, Albert Sands|Hawes, Josiah Johnson|Southworth and Hawes",American|American|American,1811 |1808 |1843,1894 |1901 |1863,ca. 1855,1853,1857,Daguerreotype,visible: 18.6 x 13.7 cm (7 5/16 x 5 3/8 in.),"The Rubel Collection, Purchase, Ann Tenenbaum and Thomas H. Lee and Lila Acheson Wallace Gifts, 1997",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/282045,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.400.39,false,true,291792,Photographs,Daguerreotype,[Young Woman with Hair Styled in Two Buns],,,,,,Artist|Artist|Photography Studio,Attributed to|Attributed to,Albert Sands Southworth|Josiah Johnson Hawes|Southworth and Hawes,"American, West Fairlee, Vermont 1811–1894 Charlestown, Massachusetts|American, Wayland, Massachusetts 1808–1901 Crawford Notch, New Hampshire|American, active 1843–1863",,"Southworth, Albert Sands|Hawes, Josiah Johnson|Southworth and Hawes",American|American|American,1811 |1808 |1843,1894 |1901 |1863,1850s,1850,1859,Daguerreotype,Image: 12.5 x 9.3 cm (4 15/16 x 3 11/16 in.) Plate: 16.5 x 13.7 cm (6 1/2 x 5 3/8 in.) Case: 1.9 x 18.1 x 15.2 cm (3/4 x 7 1/8 x 6 in.),"Bequest of Herbert Mitchell, 2008",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/291792,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.14.20,false,true,268318,Photographs,Photograph,Miss Hodges of Salem,,,,,,Photography Studio|Artist|Artist,,Southworth and Hawes|Josiah Johnson Hawes|Albert Sands Southworth,"American, active 1843–1863|American, Wayland, Massachusetts 1808–1901 Crawford Notch, New Hampshire|American, West Fairlee, Vermont 1811–1894 Charlestown, Massachusetts",,"Southworth and Hawes|Hawes, Josiah Johnson|Southworth, Albert Sands",American|American|American,1843 |1808 |1811,1863 |1901 |1894,ca. 1850,1848,1852,Daguerreotype,21.6 x 16.5 cm (8 1/2 x 6 1/2 in.),"Gift of I. N. Phelps Stokes, Edward S. Hawes, Alice Mary Hawes, and Marion Augusta Hawes, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268318,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.14.1,false,true,268306,Photographs,Photograph,Donald McKay,,,,,,Photography Studio|Artist|Artist,,Southworth and Hawes|Albert Sands Southworth|Josiah Johnson Hawes,"American, active 1843–1863|American, West Fairlee, Vermont 1811–1894 Charlestown, Massachusetts|American, Wayland, Massachusetts 1808–1901 Crawford Notch, New Hampshire",,"Southworth and Hawes|Southworth, Albert Sands|Hawes, Josiah Johnson",American|American|American,1843 |1811 |1808,1863 |1894 |1901,ca. 1850–55,1850,1855,Daguerreotype,21.6 x 16.5cm (8 1/2 x 6 1/2in.),"Gift of I. N. Phelps Stokes, Edward S. Hawes, Alice Mary Hawes, and Marion Augusta Hawes, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268306,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.14.19,false,true,268316,Photographs,Photograph,Robert Charles Winthrop,,,,,,Photography Studio|Artist|Artist,,Southworth and Hawes|Albert Sands Southworth|Josiah Johnson Hawes,"American, active 1843–1863|American, West Fairlee, Vermont 1811–1894 Charlestown, Massachusetts|American, Wayland, Massachusetts 1808–1901 Crawford Notch, New Hampshire",,"Southworth and Hawes|Southworth, Albert Sands|Hawes, Josiah Johnson",American|American|American,1843 |1811 |1808,1863 |1894 |1901,ca. 1850,1848,1852,Daguerreotype,21.6 x 16.5 cm (8 1/2 x 6 1/2 in.),"Gift of I. N. Phelps Stokes, Edward S. Hawes, Alice Mary Hawes, and Marion Augusta Hawes, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268316,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.14.26,false,true,268324,Photographs,Photograph,Elias Howe,,,,,,Photography Studio|Artist|Artist,,Southworth and Hawes|Albert Sands Southworth|Josiah Johnson Hawes,"American, active 1843–1863|American, West Fairlee, Vermont 1811–1894 Charlestown, Massachusetts|American, Wayland, Massachusetts 1808–1901 Crawford Notch, New Hampshire",,"Southworth and Hawes|Southworth, Albert Sands|Hawes, Josiah Johnson",American|American|American,1843 |1811 |1808,1863 |1894 |1901,ca. 1850,1848,1852,Daguerreotype,14.0 x 10.8 cm (5 1/2 x 4 1/4 in.),"Gift of I. N. Phelps Stokes, Edward S. Hawes, Alice Mary Hawes, and Marion Augusta Hawes, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268324,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.14.28,false,true,268326,Photographs,Photograph,Charles Sumner,,,,,,Photography Studio|Artist|Artist,,Southworth and Hawes|Albert Sands Southworth|Josiah Johnson Hawes,"American, active 1843–1863|American, West Fairlee, Vermont 1811–1894 Charlestown, Massachusetts|American, Wayland, Massachusetts 1808–1901 Crawford Notch, New Hampshire",,"Southworth and Hawes|Southworth, Albert Sands|Hawes, Josiah Johnson",American|American|American,1843 |1811 |1808,1863 |1894 |1901,ca. 1850,1848,1852,Daguerreotype,21.6 x 16.5 cm (8 1/2 x 6 1/2 in.),"Gift of I. N. Phelps Stokes, Edward S. Hawes, Alice Mary Hawes, and Marion Augusta Hawes, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268326,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2011.556.1,false,true,301969,Photographs,Photograph,[Augusta Hawes at Four Years Old],,,,,,Photography Studio|Artist|Artist,,Southworth and Hawes|Albert Sands Southworth|Josiah Johnson Hawes,"American, active 1843–1863|American, West Fairlee, Vermont 1811–1894 Charlestown, Massachusetts|American, Wayland, Massachusetts 1808–1901 Crawford Notch, New Hampshire",,"Southworth and Hawes|Southworth, Albert Sands|Hawes, Josiah Johnson",American|American|American,1843 |1811 |1808,1863 |1894 |1901,1850s,1850,1859,Daguerreotype,Plate: 12.7 x 10.7 cm (5 x 4 3/16 in.),"Gift of Isaac Lagnado, in honor of Director Thomas P. Campbell, 2011",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/301969,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1982.1182.813–.820,false,true,288237,Photographs,Stereographs,"[Group of 8 Early Stereograph Views of British Monuments, Memorials, and Tombs]",,,,,,Artist|Artist|Artist|Person in Photograph|Publisher|Person in Photograph|Person in Photograph,,Lennie|Unknown|Taylor|Samuel Johnson|Stereoscopic Gems|Sir Walter Scott|Earl Richard Beauchamp,"British, born Scotland|British|British|British, Lichfield, Staffordshire 1709–1784 London|British, Edinburgh, Scotland 1771–1832 Abbotsford, Scotland|British, 1382–1439",,"Lennie|Unknown|Taylor, Mr.|Johnson, Samuel|Stereoscopic Gems|Scott, Walter, Sir|Beauchamp, Richard Earl","British, Scottish|British|British|British, Scottish|British",1709 |1771 |1382,1784 |1832 |1439,1850s–1910s,1850,1919,Albumen silver prints,Mounts approximately: 8.4 x 17.1 cm (3 5/16 x 6 3/4 in.) to 8.5 x 17.8 cm (3 3/8 x 7 in.),"Gift of Weston J. Naef, in memory of Kathleen W. Naef and Weston J. Naef Sr., 1982",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288237,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1982.1182.647–.658,false,true,288154,Photographs,Stereographs,"[Group of 12 Stereograph Views of Celebrities, Including Popes and Presidents]",,,,,,Publisher|Photography Studio|Person in Photograph|Publisher|Publisher|Person in Photograph|Person in Photograph|Person in Photograph|Person in Photograph|Publisher|Artist|Person in Photograph|Publisher|Publisher,,Underwood & Underwood|Sun Sculpture Works and Studios|Pope Pius X|C. H. Graves|Universal Photo Art Co.|William McKinley|President John Calvin Coolidge Jr.|First Lady Grace Anna Goodhue Coolidge|Major General Adna Romanza Chaffee|H. C. White Company|Alfred Hewitt|First Lady Ida Saxton McKinley|William Hibbert|Keystone View Company,"American|American|Italian, 1835–1914|American|American, 1843–1901|American, 1872–1933|American, 1879–1957|American, 1842–1941|American|American, 1847–1907|American",,"Underwood & Underwood|Sun Sculpture Works and Studios|Pius, X Pope|Graves, C. H.|Universal Photo Art Co.|McKinley, William|Coolidge, John Calvin Jr. President|Goodhue, Coolidge Grace Anna First Lady|Chaffee, Adna Romanza Major General|H. C. White Company|Hewitt, Alfred|McKinley, Ida Saxton First Lady|Hibbert, William|Keystone View Company",American|American|Italian|American|American|American|American|American|American|American|American,1835 |1843 |1872 |1879 |1842 |1847,1914 |1901 |1933 |1957 |1941 |1907,1850s–1910s,1850,1919,Albumen silver prints,Mounts approximately: 8.9 x 17.8 cm (3 1/2 x 7 in.),"Gift of Weston J. Naef, in memory of Kathleen W. Naef and Weston J. Naef Sr., 1982",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288154,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.14.34,false,true,268333,Photographs,Photograph,John Quincy Adams,,,,,,Artist|Artist|Photography Studio|Artist,After,Josiah Johnson Hawes|Albert Sands Southworth|Southworth and Hawes|Philip Haas,"American, Wayland, Massachusetts 1808–1901 Crawford Notch, New Hampshire|American, West Fairlee, Vermont 1811–1894 Charlestown, Massachusetts|American, active 1843–1863|American",,"Hawes, Josiah Johnson|Southworth, Albert Sands|Southworth and Hawes|Haas, Philip",American|American|American|American,1808 |1811 |1843,1901 |1894 |1863,ca. 1850,1848,1852,Daguerreotype,12.0 x 9.0 cm (4 3/4 x 3 9/16 in.),"Gift of I. N. Phelps Stokes, Edward S. Hawes, Alice Mary Hawes, and Marion Augusta Hawes, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268333,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1981.1229.13,false,true,263161,Photographs,Photograph,Afghans,,,,,,Artist|Artist|Photography Studio|Person in Photograph,Person in photograph,David Octavius Hill|Robert Adamson|Hill and Adamson|Edward William Lane,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, active 1843–1848|British, Hereford 1801–1876 Worthing",,"Hill, David Octavius|Adamson, Robert|Hill and Adamson|Lane, Edward William","British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1843 |1801,1870 |1848 |1848 |1876,1843,1843,1843,Salted paper print from paper negative,,"Robert O. Dougan Collection, Gift of Warner Communications Inc., 1981",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/263161,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1982.1182.1031–.1043,false,true,288297,Photographs,Stereographs,[Group of 13 Stereograph Views of Families and Children],,,,,,Publisher|Artist|Publisher|Publisher|Artist|Publisher|Artist|Artist|Publisher|Publisher|Publisher|Artist|Publisher|Artist|Artist,,Keystone View Company|Benneville Lloyd Singley|Group Series|Universal View Co.|William H. Rau|Charles Moody|C. L. Howe|Kilburn Brothers|James M. Davis|H. C. White Company|Littleton View Company|F. G. Weller|Underwood & Underwood|Unknown|Unknown,"American, Union Township, Pennsylvania 1864–1938 Meadville, Pennsylvania|American|American, 1855–1920|American|American|American, active ca. 1865–1890|American|American|American|American",,"Keystone View Company|Singley, Benneville Lloyd|Group Series|Universal View Co.|Rau, William H.|Moody, Charles|Howe, C. L.|Kilburn Brothers|Davis, James M.|H. C. White Company|Littleton View Company|Weller., F. G.|Underwood & Underwood|Unknown|Unknown",American|American|American|American|American|American|American|American|American,1864 |1855 |1863,1938 |1920 |1892,1850s–1910s,1850,1919,Albumen silver prints,Mounts approximately: 8.2 x 17.1 cm (3 1/4 x 6 3/4 in.) to 8.9 x 17.8 cm (3 1/2 x 7 in.),"Gift of Weston J. Naef, in memory of Kathleen W. Naef and Weston J. Naef Sr., 1982",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288297,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.14.44,false,true,268344,Photographs,Photograph,William Henry Harrison,,,,,,Artist|Artist|Photography Studio|Artist,,Albert Sands Southworth|Josiah Johnson Hawes|Southworth and Hawes|Albert Gallatin Hoit,"American, West Fairlee, Vermont 1811–1894 Charlestown, Massachusetts|American, Wayland, Massachusetts 1808–1901 Crawford Notch, New Hampshire|American, active 1843–1863|American, 1809–1856",,"Southworth, Albert Sands|Hawes, Josiah Johnson|Southworth and Hawes|Hoit, Albert Gallatin",American|American|American|American,1811 |1808 |1843 |1809,1894 |1901 |1863 |1856,ca. 1850,1848,1852,Daguerreotype,8.3 x 7.0 cm (3 1/4 x 2 3/4 in.),"Gift of I. N. Phelps Stokes, Edward S. Hawes, Alice Mary Hawes, and Marion Augusta Hawes, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268344,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1982.1182.1951–.2016,false,true,288316,Photographs,Stereographs,[Group of 66 Stereograph Views of the 1893 Chicago World's Fair and Columbian Exposition],,,,,,Artist|Publisher|Artist|Subject|Artist|Artist|Person in Photograph|Publisher|Publisher|Photography Studio|Publisher,,Strohmeyer & Wyman|Underwood & Underwood|Kilburn Brothers|James M. Davis|Charles Dudley Arnold|Benneville Lloyd Singley|Stephen Grover Cleveland|Gust. Holmquist|Webster & Albee|Sun Sculpture Works and Studios|Keystone View Company,"American|American|American, active ca. 1865–1890|American, 1844–1927|American, Union Township, Pennsylvania 1864–1938 Meadville, Pennsylvania|American, 1837–1908|American|American|American",,"Strohmeyer & Wyman|Underwood & Underwood|Kilburn Brothers|Davis, James M.|Arnold, Charles D.|Singley, Benneville Lloyd|Cleveland, Stephen Grover|Holmquist, Gust.|Webster & Albee|Sun Sculpture Works and Studios|Keystone View Company",American|American|American|American|American|American|American|American|American,1863 |1844 |1864 |1837,1892 |1927 |1938 |1908,1850s–1910s,1850,1919,Albumen silver prints,Mounts approximately: 8.7 x 17.5 cm (3 7/16 x 6 7/8 in.) to 10.6 x 17.8 cm (4 3/16 x 7 in.),"Gift of Weston J. Naef, in memory of Kathleen W. Naef and Weston J. Naef Sr., 1982",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288316,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1997.382.9,false,true,282012,Photographs,Photograph,Sir David Brewster,,,,,,Artist|Artist|Person in Photograph|Photography Studio,,David Octavius Hill|Robert Adamson|Sir David Brewster|Hill and Adamson,"British, Perth, Scotland 1802–1870 Edinburgh, Scotland|British, St. Andrews, Scotland 1821–1848 St. Andrews, Scotland|British, Jedburgh, Scotland 1781–1868 Melrose|British, active 1843–1848",,"Hill, David Octavius|Adamson, Robert|Brewster, David, Sir|Hill and Adamson","British, Scottish|British, Scottish|British, Scottish|British, Scottish",1802 |1821 |1781 |1843,1870 |1848 |1868 |1848,ca. 1844,1842,1846,Salted paper print from paper negative,20.2 x 15.1 cm (7 15/16 x 5 15/16 in. ),"The Rubel Collection, Purchase, Lila Acheson Wallace Gift, 1997",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/282012,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1982.1182.208–.255,false,true,288120,Photographs,Stereographs,[Group of 48 Stereograph Views of Arizona and the Surrounding Area],,,,,,Publisher|Publisher|Photography Studio|Artist|Publisher|Publisher|Publisher|Publisher|Artist|Publisher|Artist|Publisher|Artist|Publisher,,H. C. White Company|Underwood & Underwood|Sun Sculpture Works and Studios|Kilburn Brothers|Edward Kilburn|American Stereoscopic Company|Quaker Oats Company|Continent Stereoscopic Company|Benneville Lloyd Singley|New H Series|W. S. Conant|Standard Series|Unknown|Keystone View Company,"American|American|American|American, active ca. 1865–1890|American, 1830–1884|American|American|New York|American, Union Township, Pennsylvania 1864–1938 Meadville, Pennsylvania|American|American",,"H. C. White Company|Underwood & Underwood|Sun Sculpture Works and Studios|Kilburn Brothers|Kilburn, Edward|American Stereoscopic Company|Quaker Oats Company|Continent Stereoscopic Company|Singley, Benneville Lloyd|New H Series|Conant, W. S.|Standard Series|Unknown|Keystone View Company",American|American|American|American|American|American|American|New York|American|American|American,1863 |1830 |1864,1892 |1884 |1938,1880s–1910s,1880,1919,Albumen silver prints,Mounts approximately: 8.9 x 17.8 cm (3 1/2 x 7 in.) to 10.6 x 17.4 cm (4 3/16 x 6 7/8 in.),"Gift of Weston J. Naef, in memory of Kathleen W. Naef and Weston J. Naef Sr., 1982",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288120,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1982.1182.1075–.1129,false,true,288301,Photographs,Stereographs,[Group of 55 Stereograph Views of Groups of Children],,,,,,Artist|Publisher|Publisher|Publisher|Artist|Artist|Artist|Publisher|Publisher|Publisher|Artist|Artist|Publisher|Artist|Publisher|Artist|Publisher|Artist|Publisher|Publisher|Publisher|Publisher|Artist|Artist|Publisher,,T. W. Ingersoll|Comic Series|Popular Series|Comics and Groups|R. B. Lewis|W. E. Sparrow|F. G. Weller|Underwood & Underwood|Littleton View Company|Union View Company|Strohmeyer & Wyman|J. P. King|E. & H. T. Anthony|Benneville Lloyd Singley|Universal Photo Art Co.|C. H. Graves|Universal View Co.|William H. Rau|Hegger|Charles Moody|European and American Views|S. C. Northrop|Unknown|Unknown|Keystone View Company,"American|American|American|American|American|American|American|American|American, Union Township, Pennsylvania 1864–1938 Meadville, Pennsylvania|American|American|American, 1855–1920|American|American|American|American",,"Ingersoll, T. W.|Comic Series|Popular Series|Comics and Groups|Lewis, R. B.|Sparrow, W. E.|Weller., F. G.|Underwood & Underwood|Littleton View Company|Union View Company|Strohmeyer & Wyman|King, J. P.|E. & H. T. Anthony|Singley, Benneville Lloyd|Universal Photo Art Co.|Graves, C. H.|Universal View Co.|Rau, William H.|Hegger|Moody, Charles|European and American Views|Northrop, S. C.|Unknown|Unknown|Keystone View Company",American|American|American|American|American|American|American|American|American|American|American|American|American|American|American,1864 |1855,1938 |1920,1850s–1910s,1850,1919,Albumen silver prints,Mounts approximately: 8.7 x 17.4 cm (3 7/16 x 6 7/8 in.) to 9.9 x 17.8 cm (3 7/8 x 7 in.),"Gift of Weston J. Naef, in memory of Kathleen W. Naef and Weston J. Naef Sr., 1982",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288301,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1221,false,true,286616,Photographs,Photograph,"[President Abraham Lincoln, Major General John A. McClernand (right), and E. J. Allen (Allan Pinkerton, left), Chief of the Secret Service of the United States, at Secret Service Department, Headquarters Army of the Potomac, near Antietam, Maryland]",,,,,,Artist|Person in Photograph|Person in Photograph|Person in Photograph,,Alexander Gardner|Abraham Lincoln|John Alexander McClernand|Allan Pinkerton,"American, Glasgow, Scotland 1821–1882 Washington, D.C.|American, Hardin County, Kentucky 1809–1865 Washington, D.C.|American, Breckinridge County, Kentucky 1812–1900 Springfield, Illinois|American, born Scotland, Glasgow 1819–1884 Chicago",,"Gardner, Alexander|Lincoln, Abraham|McClernand, John Alexander|Pinkerton, Allan","American, Scottish|American|American|American, born Scotland",1821 |1809 |1812-05-30|1819-08-25,1882 |1865 |1900-09-20|1884-07-01,"October 4, 1862",1862,1862,Albumen silver print from glass negative,Image: 22.4 x 18 cm (8 13/16 x 7 1/16 in.) Mount: 29.2 x 20.7 cm (11 1/2 x 8 1/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286616,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1220,false,true,286615,Photographs,Photograph,"[President Abraham Lincoln, Major General John A. McClernand (right), and E. J. Allen (Allan Pinkerton, left), Chief of the Secret Service of the United States, at Secret Service Department, Headquarters Army of the Potomac, near Antietam, Maryland]",,,,,,Artist|Person in Photograph|Person in Photograph|Person in Photograph,,Alexander Gardner|Allan Pinkerton|Abraham Lincoln|John Alexander McClernand,"American, Glasgow, Scotland 1821–1882 Washington, D.C.|American, born Scotland, Glasgow 1819–1884 Chicago|American, Hardin County, Kentucky 1809–1865 Washington, D.C.|American, Breckinridge County, Kentucky 1812–1900 Springfield, Illinois",,"Gardner, Alexander|Pinkerton, Allan|Lincoln, Abraham|McClernand, John Alexander","American, Scottish|American, born Scotland|American|American",1821 |1819-08-25|1809 |1812-05-30,1882 |1884-07-01|1865 |1900-09-20,"October 3, 1862",1862,1862,Albumen silver print from glass negative,Image: 20 x 18.5 cm (7 7/8 x 7 5/16 in.) Mount: 22.8 x 21.3 cm (9 x 8 3/8 in.) Mount: 34.1 x 27 cm (13 7/16 x 10 5/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286615,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2010.443,false,true,294320,Photographs,Photograph,[Nine Portraits in Original Passe-Partout],,,,,,Artist|Person in Photograph|Person in Photograph|Person in Photograph,,James William Bailey|Charles-Louis-Napoleon Bonaparte|William Ewart Gladstone|Albert Edward Prince of Wales,"British|French, Paris 1808–1873 Chislehurst, Kent|British, 1841–1910",,"Bailey, James William|Bonaparte, Charles-Louis-Napoleon|Gladstone, William Ewart|Edward, Albert Prince of Wales",British|French|British,1808 |1809 |1841,1873 |1898 |1910,1880s,1880,1889,Albumen silver prints from glass negatives with applied color,Frame: 34.9 x 34.9 cm (13 3/4 x 13 3/4 in.) Image: 4 x 3.5 cm (1 9/16 x 1 3/8 in.) Image: 7.7 x 6 cm (3 1/16 x 2 3/8 in.),"Funds from various donors, 2010",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/294320,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1991.1073.105,false,true,266362,Photographs,Album,Views and Costumes of Japan,,,,,,Artist,,Raimund von Stillfried,"Austrian, 1839–1911",,"Stillfried, Raimond von",Austrian,1839,1911,1860s,1860,1869,Albumen silver prints,,"Rogers Fund, 1957, transferred from the Library",,,,,,,,,,,,Albums,,http://www.metmuseum.org/art/collection/search/266362,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2006.214 (1-32),false,true,287306,Photographs,Micrograph,Mikroskopisch-Photographischer Atlas der Harnsedimente,,,,,,Artist,,Robert Ultzmann,"Austrian, 1842–1889",,"Ultzmann, Robert",Austrian,1842,1889,1869,1869,1869,Albumen silver prints,Album: 30.5 x 47 x 6.4 cm (12 x 18 1/2 x 2 1/2 in.),"Joyce F. Menschel Photography Library Fund, 2006",,,,,,,,,,,,Albums,,http://www.metmuseum.org/art/collection/search/287306,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.1098.6,false,true,631030,Photographs,Paper Negative,"David Maitland Makgill Crichton, Rankeillour",,,,,,Artist,,Hill and Adamson,"British, active 1843–1848",,Hill and Adamson,"British, Scottish",1843,1848,1843–47,1843,1847,Waxed paper negative,Image: 8 3/8 × 6 3/16 in. (21.3 × 15.7 cm),"Gift of Joyce F. Menschel, 2013",,,,,,,,,,,,Negatives,,http://www.metmuseum.org/art/collection/search/631030,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -48.105.69,false,true,269197,Photographs,Photomechanical print,A Stiff Pull,,,,,,Artist,,Peter Henry Emerson,"British, born Cuba, 1856–1936",,"Emerson, Peter Henry","British, born Cuba",1856,1936,"1880s, printed 1887",1880,1889,Photogravure,20.8 x 28.9 cm. (8 3/16 x 11 3/8 in.),"David Hunter McAlpin Fund, 1948",,,,,,,,,,,,Photographs|Prints,,http://www.metmuseum.org/art/collection/search/269197,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.68,false,true,290463,Photographs,Paper negative,Village de Murols,,,,,,Artist,,Édouard Baldus,"French, born Prussia, 1813–1889",,"Baldus, Édouard","French, born Prussia",1813,1889,1854,1854,1854,Paper negative,Image: 33.9 x 43.9 cm (13 3/8 x 17 5/16 in.) Sheet: 34.4 x 44.5 cm (13 9/16 x 17 1/2 in.),"The Horace W. Goldsmith Foundation Fund, through Joyce and Robert Menschel, 2009",,,,,,,,,,,,Negatives,,http://www.metmuseum.org/art/collection/search/290463,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.372.1–.32,false,true,286010,Photographs,Album,[Album of photographs],,,,,,Artist,,Louis-Pierre-Théophile Dubois de Nehaut,"French, active Belgium, 1799–1872",,"Dubois de Nehaut, Louis-Pierre-Théophile",Belgian,1799,1872,1854–56,1854,1856,"32 salted paper prints, waxed salted paper prints, and albumen silver prints from paper and glass negatives",5 3/4 x 7 5/8 to 9 1/4 x 12,"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Albums,,http://www.metmuseum.org/art/collection/search/286010,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1987.1100.47,true,true,265543,Photographs,Photograph,Runner in the City,,,,,,Artist,,El Lissitzky,"Russian, Pochinok 1890–1941 Moscow",,"Lissitzky, El",Russian,1890,1941,ca. 1926,1924,1928,Gelatin silver print,13.1 x 12.8 cm (5 3/16 x 5 1/16 in.),"Ford Motor Company Collection, Gift of Ford Motor Company and John C. Waddell, 1987",,,,,,,,,,,,Photographs|Collages,,http://www.metmuseum.org/art/collection/search/265543,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.551.1,false,true,283188,Photographs,Album,"Rogues, a Study of Characters",,,,,,Artist,,Samuel G. Szabó,"Hungarian, active America ca. 1854–61",,"Szabó, Samuel G.",Hungarian,1854,0061,1857,1850,1860,Salted paper prints from glass negatives,From 8.8 x 6.6 cm (3 7/16 x 2 5/8 in.) to 11.5 x 8.8 cm (4 1/2 x 3 7/16 in.),"Gilman Collection, Purchase, Ann Tenenbaum and Thomas H. Lee Gift, 2005",,,,,,,,,,,,Albums,,http://www.metmuseum.org/art/collection/search/283188,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.3,false,true,283074,Photographs,Negative; Photograph,[Young Man],,,,,,Artist,,Calvert Richard Jones,"British, Swansea, Wales 1802–1877 Bath, England",,"Jones, Calvert Richard","British, Welsh",1802,1877,late 1840s,1846,1849,Paper negative,20.9 x 15.8 cm (8 1/4 x 6 1/4 in.),"Gilman Collection, Purchase, Joseph M. Cohen Gift, 2005",,,,,,,,,,,,Negatives,,http://www.metmuseum.org/art/collection/search/283074,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.43.243,false,true,267653,Photographs,Photomechanical print,The Church or the World,,,,,,Artist,,James Craig Annan,"British, Hamilton, South Lanarkshire, Scotland 1864–1946",,"Annan, James Craig","British, Scottish",1864,1946,1893,1893,1893,Photogravure,10.5 x 12.0 cm. (4 1/8 x 4 3/4 in.),"Alfred Stieglitz Collection, 1933",,,,,,,,,,,,Photographs|Prints,,http://www.metmuseum.org/art/collection/search/267653,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -49.55.268,false,true,269385,Photographs,Photomechanical print,Toledo,,,,,,Artist,,James Craig Annan,"British, Hamilton, South Lanarkshire, Scotland 1864–1946",,"Annan, James Craig","British, Scottish",1864,1946,1914,1914,1914,Photogravure,19.9 x 13.4 cm. (7 13/16 x 5 1/4 in.),"Alfred Stieglitz Collection, 1949",,,,,,,,,,,,Photographs|Prints,,http://www.metmuseum.org/art/collection/search/269385,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -49.55.274,false,true,269392,Photographs,Photomechanical print,"The Riva Schiavoni, Venice",,,,,,Artist,,James Craig Annan,"British, Hamilton, South Lanarkshire, Scotland 1864–1946",,"Annan, James Craig","British, Scottish",1864,1946,1894,1894,1894,Photogravure,14.3 x 19.9 cm. (5 5/8 x 7 13/16 in.),"Alfred Stieglitz Collection, 1949",,,,,,,,,,,,Photographs|Prints,,http://www.metmuseum.org/art/collection/search/269392,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -52.524.3.1–.12,false,true,269582,Photographs,Photographically illustrated book,"Alfred Tennyson's Idylls of the King, and other Poems",,,,,,Artist,,Julia Margaret Cameron,"British (born India), Calcutta 1815–1879 Kalutara, Ceylon",,"Cameron, Julia Margaret","British, born India",1815,1815,1874,1874,1874,Albumen silver prints,"45.4 x 35.5 x 2.4 cm (17 7/8 x 14 x 15/16 in.), closed","David Hunter McAlpin Fund, 1952",,,,,,,,,,,,Books,,http://www.metmuseum.org/art/collection/search/269582,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -57.634.16,false,true,269947,Photographs,Autochrome,[Pan-Pacific International Exposition],,,,,,Artist,,Arnold Genthe,"American (born Germany), Berlin 1869–1942 New Milford, Connecticut",,"Genthe, Arnold","American, born Germany",1869,1942,1915,1915,1915,Autochrome,12.7 x 17.9 cm (5 x 7 1/16 in.),"Gift of Mrs. Robert Aitken, 1957",,,,,,,,,,,,Transparencies,,http://www.metmuseum.org/art/collection/search/269947,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1988.1134.1,false,true,266025,Photographs,Waxed paper negative,"The Diwan-i Khas from the Mussaman Burj, Agra Palace",,,,,,Artist,,John Murray,"British, Blackhouse, Aberdeenshire, Scotland 1809–1898 Sheringham, Norfolk county, England",,"Murray, John","British, Scottish",1809,1898,1862–64,1862,1864,Waxed paper negative with applied media,37.1 x 46.4cm (14 5/8 x 18 1/4in.) Frame: 68.7 x 122.6 cm (27 1/16 x 48 1/4 in.) (Framed with 1988.1134.2),"Purchase, Cynthia Hazen Polsky Gift, 1988",,,,,,,,,,,,Negatives,,http://www.metmuseum.org/art/collection/search/266025,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1997.382.57,false,true,282068,Photographs,Waxed paper negative,Taj Mahal and Gardens,,,,,,Artist,,John Murray,"British, Blackhouse, Aberdeenshire, Scotland 1809–1898 Sheringham, Norfolk county, England",,"Murray, John","British, Scottish",1809,1898,ca. 1855,1853,1857,Waxed paper negative,38.5 x 47.2 cm (15 3/16 x 18 9/16 in.),"The Rubel Collection, Purchase, Anonymous Gift and Cynthia Hazen Polsky Gift, 1997",,,,,,,,,,,,Negatives,,http://www.metmuseum.org/art/collection/search/282068,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.946,false,true,287604,Photographs,Negative; Photograph,Suttee Ghat Cawnpore,,,,,,Artist,,John Murray,"British, Blackhouse, Aberdeenshire, Scotland 1809–1898 Sheringham, Norfolk county, England",,"Murray, John","British, Scottish",1809,1898,1858,1858,1858,Waxed paper negative,Image: 38 x 48 cm (14 15/16 x 18 7/8 in.),"Gilman Collection, Purchase, Cynthia Hazen Polsky Gift, 2005",,,,,,,,,,,,Negatives,,http://www.metmuseum.org/art/collection/search/287604,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1224,false,true,305839,Photographs,Print,Planning the Capture of Booth and Harold,,,,,,Artist,After,Alexander Gardner,"American, Glasgow, Scotland 1821–1882 Washington, D.C.",,"Gardner, Alexander","American, Scottish",1821,1882,1865,1865,1865,Woodcut,,"Gilman Collection, Purchase, Ann Tenenbaum and Thomas H. Lee Gift, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/305839,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1973.594.55,false,true,260915,Photographs,Photograph,"Palais de Gézyret, Pavillon Exterieur",,,,,,Artist,,J. Pascal Sébah,Turkish,,"Sébah, J. Pascal",Turkish,,1890,1870s,1870,1879,Albumen silver print from glass negative,20.0 x 26.7 cm (7 7/8 x 10 1/2 in.),"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1973",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260915,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -52.524.12,false,true,269572,Photographs,Photograph,Chillon,,,,,,Artist,,John Joscelyn Coghill,"Irish, 1826–1905",,"Coghill, John Joscelyn",Irish,1826,1905,1855,1855,1855,Albumen silver print from glass negative,Approx. 15.2 x 20.3 cm (6 x 8 in.),"David Hunter McAlpin Fund, 1952",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269572,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -52.524.13,false,true,269573,Photographs,Photograph,Swiss Glacier,,,,,,Artist,,John Joscelyn Coghill,"Irish, 1826–1905",,"Coghill, John Joscelyn",Irish,1826,1905,1850s,1850,1859,Albumen silver print from glass negative,Approx. 20.3 x 15.2 cm (8 x 6 in.),"David Hunter McAlpin Fund, 1952",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269573,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -52.524.14,false,true,269574,Photographs,Photograph,View in Switzerland,,,,,,Artist,,John Joscelyn Coghill,"Irish, 1826–1905",,"Coghill, John Joscelyn",Irish,1826,1905,1850s,1850,1859,Albumen silver print from glass negative,Approx. 15.2 x 20.3 cm (6 x 8 in.),"David Hunter McAlpin Fund, 1952",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269574,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -52.524.15,false,true,269575,Photographs,Photograph,Heidelberg,,,,,,Artist,,John Joscelyn Coghill,"Irish, 1826–1905",,"Coghill, John Joscelyn",Irish,1826,1905,ca. 1855,1853,1857,Albumen silver print from glass negative,Approx. 15.2 x 20.3 cm (6 x 8 in.),"David Hunter McAlpin Fund, 1952",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269575,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -63.606.1.7,false,true,270859,Photographs,Photograph,The Castle of Chillon,,,,,,Artist,,John Joscelyn Coghill,"Irish, 1826–1905",,"Coghill, John Joscelyn",Irish,1826,1905,1855,1855,1855,Albumen silver print,,"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1963",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/270859,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.112,false,true,685454,Photographs,Carte-de-visite,[Alexander Calame],,,,,,Artist,,Vuagnat,"Swiss, active 1860s",,Vuagnat,Swiss,1859,1870,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685454,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.1041,false,true,266108,Photographs,Photograph,"[Doorway of Predikheevenkerk, Gent, Belgium]",,,,,,Artist,,Charles D'Hoy,"Belgian, 1823–1895",,"D'Hoy, Charles",Belgian,1823,1895,ca. 1858,1856,1860,Albumen silver print from glass negative,23.0 x 27.9 cm. (9 1/16 x 11 in.),"Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 1989",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/266108,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.652,false,true,286263,Photographs,X-Ray,Le pied à travers la chaussure,,,,,,Artist,,Dr. Henri van Heurck,"Belgian, 1838–1909",,"Van Heurck, Dr. Henri",Belgian,1838,1909,1896,1896,1896,Gelatin silver print,Image: 16.2 × 12.1 cm (6 3/8 × 4 3/4 in.) Mount: 18.4 × 13.4 cm (7 1/4 × 5 1/4 in.),"Gilman Collection, Purchase, Denise and Andrew Saul Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286263,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.653,false,true,286772,Photographs,X-Ray,[X-Ray of the Mummy of a Raptor],,,,,,Artist,,Dr. Henri van Heurck,"Belgian, 1838–1909",,"Van Heurck, Dr. Henri",Belgian,1838,1909,1896,1896,1896,Gelatin silver print,Image: 6 3/4 × 4 3/4 in. (17.1 × 12.1 cm),"Gilman Collection, Purchase, Denise and Andrew Saul Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286772,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.654,false,true,287290,Photographs,X-Ray,[X-Ray of a Box Compasses],,,,,,Artist,,Dr. Henri van Heurck,"Belgian, 1838–1909",,"Van Heurck, Dr. Henri",Belgian,1838,1909,1896,1896,1896,Gelatin silver print,Image: 17.2 x 11.9 cm (6 3/4 x 4 11/16 in.) Mount: 18 x 13 cm (7 1/16 x 5 1/8 in.),"Gilman Collection, Purchase, Denise and Andrew Saul Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287290,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.494.17,false,true,289289,Photographs,Photograph,Piled Stone Mountain Near Sing Chang,,,,,,Artist,,Lai Fong,"Chinese, 1839–1890",,"Lai, Fong",Chinese,1839,1890,ca. 1869,1869,1869,Albumen silver print from glass negative,Image: 8 1/8 × 11 5/16 in. (20.7 × 28.7 cm),"Gilman Collection, Purchase, Robert Rosenkranz Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289289,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.494.26,false,true,289298,Photographs,Photograph,Hisiu Peak,,,,,,Artist,,Lai Fong,"Chinese, 1839–1890",,"Lai, Fong",Chinese,1839,1890,ca. 1869,1869,1869,Albumen silver print from glass negative,Image: 9 in. × 11 7/16 in. (22.8 × 29 cm),"Gilman Collection, Purchase, Robert Rosenkranz Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289298,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.494.37,false,true,289309,Photographs,Photograph,Duck Market,,,,,,Artist,,Lai Fong,"Chinese, 1839–1890",,"Lai, Fong",Chinese,1839,1890,ca. 1869,1869,1869,Albumen silver print from glass negative,Image: 8 1/8 × 11 5/16 in. (20.7 × 28.7 cm),"Gilman Collection, Purchase, Robert Rosenkranz Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289309,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.494.39,false,true,289311,Photographs,Photograph,[Group of People Posing near River],,,,,,Artist,,Lai Fong,"Chinese, 1839–1890",,"Lai, Fong",Chinese,1839,1890,ca. 1869,1869,1869,Albumen silver print from glass negative,Image: 7 3/16 × 9 1/2 in. (18.2 × 24.1 cm),"Gilman Collection, Purchase, Robert Rosenkranz Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289311,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -53.627.1.16,false,true,306151,Photographs,"Photograph, Carte-de-visite",Empress Eugénie,,,,,,Artist,,Sergei Luvovich Levitsky,"Russian, 1819–1898",,"Levitsky, Sergei Luvovich",Russian,1819,1898,ca. 1864,1859,1869,Albumen silver print,Sheet: 10.5 × 6 cm (4 1/8 × 2 3/8 in.),"Gift of Susanna Myers, 1953",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306151,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -53.627.1.17,false,true,306152,Photographs,"Photograph, Carte-de-visite",Empress Eugénie,,,,,,Artist,,Sergei Luvovich Levitsky,"Russian, 1819–1898",,"Levitsky, Sergei Luvovich",Russian,1819,1898,ca. 1864,1859,1869,Albumen silver print,Sheet: 10.5 × 6 cm (4 1/8 × 2 3/8 in.),"Gift of Susanna Myers, 1953",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306152,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1152,false,true,285989,Photographs,Photographs,[Two Young Women at Window],,,,,,Artist,,Andrei Osipovich Karelin,"Russian, 1837–1906",,"Karelin, Andrei Osipovich",Russian,1837,1906,ca. 1870,1868,1872,Albumen silver print from glass negative,Image: 8 1/2 × 6 1/8 in. (21.6 × 15.6 cm) Mount: 16 7/16 × 12 5/8 in. (41.7 × 32 cm),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285989,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.14,false,true,283086,Photographs,Photograph,"The Hippopotamus at the Zoological Gardens, Regent's Park",,,,,,Artist,,"de Borbón, Juan","Spanish, 1822–1887",,"de Borbón, Juan",Spanish,1822,1887,1852,1852,1852,Salted paper print from glass negative,Image: 11.1 × 12 cm (4 3/8 × 4 3/4 in.) Mount: 43.8 × 30.5 cm (17 1/4 × 12 in.),"Gilman Collection, Purchase, Ann Tenenbaum and Thomas H. Lee Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283086,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -48.83.1,false,true,269224,Photographs,Photograph,Gröfin Auersperg,,,,,,Artist,,Franz Antoine,"Austrian, 1814–1882",,"Antoine, Franz",Austrian,1814,1882,1850s–60s,1850,1869,Albumen silver print from glass negative,21.8 x 17.2 cm. (8 9/16 x 6 3/4 in.),"David Hunter McAlpin Fund, 1948",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269224,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -48.83.2,false,true,269235,Photographs,Photograph,Gröfin Auersperg,,,,,,Artist,,Franz Antoine,"Austrian, 1814–1882",,"Antoine, Franz",Austrian,1814,1882,1850s–60s,1850,1869,Albumen silver print from glass negative,24.7 x 19.6 cm. (9 3/4 x 7 3/4 in.),"David Hunter McAlpin Fund, 1948",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269235,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -48.83.3,false,true,269246,Photographs,Photograph,Gröfin Auersperg,,,,,,Artist,,Franz Antoine,"Austrian, 1814–1882",,"Antoine, Franz",Austrian,1814,1882,1850s–60s,1850,1869,Albumen silver print from glass negative,25.2 x 19.6 cm. (9 15/16 x 7 3/4 in.),"David Hunter McAlpin Fund, 1948",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269246,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -48.83.4,false,true,269257,Photographs,Photograph,[Seated Man with Cane and Hat],,,,,,Artist,,Franz Antoine,"Austrian, 1814–1882",,"Antoine, Franz",Austrian,1814,1882,1850s–60s,1850,1869,Albumen silver print from glass negative,38.2 x 28.7 cm. (15 1/16 x 11 5/16 in.),"David Hunter McAlpin Fund, 1948",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269257,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -48.83.5,false,true,269263,Photographs,Photograph,[Seated Man in White Vest and Dark Coat],,,,,,Artist,,Franz Antoine,"Austrian, 1814–1882",,"Antoine, Franz",Austrian,1814,1882,1850s–60s,1850,1869,Albumen silver print from glass negative,25.5 x 21.5 cm. (10 1/16 x 8 7/16 in.),"David Hunter McAlpin Fund, 1948",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269263,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -48.83.6,false,true,269264,Photographs,Photograph,[Seated Man in White Vest and Dark Coat],,,,,,Artist,,Franz Antoine,"Austrian, 1814–1882",,"Antoine, Franz",Austrian,1814,1882,1850s–60s,1850,1869,Albumen silver print from glass negative,24.9 x 21.8 cm. (9 13/16 x 8 9/16 in.),"David Hunter McAlpin Fund, 1948",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269264,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -48.83.7,false,true,269265,Photographs,Photograph,"Betty Held, vereh. Solön-Engelsberg",,,,,,Artist,,Franz Antoine,"Austrian, 1814–1882",,"Antoine, Franz",Austrian,1814,1882,1850s–60s,1850,1869,Albumen silver print from glass negative,25.1 x 19.4 cm. (9 7/8 x 7 5/8 in.),"David Hunter McAlpin Fund, 1948",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269265,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -48.83.8,false,true,269266,Photographs,Photograph,[Portrait of an Elderly Woman],,,,,,Artist,,Franz Antoine,"Austrian, 1814–1882",,"Antoine, Franz",Austrian,1814,1882,1850s–60s,1850,1869,Albumen silver print from glass negative,16.7 x 13.4 cm. (6 9/16 x 5 1/4 in.),"David Hunter McAlpin Fund, 1948",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269266,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -48.83.9,false,true,269267,Photographs,Photograph,"Marie Antoine, geb. Woes",,,,,,Artist,,Franz Antoine,"Austrian, 1814–1882",,"Antoine, Franz",Austrian,1814,1882,1850s–60s,1850,1869,Albumen silver print from glass negative,23.8 x 21.2 cm. (9 3/8 x 8 3/6 in.),"David Hunter McAlpin Fund, 1948",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269267,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -48.83.10,false,true,269225,Photographs,Photograph,[Man Seated in Armchair],,,,,,Artist,,Franz Antoine,"Austrian, 1814–1882",,"Antoine, Franz",Austrian,1814,1882,1850s–60s,1850,1869,Albumen silver print from glass negative,22.5 x 16.8 cm. (8 7/8 x 6 5/8 in.),"David Hunter McAlpin Fund, 1948",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269225,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -48.83.11,false,true,269226,Photographs,Photograph,[Young Woman in Dotted Dress],,,,,,Artist,,Franz Antoine,"Austrian, 1814–1882",,"Antoine, Franz",Austrian,1814,1882,1850s–60s,1850,1869,Albumen silver print from glass negative,22.0 x 17.3 cm. (8 11/16 x 6 13/16 in.),"David Hunter McAlpin Fund, 1948",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269226,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -48.83.12,false,true,269227,Photographs,Photograph,Elisabeth Höusermann,,,,,,Artist,,Franz Antoine,"Austrian, 1814–1882",,"Antoine, Franz",Austrian,1814,1882,1850s–60s,1850,1869,Albumen silver print from glass negative,25.4 x 19.3 cm. (10 x 7 5/8 in.),"David Hunter McAlpin Fund, 1948",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269227,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -48.83.14,false,true,269229,Photographs,Photograph,Mathias Höusermann,,,,,,Artist,,Franz Antoine,"Austrian, 1814–1882",,"Antoine, Franz",Austrian,1814,1882,1850s–60s,1850,1869,Albumen silver print from glass negative,18.7 x 14.8 cm. (7 3/8 x 5 13/16 in.),"David Hunter McAlpin Fund, 1948",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269229,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -48.83.15,false,true,269230,Photographs,Photograph,Mathias und Elise Höusermann,,,,,,Artist,,Franz Antoine,"Austrian, 1814–1882",,"Antoine, Franz",Austrian,1814,1882,1850s–60s,1850,1869,Albumen silver print from glass negative,28.5 x 24.3 cm. (11 1/4 x 9 9/16 in.),"David Hunter McAlpin Fund, 1948",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269230,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -48.83.17,false,true,269232,Photographs,Photograph,[Group Portrait of Five Adults and Two Children in a Garden],,,,,,Artist,,Franz Antoine,"Austrian, 1814–1882",,"Antoine, Franz",Austrian,1814,1882,1850s–60s,1850,1869,Albumen silver print from glass negative,28.3 x 34.3 cm. (11 1/8 x 13 1/2 in.),"David Hunter McAlpin Fund, 1948",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269232,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -48.83.18,false,true,269233,Photographs,Photograph,[Group Portrait of Six People],,,,,,Artist,,Franz Antoine,"Austrian, 1814–1882",,"Antoine, Franz",Austrian,1814,1882,1850s–60s,1850,1869,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1948",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269233,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -48.83.19,false,true,269234,Photographs,Photograph,"[Group Portrait of Four Women, Two Men and Three Children in a Garden]",,,,,,Artist,,Franz Antoine,"Austrian, 1814–1882",,"Antoine, Franz",Austrian,1814,1882,1850s–60s,1850,1869,Albumen silver print from glass negative,2.3 x 37.8 cm. (0 15/16 x 14 7/8 in.),"David Hunter McAlpin Fund, 1948",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269234,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -48.83.20,false,true,269236,Photographs,Photograph,[Group portrait of the Antoine and Höusermann Families],,,,,,Artist,,Franz Antoine,"Austrian, 1814–1882",,"Antoine, Franz",Austrian,1814,1882,1850s–60s,1850,1869,Albumen silver print from glass negative,31.2 x 36.4 cm. (12 1/4 x 14 5/16 in.),"David Hunter McAlpin Fund, 1948",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269236,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -48.83.22,false,true,269238,Photographs,Photograph,[Mathias Höusermann seated with elbow on pedestal holding a vase of flowers],,,,,,Artist,,Franz Antoine,"Austrian, 1814–1882",,"Antoine, Franz",Austrian,1814,1882,1850s–60s,1850,1869,Albumen silver print from glass negative,37.3 x 30.5 cm. (14 11/16 x 12 in.),"David Hunter McAlpin Fund, 1948",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269238,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -48.83.23,false,true,269239,Photographs,Photograph,"Anna Wöss, Marie and Marie Antoine",,,,,,Artist,,Franz Antoine,"Austrian, 1814–1882",,"Antoine, Franz",Austrian,1814,1882,1850s–60s,1850,1869,Albumen silver print from glass negative,24.5 x 19.5 cm. (9 5/8 x 7 11/16 in.),"David Hunter McAlpin Fund, 1948",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269239,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -48.83.24,false,true,269240,Photographs,Photograph,[Group Portrait of the Antoine and Höusermann Families],,,,,,Artist,,Franz Antoine,"Austrian, 1814–1882",,"Antoine, Franz",Austrian,1814,1882,1850s–60s,1850,1869,Albumen silver print from glass negative,28.0 x 32.3 cm. (11 x 12 3/4 in.),"David Hunter McAlpin Fund, 1948",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269240,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -48.83.25,false,true,269241,Photographs,Photograph,[Group Portrait of Four Women and Three Children],,,,,,Artist,,Franz Antoine,"Austrian, 1814–1882",,"Antoine, Franz",Austrian,1814,1882,1850s–60s,1850,1869,Albumen silver print from glass negative,22.6 x 19.2 cm. (8 7/8 x 7 9/16 in.),"David Hunter McAlpin Fund, 1948",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269241,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -48.83.26,false,true,269242,Photographs,Photograph,[Seated Lady in Striped Dress with Four Little Girls],,,,,,Artist,,Franz Antoine,"Austrian, 1814–1882",,"Antoine, Franz",Austrian,1814,1882,1850s–60s,1850,1869,Coated salted paper print from glass negative,15.5 x 19.4 cm. (6 1/8 x 7 5/8 in.),"David Hunter McAlpin Fund, 1948",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269242,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -48.83.27,false,true,269243,Photographs,Photograph,"Elise Höusermann, Hermine, Marie and Marie Antoine",,,,,,Artist,,Franz Antoine,"Austrian, 1814–1882",,"Antoine, Franz",Austrian,1814,1882,1850s–60s,1850,1869,Albumen silver print from glass negative,19.0 x 21.9 cm. (7 1/2 x 8 5/8 in.),"David Hunter McAlpin Fund, 1948",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269243,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -48.83.28,false,true,269244,Photographs,Photograph,"[Three Women,Two Men, and a Child on a Picnic]",,,,,,Artist,,Franz Antoine,"Austrian, 1814–1882",,"Antoine, Franz",Austrian,1814,1882,1850s–60s,1850,1869,Albumen silver print from glass negative,16.1 x 21.1 cm. (6 5/16 x 8 5/16 in.),"David Hunter McAlpin Fund, 1948",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269244,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -48.83.29,false,true,269245,Photographs,Photograph,"[Hermine, Alfons and Eugen Antoine and Mathias Höusermann on a fallen tree]",,,,,,Artist,,Franz Antoine,"Austrian, 1814–1882",,"Antoine, Franz",Austrian,1814,1882,1850s–60s,1850,1869,Albumen silver print from glass negative,18.6 x 23.3 cm. (7 5/16 x 9 3/16 in.),"David Hunter McAlpin Fund, 1948",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269245,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -48.83.30,false,true,269247,Photographs,Photograph,Frau Hofrat Josefine Raymond,,,,,,Artist,,Franz Antoine,"Austrian, 1814–1882",,"Antoine, Franz",Austrian,1814,1882,1850s–60s,1850,1869,Albumen silver print from glass negative,28.5 x 21.5 cm. (11 1/4 x 8 7/16 in.),"David Hunter McAlpin Fund, 1948",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269247,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -48.83.31,false,true,269248,Photographs,Photograph,Hofrat Raymond,,,,,,Artist,,Franz Antoine,"Austrian, 1814–1882",,"Antoine, Franz",Austrian,1814,1882,1850s–60s,1850,1869,Albumen silver print from glass negative,34.9 x 25.0 cm. (13 3/4 x 9 13/16 in.),"David Hunter McAlpin Fund, 1948",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269248,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -48.83.33,false,true,269250,Photographs,Photograph,"[Mathias Höusermann, Marie Antoine, Elise Höusermann, and Pepe Wöss]",,,,,,Artist,,Franz Antoine,"Austrian, 1814–1882",,"Antoine, Franz",Austrian,1814,1882,1850s–60s,1850,1869,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1948",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269250,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -48.83.34,false,true,269251,Photographs,Photograph,[Portrait of Three Women and Men in a Garden],,,,,,Artist,,Franz Antoine,"Austrian, 1814–1882",,"Antoine, Franz",Austrian,1814,1882,1850s–60s,1850,1869,Albumen silver print from glass negative,23.8 x 19.0 cm (9 3/8 x 7 1/2 in.),"David Hunter McAlpin Fund, 1948",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269251,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -48.83.35,false,true,269252,Photographs,Photograph,"Alfons, Eugen, Marie, and Hermine Antoine",,,,,,Artist,,Franz Antoine,"Austrian, 1814–1882",,"Antoine, Franz",Austrian,1814,1882,1850s–60s,1850,1869,Albumen silver print from glass negative,23.1 x 18.5 cm. (9 1/16 x 7 1/4 in.),"David Hunter McAlpin Fund, 1948",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269252,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -48.83.36,false,true,269253,Photographs,Photograph,"Hermine, Marie and Marie Antoine.",,,,,,Artist,,Franz Antoine,"Austrian, 1814–1882",,"Antoine, Franz",Austrian,1814,1882,1850s–60s,1850,1869,Albumen silver print from glass negative,15.0 x 11.1 cm. (5 15/16 x 4 3/8 in.),"David Hunter McAlpin Fund, 1948",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269253,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -48.83.37,false,true,269254,Photographs,Photograph,Hermine and Marie Antoine,,,,,,Artist,,Franz Antoine,"Austrian, 1814–1882",,"Antoine, Franz",Austrian,1814,1882,1850s–60s,1850,1869,Albumen silver print from glass negative,19.2 x 11.1 cm (7 9/16 x 4 3/8 in. ),"David Hunter McAlpin Fund, 1948",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269254,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -48.83.38,false,true,269255,Photographs,Photograph,Marie Antoine (Wöss),,,,,,Artist,,Franz Antoine,"Austrian, 1814–1882",,"Antoine, Franz",Austrian,1814,1882,1850s–60s,1850,1869,Albumen silver print from glass negative,11.3 x 8.8 cm. (4 7/16 x 3 7/16 in.),"David Hunter McAlpin Fund, 1948",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269255,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -48.83.39,false,true,269256,Photographs,Photograph,"[Female Portrait, Standing, Looking Left]",,,,,,Artist,,Franz Antoine,"Austrian, 1814–1882",,"Antoine, Franz",Austrian,1814,1882,1850s–60s,1850,1869,Albumen silver print from glass negative,22.4 x 14.4 cm. (8 13/16 x 5 11/16 in.),"David Hunter McAlpin Fund, 1948",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269256,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -48.83.40,false,true,269258,Photographs,Photograph,[Portrait of Two Girls],,,,,,Artist,,Franz Antoine,"Austrian, 1814–1882",,"Antoine, Franz",Austrian,1814,1882,1850s–60s,1850,1869,Albumen silver print from glass negative,20.4 x 14.5 cm. (8 x 5 11/16 in.),"David Hunter McAlpin Fund, 1948",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269258,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -48.83.41,false,true,269259,Photographs,Photograph,"[Portrait of a Seated Woman Surrounded by Five Girls, Seated and Standing]",,,,,,Artist,,Franz Antoine,"Austrian, 1814–1882",,"Antoine, Franz",Austrian,1814,1882,1850s–60s,1850,1869,Albumen silver print from glass negative,18.4 x 13.1 cm. (7 1/4 x 5 3/16 in.),"David Hunter McAlpin Fund, 1948",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269259,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -48.83.42,false,true,269260,Photographs,Photograph,[Ten members of the Antoine family],,,,,,Artist,,Franz Antoine,"Austrian, 1814–1882",,"Antoine, Franz",Austrian,1814,1882,1850s–60s,1850,1869,Albumen silver print from glass negative,19.2 x 26.1 cm. (7 9/16 x 10 1/4 in.),"David Hunter McAlpin Fund, 1948",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269260,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -48.83.44,false,true,269262,Photographs,Photograph,Mathias Höusermann,,,,,,Artist,,Franz Antoine,"Austrian, 1814–1882",,"Antoine, Franz",Austrian,1814,1882,1850s–60s,1850,1869,Albumen silver print from glass negative,36.9 x 32.3 cm. (14 1/2 x 12 3/4 in.),"David Hunter McAlpin Fund, 1948",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269262,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2012.108,false,true,301891,Photographs,Micrograph,[Frustules of Diatoms],,,,,,Artist,Attributed to,Julius Wiesner,"Austrian, 1838–1916",,"Wiesner, Julius",Austrian,1838,1916,ca. 1870,1865,1875,Cyanotype,9.8 x 7.9 cm (3 7/8 x 3 1/8 in.),"Purchase, Steven Ames Gift, 2012",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/301891,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.505.1 (1a),false,true,288477,Photographs,Photograph,[Chinese Gentleman],,,,,,Artist,,Raimund von Stillfried,"Austrian, 1839–1911",,"Stillfried, Raimond von",Austrian,1839,1911,1870s,1870,1879,Albumen silver print from glass negative,23.7 x 19.1 cm (9 5/16 x 7 1/2 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288477,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.505.1 (2a),false,true,288481,Photographs,Photograph,Fille de Shanghai,,,,,,Artist,,Raimund von Stillfried,"Austrian, 1839–1911",,"Stillfried, Raimond von",Austrian,1839,1911,1870s,1870,1879,Albumen silver print from glass negative,23.7 x 19.2 cm (9 5/16 x 7 9/16 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288481,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.505.1 (3a),false,true,283171,Photographs,Photograph,Interprete de la Legation for the Austro Hongroise,,,,,,Artist,,Raimund von Stillfried,"Austrian, 1839–1911",,"Stillfried, Raimond von",Austrian,1839,1911,1870s,1870,1879,Albumen silver print from glass negative,23.7 x 19.2 cm (9 5/16 x 7 9/16 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283171,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.505.1 (4a),false,true,288482,Photographs,Photograph,Fille de Shanghai,,,,,,Artist,,Raimund von Stillfried,"Austrian, 1839–1911",,"Stillfried, Raimond von",Austrian,1839,1911,1870s,1870,1879,Albumen silver print from glass negative,23.7 x 19.2 cm (9 5/16 x 7 9/16 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288482,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.505.1 (5a),false,true,288012,Photographs,Photograph,Soldat de la ligne,,,,,,Artist,,Raimund von Stillfried,"Austrian, 1839–1911",,"Stillfried, Raimond von",Austrian,1839,1911,1870s,1870,1879,Albumen silver print from glass negative,23.7 x 19.2 cm (9 5/16 x 7 9/16 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288012,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.505.1 (6a),false,true,288013,Photographs,Photograph,Fille de lanxchow,,,,,,Artist,,Raimund von Stillfried,"Austrian, 1839–1911",,"Stillfried, Raimond von",Austrian,1839,1911,1870s,1870,1879,Albumen silver print from glass negative,23.7 x 19.2 cm (9 5/16 x 7 9/16 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288013,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.505.1 (7a),false,true,288485,Photographs,Photograph,[Young Chinese Gentleman],,,,,,Artist,,Raimund von Stillfried,"Austrian, 1839–1911",,"Stillfried, Raimond von",Austrian,1839,1911,1870s,1870,1879,Albumen silver print from glass negative,25.7 x 19.3 cm (10 1/8 x 7 5/8 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288485,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.505.1 (8a),false,true,288487,Photographs,Photograph,Fille de Shanghai,,,,,,Artist,,Raimund von Stillfried,"Austrian, 1839–1911",,"Stillfried, Raimond von",Austrian,1839,1911,1870s,1870,1879,Albumen silver print from glass negative,23.7 x 19.3 cm (9 5/16 x 7 5/8 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288487,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.505.1 (9a),false,true,288489,Photographs,Photograph,Vieux mendiant,,,,,,Artist,,Raimund von Stillfried,"Austrian, 1839–1911",,"Stillfried, Raimond von",Austrian,1839,1911,1870s,1870,1879,Albumen silver print from glass negative,23.7 x 19.2 cm (9 5/16 x 7 9/16 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288489,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.505.1 (10a),false,true,288491,Photographs,Photograph,Vielle mendiant,,,,,,Artist,,Raimund von Stillfried,"Austrian, 1839–1911",,"Stillfried, Raimond von",Austrian,1839,1911,1870s,1870,1879,Albumen silver print from glass negative,23.7 x 19.2 cm (9 5/16 x 7 9/16 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288491,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.505.1 (11a),false,true,288493,Photographs,Photograph,[Chinese Man],,,,,,Artist,,Raimund von Stillfried,"Austrian, 1839–1911",,"Stillfried, Raimond von",Austrian,1839,1911,1870s,1870,1879,Albumen silver print from glass negative,23.7 x 19.4 cm (9 5/16 x 7 5/8 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288493,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.505.1 (12a),false,true,288495,Photographs,Photograph,Fille de Shanghai,,,,,,Artist,,Raimund von Stillfried,"Austrian, 1839–1911",,"Stillfried, Raimond von",Austrian,1839,1911,1870s,1870,1879,Albumen silver print from glass negative,23.7 x 19.3 cm (9 5/16 x 7 5/8 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288495,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.505.1 (13a),false,true,288497,Photographs,Photograph,Vieux Chinoise de Canton,,,,,,Artist,,Raimund von Stillfried,"Austrian, 1839–1911",,"Stillfried, Raimond von",Austrian,1839,1911,1870s,1870,1879,Albumen silver print from glass negative,23.7 x 19.3 cm (9 5/16 x 7 5/8 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288497,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.505.1 (14a),false,true,288499,Photographs,Photograph,[Woman from Canton],,,,,,Artist,,Raimund von Stillfried,"Austrian, 1839–1911",,"Stillfried, Raimond von",Austrian,1839,1911,1870s,1870,1879,Albumen silver print from glass negative,23.7 x 19.3 cm (9 5/16 x 7 5/8 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288499,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.505.1 (15a),false,true,288501,Photographs,Photograph,Fille de Lanxchow,,,,,,Artist,,Raimund von Stillfried,"Austrian, 1839–1911",,"Stillfried, Raimond von",Austrian,1839,1911,1870s,1870,1879,Albumen silver print from glass negative,23.7 x 19.2 cm (9 5/16 x 7 9/16 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288501,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.505.1 (16a),false,true,288503,Photographs,Photograph,Fille de Lanxchow,,,,,,Artist,,Raimund von Stillfried,"Austrian, 1839–1911",,"Stillfried, Raimond von",Austrian,1839,1911,1870s,1870,1879,Albumen silver print from glass negative,23.7 x 19.2 cm (9 5/16 x 7 9/16 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288503,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.505.1 (17a),false,true,288506,Photographs,Photograph,Fille de Lanxchow,,,,,,Artist,,Raimund von Stillfried,"Austrian, 1839–1911",,"Stillfried, Raimond von",Austrian,1839,1911,1870s,1870,1879,Albumen silver print from glass negative,23.7 x 19.3 cm (9 5/16 x 7 5/8 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288506,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.505.1 (18a),false,true,288508,Photographs,Photograph,Fille de Shanghai,,,,,,Artist,,Raimund von Stillfried,"Austrian, 1839–1911",,"Stillfried, Raimond von",Austrian,1839,1911,1870s,1870,1879,Albumen silver print from glass negative,23.7 x 19.2 cm (9 5/16 x 7 9/16 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288508,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.505.1 (19a),false,true,288510,Photographs,Photograph,Fille de Shanghai,,,,,,Artist,,Raimund von Stillfried,"Austrian, 1839–1911",,"Stillfried, Raimond von",Austrian,1839,1911,1870s,1870,1879,Albumen silver print from glass negative,23.7 x 19.3 cm (9 5/16 x 7 5/8 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288510,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.505.1 (20a),false,true,288512,Photographs,Photograph,Fille de Lanxchow,,,,,,Artist,,Raimund von Stillfried,"Austrian, 1839–1911",,"Stillfried, Raimond von",Austrian,1839,1911,1870s,1870,1879,Albumen silver print from glass negative,23.7 x 19.3 cm (9 5/16 x 7 5/8 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288512,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.505.1 (21a),false,true,288014,Photographs,Photograph,Fille de Shanghai,,,,,,Artist,,Raimund von Stillfried,"Austrian, 1839–1911",,"Stillfried, Raimond von",Austrian,1839,1911,1870s,1870,1879,Albumen silver print from glass negative,23.7 x 19.2 cm (9 5/16 x 7 9/16 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288014,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.505.1 (22a),false,true,288515,Photographs,Photograph,[Portrait of an Old Chinese Woman],,,,,,Artist,,Raimund von Stillfried,"Austrian, 1839–1911",,"Stillfried, Raimond von",Austrian,1839,1911,1870s,1870,1879,Albumen silver print from glass negative,23.7 x 19.3 cm (9 5/16 x 7 5/8 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288515,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.505.1 (23a),false,true,288517,Photographs,Photograph,[Seated Chinese Woman with Fan],,,,,,Artist,,Raimund von Stillfried,"Austrian, 1839–1911",,"Stillfried, Raimond von",Austrian,1839,1911,1870s,1870,1879,Albumen silver print from glass negative,23.7 x 19.2 cm (9 5/16 x 7 9/16 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288517,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.505.1 (24a),false,true,288519,Photographs,Photograph,Femme de Canton,,,,,,Artist,,Raimund von Stillfried,"Austrian, 1839–1911",,"Stillfried, Raimond von",Austrian,1839,1911,1870s,1870,1879,Albumen silver print from glass negative,23.7 x 19.3 cm (9 5/16 x 7 5/8 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288519,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.505.1 (25a),false,true,288521,Photographs,Photograph,Négociant,,,,,,Artist,,Raimund von Stillfried,"Austrian, 1839–1911",,"Stillfried, Raimond von",Austrian,1839,1911,1870s,1870,1879,Albumen silver print from glass negative,23.7 x 19.3 cm (9 5/16 x 7 5/8 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288521,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.505.1 (26a),false,true,288523,Photographs,Photograph,Fille de Shanghai,,,,,,Artist,,Raimund von Stillfried,"Austrian, 1839–1911",,"Stillfried, Raimond von",Austrian,1839,1911,1870s,1870,1879,Albumen silver print from glass negative,23.7 x 19.3 cm (9 5/16 x 7 5/8 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288523,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.505.1 (27a),false,true,288525,Photographs,Photograph,[Two Chinese Men in Matching Traditional Dress],,,,,,Artist,,Raimund von Stillfried,"Austrian, 1839–1911",,"Stillfried, Raimond von",Austrian,1839,1911,1870s,1870,1879,Albumen silver print from glass negative,23.6 x 19.3 cm (9 5/16 x 7 5/8 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288525,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.505.1 (28a),false,true,288015,Photographs,Photograph,Filles de Shanghai,,,,,,Artist,,Raimund von Stillfried,"Austrian, 1839–1911",,"Stillfried, Raimond von",Austrian,1839,1911,1870s,1870,1879,Albumen silver print from glass negative,23.7 x 19.2 cm (9 5/16 x 7 9/16 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288015,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.505.1 (29a),false,true,288528,Photographs,Photograph,[Chinese Man Wearing Hat],,,,,,Artist,,Raimund von Stillfried,"Austrian, 1839–1911",,"Stillfried, Raimond von",Austrian,1839,1911,1870s,1870,1879,Albumen silver print from glass negative,23.7 x 19.2 cm (9 5/16 x 7 9/16 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288528,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.505.1 (30a),false,true,288016,Photographs,Photograph,[Chinese Woman Sitting with Basket],,,,,,Artist,,Raimund von Stillfried,"Austrian, 1839–1911",,"Stillfried, Raimond von",Austrian,1839,1911,1870s,1870,1879,Albumen silver print from glass negative,23.7 x 19.2 cm (9 5/16 x 7 9/16 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288016,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.505.1 (41a),false,true,288019,Photographs,Photograph,Brouette,,,,,,Artist,,Raimund von Stillfried,"Austrian, 1839–1911",,"Stillfried, Raimond von",Austrian,1839,1911,1870s,1870,1879,Albumen silver print from glass negative,23.7 x 19.2 cm (9 5/16 x 7 9/16 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288019,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.43.405,false,true,267821,Photographs,Photograph,Sheep,,,,,,Artist,,Hans Watzek,"Austrian, 1848–1903",,"Watzek, Hans",Austrian,1848,1903,1901,1901,1901,Gum bichromate print,50.2 x 63.6 cm (19 3/4 x 25 1/16 in.),"Alfred Stieglitz Collection, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267821,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.43.411,false,true,267828,Photographs,Photograph,[Italian Villa in Autumn],,,,,,Artist,,Hugo Henneberg,"Austrian, 1863–1918",,"Henneberg, Hugo",Austrian,1863,1918,1898,1898,1898,Gum bichromate print,54.0 x 74.5 cm (21 1/4 x 29 5/16 in.),"Alfred Stieglitz Collection, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267828,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.43.412,false,true,267829,Photographs,Photograph,Motiv aus Pommern,,,,,,Artist,,Hugo Henneberg,"Austrian, 1863–1918",,"Henneberg, Hugo",Austrian,1863,1918,"1895–96, printed 1902",1895,1896,Gum bichromate print,77.7 x 55.5 cm (30 9/16 x 21 7/8 in.),"Alfred Stieglitz Collection, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267829,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.43.413,false,true,267830,Photographs,Photograph,Pflügen,,,,,,Artist,,Hugo Henneberg,"Austrian, 1863–1918",,"Henneberg, Hugo",Austrian,1863,1918,"1890–1901, printed 1903",1890,1901,Gum bichromate print,65.8 x 95.8 cm. (25 15/16 x 37 3/4 in.),"Alfred Stieglitz Collection, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267830,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.579,false,true,285639,Photographs,Photograph,Egon Schiele,,,,,,Artist,,Anton Joseph Trcka,"Austrian, 1893–1940",,"Trcka, Anton Joseph",Austrian,1893,1940,1914,1914,1914,Gelatin silver print,Image: 22.6 x 15.3 cm (8 7/8 x 6 in.),"Gilman Collection, Purchase, Ann Tenenbaum and Thomas H. Lee Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285639,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.72,false,true,685414,Photographs,Carte-de-visite,[George Henry Boughton],,,,,,Artist,,Oliver François Xavier Sarony,"Canadian, 1820–1879",,"Sarony, Oliver François Xavier",Canadian,1820,1879,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685414,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.320,false,true,685661,Photographs,Carte-de-visite,[William Powell Frith],,,,,,Artist,,Oliver François Xavier Sarony,"Canadian, 1820–1879",,"Sarony, Oliver François Xavier",Canadian,1820,1879,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685661,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2000.288.1,false,true,283777,Photographs,Photograph,"[Claudet Family Group, Chateau de la Roche, Amboise]",,,,,,Artist,,Francis George Claudet,"Canadian, 1837–1906",,"Claudet, Francis George",Canadian,1837,1906,1856,1856,1856,Salted paper print from glass negative,17.5 x 13.4 cm (6 7/8 x 5 1/4 in.) visible,"Gift of Georgina Claudet Gilchrist and Frances Claudet Johnson, 2000",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283777,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.767,false,true,686106,Photographs,Carte-de-visite,[Sanford Thayer],,,,,,Artist,,G. J. Wood,"American?, active 1860s",,"Wood, G. J.",American?,1859,1870,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/686106,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.1098.5,false,true,631029,Photographs,Photograph,Miss Patricia Morris,,,,,,Artist,,Hill and Adamson,"British, active 1843–1848",,Hill and Adamson,"British, Scottish",1843,1848,1843–47,1843,1847,Salted paper print,Image: 8 3/8 × 6 1/8 in. (21.3 × 15.6 cm) Mount: 14 3/4 × 10 3/8 in. (37.5 × 26.4 cm),"Gift of Joyce F. Menschel, 2013",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/631029,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2011.599.1a,false,true,633310,Photographs,Photograph,The Great Elephant Saluting,,,,,,Artist,,Lala Deen Dayal,"Indian, Sardhana 1844–1905",,"Dayal, Lala Deen",Indian,1844,1905,1885–1900,1885,1900,Albumen silver print from glass negative,Image: 21.1 x 27.3 cm (8 5/16 x 10 3/4 in.) Mount: 30 x 37.7 cm (11 13/16 x 14 13/16 in.),"Gift of Cynthia Hazen Polsky, 2011",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/633310,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2011.599.1b,false,true,291060,Photographs,Photograph,The Great Elephant,,,,,,Artist,,Lala Deen Dayal,"Indian, Sardhana 1844–1905",,"Dayal, Lala Deen",Indian,1844,1905,1885–1900,1885,1900,Albumen silver print from glass negative,Image: 24.1 x 21.2 cm (9 1/2 x 8 3/8 in.) Mount: 30 x 37.7 cm (11 13/16 x 14 13/16 in.),"Gift of Cynthia Hazen Polsky, 2011",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/291060,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2011.599.2a,false,true,291679,Photographs,Photograph,Sookh-Vilas Palace Garden,,,,,,Artist,,Lala Deen Dayal,"Indian, Sardhana 1844–1905",,"Dayal, Lala Deen",Indian,1844,1905,1880–90,1880,1890,Albumen silver print from glass negative,Image: 20.3 x 27.5 cm (8 x 10 13/16 in.) Mount: 30 x 37.7 cm (11 13/16 x 14 13/16 in.),"Gift of Cynthia Hazen Polsky, 2011",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/291679,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2011.599.2b,false,true,633313,Photographs,Photograph,Sookh-Vilas Palace Garden,,,,,,Artist,,Lala Deen Dayal,"Indian, Sardhana 1844–1905",,"Dayal, Lala Deen",Indian,1844,1905,1880–90,1880,1890,Albumen silver print from glass negative,Image: 20.4 x 26.2 cm (8 1/16 x 10 5/16 in.) Mount: 30 x 37.7 cm (11 13/16 x 14 13/16 in.),"Gift of Cynthia Hazen Polsky, 2011",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/633313,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1995.368,false,true,267262,Photographs,Photograph,"Hunford Mill, Surrey",,,,,,Artist,,Henry White,"British, Scotland 1819–1903",,"White, Henry","British, Scottish",1819,1903,1855–57,1855,1857,Albumen silver print from glass negative,Image: 19.4 x 23.8 cm (7 5/8 x 9 3/8 in.) Mount: 43.4 x 59.9 cm (17 1/16 x 23 9/16 in.),"Purchase, Mrs. Harrison D. Horblit Gift, 1995",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267262,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.434,false,true,283227,Photographs,Photograph,Poling the Marsh Hay,,,,,,Artist,,Peter Henry Emerson,"British, born Cuba, 1856–1936",,"Emerson, Peter Henry","British, born Cuba",1856,1936,1886,1886,1886,Platinum print from glass negative,Image: 23.2 x 29 cm (9 1/8 x 11 7/16 in.) Mount: 28.6 x 40.9 cm (11 1/4 x 16 1/8 in.) Sheet ((Interleaving Plate Sheet)): 28 x 40.7 cm (11 x 16 in.),"Gilman Collection, Purchase, Mrs. Walter Annenberg and The Annenberg Foundation Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283227,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.722,false,true,286469,Photographs,Photograph,Setting the Bownet,,,,,,Artist,,Peter Henry Emerson,"British, born Cuba, 1856–1936",,"Emerson, Peter Henry","British, born Cuba",1856,1936,1886,1886,1886,Platinum print from glass negative,Image: 16.4 x 28.9 cm (6 7/16 x 11 3/8 in.) Mount: 28.6 x 41.1 cm (11 1/4 x 16 3/16 in.),"Gilman Collection, Purchase, Mrs. Walter Annenberg and The Annenberg Foundation Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286469,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.726,false,true,286418,Photographs,Photograph,Rowing Home the Schoof-Stuff,,,,,,Artist,,Peter Henry Emerson,"British, born Cuba, 1856–1936",,"Emerson, Peter Henry","British, born Cuba",1856,1936,1886,1886,1886,Platinum print from glass negative,Image: 13.8 x 27.9 cm (5 7/16 x 11 in.) Mount: 28.6 x 41 cm (11 1/4 x 16 1/8 in.) Sheet (Interleaving Plate Sheet): 28.2 x 40.8 cm (11 1/8 x 16 1/16 in.),"Gilman Collection, Purchase, Mrs. Walter Annenberg and The Annenberg Foundation Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286418,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.741,false,true,286671,Photographs,Photograph,Gathering Water-Lilies,,,,,,Artist,,Peter Henry Emerson,"British, born Cuba, 1856–1936",,"Emerson, Peter Henry","British, born Cuba",1856,1936,1886,1886,1886,Platinum print from glass negative,Image: 19.7 x 29.2 cm (7 3/4 x 11 1/2 in.) Mount: 28.5 x 40.9 cm (11 1/4 x 16 1/8 in.) Sheet ((Interleaving Plate Sheet)): 28.1 x 40.6 cm (11 1/16 x 16 in.),"Gilman Collection, Purchase, Mrs. Walter Annenberg and The Annenberg Foundation Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286671,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.742,false,true,286673,Photographs,Photograph,Gunner Working Up To Fowl,,,,,,Artist,,Peter Henry Emerson,"British, born Cuba, 1856–1936",,"Emerson, Peter Henry","British, born Cuba",1856,1936,1886,1886,1886,Platinum print from glass negative,Image: 18.9 x 28.8 cm (7 7/16 x 11 5/16 in.) Mount: 28.6 x 40.9 cm (11 1/4 x 16 1/8 in.) Sheet ((Interleaving Plate Sheet)): 28.2 x 40.7 cm (11 1/8 x 16 in.),"Gilman Collection, Purchase, Mrs. Walter Annenberg and The Annenberg Foundation Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286673,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.743,false,true,286676,Photographs,Photograph,Cantley: Wherries Waiting for the Turn of the Tide,,,,,,Artist,,Peter Henry Emerson,"British, born Cuba, 1856–1936",,"Emerson, Peter Henry","British, born Cuba",1856,1936,1886,1886,1886,Platinum print from glass negative,Image: 18.9 x 28.5 cm (7 7/16 x 11 1/4 in.) Mount: 28.6 x 40.9 cm (11 1/4 x 16 1/8 in.) Sheet ((Interleaving Plate Sheet)): 28.2 x 41 cm (11 1/8 x 16 1/8 in.),"Gilman Collection, Purchase, Mrs. Walter Annenberg and The Annenberg Foundation Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286676,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.744,false,true,286677,Photographs,Photograph,Water-Lilies,,,,,,Artist,,Peter Henry Emerson,"British, born Cuba, 1856–1936",,"Emerson, Peter Henry","British, born Cuba",1856,1936,1886,1886,1886,Platinum print from glass negative,Image: 12.3 x 28 cm (4 13/16 x 11 in.) Mount: 28.7 x 41 cm (11 5/16 x 16 1/8 in.) Sheet (Interleaving Plate Sheet): 28 x 40.8 cm (11 x 16 1/16 in.),"Gilman Collection, Purchase, Mrs. Walter Annenberg and The Annenberg Foundation Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286677,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.400.10,false,true,291755,Photographs,Daguerreotype,[Elderly Man Holding Ivory-topped Walking Stick],,,,,,Artist,,John Plumbe Jr.,"American, born Wales, 1809–1857",,"Plumbe Jr., John","American, born Wales",1809,1857,1840s,1840,1849,Daguerreotype,Image: 6.7 x 5.5 cm (2 5/8 x 2 3/16 in.) Plate: 8.3 x 7 cm (3 1/4 x 2 3/4 in.) Case: 1.6 x 9.2 x 8.1 cm (5/8 x 3 5/8 x 3 3/16 in.),"Bequest of Herbert Mitchell, 2008",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/291755,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.400.32,false,true,291785,Photographs,Daguerreotype,[Man with Chinstrap Beard],,,,,,Artist,,John Plumbe Jr.,"American, born Wales, 1809–1857",,"Plumbe Jr., John","American, born Wales",1809,1857,1840s,1840,1849,Daguerreotype,Image: 7.6 x 6.6 cm (3 x 2 5/8 in.) Plate: 8.3 x 7 cm (3 1/4 x 2 3/4 in.) Case: 1.6 x 9.5 x 8.1 cm (5/8 x 3 3/4 x 3 3/16 in.),"Bequest of Herbert Mitchell, 2008",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/291785,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.400.60,false,true,291813,Photographs,Daguerreotype,"Elizabeth Page Bakewell and her Grandson, Frank B. James",,,,,,Artist,,John Plumbe Jr.,"American, born Wales, 1809–1857",,"Plumbe Jr., John","American, born Wales",1809,1857,ca. 1846,1845,1848,Daguerreotype,Image: 12 x 9.1 cm (4 3/4 x 3 9/16 in.) Frame: 34.6 x 31.1 cm (13 5/8 x 12 1/4 in.) Mat: 18.1 x 14.6 cm (7 1/8 x 5 3/4 in.),"Bequest of Herbert Mitchell, 2008",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/291813,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.400.61,false,true,291814,Photographs,Daguerreotype,"Elizabeth Bakewell James and her Son, Frank B. James",,,,,,Artist,Attributed to,John Plumbe Jr.,"American, born Wales, 1809–1857",,"Plumbe Jr., John","American, born Wales",1809,1857,ca. 1846,1845,1848,Daguerreotype,Image: 11.9 x 8.9 cm (4 11/16 x 3 1/2 in.) Mat: 18.4 x 14.9 cm (7 1/4 x 5 7/8 in.) Frame: 34.8 x 31.3 cm (13 11/16 x 12 5/16 in.),"Bequest of Herbert Mitchell, 2008",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/291814,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.804,false,true,286155,Photographs,Photograph,Dr. Edward Livingston,,,,,,Artist,Attributed to,John Plumbe Jr.,"American, born Wales, 1809–1857",,"Plumbe Jr., John","American, born Wales",1809,1857,ca. 1841,1839,1843,Daguerreotype,Overall: 4 11/16 × 3 11/16 in. (11.9 × 9.4 cm) Image: 3 9/16 × 2 5/8 in. (9.1 × 6.7 cm); visible,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286155,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -18.17.1.216,false,true,282222,Photographs,Photograph,[View of the rooftops and cathedral of Vienna],,,,,,Artist,,Alois Auer,"Austrian, Wels 1813–1869 Vienna",,"Auer, Alois",Austrian,1813,1869,ca. 1853,1851,1855,Albumen silver print,,"Rogers Fund, 1918",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/282222,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -18.17.1.217,false,true,259569,Photographs,Micrograph,[Microscopic view of an insect],,,,,,Artist,,Alois Auer,"Austrian, Wels 1813–1869 Vienna",,"Auer, Alois",Austrian,1813,1869,ca. 1853,1851,1855,Albumen silver print,,"Rogers Fund, 1918",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/259569,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -61.641.2.1,false,true,270438,Photographs,Photograph,[Jerusalem],,,,,,Artist,,John Anthony,"British, born France, 1823–1901",,"Anthony, John","British, born France",1823,1901,1860s,1860,1869,Albumen silver print from glass negative,Image: 16.3 x 21.8 cm (6 7/16 x 8 9/16 in.) Mount: 27.6 x 37.3 cm (10 7/8 x 14 11/16 in.),"Gift of A. Hyatt Mayor, 1961",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/270438,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -61.641.2.3,false,true,270441,Photographs,Photograph,[Garden of Gethsemane and View of Jerusalem],,,,,,Artist,,John Anthony,"British, born France, 1823–1901",,"Anthony, John","British, born France",1823,1901,1860s,1860,1869,Albumen silver print from glass negative,Image: 16.8 x 21.4 cm (6 5/8 x 8 7/16 in.) Mount: 27.8 x 37.2 cm (10 15/16 x 14 5/8 in.),"Gift of A. Hyatt Mayor, 1961",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/270441,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -61.641.2.4,false,true,270442,Photographs,Photograph,[Jerusalem],,,,,,Artist,,John Anthony,"British, born France, 1823–1901",,"Anthony, John","British, born France",1823,1901,1857,1857,1857,Albumen silver print from glass negative,Image: 13.6 x 17 cm (5 3/8 x 6 11/16 in.) Mount: 27.9 x 37.3 cm (11 x 14 11/16 in.),"Gift of A. Hyatt Mayor, 1961",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/270442,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -61.641.2.6,false,true,270443,Photographs,Photograph,"Jerusalem, Court of the Mosque of Omar",,,,,,Artist,,John Anthony,"British, born France, 1823–1901",,"Anthony, John","British, born France",1823,1901,1857,1857,1857,Albumen silver print from glass negative,Image: 16.9 x 21.4 cm (6 5/8 x 8 7/16 in.) Mount: 28 x 37.5 cm (11 x 14 3/4 in.),"Gift of A. Hyatt Mayor, 1961",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/270443,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -61.641.2.7,false,true,270444,Photographs,Photograph,"[Dome of the Holy Sepulchre, Jerusalem]",,,,,,Artist,,John Anthony,"British, born France, 1823–1901",,"Anthony, John","British, born France",1823,1901,1860s,1860,1869,Albumen silver print from glass negative,Image: 13.9 x 17 cm (5 1/2 x 6 11/16 in.) Mount: 27.7 x 37.3 cm (10 7/8 x 14 11/16 in.),"Gift of A. Hyatt Mayor, 1961",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/270444,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -61.641.2.9,false,true,270445,Photographs,Photograph,"[Garden of Gethsemane and the Tomb of the Virgin, Jerusalem]",,,,,,Artist,,John Anthony,"British, born France, 1823–1901",,"Anthony, John","British, born France",1823,1901,1860s,1860,1869,Albumen silver print from glass negative,Image: 12.8 x 16.2 cm (5 1/16 x 6 3/8 in.) Mount: 27.7 x 37.5 cm (10 7/8 x 14 3/4 in.),"Gift of A. Hyatt Mayor, 1961",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/270445,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -63.606.1.2,false,true,270834,Photographs,Photograph,"Jerusalem, Site of the Temple on Mount Moriah",,,,,,Artist,,John Anthony,"British, born France, 1823–1901",,"Anthony, John","British, born France",1823,1901,1857,1857,1857,Albumen silver print,,"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1963",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/270834,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -61.641.2.10,false,true,270439,Photographs,Photograph,"[Tomb of the Virgin, Jerusalem]",,,,,,Artist,,John Anthony,"British, born France, 1823–1901",,"Anthony, John","British, born France",1823,1901,1860s,1860,1869,Albumen silver print from glass negative,Image: 13.9 x 17.2 cm (5 1/2 x 6 3/4 in.) Mount: 27.7 x 37.3 cm (10 7/8 x 14 11/16 in.),"Gift of A. Hyatt Mayor, 1961",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/270439,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -61.641.2.11,false,true,270440,Photographs,Photograph,"[Tomb of Absalom, Zacharias, and St. James]",,,,,,Artist,,John Anthony,"British, born France, 1823–1901",,"Anthony, John","British, born France",1823,1901,1860s,1860,1869,Albumen silver print from glass negative,Image: 15.9 x 21.3 cm (6 1/4 x 8 3/8 in.) Mount: 27.7 x 37.3 cm (10 7/8 x 14 11/16 in.),"Gift of A. Hyatt Mayor, 1961",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/270440,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -46.1.45,false,true,268988,Photographs,Photograph,Cecily Hamilton,,,,,,Artist,,Oscar Gustav Rejlander,"British, born Sweden, 1813–1875",,"Rejlander, Oscar Gustav","British, born Sweden",1813,1875,1863–1867,1863,1867,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1946",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268988,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.24,false,true,283096,Photographs,Photograph,Mr. and Miss Constable,,,,,,Artist,,Oscar Gustav Rejlander,"British, born Sweden, 1813–1875",,"Rejlander, Oscar Gustav","British, born Sweden",1813,1875,1866,1866,1866,Albumen silver print from glass negative,Image: 16.8 x 22.1 cm (6 5/8 x 8 11/16 in.) Mount: 21.2 x 28.9 cm (8 3/8 x 11 3/8 in.),"Gilman Collection, Purchase, Harriette and Noel Levine Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283096,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.159.47,false,true,306330,Photographs,Photograph,His Country's Hope,,,,,,Artist,,Oscar Gustav Rejlander,"British, born Sweden, 1813–1875",,"Rejlander, Oscar Gustav","British, born Sweden",1813,1875,1850s,1850,1859,Albumen silver print from glass negative,Mount: 10 7/8 in. × 13 7/8 in. (27.6 × 35.2 cm) Image: 6 3/16 in. × 8 in. (15.7 × 20.3 cm),"Bequest of Maurice B. Sendak, 2012",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306330,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -63.606.1.33,false,true,270849,Photographs,Photograph,Study of a Head,,,,,,Artist,,Oscar Gustav Rejlander,"British, born Sweden, 1813–1875",,"Rejlander, Oscar Gustav","British, born Sweden",1813,1875,1857,1857,1857,Albumen silver print,,"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1963",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/270849,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1106,false,true,285657,Photographs,Photograph,The Scholar's Mate,,,,,,Artist,,Oscar Gustav Rejlander,"British, born Sweden, 1813–1875",,"Rejlander, Oscar Gustav","British, born Sweden",1813,1875,1857–59,1857,1859,Albumen silver print,Mount: 16 in. × 12 7/8 in. (40.6 × 32.7 cm) Image: 7 15/16 × 6 9/16 in. (20.2 × 16.7 cm),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285657,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1164,false,true,285658,Photographs,Photograph,Ariadne,,,,,,Artist,,Oscar Gustav Rejlander,"British, born Sweden, 1813–1875",,"Rejlander, Oscar Gustav","British, born Sweden",1813,1875,1857,1857,1857,Albumen silver print from glass negative,Mount: 16 1/16 in. × 13 1/16 in. (40.8 × 33.2 cm) Image: 8 1/4 × 6 1/2 in. (21 × 16.5 cm); oval,"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285658,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2000.3,false,true,283627,Photographs,Photograph,David D'Angers,,,,,,Artist,,Édouard Baldus,"French, born Prussia, 1813–1889",,"Baldus, Édouard","French, born Prussia",1813,1889,1853,1853,1853,Salted paper print from paper negative,18.9 x 15 cm (7 7/16 x 5 7/8 in. ),"Purchase, Jennifer and Joseph Duke and Harriette and Noel Levine Gifts, 2000",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283627,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1997.52,false,true,271912,Photographs,Photograph,"The Floods of 1856, Avignon",,,,,,Artist,,Édouard Baldus,"French, born Prussia, 1813–1889",,"Baldus, Édouard","French, born Prussia",1813,1889,1856,1856,1856,Salted paper print from paper negative,30.4 x 43.8 cm (11 15/16 x 17 1/4 in. ),"Purchase, Robert Hurst, Paul F. Walter and Anonymous Gifts; Harris Brisbane Dick Fund, Rogers Fund and Gift of Mrs. Claire K. Feins, in memory of Daniel M. Feins and Linda S. Silverman, by exchange; and funds from various donors, 1997",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271912,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.67,false,true,290462,Photographs,Photograph,Village de Murols,,,,,,Artist,,Édouard Baldus,"French, born Prussia, 1813–1889",,"Baldus, Édouard","French, born Prussia",1813,1889,1854,1854,1854,Salted paper print from paper negative,Image: 33 x 43.5 cm (13 x 17 1/8 in.) Mount: 47.6 x 62.4 cm (18 3/4 x 24 9/16 in.),"The Horace W. Goldsmith Foundation Fund, through Joyce and Robert Menschel, 2009",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/290462,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -44.55.1,false,true,268839,Photographs,Photograph,Panorama de la Cité,,,,,,Artist,,Édouard Baldus,"French, born Prussia, 1813–1889",,"Baldus, Édouard","French, born Prussia",1813,1889,1860s,1860,1869,Albumen silver print from glass negative,20.3 x 28.3 cm. (8 x 11 1/8 in.),"David Hunter McAlpin Fund, 1944",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268839,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -44.55.4,false,true,268863,Photographs,Photograph,Notre-Dame (Abside),,,,,,Artist,,Édouard Baldus,"French, born Prussia, 1813–1889",,"Baldus, Édouard","French, born Prussia",1813,1889,1860s,1860,1869,Albumen silver print from glass negative,21.7 x 28.6 cm. (8 9/16 x 11 1/4 in.),"David Hunter McAlpin Fund, 1944",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268863,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -44.55.6,false,true,268865,Photographs,Photograph,Arc de triomphe de l'Ètoile,,,,,,Artist,,Édouard Baldus,"French, born Prussia, 1813–1889",,"Baldus, Édouard","French, born Prussia",1813,1889,1860s,1860,1869,Albumen silver print from glass negative,21.1 x 27.0 cm. (8 5/16 x 10 5/8 in.),"David Hunter McAlpin Fund, 1944",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268865,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -44.55.7,false,true,268866,Photographs,Photograph,Notre-Dame (façade),,,,,,Artist,,Édouard Baldus,"French, born Prussia, 1813–1889",,"Baldus, Édouard","French, born Prussia",1813,1889,1860s,1860,1869,Albumen silver print from glass negative,27.6 x 21.1 cm. (10 7/8 x 8 5/16 in.),"David Hunter McAlpin Fund, 1944",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268866,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1994.137,false,true,267002,Photographs,Photograph,[Imperial Library of the Louvre],,,,,,Artist,,Édouard Baldus,"French, born Prussia, 1813–1889",,"Baldus, Édouard","French, born Prussia",1813,1889,1856–57,1856,1857,Salted paper print from glass negative,43.9 x 34.2 cm. (17 1/4 x 13 7/16 in.),"Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 1994",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267002,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1999.228,false,true,282780,Photographs,Photograph,"Château of Princess Mathilde, Enghien",,,,,,Artist,,Édouard Baldus,"French, born Prussia, 1813–1889",,"Baldus, Édouard","French, born Prussia",1813,1889,1854–55,1854,1855,Salted paper print from paper negative,31.7 x 44.4 cm (12 1/2 x 17 1/2 in. ),"Purchase, Louis V. Bell Fund, by exchange, and The Horace W. Goldsmith Foundation Gift through Joyce and Robert Menschel, 1999",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/282780,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2006.565,false,true,287985,Photographs,Photograph,Arc antique à Orange,,,,,,Artist,,Édouard Baldus,"French, born Prussia, 1813–1889",,"Baldus, Édouard","French, born Prussia",1813,1889,1853,1853,1853,Salted paper print from paper negative,Image: 32.4 x 42.4 cm (12 3/4 x 16 11/16 in.) Mount: 48.6 x 63.5 cm (19 1/8 x 25 in.),"Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2006",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287985,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.455,false,true,288843,Photographs,Photograph,"The Floods of 1856, Church of Saint-Pothin, Lyon",,,,,,Artist,,Édouard Baldus,"French, born Prussia, 1813–1889",,"Baldus, Édouard","French, born Prussia",1813,1889,June 1856,1856,1856,Salted paper print from paper negative,Image: 32.5 x 43.5 cm (12 13/16 x 17 1/8 in.),"Funds from various donors, 2007",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288843,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -44.55.23,false,true,268854,Photographs,Photograph,Panthéon,,,,,,Artist,,Édouard Baldus,"French, born Prussia, 1813–1889",,"Baldus, Édouard","French, born Prussia",1813,1889,1860s,1860,1869,Albumen silver print from glass negative,21.7 x 27.0 cm. (8 9/16 x 10 5/8 in.),"David Hunter McAlpin Fund, 1944",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268854,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -44.55.25,false,true,268856,Photographs,Photograph,Palais de l'Industrie,,,,,,Artist,,Édouard Baldus,"French, born Prussia, 1813–1889",,"Baldus, Édouard","French, born Prussia",1813,1889,1850s–60s,1850,1869,Albumen silver print,21.6 x 27.9 cm. (8 1/2 x 11 in.),"David Hunter McAlpin Fund, 1944",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268856,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1990.1012,false,true,266222,Photographs,Photograph,Eglise d'Auvers,,,,,,Artist,,Édouard Baldus,"French, born Prussia, 1813–1889",,"Baldus, Édouard","French, born Prussia",1813,1889,"1855, printed 1855–57",1855,1855,Salted paper print from paper negative,32.4 x 43.2 cm. (12 3/4 x 17 in.),"Purchase, Rogers Fund, Joyce and Robert Menschel Gift and Harriette and Noel Levine Gift, 1990",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/266222,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1990.1127,false,true,266282,Photographs,Photograph,[Roman Arch at Orange],,,,,,Artist,,Édouard Baldus,"French, born Prussia, 1813–1889",,"Baldus, Édouard","French, born Prussia",1813,1889,1851,1851,1851,Salted paper print from paper negative,35.3 x 26.2 cm (13 7/8 x 10 5/16 in.),"Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, and Edward Pearce Casey Fund, 1990",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/266282,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1991.1042,false,true,266331,Photographs,Photograph,"Pavillon de l'Horloge, Louvre",,,,,,Artist,,Édouard Baldus,"French, born Prussia, 1813–1889",,"Baldus, Édouard","French, born Prussia",1813,1889,1852–53,1852,1853,Salted paper print from paper negative,39.5 x 27.7 cm. (15 9/16 x 10 15/16 in.),"Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 1991",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/266331,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1992.5000,true,true,266644,Photographs,Photograph,[Entrance to the Port of Boulogne],,,,,,Artist,,Édouard Baldus,"French, born Prussia, 1813–1889",,"Baldus, Édouard","French, born Prussia",1813,1889,1855,1855,1855,Salted paper print from paper negative,28.8 x 43.5 cm (11 5/16 x 17 1/8 in.),"Purchase, Louis V. Bell Fund, 1992",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/266644,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1992.5002,false,true,266646,Photographs,Photograph,Gare d'Enghien,,,,,,Artist,,Édouard Baldus,"French, born Prussia, 1813–1889",,"Baldus, Édouard","French, born Prussia",1813,1889,1855,1855,1855,Salted paper print from paper negative,Image: 31.3 x 44.3 cm. (12 5/16 x 17 7/16 in.),"Louis V. Bell Fund, 1992",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/266646,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1992.5003,false,true,266647,Photographs,Photograph,Pont en Royans,,,,,,Artist,,Édouard Baldus,"French, born Prussia, 1813–1889",,"Baldus, Édouard","French, born Prussia",1813,1889,ca. 1859,1857,1861,Salted paper print from paper negative,Image: 43.2 x 33.9 cm. (17 x 13 3/6 in.),"Louis V. Bell Fund, 1992",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/266647,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1993.125.1,false,true,266882,Photographs,Photograph,Madeleine Bourquelot de Cervignieres,,,,,,Artist,,Édouard Baldus,"French, born Prussia, 1813–1889",,"Baldus, Édouard","French, born Prussia",1813,1889,1858,1858,1858,Albumen silver print from glass negative,Image: 16.0 x 13.3 cm. (6 5/16 x 5 1/4 in.),"Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 1993",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/266882,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1993.125.2,false,true,266883,Photographs,Photograph,Pierre Bourquelot de Cervignieres,,,,,,Artist,,Édouard Baldus,"French, born Prussia, 1813–1889",,"Baldus, Édouard","French, born Prussia",1813,1889,1858,1858,1858,Albumen silver print from glass negative,Image: 17.4 x 13.7 cm. (6 7/8 x 5 3/8 in.),"Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 1993",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/266883,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1994.179.1,false,true,267017,Photographs,Stereograph,"[Portal, Church of Saint-Trophime, Arles]",,,,,,Artist,,Édouard Baldus,"French, born Prussia, 1813–1889",,"Baldus, Édouard","French, born Prussia",1813,1889,ca. 1864,1862,1866,Albumen silver print from glass negative,7.0 x 14.2 cm. (2 3/4 x 5 9/16 in.),"Gift of Pierre-Marc Richard, 1994",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267017,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1994.179.2,false,true,267018,Photographs,Stereograph,"[Church of Saint-Honorat, Arles]",,,,,,Artist,,Édouard Baldus,"French, born Prussia, 1813–1889",,"Baldus, Édouard","French, born Prussia",1813,1889,ca. 1864,1862,1866,Albumen silver print from glass negative,7.0 x 14.2 cm. (2 3/4 x 5 9/16 in.),"Gift of Pierre-Marc Richard, 1994",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267018,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.50,false,true,283131,Photographs,Photograph,Groupe dans le parc du château de La Faloise,,,,,,Artist,,Édouard Baldus,"French, born Prussia, 1813–1889",,"Baldus, Édouard","French, born Prussia",1813,1889,1857,1857,1857,Salted paper print from glass negative,Mount: 41.6 × 55.3 cm (16 3/8 × 21 3/4 in.) Image: 27.8 × 38.2 cm (10 15/16 × 15 1/16 in.),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283131,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.364.1,false,true,287310,Photographs,Photograph,Lyon,,,,,,Artist,,Édouard Baldus,"French, born Prussia, 1813–1889",,"Baldus, Édouard","French, born Prussia",1813,1889,ca. 1861,1860,1862,Albumen silver print from glass negative?,Image: 43.2 x 31.1 cm (17 x 12 1/4 in.) Mount: 46 x 60.5 cm (18 1/8 x 23 13/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287310,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.364.2,false,true,287311,Photographs,Photograph,"Lyon, Hôtel de Ville",,,,,,Artist,,Édouard Baldus,"French, born Prussia, 1813–1889",,"Baldus, Édouard","French, born Prussia",1813,1889,ca. 1861,1860,1862,Albumen silver print from glass negative,Image: 33 x 42.7 cm (13 x 16 13/16 in.) Mount: 46 x 60.5 cm (18 1/8 x 23 13/16 in.) Mat: 55.9 x 71.1 cm (22 x 28 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287311,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.364.3,false,true,287312,Photographs,Photograph,"Lyon, Gare de Perrache",,,,,,Artist,,Édouard Baldus,"French, born Prussia, 1813–1889",,"Baldus, Édouard","French, born Prussia",1813,1889,ca. 1861,1860,1862,Albumen silver print from glass negative,Image: 27.9 x 43.3 cm (11 x 17 1/16 in.) Mount: 46 x 60.5 cm (18 1/8 x 23 13/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287312,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.364.4,false,true,287313,Photographs,Photograph,Pont de la Mulatiere,,,,,,Artist,,Édouard Baldus,"French, born Prussia, 1813–1889",,"Baldus, Édouard","French, born Prussia",1813,1889,ca. 1861,1860,1862,Albumen silver print from glass negative,Image: 28 x 44.6 cm (11 x 17 9/16 in.) Mount: 46 x 60.5 cm (18 1/8 x 23 13/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287313,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.364.5,false,true,287314,Photographs,Photograph,Pont de la Mulatiere,,,,,,Artist,,Édouard Baldus,"French, born Prussia, 1813–1889",,"Baldus, Édouard","French, born Prussia",1813,1889,ca. 1861,1860,1862,Albumen silver print from glass negative,Image: 28.3 x 44.3 cm (11 1/8 x 17 7/16 in.) Mount: 46 x 60.5 cm (18 1/8 x 23 13/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287314,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.364.6,false,true,287315,Photographs,Photograph,"Lyon, Viaduc du Rhône",,,,,,Artist,,Édouard Baldus,"French, born Prussia, 1813–1889",,"Baldus, Édouard","French, born Prussia",1813,1889,ca. 1861,1860,1862,Albumen silver print from glass negative,Image: 24.3 x 42.5 cm (9 9/16 x 16 3/4 in.) Mount: 46 x 60.5 cm (18 1/8 x 23 13/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287315,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.364.7,false,true,287316,Photographs,Photograph,"Lyon, Viaduc du Rhône",,,,,,Artist,,Édouard Baldus,"French, born Prussia, 1813–1889",,"Baldus, Édouard","French, born Prussia",1813,1889,ca. 1861,1860,1862,Albumen silver print from glass negative,Image: 26 x 43.3 cm (10 1/4 x 17 1/16 in.) Mount: 46 x 60.5 cm (18 1/8 x 23 13/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287316,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.364.8,false,true,287317,Photographs,Photograph,"Givors, Viaduc",,,,,,Artist,,Édouard Baldus,"French, born Prussia, 1813–1889",,"Baldus, Édouard","French, born Prussia",1813,1889,ca. 1861,1860,1862,Albumen silver print from glass negative,Image: 27.1 x 43 cm (10 11/16 x 16 15/16 in.) Mount: 46 x 60.5 cm (18 1/8 x 23 13/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287317,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.364.9,false,true,287318,Photographs,Photograph,"Givors, Viaduc",,,,,,Artist,,Édouard Baldus,"French, born Prussia, 1813–1889",,"Baldus, Édouard","French, born Prussia",1813,1889,ca. 1861,1860,1862,Albumen silver print from glass negative,Image: 27.5 x 43.9 cm (10 13/16 x 17 5/16 in.) Mount: 46 x 60.5 cm (18 1/8 x 23 13/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287318,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.364.10,false,true,286796,Photographs,Photograph,"Vienne, Souterrain",,,,,,Artist,,Édouard Baldus,"French, born Prussia, 1813–1889",,"Baldus, Édouard","French, born Prussia",1813,1889,ca. 1861,1860,1862,Albumen silver print from glass negative,Image: 32.9 x 42.3 cm (12 15/16 x 16 5/8 in.) Mount: 46 x 60.5 cm (18 1/8 x 23 13/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286796,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.364.11,false,true,287319,Photographs,Photograph,"Vienne, St. Colombe",,,,,,Artist,,Édouard Baldus,"French, born Prussia, 1813–1889",,"Baldus, Édouard","French, born Prussia",1813,1889,ca. 1861,1860,1862,Albumen silver print from glass negative,Image: 32.5 x 42.8 cm (12 13/16 x 16 7/8 in.) Mount: 46 x 60.5 cm (18 1/8 x 23 13/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287319,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.364.12,false,true,287320,Photographs,Photograph,"Vienne, St. Jean",,,,,,Artist,,Édouard Baldus,"French, born Prussia, 1813–1889",,"Baldus, Édouard","French, born Prussia",1813,1889,ca. 1861,1860,1862,Albumen silver print from glass negative,Image: 30.8 x 43.2 cm (12 1/8 x 17 in.) Mount: 46 x 60.5 cm (18 1/8 x 23 13/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287320,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.364.13,false,true,287321,Photographs,Photograph,"Vienne, St. Maurice",,,,,,Artist,,Édouard Baldus,"French, born Prussia, 1813–1889",,"Baldus, Édouard","French, born Prussia",1813,1889,ca. 1861,1860,1862,Albumen silver print from glass negative,Image: 42.5 x 34.1 cm (16 3/4 x 13 7/16 in.) Mount: 46 x 60.5 cm (18 1/8 x 23 13/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287321,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.364.14,false,true,287322,Photographs,Photograph,"Vienne, Souterrain",,,,,,Artist,,Édouard Baldus,"French, born Prussia, 1813–1889",,"Baldus, Édouard","French, born Prussia",1813,1889,ca. 1861,1860,1862,Albumen silver print from glass negative,Image: 33 x 43 cm (13 x 16 15/16 in.) Mount: 46 x 60.5 cm (18 1/8 x 23 13/16 in.) Mat: 55.9 x 71.1 cm (22 x 28 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287322,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.364.15,false,true,287323,Photographs,Photograph,Viaduc de l'Iser,,,,,,Artist,,Édouard Baldus,"French, born Prussia, 1813–1889",,"Baldus, Édouard","French, born Prussia",1813,1889,ca. 1861,1860,1862,Albumen silver print from glass negative,Image: 26.6 x 43.7 cm (10 1/2 x 17 3/16 in.) Mount: 46 x 60.5 cm (18 1/8 x 23 13/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287323,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.364.17,false,true,287325,Photographs,Photograph,Viaduc de la Voulte,,,,,,Artist,,Édouard Baldus,"French, born Prussia, 1813–1889",,"Baldus, Édouard","French, born Prussia",1813,1889,1861 or after,1861,1862,Albumen silver print from glass negative,Image: 25.4 x 42.5 cm (10 x 16 3/4 in.) Mount: 46 x 60.5 cm (18 1/8 x 23 13/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287325,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.364.18,false,true,287326,Photographs,Photograph,Viaduc de la Voulte,,,,,,Artist,,Édouard Baldus,"French, born Prussia, 1813–1889",,"Baldus, Édouard","French, born Prussia",1813,1889,1861 or after,1861,1862,Albumen silver print from glass negative,Image: 30.5 x 43.2 cm (12 x 17 in.) Mount: 46 x 60.5 cm (18 1/8 x 23 13/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287326,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.364.19,false,true,287327,Photographs,Photograph,Viviers,,,,,,Artist,,Édouard Baldus,"French, born Prussia, 1813–1889",,"Baldus, Édouard","French, born Prussia",1813,1889,ca. 1861,1860,1862,Albumen silver print from glass negative,Image: 32.1 x 43.4 cm (12 5/8 x 17 1/16 in.) Mount: 46 x 60.5 cm (18 1/8 x 23 13/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287327,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.364.20,false,true,283135,Photographs,Photograph,Entrée du Robinet,,,,,,Artist,,Édouard Baldus,"French, born Prussia, 1813–1889",,"Baldus, Édouard","French, born Prussia",1813,1889,ca. 1861,1860,1862,Albumen silver print from glass negative,Image: 33.3 x 43.2 cm (13 1/8 x 17 in.) Mount: 46 x 60.5 cm (18 1/8 x 23 13/16 in.) Mat: 62.9 x 72.4 cm (24 3/4 x 28 1/2 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283135,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.364.21,false,true,287328,Photographs,Photograph,"Orange, Arc Antique",,,,,,Artist,,Édouard Baldus,"French, born Prussia, 1813–1889",,"Baldus, Édouard","French, born Prussia",1813,1889,ca. 1864,1863,1865,Albumen silver print from glass negative,Image: 21.7 x 28.3 cm (8 9/16 x 11 1/8 in.) Mount: 46 x 60.5 cm (18 1/8 x 23 13/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287328,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.364.22,false,true,287329,Photographs,Photograph,"Orange, Théâtre Antique",,,,,,Artist,,Édouard Baldus,"French, born Prussia, 1813–1889",,"Baldus, Édouard","French, born Prussia",1813,1889,ca. 1861,1860,1862,Albumen silver print from glass negative,Image: 42.7 x 33.9 cm (16 13/16 x 13 3/8 in.) Mount: 60.5 x 46 cm (23 13/16 x 18 1/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287329,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.364.23,false,true,287330,Photographs,Photograph,"Orange, Théâtre Antique",,,,,,Artist,,Édouard Baldus,"French, born Prussia, 1813–1889",,"Baldus, Édouard","French, born Prussia",1813,1889,ca. 1861,1860,1862,Albumen silver print from glass negative,Image: 44.1 x 34.3 cm (17 3/8 x 13 1/2 in.) Mount: 60.5 x 46 cm (23 13/16 x 18 1/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287330,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.364.24,false,true,287331,Photographs,Photograph,Avignon,,,,,,Artist,,Édouard Baldus,"French, born Prussia, 1813–1889",,"Baldus, Édouard","French, born Prussia",1813,1889,ca. 1861,1859,1862,Albumen silver print from glass negative?,Image: 31.8 x 42.2 cm (12 1/2 x 16 5/8 in.) Mount: 46 x 60.5 cm (18 1/8 x 23 13/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287331,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.364.25,false,true,287332,Photographs,Photograph,"Avignon, Pont St. Bénezet",,,,,,Artist,,Édouard Baldus,"French, born Prussia, 1813–1889",,"Baldus, Édouard","French, born Prussia",1813,1889,ca. 1864,1863,1865,Albumen silver print from glass negative,Image: 21.1 x 28.5 cm (8 5/16 x 11 1/4 in.) Mount: 46 x 60.5 cm (18 1/8 x 23 13/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287332,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.364.26,false,true,287333,Photographs,Photograph,Avignon,,,,,,Artist,,Édouard Baldus,"French, born Prussia, 1813–1889",,"Baldus, Édouard","French, born Prussia",1813,1889,1859 or after,1859,1861,Albumen silver print from glass negative,Image: 25.6 x 41.7 cm (10 1/16 x 16 7/16 in.) Mount: 46 x 60.5 cm (18 1/8 x 23 13/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287333,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.364.27,false,true,287334,Photographs,Photograph,Villeneuve les Avignon,,,,,,Artist,,Édouard Baldus,"French, born Prussia, 1813–1889",,"Baldus, Édouard","French, born Prussia",1813,1889,ca. 1862,1861,1864,Albumen silver print from glass negative,Image: 21.6 x 28.6 cm (8 1/2 x 11 1/4 in.) Mount: 46 x 60.5 cm (18 1/8 x 23 13/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287334,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.364.28,false,true,287335,Photographs,Photograph,"Avignon, Palais des Papes",,,,,,Artist,,Édouard Baldus,"French, born Prussia, 1813–1889",,"Baldus, Édouard","French, born Prussia",1813,1889,1859 or after,1859,1861,Albumen silver print from glass negative,Image: 33.6 x 43.4 cm (13 1/4 x 17 1/16 in.) Mount: 46 x 60.5 cm (18 1/8 x 23 13/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287335,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.364.29,false,true,287336,Photographs,Photograph,Vaucluse,,,,,,Artist,,Édouard Baldus,"French, born Prussia, 1813–1889",,"Baldus, Édouard","French, born Prussia",1813,1889,ca. 1862,1861,1864,Albumen silver print from glass negative?,Image: 21.9 x 27.9 cm (8 5/8 x 11 in.) Mount: 46 x 60.5 cm (18 1/8 x 23 13/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287336,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.364.30,false,true,287337,Photographs,Photograph,Saint-Rémy,,,,,,Artist,,Édouard Baldus,"French, born Prussia, 1813–1889",,"Baldus, Édouard","French, born Prussia",1813,1889,ca. 1862,1861,1864,Albumen silver print from glass negative,Image: 21 x 27.4 cm (8 1/4 x 10 13/16 in.) Mount: 46 x 60.5 cm (18 1/8 x 23 13/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287337,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.364.31,false,true,287338,Photographs,Photograph,Saint-Rémy,,,,,,Artist,,Édouard Baldus,"French, born Prussia, 1813–1889",,"Baldus, Édouard","French, born Prussia",1813,1889,ca. 1862,1861,1864,Albumen silver print from glass negative?,Image: 28.6 x 21.7 cm (11 1/4 x 8 9/16 in.) Mount: 60.5 x 46 cm (23 13/16 x 18 1/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287338,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.364.32,false,true,287339,Photographs,Photograph,Pont du Gard,,,,,,Artist,,Édouard Baldus,"French, born Prussia, 1813–1889",,"Baldus, Édouard","French, born Prussia",1813,1889,ca. 1861,1860,1862,Albumen silver print from glass negative,Image: 27.1 x 43.2 cm (10 11/16 x 17 in.) Mount: 46 x 60.5 cm (18 1/8 x 23 13/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287339,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.364.33,false,true,287340,Photographs,Photograph,"Durance, Viaduc",,,,,,Artist,,Édouard Baldus,"French, born Prussia, 1813–1889",,"Baldus, Édouard","French, born Prussia",1813,1889,before 1859,1855,1859,Albumen silver print from paper negative,Image: 31.8 x 53 cm (12 1/2 x 20 7/8 in.) Mount: 46 x 60.5 cm (18 1/8 x 23 13/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287340,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.364.34,false,true,287341,Photographs,Photograph,"Tarascon, Viaduc",,,,,,Artist,,Édouard Baldus,"French, born Prussia, 1813–1889",,"Baldus, Édouard","French, born Prussia",1813,1889,ca. 1861,1860,1862,Albumen silver print from glass negative,Image: 27 x 44.2 cm (10 5/8 x 17 3/8 in.) Mount: 46 x 60.5 cm (18 1/8 x 23 13/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287341,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.364.35,false,true,287342,Photographs,Photograph,"Tarascon, Viaduc",,,,,,Artist,,Édouard Baldus,"French, born Prussia, 1813–1889",,"Baldus, Édouard","French, born Prussia",1813,1889,before 1859,1855,1859,Albumen silver print from paper negative,Image: 37.4 x 53.2 cm (14 3/4 x 20 15/16 in.) Mount: 46 x 60.5 cm (18 1/8 x 23 13/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287342,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.364.36,false,true,287343,Photographs,Photograph,"Tarascon, Château",,,,,,Artist,,Édouard Baldus,"French, born Prussia, 1813–1889",,"Baldus, Édouard","French, born Prussia",1813,1889,ca. 1862,1861,1864,Albumen silver print from glass negative,Image: 21.6 x 28.3 cm (8 1/2 x 11 1/8 in.) Mount: 46 x 60.5 cm (18 1/8 x 23 13/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287343,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.364.37,false,true,287344,Photographs,Photograph,Maison Carrée à Nîmes,,,,,,Artist,,Édouard Baldus,"French, born Prussia, 1813–1889",,"Baldus, Édouard","French, born Prussia",1813,1889,1853,1853,1853,Albumen silver print from paper negative,Image: 33 x 44 cm (13 x 17 5/16 in.) Mount: 46 x 60.5 cm (18 1/8 x 23 13/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287344,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.364.38,false,true,287345,Photographs,Photograph,"Nîmes, Amphithéâtre",,,,,,Artist,,Édouard Baldus,"French, born Prussia, 1813–1889",,"Baldus, Édouard","French, born Prussia",1813,1889,ca. 1861,1860,1862,Albumen silver print from glass negative,Image: 32 x 42.4 cm (12 5/8 x 16 11/16 in.) Mount: 46 x 60.5 cm (18 1/8 x 23 13/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287345,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.364.39,false,true,287346,Photographs,Photograph,"Nîmes, Fontaine",,,,,,Artist,,Édouard Baldus,"French, born Prussia, 1813–1889",,"Baldus, Édouard","French, born Prussia",1813,1889,ca. 1861,1860,1862,Albumen silver print from glass negative,Image: 43.2 x 34 cm (17 x 13 3/8 in.) Mount: 60.5 x 46 cm (23 13/16 x 18 1/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287346,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.364.40,false,true,287347,Photographs,Photograph,"Nîmes, Temple de Diane",,,,,,Artist,,Édouard Baldus,"French, born Prussia, 1813–1889",,"Baldus, Édouard","French, born Prussia",1813,1889,ca. 1862,1861,1864,Albumen silver print from paper negative,Image: 20.9 x 29.8 cm (8 1/4 x 11 3/4 in.) Mount: 46 x 60.5 cm (18 1/8 x 23 13/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287347,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.364.41,false,true,285452,Photographs,Photograph,"Nîmes, Tour Magne",,,,,,Artist,,Édouard Baldus,"French, born Prussia, 1813–1889",,"Baldus, Édouard","French, born Prussia",1813,1889,1853,1853,1853,Albumen silver print from paper negative,Image: 42.9 x 33 cm (16 7/8 x 13 in.) Mount: 60.5 x 46 cm (23 13/16 x 18 1/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285452,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.364.42,false,true,287348,Photographs,Photograph,"Nîmes, Porte d'Auguste",,,,,,Artist,,Édouard Baldus,"French, born Prussia, 1813–1889",,"Baldus, Édouard","French, born Prussia",1813,1889,ca. 1864,1863,1865,Albumen silver print from glass negative,Image: 21.6 x 28.1 cm (8 1/2 x 11 1/16 in.) Mount: 46 x 60.5 cm (18 1/8 x 23 13/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287348,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.364.43,false,true,287349,Photographs,Photograph,Aigues-Mortes,,,,,,Artist,,Édouard Baldus,"French, born Prussia, 1813–1889",,"Baldus, Édouard","French, born Prussia",1813,1889,ca. 1859,1856,1862,Albumen silver print from glass negative,Image: 29.6 x 43.2 cm (11 5/8 x 17 in.) Mount: 46 x 60.5 cm (18 1/8 x 23 13/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287349,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.364.44,false,true,287350,Photographs,Photograph,St. Gilles,,,,,,Artist,,Édouard Baldus,"French, born Prussia, 1813–1889",,"Baldus, Édouard","French, born Prussia",1813,1889,1853,1853,1853,Albumen silver print from paper negative,Image: 34.5 x 42.9 cm (13 9/16 x 16 7/8 in.) Mount: 46 x 60.5 cm (18 1/8 x 23 13/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287350,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.364.45,false,true,287351,Photographs,Photograph,"Arles, St. Trophime",,,,,,Artist,,Édouard Baldus,"French, born Prussia, 1813–1889",,"Baldus, Édouard","French, born Prussia",1813,1889,1861 or after,1861,1862,Albumen silver print from glass negative,Image: 42.6 x 33.8 cm (16 3/4 x 13 5/16 in.) Mount: 60.5 x 46 cm (23 13/16 x 18 1/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287351,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.364.46,false,true,283136,Photographs,Photograph,"Arles, Cloitre St. Trophime",,,,,,Artist,,Édouard Baldus,"French, born Prussia, 1813–1889",,"Baldus, Édouard","French, born Prussia",1813,1889,ca. 1861,1860,1862,Albumen silver print from glass negative,Image: 33.2 x 42.5 cm (13 1/16 x 16 3/4 in.) Mount: 46 x 60.5 cm (18 1/8 x 23 13/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283136,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.364.47,false,true,287352,Photographs,Photograph,"Arles, Cloitre St. Trophime",,,,,,Artist,,Édouard Baldus,"French, born Prussia, 1813–1889",,"Baldus, Édouard","French, born Prussia",1813,1889,ca. 1861,1860,1862,Albumen silver print from glass negative,Image: 34 x 42.7 cm (13 3/8 x 16 13/16 in.) Mount: 46 x 60.5 cm (18 1/8 x 23 13/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287352,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.364.48,false,true,287353,Photographs,Photograph,"Arles, Amphithéâtre",,,,,,Artist,,Édouard Baldus,"French, born Prussia, 1813–1889",,"Baldus, Édouard","French, born Prussia",1813,1889,"before June 1, 1860",1858,1860,Albumen silver print from glass negative?,Image: 33.8 x 43.2 cm (13 5/16 x 17 in.) Mount: 46 x 60.5 cm (18 1/8 x 23 13/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287353,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.364.49,false,true,287354,Photographs,Photograph,"Arles, Amphithéâtre",,,,,,Artist,,Édouard Baldus,"French, born Prussia, 1813–1889",,"Baldus, Édouard","French, born Prussia",1813,1889,1860 or earlier,1857,1860,Albumen silver print from paper negative,Image: 32.3 x 42.3 cm (12 11/16 x 16 5/8 in.) Mount: 46 x 60.5 cm (18 1/8 x 23 13/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287354,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.364.50,false,true,287355,Photographs,Photograph,Théâtre Romain à Arles,,,,,,Artist,,Édouard Baldus,"French, born Prussia, 1813–1889",,"Baldus, Édouard","French, born Prussia",1813,1889,1860 or earlier,1857,1860,Albumen silver print from paper negative,Image: 33.5 x 42.4 cm (13 3/16 x 16 11/16 in.) Mount: 46 x 60.5 cm (18 1/8 x 23 13/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287355,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.364.51,false,true,287356,Photographs,Photograph,Montmajour,,,,,,Artist,,Édouard Baldus,"French, born Prussia, 1813–1889",,"Baldus, Édouard","French, born Prussia",1813,1889,ca. 1862,1861,1864,Albumen silver print from glass negative,Image: 19.6 x 28.1 cm (7 11/16 x 11 1/16 in.) Mount: 46 x 60.5 cm (18 1/8 x 23 13/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287356,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.364.52,false,true,287357,Photographs,Photograph,Viaduc de St. Chamas,,,,,,Artist,,Édouard Baldus,"French, born Prussia, 1813–1889",,"Baldus, Édouard","French, born Prussia",1813,1889,before 1859,1856,1859,Albumen silver print from paper negative,Image: 36 x 54.3 cm (14 3/16 x 21 3/8 in.) Mount: 46 x 60.5 cm (18 1/8 x 23 13/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287357,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.364.53,false,true,287358,Photographs,Photograph,Roquefavour,,,,,,Artist,,Édouard Baldus,"French, born Prussia, 1813–1889",,"Baldus, Édouard","French, born Prussia",1813,1889,ca. 1861,1860,1862,Albumen silver print from paper negative,Image: 33.6 x 42.7 cm (13 1/4 x 16 13/16 in.) Mount: 46 x 60.5 cm (18 1/8 x 23 13/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287358,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.364.54,false,true,287359,Photographs,Photograph,Souterrain de la Nerthe,,,,,,Artist,,Édouard Baldus,"French, born Prussia, 1813–1889",,"Baldus, Édouard","French, born Prussia",1813,1889,ca. 1861,1860,1862,Albumen silver print from paper negative,Image: 32.5 x 43.4 cm (12 13/16 x 17 1/16 in.) Mount: 46 x 60.5 cm (18 1/8 x 23 13/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287359,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.364.55,false,true,287360,Photographs,Photograph,Marseille,,,,,,Artist,,Édouard Baldus,"French, born Prussia, 1813–1889",,"Baldus, Édouard","French, born Prussia",1813,1889,late 1850s (?),1856,1860,Albumen silver print from paper negative,Image: 30.1 x 40.7 cm (11 7/8 x 16 in.) Mount: 46 x 60.5 cm (18 1/8 x 23 13/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287360,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.364.56,false,true,287361,Photographs,Photograph,Marseille,,,,,,Artist,,Édouard Baldus,"French, born Prussia, 1813–1889",,"Baldus, Édouard","French, born Prussia",1813,1889,late 1850s (?),1856,1860,Albumen silver print from paper negative,Image: 32.6 x 42.7 cm (12 13/16 x 16 13/16 in.) Mount: 46 x 60.5 cm (18 1/8 x 23 13/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287361,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.364.57,false,true,287362,Photographs,Photograph,Marseille,,,,,,Artist,,Édouard Baldus,"French, born Prussia, 1813–1889",,"Baldus, Édouard","French, born Prussia",1813,1889,ca. 1860,1859,1861,Albumen silver print from glass negative,Image: 27.6 x 42.9 cm (10 7/8 x 16 7/8 in.) Mount: 46 x 60.5 cm (18 1/8 x 23 13/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287362,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.364.58,false,true,287363,Photographs,Photograph,Marseille,,,,,,Artist,,Édouard Baldus,"French, born Prussia, 1813–1889",,"Baldus, Édouard","French, born Prussia",1813,1889,ca. 1861,1860,1862,Albumen silver print from glass negative,Image: 27.1 x 42.5 cm (10 11/16 x 16 3/4 in.) Mount: 46 x 60.5 cm (18 1/8 x 23 13/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287363,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.364.59,false,true,287364,Photographs,Photograph,Marseille,,,,,,Artist,,Édouard Baldus,"French, born Prussia, 1813–1889",,"Baldus, Édouard","French, born Prussia",1813,1889,ca. 1861,1860,1862,Albumen silver print from glass negative,Image: 26.7 x 40 cm (10 1/2 x 15 3/4 in.) Mount: 46 x 60.5 cm (18 1/8 x 23 13/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287364,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.364.60,false,true,287365,Photographs,Photograph,Marseille,,,,,,Artist,,Édouard Baldus,"French, born Prussia, 1813–1889",,"Baldus, Édouard","French, born Prussia",1813,1889,ca. 1861,1860,1862,Albumen silver print from glass negative,Image: 27.2 x 41.8 cm (10 11/16 x 16 7/16 in.) Mount: 46 x 60.5 cm (18 1/8 x 23 13/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287365,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.364.62,false,true,287367,Photographs,Photograph,La Ciotat,,,,,,Artist,,Édouard Baldus,"French, born Prussia, 1813–1889",,"Baldus, Édouard","French, born Prussia",1813,1889,ca. 1860,1859,1861,Albumen silver print from paper negative,Image: 32.4 x 43 cm (12 3/4 x 16 15/16 in.) Mount: 46 x 60.5 cm (18 1/8 x 23 13/16 in.) Mount: 55.9 x 71.1 cm (22 x 28 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287367,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.364.63,false,true,287368,Photographs,Photograph,"La Ciotat, Bec de l'Aigle",,,,,,Artist,,Édouard Baldus,"French, born Prussia, 1813–1889",,"Baldus, Édouard","French, born Prussia",1813,1889,ca. 1861,1860,1862,Albumen silver print from paper negative,Image: 32.1 x 42.3 cm (12 5/8 x 16 5/8 in.) Mount: 46 x 60.5 cm (18 1/8 x 23 13/16 in.) Mat: 55.9 x 71.1 cm (22 x 28 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287368,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.364.64,false,true,287283,Photographs,Photograph,Le Moine,,,,,,Artist,,Édouard Baldus,"French, born Prussia, 1813–1889",,"Baldus, Édouard","French, born Prussia",1813,1889,ca. 1861,1860,1862,Albumen silver print from paper negative,Image: 33.7 x 42.8 cm (13 1/4 x 16 7/8 in.) Mount: 46 x 60.5 cm (18 1/8 x 23 13/16 in.) Mat: 55.9 x 71.1 cm (22 x 28 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287283,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.364.65,false,true,287369,Photographs,Photograph,Bandol,,,,,,Artist,,Édouard Baldus,"French, born Prussia, 1813–1889",,"Baldus, Édouard","French, born Prussia",1813,1889,ca. 1860,1859,1861,Albumen silver print from paper negative?,Image: 33.1 x 42.9 cm (13 1/16 x 16 7/8 in.) Mount: 46 x 60.5 cm (18 1/8 x 23 13/16 in.) Mat: 55.9 x 71.1 cm (22 x 28 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287369,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.364.66,false,true,287370,Photographs,Photograph,Viaduc de Bandol,,,,,,Artist,,Édouard Baldus,"French, born Prussia, 1813–1889",,"Baldus, Édouard","French, born Prussia",1813,1889,1859 or after,1859,1862,Albumen silver print from paper negative,Image: 26.5 x 43 cm (10 7/16 x 16 15/16 in.) Mount: 46 x 60.5 cm (18 1/8 x 23 13/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287370,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.364.67,false,true,287371,Photographs,Photograph,St. Nazaire,,,,,,Artist,,Édouard Baldus,"French, born Prussia, 1813–1889",,"Baldus, Édouard","French, born Prussia",1813,1889,1859 or after,1859,1862,Albumen silver print from paper negative,Image: 32 x 43 cm (12 5/8 x 16 15/16 in.) Mount: 46 x 60.5 cm (18 1/8 x 23 13/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287371,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.364.68,false,true,287372,Photographs,Photograph,Gorges d'Ollioules,,,,,,Artist,,Édouard Baldus,"French, born Prussia, 1813–1889",,"Baldus, Édouard","French, born Prussia",1813,1889,ca. 1860,1859,1861,Albumen silver print from paper negative?,Image: 32.5 x 42 cm (12 13/16 x 16 9/16 in.) Mount: 46 x 60.5 cm (18 1/8 x 23 13/16 in.) Mat: 55.9 x 71.1 cm (22 x 28 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287372,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.364.69,false,true,285453,Photographs,Photograph,Toulon,,,,,,Artist,,Édouard Baldus,"French, born Prussia, 1813–1889",,"Baldus, Édouard","French, born Prussia",1813,1889,ca. 1861,1860,1862,Albumen silver print from glass negative,Image: 30.7 x 43.6 cm (12 1/16 x 17 3/16 in.) Mount: 46 x 60.5 cm (18 1/8 x 23 13/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285453,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.364.16.1-.2,false,true,287324,Photographs,Panorama,La Voulte,,,,,,Artist,,Édouard Baldus,"French, born Prussia, 1813–1889",,"Baldus, Édouard","French, born Prussia",1813,1889,1861,1861,1861,Albumen silver print from glass negative,Image: 26.3 x 41.7 cm (10 3/8 x 16 7/16 in.) left Image: 26.3 x 41.4 cm (10 3/8 x 16 5/16 in.) right Mount: 46 x 121 cm (18 1/8 x 47 5/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287324,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.364.61.1-.2,false,true,287366,Photographs,Panorama,Marseille,,,,,,Artist,,Édouard Baldus,"French, born Prussia, 1813–1889",,"Baldus, Édouard","French, born Prussia",1813,1889,ca. 1861,1860,1862,Albumen silver print from glass negative,Image: 29.3 x 40.8 cm (11 9/16 x 16 1/16 in.) left Image: 29.3 x 40.8 cm (11 9/16 x 16 1/16 in.) right Mount: 46 x 121 cm (18 1/8 x 47 5/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287366,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -52.658.1,false,true,269641,Photographs,Photograph,[Young Male Nude Seated on Leopard Skin],,,,,,Artist,,Guglielmo Plüshow,Italian (born Germany) 1852–1930,,"Plüshow, Guglielmo","Italian, born Germany",1852,1930,1890s–1900s,1890,1909,Albumen silver print from glass negative,22.2 x 16.2 cm. (8 3/4 x 6 3/8 in.),Museum Accession,,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269641,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2012.111,false,true,299472,Photographs,Photographs,"[Plaster Casts of Bodies, Pompeii]",,,,,,Artist,,Giorgio Sommer,"Italian, born Germany, 1834–1914",,"Sommer, Giorgio","Italian, born Germany",1834,1914,ca. 1875,1870,1880,Albumen print from glass negative,Image: 27.3 x 38.4 cm (10 3/4 x 15 1/8 in.) Sheet: 27.8 x 38.4 cm (10 15/16 x 15 1/8 in.) Mount: 47.8 x 55.8 cm (18 13/16 x 21 15/16 in.),"Purchase, Harriet Ames Charitable Trust Gift, 2012",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/299472,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -52.658.2,false,true,269642,Photographs,Photograph,[Reclining Male Nude Beside Vase],,,,,,Artist,,Wilhelm von Gloeden,"Italian, born Germany, 1886–1931",,"Gloeden, Wilhelm von","Italian, born Germany",1886,1931,1890s–1900s,1890,1909,Albumen silver print from glass negative,Image: 16.4 x 23.2cm (6 7/16 x 9 1/8in.) Mount: 27.9 x 35 cm (11 x 13 3/4 in.),Museum Accession,,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269642,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1983.1201.1,false,true,263857,Photographs,Photograph,"[Young Girl with Flowers, Sicily, Italy]",,,,,,Artist,,Wilhelm von Gloeden,"Italian, born Germany, 1886–1931",,"Gloeden, Wilhelm von","Italian, born Germany",1886,1931,1903,1903,1903,Albumen silver print from glass negative,21.9 x 16.9 cm. (8 5/8 x 6 5/8 in.),"Gift of Milton Radutzky, 1983",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/263857,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1983.1201.2,false,true,263859,Photographs,Photograph,"[Elderly Man and Young Boy at Garden Fountain, Sicily, Italy]",,,,,,Artist,,Wilhelm von Gloeden,"Italian, born Germany, 1886–1931",,"Gloeden, Wilhelm von","Italian, born Germany",1886,1931,1902,1902,1902,Albumen silver print from glass negative,16.9 x 22.6 cm. (6 5/8 x 8 7/8 in.),"Gift of Milton Radutzky, 1983",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/263859,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1983.1201.3,false,true,263860,Photographs,Photograph,"[Young Woman, Sicily, Italy]",,,,,,Artist,,Wilhelm von Gloeden,"Italian, born Germany, 1886–1931",,"Gloeden, Wilhelm von","Italian, born Germany",1886,1931,1890s–1900s,1890,1909,Albumen silver print from glass negative,22.1 x 16.6 cm. (8 11/16 x 6 9/16 in.),"Gift of Milton Radutzky, 1983",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/263860,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1983.1201.4,false,true,263861,Photographs,Photograph,"[Young Man in White Robe and Head Gear Holding Scabbard, Sicily, Italy]",,,,,,Artist,,Wilhelm von Gloeden,"Italian, born Germany, 1886–1931",,"Gloeden, Wilhelm von","Italian, born Germany",1886,1931,1890s–1900s,1890,1909,Albumen silver print from glass negative,22.3 x 16.5 cm. (8 3/4 x 6 1/2 in.),"Gift of Milton Radutzky, 1983",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/263861,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1983.1201.5,false,true,263862,Photographs,Photograph,"[Young Girl in Checked Dress with Roses, Sicily, Italy]",,,,,,Artist,,Wilhelm von Gloeden,"Italian, born Germany, 1886–1931",,"Gloeden, Wilhelm von","Italian, born Germany",1886,1931,1899,1899,1899,Albumen silver print from glass negative,22.5 x 17.0 cm. (8 7/8 x 6 11/16 in.),"Gift of Milton Radutzky, 1983",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/263862,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1983.1201.6,false,true,263863,Photographs,Photograph,"[Nude Young Child with Dog in Lap, Sicily, Italy]",,,,,,Artist,,Wilhelm von Gloeden,"Italian, born Germany, 1886–1931",,"Gloeden, Wilhelm von","Italian, born Germany",1886,1931,1890s–1900s,1890,1909,Albumen silver print from glass negative,22.5 x 16.5 cm. (8 7/8 x 6 1/2 in.),"Gift of Milton Radutzky, 1983",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/263863,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1983.1201.7,false,true,263864,Photographs,Photograph,"[Young Girl [?] with Cloak of Cloth Over Head, Sicily, Italy]",,,,,,Artist,,Wilhelm von Gloeden,"Italian, born Germany, 1886–1931",,"Gloeden, Wilhelm von","Italian, born Germany",1886,1931,1906,1906,1906,Albumen silver print from glass negative,21.8 x 16.5 cm. (8 9/16 x 6 1/2 in.),"Gift of Milton Radutzky, 1983",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/263864,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1983.1201.8,false,true,263865,Photographs,Photograph,"Mico Lo Giudice-Berbiredolu ""Il mago del mandolino""",,,,,,Artist,,Wilhelm von Gloeden,"Italian, born Germany, 1886–1931",,"Gloeden, Wilhelm von","Italian, born Germany",1886,1931,1890s–1900s,1890,1909,Albumen silver print from glass negative,22.3 x 16.9 cm. (8 3/4 x 6 5/8 in.),"Gift of Milton Radutzky, 1983",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/263865,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1983.1201.9,false,true,263866,Photographs,Photograph,"[Young Girl Wrapped in Cloth, Sicily, Italy]",,,,,,Artist,,Wilhelm von Gloeden,"Italian, born Germany, 1886–1931",,"Gloeden, Wilhelm von","Italian, born Germany",1886,1931,1890s–1900s,1890,1909,Albumen silver print from glass negative,21.7 x 16.3 cm. (8 9/16 x 6 7/16 in.),"Gift of Milton Radutzky, 1983",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/263866,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1983.1201.10,false,true,263858,Photographs,Photograph,"[Nude Study: Woman from Behind, Young Man, Sicily, Italy]",,,,,,Artist,,Wilhelm von Gloeden,"Italian, born Germany, 1886–1931",,"Gloeden, Wilhelm von","Italian, born Germany",1886,1931,1890s–1900s,1890,1909,Albumen silver print from glass negative,21.9 x 16.6 cm. (8 5/8 x 6 9/16 in.),"Gift of Milton Radutzky, 1983",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/263858,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1138,false,true,286064,Photographs,Photograph,[Two Children],,,,,,Artist,,Wilhelm von Gloeden,"Italian, born Germany, 1886–1931",,"Gloeden, Wilhelm von","Italian, born Germany",1886,1931,ca. 1900,1898,1902,Gelatin silver print,Image: 14 5/16 × 10 11/16 in. (36.4 × 27.2 cm),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286064,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1166,false,true,286608,Photographs,Photograph,[Man],,,,,,Artist,,Wilhelm von Gloeden,"Italian, born Germany, 1886–1931",,"Gloeden, Wilhelm von","Italian, born Germany",1886,1931,ca. 1900,1898,1902,Gelatin silver print,8 3/4 x 6 2/3,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286608,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.582,false,true,286232,Photographs,Photograph,"Tadeus Langier, Zakopane",,,,,,Artist,,Stanislaw Ignacy Witkiewicz,"Polish, Warsaw 1885–1939 Jeziory",,"Witkiewicz, Stanislaw Ignacy",Polish,1885,1939,1912–13,1912,1913,Gelatin silver print,Image: 12.6 x 17.6 cm (4 15/16 x 6 15/16 in.) Mount: 17.4 x 23.5 cm (6 7/8 x 9 1/4 in.),"Gilman Collection, Purchase, Denise and Andrew Saul Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286232,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.896,false,true,286234,Photographs,Photograph,"Jadwiga Janczewska, Zakopane",,,,,,Artist,,Stanislaw Ignacy Witkiewicz,"Polish, Warsaw 1885–1939 Jeziory",,"Witkiewicz, Stanislaw Ignacy",Polish,1885,1939,ca. 1913,1912,1914,Gelatin silver print,Image: 12.5 x 17.4 cm (4 15/16 x 6 7/8 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286234,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.899,false,true,286054,Photographs,Photograph,"[Self-Portrait, ""Collapse, with Lamp"", Zakopane]",,,,,,Artist,,Stanislaw Ignacy Witkiewicz,"Polish, Warsaw 1885–1939 Jeziory",,"Witkiewicz, Stanislaw Ignacy",Polish,1885,1939,ca. 1913,1912,1914,Gelatin silver print,Image: 13 x 18.1 cm (5 1/8 x 7 1/8 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286054,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.867,false,true,286118,Photographs,Photograph,Plan of the Socialist Offensive,,,,,,Artist,,Gustav Klutsis,"Russian, Latvia 1895–1938 Moscow",,"Klutsis, Gustav",Russian,1895,1938,1929–30,1929,1930,Gelatin silver print,Image: 7 3/8 × 5 1/2 in. (18.7 × 13.9 cm) Mount: 11 5/8 in. × 8 3/8 in. (29.6 × 21.2 cm),"Gilman Collection, Purchase, Denise and Andrew Saul Gift and Anonymous Gifts, by exchange, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286118,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2001.678,false,true,284734,Photographs,Photograph,"[Government Troops Firing on Demonstrators, Corner of Nevsky Prospect and Sadovaya Street, St. Petersburg, Russia]",,,,,,Artist,,Karl Karlovich Bulla,"Russian, born Germany, 1853–1929",,"Bulla, Karl Karlovich","Russian, born Germany",1853,1929,"July 4, 1917",1917,1917,Gelatin silver print,12.2 x 21.5cm (4 13/16 x 8 7/16 in.),"Purchase, Jennifer and Joseph Duke Gift, 2001",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/284734,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.82,false,true,283182,Photographs,Photograph,"Kno-Shr, Kansas Chief",,,,,,Artist,,John H. Fitzgibbon,"American, born Britain, 1816–1882",,"Fitzgibbon, John H.","American, born Britain",1816,1882,1853,1853,1853,Daguerreotype,Image: 17.9 x 14.8 cm (7 1/16 x 5 13/16 in.),"Gilman Collection, Purchase, Alfred Stieglitz Society Gifts, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283182,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1991.1203,false,true,266499,Photographs,Photograph,"Sausalito from the N.P.C.R.R. Wharf, Looking South",,,,,,Artist,,Eadweard Muybridge,"American, born Britain, 1830–1904",,"Muybridge, Eadweard","American, born Britain",1830,1904,ca. 1868,1866,1870,Albumen silver print from glass negative,9.8 x 9.5cm (3 7/8 x 3 3/4in.) Mount: 22.8 x 17.6 cm (9 x 6 15/16 in.),"Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 1991",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/266499,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.780,false,true,286079,Photographs,Photograph,"Crater of Volcano, Quetzaltenango-Guatemala",,,,,,Artist,,Eadweard Muybridge,"American, born Britain, 1830–1904",,"Muybridge, Eadweard","American, born Britain",1830,1904,1875,1875,1875,Albumen silver print from glass negative,Image: 5 3/8 × 9 1/16 in. (13.7 × 23 cm),"Gilman Collection, Purchase, Sam Salz Foundation Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286079,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.781,false,true,286562,Photographs,Photograph,"Coffee Harvesting, Las Nubes-Guatemala",,,,,,Artist,,Eadweard Muybridge,"American, born Britain, 1830–1904",,"Muybridge, Eadweard","American, born Britain",1830,1904,1875,1875,1875,Albumen silver print from glass negative,Image: 5 3/8 × 9 1/8 in. (13.7 × 23.2 cm),"Gilman Collection, Purchase, Gift of Photography in the Fine Arts, by exchange, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286562,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.101,false,true,283211,Photographs,Photograph,"Mormon Emigrant Train, Echo Canyon",,,,,,Artist,,Charles William Carter,"American, born Britain, 1832–1918",,"Carter, Charles William","American, born Britain",1832,1918,ca. 1870,1868,1872,Albumen silver print from glass negative,Image: 6.1 × 10.3 cm (2 3/8 × 4 1/16 in.),"Gilman Collection, Purchase, Marlene Nathan Meyerson Family Foundation Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283211,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -X.677,false,true,271673,Photographs,Cabinet card,[Man with Side Whiskers],,,,,,Artist,,Frederick Gutekunst,"American, born Germany, 1832–1917",,"Gutekunst, Frederick","American, born Germany",1832,1917,1870s–80s,1870,1889,Albumen silver print from glass negative,,Museum Accession,,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271673,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1985.1080.3,false,true,264322,Photographs,Photograph,"[Girl with Ringlets, Half Length]",,,,,,Artist,,Frederick Gutekunst,"American, born Germany, 1832–1917",,"Gutekunst, Frederick","American, born Germany",1832,1917,1890s,1890,1899,Gelatin silver print,14.4 x 9.4 cm. (5 11/16 x 3 11/16 in.),"Museum Accession, 1985",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264322,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1985.1080.5,false,true,264324,Photographs,Photograph,"[Girl with Ringlets, Seated, Three-Quarter Length]",,,,,,Artist,,Frederick Gutekunst,"American, born Germany, 1832–1917",,"Gutekunst, Frederick","American, born Germany",1832,1917,1890s,1890,1899,Gelatin silver print,14.2 x 9.7 cm. (5 9/16 x 3 13/16 in.),"Museum Accession, 1985",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264324,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1985.1080.6,false,true,264325,Photographs,Photograph,[Girl in Walking Costume with Hat and Muff],,,,,,Artist,,Frederick Gutekunst,"American, born Germany, 1832–1917",,"Gutekunst, Frederick","American, born Germany",1832,1917,1890s,1890,1899,Gelatin silver print,14.1 x 8.8 cm. (5 9/16 x 3 7/16 in.),"Museum Accession, 1985",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264325,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1985.1080.15,false,true,264320,Photographs,Photograph,[Girl with White Off-the-Shoulder Dress],,,,,,Artist,,Frederick Gutekunst,"American, born Germany, 1832–1917",,"Gutekunst, Frederick","American, born Germany",1832,1917,1890s,1890,1899,Gelatin silver print,14.5 x 9.5 cm. (5 11/16 x 3 3/4 in.),"Museum Accession, 1985",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264320,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2005.100.753a, b",false,true,286587,Photographs,Photograph,Dr. Joseph Parrish and an Idiot,,,,,,Artist,,Frederick Gutekunst,"American, born Germany, 1832–1917",,"Gutekunst, Frederick","American, born Germany",1832,1917,ca. 1858,1856,1860,Albumen silver print from glass negative,Image: 7 5/16 × 4 3/4 in. (18.6 × 12 cm); (a) Image: 8 in. × 5 1/4 in. (20.3 × 13.3 cm); (b) Mount: 14 7/16 × 10 5/16 in. (36.6 × 26.2 cm),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286587,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.36,false,true,669901,Photographs,Photograph,Mouth of Wisconsin River,,,,,,Artist,,Henry P. Bosse,"American, born Germany, 1844–1893",,"Bosse, Henry P.","American, born Germany",1844,1893,1885,1885,1885,Cyanotype,"Image: 10 9/16 × 13 1/2 in. (26.8 × 34.3 cm), oval Sheet: 14 7/16 × 17 3/16 in. (36.7 × 43.7 cm)","Purchase, Acquisitions Fund and Mr. and Mrs. John A. Moran Gift, in memory of Louise Chisholm Moran, 2015",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/669901,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.38,false,true,669903,Photographs,Photograph,"Old Ponton Bridge at N. McGregor, Ia.",,,,,,Artist,,Henry P. Bosse,"American, born Germany, 1844–1893",,"Bosse, Henry P.","American, born Germany",1844,1893,1885,1885,1885,Cyanotype,"Image: 10 7/16 × 13 1/8 in. (26.5 × 33.4 cm), oval Sheet: 14 1/2 × 17 3/16 in. (36.8 × 43.7 cm)","Purchase, Acquisitions Fund and Mr. and Mrs. John A. Moran Gift, in memory of Louise Chisholm Moran, 2015",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/669903,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.39,false,true,669906,Photographs,Photograph,Pine Bend,,,,,,Artist,,Henry P. Bosse,"American, born Germany, 1844–1893",,"Bosse, Henry P.","American, born Germany",1844,1893,1885,1885,1885,Cyanotype,"Image: 10 9/16 × 13 1/2 in. (26.8 × 34.3 cm), oval Sheet: 14 7/16 × 17 3/16 in. (36.7 × 43.7 cm)","Purchase, Acquisitions Fund and Mr. and Mrs. John A. Moran Gift, in memory of Louise Chisholm Moran, 2015",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/669906,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2014.715.1,false,true,669900,Photographs,Photograph,"No. 34. From Bluffs at Merrimac, Minnesota Looking Down Stream",,,,,,Artist,,Henry P. Bosse,"American, born Germany, 1844–1893",,"Bosse, Henry P.","American, born Germany",1844,1893,1885,1885,1885,Cyanotype,Sheet: 14 1/2 × 17 3/16 in. (36.8 × 43.7 cm),"Gift of Charles Wehrenberg and Sally Larsen, 2014",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/669900,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2014.715.2,false,true,669904,Photographs,Photograph,"No. 6. From South Approach of Franklin Ave Bridge, Minneapolis, Minnesota Looking Up Stream (Low Water)",,,,,,Artist,,Henry P. Bosse,"American, born Germany, 1844–1893",,"Bosse, Henry P.","American, born Germany",1844,1893,1890,1890,1890,Cyanotype,Sheet: 14 1/2 × 17 3/16 in. (36.8 × 43.7 cm),"Gift of Charles Wehrenberg and Sally Larsen, 2014",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/669904,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2014.715.3,false,true,669905,Photographs,Photograph,No. 21. Rocks and Dam below Frenchmans Bar (Low Water),,,,,,Artist,,Henry P. Bosse,"American, born Germany, 1844–1893",,"Bosse, Henry P.","American, born Germany",1844,1893,1889,1889,1889,Cyanotype,Sheet: 14 1/2 × 17 3/16 in. (36.8 × 43.7 cm),"Gift of Charles Wehrenberg and Sally Larsen, 2014",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/669905,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2014.715.4,false,true,669907,Photographs,Photograph,"No. 69. Wabasha, Minnesota",,,,,,Artist,,Henry P. Bosse,"American, born Germany, 1844–1893",,"Bosse, Henry P.","American, born Germany",1844,1893,1889,1889,1889,Cyanotype,Sheet: 14 1/2 × 17 3/16 in. (36.8 × 43.7 cm),"Gift of Charles Wehrenberg and Sally Larsen, 2014",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/669907,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2014.715.5,false,true,669908,Photographs,Photograph,"No. 88. Wingdams below Winona, Minnesota",,,,,,Artist,,Henry P. Bosse,"American, born Germany, 1844–1893",,"Bosse, Henry P.","American, born Germany",1844,1893,1889,1889,1889,Cyanotype,Sheet: 14 1/2 × 17 3/16 in. (36.8 × 43.7 cm),"Gift of Charles Wehrenberg and Sally Larsen, 2014",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/669908,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2014.715.6,false,true,669909,Photographs,Photograph,"No. 90. From bluffs at Trempealueau, Wisconsin Looking Up Stream",,,,,,Artist,,Henry P. Bosse,"American, born Germany, 1844–1893",,"Bosse, Henry P.","American, born Germany",1844,1893,1885,1885,1885,Cyanotype,Sheet: 14 1/2 × 17 3/16 in. (36.8 × 43.7 cm),"Gift of Charles Wehrenberg and Sally Larsen, 2014",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/669909,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2014.715.7,false,true,669910,Photographs,Photograph,"No. 135. Iowa State Penitentiary - Fort Madison, Iowa",,,,,,Artist,,Henry P. Bosse,"American, born Germany, 1844–1893",,"Bosse, Henry P.","American, born Germany",1844,1893,1891,1891,1891,Cyanotype,Sheet: 14 1/2 × 17 3/16 in. (36.8 × 43.7 cm),"Gift of Charles Wehrenberg and Sally Larsen, 2014",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/669910,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2014.715.8,false,true,669911,Photographs,Photograph,"No. 139a. Head of Niota Chute with Closing Dam [near Fort Madison, Iowa]",,,,,,Artist,,Henry P. Bosse,"American, born Germany, 1844–1893",,"Bosse, Henry P.","American, born Germany",1844,1893,1885,1885,1885,Cyanotype,Sheet: 14 1/2 × 17 3/16 in. (36.8 × 43.7 cm),"Gift of Charles Wehrenberg and Sally Larsen, 2014",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/669911,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2014.715.9,false,true,669912,Photographs,Photograph,No. 155a. Lower Lock Des Moines Rapids Canal,,,,,,Artist,,Henry P. Bosse,"American, born Germany, 1844–1893",,"Bosse, Henry P.","American, born Germany",1844,1893,1891,1891,1891,Cyanotype,Sheet: 14 1/2 × 17 3/16 in. (36.8 × 43.7 cm),"Gift of Charles Wehrenberg and Sally Larsen, 2014",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/669912,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2014.715.10,false,true,669913,Photographs,Photograph,No. 167. Raftboat “David Bronson”,,,,,,Artist,,Henry P. Bosse,"American, born Germany, 1844–1893",,"Bosse, Henry P.","American, born Germany",1844,1893,1885,1885,1885,Cyanotype,Sheet: 14 1/2 × 17 3/16 in. (36.8 × 43.7 cm),"Gift of Charles Wehrenberg and Sally Larsen, 2014",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/669913,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2014.715.11,false,true,669914,Photographs,Photograph,"No. 181. Marshall Ave. Bridge, Minneapolis & St. Paul",,,,,,Artist,,Henry P. Bosse,"American, born Germany, 1844–1893",,"Bosse, Henry P.","American, born Germany",1844,1893,1889,1889,1889,Cyanotype,Sheet: 14 1/2 × 17 3/16 in. (36.8 × 43.7 cm),"Gift of Charles Wehrenberg and Sally Larsen, 2014",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/669914,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2014.715.12,false,true,669915,Photographs,Photograph,"No. 185. Chicago, Milwaukee & St. Paul Rail Road Bridge at Hasting, Minnesota",,,,,,Artist,,Henry P. Bosse,"American, born Germany, 1844–1893",,"Bosse, Henry P.","American, born Germany",1844,1893,1885,1885,1885,Cyanotype,Sheet: 14 1/2 × 17 3/16 in. (36.8 × 43.7 cm),"Gift of Charles Wehrenberg and Sally Larsen, 2014",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/669915,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2014.715.13,false,true,669916,Photographs,Photograph,"No. 186. Chicago, Burlington & Northern Rail Road Bridge Across Mouth of La Croix River",,,,,,Artist,,Henry P. Bosse,"American, born Germany, 1844–1893",,"Bosse, Henry P.","American, born Germany",1844,1893,1891,1891,1891,Cyanotype,Sheet: 14 1/2 × 17 3/16 in. (36.8 × 43.7 cm),"Gift of Charles Wehrenberg and Sally Larsen, 2014",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/669916,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2014.715.14,false,true,669917,Photographs,Photograph,"No. 193a. Old Ponton Bridge at Prairie du chien, Wisconsin",,,,,,Artist,,Henry P. Bosse,"American, born Germany, 1844–1893",,"Bosse, Henry P.","American, born Germany",1844,1893,1885,1885,1885,Cyanotype,Sheet: 14 1/2 × 17 3/16 in. (36.8 × 43.7 cm),"Gift of Charles Wehrenberg and Sally Larsen, 2014",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/669917,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2014.715.15,false,true,669918,Photographs,Photograph,"No. 199. Draw Span of Chicago & North Western Rail Road Bridge at Clinton, Iowa",,,,,,Artist,,Henry P. Bosse,"American, born Germany, 1844–1893",,"Bosse, Henry P.","American, born Germany",1844,1893,1885,1885,1885,Cyanotype,Sheet: 14 1/2 × 17 3/16 in. (36.8 × 43.7 cm),"Gift of Charles Wehrenberg and Sally Larsen, 2014",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/669918,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2014.715.17,false,true,669920,Photographs,Photograph,"No. 204. Iowa Central Railway Bridge at Keithsburg, Illinois",,,,,,Artist,,Henry P. Bosse,"American, born Germany, 1844–1893",,"Bosse, Henry P.","American, born Germany",1844,1893,1889,1889,1889,Cyanotype,Sheet: 14 1/2 × 17 3/16 in. (36.8 × 43.7 cm),"Gift of Charles Wehrenberg and Sally Larsen, 2014",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/669920,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2014.715.18,false,true,669921,Photographs,Photograph,"No. 207. Wabash Rail Road Bridge at Keokuk, Iowa",,,,,,Artist,,Henry P. Bosse,"American, born Germany, 1844–1893",,"Bosse, Henry P.","American, born Germany",1844,1893,1885,1885,1885,Cyanotype,Sheet: 14 1/2 × 17 3/16 in. (36.8 × 43.7 cm),"Gift of Charles Wehrenberg and Sally Larsen, 2014",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/669921,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1027,false,true,264640,Photographs,Photograph,"General Grant's Council of War, Massaponax Church, Virginia",,,,,,Artist,,Timothy H. O'Sullivan,"American, born Ireland, 1840–1882",,"O'Sullivan, Timothy H.","American, born Ireland",1840,1882,"May 21, 1864",1864,1864,Albumen silver print from glass negative,8.7 x 10.8cm (3 7/16 x 4 1/4in.) Mount: 10.8 x 16.6cm (4 1/4 x 6 9/16in.),"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1986",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264640,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1054.19,true,true,264711,Photographs,Photograph,"Black Cañon, From Camp 8, Looking Above",,,,,,Artist,,Timothy H. O'Sullivan,"American, born Ireland, 1840–1882",,"O'Sullivan, Timothy H.","American, born Ireland",1840,1882,1871,1871,1871,Albumen silver print from glass negative,20 x 28.1 cm (7 7/8 x 11 1/16 in. ),"Purchase, Joseph Pulitzer Bequest and The Horace W. Goldsmith Foundation Gift through Joyce and Robert Menschel, 1986",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264711,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.105,false,true,283217,Photographs,Photograph,"Fissure Vent at Steamboat Springs, Nevada",,,,,,Artist,,Timothy H. O'Sullivan,"American, born Ireland, 1840–1882",,"O'Sullivan, Timothy H.","American, born Ireland",1840,1882,1867,1867,1867,Albumen silver print from glass negative,Image: 22.3 x 29 cm (8 3/4 x 11 7/16 in.) Mat: 41.3 x 47.6 cm (16 1/4 x 18 3/4 in.),"Gilman Collection, Purchase, Marlene Nathan Meyerson Family Foundation Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283217,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1199,false,true,285642,Photographs,Photograph,"[Detachment of 50th N.Y. Volunteer Engineers, Pontoon Wagon and Saddle Boat]",,,,,,Artist,,Timothy H. O'Sullivan,"American, born Ireland, 1840–1882",,"O'Sullivan, Timothy H.","American, born Ireland",1840,1882,ca. 1864,1862,1866,Albumen silver print from glass negative,Image: 6 3/4 × 8 7/8 in. (17.1 × 22.5 cm) Mount: 12 in. × 17 13/16 in. (30.5 × 45.2 cm),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285642,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1200,false,true,285643,Photographs,Photograph,Major General Pleasanton and General Custer,,,,,,Artist,,Timothy H. O'Sullivan,"American, born Ireland, 1840–1882",,"O'Sullivan, Timothy H.","American, born Ireland",1840,1882,1863,1863,1863,Albumen silver print from glass negative,Mount: 11 9/16 × 15 13/16 in. (29.4 × 40.1 cm) Image: 6 13/16 × 8 7/8 in. (17.3 × 22.5 cm),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285643,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1203,false,true,285646,Photographs,Photograph,"Volcanic Islands in Mono Lake, California",,,,,,Artist,,Timothy H. O'Sullivan,"American, born Ireland, 1840–1882",,"O'Sullivan, Timothy H.","American, born Ireland",1840,1882,1868,1868,1868,Albumen silver print from glass negative,Image: 22.3 x 29.3 cm (8 3/4 x 11 9/16 in.) Mat: 40.6 x 50.8 cm (16 x 20 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285646,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1204,false,true,285647,Photographs,Photograph,"Desert Lake, near Ragtown, Nevada",,,,,,Artist,,Timothy H. O'Sullivan,"American, born Ireland, 1840–1882",,"O'Sullivan, Timothy H.","American, born Ireland",1840,1882,1867,1867,1867,Albumen silver print from glass negative,Image: 22.3 x 29.1 cm (8 3/4 x 11 7/16 in.) Mat: 40.6 x 50.8 cm (16 x 20 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285647,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1207,false,true,285785,Photographs,Photograph,"Tufa Rocks, Pyramid Lake, Nevada",,,,,,Artist,,Timothy H. O'Sullivan,"American, born Ireland, 1840–1882",,"O'Sullivan, Timothy H.","American, born Ireland",1840,1882,1867,1867,1867,Albumen silver print from glass negative,Image: 22.1 x 29.4 cm (8 11/16 x 11 9/16 in.) Mat: 47.9 x 61 cm (18 7/8 x 24 in.),"Gilman Collection, Purchase, Ann Tenenbaum and Thomas H. Lee Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285785,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1222,false,true,285767,Photographs,Photograph,Return of Commander Selfridge and his Reconnaissance Party from an Expedition in the Interior of Darien,,,,,,Artist,,Timothy H. O'Sullivan,"American, born Ireland, 1840–1882",,"O'Sullivan, Timothy H.","American, born Ireland",1840,1882,1870,1870,1870,Albumen silver print from glass negative,9 x 11 3/8 (irregular and torn),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285767,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.372.1,false,true,286782,Photographs,Photograph,Des nouvelles occupations en Styrie,,,,,,Artist,,Louis-Pierre-Théophile Dubois de Nehaut,"French, active Belgium, 1799–1872",,"Dubois de Nehaut, Louis-Pierre-Théophile",Belgian,1799,1872,1854–56,1854,1856,Salted paper print from paper negative,Image: 20.8 x 15.9 cm (8 3/16 x 6 1/4 in.) Mount: 33.8 x 44.9 cm (13 5/16 x 17 11/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286782,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.372.2,false,true,286781,Photographs,Photograph,The Undersigned Photographer as He Was Before 1848,,,,,,Artist,,Louis-Pierre-Théophile Dubois de Nehaut,"French, active Belgium, 1799–1872",,"Dubois de Nehaut, Louis-Pierre-Théophile",Belgian,1799,1872,1854–56,1854,1856,Salted paper print from paper negative,Image: 9 13/16 × 7 15/16 in. (25 × 20.1 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286781,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.372.3,false,true,287239,Photographs,Photograph,La nièce de Mr. Tahon propriétaire de la maison n° 7,,,,,,Artist,,Louis-Pierre-Théophile Dubois de Nehaut,"French, active Belgium, 1799–1872",,"Dubois de Nehaut, Louis-Pierre-Théophile",Belgian,1799,1872,1854–56,1854,1856,Salted paper print from paper negative,Image: 9 3/4 × 7 5/16 in. (24.7 × 18.6 cm) Sheet: 13 3/8 × 18 1/8 in. (34 × 46 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287239,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.372.4,false,true,286780,Photographs,Photograph,"An ""Enfant terrible"" (No One Can Do the Impossible)",,,,,,Artist,,Louis-Pierre-Théophile Dubois de Nehaut,"French, active Belgium, 1799–1872",,"Dubois de Nehaut, Louis-Pierre-Théophile",Belgian,1799,1872,1854–56,1854,1856,Albumen silver print from paper negative,Image: 8 11/16 × 6 1/2 in. (22.1 × 16.5 cm) Sheet: 13 3/8 × 18 1/8 in. (34 × 46 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286780,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.372.5,false,true,287240,Photographs,Photograph,Mlle. Jeanne tellement tremblante que le photographe ne peut pas fixer les yeux,,,,,,Artist,,Louis-Pierre-Théophile Dubois de Nehaut,"French, active Belgium, 1799–1872",,"Dubois de Nehaut, Louis-Pierre-Théophile",Belgian,1799,1872,1854–56,1854,1856,Salted paper print from paper negative,Image: 8 9/16 × 6 7/8 in. (21.8 × 17.5 cm) Sheet: 13 3/8 × 18 1/8 in. (34 × 46 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287240,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.372.6,false,true,287241,Photographs,Photograph,Autre nièce du propriétaire du no. 7,,,,,,Artist,,Louis-Pierre-Théophile Dubois de Nehaut,"French, active Belgium, 1799–1872",,"Dubois de Nehaut, Louis-Pierre-Théophile",Belgian,1799,1872,1854–56,1854,1856,Salted paper print from paper negative,Image: 9 5/16 × 7 1/2 in. (23.7 × 19.1 cm) Sheet: 13 3/8 × 18 1/8 in. (34 × 46 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287241,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.372.7,false,true,287242,Photographs,Photograph,Visite d'un collègue de Bruxelles,,,,,,Artist,,Louis-Pierre-Théophile Dubois de Nehaut,"French, active Belgium, 1799–1872",,"Dubois de Nehaut, Louis-Pierre-Théophile",Belgian,1799,1872,1854–56,1854,1856,Salted paper print from paper negative,Image: 9 5/16 × 7 1/2 in. (23.7 × 19.1 cm) Sheet: 13 3/8 × 18 1/8 in. (34 × 46 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287242,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.372.8,false,true,287243,Photographs,Photograph,La fille de mon ami de Lille,,,,,,Artist,,Louis-Pierre-Théophile Dubois de Nehaut,"French, active Belgium, 1799–1872",,"Dubois de Nehaut, Louis-Pierre-Théophile",Belgian,1799,1872,1856,1856,1856,Salted paper print from paper negative,Image: 8 3/4 × 7 1/16 in. (22.2 × 17.9 cm) Sheet: 13 3/8 × 18 1/8 in. (34 × 46 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287243,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.372.9,false,true,287244,Photographs,Photograph,[Elderly Lady Sitting],,,,,,Artist,,Louis-Pierre-Théophile Dubois de Nehaut,"French, active Belgium, 1799–1872",,"Dubois de Nehaut, Louis-Pierre-Théophile",Belgian,1799,1872,1854–56,1854,1856,Salted paper print from paper negative,Image: 8 3/4 × 7 1/16 in. (22.2 × 18 cm) Sheet: 13 3/8 × 18 1/8 in. (34 × 46 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287244,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.372.10,false,true,287245,Photographs,Photograph,[Elderly Lady Sitting],,,,,,Artist,,Louis-Pierre-Théophile Dubois de Nehaut,"French, active Belgium, 1799–1872",,"Dubois de Nehaut, Louis-Pierre-Théophile",Belgian,1799,1872,1854–56,1854,1856,Salted paper print from paper negative,Image: 9 5/16 × 6 3/4 in. (23.7 × 17.2 cm) Sheet: 13 3/8 × 18 1/8 in. (34 × 46 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287245,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.372.11,false,true,287246,Photographs,Photograph,Madame Gihoul,,,,,,Artist,,Louis-Pierre-Théophile Dubois de Nehaut,"French, active Belgium, 1799–1872",,"Dubois de Nehaut, Louis-Pierre-Théophile",Belgian,1799,1872,1854–56,1854,1856,Albumen silver print from paper negative,Image: 7 5/16 in. × 6 in. (18.6 × 15.2 cm) Sheet: 13 3/8 × 18 1/8 in. (34 × 46 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287246,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.372.12,false,true,286784,Photographs,Photograph,[Portrait of a Woman],,,,,,Artist,,Louis-Pierre-Théophile Dubois de Nehaut,"French, active Belgium, 1799–1872",,"Dubois de Nehaut, Louis-Pierre-Théophile",Belgian,1799,1872,1854–56,1854,1856,Salted paper print from glass negative,Image: 7 1/4 × 9 1/8 in. (18.4 × 23.2 cm) Sheet: 13 3/8 × 18 1/8 in. (34 × 46 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286784,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.372.13,false,true,287247,Photographs,Photograph,En soirée - janvier 1856,,,,,,Artist,,Louis-Pierre-Théophile Dubois de Nehaut,"French, active Belgium, 1799–1872",,"Dubois de Nehaut, Louis-Pierre-Théophile",Belgian,1799,1872,1856,1856,1856,Salted paper print from paper negative,Image: 9 3/8 × 6 13/16 in. (23.8 × 17.3 cm) Sheet: 13 3/8 × 18 1/8 in. (34 × 46 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287247,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.372.14,false,true,287248,Photographs,Photograph,Madame Gihoul,,,,,,Artist,,Louis-Pierre-Théophile Dubois de Nehaut,"French, active Belgium, 1799–1872",,"Dubois de Nehaut, Louis-Pierre-Théophile",Belgian,1799,1872,1854–56,1854,1856,Salted paper print from paper negative,Image: 10 in. × 7 5/8 in. (25.4 × 19.3 cm) Sheet: 13 3/8 × 18 1/8 in. (34 × 46 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287248,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.372.15,false,true,286783,Photographs,Photograph,[Women Stacking Carrots],,,,,,Artist,,Louis-Pierre-Théophile Dubois de Nehaut,"French, active Belgium, 1799–1872",,"Dubois de Nehaut, Louis-Pierre-Théophile",Belgian,1799,1872,1854–56,1854,1856,Salted paper print from paper negative,Image: 7 1/4 × 9 1/8 in. (18.4 × 23.2 cm) Sheet: 13 3/8 × 18 1/2 in. (34 × 47 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286783,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.372.16,false,true,287249,Photographs,Photograph,"Il vient à Bruxelles, voit un appartement place du Cologne no. 7",,,,,,Artist,,Louis-Pierre-Théophile Dubois de Nehaut,"French, active Belgium, 1799–1872",,"Dubois de Nehaut, Louis-Pierre-Théophile",Belgian,1799,1872,1854–56,1854,1856,Salted paper print from paper negative,Image: 9 5/16 × 8 5/16 in. (23.6 × 21.1 cm) Sheet: 13 3/8 × 18 1/2 in. (34 × 47 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287249,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.372.17,false,true,283129,Photographs,Photograph,View of the Square in Melting Snow,,,,,,Artist,,Louis-Pierre-Théophile Dubois de Nehaut,"French, active Belgium, 1799–1872",,"Dubois de Nehaut, Louis-Pierre-Théophile",Belgian,1799,1872,1854–56,1854,1856,Salted paper print from glass negative,Image: 18 x 22.2 cm (7 1/16 x 8 3/4 in. ) Sheet: 13 3/8 × 18 1/8 in. (34 × 46 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283129,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.372.18,false,true,287250,Photographs,Photograph,Vue de droite du balcon,,,,,,Artist,,Louis-Pierre-Théophile Dubois de Nehaut,"French, active Belgium, 1799–1872",,"Dubois de Nehaut, Louis-Pierre-Théophile",Belgian,1799,1872,1854–56,1854,1856,Salted paper print from paper negative,Image: 8 13/16 × 10 3/4 in. (22.4 × 27.3 cm) Sheet: 13 3/8 × 18 1/8 in. (34 × 46 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287250,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.372.19,false,true,287251,Photographs,Photograph,Vue de face du balcon avant l'entière construction de la place,,,,,,Artist,,Louis-Pierre-Théophile Dubois de Nehaut,"French, active Belgium, 1799–1872",,"Dubois de Nehaut, Louis-Pierre-Théophile",Belgian,1799,1872,1854–56,1854,1856,Albumen silver print from paper negative,Image: 9 3/8 × 12 13/16 in. (23.8 × 32.5 cm) Sheet: 13 3/8 × 18 1/8 in. (34 × 46 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287251,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.372.20,false,true,287252,Photographs,Photograph,La place pendant les fêtes de septembre,,,,,,Artist,,Louis-Pierre-Théophile Dubois de Nehaut,"French, active Belgium, 1799–1872",,"Dubois de Nehaut, Louis-Pierre-Théophile",Belgian,1799,1872,1854–56,1854,1856,Salted paper print from paper negative,Image: 10 5/8 × 9 3/16 in. (27 × 23.3 cm) Sheet: 13 3/8 × 18 1/8 in. (34 × 46 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287252,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.372.21,false,true,287253,Photographs,Photograph,Visite d'un ami de Lille,,,,,,Artist,,Louis-Pierre-Théophile Dubois de Nehaut,"French, active Belgium, 1799–1872",,"Dubois de Nehaut, Louis-Pierre-Théophile",Belgian,1799,1872,1854,1854,1854,Salted paper print from paper negative,Image: 6 1/16 × 7 1/16 in. (15.4 × 17.9 cm) Sheet: 13 3/8 × 18 1/8 in. (34 × 46 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287253,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.372.22,false,true,287254,Photographs,Photograph,"Station de Malines, Epreuve instantanée au passage d'un train au soleil couchant",,,,,,Artist,,Louis-Pierre-Théophile Dubois de Nehaut,"French, active Belgium, 1799–1872",,"Dubois de Nehaut, Louis-Pierre-Théophile",Belgian,1799,1872,1854–56,1854,1856,Salted paper print from paper negative,Image: 6 5/8 × 8 7/16 in. (16.8 × 21.4 cm) Sheet: 13 3/8 × 18 1/8 in. (34 × 46 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287254,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.372.24,false,true,287255,Photographs,Photograph,Jardin zoologique de Bruxelles,,,,,,Artist,,Louis-Pierre-Théophile Dubois de Nehaut,"French, active Belgium, 1799–1872",,"Dubois de Nehaut, Louis-Pierre-Théophile",Belgian,1799,1872,1854–56,1854,1856,Salted paper print from paper negative,Image: 8 11/16 × 10 13/16 in. (22.1 × 27.5 cm) Sheet: 13 3/8 × 18 1/8 in. (34 × 46 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287255,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.372.25,false,true,286899,Photographs,Photograph,Zoological Garden,,,,,,Artist,,Louis-Pierre-Théophile Dubois de Nehaut,"French, active Belgium, 1799–1872",,"Dubois de Nehaut, Louis-Pierre-Théophile",Belgian,1799,1872,1854–56,1854,1856,Salted paper print from paper negative,Image: 9 3/16 × 12 1/16 in. (23.4 × 30.6 cm) Sheet: 13 3/8 × 18 1/8 in. (34 × 46 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286899,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.372.26,false,true,287256,Photographs,Photograph,"[The Bear Enclave, Zoological Gardens, Brussels]",,,,,,Artist,,Louis-Pierre-Théophile Dubois de Nehaut,"French, active Belgium, 1799–1872",,"Dubois de Nehaut, Louis-Pierre-Théophile",Belgian,1799,1872,1854–56,1854,1856,Salted paper print from paper negative,Image: 5 13/16 × 7 3/16 in. (14.8 × 18.3 cm) Sheet: 13 3/8 × 18 1/8 in. (34 × 46 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287256,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.372.27,false,true,287257,Photographs,Photograph,"[The Kiosk, Zoological Gardens, Brussels]",,,,,,Artist,,Louis-Pierre-Théophile Dubois de Nehaut,"French, active Belgium, 1799–1872",,"Dubois de Nehaut, Louis-Pierre-Théophile",Belgian,1799,1872,1854–56,1854,1856,Salted paper print from paper negative,Image: 9 1/4 × 12 1/8 in. (23.5 × 30.8 cm) Sheet: 13 3/8 × 18 1/8 in. (34 × 46 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287257,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.372.28,false,true,286779,Photographs,Photograph,"The Zoological Garden, Brussels",,,,,,Artist,,Louis-Pierre-Théophile Dubois de Nehaut,"French, active Belgium, 1799–1872",,"Dubois de Nehaut, Louis-Pierre-Théophile",Belgian,1799,1872,1854,1854,1854,Salted paper print from paper negative,Image: 9 5/16 × 11 9/16 in. (23.7 × 29.4 cm) Sheet: 13 3/8 × 18 1/8 in. (34 × 46 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286779,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.372.29,false,true,287258,Photographs,Photograph,Jardin zoologique de Bruxelles; Les trois jumeaux fils de Mr. Lebens et toute la famille,,,,,,Artist,,Louis-Pierre-Théophile Dubois de Nehaut,"French, active Belgium, 1799–1872",,"Dubois de Nehaut, Louis-Pierre-Théophile",Belgian,1799,1872,1854–56,1854,1856,Salted paper print from paper negative,Image: 5 13/16 × 7 11/16 in. (14.7 × 19.5 cm) Sheet: 13 3/8 × 18 1/8 in. (34 × 46 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287258,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.372.30,false,true,287259,Photographs,Photograph,"[Heron Pond, Zoological Gardens, Brussels]",,,,,,Artist,,Louis-Pierre-Théophile Dubois de Nehaut,"French, active Belgium, 1799–1872",,"Dubois de Nehaut, Louis-Pierre-Théophile",Belgian,1799,1872,1854–56,1854,1856,Salted paper print from paper negative,Image: 18 × 21.2 cm (7 1/16 × 8 3/8 in.) Sheet: 13 3/8 × 18 1/8 in. (34 × 46 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287259,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.372.31,false,true,287260,Photographs,Photograph,"[The Pelicans and Greenhouses, Zoological Gardens, Brussels]",,,,,,Artist,,Louis-Pierre-Théophile Dubois de Nehaut,"French, active Belgium, 1799–1872",,"Dubois de Nehaut, Louis-Pierre-Théophile",Belgian,1799,1872,1854–56,1854,1856,Salted paper print from paper negative,Image: 14.6 × 19.5 cm (5 3/4 × 7 11/16 in.) Sheet: 13 3/8 × 18 1/8 in. (34 × 46 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287260,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.372.32,false,true,283127,Photographs,Photograph,Another Impossible Task,,,,,,Artist,,Louis-Pierre-Théophile Dubois de Nehaut,"French, active Belgium, 1799–1872",,"Dubois de Nehaut, Louis-Pierre-Théophile",Belgian,1799,1872,1854,1854,1854,Salted paper print from glass negative,Image: 16.3 x 21.2 cm (6 7/16 x 8 3/8 in.) Sheet: 13 3/8 × 18 1/8 in. (34 × 46 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283127,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1987.1100.62,false,true,265589,Photographs,Multiple exposure; Photograph,In the Studio,,,,,,Artist,,El Lissitzky,"Russian, Pochinok 1890–1941 Moscow",,"Lissitzky, El",Russian,1890,1941,1923,1923,1923,Gelatin silver print,10.9 x 8.3 cm (4 5/16 x 3 1/4 in.),"Ford Motor Company Collection, Gift of Ford Motor Company and John C. Waddell, 1987",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/265589,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.151,false,true,283289,Photographs,Photograph,[Self-Portrait],,,,,,Artist,,El Lissitzky,"Russian, Pochinok 1890–1941 Moscow",,"Lissitzky, El",Russian,1890,1941,1924–25,1924,1925,Gelatin silver print,Image: 17.3 x 12.1 cm (6 13/16 x 4 3/4 in.),"Gilman Collection, Purchase, Denise and Andrew Saul Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283289,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2012.110,false,true,299469,Photographs,Photographs,[Man in Chainmail Tunic Posing as a Dying Soldier],,,,,,Artist,,Adrien Constant de Rebecque,"Swiss, Lausanne 1806–1876 Lausanne",,"Constant de Rebecque, Adrien",Swiss,1806,1876,ca. 1863,1858,1868,Albumen print from collodion glass negative,Image: 17.9 x 24.2 cm (7 1/16 x 9 1/2 in.) Mount: 27.2 x 37 cm (10 11/16 x 14 9/16 in.),"Gilman Collection, Purchase, The Howard Gilman Foundation Gift, 2012",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/299469,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1985.1168.2,false,true,264528,Photographs,Photograph,"[A Gypsy Dancing-Girl, Kathiawar]",,,,,,Artist,,E. Taurines,"probably French, active ca. 1885–1901",,"Taurines, E.",French ?,1885,1901,ca. 1915,1915,1915,Albumen silver print from glass negative,23.5 x 18.2 cm (9 1/4 x 7 3/16 in.),"Gift of Matthew Dontzin, 1985",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264528,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.400.67,false,true,291820,Photographs,Daguerreotype,[Middle-aged Man with Glasses Holding Pocket Watch],,,,,,Artist,,Antoine-François-Jean Claudet,"French, active Great Britain, 1797–1867",,"Claudet, Antione-François-Jean","French, active Great Britain",1797,1867,1844–1859,1844,1859,Daguerreotype,Image: 11.3 x 8.7 cm (4 7/16 x 3 7/16 in.) Plate: 14.6 x 12.2 cm (5 3/4 x 4 13/16 in.) Case: 2.2 x 17.8 x 15.2 cm (7/8 x 7 x 6 in.),"Bequest of Herbert Mitchell, 2008",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/291820,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2015.400.86a, b",false,true,291839,Photographs,Daguerreotype,"[Stereograph, Two Children Standing Between Furniture in a Studio Parlor Setting]",,,,,,Artist,,Antoine-François-Jean Claudet,"French, active Great Britain, 1797–1867",,"Claudet, Antione-François-Jean","French, active Great Britain",1797,1867,ca. 1855,1853,1857,Daguerreotype,"Image: 6.8 x 5.7 cm (2 11/16 x 2 1/4 in.), each Mount: 8.4 x 17.6 cm (3 5/16 x 6 15/16 in.)","Bequest of Herbert Mitchell, 2008",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/291839,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2008.528,false,true,289024,Photographs,Photograph,"Cloisters of St. Paul's, the Basilica, Outside the Walls of Rome",,,,,,Artist,,Robert Macpherson,"British, Tayside, Scotland 1811–1872 Rome",,"Macpherson, Robert","British, Scottish",1811,1811,1858 or earlier,1857,1858,Albumen silver print from glass negative,Image: 34.8 x 25.8 cm (13 11/16 x 10 3/16 in.) Mount (2nd): 52 x 36.8 cm (20 1/2 x 14 1/2 in.),"Purchase, Joyce F. Menschel Gift, 2008",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289024,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.58,false,true,283141,Photographs,Photograph,Cloaca Maxima,,,,,,Artist,,Robert Macpherson,"British, Tayside, Scotland 1811–1872 Rome",,"Macpherson, Robert","British, Scottish",1811,1811,1858 or earlier,1854,1858,Albumen silver print from glass negative,Image: 31 x 37.2 cm (12 3/16 x 14 5/8 in.),"Gilman Collection, Purchase, William Talbott Hillman Foundation Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283141,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.59,false,true,283142,Photographs,Photograph,"The Theater of Marcellus, from the Piazza Montanara",,,,,,Artist,,Robert Macpherson,"British, Tayside, Scotland 1811–1872 Rome",,"Macpherson, Robert","British, Scottish",1811,1811,1858 or earlier,1854,1858,Albumen silver print from glass negative,Image: 41.9 x 27.4 cm (16 1/2 x 10 13/16 in.),"Gilman Collection, Purchase, Alfred Stieglitz Society Gifts, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283142,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.909,false,true,285821,Photographs,Photograph,"The Valley of the Anio, with the Upper and Lower Cascatelle, Mecenas's Villa, and Distant Campagna",,,,,,Artist,,Robert Macpherson,"British, Tayside, Scotland 1811–1872 Rome",,"Macpherson, Robert","British, Scottish",1811,1811,1858 or earlier,1858,1858,Albumen silver print from glass negative,"Image: 30.6 x 40.1 cm (12 1/16 x 15 13/16 in.), oval Mount: 49.4 x 64.7 cm (19 7/16 x 25 1/2 in.)","Gilman Collection, Purchase, W. Bruce and Delaney H. Lundberg Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285821,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.963,false,true,285822,Photographs,Photograph,Falls of Terni,,,,,,Artist,,Robert Macpherson,"British, Tayside, Scotland 1811–1872 Rome",,"Macpherson, Robert","British, Scottish",1811,1811,ca. 1860,1858,1862,Albumen silver print from glass negative,"Image: 27.6 x 37.7 cm (10 7/8 x 14 13/16 in.), oval Mount: 49.5 x 60.6 cm (19 1/2 x 23 7/8 in.)","Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285822,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1098,false,true,285820,Photographs,Photograph,The Hermaphrodite,,,,,,Artist,,Robert Macpherson,"British, Tayside, Scotland 1811–1872 Rome",,"Macpherson, Robert","British, Scottish",1811,1811,ca. 1861,1858,1864,Albumen silver print from glass negative,Image: 6 3/8 in. × 13 in. (16.2 × 33 cm) Mount: 18 5/8 × 24 5/8 in. (47.3 × 62.5 cm),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285820,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1977.537,false,true,261983,Photographs,Photographs,George Frederick Watts,,,,,,Artist,,David Wilkie Wynfield,"British (born India), 1837–1887 London (?)",,"Wynfield, David Wilkie","British, born India",1837,1887,1860s,1860,1869,Albumen silver print,21.7 x 16.4 cm (8 9/16 x 6 7/16 in.),"Warner Communications Inc. Purchase Fund, 1977",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261983,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -49.55.310,false,true,269420,Photographs,Photograph,Portrait of D.O. Hill,,,,,,Artist,,Thomas Annan,"British, Dairsie, Fife, Scotland 1829–1887",,"Annan, Thomas","British, Scottish",1829,1887,"1867, printed ca. 1900s",1867,1867,Gelatin silver print,23.0 x 17.5 cm. (9 1/16 x 6 7/8 in.),"Alfred Stieglitz Collection, 1949",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269420,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.216,false,true,285739,Photographs,Photograph,Frederick Langenheim,,,,,,Artist,,William Langenheim,"American, born Germany, Schöningen 1807–1874",,"Langenheim, William","American, born Germany",1807,1874,ca. 1849–50,1847,1852,Daguerreotype,Image: 11.7 x 8.6 cm (4 5/8 x 3 3/8 in.) Case: 1.4 x 11.9 x 15.2 cm (9/16 x 4 11/16 x 6 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285739,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.217,false,true,285740,Photographs,Photograph,Frederick Langenheim,,,,,,Artist,,William Langenheim,"American, born Germany, Schöningen 1807–1874",,"Langenheim, William","American, born Germany",1807,1874,ca. 1851–53,1849,1855,Daguerreotype,Image: 8.9 x 7 cm (3 1/2 x 2 3/4 in.) Case: 1.6 x 11.9 x 9.4 cm (5/8 x 4 11/16 x 3 11/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285740,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.219,false,true,285769,Photographs,Photograph,William and Sophia Palmer Langenheim,,,,,,Artist,,William Langenheim,"American, born Germany, Schöningen 1807–1874",,"Langenheim, William","American, born Germany",1807,1874,ca. 1846–47,1844,1849,Daguerreotype,Image: 12.1 x 8.9 cm (4 3/4 x 3 1/2 in.) Case: 1.6 x 13.5 x 11.7 cm (5/8 x 5 5/16 x 4 5/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285769,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.221,false,true,286328,Photographs,Photograph,William Langenheim,,,,,,Artist,,William Langenheim,"American, born Germany, Schöningen 1807–1874",,"Langenheim, William","American, born Germany",1807,1874,ca. 1848–50,1846,1852,Daguerreotype,Image: 8.9 x 7 cm (3 1/2 x 2 3/4 in.) Case: 1.4 x 11.6 x 9.2 cm (9/16 x 4 9/16 x 3 5/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286328,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.222,false,true,286329,Photographs,Photograph,William Langenheim,,,,,,Artist,,William Langenheim,"American, born Germany, Schöningen 1807–1874",,"Langenheim, William","American, born Germany",1807,1874,1855–58,1855,1858,Daguerreotype,Image: 6.7 x 5.4 cm (2 5/8 x 2 1/8 in.) Case: 1.6 x 9.4 x 8.3 cm (5/8 x 3 11/16 x 3 1/4 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286329,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.224,false,true,286330,Photographs,Photograph,William Langenheim,,,,,,Artist,,William Langenheim,"American, born Germany, Schöningen 1807–1874",,"Langenheim, William","American, born Germany",1807,1874,ca. 1853–55,1851,1857,Daguerreotype,Image: 7.1 x 5.9 cm (2 13/16 x 2 5/16 in.) Frame: 8.4 x 7.1 cm (3 5/16 x 2 13/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286330,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.227,false,true,286326,Photographs,Photograph,William Langenheim,,,,,,Artist,,William Langenheim,"American, born Germany, Schöningen 1807–1874",,"Langenheim, William","American, born Germany",1807,1874,ca. 1849–51,1847,1853,Salted Paper Print From Paper Negative with Applied Color,Image: 16.5 x 12.1 cm (6 1/2 x 4 3/4 in.) Case: 1.3 x 19.4 x 14.4 cm (1/2 x 7 5/8 x 5 11/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286326,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.228,false,true,286273,Photographs,Photograph,Three Men Playing Cards,,,,,,Artist,,William Langenheim,"American, born Germany, Schöningen 1807–1874",,"Langenheim, William","American, born Germany",1807,1874,"March, 1842",1842,1842,Daguerreotype,Image: 7.3 x 5.7 cm (2 7/8 x 2 1/4 in.) Frame: 21.6 x 18.1 cm (8 1/2 x 7 1/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286273,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.230,false,true,286274,Photographs,Photograph,Frederick David Langenheim,,,,,,Artist,,William Langenheim,"American, born Germany, Schöningen 1807–1874",,"Langenheim, William","American, born Germany",1807,1874,ca. 1851–52,1849,1853,Daguerreotype,Image: 12.4 x 9.2 cm (4 7/8 x 3 5/8 in.) Case: 1.6 x 15.1 x 12.1 cm (5/8 x 5 15/16 x 4 3/4 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286274,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.237,false,true,285719,Photographs,Photograph,Professor Schneider's Children,,,,,,Artist,,William Langenheim,"American, born Germany, Schöningen 1807–1874",,"Langenheim, William","American, born Germany",1807,1874,ca. 1842,1840,1844,Daguerreotype,Image: 6.7 x 5.4 cm (2 5/8 x 2 1/8 in.) Case: 1.3 x 9.2 x 7.8 cm (1/2 x 3 5/8 x 3 1/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285719,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.870,false,true,285832,Photographs,Photograph,[Mrs. Thomas Ustick Walter and Her Deceased Child],,,,,,Artist,,William Langenheim,"American, born Germany, Schöningen 1807–1874",,"Langenheim, William","American, born Germany",1807,1874,ca. 1846,1844,1848,Daguerreotype,"Visible: 4 11/16 x 3 1/2, Case: 5 15/16 x 4 5/8 x 5/8","Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285832,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.215,false,true,285721,Photographs,Photograph,Frederick Langenheim,,,,,,Artist,,Frederick Langenheim,"American, born Germany, Schöningen 1809–1879",,"Langenheim, Frederick","American, born Germany",1809,1879,ca. 1850–51,1848,1853,Daguerreotype,Image: 11.4 x 8.4 cm (4 1/2 x 3 5/16 in.) Frame: 17.9 x 14.4 cm (7 1/16 x 5 11/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285721,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.494.4,false,true,289276,Photographs,Photograph,"Lovers Leap, Foochow",,,,,,Artist,Attributed to,John Thomson,"British, Edinburgh, Scotland 1837–1921 London",,"Thomson, John","British, Scottish",1837,1921,ca. 1869,1869,1869,Albumen silver print from glass negative,Image: 9 3/16 × 11 1/8 in. (23.3 × 28.3 cm),"Gilman Collection, Purchase, Robert Rosenkranz Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289276,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.494.6,false,true,289278,Photographs,Photograph,"The Cemetery, Foochow",,,,,,Artist,Attributed to,John Thomson,"British, Edinburgh, Scotland 1837–1921 London",,"Thomson, John","British, Scottish",1837,1921,ca. 1869,1869,1869,Albumen silver print from glass negative,Image: 8 1/4 × 10 7/8 in. (21 × 27.6 cm); oval,"Gilman Collection, Purchase, Robert Rosenkranz Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289278,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.494.7,false,true,289279,Photographs,Photograph,Yungfoo River,,,,,,Artist,Attributed to,John Thomson,"British, Edinburgh, Scotland 1837–1921 London",,"Thomson, John","British, Scottish",1837,1921,ca. 1869,1869,1869,Albumen silver print from glass negative,Image: 9 1/2 × 11 1/8 in. (24.1 × 28.2 cm),"Gilman Collection, Purchase, Robert Rosenkranz Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289279,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.494.8,false,true,289280,Photographs,Photograph,"White Pagoda, Foochow",,,,,,Artist,Attributed to,John Thomson,"British, Edinburgh, Scotland 1837–1921 London",,"Thomson, John","British, Scottish",1837,1921,ca. 1869,1869,1869,Albumen silver print from glass negative,Image: 9 3/8 × 10 15/16 in. (23.8 × 27.8 cm),"Gilman Collection, Purchase, Robert Rosenkranz Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289280,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.494.9,false,true,289281,Photographs,Photograph,Teahouse at Peking,,,,,,Artist,Attributed to,John Thomson,"British, Edinburgh, Scotland 1837–1921 London",,"Thomson, John","British, Scottish",1837,1921,ca. 1869,1869,1869,Albumen silver print from glass negative,Image: 9 7/16 × 11 5/16 in. (23.9 × 28.7 cm),"Gilman Collection, Purchase, Robert Rosenkranz Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289281,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.494.10,false,true,289282,Photographs,Photograph,Pagoda Island,,,,,,Artist,Attributed to,John Thomson,"British, Edinburgh, Scotland 1837–1921 London",,"Thomson, John","British, Scottish",1837,1921,ca. 1869,1869,1869,Albumen silver print from glass negative,Image: 8 1/8 × 10 11/16 in. (20.7 × 27.2 cm),"Gilman Collection, Purchase, Robert Rosenkranz Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289282,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.494.11,false,true,289283,Photographs,Photograph,Bowling Alley and Raquet Court at Foochow,,,,,,Artist,Attributed to,John Thomson,"British, Edinburgh, Scotland 1837–1921 London",,"Thomson, John","British, Scottish",1837,1921,ca. 1869,1869,1869,Albumen silver print from glass negative,Image: 8 9/16 × 11 7/16 in. (21.8 × 29 cm),"Gilman Collection, Purchase, Robert Rosenkranz Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289283,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.494.12,false,true,289284,Photographs,Photograph,Cemetery at Foochow,,,,,,Artist,Attributed to,John Thomson,"British, Edinburgh, Scotland 1837–1921 London",,"Thomson, John","British, Scottish",1837,1921,ca. 1869,1869,1869,Albumen silver print from glass negative,Image: 8 3/16 × 11 3/8 in. (20.8 × 28.9 cm),"Gilman Collection, Purchase, Robert Rosenkranz Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289284,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.494.14,false,true,289286,Photographs,Photograph,"Ming-Ming Customhouse, Rieng-Gang City",,,,,,Artist,Attributed to,John Thomson,"British, Edinburgh, Scotland 1837–1921 London",,"Thomson, John","British, Scottish",1837,1921,ca. 1869,1869,1869,Albumen silver print from glass negative,Image: 7 7/8 × 11 1/8 in. (20 × 28.3 cm),"Gilman Collection, Purchase, Robert Rosenkranz Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289286,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.494.15,false,true,289287,Photographs,Photograph,"Missionary Houses, Foochow",,,,,,Artist,Attributed to,John Thomson,"British, Edinburgh, Scotland 1837–1921 London",,"Thomson, John","British, Scottish",1837,1921,ca. 1869,1869,1869,Albumen silver print from glass negative,Image: 8 1/8 × 11 3/8 in. (20.7 × 28.9 cm),"Gilman Collection, Purchase, Robert Rosenkranz Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289287,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.494.16,false,true,289288,Photographs,Photograph,Min-ch'oi Temple & City Wall of Yen-ping,,,,,,Artist,Attributed to,John Thomson,"British, Edinburgh, Scotland 1837–1921 London",,"Thomson, John","British, Scottish",1837,1921,ca. 1869,1869,1869,Albumen silver print from glass negative,Image: 7 15/16 in. × 11 in. (20.1 × 27.9 cm),"Gilman Collection, Purchase, Robert Rosenkranz Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289288,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.494.19,false,true,289291,Photographs,Photograph,Heaven Ascending Peak near Sing-Chang,,,,,,Artist,Attributed to,John Thomson,"British, Edinburgh, Scotland 1837–1921 London",,"Thomson, John","British, Scottish",1837,1921,ca. 1869,1869,1869,Albumen silver print from glass negative,Image: 8 5/16 × 11 3/16 in. (21.1 × 28.4 cm),"Gilman Collection, Purchase, Robert Rosenkranz Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289291,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.494.20,false,true,289292,Photographs,Photograph,"Pure Spring Cave, near Sing Chang",,,,,,Artist,Attributed to,John Thomson,"British, Edinburgh, Scotland 1837–1921 London",,"Thomson, John","British, Scottish",1837,1921,ca. 1869,1869,1869,Albumen silver print from glass negative,Image: 10 1/16 × 8 3/8 in. (25.5 × 21.3 cm),"Gilman Collection, Purchase, Robert Rosenkranz Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289292,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.494.21,false,true,289293,Photographs,Photograph,[untitled],,,,,,Artist,Attributed to,John Thomson,"British, Edinburgh, Scotland 1837–1921 London",,"Thomson, John","British, Scottish",1837,1921,ca. 1869,1869,1869,Albumen silver print from glass negative,Image: 8 1/8 × 10 15/16 in. (20.7 × 27.8 cm),"Gilman Collection, Purchase, Robert Rosenkranz Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289293,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.494.22,false,true,289294,Photographs,Photograph,[untitled],,,,,,Artist,Attributed to,John Thomson,"British, Edinburgh, Scotland 1837–1921 London",,"Thomson, John","British, Scottish",1837,1921,ca. 1869,1869,1869,Albumen silver print from glass negative,Image: 8 1/16 × 11 5/16 in. (20.4 × 28.7 cm),"Gilman Collection, Purchase, Robert Rosenkranz Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289294,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.494.23,false,true,289295,Photographs,Photograph,[untitled],,,,,,Artist,Attributed to,John Thomson,"British, Edinburgh, Scotland 1837–1921 London",,"Thomson, John","British, Scottish",1837,1921,ca. 1869,1869,1869,Albumen silver print from glass negative,Image: 8 in. × 11 5/16 in. (20.3 × 28.8 cm),"Gilman Collection, Purchase, Robert Rosenkranz Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289295,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.494.24,false,true,289296,Photographs,Photograph,Flouring Mill at Yen-Ping,,,,,,Artist,Attributed to,John Thomson,"British, Edinburgh, Scotland 1837–1921 London",,"Thomson, John","British, Scottish",1837,1921,ca. 1869,1869,1869,Albumen silver print from glass negative,Image: 8 1/16 × 11 5/16 in. (20.4 × 28.7 cm),"Gilman Collection, Purchase, Robert Rosenkranz Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289296,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.494.25,false,true,289297,Photographs,Photograph,Chui Nang opposite Kien-yang city,,,,,,Artist,Attributed to,John Thomson,"British, Edinburgh, Scotland 1837–1921 London",,"Thomson, John","British, Scottish",1837,1921,ca. 1869,1869,1869,Albumen silver print from glass negative,Image: 7 15/16 × 11 1/4 in. (20.2 × 28.5 cm),"Gilman Collection, Purchase, Robert Rosenkranz Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289297,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.494.28,false,true,289300,Photographs,Photograph,"Factory and Silkworm Nursery, Foochow",,,,,,Artist,Attributed to,John Thomson,"British, Edinburgh, Scotland 1837–1921 London",,"Thomson, John","British, Scottish",1837,1921,ca. 1869,1869,1869,Albumen silver print from glass negative,Image: 6 15/16 × 9 7/16 in. (17.6 × 23.9 cm),"Gilman Collection, Purchase, Robert Rosenkranz Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289300,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.494.32,false,true,289304,Photographs,Photograph,[Mountain and Rice Fields],,,,,,Artist,Attributed to,John Thomson,"British, Edinburgh, Scotland 1837–1921 London",,"Thomson, John","British, Scottish",1837,1921,ca. 1869,1869,1869,Albumen silver print from glass negative,Image: 8 3/16 × 11 1/4 in. (20.8 × 28.5 cm),"Gilman Collection, Purchase, Robert Rosenkranz Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289304,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.494.34,false,true,289306,Photographs,Photograph,The Grand Stand Foochow,,,,,,Artist,Attributed to,John Thomson,"British, Edinburgh, Scotland 1837–1921 London",,"Thomson, John","British, Scottish",1837,1921,ca. 1869,1869,1869,Albumen silver print from glass negative,Image: 9 1/8 × 11 1/8 in. (23.1 × 28.3 cm),"Gilman Collection, Purchase, Robert Rosenkranz Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289306,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.494.36,false,true,289308,Photographs,Photograph,[Untitled],,,,,,Artist,Attributed to,John Thomson,"British, Edinburgh, Scotland 1837–1921 London",,"Thomson, John","British, Scottish",1837,1921,ca. 1869,1869,1869,Albumen silver print from glass negative,Image: 8 1/8 × 11 7/16 in. (20.7 × 29 cm),"Gilman Collection, Purchase, Robert Rosenkranz Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289308,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.494.40,false,true,289312,Photographs,Photograph,"[Village, River Min]",,,,,,Artist,Attributed to,John Thomson,"British, Edinburgh, Scotland 1837–1921 London",,"Thomson, John","British, Scottish",1837,1921,ca. 1869,1869,1869,Albumen silver print from glass negative,Image: 9 in. × 10 15/16 in. (22.8 × 27.8 cm),"Gilman Collection, Purchase, Robert Rosenkranz Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289312,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.494.44,false,true,289316,Photographs,Photograph,"Tai-Laity Montain, North River, Canton",,,,,,Artist,Attributed to,John Thomson,"British, Edinburgh, Scotland 1837–1921 London",,"Thomson, John","British, Scottish",1837,1921,ca. 1869,1869,1869,Albumen silver print from glass negative,Image: 6 5/8 × 9 7/16 in. (16.9 × 24 cm),"Gilman Collection, Purchase, Robert Rosenkranz Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289316,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.494.45,false,true,289317,Photographs,Photograph,View from Koong-Yan-Shang Temple,,,,,,Artist,Attributed to,John Thomson,"British, Edinburgh, Scotland 1837–1921 London",,"Thomson, John","British, Scottish",1837,1921,ca. 1869,1869,1869,Albumen silver print from glass negative,Image: 5 11/16 × 9 3/16 in. (14.4 × 23.3 cm),"Gilman Collection, Purchase, Robert Rosenkranz Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289317,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.494.46,false,true,289318,Photographs,Photograph,Village Road North River,,,,,,Artist,Attributed to,John Thomson,"British, Edinburgh, Scotland 1837–1921 London",,"Thomson, John","British, Scottish",1837,1921,ca. 1869,1869,1869,Albumen silver print from glass negative,Image: 6 7/8 × 9 3/16 in. (17.5 × 23.4 cm),"Gilman Collection, Purchase, Robert Rosenkranz Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289318,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.494.47,false,true,289319,Photographs,Photograph,[Panorama of Hong Kong],,,,,,Artist,Attributed to,John Thomson,"British, Edinburgh, Scotland 1837–1921 London",,"Thomson, John","British, Scottish",1837,1921,ca. 1869,1869,1869,Albumen silver print from glass negative,Image(a): 10 5/8 × 8 13/16 in. (27 × 22.4 cm) Image(b): 10 1/2 × 8 13/16 in. (26.6 × 22.4 cm),"Gilman Collection, Purchase, Robert Rosenkranz Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289319,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.494.48,false,true,289320,Photographs,Photograph,View of Canton from the River,,,,,,Artist,Attributed to,John Thomson,"British, Edinburgh, Scotland 1837–1921 London",,"Thomson, John","British, Scottish",1837,1921,ca. 1869,1869,1869,Albumen silver print from glass negative,Image: 8 1/8 × 9 13/16 in. (20.6 × 24.9 cm),"Gilman Collection, Purchase, Robert Rosenkranz Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289320,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.494.49,false,true,289321,Photographs,Photograph,View Opposite Canton,,,,,,Artist,Attributed to,John Thomson,"British, Edinburgh, Scotland 1837–1921 London",,"Thomson, John","British, Scottish",1837,1921,ca. 1869,1869,1869,Albumen silver print from glass negative,Image: 10 15/16 × 10 9/16 in. (27.8 × 26.8 cm),"Gilman Collection, Purchase, Robert Rosenkranz Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289321,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.494.51,false,true,289323,Photographs,Photograph,"West Gate, Canton",,,,,,Artist,Attributed to,John Thomson,"British, Edinburgh, Scotland 1837–1921 London",,"Thomson, John","British, Scottish",1837,1921,ca. 1869,1869,1869,Albumen silver print from glass negative,Image: 8 3/8 × 10 3/4 in. (21.3 × 27.3 cm),"Gilman Collection, Purchase, Robert Rosenkranz Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289323,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.494.52,false,true,289324,Photographs,Photograph,"Garden at the English Consulate, Canton",,,,,,Artist,Attributed to,John Thomson,"British, Edinburgh, Scotland 1837–1921 London",,"Thomson, John","British, Scottish",1837,1921,ca. 1869,1869,1869,Albumen silver print from glass negative,Image: 8 1/4 × 10 13/16 in. (21 × 27.5 cm),"Gilman Collection, Purchase, Robert Rosenkranz Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289324,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.494.53,false,true,286867,Photographs,Photograph,"Beggars at the Gate of a Temple, Canton",,,,,,Artist,Attributed to,John Thomson,"British, Edinburgh, Scotland 1837–1921 London",,"Thomson, John","British, Scottish",1837,1921,ca. 1869,1869,1869,Albumen silver print from glass negative,Image: 27.3 x 21.3 cm (10 3/4 x 8 3/8 in.),"Gilman Collection, Purchase, Robert Rosenkranz Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286867,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.494.56,false,true,289327,Photographs,Photograph,"[Rapid, Yen-Ping]",,,,,,Artist,Attributed to,John Thomson,"British, Edinburgh, Scotland 1837–1921 London",,"Thomson, John","British, Scottish",1837,1921,ca. 1869,1869,1869,Albumen silver print from glass negative,Image: 7 15/16 × 11 1/8 in. (20.1 × 28.2 cm),"Gilman Collection, Purchase, Robert Rosenkranz Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289327,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.494.58,false,true,289329,Photographs,Photograph,"Dwelling on the Water, Canton",,,,,,Artist,,John Thomson,"British, Edinburgh, Scotland 1837–1921 London",,"Thomson, John","British, Scottish",1837,1921,ca. 1869,1869,1869,Albumen silver print from glass negative,Image: 8 1/2 × 10 11/16 in. (21.6 × 27.2 cm),"Gilman Collection, Purchase, Robert Rosenkranz Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289329,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.494.59,false,true,289330,Photographs,Photograph,A Creek in Canton,,,,,,Artist,Attributed to,John Thomson,"British, Edinburgh, Scotland 1837–1921 London",,"Thomson, John","British, Scottish",1837,1921,ca. 1869,1869,1869,Albumen silver print from glass negative,Image: 8 1/2 × 10 13/16 in. (21.6 × 27.5 cm),"Gilman Collection, Purchase, Robert Rosenkranz Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289330,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.494.60,false,true,289331,Photographs,Photograph,A Creek in Canton,,,,,,Artist,Attributed to,John Thomson,"British, Edinburgh, Scotland 1837–1921 London",,"Thomson, John","British, Scottish",1837,1921,ca. 1869,1869,1869,Albumen silver print from glass negative,Image: 8 7/16 × 10 7/8 in. (21.5 × 27.6 cm),"Gilman Collection, Purchase, Robert Rosenkranz Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289331,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.494.61,false,true,289332,Photographs,Photograph,Mercantile Junks at Canton,,,,,,Artist,,John Thomson,"British, Edinburgh, Scotland 1837–1921 London",,"Thomson, John","British, Scottish",1837,1921,ca. 1869,1869,1869,Albumen silver print from glass negative,Image: 8 7/16 × 10 9/16 in. (21.4 × 26.8 cm),"Gilman Collection, Purchase, Robert Rosenkranz Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289332,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.494.62,false,true,289333,Photographs,Photograph,A Garden in Canton,,,,,,Artist,Attributed to,John Thomson,"British, Edinburgh, Scotland 1837–1921 London",,"Thomson, John","British, Scottish",1837,1921,ca. 1869,1869,1869,Albumen silver print from glass negative,Image: 8 7/16 × 10 7/8 in. (21.5 × 27.6 cm),"Gilman Collection, Purchase, Robert Rosenkranz Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289333,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.494.63,false,true,289334,Photographs,Photograph,"A Tea Pavilion, Canton",,,,,,Artist,Attributed to,John Thomson,"British, Edinburgh, Scotland 1837–1921 London",,"Thomson, John","British, Scottish",1837,1921,ca. 1869,1869,1869,Albumen silver print from glass negative,Image: 8 1/2 × 10 5/8 in. (21.6 × 27 cm),"Gilman Collection, Purchase, Robert Rosenkranz Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289334,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.494.64,false,true,289335,Photographs,Photograph,"Way to the Theater Pon-Jing-Quais Garden, Canton",,,,,,Artist,Attributed to,John Thomson,"British, Edinburgh, Scotland 1837–1921 London",,"Thomson, John","British, Scottish",1837,1921,ca. 1869,1869,1869,Albumen silver print from glass negative,Image: 8 7/16 × 10 5/8 in. (21.4 × 27 cm),"Gilman Collection, Purchase, Robert Rosenkranz Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289335,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.494.65,false,true,289336,Photographs,Photograph,Mandarin Dwelling,,,,,,Artist,Attributed to,John Thomson,"British, Edinburgh, Scotland 1837–1921 London",,"Thomson, John","British, Scottish",1837,1921,ca. 1869,1869,1869,Albumen silver print from glass negative,Image: 8 3/8 × 10 5/8 in. (21.2 × 27 cm),"Gilman Collection, Purchase, Robert Rosenkranz Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289336,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.494.66,false,true,289337,Photographs,Photograph,Madarin at Home,,,,,,Artist,Attributed to,John Thomson,"British, Edinburgh, Scotland 1837–1921 London",,"Thomson, John","British, Scottish",1837,1921,ca. 1869,1869,1869,Albumen silver print from glass negative,Image: 7 5/16 × 10 11/16 in. (18.6 × 27.1 cm),"Gilman Collection, Purchase, Robert Rosenkranz Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289337,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.494.67,false,true,289338,Photographs,Photograph,Teh Hop-Ho (Canton) and his Son,,,,,,Artist,Attributed to,John Thomson,"British, Edinburgh, Scotland 1837–1921 London",,"Thomson, John","British, Scottish",1837,1921,ca. 1869,1869,1869,Albumen silver print from glass negative,Image: 10 3/8 × 8 3/8 in. (26.4 × 21.2 cm),"Gilman Collection, Purchase, Robert Rosenkranz Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289338,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.494.68,false,true,289339,Photographs,Photograph,"A Tartar Soldier, Canton",,,,,,Artist,Attributed to,John Thomson,"British, Edinburgh, Scotland 1837–1921 London",,"Thomson, John","British, Scottish",1837,1921,ca. 1869,1869,1869,Albumen silver print from glass negative,Image: 10 11/16 × 8 3/8 in. (27.1 × 21.2 cm),"Gilman Collection, Purchase, Robert Rosenkranz Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289339,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.494.69,false,true,289340,Photographs,Photograph,"Mandarin Dwelling, Canton",,,,,,Artist,Attributed to,John Thomson,"British, Edinburgh, Scotland 1837–1921 London",,"Thomson, John","British, Scottish",1837,1921,ca. 1869,1869,1869,Albumen silver print from glass negative,Image: 8 7/16 × 10 9/16 in. (21.5 × 26.9 cm),"Gilman Collection, Purchase, Robert Rosenkranz Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289340,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.494.70,false,true,289341,Photographs,Photograph,"Garden, Canton",,,,,,Artist,Attributed to,John Thomson,"British, Edinburgh, Scotland 1837–1921 London",,"Thomson, John","British, Scottish",1837,1921,ca. 1869,1869,1869,Albumen silver print from glass negative,Image: 8 1/8 × 11 1/8 in. (20.7 × 28.2 cm),"Gilman Collection, Purchase, Robert Rosenkranz Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289341,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.494.71,false,true,289342,Photographs,Photograph,Macao,,,,,,Artist,Attributed to,John Thomson,"British, Edinburgh, Scotland 1837–1921 London",,"Thomson, John","British, Scottish",1837,1921,ca. 1869,1869,1869,Albumen silver print from glass negative,Image: 7 9/16 × 11 3/16 in. (19.2 × 28.4 cm),"Gilman Collection, Purchase, Robert Rosenkranz Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289342,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.494.72,false,true,289343,Photographs,Photograph,Fishing Boats going out Macao,,,,,,Artist,Attributed to,John Thomson,"British, Edinburgh, Scotland 1837–1921 London",,"Thomson, John","British, Scottish",1837,1921,ca. 1869,1869,1869,Albumen silver print from glass negative,Image: 7 5/16 × 10 11/16 in. (18.6 × 27.1 cm),"Gilman Collection, Purchase, Robert Rosenkranz Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289343,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.494.73,false,true,289344,Photographs,Photograph,A Street in Macao,,,,,,Artist,Attributed to,John Thomson,"British, Edinburgh, Scotland 1837–1921 London",,"Thomson, John","British, Scottish",1837,1921,ca. 1869,1869,1869,Albumen silver print from glass negative,Image: 8 5/16 × 10 5/8 in. (21.1 × 27 cm),"Gilman Collection, Purchase, Robert Rosenkranz Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289344,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.494.74,false,true,289345,Photographs,Photograph,"St. Pauls Cathedral, Macao",,,,,,Artist,Attributed to,John Thomson,"British, Edinburgh, Scotland 1837–1921 London",,"Thomson, John","British, Scottish",1837,1921,ca. 1869,1869,1869,Albumen silver print from glass negative,Image: 10 9/16 × 8 3/8 in. (26.8 × 21.2 cm),"Gilman Collection, Purchase, Robert Rosenkranz Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289345,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.494.75,false,true,289346,Photographs,Photograph,"Tomb, Macao",,,,,,Artist,Attributed to,John Thomson,"British, Edinburgh, Scotland 1837–1921 London",,"Thomson, John","British, Scottish",1837,1921,ca. 1869,1869,1869,Albumen silver print from glass negative,Image: 10 9/16 × 8 1/8 in. (26.9 × 20.6 cm),"Gilman Collection, Purchase, Robert Rosenkranz Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289346,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.494.76,false,true,289347,Photographs,Photograph,View in Camoens Garden Macao,,,,,,Artist,Attributed to,John Thomson,"British, Edinburgh, Scotland 1837–1921 London",,"Thomson, John","British, Scottish",1837,1921,ca. 1869,1869,1869,Albumen silver print from glass negative,Image: 10 3/8 × 8 1/4 in. (26.4 × 20.9 cm),"Gilman Collection, Purchase, Robert Rosenkranz Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289347,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.494.77,false,true,289348,Photographs,Photograph,Swatow,,,,,,Artist,Attributed to,John Thomson,"British, Edinburgh, Scotland 1837–1921 London",,"Thomson, John","British, Scottish",1837,1921,ca. 1869,1869,1869,Albumen silver print from glass negative,Image: 7 11/16 × 10 9/16 in. (19.5 × 26.9 cm),"Gilman Collection, Purchase, Robert Rosenkranz Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289348,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.494.78,false,true,289349,Photographs,Photograph,View on Rak-Chui opposite Swatow,,,,,,Artist,Attributed to,John Thomson,"British, Edinburgh, Scotland 1837–1921 London",,"Thomson, John","British, Scottish",1837,1921,ca. 1869,1869,1869,Albumen silver print from glass negative,Image: 6 1/4 × 10 9/16 in. (15.8 × 26.9 cm),"Gilman Collection, Purchase, Robert Rosenkranz Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289349,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.494.79,false,true,289350,Photographs,Photograph,View on Rak-Chui opposite Swatow,,,,,,Artist,Attributed to,John Thomson,"British, Edinburgh, Scotland 1837–1921 London",,"Thomson, John","British, Scottish",1837,1921,ca. 1869,1869,1869,Albumen silver print from glass negative,Image: 6 1/4 × 10 9/16 in. (15.8 × 26.9 cm),"Gilman Collection, Purchase, Robert Rosenkranz Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289350,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.494.80,false,true,289351,Photographs,Photograph,View on Rak-Chui opposite Swatow,,,,,,Artist,Attributed to,John Thomson,"British, Edinburgh, Scotland 1837–1921 London",,"Thomson, John","British, Scottish",1837,1921,ca. 1869,1869,1869,Albumen silver print from glass negative,Image: 7 3/8 × 10 15/16 in. (18.8 × 27.8 cm),"Gilman Collection, Purchase, Robert Rosenkranz Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289351,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.494.81,false,true,289352,Photographs,Photograph,View of Swatow Harbour,,,,,,Artist,Attributed to,John Thomson,"British, Edinburgh, Scotland 1837–1921 London",,"Thomson, John","British, Scottish",1837,1921,ca. 1869,1869,1869,Albumen silver print from glass negative,Image: 7 11/16 × 10 11/16 in. (19.6 × 27.2 cm),"Gilman Collection, Purchase, Robert Rosenkranz Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289352,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.494.82,false,true,289353,Photographs,Photograph,Entrance of Amoy Harbour,,,,,,Artist,Attributed to,John Thomson,"British, Edinburgh, Scotland 1837–1921 London",,"Thomson, John","British, Scottish",1837,1921,ca. 1869,1869,1869,Albumen silver print from glass negative,Image: 6 5/8 × 9 5/16 in. (16.8 × 23.7 cm),"Gilman Collection, Purchase, Robert Rosenkranz Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289353,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.494.83,false,true,289354,Photographs,Photograph,Amoy Harbour,,,,,,Artist,Attributed to,John Thomson,"British, Edinburgh, Scotland 1837–1921 London",,"Thomson, John","British, Scottish",1837,1921,ca. 1869,1869,1869,Albumen silver print from glass negative,Image: 6 11/16 × 9 1/4 in. (17 × 23.5 cm),"Gilman Collection, Purchase, Robert Rosenkranz Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289354,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.494.84,false,true,289355,Photographs,Photograph,"View on Koolangsoo Island, Amoy",,,,,,Artist,Attributed to,John Thomson,"British, Edinburgh, Scotland 1837–1921 London",,"Thomson, John","British, Scottish",1837,1921,ca. 1869,1869,1869,Albumen silver print from glass negative,Image: 6 5/8 × 9 1/4 in. (16.9 × 23.5 cm),"Gilman Collection, Purchase, Robert Rosenkranz Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289355,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.494.85,false,true,289356,Photographs,Photograph,Amoy Harbour,,,,,,Artist,,John Thomson,"British, Edinburgh, Scotland 1837–1921 London",,"Thomson, John","British, Scottish",1837,1921,ca. 1869,1869,1869,Albumen silver print from glass negative,Image: 7 1/4 × 10 3/4 in. (18.4 × 27.3 cm),"Gilman Collection, Purchase, Robert Rosenkranz Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289356,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.494.86,false,true,289357,Photographs,Photograph,View of Amoy,,,,,,Artist,Attributed to,John Thomson,"British, Edinburgh, Scotland 1837–1921 London",,"Thomson, John","British, Scottish",1837,1921,ca. 1869,1869,1869,Albumen silver print from glass negative,Image: 7 1/2 × 10 1/16 in. (19 × 25.5 cm),"Gilman Collection, Purchase, Robert Rosenkranz Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289357,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.494.87,false,true,289358,Photographs,Photograph,View of Amoy,,,,,,Artist,Attributed to,John Thomson,"British, Edinburgh, Scotland 1837–1921 London",,"Thomson, John","British, Scottish",1837,1921,ca. 1869,1869,1869,Albumen silver print from glass negative,Image: 7 1/4 × 10 1/8 in. (18.4 × 25.7 cm),"Gilman Collection, Purchase, Robert Rosenkranz Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289358,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.494.88,false,true,289359,Photographs,Photograph,"Lower Harbour, Amoy",,,,,,Artist,Attributed to,John Thomson,"British, Edinburgh, Scotland 1837–1921 London",,"Thomson, John","British, Scottish",1837,1921,ca. 1869,1869,1869,Albumen silver print from glass negative,Image: 7 3/16 × 10 1/8 in. (18.2 × 25.7 cm),"Gilman Collection, Purchase, Robert Rosenkranz Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289359,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.494.89,false,true,289360,Photographs,Photograph,"The Grand Stand, Amoy 1871",,,,,,Artist,Attributed to,John Thomson,"British, Edinburgh, Scotland 1837–1921 London",,"Thomson, John","British, Scottish",1837,1921,1871,1871,1871,Albumen silver print from glass negative,Image: 6 3/4 × 10 1/16 in. (17.2 × 25.6 cm),"Gilman Collection, Purchase, Robert Rosenkranz Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289360,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.494.90,false,true,289361,Photographs,Photograph,"Great Pagoda at Foochow, the Largest in China",,,,,,Artist,Attributed to,John Thomson,"British, Edinburgh, Scotland 1837–1921 London",,"Thomson, John","British, Scottish",1837,1921,ca. 1869,1869,1869,Albumen silver print from glass negative,Image: 9 3/4 × 13 1/4 in. (24.8 × 33.6 cm),"Gilman Collection, Purchase, Robert Rosenkranz Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289361,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.494.91,false,true,289362,Photographs,Photograph,"Peculiar shaped Rocks on Roolangsoo Island, Amoy",,,,,,Artist,,John Thomson,"British, Edinburgh, Scotland 1837–1921 London",,"Thomson, John","British, Scottish",1837,1921,ca. 1869,1869,1869,Albumen silver print from glass negative,Image: 6 3/4 × 9 3/16 in. (17.1 × 23.3 cm),"Gilman Collection, Purchase, Robert Rosenkranz Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289362,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.494.92,false,true,289363,Photographs,Photograph,Nine Arch Bridge outside Foochow,,,,,,Artist,Attributed to,John Thomson,"British, Edinburgh, Scotland 1837–1921 London",,"Thomson, John","British, Scottish",1837,1921,ca. 1869,1869,1869,Albumen silver print from glass negative,Image: 9 3/4 × 13 3/16 in. (24.7 × 33.5 cm),"Gilman Collection, Purchase, Robert Rosenkranz Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289363,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.494.93,false,true,289364,Photographs,Photograph,Amoy Houses,,,,,,Artist,Attributed to,John Thomson,"British, Edinburgh, Scotland 1837–1921 London",,"Thomson, John","British, Scottish",1837,1921,ca. 1869,1869,1869,Albumen silver print from glass negative,Image: 6 11/16 × 9 7/16 in. (17 × 24 cm),"Gilman Collection, Purchase, Robert Rosenkranz Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289364,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.494.94,false,true,289365,Photographs,Photograph,Twin Pagodas at Foochow,,,,,,Artist,Attributed to,John Thomson,"British, Edinburgh, Scotland 1837–1921 London",,"Thomson, John","British, Scottish",1837,1921,ca. 1869,1869,1869,Albumen silver print from glass negative,Image: 13 1/16 × 9 3/4 in. (33.2 × 24.8 cm),"Gilman Collection, Purchase, Robert Rosenkranz Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289365,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.494.95,false,true,289366,Photographs,Photograph,Amoy Fishing Boats,,,,,,Artist,Attributed to,John Thomson,"British, Edinburgh, Scotland 1837–1921 London",,"Thomson, John","British, Scottish",1837,1921,ca. 1869,1869,1869,Albumen silver print from glass negative,Image: 6 3/4 × 9 3/16 in. (17.2 × 23.3 cm),"Gilman Collection, Purchase, Robert Rosenkranz Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289366,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.494.96,false,true,289367,Photographs,Photograph,"Harbour of Hongkong; St Johns Cathedral Hongkong; Carriage; Tub Mending, North of China",,,,,,Artist,Attributed to,John Thomson,"British, Edinburgh, Scotland 1837–1921 London",,"Thomson, John","British, Scottish",1837,1921,ca. 1869,1869,1869,Albumen silver print from glass negative,"Image (a), (b): 3 5/8 × 6 7/8 in. (9.2 × 17.5 cm) Image (c), (d): 3 7/8 × 5 13/16 in. (9.9 × 14.8 cm)","Gilman Collection, Purchase, Robert Rosenkranz Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289367,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.494.97,false,true,289368,Photographs,Photograph,"Le-Le- Cong Josshouse, Amoy",,,,,,Artist,,John Thomson,"British, Edinburgh, Scotland 1837–1921 London",,"Thomson, John","British, Scottish",1837,1921,ca. 1869,1869,1869,Albumen silver print from glass negative,Image: 6 11/16 × 9 3/16 in. (17 × 23.4 cm),"Gilman Collection, Purchase, Robert Rosenkranz Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289368,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.494.98,false,true,289369,Photographs,Photograph,Ploughing; Carriage; Cotton Spinning; Selling Sweets,,,,,,Artist,Attributed to,John Thomson,"British, Edinburgh, Scotland 1837–1921 London",,"Thomson, John","British, Scottish",1837,1921,ca. 1869,1869,1869,Albumen silver print from glass negative,Image: 3 15/16 × 5 13/16 in. (10 × 14.8 cm); each,"Gilman Collection, Purchase, Robert Rosenkranz Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289369,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.494.99,false,true,289370,Photographs,Photograph,View over Amoy from Pe-Le-Jong,,,,,,Artist,Attributed to,John Thomson,"British, Edinburgh, Scotland 1837–1921 London",,"Thomson, John","British, Scottish",1837,1921,ca. 1869,1869,1869,Albumen silver print from glass negative,Image: 8 7/16 × 10 13/16 in. (21.5 × 27.4 cm),"Gilman Collection, Purchase, Robert Rosenkranz Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289370,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.494.100,false,true,289371,Photographs,Photograph,"Pagoda on the Execution Ground, Foochow",,,,,,Artist,Attributed to,John Thomson,"British, Edinburgh, Scotland 1837–1921 London",,"Thomson, John","British, Scottish",1837,1921,ca. 1869,1869,1869,Albumen silver print from glass negative,Image: 13 3/8 × 9 13/16 in. (33.9 × 24.9 cm),"Gilman Collection, Purchase, Robert Rosenkranz Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289371,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.494.101,false,true,289372,Photographs,Photograph,"Temple, Amoy",,,,,,Artist,,John Thomson,"British, Edinburgh, Scotland 1837–1921 London",,"Thomson, John","British, Scottish",1837,1921,ca. 1869,1869,1869,Albumen silver print from glass negative,Image: 9 in. × 6 5/8 in. (22.8 × 16.9 cm),"Gilman Collection, Purchase, Robert Rosenkranz Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289372,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.494.102,false,true,289373,Photographs,Photograph,Street in Kadhin; Foochow Creek Bridge; Country View; Shanghai,,,,,,Artist,Attributed to,John Thomson,"British, Edinburgh, Scotland 1837–1921 London",,"Thomson, John","British, Scottish",1837,1921,ca. 1869,1869,1869,Albumen silver print from glass negative,Image: 3 1/4 × 6 7/8 in. (8.2 × 17.5 cm); approx. each,"Gilman Collection, Purchase, Robert Rosenkranz Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289373,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.494.103,false,true,289374,Photographs,Photograph,Shoemaker North of China; Wandering Restaurant North of China,,,,,,Artist,Attributed to,John Thomson,"British, Edinburgh, Scotland 1837–1921 London",,"Thomson, John","British, Scottish",1837,1921,ca. 1869,1869,1869,Albumen silver print from glass negative,Image: 4 in. × 5 3/4 in. (10.1 × 14.6 cm); each,"Gilman Collection, Purchase, Robert Rosenkranz Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289374,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.494.104,false,true,289375,Photographs,Photograph,"View on the Bund; The Monument; The Club House; View from the Bund, Shanghai",,,,,,Artist,Attributed to,John Thomson,"British, Edinburgh, Scotland 1837–1921 London",,"Thomson, John","British, Scottish",1837,1921,ca. 1869,1869,1869,Albumen silver print from glass negative,Image: 3 11/16 × 6 15/16 in. (9.3 × 17.6 cm); each,"Gilman Collection, Purchase, Robert Rosenkranz Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289375,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.494.105,false,true,289376,Photographs,Photograph,The Bund in Shanghai,,,,,,Artist,Attributed to,John Thomson,"British, Edinburgh, Scotland 1837–1921 London",,"Thomson, John","British, Scottish",1837,1921,ca. 1869,1869,1869,Albumen silver print from glass negative,Image: 9 9/16 × 13 1/16 in. (24.3 × 33.2 cm),"Gilman Collection, Purchase, Robert Rosenkranz Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289376,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -46.1.61,false,true,269006,Photographs,Photograph,"[The Harbor at Valletta, Malta]",,,,,,Artist,Attributed to,Calvert Richard Jones,"British, Swansea, Wales 1802–1877 Bath, England",,"Jones, Calvert Richard","British, Welsh",1802,1877,1850s,1850,1859,Salted paper print from paper negative,,"David Hunter McAlpin Fund, 1946",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269006,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -46.1.73,false,true,269019,Photographs,Photograph,"[Temple of Concord, Rome]",,,,,,Artist,Attributed to,Calvert Richard Jones,"British, Swansea, Wales 1802–1877 Bath, England",,"Jones, Calvert Richard","British, Welsh",1802,1877,1850s,1850,1859,Salted paper print from paper negative,,"David Hunter McAlpin Fund, 1946",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269019,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -46.1.74,false,true,269020,Photographs,Photograph,"[Temple of Antonius and Faustina, San Lorenzo in Miranda, Rome]",,,,,,Artist,Attributed to,Calvert Richard Jones,"British, Swansea, Wales 1802–1877 Bath, England",,"Jones, Calvert Richard","British, Welsh",1802,1877,1850s,1850,1859,Salted paper print from paper negative,,"David Hunter McAlpin Fund, 1946",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269020,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -46.1.109,false,true,268882,Photographs,Photograph,"[Strada Levante, Valletta, Malta]",,,,,,Artist,Attributed to,Calvert Richard Jones,"British, Swansea, Wales 1802–1877 Bath, England",,"Jones, Calvert Richard","British, Welsh",1802,1877,1850s,1850,1859,Salted paper print from paper negative,Image: 21.7 x 17.4 cm (8 9/16 x 6 7/8 in.),"David Hunter McAlpin Fund, 1946",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268882,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1992.5167,false,true,266875,Photographs,Photograph,The Capitoline,,,,,,Artist,,Calvert Richard Jones,"British, Swansea, Wales 1802–1877 Bath, England",,"Jones, Calvert Richard","British, Welsh",1802,1877,1846,1846,1846,Salted paper print from a paper negative,16.2 x 21.2 cm. (6 3/8 x 8 3/6 in.),"Purchase, Hans P. Kraus Jr. Gift, and Mrs. Harrison D. Horblit and Joyce and Robert Menschel Gifts, 1992",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/266875,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.159.27,false,true,306247,Photographs,Photograph,"Saint Paul's Bay, Malta",,,,,,Artist,,Calvert Richard Jones,"British, Swansea, Wales 1802–1877 Bath, England",,"Jones, Calvert Richard","British, Welsh",1802,1877,Spring 1846,1846,1846,Salted paper print from paper negative,Image: 6 5/8 × 8 9/16 in. (16.9 × 21.7 cm) Sheet: 7 1/4 × 8 7/8 in. (18.4 × 22.6 cm),"Bequest of Maurice B. Sendak, 2012",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306247,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.159.28,false,true,306248,Photographs,Photograph,Seated Lad,,,,,,Artist,,Calvert Richard Jones,"British, Swansea, Wales 1802–1877 Bath, England",,"Jones, Calvert Richard","British, Welsh",1802,1877,1845–50,1845,1850,Salted paper print from paper negative,Image: 4 3/16 × 3 7/16 in. (10.6 × 8.7 cm),"Bequest of Maurice B. Sendak, 2012",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306248,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.159.29,false,true,306249,Photographs,Photograph,Family Group Portrait Posed in Doorway,,,,,,Artist,,Calvert Richard Jones,"British, Swansea, Wales 1802–1877 Bath, England",,"Jones, Calvert Richard","British, Welsh",1802,1877,late 1840s,1845,1849,Salted paper print from paper negative,Height: 3 3/8 in. (8.5 cm) Width: 4 1/4 in. (10.8 cm),"Bequest of Maurice B. Sendak, 2012",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306249,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.159.30,false,true,306250,Photographs,Photograph,"Portrait of the Gardener, possibly David Roderick",,,,,,Artist,,Calvert Richard Jones,"British, Swansea, Wales 1802–1877 Bath, England",,"Jones, Calvert Richard","British, Welsh",1802,1877,late 1840s,1845,1849,Salted paper print from paper negative,Height: 6 15/16 in. (17.7 cm) Width: 8 11/16 in. (22.1 cm),"Bequest of Maurice B. Sendak, 2012",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306250,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.159.31,false,true,306251,Photographs,"Photograph, contact sheet",[Contact sheet of four group portraits in a doorway],,,,,,Artist,,Calvert Richard Jones,"British, Swansea, Wales 1802–1877 Bath, England",,"Jones, Calvert Richard","British, Welsh",1802,1877,ca. 1850,1845,1855,Salted paper print from paper negative,Image: 7 3/16 in. × 9 in. (18.2 × 22.8 cm) Sheet: 7 3/8 × 9 1/8 in. (18.7 × 23.1 cm),"Bequest of Maurice B. Sendak, 2012",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306251,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.159.32,false,true,306252,Photographs,Photograph,Two Young Men Resting on a Pier,,,,,,Artist,,Calvert Richard Jones,"British, Swansea, Wales 1802–1877 Bath, England",,"Jones, Calvert Richard","British, Welsh",1802,1877,late 1840s,1845,1849,Salted paper print from paper negative,Image: 4 5/16 × 3 3/8 in. (11 × 8.6 cm) Sheet: 3 13/16 × 4 11/16 in. (9.7 × 11.9 cm),"Bequest of Maurice B. Sendak, 2012",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306252,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.159.33,false,true,306253,Photographs,Photograph,Duomo Milan,,,,,,Artist,,Calvert Richard Jones,"British, Swansea, Wales 1802–1877 Bath, England",,"Jones, Calvert Richard","British, Welsh",1802,1877,1846,1846,1846,Salted paper print from paper negative,Image: 8 1/2 × 6 5/8 in. (21.6 × 16.9 cm) Sheet: 9 7/16 × 7 11/16 in. (24 × 19.6 cm),"Bequest of Maurice B. Sendak, 2012",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306253,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.159.34,false,true,306254,Photographs,Photograph,"67. Colosseum, Rome, Second View",,,,,,Artist,,Calvert Richard Jones,"British, Swansea, Wales 1802–1877 Bath, England",,"Jones, Calvert Richard","British, Welsh",1802,1877,May 1846,1846,1846,Salted paper print from paper negative,Image: 6 7/8 × 8 9/16 in. (17.4 × 21.7 cm) Sheet: 7 5/16 × 8 7/8 in. (18.5 × 22.5 cm),"Bequest of Maurice B. Sendak, 2012",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306254,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.159.35,false,true,306255,Photographs,Photograph,Lady in Open Window with Bird Cage,,,,,,Artist,,Calvert Richard Jones,"British, Swansea, Wales 1802–1877 Bath, England",or his Circle,"Jones, Calvert Richard","British, Welsh",1802,1877,late 1840s,1845,1849,Salted paper print from paper negative,Sheet: 4 7/16 × 3 11/16 in. (11.2 × 9.4 cm) Image: 4 in. × 2 15/16 in. (10.2 × 7.5 cm),"Bequest of Maurice B. Sendak, 2012",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306255,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.851,false,true,285837,Photographs,Photograph,[Woman],,,,,,Artist,,Calvert Richard Jones,"British, Swansea, Wales 1802–1877 Bath, England",,"Jones, Calvert Richard","British, Welsh",1802,1877,1845–50,1845,1850,Salted paper print from paper negative,Image: 3 15/16 × 3 5/16 in. (10 × 8.4 cm),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285837,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.852,false,true,285838,Photographs,Photograph,[Soldier],,,,,,Artist,,Calvert Richard Jones,"British, Swansea, Wales 1802–1877 Bath, England",,"Jones, Calvert Richard","British, Welsh",1802,1877,1845–50,1845,1850,Salted paper print from paper negative,Image: 4 3/16 × 3 3/16 in. (10.6 × 8.1 cm),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285838,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2005.100.947a, b",false,true,285836,Photographs,Photograph,"Santa Lucia, Naples",,,,,,Artist,,Calvert Richard Jones,"British, Swansea, Wales 1802–1877 Bath, England",,"Jones, Calvert Richard","British, Welsh",1802,1877,1845–46,1845,1846,Salted paper prints from paper negatives,22.4 x 36.2 cm (8 13/16 x 14 1/4 in.) overall Image: 22.2 x 17 cm (8 3/4 x 6 11/16 in.) each,"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285836,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1981.1229.5,false,true,263190,Photographs,Photograph,[Thereza Dillwyn Llewelyn with Her Microscope],,,,,,Artist,,John Dillwyn Llewelyn,"British, Swansea, Wales 1810–1882 Swansea, Wales",,"Llewelyn, John Dillwyn","British, Welsh",1810,1882,ca. 1854,1852,1856,Salted paper print from glass negative,23.5 x 18.7 cm (9 1/4 x 7 3/8 in. ),"Robert O. Dougan Collection, Gift of Warner Communications Inc., 1981",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/263190,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -63.606.1.20,false,true,270835,Photographs,Photograph,"Piscator, No. 2",,,,,,Artist,,John Dillwyn Llewelyn,"British, Swansea, Wales 1810–1882 Swansea, Wales",,"Llewelyn, John Dillwyn","British, Welsh",1810,1882,1856,1856,1856,Albumen silver print,,"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1963",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/270835,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.573,false,true,285667,Photographs,Photograph,"The Wigwam, a Canadian Scene at Penllergare",,,,,,Artist,,John Dillwyn Llewelyn,"British, Swansea, Wales 1810–1882 Swansea, Wales",,"Llewelyn, John Dillwyn","British, Welsh",1810,1882,ca. 1855,1853,1857,Albumen silver print from glass negative,Image: 18.5 x 22.1 cm (7 5/16 x 8 11/16 in.) Mount: 22.1 x 36.9 cm (8 11/16 x 14 1/2 in.),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285667,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.382 (2),false,true,287892,Photographs,Photograph,Fishing for Shells,,,,,,Artist,,John Dillwyn Llewelyn,"British, Swansea, Wales 1810–1882 Swansea, Wales",,"Llewelyn, John Dillwyn","British, Welsh",1810,1882,1853–56,1853,1856,Salted paper print,Image: 15.6 × 20.5 cm (6 1/8 × 8 1/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287892,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.382 (3),false,true,287893,Photographs,Photograph,Feeding Poor Puss,,,,,,Artist,,John Dillwyn Llewelyn,"British, Swansea, Wales 1810–1882 Swansea, Wales",,"Llewelyn, John Dillwyn","British, Welsh",1810,1882,1853–56,1853,1856,Salted paper print,Image: 13.5 × 10.9 cm (5 5/16 × 4 5/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287893,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.382 (6),false,true,287896,Photographs,Photograph,Winds & Waves,,,,,,Artist,,John Dillwyn Llewelyn,"British, Swansea, Wales 1810–1882 Swansea, Wales",,"Llewelyn, John Dillwyn","British, Welsh",1810,1882,1853–56,1853,1856,Salted paper print,Image: 12 × 10.8 cm (4 3/4 × 4 1/4 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287896,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.382 (7),false,true,287897,Photographs,Photograph,Elinor and Lucy Llewelyn,,,,,,Artist,,John Dillwyn Llewelyn,"British, Swansea, Wales 1810–1882 Swansea, Wales",,"Llewelyn, John Dillwyn","British, Welsh",1810,1882,1853–56,1853,1856,Salted paper print,Image: 12.4 × 10.5 cm (4 7/8 × 4 1/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287897,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.382 (9),false,true,287899,Photographs,Photograph,Upper End of the Lake Penllergare,,,,,,Artist,,John Dillwyn Llewelyn,"British, Swansea, Wales 1810–1882 Swansea, Wales",,"Llewelyn, John Dillwyn","British, Welsh",1810,1882,1853–56,1853,1856,Salted paper print,Image: 16.9 × 21.9 cm (6 5/8 × 8 5/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287899,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.382 (11),false,true,287901,Photographs,Photograph,"[Two Women, One Kneeling and One Standing, Looking into Basket Filled with Vegetables]",,,,,,Artist,,John Dillwyn Llewelyn,"British, Swansea, Wales 1810–1882 Swansea, Wales",,"Llewelyn, John Dillwyn","British, Welsh",1810,1882,1853–56,1853,1856,Albumen silver print,Image: 19.2 × 14.9 cm (7 9/16 × 5 7/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287901,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.382 (13),false,true,287903,Photographs,Photograph,After the Storm,,,,,,Artist,,John Dillwyn Llewelyn,"British, Swansea, Wales 1810–1882 Swansea, Wales",,"Llewelyn, John Dillwyn","British, Welsh",1810,1882,1853–56,1853,1856,Salted paper print,Image: 19.5 × 15.3 cm (7 11/16 in. × 6 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287903,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.382 (23),false,true,287913,Photographs,Photograph,The Boating Party,,,,,,Artist,,John Dillwyn Llewelyn,"British, Swansea, Wales 1810–1882 Swansea, Wales",,"Llewelyn, John Dillwyn","British, Welsh",1810,1882,1853–56,1853,1856,Salted paper print,Image: 15.5 × 20 cm (6 1/8 × 7 7/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287913,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.382 (25),false,true,287915,Photographs,Photograph,Caswell,,,,,,Artist,,John Dillwyn Llewelyn,"British, Swansea, Wales 1810–1882 Swansea, Wales",,"Llewelyn, John Dillwyn","British, Welsh",1810,1882,1853–56,1853,1856,Salted paper print,Image: 15.1 × 20.4 cm (5 15/16 × 8 1/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287915,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.382 (26),false,true,287916,Photographs,Photograph,"Remember, remember the 5th of November!",,,,,,Artist,,John Dillwyn Llewelyn,"British, Swansea, Wales 1810–1882 Swansea, Wales",,"Llewelyn, John Dillwyn","British, Welsh",1810,1882,1853–56,1853,1856,Salted paper print,Image: 15.6 × 19.5 cm (6 1/8 × 7 11/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287916,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.382 (27),false,true,287917,Photographs,Photograph,Three Cliffs Bay,,,,,,Artist,,John Dillwyn Llewelyn,"British, Swansea, Wales 1810–1882 Swansea, Wales",,"Llewelyn, John Dillwyn","British, Welsh",1810,1882,1853–56,1853,1856,Salted paper print,Image: 17.8 × 15.8 cm (7 in. × 6 1/4 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287917,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.382 (31),false,true,287921,Photographs,Photograph,The Lonely Glen,,,,,,Artist,,John Dillwyn Llewelyn,"British, Swansea, Wales 1810–1882 Swansea, Wales",,"Llewelyn, John Dillwyn","British, Welsh",1810,1882,1853–56,1853,1856,Salted paper print,Image: 16 × 21 cm (6 5/16 × 8 1/4 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287921,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.382 (33),false,true,287923,Photographs,Photograph,The Sweet Water Fountain,,,,,,Artist,,John Dillwyn Llewelyn,"British, Swansea, Wales 1810–1882 Swansea, Wales",,"Llewelyn, John Dillwyn","British, Welsh",1810,1882,1853–56,1853,1856,Salted paper print,Image: 14.7 × 12 cm (14.7 × 12 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287923,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.382 (34),false,true,287924,Photographs,Photograph,The Great Torr and Crawley Rocks,,,,,,Artist,,John Dillwyn Llewelyn,"British, Swansea, Wales 1810–1882 Swansea, Wales",,"Llewelyn, John Dillwyn","British, Welsh",1810,1882,1853–56,1853,1856,Salted paper print,Image: 20.6 × 26 cm (8 1/8 × 10 1/4 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287924,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.382 (45),false,true,287935,Photographs,Photograph,Tenby Lifeboat,,,,,,Artist,,John Dillwyn Llewelyn,"British, Swansea, Wales 1810–1882 Swansea, Wales",,"Llewelyn, John Dillwyn","British, Welsh",1810,1882,1853–56,1853,1856,Salted paper print,Image: 15.9 × 20.2 cm (6 1/4 × 7 15/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287935,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.382 (82),false,true,287971,Photographs,Photograph,"The ""Juno"" in Tenby Harbour",,,,,,Artist,,John Dillwyn Llewelyn,"British, Swansea, Wales 1810–1882 Swansea, Wales",,"Llewelyn, John Dillwyn","British, Welsh",1810,1882,1853–56,1853,1856,Salted paper print,Image: 15.2 × 20.2 cm (6 in. × 7 15/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287971,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2005.100.382 (10a, b)",false,true,287900,Photographs,Photograph,Johnny and Drum; Dead Game,,,,,,Artist,,John Dillwyn Llewelyn,"British, Swansea, Wales 1810–1882 Swansea, Wales",,"Llewelyn, John Dillwyn","British, Welsh",1810,1882,1853–56,1853,1856,Salted paper print,Image: 12.7 × 10.9 cm (5 in. × 4 5/16 in.) (a) Image: 7.5 × 6.4 cm (2 15/16 × 2 1/2 in.) (b),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287900,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2005.100.382 (12a, b)",false,true,287902,Photographs,Photograph,Caswell Garden; Geneviève,,,,,,Artist,,John Dillwyn Llewelyn,"British, Swansea, Wales 1810–1882 Swansea, Wales",,"Llewelyn, John Dillwyn","British, Welsh",1810,1882,1853–56,1853,1856,Salted paper print,Image: 12.8 × 11 cm (5 1/16 × 4 5/16 in.) (a) Image: 11.5 × 8.8 cm (4 1/2 × 3 7/16 in.) (b),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287902,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2005.100.382 (16a, b)",false,true,287906,Photographs,Photograph,Water Lilies; The Photographer,,,,,,Artist,,John Dillwyn Llewelyn,"British, Swansea, Wales 1810–1882 Swansea, Wales",,"Llewelyn, John Dillwyn","British, Welsh",1810,1882,1853–56,1853,1856,Salted paper print,Image: 4.7 × 6.7 cm (1 7/8 × 2 5/8 in.) (a) Image: 13 × 10.7 cm (5 1/8 × 4 3/16 in.) (b),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287906,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2005.100.382 (61a, b)",false,true,287951,Photographs,Photograph,Lanelay; [Untitled],,,,,,Artist,,John Dillwyn Llewelyn,"British, Swansea, Wales 1810–1882 Swansea, Wales",,"Llewelyn, John Dillwyn","British, Welsh",1810,1882,1853–56,1853,1856,Salted paper print; albumen silver print,Image: 14.7 × 11.1 cm (5 13/16 × 4 3/8 in.) Image: 8.1 × 11.6 cm (3 3/16 × 4 9/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287951,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.120,false,true,283247,Photographs,Photograph,Oscar Wilde,,,,,,Artist,,Napoleon Sarony,"American (born Canada), Quebec 1821–1896 New York",,"Sarony, Napoleon","American, born Canada",1821,1896,1882,1882,1882,Albumen silver print,Image: 30.5 x 18.4 cm (12 x 7 1/4 in.),"Gilman Collection, Purchase, Ann Tenenbaum and Thomas H. Lee Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283247,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.828,false,true,285691,Photographs,Photograph,Oscar Wilde,,,,,,Artist,,Napoleon Sarony,"American (born Canada), Quebec 1821–1896 New York",,"Sarony, Napoleon","American, born Canada",1821,1896,1882,1882,1882,Albumen silver print from glass negative,Image: 12 in. × 7 1/4 in. (30.5 × 18.4 cm) Mount: 12 15/16 × 7 3/8 in. (32.8 × 18.8 cm),"Gilman Collection, Purchase, Ann Tenenbaum and Thomas H. Lee Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285691,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.622,false,true,283242,Photographs,Photograph,Countess Greffulhe,,,,,,Artist,,Otto Wegener,"French (born Sweden), Helsingborg 1849–1922 Paris",,"Wegener, Otto","French, born Sweden",1849,1922,1899,1899,1899,Gelatin silver print,Image: 26 15/16 × 15 3/8 in. (68.4 × 39 cm) Mount: 27 3/16 × 15 11/16 in. (69 × 39.8 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283242,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.538.1,false,true,291981,Photographs,Photograph,[Woman with Tea Set Playing the Koto],,,,,,Artist,,Felice Beato,"British (born Italy), Venice 1832–1909 Luxor, Egypt",,"Beato, Felice","British, born Italy",1832,1909,ca. 1860,1860,1860,Albumen silver print from glass negative,Image: 20.8 x 26.2 cm (8 3/16 x 10 5/16 in.) Mount: 34.3 x 48.3 cm (13 1/2 x 19 in.),"Gift of Isaac Lagnado, in honor of Paula J. Giardina, 2009",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/291981,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.538.2,false,true,291982,Photographs,Photograph,"""Shariki,"" or Cart-Pushing Coolies",,,,,,Artist,,Felice Beato,"British (born Italy), Venice 1832–1909 Luxor, Egypt",,"Beato, Felice","British, born Italy",1832,1909,ca. 1860,1860,1860,Albumen silver print from glass negative,Image: 20.8 x 25.4 cm (8 3/16 x 10 in.) Mount: 32.4 x 51.1 cm (12 3/4 x 20 1/8 in.),"Gift of Isaac Lagnado, in honor of Mary Stack, 2009",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/291982,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.72,false,true,283169,Photographs,Photograph,[After the Capture of the Taku Forts],,,,,,Artist,,Felice Beato,"British (born Italy), Venice 1832–1909 Luxor, Egypt",,"Beato, Felice","British, born Italy",1832,1909,1860,1860,1860,Albumen silver print from glass negative,Image: 26 x 29.9 cm (10 1/4 x 11 3/4 in.) Mount: 29.3 x 32.5 cm (11 9/16 x 12 13/16 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283169,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.566,false,true,285902,Photographs,Photograph,"Samurai, Yokohama",,,,,,Artist,,Felice Beato,"British (born Italy), Venice 1832–1909 Luxor, Egypt",,"Beato, Felice","British, born Italy",1832,1909,1864–65,1864,1865,Albumen silver print from glass negative,Image: 17.9 x 14.6 cm (7 1/16 x 5 3/4 in.),"Gilman Collection, Purchase, Robert Rosenkranz Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285902,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1994.263.20,false,true,282192,Photographs,Photograph,"[Man Standing in Front of Movie Theater, Omar, West Virginia]",,,,,,Artist,,Ben Shahn,"American (born Lithuania), Kaunas 1898–1969 New York",,"Shahn, Ben","American, born Lithuania",1898,1969,1935,1935,1935,Gelatin silver print,17.3 x 24.6 cm (6 13/16 x 9 11/16 in. ),"Walker Evans Archive, 1994",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/282192,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1994.263.21,false,true,282193,Photographs,Photograph,"[""Prepare to Meet God"" Arrow-Shaped Sign in Cornfield, Williamson, West Virginia]",,,,,,Artist,,Ben Shahn,"American (born Lithuania), Kaunas 1898–1969 New York",,"Shahn, Ben","American, born Lithuania",1898,1969,October 1935,1935,1935,Gelatin silver print,18.8 x 22.8 cm (7 3/8 x 9 in. ),"Walker Evans Archive, 1994",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/282193,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.488,false,true,286303,Photographs,Photograph,"Cotton Pickers, Pulaski County, Arkansas",,,,,,Artist,,Ben Shahn,"American (born Lithuania), Kaunas 1898–1969 New York",,"Shahn, Ben","American, born Lithuania",1898,1969,October 1935,1935,1935,Gelatin silver print,Image: 18.8 x 22.5 cm (7 3/8 x 8 7/8 in.) Mount: 35.6 x 27.8 cm (14 x 10 15/16 in.),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286303,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1987.1100.227,false,true,265274,Photographs,Photograph,[Smithland Bank],,,,,,Artist,,Ben Shahn,"American (born Lithuania), Kaunas 1898–1969 New York",,"Shahn, Ben","American, born Lithuania",1898,1969,1930s,1930,1939,Gelatin silver print,14.9 x 23.0 cm (5 7/8 x 9 1/16 in.),"Ford Motor Company Collection, Gift of Ford Motor Company and John C. Waddell, 1987",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/265274,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1987.1100.285,false,true,265337,Photographs,Photograph,"[Street Scene, Natchez, Mississippi: Two Women Walking along Sidewalk before Storefront]",,,,,,Artist,,Ben Shahn,"American (born Lithuania), Kaunas 1898–1969 New York",,"Shahn, Ben","American, born Lithuania",1898,1969,1935,1935,1935,Gelatin silver print,26.6 x 34.1 cm (10 1/2 x 13 7/16 in.),"Ford Motor Company Collection, Gift of Ford Motor Company and John C. Waddell, 1987",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/265337,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -56.517.3,false,true,269851,Photographs,Photograph,"Mrs. Greenhow and Daughter, Imprisoned in the Old Capitol, Washington",,,,,,Artist,,Alexander Gardner,"American, Glasgow, Scotland 1821–1882 Washington, D.C.",,"Gardner, Alexander","American, Scottish",1821,1882,1862,1862,1862,Albumen silver print,,"David Hunter McAlpin Fund, 1956",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269851,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.537.1,false,true,259595,Photographs,Photograph,"Burying the Dead on the Battlefield of Antietam, September 1862",,,,,,Artist,,Alexander Gardner,"American, Glasgow, Scotland 1821–1882 Washington, D.C.",,"Gardner, Alexander","American, Scottish",1821,1882,1862,1862,1862,Albumen silver print from glass negative,Image: 7.1 x 10 cm (2 13/16 x 3 15/16 in.) Mount: 7.3 x 10.6 cm (2 7/8 x 4 3/16 in.),"Purchase, Florance Waterbury Bequest, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/259595,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.537.2,false,true,259604,Photographs,Photograph,"View on the Battlefield of Antietam, September 1862",,,,,,Artist,,Alexander Gardner,"American, Glasgow, Scotland 1821–1882 Washington, D.C.",,"Gardner, Alexander","American, Scottish",1821,1882,1862,1862,1862,Albumen silver print from glass negative,Image: 7.1 x 10 cm (2 13/16 x 3 15/16 in.) Mount: 7.3 x 10.6 cm (2 7/8 x 4 3/16 in.),"Purchase, Florance Waterbury Bequest, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/259604,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.537.3,false,true,259605,Photographs,Photograph,"View in the Field, On the West Side of the Hagerstown Road, After the Battle of Antietam, Maryland, September 1862",,,,,,Artist,,Alexander Gardner,"American, Glasgow, Scotland 1821–1882 Washington, D.C.",,"Gardner, Alexander","American, Scottish",1821,1882,1862,1862,1862,Albumen silver print from glass negative,,"Purchase, Florance Waterbury Bequest, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/259605,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.537.5,false,true,259607,Photographs,Photograph,"Lutheran Church, Sharpsburgh, Maryland, September 1862",,,,,,Artist,,Alexander Gardner,"American, Glasgow, Scotland 1821–1882 Washington, D.C.",,"Gardner, Alexander","American, Scottish",1821,1882,1862,1862,1862,Albumen silver print from glass negative,,"Purchase, Florance Waterbury Bequest, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/259607,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.537.6,false,true,259608,Photographs,Photograph,"Is This Death - Antietam Battlefield, September 1862",,,,,,Artist,,Alexander Gardner,"American, Glasgow, Scotland 1821–1882 Washington, D.C.",,"Gardner, Alexander","American, Scottish",1821,1882,1862,1862,1862,Albumen silver print from glass negative,,"Purchase, Florance Waterbury Bequest, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/259608,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.537.7,false,true,259609,Photographs,Photograph,"Military Telegraphic Corps, Army of the Potomac, Berlin, October 1862",,,,,,Artist,,Alexander Gardner,"American, Glasgow, Scotland 1821–1882 Washington, D.C.",,"Gardner, Alexander","American, Scottish",1821,1882,1862,1862,1862,Albumen silver print from glass negative,Image: 6 11/16 in. × 9 in. (17 × 22.8 cm) Sheet: 10 1/16 in. × 12 in. (25.5 × 30.5 cm),"Purchase, Florance Waterbury Bequest, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/259609,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.537.8,false,true,259610,Photographs,Photograph,"Group at Secret Service Department, Headquarters, Army of the Potomac, Antietam, October 1862",,,,,,Artist,,Alexander Gardner,"American, Glasgow, Scotland 1821–1882 Washington, D.C.",,"Gardner, Alexander","American, Scottish",1821,1882,1862,1862,1862,Albumen silver print from glass negative,,"Purchase, Florance Waterbury Bequest, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/259610,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.537.9,false,true,259611,Photographs,Photograph,"Antietam Bridge, On the Sharpsburgh and Boonsboro Turnpike, No. 3, September 1862",,,,,,Artist,,Alexander Gardner,"American, Glasgow, Scotland 1821–1882 Washington, D.C.",,"Gardner, Alexander","American, Scottish",1821,1882,1862,1862,1862,Albumen silver print from glass negative,,"Purchase, Florance Waterbury Bequest, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/259611,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.537.10,false,true,259596,Photographs,Photograph,"Antietam Bridge, On the Sharpsburg and Boonsboro Turnpike, No. 1, September 1862",,,,,,Artist,,Alexander Gardner,"American, Glasgow, Scotland 1821–1882 Washington, D.C.",,"Gardner, Alexander","American, Scottish",1821,1882,1862,1862,1862,Albumen silver print from glass negative,,"Purchase, Florance Waterbury Bequest, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/259596,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.537.11,false,true,259597,Photographs,Photograph,"Antietam Bridge, On the Sharpsburg and Boonsboro Turnpike, No. 2, September 1862",,,,,,Artist,,Alexander Gardner,"American, Glasgow, Scotland 1821–1882 Washington, D.C.",,"Gardner, Alexander","American, Scottish",1821,1882,1862,1862,1862,Albumen silver print from glass negative,,"Purchase, Florance Waterbury Bequest, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/259597,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.537.12,false,true,259598,Photographs,Photograph,"Pontoon Bridge, Across the Potomac, at Berlin, Maryland, November 1862",,,,,,Artist,,Alexander Gardner,"American, Glasgow, Scotland 1821–1882 Washington, D.C.",,"Gardner, Alexander","American, Scottish",1821,1882,1862,1862,1862,Albumen silver print from glass negative,,"Purchase, Florance Waterbury Bequest, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/259598,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.537.13,false,true,259599,Photographs,Photograph,"Group at Headquarters of the Army of the Potomac, Antietam, October 1862",,,,,,Artist,,Alexander Gardner,"American, Glasgow, Scotland 1821–1882 Washington, D.C.",,"Gardner, Alexander","American, Scottish",1821,1882,1862,1862,1862,Albumen silver print from glass negative,,"Purchase, Florance Waterbury Bequest, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/259599,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.537.14,false,true,259600,Photographs,Photograph,"Burnside Bridge, Across the Antietam, near Sharpsburg, No. 1, September 1862",,,,,,Artist,,Alexander Gardner,"American, Glasgow, Scotland 1821–1882 Washington, D.C.",,"Gardner, Alexander","American, Scottish",1821,1882,1862,1862,1862,Albumen silver print from glass negative,,"Purchase, Florance Waterbury Bequest, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/259600,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.537.15,false,true,259601,Photographs,Photograph,"Group at Headquarters of the Army of the Potomac, Antietam, October 1862",,,,,,Artist,,Alexander Gardner,"American, Glasgow, Scotland 1821–1882 Washington, D.C.",,"Gardner, Alexander","American, Scottish",1821,1882,1862,1862,1862,Albumen silver print from glass negative,,"Purchase, Florance Waterbury Bequest, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/259601,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.537.16,false,true,259602,Photographs,Photograph,"Pontoon Bridge Across the Potomac, Berlin, October 1862",,,,,,Artist,,Alexander Gardner,"American, Glasgow, Scotland 1821–1882 Washington, D.C.",,"Gardner, Alexander","American, Scottish",1821,1882,1862,1862,1862,Albumen silver print from glass negative,,"Purchase, Florance Waterbury Bequest, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/259602,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1981.1158.2,false,true,263042,Photographs,Photograph,"Mrs. Greenhow and Daughter, Imprisoned in the Old Capitol, Washington",,,,,,Artist,,Alexander Gardner,"American, Glasgow, Scotland 1821–1882 Washington, D.C.",,"Gardner, Alexander","American, Scottish",1821,1882,1862,1862,1862,Albumen silver print from glass negative,,"Gift of Mrs. A. Hyatt Mayor, 1981",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/263042,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1166.2,false,true,264880,Photographs,Photograph,"Brigadier General Gustavus A. DeRussy and Staff on Steps of Arlington House, Arlington, Virginia",,,,,,Artist,,Alexander Gardner,"American, Glasgow, Scotland 1821–1882 Washington, D.C.",,"Gardner, Alexander","American, Scottish",1821,1882,May 1864,1864,1864,Albumen silver print from glass negative,17.2 x 23cm (6 3/4 x 9 1/16in.) Mount: 27.7 x 31.8cm (10 7/8 x 12 1/2in.),"A. Hyatt Mayor Purchase Fund, Marjorie Phelps Starr Bequest, 1986",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264880,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.90,false,true,283192,Photographs,Photograph,[Antietam Battlefield],,,,,,Artist,,Alexander Gardner,"American, Glasgow, Scotland 1821–1882 Washington, D.C.",,"Gardner, Alexander","American, Scottish",1821,1882,1862,1862,1862,Albumen silver print from glass negative,Image: 9.1 x 11.8cm (3 9/16 x 4 5/8in.) Mount: 4 3/16 in. × 4 15/16 in. (10.7 × 12.6 cm),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283192,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.97,false,true,283201,Photographs,Photograph,Lewis Powell [alias Lewis Payne],,,,,,Artist,,Alexander Gardner,"American, Glasgow, Scotland 1821–1882 Washington, D.C.",,"Gardner, Alexander","American, Scottish",1821,1882,"April 27, 1865",1865,1865,Albumen silver print from glass negative,22.4 × 17.4 cm (8 13/16 × 6 7/8 in.),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283201,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.243,false,true,286388,Photographs,Photographs,[Thaddeus Stevens Lying in State in the Rotunda of the Capitol at Washington],,,,,,Artist,,Alexander Gardner,"American, Glasgow, Scotland 1821–1882 Washington, D.C.",,"Gardner, Alexander","American, Scottish",1821,1882,1867,1867,1867,Albumen silver print from glass negative,Image: 16.8 x 20.5 cm (6 5/8 x 8 1/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286388,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.251,false,true,283202,Photographs,Photograph,Execution of the Conspirators,,,,,,Artist,,Alexander Gardner,"American, Glasgow, Scotland 1821–1882 Washington, D.C.",,"Gardner, Alexander","American, Scottish",1821,1882,"July 7, 1865",1865,1865,Albumen silver print from glass negative,Image: 16.8 x 24.2 cm (6 5/8 x 9 1/2 in.),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283202,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.563,false,true,286442,Photographs,Photograph,[Four Officers],,,,,,Artist,,Alexander Gardner,"American, Glasgow, Scotland 1821–1882 Washington, D.C.",,"Gardner, Alexander","American, Scottish",1821,1882,ca. 1864,1862,1866,Albumen silver print from glass negative,Image: 17.8 x 22.8 cm (7 x 9 in.),"Gilman Collection, Purchase, Sam Salz Foundation Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286442,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.785,false,true,285881,Photographs,Photograph,Queen Emma of Hawaii and Her Entourage,,,,,,Artist,,Alexander Gardner,"American, Glasgow, Scotland 1821–1882 Washington, D.C.",,"Gardner, Alexander","American, Scottish",1821,1882,1865,1865,1865,Albumen silver print from glass negative,Image: 14 1/8 × 17 15/16 in. (35.9 × 45.6 cm),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285881,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1118,false,true,286710,Photographs,Photographs,"Gardner's Gallery, 7th and D Streets, Washington, D.C.",,,,,,Artist,,Alexander Gardner,"American, Glasgow, Scotland 1821–1882 Washington, D.C.",,"Gardner, Alexander","American, Scottish",1821,1882,1864,1864,1864,Albumen silver print from glass negative,Image: 2 15/16 × 4 1/8 in. (7.5 × 10.5 cm),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286710,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1191,false,true,285846,Photographs,Photograph,"[Grand Army Review, Washington, D.C.]",,,,,,Artist,,Alexander Gardner,"American, Glasgow, Scotland 1821–1882 Washington, D.C.",,"Gardner, Alexander","American, Scottish",1821,1882,May 1865,1865,1865,Albumen silver print from glass negative,Image: 3 3/4 × 4 1/2 in. (9.5 × 11.4 cm),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285846,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1192,false,true,294721,Photographs,Photograph,"[Grand Army Review, Washington, D.C.]",,,,,,Artist,,Alexander Gardner,"American, Glasgow, Scotland 1821–1882 Washington, D.C.",,"Gardner, Alexander","American, Scottish",1821,1882,May 1865,1865,1865,Albumen silver print from glass negative,Image: 3 3/4 × 4 1/2 in. (9.5 × 11.4 cm),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/294721,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1193,false,true,283204,Photographs,Photograph,"[Grand Army Review, Pennsylvania Avenue, Washington]",,,,,,Artist,,Alexander Gardner,"American, Glasgow, Scotland 1821–1882 Washington, D.C.",,"Gardner, Alexander","American, Scottish",1821,1882,"May 23 or 24, 1865",1865,1865,Albumen silver print from glass negative,"8.8 × 9.9 cm (3 7/16 × 3 7/8 in.), irregularly trimmed","Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283204,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1194,false,true,294722,Photographs,Photograph,"[Grand Army Review, Washington, D.C.]",,,,,,Artist,,Alexander Gardner,"American, Glasgow, Scotland 1821–1882 Washington, D.C.",,"Gardner, Alexander","American, Scottish",1821,1882,May 1865,1865,1865,Albumen silver print from glass negative,Image: 3 3/4 × 20 1/2 in. (9.5 × 52.1 cm),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/294722,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1196,false,true,294725,Photographs,Photograph,"[Grand Army Review, Pennsylvania Avenue, Washington]",,,,,,Artist,,Alexander Gardner,"American, Glasgow, Scotland 1821–1882 Washington, D.C.",,"Gardner, Alexander","American, Scottish",1821,1882,"May 23 or 24, 1865",1865,1865,Albumen silver print from glass negative,"8.5 × 10.1 cm (3 3/8 × 4 in.), irregularly trimmed","Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/294725,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1197,false,true,294726,Photographs,Photograph,"[Grand Army Review, Washington]",,,,,,Artist,,Alexander Gardner,"American, Glasgow, Scotland 1821–1882 Washington, D.C.",,"Gardner, Alexander","American, Scottish",1821,1882,"May 23 or 24, 1865",1865,1865,Albumen silver print from glass negative,"8.2 × 10 cm (3 1/4 × 3 15/16 in.), irregularly trimmed","Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/294726,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1198,false,true,294724,Photographs,Photograph,"[Grand Army Review, Washington, D.C.]",,,,,,Artist,,Alexander Gardner,"American, Glasgow, Scotland 1821–1882 Washington, D.C.",,"Gardner, Alexander","American, Scottish",1821,1882,May 1865,1865,1865,Albumen silver print from glass negative,Image: 3 3/4 × 4 1/2 in. (9.6 × 11.4 cm),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/294724,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1214,false,true,286583,Photographs,Photograph,[Bearded Man in Tweed Jacket],,,,,,Artist,Attributed to,Alexander Gardner,"American, Glasgow, Scotland 1821–1882 Washington, D.C.",,"Gardner, Alexander","American, Scottish",1821,1882,early 1860s,1860,1860,Albumen silver print from glass negative,Image: 11 3/16 × 8 3/4 in. (28.4 × 22.2 cm),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286583,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1215,false,true,286584,Photographs,Photograph,Lincoln Inauguration,,,,,,Artist,,Alexander Gardner,"American, Glasgow, Scotland 1821–1882 Washington, D.C.",,"Gardner, Alexander","American, Scottish",1821,1882,"March 4 ,1865",1865,1865,Albumen silver print from glass negative,Image: 7 1/4 × 9 1/4 in. (18.4 × 23.5 cm),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286584,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1216,false,true,286585,Photographs,Photograph,General McClellan and Staff,,,,,,Artist,Attributed to,Alexander Gardner,"American, Glasgow, Scotland 1821–1882 Washington, D.C.",,"Gardner, Alexander","American, Scottish",1821,1882,ca. 1863,1858,1868,Albumen silver print from glass negative,Image: 9 × 14 in. (22.9 × 35.6 cm),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286585,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1219,false,true,286614,Photographs,Photographs,"Mill, Richmond, Virginia",,,,,,Artist,,Alexander Gardner,"American, Glasgow, Scotland 1821–1882 Washington, D.C.",,"Gardner, Alexander","American, Scottish",1821,1882,ca. 1865,1863,1867,Albumen silver print from glass negative,Image: 6 3/4 × 8 7/8 in. (17.1 × 22.5 cm),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286614,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1223,false,true,286617,Photographs,Photograph,Planning the Capture of Booth,,,,,,Artist,,Alexander Gardner,"American, Glasgow, Scotland 1821–1882 Washington, D.C.",,"Gardner, Alexander","American, Scottish",1821,1882,1865,1865,1865,Albumen silver print from glass negative,Image: 27.1 × 24.5 cm (10 11/16 × 9 5/8 in.),"Gilman Collection, Purchase, Ann Tenenbaum and Thomas H. Lee Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286617,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1225,false,true,286441,Photographs,Photograph,"Cannon, Fortress Monroe",,,,,,Artist,,Alexander Gardner,"American, Glasgow, Scotland 1821–1882 Washington, D.C.",,"Gardner, Alexander","American, Scottish",1821,1882,ca. 1864,1859,1869,Albumen silver print from glass negative,Image: 7 15/16 × 9 3/4 in. (20.2 × 24.8 cm),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286441,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1230,false,true,285619,Photographs,Photograph,Naval Blockade,,,,,,Artist,,Alexander Gardner,"American, Glasgow, Scotland 1821–1882 Washington, D.C.",,"Gardner, Alexander","American, Scottish",1821,1882,1865,1865,1865,Albumen silver print from glass negative,Image: 7 in. × 9 5/8 in. (17.8 × 24.4 cm),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285619,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1244,false,true,285756,Photographs,Photograph,[Black Soldier in Camp],,,,,,Artist,Possibly by,Alexander Gardner,"American, Glasgow, Scotland 1821–1882 Washington, D.C.",,"Gardner, Alexander","American, Scottish",1821,1882,ca. 1863,1861,1865,Albumen silver print from glass negative,Image: 6 in. × 8 7/16 in. (15.2 × 21.4 cm) Mount: 9 × 11 in. (22.9 × 27.9 cm),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285756,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -43.92,false,true,268827,Photographs,Photograph,Sir John Herschel,,,,,,Artist,,Julia Margaret Cameron,"British (born India), Calcutta 1815–1879 Kalutara, Ceylon",,"Cameron, Julia Margaret","British, born India",1815,1815,1867,1867,1867,Carbon Print,,"David Hunter McAlpin Fund, 1943",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268827,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -63.545,false,true,270819,Photographs,Photograph,Pomona,,,,,,Artist,,Julia Margaret Cameron,"British (born India), Calcutta 1815–1879 Kalutara, Ceylon",,"Cameron, Julia Margaret","British, born India",1815,1815,1872,1872,1872,Albumen silver print from glass negative,Image: 36.4 x 26.3 cm (14 5/16 x 10 3/8 in.) Mount: 49.7 x 37.4 cm (19 9/16 x 14 3/4 in.),"David Hunter McAlpin Fund, 1963",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/270819,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -35.85.1,false,true,268300,Photographs,Photograph,Henry Taylor,,,,,,Artist,,Julia Margaret Cameron,"British (born India), Calcutta 1815–1879 Kalutara, Ceylon",,"Cameron, Julia Margaret","British, born India",1815,1815,1867,1867,1867,Albumen silver print from glass negative,,"Gift of Lucy Chauncey, in memory of her father, Henry Chauncey, 1935",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268300,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -35.85.2,false,true,268301,Photographs,Photograph,"[The Lord Bishop of Winchester, Samuel Wilberforce]",,,,,,Artist,,Julia Margaret Cameron,"British (born India), Calcutta 1815–1879 Kalutara, Ceylon",,"Cameron, Julia Margaret","British, born India",1815,1815,1872,1872,1872,Albumen silver print,,"Gift of Lucy Chauncey, in memory of her father, Henry Chauncey, 1935",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268301,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -35.85.3,false,true,268302,Photographs,Photograph,Sir John Herschel,,,,,,Artist,,Julia Margaret Cameron,"British (born India), Calcutta 1815–1879 Kalutara, Ceylon",,"Cameron, Julia Margaret","British, born India",1815,1815,1867,1867,1867,Albumen silver print from glass negative,Image: 33.8 x 26.2cm (13 5/16 x 10 5/16in.) Mount: 58.3 x 46.2 cm (22 15/16 x 18 3/16 in.),"Gift of Lucy Chauncey, in memory of her father, Henry Chauncey, 1935",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268302,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -41.21.2,false,true,268702,Photographs,Photograph,La Madonna Riposata,,,,,,Artist,,Julia Margaret Cameron,"British (born India), Calcutta 1815–1879 Kalutara, Ceylon",,"Cameron, Julia Margaret","British, born India",1815,1815,1865,1865,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1941",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268702,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -41.21.3,false,true,268713,Photographs,Photograph,The Maid of Athens (May Prinsep),,,,,,Artist,,Julia Margaret Cameron,"British (born India), Calcutta 1815–1879 Kalutara, Ceylon",,"Cameron, Julia Margaret","British, born India",1815,1815,1866,1866,1866,Albumen silver print from glass negative,28.9 x 23.2 cm (11 3/8 x 9 1/8 in. ),"Harris Brisbane Dick Fund, 1941",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268713,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -41.21.4,false,true,268714,Photographs,Photograph,Daisy,,,,,,Artist,,Julia Margaret Cameron,"British (born India), Calcutta 1815–1879 Kalutara, Ceylon",,"Cameron, Julia Margaret","British, born India",1815,1815,1864,1864,1864,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1941",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268714,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -41.21.5,false,true,268715,Photographs,Photograph,Minnie Thackeray,,,,,,Artist,,Julia Margaret Cameron,"British (born India), Calcutta 1815–1879 Kalutara, Ceylon",,"Cameron, Julia Margaret","British, born India",1815,1815,1865,1865,1865,Albumen silver print from glass negative,24.8 x 19.6 cm. (9 3/4 x 7 3/4 in.),"Harris Brisbane Dick Fund, 1941",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268715,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -41.21.6,false,true,268716,Photographs,Photograph,Julia Herschel,,,,,,Artist,,Julia Margaret Cameron,"British (born India), Calcutta 1815–1879 Kalutara, Ceylon",,"Cameron, Julia Margaret","British, born India",1815,1815,1865,1865,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1941",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268716,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -41.21.7,false,true,268717,Photographs,Photograph,The Madonna Penserosa,,,,,,Artist,,Julia Margaret Cameron,"British (born India), Calcutta 1815–1879 Kalutara, Ceylon",,"Cameron, Julia Margaret","British, born India",1815,1815,1864,1864,1864,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1941",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268717,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -41.21.8,false,true,268718,Photographs,Photograph,"Alfred, Lord Tennyson",,,,,,Artist,,Julia Margaret Cameron,"British (born India), Calcutta 1815–1879 Kalutara, Ceylon",,"Cameron, Julia Margaret","British, born India",1815,1815,1865,1865,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1941",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268718,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -41.21.9,false,true,268719,Photographs,Photograph,"Charles Hay Cameron, Esq., in His Garden at Freshwater",,,,,,Artist,,Julia Margaret Cameron,"British (born India), Calcutta 1815–1879 Kalutara, Ceylon",,"Cameron, Julia Margaret","British, born India",1815,1815,1865–67,1865,1867,Albumen silver print from glass negative,33.4 x 26.7 cm (13 1/8 x 10 1/2 in. ),"Harris Brisbane Dick Fund, 1941",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268719,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.692,false,true,260359,Photographs,Photograph,[Ceylonese Group by a Tree],,,,,,Artist,,Julia Margaret Cameron,"British (born India), Calcutta 1815–1879 Kalutara, Ceylon",,"Cameron, Julia Margaret","British, born India",1815,1815,1878,1878,1878,Albumen silver print from glass negative,27 x 19.9 cm (10 5/8 x 7 13/16 in.),"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260359,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.17.17,false,true,268363,Photographs,Photograph,Thomas Carlyle,,,,,,Artist,,Julia Margaret Cameron,"British (born India), Calcutta 1815–1879 Kalutara, Ceylon",,"Cameron, Julia Margaret","British, born India",1815,1815,1867,1867,1867,Albumen silver print,,"Gift of Edith Root Grant, Edward W. Root and Eliho Root Jr., 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268363,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -41.21.11,false,true,268693,Photographs,Photograph,Contemplations,,,,,,Artist,,Julia Margaret Cameron,"British (born India), Calcutta 1815–1879 Kalutara, Ceylon",,"Cameron, Julia Margaret","British, born India",1815,1815,1864,1864,1864,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1941",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268693,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -41.21.12,false,true,268694,Photographs,Photograph,Daughters of Jerusalem,,,,,,Artist,,Julia Margaret Cameron,"British (born India), Calcutta 1815–1879 Kalutara, Ceylon",,"Cameron, Julia Margaret","British, born India",1815,1815,1865,1865,1865,Albumen silver print from glass negative,18.1 x 27.7 cm (7 1/8 x 10 7/8 in. ),"Harris Brisbane Dick Fund, 1941",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268694,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -41.21.13,false,true,268695,Photographs,Photograph,[Mary Hillier],,,,,,Artist,,Julia Margaret Cameron,"British (born India), Calcutta 1815–1879 Kalutara, Ceylon",,"Cameron, Julia Margaret","British, born India",1815,1815,ca. 1864–66,1864,1866,Albumen silver print from glass negative,19.8 x 14.8 cm (7 13/16 x 5 13/16 in. ),"Harris Brisbane Dick Fund, 1941",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268695,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -41.21.14,false,true,268696,Photographs,Photograph,Sappho,,,,,,Artist,,Julia Margaret Cameron,"British (born India), Calcutta 1815–1879 Kalutara, Ceylon",,"Cameron, Julia Margaret","British, born India",1815,1815,1865,1865,1865,Albumen silver print from glass negative,34.4 x 26.1 cm (13 9/16 x 10 1/4 in. ),"Harris Brisbane Dick Fund, 1941",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268696,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -41.21.15,false,true,268697,Photographs,Photograph,The Mountain Nymph Sweet Liberty,,,,,,Artist,,Julia Margaret Cameron,"British (born India), Calcutta 1815–1879 Kalutara, Ceylon",,"Cameron, Julia Margaret","British, born India",1815,1815,1866,1866,1866,Albumen silver print from glass negative,36.1 x 28.6 cm (14 3/16 x 11 1/4 in. ),"Harris Brisbane Dick Fund, 1941",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268697,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -41.21.17,false,true,268699,Photographs,Photograph,[James Rogers],,,,,,Artist,,Julia Margaret Cameron,"British (born India), Calcutta 1815–1879 Kalutara, Ceylon",,"Cameron, Julia Margaret","British, born India",1815,1815,1867,1867,1867,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1941",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268699,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -41.21.18,false,true,268700,Photographs,Photograph,May Prinsep,,,,,,Artist,,Julia Margaret Cameron,"British (born India), Calcutta 1815–1879 Kalutara, Ceylon",,"Cameron, Julia Margaret","British, born India",1815,1815,1868,1868,1868,Albumen silver print from glass negative,22.5 x 20.5 cm. (8 7/8 x 8 1/16 in.),"Harris Brisbane Dick Fund, 1941",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268700,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -41.21.19,false,true,268701,Photographs,Photograph,The South West Wind,,,,,,Artist,,Julia Margaret Cameron,"British (born India), Calcutta 1815–1879 Kalutara, Ceylon",,"Cameron, Julia Margaret","British, born India",1815,1815,1864,1864,1864,Albumen silver print from glass negative,25.4 x 21.6 cm (10 x 8 1/2 in. ),"Harris Brisbane Dick Fund, 1941",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268701,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -41.21.20,false,true,268703,Photographs,Photograph,The Vicar of Freshwater,,,,,,Artist,,Julia Margaret Cameron,"British (born India), Calcutta 1815–1879 Kalutara, Ceylon",,"Cameron, Julia Margaret","British, born India",1815,1815,1864,1864,1864,Albumen silver print from glass negative,26.4 x 20.8 cm (10 3/8 x 8 3/16 in. ),"Harris Brisbane Dick Fund, 1941",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268703,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -41.21.22,false,true,268705,Photographs,Photograph,Tennyson Reading,,,,,,Artist,,Julia Margaret Cameron,"British (born India), Calcutta 1815–1879 Kalutara, Ceylon",,"Cameron, Julia Margaret","British, born India",1815,1815,1865,1865,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1941",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268705,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -41.21.23,false,true,268706,Photographs,Photograph,"Henry Taylor. Author of ""Philip Van Artevelde""",,,,,,Artist,,Julia Margaret Cameron,"British (born India), Calcutta 1815–1879 Kalutara, Ceylon",,"Cameron, Julia Margaret","British, born India",1815,1815,1864,1864,1864,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1941",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268706,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -41.21.24,false,true,268707,Photographs,Photograph,Henry Taylor,,,,,,Artist,,Julia Margaret Cameron,"British (born India), Calcutta 1815–1879 Kalutara, Ceylon",,"Cameron, Julia Margaret","British, born India",1815,1815,1864,1864,1864,Albumen silver print from glass negative,25.5 x 20.1 cm (10 1/16 x 7 15/16 in. ),"Harris Brisbane Dick Fund, 1941",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268707,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -41.21.25,false,true,268708,Photographs,Photograph,Henry Taylor,,,,,,Artist,,Julia Margaret Cameron,"British (born India), Calcutta 1815–1879 Kalutara, Ceylon",,"Cameron, Julia Margaret","British, born India",1815,1815,1864,1864,1864,Albumen silver print from glass negative,26.1 x 20.8 cm (10 1/4 x 8 3/16 in. ),"Harris Brisbane Dick Fund, 1941",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268708,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -41.21.26,false,true,268709,Photographs,Photograph,Christabel,,,,,,Artist,,Julia Margaret Cameron,"British (born India), Calcutta 1815–1879 Kalutara, Ceylon",,"Cameron, Julia Margaret","British, born India",1815,1815,1866,1866,1866,Albumen silver print from glass negative,33.2 x 26.9 cm (13 1/16 x 10 9/16 in. ),"Harris Brisbane Dick Fund, 1941",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268709,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -41.21.27,false,true,268710,Photographs,Photograph,Herr Joachim,,,,,,Artist,,Julia Margaret Cameron,"British (born India), Calcutta 1815–1879 Kalutara, Ceylon",,"Cameron, Julia Margaret","British, born India",1815,1815,1868,1868,1868,Albumen silver print from glass negative,29.5 x 24.3 cm (11 5/8 x 9 9/16 in. ),"Harris Brisbane Dick Fund, 1941",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268710,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -41.21.28,false,true,268711,Photographs,Photograph,William Gifford Palgrave,,,,,,Artist,,Julia Margaret Cameron,"British (born India), Calcutta 1815–1879 Kalutara, Ceylon",,"Cameron, Julia Margaret","British, born India",1815,1815,1868,1868,1868,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1941",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268711,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -41.21.29,false,true,268712,Photographs,Photograph,"Lionel Tennyson, Freshwater",,,,,,Artist,,Julia Margaret Cameron,"British (born India), Calcutta 1815–1879 Kalutara, Ceylon",,"Cameron, Julia Margaret","British, born India",1815,1815,1869,1869,1869,Albumen silver print from glass negative,Image: 24.5 x 30.4 cm (9 5/8 x 11 15/16 in.),"Harris Brisbane Dick Fund, 1941",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268712,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -66.633.1,false,true,271077,Photographs,Photograph,"Alfred, Lord Tennyson",,,,,,Artist,,Julia Margaret Cameron,"British (born India), Calcutta 1815–1879 Kalutara, Ceylon",,"Cameron, Julia Margaret","British, born India",1815,1815,1865,1865,1865,Albumen silver print from glass negative,"Image: 22.9 x 18.4 cm (9 x 7 1/4 in.), rounded top Mount: 40.5 x 24.6 cm (15 15/16 x 9 11/16 in.), irregular","David Hunter McAlpin Fund, 1966",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271077,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -66.633.2,false,true,271078,Photographs,Photograph,"Alfred, Lord Tennyson",,,,,,Artist,,Julia Margaret Cameron,"British (born India), Calcutta 1815–1879 Kalutara, Ceylon",,"Cameron, Julia Margaret","British, born India",1815,1815,ca. 1865,1863,1867,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1966",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271078,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -69.607.1,false,true,271510,Photographs,Photograph,[Unidentified Child],,,,,,Artist,,Julia Margaret Cameron,"British (born India), Calcutta 1815–1879 Kalutara, Ceylon",,"Cameron, Julia Margaret","British, born India",1815,1815,1873,1873,1873,Albumen silver print from glass negative,33.9 x 24.3 cm (13 3/8 x 9 9/16 in. ),"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1969",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271510,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -69.607.2,false,true,271515,Photographs,Photograph,[Mary Ryan],,,,,,Artist,,Julia Margaret Cameron,"British (born India), Calcutta 1815–1879 Kalutara, Ceylon",,"Cameron, Julia Margaret","British, born India",1815,1815,1865–66,1865,1866,Albumen silver print from glass negative,33.1 x 24.7 cm (13 1/16 x 9 3/4 in. ),"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1969",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271515,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -69.607.3,false,true,271516,Photographs,Photograph,Ceylonese Woman,,,,,,Artist,,Julia Margaret Cameron,"British (born India), Calcutta 1815–1879 Kalutara, Ceylon",,"Cameron, Julia Margaret","British, born India",1815,1815,1875–79,1875,1879,Albumen silver print from glass negative,,"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1969",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271516,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -69.607.4,false,true,271517,Photographs,Photograph,"Mrs. Halford Vaugham, Freshwater",,,,,,Artist,,Julia Margaret Cameron,"British (born India), Calcutta 1815–1879 Kalutara, Ceylon",,"Cameron, Julia Margaret","British, born India",1815,1815,1873,1873,1873,Albumen silver print,33.5 x 27.3 cm (13 3/16 x 10 3/4 in. ),"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1969",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271517,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -69.607.7,false,true,271520,Photographs,Photograph,Marie Spartali,,,,,,Artist,,Julia Margaret Cameron,"British (born India), Calcutta 1815–1879 Kalutara, Ceylon",,"Cameron, Julia Margaret","British, born India",1815,1815,1868,1868,1868,Albumen silver print,,"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1969",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271520,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -69.607.8,false,true,271521,Photographs,Photograph,Aubrey de Vere,,,,,,Artist,,Julia Margaret Cameron,"British (born India), Calcutta 1815–1879 Kalutara, Ceylon",,"Cameron, Julia Margaret","British, born India",1815,1815,1866–68,1866,1868,Albumen silver print,32.5 x 26.8 cm (12 13/16 x 10 9/16 in. ),"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1969",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271521,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -69.607.9,false,true,271522,Photographs,Photograph,Beatrice,,,,,,Artist,,Julia Margaret Cameron,"British (born India), Calcutta 1815–1879 Kalutara, Ceylon",,"Cameron, Julia Margaret","British, born India",1815,1815,1866,1866,1866,Albumen silver print from glass negative,36.8 x 29.0 cm (14 1/2 x 11 7/16 in. ),"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1969",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271522,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1996.99.2,true,true,267426,Photographs,Photograph,Julia Jackson,,,,,,Artist,,Julia Margaret Cameron,"British (born India), Calcutta 1815–1879 Kalutara, Ceylon",,"Cameron, Julia Margaret","British, born India",1815,1815,1867,1867,1867,Albumen silver print from glass negative,27.4 x 20.6 cm (10 13/16 x 8 1/8 in.),"Purchase, Joseph Pulitzer Bequest, 1996",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267426,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -41.21.1.1,false,true,268690,Photographs,Photograph,Charles Hay Cameron,,,,,,Artist,,Julia Margaret Cameron,"British (born India), Calcutta 1815–1879 Kalutara, Ceylon",,"Cameron, Julia Margaret","British, born India",1815,1815,1864,1864,1864,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1941",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268690,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -41.21.1.2,false,true,268691,Photographs,Photograph,A Study,,,,,,Artist,,Julia Margaret Cameron,"British (born India), Calcutta 1815–1879 Kalutara, Ceylon",,"Cameron, Julia Margaret","British, born India",1815,1815,1864,1864,1864,Albumen silver print from glass negative,21.7 x 17.6 cm (8 9/16 x 6 15/16 in. ),"Harris Brisbane Dick Fund, 1941",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268691,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -69.607.10,false,true,271511,Photographs,Photograph,[Unidentified Woman in Profile],,,,,,Artist,,Julia Margaret Cameron,"British (born India), Calcutta 1815–1879 Kalutara, Ceylon",,"Cameron, Julia Margaret","British, born India",1815,1815,1866–68,1866,1868,Albumen silver print,33.9 x 25.8 cm (13 3/8 x 10 3/16 in. ),"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1969",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271511,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -69.607.11,false,true,271512,Photographs,Photograph,[Woman in Robes Reading a Book],,,,,,Artist,,Julia Margaret Cameron,"British (born India), Calcutta 1815–1879 Kalutara, Ceylon",,"Cameron, Julia Margaret","British, born India",1815,1815,1870,1870,1870,Albumen silver print from glass negative,35.1 x 27.3 cm (13 13/16 x 10 3/4 in. ),"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1969",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271512,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -69.607.12,false,true,271513,Photographs,Photograph,May. Freshwater,,,,,,Artist,,Julia Margaret Cameron,"British (born India), Calcutta 1815–1879 Kalutara, Ceylon",,"Cameron, Julia Margaret","British, born India",1815,1815,1870,1870,1870,Albumen silver print from glass negative,,"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1969",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271513,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -69.607.13,false,true,271514,Photographs,Photograph,[Unidentified Child],,,,,,Artist,,Julia Margaret Cameron,"British (born India), Calcutta 1815–1879 Kalutara, Ceylon",,"Cameron, Julia Margaret","British, born India",1815,1815,1873,1873,1873,Albumen silver print,32.3 x 24.4 cm (12 11/16 x 9 5/8 in. ),"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1969",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271514,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1973.502.2,false,true,260731,Photographs,Photograph,[Egeria],,,,,,Artist,,Julia Margaret Cameron,"British (born India), Calcutta 1815–1879 Kalutara, Ceylon",,"Cameron, Julia Margaret","British, born India",1815,1815,1874,1874,1874,Albumen silver print from glass negative,36.2 x 27.6 cm. (14 1/4 x 10 7/8 in.),"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260731,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.159.3,false,true,306204,Photographs,Photograph,King Lear Alotting His Kingdom to His Three Daughters,,,,,,Artist,,Julia Margaret Cameron,"British (born India), Calcutta 1815–1879 Kalutara, Ceylon",,"Cameron, Julia Margaret","British, born India",1815,1815,1872,1872,1872,Albumen silver print from glass negative,Mount: 18 1/2 × 14 7/8 in. (47 × 37.8 cm) Image: 13 1/8 × 11 1/16 in. (33.4 × 28.1 cm),"Bequest of Maurice B. Sendak, 2012",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306204,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -52.524.3.1,false,true,282128,Photographs,Photograph,Gareth and Lynette,,,,,,Artist,,Julia Margaret Cameron,"British (born India), Calcutta 1815–1879 Kalutara, Ceylon",,"Cameron, Julia Margaret","British, born India",1815,1815,1874,1874,1874,Albumen silver print from glass negative,33.9 x 28.2 cm (13 3/8 x 11 1/8 in. ),"David Hunter McAlpin Fund, 1952",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/282128,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -52.524.3.2,false,true,282119,Photographs,Photograph,Enid,,,,,,Artist,,Julia Margaret Cameron,"British (born India), Calcutta 1815–1879 Kalutara, Ceylon",,"Cameron, Julia Margaret","British, born India",1815,1815,September 1874,1874,1874,Albumen silver print from glass negative,34.2 x 26.7 cm (13 7/16 x 10 1/2 in. ),"David Hunter McAlpin Fund, 1952",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/282119,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -52.524.3.3,false,true,282120,Photographs,Photograph,And Enid Sang,,,,,,Artist,,Julia Margaret Cameron,"British (born India), Calcutta 1815–1879 Kalutara, Ceylon",,"Cameron, Julia Margaret","British, born India",1815,1815,September 1874,1874,1874,Albumen silver print from glass negative,35.4 x 28 cm (13 15/16 x 11 in. ),"David Hunter McAlpin Fund, 1952",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/282120,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -52.524.3.4,false,true,282121,Photographs,Photograph,Vivien and Merlin,,,,,,Artist,,Julia Margaret Cameron,"British (born India), Calcutta 1815–1879 Kalutara, Ceylon",,"Cameron, Julia Margaret","British, born India",1815,1815,September 1874,1874,1874,Albumen silver print from glass negative,31.9 x 28 cm (12 9/16 x 11 in. ),"David Hunter McAlpin Fund, 1952",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/282121,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -52.524.3.5,false,true,282118,Photographs,Photograph,Vivien and Merlin,,,,,,Artist,,Julia Margaret Cameron,"British (born India), Calcutta 1815–1879 Kalutara, Ceylon",,"Cameron, Julia Margaret","British, born India",1815,1815,1874,1874,1874,Albumen silver print from glass negative,30.4 x 25.3 cm (11 15/16 x 9 15/16 in. ),"David Hunter McAlpin Fund, 1952",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/282118,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -52.524.3.6,false,true,282122,Photographs,Photograph,Elaine the Lily - Maid of Astolat,,,,,,Artist,,Julia Margaret Cameron,"British (born India), Calcutta 1815–1879 Kalutara, Ceylon",,"Cameron, Julia Margaret","British, born India",1815,1815,1874,1874,1874,Albumen silver print from glass negative,34.3 x 28.4 cm (13 1/2 x 11 3/16 in. ),"David Hunter McAlpin Fund, 1952",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/282122,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -52.524.3.7,false,true,282129,Photographs,Photograph,Elaine,,,,,,Artist,,Julia Margaret Cameron,"British (born India), Calcutta 1815–1879 Kalutara, Ceylon",,"Cameron, Julia Margaret","British, born India",1815,1815,1874,1874,1874,Albumen silver print from glass negative,33.5 x 27.8 cm (13 3/16 x 10 15/16 in. ),"David Hunter McAlpin Fund, 1952",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/282129,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -52.524.3.8,false,true,282147,Photographs,Photograph,Sir Galahad and the Pale Nun,,,,,,Artist,,Julia Margaret Cameron,"British (born India), Calcutta 1815–1879 Kalutara, Ceylon",,"Cameron, Julia Margaret","British, born India",1815,1815,1874,1874,1874,Albumen silver print from glass negative,33.2 x 27.5 cm (13 1/16 x 10 13/16 in. ),"David Hunter McAlpin Fund, 1952",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/282147,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -52.524.3.9,false,true,282148,Photographs,Photograph,Queen Guinevere,,,,,,Artist,,Julia Margaret Cameron,"British (born India), Calcutta 1815–1879 Kalutara, Ceylon",,"Cameron, Julia Margaret","British, born India",1815,1815,1874,1874,1874,Albumen silver print from glass negative,34.1 x 25.5 cm (13 7/16 x 10 1/16 in. ),"David Hunter McAlpin Fund, 1952",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/282148,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1990.1074.1,false,true,266254,Photographs,Photograph,The Passing of Arthur,,,,,,Artist,,Julia Margaret Cameron,"British (born India), Calcutta 1815–1879 Kalutara, Ceylon",,"Cameron, Julia Margaret","British, born India",1815,1815,1874,1874,1874,Albumen silver print from glass negative,35.2 x 25.2 cm. (13 7/8 x 9 15/16 in.),"Bequest of James David Nelson, in memory of Samuel J. Wagstaff Jr., 1990",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/266254,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1990.1074.2,false,true,266255,Photographs,Photograph,Beatrice,,,,,,Artist,,Julia Margaret Cameron,"British (born India), Calcutta 1815–1879 Kalutara, Ceylon",,"Cameron, Julia Margaret","British, born India",1815,1815,1866,1866,1866,Albumen silver print from glass negative,34.6 x 26.3 cm. (13 5/8 x 10 3/6 in.),"Bequest of James David Nelson, in memory of Samuel J. Wagstaff Jr., 1990",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/266255,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1990.1074.3,false,true,266256,Photographs,Photograph,A Study,,,,,,Artist,,Julia Margaret Cameron,"British (born India), Calcutta 1815–1879 Kalutara, Ceylon",,"Cameron, Julia Margaret","British, born India",1815,1815,1865–66,1865,1866,Albumen silver print from glass negative,34.4 x 26.4 cm. (13 9/16 x 10 3/8 in.),"Bequest of James David Nelson, in memory of Samuel J. Wagstaff Jr., 1990",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/266256,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1990.1074.4,false,true,266257,Photographs,Photograph,Circe,,,,,,Artist,,Julia Margaret Cameron,"British (born India), Calcutta 1815–1879 Kalutara, Ceylon",,"Cameron, Julia Margaret","British, born India",1815,1815,1865,1865,1865,Albumen silver print from glass negative,25.3 x 20.1 cm. (9 15/16 x 7 15/16 in.),"Bequest of James David Nelson, in memory of Samuel J. Wagstaff Jr., 1990",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/266257,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1990.1074.5,false,true,266258,Photographs,Photograph,A Lovely Sketch,,,,,,Artist,,Julia Margaret Cameron,"British (born India), Calcutta 1815–1879 Kalutara, Ceylon",,"Cameron, Julia Margaret","British, born India",1815,1815,1873,1873,1873,Albumen silver print from glass negative,31.0 x 24.3 cm. (12 3/16 x 9 9/16 in.),"Bequest of James David Nelson, in memory of Samuel J. Wagstaff Jr., 1990",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/266258,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1990.1074.6,false,true,266259,Photographs,Photograph,English Blossoms,,,,,,Artist,,Julia Margaret Cameron,"British (born India), Calcutta 1815–1879 Kalutara, Ceylon",,"Cameron, Julia Margaret","British, born India",1815,1815,1873,1873,1873,Albumen silver print from glass negative,32.8 x 27.5 cm. (12 15/16 x 10 13/16 in.),"Bequest of James David Nelson, in memory of Samuel J. Wagstaff Jr., 1990",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/266259,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1990.1074.7,false,true,266260,Photographs,Carte-de-visite,Summer Days,,,,,,Artist,,Julia Margaret Cameron,"British (born India), Calcutta 1815–1879 Kalutara, Ceylon",,"Cameron, Julia Margaret","British, born India",1815,1815,1866–69,1866,1869,Albumen silver print from glass negative,8.4 x 5.7 cm. (3 5/16 x 2 1/4 in.),"Bequest of James David Nelson, in memory of Samuel J. Wagstaff Jr., 1990",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/266260,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1997.382.36,false,true,282041,Photographs,Photograph,"Alfred, Lord Tennyson",,,,,,Artist,,Julia Margaret Cameron,"British (born India), Calcutta 1815–1879 Kalutara, Ceylon",,"Cameron, Julia Margaret","British, born India",1815,1815,"July 4, 1866",1866,1866,Albumen silver print from glass negative,35 x 27 cm (13 3/4 x 10 5/8 in. ) irregular,"The Rubel Collection, Purchase, Lila Acheson Wallace, Michael and Jane Wilson, and Harry Kahn Gifts, 1997",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/282041,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1997.382.37,false,true,282042,Photographs,Photograph,Cassiopeia,,,,,,Artist,,Julia Margaret Cameron,"British (born India), Calcutta 1815–1879 Kalutara, Ceylon",,"Cameron, Julia Margaret","British, born India",1815,1815,1866,1866,1866,Albumen silver print from glass negative,34.9 x 27.4 cm (13 3/4 x 10 13/16 in. ),"The Rubel Collection, Purchase, Lila Acheson Wallace, Harry Kahn, and Ann Tenenbaum and Thomas H. Lee Gifts, 1997",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/282042,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1997.382.38,true,true,282043,Photographs,Photograph,"Zoe, Maid of Athens",,,,,,Artist,,Julia Margaret Cameron,"British (born India), Calcutta 1815–1879 Kalutara, Ceylon",,"Cameron, Julia Margaret","British, born India",1815,1815,1866,1866,1866,Albumen silver print from glass negative,30.1 x 24.5 cm (11 7/8 x 9 5/8 in.),"The Rubel Collection, Purchase, Lila Acheson Wallace, Ann Tenenbaum and Thomas H. Lee, and Muriel Kallis Newman Gifts, 1997",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/282043,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1997.382.39,false,true,282044,Photographs,Photograph,Sappho,,,,,,Artist,,Julia Margaret Cameron,"British (born India), Calcutta 1815–1879 Kalutara, Ceylon",,"Cameron, Julia Margaret","British, born India",1815,1815,1865,1865,1865,Albumen silver print from glass negative,35 x 27.3 cm (13 3/4 x 10 3/4 in. ),"The Rubel Collection, Purchase, Jennifer and Joseph Duke and Anonymous Gifts, 1997",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/282044,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.25,false,true,283097,Photographs,Photograph,Sir John Herschel,,,,,,Artist,,Julia Margaret Cameron,"British (born India), Calcutta 1815–1879 Kalutara, Ceylon",,"Cameron, Julia Margaret","British, born India",1815,1815,April 1867,1867,1867,Albumen silver print from glass negative,"Image: 31.8 x 24.9 cm (12 1/2 x 9 13/16 in.) Mount: 39.9 x 32.9 cm (15 11/16 x 12 15/16 in.), corners clipped","Gilman Collection, Purchase, Robert Rosenkranz Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283097,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.26,false,true,283098,Photographs,Photograph,Mrs. Herbert Duckworth,,,,,,Artist,,Julia Margaret Cameron,"British (born India), Calcutta 1815–1879 Kalutara, Ceylon",,"Cameron, Julia Margaret","British, born India",1815,1815,1867,1867,1867,Albumen silver print from glass negative,32.8 x 23.7 cm (12 15/16 x 9 5/16 in.),"Gilman Collection, Purchase, Alfred Stieglitz Society Gifts, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283098,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.27,true,true,283099,Photographs,Photograph,Philip Stanhope Worsley,,,,,,Artist,,Julia Margaret Cameron,"British (born India), Calcutta 1815–1879 Kalutara, Ceylon",,"Cameron, Julia Margaret","British, born India",1815,1815,1866,1866,1866,Albumen silver print from glass negative,"Image: 30.4 x 25 cm (11 15/16 x 9 13/16 in.) Mount: 40.9 x 30.6 cm (16 1/8 x 12 1/16 in.), irregular","Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283099,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -52.524.3.10,false,true,269583,Photographs,Photograph,The Parting of Lancelot and Guinevere,,,,,,Artist,,Julia Margaret Cameron,"British (born India), Calcutta 1815–1879 Kalutara, Ceylon",,"Cameron, Julia Margaret","British, born India",1815,1815,1874,1874,1874,Albumen silver print from glass negative,Image: 33.2 x 28.8 cm (13 1/16 x 11 5/16 in.) Mount: 44 x 33.3cm (17 5/16 x 13 1/8in.),"David Hunter McAlpin Fund, 1952",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269583,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -52.524.3.11,false,true,282149,Photographs,Photograph,King Arthur,,,,,,Artist,,Julia Margaret Cameron,"British (born India), Calcutta 1815–1879 Kalutara, Ceylon",,"Cameron, Julia Margaret","British, born India",1815,1815,September 1874,1874,1874,Albumen silver print from glass negative,35.9 x 28 cm (14 1/8 x 11 in. ),"David Hunter McAlpin Fund, 1952",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/282149,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -52.524.3.12,false,true,282150,Photographs,Photograph,The Passing of King Arthur,,,,,,Artist,,Julia Margaret Cameron,"British (born India), Calcutta 1815–1879 Kalutara, Ceylon",,"Cameron, Julia Margaret","British, born India",1815,1815,1874,1874,1874,Albumen silver print from glass negative,35 x 27.3 cm (13 3/4 x 10 3/4 in. ),"David Hunter McAlpin Fund, 1952",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/282150,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1981.1229.18,false,true,263166,Photographs,Photograph,Sir John Herschel,,,,,,Artist,,Julia Margaret Cameron,"British (born India), Calcutta 1815–1879 Kalutara, Ceylon",,"Cameron, Julia Margaret","British, born India",1815,1815,1875,1875,1875,Carbon print,,"Robert O. Dougan Collection, Gift of Warner Communications Inc., 1981",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/263166,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.239,false,true,286351,Photographs,Photograph,"Déjatch Alámayou, King Theodore's Son",,,,,,Artist,,Julia Margaret Cameron,"British (born India), Calcutta 1815–1879 Kalutara, Ceylon",,"Cameron, Julia Margaret","British, born India",1815,1815,July 1868,1868,1868,Albumen silver print from glass negative,Image: 29.2 x 23.3 cm (11 1/2 x 9 3/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286351,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.265,false,true,286404,Photographs,Photograph,[Kate Keown],,,,,,Artist,,Julia Margaret Cameron,"British (born India), Calcutta 1815–1879 Kalutara, Ceylon",,"Cameron, Julia Margaret","British, born India",1815,1815,1866,1866,1866,Albumen silver print from glass negative,Image: 29 x 29 cm (11 7/16 x 11 7/16 in.) circle,"Gilman Collection, Purchase, Jennifer and Joseph Duke Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286404,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2004.275,false,true,285429,Photographs,Photograph,[Climbing the Mast],,,,,,Artist,,László Moholy-Nagy,"American (born Hungary), Borsod 1895–1946 Chicago, Illinois",,"Moholy-Nagy, László","American, born Hungary",1895,1946,1928,1928,1928,Gelatin silver print,Image: 35.4 x 28 cm (13 15/16 x 11 in.),"Purchase, several members of The Chairman's Council Gifts, 2004",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285429,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1981.1163,false,true,263068,Photographs,Photograph,[Lucia Moholy; Negative Print],,,,,,Artist,,László Moholy-Nagy,"American (born Hungary), Borsod 1895–1946 Chicago, Illinois",,"Moholy-Nagy, László","American, born Hungary",1895,1946,1924–28,1924,1928,Gelatin silver print,23.6 x 17.5 cm (9 5/16 x 6 7/8 in.),"Warner Communications Inc. Purchase Fund, 1981",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/263068,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1183,false,true,264891,Photographs,Photograph,Fischernetze auf Isola Bella,,,,,,Artist,,László Moholy-Nagy,"American (born Hungary), Borsod 1895–1946 Chicago, Illinois",,"Moholy-Nagy, László","American, born Hungary",1895,1946,ca. 1930,1928,1932,Gelatin silver print,17.5 x 23.5 cm. (6 7/8 x 9 1/4 in.),"Gift of Sheri and Paul Siegel, 1986",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264891,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1985.1150.4,false,true,264515,Photographs,Photograph,"[Cat, Seen From Above]",,,,,,Artist,,László Moholy-Nagy,"American (born Hungary), Borsod 1895–1946 Chicago, Illinois",,"Moholy-Nagy, László","American, born Hungary",1895,1946,ca. 1926,1924,1928,Gelatin silver print,23.4 x 17.5 cm. (9 3/16 x 6 7/8 in.),"Gift of Emanuel Gerard, 1985",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264515,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1987.1100.23,false,true,265277,Photographs,Photograph,Behind the Back of the Gods,,,,,,Artist,,László Moholy-Nagy,"American (born Hungary), Borsod 1895–1946 Chicago, Illinois",,"Moholy-Nagy, László","American, born Hungary",1895,1946,1928,1928,1928,Gelatin silver print,36.0 x 27.4 cm (14 3/16 x 10 13/16 in.),"Ford Motor Company Collection, Gift of Ford Motor Company and John C. Waddell, 1987",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/265277,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.152,false,true,283290,Photographs,Photograph,"Decorating Work, Switzerland",,,,,,Artist,,László Moholy-Nagy,"American (born Hungary), Borsod 1895–1946 Chicago, Illinois",,"Moholy-Nagy, László","American, born Hungary",1895,1946,1925,1925,1925,Gelatin silver print,Image: 50.6 x 40.2 cm (19 15/16 x 15 13/16 in. ),"Gilman Collection, Purchase, Robert Rosenkranz Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283290,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.298,false,true,285944,Photographs,Photograph,"Pont Transbordeur, Marseille",,,,,,Artist,,László Moholy-Nagy,"American (born Hungary), Borsod 1895–1946 Chicago, Illinois",,"Moholy-Nagy, László","American, born Hungary",1895,1946,1929,1929,1929,Gelatin silver print,Image: 23.7 x 17.9 cm (9 5/16 x 7 1/16 in.) Mount: 25.3 x 19.2 cm (9 15/16 x 7 9/16 in.),"Gilman Collection, Purchase, Alfred Stieglitz Society Gifts, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285944,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.437,false,true,283291,Photographs,Photograph,Dolls on the Balcony,,,,,,Artist,,László Moholy-Nagy,"American (born Hungary), Borsod 1895–1946 Chicago, Illinois",,"Moholy-Nagy, László","American, born Hungary",1895,1946,1926,1926,1926,Gelatin silver print,23.5 x 17.5 cm (9 1/4 x 6 7/8 in.),"Gilman Collection, Purchase, Denise and Andrew Saul Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283291,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.444,false,true,285731,Photographs,Photograph,"From the Radio Tower, Berlin",,,,,,Artist,,László Moholy-Nagy,"American (born Hungary), Borsod 1895–1946 Chicago, Illinois",,"Moholy-Nagy, László","American, born Hungary",1895,1946,"1928, printed ca. 1940",1928,1940,Gelatin silver print,Image: 24.6 x 19.1 cm (9 11/16 x 7 1/2 in.) Mount: 38.5 x 26.8 cm (15 3/16 x 10 9/16 in.),"Gilman Collection, Purchase, Gift of Ford Motor Company and John C. Waddell, by exchange, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285731,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1987.1100.155,false,true,265194,Photographs,Photograph,7 A.M. (New Year's Morning),,,,,,Artist,,László Moholy-Nagy,"American (born Hungary), Borsod 1895–1946 Chicago, Illinois",,"Moholy-Nagy, László","American, born Hungary",1895,1946,ca. 1930,1928,1932,Gelatin silver print,27.8 x 21.3 cm (10 15/16 x 8 3/8 in.),"Ford Motor Company Collection, Gift of Ford Motor Company and John C. Waddell, 1987",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/265194,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1987.1100.158,false,true,265197,Photographs,Photogram,Fotogramm,,,,,,Artist,,László Moholy-Nagy,"American (born Hungary), Borsod 1895–1946 Chicago, Illinois",,"Moholy-Nagy, László","American, born Hungary",1895,1946,1926,1926,1926,Gelatin silver print,23.9 x 17.9 cm (9 7/16 x 7 1/16 in.),"Ford Motor Company Collection, Gift of Ford Motor Company and John C. Waddell, 1987",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/265197,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1987.1100.159,false,true,265198,Photographs,Photogram,Fotogramm,,,,,,Artist,,László Moholy-Nagy,"American (born Hungary), Borsod 1895–1946 Chicago, Illinois",,"Moholy-Nagy, László","American, born Hungary",1895,1946,1925–1928,1925,1928,Gelatin silver print,23.9 x 17.9 cm. (9 7/16 x 7 1/16 in.),"Ford Motor Company Collection, Gift of Ford Motor Company and John C. Waddell, 1987",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/265198,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1987.1100.160,false,true,265200,Photographs,Photogram,Fotogramm,,,,,,Artist,,László Moholy-Nagy,"American (born Hungary), Borsod 1895–1946 Chicago, Illinois",,"Moholy-Nagy, László","American, born Hungary",1895,1946,1925,1925,1925,Gelatin silver print,23.8 x 17.8 cm (9 3/8 x 7 in.),"Ford Motor Company Collection, Gift of Ford Motor Company and John C. Waddell, 1987",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/265200,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1987.1100.228,false,true,265275,Photographs,Photogram,Fotogramm,,,,,,Artist,,László Moholy-Nagy,"American (born Hungary), Borsod 1895–1946 Chicago, Illinois",,"Moholy-Nagy, László","American, born Hungary",1895,1946,1922,1922,1922,Gelatin silver print,17.8 x 23.7 cm (7 x 9 5/16 in.),"Ford Motor Company Collection, Gift of Ford Motor Company and John C. Waddell, 1987",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/265275,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1987.1100.230,false,true,265278,Photographs,Photograph,Target Practice (In the Name of the Law),,,,,,Artist,,László Moholy-Nagy,"American (born Hungary), Borsod 1895–1946 Chicago, Illinois",,"Moholy-Nagy, László","American, born Hungary",1895,1946,ca. 1927,1925,1929,Gelatin silver print,24.0 x 18.2 cm (9 7/16 x 7 3/16 in.),"Ford Motor Company Collection, Gift of Ford Motor Company and John C. Waddell, 1987",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/265278,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1987.1100.231,false,true,265279,Photographs,Photograph,Lucia,,,,,,Artist,,László Moholy-Nagy,"American (born Hungary), Borsod 1895–1946 Chicago, Illinois",,"Moholy-Nagy, László","American, born Hungary",1895,1946,1924–28,1924,1928,Gelatin silver print,8.2 x 5.4 cm (3 1/4 x 2 1/8 in.),"Ford Motor Company Collection, Gift of Ford Motor Company and John C. Waddell, 1987",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/265279,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1987.1100.463,false,true,265536,Photographs,Photograph,Lucia Moholy,,,,,,Artist,,László Moholy-Nagy,"American (born Hungary), Borsod 1895–1946 Chicago, Illinois",,"Moholy-Nagy, László","American, born Hungary",1895,1946,1920s,1920,1929,Gelatin silver print,10.0 x 7.2 cm. (3 15/16 x 2 13/16 in.),"Ford Motor Company Collection, Gift of Ford Motor Company and John C. Waddell, 1987",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/265536,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1987.1100.499,false,true,283698,Photographs,Photograph,Scandinavia,,,,,,Artist,,László Moholy-Nagy,"American (born Hungary), Borsod 1895–1946 Chicago, Illinois",,"Moholy-Nagy, László","American, born Hungary",1895,1946,1930,1930,1930,Gelatin silver print,23.5 x 17.1 cm (9 1/4 x 6 3/4 in.),"Ford Motor Company Collection, Gift of Ford Motor Company and John C. Waddell, 1987",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283698,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.5,false,true,283077,Photographs,Photograph,[Trees],,,,,,Artist,,Thomas Keith,"British, Kincardine, Aberdeenshire, Scotland 1827–1895 London",,"Keith, Thomas","British, Scottish",1827,1895,1854–57,1854,1857,Salted paper print from paper negative,Image: 30.3 x 22.1 cm (11 15/16 x 8 11/16 in.) Mount: 54.8 x 38.3 cm (21 9/16 x 15 1/16 in.),"Gilman Collection, Purchase, Harriette and Noel Levine Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283077,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.918,false,true,285847,Photographs,Photograph,"Tropical Scenery, Street, Chipigana",,,,,,Artist,,John Moran,"American (born England), Bolton, Lancashire 1821–1903 Pennsylvania",,"Moran, John","American, born England",1821,1903,1871,1871,1871,Albumen silver print from glass negative,Image: 20.3 x 27.9 cm (8 x 11 in.) Mount: 40.6 x 50.8 cm (16 x 20 in.),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285847,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.919,false,true,286179,Photographs,Photograph,"Tropical Scenery, Native Hut, Turbo",,,,,,Artist,,John Moran,"American (born England), Bolton, Lancashire 1821–1903 Pennsylvania",,"Moran, John","American, born England",1821,1903,1871,1871,1871,Albumen silver print from glass negative,Image: 20.3 x 27.9 cm (8 x 11 in.) Mount: 40.6 x 50.8 cm (16 x 20 in.),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286179,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.920,false,true,286180,Photographs,Photograph,"Tropical Scenery, Tropical Forest",,,,,,Artist,,John Moran,"American (born England), Bolton, Lancashire 1821–1903 Pennsylvania",,"Moran, John","American, born England",1821,1903,1871,1871,1871,Albumen silver print from glass negative,Image: 27.9 x 20.3 cm (11 x 8 in.) Mount: 50.8 x 40.6 cm (20 x 16 in.),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286180,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.921,false,true,286181,Photographs,Photograph,"Tropical Scenery, View Near Chipigana",,,,,,Artist,,John Moran,"American (born England), Bolton, Lancashire 1821–1903 Pennsylvania",,"Moran, John","American, born England",1821,1903,1871,1871,1871,Albumen silver print from glass negative,Image: 20.3 x 27.9 cm (8 x 11 in.) Mount: 40.6 x 50.8 cm (16 x 20 in.),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286181,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.922,false,true,286182,Photographs,Photograph,"Tropical Scenery, Turbo Village",,,,,,Artist,,John Moran,"American (born England), Bolton, Lancashire 1821–1903 Pennsylvania",,"Moran, John","American, born England",1821,1903,1871,1871,1871,Albumen silver print from glass negative,Image: 20.3 x 27.9 cm (8 x 11 in.) Mount: 40.6 x 50.8 cm (16 x 20 in.),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286182,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.923,false,true,286183,Photographs,Photograph,"Tropical Scenery, The Brook El Bano, Chipigana",,,,,,Artist,,John Moran,"American (born England), Bolton, Lancashire 1821–1903 Pennsylvania",,"Moran, John","American, born England",1821,1903,1871,1871,1871,Albumen silver print from glass negative,Image: 27.9 x 20.3 cm (11 x 8 in.) Mount: 50.8 x 40.6 cm (20 x 16 in.),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286183,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.926,false,true,286185,Photographs,Photograph,"Tropical Scenery, Landing, Chipigana",,,,,,Artist,,John Moran,"American (born England), Bolton, Lancashire 1821–1903 Pennsylvania",,"Moran, John","American, born England",1821,1903,1871,1871,1871,Albumen silver print from glass negative,Image: 20 x 27.6 cm (7 7/8 x 10 7/8 in.) Mount: 30.5 x 38.1 cm (12 x 15 in.),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286185,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.927,false,true,286186,Photographs,Photograph,"Tropical Scenery, Natural Arch, Cupica Bay",,,,,,Artist,,John Moran,"American (born England), Bolton, Lancashire 1821–1903 Pennsylvania",,"Moran, John","American, born England",1821,1903,1871,1871,1871,Albumen silver print from glass negative,Image: 27.6 x 20.2 cm (10 7/8 x 7 15/16 in.) Mount: 38.1 x 30.5 cm (15 x 12 in.),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286186,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.928,false,true,286187,Photographs,Photograph,"Tropical Scenery, Cascade, Limon River",,,,,,Artist,,John Moran,"American (born England), Bolton, Lancashire 1821–1903 Pennsylvania",,"Moran, John","American, born England",1821,1903,1871,1871,1871,Albumen silver print from glass negative,Image: 27.3 x 20 cm (10 3/4 x 7 7/8 in.) Mount: 38.1 x 30.5 cm (15 x 12 in.),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286187,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.929,false,true,286188,Photographs,Photograph,"Tropical Scenery, Native Hut, Turbo",,,,,,Artist,,John Moran,"American (born England), Bolton, Lancashire 1821–1903 Pennsylvania",,"Moran, John","American, born England",1821,1903,1871,1871,1871,Albumen silver print from glass negative,Image: 20 x 27.3 cm (7 7/8 x 10 3/4 in.) Mount: 30.5 x 38.1 cm (12 x 15 in.),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286188,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.930,false,true,286189,Photographs,Photograph,"Tropical Scenery, Tropical Forest",,,,,,Artist,,John Moran,"American (born England), Bolton, Lancashire 1821–1903 Pennsylvania",,"Moran, John","American, born England",1821,1903,1871,1871,1871,Albumen silver print from glass negative,Image: 27.6 x 20 cm (10 7/8 x 7 7/8 in.) Mount: 38.1 x 30.5 cm (15 x 12 in.),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286189,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.931,false,true,286190,Photographs,Photograph,"Tropical Scenery, Cathedral, Cartagena",,,,,,Artist,,John Moran,"American (born England), Bolton, Lancashire 1821–1903 Pennsylvania",,"Moran, John","American, born England",1821,1903,1871,1871,1871,Albumen silver print from glass negative,Image: 20 x 27.6 cm (7 7/8 x 10 7/8 in.) Mount: 30.5 x 38.1 cm (12 x 15 in.),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286190,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.932,false,true,286191,Photographs,Photograph,"Tropical Scenery, Darien Harbor, Chipigana",,,,,,Artist,,John Moran,"American (born England), Bolton, Lancashire 1821–1903 Pennsylvania",,"Moran, John","American, born England",1821,1903,1871,1871,1871,Albumen silver print from glass negative,Image: 20 x 27.3 cm (7 7/8 x 10 3/4 in.) Mount: 30.5 x 38.1 cm (12 x 15 in.),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286191,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.933,false,true,285991,Photographs,Photograph,"Tropical Scenery, Darien Harbor - Looking South",,,,,,Artist,,John Moran,"American (born England), Bolton, Lancashire 1821–1903 Pennsylvania",,"Moran, John","American, born England",1821,1903,1871,1871,1871,Albumen silver print from glass negative,Image: 20 x 27.6 cm (7 7/8 x 10 7/8 in.) Mount: 30.5 x 38.3 cm (12 x 15 1/16 in.),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285991,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.934,false,true,285992,Photographs,Photograph,"Tropical Scenery, The Terminus of the Proposed Canal, Limon Bay",,,,,,Artist,,John Moran,"American (born England), Bolton, Lancashire 1821–1903 Pennsylvania",,"Moran, John","American, born England",1821,1903,1871,1871,1871,Albumen silver print from glass negative,Image: 20 x 27.3 cm (7 7/8 x 10 3/4 in.) Mount: 30.5 x 38.2 cm (12 x 15 1/16 in.),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285992,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.935,false,true,285993,Photographs,Photograph,"Tropical Scenery, View of Limon Bay",,,,,,Artist,,John Moran,"American (born England), Bolton, Lancashire 1821–1903 Pennsylvania",,"Moran, John","American, born England",1821,1903,1871,1871,1871,Albumen silver print,Image: 20 x 27.3 cm (7 7/8 x 10 3/4 in.) Mount: 30.5 x 38.2 cm (12 x 15 1/16 in.),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285993,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.936,false,true,286040,Photographs,Photograph,"Tropical Scenery, Santa Maria del Real, Darien",,,,,,Artist,,John Moran,"American (born England), Bolton, Lancashire 1821–1903 Pennsylvania",,"Moran, John","American, born England",1821,1903,1871,1871,1871,Albumen silver print from glass negative,Image: 20 x 27.3 cm (7 7/8 x 10 3/4 in.) Mount: 30.5 x 38.2 cm (12 x 15 1/16 in.),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286040,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.937,false,true,286041,Photographs,Photograph,"Tropical Scenery, Cliff - Limon Bay",,,,,,Artist,,John Moran,"American (born England), Bolton, Lancashire 1821–1903 Pennsylvania",,"Moran, John","American, born England",1821,1903,1871,1871,1871,Albumen silver print from glass negative,Image: 20 x 27.3 cm (7 7/8 x 10 3/4 in.) Mount: 30.5 x 38.2 cm (12 x 15 1/16 in.),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286041,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.938,false,true,286042,Photographs,Photograph,"Tropical Scenery, Limon Bay - Low Tide",,,,,,Artist,,John Moran,"American (born England), Bolton, Lancashire 1821–1903 Pennsylvania",,"Moran, John","American, born England",1821,1903,1871,1871,1871,Albumen silver print from glass negative,Image: 20 x 27.6 cm (7 7/8 x 10 7/8 in.) Mount: 30.6 x 38.2 cm (12 1/16 x 15 1/16 in.),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286042,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.939,false,true,286560,Photographs,Photograph,"Tropical Scenery, Forest Near Turbo",,,,,,Artist,,John Moran,"American (born England), Bolton, Lancashire 1821–1903 Pennsylvania",,"Moran, John","American, born England",1821,1903,1871,1871,1871,Albumen silver print from glass negative,Image: 27.3 x 20 cm (10 3/4 x 7 7/8 in.) Mount: 38.2 x 30.6 cm (15 1/16 x 12 1/16 in.),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286560,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -53.680.6,false,true,269669,Photographs,Photograph,A Holiday Visit,,,,,,Artist,,Arnold Genthe,"American (born Germany), Berlin 1869–1942 New Milford, Connecticut",,"Genthe, Arnold","American, born Germany",1869,1942,1895–1908,1895,1908,Gelatin silver print,Image: 34 x 22.8 cm (13 3/8 x 9 in.),"Gift of Mrs. Eustace Seligman, 1953",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269669,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.43.223,false,true,267631,Photographs,Photograph,"After the Earthquake, San Francisco",,,,,,Artist,,Arnold Genthe,"American (born Germany), Berlin 1869–1942 New Milford, Connecticut",,"Genthe, Arnold","American, born Germany",1869,1942,1906,1906,1906,Gelatin silver print,13.3 x 23.5 cm (5 1/4 x 9 1/4 in.),"Alfred Stieglitz Collection, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267631,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.510.5,false,true,261240,Photographs,Photograph,"John D. Rockefeller, Jr.",,,,,,Artist,,Arnold Genthe,"American (born Germany), Berlin 1869–1942 New Milford, Connecticut",,"Genthe, Arnold","American, born Germany",1869,1942,ca. 1925,1923,1927,Gelatin silver print,Image: 19.2 x 23.8 cm (7 9/16 x 9 3/8 in.) Mount: 20.2 x 25 cm (7 15/16 x 9 13/16 in.),"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261240,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.16,false,true,283088,Photographs,Photograph,[Stag in Cart],,,,,,Artist,,Horatio Ross,"British, Rossie Castle, near Montrose, Scotland 1801–1886 Scotland",,"Ross, Horatio","British, Scottish",1801,1886,ca. 1858,1856,1860,Albumen silver print from glass negative,Image: 26.7 x 32.6 cm (10 1/2 x 12 13/16 in.) Mount: 35.7 x 43.6 cm (14 1/16 x 17 3/16 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283088,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.17,false,true,283089,Photographs,Photograph,[Tree],,,,,,Artist,,Horatio Ross,"British, Rossie Castle, near Montrose, Scotland 1801–1886 Scotland",,"Ross, Horatio","British, Scottish",1801,1886,ca. 1858,1856,1860,Albumen silver print from glass negative,Mount: 14 1/16 in. × 17 3/4 in. (35.7 × 45.1 cm) Image: 9 3/4 × 12 1/16 in. (24.7 × 30.7 cm),"Gilman Collection, Purchase, Harriette and Noel Levine Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283089,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.552 (1),false,true,288053,Photographs,Photograph,"[Miss Macrae of Inverinate, Wife of Horatio Ross]",,,,,,Artist,,Horatio Ross,"British, Rossie Castle, near Montrose, Scotland 1801–1886 Scotland",,"Ross, Horatio","British, Scottish",1801,1886,ca. 1858,1858,1858,Salted paper print,"19.8 x 15 cm (7 13/16 x 5 7/8 in.), oval","Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288053,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.552 (2),false,true,288054,Photographs,Photograph,"[Peel Ross, Son of Horatio Ross]",,,,,,Artist,,Horatio Ross,"British, Rossie Castle, near Montrose, Scotland 1801–1886 Scotland",,"Ross, Horatio","British, Scottish",1801,1886,ca. 1858,1858,1858,Salted paper print,"19.9 x 15.1 cm (7 13/16 x 5 15/16 in.), oval","Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288054,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.552 (3),false,true,288055,Photographs,Photograph,"[Edward Ross, Youngest Son of Horatio Ross]",,,,,,Artist,,Horatio Ross,"British, Rossie Castle, near Montrose, Scotland 1801–1886 Scotland",,"Ross, Horatio","British, Scottish",1801,1886,ca. 1858,1858,1858,Salted paper print,20.7 x 15.1 cm (8 1/8 x 5 15/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288055,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.552 (4),false,true,288056,Photographs,Photograph,[Ned and Colin Ross with Hunt Trophy],,,,,,Artist,,Horatio Ross,"British, Rossie Castle, near Montrose, Scotland 1801–1886 Scotland",,"Ross, Horatio","British, Scottish",1801,1886,ca. 1857,1857,1857,Salted paper print,14.9 x 19.8 cm (5 7/8 x 7 13/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288056,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.552 (5),false,true,288057,Photographs,Photograph,[Mrs. Kennedy],,,,,,Artist,,Horatio Ross,"British, Rossie Castle, near Montrose, Scotland 1801–1886 Scotland",,"Ross, Horatio","British, Scottish",1801,1886,ca. 1858,1858,1858,Salted paper print,18.4 x 14.9 cm (7 1/4 x 5 7/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288057,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.552 (6),false,true,288058,Photographs,Photograph,"[Macrae, Ross, and Warner Families Outdoors]",,,,,,Artist,,Horatio Ross,"British, Rossie Castle, near Montrose, Scotland 1801–1886 Scotland",,"Ross, Horatio","British, Scottish",1801,1886,ca. 1858,1858,1858,Salted paper print,15.3 x 18.8 cm (6 x 7 3/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288058,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.552 (7),false,true,288059,Photographs,Photograph,[Picnic Near a Stream],,,,,,Artist,,Horatio Ross,"British, Rossie Castle, near Montrose, Scotland 1801–1886 Scotland",,"Ross, Horatio","British, Scottish",1801,1886,ca. 1856,1856,1856,Salted paper print,12.9 x 18.1 cm (5 1/16 x 7 1/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288059,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.552 (8),false,true,288060,Photographs,Photograph,"[Stag Trophy Head, Killed by Ned Ross]",,,,,,Artist,,Horatio Ross,"British, Rossie Castle, near Montrose, Scotland 1801–1886 Scotland",,"Ross, Horatio","British, Scottish",1801,1886,ca. 1858,1858,1858,Albumen silver print,"17.9 x 14.9 cm (7 1/16 x 5 7/8 in.), corners trimmed diagonally","Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288060,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.552 (9),false,true,288061,Photographs,Photograph,"[Two Stags, One Shot by Mr. Ross and the Other by Mrs. Ross]",,,,,,Artist,,Horatio Ross,"British, Rossie Castle, near Montrose, Scotland 1801–1886 Scotland",,"Ross, Horatio","British, Scottish",1801,1886,ca. 1858,1858,1858,Salted paper print,"14.3 x 17.8 cm (5 5/8 x 7 in.), oval","Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288061,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.552 (10),false,true,288062,Photographs,Photograph,[Dead Roe Buck],,,,,,Artist,,Horatio Ross,"British, Rossie Castle, near Montrose, Scotland 1801–1886 Scotland",,"Ross, Horatio","British, Scottish",1801,1886,ca. 1856–59,1856,1859,Albumen silver print,14.6 x 18.5 cm (5 3/4 x 7 5/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288062,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.552 (11),false,true,288063,Photographs,Photograph,[Two Stags and Roe Buck],,,,,,Artist,,Horatio Ross,"British, Rossie Castle, near Montrose, Scotland 1801–1886 Scotland",,"Ross, Horatio","British, Scottish",1801,1886,ca. 1858,1858,1858,Salted paper print,15.9 x 20.6 cm (6 1/4 x 8 1/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288063,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.552 (12),false,true,288064,Photographs,Photograph,Jack Gralloching a Stag,,,,,,Artist,,Horatio Ross,"British, Rossie Castle, near Montrose, Scotland 1801–1886 Scotland",,"Ross, Horatio","British, Scottish",1801,1886,ca. 1856–58,1856,1858,Salted paper print,17 x 19.3 cm (6 11/16 x 7 5/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288064,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.552 (13),false,true,288065,Photographs,Photograph,[Dead Stag],,,,,,Artist,,Horatio Ross,"British, Rossie Castle, near Montrose, Scotland 1801–1886 Scotland",,"Ross, Horatio","British, Scottish",1801,1886,ca. 1856,1856,1856,Salted paper print,19.2 x 24.2 cm (7 9/16 x 9 1/2 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288065,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.552 (14),false,true,286900,Photographs,Photograph,[Dead Stag],,,,,,Artist,,Horatio Ross,"British, Rossie Castle, near Montrose, Scotland 1801–1886 Scotland",,"Ross, Horatio","British, Scottish",1801,1886,ca. 1857,1857,1857,Salted paper print,19.1 x 23.4 cm (7 1/2 x 9 3/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286900,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.552 (15),false,true,288066,Photographs,Photograph,[Dead Female Deer and Game Bird],,,,,,Artist,,Horatio Ross,"British, Rossie Castle, near Montrose, Scotland 1801–1886 Scotland",,"Ross, Horatio","British, Scottish",1801,1886,ca. 1856–59,1856,1859,Salted paper print,"17.8 x 23.8 cm (7 x 9 3/8 in.), oval","Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288066,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.552 (16),false,true,288067,Photographs,Photograph,[Stag Trophy Head Killed by Ned Ross],,,,,,Artist,,Horatio Ross,"British, Rossie Castle, near Montrose, Scotland 1801–1886 Scotland",,"Ross, Horatio","British, Scottish",1801,1886,ca. 1857,1857,1857,Albumen silver print,19.6 x 15.5 cm (7 11/16 x 6 1/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288067,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.552 (17),false,true,288068,Photographs,Photograph,[Stags Heads - Dibedale],,,,,,Artist,,Horatio Ross,"British, Rossie Castle, near Montrose, Scotland 1801–1886 Scotland",,"Ross, Horatio","British, Scottish",1801,1886,ca. 1856,1856,1856,Salted paper print,16 x 20.4 cm (6 5/16 x 8 1/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288068,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.552 (18),false,true,288069,Photographs,Photograph,[Portrait of a Seated Gentleman],,,,,,Artist,,Horatio Ross,"British, Rossie Castle, near Montrose, Scotland 1801–1886 Scotland",,"Ross, Horatio","British, Scottish",1801,1886,ca. 1856–59,1856,1859,Albumen silver print,18.1 x 14.3 cm (7 1/8 x 5 5/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288069,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.552 (19),false,true,288070,Photographs,Photograph,[Portrait of Major Anderson],,,,,,Artist,,Horatio Ross,"British, Rossie Castle, near Montrose, Scotland 1801–1886 Scotland",,"Ross, Horatio","British, Scottish",1801,1886,ca. 1856–59,1856,1859,Albumen silver print,18.9 x 15.6 cm (7 7/16 x 6 1/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288070,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.552 (20),false,true,288071,Photographs,Photograph,[Portrait of Man in Hunting Garb],,,,,,Artist,,Horatio Ross,"British, Rossie Castle, near Montrose, Scotland 1801–1886 Scotland",,"Ross, Horatio","British, Scottish",1801,1886,ca. 1856–59,1856,1859,Salted paper print,19.5 x 14.2 cm (7 11/16 x 5 9/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288071,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.552 (21),false,true,288072,Photographs,Photograph,[Old Tom],,,,,,Artist,,Horatio Ross,"British, Rossie Castle, near Montrose, Scotland 1801–1886 Scotland",,"Ross, Horatio","British, Scottish",1801,1886,ca. 1856–59,1856,1859,Salted paper print,20 x 15.1 cm (7 7/8 x 5 15/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288072,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.552 (22),false,true,288073,Photographs,Photograph,[Elegant Group Outdoors],,,,,,Artist,,Horatio Ross,"British, Rossie Castle, near Montrose, Scotland 1801–1886 Scotland",,"Ross, Horatio","British, Scottish",1801,1886,ca. 1856–59,1856,1859,Salted paper print,"14.8 x 19.7 cm (5 13/16 x 7 3/4 in.), oval","Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288073,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.552 (23),false,true,288074,Photographs,Photograph,[Peel Ross Fishing],,,,,,Artist,,Horatio Ross,"British, Rossie Castle, near Montrose, Scotland 1801–1886 Scotland",,"Ross, Horatio","British, Scottish",1801,1886,ca. 1856–59,1856,1859,Albumen silver print,"16.6 x 20.5 cm (6 9/16 x 8 1/16 in.), oval","Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288074,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.552 (24),false,true,288075,Photographs,Photograph,[Peel Ross with Hunting Trophies],,,,,,Artist,,Horatio Ross,"British, Rossie Castle, near Montrose, Scotland 1801–1886 Scotland",,"Ross, Horatio","British, Scottish",1801,1886,ca. 1856–59,1856,1859,Albumen silver print,18.7 x 22.2 cm (7 3/8 x 8 3/4 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288075,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.552 (25),false,true,288076,Photographs,Photograph,[Charlie and Peel Ross with Horse after a Hunt],,,,,,Artist,,Horatio Ross,"British, Rossie Castle, near Montrose, Scotland 1801–1886 Scotland",,"Ross, Horatio","British, Scottish",1801,1886,ca. 1856–59,1856,1859,Albumen silver print,"21.5 x 18.7 cm (8 7/16 x 7 3/8 in.), arched top","Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288076,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.552 (26),false,true,288077,Photographs,Photograph,[Colin and Horatio Ross Reading with Jessie Macrae],,,,,,Artist,,Horatio Ross,"British, Rossie Castle, near Montrose, Scotland 1801–1886 Scotland",,"Ross, Horatio","British, Scottish",1801,1886,ca. 1858,1858,1858,Albumen silver print,23.2 x 19.4 cm (9 1/8 x 7 5/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288077,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.552 (27),false,true,288078,Photographs,Photograph,[Man and Boys Fishing],,,,,,Artist,,Horatio Ross,"British, Rossie Castle, near Montrose, Scotland 1801–1886 Scotland",,"Ross, Horatio","British, Scottish",1801,1886,ca. 1856–59,1856,1859,Salted paper print,19.3 x 23.1 cm (7 5/8 x 9 1/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288078,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.552 (28),false,true,288079,Photographs,Photograph,[Stag Shot by Mrs. Ross],,,,,,Artist,,Horatio Ross,"British, Rossie Castle, near Montrose, Scotland 1801–1886 Scotland",,"Ross, Horatio","British, Scottish",1801,1886,ca. 1857,1857,1857,Salted paper print,13.5 x 23.5 cm (5 5/16 x 9 1/4 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288079,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.552 (29),false,true,288080,Photographs,Photograph,[View in the Gardens at Netherley],,,,,,Artist,,Horatio Ross,"British, Rossie Castle, near Montrose, Scotland 1801–1886 Scotland",,"Ross, Horatio","British, Scottish",1801,1886,ca. 1856–59,1856,1859,Albumen silver print,19.5 x 24.6 cm (7 11/16 x 9 11/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288080,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.552 (30),false,true,288081,Photographs,Photograph,[Colin's Royal Stag],,,,,,Artist,,Horatio Ross,"British, Rossie Castle, near Montrose, Scotland 1801–1886 Scotland",,"Ross, Horatio","British, Scottish",1801,1886,ca. 1856,1856,1856,Salted paper print,19.9 x 14.4 cm (7 13/16 x 5 11/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288081,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.552 (31),false,true,288082,Photographs,Photograph,[Spying in Glenfeshie],,,,,,Artist,,Horatio Ross,"British, Rossie Castle, near Montrose, Scotland 1801–1886 Scotland",,"Ross, Horatio","British, Scottish",1801,1886,ca. 1858,1858,1858,Salted paper print,18.7 x 23.4 cm (7 3/8 x 9 3/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288082,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.552 (32),false,true,288083,Photographs,Photograph,[Hunters Stalking a Deer],,,,,,Artist,,Horatio Ross,"British, Rossie Castle, near Montrose, Scotland 1801–1886 Scotland",,"Ross, Horatio","British, Scottish",1801,1886,ca. 1857,1857,1857,Salted paper print,13.6 x 18.9 cm (5 3/8 x 7 7/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288083,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.552 (33),false,true,288084,Photographs,Photograph,[Prize Cow and Calf],,,,,,Artist,,Horatio Ross,"British, Rossie Castle, near Montrose, Scotland 1801–1886 Scotland",,"Ross, Horatio","British, Scottish",1801,1886,ca. 1859,1859,1859,Salted paper print,15.6 x 19.7 cm (6 1/8 x 7 3/4 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288084,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.552 (34),false,true,288085,Photographs,Photograph,[Portrait of a Man in Military Regalia],,,,,,Artist,,Horatio Ross,"British, Rossie Castle, near Montrose, Scotland 1801–1886 Scotland",,"Ross, Horatio","British, Scottish",1801,1886,ca. 1859,1859,1859,Salted paper print,19.5 x 15.2 cm (7 11/16 x 6 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288085,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2011.424,false,true,298968,Photographs,Photographs,Aberdeen Portraits No. 1,,,,,,Artist,,George Washington Wilson,"British, Grampian (Baffshire), Scotland 1823–1893 Abedeen, Scotland",,"Wilson, George Washington","British, Scottish",1823,1893,1857,1857,1857,Albumen silver print from glass negative,Image: 21.3 x 17.2 cm (8 3/8 x 6 3/4 in.) Mount: 37.7 x 30 cm (14 13/16 x 11 13/16 in.) Frame: 43.2 x 35.6 cm (17 x 14 in.),"The Horace W. Goldsmith Foundation Fund, through Joyce and Robert Menschel, 2011",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/298968,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1197,false,true,265002,Photographs,Photograph,"Excised Knee Joint. A Round Musket Ball in the Inner Condyle of the Right Femur [Gardiner Lewis, Company B, Nineteenth Indiana Volunteers]",,,,,,Artist,,William Bell,"American (born England) Liverpool 1831–1910 Philadelphia, Pennsylvania",,"Bell, William","American, born Britain",1831,1910,1866–67,1866,1867,Albumen silver print from glass negative,19 x 15.3cm (7 1/2 x 6in.) Mount: 35.4 x 27.6cm (13 15/16 x 10 7/8in.),"Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 1986",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/265002,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1988.1073,false,true,265934,Photographs,Photograph,"Grand Canyon, Colorado River, Near Paria Creek, Looking West",,,,,,Artist,,William Bell,"American (born England) Liverpool 1831–1910 Philadelphia, Pennsylvania",,"Bell, William","American, born Britain",1831,1910,1872,1872,1872,Albumen silver print from glass negative,27.3 x 20.1 cm. (10 3/4 x 7 15/16 in.),"Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 1988",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/265934,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1979.675.3,false,true,262679,Photographs,Photograph,"Limestone Walls, Kanab Wash, Colorado River",,,,,,Artist,,William Bell,"American (born England) Liverpool 1831–1910 Philadelphia, Pennsylvania",,"Bell, William","American, born Britain",1831,1910,1872,1872,1872,Albumen silver print from glass negative,,"Gift of Mr. and Mrs. Weston J. Naef, in memory of Edward Dawes Meanor, 1979",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/262679,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1979.675.4,false,true,262680,Photographs,Photograph,"Grand Cañon of the Colorado River, Mouth of Kanab Wash, looking West",,,,,,Artist,,William Bell,"American (born England) Liverpool 1831–1910 Philadelphia, Pennsylvania",,"Bell, William","American, born Britain",1831,1910,1872,1872,1872,Albumen silver print from glass negative,,"Gift of Mr. and Mrs. Weston J. Naef, in memory of Edward Dawes Meanor, 1979",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/262680,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.585 (10),false,true,287291,Photographs,Photograph,"Cañon of Kanab Wash, Looking South",,,,,,Artist,,William Bell,"American (born England) Liverpool 1831–1910 Philadelphia, Pennsylvania",,"Bell, William","American, born Britain",1831,1910,1872,1872,1872,Albumen silver print from glass negative,Image: 28.2 x 20.2 cm (11 1/8 x 7 15/16 in.),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287291,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1990.1106,false,true,266273,Photographs,Photograph,"Street in Fatehpur Sikri, India",,,,,,Artist,,John Murray,"British, Blackhouse, Aberdeenshire, Scotland 1809–1898 Sheringham, Norfolk county, England",,"Murray, John","British, Scottish",1809,1898,1858–62,1858,1862,Albumen silver print from paper negative,Image: 36.8 x 45 cm (14 1/2 x 17 11/16 in.) Mount: 53.4 x 73 cm (21 x 28 3/4 in.),"Purchase, The Horace W. Goldsmith Foundation Gift through Joyce and Robert Menschel and Cynthia Hazen Polsky Gift, 1990",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/266273,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1988.1134.2,false,true,266026,Photographs,Photograph,"The Diwan-i Khas from the Mussaman Burj, Agra Palace",,,,,,Artist,,John Murray,"British, Blackhouse, Aberdeenshire, Scotland 1809–1898 Sheringham, Norfolk county, England",,"Murray, John","British, Scottish",1809,1898,1862–64,1862,1864,Albumen silver print from paper negative,40.6 x 44.3cm (16 x 17 7/16in.) Frame: 68.7 x 122.6 cm (27 1/16 x 48 1/4 in.) (Framed with 1988.1134.1),"Purchase, Cynthia Hazen Polsky Gift, 1988",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/266026,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.70,false,true,283161,Photographs,Photograph,The Chowk,,,,,,Artist,,John Murray,"British, Blackhouse, Aberdeenshire, Scotland 1809–1898 Sheringham, Norfolk county, England",,"Murray, John","British, Scottish",1809,1898,1856–57,1856,1857,Salted paper print from paper negative,Image: 37.4 x 46.5 cm (14 3/4 x 18 5/16 in.) Mount: 45.4 x 53.9 cm (17 7/8 x 21 1/4 in.),"Gilman Collection, Purchase, Cynthia Hazen Polsky Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283161,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.71,false,true,283162,Photographs,Photograph,[The Taj Mahal from the Banks of the Yamuna River],,,,,,Artist,,John Murray,"British, Blackhouse, Aberdeenshire, Scotland 1809–1898 Sheringham, Norfolk county, England",,"Murray, John","British, Scottish",1809,1898,1858–62,1858,1862,Albumen silver print from paper negative,Image: 39.9 x 44 cm (15 11/16 x 17 5/16 in.),"Gilman Collection, Purchase, Joseph M. Cohen Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283162,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.945,false,true,286085,Photographs,Photograph,"Suttee Ghat, Cawnpore",,,,,,Artist,,John Murray,"British, Blackhouse, Aberdeenshire, Scotland 1809–1898 Sheringham, Norfolk county, England",,"Murray, John","British, Scottish",1809,1898,1858,1858,1858,Albumen silver print from paper negative,Image: 33 x 43.1 cm (13 x 16 15/16 in.) Mount: 40.3 x 51.6 cm (15 7/8 x 20 5/16 in.),"Gilman Collection, Purchase, Cynthia Hazen Polsky Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286085,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1111,false,true,286082,Photographs,Photograph,"Fort Agra, The Delhi Gate",,,,,,Artist,,John Murray,"British, Blackhouse, Aberdeenshire, Scotland 1809–1898 Sheringham, Norfolk county, England",,"Murray, John","British, Scottish",1809,1898,1850s,1850,1859,Albumen silver print from paper negative,Image: 38.6 x 44.5 cm (15 3/16 x 17 1/2 in.),"Gilman Collection, Purchase, Cynthia Hazen Polsky Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286082,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.315a–c,false,true,286084,Photographs,Panorama,[The Taj Mahal from the Gateway],,,,,,Artist,,John Murray,"British, Blackhouse, Aberdeenshire, Scotland 1809–1898 Sheringham, Norfolk county, England",,"Murray, John","British, Scottish",1809,1898,January–March 1864,1864,1864,Albumen silver prints from waxed paper negatives,Image: 35 x 127 cm (13 3/4 x 50 in.) overall,"Gilman Collection, Purchase, Cynthia Hazen Polsky Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286084,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.37,false,true,669902,Photographs,Photograph,"Second Ave Rock Island, Ill. during high water",,,,,,Artist,,Henry P. Bosse,"American, born Germany, 1844–1893",,"Bosse, Henry P.","American, born Germany",1844,1893,1888,1888,1888,Cyanotype,"Image: 10 3/8 × 13 1/16 in. (26.4 × 33.2 cm), oval Sheet: 14 7/16 × 17 3/16 in. (36.7 × 43.7 cm)","Purchase, Acquisitions Fund and Mr. and Mrs. John A. Moran Gift, in memory of Louise Chisholm Moran, 2015",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/669902,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2014.715.16,false,true,669919,Photographs,Photograph,"No. 201. U.S. Government Bridge at Rock Island, Illinois (High Water)",,,,,,Artist,,Henry P. Bosse,"American, born Germany, 1844–1893",,"Bosse, Henry P.","American, born Germany",1844,1893,1888,1888,1888,Cyanotype,Sheet: 14 1/2 × 17 3/16 in. (36.8 × 43.7 cm),"Gift of Charles Wehrenberg and Sally Larsen, 2014",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/669919,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1991.1135.1,false,true,266429,Photographs,Photographically illustrated book,"Animal Locomotion. An Electro-Photographic Investigation of Consecutive Phases of Animal Movements. Commenced 1872 - Completed 1885. Volume I, Men (Nude)",,,,,,Artist,,Eadweard Muybridge,"American, born Britain, 1830–1904",,"Muybridge, Eadweard","American, born Britain",1830,1904,1880s,1880,1889,Photogravures,,"Rogers Fund, transferred from the Library",,,,,,,,,,,,Books,,http://www.metmuseum.org/art/collection/search/266429,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1991.1135.2,false,true,266432,Photographs,Photographically illustrated book,"Animal Locomotion. An Electro-Photographic Investigation of Consecutive Phases of Animal Movements. Commenced 1872 - Completed 1885. Volume II, Men (Nude)",,,,,,Artist,,Eadweard Muybridge,"American, born Britain, 1830–1904",,"Muybridge, Eadweard","American, born Britain",1830,1904,1880s,1880,1889,Photogravures,,"Rogers Fund, transferred from the Library",,,,,,,,,,,,Books,,http://www.metmuseum.org/art/collection/search/266432,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1991.1135.4,false,true,266434,Photographs,Photographically illustrated book,"Animal Locomotion. An Electro-Photographic Investigation of Consecutive Phases of Animal Movements. Commenced 1872 - Completed 1885. Volume IV, Women (Nude)",,,,,,Artist,,Eadweard Muybridge,"American, born Britain, 1830–1904",,"Muybridge, Eadweard","American, born Britain",1830,1904,1880s,1880,1889,Photogravures,,"Rogers Fund, transferred from the Library",,,,,,,,,,,,Books,,http://www.metmuseum.org/art/collection/search/266434,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1991.1135.5,false,true,266435,Photographs,Photographically illustrated book,"Animal Locomotion. An Electro-Photographic Investigation of Consecutive Phases of Animal Movements. Commenced 1872 - Completed 1885. Volume V, Man (Pelvis Cloth)",,,,,,Artist,,Eadweard Muybridge,"American, born Britain, 1830–1904",,"Muybridge, Eadweard","American, born Britain",1830,1904,1880s,1880,1889,Photogravures,,"Rogers Fund, transferred from the Library",,,,,,,,,,,,Books,,http://www.metmuseum.org/art/collection/search/266435,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1991.1135.6,false,true,266436,Photographs,Photographically illustrated book,"Animal Locomotion. An Electro-Photographic Investigation... of Animal Movements. Commenced 1872 - Completed 1885. Volume VI, Woman (Semi-Nude and Transparent Drapery) Children",,,,,,Artist,,Eadweard Muybridge,"American, born Britain, 1830–1904",,"Muybridge, Eadweard","American, born Britain",1830,1904,1880s,1880,1889,Photogravures,,"Rogers Fund, transferred from the Library",,,,,,,,,,,,Books,,http://www.metmuseum.org/art/collection/search/266436,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1991.1135.7,false,true,266437,Photographs,Photographically illustrated book,"Animal Locomotion. An Electro-Photographic Investigation... of Animal Movements. Commenced 1872 - Completed 1885. Volume VII, Men and Woman (Draped) Miscellaneous Subjects",,,,,,Artist,,Eadweard Muybridge,"American, born Britain, 1830–1904",,"Muybridge, Eadweard","American, born Britain",1830,1904,1880s,1880,1889,Photogravures,,"Rogers Fund, transferred from the Library",,,,,,,,,,,,Books,,http://www.metmuseum.org/art/collection/search/266437,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1991.1135.9,false,true,266439,Photographs,Photographically illustrated book,"Animal Locomotion. An Electro-Photographic Investigation of Consecutive Phases of Animal Movements. Commenced 1872 - Completed 1885. Volume IX, Horses",,,,,,Artist,,Eadweard Muybridge,"American, born Britain, 1830–1904",,"Muybridge, Eadweard","American, born Britain",1830,1904,1880s,1880,1889,Photogravures,,"Rogers Fund, transferred from the Library",,,,,,,,,,,,Books,,http://www.metmuseum.org/art/collection/search/266439,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1991.1135.11,false,true,266431,Photographs,Photographically illustrated book,"Animal Locomotion. An Electro-Photographic Investigation of Consecutive Phases of Animal Movements. Commenced 1872 - Completed 1885. Volume XI, Wild Animals and Birds",,,,,,Artist,,Eadweard Muybridge,"American, born Britain, 1830–1904",,"Muybridge, Eadweard","American, born Britain",1830,1904,1880s,1880,1889,Photogravures,,"Rogers Fund, transferred from the Library",,,,,,,,,,,,Books,,http://www.metmuseum.org/art/collection/search/266431,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2006.525.1,false,true,287979,Photographs,Photograph,[Studio Portrait of a Japanese Man in Western Clothing],,,,,,Artist,,Raimund von Stillfried,"Austrian, 1839–1911",,"Stillfried, Raimond von",Austrian,1839,1911,1880s,1880,1889,Albumen silver print,Image: 14.2 x 9.7 cm (5 9/16 x 3 13/16 in.) Mount: 16.6 x 10.9 cm (6 9/16 x 4 5/16 in.),"Gift of Sue Cassidy Clark, in honor of Dr. Barbara Brennen Ford, 2006",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287979,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.778,false,true,285692,Photographs,Photograph,[Advertisement for Sarony's Photographic Studies],,,,,,Artist,,Napoleon Sarony,"American (born Canada), Quebec 1821–1896 New York",,"Sarony, Napoleon","American, born Canada",1821,1896,1880s,1880,1889,Albumen silver print from glass negative; lithograph,7 3/4 x 9 5/16,"Gilman Collection, Joyce F. Menschel Photography Library Fund, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285692,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.662 (1–18),false,true,285805,Photographs,Album,[Jacob Christian Hansen Ellehammer's Experiments in Early Aviation (1905-1919)],,,,,,Artist|Artist,,Jacob Christian Hansen Ellehammer|Vilhelm Ellehammer,"Danish, 1871–1946|Danish",,"Ellehammer, Jacob Christian Hansen|Ellehammer, Vilhelm",Danish|Danish,1871,1946,1905–19,1905,1919,Gelatin silver prints from glass negatives,9 13/16 × 14 1/8 × 11/16 in. (25 × 35.8 × 1.7 cm),"Gilman Collection, Purchase, Alfred Stieglitz Society Gifts, 2005",,,,,,,,,,,,Albums,,http://www.metmuseum.org/art/collection/search/285805,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -52.639,false,true,269618,Photographs,Album,Progress of the Crystal Palace at Sydenham,,,,,,Artist|Artist,,Philip Henry Delamotte|Henry Angelo Ludovico Negretti,"British, 1821–1889|British, born Italy, 1818–1879",,"Delamotte, Philip Henry|Negretti, and Zambra","British|British, born Italy",1821 |1818,1889 |1879,1854,1854,1854,Albumen silver prints,,"David Hunter McAlpin Fund, 1952",,,,,,,,,,,,Albums,,http://www.metmuseum.org/art/collection/search/269618,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1994.128.1–.2,false,true,266992,Photographs,Photographically illustrated book,"Palais du Louvre et des Tuileries, motifs de décorations tirés des constructions éxécutées au nouveau Louvre et au palais des Tuileries ..., tomes I / II",,,,,,Artist|Artist,,Édouard Baldus|Hector Lefeul,"French, born Prussia, 1813–1889|French, 1810–1880",,"Baldus, Édouard|Lefeul, Hector","French, born Prussia|French",1813 |1810,1889 |1880,1850s–70s,1850,1879,Photogravures,,"Gift of George L. Morse, 1923, transferred from the Library",,,,,,,,,,,,Books,,http://www.metmuseum.org/art/collection/search/266992,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.37,false,true,268303,Photographs,Album,Album di disegni fotogenici,,,,,,Artist|Artist,Likely,Sebastiano Tassinari|William Henry Fox Talbot,"Italian, 1814–1888|British, Dorset 1800–1877 Lacock",,"Tassinari, Sebastiano|Talbot, William Henry Fox",Italian|British,1814 |1800,1888 |1800,1839–40,1839,1840,Photogenic drawings,28.5 x 22 cm (11 1/4 x 8 11/16 in. ),"Harris Brisbane Dick Fund, 1936",,,,,,,,,,,,Albums,,http://www.metmuseum.org/art/collection/search/268303,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2011.66.1–.15,false,true,296322,Photographs,Photogravure; X-Ray,Versuche über Photographie mittelst der Röntgen'schen Strahlen,,,,,,Artist|Artist,,Josef Maria Eder|Eduard Valenta,"Austrian, Krems an der Donau, 1855–1944 Kitzbühel|Austrian, 1857–1937",and,"Eder, Josef Maria|Valenta, Eduard",Austrian|Austrian,1855 |1857,1944 |1937,1896,1896,1896,Photogravures,50 x 36 cm (19 11/16 x 14 3/16 in.),"Purchase, Alfred Stieglitz Society Gifts, Joyce F. Menschel Photography Library Fund, and Maureen and Noel Testa Gift, 2011",,,,,,,,,,,,Portfolios,,http://www.metmuseum.org/art/collection/search/296322,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -54.550.1,false,true,269744,Photographs,Portfolio,Grotesques by Aubrey Beardsley. Facsimile Platinum Prints by Frederick H. Evans from the Twelve Original Drawings in His Collection with a Portrait Frontispiece,,,,,,Artist|Artist,After,Frederick H. Evans|Aubrey Vincent Beardsley,"British, London 1853–1943 London|British, Brighton, Sussex 1872–1898 Menton",,"Evans, Frederick Henry|Beardsley, Aubrey Vincent",British|British,1853 |1872,1943 |1898,1910s,1910,1919,Platinum prints,Sheet: 9 9/16 × 7 1/2 in. (24.3 × 19 cm) Plate: 4 × 3 1/16 in. (10.2 × 7.7 cm) Image: 1 11/16 × 1 5/16 in. (4.3 × 3.3 cm),"Gift of Gordon Conn, 1954",,,,,,,,,,,,Portfolios,,http://www.metmuseum.org/art/collection/search/269744,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.662 (17),false,true,660967,Photographs,Photograph,12 cyl. stjerneformet Motor 160HK. Forsog ikke aflluttede April 1919.,,,,,,Artist|Artist,,Jacob Christian Hansen Ellehammer|Vilhelm Ellehammer,"Danish, 1871–1946|Danish",,"Ellehammer, Jacob Christian Hansen|Ellehammer, Vilhelm",Danish|Danish,1871,1946,1905–19,1905,1919,Gelatin silver prints from glass negatives,Image: 4 3/16 × 3 1/8 in. (10.7 × 8 cm) Sheet: 9 5/8 × 12 13/16 in. (24.4 × 32.6 cm),"Gilman Collection, Purchase, Alfred Stieglitz Society Gifts, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/660967,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.662 (18),false,true,660968,Photographs,Photograph,3/10 26 Yl El Illk[?],,,,,,Artist|Artist,,Jacob Christian Hansen Ellehammer|Vilhelm Ellehammer,"Danish, 1871–1946|Danish",,"Ellehammer, Jacob Christian Hansen|Ellehammer, Vilhelm",Danish|Danish,1871,1946,1905–19,1905,1919,Gelatin silver prints from glass negatives,Image: 3 1/4 × 4 1/4 in. (8.2 × 10.8 cm) Sheet: 9 5/8 × 12 13/16 in. (24.4 × 32.6 cm),"Gilman Collection, Purchase, Alfred Stieglitz Society Gifts, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/660968,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.662 (1a),false,true,660934,Photographs,Photograph,Forsogs Stationar paa Oeu Lindholw 1905,,,,,,Artist|Artist,,Jacob Christian Hansen Ellehammer|Vilhelm Ellehammer,"Danish, 1871–1946|Danish",,"Ellehammer, Jacob Christian Hansen|Ellehammer, Vilhelm",Danish|Danish,1871,1946,1905–19,1905,1919,Gelatin silver prints from glass negatives,Image: 3 9/16 × 4 1/2 in. (9 × 11.4 cm) Sheet: 9 5/8 × 12 13/16 in. (24.4 × 32.6 cm),"Gilman Collection, Purchase, Alfred Stieglitz Society Gifts, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/660934,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.662 (1b),false,true,660936,Photographs,Photograph,Flyvernaskinin paa Banen,,,,,,Artist|Artist,,Jacob Christian Hansen Ellehammer|Vilhelm Ellehammer,"Danish, 1871–1946|Danish",,"Ellehammer, Jacob Christian Hansen|Ellehammer, Vilhelm",Danish|Danish,1871,1946,1905–19,1905,1919,Gelatin silver prints from glass negatives,Image: 3 9/16 × 4 1/2 in. (9.1 × 11.4 cm) Sheet: 9 5/8 × 12 13/16 in. (24.4 × 32.6 cm),"Gilman Collection, Purchase, Alfred Stieglitz Society Gifts, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/660936,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.662 (2a),false,true,660937,Photographs,Photograph,Pendiue Ophanguing of Motor og Flyver.,,,,,,Artist|Artist,,Jacob Christian Hansen Ellehammer|Vilhelm Ellehammer,"Danish, 1871–1946|Danish",,"Ellehammer, Jacob Christian Hansen|Ellehammer, Vilhelm",Danish|Danish,1871,1946,1905–19,1905,1919,Gelatin silver prints from glass negatives,Image: 3 9/16 × 4 7/16 in. (9 × 11.3 cm) Sheet: 9 5/8 × 12 13/16 in. (24.4 × 32.6 cm),"Gilman Collection, Purchase, Alfred Stieglitz Society Gifts, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/660937,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.662 (2b),false,true,660938,Photographs,Photograph,Banen paa Lindholm 1905.,,,,,,Artist|Artist,,Jacob Christian Hansen Ellehammer|Vilhelm Ellehammer,"Danish, 1871–1946|Danish",,"Ellehammer, Jacob Christian Hansen|Ellehammer, Vilhelm",Danish|Danish,1871,1946,1905–19,1905,1919,Gelatin silver prints from glass negatives,Image: 3 11/16 × 4 7/16 in. (9.4 × 11.3 cm) Sheet: 9 5/8 × 12 13/16 in. (24.4 × 32.6 cm),"Gilman Collection, Purchase, Alfred Stieglitz Society Gifts, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/660938,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.662 (3a),false,true,660939,Photographs,Photograph,Havari.,,,,,,Artist|Artist,,Jacob Christian Hansen Ellehammer|Vilhelm Ellehammer,"Danish, 1871–1946|Danish",,"Ellehammer, Jacob Christian Hansen|Ellehammer, Vilhelm",Danish|Danish,1871,1946,1905–19,1905,1919,Gelatin silver prints from glass negatives,Image: 4 1/8 in. × 3 in. (10.5 × 7.6 cm) Sheet: 9 5/8 × 12 13/16 in. (24.4 × 32.6 cm),"Gilman Collection, Purchase, Alfred Stieglitz Society Gifts, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/660939,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.662 (3b),false,true,660940,Photographs,Photograph,Foroget Bareflade.,,,,,,Artist|Artist,,Jacob Christian Hansen Ellehammer|Vilhelm Ellehammer,"Danish, 1871–1946|Danish",,"Ellehammer, Jacob Christian Hansen|Ellehammer, Vilhelm",Danish|Danish,1871,1946,1905–19,1905,1919,Gelatin silver prints from glass negatives,Image: 4 1/8 × 2 15/16 in. (10.5 × 7.5 cm) Sheet: 9 5/8 × 12 13/16 in. (24.4 × 32.6 cm),"Gilman Collection, Purchase, Alfred Stieglitz Society Gifts, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/660940,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.662 (4a),false,true,660941,Photographs,Photograph,"Forsog i 12m Vindhastighed, arbydende Motor.",,,,,,Artist|Artist,,Jacob Christian Hansen Ellehammer|Vilhelm Ellehammer,"Danish, 1871–1946|Danish",,"Ellehammer, Jacob Christian Hansen|Ellehammer, Vilhelm",Danish|Danish,1871,1946,1905–19,1905,1919,Gelatin silver prints from glass negatives,Image: 4 1/8 × 2 15/16 in. (10.5 × 7.4 cm) Sheet: 9 5/8 × 12 13/16 in. (24.4 × 32.6 cm),"Gilman Collection, Purchase, Alfred Stieglitz Society Gifts, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/660941,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.662 (4b),false,true,660942,Photographs,Photograph,Den 1' Flyvning i Europa Sen 12 September 1906.,,,,,,Artist|Artist,,Jacob Christian Hansen Ellehammer|Vilhelm Ellehammer,"Danish, 1871–1946|Danish",,"Ellehammer, Jacob Christian Hansen|Ellehammer, Vilhelm",Danish|Danish,1871,1946,1905–19,1905,1919,Gelatin silver prints from glass negatives,Image: 4 9/16 × 3 1/16 in. (11.6 × 7.7 cm) Sheet: 9 5/8 × 12 13/16 in. (24.4 × 32.6 cm),"Gilman Collection, Purchase, Alfred Stieglitz Society Gifts, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/660942,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.662 (5a),false,true,660943,Photographs,Photograph,"Treplau, Kollekolle. 1907.",,,,,,Artist|Artist,,Jacob Christian Hansen Ellehammer|Vilhelm Ellehammer,"Danish, 1871–1946|Danish",,"Ellehammer, Jacob Christian Hansen|Ellehammer, Vilhelm",Danish|Danish,1871,1946,1905–19,1905,1919,Gelatin silver prints from glass negatives,Image: 3 × 4 in. (7.6 × 10.2 cm) Sheet: 9 5/8 × 12 13/16 in. (24.4 × 32.6 cm),"Gilman Collection, Purchase, Alfred Stieglitz Society Gifts, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/660943,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.662 (5b),false,true,660944,Photographs,Photograph,"Treplan, Farum Su 1907.",,,,,,Artist|Artist,,Jacob Christian Hansen Ellehammer|Vilhelm Ellehammer,"Danish, 1871–1946|Danish",,"Ellehammer, Jacob Christian Hansen|Ellehammer, Vilhelm",Danish|Danish,1871,1946,1905–19,1905,1919,Gelatin silver prints from glass negatives,Image: 2 15/16 × 4 3/16 in. (7.4 × 10.6 cm) Sheet: 9 5/8 × 12 13/16 in. (24.4 × 32.6 cm),"Gilman Collection, Purchase, Alfred Stieglitz Society Gifts, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/660944,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.662 (6a),false,true,660945,Photographs,Photograph,Flyvring 1907.,,,,,,Artist|Artist,,Jacob Christian Hansen Ellehammer|Vilhelm Ellehammer,"Danish, 1871–1946|Danish",,"Ellehammer, Jacob Christian Hansen|Ellehammer, Vilhelm",Danish|Danish,1871,1946,1905–19,1905,1919,Gelatin silver prints from glass negatives,Image: 3 1/16 × 4 1/16 in. (7.7 × 10.3 cm) Sheet: 9 5/8 × 12 13/16 in. (24.4 × 32.6 cm),"Gilman Collection, Purchase, Alfred Stieglitz Society Gifts, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/660945,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.662 (6b),false,true,660946,Photographs,Photograph,"Todakker, med denne Maskine forekoges Seu 28 Tuni, Flyvring ved SX 1'officielle Flyve-Stame i Verdeu Kiel 1908. 1'Pris: 5000 Rm.",,,,,,Artist|Artist,,Jacob Christian Hansen Ellehammer|Vilhelm Ellehammer,"Danish, 1871–1946|Danish",,"Ellehammer, Jacob Christian Hansen|Ellehammer, Vilhelm",Danish|Danish,1871,1946,1905–19,1905,1919,Gelatin silver prints from glass negatives,Image: 3 1/8 × 4 1/8 in. (7.9 × 10.4 cm) Sheet: 9 5/8 × 12 13/16 in. (24.4 × 32.6 cm),"Gilman Collection, Purchase, Alfred Stieglitz Society Gifts, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/660946,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.662 (7a),false,true,660947,Photographs,Photograph,Samme Maskine Sammenfolder.,,,,,,Artist|Artist,,Jacob Christian Hansen Ellehammer|Vilhelm Ellehammer,"Danish, 1871–1946|Danish",,"Ellehammer, Jacob Christian Hansen|Ellehammer, Vilhelm",Danish|Danish,1871,1946,1905–19,1905,1919,Gelatin silver prints from glass negatives,Image: 3 1/16 × 4 1/16 in. (7.7 × 10.3 cm) Sheet: 9 5/8 × 12 13/16 in. (24.4 × 32.6 cm),"Gilman Collection, Purchase, Alfred Stieglitz Society Gifts, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/660947,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.662 (7b),false,true,660948,Photographs,Photograph,"Samme Maskine, Flyvring paa Eremitageu. 1908",,,,,,Artist|Artist,,Jacob Christian Hansen Ellehammer|Vilhelm Ellehammer,"Danish, 1871–1946|Danish",,"Ellehammer, Jacob Christian Hansen|Ellehammer, Vilhelm",Danish|Danish,1871,1946,1905–19,1905,1919,Gelatin silver prints from glass negatives,Image: 3 1/16 in. × 4 in. (7.7 × 10.2 cm) Sheet: 9 5/8 × 12 13/16 in. (24.4 × 32.6 cm),"Gilman Collection, Purchase, Alfred Stieglitz Society Gifts, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/660948,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.662 (8d),false,true,660950,Photographs,Photograph,"Flyvebaad, 1909-1910.",,,,,,Artist|Artist,,Jacob Christian Hansen Ellehammer|Vilhelm Ellehammer,"Danish, 1871–1946|Danish",,"Ellehammer, Jacob Christian Hansen|Ellehammer, Vilhelm",Danish|Danish,1871,1946,1905–19,1905,1919,Gelatin silver prints from glass negatives,Image: 2 15/16 in. × 4 in. (7.5 × 10.2 cm) Sheet: 9 5/8 × 12 13/16 in. (24.4 × 32.6 cm),"Gilman Collection, Purchase, Alfred Stieglitz Society Gifts, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/660950,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.662 (9a),false,true,660951,Photographs,Photograph,"Flyvebaad, 1909-1910.",,,,,,Artist|Artist,,Jacob Christian Hansen Ellehammer|Vilhelm Ellehammer,"Danish, 1871–1946|Danish",,"Ellehammer, Jacob Christian Hansen|Ellehammer, Vilhelm",Danish|Danish,1871,1946,1905–19,1905,1919,Gelatin silver prints from glass negatives,Image: 3 1/4 × 4 3/16 in. (8.2 × 10.7 cm) Sheet: 9 5/8 × 12 13/16 in. (24.4 × 32.6 cm),"Gilman Collection, Purchase, Alfred Stieglitz Society Gifts, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/660951,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.662 (9b),false,true,660952,Photographs,Photograph,Flyvebaad 1909-1910.,,,,,,Artist|Artist,,Jacob Christian Hansen Ellehammer|Vilhelm Ellehammer,"Danish, 1871–1946|Danish",,"Ellehammer, Jacob Christian Hansen|Ellehammer, Vilhelm",Danish|Danish,1871,1946,1905–19,1905,1919,Gelatin silver prints from glass negatives,Image: 4 1/16 × 5 5/8 in. (10.3 × 14.3 cm) Sheet: 9 5/8 × 12 13/16 in. (24.4 × 32.6 cm),"Gilman Collection, Purchase, Alfred Stieglitz Society Gifts, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/660952,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.662 (10a),false,true,660953,Photographs,Photograph,Samme Flyvebaad SammenfolSet.,,,,,,Artist|Artist,,Jacob Christian Hansen Ellehammer|Vilhelm Ellehammer,"Danish, 1871–1946|Danish",,"Ellehammer, Jacob Christian Hansen|Ellehammer, Vilhelm",Danish|Danish,1871,1946,1905–19,1905,1919,Gelatin silver prints from glass negatives,Image: 4 in. × 5 5/8 in. (10.1 × 14.3 cm) Sheet: 9 5/8 × 12 13/16 in. (24.4 × 32.6 cm),"Gilman Collection, Purchase, Alfred Stieglitz Society Gifts, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/660953,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.662 (10b),false,true,660954,Photographs,Photograph,Ieyl. skjerneformet Motor Standard Type. 30HK.,,,,,,Artist|Artist,,Jacob Christian Hansen Ellehammer|Vilhelm Ellehammer,"Danish, 1871–1946|Danish",,"Ellehammer, Jacob Christian Hansen|Ellehammer, Vilhelm",Danish|Danish,1871,1946,1905–19,1905,1919,Gelatin silver prints from glass negatives,Image: 3 1/4 × 4 7/16 in. (8.3 × 11.3 cm) Sheet: 9 5/8 × 12 13/16 in. (24.4 × 32.6 cm),"Gilman Collection, Purchase, Alfred Stieglitz Society Gifts, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/660954,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.662 (11a),false,true,660955,Photographs,Photograph,Ieyl. stjerneformet Motor Standard Type. 50HK.,,,,,,Artist|Artist,,Jacob Christian Hansen Ellehammer|Vilhelm Ellehammer,"Danish, 1871–1946|Danish",,"Ellehammer, Jacob Christian Hansen|Ellehammer, Vilhelm",Danish|Danish,1871,1946,1905–19,1905,1919,Gelatin silver prints from glass negatives,Image: 5 5/8 in. × 4 in. (14.3 × 10.1 cm) Sheet: 9 5/8 × 12 13/16 in. (24.4 × 32.6 cm),"Gilman Collection, Purchase, Alfred Stieglitz Society Gifts, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/660955,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.662 (11b),false,true,660956,Photographs,Photograph,Ellehammers roterende Aeroplan for lodret Start. 1911.,,,,,,Artist|Artist,,Jacob Christian Hansen Ellehammer|Vilhelm Ellehammer,"Danish, 1871–1946|Danish",,"Ellehammer, Jacob Christian Hansen|Ellehammer, Vilhelm",Danish|Danish,1871,1946,1905–19,1905,1919,Gelatin silver prints from glass negatives,Image: 3 1/16 in. × 4 in. (7.8 × 10.1 cm) Sheet: 9 5/8 × 12 13/16 in. (24.4 × 32.6 cm),"Gilman Collection, Purchase, Alfred Stieglitz Society Gifts, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/660956,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.662 (12a),false,true,660957,Photographs,Photograph,Samme Aeroplan.,,,,,,Artist|Artist,,Jacob Christian Hansen Ellehammer|Vilhelm Ellehammer,"Danish, 1871–1946|Danish",,"Ellehammer, Jacob Christian Hansen|Ellehammer, Vilhelm",Danish|Danish,1871,1946,1905–19,1905,1919,Gelatin silver prints from glass negatives,Image: 3 1/16 × 4 1/16 in. (7.7 × 10.3 cm) Sheet: 9 5/8 × 12 13/16 in. (24.4 × 32.6 cm),"Gilman Collection, Purchase, Alfred Stieglitz Society Gifts, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/660957,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.662 (12b),false,true,660958,Photographs,Photograph,Samme Aeroplan.,,,,,,Artist|Artist,,Jacob Christian Hansen Ellehammer|Vilhelm Ellehammer,"Danish, 1871–1946|Danish",,"Ellehammer, Jacob Christian Hansen|Ellehammer, Vilhelm",Danish|Danish,1871,1946,1905–19,1905,1919,Gelatin silver prints from glass negatives,Image: 3 1/8 × 4 1/8 in. (8 × 10.5 cm) Sheet: 9 5/8 × 12 13/16 in. (24.4 × 32.6 cm),"Gilman Collection, Purchase, Alfred Stieglitz Society Gifts, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/660958,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.662 (13a),false,true,660959,Photographs,Photograph,Samme Aeroplan svarende.,,,,,,Artist|Artist,,Jacob Christian Hansen Ellehammer|Vilhelm Ellehammer,"Danish, 1871–1946|Danish",,"Ellehammer, Jacob Christian Hansen|Ellehammer, Vilhelm",Danish|Danish,1871,1946,1905–19,1905,1919,Gelatin silver prints from glass negatives,Image: 3 5/16 × 4 7/16 in. (8.4 × 11.2 cm) Sheet: 9 5/8 × 12 13/16 in. (24.4 × 32.6 cm),"Gilman Collection, Purchase, Alfred Stieglitz Society Gifts, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/660959,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.662 (13b),false,true,660960,Photographs,Photograph,Samme Aeroplan svarende.,,,,,,Artist|Artist,,Jacob Christian Hansen Ellehammer|Vilhelm Ellehammer,"Danish, 1871–1946|Danish",,"Ellehammer, Jacob Christian Hansen|Ellehammer, Vilhelm",Danish|Danish,1871,1946,1905–19,1905,1919,Gelatin silver prints from glass negatives,Image: 3 7/16 × 4 3/8 in. (8.7 × 11.1 cm) Sheet: 9 5/8 × 12 13/16 in. (24.4 × 32.6 cm),"Gilman Collection, Purchase, Alfred Stieglitz Society Gifts, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/660960,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.662 (14a),false,true,660961,Photographs,Photograph,Samme Aeroplan svarende.,,,,,,Artist|Artist,,Jacob Christian Hansen Ellehammer|Vilhelm Ellehammer,"Danish, 1871–1946|Danish",,"Ellehammer, Jacob Christian Hansen|Ellehammer, Vilhelm",Danish|Danish,1871,1946,1905–19,1905,1919,Gelatin silver prints from glass negatives,Image: 3 1/4 × 4 1/16 in. (8.2 × 10.3 cm) Sheet: 9 5/8 × 12 13/16 in. (24.4 × 32.6 cm),"Gilman Collection, Purchase, Alfred Stieglitz Society Gifts, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/660961,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.662 (14b),false,true,660962,Photographs,Photograph,Standard Motor 80HK. Afbremsnings Prore'paa Orlogsvarftets Flyve Station 1916.,,,,,,Artist|Artist,,Jacob Christian Hansen Ellehammer|Vilhelm Ellehammer,"Danish, 1871–1946|Danish",,"Ellehammer, Jacob Christian Hansen|Ellehammer, Vilhelm",Danish|Danish,1871,1946,1905–19,1905,1919,Gelatin silver prints from glass negatives,Image: 3 5/16 × 4 3/8 in. (8.4 × 11.1 cm) Sheet: 9 5/8 × 12 13/16 in. (24.4 × 32.6 cm),"Gilman Collection, Purchase, Alfred Stieglitz Society Gifts, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/660962,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.662 (15a),false,true,660963,Photographs,Photograph,Samme Motor indbygget i en Marine Flyvebaad.,,,,,,Artist|Artist,,Jacob Christian Hansen Ellehammer|Vilhelm Ellehammer,"Danish, 1871–1946|Danish",,"Ellehammer, Jacob Christian Hansen|Ellehammer, Vilhelm",Danish|Danish,1871,1946,1905–19,1905,1919,Gelatin silver prints from glass negatives,Image: 3 3/8 × 4 5/16 in. (8.5 × 11 cm) Sheet: 9 5/8 × 12 13/16 in. (24.4 × 32.6 cm),"Gilman Collection, Purchase, Alfred Stieglitz Society Gifts, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/660963,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.662 (15b),false,true,660964,Photographs,Photograph,Samme Motor i Marine Flyvebaad.,,,,,,Artist|Artist,,Jacob Christian Hansen Ellehammer|Vilhelm Ellehammer,"Danish, 1871–1946|Danish",,"Ellehammer, Jacob Christian Hansen|Ellehammer, Vilhelm",Danish|Danish,1871,1946,1905–19,1905,1919,Gelatin silver prints from glass negatives,Image: 3 5/16 × 4 5/16 in. (8.4 × 10.9 cm) Sheet: 9 5/8 × 12 13/16 in. (24.4 × 32.6 cm),"Gilman Collection, Purchase, Alfred Stieglitz Society Gifts, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/660964,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.662 (16a),false,true,660965,Photographs,Photograph,Samme Motor i Marine Flyvebaad.,,,,,,Artist|Artist,,Jacob Christian Hansen Ellehammer|Vilhelm Ellehammer,"Danish, 1871–1946|Danish",,"Ellehammer, Jacob Christian Hansen|Ellehammer, Vilhelm",Danish|Danish,1871,1946,1905–19,1905,1919,Gelatin silver prints from glass negatives,Image: 3 3/8 × 4 5/16 in. (8.5 × 10.9 cm) Sheet: 9 5/8 × 12 13/16 in. (24.4 × 32.6 cm),"Gilman Collection, Purchase, Alfred Stieglitz Society Gifts, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/660965,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.662 (16b),false,true,660966,Photographs,Photograph,Samme Motor i Marine Flyvebaad.,,,,,,Artist|Artist,,Jacob Christian Hansen Ellehammer|Vilhelm Ellehammer,"Danish, 1871–1946|Danish",,"Ellehammer, Jacob Christian Hansen|Ellehammer, Vilhelm",Danish|Danish,1871,1946,1905–19,1905,1919,Gelatin silver prints from glass negatives,Image: 3 5/16 × 4 5/16 in. (8.4 × 10.9 cm) Sheet: 9 5/8 × 12 13/16 in. (24.4 × 32.6 cm),"Gilman Collection, Purchase, Alfred Stieglitz Society Gifts, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/660966,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.662 (8a–c),false,true,660949,Photographs,Photograph,"Standard Type, Monoplan 1909.",,,,,,Artist|Artist,,Jacob Christian Hansen Ellehammer|Vilhelm Ellehammer,"Danish, 1871–1946|Danish",,"Ellehammer, Jacob Christian Hansen|Ellehammer, Vilhelm",Danish|Danish,1871,1946,1905–19,1905,1919,Gelatin silver prints from glass negatives,Image (u.l.): 2 9/16 × 3 3/8 in. (6.5 × 8.6 cm) Image (u.r.): 2 9/16 × 3 7/16 in. (6.5 × 8.7 cm) Image (l.c.): 3 1/8 × 4 1/16 in. (7.9 × 10.3 cm) Sheet: 9 5/8 × 12 13/16 in. (24.4 × 32.6 cm),"Gilman Collection, Purchase, Alfred Stieglitz Society Gifts, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/660949,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.134a,false,true,305832,Photographs,Photomontage,Napoli Tarantella,,,,,,Artist|Artist,,Giorgio Sommer|Unknown,"Italian, born Germany, 1834–1914|Italian",,"Sommer, Giorgio|Unknown","Italian, born Germany",1834,1914,ca. 1870,1865,1875,Albumen silver print,Image: 19.7 × 25 cm (7 3/4 × 9 13/16 in.) Mount: 25.8 × 35 cm (10 3/16 × 13 3/4 in.),"Purchase, Greenwich ART Group Gift, 2013",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/305832,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2005.100.382 (1a, b)",false,true,287891,Photographs,Photograph,Papa & Mama; The Birthday Group,,,,,,Artist|Artist,,Thereza Dillwyn Llewelyn|John Dillwyn Llewelyn,"British, Swansea, Wales 1810–1882 Swansea, Wales",,"Llewelyn, Thereza Dillwyn|Llewelyn, John Dillwyn","British, Welsh",1810,1882,1853–56,1853,1856,Salted paper print,Image: 7.7 × 8.7 cm (3 1/16 × 3 7/16 in.) (a) Image: 12.6 × 11 cm (4 15/16 × 4 5/16 in.) (b),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287891,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2005.100.382 (15a, b)",false,true,287905,Photographs,Photograph,The Microscope; Thereza and Elinor,,,,,,Artist|Artist,,John Dillwyn Llewelyn|Thereza Dillwyn Llewelyn,"British, Swansea, Wales 1810–1882 Swansea, Wales",,"Llewelyn, John Dillwyn|Llewelyn, Thereza Dillwyn","British, Welsh",1810,1882,1853–56,1853,1856,Salted paper print,Image: 7.6 × 9 cm (3 in. × 3 9/16 in.) (a) Image: 13.8 × 10.5 cm (5 7/16 × 4 1/8 in.) (b),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287905,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1236,false,true,286664,Photographs,Photograph,"Copying Maps, Photographic Headquarters, Petersburg, Virginia",,,,,,Artist|Artist,Attributed to,Alexander Gardner|Unknown,"American, Glasgow, Scotland 1821–1882 Washington, D.C.",,"Gardner, Alexander|Unknown","American, Scottish",1821,1882,March 1865,1865,1865,Albumen silver print from glass negative,Image: 19 x 24.6 cm (7 1/2 x 9 11/16 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286664,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.944.23,false,true,306591,Photographs,Photograph,[The Wilderness Battlefield],,,,,,Artist|Artist,Possibly by,Unknown|Alexander Gardner,"American, Glasgow, Scotland 1821–1882 Washington, D.C.",,"Unknown|Gardner, Alexander","American, Scottish",1821,1882,1865–67,1865,1867,Albumen silver print from glass negative,Image: 4 15/16 × 3 1/4 in. (12.5 × 8.2 cm),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306591,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.944.24,false,true,306592,Photographs,Photograph,[The Wilderness Battlefield],,,,,,Artist|Artist,Possibly by,Unknown|Alexander Gardner,"American, Glasgow, Scotland 1821–1882 Washington, D.C.",,"Unknown|Gardner, Alexander","American, Scottish",1821,1882,1865–67,1865,1867,Albumen silver print from glass negative,Image: 5 in. × 3 5/16 in. (12.7 × 8.4 cm),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306592,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.382 (74a-c),false,true,287963,Photographs,Photograph,"Upper Lake, Penllengare; Rhododendrons; Brome, Morphine Tuff",,,,,,Artist|Artist,,James Knight|John Dillwyn Llewelyn,"British|British, Swansea, Wales 1810–1882 Swansea, Wales",,"Knight, James|Llewelyn, John Dillwyn","British|British, Welsh",1810,1882,1853–56,1853,1856,Albumen silver print; salted paper print,Image: 13.4 × 16.5 cm (5 1/4 × 6 1/2 in.) (a) Image: 7.4 × 6 cm (2 15/16 × 2 3/8 in.) (b) Image: 7.2 × 5.8 cm (2 13/16 × 2 5/16 in.) (c),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287963,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2005.100.382 (8a, b)",false,true,287898,Photographs,Photograph,Penllergare; Printing,,,,,,Artist|Artist,,James Knight|John Dillwyn Llewelyn,"British|British, Swansea, Wales 1810–1882 Swansea, Wales",,"Knight, James|Llewelyn, John Dillwyn","British|British, Welsh",1810,1882,1853–56,1853,1856,Salted paper print,"Image: 10.8 × 13.9 cm (4 1/4 × 5 1/2 in.) (a), oval Image: 12.6 × 10.3 cm (4 15/16 × 4 1/16 in.) (b)","Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287898,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1252,false,true,286627,Photographs,Photograph,The Wilderness Battlefield,,,,,,Artist|Artist,Possibly by,Alexander Gardner|Unknown,"American, Glasgow, Scotland 1821–1882 Washington, D.C.|American",,"Gardner, Alexander|Unknown","American, Scottish",1821,1882,1864,1864,1864,Albumen silver print from glass negative,Image: 9 x 9.5 cm (3 9/16 x 3 3/4 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286627,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.944.1,false,true,286347,Photographs,Photographs,The Wilderness Battlefield,,,,,,Artist|Artist,Possibly by,Alexander Gardner|Unknown,"American, Glasgow, Scotland 1821–1882 Washington, D.C.|American",,"Gardner, Alexander|Unknown","American, Scottish",1821,1882,1864,1864,1864,Albumen silver prints from glass negatives,From 12.5 x 7.9 cm (4 15/16 x 3 1/8 in.) to 12.5 x 9.1 cm (4 15/16 x 3 9/16 in.),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286347,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.944.2,false,true,306596,Photographs,Photographs,The Wilderness Battlefield,,,,,,Artist|Artist,Possibly by,Alexander Gardner|Unknown,"American, Glasgow, Scotland 1821–1882 Washington, D.C.|American",,"Gardner, Alexander|Unknown","American, Scottish",1821,1882,1864,1864,1864,Albumen silver print from glass negative,Image: 4 3/16 × 3 7/16 in. (10.7 × 8.7 cm),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306596,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.944.3,false,true,306597,Photographs,Photographs,The Wilderness Battlefield,,,,,,Artist|Artist,Possibly by,Alexander Gardner|Unknown,"American, Glasgow, Scotland 1821–1882 Washington, D.C.|American",,"Gardner, Alexander|Unknown","American, Scottish",1821,1882,1864,1864,1864,Albumen silver print from glass negative,Image: 4 5/8 × 3 7/16 in. (11.7 × 8.7 cm),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306597,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.944.4,false,true,306598,Photographs,Photographs,The Wilderness Battlefield,,,,,,Artist|Artist,Possibly by,Alexander Gardner|Unknown,"American, Glasgow, Scotland 1821–1882 Washington, D.C.|American",,"Gardner, Alexander|Unknown","American, Scottish",1821,1882,1864,1864,1864,Albumen silver print from glass negative,Image: 5 1/16 × 3 1/16 in. (12.8 × 7.8 cm),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306598,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.944.5,false,true,306599,Photographs,Photographs,The Wilderness Battlefield,,,,,,Artist|Artist,Possibly by,Alexander Gardner|Unknown,"American, Glasgow, Scotland 1821–1882 Washington, D.C.|American",,"Gardner, Alexander|Unknown","American, Scottish",1821,1882,1864,1864,1864,Albumen silver print from glass negative,Image: 4 7/8 × 3 11/16 in. (12.4 × 9.4 cm) Mount: 7 3/16 in. × 6 5/16 in. (18.3 × 16 cm),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306599,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.944.6,false,true,306600,Photographs,Photographs,The Wilderness Battlefield,,,,,,Artist|Artist,Possibly by,Alexander Gardner|Unknown,"American, Glasgow, Scotland 1821–1882 Washington, D.C.|American",,"Gardner, Alexander|Unknown","American, Scottish",1821,1882,1864,1864,1864,Albumen silver print from glass negative,Image: 3 1/4 × 4 15/16 in. (8.3 × 12.5 cm),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306600,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.944.7,false,true,306601,Photographs,Photographs,The Wilderness Battlefield,,,,,,Artist|Artist,Possibly by,Alexander Gardner|Unknown,"American, Glasgow, Scotland 1821–1882 Washington, D.C.|American",,"Gardner, Alexander|Unknown","American, Scottish",1821,1882,1864,1864,1864,Albumen silver print from glass negative,Image: 5 in. × 3 7/16 in. (12.7 × 8.8 cm),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306601,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.944.8,false,true,306602,Photographs,Photographs,The Wilderness Battlefield,,,,,,Artist|Artist,Possibly by,Alexander Gardner|Unknown,"American, Glasgow, Scotland 1821–1882 Washington, D.C.|American",,"Gardner, Alexander|Unknown","American, Scottish",1821,1882,1864,1864,1864,Albumen silver print from glass negative,Image: 5 1/16 × 3 11/16 in. (12.9 × 9.4 cm),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306602,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.944.9,false,true,306603,Photographs,Photographs,The Wilderness Battlefield,,,,,,Artist|Artist,Possibly by,Alexander Gardner|Unknown,"American, Glasgow, Scotland 1821–1882 Washington, D.C.|American",,"Gardner, Alexander|Unknown","American, Scottish",1821,1882,1864,1864,1864,Albumen silver print from glass negative,Image: 4 15/16 × 3 9/16 in. (12.5 × 9 cm),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306603,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.944.10,false,true,306604,Photographs,Photographs,The Wilderness Battlefield,,,,,,Artist|Artist,Possibly by,Alexander Gardner|Unknown,"American, Glasgow, Scotland 1821–1882 Washington, D.C.|American",,"Gardner, Alexander|Unknown","American, Scottish",1821,1882,1864,1864,1864,Albumen silver print from glass negative,Image: 5 in. × 3 1/4 in. (12.7 × 8.2 cm),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306604,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.944.11,false,true,306605,Photographs,Photographs,The Wilderness Battlefield,,,,,,Artist|Artist,Possibly by,Alexander Gardner|Unknown,"American, Glasgow, Scotland 1821–1882 Washington, D.C.|American",,"Gardner, Alexander|Unknown","American, Scottish",1821,1882,1864,1864,1864,Albumen silver print from glass negative,Image: 4 7/8 × 3 3/16 in. (12.4 × 8.1 cm),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306605,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.944.12,false,true,306606,Photographs,Photographs,The Wilderness Battlefield,,,,,,Artist|Artist,Possibly by,Alexander Gardner|Unknown,"American, Glasgow, Scotland 1821–1882 Washington, D.C.|American",,"Gardner, Alexander|Unknown","American, Scottish",1821,1882,1864,1864,1864,Albumen silver print from glass negative,Image: 4 15/16 × 3 5/16 in. (12.6 × 8.4 cm),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306606,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.944.13,false,true,306607,Photographs,Photographs,The Wilderness Battlefield,,,,,,Artist|Artist,Possibly by,Alexander Gardner|Unknown,"American, Glasgow, Scotland 1821–1882 Washington, D.C.|American",,"Gardner, Alexander|Unknown","American, Scottish",1821,1882,1864,1864,1864,Albumen silver print from glass negative,Image: 5 1/16 × 2 3/4 in. (12.8 × 7 cm),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306607,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.944.14,false,true,306608,Photographs,Photographs,The Wilderness Battlefield,,,,,,Artist|Artist,Possibly by,Alexander Gardner|Unknown,"American, Glasgow, Scotland 1821–1882 Washington, D.C.|American",,"Gardner, Alexander|Unknown","American, Scottish",1821,1882,1864,1864,1864,Albumen silver print from glass negative,Image: 5 in. × 3 3/16 in. (12.7 × 8.1 cm),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306608,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.944.15,false,true,306609,Photographs,Photographs,The Wilderness Battlefield,,,,,,Artist|Artist,Possibly by,Alexander Gardner|Unknown,"American, Glasgow, Scotland 1821–1882 Washington, D.C.|American",,"Gardner, Alexander|Unknown","American, Scottish",1821,1882,1864,1864,1864,Albumen silver print from glass negative,Image: 4 5/8 × 3 7/16 in. (11.8 × 8.7 cm),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306609,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.944.16,false,true,306610,Photographs,Photographs,The Wilderness Battlefield,,,,,,Artist|Artist,Possibly by,Alexander Gardner|Unknown,"American, Glasgow, Scotland 1821–1882 Washington, D.C.|American",,"Gardner, Alexander|Unknown","American, Scottish",1821,1882,1864,1864,1864,Albumen silver print from glass negative,Image: 4 13/16 × 3 5/16 in. (12.3 × 8.4 cm),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306610,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.944.17,false,true,306611,Photographs,Photographs,The Wilderness Battlefield,,,,,,Artist|Artist,Possibly by,Alexander Gardner|Unknown,"American, Glasgow, Scotland 1821–1882 Washington, D.C.|American",,"Gardner, Alexander|Unknown","American, Scottish",1821,1882,1864,1864,1864,Albumen silver print from glass negative,Image: 4 15/16 × 3 1/4 in. (12.6 × 8.2 cm),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306611,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.944.18,false,true,306612,Photographs,Photographs,"[The Wilderness Battlefield, near Spotsylvania, Virginia]",,,,,,Artist|Artist,Possibly by,Alexander Gardner|Unknown,"American, Glasgow, Scotland 1821–1882 Washington, D.C.|American",,"Gardner, Alexander|Unknown","American, Scottish",1821,1882,1865 (?),1865,1865,Albumen silver print from glass negative,,"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306612,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.944.19,false,true,306613,Photographs,Photographs,The Wilderness Battlefield,,,,,,Artist|Artist,Possibly by,Alexander Gardner|Unknown,"American, Glasgow, Scotland 1821–1882 Washington, D.C.|American",,"Gardner, Alexander|Unknown","American, Scottish",1821,1882,1864,1864,1864,Albumen silver print from glass negative,Image: 5 in. × 3 9/16 in. (12.7 × 9.1 cm),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306613,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.944.20,false,true,306614,Photographs,Photographs,The Wilderness Battlefield,,,,,,Artist|Artist,Possibly by,Alexander Gardner|Unknown,"American, Glasgow, Scotland 1821–1882 Washington, D.C.|American",,"Gardner, Alexander|Unknown","American, Scottish",1821,1882,1864,1864,1864,Albumen silver print from glass negative,Image: 4 3/4 × 3 1/8 in. (12.1 × 8 cm),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306614,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.944.21,false,true,306615,Photographs,Photographs,The Wilderness Battlefield,,,,,,Artist|Artist,Possibly by,Alexander Gardner|Unknown,"American, Glasgow, Scotland 1821–1882 Washington, D.C.|American",,"Gardner, Alexander|Unknown","American, Scottish",1821,1882,1864,1864,1864,Albumen silver print from glass negative,Image: 4 1/8 × 3 11/16 in. (10.4 × 9.3 cm),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306615,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.924,false,true,286184,Photographs,Photograph,"Infernal Rock, Chincha Islands",,,,,,Artist|Artist,Possibly by,Unknown|John Moran,"American (born England), Bolton, Lancashire 1821–1903 Pennsylvania",,"Unknown|Moran, John","American, born England",1821,1903,ca. 1870,1870,1870,Albumen silver print from glass negative,Image: 18.4 x 25.3 cm (7 1/4 x 9 15/16 in.),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286184,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1973.608.2.1,false,true,260973,Photographs,Photograph,[Pyramid at Dahshûr],,,,,,Artist|Artist,,Francis Frith|Francis Frith and Company,"British, Chesterfield, Derbyshire 1822–1898 Cannes, France|British",,"Frith, Francis|Francis Frith and Company",British|British,1822,1898,ca. 1857,1855,1859,Albumen silver print from glass negative,15.4 x 20.8 cm. (6 1/16 x 8 3/16 in.),"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1973",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260973,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1973.608.2.3,false,true,260975,Photographs,Photograph,"[Valley of the Kings, Thebes]",,,,,,Artist|Artist,,Francis Frith|Francis Frith and Company,"British, Chesterfield, Derbyshire 1822–1898 Cannes, France|British",,"Frith, Francis|Francis Frith and Company",British|British,1822,1898,"ca. 1857, printed 1870s",1855,1859,Albumen silver print from glass negative,,"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1973",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260975,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1973.608.1.26,false,true,260971,Photographs,Photograph,Banks of the Nile at Cairo,,,,,,Artist|Artist,,Francis Frith|Francis Frith and Company,"British, Chesterfield, Derbyshire 1822–1898 Cannes, France|British",,"Frith, Francis|Francis Frith and Company",British|British,1822,1898,"ca. 1857, printed 1870s",1855,1859,Albumen silver print from glass negative,,"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1973",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260971,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1999.138,false,true,282751,Photographs,Photograph,Zaragoza à Pamplona y Barcelona - Puente de Zuera,,,,,,Artist|Artist,,Juan Laurent|José Martinez Sánchez,"French, 1816–1892, active Spain, 1857–1880s|Spanish, 1808–1874",,"Laurent, Juan|Martinez, Sánchez José",French|Spanish,1816 |1808,1892 |1874,ca. 1867,1866,1868,Albumen silver print from glass negative,33.9 x 24.8 cm (13 3/8 x 9 3/4 in.),"Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, by exchange, 1999",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/282751,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2005.100.171.1, .2",false,true,283065,Photographs,Photograph,[The Reading Establishment],,,,,,Artist|Artist,Attributed to|Attributed to,Nicolaas Henneman|William Henry Fox Talbot,"Dutch, Heemskerk 1813–1898 London|British, Dorset 1800–1877 Lacock",,"Henneman, Nicolaas|Talbot, William Henry Fox",Dutch|British,1813 |1800,1898 |1800,1846,1846,1846,Salted paper prints from paper negatives,Left image: 18.6 x 22.4 cm (7 5/16 x 8 13/16 in.) Right image: 18.1 × 22 cm (7 1/8 × 8 11/16 in.) Overall sheet: 19.9 × 49.1 cm (7 13/16 × 19 5/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283065,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1982.1168.1,false,true,263461,Photographs,Photograph,"[Mezzotint portrait of a Girl in Profile, from The St. Memin Collection of Portraits]",,,,,,Artist|Artist,After,Jeremiah Gurney|Charles B. J. F. de Saint-Mémin,"American, 1812–1895 Coxsackie, New York|French, Dijon 1770–1852 Dijon",,"Gurney, Jeremiah|Saint-Mémin, Charles de",American|French,1812 |1770,1895-04-21|1852,1862,1862,1862,Salted paper print,5.6 x 5.6 cm (2 3/16 x 2 3/16 in. ),"Gift of Mrs. James Anderson, 1982",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/263461,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1982.1168.2,false,true,263462,Photographs,Photograph,"[Mezzotint portrait of a Young Man in Profile, from The St. Memin Collection of Portraits]",,,,,,Artist|Artist,After,Jeremiah Gurney|Charles B. J. F. de Saint-Mémin,"American, 1812–1895 Coxsackie, New York|French, Dijon 1770–1852 Dijon",,"Gurney, Jeremiah|Saint-Mémin, Charles de",American|French,1812 |1770,1895-04-21|1852,1862,1862,1862,Salted paper print,5.6 x 5.6 cm (2 3/16 x 2 3/16 in. ),"Gift of Mrs. James Anderson, 1982",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/263462,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2011.66.1,false,true,660039,Photographs,Photogravure; X-Ray,Frauenhand,,,,,,Artist|Artist,,Josef Maria Eder|Eduard Valenta,"Austrian, Krems an der Donau, 1855–1944 Kitzbühel|Austrian, 1857–1937",and,"Eder, Josef Maria|Valenta, Eduard",Austrian|Austrian,1855 |1857,1944 |1937,1896,1896,1896,Photogravure,Image: 10 3/16 × 4 1/4 in. (25.9 × 10.8 cm) Plate: 11 7/16 × 5 5/16 in. (29 × 13.5 cm) Sheet: 19 5/16 in. × 14 in. (49 × 35.5 cm),"Purchase, Alfred Stieglitz Society Gifts, Joyce F. Menschel Photography Library Fund, and Maureen and Noel Testa Gift, 2011",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/660039,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2011.66.2,false,true,660040,Photographs,Photogravure; X-Ray,Hand eines 8 jährigen Mädchens,,,,,,Artist|Artist,,Josef Maria Eder|Eduard Valenta,"Austrian, Krems an der Donau, 1855–1944 Kitzbühel|Austrian, 1857–1937",and,"Eder, Josef Maria|Valenta, Eduard",Austrian|Austrian,1855 |1857,1944 |1937,1896,1896,1896,Photogravure,Image: 6 7/8 × 3 5/16 in. (17.4 × 8.4 cm) Plate: 8 1/16 × 4 9/16 in. (20.5 × 11.6 cm) Sheet: 19 1/2 × 13 3/4 in. (49.5 × 34.9 cm),"Purchase, Alfred Stieglitz Society Gifts, Joyce F. Menschel Photography Library Fund, and Maureen and Noel Testa Gift, 2011",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/660040,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2011.66.3,false,true,660041,Photographs,Photogravure; X-Ray,Hand eines 4 jährigen Kindes,,,,,,Artist|Artist,,Josef Maria Eder|Eduard Valenta,"Austrian, Krems an der Donau, 1855–1944 Kitzbühel|Austrian, 1857–1937",and,"Eder, Josef Maria|Valenta, Eduard",Austrian|Austrian,1855 |1857,1944 |1937,1896,1896,1896,Photogravure,Image: 7 13/16 × 3 1/4 in. (19.9 × 8.2 cm) Plate: 8 13/16 × 4 3/16 in. (22.4 × 10.7 cm) Sheet: 19 9/16 × 13 7/8 in. (49.7 × 35.2 cm),"Purchase, Alfred Stieglitz Society Gifts, Joyce F. Menschel Photography Library Fund, and Maureen and Noel Testa Gift, 2011",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/660041,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2011.66.4,false,true,660042,Photographs,Photogravure; X-Ray,Fuss eines 17 jährigen Jünglings,,,,,,Artist|Artist,,Josef Maria Eder|Eduard Valenta,"Austrian, Krems an der Donau, 1855–1944 Kitzbühel|Austrian, 1857–1937",and,"Eder, Josef Maria|Valenta, Eduard",Austrian|Austrian,1855 |1857,1944 |1937,1896,1896,1896,Photogravure,Image: 8 1/4 in. × 7 in. (21 × 17.8 cm) Plate: 11 1/4 × 8 1/16 in. (28.5 × 20.4 cm) Sheet: 19 5/8 × 13 3/4 in. (49.9 × 35 cm),"Purchase, Alfred Stieglitz Society Gifts, Joyce F. Menschel Photography Library Fund, and Maureen and Noel Testa Gift, 2011",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/660042,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2011.66.5,false,true,660043,Photographs,Photogravure; X-Ray,X-Ray of Samples of Various Materials,,,,,,Artist|Artist,,Josef Maria Eder|Eduard Valenta,"Austrian, Krems an der Donau, 1855–1944 Kitzbühel|Austrian, 1857–1937",and,"Eder, Josef Maria|Valenta, Eduard",Austrian|Austrian,1855 |1857,1944 |1937,1896,1896,1896,Photogravure,Image: 9 1/16 × 6 15/16 in. (23 × 17.7 cm) Plate: 10 5/16 × 8 1/4 in. (26.2 × 20.9 cm) Sheet: 17 5/16 × 13 15/16 in. (44 × 35.4 cm),"Purchase, Alfred Stieglitz Society Gifts, Joyce F. Menschel Photography Library Fund, and Maureen and Noel Testa Gift, 2011",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/660043,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2011.66.6,false,true,660044,Photographs,Photogravure; X-Ray,Cameen in Goldfassung,,,,,,Artist|Artist,,Josef Maria Eder|Eduard Valenta,"Austrian, Krems an der Donau, 1855–1944 Kitzbühel|Austrian, 1857–1937",and,"Eder, Josef Maria|Valenta, Eduard",Austrian|Austrian,1855 |1857,1944 |1937,1896,1896,1896,Photogravure,Image: 4 5/8 × 6 5/8 in. (11.7 × 16.8 cm) Plate: 5 1/2 × 7 in. (14 × 17.8 cm) Sheet: 14 1/8 × 19 5/8 in. (35.8 × 49.8 cm),"Purchase, Alfred Stieglitz Society Gifts, Joyce F. Menschel Photography Library Fund, and Maureen and Noel Testa Gift, 2011",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/660044,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2011.66.7,false,true,660045,Photographs,Photogravure; X-Ray,Grüne Eidechse,,,,,,Artist|Artist,,Josef Maria Eder|Eduard Valenta,"Austrian, Krems an der Donau, 1855–1944 Kitzbühel|Austrian, 1857–1937",and,"Eder, Josef Maria|Valenta, Eduard",Austrian|Austrian,1855 |1857,1944 |1937,1896,1896,1896,Photogravure,Image: 3 5/8 × 6 1/4 in. (9.2 × 15.9 cm) Plate: 4 15/16 × 7 7/16 in. (12.5 × 18.9 cm) Sheet: 13 7/8 × 19 5/8 in. (35.2 × 49.9 cm),"Purchase, Alfred Stieglitz Society Gifts, Joyce F. Menschel Photography Library Fund, and Maureen and Noel Testa Gift, 2011",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/660045,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2011.66.8,false,true,660046,Photographs,Photogravure; X-Ray,Chamäleon cristatus,,,,,,Artist|Artist,,Josef Maria Eder|Eduard Valenta,"Austrian, Krems an der Donau, 1855–1944 Kitzbühel|Austrian, 1857–1937",and,"Eder, Josef Maria|Valenta, Eduard",Austrian|Austrian,1855 |1857,1944 |1937,1896,1896,1896,Photogravure,Image: 4 15/16 × 7 7/8 in. (12.6 × 20 cm) Plate: 6 1/8 × 9 1/8 in. (15.5 × 23.2 cm) Sheet: 13 7/8 × 19 11/16 in. (35.3 × 50 cm),"Purchase, Alfred Stieglitz Society Gifts, Joyce F. Menschel Photography Library Fund, and Maureen and Noel Testa Gift, 2011",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/660046,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2011.66.9,false,true,660047,Photographs,Photogravure; X-Ray,Zanclus cornutus / Acanthurus nigros,,,,,,Artist|Artist,,Josef Maria Eder|Eduard Valenta,"Austrian, Krems an der Donau, 1855–1944 Kitzbühel|Austrian, 1857–1937",and,"Eder, Josef Maria|Valenta, Eduard",Austrian|Austrian,1855 |1857,1944 |1937,1896,1896,1896,Photogravure,Image: 9 1/2 × 7 13/16 in. (24.2 × 19.8 cm) Plate: 10 9/16 × 8 13/16 in. (26.8 × 22.4 cm) Sheet: 19 3/4 × 13 15/16 in. (50.2 × 35.4 cm),"Purchase, Alfred Stieglitz Society Gifts, Joyce F. Menschel Photography Library Fund, and Maureen and Noel Testa Gift, 2011",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/660047,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2011.66.10,false,true,660048,Photographs,Photogravure; X-Ray,Zwei Goldfische und ein Seefisch (Christiceps argentatus),,,,,,Artist|Artist,,Josef Maria Eder|Eduard Valenta,"Austrian, Krems an der Donau, 1855–1944 Kitzbühel|Austrian, 1857–1937",and,"Eder, Josef Maria|Valenta, Eduard",Austrian|Austrian,1855 |1857,1944 |1937,1896,1896,1896,Photogravure,Image: 4 15/16 × 6 9/16 in. (12.5 × 16.6 cm) Plate: 5 13/16 × 7 9/16 in. (14.7 × 19.2 cm) Sheet: 13 7/8 × 19 5/8 in. (35.3 × 49.9 cm),"Purchase, Alfred Stieglitz Society Gifts, Joyce F. Menschel Photography Library Fund, and Maureen and Noel Testa Gift, 2011",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/660048,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2011.66.11,false,true,660049,Photographs,Photogravure; X-Ray,Solfisch (Pleuronectes solea),,,,,,Artist|Artist,,Josef Maria Eder|Eduard Valenta,"Austrian, Krems an der Donau, 1855–1944 Kitzbühel|Austrian, 1857–1937",and,"Eder, Josef Maria|Valenta, Eduard",Austrian|Austrian,1855 |1857,1944 |1937,1896,1896,1896,Photogravure,Image: 6 3/4 × 13 5/8 in. (17.1 × 34.6 cm) Plate: 8 × 14 13/16 in. (20.3 × 37.6 cm) Sheet: 13 13/16 × 19 11/16 in. (35.1 × 50 cm),"Purchase, Alfred Stieglitz Society Gifts, Joyce F. Menschel Photography Library Fund, and Maureen and Noel Testa Gift, 2011",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/660049,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2011.66.12,false,true,660050,Photographs,Photogravure; X-Ray,Frösche in Bauch und Rückenlage,,,,,,Artist|Artist,,Josef Maria Eder|Eduard Valenta,"Austrian, Krems an der Donau, 1855–1944 Kitzbühel|Austrian, 1857–1937",and,"Eder, Josef Maria|Valenta, Eduard",Austrian|Austrian,1855 |1857,1944 |1937,1896,1896,1896,Photogravure,Image: 9 in. × 6 1/2 in. (22.8 × 16.5 cm) Plate: 9 15/16 × 7 1/2 in. (25.3 × 19 cm) Sheet: 19 9/16 × 13 7/8 in. (49.7 × 35.3 cm),"Purchase, Alfred Stieglitz Society Gifts, Joyce F. Menschel Photography Library Fund, and Maureen and Noel Testa Gift, 2011",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/660050,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2011.66.13,false,true,660051,Photographs,Photogravure; X-Ray,Ratte,,,,,,Artist|Artist,,Josef Maria Eder|Eduard Valenta,"Austrian, Krems an der Donau, 1855–1944 Kitzbühel|Austrian, 1857–1937",and,"Eder, Josef Maria|Valenta, Eduard",Austrian|Austrian,1855 |1857,1944 |1937,1896,1896,1896,Photogravure,Image: 5 7/16 × 9 3/4 in. (13.8 × 24.8 cm) Plate: 6 9/16 × 10 7/8 in. (16.6 × 27.6 cm) Sheet: 13 7/8 × 19 5/8 in. (35.3 × 49.9 cm),"Purchase, Alfred Stieglitz Society Gifts, Joyce F. Menschel Photography Library Fund, and Maureen and Noel Testa Gift, 2011",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/660051,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2011.66.14,false,true,660052,Photographs,Photogravure; X-Ray,Neugeborenes Kaninchen,,,,,,Artist|Artist,,Josef Maria Eder|Eduard Valenta,"Austrian, Krems an der Donau, 1855–1944 Kitzbühel|Austrian, 1857–1937",and,"Eder, Josef Maria|Valenta, Eduard",Austrian|Austrian,1855 |1857,1944 |1937,1896,1896,1896,Photogravure,Image: 2 11/16 × 5 7/16 in. (6.9 × 13.8 cm) Plate: 4 1/4 × 6 9/16 in. (10.8 × 16.7 cm) Sheet: 14 1/16 × 19 5/8 in. (35.7 × 49.9 cm),"Purchase, Alfred Stieglitz Society Gifts, Joyce F. Menschel Photography Library Fund, and Maureen and Noel Testa Gift, 2011",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/660052,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2011.66.15,false,true,660053,Photographs,Photogravure; X-Ray,Aesculap-Schlange,,,,,,Artist|Artist,,Josef Maria Eder|Eduard Valenta,"Austrian, Krems an der Donau, 1855–1944 Kitzbühel|Austrian, 1857–1937",and,"Eder, Josef Maria|Valenta, Eduard",Austrian|Austrian,1855 |1857,1944 |1937,1896,1896,1896,Photogravure,Image: 10 3/4 × 8 9/16 in. (27.3 × 21.7 cm) Plate: 11 15/16 × 9 3/4 in. (30.3 × 24.8 cm) Sheet: 19 5/8 × 13 7/8 in. (49.9 × 35.3 cm),"Purchase, Alfred Stieglitz Society Gifts, Joyce F. Menschel Photography Library Fund, and Maureen and Noel Testa Gift, 2011",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/660053,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.1098.8,false,true,631032,Photographs,Photograph,"[Dome of the Rock, Jerusalem]",,,,,,Artist|Artist,,Felice Beato|James Robertson,"British (born Italy), Venice 1832–1909 Luxor, Egypt|British, 1813–1881",and,"Beato, Felice|Robertson, James","British, born Italy|British",1832 |1813,1909 |1881,1856–57,1856,1857,Albumen silver print,Image: 9 in. × 11 1/4 in. (22.9 × 28.6 cm) Mount: 17 5/8 in. × 22 1/2 in. (44.8 × 57.2 cm),"Gift of Joyce F. Menschel, 2013",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/631032,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1246,false,true,285890,Photographs,Photograph,"[Panorama of Camp Winfield Scott, Yorktown, Virginia]",,,,,,Artist|Artist,,Alexander Gardner|James F. Gibson,"American, Glasgow, Scotland 1821–1882 Washington, D.C.|American, born 1828",,"Gardner, Alexander|Gibson, James F.","American, Scottish|American",1821 |1828,1882 |1928,1863,1863,1863,Albumen silver prints from glass negatives,Image: 3 5/16 × 18 13/16 in. (8.4 × 47.8 cm) Mount: 5 3/8 × 20 5/8 in. (13.6 × 52.4 cm),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285890,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -52.640 (1),false,true,291374,Photographs,Photograph,Fountains Abbey. East Window and Tower,,,,,,Artist|Artist,,Joseph Cundall|Philip Henry Delamotte,"British, Norwich, Norfolk 1818–1895 Wallington, Surrey|British, 1821–1889",,"Cundall, Joseph|Delamotte, Philip Henry",British|British,1818 |1821,1895 |1889,1850s,1850,1859,Albumen silver print,Image: 28 x 23.5 cm (11 x 9 1/4 in.) Mount: 43.9 x 30 cm (17 5/16 x 11 13/16 in.),"David Hunter McAlpin Fund, 1952",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/291374,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -52.640 (2),false,true,291375,Photographs,Photograph,Fountains Abbey. General Western Front,,,,,,Artist|Artist,,Joseph Cundall|Philip Henry Delamotte,"British, Norwich, Norfolk 1818–1895 Wallington, Surrey|British, 1821–1889",,"Cundall, Joseph|Delamotte, Philip Henry",British|British,1818 |1821,1895 |1889,1850s,1850,1859,Albumen silver print,Image: 22.5 x 27.9 cm (8 7/8 x 11 in.) Mount: 43.9 x 30 cm (17 5/16 x 11 13/16 in.),"David Hunter McAlpin Fund, 1952",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/291375,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -52.640 (3),false,true,291376,Photographs,Photograph,"Fountains Abbey. The Church, Cloister and Hospitium",,,,,,Artist|Artist,,Joseph Cundall|Philip Henry Delamotte,"British, Norwich, Norfolk 1818–1895 Wallington, Surrey|British, 1821–1889",,"Cundall, Joseph|Delamotte, Philip Henry",British|British,1818 |1821,1895 |1889,1850s,1850,1859,Albumen silver print,Image: 28 x 23.4 cm (11 x 9 3/16 in.) Mount: 43.9 x 30 cm (17 5/16 x 11 13/16 in.),"David Hunter McAlpin Fund, 1952",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/291376,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -52.640 (4),false,true,291369,Photographs,Photograph,Fountains Abbey. The Refectory and Kitchen,,,,,,Artist|Artist,,Joseph Cundall|Philip Henry Delamotte,"British, Norwich, Norfolk 1818–1895 Wallington, Surrey|British, 1821–1889",,"Cundall, Joseph|Delamotte, Philip Henry",British|British,1818 |1821,1895 |1889,1850s,1850,1859,Albumen silver print,Image: 28.5 x 23.6 cm (11 1/4 x 9 5/16 in.) Mount: 43.9 x 30 cm (17 5/16 x 11 13/16 in.),"David Hunter McAlpin Fund, 1952",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/291369,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -52.640 (5),false,true,291377,Photographs,Photograph,Fountains Abbey. The Church and Chapter House,,,,,,Artist|Artist,,Joseph Cundall|Philip Henry Delamotte,"British, Norwich, Norfolk 1818–1895 Wallington, Surrey|British, 1821–1889",,"Cundall, Joseph|Delamotte, Philip Henry",British|British,1818 |1821,1895 |1889,1850s,1850,1859,Albumen silver print,Image: 27.4 x 21.7 cm (10 13/16 x 8 9/16 in.) Mount: 43.9 x 30 cm (17 5/16 x 11 13/16 in.),"David Hunter McAlpin Fund, 1952",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/291377,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -52.640 (6),false,true,291378,Photographs,Photograph,Fountains Abbey. Interior of Chapter House,,,,,,Artist|Artist,,Joseph Cundall|Philip Henry Delamotte,"British, Norwich, Norfolk 1818–1895 Wallington, Surrey|British, 1821–1889",,"Cundall, Joseph|Delamotte, Philip Henry",British|British,1818 |1821,1895 |1889,1850s,1850,1859,Albumen silver print,Image: 28.5 x 23.6 cm (11 1/4 x 9 5/16 in.) Mount: 43.9 x 30 cm (17 5/16 x 11 13/16 in.),"David Hunter McAlpin Fund, 1952",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/291378,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -52.640 (7),false,true,291379,Photographs,Photograph,"Fountains Abbey. The Chapel of the Nine Alters, Exterior",,,,,,Artist|Artist,,Joseph Cundall|Philip Henry Delamotte,"British, Norwich, Norfolk 1818–1895 Wallington, Surrey|British, 1821–1889",,"Cundall, Joseph|Delamotte, Philip Henry",British|British,1818 |1821,1895 |1889,1850s,1850,1859,Albumen silver print,Image: 23.5 x 28 cm (9 1/4 x 11 in.) Mount: 43.9 x 30 cm (17 5/16 x 11 13/16 in.),"David Hunter McAlpin Fund, 1952",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/291379,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -52.640 (8),false,true,291380,Photographs,Photograph,"Fountains Abbey. The Chapel of the Nine Alters, Interior",,,,,,Artist|Artist,,Joseph Cundall|Philip Henry Delamotte,"British, Norwich, Norfolk 1818–1895 Wallington, Surrey|British, 1821–1889",,"Cundall, Joseph|Delamotte, Philip Henry",British|British,1818 |1821,1895 |1889,1850s,1850,1859,Albumen silver print,Image: 28.6 x 23.6 cm (11 1/4 x 9 5/16 in.) Mount: 43.9 x 30 cm (17 5/16 x 11 13/16 in.),"David Hunter McAlpin Fund, 1952",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/291380,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -52.640 (9),false,true,291381,Photographs,Photograph,Fountains Abbey. Interior of the Choir,,,,,,Artist|Artist,,Joseph Cundall|Philip Henry Delamotte,"British, Norwich, Norfolk 1818–1895 Wallington, Surrey|British, 1821–1889",,"Cundall, Joseph|Delamotte, Philip Henry",British|British,1818 |1821,1895 |1889,1850s,1850,1859,Albumen silver print,Image: 28.2 x 22.8 cm (11 1/8 x 9 in.) Mount: 43.9 x 30 cm (17 5/16 x 11 13/16 in.),"David Hunter McAlpin Fund, 1952",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/291381,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -52.640 (10),false,true,291382,Photographs,Photograph,Fountains Abbey. The Echo Rock,,,,,,Artist|Artist,,Joseph Cundall|Philip Henry Delamotte,"British, Norwich, Norfolk 1818–1895 Wallington, Surrey|British, 1821–1889",,"Cundall, Joseph|Delamotte, Philip Henry",British|British,1818 |1821,1895 |1889,1850s,1850,1859,Albumen silver print,Image: 23.5 x 27.9 cm (9 1/4 x 11 in.) Mount: 43.9 x 30 cm (17 5/16 x 11 13/16 in.),"David Hunter McAlpin Fund, 1952",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/291382,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -52.640 (11),false,true,291383,Photographs,Photograph,Easby Abbey. From the East,,,,,,Artist|Artist,,Joseph Cundall|Philip Henry Delamotte,"British, Norwich, Norfolk 1818–1895 Wallington, Surrey|British, 1821–1889",,"Cundall, Joseph|Delamotte, Philip Henry",British|British,1818 |1821,1895 |1889,1850s,1850,1859,Albumen silver print,Image: 28.1 x 23.4 cm (11 1/16 x 9 3/16 in.) Mount: 43.9 x 30 cm (17 5/16 x 11 13/16 in.),"David Hunter McAlpin Fund, 1952",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/291383,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -52.640 (12),false,true,291384,Photographs,Photograph,Easby Abbey. The Refectory,,,,,,Artist|Artist,,Joseph Cundall|Philip Henry Delamotte,"British, Norwich, Norfolk 1818–1895 Wallington, Surrey|British, 1821–1889",,"Cundall, Joseph|Delamotte, Philip Henry",British|British,1818 |1821,1895 |1889,1850s,1850,1859,Albumen silver print,Image: 28 x 23.5 cm (11 x 9 1/4 in.) Mount: 43.9 x 30 cm (17 5/16 x 11 13/16 in.),"David Hunter McAlpin Fund, 1952",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/291384,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -52.640 (13),false,true,291385,Photographs,Photograph,Rivaulx Abbey. General View from the South,,,,,,Artist|Artist,,Joseph Cundall|Philip Henry Delamotte,"British, Norwich, Norfolk 1818–1895 Wallington, Surrey|British, 1821–1889",,"Cundall, Joseph|Delamotte, Philip Henry",British|British,1818 |1821,1895 |1889,1850s,1850,1859,Albumen silver print,Image: 28 x 23.5 cm (11 x 9 1/4 in.) Mount: 43.9 x 30 cm (17 5/16 x 11 13/16 in.),"David Hunter McAlpin Fund, 1952",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/291385,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -52.640 (14),false,true,291386,Photographs,Photograph,Rivaulx Abbey. Interior of the Choir,,,,,,Artist|Artist,,Joseph Cundall|Philip Henry Delamotte,"British, Norwich, Norfolk 1818–1895 Wallington, Surrey|British, 1821–1889",,"Cundall, Joseph|Delamotte, Philip Henry",British|British,1818 |1821,1895 |1889,1850s,1850,1859,Albumen silver print,Image: 28.6 x 23.6 cm (11 1/4 x 9 5/16 in.) Mount: 43.9 x 30 cm (17 5/16 x 11 13/16 in.),"David Hunter McAlpin Fund, 1952",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/291386,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -52.640 (15),false,true,291387,Photographs,Photograph,Rivaulx Abbey. Looking Across the Choir,,,,,,Artist|Artist,,Joseph Cundall|Philip Henry Delamotte,"British, Norwich, Norfolk 1818–1895 Wallington, Surrey|British, 1821–1889",,"Cundall, Joseph|Delamotte, Philip Henry",British|British,1818 |1821,1895 |1889,1850s,1850,1859,Albumen silver print,Image: 28.1 x 23.5 cm (11 1/16 x 9 1/4 in.) Mount: 43.9 x 30 cm (17 5/16 x 11 13/16 in.),"David Hunter McAlpin Fund, 1952",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/291387,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -52.640 (16),false,true,291388,Photographs,Photograph,Rivaulx Abbey. The Triforium Arches,,,,,,Artist|Artist,,Joseph Cundall|Philip Henry Delamotte,"British, Norwich, Norfolk 1818–1895 Wallington, Surrey|British, 1821–1889",,"Cundall, Joseph|Delamotte, Philip Henry",British|British,1818 |1821,1895 |1889,1850s,1850,1859,Albumen silver print,Image: 28.1 x 23.4 cm (11 1/16 x 9 3/16 in.) Mount: 43.9 x 30 cm (17 5/16 x 11 13/16 in.),"David Hunter McAlpin Fund, 1952",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/291388,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -52.640 (17),false,true,291389,Photographs,Photograph,Rivaulx Abbey. Doorway of the Refectory,,,,,,Artist|Artist,,Joseph Cundall|Philip Henry Delamotte,"British, Norwich, Norfolk 1818–1895 Wallington, Surrey|British, 1821–1889",,"Cundall, Joseph|Delamotte, Philip Henry",British|British,1818 |1821,1895 |1889,1850s,1850,1859,Albumen silver print,Image: 28.1 x 23.4 cm (11 1/16 x 9 3/16 in.) Mount: 43.9 x 30 cm (17 5/16 x 11 13/16 in.),"David Hunter McAlpin Fund, 1952",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/291389,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -52.640 (18),false,true,291390,Photographs,Photograph,Kirkstall Abbey. From the West,,,,,,Artist|Artist,,Joseph Cundall|Philip Henry Delamotte,"British, Norwich, Norfolk 1818–1895 Wallington, Surrey|British, 1821–1889",,"Cundall, Joseph|Delamotte, Philip Henry",British|British,1818 |1821,1895 |1889,1850s,1850,1859,Albumen silver print,Image: 26.3 x 23.5 cm (10 3/8 x 9 1/4 in.) Mount: 43.9 x 30 cm (17 5/16 x 11 13/16 in.),"David Hunter McAlpin Fund, 1952",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/291390,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -52.640 (19),false,true,291391,Photographs,Photograph,Kirkstall Abbey. Ruins on the South Side,,,,,,Artist|Artist,,Joseph Cundall|Philip Henry Delamotte,"British, Norwich, Norfolk 1818–1895 Wallington, Surrey|British, 1821–1889",,"Cundall, Joseph|Delamotte, Philip Henry",British|British,1818 |1821,1895 |1889,1850s,1850,1859,Albumen silver print,Image: 28.1 x 23.4 cm (11 1/16 x 9 3/16 in.) Mount: 43.9 x 30 cm (17 5/16 x 11 13/16 in.),"David Hunter McAlpin Fund, 1952",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/291391,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -52.640 (20),false,true,291392,Photographs,Photograph,Kirkstall Abbey. Doorway on the North Side,,,,,,Artist|Artist,,Joseph Cundall|Philip Henry Delamotte,"British, Norwich, Norfolk 1818–1895 Wallington, Surrey|British, 1821–1889",,"Cundall, Joseph|Delamotte, Philip Henry",British|British,1818 |1821,1895 |1889,1850s,1850,1859,Albumen silver print,Image: 28.1 x 23.4 cm (11 1/16 x 9 3/16 in.) Mount: 43.9 x 30 cm (17 5/16 x 11 13/16 in.),"David Hunter McAlpin Fund, 1952",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/291392,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -52.640 (21),false,true,291393,Photographs,Photograph,Bolton Priory. From the South,,,,,,Artist|Artist,,Joseph Cundall|Philip Henry Delamotte,"British, Norwich, Norfolk 1818–1895 Wallington, Surrey|British, 1821–1889",,"Cundall, Joseph|Delamotte, Philip Henry",British|British,1818 |1821,1895 |1889,1850s,1850,1859,Albumen silver print,Image: 28.4 x 21.6 cm (11 3/16 x 8 1/2 in.) Mount: 43.9 x 30 cm (17 5/16 x 11 13/16 in.),"David Hunter McAlpin Fund, 1952",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/291393,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -52.640 (22),false,true,291394,Photographs,Photograph,Bolton Priory. The Stepping Stones,,,,,,Artist|Artist,,Joseph Cundall|Philip Henry Delamotte,"British, Norwich, Norfolk 1818–1895 Wallington, Surrey|British, 1821–1889",,"Cundall, Joseph|Delamotte, Philip Henry",British|British,1818 |1821,1895 |1889,1850s,1850,1859,Albumen silver print,Image: 28.3 x 24.1 cm (11 1/8 x 9 1/2 in.) Mount: 43.9 x 30 cm (17 5/16 x 11 13/16 in.),"David Hunter McAlpin Fund, 1952",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/291394,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1973.616,false,true,260988,Photographs,Photograph,The Chess Players,,,,,,Artist|Artist,Likely by|Possibly by,Antoine-François-Jean Claudet|Nicolaas Henneman,"French, active Great Britain, 1797–1867|Dutch, Heemskerk 1813–1898 London",,"Claudet, Antione-François-Jean|Henneman, Nicolaas","French, active Great Britain",1797 |1813,1867 |1898,ca. 1845,1840,1850,Salted paper print from paper negative,19.5 x 14.4 cm. (7 11/16 x 5 11/16 in.),"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1973",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260988,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.159.40,false,true,306323,Photographs,Photograph,The Chess Players,,,,,,Artist|Artist,Likely by|Possibly by,Antoine-François-Jean Claudet|Nicolaas Henneman,"French, active Great Britain, 1797–1867|Dutch, Heemskerk 1813–1898 London",,"Claudet, Antione-François-Jean|Henneman, Nicolaas","French, active Great Britain|Dutch",1797 |1813,1867 |1898,ca. 1845,1840,1850,Salted paper print from paper negative,Sheet: 9 5/8 × 7 11/16 in. (24.5 × 19.6 cm) Image: 7 13/16 × 5 13/16 in. (19.8 × 14.7 cm),"Bequest of Maurice B. Sendak, 2012",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306323,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2005.100.382 (4a, b)",false,true,287894,Photographs,Photograph,The Observatory; Thereza Llewelyn,,,,,,Artist|Artist,,Miss Bush|John Dillwyn Llewelyn,"British, active 19th century|British, Swansea, Wales 1810–1882 Swansea, Wales",,"Bush Miss|Llewelyn, John Dillwyn","British|British, Welsh",1810,1882,1853–56,1853,1856,Salted paper print,Image: 9.8 × 15.6 cm (9.8 × 15.6 cm) (a) Image: 12.8 × 10.6 cm (5 1/16 × 4 3/16 in.) (b),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287894,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1981.1158.3,false,true,263051,Photographs,Photograph,"White House Landing, Pamunkey River",,,,,,Artist|Artist,,Timothy H. O'Sullivan|Mathew B. Brady,"American, born Ireland, 1840–1882|American, born Ireland, 1823?–1896 New York",,"O'Sullivan, Timothy H.|Brady, Mathew B.","American, born Ireland|American",1840 |1823,1882 |1896,1864,1864,1864,Albumen silver print,,"Gift of Mrs. A. Hyatt Mayor, 1981",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/263051,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.607,false,true,283067,Photographs,Photograph,The Fruit Sellers,,,,,,Artist|Artist,Possibly by|Possibly by,Calvert Richard Jones|William Henry Fox Talbot,"British, Swansea, Wales 1802–1877 Bath, England|British, Dorset 1800–1877 Lacock",,"Jones, Calvert Richard|Talbot, William Henry Fox","British, Welsh|British",1802 |1800,1877 |1800,ca. 1845,1843,1847,Salted paper print from paper negative,17.1 x 21.1 cm (6 3/4 x 8 5/16 in. ),"Gilman Collection, Purchase, Harriette and Noel Levine Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283067,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1973.637,false,true,261000,Photographs,Photograph,"""God"" by Baroness Elsa von Freytag-Loringhoven and Morton Schamberg",,,,,,Artist|Artist,,Morton Schamberg|Elsa von Freytag-Loringhoven,"American, Philadelphia, Pennsylvania 1881–1918 Philadelphia, Pennsylvania|German, 1874–1927",,"Schamberg, Morton|Freytag-Loringhoven, Elsa von",American|German,1881 |1874,1918 |1927,1917,1917,1917,Gelatin silver print,24.1 x 19.2 cm (9 1/2 x 7 9/16 in.),"Elisha Whittelsey Collection, Elisha Whittelsey Fund, 1973",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261000,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.80,false,true,283179,Photographs,Photograph,Frederick Langenheim,,,,,,Artist|Artist,,William Langenheim|Frederick Langenheim,"American, born Germany, Schöningen 1807–1874|American, born Germany, Schöningen 1809–1879",,"Langenheim, William|Langenheim, Frederick","American, born Germany|American, born Germany",1807 |1809,1874 |1879,ca. 1848–50,1846,1852,Daguerreotype,Image: 9 5/9 x 7 5/8; Frame: 18 x 15 x 1 3/8,"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283179,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.400.38,false,true,291791,Photographs,Daguerreotype,"[Middle-aged Man with Chinstrap Beard, Hand Tucked Inside Buttoned Jacket]",,,,,,Artist|Artist,,William Langenheim|Frederick Langenheim,"American, born Germany, Schöningen 1807–1874|American, born Germany, Schöningen 1809–1879",,"Langenheim, William|Langenheim, Frederick","American, born Germany|American, born Germany",1807 |1809,1874 |1879,1840s–50s,1840,1859,Daguerreotype,Image: 12.1 x 8.8 cm (4 3/4 x 3 7/16 in.) Plate: 14 x 10.8 cm (5 1/2 x 4 1/4 in.) Case: 1.6 x 14.9 x 11.9 cm (5/8 x 5 7/8 x 4 11/16 in.),"Bequest of Herbert Mitchell, 2008",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/291791,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.400.56,false,true,291809,Photographs,Daguerreotype,[Seated Man in Floral Vest],,,,,,Artist|Artist,,William Langenheim|Frederick Langenheim,"American, born Germany, Schöningen 1807–1874|American, born Germany, Schöningen 1809–1879",,"Langenheim, William|Langenheim, Frederick","American, born Germany|American, born Germany",1807 |1809,1874 |1879,1840s–50s,1840,1859,Daguerreotype,Image: 6.8 x 5.6 cm (2 11/16 x 2 3/16 in.) Plate: 8.3 x 7 cm (3 1/4 x 2 3/4 in.) Case: 1.7 x 9 x 7.9 cm (11/16 x 3 9/16 x 3 1/8 in.),"Bequest of Herbert Mitchell, 2008",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/291809,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.177,false,true,286315,Photographs,Photograph,Frederick Langenheim Looking at Talbotypes,,,,,,Artist|Artist,,William Langenheim|Frederick Langenheim,"American, born Germany, Schöningen 1807–1874|American, born Germany, Schöningen 1809–1879",,"Langenheim, William|Langenheim, Frederick","American, born Germany|American, born Germany",1807 |1809,1874 |1879,ca. 1849–51,1849,1851,Daguerreotype,Image: 12.1 × 8.9 cm (4 3/4 × 3 1/2 in.) Case: 1.6 × 15.2 × 11.9 cm (5/8 in. × 6 in. × 4 11/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286315,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.614a–g,false,true,283180,Photographs,Photograph,Eclipse of the Sun,,,,,,Artist|Artist,,William Langenheim|Frederick Langenheim,"American, born Germany, Schöningen 1807–1874|American, born Germany, Schöningen 1809–1879",,"Langenheim, William|Langenheim, Frederick","American, born Germany|American, born Germany",1807 |1809,1874 |1879,1854,1854,1854,Daguerreotype,From 3.2 x 2.5 cm (1 1/4 x 1 in.) to 7.2 x 5.9 cm (2 13/16 x 2 5/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283180,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.537.4,false,true,259606,Photographs,Carte-de-visite,Confederate Soldier [on the Battlefield at Antietam],,,,,,Artist|Publisher,,Alexander Gardner|Brady & Co.,"American, Glasgow, Scotland 1821–1882 Washington, D.C.|American, active 1840s–1880s",,"Gardner, Alexander|Brady & Co.","American, Scottish|American",1821 |1840,1882 |1889,September 1862,1862,1862,Albumen silver print from glass negative,Image: 6.1 x 9.8 cm (2 3/8 x 3 7/8 in.),"Purchase, Florance Waterbury Bequest, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/259606,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.395,false,true,682875,Photographs,Photograph,La Frayeur,,,,,,Artist|Artist|Person in Photograph,Person in photograph,Aquilin Schad|Pierre-Louis Pierson|Countess Virginia Oldoini Verasis di Castiglione,"Austrian, 1817–1866|French, 1822–1913|1835–1899",,"Schad, Aquilin|Pierson, Pierre-Louis|Castiglione, di, Virginia Oldoini Verasis Countess",,1817 |1822 |1835,1866 |1913 |1899,1861–64,1861,1864,Salted paper print with applied color,Image: 22 7/16 × 17 5/16 in. (57 × 44 cm) Mat: 29 1/2 × 23 5/16 in. (75 × 59.2 cm),"Purchase, The Camille M. Lownds Fund, Joyce F. Menschel Gift, Louis V. Bell and 2012 Benefit Funds, and C. Jay Moorhead Foundation Gift, 2015",,,,,,,,,,,,Photographs|Paintings,,http://www.metmuseum.org/art/collection/search/682875,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -46.122.6,false,true,269075,Photographs,Photograph,Maison au toit de chaume,,,,,,Artist|Printer,,"Unknown|Imprimerie photographique de Blanquart-Évrard, à Lille","French|French, active 1851–55",,"Unknown|Imprimerie photographique de Blanquart-Évrard, à Lille",,1851,1855,1850–53,1850,1853,Salted paper print (Blanquart-Évrard process) from paper negative,,"Harris Brisbane Dick Fund, 1946",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269075,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -46.122.7,false,true,269076,Photographs,Photograph,Nature morte au lièvre,,,,,,Artist|Printer,,"Unknown|Imprimerie photographique de Blanquart-Évrard, à Lille","French|French, active 1851–55",,"Unknown|Imprimerie photographique de Blanquart-Évrard, à Lille",,1851,1855,1853,1853,1853,Salted paper print (Blanquart-Évrard process) from paper negative,,"Harris Brisbane Dick Fund, 1946",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269076,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -46.122.8,false,true,269077,Photographs,Photograph,"Vue prise dans la vallée de Changy, aux environs de Fontainebleau",,,,,,Artist|Printer,,"Unknown|Imprimerie photographique de Blanquart-Évrard, à Lille","French|French, active 1851–55",,"Unknown|Imprimerie photographique de Blanquart-Évrard, à Lille",,1851,1855,1850–53,1850,1853,Salted paper print (Blanquart-Évrard process) from paper negative,,"Harris Brisbane Dick Fund, 1946",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269077,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -46.122.10,false,true,269058,Photographs,Photograph,Trois jeunes enfants assis autour d'un panier,,,,,,Artist|Printer,,"Unknown|Imprimerie photographique de Blanquart-Évrard, à Lille","French|French, active 1851–55",,"Unknown|Imprimerie photographique de Blanquart-Évrard, à Lille",,1851,1855,1850–53,1850,1853,Salted paper print (Blanquart-Évrard process) from paper negative,,"Harris Brisbane Dick Fund, 1946",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269058,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -46.122.11,false,true,269059,Photographs,Photograph,Sous-bois en automne,,,,,,Artist|Printer,,"Unknown|Imprimerie photographique de Blanquart-Évrard, à Lille","French|French, active 1851–55",,"Unknown|Imprimerie photographique de Blanquart-Évrard, à Lille",,1851,1855,1850–53,1850,1853,Salted paper print (Blanquart-Évrard process) from paper negative,,"Harris Brisbane Dick Fund, 1946",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269059,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -46.122.13,false,true,269061,Photographs,Photograph,Ane attaché à une charette,,,,,,Artist|Printer,,"Unknown|Imprimerie photographique de Blanquart-Évrard, à Lille","French|French, active 1851–55",,"Unknown|Imprimerie photographique de Blanquart-Évrard, à Lille",,1851,1855,1850–53,1850,1853,Salted paper print (Blanquart-Évrard process) from paper negative,,"Harris Brisbane Dick Fund, 1946",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269061,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -46.122.15,false,true,269063,Photographs,Photograph,Charette devant l'entrée d'un abri au toit de chaume,,,,,,Artist|Printer,,"Unknown|Imprimerie photographique de Blanquart-Évrard, à Lille","French|French, active 1851–55",,"Unknown|Imprimerie photographique de Blanquart-Évrard, à Lille",,1851,1855,1850–53,1850,1853,Salted paper print (Blanquart-Évrard process) from paper negative,17.9 x 22.4 cm (7 1/16 x 8 13/16 in.),"Harris Brisbane Dick Fund, 1946",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269063,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -46.122.19,false,true,269067,Photographs,Photograph,Ferme au toit de chaume,,,,,,Artist|Printer,,"Unknown|Imprimerie photographique de Blanquart-Évrard, à Lille","French|French, active 1851–55",,"Unknown|Imprimerie photographique de Blanquart-Évrard, à Lille",,1851,1855,1850–53,1850,1853,Salted paper print (Blanquart-Évrard process) from paper negative,,"Harris Brisbane Dick Fund, 1946",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269067,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.572,false,true,685911,Photographs,Carte-de-visite,[Adolph Menzel],,,,,,Photography Studio|Person in Photograph,Person in photograph,Photographische Gesellschaft|Adolph Menzel,"German, Breslau 1815–1905 Berlin",,"Photographische Gesellschaft|Menzel, Adolph",,1910 |1815,1910 |1905,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685911,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.777.1,false,true,705423,Photographs,Photograph,"The Memnonium or Rameseiom, Thebes",,,,,,Publisher|Artist,,J. Hogarth|Robert Murray,"British, Edinburgh 1822–1893 Plymouth",,"Hogarth, J.|Murray, Robert",,1822,1893,"1852–55, printed 1854–56",1852,1855,Albumen silver print from waxed paper negative,Image: 6 7/8 in. × 9 in. (17.5 × 22.9 cm) Mount: 12 in. × 15 3/8 in. (30.5 × 39.1 cm),"Gift of Charles Isaacs and Carol Nigro, 2015",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/705423,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1981.1229.49,false,true,263189,Photographs,Photograph,"Gate of Ptolemy Philomeder, B.C. 180, Karnac",,,,,,Publisher|Artist,Attributed to,J. Hogarth|Robert Murray,"British, Edinburgh 1822–1893 Plymouth",,"Hogarth, J.|Murray, Robert",,1822,1893,ca. 1856,1856,1856,Albumen silver print,Image: 9 1/8 × 7 3/8 in. (23.2 × 18.7 cm) Mount: 16 in. × 12 3/16 in. (40.6 × 31 cm),"Robert O. Dougan Collection, Gift of Warner Communications Inc., 1981",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/263189,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1981.1229.50,false,true,263191,Photographs,Photograph,"Nubian Sakkieh, or Water Wheel",,,,,,Publisher|Artist,Attributed to,J. Hogarth|Robert Murray,"British, Edinburgh 1822–1893 Plymouth",,"Hogarth, J.|Murray, Robert",,1822,1893,ca. 1856,1856,1856,Albumen silver print,Image: 6 3/4 × 8 3/16 in. (17.1 × 20.7 cm) Mount: 12 3/16 in. × 15 15/16 in. (31 × 40.5 cm),"Robert O. Dougan Collection, Gift of Warner Communications Inc., 1981",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/263191,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.778.34,false,true,700110,Photographs,Photograph,"Nubie, Grand Temple d'Isis, a Philœ, Galerie Orientale",,,,,,Publisher|Artist|Printer,,"Gide et Baudry|Maxime Du Camp|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1822–1894|French, active 1851–55",,"Gide et Baudry|Du Camp, Maxime|Imprimerie photographique de Blanquart-Évrard, à Lille",,1822 |1851,1894 |1855,"April 1850, printed 1852",1850,1850,Salted paper print from paper negative,Image: 6 9/16 × 8 9/16 in. (16.7 × 21.8 cm) Mount: 12 7/16 × 17 5/8 in. (31.6 × 44.7 cm),"Gift of Joyce F. Menschel, 2015",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/700110,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.158,false,true,267944,Photographs,Photograph,"Dead Confederate Soldier at Fort Mahone, Petersburg",,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",,1823,1896,1864,1864,1864,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267944,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.173,false,true,685515,Photographs,Carte-de-visite,[Konstantin Cretius],,,,,,Person in Photograph|Photographer,Person in photograph,Konstantin Johannes Franz Cretius|Ernst Milster,"German, Brieg 1814–1901 Berlin|German, born 1835",,"Cretius, Konstantin Johannes Franz|Milster, Ernst",,1814 |1835,1901,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685515,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.395,false,true,268206,Photographs,Photograph,"Gettysburg, Pennsylvania",,,,,,Artist|Former Attribution,Formerly attributed to,Unknown|Mathew B. Brady,"American|American, born Ireland, 1823?–1896 New York",,"Unknown|Brady, Mathew B.",,1823,1896,1863,1863,1863,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268206,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.778.38,false,true,700114,Photographs,Photograph,"Head of Cañyon de Chelle, Looking Down",,,,,,Patron|Artist,Commissioned by,Lieutenant George Montague Wheeler|Timothy H. O'Sullivan,"American, 1842–1905|American, born Ireland, 1840–1882",,"Wheeler, Lieutenant George Montague|O'Sullivan, Timothy H.",,1842 |1840,1905 |1882,1873,1873,1873,Albumen silver print,Image: 8 1/16 × 10 13/16 in. (20.5 × 27.5 cm),"Gift of Joyce F. Menschel, 2015",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/700114,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.400.659–.687,false,true,692531,Photographs,,[29 Glass Stereographs],,,,,,Artist|Artist|Artist|Artist,,Bierstadt Brothers|Various|Franklin White|James McPherson,"American, active 1860s–80s|American, active 1850s–60s|American, active 1860s",,"Bierstadt Brothers|Various|White, Franklin|McPherson, James",,1850 |1850 |1860,1900 |1869 |1869,1850s–1900s,1850,1909,Glass stereographs,Each photograph approx. 3 1/2 in. × 7 in. (8.9 × 17.8 cm),"Bequest of Herbert Mitchell, 2008",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/692531,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.778.37,false,true,700113,Photographs,Photograph,"Perched Rock, Rocker Creek, Arizona",,,,,,Patron|Artist,Commissioned by,Lieutenant George Montague Wheeler|William Bell,"American, 1842–1905|American (born England) Liverpool 1831–1910 Philadelphia, Pennsylvania",,"Wheeler, Lieutenant George Montague|Bell, William",,1842 |1831,1905 |1910,1872,1872,1872,Albumen silver print from glass negative,Image: 10 7/8 × 8 1/16 in. (27.6 × 20.5 cm) Sheet: 21 7/16 in. × 16 in. (54.4 × 40.6 cm),"Gift of Joyce F. Menschel, 2015",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/700113,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.400.688–.897,false,true,692523,Photographs,,[210 Stereographs],,,,,,Publisher|Artist|Artist|Artist,,New York Stereoscopic Company|Frederick Langenheim|William Langenheim|C. G. Hill,"American|American, born Germany, Schöningen 1809–1879|American, born Germany, Schöningen 1807–1874|American, active 1860s–80s",,"New York Stereoscopic Company|Langenheim, Frederick|Langenheim, William|Hill, C. G.",,1809 |1807 |1860,1879 |1874 |1899,1850s–1890s,1850,1899,Albumen silver prints,Each photograph approx. 3 1/2 in. × 7 in. (8.9 × 17.8 cm),"Bequest of Herbert Mitchell, 2008",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/692523,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1996.438.2,false,true,294506,Photographs,Album,[Personal Travel Album Made by the Dowager Empress Maria Feoderovna Showing Events in the Daily Life of the Russian Imperial Family],,,,,,Artist,,Dowager Empress Maria Feodorovna,"Russian, born Denmark, Copenhagen 1847–1928 Copenhagen",,"Feodorovna, Maria, Dowager Empress",,1847,1928,1916,1916,1916,Gelatin silver prints; photomechanical prints,29.5 x 42.2 x 5.7cm (11 5/8 x 16 5/8 x 2 1/4in.) each,"Gift of Prince and Princess Alexander Romanoff, 1996",,,,,,,,,,,,Albums|Prints,,http://www.metmuseum.org/art/collection/search/294506,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.533,false,true,685873,Photographs,Carte-de-visite,[Ludwig von Löfftz],,,,,,Artist,,Franz Werner,active 1860s,,"Werner, Franz",,1859,1870,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685873,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.678,false,true,686017,Photographs,Carte-de-visite,[Riefstahl ?],,,,,,Artist,,Carl Wigand,active 1860s,,"Wigand, Carl",,1859,1870,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/686017,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2016.91,false,true,702990,Photographs,Photograph,55. Athènes. Acropole. Ruines et 1ers plans (pour tableau),,,,,,Artist,,Joseph-Philibert Girault de Prangey,"French, 1804–1892",,"Girault, de Prangey Joseph-Philibert",,1804,1892,1842,1842,1842,Daguerreotype,Image: 3 3/4 × 9 7/16 in. (9.5 × 24 cm),"Purchase, Philippe de Montebello Fund, Mr. and Mrs. John A. Moran Gift, in memory of Louise Chisholm Moran, Joyce F. Menschel and Annette de la Renta Gifts, and funds from various donors, 2016",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/702990,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2016.92,false,true,702991,Photographs,Photograph,49. Athènes. Acropole. Côté O.,,,,,,Artist,,Joseph-Philibert Girault de Prangey,"French, 1804–1892",,"Girault, de Prangey Joseph-Philibert",,1804,1892,1842,1842,1842,Daguerreotype,Image: 3 3/4 × 9 7/16 in. (9.5 × 24 cm),"Purchase, Philippe de Montebello Fund, Mr. and Mrs. John A. Moran Gift, in memory of Louise Chisholm Moran, Joyce F. Menschel and Annette de la Renta Gifts, and funds from various donors, 2016",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/702991,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2016.93,false,true,702992,Photographs,Photograph,62. Athènes. Temple de Minerve Poliade.,,,,,,Artist,,Joseph-Philibert Girault de Prangey,"French, 1804–1892",,"Girault, de Prangey Joseph-Philibert",,1804,1892,1842,1842,1842,Daguerreotype,Image: 3 3/4 × 9 7/16 in. (9.5 × 24 cm),"Purchase, Philippe de Montebello Fund, Mr. and Mrs. John A. Moran Gift, in memory of Louise Chisholm Moran, Joyce F. Menschel and Annette de la Renta Gifts, and funds from various donors, 2016",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/702992,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2016.94,false,true,702993,Photographs,Photograph,56. Athènes. Caryatides. Ereckt,,,,,,Artist,,Joseph-Philibert Girault de Prangey,"French, 1804–1892",,"Girault, de Prangey Joseph-Philibert",,1804,1892,1842,1842,1842,Daguerreotype,Image: 9 7/16 × 3 3/4 in. (24 × 9.5 cm),"Purchase, Philippe de Montebello Fund, Mr. and Mrs. John A. Moran Gift, in memory of Louise Chisholm Moran, Joyce F. Menschel and Annette de la Renta Gifts, and funds from various donors, 2016",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/702993,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2016.95,false,true,702994,Photographs,Photograph,53. Athènes. Temple de Bacchus,,,,,,Artist,,Joseph-Philibert Girault de Prangey,"French, 1804–1892",,"Girault, de Prangey Joseph-Philibert",,1804,1892,1842,1842,1842,Daguerreotype,Image: 9 7/16 × 3 3/4 in. (24 × 9.5 cm),"Purchase, Philippe de Montebello Fund, Mr. and Mrs. John A. Moran Gift, in memory of Louise Chisholm Moran, Joyce F. Menschel and Annette de la Renta Gifts, and funds from various donors, 2016",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/702994,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2016.96,false,true,702995,Photographs,Photograph,73. Alexandrie. Grand Minaret,,,,,,Artist,,Joseph-Philibert Girault de Prangey,"French, 1804–1892",,"Girault, de Prangey Joseph-Philibert",,1804,1892,1842,1842,1842,Daguerreotype,Image: 9 7/16 × 3 3/4 in. (24 × 9.5 cm),"Purchase, Philippe de Montebello Fund, Mr. and Mrs. John A. Moran Gift, in memory of Louise Chisholm Moran, Joyce F. Menschel and Annette de la Renta Gifts, and funds from various donors, 2016",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/702995,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2016.97,false,true,702996,Photographs,Photograph,74. Près d'Alexandrie. Le désert.,,,,,,Artist,,Joseph-Philibert Girault de Prangey,"French, 1804–1892",,"Girault, de Prangey Joseph-Philibert",,1804,1892,1842,1842,1842,Daguerreotype,Image: 3 3/4 × 9 7/16 in. (9.5 × 24 cm),"Purchase, Philippe de Montebello Fund, Mr. and Mrs. John A. Moran Gift, in memory of Louise Chisholm Moran, Joyce F. Menschel and Annette de la Renta Gifts, and funds from various donors, 2016",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/702996,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2016.98,false,true,702997,Photographs,Photograph,68. Alexandrie. Colonne de Pompée,,,,,,Artist,,Joseph-Philibert Girault de Prangey,"French, 1804–1892",,"Girault, de Prangey Joseph-Philibert",,1804,1892,1842,1842,1842,Daguerreotype,Image: 9 7/16 × 3 3/4 in. (24 × 9.5 cm),"Purchase, Philippe de Montebello Fund, Mr. and Mrs. John A. Moran Gift, in memory of Louise Chisholm Moran, Joyce F. Menschel and Annette de la Renta Gifts, and funds from various donors, 2016",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/702997,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2016.600,false,true,726471,Photographs,Photograph,[Egypt],,,,,,Artist,,Joseph-Philibert Girault de Prangey,"French, 1804–1892",,"Girault, de Prangey Joseph-Philibert",,1804,1892,1842–44,1842,1844,Daguerreotype,Image: 3 3/4 × 9 7/16 in. (9.5 × 24 cm),"Purchase, Mr. and Mrs. John A. Moran Gift, in memory of Louise Chisholm Moran, Joyce F. Menschel Gift, Joseph Pulitzer Bequest, 2016 Benefit Fund, and Gift of Dr. Mortimer D. Sackler, Theresa Sackler and Family, 2016",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/726471,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2016.601,false,true,726472,Photographs,Photograph,"Phile, temple découvert.",,,,,,Artist,,Joseph-Philibert Girault de Prangey,"French, 1804–1892",,"Girault, de Prangey Joseph-Philibert",,1804,1892,1844,1844,1844,Daguerreotype,Image: 3 1/8 × 3 3/4 in. (8 × 9.5 cm),"Purchase, Mr. and Mrs. John A. Moran Gift, in memory of Louise Chisholm Moran, Joyce F. Menschel Gift, Joseph Pulitzer Bequest, 2016 Benefit Fund, and Gift of Dr. Mortimer D. Sackler, Theresa Sackler and Family, 2016",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/726472,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2016.602,false,true,726473,Photographs,Photograph,[Egypt],,,,,,Artist,,Joseph-Philibert Girault de Prangey,"French, 1804–1892",,"Girault, de Prangey Joseph-Philibert",,1804,1892,1842–44,1842,1844,Daguerreotype,Image: 3 3/4 × 4 3/4 in. (9.5 × 12 cm),"Purchase, Mr. and Mrs. John A. Moran Gift, in memory of Louise Chisholm Moran, Joyce F. Menschel Gift, Joseph Pulitzer Bequest, 2016 Benefit Fund, and Gift of Dr. Mortimer D. Sackler, Theresa Sackler and Family, 2016",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/726473,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2016.603,false,true,726474,Photographs,Photograph,"Kaire. Gama Soultan Ansoun, détails.",,,,,,Artist,,Joseph-Philibert Girault de Prangey,"French, 1804–1892",,"Girault, de Prangey Joseph-Philibert",,1804,1892,1842–44,1842,1844,Daguerreotype,Image: 9 7/16 × 3 3/4 in. (24 × 9.5 cm),"Purchase, Mr. and Mrs. John A. Moran Gift, in memory of Louise Chisholm Moran, Joyce F. Menschel Gift, Joseph Pulitzer Bequest, 2016 Benefit Fund, and Gift of Dr. Mortimer D. Sackler, Theresa Sackler and Family, 2016",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/726474,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2016.604,false,true,726475,Photographs,Photograph,Thebes Rhamseion,,,,,,Artist,,Joseph-Philibert Girault de Prangey,"French, 1804–1892",,"Girault, de Prangey Joseph-Philibert",,1804,1892,1844,1844,1844,Daguerreotype,Image: 7 1/2 × 9 7/16 in. (19 × 24 cm),"Purchase, Mr. and Mrs. John A. Moran Gift, in memory of Louise Chisholm Moran, Joyce F. Menschel Gift, Joseph Pulitzer Bequest, 2016 Benefit Fund, and Gift of Dr. Mortimer D. Sackler, Theresa Sackler and Family, 2016",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/726475,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2016.605,false,true,726476,Photographs,Photograph,Rome. Ponte Rotto,,,,,,Artist,,Joseph-Philibert Girault de Prangey,"French, 1804–1892",,"Girault, de Prangey Joseph-Philibert",,1804,1892,1842,1842,1842,Daguerreotype,Image: 3 3/4 × 9 7/16 in. (9.5 × 24 cm),"Purchase, Mr. and Mrs. John A. Moran Gift, in memory of Louise Chisholm Moran, Joyce F. Menschel Gift, Joseph Pulitzer Bequest, 2016 Benefit Fund, and Gift of Dr. Mortimer D. Sackler, Theresa Sackler and Family, 2016",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/726476,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2016.606,false,true,726477,Photographs,Photograph,Rome. Graecostato r fac. N. O.,,,,,,Artist,,Joseph-Philibert Girault de Prangey,"French, 1804–1892",,"Girault, de Prangey Joseph-Philibert",,1804,1892,1842,1842,1842,Daguerreotype,Image: 9 7/16 × 3 3/4 in. (24 × 9.5 cm),"Purchase, Mr. and Mrs. John A. Moran Gift, in memory of Louise Chisholm Moran, Joyce F. Menschel Gift, Joseph Pulitzer Bequest, 2016 Benefit Fund, and Gift of Dr. Mortimer D. Sackler, Theresa Sackler and Family, 2016",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/726477,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2016.607,false,true,726478,Photographs,Photograph,"Toscanella Eglise de S. Pietro, apside",,,,,,Artist,,Joseph-Philibert Girault de Prangey,"French, 1804–1892",,"Girault, de Prangey Joseph-Philibert",,1804,1892,1842,1842,1842,Daguerreotype,Image: 3 3/4 × 3 1/8 in. (9.5 × 8 cm),"Purchase, Mr. and Mrs. John A. Moran Gift, in memory of Louise Chisholm Moran, Joyce F. Menschel Gift, Joseph Pulitzer Bequest, 2016 Benefit Fund, and Gift of Dr. Mortimer D. Sackler, Theresa Sackler and Family, 2016",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/726478,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2016.608,false,true,726479,Photographs,Photograph,[Jerusalem],,,,,,Artist,,Joseph-Philibert Girault de Prangey,"French, 1804–1892",,"Girault, de Prangey Joseph-Philibert",,1804,1892,1842–44,1842,1844,Daguerreotype,Image: 3 3/4 × 9 7/16 in. (9.5 × 24 cm),"Purchase, Mr. and Mrs. John A. Moran Gift, in memory of Louise Chisholm Moran, Joyce F. Menschel Gift, Joseph Pulitzer Bequest, 2016 Benefit Fund, and Gift of Dr. Mortimer D. Sackler, Theresa Sackler and Family, 2016",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/726479,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2016.609,false,true,726480,Photographs,Photograph,"Jerusalem, près de la porte de Jaffa, chap.",,,,,,Artist,,Joseph-Philibert Girault de Prangey,"French, 1804–1892",,"Girault, de Prangey Joseph-Philibert",,1804,1892,1842–44,1842,1844,Daguerreotype,Image: 4 3/4 × 3 3/4 in. (12 × 9.5 cm),"Purchase, Mr. and Mrs. John A. Moran Gift, in memory of Louise Chisholm Moran, Joyce F. Menschel Gift, Joseph Pulitzer Bequest, 2016 Benefit Fund, and Gift of Dr. Mortimer D. Sackler, Theresa Sackler and Family, 2016",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/726480,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2016.610,false,true,726481,Photographs,Photograph,"Atlit Syrie, Chapelle [Damascus Gate, Jerusalem]",,,,,,Artist,,Joseph-Philibert Girault de Prangey,"French, 1804–1892",,"Girault, de Prangey Joseph-Philibert",,1804,1892,1842–44,1842,1844,Daguerreotype,Image: 9 7/16 × 7 1/2 in. (24 × 19 cm),"Purchase, Mr. and Mrs. John A. Moran Gift, in memory of Louise Chisholm Moran, Joyce F. Menschel Gift, Joseph Pulitzer Bequest, 2016 Benefit Fund, and Gift of Dr. Mortimer D. Sackler, Theresa Sackler and Family, 2016",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/726481,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2016.611,false,true,726482,Photographs,Photograph,"[Baalbek, Syria]",,,,,,Artist,,Joseph-Philibert Girault de Prangey,"French, 1804–1892",,"Girault, de Prangey Joseph-Philibert",,1804,1892,1842–44,1842,1844,Daguerreotype,Image: 3 3/4 × 9 7/16 in. (9.5 × 24 cm),"Purchase, Mr. and Mrs. John A. Moran Gift, in memory of Louise Chisholm Moran, Joyce F. Menschel Gift, Joseph Pulitzer Bequest, 2016 Benefit Fund, and Gift of Dr. Mortimer D. Sackler, Theresa Sackler and Family, 2016",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/726482,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2016.612,false,true,726483,Photographs,Photograph,Alep. Prise de Bab Antakieh (publiée),,,,,,Artist,,Joseph-Philibert Girault de Prangey,"French, 1804–1892",,"Girault, de Prangey Joseph-Philibert",,1804,1892,1842–44,1842,1844,Daguerreotype,Image: 7 1/2 × 9 7/16 in. (19 × 24 cm),"Purchase, Mr. and Mrs. John A. Moran Gift, in memory of Louise Chisholm Moran, Joyce F. Menschel Gift, Joseph Pulitzer Bequest, 2016 Benefit Fund, and Gift of Dr. Mortimer D. Sackler, Theresa Sackler and Family, 2016",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/726483,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2016.613,false,true,726484,Photographs,Photograph,Paris devant l’atelier de Daguerre,,,,,,Artist,,Joseph-Philibert Girault de Prangey,"French, 1804–1892",,"Girault, de Prangey Joseph-Philibert",,1804,1892,1841,1841,1841,Daguerreotype,Image: 3 3/4 × 4 3/4 in. (9.5 × 12 cm),"Purchase, Mr. and Mrs. John A. Moran Gift, in memory of Louise Chisholm Moran, Joyce F. Menschel Gift, Joseph Pulitzer Bequest, 2016 Benefit Fund, and Gift of Dr. Mortimer D. Sackler, Theresa Sackler and Family, 2016",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/726484,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2016.614,false,true,726485,Photographs,Photograph,"[Notre Dame Cathedral, Rose Window, North Transept]",,,,,,Artist,,Joseph-Philibert Girault de Prangey,"French, 1804–1892",,"Girault, de Prangey Joseph-Philibert",,1804,1892,1841,1841,1841,Daguerreotype,Image: 9 7/16 × 7 1/2 in. (24 × 19 cm),"Purchase, Mr. and Mrs. John A. Moran Gift, in memory of Louise Chisholm Moran, Joyce F. Menschel Gift, Joseph Pulitzer Bequest, 2016 Benefit Fund, and Gift of Dr. Mortimer D. Sackler, Theresa Sackler and Family, 2016",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/726485,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2016.615,false,true,726486,Photographs,Photograph,Sardes. T. de Cybèle,,,,,,Artist,,Joseph-Philibert Girault de Prangey,"French, 1804–1892",,"Girault, de Prangey Joseph-Philibert",,1804,1892,1843,1843,1843,Daguerreotype,Image: 7 1/2 × 9 7/16 in. (19 × 24 cm),"Purchase, Mr. and Mrs. John A. Moran Gift, in memory of Louise Chisholm Moran, Joyce F. Menschel Gift, Joseph Pulitzer Bequest, 2016 Benefit Fund, and Gift of Dr. Mortimer D. Sackler, Theresa Sackler and Family, 2016",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/726486,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2016.459,false,true,715974,Photographs,Photograph; Daguerreotype,[Profile of a woman with necrosis of the nose],,,,,,Artist,,Louis-Auguste Bisson,"French, 1814–1876",,"Bisson, Louis-Auguste",,1814,1876,1841–48,1841,1848,Daguerreotype,Image: 4 9/16 × 3 1/8 in. (11.6 × 7.9 cm) Plate: 7 3/16 × 5 7/8 in. (18.3 × 14.9 cm),"Funds from various donors, 2016",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/715974,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2012.352 (3),false,true,701977,Photographs,Photographs,"Assassinat des généraux Clément Tomas et Jules Lecomte, rue des Rosiers 6 à Montmartre deans la journée du 18 mars 1871",,,,,,Artist,,Ernest Eugène Appert,"French, 1831–1891",,"Appert, Ernest Eugène",,1831,1891,1870–71,1870,1871,Albumen silver print from glass negative,Sheet: 36 x 46 cm (14 3/16 x 18 1/8 in.),"Joyce F. Menschel Photography Library Fund, 2012",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/701977,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2012.352 (7),false,true,701997,Photographs,Photographs,"Massacre des dominicains d'Arcueil, route d'Italie no. 38, le 25 mai 1871, à 4 heures et demie",,,,,,Artist,,Ernest Eugène Appert,"French, 1831–1891",,"Appert, Ernest Eugène",,1831,1891,1870–71,1870,1871,Albumen silver print from glass negative,Sheet: 36 x 46 cm (14 3/16 x 18 1/8 in.),"Joyce F. Menschel Photography Library Fund, 2012",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/701997,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2012.352 (10),false,true,702000,Photographs,Photographs,"Exécution des otages, prison de la Roquette, le 24 mai 1871",,,,,,Artist,,Ernest Eugène Appert,"French, 1831–1891",,"Appert, Ernest Eugène",,1831,1891,1870–71,1870,1871,Albumen silver print from glass negative,Sheet: 36 x 46 cm (14 3/16 x 18 1/8 in.),"Joyce F. Menschel Photography Library Fund, 2012",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/702000,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2012.352 (11),false,true,702001,Photographs,Photographs,"Prison des Chantiers, le 15 août 1871, Versailles",,,,,,Artist,,Ernest Eugène Appert,"French, 1831–1891",,"Appert, Ernest Eugène",,1831,1891,1870–71,1870,1871,Albumen silver print from glass negative,Sheet: 36 x 46 cm (14 3/16 x 18 1/8 in.),"Joyce F. Menschel Photography Library Fund, 2012",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/702001,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2016.6,false,true,705511,Photographs,Photograph,"Notation of Scars, Schematic Drawings",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,ca. 1893,1888,1898,Albumen silver prints,Image (top): 6 in. × 4 1/8 in. (15.2 × 10.4 cm) Image (bottom): 2 3/16 × 4 1/8 in. (5.6 × 10.5 cm) Mount: 11 3/4 × 7 3/4 in. (29.8 × 19.7 cm),"The Horace W. Goldsmith Foundation Fund, Through Joyce and Robert Menschel, 2016",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/705511,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2016.7,false,true,705529,Photographs,Photograph,Measurement of Left Middle Finger,,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,ca. 1893,1888,1898,Albumen silver prints,Image (top): 5 7/8 × 4 7/16 in. (15 × 11.2 cm) Image (bottom): 2 7/8 × 4 3/8 in. (7.3 × 11.1 cm) Mount: 11 3/4 × 7 3/4 in. (29.8 × 19.7 cm),"The Horace W. Goldsmith Foundation Fund, Through Joyce and Robert Menschel, 2016",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/705529,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.1,false,true,306624,Photographs,Mugshot,"Adnet. Clotilde. 19 ans, née en décembre 74 à Argentant (Orne). Brodeuse. Anarchiste. Fichée le 7/1/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306624,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.2,false,true,306625,Photographs,Mugshot,"Adnet. Jeanne, Marie. Alphonsine (femme Quesnel). 22 ans, née à Argentan. Couturière. Anarchiste. 8/1/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306625,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.3,false,true,306626,Photographs,Mugshot,"Olguéni Gustave. 24 ans, né à Sala (Suède) le 24-5-69. Artiste-peintre. Anarchiste. 14-3-94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306626,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.4,false,true,306627,Photographs,Mugshot,"Alban. Jean-Louis. 35 ans, né à Paris. Plombier. Anarchiste. Fiché le 5/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306627,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.5,false,true,306628,Photographs,Mugshot,"Alicante. Philibert. 33 ans, né à Seire (Seine & Oise). Coupeur de talons. Anarchiste. 7/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306628,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.6,false,true,306629,Photographs,Mugshot,"Anacléto. Joseph, Jean-Baptiste. 36 ans, né le 14/7/57. Coiffeur. Anarchiste. 16/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306629,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.7,false,true,306630,Photographs,Mugshot,"Anceau. Aimé-Firmin. 20 ans, né le 18/2/74 à Paris XIIe. Sculpteur sur bois. Anarchiste. 17/7/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306630,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.8,false,true,306631,Photographs,Mugshot,"Arnaud. Eugène. 47 ans, né à Villeveyrac (Hérault). Ferblantier. Anarchiste. 20/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306631,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.9,false,true,306632,Photographs,Mugshot,"Augendre. Ernest. 37 ans, né à St-Pierre le Moutier (Nièvre). Maçon. Anarchiste. 1/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306632,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.10,false,true,306633,Photographs,Mugshot,"Aumaréchal. Auguste. 44 ans, né à Chateaumeillant (Cher). Ébéniste. Association de malfaiteurs. 8/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306633,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.11,false,true,306634,Photographs,Mugshot,"Auvin. Henri. 37 ans, né à St-Meme (Charente-Inférieure). Chaudronnier. Anarchiste. 1/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306634,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.12,false,true,306635,Photographs,Mugshot,"Baben. Hyppolyte, Antoine. 49 ans, né à St Sermain (Aveyron). Serrurier. Anarchiste. 9/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306635,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.13,false,true,306636,Photographs,Mugshot,"Baerisvuyl. Frédéric, Jean. 28 ans, né à Fribourg (Suisse). Ébéniste. Anarchiste. 8/1/93.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1893,1893,1893,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306636,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.14,false,true,306637,Photographs,Mugshot,"Baerisvuyl. Frédéric, Jean. 28 ans, né à Fribourg (Suisse). Ébéniste. Anarchiste. 8/1/93.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1893,1893,1893,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306637,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.15,false,true,306638,Photographs,Mugshot,"Barbichon. Jacques, Émile. 62 ans, né à Provins. Marchand de mouron. Anarchiste. 9/3/91.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1891,1891,1891,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306638,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.16,false,true,306639,Photographs,Mugshot,"Barbier. Émile, Alphonse. 36 ans, né à Paris. Peintre en bâtiment. Anarchiste. 26/2/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306639,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.17,false,true,306640,Photographs,Mugshot,"Barbier. Louis, Alexandre. 31 ans, né à Jussecourt (Marne). Comptable. Anarchiste. 27/2/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306640,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.18,false,true,306641,Photographs,Mugshot,"Barreyre. Alfred. 30 ans, né le 30/6/64 à Brassac (P. de Dôme). Gérant de restaurant. Anarchiste. 2/7/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306641,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.19,false,true,306642,Photographs,Mugshot,"Bassille. Maurice, Eugène. 19 ans, né à Paris Ille. Portefeuilliste. Anarchiste. 9/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306642,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.20,false,true,306643,Photographs,Mugshot,"Bastard. Élisée, Joseph, Michel. 22 ans, né à Birnel (Oise). Polisseur. Anarchiste. 20/8/93.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1893,1893,1893,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306643,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.21,false,true,306644,Photographs,Mugshot,"Bastard. Élisée, Joseph, Michel. 22 ans, né à Birnel (Oise). Polisseur. Anarchiste. 20/8/93.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1893,1893,1893,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306644,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.22,false,true,306645,Photographs,Mugshot,"Barla. Jean Michel. 46 ans, né à Sirié (Italie). Mécanicien. Anarchiste. 2/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306645,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.23,false,true,306646,Photographs,Mugshot,"Baudart. Joseph, Philippe. 42 ans, né à Reims le 25/3/51. Boucher. Anarchiste. 15/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306646,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.24,false,true,306647,Photographs,Mugshot,"Baumester. Augustin, Etienne. 49 ans, né le 16/1/45 à Paris VIe. Décorateur. Anarchiste. 2/7/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306647,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.25,false,true,306648,Photographs,Mugshot,"Baur. Pierre. 41 ans, né à St Leonard (Haute-Vienne). Cordonnier. Anarchiste. 1/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306648,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.26,false,true,306649,Photographs,Mugshot,"Bazin. Claudius dit César. 36 ans, né à Chatillon (Ain). Mécanicien. Anarchiste. 11/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306649,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.27,false,true,306650,Photographs,Mugshot,"Beaufort. Gilbert. 32 ans, né à Paris Xle. Ébéniste. Anarchiste. 5/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306650,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.28,false,true,306651,Photographs,Mugshot,"Beaulieu. Henri, Félix, Camille. 23 ans, né le 30/11/70 à Paris Ve. Comptable. Anarchiste. 23/5/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306651,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.29,false,true,306652,Photographs,Mugshot,"Beaulieu. Henri, Félix, Camille. 23 ans, né le 30/11/70 à Paris Ve. Comptable. Anarchiste. 23/5/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306652,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.30,false,true,306653,Photographs,Mugshot,"Becu. Lucien. 27 ans, né à la Conté d'Abigny (Pas-de-Calais). Garçon de café. Pas de motif. 22/2/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306653,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.31,false,true,306654,Photographs,Mugshot,"Bedei. Hercule. 21 ans, né à Sorli (Italie). Tailleur d'habits. Anarchiste. 1/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306654,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.32,false,true,306655,Photographs,Mugshot,"Bellemans. Eugène (ou Michel). 23 ans, né à Gand (Belgique). Tailleur d'habits. Anarchiste. 9/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306655,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.33,false,true,306656,Photographs,Mugshot,"Bellet. Eléonore. Alexandre. 29 ans, né le 23/9/65 à Salouel (Somme). Teinturier. Anarchiste. 3/7/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306656,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.34,false,true,306657,Photographs,Mugshot,"Bellon. Joseph, Alexandre. 54 ans, né à Granville (Meuse) le 19/6/39. Journalier. Anarchiste. 17/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306657,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.35,false,true,306658,Photographs,Mugshot,"Belloti. Louis. 28 ans, né à Turin. Camelot. Anarchiste. 18/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306658,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.36,false,true,306659,Photographs,Mugshot,"Benoit. Antoine. 29 ans, né à Paris Xle. Journalier. Anarchiste, vagabondage. 4/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306659,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.37,false,true,306660,Photographs,Mugshot,"Benoit. Joseph, Alexandre. 33 ans, né le 9/6/61 à Paris XIIIe. Potier d'étain. Anarchiste. 2/7/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306660,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.38,false,true,306661,Photographs,Mugshot,"Berard. Adolphe. 52 ans, né le 26/9/41 à Paris Ve. Ébéniste. Anarchiste. 16/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306661,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.39,false,true,306662,Photographs,Mugshot,"Bernaix. Louis. 29 ans, né à Clichy. Couvreur. Anarchiste. Fiché le 27/2/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306662,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.40,false,true,306663,Photographs,Mugshot,"Bernard. Paul, Auguste. 32 ans, né à Crest (Drôme). Employé. Excitation au meurtre, anarchiste. 11/2/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306663,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.41,false,true,306664,Photographs,Mugshot,"Bernard. Paul, Auguste. 32 ans, né à Crest (Drôme). Employé. Excitation au meurtre, anarchiste. 11/2/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306664,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.42,false,true,306665,Photographs,Mugshot,"Ber(h)nard. Victor. 43 ans, né à Paris. Coupeur. Anarchiste. Fiché le 28/2/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306665,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.43,false,true,306666,Photographs,Mugshot,"Berson. Samuel. 28 ans, né le 3/?/65 à Dinabourg (Russie). Tailleur d'habits. Anarchiste. 2/7/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306666,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.44,false,true,306667,Photographs,Mugshot,"Bertani. Orsini. 24 ou 25 ans, né à Florence (Italie). Sans profession. Anarchiste. 18/3/94",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306667,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.45,false,true,306668,Photographs,Mugshot,"Bertani. Orsini. 24 ou 25 ans, né à Florence (Italie). Sans profession. Anarchiste. 18/3/94",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306668,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.46,false,true,306669,Photographs,Mugshot,"Bertho. François, Élie. 26 ans, né le 30/9/67 à Jallais (Maine & Loire). Employé. Anarchiste. 9/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306669,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.47,false,true,306670,Photographs,Mugshot,"Bertout. Marie, Ismérie. 41 ans, née à Reims. Marchande de vins. Pas de motif. 26/2/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306670,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.48,false,true,306671,Photographs,Mugshot,"Biais. Fernand, Alphonse. 41 ans, né le 28/6/53 à Laval (Mayenne). Tourneur sur bois. Anarchiste. 2/7/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306671,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.49,false,true,306672,Photographs,Mugshot,"Billon. Gabriel, André, Adolphe. 20 ans, né à Boulogne /s/Seine. Typographe. Outrages. 14/8/93.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1893,1893,1893,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306672,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.50,false,true,306673,Photographs,Mugshot,"Billot. Eugène. 20 ans, né à La Charité (Nièvre). Tailleur d'habits. Anarchiste. 9/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306673,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.51,false,true,306674,Photographs,Mugshot,"Billot. Jean. 23 ans, né le 23/1/71 à Bourges (Cher). Tireur en barre. Anarchiste. 1/7/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306674,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.52,false,true,306675,Photographs,Mugshot,"Birilay. Henri, Marc, Julien. 46 ans, né à Chartres (Eure & Loir). Journalier. Anarchiste. 11/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306675,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.53,false,true,306676,Photographs,Mugshot,"Bissonier. Sébastien. 19 ans, né à St Bonnet (Allier). Journalier. Outrage à la Gendarmerie. 5/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306676,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.54,false,true,306677,Photographs,Mugshot,"Bligny. André, Eugène. 58 ans, né à Vincennes. Serrurier. Anarchiste. Fiché le 2/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306677,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.55,false,true,306678,Photographs,Mugshot,"Blay. François. 53 ans, né à St Gervais (Rhône). Tailleur. Anarchiste. 4/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306678,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.56,false,true,306679,Photographs,Mugshot,"Bocquet. Alexandre, Émile. 17 ans, né à Paris XVlle. Menuisier. Vol. Fiché le 14/4/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306679,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.57,false,true,306680,Photographs,Mugshot,"Bompeix. Eugène. 53 ans, né à St Martin d'Herbus (Haute-Vienne). Conducteur de machines. Anar. 3/7/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306680,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.58,false,true,306681,Photographs,Mugshot,"Borderie. Ferdinand, Jacques. 19 ans, né à Sarlat (Dordogne). Peintre sur métaux. Pas de motif. 1/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306681,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.59,false,true,306682,Photographs,Mugshot,"Borderie. Raoul. 18 ans, né à Castelsarazin (Tarn & Garonne). Peintre en bâtiment. Anarchiste. 1/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306682,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.60,false,true,306683,Photographs,Mugshot,"Bordes. Auguste. 15 ans, né à Paris XVIIIe. Garçon Marchand de vins. Anarchiste. 9/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306683,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.61,false,true,306684,Photographs,Mugshot,"Bordes. Guillaume, Auguste. 40 ans, né à Centrayes (Aveyron). Tailleur. Pas de motif. 29/2/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306684,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.62,false,true,306685,Photographs,Mugshot,"Borreman. Léontine, Eugénie. 23 ans, née à Paris le 25/12/70. Papetière. Anarchiste. 13/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306685,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.63,false,true,306686,Photographs,Mugshot,"Bossant. Edmond, Léon. 52 ans, né à Valenciennes (Nord). Sans profession. Anarchiste. 27/4/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306686,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.64,false,true,306687,Photographs,Mugshot,"Bossard. Célestin. 33 ans, né le 5/3/61 à Gonbretière (Vendée). Cordonnier. Anarchiste. 2/7/94",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306687,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.65,false,true,306688,Photographs,Mugshot,"Bouchenez. Adolphe. 36 ans, 27/2/94. (En rouge barrant la fiche: ""Transféré à Mazas. À faire extraire"").",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306688,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.66,false,true,306689,Photographs,Mugshot,"Bouchez. Louis. 19 ans, né le 29/8/75 à Paris XXe. Sculpteur. Anarchiste. 6/1/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306689,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.67,false,true,306690,Photographs,Mugshot,"Boulnois. Paul, Cyprien. 20 ans, né à Paris Ille. Employé de commerce. Anarchiste. 6/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306690,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.68,false,true,306691,Photographs,Mugshot,"Bourbasquet. François. 25 ans, né le 11/3/69 à St Avé (Morbihan). Garçon coiffeur. Anarchiste. 2/7/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306691,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.69,false,true,306692,Photographs,Mugshot,"Bourguoin. Hubert, Jules. 51 ans, né le 17/8/42 à Jelles (Seine & Marne). Maçon. Anarchiste. 2/7/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306692,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.70,false,true,306693,Photographs,Mugshot,"Bourlard. Joseph, Anselme. 45 ou 46 ans, né à Biemme (Belgique). Piqueur de grès. Anarchiste. 7/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306693,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.71,false,true,306694,Photographs,Mugshot,"Boutel. Joseph. Louis. 34 ans, né à Bonnay (Eure). Corroyeur. Anarchiste. 5/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306694,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.72,false,true,306695,Photographs,Mugshot,"Braun. Frédéric, Charles. 28 ans. Fiché le 22/2/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306695,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.73,false,true,306696,Photographs,Mugshot,"Breiner. Jean-Baptiste. 31 ans, né à Bar sur Aube (Aube). Mécanicien. Anarchiste. 5/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306696,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.74,false,true,306697,Photographs,Mugshot,"Bresson. Eugène, Marie. 30 ans, né le 4/7/63 à Chaumont (Haute-Marne). Avocat. Anarchiste. 20/1/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306697,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.75,false,true,306698,Photographs,Mugshot,"Breton. Ernest, Jean. 28 ans, 27/10/63. Anarchiste 24/1/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306698,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.76,false,true,306699,Photographs,Mugshot,"Briet. Albert, Louis. 44 ans, né à Lyon (Rhône). Boulanger. Anarchiste. 4/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306699,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.77,false,true,306700,Photographs,Mugshot,"Brosselin. Jean-Baptiste. 32 ans, né le 10/1/62 à Aulay (Côte d'Or). Menuisier. Anarchiste. 2/7/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306700,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.78,false,true,306701,Photographs,Mugshot,"Broggio. (Roche), Bernard. 39 ans, né en Italie. Journalier. Association de malfaiteurs. 22/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306701,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.79,false,true,306702,Photographs,Mugshot,"Bruchaesen. Etienne. 31 ans, né à Mag Levard (Hongrie). Tailleur d'habits. Anarchiste. 11/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306702,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.80,false,true,306703,Photographs,Mugshot,"Bruneau. Amédé, Jean Baptiste. 46 ans, né à Châteauroux (Indre). Cordonnier. Anarchiste. 2/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306703,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.81,false,true,306704,Photographs,Mugshot,"Brunel. Alexandre. 50 ans, né le 25/12/43 à Renaix (Belgique). Menuisier. Anarchiste. 3/7/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306704,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.82,false,true,306705,Photographs,Mugshot,"Brunet. Felix. 21 ans, né le 21/2/73 à Paris XVe. Peintre en voiture. Anarchiste. 7/7/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306705,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.83,false,true,306706,Photographs,Mugshot,"Brunet. Georges. 25 ans, né à Paris. Menuisier. Anarchiste. 4/2/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306706,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.84,false,true,306707,Photographs,Mugshot,"Buhr. Victor. 25 ans, né à Cologne (Allemagne). Peintre décorateur. Anarchiste. 2/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306707,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.85,false,true,306708,Photographs,Mugshot,"Cabuzac. Jean. 25 ans, né le 23/7/68 à Ivry la Bataille (Eure). Ciseleur. Anarchiste. 12/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306708,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.86,false,true,306709,Photographs,Mugshot,"Cana. Eugène, Louis. 22 ans, né à Paris Vllle. Monteur en bronze. Anarchiste. 2/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306709,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.87,false,true,306710,Photographs,Mugshot,"Cana. Eugène, Pierre. 47 ans, né à Paris XIe. Monteur en bronze. Anarchiste. 2/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306710,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.88,false,true,306711,Photographs,Mugshot,"Capette. Joseph, Désiré. 56 ans, né à Paris VI. Maroquinier. Anarchiste. 7/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306711,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.89,false,true,306712,Photographs,Mugshot,"Carraglia. Charles. 39 ans, né à Moceto (It). Homme de lettres. Anar, infraction à la loi du 21/6/73. 13/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306712,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.90,false,true,306713,Photographs,Mugshot,"Carteau. Auguste. 23 ans, né à St-Florent (Cher). Verrier. Anarchiste. 1/5/92.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1892,1892,1892,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306713,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.91,false,true,306714,Photographs,Mugshot,"Castallou. Charles. 53 ans, né le 4/10/41 à Paris IIe. Tapissier. Anarchiste. 16/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306714,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.92,false,true,306715,Photographs,Mugshot,"Catty. Nicolas, Pierre, François. 47 ans, né à Fressenneville (Somme). Mécanicien. Anarchiste. 10/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306715,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.93,false,true,306716,Photographs,Mugshot,"Cazal. Antoinette. 28 ans, née à Salgouz (Cantal). Couturière. Anarchiste. 28/2/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306716,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.94,false,true,306717,Photographs,Mugshot,"Ceaglio. Alexandre, Joseph. 42 ans, né à Turin (Italie). Employé de commerce. Anarchiste. 3/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306717,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.95,false,true,306718,Photographs,Mugshot,"Chambon. Raoul. 20 ans, né le 3/7/73 à Valréas (Vaucluse). Graveur. Anarchiste. 26/5/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306718,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.96,false,true,306719,Photographs,Mugshot,"Chapin. Armand, Louis. 30 ans, né à Épeigné (Indre & Loire). Charron. Anarchiste. 1/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306719,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.97,false,true,306720,Photographs,Mugshot,"Chapuis. Charles, Paul. 17 ans, né le 22/3/76 à Paris VIle. Tapissier. Anarchiste. 7/1/94",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306720,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.98,false,true,306721,Photographs,Mugshot,"Charlier. Ernile, Frédéric. 40 ans, né à Brest (Finistère). Peintre. Vol. 18/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306721,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.99,false,true,306722,Photographs,Mugshot,"Charrié. Cyprien. 26 ans, né le 7/10/67 à Paris XVIlle. Imprimeur. Anarchiste 2/7/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306722,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.100,false,true,306723,Photographs,Mugshot,"Charrié. Léon, Joseph. 27 ans, né à Paris XVllle. Garçon Plombier. Anarchiste. 1/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306723,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.101,false,true,306724,Photographs,Mugshot,"Chatel. Charles. 25 ans, né le 8/10/68 à Paris XVIIle. Anarchiste. 14/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306724,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.102,false,true,306725,Photographs,Mugshot,"Chatillon. Jean-Baptiste. 31 ans, né à Toiseron des Minard (Cher). Employé de commerce. Note du cabinet. 10/5/82",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1882,1882,1882,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306725,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.103,false,true,306726,Photographs,Mugshot,"Chauman. Nicolas. 38 ans, né à Paris XVe. Puisatier. Anarchiste. 6/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306726,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.104,false,true,306727,Photographs,Mugshot,"Chaumelin. Odilon. 22 ans, né à Paris VIe. Publiciste. Anarchiste. 24/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306727,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.105,false,true,306728,Photographs,Mugshot,"Chauvin. Émile. 18 ans, né à Paris IVe. Employé. Anarchiste. 1/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306728,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.106,false,true,306729,Photographs,Mugshot,"Chavanne. Gaston. 26 ans, né à Paris VIe. Graveur. Anarchiste. 28/2/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306729,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.107,false,true,306730,Photographs,Mugshot,"Chericotti. Paul. 35 ans, né à Milan (Italie). Marchand de volailles. Anarchiste/Assoc. de malfaiteurs. 25/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306730,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.108,false,true,306731,Photographs,Mugshot,"Chericotti. Paul. 35 ans, né à Milan (Italie). Marchand de volailles. Anarchiste/Assoc. de malfaiteurs. 25/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306731,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.109,false,true,306732,Photographs,Mugshot,"Chericotti. Paul. 35 ans, né à Milan (Italie). Marchand de volailles. Anarchiste/Assoc. de malfaiteurs. 25/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306732,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.110,false,true,306733,Photographs,Mugshot,"Chevalier. Étienne. 36 ans, né à Gémosac (Charente-Inférieure). Forgeron. Anarchiste. 11/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306733,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.111,false,true,306734,Photographs,Mugshot,"Chiroki. Eva (veuve Ortiz). 53 ans, née à Grosbitlech (Autriche). Cuisinière. Anarchiste. 21/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306734,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.112,false,true,306735,Photographs,Mugshot,"Chornat. Pierre. 50 ans, né le 20/6/44 à Letrat (Loire). Constructeur-mécanicien. Anarchiste. 2/7/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306735,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.113,false,true,306736,Photographs,Mugshot,"Clidière. François. 39 ans, né le 3/2/55 à Miales (Dordogne). Tailleur d'habits. Anarchiste. 9/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306736,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.114,false,true,306737,Photographs,Mugshot,"Cler. Henri. 31 ans, né à Paris XIe. Ébéniste. Anarchiste. 14/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306737,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.115,false,true,306738,Photographs,Mugshot,"Clouard. Paul, Jules. 35 ans, né le 20/6/58 à Peugans (Manche). Rétameur. Anarchiste. 9/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306738,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.116,false,true,306739,Photographs,Mugshot,"Cluzel. Louis. 30 ans, né le 31/8/63 à Bourg-Argental (Loire). Tailleur d'habits. Anarchiste. 2/7/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306739,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.117,false,true,306740,Photographs,Mugshot,"Collet. Edouard, Jean-Baptiste. 44 ans, né le 6/1/50 à Paris XVlle. Ciseleur. Anarchiste. 2/7/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306740,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.118,false,true,306741,Photographs,Mugshot,"Collot. Marie, Eugénie. 36 ans, né à Paris Xle. Tapissier. Anarchiste. 11/3/94",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306741,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.119,false,true,306742,Photographs,Mugshot,"Colombo. Joseph (on Jean, Octave). 19 ans, né à Paris Xlle. Monteur en bronze. Anarchiste. 10/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306742,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.120,false,true,306743,Photographs,Mugshot,"Colombet. Frédéric. 28 ans, né à Prigonnieux (Dordogne). Employé de commerce. Anarchiste. 2/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306743,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.121,false,true,306744,Photographs,Mugshot,"Couchot. Jean. 49 ans, né à Bidache (Basses-Pyrénées). Tailleurs d'habits. Anarchiste. 23/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306744,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.122,false,true,306745,Photographs,Mugshot,"Coudry. Hubert, Louis. 24 ans, né à Paris XVe. Corroyeur. Anarchiste. 7/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306745,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.123,false,true,306746,Photographs,Mugshot,"Court. Jean-Claude. 58 ans, né à Cherissey-le-M. (Haute-Savoie). Marchand de pains d'épices. Anarchiste. 17/3/94",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306746,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.124,false,true,306747,Photographs,Mugshot,"Cornu. Eugène. 25 ans, né à Paris XXe le 27/3/94. Cordonnier. Anarchiste. 2/7/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306747,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.125,false,true,306748,Photographs,Mugshot,"Cornuault. Joseph. 17 ans, né à Angers (Maine & Loire). Peintre eu bâtiment. Anarchiste. 7/1/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306748,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.126,false,true,306749,Photographs,Mugshot,"Cottée. Edouard, Eugène. 37 ans, né à Paris XVle. Artiste-peintre. Vol par complicité. 6/2/92.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1892,1892,1892,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306749,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.127,false,true,306750,Photographs,Mugshot,"Crespin. Joseph. 40 ans, né à Roquesteron (Alpes-Maritimes). Employé de banque. 25/3/93.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1893,1893,1893,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306750,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.128,false,true,306751,Photographs,Carte-de-visite; Mugshot,"Crespin. Joseph. 40 ans, né à Roquesteron (Alpes-Maritimes). Employé de banque. 25/3/93.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1893,1893,1893,Gelatin silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306751,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.129,false,true,306752,Photographs,Mugshot,"Cros. Jean. 19 ans, né à Négrin (Tarn). Tailleur d'habits. Pas de motif. 8/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306752,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.130,false,true,306753,Photographs,Mugshot,"Daguenet. Eugerne, Carolin. 39 ans, né à Granville (Manche). Ébéniste. Anarchiste. 26/2/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306753,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.131,false,true,306754,Photographs,Mugshot,"Damalix. Émile, Auguste. 37 ans, né à St-Claude (Doubs). Charpentier. Anarchiste. 19/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306754,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.132,false,true,306755,Photographs,Mugshot,"Daressy. Pierre. 39 ans, né à Lherme (Haute-Garonne). Cordonnier. Anarchiste. 28/2/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306755,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.133,false,true,306756,Photographs,Mugshot,"D'Auby. Henri. 48 (ou 49) ans, né à Montmédy (Meuse). Menuisier. Anarchiste. 28/2/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306756,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.134,false,true,306757,Photographs,Mugshot,"Dauriac. Henri, Georges. 36 ans, né à Memphis (USA). Agent d'affaires. Extortion de fonds. 22/12/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306757,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.135,false,true,306758,Photographs,Mugshot,"Dauriac. Henri, Georges. 36 ans, né à Memphis (USA). Agent d'affaires. Extortion de fonds. 22/12/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306758,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.136,false,true,306759,Photographs,Mugshot,"David. Armand, Auguste. 27 ans, né à Gien (Loiret). Faïencier. Anarchiste. 1/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306759,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.137,false,true,306760,Photographs,Mugshot,"Decker. Jacques. 43 ans, né à Grodeskersheim (Bas-Rhin). Tailleur d'habits. Anarchiste. 9/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306760,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.138,false,true,306761,Photographs,Mugshot,"Deforge. Henri, Walter. 19 ans, né à Bruxelles (Belgique). Porteur de journaux. Anarchiste. 6/1/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306761,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.139,false,true,306762,Photographs,Mugshot,"Defosse. Claude (dit Delfosse ou Lafosse). 29 ans, né à Arbeuf (Nièvre). Cocher. Anarchiste. 2/1/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306762,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.140,false,true,306763,Photographs,Mugshot,"Deguet. Victor, Adonis. 50 ans, né le 27/2/44 à Vuenpont (Aisne). Armurier. Anarchiste. 16/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306763,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.141,false,true,306764,Photographs,Mugshot,"Deherme. Marie, Adolphe. 26 ans, né à Paris XVIIe. Publiciste. Anarchiste. 9/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306764,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.142,false,true,306765,Photographs,Mugshot,"Dejernier (ou Degernier). Edouard. 45 ans, né à Gand (Belgique). Tailleur d'habits. Anarchiste. 8/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306765,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.143,false,true,306766,Photographs,Mugshot,"Dejoux. Jules. 46 ans, né le 16/6/48 à La Châtre (Indre). Maçon. Délit de presse. 2/7/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306766,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.144,false,true,306767,Photographs,Mugshot,"Delabie. Georges. 43 ans, né le 30/10/50 à Ganaches (Somme). Mécanicien. Anarchiste. 2/7/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306767,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.145,false,true,306768,Photographs,Mugshot,"De la Salle. Gabriel. 45 ans, né à Nantes (Loire-Inf.). Publiciste. Disposition du Préfet de Police. 16/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306768,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.146,false,true,306769,Photographs,Mugshot,"Delesderrier. Louis. 34 ans, né le 2/3/60 à Paris Ille. Ciseleur. Anarchiste. 16/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306769,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.147,false,true,306770,Photographs,Mugshot,"Deliège. Nicolas, François. 19 ans, né à Ixelles (Belgique). Tailleur d'habits. Anarchiste. 9/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306770,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.148,false,true,306771,Photographs,Mugshot,"Della Casa. 36 ans, né le 1/3/58 à Avoglion (Italie). Cordonnier. Anarchiste. 2/7/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306771,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.149,false,true,306772,Photographs,Mugshot,"Dery. Louis. 60 ans, né à Cobugny (Nièvre). Cordonnier. Anarchiste. 9/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306772,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.150,false,true,306773,Photographs,Mugshot,"Dodot. Émile (ou Jules). 55 ans, né à Paris Ier. Cordonnier. Anarchiste. 27/2/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306773,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.151,false,true,306774,Photographs,Mugshot,"Dufour. Louis. 37 ans, né à Port-Ste-Marie (Lot & Garonne). Bijoutier. Anarchiste.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306774,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.152,false,true,306775,Photographs,Mugshot,"Dumout. Henri, Victor. 29 ans, né à Issy (Seine). Mécanicien. Anarchiste. 8/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306775,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.153,false,true,306776,Photographs,Mugshot,"Dupit. Paul. 20 ans, né le 13/3/74 à Paris XVIIe. Garçon boucher. Anarchiste. 2/7/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306776,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.154,false,true,306777,Photographs,Mugshot,"Duprat. François, Louis. 34 ans, né à St-Martin (Gers). Marchand de vins. Anarchiste. 27/4/92.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1892,1892,1892,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306777,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.155,false,true,306778,Photographs,Mugshot,"Dupuis. Augustin. 53 ans, né le 24/6/41 à Dourdan (Seine & Oise). Charron, forgeron. Anarchiste. 3/7/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306778,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.156,false,true,306779,Photographs,Mugshot,"Dupuy. Edmond, Adolphe. 29 ans, né à Paris XIVe. Employé de commerce. Anarchiste. 1/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306779,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.157,false,true,306780,Photographs,Mugshot,"Durey. François, Louis. 43 ans, né le 25/2/51 à Lyon (Rhône). Architecte. Anarchiste. 2/7/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306780,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.158,false,true,306781,Photographs,Mugshot,"Durieux. Aléxis, Alberic. 20 ans, né le 28/2/74 à Stains (Seine). Verrier. Vol. 24/1/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306781,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.159,false,true,306782,Photographs,Mugshot,"Dutheil. Louis. 29 ans, né le 28/7/64 à Maisonnais (Ht Vienne). Tailleur d'habits. Anarchiste. 3/7/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306782,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.160,false,true,306783,Photographs,Mugshot,"Etiévant. Henri, Achille. 32 ans, né à Flammanville (Manche). Typographe. Anarchiste. 4/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306783,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.161,false,true,306784,Photographs,Mugshot,"Fauvel. Louis. 27 ans, né le 14/4/67 à Écouché (Orne). Tourneur. Anarchiste. 2/7/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306784,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.162,false,true,306785,Photographs,Mugshot,"Favre. Pierre, Maurice. 29 ans, né le 30/11/64. Ciseleur. Anarchiste. 15/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306785,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.163,false,true,306786,Photographs,Mugshot,"Favre. Sébastien. 36 ans, né à St Étienne (Loire). Négociant. Port d'arme prohibée, anarchiste. 20/2/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306786,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.164,false,true,302429,Photographs,Mugshot,Feneon. Felix. Clerk of the Galerie Berheim Jeune.,,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894–5,1894,1895,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/302429,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.165,false,true,306787,Photographs,Mugshot,"Ferter. Ernest, Charles. 31 ans, né le 23/10/62 à Melun (Seine & Marne). Fumiste. Anarchiste. 2/7/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306787,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.166,false,true,306788,Photographs,Mugshot,"Fétis. Julien. 26 ans, né à New York (USA). Couvreur. Anarchiste. 3/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306788,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.167,false,true,306789,Photographs,Mugshot,"Fischter. Joseph. 47 ans, né à Paris Ve. Imprimeur sur papiers-peints. Anarchiste. 4/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306789,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.168,false,true,306790,Photographs,Mugshot,"Forti. Alfred. 18 ans, né à Milan (Italie). Restaurateur. Anarchiste. 27/2/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306790,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.169,false,true,306791,Photographs,Mugshot,"Forti. Ernesta. 45 (ou 46) ans, née à Lodi (Italie). Laitière. Anarchiste. 27/2/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306791,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.170,false,true,306792,Photographs,Mugshot,"Fournier. Émile, Christophe. 26 ans, né à St-Martial (Creuse). Serrurier. Vol. 13/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306792,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.171,false,true,306793,Photographs,Mugshot,"Foussard. Eugène. 26 ans, né le 7/12/67 à Dangeul (Sarthe). Peintre en bâtiment. Anarchiste. 2/7/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306793,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.172,false,true,306794,Photographs,Mugshot,"Francier. Éloi. 41 ans, né le 28/10/53 à Resson-le-Long (Aisne). Ébéniste. Anarchiste. 22/5/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306794,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.173,false,true,306795,Photographs,Mugshot,"François dit Francis. 38 ans, né le 3/12/55 à Reims (Marne). Ébéniste. Anarchiste. 5/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306795,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.174,false,true,306796,Photographs,Mugshot,"Gaillard. Pierre, Auguste. 47 ans, né à Foulanges (Cantal). Employé de commerce. Anarchiste. 15/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306796,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.175,false,true,306797,Photographs,Mugshot,"Galau. Charles. 18 ans, né à Nogent s/Marne (Seine). Charron. Cris séditieux. Anarchiste. 21/2/91.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1891,1891,1891,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306797,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.176,false,true,306798,Photographs,Mugshot,"Galau (ou Gallot). Louis. 53 ans, né à Meriziès (Tarn). Charron. Anarchiste. 21/8/93.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1893,1893,1893,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306798,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.177,false,true,306799,Photographs,Mugshot,"Gama. Joseph. 41 ans, né le 5/3/42 à Paris IXe. Graveur. Anarchiste. 6/7/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306799,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.178,false,true,306800,Photographs,Mugshot,"Garnier. Anatole, Auguste. 18 ans, né à Montereau (Seine & Marne). Orfèvre. Anarchiste. 1/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306800,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.179,false,true,306801,Photographs,Mugshot,"Garnier. Auguste. 34 ans, né à Périgny (Côte-d'Or). Journalier. Anarchiste. 2/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306801,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.180,false,true,306802,Photographs,Mugshot,"Gatinet. Pierre, Adrien. 50 ans, né le 13/10/43 à Bourges (Cher). Charpentier. Anarchiste. 2/7/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306802,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.181,false,true,306803,Photographs,Mugshot,"Gauche. Henri. 24 ans, né le 7/2/60 à Paris. Rentier. Anarchiste.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1884,1884,1884,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306803,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.182,false,true,306804,Photographs,Mugshot,"Gelhausen. Jean. 55 ans, né à Grevennemache (Luxembourg). Cordonnier. Infraction à la loi du 18/12/93.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1893,1893,1893,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306804,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.183,false,true,306805,Photographs,Mugshot,"Giroux. Hippolyte. 34 ans, né à Montreuil les Mines (Saone & Loire). Mécanicien. Anarchiste. 28/2/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306805,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.184,false,true,306806,Photographs,Mugshot,"Godard. Armand, Alexandre. 18 ans, né le 11/3/75 à Paris XVIIe. Électricien. Cris séditieux. 6/1/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306806,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.185,false,true,306807,Photographs,Mugshot,"Gordon. Max. 39 ans, né le 20/8/54 à Vilna (Italie). Employé de commerce. Anarchiste. 2/7/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306807,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.186,false,true,306808,Photographs,Mugshot,"Grandidier. Louis, Auguste. 20 ans, né à St-Denis (Seine). Journalier. Anarchiste. 1/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306808,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.187,false,true,302430,Photographs,Mugshot,"Grave. Jean. 38 ans, né le 16/10/54 à Breuil (Puy de Dôme). Typographe. Anarchiste. 9/1/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/302430,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.188,false,true,306809,Photographs,Mugshot,"Grégoire. Aimé, Paul. 36 ans, né à Bruxelles (Belgique). Accordeur de piano. Anarchiste. 2/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306809,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.189,false,true,306810,Photographs,Mugshot,"Grégoire. Alphonse. 27 ans, né à La Montagne (Loire-Inférieure). Mécanicien. Anarchiste. 2/7/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306810,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.190,false,true,306811,Photographs,Mugshot,"Grugeau. Alfred, Alphonse. 26 ans, né à Tours (Indre & Loire). Cordonnier. Anarchiste. 9/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306811,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.191,false,true,306812,Photographs,Mugshot,"Guelle (ou Gueulle, dit St Denis). 72 ans, né à Beauvais (Oise). Matelassier. Anarchiste. 7/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306812,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.192,false,true,306813,Photographs,Mugshot,"Guénant. Louis, Désiré. 31 ans, né à Paris XVIIIe. Comptable. Anarchiste. 2/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306813,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.193,false,true,306814,Photographs,Mugshot,"Guerlinger. Pierre. 28 ans, né le 31/5/65 à St-Avold (Moselle). Journalier. Anarchiste. 14/4/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306814,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.194,false,true,306815,Photographs,Mugshot,"Guéry. Paul, Alphonse. 37 ans, né le 30/6/56 à Laversine (Aisne). Journalier. Anarchiste. 12/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306815,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.195,false,true,306816,Photographs,Mugshot,"Guignard. Georges, Auguste. 36 ans, né le 1/1/58 à Neuilly (Seine). Plombier. Anarchiste. 15/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306816,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.196,false,true,306817,Photographs,Mugshot,"Guillemard. Isidore, François. 46 ans, né à St-Michel des Andaines (Orne). Menuisier. Anarchiste 28/2/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306817,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.197,false,true,306818,Photographs,Mugshot,"Haesig. Léon. 18 ans, né à St-Denis. Chaudronnier. Disposition du Préfet de Police. 14/4/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306818,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.198,false,true,306819,Photographs,Mugshot,"Handrock. Frédéric(k). Vilhem. 34 ans, né à Lyeck (Allemagne). Doreur sur bois. Anarchiste. 2/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306819,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.199,false,true,306820,Photographs,Mugshot,"Hannedouche. François. 31 ans, né à Lilleris (Pas de Calais). Peintre en bâtiments. Anarchiste. 1/1/93.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1893,1893,1893,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306820,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.200,false,true,306821,Photographs,Mugshot,"Havard. Octave, Onésime. 25 ans, né à Hedouville (Calvados). Polisseur. Anarchiste. 1/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306821,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.201,false,true,306822,Photographs,Mugshot,"Hébert. Georges, Henri. 27 (ou 29) ans, né à Bayeux (Calvados). Menuisier. Anarchiste. 23/4/92.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1892,1892,1892,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306822,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.202,false,true,306823,Photographs,Mugshot,"Henon. François. 52 ans, né le 5/5/42 à Lyon (Rhône). Caissier. Anarchiste. 2/7/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306823,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.203,false,true,306824,Photographs,Mugshot,Henry. Émile. (auteur de l'attentat de l'Hotel St-Lazare),,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1890–94,1890,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306824,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.204,false,true,306825,Photographs,Mugshot,Henry. Émile. (auteur de l'attentat de l'Hotel St-Lazare),,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1890–94,1890,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306825,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.205,false,true,306826,Photographs,Mugshot,"Herman. Caroline. 33 ans, née à Paris Vllle. Couturière. Disposition du Préfet (Anarchie). 21/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306826,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.206,false,true,306827,Photographs,Mugshot,"Herouard. Henri. 17 ans, né à Paris XVIIe. Serrurier. Anarchiste. 6/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306827,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.207,false,true,306828,Photographs,Mugshot,"Hervy. Marcel, Noël. 19 ans, né à Paris XVIIIe. Raccommodeur de porcelaine. Anarchiste. 27/10/93.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1893,1893,1893,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306828,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.208,false,true,306829,Photographs,Mugshot,"Hettich (ou Hettig). Eugène. 17 ans, né le 6/1/77 à Paris XXe. Cocher. Anarchiste. 16/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306829,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.209,false,true,306830,Photographs,Mugshot,"Heurteaux. Auguste. 31 ans, né à Paris Xe. Polisseur. Anarchiste. 3/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306830,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.210,false,true,306831,Photographs,Mugshot,"Hivon. Pierre. 45 ans, né à Bourbon le Chambai (Allier). Lithographe. Anarchiste. 4/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306831,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.211,false,true,306832,Photographs,Mugshot,"Hostenbock. Joseph, Louis. 22 ans, né à Bruxelles (Belgique). Coiffeur. Anarchiste. 26/2/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306832,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.212,false,true,306833,Photographs,Mugshot,"Hourt. Jean. 34 (ou 35) ans, né à Reims (Marne). Menuisier. Anarchiste. 4/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306833,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.213,false,true,306834,Photographs,Mugshot,"Imhof. Louis, Alfred. 37 ans, né à Mex (Suisse). Journalier. Anarchiste. 1/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306834,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.214,false,true,306835,Photographs,Mugshot,"Iv(w)anowski. Casimir. 57 ans, né à Chalon-sur-Saône (Saône & Loire). Mécanicien. Anarchiste. 6/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306835,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.215,false,true,306836,Photographs,Mugshot,"Jacob. Georges, Gustave. 43 ans, né à Paris XVIIe. Journalier. Anarchiste. 27/2/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306836,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.216,false,true,306837,Photographs,Mugshot,"Jacot. Charles, Émile. 36 ans, né à Allenjoie (Doubs). Colporteur Anarchiste. 8/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306837,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.217,false,true,306838,Photographs,Mugshot,"Jacquet. Hippolyte, Edouard. 49 ans, né le 15/3/45 à Paris Ille. Sellier-maroquinier. Anarchiste. 3/7/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306838,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.218,false,true,306839,Photographs,Mugshot,"Jaffard. Julien, Ludovic. 37 ans, né le 31/5/57 à Lesterps (Charente). Journalier. Anarchiste. 2/7/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306839,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.219,false,true,306840,Photographs,Mugshot,"Jamard. Alphonse, Ernest. 51 ans, né à Paris. Distillateur. Anarchiste. 28/2/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306840,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.220,false,true,306841,Photographs,Mugshot,"Job. Eugène, François. 31 ans, né à Paris Xle. Chaisier. Anarchiste. 6/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306841,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.221,false,true,306842,Photographs,Mugshot,"Jordy. Baptiste. 61 ans, né le 8/72/32 à Labardes (Aude). Cordonnier. Anarchiste. 2/7/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306842,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.222,false,true,306843,Photographs,Mugshot,"Jourdan. Numa. 30 ans, né le 27/8/61 à Courbevoie (Seine). Teinturier. Anarchiste. 23/4/92.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1892,1892,1892,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306843,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.223,false,true,306844,Photographs,Mugshot,"Kahn. Rodolphe. 43 ans, né le 15/1/51 à Lyon (Rhône). Courtier de commerce. Anarchiste. 8/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306844,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.224,false,true,306845,Photographs,Mugshot,"Kaision. François. 39 ans, né à Reims. Mégissier. Anarchiste. 9/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306845,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.225,false,true,306846,Photographs,Mugshot,"Kern. Jacob, Hermann. 33 ans, né le 26/8/60 à Berlinger (Suisse). Comptable. Anarchiste. 10/7/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306846,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.226,false,true,306847,Photographs,Mugshot,"Kieffer. Nicolas. 35 ans, né le 8/4/59 à Haltuiller (Meurthe). Menuisier. Anarchiste. 2/7/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306847,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.227,false,true,306848,Photographs,Mugshot,"Kilchenstein. Dominique. 48 ans, né à Luneville (Meurthe & Moselle). Marchand au panier. Anarchiste. 23/4/92",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1892,1892,1892,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306848,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.228,false,true,306849,Photographs,Mugshot,"Klein. Louis. 25 ans, né le 8/8/67 à Colmar (Alsace). Employé de commerce. Anarchiste. 11/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306849,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.229,false,true,306850,Photographs,Mugshot,"Labeyrie. Romain. 19 ans, né le 13/11/74 à Cauna (Landes). Sculpteur. Anarchiste. 10/1/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306850,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.230,false,true,306851,Photographs,Mugshot,"Labrie. Oscar, Alexandre. 33 ans, né à Charenton (Seine). Marchand de vins. Anarchiste. 2/7/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306851,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.231,false,true,306852,Photographs,Mugshot,"Lagane (ou Lagasse). Lucien, Pierre. 35 ans. 22/2/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306852,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.232,false,true,306853,Photographs,Mugshot,"Lamure. Eugène, Clément. 20 ans, né le 5/11/73 à Paris Ier. Gérant de magasin. Anarchiste. 2/7/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306853,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.233,false,true,306854,Photographs,Mugshot,"Landoin. Antoine, Eugène. 33 ans, né le 16/11/60 à Quincie (Rhône). Comptable. Anarchiste. 16/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306854,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.234,false,true,306855,Photographs,Mugshot,"Landschoot. Edouard. 27 ans, né le 6/8/67 à Paris. Bijoutier. Anarchiste. 2/7/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306855,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.235,false,true,306856,Photographs,Mugshot,"Lapeyre. Louis, Pierre. 29 ans. né à Rodez (Aveyron). Employé. Anarchiste. 10/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306856,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.236,false,true,306857,Photographs,Mugshot,"Lapointe. Nicolas, Céleste. 45 ans, né à Marbach (Alsace-Lorraine). Cordonnier. Anarchiste. 2/7/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306857,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.237,false,true,306858,Photographs,Mugshot,"Large. Etienne, Louis. 20 ans, né le 1/1/74 à Lyon (Rhône). Tapissier. Anarchiste. 9/1/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306858,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.238,false,true,306859,Photographs,Mugshot,"Lassalas. Ernest, Auguste. 33 ans, né à Paris IVe. Ébéniste. Anarchiste. 2/1/94",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306859,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.239,false,true,306860,Photographs,Mugshot,"LaumesfeIt. Paul, Mathias. 35 ans, né le 29/3/59. à Paris VIe. Tailleur d'habits. Anarchiste. 2/7/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306860,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.240,false,true,306861,Photographs,Mugshot,"Leballeur. Jules, Léon. 29 (ou 30) ans, né à Rouissé Jassée (Sarthe). Cordonnier. Anarchiste. 1/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306861,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.241,false,true,306862,Photographs,Mugshot,"Leboucher. Edouard, Léon. 43 ans, né à Paris XIVe. Cordonnier. Anarchiste. 7/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306862,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.242,false,true,306863,Photographs,Mugshot,"Ledot. Julien. 41 ans, né à Bourges (Cher). Employé. Anarchiste. 1/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306863,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.243,false,true,306864,Photographs,Mugshot,"Lefebvre. Eugène, Anatole. 28 ans, né le 2/7/66 à St Pierre (Eure). Sculpteur sur bois. Anarchiste. 2/7/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306864,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.244,false,true,306865,Photographs,Mugshot,"Lefrançois. Charles, Albert. 27 ans, né le 10/11/67 à Paris XXe. Horloger. Anarchiste. 2/7/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306865,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.245,false,true,306866,Photographs,Mugshot,"Leger. Joseph. 16 ans, né à Marseille (Bouches-du-Rhône). Jardinier. Fabrication d'engins explosifs. 4/7/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306866,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.246,false,true,306867,Photographs,Mugshot,"Lelarge. Louis, Eugène. 46 ans, né le 5/4/48 à Paris Ille. Employé de commerce. Anarchiste. 2/7/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306867,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.247,false,true,306868,Photographs,Mugshot,"Leleu. Victor, Louis. 29 ans, né le 19/10/64 à Arras (Pas-de-Calais). Anarchiste. 9/1/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306868,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.248,false,true,306869,Photographs,Mugshot,"Lenfant. Émile, Jules. 26 ans, né à Choisy-le-Roi (Seine). Modeleur. Anarchiste. 2/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306869,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.249,false,true,306870,Photographs,Mugshot,"Léonard. Aimé. 30 ans, né à Chalonné/s/Loire (Maine & Loire). Mineur ou gazier. Anarchiste. 27/2/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306870,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.250,false,true,306871,Photographs,Mugshot,"Lepla. Henri, Florimond. 25 ans, né à Gand (Belgique). Anarchiste. 1/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306871,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.251,false,true,306872,Photographs,Mugshot,"Letellier. Louis, Auguste. 29 ans, né à Rouen (Seine-Inférieure). Employé. Anarchiste. 23/4/92.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1892,1892,1892,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306872,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.252,false,true,306873,Photographs,Mugshot,"Leveillé. Louis. 37 ans, né le 7/7/57 à Cliche (Seine). Forgeron. Anarchiste. 7/7/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306873,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.253,false,true,306874,Photographs,Mugshot,"Liégeois (ou Liegois), François. 30 ans, né à Vilette (Meurthe & Moselle). Cordonnier. Anarchiste. 26/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306874,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.254,false,true,306875,Photographs,Mugshot,"Livenais (ou Livenay), André. 39 ans, né à Renazé (Mayenne). Garçon de magasin. Anarchiste. 2/7/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306875,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.255,false,true,306876,Photographs,Mugshot,"Loth. Clotilde, Caroline (femme Bossant). 43 ans, née à Valenciennes. Sans profession. Anarchiste. 27/4/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306876,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.256,false,true,306877,Photographs,Mugshot,"Lothier. Gaston. 27 ans, né le 27/12/66 à St Thomas (Charente-Inférieure). Menuisier. Anarchiste. 1/1/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306877,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.257,false,true,306878,Photographs,Mugshot,"Loutrel. François. 37 ans, né à Paris XVlle. Journalier. Anarchiste. 1/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306878,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.258,false,true,302431,Photographs,Mugshot,"Luce. Maximilien. 36 ans, né le 13/3/58 à Paris VIIe. Artiste-peintre. Anarchiste. 6/7/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/302431,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.259,false,true,306879,Photographs,Mugshot,"Lustenberger. Louis, Joseph. 38 ans, le 16/3/56 à Paris XXe. Ciseleur. Anarchiste. 17/7/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306879,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.260,false,true,306880,Photographs,Mugshot,"Lutringer. Pierre, Léopold. 43 ans, né le 25/11/50 à Stenay (Meuse). Cordonnier. Anarchiste. 3/7/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306880,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.261,false,true,306881,Photographs,Mugshot,"Mahler. Jacob, Henri, Jean. 61 ans, né à Hanovre (Duché de Hesse). Couvreur. Anarchiste. 3/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306881,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.262,false,true,306882,Photographs,Mugshot,"Maillabuau. Auguste, Léon. 30 ans, né le 23/8/93 à Paris Vle. Anarchiste. 2/7/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306882,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.263,false,true,306883,Photographs,Mugshot,"Maillard. Louis. 30 ans, né à Rennes (Ille & Vilaine). Employé de commerce. Anarchiste. 27/2/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306883,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.264,false,true,306884,Photographs,Mugshot,"Maince. Émile. 19 ans, né à Levallois-Perret (Seine). Réparateur d'objets d'arts. Anarchiste. 6/1/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306884,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.265,false,true,306885,Photographs,Mugshot,"Mainfroy. Albert, Pierre. 41 ans, né le 7/4/52 à Courbevoie (Seine). Imprimeur. Anarchiste. 3/7/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306885,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.266,false,true,306886,Photographs,Mugshot,"Malpet. Jeanne (femme Pivier). 51 ans, née en mai 42. Couturière. Anarchiste. 3/7/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306886,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.268,false,true,306888,Photographs,Mugshot,"Mangin. Edmond, Émile. 33 ans, né le 19/3/61 à Senon (Meuse). Cimentier. Anarchiste. 8/7/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306888,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.269,false,true,306889,Photographs,Mugshot,"Margerand. Claude. 32 ans, né le 24/3/61 à Beaujeu (Rhône). Cordonnier. Anarchiste. 3/7/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306889,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.270,false,true,306890,Photographs,Mugshot,"Marie. Constant. 53 ans, né le 27/8/38 à Ste-Houvrince (Calvados). Garçon rnarçon. Anarchiste. 2/7/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306890,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.271,false,true,306891,Photographs,Mugshot,"Marie. Léon, Louis. 23 ans, né le 19/8/70 à Adouzeval (Calvados). Couvreur. Anarchiste. 16/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306891,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.272,false,true,306892,Photographs,Mugshot,"Martin. Constant. 53 ans, né le 5/4/39 à Santrevaux (Basses-Alpes). Crémier. Anarchiste. 27/4/92.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1892,1892,1892,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306892,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.273,false,true,306893,Photographs,Mugshot,"Martin. Pierre. 22 ans, né à St-Léger (Saône-et-Loire). Employé de commerce. Anarchiste. 27/2/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306893,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.274,false,true,306894,Photographs,Mugshot,"Martineau. Jules, Louis. 30 ans, né à Angers (Maine & Loire). Peintre en bâtiment. Anarchiste. 3/7/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306894,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.275,false,true,306895,Photographs,Mugshot,"Marty. Louis. 20 ans, né à Lacanne (Tarn). Tailleur d'habits. Anarchiste. 9/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306895,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.276,false,true,306896,Photographs,Mugshot,"Masini (dit Mazzini). Angelo, Henri. 25 ans, né le 4/9/69 à Milan (Italie). Ébéniste. Anarchiste. 1/9/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306896,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.277,false,true,306897,Photographs,Mugshot,Matha.,,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1880s–90s,1880,1899,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306897,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.278,false,true,306898,Photographs,Mugshot,Matha.,,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1880s–90s,1880,1899,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306898,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.279,false,true,306899,Photographs,Carte-de-visite; Mugshot,Mathieu. Gustave.,,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1880s–90s,1880,1899,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306899,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.280,false,true,306900,Photographs,Carte-de-visite; Mugshot,Mathieu. Gustave. (avec une barbe postiche),,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1880s–90s,1880,1899,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306900,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.281,false,true,306901,Photographs,Mugshot,"Mathon. Louis Marius. 30 ans, né à St Andiolle (Ardèche). Ferblantier. Anarchiste. 26/2/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306901,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.282,false,true,306902,Photographs,Mugshot,"Maurin. Émile, Auguste. 31 ans, né à Marseille (Bouche du Rhône). Ex photographe. Anarchiste. 2/7/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306902,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.283,false,true,306903,Photographs,Mugshot,"Mauroy. Alfred, Édouard. 34 ans, né à Paris VIIe. Dessinateur. Anarchiste. 26/2/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306903,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.284,false,true,306904,Photographs,Mugshot,"Mayence. Gustave, David. 33 ans, né le 29/5/60 à Paris XVllle. Tapissier. Anarchiste. 17/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306904,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.285,false,true,306905,Photographs,Mugshot,"Mazoldi. Frédéric, Jean-Baptiste. 54 ans, né à Bicroz (Autriche). Ferblantier. Anarchiste. 23/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306905,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.286,false,true,306906,Photographs,Mugshot,"Mentenich. François, Joseph. 22 ans, né le 3/10/71 à Paris XIIe. Ébéniste. Anarchiste. 2/7/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306906,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.287,false,true,306907,Photographs,Mugshot,"Mereaux. Émile-Louis. 33 ans, né à Laon (Aisne). Ébéniste. Anarchiste. 23/4/92.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1892,1892,1892,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306907,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.288,false,true,306908,Photographs,Mugshot,"Mérigeau. Jacques. 35 ans, né à St-Léger-les-Melles (Deux Sèvres). Ébéniste. 19/12/93.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1893,1893,1893,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306908,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.289,false,true,306909,Photographs,Mugshot,"Mermin. Camille. 33 ans, né à La Havane (Cn Espagnoles). Rep. de cou. (?). Anarchiste. 9/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306909,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.290,false,true,306910,Photographs,Mugshot,"Miaglia. Bernard. 41 ans, né à Giaglione (Italie). Cordonnier. Anarchiste. 9/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306910,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.291,false,true,306911,Photographs,Mugshot,"Miel. Eugène, Paul, Léon. 38 ans, né à Creil (Oise). Estampeur. Anarchiste. 2/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306911,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.292,false,true,306912,Photographs,Mugshot,"Millard. Victor. 53 ans, né le 5/6/40 à Moyon (Oise). Cordonnier. 16/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306912,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.293,false,true,306913,Photographs,Mugshot,"Mocquet. Georges, Gustave. 17 ans, né le 17/5/76 à Paris IXe. Tapissier. Anarchiste. 6/1/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306913,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.294,false,true,306914,Photographs,Mugshot,"Molmerret. Joseph, Camille. 28 ans, né le 20/11/65 à Lyon (Rhône). Graveur. Anarchiste. 26/5/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306914,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.295,false,true,306915,Photographs,Mugshot,"Morane. Antoine. 35 ans, né à Chalinargue (Cantal). Manœvre. Anarchiste. 6/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306915,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.296,false,true,306916,Photographs,Mugshot,"Moreau. François. 47 ans, né le 19/11/46 à Nevers (Nièvre). Menuisier: Anarchiste. 2/7/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306916,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.297,false,true,306917,Photographs,Mugshot,"Moreau. Louis. 40 ans, né le 22/10/53 à Villiers (Nièvre). Tailleur de pierre. Anarchiste. 2/7/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306917,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.298,false,true,306918,Photographs,Mugshot,"Morel. Benoit. 33 ans, né le 6/11/61 à St Laurent d'Orringt (Rhône). Ébéniste. Anarchiste. 8/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306918,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.299,false,true,306919,Photographs,Mugshot,"Morvan. Félicien. 45 ans, né le 8/6/49 à Kerity (Côte du Nord). Menuisier. Anarchiste. 2/7/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306919,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.300,false,true,306920,Photographs,Mugshot,"Moucheraud. Adrien, Eugène. 28 ans, né à Paris IVe. Imprimeur. Anarchiste. 4/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306920,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.301,false,true,306921,Photographs,Mugshot,"Moucheraud. Pierre, Yves. 27 ans, né à Paris IVe. Imprimeur. Anarchiste. 4/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306921,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.302,false,true,306922,Photographs,Mugshot,"Mouette. Charles. 32 ans, né à Paris Ile. Peintre en bâtiment. Association de malfaiteurs. 20/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306922,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.303,false,true,306923,Photographs,Mugshot,"Monzon. Lucien, Henri, Baptiste. 18 ans, né à Paris XXe. Couvreur. Anarchiste. 23/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306923,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.304,false,true,306924,Photographs,Mugshot,Mursch. Eugène. 24 ans à Schlestatt (Bas-Rhin). Ciseleur. Anarchiste. 18/3/94.,,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306924,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.305,false,true,306925,Photographs,Mugshot,"Naudet. Gervais. 40 ans, né à Echaleau (Côte-d'Or). Menuisier. Anarchiste. 10/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306925,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.306,false,true,306926,Photographs,Mugshot,"Nic. Celestin. 20 ans, né à Conflans-St-Honorine (Seine & Oise). Emballeur. 26/2/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306926,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.307,false,true,306927,Photographs,Mugshot,"Notelez. Charles, Émile. 29 ans, né à Paris XXe. Portefeuilliste. Anarchiste. 26/2/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306927,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.308,false,true,306928,Photographs,Mugshot,"308. Novi. Ernest, Théodore. 32 ans, né à Nice (Alpes-Maritimes). Architecte. Anarchiste. 27/2/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306928,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.309,false,true,306929,Photographs,Mugshot,"309. Ochart. Alphonse. 37 ans, né le 24/1/56 à Asbruch (Nord). Fabricant de chaussures. Anarchiste. 22/1/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306929,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.310,false,true,306930,Photographs,Mugshot,"310. Olivier. Philippe, Octave. 25 ans, né le 29/6/68 à Paris XVIIle. Plombier. Anarchiste. 16/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306930,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.311,false,true,306931,Photographs,Mugshot,"Ortiz. Léon. 25 ans, né à Paris. Commis d'architecte. Anarchiste. Voyage ordinairement en bicyclette.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306931,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.312,false,true,306932,Photographs,Mugshot,"Ortiz. Léon. 25 ans, né à Paris. Commis d'architecte. Anarchiste. Voyage ordinairement en bicyclette.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306932,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.313,false,true,306933,Photographs,Mugshot,"Ortiz. Léon. 25 ans, né à Paris. Commis d'architecte. Anarchiste. Voyage ordinairement en bicyclette.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306933,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.314,false,true,306934,Photographs,Mugshot,"Oudin. Clovis. 49 ans, né à Saint-Hilaire (Marne). Mécanicien. Anarchiste. 2/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306934,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.315,false,true,306935,Photographs,Mugshot,"Paget. Jean, Louis. 41 ans, né le 15/7/52 à Thonon (Haute-Savoie). Cordonnier. Anarchiste. 12/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306935,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.316,false,true,306936,Photographs,Mugshot,"Pallaz (ou Pellaz). Péronne. 28 ans, née le 11/8/66 à Aix-les-Bains (Savoie). Cuisière. Anarchiste. 8/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306936,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.317,false,true,306937,Photographs,Mugshot,"Para (ou Parra). Henri. 38 ans, né le 16/5/56 à Paris Ve. Camelot. Anarchiste. 4/9/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306937,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.318,false,true,306938,Photographs,Mugshot,"Parisis. Charles. 20 ans, né à Aubervilliers (Seine). Tailleur d'habits. Outrages anarchistes. 10/7/93.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1893,1893,1893,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306938,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.319,false,true,306939,Photographs,Mugshot,"Parisot. Louis. 37 ans, né à Saint-Avalet (Moselle). Employé au ""Petit Journal"". Anarchiste. 2/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306939,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.320,false,true,306940,Photographs,Mugshot,"Pausader. Jean, Ernest. 27 ans, né le 28/7/66 à Paris Xe. Publiciste. Anarchiste. 2/1/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306940,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.321,false,true,306941,Photographs,Mugshot,"Pelgrom. Elise (femme Schouppe). Deux photographies, dont une légendée: Reprod. faite 22/2/93.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1893,1893,1893,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306941,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.322,false,true,306942,Photographs,Mugshot,"Pelgrom. Elise (femme Schouppe). Deux photographies, dont une légendée: Reprod. faite 22/2/93.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1893,1893,1893,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306942,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.323,false,true,306943,Photographs,Mugshot,"Pemjean. Lucien, Pierre. 32 ans, né à Lyon (Rhône). Publiciste. Anarchiste. 2/1/93.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1893,1893,1893,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306943,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.324,false,true,306944,Photographs,Mugshot,"Pennelier. Casimir. Arthur. 36 ans, né à Billeuse (Somme). Clerc d'huissier. Anarchiste. 16/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306944,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.325,false,true,306945,Photographs,Mugshot,"Percheron. Auguste. 56 ans, né à Poitier (Nièvre). Écrivain public. Anarchiste. 21/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306945,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.326,false,true,306946,Photographs,Mugshot,"Pernin. François. 34 ans, né le 11/1/60 au Creuzot (Saône-et-Loire). Forgeron. Anarchiste. 2/7/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306946,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.327,false,true,306947,Photographs,Mugshot,"Perot. Gaston, Auguste. 22 ans, né à Paris XVllle. Journalier. Anarchiste. 4/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306947,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.328,false,true,306948,Photographs,Mugshot,"Perrare. Antoine. 53 ans, né à St Diaur (Rhône). Mécanicien. Anarchiste. 10/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306948,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.329,false,true,306949,Photographs,Mugshot,"Perrier (dit Theriez). Louis. 35 ans, né le 25/8/58 à Paris Vllle. Ébéniste. Anarchiste. 16/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306949,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.330,false,true,306950,Photographs,Mugshot,"Perron. Jules. 31 ans, né le 9/8/62 à Saint-Denis (Seine). Journalier. Anarchiste. 75/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306950,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.331,false,true,306951,Photographs,Mugshot,"Perrot. Jean. 33 ans, né le 18/11/61 à Tulle (Corrèze). Cordonnier. Anarchiste. 2/7/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306951,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.332,false,true,306952,Photographs,Mugshot,"Peticolin. Henri. 23 ans, né le 8/6/71 à Goersdorf (Bas-Rhin). Vernisseur. Anarchiste. 2/7/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306952,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.333,false,true,306953,Photographs,Mugshot,"Pierlay. Louis, Victor. 53 ans, né à Paris XVIle. Sculpteur. Anarchiste. 8/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306953,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.334,false,true,306954,Photographs,Mugshot,"Pierre. Joseph, Adrien. 42 ans, né à Rouen (Seine-Inférieure). Canneleur de chaises. Anarchiste. 12/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306954,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.335,false,true,306955,Photographs,Mugshot,"Pichon. Ernest. 41 ans, né le 2/11/52 à Villard-Rixoire (Jura). Terrassier. Anarchiste. 2/7/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306955,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.336,false,true,306956,Photographs,Mugshot,"Pidoux. Jean. Victor. 45 ans, né au Châtelet (Seine-et-Marne). Estampeur de métaux. Anarchiste 2/7/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306956,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.337,false,true,306957,Photographs,Mugshot,"Pioger. Louise (veuve Lefèvre). 45 ans, né à Mézières (Sarthe). Giletière. Disposition du préfet. 8/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306957,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.338,false,true,306958,Photographs,Mugshot,"Pivat. Georges, Léopold. 34 ans, né à Vauxcié (Aisne). Tailleur d'habits. Anarchiste. 9/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306958,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.339,false,true,306959,Photographs,Mugshot,"Pivier. Alexandre. 53 ans, né à Rochevan (Savoie). Tailleur d'habits. Anarchiste. 7/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306959,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.340,false,true,306960,Photographs,Mugshot,"Poirier. Jacques, Étienne. 30 ans, né à Gien (Loiret). Garçon de marchand de vins. Anarchiste. 30/4/92.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1892,1892,1892,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306960,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.341,false,true,306961,Photographs,Mugshot,"Poisson. Georges. 38 ans, né à Boulogne (Seine). Chaudronnier. Anarchiste. 6/3/94",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306961,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.342,false,true,306962,Photographs,Mugshot,"Ponchia. Charles, Albino. 32 ans, né le 1/3/62 à Montanaro (Italie). Menuisier. Anarchiste. 2/7/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306962,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.343,false,true,306963,Photographs,Mugshot,"Pouget. Émile, Jean, Joseph. 31 ans, né le 12/10/60 à Rodez (Aveyron). Publiciste. Anarchiste. 26/4/92.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1892,1892,1892,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306963,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.344,false,true,306964,Photographs,Mugshot,"Pourry. François, Nicolas. 58 ans, né à Ars-sur-Moselle (Alsace-Lorraine). Ajusteur. Anarchiste. 3/7/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306964,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.345,false,true,306965,Photographs,Mugshot,"Raboin. Émile, Pierre. 41 ans, né à Ouzoir-sur-Loire (Loiret). Distillateur. Assoc. de malfaiteurs. 28/2/94",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306965,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.346,false,true,306966,Photographs,Mugshot,"Raboin. Paul, Pierre, Augustin. 32 ans, né à Ouzoir-sur-Loire (Loiret). Journalier. Anarchiste. 23/4/92.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1892,1892,1892,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306966,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.347,false,true,306967,Photographs,Mugshot,Rampin. Pierre. 3/7/94,,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306967,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.348,false,true,306968,Photographs,Mugshot,"Ravachol. François Claudius Kœnigstein. 33 ans, né à St-Chamond (Loire). Condamné le 27/4/92.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1892,1892,1892,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306968,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.349,false,true,306969,Photographs,Mugshot,"Ravinet. Gaston. 34 ans, né à Paris XIXe. Couvreur. Anarchiste. 1/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306969,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.350,false,true,306970,Photographs,Mugshot,Reclus. Paul. Pas d'informations sur la fiche. 23/12/93,,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1893,1893,1893,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306970,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.351,false,true,306971,Photographs,Mugshot,"Recco. Grégoire. 35 ans, né à Formia (Italie). Tailleur d'habits. Anarchiste. 11/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306971,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.352,false,true,306972,Photographs,Mugshot,"Remond. Émile, Adolphe. 34 ans, né à Bagnolet (Seine). Carrier. Anarchiste. 26/2/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306972,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.353,false,true,306973,Photographs,Mugshot,"Renard. Pierre, Alfred. 46 ans, né à Flain (Haute-Saône) le 27/4/46.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1880s–90s,1880,1899,Gelatin silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306973,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.354,false,true,306974,Photographs,Mugshot,"Renaud. Jules. 41 ans, né à Anteuil (Doubs). Cordonnier. Anarchiste. 27/2/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306974,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.355,false,true,306975,Photographs,Mugshot,"Retté. Adolphe. 30 ans, né à Paris IXe. Homme de lettre. Cris séditieux. 21/1/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306975,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.356,false,true,306976,Photographs,Mugshot,"Rey. Claude. 23 ans, né le 24/5/70 au Creusot (Saone & Loire). Ébéniste. Anarchiste. 12/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306976,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.357,false,true,306977,Photographs,Mugshot,"Reytinat. Jacques, François. 48 ans, né à Mouy (Oise). Colporteur. Anarchiste. 9/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306977,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.358,false,true,306978,Photographs,Mugshot,"Ricois. Charles-Victor. 48 ans, né le 21/3/46 à Orléans. Employé au Journal Officiel. Anarchiste. 2/7/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306978,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.359,false,true,306979,Photographs,Mugshot,"Ridou. Paul, François. 27 (ou 28) ans. Ébéniste. Anarchiste. 8/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306979,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.360,false,true,306980,Photographs,Mugshot,"Rigollet. Alexandre. 41 ans, né dans le Loir-et-Cher. Terrassier. Anarchiste.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306980,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.361,false,true,306981,Photographs,Mugshot,"Ripert. Thomas. 33 ans, né à Marseille. Cocher. Anarchiste. 5/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306981,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.362,false,true,306982,Photographs,Mugshot,"Robert. Fritz, Malatesta. 24 ans, Suisse. Excitation à la haine des citoyens les uns contre les autres, expulsé.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1880s–90s,1880,1899,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306982,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.363,false,true,306983,Photographs,Mugshot,"Robillard. Guillaume, Joseph. 24 ans, né le 17/11/68 à Vaucresson. Fondeur en cuivre. Anarchiste. 2/7/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306983,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.364,false,true,306984,Photographs,Mugshot,"Robyns. Émile. 36 ans, né à Lumone (Belgique). Marchand de Pierres (?). Anarchiste. 28/2/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306984,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.365,false,true,306985,Photographs,Mugshot,"Rochet. Théophile. 24 ans, né à Rennes (Ille-et-Vilaine) le 7/6/69. Cordonnier. Anarchiste. 18/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306985,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.366,false,true,306986,Photographs,Mugshot,"Rodskidski. Eloi, Jean-Baptiste. 37 ans, né à Paris Xlle 13/12/56. Mécanicien. Anarchiste. 2/7/94",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306986,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.367,false,true,306987,Photographs,Mugshot,"Roobin. Joseph. 40 ans, né à Bourgneuf (Loire-Inférieure). Terrassier. Anarchiste. 2/3/94",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306987,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.368,false,true,306988,Photographs,Mugshot,"Rossi. Guillaume. 20 ans, né le 8/3/71 à Biel (Italie). Serrurier. Vagabondage. 15/1/95.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1895,1895,1895,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306988,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.369,false,true,306989,Photographs,Mugshot,Roubichon. Jean-Marie. né le 14/6/52 à Vannes (Morbihan). Maçon. Anarchiste. 2/7/94.,,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306989,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.370,false,true,306990,Photographs,Mugshot,"Rouif. Léon. 27 ans, né à Villethierry (Yonne). Garçon boucher. Anarchiste. 1/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306990,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.371,false,true,306991,Photographs,Mugshot,"Roussel. Henri-Louis. 28 ans, né à Paris le 2/10/65. Ardoiseur. Anarchiste. 2/7/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306991,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.372,false,true,306992,Photographs,Mugshot,"Ruaud. Jean-Baptiste. 35 ans, né à Limoges le 28/7/58. Cordonnier. Anarchiste. 4/2/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306992,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.373,false,true,306993,Photographs,Mugshot,"Sachet. Edmond. 27 ans, né à Mézières (Ardennes). Typographe. Anarchiste. 1/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306993,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.374,false,true,306994,Photographs,Mugshot,"Savard. Henri-Auguste. 29 ans, né le 7/5/65 à Paris XXe. Ciseleur. Anarchiste. 2/7/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306994,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.375,false,true,306995,Photographs,Mugshot,"Saulnier. Alphonse, Joseph. 31 ans, né à Paris XXe. Tourneur sur bois. Anarchiste. 14/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306995,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.376,false,true,306996,Photographs,Mugshot,"Schaeffer. Ignace. 42 ans, né à Berheim (Bas-Rhin) le 31/10/51. Ébéniste. Anarchiste. 2/7/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306996,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.377,false,true,306997,Photographs,Mugshot,"Schaffer. Charles. 28 ans, né à Paris Xle. Ébéniste. Association de malfaiteurs. 2/3/94",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306997,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.378,false,true,306998,Photographs,Carte-de-visite; Mugshot,"Schouppe. Placide. (dit Ricken, Franz).",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1889–94,1889,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306998,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.379,false,true,306999,Photographs,Mugshot,"Schouppe. Placide. (dit Ricken, Franz). 31 ans, né à Dickenvenne (Belgique). Mécanicien. Vol.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1889,1889,1889,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306999,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.380,false,true,307000,Photographs,Mugshot,"Schouppe. Placide. (dit Ricken, Franz). 35 ans, né à Dickenvenne (Belgique). Mécanicien. Vol.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1893,1893,1893,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/307000,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.381,false,true,307001,Photographs,Mugshot,"Schrader. Minna, Appoline. 19 ans, née à Paris XIe. Sculpteur. Association de malfaiteurs. 24/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/307001,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.382,false,true,307002,Photographs,Mugshot,"Schulé. Armand. 21 ans, né le 28/2/73 à Choisy-le-Roi. Comptable. Anarchiste. 2/7/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/307002,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.383,false,true,307003,Photographs,Mugshot,"Schwartz. Auguste. 31 ans, né à Paris Xlle le 23/1/63. Maroquinier. Anarchiste. 2/7/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/307003,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.384,false,true,307004,Photographs,Mugshot,"Segard. Émilien (dit Segard Fils). 18 ans, né à Saloüel (Somme). Peintre en voitures. Anarchiste. 2/7/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/307004,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.385,false,true,307005,Photographs,Mugshot,"Segard. Philogone. 44 ans (35 ans inscrit sur la photo), né à Salond (Somme). Journaliste. Anarchiste.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1891–95,1891,1895,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/307005,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.386,false,true,307006,Photographs,Mugshot,"Selle. Louis- Désiré-Honoré. 31 ans, né à Bougival. Cordonnier. Anarchiste. 26/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/307006,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.387,false,true,307007,Photographs,Mugshot,"Sentenac. Phillipe. 36 ans, né à Soulan (Ariège). Menuisier. Anarchiste. 7/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/307007,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.388,false,true,307008,Photographs,Mugshot,"Serre. Auguste. 37 ans, né à Anonnay (Ardêche) le 13/10/56. Mégissier. Anarchiste. 20/1/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/307008,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.389,false,true,307009,Photographs,Mugshot,"Sicard. André. 32 ans, né à Nîmes le 25/10/62. Bijoutier. Anarchiste. 2/7/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/307009,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.390,false,true,307010,Photographs,Mugshot,"Sigel. Jacques. 24 ans, né à Kuttalsheim (Bas Rhin). Bijoutier. Anarchiste. 26/2/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/307010,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.391,false,true,307011,Photographs,Mugshot,"Simonin. Joseph. 26 ans, né à Saint-Maurice (Seine). Gainier. Anarchiste. 6/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/307011,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.392,false,true,307012,Photographs,Mugshot,"Soubrié. François. 39 ans, né à Livignac-le-Haut (Aveyron). Brûleur de café. Anarchiste. 14/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/307012,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.393,false,true,307013,Photographs,Mugshot,"Soubrier. Annette (femme Chericotti). 28 ans, née à Paris Ille. Coutière. Anarchiste. 25/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/307013,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.394,false,true,307014,Photographs,Mugshot,"Solier. Auguste. 18 ans, né le 3/3/75 à Cemery-la-Ville. Dessinateur. Anarchiste. 12/1/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/307014,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.395,false,true,307015,Photographs,Mugshot,"Sost. Edmond. 30 ans, né à Serqueux (Seine-Inférieure) le 15/11/64. Ciseleur. Anarchiste. 1/7/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/307015,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.396,false,true,307016,Photographs,Mugshot,"Soulage. Alphonse, Charles. 30 ans, né à Lyon. Menuisier. Anarchiste. 1/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/307016,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.397,false,true,307017,Photographs,Mugshot,"Soulas. Honoré, Jules. 33 ans, né à Chatillon le 10/12/55. Peintre en bâtiment. Anarchiste. 27/5/89.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1889,1889,1889,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/307017,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.398,false,true,307018,Photographs,Mugshot,"Spanagel. Alfred, Vincent. 17 ans, né le 27/5/77 à Paris. Serrurier. Anarchiste. 7/7/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/307018,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.399,false,true,307019,Photographs,Mugshot,"Spanagel. Emile, Ignace. 20 ans, né le 28/2/74 à Paris XVlle. Serrurier. Anarchiste. 7/7/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/307019,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.400,false,true,307020,Photographs,Mugshot,"Springer. François. 21 ans, né le 17/9/72 à Duisburg (Allemagne). Menuisier. Anarchiste. 17/7/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/307020,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.401,false,true,307021,Photographs,Mugshot,"Surgand. Alphonse. 21 ans, né à Lyon. Réparateur de chaussures. Anarchiste. 4/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/307021,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.402,false,true,307022,Photographs,Mugshot,"Tardieu. Marius (ou Maurice). 26 ans, né le 15/7/68 à Piolène (Vaucluse). Ébéniste. Anarchiste. 2/7/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/307022,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.403,false,true,307023,Photographs,Mugshot,"Tennevin. Alexandre. 48 ans, né à Paris. Comptable. Anarchiste. 19/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/307023,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.404,false,true,307024,Photographs,Mugshot,"Terrier. Julien, François. 45 ans, né à Saint-Laurent (Mayenne). Menuisier. Anarchiste. 14/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/307024,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.405,false,true,307025,Photographs,Mugshot,"Theriez. Louis (ou Perriez). 35 ans, né le 29/8/58 à Paris. Ébéniste. Anarchiste. 16/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/307025,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.406,false,true,307026,Photographs,Mugshot,"Theuriet. Jean Baptiste. 30 ans, né à Lyon. Gérant-coiffeur. Anarchiste. 2/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/307026,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.407,false,true,307027,Photographs,Mugshot,"Thibivilliers. Eugène. 23 ans, né à Pinseux-le-Haut-Verger (Oise). Polisseur de métaux. Cris séditieux. 5/3/94",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/307027,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.408,false,true,307028,Photographs,Mugshot,"Thiebaut. Eugène. 35 (ou 36) ans, né à Château-Salin (Menthe & Moselle). Couvreur. Anarchiste. 3/3/94",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/307028,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.409,false,true,307029,Photographs,Mugshot,"Thirion. Louis, Joseph. 31 ans, né à Autrey (Vosges). Journaliste. Anarchiste. 4/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/307029,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.410,false,true,307030,Photographs,Mugshot,"Tiran. Arthur, Théodore. 29 ans, né à Briec (Finistère). Serrurier. Anarchiste. 26/2/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/307030,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.411,false,true,307031,Photographs,Mugshot,"Toesca. Calixte. 28 ans, Tour de Breuil (Alpes-Mar.). Étudiant en médecine. Association de malfaiteurs. 27/2/94",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/307031,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.412,false,true,307032,Photographs,Mugshot,"Toulet. Guy, Flavien. 41 ans, né à Boufler (Somme). Entrepreneur de peinture. Anarchiste. 23/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/307032,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.413,false,true,307033,Photographs,Mugshot,"Tournadre. Jacques (ou Eugène). 32 ans, né à Marchal (Cantal). Journaliste. Anarchiste. 3/3/94",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/307033,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.414,false,true,307034,Photographs,Mugshot,"Tournan. Pierre. 49 ans, né à Bouzouville (Moselle). Fabricant de couronnes. Anarchiste. 6/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/307034,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.415,false,true,307035,Photographs,Mugshot,"Tramcourt. Albert. 27 ans, né le 10/12/66 à Creil. Mécanicien. Anarchiste. 15/1/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/307035,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.416,false,true,307036,Photographs,Mugshot,"Tropini. Esprit, Antoine. 35 ans, né à San-Bueri (Italie). Tourneur. Anarchiste. 2/7/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/307036,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.417,false,true,307037,Photographs,Mugshot,"Trucano. Victorine (veuve Belloti). 54 ans, né à St Maurier (Italie). Chapelier. Vol anarchiste. 19/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/307037,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.418,false,true,307038,Photographs,Mugshot,"Vaury. Charles, Joseph. 43 (ou 44) ans, né le 31/3/59 à Sedan. Mécanicien. Anarchiste. 16/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/307038,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.419,false,true,307039,Photographs,Mugshot,"Vendel. Jules. 34 (ou 33) ans, né le 2/4/61 à Chevry (Ain). Garçon de cuisine. Anarchiste. 2/7/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/307039,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.420,false,true,307040,Photographs,Mugshot,"Véret. 0ctave-Jean. 19 ans, né à Paris XXe. Photographe. Anarchiste. 2/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/307040,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.421,false,true,307041,Photographs,Mugshot,"Veysseire. Michel. 25 ans, né à Montreuil. Journaliste. Anarchiste. 5/3/94",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/307041,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.422,false,true,307042,Photographs,Mugshot,"Vidal. Guillaume. 37 ans, né à Tellière (Puy de Dôme). Cocher. Anarchiste. 10/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/307042,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.423,false,true,307043,Photographs,Mugshot,"Vignaud. Antoine. 32 ans, né à Cussey (Allier). Cordonnier. Vol anarchiste. 21/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/307043,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.424,false,true,307044,Photographs,Mugshot,"Villa. Jean. 29 ans, né à Farini d'Olma (Italie). Manoeuvre. Anarchiste. 2/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/307044,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.425,false,true,307045,Photographs,Mugshot,"Villanneau. Henri, Fernand. 35 ans, né le 11/3/59 à Poitiers. Clerc de notaire. Anarchiste. 2/7/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/307045,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.426,false,true,307046,Photographs,Mugshot,"Villetard. Jules, Pierre. 42 ans, né à Ligny-le-Chastel (Yonne). Terrassier. Anarchiste. 5/3/94",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/307046,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.427,false,true,307047,Photographs,Mugshot,"Vuagniaux. Alfred. 41 ans, né à Vucheron (Suisse). Cordonnier. Anarchiste. 2/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/307047,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.428,false,true,307048,Photographs,Mugshot,"Wagner. Paul, Louis. 38 ans, né le 14/10/55. Ébéniste. Anarchiste. 2/7/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/307048,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.429,false,true,307049,Photographs,Mugshot,"Wallays. Charles. 29 ans, né à Lille. Tailleur d'habits. Anarchiste. 9/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/307049,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.430,false,true,307050,Photographs,Mugshot,"Widcoq. Alfred, François, Adolphe. 32 ans, né à Fressenneville (Somme). Mécanicien. Anarchiste. 10/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/307050,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.431,false,true,307051,Photographs,Mugshot,"Widcoq. Fulgence, Ignace. 36 ans, né à Fressenneville (Somme). Mécanicien. Anarchiste. 10/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/307051,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.432,false,true,307052,Photographs,Mugshot,"Willems. Charles, Louis. 52 ans, né à Houndchocte (Nord). Tailleur d'habits. Anarchiste. 18/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/307052,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.433,false,true,307053,Photographs,Mugshot,"Zanini. Marie (veuve Milanaccio). 28 ans, née à Turin (Italie). Cuisinière. Vol. 18/3/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/307053,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.434,false,true,307054,Photographs,Mugshot,"Zisly. Henri, Gabriel. 21 ans, né à Paris IVe. Employé de commerce. Anarchiste. 26/2/94.",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",,1853,1914,1894,1894,1894,Albumen silver print from glass negative,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/307054,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1981.1229.51.1,false,true,263192,Photographs,Photograph,Pekin. No. 923,,,,,,Artist,Attributed to,Lai Fong,"Chinese, 1839–1890",,"Lai, Fong",,1839,1890,1867,1867,1867,Albumen silver print from glass negative,,"Robert O. Dougan Collection, Gift of Warner Communications Inc., 1981",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/263192,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.494.13,false,true,289285,Photographs,Photograph,"A Portion of the Citywall, Foochow",,,,,,Artist,,Lai Fong,"Chinese, 1839–1890",,"Lai, Fong",,1839,1890,ca. 1869,1869,1869,Albumen silver print from glass negative,Image: 8 1/8 × 11 1/2 in. (20.7 × 29.2 cm),"Gilman Collection, Purchase, Robert Rosenkranz Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289285,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.494.29,false,true,289301,Photographs,Photograph,Left Road up to Yuen foo Monastery,,,,,,Artist,,Lai Fong,"Chinese, 1839–1890",,"Lai, Fong",,1839,1890,ca. 1869,1869,1869,Albumen silver print from glass negative,Image: 8 7/8 × 11 5/16 in. (22.6 × 28.7 cm),"Gilman Collection, Purchase, Robert Rosenkranz Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289301,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.494.31,false,true,289303,Photographs,Photograph,Temple of Tai-wang at Wu ü near Sing Chang Tea Mart,,,,,,Artist,,Lai Fong,"Chinese, 1839–1890",,"Lai, Fong",,1839,1890,ca. 1869,1869,1869,Albumen silver print from glass negative,Image: 8 1/16 × 11 1/4 in. (20.5 × 28.6 cm),"Gilman Collection, Purchase, Robert Rosenkranz Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289303,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.494.35,false,true,289307,Photographs,Photograph,"The Grand Stand, Foochow",,,,,,Artist,,Lai Fong,"Chinese, 1839–1890",,"Lai, Fong",,1839,1890,ca. 1869,1869,1869,Albumen silver print from glass negative,Image: 7 15/16 × 11 5/16 in. (20.2 × 28.8 cm),"Gilman Collection, Purchase, Robert Rosenkranz Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289307,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.494.38,false,true,289310,Photographs,Photograph,Culling Tea,,,,,,Artist,Attributed to,Lai Fong,"Chinese, 1839–1890",,"Lai, Fong",,1839,1890,ca. 1869,1869,1869,Albumen silver print from glass negative,Image: 6 15/16 × 9 3/8 in. (17.6 × 23.8 cm),"Gilman Collection, Purchase, Robert Rosenkranz Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289310,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2010.423.1,false,true,294322,Photographs,Postcard,"Astapovo Train Station, On the Right is the House in Which Lev Nikolayevich (Tolstoy) Died",,,,,,Artist,,Aleksey Ivanovich Saveliev,"Russian, 1883–1923",,"Saveliev, Aleksey Ivanovich",,1883,1923,1910,1910,1910,Gelatin silver print,Image: 8.9 x 13.3 cm (3 1/2 x 5 1/4 in.),"Gift of Pierre Apraxine, 2010",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/294322,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2010.423.2,false,true,294323,Photographs,Postcard,The Lowering of the Coffin into the Grave with Kneeling Mourners,,,,,,Artist,,Aleksey Ivanovich Saveliev,"Russian, 1883–1923",,"Saveliev, Aleksey Ivanovich",,1883,1923,"November 9, 1910",1910,1910,Gelatin silver print,Image: 8.9 x 13.3 cm (3 1/2 x 5 1/4 in.),"Gift of Pierre Apraxine, 2010",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/294323,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2010.423.3,false,true,294324,Photographs,Postcard,Deputation of the Yasno-Polyanskyi Peasants,,,,,,Artist,,Aleksey Ivanovich Saveliev,"Russian, 1883–1923",,"Saveliev, Aleksey Ivanovich",,1883,1923,1910,1910,1910,Gelatin silver print,Image: 8.9 x 13.3 cm (3 1/2 x 5 1/4 in.),"Gift of Pierre Apraxine, 2010",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/294324,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2010.423.4,false,true,294325,Photographs,Postcard,"En Route to the House, Visible in the Distance is the Village of Yasnaya Polyana",,,,,,Artist,,Aleksey Ivanovich Saveliev,"Russian, 1883–1923",,"Saveliev, Aleksey Ivanovich",,1883,1923,1910,1910,1910,Gelatin silver print,Image: 8.9 x 13.3 cm (3 1/2 x 5 1/4 in.),"Gift of Pierre Apraxine, 2010",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/294325,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2010.423.5,false,true,294326,Photographs,Postcard,At the Prepared Grave,,,,,,Artist,,Aleksey Ivanovich Saveliev,"Russian, 1883–1923",,"Saveliev, Aleksey Ivanovich",,1883,1923,1910,1910,1910,Gelatin silver print,Image: 8.9 x 13.3 cm (3 1/2 x 5 1/4 in.),"Gift of Pierre Apraxine, 2010",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/294326,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2010.423.6,false,true,294327,Photographs,Postcard,Peasant Carts with Funeral Wreaths,,,,,,Artist,,Aleksey Ivanovich Saveliev,"Russian, 1883–1923",,"Saveliev, Aleksey Ivanovich",,1883,1923,1910,1910,1910,Gelatin silver print,Image: 8.9 x 13.3 cm (3 1/2 x 5 1/4 in.),"Gift of Pierre Apraxine, 2010",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/294327,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1033,false,true,711640,Photographs,Photograph,[Civil War View],,,,,,Artist,,Thomas C. Roche,"American, 1826–1895",", et al","Roche, Thomas C.",,1826,1895,1860s,1860,1869,Albumen silver print from glass negative,,"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/711640,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1034,false,true,711641,Photographs,Photograph,[Civil War View],,,,,,Artist,,Thomas C. Roche,"American, 1826–1895",", et al","Roche, Thomas C.",,1826,1895,1860s,1860,1869,Albumen silver print from glass negative,,"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/711641,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1035,false,true,711642,Photographs,Photograph,[Civil War View],,,,,,Artist,,Thomas C. Roche,"American, 1826–1895",", et al","Roche, Thomas C.",,1826,1895,1860s,1860,1869,Albumen silver print from glass negative,,"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/711642,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1036,false,true,711643,Photographs,Photograph,[Civil War View],,,,,,Artist,,Thomas C. Roche,"American, 1826–1895",", et al","Roche, Thomas C.",,1826,1895,1860s,1860,1869,Albumen silver print from glass negative,,"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/711643,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1037,false,true,711644,Photographs,Photograph,[Civil War View],,,,,,Artist,,Thomas C. Roche,"American, 1826–1895",", et al","Roche, Thomas C.",,1826,1895,1860s,1860,1869,Albumen silver print from glass negative,,"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/711644,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1038,false,true,711645,Photographs,Photograph,[Civil War View],,,,,,Artist,,Thomas C. Roche,"American, 1826–1895",", et al","Roche, Thomas C.",,1826,1895,1860s,1860,1869,Albumen silver print from glass negative,,"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/711645,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1039,false,true,711646,Photographs,Photograph,[Civil War View],,,,,,Artist,,Thomas C. Roche,"American, 1826–1895",", et al","Roche, Thomas C.",,1826,1895,1860s,1860,1869,Albumen silver print from glass negative,,"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/711646,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1040,false,true,711647,Photographs,Photograph,[Civil War View],,,,,,Artist,,Thomas C. Roche,"American, 1826–1895",", et al","Roche, Thomas C.",,1826,1895,1860s,1860,1869,Albumen silver print from glass negative,,"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/711647,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1041,false,true,711648,Photographs,Photograph,[Civil War View],,,,,,Artist,,Thomas C. Roche,"American, 1826–1895",", et al","Roche, Thomas C.",,1826,1895,1860s,1860,1869,Albumen silver print from glass negative,,"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/711648,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1042,false,true,711649,Photographs,Photograph,[Civil War View],,,,,,Artist,,Thomas C. Roche,"American, 1826–1895",", et al","Roche, Thomas C.",,1826,1895,1860s,1860,1869,Albumen silver print from glass negative,,"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/711649,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1043,false,true,711650,Photographs,Photograph,[Civil War View],,,,,,Artist,,Thomas C. Roche,"American, 1826–1895",", et al","Roche, Thomas C.",,1826,1895,1860s,1860,1869,Albumen silver print from glass negative,,"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/711650,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1044,false,true,711651,Photographs,Photograph,[Civil War View],,,,,,Artist,,Thomas C. Roche,"American, 1826–1895",", et al","Roche, Thomas C.",,1826,1895,1860s,1860,1869,Albumen silver print from glass negative,,"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/711651,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1045,false,true,711652,Photographs,Photograph,[Civil War View],,,,,,Artist,,Thomas C. Roche,"American, 1826–1895",", et al","Roche, Thomas C.",,1826,1895,1860s,1860,1869,Albumen silver print from glass negative,,"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/711652,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1046,false,true,711653,Photographs,Photograph,[Civil War View],,,,,,Artist,,Thomas C. Roche,"American, 1826–1895",", et al","Roche, Thomas C.",,1826,1895,1860s,1860,1869,Albumen silver print from glass negative,,"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/711653,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1047,false,true,711654,Photographs,Photograph,[Civil War View],,,,,,Artist,,Thomas C. Roche,"American, 1826–1895",", et al","Roche, Thomas C.",,1826,1895,1860s,1860,1869,Albumen silver print from glass negative,,"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/711654,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1048,false,true,711655,Photographs,Photograph,[Civil War View],,,,,,Artist,,Thomas C. Roche,"American, 1826–1895",", et al","Roche, Thomas C.",,1826,1895,1860s,1860,1869,Albumen silver print from glass negative,,"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/711655,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1049,false,true,711656,Photographs,Photograph,[Civil War View],,,,,,Artist,,Thomas C. Roche,"American, 1826–1895",", et al","Roche, Thomas C.",,1826,1895,1860s,1860,1869,Albumen silver print from glass negative,,"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/711656,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1050,false,true,711657,Photographs,Photograph,[Civil War View],,,,,,Artist,,Thomas C. Roche,"American, 1826–1895",", et al","Roche, Thomas C.",,1826,1895,1860s,1860,1869,Albumen silver print from glass negative,,"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/711657,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1054,false,true,711661,Photographs,Photograph,[Civil War View],,,,,,Artist,,Thomas C. Roche,"American, 1826–1895",", et al","Roche, Thomas C.",,1826,1895,1860s,1860,1869,Albumen silver print from glass negative,,"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/711661,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.556(1),false,true,732748,Photographs,Photograph,Old Dominium and Uncle Tom's Tavern. Calaveras Grove,,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",,1829,1916,ca. 1878,1876,1880,Albumen silver print from glass negative,"Image: 12.5 x 12.5 cm (4 15/16 x 4 15/16 in.), circular Album page: 24 x 25.1 cm (9 7/16 x 9 7/8 in.)","Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/732748,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.556(2),false,true,732749,Photographs,Photograph,The Sentinels - Calaveras Grove,,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",,1829,1916,ca. 1878,1876,1880,Albumen silver print from glass negative,"Image: 12.5 x 12.5 cm (4 15/16 x 4 15/16 in.), circular Album page: 24 x 25.1 cm (9 7/16 x 9 7/8 in.)","Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/732749,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.556(3),false,true,732750,Photographs,Photograph,The Mother of the Forest - Calaveras Grove,,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",,1829,1916,ca. 1878,1876,1880,Albumen silver print from glass negative,"Image: 12.5 x 12.5 cm (4 15/16 x 4 15/16 in.), circular Album page: 24 x 25.1 cm (9 7/16 x 9 7/8 in.)","Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/732750,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.556(4),false,true,732751,Photographs,Photograph,The Miners Cabin - Calaveras Grove,,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",,1829,1916,ca. 1878,1876,1880,Albumen silver print from glass negative,"Image: 12.5 x 12.5 cm (4 15/16 x 4 15/16 in.), circular Album page: 24 x 25.1 cm (9 7/16 x 9 7/8 in.)","Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/732751,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.556(5),false,true,732752,Photographs,Photograph,The Mother of the Forest From the Father of the Forest - Calavaras Grove,,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",,1829,1916,ca. 1878,1876,1880,Albumen silver print from glass negative,"Image: 12.5 x 12.5 cm (4 15/16 x 4 15/16 in.), circular Album page: 24 x 25.1 cm (9 7/16 x 9 7/8 in.)","Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/732752,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.556(6),false,true,732753,Photographs,Photograph,The Mammoth Grove Hotel from the Grove - Calaveras,,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",,1829,1916,ca. 1878,1876,1880,Albumen silver print from glass negative,"Image: 12.5 x 12.5 cm (4 15/16 x 4 15/16 in.), circular Album page: 24 x 25.1 cm (9 7/16 x 9 7/8 in.)","Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/732753,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.556(7),false,true,732754,Photographs,Photograph,Looking up Among the Sugar Pines - Calaveras Grove,,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",,1829,1916,ca. 1878,1876,1880,Albumen silver print from glass negative,"Image: 12.5 x 12.5 cm (4 15/16 x 4 15/16 in.), circular Album page: 24 x 25.1 cm (9 7/16 x 9 7/8 in.)","Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/732754,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.556(8),false,true,732820,Photographs,Photograph,Pioneers Cabin - Calaveras Grove,,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",,1829,1916,ca. 1878,1876,1880,Albumen silver print from glass negative,"Image: 12.5 x 12.5 cm (4 15/16 x 4 15/16 in.), circular Album page: 24 x 25.1 cm (9 7/16 x 9 7/8 in.)","Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/732820,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.556(9),false,true,732819,Photographs,Photograph,The Three Graces Seen Through the Bryant and Seward Calaveras Grove,,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",,1829,1916,ca. 1878,1876,1880,Albumen silver print from glass negative,"Image: 12.5 x 12.5 cm (4 15/16 x 4 15/16 in.), circular Album page: 24 x 25.1 cm (9 7/16 x 9 7/8 in.)","Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/732819,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.556(10),false,true,732818,Photographs,Photograph,James King of William. Keyston State etc. Calaveras Grove,,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",,1829,1916,ca. 1878,1876,1880,Albumen silver print from glass negative,"Image: 12.5 x 12.5 cm (4 15/16 x 4 15/16 in.), circular Album page: 24 x 25.1 cm (9 7/16 x 9 7/8 in.)","Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/732818,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.556(11),false,true,732817,Photographs,Photograph,The Pride of the Forest - Calaveras Grove,,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",,1829,1916,ca. 1878,1876,1880,Albumen silver print from glass negative,"Image: 12.5 x 12.5 cm (4 15/16 x 4 15/16 in.), circular Album page: 24 x 25.1 cm (9 7/16 x 9 7/8 in.)","Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/732817,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.556(12),false,true,732816,Photographs,Photograph,Eagle's Wing - Calaveras Grove,,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",,1829,1916,ca. 1878,1876,1880,Albumen silver print from glass negative,"Image: 12.5 x 12.5 cm (4 15/16 x 4 15/16 in.), circular Album page: 24 x 25.1 cm (9 7/16 x 9 7/8 in.)","Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/732816,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.556(13),false,true,732815,Photographs,Photograph,The Father of the Forest - The Horse Back Side. Calaveras Grove,,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",,1829,1916,ca. 1878,1876,1880,Albumen silver print from glass negative,"Image: 12.5 x 12.5 cm (4 15/16 x 4 15/16 in.), circular Album page: 24 x 25.1 cm (9 7/16 x 9 7/8 in.)","Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/732815,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.556(14),false,true,732814,Photographs,Photograph,Mammoth Three Grove Hotel Calaveras Co Cal.,,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",,1829,1916,ca. 1878,1876,1880,Albumen silver print from glass negative,"Image: 12.5 x 12.5 cm (4 15/16 x 4 15/16 in.), circular Album page: 24 x 25.1 cm (9 7/16 x 9 7/8 in.)","Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/732814,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.556(15),false,true,732813,Photographs,Photograph,None,,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",,1829,1916,ca. 1878,1876,1880,Albumen silver print from glass negative,"Image: 12.5 x 12.5 cm (4 15/16 x 4 15/16 in.), circular Album page: 24 x 25.1 cm (9 7/16 x 9 7/8 in.)","Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/732813,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.556(16),false,true,732812,Photographs,Photograph,W. C. Bryant - Calaveras Grove,,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",,1829,1916,ca. 1878,1876,1880,Albumen silver print from glass negative,"Image: 12.5 x 12.5 cm (4 15/16 x 4 15/16 in.), circular Album page: 24 x 25.1 cm (9 7/16 x 9 7/8 in.)","Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/732812,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.556(17),false,true,732811,Photographs,Photograph,The Father of the Forest - C. Grove,,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",,1829,1916,ca. 1878,1876,1880,Albumen silver print from glass negative,"Image: 12.5 x 12.5 cm (4 15/16 x 4 15/16 in.), circular Album page: 24 x 25.1 cm (9 7/16 x 9 7/8 in.)","Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/732811,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.556(18),false,true,732810,Photographs,Photograph,The Empire State - C. Grove,,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",,1829,1916,ca. 1878,1876,1880,Albumen silver print from glass negative,"Image: 12.5 x 12.5 cm (4 15/16 x 4 15/16 in.), circular Album page: 24 x 25.1 cm (9 7/16 x 9 7/8 in.)","Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/732810,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.556(19),false,true,732809,Photographs,Photograph,Auld Reckie - C. Grove,,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",,1829,1916,ca. 1878,1876,1880,Albumen silver print from glass negative,"Image: 12.5 x 12.5 cm (4 15/16 x 4 15/16 in.), circular Album page: 24 x 25.1 cm (9 7/16 x 9 7/8 in.)","Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/732809,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.556(20),false,true,732808,Photographs,Photograph,Empire State. C. Grove,,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",,1829,1916,ca. 1878,1876,1880,Albumen silver print from glass negative,"Image: 12.5 x 12.5 cm (4 15/16 x 4 15/16 in.), circular Album page: 24 x 25.1 cm (9 7/16 x 9 7/8 in.)","Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/732808,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.556(21),false,true,732807,Photographs,Photograph,The Father of the Forest 450 ft C. Grove,,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",,1829,1916,ca. 1878,1876,1880,Albumen silver print from glass negative,"Image: 12.5 x 12.5 cm (4 15/16 x 4 15/16 in.), circular Album page: 24 x 25.1 cm (9 7/16 x 9 7/8 in.)","Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/732807,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.556(22),false,true,732806,Photographs,Photograph,Auld Reckie. C. Grove,,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",,1829,1916,ca. 1878,1876,1880,Albumen silver print from glass negative,"Image: 12.5 x 12.5 cm (4 15/16 x 4 15/16 in.), circular Album page: 24 x 25.1 cm (9 7/16 x 9 7/8 in.)","Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/732806,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.556(23),false,true,732805,Photographs,Photograph,"Interior of Pavilion Built on the Stump of the Tree, C. Grove",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",,1829,1916,ca. 1878,1876,1880,Albumen silver print from glass negative,"Image: 12.5 x 12.5 cm (4 15/16 x 4 15/16 in.), circular Album page: 24 x 25.1 cm (9 7/16 x 9 7/8 in.)","Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/732805,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.556(24),false,true,732804,Photographs,Photograph,The Sentinels. C. Grove,,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",,1829,1916,ca. 1878,1876,1880,Albumen silver print from glass negative,"Image: 12.5 x 12.5 cm (4 15/16 x 4 15/16 in.), circular Album page: 24 x 25.1 cm (9 7/16 x 9 7/8 in.)","Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/732804,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.556(25),false,true,732803,Photographs,Photograph,Warm Springs Hotel Lake Tahoe,,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",,1829,1916,ca. 1878,1876,1880,Albumen silver print from glass negative,"Image: 12.5 x 12.5 cm (4 15/16 x 4 15/16 in.), circular Album page: 24 x 25.1 cm (9 7/16 x 9 7/8 in.)","Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/732803,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.556(26),false,true,732802,Photographs,Photograph,"The Father of the Forest 450 Ft Long, C. Grove",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",,1829,1916,ca. 1878,1876,1880,Albumen silver print from glass negative,"Image: 12.5 x 12.5 cm (4 15/16 x 4 15/16 in.), circular Album page: 24 x 25.1 cm (9 7/16 x 9 7/8 in.)","Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/732802,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.556(27),false,true,732801,Photographs,Photograph,Donner Lake. C. P. R. R,,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",,1829,1916,ca. 1878,1876,1880,Albumen silver print from glass negative,"Image: 12.5 x 12.5 cm (4 15/16 x 4 15/16 in.), circular Album page: 24 x 25.1 cm (9 7/16 x 9 7/8 in.)","Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/732801,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.556(28),false,true,732800,Photographs,Photograph,In the Yosemite Valley,,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",,1829,1916,ca. 1878,1876,1880,Albumen silver print from glass negative,"Image: 12.5 x 12.5 cm (4 15/16 x 4 15/16 in.), circular Album page: 24 x 25.1 cm (9 7/16 x 9 7/8 in.)","Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/732800,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.556(29),false,true,732799,Photographs,Photograph,"[Yosemite National Park, California]",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",,1829,1916,ca. 1878,1876,1880,Albumen silver print from glass negative,"Image: 12.5 x 12.5 cm (4 15/16 x 4 15/16 in.), circular Album page: 24 x 25.1 cm (9 7/16 x 9 7/8 in.)","Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/732799,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.556(30),false,true,732798,Photographs,Photograph,"[Yosemite National Park, California]",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",,1829,1916,ca. 1878,1876,1880,Albumen silver print from glass negative,"Image: 12.5 x 12.5 cm (4 15/16 x 4 15/16 in.), circular Album page: 24 x 25.1 cm (9 7/16 x 9 7/8 in.)","Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/732798,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.556(31),false,true,732797,Photographs,Photograph,"[Yosemite National Park, California]",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",,1829,1916,ca. 1878,1876,1880,Albumen silver print from glass negative,"Image: 12.5 x 12.5 cm (4 15/16 x 4 15/16 in.), circular Album page: 24 x 25.1 cm (9 7/16 x 9 7/8 in.)","Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/732797,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.556(32),false,true,732796,Photographs,Photograph,"[Yosemite National Park, California]",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",,1829,1916,ca. 1878,1876,1880,Albumen silver print from glass negative,"Image: 12.5 x 12.5 cm (4 15/16 x 4 15/16 in.), circular Album page: 24 x 25.1 cm (9 7/16 x 9 7/8 in.)","Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/732796,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.556(33),false,true,732795,Photographs,Photograph,"[Yosemite National Park, California]",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",,1829,1916,ca. 1878,1876,1880,Albumen silver print from glass negative,"Image: 12.5 x 12.5 cm (4 15/16 x 4 15/16 in.), circular Album page: 24 x 25.1 cm (9 7/16 x 9 7/8 in.)","Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/732795,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.556(34),false,true,732794,Photographs,Photograph,"[Yosemite National Park, California]",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",,1829,1916,ca. 1878,1876,1880,Albumen silver print from glass negative,"Image: 12.5 x 12.5 cm (4 15/16 x 4 15/16 in.), circular Album page: 24 x 25.1 cm (9 7/16 x 9 7/8 in.)","Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/732794,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.556(35),false,true,732793,Photographs,Photograph,"[Yosemite National Park, California]",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",,1829,1916,ca. 1878,1876,1880,Albumen silver print from glass negative,"Image: 12.5 x 12.5 cm (4 15/16 x 4 15/16 in.), circular Album page: 24 x 25.1 cm (9 7/16 x 9 7/8 in.)","Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/732793,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.556(36),false,true,732792,Photographs,Photograph,"[Yosemite National Park, California]",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",,1829,1916,ca. 1878,1876,1880,Albumen silver print from glass negative,"Image: 12.5 x 12.5 cm (4 15/16 x 4 15/16 in.), circular Album page: 24 x 25.1 cm (9 7/16 x 9 7/8 in.)","Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/732792,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.556(37),false,true,732791,Photographs,Photograph,"[Yosemite National Park, California]",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",,1829,1916,ca. 1878,1876,1880,Albumen silver print from glass negative,"Image: 12.5 x 12.5 cm (4 15/16 x 4 15/16 in.), circular Album page: 24 x 25.1 cm (9 7/16 x 9 7/8 in.)","Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/732791,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.556(38),false,true,732790,Photographs,Photograph,"[Yosemite National Park, California]",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",,1829,1916,ca. 1878,1876,1880,Albumen silver print from glass negative,"Image: 12.5 x 12.5 cm (4 15/16 x 4 15/16 in.), circular Album page: 24 x 25.1 cm (9 7/16 x 9 7/8 in.)","Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/732790,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.556(39),false,true,732789,Photographs,Photograph,"[Yosemite National Park, California]",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",,1829,1916,ca. 1878,1876,1880,Albumen silver print from glass negative,"Image: 12.5 x 12.5 cm (4 15/16 x 4 15/16 in.), circular Album page: 24 x 25.1 cm (9 7/16 x 9 7/8 in.)","Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/732789,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.556(40),false,true,732788,Photographs,Photograph,"[Yosemite National Park, California]",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",,1829,1916,ca. 1878,1876,1880,Albumen silver print from glass negative,"Image: 12.5 x 12.5 cm (4 15/16 x 4 15/16 in.), circular Album page: 24 x 25.1 cm (9 7/16 x 9 7/8 in.)","Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/732788,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.556(41),false,true,732787,Photographs,Photograph,"[Yosemite National Park, California]",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",,1829,1916,ca. 1878,1876,1880,Albumen silver print from glass negative,"Image: 12.5 x 12.5 cm (4 15/16 x 4 15/16 in.), circular Album page: 24 x 25.1 cm (9 7/16 x 9 7/8 in.)","Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/732787,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.556(42),false,true,732786,Photographs,Photograph,"[Yosemite National Park, California]",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",,1829,1916,ca. 1878,1876,1880,Albumen silver print from glass negative,"Image: 12.5 x 12.5 cm (4 15/16 x 4 15/16 in.), circular Album page: 24 x 25.1 cm (9 7/16 x 9 7/8 in.)","Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/732786,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.556(43),false,true,732785,Photographs,Photograph,"[Yosemite National Park, California]",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",,1829,1916,ca. 1878,1876,1880,Albumen silver print from glass negative,"Image: 12.5 x 12.5 cm (4 15/16 x 4 15/16 in.), circular Album page: 24 x 25.1 cm (9 7/16 x 9 7/8 in.)","Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/732785,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.556(44),false,true,732784,Photographs,Photograph,"[Yosemite National Park, California]",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",,1829,1916,ca. 1878,1876,1880,Albumen silver print from glass negative,"Image: 12.5 x 12.5 cm (4 15/16 x 4 15/16 in.), circular Album page: 24 x 25.1 cm (9 7/16 x 9 7/8 in.)","Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/732784,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.556(45),false,true,732783,Photographs,Photograph,"[Yosemite National Park, California]",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",,1829,1916,ca. 1878,1876,1880,Albumen silver print from glass negative,"Image: 12.5 x 12.5 cm (4 15/16 x 4 15/16 in.), circular Album page: 24 x 25.1 cm (9 7/16 x 9 7/8 in.)","Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/732783,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.556(46),false,true,732782,Photographs,Photograph,"[Yosemite National Park, California]",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",,1829,1916,ca. 1878,1876,1880,Albumen silver print from glass negative,"Image: 12.5 x 12.5 cm (4 15/16 x 4 15/16 in.), circular Album page: 24 x 25.1 cm (9 7/16 x 9 7/8 in.)","Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/732782,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.556(47),false,true,732781,Photographs,Photograph,"[Yosemite National Park, California]",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",,1829,1916,ca. 1878,1876,1880,Albumen silver print from glass negative,"Image: 12.5 x 12.5 cm (4 15/16 x 4 15/16 in.), circular Album page: 24 x 25.1 cm (9 7/16 x 9 7/8 in.)","Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/732781,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.556(48),false,true,732780,Photographs,Photograph,"[Yosemite National Park, California]",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",,1829,1916,ca. 1878,1876,1880,Albumen silver print from glass negative,"Image: 12.5 x 12.5 cm (4 15/16 x 4 15/16 in.), circular Album page: 24 x 25.1 cm (9 7/16 x 9 7/8 in.)","Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/732780,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.556(49),false,true,732779,Photographs,Photograph,"[Yosemite National Park, California]",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",,1829,1916,ca. 1878,1876,1880,Albumen silver print from glass negative,"Image: 12.5 x 12.5 cm (4 15/16 x 4 15/16 in.), circular Album page: 24 x 25.1 cm (9 7/16 x 9 7/8 in.)","Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/732779,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.556(50),false,true,732778,Photographs,Photograph,"[Yosemite National Park, California]",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",,1829,1916,ca. 1878,1876,1880,Albumen silver print from glass negative,"Image: 12.5 x 12.5 cm (4 15/16 x 4 15/16 in.), circular Album page: 24 x 25.1 cm (9 7/16 x 9 7/8 in.)","Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/732778,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.556(51),false,true,732777,Photographs,Photograph,"[Yosemite National Park, California]",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",,1829,1916,ca. 1878,1876,1880,Albumen silver print from glass negative,"Image: 12.5 x 12.5 cm (4 15/16 x 4 15/16 in.), circular Album page: 24 x 25.1 cm (9 7/16 x 9 7/8 in.)","Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/732777,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.556(52),false,true,732776,Photographs,Photograph,"[Yosemite National Park, California]",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",,1829,1916,ca. 1878,1876,1880,Albumen silver print from glass negative,"Image: 12.5 x 12.5 cm (4 15/16 x 4 15/16 in.), circular Album page: 24 x 25.1 cm (9 7/16 x 9 7/8 in.)","Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/732776,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.556(53),false,true,732775,Photographs,Photograph,"[Yosemite National Park, California]",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",,1829,1916,ca. 1878,1876,1880,Albumen silver print from glass negative,"Image: 12.5 x 12.5 cm (4 15/16 x 4 15/16 in.), circular Album page: 24 x 25.1 cm (9 7/16 x 9 7/8 in.)","Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/732775,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.556(54),false,true,732774,Photographs,Photograph,"[Yosemite National Park, California]",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",,1829,1916,ca. 1878,1876,1880,Albumen silver print from glass negative,"Image: 12.5 x 12.5 cm (4 15/16 x 4 15/16 in.), circular Album page: 24 x 25.1 cm (9 7/16 x 9 7/8 in.)","Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/732774,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.556(55),false,true,732773,Photographs,Photograph,"[Yosemite National Park, California]",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",,1829,1916,ca. 1878,1876,1880,Albumen silver print from glass negative,"Image: 12.5 x 12.5 cm (4 15/16 x 4 15/16 in.), circular Album page: 24 x 25.1 cm (9 7/16 x 9 7/8 in.)","Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/732773,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.556(56),false,true,732772,Photographs,Photograph,"[Yosemite National Park, California]",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",,1829,1916,ca. 1878,1876,1880,Albumen silver print from glass negative,"Image: 12.5 x 12.5 cm (4 15/16 x 4 15/16 in.), circular Album page: 24 x 25.1 cm (9 7/16 x 9 7/8 in.)","Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/732772,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.556(57),false,true,732771,Photographs,Photograph,"[Yosemite National Park, California]",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",,1829,1916,ca. 1878,1876,1880,Albumen silver print from glass negative,"Image: 12.5 x 12.5 cm (4 15/16 x 4 15/16 in.), circular Album page: 24 x 25.1 cm (9 7/16 x 9 7/8 in.)","Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/732771,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.556(58),false,true,732770,Photographs,Photograph,"[Yosemite National Park, California]",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",,1829,1916,ca. 1878,1876,1880,Albumen silver print from glass negative,"Image: 12.5 x 12.5 cm (4 15/16 x 4 15/16 in.), circular Album page: 24 x 25.1 cm (9 7/16 x 9 7/8 in.)","Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/732770,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.556(59),false,true,732769,Photographs,Photograph,"[Yosemite National Park, California]",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",,1829,1916,ca. 1878,1876,1880,Albumen silver print from glass negative,"Image: 12.5 x 12.5 cm (4 15/16 x 4 15/16 in.), circular Album page: 24 x 25.1 cm (9 7/16 x 9 7/8 in.)","Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/732769,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.556(60),false,true,732768,Photographs,Photograph,"[Yosemite National Park, California]",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",,1829,1916,ca. 1878,1876,1880,Albumen silver print from glass negative,"Image: 12.5 x 12.5 cm (4 15/16 x 4 15/16 in.), circular Album page: 24 x 25.1 cm (9 7/16 x 9 7/8 in.)","Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/732768,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.556(61),false,true,732767,Photographs,Photograph,"[Yosemite National Park, California]",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",,1829,1916,ca. 1878,1876,1880,Albumen silver print from glass negative,"Image: 12.5 x 12.5 cm (4 15/16 x 4 15/16 in.), circular Album page: 24 x 25.1 cm (9 7/16 x 9 7/8 in.)","Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/732767,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.556(62),false,true,732766,Photographs,Photograph,"[Yosemite National Park, California]",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",,1829,1916,ca. 1878,1876,1880,Albumen silver print from glass negative,"Image: 12.5 x 12.5 cm (4 15/16 x 4 15/16 in.), circular Album page: 24 x 25.1 cm (9 7/16 x 9 7/8 in.)","Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/732766,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.556(63),false,true,732765,Photographs,Photograph,"[Yosemite National Park, California]",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",,1829,1916,ca. 1878,1876,1880,Albumen silver print from glass negative,"Image: 12.5 x 12.5 cm (4 15/16 x 4 15/16 in.), circular Album page: 24 x 25.1 cm (9 7/16 x 9 7/8 in.)","Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/732765,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.556(64),false,true,732764,Photographs,Photograph,"[Yosemite National Park, California]",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",,1829,1916,ca. 1878,1876,1880,Albumen silver print from glass negative,"Image: 12.5 x 12.5 cm (4 15/16 x 4 15/16 in.), circular Album page: 24 x 25.1 cm (9 7/16 x 9 7/8 in.)","Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/732764,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.556(65),false,true,732763,Photographs,Photograph,"[Yosemite National Park, California]",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",,1829,1916,ca. 1878,1876,1880,Albumen silver print from glass negative,"Image: 12.5 x 12.5 cm (4 15/16 x 4 15/16 in.), circular Album page: 24 x 25.1 cm (9 7/16 x 9 7/8 in.)","Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/732763,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.556(66),false,true,732762,Photographs,Photograph,"[Yosemite National Park, California]",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",,1829,1916,ca. 1878,1876,1880,Albumen silver print from glass negative,"Image: 12.5 x 12.5 cm (4 15/16 x 4 15/16 in.), circular Album page: 24 x 25.1 cm (9 7/16 x 9 7/8 in.)","Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/732762,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.556(67),false,true,732761,Photographs,Photograph,"[Yosemite National Park, California]",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",,1829,1916,ca. 1878,1876,1880,Albumen silver print from glass negative,"Image: 12.5 x 12.5 cm (4 15/16 x 4 15/16 in.), circular Album page: 24 x 25.1 cm (9 7/16 x 9 7/8 in.)","Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/732761,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.556(68),false,true,732760,Photographs,Photograph,"[Yosemite National Park, California]",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",,1829,1916,ca. 1878,1876,1880,Albumen silver print from glass negative,"Image: 12.5 x 12.5 cm (4 15/16 x 4 15/16 in.), circular Album page: 24 x 25.1 cm (9 7/16 x 9 7/8 in.)","Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/732760,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.556(69),false,true,732759,Photographs,Photograph,"[Yosemite National Park, California]",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",,1829,1916,ca. 1878,1876,1880,Albumen silver print from glass negative,"Image: 12.5 x 12.5 cm (4 15/16 x 4 15/16 in.), circular Album page: 24 x 25.1 cm (9 7/16 x 9 7/8 in.)","Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/732759,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.556(70),false,true,732758,Photographs,Photograph,"[Yosemite National Park, California]",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",,1829,1916,ca. 1878,1876,1880,Albumen silver print from glass negative,"Image: 12.5 x 12.5 cm (4 15/16 x 4 15/16 in.), circular Album page: 24 x 25.1 cm (9 7/16 x 9 7/8 in.)","Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/732758,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.556(71),false,true,732757,Photographs,Photograph,"[Yosemite National Park, California]",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",,1829,1916,ca. 1878,1876,1880,Albumen silver print from glass negative,"Image: 12.5 x 12.5 cm (4 15/16 x 4 15/16 in.), circular Album page: 24 x 25.1 cm (9 7/16 x 9 7/8 in.)","Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/732757,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.556(72),false,true,732756,Photographs,Photograph,"[Yosemite National Park, California]",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",,1829,1916,ca. 1878,1876,1880,Albumen silver print from glass negative,"Image: 12.5 x 12.5 cm (4 15/16 x 4 15/16 in.), circular Album page: 24 x 25.1 cm (9 7/16 x 9 7/8 in.)","Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/732756,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.556(73),false,true,732755,Photographs,Photograph,"[Yosemite National Park, California]",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",,1829,1916,ca. 1878,1876,1880,Albumen silver print from glass negative,"Image: 12.5 x 12.5 cm (4 15/16 x 4 15/16 in.), circular Album page: 24 x 25.1 cm (9 7/16 x 9 7/8 in.)","Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/732755,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.261.2,false,true,680016,Photographs,Boudoir Card; Cabinet Card,"[Self-Portrait at Glacier Bay, Alaska]",,,,,,Artist,,Frank Jay Haynes,"American, 1853–1921",,"Haynes, F. Jay",,1853,1921,1889–91,1889,1891,Albumen silver print from glass negative,Image: 8 1/8 × 5 1/16 in. (20.7 × 12.8 cm) Mount: 8 7/16 × 5 1/4 in. (21.5 × 13.4 cm),"Gift of Paul M. Hertzmann Inc., 2015",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/680016,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.778.29,false,true,700105,Photographs,Photograph,Old Time Freight Brakeman – New York Central,,,,,,Artist,,Lewis Hine,"American, 1874–1940",,"Hine, Lewis",,1874,1940,1921,1921,1921,Gelatin silver print,Image: 6 11/16 × 4 11/16 in. (17 × 11.9 cm) Sheet: 6 15/16 in. × 5 in. (17.7 × 12.7 cm),"Gift of Joyce F. Menschel, 2015",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/700105,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.777.2,false,true,705430,Photographs,Photograph,"Thèbes, Temple de Ramasseum, Colosses brisés",,,,,,Artist,,Émile Béchard,"French, active 1860s–1880s",,"Béchard, Émile",,1859,1899,1870s,1870,1879,Albumen silver print,Image: 10 9/16 × 14 7/8 in. (26.8 × 37.8 cm) Mount: 13 7/8 × 18 1/16 in. (35.2 × 45.9 cm),"Gift of Charles Isaacs and Carol Nigro, 2015",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/705430,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.400.48,false,true,291801,Photographs,Daguerreotype,Elizabeth Michael Howell,,,,,,Artist,,Addis's Lancaster Gallery,"American, active 1840s–1860s",,Addis's Lancaster Gallery,,1840,1869,1855–59,1855,1859,Daguerreotype,Image: 8.9 x 6.5 cm (3 1/2 x 2 9/16 in.) Plate: 10.8 x 8.3 cm (4 1/4 x 3 1/4 in.) Case: 1.6 x 11.9 x 9.4 cm (5/8 x 4 11/16 x 3 11/16 in.),"Bequest of Herbert Mitchell, 2008",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/291801,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.588.1.114,false,true,704975,Photographs,Photograph; Carte-de-visite,Eugénie Schlosser et Coralli,,,,,,Artist,,André-Adolphe-Eugène Disdéri,"French, Paris 1819–1889 Paris",,"Disdéri, André-Adolphe-Eugène",,1819,1889,1863,1863,1863,Albumen silver print from glass negative,Image: 7 3/8 × 9 1/4 in. (18.8 × 23.5 cm) Album page: 10 3/8 × 13 3/4 in. (26.3 × 35 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/704975,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.588.1.115,false,true,704976,Photographs,Photograph; Carte-de-visite,Eugénie Schlosser et Coralli,,,,,,Artist,,André-Adolphe-Eugène Disdéri,"French, Paris 1819–1889 Paris",,"Disdéri, André-Adolphe-Eugène",,1819,1889,1863,1863,1863,Albumen silver print from glass negative,Image: 7 3/8 × 9 1/4 in. (18.8 × 23.5 cm) Album page: 10 3/8 × 13 3/4 in. (26.3 × 35 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/704976,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2005.100.588.1.116a, b",false,true,704977,Photographs,Photograph; Carte-de-visite,Eugénie Schlosser et Coralli,,,,,,Artist,,André-Adolphe-Eugène Disdéri,"French, Paris 1819–1889 Paris",,"Disdéri, André-Adolphe-Eugène",,1819,1889,1863,1863,1863,Albumen silver print from glass negative,Image: 7 3/8 × 9 1/4 in. (18.8 × 23.5 cm) Album page: 10 3/8 × 13 3/4 in. (26.3 × 35 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/704977,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.424,false,true,684347,Photographs,Photograph,[Spread from an Ornithological Book],,,,,,Artist,,Eugène Atget,"French, Libourne 1857–1927 Paris",,"Atget, Eugène",,1857-02-02,1927-08-04,ca. 1910,1905,1915,Matte albumen silver print from glass negative,"Image: 6 7/8 × 8 15/16 in. (17.4 × 22.7 cm) Sheet: 7 1/16 × 8 15/16 in. (17.9 × 22.7 cm), irregularly trimmed","The Horace W. Goldsmith Foundation Fund, through Joyce and Robert Menschel, 2015",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/684347,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.778.31,false,true,700107,Photographs,Photograph,"Hôtel des Ambassadeurs de Hollande, 47 rue Vieille du Temple",,,,,,Artist,,Eugène Atget,"French, Libourne 1857–1927 Paris",,"Atget, Eugène",,1857-02-02,1927-08-04,1898,1898,1898,Albumen silver print,Image: 8 1/2 in. × 7 in. (21.6 × 17.8 cm),"Gift of Joyce F. Menschel, 2015",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/700107,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.778.33,false,true,700109,Photographs,Photograph,Attitudes of Animals in Motion,,,,,,Artist,,Eadweard Muybridge,"American, born Britain, 1830–1904",,"Muybridge, Eadweard",,1830,1904,"1879, printed 1881",1879,1879,Albumen silver print,Image: 6 5/16 × 9 15/16 in. (16 × 25.3 cm) Mount: 8 13/16 × 12 5/8 in. (22.4 × 32 cm),"Gift of Joyce F. Menschel, 2015",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/700109,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.452,false,true,684344,Photographs,Photograph,[Portrait of Living Man beside Dead Man],,,,,,Artist,,Louis Dodero,"Italian, active France, 1840s–60s",,"Dodero, Louis",,1820,1880,ca. 1850,1845,1855,Daguerreotype,Image: 2 5/8 × 3 7/16 in. (6.7 × 8.7 cm),"The Horace W. Goldsmith Foundation Fund, through Joyce and Robert Menschel, 2015",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/684344,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.770,false,true,286100,Photographs,Photograph,The Old Gamekeeper,,,,,,Artist,Possibly by,David Kinnebrook,"British, Norwich 1819–1865 New Zealand",,"Kinnebrook, David",,1819,1865,ca. 1844,1842,1846,Salted paper print from paper negative,6 3/4 x 5 7/8,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286100,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2012.21,false,true,296345,Photographs,Photograph,"[Sutar ka Jhopda Cave Interior, Ellora Caves]",,,,,,Artist,,Alfred William Plâté,"German (active Sri Lanka), ca. 1859 –1931 Linz",,"Plâté, Alfred William",,1859,1931,1890–1900,1890,1900,Platinum print,Image: 27.8 x 22.8 cm (10 15/16 x 9 in.),"Purchase, Robert A. Taub Gift, 2012",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/296345,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.159.56,false,true,306342,Photographs,Photograph,34. Artistical Groups in Various Poses,,,,,,Artist,,Calvert Richard Jones,"British, Swansea, Wales 1802–1877 Bath, England",,"Jones, Calvert Richard",,1802,1877,ca. 1845,1840,1850,Salted paper print from paper negative,Sheet: 3 13/16 × 4 13/16 in. (9.7 × 12.2 cm) Image: 3 9/16 × 4 5/16 in. (9 × 11 cm),"Bequest of Maurice B. Sendak, 2012",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306342,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.382 (24),false,true,287914,Photographs,Photograph,[Tree and Brush in Creek Scene],,,,,,Artist,,John Dillwyn Llewelyn,"British, Swansea, Wales 1810–1882 Swansea, Wales",,"Llewelyn, John Dillwyn",,1810,1882,1853–56,1853,1856,Salted paper print,Image: 16 × 20.8 cm (6 5/16 × 8 3/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287914,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.382 (32),false,true,287922,Photographs,Photograph,Thereza,,,,,,Artist,,John Dillwyn Llewelyn,"British, Swansea, Wales 1810–1882 Swansea, Wales",,"Llewelyn, John Dillwyn",,1810,1882,1853–56,1853,1856,Salted paper print,Image: 23.2 × 19 cm (9 1/8 × 7 1/2 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287922,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.382 (35),false,true,287925,Photographs,Photograph,Yucca Gloriosa,,,,,,Artist,,John Dillwyn Llewelyn,"British, Swansea, Wales 1810–1882 Swansea, Wales",,"Llewelyn, John Dillwyn",,1810,1882,1853–56,1853,1856,Salted paper print,Image: 20.2 × 15.7 cm (7 15/16 × 6 3/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287925,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.382 (39),false,true,287929,Photographs,Photograph,The Upper Fall,,,,,,Artist,,John Dillwyn Llewelyn,"British, Swansea, Wales 1810–1882 Swansea, Wales",,"Llewelyn, John Dillwyn",,1810,1882,1853–56,1853,1856,Albumen silver print,Image: 24.7 × 17.8 cm (9 3/4 in. × 7 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287929,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.382 (46),false,true,287936,Photographs,Photograph,Tenby Sands,,,,,,Artist,,John Dillwyn Llewelyn,"British, Swansea, Wales 1810–1882 Swansea, Wales",,"Llewelyn, John Dillwyn",,1810,1882,1856,1856,1856,Salted paper print,Image: 15.9 × 21.1 cm (6 1/4 × 8 5/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287936,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.382 (47),false,true,287937,Photographs,Photograph,Upper Lake,,,,,,Artist,,John Dillwyn Llewelyn,"British, Swansea, Wales 1810–1882 Swansea, Wales",,"Llewelyn, John Dillwyn",,1810,1882,1853–56,1853,1856,Salted paper print,Image: 19.2 × 24.1 cm (7 9/16 × 9 1/2 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287937,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.382 (48),false,true,287938,Photographs,Photograph,Gipsies,,,,,,Artist,,John Dillwyn Llewelyn,"British, Swansea, Wales 1810–1882 Swansea, Wales",,"Llewelyn, John Dillwyn",,1810,1882,1853–56,1853,1856,Albumen silver print,Image: 15.8 × 21 cm (6 1/4 × 8 1/4 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287938,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.382 (49),false,true,287939,Photographs,Photograph,"[View of a House in the Woods, with a Waterlogged Road]",,,,,,Artist,,John Dillwyn Llewelyn,"British, Swansea, Wales 1810–1882 Swansea, Wales",,"Llewelyn, John Dillwyn",,1810,1882,1853–56,1853,1856,Albumen silver print,Image: 18.5 × 22.3 cm (7 5/16 × 8 3/4 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287939,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.382 (50),false,true,287940,Photographs,Photograph,The Lewitha,,,,,,Artist,,John Dillwyn Llewelyn,"British, Swansea, Wales 1810–1882 Swansea, Wales",,"Llewelyn, John Dillwyn",,1810,1882,1853–56,1853,1856,Albumen silver print,Image: 19.4 × 23.9 cm (7 5/8 × 9 7/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287940,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.382 (51),false,true,287941,Photographs,Photograph,Dunraven Cliffs - Low Tide,,,,,,Artist,,John Dillwyn Llewelyn,"British, Swansea, Wales 1810–1882 Swansea, Wales",,"Llewelyn, John Dillwyn",,1810,1882,1853–56,1853,1856,Salted paper print,Image: 15.8 × 20.5 cm (6 1/4 × 8 1/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287941,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.382 (52),false,true,287942,Photographs,Photograph,Birthday Group,,,,,,Artist,,John Dillwyn Llewelyn,"British, Swansea, Wales 1810–1882 Swansea, Wales",,"Llewelyn, John Dillwyn",,1810,1882,1856,1856,1856,Albumen silver print,Image: 15.7 × 20.6 cm (6 3/16 × 8 1/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287942,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.382 (53),false,true,287943,Photographs,Photograph,Sea Pool with Shells and Seaweeds,,,,,,Artist,,John Dillwyn Llewelyn,"British, Swansea, Wales 1810–1882 Swansea, Wales",,"Llewelyn, John Dillwyn",,1810,1882,1853–56,1853,1856,Salted paper print,"Image: 15.2 × 20.5 cm (6 in. × 8 1/16 in.), oval","Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287943,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.382 (54),false,true,287944,Photographs,Photograph,Penrice Garden,,,,,,Artist,,John Dillwyn Llewelyn,"British, Swansea, Wales 1810–1882 Swansea, Wales",,"Llewelyn, John Dillwyn",,1810,1882,1853–56,1853,1856,Albumen silver print,Image: 19.4 × 24.6 cm (7 5/8 × 9 11/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287944,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.382 (55),false,true,287945,Photographs,Photograph,Cureuleo Meadow,,,,,,Artist,,John Dillwyn Llewelyn,"British, Swansea, Wales 1810–1882 Swansea, Wales",,"Llewelyn, John Dillwyn",,1810,1882,1853–56,1853,1856,Albumen silver print,Image: 16.1 × 21.2 cm (6 5/16 × 8 3/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287945,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.382 (56),false,true,287946,Photographs,Photograph,Oakley Cottage with Mr. St. John and Peter and Polly,,,,,,Artist,,John Dillwyn Llewelyn,"British, Swansea, Wales 1810–1882 Swansea, Wales",,"Llewelyn, John Dillwyn",,1810,1882,1853–56,1853,1856,Salted paper print,Image: 16.4 × 21.2 cm (6 7/16 × 8 3/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287946,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.382 (57),false,true,287947,Photographs,Photograph,Oakley Cottage,,,,,,Artist,,John Dillwyn Llewelyn,"British, Swansea, Wales 1810–1882 Swansea, Wales",,"Llewelyn, John Dillwyn",,1810,1882,1853–56,1853,1856,Albumen silver print,Image: 16.1 × 20.8 cm (6 5/16 × 8 3/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287947,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.382 (58),false,true,287948,Photographs,Photograph,3 Cliffs Bay with a Wave,,,,,,Artist,,John Dillwyn Llewelyn,"British, Swansea, Wales 1810–1882 Swansea, Wales",,"Llewelyn, John Dillwyn",,1810,1882,1853–56,1853,1856,Salted paper print,Image: 15.6 × 20.5 cm (6 1/8 × 8 1/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287948,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.382 (78),false,true,287967,Photographs,Photograph,The Heron,,,,,,Artist,,John Dillwyn Llewelyn,"British, Swansea, Wales 1810–1882 Swansea, Wales",,"Llewelyn, John Dillwyn",,1810,1882,1853–56,1853,1856,Salted paper print,Image: 24.3 × 18.9 cm (9 9/16 × 7 7/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287967,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.382 (84),false,true,287973,Photographs,Photograph,"The Wharfe, Yorkshire",,,,,,Artist,,John Dillwyn Llewelyn,"British, Swansea, Wales 1810–1882 Swansea, Wales",,"Llewelyn, John Dillwyn",,1810,1882,1853–56,1853,1856,Salted paper print,"Image: 19.9 cm, 24 7/8 in. (7 13/16 × 24 7/8 in.)","Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287973,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.382 (85),false,true,287974,Photographs,Photograph,Guy Fawkes,,,,,,Artist,,John Dillwyn Llewelyn,"British, Swansea, Wales 1810–1882 Swansea, Wales",,"Llewelyn, John Dillwyn",,1810,1882,1853,1853,1853,Salted paper print,Image: 15 × 20.2 cm (5 7/8 × 7 15/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287974,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.382 (30a-e),false,true,287920,Photographs,Photograph,[Figurine of Young Boy Holding Apples; Cabinet Card of a Man; Figurine of a Young Child with a Hat; Sculpture of a Man with Child; Sculpture with Animal],,,,,,Artist,,John Dillwyn Llewelyn,"British, Swansea, Wales 1810–1882 Swansea, Wales",,"Llewelyn, John Dillwyn",,1810,1882,1853–56,1853,1856,Salted paper print,"Beginning at top, moving clockwise: Image: 5.6 × 5.2 cm (2 3/16 × 2 1/16 in.) (top) Image: 7.3 × 5.4 cm (2 7/8 × 2 1/8 in.) (right) Image: 8.2 × 6.1 cm (3 1/4 × 2 3/8 in.) (bottom) Image: 7.2 × 5.4 cm (2 13/16 × 2 1/8 in.) (left) Image: 4.3 × 4.3 cm (1 11/16 × 1 11/16 in.) (center)","Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287920,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.382 (43a-d),false,true,287933,Photographs,Photograph,Gipsies; Lewitha; Upper Lake; [Untitled],,,,,,Artist,,John Dillwyn Llewelyn,"British, Swansea, Wales 1810–1882 Swansea, Wales",,"Llewelyn, John Dillwyn",,1810,1882,1853–56,1853,1856,Salted paper print,"Image: 5.5 × 5.5 cm (2 3/16 × 2 3/16 in.) (a), circular Image: 5.5 × 5.7 cm (2 3/16 × 2 1/4 in.) (b), circular Image: 5.5 × 5.6 cm (2 3/16 × 2 3/16 in.) (c), circular Image: 14.8 × 10.7 cm (5 13/16 × 4 3/16 in.) (d), oval","Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287933,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.382 (75a-c),false,true,287964,Photographs,Photograph,"Saccolabium Guttatum; Lizzie, Emily, Alice, Mrs Stratton; Emily, Etty, Alice, Lizzie, Mrs Drake, Mrs Stratton",,,,,,Artist,,John Dillwyn Llewelyn,"British, Swansea, Wales 1810–1882 Swansea, Wales",,"Llewelyn, John Dillwyn",,1810,1882,1853–56,1853,1856,Salted paper print; albumen silver print,"Image: 14.9 × 9.2 cm (5 7/8 × 3 5/8 in.) (a) Image: 9.3 × 7.1 cm (3 11/16 × 2 13/16 in.) (b) Image: 7 11/16 in., 8.3 cm (7 11/16 × 3 1/4 in.) (c)","Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287964,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.382 (83a-h),false,true,287972,Photographs,Photograph,Miss Douglas; Mlle Isaline Motte; Miss Fanny Evans; Miss Catinka Smith; Mrs Leitch née Lloyd; Miss Martin; Capt. & Mrs Hibbert; Miss Cecilia Regnell,,,,,,Artist,,John Dillwyn Llewelyn,"British, Swansea, Wales 1810–1882 Swansea, Wales",,"Llewelyn, John Dillwyn",,1810,1882,1853–56,1853,1856,Albumen silver print,"Image: 8.9 × 5.5 cm (3 1/2 × 2 3/16 in.) (a) Image: 8 × 4.8 cm (3 1/8 × 1 7/8 in.) (b), oval Image: 8.7 × 5.5 cm (3 7/16 × 2 3/16 in.) (c) Image: 8.4 × 5.3 cm (3 5/16 × 2 1/16 in.) (d), diamond Image: 8.4 × 5.3 cm (3 5/16 × 2 1/16 in.) (e), diamond Image: 8.6 × 5.3 cm (3 3/8 × 2 1/16 in.) (f) Image: 8.1 × 5.5 cm (3 3/16 × 2 3/16 in.) (g), oval Image: 9.2 × 5.7 cm (3 5/8 × 2 1/4 in.) (h)","Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287972,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2005.100.382 (37a, b)",false,true,287927,Photographs,Photograph,"Granny, Thereza, Elinor; The Shanty",,,,,,Artist,,John Dillwyn Llewelyn,"British, Swansea, Wales 1810–1882 Swansea, Wales",,"Llewelyn, John Dillwyn",,1810,1882,1853–56,1853,1856,Salted paper print,"Image: 11.1 × 9.5 cm (4 3/8 × 3 3/4 in.) (a), oval Image: 10.4 × 13.8 cm (4 1/8 × 5 7/16 in.) (a), oval","Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287927,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2005.100.382 (41a, b)",false,true,287931,Photographs,Photograph,"Penllergare; Birthday Group, Sept. 23, 1853",,,,,,Artist,,John Dillwyn Llewelyn,"British, Swansea, Wales 1810–1882 Swansea, Wales",,"Llewelyn, John Dillwyn",,1810,1882,1853–56,1853,1856,Albumen silver print,Image: 15.3 × 16.1 cm (6 in. × 6 5/16 in.) Image: 7.6 × 10.6 cm (3 in. × 4 3/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287931,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2005.100.382 (77a, b)",false,true,287966,Photographs,Photograph,Penrice; [Untitled],,,,,,Artist,,John Dillwyn Llewelyn,"British, Swansea, Wales 1810–1882 Swansea, Wales",,"Llewelyn, John Dillwyn",,1810,1882,1853–56,1853,1856,Salted paper print; albumen silver print,Image: 11.8 × 13.9 cm (4 5/8 × 5 1/2 in.) (a) Image: 8.3 × 5.2 cm (3 1/4 × 2 1/16 in.) (b),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287966,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2005.100.382 (79a, b)",false,true,287968,Photographs,Photograph,Coln Church + Mr Kent; 3 Miss Wallingtons,,,,,,Artist,,John Dillwyn Llewelyn,"British, Swansea, Wales 1810–1882 Swansea, Wales",,"Llewelyn, John Dillwyn",,1810,1882,1853–56,1853,1856,Albumen silver print,Image: 15.5 × 14.5 cm (6 1/8 × 5 11/16 in.) (a) Image: 8.1 × 6.7 cm (3 3/16 × 2 5/8 in.) (b),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287968,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2005.100.382 (80a, b)",false,true,287969,Photographs,Photograph,Amy & Essie Dillwyn; TSM Meggie & Mary,,,,,,Artist,,John Dillwyn Llewelyn,"British, Swansea, Wales 1810–1882 Swansea, Wales",,"Llewelyn, John Dillwyn",,1810,1882,1861,1861,1861,Salted paper print; albumen silver print,Image: 13.9 × 10.4 cm (5 1/2 × 4 1/8 in.) (a) Image: 6.8 × 5.5 cm (2 11/16 × 2 3/16 in.) (b),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287969,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1254,false,true,728494,Photographs,Photograph,The Wilderness Battlefield,,,,,,Artist|Artist,Possibly by,Alexander Gardner|Unknown,"American, Glasgow, Scotland 1821–1882 Washington, D.C.|American",,"Gardner, Alexander|Unknown",,1821,1882,1864,1864,1864,Albumen silver print from glass negative,Image: 12.8 × 8.7 cm (5 1/16 × 3 7/16 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/728494,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1253,false,true,728493,Photographs,Photograph,The Wilderness Battlefield,,,,,,Artist|Artist,Possibly by,Unknown|Alexander Gardner,"American|American, Glasgow, Scotland 1821–1882 Washington, D.C.",,"Unknown|Gardner, Alexander",,1821,1882,1864,1864,1864,Albumen silver print from glass negative,Image: 10.9 × 9.5 cm (4 5/16 × 3 3/4 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/728493,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1996.158.3,false,true,267330,Photographs,Photograph,Marius Bourotte,,,,,,Artist|Publisher,,Unknown|Le Petit Parisien,"French|French, active 1876–1944",,Unknown|Le Petit Parisien,,1876,1944,1929,1929,1929,Gelatin silver print with applied color,11.6 x 16.2 cm. (4 9/16 x 6 3/8 in.),"Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 1996",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267330,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1994.197.6 (1),false,true,289171,Photographs,Photographically illustrated book,Westminster Abbey,,,,,,Artist,,Nicolaas Henneman,"Dutch, Heemskerk 1813–1898 London",,"Henneman, Nicolaas",Dutch,1813,1898,before May 1845,1845,1846,Salted paper print from paper negative,,"Gift of Jean Horblit, in memory of Harrison D. Horblit, 1994",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289171,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.440,false,true,685780,Photographs,Carte-de-visite,[Jozef Israels],,,,,,Artist,,Willem Frederik Vinkenbos,"Dutch, Amsterdam 1831–1896 The Hague",,"Vinkenbos, Willem Frederik",Dutch,1831,1896,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685780,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.387.1–.11,false,true,283246,Photographs,Album,Série des Roses,,,,,,Artist|Person in Photograph,,Pierre-Louis Pierson|Countess Virginia Oldoini Verasis di Castiglione,"French, 1822–1913|1835–1899",,"Pierson, Pierre-Louis|Castiglione, di, Virginia Oldoini Verasis Countess",French,1822 |1835,1913 |1899,1895,1895,1895,Albumen silver print from glass negative,Approximately 14.3 x 9.9 cm (5 5/8 x 3 7/8 in.) each,"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Albums,,http://www.metmuseum.org/art/collection/search/283246,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.388.1–.13,false,true,286855,Photographs,Album,Série des Roses,,,,,,Artist|Person in Photograph,,Pierre-Louis Pierson|Countess Virginia Oldoini Verasis di Castiglione,"French, 1822–1913|1835–1899",,"Pierson, Pierre-Louis|Castiglione, di, Virginia Oldoini Verasis Countess",French,1822 |1835,1913 |1899,1895,1895,1895,Albumen silver print from glass negative,Approximately 14.3 x 9.9 cm (5 5/8 x 3 7/8 in.) each,"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Albums,,http://www.metmuseum.org/art/collection/search/286855,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.587.1–.29,false,true,286698,Photographs,Album,[Halévy Family Album],,,,,,Artist|Maker|Person in Photograph|Person in Photograph|Artist|Person in Photograph|Person in Photograph|Person in Photograph|Artist|Person in Photograph|Person in Photograph|Person in Photograph|Person in Photograph,Assembled by|Person in photograph|Person in photograph|Person in photograph|Person in photograph|Person in photograph|Person in photograph|Person in photograph|Person in photograph|Person in photograph,Hortense Howland|Ludovic Halévy|Henri-René-Albert-Guy-de Maupassant|Comtesse Laure de Chevigné|Marquis Alfred du Lau d'Allemans|Edgar Degas|Geneviève Halévy|Ludovic Halévy|Unknown|Charles Haas|Marquis Alfred du Lau d'Allemans|Louise Halévy|Émile Straus,"French, 1835–1920|French, 1834–1908|French, Dieppe, Normandy 1850–1893 Paris|1859–1936|1833–1919|French, Paris 1834–1917 Paris|French, 1849–1926|French, 1834–1908|French|1833–1902|1833–1919|French, 1847–1930",,"Howland, Hortense|Halévy, Ludovic|Maupassant, Henri-René-Albert-Guy-de|de Chevigné, Laure Comtesse|du Lau d'Allemans, Alfred Marquis|Degas, Edgar|Halévy, Geneviève|Halévy, Ludovic|Unknown|Haas, Charles|du Lau d'Allemans, Alfred Marquis|Halévy, Louise|Straus, Émile",French,1835 |1834 |1850 |1859 |1833 |1834 |1849 |1834 |1833 |1833 |1847,1920 |1908 |1893 |1936 |1919 |1917 |1926 |1908 |1902 |1919 |1930,1886–89,1886,1889,Gelatin silver print,Album: 10 1/4 x 13 x 1 5/8 inches Print sizes vary: 7.4 x 6.4 to 13 x 18.3,"Gilman Collection, Purchase, Mrs. Walter Annenberg and The Annenberg Foundation Gift, 2005",,,,,,,,,,,,Albums,,http://www.metmuseum.org/art/collection/search/286698,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.611,false,true,283101,Photographs,Photograph,Louis-Jacques-Mandé Daguerre,,,,,,Artist|Person in Photograph,,Pierre-Ambrose Richebourg|Louis-Jacques-Mandé Daguerre,"French, 1810–1893",,"Richebourg, Pierre-Ambrose|Daguerre, Louis-Jacques-Mandé",French,1810,1893,ca. 1844,1842,1846,Daguerreotype,8.9 x 7 cm (3 1/2 x 2 3/4 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283101,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.607.1,false,true,261799,Photographs,Photograph,"Boûlâk, Carrefour (Mosquée, Café, etc.)",,,,,,Artist|Printer,,Félix Teynard|Imprimerie Photographique de H. de Fonteny et Cie,"French, 1817–1892",,"Teynard, Félix|Imprimerie Photographique de H. de Fonteny et Cie",French,1817,1892,"1851–52, printed 1853–54",1851,1852,Salted paper print from paper negative,24.1 x 30.5 cm (9 1/2 x 12 in.),"Purchase, Lila Acheson Wallace Gift, 1976",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261799,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.607.2,false,true,261816,Photographs,Photograph,"Le Kaire, Mosquées d'Iscander-Pacha et du Sultan Haçan",,,,,,Artist|Printer,,Félix Teynard|Imprimerie Photographique de H. de Fonteny et Cie,"French, 1817–1892",,"Teynard, Félix|Imprimerie Photographique de H. de Fonteny et Cie",French,1817,1892,"1851–52, printed 1853–54",1851,1852,Salted paper print from paper negative,23.7 x 30.6 cm. (9 5/16 x 12 1/16 in.),"Purchase, Lila Acheson Wallace Gift, 1976",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261816,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.607.3,false,true,261827,Photographs,Photograph,"Le Kaire, Mosquée du Sultan Haçan (le Tombeau)",,,,,,Artist|Printer,,Félix Teynard|Imprimerie Photographique de H. de Fonteny et Cie,"French, 1817–1892",,"Teynard, Félix|Imprimerie Photographique de H. de Fonteny et Cie",French,1817,1892,"1851–52, printed 1853–54",1851,1852,Salted paper print from paper negative,25.2 x 30.5 cm. (9 15/16 x 12 in.),"Purchase, Lila Acheson Wallace Gift, 1976",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261827,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.607.4,false,true,261838,Photographs,Photograph,"Le Kaire, Mosquée Nâcéryeh",,,,,,Artist|Printer,,Félix Teynard|Imprimerie Photographique de H. de Fonteny et Cie,"French, 1817–1892",,"Teynard, Félix|Imprimerie Photographique de H. de Fonteny et Cie",French,1817,1892,"1851–52, printed 1853–54",1851,1852,Salted paper print from paper negative,24.3 x 30.4 cm. (9 9/16 x 12 in.),"Purchase, Lila Acheson Wallace Gift, 1976",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261838,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.607.5,false,true,261849,Photographs,Photograph,"Le Kaire, Mosquée d'Amrou - Intérieur - Côté du Sanctuaire",,,,,,Artist|Printer,,Félix Teynard|Imprimerie Photographique de H. de Fonteny et Cie,"French, 1817–1892",,"Teynard, Félix|Imprimerie Photographique de H. de Fonteny et Cie",French,1817,1892,"1851–52, printed 1853–54",1851,1852,Salted paper print from paper negative,24.0 x 30.5 cm. (9 7/16 x 12 in.),"Purchase, Lila Acheson Wallace Gift, 1976",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261849,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.607.6,false,true,261860,Photographs,Photograph,"Le Kaire, Tombeaux de Sultans Mamelouks",,,,,,Artist|Printer,,Félix Teynard|Imprimerie Photographique de H. de Fonteny et Cie,"French, 1817–1892",,"Teynard, Félix|Imprimerie Photographique de H. de Fonteny et Cie",French,1817,1892,"1851–52, printed 1853–54",1851,1852,Salted paper print from paper negative,24.3 x 30.7 cm. (9 9/16 x 12 1/16 in.),"Purchase, Lila Acheson Wallace Gift, 1976",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261860,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.607.7,false,true,261871,Photographs,Photograph,"Le Kaire, Cimetière des Mamelouks",,,,,,Artist|Printer,,Félix Teynard|Imprimerie Photographique de H. de Fonteny et Cie,"French, 1817–1892",,"Teynard, Félix|Imprimerie Photographique de H. de Fonteny et Cie",French,1817,1892,"1851–52, printed 1853–54",1851,1852,Salted paper print from paper negative,24.0 x 30.0 cm. (9 7/16 x 11 13/16 in.),"Purchase, Lila Acheson Wallace Gift, 1976",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261871,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.607.8,false,true,261882,Photographs,Photograph,"Djîzeh (Nécropole de Memphis), Sphinx et Pyramides",,,,,,Artist|Printer,,Félix Teynard|Imprimerie Photographique de H. de Fonteny et Cie,"French, 1817–1892",,"Teynard, Félix|Imprimerie Photographique de H. de Fonteny et Cie",French,1817,1892,"1851–52, printed 1853–54",1851,1852,Salted paper print from paper negative,23.9 x 30.0 cm. (9 7/16 x 11 13/16 in.),"Purchase, Lila Acheson Wallace Gift, 1976",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261882,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.607.9,false,true,261893,Photographs,Photograph,"Djîzeh (Nécropole de Memphis), Pyramide de Chéops (Grande Pyramide)",,,,,,Artist|Printer,,Félix Teynard|Imprimerie Photographique de H. de Fonteny et Cie,"French, 1817–1892",,"Teynard, Félix|Imprimerie Photographique de H. de Fonteny et Cie",French,1817,1892,"1851–52, printed 1853–54",1851,1852,Salted paper print from paper negative,24.4 x 30.1 cm. (9 5/8 x 11 7/8 in.),"Purchase, Lila Acheson Wallace Gift, 1976",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261893,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.607.10,false,true,261800,Photographs,Photograph,"Djîzeh (Nécropole de Memphis), Pyramide de Chéphren",,,,,,Artist|Printer,,Félix Teynard|Imprimerie Photographique de H. de Fonteny et Cie,"French, 1817–1892",,"Teynard, Félix|Imprimerie Photographique de H. de Fonteny et Cie",French,1817,1892,"1851–52, printed 1853–54",1851,1852,Salted paper print from paper negative,24.2 x 30.2 cm. (9 1/2 x 11 7/8 in.),"Purchase, Lila Acheson Wallace Gift, 1976",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261800,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.607.11,false,true,261807,Photographs,Photograph,"Abâzîz, Intérieur d'un Village Arabe",,,,,,Artist|Printer,,Félix Teynard|Imprimerie Photographique de H. de Fonteny et Cie,"French, 1817–1892",,"Teynard, Félix|Imprimerie Photographique de H. de Fonteny et Cie",French,1817,1892,"1851–52, printed 1853–54",1851,1852,Salted paper print from paper negative,24.0 x 30.3 cm. (9 7/16 x 11 15/16 in.),"Purchase, Lila Acheson Wallace Gift, 1976",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261807,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.607.12,false,true,261808,Photographs,Photograph,"El-Nâcérah, Dattiers, Rives du Nil et Barques",,,,,,Artist|Printer,,Félix Teynard|Imprimerie Photographique de H. de Fonteny et Cie,"French, 1817–1892",,"Teynard, Félix|Imprimerie Photographique de H. de Fonteny et Cie",French,1817,1892,"1851–52, printed 1853–54",1851,1852,Salted paper print from paper negative,23.2 x 23.9 cm. (9 1/8 x 9 7/16 in.),"Purchase, Lila Acheson Wallace Gift, 1976",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261808,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.607.13,false,true,261809,Photographs,Photograph,"Béni-Haçan, Architecture Hypogéene - Tombeau d'Amoneï",,,,,,Artist|Printer,,Félix Teynard|Imprimerie Photographique de H. de Fonteny et Cie,"French, 1817–1892",,"Teynard, Félix|Imprimerie Photographique de H. de Fonteny et Cie",French,1817,1892,"1851–52, printed 1853–54",1851,1852,Salted paper print from paper negative,24.5 x 30.3 cm. (9 5/8 x 11 15/16 in.),"Purchase, Lila Acheson Wallace Gift, 1976",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261809,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.607.14,false,true,261810,Photographs,Photograph,"Béni-Haçan, Architecture Hypogéene - Tombeau de Névothph",,,,,,Artist|Printer,,Félix Teynard|Imprimerie Photographique de H. de Fonteny et Cie,"French, 1817–1892",,"Teynard, Félix|Imprimerie Photographique de H. de Fonteny et Cie",French,1817,1892,"1851–52, printed 1853–54",1851,1852,Salted paper print from paper negative,23.8 x 30.4 cm. (9 3/8 x 12 in.),"Purchase, Lila Acheson Wallace Gift, 1976",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261810,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.607.15,false,true,261811,Photographs,Photograph,"Syout, Habitations Arabes sur le Bord du Nil",,,,,,Artist|Printer,,Félix Teynard|Imprimerie Photographique de H. de Fonteny et Cie,"French, 1817–1892",,"Teynard, Félix|Imprimerie Photographique de H. de Fonteny et Cie",French,1817,1892,"1851–52, printed 1853–54",1851,1852,Salted paper print from paper negative,23.9 x 30.0 cm. (9 7/16 x 11 13/16 in.),"Purchase, Lila Acheson Wallace Gift, 1976",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261811,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.607.16,false,true,261812,Photographs,Photograph,"Syout, Constructions Modernes - le Divan, etc.",,,,,,Artist|Printer,,Félix Teynard|Imprimerie Photographique de H. de Fonteny et Cie,"French, 1817–1892",,"Teynard, Félix|Imprimerie Photographique de H. de Fonteny et Cie",French,1817,1892,"1851–52, printed 1853–54",1851,1852,Salted paper print from paper negative,23.7 x 29.8 cm. (9 5/16 x 11 3/4 in.),"Purchase, Lila Acheson Wallace Gift, 1976",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261812,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.607.17,false,true,261813,Photographs,Photograph,"Syout, Pont Sur le Grand Canal",,,,,,Artist|Printer,,Félix Teynard|Imprimerie Photographique de H. de Fonteny et Cie,"French, 1817–1892",,"Teynard, Félix|Imprimerie Photographique de H. de Fonteny et Cie",French,1817,1892,"1851–52, printed 1853–54",1851,1852,Salted paper print from paper negative,23.7 x 30.4 cm. (9 5/16 x 12 in.),"Purchase, Lila Acheson Wallace Gift, 1976",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261813,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.607.18,false,true,261814,Photographs,Photograph,"Syout (Lycopolis), Statue Appartenant au Docteaur Cuny",,,,,,Artist|Printer,,Félix Teynard|Imprimerie Photographique de H. de Fonteny et Cie,"French, 1817–1892",,"Teynard, Félix|Imprimerie Photographique de H. de Fonteny et Cie",French,1817,1892,"1851–52, printed 1853–54",1851,1852,Salted paper print from paper negative,23.6 x 15.4 cm. (9 5/16 x 6 1/16 in.),"Purchase, Lila Acheson Wallace Gift, 1976",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261814,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.607.19,false,true,261815,Photographs,Photograph,"Syout, Caravansérail en Ruines",,,,,,Artist|Printer,,Félix Teynard|Imprimerie Photographique de H. de Fonteny et Cie,"French, 1817–1892",,"Teynard, Félix|Imprimerie Photographique de H. de Fonteny et Cie",French,1817,1892,"1851–52, printed 1853–54",1851,1852,Salted paper print from paper negative,24.7 x 30.3 cm. (9 3/4 x 11 15/16 in.),"Purchase, Lila Acheson Wallace Gift, 1976",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261815,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.607.20,false,true,261817,Photographs,Photograph,"Souâdj, Cimetière Musulman et Tombeau de Mouràd-Bey",,,,,,Artist|Printer,,Félix Teynard|Imprimerie Photographique de H. de Fonteny et Cie,"French, 1817–1892",,"Teynard, Félix|Imprimerie Photographique de H. de Fonteny et Cie",French,1817,1892,"1851–52, printed 1853–54",1851,1852,Salted paper print from paper negative,24.0 x 30.0 cm. (9 7/16 x 11 13/16 in.),"Purchase, Lila Acheson Wallace Gift, 1976",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261817,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.607.21,false,true,261818,Photographs,Photograph,"Souâdj, Tombeau de Mouràd-Bey - Entrée de la Mosquée",,,,,,Artist|Printer,,Félix Teynard|Imprimerie Photographique de H. de Fonteny et Cie,"French, 1817–1892",,"Teynard, Félix|Imprimerie Photographique de H. de Fonteny et Cie",French,1817,1892,"1851–52, printed 1853–54",1851,1852,Salted paper print from paper negative,23.9 x 30.5 cm. (9 7/16 x 12 in.),"Purchase, Lila Acheson Wallace Gift, 1976",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261818,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.607.22,false,true,261819,Photographs,Photograph,"Djirdjeh, Mosquée en Ruines Sur le Bord du Nil",,,,,,Artist|Printer,,Félix Teynard|Imprimerie Photographique de H. de Fonteny et Cie,"French, 1817–1892",,"Teynard, Félix|Imprimerie Photographique de H. de Fonteny et Cie",French,1817,1892,"1851–52, printed 1853–54",1851,1852,Salted paper print from paper negative,29.7 x 25.1 cm. (11 11/16 x 9 7/8 in.),"Purchase, Lila Acheson Wallace Gift, 1976",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261819,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.607.23,false,true,261820,Photographs,Photograph,"Dendérah (Tentyris), Temple d'Athôr - Vue Générale",,,,,,Artist|Printer,,Félix Teynard|Imprimerie Photographique de H. de Fonteny et Cie,"French, 1817–1892",,"Teynard, Félix|Imprimerie Photographique de H. de Fonteny et Cie",French,1817,1892,"1851–52, printed 1853–54",1851,1852,Salted paper print from paper negative,23.8 x 29.9 cm. (9 3/8 x 11 3/4 in.),"Purchase, Lila Acheson Wallace Gift, 1976",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261820,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.607.24,false,true,261821,Photographs,Photograph,"Dendérah (Tentyris), Temple d'Athôr - Face Postérieure",,,,,,Artist|Printer,,Félix Teynard|Imprimerie Photographique de H. de Fonteny et Cie,"French, 1817–1892",,"Teynard, Félix|Imprimerie Photographique de H. de Fonteny et Cie",French,1817,1892,"1851–52, printed 1853–54",1851,1852,Salted paper print from paper negative,24.7 x 30.0 cm. (9 3/4 x 11 13/16 in.),"Purchase, Lila Acheson Wallace Gift, 1976",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261821,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.607.25,false,true,261822,Photographs,Photograph,"Dendérah (Tentyris), Temple d'Athôr - Face Postérieure - Cléopatre et Cæsarion",,,,,,Artist|Printer,,Félix Teynard|Imprimerie Photographique de H. de Fonteny et Cie,"French, 1817–1892",,"Teynard, Félix|Imprimerie Photographique de H. de Fonteny et Cie",French,1817,1892,"1851–52, printed 1853–54",1851,1852,Salted paper print from paper negative,30.0 x 25.4 cm. (11 13/16 x 10 in.),"Purchase, Lila Acheson Wallace Gift, 1976",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261822,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.607.26,false,true,261823,Photographs,Photograph,"Dendérah (Tentyris), Temple d'Athôr - Sanctuaire Placé a l'Angle Sud-Ouest de la Plateforme Inférieure",,,,,,Artist|Printer,,Félix Teynard|Imprimerie Photographique de H. de Fonteny et Cie,"French, 1817–1892",,"Teynard, Félix|Imprimerie Photographique de H. de Fonteny et Cie",French,1817,1892,"1851–52, printed 1853–54",1851,1852,Salted paper print from paper negative,24.1 x 30.8 cm. (9 1/2 x 12 1/8 in.),"Purchase, Lila Acheson Wallace Gift, 1976",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261823,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.607.27,false,true,261824,Photographs,Photograph,"Dendérah (Tentyris), Mammisi - Décoration Extérieure de la Face Sud",,,,,,Artist|Printer,,Félix Teynard|Imprimerie Photographique de H. de Fonteny et Cie,"French, 1817–1892",,"Teynard, Félix|Imprimerie Photographique de H. de Fonteny et Cie",French,1817,1892,"1851–52, printed 1853–54",1851,1852,Salted paper print from paper negative,24.0 x 30.0 cm. (9 7/16 x 11 13/16 in.),"Purchase, Lila Acheson Wallace Gift, 1976",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261824,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.607.28,false,true,261825,Photographs,Photograph,"Louksor, Petit Bras du Nil - Barque de Voyageurs",,,,,,Artist|Printer,,Félix Teynard|Imprimerie Photographique de H. de Fonteny et Cie,"French, 1817–1892",,"Teynard, Félix|Imprimerie Photographique de H. de Fonteny et Cie",French,1817,1892,"1851–52, printed 1853–54",1851,1852,Salted paper print from paper negative,24.4 x 30.7 cm. (9 5/8 x 12 1/16 in.),"Purchase, Lila Acheson Wallace Gift, 1976",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261825,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.607.29,false,true,261826,Photographs,Photograph,"Louksor (Thèbes), Vue Générale des Ruines",,,,,,Artist|Printer,,Félix Teynard|Imprimerie Photographique de H. de Fonteny et Cie,"French, 1817–1892",,"Teynard, Félix|Imprimerie Photographique de H. de Fonteny et Cie",French,1817,1892,"1851–52, printed 1853–54",1851,1852,Salted paper print from paper negative,24.7 x 30.1 cm. (9 3/4 x 11 7/8 in.),"Purchase, Lila Acheson Wallace Gift, 1976",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261826,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.607.30,false,true,261828,Photographs,Photograph,"Louksor (Thèbes), Construction Antérieure - Pylône, Colosses et Obélisque",,,,,,Artist|Printer,,Félix Teynard|Imprimerie Photographique de H. de Fonteny et Cie,"French, 1817–1892",,"Teynard, Félix|Imprimerie Photographique de H. de Fonteny et Cie",French,1817,1892,"1851–52, printed 1853–54",1851,1852,Salted paper print from paper negative,23.8 x 29.9 cm. (9 3/8 x 11 3/4 in.),"Purchase, Lila Acheson Wallace Gift, 1976",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261828,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.607.31,false,true,261829,Photographs,Photograph,Louksor (Thèbes). Construction Centrale - Grande Colonnade,,,,,,Artist|Printer,,Félix Teynard|Imprimerie Photographique de H. de Fonteny et Cie,"French, 1817–1892",,"Teynard, Félix|Imprimerie Photographique de H. de Fonteny et Cie",French,1817,1892,"1851–52, printed 1853–54",1851,1852,Salted paper print from paper negative,24.8 x 30.3 cm. (9 3/4 x 11 15/16 in.),"Purchase, Lila Acheson Wallace Gift, 1976",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261829,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.607.32,false,true,261830,Photographs,Photograph,"Louksor (Thèbes), Construction Postérieure - Galeries Parallèles",,,,,,Artist|Printer,,Félix Teynard|Imprimerie Photographique de H. de Fonteny et Cie,"French, 1817–1892",,"Teynard, Félix|Imprimerie Photographique de H. de Fonteny et Cie",French,1817,1892,"1851–52, printed 1853–54",1851,1852,Salted paper print from paper negative,23.2 x 29.7 cm. (9 1/8 x 11 11/16 in.),"Purchase, Lila Acheson Wallace Gift, 1976",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261830,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.607.33,false,true,261831,Photographs,Photograph,"Louksor, Dattiers et Jardin de l'Expédition du Louksor",,,,,,Artist|Printer,,Félix Teynard|Imprimerie Photographique de H. de Fonteny et Cie,"French, 1817–1892",,"Teynard, Félix|Imprimerie Photographique de H. de Fonteny et Cie",French,1817,1892,"1851–52, printed 1853–54",1851,1852,Salted paper print from paper negative,23.7 x 30.3 cm. (9 5/16 x 11 15/16 in.),"Purchase, Lila Acheson Wallace Gift, 1976",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261831,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.607.34,false,true,261832,Photographs,Photograph,"Médînet-Abou (Thèbes), Construction Antérieures - Vue Générale",,,,,,Artist|Printer,,Félix Teynard|Imprimerie Photographique de H. de Fonteny et Cie,"French, 1817–1892",,"Teynard, Félix|Imprimerie Photographique de H. de Fonteny et Cie",French,1817,1892,"1851–52, printed 1853–54",1851,1852,Salted paper print from paper negative,24.2 x 30.2 cm. (9 1/2 x 11 7/8 in.),"Purchase, Lila Acheson Wallace Gift, 1976",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261832,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.607.35,false,true,261833,Photographs,Photograph,"Médînet-Abou (Thèbes), Construction Antérieures - Entrée Principale",,,,,,Artist|Printer,,Félix Teynard|Imprimerie Photographique de H. de Fonteny et Cie,"French, 1817–1892",,"Teynard, Félix|Imprimerie Photographique de H. de Fonteny et Cie",French,1817,1892,"1851–52, printed 1853–54",1851,1852,Salted paper print from paper negative,24.5 x 30.5 cm. (9 5/8 x 12 in.),"Purchase, Lila Acheson Wallace Gift, 1976",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261833,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.607.36,false,true,261834,Photographs,Photograph,"Médînet-Abou (Thèbes), Constructions Postérieures - Fragment de Sculptures Sur la Face Nord-Est",,,,,,Artist|Printer,,Félix Teynard|Imprimerie Photographique de H. de Fonteny et Cie,"French, 1817–1892",,"Teynard, Félix|Imprimerie Photographique de H. de Fonteny et Cie",French,1817,1892,"1851–52, printed 1853–54",1851,1852,Salted paper print from paper negative,24.6 x 30.1 cm. (9 11/16 x 11 7/8 in.),"Purchase, Lila Acheson Wallace Gift, 1976",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261834,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.607.37,false,true,261835,Photographs,Photograph,Médînet-Abou (Thèbes). Constructions Postérieures - Deuxiéme Cour,,,,,,Artist|Printer,,Félix Teynard|Imprimerie Photographique de H. de Fonteny et Cie,"French, 1817–1892",,"Teynard, Félix|Imprimerie Photographique de H. de Fonteny et Cie",French,1817,1892,"1851–52, printed 1853–54",1851,1852,Salted paper print from paper negative,25.0 x 30.3 cm. (9 13/16 x 11 15/16 in.),"Purchase, Lila Acheson Wallace Gift, 1976",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261835,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.607.38,false,true,261836,Photographs,Photograph,"Médînet-Abou (Thèbes), Constructions Postérieures - Deuxieme Cour - Galerie Nord-Ouest",,,,,,Artist|Printer,,Félix Teynard|Imprimerie Photographique de H. de Fonteny et Cie,"French, 1817–1892",,"Teynard, Félix|Imprimerie Photographique de H. de Fonteny et Cie",French,1817,1892,"1851–52, printed 1853–54",1851,1852,Salted paper print from paper negative,30.3 x 24.6 cm. (11 15/16 x 9 11/16 in.),"Purchase, Lila Acheson Wallace Gift, 1976",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261836,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.607.39,false,true,261837,Photographs,Photograph,"Médînet-Abou (Thèbes), Construction Postérieures - Deuxiéme Cour - Galerie Sud-Ouest",,,,,,Artist|Printer,,Félix Teynard|Imprimerie Photographique de H. de Fonteny et Cie,"French, 1817–1892",,"Teynard, Félix|Imprimerie Photographique de H. de Fonteny et Cie",French,1817,1892,"1851–52, printed 1853–54",1851,1852,Salted paper print from paper negative,30.5 x 25.1 cm. (12 x 9 7/8 in.),"Purchase, Lila Acheson Wallace Gift, 1976",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261837,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.607.40,false,true,261839,Photographs,Photograph,"Gournah (Thèbes), Colosses (Celui de Droite, Dit de Memnon)",,,,,,Artist|Printer,,Félix Teynard|Imprimerie Photographique de H. de Fonteny et Cie,"French, 1817–1892",,"Teynard, Félix|Imprimerie Photographique de H. de Fonteny et Cie",French,1817,1892,"1851–52, printed 1853–54",1851,1852,Salted paper print from paper negative,23.9 x 30.5 cm. (9 7/16 x 12 in.),"Purchase, Lila Acheson Wallace Gift, 1976",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261839,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.607.41,false,true,261840,Photographs,Photograph,"Gournah (Thèbes), Colosse de Gauche - Décoration de la Face Nord-Est du Trône",,,,,,Artist|Printer,,Félix Teynard|Imprimerie Photographique de H. de Fonteny et Cie,"French, 1817–1892",,"Teynard, Félix|Imprimerie Photographique de H. de Fonteny et Cie",French,1817,1892,"1851–52, printed 1853–54",1851,1852,Salted paper print from paper negative,24.4 x 30.0 cm. (9 5/8 x 11 13/16 in.),"Purchase, Lila Acheson Wallace Gift, 1976",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261840,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.607.42,false,true,261841,Photographs,Photograph,"Gournah (Thèbes), Palais Dit le Memnonium",,,,,,Artist|Printer,,Félix Teynard|Imprimerie Photographique de H. de Fonteny et Cie,"French, 1817–1892",,"Teynard, Félix|Imprimerie Photographique de H. de Fonteny et Cie",French,1817,1892,"1851–52, printed 1853–54",1851,1852,Salted paper print from paper negative,24.5 x 30.1 cm. (9 5/8 x 11 7/8 in.),"Purchase, Lila Acheson Wallace Gift, 1976",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261841,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.607.43,false,true,261842,Photographs,Photograph,"Karnak, Groupe de Dattiers Vu du Point A",,,,,,Artist|Printer,,Félix Teynard|Imprimerie Photographique de H. de Fonteny et Cie,"French, 1817–1892",,"Teynard, Félix|Imprimerie Photographique de H. de Fonteny et Cie",French,1817,1892,"1851–52, printed 1853–54",1851,1852,Salted paper print from paper negative,24.5 x 30.9 cm. (9 5/8 x 12 3/16 in.),"Purchase, Lila Acheson Wallace Gift, 1976",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261842,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.607.44,false,true,261843,Photographs,Photograph,"Karnak (Thèbes), Vue Générale des Ruines Prise du Point B",,,,,,Artist|Printer,,Félix Teynard|Imprimerie Photographique de H. de Fonteny et Cie,"French, 1817–1892",,"Teynard, Félix|Imprimerie Photographique de H. de Fonteny et Cie",French,1817,1892,"1851–52, printed 1853–54",1851,1852,Salted paper print from paper negative,23.6 x 30.4 cm. (9 5/16 x 12 in.),"Purchase, Lila Acheson Wallace Gift, 1976",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261843,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.607.45,false,true,261844,Photographs,Photograph,"Karnak (Thèbes), Grande Porte du Sud Vue du Point C",,,,,,Artist|Printer,,Félix Teynard|Imprimerie Photographique de H. de Fonteny et Cie,"French, 1817–1892",,"Teynard, Félix|Imprimerie Photographique de H. de Fonteny et Cie",French,1817,1892,"1851–52, printed 1853–54",1851,1852,Salted paper print from paper negative,24.0 x 30.3 cm. (9 7/16 x 11 15/16 in.),"Purchase, Lila Acheson Wallace Gift, 1976",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261844,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.607.46,false,true,261845,Photographs,Photograph,"Karnak (Thèbes), Vue Générale des Ruines Prise du Sud-Est, en T",,,,,,Artist|Printer,,Félix Teynard|Imprimerie Photographique de H. de Fonteny et Cie,"French, 1817–1892",,"Teynard, Félix|Imprimerie Photographique de H. de Fonteny et Cie",French,1817,1892,"1851–52, printed 1853–54",1851,1852,Salted paper print from paper negative,23.6 x 30.5 cm. (9 5/16 x 12 in.),"Purchase, Lila Acheson Wallace Gift, 1976",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261845,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.607.47,false,true,261846,Photographs,Photograph,"Karnak (Thèbes), Vue Générale des Ruines Prise du Nord-Est, en V",,,,,,Artist|Printer,,Félix Teynard|Imprimerie Photographique de H. de Fonteny et Cie,"French, 1817–1892",,"Teynard, Félix|Imprimerie Photographique de H. de Fonteny et Cie",French,1817,1892,"1851–52, printed 1853–54",1851,1852,Salted paper print from paper negative,24.3 x 30.4 cm. (9 9/16 x 12 in.),"Purchase, Lila Acheson Wallace Gift, 1976",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261846,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.607.48,false,true,261847,Photographs,Photograph,"Karnak (Thèbes), Enciente du Palais Vue du Point H",,,,,,Artist|Printer,,Félix Teynard|Imprimerie Photographique de H. de Fonteny et Cie,"French, 1817–1892",,"Teynard, Félix|Imprimerie Photographique de H. de Fonteny et Cie",French,1817,1892,"1851–52, printed 1853–54",1851,1852,Salted paper print from paper negative,23.8 x 30.7 cm (9 3/8 x 12 1/16 in.),"Purchase, Lila Acheson Wallace Gift, 1976",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261847,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.607.49,false,true,261848,Photographs,Photograph,"Karnak (Thèbes), Enciente du Palais - Détailes de Sculptures au Point N",,,,,,Artist|Printer,,Félix Teynard|Imprimerie Photographique de H. de Fonteny et Cie,"French, 1817–1892",,"Teynard, Félix|Imprimerie Photographique de H. de Fonteny et Cie",French,1817,1892,"1851–52, printed 1853–54",1851,1852,Salted paper print from paper negative,24.2 x 30.1 cm. (9 1/2 x 11 7/8 in.),"Purchase, Lila Acheson Wallace Gift, 1976",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261848,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.607.50,false,true,261850,Photographs,Photograph,"Karnak (Thèbes), Enciente du Palais - Détails de Sculptures au Point O",,,,,,Artist|Printer,,Félix Teynard|Imprimerie Photographique de H. de Fonteny et Cie,"French, 1817–1892",,"Teynard, Félix|Imprimerie Photographique de H. de Fonteny et Cie",French,1817,1892,"1851–52, printed 1853–54",1851,1852,Salted paper print from paper negative,24.5 x 30.2 cm. (9 5/8 x 11 7/8 in.),"Purchase, Lila Acheson Wallace Gift, 1976",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261850,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.607.51,false,true,261851,Photographs,Photograph,"Karnak (Thèbes), Cour du Palais - Vue Prise de Point I",,,,,,Artist|Printer,,Félix Teynard|Imprimerie Photographique de H. de Fonteny et Cie,"French, 1817–1892",,"Teynard, Félix|Imprimerie Photographique de H. de Fonteny et Cie",French,1817,1892,"1851–52, printed 1853–54",1851,1852,Salted paper print from paper negative,30.7 x 25.2 cm. (12 1/16 x 9 15/16 in.),"Purchase, Lila Acheson Wallace Gift, 1976",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261851,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.607.52,false,true,261852,Photographs,Photograph,"Karnak (Thèbes), Palais - Salle Hypostyle - Vue Générale Prise du Point Q",,,,,,Artist|Printer,,Félix Teynard|Imprimerie Photographique de H. de Fonteny et Cie,"French, 1817–1892",,"Teynard, Félix|Imprimerie Photographique de H. de Fonteny et Cie",French,1817,1892,"1851–52, printed 1853–54",1851,1852,Salted paper print from paper negative,24.7 x 30.5 cm. (9 3/4 x 12 in.),"Purchase, Lila Acheson Wallace Gift, 1976",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261852,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.607.53,false,true,261853,Photographs,Photograph,"Karnak (Thèbes), Palais - Salle Hypostyle - Colonnade Centrale Vue du Point J",,,,,,Artist|Printer,,Félix Teynard|Imprimerie Photographique de H. de Fonteny et Cie,"French, 1817–1892",,"Teynard, Félix|Imprimerie Photographique de H. de Fonteny et Cie",French,1817,1892,"1851–52, printed 1853–54",1851,1852,Salted paper print from paper negative,30.2 x 24.4 cm. (11 7/8 x 9 5/8 in.),"Purchase, Lila Acheson Wallace Gift, 1976",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261853,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.607.54,false,true,261854,Photographs,Photograph,"Karnak (Thèbes), Palais - Salle Hypostyle - Colonnade Centrale - Décoration d'un Fut",,,,,,Artist|Printer,,Félix Teynard|Imprimerie Photographique de H. de Fonteny et Cie,"French, 1817–1892",,"Teynard, Félix|Imprimerie Photographique de H. de Fonteny et Cie",French,1817,1892,"1851–52, printed 1853–54",1851,1852,Salted paper print from paper negative,25.0 x 16.3 cm. (9 13/16 x 6 7/16 in.),"Purchase, Lila Acheson Wallace Gift, 1976",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261854,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.607.55,false,true,261855,Photographs,Photograph,"Karnak (Thèbes), Palais - Salle Hypostyle - Colonnade Centrale - Chapiteaux",,,,,,Artist|Printer,,Félix Teynard|Imprimerie Photographique de H. de Fonteny et Cie,"French, 1817–1892",,"Teynard, Félix|Imprimerie Photographique de H. de Fonteny et Cie",French,1817,1892,"1851–52, printed 1853–54",1851,1852,Salted paper print from paper negative,30.3 x 25.4 cm. (11 15/16 x 10 in.),"Purchase, Lila Acheson Wallace Gift, 1976",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261855,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.607.56,false,true,261856,Photographs,Photograph,"Karnak (Thèbes), Palais - Salle Hypostyle - Fenêtre et Chapiteaux des Galleries Latérales",,,,,,Artist|Printer,,Félix Teynard|Imprimerie Photographique de H. de Fonteny et Cie,"French, 1817–1892",,"Teynard, Félix|Imprimerie Photographique de H. de Fonteny et Cie",French,1817,1892,"1851–52, printed 1853–54",1851,1852,Salted paper print from paper negative,30.2 x 25.0 cm (11 7/8 x 9 13/16 in.),"Purchase, Lila Acheson Wallace Gift, 1976",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261856,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.607.57,false,true,261857,Photographs,Photograph,"Karnak (Thèbes), Palais - Salle Hypostyle - Fenêtre et Chapiteaux des Galeries Latérales",,,,,,Artist|Printer,,Félix Teynard|Imprimerie Photographique de H. de Fonteny et Cie,"French, 1817–1892",,"Teynard, Félix|Imprimerie Photographique de H. de Fonteny et Cie",French,1817,1892,"1851–52, printed 1853–54",1851,1852,Salted paper print from paper negative,30.7 x 25.5 cm. (12 1/16 x 10 1/16 in.),"Purchase, Lila Acheson Wallace Gift, 1976",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261857,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.607.58,false,true,261858,Photographs,Photograph,"Karnak (Thèbes), Palais - Salle Hypostyle - Décoration de la Paroi Intérieure au Point L",,,,,,Artist|Printer,,Félix Teynard|Imprimerie Photographique de H. de Fonteny et Cie,"French, 1817–1892",,"Teynard, Félix|Imprimerie Photographique de H. de Fonteny et Cie",French,1817,1892,"1851–52, printed 1853–54",1851,1852,Salted paper print from paper negative,30.1 x 25.0 cm. (11 7/8 x 9 13/16 in.),"Purchase, Lila Acheson Wallace Gift, 1976",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261858,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.607.59,false,true,261859,Photographs,Photograph,"Karnak (Thèbes), Palais - Salle Hypostyle - Décoration de la Paroi Intérieure au Point M",,,,,,Artist|Printer,,Félix Teynard|Imprimerie Photographique de H. de Fonteny et Cie,"French, 1817–1892",,"Teynard, Félix|Imprimerie Photographique de H. de Fonteny et Cie",French,1817,1892,"1851–52, printed 1853–54",1851,1852,Salted paper print from paper negative,23.3 x 30.5 cm. (9 3/16 x 12 in.),"Purchase, Lila Acheson Wallace Gift, 1976",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261859,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.607.60,false,true,261861,Photographs,Photograph,"Karnak (Thèbes), Palais - Construction de Granit - Pilier Sculpté, au Point P",,,,,,Artist|Printer,,Félix Teynard|Imprimerie Photographique de H. de Fonteny et Cie,"French, 1817–1892",,"Teynard, Félix|Imprimerie Photographique de H. de Fonteny et Cie",French,1817,1892,"1851–52, printed 1853–54",1851,1852,Salted paper print from paper negative,26.8 x 13.5 cm. (10 9/16 x 5 5/16 in.),"Purchase, Lila Acheson Wallace Gift, 1976",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261861,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.607.61,false,true,261862,Photographs,Photograph,"Karnak (Thèbes), Palais - Construction de Granit - Décoration Sculptée et Piente au Point R",,,,,,Artist|Printer,,Félix Teynard|Imprimerie Photographique de H. de Fonteny et Cie,"French, 1817–1892",,"Teynard, Félix|Imprimerie Photographique de H. de Fonteny et Cie",French,1817,1892,"1851–52, printed 1853–54",1851,1852,Salted paper print from paper negative,23.7 x 30.5 cm. (9 5/16 x 12 in.),"Purchase, Lila Acheson Wallace Gift, 1976",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261862,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.607.62,false,true,261863,Photographs,Photograph,"Karnak (Thèbes), Palais - Partie Posterieure - Fragment de Colonnades Vu du Point S",,,,,,Artist|Printer,,Félix Teynard|Imprimerie Photographique de H. de Fonteny et Cie,"French, 1817–1892",,"Teynard, Félix|Imprimerie Photographique de H. de Fonteny et Cie",French,1817,1892,"1851–52, printed 1853–54",1851,1852,Salted paper print from paper negative,23.9 x 30.6 cm. (9 7/16 x 12 1/16 in.),"Purchase, Lila Acheson Wallace Gift, 1976",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261863,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.607.63,false,true,261864,Photographs,Photograph,"Karnak (Thèbes), Édifice en Ruines - Sculptures du la Paroi Intèrieure, en U",,,,,,Artist|Printer,,Félix Teynard|Imprimerie Photographique de H. de Fonteny et Cie,"French, 1817–1892",,"Teynard, Félix|Imprimerie Photographique de H. de Fonteny et Cie",French,1817,1892,"1851–52, printed 1853–54",1851,1852,Salted paper print from paper negative,24.9 x 30.3 cm. (9 13/16 x 11 15/16 in.),"Purchase, Lila Acheson Wallace Gift, 1976",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261864,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.607.64,false,true,261865,Photographs,Photograph,"Karnak (Thèbes), Grande Porte du Nord Vue du Point X",,,,,,Artist|Printer,,Félix Teynard|Imprimerie Photographique de H. de Fonteny et Cie,"French, 1817–1892",,"Teynard, Félix|Imprimerie Photographique de H. de Fonteny et Cie",French,1817,1892,"1851–52, printed 1853–54",1851,1852,Salted paper print from paper negative,23.7 x 30.6 cm. (9 5/16 x 12 1/16 in.),"Purchase, Lila Acheson Wallace Gift, 1976",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261865,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.607.65,false,true,261866,Photographs,Photograph,"Karnak (Thèbes), Troisième Pylône - Colosse de Spath Calcaire, en D",,,,,,Artist|Printer,,Félix Teynard|Imprimerie Photographique de H. de Fonteny et Cie,"French, 1817–1892",,"Teynard, Félix|Imprimerie Photographique de H. de Fonteny et Cie",French,1817,1892,"1851–52, printed 1853–54",1851,1852,Salted paper print from paper negative,24.0 x 30.3 cm. (9 7/16 x 11 15/16 in.),"Purchase, Lila Acheson Wallace Gift, 1976",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261866,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.607.66,false,true,261867,Photographs,Photograph,"Karnak (Thèbes), Premier Pylône - Ruines de la Porte et des Colosses, Vues du Point E",,,,,,Artist|Printer,,Félix Teynard|Imprimerie Photographique de H. de Fonteny et Cie,"French, 1817–1892",,"Teynard, Félix|Imprimerie Photographique de H. de Fonteny et Cie",French,1817,1892,"1851–52, printed 1853–54",1851,1852,Salted paper print from paper negative,24.3 x 30.5 cm. (9 9/16 x 12 in.),"Purchase, Lila Acheson Wallace Gift, 1976",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261867,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.607.67,false,true,261868,Photographs,Photograph,"Karnak (Thèbes), Avenue de Sphinx - Vue Générale Prise du Point G",,,,,,Artist|Printer,,Félix Teynard|Imprimerie Photographique de H. de Fonteny et Cie,"French, 1817–1892",,"Teynard, Félix|Imprimerie Photographique de H. de Fonteny et Cie",French,1817,1892,"1851–52, printed 1853–54",1851,1852,Salted paper print from paper negative,24.1 x 30.5 cm. (9 1/2 x 12 in.),"Purchase, Lila Acheson Wallace Gift, 1976",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261868,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.607.68,false,true,261869,Photographs,Photograph,"Karnak (Thèbes), Sphinx a Tête Humaine et a Tête de Bélier, en Y",,,,,,Artist|Printer,,Félix Teynard|Imprimerie Photographique de H. de Fonteny et Cie,"French, 1817–1892",,"Teynard, Félix|Imprimerie Photographique de H. de Fonteny et Cie",French,1817,1892,"1851–52, printed 1853–54",1851,1852,Salted paper print from paper negative,24.0 x 30.6 cm. (9 7/16 x 12 1/16 in.),"Purchase, Lila Acheson Wallace Gift, 1976",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261869,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.607.69,false,true,261870,Photographs,Photograph,"Erment (Hermonthis), Vue Générale des Ruines -Temple et Mammisi",,,,,,Artist|Printer,,Félix Teynard|Imprimerie Photographique de H. de Fonteny et Cie,"French, 1817–1892",,"Teynard, Félix|Imprimerie Photographique de H. de Fonteny et Cie",French,1817,1892,"1851–52, printed 1853–54",1851,1852,Salted paper print from paper negative,24.3 x 30.2 cm. (9 9/16 x 11 7/8 in.),"Purchase, Lila Acheson Wallace Gift, 1976",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261870,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.607.70,false,true,261872,Photographs,Photograph,"Esneh (Latopolis), Construction Ensablée - Paroi Extérieure - Corniche et Sculptures",,,,,,Artist|Printer,,Félix Teynard|Imprimerie Photographique de H. de Fonteny et Cie,"French, 1817–1892",,"Teynard, Félix|Imprimerie Photographique de H. de Fonteny et Cie",French,1817,1892,"1851–52, printed 1853–54",1851,1852,Salted paper print from paper negative,23.8 x 30.5 cm. (9 3/8 x 12 in.),"Purchase, Lila Acheson Wallace Gift, 1976",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261872,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.607.71,false,true,261873,Photographs,Photograph,"Esneh (Latopolis), Construction Ensablée - Architrave, Futs, et Chapiteaux",,,,,,Artist|Printer,,Félix Teynard|Imprimerie Photographique de H. de Fonteny et Cie,"French, 1817–1892",,"Teynard, Félix|Imprimerie Photographique de H. de Fonteny et Cie",French,1817,1892,"1851–52, printed 1853–54",1851,1852,Salted paper print from paper negative,25.0 x 30.7 cm. (9 13/16 x 12 1/16 in.),"Purchase, Lila Acheson Wallace Gift, 1976",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261873,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.607.72,false,true,261874,Photographs,Photograph,"Esneh, Dattiers, Sycomore et Café Sur le Bord du Nil",,,,,,Artist|Printer,,Félix Teynard|Imprimerie Photographique de H. de Fonteny et Cie,"French, 1817–1892",,"Teynard, Félix|Imprimerie Photographique de H. de Fonteny et Cie",French,1817,1892,"1851–52, printed 1853–54",1851,1852,Salted paper print from paper negative,23.8 x 30.5 cm. (9 3/8 x 12 in.),"Purchase, Lila Acheson Wallace Gift, 1976",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261874,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.607.73,false,true,261875,Photographs,Photograph,"El-Kab (Éléthya), Vue Générale de l'Hémi-Spéos",,,,,,Artist|Printer,,Félix Teynard|Imprimerie Photographique de H. de Fonteny et Cie,"French, 1817–1892",,"Teynard, Félix|Imprimerie Photographique de H. de Fonteny et Cie",French,1817,1892,"1851–52, printed 1853–54",1851,1852,Salted paper print from paper negative,23.9 x 30.5 cm. (9 7/16 x 12 in.),"Purchase, Lila Acheson Wallace Gift, 1976",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261875,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.607.74,false,true,261876,Photographs,Photograph,"El-Kab (Éléthya), Architecture Hypogéenne - Tombeau de Phapé - Sculptures Pientes",,,,,,Artist|Printer,,Félix Teynard|Imprimerie Photographique de H. de Fonteny et Cie,"French, 1817–1892",,"Teynard, Félix|Imprimerie Photographique de H. de Fonteny et Cie",French,1817,1892,"1851–52, printed 1853–54",1851,1852,Salted paper print from paper negative,25.2 x 30.2 cm. (9 15/16 x 11 7/8 in.),"Purchase, Lila Acheson Wallace Gift, 1976",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261876,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.607.75,false,true,261877,Photographs,Photograph,"Edfou (Apollonopolis Magna), Vue Générale du Temple",,,,,,Artist|Printer,,Félix Teynard|Imprimerie Photographique de H. de Fonteny et Cie,"French, 1817–1892",,"Teynard, Félix|Imprimerie Photographique de H. de Fonteny et Cie",French,1817,1892,"1851–52, printed 1853–54",1851,1852,Salted paper print from paper negative,23.9 x 30.4 cm. (9 7/16 x 12 in.),"Purchase, Lila Acheson Wallace Gift, 1976",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261877,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.607.76,false,true,261878,Photographs,Photograph,"Edfou (Apollonopolis Magna), Galerie Latérale de la Cour et Pronaos",,,,,,Artist|Printer,,Félix Teynard|Imprimerie Photographique de H. de Fonteny et Cie,"French, 1817–1892",,"Teynard, Félix|Imprimerie Photographique de H. de Fonteny et Cie",French,1817,1892,"1851–52, printed 1853–54",1851,1852,Salted paper print from paper negative,24.7 x 30.8 cm. (9 3/4 x 12 1/8 in.),"Purchase, Lila Acheson Wallace Gift, 1976",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261878,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.607.77,false,true,261879,Photographs,Photograph,"Edfou, Aspect Générale de la Ville Vue de la Plateforme Centrale du Pylône",,,,,,Artist|Printer,,Félix Teynard|Imprimerie Photographique de H. de Fonteny et Cie,"French, 1817–1892",,"Teynard, Félix|Imprimerie Photographique de H. de Fonteny et Cie",French,1817,1892,"1851–52, printed 1853–54",1851,1852,Salted paper print from paper negative,30.5 x 25.0 cm. (12 x 9 13/16 in.),"Purchase, Lila Acheson Wallace Gift, 1976",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261879,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.607.78,false,true,261880,Photographs,Photograph,"Djébel Selséleh (Silsilis), Steles Architecturales Taillées Dans les Carriéres",,,,,,Artist|Printer,,Félix Teynard|Imprimerie Photographique de H. de Fonteny et Cie,"French, 1817–1892",,"Teynard, Félix|Imprimerie Photographique de H. de Fonteny et Cie",French,1817,1892,"1851–52, printed 1853–54",1851,1852,Salted paper print from paper negative,23.5 x 30.3 cm. (9 1/4 x 11 15/16 in.),"Purchase, Lila Acheson Wallace Gift, 1976",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261880,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.607.79,false,true,261881,Photographs,Photograph,"Kôm-Ombou (Ombos), Vue Générale des Ruines",,,,,,Artist|Printer,,Félix Teynard|Imprimerie Photographique de H. de Fonteny et Cie,"French, 1817–1892",,"Teynard, Félix|Imprimerie Photographique de H. de Fonteny et Cie",French,1817,1892,"1851–52, printed 1853–54",1851,1852,Salted paper print from paper negative,23.8 x 30.7 cm. (9 3/8 x 12 1/16 in.),"Purchase, Lila Acheson Wallace Gift, 1976",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261881,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.607.80,false,true,261883,Photographs,Photograph,"Assouan, Ruines de l'Ancienne Enciente Arabe, au Sud-Est de la Ville",,,,,,Artist|Printer,,Félix Teynard|Imprimerie Photographique de H. de Fonteny et Cie,"French, 1817–1892",,"Teynard, Félix|Imprimerie Photographique de H. de Fonteny et Cie",French,1817,1892,"1851–52, printed 1853–54",1851,1852,Salted paper print from paper negative,23.8 x 30.4 cm. (9 3/8 x 12 in.),"Purchase, Lila Acheson Wallace Gift, 1976",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261883,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.607.81,false,true,261884,Photographs,Photograph,"Assouan (Syène), Carrières de Granit - Ancien Système d'Extraction",,,,,,Artist|Printer,,Félix Teynard|Imprimerie Photographique de H. de Fonteny et Cie,"French, 1817–1892",,"Teynard, Félix|Imprimerie Photographique de H. de Fonteny et Cie",French,1817,1892,"1851–52, printed 1853–54",1851,1852,Salted paper print from paper negative,23.8 x 30.3 cm. (9 3/8 x 11 15/16 in.),"Purchase, Lila Acheson Wallace Gift, 1976",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261884,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.607.82,false,true,261885,Photographs,Photograph,"Assouan, Cimetière Arabe - Inscription Funéraires",,,,,,Artist|Printer,,Félix Teynard|Imprimerie Photographique de H. de Fonteny et Cie,"French, 1817–1892",,"Teynard, Félix|Imprimerie Photographique de H. de Fonteny et Cie",French,1817,1892,"1851–52, printed 1853–54",1851,1852,Salted paper print from paper negative,23.7 x 30.9 cm. (9 5/16 x 12 3/16 in.),"Purchase, Lila Acheson Wallace Gift, 1976",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261885,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.607.83,false,true,261886,Photographs,Photograph,"Première Cataracte, Vue Générale Prise de la Point Méridionale de l'Ile d'Éléphantine",,,,,,Artist|Printer,,Félix Teynard|Imprimerie Photographique de H. de Fonteny et Cie,"French, 1817–1892",,"Teynard, Félix|Imprimerie Photographique de H. de Fonteny et Cie",French,1817,1892,"1851–52, printed 1853–54",1851,1852,Salted paper print from paper negative,24.2 x 30.7 cm. (9 1/2 x 12 1/16 in.),"Purchase, Lila Acheson Wallace Gift, 1976",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261886,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.607.84,false,true,261887,Photographs,Photograph,"Première Cataracte, Montagnes Granitiques Couvertes de Sables",,,,,,Artist|Printer,,Félix Teynard|Imprimerie Photographique de H. de Fonteny et Cie,"French, 1817–1892",,"Teynard, Félix|Imprimerie Photographique de H. de Fonteny et Cie",French,1817,1892,"1851–52, printed 1853–54",1851,1852,Salted paper print from paper negative,23.6 x 30.3 cm. (9 5/16 x 11 15/16 in.),"Purchase, Lila Acheson Wallace Gift, 1976",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261887,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.607.85,false,true,261888,Photographs,Photograph,"Ile de Fîleh (Philæ), Vue Générale Prise du Sud-Est au Point B",,,,,,Artist|Printer,,Félix Teynard|Imprimerie Photographique de H. de Fonteny et Cie,"French, 1817–1892",,"Teynard, Félix|Imprimerie Photographique de H. de Fonteny et Cie",French,1817,1892,"1851–52, printed 1853–54",1851,1852,Salted paper print from paper negative,24.2 x 30.5 cm. (9 1/2 x 12 in.),"Purchase, Lila Acheson Wallace Gift, 1976",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261888,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.607.86,false,true,261889,Photographs,Photograph,"Ile de Fîleh (Philæ), Édifice de l'Est - Vue Générale Prise du Point C",,,,,,Artist|Printer,,Félix Teynard|Imprimerie Photographique de H. de Fonteny et Cie,"French, 1817–1892",,"Teynard, Félix|Imprimerie Photographique de H. de Fonteny et Cie",French,1817,1892,"1851–52, printed 1853–54",1851,1852,Salted paper print from paper negative,20.8 x 30.5 cm. (8 3/16 x 12 in.),"Purchase, Lila Acheson Wallace Gift, 1976",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261889,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.607.87,false,true,261890,Photographs,Photograph,"Ile de Fîleh (Philæ), Édifice de l'Est - Face Latérale Vue du Point D",,,,,,Artist|Printer,,Félix Teynard|Imprimerie Photographique de H. de Fonteny et Cie,"French, 1817–1892",,"Teynard, Félix|Imprimerie Photographique de H. de Fonteny et Cie",French,1817,1892,"1851–52, printed 1853–54",1851,1852,Salted paper print from paper negative,23.5 x 30.3 cm. (9 1/4 x 11 15/16 in.),"Purchase, Lila Acheson Wallace Gift, 1976",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261890,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.607.88,false,true,261891,Photographs,Photograph,"Ile de Fîleh (Philæ), Édifice de l'Est - Façade Occidentale - Vue du Point E",,,,,,Artist|Printer,,Félix Teynard|Imprimerie Photographique de H. de Fonteny et Cie,"French, 1817–1892",,"Teynard, Félix|Imprimerie Photographique de H. de Fonteny et Cie",French,1817,1892,"1851–52, printed 1853–54",1851,1852,Salted paper print from paper negative,25.1 x 30.5 cm. (9 7/8 x 12 in.),"Purchase, Lila Acheson Wallace Gift, 1976",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261891,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.607.89,false,true,261892,Photographs,Photograph,"Ile de Fîleh (Philæ), Édifice du Sud et Partie de la Colonnade Occidentale Vue du Point V",,,,,,Artist|Printer,,Félix Teynard|Imprimerie Photographique de H. de Fonteny et Cie,"French, 1817–1892",,"Teynard, Félix|Imprimerie Photographique de H. de Fonteny et Cie",French,1817,1892,"1851–52, printed 1853–54",1851,1852,Salted paper print from paper negative,23.7 x 30.5 cm. (9 5/16 x 12 in.),"Purchase, Lila Acheson Wallace Gift, 1976",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261892,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.607.90,false,true,261894,Photographs,Photograph,"Ile de Fîleh (Philæ), Colonnade Occidentale - Ruines Vues du Point L",,,,,,Artist|Printer,,Félix Teynard|Imprimerie Photographique de H. de Fonteny et Cie,"French, 1817–1892",,"Teynard, Félix|Imprimerie Photographique de H. de Fonteny et Cie",French,1817,1892,"1851–52, printed 1853–54",1851,1852,Salted paper print from paper negative,23.2 x 30.0 cm. (9 1/8 x 11 13/16 in.),"Purchase, Lila Acheson Wallace Gift, 1976",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261894,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.607.91,false,true,261895,Photographs,Photograph,"Ile de Fîleh (Philæ), Premier Pylône - Vue Prise de la Plate-Forme de la Colonnade Orientale en P",,,,,,Artist|Printer,,Félix Teynard|Imprimerie Photographique de H. de Fonteny et Cie,"French, 1817–1892",,"Teynard, Félix|Imprimerie Photographique de H. de Fonteny et Cie",French,1817,1892,"1851–52, printed 1853–54",1851,1852,Salted paper print from paper negative,24.3 x 30.5 cm. (9 9/16 x 12 in.),"Purchase, Lila Acheson Wallace Gift, 1976",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261895,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.607.92,false,true,261896,Photographs,Photograph,"Ile de Fîleh (Philæ), Vue Génèrale Prise du Point I, Sur La Plateforme du Pylône",,,,,,Artist|Printer,,Félix Teynard|Imprimerie Photographique de H. de Fonteny et Cie,"French, 1817–1892",,"Teynard, Félix|Imprimerie Photographique de H. de Fonteny et Cie",French,1817,1892,"1851–52, printed 1853–54",1851,1852,Salted paper print from paper negative,23.8 x 30.7 cm. (9 3/8 x 12 1/16 in.),"Purchase, Lila Acheson Wallace Gift, 1976",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261896,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.607.93,false,true,261897,Photographs,Photograph,"Ile de Fîleh (Philæ), Deuxième Pylône - Partie Orientale Vue de la Plateforme Inférieure du Premier Pylône, du Point G",,,,,,Artist|Printer,,Félix Teynard|Imprimerie Photographique de H. de Fonteny et Cie,"French, 1817–1892",,"Teynard, Félix|Imprimerie Photographique de H. de Fonteny et Cie",French,1817,1892,"1851–52, printed 1853–54",1851,1852,Salted paper print from paper negative,30.2 x 23.4 cm. (11 7/8 x 9 3/16 in.),"Purchase, Lila Acheson Wallace Gift, 1976",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261897,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.607.94,false,true,261898,Photographs,Photograph,"Environs de Fîleh, Palmier Doum sur la Rive Orientale du Nil",,,,,,Artist|Printer,,Félix Teynard|Imprimerie Photographique de H. de Fonteny et Cie,"French, 1817–1892",,"Teynard, Félix|Imprimerie Photographique de H. de Fonteny et Cie",French,1817,1892,"1851–52, printed 1853–54",1851,1852,Salted paper print from paper negative,23.7 x 30.8 cm. (9 5/16 x 12 1/8 in.),"Purchase, Lila Acheson Wallace Gift, 1976",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261898,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.607.95,false,true,261899,Photographs,Photograph,"Débôd (Parembole), Vue Générale des Ruines",,,,,,Artist|Printer,,Félix Teynard|Imprimerie Photographique de H. de Fonteny et Cie,"French, 1817–1892",,"Teynard, Félix|Imprimerie Photographique de H. de Fonteny et Cie",French,1817,1892,"1851–52, printed 1853–54",1851,1852,Salted paper print from paper negative,24.1 x 30.7 cm. (9 1/2 x 12 1/16 in.),"Purchase, Lila Acheson Wallace Gift, 1976",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261899,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.607.96,false,true,261900,Photographs,Photograph,"Kardâcy, Sanctuaire, Niches et Inscriptions Taillées dans les Carrières",,,,,,Artist|Printer,,Félix Teynard|Imprimerie Photographique de H. de Fonteny et Cie,"French, 1817–1892",,"Teynard, Félix|Imprimerie Photographique de H. de Fonteny et Cie",French,1817,1892,"1851–52, printed 1853–54",1851,1852,Salted paper print from paper negative,23.7 x 30.6 cm. (9 5/16 x 12 1/16 in.),"Purchase, Lila Acheson Wallace Gift, 1976",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261900,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.607.97,false,true,261901,Photographs,Photograph,"Tâfah, Rochers Granitiques sur les Rives du Nil",,,,,,Artist|Printer,,Félix Teynard|Imprimerie Photographique de H. de Fonteny et Cie,"French, 1817–1892",,"Teynard, Félix|Imprimerie Photographique de H. de Fonteny et Cie",French,1817,1892,"1851–52, printed 1853–54",1851,1852,Salted paper print from paper negative,24.9 x 30.1 cm. (9 13/16 x 11 7/8 in.),"Purchase, Lila Acheson Wallace Gift, 1976",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261901,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.607.98,false,true,261902,Photographs,Photograph,"Kalabcheh (Talmis), Ruines du temple - Façade et Mur d'Enceinte",,,,,,Artist|Printer,,Félix Teynard|Imprimerie Photographique de H. de Fonteny et Cie,"French, 1817–1892",,"Teynard, Félix|Imprimerie Photographique de H. de Fonteny et Cie",French,1817,1892,"1851–52, printed 1853–54",1851,1852,Salted paper print from paper negative,23.6 x 30.3 cm. (9 5/16 x 11 15/16 in.),"Purchase, Lila Acheson Wallace Gift, 1976",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261902,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.607.99,false,true,261903,Photographs,Photograph,"Dandour, Vue Générale des Ruines",,,,,,Artist|Printer,,Félix Teynard|Imprimerie Photographique de H. de Fonteny et Cie,"French, 1817–1892",,"Teynard, Félix|Imprimerie Photographique de H. de Fonteny et Cie",French,1817,1892,"1851–52, printed 1853–54",1851,1852,Salted paper print from paper negative,23.2 x 30.2 cm. (9 1/8 x 11 7/8 in.),"Purchase, Lila Acheson Wallace Gift, 1976",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261903,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.607.100,false,true,261801,Photographs,Photograph,"Djerf-Hocein (Tutzis), Hemi-Spéos, Colosses de la Partie Extérieure",,,,,,Artist|Printer,,Félix Teynard|Imprimerie Photographique de H. de Fonteny et Cie,"French, 1817–1892",,"Teynard, Félix|Imprimerie Photographique de H. de Fonteny et Cie",French,1817,1892,"1851–52, printed 1853–54",1851,1852,Salted paper print from paper negative,23.7 x 30.4 cm. (9 5/16 x 12 in.),"Purchase, Lila Acheson Wallace Gift, 1976",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261801,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.607.101,false,true,261802,Photographs,Photograph,"Dakkeh (Pselcis), Vue Générale du Temple",,,,,,Artist|Printer,,Félix Teynard|Imprimerie Photographique de H. de Fonteny et Cie,"French, 1817–1892",,"Teynard, Félix|Imprimerie Photographique de H. de Fonteny et Cie",French,1817,1892,"1851–52, printed 1853–54",1851,1852,Salted paper print from paper negative,23.1 x 30.2 cm. (9 1/16 x 11 7/8 in.),"Purchase, Lila Acheson Wallace Gift, 1976",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261802,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.607.102,false,true,261803,Photographs,Photograph,"Korósko, Sycomores et Campement d'une Caravane pour le Sennâr",,,,,,Artist|Printer,,Félix Teynard|Imprimerie Photographique de H. de Fonteny et Cie,"French, 1817–1892",,"Teynard, Félix|Imprimerie Photographique de H. de Fonteny et Cie",French,1817,1892,"1851–52, printed 1853–54",1851,1852,Salted paper print from paper negative,23.4 x 29.5 cm. (9 3/16 x 11 5/8 in.),"Purchase, Lila Acheson Wallace Gift, 1976",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261803,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.607.103,false,true,261804,Photographs,Photograph,"Deîr, Carrefour et Habitation Particulière",,,,,,Artist|Printer,,Félix Teynard|Imprimerie Photographique de H. de Fonteny et Cie,"French, 1817–1892",,"Teynard, Félix|Imprimerie Photographique de H. de Fonteny et Cie",French,1817,1892,"1851–52, printed 1853–54",1851,1852,Salted paper print from paper negative,23.6 x 30.2 cm. (9 5/16 x 11 7/8 in.),"Purchase, Lila Acheson Wallace Gift, 1976",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261804,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.607.104,false,true,261805,Photographs,Photograph,"Abou-Sembil, Petit Spéos - Partie Gauche de la Façade",,,,,,Artist|Printer,,Félix Teynard|Imprimerie Photographique de H. de Fonteny et Cie,"French, 1817–1892",,"Teynard, Félix|Imprimerie Photographique de H. de Fonteny et Cie",French,1817,1892,"1851–52, printed 1853–54",1851,1852,Salted paper print from paper negative,23.3 x 30.5 cm. (9 3/16 x 12 in.),"Purchase, Lila Acheson Wallace Gift, 1976",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261805,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.607.105,false,true,261806,Photographs,Photograph,"Abou Sembil, Grand Spéos - Statues Colossales, Vues de Trois-Quarts",,,,,,Artist|Printer,,Félix Teynard|Imprimerie Photographique de H. de Fonteny et Cie,"French, 1817–1892",,"Teynard, Félix|Imprimerie Photographique de H. de Fonteny et Cie",French,1817,1892,"1851–52, printed 1853–54",1851,1852,Salted paper print from paper negative,30.0 x 24.4 cm. (11 13/16 x 9 5/8 in.),"Purchase, Lila Acheson Wallace Gift, 1976",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261806,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.63,false,true,685405,Photographs,Carte-de-visite,[Unknown Subject],,,,,,Photography Studio,,Walery Frères,"French, active 1860s–1870s",,Walery Frères,French,1860,1879,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685405,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.227,false,true,685568,Photographs,Carte-de-visite,[Unknown Subject],,,,,,Photography Studio,,Walery Frères,"French, active 1860s–1870s",,Walery Frères,French,1860,1879,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685568,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.200,false,true,285700,Photographs,Photograph,[Countess de Castiglione],,,,,,Person in Photograph|Artist,,Countess Virginia Oldoini Verasis di Castiglione|Pierre-Louis Pierson,"1835–1899|French, 1822–1913",,"Castiglione, di, Virginia Oldoini Verasis Countess|Pierson, Pierre-Louis",French,1835 |1822,1899 |1913,1858,1858,1858,Albumen silver print from glass negative,Image: 27.8 x 20.9 cm (10 15/16 x 8 1/4 in.) Mount: 29 x 22 cm (11 7/16 x 8 11/16 in.) Mount (2nd): 45 x 35.3 cm (17 11/16 x 13 7/8 in.) Mat: 50.8 x 40.6 cm (20 x 16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285700,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.392,false,true,285701,Photographs,Photograph,[Countess de Castiglione as Elvira at the Cheval Glass],,,,,,Person in Photograph|Artist,,Countess Virginia Oldoini Verasis di Castiglione|Pierre-Louis Pierson,"1835–1899|French, 1822–1913",,"Castiglione, di, Virginia Oldoini Verasis Countess|Pierson, Pierre-Louis",French,1835 |1822,1899 |1913,1861–67,1861,1867,Salted paper print from glass negative,Image: 14.5 x 15.4 cm (5 11/16 x 6 1/16 in.) Mount: 17.1 x 17.3 cm (6 3/4 x 6 13/16 in.) Mat: 43.2 x 35.6 cm (17 x 14 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285701,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.405,false,true,286837,Photographs,Photograph,La Comtesse de Castiglione en Reine de la Nuit,,,,,,Person in Photograph|Artist,,Countess Virginia Oldoini Verasis di Castiglione|Pierre-Louis Pierson,"1835–1899|French, 1822–1913",,"Castiglione, di, Virginia Oldoini Verasis Countess|Pierson, Pierre-Louis",French,1835 |1822,1899 |1913,1863–67,1863,1867,Albumen silver print from glass negative,Image: 10.5 x 7.4 cm (4 1/8 x 2 15/16 in.) Mount: 12.2 x 9 cm (4 13/16 x 3 9/16 in.) Mat: 35.6 x 27.9 cm (14 x 11 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286837,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.423,false,true,286787,Photographs,Photograph,[Trying for Snapshots],,,,,,Person in Photograph|Artist,,Countess Virginia Oldoini Verasis di Castiglione|Pierre-Louis Pierson,"1835–1899|French, 1822–1913",,"Castiglione, di, Virginia Oldoini Verasis Countess|Pierson, Pierre-Louis",French,1835 |1822,1899 |1913,1861–67,1861,1867,Albumen silver print from glass negative,Image: 9.6 x 7.7 cm (3 3/4 x 3 1/16 in.) Mount: 12.1 x 8.9 cm (4 3/4 x 3 1/2 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286787,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.387.1,false,true,288106,Photographs,Photograph,"[Countess de Castiglione, from Série des Roses]",,,,,,Person in Photograph|Artist,,Countess Virginia Oldoini Verasis di Castiglione|Pierre-Louis Pierson,"1835–1899|French, 1822–1913",,"Castiglione, di, Virginia Oldoini Verasis Countess|Pierson, Pierre-Louis",French,1835 |1822,1899 |1913,1895,1895,1895,Albumen silver print from glass negative,Approximately 14.3 x 9.9 cm (5 5/8 x 3 7/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288106,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.387.2,false,true,288107,Photographs,Photograph,"[Countess de Castiglione, from Série des Roses]",,,,,,Person in Photograph|Artist,,Countess Virginia Oldoini Verasis di Castiglione|Pierre-Louis Pierson,"1835–1899|French, 1822–1913",,"Castiglione, di, Virginia Oldoini Verasis Countess|Pierson, Pierre-Louis",French,1835 |1822,1899 |1913,1895,1895,1895,Albumen silver print from glass negative,Approximately 14.3 x 9.9 cm (5 5/8 x 3 7/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288107,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.387.3,false,true,288108,Photographs,Photograph,"[Countess de Castiglione, from Série des Roses]",,,,,,Person in Photograph|Artist,,Countess Virginia Oldoini Verasis di Castiglione|Pierre-Louis Pierson,"1835–1899|French, 1822–1913",,"Castiglione, di, Virginia Oldoini Verasis Countess|Pierson, Pierre-Louis",French,1835 |1822,1899 |1913,1895,1895,1895,Albumen silver print from glass negative,Approximately 14.3 x 9.9 cm (5 5/8 x 3 7/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288108,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.387.4,false,true,288109,Photographs,Photograph,"[Countess de Castiglione, from Série des Roses]",,,,,,Person in Photograph|Artist,,Countess Virginia Oldoini Verasis di Castiglione|Pierre-Louis Pierson,"1835–1899|French, 1822–1913",,"Castiglione, di, Virginia Oldoini Verasis Countess|Pierson, Pierre-Louis",French,1835 |1822,1899 |1913,1895,1895,1895,Albumen silver print from glass negative,Approximately 14.3 x 9.9 cm (5 5/8 x 3 7/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288109,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.387.6,false,true,288111,Photographs,Photograph,"[Countess de Castiglione, from Série des Roses]",,,,,,Person in Photograph|Artist,,Countess Virginia Oldoini Verasis di Castiglione|Pierre-Louis Pierson,"1835–1899|French, 1822–1913",,"Castiglione, di, Virginia Oldoini Verasis Countess|Pierson, Pierre-Louis",French,1835 |1822,1899 |1913,1895,1895,1895,Albumen silver print from glass negative,Approximately 14.3 x 9.9 cm (5 5/8 x 3 7/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288111,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.387.7,false,true,288112,Photographs,Photograph,"[Countess de Castiglione, from Série des Roses]",,,,,,Person in Photograph|Artist,,Countess Virginia Oldoini Verasis di Castiglione|Pierre-Louis Pierson,"1835–1899|French, 1822–1913",,"Castiglione, di, Virginia Oldoini Verasis Countess|Pierson, Pierre-Louis",French,1835 |1822,1899 |1913,1895,1895,1895,Albumen silver print from glass negative,Approximately 14.3 x 9.9 cm (5 5/8 x 3 7/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288112,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.387.8,false,true,288113,Photographs,Photograph,"[Countess de Castiglione, from Série des Roses]",,,,,,Person in Photograph|Artist,,Countess Virginia Oldoini Verasis di Castiglione|Pierre-Louis Pierson,"1835–1899|French, 1822–1913",,"Castiglione, di, Virginia Oldoini Verasis Countess|Pierson, Pierre-Louis",French,1835 |1822,1899 |1913,1895,1895,1895,Albumen silver print from glass negative,Approximately 14.3 x 9.9 cm (5 5/8 x 3 7/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288113,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.387.9,false,true,288114,Photographs,Photograph,"[Countess de Castiglione, from Série des Roses]",,,,,,Person in Photograph|Artist,,Countess Virginia Oldoini Verasis di Castiglione|Pierre-Louis Pierson,"1835–1899|French, 1822–1913",,"Castiglione, di, Virginia Oldoini Verasis Countess|Pierson, Pierre-Louis",French,1835 |1822,1899 |1913,1895,1895,1895,Albumen silver print from glass negative,Approximately 14.3 x 9.9 cm (5 5/8 x 3 7/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288114,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.388.1,false,true,288124,Photographs,Photograph,[Countess de Castiglione],,,,,,Person in Photograph|Artist,,Countess Virginia Oldoini Verasis di Castiglione|Pierre-Louis Pierson,"1835–1899|French, 1822–1913",,"Castiglione, di, Virginia Oldoini Verasis Countess|Pierson, Pierre-Louis",French,1835 |1822,1899 |1913,1895,1895,1895,Albumen silver print from glass negative,Approximately 14.3 x 9.9 cm (5 5/8 x 3 7/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288124,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.388.2,false,true,288125,Photographs,Photograph,[Countess de Castiglione],,,,,,Person in Photograph|Artist,,Countess Virginia Oldoini Verasis di Castiglione|Pierre-Louis Pierson,"1835–1899|French, 1822–1913",,"Castiglione, di, Virginia Oldoini Verasis Countess|Pierson, Pierre-Louis",French,1835 |1822,1899 |1913,1895,1895,1895,Albumen silver print from glass negative,Approximately 14.3 x 9.9 cm (5 5/8 x 3 7/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288125,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.388.4,false,true,288127,Photographs,Photograph,[Countess de Castiglione],,,,,,Person in Photograph|Artist,,Countess Virginia Oldoini Verasis di Castiglione|Pierre-Louis Pierson,"1835–1899|French, 1822–1913",,"Castiglione, di, Virginia Oldoini Verasis Countess|Pierson, Pierre-Louis",French,1835 |1822,1899 |1913,1895,1895,1895,Albumen silver print from glass negative,Approximately 14.3 x 9.9 cm (5 5/8 x 3 7/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288127,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.388.5,false,true,288128,Photographs,Photograph,[Countess de Castiglione],,,,,,Person in Photograph|Artist,,Countess Virginia Oldoini Verasis di Castiglione|Pierre-Louis Pierson,"1835–1899|French, 1822–1913",,"Castiglione, di, Virginia Oldoini Verasis Countess|Pierson, Pierre-Louis",French,1835 |1822,1899 |1913,1895,1895,1895,Albumen silver print from glass negative,Approximately 14.3 x 9.9 cm (5 5/8 x 3 7/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288128,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.388.6,false,true,288129,Photographs,Photograph,[Countess de Castiglione],,,,,,Person in Photograph|Artist,,Countess Virginia Oldoini Verasis di Castiglione|Pierre-Louis Pierson,"1835–1899|French, 1822–1913",,"Castiglione, di, Virginia Oldoini Verasis Countess|Pierson, Pierre-Louis",French,1835 |1822,1899 |1913,1895,1895,1895,Albumen silver print from glass negative,Approximately 14.3 x 9.9 cm (5 5/8 x 3 7/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288129,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.388.7,false,true,288130,Photographs,Photograph,[Countess de Castiglione],,,,,,Person in Photograph|Artist,,Countess Virginia Oldoini Verasis di Castiglione|Pierre-Louis Pierson,"1835–1899|French, 1822–1913",,"Castiglione, di, Virginia Oldoini Verasis Countess|Pierson, Pierre-Louis",French,1835 |1822,1899 |1913,1895,1895,1895,Albumen silver print from glass negative,Approximately 14.3 x 9.9 cm (5 5/8 x 3 7/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288130,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.388.8,false,true,288131,Photographs,Photograph,[Countess de Castiglione],,,,,,Person in Photograph|Artist,,Countess Virginia Oldoini Verasis di Castiglione|Pierre-Louis Pierson,"1835–1899|French, 1822–1913",,"Castiglione, di, Virginia Oldoini Verasis Countess|Pierson, Pierre-Louis",French,1835 |1822,1899 |1913,1895,1895,1895,Albumen silver print from glass negative,Approximately 14.3 x 9.9 cm (5 5/8 x 3 7/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288131,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.388.9,false,true,288132,Photographs,Photograph,[Countess de Castiglione],,,,,,Person in Photograph|Artist,,Countess Virginia Oldoini Verasis di Castiglione|Pierre-Louis Pierson,"1835–1899|French, 1822–1913",,"Castiglione, di, Virginia Oldoini Verasis Countess|Pierson, Pierre-Louis",French,1835 |1822,1899 |1913,1895,1895,1895,Albumen silver print from glass negative,Approximately 14.3 x 9.9 cm (5 5/8 x 3 7/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288132,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.424.2,false,true,286770,Photographs,Photograph,Ti-fille Brune,,,,,,Person in Photograph|Artist,,Countess Virginia Oldoini Verasis di Castiglione|Pierre-Louis Pierson,"1835–1899|French, 1822–1913",,"Castiglione, di, Virginia Oldoini Verasis Countess|Pierson, Pierre-Louis",French,1835 |1822,1899 |1913,1895,1895,1895,Albumen silver print from glass negative,Image: 14.3 x 9.8 cm (5 5/8 x 3 7/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286770,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.387.10,false,true,288115,Photographs,Photograph,"[Countess de Castiglione, from Série des Roses]",,,,,,Person in Photograph|Artist,,Countess Virginia Oldoini Verasis di Castiglione|Pierre-Louis Pierson,"1835–1899|French, 1822–1913",,"Castiglione, di, Virginia Oldoini Verasis Countess|Pierson, Pierre-Louis",French,1835 |1822,1899 |1913,1895,1895,1895,Albumen silver print from glass negative,Approximately 14.3 x 9.9 cm (5 5/8 x 3 7/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288115,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.388.10,false,true,288133,Photographs,Photograph,[Countess de Castiglione],,,,,,Person in Photograph|Artist,,Countess Virginia Oldoini Verasis di Castiglione|Pierre-Louis Pierson,"1835–1899|French, 1822–1913",,"Castiglione, di, Virginia Oldoini Verasis Countess|Pierson, Pierre-Louis",French,1835 |1822,1899 |1913,1895,1895,1895,Albumen silver print from glass negative,Approximately 14.3 x 9.9 cm (5 5/8 x 3 7/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288133,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.388.11,false,true,288219,Photographs,Photograph,[Countess de Castiglione],,,,,,Person in Photograph|Artist,,Countess Virginia Oldoini Verasis di Castiglione|Pierre-Louis Pierson,"1835–1899|French, 1822–1913",,"Castiglione, di, Virginia Oldoini Verasis Countess|Pierson, Pierre-Louis",French,1835 |1822,1899 |1913,1895,1895,1895,Albumen silver print from glass negative,Approximately 14.3 x 9.9 cm (5 5/8 x 3 7/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288219,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.388.12,false,true,288220,Photographs,Photograph,[Countess de Castiglione],,,,,,Person in Photograph|Artist,,Countess Virginia Oldoini Verasis di Castiglione|Pierre-Louis Pierson,"1835–1899|French, 1822–1913",,"Castiglione, di, Virginia Oldoini Verasis Countess|Pierson, Pierre-Louis",French,1835 |1822,1899 |1913,1895,1895,1895,Albumen silver print from glass negative,Approximately 14.3 x 9.9 cm (5 5/8 x 3 7/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288220,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2005.100.424.1, .3",false,true,286564,Photographs,Photograph,Ti-fille Brune,,,,,,Person in Photograph|Artist,,Countess Virginia Oldoini Verasis di Castiglione|Pierre-Louis Pierson,"1835–1899|French, 1822–1913",,"Castiglione, di, Virginia Oldoini Verasis Countess|Pierson, Pierre-Louis",French,1835 |1822,1899 |1913,1895,1895,1895,Albumen silver print from glass negative,Image: 14.3 x 9.8 cm (5 5/8 x 3 7/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286564,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.258–.262,false,true,289066,Photographs,Photograph,[Album page with ten photographs of La Comtesse mounted recto and verso],,,,,,Person in Photograph|Artist,,Countess Virginia Oldoini Verasis di Castiglione|Pierre-Louis Pierson,"1835–1899|French, 1822–1913",,"Castiglione, di, Virginia Oldoini Verasis Countess|Pierson, Pierre-Louis",French,1835 |1822,1899 |1913,1861–67,1861,1867,Albumen silver prints from glass negative,10.8 x 8.6 cm (4 1/4 x 3 3/8 in.) to 2.5 x 3.5 cm (1 x 1 3/8 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289066,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -48.188,false,true,269214,Photographs,Photograph,Scherzo di Follia,,,,,,Artist|Person in Photograph,,Pierre-Louis Pierson|Countess Virginia Oldoini Verasis di Castiglione,"French, 1822–1913|1835–1899",,"Pierson, Pierre-Louis|Castiglione, di, Virginia Oldoini Verasis Countess",French,1822 |1835,1913 |1899,"1863–66, printed 1940s",1863,1866,Gelatin silver print from glass negative,18.7 x 12.5 cm. (7 3/8 x 4 15/16 in.),"Gift of George Davis, 1948",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269214,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.66,false,true,261518,Photographs,Carte-de-visite,Convalescente (autre),,,,,,Artist|Person in Photograph,Person in photograph,Pierre-Louis Pierson|Countess Virginia Oldoini Verasis di Castiglione,"French, 1822–1913|1835–1899",,"Pierson, Pierre-Louis|Castiglione, di, Virginia Oldoini Verasis Countess",French,1822 |1835,1913 |1899,1860s,1860,1869,Albumen silver print from glass negative,10.4 x 6.3 cm (4 1/8 x 2 1/2 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261518,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.68,false,true,261520,Photographs,Carte-de-visite,Convalescente (autre),,,,,,Artist|Person in Photograph,Person in photograph,Pierre-Louis Pierson|Countess Virginia Oldoini Verasis di Castiglione,"French, 1822–1913|1835–1899",,"Pierson, Pierre-Louis|Castiglione, di, Virginia Oldoini Verasis Countess",French,1822 |1835,1913 |1899,1860s,1860,1869,Albumen silver print from glass negative,9.9 x 6.9 cm (3 7/8 x 2 11/16 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261520,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.183,false,true,261364,Photographs,Photograph,[La Comtesse at Table with Hand to Face],,,,,,Artist|Person in Photograph,,Pierre-Louis Pierson|Countess Virginia Oldoini Verasis di Castiglione,"French, 1822–1913|1835–1899",,"Pierson, Pierre-Louis|Castiglione, di, Virginia Oldoini Verasis Countess",French,1822 |1835,1913 |1899,1860s,1860,1869,Albumen silver print from glass negative,8.9 x 12.1 cm (3 1/2 x 4 3/4 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261364,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.184,false,true,261365,Photographs,Photograph,[La Comtesse],,,,,,Artist|Person in Photograph,,Pierre-Louis Pierson|Countess Virginia Oldoini Verasis di Castiglione,"French, 1822–1913|1835–1899",,"Pierson, Pierre-Louis|Castiglione, di, Virginia Oldoini Verasis Countess",French,1822 |1835,1913 |1899,1860s,1860,1869,Albumen silver print from glass negative,11.7 x 16.2 cm (4 5/8 x 6 3/8 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261365,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.189,false,true,261370,Photographs,Photograph,[La Comtesse in robe de piqué or as Judith (?)],,,,,,Artist|Person in Photograph,,Pierre-Louis Pierson|Countess Virginia Oldoini Verasis di Castiglione,"French, 1822–1913|1835–1899",,"Pierson, Pierre-Louis|Castiglione, di, Virginia Oldoini Verasis Countess",French,1822 |1835,1913 |1899,1860s,1860,1869,Albumen silver print from glass negative with applied color,12.4 x 8.8 cm (4 7/8 x 3 7/16 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261370,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.190,false,true,261372,Photographs,Photograph,[La Comtesse in robe de piqué‚ or as Judith (?)],,,,,,Artist|Person in Photograph,,Pierre-Louis Pierson|Countess Virginia Oldoini Verasis di Castiglione,"French, 1822–1913|1835–1899",,"Pierson, Pierre-Louis|Castiglione, di, Virginia Oldoini Verasis Countess",French,1822 |1835,1913 |1899,1860s,1860,1869,Albumen silver print from glass negative with applied color,12.3 x 8.7 cm (4 13/16 x 3 7/16 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261372,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.191,false,true,261373,Photographs,Photograph,[La Comtesse in robe de piqué or as Judith (?)],,,,,,Artist|Person in Photograph,,Pierre-Louis Pierson|Countess Virginia Oldoini Verasis di Castiglione,"French, 1822–1913|1835–1899",,"Pierson, Pierre-Louis|Castiglione, di, Virginia Oldoini Verasis Countess",French,1822 |1835,1913 |1899,1860s,1860,1869,Albumen silver print from glass negative with applied color,12.2 x 8.5 cm (4 13/16 x 3 3/8 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261373,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.192,false,true,261374,Photographs,Photograph,[La Comtesse in robe de piqué or as Judith (?)],,,,,,Artist|Person in Photograph,,Pierre-Louis Pierson|Countess Virginia Oldoini Verasis di Castiglione,"French, 1822–1913|1835–1899",,"Pierson, Pierre-Louis|Castiglione, di, Virginia Oldoini Verasis Countess",French,1822 |1835,1913 |1899,1860s,1860,1869,Albumen silver print from glass negative with applied color,12.4 x 8.7 cm (4 7/8 x 3 7/16 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261374,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.257,false,true,261454,Photographs,Photograph,[La Comtesse Reclining in Dark Dress with Chain Around Neck],,,,,,Artist|Person in Photograph,,Pierre-Louis Pierson|Countess Virginia Oldoini Verasis di Castiglione,"French, 1822–1913|1835–1899",,"Pierson, Pierre-Louis|Castiglione, di, Virginia Oldoini Verasis Countess",French,1822 |1835,1913 |1899,1861–65,1861,1865,Albumen silver print from glass negative,10.2 x 12.0 cm (4 x 4 3/4 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261454,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.268,false,true,261466,Photographs,Photograph,[La Comtesse in Ermine Cape],,,,,,Artist|Person in Photograph,,Pierre-Louis Pierson|Countess Virginia Oldoini Verasis di Castiglione,"French, 1822–1913|1835–1899",,"Pierson, Pierre-Louis|Castiglione, di, Virginia Oldoini Verasis Countess",French,1822 |1835,1913 |1899,1860s,1860,1869,Albumen silver print from glass negative,9.2 x 14.9 cm (3 5/8 x 5 7/8 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261466,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.269,false,true,261467,Photographs,Photograph,"[La Comtesse in Hat with Veil and Cape with Fringe, Serie à la Ristori]",,,,,,Artist|Person in Photograph,,Pierre-Louis Pierson|Countess Virginia Oldoini Verasis di Castiglione,"French, 1822–1913|1835–1899",,"Pierson, Pierre-Louis|Castiglione, di, Virginia Oldoini Verasis Countess",French,1822 |1835,1913 |1899,1860s,1860,1869,Albumen silver print from glass negative,9.5 x 13.7 cm (3 3/4 x 5 3/8 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261467,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.270,false,true,261469,Photographs,Photograph,[La Comtesse in Cape with Fringe; Serie à la Ristori],,,,,,Artist|Person in Photograph,,Pierre-Louis Pierson|Countess Virginia Oldoini Verasis di Castiglione,"French, 1822–1913|1835–1899",,"Pierson, Pierre-Louis|Castiglione, di, Virginia Oldoini Verasis Countess",French,1822 |1835,1913 |1899,1860s,1860,1869,Albumen silver print from glass negative,8.3 x 13.0 cm (3 1/4 x 5 1/8 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261469,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.271,false,true,261471,Photographs,Photograph,[La Comtesse with Group on a Rocky Beach],,,,,,Artist|Person in Photograph,,Pierre-Louis Pierson|Countess Virginia Oldoini Verasis di Castiglione,"French, 1822–1913|1835–1899",,"Pierson, Pierre-Louis|Castiglione, di, Virginia Oldoini Verasis Countess",French,1822 |1835,1913 |1899,1860s,1860,1869,Albumen silver print from glass negative,21.3 x 17.8 cm (8 3/8 x 7 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261471,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.272,false,true,261472,Photographs,Photograph,[La Comtesse décolletée; Roses mousseuses],,,,,,Artist|Person in Photograph,,Pierre-Louis Pierson|Countess Virginia Oldoini Verasis di Castiglione,"French, 1822–1913|1835–1899",,"Pierson, Pierre-Louis|Castiglione, di, Virginia Oldoini Verasis Countess",French,1822 |1835,1913 |1899,1861–67,1861,1867,Salted paper print from glass negative,44.8 x 29.2 cm (17 5/8 x 11 1/2 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261472,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.273,false,true,261473,Photographs,Photograph,[La Comtesse at Table holding Fan],,,,,,Artist|Person in Photograph,,Pierre-Louis Pierson|Countess Virginia Oldoini Verasis di Castiglione,"French, 1822–1913|1835–1899",,"Pierson, Pierre-Louis|Castiglione, di, Virginia Oldoini Verasis Countess",French,1822 |1835,1913 |1899,1860s,1860,1869,Salted paper print from glass negative,54.9 x 36.2 cm (21 5/8 x 14 1/4 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261473,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.274,false,true,261474,Photographs,Photograph,[La Comtesse in Lace Shawl],,,,,,Artist|Person in Photograph,,Pierre-Louis Pierson|Countess Virginia Oldoini Verasis di Castiglione,"French, 1822–1913|1835–1899",,"Pierson, Pierre-Louis|Castiglione, di, Virginia Oldoini Verasis Countess",French,1822 |1835,1913 |1899,1860s,1860,1869,Salted paper print from glass negative,54.6 x 36.2 cm (21 1/2 x 14 1/4 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261474,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.275,false,true,261475,Photographs,Photograph,[La Comtesse at Table with Hand to Face],,,,,,Artist|Person in Photograph,,Pierre-Louis Pierson|Countess Virginia Oldoini Verasis di Castiglione,"French, 1822–1913|1835–1899",,"Pierson, Pierre-Louis|Castiglione, di, Virginia Oldoini Verasis Countess",French,1822 |1835,1913 |1899,1860s,1860,1869,Salted paper print from glass negative,,"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261475,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.119,false,true,283245,Photographs,Photograph,The Gaze,,,,,,Artist|Person in Photograph,,Pierre-Louis Pierson|Countess Virginia Oldoini Verasis di Castiglione,"French, 1822–1913|1835–1899",,"Pierson, Pierre-Louis|Castiglione, di, Virginia Oldoini Verasis Countess",French,1822 |1835,1913 |1899,1856–57,1856,1857,Albumen silver print from glass negative,Image: 9 x 6.6 cm (3 9/16 x 2 5/8 in.) Mount: 12.9 x 9.1 cm (5 1/16 x 3 9/16 in.) Mat: 21.9 x 15.9 cm (8 5/8 x 6 1/4 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283245,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.194,false,true,285650,Photographs,Photograph,The White Nun,,,,,,Artist|Person in Photograph,,Pierre-Louis Pierson|Countess Virginia Oldoini Verasis di Castiglione,"French, 1822–1913|1835–1899",,"Pierson, Pierre-Louis|Castiglione, di, Virginia Oldoini Verasis Countess",French,1822 |1835,1913 |1899,1856–57,1856,1857,Albumen silver print from glass negative,Image: 19.1 x 13.8 cm (7 1/2 x 5 7/16 in.) Mount: 19.1 x 14.4 cm (7 1/2 x 5 11/16 in.) Mat: 43.2 x 35.6 cm (17 x 14 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285650,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.195,false,true,285614,Photographs,Photograph,Béatrix,,,,,,Artist|Person in Photograph,,Pierre-Louis Pierson|Countess Virginia Oldoini Verasis di Castiglione,"French, 1822–1913|1835–1899",,"Pierson, Pierre-Louis|Castiglione, di, Virginia Oldoini Verasis Countess",French,1822 |1835,1913 |1899,1856–57,1856,1857,Albumen silver print from glass negative,Image: 10.5 x 7 cm (4 1/8 x 2 3/4 in.) Mount: 12.2 x 9.1 cm (4 13/16 x 3 9/16 in.) Mat: 35.6 x 27.9 cm (14 x 11 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285614,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.197,false,true,285652,Photographs,Photograph,[The Opera Ball],,,,,,Artist|Person in Photograph,,Pierre-Louis Pierson|Countess Virginia Oldoini Verasis di Castiglione,"French, 1822–1913|1835–1899",,"Pierson, Pierre-Louis|Castiglione, di, Virginia Oldoini Verasis Countess",French,1822 |1835,1913 |1899,"1861–67, printed 1895–1910",1861,1910,Gelatin silver print from glass negative,Image: 36 x 27.9 cm (14 3/16 x 11 in.) Mat: 57.2 x 47 cm (22 1/2 x 18 1/2 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285652,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.198,false,true,285608,Photographs,Photograph,Scherzo di Follia,,,,,,Artist|Person in Photograph,,Pierre-Louis Pierson|Countess Virginia Oldoini Verasis di Castiglione,"French, 1822–1913|1835–1899",,"Pierson, Pierre-Louis|Castiglione, di, Virginia Oldoini Verasis Countess",French,1822 |1835,1913 |1899,"1861–67, printed ca. 1930",1861,1932,Gelatin silver print from glass negative,Image: 39.8 x 29.8 cm (15 11/16 x 11 3/4 in.) Mat: 61 x 50.8 cm (24 x 20 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285608,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.389,false,true,285662,Photographs,Photograph,Le Manteau d'Hermine,,,,,,Artist|Person in Photograph,,Pierre-Louis Pierson|Countess Virginia Oldoini Verasis di Castiglione,"French, 1822–1913|1835–1899",,"Pierson, Pierre-Louis|Castiglione, di, Virginia Oldoini Verasis Countess",French,1822 |1835,1913 |1899,"August 31, 1895",1895,1895,Albumen silver print from glass negative,Image: 14.4 x 10 cm (5 11/16 x 3 15/16 in.) Mat: 35.6 x 27.9 cm (14 x 11 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285662,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.390,false,true,286841,Photographs,Photograph,Rachel,,,,,,Artist|Person in Photograph,,Pierre-Louis Pierson|Countess Virginia Oldoini Verasis di Castiglione,"French, 1822–1913|1835–1899",,"Pierson, Pierre-Louis|Castiglione, di, Virginia Oldoini Verasis Countess",French,1822 |1835,1913 |1899,"September 1, 1893",1893,1893,Albumen silver print from glass negative,Image: 14.6 x 9.8 cm (5 3/4 x 3 7/8 in.) Mat: 35.6 x 27.9 cm (14 x 11 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286841,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.393,false,true,286827,Photographs,Cabinet card,Sculptural Shoulders,,,,,,Artist|Person in Photograph,,Pierre-Louis Pierson|Countess Virginia Oldoini Verasis di Castiglione,"French, 1822–1913|1835–1899",,"Pierson, Pierre-Louis|Castiglione, di, Virginia Oldoini Verasis Countess",French,1822 |1835,1913 |1899,1861–67,1861,1867,Albumen silver print from glass negative,Image: 12 x 8.5 cm (4 3/4 x 3 3/8 in.) Mount: 15.4 x 9.6 cm (6 1/16 x 3 3/4 in.) Mat: 35.6 x 27.9 cm (14 x 11 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286827,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.394,false,true,286828,Photographs,Carte-de-visite,La Dogaresse,,,,,,Artist|Person in Photograph,,Pierre-Louis Pierson|Countess Virginia Oldoini Verasis di Castiglione,"French, 1822–1913|1835–1899",,"Pierson, Pierre-Louis|Castiglione, di, Virginia Oldoini Verasis Countess",French,1822 |1835,1913 |1899,1861–67,1861,1867,Albumen silver print from glass negative,Image: 8.9 x 5.4 cm (3 1/2 x 2 1/8 in.) Mount: 10.7 x 6.2 cm (4 3/16 x 2 7/16 in.) Mat: 25.4 x 20.3 cm (10 x 8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286828,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.403,false,true,286835,Photographs,Photograph,La Marquise Mathilde,,,,,,Artist|Person in Photograph,,Pierre-Louis Pierson|Countess Virginia Oldoini Verasis di Castiglione,"French, 1822–1913|1835–1899",,"Pierson, Pierre-Louis|Castiglione, di, Virginia Oldoini Verasis Countess",French,1822 |1835,1913 |1899,1861–66,1861,1866,Albumen silver print from glass negative,Image: 9.9 x 7.5 cm (3 7/8 x 2 15/16 in.) Mount: 12.2 x 8.9 cm (4 13/16 x 3 1/2 in.) Mat: 35.6 x 27.9 cm (14 x 11 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286835,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.404,false,true,286836,Photographs,Photograph,La Dame de Cœurs,,,,,,Artist|Person in Photograph,,Pierre-Louis Pierson|Countess Virginia Oldoini Verasis di Castiglione,"French, 1822–1913|1835–1899",,"Pierson, Pierre-Louis|Castiglione, di, Virginia Oldoini Verasis Countess",French,1822 |1835,1913 |1899,1861–63,1861,1863,Albumen silver print from glass negative,Image: 10.5 x 7.4 cm (4 1/8 x 2 15/16 in.) Mount: 12.3 x 9.1 cm (4 13/16 x 3 9/16 in.) Mat: 35.6 x 27.9 cm (14 x 11 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286836,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.411,false,true,286785,Photographs,Photograph,[Countess de Castiglione],,,,,,Artist|Person in Photograph,,Pierre-Louis Pierson|Countess Virginia Oldoini Verasis di Castiglione,"French, 1822–1913|1835–1899",,"Pierson, Pierre-Louis|Castiglione, di, Virginia Oldoini Verasis Countess",French,1822 |1835,1913 |1899,"August 31, 1895",1895,1895,Albumen silver print from glass negative,Image: 14.3 x 10.1 cm (5 5/8 x 4 in.) Mat: 59.8 x 49.8 cm (23 9/16 x 19 5/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286785,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.412,false,true,286843,Photographs,Photograph,[Countess de Castiglione],,,,,,Artist|Person in Photograph,,Pierre-Louis Pierson|Countess Virginia Oldoini Verasis di Castiglione,"French, 1822–1913|1835–1899",,"Pierson, Pierre-Louis|Castiglione, di, Virginia Oldoini Verasis Countess",French,1822 |1835,1913 |1899,"August 31, 1895",1895,1895,Albumen silver print from glass negative,Image: 14.3 x 10.1 cm (5 5/8 x 4 in.) Mat: 59.8 x 49.8 cm (23 9/16 x 19 5/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286843,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.413,false,true,286844,Photographs,Photograph,L'Armoire,,,,,,Artist|Person in Photograph,,Pierre-Louis Pierson|Countess Virginia Oldoini Verasis di Castiglione,"French, 1822–1913|1835–1899",,"Pierson, Pierre-Louis|Castiglione, di, Virginia Oldoini Verasis Countess",French,1822 |1835,1913 |1899,"September 1, 1893",1893,1893,Albumen silver print from glass negative,Image: 14.8 x 9.8 cm (5 13/16 x 3 7/8 in.) Mat: 59.8 x 49.8 cm (23 9/16 x 19 5/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286844,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.414,false,true,286845,Photographs,Photograph,Ristori,,,,,,Artist|Person in Photograph,,Pierre-Louis Pierson|Countess Virginia Oldoini Verasis di Castiglione,"French, 1822–1913|1835–1899",,"Pierson, Pierre-Louis|Castiglione, di, Virginia Oldoini Verasis Countess",French,1822 |1835,1913 |1899,"September 1, 1893",1893,1893,Albumen silver print from glass negative,Image: 14.3 x 9.8 cm (5 5/8 x 3 7/8 in.) Mat: 59.8 x 49.8 cm (23 9/16 x 19 5/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286845,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.415,false,true,286846,Photographs,Photograph,Madame Douane,,,,,,Artist|Person in Photograph,,Pierre-Louis Pierson|Countess Virginia Oldoini Verasis di Castiglione,"French, 1822–1913|1835–1899",,"Pierson, Pierre-Louis|Castiglione, di, Virginia Oldoini Verasis Countess",French,1822 |1835,1913 |1899,"September 1, 1893",1893,1893,Albumen silver print from glass negative,Image: 14.4 x 10.1 cm (5 11/16 x 4 in.) Mat: 59.8 x 49.8 cm (23 9/16 x 19 5/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286846,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.416,false,true,286847,Photographs,Photograph,Torino Aosta,,,,,,Artist|Person in Photograph,,Pierre-Louis Pierson|Countess Virginia Oldoini Verasis di Castiglione,"French, 1822–1913|1835–1899",,"Pierson, Pierre-Louis|Castiglione, di, Virginia Oldoini Verasis Countess",French,1822 |1835,1913 |1899,"September 1, 1893",1893,1893,Albumen silver print from glass negative,Image: 14.9 x 9.9 cm (5 7/8 x 3 7/8 in.) Mat: 59.8 x 49.8 cm (23 9/16 x 19 5/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286847,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.418,false,true,286822,Photographs,Photograph,[Standing at the Prie-Dieu],,,,,,Artist|Person in Photograph,,Pierre-Louis Pierson|Countess Virginia Oldoini Verasis di Castiglione,"French, 1822–1913|1835–1899",,"Pierson, Pierre-Louis|Castiglione, di, Virginia Oldoini Verasis Countess",French,1822 |1835,1913 |1899,1861–64,1861,1864,Albumen silver print from glass negative,Image: 10.5 x 8.5 cm (4 1/8 x 3 3/8 in.) Mount: 10.5 x 8.5 cm (4 1/8 x 3 3/8 in.) Mat: 35.6 x 27.9 cm (14 x 11 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286822,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.419,false,true,286823,Photographs,Photograph,Anne Boleyn,,,,,,Artist|Person in Photograph,,Pierre-Louis Pierson|Countess Virginia Oldoini Verasis di Castiglione,"French, 1822–1913|1835–1899",,"Pierson, Pierre-Louis|Castiglione, di, Virginia Oldoini Verasis Countess",French,1822 |1835,1913 |1899,1861–65,1861,1865,Albumen silver print from glass negative,Image: 13.2 x 10.1 cm (5 3/16 x 4 in.) Mount: 14.3 x 10.6 cm (5 5/8 x 4 3/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286823,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.420,false,true,286824,Photographs,Cabinet card,[Standing with a Rosary],,,,,,Artist|Person in Photograph,,Pierre-Louis Pierson|Countess Virginia Oldoini Verasis di Castiglione,"French, 1822–1913|1835–1899",,"Pierson, Pierre-Louis|Castiglione, di, Virginia Oldoini Verasis Countess",French,1822 |1835,1913 |1899,1861–67,1861,1867,Albumen silver print from glass negative,Image: 11.7 x 8.5 cm (4 5/8 x 3 3/8 in.) Mount: 15.5 x 10 cm (6 1/8 x 3 15/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286824,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.421,false,true,286825,Photographs,Photograph,La Reine d'Étrurie,,,,,,Artist|Person in Photograph,,Pierre-Louis Pierson|Countess Virginia Oldoini Verasis di Castiglione,"French, 1822–1913|1835–1899",,"Pierson, Pierre-Louis|Castiglione, di, Virginia Oldoini Verasis Countess",French,1822 |1835,1913 |1899,1863–67,1863,1867,Albumen silver print from glass negative,Image: 11.3 x 9.4 cm (4 7/16 x 3 11/16 in.) Mount: 26.8 x 21 cm (10 9/16 x 8 1/4 in.) Mat: 35.6 x 27.9 cm (14 x 11 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286825,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.422,false,true,286826,Photographs,Photograph,"[Profile with Chignon, Large]",,,,,,Artist|Person in Photograph,,Pierre-Louis Pierson|Countess Virginia Oldoini Verasis di Castiglione,"French, 1822–1913|1835–1899",,"Pierson, Pierre-Louis|Castiglione, di, Virginia Oldoini Verasis Countess",French,1822 |1835,1913 |1899,1859,1859,1859,Albumen silver print from glass negative,Image: 26.3 x 21.2 cm (10 3/8 x 8 3/8 in.) Mount: 30.9 x 23.8 cm (12 3/16 x 9 3/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286826,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.426,false,true,285653,Photographs,Photograph,La Mà,,,,,,Artist|Person in Photograph,,Pierre-Louis Pierson|Countess Virginia Oldoini Verasis di Castiglione,"French, 1822–1913|1835–1899",,"Pierson, Pierre-Louis|Castiglione, di, Virginia Oldoini Verasis Countess",French,1822 |1835,1913 |1899,"August 21, 1895",1895,1895,Albumen silver print from glass negative,Image: 14.1 x 10.1 cm (5 9/16 x 4 in.) Mount: 14.1 x 10.1 cm (5 9/16 x 4 in.) Mat: 35.6 x 27.9 cm (14 x 11 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285653,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.427,false,true,286848,Photographs,Photograph,Baisemain,,,,,,Artist|Person in Photograph,,Pierre-Louis Pierson|Countess Virginia Oldoini Verasis di Castiglione,"French, 1822–1913|1835–1899",,"Pierson, Pierre-Louis|Castiglione, di, Virginia Oldoini Verasis Countess",French,1822 |1835,1913 |1899,"September 1, 1893",1893,1893,Albumen silver print from glass negative,Image: 14 x 9.9 cm (5 1/2 x 3 7/8 in.) Mount: 14 x 9.9 cm (5 1/2 x 3 7/8 in.) Mat: 35.6 x 27.9 cm (14 x 11 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286848,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.428,false,true,286849,Photographs,Photograph,Arrivo,,,,,,Artist|Person in Photograph,,Pierre-Louis Pierson|Countess Virginia Oldoini Verasis di Castiglione,"French, 1822–1913|1835–1899",,"Pierson, Pierre-Louis|Castiglione, di, Virginia Oldoini Verasis Countess",French,1822 |1835,1913 |1899,"September 1, 1893",1893,1893,Albumen silver print from glass negative,Image: 14.7 x 9.8 cm (5 13/16 x 3 7/8 in.) Mount: 14.7 x 9.8 cm (5 13/16 x 3 7/8 in.) Mat: 35.6 x 27.9 cm (14 x 11 in.) Frame: 37.3 x 30 cm (14 11/16 x 11 13/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286849,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.429,false,true,286850,Photographs,Photograph,[Countess de Castiglione],,,,,,Artist|Person in Photograph,,Pierre-Louis Pierson|Countess Virginia Oldoini Verasis di Castiglione,"French, 1822–1913|1835–1899",,"Pierson, Pierre-Louis|Castiglione, di, Virginia Oldoini Verasis Countess",French,1822 |1835,1913 |1899,1895,1895,1895,Albumen silver print from glass negative,Image: 15.1 x 10.1 cm (5 15/16 x 4 in.) Mount: 15.2 x 10.2 cm (6 x 4 in.) Mat: 35.6 x 27.9 cm (14 x 11 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286850,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.430,false,true,286851,Photographs,Photograph,Au Bureau,,,,,,Artist|Person in Photograph,,Pierre-Louis Pierson|Countess Virginia Oldoini Verasis di Castiglione,"French, 1822–1913|1835–1899",,"Pierson, Pierre-Louis|Castiglione, di, Virginia Oldoini Verasis Countess",French,1822 |1835,1913 |1899,"September 1, 1893",1893,1893,Albumen silver print from glass negative,Image: 13.7 x 9.9 cm (5 3/8 x 3 7/8 in.) Mount: 13.7 x 10 cm (5 3/8 x 3 15/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286851,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.431,false,true,286852,Photographs,Photograph,À la Barre,,,,,,Artist|Person in Photograph,,Pierre-Louis Pierson|Countess Virginia Oldoini Verasis di Castiglione,"French, 1822–1913|1835–1899",,"Pierson, Pierre-Louis|Castiglione, di, Virginia Oldoini Verasis Countess",French,1822 |1835,1913 |1899,"September 1, 1893",1893,1893,Albumen silver print from glass negative,Image: 13.8 x 10 cm (5 7/16 x 3 15/16 in.) Mount: 13.8 x 10 cm (5 7/16 x 3 15/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286852,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.432,false,true,286853,Photographs,Photograph,Roses Compiègne,,,,,,Artist|Person in Photograph,,Pierre-Louis Pierson|Countess Virginia Oldoini Verasis di Castiglione,"French, 1822–1913|1835–1899",,"Pierson, Pierre-Louis|Castiglione, di, Virginia Oldoini Verasis Countess",French,1822 |1835,1913 |1899,1895,1895,1895,Albumen silver print from glass negative,Image: 14.9 x 10.1 cm (5 7/8 x 4 in.) Mount: 15 x 10.1 cm (5 7/8 x 4 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286853,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.433,false,true,286854,Photographs,Photograph,Coin Noir de la Colonne,,,,,,Artist|Person in Photograph,,Pierre-Louis Pierson|Countess Virginia Oldoini Verasis di Castiglione,"French, 1822–1913|1835–1899",,"Pierson, Pierre-Louis|Castiglione, di, Virginia Oldoini Verasis Countess",French,1822 |1835,1913 |1899,"September 1, 1893",1893,1893,Albumen silver print from glass negative,Image: 9.8 x 14.2 cm (3 7/8 x 5 9/16 in.) Mount: 9.9 x 14.2 cm (3 7/8 x 5 9/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286854,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.387.5,false,true,288110,Photographs,Photograph,"[Countess de Castiglione, from Série des Roses]",,,,,,Artist|Person in Photograph,,Pierre-Louis Pierson|Countess Virginia Oldoini Verasis di Castiglione,"French, 1822–1913|1835–1899",,"Pierson, Pierre-Louis|Castiglione, di, Virginia Oldoini Verasis Countess",French,1822 |1835,1913 |1899,1895,1895,1895,Albumen silver print from glass negative,Approximately 14.3 x 9.9 cm (5 5/8 x 3 7/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288110,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.388.3,false,true,288126,Photographs,Photograph,[Countess de Castiglione],,,,,,Artist|Person in Photograph,,Pierre-Louis Pierson|Countess Virginia Oldoini Verasis di Castiglione,"French, 1822–1913|1835–1899",,"Pierson, Pierre-Louis|Castiglione, di, Virginia Oldoini Verasis Countess",French,1822 |1835,1913 |1899,1895,1895,1895,Albumen silver print from glass negative,Approximately 14.3 x 9.9 cm (5 5/8 x 3 7/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288126,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.410 (3a),false,true,288157,Photographs,Photograph,[Countess de Castiglione as the Queen of Etruria],,,,,,Artist|Artist|Person in Photograph,Painted and retouched by|Person in photograph,Pierre-Louis Pierson|Marck|Countess Virginia Oldoini Verasis di Castiglione,"French, 1822–1913|1835–1899",", et al","Pierson, Pierre-Louis|Marck|Castiglione, di, Virginia Oldoini Verasis Countess",French,1822 |1835,1913 |1899,before 1865,1855,1865,Albumen silver print from glass negative,Window: 7 1/2 × 5 1/2 in. (19 × 14 cm) Image: 4 in. × 2 13/16 in. (10.2 × 7.2 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288157,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.410 (3b),false,true,288158,Photographs,Photograph,[Countess de Castiglione as Anne Boleyn],,,,,,Artist|Artist|Person in Photograph,Painted and retouched by|Person in photograph,Pierre-Louis Pierson|Marck|Countess Virginia Oldoini Verasis di Castiglione,"French, 1822–1913|1835–1899",", et al","Pierson, Pierre-Louis|Marck|Castiglione, di, Virginia Oldoini Verasis Countess",French,1822 |1835,1913 |1899,before 1865,1855,1865,Albumen silver print from glass negative,Window: 7 1/2 × 5 1/2 in. (19 × 14 cm) Image: 4 in. × 2 13/16 in. (10.2 × 7.2 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288158,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.263–.267,false,true,684336,Photographs,Photograph,[Album page with ten photographs of La Comtesse mounted recto and verso],,,,,,Artist|Person in Photograph,,Pierre-Louis Pierson|Countess Virginia Oldoini Verasis di Castiglione,"French, 1822–1913|1835–1899",,"Pierson, Pierre-Louis|Castiglione, di, Virginia Oldoini Verasis Countess",French,1822 |1835,1913 |1899,1861–67,1861,1867,Albumen silver prints from glass negative,10.8 x 8.6 cm (4 1/4 x 3 3/8 in.) to 2.5 x 3.5 cm (1 x 1 3/8 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/684336,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.391.1–.2,false,true,286842,Photographs,Photograph,Le Pé,,,,,,Artist|Person in Photograph,,Pierre-Louis Pierson|Countess Virginia Oldoini Verasis di Castiglione,"French, 1822–1913|1835–1899",,"Pierson, Pierre-Louis|Castiglione, di, Virginia Oldoini Verasis Countess",French,1822 |1835,1913 |1899,"August 1, 1894",1894,1894,Albumen silver print from glass negative,Image: 14.4 x 10 cm (5 11/16 x 3 15/16 in.) Mat: 35.6 x 27.9 cm (14 x 11 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286842,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.71,false,true,287478,Photographs,Photograph,[Empress Eugénie's Poodle],,,,,,Photography Studio,,André-Adolphe-Eugène Disdéri,"French, Paris 1819–1889 Paris",,"Disdéri, André-Adolphe-Eugène",French,1819,1889,1850s,1850,1859,Salted paper print from collodion glass negative,Image: 20.7 x 20.8 cm (8 1/8 x 8 3/16 in.) Sheet: 22.2 x 29 cm (8 3/4 x 11 7/16 in.),"Gilman Collection, Purchase, The Howard Gilman Foundation Gift, 2007",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287478,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -46.122.20,false,true,269069,Photographs,Photograph,Ruines gothiques,,,,,,Artist|Printer,,"A. Fays|Imprimerie photographique de Blanquart-Évrard, à Lille","French|French, active 1851–55",,"Fays, A.|Imprimerie photographique de Blanquart-Évrard, à Lille",French,1851,1855,1850–53,1850,1853,Salted paper print (Blanquart-Évrard process) from paper negative,,"Harris Brisbane Dick Fund, 1946",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269069,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.395,false,true,285479,Photographs,Photograph,Ermitage de Passy,,,,,,Artist|Artist|Person in Photograph,Painted and retouched by,Pierre-Louis Pierson|Unknown|Countess Virginia Oldoini Verasis di Castiglione,"French, 1822–1913|French|1835–1899",,"Pierson, Pierre-Louis|Unknown|Castiglione, di, Virginia Oldoini Verasis Countess",French,1822 |1835,1913 |1899,1863,1863,1863,Albumen silver print from glass negative,Image: 12 x 8.5 cm (4 3/4 x 3 3/8 in.) Mount: 13 x 9.6 cm (5 1/8 x 3 3/4 in.) Mat: 35.6 x 27.9 cm (14 x 11 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285479,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.396,false,true,285649,Photographs,Photograph,The Red Bow,,,,,,Artist|Artist|Person in Photograph,Painted and retouched by,Pierre-Louis Pierson|Unknown|Countess Virginia Oldoini Verasis di Castiglione,"French, 1822–1913|French|1835–1899",,"Pierson, Pierre-Louis|Unknown|Castiglione, di, Virginia Oldoini Verasis Countess",French,1822 |1835,1913 |1899,1861–67,1861,1867,Albumen silver print from glass negative,Image: 18.7 x 13.6 cm (7 3/8 x 5 3/8 in.) Mount: 32.3 x 23.8 cm (12 11/16 x 9 3/8 in.) Mat: 50.8 x 40.6 cm (20 x 16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285649,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.397,false,true,286829,Photographs,Photograph,Les Cothurnes,,,,,,Artist|Artist|Person in Photograph,Painted and retouched by,Pierre-Louis Pierson|Unknown|Countess Virginia Oldoini Verasis di Castiglione,"French, 1822–1913|French|1835–1899",,"Pierson, Pierre-Louis|Unknown|Castiglione, di, Virginia Oldoini Verasis Countess",French,1822 |1835,1913 |1899,1861–67,1861,1867,Albumen silver print from glass negative,Image: 5.4 x 7.5 cm (2 1/8 x 2 15/16 in.) Mount: 6.8 x 8.6 cm (2 11/16 x 3 3/8 in.) Mat: 35.6 x 27.9 cm (14 x 11 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286829,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.398,false,true,286830,Photographs,Photograph,Les Cothurnes,,,,,,Artist|Artist|Person in Photograph,Painted and retouched by,Pierre-Louis Pierson|Unknown|Countess Virginia Oldoini Verasis di Castiglione,"French, 1822–1913|French|1835–1899",,"Pierson, Pierre-Louis|Unknown|Castiglione, di, Virginia Oldoini Verasis Countess",French,1822 |1835,1913 |1899,1861–67,1861,1867,Albumen silver print from glass negative,Image: 5.4 x 7.5 cm (2 1/8 x 2 15/16 in.) Mount: 6.8 x 8.8 cm (2 11/16 x 3 7/16 in.) Mat: 35.6 x 27.9 cm (14 x 11 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286830,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.399,false,true,286831,Photographs,Photograph,La Reine d'Étrurie,,,,,,Artist|Artist|Person in Photograph,Painted and retouched by,Pierre-Louis Pierson|Unknown|Countess Virginia Oldoini Verasis di Castiglione,"French, 1822–1913|French|1835–1899",,"Pierson, Pierre-Louis|Unknown|Castiglione, di, Virginia Oldoini Verasis Countess",French,1822 |1835,1913 |1899,1863–67,1863,1867,Albumen silver print from glass negative,Image: 10.5 x 8.3 cm (4 1/8 x 3 1/4 in.) Mount: 12.9 x 9.4 cm (5 1/16 x 3 11/16 in.) Mat: 35.6 x 27.9 cm (14 x 11 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286831,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.400,false,true,286832,Photographs,Photograph,[La Finlandaise],,,,,,Artist|Artist|Person in Photograph,Painted and retouched by,Pierre-Louis Pierson|Unknown|Countess Virginia Oldoini Verasis di Castiglione,"French, 1822–1913|French|1835–1899",,"Pierson, Pierre-Louis|Unknown|Castiglione, di, Virginia Oldoini Verasis Countess",French,1822 |1835,1913 |1899,1861–67,1861,1867,Albumen silver print from glass negative,Image: 12.2 x 8.8 cm (4 13/16 x 3 7/16 in.) Mount: 13.9 x 10.9 cm (5 1/2 x 4 5/16 in.) Mat: 35.6 x 27.9 cm (14 x 11 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286832,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.401,false,true,286833,Photographs,Photograph,Costigliole,,,,,,Artist|Artist|Person in Photograph,Painted and retouched by,Pierre-Louis Pierson|Unknown|Countess Virginia Oldoini Verasis di Castiglione,"French, 1822–1913|French|1835–1899",,"Pierson, Pierre-Louis|Unknown|Castiglione, di, Virginia Oldoini Verasis Countess",French,1822 |1835,1913 |1899,1862–67,1862,1867,Albumen silver print from glass negative,Image: 12.5 x 9 cm (4 15/16 x 3 9/16 in.) Mount: 14.5 x 10.7 cm (5 11/16 x 4 3/16 in.) Mat: 35.6 x 43.2 cm (14 x 17 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286833,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.402,false,true,286834,Photographs,Photograph,Costigliole,,,,,,Artist|Artist|Person in Photograph,Painted and retouched by,Pierre-Louis Pierson|Unknown|Countess Virginia Oldoini Verasis di Castiglione,"French, 1822–1913|French|1835–1899",,"Pierson, Pierre-Louis|Unknown|Castiglione, di, Virginia Oldoini Verasis Countess",French,1822 |1835,1913 |1899,1862–67,1862,1867,Albumen silver print from glass negative,Image: 12.5 x 8.9 cm (4 15/16 x 3 1/2 in.) Mount: 14.4 x 10.9 cm (5 11/16 x 4 5/16 in.) Mat: 35.6 x 43.2 cm (14 x 17 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286834,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.406,false,true,285609,Photographs,Photograph,La Frayeur,,,,,,Artist|Artist|Person in Photograph,Painted and retouched by,Pierre-Louis Pierson|Unknown|Countess Virginia Oldoini Verasis di Castiglione,"French, 1822–1913|French|1835–1899",,"Pierson, Pierre-Louis|Unknown|Castiglione, di, Virginia Oldoini Verasis Countess",French,1822 |1835,1913 |1899,1861–67,1861,1867,Salted paper print from glass negative with applied color,Image: 23.6 x 17 cm (9 5/16 x 6 11/16 in.) Mount: 31.7 x 23.8 cm (12 1/2 x 9 3/8 in.) Mat: 57.2 x 47 cm (22 1/2 x 18 1/2 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285609,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.407,false,true,286838,Photographs,Photograph,Béatrix,,,,,,Artist|Artist|Person in Photograph,Painted and retouched by,Pierre-Louis Pierson|Unknown|Countess Virginia Oldoini Verasis di Castiglione,"French, 1822–1913|French|1835–1899",,"Pierson, Pierre-Louis|Unknown|Castiglione, di, Virginia Oldoini Verasis Countess",French,1822 |1835,1913 |1899,"1856–57, printed 1861–67",1856,1867,Salted paper print from glass negative,Image: 23.2 x 17.8 cm (9 1/8 x 7 in.) Mount: 31.7 x 23.8 cm (12 1/2 x 9 3/8 in.) Mat: 57.2 x 47 cm (22 1/2 x 18 1/2 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286838,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.408a–d,false,true,286839,Photographs,Photograph,"[Variations on the ""Elvira"" and ""Ritrosetta"" Dresses]",,,,,,Artist|Artist|Person in Photograph,Painted and retouched by,Pierre-Louis Pierson|Unknown|Countess Virginia Oldoini Verasis di Castiglione,"French, 1822–1913|French|1835–1899",,"Pierson, Pierre-Louis|Unknown|Castiglione, di, Virginia Oldoini Verasis Countess",French,1822 |1835,1913 |1899,1861–67,1861,1867,Albumen silver print from glass negative,Image: 12.4 x 8.8 cm (4 7/8 x 3 7/16 in.) A Image: 12.5 x 8.9 cm (4 15/16 x 3 1/2 in.) B Image: 12.3 x 8.8 cm (4 13/16 x 3 7/16 in.) C Image: 12.4 x 8.9 cm (4 7/8 x 3 1/2 in.) D Mount: 32.2 x 24 cm (12 11/16 x 9 7/16 in.) Mat: 50.8 x 40.6 cm (20 x 16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286839,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.409a–d,false,true,286840,Photographs,Photograph,"[Variations on the ""Elvira"" Dress]",,,,,,Artist|Artist|Person in Photograph,Painted and retouched by,Pierre-Louis Pierson|Unknown|Countess Virginia Oldoini Verasis di Castiglione,"French, 1822–1913|French|1835–1899",,"Pierson, Pierre-Louis|Unknown|Castiglione, di, Virginia Oldoini Verasis Countess",French,1822 |1835,1913 |1899,1861–67,1861,1867,Albumen silver print from glass negative,Image: 12.5 x 8.9 cm (4 15/16 x 3 1/2 in.) A Image: 12.5 x 8.7 cm (4 15/16 x 3 7/16 in.) B Image: 12.3 x 8.9 cm (4 13/16 x 3 1/2 in.) C Image: 12.3 x 8.8 cm (4 13/16 x 3 7/16 in.) D Mount: 32.2 x 24 cm (12 11/16 x 9 7/16 in.) Mat: 50.8 x 40.6 cm (20 x 16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286840,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2005.100.714.1, .2",false,true,286111,Photographs,Photograph,[Auguste Vacquerie at Marine Terrace],,,,,,Artist|Author,,Charles Victor Hugo|Auguste Vacquerie,"French, 1826–1871|French, 1819–1855",,"Hugo, Charles Victor|Vacquerie, Auguste",French,1826 |1819,1871 |1855,1855,1855,1855,Salted paper print from glass negative,Image: 4 1/8 × 2 15/16 in. (10.5 × 7.4 cm) Mount: 8 5/8 × 5 13/16 in. (21.9 × 14.8 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286111,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.187,false,true,306174,Photographs,Photograph,Arak el Emir,,,,,,Artist|Printer,Possibly printed by,Louis Vignes|Charles Nègre,"French, 1831–1896|French, 1820–1880",,"Vignes, Louis|Nègre, Charles",French,1831 |1820,1896 |1880,1864,1864,1864,Albumen silver print from glass negative,Image: 19 × 24.7 cm (7 1/2 × 9 3/4 in.) Mount: 27.7 × 35.7 cm (10 7/8 × 14 1/16 in.),"Purchase, Susan and Thomas Dunn Gift, 2013",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306174,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -46.122.9,false,true,269078,Photographs,Photograph,Joueurs de vielle,,,,,,Artist|Printer,,"Louis-Désiré Blanquart-Évrard|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1802–1872|French, active 1851–55",,"Blanquart-Évrard, Louis-Désiré|Imprimerie photographique de Blanquart-Évrard, à Lille",French,1802 |1851,1872 |1855,1850–53,1850,1853,Salted paper print from paper negative,,"Harris Brisbane Dick Fund, 1946",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269078,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -46.122.14,false,true,269062,Photographs,Photograph,"Nature morte: chaudron, cruche, et légumes, sur une table à trétaux",,,,,,Artist|Printer,,"Henri Victor Regnault|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1810–1878|French, active 1851–55",,"Regnault, Henri Victor|Imprimerie photographique de Blanquart-Évrard, à Lille",French,1810 |1851,1878 |1855,1850–53,1850,1853,Salted paper print (Blanquart-Évrard process) from paper negative,,"Harris Brisbane Dick Fund, 1946",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269062,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1977.673.10,false,true,262126,Photographs,Photograph,"Nubie. Grand Temple d'Isis, A Philoe. Vue générale prise du nord",,,,,,Artist|Printer,,"Maxime Du Camp|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1822–1894|French, active 1851–55",,"Du Camp, Maxime|Imprimerie photographique de Blanquart-Évrard, à Lille",French,1822 |1851,1894 |1855,1850,1850,1850,Salted paper print (Blanquart-Évrard process) from paper negative,16.8 x 23.1 cm. (6 5/8 x 9 1/16 in.),"Robert O. Dougan Collection, Gift of Warner Communications Inc., 1973",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/262126,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1977.673.11,false,true,262127,Photographs,Photograph,"Nubie. Grand Temple d'Isis, A Philoe",,,,,,Artist|Printer,,"Maxime Du Camp|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1822–1894|French, active 1851–55",,"Du Camp, Maxime|Imprimerie photographique de Blanquart-Évrard, à Lille",French,1822 |1851,1894 |1855,1850,1850,1850,Salted paper print (Blanquart-Évrard process) from paper negative,16.0 x 22.4 cm. (6 5/16 x 8 13/16 in.),"Robert O. Dougan Collection, Gift of Warner Communications Inc., 1977",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/262127,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1981.1229.6.1,false,true,263214,Photographs,Photograph,Égypte Moyenne. Pyramide de Chéphren,,,,,,Artist|Printer,,"Maxime Du Camp|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1822–1894|French, active 1851–55",,"Du Camp, Maxime|Imprimerie photographique de Blanquart-Évrard, à Lille",French,1822 |1851,1894 |1855,1850,1850,1850,Salted paper print (Blanquart-Évrard process) from paper negative,,"Robert O. Dougan Collection, Gift of Warner Communications Inc., 1981",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/263214,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1981.1229.6.2,false,true,263225,Photographs,Photograph,Haute-Égypte. Girgeh. Mosquèe d'Aly-Bey,,,,,,Artist|Printer,,"Maxime Du Camp|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1822–1894|French, active 1851–55",,"Du Camp, Maxime|Imprimerie photographique de Blanquart-Évrard, à Lille",French,1822 |1851,1894 |1855,1850,1850,1850,Salted paper print (Blanquart-Évrard process) from paper negative,,"Robert O. Dougan Collection, Gift of Warner Communications Inc., 1981",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/263225,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1981.1229.6.4,false,true,263234,Photographs,Photograph,Thebes. Palais de Karnak. Sculptures extérieures du Sanctuaire de granit,,,,,,Artist|Printer,,"Maxime Du Camp|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1822–1894|French, active 1851–55",,"Du Camp, Maxime|Imprimerie photographique de Blanquart-Évrard, à Lille",French,1822 |1851,1894 |1855,1850,1850,1850,Salted paper print (Blanquart-Évrard process) from paper negative,,"Robert O. Dougan Collection, Gift of Warner Communications Inc., 1981",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/263234,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1981.1229.6.5,false,true,263235,Photographs,Photograph,Thebes. Palais de Karnak. Sanctuaire de granit et salle Hypostyle,,,,,,Artist|Printer,,"Maxime Du Camp|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1822–1894|French, active 1851–55",,"Du Camp, Maxime|Imprimerie photographique de Blanquart-Évrard, à Lille",French,1822 |1851,1894 |1855,1850,1850,1850,Salted paper print (Blanquart-Évrard process) from paper negative,,"Robert O. Dougan Collection, Gift of Warner Communications Inc., 1981",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/263235,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1981.1229.6.6,false,true,263236,Photographs,Photograph,Thebes. Médinet-Habou. Partie orientale du Péristyle du Palais de Ramsès-Méiamoun,,,,,,Artist|Printer,,"Maxime Du Camp|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1822–1894|French, active 1851–55",,"Du Camp, Maxime|Imprimerie photographique de Blanquart-Évrard, à Lille",French,1822 |1851,1894 |1855,1850,1850,1850,Salted paper print (Blanquart-Évrard process) from paper negative,,"Robert O. Dougan Collection, Gift of Warner Communications Inc., 1981",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/263236,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1981.1229.6.7,false,true,263237,Photographs,Photograph,Thebes. Médinet-Habou. Runes de la ville de Papa,,,,,,Artist|Printer,,"Maxime Du Camp|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1822–1894|French, active 1851–55",,"Du Camp, Maxime|Imprimerie photographique de Blanquart-Évrard, à Lille",French,1822 |1851,1894 |1855,1850,1850,1850,Salted paper print (Blanquart-Évrard process) from paper negative,,"Robert O. Dougan Collection, Gift of Warner Communications Inc., 1981",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/263237,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1981.1229.6.10,false,true,263215,Photographs,Photograph,Haute-Égypte. Entrée de la première Cataracte,,,,,,Artist|Printer,,"Maxime Du Camp|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1822–1894|French, active 1851–55",,"Du Camp, Maxime|Imprimerie photographique de Blanquart-Évrard, à Lille",French,1822 |1851,1894 |1855,1850,1850,1850,Salted paper print (Blanquart-Évrard process) from paper negative,,"Robert O. Dougan Collection, Gift of Warner Communications Inc., 1981",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/263215,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1981.1229.6.12,false,true,263217,Photographs,Photograph,Nubie. Philoe,,,,,,Artist|Printer,,"Maxime Du Camp|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1822–1894|French, active 1851–55",,"Du Camp, Maxime|Imprimerie photographique de Blanquart-Évrard, à Lille",French,1822 |1851,1894 |1855,1850,1850,1850,Salted paper print (Blanquart-Évrard process) from paper negative,,"Robert O. Dougan Collection, Gift of Warner Communications Inc., 1981",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/263217,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1981.1229.6.13,false,true,263218,Photographs,Photograph,Nubie. Grand Temple D'Isis A Philoe. Muraille occidentale,,,,,,Artist|Printer,,"Maxime Du Camp|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1822–1894|French, active 1851–55",,"Du Camp, Maxime|Imprimerie photographique de Blanquart-Évrard, à Lille",French,1822 |1851,1894 |1855,1850,1850,1850,Salted paper print (Blanquart-Évrard process) from paper negative,,"Robert O. Dougan Collection, Gift of Warner Communications Inc., 1981",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/263218,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1981.1229.6.14,false,true,263219,Photographs,Photograph,Nubie. Rive Orientale du Nil (Village de Bab). Vue prise au sud de Philoe,,,,,,Artist|Printer,,"Maxime Du Camp|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1822–1894|French, active 1851–55",,"Du Camp, Maxime|Imprimerie photographique de Blanquart-Évrard, à Lille",French,1822 |1851,1894 |1855,1850,1850,1850,Salted paper print (Blanquart-Évrard process) from paper negative,,"Robert O. Dougan Collection, Gift of Warner Communications Inc., 1981",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/263219,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1981.1229.6.15,false,true,263220,Photographs,Photograph,Nubie. Temple et Village de Débôd. Parembole de l'itinéraire d'Antonin,,,,,,Artist|Printer,,"Maxime Du Camp|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1822–1894|French, active 1851–55",,"Du Camp, Maxime|Imprimerie photographique de Blanquart-Évrard, à Lille",French,1822 |1851,1894 |1855,1850,1850,1850,Salted paper print (Blanquart-Évrard process) from paper negative,,"Robert O. Dougan Collection, Gift of Warner Communications Inc., 1981",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/263220,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1981.1229.6.16,false,true,263221,Photographs,Photograph,Nubie. Temple de Déböd. Parembole de l'itinéraire d'Antonin,,,,,,Artist|Printer,,"Maxime Du Camp|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1822–1894|French, active 1851–55",,"Du Camp, Maxime|Imprimerie photographique de Blanquart-Évrard, à Lille",French,1822 |1851,1894 |1855,1850,1850,1850,Salted paper print (Blanquart-Évrard process) from paper negative,,"Robert O. Dougan Collection, Gift of Warner Communications Inc., 1981",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/263221,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1981.1229.6.17,false,true,263222,Photographs,Photograph,Nubie. Kalabscheh. Sculptures de la Facade postérieure du Temple,,,,,,Artist|Printer,,"Maxime Du Camp|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1822–1894|French, active 1851–55",,"Du Camp, Maxime|Imprimerie photographique de Blanquart-Évrard, à Lille",French,1822 |1851,1894 |1855,1850,1850,1850,Salted paper print (Blanquart-Évrard process) from paper negative,,"Robert O. Dougan Collection, Gift of Warner Communications Inc., 1981",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/263222,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1981.1229.6.19,false,true,263224,Photographs,Photograph,Nubie. Hémi-Spéos de Sébour. Pylônes,,,,,,Artist|Printer,,"Maxime Du Camp|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1822–1894|French, active 1851–55",,"Du Camp, Maxime|Imprimerie photographique de Blanquart-Évrard, à Lille",French,1822 |1851,1894 |1855,1850,1850,1850,Salted paper print (Blanquart-Évrard process) from paper negative,,"Robert O. Dougan Collection, Gift of Warner Communications Inc., 1981",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/263224,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1981.1229.6.20,false,true,263226,Photographs,Photograph,Nubie. Forteresse D'Ibrym (Ancienne Premmis). Vue prise au sud.,,,,,,Artist|Printer,,"Maxime Du Camp|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1822–1894|French, active 1851–55",,"Du Camp, Maxime|Imprimerie photographique de Blanquart-Évrard, à Lille",French,1822 |1851,1894 |1855,1850,1850,1850,Salted paper print (Blanquart-Évrard process) from paper negative,,"Robert O. Dougan Collection, Gift of Warner Communications Inc., 1981",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/263226,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1981.1229.6.21,false,true,263227,Photographs,Photograph,Nubie. Ibsamboul. Partie septentrionale du Spéos d'Hathor,,,,,,Artist|Printer,,"Maxime Du Camp|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1822–1894|French, active 1851–55",,"Du Camp, Maxime|Imprimerie photographique de Blanquart-Évrard, à Lille",French,1822 |1851,1894 |1855,1850,1850,1850,Salted paper print (Blanquart-Évrard process) from paper negative,,"Robert O. Dougan Collection, Gift of Warner Communications Inc., 1981",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/263227,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1981.1229.6.22,false,true,263228,Photographs,Photograph,Nubie. Ibsamboul. Entrée du Spéos d'Hathor,,,,,,Artist|Printer,,"Maxime Du Camp|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1822–1894|French, active 1851–55",,"Du Camp, Maxime|Imprimerie photographique de Blanquart-Évrard, à Lille",French,1822 |1851,1894 |1855,1850,1850,1850,Salted paper print (Blanquart-Évrard process) from paper negative,,"Robert O. Dougan Collection, Gift of Warner Communications Inc., 1981",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/263228,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1981.1229.6.23,false,true,263229,Photographs,Photograph,Nubie. Vue Cavalière de la Seconde Cataracte,,,,,,Artist|Printer,,"Maxime Du Camp|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1822–1894|French, active 1851–55",,"Du Camp, Maxime|Imprimerie photographique de Blanquart-Évrard, à Lille",French,1822 |1851,1894 |1855,1850,1850,1850,Salted paper print (Blanquart-Évrard process) from paper negative,,"Robert O. Dougan Collection, Gift of Warner Communications Inc., 1981",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/263229,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1981.1229.6.24,false,true,263230,Photographs,Photograph,Nubie. Seconde Cataracte. Dgebel-Abouoir,,,,,,Artist|Printer,,"Maxime Du Camp|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1822–1894|French, active 1851–55",,"Du Camp, Maxime|Imprimerie photographique de Blanquart-Évrard, à Lille",French,1822 |1851,1894 |1855,1850,1850,1850,Salted paper print (Blanquart-Évrard process) from paper negative,,"Robert O. Dougan Collection, Gift of Warner Communications Inc., 1981",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/263230,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1981.1229.6.25,false,true,263231,Photographs,Photograph,Palestine. Jérusalem. Partie occidentale des Murailles,,,,,,Artist|Printer,,"Maxime Du Camp|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1822–1894|French, active 1851–55",,"Du Camp, Maxime|Imprimerie photographique de Blanquart-Évrard, à Lille",French,1822 |1851,1894 |1855,1850,1850,1850,Salted paper print (Blanquart-Évrard process) from paper negative,,"Robert O. Dougan Collection, Gift of Warner Communications Inc., 1981",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/263231,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1981.1229.6.26,false,true,263232,Photographs,Photograph,Palestine. Jérusalem. Mosquée d'Omar,,,,,,Artist|Printer,,"Maxime Du Camp|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1822–1894|French, active 1851–55",,"Du Camp, Maxime|Imprimerie photographique de Blanquart-Évrard, à Lille",French,1822 |1851,1894 |1855,1850,1850,1850,Salted paper print (Blanquart-Évrard process) from paper negative,,"Robert O. Dougan Collection, Gift of Warner Communications Inc., 1981",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/263232,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -46.122.3,false,true,269072,Photographs,Photograph,Homme allongé au pied d'un chàtaignier,,,,,,Artist|Printer,,"Charles Marville|Imprimerie photographique de Blanquart-Évrard, à Lille","French, Paris 1813–1879 Paris|French, active 1851–55",,"Marville, Charles|Imprimerie photographique de Blanquart-Évrard, à Lille",French,1813 |1851,1879 |1855,1850–53,1850,1853,Salted paper print (Blanquart-Évrard process) from paper negative,20.9 x 16.2 cm (8 1/4 x 6 3/8 in.),"Harris Brisbane Dick Fund, 1946",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269072,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -46.122.5,false,true,269074,Photographs,Photograph,Allée bordée d'arbres,,,,,,Artist|Printer,,"Charles Marville|Imprimerie photographique de Blanquart-Évrard, à Lille","French, Paris 1813–1879 Paris|French, active 1851–55",,"Marville, Charles|Imprimerie photographique de Blanquart-Évrard, à Lille",French,1813 |1851,1879 |1855,1850–53,1850,1853,Salted paper print (Blanquart-Évrard process) from paper negative,,"Harris Brisbane Dick Fund, 1946",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269074,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -46.122.17,false,true,269065,Photographs,Photograph,Carrière,,,,,,Artist|Printer,,"Charles Marville|Imprimerie photographique de Blanquart-Évrard, à Lille","French, Paris 1813–1879 Paris|French, active 1851–55",,"Marville, Charles|Imprimerie photographique de Blanquart-Évrard, à Lille",French,1813 |1851,1879 |1855,1850–53,1850,1853,Salted paper print (Blanquart-Évrard process) from paper negative,,"Harris Brisbane Dick Fund, 1946",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269065,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -46.122.4,false,true,269073,Photographs,Photograph,Mt. Liban. Tronc d'un des Cèdres de Salomon,,,,,,Artist|Printer,,"Ernest Benecke|Imprimerie photographique de Blanquart-Évrard, à Lille","German, born England, 1817–1894|French, active 1851–55",,"Benecke, Ernest|Imprimerie photographique de Blanquart-Évrard, à Lille",French,1817 |1851,1894 |1855,1850–53,1850,1853,Salted paper print (Blanquart-Évrard process) from paper negative,,"Harris Brisbane Dick Fund, 1946",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269073,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2000.235,false,true,283727,Photographs,Waxed paper negative,Jacques-Joseph Ebelman on his Deathbed,,,,,,Artist,,Louis-Rémy Robert,"French, 1810–1882",,"Robert, Louis-Rémy",French,1810,1882,"March 31, 1852",1852,1852,Waxed paper negative,19 x 24.5 cm (7 1/2 x 9 5/8 in.),"Purchase, The Hite Foundation Gift, 2000",,,,,,,,,,,,Negatives,,http://www.metmuseum.org/art/collection/search/283727,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2006.248,false,true,287309,Photographs,Paper negative,[Gardens of the Château de Saint-Cloud],,,,,,Artist,,Louis-Rémy Robert,"French, 1810–1882",,"Robert, Louis-Rémy",French,1810,1882,ca. 1853,1851,1855,Paper negative,Sheet: 32.6 x 23.8 cm (12 13/16 x 9 3/8 in.),"Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2006",,,,,,,,,,,,Negatives,,http://www.metmuseum.org/art/collection/search/287309,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.686,false,true,286771,Photographs,Negative; Photograph,"[Village Scene, Brittany]",,,,,,Artist,,Louis-Rémy Robert,"French, 1810–1882",,"Robert, Louis-Rémy",French,1810,1882,ca. 1854,1852,1856,Paper negative,Image: 12 3/4 × 10 3/16 in. (32.4 × 25.8 cm) (image only),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Negatives,,http://www.metmuseum.org/art/collection/search/286771,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.868,false,true,286247,Photographs,Negative; Photograph,[Fountain at Versailles],,,,,,Artist,,Louis-Rémy Robert,"French, 1810–1882",,"Robert, Louis-Rémy",French,1810,1882,ca. 1851,1849,1853,Paper negative,Image: 10 5/16 × 12 11/16 in. (26.2 × 32.3 cm) (image only),"Gilman Collection, Purchase, Jennifer and Joseph Duke Gift, 2005",,,,,,,,,,,,Negatives,,http://www.metmuseum.org/art/collection/search/286247,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.877,false,true,286248,Photographs,Negative; Photograph,[Still Life],,,,,,Artist,,Louis-Rémy Robert,"French, 1810–1882",,"Robert, Louis-Rémy",French,1810,1882,1850,1850,1850,Paper negative,Sheet: 10 5/8 × 7 3/8 in. (27 × 18.7 cm) Image: 10 3/8 × 7 1/16 in. (26.4 × 18 cm),"Gilman Collection, Purchase, Anonymous Gifts, by exchange, 2005",,,,,,,,,,,,Negatives,,http://www.metmuseum.org/art/collection/search/286248,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.863,false,true,286571,Photographs,Negative; Photograph,"[Bas-Relief, Arch of Constantine, Rome]",,,,,,Artist,,Frédéric Flachéron,"French, 1813–1883",,"Flachéron, Frédéric",French,1813,1883,1849,1849,1849,Paper negative,Image: 12 3/4 × 9 15/16 in. (32.4 × 25.2 cm),"Gilman Collection, Purchase, Anonymous Gifts, by exchange, 2005",,,,,,,,,,,,Negatives,,http://www.metmuseum.org/art/collection/search/286571,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2000.208.2,false,true,283722,Photographs,Paper negative,[Boy in Uniform],,,,,,Artist,,Jean-Baptiste Frénet,"French, 1814–1889",,"Frénet, Jean-Baptiste",French,1814,1889,ca. 1855,1853,1857,Paper negative,24.5 x 17.9 cm (9 5/8 x 7 1/16 in. ),"Purchase, Noel and Harriette Levine Gift and Jennifer and Joseph Duke Gift, 2000",,,,,,,,,,,,Negatives,,http://www.metmuseum.org/art/collection/search/283722,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -46.122.21,false,true,269070,Photographs,Photomechanical print,[Man and Boy],,,,,,Artist,,Armand-Hippolyte-Louis Fizeau,"French, 1819–1896",,"Fizeau, Armand-Hippolyte-Louis",French,1819,1896,ca. 1841,1840,1842,Photogravure,,"Harris Brisbane Dick Fund, 1946",,,,,,,,,,,,Photographs|Prints,,http://www.metmuseum.org/art/collection/search/269070,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -46.122.22,false,true,269071,Photographs,Photomechanical print,"St. Sulpice, Paris",,,,,,Artist,,Armand-Hippolyte-Louis Fizeau,"French, 1819–1896",,"Fizeau, Armand-Hippolyte-Louis",French,1819,1896,ca. 1841,1840,1842,Photogravure,,"Harris Brisbane Dick Fund, 1946",,,,,,,,,,,,Photographs|Prints,,http://www.metmuseum.org/art/collection/search/269071,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1998.475,false,true,282737,Photographs,Album,Le Midi de la France,,,,,,Artist,,Charles Nègre,"French, 1820–1880",,"Nègre, Charles",French,1820,1880,1850s–60s,1850,1869,Albumen silver prints,24.4 x 29.4 x 6.3 cm (9 5/8 x 11 9/16 x 2 1/2 in. ),"Gift of W. Bruce and Delaney H. Lundberg, 1998",,,,,,,,,,,,Albums,,http://www.metmuseum.org/art/collection/search/282737,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1995.562,false,true,267296,Photographs,Photomechanical print,"[Chartres Cathedral, Central Portal of the South Transept; The Last Judgment]",,,,,,Artist,,Charles Nègre,"French, 1820–1880",,"Nègre, Charles",French,1820,1880,"1855, printed 1857",1855,1855,Photogravure,60.0 x 48.5 cm. (23 5/8 x 19 1/16 in.),"Gift of Charles Isaacs, 1995",,,,,,,,,,,,Photographs|Prints,,http://www.metmuseum.org/art/collection/search/267296,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.1–.175,false,true,286683,Photographs,Portfolio,"Égypte, Nubie, Syrie: Paysages et Monuments",,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,1849–50,1849,1850,Salted paper prints from paper negatives,49 x 33 x 6 cm (19 5/16 x 13 x 2 3/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Portfolios,,http://www.metmuseum.org/art/collection/search/286683,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.892,false,true,285681,Photographs,Negative; Photograph,[Model of a P.L.M. Locomotive],,,,,,Artist,,Dominique Roman,"French, 1824–1911",,"Roman, Dominique",French,1824,1911,ca. 1855,1853,1857,Paper negative,Image: 12 5/16 × 18 7/8 in. (31.2 × 48 cm),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Negatives,,http://www.metmuseum.org/art/collection/search/285681,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.270,false,true,307069,Photographs,Waxed Paper Negative,"[Palace of the Dey of Algiers, Algeria]",,,,,,Artist,,Gustave de Beaucorps,"French, 1825–1906",,"Beaucorps, Gustave de",French,1825,1906,1859,1859,1859,Waxed paper negative,"Image: 28.7 × 38.6 cm (11 5/16 × 15 3/16 in.) Sheet: 29.1 × 39.8 cm (11 7/16 × 15 11/16 in.), irregularly trimmed","The Horace W. Goldsmith Foundation Fund, through Joyce and Robert Menschel, 2013",,,,,,,,,,,,Negatives,,http://www.metmuseum.org/art/collection/search/307069,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2012.352 (1–14),false,true,302505,Photographs,Photographs,Souvenir 1870–71,,,,,,Artist,,Ernest Eugène Appert,"French, 1831–1891",,"Appert, Ernest Eugène",French,1831,1891,1870–71,1870,1871,Albumen silver prints from glass negatives,Sheet: 36 x 46 cm (14 3/16 x 18 1/8 in.),"Joyce F. Menschel Photography Library Fund, 2012",,,,,,,,,,,,Albums,,http://www.metmuseum.org/art/collection/search/302505,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2001.483.1–.172,false,true,284718,Photographs,Album,[Album of Paris Crime Scenes],,,,,,Artist,Attributed to,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",French,1853,1914,1901–8,1901,1908,Gelatin silver prints,Overall: 24.3 x 31cm (9 9/16 x 12 3/16in.) Page: 23 x 29 cm (9 1/16 x 11 7/16 in.),"Gilman Collection, Purchase, The Howard Gilman Foundation Gift, 2001",,,,,,,,,,,,Albums,,http://www.metmuseum.org/art/collection/search/284718,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1240,false,true,286575,Photographs,Negative; Photograph,The Laundry,,,,,,Artist,,Louis-Adolphe Humbert de Molard,"French, Paris 1800–1874",,"Humbert de Molard, Louis-Adolphe",French,1800,1874,1840s,1840,1849,Albumen paper negative,Image: 5 13/16 × 5 5/16 in. (14.7 × 13.5 cm),"Gilman Collection, Purchase, Ann Tenenbaum and Thomas H. Lee Gift, 2005",,,,,,,,,,,,Negatives,,http://www.metmuseum.org/art/collection/search/286575,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.588.3.1–.155,false,true,286390,Photographs,Album,Demi-Monde II 57,,,,,,Artist,,André-Adolphe-Eugène Disdéri,"French, Paris 1819–1889 Paris",,"Disdéri, André-Adolphe-Eugène",French,1819,1889,1858–68,1858,1868,Albumen silver print from glass negative,,"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Albums,,http://www.metmuseum.org/art/collection/search/286390,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.371,false,true,283117,Photographs,Photograph; Album,"[Album Containing Photographs, Engravings, Drawings, and Publications Pertaining to Alexandre Dumas]",,,,,,Artist,,Nadar,"French, Paris 1820–1910 Paris",,Nadar,French,1820,1910,November 1855,1855,1855,Salted paper print from glass negative,Image: 9 3/8 × 7 5/16 in. (23.8 × 18.5 cm) Sheet: 11 1/2 × 7 5/8 in. (29.2 × 19.4 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Albums,,http://www.metmuseum.org/art/collection/search/283117,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.501 (1-47),false,true,283168,Photographs,Album,Cochinchine et Cambodge,,,,,,Artist,,Emile Gsell,"French, Sainte-Marie-aux-Mines 1838–1879 Vietnam",,"Gsell, Emile",French,1838,1879,1866,1866,1866,Albumen silver prints from glass negatives,44.8 x 37.2 x 3.4 cm (17 5/8 x 14 5/8 x 1 5/16 in.),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Albums,,http://www.metmuseum.org/art/collection/search/283168,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.590.2,false,true,675524,Photographs,Photograph,"Portail Saint-Trophime; Entrée Du Cloître, Arles, Église Métropolitaine de Saint-Trophime",,,,,,Artist,,Charles Nègre,"French, 1820–1880",,"Nègre, Charles",French,1820,1880,1852,1852,1852,Relief print,Image: 6 15/16 × 5 7/8 in. (17.6 × 15 cm) Mount: 15 7/8 × 7 1/2 in. (40.4 × 19.1 cm),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/675524,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2012.481.18,false,true,301936,Photographs,Daguerreotype,[Woman with Four Children],,,,,,Artist,,Alexandre Bertrand,"French, born 1822",,"Bertrand, Alexandre",French,1822,1922,1850s,1850,1859,Daguerreotype,Image: 14.6 × 11 cm (5 3/4 × 4 5/16 in.) Frame: 37 × 31.5 cm (14 9/16 × 12 3/8 in.),"Gift of Joyce F. Menschel, 2012",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/301936,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2010.23,true,true,291739,Photographs,Photograph,[The Salon of Baron Gros],,,,,,Artist,,Baron Jean-Baptiste-Louis Gros,"French, 1793–1870",,"Gros, Jean-Baptiste-Louis",French,1793,1870,1850–57,1850,1857,Daguerreotype,Image: 22 x 17.1 cm (8 11/16 x 6 3/4 in.),"Purchase, Fletcher Fund, Joyce F. Menschel Gift, Louis V. Bell Fund, Alfred Stieglitz Society and W. Bruce and Delaney H. Lundberg Gifts, 2010",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/291739,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1993.69.1,false,true,266980,Photographs,Photograph,[Reclining Female Nude],,,,,,Artist,,Julien Vallou de Villeneuve,"French, 1795–1866",,"Vallou-de-Villeneuve, Julien",French,1795,1866,ca. 1853,1851,1855,Salted paper print from paper negative,Image: 11.8 x 16.0 cm (4 5/8 x 6 5/16 in.),"Purchase, Lila Acheson Wallace Gift, 1993",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/266980,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1993.69.2,false,true,266981,Photographs,Photograph,[Standing Female Nude],,,,,,Artist,,Julien Vallou de Villeneuve,"French, 1795–1866",,"Vallou-de-Villeneuve, Julien",French,1795,1866,ca. 1853,1851,1855,Salted paper print from paper negative,Image: 12.0 x 16.0 cm. (4 3/4 x 6 5/16 in.),"Purchase, Lila Acheson Wallace Gift, 1993",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/266981,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1993.69.3,true,true,266982,Photographs,Photograph,"[Female Nude, Reclining, in Profile]",,,,,,Artist,,Julien Vallou de Villeneuve,"French, 1795–1866",,"Vallou-de-Villeneuve, Julien",French,1795,1866,ca. 1853,1851,1855,Salted paper print from paper negative,11.2 x 15.5 cm (4 7/16 x 6 1/8 in.),"Purchase, Lila Acheson Wallace Gift, 1993",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/266982,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.40,false,true,283114,Photographs,Photograph,[Reclining Nude],,,,,,Artist,,Julien Vallou de Villeneuve,"French, 1795–1866",,"Vallou-de-Villeneuve, Julien",French,1795,1866,1851–53,1851,1853,Salted paper print from paper negative,Image: 4 13/16 × 6 5/16 in. (12.3 × 16.1 cm) Mount: 10 11/16 in. × 14 in. (27.2 × 35.5 cm),"Gilman Collection, Purchase, Mrs. Walter Annenberg and The Annenberg Foundation Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283114,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.32,false,true,285909,Photographs,Photograph,"[Windmills, Montmartre]",,,,,,Artist,,Hippolyte Bayard,"French, 1801–1887",,"Bayard, Hippolyte",French,1801,1887,1839,1839,1839,Direct positive on paper,"Image: 9.3 × 10.6 cm (3 11/16 × 4 3/16 in.), irregularly trimmed","Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285909,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.379,false,true,285907,Photographs,Photograph,[Still Life with Statuary],,,,,,Artist,,Hippolyte Bayard,"French, 1801–1887",,"Bayard, Hippolyte",French,1801,1887,Early 1850s,1850,1855,Albumen silver print from glass negative,Image: 26.2 x 20.2 cm (10 5/16 x 7 15/16 in.),"Gilman Collection, Purchase, William Talbott Hillman Foundation Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285907,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -38.22,false,true,268620,Photographs,Photograph,The Birth of Venus,,,,,,Artist,,Abel Niépce de St. Victor,"French, 1805–1870",,"Niépce de St. Victor, Abel",French,1805,1870,1855,1855,1855,Photogravure,5.4 x 4.1 cm. (2 1/8 x 1 5/8 in.),"Gift of Harry Stone, 1938",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268620,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.189,false,true,285971,Photographs,Photograph,Monsieur Itier's Cange Under Sail on the Nile,,,,,,Artist,,Andre-Victor-Alcide-Jules Itier,"French, 1805–1877",,"Itier, Andre-Victor-Alcide-Jules",French,1805,1877,1845–46,1845,1846,Daguerreotype,Image (visible): 4 3/8 × 5 15/16 in. (11.1 × 15.1 cm) Overall: 6 5/8 × 8 1/4 in. (16.8 × 21 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285971,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.39,false,true,283113,Photographs,Photograph,[Gardens of Saint-Cloud],,,,,,Artist,,Henri Victor Regnault,"French, 1810–1878",,"Regnault, Henri Victor",French,1810,1878,before 1855,1855,1855,Salted paper print from paper negative,Image: 16 1/8 × 14 1/16 in. (41 × 35.7 cm) Sheet: 19 in. × 15 3/16 in. (48.3 × 38.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283113,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1995.3,false,true,267230,Photographs,Photograph,[Henriette-Caroline-Victoire Robert],,,,,,Artist,,Louis-Rémy Robert,"French, 1810–1882",,"Robert, Louis-Rémy",French,1810,1882,1850s,1850,1859,Salted paper print from paper negative,23.1 x 17.4 cm (9 1/16 x 6 7/8 in.),"Purchase, Joyce and Robert Menschel Gift and The Horace W. Goldsmith Foundation Gift through Joyce and Robert Menschel, 1995",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267230,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1995.104,false,true,267132,Photographs,Photograph,[Gardens of the Chàteau de Saint-Cloud],,,,,,Artist,,Louis-Rémy Robert,"French, 1810–1882",,"Robert, Louis-Rémy",French,1810,1882,ca. 1853,1851,1855,Salted paper print from paper negative,31.1 x 20.0 cm. (12 1/4 x 7 7/8 in.),"Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 1995",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267132,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1987.1052,false,true,265090,Photographs,Photograph,[Table Top Still Life with Model Cathedral and Small Sculptures],,,,,,Artist,,Louis-Rémy Robert,"French, 1810–1882",,"Robert, Louis-Rémy",French,1810,1882,ca. 1856,1854,1858,Salted paper print from glass negative,20.0 x 26.1 cm (7 7/8 x 10 1/4 in.),"Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 1987",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/265090,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1991.1044,true,true,266332,Photographs,Photograph,Alfred Thompson Gobert,,,,,,Artist,,Louis-Rémy Robert,"French, 1810–1882",,"Robert, Louis-Rémy",French,1810,1882,1849–55,1849,1855,Salted paper print from paper negative,22.6 x 16.8 cm (8 7/8 x 6 5/8 in.),"Purchase, Joyce and Robert Menschel, Mrs. Harrison D. Horblit and Paul F. Walter Gifts, and Rogers Fund, 1991",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/266332,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1996.363.2,true,true,271963,Photographs,Photograph,Romesnil,,,,,,Artist,,Louis-Rémy Robert,"French, 1810–1882",,"Robert, Louis-Rémy",French,1810,1882,1850–55,1850,1855,Salted paper print from paper negative,27 x 35.2 cm (10 5/8 x 13 7/8 in.),"Purchase, Joseph Pulitzer Bequest, 1996",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271963,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.38,false,true,283112,Photographs,Photograph,"[The Large Tree at La Verrerie, Romesnil]",,,,,,Artist,,Louis-Rémy Robert,"French, 1810–1882",,"Robert, Louis-Rémy",French,1810,1882,ca. 1852,1850,1854,Salted paper print from paper negative,Image: 10 3/8 × 12 1/2 in. (26.3 × 31.8 cm),"Gilman Collection, Purchase, Mrs. Walter Annenberg and The Annenberg Foundation Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283112,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.630,false,true,286246,Photographs,Photograph,"[Village Scene, Brittany]",,,,,,Artist,,Louis-Rémy Robert,"French, 1810–1882",,"Robert, Louis-Rémy",French,1810,1882,ca. 1854,1852,1856,Salted paper print from paper negative,Image: 31.8 x 26.4 cm (12 1/2 x 10 3/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286246,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1998.334.1,false,true,284087,Photographs,Photograph,"Barricades de la Commune, avril 71. Coin de la place Hotel de Ville & de la rue de Rivoli",,,,,,Artist,,Pierre-Ambrose Richebourg,"French, 1810–1893",,"Richebourg, Pierre-Ambrose",French,1810,1893,1871,1871,1871,Albumen silver print,10.6 x 10 cm (4 3/16 x 3 15/16 in.),"Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 1998",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/284087,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2004.106,false,true,285343,Photographs,Photograph,[Study of Leaves on a Background of Floral Lace],,,,,,Artist,,Charles Hippolyte Aubry,"French, 1811–1877",,"Aubry, Charles",French,1811,1877,1864,1864,1864,Albumen silver print from glass negative,Image: 46.7 x 36.7 cm (18 3/8 x 14 7/16 in.) Mount: 56.7 x 45.3 cm (22 5/16 x 17 13/16 in.),"Gilman Collection, Purchase, Howard Gilman Foundation Gift, 2004",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285343,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -59.600.57,false,true,270200,Photographs,Photograph,[The Tuileries after the Commune],,,,,,Artist,,Hippolyte-Auguste Collard,"French, 1811–1887",,"Collard, Hippolyte-Auguste",French,1811,1887,1871,1871,1871,Albumen silver print from glass negative,,"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1959",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/270200,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -59.600.58,false,true,270201,Photographs,Photograph,[The Vendôme Column After Being Torn Down by the Communards],,,,,,Artist,,Hippolyte-Auguste Collard,"French, 1811–1887",,"Collard, Hippolyte-Auguste",French,1811,1887,1871,1870,1879,Albumen silver print from glass negative,21.7 x 30.9 cm (8 9/16 x 12 3/16 in. ),"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1959",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/270201,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -59.600.59,false,true,270202,Photographs,Photograph,[The Hötel de Ville after the Commune],,,,,,Artist,,Hippolyte-Auguste Collard,"French, 1811–1887",,"Collard, Hippolyte-Auguste",French,1811,1887,1871,1871,1871,Albumen silver print from glass negative,,"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1959",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/270202,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -59.600.60,false,true,270203,Photographs,Photograph,[The Tuileries After Its Destruction by the Communards],,,,,,Artist,,Hippolyte-Auguste Collard,"French, 1811–1887",,"Collard, Hippolyte-Auguste",French,1811,1887,1871,1871,1871,Albumen silver print from glass negative,20.8 x 27 cm (8 3/16 x 10 5/8 in. ),"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1959",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/270203,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -59.600.61,false,true,270204,Photographs,Photograph,[Barricades pres de Ministere de la Marine et l'Hötel Crillon],,,,,,Artist,,Hippolyte-Auguste Collard,"French, 1811–1887",,"Collard, Hippolyte-Auguste",French,1811,1887,1871,1871,1871,Albumen silver print from glass negative,21.7 x 30.9 cm (8 9/16 x 12 3/16 in. ),"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1959",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/270204,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.436,false,true,285611,Photographs,Photograph,"[Landscape, Arras]",,,,,,Artist,,Adalbert Cuvelier,"French, 1812–1871",,"Cuvelier, Albert",French,1812,1871,1852,1852,1852,Salted paper print from paper negative,Image: 16.3 × 14.1 cm (6 7/16 × 5 9/16 in.) Mount: 20.4 × 17.5 cm (8 1/16 × 6 7/8 in.),"Gilman Collection, Purchase, Mrs. Walter Annenberg and The Annenberg Foundation Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285611,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1995.96.1,false,true,282154,Photographs,Photograph,Vue générale de Rouen,,,,,,Artist,,Edmond Bacot,"French, 1814–1875",,"Bacot, Edmond",French,1814,1875,1852–54,1852,1854,Salted paper print from glass negative,21.9 x 33.3 cm (8 5/8 x 13 1/8 in. ),"Harris Brisbane Dick Fund, 1995",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/282154,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1995.96.2,false,true,282155,Photographs,Photograph,Vue générale de la Cathédrale de Rouen,,,,,,Artist,,Edmond Bacot,"French, 1814–1875",,"Bacot, Edmond",French,1814,1875,1852–54,1852,1854,Salted paper print from glass negative,33.6 x 25.1 cm (13 1/4 x 9 7/8 in. ),"Harris Brisbane Dick Fund, 1995",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/282155,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1995.96.3,false,true,282156,Photographs,Photograph,"Bas du Portail, Côté de la Place, Cathédrale de Rouen",,,,,,Artist,,Edmond Bacot,"French, 1814–1875",,"Bacot, Edmond",French,1814,1875,1852–54,1852,1854,Salted paper print from glass negative,26.2 x 35 cm (10 5/16 x 13 3/4 in. ),"Harris Brisbane Dick Fund, 1995",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/282156,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1995.96.4,false,true,282157,Photographs,Photograph,"Haut de Portail, Côté de la Place, Cathédrale de Rouen",,,,,,Artist,,Edmond Bacot,"French, 1814–1875",,"Bacot, Edmond",French,1814,1875,1852–54,1852,1854,Salted paper print from glass negative,34.6 x 26 cm (13 5/8 x 10 1/4 in. ),"Harris Brisbane Dick Fund, 1995",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/282157,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1995.96.5,false,true,282158,Photographs,Photograph,"Portail de la Calende, Rouen Cathédral",,,,,,Artist,,Edmond Bacot,"French, 1814–1875",,"Bacot, Edmond",French,1814,1875,1852–54,1852,1854,Salted paper print from glass negative,34.4 x 26 cm (13 9/16 x 10 1/4 in. ),"Harris Brisbane Dick Fund, 1995",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/282158,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1995.96.6,false,true,282159,Photographs,Photograph,Vue générale de Saint-Ouen,,,,,,Artist,,Edmond Bacot,"French, 1814–1875",,"Bacot, Edmond",French,1814,1875,1852–54,1852,1854,Salted paper print from glass negative,24.8 x 31.1 cm (9 3/4 x 12 1/4 in. ),"Harris Brisbane Dick Fund, 1995",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/282159,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1995.96.7,false,true,282160,Photographs,Photograph,"Portail de Saint-Ouen, Rouen",,,,,,Artist,,Edmond Bacot,"French, 1814–1875",,"Bacot, Edmond",French,1814,1875,1852–54,1852,1854,Salted paper print from glass negative,34.7 x 26.3 cm (13 11/16 x 10 3/8 in. ),"Harris Brisbane Dick Fund, 1995",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/282160,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1995.96.8,false,true,282161,Photographs,Photograph,"Tour de Saint-Ouen, Rouen",,,,,,Artist,,Edmond Bacot,"French, 1814–1875",,"Bacot, Edmond",French,1814,1875,1852–54,1852,1854,Salted paper print from glass negative,33.7 x 24.9 cm (13 1/4 x 9 13/16 in. ),"Harris Brisbane Dick Fund, 1995",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/282161,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1995.96.9,false,true,282162,Photographs,Photograph,"Portail des Marmousets, Saint-Ouen de Rouen",,,,,,Artist,,Edmond Bacot,"French, 1814–1875",,"Bacot, Edmond",French,1814,1875,1852–54,1852,1854,Salted paper print from glass negative,34 x 26.1 cm (13 3/8 x 10 1/4 in. ),"Harris Brisbane Dick Fund, 1995",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/282162,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1995.96.10,true,true,282163,Photographs,Photograph,"Saint-Maclou, Rouen",,,,,,Artist,,Edmond Bacot,"French, 1814–1875",,"Bacot, Edmond",French,1814,1875,1852–54,1852,1854,Salted paper print from glass negative,34.4 x 25.6 cm (13 9/16 x 10 1/16 in.),"Harris Brisbane Dick Fund, 1995",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/282163,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1995.96.11,false,true,282164,Photographs,Photograph,"Escalier de la Basse Vieille Cour, Rouen",,,,,,Artist,,Edmond Bacot,"French, 1814–1875",,"Bacot, Edmond",French,1814,1875,1852–54,1852,1854,Salted paper print from glass negative,30.5 x 21.9 cm (12 x 8 5/8 in. ),"Harris Brisbane Dick Fund, 1995",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/282164,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1995.96.12,false,true,282165,Photographs,Photograph,"Fontaine de la Croix de Pierre, Rouen",,,,,,Artist,,Edmond Bacot,"French, 1814–1875",,"Bacot, Edmond",French,1814,1875,1852–54,1852,1854,Salted paper print from glass negative,30.9 x 22.6 cm (12 3/16 x 8 7/8 in. ),"Harris Brisbane Dick Fund, 1995",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/282165,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1995.96.13,false,true,267316,Photographs,Photograph,"Cloître Saint-Amand, Rouen",,,,,,Artist,,Edmond Bacot,"French, 1814–1875",,"Bacot, Edmond",French,1814,1875,1852–53,1852,1853,Salted paper print from glass negative,34.5 x 26.5 cm (13 9/16 x 10 7/16 in.),"Harris Brisbane Dick Fund, 1995",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267316,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1995.96.14,false,true,282166,Photographs,Photograph,"Hôtel du Bourgtheroulde, Rouen",,,,,,Artist,,Edmond Bacot,"French, 1814–1875",,"Bacot, Edmond",French,1814,1875,1852–54,1852,1854,Salted paper print from glass negative,33.6 x 24.9 cm (13 1/4 x 9 13/16 in. ),"Harris Brisbane Dick Fund, 1995",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/282166,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1995.96.15,false,true,282167,Photographs,Photograph,"Tourelle du Palais de Justice, Rouen",,,,,,Artist,,Edmond Bacot,"French, 1814–1875",,"Bacot, Edmond",French,1814,1875,1852–54,1852,1854,Salted paper print from glass negative,34.8 x 25.8 cm (13 11/16 x 10 3/16 in. ),"Harris Brisbane Dick Fund, 1995",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/282167,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1995.96.16,false,true,282168,Photographs,Photograph,"Fragment du Palais de Justice, Rouen",,,,,,Artist,,Edmond Bacot,"French, 1814–1875",,"Bacot, Edmond",French,1814,1875,1852–54,1852,1854,Salted paper print from glass negative,34.7 x 24.2 cm (13 11/16 x 9 1/2 in. ),"Harris Brisbane Dick Fund, 1995",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/282168,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1995.96.17,false,true,282169,Photographs,Photograph,"Fragment du Palais de Justice, Rouen",,,,,,Artist,,Edmond Bacot,"French, 1814–1875",,"Bacot, Edmond",French,1814,1875,1852–54,1852,1854,Salted paper print from glass negative,34.8 x 26 cm (13 11/16 x 10 1/4 in. ),"Harris Brisbane Dick Fund, 1995",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/282169,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1995.96.18,false,true,282170,Photographs,Photograph,"Notre Dame de Bonsecours, près Rouen",,,,,,Artist,,Edmond Bacot,"French, 1814–1875",,"Bacot, Edmond",French,1814,1875,1852–54,1852,1854,Salted paper print from glass negative,32.8 x 26.1 cm (12 15/16 x 10 1/4 in. ),"Harris Brisbane Dick Fund, 1995",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/282170,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1995.96.19,false,true,282171,Photographs,Photograph,"Saint-Georges de Boscherville, près Rouen",,,,,,Artist,,Edmond Bacot,"French, 1814–1875",,"Bacot, Edmond",French,1814,1875,1852–54,1852,1854,Salted paper print from glass negative,34.1 x 25.4 cm (13 7/16 x 10 in. ),"Harris Brisbane Dick Fund, 1995",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/282171,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1995.96.20,false,true,282172,Photographs,Photograph,"Cathédrale de Louviers, vue générale",,,,,,Artist,,Edmond Bacot,"French, 1814–1875",,"Bacot, Edmond",French,1814,1875,1852–54,1852,1854,Salted paper print from glass negative,32.6 x 25.4 cm (12 13/16 x 10 in. ),"Harris Brisbane Dick Fund, 1995",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/282172,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1995.96.21,false,true,282173,Photographs,Photograph,Portail de la Cathédrale de Louviers,,,,,,Artist,,Edmond Bacot,"French, 1814–1875",,"Bacot, Edmond",French,1814,1875,1852–54,1852,1854,Salted paper print from glass negative,32.7 x 24.5 cm (12 7/8 x 9 5/8 in. ),"Harris Brisbane Dick Fund, 1995",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/282173,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1995.96.22,false,true,282175,Photographs,Photograph,Portail de la Cathédrale de Louviers,,,,,,Artist,,Edmond Bacot,"French, 1814–1875",,"Bacot, Edmond",French,1814,1875,1852–54,1852,1854,Salted paper print from glass negative,34.3 x 25.4 cm (13 1/2 x 10 in. ),"Harris Brisbane Dick Fund, 1995",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/282175,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1995.96.23,false,true,282174,Photographs,Photograph,Château de Martainville,,,,,,Artist,,Edmond Bacot,"French, 1814–1875",,"Bacot, Edmond",French,1814,1875,1852–54,1852,1854,Salted paper print from glass negative,24.1 x 31.8 cm (9 1/2 x 12 1/2 in. ),"Harris Brisbane Dick Fund, 1995",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/282174,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1995.96.24,false,true,282176,Photographs,Photograph,"Rue des Petits Murs, Caen",,,,,,Artist,,Edmond Bacot,"French, 1814–1875",,"Bacot, Edmond",French,1814,1875,1852–54,1852,1854,Salted paper print from glass negative,33.7 x 25.6 cm (13 1/4 x 10 1/16 in. ),"Harris Brisbane Dick Fund, 1995",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/282176,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1995.96.25,false,true,282177,Photographs,Photograph,"Abside de Saint-Pierre, Caen",,,,,,Artist,,Edmond Bacot,"French, 1814–1875",,"Bacot, Edmond",French,1814,1875,1852–54,1852,1854,Salted paper print from glass negative,34.2 x 25.3 cm (13 7/16 x 9 15/16 in. ),"Harris Brisbane Dick Fund, 1995",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/282177,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1995.96.26,false,true,282178,Photographs,Photograph,Vue de l'Odon,,,,,,Artist,,Edmond Bacot,"French, 1814–1875",,"Bacot, Edmond",French,1814,1875,1852–54,1852,1854,Salted paper print from glass negative,33.9 x 25.6 cm (13 3/8 x 10 1/16 in. ),"Harris Brisbane Dick Fund, 1995",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/282178,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1995.96.27,false,true,282179,Photographs,Photograph,"Poissonerie, Caen",,,,,,Artist,,Edmond Bacot,"French, 1814–1875",,"Bacot, Edmond",French,1814,1875,1852–54,1852,1854,Salted paper print from glass negative,24.6 x 33.5 cm (9 11/16 x 13 3/16 in. ),"Harris Brisbane Dick Fund, 1995",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/282179,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1995.96.28,false,true,282180,Photographs,Photograph,"Abbaye aux Dames et Hospice, Caen",,,,,,Artist,,Edmond Bacot,"French, 1814–1875",,"Bacot, Edmond",French,1814,1875,1852–54,1852,1854,Salted paper print from glass negative,25.8 x 34.4 cm (10 3/16 x 13 9/16 in. ),"Harris Brisbane Dick Fund, 1995",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/282180,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.28,false,true,283102,Photographs,Photograph,[Dog],,,,,,Artist,,Louis-Auguste Bisson,"French, 1814–1876",,"Bisson, Louis-Auguste",French,1814,1876,1841–49,1841,1849,Daguerreotype,7.6 x 10.3 cm (3 x 4 1/16 in.),"Gilman Collection, Purchase, Jennifer and Joseph Duke Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283102,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2000.208.1,false,true,283721,Photographs,Photograph,[Boy in Uniform],,,,,,Artist,,Jean-Baptiste Frénet,"French, 1814–1889",,"Frénet, Jean-Baptiste",French,1814,1889,ca. 1855,1853,1857,Salted paper print from glass negative,24.5 x 17.9 cm (9 5/8 x 7 1/16 in. ),"Purchase, Harriette and Noel Levine Gift and Jennifer and Joseph Duke Gift, 2000",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283721,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -53.704.8,false,true,269671,Photographs,Photograph,Colonne Vendôme,,,,,,Artist,,Franck,"French, 1816–1906",,Franck,French,1816,1906,1871,1871,1871,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1953",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269671,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.380,false,true,286595,Photographs,Photograph,Canal St. Martin,,,,,,Artist,,Franck,"French, 1816–1906",,Franck,French,1816,1906,1860,1860,1860,Albumen silver print from glass negative,Image: 18.6 x 25.2 cm (7 5/16 x 9 15/16 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286595,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2008.98,false,true,288428,Photographs,Photograph,"Deuxième Cataracte, Rocher d'Abouçir, Rapides et Ilots Granitiques",,,,,,Artist,,Félix Teynard,"French, 1817–1892",,"Teynard, Félix",French,1817,1892,1851,1851,1851,Salted paper print from paper negative,Image: 24.9 x 30.7 cm (9 13/16 x 12 1/16 in.) Mount: 39.9 x 51.9 cm (15 11/16 x 20 7/16 in.),"Purchase, Susan and Thomas Dunn Gift and funds from various donors, 2008",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288428,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1996.530,false,true,272026,Photographs,Photograph,"Louksor (Thèbes), Vue Générale des Ruines",,,,,,Artist,,Félix Teynard,"French, 1817–1892",,"Teynard, Félix",French,1817,1892,1851–52,1851,1852,Salted paper print from paper negative,25.4 x 30.8 cm (10 x 12 1/8 in. ),"Gift of Hans P. Kraus Jr. and Mariana Cooke, 1997",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/272026,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.431,false,true,307071,Photographs,Photograph,"Dandoûr, Nubie",,,,,,Artist,,Félix Teynard,"French, 1817–1892",,"Teynard, Félix",French,1817,1892,1851,1851,1851,Salted paper print from paper negative,Image: 23.9 × 31.1 cm (9 7/16 × 12 1/4 in.),"Funds from various donors, 2013",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/307071,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.60,false,true,283145,Photographs,Photograph,"Abo-Sembil, Grand Spéos, Statues Colossales vues de Face (Parte Inférieure)",,,,,,Artist,,Félix Teynard,"French, 1817–1892",,"Teynard, Félix",French,1817,1892,1851–52,1851,1852,Salted paper print from paper negative,Image: 24.3 x 30.8 cm (9 9/16 x 12 1/8 in.) Mount: 37.9 x 50.1 cm (14 15/16 x 19 3/4 in.),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283145,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.61,false,true,283146,Photographs,Photograph,"Béni Haçan, Architecture Hypogéenne, Tombeau d'Amoneï",,,,,,Artist,,Félix Teynard,"French, 1817–1892",,"Teynard, Félix",French,1817,1892,1851–52,1851,1852,Salted paper print from paper negative,Image: 24.4 x 30.6 cm (9 5/8 x 12 1/16 in.) Mount: 40.4 x 52.2 cm (15 7/8 x 20 9/16 in.),"Gilman Collection, Purchase, William Talbott Hillman Foundation Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283146,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.62,false,true,283147,Photographs,Photograph,"Séboûah, Temple, Colosse et Sphinx de la Partie Gauche de l'Avenue",,,,,,Artist,,Félix Teynard,"French, 1817–1892",,"Teynard, Félix",French,1817,1892,1851–52,1851,1852,Salted paper print from paper negative,Image: 24 × 31.1 cm (9 7/16 × 12 1/4 in.) Mount: 37.9 × 50.2 cm (14 15/16 × 19 3/4 in.),"Gilman Collection, Purchase, Ann Tenenbaum and Thomas H. Lee Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283147,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.688,false,true,285952,Photographs,Photograph,"Louksor (Thèbes), Construction Postérieure - Galeries - Parallèles",,,,,,Artist,,Félix Teynard,"French, 1817–1892",,"Teynard, Félix",French,1817,1892,1851–52,1851,1852,Salted paper print from paper negative,Image: 24 x 30.2 cm (9 7/16 x 11 7/8 in.) Mount: 40.2 x 52.3 cm (15 13/16 x 20 9/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285952,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.689,false,true,286152,Photographs,Photograph,"Le Kaire, Mosquée Nâcéryeh",,,,,,Artist,,Félix Teynard,"French, 1817–1892",,"Teynard, Félix",French,1817,1892,published 1851,1851,1851,Salted paper print from paper negative,Image: 24 x 30.5 cm (9 7/16 x 12 in.) Mount: 40.3 x 52.2 cm (15 7/8 x 20 9/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286152,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.690,false,true,285955,Photographs,Photograph,"Abou Sembil, Grand Spéos - Statues Colossales, Vues de Trois-Quarts",,,,,,Artist,,Félix Teynard,"French, 1817–1892",,"Teynard, Félix",French,1817,1892,1851–52,1851,1852,Salted paper print from paper negative,Image: 30 x 24.4 cm (11 13/16 x 9 5/8 in.) Mount: 51.6 x 39.9 cm (20 5/16 x 15 11/16 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285955,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.691,false,true,286150,Photographs,Photograph,"Dakkeh, Village et Rives du Nil",,,,,,Artist,,Félix Teynard,"French, 1817–1892",,"Teynard, Félix",French,1817,1892,1851–52,1851,1852,Salted paper print from paper negative,Image: 23.7 x 30.9 cm (9 5/16 x 12 3/16 in.) Mount: 40 x 51.8 cm (15 3/4 x 20 3/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286150,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.693,false,true,285946,Photographs,Photograph,"Louksor (Thèbes), Construction Antérieure - Pylône Colosses et Obélisque",,,,,,Artist,,Félix Teynard,"French, 1817–1892",,"Teynard, Félix",French,1817,1892,1851–52,1851,1852,Salted paper print from paper negative,Image: 24.3 x 30.4 cm (9 9/16 x 11 15/16 in.) Mount: 40.3 x 52.1 cm (15 7/8 x 20 1/2 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285946,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.694,false,true,285945,Photographs,Photograph,"Ile de Fîleh (Philæ), Vue Génèrale Prise du Point I, Sur La Plateforme du Pylône",,,,,,Artist,,Félix Teynard,"French, 1817–1892",,"Teynard, Félix",French,1817,1892,1851–52,1851,1852,Salted paper print from paper negative,Image: 24 x 30.5 cm (9 7/16 x 12 in.) Mount: 39.6 x 51.8 cm (15 9/16 x 20 3/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285945,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.695,false,true,285954,Photographs,Photograph,"Louksor, Petit Bras du Nil - Barque de Voyageurs",,,,,,Artist,,Félix Teynard,"French, 1817–1892",,"Teynard, Félix",French,1817,1892,1851–52,1851,1852,Salted paper print from paper negative,Image: 24.2 x 30.7 cm (9 1/2 x 12 1/16 in.) Mount: 40.5 x 52.4 cm (15 15/16 x 20 5/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285954,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.696,false,true,285948,Photographs,Photograph,"Abâzîz, Intérieure d'un Village Arabe",,,,,,Artist,,Félix Teynard,"French, 1817–1892",,"Teynard, Félix",French,1817,1892,1851–52,1851,1852,Salted paper print from paper negative,Image: 23.6 x 30.3 cm (9 5/16 x 11 15/16 in.) Mount: 39.7 x 51.7 cm (15 5/8 x 20 3/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285948,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.697,false,true,286091,Photographs,Photograph,"Ile de Fîleh (Philæ), Premier Pylône, Inscription Française Gravée Sur L'Ébrasement Oriental, En M",,,,,,Artist,,Félix Teynard,"French, 1817–1892",,"Teynard, Félix",French,1817,1892,1851–52,1851,1852,Salted paper print from paper negative,Image: 23.7 x 30.6 cm (9 5/16 x 12 1/16 in.) Mount: 39.9 x 51.7 cm (15 11/16 x 20 3/8 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286091,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.925,false,true,286153,Photographs,Photograph,"Sébôuah, Vue Générale du Temple",,,,,,Artist,,Félix Teynard,"French, 1817–1892",,"Teynard, Félix",French,1817,1892,published 1851,1851,1851,Salted paper print from paper negative,Image: 23.8 x 30.6 cm (9 3/8 x 12 1/16 in.) Mount: 39.8 x 51.5 cm (15 11/16 x 20 1/4 in.),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286153,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.940,false,true,286151,Photographs,Photograph,"Louksor (Thèbes), Vue Générale des Ruines",,,,,,Artist,,Félix Teynard,"French, 1817–1892",,"Teynard, Félix",French,1817,1892,1851–52,1851,1852,Salted paper print from paper negative,Image: 23.6 x 30.5 cm (9 5/16 x 12 in.) Mount: 40.2 x 51.8 cm (15 13/16 x 20 3/8 in.),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286151,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.941,false,true,285947,Photographs,Photograph,"Esneh (Latopolis), Construction Ensablée - Architrave, Fûts et Chapiteaux",,,,,,Artist,,Félix Teynard,"French, 1817–1892",,"Teynard, Félix",French,1817,1892,1851–52,1851,1852,Salted paper print from paper negative,Image: 24.9 x 30.7 cm (9 13/16 x 12 1/16 in.) Mount: 40.1 x 51.8 cm (15 13/16 x 20 3/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285947,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.942,false,true,285956,Photographs,Photograph,"Ile de Fîleh (Philæ), Vu Générale Prise du Nord-Ouest au Point A",,,,,,Artist,,Félix Teynard,"French, 1817–1892",,"Teynard, Félix",French,1817,1892,1851–52,1851,1852,Salted paper print from paper negative,Image: 23.9 x 31.2 cm (9 7/16 x 12 5/16 in.) Mount: 39.8 x 51.6 cm (15 11/16 x 20 5/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285956,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.698,false,true,285640,Photographs,Photographs,Young Nuba Woman,,,,,,Artist,,Pierre Trémaux,"French, 1818–1895",,"Trémaux, Pierre",French,1818,1895,1853–54,1853,1854,Salted paper print from paper negative,Image: 10 9/16 × 8 1/4 in. (26.9 × 21 cm) Mount: 13 11/16 × 9 5/16 in. (34.7 × 23.6 cm),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285640,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1998.132,true,true,282234,Photographs,Photograph,The Refectory of the Imperial Asylum at Vincennes,,,,,,Artist,,Charles Nègre,"French, 1820–1880",,"Nègre, Charles",French,1820,1880,1858–59,1858,1859,Salted paper print from glass negative,34.2 x 42.5 cm (13 7/16 x 16 3/4 in.),"Gift of Hans P. Kraus Jr. and Mariana Cook, in honor of André and Marie-Thérèse Jammes, 1998",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/282234,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2000.286,false,true,283736,Photographs,Photograph,A Street in Grasse,,,,,,Artist,,Charles Nègre,"French, 1820–1880",,"Nègre, Charles",French,1820,1880,1852,1852,1852,Salted paper print from paper negative,32.9 x 23.8 cm (12 15/16 x 9 3/8 in. ),"Purchase, Jennifer and Joseph Duke Gift and several members of The Chairman's Council Gifts, 2000",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283736,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1997.382.63,false,true,282074,Photographs,Photograph,Tarascon,,,,,,Artist,,Charles Nègre,"French, 1820–1880",,"Nègre, Charles",French,1820,1880,ca. 1852,1850,1854,Albumen silver print from paper negative,23.6 x 29.5 cm (9 5/16 x 11 5/8 in.),"The Rubel Collection, Purchase, Lila Acheson Wallace Gift, 1997",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/282074,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.52,false,true,283133,Photographs,Photograph,Spartan Soldier,,,,,,Artist,,Charles Nègre,"French, 1820–1880",,"Nègre, Charles",French,1820,1880,1859,1859,1859,Albumen silver print from glass negative,Image: 35.3 x 43.5 cm (13 7/8 x 17 1/8 in.),"Gilman Collection, Purchase, Harriette and Noel Levine Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283133,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.204,false,true,285853,Photographs,Photograph,"Arles, Porte des Châtaignes",,,,,,Artist,,Charles Nègre,"French, 1820–1880",,"Nègre, Charles",French,1820,1880,1852,1850,1854,Salted paper print from a paper negative,Image: 22.1 x 32.1 cm (8 11/16 x 12 5/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285853,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.264,false,true,286170,Photographs,Photograph,"[Lord Brougham and his Family, Cannes]",,,,,,Artist,,Charles Nègre,"French, 1820–1880",,"Nègre, Charles",French,1820,1880,1862,1862,1862,Albumen silver print from glass negative,Image: 24.8 x 34 cm (9 3/4 x 13 3/8 in.),"Gilman Collection, Purchase, Harriette and Noel Levine Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286170,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.314,false,true,286169,Photographs,Photograph,[Still Life with Game Birds],,,,,,Artist,,Charles Nègre,"French, 1820–1880",,"Nègre, Charles",French,1820,1880,1859,1857,1861,Albumen silver print from glass negative,Image: 43.8 x 34.9 cm (17 1/4 x 13 3/4 in.),"Gilman Collection, Purchase, Jennifer and Joseph Duke Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286169,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.327,false,true,286165,Photographs,Photograph,[The 15th of August. Imperial Asylum at Vincennes],,,,,,Artist,,Charles Nègre,"French, 1820–1880",,"Nègre, Charles",French,1820,1880,1858,1857,1861,Albumen silver print from glass negative,"Image: 32.3 x 44.2 cm (12 11/16 x 17 3/8 in.), oval Mount: 47.5 x 60.5 cm (18 11/16 x 23 13/16 in.)","Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286165,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.328,false,true,286164,Photographs,Photograph,[Self-Portrait in Eastern Costume],,,,,,Artist,,Charles Nègre,"French, 1820–1880",,"Nègre, Charles",French,1820,1880,1855–60,1851,1852,Albumen silver print from glass negative,Image: 18.7 x 13.8 cm (7 3/8 x 5 7/16 in.) Mount: 38.1 x 28.9 cm (15 x 11 3/8 in.),"Gilman Collection, Purchase, Harriette and Noel Levine Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286164,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.560,false,true,286173,Photographs,Photograph,[Family Group],,,,,,Artist,,Charles Nègre,"French, 1820–1880",,"Nègre, Charles",French,1820,1880,1854,1854,1854,Salted paper print from glass negative,Mount: 7 3/8 in. × 8 9/16 in. (18.8 × 21.7 cm) Image: 5 11/16 × 7 1/16 in. (14.4 × 18 cm),"Gilman Collection, Purchase, Jennifer and Joseph Duke Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286173,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.755,false,true,286171,Photographs,Photograph,"Asile impériale de Vincennes, salle de jeu",,,,,,Artist,,Charles Nègre,"French, 1820–1880",,"Nègre, Charles",French,1820,1880,1858–59,1858,1859,Albumen silver print from glass negative,Image: 8 3/4 × 7 3/16 in. (22.3 × 18.2 cm),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286171,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.796,false,true,286167,Photographs,Photograph,"Asile Impériale de Vincennes, la pharmacie",,,,,,Artist,,Charles Nègre,"French, 1820–1880",,"Nègre, Charles",French,1820,1880,1858–59,1858,1859,Albumen silver print from glass negative,"Image: 17 x 17 cm (6 11/16 x 6 11/16 in.), circular","Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286167,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.832,false,true,285852,Photographs,Photograph,[Trees and Waterfalls],,,,,,Artist,,Charles Nègre,"French, 1820–1880",,"Nègre, Charles",French,1820,1880,1860–65,1860,1865,Albumen silver print from glass negative,Image: 13.3 x 10; Mount: 18.9 x 15.6,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285852,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2005.100.590.1a, b",false,true,286168,Photographs,Photograph,"Profil du Portail; Entrée Du Cloitre, Arles, Eglise Metropolitaine de Saint-Trophime",,,,,,Artist,,Charles Nègre,"French, 1820–1880",,"Nègre, Charles",French,1820,1880,1852,1852,1852,Salted paper prints from paper negatives,Image: 6 7/8 × 5 7/8 in. (17.5 × 15 cm) (each) Mount: 15 7/8 × 11 7/16 in. (40.3 × 29 cm),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286168,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -40.102.9,false,true,268659,Photographs,Photograph,[Rocky Hillside],,,,,,Artist,,Victor Prevost,"French, 1820–1881",,"Prevost, Victor",French,1820,1881,1850s,1850,1859,Salted paper print from paper negative,Image: 12 5/16 × 9 13/16 in. (31.2 × 24.9 cm),"Gift of John Goldsmith Phillips, 1940",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268659,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2000.13,true,true,283626,Photographs,Photograph,"[Oak Tree and Rocks, Forest of Fontainebleau]",,,,,,Artist,,Gustave Le Gray,"French, 1820–1884",,"Le Gray, Gustave",French,1820,1884,1849–52,1849,1852,Salted paper print from paper negative,25.2 x 35.7 cm (9 15/16 x 14 1/16 in.),"Purchase, Jennifer and Joseph Duke and Lila Acheson Wallace Gifts, 2000",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283626,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.646,true,true,261941,Photographs,Photograph,"[The Great Wave, Sète]",,,,,,Artist,,Gustave Le Gray,"French, 1820–1884",,"Le Gray, Gustave",French,1820,1884,1857,1857,1857,Albumen silver print from glass negative,33.7 x 41.4 cm (13 1/4 x 16 5/16 in.),"Gift of John Goldsmith Phillips, 1976",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261941,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1996.293,false,true,271964,Photographs,Photograph,"Hotel de Cluny, Paris",,,,,,Artist,,Gustave Le Gray,"French, 1820–1884",,"Le Gray, Gustave",French,1820,1884,ca. 1851,1851,1851,Salted paper print from paper negative,32.4 x 24.2 cm (12 3/4 x 9 1/2 in. ),"Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel and Rogers Fund, 1996",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271964,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2002.257,false,true,284986,Photographs,Photograph,[Soldier and Military Camel],,,,,,Artist,,Gustave Le Gray,"French, 1820–1884",,"Le Gray, Gustave",French,1820,1884,1866,1866,1866,Albumen silver print from glass negative,Image: 24.2 x 30.7 cm (9 1/2 x 12 1/16 in.) Mount: 37.7 x 43.4 cm (14 13/16 x 17 1/16 in.),"Purchase, Alfred Stieglitz Society Gifts, 2002",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/284986,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1987.1011,false,true,265065,Photographs,Photograph,"Tree Study, Forest of Fontainebleau",,,,,,Artist,,Gustave Le Gray,"French, 1820–1884",,"Le Gray, Gustave",French,1820,1884,ca. 1856,1854,1858,Albumen silver print from glass negative,31.8 x 41.4 cm. (12 1/2 x 16 5/16 in.),"Purchase, Joyce and Robert Menschel, The Howard Gilman Foundation, Harrison D. Horblit, Harriette and Noel Levine and Paul F. Walter Gifts and David Hunter McAlpin Fund; and Gift of Mr. and Mrs. Harry H. Lunn Jr., 1987",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/265065,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1991.1058,false,true,266351,Photographs,Photograph,Portail milieu d'Aubeterre,,,,,,Artist,,Gustave Le Gray,"French, 1820–1884",,"Le Gray, Gustave",French,1820,1884,1851,1851,1851,Salted paper print from paper negative,Image: 23.3 x 28.1 cm. (9 3/16 x 11 1/16 in.),"Edward Pearce Casey Fund, 1991",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/266351,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1996.99.1,false,true,267425,Photographs,Photograph,Mediterranean with Mount Agde,,,,,,Artist,,Gustave Le Gray,"French, 1820–1884",,"Le Gray, Gustave",French,1820,1884,1857,1857,1857,Albumen silver print from two glass negatives,31.8 x 40.9 cm. (12 1/2 x 16 1/8 in.),"Purchase, Joseph Pulitzer Bequest, 1996",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267425,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.645.1,false,true,261937,Photographs,Photograph,Brig on the Water,,,,,,Artist,,Gustave Le Gray,"French, 1820–1884",,"Le Gray, Gustave",French,1820,1884,1856,1856,1856,Albumen silver print from glass negative,32.1 x 40.5 cm (12 5/8 x 15 15/16 in. ),"Gift of A. Hyatt Mayor, 1976",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261937,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.440.1,false,true,291659,Photographs,Panorama,Pyramides de Gizèh,,,,,,Artist,,Gustave Le Gray,"French, 1820–1884",,"Le Gray, Gustave",French,1820,1884,1865–69,1865,1869,Albumen silver print from glass negative,Image: 31.1 x 41.8 cm (12 1/4 x 16 7/16 in.) Mount: 45.9 x 62.5 cm (18 1/16 x 24 5/8 in.),"Gift of Robert Shapazian, 2009",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/291659,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.440.2,false,true,291694,Photographs,Panorama,Pyramides de Gizèh,,,,,,Artist,,Gustave Le Gray,"French, 1820–1884",,"Le Gray, Gustave",French,1820,1884,1865–69,1865,1869,Albumen silver print from glass negative,Image: 31.1 x 41.8 cm (12 1/4 x 16 7/16 in.) Mount: 45.9 x 62.5 cm (18 1/16 x 24 5/8 in.),"Gift of Robert Shapazian, 2009",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/291694,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.440.3,false,true,291695,Photographs,Panorama,Pyramides de Gizèh,,,,,,Artist,,Gustave Le Gray,"French, 1820–1884",,"Le Gray, Gustave",French,1820,1884,1865–69,1865,1869,Albumen silver print from glass negative,Image: 31.1 x 41.8 cm (12 1/4 x 16 7/16 in.) Mount: 45.9 x 62.5 cm (18 1/16 x 24 5/8 in.),"Gift of Robert Shapazian, 2009",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/291695,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.46,false,true,283122,Photographs,Photograph,Chêne dans les rochers à Fontainebleau,,,,,,Artist,,Gustave Le Gray,"French, 1820–1884",,"Le Gray, Gustave",French,1820,1884,1849–52,1849,1852,Salted paper print from waxed-paper negative,Image: 25.4 x 36.2 cm (10 x 14 1/4 in.) Mount: 31.8 x 46.2 cm (12 1/2 x 18 3/16 in.),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283122,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.47,false,true,283123,Photographs,Photograph,"Fontainebleau, chemin sablonneux montant",,,,,,Artist,,Gustave Le Gray,"French, 1820–1884",,"Le Gray, Gustave",French,1820,1884,ca. 1856,1854,1858,Albumen silver print from glass negative,Image: 29.9 x 37.7 cm (11 3/4 x 14 13/16 in.) Mount: 53.3 x 63.8 cm (21 x 25 1/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283123,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.48,false,true,283124,Photographs,Photograph,Mer Méditerranée - Sète,,,,,,Artist,,Gustave Le Gray,"French, 1820–1884",,"Le Gray, Gustave",French,1820,1884,1857,1857,1857,Albumen silver print from two glass negatives,Image: 32.1 x 41.9 cm (12 5/8 x 16 1/2 in.) Mount: 52.7 × 67.3 cm (20 3/4 × 26 1/2 in.),"Gilman Collection, Purchase, Robert Rosenkranz Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283124,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.49,false,true,283125,Photographs,Photograph,"[Cavalry Maneuvers, Camp de Châlons]",,,,,,Artist,,Gustave Le Gray,"French, 1820–1884",,"Le Gray, Gustave",French,1820,1884,1857,1857,1857,Albumen silver print from glass negative,Image: 26.7 x 33 cm (10 1/2 x 13 in.),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283125,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.159.37,false,true,306320,Photographs,Photograph,"The French and English Fleets, Cherbourg",,,,,,Artist,,Gustave Le Gray,"French, 1820–1884",,"Le Gray, Gustave",French,1820,1884,August 1858,1858,1858,Albumen silver print from glass negative,Mount: 21 in. × 26 3/4 in. (53.3 × 68 cm) Image: 11 13/16 in. × 16 in. (30 × 40.7 cm),"Bequest of Maurice B. Sendak, 2012",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306320,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.159.38,false,true,306321,Photographs,Photograph,"Hollow Oak Tree, Fontainebleau",,,,,,Artist,,Gustave Le Gray,"French, 1820–1884",,"Le Gray, Gustave",French,1820,1884,1855–57,1855,1857,Albumen silver print from glass negative,Mount: 21 9/16 in. × 27 3/8 in. (54.8 × 69.6 cm) Image: 12 3/8 × 14 13/16 in. (31.5 × 37.7 cm),"Bequest of Maurice B. Sendak, 2012",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306321,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.190,false,true,286349,Photographs,Photograph,[View from Photographer's Studio],,,,,,Artist,,Gustave Le Gray,"French, 1820–1884",,"Le Gray, Gustave",French,1820,1884,1851–54,1851,1854,Salted paper print from paper negative,26.1 x 35.1 cm (10 1/4 x 13 13/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286349,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.191,false,true,286028,Photographs,Photographs,"Fête de S. A. Ismaïl Pacha à bord des bateaux de LL. A A. les princes, janvier 1867",,,,,,Artist,,Gustave Le Gray,"French, 1820–1884",,"Le Gray, Gustave",French,1820,1884,1867,1867,1867,Albumen print from paper negative,Image: 30.6 x 40.2 cm (12 1/16 x 15 13/16 in.) Mount: 50 x 64.8 cm (19 11/16 x 25 1/2 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286028,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.192,false,true,285473,Photographs,Photograph,Temple of Edfu,,,,,,Artist,,Gustave Le Gray,"French, 1820–1884",,"Le Gray, Gustave",French,1820,1884,1867,1867,1867,Albumen silver print from paper negative,Image: 31.4 x 41 cm (12 3/8 x 16 1/8 in.) Mount: 50.1 x 64.6 cm (19 3/4 x 25 7/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285473,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.258,false,true,285465,Photographs,Photograph,L'impératrice Eugénie en prière,,,,,,Artist,,Gustave Le Gray,"French, 1820–1884",,"Le Gray, Gustave",French,1820,1884,1856,1856,1856,Albumen silver print from a collodion glass negative,Image: 23.4 x 18.3 cm (9 3/16 x 7 3/16 in.) Mount: 40.1 × 27.5 cm (15 13/16 × 10 13/16 in.),"Gilman Collection, Purchase, Harriette and Noel Levine Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285465,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.273,false,true,285467,Photographs,Photograph,"La Reine Hortense - Yacht de l'empereur, Havre",,,,,,Artist,,Gustave Le Gray,"French, 1820–1884",,"Le Gray, Gustave",French,1820,1884,1856,1856,1856,Albumen silver print from glass negative,Image: 31.8 x 41.3 cm (12 1/2 x 16 1/4 in.) Mount: 53.1 x 66.3 cm (20 7/8 x 26 1/8 in.),"Gilman Collection, Purchase, Jennifer and Joseph Duke Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285467,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.274,false,true,285791,Photographs,Photograph,Nu féminin allongé sur un canapé Récamier,,,,,,Artist,,Gustave Le Gray,"French, 1820–1884",,"Le Gray, Gustave",French,1820,1884,ca. 1856,1854,1858,Albumen silver print from glass negative,Image: 21.7 x 32.9 cm (8 9/16 x 12 15/16 in.),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285791,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.311,false,true,285471,Photographs,Photograph,The Salon of 1852,,,,,,Artist,,Gustave Le Gray,"French, 1820–1884",,"Le Gray, Gustave",French,1820,1884,1852,1852,1852,Salted paper print from waxed-paper negative,Image: 24.1 × 38 cm (9 1/2 × 14 15/16 in.) Mount: 37.5 × 40.9 cm (14 3/4 × 16 1/8 in.),"Gilman Collection, Purchase, Mrs. Walter Annenberg and The Annenberg Foundation Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285471,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.612,false,true,283126,Photographs,Photograph,Scène près d'un étang au moulin du Petit-Mourmelon,,,,,,Artist,,Gustave Le Gray,"French, 1820–1884",,"Le Gray, Gustave",French,1820,1884,1857,1857,1857,Albumen silver print from glass negative,Mount: 18 9/16 × 24 9/16 in. (47.2 × 62.4 cm) Image: 11 in. × 13 7/8 in. (27.9 × 35.2 cm),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283126,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.628,false,true,285461,Photographs,Photograph,"[View of the Seine, Paris]",,,,,,Artist,,Gustave Le Gray,"French, 1820–1884",,"Le Gray, Gustave",French,1820,1884,1857,1857,1857,Albumen silver print from glass negative,Image: 38.5 x 50.9; Mount: 49.5 x 60.7,"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285461,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.678,false,true,286160,Photographs,Photograph,Portrait de Pitre-Chevalier,,,,,,Artist,,Gustave Le Gray,"French, 1820–1884",,"Le Gray, Gustave",French,1820,1884,ca. 1853,1851,1855,Salted paper print from glass negative,"Image: 17.5 x 13.4 cm (6 7/8 x 5 1/4 in.), irregularly trimmed Mount: 17.5 x 13.4 cm (6 7/8 x 5 1/4 in.), irregularly trimmed","Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286160,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.679,false,true,285792,Photographs,Photograph,Vue de la Plaine de Thèbes prise du temple de Karnac,,,,,,Artist,,Gustave Le Gray,"French, 1820–1884",,"Le Gray, Gustave",French,1820,1884,1867,1867,1867,Albumen silver print from paper negative,Image: 32 × 41.5 cm (12 5/8 × 16 5/16 in.) Mount: 19 13/16 × 25 9/16 in. (50.3 × 65 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285792,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.680,false,true,286350,Photographs,Photograph,[An Italian Street Musician],,,,,,Artist,,Gustave Le Gray,"French, 1820–1884",,"Le Gray, Gustave",French,1820,1884,ca. 1856,1854,1858,Albumen silver print from glass negative,"Image: 34 x 26.4 cm (13 3/8 x 10 3/8 in.), partially obscured by overmat Mat: 49.6 x 38.1 cm (19 1/2 x 15 in.)","Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286350,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.850,false,true,286196,Photographs,Photograph,Portrait de Louis-Napoléon Bonaparte en Prince-Président,,,,,,Artist,,Gustave Le Gray,"French, 1820–1884",,"Le Gray, Gustave",French,1820,1884,1852,1852,1852,Albumen silver print from paper negative,Image: 20.2 x 14.7 cm (7 15/16 x 5 13/16 in.) Mount: 48.1 x 38.6 cm (18 15/16 x 15 3/16 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286196,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.964,false,true,286195,Photographs,Photographs,La Messe au Camp de Châlons,,,,,,Artist,,Gustave Le Gray,"French, 1820–1884",,"Le Gray, Gustave",French,1820,1884,1857,1857,1857,Albumen silver print from glass negative,28.6 x 35.8 cm (11 1/4 x 14 1/8 in.),"Gilman Collection, Purchase, Mrs. Walter Annenberg and The Annenberg Foundation Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286195,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.965,false,true,285628,Photographs,Photographs,"[Zouaves, Camp de Châlons]",,,,,,Artist,,Gustave Le Gray,"French, 1820–1884",,"Le Gray, Gustave",French,1820,1884,1857,1857,1857,Albumen silver print from glass negative,Image: 27.9 x 35.6 cm (11 x 14 in.),"Gilman Collection, Purchase, Mrs. Walter Annenberg and The Annenberg Foundation Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285628,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.181,false,true,261362,Photographs,Photograph,[La Comtesse in Lace Shawl],,,,,,Artist,,Alphonse (Jean-Baptiste) Bernoud,"French, 1820–1889",,"Bernoud, Alphonse",French,1820,1889,1860s,1860,1869,Albumen silver print from glass negative,5.1 x 8.6 cm (2 x 3 3/8 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261362,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.503.1,false,true,289455,Photographs,Photograph,Vue du château de Pau,,,,,,Artist,,Joseph Vigier,"French, 1821–1862",,"Vigier, Joseph",French,1821,1862,1853,1853,1853,Salted paper print from paper negative,Sheet: 12 in. × 18 9/16 in. (30.5 × 47.2 cm) Image: 9 3/4 × 13 3/4 in. (24.7 × 35 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289455,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.503.2,false,true,289456,Photographs,Photograph,Route de Pierrefitte à Luz St Sauveur,,,,,,Artist,,Joseph Vigier,"French, 1821–1862",,"Vigier, Joseph",French,1821,1862,1853,1853,1853,Salted paper print from paper negative,Sheet: 12 in. × 18 9/16 in. (30.5 × 47.2 cm) Image: 9 3/4 × 13 3/4 in. (24.7 × 35 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289456,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.503.3,false,true,289457,Photographs,Photograph,Vallé de Lur prise du chemin de Sasie à St-Sauveur,,,,,,Artist,,Joseph Vigier,"French, 1821–1862",,"Vigier, Joseph",French,1821,1862,1850,1850,1850,Salted paper print from paper negative,Sheet: 12 in. × 18 9/16 in. (30.5 × 47.2 cm) Image: 10 1/4 × 14 7/16 in. (26 × 36.7 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289457,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.503.4,false,true,289458,Photographs,Photograph,Vue des Eaux de Saint-Sauveur,,,,,,Artist,,Joseph Vigier,"French, 1821–1862",,"Vigier, Joseph",French,1821,1862,1853,1853,1853,Salted paper print from paper negative,Sheet: 12 in. × 18 9/16 in. (30.5 × 47.2 cm) Image: 10 13/16 × 14 5/8 in. (27.4 × 37.1 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289458,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.503.5,false,true,289459,Photographs,Photograph,Vallée d'Argelès près de la ferme de Despourreins. St-Sauveur,,,,,,Artist,,Joseph Vigier,"French, 1821–1862",,"Vigier, Joseph",French,1821,1862,1853,1853,1853,Salted paper print from paper negative,Sheet: 12 in. × 18 9/16 in. (30.5 × 47.2 cm) Image: 10 9/16 × 14 1/16 in. (26.9 × 35.7 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289459,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.503.6,false,true,289460,Photographs,Photograph,Pont de Sia Route de Gavarnie St Sauveur,,,,,,Artist,,Joseph Vigier,"French, 1821–1862",,"Vigier, Joseph",French,1821,1862,1853,1853,1853,Salted paper print from paper negative,Sheet: 12 in. × 18 9/16 in. (30.5 × 47.2 cm) Image: 14 1/16 × 10 9/16 in. (35.7 × 26.8 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289460,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.503.7,false,true,289461,Photographs,Photograph,"Le chaos en allant à Gavarnie, St-Sauveur",,,,,,Artist,,Joseph Vigier,"French, 1821–1862",,"Vigier, Joseph",French,1821,1862,1853,1853,1853,Salted paper print from paper negative,Image: 10 7/8 × 14 9/16 in. (27.6 × 37 cm) Sheet: 12 in. × 18 9/16 in. (30.5 × 47.2 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289461,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.503.8,false,true,283111,Photographs,Photograph,"Sentier du chaos, St-Sauveur",,,,,,Artist,,Joseph Vigier,"French, 1821–1862",,"Vigier, Joseph",French,1821,1862,1853,1853,1853,Salted paper print from paper negative,Image: 9 13/16 × 14 1/2 in. (24.9 × 36.9 cm) Sheet: 12 in. × 18 9/16 in. (30.5 × 47.2 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283111,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.503.9,false,true,289462,Photographs,Photograph,"Blocs dans le chaos, St-Sauveur",,,,,,Artist,,Joseph Vigier,"French, 1821–1862",,"Vigier, Joseph",French,1821,1862,1853,1853,1853,Salted paper print from paper negative,Image: 10 1/16 × 13 9/16 in. (25.5 × 34.4 cm) Sheet: 12 in. × 18 9/16 in. (30.5 × 47.2 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289462,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.503.10,false,true,289463,Photographs,Photograph,"Village de Gèdres, Route de Gavarnie",,,,,,Artist,,Joseph Vigier,"French, 1821–1862",,"Vigier, Joseph",French,1821,1862,1853,1853,1853,Salted paper print from paper negative,Sheet: 12 in. × 18 9/16 in. (30.5 × 47.2 cm) Image: 9 15/16 × 14 5/16 in. (25.3 × 36.3 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289463,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.503.11,false,true,289464,Photographs,Photograph,"Cirque de Gavarnie, St Sauveur",,,,,,Artist,,Joseph Vigier,"French, 1821–1862",,"Vigier, Joseph",French,1821,1862,1853,1853,1853,Salted paper print from paper negative,Image: 9 9/16 × 14 9/16 in. (24.3 × 37 cm) Sheet: 12 in. × 18 9/16 in. (30.5 × 47.2 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289464,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.503.12,false,true,289465,Photographs,Photograph,"Gorge d'Estaubé prise des gloriettes d'Héas, St-Sauveur",,,,,,Artist,,Joseph Vigier,"French, 1821–1862",,"Vigier, Joseph",French,1821,1862,1853,1853,1853,Salted paper print from paper negative,Image: 9 5/8 × 14 5/16 in. (24.5 × 36.4 cm) Sheet: 12 in. × 18 9/16 in. (30.5 × 47.2 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289465,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.503.13,false,true,289466,Photographs,Photograph,"Vallée et chapelle d'Héas, St-Sauveur",,,,,,Artist,,Joseph Vigier,"French, 1821–1862",,"Vigier, Joseph",French,1821,1862,1853,1853,1853,Salted paper print from paper negative,Image: 9 1/2 × 14 1/2 in. (24.1 × 36.8 cm) Sheet: 12 in. × 18 9/16 in. (30.5 × 47.2 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289466,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.503.14,false,true,289467,Photographs,Photograph,"Cauterets, Pont d'Espagne",,,,,,Artist,,Joseph Vigier,"French, 1821–1862",,"Vigier, Joseph",French,1821,1862,1853,1853,1853,Salted paper print from paper negative,Image: 14 5/16 × 9 1/2 in. (36.4 × 24.2 cm) Sheet: 12 in. × 18 9/16 in. (30.5 × 47.2 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289467,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.503.15,false,true,289468,Photographs,Photograph,"Lac de Gaube, Cauterets",,,,,,Artist,,Joseph Vigier,"French, 1821–1862",,"Vigier, Joseph",French,1821,1862,1853,1853,1853,Salted paper print from paper negative,Image: 9 9/16 × 14 5/16 in. (24.3 × 36.3 cm) Sheet: 12 in. × 18 9/16 in. (30.5 × 47.2 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289468,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.503.16,false,true,289469,Photographs,Photograph,Vue de la Vallée de Luchon prise de la tour de Castelvieilh,,,,,,Artist,,Joseph Vigier,"French, 1821–1862",,"Vigier, Joseph",French,1821,1862,1853,1853,1853,Salted paper print from paper negative,Image: 9 5/8 × 14 5/16 in. (24.5 × 36.4 cm) Sheet: 12 in. × 18 9/16 in. (30.5 × 47.2 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289469,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.503.17,false,true,289470,Photographs,Photograph,Bains de la Raillière à Cauterets,,,,,,Artist,,Joseph Vigier,"French, 1821–1862",,"Vigier, Joseph",French,1821,1862,1853,1853,1853,Salted paper print from paper negative,Image: 9 1/2 × 14 7/16 in. (24.1 × 36.7 cm) Sheet: 12 in. × 18 9/16 in. (30.5 × 47.2 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289470,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.503.18,false,true,289471,Photographs,Photograph,"Gorge d'Astos, prise en revenant du lac d'Oo, Luchon",,,,,,Artist,,Joseph Vigier,"French, 1821–1862",,"Vigier, Joseph",French,1821,1862,1853,1853,1853,Salted paper print from paper negative,Image: 9 3/4 × 14 5/16 in. (24.7 × 36.3 cm) Sheet: 12 in. × 18 9/16 in. (30.5 × 47.2 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289471,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.503.19,false,true,289472,Photographs,Photograph,Vallée de Bosost prise de la capilla San Antonio,,,,,,Artist,,Joseph Vigier,"French, 1821–1862",,"Vigier, Joseph",French,1821,1862,1853,1853,1853,Salted paper print from paper negative,Image: 9 5/8 × 14 9/16 in. (24.4 × 37 cm) Sheet: 12 in. × 18 9/16 in. (30.5 × 47.2 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289472,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.503.20,false,true,289473,Photographs,Photograph,"Pont de l'ardoise pris en revenant de la cascade des Parisiens, Luchon",,,,,,Artist,,Joseph Vigier,"French, 1821–1862",,"Vigier, Joseph",French,1821,1862,1853,1853,1853,Salted paper print from paper negative,Image: 9 5/8 × 13 7/8 in. (24.4 × 35.3 cm) Sheet: 12 in. × 18 9/16 in. (30.5 × 47.2 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289473,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.503.21,false,true,289474,Photographs,Photograph,Village de Montaubant pris de Luchon,,,,,,Artist,,Joseph Vigier,"French, 1821–1862",,"Vigier, Joseph",French,1821,1862,1853,1853,1853,Salted paper print from paper negative,Image: 9 3/4 × 13 3/8 in. (24.8 × 34 cm) Sheet: 12 in. × 18 9/16 in. (30.5 × 47.2 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289474,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.503.22,false,true,289475,Photographs,Photograph,"Torrent de la cascade des Demoiselles, Bagnères de Luchon.",,,,,,Artist,,Joseph Vigier,"French, 1821–1862",,"Vigier, Joseph",French,1821,1862,1853,1853,1853,Salted paper print from paper negative,Image: 14 1/8 × 9 13/16 in. (35.8 × 25 cm) Sheet: 12 in. × 18 9/16 in. (30.5 × 47.2 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289475,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.503.23,false,true,289476,Photographs,Photograph,"Vue du lac d'Oo ou Seculejo, Bagnère de Luchon",,,,,,Artist,,Joseph Vigier,"French, 1821–1862",,"Vigier, Joseph",French,1821,1862,1853,1853,1853,Salted paper print from paper negative,Image: 10 1/8 × 14 1/16 in. (25.7 × 35.7 cm) Sheet: 12 in. × 18 9/16 in. (30.5 × 47.2 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289476,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.503.24,false,true,289477,Photographs,Photograph,Vue de la partie de la Maladetta et des montagnes du haut du port de Vénasque,,,,,,Artist,,Joseph Vigier,"French, 1821–1862",,"Vigier, Joseph",French,1821,1862,1853,1853,1853,Salted paper print from paper negative,Image: 9 5/8 × 14 3/16 in. (24.4 × 36 cm) Sheet: 12 in. × 18 9/16 in. (30.5 × 47.2 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289477,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.1,false,true,287066,Photographs,Photograph,"Vue générale du Kaire, prise de la Mosquée Tegloun",,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,December 1849–January 1850,1849,1850,Salted paper print from paper negative,Mount: 47 x 32 cm (18 1/2 x 12 5/8 in.) Image: 6 1/8 × 5 3/8 in. (15.5 × 13.6 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287066,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.2,false,true,287067,Photographs,Photograph,"Vue prise du quartier Franc, au Kaire",,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,December 1849–January 1850,1849,1850,Salted paper print from paper negative,Image: 5 9/16 × 8 1/4 in. (14.2 × 21 cm) Mount: 12 3/16 × 18 11/16 in. (31 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287067,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.3,false,true,287068,Photographs,Photograph,"Vue prise d'un Jardin du quartier Franc, au Kaire",,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,December 1849–January 1850,1849,1850,Salted paper print from paper negative,Image: 6 in. × 8 7/8 in. (15.3 × 22.5 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287068,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.5,false,true,287069,Photographs,Photograph,"Dattiers et Maison du quartier Franc, au Kaire",,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,December 1849–January 1850,1849,1850,Salted paper print from paper negative,Image: 8 11/16 × 6 5/16 in. (22 × 16 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287069,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.6,false,true,287070,Photographs,Photograph,"Minaret occidental de la Mosqée du Khalif Hakem, au Kaire",,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,"January 9, 1850",1850,1850,Salted paper print from paper negative,Image: 8 7/8 × 6 1/8 in. (22.5 × 15.5 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287070,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.7,false,true,287071,Photographs,Photograph,"Minaret oriental de la Mosquée du Khalif Hakem, au Kaire",,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,"December 27, 1849",1849,1849,Salted paper print from paper negative,Image: 8 7/16 × 6 1/8 in. (21.5 × 15.5 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287071,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.8,false,true,287072,Photographs,Photograph,"Sibyl ou Fontaine et Ecole de Souk-el-asr, au Kaire",,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,December 1849–January 1850,1849,1850,Salted paper print from paper negative,Image: 8 3/8 × 6 1/4 in. (21.2 × 15.8 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287072,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.9,false,true,287073,Photographs,Photograph,"Minaret penché de la Mosquée de Bibars, au Kaire",,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,December 1849–January 1850,1849,1850,Salted paper print from paper negative,Image: 8 15/16 × 6 1/4 in. (22.7 × 15.8 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287073,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.10,false,true,287074,Photographs,Photograph,"Vue d'une Mosquée ruinée près de Bab-Saïda, au Kaire",,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,December 1849–January 1850,1849,1850,Salted paper print from paper negative,Image: 6 1/4 × 8 9/16 in. (15.8 × 21.8 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287074,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.11,false,true,287075,Photographs,Photograph,"Vue générale de la Mosquée et du Tombeau de Sultan Bezkouk, El-Melek-el-Dâher, au Kaire",,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,December 1849–January 1850,1849,1850,Salted paper print from paper negative,Image: 6 1/8 × 8 5/16 in. (15.6 × 21.1 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287075,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.12,false,true,287076,Photographs,Photograph,"Entrée du Tombeau de Sultan Bezkouk, au Kaire",,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,December 1849–January 1850,1849,1850,Salted paper print from paper negative,Image: 8 3/4 × 6 5/16 in. (22.3 × 16 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287076,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.13,false,true,287077,Photographs,Photograph,"Tombeau du Sultan Kaït-Bay, au Kaire",,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,December 1849–January 1850,1849,1850,Salted paper print from paper negative,Image: 6 in. × 8 9/16 in. (15.3 × 21.8 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287077,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.14,false,true,287078,Photographs,Photograph,"Tombeau du Sultan El-Goury, au Kaire",,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,December 1849–January 1850,1849,1850,Salted paper print from paper negative,Image: 6 9/16 × 8 11/16 in. (16.6 × 22 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287078,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.15,false,true,287079,Photographs,Photograph,"Mosquée et Tombeau des Ayoubites, au Kaire",,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,December 1849–January 1850,1849,1850,Salted paper print from paper negative,Image: 6 in. × 8 9/16 in. (15.3 × 21.8 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287079,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.16,false,true,287080,Photographs,Photograph,"Tombeau des Sultans Mamelouks, au Kaire",,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,December 1849–January 1850,1849,1850,Salted paper print from paper negative,Image: 6 3/8 × 8 9/16 in. (16.2 × 21.8 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287080,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.17,false,true,287081,Photographs,Photograph,Armes et ustensiles du Kaire,,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,December 1849–January 1850,1849,1850,Salted paper print from paper negative,Image: 8 7/8 × 6 5/16 in. (22.5 × 16 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287081,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.18,false,true,287082,Photographs,Photograph,Vue de la grande pyramide (Chéops) prise à l'angle S.E.,,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,December 1849,1849,1849,Salted paper print from paper negative,Image: 6 3/8 × 8 9/16 in. (16.2 × 21.8 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287082,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.19,false,true,287083,Photographs,Photograph,"Vue de la seconde Pyramide, prise au Sud-Est",,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,December 1849,1849,1849,Salted paper print from paper negative,Image: 5 7/8 × 8 9/16 in. (15 × 21.7 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287083,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.20,false,true,287084,Photographs,Photograph,Vue du grand Sphinx et de la grande pyramide de Menkazeh (Mycerinus),,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,December 1849,1849,1849,Salted paper print from paper negative,Image: 6 3/8 × 8 7/16 in. (16.2 × 21.4 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287084,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.21,false,true,287085,Photographs,Photograph,"Profile du grande Sphinx, pris du Sud",,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,December 1849,1849,1849,Salted paper print from paper negative,Image: 6 1/8 × 8 11/16 in. (15.6 × 22 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287085,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.22,false,true,287086,Photographs,Photograph,Vue prise à Béni-Souef,,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,1850,1850,1850,Salted paper print from paper negative,Image: 5 3/4 × 8 7/8 in. (14.6 × 22.6 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287086,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.23,false,true,287087,Photographs,Photograph,Vue du Village de Garara,,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,1850,1850,1850,Salted paper print from paper negative,Image: 6 1/8 × 8 7/16 in. (15.6 × 21.5 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287087,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.24,false,true,287088,Photographs,Photograph,"Tombeau de Sidi-Ambarek, à Garara",,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,1850,1850,1850,Salted paper print from paper negative,Image: 5 1/2 × 8 3/8 in. (14 × 21.3 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287088,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.25,false,true,287089,Photographs,Photograph,Vue de Djebel-el-teir et du Convent de la Poulie,,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,1850,1850,1850,Salted paper print from paper negative,Image: 5 5/8 × 8 3/8 in. (14.3 × 21.3 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287089,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.26,false,true,287090,Photographs,Photograph,Vue de Syout - Palais du Pacha,,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,1850,1850,1850,Salted paper print from paper negative,Image: 5 7/16 × 8 9/16 in. (13.8 × 21.7 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287090,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.27,false,true,287091,Photographs,Photograph,"Vue du Divan et du Palais du Gouverneur, à Syout",,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,1849–50,1849,1850,Salted paper print from paper negative,Image: 5 9/16 × 8 1/2 in. (14.2 × 21.6 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287091,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.28,false,true,287092,Photographs,Photograph,Vue générale du Cimetière de Siout,,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,1849–50,1849,1850,Salted paper print from paper negative,Image: 6 9/16 × 8 9/16 in. (16.7 × 21.7 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287092,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.29,false,true,287093,Photographs,Photograph,Tombeaux Musulmans à Siout,,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,1849–50,1849,1850,Salted paper print from paper negative,Image: 6 5/16 × 8 9/16 in. (16 × 21.7 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287093,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.30,false,true,287094,Photographs,Photograph,"Ancienne Nécropole de Lycopolis, à Syout",,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,1849–50,1849,1850,Salted paper print from paper negative,Image: 6 5/16 × 8 9/16 in. (16 × 21.7 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287094,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.31,false,true,287095,Photographs,Photograph,"Mosquée d'El-Arif et Tombeau de Mourad-Bey, à Souhadj",,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,1849–50,1849,1850,Salted paper print from paper negative,Image: 5 1/8 in. × 8 in. (13 × 20.3 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287095,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.32,false,true,287096,Photographs,Photograph,Vue de Girgeh et du littoral enlevé - par l'inondation du Nil,,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,1849–50,1849,1850,Salted paper print from paper negative,Image: 5 5/8 × 8 3/16 in. (14.3 × 20.8 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287096,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.33,false,true,287097,Photographs,Photograph,"Mosquée d'Ali-Bey, à Girgeh",,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,1849–50,1849,1850,Salted paper print from paper negative,Image: 6 7/16 × 8 3/4 in. (16.3 × 22.3 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287097,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.34,false,true,287098,Photographs,Photograph,Mosquée de Haou (Diospolis parva),,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,1849–50,1849,1850,Salted paper print from paper negative,Image: 5 11/16 × 7 7/8 in. (14.5 × 20 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287098,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.35,false,true,287099,Photographs,Photograph,"Vue du Village de Hamarneh, près de Dendérah (Rive droite)",,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,1849–50,1849,1850,Salted paper print from paper negative,Image: 5 13/16 × 8 9/16 in. (14.7 × 21.7 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287099,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.36,false,true,287100,Photographs,Photograph,"Bois de Dattiers et de Doums, à Hamarneh",,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,1849–50,1849,1850,Salted paper print from paper negative,Image: 6 3/8 × 8 3/8 in. (16.2 × 21.2 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287100,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.37,false,true,287101,Photographs,Photograph,Palmiers Doums à Hamarneh,,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,1849–50,1849,1850,Salted paper print from paper negative,Image: 6 5/8 × 8 3/8 in. (16.9 × 21.2 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287101,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.38,false,true,287102,Photographs,Photograph,Façade du Temple d'Athor à Dendérah (Tentyris),,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,1849–50,1849,1850,Salted paper print from paper negative,Image: 6 7/16 × 8 3/8 in. (16.4 × 21.2 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287102,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.39,false,true,287103,Photographs,Photograph,Façade postérieure de grande Temple de Dendérah (Tentyris),,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,1849–50,1849,1850,Salted paper print from paper negative,Image: 6 7/16 × 8 3/8 in. (16.4 × 21.2 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287103,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.40,false,true,287104,Photographs,Photograph,"Hypètre d'Athor, sur la Terrasse du grande Temple de Dendérah (Tentyris)",,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,1849–50,1849,1850,Salted paper print from paper negative,Image: 6 7/16 × 8 3/8 in. (16.4 × 21.2 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287104,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.41,false,true,287105,Photographs,Photograph,Bas-reliefs de la façade du Temple de Dendérah (Tentyris),,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,1849–50,1849,1850,Salted paper print from paper negative,Image: 6 1/8 × 8 9/16 in. (15.5 × 21.8 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287105,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.42,false,true,287106,Photographs,Photograph,Sculptures sur la façade postérieure du grande Temple de Dendérah (Tentyris),,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,1849–50,1849,1850,Salted paper print from paper negative,Image: 6 1/8 × 8 9/16 in. (15.5 × 21.8 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287106,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.43,false,true,287107,Photographs,Photograph,"Vue générale des Ruines de Louxor, prise de l'Ouest (Thèbes)",,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,1849–50,1849,1850,Salted paper print from paper negative,Image: 5 1/2 × 8 7/16 in. (14 × 21.5 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287107,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.44,false,true,287108,Photographs,Photograph,"Habitation de l'équipage de l'allège de Luxor, bâtie sur la terrasse du Palais",,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,1849–50,1849,1850,Salted paper print from paper negative,Image: 5 13/16 × 8 9/16 in. (14.7 × 21.7 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287108,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.45,false,true,287109,Photographs,Photograph,"Pigeonniers bâtis sur la colonnade méridionale du palais d'Aménophis III, à Louxor, Thèbes",,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,1849–50,1849,1850,Salted paper print from paper negative,Image: 5 7/8 × 8 1/2 in. (15 × 21.6 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287109,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.46,false,true,287110,Photographs,Photograph,"Vue d'une partie du village de Louxor, Thèbes",,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,1849–50,1849,1850,Salted paper print from paper negative,Image: 5 5/8 × 8 1/16 in. (14.3 × 20.4 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287110,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.47,false,true,287111,Photographs,Photograph,"Grande Colonnade du Palais d'Aménophis III, à Luxor, Thèbes",,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,1849–50,1849,1850,Salted paper print from paper negative,Image: 6 9/16 × 8 1/2 in. (16.6 × 21.6 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287111,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.48,false,true,287112,Photographs,Photograph,"Palais et Village de Louxor, pris du Sud, Thèbes",,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,1849–50,1849,1850,Salted paper print from paper negative,Image: 6 5/16 × 8 3/8 in. (16 × 21.2 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287112,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.49,false,true,287113,Photographs,Photograph,"Groupe de colonnes du Palais de Louxor, Thèbes",,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,1849–50,1849,1850,Salted paper print from paper negative,Image: 8 7/16 × 6 9/16 in. (21.4 × 16.6 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287113,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.50,false,true,287114,Photographs,Photograph,"Propylone du Temple de Khons, à Karnac, Thèbes",,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,1849–50,1849,1850,Salted paper print from paper negative,Image: 7 11/16 × 6 5/16 in. (19.5 × 16 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287114,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.51,false,true,287115,Photographs,Photograph,"Intérieur du Temple de Khons, à Karnac, Thèbes",,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,1849–50,1849,1850,Salted paper print from paper negative,Image: 8 1/4 × 6 1/2 in. (21 × 16.5 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287115,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.52,false,true,287116,Photographs,Photograph,"Vue générale du Temple de Khons, à Karnac, Thèbes",,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,1849–50,1849,1850,Salted paper print from paper negative,Image: 6 5/16 × 8 1/4 in. (16 × 21 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287116,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.53,false,true,287117,Photographs,Photograph,Vue des Pylones du Temple Khons et d'une partie du village de Karnac,,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,1849–50,1849,1850,Salted paper print from paper negative,Image: 5 1/4 × 8 1/8 in. (13.4 × 20.6 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287117,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.54,false,true,287118,Photographs,Photograph,"Vue des propylées du palais de Karnac, prise du Sud-Est",,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,1849–50,1849,1850,Salted paper print from paper negative,Image: 5 1/4 × 8 1/8 in. (13.4 × 20.6 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287118,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.55,false,true,287119,Photographs,Photograph,"Vue générale des ruines du Palais de Karnac, prise du Nord",,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,1849–50,1849,1850,Salted paper print from paper negative,Image: 6 in. × 8 3/8 in. (15.3 × 21.2 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287119,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.56,false,true,287120,Photographs,Photograph,"Grand Pylone du Palais de Karnac, Thèbes",,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,1849–50,1849,1850,Salted paper print from paper negative,Image: 5 7/8 × 8 1/8 in. (14.9 × 20.7 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287120,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.57,false,true,287121,Photographs,Photograph,"Cour des bubastites et entrée de la Salle Hypostyle du palais de Karnac, Thèbes",,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,1849–50,1849,1850,Salted paper print from paper negative,Image: 8 7/16 × 6 9/16 in. (21.5 × 16.6 cm) Mount: 18 11/16 × 12 5/16 in. (47.5 × 31.2 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287121,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.58,false,true,287122,Photographs,Photograph,Ruines de la Salle Hypostyle du Palais de Karnac - Vue prise du Sud-Ouest,,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,1849–50,1849,1850,Salted paper print from paper negative,Image: 5 3/16 × 7 15/16 in. (13.1 × 20.2 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287122,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.59,false,true,287123,Photographs,Photograph,"Vue de la Salle Hypostyle du palais de Karnac, prise sur l'angle N.E.",,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,1849–50,1849,1850,Salted paper print from paper negative,Image: 6 1/8 × 8 1/4 in. (15.5 × 21 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287123,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.60,false,true,287124,Photographs,Photograph,Porte Septentrionale de la Salle Hypostyle du Palais de Karnac,,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,1849–50,1849,1850,Salted paper print from paper negative,Image: 6 1/8 × 8 1/4 in. (15.5 × 21 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287124,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.61,false,true,287125,Photographs,Photograph,Porte méridionale de la Salle Hypostyle du Palais de Karnac (Thèbes),,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,1849–50,1849,1850,Salted paper print from paper negative,Image: 6 in. × 8 3/8 in. (15.3 × 21.3 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287125,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.62,false,true,287126,Photographs,Photograph,Obélisques du Palais de Karnac (Thèbes),,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,1849–50,1849,1850,Salted paper print from paper negative,Image: 7 11/16 × 6 5/8 in. (19.5 × 16.8 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287126,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.63,false,true,287127,Photographs,Photograph,Pilier du Sanctuaire de granit du Palais de Karnac (Bas-relief représentant Thotmès III et la Déesse Athor),,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,1849–50,1849,1850,Salted paper print from paper negative,Image: 8 3/16 × 6 1/4 in. (20.8 × 15.8 cm) Mount: 18 11/16 × 12 5/16 in. (47.5 × 31.2 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287127,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.64,false,true,287128,Photographs,Photograph,Pilier du Sanctuaire de granit du Palais de Karnac (Bas-relief représentant le Pharaon Thotmès III et la Déesse Nauth),,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,1849–50,1849,1850,Salted paper print from paper negative,Image: 8 3/16 × 6 1/4 in. (20.8 × 15.8 cm) Mount: 18 11/16 × 12 5/16 in. (47.5 × 31.2 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287128,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.65,false,true,287129,Photographs,Photograph,Sculptures extérieures du Sanctuaire de granit du Palais de Karnac (Thèbes) (Philippe-Aridée conduisant la Barc de Mauth),,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,1849–50,1849,1850,Salted paper print from paper negative,Image: 6 3/8 × 8 1/4 in. (16.2 × 21 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287129,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.66,false,true,287130,Photographs,Photograph,Sculptures extérieures du Santuaire de granit du palais de Karnac (Sacre de Philippe-Aridée par les Dieux Thot et Hor-hat),,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,1849–50,1849,1850,Salted paper print from paper negative,Image: 8 1/4 × 6 7/16 in. (21 × 16.3 cm) Mount: 18 11/16 × 12 5/16 in. (47.5 × 31.2 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287130,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.67,false,true,287131,Photographs,Photograph,Sculptures extérieures du Sanctuaire de granit du Palais de Karnac (Ammon assurant la couronne sur la tête de Philippe-Aridée),,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,1849–50,1849,1850,Salted paper print from paper negative,Image: 6 1/8 in. × 8 in. (15.5 × 20.3 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287131,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.68,false,true,287132,Photographs,Photograph,Ruines du Palais de Karnac - Vue prise à l'extrêmité du Sanctuaire,,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,1849–50,1849,1850,Salted paper print from paper negative,Image: 6 3/8 × 8 7/16 in. (16.2 × 21.5 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287132,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.69,false,true,287133,Photographs,Photograph,Promenoir de Thoutmès III - Dernières galeries du Palais de Karnac (Thèbes),,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,1849–50,1849,1850,Salted paper print from paper negative,Image: 5 13/16 in. × 8 in. (14.8 × 20.3 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287133,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.70,false,true,287134,Photographs,Photograph,Vue générale des Ruines du Palais de Karnac (prise à l'Est) - Thèbes,,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,1849–50,1849,1850,Salted paper print from paper negative,Image: 5 11/16 × 8 7/16 in. (14.4 × 21.4 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287134,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.71,false,true,287135,Photographs,Photograph,Palais de Gournah (Ménephtéun) à Thèbes,,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,1849–50,1849,1850,Salted paper print from paper negative,Image: 5 11/16 × 8 11/16 in. (14.5 × 22 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287135,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.72,false,true,287136,Photographs,Photograph,"Vue générale des Ruines du Rhamesseum, à Thèbes (Tombeau d'Osymandian)",,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,1849–50,1849,1850,Salted paper print from paper negative,Image: 5 11/16 × 8 11/16 in. (14.5 × 22 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287136,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.73,false,true,286898,Photographs,Photograph,Colosses du Ramesséum,,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,1850,1850,1850,Salted paper print from paper negative,Image: 16.4 x 21.1 cm (6 7/16 x 8 5/16 in.) Mount: 31.2 x 43.8 cm (12 5/16 x 17 1/4 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286898,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.74,false,true,287137,Photographs,Photograph,Vue générale de la Nécropole de Thèbes (Gournah),,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,1849–50,1849,1850,Salted paper print from paper negative,Mount: Approximately 32 x 47 cm (12 5/8 x 18 1/2 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287137,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.75,false,true,287138,Photographs,Photograph,"Vue des deux colosses de l'Aménophéum, à Thèbes",,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,1849–50,1849,1850,Salted paper print from paper negative,Image: 6 1/8 × 7 7/8 in. (15.5 × 20 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287138,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.76,false,true,287139,Photographs,Photograph,"Colosse restauré d' Aménophis III, à Thèbes (Statue vocale ou Colosse de Memnon)",,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,1849–50,1849,1850,Salted paper print from paper negative,Image: 7 13/16 × 6 5/16 in. (19.8 × 16 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287139,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.77,false,true,287140,Photographs,Photograph,"Colosse monolithe d'Amenophis III, à Thèbes",,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,1849–50,1849,1850,Salted paper print from paper negative,Image: 7 11/16 × 6 1/4 in. (19.6 × 15.9 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287140,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.78,false,true,287141,Photographs,Photograph,"Siège du colosse monolithe d'Aménophis III, à Thèbes (Détails des sculptures)",,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,1849–50,1849,1850,Salted paper print from paper negative,Image: 9 1/8 × 6 7/16 in. (23.1 × 16.4 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287141,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.79,false,true,287142,Photographs,Photograph,"Vue générale de Médinet-habou, prise de l'Est (Thèbes)",,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,1849–50,1849,1850,Salted paper print from paper negative,Image: 6 7/16 × 8 1/16 in. (16.3 × 20.4 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287142,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.80,false,true,287143,Photographs,Photograph,"Propylées du Thoutmoséum, à Médinet-habou (Thèbes)",,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,1849–50,1849,1850,Salted paper print from paper negative,Image: 8 7/16 × 6 7/16 in. (21.4 × 16.4 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287143,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.81,false,true,287144,Photographs,Photograph,"Pavillon ou Gynecée de Rhamsès-Meiamoun, à Médinet-habou",,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,1849–50,1849,1850,Salted paper print from paper negative,Image: 6 5/16 × 8 3/4 in. (16 × 22.2 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287144,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.82,false,true,287145,Photographs,Photograph,"Façade latérale du Gynecée de Rhamsès-Meiamoun, Palais de Médinet-Habou, à Thèbes",,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,1849–50,1849,1850,Salted paper print from paper negative,Image: 9 in. × 6 9/16 in. (22.8 × 16.7 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287145,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.83,false,true,287146,Photographs,Photograph,"Cour du Palais Rhamsès-Meiamoun, à Médinet-habou (Thèbes) (Restes d'une Eglise Copte)",,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,1849–50,1849,1850,Salted paper print from paper negative,Image: 6 3/8 × 8 3/8 in. (16.2 × 21.2 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287146,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.84,false,true,287147,Photographs,Photograph,"Piliers dans la cour du palais de Rhamsès-Meiamoun, à Médinet-habou (Thèbes)",,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,1849–50,1849,1850,Salted paper print from paper negative,Image: 8 3/8 in. × 6 in. (21.2 × 15.3 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287147,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.85,false,true,287148,Photographs,Photograph,"Ruines d'une ville chrétienne, à Médinet-habou (Thèbes)",,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,1849–50,1849,1850,Salted paper print from paper negative,Image: 6 1/2 × 8 15/16 in. (16.5 × 22.7 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287148,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.86,false,true,287149,Photographs,Photograph,Vue du Village d'Herment,,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,1849–50,1849,1850,Salted paper print from paper negative,Image: 6 in. × 8 3/8 in. (15.3 × 21.3 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287149,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.87,false,true,287150,Photographs,Photograph,Ruines du Temple d'Herment (Hermentis),,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,1849–50,1849,1850,Salted paper print from paper negative,Mount: Approximately 32 x 47 cm (12 5/8 x 18 1/2 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287150,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.88,false,true,287151,Photographs,Photograph,Tombeaux Musulmans à Herment,,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,1849–50,1849,1850,Salted paper print from paper negative,Mount: Approximately 32 x 47 cm (12 5/8 x 18 1/2 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287151,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.89,false,true,287152,Photographs,Photograph,"Tombeau de Hadji-Abdallah-el-Marabout, à Herment",,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,1849–50,1849,1850,Salted paper print from paper negative,Image: 6 1/4 × 8 1/2 in. (15.8 × 21.6 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287152,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.90,false,true,287153,Photographs,Photograph,Vue générale d'Esné (No. 1 extrêmité Sud),,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,1849–50,1849,1850,Salted paper print from paper negative,Image: 5 1/2 × 8 1/2 in. (14 × 21.6 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287153,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.91,false,true,287154,Photographs,Photograph,"Vue générale d'Esné (No. 2, partie médiale)",,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,1849–50,1849,1850,Salted paper print from paper negative,Image: 5 7/16 × 8 3/4 in. (13.8 × 22.2 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287154,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.92,false,true,287155,Photographs,Photograph,"Vue générale d'Esné (No. 3, pointe Nord)",,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,1849–50,1849,1850,Salted paper print from paper negative,Image: 5 7/16 × 7 7/8 in. (13.8 × 20 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287155,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.93,false,true,287156,Photographs,Photograph,"Palais de Mehemet-Ali, à Esné",,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,1849–50,1849,1850,Salted paper print from paper negative,Image: 5 13/16 × 8 7/16 in. (14.8 × 21.5 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287156,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.94,false,true,287157,Photographs,Photograph,Vue du Village d'Edfou,,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,1849–50,1849,1850,Salted paper print from paper negative,Image: 5 13/16 × 8 1/8 in. (14.8 × 20.6 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287157,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.95,false,true,287158,Photographs,Photograph,Ruines du Temple de Koum-Ombou (Ombos),,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,1849–50,1849,1850,Salted paper print from paper negative,Image: 6 5/8 × 8 3/4 in. (16.8 × 22.2 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287158,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.96,false,true,287159,Photographs,Photograph,"Vue de l'île d'Eléphantine, en face d'Assouan",,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,1849–50,1849,1850,Salted paper print from paper negative,Image: 5 11/16 × 8 11/16 in. (14.4 × 22 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287159,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.97,false,true,287160,Photographs,Photograph,Entrée de la première Cataracte près d'Assouan,,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,1849–50,1849,1850,Salted paper print from paper negative,Image: 5 15/16 × 8 11/16 in. (15.1 × 22 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287160,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.98,false,true,287161,Photographs,Photograph,Vue prise à la première Cataracte (Rive droite),,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,1849–50,1849,1850,Salted paper print from paper negative,Image: 6 5/8 × 8 7/8 in. (16.9 × 22.6 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287161,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.99,false,true,287162,Photographs,Photograph,"Vue de la première Cataracte, prise à l'Ouest, entre Assouan et Philae",,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,1849–50,1849,1850,Salted paper print from paper negative,Image: 6 in. × 8 1/4 in. (15.2 × 21 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287162,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.100,false,true,287163,Photographs,Photograph,Sortie de la première Cataracte (côté S.E.),,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,1849–50,1849,1850,Salted paper print from paper negative,Image: 6 5/16 × 8 9/16 in. (16 × 21.8 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287163,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.101,false,true,287164,Photographs,Photograph,Vue prise au Nord de Philae - Village de Kounoço,,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,1849–50,1849,1850,Salted paper print from paper negative,Image: 5 11/16 × 8 3/16 in. (14.5 × 20.8 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287164,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.102,false,true,287165,Photographs,Photograph,Vue prise du Nord-Est de Philae - Village de Kolokina,,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,1849–50,1849,1850,Salted paper print from paper negative,Image: 5 11/16 × 8 3/16 in. (14.5 × 20.8 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287165,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.103,false,true,287166,Photographs,Photograph,Vue prise à l'Est de Philae - Village de Abou-Kouli; Route d'Assouan,,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,1849–50,1849,1850,Salted paper print from paper negative,Image: 5 3/8 × 8 3/16 in. (13.6 × 20.8 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287166,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.104,false,true,287167,Photographs,Photograph,Vue prise au Sud-Est de Philae - Cherk-el-Hesseh,,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,1849–50,1849,1850,Salted paper print from paper negative,Image: 6 in. × 7 15/16 in. (15.3 × 20.1 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287167,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.105,false,true,287168,Photographs,Photograph,Vue prise au Sud-Est de Philae - Village d'El-Bâb,,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,1849–50,1849,1850,Salted paper print from paper negative,Image: 5 5/8 × 8 1/8 in. (14.3 × 20.7 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287168,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.106,false,true,287169,Photographs,Photograph,"Vue générale de l'île de Philae, prise de la pointe Sud de l'île de Begueh",,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,1849–50,1849,1850,Salted paper print from paper negative,Image: 6 9/16 × 8 7/16 in. (16.6 × 21.4 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287169,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.107,false,true,287170,Photographs,Photograph,"Vue générale de l'île de Philae, prise de l'île de Begueh (Ouest)",,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,1849–50,1849,1850,Salted paper print from paper negative,Image: 6 in. × 7 7/8 in. (15.2 × 20 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287170,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.108,false,true,287171,Photographs,Photograph,"Vue générale de l'île de Philae, prise de l'Est",,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,1849–50,1849,1850,Salted paper print from paper negative,Image: 5 9/16 × 8 11/16 in. (14.1 × 22 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287171,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.109,false,true,287172,Photographs,Photograph,"Grand Hypètre ou Typhonium, à Philae - Vue prise de l'Est",,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,1849–50,1849,1850,Salted paper print from paper negative,Image: 6 5/8 × 8 3/4 in. (16.8 × 22.2 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287172,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.110,false,true,287173,Photographs,Photograph,"Ruines d'un Arc-de-triomphe Romain, à Philae",,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,April 1850,1850,1850,Salted paper print from paper negative,Image: 6 3/16 in. × 8 in. (15.7 × 20.3 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287173,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.111,false,true,287174,Photographs,Photograph,Ensemble du Temple d'Isis à Philae - Vue prise au Nord,,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,April 1850,1850,1850,Salted paper print from paper negative,Image: 6 in. × 8 3/4 in. (15.2 × 22.2 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287174,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.112,false,true,287175,Photographs,Photograph,"Dromos et Pylones du grand Temple d'Isis, à Philae",,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,"April 13, 1850",1850,1850,Salted paper print from paper negative,Image: 6 9/16 × 8 3/4 in. (16.6 × 22.2 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287175,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.113,false,true,287176,Photographs,Photograph,"Colonnade latérale de la cour du Temple d'Isis, à Philae",,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,April 1850,1850,1850,Salted paper print from paper negative,Image: 6 5/8 × 8 13/16 in. (16.8 × 22.4 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287176,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.114,false,true,287177,Photographs,Photograph,"Second Pylone du Temple d'Isis, à Philae",,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,"April 13, 1850",1850,1850,Salted paper print from paper negative,Image: 9 5/16 × 6 9/16 in. (23.6 × 16.6 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287177,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.115,false,true,287178,Photographs,Photograph,"Proseynème scellé dans le second Pylone du Temple d'Isis, à Philae",,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,"April 15, 1850",1850,1850,Salted paper print from paper negative,Image: 6 7/16 × 8 9/16 in. (16.4 × 21.8 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287178,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.116,false,true,287179,Photographs,Photograph,"Bas-relief pris sur la muraille occidentale du grand Temple d'Isis, à Philae",,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,April 1850,1850,1850,Salted paper print from paper negative,Image: 9 1/4 × 7 1/16 in. (23.5 × 17.9 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287179,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.117,false,true,287180,Photographs,Photograph,Thot Ibiocéphale (Dieu des Lettres) à Philae,,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,April 1850,1850,1850,Salted paper print from paper negative,Image: 9 1/8 × 6 1/2 in. (23.2 × 16.5 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287180,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.118,false,true,287181,Photographs,Photograph,"Inscription Démotique; second Pylone du Temple d'Isis, à Philae",,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,"April 15, 1850",1850,1850,Salted paper print from paper negative,Image: 6 1/4 × 9 1/8 in. (15.8 × 23.2 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287181,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.119,false,true,287182,Photographs,Photograph,"Ruines et Village de Begueh, petite île à l'Ouest de Philae",,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,"April 11, 1850",1850,1850,Salted paper print from paper negative,Image: 5 7/8 × 8 3/8 in. (14.9 × 21.2 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287182,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.120,false,true,287183,Photographs,Photograph,"Mosquée de Belal, au Sud de Philae (Rive droite)",,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,"April 11, 1850",1850,1850,Salted paper print from paper negative,Image: 6 1/4 × 8 9/16 in. (15.9 × 21.7 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287183,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.121,false,true,287184,Photographs,Photograph,Vue générale du Temple et du Village de Déboude (Parembole),,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,"April 10, 1850",1850,1850,Salted paper print from paper negative,Image: 5 5/8 × 8 11/16 in. (14.3 × 22 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287184,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.122,false,true,287185,Photographs,Photograph,Pronaos du Temple de Déboude (Parembole),,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,"April 10, 1850",1850,1850,Salted paper print from paper negative,Image: 6 5/16 × 8 9/16 in. (16 × 21.8 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287185,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.123,false,true,287186,Photographs,Photograph,Vue du Temple de Kardassy,,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,"April 9, 1850",1850,1850,Salted paper print from paper negative,Image: 6 9/16 × 8 3/4 in. (16.6 × 22.2 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287186,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.124,false,true,287187,Photographs,Photograph,Vue du Temple de Tafah (Taphis),,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,"April 9, 1850",1850,1850,Salted paper print from paper negative,Image: 6 15/16 × 8 9/16 in. (17.7 × 21.7 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287187,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.125,false,true,287188,Photographs,Photograph,"Vue générale du Temple de Kalabcheh (Talmis), prise de la montagne",,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,"April 8, 1850",1850,1850,Salted paper print from paper negative,Image: 6 9/16 × 8 11/16 in. (16.6 × 22 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287188,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.126,false,true,287189,Photographs,Photograph,Temple de Kalabcheh - Entre colonnement médial du Pronaos,,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,"April 8, 1850",1850,1850,Salted paper print from paper negative,Image: 8 7/8 × 6 9/16 in. (22.5 × 16.7 cm) Mount: 18 11/16 × 12 5/16 in. (47.5 × 31.2 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287189,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.127,false,true,287190,Photographs,Photograph,Ptolémée-Cæsarion - Bas-relief du Temple de Kalabcheh (Talmis),,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,"April 8, 1850",1850,1850,Salted paper print from paper negative,Image: 8 7/8 × 6 9/16 in. (22.5 × 16.7 cm) Mount: 18 11/16 × 12 5/16 in. (47.5 × 31.2 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287190,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.128,false,true,287191,Photographs,Photograph,Isis et Horus-Arsiési - Bas-relief du Temple de Kalabcheh (Talmis),,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,"April 8, 1850",1850,1850,Salted paper print from paper negative,Image: 8 9/16 × 6 7/16 in. (21.7 × 16.3 cm) Image: 8 7/8 × 6 9/16 in. (22.5 × 16.7 cm) Mount: 18 11/16 × 12 5/16 in. (47.5 × 31.2 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287191,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.129,false,true,287192,Photographs,Photograph,Vue du Village d'Abou-hor (Tropique du Cancer),,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,April 1850,1850,1850,Salted paper print from paper negative,Image: 5 3/4 × 8 9/16 in. (14.6 × 21.8 cm) Image: 8 7/8 × 6 9/16 in. (22.5 × 16.7 cm) Mount: 18 11/16 × 12 5/16 in. (47.5 × 31.2 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287192,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.130,false,true,287193,Photographs,Photograph,Vue prise au Village d'Abou-hor (Tropique du Cancer),,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,April 1850,1850,1850,Salted paper print from paper negative,Image: 6 1/16 × 8 13/16 in. (15.4 × 22.4 cm) Image: 8 7/8 × 6 9/16 in. (22.5 × 16.7 cm) Mount: 18 11/16 × 12 5/16 in. (47.5 × 31.2 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287193,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.131,false,true,287194,Photographs,Photograph,Propylon du Temple de Dandour (Tropique du Cancer),,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,April 1850,1850,1850,Salted paper print from paper negative,Image: 8 7/8 × 6 5/8 in. (22.5 × 16.8 cm) Image: 8 7/8 × 6 9/16 in. (22.5 × 16.7 cm) Mount: 18 11/16 × 12 5/16 in. (47.5 × 31.2 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287194,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.132,false,true,287195,Photographs,Photograph,Vue du pronaos du Temple de Dandour (Tropique du Cancer),,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,"April 7, 1850",1850,1850,Salted paper print from paper negative,Image: 6 9/16 × 8 1/2 in. (16.6 × 21.6 cm) Image: 8 7/8 × 6 9/16 in. (22.5 × 16.7 cm) Mount: 18 11/16 × 12 5/16 in. (47.5 × 31.2 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287195,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.133,false,true,287196,Photographs,Photograph,"Vue générale du Temple de Dakkeh (Pselcis), prise au Nord",,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,"April 5, 1850",1850,1850,Salted paper print from paper negative,Image: 6 in. × 8 7/16 in. (15.3 × 21.4 cm) Image: 8 7/8 × 6 9/16 in. (22.5 × 16.7 cm) Mount: 18 11/16 × 12 5/16 in. (47.5 × 31.2 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287196,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.134,false,true,287197,Photographs,Photograph,Vue de la façade du pronaos du Temple de Dakkeh (Pselcis),,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,"April 5, 1850",1850,1850,Salted paper print from paper negative,Image: 6 3/4 × 8 3/4 in. (17.1 × 22.3 cm) Image: 8 7/8 × 6 9/16 in. (22.5 × 16.7 cm) Mount: 18 11/16 × 12 5/16 in. (47.5 × 31.2 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287197,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.135,false,true,287198,Photographs,Photograph,Vue du Temple de Maharakka (Hiéra-Sycaminos),,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,"April 5, 1850",1850,1850,Salted paper print from paper negative,Image: 6 3/16 × 8 3/4 in. (15.7 × 22.2 cm) Image: 8 7/8 × 6 9/16 in. (22.5 × 16.7 cm) Mount: 18 11/16 × 12 5/16 in. (47.5 × 31.2 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287198,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.136,false,true,287199,Photographs,Photograph,Dromos du Temple de Sébona,,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,"April 3, 1850",1850,1850,Salted paper print from paper negative,Image: 6 1/8 × 8 15/16 in. (15.5 × 22.7 cm) Image: 8 7/8 × 6 9/16 in. (22.5 × 16.7 cm) Mount: 18 11/16 × 12 5/16 in. (47.5 × 31.2 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287199,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.137,false,true,287200,Photographs,Photograph,Pylones du Temple de Sébona,,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,"April 3, 1850",1850,1850,Salted paper print from paper negative,Image: 6 1/2 × 8 3/4 in. (16.5 × 22.3 cm) Image: 8 7/8 × 6 9/16 in. (22.5 × 16.7 cm) Mount: 18 11/16 × 12 5/16 in. (47.5 × 31.2 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287200,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.138,false,true,287201,Photographs,Photograph,Vue du Temple d'Amada - Coupole ruinée d'une Eglise Copte,,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,"April 2, 1850",1850,1850,Salted paper print from paper negative,Image: 8 7/8 × 6 9/16 in. (22.5 × 16.7 cm) Mount: 18 11/16 × 12 5/16 in. (47.5 × 31.2 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287201,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.139,false,true,287202,Photographs,Photograph,Vue de la Fortresse d'Ibrym,,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,"March 31, 1850",1850,1850,Salted paper print from paper negative,Image: 8 7/8 × 6 9/16 in. (22.5 × 16.7 cm) Mount: 18 11/16 × 12 5/16 in. (47.5 × 31.2 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287202,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.140,false,true,287203,Photographs,Photograph,"Vue générale des Spéos de Phré et d'Athor, à Abousembil, prise de l'île",,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,"March 29, 1850",1850,1850,Salted paper print from paper negative,Image: 5 1/4 × 8 7/16 in. (13.3 × 21.5 cm) Mount: 18 11/16 × 12 5/16 in. (47.5 × 31.2 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287203,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.141,false,true,287204,Photographs,Photograph,"Entrée du Spéos d'Athor, à Abousembil",,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,March 1850,1850,1850,Salted paper print from paper negative,Image: 8 5/8 × 6 3/8 in. (21.9 × 16.2 cm) Mount: 18 11/16 × 12 5/16 in. (47.5 × 31.2 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287204,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.142,false,true,287205,Photographs,Photograph,"Moitié de la façade du Spéos d'Athor, à Abousembil (partie Septentrionale)",,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,March 1850,1850,1850,Salted paper print from paper negative,Image: 6 3/4 × 9 1/16 in. (17.2 × 23 cm) Mount: 18 11/16 × 12 5/16 in. (47.5 × 31.2 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287205,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.143,false,true,287206,Photographs,Photograph,"Moitié de la façade du Spéos d'Athor, à Abousembil (partie méridionale)",,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,"March 28, 1850",1850,1850,Salted paper print from paper negative,Image: 6 5/8 × 8 11/16 in. (16.9 × 22.1 cm) Mount: 18 11/16 × 12 5/16 in. (47.5 × 31.2 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287206,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.144,false,true,287207,Photographs,Photograph,"Vue de la façade du Spéos de Phré, à Abousembil",,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,March 1850,1850,1850,Salted paper print from paper negative,Image: 6 5/8 × 8 11/16 in. (16.9 × 22.1 cm) Mount: 18 11/16 × 12 5/16 in. (47.5 × 31.2 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287207,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.145,false,true,287208,Photographs,Photograph,"Colosse oriental du Spéos de Phré, à Abousembil (Portrait de Rhamsès-le-grand)",,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,March 1850,1850,1850,Salted paper print from paper negative,Image: 8 7/16 × 6 9/16 in. (21.5 × 16.6 cm) Mount: 18 11/16 × 12 5/16 in. (47.5 × 31.2 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287208,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.146,false,true,287209,Photographs,Photograph,"Profil du Colosse oriental du Spéos de Phré, à Abousembil (Portrait de Rhamsès-le-grand)",,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,"March 29, 1850",1850,1850,Salted paper print from paper negative,Image: 8 7/16 × 6 9/16 in. (21.5 × 16.6 cm) Mount: 18 11/16 × 12 5/16 in. (47.5 × 31.2 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287209,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.147,false,true,287210,Photographs,Photograph,Décoration de l'entrée du grand Spéos d'Abousembil (Le Dieu Phré recevant les offrandes de Rhamsès-le-grand),,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,March 1850,1850,1850,Salted paper print from paper negative,Image: 8 11/16 × 6 5/8 in. (22 × 16.8 cm) Mount: 18 11/16 × 12 5/16 in. (47.5 × 31.2 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287210,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.148,false,true,287211,Photographs,Photograph,"Colosse médial du Spéos d'Phré, à Abousembil",,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,"March 29, 1850",1850,1850,Salted paper print from paper negative,Image: 8 3/8 × 6 5/8 in. (21.2 × 16.8 cm) Mount: 18 11/16 × 12 5/16 in. (47.5 × 31.2 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287211,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.149,false,true,283143,Photographs,Photograph,"Westernmost Colossus of the Temple of Re, Abu Simbel",,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,1850,1850,1850,Salted paper print from paper negative,Image: 9 in. × 6 1/2 in. (22.8 × 16.5 cm) Mount: 18 11/16 × 12 5/16 in. (47.5 × 31.2 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283143,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.150,false,true,287212,Photographs,Photograph,"Vue cavalière de la seconde cataracte, prise du haut de Djebel-Aboucir",,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,"March 24, 1850",1850,1850,Salted paper print from paper negative,Image: 7 7/8 × 6 5/16 in. (20 × 16 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287212,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.151,false,true,287213,Photographs,Photograph,Vue des rapides de la Seconde Cataracte,,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,March 1850,1850,1850,Salted paper print from paper negative,Image: 4 13/16 × 8 1/2 in. (12.3 × 21.6 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287213,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.152,false,true,287214,Photographs,Photograph,Vue prise à la Seconde Cataracte,,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,March 1850,1850,1850,Salted paper print from paper negative,Image: 5 1/2 × 8 11/16 in. (14 × 22 cm) Mount: 12 5/16 × 18 11/16 in. (31.3 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287214,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.153,false,true,287215,Photographs,Photograph,Batu-el-Hadjar. Vue prise à la Seconde Cataracte,,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,March 1850,1850,1850,Salted paper print from paper negative,Image: 6 in. × 8 7/16 in. (15.2 × 21.5 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287215,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.154,false,true,287216,Photographs,Photograph,Djebel-Aboucir - Rive gauche de la Seconde Cataracte,,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,"March 25, 1850",1850,1850,Salted paper print from paper negative,Image: 6 in. × 8 11/16 in. (15.2 × 22 cm) Mount: 12 5/16 in. × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287216,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.155,false,true,287217,Photographs,Photograph,"Vue générale des ruines de Baâlbek, prise à l'Est",,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,September 1850,1850,1850,Salted paper print from paper negative,Image: 16.2 × 21.7 cm (6 3/8 × 8 9/16 in.) Mount: 12 5/8 × 18 1/2 in. (32 × 47 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287217,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.156,false,true,287218,Photographs,Photograph,Intérieur de l'enceinte du Temple de Baalbek (Héliopolis),,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,"September 15, 1850",1850,1850,Salted paper print from paper negative,Image: 6 1/4 × 8 1/8 in. (15.8 × 20.7 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287218,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.157,false,true,287219,Photographs,Photograph,"Hémicycle de l'enceinte des Temples, à Baâlbek",,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,"September 15, 1850",1850,1850,Salted paper print from paper negative,Image: 8 5/8 × 6 1/2 in. (21.9 × 16.5 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287219,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.158,false,true,287220,Photographs,Photograph,"Colonnade du Temple du Soleil, à Baâlbek (Héliopolis)",,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,"September 15, 1850",1850,1850,Salted paper print from paper negative,Image: 6 9/16 × 8 1/4 in. (16.7 × 21 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287220,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.159,false,true,287221,Photographs,Photograph,"Colonnade du Temple du Soleil, à Baâlbek (Héliopolis)",,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,September 1850,1850,1850,Salted paper print from paper negative,Image: 8 9/16 × 6 9/16 in. (21.7 × 16.7 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287221,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.160,false,true,287222,Photographs,Photograph,"Porte du Temple de Jupiter, à Baalbek (Héliopolis)",,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,September 1850,1850,1850,Salted paper print from paper negative,Image: 8 3/4 × 6 9/16 in. (22.3 × 16.7 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287222,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.161,false,true,287223,Photographs,Photograph,"Colonnade intérieure du Naos du Temple de Jupiter, à Baâlbek (Héliopolis)",,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,"September 15, 1850",1850,1850,Salted paper print from paper negative,Image: 8 3/4 × 6 1/2 in. (22.2 × 16.5 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287223,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.162,false,true,287224,Photographs,Photograph,"Temple de Jupiter, à Baâlbek (Héliopolis)",,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,"September 15, 1850",1850,1850,Salted paper print from paper negative,Image: 8 3/4 × 6 1/2 in. (22.2 × 16.5 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287224,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.163,false,true,287225,Photographs,Photograph,"Vue du Temple de Jupiter, à Baâlbek (Héliopolis)",,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,"September 15, 1850",1850,1850,Salted paper print from paper negative,Image: 6 7/16 × 8 3/8 in. (16.4 × 21.3 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287225,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.164,false,true,287226,Photographs,Photograph,"Colonnade occidental du Temple de Jupiter, à Baâlbek (Héliopolis)",,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,"September 15, 1850",1850,1850,Salted paper print from paper negative,Image: 6 11/16 × 8 11/16 in. (17 × 22 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287226,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.165,false,true,287227,Photographs,Photograph,Château de David (Daoud Kalessy) et murailles de Jérusalem,,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,August 1850,1850,1850,Salted paper print from paper negative,Image: 6 1/8 × 8 9/16 in. (15.5 × 21.8 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287227,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.166,false,true,287228,Photographs,Photograph,Koubbé-Nébi-Monça - Coupole de Moïse à Jérusalem,,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,August 1850,1850,1850,Salted paper print from paper negative,Image: 6 1/4 × 9 3/16 in. (15.8 × 23.3 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287228,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.167,false,true,287229,Photographs,Photograph,Vue prise au Nord-Ouest de Jérusalem,,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,August 1850,1850,1850,Salted paper print from paper negative,Image: 6 1/4 × 9 3/16 in. (15.8 × 23.3 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287229,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.168,false,true,287230,Photographs,Photograph,Vue de la Piscine Probatique et d'un quartier de Jérusalem,,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,August 1850,1850,1850,Salted paper print from paper negative,Image: 6 1/4 × 9 3/16 in. (15.8 × 23.3 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287230,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.169,false,true,287231,Photographs,Photograph,Vue de la Mosquée d'El-Melouyeh et d'un quartier de Jérusalem,,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,August 1850,1850,1850,Salted paper print from paper negative,Image: 6 7/16 × 8 1/4 in. (16.4 × 21 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287231,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.170,false,true,287232,Photographs,Photograph,"Façade de l'Eglise du St. Sépulcre, à Jérusalem (No. 1)",,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,"August 19, 1850",1850,1850,Salted paper print from paper negative,Image: 6 11/16 × 9 3/8 in. (17 × 23.8 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287232,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.171,false,true,287233,Photographs,Photograph,"Façade de l'Eglise du St. Sépulcre, à Jérusalem (No. 2 partie supérieure)",,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,August 1850,1850,1850,Salted paper print from paper negative,Image: 6 11/16 × 9 3/16 in. (17 × 23.4 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287233,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.172,false,true,287234,Photographs,Photograph,"La Mosquée d'Omar, à Jérusalem",,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,August 1850,1850,1850,Salted paper print from paper negative,Image: 6 11/16 × 9 1/4 in. (17 × 23.5 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287234,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.173,false,true,287235,Photographs,Photograph,La Porte dorée à Jérusalem,,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,August 1850,1850,1850,Salted paper print from paper negative,Image: 9 5/16 × 6 3/8 in. (23.7 × 16.2 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287235,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.376.174,false,true,287236,Photographs,Photograph,"Mosquée de Sultan Haçan, Place de Roumelich, au Kaire",,,,,,Artist,,Maxime Du Camp,"French, 1822–1894",,"Du Camp, Maxime",French,1822,1894,"December 13, 1849",1849,1849,Salted paper print from paper negative,Image: 8 1/2 × 6 7/16 in. (21.6 × 16.3 cm) Mount: 12 5/16 × 18 11/16 in. (31.2 × 47.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287236,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2003.538,false,true,285271,Photographs,Photograph,Viscountess Vilain,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1857,1857,1857,Salted paper print from glass negative,Image: 29.1 x 22.5 cm (11 7/16 x 8 7/8 in.),"Purchase, Harriette and Noel Levine Gift, 2003",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285271,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.1,false,true,261268,Photographs,Photograph,L'Ecstase,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,5.1 x 8.6 cm (2 x 3 3/8 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261268,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.3,false,true,261478,Photographs,Photograph,Aux écoutes,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,5.1 x 8.6 cm. (2 x 3 3/8 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261478,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.4,false,true,261489,Photographs,Photograph,L'Interrogation,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,5.1 x 8.6 cm. (2 x 3 3/8 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261489,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.5,false,true,261500,Photographs,Photograph,Les beau décolleté,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,5.1 x 8.6 cm. (2 x 3 3/8 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261500,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.6,false,true,261511,Photographs,Photograph,Méditation,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,5.1 x 8.6 cm. (2 x 3 3/8 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261511,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.7,false,true,261522,Photographs,Photograph,Le beau bras,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,5.1 x 8.6 cm. (2 x 3 3/8 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261522,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.8,false,true,261533,Photographs,Photograph,La Psyché,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,5.1 x 8.6 cm. (2 x 3 3/8 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261533,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.9,false,true,261544,Photographs,Photograph,Réverie,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,5.1 x 8.6 cm. (2 x 3 3/8 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261544,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.13,false,true,261302,Photographs,Photograph,Derelitta,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,7.3 x 10.8 cm. (2 7/8 x 4 1/4 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261302,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.14,false,true,261313,Photographs,Photograph,Derelitta,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,7.3 x 10.2 cm. (2 7/8 x 4 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261313,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.15,false,true,261324,Photographs,Photograph,Derelitta,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,7.8 x 9.5 cm. (3 1/16 x 3 3/4 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261324,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.16,false,true,261335,Photographs,Photograph,Stella,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,7.6 x 10.2 cm. (3 x 4 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261335,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.17,false,true,261346,Photographs,Photograph,La robe de moiré,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,7.3 x 10.2 cm (2 7/8 x 4 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261346,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.18,false,true,261359,Photographs,Photograph,Stella (autre),,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,7.3 x 10.2 cm. (2 7/8 x 4 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261359,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.19,false,true,261371,Photographs,Photograph,Les épaules tombantes,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,7.6 x 10.2 cm. (3 x 4 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261371,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.20,false,true,261391,Photographs,Photograph,Bal,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,7.9 x 10.8 cm. (3 1/8 x 4 1/4 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261391,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.21,false,true,261402,Photographs,Photograph,Bal,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,7.3 x 10.2 cm. (2 7/8 x 4 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261402,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.22,false,true,261413,Photographs,Photograph,Lucréce (ou la Vestale),,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,7.6 x 10.2 cm. (3 x 4 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261413,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.23,false,true,261424,Photographs,Photograph,Les rubans découpé,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,6.7 x 9.5 cm. (2 5/8 x 3 3/4 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261424,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.24,false,true,261435,Photographs,Photograph,Le dos,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,7.6 x 10.2 cm. (3 x 4 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261435,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.25,false,true,261446,Photographs,Photograph,Le regard,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,8.6 x 12.1 cm. (3 3/8 x 4 3/4 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261446,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.26,false,true,261457,Photographs,Photograph,Le chapeau à brides,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,7.6 x 11.7 cm. (3 x 4 5/8 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261457,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.27,false,true,261468,Photographs,Photograph,Le chapeau à brides,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,7.3 x 11.1 cm. (2 7/8 x 4 3/8 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261468,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.28,false,true,261476,Photographs,Photograph,L'Espagnole,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,6.7 x 8.9 cm. (2 5/8 x 3 1/2 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261476,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.29,false,true,261477,Photographs,Photograph,L'Ancre,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,5.4 x 8.6 cm. (2 1/8 x 3 3/8 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261477,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.30,false,true,261479,Photographs,Photograph,L'Orage,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,6.0 x 8.6 cm. (2 3/8 x 3 3/8 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261479,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.31,false,true,261480,Photographs,Photograph,L'accoudée,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1856–57,1856,1857,Albumen silver print from glass negative,11.1 x 7.6 cm (4 3/8 x 3 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261480,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.32,false,true,261481,Photographs,Photograph,Priére,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,7.3 x 10.5 cm. (2 7/8 x 4 1/8 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261481,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.33,false,true,261482,Photographs,Photograph,La robe d'été,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,7.6 x 11.4 cm. (3 x 4 1/2 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261482,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.34,false,true,261483,Photographs,Photograph,La Frayeur,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,9.8 x 10.8 cm. (3 7/8 x 4 1/4 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261483,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.35,false,true,261484,Photographs,Photograph,La peignoir plisié,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,8.6 x 11.1 cm. (3 3/8 x 4 3/8 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261484,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.36,false,true,261485,Photographs,Photograph,Le peignoir plisié,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,10.2 x 10.2 cm. (4 x 4 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261485,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.37,false,true,261486,Photographs,Photograph,Le Chapelet,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,8.9 x 12.4 cm. (3 1/2 x 4 7/8 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261486,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.38,false,true,261487,Photographs,Photograph,La robe de soie,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,8.6 x 11.4 cm. (3 3/8 x 4 1/2 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261487,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.39,false,true,261488,Photographs,Photograph,Petite Reine d'Etrurie,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,8.9 x 11.7 cm. (3 1/2 x 4 5/8 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261488,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.40,false,true,261490,Photographs,Photograph,La Nonne blanche,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261490,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.41,false,true,261491,Photographs,Photograph,La casagne de velours,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261491,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.42,false,true,261492,Photographs,Photograph,Le chàle de dentelles,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261492,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.43,false,true,261493,Photographs,Photograph,Beatrice,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261493,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.44,false,true,261494,Photographs,Photograph,La robe bouffante,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261494,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.45,false,true,261495,Photographs,Photograph,La cape,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261495,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.46,false,true,261496,Photographs,Photograph,Funerale,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261496,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.47,false,true,261497,Photographs,Photograph,Funerale,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261497,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.48,false,true,261498,Photographs,Photograph,Le chapeau à plumes,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261498,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.49,false,true,261499,Photographs,Photograph,Le peignoir plisie (autre),,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261499,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.50,false,true,261501,Photographs,Photograph,L'Agrèable,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261501,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.51,false,true,261502,Photographs,Photograph,Costigliole,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261502,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.52,false,true,261503,Photographs,Photograph,"Le noeud de dentelle. ""Ritrosetta""",,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261503,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.53,false,true,261504,Photographs,Photograph,"Le noeud de dentelle. ""Ritrosetta""",,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261504,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.54,false,true,261505,Photographs,Photograph,Le Chapelet (autre),,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261505,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.55,false,true,261506,Photographs,Photograph,Le voile,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261506,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.56,false,true,261507,Photographs,Photograph,L'hermine,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261507,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.57,false,true,261508,Photographs,Photograph,L'hermine,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,Image: 12.2 x 18.6 cm (4 13/16 x 7 5/16 in.) Mount: 17.4 x 13.6 cm (6 7/8 x 5 3/8 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261508,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.58,false,true,261509,Photographs,Photograph,Le chapelet (autre),,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261509,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.59,false,true,261510,Photographs,Photograph,Le chapeau à plume (autre),,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261510,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.60,false,true,261512,Photographs,Photograph,"La laçon de dessin. ""L'artiste""",,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261512,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.61,false,true,261513,Photographs,Photograph,Le Repos,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,10.2 x 7.7 cm (4 x 3 1/16 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261513,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.62,false,true,261514,Photographs,Photograph,L'Algérienne,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,10.5 x 11.4 cm (4 1/8 x 4 1/2 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261514,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.63,false,true,261515,Photographs,Photograph,Repos (autre),,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,10.2 x 7.6 cm (4 x 3 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261515,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.64,false,true,261516,Photographs,Photograph,Convalescente (colorieè),,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1861–67,1861,1867,Albumen silver print from glass negative overpainted with watercolor,22.1 x 16.2 cm (8 11/16 x 6 3/8 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261516,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.65,false,true,261517,Photographs,Photograph,L'Allongée,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,7.5 x 10.6 cm (2 15/16 x 4 3/16 in.) oval,"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261517,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.67,false,true,261519,Photographs,Photograph,Repos (autre),,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261519,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.69,false,true,261521,Photographs,Photograph,La Chemise,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Salted paper print from glass negative,,"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261521,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.70,false,true,261523,Photographs,Photograph,La Coucher,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Salted paper print from glass negative,,"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261523,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.71,false,true,261524,Photographs,Photograph,La robe de taffetas,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261524,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.72,false,true,261525,Photographs,Photograph,La robe de taffetas,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261525,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.73,false,true,261526,Photographs,Photograph,Les yeux mirés,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,Image: 17.4 x 12.8 cm (6 7/8 x 5 1/16 in.) Mount: 23.5 x 20.6 cm (9 1/4 x 8 1/8 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261526,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.74,false,true,261527,Photographs,Photograph,Les yeux mirés,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261527,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.78,false,true,261531,Photographs,Photograph,Marie Stuart,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261531,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.79,false,true,261532,Photographs,Photograph,Marie Stuart,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261532,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.80,false,true,261534,Photographs,Photograph,Marie Stuart,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261534,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.81,false,true,261535,Photographs,Photograph,Cauchoise,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261535,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.82,false,true,261536,Photographs,Photograph,La Marquise Mathilde,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261536,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.83,false,true,261537,Photographs,Photograph,Cauchoise (autre),,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261537,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.84,false,true,261538,Photographs,Photograph,Mathilde (autre),,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261538,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.85,false,true,261539,Photographs,Photograph,Mathilde,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261539,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.86,false,true,261540,Photographs,Photograph,Mathilde,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261540,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.87,false,true,261541,Photographs,Photograph,Judith,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261541,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.88,false,true,261542,Photographs,Photograph,Judith,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261542,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.89,false,true,261543,Photographs,Photograph,Judith,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261543,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.90,false,true,261545,Photographs,Photograph,Judith,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261545,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.91,false,true,261546,Photographs,Photograph,Judith,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261546,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.92,false,true,261547,Photographs,Photograph,Judith,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261547,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.93,false,true,261548,Photographs,Photograph,Reine d'Etrurie (colorieè),,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261548,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.94,false,true,261549,Photographs,Photograph,Reine d'Etrurie (colorieè),,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Salted paper print from glass negative,,"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261549,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.96,false,true,261551,Photographs,Photograph,Livetta,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261551,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.97,false,true,261552,Photographs,Photograph,Nonne blanche (tete),,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261552,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.98,false,true,261553,Photographs,Photograph,Nonne blanche (en pied),,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261553,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.99,false,true,261554,Photographs,Photograph,Soeur Elize,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261554,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.100,false,true,261270,Photographs,Photograph,Le noeud rouge,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261270,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.101,false,true,261271,Photographs,Photograph,"La Bisi. Boudoir, robe velour gris, moire rose, fleurs roses, feuilles grises (de sa mai, au revers.)",,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,11.4 x 10.7 cm (4 1/2 x 4 3/16 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261271,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.116,false,true,261287,Photographs,Photograph,Ritrosetta,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1861–64,1861,1864,Salted paper print from glass negative with applied color,14.5 x 13.1 cm (5 11/16 x 5 3/16 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261287,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.118,false,true,261289,Photographs,Photograph,Derelitta (peintre),,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261289,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.119,false,true,261290,Photographs,Photograph,La Frayeur,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1861–67,1861,1867,Salted paper print from glass negative with applied color,12.7 x 15.1 cm (5 x 5 15/16 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261290,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.120,false,true,261292,Photographs,Photograph,Virginie,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Salted paper print from glass negative with applied color,8.7 x 13.6 cm (3 7/16 x 5 3/8 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261292,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.127,false,true,261299,Photographs,Photograph,[Study of Legs],,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1861–67,1861,1867,Albumen silver print from glass negative,11.4 x 13.7 cm (4 1/2 x 5 3/8 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261299,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.128,false,true,261300,Photographs,Photograph,Les jambes,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,11.7 x 13.0 cm (4 5/8 x 5 1/8 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261300,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.129,false,true,261301,Photographs,Photograph,Les jambes,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,11.1 x 14.9 cm (4 3/8 x 5 7/8 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261301,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.130,false,true,261303,Photographs,Photograph,Sèriè à la Ristori,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261303,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.131,false,true,261304,Photographs,Photograph,Sèriè à la Ristori,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261304,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.132,false,true,261305,Photographs,Photograph,Sèriè à la Ristori,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261305,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.133,false,true,261306,Photographs,Photograph,Sèriè à la Ristori,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261306,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.134,false,true,261307,Photographs,Photograph,Sèriè à la Ristori,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261307,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.135,false,true,261308,Photographs,Photograph,Sèriè à la Ristori,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261308,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.136,false,true,261309,Photographs,Photograph,Sèriè à la Ristori,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,9.8 x 13.3 cm. (3 7/8 x 5 1/4 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261309,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.137,false,true,261310,Photographs,Photograph,Sèriè à la Ristori,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261310,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.138,false,true,261311,Photographs,Photograph,Sèriè à la Ristori,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261311,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.139,false,true,261312,Photographs,Photograph,Sèriè à la Ristori,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261312,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.140,false,true,261314,Photographs,Photograph,Sèriè à la Ristori,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261314,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.141,false,true,261315,Photographs,Photograph,Sèriè à la Ristori,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261315,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.142,false,true,261316,Photographs,Photograph,Sèriè à la Ristori,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261316,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.143,false,true,261317,Photographs,Photograph,Sèriè à la Ristori,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,9.8 x 13.3 cm. (3 7/8 x 5 1/4 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261317,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.144,false,true,261318,Photographs,Photograph,Sèriè à la Ristori,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261318,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.145,false,true,261319,Photographs,Photograph,Sèriè à la Ristori,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261319,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.146,false,true,261320,Photographs,Photograph,Sèriè à la Ristori,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261320,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.147,false,true,261321,Photographs,Photograph,Le pardessus dècoré,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,9.8 x 13.3 cm. (3 7/8 x 5 1/4 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261321,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.148,false,true,261322,Photographs,Photograph,Le pardessus dècoré,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261322,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.149,false,true,261323,Photographs,Photograph,Le pardessus dècoré,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261323,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.150,false,true,261325,Photographs,Photograph,Le Caracul (L'Astrakhan),,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261325,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.151,false,true,261326,Photographs,Photograph,Le Caracul (L'Astrakhan),,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261326,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.152,false,true,261327,Photographs,Photograph,Le Caracul (L'Astrakhan),,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261327,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.153,false,true,261328,Photographs,Photograph,Le Caracul (L'Astrakhan),,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261328,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.154,false,true,261329,Photographs,Photograph,Le Caracul (L'Astrakhan),,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261329,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.155,false,true,261330,Photographs,Photograph,Le Caracul (L'Astrakhan),,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261330,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.156,false,true,261331,Photographs,Photograph,Le Caracul (L'Astrakhan),,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261331,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.157,false,true,261332,Photographs,Photograph,Le Caracul (L'Astrakhan),,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261332,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.158,false,true,261333,Photographs,Photograph,Le Caracul (L'Astrakhan),,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261333,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.159,false,true,261334,Photographs,Photograph,Le Caracul (L'Astrakhan),,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261334,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.160,false,true,261336,Photographs,Photograph,Le Caracul (L'Astrakhan),,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261336,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.161,false,true,261337,Photographs,Photograph,Le Caracul (L'Astrakhan),,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261337,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.162,false,true,261338,Photographs,Photograph,Le Caracul (L'Astrakhan),,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261338,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.163,false,true,261339,Photographs,Photograph,Le Caracul (L'Astrakhan),,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261339,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.164,false,true,261340,Photographs,Photograph,Le Caracul (L'Astrakhan),,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261340,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.165,false,true,261341,Photographs,Photograph,Le Caracul (L'Astrakhan),,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261341,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.166,false,true,261342,Photographs,Photograph,Les étoiles de jois,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261342,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.167,false,true,261343,Photographs,Photograph,Les edoiles de jois,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261343,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.168,false,true,261344,Photographs,Photograph,Les étoiles de jois,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261344,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.169,false,true,261345,Photographs,Photograph,Les étoiles de jois,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261345,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.170,false,true,261347,Photographs,Photograph,Les Chiens,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Gelatin silver print,13.7 x 10.2 cm (5 3/8 x 4 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261347,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.171,false,true,261348,Photographs,Photograph,Les Chiens,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,10.2 x 14.3 cm (4 x 5 5/8 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261348,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.172,false,true,261349,Photographs,Photograph,Les Chiens,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Gelatin silver print,13.7 x 9.8 cm (5 3/8 x 3 7/8 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261349,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.173,false,true,261350,Photographs,Photograph,Les Chiens,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,11.7 x 8.9 cm (4 5/8 x 3 1/2 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261350,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.174,false,true,261352,Photographs,Photograph,Les Chiens,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,10.2 x 13.7 cm (4 x 5 3/8 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261352,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.175,false,true,261354,Photographs,Photograph,Les Chiens,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,9.2 x 13.3 cm (3 5/8 x 5 1/4 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261354,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.176,false,true,261355,Photographs,Photograph,Les Chiens,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1875–80,1875,1880,Albumen silver print from glass negative,22 x 16.8 cm (8 11/16 x 6 5/8 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261355,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.177,false,true,261356,Photographs,Photograph,Les dernieres,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261356,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.178,false,true,261357,Photographs,Photograph,Les dernieres,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261357,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.179,false,true,261358,Photographs,Photograph,Les dernieres,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261358,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.180,false,true,261360,Photographs,Photograph,Les dernieres,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261360,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.185,false,true,261366,Photographs,Photograph,[Reine d'Etrurie],,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1863–67,1863,1867,Albumen silver print from glass negative retouched with gouache,12.4 x 8.9 cm (4 7/8 x 3 1/2 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261366,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.186,false,true,261367,Photographs,Photograph,[Reine d'Etrurie],,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative with applied color,12.3 x 8.9 cm (4 13/16 x 3 1/2 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261367,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.187,false,true,261368,Photographs,Photograph,[Reine d'Etrurie],,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative with applied color,12.3 x 8.9 cm (4 13/16 x 3 1/2 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261368,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.188,false,true,261369,Photographs,Photograph,[Reine d'Etrurie],,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative with applied color,12.4 x 8.9 cm (4 7/8 x 3 1/2 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261369,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.194,false,true,261384,Photographs,Photograph,L'Accouchée,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Salted paper print from glass negative,18.1 x 22.5 cm. (7 1/8 x 8 7/8 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261384,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.195,false,true,261385,Photographs,Photograph,En famille,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Salted paper print from glass negative,17.8 x 21.9 cm. (7 x 8 5/8 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261385,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.196,false,true,261386,Photographs,Photograph,La Mere et L'Epoux,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,13.3 x 16.8 cm. (5 1/4 x 6 5/8 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261386,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.197,false,true,261387,Photographs,Photograph,La Colombe et le Tigre,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,14.9 x 17.8 cm. (5 7/8 x 7 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261387,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.198,false,true,261388,Photographs,Photograph,La capuche,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,9.8 x 12.7 cm. (3 7/8 x 5 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261388,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.199,false,true,261389,Photographs,Photograph,La petite chemise,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,8.9 x 11.4 cm. (3 1/2 x 4 1/2 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261389,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.200,false,true,261392,Photographs,Photograph,Le derrière,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,9.5 x 12.7 cm. (3 3/4 x 5 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261392,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.201,false,true,261393,Photographs,Photograph,La ?aurier rose,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,10.8 x 13.0 cm. (4 1/4 x 5 1/8 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261393,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.202,false,true,261394,Photographs,Photograph,La veste de cygne,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,5.1 x 8.6 cm. (2 x 3 3/8 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261394,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.203,false,true,261395,Photographs,Photograph,Autre chaise rustique,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,5.7 x 7.6 cm. (2 1/4 x 3 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261395,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.204,false,true,261396,Photographs,Photograph,Le bournous (colorie),,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,10.8 x 13.0 cm. (4 1/4 x 5 1/8 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261396,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.205,false,true,261397,Photographs,Photograph,Le treillage,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,7.0 x 9.8 cm. (2 3/4 x 3 7/8 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261397,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.206,false,true,261398,Photographs,Photograph,La veste de Cygne (autre),,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,5.1 x 2.5 cm. (2 x 1 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261398,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.207,false,true,261399,Photographs,Photograph,La veste de Cygne,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,8.6 x 12.1 cm. (3 3/8 x 4 3/4 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261399,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.208,false,true,261400,Photographs,Photograph,Le dos,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,8.3 x 10.2 cm. (3 1/4 x 4 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261400,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.209,false,true,261401,Photographs,Photograph,Encore la chaise rustique,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,11.1 x 14.0 cm. (4 3/8 x 5 1/2 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261401,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.210,false,true,261403,Photographs,Photograph,Le reflet (profile),,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,7.6 x 10.2 cm. (3 x 4 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261403,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.212,false,true,261404,Photographs,Photograph,Le fauteuil,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,6.0 x 9.5 cm. (2 3/8 x 3 3/4 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261404,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.213,false,true,261406,Photographs,Photograph,La fouriure,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,15.9 x 9.5 cm. (6 1/4 x 3 3/4 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261406,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.214,false,true,261407,Photographs,Photograph,La fouriure,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,7.3 x 10.2 cm. (2 7/8 x 4 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261407,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.215,false,true,261408,Photographs,Photograph,La frisure,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,7.6 x 9.8 cm. (3 x 3 7/8 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261408,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.216,false,true,261409,Photographs,Photograph,Les jambes croisées,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,8.9 x 12.4 cm. (3 1/2 x 4 7/8 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261409,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.217,false,true,261410,Photographs,Photograph,L'Ecossais,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,8.6 x 11.4 cm. (3 3/8 x 4 1/2 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261410,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.218,false,true,261411,Photographs,Photograph,Le liseur,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,8.6 x 12.1 cm. (3 3/8 x 4 3/4 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261411,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.219,false,true,261412,Photographs,Photograph,L'Echevelé,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,8.6 x 12.1 cm. (3 3/8 x 4 3/4 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261412,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.220,false,true,261414,Photographs,Photograph,Le chemise Russe,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,8.6 x 12.1 cm. (3 3/8 x 4 3/4 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261414,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.221,false,true,261415,Photographs,Photograph,Le chemise Russe,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,8.6 x 11.4 cm. (3 3/8 x 4 1/2 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261415,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.222,false,true,261416,Photographs,Photograph,Le montagnard,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,8.6 x 11.4 cm. (3 3/8 x 4 1/2 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261416,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.223,false,true,261417,Photographs,Photograph,Le montagnard,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,8.6 x 11.7 cm. (3 3/8 x 4 5/8 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261417,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.224,false,true,261418,Photographs,Photograph,Le montagnard,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,8.6 x 12.1 cm. (3 3/8 x 4 3/4 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261418,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.225,false,true,261419,Photographs,Photograph,Le montagnard,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,8.6 x 12.1 cm (3 3/8 x 4 3/4 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261419,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.226,false,true,261420,Photographs,Photograph,L'Enfant blanc,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,8.6 x 12.1 cm. (3 3/8 x 4 3/4 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261420,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.227,false,true,261421,Photographs,Photograph,L'Enfant blanc,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,8.6 x 12.1 cm. (3 3/8 x 4 3/4 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261421,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.228,false,true,261422,Photographs,Photograph,Le petit Russe,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,8.3 x 11.7 cm. (3 1/4 x 4 5/8 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261422,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.229,false,true,261423,Photographs,Photograph,Le petit Russe,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,8.3 x 11.4 cm. (3 1/4 x 4 1/2 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261423,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.230,false,true,261425,Photographs,Photograph,Le petit Russe,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,8.6 x 11.7 cm. (3 3/8 x 4 5/8 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261425,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.231,false,true,261426,Photographs,Photograph,Le petit Russe,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261426,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.232,false,true,261427,Photographs,Photograph,Le petit Russe,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,8.6 x 11.4 cm. (3 3/8 x 4 1/2 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261427,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.233,false,true,261428,Photographs,Photograph,Le petit Russe,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,9.5 x 11.7 cm. (3 3/4 x 4 5/8 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261428,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.234,false,true,261429,Photographs,Photograph,Le petit Russe,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,9.5 x 13.7 cm. (3 3/4 x 5 3/8 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261429,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.235,false,true,261430,Photographs,Photograph,Le petit Russe,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,9.8 x 13.3 cm (3 7/8 x 5 1/4 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261430,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.236,false,true,261431,Photographs,Photograph,Le petit Russe,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261431,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.237,false,true,261432,Photographs,Photograph,Le Grand Russe,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,9.8 x 13.3 cm. (3 7/8 x 5 1/4 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261432,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.238,false,true,261433,Photographs,Photograph,Le Grand Russe,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,9.8 x 13.3 cm. (3 7/8 x 5 1/4 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261433,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.239,false,true,261434,Photographs,Photograph,Le Grand Russe,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,9.8 x 13.3 cm. (3 7/8 x 5 1/4 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261434,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.240,false,true,261436,Photographs,Photograph,Le Grand Russe,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,9.8 x 13.3 cm. (3 7/8 x 5 1/4 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261436,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.241,false,true,261437,Photographs,Photograph,Le Grand Russe,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,9.8 x 13.3 cm. (3 7/8 x 5 1/4 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261437,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.242,false,true,261438,Photographs,Photograph,Le Grand Russe,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,9.8 x 13.3 cm. (3 7/8 x 5 1/4 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261438,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.243,false,true,261439,Photographs,Photograph,Le Grand Russe,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261439,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.244,false,true,261440,Photographs,Photograph,Le Grand Russe,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,9.8 x 13.3 cm. (3 7/8 x 5 1/4 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261440,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.245,false,true,261441,Photographs,Photograph,Le Grand Russe,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,9.8 x 13.3 cm. (3 7/8 x 5 1/4 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261441,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.251,false,true,261448,Photographs,Photograph,La fillette,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,11.8 x 8.9 cm (4 5/8 x 3 1/2 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261448,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.201,false,true,285651,Photographs,Photograph,Giorgio de Castiglione,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,"1861, printed 1895–1910",1861,1910,Gelatin silver print from glass negative,Image: 36 x 27.9 cm (14 3/16 x 11 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285651,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.173a,false,true,261351,Photographs,Photograph,Les Chiens,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,11.4 x 8.6 cm (4 1/2 x 3 3/8 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261351,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.174a,false,true,261353,Photographs,Photograph,Les Chiens,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Waxed albumen silver print from glass negative with applied color,8.3 x 5 cm (3 1/4 x 1 15/16 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261353,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.176a,false,true,282738,Photographs,Photograph,Les Chiens,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1875–80,1875,1880,Albumen silver print from glass negative,22.7 x 16.8 cm (8 15/16 x 6 5/8 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/282738,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.212a,false,true,261405,Photographs,Photograph,Le fauteuil,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,7.6 x 10.2 cm. (3 x 4 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261405,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.193.1,false,true,261375,Photographs,Photograph,La chaise rustique,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print,,"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261375,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.193.2,false,true,261376,Photographs,Photograph,Le tambour,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261376,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.193.3,false,true,261377,Photographs,Photograph,Chaise rustique (autre),,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261377,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.193.4,false,true,261378,Photographs,Photograph,Chaise rustique,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261378,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.193.5,false,true,261379,Photographs,Photograph,Chaise rustique,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261379,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.193.6,false,true,261380,Photographs,Photograph,Le furieuse,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261380,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.193.7,false,true,261381,Photographs,Photograph,Le bournous,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261381,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.193.8,false,true,261382,Photographs,Photograph,Tambour (autre),,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261382,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.193.9,false,true,261383,Photographs,Photograph,La tête renversée,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261383,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"1975.548.95a, b",false,true,261550,Photographs,Photograph,Piede de Judith,,,,,,Artist,,Pierre-Louis Pierson,"French, 1822–1913",,"Pierson, Pierre-Louis",French,1822,1913,1860s,1860,1869,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261550,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.400.51,false,true,291804,Photographs,Daguerreotype,[Reclining Female Nude as Danae],,,,,,Artist,Attributed to,Bruno Braquehais,"French, 1823–1875",,"Braquehais, Bruno",French,1823,1875,1850s,1852,1858,Daguerreotype,Image: 8 x 6 cm (3 1/8 x 2 3/8 in.) Mount: 12.9 x 11.1 cm (5 1/16 x 4 3/8 in.),"Bequest of Herbert Mitchell, 2008",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/291804,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.279,false,true,286294,Photographs,Photograph,[Nude Before a Mirror],,,,,,Artist,Attributed to,Bruno Braquehais,"French, 1823–1875",,"Braquehais, Bruno",French,1823,1875,ca. 1857,1855,1859,Albumen silver print from glass negative,Image: 22.3 × 17.9 cm (8 3/4 × 7 1/16 in.) Mount: 50.1 × 39.3 cm (19 3/4 × 15 1/2 in.),"Gilman Collection, Purchase, Mr. and Mrs. Henry R. Kravis Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286294,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.45,false,true,283120,Photographs,Photograph,Emmanuel Frémiet,,,,,,Artist,,Adrien Tournachon,"French, 1825–1903",,"Tournachon, Adrien",French,1825,1903,1854–55,1854,1855,Salted paper print from glass negative,24.6 x 17.3cm (9 11/16 x 6 13/16in.),"Gilman Collection, Purchase, Alfred Stieglitz Society Gifts, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283120,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.634,false,true,286272,Photographs,Photograph,"[Bull from Glane, Canton of Fribourg]",,,,,,Artist,,Adrien Tournachon,"French, 1825–1903",,"Tournachon, Adrien",French,1825,1903,1856,1856,1856,Salted paper print from glass negative,Image: 7 1/2 × 10 9/16 in. (19.1 × 26.8 cm) Mount: 10 5/8 × 12 13/16 in. (27 × 32.5 cm),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286272,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.176,false,true,286657,Photographs,Photograph,Auguste Vacquerie,,,,,,Artist,,Charles Victor Hugo,"French, 1826–1871",,"Hugo, Charles Victor",French,1826,1871,1853–56,1853,1856,Salted paper print from paper negative,Image: 9.2 x 7.6 cm (3 5/8 x 3 in.) Mount: 24.8 x 18.4 cm (9 3/4 x 7 1/4 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286657,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1997.382.68,false,true,282079,Photographs,Photograph,[Man in a Forest Landscape],,,,,,Artist,,Constant Alexandre Famin,"French, 1827–1888",,"Famin, Constant Alexandre",French,1827,1888,ca. 1870,1868,1872,Albumen silver print from glass negative,33.7 x 25.3 cm (13 1/4 x 9 15/16 in. ),"The Rubel Collection, Purchase, Lila Acheson Wallace Gift, 1997",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/282079,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2008.256,false,true,289145,Photographs,Photograph,Gustave Le Gray,,,,,,Artist,,Alphonse Delaunay,"French, 1827–1906",,"Delaunay, Alphonse",French,1827,1906,1854,1854,1854,Salted paper print from glass negative,Image: 22.1 x 16.5 cm (8 11/16 x 6 1/2 in.) Mount: 44 x 34.2 cm (17 5/16 x 13 7/16 in.),"Purchase, Daniel Blau Gift and 2007 Benefit Fund, 2008",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289145,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.236,false,true,680014,Photographs,Photograph,"Second Palace at Mitla, Mexico.",,,,,,Artist,,Désiré Charnay,"French, 1828–1915",,"Charnay, Désiré",French,1828,1915,February 1860,1860,1860,Albumen silver print from glass negative,Image: 13 1/8 × 16 7/8 in. (33.3 × 42.9 cm) Mount: 21 3/16 in. × 25 in. (53.8 × 63.5 cm),"Purchase, Mr. and Mrs. John A. Moran Gift, in memory of Louise Chisholm Moran, 2015",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/680014,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.73,false,true,283172,Photographs,Photograph,"Raharla, Minister to the Queen",,,,,,Artist,,Désiré Charnay,"French, 1828–1915",,"Charnay, Désiré",French,1828,1915,1863,1863,1863,Albumen silver print from glass negative,Image: 19.4 × 12 cm (7 5/8 × 4 3/4 in.) Mount: 28.7 × 23.1 cm (11 5/16 × 9 1/8 in.),"Gilman Collection, Purchase, Joyce F. Menschel Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283172,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.74,false,true,283173,Photographs,Photograph,[Family Group],,,,,,Artist,,Désiré Charnay,"French, 1828–1915",,"Charnay, Désiré",French,1828,1915,1863,1863,1863,Albumen silver print from glass negative,Image: 21.3 × 16.8 cm (8 3/8 × 6 5/8 in.) Mount: 28.8 × 23.2 cm (11 5/16 × 9 1/8 in.),"Gilman Collection, Purchase, Alfred Stieglitz Society Gifts, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283173,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.565,false,true,286467,Photographs,Photograph,"La Prison, à Chichen-Itza",,,,,,Artist,,Désiré Charnay,"French, 1828–1915",,"Charnay, Désiré",French,1828,1915,1857–89,1857,1889,Albumen silver print from glass negative,Image: 33.3 x 42.4 cm (13 1/8 x 16 11/16 in.) Mount: 54 x 70.8 cm (21 1/4 x 27 7/8 in.),"Gilman Collection, Purchase, Jennifer and Joseph Duke Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286467,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.732,false,true,286280,Photographs,Photograph,"Femmes Betsimisaraka, Madagascar",,,,,,Artist,,Désiré Charnay,"French, 1828–1915",,"Charnay, Désiré",French,1828,1915,1863,1863,1863,Albumen silver print from glass negative,Image: 19.7 x 17 cm (7 3/4 x 6 11/16 in.) Mount: 28.7 x 23.1 cm (11 5/16 x 9 1/8 in.),"Gilman Collection, Purchase, Sam Salz Foundation Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286280,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.733,false,true,286146,Photographs,Photograph,Ile de la Réunion,,,,,,Artist,,Désiré Charnay,"French, 1828–1915",,"Charnay, Désiré",French,1828,1915,1863,1863,1863,Albumen silver print from glass negative,Image: 27.8 x 20.4 cm (10 15/16 x 8 1/16 in.) Mount: 35.8 x 27 cm (14 1/8 x 10 5/8 in.),"Gilman Collection, Purchase, Sam Salz Foundation Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286146,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.734,false,true,286691,Photographs,Photograph,Fougère arborescente,,,,,,Artist,,Désiré Charnay,"French, 1828–1915",,"Charnay, Désiré",French,1828,1915,1863,1863,1863,Albumen silver print from glass negative,Image: 28.7 x 22.8 cm (11 5/16 x 9 in.) Mount: 35.5 x 27.4 cm (14 x 10 13/16 in.),"Gilman Collection, Purchase, Sam Salz Foundation Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286691,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.735,false,true,286282,Photographs,Photograph,Baobab à Mohéli,,,,,,Artist,,Désiré Charnay,"French, 1828–1915",,"Charnay, Désiré",French,1828,1915,1863,1863,1863,Albumen silver print from glass negative,Image: 28.7 x 22.8 cm (11 5/16 x 9 in.) Mount: 35.4 x 26.9 cm (13 15/16 x 10 9/16 in.),"Gilman Collection, Purchase, Sam Salz Foundation Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286282,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.491.1 (8),false,true,287607,Photographs,Photograph,"[The Countess Canning, Simla]",,,,,,Artist,,Jean Baptiste Oscar Mallitte,"French, 1829–1905",,"Mallitte, Jean Baptiste Oscar",French,1829,1905,1861,1861,1861,Albumen silver print,Mount: 33 x 26.1 cm (13 x 10 1/4 in.) Image: 20.7 x 14.1 cm (8 1/8 x 5 9/16 in.),"Gilman Collection, Purchase, Cynthia Hazen Polsky Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287607,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.491.1 (46),false,true,287646,Photographs,Photograph,"[Group portrait at the Governor Generals Camp, L-R: Maj Jones, Mr. Walters, The Governor Generals Chaplain, J.C.S. and Sir E.Campbell Bart., 60th Rifles Mry. Sry. to G.G.]",,,,,,Artist,,Jean Baptiste Oscar Mallitte,"French, 1829–1905",,"Mallitte, Jean Baptiste Oscar",French,1829,1905,1858–61,1858,1861,Albumen silver print,Image: 20.5 x 24.7 cm (8 1/16 x 9 3/4 in.) Mount: 33 x 26 cm (13 x 10 1/4 in.) Print mounted vertically.,"Gilman Collection, Purchase, Cynthia Hazen Polsky Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287646,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.491.1 (52),false,true,287652,Photographs,Photograph,[Lady Canning],,,,,,Artist,,Jean Baptiste Oscar Mallitte,"French, 1829–1905",,"Mallitte, Jean Baptiste Oscar",French,1829,1905,1858–61,1858,1861,Albumen silver print,Image: 16 x 19.7 cm (6 5/16 x 7 3/4 in.) Mount: 33.1 x 26 cm (13 1/16 x 10 1/4 in.) Print mounted vertically.,"Gilman Collection, Purchase, Cynthia Hazen Polsky Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287652,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.491.1 (60),false,true,287660,Photographs,Photograph,"[Metcalfe House, Delhi]",,,,,,Artist,,Jean Baptiste Oscar Mallitte,"French, 1829–1905",,"Mallitte, Jean Baptiste Oscar",French,1829,1905,1858–61,1858,1861,Albumen silver print,Image: 21 x 28.4 cm (8 1/4 x 11 3/16 in.) Mount: 33 x 26 cm (13 x 10 1/4 in.) Pritn mounted vertically.,"Gilman Collection, Purchase, Cynthia Hazen Polsky Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287660,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.491.1 (50a),false,true,287650,Photographs,Photograph,"[Lady Canning on her Black Arab and Lord Clyde, Commander in Chief]",,,,,,Artist,,Jean Baptiste Oscar Mallitte,"French, 1829–1905",,"Mallitte, Jean Baptiste Oscar",French,1829,1905,1858–61,1858,1861,Albumen silver print,Image: 14.9 x 19.2 cm (5 7/8 x 7 9/16 in.) Mount: 33.1 x 26 cm (13 1/16 x 10 1/4 in.),"Gilman Collection, Purchase, Cynthia Hazen Polsky Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287650,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.491.1 (56b),false,true,287705,Photographs,Photograph,"[Campbell Twins in a Shigram, Governor General's Camp]",,,,,,Artist,,Jean Baptiste Oscar Mallitte,"French, 1829–1905",,"Mallitte, Jean Baptiste Oscar",French,1829,1905,1858–61,1858,1861,Albumen silver print,Image: 9.7 x 19.6 cm (3 13/16 x 7 11/16 in.) Mount: 33 x 26 cm (13 x 10 1/4 in.),"Gilman Collection, Purchase, Cynthia Hazen Polsky Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287705,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.1144,false,true,266168,Photographs,Photographs,[The Corpse of Emperor Maximilian I of Mexico],,,,,,Artist,,François Aubert,"French, 1829–1906",,"Aubert, François",French,1829,1906,1867,1867,1867,Albumen silver print,,"Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 1989",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/266168,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.212,false,true,285871,Photographs,Photograph,[The Scene of the Execution of Emperor Maximilian I of Mexico],,,,,,Artist,,François Aubert,"French, 1829–1906",,"Aubert, François",French,1829,1906,1867,1867,1867,Albumen silver print from glass negative,Image: 22.1 × 16.1 cm (8 11/16 × 6 5/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285871,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.213,false,true,285712,Photographs,Photographs,"The Shirt of the Emperor, Worn during His Execution",,,,,,Artist,,François Aubert,"French, 1829–1906",,"Aubert, François",French,1829,1906,1867,1867,1867,Albumen silver print from glass negative,Image: 22.2 × 15.8 cm (8 3/4 × 6 1/4 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285712,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.580.1,false,true,286895,Photographs,Photograph,[Emperor Maximilian's Firing Squad],,,,,,Artist,,François Aubert,"French, 1829–1906",,"Aubert, François",French,1829,1906,1867,1867,1867,Albumen silver print from glass negative,Image: 11.4 x 14.2 cm (4 7/16 x 5 9/16 in.) Mount: 18.2 x 22.1 cm (7 3/16 x 8 11/16 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286895,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.820,false,true,286544,Photographs,Photograph,[Bird in Flight],,,,,,Artist,,Etienne-Jules Marey,"French, 1830–1904",,"Marey, Etienne-Jules",French,1830,1904,1886,1886,1886,Albumen silver print from glass negative,Image: 3.3 x 17.3 cm (1 5/16 x 6 13/16 in.),"Gilman Collection, Purchase, Denise and Andrew Saul Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286544,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.321,false,true,286606,Photographs,Photograph,"Tomb of Ptahmose, Saqqara (Memphis)",,,,,,Artist,,Théodule Deveria,"French, 1831–1871",,"Deveria, Théodule",French,1831,1871,1859,1859,1859,Albumen silver print from paper negative,"Image: 8 3/8 × 11 1/16 in. (21.2 × 28.1 cm), irregularly trimmed Sheet: 8 7/16 × 11 5/16 in. (21.4 × 28.8 cm), irregularly trimmed","Gilman Collection, Purchase, Joseph M. Cohen Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286606,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2010.317,false,true,292057,Photographs,Photograph,Femme turque en toilette de ville,,,,,,Artist,,Félix Bonfils,"French, 1831–1885",,"Bonfils, Félix",French,1831,1885,1870s,1870,1879,Albumen silver print from glass negative,Image: 22.2 x 16.4 cm (8 3/4 x 6 7/16 in.),"Funds from various donors, 2010",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/292057,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.561.1,false,true,261776,Photographs,Photograph,Temple d'Andour,,,,,,Artist,,Félix Bonfils,"French, 1831–1885",,"Bonfils, Félix",French,1831,1885,ca. 1870,1868,1872,Albumen silver print from glass negative,17.3 x 23.0 cm (6 13/16 x 9 1/16 in.),"Gift of Daniel Wolf, in memory of Diane R. Wolf, 1976",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261776,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.561.2,false,true,261777,Photographs,Photograph,Pyramides et le Sphinx,,,,,,Artist,,Félix Bonfils,"French, 1831–1885",,"Bonfils, Félix",French,1831,1885,"1860s–70s, printed ca. 1870",1860,1879,Albumen silver print,17.3 x 23.1 cm. (6 13/16 x 9 1/16 in.),"Gift of Daniel Wolf, in memory of Diane R. Wolf, 1976",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261777,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1973.594.87,false,true,260950,Photographs,Photograph,[The Roman Forum],,,,,,Artist,,Félix Bonfils,"French, 1831–1885",,"Bonfils, Félix",French,1831,1885,ca. 1870s,1868,1872,Albumen silver print,,"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1973",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260950,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1981.1128.1,false,true,262989,Photographs,Photograph,The Temple of the Sun at Baalbec,,,,,,Artist,,Félix Bonfils,"French, 1831–1885",,"Bonfils, Félix",French,1831,1885,ca. 1870,1868,1872,Albumen silver print,,"Gift of Douglas Dillon, 1981",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/262989,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1981.1128.2,false,true,262990,Photographs,Photograph,"[Details of the Colonnade of the Parthenon, Athens]",,,,,,Artist,,Félix Bonfils,"French, 1831–1885",,"Bonfils, Félix",French,1831,1885,ca. 1870s,1868,1872,Albumen silver print,Image: 22.3 x 27.7 cm (8 3/4 x 10 7/8 in.) Mount: 40.5 x 50.6 cm (15 15/16 x 19 15/16 in.),"Gift of Douglas Dillon, 1981",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/262990,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1981.1194.4,false,true,263132,Photographs,Photograph,Caire près du Mokkatam,,,,,,Artist,,Félix Bonfils,"French, 1831–1885",,"Bonfils, Félix",French,1831,1885,1870s,1870,1879,Albumen silver print from glass negative,,"Gift of Mrs. John L. Swayze, 1981",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/263132,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1981.1194.5,false,true,263133,Photographs,Photograph,"Caire. Mosquée el-Arhar, détails de la porte",,,,,,Artist,,Félix Bonfils,"French, 1831–1885",,"Bonfils, Félix",French,1831,1885,1870s,1870,1879,Albumen silver print from glass negative,,"Gift of Mrs. John L. Swayze, 1981",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/263133,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1981.1194.6,false,true,263134,Photographs,Photograph,Rue du Caire,,,,,,Artist,,Félix Bonfils,"French, 1831–1885",,"Bonfils, Félix",French,1831,1885,1870s,1870,1879,Albumen silver print from glass negative,,"Gift of Mrs. John L. Swayze, 1981",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/263134,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1981.1194.7,false,true,263135,Photographs,Photograph,Saîs coureurs au Caire,,,,,,Artist,,Félix Bonfils,"French, 1831–1885",,"Bonfils, Félix",French,1831,1885,1870s,1870,1879,Albumen silver print from glass negative,,"Gift of Mrs. John L. Swayze, 1981",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/263135,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1981.1194.8,false,true,263136,Photographs,Photograph,Le Caire - Intérieur de la mosquée El Bordei,,,,,,Artist,,Félix Bonfils,"French, 1831–1885",,"Bonfils, Félix",French,1831,1885,1870s,1870,1879,Albumen silver print from glass negative,,"Gift of Mrs. John L. Swayze, 1981",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/263136,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1985.1123.8,false,true,264380,Photographs,Photograph,Caire. Allée de Pyramides,,,,,,Artist,,Félix Bonfils,"French, 1831–1885",,"Bonfils, Félix",French,1831,1885,ca. 1870,1868,1872,Albumen silver print,,"Gift of Weston J. Naef, 1985",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264380,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1982.1153.13,false,true,263432,Photographs,Photograph,"Cous du Kadisha, Monte Libon",,,,,,Artist,,Félix Bonfils,"French, 1831–1885",,"Bonfils, Félix",French,1831,1885,"1860s–80s, printed ca. 1870",1860,1889,Albumen silver print,,"Gift of Mrs. John L. Swayze, 1982",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/263432,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1982.1153.14,false,true,263433,Photographs,Photograph,Intérieur de la Porte de Jaffa,,,,,,Artist,,Félix Bonfils,"French, 1831–1885",,"Bonfils, Félix",French,1831,1885,ca. 1870,1868,1872,Albumen silver print,Image: 28 x 22.6 cm (11 x 8 7/8 in.),"Gift of Mrs. John L. Swayze, 1982",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/263433,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1982.1153.15,false,true,263434,Photographs,Photograph,Promenade des Pins á Beyrouth,,,,,,Artist,,Félix Bonfils,"French, 1831–1885",,"Bonfils, Félix",French,1831,1885,ca. 1870,1868,1872,Albumen silver print,,"Gift of Mrs. John L. Swayze, 1982",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/263434,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1982.1153.20,false,true,263440,Photographs,Photograph,Beyrouth. Vu du collège américain,,,,,,Artist,,Félix Bonfils,"French, 1831–1885",,"Bonfils, Félix",French,1831,1885,ca. 1870,1868,1872,Albumen silver print,,"Gift of Mrs. John L. Swayze, 1982",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/263440,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1982.1153.21,false,true,263441,Photographs,Photograph,Vue générale de Bethany - General view of Bethany,,,,,,Artist,,Félix Bonfils,"French, 1831–1885",,"Bonfils, Félix",French,1831,1885,ca. 1880,1878,1882,Albumen silver print,,"Gift of Mrs. John L. Swayze, 1982",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/263441,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1982.1153.22,false,true,263442,Photographs,Photograph,Les ponts du fleurs du chien,,,,,,Artist,,Félix Bonfils,"French, 1831–1885",,"Bonfils, Félix",French,1831,1885,ca. 1880,1878,1882,Albumen silver print,,"Gift of Mrs. John L. Swayze, 1982",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/263442,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1982.1153.23,false,true,263443,Photographs,Photograph,Panorama de Jaffa,,,,,,Artist,,Félix Bonfils,"French, 1831–1885",,"Bonfils, Félix",French,1831,1885,ca. 1880,1878,1882,Albumen silver print,,"Gift of Mrs. John L. Swayze, 1982",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/263443,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.501,false,true,685841,Photographs,Carte-de-visite,[John Leech],,,,,,Artist,,Camille Silvy,"French, 1835–1869",,"Silvy, Camille",French,1835,1869,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685841,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1014.2,false,true,264624,Photographs,Photograph,[Roses],,,,,,Artist,,Eugène Chauvigné,"French, 1837–1894",,"Chauvigné, Eugène",French,1837,1894,ca. 1875,1873,1877,Albumen silver print from glass negative,24.0 x 17.9 cm. (9 7/16 x 7 1/16 in.),"Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 1986",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264624,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.366,false,true,685706,Photographs,Carte-de-visite,[Francois Adolphe Grison],,,,,,Artist,,Émile Schweitzer,"French, 1837–1903",,"Schweitzer, Émile",French,1837,1903,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685706,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.186,false,true,306173,Photographs,Photograph,Prenant un Ris à Bord de L'Astrée,,,,,,Artist,,Félix Auguste Leclerc,"French, 1838–1896",,"Leclerc, Félix Auguste",French,1838,1896,1871,1871,1871,Albumen silver print from glass negative,Image: 25 × 18.7 cm (9 13/16 × 7 3/8 in.) Mount: 44.7 × 32.4 cm (17 5/8 × 12 3/4 in.),"Purchase, The Buddy Taub Foundation Gift, Dennis A. Roach and Jill Roach, Directors, 2013",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306173,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.848,false,true,285885,Photographs,Photograph,[Emmanuel Frémiet],,,,,,Artist,Possibly by,Edmond Bénard,"French, 1838–1907",,"Bénard, Edmond",French,1838,1907,1880s–90s,1880,1899,Albumen silver print from glass negative,Image: 20.6 × 26.4 cm (8 1/8 × 10 3/8 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285885,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1995.9,false,true,267314,Photographs,Photograph,Le Nouvel Opéra de Paris (Sculpture Ornementale),,,,,,Artist,,Louis-Emile Durandelle,"French, 1839–1917",,"Durandelle, Louis-Émile",French,1839,1917,1865–72,1865,1872,Albumen silver print from glass negative,38.3 x 27.9 cm. (15 1/16 x 11 in.),"Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 1995",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267314,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -49.50.353,false,true,269271,Photographs,Photograph,[Charles Garnier in the Drafting Room While Designing the New Paris Opera],,,,,,Artist,,Louis-Emile Durandelle,"French, 1839–1917",,"Durandelle, Louis-Émile",French,1839,1917,ca. 1870,1868,1872,Albumen silver print from glass negative,,"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1949",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269271,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1988.1072,false,true,265933,Photographs,Photograph,La Tour St. Jacques La Boucherie à Paris,,,,,,Artist,,Charles Soulier,"French, 1840–1875",,"Soulier, Charles",French,1840,1875,ca. 1867,1865,1869,Albumen silver print from glass negative,40.7 x 30.6 cm. (16 x 12 1/16 in.),"Edward Pearce Casey Fund, 1988",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/265933,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.32.1,false,true,289160,Photographs,Photograph,Grande Salle du Conseil d'Etat,,,,,,Artist,,Charles Soulier,"French, 1840–1875",,"Soulier, Charles",French,1840,1875,May 1871,1871,1871,Albumen silver print from glass negative,Image: 19 x 24.9 cm (7 1/2 x 9 13/16 in.) Mount: 34.7 x 47.2 cm (13 11/16 x 18 9/16 in.),"Gift of Paula and Robert Hershkowitz, in memory of Sam Wagstaff, 2009",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289160,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.32.2,false,true,289161,Photographs,Photograph,Maisons de la porte d'Auteuil,,,,,,Artist,,Charles Soulier,"French, 1840–1875",,"Soulier, Charles",French,1840,1875,May 1871,1871,1871,Albumen silver print from glass negative,Image: 19.2 x 24.9 cm (7 9/16 x 9 13/16 in.) Mount: 34.7 x 47.5 cm (13 11/16 x 18 11/16 in.),"Gift of Paula and Robert Hershkowitz, in memory of Sam Wagstaff, 2009",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289161,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2008.527,false,true,289070,Photographs,Photograph,[Group of Adults and Children on a Village Street in the Auvergne],,,,,,Artist,,Felix Thiollier,"French, 1842–1914",,"Thiollier, Felix",French,1842,1914,ca. 1910,1905,1914,Gelatin silver print,29.4 x 39.4 cm (11 9/16 x 15 1/2 in.),"Twentieth-Century Photography Fund, 2008",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289070,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2010.1,false,true,291656,Photographs,Photograph,[Fencer],,,,,,Artist,,Georges Demeny,"French, 1850–1917",,"Demeny, Georges",French,1850,1917,1906,1906,1906,Gelatin silver print,Image: 17.5 x 26.5 x 12.8 cm (6 7/8 x 10 7/16 in.) Mount: 20.1 x 28.7 cm (7 15/16 x 11 5/16 in.),"Purchase, Alfred Stieglitz Society Gifts, 2010",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/291656,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.16,false,true,289245,Photographs,Photograph,"Tableau synoptic des traits physionomiques: pour servir a l'étude du ""portrait parlé""",,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",French,1853,1914,ca. 1909,1909,1909,Gelatin silver print,Image: 39.4 x 29.5 cm (15 1/2 x 11 5/8 in.),"Twentieth-Century Photography Fund, 2009",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289245,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.375.1–.434,false,true,286605,Photographs,Mugshot,[Mugshots of Suspected Anarchists from French Police Files],,,,,,Artist,,Alphonse Bertillon,"French, 1853–1914",,"Bertillon, Alphonse",French,1853,1914,1891–95,1891,1895,Albumen silver prints and gelatin silver prints,10.5 x 7 x 0.5 cm (4 1/8 x 2 3/4 x 3/16 in.) each,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286605,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.43.254,false,true,267665,Photographs,Photograph,[Study in Orange],,,,,,Artist,,René Le Bègue,"French, 1857–1914",,"Le Bègue, René",French,1857,1914,1904,1904,1904,Gum bichromate print,22.0 x 14.3 cm. (8 11/16 x 5 5/8 in.),"Alfred Stieglitz Collection, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267665,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.43.258,false,true,267669,Photographs,Photograph,Académie,,,,,,Artist,,René Le Bègue,"French, 1857–1914",,"Le Bègue, René",French,1857,1914,1902,1902,1902,Gum bichromate print,24.1 x 18.0 cm. (9 1/2 x 7 1/16 in.),"Alfred Stieglitz Collection, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267669,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -49.55.220,false,true,269345,Photographs,Photograph,[Study in Orange],,,,,,Artist,,René Le Bègue,"French, 1857–1914",,"Le Bègue, René",French,1857,1914,1903,1903,1903,Gum bichromate print,25.7 x 19.7 cm. (10 1/8 x 7 3/4 in.),"Alfred Stieglitz Collection, 1949",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269345,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.118,false,true,283240,Photographs,Photograph,"[Puyo, Robert Demachy, and Paul de Singly with Model]",,,,,,Artist,,Emile Joachim Constant Puyo,"French, 1857–1933",,"Puyo, Emile Joachim Constant",French,1857,1933,1909,1909,1909,Platinum print,Image: 2 11/16 × 3 3/16 in. (6.9 × 8.1 cm) Mount: 3 1/8 × 3 1/2 in. (7.9 × 8.9 cm),"Gilman Collection, Purchase, Harriette and Noel Levine Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283240,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1148,false,true,285751,Photographs,Photograph,[Montmartre],,,,,,Artist,,Emile Joachim Constant Puyo,"French, 1857–1933",,"Puyo, Emile Joachim Constant",French,1857,1933,ca. 1906,1904,1908,Bromoil print,Image: 11 7/16 × 8 7/8 in. (29.1 × 22.6 cm),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285751,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.43.56,false,true,267853,Photographs,Photograph,Panel,,,,,,Artist,,Robert Demachy,"French, 1859–1936",,"Demachy, Robert",French,1859,1936,1898,1898,1898,Gum bichromate print,14.7 x 20.2 cm (5 13/16 x 7 15/16 in. ),"Alfred Stieglitz Collection, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267853,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.43.57,false,true,267854,Photographs,Photograph,Académie,,,,,,Artist,,Robert Demachy,"French, 1859–1936",,"Demachy, Robert",French,1859,1936,1900,1900,1900,Gum bichromate print,22.2 x 17.0 cm. (8 3/4 x 6 11/16 in.),"Alfred Stieglitz Collection, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267854,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.43.470,false,true,267843,Photographs,Photograph,Honfleur,,,,,,Artist,,Robert Demachy,"French, 1859–1936",,"Demachy, Robert",French,1859,1936,1905,1900,1909,Gum bichromate print,15.1 x 21.4 cm. (5 15/16 x 8 7/16 in.),"Alfred Stieglitz Collection, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267843,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -49.55.205,false,true,269328,Photographs,Photograph,The Crowd,,,,,,Artist,,Robert Demachy,"French, 1859–1936",,"Demachy, Robert",French,1859,1936,1910,1910,1910,Oil print,15.8 x 22.8 cm. (6 1/4 x 9 in.),"Alfred Stieglitz Collection, 1949",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269328,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -49.55.206,false,true,269329,Photographs,Photograph,Dans les coulisses,,,,,,Artist,,Robert Demachy,"French, 1859–1936",,"Demachy, Robert",French,1859,1936,ca. 1897,1895,1899,Gum bichromate print,36.7 x 18.8 cm. (14 7/16 x 7 3/8 in.),"Alfred Stieglitz Collection, 1949",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269329,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2008.666.3,false,true,289550,Photographs,Photograph,Struggle,,,,,,Artist,,Robert Demachy,"French, 1859–1936",,"Demachy, Robert",French,1859,1936,1903 or earlier,1900,1903,Gum bichromate print,Image: 17.4 x 11.6 cm (6 7/8 x 4 9/16 in.) Mount: 17.7 x 11.9 cm (6 15/16 x 4 11/16 in.) Mount (2nd): 39.6 x 29.6 cm (15 9/16 x 11 5/8 in.),"Gift of Isaac Lagnado, in honor of Thomas P. Campbell, 2008",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289550,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.115,false,true,283233,Photographs,Photograph,[Auguste Rodin's The Clenched Hand],,,,,,Artist,,Eugène Druet,"French, 1868–1917",,"Druet, Eugène",French,1868,1917,before 1898,1890,1898,Gelatin silver print,Image: 29.9 x 39.5 cm (11 3/4 x 15 9/16 in.),"Gilman Collection, Purchase, Mr. and Mrs. Henry R. Kravis Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283233,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.186,false,true,286722,Photographs,Photograph,"[Nijinsky in ""Danse siamoise"" from the ""Orientales""]",,,,,,Artist,,Eugène Druet,"French, 1868–1917",,"Druet, Eugène",French,1868,1917,1910,1910,1910,Gelatin silver print,Image: 20.3 x 14.6 cm (8 x 5 3/4 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286722,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.621,false,true,283234,Photographs,Photograph,[Study of a Sculpture],,,,,,Artist,Attributed to,Eugène Druet,"French, 1868–1917",,"Druet, Eugène",French,1868,1917,ca. 1900,1898,1902,Gelatin silver print,39.7 x 29.8 cm (15 5/8 x 11 3/4 in.),"Gilman Collection, Purchase, Mr. and Mrs. Henry R. Kravis Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283234,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2001.8,false,true,284433,Photographs,Photograph,[Materialization of a Woman's Face Produced by the Medium Eva C.],,,,,,Artist,,Gustave Geley,"French, 1868–1924",,"Geley, Gustave",French,1868,1924,"February 26, 1918",1918,1918,Gelatin silver print,17.9 x 13 cm (7 1/16 x 5 1/8 in.),"Gilman Collection, Purchase, The Howard Gilman Foundation Gift, 2001",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/284433,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1090,false,true,286701,Photographs,Photograph,[Construction for the Universal Exhibition of 1855],,,,,,Artist,,Bertsch et D'Arnaud,"French, active 1850s",,Bertsch et D'Arnaud,French,1850,1859,1855,1855,1855,Salted paper print from glass negative,Image: 20.7 × 16.4 cm (8 1/8 × 6 7/16 in.) Mount: 31 × 25.1 cm (12 3/16 × 9 7/8 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286701,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.651,false,true,287289,Photographs,Photograph,[Spiral of Lightning in a Thunderstorm],,,,,,Artist,,Charles Moussette,"French, active 1880s",,"Moussette, Charles",French,1880,1889,"May 12, 1886",1886,1886,Albumen silver print from glass negative,Mount: 9 13/16 in. × 6 7/8 in. (25 × 17.5 cm) Image: 6 5/8 × 4 5/8 in. (16.9 × 11.7 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287289,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.51,false,true,283132,Photographs,Photograph,"Leonardo da Vinci, Drawing for Christ in ""The Last Supper""",,,,,,Artist,,Léon Gérard,"French, active 1857–61",,"Gérard, Léon",French,1857,1861,1857–61,1857,1861,Albumen silver print from paper negative,Image: 36.3 x 26.6 cm (14 5/16 x 10 1/2 in.) Mount: 59.2 x 42.6 cm (23 5/16 x 16 3/4 in.),"Gilman Collection, Purchase, Denise and Andrew Saul Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283132,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.460.13,false,true,290481,Photographs,Photograph,"Nuremburg, Intérieur de la Cour du Burg impérial",,,,,,Artist,,Léon Gérard,"French, active 1857–61",,"Gérard, Léon",French,1857,1861,1857,1857,1857,Albumen silver print from glass negative,Image: 34.9 x 26.9 cm (13 3/4 x 10 9/16 in.) Mount: 59.1 x 43.2 cm (23 1/4 x 17 in.),"Gift of Paul F. Walter, 2009",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/290481,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1997.382.46,true,true,282051,Photographs,Photograph,[Two Standing Female Nudes],,,,,,Artist,,Félix-Jacques-Antoine Moulin,"French, 1800–after 1875",,"Moulin, Félix-Jacques-Antoine",French,1800,1875,ca. 1850,1848,1852,Daguerreotype,visible: 14.5 x 11.1 cm (5 11/16 x 4 3/8 in.),"The Rubel Collection, Purchase, Anonymous Gift and Lila Acheson Wallace Gift, 1997",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/282051,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1105,false,true,286524,Photographs,Photograph,Nude,,,,,,Artist,,Félix-Jacques-Antoine Moulin,"French, 1800–after 1875",,"Moulin, Félix-Jacques-Antoine",French,1800,1875,ca. 1850,1848,1852,Salted paper print from paper negative,Image: 8 13/16 × 6 3/4 in. (22.4 × 17.2 cm) Mount: 11 5/8 × 8 1/16 in. (29.6 × 20.5 cm),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286524,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1991.1075,false,true,266384,Photographs,Photograph,[Standing Male Nude],,,,,,Artist,,Charles Alphonse Marlé,"French, 1821–after 1867",,"Marlé, C.",French,1821,1921,ca. 1855,1853,1857,Salted paper print from paper negative,Image: 25.7 x 17.6 cm (10 1/8 x 6 15/16 in.),"Purchase, Ezra Mack Gift and The Horace W. Goldsmith Foundation Gift through Joyce and Robert Menschel, 1991",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/266384,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.263,false,true,285463,Photographs,Photograph,"Shelling Beans, Argentelle",,,,,,Artist,,Louis-Adolphe Humbert de Molard,"French, Paris 1800–1874",,"Humbert de Molard, Louis-Adolphe",French,1800,1874,1851,1851,1851,Salted paper print from paper negative,Image: 22.3 x 17.7 cm (8 3/4 x 6 15/16 in.),"Gilman Collection, Purchase, Mr. and Mrs. Henry R. Kravis Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285463,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.266,false,true,286023,Photographs,Photograph,"[Pont d'Ouilly on the Orne River, Normandy]",,,,,,Artist,,Louis-Adolphe Humbert de Molard,"French, Paris 1800–1874",,"Humbert de Molard, Louis-Adolphe",French,1800,1874,1850–51,1850,1851,Salted paper print from paper negative,Image: 17.9 x 22.2 cm (7 1/16 x 8 3/4 in.) Mount: 21 x 24 cm (8 1/4 x 9 7/16 in.),"Gilman Collection, Purchase, Heidi S. Steiger Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286023,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.286,false,true,286022,Photographs,Photograph,Self-Portrait,,,,,,Artist,,Louis-Adolphe Humbert de Molard,"French, Paris 1800–1874",,"Humbert de Molard, Louis-Adolphe",French,1800,1874,1846–47,1846,1847,Daguerreotype,Image: 13.3 x 10.3 cm (5 1/4 x 4 1/16 in.),"Gilman Collection, Purchase, Joseph M. Cohen Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286022,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.758,false,true,286127,Photographs,Photograph,Louise-Marie-Julie,,,,,,Artist,,Louis-Adolphe Humbert de Molard,"French, Paris 1800–1874",,"Humbert de Molard, Louis-Adolphe",French,1800,1874,1849,1849,1849,Salted paper print from paper negative,16.6 x 12.9 cm (6 9/16 x 5 1/16 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286127,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.797,false,true,286024,Photographs,Photograph,Henriette-Reneé Patu,,,,,,Artist,,Louis-Adolphe Humbert de Molard,"French, Paris 1800–1874",,"Humbert de Molard, Louis-Adolphe",French,1800,1874,ca. 1845,1843,1847,Waxed salted paper print from paper negative,Image: 6 7/8 × 5 3/16 in. (17.5 × 13.2 cm),"Gilman Collection, Purchase, Ann Tenenbaum and Thomas H. Lee Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286024,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.898,false,true,286292,Photographs,Photograph,[Female Nude],,,,,,Artist,Circle of,Louis-Adolphe Humbert de Molard,"French, Paris 1800–1874",,"Humbert de Molard, Louis-Adolphe",French,1800,1874,ca. 1855,1853,1857,Salted paper print from paper negative,"14 x 11 cm (5 1/2 x 4 5/16 in.), corners clipped","Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286292,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1241,false,true,302627,Photographs,Photograph,The Laundry,,,,,,Artist,,Louis-Adolphe Humbert de Molard,"French, Paris 1800–1874",,"Humbert de Molard, Louis-Adolphe",French,1800,1874,1840s,1840,1849,Salted paper print,Image: 7 5/16 × 5 1/2 in. (18.6 × 14 cm),"Gilman Collection, Purchase, Ann Tenenbaum and Thomas H. Lee Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/302627,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.17,false,true,685359,Photographs,Carte-de-visite,[? Aubert],,,,,,Artist,,Émile Tourtin,"French, active 1860s–70s",,"Tourtin, Émile",French,1860,1880,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685359,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.466,false,true,685806,Photographs,Carte-de-visite,[Unknown Subject],,,,,,Artist,,Émile Tourtin,"French, active 1860s–70s",,"Tourtin, Émile",French,1860,1880,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685806,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.516,false,true,685856,Photographs,Carte-de-visite,[Unknown Subject],,,,,,Artist,,Émile Tourtin,"French, active 1860s–70s",,"Tourtin, Émile",French,1860,1880,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685856,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.784,false,true,686123,Photographs,Carte-de-visite,[Unknown Subject],,,,,,Artist,,Émile Tourtin,"French, active 1860s–70s",,"Tourtin, Émile",French,1860,1880,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/686123,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.860,false,true,686199,Photographs,Carte-de-visite,[Unknown Subject],,,,,,Artist,,Émile Tourtin,"French, active 1860s–70s",,"Tourtin, Émile",French,1860,1880,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/686199,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2008.145,false,true,289044,Photographs,Photograph,"Tubular Jetty, Mouth of the Adour, Port of Bayonne",,,,,,Artist,,Louis Lafon,"French, active 1870s–90s",,"Lafon, Louis",French,1870,1899,1892,1892,1892,Albumen silver print from glass negative,Image: 36.5 x 47.3 cm (14 3/8 x 18 5/8 in.),"Purchase, Alfred Stieglitz Society Gifts, 2008",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289044,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2008.146,false,true,289045,Photographs,Photograph,Lessart Viaduct on the Rance River,,,,,,Artist,,Louis Lafon,"French, active 1870s–90s",,"Lafon, Louis",French,1870,1899,October 1879,1879,1879,Albumen silver print from glass negative,Image: 36.9 x 47.6 cm (14 1/2 x 18 3/4 in.),"Purchase, Alfred Stieglitz Society Gifts, 2008",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289045,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2008.147,false,true,289106,Photographs,Photograph,The Foundry,,,,,,Artist,,Louis Lafon,"French, active 1870s–90s",,"Lafon, Louis",French,1870,1899,1870s–80s,1870,1889,Albumen silver print from glass negative,Image: 36.6 x 47.9 cm (14 7/16 x 18 7/8 in.),"Purchase, Alfred Stieglitz Society Gifts, 2008",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289106,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2008.558.1,false,true,289717,Photographs,Photograph,[Factory Interior],,,,,,Artist,,Louis Lafon,"French, active 1870s–90s",,"Lafon, Louis",French,1870,1899,ca. 1880,1875,1885,Albumen silver print from glass negative,Image: 36.7 x 47 cm (14 7/16 x 18 1/2 in.),"Gift of Charles Isaacs, 2008",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289717,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2008.558.2,false,true,289718,Photographs,Photograph,Forges,,,,,,Artist,,Louis Lafon,"French, active 1870s–90s",,"Lafon, Louis",French,1870,1899,ca. 1880,1875,1885,Albumen silver print from glass negative,Image: 35.2 x 45.5 cm (13 7/8 x 17 15/16 in.),"Gift of Charles Isaacs, 2008",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289718,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2008.558.3,false,true,289719,Photographs,Photograph,Cour des Ateliers,,,,,,Artist,,Louis Lafon,"French, active 1870s–90s",,"Lafon, Louis",French,1870,1899,ca. 1880,1875,1885,Albumen silver print from glass negative,Image: 37 x 46.6 cm (14 9/16 x 18 3/8 in.),"Gift of Charles Isaacs, 2008",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289719,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2002.9,false,true,284818,Photographs,Photograph,"[Angel of the Passion, Sainte-Chapelle, Paris]",,,,,,Artist,,Auguste Mestral,"French, Rans 1812–1884 Rans",,"Mestral, Auguste",French,1812,1884,1852–53,1852,1853,Salted paper print from paper negative,32.9 x 20.3 cm (12 15/16 x 8 in.),"Gilman Collection, Purchase, The Howard Gilman Foundation Gift, 2002",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/284818,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2000.290,false,true,283761,Photographs,Photograph,"[Sculpture of Virgin and Child, Notre Dame, Paris]",,,,,,Artist,,Auguste Mestral,"French, Rans 1812–1884 Rans",,"Mestral, Auguste",French,1812,1884,ca. 1851,1850,1852,Salted paper print from paper negative,35.1 x 27.6 cm (13 13/16 x 10 7/8 in. ),"Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2000",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283761,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1,true,true,283121,Photographs,Photograph,[Woman Seen from the Back],,,,,,Artist,,Onésipe Aguado de las Marismas,"French, Evry 1830–1893 Paris",,"Aguado de las Marismas, Onésipe",French,1830,1893,ca. 1862,1860,1864,Salted paper print from glass negative,Image: 30.8 × 25.7 cm (12 1/8 × 10 1/8 in.) Mount: 39.4 × 31 cm (15 1/2 × 12 3/16 in.),"Gilman Collection, Purchase, Joyce F. Menschel Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283121,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.715,false,true,286694,Photographs,Photograph,[Rustic Building with Man under Trellis],,,,,,Artist,,André Giroux,"French, Paris 1801–1879 Paris",,"Giroux, André",French,1801,1879,ca. 1853,1851,1855,Salted paper print from glass negative,Image: 8 11/16 × 11 3/16 in. (22.1 × 28.4 cm) Mount: 18 9/16 × 23 13/16 in. (47.1 × 60.5 cm),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286694,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1997.107,false,true,282181,Photographs,Photograph,Rue Neuve-Coquenard (from the Rue Lamartine),,,,,,Artist,,Charles Marville,"French, Paris 1813–1879 Paris",,"Marville, Charles",French,1813,1879,1870s,1870,1879,Albumen silver print,32.6 x 27 cm (12 13/16 x 10 5/8 in. ),"Purchase, Harris Brisbane Dick Fund and Warner Communications Inc. Purchase Fund, by exchange, 1997",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/282181,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2000.292,false,true,283734,Photographs,Photograph,"[South Portal, Chartres Cathedral]",,,,,,Artist,,Charles Marville,"French, Paris 1813–1879 Paris",,"Marville, Charles",French,1813,1879,1854,1854,1854,Salted paper print from paper negative,21.5 x 15.5 cm (8 7/16 x 6 1/8 in.),"Purchase, Jennifer and Joseph Duke Gift and The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2000",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283734,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.167,false,true,288026,Photographs,Photograph,Arts et Métiers (Ancien Modèle),,,,,,Artist,,Charles Marville,"French, Paris 1813–1879 Paris",,"Marville, Charles",French,1813,1879,1864,1864,1864,Albumen silver print from glass negative,Image: 36.6 x 24.1 cm (14 7/16 x 9 1/2 in.) Mount: 63 x 45 cm (24 13/16 x 17 11/16 in.),"Purchase, Alfred Stieglitz Society Gifts, 2007",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288026,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2008.668,false,true,289721,Photographs,Photograph,Rue du Haut-Pave (Pantheon in Distance),,,,,,Artist,,Charles Marville,"French, Paris 1813–1879 Paris",,"Marville, Charles",French,1813,1879,1865–69,1865,1869,Albumen silver print from glass negative,Image: 20.6 x 37 cm (8 1/8 x 14 9/16 in.) Mount: 42.4 x 50.5 cm (16 11/16 x 19 7/8 in.),"Gift of Howard Greenberg, Paula and Robert Hershkowitz, Charles Isaacs, and Hans P. Kraus Jr., in honor of Philippe de Montebello, 2008",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289721,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1141,false,true,264863,Photographs,Photograph,[Rue de Constantine],,,,,,Artist,,Charles Marville,"French, Paris 1813–1879 Paris",,"Marville, Charles",French,1813,1879,ca. 1865,1863,1867,Albumen silver print from glass negative,27.3 x 36.8 cm (10 3/4 x 14 1/2 in.),"Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 1986",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264863,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1987.1094,false,true,265131,Photographs,Photographs,[Cloud Study over Paris],,,,,,Artist,,Charles Marville,"French, Paris 1813–1879 Paris",,"Marville, Charles",French,1813,1879,1850s,1850,1859,Albumen silver print,16.7 x 20.6 cm. (6 9/16 x 8 1/8 in.),"Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 1987",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/265131,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1988.1071,false,true,265932,Photographs,Photograph,La Bièvre,,,,,,Artist,,Charles Marville,"French, Paris 1813–1879 Paris",,"Marville, Charles",French,1813,1879,ca. 1865,1863,1867,Albumen silver print from glass negative,27.8 x 37.6 cm. (10 15/16 x 14 13/16 in.),"Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 1988",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/265932,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2010.513.2,false,true,283713,Photographs,Photograph,Rue Traversine (from the Rue d'Arras),,,,,,Artist,,Charles Marville,"French, Paris 1813–1879 Paris",,"Marville, Charles",French,1813,1879,ca. 1868,1866,1870,Albumen silver print from glass negative,34.8 x 27.5 cm (13 11/16 x 10 13/16 in. ),"Gift of Howard Stein, 2010",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283713,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2010.513.3,false,true,283714,Photographs,Photograph,Rue du Chat-qui-Pêche (from the Rue de la Huchette),,,,,,Artist,,Charles Marville,"French, Paris 1813–1879 Paris",,"Marville, Charles",French,1813,1879,ca. 1868,1866,1870,Albumen silver print from glass negative,35.9 x 27.2 cm (14 1/8 x 10 11/16 in. ),"Gift of Howard Stein, 2010",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283714,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.205,false,true,286547,Photographs,Photograph,Le Chat Momifié (trouvé dans les fouilles de Saint-Germain-en-Laye),,,,,,Artist,,Charles Marville,"French, Paris 1813–1879 Paris",,"Marville, Charles",French,1813,1879,ca. 1862,1860,1864,Albumen silver print from glass negative,Mount: 19 13/16 × 9 15/16 in. (50.3 × 25.3 cm) Image: 11 1/2 × 11 1/4 in. (29.2 × 28.6 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286547,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.351,false,true,286546,Photographs,Photograph,Etude de ciel,,,,,,Artist,,Charles Marville,"French, Paris 1813–1879 Paris",,"Marville, Charles",French,1813,1879,1855–56,1855,1856,Salted paper print from glass negative,Image: 16 x 21 cm (6 5/16 x 8 1/4 in.) Mount: 25.9 x 31 cm (10 3/16 x 12 3/16 in.),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286546,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.353,false,true,286336,Photographs,Photograph,Etude de ciel,,,,,,Artist,,Charles Marville,"French, Paris 1813–1879 Paris",,"Marville, Charles",French,1813,1879,1855–56,1855,1856,Albumen silver print from glass negative,Image: 15.4 x 21 cm (6 1/16 x 8 1/4 in.) Mount: 31.1 x 43.5 cm (12 1/4 x 17 1/8 in.),"Gilman Collection, Purchase, Ann Tenenbaum and Thomas H. Lee Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286336,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.355,false,true,286338,Photographs,Photographs,"Rue du Contrat-Social, de la rue de la Tonnellerie",,,,,,Artist,,Charles Marville,"French, Paris 1813–1879 Paris",,"Marville, Charles",French,1813,1879,1864–1865,1864,1865,Albumen silver print from glass negative,Image: 22.5 x 37.1 cm (8 7/8 x 14 5/8 in.) Mount: 41.3 x 60.3 cm (16 1/4 x 23 3/4 in.),"Gilman Collection, Purchase, Mr. and Mrs. Henry R. Kravis Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286338,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.357,false,true,286340,Photographs,Photograph,"[Allegorical Sculpture of Industry, Pont du Carrousel]",,,,,,Artist,,Charles Marville,"French, Paris 1813–1879 Paris",,"Marville, Charles",French,1813,1879,1852,1852,1852,Salted paper print from paper negative,Image: 21.1 x 15.2 cm (8 5/16 x 6 in.) Mount: 42.7 x 27.8 cm (16 13/16 x 10 15/16 in.),"Gilman Collection, Purchase, Mr. and Mrs. Henry R. Kravis Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286340,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.358,false,true,285698,Photographs,Photograph,"Rue Estienne, de la rue Boucher",,,,,,Artist,,Charles Marville,"French, Paris 1813–1879 Paris",,"Marville, Charles",French,1813,1879,1862–65,1862,1865,Albumen silver print from glass negative,Image: 34.3 x 27.1 cm (13 1/2 x 10 11/16 in.) Mount: 23 11/16 × 16 5/16 in. (60.2 × 41.4 cm),"Gilman Collection, Purchase, Mr. and Mrs. Henry R. Kravis Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285698,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.362,false,true,285699,Photographs,Photograph,Impasse Briare (de la Cité Coquenard),,,,,,Artist,,Charles Marville,"French, Paris 1813–1879 Paris",,"Marville, Charles",French,1813,1879,1860s,1860,1869,Albumen silver print from glass negative,Image: 62.9 x 45.2 cm (24 3/4 x 17 13/16 in.) Mount: 24 13/16 × 17 11/16 in. (63 × 45 cm),"Gilman Collection, Purchase, Mr. and Mrs. Henry R. Kravis Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285699,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.378,false,true,286545,Photographs,Photograph,Cour Saint-Guillaume,,,,,,Artist,,Charles Marville,"French, Paris 1813–1879 Paris",,"Marville, Charles",French,1813,1879,ca. 1865,1863,1867,Albumen silver print from glass negative,Image: 34.2 x 27.2 cm (13 7/16 x 10 11/16 in.) Mount: 62.9 x 45.2 cm (24 3/4 x 17 13/16 in.),"Gilman Collection, Purchase, Alfred Stieglitz Society Gifts, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286545,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1990.1130,true,true,266284,Photographs,Photograph,"[Large Figures on the North Porch, Chartres Cathedral]",,,,,,Artist,,Henri-Jean-Louis Le Secq,"French, Paris 1818–1882 Paris",,"Le Secq, Henri-Jean-Louis",French,1818,1882,1852,1852,1852,Salted paper print from paper negative,32.8 x 22.1 cm (12 15/16 x 8 11/16 in.),"Purchase, The Howard Gilman Foundation and Harriette and Noel Levine Gifts, Samuel J. Wagstaff Jr. Bequest, and Rogers Fund, 1990",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/266284,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.35,false,true,283108,Photographs,Photograph,Wooden Staircase at Chartres,,,,,,Artist,,Henri-Jean-Louis Le Secq,"French, Paris 1818–1882 Paris",,"Le Secq, Henri-Jean-Louis",French,1818,1882,1852,1852,1852,Salted paper print from paper negative,Mount: 19 13/16 in. × 13 7/8 in. (50.3 × 35.2 cm) Image: 12 13/16 × 9 3/16 in. (32.6 × 23.4 cm),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283108,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -63.661.1,false,true,270873,Photographs,Carte-de-visite,Alphonse Karr,,,,,,Artist,,André-Adolphe-Eugène Disdéri,"French, Paris 1819–1889 Paris",,"Disdéri, André-Adolphe-Eugène",French,1819,1889,1850s,1850,1859,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1963",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/270873,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -47.149.40,false,true,269111,Photographs,Carte-de-visite,The Imperial Court of Napoleon III,,,,,,Artist,,André-Adolphe-Eugène Disdéri,"French, Paris 1819–1889 Paris",,"Disdéri, André-Adolphe-Eugène",French,1819,1889,"ca. 1866, printed 1940s",1861,1871,Gelatin silver print,Image: 12.8 x 8 cm (5 1/16 x 3 1/8 in.) Frame: 35.6 x 27.9 cm (14 x 11 in.),"David Hunter McAlpin Fund, 1947",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269111,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1995.170.1,false,true,267168,Photographs,Photograph,Prince Lobkowitz,,,,,,Artist,,André-Adolphe-Eugène Disdéri,"French, Paris 1819–1889 Paris",,"Disdéri, André-Adolphe-Eugène",French,1819,1889,1858,1858,1858,Albumen silver print from glass negative,20.0 x 23.2 cm (7 7/8 x 9 1/8 in.),"Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 1995",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267168,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.10,false,true,261269,Photographs,Photograph,La taille,,,,,,Artist,,André-Adolphe-Eugène Disdéri,"French, Paris 1819–1889 Paris",,"Disdéri, André-Adolphe-Eugène",French,1819,1889,1860s,1860,1869,Albumen silver print from glass negative,5.1 x 8.6 cm. (2 x 3 3/8 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261269,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.11,false,true,261280,Photographs,Photograph,Le deux roses,,,,,,Artist,,André-Adolphe-Eugène Disdéri,"French, Paris 1819–1889 Paris",,"Disdéri, André-Adolphe-Eugène",French,1819,1889,1860s,1860,1869,Albumen silver print from glass negative,5.1 x 8.6 cm. (2 x 3 3/8 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261280,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.12,false,true,261291,Photographs,Photograph,La robe écossaise,,,,,,Artist,,André-Adolphe-Eugène Disdéri,"French, Paris 1819–1889 Paris",,"Disdéri, André-Adolphe-Eugène",French,1819,1889,1860s,1860,1869,Albumen silver print from glass negative,5.1 x 8.6 cm. (2 x 3 3/8 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261291,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.242,false,true,286378,Photographs,Photographs,[Profile of the Prince Imperial],,,,,,Artist,,André-Adolphe-Eugène Disdéri,"French, Paris 1819–1889 Paris",,"Disdéri, André-Adolphe-Eugène",French,1819,1889,1863,1863,1863,Albumen silver print from glass negative,Image: 25.1 x 18.3 cm (9 7/8 x 7 3/16 in.) Mount: 28.2 x 20.5 cm (11 1/8 x 8 1/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286378,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.284,false,true,286594,Photographs,Photograph,The Juggler Manoel,,,,,,Artist,,André-Adolphe-Eugène Disdéri,"French, Paris 1819–1889 Paris",,"Disdéri, André-Adolphe-Eugène",French,1819,1889,1861,1861,1861,Albumen silver print from glass negative,Image: 19.9 × 23.2 cm (7 13/16 × 9 1/8 in.),"Gilman Collection, Purchase, Ann Tenenbaum and Thomas H. Lee Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286594,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1163,false,true,286389,Photographs,Photograph,Caroline Rosati,,,,,,Artist,,André-Adolphe-Eugène Disdéri,"French, Paris 1819–1889 Paris",,"Disdéri, André-Adolphe-Eugène",French,1819,1889,ca. 1860,1858,1862,Salted paper print from glass negative,Image: 29.8 × 23.6 cm (11 3/4 × 9 5/16 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286389,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.588.4,false,true,294508,Photographs,Photograph,Dulare,,,,,,Artist,,André-Adolphe-Eugène Disdéri,"French, Paris 1819–1889 Paris",,"Disdéri, André-Adolphe-Eugène",French,1819,1889,February 1865–75,1865,1875,Albumen silver print from glass negative,Image: 20.3 × 12.1 cm (8 × 4 3/4 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/294508,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.588.5,false,true,294509,Photographs,Photograph,Honoune Keller,,,,,,Artist,,André-Adolphe-Eugène Disdéri,"French, Paris 1819–1889 Paris",,"Disdéri, André-Adolphe-Eugène",French,1819,1889,1864–65,1864,1865,Albumen silver print from glass negative,Image: 19.4 × 12.5 cm (7 5/8 × 4 15/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/294509,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.588.6,false,true,294510,Photographs,Photograph,Louis Revoil,,,,,,Artist,,André-Adolphe-Eugène Disdéri,"French, Paris 1819–1889 Paris",,"Disdéri, André-Adolphe-Eugène",French,1819,1889,February 1865–75,1865,1875,Albumen silver print from glass negative,Image: 19.5 × 24.5 cm (7 11/16 × 9 5/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/294510,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.588.7,false,true,294511,Photographs,Photograph,"du Beaumenil, Forsyth",,,,,,Artist,,André-Adolphe-Eugène Disdéri,"French, Paris 1819–1889 Paris",,"Disdéri, André-Adolphe-Eugène",French,1819,1889,"October 1862–February 11, 1863",1862,1863,Albumen silver print from glass negative,Image: 19.3 × 23.6 cm (7 5/8 × 9 5/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/294511,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.588.8,false,true,294512,Photographs,Photograph,Berthe,,,,,,Artist,,André-Adolphe-Eugène Disdéri,"French, Paris 1819–1889 Paris",,"Disdéri, André-Adolphe-Eugène",French,1819,1889,"January 22, 1862– April 1862",1862,1862,Albumen silver print from glass negative,Image: 20.1 × 23.3 cm (7 15/16 × 9 3/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/294512,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.588.9,false,true,294513,Photographs,Photograph,Berthe,,,,,,Artist,,André-Adolphe-Eugène Disdéri,"French, Paris 1819–1889 Paris",,"Disdéri, André-Adolphe-Eugène",French,1819,1889,"January 22, 1862–April 1862",1862,1862,Albumen silver print from glass negative,Image: 18.5 × 22.7 cm (7 5/16 × 8 15/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/294513,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.588.10,false,true,294514,Photographs,Photograph; Carte-de-visite,Schneider,,,,,,Artist,,André-Adolphe-Eugène Disdéri,"French, Paris 1819–1889 Paris",,"Disdéri, André-Adolphe-Eugène",French,1819,1889,May–August 1863,1863,1863,Albumen silver print from glass negative,Image: 18.8 × 24.3 cm (7 3/8 × 9 9/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/294514,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.588.11,false,true,294515,Photographs,Photograph; Carte-de-visite,V Queniaus,,,,,,Artist,,André-Adolphe-Eugène Disdéri,"French, Paris 1819–1889 Paris",,"Disdéri, André-Adolphe-Eugène",French,1819,1889,May–June 1860,1860,1860,Albumen silver print from glass negative,Image: 19.9 × 23.1 cm (7 13/16 × 9 1/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/294515,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.588.12,false,true,294727,Photographs,Photograph; Carte-de-visite,Taglione,,,,,,Artist,,André-Adolphe-Eugène Disdéri,"French, Paris 1819–1889 Paris",,"Disdéri, André-Adolphe-Eugène",French,1819,1889,September 1857–November 1858,1857,1858,Albumen silver print from glass negative,Image: 19.9 × 23.3 cm (7 13/16 × 9 3/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/294727,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.588.13,false,true,294728,Photographs,Photograph; Carte-de-visite,Danvers,,,,,,Artist,,André-Adolphe-Eugène Disdéri,"French, Paris 1819–1889 Paris",,"Disdéri, André-Adolphe-Eugène",French,1819,1889,"February–July 14, 1864",1864,1864,Albumen silver print from glass negative,Image: 20 × 24.2 cm (7 7/8 × 9 1/2 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/294728,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.588.14,false,true,294729,Photographs,Photograph; Carte-de-visite,A Mlle Schlusser,,,,,,Artist,,André-Adolphe-Eugène Disdéri,"French, Paris 1819–1889 Paris",,"Disdéri, André-Adolphe-Eugène",French,1819,1889,May–August 1861,1861,1861,Albumen silver print from glass negative,Image: 19.9 × 23.7 cm (7 13/16 × 9 5/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/294729,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.588.2.2,false,true,306153,Photographs,Photograph,Louise Abigdon,,,,,,Artist,,André-Adolphe-Eugène Disdéri,"French, Paris 1819–1889 Paris",,"Disdéri, André-Adolphe-Eugène",French,1819,1889,1866,1866,1866,Albumen silver print from glass negative,"Image: 18.4 × 24.8 cm (7 1/4 in., 24.8 cm) Sheet: 26.2 × 34.8 cm (10 5/16 × 13 11/16 in.)","Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306153,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.588.3.5,false,true,306158,Photographs,Photograph,Finali,,,,,,Artist,,André-Adolphe-Eugène Disdéri,"French, Paris 1819–1889 Paris",,"Disdéri, André-Adolphe-Eugène",French,1819,1889,1866,1866,1866,Albumen silver print from glass negative,Image: 7 3/4 × 9 1/8 in. (19.7 × 23.1 cm) Album page: 10 3/8 × 13 3/4 in. (26.3 × 35 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306158,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.588.2.53,false,true,306154,Photographs,Photograph,Beresford,,,,,,Artist,,André-Adolphe-Eugène Disdéri,"French, Paris 1819–1889 Paris",,"Disdéri, André-Adolphe-Eugène",French,1819,1889,1867,1867,1867,Albumen silver print from glass negative,Image: 18.4 × 24.8 cm (7 1/4 × 9 3/4 in.) Album page: 26.2 × 34.8 cm (10 5/16 × 13 11/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306154,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.588.2.78,false,true,306155,Photographs,Photograph,Berthe,,,,,,Artist,,André-Adolphe-Eugène Disdéri,"French, Paris 1819–1889 Paris",,"Disdéri, André-Adolphe-Eugène",French,1819,1889,1862,1862,1862,Albumen silver print from glass negative,Image: 18.4 × 24.8 cm (7 1/4 × 9 3/4 in.) Album page: 26.2 × 34.8 cm (10 5/16 × 13 11/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306155,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.588.3.10,false,true,306159,Photographs,Photograph,Gabrielle; M. Gutierrez de Estrada,,,,,,Artist,,André-Adolphe-Eugène Disdéri,"French, Paris 1819–1889 Paris",,"Disdéri, André-Adolphe-Eugène",French,1819,1889,1867,1867,1867,Albumen silver print from glass negative,Image: 7 9/16 × 9 1/4 in. (19.2 × 23.5 cm) Sheet: 10 3/8 × 13 3/4 in. (26.3 × 35 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306159,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.588.3.23,false,true,306160,Photographs,Photograph,Héloise,,,,,,Artist,,André-Adolphe-Eugène Disdéri,"French, Paris 1819–1889 Paris",,"Disdéri, André-Adolphe-Eugène",French,1819,1889,1863,1863,1863,Albumen silver print from glass negative,Image: 7 9/16 × 9 1/4 in. (19.2 × 23.5 cm) Album page: 10 3/8 × 13 3/4 in. (26.3 × 35 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306160,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.588.3.35,false,true,306161,Photographs,Photograph,Valois,,,,,,Artist,,André-Adolphe-Eugène Disdéri,"French, Paris 1819–1889 Paris",,"Disdéri, André-Adolphe-Eugène",French,1819,1889,1866,1866,1866,Albumen silver print from glass negative,Image: 7 9/16 × 9 1/4 in. (19.2 × 23.5 cm) Album page: 10 3/8 × 13 3/4 in. (26.3 × 35 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306161,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.588.3.74,false,true,306162,Photographs,Photograph,Rosalie Léon,,,,,,Artist,,André-Adolphe-Eugène Disdéri,"French, Paris 1819–1889 Paris",,"Disdéri, André-Adolphe-Eugène",French,1819,1889,1866,1866,1866,Albumen silver print from glass negative,Image: 7 1/2 × 10 1/16 in. (19 × 25.6 cm) Album page: 10 3/8 × 13 3/4 in. (26.3 × 35 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306162,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.588.3.85,false,true,306163,Photographs,Photograph,Mangin,,,,,,Artist,,André-Adolphe-Eugène Disdéri,"French, Paris 1819–1889 Paris",,"Disdéri, André-Adolphe-Eugène",French,1819,1889,1861,1861,1861,Albumen silver print from glass negative,Image: 7 1/2 × 9 7/16 in. (19 × 24 cm) Album page: 10 3/8 × 13 3/4 in. (26.3 × 35 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306163,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2011.531.1–.164,false,true,299315,Photographs,Album,Costumes V,,,,,,Artist,,André-Adolphe-Eugène Disdéri,"French, Paris 1819–1889 Paris",,"Disdéri, André-Adolphe-Eugène",French,1819,1889,1863–67,1863,1867,Albumen silver prints from glass negatives,,"Museum Accession, transferred from the Costume Institute Library",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/299315,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.588.2.106,false,true,306156,Photographs,Photograph,Boudet,,,,,,Artist,,André-Adolphe-Eugène Disdéri,"French, Paris 1819–1889 Paris",,"Disdéri, André-Adolphe-Eugène",French,1819,1889,1866,1866,1866,Albumen silver print from glass negative,Image: 18.4 × 24.8 cm (7 1/4 × 9 3/4 in.) Album page: 26.2 × 34.8 cm (10 5/16 × 13 11/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306156,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.588.2.142,false,true,306157,Photographs,Photograph,Esther David,,,,,,Artist,,André-Adolphe-Eugène Disdéri,"French, Paris 1819–1889 Paris",,"Disdéri, André-Adolphe-Eugène",French,1819,1889,1866,1866,1866,Albumen silver print from glass negative,Image: 18.4 × 24.8 cm (7 1/4 × 9 3/4 in.) Album page: 26.2 × 34.8 cm (10 5/16 × 13 11/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306157,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.588.3.109,false,true,306164,Photographs,Photograph,Amélie and Elise Gitteri,,,,,,Artist,,André-Adolphe-Eugène Disdéri,"French, Paris 1819–1889 Paris",,"Disdéri, André-Adolphe-Eugène",French,1819,1889,1866,1866,1866,Albumen silver print from glass negative,Image: 7 1/2 × 9 7/16 in. (19 × 24 cm) Album page: 10 3/8 × 13 3/4 in. (26.3 × 35 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306164,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.588.3.110,false,true,306165,Photographs,Photograph,Clara Silvois,,,,,,Artist,,André-Adolphe-Eugène Disdéri,"French, Paris 1819–1889 Paris",,"Disdéri, André-Adolphe-Eugène",French,1819,1889,1861,1861,1861,Albumen silver print from glass negative,Image: 7 1/2 × 9 7/16 in. (19 × 24 cm) Album page: 10 3/8 × 13 3/4 in. (26.3 × 35 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306165,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.588.3.134,false,true,306166,Photographs,Photograph,A. Sardou,,,,,,Artist,,André-Adolphe-Eugène Disdéri,"French, Paris 1819–1889 Paris",,"Disdéri, André-Adolphe-Eugène",French,1819,1889,1867,1867,1867,Albumen silver print from glass negative,Image: 7 1/2 × 9 7/16 in. (19 × 24 cm) Album page: 10 3/8 × 13 3/4 in. (26.3 × 35 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306166,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.588.3.152,false,true,306167,Photographs,Photograph,Léontine Walter,,,,,,Artist,,André-Adolphe-Eugène Disdéri,"French, Paris 1819–1889 Paris",,"Disdéri, André-Adolphe-Eugène",French,1819,1889,1863,1863,1863,Albumen silver print from glass negative,Image: 7 1/2 × 9 1/4 in. (19 × 23.5 cm) Album page: 10 3/8 × 13 3/4 in. (26.3 × 35 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306167,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1993.233,false,true,266902,Photographs,Photograph,Gioacchino Rossini,,,,,,Artist,,Nadar,"French, Paris 1820–1910 Paris",,Nadar,French,1820,1910,March 1856,1856,1856,Salted paper print from glass negative,Image: 24.6 x 18.3 cm (9 11/16 x 7 3/16 in.),"Purchase, Marie-Thérèse and André Jammes and Annalee Newman Gifts, The Horace W. Goldsmith Foundation Gift through Joyce and Robert Menschel, and Nancy and Edwin Marks Gift, 1993",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/266902,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1991.1174,false,true,266459,Photographs,Photograph,[Standing Female Nude],,,,,,Artist,,Nadar,"French, Paris 1820–1910 Paris",,Nadar,French,1820,1910,1860–61,1860,1861,Salted paper print from glass negative,Image: 20.2 x 13.3 cm (7 15/16 x 5 1/4 in.),"Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 1991",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/266459,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1991.1198,true,true,266480,Photographs,Photograph,Eugène Pelletan,,,,,,Artist,,Nadar,"French, Paris 1820–1910 Paris",,Nadar,French,1820,1910,1855–59,1855,1859,Salted paper print from glass negative,23.5 x 17.6 cm (9 1/4 x 6 15/16 in.),"Purchase, The Howard Gilman Foundation Gift and Rogers Fund, 1991",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/266480,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1991.1199,false,true,266481,Photographs,Photograph,Pierre-Luc-Charles Cicéri,,,,,,Artist,,Nadar,"French, Paris 1820–1910 Paris",,Nadar,French,1820,1910,1855–60,1855,1860,Salted paper print from glass negative,23.5 x 18.7 cm. (9 1/4 x 7 3/8 in.),"Gift of Marie-Thérèse and André Jammes, in memory of Samuel J. Wagstaff Jr., 1991",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/266481,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -58.553.28,false,true,270013,Photographs,Cabinet card,Jean-François Millet,,,,,,Artist,,Nadar,"French, Paris 1820–1910 Paris",,Nadar,French,1820,1910,"1856–58, printed ca. 1900",1856,1858,Albumen silver print from glass negative,,"Gift of M. Knoedler & Co. Inc., 1958",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/270013,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2010.457.1,false,true,294431,Photographs,Photograph,Hermaphrodite,,,,,,Artist,,Nadar,"French, Paris 1820–1910 Paris",,Nadar,French,1820,1910,1860,1860,1860,Albumen silver print from glass negative,Image: 23.9 x 19.2 cm (9 7/16 x 7 9/16 in.),"Bequest of Robert Shapazian, 2010",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/294431,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.42,false,true,283116,Photographs,Photograph,"[Seated Model, Partially Draped]",,,,,,Artist,,Nadar,"French, Paris 1820–1910 Paris",,Nadar,French,1820,1910,1856–59,1856,1859,Salted paper print from glass negative,Image: 11.2 x 10.5 cm (4 7/16 x 4 1/8 in.) Mount: 12.2 x 10.8 cm (4 13/16 x 4 1/4 in.),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283116,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.159.46,false,true,306329,Photographs,Photograph,Jean-Francois Millet,,,,,,Artist,,Nadar,"French, Paris 1820–1910 Paris",,Nadar,French,1820,1910,1856–58,1856,1858,Salted paper print from paper negative,Mount: 15 7/8 in. × 11 5/8 in. (40.4 × 29.5 cm) Image: 10 9/16 × 8 3/8 in. (26.8 × 21.3 cm),"Bequest of Maurice B. Sendak, 2012",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306329,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.256,false,true,285631,Photographs,Photograph,Théophile Gautier,,,,,,Artist,,Nadar,"French, Paris 1820–1910 Paris",,Nadar,French,1820,1910,ca. 1856,1854,1858,Salted paper print from glass negative,Image: 24.8 x 19.7 cm (9 3/4 x 7 3/4 in.) oval Mount: 26.9 x 21.7 cm (10 9/16 x 8 9/16 in.),"Gilman Collection, Purchase, Harriette and Noel Levine Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285631,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.267,false,true,285630,Photographs,Photograph,[Self Portrait in American Indian Costume],,,,,,Artist,,Nadar,"French, Paris 1820–1910 Paris",,Nadar,French,1820,1910,1863,1863,1863,Salted paper print from glass negative,"Image: 22.1 x 13.2 cm (8 11/16 x 5 3/16 in.), arched, unevenly trimmed Mount: 30.8 x 21.3 cm (12 1/8 x 8 3/8 in.)","Gilman Collection, Purchase, Harriette and Noel Levine Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285630,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.313,false,true,286163,Photographs,Photograph,"[Nadar with His Wife, Ernestine, in a Balloon]",,,,,,Artist,,Nadar,"French, Paris 1820–1910 Paris",,Nadar,French,1820,1910,"ca. 1865, printed 1890s",1863,1867,Gelatin silver print from glass negative,Image: 9 x 7.8 cm (3 9/16 x 3 1/16 in.) Mount: 23 x 19.9 cm (9 1/16 x 7 13/16 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286163,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.723,false,true,286070,Photographs,Photograph,Jules Janin,,,,,,Artist,,Nadar,"French, Paris 1820–1910 Paris",,Nadar,French,1820,1910,ca. 1856,1854,1858,Albumen silver print from glass negative,Image: 9 1/4 × 7 3/8 in. (23.5 × 18.7 cm) Mount: 9 5/8 × 7 3/4 in. (24.5 × 19.7 cm),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286070,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.798,false,true,285632,Photographs,Photograph,[Paul Nadar at the Breast of His Wet Nurse],,,,,,Artist,,Nadar,"French, Paris 1820–1910 Paris",,Nadar,French,1820,1910,1856,1856,1856,Salted paper print from glass negative,Image: 10 15/16 × 8 7/8 in. (27.8 × 22.5 cm),"Gilman Collection, Purchase, Jennifer and Joseph Duke Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285632,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.813,false,true,286161,Photographs,Photograph,"Catacombs, Paris",,,,,,Artist,,Nadar,"French, Paris 1820–1910 Paris",,Nadar,French,1820,1910,April 1862,1862,1862,Albumen silver print from glass negative,Image: 23.7 x 18.6 cm (9 5/16 x 7 5/16 in.) Mount: 46 x 33.6 cm (18 1/8 x 13 1/4 in.),"Gilman Collection, Purchase, Denise and Andrew Saul Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286161,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1998.56,false,true,282191,Photographs,Photograph,Daniel Halévy,,,,,,Artist,,Edgar Degas,"French, Paris 1834–1917 Paris",,"Degas, Edgar",French,1834,1917,1895,1895,1895,Gelatin silver print from glass negative,40 x 28.7 cm (15 3/4 x 11 5/16 in.),"Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 1998",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/282191,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2004.335,false,true,285444,Photographs,Photograph,[Self-Portrait with Christine and Yvonne Lerolle],,,,,,Artist,,Edgar Degas,"French, Paris 1834–1917 Paris",,"Degas, Edgar",French,1834,1917,probably 1895–96,1895,1896,Gelatin silver print,Image: 37.1 x 29.3 cm (14 5/8 x 11 9/16 in.) Mount: 55.4 x 45.5 cm (21 13/16 x 17 15/16 in.),"Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel and Rogers Fund, 2004",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285444,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1983.1092,false,true,263608,Photographs,Photograph,"Paul Poujaud, Mme. Arthur Fontaine, and Degas",,,,,,Artist,,Edgar Degas,"French, Paris 1834–1917 Paris",,"Degas, Edgar",French,1834,1917,1895,1895,1895,Gelatin silver print,29.4 x 40.5 cm. (11 9/16 x 15 15/16 in.),"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1983",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/263608,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2000.655.1,false,true,284452,Photographs,Photograph,"Paule Gobillard, Jeannie Gobillard, Julie Manet, and Geneviève Mallarmé",,,,,,Artist,,Edgar Degas,"French, Paris 1834–1917 Paris",,"Degas, Edgar",French,1834,1917,1895,1895,1895,Gelatin silver print,28.4 x 38.9 cm (11 3/16 x 15 5/16 in.),"Gift of Paul F. Walter, 2000",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/284452,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2000.655.2,false,true,284453,Photographs,Photograph,"Street Scene, La-Queue-en-Brie (Val-de-Marne)",,,,,,Artist,,Edgar Degas,"French, Paris 1834–1917 Paris",,"Degas, Edgar",French,1834,1917,ca. 1896,1894,1898,Gelatin silver print,28.5 x 39.5 cm (11 1/4 x 15 9/16 in. ),"Gift of Paul F. Walter, 2000",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/284453,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2010.457.4a,false,true,294434,Photographs,Photograph,[Self-Portrait with Zoé Closier],,,,,,Artist,,Edgar Degas,"French, Paris 1834–1917 Paris",,"Degas, Edgar",French,1834,1917,probably 1895,1895,1895,Gelatin silver print,Image: 5.8 x 8.8 cm (2 5/16 x 3 7/16 in.) Mount: 15 x 12 cm (5 7/8 x 4 3/4 in.),"Bequest of Robert Shapazian, 2010",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/294434,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2010.457.4b,false,true,296287,Photographs,Photograph,[Self-Portrait in Library (Hand to Chin)],,,,,,Artist,,Edgar Degas,"French, Paris 1834–1917 Paris",,"Degas, Edgar",French,1834,1917,probably 1895,1895,1895,Gelatin silver print,Image: 5.8 x 8.7 cm (2 5/16 x 3 7/16 in.) Mount: 15 x 12 cm (5 7/8 x 4 3/4 in.),"Bequest of Robert Shapazian, 2010",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/296287,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1167,false,true,286039,Photographs,Photograph,Denise Zola,,,,,,Artist,,Émile Zola,"French, Paris 1840–1902 Paris",,"Zola, Émile",French,1840,1902,ca. 1900,1898,1902,Gelatin silver print,Image: 8 7/8 × 6 9/16 in. (22.5 × 16.6 cm) Mount: 9 7/16 × 7 1/16 in. (24 × 18 cm),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286039,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.799 (1),false,true,288397,Photographs,Photograph,Porta del Popolo,,,,,,Artist,,Eugène Constant,"French, active Italy, 1848–55",,"Constant, Eugène",French,1848,1855,1848–52,1848,1852,Albumen print from glass negative,Image: 8 11/16 × 11 5/16 in. (22 × 28.7 cm) Sheet: 12 1/8 × 18 1/2 in. (30.8 × 47 cm),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288397,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.799 (2),false,true,288398,Photographs,Photograph,Piazza del Popolo,,,,,,Artist,,Eugène Constant,"French, active Italy, 1848–55",,"Constant, Eugène",French,1848,1855,1848–52,1848,1852,Albumen print from glass negative,Image: 8 11/16 × 11 5/16 in. (22 × 28.7 cm) Sheet: 12 1/8 × 18 1/2 in. (30.8 × 47 cm),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288398,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.799 (3),false,true,288399,Photographs,Photograph,Villa Medici,,,,,,Artist,,Eugène Constant,"French, active Italy, 1848–55",,"Constant, Eugène",French,1848,1855,1848–52,1848,1852,Albumen print from glass negative,Image: 8 11/16 × 11 5/16 in. (22 × 28.7 cm) Sheet: 12 1/8 × 18 1/2 in. (30.8 × 47 cm),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288399,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.799 (4),false,true,288400,Photographs,Photograph,Fontana di Trevi,,,,,,Artist,,Eugène Constant,"French, active Italy, 1848–55",,"Constant, Eugène",French,1848,1855,1848–52,1848,1852,Albumen print from glass negative,Image: 11 1/4 × 8 9/16 in. (28.6 × 21.7 cm) Sheet: 18 1/2 × 12 1/8 in. (47 × 30.8 cm),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288400,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.799 (5),false,true,288401,Photographs,Photograph,Monte Cavallo,,,,,,Artist,,Eugène Constant,"French, active Italy, 1848–55",,"Constant, Eugène",French,1848,1855,1848–52,1848,1852,Albumen print from glass negative,Image: 8 11/16 × 11 5/16 in. (22 × 28.7 cm) Sheet: 12 1/8 × 18 1/2 in. (30.8 × 47 cm),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288401,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.799 (6),false,true,288402,Photographs,Photograph,S. Giovanni Laterano,,,,,,Artist,,Eugène Constant,"French, active Italy, 1848–55",,"Constant, Eugène",French,1848,1855,1848–52,1848,1852,Albumen print from glass negative,Image: 8 11/16 × 11 5/16 in. (22 × 28.7 cm) Sheet: 12 1/8 × 18 1/2 in. (30.8 × 47 cm),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288402,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.799 (7),false,true,288403,Photographs,Photograph,Panteon,,,,,,Artist,,Eugène Constant,"French, active Italy, 1848–55",,"Constant, Eugène",French,1848,1855,1848–52,1848,1852,Albumen print from glass negative,Image: 8 11/16 × 11 5/16 in. (22 × 28.7 cm) Sheet: 12 1/8 × 18 1/2 in. (30.8 × 47 cm),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288403,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.799 (8),false,true,288404,Photographs,Photograph,Ponte e Castel S. Angelo,,,,,,Artist,,Eugène Constant,"French, active Italy, 1848–55",,"Constant, Eugène",French,1848,1855,1848–52,1848,1852,Albumen print from glass negative,Image: 8 11/16 × 11 5/16 in. (22 × 28.7 cm) Sheet: 12 1/8 × 18 1/2 in. (30.8 × 47 cm),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288404,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.799 (9),false,true,288405,Photographs,Photograph,Veduta di Castel St Angelo. S. Pietro,,,,,,Artist,,Eugène Constant,"French, active Italy, 1848–55",,"Constant, Eugène",French,1848,1855,1848–52,1848,1852,Albumen print from glass negative,Image: 8 11/16 × 11 5/16 in. (22 × 28.7 cm) Sheet: 12 1/8 × 18 1/2 in. (30.8 × 47 cm),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288405,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.799 (10),false,true,288406,Photographs,Photograph,S. Pietro in Vaticano,,,,,,Artist,,Eugène Constant,"French, active Italy, 1848–55",,"Constant, Eugène",French,1848,1855,1848–52,1848,1852,Albumen print from glass negative,Image: 8 11/16 × 11 5/16 in. (22 × 28.7 cm) Sheet: 12 1/8 × 18 1/2 in. (30.8 × 47 cm),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288406,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.799 (11),false,true,288407,Photographs,Photograph,Foro e Colonna di Trajano,,,,,,Artist,,Eugène Constant,"French, active Italy, 1848–55",,"Constant, Eugène",French,1848,1855,1848–52,1848,1852,Albumen print from glass negative,Image: 11 5/16 × 8 11/16 in. (28.8 × 22 cm) Sheet: 18 1/2 × 12 1/8 in. (47 × 30.8 cm),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288407,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.799 (12),false,true,288408,Photographs,Photograph,Tempio di Vesta,,,,,,Artist,,Eugène Constant,"French, active Italy, 1848–55",,"Constant, Eugène",French,1848,1855,1848–52,1848,1852,Albumen print from glass negative,Image: 6 5/16 × 8 7/16 in. (16 × 21.5 cm) Sheet: 12 1/8 × 18 1/2 in. (30.8 × 47 cm),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288408,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.799 (13),false,true,288409,Photographs,Photograph,Fontana delle Tartarughe,,,,,,Artist,,Eugène Constant,"French, active Italy, 1848–55",,"Constant, Eugène",French,1848,1855,1848–52,1848,1852,Albumen print from glass negative,Image: 11 5/16 × 8 11/16 in. (28.8 × 22 cm) Sheet: 18 1/2 × 12 1/8 in. (47 × 30.8 cm),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288409,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.799 (14),false,true,288410,Photographs,Photograph,"L'arco degli argentari, adossato alla chiesa di San Giorgio al Velabro",,,,,,Artist,,Eugène Constant,"French, active Italy, 1848–55",,"Constant, Eugène",French,1848,1855,1848–52,1848,1852,Albumen print from glass negative,Image: 11 5/16 × 8 11/16 in. (28.8 × 22 cm) Sheet: 18 1/2 × 12 1/8 in. (47 × 30.8 cm),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288410,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.799 (15),false,true,288411,Photographs,Photograph,Uno dei Colossi di Campedoglio,,,,,,Artist,,Eugène Constant,"French, active Italy, 1848–55",,"Constant, Eugène",French,1848,1855,1848–52,1848,1852,Albumen print from glass negative,Image: 11 5/16 × 8 11/16 in. (28.8 × 22 cm) Sheet: 18 1/2 × 12 1/8 in. (47 × 30.8 cm),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288411,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.799 (16),false,true,288412,Photographs,Photograph,Campedoglio,,,,,,Artist,,Eugène Constant,"French, active Italy, 1848–55",,"Constant, Eugène",French,1848,1855,1848–52,1848,1852,Albumen print from glass negative,Image: 8 11/16 × 11 5/16 in. (22 × 28.7 cm) Sheet: 12 1/8 × 18 1/2 in. (30.8 × 47 cm),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288412,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.799 (17),false,true,288413,Photographs,Photograph,Arco di Settimio Severo,,,,,,Artist,,Eugène Constant,"French, active Italy, 1848–55",,"Constant, Eugène",French,1848,1855,1848–52,1848,1852,Albumen print from glass negative,Image: 8 11/16 × 11 5/16 in. (22 × 28.7 cm) Sheet: 12 1/8 × 18 1/2 in. (30.8 × 47 cm),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288413,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.799 (18),false,true,288414,Photographs,Photograph,Veduta del Foro Romano,,,,,,Artist,,Eugène Constant,"French, active Italy, 1848–55",,"Constant, Eugène",French,1848,1855,1848–52,1848,1852,Albumen print from glass negative,Image: 8 11/16 × 11 5/16 in. (22 × 28.7 cm) Sheet: 12 1/8 × 18 1/2 in. (30.8 × 47 cm),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288414,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.799 (19),false,true,288415,Photographs,Photograph,Campo Vaccino (Foro Romano),,,,,,Artist,,Eugène Constant,"French, active Italy, 1848–55",,"Constant, Eugène",French,1848,1855,1848–52,1848,1852,Albumen print from glass negative,Image: 8 1/16 × 11 3/16 in. (20.5 × 28.4 cm) Sheet: 12 1/8 × 18 1/2 in. (30.8 × 47 cm),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288415,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.799 (20),false,true,288416,Photographs,Photograph,Arco di Giano,,,,,,Artist,,Eugène Constant,"French, active Italy, 1848–55",,"Constant, Eugène",French,1848,1855,1848–52,1848,1852,Albumen print from glass negative,Image: 8 11/16 × 11 5/16 in. (22 × 28.7 cm) Sheet: 12 1/8 × 18 1/2 in. (30.8 × 47 cm),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288416,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.799 (21),false,true,287890,Photographs,Photograph,Tempo della Fortuna Virile,,,,,,Artist,,Eugène Constant,"French, active Italy, 1848–55",,"Constant, Eugène",French,1848,1855,1848–52,1848,1852,Albumen print from glass negative,Image: 8 11/16 × 11 5/16 in. (22 × 28.7 cm) Sheet: 12 1/8 × 18 1/2 in. (30.8 × 47 cm),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287890,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.799 (22),false,true,288417,Photographs,Photograph,Tempio della Concordia,,,,,,Artist,,Eugène Constant,"French, active Italy, 1848–55",,"Constant, Eugène",French,1848,1855,1848–52,1848,1852,Albumen print from glass negative,Image: 8 11/16 × 11 5/16 in. (22 × 28.7 cm) Sheet: 12 1/8 × 18 1/2 in. (30.8 × 47 cm),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288417,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.799 (23),false,true,288418,Photographs,Photograph,Rovine del Foro,,,,,,Artist,,Eugène Constant,"French, active Italy, 1848–55",,"Constant, Eugène",French,1848,1855,1848–52,1848,1852,Albumen print from glass negative,Image: 11 5/16 × 8 11/16 in. (28.7 × 22 cm) Sheet: 18 1/2 × 12 1/8 in. (47 × 30.8 cm),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288418,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.799 (24),false,true,288419,Photographs,Photograph,Tempio di Antonino e Faustina,,,,,,Artist,,Eugène Constant,"French, active Italy, 1848–55",,"Constant, Eugène",French,1848,1855,1848–52,1848,1852,Albumen print from glass negative,Image: 11 5/16 × 8 11/16 in. (28.7 × 22 cm) Sheet: 18 1/2 × 12 1/8 in. (47 × 30.8 cm),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288419,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.799 (25),false,true,288420,Photographs,Photograph,Tempio della Pace,,,,,,Artist,,Eugène Constant,"French, active Italy, 1848–55",,"Constant, Eugène",French,1848,1855,1848–52,1848,1852,Albumen print from glass negative,Image: 8 11/16 × 11 5/16 in. (22 × 28.7 cm) Sheet: 12 1/8 × 18 1/2 in. (30.8 × 47 cm),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288420,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.799 (26),false,true,288421,Photographs,Photograph,Arco di Tito,,,,,,Artist,,Eugène Constant,"French, active Italy, 1848–55",,"Constant, Eugène",French,1848,1855,1848–52,1848,1852,Album print from glass negative,Image: 6 7/16 × 8 7/16 in. (16.4 × 21.5 cm) Sheet: 12 1/8 × 18 1/2 in. (30.8 × 47 cm),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288421,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.799 (27),false,true,288422,Photographs,Photograph,Bassorilievo dell'arco di Tito,,,,,,Artist,,Eugène Constant,"French, active Italy, 1848–55",,"Constant, Eugène",French,1848,1855,1848–52,1848,1852,Albumen print from glass negative,Image: 11 5/16 × 8 5/8 in. (28.8 × 21.9 cm) Sheet: 18 1/2 × 12 1/8 in. (47 × 30.8 cm),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288422,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.799 (28),false,true,288423,Photographs,Photograph,Bassorilievo dell'arco di Tito,,,,,,Artist,,Eugène Constant,"French, active Italy, 1848–55",,"Constant, Eugène",French,1848,1855,1848–52,1848,1852,Albumen print from glass negative,Image: 11 5/16 × 8 5/8 in. (28.8 × 21.9 cm) Sheet: 18 1/2 × 12 1/8 in. (47 × 30.8 cm),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288423,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.799 (29),false,true,288424,Photographs,Photograph,Colosseo (Anfiteatro di Flavio),,,,,,Artist,,Eugène Constant,"French, active Italy, 1848–55",,"Constant, Eugène",French,1848,1855,1848–52,1848,1852,Salted paper print from glass negative,Image: 6 7/16 × 8 11/16 in. (16.3 × 22 cm) Sheet: 12 1/8 × 18 1/2 in. (30.8 × 47 cm),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288424,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.799 (30),false,true,288425,Photographs,Photograph,Arco di Constantino,,,,,,Artist,,Eugène Constant,"French, active Italy, 1848–55",,"Constant, Eugène",French,1848,1855,1848–52,1848,1852,Albumen print from glass negative,Image: 8 11/16 × 11 5/16 in. (22.1 × 28.7 cm) Sheet: 12 1/8 × 18 1/2 in. (30.8 × 47 cm),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288425,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2011.315,false,true,294767,Photographs,Photograph,Ancienne Ferme à Pérenchies,,,,,,Artist,,Alphonse Le Blondel,"French, Bréhal 1814–1875 Lille",,"Le Blondel, Alphonse",French,1814,1875,1854,1854,1854,Salted paper print from paper negative,Image: 23.5 x 29.4 cm (9 1/4 x 11 9/16 in.) Mount: 26 x 37.4 cm (10 1/4 x 14 3/4 in.),"Purchase, Alfred Stieglitz Society Gifts, 2011",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/294767,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.31,false,true,283105,Photographs,Photograph,[Postmortem],,,,,,Artist,,Alphonse Le Blondel,"French, Bréhal 1814–1875 Lille",,"Le Blondel, Alphonse",French,1814,1875,ca. 1850,1848,1852,Daguerreotype,Image (visible): 3 1/2 × 4 11/16 in. (8.9 × 11.9 cm) Overall: 6 11/16 × 8 1/4 in. (17 × 21 cm),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283105,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -58.648.3a,false,true,270074,Photographs,Photograph,Etienne Carjat,,,,,,Artist,,Étienne Carjat,"French, Fareins 1828–1906 Paris",,"Carjat, Étienne",French,1828,1906,1870s,1870,1879,Albumen silver print,,"Gift of Helen and Janos Scholz, 1958",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/270074,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.633.1,false,true,261621,Photographs,Photograph,Jean Baptiste Camille Corot,,,,,,Artist,,Étienne Carjat,"French, Fareins 1828–1906 Paris",,"Carjat, Étienne",French,1828,1906,1870,1870,1870,Albumen silver print,,"Gift of M. Roy Fisher, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261621,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2012.446.2,false,true,420964,Photographs,Print,"Henry Monnier, from Galerie contemporaine, littéraire, artistique",,,,,,Artist,,Étienne Carjat,"French, Fareins 1828–1906 Paris",,"Carjat, Étienne",French,1828,1906,1876–1881,1876,1881,Woodburytype,9 1/2 x 7 1/2 in. (24.1 x 19.1 cm),"Gift of Eric G. Carlson, in honor of Patricia Mainardi, 2012",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/420964,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.41,false,true,283115,Photographs,Photograph,[Seated Female Nude],,,,,,Artist,,Eugène Durieu,"French, Nîmes 1800–1874 Geneva",,"Durieu, Eugène",French,1800,1874,1853–54,1853,1854,Albumen silver print from glass negative,Image: 6 13/16 × 4 11/16 in. (17.3 × 11.9 cm) Mount: 13 13/16 × 10 9/16 in. (35.1 × 26.9 cm),"Gilman Collection, Purchase, Mr. and Mrs. Henry R. Kravis Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283115,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.569,false,true,286255,Photographs,Photograph,[Nude Study],,,,,,Artist,,Eugène Durieu,"French, Nîmes 1800–1874 Geneva",,"Durieu, Eugène",French,1800,1874,1853–54,1853,1854,Albumen silver print from glass negative,Image: 20.5 x 12.8 cm (8 1/16 x 5 1/16 in.) Mount: 32.3 x 27 cm (12 11/16 x 10 5/8 in.),"Gilman Collection, Purchase, Mr. and Mrs. Henry R. Kravis Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286255,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.724,false,true,286140,Photographs,Photograph,[Nude],,,,,,Artist,Possibly by,Eugène Durieu,"French, Nîmes 1800–1874 Geneva",,"Durieu, Eugène",French,1800,1874,ca. 1851,1849,1853,Salted paper print from paper negative,4 5/16 x 6 1/8,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286140,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.254,false,true,286667,Photographs,Photograph,"Wassileh and Lhedeh, Ghawagea",,,,,,Artist,,Ernest Benecke,"German, born England, 1817–1894",,"Benecke, Ernest",French,1817,1894,1852,1852,1852,Salted paper print from paper negative,Mount: 17 5/8 in. × 12 7/16 in. (44.8 × 31.6 cm) Image: 8 9/16 × 6 7/8 in. (21.8 × 17.4 cm),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286667,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.257,false,true,285462,Photographs,Photograph,"[Children from the Village of Kalabshah, Nubia]",,,,,,Artist,,Ernest Benecke,"German, born England, 1817–1894",,"Benecke, Ernest",French,1817,1894,1852,1852,1852,Salted paper print from paper negative,Image: 17 x 21.5 cm (6 11/16 x 8 7/16 in.),"Gilman Collection, Purchase, Alfred Stieglitz Society Gifts, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285462,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.262,false,true,286501,Photographs,Photograph,"Autopsy of the First Crocodile Onboard, Upper Egypt",,,,,,Artist,,Ernest Benecke,"German, born England, 1817–1894",,"Benecke, Ernest",French,1817,1894,1852,1852,1852,Salted paper print from paper negative,Image: 17.5 x 21.3 cm (6 7/8 x 8 3/8 in.) Mount: 31.5 x 45.1 cm (12 3/8 x 17 3/4 in.),"Gilman Collection, Purchase, William Talbott Hillman Foundation Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286501,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.318,false,true,286268,Photographs,Photograph,[Two Women],,,,,,Artist,,Ernest Benecke,"German, born England, 1817–1894",,"Benecke, Ernest",French,1817,1894,1852,1852,1852,Salted paper print from paper negative,Image: 8 1/4 × 6 1/8 in. (21 × 15.6 cm) Mount: 17 1/16 × 12 3/16 in. (43.4 × 30.9 cm),"Gilman Collection, Purchase, Sam Salz Foundation Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286268,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.319,false,true,286666,Photographs,Photograph,Egyptian Musicians (Rawabí) and Almée,,,,,,Artist,,Ernest Benecke,"German, born England, 1817–1894",,"Benecke, Ernest",French,1817,1894,"February 20, 1852",1852,1852,Salted paper print from paper negative,Image: 6 9/16 × 8 1/8 in. (16.7 × 20.6 cm) Mount: 12 3/8 × 17 5/8 in. (31.4 × 44.8 cm),"Gilman Collection, Purchase, Joseph M. Cohen Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286666,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.320,false,true,286502,Photographs,Photograph,Abu Nabut and Negro Slaves in Cairo,,,,,,Artist,,Ernest Benecke,"German, born England, 1817–1894",,"Benecke, Ernest",French,1817,1894,"April 22, 1852",1852,1852,Salted paper print from paper negative,Image: 21.8 x 17.2 cm (8 9/16 x 6 3/4 in.) Mount: 44.6 x 31.7 cm (17 9/16 x 12 1/2 in.),"Gilman Collection, Purchase, Ann Tenenbaum and Thomas H. Lee Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286502,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -62.548,false,true,270485,Photographs,Photograph,"Shop front of ""Courone d'or,"" Quai Bourbon",,,,,,Artist,,Eugène Atget,"French, Libourne 1857–1927 Paris",,"Atget, Eugène",French,1857-02-02,1927-08-04,1922,1922,1922,Gelatin silver print,Image: 17.7 x 22.4 cm (6 15/16 x 8 13/16 in.),"David Hunter McAlpin Fund, 1962",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/270485,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1994.271,true,true,267042,Photographs,Photograph,Avenue des Gobelins,,,,,,Artist,,Eugène Atget,"French, Libourne 1857–1927 Paris",,"Atget, Eugène",French,1857-02-02,1927-08-04,1927,1927,1927,Gelatin silver print from glass negative,36.8 x 28.6 cm (14 1/2 x 11 1/4 in.),"Purchase, Rogers Fund, and Joyce and Robert Menschel and Harriette and Noel Levine Gifts, 1994",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267042,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -56.610.1,false,true,269874,Photographs,Photograph,St. Cloud,,,,,,Artist,,Eugène Atget,"French, Libourne 1857–1927 Paris",,"Atget, Eugène",French,1857-02-02,1927-08-04,"1915–19, printed 1956",1915,1919,Gelatin silver print,,"David Hunter McAlpin Fund, 1956",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269874,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -56.610.2,false,true,269885,Photographs,Photograph,Nenuphars,,,,,,Artist,,Eugène Atget,"French, Libourne 1857–1927 Paris",,"Atget, Eugène",French,1857-02-02,1927-08-04,"1890s–1920s, printed 1956",1890,1929,Gelatin silver print,,"David Hunter McAlpin Fund, 1956",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269885,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -56.610.3,false,true,269887,Photographs,Photograph,Men's Fashions,,,,,,Artist,,Eugène Atget,"French, Libourne 1857–1927 Paris",,"Atget, Eugène",French,1857-02-02,1927-08-04,"1925, printed 1956",1925,1925,Gelatin silver print,,"David Hunter McAlpin Fund, 1956",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269887,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -56.610.4,false,true,269888,Photographs,Photograph,Eclipse,,,,,,Artist,,Eugène Atget,"French, Libourne 1857–1927 Paris",,"Atget, Eugène",French,1857-02-02,1927-08-04,"1911, printed 1956",1911,1911,Gelatin silver print,,"David Hunter McAlpin Fund, 1956",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269888,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -56.610.5,false,true,269889,Photographs,Photograph,Paris Interior,,,,,,Artist,,Eugène Atget,"French, Libourne 1857–1927 Paris",,"Atget, Eugène",French,1857-02-02,1927-08-04,"ca. 1910, printed 1956",1908,1912,Gelatin silver print,,"David Hunter McAlpin Fund, 1956",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269889,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -56.610.6,false,true,269890,Photographs,Photograph,Pompe Funebre (1e Classe),,,,,,Artist,,Eugène Atget,"French, Libourne 1857–1927 Paris",,"Atget, Eugène",French,1857-02-02,1927-08-04,"1910, printed 1956",1910,1910,Gelatin silver print,,"David Hunter McAlpin Fund, 1956",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269890,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -56.610.7,false,true,269891,Photographs,Photograph,Carrousel,,,,,,Artist,,Eugène Atget,"French, Libourne 1857–1927 Paris",,"Atget, Eugène",French,1857-02-02,1927-08-04,"1923, printed 1956",1923,1923,Gelatin silver print,,"David Hunter McAlpin Fund, 1956",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269891,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -56.610.8,false,true,269892,Photographs,Photograph,Marchand Abat-Jours,,,,,,Artist,,Eugène Atget,"French, Libourne 1857–1927 Paris",,"Atget, Eugène",French,1857-02-02,1927-08-04,"1899–1900, printed 1956",1899,1900,Gelatin silver print,,"David Hunter McAlpin Fund, 1956",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269892,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -56.610.9,false,true,269893,Photographs,Photograph,"Rue St. Rustique, Montmartre",,,,,,Artist,,Eugène Atget,"French, Libourne 1857–1927 Paris",,"Atget, Eugène",French,1857-02-02,1927-08-04,"1922, printed 1956",1922,1922,Gelatin silver print,,"David Hunter McAlpin Fund, 1956",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269893,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1202,false,true,265031,Photographs,Photograph,"A la Biche, rue Geoffrey Hilaire",,,,,,Artist,,Eugène Atget,"French, Libourne 1857–1927 Paris",,"Atget, Eugène",French,1857-02-02,1927-08-04,1922,1922,1922,Matte albumen silver print,,"Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 1986",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/265031,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.1031,false,true,266094,Photographs,Photograph,"Versailles, La Terre par Massou",,,,,,Artist,,Eugène Atget,"French, Libourne 1857–1927 Paris",,"Atget, Eugène",French,1857-02-02,1927-08-04,1922–23,1922,1923,Albumen silver print from glass negative,21.7 x 17.6 cm. (8 9/16 x 6 15/16 in.),"Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 1989",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/266094,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1991.1233,true,true,266538,Photographs,Photograph,"15, rue Maître-Albert",,,,,,Artist,,Eugène Atget,"French, Libourne 1857–1927 Paris",,"Atget, Eugène",French,1857-02-02,1927-08-04,1912,1912,1912,Gelatin silver print from glass negative,23.2 x 17.6 cm (9 1/8 x 6 15/16 in.),"Rogers Fund, 1991",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/266538,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1992.5116,false,true,266754,Photographs,Photograph,"Hôtel de Lauzun, Quai d'Anjou",,,,,,Artist,,Eugène Atget,"French, Libourne 1857–1927 Paris",,"Atget, Eugène",French,1857-02-02,1927-08-04,1904–05,1904,1905,Albumen silver print from glass negative,21.6 x 17.8 cm. (8 1/2 x 7 in.),"Gift of Virginia M. Zabriskie, 1992",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/266754,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1992.5152,true,true,266853,Photographs,Photograph,"Versailles, France",,,,,,Artist,,Eugène Atget,"French, Libourne 1857–1927 Paris",,"Atget, Eugène",French,1857-02-02,1927-08-04,1923,1923,1923,Albumen silver print from glass negative,17.8 x 21.9 cm (7 x 8 5/8 in.),"The Samuel J. Wagstaff Jr. Memorial and David Hunter McAlpin Funds, The Horace W. Goldsmith Foundation Gift through Joyce and Robert Menschel, and Paul F. Walter, and Mr. and Mrs. John Walsh Gifts, 1992",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/266853,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -56.610.10,false,true,269875,Photographs,Photograph,Maison Close,,,,,,Artist,,Eugène Atget,"French, Libourne 1857–1927 Paris",,"Atget, Eugène",French,1857-02-02,1927-08-04,"1921, printed 1956",1921,1921,Gelatin silver print,,"David Hunter McAlpin Fund, 1956",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269875,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -56.610.11,false,true,269876,Photographs,Photograph,Bar de Cabaret,,,,,,Artist,,Eugène Atget,"French, Libourne 1857–1927 Paris",,"Atget, Eugène",French,1857-02-02,1927-08-04,"1900–1911, printed 1956",1900,1911,Gelatin silver print,,"David Hunter McAlpin Fund, 1956",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269876,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -56.610.12,false,true,269877,Photographs,Photograph,Street Paver,,,,,,Artist,,Eugène Atget,"French, Libourne 1857–1927 Paris",,"Atget, Eugène",French,1857-02-02,1927-08-04,"1899–1900, printed 1956",1899,1900,Gelatin silver print,,"David Hunter McAlpin Fund, 1956",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269877,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -56.610.13,false,true,269878,Photographs,Photograph,"Cour, rue de Valence",,,,,,Artist,,Eugène Atget,"French, Libourne 1857–1927 Paris",,"Atget, Eugène",French,1857-02-02,1927-08-04,"1922, printed 1956",1922,1922,Gelatin silver print,,"David Hunter McAlpin Fund, 1956",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269878,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -56.610.14,false,true,269879,Photographs,Photograph,Ragpickers' Hut,,,,,,Artist,,Eugène Atget,"French, Libourne 1857–1927 Paris",,"Atget, Eugène",French,1857-02-02,1927-08-04,"1910, printed 1956",1910,1910,Gelatin silver print,,"David Hunter McAlpin Fund, 1956",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269879,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -56.610.15,false,true,269880,Photographs,Photograph,Mannequin,,,,,,Artist,,Eugène Atget,"French, Libourne 1857–1927 Paris",,"Atget, Eugène",French,1857-02-02,1927-08-04,"1926–27, printed 1956",1926,1927,Gelatin silver print,,"David Hunter McAlpin Fund, 1956",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269880,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -56.610.16,false,true,269881,Photographs,Photograph,Street Musicians,,,,,,Artist,,Eugène Atget,"French, Libourne 1857–1927 Paris",,"Atget, Eugène",French,1857-02-02,1927-08-04,"1898–99, printed 1956",1898,1899,Gelatin silver print,,"David Hunter McAlpin Fund, 1956",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269881,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -56.610.17,false,true,269882,Photographs,Photograph,"Boucherie, Rue Christine",,,,,,Artist,,Eugène Atget,"French, Libourne 1857–1927 Paris",,"Atget, Eugène",French,1857-02-02,1927-08-04,"1920s, printed 1956",1900,1929,Gelatin silver print,,"David Hunter McAlpin Fund, 1956",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269882,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -56.610.18,false,true,269883,Photographs,Photograph,"Faucheurs, Somme",,,,,,Artist,,Eugène Atget,"French, Libourne 1857–1927 Paris",,"Atget, Eugène",French,1857-02-02,1927-08-04,"1890–98, printed 1956",1890,1898,Gelatin silver print,,"David Hunter McAlpin Fund, 1956",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269883,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -56.610.19,false,true,269884,Photographs,Photograph,Environs of Paris,,,,,,Artist,,Eugène Atget,"French, Libourne 1857–1927 Paris",,"Atget, Eugène",French,1857-02-02,1927-08-04,"1920s, printed 1956",1920,1929,Gelatin silver print,,"David Hunter McAlpin Fund, 1956",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269884,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -56.610.20,false,true,269886,Photographs,Photograph,Masque Antique,,,,,,Artist,,Eugène Atget,"French, Libourne 1857–1927 Paris",,"Atget, Eugène",French,1857-02-02,1927-08-04,"1890s–1920s, printed 1956",1890,1929,Gelatin silver print,,"David Hunter McAlpin Fund, 1956",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269886,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.594.1,false,true,259729,Photographs,Photograph,St. Cloud near Paris,,,,,,Artist,,Eugène Atget,"French, Libourne 1857–1927 Paris",,"Atget, Eugène",French,1857-02-02,1927-08-04,1923,1923,1923,Gelatin silver print,Image: 18.2 x 22.2 cm (7 3/16 x 8 3/4 in.),"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, by exchange, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/259729,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.594.2,false,true,259730,Photographs,Photograph,"Rue Laplace and Rue Valette, Paris",,,,,,Artist,,Eugène Atget,"French, Libourne 1857–1927 Paris",,"Atget, Eugène",French,1857-02-02,1927-08-04,1926,1926,1926,Gelatin silver print,Image: 22 x 17.6 cm (8 11/16 x 6 15/16 in.),"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, by exchange, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/259730,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.645.1,false,true,261632,Photographs,Photograph,"Coin rue du Cimitière, Saint-Benoît",,,,,,Artist,,Eugène Atget,"French, Libourne 1857–1927 Paris",,"Atget, Eugène",French,1857-02-02,1927-08-04,1923,1923,1923,Gelatin silver print,,"Gift of A. Hyatt Mayor, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261632,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1997.398.2,false,true,282111,Photographs,Photograph,"Hotel de Sens, rue de l'Hôtel de Ville, Paris",,,,,,Artist,,Eugène Atget,"French, Libourne 1857–1927 Paris",,"Atget, Eugène",French,1857-02-02,1927-08-04,early 1900s,1900,1909,Albumen silver print from glass negative,16.7 x 20.5 cm (6 9/16 x 8 1/16 in. ),"The Rubel Collection, Gift of William Rubel, 1997",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/282111,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1990.1026.1,false,true,266231,Photographs,Photograph,Intérieur Rue de Vaugirard,,,,,,Artist,,Eugène Atget,"French, Libourne 1857–1927 Paris",,"Atget, Eugène",French,1857-02-02,1927-08-04,1910,1910,1910,Albumen silver print from glass negative,22.0 x 17.9 cm. (8 11/16 x 7 1/16 in.),"Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 1990",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/266231,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1990.1026.2,false,true,266232,Photographs,Photograph,Cuisine,,,,,,Artist,,Eugène Atget,"French, Libourne 1857–1927 Paris",,"Atget, Eugène",French,1857-02-02,1927-08-04,ca. 1910,1908,1912,Albumen silver print from glass negative,21.4 x 17.2 cm. (8 7/16 x 6 3/4 in.),"Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 1990",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/266232,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1990.1026.3,false,true,266233,Photographs,Photograph,[Atget's Work Room with Contact Printing Frames],,,,,,Artist,,Eugène Atget,"French, Libourne 1857–1927 Paris",,"Atget, Eugène",French,1857-02-02,1927-08-04,ca. 1910,1908,1912,Albumen silver print from glass negative,20.9 x 17.3 cm. (8 1/4 x 6 13/16 in.),"Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 1990",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/266233,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.129,false,true,283261,Photographs,Photograph,Organ-grinder,,,,,,Artist,,Eugène Atget,"French, Libourne 1857–1927 Paris",,"Atget, Eugène",French,1857-02-02,1927-08-04,1898–99,1898,1899,Matte albumen silver print from glass negative,Image: 22.4 × 17.6 cm (22.4 × 17.6 cm) Sheet: 22.5 × 18 cm (8 7/8 × 7 1/16 in.),"Gilman Collection, Purchase, Ann Tenenbaum and Thomas H. Lee Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283261,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.132,false,true,283264,Photographs,Photograph,"Versailles, The Orangerie Staircase",,,,,,Artist,,Eugène Atget,"French, Libourne 1857–1927 Paris",,"Atget, Eugène",French,1857-02-02,1927-08-04,1901,1901,1901,Albumen silver print from glass negative,Image: 16.9 x 21.7cm (6 5/8 x 8 9/16in.) Mount: 32.8 x 25.3 cm (12 15/16 x 9 15/16 in.),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283264,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.133,false,true,283265,Photographs,Photograph,"Café, Avenue de la Grande-Armée",,,,,,Artist,,Eugène Atget,"French, Libourne 1857–1927 Paris",,"Atget, Eugène",French,1857-02-02,1927-08-04,1924–25,1924,1925,Matte albumen silver print from glass negative,17.6 × 22.7 cm (6 15/16 × 8 15/16 in.) Sheet: 17.9 × 22.8 cm (17.9 × 22.8 cm),"Gilman Collection, Purchase, Ann Tenenbaum and Thomas H. Lee Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283265,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.134,false,true,283266,Photographs,Photograph,Rue Asselin,,,,,,Artist,,Eugène Atget,"French, Libourne 1857–1927 Paris",,"Atget, Eugène",French,1857-02-02,1927-08-04,1924–25,1924,1925,Matte albumen silver print from glass negative,22.9 x 17.6cm (9 x 6 15/16in.) Sheet: 23.1 × 18 cm (9 1/8 × 7 1/16 in.),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283266,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.344,false,true,286477,Photographs,Photograph,Boutique Fleurs,,,,,,Artist,,Eugène Atget,"French, Libourne 1857–1927 Paris",,"Atget, Eugène",French,1857-02-02,1927-08-04,1925,1924,1924,Matte albumen silver print from glass negative,Image: 22.5 × 17.7 cm (8 13/16 × 6 15/16 in.) Sheet: 22.5 × 18.2 cm (8 7/8 × 7 3/16 in.),"Gilman Collection, Purchase, Ann Tenenbaum and Thomas H. Lee Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286477,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.435,false,true,285848,Photographs,Photograph,"Etang, Ville-d'Avray",,,,,,Artist,,Eugène Atget,"French, Libourne 1857–1927 Paris",,"Atget, Eugène",French,1857-02-02,1927-08-04,1923–25,1923,1925,Matte albumen silver print from glass negative,Image: 17.4 x 22.5 cm (6 7/8 x 8 7/8 in.),"Gilman Collection, Purchase, Ann Tenenbaum and Thomas H. Lee Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285848,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.511,false,true,286216,Photographs,Photograph,"Boulevard de Strasbourg, Corsets, Paris",,,,,,Artist,,Eugène Atget,"French, Libourne 1857–1927 Paris",,"Atget, Eugène",French,1857-02-02,1927-08-04,1912,1912,1912,Gelatin silver print from glass negative,Image: 22.4 x 17.5 cm (8 13/16 x 6 7/8 in.) Mount: 36.7 x 28.7 cm (14 7/16 x 11 5/16 in.),"Gilman Collection, Purchase, Ann Tenenbaum and Thomas H. Lee Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286216,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.512,false,true,285679,Photographs,Photograph,Versailles,,,,,,Artist,,Eugène Atget,"French, Libourne 1857–1927 Paris",,"Atget, Eugène",French,1857-02-02,1927-08-04,1924–25,1924,1925,Salted paper print from glass negative,Image: 17.5 x 21.9 cm (6 7/8 x 8 5/8 in.) Sheet: 18 × 21.9 cm (7 1/16 × 8 5/8 in.) Mat: 16 × 20 in. (40.6 × 50.8 cm),"Gilman Collection, Purchase, Ann Tenenbaum and Thomas H. Lee Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285679,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.513,false,true,286674,Photographs,Photograph,"Boutique Journaux, Rue de Sèvres, Paris",,,,,,Artist,,Eugène Atget,"French, Libourne 1857–1927 Paris",,"Atget, Eugène",French,1857-02-02,1927-08-04,1910–11,1910,1911,Matte albumen silver print from glass negative,Image: 22.7 x 18 cm (8 15/16 x 7 1/16 in.),"Gilman Collection, Purchase, Mr. and Mrs. Henry R. Kravis Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286674,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.514,false,true,285764,Photographs,Photograph,Versailles,,,,,,Artist,,Eugène Atget,"French, Libourne 1857–1927 Paris",,"Atget, Eugène",French,1857-02-02,1927-08-04,1922–27,1922,1927,Albumen silver print from glass negative,Image: 21.3 x 17.7 cm (8 3/8 x 6 15/16 in.) Mount: 31.4 x 29.3 cm (12 3/8 x 11 9/16 in.),"Gilman Collection, Purchase, Mr. and Mrs. Henry R. Kravis Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285764,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.515,false,true,286680,Photographs,Photograph,"Cabaret de l'Homme Armé, 25 rue des Blancs-Manteaux",,,,,,Artist,,Eugène Atget,"French, Libourne 1857–1927 Paris",,"Atget, Eugène",French,1857-02-02,1927-08-04,1900,1900,1900,Albumen silver print from glass negative,Image: 21.3 x 17.4 cm (8 3/8 x 6 7/8 in.) Mount: 35.4 x 27.3 cm (13 15/16 x 10 3/4 in.),"Gilman Collection, Purchase, Mrs. Walter Annenberg and The Annenberg Foundation Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286680,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.516,false,true,286197,Photographs,Photograph,"Rouen, Place Eau-de-Robec",,,,,,Artist,,Eugène Atget,"French, Libourne 1857–1927 Paris",,"Atget, Eugène",French,1857-02-02,1927-08-04,1908,1908,1908,Matte albumen silver print from glass negative,Image: 22.7 x 17.4 cm (8 15/16 x 6 7/8 in.) Sheet: 22.9 × 17.7 cm (9 in. × 6 15/16 in.),"Gilman Collection, Purchase, Sam Salz Foundation Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286197,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.517,false,true,285966,Photographs,Photograph,Grand Trianon,,,,,,Artist,,Eugène Atget,"French, Libourne 1857–1927 Paris",,"Atget, Eugène",French,1857-02-02,1927-08-04,1903,1903,1903,Matte albumen silver print from glass negative,Image: 17.2 x 23.15,"Gilman Collection, Purchase, Ann Tenenbaum and Thomas H. Lee Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285966,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.518,false,true,286577,Photographs,Photograph,Lys,,,,,,Artist,,Eugène Atget,"French, Libourne 1857–1927 Paris",,"Atget, Eugène",French,1857-02-02,1927-08-04,before 1900,1892,1900,Albumen silver print from glass negatives,Image: 21.9 x 17.7 cm (8 5/8 x 6 15/16 in.) Sheet: 22 × 18.2 cm (8 11/16 × 7 3/16 in.),"Gilman Collection, Purchase, Jennifer and Joseph Duke Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286577,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.519,false,true,286211,Photographs,Photograph,"St. Cloud, Fin mai, 7 h. soir",,,,,,Artist,,Eugène Atget,"French, Libourne 1857–1927 Paris",,"Atget, Eugène",French,1857-02-02,1927-08-04,1922,1922,1922,Matte albumen silver print from glass negative,Image: 21.8 × 17.3 cm (8 9/16 × 6 13/16 in.) Mount: 35.5 × 28.6 cm (14 in. × 11 1/4 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286211,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.520,false,true,285738,Photographs,Photograph,Notre-Dame depuis le quai de la Tournelle,,,,,,Artist,,Eugène Atget,"French, Libourne 1857–1927 Paris",,"Atget, Eugène",French,1857-02-02,1927-08-04,1923,1923,1923,Albumen silver print from glass negative,"Image: 17.6 × 22.1 cm (6 15/16 × 8 11/16 in.) Sheet: 18.2 × 22.1 cm (7 3/16 in., 22.1 cm)","Gilman Collection, Purchase, Joseph M. Cohen Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285738,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.521,false,true,286644,Photographs,Photograph,Water Lilies,,,,,,Artist,,Eugène Atget,"French, Libourne 1857–1927 Paris",,"Atget, Eugène",French,1857-02-02,1927-08-04,1910 or earlier,1910,1910,Gelatin silver print from glass negative,Image: 17.6 x 22.7 cm (6 15/16 x 8 15/16 in.),"Gilman Collection, Purchase, Mrs. Walter Annenberg and The Annenberg Foundation Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286644,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.522,false,true,286417,Photographs,Photograph,"Maison Close, Versailles",,,,,,Artist,,Eugène Atget,"French, Libourne 1857–1927 Paris",,"Atget, Eugène",French,1857-02-02,1927-08-04,ca. 1921,1919,1923,Albumen silver print from glass negative,Image: 22 × 17.7 cm (8 11/16 × 6 15/16 in.) Sheet: 22 × 18.3 cm (8 11/16 × 7 3/16 in.),"Gilman Collection, Purchase, Ann Tenenbaum and Thomas H. Lee Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286417,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.523,false,true,285676,Photographs,Photograph,"La Bièvre, Boulevard d'Italie 13ème disparue en 1891; aujourd'hui rue Edmond Gondinet",,,,,,Artist,,Eugène Atget,"French, Libourne 1857–1927 Paris",,"Atget, Eugène",French,1857-02-02,1927-08-04,ca. 1898,1896,1900,Albumen silver print from glass negative,Image: 21.5 x 17.1 cm (8 7/16 x 6 3/4 in.) Mount: 37.2 x 28.8 cm (14 5/8 x 11 5/16 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285676,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.524,false,true,285812,Photographs,Photograph,Joueur de Guitare,,,,,,Artist,,Eugène Atget,"French, Libourne 1857–1927 Paris",,"Atget, Eugène",French,1857-02-02,1927-08-04,1900,1900,1900,Albumen silver print from glass negative,Image: 22.2 x 17.3 cm (8 3/4 x 6 13/16 in.),"Gilman Collection, Purchase, Ann Tenenbaum and Thomas H. Lee Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285812,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.525,false,true,285809,Photographs,Photograph,"Parc de Saint-Cloud, Paris",,,,,,Artist,,Eugène Atget,"French, Libourne 1857–1927 Paris",,"Atget, Eugène",French,1857-02-02,1927-08-04,1915–20,1913,1922,Matte albumen silver print from glass negative,Image: 17.2 x 22.5 cm (6 3/4 x 8 7/8 in.) Mount: 36.7 x 28.6 cm (14 7/16 x 11 1/4 in.),"Gilman Collection, Purchase, Ann Tenenbaum and Thomas H. Lee Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285809,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.526,false,true,285810,Photographs,Photograph,Facteur,,,,,,Artist,,Eugène Atget,"French, Libourne 1857–1927 Paris",,"Atget, Eugène",French,1857-02-02,1927-08-04,ca. 1900,1898,1902,Albumen silver print from glass negative,21.7 x 16.7 cm (8 9/16 x 6 9/16 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285810,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.527,false,true,286380,Photographs,Photograph,Small Market in Front of the Church in Place Saint-Médard,,,,,,Artist,,Eugène Atget,"French, Libourne 1857–1927 Paris",,"Atget, Eugène",French,1857-02-02,1927-08-04,1898,1898,1898,Gelatin silver print from glass negative,22.1 x 16.9 cm (8 11/16 x 6 5/8 in.),"Gilman Collection, Purchase, Ann Tenenbaum and Thomas H. Lee Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286380,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.528,false,true,285850,Photographs,Photograph,Vannier,,,,,,Artist,,Eugène Atget,"French, Libourne 1857–1927 Paris",,"Atget, Eugène",French,1857-02-02,1927-08-04,1910–12,1910,1912,Gelatin silver print from glass negative,Image: 16.3 x 22.3 cm (6 7/16 x 8 3/4 in.) Mount: 36.8 x 29.6 cm (14 1/2 x 11 5/8 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285850,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.529,false,true,286679,Photographs,Photograph,Jardin du Luxembourg,,,,,,Artist,,Eugène Atget,"French, Libourne 1857–1927 Paris",,"Atget, Eugène",French,1857-02-02,1927-08-04,1902,1902,1902,Albumen silver print from glass negative,Image: 22.1 × 17.5 cm (8 11/16 × 6 7/8 in.) Sheet: 22.1 × 17.7 cm (8 11/16 × 6 15/16 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286679,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.530,false,true,286675,Photographs,Photograph,"Terre-plein du Pont Neuf, Matinée d'Hiver",,,,,,Artist,,Eugène Atget,"French, Libourne 1857–1927 Paris",,"Atget, Eugène",French,1857-02-02,1927-08-04,1925,1925,1925,Gelatin silver print from glass negative,Image: 17.5 x 22.8 cm (6 7/8 x 9 in.) Sheet: 17.8 × 22.8 cm (7 × 9 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286675,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.531,false,true,285678,Photographs,Photograph,"Fontaine, Sceaux",,,,,,Artist,,Eugène Atget,"French, Libourne 1857–1927 Paris",,"Atget, Eugène",French,1857-02-02,1927-08-04,ca. 1921,1919,1923,Gelatin silver print from glass negative,Image: 22.1 x 17.3 cm (8 11/16 x 6 13/16 in.) Mount: 37.1 x 28.8 cm (14 5/8 x 11 5/16 in.),"Gilman Collection, Purchase, Sam Salz Foundation Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285678,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.532,false,true,286125,Photographs,Photograph,"Le Château, fin Octobre, le soir, effet d'orage, vue prise du Parterre du Nord",,,,,,Artist,,Eugène Atget,"French, Libourne 1857–1927 Paris",,"Atget, Eugène",French,1857-02-02,1927-08-04,1903,1903,1903,Albumen silver print from glass negative,Image: 17.4 × 21.6 cm (6 7/8 × 8 1/2 in.) Sheet: 17.6 × 21.6 cm (6 15/16 × 8 1/2 in.),"Gilman Collection, Purchase, William Talbott Hillman Foundation Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286125,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.533,false,true,286669,Photographs,Photograph,Versailles - Cour du Parc,,,,,,Artist,,Eugène Atget,"French, Libourne 1857–1927 Paris",,"Atget, Eugène",French,1857-02-02,1927-08-04,1902,1902,1902,Albumen silver print from glass negative,Image: 21.5 × 17.5 cm (8 7/16 × 6 7/8 in.) Sheet: 21.5 × 17.9 cm (8 7/16 × 7 1/16 in.),"Gilman Collection, Purchase, Mr. and Mrs. Henry R. Kravis Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286669,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.534,false,true,286670,Photographs,Photograph,"St. Denis, Ancien Relais de la Poste d'Ecouen, Hôtel du Grand Cerf",,,,,,Artist,,Eugène Atget,"French, Libourne 1857–1927 Paris",,"Atget, Eugène",French,1857-02-02,1927-08-04,ca. 1900,1898,1902,Albumen silver print from glass negative,Image: 17.4 × 21.8 cm (6 7/8 × 8 9/16 in.) Sheet: 17.6 × 21.8 cm (6 15/16 × 8 9/16 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286670,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.535,false,true,286693,Photographs,Photograph,Rue de la Montagne-Sainte-Geneviève,,,,,,Artist,,Eugène Atget,"French, Libourne 1857–1927 Paris",,"Atget, Eugène",French,1857-02-02,1927-08-04,1924,1924,1924,Matte albumen silver print from glass negative,Image: 17.4 x 22.1 cm (6 7/8 x 8 11/16 in.) Sheet: 17.7 × 22.5 cm (17.7 × 22.5 cm),"Gilman Collection, Purchase, Mr. and Mrs. Henry R. Kravis Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286693,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.536,false,true,286700,Photographs,Photograph,Boulevard de Strasbourg,,,,,,Artist,,Eugène Atget,"French, Libourne 1857–1927 Paris",,"Atget, Eugène",French,1857-02-02,1927-08-04,1912,1912,1912,Matte albumen silver print from glass negative,Image: 22.7 × 17.7 cm (8 15/16 × 6 15/16 in.) Sheet: 23 × 18 cm (9 1/16 × 7 1/16 in.),"Gilman Collection, Purchase, Ann Tenenbaum and Thomas H. Lee Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286700,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.537,false,true,286317,Photographs,Photograph,"Quai d'Anjou, 6h du matin",,,,,,Artist,,Eugène Atget,"French, Libourne 1857–1927 Paris",,"Atget, Eugène",French,1857-02-02,1927-08-04,1924,1924,1924,Albumen silver print from glass negative,17.7 x 22.8 cm (6 15/16 x 8 15/16 in.),"Gilman Collection, Purchase, William Talbott Hillman Foundation Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286317,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.538,false,true,286682,Photographs,Photograph,"Fête, Avenue de Breteuil",,,,,,Artist,,Eugène Atget,"French, Libourne 1857–1927 Paris",,"Atget, Eugène",French,1857-02-02,1927-08-04,1913,1913,1913,Matte albumen silver print from glass negative,Image: 17.5 x 23 cm (6 7/8 x 9 1/16 in.) Sheet: 18 × 23 cm (7 1/16 × 9 1/16 in.),"Gilman Collection, Purchase, Ann Tenenbaum and Thomas H. Lee Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286682,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.539,false,true,286032,Photographs,Photograph,Agave du Mexique,,,,,,Artist,,Eugène Atget,"French, Libourne 1857–1927 Paris",,"Atget, Eugène",French,1857-02-02,1927-08-04,1920s,1920,1929,Albumen silver print from glass negative,Image: 17.7 x 22.2 cm (6 15/16 x 8 3/4 in.) Sheet: 18 × 22.2 cm (7 1/16 × 8 3/4 in.),"Gilman Collection, Purchase, Denise and Andrew Saul Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286032,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.540,false,true,286678,Photographs,Photograph,97 Rue du Bac,,,,,,Artist,,Eugène Atget,"French, Libourne 1857–1927 Paris",,"Atget, Eugène",French,1857-02-02,1927-08-04,1903,1903,1903,Albumen silver print from glass negative,21.5 x 17 cm (8 7/16 x 6 11/16 in.),"Gilman Collection, Purchase, Mrs. Walter Annenberg and The Annenberg Foundation Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286678,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.541,false,true,285706,Photographs,Photograph,Boats at La Rochelle,,,,,,Artist,,Eugène Atget,"French, Libourne 1857–1927 Paris",,"Atget, Eugène",French,1857-02-02,1927-08-04,ca. 1896,1894,1898,Matte albumen silver print from glass negative,"Image: 16.6 x 22.5 cm (6 9/16 x 8 7/8 in.) Mount: 35.5 x 30.4 cm (14 x 11 15/16 in.), modern mount","Gilman Collection, Purchase, Ann Tenenbaum and Thomas H. Lee Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285706,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.542,false,true,286662,Photographs,Photograph,Jardin des Plantes,,,,,,Artist,,Eugène Atget,"French, Libourne 1857–1927 Paris",,"Atget, Eugène",French,1857-02-02,1927-08-04,1898–1900,1898,1900,Albumen silver print from glass negative,Image: 17.5 × 22 cm (6 7/8 × 8 11/16 in.) Sheet: 17.8 × 22 cm (17.8 × 22 cm),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286662,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.543,false,true,285620,Photographs,Photograph,Trianon,,,,,,Artist,,Eugène Atget,"French, Libourne 1857–1927 Paris",,"Atget, Eugène",French,1857-02-02,1927-08-04,1923–25,1923,1925,Matte albumen silver print from glass negative,Image: 17 x 22.6 cm (6 11/16 x 8 7/8 in.) Mount: 19 x 25.2 cm (7 1/2 x 9 15/16 in.),"Gilman Collection, Purchase, Mrs. Walter Annenberg and The Annenberg Foundation Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285620,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.544,false,true,285818,Photographs,Photograph,Blés,,,,,,Artist,,Eugène Atget,"French, Libourne 1857–1927 Paris",,"Atget, Eugène",French,1857-02-02,1927-08-04,1900,1900,1900,Albumen silver print from glass negative,Image: 22.5 × 17.4 cm (8 7/8 × 6 7/8 in.) Sheet: 22.5 × 17.8 cm (8 7/8 in. × 7 in.),"Gilman Collection, Purchase, Mrs. Walter Annenberg and The Annenberg Foundation Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285818,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.545,false,true,285724,Photographs,Photograph,[Window],,,,,,Artist,,Eugène Atget,"French, Libourne 1857–1927 Paris",,"Atget, Eugène",French,1857-02-02,1927-08-04,1913,1913,1913,Albumen silver print from glass negative,21.5 x 17.4 cm (8 7/16 x 6 7/8 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285724,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.546,false,true,285845,Photographs,Photograph,Saint-Cloud,,,,,,Artist,,Eugène Atget,"French, Libourne 1857–1927 Paris",,"Atget, Eugène",French,1857-02-02,1927-08-04,July 1921,1921,1921,Albumen silver print from glass negative,Image: 17.5 x 21.5 cm (6 7/8 x 8 7/16 in.) Mount: 37.1 x 28.7 cm (14 5/8 x 11 5/16 in.),"Gilman Collection, Purchase, Ann Tenenbaum and Thomas H. Lee Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285845,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.547,false,true,286609,Photographs,Photograph,Parc de Sceaux,,,,,,Artist,,Eugène Atget,"French, Libourne 1857–1927 Paris",,"Atget, Eugène",French,1857-02-02,1927-08-04,1925,1925,1925,Gelatin silver print from glass negative,Image: 17.7 x 22.5 cm (6 15/16 x 8 7/8 in.),"Gilman Collection, Purchase, Denise and Andrew Saul Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286609,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.548,false,true,286045,Photographs,Photograph,"Marchand de Vin, Rue Boyer, Paris",,,,,,Artist,,Eugène Atget,"French, Libourne 1857–1927 Paris",,"Atget, Eugène",French,1857-02-02,1927-08-04,1910–11,1910,1911,Albumen silver print from glass negative,Image: 21.5 x 17.6 cm (8 7/16 x 6 15/16 in.) Sheet: 21.5 × 18.1 cm (8 7/16 × 7 1/8 in.),"Gilman Collection, Purchase, Joseph M. Cohen Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286045,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.549,false,true,286476,Photographs,Photograph,"Boutique de fruits et légumes, Rue Mouffetard",,,,,,Artist,,Eugène Atget,"French, Libourne 1857–1927 Paris",,"Atget, Eugène",French,1857-02-02,1927-08-04,1925,1925,1925,Gelatin silver print from glass negative,Image: 22.6 x 17.9 cm (8 7/8 x 7 in.),"Gilman Collection, Purchase, Ann Tenenbaum and Thomas H. Lee Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286476,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.550,false,true,285891,Photographs,Photograph,"Cour, 7 rue de Valence",,,,,,Artist,,Eugène Atget,"French, Libourne 1857–1927 Paris",,"Atget, Eugène",French,1857-02-02,1927-08-04,1922,1922,1922,Gelatin silver print from glass negative,Image: 17.2 x 22.7 cm (6 3/4 x 8 15/16 in.) Mount: 36.7 x 28.7 cm (14 7/16 x 11 5/16 in.),"Gilman Collection, Purchase, Ann Tenenbaum and Thomas H. Lee Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285891,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1987.1100.113,false,true,265148,Photographs,Photograph,Avenue des Gobelins,,,,,,Artist,,Eugène Atget,"French, Libourne 1857–1927 Paris",,"Atget, Eugène",French,1857-02-02,1927-08-04,1925,1925,1925,Gelatin silver print from glass negative,21.9 x 17.3 cm (8 5/8 x 6 13/16 in.),"Ford Motor Company Collection, Gift of Ford Motor Company and John C. Waddell, 1987",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/265148,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1987.1100.353,false,true,265413,Photographs,Photograph,Versailles,,,,,,Artist,,Eugène Atget,"French, Libourne 1857–1927 Paris",,"Atget, Eugène",French,1857-02-02,1927-08-04,1923,1923,1923,Albumen silver print from glass negative,16.9 x 21.5 cm (6 5/8 x 8 7/16 in.),"Ford Motor Company Collection, Gift of Ford Motor Company and John C. Waddell, 1987",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/265413,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.175,false,true,285969,Photographs,Photograph,"Compiègne, Présentation du Prince Impérial",,,,,,Artist,,Olympe Aguado de las Marismas,"French, Paris 1827–1894 Compiegne",,"Aguado de las Marismas, Olympe",French,1827,1894,1856,1856,1856,Albumen silver print from glass negative,Image: 11.3 × 18.8 cm (4 7/16 × 7 3/8 in.) Mount: 30.7 × 42.3 cm (12 1/16 × 16 5/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285969,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.249,false,true,286647,Photographs,Photograph,"[The Artist, His Mother, and Friends in Fishing Garb]",,,,,,Artist,,Olympe Aguado de las Marismas,"French, Paris 1827–1894 Compiegne",,"Aguado de las Marismas, Olympe",French,1827,1894,ca. 1860,1858,1862,Albumen silver print from glass negative,"Image: 17.7 × 14.6 cm (6 15/16 × 5 3/4 in.) Sheet: 19 × 14.6 cm (7 1/2 × 5 3/4 in.), irregularly trimmed","Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286647,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.283,false,true,286649,Photographs,Photograph,Quelques familiers de Louis Robert,,,,,,Artist,,Olympe Aguado de las Marismas,"French, Paris 1827–1894 Compiegne",,"Aguado de las Marismas, Olympe",French,1827,1894,ca. 1860,1858,1862,Albumen silver print from glass negative,Image: 17.1 × 14.3 cm (6 3/4 × 5 5/8 in.) Sheet: 17.5 × 14.3 cm (6 7/8 × 5 5/8 in.),"Gilman Collection, Purchase, Mrs. Walter Annenberg and The Annenberg Foundation Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286649,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.825,false,true,286697,Photographs,Photograph,Emperor Napoleon III,,,,,,Artist,,Olympe Aguado de las Marismas,"French, Paris 1827–1894 Compiegne",,"Aguado de las Marismas, Olympe",French,1827,1894,March 1860,1860,1860,Albumen silver print,"Image: 13.7 × 9.1 cm (13.7 × 9.1 cm), arched top Sheet: 14.2 × 9.6 cm (14.2 × 9.6 cm)","Gilman Collection, Purchase, Gift of The Howard Gilman Foundation, by exchange, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286697,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.826,false,true,286113,Photographs,Photograph,Empress Eugénie,,,,,,Artist,,Olympe Aguado de las Marismas,"French, Paris 1827–1894 Compiegne",,"Aguado de las Marismas, Olympe",French,1827,1894,1860,1860,1860,Albumen silver print from glass negative,"Image: 13.6 × 9.2 cm (5 3/8 × 3 5/8 in.), arched top Sheet: 14.1 × 9.5 cm (5 9/16 × 3 3/4 in.)","Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286113,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.882,false,true,286650,Photographs,Photograph,Louis Robert and Olympe Aguado,,,,,,Artist,,Olympe Aguado de las Marismas,"French, Paris 1827–1894 Compiegne",,"Aguado de las Marismas, Olympe",French,1827,1894,ca. 1860,1858,1862,Albumen silver print from glass negative,Image: 14.7 x 19.1 cm (5 13/16 x 7 1/2 in.) Mount: 23.2 x 29.4 cm (9 1/8 x 11 9/16 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286650,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1987.1161,true,true,265726,Photographs,Photograph,"[Flower Study, Rose of Sharon]",,,,,,Artist,,Adolphe Braun,"French, Besançon 1811–1877 Dornach",,"Braun, Adolphe",French,1811,1877,ca. 1854,1852,1856,Albumen silver print from glass negative,37.5 x 41.9 cm (14 3/4 x 16 1/2 in.),"Gift of Gilman Paper Company, in memory of Samuel J. Wagstaff Jr., 1987",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/265726,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -47.149.53,false,true,269118,Photographs,Photograph,Pheasant and Grouse,,,,,,Artist,,Adolphe Braun,"French, Besançon 1811–1877 Dornach",,"Braun, Adolphe",French,1811,1877,ca. 1865,1863,1867,Carbon print,Image: 80.3 x 47.5 cm (31 5/8 x 18 11/16 in.),"Gift of E. S. Herrmann, 1947",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269118,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1973.607.1,false,true,260967,Photographs,Photograph,"Haute-Egypt, Salle Hypostyle à Karnak",,,,,,Artist,,Adolphe Braun,"French, Besançon 1811–1877 Dornach",,"Braun, Adolphe",French,1811,1877,ca. 1870,1868,1872,Albumen silver print,,"Gift of Weston J. Naef, 1973",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260967,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2011.537,false,true,296359,Photographs,Photographs,"Château de St.-Germain-en-Laye, Intérieur de la cour, Chapelle",,,,,,Artist,,Médéric Mieusement,"French, Gonneville-la-Mallet 1840–1905 Pornic",,"Mieusement, Médéric",French,1840,1905,1862–67,1862,1867,Albumen silver print from glass negative,Image: 37.5 x 28.2 cm (14 3/4 x 11 1/8 in.) Mount: 60.3 x 48 cm (23 3/4 x 18 7/8 in.),"Purchase, Peter C. Bunnell Gift, 2011",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/296359,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -46.167,false,true,269086,Photographs,Photograph,[Bearded Man with Magnifying Glass Examining a Manuscript],,,,,,Artist,,Antoine-Samuel Adam-Salomon,"French, La Ferté-sous-Jouarre 1811–1881 Paris",,"Adam-Salomon, Antoine-Samuel",French,1811,1881,1870s,1870,1879,Albumen silver print from glass negative,,"Museum Accession, 1946",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269086,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.497,false,true,285816,Photographs,Photograph,[Photographic Advertisement],,,,,,Artist,,Emile Gsell,"French, Sainte-Marie-aux-Mines 1838–1879 Vietnam",,"Gsell, Emile",French,1838,1879,1860s,1860,1869,Albumen silver print from glass negative,Image: 23.5 x 31.8 cm (9 1/4 x 12 1/2 in.),"Gilman Collection, Purchase, Cynthia Hazen Polsky Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285816,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.501 (1),false,true,288171,Photographs,Photograph,"Costumes de Théâtre, Saïgon, Cochinchine",,,,,,Artist,,Emile Gsell,"French, Sainte-Marie-aux-Mines 1838–1879 Vietnam",,"Gsell, Emile",French,1838,1879,1866,1866,1866,Albumen silver print from glass negative,24.4 x 31.1 cm (9 5/8 x 12 1/4 in.),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288171,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.501 (2),false,true,288172,Photographs,Photograph,"Femme Annamite, Saïgon, Cochinchine",,,,,,Artist,,Emile Gsell,"French, Sainte-Marie-aux-Mines 1838–1879 Vietnam",,"Gsell, Emile",French,1838,1879,1866,1866,1866,Albumen silver print from glass negative,20 x 16.7 cm (7 7/8 x 6 9/16 in.),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288172,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.501 (3),false,true,288173,Photographs,Photograph,"Prisonniers conduits par un Mata, Cochinchine",,,,,,Artist,,Emile Gsell,"French, Sainte-Marie-aux-Mines 1838–1879 Vietnam",,"Gsell, Emile",French,1838,1879,1866,1866,1866,Albumen silver print from glass negative,24.2 x 21 cm (9 1/2 x 8 1/4 in.),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288173,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.501 (4),false,true,288174,Photographs,Photograph,"Riche Annamite montant à cheval, Cochinchine",,,,,,Artist,,Emile Gsell,"French, Sainte-Marie-aux-Mines 1838–1879 Vietnam",,"Gsell, Emile",French,1838,1879,1866,1866,1866,Albumen silver print from glass negative,19.1 x 25 cm (7 1/2 x 9 13/16 in.),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288174,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.501 (5),false,true,288175,Photographs,Photograph,"Enterrement, Cochinchine",,,,,,Artist,,Emile Gsell,"French, Sainte-Marie-aux-Mines 1838–1879 Vietnam",,"Gsell, Emile",French,1838,1879,1866,1866,1866,Albumen silver print from glass negative,22.5 x 30.9 cm (8 7/8 x 12 3/16 in.),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288175,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.501 (6),false,true,288176,Photographs,Photograph,"Cérémonie religieuse dans la Pagode Chinoise de Cholen, Saïgon, Cochinchine",,,,,,Artist,,Emile Gsell,"French, Sainte-Marie-aux-Mines 1838–1879 Vietnam",,"Gsell, Emile",French,1838,1879,1866,1866,1866,Albumen silver print from glass negative,22.3 x 33.4 cm (8 3/4 x 13 1/8 in.),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288176,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.501 (7),false,true,288177,Photographs,Photograph,"Cortège d'un mariage, Cochinchine",,,,,,Artist,,Emile Gsell,"French, Sainte-Marie-aux-Mines 1838–1879 Vietnam",,"Gsell, Emile",French,1838,1879,1866,1866,1866,Albumen silver print from glass negative,20.7 x 31.1 cm (8 1/8 x 12 1/4 in.),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288177,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.501 (8),false,true,288178,Photographs,Photograph,"Vue de la Ville Chinoise (Cholen) Feuille No. 3, Saïgon, Cochinchine",,,,,,Artist,,Emile Gsell,"French, Sainte-Marie-aux-Mines 1838–1879 Vietnam",,"Gsell, Emile",French,1838,1879,1866,1866,1866,Albumen silver print from glass negative,22.3 x 30.1 cm (8 3/4 x 11 7/8 in.),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288178,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.501 (9),false,true,288179,Photographs,Photograph,"Vue de la Ville Chinoise (Cholen) Feuille No. 2, Saïgon, Cochinchine",,,,,,Artist,,Emile Gsell,"French, Sainte-Marie-aux-Mines 1838–1879 Vietnam",,"Gsell, Emile",French,1838,1879,1866,1866,1866,Albumen silver print from glass negative,23.5 x 31.4 cm (9 1/4 x 12 3/8 in.),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288179,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.501 (10),false,true,288180,Photographs,Photograph,"Vue de la Ville Chinoise (Cholen) Feuille No. 2, Saïgon, Cochinchine",,,,,,Artist,,Emile Gsell,"French, Sainte-Marie-aux-Mines 1838–1879 Vietnam",,"Gsell, Emile",French,1838,1879,1866,1866,1866,Albumen silver print from glass negative,22.3 x 30.4 cm (8 3/4 x 11 15/16 in.),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288180,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.501 (11),false,true,288181,Photographs,Photograph,"Vue de la Ville Chinoise (Cholen) Feuille No. 6, Saïgon, Cochinchine",,,,,,Artist,,Emile Gsell,"French, Sainte-Marie-aux-Mines 1838–1879 Vietnam",,"Gsell, Emile",French,1838,1879,1866,1866,1866,Albumen silver print from glass negative,23.8 x 31.2 cm (9 3/8 x 12 5/16 in.),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288181,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.501 (12),false,true,288182,Photographs,Photograph,Vue de la Ville Chinoise (Cholen) Feuille No. 4,,,,,,Artist,,Emile Gsell,"French, Sainte-Marie-aux-Mines 1838–1879 Vietnam",,"Gsell, Emile",French,1838,1879,1866,1866,1866,Albumen silver print from glass negative,23.9 x 30 cm (9 7/16 x 11 13/16 in.),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288182,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.501 (13),false,true,288183,Photographs,Photograph,"Vue de la Ville Chinoise (Cholen) Feuille No. 5, Saïgon, Cochinchine",,,,,,Artist,,Emile Gsell,"French, Sainte-Marie-aux-Mines 1838–1879 Vietnam",,"Gsell, Emile",French,1838,1879,1866,1866,1866,Albumen silver print from glass negative,24.4 x 30.4 cm (9 5/8 x 11 15/16 in.),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288183,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.501 (14),false,true,288184,Photographs,Photograph,"Bonzes de la Pagoda Chinoise (Cholen), Saïgon, Cochinchine",,,,,,Artist,,Emile Gsell,"French, Sainte-Marie-aux-Mines 1838–1879 Vietnam",,"Gsell, Emile",French,1838,1879,1866,1866,1866,Albumen silver print from glass negative,24.1 x 21.5 cm (9 1/2 x 8 7/16 in.),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288184,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.501 (15),false,true,288185,Photographs,Photograph,"Femmes Annamites, Cochinchine",,,,,,Artist,,Emile Gsell,"French, Sainte-Marie-aux-Mines 1838–1879 Vietnam",,"Gsell, Emile",French,1838,1879,1866,1866,1866,Albumen silver print from glass negative,26.3 x 23 cm (10 3/8 x 9 1/16 in.),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288185,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.501 (16),false,true,288186,Photographs,Photograph,"Musiciens Annamites, Saïgon, Cochinchine",,,,,,Artist,,Emile Gsell,"French, Sainte-Marie-aux-Mines 1838–1879 Vietnam",,"Gsell, Emile",French,1838,1879,1866,1866,1866,Albumen silver print from glass negative,23.1 x 27.9 cm (9 1/8 x 11 in.),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288186,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.501 (17),false,true,288187,Photographs,Photograph,"Chef de Village en Costume officiel, Cochinchine",,,,,,Artist,,Emile Gsell,"French, Sainte-Marie-aux-Mines 1838–1879 Vietnam",,"Gsell, Emile",French,1838,1879,1866,1866,1866,Albumen silver print from glass negative,20.8 x 16.3 cm (8 3/16 x 6 7/16 in.),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288187,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.501 (18),false,true,288188,Photographs,Photograph,"Marchands de Fruits, Cochinchine",,,,,,Artist,,Emile Gsell,"French, Sainte-Marie-aux-Mines 1838–1879 Vietnam",,"Gsell, Emile",French,1838,1879,1866,1866,1866,Albumen silver print from glass negative,15.3 x 20.1 cm (6 x 7 15/16 in.),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288188,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.501 (19),false,true,288189,Photographs,Photograph,"Miliciens mangeant le riz, Cochinchine",,,,,,Artist,,Emile Gsell,"French, Sainte-Marie-aux-Mines 1838–1879 Vietnam",,"Gsell, Emile",French,1838,1879,1866,1866,1866,Albumen silver print from glass negative,21.1 x 16.9 cm (8 5/16 x 6 5/8 in.),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288189,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.501 (20),false,true,288190,Photographs,Photograph,Pagode de la Ville Chinoise,,,,,,Artist,,Emile Gsell,"French, Sainte-Marie-aux-Mines 1838–1879 Vietnam",,"Gsell, Emile",French,1838,1879,1866,1866,1866,Albumen silver print from glass negative,14.6 x 22.8 cm (5 3/4 x 9 in.),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288190,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.501 (21),false,true,288191,Photographs,Photograph,Vue de l'Etablissement des Messageries Impériales,,,,,,Artist,,Emile Gsell,"French, Sainte-Marie-aux-Mines 1838–1879 Vietnam",,"Gsell, Emile",French,1838,1879,1866,1866,1866,Albumen silver print from glass negative,13.2 x 25 cm (5 3/16 x 9 13/16 in.),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288191,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.501 (22),false,true,288192,Photographs,Photograph,Tombeau de l'Evêque d'Adran,,,,,,Artist,,Emile Gsell,"French, Sainte-Marie-aux-Mines 1838–1879 Vietnam",,"Gsell, Emile",French,1838,1879,1866,1866,1866,Albumen silver print from glass negative,16.9 x 22.7 cm (6 5/8 x 8 15/16 in.),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288192,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.501 (23),false,true,288193,Photographs,Photograph,Matas (Miliciens indigènes),,,,,,Artist,,Emile Gsell,"French, Sainte-Marie-aux-Mines 1838–1879 Vietnam",,"Gsell, Emile",French,1838,1879,1866,1866,1866,Albumen silver print from glass negative,26.1 x 19.8 cm (10 1/4 x 7 13/16 in.),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288193,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.501 (24),false,true,288194,Photographs,Photograph,Vue de Saïgon (Feuille No. 2),,,,,,Artist,,Emile Gsell,"French, Sainte-Marie-aux-Mines 1838–1879 Vietnam",,"Gsell, Emile",French,1838,1879,1866,1866,1866,Albumen silver print from glass negative,24 x 33.5 cm (9 7/16 x 13 3/16 in.),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288194,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.501 (25),false,true,288195,Photographs,Photograph,Vue de Saïgon (Feuille No. 3),,,,,,Artist,,Emile Gsell,"French, Sainte-Marie-aux-Mines 1838–1879 Vietnam",,"Gsell, Emile",French,1838,1879,1866,1866,1866,Albumen silver print from glass negative,24.7 x 33.7 cm (9 3/4 x 13 1/4 in.),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288195,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.501 (26),false,true,288196,Photographs,Photograph,Vue de Saïgon (Feuille No. 1),,,,,,Artist,,Emile Gsell,"French, Sainte-Marie-aux-Mines 1838–1879 Vietnam",,"Gsell, Emile",French,1838,1879,1866,1866,1866,Albumen silver print from glass negative,24.7 x 34.2 cm (9 3/4 x 13 7/16 in.),"Gilman Collection, Purchase, The Horace W. Goldsmith Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288196,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.501 (27),false,true,288197,Photographs,Photograph,Portique d'entrée de la Grande Galerie de la Pagode,,,,,,Artist,,Emile Gsell,"French, Sainte-Marie-aux-Mines 1838–1879 Vietnam",,"Gsell, Emile",French,1838,1879,1866,1866,1866,Albumen silver print from glass negative,16.9 x 22.5 cm (6 5/8 x 8 7/8 in.),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288197,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.501 (28),false,true,288198,Photographs,Photograph,Façade Ouest de la Grande Pagode,,,,,,Artist,,Emile Gsell,"French, Sainte-Marie-aux-Mines 1838–1879 Vietnam",,"Gsell, Emile",French,1838,1879,1866,1866,1866,Albumen silver print from glass negative,25.1 x 30.3 cm (9 7/8 x 11 15/16 in.),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288198,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.501 (29),false,true,288199,Photographs,Photograph,Bonzerie de la Grande Pagode,,,,,,Artist,,Emile Gsell,"French, Sainte-Marie-aux-Mines 1838–1879 Vietnam",,"Gsell, Emile",French,1838,1879,1866,1866,1866,Albumen silver print from glass negative,26 x 32 cm (10 1/4 x 12 5/8 in.),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288199,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.501 (30),false,true,288200,Photographs,Photograph,Façade Nord de la Grand Pagode,,,,,,Artist,,Emile Gsell,"French, Sainte-Marie-aux-Mines 1838–1879 Vietnam",,"Gsell, Emile",French,1838,1879,1866,1866,1866,Albumen silver print from glass negative,25.2 x 31.1 cm (9 15/16 x 12 1/4 in.),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288200,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.501 (31),false,true,288201,Photographs,Photograph,Grande Pagode - Edicule Nord,,,,,,Artist,,Emile Gsell,"French, Sainte-Marie-aux-Mines 1838–1879 Vietnam",,"Gsell, Emile",French,1838,1879,1866,1866,1866,Albumen silver print from glass negative,25.6 x 30.9 cm (10 1/16 x 12 3/16 in.),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288201,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.501 (32),false,true,288202,Photographs,Photograph,Edicule extérieur Sud,,,,,,Artist,,Emile Gsell,"French, Sainte-Marie-aux-Mines 1838–1879 Vietnam",,"Gsell, Emile",French,1838,1879,1866,1866,1866,Albumen silver print from glass negative,16.5 x 22.1 cm (6 1/2 x 8 11/16 in.),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288202,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.501 (33),false,true,288203,Photographs,Photograph,Grande Pagode Porte Ouest de la 1ère enceinte,,,,,,Artist,,Emile Gsell,"French, Sainte-Marie-aux-Mines 1838–1879 Vietnam",,"Gsell, Emile",French,1838,1879,1866,1866,1866,Albumen silver print from glass negative,16.4 x 22.9 cm (6 7/16 x 9 in.),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288203,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.501 (34),false,true,286880,Photographs,Photograph,Angle d'Une Cour Intérieure de la Grande Pagode,,,,,,Artist,,Emile Gsell,"French, Sainte-Marie-aux-Mines 1838–1879 Vietnam",,"Gsell, Emile",French,1838,1879,1866,1866,1866,Albumen silver print from glass negative,22.7 x 17.7cm (8 15/16 x 6 15/16in.),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286880,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.501 (35),false,true,288204,Photographs,Photograph,Grande Pagode - Edicule extérieur Sud,,,,,,Artist,,Emile Gsell,"French, Sainte-Marie-aux-Mines 1838–1879 Vietnam",,"Gsell, Emile",French,1838,1879,1866,1866,1866,Albumen silver print from glass negative,16.5 x 22 cm (6 1/2 x 8 11/16 in.),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288204,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.501 (36),false,true,288205,Photographs,Photograph,Grande Pagode - Colonnade de l'Esplanade,,,,,,Artist,,Emile Gsell,"French, Sainte-Marie-aux-Mines 1838–1879 Vietnam",,"Gsell, Emile",French,1838,1879,1866,1866,1866,Albumen silver print from glass negative,17.3 x 12 cm (6 13/16 x 4 3/4 in.),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288205,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.501 (37),false,true,288206,Photographs,Photograph,Bonzes de la Grande Pagode,,,,,,Artist,,Emile Gsell,"French, Sainte-Marie-aux-Mines 1838–1879 Vietnam",,"Gsell, Emile",French,1838,1879,1866,1866,1866,Albumen silver prints from glass negatives,17 x 10.9 cm (6 11/16 x 4 5/16 in.),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288206,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.501 (38),false,true,288207,Photographs,Photograph,Bas-relief de la Grande Galerie de la Pagode,,,,,,Artist,,Emile Gsell,"French, Sainte-Marie-aux-Mines 1838–1879 Vietnam",,"Gsell, Emile",French,1838,1879,1866,1866,1866,Albumen silver prints from glass negatives,12.1 x 17.2 cm (4 3/4 x 6 3/4 in.),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288207,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.501 (39),false,true,288208,Photographs,Photograph,Bas-relief de la Grande Galerie de la Pagode,,,,,,Artist,,Emile Gsell,"French, Sainte-Marie-aux-Mines 1838–1879 Vietnam",,"Gsell, Emile",French,1838,1879,1866,1866,1866,Albumen silver prints from glass negatives,12.1 x 17.2 cm (4 3/4 x 6 3/4 in.),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288208,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.501 (40),false,true,288209,Photographs,Photograph,Grande Pagode - Tour Nord du 2e Etage,,,,,,Artist,,Emile Gsell,"French, Sainte-Marie-aux-Mines 1838–1879 Vietnam",,"Gsell, Emile",French,1838,1879,1866,1866,1866,Albumen silver prints from glass negatives,24.2 x 17 cm (9 1/2 x 6 11/16 in.),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288209,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.501 (41),false,true,288210,Photographs,Photograph,Détail Décoratif de la Grande Pagode,,,,,,Artist,,Emile Gsell,"French, Sainte-Marie-aux-Mines 1838–1879 Vietnam",,"Gsell, Emile",French,1838,1879,1866,1866,1866,Albumen silver prints from glass negatives,16.4 x 11.2 cm (6 7/16 x 4 7/16 in.),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288210,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.501 (42),false,true,288211,Photographs,Photograph,"S. M. Norodon, Roi du Cambodge",,,,,,Artist,,Emile Gsell,"French, Sainte-Marie-aux-Mines 1838–1879 Vietnam",,"Gsell, Emile",French,1838,1879,1866,1866,1866,Albumen silver prints from glass negatives,23.3 x 19.7 cm (9 3/16 x 7 3/4 in.),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288211,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.501 (43),false,true,288212,Photographs,Photograph,"Phra-Kéo-Pha, Frère du Roi",,,,,,Artist,,Emile Gsell,"French, Sainte-Marie-aux-Mines 1838–1879 Vietnam",,"Gsell, Emile",French,1838,1879,1866,1866,1866,Albumen silver prints from glass negatives,24.8 x 20.8 cm (9 3/4 x 8 3/16 in.),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288212,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.501 (44),false,true,288213,Photographs,Photograph,Prince Cambodgien et son Cortège,,,,,,Artist,,Emile Gsell,"French, Sainte-Marie-aux-Mines 1838–1879 Vietnam",,"Gsell, Emile",French,1838,1879,1866,1866,1866,Albumen silver prints from glass negatives,24.2 x 30 cm (9 1/2 x 11 13/16 in.),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288213,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.501 (45),false,true,288214,Photographs,Photograph,Femmes du Prince Phra-Kéo-Pha,,,,,,Artist,,Emile Gsell,"French, Sainte-Marie-aux-Mines 1838–1879 Vietnam",,"Gsell, Emile",French,1838,1879,1866,1866,1866,Albumen silver prints from glass negatives,19.9 x 27.7 cm (7 13/16 x 10 7/8 in.),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288214,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.501 (46),false,true,288215,Photographs,Photograph,Nam-Vian - Tombeaux,,,,,,Artist,,Emile Gsell,"French, Sainte-Marie-aux-Mines 1838–1879 Vietnam",,"Gsell, Emile",French,1838,1879,1866,1866,1866,Albumen silver prints from glass negatives,16.6 x 22.7 cm (6 9/16 x 8 15/16 in.),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288215,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.501 (47),false,true,288216,Photographs,Photograph,Nam-Vian - Tombeaux,,,,,,Artist,,Emile Gsell,"French, Sainte-Marie-aux-Mines 1838–1879 Vietnam",,"Gsell, Emile",French,1838,1879,1866,1866,1866,Albumen silver prints from glass negatives,15.8 x 22 cm (6 1/4 x 8 11/16 in.),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288216,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2008.144,false,true,289043,Photographs,Photograph,[Construction Site],,,,,,Artist,,Louis Lafon,"French, active 1870s–90s",,"Lafon, Louis",French,1870,1899,1880s,1880,1889,Albumen silver print from glass negative,Image: 36.8 x 47.8 cm (14 1/2 x 18 13/16 in.),"Purchase, Alfred Stieglitz Society Gifts, 2008",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289043,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.410 (2a),false,true,288155,Photographs,Photograph,[Unknown Sitter],,,,,,Artist|Artist,Painted and retouched by,Pierre-Louis Pierson|Marck,"French, 1822–1913",", et al","Pierson, Pierre-Louis|Marck",French,1822,1913,before 1865,1855,1865,Albumen silver print from glass negative,Image: 6 5/16 × 4 3/4 in. (16 × 12 cm) Mount: 14 3/16 in. × 11 in. (36 × 28 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288155,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.410 (2b),false,true,288156,Photographs,Photograph,[Marie de Morny],,,,,,Artist|Artist,Painted and retouched by,Pierre-Louis Pierson|Marck,"French, 1822–1913",", et al","Pierson, Pierre-Louis|Marck",French,1822,1913,before 1865,1855,1865,Albumen silver print from glass negative,Window: 7 1/2 × 5 1/2 in. (19 × 14 cm) Image: 4 in. × 2 13/16 in. (10.2 × 7.2 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288156,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.410 (5a-d),false,true,288160,Photographs,Cartes-de-visite,[Unknown Sitters and Duchesse de Morny],,,,,,Artist|Artist,Painted and retouched by,Pierre-Louis Pierson|Marck,"French, 1822–1913",", et al","Pierson, Pierre-Louis|Marck",French,1822,1913,before 1865,1855,1865,Albumen silver prints from glass negatives,Image: 3 3/8 in. × 2 in. (8.6 × 5.1 cm) (each),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288160,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.410 (5e-h),false,true,288161,Photographs,Cartes-de-visite,"[Pourtalès, Metternich, Grande Duchesse de Mecklemboury, and Unknown Sitter]",,,,,,Artist|Artist,Painted and retouched by,Pierre-Louis Pierson|Marck,"French, 1822–1913",", et al","Pierson, Pierre-Louis|Marck",French,1822,1913,before 1865,1855,1865,Albumen silver prints from glass negatives,Image: 3 3/8 in. × 2 in. (8.6 × 5.1 cm) (each),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288161,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.410 (6a-d),false,true,288162,Photographs,Cartes-de-visite,[Anna Murat and Unknown Sitters],,,,,,Artist|Artist,Painted and retouched by,Pierre-Louis Pierson|Marck,"French, 1822–1913",", et al","Pierson, Pierre-Louis|Marck",French,1822,1913,before 1865,1855,1865,Albumen silver prints from glass negatives,Image: 3 3/8 in. × 2 in. (8.6 × 5.1 cm) (each),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288162,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.410 (6e-h),false,true,288163,Photographs,Cartes-de-visite,[Comtesse Walewska and Princesse Jaochim Murat],,,,,,Artist|Artist,Painted and retouched by,Pierre-Louis Pierson|Marck,"French, 1822–1913",", et al","Pierson, Pierre-Louis|Marck",French,1822,1913,before 1865,1855,1865,Albumen silver prints from glass negatives,Image: 3 3/8 in. × 2 in. (8.6 × 5.1 cm) (each),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288163,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.410 (7a-d),false,true,288164,Photographs,Cartes-de-visite,[Unknown Sitters],,,,,,Artist|Artist,Painted and retouched by,Pierre-Louis Pierson|Marck,"French, 1822–1913",", et al","Pierson, Pierre-Louis|Marck",French,1822,1913,before 1865,1855,1865,Albumen silver prints from glass negatives,Image: 3 3/8 in. × 2 in. (8.6 × 5.1 cm) (each),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288164,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.410 (7e-h),false,true,288165,Photographs,Cartes-de-visite,[Unknown Sitters],,,,,,Artist|Artist,Painted and retouched by,Pierre-Louis Pierson|Marck,"French, 1822–1913",", et al","Pierson, Pierre-Louis|Marck",French,1822,1913,before 1865,1855,1865,Albumen silver prints from glass negatives,Image: 3 3/8 in. × 2 in. (8.6 × 5.1 cm) (each),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288165,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.410 (8a-d),false,true,288166,Photographs,Cartes-de-visite,[Unknown Sitters],,,,,,Artist|Artist,Painted and retouched by,Pierre-Louis Pierson|Marck,"French, 1822–1913",", et al","Pierson, Pierre-Louis|Marck",French,1822,1913,before 1865,1855,1865,Albumen silver prints from glass negatives,Image: 3 3/8 in. × 2 in. (8.6 × 5.1 cm) (each),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288166,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.410 (8e-g),false,true,288167,Photographs,Cartes-de-visite,[Madame Demidoff and Unknown Sitters],,,,,,Artist|Artist,Painted and retouched by,Pierre-Louis Pierson|Marck,"French, 1822–1913",", et al","Pierson, Pierre-Louis|Marck",French,1822,1913,before 1865,1855,1865,Albumen silver prints from glass negatives,Image: 3 3/8 in. × 2 in. (8.6 × 5.1 cm) (each),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288167,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1097,false,true,285685,Photographs,Photograph,Ambassade Cochinchinoise à Paris,,,,,,Artist|Artist,,Louis Rousseau|Philippe Jacques Potteau,"French, 1807–1876, active 1860s",,"Rousseau, Louis|Potteau, Philippe Jacques",French,1807,1876,1863,1863,1863,Albumen silver print from glass negative,Image: 7 in. × 4 15/16 in. (17.8 × 12.5 cm) Mount: 14 13/16 × 11 1/8 in. (37.7 × 28.2 cm),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285685,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.759.11,false,true,259815,Photographs,Photograph,[Reproduction of Napoleon on the Battlefield of Eylau by Antoine-Jean Gros],,,,,,Artist|Artist,After,Unknown|baron Antoine Jean Gros,"French|French, Paris 1771–1835 Meudon",,"Unknown|Gros, Antoine Jean, baron",French,1771,1835,1850s,1850,1859,Albumen silver print,,"Museum Accession, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/259815,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1978.602.2,false,true,262236,Photographs,Photograph,Nigella Damascena Spinnenkopf,,,,,,Artist|Printer,,Karl Blossfeldt|Jürgen Wilde,"German, 1865–1932",,"Blossfeldt, Karl|Wilde, Jürgen",German,1865,1932,"ca. 1932, printed 1976",1930,1934,Gelatin silver print,25.2 x 19.3 cm. (9 15/16 x 7 5/8 in.),"Warner Communications Inc. Purchase Fund, 1978",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/262236,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.267,false,true,685608,Photographs,Carte-de-visite,[Otto Eurmann?],,,,,,Photography Studio,,G. & A. Overbeck,"German, active 1860s",,G. & A. Overbeck,German,1855,1870,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685608,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.39,false,true,685381,Photographs,Carte-de-visite,[Oskar Begas],,,,,,Photography Studio,,Loescher & Petsch,"German, active ca. 1860–90",,Loescher & Petsch,German,1860,1890,before 1883,1860,1883,Albumen silver print,Approx. 10.2 x 6.4 cm (4 in. × 2 1/2 in.) (10.2 × 6.4 cm),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685381,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.517,false,true,685857,Photographs,Carte-de-visite,[Unknown Subject],,,,,,Photography Studio,,Loescher & Petsch,"German, active ca. 1860–90",,Loescher & Petsch,German,1860,1890,after 1867,1867,1890,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685857,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.547,false,true,685887,Photographs,Carte-de-visite,[Charles Mandel],,,,,,Photography Studio,,Loescher & Petsch,"German, active ca. 1860–90",,Loescher & Petsch,German,1860,1890,after 1867,1867,1890,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685887,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.629,false,true,685968,Photographs,Carte-de-visite,[Ludwig Johann Passini],,,,,,Photography Studio,,Loescher & Petsch,"German, active ca. 1860–90",,Loescher & Petsch,German,1860,1890,after 1867,1867,1890,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685968,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.829,false,true,686168,Photographs,Carte-de-visite,[Rildhauer Wolff],,,,,,Photography Studio,,Loescher & Petsch,"German, active ca. 1860–90",,Loescher & Petsch,German,1860,1890,after 1867,1867,1890,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/686168,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.856,false,true,686195,Photographs,Carte-de-visite,Carl Becker,,,,,,Person in Photograph|Photography Studio,Person in photograph,Carl Becker|Loescher & Petsch,"German, active ca. 1860–90",,Becker Carl|Loescher & Petsch,German,1898 |1860,1898 |1890,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/686195,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.890,false,true,686229,Photographs,Carte-de-visite,[Karl Heffeck],,,,,,Photography Studio,,Loescher & Petsch,"German, active ca. 1860–90",,Loescher & Petsch,German,1860,1890,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/686229,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.675,false,true,686014,Photographs,Carte-de-visite,Gustav Karl Ludwig Richter,,,,,,Person in Photograph|Photography Studio,Person in photograph,Gustav Karl Ludwig Richter|Loescher & Petsch,"German, 1823–1884|German, active ca. 1860–90",,"Richter, Gustav Karl Ludwig|Loescher & Petsch",German,1823 |1860,1884 |1890,after 1867,1867,1890,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/686014,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.83,false,true,685425,Photographs,Carte-de-visite,[Meyer George von Bremen],,,,,,Artist|Person in Photograph,Person in photograph,Heinrich Graf|Johann Georg Meyer,"German, active 1860s|German, Bremen 1813–1880 Berlin",,"Graf, Heinrich|Meyer, Johann Georg",German,1813,1880,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685425,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.84,false,true,685426,Photographs,Carte-de-visite,[Meyer George von Bremen],,,,,,Artist|Person in Photograph,Person in photograph,Heinrich Graf|Johann Georg Meyer,"German, active 1860s|German, Bremen 1813–1880 Berlin",,"Graf, Heinrich|Meyer, Johann Georg",German,1813,1880,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685426,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.579,false,true,685918,Photographs,Carte-de-visite,[Paul Friedrich Meyerheim],,,,,,Person in Photograph|Photography Studio,Person in photograph,Paul Friedrich Meyerheim|Loescher & Petsch,"German, Berlin 1842–1915 Berlin|German, active ca. 1860–90",,"Meyerheim, Paul Friedrich|Loescher & Petsch",German,1842 |1860,1915 |1890,after 1867,1867,1890,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685918,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.299,false,true,286358,Photographs,Photograph,Blumenbachia hieronymi,,,,,,Artist,,Karl Blossfeldt,"German, 1865–1932",,"Blossfeldt, Karl",German,1865,1932,1915–25,1915,1925,Gelatin silver print,Image: 29.8 x 23.8 cm (11 3/4 x 9 3/8 in.),"Gilman Collection, Purchase, Denise and Andrew Saul Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286358,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1987.1100.246,false,true,265295,Photographs,Photograph,"[Arrangement of 12 Female Mannequin Heads, Each with Distinct Physiognomy and Period Hair Style]",,,,,,Artist,,Peter Weller,"German, 1868–1940",,"Weller, Peter",German,1868,1940,1920s–30s,1920,1939,Gelatin silver print,16.9 x 23.3 cm (6 5/8 x 9 3/16 in.),"Ford Motor Company Collection, Gift of Ford Motor Company and John C. Waddell, 1987",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/265295,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.188,false,true,285709,Photographs,Photograph,[Adolph Hitler Leaving Landsberg Prison],,,,,,Artist,,Heinrich Hoffmann,"German, 1885–1957",,"Hoffmann, Heinrich",German,1885,1957,"December 20, 1924",1924,1924,Gelatin silver print,22.2 x 16.4 cm (8 3/4 x 6 7/16 in.) Mount: 29.3 × 22 cm (11 9/16 × 8 11/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285709,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1987.1100.334,false,true,265392,Photographs,Photograph,Junge vom Hiddensee,,,,,,Artist,,Aenne Biermann,"German, 1898–1933",,"Biermann, Aenne",German,1898,1933,ca. 1930,1928,1932,Gelatin silver print,23.5 x 17.6 cm (9 1/4 x 6 15/16 in.),"Ford Motor Company Collection, Gift of Ford Motor Company and John C. Waddell, 1987",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/265392,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.182,false,true,286038,Photographs,Photograph,Tanzbar,,,,,,Artist,,Yva (Else Simon),"German, 1900–1942",,"Simon, Else",German,1900,1942,ca. 1930,1928,1932,Gelatin silver print,Image: 24.3 x 17.8 cm (9 9/16 x 7 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286038,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.1033,false,true,266095,Photographs,Photograph,"Juggernaut Car, Madras",,,,,,Artist,,Frederick Fiebig,"German, active 1840s–50s",,"Fiebig, Frederick",German,1840,1859,1850s,1850,1859,Salted paper print from paper negative,Image: 23.9 x 18.3 cm (9 7/16 x 7 3/16 in.),"Purchase, Cynthia Hazen Polsky Gift and The Horace W. Goldsmith Foundation Gift through Joyce and Robert Menschel, 1989",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/266095,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.571,false,true,685910,Photographs,Carte-de-visite,Louis Leloir,,,,,,Artist,,W. Severin,"German, active 1840s–70s",,"Severin, W.",German,1800,1890,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685910,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.11,false,true,685353,Photographs,Carte-de-visite,[Wilhelm Amberg],,,,,,Artist,,Loescher & Petsch,"German, active ca. 1860–90",,Loescher & Petsch,German,1860,1890,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685353,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1983.1020,false,true,263541,Photographs,Photograph,Tête du Sphynx,,,,,,Artist,,Wilhelm Hammerschmidt,"German, born Prussia, died 1869",,"Hammerschmidt, Wilhelm",German,,1869,ca. 1860,1858,1862,Albumen silver print from glass negative,23.8 x 31.5 cm. (9 3/8 x 12 3/8 in.),"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1983",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/263541,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.1076,false,true,266127,Photographs,Photograph,"Porte de la Mosque, Sultan Hassan. Partie Inférieure",,,,,,Artist,,Wilhelm Hammerschmidt,"German, born Prussia, died 1869",,"Hammerschmidt, Wilhelm",German,,1869,ca. 1860,1858,1862,Albumen silver print from glass negative,23.5 x 31.2 cm. (9 1/4 x 12 1/4 in.),"Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 1989",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/266127,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1992.5050,false,true,266692,Photographs,Photograph,Temple de Dandour en Nubie,,,,,,Artist,,Wilhelm Hammerschmidt,"German, born Prussia, died 1869",,"Hammerschmidt, Wilhelm",German,,1869,1860s,1860,1869,Albumen silver print from glass negative,23.7 x 31.3 cm. (9 5/16 x 12 5/16 in.),"Gift of Ezra Mack, 1992",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/266692,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1987.1100.61,false,true,265588,Photographs,Photograph,Madame Elena Vacarescu,,,,,,Artist,,Erich Salomon,"German, Berlin 1886–1944 Auschwitz, Poland",,"Salomon, Erich",German,1886,1944,1928,1928,1928,Gelatin silver print,12.6 x 17.6 cm (4 15/16 x 6 15/16 in.),"Ford Motor Company Collection, Gift of Ford Motor Company and John C. Waddell, 1987",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/265588,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1987.1100.266,false,true,265317,Photographs,Photograph,"[Two Women, Seated and in Conversation, in Interior Setting]",,,,,,Artist,,Erich Salomon,"German, Berlin 1886–1944 Auschwitz, Poland",,"Salomon, Erich",German,1886,1944,1920s–30s,1920,1939,Gelatin silver print,16.9 x 22.8 cm. (6 5/8 x 9 in.),"Ford Motor Company Collection, Gift of Ford Motor Company and John C. Waddell, 1987",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/265317,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1987.1100.268,false,true,265319,Photographs,Photograph,[Five Gentlemen Conversing around Table],,,,,,Artist,,Erich Salomon,"German, Berlin 1886–1944 Auschwitz, Poland",,"Salomon, Erich",German,1886,1944,1920s–30s,1920,1939,Gelatin silver print,17.4 x 23.2 cm. (6 7/8 x 9 1/8 in.),"Ford Motor Company Collection, Gift of Ford Motor Company and John C. Waddell, 1987",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/265319,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1992.5013,false,true,266656,Photographs,Photographs,Horse,,,,,,Artist,,Ottomar Anschütz,"German, Lissa (Leszno, Poland) 1846–1907 Berlin",,"Anschütz, Ottomar",German,1846,1907,1884,1884,1884,Albumen silver print,,"Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 1992",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/266656,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1982.1182.965,false,true,288290,Photographs,Stereographs,"Statue of Queen Anne, St. Paul's, London",,,,,,Publisher|Artist|Person in Photograph,,European and American Views|Unknown|Queen Anne Stuart,"British, 1665–1714",,"European and American Views|Unknown|Stuart, Anne Queen",British,1665,1714,1850s–1910s,1850,1919,Albumen silver prints,Mount: 8.8 x 17.8 cm (3 7/16 x 7 in.),"Gift of Weston J. Naef, in memory of Kathleen W. Naef and Weston J. Naef Sr., 1982",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288290,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.13,false,true,685355,Photographs,Carte-de-visite,[Richard Ansdell],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685355,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.18,false,true,685360,Photographs,Carte-de-visite,[John Ballantyne],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685360,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.19,false,true,685361,Photographs,Carte-de-visite,[Edward Charles Barnes],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685361,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.22,false,true,685364,Photographs,Carte-de-visite,[Sir Charles Barry],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685364,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.23,false,true,685365,Photographs,Carte-de-visite,[Edward Middleton Barry],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685365,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.35,false,true,685377,Photographs,Carte-de-visite,[Charles Baxter],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685377,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.48,false,true,685390,Photographs,Carte-de-visite,[Charles Henry Bennett],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685390,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.91,false,true,685433,Photographs,Carte-de-visite,[Sir David Brewster],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685433,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.101,false,true,685443,Photographs,Carte-de-visite,[John Burnet],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685443,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.102,false,true,685444,Photographs,Carte-de-visite,[Frederic William Burton],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685444,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.113,false,true,685455,Photographs,Carte-de-visite,[Philip Hermogenes Calderon],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685455,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.115,false,true,685457,Photographs,Carte-de-visite,[William Callow],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685457,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.135,false,true,685477,Photographs,Carte-de-visite,[Adelaide Claxton],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685477,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.136,false,true,685478,Photographs,Carte-de-visite,[Florence Anne Claxton],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685478,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.137,false,true,685479,Photographs,Carte-de-visite,[Marshall C. Claxton],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685479,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.148,false,true,685490,Photographs,Carte-de-visite,[George Vicat Cole],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685490,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.149,false,true,685491,Photographs,Carte-de-visite,[William Collingwood],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685491,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.155,false,true,685497,Photographs,Carte-de-visite,[Edward William Cooke],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685497,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.158,false,true,685500,Photographs,Carte-de-visite,[Thomas Sidney Cooper],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685500,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.159,false,true,685501,Photographs,Carte-de-visite,[Charles West Cope],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685501,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.166,false,true,685508,Photographs,Carte-de-visite,[Samuel Cousins],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685508,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.181,false,true,685522,Photographs,Carte-de-visite,[James Francis Danby],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685522,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.182,false,true,685523,Photographs,Carte-de-visite,[Thomas Danby],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685523,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.235,false,true,685576,Photographs,Carte-de-visite,[William Charles Thomas Dobson],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685576,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.237,false,true,685578,Photographs,Carte-de-visite,[George Thomas Doo],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685578,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.242,false,true,685583,Photographs,Carte-de-visite,[Richard Doyle],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685583,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.247,false,true,685588,Photographs,Carte-de-visite,[Edward Duncan],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685588,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.256,false,true,685597,Photographs,Carte-de-visite,[Joseph Durham],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685597,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.258,false,true,685599,Photographs,Carte-de-visite,[William Dyce],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685599,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.260,false,true,685601,Photographs,Carte-de-visite,[Sir Charles Lock Eastlake],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685601,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.266,false,true,685607,Photographs,Carte-de-visite,[Alfred Elmore],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685607,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.271,false,true,685612,Photographs,Carte-de-visite,[William Etty],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685612,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.273,false,true,685614,Photographs,Carte-de-visite,[Thomas Faed],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685614,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.277,false,true,685618,Photographs,Carte-de-visite,[Frederick William Fairholt],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685618,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.284,false,true,685625,Photographs,Carte-de-visite,[Unknown Subject],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685625,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.289,false,true,685630,Photographs,Carte-de-visite,[Sir William Boxall],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685630,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.290,false,true,685631,Photographs,Carte-de-visite,[George Price Boyce],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685631,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.317,false,true,685658,Photographs,Carte-de-visite,[Alfred Downing Fripp],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685658,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.321,false,true,685662,Photographs,Carte-de-visite,[William Powell Frith],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685662,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.324,false,true,685665,Photographs,Carte-de-visite,[William Edward Frost],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685665,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.339,false,true,685679,Photographs,Carte-de-visite,[John Gibson],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685679,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.347,false,true,685687,Photographs,Carte-de-visite,[Margaret Gillies],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685687,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.354,false,true,685694,Photographs,Carte-de-visite,[Edward Alfred Goodall],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685694,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.355,false,true,685695,Photographs,Carte-de-visite,[Frederick Goodall],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685695,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.363,false,true,685703,Photographs,Carte-de-visite,[Robert Graves],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685703,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.371,false,true,685711,Photographs,Carte-de-visite,[Unknown Subject],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685711,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.372,false,true,685712,Photographs,Carte-de-visite,[Carl Haag],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685712,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.378,false,true,685718,Photographs,Carte-de-visite,[Robert Hannah],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685718,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.386,false,true,685726,Photographs,Carte-de-visite,[Solomon Alexander Hart],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685726,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.388,false,true,685728,Photographs,Carte-de-visite,[William Harvey],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685728,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.391,false,true,685731,Photographs,Carte-de-visite,[Edwin Hayes],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685731,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.406,false,true,685746,Photographs,Carte-de-visite,[George Edwards Hering],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685746,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.416,false,true,685756,Photographs,Carte-de-visite,[James Clarke Hook],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685756,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.420,false,true,685760,Photographs,Carte-de-visite,[John Callcott Horsley],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685760,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.426,false,true,685766,Photographs,Carte-de-visite,[Unknown Subject],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685766,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.445,false,true,685785,Photographs,Carte-de-visite,[Samuel Philips Jackson],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685785,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.455,false,true,685795,Photographs,Carte-de-visite,[Joseph John Jenkins],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685795,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.459,false,true,685799,Photographs,Carte-de-visite,[Alexander Johnston],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685799,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.460,false,true,685800,Photographs,Carte-de-visite,[E.B. Jones],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685800,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.461,false,true,685801,Photographs,Carte-de-visite,[George Jones],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685801,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.464,false,true,685804,Photographs,Carte-de-visite,[HenryJutsum],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685804,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.477,false,true,685817,Photographs,Carte-de-visite,[John Prescott Knight],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685817,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.482,false,true,685822,Photographs,Carte-de-visite,[Edward S. ? Kuntze],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685822,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.493,false,true,685833,Photographs,Carte-de-visite,[Charles Landseer],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685833,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.494,false,true,685834,Photographs,Carte-de-visite,[Thomas Landseer],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685834,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.495,false,true,685835,Photographs,Carte-de-visite,[Richard James Lane],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685835,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.500,false,true,685840,Photographs,Carte-de-visite,[Frederick Richard Lee],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685840,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.506,false,true,685846,Photographs,Carte-de-visite,[Sir Frederic Leighton],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685846,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.509,false,true,685849,Photographs,Carte-de-visite,[Henry LeJeune],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685849,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.515,false,true,685855,Photographs,Carte-de-visite,[George Dunlop Leslie],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685855,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.535,false,true,685875,Photographs,Carte-de-visite,[Charles Lucy],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685875,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.538,false,true,685878,Photographs,Carte-de-visite,[Egron Sellif Lundgren],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685878,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.540,false,true,685880,Photographs,Carte-de-visite,[Unknown Subject],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685880,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.554,false,true,685894,Photographs,Carte-de-visite,[William Calder Marshall],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685894,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.555,false,true,685895,Photographs,Carte-de-visite,[George Mason],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685895,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.556,false,true,685896,Photographs,Carte-de-visite,[Gerald Massey],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685896,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.561,false,true,685900,Photographs,Carte-de-visite,[David Hall McKewan],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685900,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.580,false,true,685919,Photographs,Carte-de-visite,[John Everett Millais],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685919,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.581,false,true,685920,Photographs,Carte-de-visite,[John Everett Millais],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685920,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.587,false,true,685926,Photographs,Carte-de-visite,[John Henry Mole],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685926,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.588,false,true,685927,Photographs,Carte-de-visite,[John Henry Mole],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685927,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.593,false,true,685932,Photographs,Carte-de-visite,[Unknown Subject],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685932,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.601,false,true,685940,Photographs,Carte-de-visite,[Unknown Subject],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685940,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.606,false,true,685945,Photographs,Carte-de-visite,[Paul Jacob Naftel],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685945,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.607,false,true,685946,Photographs,Carte-de-visite,[Joseph Nash],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685946,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.609,false,true,685948,Photographs,Carte-de-visite,[Alfred Pizzey Newton],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685948,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.610,false,true,685949,Photographs,Carte-de-visite,[Erskine Nicol],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685949,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.613,false,true,685952,Photographs,Carte-de-visite,[Matthew Noble],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685952,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.617,false,true,685956,Photographs,Carte-de-visite,[William Quiller Orchardson],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685956,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.624,false,true,685963,Photographs,Carte-de-visite,[Samuel Palmer],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685963,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.637,false,true,685976,Photographs,Carte-de-visite,[John Pettie],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685976,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.641,false,true,685980,Photographs,Carte-de-visite,[Frederick Richard Pickersgill],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685980,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.642,false,true,685981,Photographs,Carte-de-visite,[Frederick Richard Pickersgill],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685981,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.649,false,true,685988,Photographs,Carte-de-visite,[Paul Falconer Poole],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685988,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.653,false,true,685992,Photographs,Carte-de-visite,[Edward John Poynter],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685992,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.659,false,true,685998,Photographs,Carte-de-visite,[Unknown Subject],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685998,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.660,false,true,685999,Photographs,Carte-de-visite,[James Baker Pyne],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685999,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.664,false,true,686003,Photographs,Carte-de-visite,[S.Read],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/686003,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.665,false,true,686004,Photographs,Carte-de-visite,[Richard Redgrave],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/686004,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.674,false,true,686013,Photographs,Carte-de-visite,[George Richmond],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/686013,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.688,false,true,686027,Photographs,Carte-de-visite,[William H. Robinson?],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/686027,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.701,false,true,686040,Photographs,Carte-de-visite,[George Augustus Sala],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/686040,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.703,false,true,686042,Photographs,Carte-de-visite,[James Sant],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/686042,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.718,false,true,686057,Photographs,Carte-de-visite,[George Gilbert Scott],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/686057,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.729,false,true,686068,Photographs,Carte-de-visite,[Arthur Sketchley],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/686068,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.732,false,true,686071,Photographs,Carte-de-visite,[Sydney Smirke],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/686071,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.733,false,true,686072,Photographs,Carte-de-visite,[Collingwood Smith],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/686072,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.734,false,true,686073,Photographs,Carte-de-visite,[George Smith],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/686073,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.735,false,true,686074,Photographs,Carte-de-visite,[Unknown Subject],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/686074,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.745,false,true,686084,Photographs,Carte-de-visite,[Unknown Subject],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/686084,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.750,false,true,686089,Photographs,Carte-de-visite,[Lumb Stocks],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/686089,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.751,false,true,686090,Photographs,Carte-de-visite,[Marcus Stone],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/686090,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.754,false,true,686093,Photographs,Carte-de-visite,[George Edward ?],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/686093,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.764,false,true,686103,Photographs,Carte-de-visite,[Frederick Taylor],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/686103,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.766,false,true,686105,Photographs,Carte-de-visite,[John Tenniel],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/686105,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.769,false,true,686108,Photographs,Carte-de-visite,[Unknown Subject],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/686108,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.803,false,true,686142,Photographs,Carte-de-visite,[E.M.Ward],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/686142,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.807,false,true,686146,Photographs,Carte-de-visite,[George Frederick Watts],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/686146,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.808,false,true,686147,Photographs,Carte-de-visite,[George Frederick Watts],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/686147,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.810,false,true,686149,Photographs,Carte-de-visite,[Thomas Webster],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/686149,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.811,false,true,686150,Photographs,Carte-de-visite,[Henry Weekes],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/686150,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.812,false,true,686151,Photographs,Carte-de-visite,[Henry Weekes],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/686151,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.814,false,true,686153,Photographs,Carte-de-visite,[Henry Tanworth Wells],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/686153,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.818,false,true,686157,Photographs,Carte-de-visite,[Richard Westmacott],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/686157,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.820,false,true,686159,Photographs,Carte-de-visite,[Josiah Wood Whymper],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/686159,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.826,false,true,686165,Photographs,Carte-de-visite,[Henry Brittan Willis],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/686165,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.830,false,true,686169,Photographs,Carte-de-visite,[W.F. Woodington],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/686169,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.835,false,true,686174,Photographs,Carte-de-visite,[William Frederick Yeames],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/686174,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.863,false,true,686202,Photographs,Carte-de-visite,[Unknown Subject],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/686202,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.864,false,true,686203,Photographs,Carte-de-visite,[Unknown Subject],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/686203,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.867,false,true,686206,Photographs,Carte-de-visite,[Unknown Subject],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/686206,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.892,false,true,686231,Photographs,Carte-de-visite,[Professor Owen],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/686231,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.917,false,true,686256,Photographs,Carte-de-visite,[Henry O'Neil],,,,,,Photography Studio,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/686256,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.1098.4,false,true,631028,Photographs,Photograph,"Field Marshall Lord Raglan, Crimea",,,,,,Artist|Publisher|Publisher,,"Roger Fenton|P. & D. Colnaghi & Co.|Thomas Agnew & Sons, Ltd.","British, 1819–1869|London",,"Fenton, Roger|Colnaghi & Co., P. & D.|Agnew, Thomas & Sons, Ltd.",British,1819 |1760,1819 |9999,1855,1855,1855,Salted paper print from collodion glass negative,Image: 7 13/16 × 5 7/8 in. (19.9 × 14.9 cm) Mount: 23 5/8 × 17 1/4 in. (60 × 43.8 cm),"Gift of Joyce F. Menschel, 2013",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/631028,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -69.635.1,false,true,271528,Photographs,Photograph,Don Quixote in His Study,,,,,,Artist|Printer,,William Frederick Lake Price|J. Spencer,"British, London 1810–1896 Lee, Kent",,"Price, William Frederick Lake|Spencer, J.",British,1810-10-10,1896-12-09,1857,1857,1857,Albumen silver print from glass negative,Image: 31.9 x 28 cm (12 9/16 x 11 in.) Mount: 42.6 x 33.3 cm (16 3/4 x 13 1/8 in.),"Gift of A. Hyatt Mayor, 1969",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271528,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.772,false,true,285943,Photographs,Photograph,Louis-Jacques-Mandé Daguerre,,,,,,Artist|Person in Photograph,,John Jabez Edwin Mayall|Louis-Jacques-Mandé Daguerre,"British, Oldham, Lancashire 1813–1901 West Sussex",,"Mayall, John Jabez Edwin|Daguerre, Louis-Jacques-Mandé",British,1813,1901,ca. 1860,1858,1862,Albumen silver print from glass negative,Image: 7 7/16 × 5 5/16 in. (18.9 × 13.5 cm) Mount: 9 5/8 in. × 6 13/16 in. (24.5 × 17.3 cm),"Gilman Collection, Purchase, Warner Communications Inc. Purchase Fund, by exchange, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285943,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.553,false,true,685893,Photographs,Carte-de-visite,[Baron Carlo (Charles) Marochetti],,,,,,Photography Studio|Person in Photograph,Person in photograph,John and Charles Watkins|Baron Charles Marochetti,"British, active 1867–71|Italian, Turin 1805–1867",,"John and Charles Watkins|Marochetti, Charles Baron",British,1840 |1805,1875 |1867,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685893,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.528,false,true,685868,Photographs,Carte-de-visite,[John Linnel],,,,,,Artist|Person in Photograph,Person in photograph,Eotto's School of Photography|John Linnell,"British, active 1860s|British, London 1792–1882 Redhill, Surrey",,"Eotto's School of Photography|Linnell, John",British,1792,1882,1863,1863,1863,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685868,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.523,false,true,685863,Photographs,Carte-de-visite,[John Frederick Lewis],,,,,,Photography Studio|Person in Photograph,Person in photograph,John and Charles Watkins|John Frederick Lewis,"British, active 1867–71|British, London 1805–1876 Walton-on-Thames",,"John and Charles Watkins|Lewis, John Frederick",British,1840 |1805,1875 |1876,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685863,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.686,false,true,686025,Photographs,Carte-de-visite,[David Roberts],,,,,,Person in Photograph|Photography Studio,Person in photograph,David Roberts|John and Charles Watkins,"British, Stockbridge, Scotland 1796–1864 London|British, active 1867–71",,"Roberts, David|John and Charles Watkins",British,1796 |1840,1864 |1875,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/686025,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.180,false,true,685521,Photographs,Carte-de-visite,[Francis Danby],,,,,,Photography Studio|Person in Photograph,Person in photograph,John and Charles Watkins|Francis Danby,"British, active 1867–71|Irish, Killinick, County Wexford 1793–1861 Exmouth",,"John and Charles Watkins|Danby, Francis",British,1840 |1793,1875 |1861,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685521,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.55,false,true,283138,Photographs,Negative; Photograph,"Temple of Wingless Victory, Lately Restored",,,,,,Artist,,George Wilson Bridges,"British, 1788–1864",,"Bridges, George Wilson",British,1788,1864,1848,1848,1848,Paper negative,Image: 16.7 x 20.6 cm (6 9/16 x 8 1/8 in.),"Gilman Collection, Purchase, William Talbott Hillman Foundation Gift, 2005",,,,,,,,,,,,Negatives,,http://www.metmuseum.org/art/collection/search/283138,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1997.382.6,false,true,282010,Photographs,Negatives; Photographs,[Botanical Specimens],,,,,,Artist,Attributed to,Robert Hunt,"British, 1807–1887",,"Hunt, Robert",British,1807,1887,ca. 1841,1839,1843,Photogenic drawing negatives on paper and fabric,"Sheet: 11 1/8 × 11 5/16 in. (28.2 × 28.8 cm), album page Top Left Image: 4 1/8 × 3 15/16 in. (10.5 × 10 cm), irregularly trimmed Top Right Image: 4 1/2 × 3 11/16 in. (11.4 × 9.4 cm), irregularly trimmed Bottom Left Image: 1 7/8 × 3 3/8 in. (4.8 × 8.6 cm) Bottom Right Image: 2 9/16 × 3 11/16 in. (6.5 × 9.3 cm), irregularly trimmed","The Rubel Collection, Purchase, Anonymous Gift, 1997",,,,,,,,,,,,Negatives,,http://www.metmuseum.org/art/collection/search/282010,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.259,false,true,285469,Photographs,Negative; Photograph,"Crystal Palace, Hyde Park, Transept",,,,,,Artist,,Benjamin Brecknell Turner,"British, 1815–1894",,"Turner, Benjamin Brecknell",British,1815,1894,1852,1852,1852,Paper negative,Image: 30.2 x 40.1 cm (11 7/8 x 15 13/16 in.),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Negatives,,http://www.metmuseum.org/art/collection/search/285469,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.883,false,true,286446,Photographs,Negative; Photograph,"[Rural Manor, Possibly Bredicot]",,,,,,Artist,,Benjamin Brecknell Turner,"British, 1815–1894",,"Turner, Benjamin Brecknell",British,1815,1894,1852–54,1852,1854,Waxed paper negative,11 5/8 x 15 1/2,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Negatives,,http://www.metmuseum.org/art/collection/search/286446,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.801 (1–47),false,true,285799,Photographs,Album,Views of the Crystal Palace,,,,,,Artist,Attributed to,Philip Henry Delamotte,"British, 1821–1889",,"Delamotte, Philip Henry",British,1821,1889,ca. 1859,1857,1861,Albumen silver prints from glass negatives,12 1/16 × 10 5/16 × 1 in. (30.7 × 26.2 × 2.5 cm),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Albums,,http://www.metmuseum.org/art/collection/search/285799,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1997.382.5,false,true,282008,Photographs,Paper negative,[Chicken Feathers],,,,,,Artist,,Nevil Story Maskelyne,"British, 1823–1911",,"Maskelyne, Nevil Story",British,1823,1911,ca. 1840,1838,1842,Photogenic drawing negative,"Image: 7 5/16 × 7 5/8 in. (18.6 × 19.3 cm), irregularly trimmed","The Rubel Collection, Purchase, Lila Acheson Wallace and Anonymous Gifts, 1997",,,,,,,,,,,,Negatives,,http://www.metmuseum.org/art/collection/search/282008,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.284,false,true,288086,Photographs,Cabinet cards,[Follett Family Album of Children Costumed for a Fancy Dress Ball],,,,,,Artist,,Owen Angel,"British, ca. 1821–1909",,"Angel, Owen",British,1821,1909,ca. 1880,1875,1885,Albumen silver prints from glass negatives with applied color,Album: 28.6 x 22.9 x 4.4 cm (11 1/4 x 9 x 1 3/4 in.) Case,"Purchase, Joseph M. Cohen, William Talbott Hillman Foundation, Robert and Joyce Menschel Family Foundation, Robert D. and Virginia R. Joffe, Paula and Ira M. Resnick, and Maureen and Noel Testa Gifts, 2007",,,,,,,,,,,,Albums,,http://www.metmuseum.org/art/collection/search/288086,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2005.100.623a, b",false,true,283248,Photographs,Photograph; Photomechanical print,Aubrey Beardsley,,,,,,Artist,,Frederick H. Evans,"British, London 1853–1943 London",,"Evans, Frederick Henry",British,1853,1943,ca. 1894,1892,1896,Platinum print; photogravure,13.6 x 9.7 cm (5 3/8 x 3 13/16 in.); 12.3 x 9.5 cm (4 13/16 x 3 3/4 in.),"Gilman Collection, Purchase, Mr. and Mrs. Henry R. Kravis Gift, 2005",,,,,,,,,,,,Photographs|Prints,,http://www.metmuseum.org/art/collection/search/283248,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1991.1073.88,false,true,266372,Photographs,Photographically illustrated book,"Egypt, Sinai and Palestine. Supplementary Volume",,,,,,Artist,,Francis Frith,"British, Chesterfield, Derbyshire 1822–1898 Cannes, France",,"Frith, Francis",British,1822,1898,1860s,1860,1869,Albumen silver prints,,"Rogers Fund, 1908, transferred from the Library",,,,,,,,,,,,Books,,http://www.metmuseum.org/art/collection/search/266372,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1991.1073.91,false,true,266375,Photographs,Photographically illustrated book,Sinai and Palestine,,,,,,Artist,,Francis Frith,"British, Chesterfield, Derbyshire 1822–1898 Cannes, France",,"Frith, Francis",British,1822,1898,1860s,1860,1869,Albumen silver prints,,"Rogers Fund, 1908, transferred from the Library",,,,,,,,,,,,Books,,http://www.metmuseum.org/art/collection/search/266375,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -54.550.2,false,true,269745,Photographs,Print,[Peacock],,,,,,Artist,,Frederick H. Evans,"British, London 1853–1943 London",,"Evans, Frederick Henry",British,1853,1943,1925,1925,1925,Lithograph,,"Gift of Gordon Conn, 1954",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/269745,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1066.1–.86,false,true,286659,Photographs,Photograph,[86 Stereographic Views of The International Exhibition of 1862],,,,,,Artist,,William England,British,,"England, William",British,,1896,1862,1862,1862,Albumen silver prints from glass negatives,,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286659,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.400.83,false,true,291836,Photographs,Daguerreotype,[Stereograph Still-life of Game with Rake and Onion Jar],,,,,,Artist,,T. R. Williams,"British, born 1825",,"Williams, T. R.",British,1825,1825,1854 or later,1854,1860,Daguerreotype,"Image: 7.1 x 6 cm (2 13/16 x 2 3/8 in.), each Mount: 8.3 x 17.1 cm (3 1/4 x 6 3/4 in.)","Bequest of Herbert Mitchell, 2008",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/291836,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.400.88,false,true,291841,Photographs,Daguerreotype,[Stereograph Still-life of Fowl with Initialed Barrel and Root Vegetables],,,,,,Artist,,T. R. Williams,"British, born 1825",,"Williams, T. R.",British,1825,1825,1850s,1850,1859,Daguerreotype,"Image: 6.8 x 5.6 cm (2 11/16 x 2 3/16 in.), each Mount: 8.3 x 17.5 cm (3 1/4 x 6 7/8 in.)","Bequest of Herbert Mitchell, 2008",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/291841,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.400.92,false,true,291845,Photographs,Daguerreotype,"[Stereograph Still-life with Cockatoo, Ornamental Ball, Lace, Peacock Feathers]",,,,,,Artist,,T. R. Williams,"British, born 1825",,"Williams, T. R.",British,1825,1825,1850s,1850,1859,Daguerreotype,"Image: 5.9 x 7.1 cm (2 5/16 x 2 13/16 in.), each Mount: 8.3 x 17.3 cm (3 1/4 x 6 13/16 in.)","Bequest of Herbert Mitchell, 2008",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/291845,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.400.93,false,true,291846,Photographs,Daguerreotype,"[Stereograph Still-life with Cockatoo, Ornamental Ball, Lace, Statuette]",,,,,,Artist,,T. R. Williams,"British, born 1825",,"Williams, T. R.",British,1825,1825,1850s,1850,1859,Daguerreotype,"Image: 7.1 x 5.9 cm (2 13/16 x 2 5/16 in.), each Mount: 8.4 x 17.3 cm (3 5/16 x 6 13/16 in.)","Bequest of Herbert Mitchell, 2008",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/291846,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.400.94,false,true,291847,Photographs,Daguerreotype,"[Stereograph Still-life with Cockatoo, Mirror, Ornamental Ball, Vases, and Lace]",,,,,,Artist,,T. R. Williams,"British, born 1825",,"Williams, T. R.",British,1825,1825,1850s,1850,1859,Daguerreotype,"Image: 7 x 5.9 cm (2 3/4 x 2 5/16 in.), each Mount: 8.3 x 17.5 cm (3 1/4 x 6 7/8 in.)","Bequest of Herbert Mitchell, 2008",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/291847,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1994.519,false,true,267097,Photographs,Photograph,"Portal of the Convent of Sancti Spiritu, Salamanca",,,,,,Artist,,Charles Clifford,"Welsh, 1819–1863",,"Clifford, Charles",British,1819,1863,1853,1853,1853,Albumen silver print from paper negative,Image: 39.9 x 31.4 cm (15 11/16 x 12 3/8 in.) Mount: 61.8 x 47cm (24 5/16 x 18 1/2in.),"Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 1994",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267097,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1988.1052,false,true,265920,Photographs,Photograph,"[Puente del Diablo, Martorell]",,,,,,Artist,,Charles Clifford,"Welsh, 1819–1863",,"Clifford, Charles",British,1819,1863,ca. 1856,1854,1858,Albumen silver print from glass negative,Image: 28.4 x 41.5 cm. (11 3/16 x 16 5/16 in.),"Purchase, Joyce and Robert Menschel Gift, 1988",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/265920,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.1047,false,true,266109,Photographs,Photograph,"[Cloisters of the Church of Saint John of the Kings, Toledo, Spain]",,,,,,Artist,,Charles Clifford,"Welsh, 1819–1863",,"Clifford, Charles",British,1819,1863,ca. 1858,1856,1860,Albumen silver print from glass negative,41.6 x 31.7 cm. (16 3/8 x 12 1/2 in.),"Purchase, Harriette and Noel Levine Gift, 1989",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/266109,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.1048,false,true,266110,Photographs,Photograph,"[Madrid. Facade of the Hospital of ""La Latina""]",,,,,,Artist,,Charles Clifford,"Welsh, 1819–1863",,"Clifford, Charles",British,1819,1863,ca. 1857,1855,1859,Albumen silver print from glass negative,37.7 x 28.2 cm. (14 13/16 x 11 1/8 in.),"Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 1989",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/266110,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.250.1,false,true,288044,Photographs,Photograph,Zaragoza: Porta de los Gigantes,,,,,,Artist,,Charles Clifford,"Welsh, 1819–1863",,"Clifford, Charles",British,1819,1863,1860,1860,1860,Albumen silver print from glass negative,Image: 41 x 31.7 cm (16 1/8 x 12 1/2 in.) Mount: 51 x 47.5 cm (20 1/16 x 18 11/16 in.),"Gift of C. David and Mary Robinson, 2007",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288044,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.250.2,false,true,288043,Photographs,Photograph,"[The Lion Court at the Alhambra, Viewed from Beneath the Portico Temple]",,,,,,Artist,,Charles Clifford,"Welsh, 1819–1863",,"Clifford, Charles",British,1819,1863,1862,1862,1862,Albumen silver print from glass negative,Image: 42 x 32.1 cm (16 9/16 x 12 5/8 in.) Mount: 63.3 x 46.3 cm (24 15/16 x 18 1/4 in.),"Gift of C. David and Mary Robinson, 2007",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288043,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.65,false,true,283154,Photographs,Photograph,"Principal Doorway of the Carthusian Monastery, Burgos",,,,,,Artist,,Charles Clifford,"Welsh, 1819–1863",,"Clifford, Charles",British,1819,1863,1853,1853,1853,Albumen silver print from paper negative,Image: 33.9 x 28.4cm (13 3/8 x 11 3/16in.) Mat: 71.1 x 55.9 cm (28 x 22 in.) Frame: 81.3 x 66 cm (32 x 26 in.),"Gilman Collection, Purchase, Alfred Stieglitz Society Gifts, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283154,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.250,false,true,283156,Photographs,Photograph,"The Walnut Tree of Emperor Charles V, Yuste",,,,,,Artist,,Charles Clifford,"Welsh, 1819–1863",,"Clifford, Charles",British,1819,1863,1858,1858,1858,Albumen silver print from glass negative,Image: 41.6 × 30 cm (16 3/8 × 11 13/16 in.) Mount: 62.3 × 47.4 cm (24 1/2 × 18 11/16 in.),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283156,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.561,false,true,286661,Photographs,Photograph,[The Armor of Philip III],,,,,,Artist,,Charles Clifford,"Welsh, 1819–1863",,"Clifford, Charles",British,1819,1863,1866,1866,1866,Albumen silver print from glass negative,Image: 33.3 x 23 cm (13 1/8 x 9 1/16 in.) Mount: 57.2 x 45.7 cm (22 1/2 x 18 in.),"Gilman Collection, Purchase, Mr. and Mrs. Henry R. Kravis Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286661,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.504.12,false,true,292002,Photographs,Photograph,"Baleares, Aldeanos de Palma y sus alrrededores",,,,,,Artist,,Charles Clifford,"Welsh, 1819–1863",,"Clifford, Charles",British,1819,1863,1860,1860,1860,Albumen silver print from glass negative,Image: 23.9 × 27.6 cm (9 7/16 × 10 7/8 in.) Mount: 42.3 × 58.1 cm (16 5/8 × 22 7/8 in.),"Gilman Collection, Purchase, Mr. and Mrs. Henry R. Kravis Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/292002,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.504.31,false,true,292003,Photographs,Photograph,"Monserrat, Vista general de la montaña desde Monistrol",,,,,,Artist,,Charles Clifford,"Welsh, 1819–1863",,"Clifford, Charles",British,1819,1863,1860,1860,1860,Albumen silver print from glass negative,Image: 31.7 × 41.5 cm (12 1/2 × 16 5/16 in.) Mount: 42.3 × 58.2 cm (16 5/8 × 22 15/16 in.),"Gilman Collection, Purchase, Mr. and Mrs. Henry R. Kravis Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/292003,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.504.46,false,true,283155,Photographs,Photograph,"Zaragoza, Patio de la Casa Conocida con el Nombre de los Infantes",,,,,,Artist,,Charles Clifford,"Welsh, 1819–1863",,"Clifford, Charles",British,1819,1863,1860,1860,1860,Albumen silver print from glass negative,Image: 30.9 × 42.5 cm (12 3/16 × 16 3/4 in.) Mount: 42.3 × 58 cm (16 5/8 × 22 13/16 in.),"Gilman Collection, Purchase, Mr. and Mrs. Henry R. Kravis Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283155,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -46.1.79,false,true,269025,Photographs,Photograph,"[Colosseum, Rome]",,,,,,Artist,,George Wilson Bridges,"British, 1788–1864",,"Bridges, George Wilson",British,1788,1864,1850s,1850,1859,Salted paper print from paper negative,,"David Hunter McAlpin Fund, 1946",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269025,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.159.1,false,true,306202,Photographs,Photograph,"Garden of Selvia, Syracuse, Sicily",,,,,,Artist,,George Wilson Bridges,"British, 1788–1864",,"Bridges, George Wilson",British,1788,1864,1846,1846,1846,Salted paper print from paper negative,Image: 6 15/16 × 8 9/16 in. (17.7 × 21.7 cm) Sheet: 7 5/16 × 8 13/16 in. (18.5 × 22.4 cm),"Bequest of Maurice B. Sendak, 2012",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306202,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.159.2,false,true,306203,Photographs,Photograph,"Benedictine Convent, Catania",,,,,,Artist,,George Wilson Bridges,"British, 1788–1864",,"Bridges, George Wilson",British,1788,1864,1846,1846,1846,Salted paper print from paper negative,Image: 6 3/4 × 8 9/16 in. (17.1 × 21.7 cm) Sheet: 7 11/16 × 9 13/16 in. (19.5 × 24.9 cm),"Bequest of Maurice B. Sendak, 2012",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306203,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.661,false,true,286361,Photographs,Photograph,[The Photographer before his Tent on the Site of the Pyramid of Khafre (Chephren)],,,,,,Artist,,George Wilson Bridges,"British, 1788–1864",,"Bridges, George Wilson",British,1788,1864,1851,1851,1851,Salted paper print from paper negative,Mount: 10 3/8 in. × 13 9/16 in. (26.4 × 34.5 cm) Image: 6 7/16 × 8 7/16 in. (16.3 × 21.4 cm),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286361,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.846a,false,true,286359,Photographs,Photograph,Temple of Victory,,,,,,Artist,,George Wilson Bridges,"British, 1788–1864",,"Bridges, George Wilson",British,1788,1864,ca. 1848,1846,1850,Salted paper print from paper negative,Mount: 10 7/16 in. × 13 5/8 in. (26.5 × 34.6 cm) Image: 6 7/8 × 8 1/4 in. (17.5 × 21 cm),"Gilman Collection, Purchase, Joseph M. Cohen Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286359,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2004.172,false,true,285421,Photographs,Photogram,Spiraea aruncus (Tyrol),,,,,,Artist,,Anna Atkins,"British, 1799–1871",,"Atkins, Anna",British,1799,1871,1851–54,1851,1854,Cyanotype,Image: 35.1 x 24.6 cm (13 13/16 x 9 11/16 in.),"Purchase, Alfred Stieglitz Society Gifts, 2004",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285421,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -63.606.1.21,false,true,270836,Photographs,Photograph,Still Life and Embroidery,,,,,,Artist,,Robert Wilfred Skeffington Lutwidge,"British, 1802–1873",,"Lutwidge, Robert Wilfred Skeffington",British,1802,1873,1856,1856,1856,Albumen silver print,,"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1963",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/270836,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.382 (40),false,true,287930,Photographs,Photograph,Mr and Mrs W. Beach,,,,,,Artist,,Jane Martha St. John,"British, 1803–1882",,"St., John Jane Martha",British,1803,1882,1853–56,1853,1856,Salted paper print,Image: 15.7 × 13.8 cm (6 3/16 × 5 7/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287930,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.382 (66),false,true,287956,Photographs,Photograph,"Stone Pines, Villa Pamfili Doria, Rome",,,,,,Artist,,Jane Martha St. John,"British, 1803–1882",,"St., John Jane Martha",British,1803,1882,1856,1856,1856,Albumen silver print from paper negative,Image: 20 x 24.8 cm (7 7/8 x 9 3/4 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287956,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.382 (67),false,true,287957,Photographs,Photograph,"The Forum, Rome",,,,,,Artist,Possibly by,Jane Martha St. John,"British, 1803–1882",,"St., John Jane Martha",British,1803,1882,1853–56,1853,1856,Albumen silver print,Image: 17.5 × 24.9 cm (6 7/8 × 9 13/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287957,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.382 (68),false,true,287727,Photographs,Photograph,The Colosseum,,,,,,Artist,,Jane Martha St. John,"British, 1803–1882",,"St., John Jane Martha",British,1803,1882,1856,1856,1856,Albumen silver print from paper negative,Image: 19.5 x 25 cm (7 11/16 x 9 13/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287727,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.382 (69),false,true,287958,Photographs,Photograph,Bridge of Augustus at Nani,,,,,,Artist,Possibly by,Jane Martha St. John,"British, 1803–1882",,"St., John Jane Martha",British,1803,1882,1853–56,1853,1856,Albumen silver print,Image: 19.2 × 25 cm (7 9/16 × 9 13/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287958,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.382 (70),false,true,287959,Photographs,Photograph,St. Peters from the Pincian Hill,,,,,,Artist,Possibly by,Jane Martha St. John,"British, 1803–1882",,"St., John Jane Martha",British,1803,1882,1853–56,1853,1856,Albumen silver print,Image: 19.1 × 24.7 cm (7 1/2 × 9 3/4 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287959,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.382 (71),false,true,287960,Photographs,Photograph,"Pincian Garden, Rome",,,,,,Artist,Possibly by,Jane Martha St. John,"British, 1803–1882",,"St., John Jane Martha",British,1803,1882,1853–56,1853,1856,Albumen silver print,Image: 19.3 × 25 cm (7 5/8 × 9 13/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287960,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.382 (72),false,true,287961,Photographs,Photograph,"Pincian Garden, Rome",,,,,,Artist,Possibly by,Jane Martha St. John,"British, 1803–1882",,"St., John Jane Martha",British,1803,1882,1853–56,1853,1856,Albumen silver print,19.5 24.8,"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287961,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.382 (73),false,true,287962,Photographs,Photograph,"Old Cypress Trees in Carthusian Convent, Rome",,,,,,Artist,Possibly by,Jane Martha St. John,"British, 1803–1882",,"St., John Jane Martha",British,1803,1882,1853–56,1853,1856,Albumen silver print,Image: 24.9 × 19.7 cm (9 13/16 × 7 3/4 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287962,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.382 (81),false,true,287970,Photographs,Photograph,[Arch of Titus],,,,,,Artist,,Jane Martha St. John,"British, 1803–1882",,"St., John Jane Martha",British,1803,1882,1853–56,1853,1856,Albumen silver print,Image: 19.2 × 25 cm (7 9/16 × 9 13/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287970,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2005.100.382 (42a, b)",false,true,287932,Photographs,Photograph,"Harry Strangways, present Lord Ilchester; From a Picture Taken from a Church at Kertch",,,,,,Artist,,Jane Martha St. John,"British, 1803–1882",,"St., John Jane Martha",British,1803,1882,1853–56,1853,1856,Salted paper print; albumen silver print,Image: 14.8 × 12 cm (5 13/16 × 4 3/4 in.) Image: 10 × 9.1 cm (3 15/16 × 3 9/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287932,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2005.100.382 (60a, b)",false,true,287950,Photographs,Photograph,Clematis Cerulea; Hollyhocks,,,,,,Artist,,Jane Martha St. John,"British, 1803–1882",,"St., John Jane Martha",British,1803,1882,1853–56,1853,1856,Albumen silver print,Image: 145 × 11.4 cm (57 1/16 × 4 1/2 in.) (a) Image: 14.5 × 10.7 cm (5 11/16 × 4 3/16 in.) (b),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287950,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -63.606.1.1,false,true,270823,Photographs,Photograph,"Copy of a Bust of Her Majesty Queen Victoria, by Joseph Durham, Esq. F.S.A.",,,,,,Artist,,Hugh Welch Diamond,"British, 1808–1886",,"Diamond, Hugh Welch",British,1808,1886,1857,1857,1857,Albumen silver print from glass negative,,"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1963",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/270823,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.19,false,true,283091,Photographs,Photograph,"Patient, Surrey County Lunatic Asylum",,,,,,Artist,,Hugh Welch Diamond,"British, 1808–1886",,"Diamond, Hugh Welch",British,1808,1886,1850–58,1850,1858,Albumen silver print from glass negative,Mat: 11 1/4 × 9 1/8 in. (28.6 × 23.2 cm) Image: 7 1/2 × 5 1/2 in. (19.1 × 14 cm),"Gilman Collection, Purchase, Ann Tenenbaum and Thomas H. Lee Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283091,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.812,false,true,285964,Photographs,Photograph,"[Patient, Surrey County Lunatic Asylum]",,,,,,Artist,,Hugh Welch Diamond,"British, 1808–1886",,"Diamond, Hugh Welch",British,1808,1886,1850–55,1850,1855,Albumen silver print from glass negative,Image: 18.2 x 12.9 cm (7 3/16 x 5 1/16 in.),"Gilman Collection, Purchase, Anonymous Gifts, by exchange, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285964,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -63.606.1.16,false,true,270830,Photographs,Photograph,Peasants of the Alto-Douro,,,,,,Artist,,Joseph James Forrester,"British, 1809–1862",,"Forrester, Joseph James",British,1809,1862,1856,1856,1856,Albumen silver print from glass negative,Image: 18.5 x 15.1 cm (7 5/16 x 5 15/16 in.) Mount: 44 x 30.4 cm (17 5/16 x 11 15/16 in.),"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1963",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/270830,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -63.606.1.25,false,true,270840,Photographs,Photograph,Study for a Picture,,,,,,Artist,,Thomas George Mackinlay,"British, 1809–1865",,"Mackinlay, Thomas George Rev.",British,1809,1865,1856,1856,1856,Albumen silver print,,"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1963",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/270840,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -63.606.1.31,false,true,270847,Photographs,Photograph,"Church Porch, Earlham, near Norwich",,,,,,Artist,,William Harcourt Ranking,"British, 1814–1867",,"Ranking, William Harcourt Dr.",British,1814,1867,1857,1857,1857,Albumen silver print,,"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1963",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/270847,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -63.606.1.38,false,true,270854,Photographs,Photograph,Bonchurch,,,,,,Artist,,Benjamin Brecknell Turner,"British, 1815–1894",,"Turner, Benjamin Brecknell",British,1815,1894,1850s,1850,1859,Gelatin silver print,,"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1963",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/270854,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.756,false,true,285627,Photographs,Photograph,"Pepperharrow Park, Surrey",,,,,,Artist,,Benjamin Brecknell Turner,"British, 1815–1894",,"Turner, Benjamin Brecknell",British,1815,1894,1852–54,1852,1854,Albumen silver print from paper negative,11 1/4 x 15 1/4,"Gilman Collection, Purchase, Ann Tenenbaum and Thomas H. Lee Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285627,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -63.606.1.3,false,true,270845,Photographs,Photograph,Wild Flowers,,,,,,Artist,,Mark Anthony,"British, 1817–1886",,"Anthony, Mark",British,1817,1886,ca. 1857,1855,1859,Albumen silver print,Image: 21.4 x 16.1 cm (8 7/16 x 6 5/16 in.) Mount: 44 x 30.4 cm (17 5/16 x 11 15/16 in.),"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1963",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/270845,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -X.663.1,false,true,271657,Photographs,Stereograph,The Temple. Collection of Antiquities,,,,,,Artist,,Roger Fenton,"British, 1819–1869",,"Fenton, Roger",British,1819,1819,1850s,1850,1859,Albumen silver print from glass negative,,Museum Accession,,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271657,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -X.663.2,false,true,271658,Photographs,Stereograph,"The Megatherium, British Museum",,,,,,Artist,,Roger Fenton,"British, 1819–1869",,"Fenton, Roger",British,1819,1819,1850s,1850,1859,Albumen silver print from glass negative,,Museum Accession,,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271658,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -X.663.3,false,true,271659,Photographs,Stereograph,"The Lycian Saloon, British Museum",,,,,,Artist,,Roger Fenton,"British, 1819–1869",,"Fenton, Roger",British,1819,1819,1850s,1850,1859,Albumen silver print from glass negative,,Museum Accession,,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271659,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1988.1027,false,true,265901,Photographs,Photograph,"Falls of the Llugwy, at Pont-y-Pair",,,,,,Artist,,Roger Fenton,"British, 1819–1869",,"Fenton, Roger",British,1819,1819,1857,1857,1857,Albumen silver print from glass negative,35.8 x 42.9 cm (14 1/16 x 16 7/8 in.),"Purchase, Louis V. Bell Fund and Mrs. Jackson Burke Gift, 1988",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/265901,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -53.531.48,false,true,269654,Photographs,Photograph,"The Council of War on the Morning of the Taking of the Mamelon. Lord Raglan, Omar Pasha, Marshal Pélissier",,,,,,Artist,,Roger Fenton,"British, 1819–1869",,"Fenton, Roger",British,1819,1819,1855,1855,1855,Albumen silver print from glass negative,,"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1953",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269654,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.6,false,true,283078,Photographs,Photograph,"Roslin Chapel, South Porch",,,,,,Artist,,Roger Fenton,"British, 1819–1869",,"Fenton, Roger",British,1819,1819,1856,1856,1856,Salted paper print from glass negative,Image: 35.8 x 43.3 cm (14 1/8 x 17 1/16 in.) Mount: 41 x 48.2 cm (16 1/8 x 19 in.),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283078,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.7,false,true,283079,Photographs,Photograph,Rievaulx Abbey,,,,,,Artist,,Roger Fenton,"British, 1819–1869",,"Fenton, Roger",British,1819,1819,1854,1854,1854,Albumen silver print from glass negative,Image: 35.7 x 29.8 cm (14 1/16 x 11 3/4 in.) Mount: 40.3 x 34.5 cm (15 7/8 x 13 9/16 in.),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283079,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.8,false,true,283080,Photographs,Photograph,"Wharfe and Pool, Below the Strid",,,,,,Artist,,Roger Fenton,"British, 1819–1869",,"Fenton, Roger",British,1819,1819,1854,1854,1854,Salted paper print,Image: 34.5 x 28.2 cm (13 9/16 x 11 1/8 in.) Mount: 56.8 x 44.9 cm (22 3/8 x 17 11/16 in.),"Gilman Collection, Purchase, W. Bruce and Delaney H. Lundberg Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283080,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.9,false,true,283081,Photographs,Photograph,"Salisbury Cathedral - The Nave, from the South Transept",,,,,,Artist,,Roger Fenton,"British, 1819–1869",,"Fenton, Roger",British,1819,1819,1858,1858,1858,Albumen silver print from glass negative,30.2 x 31.1 cm (11 7/8 x 12 1/4 in. ),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283081,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.460.8,false,true,290476,Photographs,Photograph,[Royal Children in Tableau of the Seasons],,,,,,Artist,,Roger Fenton,"British, 1819–1869",,"Fenton, Roger",British,1819,1819,1854,1854,1854,Albumen silver print from glass negative,15.5 x 16 cm (6 1/8 x 6 5/16 in.),"Gift of Paul F. Walter, 2009",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/290476,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.460.9,false,true,290477,Photographs,Photograph,Group at Head Quarters,,,,,,Artist,,Roger Fenton,"British, 1819–1869",,"Fenton, Roger",British,1819,1819,1855,1855,1855,Salted paper print from glass negative,Image: 19.2 x 16.4 cm (7 9/16 x 6 7/16 in.) Mount: 57.7 x 40.2 cm (22 11/16 x 15 13/16 in.),"Gift of Paul F. Walter, 2009",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/290477,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1997.382.33,false,true,282038,Photographs,Photograph,Valley of the Ribble and Pendle Hill,,,,,,Artist,,Roger Fenton,"British, 1819–1869",,"Fenton, Roger",British,1819,1819,1859,1859,1859,Albumen silver print from glass negative,32.1 x 42.5 cm (12 5/8 x 16 3/4 in.),"The Rubel Collection, Purchase, Lila Acheson Wallace and Ann Tenenbaum and Thomas H. Lee Gifts, 1997",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/282038,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1997.382.34,true,true,282039,Photographs,Photograph,[Reclining Odalisque],,,,,,Artist,,Roger Fenton,"British, 1819–1869",,"Fenton, Roger",British,1819,1819,1858,1858,1858,Salted paper print from glass negative,28.5 x 39 cm (11 1/4 x 15 3/8 in.),"The Rubel Collection, Purchase, Lila Acheson Wallace, Anonymous, Joyce and Robert Menschel, Jennifer and Joseph Duke, and Ann Tenenbaum and Thomas H. Lee Gifts, 1997",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/282039,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1997.382.35,true,true,282040,Photographs,Photograph,[Landscape with Clouds],,,,,,Artist,,Roger Fenton,"British, 1819–1869",,"Fenton, Roger",British,1819,1819,probably 1856,1856,1856,Salted paper print from glass negative,"Image: 31.4 x 44.3 cm (12 3/8 x 17 7/16 in.), irregular","The Rubel Collection, Purchase, Anonymous Gift, Curator's Discretionary Grant from The Judith Rothschild Foundation, and Thomas Walther Gift, 1997",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/282040,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.15,false,true,283087,Photographs,Photograph,[Still Life with Fruit],,,,,,Artist,,Roger Fenton,"British, 1819–1869",,"Fenton, Roger",British,1819,1819,1860,1860,1860,Albumen silver print from glass negative,35.2 x 43.1cm (13 7/8 x 16 15/16in.) Mount: 46.6 x 60.3 cm (18 3/8 x 23 3/4 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283087,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.66,false,true,283157,Photographs,Photograph,"Moscow, Domes of Churches in the Kremlin",,,,,,Artist,,Roger Fenton,"British, 1819–1869",,"Fenton, Roger",British,1819,1819,1852,1852,1852,Salted paper print from paper negative,Image: 17.9 x 21.6 cm (7 1/16 x 8 1/2 in.) Mount: 43.7 x 60.1 cm (17 3/16 x 23 11/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283157,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.67,false,true,283158,Photographs,Photograph,"Landing Place, Railway Stores, Balaklava",,,,,,Artist,,Roger Fenton,"British, 1819–1869",,"Fenton, Roger",British,1819,1819,1855,1855,1855,Salted paper print from glass negative,Image: 27.9 x 36.4 cm (11 x 14 5/16 in.) Mount: 43.5 x 58.9 cm (17 1/8 x 23 3/16 in.),"Gilman Collection, Purchase, Alfred Stieglitz Society Gifts, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283158,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.68,false,true,283159,Photographs,Photograph,Sebastopol from Cathcart's Hill,,,,,,Artist,,Roger Fenton,"British, 1819–1869",,"Fenton, Roger",British,1819,1819,1855,1855,1855,Salted paper print from glass negative,Image: 21.9 x 34.7 cm (8 5/8 x 13 11/16 in.) Mount: 41.7 x 57.2 cm (16 7/16 x 22 1/2 in.),"Gilman Collection, Purchase, Mrs. Walter Annenberg and The Annenberg Foundation Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283159,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -63.606.1.14,false,true,270828,Photographs,Photograph,Birth of St. John,,,,,,Artist,,Roger Fenton,"British, 1819–1869",,"Fenton, Roger",British,1819,1819,1857,1857,1857,Albumen silver print,,"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1963",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/270828,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.187,false,true,286419,Photographs,Photograph,Cooking House of the 8th Hussars,,,,,,Artist,,Roger Fenton,"British, 1819–1869",,"Fenton, Roger",British,1819,1819,1855,1855,1855,Salted paper print from glass negative,Image: 15.9 x 20.2 cm (6 1/4 x 7 15/16 in.) Mount: 43.7 x 59.4 cm (17 3/16 x 23 3/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286419,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.275,false,true,286435,Photographs,Photograph,"Rievaulx Abbey, the High Altar",,,,,,Artist,,Roger Fenton,"British, 1819–1869",,"Fenton, Roger",British,1819,1819,1854,1854,1854,Albumen silver print from glass negative,Image: 29.5 x 36.5 cm (11 5/8 x 14 3/8 in.) Mount: 48 x 61.5 cm (18 7/8 x 24 3/16 in.),"Gilman Collection, Purchase, William Talbott Hillman Foundation Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286435,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.282,false,true,286715,Photographs,Photograph,[Lady on Horseback],,,,,,Artist,,Roger Fenton,"British, 1819–1869",,"Fenton, Roger",British,1819,1819,1850s,1850,1859,Salted paper print from glass negative,"21.3 x 22.9 cm (8 3/8 x 9 in.), irregularly trimmed","Gilman Collection, Purchase, Harriette and Noel Levine Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286715,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.285,false,true,286386,Photographs,Photograph,[Self-Portrait],,,,,,Artist,,Roger Fenton,"British, 1819–1869",,"Fenton, Roger",British,1819,1819,February 1852,1852,1852,Albumen silver print from glass negative,Image: 12.2 x 9 cm (4 13/16 x 3 9/16 in.),"Gilman Collection, Purchase, Harriette and Noel Levine Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286386,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.323,false,true,286569,Photographs,Photograph,Omar Pasha,,,,,,Artist,,Roger Fenton,"British, 1819–1869",,"Fenton, Roger",British,1819,1819,1855,1855,1855,Salted paper print from glass negative,Image: 17.9 x 13.4 cm (7 1/16 x 5 1/4 in.) Mount: 59.3 x 42.3 cm (23 3/8 x 16 5/8 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286569,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.348,false,true,286462,Photographs,Photograph,South Front of the Kremlin from the Old Bridge,,,,,,Artist,,Roger Fenton,"British, 1819–1869",,"Fenton, Roger",British,1819,1819,1852,1852,1852,Salted paper print from paper negative,Image: 17.7 × 21.1 cm (6 15/16 × 8 5/16 in.),"Gilman Collection, Purchase, Harriette and Noel Levine Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286462,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.554,false,true,286568,Photographs,Photograph,"Landing Place, Ordnance Wharf, Balaklava",,,,,,Artist,,Roger Fenton,"British, 1819–1869",,"Fenton, Roger",British,1819,1819,1855,1855,1855,Salted paper print from glass negative,Image: 26 x 35.1 cm (10 1/4 x 13 13/16 in.) Mount: 43.2 x 59.4 cm (17 x 23 3/8 in.),"Gilman Collection, Purchase, Joseph M. Cohen Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286568,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.663,false,true,285999,Photographs,Photograph,Captain Burnsby of the Grenadier Guards,,,,,,Artist,,Roger Fenton,"British, 1819–1869",,"Fenton, Roger",British,1819,1819,1855,1855,1855,Salted paper print from glass negative,Image: 16.3 x 15.6 cm (6 7/16 x 6 1/8 in.) Mount: 16.1 x 16.1 cm (6 5/16 x 6 5/16 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285999,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.664,false,true,286393,Photographs,Photograph,Henry Duberly and Mrs. Duberly,,,,,,Artist,,Roger Fenton,"British, 1819–1869",,"Fenton, Roger",British,1819,1819,1855,1855,1855,Salted paper print from glass negative,Image: 15.9 x 15.8 cm (6 1/4 x 6 1/4 in.) Mount: 16 x 15.9 cm (6 5/16 x 6 1/4 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286393,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.665,false,true,286391,Photographs,Photograph,Major General A. H. King,,,,,,Artist,,Roger Fenton,"British, 1819–1869",,"Fenton, Roger",British,1819,1819,1855,1855,1855,Salted paper print from glass negative,Image: 17.8 x 14.7 cm (7 x 5 13/16 in.) Mount: 18.3 x 15 cm (7 3/16 x 5 7/8 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286391,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.666,false,true,286567,Photographs,Photograph,"Cossack Bay, Balaklava",,,,,,Artist,,Roger Fenton,"British, 1819–1869",,"Fenton, Roger",British,1819,1819,1855,1855,1855,Salted paper print from glass negative,Image: 28.5 x 36.2 cm (11 1/4 x 14 1/4 in.) Mount: 43.2 x 59.7 cm (17 x 23 1/2 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286567,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.667,false,true,286566,Photographs,Photograph,"The Genoese Castle, Balaklava",,,,,,Artist,,Roger Fenton,"British, 1819–1869",,"Fenton, Roger",British,1819,1819,1855,1855,1855,Salted paper print from glass negative,Image: 27.4 x 34.8 cm (10 13/16 x 13 11/16 in.) Mount: 43.5 x 59.8 cm (17 1/8 x 23 9/16 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286566,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.668,false,true,286716,Photographs,Photograph,"Moscow, the Kremlin in the Distance",,,,,,Artist,,Roger Fenton,"British, 1819–1869",,"Fenton, Roger",British,1819,1819,1852,1852,1852,Salted paper print from paper negative,Image: 17.7 x 21.8 cm (6 15/16 x 8 9/16 in.) Mount (2nd): 43.8 x 59.1 cm (17 1/4 x 23 1/4 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286716,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.669,false,true,286008,Photographs,Photograph,[Orientalist Study of a Woman],,,,,,Artist,,Roger Fenton,"British, 1819–1869",,"Fenton, Roger",British,1819,1819,1858,1858,1858,Albumen silver print from glass negative,"26.2 x 18.2 cm (10 5/16 x 7 3/16 in.), irregularly trimmed","Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286008,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.671,false,true,286035,Photographs,Photograph,Lieutenant General Sir J. L. Pennefather and Staff,,,,,,Artist,,Roger Fenton,"British, 1819–1869",,"Fenton, Roger",British,1819,1819,1855,1855,1855,Salted paper print from glass negative,Image: 14.4 x 20.8 cm (5 11/16 x 8 3/16 in.) Mount: 40.2 x 57.8 cm (15 13/16 x 22 3/4 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286035,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.672,false,true,286712,Photographs,Photograph,Aelius Caesar,,,,,,Artist,,Roger Fenton,"British, 1819–1869",,"Fenton, Roger",British,1819,1819,1854–58,1854,1858,Salted paper print from glass negative,36.8 x 29.2 cm (14 1/2 x 11 1/2 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286712,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.673,false,true,286719,Photographs,Photograph,Laughing Satyr,,,,,,Artist,,Roger Fenton,"British, 1819–1869",,"Fenton, Roger",British,1819,1819,1854–58,1854,1858,Salted paper print from glass negative,29.7 x 26.6 cm (11 11/16 x 10 1/2 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286719,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.674,false,true,286565,Photographs,Photograph,"Ely Cathedral, from the Grammar School",,,,,,Artist,,Roger Fenton,"British, 1819–1869",,"Fenton, Roger",British,1819,1819,1857,1857,1857,Albumen silver print from glass negative,Image: 35.4 x 44.3 cm (13 15/16 x 17 7/16 in.) Mount: 40.2 x 48.3 cm (15 13/16 x 19 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286565,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.1098.9,false,true,631050,Photographs,Photograph,Harbor Scene,,,,,,Artist,,Thomas Sutton,"British, 1819–1875",,"Sutton, Thomas",British,1819,1875,ca. 1855,1850,1860,Salted paper print from waxed paper negative,Image: 7 3/8 × 9 7/8 in. (18.7 × 25.1 cm) Mount: 10 1/2 in. × 13 in. (26.7 × 33 cm),"Gift of Joyce F. Menschel, 2013",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/631050,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -63.606.1.36,false,true,270852,Photographs,Photograph,Part of Tenby Town and Harbour,,,,,,Artist,,George Stokes,"British, 1819–1903",,"Stokes, George",British,1819,1903,1853,1853,1853,Albumen silver print,,"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1963",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/270852,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -63.606.1.22,false,true,270837,Photographs,Photograph,"Newark Abbey, near Chertsey",,,,,,Artist,,John Richardson Major,"British, 1821–1871",,"Major, John Richardson",British,1821,1871,1856,1856,1856,Albumen silver print,,"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1963",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/270837,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -52.524.7,false,true,269585,Photographs,Photograph,Evening,,,,,,Artist,,Philip Henry Delamotte,"British, 1821–1889",,"Delamotte, Philip Henry",British,1821,1889,1854,1854,1854,Albumen silver print from glass negative,Image: 21.3 x 16.4 cm (8 3/8 x 6 7/16 in.) Mount: 35.3 x 24.9 cm (13 7/8 x 9 13/16 in.),"David Hunter McAlpin Fund, 1952",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269585,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -52.639.6,false,true,269623,Photographs,Photograph,[The Grounds Looking Towards Penge],,,,,,Artist,,Philip Henry Delamotte,"British, 1821–1889",,"Delamotte, Philip Henry",British,1821,1889,1854,1854,1854,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1952",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269623,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -52.639.19,false,true,269619,Photographs,Photograph,"[Storeroom with Artisans and Plaster Casts, Crystal Palace]",,,,,,Artist,,Philip Henry Delamotte,"British, 1821–1889",,"Delamotte, Philip Henry",British,1821,1889,1852,1852,1852,Albumen silver print from glass negative,Image: 22.8 × 28.1 cm (9 in. × 11 1/16 in.) Mount: 36.9 × 52.7 cm (14 1/2 × 20 3/4 in.),"David Hunter McAlpin Fund, 1952",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269619,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -52.639.34,false,true,269621,Photographs,Photograph,The Upper Gallery,,,,,,Artist,,Philip Henry Delamotte,"British, 1821–1889",,"Delamotte, Philip Henry",British,1821,1889,1854,1854,1854,Albumen silver print from glass negative,Image: 27.5 x 23.2 cm (10 13/16 x 9 1/8 in.) Mount: 52.2 x 36.8 cm (20 9/16 x 14 1/2 in.),"David Hunter McAlpin Fund, 1952",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269621,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -52.639.59,false,true,269622,Photographs,Photograph,[Carving a Sphinx],,,,,,Artist,,Philip Henry Delamotte,"British, 1821–1889",,"Delamotte, Philip Henry",British,1821,1889,1854,1854,1854,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1952",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269622,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -63.606.1.12,false,true,270826,Photographs,Photograph,"View in Central Hall, Art Treasures Exhibition, Manchester",,,,,,Artist,,Philip Henry Delamotte,"British, 1821–1889",,"Delamotte, Philip Henry",British,1821,1889,1857,1857,1857,Albumen silver print,,"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1963",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/270826,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.801 (1a–d),false,true,288389,Photographs,Photograph,[Exterior View of Facade and Fountains; Exterior View of Side Pavilion; Exterior Side View of Central Transept; Exterior Side View of Central Trancept with Reclining Figure in Foreground],,,,,,Artist,Attributed to,Philip Henry Delamotte,"British, 1821–1889",,"Delamotte, Philip Henry",British,1821,1889,ca. 1859,1857,1861,Albumen silver print from glass negative,"7.9 x 8.1 cm (3 1/8 x 3 3/16 in.), each","Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288389,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.801 (2a–d),false,true,288393,Photographs,Photograph,[Two Gentlemen Seated on the Grounds of the Palace; The Palace from the Rosary; Cascades and North End of Palace; General View of Gardens and Fountains],,,,,,Artist,Attributed to,Philip Henry Delamotte,"British, 1821–1889",,"Delamotte, Philip Henry",British,1821,1889,ca. 1859,1857,1861,Albumen silver print from glass negative,"7.9 x 8.1 cm (3 1/8 x 3 3/16 in.), each","Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288393,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.801 (3a–d),false,true,288429,Photographs,Photograph,[View of Fountains; Nave Looking North; Screen of the Kings and Queens of England],,,,,,Artist,Attributed to,Philip Henry Delamotte,"British, 1821–1889",,"Delamotte, Philip Henry",British,1821,1889,ca. 1859,1857,1861,Albumen silver print from glass negative,"7.9 x 8.1 cm (3 1/8 x 3 3/16 in.), each","Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288429,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.801 (4a–d),false,true,288433,Photographs,Photograph,"[Mammoth Tree; General View of Nave, Looking South; View from North Gallery; Classical Fountain]",,,,,,Artist,Attributed to,Philip Henry Delamotte,"British, 1821–1889",,"Delamotte, Philip Henry",British,1821,1889,ca. 1859,1857,1861,Albumen silver print from glass negative,"7.9 x 8.1 cm (3 1/8 x 3 3/16 in.), each","Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288433,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.801 (5a–d),false,true,288437,Photographs,Photograph,"[View of Fountain and Byzantine Court; Bronze Fountain in Northern Nave; Monti's Fountain, and Alhambra Court; Alhambra Court from North Transept]",,,,,,Artist,Attributed to,Philip Henry Delamotte,"British, 1821–1889",,"Delamotte, Philip Henry",British,1821,1889,ca. 1859,1857,1861,Albumen silver print from glass negative,"7.9 x 8.1 cm (3 1/8 x 3 3/16 in.), each","Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288437,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.801 (6a–d),false,true,288442,Photographs,Photograph,[Alhambra Court Facade Towards the Nave; Entryway to the Alhambra Court; Side View of Alhambra Court; Alhambra Court Looking Towards the North],,,,,,Artist,Attributed to,Philip Henry Delamotte,"British, 1821–1889",,"Delamotte, Philip Henry",British,1821,1889,ca. 1859,1857,1861,Albumen silver print from glass negative,"7.9 x 8.1 cm (3 1/8 x 3 3/16 in.), each","Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288442,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.801 (7a–d),false,true,288447,Photographs,Photograph,[Alhambra and Court of Lions; View in South Transept],,,,,,Artist,Attributed to,Philip Henry Delamotte,"British, 1821–1889",,"Delamotte, Philip Henry",British,1821,1889,ca. 1859,1857,1861,Albumen silver print from glass negative,"7.9 x 8.1 cm (3 1/8 x 3 3/16 in.), each","Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288447,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.801 (8a–d),false,true,288450,Photographs,Photograph,[View From Music Court to South Transept; View in Tropical Department; View of the Egyptian Court; View of North Transept],,,,,,Artist,Attributed to,Philip Henry Delamotte,"British, 1821–1889",,"Delamotte, Philip Henry",British,1821,1889,ca. 1859,1857,1861,Albumen silver print from glass negative,"7.9 x 8.1 cm (3 1/8 x 3 3/16 in.), each","Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288450,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.801 (9a–d),false,true,288454,Photographs,Photograph,[View in Tropical Department; View of Egyptian Sphinxes],,,,,,Artist,Attributed to,Philip Henry Delamotte,"British, 1821–1889",,"Delamotte, Philip Henry",British,1821,1889,ca. 1859,1857,1861,Albumen silver print from glass negative,"7.9 x 8.1 cm (3 1/8 x 3 3/16 in.), each","Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288454,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.801 (10a–d),false,true,288458,Photographs,Photograph,[View of Sphinxes Among Foliage; View of Tropical Foliage; Egyptian Court from North-east Gallery; Colossal Egyptian Figures],,,,,,Artist,Attributed to,Philip Henry Delamotte,"British, 1821–1889",,"Delamotte, Philip Henry",British,1821,1889,ca. 1859,1857,1861,Albumen silver print from glass negative,"7.9 x 8.1 cm (3 1/8 x 3 3/16 in.), each","Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288458,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.801 (11a–d),false,true,288462,Photographs,Photograph,"[Egyptian Court, Eastern Wall of Principal Court; Egyptian Court, Principal Facade towards the Nave; Lions in the Egyptian Court; [Colonnade Adorned with Egyptian Paintings]",,,,,,Artist,Attributed to,Philip Henry Delamotte,"British, 1821–1889",,"Delamotte, Philip Henry",British,1821,1889,ca. 1859,1857,1861,Albumen silver print from glass negative,"7.9 x 8.1 cm (3 1/8 x 3 3/16 in.), each","Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288462,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.801 (12a–d),false,true,288469,Photographs,Photograph,[View Across the Egyptian Court; View through Egyptian Columns into Classical Sculpture Gallery; Side View of Egyptian Colonnade; Facade of the Hall of Columns],,,,,,Artist,Attributed to,Philip Henry Delamotte,"British, 1821–1889",,"Delamotte, Philip Henry",British,1821,1889,ca. 1859,1857,1861,Albumen silver print from glass negative,"7.9 x 8.1 cm (3 1/8 x 3 3/16 in.), each","Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288469,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.801 (13a–d),false,true,288473,Photographs,Photograph,[Interior of Egyptian Court; Classical Sculpture Gallery with Discus-Thrower; View of Egyptian Court from Classical Sculpture Gallery; Foliage in the Egyptian Court],,,,,,Artist,Attributed to,Philip Henry Delamotte,"British, 1821–1889",,"Delamotte, Philip Henry",British,1821,1889,ca. 1859,1857,1861,Albumen silver print from glass negative,"7.9 x 8.1 cm (3 1/8 x 3 3/16 in.), each","Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288473,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.801 (14a–d),false,true,288479,Photographs,Photograph,"[Elevated View of Egyptian Court; Ninevah Court; Monti's Fountain, and Nineveh Court; Assyrian Court with Workers]",,,,,,Artist,Attributed to,Philip Henry Delamotte,"British, 1821–1889",,"Delamotte, Philip Henry",British,1821,1889,ca. 1859,1857,1861,Albumen silver print from glass negative,"7.9 x 8.1 cm (3 1/8 x 3 3/16 in.), each","Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288479,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.801 (15a–d),false,true,288564,Photographs,Photograph,"[Assyrian Court, Facade Towards the Nave; Elevated View of Assyrian Court; Greek and Roman Sculpture Court; The Three Graces]",,,,,,Artist,Attributed to,Philip Henry Delamotte,"British, 1821–1889",,"Delamotte, Philip Henry",British,1821,1889,ca. 1859,1857,1861,Albumen silver print from glass negative,"7.9 x 8.1 cm (3 1/8 x 3 3/16 in.), each","Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288564,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.801 (16a–d),false,true,288570,Photographs,Photograph,"[Medieval Court; Statue of Coleoni; Equestrian Statue of Gattamelata by Donatello, from the Court of Monuments of Christian Art; Equestrian Statue of Colleone]",,,,,,Artist,Attributed to,Philip Henry Delamotte,"British, 1821–1889",,"Delamotte, Philip Henry",British,1821,1889,ca. 1859,1857,1861,Albumen silver print from glass negative,"7.9 x 8.1 cm (3 1/8 x 3 3/16 in.), each","Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288570,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.801 (17a–d),false,true,288575,Photographs,Photograph,"[Statue of a Horse; Roman Court, Portrait Busts of Emperors; Doorway of Roman Court, Flanked by Portrait Bust of Nero; Greek Court of Philosophers, Statesmen, and Generals]",,,,,,Artist,Attributed to,Philip Henry Delamotte,"British, 1821–1889",,"Delamotte, Philip Henry",British,1821,1889,ca. 1859,1857,1861,Albumen silver print from glass negative,"7.9 x 8.1 cm (3 1/8 x 3 3/16 in.), each","Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288575,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.801 (18a–d),false,true,288579,Photographs,Photograph,[View in Court of Christian Monuments; Views of Greek and Roman Sculpture Court],,,,,,Artist,Attributed to,Philip Henry Delamotte,"British, 1821–1889",,"Delamotte, Philip Henry",British,1821,1889,ca. 1859,1857,1861,Albumen silver print from glass negative,"7.9 x 8.1 cm (3 1/8 x 3 3/16 in.), each","Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288579,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.801 (19a–d),false,true,288583,Photographs,Photograph,[Views of Greek Sculpture Court including Bust of Minerva],,,,,,Artist,Attributed to,Philip Henry Delamotte,"British, 1821–1889",,"Delamotte, Philip Henry",British,1821,1889,ca. 1859,1857,1861,Albumen silver print from glass negative,"7.9 x 8.1 cm (3 1/8 x 3 3/16 in.), each","Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288583,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.801 (20a–d),false,true,288587,Photographs,Photograph,"[Views in Greek Sculpture Gallery, Including A Pieta, Apollo Belvedere, Niobe and her Family, Priest of Bacchus and Farnese Torso]",,,,,,Artist,Attributed to,Philip Henry Delamotte,"British, 1821–1889",,"Delamotte, Philip Henry",British,1821,1889,ca. 1859,1857,1861,Albumen silver print from glass negative,"7.9 x 8.1 cm (3 1/8 x 3 3/16 in.), each","Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288587,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.801 (21a–d),false,true,288591,Photographs,Photograph,[Sculptures in Roman Court; Sacrificial Altar; Greek Sculpture Gallery; Statues in Greek and Roman Sculpture Court],,,,,,Artist,Attributed to,Philip Henry Delamotte,"British, 1821–1889",,"Delamotte, Philip Henry",British,1821,1889,ca. 1859,1857,1861,Albumen silver print from glass negative,"7.9 x 8.1 cm (3 1/8 x 3 3/16 in.), each","Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288591,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.801 (22a–d),false,true,288595,Photographs,Photograph,"[Greek Court; Statue of Minerva; Ludovisi Mars; Iris, Hecate, or Lucifera]",,,,,,Artist,Attributed to,Philip Henry Delamotte,"British, 1821–1889",,"Delamotte, Philip Henry",British,1821,1889,ca. 1859,1857,1861,Albumen silver print from glass negative,"7.9 x 8.1 cm (3 1/8 x 3 3/16 in.), each","Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288595,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.801 (23a–d),false,true,288599,Photographs,Photograph,[Greek Court with Farnese Torso of a Youth; View of a Classical Fountain and Pool; Roman Gallery with Apollo Belvedere and Model of the Roman Forum; Roman Sculpture Court],,,,,,Artist,Attributed to,Philip Henry Delamotte,"British, 1821–1889",,"Delamotte, Philip Henry",British,1821,1889,ca. 1859,1857,1861,Albumen silver print from glass negative,"7.9 x 8.1 cm (3 1/8 x 3 3/16 in.), each","Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288599,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.801 (24a–d),false,true,288603,Photographs,Photograph,"[Sculpture Court Flanked by Torso of Marsyas and Sacrificial Altar; Sculpture Court with Bust of Caracalla; Greek Court with Sculptures of Mercury, Faun, and Ariadne; Roman Court with Three Sculptures of Venus]",,,,,,Artist,Attributed to,Philip Henry Delamotte,"British, 1821–1889",,"Delamotte, Philip Henry",British,1821,1889,ca. 1859,1857,1861,Albumen silver print from glass negative,"7.9 x 8.1 cm (3 1/8 x 3 3/16 in.), each","Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288603,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.801 (25a–d),false,true,288607,Photographs,Photograph,"[Greek Court with Sculpture of Discobolus; Roman Court with Sculptures of Gladiator, Mercury and Fauns; Italian Sculpture near the Great Transept]",,,,,,Artist,Attributed to,Philip Henry Delamotte,"British, 1821–1889",,"Delamotte, Philip Henry",British,1821,1889,ca. 1859,1857,1861,Albumen silver print from glass negative,"7.9 x 8.1 cm (3 1/8 x 3 3/16 in.), each","Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288607,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.801 (26a–d),false,true,288611,Photographs,Photograph,[Roman Court with Sculptures of Posidonius and Wounded Gladiator; Sculpture of Geoffrey Chaucer by Marshall; Sculpture of Shakespeare by Roubilliac],,,,,,Artist,Attributed to,Philip Henry Delamotte,"British, 1821–1889",,"Delamotte, Philip Henry",British,1821,1889,ca. 1859,1857,1861,Albumen silver print from glass negative,"7.9 x 8.1 cm (3 1/8 x 3 3/16 in.), each","Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288611,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.801 (27a–d),false,true,288615,Photographs,Photograph,[Sculptures of Abraham Duquesne; an Ancient Briton; David with his Slingshot; a Hunter],,,,,,Artist,Attributed to,Philip Henry Delamotte,"British, 1821–1889",,"Delamotte, Philip Henry",British,1821,1889,ca. 1859,1857,1861,Albumen silver print from glass negative,"7.9 x 8.1 cm (3 1/8 x 3 3/16 in.), each","Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288615,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.801 (28a–d),false,true,288619,Photographs,Photograph,"[Sculptures of a Dancing Faun, a Neapolitan Improvisatore, Homer, and Thucydides]",,,,,,Artist,Attributed to,Philip Henry Delamotte,"British, 1821–1889",,"Delamotte, Philip Henry",British,1821,1889,ca. 1859,1857,1861,Albumen silver print from glass negative,"7.9 x 8.1 cm (3 1/8 x 3 3/16 in.), each","Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288619,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.801 (29a–d),false,true,288623,Photographs,Photograph,"[Sculptures of Hector, a Dancing Girl, Corinna, and Dorothea]",,,,,,Artist,Attributed to,Philip Henry Delamotte,"British, 1821–1889",,"Delamotte, Philip Henry",British,1821,1889,ca. 1859,1857,1861,Albumen silver print from glass negative,"7.9 x 8.1 cm (3 1/8 x 3 3/16 in.), each","Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288623,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.801 (30a–d),false,true,288627,Photographs,Photograph,"[Sculptures of the Tired Hunter, a Nymph Preparing to Bathe, Godiva, and an Allegorical Figure of Night]",,,,,,Artist,Attributed to,Philip Henry Delamotte,"British, 1821–1889",,"Delamotte, Philip Henry",British,1821,1889,ca. 1859,1857,1861,Albumen silver print from glass negative,"7.9 x 8.1 cm (3 1/8 x 3 3/16 in.), each","Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288627,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.801 (31a–d),false,true,288631,Photographs,Photograph,"[Sculptures of Andromeda, the Toilet of Atalanta, Corinna, and a Naiad]",,,,,,Artist,Attributed to,Philip Henry Delamotte,"British, 1821–1889",,"Delamotte, Philip Henry",British,1821,1889,ca. 1859,1857,1861,Albumen silver print from glass negative,"7.9 x 8.1 cm (3 1/8 x 3 3/16 in.), each","Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288631,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.801 (32a–d),false,true,288635,Photographs,Photograph,"[Sculptures of Sabrina, an Allegorical Figure of Morning, a Nereide, and Eve Listening]",,,,,,Artist,Attributed to,Philip Henry Delamotte,"British, 1821–1889",,"Delamotte, Philip Henry",British,1821,1889,ca. 1859,1857,1861,Albumen silver print from glass negative,"7.9 x 8.1 cm (3 1/8 x 3 3/16 in.), each","Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288635,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.801 (33a–d),false,true,288639,Photographs,Photograph,"[Sculptures of the Medici Venus and Pomona, Venus, Esmeralda, and the Mourners]",,,,,,Artist,Attributed to,Philip Henry Delamotte,"British, 1821–1889",,"Delamotte, Philip Henry",British,1821,1889,ca. 1859,1857,1861,Albumen silver print from glass negative,"7.9 x 8.1 cm (3 1/8 x 3 3/16 in.), each","Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288639,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.801 (34a–d),false,true,288643,Photographs,Photograph,"[Sculptures of Minerva Protecting a Warrior, Una and the Lion, Children with a Pony and a Hound, and Child Play]",,,,,,Artist,Attributed to,Philip Henry Delamotte,"British, 1821–1889",,"Delamotte, Philip Henry",British,1821,1889,ca. 1859,1857,1861,Albumen silver print from glass negative,"7.9 x 8.1 cm (3 1/8 x 3 3/16 in.), each","Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288643,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.801 (35a–d),false,true,288647,Photographs,Photograph,"[Sculptures of Cain, a Hunter Defending his Family, the Massacre of the Innocents, and Allegorical Figures of the First Whisper of Love]",,,,,,Artist,Attributed to,Philip Henry Delamotte,"British, 1821–1889",,"Delamotte, Philip Henry",British,1821,1889,ca. 1859,1857,1861,Albumen silver print from glass negative,"7.9 x 8.1 cm (3 1/8 x 3 3/16 in.), each","Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288647,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.801 (36a–d),false,true,288651,Photographs,Photograph,"[Sculptures of Hylas and the Nymphs, Allegorical Figures of the Three Fates, Zephyr wooing Flora, and Michelangelo's Bacchus and Donatello's St. George]",,,,,,Artist,Attributed to,Philip Henry Delamotte,"British, 1821–1889",,"Delamotte, Philip Henry",British,1821,1889,ca. 1859,1857,1861,Albumen silver print from glass negative,"7.9 x 8.1 cm (3 1/8 x 3 3/16 in.), each","Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288651,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.801 (37a–d),false,true,288655,Photographs,Photograph,[Court of French and Italian Sculpture; Avenue in Front of Fine Arts Courts; View into Classical Sculpture Gallery; Avenue in Front of Sheffield Court],,,,,,Artist,Attributed to,Philip Henry Delamotte,"British, 1821–1889",,"Delamotte, Philip Henry",British,1821,1889,ca. 1859,1857,1861,Albumen silver print from glass negative,"7.9 x 8.1 cm (3 1/8 x 3 3/16 in.), each","Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288655,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.801 (38a–d),false,true,288682,Photographs,Photograph,[Court of Ancient Monuments; German Medieval Court; View with Statue of Albert of Bavaria; Elevated View of Central Transept],,,,,,Artist,Attributed to,Philip Henry Delamotte,"British, 1821–1889",,"Delamotte, Philip Henry",British,1821,1889,ca. 1859,1857,1861,Albumen silver print from glass negative,"7.9 x 8.1 cm (3 1/8 x 3 3/16 in.), each","Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288682,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.801 (39a–d),false,true,288686,Photographs,Photograph,[Entryway to the Renaissance Court; Doorway from an Old Palace of the Dorias; The Ghiberti Gates; View in Medieval Court],,,,,,Artist,Attributed to,Philip Henry Delamotte,"British, 1821–1889",,"Delamotte, Philip Henry",British,1821,1889,ca. 1859,1857,1861,Albumen silver print from glass negative,"7.9 x 8.1 cm (3 1/8 x 3 3/16 in.), each","Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288686,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.801 (40a–d),false,true,288690,Photographs,Photograph,[Medieval Court; The Walsingham Font; Entrance to English Medieval Court],,,,,,Artist,Attributed to,Philip Henry Delamotte,"British, 1821–1889",,"Delamotte, Philip Henry",British,1821,1889,ca. 1859,1857,1861,Albumen silver print from glass negative,"7.9 x 8.1 cm (3 1/8 x 3 3/16 in.), each","Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288690,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.801 (41a–d),false,true,288694,Photographs,Photograph,"[The Rochester Doorway; Vestibule, Garden Side of English Medieval Court; Byzantine Court Exterior and Interior]",,,,,,Artist,Attributed to,Philip Henry Delamotte,"British, 1821–1889",,"Delamotte, Philip Henry",British,1821,1889,ca. 1859,1857,1861,Albumen silver print from glass negative,"7.9 x 8.1 cm (3 1/8 x 3 3/16 in.), each","Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288694,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.801 (42a–d),false,true,288698,Photographs,Photograph,[Views of Byzantine Court with Royal Effigies],,,,,,Artist,Attributed to,Philip Henry Delamotte,"British, 1821–1889",,"Delamotte, Philip Henry",British,1821,1889,ca. 1859,1857,1861,Albumen silver print from glass negative,"7.9 x 8.1 cm (3 1/8 x 3 3/16 in.), each","Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288698,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.801 (43a–d),false,true,288702,Photographs,Photograph,"[Façade, Views, and Entrance Loggia of the Renaissance Court]",,,,,,Artist,Attributed to,Philip Henry Delamotte,"British, 1821–1889",,"Delamotte, Philip Henry",British,1821,1889,ca. 1859,1857,1861,Albumen silver print from glass negative,"7.9 x 8.1 cm (3 1/8 x 3 3/16 in.), each","Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288702,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.801 (44a–d),false,true,288723,Photographs,Photograph,[Tomb of Lorenzo de Medici; Tomb of Giuliano de Medici; Court of Christian Monuments; German Medieval Vestibule],,,,,,Artist,Attributed to,Philip Henry Delamotte,"British, 1821–1889",,"Delamotte, Philip Henry",British,1821,1889,ca. 1859,1857,1861,Albumen silver print from glass negative,"7.9 x 8.1 cm (3 1/8 x 3 3/16 in.), each","Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288723,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.801 (45a–d),false,true,288727,Photographs,Photograph,[Medieval Court; Entryway to Byzantine Court; Sheffield Court; French and Italian Mediaeval Vestibule],,,,,,Artist,Attributed to,Philip Henry Delamotte,"British, 1821–1889",,"Delamotte, Philip Henry",British,1821,1889,ca. 1859,1857,1861,Albumen silver print from glass negative,"7.9 x 8.1 cm (3 1/8 x 3 3/16 in.), each","Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288727,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.801 (46a–d),false,true,288731,Photographs,Photograph,[Entryway of Renaissance Court; Medieval Vestibule; View of the Renaissance Court; Room of Classical Reliefs and Sarcophagi],,,,,,Artist,Attributed to,Philip Henry Delamotte,"British, 1821–1889",,"Delamotte, Philip Henry",British,1821,1889,ca. 1859,1857,1861,Albumen silver print from glass negative,"7.9 x 8.1 cm (3 1/8 x 3 3/16 in.), each","Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288731,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.801 (47a–b),false,true,288735,Photographs,Photograph,[View of Large Plant with Sign Requesting Viewers Not to Touch; Telescope Gallery],,,,,,Artist,Attributed to,Philip Henry Delamotte,"British, 1821–1889",,"Delamotte, Philip Henry",British,1821,1889,ca. 1859,1857,1861,Albumen silver print from glass negative,"7.9 x 8.1 cm (3 1/8 x 3 3/16 in.), each","Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288735,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.23,false,true,283095,Photographs,Photograph,Photographic Study,,,,,,Artist,,Clementina Hawarden,"British, 1822–1865",,"Hawarden, Lady Clementina",British,1822,1865,early 1860s,1860,1864,Albumen silver print from glass negative,20.1 x 14.4 cm (7 15/16 x 5 11/16 in.),"Gilman Collection, Purchase, Harriette and Noel Levine Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283095,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.775,false,true,286598,Photographs,Photograph,Photographic Study,,,,,,Artist,,Clementina Hawarden,"British, 1822–1865",,"Hawarden, Lady Clementina",British,1822,1865,Early 1860s,1860,1865,Albumen silver print from glass negative,Image: 3 1/4 × 2 9/16 in. (8.3 × 6.5 cm),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286598,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.620,false,true,296278,Photographs,Photograph,Frontispiece of Inquiries into Human Faculty and its Development,,,,,,Artist,,Francis Galton,"British, 1822–1911",,"Galton, Francis",British,1822,1911,1883,1883,1883,Albumen silver print from glass negative,Image: 20 x 11.8 cm (7 7/8 x 4 5/8 in.) Frame: 35.6 x 27.9 cm (14 x 11 in.),"Joyce F. Menschel Photography Library Fund, 2002, transferred from the Joyce F. Menschel Photography Library",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/296278,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.400.65,false,true,291818,Photographs,Daguerreotype,[Seated Middle-aged Man in Bow Tie and Jacket],,,,,,Artist,,John Watkins,"British, 1823–1874",,"Watkins, John",British,1823,1874,1850s,1850,1859,Daguerreotype,Image: 14 x 10 cm (5 1/2 x 3 15/16 in.) Plate: 19.1 x 15.2 cm (7 1/2 x 6 in.) Case: 1.9 x 20.2 x 16.5 cm (3/4 x 7 15/16 x 6 1/2 in.),"Bequest of Herbert Mitchell, 2008",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/291818,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.771,false,true,286549,Photographs,Photograph,Sultan,,,,,,Artist,,Nevil Story Maskelyne,"British, 1823–1911",,"Maskelyne, Nevil Story",British,1823,1911,mid-1850s,1853,1857,Salted paper print from glass negative,Image: 5 3/4 × 6 7/8 in. (14.6 × 17.5 cm); corners trimmed,"Gilman Collection, Purchase, Joseph M. Cohen Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286549,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.400.58,false,true,291811,Photographs,Daguerreotype,"[Two Young Men in Bow Ties, One Seated Holding a Book, One Standing]",,,,,,Artist,,Robert Boning,"British, 1826–1878",,"Boning, Robert",British,1826,1878,1850s,1850,1859,Daguerreotype,Image: 9.3 x 7 cm (3 11/16 x 2 3/4 in.) Plate: 10.8 x 8.3 cm (4 1/4 x 3 1/4 in.) Case: 1.1 x 11.7 x 9.5 cm (7/16 x 4 5/8 x 3 3/4 in.),"Bequest of Herbert Mitchell, 2008",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/291811,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -63.606.1.27,false,true,270842,Photographs,Photograph,"The Mouth of the East and West Lyn, Lynmouth, North Devon",,,,,,Artist,,Henry Pollock,"British, 1826–1889",,"Pollock, Henry",British,1826,1889,1856,1856,1856,Albumen silver print,,"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1963",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/270842,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2012.112,false,true,301892,Photographs,Photograph,"Oak Struck by Lightning, Badger, 1856.",,,,,,Artist,,Alfred Capel Cure,"British, 1826–1896",,"Capel, Cure Alfred",British,1826,1896,1856,1856,1856,Albumen silver print from paper negative,Image: 18 x 22.7 cm (7 1/16 x 8 15/16 in.) Mount: 25.2 x 30.5 cm (9 15/16 x 12 in.),"Purchase, The Buddy Taub Foundation Gift, Dennis A. Roach and Jill Roach, Directors, 2012",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/301892,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.460.2,false,true,290470,Photographs,Photograph,"Hurstmonceaux, Sussex",,,,,,Artist,,Alfred Capel Cure,"British, 1826–1896",,"Capel, Cure Alfred",British,1826,1896,1857,1857,1857,Albumen silver print from paper negative,Image: 21.2 x 27.2 cm (8 3/8 x 10 11/16 in.) Mount: 32.5 x 37.9 cm (12 13/16 x 14 15/16 in.),"Gift of Paul F. Walter, 2009",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/290470,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.460.3,false,true,290471,Photographs,Photograph,Thornton,,,,,,Artist,,Alfred Capel Cure,"British, 1826–1896",,"Capel, Cure Alfred",British,1826,1896,1860,1860,1860,Albumen silver print from paper negative,Image: 21.4 x 26.9 cm (8 7/16 x 10 9/16 in.) Mount: 32.5 x 37.9 cm (12 13/16 x 14 15/16 in.),"Gift of Paul F. Walter, 2009",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/290471,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.460.4,false,true,290472,Photographs,Photograph,Montacute House near Yeovil,,,,,,Artist,,Alfred Capel Cure,"British, 1826–1896",,"Capel, Cure Alfred",British,1826,1896,1857–60,1857,1860,Albumen silver print from paper negatives,Image: 21 x 27.1 cm (8 1/4 x 10 11/16 in.) (clippped corners) Mount: 32.5 x 37.8 cm (12 13/16 x 14 7/8 in.),"Gift of Paul F. Walter, 2009",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/290472,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.460.5,false,true,290473,Photographs,Photograph,"Interior, Tintern",,,,,,Artist,,Alfred Capel Cure,"British, 1826–1896",,"Capel, Cure Alfred",British,1826,1896,1857,1857,1857,Albumen silver print from paper negative,Image: 22 x 27.2 cm (8 11/16 x 10 11/16 in.) Mount: 32.5 x 37.9 cm (12 13/16 x 14 15/16 in.),"Gift of Paul F. Walter, 2009",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/290473,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1987.1183.39,false,true,265773,Photographs,Photograph,Peterborough,,,,,,Artist,,Alfred Capel Cure,"British, 1826–1896",,"Capel, Cure Alfred",British,1826,1896,1860,1860,1860,Albumen silver print from paper negative,21.3 x 26.9 cm. (8 3/8 x 10 9/16 in.),"Gift of Paul F. Walter, in memory of Christopher Hemphill, 1987",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/265773,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1987.1183.40,false,true,265775,Photographs,Photograph,Buildwas Abbey,,,,,,Artist,,Alfred Capel Cure,"British, 1826–1896",,"Capel, Cure Alfred",British,1826,1896,1858,1858,1858,Albumen silver print from paper negative,21.3 x 26.8 cm. (8 3/8 x 10 9/16 in.),"Gift of Paul F. Walter, in memory of Christopher Hemphill, 1987",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/265775,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1987.1183.41,false,true,265776,Photographs,Photograph,Willey,,,,,,Artist,,Alfred Capel Cure,"British, 1826–1896",,"Capel, Cure Alfred",British,1826,1896,1850s–60s,1850,1869,Albumen silver print from paper negative,17.4 x 22.0 cm. (6 7/8 x 8 11/16 in.),"Gift of Paul F. Walter, in memory of Christopher Hemphill, 1987",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/265776,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1987.1183.42,false,true,265777,Photographs,Photograph,Falaise Castle,,,,,,Artist,,Alfred Capel Cure,"British, 1826–1896",,"Capel, Cure Alfred",British,1826,1896,1856,1856,1856,Albumen silver print from paper negative,21.2 x 27.2 cm. (8 3/6 x 10 11/16 in.),"Gift of Paul F. Walter, in memory of Christopher Hemphill, 1987",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/265777,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1987.1183.43,false,true,265778,Photographs,Photograph,Malmesbury,,,,,,Artist,,Alfred Capel Cure,"British, 1826–1896",,"Capel, Cure Alfred",British,1826,1896,1850s–60s,1850,1869,Albumen silver print from paper negative,20.9 x 27.2 cm. (8 1/4 x 10 11/16 in.),"Gift of Paul F. Walter, in memory of Christopher Hemphill, 1987",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/265778,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1987.1183.44,false,true,265779,Photographs,Photograph,Wenlock Abbey,,,,,,Artist,,Alfred Capel Cure,"British, 1826–1896",,"Capel, Cure Alfred",British,1826,1896,1858,1858,1858,Albumen silver print from paper negative,21.4 x 27.2 cm. (8 7/16 x 10 11/16 in.),"Gift of Paul F. Walter, in memory of Christopher Hemphill, 1987",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/265779,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1987.1183.45,false,true,265780,Photographs,Photograph,"[photo-reproduction of Hogarth's print illustrating the Dunciad, Book I, line III]",,,,,,Artist,,Alfred Capel Cure,"British, 1826–1896",,"Capel, Cure Alfred",British,1826,1896,1850s–60s,1850,1869,Albumen silver print from paper negative,15.9 x 17.5 cm. (6 1/4 x 6 7/8 in.),"Gift of Paul F. Walter, in memory of Christopher Hemphill, 1987",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/265780,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1987.1183.46,false,true,265781,Photographs,Photograph,"St. Osyths, Essex",,,,,,Artist,,Alfred Capel Cure,"British, 1826–1896",,"Capel, Cure Alfred",British,1826,1896,1856,1856,1856,Albumen silver print from paper negative,20.8 x 26.8 cm. (8 3/16 x 10 9/16 in.),"Gift of Paul F. Walter, in memory of Christopher Hemphill, 1987",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/265781,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1987.1183.47,false,true,265782,Photographs,Photograph,Wells,,,,,,Artist,,Alfred Capel Cure,"British, 1826–1896",,"Capel, Cure Alfred",British,1826,1896,1857,1857,1857,Albumen silver print from paper negative,20.2 x 27.3 cm. (7 15/16 x 10 3/4 in.),"Gift of Paul F. Walter, in memory of Christopher Hemphill, 1987",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/265782,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1987.1183.48,false,true,265783,Photographs,Photograph,Thornton,,,,,,Artist,,Alfred Capel Cure,"British, 1826–1896",,"Capel, Cure Alfred",British,1826,1896,1860,1860,1860,Albumen silver print from paper negative,21.5 x 27.4 cm. (8 7/16 x 10 13/16 in.),"Gift of Paul F. Walter, in memory of Christopher Hemphill, 1987",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/265783,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1987.1183.49,false,true,265784,Photographs,Photograph,"West Front, Wells",,,,,,Artist,,Alfred Capel Cure,"British, 1826–1896",,"Capel, Cure Alfred",British,1826,1896,1857,1857,1857,Albumen silver print from paper negative,20.6 x 27.0 cm. (8 1/8 x 10 5/8 in.),"Gift of Paul F. Walter, in memory of Christopher Hemphill, 1987",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/265784,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1987.1183.50,false,true,265786,Photographs,Photograph,"Crowland Abbey, the West Front Under Repair",,,,,,Artist,,Alfred Capel Cure,"British, 1826–1896",,"Capel, Cure Alfred",British,1826,1896,1860,1860,1860,Albumen silver print from paper negative,21.8 x 27.0 cm. (8 9/16 x 10 5/8 in.),"Gift of Paul F. Walter, in memory of Christopher Hemphill, 1987",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/265786,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1987.1183.51,false,true,265787,Photographs,Photograph,Byland Abbey,,,,,,Artist,,Alfred Capel Cure,"British, 1826–1896",,"Capel, Cure Alfred",British,1826,1896,1856,1856,1856,Albumen silver print from paper negative,21.1 x 27.0 cm. (8 5/16 x 10 5/8 in.),"Gift of Paul F. Walter, in memory of Christopher Hemphill, 1987",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/265787,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1987.1183.52,false,true,265788,Photographs,Photograph,Haughmond Abbey,,,,,,Artist,,Alfred Capel Cure,"British, 1826–1896",,"Capel, Cure Alfred",British,1826,1896,1858,1858,1858,Albumen silver print from paper negative,21.9 x 27.4 cm. (8 5/8 x 10 13/16 in.),"Gift of Paul F. Walter, in memory of Christopher Hemphill, 1987",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/265788,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1987.1183.53,false,true,265789,Photographs,Photograph,Thornton College - Lincolnshire,,,,,,Artist,,Alfred Capel Cure,"British, 1826–1896",,"Capel, Cure Alfred",British,1826,1896,1860,1860,1860,Albumen silver print from paper negative,21.6 x 27.1 cm. (8 1/2 x 10 11/16 in.),"Gift of Paul F. Walter, in memory of Christopher Hemphill, 1987",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/265789,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1987.1183.54,false,true,265790,Photographs,Photograph,West Front - Peterboro,,,,,,Artist,,Alfred Capel Cure,"British, 1826–1896",,"Capel, Cure Alfred",British,1826,1896,1856,1856,1856,Albumen silver print from paper negative,21.0 x 27.1 cm. (8 1/4 x 10 11/16 in.),"Gift of Paul F. Walter, in memory of Christopher Hemphill, 1987",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/265790,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1987.1183.55,false,true,265791,Photographs,Photograph,"St. Pierre, Caen",,,,,,Artist,,Alfred Capel Cure,"British, 1826–1896",,"Capel, Cure Alfred",British,1826,1896,1856,1856,1856,Albumen silver print from paper negative,19.8 x 25.2 cm. (7 13/16 x 9 15/16 in.),"Gift of Paul F. Walter, in memory of Christopher Hemphill, 1987",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/265791,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1987.1183.56,false,true,265792,Photographs,Photograph,"St. Peter's in the East, Oxford",,,,,,Artist,,Alfred Capel Cure,"British, 1826–1896",,"Capel, Cure Alfred",British,1826,1896,1859,1859,1859,Albumen silver print from paper negative,21.7 x 27.5 cm. (8 9/16 x 10 13/16 in.),"Gift of Paul F. Walter, in memory of Christopher Hemphill, 1987",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/265792,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1987.1183.57,false,true,265793,Photographs,Photograph,"Keep of Tattershall Castle, Lincolnshire - 2nd Fortescue",,,,,,Artist,,Alfred Capel Cure,"British, 1826–1896",,"Capel, Cure Alfred",British,1826,1896,1860,1860,1860,Albumen silver print from paper negative,21.6 x 26.8 cm. (8 1/2 x 10 9/16 in.),"Gift of Paul F. Walter, in memory of Christopher Hemphill, 1987",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/265793,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1987.1183.58,false,true,265794,Photographs,Photograph,"""Peter""",,,,,,Artist,,Alfred Capel Cure,"British, 1826–1896",,"Capel, Cure Alfred",British,1826,1896,1860,1860,1860,Albumen silver print from glass negative,10.2 x 14.2 cm. (4 x 5 9/16 in.),"Gift of Paul F. Walter, in memory of Christopher Hemphill, 1987",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/265794,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1987.1183.59,false,true,265795,Photographs,Photograph,Reverend L. C. Cure and His Pony,,,,,,Artist,,Alfred Capel Cure,"British, 1826–1896",,"Capel, Cure Alfred",British,1826,1896,1859,1859,1859,Albumen silver print from glass negative,11.3 x 14.7 cm. (4 7/16 x 5 13/16 in.),"Gift of Paul F. Walter, in memory of Christopher Hemphill, 1987",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/265795,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1987.1183.60,false,true,265797,Photographs,Photograph,Blake House,,,,,,Artist,,Alfred Capel Cure,"British, 1826–1896",,"Capel, Cure Alfred",British,1826,1896,1860,1860,1860,Albumen silver print from glass negative,"17.2 x 23.9 cm. (6 3/4 x 9 7/16 in.), rounded corners at top","Gift of Paul F. Walter, in memory of Christopher Hemphill, 1987",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/265797,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1987.1183.61,false,true,265798,Photographs,Photograph,[Vignetted portrait of two children],,,,,,Artist,,Alfred Capel Cure,"British, 1826–1896",,"Capel, Cure Alfred",British,1826,1896,1850s–60s,1850,1869,Albumen silver print from paper negative,"8.9 x 7.9 cm. (3 1/2 x 3 1/8 in.), oval","Gift of Paul F. Walter, in memory of Christopher Hemphill, 1987",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/265798,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1987.1183.62,false,true,265799,Photographs,Photograph,"[Vignetted portrait, woman holding a baby]",,,,,,Artist,,Alfred Capel Cure,"British, 1826–1896",,"Capel, Cure Alfred",British,1826,1896,1850s–60s,1850,1869,Albumen silver print from glass negative,8.8 x 8.0 cm. (3 7/16 x 3 1/8 in.),"Gift of Paul F. Walter, in memory of Christopher Hemphill, 1987",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/265799,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1987.1183.63,false,true,265800,Photographs,Photograph,Harry,,,,,,Artist,,Alfred Capel Cure,"British, 1826–1896",,"Capel, Cure Alfred",British,1826,1896,1858,1858,1858,Albumen silver print from glass negative,13.8 x 9.9 cm. (5 7/16 x 3 7/8 in.),"Gift of Paul F. Walter, in memory of Christopher Hemphill, 1987",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/265800,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1987.1183.64,false,true,265801,Photographs,Photograph,Nephews,,,,,,Artist,,Alfred Capel Cure,"British, 1826–1896",,"Capel, Cure Alfred",British,1826,1896,1860,1860,1860,Albumen silver print from glass negative,12.0 x 6.1 cm. (4 3/4 x 2 3/8 in.),"Gift of Paul F. Walter, in memory of Christopher Hemphill, 1987",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/265801,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1987.1183.65,false,true,265802,Photographs,Photograph,Harry,,,,,,Artist,,Alfred Capel Cure,"British, 1826–1896",,"Capel, Cure Alfred",British,1826,1896,1860,1860,1860,Albumen silver print from paper negative,13.9 x 11.0 cm. (5 1/2 x 4 5/16 in.),"Gift of Paul F. Walter, in memory of Christopher Hemphill, 1987",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/265802,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1987.1183.66,false,true,265803,Photographs,Photograph,Gateway - Bury St. Edmond's,,,,,,Artist,,Alfred Capel Cure,"British, 1826–1896",,"Capel, Cure Alfred",British,1826,1896,1858,1858,1858,Albumen silver print from paper negative,"21.8 x 26.8 cm. (8 9/16 x 10 9/16 in.), top corners trimmed","Gift of Paul F. Walter, in memory of Christopher Hemphill, 1987",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/265803,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1987.1183.67,false,true,265804,Photographs,Photograph,St. Osyth's Priory,,,,,,Artist,,Alfred Capel Cure,"British, 1826–1896",,"Capel, Cure Alfred",British,1826,1896,1856,1856,1856,Albumen silver print from paper negative,21.1 x 27.2 cm. (8 5/16 x 10 11/16 in.),"Gift of Paul F. Walter, in memory of Christopher Hemphill, 1987",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/265804,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1987.1183.68,false,true,265805,Photographs,Photograph,"Village of Andelys - Chateau Gaillard, Coeur de Lion",,,,,,Artist,,Alfred Capel Cure,"British, 1826–1896",,"Capel, Cure Alfred",British,1826,1896,1856,1856,1856,Albumen silver print from paper negative,20.6 x 27.2 cm. (8 1/8 x 10 11/16 in.),"Gift of Paul F. Walter, in memory of Christopher Hemphill, 1987",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/265805,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1987.1183.69,false,true,265806,Photographs,Photograph,Wenlock,,,,,,Artist,,Alfred Capel Cure,"British, 1826–1896",,"Capel, Cure Alfred",British,1826,1896,1850s–60s,1850,1869,Albumen silver print from paper negative,21.6 x 26.9 cm. (8 1/2 x 10 9/16 in.),"Gift of Paul F. Walter, in memory of Christopher Hemphill, 1987",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/265806,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1987.1183.70,false,true,265808,Photographs,Photograph,Nether Hall,,,,,,Artist,,Alfred Capel Cure,"British, 1826–1896",,"Capel, Cure Alfred",British,1826,1896,1857,1857,1857,Albumen silver print from paper negative,21.5 x 27.4 cm. (8 7/16 x 10 13/16 in.),"Gift of Paul F. Walter, in memory of Christopher Hemphill, 1987",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/265808,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1987.1183.71,false,true,265809,Photographs,Photograph,Blake House,,,,,,Artist,,Alfred Capel Cure,"British, 1826–1896",,"Capel, Cure Alfred",British,1826,1896,1860,1860,1860,Albumen silver print from paper negative,19.8 x 23.9 cm. (7 13/16 x 9 7/16 in.),"Gift of Paul F. Walter, in memory of Christopher Hemphill, 1987",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/265809,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1987.1183.72,false,true,265810,Photographs,Photograph,"Cloisters, Magdalen",,,,,,Artist,,Alfred Capel Cure,"British, 1826–1896",,"Capel, Cure Alfred",British,1826,1896,1859,1859,1859,Albumen silver print from paper negative,21.1 x 27.1 cm. (8 5/16 x 10 11/16 in.),"Gift of Paul F. Walter, in memory of Christopher Hemphill, 1987",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/265810,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1987.1183.73,false,true,265811,Photographs,Photograph,"Green Court, Raglan",,,,,,Artist,,Alfred Capel Cure,"British, 1826–1896",,"Capel, Cure Alfred",British,1826,1896,1858,1858,1858,Albumen silver print from paper negative,21.0 x 27.0 cm. (8 1/4 x 10 5/8 in.),"Gift of Paul F. Walter, in memory of Christopher Hemphill, 1987",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/265811,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1987.1183.74,false,true,265812,Photographs,Photograph,"Town Hall, Cirencester",,,,,,Artist,,Alfred Capel Cure,"British, 1826–1896",,"Capel, Cure Alfred",British,1826,1896,1858,1858,1858,Albumen silver print from paper negative,21.8 x 27.1 cm. (8 9/16 x 10 11/16 in.),"Gift of Paul F. Walter, in memory of Christopher Hemphill, 1987",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/265812,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1987.1183.75,false,true,265813,Photographs,Photograph,"Conventual Buildings, Bury",,,,,,Artist,,Alfred Capel Cure,"British, 1826–1896",,"Capel, Cure Alfred",British,1826,1896,1858,1858,1858,Albumen silver print from paper negative,20.2 x 26.9 cm. (7 15/16 x 10 9/16 in.),"Gift of Paul F. Walter, in memory of Christopher Hemphill, 1987",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/265813,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1987.1183.76,false,true,265814,Photographs,Photograph,Layer Marney,,,,,,Artist,,Alfred Capel Cure,"British, 1826–1896",,"Capel, Cure Alfred",British,1826,1896,1857,1857,1857,Albumen silver print from paper negative,21.2 x 27.2 cm. (8 3/6 x 10 11/16 in.),"Gift of Paul F. Walter, in memory of Christopher Hemphill, 1987",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/265814,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1987.1183.77,false,true,265815,Photographs,Photograph,"Talbot's Tower, Falaise Castle",,,,,,Artist,,Alfred Capel Cure,"British, 1826–1896",,"Capel, Cure Alfred",British,1826,1896,1856,1856,1856,Albumen silver print from paper negative,21.3 x 27.8 cm. (8 3/8 x 10 15/16 in.),"Gift of Paul F. Walter, in memory of Christopher Hemphill, 1987",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/265815,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1987.1183.78,false,true,265816,Photographs,Photograph,"American Creeper, Blake House",,,,,,Artist,,Alfred Capel Cure,"British, 1826–1896",,"Capel, Cure Alfred",British,1826,1896,1860,1860,1860,Albumen silver print from paper negative,15.7 x 20.6 cm. (6 3/16 x 8 1/8 in.),"Gift of Paul F. Walter, in memory of Christopher Hemphill, 1987",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/265816,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1987.1183.79,false,true,265817,Photographs,Photograph,Brevet Lieutenant Colonel Cure,,,,,,Artist,,Alfred Capel Cure,"British, 1826–1896",,"Capel, Cure Alfred",British,1826,1896,1856,1856,1856,Albumen silver print from glass negative,21.0 x 13.6 cm. (8 1/4 x 5 3/6 in.),"Gift of Paul F. Walter, in memory of Christopher Hemphill, 1987",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/265817,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -63.606.1.15,false,true,270829,Photographs,Photograph,"The Meeting of the Waters, Killarney",,,,,,Artist,,Lord Otho Fitzgerald,"British, 1827–1882",,"Fitzgerald, Otho Lord",British,1827,1882,1854,1854,1854,Albumen silver print,,"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1963",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/270829,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -63.606.1.11,false,true,270825,Photographs,Photograph,"Wood-scene, Norton, Cheshire",,,,,,Artist,,Thomas Davies,"British, 1830–1880",,"Davies, Thomas",British,1830,1880,1856,1856,1856,Albumen silver print,,"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1963",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/270825,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -63.606.1.32,false,true,270848,Photographs,Photograph,Sparrowe's House,,,,,,Artist,,Robert Charles Ransome,"British, 1830–1886",,"Ransome, Robert Charles",British,1830,1886,1853,1853,1853,Salted paper print,,"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1963",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/270848,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -63.606.1.35,false,true,270851,Photographs,Photograph,The Time of Promise,,,,,,Artist,,George Shadbolt,"British, 1830–1901",,"Shadbolt, George",British,1830,1901,1857,1857,1857,Albumen silver print,,"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1963",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/270851,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1988.1033,false,true,265905,Photographs,Photograph,In the Valley of the Mole,,,,,,Artist,,Robert Howlett,"British, 1831–1858",,"Howlett, Robert",British,1831,1831,1855,1855,1855,Albumen silver print from glass negative,Image: 20.4 x 25.5 cm (8 1/16 x 10 1/16 in.) Mount: 30.5 x 43.7 cm (12 x 17 3/16 in.),"Purchase, Harrison D. Horblit and Harriette and Noel Levine Gifts and David Hunter McAlpin Fund, 1988",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/265905,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.11,false,true,283083,Photographs,Photograph,[Isambard Kingdom Brunel Standing Before the Launching Chains of the Great Eastern],,,,,,Artist,,Robert Howlett,"British, 1831–1858",,"Howlett, Robert",British,1831,1831,"1857, printed 1863–64",1857,1857,Albumen silver print from glass negative,Image: 27.9 x 21.5 cm (11 x 8 7/16 in.) Mount: 36.4 x 26.6 cm (14 5/16 x 10 1/2 in.),"Gilman Collection, Purchase, Harriette and Noel Levine Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283083,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.839,false,true,285910,Photographs,Photograph,"[Men at Work Beside the Launching Chains of the ""Great Eastern""]",,,,,,Artist,,Robert Howlett,"British, 1831–1858",,"Howlett, Robert",British,1831,1831,"November 18, 1857",1857,1857,Albumen silver print from glass negative,Dome topped: 11 1/8 x 13 15/16 Mount: 16 15/16 × 21 5/16 in. (43 × 54.2 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285910,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1981.1229.51.2,false,true,263193,Photographs,Photograph,Opium smoker,,,,,,Artist,,William Thomas Saunders,"British, 1832–1892",,"Saunders, William Thomas",British,1832,1892,1867,1867,1867,Albumen silver print from glass negative,,"Robert O. Dougan Collection, Gift of Warner Communications Inc., 1981",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/263193,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1981.1229.51.3,false,true,263194,Photographs,Photograph,Pekin-car,,,,,,Artist,,William Thomas Saunders,"British, 1832–1892",,"Saunders, William Thomas",British,1832,1892,1867,1867,1867,Albumen silver print from glass negative,,"Robert O. Dougan Collection, Gift of Warner Communications Inc., 1981",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/263194,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1993.331,false,true,266921,Photographs,Photograph,The Manirung Pass,,,,,,Artist,,Samuel Bourne,"British, 1834–1912",,"Bourne, Samuel",British,1834,1912,1866,1866,1866,Albumen silver print from glass negative,Image: 23.5 x 29.7 cm. (9 1/4 x 11 11/16 in.),"Purchase, Cynthia Hazen Polsky Gift, 1993",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/266921,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1993.189.2.1,false,true,266893,Photographs,Photograph,[Mussucks for Crossing the Beas River Below Bajoura],,,,,,Artist,,Samuel Bourne,"British, 1834–1912",,"Bourne, Samuel",British,1834,1912,1866,1866,1866,Albumen silver print from glass negative,23.7 x 29.8 cm. (9 5/16 x 11 3/4 in.),"Purchase, Cynthia Hazen Polsky Gift, 1993",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/266893,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.499 (99),false,true,286879,Photographs,Photograph,The Manirung Pass,,,,,,Artist,,Samuel Bourne,"British, 1834–1912",,"Bourne, Samuel",British,1834,1912,1860s,1860,1869,Albumen silver print from glass negative,23.7 x 29.6 cm (9 5/16 x 11 5/8 in.),"Gilman Collection, Purchase, Cynthia Hazen Polsky Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286879,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -63.606.1.29,false,true,270844,Photographs,Photograph,"On the Road to Watersmeet, near Lynton, North Devon",,,,,,Artist,,Arthur Julius Pollock,"British, 1835–1890",,"Pollock, Arthur Julius",British,1835,1890,1856,1856,1856,Albumen silver print,,"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1963",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/270844,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.491.1 (9a),false,true,287608,Photographs,Photograph,"[The Viscountess Canning, Barrackpore]",,,,,,Artist,,John Constantine Stanley,"British, 1837–1878",,"Stanley, John Constantine",British,1837,1878,1858,1858,1858,Albumen silver print,Image: 13.3 x 9.6 cm (5 1/4 x 3 3/4 in.) Mount: 33 x 26 cm (13 x 10 1/4 in.),"Gilman Collection, Purchase, Cynthia Hazen Polsky Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287608,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.491.1 (9b),false,true,287681,Photographs,Photograph,"[The Viscountess Canning, Barrackpore]",,,,,,Artist,,John Constantine Stanley,"British, 1837–1878",,"Stanley, John Constantine",British,1837,1878,1858,1858,1858,Albumen silver print,Image: 15 x 12.5 cm (5 7/8 x 4 15/16 in.) Mount: 33 x 26 cm (13 x 10 1/4 in.),"Gilman Collection, Purchase, Cynthia Hazen Polsky Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287681,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.491.1 (14a),false,true,287613,Photographs,Photograph,"[N.E. Gate of Government House, Calcutta]",,,,,,Artist,,John Constantine Stanley,"British, 1837–1878",,"Stanley, John Constantine",British,1837,1878,1858–61,1858,1861,Albumen silver print,Image: 14.1 x 20.7 cm (5 9/16 x 8 1/8 in.) Mount: 33 x 26.4 cm (13 x 10 3/8 in.),"Gilman Collection, Purchase, Cynthia Hazen Polsky Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287613,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.491.1 (14b),false,true,287682,Photographs,Photograph,"[Great Sikh Gun taken at Ferozshah on the Night of December 21, 1845, Government House, Calcutta]",,,,,,Artist,,John Constantine Stanley,"British, 1837–1878",,"Stanley, John Constantine",British,1837,1878,1858–61,1858,1861,Albumen silver print,Image: 15.4 x 20.8 cm (6 1/16 x 8 3/16 in.) Mount: 33 x 26.4 cm (13 x 10 3/8 in.),"Gilman Collection, Purchase, Cynthia Hazen Polsky Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287682,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.491.1 (16a),false,true,287616,Photographs,Photograph,"[View of Chowringhee from Government House, Calcutta]",,,,,,Artist,,John Constantine Stanley,"British, 1837–1878",,"Stanley, John Constantine",British,1837,1878,1858–61,1858,1861,Albumen silver print,Image: 11.1 x 15.8 cm (4 3/8 x 6 1/4 in.) Mount: 33 x 26 cm (13 x 10 1/4 in.),"Gilman Collection, Purchase, Cynthia Hazen Polsky Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287616,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.491.1 (16b),false,true,287684,Photographs,Photograph,[The Maidan from Government House During the Rains],,,,,,Artist,,John Constantine Stanley,"British, 1837–1878",,"Stanley, John Constantine",British,1837,1878,1858–61,1858,1861,Albumen silver print,Image: 13.8 x 20.9 cm (5 7/16 x 8 1/4 in.) Mount: 33 x 26 cm (13 x 10 1/4 in.),"Gilman Collection, Purchase, Cynthia Hazen Polsky Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287684,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.491.1 (17a),false,true,287617,Photographs,Photograph,"[Kitchen and Stables of Government House, Calcutta]",,,,,,Artist,,John Constantine Stanley,"British, 1837–1878",,"Stanley, John Constantine",British,1837,1878,1858–61,1858,1861,Albumen silver print,Image: 12.2 x 18.8 cm (4 13/16 x 7 3/8 in.) Mount: 33 x 26.2 cm (13 x 10 5/16 in.),"Gilman Collection, Purchase, Cynthia Hazen Polsky Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287617,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.491.1 (17b),false,true,287685,Photographs,Photograph,"[Spence's Hotel & St. John's Cathedral, Calcutta]",,,,,,Artist,,John Constantine Stanley,"British, 1837–1878",,"Stanley, John Constantine",British,1837,1878,1858–61,1858,1861,Albumen silver print,Image: 15.5 x 20.1 cm (6 1/8 x 7 15/16 in.) Mount: 33 x 26.2 cm (13 x 10 5/16 in.),"Gilman Collection, Purchase, Cynthia Hazen Polsky Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287685,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.491.1 (37a),false,true,287637,Photographs,Photograph,"[Gunpowder Agents Bungalow, Ishapoor.]",,,,,,Artist,,John Constantine Stanley,"British, 1837–1878",,"Stanley, John Constantine",British,1837,1878,1858–61,1858,1861,Albumen silver print,Image: 12 x 17.2 cm (4 3/4 x 6 3/4 in.) Mount: 33.1 x 26 cm (13 1/16 x 10 1/4 in.),"Gilman Collection, Purchase, Cynthia Hazen Polsky Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287637,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.491.1 (37b),false,true,287693,Photographs,Photograph,"[Gunpowder Agents Bungalow, Ishapoor]",,,,,,Artist,,John Constantine Stanley,"British, 1837–1878",,"Stanley, John Constantine",British,1837,1878,1858–61,1858,1861,Albumen silver print,Image: 11 x 18 cm (4 5/16 x 7 1/16 in.) Mount: 33.1 x 26 cm (13 1/16 x 10 1/4 in.),"Gilman Collection, Purchase, Cynthia Hazen Polsky Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287693,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.491.1 (40a),false,true,287640,Photographs,Photograph,"[Government House, Allahabad]",,,,,,Artist,,John Constantine Stanley,"British, 1837–1878",,"Stanley, John Constantine",British,1837,1878,1858,1858,1858,Albumen silver print,Image: 7.1 x 7.6 cm (2 13/16 x 3 in.) Mount: 33 x 26 cm (13 x 10 1/4 in.),"Gilman Collection, Purchase, Cynthia Hazen Polsky Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287640,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.491.1 (40b),false,true,287696,Photographs,Photograph,"[Gardens, Government House, Allahabad]",,,,,,Artist,,John Constantine Stanley,"British, 1837–1878",,"Stanley, John Constantine",British,1837,1878,1858,1858,1858,Albumen silver print,Image: 7 x 7.8 cm (2 3/4 x 3 1/16 in.) Mount: 33 x 26 cm (13 x 10 1/4 in.),"Gilman Collection, Purchase, Cynthia Hazen Polsky Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287696,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.491.1 (40c),false,true,287697,Photographs,Photograph,"[Countess Canning with Guest, Government House, Allahabad]",,,,,,Artist,,John Constantine Stanley,"British, 1837–1878",,"Stanley, John Constantine",British,1837,1878,1858,1858,1858,Albumen silver print,Image: 7.6 x 6.9 cm (3 x 2 11/16 in.) Mount: 33 x 26 cm (13 x 10 1/4 in.),"Gilman Collection, Purchase, Cynthia Hazen Polsky Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287697,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.491.1 (40d),false,true,287698,Photographs,Photograph,"[Countess Canning with Guests, Government House, Allahabad]",,,,,,Artist,,John Constantine Stanley,"British, 1837–1878",,"Stanley, John Constantine",British,1837,1878,1858,1858,1858,Albumen silver print,Image: 7.1 x 7.5 cm (2 13/16 x 2 15/16 in.) Mount: 33 x 26 cm (13 x 10 1/4 in.),"Gilman Collection, Purchase, Cynthia Hazen Polsky Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287698,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.491.1 (40e),false,true,287699,Photographs,Photograph,"[Man and Horse, Government House, Allahabad]",,,,,,Artist,,John Constantine Stanley,"British, 1837–1878",,"Stanley, John Constantine",British,1837,1878,1858,1858,1858,Albumen silver print,Image: 6.6 x 6.9 cm (2 5/8 x 2 11/16 in.) Mount: 33 x 26 cm (13 x 10 1/4 in.),"Gilman Collection, Purchase, Cynthia Hazen Polsky Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287699,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.491.1 (40f),false,true,287700,Photographs,Photograph,"[Countess Canning with Guests, Government House, Allahabad]",,,,,,Artist,,John Constantine Stanley,"British, 1837–1878",,"Stanley, John Constantine",British,1837,1878,1858,1858,1858,Albumen silver print,Image: 7 x 7.5 cm (2 3/4 x 2 15/16 in.) Mount: 33 x 26 cm (13 x 10 1/4 in.),"Gilman Collection, Purchase, Cynthia Hazen Polsky Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287700,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.1017,false,true,266091,Photographs,Photograph,[Indian Barber],,,,,,Artist,,Willoughby Wallace Hooper,"British, 1837–1912",,"Hooper, Willoughby Wallace",British,1837,1912,1860s,1860,1869,Albumen silver print from glass negative,14.9 x 19.4 cm. (5 7/8 x 7 5/8 in.),"Purchase, Cynthia Hazen Polsky Gift, 1989",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/266091,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1985.1123.18,false,true,264378,Photographs,Photograph,"Graia, On the Red Sea, Near Ezion-Geber, Port of King Solomon",,,,,,Artist,,Frank Mason Good,"British, 1839–1928",,"Good, Frank Mason",British,1839,1928,1870s,1870,1879,Albumen silver print,,"Gift of Weston J. Naef, 1985",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264378,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -41.21.10,false,true,268692,Photographs,Photograph,Julia Margaret Cameron,,,,,,Artist,,Henry Herschel Hay Cameron,"British, 1852–1911",,"Cameron, Henry Herschel Hay",British,1852,1911,1870,1870,1870,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1941",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268692,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -49.74.237,false,true,269472,Photographs,Photograph,"Deir el Bahari, Egypt",,,,,,Artist,,Lord Carnarvon,"British, 1866–1923",,"Carnarvon, Lord",British,1866,1923,1914,1914,1914,Gum bichromate over platinum print,,"Howard Carter Bequest, 1939",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269472,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -49.74.238,false,true,269473,Photographs,Photograph,"Dier el Bahari, Egypt",,,,,,Artist,,Lord Carnarvon,"British, 1866–1923",,"Carnarvon, Lord",British,1866,1923,1914,1914,1914,Gum bichromate over platinum print,,"Howard Carter Bequest, 1939",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269473,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.970,false,true,283250,Photographs,Photograph,[Cavorting by the Pool at Garsington],,,,,,Artist,,Lady Ottoline Violet Anne Cavendish-Bentinck Morrell,"British, 1873–1938",,"Morrell, Lady Ottoline",British,1873,1938,ca. 1916,1915,1917,Gelatin silver prints,8.8 x 6.2 cm (3 7/16 x 2 7/16 in.) and 8.8 x 6.3 cm (3 7/16 x 2 7/16 in.),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283250,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.971,false,true,291058,Photographs,Photograph,[Cavorting by the Pool at Garsington],,,,,,Artist,,Lady Ottoline Violet Anne Cavendish-Bentinck Morrell,"British, 1873–1938",,"Morrell, Lady Ottoline",British,1873,1938,ca. 1916,1915,1917,Gelatin silver print,8.8 x 6.3 cm (3 7/16 x 2 7/16 in.),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/291058,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -46.1.106,false,true,268879,Photographs,Photograph,[Tree in Yard],,,,,,Artist,Attributed to,Samuel Buckle,"British, 1809?–1860",,"Buckle, Samuel",British,1809,1860,1850s,1850,1859,Salted paper print from paper negative,,"David Hunter McAlpin Fund, 1946",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268879,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.491.1 (2),false,true,287592,Photographs,Photograph,"[The Countess Canning, Calcutta]",,,,,,Artist,,Josiah Rowe,"British, ca. 1809–1874",,"Rowe, Josiah",British,1809,1874,1861,1861,1861,Albumen silver print,Image: 24.6 x 22.9 cm (9 11/16 x 9 in.) Mount: 33 x 26.2 cm (13 x 10 5/16 in.),"Gilman Collection, Purchase, Cynthia Hazen Polsky Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287592,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.491.1 (4),false,true,287594,Photographs,Photograph,"[The Countess Canning, Calcutta]",,,,,,Artist,,Josiah Rowe,"British, ca. 1809–1874",,"Rowe, Josiah",British,1809,1874,1861,1861,1861,Albumen silver print from glass negative,Image: 23.6 x 22.4 cm (9 5/16 x 8 13/16 in.) Mount: 33 x 26.2 cm (13 x 10 5/16 in.),"Gilman Collection, Purchase, Cynthia Hazen Polsky Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287594,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.499,false,true,685839,Photographs,Carte-de-visite,[Benjamin William Leader],,,,,,Artist,,John and Charles Watkins,"British, active 1867–71",,John and Charles Watkins,British,1840,1875,1867–1870,1867,1870,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685839,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.1126,false,true,266157,Photographs,Photograph,"[House with Woman on Balcony, Man Standing Below]",,,,,,Artist,,Richard Dykes Alexander,"British, Ipswich 1788–1865",,"Alexander, Richard Dykes",British,1788,1865,ca. 1857,1855,1859,Salted paper print from glass negative,15.2 x 20.6 cm. (6 x 8 1/8 in.),"Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 1989",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/266157,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1971.590.2,false,true,259897,Photographs,Photograph,[Rocky Inlet with Seascape],,,,,,Artist,,Francis Bedford,"British, London 1816–1894 London",,"Bedford, Francis",British,1816,1894,1870s,1870,1879,Albumen silver print,,"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1971",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/259897,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1971.590.3,false,true,259898,Photographs,Photograph,[View Through Rocks' Of Tower On Hill],,,,,,Artist,,Francis Bedford,"British, London 1816–1894 London",,"Bedford, Francis",British,1816,1894,1870s,1870,1879,Albumen silver print,,"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1971",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/259898,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1971.590.4,false,true,259899,Photographs,Photograph,"Monuments and Chancel Steps, Tenby Church",,,,,,Artist,,Francis Bedford,"British, London 1816–1894 London",,"Bedford, Francis",British,1816,1894,1870s,1870,1879,Albumen silver print,,"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1971",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/259899,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1971.590.5,false,true,259900,Photographs,Photograph,Gosceau Rock and the Croft,,,,,,Artist,,Francis Bedford,"British, London 1816–1894 London",,"Bedford, Francis",British,1816,1894,1870s,1870,1879,Albumen silver print,,"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1971",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/259900,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.567.1,false,true,260056,Photographs,Photograph,Colwyn Bay. Rustic Bridge in the Wood,,,,,,Artist,,Francis Bedford,"British, London 1816–1894 London",,"Bedford, Francis",British,1816,1894,1870s,1870,1879,Albumen silver print from glass negative,,"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260056,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.567.2,false,true,260067,Photographs,Photograph,"Chargford, Holy S. Mill",,,,,,Artist,,Francis Bedford,"British, London 1816–1894 London",,"Bedford, Francis",British,1816,1894,1870s,1870,1879,Albumen silver print from glass negative,,"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260067,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.567.3,false,true,260069,Photographs,Photograph,Morte Point from Barraoane Bay,,,,,,Artist,,Francis Bedford,"British, London 1816–1894 London",,"Bedford, Francis",British,1816,1894,1870s,1870,1879,Albumen silver print from glass negative,,"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260069,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.567.4,false,true,260070,Photographs,Photograph,Pensarn Beach,,,,,,Artist,,Francis Bedford,"British, London 1816–1894 London",,"Bedford, Francis",British,1816,1894,1870s,1870,1879,Albumen silver print from glass negative,,"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260070,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.567.5,false,true,260071,Photographs,Photograph,Worcester. From the Severn,,,,,,Artist,,Francis Bedford,"British, London 1816–1894 London",,"Bedford, Francis",British,1816,1894,1870s,1870,1879,Albumen silver print from glass negative,,"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260071,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.567.6,false,true,260072,Photographs,Photograph,"Wrexham, Rossett Mill",,,,,,Artist,,Francis Bedford,"British, London 1816–1894 London",,"Bedford, Francis",British,1816,1894,1870s,1870,1879,Albumen silver print from glass negative,,"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260072,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.567.7,false,true,260073,Photographs,Photograph,The Wye and Symond's Yat. From Rocklands,,,,,,Artist,,Francis Bedford,"British, London 1816–1894 London",,"Bedford, Francis",British,1816,1894,1870s,1870,1879,Albumen silver print from glass negative,,"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260073,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.567.8,false,true,260074,Photographs,Photograph,"Stradford-on-Avon Church, from the Avon",,,,,,Artist,,Francis Bedford,"British, London 1816–1894 London",,"Bedford, Francis",British,1816,1894,1870s,1870,1879,Albumen silver print from glass negative,,"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260074,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.567.9,false,true,260075,Photographs,Photograph,"Ilfracombe, Capstone Parade",,,,,,Artist,,Francis Bedford,"British, London 1816–1894 London",,"Bedford, Francis",British,1816,1894,1870s,1870,1879,Albumen silver print from glass negative,,"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260075,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -63.606.1.5,false,true,270857,Photographs,Photograph,"At Pont y pair, Bettws-y-Coed, North Wales",,,,,,Artist,,Francis Bedford,"British, London 1816–1894 London",,"Bedford, Francis",British,1816,1894,1856,1856,1856,Albumen silver print,,"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1963",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/270857,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.567.10,false,true,260057,Photographs,Photograph,"Sidmount, West end of Esplanade",,,,,,Artist,,Francis Bedford,"British, London 1816–1894 London",,"Bedford, Francis",British,1816,1894,1870s,1870,1879,Albumen silver print from glass negative,,"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260057,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.567.11,false,true,260058,Photographs,Photograph,"Ilfracombe, The Victorian Promenade",,,,,,Artist,,Francis Bedford,"British, London 1816–1894 London",,"Bedford, Francis",British,1816,1894,1870s,1870,1879,Albumen silver print from glass negative,,"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260058,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.567.12,false,true,260059,Photographs,Photograph,Glen Lun. The Rustic Bridge,,,,,,Artist,,Francis Bedford,"British, London 1816–1894 London",,"Bedford, Francis",British,1816,1894,1870s,1870,1879,Albumen silver print from glass negative,,"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260059,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.567.13,false,true,260060,Photographs,Photograph,"Rhyl, from the Sea",,,,,,Artist,,Francis Bedford,"British, London 1816–1894 London",,"Bedford, Francis",British,1816,1894,1870s,1870,1879,Albumen silver print from glass negative,,"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260060,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.567.14,false,true,260061,Photographs,Photograph,"Clovelly, The New Inn and Street",,,,,,Artist,,Francis Bedford,"British, London 1816–1894 London",,"Bedford, Francis",British,1816,1894,1870s,1870,1879,Albumen silver print from glass negative,,"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260061,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.567.15,false,true,260062,Photographs,Photograph,Old Barmouth,,,,,,Artist,,Francis Bedford,"British, London 1816–1894 London",,"Bedford, Francis",British,1816,1894,1870s,1870,1879,Albumen silver print from glass negative,,"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260062,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.567.16,false,true,260063,Photographs,Photograph,"Torquay, Hesketh Crescent and Meadfoot",,,,,,Artist,,Francis Bedford,"British, London 1816–1894 London",,"Bedford, Francis",British,1816,1894,1870s,1870,1879,Albumen silver print from glass negative,,"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260063,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.567.17,false,true,260064,Photographs,Photograph,Rhyl. The Pavilion and Pier,,,,,,Artist,,Francis Bedford,"British, London 1816–1894 London",,"Bedford, Francis",British,1816,1894,1870s,1870,1879,Albumen silver print from glass negative,,"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260064,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.567.18,false,true,260065,Photographs,Photograph,Barmouth. Marine Terrace and Esplanade,,,,,,Artist,,Francis Bedford,"British, London 1816–1894 London",,"Bedford, Francis",British,1816,1894,1870s,1870,1879,Albumen silver print from glass negative,,"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260065,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.567.19,false,true,260066,Photographs,Photograph,Colwyn Bay. The Pool in the Wood,,,,,,Artist,,Francis Bedford,"British, London 1816–1894 London",,"Bedford, Francis",British,1816,1894,1870s,1870,1879,Albumen silver print from glass negative,,"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260066,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.567.20,false,true,260068,Photographs,Photograph,"Abergele, Tan-yr-ogo Cave",,,,,,Artist,,Francis Bedford,"British, London 1816–1894 London",,"Bedford, Francis",British,1816,1894,1870s,1870,1879,Albumen silver print from glass negative,,"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260068,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.717,false,true,285960,Photographs,Photographs,Pensarn Beach,,,,,,Artist,,Francis Bedford,"British, London 1816–1894 London",,"Bedford, Francis",British,1816,1894,1860s,1860,1869,Albumen silver print,Image: 12.6 × 19.9 cm (4 15/16 × 7 13/16 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285960,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1994.215,false,true,267034,Photographs,Photograph,"Walter Churcher, ""Churcher Smileth""",,,,,,Artist,,Frederick H. Evans,"British, London 1853–1943 London",,"Evans, Frederick Henry",British,1853,1943,1900–1905,1900,1905,Platinum print,22.2 x 11.4 cm. (8 3/4 x 4 1/2 in.),"Gift of Joel Snyder, 1994",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267034,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -54.550.3,false,true,269746,Photographs,Photograph,[Angels with Interlace],,,,,,Artist,,Frederick H. Evans,"British, London 1853–1943 London",,"Evans, Frederick Henry",British,1853,1943,1900s–1910s,1900,1919,Platinum print,Image (visible): 3 9/16 × 8 15/16 in. (9.1 × 22.7 cm) Image (overall): 4 × 10 in. (10.1 × 25.4 cm) Overmat: 7 × 13 in. (17.8 × 33 cm),"Gift of Gordon Conn, 1954",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269746,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -54.550.5,false,true,269748,Photographs,Photograph,"[Needlework Altar Cloth, Durham]",,,,,,Artist,,Frederick H. Evans,"British, London 1853–1943 London",,"Evans, Frederick Henry",British,1853,1943,1911-1912,1911,1912,Platinum print on fabric,,"Gift of Gordon Conn, 1954",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269748,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -54.550.6,false,true,269749,Photographs,Photograph,Un Bon Viveur,,,,,,Artist,,Frederick H. Evans,"British, London 1853–1943 London",,"Evans, Frederick Henry",British,1853,1943,1890s–1900s,1890,1909,Platinum print,,"Gift of Gordon Conn, 1954",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269749,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -68.637.4,false,true,271352,Photographs,Photograph,Alvin Langdon Coburn,,,,,,Artist,,Frederick H. Evans,"British, London 1853–1943 London",,"Evans, Frederick Henry",British,1853,1943,ca. 1901,1899,1903,Platinum print,,"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1968",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271352,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -68.637.5,false,true,271353,Photographs,Photograph,Gloucester Cathedral: North Transept,,,,,,Artist,,Frederick H. Evans,"British, London 1853–1943 London",,"Evans, Frederick Henry",British,1853,1943,1900s–1910s,1900,1919,Platinum print,,"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1968",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271353,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -68.637.6,false,true,271354,Photographs,Photograph,Wells Cathedral from the Moat Path,,,,,,Artist,,Frederick H. Evans,"British, London 1853–1943 London",,"Evans, Frederick Henry",British,1853,1943,1900s–1910s,1900,1919,Platinum print,,"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1968",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271354,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -68.637.7,false,true,271355,Photographs,Photograph,"Maison Jeanne d'Arc, Rouen",,,,,,Artist,,Frederick H. Evans,"British, London 1853–1943 London",,"Evans, Frederick Henry",British,1853,1943,1900s,1900,1909,Platinum print,,"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1968",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271355,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -68.688.1,false,true,271404,Photographs,Photograph,"""Castle in the Air""",,,,,,Artist,,Frederick H. Evans,"British, London 1853–1943 London",,"Evans, Frederick Henry",British,1853,1943,ca. 1906,1904,1908,Platinum print,,"David Hunter McAlpin Fund, 1968",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271404,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -68.688.3,false,true,271406,Photographs,Photograph,[Trees],,,,,,Artist,,Frederick H. Evans,"British, London 1853–1943 London",,"Evans, Frederick Henry",British,1853,1943,1900s–1910s,1900,1919,Gum bichromate print,,"David Hunter McAlpin Fund, 1968",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271406,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -69.616.1,false,true,271523,Photographs,Photograph,In the New Forest,,,,,,Artist,,Frederick H. Evans,"British, London 1853–1943 London",,"Evans, Frederick Henry",British,1853,1943,1919,1919,1919,Platinum print,,"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1969",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271523,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -69.616.2,false,true,271524,Photographs,Photograph,Bude,,,,,,Artist,,Frederick H. Evans,"British, London 1853–1943 London",,"Evans, Frederick Henry",British,1853,1943,1910s–20s,1910,1929,Platinum print,,"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1969",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271524,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -69.616.3,false,true,271525,Photographs,Photograph,Bude,,,,,,Artist,,Frederick H. Evans,"British, London 1853–1943 London",,"Evans, Frederick Henry",British,1853,1943,1910s–20s,1910,1929,Platinum print,,"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1969",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271525,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -69.616.4,false,true,271526,Photographs,Photograph,Dandelions,,,,,,Artist,,Frederick H. Evans,"British, London 1853–1943 London",,"Evans, Frederick Henry",British,1853,1943,1900s–1920s,1900,1929,Platinum print,,"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1969",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271526,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -69.616.5,false,true,271527,Photographs,Photograph,A Stalk of Berbery,,,,,,Artist,,Frederick H. Evans,"British, London 1853–1943 London",,"Evans, Frederick Henry",British,1853,1943,1900s–1910s,1900,1919,Platinum print,,"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1969",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271527,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.43.283,false,true,271766,Photographs,Photograph,Height and Light in Bourges Cathedral,,,,,,Artist,,Frederick H. Evans,"British, London 1853–1943 London",,"Evans, Frederick Henry",British,1853,1943,1903,1903,1903,Platinum print,12.0 x 7.4 cm (4 3/4 x 2 15/16 in.),"Alfred Stieglitz Collection, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271766,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -49.55.239,false,true,271764,Photographs,Photograph,George Bernard Shaw,,,,,,Artist,,Frederick H. Evans,"British, London 1853–1943 London",,"Evans, Frederick Henry",British,1853,1943,1901,1901,1901,Platinum print,Image: 9 in. × 6 1/8 in. (22.8 × 15.6 cm) Border 1: 10 11/16 × 7 9/16 in. (27.2 × 19.2 cm) Border 2: 10 7/8 × 7 3/4 in. (27.7 × 19.7 cm) Mount: 14 3/16 × 11 1/4 in. (36 × 28.6 cm),"Alfred Stieglitz Collection, 1949",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271764,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1997.398.3,false,true,282112,Photographs,Photograph,Lincoln Cathedral: From the Castle,,,,,,Artist,,Frederick H. Evans,"British, London 1853–1943 London",,"Evans, Frederick Henry",British,1853,1943,1896,1896,1896,Photogravure,21 x 15.8 cm (8 1/4 x 6 1/4 in. ),"The Rubel Collection, Gift of William Rubel, 1997",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/282112,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.159.8,false,true,306209,Photographs,Photograph,Redlands Woods,,,,,,Artist,,Frederick H. Evans,"British, London 1853–1943 London",,"Evans, Frederick Henry",British,1853,1943,1893,1893,1893,Platinum print,Mount: 9 15/16 in. × 6 11/16 in. (25.3 × 17 cm) Image: 6 1/16 × 4 7/16 in. (15.4 × 11.2 cm),"Bequest of Maurice B. Sendak, 2012",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306209,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1987.1183.1,false,true,265736,Photographs,Photograph,"Organ Screen, York Minster",,,,,,Artist,,Frederick H. Evans,"British, London 1853–1943 London",,"Evans, Frederick Henry",British,1853,1943,ca. 1904,1902,1906,Platinum print,20.3 x 24.4 cm. (8 x 9 5/8 in.),"Gift of Paul F. Walter, in memory of Christopher Hemphill, 1987",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/265736,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1987.1183.2,false,true,265747,Photographs,Photograph,The Little Cloisters,,,,,,Artist,,Frederick H. Evans,"British, London 1853–1943 London",,"Evans, Frederick Henry",British,1853,1943,ca. 1900,1898,1902,Platinum print,11.4 x 15.1 cm. (4 1/2 x 5 15/16 in.),"Gift of Paul F. Walter, in memory of Christopher Hemphill, 1987",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/265747,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.779,false,true,286415,Photographs,Photograph,On a French River,,,,,,Artist,,Frederick H. Evans,"British, London 1853–1943 London",,"Evans, Frederick Henry",British,1853,1943,ca. 1902,1900,1904,Gelatin silver print,Image: 24.8 x 13.1 cm (9 3/4 x 5 3/16 in.) Mount: 45.5 x 30.2 cm (17 15/16 x 11 7/8 in.),"Gilman Collection, Purchase, Mrs. Walter Annenberg and The Annenberg Foundation Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286415,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.906,false,true,286643,Photographs,Photograph,"""In Sure and Certain Hope."" York Minster",,,,,,Artist,,Frederick H. Evans,"British, London 1853–1943 London",,"Evans, Frederick Henry",British,1853,1943,1902,1902,1902,Platinum print,"Image: 20 x 14.8 cm (7 7/8 x 5 13/16 in.) Mount: 26.4 x 18.5 cm (10 3/8 x 7 5/16 in.), irregularly trimmed","Gilman Collection, Purchase, Alfred Stieglitz Society Gifts, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286643,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2010.365,false,true,294742,Photographs,Photograph,The Great British Advance in the West: A Raiding Party Waiting for the Word to Go.,,,,,,Artist,,John Warwick Brooke,"British, London 1886–1929 London",,"Brooke, John Warwick",British,1886,1929,1914–18,1914,1918,Gelatin silver print,Image: 14 x 19.1 cm (5 1/2 x 7 1/2 in.),"Twentieth-Century Photography Fund, 2010",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/294742,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1992.5030,false,true,266672,Photographs,Photograph,French Machinery,,,,,,Artist,,Charles Thurston Thompson,"British, Peckham 1816–1868 Paris",,"Thompson, Charles Thurston",British,1816,1868,1855,1855,1855,Salted paper print from glass negative,22.0 x 28.7 cm (8 11/16 x 11 5/16 in.),"Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 1992",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/266672,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -63.606.1.10,false,true,270824,Photographs,Photograph,"The Court of Lions in the Alhambra, Spain",,,,,,Artist,,John Gregory Crace,"British, London 1809–1889 Dulwich",,"Crace, John Gregory",British,1809,1889,1855,1855,1855,Albumen silver print,,"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1963",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/270824,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.21,false,true,283093,Photographs,Photograph,St. George and the Dragon,,,,,,Artist,,Lewis Carroll,"British, Daresbury, Cheshire 1832–1898 Guildford",,"Carroll, Lewis",British,1832,1898,"June 26, 1875",1875,1875,Albumen silver print from glass negative,Image: 11.7 × 16 cm (4 5/8 × 6 5/16 in.),"Gilman Collection, Purchase, Ann Tenenbaum and Thomas H. Lee Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283093,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.400.64,false,true,291817,Photographs,Daguerreotype,[Seated Man Pointing to a Passage in an Open Book],,,,,,Artist,,John Jabez Edwin Mayall,"British, Oldham, Lancashire 1813–1901 West Sussex",,"Mayall, John Jabez Edwin",British,1813,1901,1850s,1853,1859,Daguerreotype,Image: 15.3 x 10.8 cm (6 x 4 1/4 in.) Plate: 19.7 x 15.2 cm (7 3/4 x 6 in.) Case: 2.1 x 21 x 16.5 cm (13/16 x 8 1/4 x 6 1/2 in.),"Bequest of Herbert Mitchell, 2008",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/291817,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -63.606.1.23,false,true,270838,Photographs,Photograph,Dr. Livingstone,,,,,,Artist,,John Jabez Edwin Mayall,"British, Oldham, Lancashire 1813–1901 West Sussex",,"Mayall, John Jabez Edwin",British,1813,1901,1857,1857,1857,Albumen silver print,,"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1963",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/270838,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1988.1048,false,true,265917,Photographs,Photograph,The Jewels of the Pagoda,,,,,,Artist,,Linnaeus Tripe,"British, Devonport (Plymouth Dock) 1822–1902 Devonport",,"Tripe, Linnaeus",British,1822,1902,January 1858,1858,1858,Albumen silver print from waxed paper negative,Image: 26.4 x 36.9 cm (10 3/8 x 14 1/2 in.),"Purchase, Cynthia Hazen Polsky Gift, 1988",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/265917,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1991.1152,false,true,266450,Photographs,Photograph,Elliot Marbles and Other Sculpture from the Central Museum Madras: Group 26,,,,,,Artist,,Linnaeus Tripe,"British, Devonport (Plymouth Dock) 1822–1902 Devonport",,"Tripe, Linnaeus",British,1822,1902,May–June 1858,1858,1858,Albumen silver print from dry collodion on glass negative,Image: 23.4 x 29.9 cm (9 3/16 x 11 3/4 in.) Mount: 33.1 x 45 cm (13 1/16 x 17 11/16 in.) Mat: 18 1/2 × 22 1/2 in. (47 × 57.1 cm),"Purchase, Cynthia Hazen Polsky Gift and The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 1991",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/266450,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2012.323.1,false,true,302510,Photographs,Panorama,No. 1. Prome. General View.,,,,,,Artist,,Linnaeus Tripe,"British, Devonport (Plymouth Dock) 1822–1902 Devonport",,"Tripe, Linnaeus",British,1822,1902,"August 7, 1855",1855,1855,Albumen silver prints from waxed(?) paper negatives,"Image: 24.4 x 60.8 cm (9 5/8 x 23 15/16 in.), overall Mount: 45.7 x 108 cm (18 x 42 1/2 in.), overall","Purchase, The Buddy Taub Foundation, Dennis A. Roach and Jill Roach, Directors, and Alfred Stieglitz Society Gifts, 2012",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/302510,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2012.323.5,false,true,302635,Photographs,Photograph,No. 7. Ye-nan-gyoung. Pagoda and Kyoung.,,,,,,Artist,,Linnaeus Tripe,"British, Devonport (Plymouth Dock) 1822–1902 Devonport",,"Tripe, Linnaeus",British,1822,1902,"August 14–16, 1855",1855,1855,Albumen silver print from waxed paper negative,Image: 23.7 x 33.8 cm (9 5/16 x 13 5/16 in.) Mount: 45.6 x 58.3 cm (17 15/16 x 22 15/16 in.),"Purchase, The Buddy Taub Foundation, Dennis A. Roach and Jill Roach, Directors, and Alfred Stieglitz Society Gifts, 2012",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/302635,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2012.323.6,false,true,302636,Photographs,Photograph,Pugahm Myo: Thapinyu Pagoda,,,,,,Artist,,Linnaeus Tripe,"British, Devonport (Plymouth Dock) 1822–1902 Devonport",,"Tripe, Linnaeus",British,1822,1902,"August 20–24, 1855",1855,1855,Albumen silver print from waxed paper negative,Image: 25.1 x 34.5 cm (9 7/8 x 13 9/16 in.) Mount: 45.6 x 58.3 cm (17 15/16 x 22 15/16 in.) Mat: 20 × 24 in. (50.8 × 61 cm),"Purchase, The Buddy Taub Foundation, Dennis A. Roach and Jill Roach, Directors, and Alfred Stieglitz Society Gifts, 2012",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/302636,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2012.323.8,false,true,302638,Photographs,Photograph,Pugahm Myo: Distant View of Gauda-palen Pagoda,,,,,,Artist,,Linnaeus Tripe,"British, Devonport (Plymouth Dock) 1822–1902 Devonport",,"Tripe, Linnaeus",British,1822,1902,"August 20–24, 1855",1855,1855,Albumen silver print from waxed(?) paper negative,Image: 25.3 x 34.1 cm (9 15/16 x 13 7/16 in.) Mount: 45.6 x 58.3 cm (17 15/16 x 22 15/16 in.) Mat: 20 × 24 in. (50.8 × 61 cm),"Purchase, The Buddy Taub Foundation, Dennis A. Roach and Jill Roach, Directors, and Alfred Stieglitz Society Gifts, 2012",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/302638,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1997.382.60,false,true,282071,Photographs,Photograph,Virabadra Drug as seen from near the site of the last view,,,,,,Artist,,Linnaeus Tripe,"British, Devonport (Plymouth Dock) 1822–1902 Devonport",,"Tripe, Linnaeus",British,1822,1902,December 1857–January 1858,1857,1858,Salted paper print from waxed paper negative,27.7 x 38.2 cm (10 7/8 x 15 1/16 in. ),"The Rubel Collection, Purchase, Cynthia Hazen Polsky and Lila Acheson Wallace Gifts, 1997",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/282071,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1997.382.61,false,true,282072,Photographs,Photograph,Central Museum Madras: Group 27,,,,,,Artist,,Linnaeus Tripe,"British, Devonport (Plymouth Dock) 1822–1902 Devonport",,"Tripe, Linnaeus",British,1822,1902,May–June 1858,1858,1858,Albumen silver print from dry collodion on glass negative,Image: 25.8 x 23.2 cm (10 3/16 x 9 1/8 in.) Mount: 45.1 x 33 cm (17 3/4 x 13 in.) Mat: 20 × 24 in. (50.8 × 61 cm),"The Rubel Collection, Purchase, Lila Acheson Wallace and Richard and Ronay Menschel Gifts, 1997",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/282072,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2012.323.10,false,true,302640,Photographs,Photograph,Tsagain Myo: A Roadway,,,,,,Artist,,Linnaeus Tripe,"British, Devonport (Plymouth Dock) 1822–1902 Devonport",,"Tripe, Linnaeus",British,1822,1902,"August 29–30, 1855",1855,1855,Albumen silver print from waxed(?) paper negative,Image: 24.5 x 34.1 cm (9 5/8 x 13 7/16 in.) Mount: 45.6 x 58.3 cm (17 15/16 x 22 15/16 in.) Mat: 20 × 24 in. (50.8 × 61 cm),"Purchase, The Buddy Taub Foundation, Dennis A. Roach and Jill Roach, Directors, and Alfred Stieglitz Society Gifts, 2012",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/302640,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2012.323.11,false,true,302641,Photographs,Photograph,Tsagain Myo: Litters under a shed.,,,,,,Artist,,Linnaeus Tripe,"British, Devonport (Plymouth Dock) 1822–1902 Devonport",,"Tripe, Linnaeus",British,1822,1902,"August 29–30, 1855",1855,1855,Albumen silver print from waxed(?) paper negative,Image: 27 x 34.5 cm (10 5/8 x 13 9/16 in.) Mount: 45.6 x 58.3 cm (17 15/16 x 22 15/16 in.) Mat: 20 × 24 in. (50.8 × 61 cm),"Purchase, The Buddy Taub Foundation, Dennis A. Roach and Jill Roach, Directors, and Alfred Stieglitz Society Gifts, 2012",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/302641,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2012.323.14,false,true,302644,Photographs,Photograph,Amerapoora: Corner of Mygabhoodee-tee Kyoung,,,,,,Artist,,Linnaeus Tripe,"British, Devonport (Plymouth Dock) 1822–1902 Devonport",,"Tripe, Linnaeus",British,1822,1902,"September 1–October 21, 1855",1855,1855,Albumen silver print from waxed paper negative,Image: 27.3 x 34.4 cm (10 3/4 x 13 9/16 in.) Mount: 45.6 x 58.4 cm (17 15/16 x 23 in.) Mat: 20 × 24 in. (50.8 × 61 cm),"Purchase, The Buddy Taub Foundation, Dennis A. Roach and Jill Roach, Directors, and Alfred Stieglitz Society Gifts, 2012",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/302644,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2012.323.15,false,true,302645,Photographs,Photograph,Amerapoora: Wooden Bridge,,,,,,Artist,,Linnaeus Tripe,"British, Devonport (Plymouth Dock) 1822–1902 Devonport",,"Tripe, Linnaeus",British,1822,1902,"September 1–October 21, 1855",1855,1855,"Purchase, The Buddy Taub Foundation, Dennis A. Roach and Jill Roach, Directors, and Alfred Stieglitz Society Gifts, 2012",Image: 22.3 x 32.4 cm (8 3/4 x 12 3/4 in.) Mount: 45.6 x 58.4 cm (17 15/16 x 23 in.) Mat: 20 × 24 in. (50.8 × 61 cm),"Purchase, The Buddy Taub Foundation, Dennis A. Roach and Jill Roach, Directors, and Alfred Stieglitz Society Gifts, 2012",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/302645,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2012.323.20,false,true,302650,Photographs,Photograph,Amerapoora: Shwe-doung-dyk Pagoda,,,,,,Artist,,Linnaeus Tripe,"British, Devonport (Plymouth Dock) 1822–1902 Devonport",,"Tripe, Linnaeus",British,1822,1902,"September 1–October 21, 1855",1855,1855,Albumen silver print from waxed paper negative,Image: 25.8 x 34.6 cm (10 3/16 x 13 5/8 in.) Mount: 45.5 x 58.3 cm (17 15/16 x 22 15/16 in.) Mat: 20 × 24 in. (50.8 × 61 cm),"Purchase, The Buddy Taub Foundation, Dennis A. Roach and Jill Roach, Directors, and Alfred Stieglitz Society Gifts, 2012",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/302650,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2012.323.24,false,true,302654,Photographs,Photograph,Amerapoora: Part of Balcony on the South Side of Maha-oung-meeay-liy-mhan Kyoung,,,,,,Artist,,Linnaeus Tripe,"British, Devonport (Plymouth Dock) 1822–1902 Devonport",,"Tripe, Linnaeus",British,1822,1902,"September 1–October 21, 1855",1855,1855,Albumen silver print from waxed paper negative,Image: 26.9 x 34.7 cm (10 9/16 x 13 11/16 in.) Mount: 45.6 x 58.3 cm (17 15/16 x 22 15/16 in.) Mat: 20 × 24 in. (50.8 × 61 cm),"Purchase, The Buddy Taub Foundation, Dennis A. Roach and Jill Roach, Directors, and Alfred Stieglitz Society Gifts, 2012",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/302654,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2012.323.30,false,true,302660,Photographs,Photograph,Rangoon: Henzas on the East Side of the Shwe Dagon Pagoda,,,,,,Artist,,Linnaeus Tripe,"British, Devonport (Plymouth Dock) 1822–1902 Devonport",,"Tripe, Linnaeus",British,1822,1902,November 1855,1855,1855,Albumen silver print from waxed paper negative,Image: 26.1 x 34.3 cm (10 1/4 x 13 1/2 in.) Mount: 45.7 x 58.4 cm (18 x 23 in.) Mat: 20 × 24 in. (50.8 × 61 cm),"Purchase, The Buddy Taub Foundation, Dennis A. Roach and Jill Roach, Directors, and Alfred Stieglitz Society Gifts, 2012",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/302660,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.767,false,true,285626,Photographs,Photograph,"Amerapoora, Palace of the White Elephant",,,,,,Artist,,Linnaeus Tripe,"British, Devonport (Plymouth Dock) 1822–1902 Devonport",,"Tripe, Linnaeus",British,1822,1902,1 September–21 October 1855,1855,1855,Salted paper print from waxed(?) paper negative,Image: 24.1 x 33.5 cm (9 1/2 x 13 3/16 in.),"Gilman Collection, Purchase, Cynthia Hazen Polsky Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285626,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.776,false,true,285623,Photographs,Photograph,"Amerapoora, Barracks of the Burmese Guard",,,,,,Artist,,Linnaeus Tripe,"British, Devonport (Plymouth Dock) 1822–1902 Devonport",,"Tripe, Linnaeus",British,1822,1902,1 September–21 October 1855,1855,1855,Salted paper print from waxed paper negative,Image: 26 x 34.4 cm (10 1/4 x 13 9/16 in.),"Gilman Collection, Purchase, Cynthia Hazen Polsky Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285623,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.381.1.1,false,true,287261,Photographs,Photograph,The Elephant Rock,,,,,,Artist,,Linnaeus Tripe,"British, Devonport (Plymouth Dock) 1822–1902 Devonport",,"Tripe, Linnaeus",British,1822,1902,January–February 1858,1858,1858,Albumen silver print from waxed paper negative,Image: 23.7 x 35.5 cm (9 5/16 x 14 in.) Mount: 45.3 x 57.4 cm (17 13/16 x 22 5/8 in.),"Gilman Collection, Purchase, Cynthia Hazen Polsky Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287261,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.381.1.2,false,true,287262,Photographs,Photograph,South East Angle of the Tirambur Pagoda,,,,,,Artist,,Linnaeus Tripe,"British, Devonport (Plymouth Dock) 1822–1902 Devonport",,"Tripe, Linnaeus",British,1822,1902,January–February 1858,1858,1858,Albumen silver print from waxed paper negative,Image: 26.3 x 36.4 cm (10 3/8 x 14 5/16 in.) Mount: 45 x 56.5 cm (17 11/16 x 22 1/4 in.),"Gilman Collection, Purchase, Cynthia Hazen Polsky Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287262,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.381.1.3,false,true,287263,Photographs,Photograph,View of the N. E. Angle of the Tirambur Pagoda.,,,,,,Artist,,Linnaeus Tripe,"British, Devonport (Plymouth Dock) 1822–1902 Devonport",,"Tripe, Linnaeus",British,1822,1902,January–February 1858,1858,1858,Albumen silver print from waxed paper negative,Image: 24.2 x 36.2 cm (9 1/2 x 14 1/4 in.) Mount: 45.2 x 57.3 cm (17 13/16 x 22 9/16 in.),"Gilman Collection, Purchase, Cynthia Hazen Polsky Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287263,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.381.1.4,false,true,286803,Photographs,Photograph,The Teppa-kulam,,,,,,Artist,,Linnaeus Tripe,"British, Devonport (Plymouth Dock) 1822–1902 Devonport",,"Tripe, Linnaeus",British,1822,1902,1858,1858,1858,Albumen silver print from waxed paper negative,Image: 26 x 37.5 cm (10 1/4 x 14 3/4 in.) Mount: 45.2 x 56.2 cm (17 13/16 x 22 1/8 in.),"Gilman Collection, Purchase, Cynthia Hazen Polsky Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286803,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.381.1.5,false,true,287264,Photographs,Photograph,The Tamukkam or Tamkam,,,,,,Artist,,Linnaeus Tripe,"British, Devonport (Plymouth Dock) 1822–1902 Devonport",,"Tripe, Linnaeus",British,1822,1902,January–March 1858,1858,1858,Albumen silver print from waxedpaper negative,Image: 26 x 37.2 cm (10 1/4 x 14 5/8 in.) Mount: 45.2 x 57.5 cm (17 13/16 x 22 5/8 in.),"Gilman Collection, Purchase, Cynthia Hazen Polsky Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287264,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.381.1.6,false,true,287265,Photographs,Photograph,The Causeway Across the Vaigai River,,,,,,Artist,,Linnaeus Tripe,"British, Devonport (Plymouth Dock) 1822–1902 Devonport",,"Tripe, Linnaeus",British,1822,1902,January–March 1858,1858,1858,Albumen silver print from waxed paper negative,Image: 24.9 x 35 cm (9 13/16 x 13 3/4 in.) Mount: 45.2 x 57.5 cm (17 13/16 x 22 5/8 in.),"Gilman Collection, Purchase, Cynthia Hazen Polsky Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287265,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.381.1.7,false,true,287266,Photographs,Photograph,The Neerali Mundapam,,,,,,Artist,,Linnaeus Tripe,"British, Devonport (Plymouth Dock) 1822–1902 Devonport",,"Tripe, Linnaeus",British,1822,1902,January–March 1858,1858,1858,Albumen silver print from paper negative,Image: 23.9 x 34.8 cm (9 7/16 x 13 11/16 in.) Mount: 45.3 x 57.4 cm (17 13/16 x 22 5/8 in.),"Gilman Collection, Purchase, Cynthia Hazen Polsky Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287266,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.381.1.8,false,true,287267,Photographs,Photograph,The Raya Gopuram from E.,,,,,,Artist,,Linnaeus Tripe,"British, Devonport (Plymouth Dock) 1822–1902 Devonport",,"Tripe, Linnaeus",British,1822,1902,January–March 1858,1858,1858,Albumen silver print from waxed paper negative,Image: 27 x 36.5 cm (10 5/8 x 14 3/8 in.) Mount: 45.4 x 57.2 cm (17 7/8 x 22 1/2 in.),"Gilman Collection, Purchase, Cynthia Hazen Polsky Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287267,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.381.1.9,false,true,286883,Photographs,Photograph,"Pillars in the Recessed Portico in the Roya Gopuram with the Base of One of the Four Sculptured Monoliths, Madura",,,,,,Artist,,Linnaeus Tripe,"British, Devonport (Plymouth Dock) 1822–1902 Devonport",,"Tripe, Linnaeus",British,1822,1902,January–March 1858,1858,1858,Albumen silver print from waxed paper negative,Image: 35.8 x 30.2 cm (14 1/8 x 11 7/8 in.) Mount: 45.3 x 57.5 cm (17 13/16 x 22 5/8 in.),"Gilman Collection, Purchase, Cynthia Hazen Polsky Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286883,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.381.2.1,false,true,287269,Photographs,Photograph,The Great Pagoda,,,,,,Artist,,Linnaeus Tripe,"British, Devonport (Plymouth Dock) 1822–1902 Devonport",,"Tripe, Linnaeus",British,1822,1902,January–March 1858,1858,1858,Albumen silver print from waxed paper negative,Image: 33.6 x 30.1 cm (13 1/4 x 11 7/8 in.) Mount: 57 x 45 cm (22 7/16 x 17 11/16 in.),"Gilman Collection, Purchase, Cynthia Hazen Polsky Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287269,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.381.2.2,false,true,287270,Photographs,Photograph,The Viravasuntarayan Munapam,,,,,,Artist,,Linnaeus Tripe,"British, Devonport (Plymouth Dock) 1822–1902 Devonport",,"Tripe, Linnaeus",British,1822,1902,January–March 1858,1858,1858,Albumen silver print from waxed paper negative,Image: 29.5 x 37 cm (11 5/8 x 14 9/16 in.) Mount: 46 x 57.1 cm (18 1/8 x 22 1/2 in.),"Gilman Collection, Purchase, Cynthia Hazen Polsky Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287270,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.381.2.3,false,true,287271,Photographs,Photograph,Entrance to the Thousand Pillared Mundapam in the Great Pagoda,,,,,,Artist,,Linnaeus Tripe,"British, Devonport (Plymouth Dock) 1822–1902 Devonport",,"Tripe, Linnaeus",British,1822,1902,January–March 1858,1858,1858,Albumen silver print from waxed paper negative,Image: 33 x 30.7 cm (13 x 12 1/16 in.) Mount: 57.1 x 45 cm (22 1/2 x 17 11/16 in.),"Gilman Collection, Purchase, Cynthia Hazen Polsky Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287271,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.381.2.4,false,true,287272,Photographs,Photograph,The Muduramiar Mundapam,,,,,,Artist,,Linnaeus Tripe,"British, Devonport (Plymouth Dock) 1822–1902 Devonport",,"Tripe, Linnaeus",British,1822,1902,January–March 1858,1858,1858,Albumen silver print from waxed paper negative,Image: 34.7 x 28.3 cm (13 11/16 x 11 1/8 in.) Mount: 57 x 45.2 cm (22 7/16 x 17 13/16 in.),"Gilman Collection, Purchase, Cynthia Hazen Polsky Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287272,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.381.2.5,false,true,287273,Photographs,Photograph,Side Colonnade in the Muroothappa Sarvacar Mundapam,,,,,,Artist,,Linnaeus Tripe,"British, Devonport (Plymouth Dock) 1822–1902 Devonport",,"Tripe, Linnaeus",British,1822,1902,January–March 1858,1858,1858,Albumen silver print from waxed paper negative,Image: 36.5 x 29.1 cm (14 3/8 x 11 7/16 in.) Mount: 57.1 x 45.1 cm (22 1/2 x 17 3/4 in.),"Gilman Collection, Purchase, Cynthia Hazen Polsky Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287273,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.381.2.6,false,true,287274,Photographs,Photograph,The Inner Facade of the Gateway of the East Gopuram,,,,,,Artist,,Linnaeus Tripe,"British, Devonport (Plymouth Dock) 1822–1902 Devonport",,"Tripe, Linnaeus",British,1822,1902,January–March 1858,1858,1858,Albumen silver print from waxed paper negative,Image: 29.2 x 34 cm (11 1/2 x 13 3/8 in.) Mount (2nd): 45 x 57.1 cm (17 11/16 x 22 1/2 in.),"Gilman Collection, Purchase, Cynthia Hazen Polsky Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287274,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.381.2.7,false,true,287275,Photographs,Photograph,The Kulayana Mundapam,,,,,,Artist,,Linnaeus Tripe,"British, Devonport (Plymouth Dock) 1822–1902 Devonport",,"Tripe, Linnaeus",British,1822,1902,January–March 1858,1858,1858,Albumen silver print from waxed paper negative,Image: 27 x 37.7 cm (10 5/8 x 14 13/16 in.) Mount: 45 x 57.1 cm (17 11/16 x 22 1/2 in.),"Gilman Collection, Purchase, Cynthia Hazen Polsky Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287275,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.381.2.8,false,true,287276,Photographs,Photograph,Tatta Suddhi Mundapam,,,,,,Artist,,Linnaeus Tripe,"British, Devonport (Plymouth Dock) 1822–1902 Devonport",,"Tripe, Linnaeus",British,1822,1902,January–March 1858,1858,1858,Albumen silver print from waxed paper negative,Image: 24 x 36.8 cm (9 7/16 x 14 1/2 in.) Mount: 45 x 57.1 cm (17 11/16 x 22 1/2 in.),"Gilman Collection, Purchase, Cynthia Hazen Polsky Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287276,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.381.2.9,false,true,287277,Photographs,Photograph,The Western Gopuram,,,,,,Artist,,Linnaeus Tripe,"British, Devonport (Plymouth Dock) 1822–1902 Devonport",,"Tripe, Linnaeus",British,1822,1902,January–March 1858,1858,1858,Albumen silver print from waxed paper negative,Image: 36.1 x 28.5 cm (14 3/16 x 11 1/4 in.) Mount: 42 x 39 cm (16 9/16 x 15 3/8 in.),"Gilman Collection, Purchase, Cynthia Hazen Polsky Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287277,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.381.1.10,false,true,287268,Photographs,Photograph,The Raya Gopuram from W,,,,,,Artist,,Linnaeus Tripe,"British, Devonport (Plymouth Dock) 1822–1902 Devonport",,"Tripe, Linnaeus",British,1822,1902,January–March 1858,1858,1858,Albumen silver print from waxed paper negative,Image: 27.3 x 36.3 cm (10 3/4 x 14 5/16 in.) Mount: 45 x 57.8 cm (17 11/16 x 22 3/4 in.),"Gilman Collection, Purchase, Cynthia Hazen Polsky Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287268,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.381.2.10,false,true,287278,Photographs,Photograph,"The Outer Prakarum, or Corridor Around the Temple of the God Sundareshawara",,,,,,Artist,,Linnaeus Tripe,"British, Devonport (Plymouth Dock) 1822–1902 Devonport",,"Tripe, Linnaeus",British,1822,1902,January–March 1858,1858,1858,Albumen silver print from dry collodion on glass negative,Image: 34.8 x 24.2 cm (13 11/16 x 9 1/2 in.) Mount: 57 x 45.1 cm (22 7/16 x 17 3/4 in.),"Gilman Collection, Purchase, Cynthia Hazen Polsky Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287278,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.381.2.11,false,true,283163,Photographs,Photograph,Madura. The Great Pagoda Jewels.,,,,,,Artist,,Linnaeus Tripe,"British, Devonport (Plymouth Dock) 1822–1902 Devonport",,"Tripe, Linnaeus",British,1822,1902,January–February 1858,1858,1858,Albumen silver print from dry collodion on glass negative,Image: 21.9 x 30.1 cm (8 5/8 x 11 7/8 in.) Mount: 45.1 x 57.3 cm (17 3/4 x 22 9/16 in.) Mat: 21 1/8 × 23 15/16 in. (53.6 × 60.8 cm),"Gilman Collection, Purchase, Cynthia Hazen Polsky Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283163,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.381.2.12,false,true,287279,Photographs,Photograph,Outer Prakarum on the North Side of the Temple of the God Sundareshwara,,,,,,Artist,,Linnaeus Tripe,"British, Devonport (Plymouth Dock) 1822–1902 Devonport",,"Tripe, Linnaeus",British,1822,1902,January–March 1858,1858,1858,Albumen silver print from dry collodion on glass negative,Image: 26.1 x 34 cm (10 1/4 x 13 3/8 in.) Mount: 45.1 x 57 cm (17 3/4 x 22 7/16 in.),"Gilman Collection, Purchase, Cynthia Hazen Polsky Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287279,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.381.2.13,false,true,287280,Photographs,Photograph,View of the Sacred Tank in the Great Pagoda,,,,,,Artist,,Linnaeus Tripe,"British, Devonport (Plymouth Dock) 1822–1902 Devonport",,"Tripe, Linnaeus",British,1822,1902,January–March 1858,1858,1858,Albumen silver print from waxed paper negative,Image: 25.8 x 35.1 cm (10 3/16 x 13 13/16 in.) Mount: 45 x 57 cm (17 11/16 x 22 7/16 in.),"Gilman Collection, Purchase, Cynthia Hazen Polsky Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287280,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.381.2.14,false,true,287281,Photographs,Photograph,View of the Sacred Tank in the Great Pagoda,,,,,,Artist,,Linnaeus Tripe,"British, Devonport (Plymouth Dock) 1822–1902 Devonport",,"Tripe, Linnaeus",British,1822,1902,January–March 1858,1858,1858,Albumen silver print from waxed paper negative,Image: 29.1 x 37.5 cm (11 7/16 x 14 3/4 in.) Mount: 45 x 57 cm (17 11/16 x 22 7/16 in.),"Gilman Collection, Purchase, Cynthia Hazen Polsky Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287281,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.381.2.15,false,true,287282,Photographs,Photograph,Entrance to the Temple of Minakshi in the Great Pagoda,,,,,,Artist,,Linnaeus Tripe,"British, Devonport (Plymouth Dock) 1822–1902 Devonport",,"Tripe, Linnaeus",British,1822,1902,January–March 1858,1858,1858,Albumen silver print from waxed paper negative,Image: 30.5 x 37 cm (12 x 14 9/16 in.) Mount: 45.2 x 57 cm (17 13/16 x 22 7/16 in.),"Gilman Collection, Purchase, Cynthia Hazen Polsky Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287282,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2011.422,false,true,292071,Photographs,Photograph,[Grenadier Guards Drummer],,,,,,Artist,,Joseph Cundall,"British, Norwich, Norfolk 1818–1895 Wallington, Surrey",,"Cundall, Joseph",British,1818,1895,ca. 1856,1854,1856,Salted paper print from glass negative,Image: 23.1 x 17.4 cm (9 1/8 x 6 7/8 in.) Mount: 41.5 x 31.9 cm (16 5/16 x 12 9/16 in.),"Purchase, Alfred Stieglitz Society Gifts, 2011",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/292071,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -52.524.16,false,true,269576,Photographs,Photograph,Cottage at Jersey,,,,,,Artist,,Joseph Cundall,"British, Norwich, Norfolk 1818–1895 Wallington, Surrey",,"Cundall, Joseph",British,1818,1895,1855,1855,1855,Albumen silver print from glass negative,23.5 x 19.1 cm (9 1/4 x 7 1/2 in.),"David Hunter McAlpin Fund, 1952",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269576,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.159.7,false,true,306208,Photographs,Photograph,The Alms House,,,,,,Artist,,Joseph Cundall,"British, Norwich, Norfolk 1818–1895 Wallington, Surrey",,"Cundall, Joseph",British,1818,1895,1855,1855,1855,Albumen silver print from glass negative,Mount: 17 1/4 in. × 11 15/16 in. (43.8 × 30.4 cm) Image: 9 5/16 × 7 5/16 in. (23.7 × 18.5 cm),"Bequest of Maurice B. Sendak, 2012",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306208,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -63.606.1.9,false,true,270861,Photographs,Photograph,Highlanders,,,,,,Artist,,Joseph Cundall,"British, Norwich, Norfolk 1818–1895 Wallington, Surrey",,"Cundall, Joseph",British,1818,1895,1856,1856,1856,Albumen silver print,,"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1963",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/270861,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.949,false,true,283241,Photographs,Photograph,[Loie Fuller Dancing],,,,,,Artist,,Samuel Joshua Beckett,"British, Shadwell, Stepney [London] 1870–1940 Bournemouth",,"Beckett, Samuel Joshua",British,1870,1940,ca. 1900,1899,1901,Gelatin silver print,7.7 x 10.2 cm (3 1/16 x 4 in.),"Gilman Collection, Purchase, Mrs. Walter Annenberg and The Annenberg Foundation Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283241,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.950,false,true,287806,Photographs,Photograph,[Loie Fuller Dancing],,,,,,Artist,,Samuel Joshua Beckett,"British, Shadwell, Stepney [London] 1870–1940 Bournemouth",,"Beckett, Samuel Joshua",British,1870,1940,ca. 1900,1899,1901,Gelatin silver print,"10.3 x 13.3 cm (4 x 5 1/4 in.), irregularly trimmed","Gilman Collection, Purchase, Mrs. Walter Annenberg and The Annenberg Foundation Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287806,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.951,false,true,287807,Photographs,Photograph,[Loie Fuller Dancing],,,,,,Artist,,Samuel Joshua Beckett,"British, Shadwell, Stepney [London] 1870–1940 Bournemouth",,"Beckett, Samuel Joshua",British,1870,1940,ca. 1900,1899,1901,Gelatin silver print,10.1 x 12.5 cm (4 x 4 15/16 in.),"Gilman Collection, Purchase, Mrs. Walter Annenberg and The Annenberg Foundation Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287807,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.952,false,true,285696,Photographs,Photograph,Loie Fuller Dancing,,,,,,Artist,,Samuel Joshua Beckett,"British, Shadwell, Stepney [London] 1870–1940 Bournemouth",,"Beckett, Samuel Joshua",British,1870,1940,ca. 1900,1898,1902,Gelatin silver print,10.9 x 14.3 cm (4 5/16 x 5 5/8 in.),"Gilman Collection, Purchase, Mrs. Walter Annenberg and The Annenberg Foundation Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285696,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.953,false,true,287808,Photographs,Photograph,Loie Fuller Dancing,,,,,,Artist,,Samuel Joshua Beckett,"British, Shadwell, Stepney [London] 1870–1940 Bournemouth",,"Beckett, Samuel Joshua",British,1870,1940,ca. 1900,1898,1902,Gelatin silver print,Image: 4 in. × 5 11/16 in. (10.2 × 14.4 cm),"Gilman Collection, Purchase, Mrs. Walter Annenberg and The Annenberg Foundation Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287808,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.954,false,true,287809,Photographs,Photograph,Loie Fuller Dancing,,,,,,Artist,,Samuel Joshua Beckett,"British, Shadwell, Stepney [London] 1870–1940 Bournemouth",,"Beckett, Samuel Joshua",British,1870,1940,ca. 1900,1898,1902,Gelatin silver print,Image: 5 15/16 × 4 3/8 in. (15.1 × 11.1 cm),"Gilman Collection, Purchase, Mrs. Walter Annenberg and The Annenberg Foundation Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287809,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.955,false,true,287810,Photographs,Photograph,Loie Fuller Dancing,,,,,,Artist,,Samuel Joshua Beckett,"British, Shadwell, Stepney [London] 1870–1940 Bournemouth",,"Beckett, Samuel Joshua",British,1870,1940,ca. 1900,1898,1902,Gelatin silver print,Image: 3 1/16 in. × 4 in. (7.7 × 10.2 cm),"Gilman Collection, Purchase, Mrs. Walter Annenberg and The Annenberg Foundation Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287810,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.956,false,true,287811,Photographs,Photograph,Loie Fuller Dancing,,,,,,Artist,,Samuel Joshua Beckett,"British, Shadwell, Stepney [London] 1870–1940 Bournemouth",,"Beckett, Samuel Joshua",British,1870,1940,ca. 1900,1898,1902,Gelatin silver print,Image: 3 15/16 × 5 13/16 in. (10 × 14.7 cm),"Gilman Collection, Purchase, Mrs. Walter Annenberg and The Annenberg Foundation Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287811,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.957,false,true,287812,Photographs,Photograph,Loie Fuller Dancing,,,,,,Artist,,Samuel Joshua Beckett,"British, Shadwell, Stepney [London] 1870–1940 Bournemouth",,"Beckett, Samuel Joshua",British,1870,1940,ca. 1900,1898,1902,Gelatin silver print,Image: 3 × 4 in. (7.6 × 10.1 cm),"Gilman Collection, Purchase, Mrs. Walter Annenberg and The Annenberg Foundation Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287812,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.958,false,true,287813,Photographs,Photograph,Loie Fuller Dancing,,,,,,Artist,,Samuel Joshua Beckett,"British, Shadwell, Stepney [London] 1870–1940 Bournemouth",,"Beckett, Samuel Joshua",British,1870,1940,ca. 1900,1898,1902,Gelatin silver print,Image: 4 in. × 5 11/16 in. (10.2 × 14.5 cm),"Gilman Collection, Purchase, Mrs. Walter Annenberg and The Annenberg Foundation Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287813,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.959,false,true,287814,Photographs,Photograph,Loie Fuller Dancing,,,,,,Artist,,Samuel Joshua Beckett,"British, Shadwell, Stepney [London] 1870–1940 Bournemouth",,"Beckett, Samuel Joshua",British,1870,1940,ca. 1900,1898,1902,Gelatin silver print,Image: 3 15/16 × 5 3/8 in. (10 × 13.7 cm),"Gilman Collection, Purchase, Mrs. Walter Annenberg and The Annenberg Foundation Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287814,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.960,false,true,287815,Photographs,Photograph,Loie Fuller Dancing,,,,,,Artist,,Samuel Joshua Beckett,"British, Shadwell, Stepney [London] 1870–1940 Bournemouth",,"Beckett, Samuel Joshua",British,1870,1940,ca. 1900,1898,1902,Gelatin silver print,Image: 4 in. × 5 1/8 in. (10.2 × 13 cm),"Gilman Collection, Purchase, Mrs. Walter Annenberg and The Annenberg Foundation Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287815,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.961,false,true,287816,Photographs,Photograph,Loie Fuller Dancing,,,,,,Artist,,Samuel Joshua Beckett,"British, Shadwell, Stepney [London] 1870–1940 Bournemouth",,"Beckett, Samuel Joshua",British,1870,1940,ca. 1900,1898,1902,Gelatin silver print,Image: 5 15/16 × 4 7/16 in. (15.1 × 11.2 cm),"Gilman Collection, Purchase, Mrs. Walter Annenberg and The Annenberg Foundation Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287816,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2004.217,false,true,285424,Photographs,Photograph,"[Egyptian Obelisk, ""Cleopatra's Needle,"" in Alexandria, Egypt]",,,,,,Artist,Attributed to,Francis Frith,"British, Chesterfield, Derbyshire 1822–1898 Cannes, France",,"Frith, Francis",British,1822,1898,ca. 1870,1861,1879,Albumen silver print from glass negative,Image: 15.5 x 20.8 cm (6 1/8 x 8 3/16 in.),"Funds from various donors, 2004",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285424,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.595.1,false,true,260089,Photographs,Photograph,Distant View of the Cedars of Lebanon,,,,,,Artist,,Francis Frith,"British, Chesterfield, Derbyshire 1822–1898 Cannes, France",,"Frith, Francis",British,1822,1898,ca. 1857,1855,1859,Albumen silver print from glass negative,,"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260089,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.595.2,false,true,260090,Photographs,Photograph,Cedars of Lebanon,,,,,,Artist,,Francis Frith,"British, Chesterfield, Derbyshire 1822–1898 Cannes, France",,"Frith, Francis",British,1822,1898,ca. 1857,1855,1859,Albumen silver print from glass negative,Image: 15.4 x 20.7 cm (6 1/16 x 8 1/8 in.) Mount: 30.5 x 38.5 cm (12 x 15 3/16 in.),"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260090,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.674.1,false,true,260348,Photographs,Photograph,[Six East Indian Men],,,,,,Artist,,Francis Frith,"British, Chesterfield, Derbyshire 1822–1898 Cannes, France",,"Frith, Francis",British,1822,1898,1870s,1870,1879,Albumen silver print from glass negative,,"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260348,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.674.2,false,true,260349,Photographs,Photograph,Rajpoots,,,,,,Artist,,Francis Frith,"British, Chesterfield, Derbyshire 1822–1898 Cannes, France",,"Frith, Francis",British,1822,1898,1870s,1870,1879,Albumen silver print from glass negative,,"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260349,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.674.3,false,true,260350,Photographs,Photograph,[Three East Indian Women],,,,,,Artist,,Francis Frith,"British, Chesterfield, Derbyshire 1822–1898 Cannes, France",,"Frith, Francis",British,1822,1898,1870s,1870,1879,Albumen silver print from glass negative,,"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260350,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.674.4,false,true,260351,Photographs,Photograph,[Four East Indian Men],,,,,,Artist,,Francis Frith,"British, Chesterfield, Derbyshire 1822–1898 Cannes, France",,"Frith, Francis",British,1822,1898,1870s,1870,1879,Albumen silver print from glass negative,,"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260351,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.674.5,false,true,260352,Photographs,Photograph,Marwaree Brokers,,,,,,Artist,,Francis Frith,"British, Chesterfield, Derbyshire 1822–1898 Cannes, France",,"Frith, Francis",British,1822,1898,1870s,1870,1879,Albumen silver print from glass negative,,"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260352,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.674.6,false,true,260353,Photographs,Photograph,Mehmans,,,,,,Artist,,Francis Frith,"British, Chesterfield, Derbyshire 1822–1898 Cannes, France",,"Frith, Francis",British,1822,1898,1870s,1870,1879,Albumen silver print from glass negative,,"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260353,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1973.502.1,false,true,260730,Photographs,Photograph,"Elephanta from Water Cave, Coombe Martin Bay, Watermouth",,,,,,Artist,,Francis Frith,"British, Chesterfield, Derbyshire 1822–1898 Cannes, France",,"Frith, Francis",British,1822,1898,1870s,1870,1879,Albumen silver print from glass negative,,"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1973",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260730,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1973.502.4,false,true,260733,Photographs,Photograph,Watermouth,,,,,,Artist,,Francis Frith,"British, Chesterfield, Derbyshire 1822–1898 Cannes, France",,"Frith, Francis",British,1822,1898,1870s,1870,1879,Albumen silver print from glass negative,,"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1973",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260733,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1973.502.5,false,true,260734,Photographs,Photograph,Coombe Martin Bay,,,,,,Artist,,Francis Frith,"British, Chesterfield, Derbyshire 1822–1898 Cannes, France",,"Frith, Francis",British,1822,1898,1870s,1870,1879,Albumen silver print from glass negative,,"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1973",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260734,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1973.597.2,false,true,260957,Photographs,Photograph,"The Written Valley, Sinai",,,,,,Artist,,Francis Frith,"British, Chesterfield, Derbyshire 1822–1898 Cannes, France",,"Frith, Francis",British,1822,1898,ca. 1857,1855,1859,Albumen silver print from glass negative,,"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1973",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260957,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1973.597.3,false,true,260958,Photographs,Photograph,"Mount Hermon, The Mount of Transfiguration",,,,,,Artist,,Francis Frith,"British, Chesterfield, Derbyshire 1822–1898 Cannes, France",,"Frith, Francis",British,1822,1898,ca. 1857,1855,1859,Albumen silver print from glass negative,,"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1973",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260958,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1973.597.4,false,true,260959,Photographs,Photograph,Bethel,,,,,,Artist,,Francis Frith,"British, Chesterfield, Derbyshire 1822–1898 Cannes, France",,"Frith, Francis",British,1822,1898,ca. 1857,1855,1859,Albumen silver print from glass negative,,"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1973",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260959,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1973.597.5,false,true,260960,Photographs,Photograph,The Fountain of Jerico and Probable Site of the City,,,,,,Artist,,Francis Frith,"British, Chesterfield, Derbyshire 1822–1898 Cannes, France",,"Frith, Francis",British,1822,1898,ca. 1857,1855,1859,Albumen silver print from glass negative,,"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1973",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260960,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1973.597.6,false,true,260961,Photographs,Photograph,"Mount Moriah, Jerusalem, from the Well of En Rogel",,,,,,Artist,,Francis Frith,"British, Chesterfield, Derbyshire 1822–1898 Cannes, France",,"Frith, Francis",British,1822,1898,ca. 1857,1855,1859,Albumen silver print from glass negative,,"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1973",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260961,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1973.597.7,false,true,260962,Photographs,Photograph,"Principal Source of the Jordan, Flowing From a Cave Near Banias, Near the Site of the Northern City of Dan, the Frontier Town of Israel",,,,,,Artist,,Francis Frith,"British, Chesterfield, Derbyshire 1822–1898 Cannes, France",,"Frith, Francis",British,1822,1898,ca. 1857,1855,1859,Albumen silver print from glass negative,,"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1973",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260962,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -66.640.2.1,false,true,271097,Photographs,Photograph,"Wady Kardassy, Nubia",,,,,,Artist,,Francis Frith,"British, Chesterfield, Derbyshire 1822–1898 Cannes, France",,"Frith, Francis",British,1822,1898,1857,1857,1857,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1966",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271097,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -66.640.2.3,false,true,271098,Photographs,Photograph,"The Largest of the Cedars, Mount Lebannon",,,,,,Artist,,Francis Frith,"British, Chesterfield, Derbyshire 1822–1898 Cannes, France",,"Frith, Francis",British,1822,1898,1857,1857,1857,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1966",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271098,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -66.640.2.4,false,true,271100,Photographs,Photograph,"Portrait, Turkish Summer Costume",,,,,,Artist,,Francis Frith,"British, Chesterfield, Derbyshire 1822–1898 Cannes, France",,"Frith, Francis",British,1822,1898,1857,1857,1857,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1966",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271100,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -66.640.2.6,false,true,271101,Photographs,Photograph,"The Sphynx and Great Pyramid, Geezeh",,,,,,Artist,,Francis Frith,"British, Chesterfield, Derbyshire 1822–1898 Cannes, France",,"Frith, Francis",British,1822,1898,1857,1857,1857,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1966",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271101,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -66.640.2.9,false,true,271102,Photographs,Photograph,"The Mosque of Omar, Jerusalem",,,,,,Artist,,Francis Frith,"British, Chesterfield, Derbyshire 1822–1898 Cannes, France",,"Frith, Francis",British,1822,1898,1857,1857,1857,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1966",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271102,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -66.640.1.23,false,true,271085,Photographs,Photograph,The Approach to Phil‘,,,,,,Artist,,Francis Frith,"British, Chesterfield, Derbyshire 1822–1898 Cannes, France",,"Frith, Francis",British,1822,1898,1857,1857,1857,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1966",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271085,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -66.640.1.27,false,true,271086,Photographs,Photograph,The Statues of Memnon. Plain of Thebes,,,,,,Artist,,Francis Frith,"British, Chesterfield, Derbyshire 1822–1898 Cannes, France",,"Frith, Francis",British,1822,1898,1857,1857,1857,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1966",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271086,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -66.640.1.28,false,true,271087,Photographs,Photograph,"Nablous, The Ancient Shechem",,,,,,Artist,,Francis Frith,"British, Chesterfield, Derbyshire 1822–1898 Cannes, France",,"Frith, Francis",British,1822,1898,1857,1857,1857,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1966",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271087,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -66.640.1.30,false,true,271088,Photographs,Photograph,Colossi and Sphynx at Wady Saboua,,,,,,Artist,,Francis Frith,"British, Chesterfield, Derbyshire 1822–1898 Cannes, France",,"Frith, Francis",British,1822,1898,1857,1857,1857,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1966",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271088,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -66.640.1.46,false,true,271092,Photographs,Photograph,Damascus,,,,,,Artist,,Francis Frith,"British, Chesterfield, Derbyshire 1822–1898 Cannes, France",,"Frith, Francis",British,1822,1898,1857,1857,1857,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1966",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271092,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -66.640.1.53,false,true,271093,Photographs,Photograph,"Valley of the Tombs of the Kings, Thebes",,,,,,Artist,,Francis Frith,"British, Chesterfield, Derbyshire 1822–1898 Cannes, France",,"Frith, Francis",British,1822,1898,1857,1857,1857,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1966",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271093,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -66.640.1.54,false,true,271094,Photographs,Photograph,View at Girgeh,,,,,,Artist,,Francis Frith,"British, Chesterfield, Derbyshire 1822–1898 Cannes, France",,"Frith, Francis",British,1822,1898,1857,1857,1857,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1966",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271094,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -66.640.1.60,false,true,271095,Photographs,Photograph,The Statues of Memnon. Plain of Thebes,,,,,,Artist,,Francis Frith,"British, Chesterfield, Derbyshire 1822–1898 Cannes, France",,"Frith, Francis",British,1822,1898,1857,1857,1857,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1966",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271095,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -66.640.2.32,false,true,271099,Photographs,Photograph,"Abou Simbel, Nubia",,,,,,Artist,,Francis Frith,"British, Chesterfield, Derbyshire 1822–1898 Cannes, France",,"Frith, Francis",British,1822,1898,1857,1857,1857,Albumen silver print from glass negative,,"David Hunter McAlpin Fund, 1966",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271099,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.555,false,true,286141,Photographs,Photographically illustrated book,The Great Pyramid and The Great Sphinx,,,,,,Artist,,Francis Frith,"British, Chesterfield, Derbyshire 1822–1898 Cannes, France",,"Frith, Francis",British,1822,1898,1858,1858,1858,Albumen silver print from glass negative,Image: 38.7 x 49.3 cm (15 1/4 x 19 7/16 in.),"Gilman Collection, Purchase, William Talbott Hillman Foundation Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286141,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.633,false,true,286142,Photographs,Photograph,"The Rameseum of El-Kurneh, Thebes",,,,,,Artist,,Francis Frith,"British, Chesterfield, Derbyshire 1822–1898 Cannes, France",,"Frith, Francis",British,1822,1898,1857,1857,1857,Albumen silver print from glass negative,Image: 37.9 x 47.7 cm (14 15/16 x 18 3/4 in.) Mount: 59.9 × 73.8 cm (23 9/16 in. × 29 1/16 in.),"Gilman Collection, Purchase, Anonymous Gifts, by exchange, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286142,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.18,false,true,283090,Photographs,Photograph,"""She Never Told Her Love""",,,,,,Artist,,Henry Peach Robinson,"British, Ludlow, Shropshire 1830–1901 Tunbridge Wells, Kent",,"Robinson, Henry Peach",British,1830,1901,1857,1857,1857,Albumen silver print from glass negative,18 x 23.2cm (7 1/16 x 9 1/8in.) Frame: 41.6 x 46.4 cm (16 3/8 x 18 1/4 in.),"Gilman Collection, Purchase, Jennifer and Joseph Duke Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283090,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1132,false,true,286250,Photographs,Photograph,Fear,,,,,,Artist,,Henry Peach Robinson,"British, Ludlow, Shropshire 1830–1901 Tunbridge Wells, Kent",,"Robinson, Henry Peach",British,1830,1901,ca. 1860,1858,1862,Albumen silver print,Image: 7 15/16 × 6 1/16 in. (20.2 × 15.4 cm),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286250,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.760.4,false,true,259827,Photographs,Photograph,"Carro con buoi, Perugia",,,,,,Artist,,James Anderson,"British, 1813–1877",,"Anderson, James",British,1813,1877,1880s,1880,1889,Albumen silver print,,"Museum Accession, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/259827,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.384 (1-48),false,true,285734,Photographs,Album,[Album of Spirit Photographs],,,,,,Artist|Artist,,Frederick Hudson|Mr. Reeve,"British, died 1889",,"Hudson, Frederick|Reeve",British,,1889,1872,1870,1880,Albumen silver prints from glass negatives,25.4 x 19.1 cm (10 x 7 1/2 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Albums,,http://www.metmuseum.org/art/collection/search/285734,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -46.1.53,false,true,268997,Photographs,Photograph,"Lacock Abbey, Cloisters, September 12, 1855 [?]",,,,,,Artist|Artist,Possibly by,Charles Henry Talbot|Unknown,"British, 1842–1916|British",,"Talbot, Charles Henry|Unknown",British,1842,1916,"September 12, 1855 [?]",1853,1858,Salted paper print from paper negative,,"David Hunter McAlpin Fund, 1946",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268997,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2000.134,false,true,283685,Photographs,Photograph,"Catacombs, Convento dei Cappucini, Palermo",,,,,,Photography Studio|Artist,,Eugenio Interguglielmi & Company|Eugenio Interguglielmi,"Italian, active 1895–1981|Italian, Palermo 1850–1911 Palermo",,"Interguglielmi, Eugenio & Company|Interguglielmi, Eugenio",Italian,1850,1911,ca. 1895,1895,1895,Albumen silver print from glass negative,18.6 x 25.3 cm (7 5/16 x 9 15/16 in. ),"Funds from various donors, 2000",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283685,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1994.263.168,false,true,284874,Photographs,Print,Veduta del Tempio di Giove Tonante,,,,,,Artist,,Giovanni Battista Piranesi,"Italian, Mogliano Veneto 1720–1778 Rome",,"Piranesi, Giovanni Battista",Italian,1720,1778,1740s–60s,1740,1769,Engraving,Image: 38 x 59.5 cm (14 15/16 x 23 7/16 in.),"Walker Evans Archive, 1994",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/284874,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1095,false,true,286681,Photographs,Photograph,Tempio di Vesta,,,,,,Artist,,Pietro Dovizielli,"Italian, 1804–1885",,"Dovizielli, Pietro",Italian,1804,1885,1850s,1850,1859,Salted paper print from paper negative,Mount: 19 3/8 in. × 27 1/16 in. (49.2 × 68.7 cm) Image: 11 9/16 × 15 1/2 in. (29.3 × 39.3 cm),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286681,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1096,false,true,286126,Photographs,Photograph,Interno del Colosseo,,,,,,Artist,,Pietro Dovizielli,"Italian, 1804–1885",,"Dovizielli, Pietro",Italian,1804,1885,ca. 1859,1854,1864,Salted paper print from paper negative,Image: 30.4 × 38.7 cm (11 15/16 × 15 1/4 in.) Mount: 49.2 × 63.7 cm (19 3/8 × 25 1/16 in.),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286126,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.548.2,false,true,261390,Photographs,Photograph,La Simple,,,,,,Artist,,Duroni et Murer,"Italian, 1807–1870",,Duroni et Murer,Italian,1807,1870,1860s,1860,1869,Albumen silver print from glass negative,5.1 x 8.9 cm. (2 x 3 1/2 in.),"David Hunter McAlpin Fund, 1975",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261390,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -62.600.64,false,true,270616,Photographs,Photograph,[Tree in Formal Garden Outside Palazzo],,,,,,Artist,,Giacomo Caneva,"Italian, 1812–1865",,"Caneva, Giacomo",Italian,1812,1865,1860s–70s,1860,1879,Albumen silver print from glass negative,,"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1962",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/270616,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -62.600.68,false,true,270620,Photographs,Photograph,[Roman Ruins],,,,,,Artist,Attributed to,Giacomo Caneva,"Italian, 1812–1865",,"Caneva, Giacomo",Italian,1812,1865,1860s,1860,1869,Albumen silver print,,"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1962",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/270620,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.57,false,true,283140,Photographs,Photograph,[Carlotta Cortudino],,,,,,Artist,,Giacomo Caneva,"Italian, 1812–1865",,"Caneva, Giacomo",Italian,1812,1865,ca. 1852,1850,1854,Salted paper print from paper negative,Image: 5 5/16 × 7 13/16 in. (13.5 × 19.9 cm) Mount: 11 5/8 × 11 5/8 in. (29.6 × 29.5 cm),"Gilman Collection, Purchase, Joseph M. Cohen Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283140,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.570,false,true,286574,Photographs,Photograph,[Vesuvius from Mergellina],,,,,,Artist,,Giacomo Caneva,"Italian, 1812–1865",,"Caneva, Giacomo",Italian,1812,1865,ca. 1855,1853,1857,Salted paper print from paper negative,Image: 13.1 x 27.9 cm (5 3/16 x 11 in.) Mount: 26.7 x 43.5 cm (10 1/2 x 17 1/8 in.),"Gilman Collection, Purchase, Mrs. Walter Annenberg and The Annenberg Foundation Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286574,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.855,false,true,286270,Photographs,Photograph,Veduta d'alla Villa Medici od Academia di Francia,,,,,,Artist,,Giacomo Caneva,"Italian, 1812–1865",,"Caneva, Giacomo",Italian,1812,1865,ca. 1852,1847,1857,Salted paper print from paper negative,Image: 20 × 27.2 cm (7 7/8 × 10 11/16 in.) Mount: 31 × 46.2 cm (12 3/16 × 18 3/16 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286270,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.37 (45),false,true,289233,Photographs,Photograph,Primo tentativo fatto in Italia del Sig. Tassinari farmacista e chimico in Castel Bolognese,,,,,,Artist,Likely,Sebastiano Tassinari,"Italian, 1814–1888",,"Tassinari, Sebastiano",Italian,1814,1888,1839–40,1839,1840,Photogenic drawing,"10.2 x 10.5 cm (4 x 4 1/8 in.), irregularly trimmed","Harris Brisbane Dick Fund, 1936",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289233,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.37 (44a),false,true,289232,Photographs,Photograph,[Photogenic Drawing from Leaf],,,,,,Artist,Likely,Sebastiano Tassinari,"Italian, 1814–1888",,"Tassinari, Sebastiano",Italian,1814,1888,1839–40,1839,1840,Photogenic drawing,"10.6 x 14 cm (4 3/16 x 5 1/2 in.), irregularly trimmed","Harris Brisbane Dick Fund, 1936",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289232,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.37 (44b),false,true,289234,Photographs,Photograph,[Photogenic Drawing from Leaf],,,,,,Artist,Likely,Sebastiano Tassinari,"Italian, 1814–1888",,"Tassinari, Sebastiano",Italian,1814,1888,1839–40,1839,1840,Photogenic drawing,"10.6 x 14 cm (4 3/16 x 5 1/2 in.), irregularly trimmed","Harris Brisbane Dick Fund, 1936",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289234,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -67.606.1,false,true,271262,Photographs,Photograph,Processione sulla facciata della grande scala,,,,,,Artist,,Luigi Pesce,"Italian, 1818–1891",,"Pesce, Luigi",Italian,1818,1891,1858,1858,1858,Salted paper print from paper negative,,"Gift of Kay Gregory, 1967",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271262,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -67.606.2,false,true,271273,Photographs,Photograph,Processione sulla facciata della Grande Scala,,,,,,Artist,,Luigi Pesce,"Italian, 1818–1891",,"Pesce, Luigi",Italian,1818,1891,1858,1858,1858,Salted paper print from paper negative,,"Gift of Kay Gregory, 1967",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271273,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -67.606.3,false,true,271277,Photographs,Photograph,Processione sulla facciata della Grande Scala,,,,,,Artist,,Luigi Pesce,"Italian, 1818–1891",,"Pesce, Luigi",Italian,1818,1891,1858,1858,1858,Salted paper print from paper negative,,"Gift of Kay Gregory, 1967",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271277,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -67.606.4,false,true,271278,Photographs,Photograph,Processione sulla facciata della Grande Scala,,,,,,Artist,,Luigi Pesce,"Italian, 1818–1891",,"Pesce, Luigi",Italian,1818,1891,1858,1858,1858,Salted paper print from paper negative,,"Gift of Kay Gregory, 1967",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271278,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -67.606.5,false,true,271279,Photographs,Photograph,Teheran,,,,,,Artist,,Luigi Pesce,"Italian, 1818–1891",,"Pesce, Luigi",Italian,1818,1891,1858,1858,1858,Salted paper print from paper negative,,"Gift of Kay Gregory, 1967",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271279,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -67.606.6,false,true,271280,Photographs,Photograph,Amharet es Schah,,,,,,Artist,,Luigi Pesce,"Italian, 1818–1891",,"Pesce, Luigi",Italian,1818,1891,1858,1858,1858,Salted paper print from paper negative,,"Gift of Kay Gregory, 1967",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271280,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -67.606.7,false,true,271281,Photographs,Photograph,"[Peacock's Throne Room, Teheran]",,,,,,Artist,,Luigi Pesce,"Italian, 1818–1891",,"Pesce, Luigi",Italian,1818,1891,1858,1858,1858,Salted paper print from paper negative,,"Gift of Kay Gregory, 1967",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271281,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -67.606.8,false,true,271282,Photographs,Photograph,"Outer Entrance to the King's Palace, Teheran",,,,,,Artist,,Luigi Pesce,"Italian, 1818–1891",,"Pesce, Luigi",Italian,1818,1891,1858,1858,1858,Salted paper print from paper negative,,"Gift of Kay Gregory, 1967",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271282,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -67.606.9,false,true,271283,Photographs,Photograph,Teheran. Prime Minister's House (Nezanneh),,,,,,Artist,,Luigi Pesce,"Italian, 1818–1891",,"Pesce, Luigi",Italian,1818,1891,1858,1858,1858,Salted paper print from paper negative,,"Gift of Kay Gregory, 1967",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271283,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -67.606.10,false,true,271263,Photographs,Photograph,Tomb of the Khan of Khiva at Teheran,,,,,,Artist,,Luigi Pesce,"Italian, 1818–1891",,"Pesce, Luigi",Italian,1818,1891,1859,1859,1859,Albumen silver print from paper negative,,"Gift of Kay Gregory, 1967",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271263,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -67.606.11,false,true,271264,Photographs,Photograph,"British Legation, Teheran",,,,,,Artist,,Luigi Pesce,"Italian, 1818–1891",,"Pesce, Luigi",Italian,1818,1891,1858,1858,1858,Salted paper print from paper negative,,"Gift of Kay Gregory, 1967",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271264,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -67.606.12,false,true,271265,Photographs,Photograph,"Zerghiandeh. Russian Minister's Country House, Teheran",,,,,,Artist,,Luigi Pesce,"Italian, 1818–1891",,"Pesce, Luigi",Italian,1818,1891,1858,1858,1858,Salted paper print from paper negative,,"Gift of Kay Gregory, 1967",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271265,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -67.606.13,false,true,271266,Photographs,Photograph,Bagh-takt a chiraz,,,,,,Artist,,Luigi Pesce,"Italian, 1818–1891",,"Pesce, Luigi",Italian,1818,1891,1858,1858,1858,Salted paper print from paper negative,,"Gift of Kay Gregory, 1967",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271266,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -67.606.14,false,true,271267,Photographs,Photograph,"Yran, Piazza a Isphaan",,,,,,Artist,,Luigi Pesce,"Italian, 1818–1891",,"Pesce, Luigi",Italian,1818,1891,1858,1858,1858,Salted paper print from paper negative,,"Gift of Kay Gregory, 1967",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271267,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -67.606.15,false,true,271268,Photographs,Photograph,Bassirilieve a Maksci Reste,,,,,,Artist,,Luigi Pesce,"Italian, 1818–1891",,"Pesce, Luigi",Italian,1818,1891,1858,1858,1858,Salted paper print from paper negative,,"Gift of Kay Gregory, 1967",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271268,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -67.606.16,false,true,271269,Photographs,Photograph,"Ruine sulla prima terrazza, Persepolis",,,,,,Artist,,Luigi Pesce,"Italian, 1818–1891",,"Pesce, Luigi",Italian,1818,1891,1858,1858,1858,Salted paper print from paper negative,,"Gift of Kay Gregory, 1967",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271269,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -67.606.17,false,true,271270,Photographs,Photograph,Tomba sulla rocca a Persepolis,,,,,,Artist,,Luigi Pesce,"Italian, 1818–1891",,"Pesce, Luigi",Italian,1818,1891,1858,1858,1858,Salted paper print from paper negative,,"Gift of Kay Gregory, 1967",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271270,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -67.606.18,false,true,271271,Photographs,Photograph,Tombe de Ciro a Morgab,,,,,,Artist,,Luigi Pesce,"Italian, 1818–1891",,"Pesce, Luigi",Italian,1818,1891,1858,1858,1858,Salted paper print from paper negative,,"Gift of Kay Gregory, 1967",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271271,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -67.606.19,false,true,271272,Photographs,Photograph,L'antica porta d'ingrezza a Persepolis],,,,,,Artist,,Luigi Pesce,"Italian, 1818–1891",,"Pesce, Luigi",Italian,1818,1891,1858,1858,1858,Salted paper print from paper negative,,"Gift of Kay Gregory, 1967",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271272,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -67.606.20,false,true,271274,Photographs,Photograph,"Ruine sulla terza terazza, Persepolis",,,,,,Artist,,Luigi Pesce,"Italian, 1818–1891",,"Pesce, Luigi",Italian,1818,1891,1858,1858,1858,Salted paper print from paper negative,,"Gift of Kay Gregory, 1967",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271274,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -67.606.21,false,true,271275,Photographs,Photograph,Porta d'entrata alla ruine de Persepolis,,,,,,Artist,,Luigi Pesce,"Italian, 1818–1891",,"Pesce, Luigi",Italian,1818,1891,1858,1858,1858,Salted paper print from paper negative,,"Gift of Kay Gregory, 1967",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271275,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -67.606.22,false,true,271276,Photographs,Photograph,Veduta generale di Persepolis presa dalla Montagna,,,,,,Artist,,Luigi Pesce,"Italian, 1818–1891",,"Pesce, Luigi",Italian,1818,1891,1858,1858,1858,Salted paper print from paper negative,,"Gift of Kay Gregory, 1967",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271276,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1977.683.2,false,true,652099,Photographs,Photograph,"[Golestan Palace, Interior, Teheran, Iran]",,,,,,Artist,,Luigi Pesce,"Italian, 1818–1891",,"Pesce, Luigi",Italian,1818,1891,1840s–60s,1840,1869,,,"Gift of Charles K. and Irma B. Wilkinson, 1977",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/652099,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1977.683.3,false,true,652100,Photographs,Photograph,"[Golestan, The Peacock Throne, Teheran, Iran] (Takht-I Taous)",,,,,,Artist,,Luigi Pesce,"Italian, 1818–1891",,"Pesce, Luigi",Italian,1818,1891,1840s–60s,1840,1869,,,"Gift of Charles K. and Irma B. Wilkinson, 1977",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/652100,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1977.683.4,false,true,652101,Photographs,Photograph,"[Golestan Palace, Teheran, Iran] (Takht-i Marmor)",,,,,,Artist,,Luigi Pesce,"Italian, 1818–1891",,"Pesce, Luigi",Italian,1818,1891,1840s–60s,1840,1869,,,"Gift of Charles K. and Irma B. Wilkinson, 1977",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/652101,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1977.683.5,false,true,652102,Photographs,Photograph,"[Golestan, Le Salon et Fete de l'equinode, Teheran, Iran (le Pavillion du Trone)]",,,,,,Artist,Possibly by,Luigi Pesce,"Italian, 1818–1891",,"Pesce, Luigi",Italian,1818,1891,1840s–60s,1840,1869,,,"Gift of Charles K. and Irma B. Wilkinson, 1977",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/652102,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1977.683.6,false,true,652103,Photographs,Photograph,"[Gate of Government, Teheran, Iran]",,,,,,Artist,,Luigi Pesce,"Italian, 1818–1891",,"Pesce, Luigi",Italian,1818,1891,1840s–60s,1840,1869,,,"Gift of Charles K. and Irma B. Wilkinson, 1977",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/652103,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1977.683.7,false,true,652104,Photographs,Photograph,"[Plaza of Canons, Teheran, Iran] (Maydan-i Top-khaneh)",,,,,,Artist,Possibly by,Luigi Pesce,"Italian, 1818–1891",,"Pesce, Luigi",Italian,1818,1891,1840s–60s,1840,1869,,,"Gift of Charles K. and Irma B. Wilkinson, 1977",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/652104,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1977.683.8,false,true,262204,Photographs,Photograph,"[The New Gate, Teheran]",,,,,,Artist,,Luigi Pesce,"Italian, 1818–1891",,"Pesce, Luigi",Italian,1818,1891,1850s,1850,1859,Albumen silver print from paper negative,,"Gift of Charles K. and Irma B. Wilkinson, 1977",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/262204,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1977.683.9,false,true,652106,Photographs,Photograph,"[South Gate of the Arq, Teheran, Iran]",,,,,,Artist,,Luigi Pesce,"Italian, 1818–1891",,"Pesce, Luigi",Italian,1818,1891,1840s–60s,1840,1869,,,"Gift of Charles K. and Irma B. Wilkinson, 1977",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/652106,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1977.683.10,false,true,262200,Photographs,Photograph,[Palace of the Shah],,,,,,Artist,,Luigi Pesce,"Italian, 1818–1891",,"Pesce, Luigi",Italian,1818,1891,1850s,1850,1859,Albumen silver print from paper negative,,"Gift of Charles K. and Irma B. Wilkinson, 1977",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/262200,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1977.683.11,false,true,652107,Photographs,Photograph,"[Summer residence (Qasr) of the Shah, Emarat-e xoruji, Teheran, Iran]",,,,,,Artist,Possibly by,Luigi Pesce,"Italian, 1818–1891",,"Pesce, Luigi",Italian,1818,1891,1840s–60s,1840,1869,,,"Gift of Charles K. and Irma B. Wilkinson, 1977",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/652107,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1977.683.12,false,true,652108,Photographs,Photograph,"[Palace of the Shah, Teheran, Iran]",,,,,,Artist,Possibly by,Luigi Pesce,"Italian, 1818–1891",,"Pesce, Luigi",Italian,1818,1891,1840s–60s,1840,1869,,,"Gift of Charles K. and Irma B. Wilkinson, 1977",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/652108,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1977.683.13,false,true,652109,Photographs,Photograph,"[Palace of the Shah, Paying respects to the Shah/Fete de Salam, Teheran, Iran [same as 12] ]",,,,,,Artist,Possibly by,Luigi Pesce,"Italian, 1818–1891",,"Pesce, Luigi",Italian,1818,1891,1840s–60s,1840,1869,,,"Gift of Charles K. and Irma B. Wilkinson, 1977",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/652109,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1977.683.14,false,true,652110,Photographs,Photograph,"[Palace of the Shah, Teheran, Iran]",,,,,,Artist,Possibly by,Luigi Pesce,"Italian, 1818–1891",,"Pesce, Luigi",Italian,1818,1891,1840s–60s,1840,1869,,,"Gift of Charles K. and Irma B. Wilkinson, 1977",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/652110,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1977.683.15,false,true,652111,Photographs,Photograph,"[The Sublime Porte, Teheran, Iran]",,,,,,Artist,,Luigi Pesce,"Italian, 1818–1891",,"Pesce, Luigi",Italian,1818,1891,1840s–60s,1840,1869,,,"Gift of Charles K. and Irma B. Wilkinson, 1977",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/652111,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1977.683.16,false,true,652112,Photographs,Photograph,"[Mosque of Nasser-eddin Shah, Teheran, Iran]",,,,,,Artist,,Luigi Pesce,"Italian, 1818–1891",,"Pesce, Luigi",Italian,1818,1891,1840s–60s,1840,1869,,,"Gift of Charles K. and Irma B. Wilkinson, 1977",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/652112,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1977.683.17,false,true,652113,Photographs,Photograph,"[Tomb of Khan of Khiva, Uzbekistan]",,,,,,Artist,Possibly by,Luigi Pesce,"Italian, 1818–1891",,"Pesce, Luigi",Italian,1818,1891,1840s–60s,1840,1869,,,"Gift of Charles K. and Irma B. Wilkinson, 1977",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/652113,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1977.683.19,false,true,652115,Photographs,Photograph,[Departure for the huntTomb of Khan of Khiva],,,,,,Artist,Possibly by,Luigi Pesce,"Italian, 1818–1891",,"Pesce, Luigi",Italian,1818,1891,1840s–60s,1840,1869,Salted paper print,,"Gift of Charles K. and Irma B. Wilkinson, 1977",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/652115,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1977.683.20,false,true,652116,Photographs,Photograph,[Armenian Woman of Teheran],,,,,,Artist,Possibly by,Luigi Pesce,"Italian, 1818–1891",,"Pesce, Luigi",Italian,1818,1891,1840s–60s,1840,1869,Albumen silver print,,"Gift of Charles K. and Irma B. Wilkinson, 1977",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/652116,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1977.683.21,false,true,262201,Photographs,Photograph,[In the Mosque of the Damegan/The Eunuchs],,,,,,Artist,Possibly by,Luigi Pesce,"Italian, 1818–1891",,"Pesce, Luigi",Italian,1818,1891,1850s,1850,1859,Albumen silver print from paper negative,16.3 x 23.2 cm. (6 7/16 x 9 1/8 in.),"Gift of Charles K. and Irma B. Wilkinson, 1977",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/262201,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1977.683.25,false,true,652118,Photographs,Photograph,"Portrait of Ardeshir Mirza, uncle of the king",,,,,,Artist,Possibly by,Luigi Pesce,"Italian, 1818–1891",,"Pesce, Luigi",Italian,1818,1891,1840s–60s,1840,1869,Salted paper print,,"Gift of Charles K. and Irma B. Wilkinson, 1977",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/652118,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1977.683.26,false,true,652119,Photographs,Photograph,"[A Persian revue in a painting that once belonged to Ardeshir Mirza, uncle of the king.]",,,,,,Artist,Possibly by,Luigi Pesce,"Italian, 1818–1891",,"Pesce, Luigi",Italian,1818,1891,1840s–60s,1840,1869,Albumen silver print,,"Gift of Charles K. and Irma B. Wilkinson, 1977",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/652119,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1977.683.27,false,true,652120,Photographs,Photograph,"[Fath-Ali Shah, Painting that Once Belonged to Hmah [?] Saula, Uncle of the King.]",,,,,,Artist,Possibly by,Luigi Pesce,"Italian, 1818–1891",,"Pesce, Luigi",Italian,1818,1891,1840s–60s,1840,1869,Albumen silver print,,"Gift of Charles K. and Irma B. Wilkinson, 1977",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/652120,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1977.683.28,false,true,652121,Photographs,Photograph,"[RAYY, Tower of Toghrul, 1139.]",,,,,,Artist,Possibly by,Luigi Pesce,"Italian, 1818–1891",,"Pesce, Luigi",Italian,1818,1891,1840s–60s,1840,1869,Albumen silver print,,"Gift of Charles K. and Irma B. Wilkinson, 1977",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/652121,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1977.683.29,false,true,652122,Photographs,Photograph,[A General View of MESHED from the roof of a hamam.],,,,,,Artist,Possibly by,Luigi Pesce,"Italian, 1818–1891",,"Pesce, Luigi",Italian,1818,1891,1840s–60s,1840,1869,Albumen silver print,,"Gift of Charles K. and Irma B. Wilkinson, 1977",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/652122,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1977.683.30,false,true,652123,Photographs,Photograph,[Principal Gate of MESHED],,,,,,Artist,Possibly by,Luigi Pesce,"Italian, 1818–1891",,"Pesce, Luigi",Italian,1818,1891,1840s–60s,1840,1869,Albumen silver print,,"Gift of Charles K. and Irma B. Wilkinson, 1977",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/652123,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1977.683.31,false,true,652124,Photographs,Photograph,"[New Court of Imam Riza, MESHED]",,,,,,Artist,Possibly by,Luigi Pesce,"Italian, 1818–1891",,"Pesce, Luigi",Italian,1818,1891,1840s–60s,1840,1869,Albumen silver print,,"Gift of Charles K. and Irma B. Wilkinson, 1977",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/652124,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1977.683.32,false,true,302221,Photographs,Photograph,"[Main Gate of Imam Riza, Mashhad, Iran]",,,,,,Artist,Possibly by,Luigi Pesce,"Italian, 1818–1891",,"Pesce, Luigi",Italian,1818,1891,1850s,1850,1859,Albumen silver print from paper negative,Image: 16.8 x 21.1 cm (6 5/8 x 8 5/16 in.) Sheet: 17.2 x 21.1 cm (6 3/4 x 8 5/16 in.),"Gift of Charles K. and Irma B. Wilkinson, 1977",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/302221,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1977.683.33,false,true,652126,Photographs,Photograph,[Old Court of Imam Riza MESHED],,,,,,Artist,Possibly by,Luigi Pesce,"Italian, 1818–1891",,"Pesce, Luigi",Italian,1818,1891,1840s–60s,1840,1869,Albumen silver print,,"Gift of Charles K. and Irma B. Wilkinson, 1977",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/652126,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1977.683.34,false,true,652127,Photographs,Photograph,"[Court of the mosque Gawhar Shad, MESHED, 1418 (?)]",,,,,,Artist,Possibly by,Luigi Pesce,"Italian, 1818–1891",,"Pesce, Luigi",Italian,1818,1891,1840s–60s,1840,1869,Albumen silver print,,"Gift of Charles K. and Irma B. Wilkinson, 1977",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/652127,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1977.683.35,false,true,652128,Photographs,Photograph,"[Nadir Shah's Golden Gate and Minaret. Otherwise known as the Golden Iwan of Ali Shir Nawai, late 15th Century with Restorations. MESHED]",,,,,,Artist,Possibly by,Luigi Pesce,"Italian, 1818–1891",,"Pesce, Luigi",Italian,1818,1891,1840s–60s,1840,1869,Albumen silver print,,"Gift of Charles K. and Irma B. Wilkinson, 1977",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/652128,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1977.683.36,false,true,652129,Photographs,Photograph,[Mosque of the Shah],,,,,,Artist,Possibly by,Luigi Pesce,"Italian, 1818–1891",,"Pesce, Luigi",Italian,1818,1891,1840s–60s,1840,1869,Albumen silver print,,"Gift of Charles K. and Irma B. Wilkinson, 1977",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/652129,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1977.683.37,false,true,652130,Photographs,Photograph,[Tomb of Kogin Baba],,,,,,Artist,Possibly by,Luigi Pesce,"Italian, 1818–1891",,"Pesce, Luigi",Italian,1818,1891,1840s–60s,1840,1869,Albumen silver print,,"Gift of Charles K. and Irma B. Wilkinson, 1977",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/652130,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1977.683.38,false,true,652131,Photographs,Photograph,[Tomb of Seeh-i Mumin],,,,,,Artist,Possibly by,Luigi Pesce,"Italian, 1818–1891",,"Pesce, Luigi",Italian,1818,1891,1840s–60s,1840,1869,Albumen silver print,,"Gift of Charles K. and Irma B. Wilkinson, 1977",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/652131,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1977.683.39,false,true,652132,Photographs,Photograph,"[Bastam, Tomb Tower (built 1313), Khorasan]",,,,,,Artist,Possibly by,Luigi Pesce,"Italian, 1818–1891",,"Pesce, Luigi",Italian,1818,1891,1840s–60s,1840,1869,Albumen silver print,,"Gift of Charles K. and Irma B. Wilkinson, 1977",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/652132,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1977.683.40,false,true,652133,Photographs,Photograph,"[Tomb of Bayazid, BISTAM]",,,,,,Artist,Possibly by,Luigi Pesce,"Italian, 1818–1891",,"Pesce, Luigi",Italian,1818,1891,1840s–60s,1840,1869,Albumen silver print,,"Gift of Charles K. and Irma B. Wilkinson, 1977",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/652133,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1977.683.41,false,true,652134,Photographs,Photograph,[Cemetry of MESHED],,,,,,Artist,Possibly by,Luigi Pesce,"Italian, 1818–1891",,"Pesce, Luigi",Italian,1818,1891,1840s–60s,1840,1869,Albumen silver print,,"Gift of Charles K. and Irma B. Wilkinson, 1977",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/652134,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1977.683.42,false,true,652135,Photographs,Photograph,[View of TABRIZ],,,,,,Artist,Possibly by,Luigi Pesce,"Italian, 1818–1891",,"Pesce, Luigi",Italian,1818,1891,1840s–60s,1840,1869,Albumen silver print,,"Gift of Charles K. and Irma B. Wilkinson, 1977",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/652135,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1977.683.43,false,true,652136,Photographs,Photograph,"[Blue Mosque of TABRIZ, 1465.]",,,,,,Artist,Possibly by,Luigi Pesce,"Italian, 1818–1891",,"Pesce, Luigi",Italian,1818,1891,1840s–60s,1840,1869,Albumen silver print,,"Gift of Charles K. and Irma B. Wilkinson, 1977",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/652136,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1977.683.44,false,true,652137,Photographs,Photograph,[A Persian Citadel in the Environs of Sultaniye],,,,,,Artist,Possibly by,Luigi Pesce,"Italian, 1818–1891",,"Pesce, Luigi",Italian,1818,1891,1840s–60s,1840,1869,Albumen silver print,,"Gift of Charles K. and Irma B. Wilkinson, 1977",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/652137,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1977.683.45,false,true,652138,Photographs,Photograph,"[Tomb of Oljaetu, 1305-1313.]",,,,,,Artist,Possibly by,Luigi Pesce,"Italian, 1818–1891",,"Pesce, Luigi",Italian,1818,1891,1840s–60s,1840,1869,Albumen silver print,,"Gift of Charles K. and Irma B. Wilkinson, 1977",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/652138,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1977.683.46,false,true,652139,Photographs,Photograph,[Mosque at Sultaniye],,,,,,Artist,Possibly by,Luigi Pesce,"Italian, 1818–1891",,"Pesce, Luigi",Italian,1818,1891,1840s–60s,1840,1869,Albumen silver print,,"Gift of Charles K. and Irma B. Wilkinson, 1977",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/652139,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1977.683.47,false,true,652140,Photographs,Photograph,"[Mosque at Sultaniye, [same as 46] ]",,,,,,Artist,Possibly by,Luigi Pesce,"Italian, 1818–1891",,"Pesce, Luigi",Italian,1818,1891,1840s–60s,1840,1869,Albumen silver print,,"Gift of Charles K. and Irma B. Wilkinson, 1977",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/652140,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1977.683.48,false,true,652141,Photographs,Photograph,"[View of Kermanshah, Capital of Kurdistan]",,,,,,Artist,Possibly by,Luigi Pesce,"Italian, 1818–1891",,"Pesce, Luigi",Italian,1818,1891,1840s–60s,1840,1869,Salted paper print,,"Gift of Charles K. and Irma B. Wilkinson, 1977",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/652141,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1977.683.49,false,true,652142,Photographs,Photograph,[View of Koum],,,,,,Artist,Possibly by,Luigi Pesce,"Italian, 1818–1891",,"Pesce, Luigi",Italian,1818,1891,1840s–60s,1840,1869,Albumen silver print,,"Gift of Charles K. and Irma B. Wilkinson, 1977",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/652142,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1977.683.50,false,true,652143,Photographs,Photograph,[Mosque of Koum],,,,,,Artist,Possibly by,Luigi Pesce,"Italian, 1818–1891",,"Pesce, Luigi",Italian,1818,1891,1840s–60s,1840,1869,Albumen silver print,,"Gift of Charles K. and Irma B. Wilkinson, 1977",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/652143,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1977.683.51,false,true,652144,Photographs,Photograph,[The Bridge at Dizfoul],,,,,,Artist,Possibly by,Luigi Pesce,"Italian, 1818–1891",,"Pesce, Luigi",Italian,1818,1891,1840s–60s,1840,1869,Salted paper print,,"Gift of Charles K. and Irma B. Wilkinson, 1977",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/652144,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1977.683.52,false,true,652145,Photographs,Photograph,"[Ruins, Dizfoul]",,,,,,Artist,Possibly by,Luigi Pesce,"Italian, 1818–1891",,"Pesce, Luigi",Italian,1818,1891,1840s–60s,1840,1869,Albumen silver print,,"Gift of Charles K. and Irma B. Wilkinson, 1977",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/652145,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1977.683.53,false,true,652146,Photographs,Photograph,"[The Tower of 'Chihil Dukhtaran', Mausoleum of 40 daughters, 1056.]",,,,,,Artist,Possibly by,Luigi Pesce,"Italian, 1818–1891",,"Pesce, Luigi",Italian,1818,1891,1840s–60s,1840,1869,Albumen silver print,,"Gift of Charles K. and Irma B. Wilkinson, 1977",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/652146,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1977.683.54,false,true,652147,Photographs,Photograph,"[Minaret of the Mosque of 40 Columns, Chehel Dokhtar, 359b.]",,,,,,Artist,Possibly by,Luigi Pesce,"Italian, 1818–1891",,"Pesce, Luigi",Italian,1818,1891,1840s–60s,1840,1869,Albumen silver print,,"Gift of Charles K. and Irma B. Wilkinson, 1977",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/652147,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1977.683.55,false,true,652148,Photographs,Photograph,"[Minaret of the Chief Mosque at Damghan, 1026–1029]",,,,,,Artist,Possibly by,Luigi Pesce,"Italian, 1818–1891",,"Pesce, Luigi",Italian,1818,1891,1840s–60s,1840,1869,Albumen silver print,,"Gift of Charles K. and Irma B. Wilkinson, 1977",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/652148,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1977.683.56,false,true,652149,Photographs,Photograph,"[Tag-e bustan, Kermanshah, Kurdestan]",,,,,,Artist,Possibly by,Luigi Pesce,"Italian, 1818–1891",,"Pesce, Luigi",Italian,1818,1891,1840s–60s,1840,1869,Albumen silver print,,"Gift of Charles K. and Irma B. Wilkinson, 1977",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/652149,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1977.683.57,false,true,652150,Photographs,Photograph,"[Ruins of Tus, Khorasan]",,,,,,Artist,Possibly by,Luigi Pesce,"Italian, 1818–1891",,"Pesce, Luigi",Italian,1818,1891,1840s–60s,1840,1869,Albumen silver print,,"Gift of Charles K. and Irma B. Wilkinson, 1977",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/652150,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1977.683.58,false,true,652151,Photographs,Photograph,"[Other ruins in the town of Tus, Khorasan]",,,,,,Artist,Possibly by,Luigi Pesce,"Italian, 1818–1891",,"Pesce, Luigi",Italian,1818,1891,1840s–60s,1840,1869,Albumen silver print,,"Gift of Charles K. and Irma B. Wilkinson, 1977",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/652151,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1977.683.59,false,true,652152,Photographs,Photograph,"(1) [Tag-e Bustan, Kermanshah]",,,,,,Artist,,Luigi Pesce,"Italian, 1818–1891",,"Pesce, Luigi",Italian,1818,1891,1840s–60s,1840,1869,Albumen silver print,,"Gift of Charles K. and Irma B. Wilkinson, 1977",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/652152,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1977.683.60,false,true,652153,Photographs,Photograph,(2) [Persepolis],,,,,,Artist,,Luigi Pesce,"Italian, 1818–1891",,"Pesce, Luigi",Italian,1818,1891,1840s–60s,1840,1869,Albumen silver print,,"Gift of Charles K. and Irma B. Wilkinson, 1977",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/652153,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1977.683.61,false,true,652154,Photographs,Photograph,(3) [Persepolis (?)],,,,,,Artist,,Luigi Pesce,"Italian, 1818–1891",,"Pesce, Luigi",Italian,1818,1891,1840s–60s,1840,1869,Albumen silver print,,"Gift of Charles K. and Irma B. Wilkinson, 1977",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/652154,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1977.683.62,false,true,652155,Photographs,Photograph,"(4) [Naksh-i Rustam, Near Persepolis]",,,,,,Artist,,Luigi Pesce,"Italian, 1818–1891",,"Pesce, Luigi",Italian,1818,1891,1840s–60s,1840,1869,Albumen silver print,,"Gift of Charles K. and Irma B. Wilkinson, 1977",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/652155,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1977.683.63,false,true,652156,Photographs,Photograph,(5) [Persepolis],,,,,,Artist,,Luigi Pesce,"Italian, 1818–1891",,"Pesce, Luigi",Italian,1818,1891,1840s–60s,1840,1869,Albumen silver print,,"Gift of Charles K. and Irma B. Wilkinson, 1977",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/652156,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1977.683.64,false,true,652157,Photographs,Photograph,"(6) [Naksh-i Rustam, Near Persepolis]",,,,,,Artist,,Luigi Pesce,"Italian, 1818–1891",,"Pesce, Luigi",Italian,1818,1891,1840s–60s,1840,1869,Albumen silver print,,"Gift of Charles K. and Irma B. Wilkinson, 1977",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/652157,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1977.683.65,false,true,652158,Photographs,Photograph,"(7) [Tag-e Bustan, Crowning Ceremony of Shapour II, Kermanshah]",,,,,,Artist,,Luigi Pesce,"Italian, 1818–1891",,"Pesce, Luigi",Italian,1818,1891,1840s–60s,1840,1869,Albumen silver print,,"Gift of Charles K. and Irma B. Wilkinson, 1977",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/652158,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1977.683.67,false,true,652160,Photographs,Photograph,(9) Untitled,,,,,,Artist,,Luigi Pesce,"Italian, 1818–1891",,"Pesce, Luigi",Italian,1818,1891,1840s–60s,1840,1869,Albumen silver print,,"Gift of Charles K. and Irma B. Wilkinson, 1977",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/652160,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1977.683.68,false,true,652161,Photographs,Photograph,"(10) [Gate of all Nations, Persepolis, Fars]",,,,,,Artist,,Luigi Pesce,"Italian, 1818–1891",,"Pesce, Luigi",Italian,1818,1891,1840s–60s,1840,1869,Albumen silver print,,"Gift of Charles K. and Irma B. Wilkinson, 1977",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/652161,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1977.683.69,false,true,652162,Photographs,Photograph,(11) [Naksh-i Rustam],,,,,,Artist,,Luigi Pesce,"Italian, 1818–1891",,"Pesce, Luigi",Italian,1818,1891,1840s–60s,1840,1869,Albumen silver print,,"Gift of Charles K. and Irma B. Wilkinson, 1977",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/652162,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1977.683.70,false,true,652163,Photographs,Photograph,"(12) [Persepolis, (W: before restoration)",,,,,,Artist,,Luigi Pesce,"Italian, 1818–1891",,"Pesce, Luigi",Italian,1818,1891,1840s–60s,1840,1869,Albumen silver print,,"Gift of Charles K. and Irma B. Wilkinson, 1977",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/652163,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1977.683.71,false,true,652164,Photographs,Photograph,(13) [Persepolis],,,,,,Artist,,Luigi Pesce,"Italian, 1818–1891",,"Pesce, Luigi",Italian,1818,1891,1840s–60s,1840,1869,Albumen silver print,,"Gift of Charles K. and Irma B. Wilkinson, 1977",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/652164,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1977.683.72,false,true,262203,Photographs,Photograph,[Persepolis],,,,,,Artist,,Luigi Pesce,"Italian, 1818–1891",,"Pesce, Luigi",Italian,1818,1891,1850s,1850,1859,Albumen silver print from paper negative,,"Gift of Charles K. and Irma B. Wilkinson, 1977",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/262203,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1977.683.73,false,true,652165,Photographs,Photograph,"(15) [Gate of all Nations, Persepolis, Fars]",,,,,,Artist,,Luigi Pesce,"Italian, 1818–1891",,"Pesce, Luigi",Italian,1818,1891,1840s–60s,1840,1869,Albumen silver print,,"Gift of Charles K. and Irma B. Wilkinson, 1977",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/652165,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1977.683.74,false,true,652166,Photographs,Photograph,"(16) [Apadana Hall Eastern Stairway, Persepolis, Fars]",,,,,,Artist,Possibly by,Luigi Pesce,"Italian, 1818–1891",,"Pesce, Luigi",Italian,1818,1891,1840s–60s,1840,1869,Albumen silver print,,"Gift of Charles K. and Irma B. Wilkinson, 1977",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/652166,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1977.683.75,false,true,652167,Photographs,Photograph,(17) Untitled,,,,,,Artist,,Luigi Pesce,"Italian, 1818–1891",,"Pesce, Luigi",Italian,1818,1891,1840s–60s,1840,1869,Albumen silver print,,"Gift of Charles K. and Irma B. Wilkinson, 1977",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/652167,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1977.683.76,false,true,652168,Photographs,Photograph,"(18) [Inscription, Old Persian in Cuneiform]",,,,,,Artist,,Luigi Pesce,"Italian, 1818–1891",,"Pesce, Luigi",Italian,1818,1891,1840s–60s,1840,1869,Albumen silver print,,"Gift of Charles K. and Irma B. Wilkinson, 1977",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/652168,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1987.1100.456,false,true,265528,Photographs,Photograph,"[Shirtless Man with Arm Raised in Fascist Salute with Superimposed Cross, Circle, and Spray of Flowers]",,,,,,Artist,,Mario Castagneri,"Italian, 1892–1940",,"Castagneri, Mario",Italian,1892,1940,ca. 1930,1928,1932,Gelatin silver print,23.0 x 28.8 cm (9 1/16 x 11 5/16 in.),"Ford Motor Company Collection, Gift of Ford Motor Company and John C. Waddell, 1987",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/265528,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1987.1100.43,false,true,265498,Photographs,Photograph,"Stairs, Mexico City",,,,,,Artist,,Tina Modotti,"Italian, 1896–1942",,"Modotti, Tina",Italian,1896,1942,1924–26,1924,1926,Gelatin silver print,18.4 x 23.9 cm (7 1/4 x 9 7/16 in.),"Ford Motor Company Collection, Gift of Ford Motor Company and John C. Waddell, 1987",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/265498,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.657,false,true,285802,Photographs,Carte-de-visite,Vittorio Emanuele II,,,,,,Artist,,Cesare Bernieri,"Italian, active Turin 1861–70",,"Bernieri, Cesare",Italian,1861,1870,1867,1867,1867,Albumen silver print from glass negative,Image: 6.2 x 4.7 cm (2 7/16 x 1 7/8 in.) oval Sheet: 10.1 x 5.9 cm (4 x 2 5/16 in.) Mount: 10.6 x 6.4 cm (4 3/16 x 2 1/2 in.) Mat: 21.9 x 15.7 cm (8 5/8 x 6 3/16 in.) Case: 22.9 x 17.1 x 1.3 cm (9 x 6 3/4 x 1/2 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285802,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1981.1194.1,false,true,263104,Photographs,Photograph,"Medinet Habou, 2me cour cote sudouest",,,,,,Artist,,Antonio Beato,"British, born Corfu, 1834–1906",,"Beato, Antonio",Italian,1834,1906,1870s,1870,1879,Albumen silver print from glass negative,,"Gift of Mrs. John L. Swayze, 1981",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/263104,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1981.1194.2,false,true,263115,Photographs,Photograph,"Luxor, vue du temple cote ouest",,,,,,Artist,,Antonio Beato,"British, born Corfu, 1834–1906",,"Beato, Antonio",Italian,1834,1906,1870s,1870,1879,Albumen silver print from glass negative,,"Gift of Mrs. John L. Swayze, 1981",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/263115,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1981.1194.3,false,true,263126,Photographs,Photograph,Temple a Denderah,,,,,,Artist,,Antonio Beato,"British, born Corfu, 1834–1906",,"Beato, Antonio",Italian,1834,1906,1870s,1870,1879,Albumen silver print from glass negative,,"Gift of Mrs. John L. Swayze, 1981",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/263126,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.562,false,true,286136,Photographs,Photograph,Interno del Colosseo,,,,,,Artist,,Giovanni Battista Altadonna,"Italian, Borgo Valsugana 1824–1890 Trento",,"Altadonna, Giovanni Battista",Italian,1824,1890,1850s,1850,1859,Albumen silver print from glass negative,"Image: 19.1 x 24.9 cm (7 1/2 x 9 13/16 in.), oval Mount: 26.8 x 35.7 cm (10 9/16 x 14 1/16 in.)","Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286136,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.148,false,true,260157,Photographs,Photomechanical print,Minuet,,,,,,Artist|Printer,,"Frank Eugene|F. Bruckmann Verlag, Munich","American, New York 1865–1936 Munich",,"Eugene, Frank|F. Bruckmann Verlag, Munich",American,1865,1936,"1900, printed 1909",1900,1900,Photogravure,12.7 x 17.7 cm (5 x 7 in.),"Rogers Fund, 1972",,,,,,,,,,,,Photographs|Prints,,http://www.metmuseum.org/art/collection/search/260157,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.46,false,true,301949,Photographs,"Photograph, corsage",[Mourning Corsage with Portrait of Abraham Lincoln],,,,,,Maker|Photography Studio,After,Unknown|Brady & Co.,"American|American, active 1840s–1880s",,Unknown|Brady & Co.,American,1840,1889,April 1865,1865,1865,Black and white silk with tintype set inside brass button,20 x 9 cm (7 7/8 x 3 9/16 in.) Image: 2 x 2 cm (13/16 x 13/16 in.),"Purchase, Alfred Stieglitz Society Gifts, 2013",,,,,,,,,,,,Assemblages,,http://www.metmuseum.org/art/collection/search/301949,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.176,false,true,260188,Photographs,Postcard,"Luitpold u. Albrecht, Kgl. Prinzen v. Bayern",,,,,,Artist|Printer,,Frank Eugene|Böhm Publishers,"American, New York 1865–1936 Munich",,"Eugene, Frank|Böhm Publishers",American,1865,1936,1908,1908,1908,Halftone,,"Rogers Fund, 1972",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/260188,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.48,false,true,268239,Photographs,Photograph,"Winter Quarters, Fort Brady",,,,,,Former Attribution,Formerly attributed to,William Frank Browne,American,,"Browne, William Frank",American,,1867,ca. 1865,1863,1867,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268239,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.737.11,false,true,260387,Photographs,Photograph,"Abandoned Farm in the Dustbowl, Coldwater District, near Dalhart, Texas, June",,,,,,Artist|Printer,,Dorothea Lange|Library of Congress,"American, 1895–1965",,"Lange, Dorothea|Library Of Congress",American,1895,1965,"1938, printed ca. 1972",1938,1938,Gelatin silver print,,"Gift of Phyllis D. Massar, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260387,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1987.1100.260,false,true,265311,Photographs,Photograph,"[Open-Air Barber at Work and Waiting Customers before Mileston, Mississippi, Post Office]",,,,,,Artist|Printer,,Marion Post Wolcott|F.S.A. studio,"American, 1910–1990",,"Wolcott, Marion Post|F.S.A. studio",American,1910,1990,1939,1939,1939,Gelatin silver print,25.0 x 32.1 cm (9 13/16 x 12 5/8 in.),"Ford Motor Company Collection, Gift of Ford Motor Company and John C. Waddell, 1987",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/265311,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.117,false,true,685459,Photographs,Carte-de-visite,[Charles Calverley],,,,,,Photography Studio,,Thompson Gallery,"American, active 1860s",,Thompson Gallery,American,1860,1869,1864–66,1864,1866,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685459,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.263,false,true,685604,Photographs,Carte-de-visite,[Elliott and Palmer ?],,,,,,Photography Studio,,Thompson Gallery,"American, active 1860s",,Thompson Gallery,American,1860,1869,1864–66,1864,1866,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685604,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.759,false,true,686098,Photographs,Carte-de-visite,[James Suydam],,,,,,Photography Studio,,Rintoul & Rockwood,"American, active 1860s",,Rintoul & Rockwood,American,1859,1870,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/686098,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.133,false,true,305831,Photographs,Photomontage; Cabinet card,[Man Cutting Watermelon],,,,,,Photography Studio,,Johnson,"American, active 1898–1903",,Johnson,American,1898,1903,1898–1903,1898,1903,Carbon print,Image: 9.4 × 13.2 cm (3 11/16 × 5 3/16 in.) Mount: 10.9 × 16.6 cm (4 5/16 × 6 9/16 in.),"Purchase, Greenwich ART Group Gift, 2013",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/305831,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.55,false,true,301993,Photographs,Photograph,[Major General William Tecumseh Sherman Wearing Mourning Armband],,,,,,Photography Studio,,Brady & Co.,"American, active 1840s–1880s",,Brady & Co.,American,1840,1889,1865,1865,1865,Albumen silver print from glass negative,Image: 8.5 x 5.4 cm (3 3/8 x 2 1/8 in.) Mount: 10.2 x 6.1 cm (4 x 2 3/8 in.),"Purchase, Alfred Stieglitz Society Gifts, 2013",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/301993,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.86,false,true,283187,Photographs,Photograph,[Senator and Mrs. James Henry Lane],,,,,,Photography Studio,,Brady & Co.,"American, active 1840s–1880s",,Brady & Co.,American,1840,1889,1861–66,1861,1866,Albumen silver print from glass negative,22.8 × 19.7 cm (9 × 7 3/4 in.),"Gilman Collection, Purchase, Alfred Stieglitz Society Gifts, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283187,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.98,false,true,283205,Photographs,Photograph,Relics of Andersonville Prison,,,,,,Photography Studio,,Brady & Co.,"American, active 1840s–1880s",,Brady & Co.,American,1840,1889,June 1866,1866,1866,Albumen silver print from glass negative,Image:22.1 x 18.9cm (8 11/16 x 7 7/16in.) Mount: 13 9/16 × 10 9/16 in. (34.5 × 26.9 cm),"Gilman Collection, Purchase, Joseph M. Cohen Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283205,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.400.22,false,true,291775,Photographs,Daguerreotype,[Bearded Man],,,,,,Photography Studio,,Brady & Co.,"American, active 1840s–1880s",,Brady & Co.,American,1840,1889,1853–57,1853,1857,Daguerreotype,Image: 9.2 x 6.8 cm (3 5/8 x 2 11/16 in.) Plate: 10.8 x 8.3 cm (4 1/4 x 3 1/4 in.) Case: 1.9 x 12.1 x 9.5 cm (3/4 x 4 3/4 x 3 3/4 in.),"Bequest of Herbert Mitchell, 2008",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/291775,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.400.77,false,true,291830,Photographs,Daguerreotype,Francis Alofsen,,,,,,Photography Studio,,Brady & Co.,"American, active 1840s–1880s",,Brady & Co.,American,1840,1889,1855,1855,1855,Daguerreotype,Image: 7 x 5.8 cm (2 3/4 x 2 5/16 in.) Plate: 8.3 x 7 cm (3 1/4 x 2 3/4 in.) Case: 2.1 x 9.4 x 8.3 cm (13/16 x 3 11/16 x 3 1/4 in.),"Bequest of Herbert Mitchell, 2008",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/291830,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.77,false,true,260289,Photographs,Photograph,Minuet,,,,,,Artist|Printer,,"Frank Eugene|F. Bruckmann Verlag, Munich","American, New York 1865–1936 Munich",,"Eugene, Frank|F. Bruckmann Verlag, Munich",American,1865,1936,"1900, printed 1909",1900,1900,Photogravure,12.7 x 17.7 cm (5 x 7 in.),"Rogers Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260289,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.58,false,true,302000,Photographs,Carte-de-visite,[Young Woman with Beckers Tabletop Stereoscope],,,,,,Photography Studio,,Porter Photograph Parlors,"American, active Janesville, Wisconsin, 1860s",,Porter Photograph Parlors,American,1860,1869,1864–66,1864,1866,Albumen silver print from glass negative,Image: 9.1 x 5.2 cm (3 9/16 x 2 1/16 in.),"Purchase, Alfred Stieglitz Society Gifts, 2013",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/302000,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2012.12,false,true,299481,Photographs,Medal; Button,[Presidential Campaign Medal with portraits of Abraham Lincoln and Hannibal Hamlin],,,,,,Maker|Artist,After,Unknown|Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Unknown|Brady, Mathew B.",American,1823,1896,1860,1860,1860,Tintype,"Image: 1.4 cm (9/16 in.), diameter Overall: 2.4 cm (15/16 in.), diameter","Purchase, The Overbrook Foundation Gift, 2012",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/299481,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.1,false,true,267879,Photographs,Photograph,"U.S. Monitor Onondaga, James River",,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267879,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.2,false,true,267990,Photographs,Photograph,"Co. B, 30th Pennsylvania Infantry",,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267990,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.4,false,true,268211,Photographs,Photograph,"Company C, 9th Indiana Infantry (Sherman's Veterans)",,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268211,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.6,false,true,268252,Photographs,Photograph,"Potomac Creek Railroad Bridge, A.C. & F. Railroad",,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268252,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.9,false,true,268285,Photographs,Photograph,"Ironclad fleet on James River below Rebel ""Howlett House Battery""",,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1865,1865,1865,Albumen silver print from glass negative,Image: 16.8 x 20.3 cm (6 5/8 x 8 in.),"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268285,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.23,false,true,268023,Photographs,Photograph,"Pennsylvania Light Artillery, Keystone Battery",,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268023,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.24,false,true,268034,Photographs,Photograph,Camp of 30th Pennsylvania Infantry,,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268034,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.27,false,true,268067,Photographs,Photograph,"Camp of 34th Massachusetts Infantry near Fort Lyon, Virginia",,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268067,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.29,false,true,268089,Photographs,Photograph,"Pennsylvania Light Artillery, Keystone Battery",,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268089,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.30,false,true,268101,Photographs,Photograph,"Campbell Hospital, D.C.",,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268101,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.31,false,true,268112,Photographs,Photograph,"Co. E, 21st Michigan Infantry. Sherman's Volunteers",,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,16.9 x 22.9 cm (6 5/8 x 9 in.),"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268112,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.33,false,true,268134,Photographs,Photograph,"Co. D, 21st Michigan Infantry. Sherman's Volunteers",,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268134,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.36,false,true,268167,Photographs,Photograph,"Pennsylvania Light Artillery, Keystone Battery",,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268167,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.37,false,true,268178,Photographs,Photograph,Washington. Harewood Hospital,,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268178,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.38,false,true,268189,Photographs,Photograph,"[Maneuvers, Winter Quarters]. Brady album, p. 129",,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,12.6 x 19.8 cm (4 15/16 x 7 13/16 in.),"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268189,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.39,false,true,268200,Photographs,Photograph,"[Encampment with shacks and laundry]. Brady album, p. 129",,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268200,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.41,false,true,268223,Photographs,Photograph,Land Battery of Naval Guns,,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268223,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.42,false,true,268233,Photographs,Photograph,"[Playing Cards, Winter Quarters]. Brady album, p. 129",,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268233,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.43,false,true,268234,Photographs,Photograph,"Transports, Tennessee River",,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1864,1864,1864,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268234,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.44,false,true,268235,Photographs,Photograph,"Transports, Tennessee River at Chattanooga",,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,ca. 1864,1862,1866,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268235,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.47,false,true,268238,Photographs,Photograph,"U.S. Monitor Onondaga, James River",,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268238,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.78,false,true,268272,Photographs,Photograph,"U.S. Monitor Onondaga, James River",,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268272,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.79,false,true,268273,Photographs,Photograph,"U.S. Monitor Onondaga, James River",,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268273,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.81,false,true,268276,Photographs,Photograph,"U.S. Monitor ""Saugus"" and Gunboat ""Mendota"", Appomattox River",,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268276,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.82,false,true,268277,Photographs,Photograph,"U.S. Monitor ""Mahopac"" on the Appomattox River",,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1864,1864,1864,Albumen silver print from glass negative,Image: 12.9 x 20.1 cm (5 1/16 x 7 15/16 in.),"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268277,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.83,false,true,268278,Photographs,Photograph,"U.S. Monitor Lehigh, James River",,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268278,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.84,false,true,268279,Photographs,Photograph,"""Mendota"", 100 lb. Parrott Gun",,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268279,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.85,false,true,268280,Photographs,Photograph,"U.S. Gunboat ""Mendota"", James River",,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268280,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.86,false,true,268281,Photographs,Photograph,Camp of 44th New York Infantry,,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268281,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.87,false,true,268282,Photographs,Photograph,"Ward in Hospital. Convalescent Camp, Alexandria Virginia",,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268282,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.88,false,true,268283,Photographs,Photograph,Convalescent Camp near Alexandria,,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,12.6 x 20 cm (4 15/16 x 7 7/8 in. ),"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268283,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.89,false,true,268284,Photographs,Photograph,"Barracks at Alexandria, Virginia",,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,13.1 x 20.1 cm (5 3/16 x 7 15/16 in. ),"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268284,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.97,false,true,268293,Photographs,Photograph,Camp of 44th New York Infantry,,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268293,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.116,false,true,267898,Photographs,Photograph,Fortifications at City Point,,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267898,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.117,false,true,267899,Photographs,Photograph,"Summer Headquarters of General Grant, City Point, Virginia",,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267899,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.121,false,true,267904,Photographs,Photograph,"Sanitary Commission Office. Convalescent Camp, Alexandria, Virginia",,,,,,Former Attribution,Former attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267904,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.122,false,true,267905,Photographs,Photograph,Convalescent Camp near Alexandria,,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,13.2 x 21 cm (5 3/16 x 8 1/4 in. ),"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267905,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.125,false,true,267908,Photographs,Photograph,"U.S. Gunboat ""Mendota"", James River",,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267908,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.126,false,true,267909,Photographs,Photograph,"U.S. Gunboat ""Mendota"", James River",,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,15.1 x 21.1 cm (5 15/16 x 8 5/16 in.),"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267909,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.127,false,true,267910,Photographs,Photograph,"[Crew of U.S. Monitor ""Saugus""]. Brady album, p. 172",,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267910,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.128,false,true,267911,Photographs,Photograph,"""General Grant"" at Kingston Gap, Tennessee River",,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267911,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.129,false,true,267912,Photographs,Photograph,"U.S. Ship ""Mendota"", James River",,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267912,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.131,false,true,267915,Photographs,Photograph,"Frigates ""Santee"" and ""Constitution"" off Naval Academy, Annapolis",,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267915,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.132,false,true,267916,Photographs,Photograph,"U.S. Monitor ""Casco"" on James River, taken from a lookout tower on bank.",,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267916,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.135,false,true,267919,Photographs,Photograph,Ruins of RR Bridge. Bull Run,,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,ca. 1862,1860,1864,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267919,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.141,false,true,267926,Photographs,Photograph,139th Pennsylvania Infantry,,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,12.5 x 19.1 cm (4 15/16 x 7 1/2 in.),"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267926,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.142,false,true,267927,Photographs,Photograph,"U.S. Transport ""Wau Katchie""",,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,17.4 x 21.1 cm (6 7/8 x 8 5/16 in.),"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267927,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.155,false,true,267941,Photographs,Photograph,"Fort Burnham, front of Petersburg",,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267941,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.161,false,true,267948,Photographs,Photograph,"U.S. Gunboat ""Saginaw"" and Monitor ""Onondaga""",,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267948,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.163,false,true,267950,Photographs,Photograph,164th New York Infantry,,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,ca. 1861,1859,1863,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267950,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.164,false,true,267951,Photographs,Photograph,"[Regiments - unidentified]. Brady album, p. 156",,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,13.5 x 20.2 cm (5 5/16 x 7 15/16 in.),"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267951,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.171,false,true,267959,Photographs,Photograph,"Transports, Tennessee River",,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1864,1864,1864,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267959,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.173,false,true,267961,Photographs,Photograph,"""Building Winter Quarters""",,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267961,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.176,false,true,267964,Photographs,Photograph,[Regiments - unidentified],,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,12.7 x 20.1 cm (5 x 7 15/16 in.),"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267964,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.177,false,true,267965,Photographs,Photograph,"""At the Sutler's Store""",,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267965,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.178,false,true,267966,Photographs,Photograph,"Co. B, 170th New York Volunteers",,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,ca. 1861,1859,1863,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267966,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.179,false,true,267967,Photographs,Photograph,"Camp of 34th Massachusetts Infantry, Miner's Hill, VA. Skirmish Drill.",,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1862–63,1862,1863,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267967,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.181,false,true,267970,Photographs,Photograph,"Fort Ellsworth, Alexandria, Virginia",,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267970,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.185,false,true,267974,Photographs,Photograph,"Camp of 34th Massachusetts Infantry near Fort Lyon, Virginia",,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267974,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.187,false,true,267976,Photographs,Photograph,"[Unidentified camp with ruined chimneys in background]. Brady album, p. 130",,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,11 x 20.1 cm (4 5/16 x 7 15/16 in.),"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267976,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.188,false,true,267977,Photographs,Photograph,"[Earthworks at the edge of a forest]. Brady album, p. 132",,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267977,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.189,false,true,267978,Photographs,Photograph,Cannon,,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267978,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.191,false,true,267981,Photographs,Photograph,"[Maneuvers, Union Cavalry, Winter Quarters]. Brady album, p. 127",,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267981,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.192,false,true,267982,Photographs,Photograph,139th Pennsylvania Infantry,,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267982,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.193,false,true,267983,Photographs,Photograph,Heavy Artillery,,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267983,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.194,false,true,267984,Photographs,Photograph,"[Regiments - unidentified]. Brady album, p. 156",,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267984,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.195,false,true,267985,Photographs,Photograph,139th Pennsylvania Infantry,,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267985,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.196,false,true,267986,Photographs,Photograph,Fork,,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267986,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.197,false,true,267987,Photographs,Photograph,"[Landscape with army encampment in the distance]. Brady album, p. 125",,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267987,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.198,false,true,267988,Photographs,Photograph,Fortifications,,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,15.5 x 20.8 cm (6 1/8 x 8 3/16 in.),"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267988,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.199,false,true,267989,Photographs,Photograph,A Photographer's Store (Brady's?),,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267989,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.205,false,true,267997,Photographs,Photograph,Heavy Artillery,,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267997,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.206,false,true,267998,Photographs,Photograph,"[Regiments - unidentified]. Brady album, p. 157",,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267998,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.208,false,true,268000,Photographs,Photograph,"[Winter Quarters, troops with row of cabins]. Brady album, p. 128",,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,12.9 x 20.3 cm (5 1/16 x 8 in.),"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268000,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.209,false,true,268001,Photographs,Photograph,"[Winter Quarters, troops with row of cabins]. Brady album, p. 128",,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268001,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.217,false,true,268010,Photographs,Photograph,"Unloading Supplies for U.S. Military Railroad opposite Richmond, Virginia",,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,ca. 1865,1863,1867,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268010,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.232,false,true,268026,Photographs,Photograph,21st Michigan Infantry. Sherman's Volunteers,,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268026,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.235,false,true,268029,Photographs,Photograph,30th Pennsylvania Infantry,,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,15.9 x 20.3 cm (6 1/4 x 8 in.),"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268029,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.236,false,true,268030,Photographs,Photograph,"Maryland Heights, near Harper's Ferry, New York State Militia",,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268030,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.237,false,true,268031,Photographs,Photograph,"Co. A, 30th Pennsylvania Infantry, Camp Mott Hooton",,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268031,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.238,false,true,268032,Photographs,Photograph,30th Pennsylvania Infantry,,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268032,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.239,false,true,268033,Photographs,Photograph,139th Pennsylvania Infantry,,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,12.5 x 20.2 cm (4 15/16 x 7 15/16 in.),"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268033,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.240,false,true,268035,Photographs,Photograph,Camp of 153rd New York Infantry,,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,ca. 1861,1859,1863,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268035,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.242,false,true,268037,Photographs,Photograph,"Headquarters, Co. F, 11th Rhode Island Infantry, Miner's Hill, Virginia",,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1862,1862,1862,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268037,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.243,false,true,268038,Photographs,Photograph,153rd New York Infantry,,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,ca. 1861,1859,1863,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268038,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.244,false,true,268039,Photographs,Photograph,30th Pennsylvania Infantry,,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268039,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.246,false,true,268041,Photographs,Photograph,Camp of 153rd New York Infantry,,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,ca. 1861,1859,1863,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268041,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.247,false,true,268042,Photographs,Photograph,44th Indiana Infantry,,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268042,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.248,false,true,268043,Photographs,Photograph,"Stoneman's Station, Virginia",,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268043,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.249,false,true,268044,Photographs,Photograph,"Stoneman's Station, Virginia",,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,12.6 x 20 cm (4 15/16 x 7 7/8 in.),"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268044,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.250,false,true,268046,Photographs,Photograph,"Stoneman's Station, Virginia. Quartermaster Dept.",,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,13 x 19.8 cm (5 1/8 x 7 13/16 in. ),"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268046,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.251,false,true,268047,Photographs,Photograph,"Stoneman's Station, Virginia. Commissary Dept.",,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268047,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.265,false,true,268062,Photographs,Photograph,U.S. Gunboat at Kingston Gap,,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268062,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.267,false,true,268064,Photographs,Photograph,Camp of 44th New York Infantry,,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268064,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.268,false,true,268065,Photographs,Photograph,"Hunting Creek, near Alexandria, Virginia",,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,14.8 x 20.6 cm (5 13/16 x 8 1/8 in. ),"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268065,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.276,false,true,268074,Photographs,Photograph,"[Mitchell's Plantation, Hopewell, Virginia]",,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1863–64,1863,1864,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268074,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.277,false,true,268075,Photographs,Photograph,Union Cavalry Winter Quarters,,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,15.8 x 21 cm (6 1/4 x 8 1/4 in.),"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268075,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.282,false,true,268081,Photographs,Photograph,"Camp of Construction Corps, U.S. Military Rail Road, near Manchester, Virginia",,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,ca. 1865,1863,1867,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268081,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.283,false,true,268082,Photographs,Photograph,"Hanover Junction, Pennsylvania",,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268082,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.285,false,true,268084,Photographs,Photograph,"Fort Johnson, James Island, looking toward Fort Sumter",,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268084,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.286,false,true,268085,Photographs,Photograph,"Hanover Junction Station, Pennsylvania",,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268085,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.288,false,true,268087,Photographs,Photograph,"Falls Church, Virginia",,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,16.6 x 20.6 cm (6 9/16 x 8 1/8 in. ),"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268087,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.290,false,true,268090,Photographs,Photograph,"Camp Barry near Bladensberg, Maryland. Artillery Depot",,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,15.8 x 21 cm (6 1/4 x 8 1/4 in. ),"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268090,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.293,false,true,268093,Photographs,Photograph,"Manchester, Virginia",,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268093,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.296,false,true,268096,Photographs,Photograph,"Fort Putnam, South Carolina",,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268096,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.297,false,true,268097,Photographs,Photograph,"Fort Johnson, James Island, South Carolina",,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268097,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.298,false,true,268098,Photographs,Photograph,"Geisboro D.C., Barracks at Fort Carroll",,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1863–64,1863,1864,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268098,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.299,false,true,268099,Photographs,Photograph,"Camp Barry near Bladensberg, Maryland. Stables",,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268099,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.300,false,true,268102,Photographs,Photograph,"Washington, D.C.",,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268102,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.301,false,true,268103,Photographs,Photograph,Washington. Harewood Hospital,,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268103,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.302,false,true,268104,Photographs,Photograph,Washington. Harewood Hospital,,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268104,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.303,false,true,268105,Photographs,Photograph,Washington. Harewood Hospital,,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268105,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.304,false,true,268106,Photographs,Photograph,Washington. Harewood Hospital,,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268106,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.305,false,true,268107,Photographs,Photograph,"Campbell Hospital near Washington, D.C.",,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268107,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.309,false,true,268111,Photographs,Photograph,Washington. Harewood Hospital,,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,15.8 x 21.1 cm (6 1/4 x 8 5/16 in. ),"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268111,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.310,false,true,268113,Photographs,Photograph,"Battery Rodgers, Potomac River near Washington",,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,16.6 x 20.6 cm (6 9/16 x 8 1/8 in. ),"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268113,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.311,false,true,268114,Photographs,Photograph,"Distant View of Arsenal, Washington, D.C.",,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,15.5 x 20.7 cm (6 1/8 x 8 1/8 in.),"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268114,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.314,false,true,268117,Photographs,Photograph,"[Wagon in a landscape with army encampment in the distance]. Brady album, p. 125",,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268117,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.315,false,true,268118,Photographs,Photograph,[Fortifications],,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268118,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.317,false,true,268120,Photographs,Photograph,Fort Sumter,,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268120,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.318,false,true,268121,Photographs,Photograph,"6th Vermont Infantry, Camp Griffen",,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268121,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.319,false,true,268122,Photographs,Photograph,"U.S. Monitor ""Saugus""",,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268122,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.320,false,true,268124,Photographs,Photograph,Officers of U.S.S. Hunchback,,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268124,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.321,false,true,268125,Photographs,Photograph,Officers of U.S.S. Hunchback,,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268125,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.322,false,true,268126,Photographs,Photograph,Officers of U.S.S. Hunchback,,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268126,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.323,false,true,268127,Photographs,Photograph,Officers of U.S.S. Hunchback,,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268127,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.324,false,true,268128,Photographs,Photograph,Officers of U.S.S. Hunchback,,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268128,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.326,false,true,268130,Photographs,Photograph,"Officers of ""Mendota""",,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268130,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.328,false,true,268132,Photographs,Photograph,[Encampment on a bluff],,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,9.8 x 10.7 cm (3 7/8 x 4 3/16 in.),"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268132,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.331,false,true,268136,Photographs,Photograph,22nd New York State Militia,,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,ca. 1861,1859,1863,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268136,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.333,false,true,268138,Photographs,Photograph,Officers of U.S.S. Hunchback,,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268138,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.336,false,true,268141,Photographs,Photograph,Cannon,,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268141,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.344,false,true,268150,Photographs,Photograph,"U.S. Monitor Lehigh, James River",,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268150,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.345,false,true,268151,Photographs,Photograph,"U.S. Monitor Lehigh, James River",,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268151,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.350,false,true,268157,Photographs,Photograph,23rd New York Infantry,,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,ca. 1861,1859,1863,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268157,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.351,false,true,268158,Photographs,Photograph,33rd New York Infantry,,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,ca. 1861,1859,1863,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268158,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.354,false,true,268161,Photographs,Photograph,Officers of U.S.S. Hunchback,,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268161,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.356,false,true,268163,Photographs,Photograph,"[Herd of Horses]. Brady album, p. 123",,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268163,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.357,false,true,268164,Photographs,Photograph,"[View of a small town with wooden sheds in distance]. Brady album, p. 123",,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268164,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.360,false,true,268168,Photographs,Photograph,[Four men in camp under a lean-to of pine boughs],,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268168,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.363,false,true,268171,Photographs,Photograph,"[Encampment alongside a stand of trees]. Brady album, p. 123",,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268171,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.364,false,true,268172,Photographs,Photograph,"[White picket fence with buildings in background]. Brady album, p. 123",,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268172,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.367,false,true,268175,Photographs,Photograph,"[Roads leading into a small town]. Brady album, p. 123",,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268175,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.369,false,true,268177,Photographs,Photograph,74th New York Infantry,,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861,1861,1861,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268177,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.370,false,true,268179,Photographs,Photograph,"26th New York Infantry at Fort Lyon, near Alexandria, Virginia",,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268179,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.371,false,true,268180,Photographs,Photograph,Fort Sumter,,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268180,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.372,false,true,268181,Photographs,Photograph,"Camp Northumberland, 91st Pennsylvania Infantry",,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861,1861,1861,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268181,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.373,false,true,268182,Photographs,Photograph,"Navy Yard, Washington, 71st New York State Militia",,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268182,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.375,false,true,268184,Photographs,Photograph,Fort Johnson,,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268184,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.376,false,true,268185,Photographs,Photograph,"Fort Gaines, Officers of the 55th New York Volunteers",,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268185,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.377,false,true,268186,Photographs,Photograph,"[Pleasant Valley Winery, New York]",,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1860,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268186,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.378,false,true,268187,Photographs,Photograph,Fort Sumter,,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268187,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.379,false,true,268188,Photographs,Photograph,Swamp Angel Battery,,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268188,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.380,false,true,268190,Photographs,Photograph,"Camp Jameson, Hall's Hill, 22nd Maine Infantry",,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268190,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.382,false,true,268192,Photographs,Photograph,Sally at Fort Richardson,,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1862,1862,1862,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268192,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.384,false,true,268194,Photographs,Photograph,"Fort Totten, N.W. of Washington",,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1865,1865,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268194,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.385,false,true,268195,Photographs,Photograph,"[Cache of Cannon]. Brady album, p. 123",,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268195,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.386,false,true,268196,Photographs,Photograph,"[House framed by trees]. Brady album, p. 123",,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268196,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.390,false,true,268201,Photographs,Photograph,Gettysburg. John Burns House,,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1863,1863,1863,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268201,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.391,false,true,268202,Photographs,Panorama,Gettysburg Wheat Field,,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1863,1863,1863,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268202,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.392,false,true,268203,Photographs,Photograph,"Gettysburg, Pennsylvania",,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1863,1863,1863,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268203,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.394,false,true,268205,Photographs,Photograph,Gettysburg from the West,,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1863,1863,1863,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268205,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.562,false,true,283979,Photographs,Photograph,Confederate Torpedoes Taken from James River,,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1864,1864,1864,Albumen silver print from glass negative,13.3 x 20.6 cm (5 1/4 x 8 1/8 in. ),"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283979,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"33.65.352, .391",false,true,268159,Photographs,Panorama,Wheat–Field in Which General Reynolds Was Shot,,,,,,Former Attribution,Formerly attributed to,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,July 1863,1863,1863,Albumen silver prints from glass negatives,Panorama: 15.7 × 40.9 cm (6 3/16 × 16 1/8 in.),"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268159,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.306,false,true,268108,Photographs,Photograph,Washington. Armory Square Hospital,,,,,,Former Attribution|Artist,Formerly attributed to,Mathew B. Brady|Unknown,"American, born Ireland, 1823?–1896 New York|American",,"Brady, Mathew B.|Unknown",American,1823,1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268108,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.307,false,true,268109,Photographs,Photograph,"Armory Square Hospital, Washington",,,,,,Former Attribution|Artist,Formerly attributed to,Mathew B. Brady|Unknown,"American, born Ireland, 1823?–1896 New York|American",,"Brady, Mathew B.|Unknown",American,1823,1896,1863–65,1863,1865,Albumen silver print from glass negative,Image: 17.3 × 20 cm (6 13/16 × 7 7/8 in.),"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268109,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.102,false,true,267883,Photographs,Photograph,"Pontoon Bridge, Broadway Landing, Appomattox River",,,,,,Artist|Former Attribution,Formerly attributed to,William Frank Browne|Mathew B. Brady,"American|American, born Ireland, 1823?–1896 New York",,"Browne, William Frank|Brady, Mathew B.",American,1823,1867 |1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267883,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.104,false,true,267885,Photographs,Photograph,"Broadway Landing, Appomattox River",,,,,,Artist|Former Attribution,Formerly attributed to,William Frank Browne|Mathew B. Brady,"American|American, born Ireland, 1823?–1896 New York",,"Browne, William Frank|Brady, Mathew B.",American,1823,1867 |1896,1864,1864,1864,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267885,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.110,false,true,267892,Photographs,Photograph,"Broadway Landing, Appomattox River",,,,,,Artist|Former Attribution,Formerly attributed to,William Frank Browne|Mathew B. Brady,"American|American, born Ireland, 1823?–1896 New York",,"Browne, William Frank|Brady, Mathew B.",American,1823,1867 |1896,1864,1864,1864,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267892,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.136,false,true,267920,Photographs,Photograph,"Fort Brady, Virginia, near Dutch Gap",,,,,,Artist|Former Attribution,Formerly attributed to,William Frank Browne|Mathew B. Brady,"American|American, born Ireland, 1823?–1896 New York",,"Browne, William Frank|Brady, Mathew B.",American,1823,1867 |1896,1865,1865,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267920,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.137,false,true,267921,Photographs,Photograph,"Near Dutch Gap, Virginia. Fort Brady",,,,,,Artist|Former Attribution,Formerly attributed to,William Frank Browne|Mathew B. Brady,"American|American, born Ireland, 1823?–1896 New York",,"Browne, William Frank|Brady, Mathew B.",American,1823,1867 |1896,ca. 1865,1863,1867,Albumen silver print from glass negative,15.8 x 20.6 cm (6 1/4 x 8 1/8 in. ),"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267921,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.138,false,true,267922,Photographs,Photograph,Distant View of Fort Brady,,,,,,Artist|Former Attribution,Formerly attributed to,William Frank Browne|Mathew B. Brady,"American|American, born Ireland, 1823?–1896 New York",,"Browne, William Frank|Brady, Mathew B.",American,1823,1867 |1896,ca. 1865,1863,1867,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267922,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.143,false,true,267928,Photographs,Photograph,Fort Darling. Masked Battery and Obstruction in James River,,,,,,Artist|Former Attribution,Formerly attributed to,William Frank Browne|Mathew B. Brady,"American|American, born Ireland, 1823?–1896 New York",,"Browne, William Frank|Brady, Mathew B.",American,1823,1867 |1896,ca. 1865,1863,1867,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267928,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.166,false,true,267953,Photographs,Photograph,"Fort Brady, Building Quarters",,,,,,Artist|Former Attribution,Formerly attributed to,William Frank Browne|Mathew B. Brady,"American|American, born Ireland, 1823?–1896 New York",,"Browne, William Frank|Brady, Mathew B.",American,1823,1867 |1896,ca. 1865,1863,1867,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267953,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.218,false,true,268011,Photographs,Photograph,"Fort Darling, James River",,,,,,Artist|Former Attribution,Formerly attributed to,William Frank Browne|Mathew B. Brady,"American|American, born Ireland, 1823?–1896 New York",,"Browne, William Frank|Brady, Mathew B.",American,1823,1867 |1896,ca. 1865,1863,1867,Albumen silver print from glass negative,15.1 x 21.4 cm (5 15/16 x 8 7/16 in. ),"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268011,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.263,false,true,268060,Photographs,Photograph,Obstructions in James River near Drewry's Bluff,,,,,,Artist|Former Attribution,Formerly attributed to,William Frank Browne|Mathew B. Brady,"American|American, born Ireland, 1823?–1896 New York",,"Browne, William Frank|Brady, Mathew B.",American,1823,1867 |1896,ca. 1865,1863,1867,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268060,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.270,false,true,268068,Photographs,Photograph,"Broadway Landing, Appomattox River",,,,,,Artist|Former Attribution,Formerly attributed to,William Frank Browne|Mathew B. Brady,"American|American, born Ireland, 1823?–1896 New York",,"Browne, William Frank|Brady, Mathew B.",American,1823,1867 |1896,1864,1864,1864,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268068,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.480,false,true,283897,Photographs,Photograph,"Fort Darling, James River",,,,,,Artist|Former Attribution,Attributed to|Formerly attributed to,William Frank Browne|Mathew B. Brady,"American|American, born Ireland, 1823?–1896 New York",,"Browne, William Frank|Brady, Mathew B.",American,1823,1867 |1896,1865 (?),1865,1865,Albumen silver print from glass negative,16.4 x 20.9 cm (6 7/16 x 8 1/4 in.),"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283897,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.5,false,true,268241,Photographs,Photograph,Lewis House. Battlefield of Bull Run,,,,,,Artist|Former Attribution,Formerly attributed to,George N. Barnard|Mathew B. Brady,"American, 1819–1902|American, born Ireland, 1823?–1896 New York",,"Barnard, George N.|Brady, Mathew B.",American,1819 |1823,1902 |1896,1861–62,1861,1862,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268241,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.51,false,true,268243,Photographs,Photograph,"St. Michael's Church, Charleston, S.C.",,,,,,Artist|Former Attribution,Formerly attributed to,George N. Barnard|Mathew B. Brady,"American, 1819–1902|American, born Ireland, 1823?–1896 New York",,"Barnard, George N.|Brady, Mathew B.",American,1819 |1823,1902 |1896,ca. 1864,1862,1866,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268243,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.53,false,true,268245,Photographs,Photograph,"Hibernian Hall, Charleston",,,,,,Artist|Former Attribution,Formerly attributed to,George N. Barnard|Mathew B. Brady,"American, 1819–1902|American, born Ireland, 1823?–1896 New York",,"Barnard, George N.|Brady, Mathew B.",American,1819 |1823,1902 |1896,ca. 1864,1862,1866,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268245,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.106,false,true,267887,Photographs,Photograph,"Ruins of Mrs. Henry's House, Battlefield of Bull Run",,,,,,Artist|Former Attribution,Formerly attributed to,George N. Barnard|Mathew B. Brady,"American, 1819–1902|American, born Ireland, 1823?–1896 New York",,"Barnard, George N.|Brady, Mathew B.",American,1819 |1823,1902 |1896,March 1862,1862,1862,Albumen silver print from glass negative,Image: 15.9 × 20.9 cm (6 1/4 × 8 1/4 in.),"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267887,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.180,false,true,267969,Photographs,Photograph,Bull Run. Matthews House,,,,,,Artist|Former Attribution,Formerly attributed to,George N. Barnard|Mathew B. Brady,"American, 1819–1902|American, born Ireland, 1823?–1896 New York",,"Barnard, George N.|Brady, Mathew B.",American,1819 |1823,1902 |1896,1861–62,1861,1862,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267969,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.207,false,true,267999,Photographs,Photograph,"Bull Run, Mrs. Henry's House, 21 July 1861",,,,,,Artist|Former Attribution,Formerly attributed to,George N. Barnard|Mathew B. Brady,"American, 1819–1902|American, born Ireland, 1823?–1896 New York",,"Barnard, George N.|Brady, Mathew B.",American,1819 |1823,1902 |1896,1861,1861,1861,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267999,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.284,false,true,268083,Photographs,Photograph,"Commissary Headquarters, Rocky Face Ridge",,,,,,Artist|Former Attribution,Formerly attributed to,George N. Barnard|Mathew B. Brady,"American, 1819–1902|American, born Ireland, 1823?–1896 New York",,"Barnard, George N.|Brady, Mathew B.",American,1819 |1823,1902 |1896,ca. 1864,1862,1866,Albumen silver print from glass negative,12.7 x 20.1 cm (5 x 7 15/16 in. ),"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268083,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.295,false,true,268095,Photographs,Photograph,Rebel Fortifications in front of Atlanta,,,,,,Artist|Former Attribution,Formerly attributed to,George N. Barnard|Mathew B. Brady,"American, 1819–1902|American, born Ireland, 1823?–1896 New York",,"Barnard, George N.|Brady, Mathew B.",American,1819 |1823,1902 |1896,ca. 1864,1862,1866,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268095,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.366,false,true,268174,Photographs,Photograph,View on Tennessee River,,,,,,Artist|Former Attribution,Formerly attributed to,George N. Barnard|Mathew B. Brady,"American, 1819–1902|American, born Ireland, 1823?–1896 New York",,"Barnard, George N.|Brady, Mathew B.",American,1819 |1823,1902 |1896,ca. 1864,1862,1866,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268174,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.389,false,true,268199,Photographs,Photograph,Tennessee River at Bridgeport,,,,,,Artist|Former Attribution,Formerly attributed to,George N. Barnard|Mathew B. Brady,"American, 1819–1902|American, born Ireland, 1823?–1896 New York",,"Barnard, George N.|Brady, Mathew B.",American,1819 |1823,1902 |1896,ca. 1864,1862,1866,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268199,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.397,false,true,268208,Photographs,Photograph,"U.S. Transport in Rapids, Tennessee River/The Suck - Tennessee River below Chattanooga, looking down stream",,,,,,Artist|Former Attribution,Formerly attributed to,George N. Barnard|Mathew B. Brady,"American, 1819–1902|American, born Ireland, 1823?–1896 New York",,"Barnard, George N.|Brady, Mathew B.",American,1819 |1823,1902 |1896,ca. 1864,1862,1866,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268208,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.399,false,true,268210,Photographs,Photograph,"Chattanooga, Tennessee",,,,,,Artist|Former Attribution,Formerly attributed to,George N. Barnard|Mathew B. Brady,"American, 1819–1902|American, born Ireland, 1823?–1896 New York",,"Barnard, George N.|Brady, Mathew B.",American,1819 |1823,1902 |1896,ca. 1864,1862,1866,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268210,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.400,false,true,268213,Photographs,Photograph,Camp near Chattanooga,,,,,,Artist|Former Attribution,Formerly attributed to,George N. Barnard|Mathew B. Brady,"American, 1819–1902|American, born Ireland, 1823?–1896 New York",,"Barnard, George N.|Brady, Mathew B.",American,1819 |1823,1902 |1896,ca. 1864,1862,1866,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268213,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.401,false,true,268214,Photographs,Photograph,Bridge Builders Camp opposite Chattanooga,,,,,,Artist|Former Attribution,Formerly attributed to,George N. Barnard|Mathew B. Brady,"American, 1819–1902|American, born Ireland, 1823?–1896 New York",,"Barnard, George N.|Brady, Mathew B.",American,1819 |1823,1902 |1896,ca. 1864,1862,1866,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268214,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.402,false,true,268215,Photographs,Photograph,"Crutchfield House, Chattanooga, Tennessee",,,,,,Artist|Former Attribution,Formerly attributed to,George N. Barnard|Mathew B. Brady,"American, 1819–1902|American, born Ireland, 1823?–1896 New York",,"Barnard, George N.|Brady, Mathew B.",American,1819 |1823,1902 |1896,ca. 1864,1862,1866,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268215,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.403,false,true,268216,Photographs,Photograph,"Chattanooga, Tennessee. Government Stable",,,,,,Artist|Former Attribution,Formerly attributed to,George N. Barnard|Mathew B. Brady,"American, 1819–1902|American, born Ireland, 1823?–1896 New York",,"Barnard, George N.|Brady, Mathew B.",American,1819 |1823,1902 |1896,ca. 1864,1862,1866,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268216,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.404,false,true,268217,Photographs,Photograph,"Chattanooga, Tennessee",,,,,,Artist|Former Attribution,Formerly attributed to,George N. Barnard|Mathew B. Brady,"American, 1819–1902|American, born Ireland, 1823?–1896 New York",,"Barnard, George N.|Brady, Mathew B.",American,1819 |1823,1902 |1896,ca. 1864,1862,1866,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268217,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.405,false,true,268218,Photographs,Photograph,"Chief Commissary's Office, Chattanooga",,,,,,Artist|Former Attribution,Formerly attributed to,George N. Barnard|Mathew B. Brady,"American, 1819–1902|American, born Ireland, 1823?–1896 New York",,"Barnard, George N.|Brady, Mathew B.",American,1819 |1823,1902 |1896,ca. 1864,1862,1866,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268218,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.406,false,true,268219,Photographs,Photograph,Bridge Across Tennessee River at Chattanooga,,,,,,Artist|Former Attribution,Formerly attributed to,George N. Barnard|Mathew B. Brady,"American, 1819–1902|American, born Ireland, 1823?–1896 New York",,"Barnard, George N.|Brady, Mathew B.",American,1819 |1823,1902 |1896,ca. 1864,1862,1866,Albumen silver print from glass negative,8.5 x 8.9 cm (3 3/8 x 3 1/2 in. ),"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268219,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.407,false,true,268220,Photographs,Photograph,View on Tennessee River looking toward Chattanooga,,,,,,Artist|Former Attribution,Formerly attributed to,George N. Barnard|Mathew B. Brady,"American, 1819–1902|American, born Ireland, 1823?–1896 New York",,"Barnard, George N.|Brady, Mathew B.",American,1819 |1823,1902 |1896,ca. 1864,1862,1866,Albumen silver print from glass negative,7.9 x 9.1 cm (3 1/8 x 3 9/16 in. ),"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268220,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.408,false,true,268221,Photographs,Photograph,Bridge over Tennessee River at Chattanooga,,,,,,Artist|Former Attribution,Formerly attributed to,George N. Barnard|Mathew B. Brady,"American, 1819–1902|American, born Ireland, 1823?–1896 New York",,"Barnard, George N.|Brady, Mathew B.",American,1819 |1823,1902 |1896,ca. 1864,1862,1866,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268221,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.409,false,true,268222,Photographs,Photograph,Bridge Across Tennessee River at Chattanooga,,,,,,Former Attribution|Artist,Formerly attributed to,George N. Barnard|Mathew B. Brady,"American, 1819–1902|American, born Ireland, 1823?–1896 New York",,"Barnard, George N.|Brady, Mathew B.",American,1819 |1823,1902 |1896,ca. 1864,1862,1866,Albumen silver print from glass negative,13.4 x 20.5 cm (5 1/4 x 8 1/16 in. ),"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268222,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.410,false,true,268224,Photographs,Photograph,"Chattanooga, Tennessee",,,,,,Artist|Former Attribution,Formerly attributed to,George N. Barnard|Mathew B. Brady,"American, 1819–1902|American, born Ireland, 1823?–1896 New York",,"Barnard, George N.|Brady, Mathew B.",American,1819 |1823,1902 |1896,ca. 1864,1862,1866,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268224,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.411,false,true,268225,Photographs,Photograph,Tennessee River at Chattanooga (81 Lookout Mountain Spur),,,,,,Artist|Former Attribution,Formerly attributed to,George N. Barnard|Mathew B. Brady,"American, 1819–1902|American, born Ireland, 1823?–1896 New York",,"Barnard, George N.|Brady, Mathew B.",American,1819 |1823,1902 |1896,ca. 1864,1862,1866,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268225,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.412,false,true,268226,Photographs,Photograph,"Provost Marshals Headquarters, Chattanooga",,,,,,Artist|Former Attribution,Formerly attributed to,George N. Barnard|Mathew B. Brady,"American, 1819–1902|American, born Ireland, 1823?–1896 New York",,"Barnard, George N.|Brady, Mathew B.",American,1819 |1823,1902 |1896,ca. 1864,1862,1866,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268226,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.413,false,true,268227,Photographs,Photograph,"Headquarters of General Sherman or Thomas, Chattanooga",,,,,,Artist|Former Attribution,Formerly attributed to,George N. Barnard|Mathew B. Brady,"American, 1819–1902|American, born Ireland, 1823?–1896 New York",,"Barnard, George N.|Brady, Mathew B.",American,1819 |1823,1902 |1896,ca. 1864,1862,1866,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268227,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.415,false,true,268229,Photographs,Photograph,"Lookout Mountain, Tennessee",,,,,,Artist|Former Attribution,Formerly attributed to,George N. Barnard|Mathew B. Brady,"American, 1819–1902|American, born Ireland, 1823?–1896 New York",,"Barnard, George N.|Brady, Mathew B.",American,1819 |1823,1902 |1896,ca. 1864,1862,1866,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268229,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.148,false,true,267933,Photographs,Photograph,"Fort Mahone, Petersburg, Virginia",,,,,,Artist|Former Attribution,Formerly attributed to,Thomas C. Roche|Mathew B. Brady,"American, 1826–1895|American, born Ireland, 1823?–1896 New York",,"Roche, Thomas C.|Brady, Mathew B.",American,1826 |1823,1895 |1896,1864,1864,1864,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267933,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.334,false,true,268139,Photographs,Photograph,"Dead Confederates, Fort Mahone",,,,,,Artist|Former Attribution,Formerly attributed to,Thomas C. Roche|Mathew B. Brady,"American, 1826–1895|American, born Ireland, 1823?–1896 New York",,"Roche, Thomas C.|Brady, Mathew B.",American,1826 |1823,1895 |1896,1864,1864,1864,Albumen silver print from glass negative,9.9 x 9.4 cm (3 7/8 x 3 11/16 in. ),"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268139,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.340,false,true,268146,Photographs,Photograph,"Dead Confederates, Fort Mahone",,,,,,Artist|Former Attribution,Formerly attributed to,Thomas C. Roche|Mathew B. Brady,"American, 1826–1895|American, born Ireland, 1823?–1896 New York",,"Roche, Thomas C.|Brady, Mathew B.",American,1826 |1823,1895 |1896,1864,1864,1864,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268146,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.7,false,true,268263,Photographs,Photograph,"City Point, Virginia. Terminus of U.S. Military Railroad",,,,,,Artist|Former Attribution,Formerly attributed to,Andrew Joseph Russell|Mathew B. Brady,"American, 1830–1902|American, born Ireland, 1823?–1896 New York",,"Russell, Andrew Joseph|Brady, Mathew B.",American,1830 |1823,1902 |1896,1861–65,1861,1865,Albumen silver print from glass negative,16.7 x 21.8 cm (6 9/16 x 8 9/16 in. ),"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268263,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.8,false,true,268274,Photographs,Photograph,"Burial of the Dead, Fredericksburg",,,,,,Artist|Former Attribution,Formerly attributed to,Andrew Joseph Russell|Mathew B. Brady,"American, 1830–1902|American, born Ireland, 1823?–1896 New York",,"Russell, Andrew Joseph|Brady, Mathew B.",American,1830 |1823,1902 |1896,1863,1863,1863,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268274,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.14,false,true,267924,Photographs,Photograph,"Pontoon Bridge at Deep Bottom, James River",,,,,,Artist|Former Attribution,Formerly attributed to,Andrew Joseph Russell|Mathew B. Brady,"American, 1830–1902|American, born Ireland, 1823?–1896 New York",,"Russell, Andrew Joseph|Brady, Mathew B.",American,1830 |1823,1902 |1896,1864,1864,1864,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267924,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.15,false,true,267935,Photographs,Photograph,"City Point, Virginia",,,,,,Artist|Former Attribution,Formerly attributed to,Andrew Joseph Russell|Mathew B. Brady,"American, 1830–1902|American, born Ireland, 1823?–1896 New York",,"Russell, Andrew Joseph|Brady, Mathew B.",American,1830 |1823,1902 |1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267935,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.17,false,true,267957,Photographs,Photograph,"End of the Bridge after Burnside's Attack, Fredericksburg, Virginia",,,,,,Artist|Former Attribution,Formerly attributed to,Andrew Joseph Russell|Mathew B. Brady,"American, 1830–1902|American, born Ireland, 1823?–1896 New York",,"Russell, Andrew Joseph|Brady, Mathew B.",American,1830 |1823,1902 |1896,1863,1863,1863,Albumen silver print from glass negative,Image: 13.1 × 20.6 cm (5 3/16 × 8 1/8 in.),"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267957,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.18,false,true,267968,Photographs,Photograph,"Pontoon Bridge at Deep Bottom, James River",,,,,,Artist|Former Attribution,Formerly attributed to,Andrew Joseph Russell|Mathew B. Brady,"American, 1830–1902|American, born Ireland, 1823?–1896 New York",,"Russell, Andrew Joseph|Brady, Mathew B.",American,1830 |1823,1902 |1896,1864,1864,1864,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267968,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.21,false,true,268002,Photographs,Photograph,"Abandoned Camp, Falmouth, Virginia",,,,,,Artist|Former Attribution,Formerly attributed to,Andrew Joseph Russell|Mathew B. Brady,"American, 1830–1902|American, born Ireland, 1823?–1896 New York",,"Russell, Andrew Joseph|Brady, Mathew B.",American,1830 |1823,1902 |1896,1862,1862,1862,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268002,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.34,false,true,268145,Photographs,Photograph,"Headquarters, 10th Army Corps, Hatcher's Farm, Virginia",,,,,,Artist|Former Attribution,Formerly attributed to,Andrew Joseph Russell|Mathew B. Brady,"American, 1830–1902|American, born Ireland, 1823?–1896 New York",,"Russell, Andrew Joseph|Brady, Mathew B.",American,1830 |1823,1902 |1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268145,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.45,false,true,268236,Photographs,Photograph,"Pontoon Bridge at Deep Bottom, James River",,,,,,Artist|Former Attribution,Formerly attributed to,Andrew Joseph Russell|Mathew B. Brady,"American, 1830–1902|American, born Ireland, 1823?–1896 New York",,"Russell, Andrew Joseph|Brady, Mathew B.",American,1830 |1823,1902 |1896,1864,1864,1864,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268236,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.46,false,true,268237,Photographs,Photograph,"Pontoon Bridge at Deep Bottom, James River",,,,,,Artist|Former Attribution,Formerly attributed to,Andrew Joseph Russell|Mathew B. Brady,"American, 1830–1902|American, born Ireland, 1823?–1896 New York",,"Russell, Andrew Joseph|Brady, Mathew B.",American,1830 |1823,1902 |1896,1864,1864,1864,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268237,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.49,false,true,268240,Photographs,Photograph,"Military Railroad Camp, City Point, Virginia",,,,,,Artist|Former Attribution,Formerly attributed to,Andrew Joseph Russell|Mathew B. Brady,"American, 1830–1902|American, born Ireland, 1823?–1896 New York",,"Russell, Andrew Joseph|Brady, Mathew B.",American,1830 |1823,1902 |1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268240,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.58,false,true,268250,Photographs,Photograph,Entrenchments on left of Bermuda Hundred Lines,,,,,,Artist|Former Attribution,Formerly attributed to,Andrew Joseph Russell|Mathew B. Brady,"American, 1830–1902|American, born Ireland, 1823?–1896 New York",,"Russell, Andrew Joseph|Brady, Mathew B.",American,1830 |1823,1902 |1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268250,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.59,false,true,268251,Photographs,Photograph,"Port Royal, Rappahannock River",,,,,,Artist|Former Attribution,Formerly attributed to,Andrew Joseph Russell|Mathew B. Brady,"American, 1830–1902|American, born Ireland, 1823?–1896 New York",,"Russell, Andrew Joseph|Brady, Mathew B.",American,1830 |1823,1902 |1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268251,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.60,false,true,268253,Photographs,Photograph,"Port Royal, Rappahannock River",,,,,,Artist|Former Attribution,Formerly attributed to,Andrew Joseph Russell|Mathew B. Brady,"American, 1830–1902|American, born Ireland, 1823?–1896 New York",,"Russell, Andrew Joseph|Brady, Mathew B.",American,1830 |1823,1902 |1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268253,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.61,false,true,268254,Photographs,Photograph,"Wagon Train at Port Royal, Rappahannock River",,,,,,Artist|Former Attribution,Formerly attributed to,Andrew Joseph Russell|Mathew B. Brady,"American, 1830–1902|American, born Ireland, 1823?–1896 New York",,"Russell, Andrew Joseph|Brady, Mathew B.",American,1830 |1823,1902 |1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268254,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.62,false,true,268255,Photographs,Photograph,Crow's Nest Signal Tower near Bermuda Hundred,,,,,,Artist|Former Attribution,Formerly attributed to,Andrew Joseph Russell|Mathew B. Brady,"American, 1830–1902|American, born Ireland, 1823?–1896 New York",,"Russell, Andrew Joseph|Brady, Mathew B.",American,1830 |1823,1902 |1896,1861–65,1861,1865,Albumen silver print from glass negative,20.3 x 12.9 cm (8 x 5 1/16 in.),"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268255,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.63,false,true,268256,Photographs,Photograph,Extreme Left of Bermuda Hundred Lines,,,,,,Artist|Former Attribution,Formerly attributed to,Andrew Joseph Russell|Mathew B. Brady,"American, 1830–1902|American, born Ireland, 1823?–1896 New York",,"Russell, Andrew Joseph|Brady, Mathew B.",American,1830 |1823,1902 |1896,1861–65,1861,1865,Albumen silver print from glass negative,16.4 x 21.1 cm (6 7/16 x 8 5/16 in. ),"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268256,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.70,false,true,268264,Photographs,Photograph,"Pontoon Bridge, James River",,,,,,Artist|Former Attribution,Formerly attributed to,Andrew Joseph Russell|Mathew B. Brady,"American, 1830–1902|American, born Ireland, 1823?–1896 New York",,"Russell, Andrew Joseph|Brady, Mathew B.",American,1830 |1823,1902 |1896,1864,1864,1864,Albumen silver print from glass negative,12.7 x 19.7 cm (5 x 7 3/4 in.),"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268264,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.71,false,true,268265,Photographs,Photograph,Wilcox Landing,,,,,,Artist|Former Attribution,Formerly attributed to,Andrew Joseph Russell|Mathew B. Brady,"American, 1830–1902|American, born Ireland, 1823?–1896 New York",,"Russell, Andrew Joseph|Brady, Mathew B.",American,1830 |1823,1902 |1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268265,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.72,false,true,268266,Photographs,Photograph,"Confederate Prisoners for Exchange, Cox's Landing, James River",,,,,,Artist|Former Attribution,Formerly attributed to,Andrew Joseph Russell|Mathew B. Brady,"American, 1830–1902|American, born Ireland, 1823?–1896 New York",,"Russell, Andrew Joseph|Brady, Mathew B.",American,1830 |1823,1902 |1896,1864,1864,1864,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268266,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.73,false,true,268267,Photographs,Photograph,"Pontoon Bridge Train, James River",,,,,,Artist|Former Attribution,Formerly attributed to,Andrew Joseph Russell|Mathew B. Brady,"American, 1830–1902|American, born Ireland, 1823?–1896 New York",,"Russell, Andrew Joseph|Brady, Mathew B.",American,1830 |1823,1902 |1896,1864,1864,1864,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268267,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.75,false,true,268269,Photographs,Photograph,"Pontoon Bridge, James River",,,,,,Artist|Former Attribution,Formerly attributed to,Andrew Joseph Russell|Mathew B. Brady,"American, 1830–1902|American, born Ireland, 1823?–1896 New York",,"Russell, Andrew Joseph|Brady, Mathew B.",American,1830 |1823,1902 |1896,1864,1864,1864,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268269,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.76,false,true,268270,Photographs,Photograph,"Bermuda Hundred Landing, James River",,,,,,Artist|Former Attribution,Formerly attributed to,Andrew Joseph Russell|Mathew B. Brady,"American, 1830–1902|American, born Ireland, 1823?–1896 New York",,"Russell, Andrew Joseph|Brady, Mathew B.",American,1830 |1823,1902 |1896,1864,1864,1864,Albumen silver print from glass negative,14.6 x 16.5 cm (5 3/4 x 6 1/2 in. ),"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268270,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.90,false,true,268286,Photographs,Photograph,Street in Fredericksburg,,,,,,Artist|Former Attribution,Formerly attributed to,Andrew Joseph Russell|Mathew B. Brady,"American, 1830–1902|American, born Ireland, 1823?–1896 New York",,"Russell, Andrew Joseph|Brady, Mathew B.",American,1830 |1823,1902 |1896,1863,1863,1863,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268286,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.92,false,true,268288,Photographs,Photograph,"Headquarters of Capt. E.E. Camp, A.Q.M., at City Point, Virginia",,,,,,Artist|Former Attribution,Formerly attributed to,Andrew Joseph Russell|Mathew B. Brady,"American, 1830–1902|American, born Ireland, 1823?–1896 New York",,"Russell, Andrew Joseph|Brady, Mathew B.",American,1830 |1823,1902 |1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268288,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.93,false,true,268289,Photographs,Photograph,"City Point, Virginia. James River",,,,,,Artist|Former Attribution,Formerly attributed to,Andrew Joseph Russell|Mathew B. Brady,"American, 1830–1902|American, born Ireland, 1823?–1896 New York",,"Russell, Andrew Joseph|Brady, Mathew B.",American,1830 |1823,1902 |1896,1864,1864,1864,Albumen silver print from glass negative,12.2 x 22.7 cm (4 13/16 x 8 15/16 in. ),"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268289,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.94,false,true,268290,Photographs,Photograph,Bridge. U.S. Military Railroad at City Point,,,,,,Artist|Former Attribution,,Andrew Joseph Russell|Mathew B. Brady,"American, 1830–1902|American, born Ireland, 1823?–1896 New York",,"Russell, Andrew Joseph|Brady, Mathew B.",American,1830 |1823,1902 |1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268290,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.95,false,true,268291,Photographs,Photograph,"City Point, Virginia",,,,,,Artist|Former Attribution,,Andrew Joseph Russell|Mathew B. Brady,"American, 1830–1902|American, born Ireland, 1823?–1896 New York",,"Russell, Andrew Joseph|Brady, Mathew B.",American,1830 |1823,1902 |1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268291,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.96,false,true,268292,Photographs,Photograph,"Fredericksburg, Virginia",,,,,,Artist|Former Attribution,,Andrew Joseph Russell|Mathew B. Brady,"American, 1830–1902|American, born Ireland, 1823?–1896 New York",,"Russell, Andrew Joseph|Brady, Mathew B.",American,1830 |1823,1902 |1896,1863,1863,1863,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268292,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.98,false,true,268294,Photographs,Photograph,"Pontoon Bridge at Deep Bottom, James River",,,,,,Artist|Former Attribution,,Andrew Joseph Russell|Mathew B. Brady,"American, 1830–1902|American, born Ireland, 1823?–1896 New York",,"Russell, Andrew Joseph|Brady, Mathew B.",American,1830 |1823,1902 |1896,1864,1864,1864,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268294,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.99,false,true,268295,Photographs,Photograph,James River,,,,,,Artist|Former Attribution,,Andrew Joseph Russell|Mathew B. Brady,"American, 1830–1902|American, born Ireland, 1823?–1896 New York",,"Russell, Andrew Joseph|Brady, Mathew B.",American,1830 |1823,1902 |1896,1864,1864,1864,Albumen silver print from glass negative,9 x 20 cm (3 9/16 x 7 7/8 in.),"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268295,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.100,false,true,267881,Photographs,Photograph,"City Point, Virginia",,,,,,Artist|Former Attribution,,Andrew Joseph Russell|Mathew B. Brady,"American, 1830–1902|American, born Ireland, 1823?–1896 New York",,"Russell, Andrew Joseph|Brady, Mathew B.",American,1830 |1823,1902 |1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267881,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.101,false,true,267882,Photographs,Photograph,"Terminus of U.S. Military Railroad, City Point, Virginia",,,,,,Artist|Former Attribution,,Andrew Joseph Russell|Mathew B. Brady,"American, 1830–1902|American, born Ireland, 1823?–1896 New York",,"Russell, Andrew Joseph|Brady, Mathew B.",American,1830 |1823,1902 |1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267882,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.103,false,true,267884,Photographs,Photograph,"Excavating for ""Y"" at Devereaux Station, Orange & Alexandria Railroad",,,,,,Artist|Former Attribution,,Andrew Joseph Russell|Mathew B. Brady,"American, 1830–1902|American, born Ireland, 1823?–1896 New York",,"Russell, Andrew Joseph|Brady, Mathew B.",American,1830 |1823,1902 |1896,1863,1863,1863,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267884,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.112,false,true,267894,Photographs,Photograph,"City Point, Virginia",,,,,,Artist|Former Attribution,Formerly attributed to,Andrew Joseph Russell|Mathew B. Brady,"American, 1830–1902|American, born Ireland, 1823?–1896 New York",,"Russell, Andrew Joseph|Brady, Mathew B.",American,1830 |1823,1902 |1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267894,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.113,false,true,267895,Photographs,Photograph,"Headquarters of Capt. H.B. Blood, A.Q.M., at City Point, Virginia",,,,,,Artist|Former Attribution,Formerly attributed to,Andrew Joseph Russell|Mathew B. Brady,"American, 1830–1902|American, born Ireland, 1823?–1896 New York",,"Russell, Andrew Joseph|Brady, Mathew B.",American,1830 |1823,1902 |1896,1861–65,1861,1865,Albumen silver print from glass negative,12.8 x 18.9 cm (5 1/16 x 7 7/16 in. ),"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267895,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.114,false,true,267896,Photographs,Photograph,"[Wharves on the James River, City Point]. Brady album, p. 10",,,,,,Artist|Former Attribution,Formerly attributed to,Andrew Joseph Russell|Mathew B. Brady,"American, 1830–1902|American, born Ireland, 1823?–1896 New York",,"Russell, Andrew Joseph|Brady, Mathew B.",American,1830 |1823,1902 |1896,1861–65,1861,1865,Albumen silver print from glass negative,13.8 x 20.9 cm (5 7/16 x 8 1/4 in. ),"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267896,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.115,false,true,267897,Photographs,Photograph,"Commissary Department, City Point, Virginia",,,,,,Artist|Former Attribution,Formerly attributed to,Andrew Joseph Russell|Mathew B. Brady,"American, 1830–1902|American, born Ireland, 1823?–1896 New York",,"Russell, Andrew Joseph|Brady, Mathew B.",American,1830 |1823,1902 |1896,1861–65,1861,1865,Albumen silver print from glass negative,16 x 22.9 cm (6 5/16 x 9 in. ),"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267897,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.118,false,true,267900,Photographs,Panorama,"Looking Towards Marye's Heights, Fredericksburg",,,,,,Artist|Former Attribution,Formerly attributed to,Andrew Joseph Russell|Mathew B. Brady,"American, 1830–1902|American, born Ireland, 1823?–1896 New York",,"Russell, Andrew Joseph|Brady, Mathew B.",American,1830 |1823,1902 |1896,1864,1864,1864,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267900,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.119,false,true,267901,Photographs,Photograph,"Burial of the Dead, Fredericksburg",,,,,,Artist|Former Attribution,Formerly attributed to,Andrew Joseph Russell|Mathew B. Brady,"American, 1830–1902|American, born Ireland, 1823?–1896 New York",,"Russell, Andrew Joseph|Brady, Mathew B.",American,1830 |1823,1902 |1896,1863,1863,1863,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267901,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.120,false,true,267903,Photographs,Photograph,"Battery Going into Action, Fredericksburg, December 13, 1862",,,,,,Artist|Former Attribution,Formerly attributed to,Andrew Joseph Russell|Mathew B. Brady,"American, 1830–1902|American, born Ireland, 1823?–1896 New York",,"Russell, Andrew Joseph|Brady, Mathew B.",American,1830 |1823,1902 |1896,1862,1862,1862,Albumen silver print from glass negative,14.7 x 19.9 cm (5 13/16 x 7 13/16 in. ),"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267903,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.170,false,true,267958,Photographs,Photograph,Pontoon Bridge,,,,,,Artist|Former Attribution,Formerly attributed to,Andrew Joseph Russell|Mathew B. Brady,"American, 1830–1902|American, born Ireland, 1823?–1896 New York",,"Russell, Andrew Joseph|Brady, Mathew B.",American,1830 |1823,1902 |1896,1861–65,1861,1865,Albumen silver print from glass negative,13.3 x 20.4 cm (5 1/4 x 8 1/16 in.),"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267958,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.175,false,true,267963,Photographs,Photograph,"Confederate Method of Destroying Rail Roads at McCloud Mill, Virginia",,,,,,Artist|Former Attribution,Formerly attributed to,Andrew Joseph Russell|Mathew B. Brady,"American, 1830–1902|American, born Ireland, 1823?–1896 New York",,"Russell, Andrew Joseph|Brady, Mathew B.",American,1830 |1823,1902 |1896,1863,1863,1863,Albumen silver print from glass negative,Image: 16.2 × 19.6 cm (6 3/8 × 7 11/16 in.),"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267963,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.183,false,true,267972,Photographs,Photograph,"General Butler's Headquarters, Chapin's Farm, Virginia",,,,,,Artist|Former Attribution,Formerly attributed to,Andrew Joseph Russell|Mathew B. Brady,"American, 1830–1902|American, born Ireland, 1823?–1896 New York",,"Russell, Andrew Joseph|Brady, Mathew B.",American,1830 |1823,1902 |1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267972,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.210,false,true,268003,Photographs,Photograph,Confederate Trestle Work on Alexandria Railroad,,,,,,Artist|Former Attribution,Formerly attributed to,Andrew Joseph Russell|Mathew B. Brady,"American, 1830–1902|American, born Ireland, 1823?–1896 New York",,"Russell, Andrew Joseph|Brady, Mathew B.",American,1830 |1823,1902 |1896,1861–65,1861,1865,Albumen silver print from glass negative,15.2 x 21.1 cm (6 x 8 5/16 in.),"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268003,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.262,false,true,268059,Photographs,Photograph,"Deep Bottom, James River",,,,,,Artist|Former Attribution,Formerly attributed to,Andrew Joseph Russell|Mathew B. Brady,"American, 1830–1902|American, born Ireland, 1823?–1896 New York",,"Russell, Andrew Joseph|Brady, Mathew B.",American,1830 |1823,1902 |1896,1864,1864,1864,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268059,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.264,false,true,268061,Photographs,Photograph,"Unloading Supplies at Port Royal, Virginia",,,,,,Artist|Former Attribution,Formerly attributed to,Andrew Joseph Russell|Mathew B. Brady,"American, 1830–1902|American, born Ireland, 1823?–1896 New York",,"Russell, Andrew Joseph|Brady, Mathew B.",American,1830 |1823,1902 |1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268061,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.269,false,true,268066,Photographs,Photograph,"Army Wagon and Forge, City Point, Virginia",,,,,,Artist|Former Attribution,Formerly attributed to,Andrew Joseph Russell|Mathew B. Brady,"American, 1830–1902|American, born Ireland, 1823?–1896 New York",,"Russell, Andrew Joseph|Brady, Mathew B.",American,1830 |1823,1902 |1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268066,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.271,false,true,268069,Photographs,Photograph,"[Orange and Alexandria Railroad Bridge, near Union Mills, Virginia]",,,,,,Artist|Former Attribution,Formerly attributed to,Andrew Joseph Russell|Mathew B. Brady,"American, 1830–1902|American, born Ireland, 1823?–1896 New York",,"Russell, Andrew Joseph|Brady, Mathew B.",American,1830 |1823,1902 |1896,ca. 1863,1860,1865,Albumen silver print from glass negative,Image: 12.3 x 20.1 cm (4 13/16 x 7 15/16 in.),"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268069,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.275,false,true,268073,Photographs,Photograph,"Removing Dead from Battlefield, Marye's Heights, May 2, 1864",,,,,,Artist|Former Attribution,Formerly attributed to,Andrew Joseph Russell|Mathew B. Brady,"American, 1830–1902|American, born Ireland, 1823?–1896 New York",,"Russell, Andrew Joseph|Brady, Mathew B.",American,1830 |1823,1902 |1896,1864,1864,1864,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268073,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.278,false,true,268076,Photographs,Photograph,"Bridge on Orange and Alexandria Rail Road, as Repaired by Army Engineers under Colonel Herman Haupt",,,,,,Artist|Former Attribution,Formerly attributed to,Andrew Joseph Russell|Mathew B. Brady,"American, 1830–1902|American, born Ireland, 1823?–1896 New York",,"Russell, Andrew Joseph|Brady, Mathew B.",American,1830 |1823,1902 |1896,1865,1865,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268076,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.279,false,true,268077,Photographs,Photograph,"Locomotive #56, U.S. Military Railroad/City Point. Troops Ready to be Taken to the Front by Rail",,,,,,Artist|Former Attribution,Formerly attributed to,Andrew Joseph Russell|Mathew B. Brady,"American, 1830–1902|American, born Ireland, 1823?–1896 New York",,"Russell, Andrew Joseph|Brady, Mathew B.",American,1830 |1823,1902 |1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268077,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.280,false,true,268079,Photographs,Photograph,"Locomotive #133, U.S.M.R.R.",,,,,,Artist|Former Attribution,Formerly attributed to,Andrew Joseph Russell|Mathew B. Brady,"American, 1830–1902|American, born Ireland, 1823?–1896 New York",,"Russell, Andrew Joseph|Brady, Mathew B.",American,1830 |1823,1902 |1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268079,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.289,false,true,268088,Photographs,Photograph,Government Saw Mill (Chattanooga),,,,,,Artist|Former Attribution,Formerly attributed to,Andrew Joseph Russell|Mathew B. Brady,"American, 1830–1902|American, born Ireland, 1823?–1896 New York",,"Russell, Andrew Joseph|Brady, Mathew B.",American,1830 |1823,1902 |1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268088,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.291,false,true,268091,Photographs,Photograph,"Fort Beauregard, Manassas, VA",,,,,,Artist|Former Attribution,Formerly attributed to,Andrew Joseph Russell|Mathew B. Brady,"American, 1830–1902|American, born Ireland, 1823?–1896 New York",,"Russell, Andrew Joseph|Brady, Mathew B.",American,1830 |1823,1902 |1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268091,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.308,false,true,268110,Photographs,Photograph,"Camp of Construction Corps, U.S. Military Railroad at City Point",,,,,,Artist|Former Attribution,Formerly attributed to,Andrew Joseph Russell|Mathew B. Brady,"American, 1830–1902|American, born Ireland, 1823?–1896 New York",,"Russell, Andrew Joseph|Brady, Mathew B.",American,1830 |1823,1902 |1896,1861–65,1861,1865,Albumen silver print from glass negative,14.4 x 20.8 cm (5 11/16 x 8 3/16 in. ),"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268110,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.316,false,true,268119,Photographs,Photograph,"Lower Pontoon Bridge, Deep Bottom, James River",,,,,,Artist|Former Attribution,Formerly attributed to,Andrew Joseph Russell|Mathew B. Brady,"American, 1830–1902|American, born Ireland, 1823?–1896 New York",,"Russell, Andrew Joseph|Brady, Mathew B.",American,1830 |1823,1902 |1896,1864,1864,1864,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268119,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.325,false,true,268129,Photographs,Photograph,"Cox's Landing, James River",,,,,,Artist|Former Attribution,Formerly attributed to,Andrew Joseph Russell|Mathew B. Brady,"American, 1830–1902|American, born Ireland, 1823?–1896 New York",,"Russell, Andrew Joseph|Brady, Mathew B.",American,1830 |1823,1902 |1896,1864,1864,1864,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268129,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.337,false,true,268142,Photographs,Photograph,"Fort Brady, Virginia",,,,,,Artist|Former Attribution,Formerly attributed to,Andrew Joseph Russell|Mathew B. Brady,"American, 1830–1902|American, born Ireland, 1823?–1896 New York",,"Russell, Andrew Joseph|Brady, Mathew B.",American,1830 |1823,1902 |1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268142,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.339,false,true,268144,Photographs,Photograph,"Artillery Camp, City Point, Virginia",,,,,,Artist|Former Attribution,Formerly attributed to,Andrew Joseph Russell|Mathew B. Brady,"American, 1830–1902|American, born Ireland, 1823?–1896 New York",,"Russell, Andrew Joseph|Brady, Mathew B.",American,1830 |1823,1902 |1896,1861–65,1861,1865,Albumen silver print from glass negative,9.6 x 9.9 cm (3 3/4 x 3 7/8 in. ),"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268144,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.341,false,true,268147,Photographs,Photograph,Extreme Left of Bermuda Hundred Lines,,,,,,Artist|Former Attribution,Formerly attributed to,Andrew Joseph Russell|Mathew B. Brady,"American, 1830–1902|American, born Ireland, 1823?–1896 New York",,"Russell, Andrew Joseph|Brady, Mathew B.",American,1830 |1823,1902 |1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268147,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.343,false,true,268149,Photographs,Photograph,Entrenchments on left of Bermuda Hundred Lines,,,,,,Artist|Former Attribution,Formerly attributed to,Andrew Joseph Russell|Mathew B. Brady,"American, 1830–1902|American, born Ireland, 1823?–1896 New York",,"Russell, Andrew Joseph|Brady, Mathew B.",American,1830 |1823,1902 |1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268149,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.353,false,true,268160,Photographs,Photograph,"Camp of Construction Corps, U.S. Military Railroad, at City Point",,,,,,Artist|Former Attribution,Formerly attributed to,Andrew Joseph Russell|Mathew B. Brady,"American, 1830–1902|American, born Ireland, 1823?–1896 New York",,"Russell, Andrew Joseph|Brady, Mathew B.",American,1830 |1823,1902 |1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268160,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.358,false,true,268165,Photographs,Photograph,"Fort Brady, James River",,,,,,Artist|Former Attribution,Formerly attributed to,Andrew Joseph Russell|Mathew B. Brady,"American, 1830–1902|American, born Ireland, 1823?–1896 New York",,"Russell, Andrew Joseph|Brady, Mathew B.",American,1830 |1823,1902 |1896,1864,1864,1864,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268165,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.359,false,true,268166,Photographs,Photograph,"Fort Brady, Virginia",,,,,,Artist|Former Attribution,Formerly attributed to,Andrew Joseph Russell|Mathew B. Brady,"American, 1830–1902|American, born Ireland, 1823?–1896 New York",,"Russell, Andrew Joseph|Brady, Mathew B.",American,1830 |1823,1902 |1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268166,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.361,false,true,268169,Photographs,Photograph,"Fort Brady, James River",,,,,,Artist|Former Attribution,Formerly attributed to,Andrew Joseph Russell|Mathew B. Brady,"American, 1830–1902|American, born Ireland, 1823?–1896 New York",,"Russell, Andrew Joseph|Brady, Mathew B.",American,1830 |1823,1902 |1896,1864,1864,1864,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268169,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.362,false,true,268170,Photographs,Photograph,"Camp of Laborers, City Point",,,,,,Artist|Former Attribution,Formerly attributed to,Andrew Joseph Russell|Mathew B. Brady,"American, 1830–1902|American, born Ireland, 1823?–1896 New York",,"Russell, Andrew Joseph|Brady, Mathew B.",American,1830 |1823,1902 |1896,1861–65,1861,1865,Albumen silver print from glass negative,10.4 x 9.5 cm (4 1/8 x 3 3/4 in. ),"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268170,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.381,false,true,268191,Photographs,Photograph,"Camp of Construction Corps, U.S. Military Railroad at City Point",,,,,,Artist|Former Attribution,Formerly attributed to,Andrew Joseph Russell|Mathew B. Brady,"American, 1830–1902|American, born Ireland, 1823?–1896 New York",,"Russell, Andrew Joseph|Brady, Mathew B.",American,1830 |1823,1902 |1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268191,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.383,false,true,268193,Photographs,Photograph,"Manassas, Virginia",,,,,,Artist|Former Attribution,Formerly attributed to,Andrew Joseph Russell|Mathew B. Brady,"American, 1830–1902|American, born Ireland, 1823?–1896 New York",,"Russell, Andrew Joseph|Brady, Mathew B.",American,1830 |1823,1902 |1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268193,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.387,false,true,268197,Photographs,Photograph,Fortifications at Manassas,,,,,,Artist|Former Attribution,Formerly attributed to,Andrew Joseph Russell|Mathew B. Brady,"American, 1830–1902|American, born Ireland, 1823?–1896 New York",,"Russell, Andrew Joseph|Brady, Mathew B.",American,1830 |1823,1902 |1896,1861–65,1861,1865,Albumen silver print from glass negative,5.6 x 8.2 cm (2 3/16 x 3 1/4 in. ),"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268197,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.432,false,true,283849,Photographs,Photograph,Bridge. U.S. Military Railroad at City Point,,,,,,Artist|Former Attribution,Attributed to|Formerly attributed to,Andrew Joseph Russell|Mathew B. Brady,"American, 1830–1902|American, born Ireland, 1823?–1896 New York",,"Russell, Andrew Joseph|Brady, Mathew B.",American,1830 |1823,1902 |1896,1861–65,1861,1865,Albumen silver print from glass negative,16.4 x 21.3 cm (6 7/16 x 8 3/8 in.),"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283849,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -41.142.3,false,true,271897,Photographs,Photograph,Two Pupils in Greek Dress,,,,,,Artist|Printer,,Thomas Eakins|Susan Macdowell Eakins,"American, Philadelphia, Pennsylvania 1844–1916 Philadelphia, Pennsylvania|1851–1938",,"Eakins, Thomas|Eakins, Susan Macdowell",American,1844 |1851,1916 |1938,1880s,1880,1889,Platinum print,22.4 x 16.7 cm (8 13/16 x 6 9/16 in.),"Gift of Charles Bregler, 1941",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271897,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1990.1067,false,true,266252,Photographs,Photographically illustrated book,Ichnographs from the Sandstone of Connecticut River,,,,,,Artist,,James Deane,"American, 1801–1858",,"Deane, James",American,1801,1858,published 1861,1850,1869,Salted paper prints,,"Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 1990",,,,,,,,,,,,Books,,http://www.metmuseum.org/art/collection/search/266252,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.334,false,true,286059,Photographs,Photograph,California News,,,,,,Artist,,Gabriel Harrison,"American, 1818–1902",,"Harrison, Gabriel",American,1818,1902,ca. 1850,1848,1852,Daguerreotype,Image: 14 x 10.5 cm (5 1/2 x 4 1/8 in.) 6 × 4 13/16 in. (15.3 × 12.2 cm),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Pastels & Oil Sketches on Paper,,http://www.metmuseum.org/art/collection/search/286059,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.525 (1–61),false,true,259583,Photographs,Album,Photographic Views of Sherman's Campaign,,,,,,Artist,,George N. Barnard,"American, 1819–1902",,"Barnard, George N.",American,1819,1902,1860s,1860,1869,,,"Pfeiffer and Rogers Funds, 1970",,,,,,,,,,,,Albums,,http://www.metmuseum.org/art/collection/search/259583,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.787,false,true,285757,Photographs,Negative; Photograph,"Ordnance Wharf, City Point, Virginia",,,,,,Artist,,Thomas C. Roche,"American, 1826–1895",,"Roche, Thomas C.",American,1826,1895,1865,1865,1865,Collodion glass negative,Image: 21.6 × 25.6 cm (8 1/2 × 10 1/16 in.),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Negatives,,http://www.metmuseum.org/art/collection/search/285757,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.556 (1–73),false,true,286455,Photographs,Album,"[Trees in Calaveras Grove and Views of Yosemite, California]",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,ca. 1878,1876,1880,Albumen silver prints from glass negatives,Image: approximately 12.5 x 12.5 cm (4 15/16 x 4 15/16 in.) each Mount: 24 x 25.1 cm (9 7/16 x 9 7/8 in.) each Album: 24.8 x 26 x 3.2 cm (9 3/4 x 10 1/4 x 1 1/4 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Albums,,http://www.metmuseum.org/art/collection/search/286455,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1117,false,true,285972,Photographs,Photomechanical print,[Native American with a Medal of President Garfield],,,,,,Artist,,William Henry Jackson,"American, 1843–1942",,"Jackson, William Henry",American,1843,1942,1890–1910,1890,1910,Photochrom,In mat: 12 x 9 13/16,"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs|Prints,,http://www.metmuseum.org/art/collection/search/285972,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1291,false,true,285857,Photographs,Autochrome,[Bananas],,,,,,Artist,,Frederick Dellenbaugh,"American, 1853–1935",,"Dellenbaugh, Frederick",American,1853,1935,ca. 1908,1906,1910,Autochrome,8.2 x 10.7 cm (3 1/4 x 4 1/4 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Transparencies,,http://www.metmuseum.org/art/collection/search/285857,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -54.561.2,false,true,269751,Photographs,Photomechanical print,Old Woman Praying,,,,,,Artist,,Rudolph Eickemeyer,"American, 1862–1932",,"Eickemeyer, Jr., Rudolph",American,1862,1932,ca. 1900,1898,1902,Photogravure,,"Gift of Mrs. William H. Schubart, 1954",,,,,,,,,,,,Photographs|Prints,,http://www.metmuseum.org/art/collection/search/269751,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.644.5,false,true,260328,Photographs,Photomechanical print,The Bridal Rose,,,,,,Artist,,Rudolph Eickemeyer,"American, 1862–1932",,"Eickemeyer, Jr., Rudolph",American,1862,1932,1900,1900,1900,Photogravure,,"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1972",,,,,,,,,,,,Photographs|Prints,,http://www.metmuseum.org/art/collection/search/260328,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1987.1100.275,false,true,265327,Photographs,Photomechanical print,[Prison Work Crew (ca. 9 Members) Digging Trench and 1 Guard],,,,,,Artist,,Doris Ulmann,"American, 1882–1934",,"Ulmann, Doris",American,1882,1934,"1929–30, printed 1934",1929,1930,Photogravure from glass negative,20.3 x 28.7 cm (8 x 11 5/16 in.),"Ford Motor Company Collection, Gift of Ford Motor Company and John C. Waddell, 1987",,,,,,,,,,,,Photographs|Prints,,http://www.metmuseum.org/art/collection/search/265327,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -55.635.11,false,true,271736,Photographs,Autochrome,"Emmy and Kitty - Tutzing, Bavaria",,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1907,1907,1907,Autochrome,18.1 x 13.1 cm,"Alfred Stieglitz Collection, 1955",,,,,,,,,,,,Transparencies,,http://www.metmuseum.org/art/collection/search/271736,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -55.635.12,false,true,271737,Photographs,Autochrome,Stieglitz and Emmy,,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1907,1907,1907,Autochrome,13.2 x 18 cm,"Alfred Stieglitz Collection, 1955",,,,,,,,,,,,Transparencies,,http://www.metmuseum.org/art/collection/search/271737,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.60,false,true,260271,Photographs,Photomechanical print,Baroness von P. --- Kimono,,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1900s,1900,1909,Photogravure,,"Rogers Fund, 1972",,,,,,,,,,,,Photographs|Prints,,http://www.metmuseum.org/art/collection/search/260271,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.62,false,true,260273,Photographs,Photomechanical print,The Summer Song,,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,ca. 1908,1906,1910,Halftone,,"Rogers Fund, 1972",,,,,,,,,,,,Photographs|Prints,,http://www.metmuseum.org/art/collection/search/260273,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.119,false,true,260126,Photographs,Photomechanical print,H.R.H. Princess Rupprecht with Prince Leopold and Albrecht Johann,,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,"1900–1908, printed 1910",1900,1908,Photogravure,,"Rogers Fund, 1972",,,,,,,,,,,,Photographs|Prints,,http://www.metmuseum.org/art/collection/search/260126,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.136,false,true,260144,Photographs,Photomechanical print,[Park with Fence],,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1886,1886,1886,Photogravure,16.3 x 11.9 cm (6 7/16 x 4 11/16 in.),"Rogers Fund, 1972",,,,,,,,,,,,Photographs|Prints,,http://www.metmuseum.org/art/collection/search/260144,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.139,false,true,260147,Photographs,Photomechanical print,The Man in Armor,,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,"1898, printed 1910",1898,1898,Photogravure,,"Rogers Fund, 1972",,,,,,,,,,,,Photographs|Prints,,http://www.metmuseum.org/art/collection/search/260147,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.147,false,true,260156,Photographs,Photomechanical print,The Archer II,,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1900s,1900,1909,Photogravure,,"Rogers Fund, 1972",,,,,,,,,,,,Photographs|Prints,,http://www.metmuseum.org/art/collection/search/260156,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.152,false,true,260162,Photographs,Photomechanical print,Keep a Movin'! For Monkey * Fan * Tiger or Dove: Dough or Fame: Same Old Game as Love,,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1900s,1900,1909,Photogravure,12.2 x 17.9 cm (4 13/16 x 7 1/16 in.),"Rogers Fund, 1972",,,,,,,,,,,,Photographs|Prints,,http://www.metmuseum.org/art/collection/search/260162,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.153,false,true,260163,Photographs,Photomechanical print,Nude Man with Harp,,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1908–10,1908,1910,Photogravure,,"Rogers Fund, 1972",,,,,,,,,,,,Photographs|Prints,,http://www.metmuseum.org/art/collection/search/260163,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -55.635.14,false,true,269800,Photographs,Autochrome,Mrs. Selma Schubart,,,,,,Artist,,Alfred Stieglitz,"American, Hoboken, New Jersey 1864–1946 New York",,"Stieglitz, Alfred",American,1864,1946,1907,1907,1907,Autochrome,Plate: 17.8 × 12.8 cm (7 × 5 1/16 in.) Image: 16.5 × 11.6 cm (6 1/2 × 4 9/16 in.),"Alfred Stieglitz Collection, 1955",,,,,,,,,,,,Transparencies,,http://www.metmuseum.org/art/collection/search/269800,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -55.635.16,false,true,269802,Photographs,Autochrome,Walkowitz at Lake George,,,,,,Artist,,Alfred Stieglitz,"American, Hoboken, New Jersey 1864–1946 New York",,"Stieglitz, Alfred",American,1864,1946,1916,1916,1916,Autochrome,12.7 x 17.8 cm (5 x 7 in.),"Alfred Stieglitz Collection, 1955",,,,,,,,,,,,Transparencies,,http://www.metmuseum.org/art/collection/search/269802,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -55.635.17,false,true,269803,Photographs,Autochrome,[Man in Red Sweater],,,,,,Artist,,Alfred Stieglitz,"American, Hoboken, New Jersey 1864–1946 New York",,"Stieglitz, Alfred",American,1864,1946,1907,1907,1907,Autochrome,17.9 x 12.8 cm (7 1/16 x 5 1/16 in.),"Alfred Stieglitz Collection, 1955",,,,,,,,,,,,Transparencies,,http://www.metmuseum.org/art/collection/search/269803,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -55.635.18,false,true,269804,Photographs,Autochrome,Frank Eugene,,,,,,Artist,,Alfred Stieglitz,"American, Hoboken, New Jersey 1864–1946 New York",,"Stieglitz, Alfred",American,1864,1946,1907,1907,1907,Autochrome,18 x 13 cm (7 1/16 x 5 1/8 in.),"Alfred Stieglitz Collection, 1955",,,,,,,,,,,,Transparencies,,http://www.metmuseum.org/art/collection/search/269804,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.476,false,true,285938,Photographs,Autochrome,[Two Men Playing Chess],,,,,,Artist,,Alfred Stieglitz,"American, Hoboken, New Jersey 1864–1946 New York",,"Stieglitz, Alfred",American,1864,1946,June 1907,1907,1907,Autochrome,9 x 12 cm (3.5 x 4.7 in.),"Gilman Collection, Purchase, Mr. and Mrs. Henry R. Kravis Gift, 2005",,,,,,,,,,,,Transparencies,,http://www.metmuseum.org/art/collection/search/285938,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.478,false,true,286176,Photographs,Autochrome,Portrait of Kitty,,,,,,Artist,,Alfred Stieglitz,"American, Hoboken, New Jersey 1864–1946 New York",,"Stieglitz, Alfred",American,1864,1946,ca. 1911,1909,1913,Autochrome,17.8 x 12.7 cm (7x 5 in.),"Gilman Collection, Purchase, Mrs. Walter Annenberg and The Annenberg Foundation Gift, 2005",,,,,,,,,,,,Transparencies,,http://www.metmuseum.org/art/collection/search/286176,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -49.55.19,false,true,269310,Photographs,Photomechanical print,Two Towers - New York,,,,,,Artist,,Alfred Stieglitz,"American, Hoboken, New Jersey 1864–1946 New York",,"Stieglitz, Alfred",American,1864,1946,"1911, printed in or before 1913",1911,1911,Photogravure,32.7 x 25.3 cm. (12 7/8 x 9 15/16 in.),"Alfred Stieglitz Collection, 1949",,,,,,,,,,,,Photographs|Prints,,http://www.metmuseum.org/art/collection/search/269310,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -58.577.1,false,true,270030,Photographs,Photomechanical print,Two Towers - New York,,,,,,Artist,,Alfred Stieglitz,"American, Hoboken, New Jersey 1864–1946 New York",,"Stieglitz, Alfred",American,1864,1946,"1911, printed 1913",1911,1911,Photogravure,19.5 x 15.9 cm. (7 11/16 x 6 1/4 in.),"Gift of J. B. Neumann, 1958",,,,,,,,,,,,Photographs|Prints,,http://www.metmuseum.org/art/collection/search/270030,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -58.577.2,false,true,270041,Photographs,Photomechanical print,Old and New New York,,,,,,Artist,,Alfred Stieglitz,"American, Hoboken, New Jersey 1864–1946 New York",,"Stieglitz, Alfred",American,1864,1946,"1910, printed 1911",1910,1910,Photogravure,20.2 x 15.8 cm. (7 15/16 x 6 1/4 in.),"Gift of J. B. Neumann, 1958",,,,,,,,,,,,Photographs|Prints,,http://www.metmuseum.org/art/collection/search/270041,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.43.419,false,true,267836,Photographs,Photomechanical print,The Steerage,,,,,,Artist,,Alfred Stieglitz,"American, Hoboken, New Jersey 1864–1946 New York",,"Stieglitz, Alfred",American,1864,1946,"1907, printed in or before 1913",1907,1907,Photogravure,32.2 x 25.8 cm (12 11/16 x 10 3/16 in.),"Alfred Stieglitz Collection, 1933",,,,,,,,,,,,Photographs|Prints,,http://www.metmuseum.org/art/collection/search/267836,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -58.577.18,false,true,270039,Photographs,Photomechanical print,Snapshot - In the New York Central Yards,,,,,,Artist,,Alfred Stieglitz,"American, Hoboken, New Jersey 1864–1946 New York",,"Stieglitz, Alfred",American,1864,1946,"1903, printed 1907",1903,1903,Photogravure,19.4 x 15.9 cm (7 5/8 x 6 1/4 in.),"Gift of J. B. Neumann, 1958",,,,,,,,,,,,Photographs|Prints,,http://www.metmuseum.org/art/collection/search/270039,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -58.577.20,false,true,270042,Photographs,Photomechanical print,Winter - Fifth Avenue,,,,,,Artist,,Alfred Stieglitz,"American, Hoboken, New Jersey 1864–1946 New York",,"Stieglitz, Alfred",American,1864,1946,"1893, printed 1905",1893,1893,Photogravure,21.8 x 15.4 cm. (8 9/16 x 6 1/16 in.),"Gift of J. B. Neumann, 1958",,,,,,,,,,,,Photographs|Prints,,http://www.metmuseum.org/art/collection/search/270042,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -58.577.21,false,true,270043,Photographs,Photomechanical print,Nearing Land,,,,,,Artist,,Alfred Stieglitz,"American, Hoboken, New Jersey 1864–1946 New York",,"Stieglitz, Alfred",American,1864,1946,"1904, printed 1905",1904,1904,Photogravure,21.6 x 17.4 cm. (8 1/2 x 6 7/8 in.),"Gift of J. B. Neumann, 1958",,,,,,,,,,,,Photographs|Prints,,http://www.metmuseum.org/art/collection/search/270043,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -58.577.28,false,true,270050,Photographs,Photomechanical print,The City across the River,,,,,,Artist,,Alfred Stieglitz,"American, Hoboken, New Jersey 1864–1946 New York",,"Stieglitz, Alfred",American,1864,1946,"1910, printed 1911",1910,1910,Photogravure,20.0 x 16.0 cm. (7 7/8 x 6 5/16 in.),"Gift of J. B. Neumann, 1958",,,,,,,,,,,,Photographs|Prints,,http://www.metmuseum.org/art/collection/search/270050,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -58.577.30,false,true,270053,Photographs,Photomechanical print,Lower Manhattan,,,,,,Artist,,Alfred Stieglitz,"American, Hoboken, New Jersey 1864–1946 New York",,"Stieglitz, Alfred",American,1864,1946,"1910, printed 1911",1910,1910,Photogravure,16.0 x 19.8 cm. (6 5/16 x 7 13/16 in.),"Gift of J. B. Neumann, 1958",,,,,,,,,,,,Photographs|Prints,,http://www.metmuseum.org/art/collection/search/270053,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -58.577.31,false,true,270054,Photographs,Photomechanical print,The Aeroplane,,,,,,Artist,,Alfred Stieglitz,"American, Hoboken, New Jersey 1864–1946 New York",,"Stieglitz, Alfred",American,1864,1946,"1910, printed 1911",1910,1910,Photogravure,14.5 x 17.5 cm (5 11/16 x 6 7/8 in.),"Gift of J. B. Neumann, 1958",,,,,,,,,,,,Photographs|Prints,,http://www.metmuseum.org/art/collection/search/270054,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -58.577.34,false,true,270057,Photographs,Photomechanical print,The Swimming Lesson,,,,,,Artist,,Alfred Stieglitz,"American, Hoboken, New Jersey 1864–1946 New York",,"Stieglitz, Alfred",American,1864,1946,"1906, printed 1911",1906,1906,Photogravure,14.8 x 23.0 cm. (5 13/16 x 9 1/16 in.),"Gift of J. B. Neumann, 1958",,,,,,,,,,,,Photographs|Prints,,http://www.metmuseum.org/art/collection/search/270057,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -58.577.36,false,true,270059,Photographs,Photomechanical print,"A Snapshot, Paris",,,,,,Artist,,Alfred Stieglitz,"American, Hoboken, New Jersey 1864–1946 New York",,"Stieglitz, Alfred",American,1864,1946,"1911, printed 1912",1911,1911,Photogravure,13.8 x 17.4 cm. (5 7/16 x 6 7/8 in.),"Gift of J. B. Neumann, 1958",,,,,,,,,,,,Photographs|Prints,,http://www.metmuseum.org/art/collection/search/270059,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1987.1100.116,false,true,265151,Photographs,Photograph,[View of Rooftops],,,,,,Artist,,Morton Schamberg,"American, Philadelphia, Pennsylvania 1881–1918 Philadelphia, Pennsylvania",,"Schamberg, Morton",American,1881,1918,1917,1917,1917,Gelatin silver print,23.7 x 19.0 cm (9 5/16 x 7 1/2 in.),"Ford Motor Company Collection, Gift of Ford Motor Company and John C. Waddell, 1987",,,,,,,,,,,,Pastels & Oil Sketches on Paper,,http://www.metmuseum.org/art/collection/search/265151,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.83,false,true,283183,Photographs,Photograph,[The Great Man Has Fallen],,,,,,Artist,,Robert H. Vance,"American, died 1876",,"Vance, Robert H.",American,1876,1876,1856,1856,1856,Daguerreotype,Image: 13.5 x 18.8 cm (5 5/16 x 7 3/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283183,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.207,false,true,286346,Photographs,Photograph,[View in New Hampshire],,,,,,Artist,,Samuel Bemis,"American, 1789–1881",,"Bemis, Samuel",American,1789,1881,1840–41,1840,1841,Daguerreotype,Image: 5 3/4 × 7 7/8 in. (14.6 × 20 cm) Frame: 9 5/16 × 11 7/16 in. (23.6 × 29 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286346,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1135,false,true,285732,Photographs,Photograph,[Gentleman],,,,,,Artist,,Henry Fitz Jr.,"American, 1808–1863",,"Fitz Jr., Henry",American,1808,1863,ca. 1840,1838,1842,Daguerreotype,Approx. ninth-plate,"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285732,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.400.50,false,true,291803,Photographs,Daguerreotype,"[Man Wearing Coat and Gloves, Holding Hat, Seated in Front of Painted Outdoor Backdrop]",,,,,,Artist,Attributed to,Samuel Broadbent Jr.,"American, 1810–1880",,"Broadbent, Samuel, Jr.",American,1810,1880,1840s,1840,1849,Daguerreotype,Image: 12 x 9 cm (4 3/4 x 3 9/16 in.) Plate: 14 x 10.8 cm (5 1/2 x 4 1/4 in.) Case: 2.2 x 15.2 x 12.4 cm (7/8 x 6 x 4 7/8 in.),"Bequest of Herbert Mitchell, 2008",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/291803,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.400.52,false,true,291805,Photographs,Daguerreotype,"Josiah Bunting, 85, with George M. Bunting, 17 Months",,,,,,Artist,,Samuel Broadbent Jr.,"American, 1810–1880",,"Broadbent, Samuel, Jr.",American,1810,1880,1850s,1850,1859,Daguerreotype,Image: 8.8 x 6.8 cm (3 7/16 x 2 11/16 in.) Plate: 10.8 x 8.3 cm (4 1/4 x 3 1/4 in.) Case: 2.2 x 11.7 x 9.2 cm (7/8 x 4 5/8 x 3 5/8 in.),"Bequest of Herbert Mitchell, 2008",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/291805,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.869,false,true,286550,Photographs,Photograph,"[Landscape, Pride's Crossing]",,,,,,Artist,,Samuel Masury,"American, 1818–1874",,"Masury, Samuel",American,1818,1874,ca. 1856,1854,1858,Salted paper print from paper negative,10 1/8 x 13 1/2,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286550,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1980.1056.1,false,true,262760,Photographs,Stereograph,Broadway on a Rainy Day,,,,,,Artist,,Edward Anthony,"American, 1818–1888",,"Anthony, Edward",American,1818,1888,1859,1859,1859,Albumen silver print,8.3 x 15.7 cm (3 1/4 x 6 3/16 in.),"Warner Communications Inc. Purchase Fund, 1980",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/262760,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1980.1056.4,false,true,262763,Photographs,Stereograph,[Broadway with horse-drawn carriages],,,,,,Artist,,Edward Anthony,"American, 1818–1888",,"Anthony, Edward",American,1818,1888,ca. 1860s,1858,1862,Albumen silver print,,"Warner Communications Inc. Purchase Fund, 1980",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/262763,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.457.3626,false,true,291959,Photographs,Photograph,Return of the Japanese Embassy from City Hall,,,,,,Artist,,Edward Anthony,"American, 1818–1888",,"Anthony, Edward",American,1818,1888,1860,1860,1860,Albumen silver print from glass negative,Image: 7.5 x 15 cm (2 15/16 x 5 7/8 in.) Mount: 8.2 x 17.1 cm (3 1/4 x 6 3/4 in.),"Herbert Mitchell Collection, 2007",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/291959,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.457.3682,false,true,291961,Photographs,Photograph,"The Embassy Leave the Metropolitan for the City Hall, the Seventh Regiment Form a Hollow Square With the Carriages of the Embassy in the Middle",,,,,,Artist,,Edward Anthony,"American, 1818–1888",,"Anthony, Edward",American,1818,1888,1860,1860,1860,Albumen silver print from glass negative,Image: 7.5 x 14.2 cm (2 15/16 x 5 9/16 in.) Mount: 8.2 x 17.4 cm (3 1/4 x 6 7/8 in.),"Herbert Mitchell Collection, 2007",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/291961,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.457.3683,false,true,291962,Photographs,Photograph,The Populace Begin to Gather in Front of the City Hall to Witness the Arrival of the Embassy on Their Visit to the Governor and Mayor,,,,,,Artist,,Edward Anthony,"American, 1818–1888",,"Anthony, Edward",American,1818,1888,1860,1860,1860,Albumen silver print from glass negative,Image: 7.6 x 14.2 cm (3 x 5 9/16 in.) Mount: 8.2 x 17.3 cm (3 1/4 x 6 13/16 in.),"Herbert Mitchell Collection, 2007",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/291962,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2012.19,false,true,301887,Photographs,Photograph,"Savannah, Georgia, No. 2",,,,,,Artist,,George N. Barnard,"American, 1819–1902",,"Barnard, George N.",American,1819,1902,1866,1866,1866,Albumen silver print from glass negative,Image: 25.5 x 36.1 cm (10 1/16 x 14 3/16 in.) Mount: 40.9 x 51.1 cm (16 1/8 x 20 1/8 in.),"The Horace W. Goldsmith Foundation Fund, through Joyce and Robert Menschel, 2012",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/301887,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -51.611.484,false,true,269550,Photographs,Photograph,"Rebel Works in Front of Atlanta, Georgia, No. 2",,,,,,Artist,,George N. Barnard,"American, 1819–1902",,"Barnard, George N.",American,1819,1902,1864,1864,1864,Albumen silver print,,"Gift of Mrs. Robert Ingersoll Aitken, 1951",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269550,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.525 (1),false,true,294445,Photographs,Photograph,Sherman and His Generals,,,,,,Artist,,George N. Barnard,"American, 1819–1902",,"Barnard, George N.",American,1819,1902,1860s,1860,1869,Albumen silver print from glass negative,,"Pfeiffer and Rogers Funds, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/294445,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.525 (2),false,true,294446,Photographs,Photograph,"The Capitol, Nashville, Tennessee",,,,,,Artist,,George N. Barnard,"American, 1819–1902",,"Barnard, George N.",American,1819,1902,1860s,1860,1869,Albumen silver print from glass negative,,"Pfeiffer and Rogers Funds, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/294446,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.525 (3),false,true,294447,Photographs,Photograph,Nashville from the Capitol,,,,,,Artist,,George N. Barnard,"American, 1819–1902",,"Barnard, George N.",American,1819,1902,1860s,1860,1869,Albumen silver print from glass negative,,"Pfeiffer and Rogers Funds, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/294447,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.525 (4),false,true,294448,Photographs,Photograph,Trestle Bridge at Whiteside,,,,,,Artist,,George N. Barnard,"American, 1819–1902",,"Barnard, George N.",American,1819,1902,1860s,1860,1869,Albumen silver print from glass negative,,"Pfeiffer and Rogers Funds, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/294448,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.525 (5),false,true,294449,Photographs,Photograph,Whiteside Valley Below the Bridge,,,,,,Artist,,George N. Barnard,"American, 1819–1902",,"Barnard, George N.",American,1819,1902,1860s,1860,1869,Albumen silver print from glass negative,,"Pfeiffer and Rogers Funds, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/294449,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.525 (6),false,true,294450,Photographs,Photograph,"Pass in the Racoon Range, Whiteside No. 1",,,,,,Artist,,George N. Barnard,"American, 1819–1902",,"Barnard, George N.",American,1819,1902,1860s,1860,1869,Albumen silver print from glass negative,,"Pfeiffer and Rogers Funds, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/294450,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.525 (7),false,true,294451,Photographs,Photograph,"Pass in the Raccoon Range, Whiteside No. 2",,,,,,Artist,,George N. Barnard,"American, 1819–1902",,"Barnard, George N.",American,1819,1902,1860s,1860,1869,Albumen silver print from glass negative,,"Pfeiffer and Rogers Funds, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/294451,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.525 (8),false,true,294452,Photographs,Photograph,Chattanooga from the North,,,,,,Artist,,George N. Barnard,"American, 1819–1902",,"Barnard, George N.",American,1819,1902,1860s,1860,1869,Albumen silver print from glass negative,,"Pfeiffer and Rogers Funds, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/294452,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.525 (9),false,true,294453,Photographs,Photograph,Mission Ridge from Orchard Knob,,,,,,Artist,,George N. Barnard,"American, 1819–1902",,"Barnard, George N.",American,1819,1902,1860s,1860,1869,Albumen silver print from glass negative,,"Pfeiffer and Rogers Funds, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/294453,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.281,false,true,286658,Photographs,Photograph,"Bonaventure Cemetery, Four Miles from Savannah",,,,,,Artist,,George N. Barnard,"American, 1819–1902",,"Barnard, George N.",American,1819,1902,1866,1866,1866,Albumen silver print from glass negative,Image: 34 x 26.4 cm (13 3/8 x 10 3/8 in.),"Gilman Collection, Purchase, Ann Tenenbaum and Thomas H. Lee Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286658,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.525 (10),false,true,294454,Photographs,Photograph,Orchard Knob from Mission Ridge,,,,,,Artist,,George N. Barnard,"American, 1819–1902",,"Barnard, George N.",American,1819,1902,1860s,1860,1869,Albumen silver print from glass negative,,"Pfeiffer and Rogers Funds, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/294454,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.525 (11),false,true,294455,Photographs,Photograph,The Crest of Mission Ridge,,,,,,Artist,,George N. Barnard,"American, 1819–1902",,"Barnard, George N.",American,1819,1902,1860s,1860,1869,Albumen silver print from glass negative,,"Pfeiffer and Rogers Funds, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/294455,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.525 (12),false,true,294456,Photographs,Photograph,Mission Ridge Scene of Sherman's Attack,,,,,,Artist,,George N. Barnard,"American, 1819–1902",,"Barnard, George N.",American,1819,1902,1860s,1860,1869,Albumen silver print from glass negative,,"Pfeiffer and Rogers Funds, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/294456,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.525 (13),false,true,294457,Photographs,Photograph,Chattanooga Valley from Lookout Mountain,,,,,,Artist,,George N. Barnard,"American, 1819–1902",,"Barnard, George N.",American,1819,1902,1860s,1860,1869,Albumen silver print from glass negative,,"Pfeiffer and Rogers Funds, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/294457,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.525 (14),false,true,294458,Photographs,Photograph,Chattanooga Valley from Lookout Mountain No. 2,,,,,,Artist,,George N. Barnard,"American, 1819–1902",,"Barnard, George N.",American,1819,1902,1860s,1860,1869,Albumen silver print from glass negative,,"Pfeiffer and Rogers Funds, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/294458,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.525 (15),false,true,294459,Photographs,Photograph,"Lu-La Lake, Lookout Mountain",,,,,,Artist,,George N. Barnard,"American, 1819–1902",,"Barnard, George N.",American,1819,1902,1864 or 1866,1864,1866,Albumen silver print from glass negative,Image: 25.6 × 35.9 cm (10 1/16 × 14 1/8 in.),"Pfeiffer and Rogers Funds, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/294459,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.525 (16),false,true,294460,Photographs,Photograph,"The John Ross House, Ringold, Georgia",,,,,,Artist,,George N. Barnard,"American, 1819–1902",,"Barnard, George N.",American,1819,1902,1860s,1860,1869,Albumen silver print from glass negative,,"Pfeiffer and Rogers Funds, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/294460,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.525 (17),false,true,294461,Photographs,Photograph,"Ringold, Georgia",,,,,,Artist,,George N. Barnard,"American, 1819–1902",,"Barnard, George N.",American,1819,1902,1860s,1860,1869,Albumen silver print from glass negative,,"Pfeiffer and Rogers Funds, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/294461,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.525 (18),false,true,294462,Photographs,Photograph,"Buzzard Roost, Georgia",,,,,,Artist,,George N. Barnard,"American, 1819–1902",,"Barnard, George N.",American,1819,1902,1860s,1860,1869,Albumen silver print from glass negative,,"Pfeiffer and Rogers Funds, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/294462,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.525 (19),false,true,294463,Photographs,Photograph,"Battle Ground of Resacca, Georgia No. 1",,,,,,Artist,,George N. Barnard,"American, 1819–1902",,"Barnard, George N.",American,1819,1902,1860s,1860,1869,Albumen silver print from glass negative,,"Pfeiffer and Rogers Funds, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/294463,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.525 (20),false,true,294464,Photographs,Photograph,"Battle Ground of Resacca, Georgia No. 2",,,,,,Artist,,George N. Barnard,"American, 1819–1902",,"Barnard, George N.",American,1819,1902,1860s,1860,1869,Albumen silver print from glass negative,,"Pfeiffer and Rogers Funds, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/294464,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.525 (21),false,true,294465,Photographs,Photograph,"Battle Ground of Resacca, Georgia No. 3",,,,,,Artist,,George N. Barnard,"American, 1819–1902",,"Barnard, George N.",American,1819,1902,1860s,1860,1869,Albumen silver print from glass negative,,"Pfeiffer and Rogers Funds, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/294465,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.525 (22),false,true,294466,Photographs,Photograph,"Battle Ground of Resacca, Georgia No. 4",,,,,,Artist,,George N. Barnard,"American, 1819–1902",,"Barnard, George N.",American,1819,1902,1860s,1860,1869,Albumen silver print from glass negative,,"Pfeiffer and Rogers Funds, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/294466,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.525 (23),false,true,294467,Photographs,Photograph,Defences of the Etawah Bridge,,,,,,Artist,,George N. Barnard,"American, 1819–1902",,"Barnard, George N.",American,1819,1902,1860s,1860,1869,Albumen silver print from glass negative,,"Pfeiffer and Rogers Funds, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/294467,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.525 (24),false,true,294468,Photographs,Photograph,Allatoona from the Etawah,,,,,,Artist,,George N. Barnard,"American, 1819–1902",,"Barnard, George N.",American,1819,1902,1860s,1860,1869,Albumen silver print from glass negative,,"Pfeiffer and Rogers Funds, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/294468,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.525 (25),false,true,294469,Photographs,Photograph,"Battle Field of New Hope Church, Georgia No. 1",,,,,,Artist,,George N. Barnard,"American, 1819–1902",,"Barnard, George N.",American,1819,1902,1860s,1860,1869,Albumen silver print from glass negative,,"Pfeiffer and Rogers Funds, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/294469,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.525 (26),false,true,294470,Photographs,Photograph,"Battle Field of New Hope Church, Georgia No. 2",,,,,,Artist,,George N. Barnard,"American, 1819–1902",,"Barnard, George N.",American,1819,1902,1860s,1860,1869,Albumen silver print from glass negative,,"Pfeiffer and Rogers Funds, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/294470,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.525 (27),false,true,294471,Photographs,Photograph,"The ""Hell Hole"" New Hope Church, Georgia",,,,,,Artist,,George N. Barnard,"American, 1819–1902",,"Barnard, George N.",American,1819,1902,1860s,1860,1869,Albumen silver print from glass negative,,"Pfeiffer and Rogers Funds, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/294471,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.525 (28),false,true,294472,Photographs,Photograph,"The Allatoona Pass, Georgia",,,,,,Artist,,George N. Barnard,"American, 1819–1902",,"Barnard, George N.",American,1819,1902,1860s,1860,1869,Albumen silver print from glass negative,,"Pfeiffer and Rogers Funds, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/294472,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.525 (29),false,true,294473,Photographs,Photograph,"The Allatoona Pass Looking North, Georgia",,,,,,Artist,,George N. Barnard,"American, 1819–1902",,"Barnard, George N.",American,1819,1902,1860s,1860,1869,Albumen silver print from glass negative,,"Pfeiffer and Rogers Funds, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/294473,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.525 (30),false,true,294474,Photographs,Photograph,Pine Mountain,,,,,,Artist,,George N. Barnard,"American, 1819–1902",,"Barnard, George N.",American,1819,1902,1860s,1860,1869,Albumen silver print from glass negative,,"Pfeiffer and Rogers Funds, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/294474,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.525 (31),false,true,294475,Photographs,Photograph,"The Front of Kenesaw Mountain, Georgia",,,,,,Artist,,George N. Barnard,"American, 1819–1902",,"Barnard, George N.",American,1819,1902,1860s,1860,1869,Albumen silver print from glass negative,,"Pfeiffer and Rogers Funds, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/294475,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.525 (32),false,true,294476,Photographs,Photograph,"View of Kenesaw Mountain, Georgia",,,,,,Artist,,George N. Barnard,"American, 1819–1902",,"Barnard, George N.",American,1819,1902,1860s,1860,1869,Albumen silver print from glass negative,,"Pfeiffer and Rogers Funds, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/294476,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.525 (33),false,true,294477,Photographs,Photograph,"South Bank of the Chattahoochie, Georgia",,,,,,Artist,,George N. Barnard,"American, 1819–1902",,"Barnard, George N.",American,1819,1902,1860s,1860,1869,Albumen silver print from glass negative,,"Pfeiffer and Rogers Funds, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/294477,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.525 (34),false,true,294478,Photographs,Photograph,"The Battle Field of Peach Tree Creek, Georgia",,,,,,Artist,,George N. Barnard,"American, 1819–1902",,"Barnard, George N.",American,1819,1902,1860s,1860,1869,Albumen silver print from glass negative,,"Pfeiffer and Rogers Funds, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/294478,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.525 (35),false,true,294479,Photographs,Photograph,Scene of General McPherson's Death,,,,,,Artist,,George N. Barnard,"American, 1819–1902",,"Barnard, George N.",American,1819,1902,1864 or 1866,1864,1866,Albumen silver print from glass negative,Image: 25.4 × 36.1 cm (10 × 14 3/16 in.),"Pfeiffer and Rogers Funds, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/294479,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.525 (36),false,true,294480,Photographs,Photograph,"Battle Field of Atlanta, Georgia, July 22nd 1864 No. 1",,,,,,Artist,,George N. Barnard,"American, 1819–1902",,"Barnard, George N.",American,1819,1902,1860s,1860,1869,Albumen silver print from glass negative,,"Pfeiffer and Rogers Funds, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/294480,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.525 (37),false,true,294481,Photographs,Photograph,"Battle Field of Atlanta, Georgia, July 22nd 1864 No. 2",,,,,,Artist,,George N. Barnard,"American, 1819–1902",,"Barnard, George N.",American,1819,1902,1860s,1860,1869,Albumen silver print from glass negative,,"Pfeiffer and Rogers Funds, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/294481,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.525 (38),false,true,294482,Photographs,Photograph,"The Potter House, Atlanta",,,,,,Artist,,George N. Barnard,"American, 1819–1902",,"Barnard, George N.",American,1819,1902,1860s,1860,1869,Albumen silver print from glass negative,,"Pfeiffer and Rogers Funds, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/294482,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.525 (39),false,true,294483,Photographs,Photograph,"Rebel Works in Front of Atlanta, Georgia No. 1",,,,,,Artist,,George N. Barnard,"American, 1819–1902",,"Barnard, George N.",American,1819,1902,1860s,1860,1869,Albumen silver print from glass negative,,"Pfeiffer and Rogers Funds, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/294483,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.525 (40),false,true,294484,Photographs,Photograph,"Rebel Works in Front of Atlanta, Georgia No. 2",,,,,,Artist,,George N. Barnard,"American, 1819–1902",,"Barnard, George N.",American,1819,1902,1860s,1860,1869,Albumen silver print from glass negative,,"Pfeiffer and Rogers Funds, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/294484,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.525 (41),false,true,294485,Photographs,Photograph,"Rebel Works in Front of Atlanta, Georgia No. 3",,,,,,Artist,,George N. Barnard,"American, 1819–1902",,"Barnard, George N.",American,1819,1902,1860s,1860,1869,Albumen silver print from glass negative,,"Pfeiffer and Rogers Funds, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/294485,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.525 (42),false,true,294486,Photographs,Photograph,"Rebel Works in Front of Atlanta, Georgia No. 4",,,,,,Artist,,George N. Barnard,"American, 1819–1902",,"Barnard, George N.",American,1819,1902,1860s,1860,1869,Albumen silver print from glass negative,,"Pfeiffer and Rogers Funds, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/294486,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.525 (43),false,true,294487,Photographs,Photograph,"Rebel Works in Front of Atlanta, Georgia No. 5",,,,,,Artist,,George N. Barnard,"American, 1819–1902",,"Barnard, George N.",American,1819,1902,1860s,1860,1869,Albumen silver print from glass negative,,"Pfeiffer and Rogers Funds, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/294487,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.525 (44),false,true,294488,Photographs,Photograph,Destruction of Hood's Ordinance Train,,,,,,Artist,,George N. Barnard,"American, 1819–1902",,"Barnard, George N.",American,1819,1902,1860s,1860,1869,Albumen silver print from glass negative,,"Pfeiffer and Rogers Funds, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/294488,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.525 (45),false,true,294489,Photographs,Photograph,"City of Atlanta, Georgia No. 1",,,,,,Artist,,George N. Barnard,"American, 1819–1902",,"Barnard, George N.",American,1819,1902,1860s,1860,1869,Albumen silver print from glass negative,,"Pfeiffer and Rogers Funds, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/294489,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.525 (46),false,true,294490,Photographs,Photograph,"City of Atlanta, Georgia No. 2",,,,,,Artist,,George N. Barnard,"American, 1819–1902",,"Barnard, George N.",American,1819,1902,1866,1866,1866,Albumen silver print from glass negative,Image: 25.6 × 35.5 cm (10 1/16 × 14 in.),"Pfeiffer and Rogers Funds, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/294490,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.525 (47),false,true,294491,Photographs,Photograph,"Savanah River, Near Savanah, Georgia",,,,,,Artist,,George N. Barnard,"American, 1819–1902",,"Barnard, George N.",American,1819,1902,1860s,1860,1869,Albumen silver print from glass negative,,"Pfeiffer and Rogers Funds, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/294491,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.525 (48),false,true,294492,Photographs,Photograph,"Buen-Ventura, Savanah, Georgia",,,,,,Artist,,George N. Barnard,"American, 1819–1902",,"Barnard, George N.",American,1819,1902,1860s,1860,1869,Albumen silver print from glass negative,,"Pfeiffer and Rogers Funds, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/294492,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.525 (49),false,true,294493,Photographs,Photograph,"Savanah, Georgia No. 1",,,,,,Artist,,George N. Barnard,"American, 1819–1902",,"Barnard, George N.",American,1819,1902,1860s,1860,1869,Albumen silver print from glass negative,,"Pfeiffer and Rogers Funds, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/294493,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.525 (50),false,true,294494,Photographs,Photograph,"Savanah, Georgia No. 2",,,,,,Artist,,George N. Barnard,"American, 1819–1902",,"Barnard, George N.",American,1819,1902,1860s,1860,1869,Albumen silver print from glass negative,,"Pfeiffer and Rogers Funds, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/294494,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.525 (51),false,true,294495,Photographs,Photograph,"Fountain, Savanah, Georgia",,,,,,Artist,,George N. Barnard,"American, 1819–1902",,"Barnard, George N.",American,1819,1902,1860s,1860,1869,Albumen silver print from glass negative,,"Pfeiffer and Rogers Funds, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/294495,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.525 (52),false,true,294496,Photographs,Photograph,"The New Capitol, Columbia, South Carolina",,,,,,Artist,,George N. Barnard,"American, 1819–1902",,"Barnard, George N.",American,1819,1902,1860s,1860,1869,Albumen silver print from glass negative,,"Pfeiffer and Rogers Funds, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/294496,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.525 (53),false,true,294497,Photographs,Photograph,Columbia from the Capitol,,,,,,Artist,,George N. Barnard,"American, 1819–1902",,"Barnard, George N.",American,1819,1902,1860s,1860,1869,Albumen silver print from glass negative,,"Pfeiffer and Rogers Funds, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/294497,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.525 (54),false,true,294498,Photographs,Photograph,"Ruins in Columbia, South Carolina",,,,,,Artist,,George N. Barnard,"American, 1819–1902",,"Barnard, George N.",American,1819,1902,1860s,1860,1869,Albumen silver print from glass negative,,"Pfeiffer and Rogers Funds, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/294498,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.525 (55),false,true,294499,Photographs,Photograph,"Ruins in Columbia, South Carolina No. 2",,,,,,Artist,,George N. Barnard,"American, 1819–1902",,"Barnard, George N.",American,1819,1902,1860s,1860,1869,Albumen silver print from glass negative,,"Pfeiffer and Rogers Funds, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/294499,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.525 (56),false,true,294500,Photographs,Photograph,Fort Sumpter,,,,,,Artist,,George N. Barnard,"American, 1819–1902",,"Barnard, George N.",American,1819,1902,1860s,1860,1869,Albumen silver print from glass negative,,"Pfeiffer and Rogers Funds, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/294500,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.525 (57),false,true,294501,Photographs,Photograph,Interior View of Fort Sumpter,,,,,,Artist,,George N. Barnard,"American, 1819–1902",,"Barnard, George N.",American,1819,1902,1860s,1860,1869,Albumen silver print from glass negative,,"Pfeiffer and Rogers Funds, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/294501,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.525 (58),false,true,294502,Photographs,Photograph,Exterior View of Fort Sumpter,,,,,,Artist,,George N. Barnard,"American, 1819–1902",,"Barnard, George N.",American,1819,1902,1860s,1860,1869,Albumen silver print from glass negative,,"Pfeiffer and Rogers Funds, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/294502,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.525 (59),false,true,294503,Photographs,Photograph,"Ruins of the Pinckney Mansion, Charleston, South Carolina",,,,,,Artist,,George N. Barnard,"American, 1819–1902",,"Barnard, George N.",American,1819,1902,1860s,1860,1869,Albumen silver print from glass negative,,"Pfeiffer and Rogers Funds, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/294503,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.525 (60),false,true,294504,Photographs,Photograph,"Ruins in Charleston, South Carolina",,,,,,Artist,,George N. Barnard,"American, 1819–1902",,"Barnard, George N.",American,1819,1902,1860s,1860,1869,Albumen silver print from glass negative,,"Pfeiffer and Rogers Funds, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/294504,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.525 (61),false,true,294505,Photographs,Photograph,"Ruins of the R.R. Depot, Charleston, South Carolina",,,,,,Artist,,George N. Barnard,"American, 1819–1902",,"Barnard, George N.",American,1819,1902,1860s,1860,1869,Albumen silver print from glass negative,,"Pfeiffer and Rogers Funds, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/294505,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1232,false,true,285727,Photographs,Photograph,"Quaker Gun, Centreville, Virginia",,,,,,Artist,,George N. Barnard,"American, 1819–1902",,"Barnard, George N.",American,1819,1902,March 1862,1862,1862,Albumen silver print from glass negative,Image: 8.1 × 9.4 cm (3 3/16 × 3 11/16 in.) Mount: 10.7 × 12.6 cm (4 3/16 × 4 15/16 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285727,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1997.382.52,false,true,282057,Photographs,Photograph,Niagara Falls,,,,,,Artist,Possibly by,Silas A. Holmes,"American, 1820–1886",,"Holmes, Silas S.",American,1820,1886,ca. 1855,1853,1857,Salted paper print from glass negative,30.5 x 40.4 cm (12 x 15 7/8 in. ) irregular,"The Rubel Collection, Purchase, Lila Acheson Wallace Gift, 1997",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/282057,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2011.556.3,false,true,301973,Photographs,Photograph,[Rev. Mr. Frederick T. Gray and Deacons of Old Bullfinch Street Church],,,,,,Artist,Attributed to,John Adams Whipple,"American, 1822–1891",,"Whipple, John Adams",American,1822,1891,ca. 1845,1840,1850,Daguerreotype,Plate: 10.8 x 14 cm (4 1/4 x 5 1/2 in.),"Gift of Isaac Lagnado, in honor of Elliott Cohen, 2011",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/301973,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1997.382.41,true,true,282046,Photographs,Photograph,[Cornelius Conway Felton with His Hat and Coat],,,,,,Artist,,John Adams Whipple,"American, 1822–1891",,"Whipple, John Adams",American,1822,1891,early 1850s,1850,1853,Daguerreotype,visible: 8.3 x 7 cm (3 1/4 x 2 3/4 in.) each,"The Rubel Collection, Purchase, Lila Acheson Wallace, W. Bruce and Delaney H. Lundberg, and Ann Tenenbaum and Thomas H. Lee Gifts, 1997",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/282046,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.76,false,true,283175,Photographs,Photograph,[Hypnotism],,,,,,Artist,,John Adams Whipple,"American, 1822–1891",,"Whipple, John Adams",American,1822,1891,ca. 1845,1843,1847,Daguerreotype,Image: 13.3 x 18.4 cm (5 1/4 x 7 1/4 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283175,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.460.14,false,true,290482,Photographs,Photograph,National Congregational Council at Plymouth Rock,,,,,,Artist,,John Adams Whipple,"American, 1822–1891",,"Whipple, John Adams",American,1822,1891,"June 22, 1865",1865,1865,Albumen silver print from glass negative,Image: 37.5 x 47.6 cm (14 3/4 x 18 3/4 in.) Mount: 45.7 x 55.9 cm (18 x 22 in.),"Gift of Paul F. Walter, 2009",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/290482,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.400.43,false,true,291796,Photographs,Daguerreotype,[Double Plate: Two Men with Sideburns],,,,,,Artist,Possibly by,John Adams Whipple,"American, 1822–1891",,"Whipple, John Adams",American,1822,1891,1850s,1850,1859,Daguerreotype,"Image: 6.5 x 5.3 cm (2 9/16 x 2 1/16 in.), each Plate: 8.3 x 7 cm (3 1/4 x 2 3/4 in.), each Case: 1.9 x 9.5 x 8.3 cm (3/4 x 3 3/4 x 3 1/4 in.)","Bequest of Herbert Mitchell, 2008",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/291796,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.856,false,true,286231,Photographs,Photograph,[Two Elderly Men Conversing],,,,,,Artist,,John Adams Whipple,"American, 1822–1891",,"Whipple, John Adams",American,1822,1891,ca. 1850,1848,1852,Daguerreotype,oberall: 9 1/16 × 6 15/16 in. (23 × 17.7 cm) Image: 7 1/4 × 5 1/4 in. (18.4 × 13.4 cm); visible,"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286231,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.857,false,true,286237,Photographs,Photograph,[Self-Portrait with Artist's Brother],,,,,,Artist,,John Adams Whipple,"American, 1822–1891",,"Whipple, John Adams",American,1822,1891,1840s,1840,1849,Daguerreotype,Image: 1 7/8 × 1 7/16 in. (4.7 × 3.7 cm); visible,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286237,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.858,false,true,286230,Photographs,Photograph,[Self-Portrait with Wife and Two Daughters],,,,,,Artist,,John Adams Whipple,"American, 1822–1891",,"Whipple, John Adams",American,1822,1891,1854,1854,1854,Daguerreotype,Overall: 6 × 4 3/4 in. (15.2 × 12 cm) Image: 4 13/16 × 3 9/16 in. (12.3 × 9 cm); visible,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286230,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.56,false,true,301998,Photographs,Photograph,General George McClellan,,,,,,Artist,,Charles DeForest Fredricks,"American, 1823–1894",,"Fredricks, Charles DeForest",American,1823,1894,1862 (?),1862,1862,Albumen silver print from glass negative,Image: 9.1 x 5.4 cm (3 9/16 x 2 1/8 in.) Mount: 10.2 x 6.1 cm (4 x 2 3/8 in.),"Purchase, Alfred Stieglitz Society Gifts, 2013",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/301998,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2014.210,false,true,647196,Photographs,Photograph,[Clown],,,,,,Artist,,Charles DeForest Fredricks,"American, 1823–1894",,"Fredricks, Charles DeForest",American,1823,1894,ca. 1860,1855,1865,Albumen silver print,Image: 8 1/16 × 6 13/16 in. (20.5 × 17.3 cm) Mount: 13 5/16 × 10 1/2 in. (33.8 × 26.7 cm),"Gift of Howard Greenberg, 2014",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/647196,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.49,false,true,301963,Photographs,Carte-de-visite,"[Wounded Soldiers on Cots, possibly at Harewood Hospital]",,,,,,Artist,,Reed Brockway Bontecou,"American, 1824–1907",,"Bontecou, Reed Brockway",American,1824,1907,1865,1865,1865,Albumen silver print from glass negative,Image: 9.4 x 5.7 cm (3 11/16 x 2 1/4 in.) Mount: 10.2 x 6.1 cm (4 x 2 3/8 in.),"Purchase, Alfred Stieglitz Society Gifts, 2013",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/301963,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1992.5127,false,true,266764,Photographs,Photograph,Judson C. Albright,,,,,,Artist,,Reed Brockway Bontecou,"American, 1824–1907",,"Bontecou, Reed Brockway",American,1824,1907,1865,1865,1865,Albumen silver print from glass negative,,"Gift of Stanley B. Burns, M.D. and The Burns Archive, 1992",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/266764,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1992.5128,false,true,266765,Photographs,Photograph,Frederick A. Bentley,,,,,,Artist,,Reed Brockway Bontecou,"American, 1824–1907",,"Bontecou, Reed Brockway",American,1824,1907,1865,1865,1865,Albumen silver print from glass negative,,"Gift of Stanley B. Burns, M.D. and The Burns Archive, 1992",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/266765,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1992.5129,false,true,266766,Photographs,Photograph,John A. Dixon,,,,,,Artist,,Reed Brockway Bontecou,"American, 1824–1907",,"Bontecou, Reed Brockway",American,1824,1907,1865,1865,1865,Albumen silver print from glass negative,,"Gift of Stanley B. Burns, M.D. and The Burns Archive, 1992",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/266766,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1992.5130,false,true,266767,Photographs,Photograph,Frederick Hohmann,,,,,,Artist,,Reed Brockway Bontecou,"American, 1824–1907",,"Bontecou, Reed Brockway",American,1824,1907,1865,1865,1865,Albumen silver print from glass negative,,"Gift of Stanley B. Burns, M.D. and The Burns Archive, 1992",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/266767,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1992.5131,false,true,266768,Photographs,Photograph,"Private John Parkhurst, Company E, Second New York Heavy Artillery",,,,,,Artist,,Reed Brockway Bontecou,"American, 1824–1907",,"Bontecou, Reed Brockway",American,1824,1907,1865,1865,1865,Albumen silver print from glass negative,"Image: 18.9 × 13.1 cm (7 7/16 × 5 3/16 in.), oval","Gift of Stanley B. Burns, M.D. and The Burns Archive, 1992",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/266768,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1992.5132,false,true,266769,Photographs,Photograph,Stephen D. Wilbur,,,,,,Artist,,Reed Brockway Bontecou,"American, 1824–1907",,"Bontecou, Reed Brockway",American,1824,1907,1865,1865,1865,Albumen silver print from glass negative,,"Gift of Stanley B. Burns, M.D. and The Burns Archive, 1992",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/266769,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1992.5133,false,true,266770,Photographs,Photograph,Herman Rice,,,,,,Artist,,Reed Brockway Bontecou,"American, 1824–1907",,"Bontecou, Reed Brockway",American,1824,1907,1865,1865,1865,Albumen silver print from glass negative,,"Gift of Stanley B. Burns, M.D. and The Burns Archive, 1992",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/266770,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1992.5134,false,true,266771,Photographs,Photograph,"Private Samuel Shoop, Company F, 200th Pennsylvania Infantry",,,,,,Artist,,Reed Brockway Bontecou,"American, 1824–1907",,"Bontecou, Reed Brockway",American,1824,1907,April–May 1865,1865,1865,Albumen silver print from glass negative,Image: 18.9 × 13.1 cm (7 7/16 × 5 3/16 in.),"Gift of Stanley B. Burns, M.D. and The Burns Archive, 1992",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/266771,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1992.5135,false,true,266772,Photographs,Photograph,"Private Jacob F. Simmons, Company H, Eighty-second Pennsylvania Volunteers",,,,,,Artist,,Reed Brockway Bontecou,"American, 1824–1907",,"Bontecou, Reed Brockway",American,1824,1907,April–May 1865,1865,1865,Albumen silver print from glass negative,"Image: 18.7 × 13 cm (7 3/8 × 5 1/8 in.), oval Mount: 29.5 × 24.1 cm (11 5/8 × 9 1/2 in.)","Gift of Stanley B. Burns, M.D. and The Burns Archive, 1992",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/266772,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1992.5136,false,true,266773,Photographs,Photograph,"Corporal Israel Spotts, Company G, 200th Pennsylvania Volunteers",,,,,,Artist,,Reed Brockway Bontecou,"American, 1824–1907",,"Bontecou, Reed Brockway",American,1824,1907,April–May 1865,1865,1865,Albumen silver print from glass negative,"Image: 18.9 x 13.1 cm (7 7/16 x 5 3/16 in.), oval Mount: 29.8 x 24.3 cm (11 3/4 x 9 9/16 in.)","Gift of Stanley B. Burns, M.D. and The Burns Archive, 1992",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/266773,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1992.5137,false,true,266774,Photographs,Photograph,"Private James H. Stokes, Company H, 185th New York Volunteers",,,,,,Artist,,Reed Brockway Bontecou,"American, 1824–1907",,"Bontecou, Reed Brockway",American,1824,1907,April–May 1865,1865,1865,Albumen silver print from glass negative,"Image: 18.8 × 13 cm (7 3/8 × 5 1/8 in.), oval","Gift of Stanley B. Burns, M.D. and The Burns Archive, 1992",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/266774,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1992.5138,false,true,266775,Photographs,Photograph,Robert Stevenson,,,,,,Artist,,Reed Brockway Bontecou,"American, 1824–1907",,"Bontecou, Reed Brockway",American,1824,1907,1865,1865,1865,Albumen silver print from glass negative,,"Gift of Stanley B. Burns, M.D. and The Burns Archive, 1992",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/266775,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1992.5139,false,true,266776,Photographs,Photograph,"Privat Dennis Sullivan, Company E, Second Virginia Cavalry",,,,,,Artist,,Reed Brockway Bontecou,"American, 1824–1907",,"Bontecou, Reed Brockway",American,1824,1907,April 1865,1865,1865,Albumen silver print from glass negative,"Image: 13.1 × 18.9 cm (5 3/16 × 7 7/16 in.), oval","Gift of Stanley B. Burns, M.D. and The Burns Archive, 1992",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/266776,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1992.5140,false,true,266777,Photographs,Photograph,Andrew Wagoner,,,,,,Artist,,Reed Brockway Bontecou,"American, 1824–1907",,"Bontecou, Reed Brockway",American,1824,1907,1865,1865,1865,Albumen silver print from glass negative,,"Gift of Stanley B. Burns, M.D. and The Burns Archive, 1992",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/266777,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1992.5141,false,true,266778,Photographs,Photograph,Henry Yon,,,,,,Artist,,Reed Brockway Bontecou,"American, 1824–1907",,"Bontecou, Reed Brockway",American,1824,1907,1865,1865,1865,Albumen silver print from glass negative,,"Gift of Stanley B. Burns, M.D. and The Burns Archive, 1992",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/266778,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1992.5142,false,true,266779,Photographs,Photograph,Frederick Pilgrim,,,,,,Artist,,Reed Brockway Bontecou,"American, 1824–1907",,"Bontecou, Reed Brockway",American,1824,1907,1865,1865,1865,Albumen silver print from glass negative,,"Gift of Stanley B. Burns, M.D. and The Burns Archive, 1992",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/266779,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1992.5143,false,true,266780,Photographs,Photograph,Frederick Pilgrim,,,,,,Artist,,Reed Brockway Bontecou,"American, 1824–1907",,"Bontecou, Reed Brockway",American,1824,1907,1865,1865,1865,Albumen silver print from glass negative,,"Gift of Stanley B. Burns, M.D. and The Burns Archive, 1992",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/266780,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.99,false,true,283206,Photographs,Photograph,"Private George Ruoss, Co. G, 7th New York Volunteers",,,,,,Artist,,Reed Brockway Bontecou,"American, 1824–1907",,"Bontecou, Reed Brockway",American,1824,1907,1865–1866,1865,1866,Albumen silver print from glass negative,Image: 16.6 × 21.7 cm (6 9/16 × 8 9/16 in.) Sheet: 19.3 × 24.2 cm (7 5/8 × 9 1/2 in.) Mount: 27.7 × 35.4 cm (10 7/8 × 13 15/16 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283206,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1981.1229.4,false,true,263187,Photographs,Photograph,[Boston from a Hot-Air Balloon],,,,,,Artist,,James Wallace Black,"American, 1825–1896",,"Black, James Wallace",American,1825,1896,1860s,1860,1869,Albumen silver print from glass negative,25.6 x 20.2 cm. (10 1/16 x 7 15/16 in.),"Robert O. Dougan Collection, Gift of Warner Communications Inc., 1981",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/263187,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.87,false,true,283189,Photographs,Photograph,"Boston, as the Eagle and the Wild Goose See It",,,,,,Artist,,James Wallace Black,"American, 1825–1896",,"Black, James Wallace",American,1825,1896,1860,1860,1860,Albumen silver print from glass negative,"Image: 18.5 x 16.7 cm (7 5/16 x 6 9/16 in.), irregularly trimmed Mount: 20.3 x 17 cm (8 x 6 11/16 in.), irregularly trimmed","Gilman Collection, Purchase, Ann Tenenbaum and Thomas H. Lee Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283189,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1147,false,true,286653,Photographs,Photograph,Four Generations,,,,,,Artist,,James Wallace Black,"American, 1825–1896",,"Black, James Wallace",American,1825,1896,ca. 1860,1858,1862,Salted paper print,Image (Oval): 28.1 × 22.4 cm (11 1/16 × 8 13/16 in.) Mount (Oval): 27.8 × 22.2 cm (10 15/16 × 8 3/4 in.),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286653,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1247,false,true,286300,Photographs,Photograph,[Victorian House],,,,,,Artist,,James Wallace Black,"American, 1825–1896",,"Black, James Wallace",American,1825,1896,ca. 1860,1855,1865,Albumen silver print from glass negative,Image: 24.8 × 32.5 cm (9 3/4 × 12 13/16 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286300,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1248,false,true,286299,Photographs,Photograph,"[Washington Street, Boston]",,,,,,Artist,,James Wallace Black,"American, 1825–1896",,"Black, James Wallace",American,1825,1896,ca. 1860,1855,1865,Albumen silver print from glass negative,"Image: 18 × 15.5 cm (7 1/16 × 6 1/8 in.), dome top","Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286299,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.94,false,true,283198,Photographs,Photograph,"[Ordnance Wharf, City Point, Virginia]",,,,,,Artist,,Thomas C. Roche,"American, 1826–1895",,"Roche, Thomas C.",American,1826,1895,1865,1865,1865,Albumen silver print from glass negative,Image: 21.7 x 25.5 cm (8 9/16 x 10 1/16 in.),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283198,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2010.518,false,true,294766,Photographs,Photograph,[Winter Scene with Trestle Bridge Along the Atlantic & Great Western Railway],,,,,,Artist,,James Fitzallen Ryder,"American, 1826–1904",,"Ryder, James Fitzallen",American,1826,1904,1862–64,1862,1864,Albumen silver print from glass negative,Image: 18.7 x 23.8 cm (7 3/8 x 9 3/8 in.),"Gift of Mary and Dan Solomon, in honor of Hans P. Kraus Jr., 2010",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/294766,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1979.622,true,true,262612,Photographs,Photograph,"View on the Columbia, Cascades",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,1867,1867,1867,Albumen silver print from glass negative,40.0 x 52.4 cm (15 3/4 x 20 5/8 in.),"Warner Communications Inc. Purchase Fund and Harris Brisbane Dick Fund, 1979",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/262612,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.1082,false,true,266132,Photographs,Photograph,"Devil's Canyon, Geysers, Looking Down",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,1868–70,1868,1870,Albumen silver print from glass negative,39.8 x 52.4 cm. (15 11/16 x 20 5/8 in.),"Rogers Fund, 1989",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/266132,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.1083,true,true,266133,Photographs,Photograph,"The Town on the Hill, New Almaden",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,1863,1863,1863,Albumen silver print from glass negative,39.7 x 52.3 cm (15 5/8 x 20 9/16 in.),"Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 1989",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/266133,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.540.4,false,true,259681,Photographs,Photograph,"El Capitan, Yosemite",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,1865–66,1865,1866,Albumen silver print from glass negative,,"Rogers Fund, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/259681,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.540.6,false,true,259683,Photographs,Photograph,"Section of the Grizzly Giant with Galen Clark, Mariposa Grove, Yosemite",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,1865–66,1865,1866,Albumen silver print from glass negative,Image: 20 5/8 × 15 11/16 in. (52.4 × 39.8 cm) Sheet: 23 13/16 × 19 1/16 in. (60.5 × 48.4 cm),"Rogers Fund, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/259683,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.643.1,false,true,260314,Photographs,Photograph,"Vernal Fall, Yosemite",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,"1865–66, printed ca. 1875",1865,1866,Albumen silver print from glass negative,,"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260314,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.643.3,false,true,260317,Photographs,Photograph,"Section of the Grizzly Giant with Galen Clark, Mariposa Grove, Yosemite",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,"1865–66, printed ca. 1876",1865,1866,Albumen silver print from glass negative,52.2 x 40.6 cm. (20 9/16 x 16 in.),"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260317,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1189.1,false,true,264896,Photographs,Photograph,"Residence of Charles Bernard. 312 Oak Street, San Francisco, California",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,ca. 1876,1874,1878,Albumen silver print from glass negative,,"Gift of Carole and Irwin Lainoff, Ruth P. Lasser and Joseph R. Lasser, Mr. and Mrs. John T. Marvin, Martin E. and Joan Messinger, Richard L. Yett and Sheri and Paul Siegel, 1986",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264896,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1189.2,false,true,264909,Photographs,Photograph,San Francisco,,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,"1864, printed ca. 1876",1864,1864,Albumen silver print from glass negative,,"Gift of Carole and Irwin Lainoff, Ruth P. Lasser and Joseph R. Lasser, Mr. and Mrs. John T. Marvin, Martin E. and Joan Messinger, Richard L. Yett and Sheri and Paul Siegel, 1986",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264909,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1189.3,false,true,264920,Photographs,Photograph,"San Francisco, from California and Powell Street",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,"1864, printed ca. 1876",1864,1864,Albumen silver print from glass negative,,"Gift of Carole and Irwin Lainoff, Ruth P. Lasser and Joseph R. Lasser, Mr. and Mrs. John T. Marvin, Martin E. and Joan Messinger, Richard L. Yett and Sheri and Paul Siegel, 1986",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264920,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1189.4,false,true,264931,Photographs,Photograph,"San Francisco, from Rincon Hill",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,"1864, printed ca. 1876",1864,1864,Albumen silver print from glass negative,,"Gift of Carole and Irwin Lainoff, Ruth P. Lasser and Joseph R. Lasser, Mr. and Mrs. John T. Marvin, Martin E. and Joan Messinger, Richard L. Yett and Sheri and Paul Siegel, 1986",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264931,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1189.5,false,true,264942,Photographs,Photograph,"San Francisco, from California and Powell Streets",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,"1864, printed ca. 1876",1864,1864,Albumen silver print from glass negative,,"Gift of Carole and Irwin Lainoff, Ruth P. Lasser and Joseph R. Lasser, Mr. and Mrs. John T. Marvin, Martin E. and Joan Messinger, Richard L. Yett and Sheri and Paul Siegel, 1986",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264942,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1189.6,false,true,264953,Photographs,Photograph,South Side of California Street,,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,"1864, printed ca. 1876",1864,1864,Albumen silver print from glass negative,,"Gift of Carole and Irwin Lainoff, Ruth P. Lasser and Joseph R. Lasser, Mr. and Mrs. John T. Marvin, Martin E. and Joan Messinger, Richard L. Yett and Sheri and Paul Siegel, 1986",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264953,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1189.7,false,true,264964,Photographs,Photograph,North Side of California Street,,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,"1864, printed ca. 1876",1864,1864,Albumen silver print from glass negative,,"Gift of Carole and Irwin Lainoff, Ruth P. Lasser and Joseph R. Lasser, Mr. and Mrs. John T. Marvin, Martin E. and Joan Messinger, Richard L. Yett and Sheri and Paul Siegel, 1986",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264964,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1189.8,false,true,264975,Photographs,Photograph,"First Street, San Francisco",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,"1864, printed ca. 1876",1864,1864,Albumen silver print from glass negative,,"Gift of Carole and Irwin Lainoff, Ruth P. Lasser and Joseph R. Lasser, Mr. and Mrs. John T. Marvin, Martin E. and Joan Messinger, Richard L. Yett and Sheri and Paul Siegel, 1986",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264975,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1189.9,false,true,264986,Photographs,Photograph,The Golden Gate,,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,"1868–69, printed ca. 1876",1868,1869,Albumen silver print from glass negative,,"Gift of Carole and Irwin Lainoff, Ruth P. Lasser and Joseph R. Lasser, Mr. and Mrs. John T. Marvin, Martin E. and Joan Messinger, Richard L. Yett and Sheri and Paul Siegel, 1986",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264986,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1189.10,false,true,264897,Photographs,Photograph,"Alcatraz Island, San Francisco",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,"1868–69, printed ca. 1876",1868,1869,Albumen silver print from glass negative,,"Gift of Carole and Irwin Lainoff, Ruth P. Lasser and Joseph R. Lasser, Mr. and Mrs. John T. Marvin, Martin E. and Joan Messinger, Richard L. Yett and Sheri and Paul Siegel, 1986",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264897,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1189.11,false,true,264900,Photographs,Photograph,Cliff House and Seal Rock,,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,"1868–69, printed ca. 1876",1868,1869,Albumen silver print from glass negative,,"Gift of Carole and Irwin Lainoff, Ruth P. Lasser and Joseph R. Lasser, Mr. and Mrs. John T. Marvin, Martin E. and Joan Messinger, Richard L. Yett and Sheri and Paul Siegel, 1986",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264900,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1189.12,false,true,264901,Photographs,Photograph,Seal Rocks,,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,"1868–69, printed ca. 1876",1868,1869,Albumen silver print from glass negative,,"Gift of Carole and Irwin Lainoff, Ruth P. Lasser and Joseph R. Lasser, Mr. and Mrs. John T. Marvin, Martin E. and Joan Messinger, Richard L. Yett and Sheri and Paul Siegel, 1986",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264901,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1189.13,false,true,264902,Photographs,Photograph,"Oakland, from Military Academy",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,"1864, printed ca. 1876",1864,1864,Albumen silver print from glass negative,,"Gift of Carole and Irwin Lainoff, Ruth P. Lasser and Joseph R. Lasser, Mr. and Mrs. John T. Marvin, Martin E. and Joan Messinger, Richard L. Yett and Sheri and Paul Siegel, 1986",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264902,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1189.14,false,true,264903,Photographs,Photograph,General View of Yosemite,,,,,,Artist,Attributed to,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,"ca. 1872, printed ca. 1876",1870,1874,Albumen silver print from glass negative,,"Gift of Carole and Irwin Lainoff, Ruth P. Lasser and Joseph R. Lasser, Mr. and Mrs. John T. Marvin, Martin E. and Joan Messinger, Richard L. Yett and Sheri and Paul Siegel, 1986",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264903,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1189.15,false,true,264904,Photographs,Photograph,Yosemite Valley,,,,,,Artist,Attributed to,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,"ca. 1872, printed ca. 1876",1870,1874,Albumen silver print from glass negative,,"Gift of Carole and Irwin Lainoff, Ruth P. Lasser and Joseph R. Lasser, Mr. and Mrs. John T. Marvin, Martin E. and Joan Messinger, Richard L. Yett and Sheri and Paul Siegel, 1986",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264904,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1189.16,false,true,264905,Photographs,Photograph,Yosemite Valley from Union Point,,,,,,Artist,Attributed to,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,"ca. 1872, printed ca. 1876",1870,1874,Albumen silver print from glass negative,,"Gift of Carole and Irwin Lainoff, Ruth P. Lasser and Joseph R. Lasser, Mr. and Mrs. John T. Marvin, Martin E. and Joan Messinger, Richard L. Yett and Sheri and Paul Siegel, 1986",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264905,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1189.17,false,true,264906,Photographs,Photograph,Yosemite Valley from Glacier Point,,,,,,Artist,Attributed to,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,"ca. 1872, printed ca. 1876",1870,1874,Albumen silver print from glass negative,,"Gift of Carole and Irwin Lainoff, Ruth P. Lasser and Joseph R. Lasser, Mr. and Mrs. John T. Marvin, Martin E. and Joan Messinger, Richard L. Yett and Sheri and Paul Siegel, 1986",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264906,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1189.18,false,true,264907,Photographs,Photograph,Looking Up Yosemite Valley,,,,,,Artist,Attributed to,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,"ca. 1872, printed ca. 1876",1870,1874,Albumen silver print from glass negative,,"Gift of Carole and Irwin Lainoff, Ruth P. Lasser and Joseph R. Lasser, Mr. and Mrs. John T. Marvin, Martin E. and Joan Messinger, Richard L. Yett and Sheri and Paul Siegel, 1986",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264907,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1189.19,false,true,264908,Photographs,Photograph,Looking Down Yosemite Valley,,,,,,Artist,Attributed to,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,"ca. 1872, printed ca. 1876",1870,1874,Albumen silver print from glass negative,,"Gift of Carole and Irwin Lainoff, Ruth P. Lasser and Joseph R. Lasser, Mr. and Mrs. John T. Marvin, Martin E. and Joan Messinger, Richard L. Yett and Sheri and Paul Siegel, 1986",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264908,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1189.20,false,true,264910,Photographs,Photograph,"Yosemite Falls, 2,634 feet",,,,,,Artist,Attributed to,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,"ca. 1872, printed ca. 1876",1870,1874,Albumen silver print from glass negative,,"Gift of Carole and Irwin Lainoff, Ruth P. Lasser and Joseph R. Lasser, Mr. and Mrs. John T. Marvin, Martin E. and Joan Messinger, Richard L. Yett and Sheri and Paul Siegel, 1986",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264910,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1189.21,false,true,264911,Photographs,Photograph,"Yosemite Falls, 2,634 feet",,,,,,Artist,Attributed to,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,"ca. 1872, printed ca. 1876",1870,1874,Albumen silver print from glass negative,,"Gift of Carole and Irwin Lainoff, Ruth P. Lasser and Joseph R. Lasser, Mr. and Mrs. John T. Marvin, Martin E. and Joan Messinger, Richard L. Yett and Sheri and Paul Siegel, 1986",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264911,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1189.22,false,true,264912,Photographs,Photograph,"Cathedral Rocks, Yosemite",,,,,,Artist,Attributed to,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,"ca. 1872, printed ca. 1876",1870,1874,Albumen silver print from glass negative,,"Gift of Carole and Irwin Lainoff, Ruth P. Lasser and Joseph R. Lasser, Mr. and Mrs. John T. Marvin, Martin E. and Joan Messinger, Richard L. Yett and Sheri and Paul Siegel, 1986",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264912,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1189.23,false,true,264913,Photographs,Photograph,Cathedral Rocks and Spires,,,,,,Artist,Attributed to,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,"ca. 1872, printed ca. 1876",1870,1874,Albumen silver print from glass negative,,"Gift of Carole and Irwin Lainoff, Ruth P. Lasser and Joseph R. Lasser, Mr. and Mrs. John T. Marvin, Martin E. and Joan Messinger, Richard L. Yett and Sheri and Paul Siegel, 1986",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264913,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1189.24,false,true,264914,Photographs,Photograph,"Cathedral Rocks, Yosemite",,,,,,Artist,Attributed to,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,"ca. 1872, printed ca. 1876",1870,1874,Albumen silver print from glass negative,,"Gift of Carole and Irwin Lainoff, Ruth P. Lasser and Joseph R. Lasser, Mr. and Mrs. John T. Marvin, Martin E. and Joan Messinger, Richard L. Yett and Sheri and Paul Siegel, 1986",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264914,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1189.25,false,true,264915,Photographs,Photograph,"The Domes, Yosemite",,,,,,Artist,Attributed to,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,"ca. 1872, printed ca. 1876",1870,1874,Albumen silver print from glass negative,,"Gift of Carole and Irwin Lainoff, Ruth P. Lasser and Joseph R. Lasser, Mr. and Mrs. John T. Marvin, Martin E. and Joan Messinger, Richard L. Yett and Sheri and Paul Siegel, 1986",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264915,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1189.26,false,true,264916,Photographs,Photograph,"The Domes, Yosemite",,,,,,Artist,Attributed to,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,"ca. 1872, printed ca. 1876",1870,1874,Albumen silver print from glass negative,,"Gift of Carole and Irwin Lainoff, Ruth P. Lasser and Joseph R. Lasser, Mr. and Mrs. John T. Marvin, Martin E. and Joan Messinger, Richard L. Yett and Sheri and Paul Siegel, 1986",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264916,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1189.27,false,true,264917,Photographs,Photograph,"North and South Dome, Yosemite",,,,,,Artist,Attributed to,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,"ca. 1872, printed ca. 1876",1870,1874,Albumen silver print from glass negative,,"Gift of Carole and Irwin Lainoff, Ruth P. Lasser and Joseph R. Lasser, Mr. and Mrs. John T. Marvin, Martin E. and Joan Messinger, Richard L. Yett and Sheri and Paul Siegel, 1986",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264917,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1189.28,false,true,264918,Photographs,Photograph,"North and South Dome, Yosemite",,,,,,Artist,Attributed to,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,"ca. 1872, printed ca. 1876",1870,1874,Albumen silver print from glass negative,,"Gift of Carole and Irwin Lainoff, Ruth P. Lasser and Joseph R. Lasser, Mr. and Mrs. John T. Marvin, Martin E. and Joan Messinger, Richard L. Yett and Sheri and Paul Siegel, 1986",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264918,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1189.29,false,true,264919,Photographs,Photograph,"North Dome, 3,725 feet, Yosemite",,,,,,Artist,Attributed to,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,"ca. 1872, printed ca. 1876",1870,1874,Albumen silver print from glass negative,,"Gift of Carole and Irwin Lainoff, Ruth P. Lasser and Joseph R. Lasser, Mr. and Mrs. John T. Marvin, Martin E. and Joan Messinger, Richard L. Yett and Sheri and Paul Siegel, 1986",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264919,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1189.30,false,true,264921,Photographs,Photograph,"South Dome, Yosemite",,,,,,Artist,Attributed to,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,"ca. 1872, printed ca. 1876",1870,1874,Albumen silver print from glass negative,,"Gift of Carole and Irwin Lainoff, Ruth P. Lasser and Joseph R. Lasser, Mr. and Mrs. John T. Marvin, Martin E. and Joan Messinger, Richard L. Yett and Sheri and Paul Siegel, 1986",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264921,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1189.31,false,true,264922,Photographs,Photograph,"South Dome, 6,000 feet",,,,,,Artist,Attributed to,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,"ca. 1872, printed ca. 1876",1870,1874,Albumen silver print from glass negative,,"Gift of Carole and Irwin Lainoff, Ruth P. Lasser and Joseph R. Lasser, Mr. and Mrs. John T. Marvin, Martin E. and Joan Messinger, Richard L. Yett and Sheri and Paul Siegel, 1986",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264922,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1189.32,false,true,264923,Photographs,Photograph,"South Dome, 6,000 feet",,,,,,Artist,Attributed to,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,"ca. 1872, printed ca. 1876",1870,1874,Albumen silver print from glass negative,,"Gift of Carole and Irwin Lainoff, Ruth P. Lasser and Joseph R. Lasser, Mr. and Mrs. John T. Marvin, Martin E. and Joan Messinger, Richard L. Yett and Sheri and Paul Siegel, 1986",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264923,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1189.33,false,true,264924,Photographs,Photograph,"South Dome, 6,000 feet, Yosemite",,,,,,Artist,Attributed to,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,"ca. 1872, printed ca. 1876",1870,1874,Albumen silver print from glass negative,Image: 33.5 x 26.8 cm (13 3/16 x 10 9/16 in.),"Gift of Carole and Irwin Lainoff, Ruth P. Lasser and Joseph R. Lasser, Mr. and Mrs. John T. Marvin, Martin E. and Joan Messinger, Richard L. Yett and Sheri and Paul Siegel, 1986",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264924,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1189.34,false,true,264925,Photographs,Photograph,Merced River,,,,,,Artist,Attributed to,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,"ca. 1872, printed ca. 1876",1870,1874,Albumen silver print from glass negative,,"Gift of Carole and Irwin Lainoff, Ruth P. Lasser and Joseph R. Lasser, Mr. and Mrs. John T. Marvin, Martin E. and Joan Messinger, Richard L. Yett and Sheri and Paul Siegel, 1986",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264925,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1189.35,false,true,264926,Photographs,Photograph,"Merced River, Yosemite",,,,,,Artist,Attributed to,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,"ca. 1872, printed ca. 1876",1870,1874,Albumen silver print from glass negative,,"Gift of Carole and Irwin Lainoff, Ruth P. Lasser and Joseph R. Lasser, Mr. and Mrs. John T. Marvin, Martin E. and Joan Messinger, Richard L. Yett and Sheri and Paul Siegel, 1986",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264926,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1189.36,false,true,264927,Photographs,Photograph,"Merced River, Yosemite",,,,,,Artist,Attributed to,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,"ca. 1872, printed ca. 1876",1870,1874,Albumen silver print from glass negative,,"Gift of Carole and Irwin Lainoff, Ruth P. Lasser and Joseph R. Lasser, Mr. and Mrs. John T. Marvin, Martin E. and Joan Messinger, Richard L. Yett and Sheri and Paul Siegel, 1986",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264927,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1189.37,false,true,264928,Photographs,Photograph,"Mirror Lake, Yosemite",,,,,,Artist,Attributed to,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,"ca. 1872, printed ca. 1876",1870,1874,Albumen silver print from glass negative,,"Gift of Carole and Irwin Lainoff, Ruth P. Lasser and Joseph R. Lasser, Mr. and Mrs. John T. Marvin, Martin E. and Joan Messinger, Richard L. Yett and Sheri and Paul Siegel, 1986",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264928,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1189.38,false,true,264929,Photographs,Photograph,"Mirror View of the Three Brothers, Yosemite",,,,,,Artist,Attributed to,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,"ca. 1872, printed ca. 1876",1870,1874,Albumen silver print from glass negative,,"Gift of Carole and Irwin Lainoff, Ruth P. Lasser and Joseph R. Lasser, Mr. and Mrs. John T. Marvin, Martin E. and Joan Messinger, Richard L. Yett and Sheri and Paul Siegel, 1986",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264929,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1189.39,false,true,264930,Photographs,Photograph,"Mirror View of Cathedral Rocks, Yosemite",,,,,,Artist,Attributed to,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,"ca. 1872, printed ca. 1876",1870,1874,Albumen silver print from glass negative,,"Gift of Carole and Irwin Lainoff, Ruth P. Lasser and Joseph R. Lasser, Mr. and Mrs. John T. Marvin, Martin E. and Joan Messinger, Richard L. Yett and Sheri and Paul Siegel, 1986",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264930,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1189.40,false,true,264932,Photographs,Photograph,"Cap of Liberty and Nevada Fall, Yosemite",,,,,,Artist,Attributed to,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,"ca. 1872, printed ca. 1876",1870,1874,Albumen silver print from glass negative,,"Gift of Carole and Irwin Lainoff, Ruth P. Lasser and Joseph R. Lasser, Mr. and Mrs. John T. Marvin, Martin E. and Joan Messinger, Richard L. Yett and Sheri and Paul Siegel, 1986",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264932,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1189.41,false,true,264933,Photographs,Photograph,"Nevada Fall, 700 feet, Yosemite",,,,,,Artist,Attributed to,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,"ca. 1872, printed ca. 1876",1870,1874,Albumen silver print from glass negative,Image: 33.6 × 26.7 cm (13 1/4 × 10 1/2 in.) Sheet: 42.3 × 33 cm (16 5/8 × 13 in.),"Gift of Carole and Irwin Lainoff, Ruth P. Lasser and Joseph R. Lasser, Mr. and Mrs. John T. Marvin, Martin E. and Joan Messinger, Richard L. Yett and Sheri and Paul Siegel, 1986",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264933,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1189.42,false,true,264934,Photographs,Photograph,"Lower Yosemite Fall, 1,600 feet",,,,,,Artist,Attributed to,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,"ca. 1872, printed ca. 1876",1870,1874,Albumen silver print from glass negative,,"Gift of Carole and Irwin Lainoff, Ruth P. Lasser and Joseph R. Lasser, Mr. and Mrs. John T. Marvin, Martin E. and Joan Messinger, Richard L. Yett and Sheri and Paul Siegel, 1986",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264934,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1189.43,false,true,264935,Photographs,Photograph,"Bridal Veil Fall, 940 feet, Yosemite",,,,,,Artist,Attributed to,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,"ca. 1872, printed ca. 1876",1870,1874,Albumen silver print from glass negative,,"Gift of Carole and Irwin Lainoff, Ruth P. Lasser and Joseph R. Lasser, Mr. and Mrs. John T. Marvin, Martin E. and Joan Messinger, Richard L. Yett and Sheri and Paul Siegel, 1986",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264935,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1189.44,false,true,264936,Photographs,Photograph,"Washington Tower, Yosemite",,,,,,Artist,Attributed to,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,"ca. 1872, printed ca. 1876",1870,1874,Albumen silver print from glass negative,,"Gift of Carole and Irwin Lainoff, Ruth P. Lasser and Joseph R. Lasser, Mr. and Mrs. John T. Marvin, Martin E. and Joan Messinger, Richard L. Yett and Sheri and Paul Siegel, 1986",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264936,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1189.45,false,true,264937,Photographs,Photograph,"Washington Tower, Yosemite",,,,,,Artist,Attributed to,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,"ca. 1872, printed ca. 1876",1870,1874,Albumen silver print from glass negative,,"Gift of Carole and Irwin Lainoff, Ruth P. Lasser and Joseph R. Lasser, Mr. and Mrs. John T. Marvin, Martin E. and Joan Messinger, Richard L. Yett and Sheri and Paul Siegel, 1986",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264937,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1189.46,false,true,264938,Photographs,Photograph,"Eagle Point, 4,000 feet, Yosemite",,,,,,Artist,Attributed to,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,"ca. 1872, printed ca. 1876",1870,1874,Albumen silver print from glass negative,,"Gift of Carole and Irwin Lainoff, Ruth P. Lasser and Joseph R. Lasser, Mr. and Mrs. John T. Marvin, Martin E. and Joan Messinger, Richard L. Yett and Sheri and Paul Siegel, 1986",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264938,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1189.47,false,true,264939,Photographs,Photograph,"Vernal Falls, 350 feet, Yosemite",,,,,,Artist,Attributed to,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,"ca. 1872, printed ca. 1876",1870,1874,Albumen silver print from glass negative,,"Gift of Carole and Irwin Lainoff, Ruth P. Lasser and Joseph R. Lasser, Mr. and Mrs. John T. Marvin, Martin E. and Joan Messinger, Richard L. Yett and Sheri and Paul Siegel, 1986",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264939,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1189.48,false,true,264940,Photographs,Photograph,"Mirror View of El Capitan, Yosemite",,,,,,Artist,Attributed to,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,"ca. 1872, printed ca. 1876",1870,1874,Albumen silver print from glass negative,,"Gift of Carole and Irwin Lainoff, Ruth P. Lasser and Joseph R. Lasser, Mr. and Mrs. John T. Marvin, Martin E. and Joan Messinger, Richard L. Yett and Sheri and Paul Siegel, 1986",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264940,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1189.49,false,true,264941,Photographs,Photograph,"Mirror View of Sentinel Rock, Yosemite",,,,,,Artist,Attributed to,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,"ca. 1872, printed ca. 1876",1870,1874,Albumen silver print from glass negative,Image: 13 1/4 × 10 1/2 in. (33.7 × 26.6 cm) Sheet: 16 5/8 × 13 7/16 in. (42.2 × 34.2 cm),"Gift of Carole and Irwin Lainoff, Ruth P. Lasser and Joseph R. Lasser, Mr. and Mrs. John T. Marvin, Martin E. and Joan Messinger, Richard L. Yett and Sheri and Paul Siegel, 1986",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264941,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1189.50,false,true,264943,Photographs,Photograph,"Sentinel Rock, Yosemite",,,,,,Artist,Attributed to,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,"ca. 1872, printed ca. 1876",1870,1874,Albumen silver print from glass negative,,"Gift of Carole and Irwin Lainoff, Ruth P. Lasser and Joseph R. Lasser, Mr. and Mrs. John T. Marvin, Martin E. and Joan Messinger, Richard L. Yett and Sheri and Paul Siegel, 1986",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264943,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1189.51,false,true,264944,Photographs,Photograph,"Sentinel Rock, Yosemite",,,,,,Artist,Attributed to,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,"ca. 1872, printed ca. 1876",1870,1874,Albumen silver print from glass negative,,"Gift of Carole and Irwin Lainoff, Ruth P. Lasser and Joseph R. Lasser, Mr. and Mrs. John T. Marvin, Martin E. and Joan Messinger, Richard L. Yett and Sheri and Paul Siegel, 1986",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264944,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1189.52,false,true,264945,Photographs,Photograph,"Magic Tower, Yosemite",,,,,,Artist,Attributed to,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,"ca. 1872, printed ca. 1876",1870,1874,Albumen silver print from glass negative,,"Gift of Carole and Irwin Lainoff, Ruth P. Lasser and Joseph R. Lasser, Mr. and Mrs. John T. Marvin, Martin E. and Joan Messinger, Richard L. Yett and Sheri and Paul Siegel, 1986",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264945,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1189.53,false,true,264946,Photographs,Photograph,"Eagle Point, Yosemite",,,,,,Artist,Attributed to,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,"ca. 1872, printed ca. 1876",1870,1874,Albumen silver print from glass negative,,"Gift of Carole and Irwin Lainoff, Ruth P. Lasser and Joseph R. Lasser, Mr. and Mrs. John T. Marvin, Martin E. and Joan Messinger, Richard L. Yett and Sheri and Paul Siegel, 1986",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264946,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1189.54,false,true,264947,Photographs,Photograph,"The Three Brothers, Yosemite",,,,,,Artist,Attributed to,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,"ca. 1872, printed ca. 1876",1870,1874,Albumen silver print from glass negative,,"Gift of Carole and Irwin Lainoff, Ruth P. Lasser and Joseph R. Lasser, Mr. and Mrs. John T. Marvin, Martin E. and Joan Messinger, Richard L. Yett and Sheri and Paul Siegel, 1986",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264947,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1189.55,false,true,264948,Photographs,Photograph,"The Three Brothers, Yosemite",,,,,,Artist,Attributed to,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,"ca. 1872, printed ca. 1876",1870,1874,Albumen silver print from glass negative,,"Gift of Carole and Irwin Lainoff, Ruth P. Lasser and Joseph R. Lasser, Mr. and Mrs. John T. Marvin, Martin E. and Joan Messinger, Richard L. Yett and Sheri and Paul Siegel, 1986",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264948,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1189.56,false,true,264949,Photographs,Photograph,The Hotel from Mammoth Grove,,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,"ca. 1872, printed ca. 1876",1870,1874,Albumen silver print from glass negative,,"Gift of Carole and Irwin Lainoff, Ruth P. Lasser and Joseph R. Lasser, Mr. and Mrs. John T. Marvin, Martin E. and Joan Messinger, Richard L. Yett and Sheri and Paul Siegel, 1986",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264949,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1189.57,false,true,264950,Photographs,Photograph,Mammoth Grove Hotel,,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,"ca. 1872, printed ca. 1876",1870,1874,Albumen silver print from glass negative,Image: 25.9 x 32.4 cm (10 3/16 x 12 3/4 in.),"Gift of Carole and Irwin Lainoff, Ruth P. Lasser and Joseph R. Lasser, Mr. and Mrs. John T. Marvin, Martin E. and Joan Messinger, Richard L. Yett and Sheri and Paul Siegel, 1986",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264950,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1189.58,false,true,264951,Photographs,Photograph,"The Tripod, 94 feet circumference",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,"1865–66, printed ca. 1876",1865,1866,Albumen silver print from glass negative,,"Gift of Carole and Irwin Lainoff, Ruth P. Lasser and Joseph R. Lasser, Mr. and Mrs. John T. Marvin, Martin E. and Joan Messinger, Richard L. Yett and Sheri and Paul Siegel, 1986",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264951,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1189.59,false,true,264952,Photographs,Photograph,Cosmopolitan Saloon,,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,"ca. 1872, printed ca. 1876",1870,1874,Albumen silver print from glass negative,,"Gift of Carole and Irwin Lainoff, Ruth P. Lasser and Joseph R. Lasser, Mr. and Mrs. John T. Marvin, Martin E. and Joan Messinger, Richard L. Yett and Sheri and Paul Siegel, 1986",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264952,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1189.60,false,true,264954,Photographs,Photograph,The House over a Stump of a Big Tree,,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,"1865–66, printed ca. 1876",1865,1866,Albumen silver print from glass negative,,"Gift of Carole and Irwin Lainoff, Ruth P. Lasser and Joseph R. Lasser, Mr. and Mrs. John T. Marvin, Martin E. and Joan Messinger, Richard L. Yett and Sheri and Paul Siegel, 1986",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264954,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1189.61,false,true,264955,Photographs,Photograph,The House Built over the Stump of a Big Tree,,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,"1865–66, printed ca. 1876",1865,1866,Albumen silver print from glass negative,,"Gift of Carole and Irwin Lainoff, Ruth P. Lasser and Joseph R. Lasser, Mr. and Mrs. John T. Marvin, Martin E. and Joan Messinger, Richard L. Yett and Sheri and Paul Siegel, 1986",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264955,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1189.62,false,true,264956,Photographs,Photograph,"The Sentinels, 315 feet, Yosemite",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,"ca. 1872, printed ca. 1876",1870,1874,Albumen silver print from glass negative,,"Gift of Carole and Irwin Lainoff, Ruth P. Lasser and Joseph R. Lasser, Mr. and Mrs. John T. Marvin, Martin E. and Joan Messinger, Richard L. Yett and Sheri and Paul Siegel, 1986",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264956,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1189.63,false,true,264957,Photographs,Photograph,"The Sentinel, 315 feet",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,"1865–66, printed ca. 1876",1865,1866,Albumen silver print from glass negative,,"Gift of Carole and Irwin Lainoff, Ruth P. Lasser and Joseph R. Lasser, Mr. and Mrs. John T. Marvin, Martin E. and Joan Messinger, Richard L. Yett and Sheri and Paul Siegel, 1986",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264957,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1189.64,false,true,264958,Photographs,Photograph,Father of the Forest,,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,"1865–66, printed ca. 1876",1865,1866,Albumen silver print from glass negative,,"Gift of Carole and Irwin Lainoff, Ruth P. Lasser and Joseph R. Lasser, Mr. and Mrs. John T. Marvin, Martin E. and Joan Messinger, Richard L. Yett and Sheri and Paul Siegel, 1986",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264958,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1189.65,false,true,264959,Photographs,Photograph,"The Father of the Forest, 112 feet circumference, Calaveras Grove",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,"1865–66, printed ca. 1876",1865,1866,Albumen silver print from glass negative,,"Gift of Carole and Irwin Lainoff, Ruth P. Lasser and Joseph R. Lasser, Mr. and Mrs. John T. Marvin, Martin E. and Joan Messinger, Richard L. Yett and Sheri and Paul Siegel, 1986",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264959,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1189.66,false,true,264960,Photographs,Photograph,Mother of the Forest,,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,"1865–66, printed ca. 1876",1865,1866,Albumen silver print from glass negative,,"Gift of Carole and Irwin Lainoff, Ruth P. Lasser and Joseph R. Lasser, Mr. and Mrs. John T. Marvin, Martin E. and Joan Messinger, Richard L. Yett and Sheri and Paul Siegel, 1986",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264960,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1189.67,false,true,264961,Photographs,Photograph,"Section of the Grizzly Giant, 101 feet circumference",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,"1865–66, printed ca. 1876",1865,1866,Albumen silver print from glass negative,Image: 33.5 x 26.6 cm (13 3/16 x 10 1/2 in.),"Gift of Carole and Irwin Lainoff, Ruth P. Lasser and Joseph R. Lasser, Mr. and Mrs. John T. Marvin, Martin E. and Joan Messinger, Richard L. Yett and Sheri and Paul Siegel, 1986",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264961,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1189.68,false,true,264962,Photographs,Photograph,Pioneer's Cabin,,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,"1865–66, printed ca. 1876",1865,1866,Albumen silver print from glass negative,,"Gift of Carole and Irwin Lainoff, Ruth P. Lasser and Joseph R. Lasser, Mr. and Mrs. John T. Marvin, Martin E. and Joan Messinger, Richard L. Yett and Sheri and Paul Siegel, 1986",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264962,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1189.69,false,true,264963,Photographs,Photograph,"The Three Graces, 272 feet",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,"1865–66, printed ca. 1876",1865,1866,Albumen silver print from glass negative,,"Gift of Carole and Irwin Lainoff, Ruth P. Lasser and Joseph R. Lasser, Mr. and Mrs. John T. Marvin, Martin E. and Joan Messinger, Richard L. Yett and Sheri and Paul Siegel, 1986",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264963,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1189.70,false,true,264965,Photographs,Photograph,Looking Up Pluto's Chimney,,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,"1865–66, printed ca. 1876",1865,1866,Albumen silver print from glass negative,,"Gift of Carole and Irwin Lainoff, Ruth P. Lasser and Joseph R. Lasser, Mr. and Mrs. John T. Marvin, Martin E. and Joan Messinger, Richard L. Yett and Sheri and Paul Siegel, 1986",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264965,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1189.71,false,true,264966,Photographs,Photograph,"Hutchings Hotel, Yosemite",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,"ca. 1872, printed ca. 1876",1870,1874,Albumen silver print from glass negative,,"Gift of Carole and Irwin Lainoff, Ruth P. Lasser and Joseph R. Lasser, Mr. and Mrs. John T. Marvin, Martin E. and Joan Messinger, Richard L. Yett and Sheri and Paul Siegel, 1986",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264966,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1189.72,false,true,264967,Photographs,Photograph,Geyser Road,,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,"1868–70, printed ca. 1876",1868,1870,Albumen silver print from glass negative,,"Gift of Carole and Irwin Lainoff, Ruth P. Lasser and Joseph R. Lasser, Mr. and Mrs. John T. Marvin, Martin E. and Joan Messinger, Richard L. Yett and Sheri and Paul Siegel, 1986",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264967,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1189.73,false,true,264968,Photographs,Photograph,Sulphur Creek and Flume-road to Geysers,,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,"1868–70, printed ca. 1876",1868,1870,Albumen silver print from glass negative,,"Gift of Carole and Irwin Lainoff, Ruth P. Lasser and Joseph R. Lasser, Mr. and Mrs. John T. Marvin, Martin E. and Joan Messinger, Richard L. Yett and Sheri and Paul Siegel, 1986",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264968,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1189.74,false,true,264969,Photographs,Photograph,Sulphur Creek and Road to Geysers,,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,"1868–70, printed ca. 1876",1868,1870,Albumen silver print from glass negative,,"Gift of Carole and Irwin Lainoff, Ruth P. Lasser and Joseph R. Lasser, Mr. and Mrs. John T. Marvin, Martin E. and Joan Messinger, Richard L. Yett and Sheri and Paul Siegel, 1986",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264969,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1189.75,false,true,264970,Photographs,Photograph,"Hot Sulphur Springs, Santa Barbara",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,"1876, printed ca. 1876",1876,1876,Albumen silver print from glass negative,,"Gift of Carole and Irwin Lainoff, Ruth P. Lasser and Joseph R. Lasser, Mr. and Mrs. John T. Marvin, Martin E. and Joan Messinger, Richard L. Yett and Sheri and Paul Siegel, 1986",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264970,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1189.76,false,true,264971,Photographs,Photograph,"Hot Sulphur Springs, Santa Barbara",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,"1876, printed ca. 1876",1876,1876,Albumen silver print from glass negative,,"Gift of Carole and Irwin Lainoff, Ruth P. Lasser and Joseph R. Lasser, Mr. and Mrs. John T. Marvin, Martin E. and Joan Messinger, Richard L. Yett and Sheri and Paul Siegel, 1986",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264971,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1189.77,false,true,264972,Photographs,Photograph,Santa Barbara and Mission Church,,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,"1876, printed ca. 1876",1876,1876,Albumen silver print from glass negative,,"Gift of Carole and Irwin Lainoff, Ruth P. Lasser and Joseph R. Lasser, Mr. and Mrs. John T. Marvin, Martin E. and Joan Messinger, Richard L. Yett and Sheri and Paul Siegel, 1986",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264972,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1189.78,false,true,264973,Photographs,Photograph,"Old Mission Church, Santa Barbara",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,"1876, printed ca. 1876",1876,1876,Albumen silver print from glass negative,,"Gift of Carole and Irwin Lainoff, Ruth P. Lasser and Joseph R. Lasser, Mr. and Mrs. John T. Marvin, Martin E. and Joan Messinger, Richard L. Yett and Sheri and Paul Siegel, 1986",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264973,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1189.79,false,true,264974,Photographs,Photograph,San Luis Obispo,,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,"1876, printed ca. 1876",1876,1876,Albumen silver print from glass negative,,"Gift of Carole and Irwin Lainoff, Ruth P. Lasser and Joseph R. Lasser, Mr. and Mrs. John T. Marvin, Martin E. and Joan Messinger, Richard L. Yett and Sheri and Paul Siegel, 1986",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264974,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1189.80,false,true,264976,Photographs,Photograph,"Steel's Ranch, San Luis Obispo",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,"1876, printed ca. 1876",1876,1876,Albumen silver print from glass negative,,"Gift of Carole and Irwin Lainoff, Ruth P. Lasser and Joseph R. Lasser, Mr. and Mrs. John T. Marvin, Martin E. and Joan Messinger, Richard L. Yett and Sheri and Paul Siegel, 1986",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264976,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1189.81,false,true,264977,Photographs,Photograph,San Diego,,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,"1876, printed ca. 1876",1876,1876,Albumen silver print from glass negative,,"Gift of Carole and Irwin Lainoff, Ruth P. Lasser and Joseph R. Lasser, Mr. and Mrs. John T. Marvin, Martin E. and Joan Messinger, Richard L. Yett and Sheri and Paul Siegel, 1986",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264977,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1189.82,false,true,264978,Photographs,Photograph,Los Angeles,,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,"1876, printed ca. 1876",1876,1876,Albumen silver print from glass negative,,"Gift of Carole and Irwin Lainoff, Ruth P. Lasser and Joseph R. Lasser, Mr. and Mrs. John T. Marvin, Martin E. and Joan Messinger, Richard L. Yett and Sheri and Paul Siegel, 1986",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264978,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1189.83,false,true,264979,Photographs,Photograph,"Lake Vineyard and Orange Grove, Los Angeles",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,"1876, printed ca. 1876",1876,1876,Albumen silver print from glass negative,,"Gift of Carole and Irwin Lainoff, Ruth P. Lasser and Joseph R. Lasser, Mr. and Mrs. John T. Marvin, Martin E. and Joan Messinger, Richard L. Yett and Sheri and Paul Siegel, 1986",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264979,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1189.84,false,true,264980,Photographs,Photograph,Vineyard of Camulos Ranch,,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,"1876, printed ca. 1876",1876,1876,Albumen silver print from glass negative,,"Gift of Carole and Irwin Lainoff, Ruth P. Lasser and Joseph R. Lasser, Mr. and Mrs. John T. Marvin, Martin E. and Joan Messinger, Richard L. Yett and Sheri and Paul Siegel, 1986",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264980,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1189.85,false,true,264981,Photographs,Photograph,Santa Margarita Ranch,,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,"1876, printed ca. 1876",1876,1876,Albumen silver print from glass negative,,"Gift of Carole and Irwin Lainoff, Ruth P. Lasser and Joseph R. Lasser, Mr. and Mrs. John T. Marvin, Martin E. and Joan Messinger, Richard L. Yett and Sheri and Paul Siegel, 1986",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264981,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1189.86,false,true,264982,Photographs,Photograph,Virginia City,,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,"1875, printed ca. 1876",1875,1875,Albumen silver print from glass negative,,"Gift of Carole and Irwin Lainoff, Ruth P. Lasser and Joseph R. Lasser, Mr. and Mrs. John T. Marvin, Martin E. and Joan Messinger, Richard L. Yett and Sheri and Paul Siegel, 1986",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264982,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1189.87,false,true,264983,Photographs,Photograph,Virginia City,,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,"1875, printed ca. 1876",1875,1875,Albumen silver print from glass negative,,"Gift of Carole and Irwin Lainoff, Ruth P. Lasser and Joseph R. Lasser, Mr. and Mrs. John T. Marvin, Martin E. and Joan Messinger, Richard L. Yett and Sheri and Paul Siegel, 1986",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264983,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1189.88,false,true,264984,Photographs,Photograph,Sutro Tunnel's Road to Virginia City,,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,"1875, printed ca. 1876",1875,1875,Albumen silver print from glass negative,,"Gift of Carole and Irwin Lainoff, Ruth P. Lasser and Joseph R. Lasser, Mr. and Mrs. John T. Marvin, Martin E. and Joan Messinger, Richard L. Yett and Sheri and Paul Siegel, 1986",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264984,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1189.89,false,true,264985,Photographs,Photograph,Road View to Sutro Tunnel,,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,"1875, printed ca. 1876",1875,1875,Albumen silver print from glass negative,,"Gift of Carole and Irwin Lainoff, Ruth P. Lasser and Joseph R. Lasser, Mr. and Mrs. John T. Marvin, Martin E. and Joan Messinger, Richard L. Yett and Sheri and Paul Siegel, 1986",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264985,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1189.90,false,true,264987,Photographs,Photograph,Buildings of Sutro Tunnel,,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,"1875, printed ca. 1876",1875,1875,Albumen silver print from glass negative,,"Gift of Carole and Irwin Lainoff, Ruth P. Lasser and Joseph R. Lasser, Mr. and Mrs. John T. Marvin, Martin E. and Joan Messinger, Richard L. Yett and Sheri and Paul Siegel, 1986",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264987,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1189.91,false,true,264988,Photographs,Photograph,Sutro Tunnel Shaft No. 2,,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,"1875, printed ca. 1876",1875,1875,Albumen silver print from glass negative,,"Gift of Carole and Irwin Lainoff, Ruth P. Lasser and Joseph R. Lasser, Mr. and Mrs. John T. Marvin, Martin E. and Joan Messinger, Richard L. Yett and Sheri and Paul Siegel, 1986",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264988,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1189.92,false,true,264989,Photographs,Photograph,Sutro Tunnel Shaft No. 2,,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,"1875, printed ca. 1876",1875,1875,Albumen silver print from glass negative,,"Gift of Carole and Irwin Lainoff, Ruth P. Lasser and Joseph R. Lasser, Mr. and Mrs. John T. Marvin, Martin E. and Joan Messinger, Richard L. Yett and Sheri and Paul Siegel, 1986",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264989,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1189.93,false,true,264990,Photographs,Photograph,Sutro Tunnel Shaft No. 3,,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,"1875, printed ca. 1876",1875,1875,Albumen silver print from glass negative,,"Gift of Carole and Irwin Lainoff, Ruth P. Lasser and Joseph R. Lasser, Mr. and Mrs. John T. Marvin, Martin E. and Joan Messinger, Richard L. Yett and Sheri and Paul Siegel, 1986",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264990,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1189.94,false,true,264991,Photographs,Photograph,"Eureka Quartz Mill and Flume, Nevada",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,"1875, printed ca. 1876",1875,1875,Albumen silver print from glass negative,,"Gift of Carole and Irwin Lainoff, Ruth P. Lasser and Joseph R. Lasser, Mr. and Mrs. John T. Marvin, Martin E. and Joan Messinger, Richard L. Yett and Sheri and Paul Siegel, 1986",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264991,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1189.95,false,true,264992,Photographs,Photograph,"Eureka Quartz Mill and Flume, Nevada",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,"1875, printed ca. 1876",1875,1875,Albumen silver print from glass negative,,"Gift of Carole and Irwin Lainoff, Ruth P. Lasser and Joseph R. Lasser, Mr. and Mrs. John T. Marvin, Martin E. and Joan Messinger, Richard L. Yett and Sheri and Paul Siegel, 1986",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264992,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1189.96,false,true,264993,Photographs,Photograph,"Passage of the Dalles, Oregon",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,"1867, printed ca. 1876",1867,1867,Albumen silver print from glass negative,,"Gift of Carole and Irwin Lainoff, Ruth P. Lasser and Joseph R. Lasser, Mr. and Mrs. John T. Marvin, Martin E. and Joan Messinger, Richard L. Yett and Sheri and Paul Siegel, 1986",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264993,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1189.97,false,true,264994,Photographs,Photograph,"Cascades, Oregon",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,"1867, printed ca. 1876",1867,1867,Albumen silver print from glass negative,,"Gift of Carole and Irwin Lainoff, Ruth P. Lasser and Joseph R. Lasser, Mr. and Mrs. John T. Marvin, Martin E. and Joan Messinger, Richard L. Yett and Sheri and Paul Siegel, 1986",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264994,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1189.98,false,true,264995,Photographs,Photograph,"Cape Horn, Oregon",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,"1867, printed ca. 1876",1867,1867,Albumen silver print from glass negative,,"Gift of Carole and Irwin Lainoff, Ruth P. Lasser and Joseph R. Lasser, Mr. and Mrs. John T. Marvin, Martin E. and Joan Messinger, Richard L. Yett and Sheri and Paul Siegel, 1986",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264995,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1189.99,false,true,264996,Photographs,Photograph,"Islands in the Upper Cascades, Oregon",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,"1867, printed ca. 1876",1867,1867,Albumen silver print from glass negative,,"Gift of Carole and Irwin Lainoff, Ruth P. Lasser and Joseph R. Lasser, Mr. and Mrs. John T. Marvin, Martin E. and Joan Messinger, Richard L. Yett and Sheri and Paul Siegel, 1986",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264996,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.107,false,true,283220,Photographs,Photograph,"Sugar Loaf Islands, Farallons",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,1868–69,1868,1869,Albumen silver print from glass negative,Image: 40 x 52.4; Mount: 55.3 x 66.3,"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283220,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.108,false,true,283221,Photographs,Photograph,"Multnomah Falls Cascade, Columbia River",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,1867,1867,1867,Albumen silver print from glass negative,Image: 52.4 x 40 cm (20 5/8 x 15 3/4 in.) Mount: 64.7 x 49.5 cm (25 1/2 x 19 1/2 in.),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283221,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.109,true,true,283222,Photographs,Photograph,Cape Horn near Celilo,,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,1867,1867,1867,Albumen silver print from glass negative,Image: 40 x 52.4cm (15 3/4 x 20 5/8in.) Mount: 54.5 x 68.6 cm (21 7/16 x 27 in.),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283222,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.110,false,true,283223,Photographs,Photograph,"Strait of Carquennes, from South Vallejo",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,1868–69,1868,1869,Albumen silver print from glass negative,Image: 40.3 x 52.5 cm (15 7/8 x 20 11/16 in.) Mount: 54.4 x 66.4 cm (21 7/16 x 26 1/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283223,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.174,false,true,285468,Photographs,Photograph,"Multnomah Falls, Oregon",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,"1867, printed later",1867,1867,Albumen silver print from glass negative,Image: 52.1 x 38.7 cm (20 1/2 x 15 1/4 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285468,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.339,false,true,285987,Photographs,Photograph,"Indian Sweat House, Mendicino County, Colorado",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,1863,1863,1863,Albumen silver print from glass negative,Image: 40 x 52.4 cm (15 3/4 x 20 5/8 in.) Mount: 55.1 x 68.3 cm (21 11/16 x 26 7/8 in.),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285987,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.493,false,true,286513,Photographs,Photograph,"Cape Horn, Columbia River, Oregon",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,1867,1867,1867,Albumen silver print from glass negative,Image: 52.1 x 39 cm (20 1/2 x 15 3/8 in.) Frame: 76.2 x 63.5 cm (30 x 25 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286513,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.618,false,true,283219,Photographs,Photograph,"The Grisly Giant, Mariposa Grove, Yosemite",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,1861,1861,1861,Albumen silver print from glass negative,Image: 52.3 x 40.7; Mount: 61.4 x 54.1,"Gilman Collection, Purchase, Gift of The Howard Gilman Foundation, by exchange, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283219,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1189.100,false,true,264898,Photographs,Photograph,"Tooth Bridge, Oregon",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,"1867, printed ca. 1876",1867,1867,Albumen silver print from glass negative,,"Gift of Carole and Irwin Lainoff, Ruth P. Lasser and Joseph R. Lasser, Mr. and Mrs. John T. Marvin, Martin E. and Joan Messinger, Richard L. Yett and Sheri and Paul Siegel, 1986",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264898,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1189.101,false,true,264899,Photographs,Photograph,"Castle Rock, Oregon",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,"1867, printed ca. 1876",1867,1867,Albumen silver print from glass negative,,"Gift of Carole and Irwin Lainoff, Ruth P. Lasser and Joseph R. Lasser, Mr. and Mrs. John T. Marvin, Martin E. and Joan Messinger, Richard L. Yett and Sheri and Paul Siegel, 1986",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264899,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1183,false,true,285825,Photographs,Photograph,"2637 Ft. Yosemite Fall, Front View",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,1861,1861,1861,Albumen silver print from glass negative,"Image: 40.9 x 51.3 cm (16 1/8 x 20 3/16 in.), arch-topped Mount: 53.6 x 67 cm (21 1/8 x 26 3/8 in.)","Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285825,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1184,false,true,285714,Photographs,Photograph,Mt. Broderick and Nevada Fall. Fall = 700 ft.,,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,"1861, Yosemite",1861,1861,Albumen silver print from glass negative,Image: 41.8 × 52.1 cm (16 7/16 × 20 1/2 in.) Mount: 53.9 × 66.9 cm (21 1/4 × 26 5/16 in.),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285714,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1185,false,true,285715,Photographs,Photograph,North Dome on left - Royal Arches - Washington Column,,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,"1861, Yosemite",1861,1861,Albumen silver print from glass negative,Image: 42.5 × 51.5 cm (16 3/4 × 20 1/4 in.) Mount: 53.8 × 67.3 cm (21 3/16 × 26 1/2 in.),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285715,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1186,false,true,286077,Photographs,Photograph,Yosemite Fall. Down the Valley. 2637 Ft.,,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,"1861, Yosemite",1861,1861,Albumen silver print from glass negative,Image: 44 × 50.9 cm (17 5/16 × 20 1/16 in.) Mount: 53.5 × 67 cm (21 1/16 × 26 3/8 in.),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286077,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1187,false,true,285860,Photographs,Photograph,"Cascade, Nevada Fall on Left, View above Vernal Fall",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,1861,1861,1861,Albumen silver print from glass negative,"Image: 41.2 x 52.4 cm (16 1/4 x 20 5/8 in.), arch-topped Mount: 53.5 x 66.7 cm (21 1/16 x 26 1/4 in.)","Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285860,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1188,false,true,286423,Photographs,Photograph,Sentinel. Front View: 3270,,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,"1861, Yosemite",1861,1861,Albumen silver print from glass negative,Image: 43.1 × 51.6 cm (16 15/16 × 20 5/16 in.) Mount: 53.9 × 67 cm (21 1/4 × 26 3/8 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286423,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1189,false,true,286425,Photographs,Photograph,Cathedral Rock,,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,1861,1861,1861,Albumen silver print from glass negative,"Image: 42.1 x 50.8 cm (16 9/16 x 20 in.), arch-topped Mount: 53.7 x 66.7 cm (21 1/8 x 26 1/4 in.)","Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286425,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1190,false,true,286427,Photographs,Photograph,"Mt. Broderick in Distant Centre, Piroyac, Falling Chrystals, Vernal Fall",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,1861,1861,1861,Albumen silver print from glass negative,"Image: 42.4 x 51.5 cm (16 11/16 x 20 1/4 in.), arch-topped Mount: 53.4 x 67 cm (21 x 26 3/8 in.)","Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286427,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1256,false,true,286052,Photographs,Photograph,"Pohono, Bridal Veil, 900 Feet, Yosemite",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,1861,1861,1861,Albumen silver print from glass negative,Image: 16 1/2 × 20 9/16 in. (41.9 × 52.3 cm) Mount: 21 5/16 in. × 26 7/16 in. (54.1 × 67.2 cm),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286052,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1257,false,true,285824,Photographs,Photograph,"Up the Valley, North Dome in Center, Sentinel on Left",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,1861,1861,1861,Albumen silver print from glass negative,Image: 16 1/2 × 20 9/16 in. (41.9 × 52.3 cm) Mount: 21 5/16 in. × 26 7/16 in. (54.1 × 67.2 cm),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285824,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1258,false,true,285994,Photographs,Photograph,"River View, Down the Valley, Cathedral Rock on Left",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,"1861, Yosemite",1861,1861,Albumen silver print from glass negative,Image: 15 9/16 × 20 9/16 in. (39.5 × 52.2 cm) Mount: 21 1/16 × 26 1/2 in. (53.5 × 67.3 cm),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285994,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1259,false,true,285713,Photographs,Photograph,"Camp Grove, Near Sentinel",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,"1861, Yosemite",1861,1861,Albumen silver print from glass negative,Image: 15 1/2 × 19 5/8 in. (39.3 × 49.9 cm) Mount: 21 1/4 in. × 26 3/8 in. (54 × 67 cm),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285713,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1260,false,true,286458,Photographs,Photograph,"Tasayac, or the Half Dome, 4967 Feet",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,"1861, Yosemite",1861,1861,Albumen silver print from glass negative,Image: 15 13/16 × 20 1/4 in. (40.1 × 51.5 cm) Image: 21 1/4 × 26 3/8 in. (54 × 67 cm),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286458,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1261,false,true,286459,Photographs,Photograph,"Nevada Fall, 700 Feet",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,1861,1861,1861,Albumen silver print from glass negative,Image: 42.2 x 51; Mount: 53.6 x 67,"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286459,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1262,false,true,286460,Photographs,Photograph,"Tutucanula, El Capitan",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,1861,1861,1861,Albumen silver print from glass negative,Image: 51.3 x 41.4; Mount: 66.5 x 53.6,"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286460,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1263,false,true,286072,Photographs,Photograph,"River View, Sentinel, 3270 Feet",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,1861,1861,1861,Albumen silver print from glass negative,Image: 52.1 x 41.6; Mount: 60.8 x 53.6,"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286072,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1264,false,true,286073,Photographs,Photograph,"Three Brothers, Front View, 4480 Feet",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,1861,1861,1861,Albumen silver print from glass negative,Image: 20 3/8 × 16 1/8 in. (51.7 × 40.9 cm) Mount: 26 5/16 in. × 21 1/8 in. (66.9 × 53.6 cm),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286073,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1265,false,true,286074,Photographs,Photograph,"Outline View of the Half Dome, 4967 Feet",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,1861,1861,1861,Albumen silver print from glass negative,Image: 20 1/16 × 16 5/16 in. (51 × 41.5 cm) Mount: 21 1/4 × 26 3/8 in. (54 × 67 cm),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286074,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1266,false,true,286075,Photographs,Photograph,"River View, Sentinel, 3270 Feet",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,1861,1861,1861,Albumen silver print from glass negative,Image: 20 1/4 × 16 7/16 in. (51.5 × 41.7 cm) Mount: 21 1/4 × 26 3/8 in. (54 × 67 cm),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286075,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1267,false,true,286076,Photographs,Photograph,Cathedral Towers,,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,1861,1861,1861,Albumen silver print from glass negative,Image: 20 3/8 × 16 1/8 in. (51.7 × 40.9 cm) Mount: 26 5/16 in. × 21 1/8 in. (66.9 × 53.6 cm),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286076,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1268,false,true,286078,Photographs,Photograph,The Lake at the Foot of Half Dome,,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,1861,1861,1861,Albumen silver print from glass negative,Image: 16 1/2 × 20 9/16 in. (41.9 × 52.3 cm) Mount: 21 5/16 in. × 26 7/16 in. (54.1 × 67.2 cm),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286078,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1269,false,true,285858,Photographs,Photograph,"Cathedral Rock, Down the Valley",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,1861,1861,1861,Albumen silver print from glass negative,Image: 16 1/2 × 20 9/16 in. (41.9 × 52.3 cm) Mount: 21 5/16 in. × 26 7/16 in. (54.1 × 67.2 cm),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285858,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1270,false,true,285859,Photographs,Photograph,"Section of Grisly Giant, Mariposa Grove",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,1861,1861,1861,Albumen silver print from glass negative,Image: 16 5/16 × 20 11/16 in. (41.5 × 52.5 cm) Mount: 21 1/4 × 26 3/8 in. (54 × 67 cm),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285859,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1271,false,true,285861,Photographs,Photograph,"River View Down Valley, Cathedral Rock on Left",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,1861,1861,1861,Albumen silver print from glass negative,Image: 15 9/16 × 20 9/16 in. (39.5 × 52.2 cm) Mount: 21 1/16 × 26 1/2 in. (53.5 × 67.3 cm),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285861,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1272,false,true,286510,Photographs,Photograph,"Tacoye, The North Dome, 3729 Feet",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,1861,1861,1861,Albumen silver print from glass negative,Image: 19 15/16 × 16 5/16 in. (50.6 × 41.4 cm) Mount: 21 1/4 × 26 3/8 in. (54 × 67 cm),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286510,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1273,false,true,286424,Photographs,Photograph,"Cathedral Rock, River View",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,1861,1861,1861,Albumen silver print from glass negative,Image: 41.5 x 59.6; Mount: 53.4 x 67,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286424,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1274,false,true,286426,Photographs,Photograph,"Yosemite Falls, River View, 2637 Feet",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,1861,1861,1861,Albumen silver print from glass negative,Image: 16 1/2 × 19 1/2 in. (41.9 × 49.5 cm) Mount: 21 1/4 × 26 3/8 in. (54 × 67 cm),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286426,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1275,false,true,286428,Photographs,Photograph,"Pompomasos (Leaping Frogs), Three Brothers",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,1861,1861,1861,Albumen silver print from glass negative,Image: 42.4 x 52.2; Mount: 53.7 x 69.3,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286428,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1276,false,true,286457,Photographs,Photograph,"Tutucanula, El Capitan, 4000 Feet",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,1861,1861,1861,Albumen silver print from glass negative,Image: 16 1/2 × 19 1/2 in. (41.9 × 49.5 cm) Mount: 21 1/4 × 26 3/8 in. (54 × 67 cm),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286457,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.1084.1–.3,false,true,689997,Photographs,Panorama,"View from the Sentinel Dome, Yosemite",,,,,,Artist,,Carleton E. Watkins,"American, 1829–1916",,"Watkins, Carleton E.",American,1829,1916,1865–66,1865,1866,Albumen silver prints from glass negatives,Image 1: 40.3 × 52.1 cm (15 7/8 × 20 1/2 in.) Mount 1: 53.4 x 68.7cm (21 x 27 1/16in.) Image 2: 40.0 x 52.5 cm (15 3/4 x 20 11/16 in.) Mount 2: 53.3 x 68.8cm (21 x 27 1/16in.) Image 3: 40.8 x 52.5cm (16 1/16 x 20 11/16in.) Mount 3: 53.2 x 68.7cm (20 15/16 x 27 1/16in.),"Purchase, Joseph Pulitzer Bequest, 1989",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/689997,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1196,false,true,265001,Photographs,Photograph,"Hanging Rock, Foot of Echo Cañon",,,,,,Artist,,Andrew Joseph Russell,"American, 1830–1902",,"Russell, Andrew Joseph",American,1830,1902,1867–68,1867,1868,Albumen silver print from glass negative,Image: 21.3 x 28.2 cm (8 3/8 x 11 1/8 in.),"Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 1986",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/265001,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.91,false,true,283193,Photographs,Photograph,"Slave Pen, Alexandria, Virginia",,,,,,Artist,,Andrew Joseph Russell,"American, 1830–1902",,"Russell, Andrew Joseph",American,1830,1902,1863,1863,1863,Albumen silver print from glass negative,Image: 25.6 x 36.5cm (10 1/16 x 14 3/8in.) Mount: 12 3/16 × 16 9/16 in. (31 × 42 cm),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283193,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1237,false,true,286065,Photographs,Photograph,"Fort Laramie, Wyoming",,,,,,Artist,Attributed to,Ridgway Glover,"American, 1831–1866",,"Glover, Ridgway",American,1831,1866,ca. 1866,1864,1868,Albumen silver print from glass negative,"Image: 12.8 × 17.8 cm (5 1/16 × 7 in.), oval Mount: 20.3 × 25.3 cm (8 × 9 15/16 in.)","Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286065,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1980.1056.5,false,true,262764,Photographs,Stereograph,"[Ship in Ice, Greenland Expedition]",,,,,,Artist,,Isaac Israel Hayes,"American, 1832–1881",,"Hayes, Isaac Israel",American,1832,1881,ca. 1859,1857,1861,Albumen silver print from glass negative,,"Warner Communications Inc. Purchase Fund, 1980",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/262764,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1980.1056.7,false,true,262766,Photographs,Stereograph,Esquimau,,,,,,Artist,,Isaac Israel Hayes,"American, 1832–1881",,"Hayes, Isaac Israel",American,1832,1881,ca. 1859,1857,1861,Albumen silver print from glass negative,,"Warner Communications Inc. Purchase Fund, 1980",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/262766,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.458,false,true,685798,Photographs,Carte-de-visite,[E. Johnson],,,,,,Artist,,George Gardner Rockwood,"American, 1832–1911",,"Rockwood, George Gardner",American,1832,1911,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685798,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.471,false,true,685811,Photographs,Carte-de-visite,[John Frederick Kensett],,,,,,Artist,,George Gardner Rockwood,"American, 1832–1911",,"Rockwood, George Gardner",American,1832,1911,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685811,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.725,false,true,686064,Photographs,Carte-de-visite,[Aaron Draper Shattuck],,,,,,Artist,,George Gardner Rockwood,"American, 1832–1911",,"Rockwood, George Gardner",American,1832,1911,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/686064,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.752,false,true,686091,Photographs,Carte-de-visite,[W.O.? Stone],,,,,,Artist,,George Gardner Rockwood,"American, 1832–1911",,"Rockwood, George Gardner",American,1832,1911,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/686091,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.771,false,true,686110,Photographs,Carte-de-visite,[Laurent ?],,,,,,Artist,,George Gardner Rockwood,"American, 1832–1911",,"Rockwood, George Gardner",American,1832,1911,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/686110,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.804,false,true,686143,Photographs,Carte-de-visite,[Unknown Subject],,,,,,Artist,,George Gardner Rockwood,"American, 1832–1911",,"Rockwood, George Gardner",American,1832,1911,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/686143,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.855,false,true,686194,Photographs,Carte-de-visite,[Unknown Subject],,,,,,Artist,,George Gardner Rockwood,"American, 1832–1911",,"Rockwood, George Gardner",American,1832,1911,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/686194,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.391,false,true,288841,Photographs,Photograph,"[Lula Lake and Upper Falls on Rock Creek, near Lookout Mountain, Georgia]",,,,,,Artist,,Isaac H. Bonsall,"American, 1833–1909",,"Bonsall, Isaac H.",American,1833,1909,1864–65,1864,1865,Albumen silver print from glass negative,Image: 26.5 x 33.7 cm (10 7/16 x 13 1/4 in.),"Purchase, Celia Tompkins Hegyi Gift, 2007",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288841,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.392,false,true,288840,Photographs,Photograph,"Lulah Falls, Lookout Mountain, Georgia",,,,,,Artist,,Isaac H. Bonsall,"American, 1833–1909",,"Bonsall, Isaac H.",American,1833,1909,1864–65,1864,1865,Albumen silver print from glass negative,Image: 21.2 x 26.6 cm (8 3/8 x 10 1/2 in.),"Purchase, Celia Tompkins Hegyi Gift, 2007",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288840,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.238,false,true,286559,Photographs,Photograph,Deck of U.S. Ship Vermont,,,,,,Artist,,Henry P. Moore,"American, 1833–1911",,"Moore, Henry P.",American,1833,1911,ca. 1863,1861,1865,Albumen silver print from glass negative,Image: 13.3 x 20.2 cm (5 1/4 x 7 15/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286559,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.897,false,true,286558,Photographs,Photograph,"Contrabands Aboard U.S. Ship Vermont, Port Royal, South Carolina",,,,,,Artist,,Henry P. Moore,"American, 1833–1911",,"Moore, Henry P.",American,1833,1911,1861,1861,1861,Albumen silver print from glass negative,Image: 12.9 × 20.8 cm (5 1/16 × 8 3/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286558,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1137,false,true,286557,Photographs,Photograph,"Negroes (Gwine to de Field), Hopkinson's Plantation, Edisto Island, South Carolina",,,,,,Artist,,Henry P. Moore,"American, 1833–1911",,"Moore, Henry P.",American,1833,1911,1862,1862,1862,Albumen silver print from glass negative,Image: 15.2 × 20.4 cm (6 × 8 1/16 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286557,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.52,false,true,301982,Photographs,Photograph,"Frank Wyatt, One of General Dodge's Band, Corinth, Mississippi",,,,,,Artist,,George W. Armstead,"American, 1833–1912",,"Armstead, George W.",American,1833,1912,"September 18, 1863",1863,1863,Albumen silver print from glass negative,Image: 9 x 5.4 cm (3 9/16 x 2 1/8 in.),"Purchase, Alfred Stieglitz Society Gifts, 2013",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/301982,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1139,false,true,285746,Photographs,Photograph,"[Hudson River Seen from United State Military Academy at West Point, New York]",,,,,,Artist,,George Kendall Warren,"American, 1834–1884",,"Warren, George Kendall",American,1834,1884,1867,1867,1867,Albumen silver print,Image: 6 1/4 × 9 1/8 in. (15.9 × 23.2 cm) Mount: 10 1/8 in. × 13 3/16 in. (25.7 × 33.5 cm),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285746,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1140,false,true,285745,Photographs,Photograph,"""Sam,"" the Black Peddlar",,,,,,Artist,,George Kendall Warren,"American, 1834–1884",,"Warren, George Kendall",American,1834,1884,ca. 1858,1856,1860,Salted paper print,Image: 6 9/16 in. × 5 in. (16.7 × 12.7 cm) Mount: 11 7/16 in. × 8 1/2 in. (29.1 × 21.6 cm),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285745,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -46.29.9,false,true,269088,Photographs,Carte-de-visite,Steamer R.E. Lee Racing with Natches When Nearing St. Louis,,,,,,Artist,,Robert Benecke,"American, 1835–1903",,"Benecke, Robert",American,1835,1903,ca. 1870,1868,1872,Albumen silver print,,"Gift of Mrs. Lawrence Fowler, 1946",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269088,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1245,false,true,286312,Photographs,Photograph,"Ma-ni-mic, Cheyenne Chief",,,,,,Artist,,William Stinson Soule,"American, 1836–1908",,"Soule, William Stinson",American,1836,1908,1869–74,1869,1874,Albumen silver print from glass negative,Image: 7 7/16 × 5 3/8 in. (18.9 × 13.7 cm) Mount: 14 3/4 in. × 10 7/8 in. (37.4 × 27.7 cm),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286312,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1999.367.5,false,true,283336,Photographs,Photograph,[Wisconsin Landscape],,,,,,Artist,,Henry Hamilton Bennett,"American, 1843–1908",,"Bennett, Henry Hamilton",American,1843,1908,1889,1889,1889,Albumen silver print,17 x 22 cm (6 11/16 x 8 11/16 in. ),"Bequest of Winthrop Edey, 1999",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283336,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1974.530,false,true,261106,Photographs,Photograph,"Mammoth Hot Springs, Pulpit Terraces",,,,,,Artist,,William Henry Jackson,"American, 1843–1942",,"Jackson, William Henry",American,1843,1942,ca. 1883,1881,1885,Albumen silver print from glass negative,43.0 x 53.1 cm. (16 15/16 x 20 15/16 in.),"Rogers Fund, 1974",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261106,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1973.641.3,false,true,261003,Photographs,Photograph,"Tantalus Cañon, Utah",,,,,,Artist,,William Henry Jackson,"American, 1843–1942",,"Jackson, William Henry",American,1843,1942,1870s,1870,1879,Albumen silver print from glass negative,,"Gift of A. Hyatt Mayor, 1973",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261003,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.1098.7,false,true,631031,Photographs,Photograph,"Hanging Rock, Clear Creek Canyon",,,,,,Artist,,William Henry Jackson,"American, 1843–1942",,"Jackson, William Henry",American,1843,1942,1870s,1870,1879,Albumen silver print,Image: 21 1/4 × 17 7/16 in. (54 × 44.3 cm) Mount: 27 7/8 in. × 22 in. (70.8 × 55.9 cm),"Gift of Joyce F. Menschel, 2013",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/631031,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -42.120.1,false,true,268759,Photographs,Photograph,White Sailboat in Long Island Sound,,,,,,Artist,,Charles E. Bolles,"American, 1845–1919",,"Bolles, Charles E.",American,1845,1919,"1890s, printed 1897",1890,1899,Platinum print,,"Gift of Marion P. Bolles, 1942",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268759,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -42.120.2,false,true,268760,Photographs,Photograph,Sailboat,,,,,,Artist,,Charles E. Bolles,"American, 1845–1919",,"Bolles, Charles E.",American,1845,1919,"1890s, printed 1897",1890,1899,Platinum print,,"Gift of Marion P. Bolles, 1942",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268760,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -42.120.3,false,true,268761,Photographs,Photograph,"Hauling in Nets, Long Island Sound",,,,,,Artist,,Charles E. Bolles,"American, 1845–1919",,"Bolles, Charles E.",American,1845,1919,"1890s, printed 1897",1890,1899,Platinum print,,"Gift of Marion P. Bolles, 1942",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268761,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.572,false,true,285710,Photographs,Photograph,Otoe Delegation,,,,,,Artist,,Charles Milton Bell,"American, 1848–1893",,"Bell, Charles Milton",American,1848,1893,1881,1881,1881,Albumen silver print from glass negative,24 x 28.8 cm (9 7/16 x 11 5/16 in.),"Gilman Collection, Purchase, Joseph M. Cohen Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285710,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.619,false,true,283224,Photographs,Photograph,Red Cloud,,,,,,Artist,,Charles Milton Bell,"American, 1848–1893",,"Bell, Charles Milton",American,1848,1893,1880,1880,1880,Albumen silver print from glass negative,Image: 14 × 9.9 cm (5 1/2 × 3 7/8 in.) Mount: 16.5 × 10.8 cm (6 1/2 × 4 1/4 in.),"Gilman Collection, Purchase, Sam Salz Foundation Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283224,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -46.164,false,true,269083,Photographs,Photograph,Mr. and Mrs. Charles E. Tiffany in Louis C. Tiffany's Studio,,,,,,Artist,,George Collins Cox,"American, 1851–1902",,"Cox, George Collins",American,1851,1902,ca. 1890,1888,1892,Platinum print (?),18.8 x 23.6 cm (7 3/8 x 9 5/16 in. ),"Museum Accession, 1946",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269083,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -46.165,false,true,269084,Photographs,Photograph,"Frances and Ethel de Forest, daughters of Robert de Forest",,,,,,Artist,,George Collins Cox,"American, 1851–1902",,"Cox, George Collins",American,1851,1902,ca. 1890,1888,1892,Albumen silver print,19.2 x 23.7 cm (7 9/16 x 9 5/16 in. ),"Museum Accession, 1946",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269084,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.43.132,true,true,267530,Photographs,Photograph,Blessed Art Thou among Women,,,,,,Artist,,Gertrude Käsebier,"American, 1852–1934",,"Käsebier, Gertrude",American,1852,1934,1899,1899,1899,Platinum print,23 x 13.2 cm (9 1/16 x 5 3/16 in.),"Alfred Stieglitz Collection, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267530,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.43.136,false,true,267534,Photographs,Photograph,The Sketch,,,,,,Artist,,Gertrude Käsebier,"American, 1852–1934",,"Käsebier, Gertrude",American,1852,1934,1903,1903,1903,Platinum print,15.3 x 20.7 cm. (6 x 8 1/8 in.),"Alfred Stieglitz Collection, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267534,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.43.142,false,true,267541,Photographs,Photograph,Happy Days,,,,,,Artist,,Gertrude Käsebier,"American, 1852–1934",,"Käsebier, Gertrude",American,1852,1934,1902,1902,1902,Platinum print,19.8 x 14.9 cm. (7 13/16 x 5 7/8 in.),"Alfred Stieglitz Collection, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267541,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -49.55.188,false,true,269308,Photographs,Photograph,[F. Holland Day],,,,,,Artist,,Gertrude Käsebier,"American, 1852–1934",,"Käsebier, Gertrude",American,1852,1934,ca. 1898,1896,1900,Platinum print,18.0 x 13.6 cm (7 1/16 x 5 3/8 in.),"Alfred Stieglitz Collection, 1949",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269308,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1984.1024.1,false,true,263962,Photographs,Photograph,"William M. Ivins, Jr.",,,,,,Artist,,Gertrude Käsebier,"American, 1852–1934",,"Käsebier, Gertrude",American,1852,1934,ca. 1910,1905,1915,Platinum print,Image: 7 5/8 × 5 5/16 in. (19.3 × 13.5 cm) Sheet: 7 5/8 × 5 5/16 in. (19.3 × 13.5 cm),"Gift of Barbara Ivins, 1984",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/263962,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.889,false,true,285990,Photographs,Photograph,Baron Adolph de Meyer,,,,,,Artist,,Gertrude Käsebier,"American, 1852–1934",,"Käsebier, Gertrude",American,1852,1934,1903,1903,1903,Platinum print,"Image: 21.7 x 13.9 cm (8 9/16 x 5 1/2 in.), irregularly trimmed Mount: 22.1 x 14.2 cm (8 11/16 x 5 9/16 in.)","Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285990,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.905,false,true,286366,Photographs,Photograph,"Turner Family, Woburn, Massachusetts",,,,,,Artist,,Gertrude Käsebier,"American, 1852–1934",,"Käsebier, Gertrude",American,1852,1934,ca. 1910,1908,1912,Gelatin silver print on tissue,33.2 x 26.1 cm (13 1/16 x 10 1/4 in.),"Gilman Collection, Purchase, Harriette and Noel Levine Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286366,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1983.1041.2,false,true,263549,Photographs,Photograph,[Group with Horse-Drawn Carriage],,,,,,Artist,,Christian Barthelmess,"American, 1854–1906",,"Barthelmess, Christian",American,1854,1906,1890s,1890,1899,Albumen silver print from glass negative,10.2 x 15.8 cm. (4 x 6 1/4 in.),"David Hunter McAlpin Fund, 1983",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/263549,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.113,false,true,283229,Photographs,Photograph,[Children Fishing],,,,,,Artist,,William James Mullins,"American, 1860–1917",,"Mullins, William James",American,1860,1917,ca. 1900,1899,1901,Platinum print,Image: 9.1 x 25.7cm (3 9/16 x 10 1/8in.) Mount: 9.8 × 26.4 cm (3 7/8 in. × 10 3/8 in.),"Gilman Collection, Purchase, Jennifer and Joseph Duke Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283229,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -30.89.1,false,true,267474,Photographs,Photograph,"Oak, Mission Ridge, Santa Barbara, California",,,,,,Artist,,Henry Ravell,"American, 1860–1930",,"Ravell, Henry",American,1860,1930,1910s,1910,1919,Gum bichromate print,,"Gift of Mrs. Florence D. R. Lothrop, in memory of her brother, Henry Ravell, 1930",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267474,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -30.89.2,false,true,267481,Photographs,Photograph,"Cypress, Pebble Beach, California",,,,,,Artist,,Henry Ravell,"American, 1860–1930",,"Ravell, Henry",American,1860,1930,1910s,1910,1919,Gum bichromate print,,"Gift of Mrs. Florence D. R. Lothrop, in memory of her brother, Henry Ravell, 1930",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267481,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -30.89.3,false,true,267482,Photographs,Photograph,"Cypress, Pebble Beach, California",,,,,,Artist,,Henry Ravell,"American, 1860–1930",,"Ravell, Henry",American,1860,1930,1910s,1910,1919,Gum bichromate print,,"Gift of Mrs. Florence D. R. Lothrop, in memory of her brother, Henry Ravell, 1930",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267482,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -30.89.4,false,true,267483,Photographs,Photograph,"Cypress, Pebble Beach, California",,,,,,Artist,,Henry Ravell,"American, 1860–1930",,"Ravell, Henry",American,1860,1930,1910s,1910,1919,Gum bichromate print,,"Gift of Mrs. Florence D. R. Lothrop, in memory of her brother, Henry Ravell, 1930",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267483,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -30.89.5,false,true,267484,Photographs,Photograph,"Tortilla Women in the Plaza, Mexico",,,,,,Artist,,Henry Ravell,"American, 1860–1930",,"Ravell, Henry",American,1860,1930,1910s,1910,1919,Gum bichromate print,,"Gift of Mrs. Florence D. R. Lothrop, in memory of her brother, Henry Ravell, 1930",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267484,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -30.89.6,false,true,267485,Photographs,Photograph,"Cypress at Pebble Beach, California",,,,,,Artist,,Henry Ravell,"American, 1860–1930",,"Ravell, Henry",American,1860,1930,1910s,1910,1919,Gum bichromate print,,"Gift of Mrs. Florence D. R. Lothrop, in memory of her brother, Henry Ravell, 1930",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267485,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -30.89.7,false,true,267486,Photographs,Photograph,"Cypress, Pebble Beach, California",,,,,,Artist,,Henry Ravell,"American, 1860–1930",,"Ravell, Henry",American,1860,1930,1910s,1910,1919,Gum bichromate print,,"Gift of Mrs. Florence D. R. Lothrop, in memory of her brother, Henry Ravell, 1930",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267486,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -30.89.8,false,true,267487,Photographs,Photograph,"Cypress, Pebble Beach, California",,,,,,Artist,,Henry Ravell,"American, 1860–1930",,"Ravell, Henry",American,1860,1930,1910s,1910,1919,Gum bichromate print,,"Gift of Mrs. Florence D. R. Lothrop, in memory of her brother, Henry Ravell, 1930",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267487,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -30.89.9,false,true,267488,Photographs,Photograph,Fog and Cypress Trees,,,,,,Artist,,Henry Ravell,"American, 1860–1930",,"Ravell, Henry",American,1860,1930,1910s,1910,1919,Gum bichromate print,,"Gift of Mrs. Florence D. R. Lothrop, in memory of her brother, Henry Ravell, 1930",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267488,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -30.89.10,false,true,267475,Photographs,Photograph,"Jesuit Church, Guanajuato, Mexico",,,,,,Artist,,Henry Ravell,"American, 1860–1930",,"Ravell, Henry",American,1860,1930,1910s,1910,1919,Gum bichromate print,,"Gift of Mrs. Florence D. R. Lothrop, in memory of her brother, Henry Ravell, 1930",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267475,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -30.89.11,false,true,267476,Photographs,Photograph,Pottery Sellers by the Church Door,,,,,,Artist,,Henry Ravell,"American, 1860–1930",,"Ravell, Henry",American,1860,1930,1910s,1910,1919,Gum bichromate print,,"Gift of Mrs. Florence D. R. Lothrop, in memory of her brother, Henry Ravell, 1930",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267476,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -30.89.12,false,true,267477,Photographs,Photograph,"A Church Dome at Cuernavaca, Mexico",,,,,,Artist,,Henry Ravell,"American, 1860–1930",,"Ravell, Henry",American,1860,1930,1910s,1910,1919,Gum bichromate print,,"Gift of Mrs. Florence D. R. Lothrop, in memory of her brother, Henry Ravell, 1930",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267477,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -30.89.13,false,true,267478,Photographs,Photograph,Lunching in the Market Place,,,,,,Artist,,Henry Ravell,"American, 1860–1930",,"Ravell, Henry",American,1860,1930,1910s,1910,1919,Gum or carbon transfer with applied media,,"Gift of Mrs. Florence D. R. Lothrop, in memory of her brother, Henry Ravell, 1930",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267478,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -30.89.14,false,true,267479,Photographs,Photograph,Water and Trees of the Viga Canal near Mexico City,,,,,,Artist,,Henry Ravell,"American, 1860–1930",,"Ravell, Henry",American,1860,1930,1910s,1910,1919,Gum bichromate print,,"Gift of Mrs. Florence D. R. Lothrop, in memory of her brother, Henry Ravell, 1930",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267479,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -30.89.15,false,true,267480,Photographs,Photograph,"The Plaza, Market Day, Taxco, Mexico",,,,,,Artist,,Henry Ravell,"American, 1860–1930",,"Ravell, Henry",American,1860,1930,1910s,1910,1919,Gum bichromate print,,"Gift of Mrs. Florence D. R. Lothrop, in memory of her brother, Henry Ravell, 1930",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267480,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.644.2,false,true,260325,Photographs,Photograph,"A Study, No. 1",,,,,,Artist,,Rudolph Eickemeyer,"American, 1862–1932",,"Eickemeyer, Jr., Rudolph",American,1862,1932,1901,1901,1901,Gelatin silver print,,"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260325,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.644.3,false,true,260326,Photographs,Photograph,[Ships Dockside in a Harbor],,,,,,Artist,,Rudolph Eickemeyer,"American, 1862–1932",,"Eickemeyer, Jr., Rudolph",American,1862,1932,ca. 1900,1898,1902,Platinum print,,"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260326,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.644.4,false,true,260327,Photographs,Photograph,[Ships on a Beach with Two Long Boats and Two Men Sweeping],,,,,,Artist,,Rudolph Eickemeyer,"American, 1862–1932",,"Eickemeyer, Jr., Rudolph",American,1862,1932,ca. 1900,1898,1902,Platinum print,,"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260327,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.482,false,true,286256,Photographs,Photograph,Tired Butterfly,,,,,,Artist,,Rudolph Eickemeyer,"American, 1862–1932",,"Eickemeyer, Jr., Rudolph",American,1862,1932,1902,1902,1902,Carbon print,Image: 19.1 x 24.1 cm (7 1/2 x 9 1/2 in.) Sheet: 22.4 x 25.7 cm (8 13/16 x 10 1/8 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286256,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.150,false,true,669361,Photographs,Photograph; Photomicrograph,[Snow Crystal],,,,,,Artist,,Wilson Alwyn Bentley,"American, 1865–1931",,"Bentley, Wilson Alwyn",American,1865,1931,1890s–1920s,1890,1929,Gelatin silver print,Image: 2 15/16 × 3 1/8 in. (7.5 × 8 cm) Sheet: 3 1/16 × 3 15/16 in. (7.7 × 10 cm),"Purchase, Alfred Stieglitz Society Gifts, 2015",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/669361,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.151,false,true,670594,Photographs,Photograph; Photomicrograph,[Snow Crystal],,,,,,Artist,,Wilson Alwyn Bentley,"American, 1865–1931",,"Bentley, Wilson Alwyn",American,1865,1931,1890s–1920s,1890,1929,Gelatin silver print,Image: 2 15/16 × 3 1/8 in. (7.5 × 8 cm) Sheet: 2 15/16 in. × 4 in. (7.5 × 10.1 cm),"Purchase, Alfred Stieglitz Society Gifts, 2015",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/670594,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.152,false,true,670606,Photographs,Photograph; Photomicrograph,[Snow Crystal],,,,,,Artist,,Wilson Alwyn Bentley,"American, 1865–1931",,"Bentley, Wilson Alwyn",American,1865,1931,1890s–1920s,1890,1929,Gelatin silver print,Image: 3 in. × 3 3/16 in. (7.6 × 8.1 cm) Sheet: 3 × 4 in. (7.6 × 10.1 cm),"Purchase, Alfred Stieglitz Society Gifts, 2015",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/670606,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.153,false,true,670607,Photographs,Photograph; Photomicrograph,[Snow Crystal],,,,,,Artist,,Wilson Alwyn Bentley,"American, 1865–1931",,"Bentley, Wilson Alwyn",American,1865,1931,1890s–1920s,1890,1929,Gelatin silver print,Image: 3 in. × 3 1/4 in. (7.6 × 8.3 cm) Sheet: 3 × 4 in. (7.6 × 10.1 cm),"Purchase, Alfred Stieglitz Society Gifts, 2015",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/670607,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.154,false,true,670608,Photographs,Photograph; Photomicrograph,[Snow Crystal],,,,,,Artist,,Wilson Alwyn Bentley,"American, 1865–1931",,"Bentley, Wilson Alwyn",American,1865,1931,1890s–1920s,1890,1929,Gelatin silver print,Image: 2 15/16 × 3 1/8 in. (7.4 × 8 cm) Sheet: 3 × 4 in. (7.6 × 10.1 cm),"Purchase, Alfred Stieglitz Society Gifts, 2015",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/670608,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.155,false,true,670609,Photographs,Photograph; Photomicrograph,[Snow Crystal],,,,,,Artist,,Wilson Alwyn Bentley,"American, 1865–1931",,"Bentley, Wilson Alwyn",American,1865,1931,1890s–1920s,1890,1929,Gelatin silver print,Image: 3 in. × 3 1/8 in. (7.6 × 7.9 cm) Sheet: 3 × 4 in. (7.6 × 10.1 cm),"Purchase, Alfred Stieglitz Society Gifts, 2015",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/670609,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.156,false,true,670610,Photographs,Photograph; Photomicrograph,[Snow Crystal],,,,,,Artist,,Wilson Alwyn Bentley,"American, 1865–1931",,"Bentley, Wilson Alwyn",American,1865,1931,1890s–1920s,1890,1929,Gelatin silver print,Image: 2 7/8 × 2 15/16 in. (7.3 × 7.4 cm) Sheet: 3 × 4 in. (7.6 × 10.1 cm),"Purchase, Alfred Stieglitz Society Gifts, 2015",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/670610,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.157,false,true,670611,Photographs,Photograph; Photomicrograph,[Snow Crystal],,,,,,Artist,,Wilson Alwyn Bentley,"American, 1865–1931",,"Bentley, Wilson Alwyn",American,1865,1931,1890s–1920s,1890,1929,Gelatin silver print,Image: 2 15/16 in. × 3 in. (7.5 × 7.6 cm) Sheet: 3 1/4 in. × 4 in. (8.3 × 10.1 cm),"Purchase, Alfred Stieglitz Society Gifts, 2015",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/670611,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.158,false,true,670612,Photographs,Photograph; Photomicrograph,[Dew on a Blade of Grass],,,,,,Artist,,Wilson Alwyn Bentley,"American, 1865–1931",,"Bentley, Wilson Alwyn",American,1865,1931,1890s–1920s,1890,1929,Gelatin silver print,Image: 4 in. × 2 15/16 in. (10.1 × 7.5 cm),"Purchase, Alfred Stieglitz Society Gifts, 2015",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/670612,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.159,false,true,670613,Photographs,Photograph; Photomicrograph,[Snow Crystal],,,,,,Artist,,Wilson Alwyn Bentley,"American, 1865–1931",,"Bentley, Wilson Alwyn",American,1865,1931,1890s–1920s,1890,1929,Gelatin silver print,Image: 2 15/16 × 3 7/16 in. (7.5 × 8.8 cm) Sheet: 2 15/16 in. × 4 in. (7.5 × 10.2 cm),"Purchase, Alfred Stieglitz Society Gifts, 2015",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/670613,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.160,false,true,670614,Photographs,Photograph; Photomicrograph,[Snow Crystal],,,,,,Artist,,Wilson Alwyn Bentley,"American, 1865–1931",,"Bentley, Wilson Alwyn",American,1865,1931,1890s–1920s,1890,1929,Gelatin silver print,Image: 2 15/16 × 3 1/4 in. (7.4 × 8.2 cm) Sheet: 3 × 4 in. (7.6 × 10.1 cm),"Purchase, Alfred Stieglitz Society Gifts, 2015",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/670614,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.161,false,true,670615,Photographs,Photograph; Photomicrograph,[Snow Crystal],,,,,,Artist,,Wilson Alwyn Bentley,"American, 1865–1931",,"Bentley, Wilson Alwyn",American,1865,1931,1890s–1920s,1890,1929,Gelatin silver print,Image: 2 15/16 × 3 7/16 in. (7.4 × 8.8 cm) Sheet: 2 15/16 in. × 4 in. (7.4 × 10.1 cm),"Purchase, Alfred Stieglitz Society Gifts, 2015",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/670615,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.162,false,true,670616,Photographs,Photograph; Photomicrograph,[Snow Crystal],,,,,,Artist,,Wilson Alwyn Bentley,"American, 1865–1931",,"Bentley, Wilson Alwyn",American,1865,1931,1890s–1920s,1890,1929,Gelatin silver print,Image: 2 7/8 in. × 3 in. (7.3 × 7.6 cm) Sheet: 2 15/16 in. × 4 in. (7.5 × 10.1 cm),"Purchase, Alfred Stieglitz Society Gifts, 2015",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/670616,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.163,false,true,670617,Photographs,Photograph; Photomicrograph,[Snow Crystal],,,,,,Artist,,Wilson Alwyn Bentley,"American, 1865–1931",,"Bentley, Wilson Alwyn",American,1865,1931,1890s–1920s,1890,1929,Gelatin silver print,,"Purchase, Alfred Stieglitz Society Gifts, 2015",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/670617,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.164,false,true,670618,Photographs,Photograph; Photomicrograph,[Snow Crystal],,,,,,Artist,,Wilson Alwyn Bentley,"American, 1865–1931",,"Bentley, Wilson Alwyn",American,1865,1931,1890s–1920s,1890,1929,Gelatin silver print,Image: 2 3/4 × 2 7/8 in. (7 × 7.3 cm) Sheet: 3 × 4 in. (7.6 × 10.2 cm),"Purchase, Alfred Stieglitz Society Gifts, 2015",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/670618,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.165,false,true,670619,Photographs,Photograph; Photomicrograph,[Snow Crystal],,,,,,Artist,,Wilson Alwyn Bentley,"American, 1865–1931",,"Bentley, Wilson Alwyn",American,1865,1931,1890s–1920s,1890,1929,Gelatin silver print,Image: 2 15/16 × 3 1/4 in. (7.5 × 8.2 cm) Sheet: 2 15/16 × 4 1/16 in. (7.5 × 10.3 cm),"Purchase, Alfred Stieglitz Society Gifts, 2015",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/670619,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.166,false,true,670620,Photographs,Photograph; Photomicrograph,[Snow Crystal],,,,,,Artist,,Wilson Alwyn Bentley,"American, 1865–1931",,"Bentley, Wilson Alwyn",American,1865,1931,1890s–1920s,1890,1929,Gelatin silver print,Image: 3 in. × 3 3/16 in. (7.6 × 8.1 cm) Sheet: 3 × 4 in. (7.6 × 10.2 cm),"Purchase, Alfred Stieglitz Society Gifts, 2015",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/670620,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.167,false,true,670621,Photographs,Photograph; Photomicrograph,[Snow Crystal],,,,,,Artist,,Wilson Alwyn Bentley,"American, 1865–1931",,"Bentley, Wilson Alwyn",American,1865,1931,1890s–1920s,1890,1929,Gelatin silver print,Image: 3 in. × 3 5/16 in. (7.6 × 8.4 cm) Sheet: 3 × 4 in. (7.6 × 10.1 cm),"Purchase, Alfred Stieglitz Society Gifts, 2015",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/670621,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.168,false,true,670622,Photographs,Photograph; Photomicrograph,[Snow Crystal],,,,,,Artist,,Wilson Alwyn Bentley,"American, 1865–1931",,"Bentley, Wilson Alwyn",American,1865,1931,1890s–1920s,1890,1929,Gelatin silver print,Image: 2 15/16 × 3 5/16 in. (7.4 × 8.4 cm) Sheet: 3 × 4 in. (7.6 × 10.2 cm),"Purchase, Alfred Stieglitz Society Gifts, 2015",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/670622,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.169,false,true,670623,Photographs,Photograph; Photomicrograph,[Snow Crystal],,,,,,Artist,,Wilson Alwyn Bentley,"American, 1865–1931",,"Bentley, Wilson Alwyn",American,1865,1931,1890s–1920s,1890,1929,Gelatin silver print,Image: 2 15/16 in. × 3 in. (7.5 × 7.6 cm) Sheet: 2 15/16 × 3 7/8 in. (7.5 × 9.9 cm),"Purchase, Alfred Stieglitz Society Gifts, 2015",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/670623,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.170,false,true,670624,Photographs,Photograph; Photomicrograph,[Snow Crystal],,,,,,Artist,,Wilson Alwyn Bentley,"American, 1865–1931",,"Bentley, Wilson Alwyn",American,1865,1931,1890s–1920s,1890,1929,Gelatin silver print,Image: 2 15/16 × 3 1/4 in. (7.5 × 8.3 cm) Sheet: 3 × 4 in. (7.6 × 10.1 cm),"Purchase, Alfred Stieglitz Society Gifts, 2015",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/670624,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.171,false,true,670625,Photographs,Photograph; Photomicrograph,[Snow Crystal],,,,,,Artist,,Wilson Alwyn Bentley,"American, 1865–1931",,"Bentley, Wilson Alwyn",American,1865,1931,1890s–1920s,1890,1929,Gelatin silver print,Image: 3 in. × 3 1/16 in. (7.6 × 7.7 cm) Sheet: 3 × 4 in. (7.6 × 10.1 cm),"Purchase, Alfred Stieglitz Society Gifts, 2015",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/670625,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.172,false,true,670626,Photographs,Photograph; Photomicrograph,[Snow Crystal],,,,,,Artist,,Wilson Alwyn Bentley,"American, 1865–1931",,"Bentley, Wilson Alwyn",American,1865,1931,1890s–1920s,1890,1929,Gelatin silver print,Image: 2 15/16 × 2 3/4 in. (7.4 × 7 cm) Sheet: 2 15/16 × 3 15/16 in. (7.5 × 10 cm),"Purchase, Alfred Stieglitz Society Gifts, 2015",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/670626,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.173,false,true,670627,Photographs,Photograph; Photomicrograph,[Snow Crystal],,,,,,Artist,,Wilson Alwyn Bentley,"American, 1865–1931",,"Bentley, Wilson Alwyn",American,1865,1931,1890s–1920s,1890,1929,Gelatin silver print,Image: 2 15/16 in. × 3 in. (7.4 × 7.6 cm) Sheet: 3 1/16 in. × 4 in. (7.7 × 10.2 cm),"Purchase, Alfred Stieglitz Society Gifts, 2015",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/670627,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.174,false,true,670628,Photographs,Photograph; Photomicrograph,[Snow Crystal],,,,,,Artist,,Wilson Alwyn Bentley,"American, 1865–1931",,"Bentley, Wilson Alwyn",American,1865,1931,1890s–1920s,1890,1929,Gelatin silver print,Image: 2 15/16 × 3 1/2 in. (7.4 × 8.9 cm) Sheet: 3 × 4 in. (7.6 × 10.1 cm),"Purchase, Alfred Stieglitz Society Gifts, 2015",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/670628,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.175,false,true,670629,Photographs,Photograph; Photomicrograph,[Snow Crystal],,,,,,Artist,,Wilson Alwyn Bentley,"American, 1865–1931",,"Bentley, Wilson Alwyn",American,1865,1931,1890s–1920s,1890,1929,Gelatin silver print,Image: 3 in. × 3 3/16 in. (7.6 × 8.1 cm) Sheet: 3 × 4 in. (7.6 × 10.1 cm),"Purchase, Alfred Stieglitz Society Gifts, 2015",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/670629,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.176,false,true,670630,Photographs,Photograph; Photomicrograph,[Snow Crystal],,,,,,Artist,,Wilson Alwyn Bentley,"American, 1865–1931",,"Bentley, Wilson Alwyn",American,1865,1931,1890s–1920s,1890,1929,Gelatin silver print,Image: 2 15/16 in. × 3 in. (7.4 × 7.6 cm) Sheet: 3 × 4 in. (7.6 × 10.1 cm),"Purchase, Alfred Stieglitz Society Gifts, 2015",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/670630,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.177,false,true,670631,Photographs,Photograph; Photomicrograph,[Snow Crystal],,,,,,Artist,,Wilson Alwyn Bentley,"American, 1865–1931",,"Bentley, Wilson Alwyn",American,1865,1931,1890s–1920s,1890,1929,Gelatin silver print,Image: 2 13/16 × 2 15/16 in. (7.2 × 7.5 cm) Sheet: 2 7/8 × 3 5/8 in. (7.3 × 9.2 cm),"Purchase, Alfred Stieglitz Society Gifts, 2015",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/670631,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.178,false,true,670632,Photographs,Photograph; Photomicrograph,[Snow Crystal],,,,,,Artist,,Wilson Alwyn Bentley,"American, 1865–1931",,"Bentley, Wilson Alwyn",American,1865,1931,1890s–1920s,1890,1929,Gelatin silver print,Image: 3 × 3 in. (7.6 × 7.6 cm) Sheet: 3 1/4 in. × 4 in. (8.2 × 10.1 cm),"Purchase, Alfred Stieglitz Society Gifts, 2015",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/670632,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.179,false,true,670633,Photographs,Photograph; Photomicrograph,[Snow Crystal],,,,,,Artist,,Wilson Alwyn Bentley,"American, 1865–1931",,"Bentley, Wilson Alwyn",American,1865,1931,1890s–1920s,1890,1929,Gelatin silver print,Image: 2 7/8 in. × 3 in. (7.3 × 7.6 cm) Sheet: 3 × 4 in. (7.6 × 10.1 cm),"Purchase, Alfred Stieglitz Society Gifts, 2015",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/670633,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.180,false,true,670634,Photographs,Photograph; Photomicrograph,[Snow Crystal],,,,,,Artist,,Wilson Alwyn Bentley,"American, 1865–1931",,"Bentley, Wilson Alwyn",American,1865,1931,1890s–1920s,1890,1929,Gelatin silver print,Image: 2 7/8 × 2 15/16 in. (7.3 × 7.5 cm) Sheet: 2 15/16 in. × 4 in. (7.5 × 10.2 cm),"Purchase, Alfred Stieglitz Society Gifts, 2015",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/670634,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.181,false,true,670635,Photographs,Photograph; Photomicrograph,[Snow Crystal],,,,,,Artist,,Wilson Alwyn Bentley,"American, 1865–1931",,"Bentley, Wilson Alwyn",American,1865,1931,1890s–1920s,1890,1929,Gelatin silver print,Image: 2 15/16 × 3 1/4 in. (7.5 × 8.3 cm) Sheet: 3 × 4 in. (7.6 × 10.1 cm),"Purchase, Alfred Stieglitz Society Gifts, 2015",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/670635,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.182,false,true,670636,Photographs,Photograph; Photomicrograph,[Snow Crystal],,,,,,Artist,,Wilson Alwyn Bentley,"American, 1865–1931",,"Bentley, Wilson Alwyn",American,1865,1931,1890s–1920s,1890,1929,Gelatin silver print,Image: 2 15/16 × 2 7/8 in. (7.4 × 7.3 cm) Sheet: 2 15/16 in. × 4 in. (7.5 × 10.2 cm),"Purchase, Alfred Stieglitz Society Gifts, 2015",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/670636,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.183,false,true,670637,Photographs,Photograph; Photomicrograph,[Snow Crystal],,,,,,Artist,,Wilson Alwyn Bentley,"American, 1865–1931",,"Bentley, Wilson Alwyn",American,1865,1931,1890s–1920s,1890,1929,Gelatin silver print,,"Purchase, Alfred Stieglitz Society Gifts, 2015",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/670637,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.184,false,true,670638,Photographs,Photograph; Photomicrograph,[Snow Crystal],,,,,,Artist,,Wilson Alwyn Bentley,"American, 1865–1931",,"Bentley, Wilson Alwyn",American,1865,1931,1890s–1920s,1890,1929,Gelatin silver print,Image: 2 15/16 × 3 1/8 in. (7.4 × 7.9 cm) Sheet: 3 × 4 in. (7.6 × 10.2 cm),"Purchase, Alfred Stieglitz Society Gifts, 2015",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/670638,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.185,false,true,670639,Photographs,Photograph; Photomicrograph,[Snow Crystal],,,,,,Artist,,Wilson Alwyn Bentley,"American, 1865–1931",,"Bentley, Wilson Alwyn",American,1865,1931,1890s–1920s,1890,1929,Gelatin silver print,Image: 2 15/16 × 3 1/4 in. (7.4 × 8.2 cm) Sheet: 3 × 4 in. (7.6 × 10.1 cm),"Purchase, Alfred Stieglitz Society Gifts, 2015",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/670639,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.186,false,true,670640,Photographs,Photograph; Photomicrograph,[Snow Crystal],,,,,,Artist,,Wilson Alwyn Bentley,"American, 1865–1931",,"Bentley, Wilson Alwyn",American,1865,1931,1890s–1920s,1890,1929,Gelatin silver print,Image: 2 13/16 in. × 3 in. (7.1 × 7.6 cm) Sheet: 2 13/16 in. × 4 in. (7.2 × 10.1 cm),"Purchase, Alfred Stieglitz Society Gifts, 2015",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/670640,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.187,false,true,670641,Photographs,Photograph; Photomicrograph,[Snow Crystal],,,,,,Artist,,Wilson Alwyn Bentley,"American, 1865–1931",,"Bentley, Wilson Alwyn",American,1865,1931,1890s–1920s,1890,1929,Gelatin silver print,Image: 2 15/16 in. × 3 in. (7.5 × 7.6 cm) Sheet: 3 1/8 in. × 4 in. (7.9 × 10.1 cm),"Purchase, Alfred Stieglitz Society Gifts, 2015",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/670641,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.188,false,true,670642,Photographs,Photograph; Photomicrograph,[Snow Crystal],,,,,,Artist,,Wilson Alwyn Bentley,"American, 1865–1931",,"Bentley, Wilson Alwyn",American,1865,1931,1890s–1920s,1890,1929,Gelatin silver print,Image: 2 13/16 × 2 7/8 in. (7.2 × 7.3 cm) Sheet: 3 1/16 in. × 4 in. (7.7 × 10.2 cm),"Purchase, Alfred Stieglitz Society Gifts, 2015",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/670642,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.189,false,true,670643,Photographs,Photograph; Photomicrograph,[Snow Crystal],,,,,,Artist,,Wilson Alwyn Bentley,"American, 1865–1931",,"Bentley, Wilson Alwyn",American,1865,1931,1890s–1920s,1890,1929,Gelatin silver print,Image: 2 13/16 × 2 15/16 in. (7.1 × 7.4 cm) Sheet: 2 15/16 in. × 4 in. (7.5 × 10.1 cm),"Purchase, Alfred Stieglitz Society Gifts, 2015",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/670643,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.190,false,true,670644,Photographs,Photograph; Photomicrograph,[Snow Crystal],,,,,,Artist,,Wilson Alwyn Bentley,"American, 1865–1931",,"Bentley, Wilson Alwyn",American,1865,1931,1890s–1920s,1890,1929,Gelatin silver print,Image: 2 15/16 × 3 1/8 in. (7.5 × 7.9 cm) Sheet: 3 in. × 4 1/16 in. (7.6 × 10.3 cm),"Purchase, Alfred Stieglitz Society Gifts, 2015",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/670644,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.191,false,true,670645,Photographs,Photograph; Photomicrograph,[Snow Crystal],,,,,,Artist,,Wilson Alwyn Bentley,"American, 1865–1931",,"Bentley, Wilson Alwyn",American,1865,1931,1890s–1920s,1890,1929,Gelatin silver print,Image: 2 3/4 in. × 3 in. (7 × 7.6 cm) Sheet: 2 13/16 in. × 4 in. (7.2 × 10.1 cm),"Purchase, Alfred Stieglitz Society Gifts, 2015",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/670645,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.192,false,true,670646,Photographs,Photograph; Photomicrograph,[Snow Crystal],,,,,,Artist,,Wilson Alwyn Bentley,"American, 1865–1931",,"Bentley, Wilson Alwyn",American,1865,1931,1890s–1920s,1890,1929,Gelatin silver print,Image: 2 13/16 × 2 7/8 in. (7.1 × 7.3 cm) Sheet: 2 15/16 in. × 4 in. (7.5 × 10.1 cm),"Purchase, Alfred Stieglitz Society Gifts, 2015",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/670646,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.193,false,true,670647,Photographs,Photograph; Photomicrograph,[Snow Crystal],,,,,,Artist,,Wilson Alwyn Bentley,"American, 1865–1931",,"Bentley, Wilson Alwyn",American,1865,1931,1890s–1920s,1890,1929,Gelatin silver print,Image: 3 in. × 3 1/4 in. (7.6 × 8.3 cm) Sheet: 3 × 4 in. (7.6 × 10.1 cm),"Purchase, Alfred Stieglitz Society Gifts, 2015",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/670647,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.194,false,true,670648,Photographs,Photograph; Photomicrograph,[Snow Crystal],,,,,,Artist,,Wilson Alwyn Bentley,"American, 1865–1931",,"Bentley, Wilson Alwyn",American,1865,1931,1890s–1920s,1890,1929,Gelatin silver print,Image: 2 15/16 × 3 3/16 in. (7.5 × 8.1 cm) Sheet: 3 × 4 in. (7.6 × 10.1 cm),"Purchase, Alfred Stieglitz Society Gifts, 2015",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/670648,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.195,false,true,670649,Photographs,Photograph; Photomicrograph,[Snow Crystal],,,,,,Artist,,Wilson Alwyn Bentley,"American, 1865–1931",,"Bentley, Wilson Alwyn",American,1865,1931,1890s–1920s,1890,1929,Gelatin silver print,Image: 2 13/16 × 2 7/8 in. (7.1 × 7.3 cm) Sheet: 3 in. × 3 11/16 in. (7.6 × 9.4 cm),"Purchase, Alfred Stieglitz Society Gifts, 2015",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/670649,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.196,false,true,670650,Photographs,Photograph; Photomicrograph,[Snow Crystal],,,,,,Artist,,Wilson Alwyn Bentley,"American, 1865–1931",,"Bentley, Wilson Alwyn",American,1865,1931,1890s–1920s,1890,1929,Gelatin silver print,Image: 3 in. × 3 1/8 in. (7.6 × 8 cm) Sheet: 3 × 4 in. (7.6 × 10.1 cm),"Purchase, Alfred Stieglitz Society Gifts, 2015",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/670650,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.197,false,true,670651,Photographs,Photograph; Photomicrograph,[Snow Crystal],,,,,,Artist,,Wilson Alwyn Bentley,"American, 1865–1931",,"Bentley, Wilson Alwyn",American,1865,1931,1890s–1920s,1890,1929,Gelatin silver print,Image: 2 7/8 × 3 1/16 in. (7.3 × 7.8 cm) Sheet: 3 × 4 in. (7.6 × 10.1 cm),"Purchase, Alfred Stieglitz Society Gifts, 2015",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/670651,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.198,false,true,670652,Photographs,Photograph; Photomicrograph,[Snow Crystal],,,,,,Artist,,Wilson Alwyn Bentley,"American, 1865–1931",,"Bentley, Wilson Alwyn",American,1865,1931,1890s–1920s,1890,1929,Gelatin silver print,Image: 2 15/16 in. × 3 in. (7.4 × 7.6 cm) Sheet: 3 × 4 in. (7.6 × 10.1 cm),"Purchase, Alfred Stieglitz Society Gifts, 2015",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/670652,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.199,false,true,670653,Photographs,Photograph; Photomicrograph,[Snow Crystal],,,,,,Artist,,Wilson Alwyn Bentley,"American, 1865–1931",,"Bentley, Wilson Alwyn",American,1865,1931,1890s–1920s,1890,1929,Gelatin silver print,Image: 2 15/16 × 3 1/8 in. (7.4 × 8 cm) Sheet: 3 in. × 3 15/16 in. (7.6 × 10 cm),"Purchase, Alfred Stieglitz Society Gifts, 2015",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/670653,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.200,false,true,670654,Photographs,Photograph; Photomicrograph,[Snow Crystal],,,,,,Artist,,Wilson Alwyn Bentley,"American, 1865–1931",,"Bentley, Wilson Alwyn",American,1865,1931,1890s–1920s,1890,1929,Gelatin silver print,Image: 2 15/16 × 3 1/8 in. (7.4 × 7.9 cm) Sheet: 3 × 4 in. (7.6 × 10.1 cm),"Purchase, Alfred Stieglitz Society Gifts, 2015",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/670654,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.201,false,true,669362,Photographs,Photograph,[Frost],,,,,,Artist,,Wilson Alwyn Bentley,"American, 1865–1931",,"Bentley, Wilson Alwyn",American,1865,1931,ca. 1910,1905,1915,Gelatin silver print,Image: 3 in. × 3 11/16 in. (7.6 × 9.4 cm) Sheet: 3 1/16 × 3 15/16 in. (7.7 × 10 cm),"Purchase, Alfred Stieglitz Society Gifts, 2015",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/669362,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.202,false,true,669363,Photographs,Photograph,[Dew on a Spider Web],,,,,,Artist,,Wilson Alwyn Bentley,"American, 1865–1931",,"Bentley, Wilson Alwyn",American,1865,1931,ca. 1910,1905,1915,Gelatin silver print,Image: 3 in. × 3 5/8 in. (7.6 × 9.2 cm) Sheet: 3 in. × 4 7/16 in. (7.6 × 11.2 cm),"Purchase, Alfred Stieglitz Society Gifts, 2015",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/669363,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.203,false,true,669364,Photographs,Photograph,[Self-Portrait with Camera],,,,,,Artist,,Wilson Alwyn Bentley,"American, 1865–1931",,"Bentley, Wilson Alwyn",American,1865,1931,ca. 1910,1905,1915,Gelatin silver print,Image: 2 13/16 × 3 3/4 in. (7.1 × 9.5 cm) Sheet: 3 in. × 4 1/16 in. (7.6 × 10.3 cm),"Purchase, Alfred Stieglitz Society Gifts, 2015",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/669364,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.55.3,false,true,286768,Photographs,Photograph,[Snow Crystal],,,,,,Artist,,Wilson Alwyn Bentley,"American, 1865–1931",,"Bentley, Wilson Alwyn",American,1865,1931,ca. 1910,1905,1915,Gelatin silver print,Image: 7.4 x 9 cm (2 15/16 x 3 9/16 in.),"Josh Rosenthal Fund, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286768,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2010.188,false,true,292061,Photographs,Postcard,Carving One of Our Watermelons,,,,,,Artist,,William H. Martin,"American, 1865–1940",,"Martin, William H.",American,1865,1940,1909,1909,1909,Gelatin silver print,Image: 8.7 x 14 cm (3 7/16 x 5 1/2 in.) Frame: 55.9 x 71.1 cm (22 x 28 in.) (Multiple postcards in frame),"Twentieth-Century Photography Fund, 2010",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/292061,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.460.5,false,true,288710,Photographs,Postcard,A Load of Fancy Poultry,,,,,,Artist,,William H. Martin,"American, 1865–1940",,"Martin, William H.",American,1865,1940,1909,1909,1909,Gelatin silver print,Image: 8.4 x 14 cm (3 5/16 x 5 1/2 in.) Frame: 55.9 x 71.1 cm (22 x 28 in.) (Multiple postcards in frame),"Gift of Charles Isaacs and Carol Nigro, 2007",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288710,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.460.6,false,true,288711,Photographs,Postcard,A Unique Bungalow,,,,,,Artist,,William H. Martin,"American, 1865–1940",,"Martin, William H.",American,1865,1940,1909,1909,1909,Gelatin silver print,Image: 8.4 x 14 cm (3 5/16 x 5 1/2 in.) Frame: 55.9 x 71.1 cm (22 x 28 in.) (Multiple postcards in frame),"Gift of Charles Isaacs and Carol Nigro, 2007",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288711,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.460.7,false,true,288712,Photographs,Postcard,Great Sport Shooting Rabbits in Iowa,,,,,,Artist,,William H. Martin,"American, 1865–1940",,"Martin, William H.",American,1865,1940,1909,1909,1909,Gelatin silver print,Image: 8.4 x 14 cm (3 5/16 x 5 1/2 in.) Frame: 55.9 x 71.1 cm (22 x 28 in.) (Multiple postcards in frame),"Gift of Charles Isaacs and Carol Nigro, 2007",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288712,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.460.8,false,true,288713,Photographs,Postcard,Harvesting Wheat in Iowa,,,,,,Artist,,William H. Martin,"American, 1865–1940",,"Martin, William H.",American,1865,1940,1909,1909,1909,Gelatin silver print,Image: 8.4 x 14 cm (3 5/16 x 5 1/2 in.) Frame: 55.9 x 71.1 cm (22 x 28 in.) (Multiple postcards in frame),"Gift of Charles Isaacs and Carol Nigro, 2007",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288713,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -61.608.20,false,true,270412,Photographs,Photograph,"Maxim Gorky and Zena Peschkoff, His Adopted Son",,,,,,Artist,,Alice Boughton,"American, 1865–1943",,"Boughton, Alice",American,1865,1943,ca. 1910,1908,1912,Platinum print,,"Gift of Miss Elma Loines, 1961",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/270412,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1987.1100.432.1,false,true,265501,Photographs,Photograph,[Man Sitting at Edge of Swimming Pool with Surface Water Reflections],,,,,,Artist,,Louis Fleckenstein,"American, 1866–1943",,"Fleckenstein, Louis",American,1866,1943,1931,1931,1931,Gelatin silver print,25.2 x 20.1 cm (9 15/16 x 7 15/16 in.),"Ford Motor Company Collection, Gift of Ford Motor Company and John C. Waddell, 1987",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/265501,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1987.1100.432.2,false,true,265502,Photographs,Photograph,[The Boy Scouts Swimming Pool at Idyllwild],,,,,,Artist,,Louis Fleckenstein,"American, 1866–1943",,"Fleckenstein, Louis",American,1866,1943,1931,1931,1931,Gelatin silver print,7.8 x 5.4 cm (3 1/16 x 2 1/8 in.),"Ford Motor Company Collection, Gift of Ford Motor Company and John C. Waddell, 1987",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/265502,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1977.630.45,false,true,262075,Photographs,Photograph,[Road Through Flooded Land],,,,,,Artist,,Morgan Whitney,"American, 1869–1913",,"Whitney, Morgan",American,1869,1913,1890s–1900s,1890,1909,Platinum print,16.7 x 11.5 cm. (6 9/16 x 4 1/2 in.),"Gift of Mr. and Mrs. Morgan Whitney, 1977",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/262075,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.43.174,false,true,267576,Photographs,Photograph,A Sioux Chief,,,,,,Artist,,Joseph T. Keiley,"American, 1869–1914",,"Keiley, Joseph T.",American,1869,1914,ca. 1898,1896,1900,Platinum print,19.3 x 14.1 cm. (7 5/8 x 5 9/16 in.),"Alfred Stieglitz Collection, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267576,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.43.179,false,true,267581,Photographs,Photograph,[The Averted Head - A Study in Flesh Tones],,,,,,Artist,,Joseph T. Keiley,"American, 1869–1914",,"Keiley, Joseph T.",American,1869,1914,1899,1899,1899,Platinum print,16.5 x 10.5 cm. (6 1/2 x 4 1/8 in.),"Alfred Stieglitz Collection, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267581,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.43.185,false,true,267588,Photographs,Photograph,A Bacchante,,,,,,Artist,,Joseph T. Keiley,"American, 1869–1914",,"Keiley, Joseph T.",American,1869,1914,1899,1899,1899,Platinum-palladium print,24.5 x 19.3 cm. (9 5/8 x 7 5/8 in.),"Alfred Stieglitz Collection, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267588,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.43.187,false,true,267590,Photographs,Photograph,Indian Head,,,,,,Artist,,Joseph T. Keiley,"American, 1869–1914",,"Keiley, Joseph T.",American,1869,1914,1898,1898,1898,Platinum print,19.8 x 14.5 cm. (7 13/16 x 5 11/16 in.),"Alfred Stieglitz Collection, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267590,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.43.417,false,true,267834,Photographs,Photograph,A Sioux Chief,,,,,,Artist,,Joseph T. Keiley,"American, 1869–1914",,"Keiley, Joseph T.",American,1869,1914,1898,1898,1898,Platinum print,19.5 x 13.0 cm. (7 11/16 x 5 1/8 in.),"Alfred Stieglitz Collection, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267834,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.291,false,true,285686,Photographs,Photograph,In the Circus,,,,,,Artist,,Harry Cogswell Rubincam,"American, 1871–1940",,"Rubincam, Harry Cogswell",American,1871,1940,1905,1905,1905,Platinum print,"Image: 21.2 x 15.7 cm (8 3/8 x 6 3/16 in.), irregular Sheet: 21.6 x 16.4 cm (8 1/2 x 6 7/16 in.) Mount: 37.8 x 28.8 cm (14 7/8 x 11 5/16 in.)","Gilman Collection, Purchase, Ann Tenenbaum and Thomas H. Lee Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285686,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.126,false,true,283258,Photographs,Photograph,"[Lynching, Russellville, Kentucky]",,,,,,Artist,,Minor B. Wade,"American, 1874–1932",,"Wade, Minor B.",American,1874,1932,1908,1908,1908,Gelatin silver print,Image: 11.8 x 9.1 cm (4 5/8 x 3 9/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283258,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1998.91,false,true,282210,Photographs,Photograph,Midnight at the Bowery Mission Bread Line,,,,,,Artist,,Lewis Hine,"American, 1874–1940",,"Hine, Lewis",American,1874,1940,1906–7,1906,1907,Gelatin silver print,11.5 x 14.9 cm (4 1/2 x 5 7/8 in. ),"Gift of John C. Waddell, 1998",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/282210,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.338,false,true,286820,Photographs,Photograph,"Jo Lehman, a 7 year old newsboy. 824 Third Ave., N.Y. City. He was selling in this Saloon. I asked him about the badge he was wearing. ""Oh! Dat's me bruder's,"" he said. Location: New York, New York.",,,,,,Artist,,Lewis Hine,"American, 1874–1940",,"Hine, Lewis",American,1874,1940,July 1910,1910,1910,Gelatin silver print,Image: 11.6 x 9.5 cm (4 9/16 x 3 3/4 in.),"Gilman Collection, Purchase, The Howard Gilman Foundation Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286820,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2014.438,false,true,650860,Photographs,Photograph,"The Morning Attendance at the Mill School, Huntsville, Alabama",,,,,,Artist,,Lewis Hine,"American, 1874–1940",,"Hine, Lewis",American,1874,1940,December 1913,1913,1913,Gelatin silver print,Image: 4 7/16 × 6 7/16 in. (11.3 × 16.4 cm) Sheet: 4 15/16 × 6 15/16 in. (12.5 × 17.6 cm),"Purchase, The Overbrook Foundation Gift and funds from various donors, 2014",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/650860,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -54.549.56,false,true,269725,Photographs,Photograph,Steamfitter,,,,,,Artist,,Lewis Hine,"American, 1874–1940",,"Hine, Lewis",American,1874,1940,1910s,1910,1919,Gelatin silver print,Image: 34.5 × 24.7 cm (13 9/16 × 9 3/4 in.) Sheet: 35.4 × 27.9 cm (13 15/16 × 11 in.),"Gift of Clarence McK. Lewis, 1954",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269725,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.588.1,false,true,259722,Photographs,Photograph,"Boy carrying hats. Blee[c]ker St., N.Y.",,,,,,Artist,,Lewis Hine,"American, 1874–1940",,"Hine, Lewis",American,1874,1940,February 1912,1912,1912,Gelatin silver print,11.5 x 14.7 cm (4 1/2 x 5 13/16 in.),"Bequest of Edwin De T. Bechtel, by exchange, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/259722,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.588.6,false,true,259727,Photographs,Photograph,"Ivey Mill, Hickory, N.C. Little one, 3 years old, who visits and plays in the mill. Daughter of the overseer.",,,,,,Artist,,Lewis Hine,"American, 1874–1940",,"Hine, Lewis",American,1874,1940,November 1898,1908,1908,Gelatin silver print,Image: 11.7 x 16.9 cm (4 5/8 x 6 5/8 in.),"Bequest of Edwin De T. Bechtel, by exchange, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/259727,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.727.1,true,true,259797,Photographs,Photograph,"11:00 A.M. Monday, May 9th, 1910. Newsies at Skeeter's Branch, Jefferson near Franklin. They were all smoking. Location: St. Louis, Missouri.",,,,,,Artist,,Lewis Hine,"American, 1874–1940",,"Hine, Lewis",American,1874,1940,"May 9, 1910",1910,1910,Gelatin silver print,9.1 x 11.9 cm (3 9/16 x 4 11/16 in.),"Gift of Phyllis D. Massar, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/259797,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2011.553.7,false,true,301919,Photographs,Photograph,"Addie Card, 12 years. Spinner in North Pownal Cotton Mill. Girls in mill say she is ten years. She admitted to me she was twelve; that she started during school vacation and now would ""stay"". Location: Vermont",,,,,,Artist,,Lewis Hine,"American, 1874–1940",,"Hine, Lewis",American,1874,1940,1910,1910,1910,Gelatin silver print,Image: 17.7 x 12.7 cm (6 15/16 x 5 in.) Sheet: 16.8 x 11.9 cm (6 5/8 x 4 11/16 in.),"Gift of Joyce F. Menschel, 2011",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/301919,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2011.553.8,false,true,301920,Photographs,Photograph,"Mill Children #440, South Carolina",,,,,,Artist,,Lewis Hine,"American, 1874–1940",,"Hine, Lewis",American,1874,1940,1908,1908,1908,Gelatin silver print,Image: 11.9 x 16.9 cm (4 11/16 x 6 5/8 in.),"Gift of Joyce F. Menschel, 2011",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/301920,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1993.43.289,false,true,266949,Photographs,Photograph,Three National Child Labor Committee Exhibition Panels,,,,,,Artist,,Lewis Hine,"American, 1874–1940",,"Hine, Lewis",American,1874,1940,1913–14,1913,1914,Gelatin silver print,8.9 x 15.0 cm (3 1/2 x 5 15/16 in.),"Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 1993",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/266949,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.128,false,true,283260,Photographs,Photograph,"Newsboy asleep on stairs with papers, Jersey City, New Jersey",,,,,,Artist,,Lewis Hine,"American, 1874–1940",,"Hine, Lewis",American,1874,1940,February 1912,1912,1912,Gelatin silver print,Image: 11.5 x 16.8 cm (4 1/2 x 6 5/8 in.),"Gilman Collection, Purchase, Ann Tenenbaum and Thomas H. Lee Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283260,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.730,false,true,285844,Photographs,Photograph,"Addie Card, 12 years. Spinner in North Pownal Cotton Mill. Girls in mill say she is ten years. She admitted to me she was twelve; that she started during school vacation and now would ""stay"". Location: Vermont",,,,,,Artist,,Lewis Hine,"American, 1874–1940",,"Hine, Lewis",American,1874,1940,August 1910,1910,1910,Gelatin silver print,Image: 24.4 x 19.3 cm (9 5/8 x 7 5/8 in.) Sheet: 25.4 x 20.4 cm (10 x 8 1/16 in.),"Gilman Collection, Purchase, Anonymous Gifts, by exchange, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285844,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1987.1100.119,false,true,265154,Photographs,Photograph,"Icarus, Empire State Building",,,,,,Artist,,Lewis Hine,"American, 1874–1940",,"Hine, Lewis",American,1874,1940,1930,1930,1930,Gelatin silver print,18.7 x 23.7 cm (7 3/8 x 9 5/16 in.),"Ford Motor Company Collection, Gift of Ford Motor Company and John C. Waddell, 1987",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/265154,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1987.1100.146,false,true,265184,Photographs,Photograph,Steamfitter,,,,,,Artist,,Lewis Hine,"American, 1874–1940",,"Hine, Lewis",American,1874,1940,1921,1921,1921,Gelatin silver print,42.1 x 30.9 cm (16 9/16 x 12 3/16 in.),"Ford Motor Company Collection, Gift of Ford Motor Company and John C. Waddell, 1987",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/265184,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1987.1100.325,false,true,265382,Photographs,Photograph,Empire State Building,,,,,,Artist,,Lewis Hine,"American, 1874–1940",,"Hine, Lewis",American,1874,1940,1930s,1930,1939,Gelatin silver print,10.0 x 12.2 cm (3 15/16 x 4 13/16 in.),"Ford Motor Company Collection, Gift of Ford Motor Company and John C. Waddell, 1987",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/265382,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1987.1100.486,false,true,265559,Photographs,Photograph,"Icarus, Empire State Building",,,,,,Artist,,Lewis Hine,"American, 1874–1940",,"Hine, Lewis",American,1874,1940,1930,1930,1930,Gelatin silver print,9.1 x 11.5 cm (3 9/16 x 4 1/2 in.),"Ford Motor Company Collection, Gift of Ford Motor Company and John C. Waddell, 1987",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/265559,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.660.11,false,true,259735,Photographs,Photograph,"Financial District, From the Hotel Bossert",,,,,,Artist,,Samuel H. Gottscho,"American, 1875–1971",,"Gottscho, Samuel H.",American,1875,1971,"1933, printed later",1933,1933,Gelatin silver print,16.7 x 24 cm (6 9/16 x 9 7/16 in. ),"Purchase, Florance Waterbury Bequest, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/259735,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1987.1100.96,false,true,265626,Photographs,Photograph,"[Locomotive, with Entrance to Perisphere of 1939 New York World's Fair in Background]",,,,,,Artist,,Samuel H. Gottscho,"American, 1875–1971",,"Gottscho, Samuel H.",American,1875,1971,ca. 1939,1937,1941,Gelatin silver print,33.0 x 25.4 cm (13 x 10 in.),"Ford Motor Company Collection, Gift of Ford Motor Company and John C. Waddell, 1987",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/265626,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1987.1100.402,false,true,265468,Photographs,Photograph,"[Fountains, 1939 New York World's Fair, with Trylon and Perisphere in Background]",,,,,,Artist,,Samuel H. Gottscho,"American, 1875–1971",,"Gottscho, Samuel H.",American,1875,1971,ca. 1939,1937,1941,Gelatin silver print,31.7 x 25.4 cm (12 1/2 x 10 in.),"Ford Motor Company Collection, Gift of Ford Motor Company and John C. Waddell, 1987",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/265468,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1987.1100.403,false,true,265469,Photographs,Photograph,"[1939 New York World's Fair, Entrance to Perisphere]",,,,,,Artist,,Samuel H. Gottscho,"American, 1875–1971",,"Gottscho, Samuel H.",American,1875,1971,ca. 1939,1937,1941,Gelatin silver print,31.4 x 25.5 cm. (12 3/8 x 10 1/16 in.),"Ford Motor Company Collection, Gift of Ford Motor Company and John C. Waddell, 1987",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/265469,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1987.1100.404,false,true,265470,Photographs,Photograph,"Trylon and Perisphere, New York World's Fair",,,,,,Artist,,Samuel H. Gottscho,"American, 1875–1971",,"Gottscho, Samuel H.",American,1875,1971,ca. 1939,1937,1941,Gelatin silver print,33.0 x 21.6 cm (13 x 8 1/2 in.),"Ford Motor Company Collection, Gift of Ford Motor Company and John C. Waddell, 1987",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/265470,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1987.1100.243,false,true,265292,Photographs,Photograph,[Old Man with Boy],,,,,,Artist,,Doris Ulmann,"American, 1882–1934",,"Ulmann, Doris",American,1882,1934,1920s–30s,1920,1939,Platinum print,20.4 x 15.4 cm (8 x 6 1/16 in.),"Ford Motor Company Collection, Gift of Ford Motor Company and John C. Waddell, 1987",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/265292,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1987.1100.274,false,true,265326,Photographs,Photograph,[Bell Ringer Outside a Church],,,,,,Artist,,Doris Ulmann,"American, 1882–1934",,"Ulmann, Doris",American,1882,1934,1920s–30s,1920,1939,Platinum print,20.1 x 15.1 cm (7 15/16 x 5 15/16 in.),"Ford Motor Company Collection, Gift of Ford Motor Company and John C. Waddell, 1987",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/265326,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1987.1100.276,false,true,265328,Photographs,Photograph,[Man with Bridle],,,,,,Artist,,Doris Ulmann,"American, 1882–1934",,"Ulmann, Doris",American,1882,1934,1920s–30s,1920,1939,Platinum print,20.4 x 15.1 cm (8 x 5 15/16 in.),"Ford Motor Company Collection, Gift of Ford Motor Company and John C. Waddell, 1987",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/265328,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1987.1100.284,false,true,265336,Photographs,Photograph,The Corn Crib,,,,,,Artist,,Doris Ulmann,"American, 1882–1934",,"Ulmann, Doris",American,1882,1934,1918,1918,1918,Bromoil print,20.5 x 15.9 cm (8 1/16 x 6 1/4 in.),"Ford Motor Company Collection, Gift of Ford Motor Company and John C. Waddell, 1987",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/265336,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1987.1100.271,false,true,265323,Photographs,Photograph,Grotesque Shadows,,,,,,Artist,,Roland E. Schneider,"American, 1884–1934",,"Schneider, Roland E.",American,1884,1934,1920s,1920,1929,Gelatin silver print,23.3 x 18.8 cm (9 3/16 x 7 3/8 in.),"Ford Motor Company Collection, Gift of Ford Motor Company and John C. Waddell, 1987",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/265323,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2001.390,false,true,284645,Photographs,Photograph,"Alabama Plow Girl, near Eutaw, Alabama",,,,,,Artist,,Dorothea Lange,"American, 1895–1965",,"Lange, Dorothea",American,1895,1965,1936,1936,1936,Gelatin silver print,19.1 x 19.4 cm (7 1/2 x 7 5/8 in. ),"Purchase, Alfred Stieglitz Society Gifts, 2001",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/284645,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2000.351.3,false,true,284150,Photographs,Photograph,"[Migrant Pea Picker's Makeshift Home, Nipomo, California]",,,,,,Artist,,Dorothea Lange,"American, 1895–1965",,"Lange, Dorothea",American,1895,1965,February 1936,1936,1936,Gelatin silver print,18.2 x 24.5 cm (7 3/16 x 9 5/8 in. ),"Purchase, Jennifer and Joseph Duke Gift, 2000",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/284150,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2001.296,false,true,284650,Photographs,Photograph,"[African American Mother and Child on Bed in their Cabin near Jefferson, Texas]",,,,,,Artist,,Russell Lee,"American, 1903–1986",,"Lee, Russell",American,1903,1986,1939,1939,1939,Gelatin silver print,18.2 x 24.3 cm (7 3/16 x 9 9/16 in. ),"Purchase, Alfred Stieglitz Society Gifts, 2001",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/284650,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2001.297,false,true,284653,Photographs,Photograph,"[Cotton Pickers with Knee Pads, Lehi, Arkansas]",,,,,,Artist,,Russell Lee,"American, 1903–1986",,"Lee, Russell",American,1903,1986,1938,1938,1938,Gelatin silver print,16.5 x 24.2 cm (6 1/2 x 9 1/2 in. ),"Purchase, Alfred Stieglitz Society Gifts, 2001",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/284653,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2001.298,false,true,284663,Photographs,Photograph,"[African-American Family at Gee's Bend, Alabama]",,,,,,Artist,,Arthur Rothstein,"American, 1915–1985",,"Rothstein, Arthur",American,1915,1985,1937,1937,1937,Gelatin silver print,18.1 x 24.1 cm (7 1/8 x 9 1/2 in. ),"Purchase, Alfred Stieglitz Society Gifts, 2001",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/284663,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1987.1100.64,false,true,265591,Photographs,Photograph,"Son of Sharecropper - Mississippi Country, Arkansas",,,,,,Artist,,Arthur Rothstein,"American, 1915–1985",,"Rothstein, Arthur",American,1915,1985,1935,1935,1935,Gelatin silver print,25.5 x 20.2 cm (10 1/16 x 7 15/16 in.),"Ford Motor Company Collection, Gift of Ford Motor Company and John C. Waddell, 1987",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/265591,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2011.478,false,true,299311,Photographs,Carte-de-visite,"Rebecca, Charley and Rosa, Slave Children from New Orleans",,,,,,Artist,,Myron H. Kimball,"American, active 1860s",,"Kimball, Myron H.",American,1860,1860,1863–64,1863,1864,Albumen silver print from glass negative,Image: 8.4 x 5.4 cm (3 5/16 x 2 1/8 in.) Mount: 10.1 x 6.2 cm (4 x 2 7/16 in.),"The Horace W. Goldsmith Foundation Fund, through Joyce and Robert Menschel, 2011",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/299311,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2014.273,false,true,645493,Photographs,Photograph,"A Burial Party, Cold Harbor, Virginia.",,,,,,Artist,,John Reekie,"American, active 1860s",,"Reekie, John",American,1860,1869,April 1865,1865,1865,Albumen silver print from glass negative,Image: 7 × 9 in. (17.8 × 22.9 cm) Mount: 12 5/8 × 17 1/2 in. (32 × 44.4 cm),"Purchase, W. Bruce and Delaney H. Lundberg Gift and The Horace W. Goldsmith Foundation Fund, through Joyce and Robert Menschel, 2014",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/645493,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.36,false,true,685378,Photographs,Carte-de-visite,[William Holbrook Beard],,,,,,Artist,,J. T. Upson,"American, active 1860s",,"Upson, J. T.",American,1859,1870,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685378,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.50,false,true,685392,Photographs,Carte-de-visite,[Eugene Benson],,,,,,Artist,,Maurice Stadtfeld,"American, active 1860s",,"Stadtfeld, Maurice",American,1860,1860,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685392,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.92,false,true,283194,Photographs,Photograph,Emancipated Slaves Brought from Louisiana by Colonel George H. Banks,,,,,,Artist,,Myron H. Kimball,"American, active 1860s",,"Kimball, Myron H.",American,1860,1860,December 1863,1863,1863,Albumen silver print from glass negative,"Image: 13.2 x 18.3cm (5 3/16 x 7 3/16in.), oblong oval Mat: 19.9 x 25.2 cm (7 13/16 x 9 15/16 in.)","Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283194,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.286,false,true,685627,Photographs,Carte-de-visite,[George Augustus Baker],,,,,,Artist,,Maurice Stadtfeld,"American, active 1860s",,"Stadtfeld, Maurice",American,1860,1860,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685627,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.402,false,true,685742,Photographs,Carte-de-visite,[William John Hennessy],,,,,,Artist,,Maurice Stadtfeld,"American, active 1860s",,"Stadtfeld, Maurice",American,1860,1860,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/685742,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.737,false,true,686076,Photographs,Carte-de-visite,[Morrell],,,,,,Artist,,Maurice Stadtfeld,"American, active 1860s",,"Stadtfeld, Maurice",American,1860,1860,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/686076,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.815,false,true,686154,Photographs,Carte-de-visite,[A.H. Wenzler],,,,,,Artist,,Maurice Stadtfeld,"American, active 1860s",,"Stadtfeld, Maurice",American,1860,1860,1860s,1860,1869,Albumen silver print,Approx. 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/686154,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1231,false,true,285894,Photographs,Photograph,"Duryea Zouaves, Fort Schuyler Adjuant Mess",,,,,,Artist,,Stacy,"American, active 1860s",,Stacy,American,1860,1869,"May 18, 1861",1861,1861,Albumen silver print from glass negative,5 9/16 x 7 1/2,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285894,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.457.3681,false,true,291274,Photographs,Photographs,Reception of the Japanese Embassy at the Battery,,,,,,Artist,Attributed to,George Stacy,"American, active 1860s",,"Stacy, George",American,1860,1860,1860,1860,1860,Albumen silver print from glass negative,"Image: 7.4 x 14.6 cm (2 15/16 x 5 3/4 in.), arch-topped Mount: 18.4 x 17.5 cm (7 1/4 x 6 7/8 in.)","Herbert Mitchell Collection, 2007",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/291274,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.386,false,true,285781,Photographs,Photograph,James Hyatt Inhaling Chlorine Gas,,,,,,Artist,,Peter Welling,"American, active c. 1850s",,"Welling, Peter",American,1850,1859,1850–55,1850,1855,Daguerreotype,Image: 2 11/16 × 2 3/16 in. (6.9 × 5.6 cm); visible Overall: 3 5/8 × 3 1/8 in. (9.2 × 8 cm),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285781,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.329,false,true,285986,Photographs,Photographs,"The Lincoln Funeral Train, Philadelphia",,,,,,Artist,,Charles L. Philippi,"American, active 1861–74",,"Philippi, Charles L.",American,1861,1874,"April 22–24, 1865",1865,1865,Albumen silver print from glass negative,Image: 24 x 33.8 cm (9 7/16 x 13 5/16 in.) Mount: 26.5 x 34.8 cm (10 7/16 x 13 11/16 in.),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285986,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2000.124,false,true,283717,Photographs,Photograph,"Gibson's Breaker, Rushdale, Pennsylvania",,,,,,Artist,,Thomas H. Johnson,"American, active 1860s–70s",,"Johnson, Thomas H.",American,1860,1870,1860s,1860,1869,Albumen silver print from glass negative,30.3 x 38.7 cm (11 15/16 x 15 1/4 in. ),"Purchase, Alfred Stieglitz Society Gifts, 2000",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283717,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1145,false,true,286517,Photographs,Cabinet card,Musical Mokes,,,,,,Artist,,J. Wood,"American, active 1870s–80s",,"Wood, J.",American,1870,1889,1860s,1860,1869,Albumen silver print from glass negative,Image: 5 7/8 × 3 7/8 in. (14.9 × 9.8 cm),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286517,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1142,false,true,286633,Photographs,Photograph,12 O'clock in the Deadening,,,,,,Artist,,John Horgan Jr.,"American, active 1880s–90s",,"Horgan Jr, John",American,1880,1899,ca. 1891,1889,1893,Albumen silver print from glass negative,Image: 17 in. × 19 1/4 in. (43.2 × 48.9 cm) Mount: 20 1/4 × 24 3/16 in. (51.5 × 61.5 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286633,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.400.41,false,true,291794,Photographs,Daguerreotype,[Young Man],,,,,,Artist,,Knickerbocker Gallery,"American, active ca. 1841–59",,Knickerbocker Gallery,American,1841,1859,1850s,1850,1859,Daguerreotype,Image: 7 x 5.7 cm (2 3/4 x 2 1/4 in.) Plate: 8.3 x 7 cm (3 1/4 x 2 3/4 in.) Case: 1.6 x 9.2 x 7.9 cm (5/8 x 3 5/8 x 3 1/8 in.),"Bequest of Herbert Mitchell, 2008",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/291794,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2011.477,false,true,299310,Photographs,Carte-de-visite,"Learning is Wealth—Wilson, Charley, Rebecca, and Rosa, Slaves from New Orleans",,,,,,Artist,,Charles Paxson,"American, active New York, 1860s",,"Paxson, Charles",American,1860,1869,1863–64,1863,1864,Albumen silver print from glass negative,Image: 8.5 x 5.3 cm (3 3/8 x 2 1/16 in.) Mount: 10.1 x 6.1 cm (4 x 2 3/8 in.),"The Horace W. Goldsmith Foundation Fund, through Joyce and Robert Menschel, 2011",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/299310,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1982.1182.1914,false,true,288312,Photographs,Stereographs,"German's Day, California Midwinter Exposition",,,,,,Artist,,Kilburn Brothers,"American, active ca. 1865–1890",,Kilburn Brothers,American,1863,1892,1850s–1910s,1850,1919,Albumen silver prints,Mount: 8.9 x 17.8 cm (3 1/2 x 7 in.),"Gift of Weston J. Naef, in memory of Kathleen W. Naef and Weston J. Naef Sr., 1982",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288312,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2004.454.2,false,true,285488,Photographs,Photograph,"[Interborough Rapid Transit (IRT) Construction, Broadway Looking North at 101st Street, New York City]",,,,,,Artist,,William B.,"American, active ca. 1900–1939",,"B., William",American,1900,1939,1900,1900,1900,Platinum print,Image: 19 x 24 cm (7 1/2 x 9 7/16 in.),"Purchase, Marlene Nathan Meyerson Family Foundation Gift, 2004",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285488,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2004.454.10,false,true,285486,Photographs,Photograph,"[Interborough Rapid Transit (IRT) Construction, 25th Street and Fourth Avenue, New York City]",,,,,,Artist,,W. R. C.,"American, active ca. 1900s–1930s",,"C., W. R.",American,1900,1939,1906,1906,1906,Platinum print,Image: 19 x 24 cm (7 1/2 x 9 7/16 in.),"Purchase, Marlene Nathan Meyerson Family Foundation Gift, 2004",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285486,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -57.670.2,false,true,269959,Photographs,Photograph,"[Stern-Wheeler Arriving at Silver Springs, Florida, after an Overnight Run up the St. Johns, Oklawaha, & Silver Rivers]",,,,,,Artist,,George Barker,"American, born Canada, 1844–1894",,"Barker, George",American,1844,1894,1886,1886,1886,Albumen silver print from glass negative,41.3 x 51.3 cm. (16 1/4 x 20 3/16 in.),"Gift of A. Hyatt Mayor, 1957",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269959,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.494.50,false,true,289322,Photographs,Photograph,Canton City,,,,,,Artist,,Milton M. Miller,"American, active China, 1830–1899",,"Miller, Milton",American,1830,1899,ca. 1869,1869,1869,Albumen silver print from glass negative,Image: 8 1/4 × 11 7/16 in. (20.9 × 29 cm),"Gilman Collection, Purchase, Robert Rosenkranz Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289322,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.494.54,false,true,289325,Photographs,Photograph,Mandarin Wife,,,,,,Artist,,Milton M. Miller,"American, active China, 1830–1899",,"Miller, Milton",American,1830,1899,1860–1863,1860,1863,Albumen silver print from glass negative,Image: 9 in. × 6 3/4 in. (22.9 × 17.2 cm),"Gilman Collection, Purchase, Robert Rosenkranz Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289325,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.494.55,false,true,289326,Photographs,Photograph,Madarin Wife,,,,,,Artist,,Milton M. Miller,"American, active China, 1830–1899",,"Miller, Milton",American,1830,1899,1861–1863,1861,1863,Albumen silver print from glass negative,Image: 11 1/8 × 8 3/4 in. (28.3 × 22.2 cm),"Gilman Collection, Purchase, Robert Rosenkranz Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289326,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.494.57,false,true,289328,Photographs,Photograph,Mandarin with Family,,,,,,Artist,,Milton M. Miller,"American, active China, 1830–1899",,"Miller, Milton",American,1830,1899,1860–1863,1860,1863,Albumen silver print from glass negative,Image: 8 3/16 × 11 1/8 in. (20.8 × 28.2 cm),"Gilman Collection, Purchase, Robert Rosenkranz Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/289328,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2004.251,false,true,285438,Photographs,Photograph,"[Waterfall, Constantine]",,,,,,Artist,,John Beasley Greene,"American, active France, 1832–1856",,"Greene, John Beasley",American,1832,1856,1856,1856,1856,Salted paper print from paper negative,Image: 23.3 x 30.1 cm (9 3/16 x 11 7/8 in.),"Purchase, Alfred Stieglitz Society Gifts, Anonymous Foundation Gift, W. Bruce and Delaney H. Lundberg Gift, and Marian and James H. Cohen Gift, in memory of their son, Michael Harrison Cohen, 2004",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285438,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.1063,true,true,266121,Photographs,Photograph,Medinet-Habu,,,,,,Artist,,John Beasley Greene,"American, active France, 1832–1856",,"Greene, John Beasley",American,1832,1856,1854,1854,1854,Salted paper print from paper negative,23.4 x 30.1 cm (9 3/16 x 11 7/8 in.),"Purchase, The Howard Gilman Foundation Gift, 1989",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/266121,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.63,false,true,283148,Photographs,Photograph,[The Nile in front of the Theban Hills],,,,,,Artist,,John Beasley Greene,"American, active France, 1832–1856",,"Greene, John Beasley",American,1832,1856,1853–54,1853,1854,Salted paper print from paper negative,Mount: 18 7/16 × 24 1/8 in. (46.9 × 61.2 cm) Image: 8 3/4 × 11 7/8 in. (22.3 × 30.2 cm),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283148,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.64,false,true,283149,Photographs,Photograph,Dakkeh,,,,,,Artist,,John Beasley Greene,"American, active France, 1832–1856",,"Greene, John Beasley",American,1832,1856,1853–54,1853,1854,Salted paper print from paper negative,Image: 9 3/16 × 11 7/8 in. (23.4 × 30.2 cm) Mount: 18 1/2 × 24 1/8 in. (47 × 61.2 cm),"Gilman Collection, Purchase, Joseph M. Cohen Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283149,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.276,false,true,286665,Photographs,Photograph,[Excavations near the Sphinx],,,,,,Artist,,John Beasley Greene,"American, active France, 1832–1856",,"Greene, John Beasley",American,1832,1856,1853,1853,1853,Salted paper print from paper negative,Image: 22.3 x 30.2 cm (8 3/4 x 11 7/8 in.) Mount: 18 11/16 × 24 5/16 in. (47.5 × 61.8 cm),"Gilman Collection, Purchase, Ann Tenenbaum and Thomas H. Lee Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286665,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.759,false,true,286131,Photographs,Photograph,"Etude de Palmiers, Bords du Nil, Kalabschi",,,,,,Artist,,John Beasley Greene,"American, active France, 1832–1856",,"Greene, John Beasley",American,1832,1856,1853–54,1853,1854,Salted paper print from paper negative,9 1/4 x 11 3/4,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286131,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.760,false,true,286130,Photographs,Photograph,[Island of Philae],,,,,,Artist,,John Beasley Greene,"American, active France, 1832–1856",,"Greene, John Beasley",American,1832,1856,1853–54,1853,1854,Salted paper print from paper negative,9 x 11 3/4,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286130,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.761,false,true,286135,Photographs,Photograph,Temple de Deboud,,,,,,Artist,,John Beasley Greene,"American, active France, 1832–1856",,"Greene, John Beasley",American,1832,1856,1853–54,1853,1854,Salted paper print from paper negative,9 1/8 x 11,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286135,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.762,false,true,286690,Photographs,Photograph,"[Boat in Harbor, Algeria]",,,,,,Artist,,John Beasley Greene,"American, active France, 1832–1856",,"Greene, John Beasley",American,1832,1856,1853–54,1853,1854,Salted paper print from paper negative,8 7/8 x 11 1/8,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286690,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.763,false,true,286689,Photographs,Photograph,"[Antiquities in the Museum at Cherchell, Algeria]",,,,,,Artist,,John Beasley Greene,"American, active France, 1832–1856",,"Greene, John Beasley",American,1832,1856,1853–54,1853,1854,Salted paper print from paper negative,Image: 11 7/16 × 8 15/16 in. (29.1 × 22.7 cm),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286689,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.764,false,true,286688,Photographs,Photograph,"[Constantine, Algeria]",,,,,,Artist,,John Beasley Greene,"American, active France, 1832–1856",,"Greene, John Beasley",American,1832,1856,1853–54,1853,1854,Salted paper print from paper negative,Image: 9 3/16 × 11 13/16 in. (23.4 × 30 cm),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286688,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.765,false,true,286129,Photographs,Photograph,"[Fragment of an Egyptian Statue in the Museum at Cherchell, Algeria]",,,,,,Artist,,John Beasley Greene,"American, active France, 1832–1856",,"Greene, John Beasley",American,1832,1856,1856,1856,1856,Salted paper print from paper negative,11 3/8 x 9 1/8,"Gilman Collection, Purchase, Mr. and Mrs. Henry R. Kravis Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286129,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.766,false,true,286124,Photographs,Photograph,"[Tents, Algeria]",,,,,,Artist,,John Beasley Greene,"American, active France, 1832–1856",,"Greene, John Beasley",American,1832,1856,1856,1856,1856,Salted paper print from paper negative,Image: 8 9/16 × 11 3/4 in. (21.7 × 29.8 cm),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286124,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.783,false,true,286094,Photographs,Photograph,"[Bank of the Rhumel, near Constantine, Algeria]",,,,,,Artist,,John Beasley Greene,"American, active France, 1832–1856",,"Greene, John Beasley",American,1832,1856,ca. 1855–56,1853,1858,Salted paper print from paper negative,9 3/8 x 12 1/8,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286094,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.849,false,true,285974,Photographs,Photograph,"Amada, Temple",,,,,,Artist,,John Beasley Greene,"American, active France, 1832–1856",,"Greene, John Beasley",American,1832,1856,1853–54,1853,1854,Salted paper print from paper negative,Image: 8 3/4 × 11 3/4 in. (22.3 × 29.9 cm) Mount: 18 9/16 × 24 1/8 in. (47.1 × 61.2 cm),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285974,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.400.28,false,true,291781,Photographs,Daguerreotype,[Seated Elderly Woman Wearing Plaid Dress and Bonnet],,,,,,Artist,,William Hardy Kent,"American, England 1819–1907 England",,"Kent, William Hardy",American,1819,1819,1854–60,1854,1860,Daguerreotype,Image: 9.2 x 6.4 cm (3 5/8 x 2 1/2 in.) Plate: 10.8 x 8.3 cm (4 1/4 x 3 1/4 in.) Frame: 11.7 x 9.4 cm (4 5/8 x 3 11/16 in.),"Bequest of Herbert Mitchell, 2008",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/291781,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.400.29,false,true,291782,Photographs,Daguerreotype,[Seated Middle-aged Woman Dressed in Finery],,,,,,Artist,,William Hardy Kent,"American, England 1819–1907 England",,"Kent, William Hardy",American,1819,1819,1854–60,1854,1860,Daguerreotype,Image: 9 x 6.3 cm (3 9/16 x 2 1/2 in.) Plate: 10.8 x 8.3 cm (4 1/4 x 3 1/4 in.) Frame: 12.1 x 9.2 cm (4 3/4 x 3 5/8 in.),"Bequest of Herbert Mitchell, 2008",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/291782,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.43.66,false,true,267861,Photographs,Photograph,Song of the Lily,,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1897,1897,1897,Platinum print,17.0 x 12.1 cm. (6 11/16 x 4 3/4 in.),"Alfred Stieglitz Collection, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267861,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.43.68,false,true,267863,Photographs,Photograph,Frau Willi Geiger (Clara),,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1900–1908,1900,1908,Platinum print,11.9 x 17.0 cm. (4 11/16 x 6 11/16 in.),"Alfred Stieglitz Collection, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267863,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.43.69,false,true,271730,Photographs,Photograph,Brigitta Wenz,,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1900,1900,1900,Platinum print,12.0 x 16.8 cm. (4 3/4 x 6 5/8 in.),"Alfred Stieglitz Collection, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271730,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.43.70,false,true,267865,Photographs,Photograph,Summer,,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1898,1898,1898,Platinum print,17.7 x 6.3 cm. (7 x 2 1/2 in.),"Alfred Stieglitz Collection, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267865,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.43.74,false,true,271746,Photographs,Photograph,Dr. Emanuel Lasker and His Brother,,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1907,1907,1907,Platinum print,15.5 x 12.1 cm. (6 1/8 x 4 3/4 in.),"Alfred Stieglitz Collection, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271746,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.43.76,false,true,271741,Photographs,Photograph,The Man in Armor (Self-Portrait),,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1898,1898,1898,Platinum print,17.3 x 11.9 cm. (6 13/16 x 4 11/16 in.),"Alfred Stieglitz Collection, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271741,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.43.87,false,true,271733,Photographs,Photograph,"Stieglitz, Steichen and Kuehn Admiring the Work of Frank Eugene",,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1907,1907,1907,Platinum print,12.1 x 16.8 cm. (4 3/4 x 6 5/8 in.),"Alfred Stieglitz Collection, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271733,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -55.635.7,false,true,271750,Photographs,Photograph,Alfred Stieglitz,,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1907,1907,1907,Platinum print,16.5 x 11.8 cm. (6 1/2 x 4 5/8 in.),"Alfred Stieglitz Collection, 1955",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271750,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.33,false,true,260240,Photographs,Photograph,Adam and Eve,,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,"1900s, printed 1909",1900,1909,Photogravure,17.8 x 12.8 cm. (7 x 5 1/16 in.),"Rogers Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260240,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.61,false,true,260272,Photographs,Photograph,Miss Gladys Lawrence - The Seashell,,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1910–13,1910,1913,Platinum print,,"Rogers Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260272,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.63,false,true,260274,Photographs,Photograph,The Guitar Player,,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,ca. 1908,1906,1910,Platinum print,,"Rogers Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260274,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.64,false,true,260275,Photographs,Photograph,The Oriental Bride,,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1900s,1900,1909,Platinum print,,"Rogers Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260275,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.65,false,true,260276,Photographs,Photograph,Joachim's Daughter,,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1899,1899,1899,Platinum print,,"Rogers Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260276,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.66,false,true,260277,Photographs,Photograph,Snakecharmer,,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1900–1908,1900,1908,Platinum print,,"Rogers Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260277,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.67,false,true,260278,Photographs,Photograph,Snakecharmer,,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1900–1908,1900,1908,Platinum print,,"Rogers Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260278,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.68,false,true,260279,Photographs,Photograph,Miss Nan N. - Indian Festival,,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1900s,1900,1909,Platinum print,,"Rogers Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260279,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.69,false,true,260280,Photographs,Photograph,The Misses Ide in Samoa,,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,ca. 1908,1906,1910,Platinum print,11.8 x 16.1 cm (4 5/8 x 6 5/16 in.),"Rogers Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260280,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.70,false,true,260282,Photographs,Photograph,Miss Ide,,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1890–1903,1890,1903,Platinum print,,"Rogers Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260282,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.71,false,true,260283,Photographs,Photograph,Baroness von W. of Vienna,,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1900s,1900,1909,Platinum print,,"Rogers Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260283,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.72,false,true,260284,Photographs,Photograph,Mirzl Wach. The Sister of Charity,,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1900s,1900,1909,Platinum print,,"Rogers Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260284,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.73,false,true,260285,Photographs,Photograph,Emmy G.,,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1900–1908,1900,1908,Platinum print,,"Rogers Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260285,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.74,false,true,260286,Photographs,Photograph,Emmy Geiger,,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1900–1908,1900,1908,Platinum print,11.9 x 16.4 cm (4 11/16 x 6 7/16 in.),"Rogers Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260286,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.75,false,true,260287,Photographs,Photograph,Emmy G.,,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1900–1908,1900,1908,Platinum print,,"Rogers Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260287,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.76,false,true,260288,Photographs,Photograph,Mrs. Wilm,,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1909,1909,1909,Platinum print,,"Rogers Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260288,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.78,false,true,260290,Photographs,Photograph,Lisl Bosse,,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1900s,1900,1909,Platinum print,,"Rogers Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260290,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.79,false,true,260291,Photographs,Photograph,Hortensia,,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1898,1898,1898,Platinum print,,"Rogers Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260291,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.81,false,true,260294,Photographs,Photograph,Miss Lilian C. Wiver in Her Workshop,,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1900s,1900,1909,Platinum print,,"Rogers Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260294,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.82,false,true,260295,Photographs,Photograph,Miss Lilian C. Wiver with Her Angora,,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1900s,1900,1909,Platinum print,Image: 16.7 × 12 cm (6 9/16 × 4 3/4 in.) Mount: 25.3 × 20 cm (9 15/16 × 7 7/8 in.),"Rogers Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260295,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.83,false,true,260296,Photographs,Photograph,Thilda H. - The Veiled Lady,,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1900s,1900,1909,Platinum print,,"Rogers Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260296,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.84,false,true,260297,Photographs,Photograph,Sweet Alice (Ben Bolt),,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1900s,1900,1909,Platinum print,,"Rogers Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260297,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.85,false,true,260298,Photographs,Photograph,The Painter's Wife,,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1900s,1900,1909,Platinum print,,"Rogers Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260298,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.86,false,true,260299,Photographs,Photograph,The Diva at Home,,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1900s,1900,1909,Platinum print,,"Rogers Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260299,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.87,false,true,260300,Photographs,Photograph,The Diva and Her Most Trusty Friend and Companion,,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1900–1908,1900,1908,Platinum print,,"Rogers Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260300,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.89,false,true,260302,Photographs,Photograph,On the Wabash (Miss D.),,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1900s,1900,1909,Platinum print,,"Rogers Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260302,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.90,false,true,260304,Photographs,Photograph,Frau Clara G.,,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1900–1908,1900,1908,Platinum print,,"Rogers Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260304,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.91,false,true,260305,Photographs,Photograph,Miss L.L.L.,,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1900s,1900,1909,Platinum print,,"Rogers Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260305,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.92,false,true,260306,Photographs,Photograph,Lise Lotte Lindström,,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1900s,1900,1909,Platinum print,,"Rogers Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260306,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.93,false,true,260307,Photographs,Photograph,Baroness Haltvayne,,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1900s,1900,1909,Platinum print,,"Rogers Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260307,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.94,false,true,260308,Photographs,Photograph,Maria von Seidl,,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1900s,1900,1909,Platinum print,,"Rogers Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260308,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.95,false,true,260309,Photographs,Photograph,Marie Struthers,,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1890–1903,1890,1903,Platinum print,,"Rogers Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260309,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.96,false,true,260310,Photographs,Photograph,The Pearl Necklace,,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1900s,1900,1909,Platinum print,,"Rogers Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260310,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.97,false,true,260311,Photographs,Photograph,Dolly Varden,,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1900s,1900,1909,Platinum print,,"Rogers Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260311,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.98,false,true,260312,Photographs,Photograph,Dolly Varden,,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1900s,1900,1909,Platinum print,,"Rogers Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260312,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.99,false,true,260313,Photographs,Photograph,Lydia Leslie Lydie - Candlestick,,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1900s,1900,1909,Platinum print,,"Rogers Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260313,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.100,false,true,260106,Photographs,Photograph,Fritzi von Derra - The Exotic Dancer,,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1900s,1900,1909,Platinum print,,"Rogers Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260106,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.101,false,true,260107,Photographs,Photograph,Fritzi von Derra - Greek Dancer,,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1900s,1900,1909,Platinum print,,"Rogers Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260107,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.102,false,true,260108,Photographs,Photograph,Fritzi von Derra - The Greek Dancer,,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1900s,1900,1909,Platinum print,,"Rogers Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260108,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.103,false,true,260109,Photographs,Photograph,Fritzi von Derra - The Greek Dancer,,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1900s,1900,1909,Platinum print,,"Rogers Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260109,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.104,false,true,260110,Photographs,Photograph,Fritzi von Derra - The Greek Dancer,,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1900s,1900,1909,Platinum print,,"Rogers Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260110,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.105,false,true,260111,Photographs,Photograph,Fritzi von Derra - The Oriental Dancer,,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1900s,1900,1909,Platinum print,,"Rogers Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260111,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.106,false,true,260112,Photographs,Photograph,The Baronin B. and Miss M. - Rosenkavalier,,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1900s,1900,1909,Platinum print,,"Rogers Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260112,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.107,false,true,260113,Photographs,Photograph,"""Fisherman's Luck"" - Henry Heyligers and Wife",,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1900s,1900,1909,Platinum print,,"Rogers Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260113,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.108,false,true,260114,Photographs,Photograph,Frau Frieda and Franz S,,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1900s,1900,1909,Platinum print,,"Rogers Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260114,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.109,false,true,260115,Photographs,Photograph,Four Sisters,,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1900s,1900,1909,Platinum print,,"Rogers Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260115,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.110,false,true,260117,Photographs,Photograph,The Cake Walk,,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1900s,1900,1909,Platinum print,,"Rogers Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260117,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.111,false,true,260118,Photographs,Photograph,Ritual Vestalis,,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1900s,1900,1909,Platinum print,,"Rogers Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260118,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.112,false,true,260119,Photographs,Photograph,Slumbering Maidens,,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1900s,1900,1909,Platinum print,,"Rogers Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260119,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.113,false,true,260120,Photographs,Photograph,The Graduating Class,,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1913,1913,1913,Platinum print,,"Rogers Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260120,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.114,false,true,260121,Photographs,Photograph,"Misses Weaver H. Patties' School for Girls, Munich",,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1912,1912,1912,Platinum print,,"Rogers Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260121,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.115,false,true,260122,Photographs,Photograph,Marie R. and Cryma,,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1900s,1900,1909,Platinum print,,"Rogers Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260122,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.116,false,true,260123,Photographs,Photograph,SKH Prinzregent Ludwig von Bayern,,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1912–13,1912,1913,Platinum print,23.1 x 15.8 cm (9 1/16 x 6 3/16 in.),"Rogers Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260123,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.117,false,true,260124,Photographs,Photograph,Crown Prince Ludwig III of Bavaria,,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1912,1912,1912,Platinum print,,"Rogers Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260124,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.118,false,true,260125,Photographs,Photograph,Crown Prince Rupprecht of Bavaria,,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1900–1908,1900,1908,Platinum print,,"Rogers Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260125,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.120,false,true,260128,Photographs,Photograph,H.R.H. Prince Leopold and Prince Johann Albrecht of Bavaria,,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1900–1908,1900,1908,Platinum print,,"Rogers Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260128,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.121,false,true,260129,Photographs,Photograph,H.R.H. Prince Leopold of Bavaria,,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1900–1908,1900,1908,Platinum print,,"Rogers Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260129,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.122,false,true,260130,Photographs,Photograph,H.R.H. Prince Leopold and His Hobbyhorse,,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1900–1908,1900,1908,Platinum print,,"Rogers Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260130,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.123,false,true,260131,Photographs,Photograph,"H.R.H. Prince Albrecht Johann. ""With Neither Crown Nor Scepter""",,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1900–1908,1900,1908,Platinum print,,"Rogers Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260131,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.124,false,true,260132,Photographs,Photograph,"H.R.H. King Friedrich August of Saxony, Taken Shortly Before Dethronement",,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,ca. 1913,1911,1915,Platinum print,,"Rogers Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260132,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.125,false,true,260133,Photographs,Photograph,Dr. Paul Heyse,,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1900–1914,1900,1914,Platinum print,,"Rogers Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260133,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.126,false,true,260134,Photographs,Photograph,"Sigmund von Hausegger, Kapellmeister",,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1910–24,1910,1924,Platinum print,,"Rogers Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260134,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.127,false,true,260135,Photographs,Photograph,Professor Fritz von Miller,,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1910–13,1910,1913,Platinum print,,"Rogers Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260135,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.128,false,true,260136,Photographs,Photograph,Professor Georg Witkowski,,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1900s,1900,1909,Platinum print,,"Rogers Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260136,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.129,false,true,260137,Photographs,Photograph,Jesko von Puttkamer,,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1910–23,1910,1923,Platinum print,,"Rogers Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260137,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.131,false,true,260139,Photographs,Photograph,Joseph Pennell,,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1910–24,1910,1924,Platinum print,,"Rogers Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260139,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.132,false,true,260140,Photographs,Photograph,"Stieglitz, Steichen, Smith and Kuehn Admiring the Work of Frank Eugene",,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1907,1907,1907,Platinum print,Image: 10.2 x 16.2 cm (4 x 6 3/8 in.) Mount: 25.2 x 17.5 cm (9 15/16 x 6 7/8 in.),"Rogers Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260140,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.133,false,true,260141,Photographs,Photograph,Josef Geis as Beckmeser,,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1900s,1900,1909,Platinum print,,"Rogers Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260141,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.134,false,true,260142,Photographs,Photograph,[Farmyard],,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1886,1886,1886,Gum bichromate print,14.3 x 19.4 cm (5 5/8 x 7 5/8 in.),"Rogers Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260142,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.135,false,true,260143,Photographs,Photograph,[Landscape with River and Trees],,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1886,1886,1886,Gum bichromate print,19.3 x 16.0 cm (7 5/8 x 6 5/16 in.),"Rogers Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260143,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.137,false,true,260145,Photographs,Photograph,Frank Jefferson,,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1898,1898,1898,Platinum print,Image: 16.8 × 11.8 cm (6 5/8 × 4 5/8 in.),"Rogers Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260145,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.138,false,true,260146,Photographs,Photograph,The Man in Armor,,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1898,1898,1898,Platinum print,17.6 x 12.9 cm (6 15/16 x 5 1/16 in.),"Rogers Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260146,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.140,false,true,260149,Photographs,Photograph,Alfred Stieglitz,,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1890s,1890,1899,Platinum print,16.4 x 11.0 cm (6 7/16 x 4 5/16 in.),"Rogers Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260149,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.143,false,true,260152,Photographs,Photograph,Fredy,,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1901,1901,1901,Platinum print,,"Rogers Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260152,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.144,false,true,260153,Photographs,Photograph,Fredy,,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1901,1901,1901,Platinum print,,"Rogers Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260153,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.145,false,true,260154,Photographs,Photograph,Fredy,,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1901,1901,1901,Platinum print,,"Rogers Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260154,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.146,false,true,260155,Photographs,Photograph,Fredy,,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1901,1901,1901,Platinum print,,"Rogers Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260155,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.149,false,true,260158,Photographs,Photograph,Emanuel von Seidl,,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1900–1908,1900,1908,Platinum print,,"Rogers Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260158,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.150,false,true,260160,Photographs,Photograph,The Studio,,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1910s,1910,1919,Platinum print,,"Rogers Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260160,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.151,false,true,260161,Photographs,Postcard,The Studio,,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1910s,1910,1919,Platinum print,,"Rogers Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260161,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.154,false,true,260164,Photographs,Photograph,[Anna Königer Smith?],,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1900s,1900,1909,Platinum print,,"Rogers Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260164,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.155,false,true,260165,Photographs,Photograph,Gustel Königer,,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1909,1909,1909,Platinum print,,"Rogers Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260165,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.156,false,true,260166,Photographs,Photograph,Gustel Königer,,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1909,1909,1909,Platinum print,,"Rogers Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260166,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.157,false,true,260167,Photographs,Photograph,Anne,,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1911,1911,1911,Platinum print,,"Rogers Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260167,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.158,false,true,260168,Photographs,Photograph,Friedel Wearing a Kimono,,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1911,1911,1911,Platinum print,,"Rogers Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260168,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.159,false,true,260169,Photographs,Photograph,Friedel Wearing a Kimono,,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1911,1911,1911,Platinum print,,"Rogers Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260169,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.160,false,true,260171,Photographs,Photograph,Friedel Wearing a Kimono,,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1911,1911,1911,Platinum print,,"Rogers Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260171,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.161,false,true,260172,Photographs,Photograph,Ellen,,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1909,1909,1909,Platinum print,,"Rogers Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260172,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.162,false,true,260173,Photographs,Photograph,Anne Köninger and Frederick L. Smith,,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1912,1912,1912,Platinum print,,"Rogers Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260173,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.164,false,true,260175,Photographs,Photograph,Anne Königer and Frederick L. Smith,,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1912,1912,1912,Platinum print,,"Rogers Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260175,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.166,false,true,260177,Photographs,Photograph,Anne Königer and Frederick L. Smith,,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1912,1912,1912,Platinum print,,"Rogers Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260177,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.167,false,true,260178,Photographs,Photograph,Anne Königer and Frederick L. Smith,,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1912,1912,1912,Platinum print,,"Rogers Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260178,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.168,false,true,260179,Photographs,Photograph,Kal Smith,,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1912,1912,1912,Platinum print,17.0 x 11.8 cm (6 11/16 x 4 5/8 in.),"Rogers Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260179,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.169,false,true,260180,Photographs,Photograph,Kal Smith,,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1912,1912,1912,Platinum print,,"Rogers Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260180,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.170,false,true,260182,Photographs,Photograph,"Count and Countes LaRosée, Bride and Bridegroom",,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1900–1911,1900,1911,Platinum print,,"Rogers Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260182,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.171,false,true,260183,Photographs,Photograph,Johanna,,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1919,1919,1919,Platinum print,,"Rogers Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260183,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.172,false,true,260184,Photographs,Photograph,Johanna,,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1921,1921,1921,Platinum print,,"Rogers Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260184,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.173,false,true,260185,Photographs,Photograph,Frank Eugene with Herr von Martine and Others,,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1920,1920,1920,Platinum print,,"Rogers Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260185,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.174,false,true,260186,Photographs,Photograph,Self-Portrait with Dolls,,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1923,1923,1923,Platinum print,,"Rogers Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260186,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.175,false,true,260187,Photographs,Photograph,"Learning How to ""Sit Up!""",,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1900s,1900,1909,Platinum print,Image: 9.5 x 4.4 cm (3 3/4 x 1 3/4 in.),"Rogers Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260187,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.177,false,true,260189,Photographs,Photograph,Self-Portrait,,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1924,1924,1924,Platinum print,,"Rogers Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260189,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.179,false,true,260191,Photographs,Photograph,"Mein Grossvater Selinger, 93 Jahre",,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1921,1921,1921,Platinum print,,"Rogers Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260191,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.191,false,true,260205,Photographs,Photograph,Frank Eugene,,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1936,1936,1936,Platinum print,,"Rogers Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260205,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.192,false,true,260206,Photographs,Photograph,Frank Eugene,,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1936,1936,1936,Platinum print,,"Rogers Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260206,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.195,false,true,260209,Photographs,Photograph,House and Church in Germany,,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1920,1920,1920,Platinum print,,"Rogers Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260209,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.196,false,true,260210,Photographs,Photograph,Anne Königer Smith,,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,1912,1912,1912,Platinum print,,"Rogers Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260210,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.633.197,false,true,260211,Photographs,Photograph,Frederick L. Smith,,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,ca. 1915,1915,1915,Platinum print,16.2 x 11.3 cm (6 3/8 x 4 7/16 in.),"Rogers Fund, 1972",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/260211,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.721,false,true,286684,Photographs,Photograph,The Great White Cloud,,,,,,Artist,,Frank Eugene,"American, New York 1865–1936 Munich",,"Eugene, Frank",American,1865,1936,ca. 1910,1908,1912,Platinum print,Image: 11.6 × 16.1 cm (4 9/16 × 6 5/16 in.) Mount: 34 × 26.9 cm (13 3/8 × 10 9/16 in.),"Gilman Collection, Purchase, Sam Salz Foundation Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286684,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -53.679.1805,false,true,269660,Photographs,Photograph,Apple Blossoms,,,,,,Artist,,Louis Comfort Tiffany,"American, New York 1848–1933 New York",,"Tiffany, Louis Comfort",American,1848,1933,1890s–1900s,1890,1909,Albumen silver print,,"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1953",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269660,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2010.34,false,true,291651,Photographs,Photograph,"Artillery, Quartermaster Sergeant",,,,,,Artist,Attributed to,Oliver H. Willard,"American, active 1850s–70s, died 1875",,"Willard, Oliver H.",American,1775,1875,1866,1866,1866,Albumen silver print from glass negative,Image: 20.3 × 14.8 cm (8 × 5 13/16 in.) Mount: 33.3 x 25.9 cm (13 1/8 x 10 3/16 in.),"The Horace W. Goldsmith Foundation Fund, through Joyce and Robert Menschel, 2010",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/291651,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2010.35,false,true,291652,Photographs,Photograph,"Artillery, Musician",,,,,,Artist,Attributed to,Oliver H. Willard,"American, active 1850s–70s, died 1875",,"Willard, Oliver H.",American,1775,1875,1866,1866,1866,Albumen silver print from glass negative,Image: 19.9 x 14.9 cm (7 13/16 x 5 7/8 in.) Mount: 33.3 x 25.9 cm (13 1/8 x 10 3/16 in.),"The Horace W. Goldsmith Foundation Fund, through Joyce and Robert Menschel, 2010",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/291652,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2010.36,false,true,291653,Photographs,Photograph,"Light Artillery, Sergeant Major",,,,,,Artist,Attributed to,Oliver H. Willard,"American, active 1850s–70s, died 1875",,"Willard, Oliver H.",American,1775,1875,1866,1866,1866,Albumen silver print from glass negative,Image: 20.3 x 15.2 cm (8 x 6 in.) Mount: 33.3 x 25.7 cm (13 1/8 x 10 1/8 in.),"Purchase, Saundra B. Lane Gift, in honor of Charles Isaacs, 2010",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/291653,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2010.37,false,true,291654,Photographs,Photograph,"Ordnance, Private",,,,,,Artist,Attributed to,Oliver H. Willard,"American, active 1850s–70s, died 1875",,"Willard, Oliver H.",American,1775,1875,1866,1866,1866,Albumen silver print from glass negative,Image: 20.2 x 15 cm (7 15/16 x 5 7/8 in.) Mount: 33.3 x 25.9 cm (13 1/8 x 10 3/16 in.),"The Horace W. Goldsmith Foundation Fund, through Joyce and Robert Menschel, 2010",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/291654,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2010.38,false,true,291655,Photographs,Photograph,"Fatigue, Marching Order",,,,,,Artist,Attributed to,Oliver H. Willard,"American, active 1850s–70s, died 1875",,"Willard, Oliver H.",American,1775,1875,1866,1866,1866,Albumen silver print from glass negative,Image: 20.1 x 15 cm (7 15/16 x 5 7/8 in.) Mount: 33.2 x 25.7 cm (13 1/16 x 10 1/8 in.),"The Horace W. Goldsmith Foundation Fund, through Joyce and Robert Menschel, 2010",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/291655,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1999.185,false,true,282767,Photographs,Photograph,[Man Holding Patent Office Book],,,,,,Artist,Attributed to,Oliver H. Willard,"American, active 1850s–70s, died 1875",,"Willard, Oliver H.",American,1775,1875,ca. 1857,1856,1858,Salted paper print from glass negative,21.6 x 16.5 cm (8 1/2 x 6 1/2 in. ),"Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 1999",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/282767,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -X.676,false,true,271672,Photographs,Cabinet card,[The British Soprano Euphrosyne Parepa-Rosa (1836-1874)],,,,,,Artist,,Jeremiah Gurney,"American, 1812–1895 Coxsackie, New York",,"Gurney, Jeremiah",American,1812,1895-04-21,1870s,1870,1879,Albumen silver print from glass negative,13.3 x 8.3 cm (5 1/4 x 3 1/4 in. ),Museum Accession,,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271672,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -X.701.4,false,true,271712,Photographs,Cabinet card,"[Wohes Family, New York]",,,,,,Artist,,Jeremiah Gurney,"American, 1812–1895 Coxsackie, New York",,"Gurney, Jeremiah",American,1812,1895-04-21,1870s,1870,1870,Albumen silver print from glass negative,13.7 x 9.2 cm. (5 3/8 x 3 5/8 in.),Museum Accession,,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271712,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.400.23,false,true,291776,Photographs,Daguerreotype,[Pair of Portraits of Man and Woman (Husband and Wife?)],,,,,,Artist,,Jeremiah Gurney,"American, 1812–1895 Coxsackie, New York",,"Gurney, Jeremiah",American,1812,1895-04-21,1852–60,1852,1860,Daguerreotype,"Image: 9 x 6.4 cm (3 9/16 x 2 1/2 in.), each Plate: 10.8 x 8.3 cm (4 1/4 x 3 1/4 in.), each Case: 1.9 x 11.9 x 9.5 cm (3/4 x 4 11/16 x 3 3/4 in.)","Bequest of Herbert Mitchell, 2008",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/291776,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.325,false,true,285472,Photographs,Photograph,[Two Girls in Identical Dresses],,,,,,Artist,,Jeremiah Gurney,"American, 1812–1895 Coxsackie, New York",,"Gurney, Jeremiah",American,1812,1895-04-21,ca. 1857,1852,1863,Daguerreotype,Image: 11.3 x 8.2 cm (4 7/16 x 3 1/4 in.),"Gilman Collection, Purchase, Marlene Nathan Meyerson Family Foundation Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285472,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1136,false,true,286062,Photographs,carte-de-visite,"James B. ""Wild Bill"" Hickock",,,,,,Artist,,Jeremiah Gurney,"American, 1812–1895 Coxsackie, New York",,"Gurney, Jeremiah",American,1812,1895-04-21,ca. 1873,1871,1875,Albumen silver print from glass negative,Image: 3 5/8 × 2 1/4 in. (9.2 × 5.7 cm) Mount: 4 1/8 × 2 1/2 in. (10.4 × 6.3 cm),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286062,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.48,false,true,301954,Photographs,Photograph,"[Camp Scene with Soldiers of the 22nd New York State Militia, Harper's Ferry, Virginia]",,,,,,Artist,,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1862,1862,1862,Albumen silver print from glass negative,Image: 8.7 x 5.8 cm (3 7/16 x 2 5/16 in.) Mount: 10.2 x 6.1 cm (4 x 2 3/8 in.),"Purchase, Alfred Stieglitz Society Gifts, 2013",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/301954,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -56.517.4,false,true,269852,Photographs,Photograph,President Martin Van Buren,,,,,,Artist,,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1855–58,1855,1858,Salted paper print from glass negative,48.3 x 39.7 cm (19 x 15 5/8 in.),"David Hunter McAlpin Fund, 1956",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269852,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1997.382.48,false,true,282053,Photographs,Photograph,Grenville Kane,,,,,,Artist,,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,late 1850s,1857,1859,Ambrotype,visible: 12.2 x 8.9 cm (4 13/16 x 3 1/2 in.),"The Rubel Collection, Purchase, Lila Acheson Wallace Gift, 1997",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/282053,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.84,false,true,283184,Photographs,Photograph,[Commodore Matthew Calbraith Perry],,,,,,Artist,,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1856–58,1856,1858,Salted paper print from glass negative,Image : 33.1 x 28.4cm (13 1/16 x 11 3/16in.) Mount: 18 1/8 in. × 14 in. (46 × 35.5 cm),"Gilman Collection, Purchase, Joseph M. Cohen Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283184,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.85,false,true,283186,Photographs,Photograph,[Portrait of a Man],,,,,,Artist,,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,ca. 1857,1855,1859,Salted paper print from glass negative,Mount: 11 3/16 in. × 9 1/8 in. (28.4 × 23.2 cm) Image: 9 15/16 × 7 9/16 in. (25.3 × 19.2 cm),"Gilman Collection, Purchase, Harriette and Noel Levine Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283186,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.280,false,true,286046,Photographs,Photographs,Lilliputian Souvenir,,,,,,Artist,,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1862–63,1862,1863,Albumen silver print from glass negative,Image: 7.7 x 4.5 cm (3 1/16 x 1 3/4 in.) each Mount: 8.8 x 5.2 cm (3 7/16 x 2 1/16 in.) each Mount (2nd): 51.7 x 41.7 cm (20 3/8 x 16 7/16 in.),"Gilman Collection, Purchase, Ann Tenenbaum and Thomas H. Lee Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286046,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.496,false,true,286624,Photographs,Photograph,Peter Force,,,,,,Artist,,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,ca. 1858,1856,1860,Salted paper print from glass negative,Image: 34.4 × 27.2 cm (13 9/16 × 10 11/16 in.) Mount: 50.4 × 47.3 cm (19 13/16 × 18 5/8 in.),"Gilman Collection, Purchase, Joseph M. Cohen Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286624,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.567,false,true,285877,Photographs,Photograph,Madame Medori,,,,,,Artist,,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,ca. 1857,1855,1859,Salted paper print from glass negative,Image: 20.6 x 15.6 cm (8 1/8 x 6 1/8 in.),"Gilman Collection, Purchase, Mrs. Walter Annenberg and The Annenberg Foundation Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285877,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.754,false,true,286586,Photographs,Cabinet card,Frederick Douglass,,,,,,Artist,,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,ca. 1880,1878,1882,Albumen silver print from glass negative,Image: 14.7 × 10.2 cm (5 13/16 × 4 in.) Mount: 16.5 × 10.8 cm (6 1/2 × 4 1/4 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286586,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1108,false,true,286620,Photographs,Photograph,"Lieutenent General Scott, General-in-Chief U.S. Army, & Staff",,,,,,Artist,,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,"September 6, 1861",1861,1861,Albumen silver print from glass negative,Image: 26.4 × 36.5 cm (10 3/8 × 14 3/8 in.) Mount: 35.6 × 43.1 cm (14 × 16 15/16 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286620,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1115,false,true,286097,Photographs,Photograph,"[Japanese Embassy, Navy Yard, Washington, DC]",,,,,,Artist,,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1860,1860,1860,Albumen silver print from glass negative,Image: 37.5 x 48.1 cm (14 3/4 x 18 15/16 in.) Mount: 41.8 x 49.6 cm (16 7/16 x 19 1/2 in.),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286097,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1125,false,true,285889,Photographs,Photograph,"Second Corps Hospital, Washington, D.C.",,,,,,Artist,,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,ca. 1863,1861,1865,Albumen silver print from glass negative,Image: 7.7 × 9.7 cm (3 1/16 × 3 13/16 in.) Mount: 22.9 × 26.8 cm (9 × 10 9/16 in.),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285889,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1129,false,true,285835,Photographs,Photograph,"Fortifications, Manassas, Occupied by 13th Mass.",,,,,,Artist,,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1862,1862,1862,Albumen silver print from glass negative,Image: 7 5/16 × 9 1/4 in. (18.6 × 23.5 cm) Mount: 10 5/8 × 13 5/8 in. (27 × 34.6 cm),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285835,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1130,false,true,286262,Photographs,Photograph,Ruins of Stone Bridge - Bull Run,,,,,,Artist,,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1862,1862,1862,Albumen silver print from glass negative,Image: 18.8 × 23.1 cm (7 3/8 × 9 1/8 in.) Mount: 26.9 × 34.7 cm (10 9/16 × 13 11/16 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286262,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1131,false,true,285834,Photographs,Photograph,Stone Bridge - Bull Run,,,,,,Artist,,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1862,1862,1862,Albumen silver print from glass negative,Image: 18.4 × 23.1 cm (7 1/4 × 9 1/8 in.) Mount: 27 × 34.7 cm (10 5/8 × 13 11/16 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285834,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1213,false,true,286582,Photographs,Photograph,General Robert E. Lee,,,,,,Artist,,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1865,1865,1865,Albumen silver print from glass negative,Image: 14 × 9.3 cm (5 1/2 × 3 11/16 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286582,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1226,false,true,286343,Photographs,Photograph,Major General David E. Twiggs,,,,,,Artist,,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,ca. 1859,1857,1861,Salted paper print from glass negative,Image: 22.5 × 14.9 cm (8 7/8 × 5 7/8 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286343,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1233,false,true,286629,Photographs,Photograph,Edward Everett,,,,,,Artist,,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,ca. 1858,1853,1863,Salted paper print from glass negative,Image: 47.1 × 38.5 cm (18 9/16 × 15 3/16 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286629,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1234,false,true,285959,Photographs,Photograph,General William Ward and Staff,,,,,,Artist,,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,ca. 1861,1856,1866,Albumen silver print from glass negative,Image: 24.6 × 19 cm (9 11/16 × 7 1/2 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285959,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1218a,false,true,286612,Photographs,Photographs,Robert E. Lee,,,,,,Artist,,Mathew B. Brady,"American, born Ireland, 1823?–1896 New York",,"Brady, Mathew B.",American,1823,1896,1869,1869,1869,Albumen silver print from glass negative,"Mount: 32.1 × 30.6 cm (12 5/8 × 12 1/16 in.) Image: 20.6 × 15.6 cm (8 1/8 × 6 1/8 in.), oval","Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286612,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -28.126,false,true,267441,Photographs,Multiple exposure; Photograph,Dorothy True,,,,,,Artist,,Alfred Stieglitz,"American, Hoboken, New Jersey 1864–1946 New York",,"Stieglitz, Alfred",American,1864,1946,1919,1919,1919,Gelatin silver print,24.3 x 19.3 cm (9 9/16 x 7 5/8 in.),"Gift of Paul Rosenfeld, 1928",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267441,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -28.129,false,true,267460,Photographs,Photograph,Georgia O'Keeffe – Hands and Thimble,,,,,,Artist,,Alfred Stieglitz,"American, Hoboken, New Jersey 1864–1946 New York",,"Stieglitz, Alfred",American,1864,1946,1919,1919,1919,Palladium print,23.5 x 18.4 cm (9 1/4 x 7 1/4 in.),"Gift of Mrs. Rebecca S. Strand, 1928",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267460,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -49.55.6,false,true,269458,Photographs,Photograph,The Terminal,,,,,,Artist,,Alfred Stieglitz,"American, Hoboken, New Jersey 1864–1946 New York",,"Stieglitz, Alfred",American,1864,1946,"1893, printed 1920s–30s",1893,1893,Gelatin silver print,8.9 x 11.5 cm (3 1/2 x 4 1/2 in.),"Alfred Stieglitz Collection, 1949",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269458,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -49.55.7,false,true,269459,Photographs,Photograph,An Icy Night,,,,,,Artist,,Alfred Stieglitz,"American, Hoboken, New Jersey 1864–1946 New York",,"Stieglitz, Alfred",American,1864,1946,"1898, printed 1920–1939",1898,1898,Gelatin silver print from glass negative,9.2 x 11.8 cm. (3 5/8 x 4 5/8 in.),"Alfred Stieglitz Collection, 1949",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269459,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -49.55.8,false,true,269460,Photographs,Photograph,"The Street, Fifth Avenue",,,,,,Artist,,Alfred Stieglitz,"American, Hoboken, New Jersey 1864–1946 New York",,"Stieglitz, Alfred",American,1864,1946,"1900–1901, printed 1903–4",1900,1901,Photogravure,30.6 x 23.3 cm. (12 1/16 x 9 3/16 in.),"Alfred Stieglitz Collection, 1949",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269460,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -49.55.9,false,true,269461,Photographs,Photograph,The Hand of Man,,,,,,Artist,,Alfred Stieglitz,"American, Hoboken, New Jersey 1864–1946 New York",,"Stieglitz, Alfred",American,1864,1946,"1902, printed 1910",1902,1902,Photogravure,24.2 x 31.9 cm (9 1/2 x 12 9/16 in.),"Alfred Stieglitz Collection, 1949",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269461,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.469,false,true,288987,Photographs,Photograph,Margaret Treadwell,,,,,,Artist,,Alfred Stieglitz,"American, Hoboken, New Jersey 1864–1946 New York",,"Stieglitz, Alfred",American,1864,1946,1921,1921,1921,Platinum-palladium print,25.4 x 20.3 cm (10 x 8 in. ),"Gift of John Pritzker, 2007",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288987,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -28.127.1,false,true,267442,Photographs,Photograph,Georgia O'Keeffe,,,,,,Artist,,Alfred Stieglitz,"American, Hoboken, New Jersey 1864–1946 New York",,"Stieglitz, Alfred",American,1864,1946,1918,1918,1918,Platinum-palladium print,24.5 x 19.6 cm (9 5/8 x 7 11/16 in. ),"Gift of David A. Schulte, 1928",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267442,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -28.127.6,false,true,267447,Photographs,Photograph,"Music – A Sequence of Ten Cloud Photographs, No. 1",,,,,,Artist,,Alfred Stieglitz,"American, Hoboken, New Jersey 1864–1946 New York",,"Stieglitz, Alfred",American,1864,1946,1922,1922,1922,Platinum print,19.2 x 24.1 cm. (7 9/16 x 9 1/2 in.),"Gift of David A. Schulte, 1928",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267447,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -28.127.7,false,true,267448,Photographs,Photograph,The Dancing Trees,,,,,,Artist,,Alfred Stieglitz,"American, Hoboken, New Jersey 1864–1946 New York",,"Stieglitz, Alfred",American,1864,1946,1922,1922,1922,Palladium print,24.2 x 19.3 cm (9 1/2 x 7 5/8 in.),"Gift of David A. Schulte, 1928",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267448,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -28.128.4,false,true,267454,Photographs,Photograph,Equivalent,,,,,,Artist,,Alfred Stieglitz,"American, Hoboken, New Jersey 1864–1946 New York",,"Stieglitz, Alfred",American,1864,1946,1925,1925,1925,Gelatin silver print,11.8 x 9.2 cm (4 5/8 x 3 5/8 in.),"Alfred Stieglitz Collection, 1928",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267454,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -28.128.5,false,true,267455,Photographs,Photograph,Songs of the Sky,,,,,,Artist,,Alfred Stieglitz,"American, Hoboken, New Jersey 1864–1946 New York",,"Stieglitz, Alfred",American,1864,1946,1924,1924,1924,Gelatin silver print,9.2 x 11.8 cm (3 5/8 x 4 5/8 in.),"Alfred Stieglitz Collection, 1928",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267455,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -28.128.7,false,true,267457,Photographs,Photograph,Equivalent,,,,,,Artist,,Alfred Stieglitz,"American, Hoboken, New Jersey 1864–1946 New York",,"Stieglitz, Alfred",American,1864,1946,1925,1925,1925,Gelatin silver print,11.8 x 9.2 cm (4 5/8 x 3 5/8 in.),"Alfred Stieglitz Collection, 1928",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267457,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -28.128.8,false,true,267458,Photographs,Photograph,Equivalent,,,,,,Artist,,Alfred Stieglitz,"American, Hoboken, New Jersey 1864–1946 New York",,"Stieglitz, Alfred",American,1864,1946,1925,1925,1925,Gelatin silver print,9.3 x 11.9 cm.(3 11/16 x 4 11/16 in.),"Alfred Stieglitz Collection, 1928",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267458,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -28.128.9,false,true,267459,Photographs,Photograph,Equivalent,,,,,,Artist,,Alfred Stieglitz,"American, Hoboken, New Jersey 1864–1946 New York",,"Stieglitz, Alfred",American,1864,1946,1925,1925,1925,Gelatin silver print,11.9 x 9.1 cm (4 11/16 x 3 9/16 in.),"Alfred Stieglitz Collection, 1928",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267459,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -28.130.1,false,true,267461,Photographs,Photograph,Georgia O'Keeffe — Hand and Breasts,,,,,,Artist,,Alfred Stieglitz,"American, Hoboken, New Jersey 1864–1946 New York",,"Stieglitz, Alfred",American,1864,1946,1919,1919,1919,Palladium print,18.2 x 23.1 cm (7 3/16 x 9 1/8 in. ),"Gift of Mrs. Alma Wertheim, 1928",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267461,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -28.130.2,false,true,267462,Photographs,Photograph,Georgia O'Keeffe—Torso,,,,,,Artist,,Alfred Stieglitz,"American, Hoboken, New Jersey 1864–1946 New York",,"Stieglitz, Alfred",American,1864,1946,1918,1918,1918,Gelatin silver print,23.6 x 18.8 cm (9 5/16 x 7 3/8 in.),"Gift of Mrs. Alma Wertheim, 1928",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267462,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -49.55.12,false,true,269280,Photographs,Photograph,After Working Hours - The Ferry Boat,,,,,,Artist,,Alfred Stieglitz,"American, Hoboken, New Jersey 1864–1946 New York",,"Stieglitz, Alfred",American,1864,1946,"1910, printed in or before 1913",1910,1910,Photogravure,33.2 x 25.9 cm. (13 1/16 x 10 3/16 in.),"Alfred Stieglitz Collection, 1949",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269280,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -49.55.13,false,true,269281,Photographs,Photograph,The Steerage,,,,,,Artist,,Alfred Stieglitz,"American, Hoboken, New Jersey 1864–1946 New York",,"Stieglitz, Alfred",American,1864,1946,"1907, printed 1915",1907,1907,Photogravure,32.2 x 25.8 cm. (12 11/16 x 10 3/16 in.),"Alfred Stieglitz Collection, 1949",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269281,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -49.55.15,false,true,269283,Photographs,Photograph,The City of Ambitions,,,,,,Artist,,Alfred Stieglitz,"American, Hoboken, New Jersey 1864–1946 New York",,"Stieglitz, Alfred",American,1864,1946,"1910, printed 1910–13",1910,1910,Photogravure,33.8 x 26.0 cm (13 5/16 x 10 1/4 in.),"Alfred Stieglitz Collection, 1949",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269283,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -49.55.16,false,true,269284,Photographs,Photograph,The Flatiron,,,,,,Artist,,Alfred Stieglitz,"American, Hoboken, New Jersey 1864–1946 New York",,"Stieglitz, Alfred",American,1864,1946,"1903, printed in or before 1910",1903,1903,Photogravure,32.8 x 16.7 cm (12 15/16 x 6 9/16 in.),"Alfred Stieglitz Collection, 1949",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269284,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -49.55.17,false,true,269290,Photographs,Photograph,Old and New New York,,,,,,Artist,,Alfred Stieglitz,"American, Hoboken, New Jersey 1864–1946 New York",,"Stieglitz, Alfred",American,1864,1946,"1910, printed in or before 1913",1910,1910,Photogravure,33.2 x 25.5 cm (13 1/16 x 10 1/16 in.),"Alfred Stieglitz Collection, 1949",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269290,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -49.55.18,false,true,269300,Photographs,Photograph,The Terminal,,,,,,Artist,,Alfred Stieglitz,"American, Hoboken, New Jersey 1864–1946 New York",,"Stieglitz, Alfred",American,1864,1946,"1893, printed 1913 or before",1893,1893,Photogravure,25.5 x 33.5 cm (10 1/16 x 13 3/16 in.),"Alfred Stieglitz Collection, 1949",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269300,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -49.55.21,false,true,269333,Photographs,Photograph,Shadows in Lake,,,,,,Artist,,Alfred Stieglitz,"American, Hoboken, New Jersey 1864–1946 New York",,"Stieglitz, Alfred",American,1864,1946,1916,1916,1916,Gelatin silver print,11.5 x 9.1 cm (4 1/2 x 3 9/16 in.),"Alfred Stieglitz Collection, 1949",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269333,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -49.55.22,false,true,269344,Photographs,Photograph,Rebecca Salsbury Strand,,,,,,Artist,,Alfred Stieglitz,"American, Hoboken, New Jersey 1864–1946 New York",,"Stieglitz, Alfred",American,1864,1946,1922,1922,1922,Gelatin silver print,9.3 x 11.5 cm. (3 11/16 x 4 1/2 in.),"Alfred Stieglitz Collection, 1949",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269344,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -49.55.23,false,true,269355,Photographs,Photograph,Katherine,,,,,,Artist,,Alfred Stieglitz,"American, Hoboken, New Jersey 1864–1946 New York",,"Stieglitz, Alfred",American,1864,1946,1921,1921,1921,Gelatin silver print,10.4 x 8.2 cm. (4 1/16 x 3 1/4 in.),"Alfred Stieglitz Collection, 1949",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269355,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -49.55.24,false,true,269360,Photographs,Photograph,Spiritual America,,,,,,Artist,,Alfred Stieglitz,"American, Hoboken, New Jersey 1864–1946 New York",,"Stieglitz, Alfred",American,1864,1946,1923,1923,1923,Gelatin silver print,11.6 x 9.2 cm. (4 9/16 x 3 5/8 in.),"Alfred Stieglitz Collection, 1949",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269360,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -49.55.26,false,true,269376,Photographs,Photograph,Equivalent,,,,,,Artist,,Alfred Stieglitz,"American, Hoboken, New Jersey 1864–1946 New York",,"Stieglitz, Alfred",American,1864,1946,1926,1926,1926,Gelatin silver print,11.6 x 9.2 cm (4 9/16 x 3 5/8 in.),"Alfred Stieglitz Collection, 1949",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269376,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -49.55.29,false,true,269404,Photographs,Photograph,Equivalent,,,,,,Artist,,Alfred Stieglitz,"American, Hoboken, New Jersey 1864–1946 New York",,"Stieglitz, Alfred",American,1864,1946,1926,1926,1926,Gelatin silver print,11.8 x 9.2 cm (4 5/8 x 3 5/8 in.),"Alfred Stieglitz Collection, 1949",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269404,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -49.55.30,false,true,269408,Photographs,Photograph,"Equivalent, Set C2 No. 1",,,,,,Artist,,Alfred Stieglitz,"American, Hoboken, New Jersey 1864–1946 New York",,"Stieglitz, Alfred",American,1864,1946,1929,1929,1929,Gelatin silver print,11.6 x 9.4 cm. (4 9/16 x 3 11/16 in.),"Alfred Stieglitz Collection, 1949",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269408,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -49.55.31,false,true,269419,Photographs,Photograph,"Equivalent, Set C2 No. 2",,,,,,Artist,,Alfred Stieglitz,"American, Hoboken, New Jersey 1864–1946 New York",,"Stieglitz, Alfred",American,1864,1946,1929,1929,1929,Gelatin silver print,11.7 x 9.3 cm (4 5/8 x 3 11/16 in.),"Alfred Stieglitz Collection, 1949",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269419,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -49.55.33,false,true,269438,Photographs,Photograph,"Equivalent, Set C2 No. 4",,,,,,Artist,,Alfred Stieglitz,"American, Hoboken, New Jersey 1864–1946 New York",,"Stieglitz, Alfred",American,1864,1946,1929,1929,1929,Gelatin silver print,11.8 x 9.4 cm. (4 5/8 x 3 11/16 in.),"Alfred Stieglitz Collection, 1949",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269438,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -49.55.34,false,true,269441,Photographs,Photograph,"Equivalent, Set C2 No. 5",,,,,,Artist,,Alfred Stieglitz,"American, Hoboken, New Jersey 1864–1946 New York",,"Stieglitz, Alfred",American,1864,1946,1929,1929,1929,Gelatin silver print,11.7 x 9.3 cm. (4 5/8 x 3 11/16 in.),"Alfred Stieglitz Collection, 1949",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269441,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -49.55.35,true,true,269442,Photographs,Photograph,From the Back Window – 291,,,,,,Artist,,Alfred Stieglitz,"American, Hoboken, New Jersey 1864–1946 New York",,"Stieglitz, Alfred",American,1864,1946,1915,1915,1915,Platinum print,25.1 x 20.2 cm (9 7/8 x 7 15/16 in.),"Alfred Stieglitz Collection, 1949",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269442,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -49.55.36,false,true,269443,Photographs,Photograph,291 – Picasso-Braque Exhibition,,,,,,Artist,,Alfred Stieglitz,"American, Hoboken, New Jersey 1864–1946 New York",,"Stieglitz, Alfred",American,1864,1946,1915,1915,1915,Platinum print,19.4 x 24.4 cm (7 5/8 x 9 5/8 in.),"Alfred Stieglitz Collection, 1949",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269443,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -49.55.37,false,true,269444,Photographs,Photograph,Leo Stein,,,,,,Artist,,Alfred Stieglitz,"American, Hoboken, New Jersey 1864–1946 New York",,"Stieglitz, Alfred",American,1864,1946,1917,1917,1917,Platinum print,24.6 x 19.7 cm. (9 11/16 x 7 3/4 in.),"Alfred Stieglitz Collection, 1949",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269444,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -49.55.38,false,true,269445,Photographs,Photograph,Hodge Kirnon,,,,,,Artist,,Alfred Stieglitz,"American, Hoboken, New Jersey 1864–1946 New York",,"Stieglitz, Alfred",American,1864,1946,1917,1917,1917,Palladium print,24.6 x 19.9 cm. (9 11/16 x 7 13/16 in.),"Alfred Stieglitz Collection, 1949",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269445,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -49.55.39,false,true,269446,Photographs,Photograph,John Marin,,,,,,Artist,,Alfred Stieglitz,"American, Hoboken, New Jersey 1864–1946 New York",,"Stieglitz, Alfred",American,1864,1946,1921–22,1921,1922,Palladium print,24.2 x 19.3 cm. (9 1/2 x 7 5/8 in.),"Alfred Stieglitz Collection, 1949",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269446,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -49.55.40,false,true,269448,Photographs,Photograph,Katherine Dudley,,,,,,Artist,,Alfred Stieglitz,"American, Hoboken, New Jersey 1864–1946 New York",,"Stieglitz, Alfred",American,1864,1946,1922,1922,1922,Gelatin silver print,24.7 x 19.4 cm. (9 3/4 x 7 5/8 in.),"Alfred Stieglitz Collection, 1949",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269448,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -49.55.41,false,true,269449,Photographs,Photograph,House and Grape Leaves,,,,,,Artist,,Alfred Stieglitz,"American, Hoboken, New Jersey 1864–1946 New York",,"Stieglitz, Alfred",American,1864,1946,1934,1934,1934,Gelatin silver print,24.2 x 19.3 cm (9 1/2 x 7 5/8 in.),"Alfred Stieglitz Collection, 1949",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269449,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -49.55.44,false,true,269452,Photographs,Photograph,Equivalent 27C,,,,,,Artist,,Alfred Stieglitz,"American, Hoboken, New Jersey 1864–1946 New York",,"Stieglitz, Alfred",American,1864,1946,1933,1933,1933,Gelatin silver print,24.0 x 19.1 cm. (9 7/16 x 7 1/2 in.),"Alfred Stieglitz Collection, 1949",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269452,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -49.55.45,false,true,269453,Photographs,Photograph,"From My Window at the Shelton, West",,,,,,Artist,,Alfred Stieglitz,"American, Hoboken, New Jersey 1864–1946 New York",,"Stieglitz, Alfred",American,1864,1946,1931,1931,1931,Gelatin silver print,24.2 x 19.1 cm (9 1/2 x 7 1/2 in.),"Alfred Stieglitz Collection, 1949",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269453,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -49.55.46,false,true,269454,Photographs,Photograph,"From My Window at An American Place, North",,,,,,Artist,,Alfred Stieglitz,"American, Hoboken, New Jersey 1864–1946 New York",,"Stieglitz, Alfred",American,1864,1946,1931,1931,1931,Gelatin silver print,18.9 x 24.0 cm (7 7/16 x 9 7/16 in.),"Alfred Stieglitz Collection, 1949",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269454,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -49.55.47,false,true,269455,Photographs,Photograph,"From My Window at An American Place, North",,,,,,Artist,,Alfred Stieglitz,"American, Hoboken, New Jersey 1864–1946 New York",,"Stieglitz, Alfred",American,1864,1946,1931,1931,1931,Gelatin silver print,23.5 x 18.6 cm (9 1/4 x 7 5/16 in.),"Alfred Stieglitz Collection, 1949",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269455,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -49.55.48,false,true,269456,Photographs,Photograph,"From My Window at An American Place, Southwest",,,,,,Artist,,Alfred Stieglitz,"American, Hoboken, New Jersey 1864–1946 New York",,"Stieglitz, Alfred",American,1864,1946,1932,1932,1932,Gelatin silver print,19.2 x 24.1 cm (7 9/16 x 9 1/2 in.),"Alfred Stieglitz Collection, 1949",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269456,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -58.577.5,false,true,270062,Photographs,Photograph,The Street - Design for a Poster,,,,,,Artist,,Alfred Stieglitz,"American, Hoboken, New Jersey 1864–1946 New York",,"Stieglitz, Alfred",American,1864,1946,"1900–1901, printed 1903",1900,1901,Photogravure,17.7 x 13.3 cm. (7 x 5 1/4 in.),"Gift of J. B. Neumann, 1958",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/270062,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1997.61.2,false,true,271640,Photographs,Photograph,Georgia O'Keeffe—Hands,,,,,,Artist,,Alfred Stieglitz,"American, Hoboken, New Jersey 1864–1946 New York",,"Stieglitz, Alfred",American,1864,1946,1917,1917,1917,Platinum print,22.6 x 16.8 cm (8 7/8 x 6 5/8 in.),"Gift of Georgia O'Keeffe, through the generosity of The Georgia O'Keeffe Foundation and Jennifer and Joseph Duke, 1997",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271640,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1997.61.6,false,true,271591,Photographs,Photograph,Georgia O'Keeffe,,,,,,Artist,,Alfred Stieglitz,"American, Hoboken, New Jersey 1864–1946 New York",,"Stieglitz, Alfred",American,1864,1946,1918,1918,1918,Palladium print,24.5 x 19.2 cm (9 5/8 x 7 9/16 in. ),"Gift of Georgia O'Keeffe, through the generosity of The Georgia O'Keeffe Foundation and Jennifer and Joseph Duke, 1997",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271591,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -28.128.10,false,true,267450,Photographs,Photograph,Rain Drops,,,,,,Artist,,Alfred Stieglitz,"American, Hoboken, New Jersey 1864–1946 New York",,"Stieglitz, Alfred",American,1864,1946,1927,1927,1927,Gelatin silver print,9.2 x 11.7 cm. (3 5/8 x 4 5/8 in.),"Alfred Stieglitz Collection, 1928",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267450,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -28.128.11,false,true,267451,Photographs,Photograph,Equivalents,,,,,,Artist,,Alfred Stieglitz,"American, Hoboken, New Jersey 1864–1946 New York",,"Stieglitz, Alfred",American,1864,1946,1927,1927,1927,Gelatin silver print,9.1 x 11.8 cm. (3 9/16 x 4 5/8 in.),"Alfred Stieglitz Collection, 1928",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267451,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -58.577.11,false,true,270032,Photographs,Photogram,The Terminal,,,,,,Artist,,Alfred Stieglitz,"American, Hoboken, New Jersey 1864–1946 New York",,"Stieglitz, Alfred",American,1864,1946,"1893, printed 1911",1893,1893,Photogravure,12.1 x 16.0 cm (4 3/4 x 6 5/16 in.),"Gift of J. B. Neumann, 1958",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/270032,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -58.577.16,false,true,270037,Photographs,Photograph,"Snapshot - From My Window, Berlin",,,,,,Artist,,Alfred Stieglitz,"American, Hoboken, New Jersey 1864–1946 New York",,"Stieglitz, Alfred",American,1864,1946,"1890s–1900s, printed 1907",1890,1909,Photogravure,21.1 x 17.0 cm. (8 5/16 x 6 11/16 in.),"Gift of J. B. Neumann, 1958",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/270037,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -58.577.37,false,true,270060,Photographs,Photograph,The Flat-iron,,,,,,Artist,,Alfred Stieglitz,"American, Hoboken, New Jersey 1864–1946 New York",,"Stieglitz, Alfred",American,1864,1946,1903,1903,1903,Photogravure,17.0 x 8.4 cm. (6 11/16 x 3 5/16 in.),"Gift of J. B. Neumann, 1958",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/270060,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1997.61.11,false,true,271588,Photographs,Photograph,Georgia O'Keeffe,,,,,,Artist,,Alfred Stieglitz,"American, Hoboken, New Jersey 1864–1946 New York",,"Stieglitz, Alfred",American,1864,1946,1919–21,1919,1921,Palladium print,24.1 x 19.5 cm (9 1/2 x 7 11/16 in. ),"Gift of Georgia O'Keeffe, through the generosity of The Georgia O'Keeffe Foundation and Jennifer and Joseph Duke, 1997",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271588,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1997.61.18,false,true,271617,Photographs,Photograph,Georgia O'Keeffe — Hands,,,,,,Artist,,Alfred Stieglitz,"American, Hoboken, New Jersey 1864–1946 New York",,"Stieglitz, Alfred",American,1864,1946,1919,1919,1919,Palladium print,22.9 x 18.9 cm (9 x 7 7/16 in. ),"Gift of Georgia O'Keeffe, through the generosity of The Georgia O'Keeffe Foundation and Jennifer and Joseph Duke, 1997",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271617,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1997.61.19,true,true,271615,Photographs,Photograph,Georgia O'Keeffe — Neck,,,,,,Artist,,Alfred Stieglitz,"American, Hoboken, New Jersey 1864–1946 New York",,"Stieglitz, Alfred",American,1864,1946,1921,1921,1921,Palladium print,23.6 x 19.2 cm (9 5/16 x 7 9/16 in.),"Gift of Georgia O'Keeffe, through the generosity of The Georgia O'Keeffe Foundation and Jennifer and Joseph Duke, 1997",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271615,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1997.61.20,false,true,271607,Photographs,Photograph,Georgia O'Keeffe,,,,,,Artist,,Alfred Stieglitz,"American, Hoboken, New Jersey 1864–1946 New York",,"Stieglitz, Alfred",American,1864,1946,1918,1918,1918,Palladium print,22.8 x 18.6 cm (9 x 7 5/16 in. ),"Gift of Georgia O'Keeffe, through the generosity of The Georgia O'Keeffe Foundation and Jennifer and Joseph Duke, 1997",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271607,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1997.61.21,false,true,271629,Photographs,Photograph,Georgia O'Keeffe,,,,,,Artist,,Alfred Stieglitz,"American, Hoboken, New Jersey 1864–1946 New York",,"Stieglitz, Alfred",American,1864,1946,1918,1918,1918,Palladium print,23.2 x 19.2 cm (9 1/8 x 7 9/16 in.),"Gift of Georgia O'Keeffe, through the generosity of The Georgia O'Keeffe Foundation and Jennifer and Joseph Duke, 1997",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271629,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1997.61.23,false,true,271623,Photographs,Photograph,Georgia O'Keeffe — Breasts,,,,,,Artist,,Alfred Stieglitz,"American, Hoboken, New Jersey 1864–1946 New York",,"Stieglitz, Alfred",American,1864,1946,1919,1919,1919,Palladium print,24.4 x 19.3 cm (9 5/8 x 7 5/8 in. ),"Gift of Georgia O'Keeffe, through the generosity of The Georgia O'Keeffe Foundation and Jennifer and Joseph Duke, 1997",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271623,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1997.61.24,false,true,271585,Photographs,Photograph,Georgia O'Keeffe,,,,,,Artist,,Alfred Stieglitz,"American, Hoboken, New Jersey 1864–1946 New York",,"Stieglitz, Alfred",American,1864,1946,1924,1924,1924,Palladium print,23.8 x 19.3 cm (9 3/8 x 7 5/8 in. ),"Gift of Georgia O'Keeffe, through the generosity of The Georgia O'Keeffe Foundation and Jennifer and Joseph Duke, 1997",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271585,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1997.61.25,true,true,271570,Photographs,Photograph,Georgia O'Keeffe,,,,,,Artist,,Alfred Stieglitz,"American, Hoboken, New Jersey 1864–1946 New York",,"Stieglitz, Alfred",American,1864,1946,1918,1918,1918,Palladium print,11.7 x 9 cm (4 5/8 x 3 9/16 in.),"Gift of Georgia O'Keeffe, through the generosity of The Georgia O'Keeffe Foundation and Jennifer and Joseph Duke, 1997",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271570,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1997.61.26,false,true,271592,Photographs,Photograph,Georgia O'Keeffe—Hand,,,,,,Artist,,Alfred Stieglitz,"American, Hoboken, New Jersey 1864–1946 New York",,"Stieglitz, Alfred",American,1864,1946,1918,1918,1918,Platinum print,11.8 x 9.1 cm (4 5/8 x 3 9/16 in. ),"Gift of Georgia O'Keeffe, through the generosity of The Georgia O'Keeffe Foundation and Jennifer and Joseph Duke, 1997",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271592,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1997.61.29,false,true,271605,Photographs,Photograph,Georgia O'Keeffe,,,,,,Artist,,Alfred Stieglitz,"American, Hoboken, New Jersey 1864–1946 New York",,"Stieglitz, Alfred",American,1864,1946,1921,1921,1921,Palladium print,23.5 x 18.1 cm (9 1/4 x 7 1/8 in. ),"Gift of Georgia O'Keeffe, through the generosity of The Georgia O'Keeffe Foundation and Jennifer and Joseph Duke, 1997",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271605,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1997.61.31,false,true,271598,Photographs,Photograph,Georgia O'Keeffe,,,,,,Artist,,Alfred Stieglitz,"American, Hoboken, New Jersey 1864–1946 New York",,"Stieglitz, Alfred",American,1864,1946,1918,1918,1918,Gelatin silver print,23.5 x 19 cm (9 1/4 x 7 1/2 in. ),"Gift of Georgia O'Keeffe, through the generosity of The Georgia O'Keeffe Foundation and Jennifer and Joseph Duke, 1997",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271598,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1997.61.36,false,true,271634,Photographs,Photograph,Georgia O'Keeffe,,,,,,Artist,,Alfred Stieglitz,"American, Hoboken, New Jersey 1864–1946 New York",,"Stieglitz, Alfred",American,1864,1946,1932,1932,1932,Gelatin silver print,23.5 x 18.9 cm (9 1/4 x 7 7/16 in. ),"Gift of Georgia O'Keeffe, through the generosity of The Georgia O'Keeffe Foundation and Jennifer and Joseph Duke, 1997",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271634,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1997.61.37,false,true,271610,Photographs,Photograph,Georgia O'Keeffe—Hands and Horse Skull,,,,,,Artist,,Alfred Stieglitz,"American, Hoboken, New Jersey 1864–1946 New York",,"Stieglitz, Alfred",American,1864,1946,1931,1931,1931,Gelatin silver print,19.2 x 24 cm (7 9/16 x 9 7/16 in. ),"Gift of Georgia O'Keeffe, through the generosity of The Georgia O'Keeffe Foundation and Jennifer and Joseph Duke, 1997",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271610,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1997.61.39,false,true,271582,Photographs,Photograph,Georgia O'Keeffe—Hand and Wheel,,,,,,Artist,,Alfred Stieglitz,"American, Hoboken, New Jersey 1864–1946 New York",,"Stieglitz, Alfred",American,1864,1946,1933,1933,1933,Gelatin silver print,24.1 x 19.5 cm (9 1/2 x 7 11/16 in.),"Gift of Georgia O'Keeffe, through the generosity of The Georgia O'Keeffe Foundation and Jennifer and Joseph Duke, 1997",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271582,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1997.61.40,false,true,271579,Photographs,Photograph,Georgia O'Keeffe,,,,,,Artist,,Alfred Stieglitz,"American, Hoboken, New Jersey 1864–1946 New York",,"Stieglitz, Alfred",American,1864,1946,1933,1933,1933,Gelatin silver print,23.9 x 18.9 cm (9 7/16 x 7 7/16 in. ),"Gift of Georgia O'Keeffe, through the generosity of The Georgia O'Keeffe Foundation and Jennifer and Joseph Duke, 1997",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271579,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1997.61.43,false,true,271600,Photographs,Photograph,Georgia O'Keeffe,,,,,,Artist,,Alfred Stieglitz,"American, Hoboken, New Jersey 1864–1946 New York",,"Stieglitz, Alfred",American,1864,1946,1930,1930,1930,Gelatin silver print,24.1 x 18.8 cm (9 1/2 x 7 3/8 in. ),"Gift of Georgia O'Keeffe, through the generosity of The Georgia O'Keeffe Foundation and Jennifer and Joseph Duke, 1997",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271600,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1997.61.55,false,true,271572,Photographs,Photograph,Georgia O'Keeffe—Feet,,,,,,Artist,,Alfred Stieglitz,"American, Hoboken, New Jersey 1864–1946 New York",,"Stieglitz, Alfred",American,1864,1946,1918,1918,1918,Platinum print,24.1 x 19.5 cm (9 1/2 x 7 11/16 in. ),"Gift of Georgia O'Keeffe, through the generosity of The Georgia O'Keeffe Foundation and Jennifer and Joseph Duke, 1997",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271572,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1997.61.61,false,true,271604,Photographs,Photograph,Georgia O'Keeffe,,,,,,Artist,,Alfred Stieglitz,"American, Hoboken, New Jersey 1864–1946 New York",,"Stieglitz, Alfred",American,1864,1946,1919–21,1919,1921,Palladium print,23.7 x 19 cm (9 5/16 x 7 1/2 in. ),"Gift of Georgia O'Keeffe, through the generosity of The Georgia O'Keeffe Foundation and Jennifer and Joseph Duke, 1997",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271604,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1997.61.63,false,true,271603,Photographs,Photograph,Georgia O'Keeffe,,,,,,Artist,,Alfred Stieglitz,"American, Hoboken, New Jersey 1864–1946 New York",,"Stieglitz, Alfred",American,1864,1946,ca. 1920,1918,1922,Gelatin silver print,9.2 x 11.5 cm (3 5/8 x 4 1/2 in. ),"Gift of Georgia O'Keeffe, through the generosity of The Georgia O'Keeffe Foundation and Jennifer and Joseph Duke, 1997",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271603,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1997.61.64,false,true,271590,Photographs,Photograph,Georgia O'Keeffe with Matisse Sculpture,,,,,,Artist,,Alfred Stieglitz,"American, Hoboken, New Jersey 1864–1946 New York",,"Stieglitz, Alfred",American,1864,1946,1921,1921,1921,Palladium print,24.4 x 19.2 cm (9 5/8 x 7 9/16 in. ),"Gift of Georgia O'Keeffe, through the generosity of The Georgia O'Keeffe Foundation and Jennifer and Joseph Duke, 1997",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271590,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1997.61.66,false,true,271587,Photographs,Photograph,Georgia O'Keeffe,,,,,,Artist,,Alfred Stieglitz,"American, Hoboken, New Jersey 1864–1946 New York",,"Stieglitz, Alfred",American,1864,1946,1922,1922,1922,Palladium print,18.9 x 24 cm (7 7/16 x 9 7/16 in. ),"Gift of Georgia O'Keeffe, through the generosity of The Georgia O'Keeffe Foundation and Jennifer and Joseph Duke, 1997",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271587,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1997.61.69,false,true,271602,Photographs,Photograph,Georgia O'Keeffe,,,,,,Artist,,Alfred Stieglitz,"American, Hoboken, New Jersey 1864–1946 New York",,"Stieglitz, Alfred",American,1864,1946,1932,1932,1932,Gelatin silver print,19.1 x 24 cm (7 1/2 x 9 7/16 in.),"Gift of Georgia O'Keeffe, through the generosity of The Georgia O'Keeffe Foundation and Jennifer and Joseph Duke, 1997",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271602,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1997.61.71,false,true,271624,Photographs,Photograph,Georgia O'Keeffe – Torso,,,,,,Artist,,Alfred Stieglitz,"American, Hoboken, New Jersey 1864–1946 New York",,"Stieglitz, Alfred",American,1864,1946,1931,1931,1931,Gelatin silver print,10.2 x 23.7 cm (4 x 9 5/16 in.),"Gift of Georgia O'Keeffe, through the generosity of The Georgia O'Keeffe Foundation and Jennifer and Joseph Duke, 1997",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271624,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1997.61.74,false,true,271614,Photographs,Photograph,[Margaret Prosser's Clasped Hands in Lap],,,,,,Artist,,Alfred Stieglitz,"American, Hoboken, New Jersey 1864–1946 New York",,"Stieglitz, Alfred",American,1864,1946,1933,1933,1933,Gelatin silver print,9.2 x 11.5 cm (3 5/8 x 4 1/2 in. ),"Gift of Georgia O'Keeffe, through the generosity of The Georgia O'Keeffe Foundation and Jennifer and Joseph Duke, 1997",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271614,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.159.49,false,true,306332,Photographs,Photograph,"Kitty Stieglitz, Central Park, New York",,,,,,Artist,,Alfred Stieglitz,"American, Hoboken, New Jersey 1864–1946 New York",,"Stieglitz, Alfred",American,1864,1946,1901,1901,1901,Photogravure,Mount: 10 3/8 in. × 6 15/16 in. (26.4 × 17.6 cm) Image: 4 13/16 × 6 3/16 in. (12.2 × 15.7 cm),"Bequest of Maurice B. Sendak, 2012",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/306332,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1987.1100.11,false,true,265144,Photographs,Photograph,"From My Window at the Shelton, North",,,,,,Artist,,Alfred Stieglitz,"American, Hoboken, New Jersey 1864–1946 New York",,"Stieglitz, Alfred",American,1864,1946,1931,1931,1931,Gelatin silver print,24.2 x 19.2 cm (9 1/2 x 7 9/16 in.),"Ford Motor Company Collection, Gift of Ford Motor Company and John C. Waddell, 1987",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/265144,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.178,false,true,285933,Photographs,Photograph,"Spring Showers, the Coach",,,,,,Artist,,Alfred Stieglitz,"American, Hoboken, New Jersey 1864–1946 New York",,"Stieglitz, Alfred",American,1864,1946,1899–1900,1899,1900,Platinum print,Image: 3 3/8 × 1 13/16 in. (8.6 × 4.6 cm) Mount (1): 3 9/16 in. × 2 in. (9 × 5.1 cm) Mount (2): 6 13/16 × 3 15/16 in. (17.3 × 10 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285933,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.290,false,true,283271,Photographs,Photograph,Marsden Hartley,,,,,,Artist,,Alfred Stieglitz,"American, Hoboken, New Jersey 1864–1946 New York",,"Stieglitz, Alfred",American,1864,1946,1916,1916,1916,Gelatin silver print,"Image: 24.8 x 19.8cm (9 3/4 x 7 13/16in.) Mount: 55.8 cm, 46 3/16 in. (21 15/16 in., 117.3 cm)","Gilman Collection, Purchase, Gift of Marsden Hartley, by exchange, and Gift of Grace M. Mayer, by exchange, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283271,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.459,false,true,285929,Photographs,Photograph,The Hand of Man,,,,,,Artist,,Alfred Stieglitz,"American, Hoboken, New Jersey 1864–1946 New York",,"Stieglitz, Alfred",American,1864,1946,"1902, printed 1920s–30s",1902,1902,Gelatin silver print,Image: 8.8 x 11.8 cm (3 7/16 x 4 5/8 in.) Mount: 8.8 x 11.8 cm (3 7/16 x 4 5/8 in.) Mount (2nd): 31.3 x 24.7 cm (12 5/16 x 9 3/4 in.),"Gilman Collection, Purchase, Ann Tenenbaum and Thomas H. Lee Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285929,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.479,false,true,286177,Photographs,Photograph,Grass,,,,,,Artist,,Alfred Stieglitz,"American, Hoboken, New Jersey 1864–1946 New York",,"Stieglitz, Alfred",American,1864,1946,1933,1933,1933,Gelatin silver print,Image: 14.9 x 18.6 cm (5 7/8 x 7 5/16 in.) Mount: 14.9 x 18.6 cm (5 7/8 x 7 5/16 in.) Mount (2nd): 34.7 x 27.5 cm (13 11/16 x 10 13/16 in.),"Gilman Collection, Purchase, Ann Tenenbaum and Thomas H. Lee Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286177,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1987.1100.103,false,true,265137,Photographs,Photograph,Gable and Apples,,,,,,Artist,,Alfred Stieglitz,"American, Hoboken, New Jersey 1864–1946 New York",,"Stieglitz, Alfred",American,1864,1946,1922,1922,1922,Gelatin silver print,11.4 x 9.0 cm (4 1/2 x 3 9/16 in.),"Ford Motor Company Collection, Gift of Ford Motor Company and John C. Waddell, 1987",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/265137,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1987.1100.457,false,true,265529,Photographs,Photograph,Equivalent No. 314,,,,,,Artist,,Alfred Stieglitz,"American, Hoboken, New Jersey 1864–1946 New York",,"Stieglitz, Alfred",American,1864,1946,1926,1926,1926,Gelatin silver print,11.9 x 9.1 cm (4 11/16 x 3 9/16 in.),"Ford Motor Company Collection, Gift of Ford Motor Company and John C. Waddell, 1987",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/265529,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1149,false,true,285942,Photographs,Photograph,"Fragment from Balustrade of the Temple of Athena Nike, Acropolis, Athens",,,,,,Artist,,William James Stillman,"American, Schenectady, New York 1828–1901 Surrey",,"Stillman, William James",American,1828,1901,ca. 1882,1877,1887,Albumen silver print from glass negative,Image: 10 1/2 × 8 1/8 in. (26.7 × 20.6 cm) Mount: 15 7/8 in. × 13 15/16 in. (40.4 × 35.4 cm),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285942,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.240,false,true,681809,Photographs,Tintype,[Ku Klux Klansman with Two Pistols and a Sword],,,,,,Artist,,Owen A. Kenefick,"American, Lawrence, Massachusetts ca. 1858–after 1930",,"Kenefick, Owen A.",American,1850,1950,1880–1905,1880,1905,Tintype,Tintype: 3 1/2 × 2 9/16 in. (8.9 × 6.5 cm) Passe-Partout: 4 7/8 × 3 1/8 in. (12.4 × 7.9 cm) Paper Folder (Open): 4 7/8 × 6 1/4 in. (12.4 × 15.9 cm),"The Horace W. Goldsmith Foundation Fund, through Joyce and Robert Menschel, 2015",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/681809,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.75,false,true,283174,Photographs,Photograph,[Portrait of a Young Man],,,,,,Artist,,Samuel F. B. Morse,"American, Charlestown, Massachusetts 1791–1872 New York",,"Morse, Samuel F. B.",American,1791,1872,1840,1840,1840,Daguerreotype,"Image: 5 x 4.2 cm (1 15/16 x 1 5/8 in.), oval","Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283174,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1110,false,true,286619,Photographs,Photograph,"[Fortifications Near Charleston, South Carolina]",,,,,,Artist,Attributed to,George Smith Cook,"American, Stratford, Connecticut 1819–1902 Bel Air, Virginia",,"Cook, George Smith",American,1819,1902,ca. 1861,1861,1861,Albumen silver print from glass negative,Image: 15 × 20.6 cm (5 7/8 × 8 1/8 in.) Mount: 23.2 × 27.1 cm (9 1/8 × 10 11/16 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286619,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2001.389,false,true,284644,Photographs,Photograph,Grave,,,,,,Artist,,Walker Evans,"American, St. Louis, Missouri 1903–1975 New Haven, Connecticut",,"Evans, Walker",American,1903,1975,1936,1936,1936,Gelatin silver print,19.4 x 24.2 cm (7 5/8 x 9 1/2 in. ),"Purchase, Alfred Stieglitz Society Gifts, 2001",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/284644,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1987.1100.482,true,true,265556,Photographs,Photograph,"Penny Picture Display, Savannah",,,,,,Artist,,Walker Evans,"American, St. Louis, Missouri 1903–1975 New Haven, Connecticut",,"Evans, Walker",American,1903,1975,1936,1936,1936,Gelatin silver print,24.7 x 19.3 cm (9 3/4 x 7 5/8 in.),"Ford Motor Company Collection, Gift of Ford Motor Company and John C. Waddell, 1987",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/265556,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.43.147,false,true,267546,Photographs,Photograph,[Mrs. James Brown Potter or Mrs Potter Palmer],,,,,,Artist,,F. Holland Day,"American, Norwood, Massachusetts 1864–1933 Norwood, Massachusetts",,"Day, F. Holland",American,1864,1933,1896,1896,1896,Platinum print,13.4 x 10.8 cm. (5 1/4 x 4 1/4 in.),"Alfred Stieglitz Collection, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267546,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.43.148,false,true,267547,Photographs,Photograph,Zaida Ben-Yusuf,,,,,,Artist,,F. Holland Day,"American, Norwood, Massachusetts 1864–1933 Norwood, Massachusetts",,"Day, F. Holland",American,1864,1933,1898,1898,1898,Platinum print,Image: 16.3 x 10.9 cm (6 7/16 x 4 5/16 in.) Mount: 17.1 x 11.5 cm (6 3/4 x 4 1/2 in.),"Alfred Stieglitz Collection, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267547,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.43.151,false,true,267551,Photographs,Photograph,Kahlil Gibran with Book,,,,,,Artist,,F. Holland Day,"American, Norwood, Massachusetts 1864–1933 Norwood, Massachusetts",,"Day, F. Holland",American,1864,1933,1896,1896,1896,Platinum print,15.9 x 12.0 cm. (6 1/4 x 4 3/4 in.),"Alfred Stieglitz Collection, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267551,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.43.157,false,true,267557,Photographs,Photograph,An Ethiopian Chief,,,,,,Artist,,F. Holland Day,"American, Norwood, Massachusetts 1864–1933 Norwood, Massachusetts",,"Day, F. Holland",American,1864,1933,ca. 1897,1895,1899,Platinum print,18.1 x 18.4 cm. (7 1/8 x 7 1/4 in.),"Alfred Stieglitz Collection, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267557,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.43.158,false,true,267558,Photographs,Photograph,Menelek,,,,,,Artist,,F. Holland Day,"American, Norwood, Massachusetts 1864–1933 Norwood, Massachusetts",,"Day, F. Holland",American,1864,1933,1897,1897,1897,Platinum print,24.5 x 19.5 cm (9 5/8 x 7 11/16 in.),"Alfred Stieglitz Collection, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267558,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.43.159,false,true,267559,Photographs,Photograph,Portrait,,,,,,Artist,,F. Holland Day,"American, Norwood, Massachusetts 1864–1933 Norwood, Massachusetts",,"Day, F. Holland",American,1864,1933,1898,1898,1898,Platinum print,15.7 x 9.8 cm. (6 3/16 x 3 7/8 in.),"Alfred Stieglitz Collection, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267559,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.43.160,false,true,267561,Photographs,Photograph,The Vigil,,,,,,Artist,,F. Holland Day,"American, Norwood, Massachusetts 1864–1933 Norwood, Massachusetts",,"Day, F. Holland",American,1864,1933,1899,1899,1899,Platinum print,Image: 16.2 x 11 cm (6 3/8 x 4 5/16 in.) Mount: 30.9 x 23.1 cm (12 3/16 x 9 1/8 in.),"Alfred Stieglitz Collection, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267561,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.43.161,false,true,267562,Photographs,Photograph,[Boy Piping],,,,,,Artist,,F. Holland Day,"American, Norwood, Massachusetts 1864–1933 Norwood, Massachusetts",,"Day, F. Holland",American,1864,1933,ca. 1896,1894,1898,Platinum print,11.8 x 16.6 cm. (4 5/8 x 6 9/16 in.),"Alfred Stieglitz Collection, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267562,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.43.165,false,true,267566,Photographs,Photograph,Portrait of a Man with Book,,,,,,Artist,,F. Holland Day,"American, Norwood, Massachusetts 1864–1933 Norwood, Massachusetts",,"Day, F. Holland",American,1864,1933,1896,1896,1896,Platinum print,Image: 16 x 11.7 cm (6 5/16 x 4 5/8 in.) Mount: 16.3 x 11.9 cm (6 7/16 x 4 11/16 in.) Mount (2nd): 16.6 x 12.1 cm (6 9/16 x 4 3/4 in.) Mount (3rd): 19 x 14.6 cm (7 1/2 x 5 3/4 in.) Mount (4th): 19.2 x 14.9 cm (7 9/16 x 5 7/8 in.) Mount (5th): 36 x 27.8 cm (14 3/16 x 10 15/16 in.),"Alfred Stieglitz Collection, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267566,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.43.166,false,true,267567,Photographs,Photograph,Ebony and Ivory,,,,,,Artist,,F. Holland Day,"American, Norwood, Massachusetts 1864–1933 Norwood, Massachusetts",,"Day, F. Holland",American,1864,1933,ca. 1897,1895,1899,Platinum print,18.3 x 20.0 cm (7 3/16 x 7 7/8 in.),"Alfred Stieglitz Collection, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267567,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.43.355,false,true,267770,Photographs,Photograph,[Draped Nude Lounging on the Grass],,,,,,Artist,,F. Holland Day,"American, Norwood, Massachusetts 1864–1933 Norwood, Massachusetts",,"Day, F. Holland",American,1864,1933,ca. 1897,1895,1899,Platinum print,Image: 11.8 x 15.8 cm (4 5/8 x 6 1/4 in.) Mount: 15.2 x 19.3 cm (6 x 7 5/8 in.),"Alfred Stieglitz Collection, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267770,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.43.356,false,true,267771,Photographs,Photograph,The Entombment,,,,,,Artist,,F. Holland Day,"American, Norwood, Massachusetts 1864–1933 Norwood, Massachusetts",,"Day, F. Holland",American,1864,1933,1898,1898,1898,Platinum print,6.7 x 16.6 cm. (2 5/8 x 6 9/16 in.),"Alfred Stieglitz Collection, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267771,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.43.357,false,true,267772,Photographs,Photograph,The Honey Gatherer,,,,,,Artist,,F. Holland Day,"American, Norwood, Massachusetts 1864–1933 Norwood, Massachusetts",,"Day, F. Holland",American,1864,1933,ca. 1898,1896,1900,Platinum print,Image: 12.5 cm (4 15/16 in.) diameter Mount: 14 x 13.6 cm (5 1/2 x 5 3/8 in.) Mount (2nd): 14.7 x 14.4 cm (5 13/16 x 5 11/16 in.),"Alfred Stieglitz Collection, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267772,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.43.358,false,true,267773,Photographs,Photograph,[Mrs. James Brown Potter or Mrs. Potter Palmer],,,,,,Artist,,F. Holland Day,"American, Norwood, Massachusetts 1864–1933 Norwood, Massachusetts",,"Day, F. Holland",American,1864,1933,1896,1896,1896,Platinum print,14.2 x 11.9 cm. (5 9/16 x 4 11/16 in.),"Alfred Stieglitz Collection, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267773,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.43.361,false,true,267777,Photographs,Photograph,Kahlil Gibran with Book,,,,,,Artist,,F. Holland Day,"American, Norwood, Massachusetts 1864–1933 Norwood, Massachusetts",,"Day, F. Holland",American,1864,1933,1896,1896,1896,Platinum print,16.0 x 11.8 cm. (6 5/16 x 4 5/8 in.),"Alfred Stieglitz Collection, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267777,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.43.367,false,true,267783,Photographs,Photograph,Zaîda Ben-Yusuf,,,,,,Artist,,F. Holland Day,"American, Norwood, Massachusetts 1864–1933 Norwood, Massachusetts",,"Day, F. Holland",American,1864,1933,1890s,1890,1899,Platinum print,16.5 x 12.2 cm. (6 1/2 x 4 13/16 in.),"Alfred Stieglitz Collection, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267783,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -49.55.175,false,true,269295,Photographs,Photograph,The Seven Words,,,,,,Artist,,F. Holland Day,"American, Norwood, Massachusetts 1864–1933 Norwood, Massachusetts",,"Day, F. Holland",American,1864,1933,1898,1898,1898,Platinum print,each approx: 14.0 x 11.5 cm (5 1/2 x 4 1/2 in.),"Alfred Stieglitz Collection, 1949",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269295,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -49.55.222,false,true,269347,Photographs,Photograph,The Seven Words,,,,,,Artist,,F. Holland Day,"American, Norwood, Massachusetts 1864–1933 Norwood, Massachusetts",,"Day, F. Holland",American,1864,1933,1898,1898,1898,Platinum print,7.9 x 32.8 cm. (3 1/8 x 12 15/16 in.),"Alfred Stieglitz Collection, 1949",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269347,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2011.366,false,true,294845,Photographs,Photograph,[Man Serving Head on a Platter],,,,,,Artist,,William Robert Bowles,"American, Campbellsville, Kentucky 1861–1918 Hopkinsville, Kentucky",,"Bowles, William Robert",American,1861,1918,ca. 1900,1895,1905,Gelatin silver print,Image: 9.8 x 13.9 cm (3 7/8 x 5 1/2 in.) Mount: 14.6 x 17.5 cm (5 3/4 x 6 7/8 in.) Frame: 27.9 x 35.6 cm (11 x 14 in.),"Twentieth-Century Photography Fund, 2011",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/294845,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -44.75.7,false,true,271868,Photographs,Photograph,[Three Boys Wading in a Creek],,,,,,Artist,,Thomas Eakins,"American, Philadelphia, Pennsylvania 1844–1916 Philadelphia, Pennsylvania",,"Eakins, Thomas",American,1844,1916,1883,1883,1883,Platinum print,,"Gift of Charles Bregler, 1944",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271868,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -41.142.2,false,true,271896,Photographs,Photograph,"[William H. Macdowell and Margaret Eakins in Saltville (or Clinch Mountain), Virginia]",,,,,,Artist,Attributed to,Thomas Eakins,"American, Philadelphia, Pennsylvania 1844–1916 Philadelphia, Pennsylvania",,"Eakins, Thomas",American,1844,1916,1880–82,1880,1882,Platinum print,"Image: 27.4 x 20 cm (10 13/16 x 7 7/8 in.), irregular","Gift of Charles Bregler, 1941",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271896,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -43.87.17,true,true,271885,Photographs,Photograph,Two Pupils in Greek Dress,,,,,,Artist,,Thomas Eakins,"American, Philadelphia, Pennsylvania 1844–1916 Philadelphia, Pennsylvania",,"Eakins, Thomas",American,1844,1916,1883,1883,1883,Platinum print,36.8 x 26.7 cm. (14 1/2 x 10 1/2 in.),"David Hunter McAlpin Fund, 1943",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271885,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -43.87.20,false,true,271887,Photographs,Photograph,"Bill Duckett Nude, at the Art Students’ League of Philadelphia",,,,,,Artist,,Thomas Eakins,"American, Philadelphia, Pennsylvania 1844–1916 Philadelphia, Pennsylvania",,"Eakins, Thomas",American,1844,1916,ca. 1889,1884,1894,Platinum print,23.3 x 22 cm (9 3/16 x 8 11/16 in.),"David Hunter McAlpin Fund, 1943",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271887,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -43.87.22,false,true,271889,Photographs,Photograph,"[Thomas Eakins, Nude, Playing Pipes]",,,,,,Artist,,Thomas Eakins,"American, Philadelphia, Pennsylvania 1844–1916 Philadelphia, Pennsylvania",,"Eakins, Thomas",American,1844,1916,ca. 1883,1883,1883,Platinum print,22.7 x 16.6 cm (8 15/16 x 6 9/16 in.) irregular,"David Hunter McAlpin Fund, 1943",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271889,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -43.87.23,true,true,271890,Photographs,Photograph,[Thomas Eakins and John Laurie Wallace on a Beach],,,,,,Artist,,Thomas Eakins,"American, Philadelphia, Pennsylvania 1844–1916 Philadelphia, Pennsylvania",,"Eakins, Thomas",American,1844,1916,ca. 1883,1882,1884,Platinum print,"25.5 x 20.4 cm (10 1/16 x 8 1/16 in.), irregular","David Hunter McAlpin Fund, 1943",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271890,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -43.87.24,false,true,271891,Photographs,Photograph,[Nude Men in the Garden],,,,,,Artist,,Thomas Eakins,"American, Philadelphia, Pennsylvania 1844–1916 Philadelphia, Pennsylvania",,"Eakins, Thomas",American,1844,1916,1870s,1870,1879,Platinum print,,"David Hunter McAlpin Fund, 1943",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271891,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -43.87.25,false,true,271892,Photographs,Photograph,[Female Nude from the Back],,,,,,Artist,,Thomas Eakins,"American, Philadelphia, Pennsylvania 1844–1916 Philadelphia, Pennsylvania",,"Eakins, Thomas",American,1844,1916,ca. 1889,1887,1891,Platinum print,7.1 x 13.4 cm (2 13/16 x 5 1/4 in.),"David Hunter McAlpin Fund, 1943",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271892,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -44.75.10,false,true,271873,Photographs,Photograph,"[Man Walking, ""Stroboscopic"" Photograph]",,,,,,Artist,,Thomas Eakins,"American, Philadelphia, Pennsylvania 1844–1916 Philadelphia, Pennsylvania",,"Eakins, Thomas",American,1844,1916,"1880s, printed 1930s–40s",1880,1889,Gelatin silver print,,"Gift of Charles Bregler, 1944",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271873,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1983.1103,false,true,271854,Photographs,Photograph,[Betty Reynolds with Doll on Lap],,,,,,Artist,,Thomas Eakins,"American, Philadelphia, Pennsylvania 1844–1916 Philadelphia, Pennsylvania",,"Eakins, Thomas",American,1844,1916,ca. 1885,1883,1887,Gelatin silver print,5.5 x 5.3 cm (2 3/16 x 2 1/16 in.),"David Hunter McAlpin Fund, 1983",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271854,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1979.547.2,false,true,271856,Photographs,Photograph,[Mrs. Eakins or Her Sister Doll],,,,,,Artist,,Thomas Eakins,"American, Philadelphia, Pennsylvania 1844–1916 Philadelphia, Pennsylvania",,"Eakins, Thomas",American,1844,1916,1880s–90s,1880,1899,Albumen silver print,,"Gift of Julius Ravzin, 1979",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271856,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1985.1027.12,false,true,271805,Photographs,Photograph,"Katie Crowell in Avondale, Pennsylvania",,,,,,Artist,,Thomas Eakins,"American, Philadelphia, Pennsylvania 1844–1916 Philadelphia, Pennsylvania",,"Eakins, Thomas",American,1844,1916,1887,1887,1887,Albumen silver print,,"Gift of Arthur and Carol Goldberg, 1985",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271805,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1985.1027.17,false,true,271810,Photographs,Photograph,[Four Cats],,,,,,Artist,,Thomas Eakins,"American, Philadelphia, Pennsylvania 1844–1916 Philadelphia, Pennsylvania",,"Eakins, Thomas",American,1844,1916,ca. 1895,1893,1897,Platinum print,5.5 x 13.6 cm (2 3/16 x 5 3/8 in.),"Gift of Joan and Martin E. Messinger, 1985",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271810,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1985.1027.20,false,true,271813,Photographs,Photograph,Self-Portrait,,,,,,Artist,,Thomas Eakins,"American, Philadelphia, Pennsylvania 1844–1916 Philadelphia, Pennsylvania",,"Eakins, Thomas",American,1844,1916,1889–94,1889,1894,Platinum print,,"Gift of Mimi and Ariel Halpern, 1985",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271813,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1985.1027.24,false,true,271817,Photographs,Photograph,William J. Crowell with Ella,,,,,,Artist,,Thomas Eakins,"American, Philadelphia, Pennsylvania 1844–1916 Philadelphia, Pennsylvania",,"Eakins, Thomas",American,1844,1916,1880,1880,1880,Albumen silver print,9.5 x 7.2 cm (3 3/4 x 2 13/16 in.),"Gift of Harry D. Nelson Jr., 1985",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271817,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1985.1027.41,false,true,271834,Photographs,Photograph,[Cornfield in Back of the Barn],,,,,,Artist,,Thomas Eakins,"American, Philadelphia, Pennsylvania 1844–1916 Philadelphia, Pennsylvania",,"Eakins, Thomas",American,1844,1916,1883,1883,1883,Albumen silver print,,"Gift of Joan and Martin E. Messinger, 1985",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271834,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1985.1027.42,false,true,271835,Photographs,Photograph,"[Two Boys Playing at the Creek, July 4, 1883]",,,,,,Artist,,Thomas Eakins,"American, Philadelphia, Pennsylvania 1844–1916 Philadelphia, Pennsylvania",,"Eakins, Thomas",American,1844,1916,1883,1883,1883,Albumen silver print,8.8 x 11.1 cm (3 7/16 x 4 3/8 in.),"Gift of Joseph R. Lasser and Ruth P. Lasser, 1985",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271835,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1985.1027.43,false,true,271836,Photographs,Photograph,"[Three Children and a Dog Playing in the Creek, July 4, 1883]",,,,,,Artist,,Thomas Eakins,"American, Philadelphia, Pennsylvania 1844–1916 Philadelphia, Pennsylvania",,"Eakins, Thomas",American,1844,1916,1883,1883,1883,Albumen silver print,8.9 x 11.2 cm (3 1/2 x 4 7/16 in.),"Gift of Robert D. English, 1985",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271836,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1985.1027.45,false,true,271838,Photographs,Photograph,"[Three Children and a Dog Playing in the Creek, July 4, 1883]",,,,,,Artist,,Thomas Eakins,"American, Philadelphia, Pennsylvania 1844–1916 Philadelphia, Pennsylvania",,"Eakins, Thomas",American,1844,1916,1883,1883,1883,Albumen silver print,,"Gift of Daniel P. and Nancy C. Paduano, 1985",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271838,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1985.1027.51,false,true,271844,Photographs,Photograph,"[Frances Crowell with Unidentified Boy, Katie, James, and Frances Crowell]",,,,,,Artist,,Thomas Eakins,"American, Philadelphia, Pennsylvania 1844–1916 Philadelphia, Pennsylvania",,"Eakins, Thomas",American,1844,1916,1890,1890,1890,Platinum print,7.8 x 9.2 cm (3 1/16 x 3 5/8 in.),"Gift of Joseph R. Lasser and Ruth P. Lasser, 1985",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271844,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1985.1027.59,false,true,271852,Photographs,Photograph,"[Thomas Eakins's Horse Billy and Two Crowell Children at Avondale, Pennsylvania]",,,,,,Artist,,Thomas Eakins,"American, Philadelphia, Pennsylvania 1844–1916 Philadelphia, Pennsylvania",,"Eakins, Thomas",American,1844,1916,ca. 1892,1890,1894,Platinum print,8.1 x 9.8 cm (3 3/16 x 3 7/8 in.) irregular,"Gift of Joseph R. Lasser and Ruth P. Lasser, 1985",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271852,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.589,false,true,285754,Photographs,Photograph,[Self-Portrait],,,,,,Artist,,Thomas Eakins,"American, Philadelphia, Pennsylvania 1844–1916 Philadelphia, Pennsylvania",,"Eakins, Thomas",American,1844,1916,ca. 1880,1878,1882,Platinum print,Image: 15.7 x 10 cm (6 3/16 x 3 15/16 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285754,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1109,false,true,285842,Photographs,Photograph,[African-American Man],,,,,,Artist,,Thomas Eakins,"American, Philadelphia, Pennsylvania 1844–1916 Philadelphia, Pennsylvania",,"Eakins, Thomas",American,1844,1916,ca. 1884,1882,1886,Gelatin silver print,Image: 3 13/16 × 2 11/16 in. (9.7 × 6.8 cm),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285842,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1974.671,false,true,261176,Photographs,Photograph,Herbert,,,,,,Artist,,Morton Schamberg,"American, Philadelphia, Pennsylvania 1881–1918 Philadelphia, Pennsylvania",,"Schamberg, Morton",American,1881,1918,1912,1912,1912,Gelatin silver print,,"Gift of Jean Loeb Whitehill, 1974",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261176,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.43.351,false,true,267766,Photographs,Photograph,Portrait Study,,,,,,Artist,,Morton Schamberg,"American, Philadelphia, Pennsylvania 1881–1918 Philadelphia, Pennsylvania",,"Schamberg, Morton",American,1881,1918,1917,1917,1917,Gelatin silver print,24.1 x 17.7 cm. (9 1/2 x 7 in.),"Alfred Stieglitz Collection, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267766,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1973.664.1,false,true,261015,Photographs,Photograph,Jeanne,,,,,,Artist,,Morton Schamberg,"American, Philadelphia, Pennsylvania 1881–1918 Philadelphia, Pennsylvania",,"Schamberg, Morton",American,1881,1918,1917,1917,1917,Gelatin silver print,,"Gift of Jean Loeb Whitehill, 1973",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261015,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1973.664.2,false,true,261026,Photographs,Photograph,Jeanne,,,,,,Artist,,Morton Schamberg,"American, Philadelphia, Pennsylvania 1881–1918 Philadelphia, Pennsylvania",,"Schamberg, Morton",American,1881,1918,1917,1917,1917,Gelatin silver print,,"Gift of Jean Loeb Whitehill, 1973",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261026,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1973.664.3,false,true,261027,Photographs,Photograph,Jeanne,,,,,,Artist,,Morton Schamberg,"American, Philadelphia, Pennsylvania 1881–1918 Philadelphia, Pennsylvania",,"Schamberg, Morton",American,1881,1918,1917,1917,1917,Gelatin silver print,,"Gift of Jean Loeb Whitehill, 1973",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261027,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1973.664.4,false,true,261028,Photographs,Photograph,Jeanne and Regena,,,,,,Artist,,Morton Schamberg,"American, Philadelphia, Pennsylvania 1881–1918 Philadelphia, Pennsylvania",,"Schamberg, Morton",American,1881,1918,1917,1917,1917,Gelatin silver print,,"Gift of Jean Loeb Whitehill, 1973",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261028,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1973.664.5,false,true,261029,Photographs,Photograph,Regena,,,,,,Artist,,Morton Schamberg,"American, Philadelphia, Pennsylvania 1881–1918 Philadelphia, Pennsylvania",,"Schamberg, Morton",American,1881,1918,1917,1917,1917,Gelatin silver print,,"Gift of Jean Loeb Whitehill, 1973",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261029,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1973.664.6,false,true,261030,Photographs,Photograph,Regena,,,,,,Artist,,Morton Schamberg,"American, Philadelphia, Pennsylvania 1881–1918 Philadelphia, Pennsylvania",,"Schamberg, Morton",American,1881,1918,1912,1912,1912,Gelatin silver print,,"Gift of Jean Loeb Whitehill, 1973",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261030,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1973.664.7,false,true,261031,Photographs,Photograph,Regena and Herbert,,,,,,Artist,,Morton Schamberg,"American, Philadelphia, Pennsylvania 1881–1918 Philadelphia, Pennsylvania",,"Schamberg, Morton",American,1881,1918,1912,1912,1912,Gelatin silver print,,"Gift of Jean Loeb Whitehill, 1973",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261031,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1973.664.8,false,true,261032,Photographs,Photograph,Herbert,,,,,,Artist,,Morton Schamberg,"American, Philadelphia, Pennsylvania 1881–1918 Philadelphia, Pennsylvania",,"Schamberg, Morton",American,1881,1918,1912,1912,1912,Gelatin silver print,,"Gift of Jean Loeb Whitehill, 1973",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261032,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1973.664.9,false,true,261033,Photographs,Photograph,Herbert,,,,,,Artist,,Morton Schamberg,"American, Philadelphia, Pennsylvania 1881–1918 Philadelphia, Pennsylvania",,"Schamberg, Morton",American,1881,1918,1912,1912,1912,Gelatin silver print,,"Gift of Jean Loeb Whitehill, 1973",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261033,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1973.664.10,false,true,261016,Photographs,Photograph,Jeanne,,,,,,Artist,,Morton Schamberg,"American, Philadelphia, Pennsylvania 1881–1918 Philadelphia, Pennsylvania",,"Schamberg, Morton",American,1881,1918,1912,1912,1912,Gelatin silver print,,"Gift of Jean Loeb Whitehill, 1973",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261016,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1973.664.11,false,true,261017,Photographs,Photograph,Jeanne,,,,,,Artist,,Morton Schamberg,"American, Philadelphia, Pennsylvania 1881–1918 Philadelphia, Pennsylvania",,"Schamberg, Morton",American,1881,1918,1912,1912,1912,Gelatin silver print,,"Gift of Jean Loeb Whitehill, 1973",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261017,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1973.664.12,false,true,261018,Photographs,Photograph,Jeanne and Richard,,,,,,Artist,,Morton Schamberg,"American, Philadelphia, Pennsylvania 1881–1918 Philadelphia, Pennsylvania",,"Schamberg, Morton",American,1881,1918,1912,1912,1912,Gelatin silver print,,"Gift of Jean Loeb Whitehill, 1973",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261018,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1973.664.13,false,true,261019,Photographs,Photograph,Jeanne and Richard,,,,,,Artist,,Morton Schamberg,"American, Philadelphia, Pennsylvania 1881–1918 Philadelphia, Pennsylvania",,"Schamberg, Morton",American,1881,1918,1912,1912,1912,Gelatin silver print,,"Gift of Jean Loeb Whitehill, 1973",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261019,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1973.664.14,false,true,261020,Photographs,Photograph,Richard,,,,,,Artist,,Morton Schamberg,"American, Philadelphia, Pennsylvania 1881–1918 Philadelphia, Pennsylvania",,"Schamberg, Morton",American,1881,1918,1912,1912,1912,Gelatin silver print,,"Gift of Jean Loeb Whitehill, 1973",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261020,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1973.664.15,false,true,261021,Photographs,Photograph,Etellea,,,,,,Artist,,Morton Schamberg,"American, Philadelphia, Pennsylvania 1881–1918 Philadelphia, Pennsylvania",,"Schamberg, Morton",American,1881,1918,1912,1912,1912,Gelatin silver print,,"Gift of Jean Loeb Whitehill, 1973",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261021,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1973.664.16,false,true,261022,Photographs,Photograph,Jeanne,,,,,,Artist,,Morton Schamberg,"American, Philadelphia, Pennsylvania 1881–1918 Philadelphia, Pennsylvania",,"Schamberg, Morton",American,1881,1918,1915,1915,1915,Gelatin silver print,,"Gift of Jean Loeb Whitehill, 1973",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261022,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1973.664.17,false,true,261023,Photographs,Photograph,Jeanne,,,,,,Artist,,Morton Schamberg,"American, Philadelphia, Pennsylvania 1881–1918 Philadelphia, Pennsylvania",,"Schamberg, Morton",American,1881,1918,1912,1912,1912,Gelatin silver print,,"Gift of Jean Loeb Whitehill, 1973",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261023,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1973.664.18,false,true,261024,Photographs,Photograph,Morton Schamberg,,,,,,Artist,,Morton Schamberg,"American, Philadelphia, Pennsylvania 1881–1918 Philadelphia, Pennsylvania",,"Schamberg, Morton",American,1881,1918,ca. 1912,1910,1914,Gelatin silver print,,"Gift of Jean Loeb Whitehill, 1973",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261024,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1973.664.19,false,true,261025,Photographs,Photograph,"Morton, Richard, Jeanne, Gilbert, Jesse, Etellea, Yvonne, Henry, Regena, and Herbert",,,,,,Artist,,Morton Schamberg,"American, Philadelphia, Pennsylvania 1881–1918 Philadelphia, Pennsylvania",,"Schamberg, Morton",American,1881,1918,1912,1912,1912,Gelatin silver print,,"Gift of Jean Loeb Whitehill, 1973",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/261025,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -47.62.2,false,true,271903,Photographs,Glass positive,[Man on a Ladder],,,,,,Artist,,Thomas Eakins,"American, Philadelphia, Pennsylvania 1844–1916 Philadelphia, Pennsylvania",,"Eakins, Thomas",American,1844,1916,1880s,1880,1889,Glass positive,,"Gift of Charles Bregler, 1947",,,,,,,,,,,,Transparencies,,http://www.metmuseum.org/art/collection/search/271903,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -47.62.3,false,true,271904,Photographs,Glass positive,Margaret Eakins,,,,,,Artist,,Thomas Eakins,"American, Philadelphia, Pennsylvania 1844–1916 Philadelphia, Pennsylvania",,"Eakins, Thomas",American,1844,1916,1880s,1880,1889,Glass positive,,"Gift of Charles Bregler, 1947",,,,,,,,,,,,Transparencies,,http://www.metmuseum.org/art/collection/search/271904,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -47.62.4,false,true,271905,Photographs,Glass positive,[Female portrait],,,,,,Artist,,Thomas Eakins,"American, Philadelphia, Pennsylvania 1844–1916 Philadelphia, Pennsylvania",,"Eakins, Thomas",American,1844,1916,1880s,1880,1889,Glass positive,,"Gift of Charles Bregler, 1947",,,,,,,,,,,,Transparencies,,http://www.metmuseum.org/art/collection/search/271905,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -47.62.5,false,true,271902,Photographs,Glass positive,Frank MacDowell,,,,,,Artist,,Thomas Eakins,"American, Philadelphia, Pennsylvania 1844–1916 Philadelphia, Pennsylvania",,"Eakins, Thomas",American,1844,1916,1880s,1880,1889,Glass positive,,"Gift of Charles Bregler, 1947",,,,,,,,,,,,Transparencies,,http://www.metmuseum.org/art/collection/search/271902,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -44.75.9,false,true,271870,Photographs,Photograph,Edmund Quinn Fencing,,,,,,Artist,Circle of,Thomas Eakins,"American, Philadelphia, Pennsylvania 1844–1916 Philadelphia, Pennsylvania",,"Eakins, Thomas",American,1844,1916,1880s,1880,1889,Gelatin silver print,Image: 9.1 x 11 cm (3 9/16 x 4 5/16 in.),"Gift of Charles Bregler, 1944",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271870,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -41.142.4,false,true,271898,Photographs,Photograph,[Woman Playing Cello],,,,,,Artist,,Thomas Eakins,"American, Philadelphia, Pennsylvania 1844–1916 Philadelphia, Pennsylvania",,"Eakins, Thomas",American,1844,1916,1880s,1880,1889,Platinum print,,"Gift of Charles Bregler, 1941",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271898,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -43.87.10,false,true,271878,Photographs,Photograph,Mary Macdowell,,,,,,Artist,,Thomas Eakins,"American, Philadelphia, Pennsylvania 1844–1916 Philadelphia, Pennsylvania",,"Eakins, Thomas",American,1844,1916,1880s,1880,1889,Platinum print,,"David Hunter McAlpin Fund, 1943",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271878,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -43.87.11,false,true,271879,Photographs,Photograph,Elizabeth MacDowell Kenton,,,,,,Artist,,Thomas Eakins,"American, Philadelphia, Pennsylvania 1844–1916 Philadelphia, Pennsylvania",,"Eakins, Thomas",American,1844,1916,1880s,1880,1889,Albumen silver print,,"David Hunter McAlpin Fund, 1943",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271879,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -43.87.14,false,true,271882,Photographs,Photograph,[Woman in White Laced-bodice Dress in Studio of Thomas Eakins],,,,,,Artist,,Thomas Eakins,"American, Philadelphia, Pennsylvania 1844–1916 Philadelphia, Pennsylvania",,"Eakins, Thomas",American,1844,1916,1880s,1880,1889,Platinum print,23.3 x 14.9 cm (9 3/16 x 5 7/8 in.),"David Hunter McAlpin Fund, 1943",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271882,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -43.87.21,false,true,271888,Photographs,Photograph,[Standing Male Nude with Pipes],,,,,,Artist,,Thomas Eakins,"American, Philadelphia, Pennsylvania 1844–1916 Philadelphia, Pennsylvania",,"Eakins, Thomas",American,1844,1916,1880s,1880,1889,Platinum print,22.9 x 17.3 cm (9 x 6 13/16 in.),"David Hunter McAlpin Fund, 1943",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271888,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -44.75.11,false,true,271869,Photographs,Photograph,[Thomas Eakins in Swim Suit],,,,,,Artist,,Thomas Eakins,"American, Philadelphia, Pennsylvania 1844–1916 Philadelphia, Pennsylvania",,"Eakins, Thomas",American,1844,1916,1880s,1880,1889,Albumen silver print,,"Gift of Charles Bregler, 1944",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271869,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1979.547.1,false,true,271855,Photographs,Photograph,Mr. MacDowell,,,,,,Artist,,Thomas Eakins,"American, Philadelphia, Pennsylvania 1844–1916 Philadelphia, Pennsylvania",,"Eakins, Thomas",American,1844,1916,1880s,1880,1889,Albumen silver print,,"Gift of Julius Ravzin, 1979",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271855,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1979.547.3,false,true,271857,Photographs,Photograph,[Mrs. Louis Kentin in Empire Dress],,,,,,Artist,,Thomas Eakins,"American, Philadelphia, Pennsylvania 1844–1916 Philadelphia, Pennsylvania",,"Eakins, Thomas",American,1844,1916,1880s,1880,1889,Albumen silver print,,"Gift of Julius Ravzin, 1979",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271857,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1985.1027.11,false,true,271804,Photographs,Photograph,[Thomas Eakins's Dog Harry and Another Setter],,,,,,Artist,,Thomas Eakins,"American, Philadelphia, Pennsylvania 1844–1916 Philadelphia, Pennsylvania",,"Eakins, Thomas",American,1844,1916,1880s,1880,1889,Platinum print,8.5 x 10.4 cm (3 3/8 x 4 1/8 in.),"Gift of John T. Marvin, 1985",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/271804,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2011.199,false,true,296297,Photographs,Cabinet card,[Man in Bottle],,,,,,Artist,,John C. Higgins,"American, active 1880s–90s",,"Higgins, John C.",American,1880,1899,ca. 1888,1883,1893,Albumen silver print from glass negative,Image: 13.5 x 10 cm (5 5/16 x 3 15/16 in.) Mount: 16.4 x 10.6 cm (6 7/16 x 4 3/16 in.) Frame: 35.6 x 27.9 cm (14 x 11 in.),"Purchase, Susan and Thomas Dunn Gift, 2011",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/296297,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.460.1,false,true,290469,Photographs,Photograph,Niagara Falls,,,,,,Artist,,George Barker,"American, born Canada, 1844–1894",,"Barker, George",American,1844,1894,ca. 1888,1885,1890,Albumen silver print from glass negative,Image: 49.1 x 42.5 cm (19 5/16 x 16 3/4 in.) Sheet: 50.8 x 43.3 cm (20 x 17 1/16 in.) Mount: 54.7 x 48.8 cm (21 9/16 x 19 3/16 in.),"Gift of Paul F. Walter, 2009",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/290469,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1174.3,false,true,302666,Photographs,Carte-de-visite,"The Evacuation of Fort Sumter, April 1861",,,,,,Publisher,,Edward Anthony,"American, 1818–1888",,"Anthony, Edward",American,1818,1888,April 1861,1861,1861,Albumen silver print from glass negative,Image: 3 1/8 × 1 15/16 in. (7.9 × 5 cm) Mount: 4 3/4 in. × 3 3/8 in. (12 × 8.5 cm),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/302666,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1174.5,false,true,302668,Photographs,Carte-de-visite,"The Evacuation of Fort Sumter, April 1861",,,,,,Publisher,,Edward Anthony,"American, 1818–1888",,"Anthony, Edward",American,1818,1888,April 1861,1861,1861,Albumen silver print from glass negative,Image: 1 15/16 × 3 1/8 in. (5 × 7.9 cm) Mount: 3 3/8 in. × 4 3/4 in. (8.5 × 12 cm),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/302668,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1174.6,false,true,302669,Photographs,Carte-de-visite,"The Evacuation of Fort Sumter, April 1861",,,,,,Publisher,,Edward Anthony,"American, 1818–1888",,"Anthony, Edward",American,1818,1888,April 1861,1861,1861,Albumen silver print from glass negative,Image: 1 15/16 × 3 1/8 in. (5 × 7.9 cm) Mount: 3 3/8 in. × 4 3/4 in. (8.5 × 12 cm),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/302669,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1174.10,false,true,302673,Photographs,Carte-de-visite,"The Evacuation of Fort Sumter, April 1861",,,,,,Publisher,,Edward Anthony,"American, 1818–1888",,"Anthony, Edward",American,1818,1888,April 1861,1861,1861,Albumen silver print from glass negative,Image: 1 15/16 × 3 1/8 in. (5 × 7.9 cm) Mount: 3 3/8 in. × 4 3/4 in. (8.5 × 12 cm),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/302673,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1174.12,false,true,302675,Photographs,Carte-de-visite,"The Evacuation of Fort Sumter, April 1861",,,,,,Publisher,,Edward Anthony,"American, 1818–1888",,"Anthony, Edward",American,1818,1888,April 1861,1861,1861,Albumen silver print from glass negative,Image: 1 15/16 × 3 1/8 in. (5 × 7.9 cm) Mount: 3 3/8 in. × 4 3/4 in. (8.5 × 12 cm),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/302675,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1174.14,false,true,302677,Photographs,Carte-de-visite,"The Evacuation of Fort Sumter, April 1861",,,,,,Publisher,,Edward Anthony,"American, 1818–1888",,"Anthony, Edward",American,1818,1888,April 1861,1861,1861,Albumen silver print from glass negative,Image: 1 15/16 × 3 1/8 in. (5 × 7.9 cm) Mount: 3 3/8 in. × 4 3/4 in. (8.5 × 12 cm),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/302677,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1982.1182.939,false,true,288280,Photographs,Stereographs,"Tower of London, London, England",,,,,,Artist|Publisher,,Benneville Lloyd Singley|Keystone View Company,"American, Union Township, Pennsylvania 1864–1938 Meadville, Pennsylvania",,"Singley, Benneville Lloyd|Keystone View Company",American,1864,1938,1850s–1910s,1850,1919,Albumen silver prints,Mount: 8.9 x 17.9 cm (3 1/2 x 7 1/16 in.),"Gift of Weston J. Naef, in memory of Kathleen W. Naef and Weston J. Naef Sr., 1982",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288280,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1982.1182.958–.962,false,true,288288,Photographs,Stereographs,"[Group of 5 Stereograph Views of the Thames River at Night, London, England]",,,,,,Artist|Publisher,,Benneville Lloyd Singley|Keystone View Company,"American, Union Township, Pennsylvania 1864–1938 Meadville, Pennsylvania",,"Singley, Benneville Lloyd|Keystone View Company",American,1864,1938,1850s–1910s,1850,1919,Albumen silver prints,,"Gift of Weston J. Naef, in memory of Kathleen W. Naef and Weston J. Naef Sr., 1982",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288288,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1982.1182.992–.995,false,true,288294,Photographs,Stereographs,[Group of 4 Stereograph Views of Babies],,,,,,Artist|Publisher,,Benneville Lloyd Singley|Keystone View Company,"American, Union Township, Pennsylvania 1864–1938 Meadville, Pennsylvania",,"Singley, Benneville Lloyd|Keystone View Company",American,1864,1938,1850s–1910s,1850,1919,Albumen silver prints,Mounts approximately: 8.9 x 17.8 cm (3 1/2 x 7 in.),"Gift of Weston J. Naef, in memory of Kathleen W. Naef and Weston J. Naef Sr., 1982",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288294,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.505.1 (1b),false,true,288478,Photographs,Photograph,[Young Japanese Woman],,,,,,Artist,,Suzuki Shin'ichi,"Japanese, 1835–1919",,"Suzuki, Shin'ichi",Japanese,1835,1919,1870s,1870,1879,Albumen silver print from glass negative,24.8 x 19.8 cm (9 3/4 x 7 13/16 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288478,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.505.1 (2b),false,true,283170,Photographs,Photograph,Actor in Samurai Armor,,,,,,Artist,,Suzuki Shin'ichi,"Japanese, 1835–1919",,"Suzuki, Shin'ichi",Japanese,1835,1919,1870s,1870,1879,Albumen silver print from glass negative,24.6 x 18.9 cm (9 11/16 x 7 7/16 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283170,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.505.1 (4b),false,true,288483,Photographs,Photograph,[Woman in Traditional Japanese Garment Photographed from Behind],,,,,,Artist,,Suzuki Shin'ichi,"Japanese, 1835–1919",,"Suzuki, Shin'ichi",Japanese,1835,1919,1870s,1870,1879,Albumen silver print from glass negative,25.1 x 20 cm (9 7/8 x 7 7/8 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288483,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.505.1 (5b),false,true,288484,Photographs,Photograph,[Young Japanese Woman],,,,,,Artist,,Suzuki Shin'ichi,"Japanese, 1835–1919",,"Suzuki, Shin'ichi",Japanese,1835,1919,1870s,1870,1879,Albumen silver print from glass negative,25 x 19.9 cm (9 13/16 x 7 13/16 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288484,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.505.1 (9b),false,true,288490,Photographs,Photograph,"Street Minstrel, Gose",,,,,,Artist,,Suzuki Shin'ichi,"Japanese, 1835–1919",,"Suzuki, Shin'ichi",Japanese,1835,1919,1870s,1870,1879,Albumen silver print from glass negative,25.2 x 20.2 cm (9 15/16 x 7 15/16 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288490,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.505.1 (10b),false,true,288492,Photographs,Photograph,Farm laborer with rain coat (mino),,,,,,Artist,,Suzuki Shin'ichi,"Japanese, 1835–1919",,"Suzuki, Shin'ichi",Japanese,1835,1919,1870s,1870,1879,Albumen silver print from glass negative,25.2 x 19.7 cm (9 15/16 x 7 3/4 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288492,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.505.1 (11b),false,true,288494,Photographs,Photograph,Professional Singers,,,,,,Artist,,Suzuki Shin'ichi,"Japanese, 1835–1919",,"Suzuki, Shin'ichi",Japanese,1835,1919,1870s,1870,1879,Albumen silver print from glass negative,25.1 x 20.1 cm (9 7/8 x 7 15/16 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288494,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.505.1 (12b),false,true,288496,Photographs,Photograph,Newsman,,,,,,Artist,,Suzuki Shin'ichi,"Japanese, 1835–1919",,"Suzuki, Shin'ichi",Japanese,1835,1919,1870s,1870,1879,Albumen silver print from glass negative,25.3 x 20.1 cm (9 15/16 x 7 15/16 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288496,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.505.1 (13b),false,true,288498,Photographs,Photograph,Tea House waitress,,,,,,Artist,,Suzuki Shin'ichi,"Japanese, 1835–1919",,"Suzuki, Shin'ichi",Japanese,1835,1919,1870s,1870,1879,Albumen silver print from glass negative,25.1 x 20.2 cm (9 7/8 x 7 15/16 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288498,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.505.1 (14b),false,true,288500,Photographs,Photograph,La Toilette,,,,,,Artist,,Suzuki Shin'ichi,"Japanese, 1835–1919",,"Suzuki, Shin'ichi",Japanese,1835,1919,1870s,1870,1879,Albumen silver print from glass negative,25.2 x 20.3 cm (9 15/16 x 8 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288500,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.505.1 (15b),false,true,288502,Photographs,Photograph,[Japanese Women in Traditional Dress],,,,,,Artist,,Suzuki Shin'ichi,"Japanese, 1835–1919",,"Suzuki, Shin'ichi",Japanese,1835,1919,1870s,1870,1879,Albumen silver print from glass negative,25.2 x 20 cm (9 15/16 x 7 7/8 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288502,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.505.1 (16b),false,true,288505,Photographs,Photograph,[Japanese Women in Traditional Dress],,,,,,Artist,,Suzuki Shin'ichi,"Japanese, 1835–1919",,"Suzuki, Shin'ichi",Japanese,1835,1919,1870s,1870,1879,Albumen silver print from glass negative,25.2 x 20 cm (9 15/16 x 7 7/8 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288505,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.505.1 (17b),false,true,288507,Photographs,Photograph,[Japanese Woman in Traditional Dress],,,,,,Artist,,Suzuki Shin'ichi,"Japanese, 1835–1919",,"Suzuki, Shin'ichi",Japanese,1835,1919,1870s,1870,1879,Albumen silver print from glass negative,25.2 x 19.7 cm (9 15/16 x 7 3/4 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288507,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.505.1 (18b),false,true,288509,Photographs,Photograph,Osaki Kioto [illegible] dancer,,,,,,Artist,,Suzuki Shin'ichi,"Japanese, 1835–1919",,"Suzuki, Shin'ichi",Japanese,1835,1919,1870s,1870,1879,Albumen silver print from glass negative,25.2 x 19.9 cm (9 15/16 x 7 13/16 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288509,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.505.1 (19b),false,true,288511,Photographs,Photograph,[Two Japanese Women in Traditional Dress],,,,,,Artist,,Suzuki Shin'ichi,"Japanese, 1835–1919",,"Suzuki, Shin'ichi",Japanese,1835,1919,1870s,1870,1879,Albumen silver print from glass negative,25.2 x 19.9 cm (9 15/16 x 7 13/16 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288511,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.505.1 (20b),false,true,288513,Photographs,Photograph,[Two Japanese Women in Traditional Dress with Fan and Screen],,,,,,Artist,,Suzuki Shin'ichi,"Japanese, 1835–1919",,"Suzuki, Shin'ichi",Japanese,1835,1919,1870s,1870,1879,Albumen silver print from glass negative,25.2 x 19.7 cm (9 15/16 x 7 3/4 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288513,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.505.1 (21b),false,true,288514,Photographs,Photograph,[Japanese Man Preparing a Fish],,,,,,Artist,,Suzuki Shin'ichi,"Japanese, 1835–1919",,"Suzuki, Shin'ichi",Japanese,1835,1919,1870s,1870,1879,Albumen silver print from glass negative,25.2 x 19.9 cm (9 15/16 x 7 13/16 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288514,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.505.1 (22b),false,true,288516,Photographs,Photograph,[A Japanese Woman and a Japanese Boy in Traditional Dress],,,,,,Artist,,Suzuki Shin'ichi,"Japanese, 1835–1919",,"Suzuki, Shin'ichi",Japanese,1835,1919,1870s,1870,1879,Albumen silver print from glass negative,25.2 x 20.1 cm (9 15/16 x 7 15/16 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288516,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.505.1 (23b),false,true,288518,Photographs,Photograph,[Japanese Woman with Parasol],,,,,,Artist,,Suzuki Shin'ichi,"Japanese, 1835–1919",,"Suzuki, Shin'ichi",Japanese,1835,1919,1870s,1870,1879,Albumen silver print from glass negative,25.1 x 20.3 cm (9 7/8 x 8 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288518,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.505.1 (24b),false,true,288520,Photographs,Photograph,Florist,,,,,,Artist,,Suzuki Shin'ichi,"Japanese, 1835–1919",,"Suzuki, Shin'ichi",Japanese,1835,1919,1870s,1870,1879,Albumen silver print from glass negative,25.1 x 20.3 cm (9 7/8 x 8 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288520,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.505.1 (25b),false,true,288522,Photographs,Photograph,[Two Japanese Women Posing with Fans],,,,,,Artist,,Suzuki Shin'ichi,"Japanese, 1835–1919",,"Suzuki, Shin'ichi",Japanese,1835,1919,1870s,1870,1879,Albumen silver print from glass negative,25.2 x 20.1 cm (9 15/16 x 7 15/16 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288522,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.505.1 (26b),false,true,288524,Photographs,Photograph,[Japanese Woman in Traditional Dress Posing with a Child on her Back],,,,,,Artist,,Suzuki Shin'ichi,"Japanese, 1835–1919",,"Suzuki, Shin'ichi",Japanese,1835,1919,1870s,1870,1879,Albumen silver print from glass negative,25 x 20.2 cm (9 13/16 x 7 15/16 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288524,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.505.1 (27b),false,true,288526,Photographs,Photograph,[Japanese Woman in Traditional Dress Posing with Cat and Instrument],,,,,,Artist,,Suzuki Shin'ichi,"Japanese, 1835–1919",,"Suzuki, Shin'ichi",Japanese,1835,1919,1870s,1870,1879,Albumen silver print from glass negative,25.2 x 20 cm (9 15/16 x 7 7/8 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288526,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.505.1 (28b),false,true,288527,Photographs,Photograph,[Japanese Woman in Traditional Dress Posing Outdoors],,,,,,Artist,,Suzuki Shin'ichi,"Japanese, 1835–1919",,"Suzuki, Shin'ichi",Japanese,1835,1919,1870s,1870,1879,Albumen silver print from glass negative,25.2 x 19.8 cm (9 15/16 x 7 13/16 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288527,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.505.1 (29b),false,true,288529,Photographs,Photograph,Cobbler,,,,,,Artist,,Suzuki Shin'ichi,"Japanese, 1835–1919",,"Suzuki, Shin'ichi",Japanese,1835,1919,1870s,1870,1879,Albumen silver print from glass negative,25.3 x 20.2 cm (9 15/16 x 7 15/16 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288529,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.505.1 (30b),false,true,288530,Photographs,Photograph,[Two Japanese Men in Traditional Dress],,,,,,Artist,,Suzuki Shin'ichi,"Japanese, 1835–1919",,"Suzuki, Shin'ichi",Japanese,1835,1919,1870s,1870,1879,Albumen silver print from glass negative,20.1 x 25.2 cm (7 15/16 x 9 15/16 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288530,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.505.1 (40b),false,true,288548,Photographs,Photograph,[Japanese Woman in Carriage],,,,,,Artist,,Suzuki Shin'ichi,"Japanese, 1835–1919",,"Suzuki, Shin'ichi",Japanese,1835,1919,1870s,1870,1879,Albumen silver print from glass negative,20.1 x 25.2 cm (7 15/16 x 9 15/16 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288548,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.505.1 (41b),false,true,288549,Photographs,Photograph,[Landscape with Buddha Sculpture],,,,,,Artist,,Suzuki Shin'ichi,"Japanese, 1835–1919",,"Suzuki, Shin'ichi",Japanese,1835,1919,1870s,1870,1879,Albumen silver print from glass negative,25.1 x 20.1 cm (9 7/8 x 7 15/16 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288549,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.505.1 (43b),false,true,288553,Photographs,Photograph,[Buddha Sculpture],,,,,,Artist,,Suzuki Shin'ichi,"Japanese, 1835–1919",,"Suzuki, Shin'ichi",Japanese,1835,1919,1870s,1870,1879,Albumen silver print from glass negative,25.2 x 20 cm (9 15/16 x 7 7/8 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288553,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2004.283a, b",false,true,285443,Photographs,Photograph,[Geisha with Attendant],,,,,,Artist,,Yokoyama Matsusaburō,"Japanese, 1838–1884",,"Yokoyama, Matsusaburō",Japanese,1838,1884,1860s,1860,1869,Ambrotype,Image: 10 x 7.6 cm (3 15/16 x 3 in.) Case: 1.3 x 11.1 x 8.6 cm (1/2 x 4 3/8 x 3 3/8 in.),"Funds from various donors, 2004",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285443,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1064.1,false,true,264743,Photographs,Photograph,"Mutsuhito, The Meiji Emperor",,,,,,Artist,,Uchida Kuichi,"Japanese, 1844–1875",,"Uchida, Kuichi",Japanese,1844,1875,1872,1872,1872,Albumen silver print from glass negative,25.1 x 19.5 cm (9 7/8 x 7 11/16 in.),"The Elisha Whittelsey Collection, The Elisha Whittelsey Fund, 1986",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/264743,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2004.282a, b",false,true,285442,Photographs,Photograph,Kenji Morita,,,,,,Artist,,Fujita,"Japanese, active 1880s",,Fujita,Japanese,1880,1880,1886,1886,1886,Ambrotype,Image: 9.6 x 6.8 cm (3 3/4 x 2 11/16 in.) Case: 1.3 x 11.1 x 8.3 cm (1/2 x 4 3/8 x 3 1/4 in.),"Funds from various donors, 2004",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285442,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.454.1.27,false,true,291461,Photographs,Album,"Barracks Post, Place de la Bastille; Canal Tunnel and July Column",,,,,,Artist|Author,,Alphonse J. Liébert|Alfred d'Aunay,"French, 1827–1913|French",,"Liébert, Alphonse J.|d'Aunay, Alfred",French|French,1827,1913,1871,1871,1871,Albumen silver print from glass negative,"Images approx.: 19 x 25 cm (7 1/2 x 9 13/16 in.), or the reverse Mounts: 32.8 x 41.3 cm (12 15/16 x 16 1/4 in.), or the reverse","Joyce F. Menschel Photography Library Fund, 2007",,,,,,,,,,,,Albums,,http://www.metmuseum.org/art/collection/search/291461,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.454.1.1–.33,false,true,288894,Photographs,Album,"Les Ruines de Paris et de ses Environs 1870-1871: Cent Photographies: Premier Volume. Par A. Liébert, text par Alfred d'Aunay.",,,,,,Artist|Author,,Alphonse J. Liébert|Alfred d'Aunay,"French, 1827–1913|French",,"Liébert, Alphonse J.|d'Aunay, Alfred",French|French,1827,1913,1870–71,1870,1871,Albumen silver prints from glass negatives,"Images approx.: 19 x 25 cm (7 1/2 x 9 13/16 in.), or the reverse Mounts: 32.8 x 41.3 cm (12 15/16 x 16 1/4 in.), or the reverse","Joyce F. Menschel Photography Library Fund, 2007",,,,,,,,,,,,Albums,,http://www.metmuseum.org/art/collection/search/288894,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.454.2.1–.32,false,true,288895,Photographs,Album,"Les Ruines de Paris et de ses Environs 1870-1871: Cent Photographies: Second Volume. Par A. Liébert, text par Alfred d'Aunay.",,,,,,Artist|Author,,Alphonse J. Liébert|Alfred d'Aunay,"French, 1827–1913|French",,"Liébert, Alphonse J.|d'Aunay, Alfred",French|French,1827,1913,1870–71,1870,1871,Albumen silver prints from glass negatives,"Images approx.: 19 x 25 cm (7 1/2 x 9 13/16 in.), or the reverse Mounts: 32.8 x 41.3 cm (12 15/16 x 16 1/4 in.), or the reverse","Joyce F. Menschel Photography Library Fund, 2007",,,,,,,,,,,,Albums,,http://www.metmuseum.org/art/collection/search/288895,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1993.270,false,true,266917,Photographs,Album,Album d'Études–Poses,,,,,,Artist|Editor,,Louis Igout|A. Calavas,"French, 1837–1881|French",,"Igout, Louis|Calavas, A.",French|French,1837,1881,ca. 1880,1878,1882,Albumen silver prints from glass negatives,,"Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 1993",,,,,,,,,,,,Albums,,http://www.metmuseum.org/art/collection/search/266917,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.1–.179,false,true,285688,Photographs,Portfolio,"Jerusalem, Etude et reproduction photographique des monuments de la ville sainte depuis l' époque judaique jusqu'à nos jours 1856",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,"1854, printed 1856",1854,1859,Salted paper prints from paper negatives,,"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Portfolios,,http://www.metmuseum.org/art/collection/search/285688,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.175–.179,false,true,681092,Photographs,Portfolio,"Jerusalem, Etude et reproduction photographique des monuments de la ville sainte depuis l' époque judaique jusqu'à nos jours 1856",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Ink on paper,,"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Portfolios,,http://www.metmuseum.org/art/collection/search/681092,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -64.677.4,false,true,270956,Photographs,Photomechanical print,Charles Baudelaire,,,,,,Printer|Artist,,Goupil et Cie|Étienne Carjat,"French, active 1850–84|French, Fareins 1828–1906 Paris",,"Goupil et Cie|Carjat, Étienne",French|French,1850 |1828,1884 |1906,ca. 1863,1861,1865,Woodburytype,,"David Hunter McAlpin Fund, 1964",,,,,,,,,,,,Photographs|Prints,,http://www.metmuseum.org/art/collection/search/270956,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.454.1.4,false,true,291458,Photographs,Photograph,"Tuileries Palace, Burned. General View",,,,,,Artist|Author,,Alphonse J. Liébert|Alfred d'Aunay,"French, 1827–1913|French",,"Liébert, Alphonse J.|d'Aunay, Alfred",French|French,1827,1913,1871,1871,1871,Albumen silver print from glass negative,"Images approx.: 19 x 25 cm (7 1/2 x 9 13/16 in.), or the reverse Mounts: 32.8 x 41.3 cm (12 15/16 x 16 1/4 in.), or the reverse","Joyce F. Menschel Photography Library Fund, 2007",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/291458,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.454.1.12,false,true,291459,Photographs,Photograph,"Finance Ministry, Burned. Exterior View",,,,,,Artist|Author,,Alphonse J. Liébert|Alfred d'Aunay,"French, 1827–1913|French",,"Liébert, Alphonse J.|d'Aunay, Alfred",French|French,1827,1913,1871,1871,1871,Albumen silver print from glass negative,"Images approx.: 19 x 25 cm (7 1/2 x 9 13/16 in.), or the reverse Mounts: 32.8 x 41.3 cm (12 15/16 x 16 1/4 in.), or the reverse","Joyce F. Menschel Photography Library Fund, 2007",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/291459,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.454.1.26,false,true,291460,Photographs,Photograph,"Place de la Bastille, Burned",,,,,,Artist|Author,,Alphonse J. Liébert|Alfred d'Aunay,"French, 1827–1913|French",,"Liébert, Alphonse J.|d'Aunay, Alfred",French|French,1827,1913,1871,1871,1871,Albumen silver print from glass negative,"Images approx.: 19 x 25 cm (7 1/2 x 9 13/16 in.), or the reverse Mounts: 32.8 x 41.3 cm (12 15/16 x 16 1/4 in.), or the reverse","Joyce F. Menschel Photography Library Fund, 2007",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/291460,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.454.2.3,false,true,291457,Photographs,Photograph,Neuilly Bombarded. General View of the rue de Chezy,,,,,,Author|Artist,,Alfred d'Aunay|Alphonse J. Liébert,"French|French, 1827–1913",,"d'Aunay, Alfred|Liébert, Alphonse J.",French|French,1827,1913,1871,1871,1871,Albumen silver print from glass negative,"Images approx.: 19 x 25 cm (7 1/2 x 9 13/16 in.), or the reverse Mounts: 32.8 x 41.3 cm (12 15/16 x 16 1/4 in.), or the reverse","Joyce F. Menschel Photography Library Fund, 2007",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/291457,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.33,false,true,283106,Photographs,Photograph,[Henriette-Caroline-Victoire Robert],,,,,,Artist|Person in Photograph,Person in photograph,Louis-Rémy Robert|Henriette-Caroline-Victoire Robert,"French, 1810–1882|French, 1834–1934",,"Robert, Louis-Rémy|Robert, Henriette-Caroline-Victoire",French|French,1810 |1834,1882 |1934,ca. 1850,1848,1852,Salted paper print from paper negative,Mount: 12 13/16 in. × 10 3/8 in. (32.5 × 26.3 cm) Image: 8 11/16 × 5 13/16 in. (22 × 14.7 cm),"Gilman Collection, Purchase, Mrs. Walter Annenberg and The Annenberg Foundation Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283106,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.46,false,true,286945,Photographs,Photograph,"Jérusalem, Vallée de Josaphat, Tombeau d'Absalon",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 32.2 x 23.2 cm (12 11/16 x 9 1/8 in.) Mount: 59.8 x 44.8 cm (23 9/16 x 17 5/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286945,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.47,false,true,286946,Photographs,Photograph,"Jérusalem, Vallée de Josaphat, Détails du Tombeau d'Absalom",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 33 x 23.1 cm (13 x 9 1/8 in.) Mount: 60 x 44.6 cm (23 5/8 x 17 9/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286946,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.48,false,true,286947,Photographs,Photograph,"Jérusalem, Vallée de Josaphat, Fronton du Tombeau de Josaphat",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 23.5 x 32.5 cm (9 1/4 x 12 13/16 in.) Mount: 44.8 x 59.5 cm (17 5/8 x 23 7/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286947,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.49,false,true,286948,Photographs,Photograph,"Jérusalem, Vallée de Josaphat, Grotte sépulcrale, 1",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 23.2 x 33.5 cm (9 1/8 x 13 3/16 in.) Mount: 44.8 x 59.8 cm (17 5/8 x 23 9/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286948,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.50,false,true,286949,Photographs,Photograph,"Jérusalem, Vallée de Josaphat, Grottes sépulcrales, 2",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 22.7 x 33.2 cm (8 15/16 x 13 1/16 in.) Mount: 44.7 x 60.4 cm (17 5/8 x 23 3/4 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286949,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.51,false,true,286950,Photographs,Photograph,"Jérusalem, Tombeau des rois de Juda, Cour extérieure",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 23.2 x 32.2 cm (9 1/8 x 12 11/16 in.) Mount: 44.6 x 59.9 cm (17 9/16 x 23 9/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286950,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.52,false,true,286951,Photographs,Photograph,"Jérusalem, Tombeau des rois de Juda, Intérieur de la cour",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 23.4 x 31.9 cm (9 3/16 x 12 9/16 in.) Mount: 44.6 x 60.1 cm (17 9/16 x 23 11/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286951,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.53,false,true,286952,Photographs,Photograph,"Jérusalem, Tombeau des rois de Juda, Frise supérieure et centrale",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 23.2 x 32.2 cm (9 1/8 x 12 11/16 in.) Mount: 44.5 x 59.7 cm (17 1/2 x 23 1/2 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286952,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.54,false,true,286953,Photographs,Photograph,"Jérusalem, Tombeau des rois de Juda, Encadrement de feuillages et de fruits",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 23.4 x 32.3 cm (9 3/16 x 12 11/16 in.) Mount: 45.1 x 59.9 cm (17 3/4 x 23 9/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286953,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.55,false,true,286954,Photographs,Photograph,"Jérusalem, Tombeau des rois de Juda, Fragments d'un sarcophage",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 23.6 x 33.6 cm (9 5/16 x 13 1/4 in.) Mount: 44.8 x 59.9 cm (17 5/8 x 23 9/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286954,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.56,false,true,286955,Photographs,Photograph,"Jérusalem, Tombeau des rois de Juda, Couvercle de sarcophage et fragment d'une porte en pierre",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 23.3 x 33.2 cm (9 3/16 x 13 1/16 in.) Mount: 44.8 x 59.7 cm (17 5/8 x 23 1/2 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286955,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.57,false,true,286956,Photographs,Photograph,"Jérusalem, Escalier antique taillé dans le roc, conduisant à l'ancienne Porte du Fumier",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 33.3 x 23.5 cm (13 1/8 x 9 1/4 in.) Mount: 59.9 x 44.7 cm (23 9/16 x 17 5/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286956,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.58,false,true,286957,Photographs,Photograph,"Jérusalem, Tombeau des Juges, Vue générale",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 23.2 x 33.3 cm (9 1/8 x 13 1/8 in.) Mount: 45.2 x 60.2 cm (17 13/16 x 23 11/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286957,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.59,false,true,286958,Photographs,Photograph,"Jérusalem, Tombeau des Juges, Détails",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 23.2 x 32.5 cm (9 1/8 x 12 13/16 in.) Mount: 44.8 x 60.1 cm (17 5/8 x 23 11/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286958,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.60,false,true,286959,Photographs,Photograph,"Jérusalem, Tombeau Juif, à trois milles Nord-Nord-Ouest de Jérusalem",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 23 x 33.3 cm (9 1/16 x 13 1/8 in.) Mount: 45 x 60.3 cm (17 11/16 x 23 3/4 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286959,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.61,false,true,286960,Photographs,Photograph,"Jérusalem, Tombeau Juif, Détails",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 23.7 x 33.6 cm (9 5/16 x 13 1/4 in.) Mount: 44.9 x 60.4 cm (17 11/16 x 23 3/4 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286960,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.62,false,true,286961,Photographs,Photograph,"Jérusalem, Tombeau de Salomon, Vue générale",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 22.9 x 32.7 cm (9 x 12 7/8 in.) Mount: 44.7 x 60.1 cm (17 5/8 x 23 11/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286961,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.63,false,true,286962,Photographs,Photograph,"Jérusalem, Tombeau de Salomon, Détails",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 23 x 33.1 cm (9 1/16 x 13 1/16 in.) Mount: 44.8 x 60.1 cm (17 5/8 x 23 11/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286962,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.64,false,true,286963,Photographs,Photograph,"Jérusalem, Birket-Mamillah",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 23.7 x 32.8 cm (9 5/16 x 12 15/16 in.) Mount: 44.6 x 60.1 cm (17 9/16 x 23 11/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286963,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.65,false,true,286964,Photographs,Photograph,"Jérusalem, Birket-es-Soutlan",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 23.2 x 32.8 cm (9 1/8 x 12 15/16 in.) Mount: 44.8 x 60.1 cm (17 5/8 x 23 11/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286964,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.66,false,true,286965,Photographs,Photograph,"Jérusalem, Birket-Hammam-el-Batrak",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 23.5 x 33.2 cm (9 1/4 x 13 1/16 in.) Mount: 44.8 x 59.5 cm (17 5/8 x 23 7/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286965,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.67,false,true,286966,Photographs,Photograph,"Jérusalem, Carrière à la Porte de Damas",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 23.5 x 33.4 cm (9 1/4 x 13 1/8 in.) Mount: 44.7 x 59.4 cm (17 5/8 x 23 3/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286966,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.68,false,true,286967,Photographs,Photograph,"Jérusalem, Birket-Hammam-Setty-Mariam",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 23.1 x 33.1 cm (9 1/8 x 13 1/16 in.) Mount: 44.5 x 59.6 cm (17 1/2 x 23 7/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286967,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.69,false,true,286968,Photographs,Photograph,"Jérusalem, Tour de David",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 23.4 x 32.9 cm (9 3/16 x 12 15/16 in.) Mount: 44.6 x 59.6 cm (17 9/16 x 23 7/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286968,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.70,false,true,286969,Photographs,Photograph,"Jérusalem, Restes de scupltures judaïques",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 23.5 x 33 cm (9 1/4 x 13 in.) Mount: 44.5 x 60.6 cm (17 1/2 x 23 7/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286969,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.71,false,true,283150,Photographs,Photograph,"Jérusalem, Sarcophage judaïque",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 23.2 × 32.7 cm (9 1/8 × 12 7/8 in.) Mount: 44.5 x 59.7 cm (17 1/2 x 23 1/2 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283150,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.72,false,true,286970,Photographs,Photograph,"Jérusalem, Casque trouvé dans le Jourdain, 1",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 23.5 x 33.3 cm (9 1/4 x 13 1/8 in.) Mount: 45.1 x 60.2 cm (17 3/4 x 23 11/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286970,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.73,false,true,286971,Photographs,Photograph,"Jérusalem, Casque trouvé dans le Jourdain, 2",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 23.2 x 33 cm (9 1/8 x 13 in.) Mount: 44.7 x 59.9 cm (17 5/8 x 23 9/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286971,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.74,false,true,286972,Photographs,Photograph,"Jérusalem, Fragments judaïque et romain",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 23.5 x 30.9 cm (9 1/4 x 12 3/16 in.) Mount: 44.8 x 59.7 cm (17 5/8 x 23 1/2 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286972,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.75,false,true,286973,Photographs,Photograph,"Jérusalem, Forteresse de Soin",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 23.5 x 33.3 cm (9 1/4 x 13 1/8 in.) Mount: 44.6 x 60 cm (17 9/16 x 23 5/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286973,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.76,false,true,286974,Photographs,Photograph,"Jérusalem, Arc de l'Ecce-Homo",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 33.2 x 23.5 cm (13 1/16 x 9 1/4 in.) Mount: 59.9 x 45.2 cm (23 9/16 x 17 13/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286974,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.77,false,true,286975,Photographs,Photograph,"Jérusalem, Arc de l'Ecce-Homo, Détails",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 33.3 x 23.3 cm (13 1/8 x 9 3/16 in.) Mount: 59.8 x 44.8 cm (23 9/16 x 17 5/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286975,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.78,false,true,286976,Photographs,Photograph,"Jérusalem, Fontaine de Saint-Philippe",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 33.7 x 23.6 cm (13 1/4 x 9 5/16 in.) Mount: 60.3 x 44.5 cm (23 3/4 x 17 1/2 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286976,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.79,false,true,286977,Photographs,Photograph,"Jérusalem, Colonne de la Porte judiciaire",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 33.2 x 23.6 cm (13 1/16 x 9 5/16 in.) Mount: 59.9 x 44.5 cm (23 9/16 x 17 1/2 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286977,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.80,false,true,286978,Photographs,Photograph,"Jérusalem, Grotte de Jérémie",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 22.8 x 32.5 cm (9 x 12 13/16 in.) Mount: 44.7 x 59.3 cm (17 5/8 x 23 3/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286978,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.81,false,true,286979,Photographs,Photograph,"Jérusalem, Via Dolorosa, Reste antique",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 33.3 x 23.3 cm (13 1/8 x 9 3/16 in.) Mount: 60.2 x 44.8 cm (23 11/16 x 17 5/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286979,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.83,false,true,286980,Photographs,Photograph,"Jérusalem, Saint-Sépulcre, Entrée principale",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 23.4 x 32.4 cm (9 3/16 x 12 3/4 in.) Mount: 45 x 60.1 cm (17 11/16 x 23 11/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286980,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.84,false,true,286981,Photographs,Photograph,"Jérusalem, Saint-Sépulcre, Bas-relief (porte d'entrée)",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 23.4 x 31.6 cm (9 3/16 x 12 7/16 in.) Mount: 45.1 x 59.9 cm (17 3/4 x 23 9/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286981,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.85,false,true,286982,Photographs,Photograph,"Jérusalem, Saint-Sépulcre, Bas-relief (porte murée)",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 23.6 x 32.8 cm (9 5/16 x 12 15/16 in.) Mount: 44.3 x 60.5 cm (17 7/16 x 23 13/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286982,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.87,false,true,286983,Photographs,Photograph,"Jérusalem, Saint-Sépulcre, Détails de la porte",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 32.3 x 23.3 cm (12 11/16 x 9 3/16 in.) Mount: 60.3 x 44.7 cm (23 3/4 x 17 5/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286983,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.88,false,true,286984,Photographs,Photograph,"Jérusalem, Saint-Sépulcre, Détails des chapiteaux de la porte principale",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 32.9 x 23.2 cm (12 15/16 x 9 1/8 in.) Mount: 59.6 x 44.4 cm (23 7/16 x 17 1/2 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286984,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.89,false,true,286985,Photographs,Photograph,"Jérusalem, Saint-Sépulcre, Partie supérieure de la façade",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 23.4 x 32.9 cm (9 3/16 x 12 15/16 in.) Mount: 44.6 x 60 cm (17 9/16 x 23 5/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286985,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.91,false,true,286986,Photographs,Photograph,"Jérusalem, Saint-Sépulcre, Chapelle du Calvaire",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 32.5 x 23.4 cm (12 13/16 x 9 3/16 in.) Mount: 59.8 x 44.6 cm (23 9/16 x 17 9/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286986,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.92,false,true,286987,Photographs,Photograph,"Jérusalem, Saint-Sépulcre, Détails de la Chapelle du Calvaire",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 32.7 x 23.4 cm (12 7/8 x 9 3/16 in.) Mount: 59.9 x 44.6 cm (23 9/16 x 17 9/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286987,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.93,false,true,286988,Photographs,Photograph,"Jérusalem, Saint-Sépulcre, Intérieur de la Chapelle du Calvaire",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 32.9 x 23.4 cm (12 15/16 x 9 3/16 in.) Mount: 59.9 x 44.7 cm (23 9/16 x 17 5/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286988,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.94,false,true,286989,Photographs,Photograph,"Jérusalem, Saint-Sépulcre, Détails de la façade",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 22.7 × 33.1 cm (8 15/16 × 13 1/16 in.) Mount: 59.9 x 44.6 cm (23 9/16 x 17 9/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286989,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.95,false,true,286990,Photographs,Photograph,"Jérusalem, Saint-Sépulcre, Vue générale, 1",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 23.1 × 33.1 cm (9 1/8 × 13 1/16 in.) Mount: 60 x 44.6 cm (23 5/8 x 17 9/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286990,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.98,false,true,286991,Photographs,Photograph,"Jérusalem, Saint-Sépulcre, Clocher",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 32.9 x 23.5 cm (12 15/16 x 9 1/4 in.) Mount: 60 x 45.2 cm (23 5/8 x 17 13/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286991,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.99,false,true,286992,Photographs,Photograph,"Jérusalem, Saint-Sépulcre, Détails du Clocher",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 32.5 × 23.5 cm (12 13/16 × 9 1/4 in.) Mount: 60.2 x 44.6 cm (23 11/16 x 17 9/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286992,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.101,false,true,286993,Photographs,Photograph,"Jérusalem, Saint-Sépulcre, Porte Ouest",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 33.3 × 23.7 cm (13 1/8 × 9 5/16 in.) Mount: 59.6 x 44.7 cm (23 7/16 x 17 5/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286993,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.102,false,true,286994,Photographs,Photograph,"Jérusalem, Saint-Sépulcre, Face Ouest, Rue du Patriarche",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 32.4 × 23.8 cm (12 3/4 × 9 3/8 in.) Mount: 59.5 x 45 cm (23 7/16 x 17 11/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286994,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.103,false,true,286995,Photographs,Photograph,"Jérusalem, Saint-Sépulcre, Colonne du parvis",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 32.6 x 23 cm (12 13/16 x 9 1/16 in.) Mount: 59.6 x 45 cm (23 7/16 x 17 11/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286995,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.104,false,true,286996,Photographs,Photograph,"Jérusalem, Epée de Godefroy de Bouillon",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 32.6 x 23.1 cm (12 13/16 x 9 1/8 in.) Mount: 60 x 44.5 cm (23 5/8 x 17 1/2 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286996,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.106,false,true,286997,Photographs,Photograph,"Jérusalem, Sainte-Marie-la-Grande, Portail",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 21.7 × 33.4 cm (8 9/16 × 13 1/8 in.) Mount: 44.9 x 59.4 cm (17 11/16 x 23 3/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286997,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.107,false,true,286998,Photographs,Photograph,"Jérusalem, Sainte-Marie-la-Grande, Détails de la porte",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 23.3 x 32.7 cm (9 3/16 x 12 7/8 in.) Mount: 44.7 x 60.3 cm (17 5/8 x 23 3/4 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286998,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.108,false,true,286999,Photographs,Photograph,"Jérusalem, Sainte-Marie-la-Grande, Cloître",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 23 x 32.9 cm (9 1/16 x 12 15/16 in.) Mount: 44.3 x 60.4 cm (17 7/16 x 23 3/4 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286999,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.109,false,true,287000,Photographs,Photograph,"Jérusalem, Sainte-Marie-la-Grande, Vue générale",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 23.5 x 33.5 cm (9 1/4 x 13 3/16 in.) Mount: 44.9 x 59.7 cm (17 11/16 x 23 1/2 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287000,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.110,false,true,287001,Photographs,Photograph,"Jérusalem, Sainte-Marie-la-Latine",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 23.4 x 32.7 cm (9 3/16 x 12 7/8 in.) Mount: 44.9 x 61 cm (17 11/16 x 24 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287001,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.111,false,true,287002,Photographs,Photograph,"Jérusalem, Enceinte de l'Hopital des Chevaliers-de-Saint-Jean, Côté Sud",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 23.4 x 33.1 cm (9 3/16 x 13 1/16 in.) Mount: 45.1 x 59.8 cm (17 3/4 x 23 9/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287002,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.112,false,true,287003,Photographs,Photograph,"Jérusalem, Fontaine du Couvent grec",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 23.4 x 32.3 cm (9 3/16 x 12 11/16 in.) Mount: 44.8 x 60 cm (17 5/8 x 23 5/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287003,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.113,false,true,287004,Photographs,Photograph,"Jérusalem, Église Sainte-Anne, Vue générale",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 33.2 x 23.7 cm (13 1/16 x 9 5/16 in.) Mount: 60.4 x 44.8 cm (23 3/4 x 17 5/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287004,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.114,false,true,287005,Photographs,Photograph,"Jérusalem, Église Sainte-Anne, Façade",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 32.5 x 23.4 cm (12 13/16 x 9 3/16 in.) Mount: 59.3 x 44.6 cm (23 3/8 x 17 9/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287005,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.115,false,true,287006,Photographs,Photograph,"Jérusalem, Église Sainte-Anne, Détails du portail",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 23.2 x 32.7 cm (9 1/8 x 12 7/8 in.) Mount: 45.1 x 59.8 cm (17 3/4 x 23 9/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287006,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.116,false,true,287007,Photographs,Photograph,"Jérusalem, Tombeau de la Vierge",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 23.5 x 33 cm (9 1/4 x 13 in.) Mount: 44.6 x 60.1 cm (17 9/16 x 23 11/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287007,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.117,false,true,287008,Photographs,Photograph,"Jérusalem, Chapelle de l'Ascension",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 23.2 x 33.3 cm (9 1/8 x 13 1/8 in.) Mount: 44.7 x 59.5 cm (17 5/8 x 23 7/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287008,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.118,false,true,287009,Photographs,Photograph,"Jérusalem, Église de Sainte-Marie-Madeleine",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 23.4 x 33.2 cm (9 3/16 x 13 1/16 in.) Mount: 45 x 59.3 cm (17 11/16 x 23 3/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287009,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.119,false,true,287010,Photographs,Photograph,"Jérusalem, Palais de rois de Jérusalem, Vue générale",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 23.3 x 33.3 cm (9 3/16 x 13 1/8 in.) Mount: 44.8 x 59.6 cm (17 5/8 x 23 7/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287010,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.120,false,true,287011,Photographs,Photograph,"Jérusalem, Palais de rois de Jérusalem, Entrée principale",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 32.9 x 23.3 cm (12 15/16 x 9 3/16 in.) Mount: 59.7 x 44.7 cm (23 1/2 x 17 5/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287011,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.121,false,true,287012,Photographs,Photograph,"Jérusalem, Auberge d'Allemagne",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 33.2 x 23.4 cm (13 1/16 x 9 3/16 in.) Mount: 60.1 x 44.7 cm (23 11/16 x 17 5/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287012,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.122,false,true,287013,Photographs,Photograph,"Jérusalem, Couvent Arménien, Ornements, 1",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 31 x 22.9 cm (12 3/16 x 9 in.) Mount: 59.9 x 44.8 cm (23 9/16 x 17 5/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287013,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.123,false,true,287014,Photographs,Photograph,"Jérusalem, Couvent Arménien, Ornements, 2",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 23.1 x 33 cm (9 1/8 x 13 in.) Mount: 44.9 x 60 cm (17 11/16 x 23 5/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287014,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.124,false,true,287015,Photographs,Photograph,"Jérusalem, Chapelle anglaise",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 23.5 x 32.6 cm (9 1/4 x 12 13/16 in.) Mount: 44.8 x 60 cm (17 5/8 x 23 5/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287015,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.125,false,true,287016,Photographs,Photograph,"Jérusalem, Beit-Lehem, Vue générale",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 22.6 x 27.6 cm (8 7/8 x 10 7/8 in.) Mount: 44.6 x 60.3 cm (17 9/16 x 23 3/4 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287016,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.126,false,true,287017,Photographs,Photograph,"Beit-Lehem, Mosaïque de l'Église I",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,,"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287017,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.127,false,true,287018,Photographs,Photograph,"Beit-Lehem, Mosaïque de l'Église II",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,,"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287018,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.128,false,true,287019,Photographs,Photograph,"Jérusalem, Mosquée d'Omar, côté ouest",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 23.4 x 33 cm (9 3/16 x 13 in.) Mount: 44.5 x 59.3 cm (17 1/2 x 23 3/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287019,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.129,false,true,287020,Photographs,Photograph,"Jérusalem, Mosquée d'Omar, côté est, Intérieur de l'enceinte, 1",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 22.1 x 26.8 cm (8 11/16 x 10 9/16 in.) Mount: 44.8 x 60.3 cm (17 5/8 x 23 3/4 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287020,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.130,false,true,287021,Photographs,Photograph,"Jérusalem, Mosquée d'Omar, côté Est, Intérieur de l'enceinte, 2",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 22.6 x 33 cm (8 7/8 x 13 in.) Mount: 44.8 x 60.2 cm (17 5/8 x 23 11/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287021,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.131,false,true,287022,Photographs,Photograph,"Jérusalem, Mosquée d'Omar, côté Nord, Intérieur de l'enceinte",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 23.4 x 32.8 cm (9 3/16 x 12 15/16 in.) Mount: 44.5 x 59.8 cm (17 1/2 x 23 9/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287022,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.132,false,true,287023,Photographs,Photograph,"Jérusalem, Mosquée d'Omar, côté Ouest, Intérieur de l'enceinte",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 22.8 x 32.5 cm (9 x 12 13/16 in.) Mount: 44.6 x 59.6 cm (17 9/16 x 23 7/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287023,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.133,false,true,287024,Photographs,Photograph,"Jérusalem, Minaret de la Rue du Patriarche",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 33 x 23.6 cm (13 x 9 5/16 in.) Mount: 60.3 x 44.6 cm (23 3/4 x 17 9/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287024,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.134,false,true,287025,Photographs,Photograph,"Jérusalem, Minaret de l'ancienne mosquée d'Abd-es-Samed",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 32.8 x 23.3 cm (12 15/16 x 9 3/16 in.) Mount: 59.4 x 45.1 cm (23 3/8 x 17 3/4 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287025,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.135,false,true,287026,Photographs,Photograph,"Jérusalem, Porte de Jaffa, Vue extérieure",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 31.8 x 23 cm (12 1/2 x 9 1/16 in.) Mount: 59.3 x 44.9 cm (23 3/8 x 17 11/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287026,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.136,false,true,287027,Photographs,Photograph,"Jérusalem, Porte de Jaffa, Vue générale",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,,"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287027,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.137,false,true,287028,Photographs,Photograph,"Jérusalem, Porte de Jaffa, Intérieur",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 31.9 x 23.1 cm (12 9/16 x 9 1/8 in.) Mount: 59.6 x 44.9 cm (23 7/16 x 17 11/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287028,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.138,false,true,287029,Photographs,Photograph,"Jérusalem, Porte de Jaffa, Inscription",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,,"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287029,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.139,false,true,287030,Photographs,Photograph,"Jérusalem, Porte de Jaffa, Inscription de la fausse porte",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 23.5 x 33.2 cm (9 1/4 x 13 1/16 in.) Mount: 44.5 x 59.5 cm (17 1/2 x 23 7/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287030,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.140,false,true,287031,Photographs,Photograph,"Jérusalem, Porte de David, Vue extérieure",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 32.8 x 23.5 cm (12 15/16 x 9 1/4 in.) Mount: 59.8 x 44.8 cm (23 9/16 x 17 5/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287031,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.141,false,true,287032,Photographs,Photograph,"Jérusalem, Porte de David, Intérieur",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 33.1 x 23.7 cm (13 1/16 x 9 5/16 in.) Mount: 60.2 x 44.4 cm (23 11/16 x 17 1/2 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287032,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.142,false,true,287033,Photographs,Photograph,"Jérusalem, Porte de Mograbins",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,,"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287033,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.143,false,true,287034,Photographs,Photograph,"Jérusalem, Porte Saint-Étienne, Vue extérieure",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 32.4 x 23.7 cm (12 3/4 x 9 5/16 in.) Mount: 59.7 x 44.7 cm (23 1/2 x 17 5/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287034,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.144,false,true,287035,Photographs,Photograph,"Jérusalem, Porte Saint-Étienne, Intérieur",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 23 x 32.7 cm (9 1/16 x 12 7/8 in.) Mount: 45 x 59.8 cm (17 11/16 x 23 9/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287035,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.145,false,true,287036,Photographs,Photograph,"Jérusalem, Porte d'Hérode",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 23.1 x 33 cm (9 1/8 x 13 in.) Mount: 44.8 x 60.3 cm (17 5/8 x 23 3/4 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287036,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.146,false,true,287037,Photographs,Photograph,"Jérusalem, Porte de Damas, Vue extérieure",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 22.8 x 32.4 cm (9 x 12 3/4 in.) Mount: 44.4 x 59.6 cm (17 1/2 x 23 7/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287037,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.147,false,true,287038,Photographs,Photograph,"Jérusalem, Porte de Damas, Intérieur",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 23.1 x 32.6 cm (9 1/8 x 12 13/16 in.) Mount: 44.5 x 59.5 cm (17 1/2 x 23 7/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287038,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.148,false,true,287039,Photographs,Photograph,"Jérusalem, Escalier arabe de Sainte-Marie-la-Grande",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 32.9 x 23.4 cm (12 15/16 x 9 3/16 in.) Mount: 59.6 x 44.7 cm (23 7/16 x 17 5/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287039,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.149,false,true,287040,Photographs,Photograph,"Jérusalem, Escalier arabe de Sainte-Marie-la-Grande, Détails de la partie supérieure",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 32.7 x 23.4 cm (12 7/8 x 9 3/16 in.) Mount: 59.5 x 44.6 cm (23 7/16 x 17 9/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287040,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.150,false,true,287041,Photographs,Photograph,"Jérusalem, Hospital de Sainte-Hélène, Face Sud",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 32.5 x 23.7 cm (12 13/16 x 9 5/16 in.) Mount: 59.6 x 44.6 cm (23 7/16 x 17 9/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287041,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.151,false,true,287042,Photographs,Photograph,"Jérusalem, Hospital de Sainte-Hélène, Intérieur",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 23 x 33 cm (9 1/16 x 13 in.) Mount: 44.8 x 60.4 cm (17 5/8 x 23 3/4 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287042,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.152,false,true,287043,Photographs,Photograph,"Jérusalem, Fontaine Arabe, 1",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,,"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287043,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.153,false,true,287044,Photographs,Photograph,"Jérusalem, Fontaine Arabe, 2",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 33.1 x 23.5 cm (13 1/16 x 9 1/4 in.) Mount: 59.8 x 44.8 cm (23 9/16 x 17 5/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287044,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.154,false,true,287045,Photographs,Photograph,"Jérusalem, Fontaine Arabe, 3",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 33.1 x 23.5 cm (13 1/16 x 9 1/4 in.) Mount: 60.3 x 44.6 cm (23 3/4 x 17 9/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287045,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.155,false,true,287046,Photographs,Photograph,"Jérusalem, Fontaine Arabe, 4",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 32.8 x 23.4 cm (12 15/16 x 9 3/16 in.) Mount: 59.6 x 45 cm (23 7/16 x 17 11/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287046,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.156,false,true,287047,Photographs,Photograph,"Jérusalem, Rue du quartier arabe, 1",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 32.7 x 23.6 cm (12 7/8 x 9 5/16 in.) Mount: 59.6 x 44.6 cm (23 7/16 x 17 9/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287047,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.157,false,true,287048,Photographs,Photograph,"Jérusalem, Rue du quartier arabe, 2",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 33.5 x 23.6 cm (13 3/16 x 9 5/16 in.) Mount: 60.3 x 45 cm (23 3/4 x 17 11/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287048,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.158,false,true,287049,Photographs,Photograph,"Jérusalem, Ornements arabes",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 23.4 x 32.1 cm (9 3/16 x 12 5/8 in.) Mount: 44.8 x 60 cm (17 5/8 x 23 5/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287049,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.159,false,true,287050,Photographs,Photograph,"Jérusalem, Fenêtre arabe",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 33.5 x 23.3 cm (13 3/16 x 9 3/16 in.) Mount: 60.1 x 44.8 cm (23 11/16 x 17 5/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287050,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.160,false,true,287051,Photographs,Photograph,"Jérusalem, Détails de la porte d'un Dôme sépulcral",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 23 x 32.8 cm (9 1/16 x 12 15/16 in.) Mount: 44.8 x 59.9 cm (17 5/8 x 23 9/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287051,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.161,false,true,287052,Photographs,Photograph,"Jérusalem, Tombeau arabe",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 23.2 x 32.9 cm (9 1/8 x 12 15/16 in.) Mount: 44.9 x 60.4 cm (17 11/16 x 23 3/4 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287052,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.162,false,true,287053,Photographs,Photograph,"Jérusalem, Porte de la citadelle",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 32.9 x 23.1 cm (12 15/16 x 9 1/8 in.) Mount: 59.6 x 44.7 cm (23 7/16 x 17 5/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287053,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.163,false,true,287054,Photographs,Photograph,"Jérusalem, Porte de la citadelle, Inscription",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 22.5 x 33 cm (8 7/8 x 13 in.) Mount: 45 x 60.4 cm (17 11/16 x 23 3/4 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287054,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.164,false,true,287055,Photographs,Photograph,"Jérusalem, Aqueduc de Ponce-Pilate, Inscription",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 22.8 x 32.8 cm (9 x 12 15/16 in.) Mount: 45 x 59.4 cm (17 11/16 x 23 3/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287055,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.165,false,true,287056,Photographs,Photograph,"Jérusalem, Maison du mauvais riche",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 33.5 x 23.6 cm (13 3/16 x 9 5/16 in.) Mount: 60.3 x 44.8 cm (23 3/4 x 17 5/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287056,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.166,false,true,287057,Photographs,Photograph,"Jérusalem, Mont Sion",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 22.9 x 33.3 cm (9 x 13 1/8 in.) Mount: 45 x 59.5 cm (17 11/16 x 23 7/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287057,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.167,false,true,287058,Photographs,Photograph,"Jérusalem, Forteresse de David (citadelle), Face Ouest",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 32.6 x 23.7 cm (12 13/16 x 9 5/16 in.) Mount: 44.4 x 59.5 cm (17 1/2 x 23 7/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287058,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.168,false,true,287059,Photographs,Photograph,"Jérusalem, Pins du Couvent arménien",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 23 x 32.9 cm (9 1/16 x 12 15/16 in.) Mount: 44.5 x 59.4 cm (17 1/2 x 23 3/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287059,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.169,false,true,287060,Photographs,Photograph,"Jérusalem, Côté Est de Jérusalem",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 22.6 x 32.2 cm (8 7/8 x 12 11/16 in.) Mount: 44.9 x 60.4 cm (17 11/16 x 23 3/4 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287060,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.170,false,true,287061,Photographs,Photograph,"Jérusalem, Côté Sud de Jérusalem",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 23.4 x 33 cm (9 3/16 x 13 in.) Mount: 44.8 x 59.7 cm (17 5/8 x 23 1/2 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287061,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.171,false,true,287062,Photographs,Photograph,"Jérusalem, Chemin de Naplouse",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 23.1 x 33 cm (9 1/8 x 13 in.) Mount: 44.8 x 59.5 cm (17 5/8 x 23 7/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287062,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.172,false,true,287063,Photographs,Photograph,"Jérusalem, Chemin de Beit-Lehem",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 23.3 x 32.7 cm (9 3/16 x 12 7/8 in.) Mount: 44.5 x 59.5 cm (17 1/2 x 23 7/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287063,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.173,false,true,287064,Photographs,Photograph,"Jérusalem, Vue générale de la Vallée de Hinnom",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 23.5 x 33.5 cm (9 1/4 x 13 3/16 in.) Mount: 44.5 x 59.5 cm (17 1/2 x 23 7/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287064,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.174,false,true,287065,Photographs,Photograph,"Jérusalem, Côté Nord de Jérusalem",,,,,,Artist|Printer,,"Auguste Salzmann|Imprimerie photographique de Blanquart-Évrard, à Lille","French, 1824–1872|French, active 1851–55",,"Salzmann, Auguste|Imprimerie photographique de Blanquart-Évrard, à Lille",French|French,1824 |1851,1872 |1855,1854,1854,1859,Salted paper print from paper negative,Image: 22.8 x 32.6 cm (9 x 12 13/16 in.) Mount: 45.1 x 60.1 cm (17 3/4 x 23 11/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/287065,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.1,false,true,286901,Photographs,Photograph,"Jérusalem, Enceinte du Temple, Côté Ouest, Heit-el-Morharby",,,,,,Printer|Artist,,"Imprimerie photographique de Blanquart-Évrard, à Lille|Auguste Salzmann","French, active 1851–55|French, 1824–1872",,"Imprimerie photographique de Blanquart-Évrard, à Lille|Salzmann, Auguste",French|French,1851 |1824,1855 |1872,1854,1854,1859,Salted paper print from paper negative,Image: 23.2 × 33.2 cm (9 1/8 × 13 1/16 in.) Mount: 44.1 x 60 cm (17 3/8 x 23 5/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286901,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.2,false,true,286902,Photographs,Photograph,"Jérusalem, Enceinte du Temple, Arche du Pont Salomonien qui reliait Moria à Sion",,,,,,Printer|Artist,,"Imprimerie photographique de Blanquart-Évrard, à Lille|Auguste Salzmann","French, active 1851–55|French, 1824–1872",,"Imprimerie photographique de Blanquart-Évrard, à Lille|Salzmann, Auguste",French|French,1851 |1824,1855 |1872,1854,1854,1859,Salted paper print from paper negative,Image: 23.2 x 32.5 cm (9 1/8 x 12 13/16 in.) Mount: 44.6 x 59.6 cm (17 9/16 x 23 7/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286902,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.3,false,true,286903,Photographs,Photograph,"Jérusalem, Enceinte du Temple, Mosquée El-Aksa, angle Sud-Ouest",,,,,,Printer|Artist,,"Imprimerie photographique de Blanquart-Évrard, à Lille|Auguste Salzmann","French, active 1851–55|French, 1824–1872",,"Imprimerie photographique de Blanquart-Évrard, à Lille|Salzmann, Auguste",French|French,1851 |1824,1855 |1872,1854,1854,1859,Salted paper print from paper negative,Image: 23.1 x 32.6 cm (9 1/8 x 12 13/16 in.) Mount: 44.6 x 60 cm (17 9/16 x 23 5/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286903,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.4,false,true,286904,Photographs,Photograph,"Jérusalem, Enceinte du Temple, Face sud de l'angle Sud-Est",,,,,,Printer|Artist,,"Imprimerie photographique de Blanquart-Évrard, à Lille|Auguste Salzmann","French, active 1851–55|French, 1824–1872",,"Imprimerie photographique de Blanquart-Évrard, à Lille|Salzmann, Auguste",French|French,1851 |1824,1855 |1872,1854,1854,1859,Salted paper print from paper negative,Image: 23.3 x 32.8 cm (9 3/16 x 12 15/16 in.) Mount: 44.7 x 60.5 cm (17 5/8 x 23 13/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286904,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.5,false,true,286905,Photographs,Photograph,"Jérusalem, Enceinte du Temple, Vue générale de la face Sud 1",,,,,,Printer|Artist,,"Imprimerie photographique de Blanquart-Évrard, à Lille|Auguste Salzmann","French, active 1851–55|French, 1824–1872",,"Imprimerie photographique de Blanquart-Évrard, à Lille|Salzmann, Auguste",French|French,1851 |1824,1855 |1872,1854,1854,1859,Salted paper print from paper negative,Image: 23 x 31.7 cm (9 1/16 x 12 1/2 in.) Mount: 44.7 x 60.2 cm (17 5/8 x 23 11/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286905,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.6,false,true,286906,Photographs,Photograph,"Jérusalem, Enceinte du Temple, Vue générale de la face Sud 2",,,,,,Printer|Artist,,"Imprimerie photographique de Blanquart-Évrard, à Lille|Auguste Salzmann","French, active 1851–55|French, 1824–1872",,"Imprimerie photographique de Blanquart-Évrard, à Lille|Salzmann, Auguste",French|French,1851 |1824,1855 |1872,1854,1854,1859,Salted paper print from paper negative,Image: 23.1 x 32.7 cm (9 1/8 x 12 7/8 in.) Mount: 44.7 x 60 cm (17 5/8 x 23 5/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286906,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.7,false,true,286907,Photographs,Photograph,"Jérusalem, Enceinte du Temple, Porte hérodienne",,,,,,Printer|Artist,,"Imprimerie photographique de Blanquart-Évrard, à Lille|Auguste Salzmann","French, active 1851–55|French, 1824–1872",,"Imprimerie photographique de Blanquart-Évrard, à Lille|Salzmann, Auguste",French|French,1851 |1824,1855 |1872,1854,1854,1859,Salted paper print from paper negative,Image: 32.9 x 23.1 cm (12 15/16 x 9 1/8 in.) Mount: 59.5 x 44.5 cm (23 7/16 x 17 1/2 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286907,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.8,false,true,286908,Photographs,Photograph,"Jérusalem, Enceinte du Temple, Triple porte romaine",,,,,,Printer|Artist,,"Imprimerie photographique de Blanquart-Évrard, à Lille|Auguste Salzmann","French, active 1851–55|French, 1824–1872",,"Imprimerie photographique de Blanquart-Évrard, à Lille|Salzmann, Auguste",French|French,1851 |1824,1855 |1872,1854,1854,1859,Salted paper print from paper negative,Image: 23.4 x 31.9 cm (9 3/16 x 12 9/16 in.) Mount: 44.6 x 59.6 cm (17 9/16 x 23 7/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286908,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.9,false,true,286909,Photographs,Photograph,"Jérusalem, Enceinte du Temple, Moulure judaïque formant pied-droit de l'une des portes romaines",,,,,,Printer|Artist,,"Imprimerie photographique de Blanquart-Évrard, à Lille|Auguste Salzmann","French, active 1851–55|French, 1824–1872",,"Imprimerie photographique de Blanquart-Évrard, à Lille|Salzmann, Auguste",French|French,1851 |1824,1855 |1872,1854,1854,1859,Salted paper print from paper negative,Image: 32.7 x 22.7 cm (12 7/8 x 8 15/16 in.) Mount: 60 x 44.9 cm (23 5/8 x 17 11/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286909,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.10,false,true,286910,Photographs,Photograph,"Jérusalem, Enceinte du Temple, Face Sud de l'angle Sud-Est",,,,,,Printer|Artist,,"Imprimerie photographique de Blanquart-Évrard, à Lille|Auguste Salzmann","French, active 1851–55|French, 1824–1872",,"Imprimerie photographique de Blanquart-Évrard, à Lille|Salzmann, Auguste",French|French,1851 |1824,1855 |1872,1854,1854,1859,Salted paper print from paper negative,Image: 33.1 x 23.6 cm (13 1/16 x 9 5/16 in.) Mount: 59.8 x 44.6 cm (23 9/16 x 17 9/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286910,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.11,false,true,286911,Photographs,Photograph,"Jérusalem, Enceinte du Temple, Face Est de l'angle Sud-Est",,,,,,Printer|Artist,,"Imprimerie photographique de Blanquart-Évrard, à Lille|Auguste Salzmann","French, active 1851–55|French, 1824–1872",,"Imprimerie photographique de Blanquart-Évrard, à Lille|Salzmann, Auguste",French|French,1851 |1824,1855 |1872,1854,1854,1859,Salted paper print from paper negative,Image: 33 x 23.5 cm (13 x 9 1/4 in.) Mount: 60 x 44.7 cm (23 5/8 x 17 5/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286911,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.12,false,true,286912,Photographs,Photograph,"Jérusalem, Enceinte du Temple, Vue générale de la face Est, Pl. 1",,,,,,Printer|Artist,,"Imprimerie photographique de Blanquart-Évrard, à Lille|Auguste Salzmann","French, active 1851–55|French, 1824–1872",,"Imprimerie photographique de Blanquart-Évrard, à Lille|Salzmann, Auguste",French|French,1851 |1824,1855 |1872,1854,1854,1859,Salted paper print from paper negative,Image: 23.1 x 31.8 cm (9 1/8 x 12 1/2 in.) Mount: 44.5 x 60 cm (17 1/2 x 23 5/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286912,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.13,false,true,286913,Photographs,Photograph,"Jérusalem, Enceinte du Temple, Vue générale de la face Est, Pl. 2",,,,,,Printer|Artist,,"Imprimerie photographique de Blanquart-Évrard, à Lille|Auguste Salzmann","French, active 1851–55|French, 1824–1872",,"Imprimerie photographique de Blanquart-Évrard, à Lille|Salzmann, Auguste",French|French,1851 |1824,1855 |1872,1854,1854,1859,Salted paper print from paper negative,Image: 22.5 x 32.9 cm (8 7/8 x 12 15/16 in.) Mount: 44.8 x 60.5 cm (17 5/8 x 23 13/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286913,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.14,false,true,286914,Photographs,Photograph,"Jérusalem, Enceinte du Temple, Vue générale de la face Est, Pl. 3",,,,,,Printer|Artist,,"Imprimerie photographique de Blanquart-Évrard, à Lille|Auguste Salzmann","French, active 1851–55|French, 1824–1872",,"Imprimerie photographique de Blanquart-Évrard, à Lille|Salzmann, Auguste",French|French,1851 |1824,1855 |1872,1854,1854,1859,Salted paper print from paper negative,Image: 22.8 × 33.2 cm (9 in. × 13 1/16 in.) Mount: 44.8 x 58.8 cm (17 5/8 x 23 1/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286914,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.15,false,true,286915,Photographs,Photograph,"Jérusalem, Enceinte du Temple, Poterne de Josaphat",,,,,,Printer|Artist,,"Imprimerie photographique de Blanquart-Évrard, à Lille|Auguste Salzmann","French, active 1851–55|French, 1824–1872",,"Imprimerie photographique de Blanquart-Évrard, à Lille|Salzmann, Auguste",French|French,1851 |1824,1855 |1872,1854,1854,1859,Salted paper print from paper negative,Image: 32.3 x 23.2 cm (12 11/16 x 9 1/8 in.) Mount: 60 x 44.7 cm (23 5/8 x 17 5/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286915,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.16,false,true,286916,Photographs,Photograph,"Jérusalem, Enceinte du Temple, Porte Dorée",,,,,,Printer|Artist,,"Imprimerie photographique de Blanquart-Évrard, à Lille|Auguste Salzmann","French, active 1851–55|French, 1824–1872",,"Imprimerie photographique de Blanquart-Évrard, à Lille|Salzmann, Auguste",French|French,1851 |1824,1855 |1872,1854,1854,1859,Salted paper print from paper negative,Image: 32 x 22.4 cm (12 5/8 x 8 13/16 in.) Mount: 60 x 44.8 cm (23 5/8 x 17 5/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286916,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.17,false,true,286917,Photographs,Photograph,"Jérusalem, Enceinte du Temple, Détails de la Porte Dorée",,,,,,Printer|Artist,,"Imprimerie photographique de Blanquart-Évrard, à Lille|Auguste Salzmann","French, active 1851–55|French, 1824–1872",,"Imprimerie photographique de Blanquart-Évrard, à Lille|Salzmann, Auguste",French|French,1851 |1824,1855 |1872,1854,1854,1859,Salted paper print from paper negative,Image: 32.2 x 22.9 cm (12 11/16 x 9 in.) Mount: 60 x 44.7 cm (23 5/8 x 17 5/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286917,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.18,false,true,286918,Photographs,Photograph,"Jérusalem, Enceinte du Temple, Face Est de l'angle Nord-Est",,,,,,Printer|Artist,,"Imprimerie photographique de Blanquart-Évrard, à Lille|Auguste Salzmann","French, active 1851–55|French, 1824–1872",,"Imprimerie photographique de Blanquart-Évrard, à Lille|Salzmann, Auguste",French|French,1851 |1824,1855 |1872,1854,1854,1859,Salted paper print from paper negative,Image: 22.8 x 31.8 cm (9 x 12 1/2 in.) Mount: 44.5 x 60 cm (17 1/2 x 23 5/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286918,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.19,false,true,286919,Photographs,Photograph,"Jérusalem, Enceinte du Temple, Face Nord de l'angle Nord-Est",,,,,,Printer|Artist,,"Imprimerie photographique de Blanquart-Évrard, à Lille|Auguste Salzmann","French, active 1851–55|French, 1824–1872",,"Imprimerie photographique de Blanquart-Évrard, à Lille|Salzmann, Auguste",French|French,1851 |1824,1855 |1872,1854,1854,1859,Salted paper print from paper negative,Image: 32.1 x 22.7 cm (12 5/8 x 8 15/16 in.) Mount: 59.5 x 44.7 cm (23 7/16 x 17 5/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286919,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.20,false,true,286920,Photographs,Photograph,"Jérusalem, Enceinte du Temple, Piscine probatique, 1",,,,,,Printer|Artist,,"Imprimerie photographique de Blanquart-Évrard, à Lille|Auguste Salzmann","French, active 1851–55|French, 1824–1872",,"Imprimerie photographique de Blanquart-Évrard, à Lille|Salzmann, Auguste",French|French,1851 |1824,1855 |1872,1854,1854,1859,Salted paper print from paper negative,Image: 22.7 x 32.8 cm (8 15/16 x 12 15/16 in.) Mount: 44.4 x 59.9 cm (17 1/2 x 23 9/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286920,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.21,false,true,286921,Photographs,Photograph,"Jérusalem, Enceinte du Temple, Piscine probatique, 2",,,,,,Printer|Artist,,"Imprimerie photographique de Blanquart-Évrard, à Lille|Auguste Salzmann","French, active 1851–55|French, 1824–1872",,"Imprimerie photographique de Blanquart-Évrard, à Lille|Salzmann, Auguste",French|French,1851 |1824,1855 |1872,1854,1854,1859,Salted paper print from paper negative,Image: 23.1 x 32.4 cm (9 1/8 x 12 3/4 in.) Mount: 44.7 x 60 cm (17 5/8 x 23 5/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286921,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.22,false,true,286922,Photographs,Photograph,"Jérusalem, Enceinte du Temple, Détails de l'appareil de la piscine probatique",,,,,,Printer|Artist,,"Imprimerie photographique de Blanquart-Évrard, à Lille|Auguste Salzmann","French, active 1851–55|French, 1824–1872",,"Imprimerie photographique de Blanquart-Évrard, à Lille|Salzmann, Auguste",French|French,1851 |1824,1855 |1872,1854,1854,1859,Salted paper print from paper negative,Image: 22.8 x 32.5 cm (9 x 12 13/16 in.) Mount: 44.6 x 60.2 cm (17 9/16 x 23 11/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286922,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.23,false,true,286923,Photographs,Photograph,"Jérusalem, Enceinte du Temple, Angle Nord-Ouest et minaret élevé en l'an 697 de l'Hégire",,,,,,Printer|Artist,,"Imprimerie photographique de Blanquart-Évrard, à Lille|Auguste Salzmann","French, active 1851–55|French, 1824–1872",,"Imprimerie photographique de Blanquart-Évrard, à Lille|Salzmann, Auguste",French|French,1851 |1824,1855 |1872,1854,1854,1859,Salted paper print from paper negative,Image: 33.1 x 23.4 cm (13 1/16 x 9 3/16 in.) Mount: 60.3 x 44.6 cm (23 3/4 x 17 9/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286923,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.24,false,true,286924,Photographs,Photograph,"Jérusalem, Vallée de Hinnom, Tombeaux antiques",,,,,,Printer|Artist,,"Imprimerie photographique de Blanquart-Évrard, à Lille|Auguste Salzmann","French, active 1851–55|French, 1824–1872",,"Imprimerie photographique de Blanquart-Évrard, à Lille|Salzmann, Auguste",French|French,1851 |1824,1855 |1872,1854,1854,1859,Salted paper print from paper negative,Image: 23.4 x 33 cm (9 3/16 x 13 in.) Mount: 44.6 x 60.1 cm (17 9/16 x 23 11/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286924,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.25,false,true,286925,Photographs,Photograph,"Jérusalem, Vallée de Hinnom, Inscription tumulaire grecque, 1",,,,,,Printer|Artist,,"Imprimerie photographique de Blanquart-Évrard, à Lille|Auguste Salzmann","French, active 1851–55|French, 1824–1872",,"Imprimerie photographique de Blanquart-Évrard, à Lille|Salzmann, Auguste",French|French,1851 |1824,1855 |1872,1854,1854,1859,Salted paper print from paper negative,Image: 23.5 x 32.7 cm (9 1/4 x 12 7/8 in.) Mount: 44.7 x 60.2 cm (17 5/8 x 23 11/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286925,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.26,false,true,286926,Photographs,Photograph,"Jérusalem, Vallée de Hinnom, Inscription tumulaire grecque, 2",,,,,,Printer|Artist,,"Imprimerie photographique de Blanquart-Évrard, à Lille|Auguste Salzmann","French, active 1851–55|French, 1824–1872",,"Imprimerie photographique de Blanquart-Évrard, à Lille|Salzmann, Auguste",French|French,1851 |1824,1855 |1872,1854,1854,1859,Salted paper print from paper negative,Image: 23 x 32.5 cm (9 1/16 x 12 13/16 in.) Mount: 44.5 x 59.8 cm (17 1/2 x 23 9/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286926,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.27,false,true,286927,Photographs,Photograph,"Jérusalem, Vallée de Hinnom, Retraite des Apôtres",,,,,,Printer|Artist,,"Imprimerie photographique de Blanquart-Évrard, à Lille|Auguste Salzmann","French, active 1851–55|French, 1824–1872",,"Imprimerie photographique de Blanquart-Évrard, à Lille|Salzmann, Auguste",French|French,1851 |1824,1855 |1872,1854,1854,1859,Salted paper print from paper negative,Image: 23.5 x 32.8 cm (9 1/4 x 12 15/16 in.) Mount: 44.8 x 59.7 cm (17 5/8 x 23 1/2 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286927,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.28,false,true,286928,Photographs,Photograph,"Jérusalem, Vallée de Hinnom, Détails de la frise de la retraite des Apôtres",,,,,,Printer|Artist,,"Imprimerie photographique de Blanquart-Évrard, à Lille|Auguste Salzmann","French, active 1851–55|French, 1824–1872",,"Imprimerie photographique de Blanquart-Évrard, à Lille|Salzmann, Auguste",French|French,1851 |1824,1855 |1872,1854,1854,1859,Salted paper print from paper negative,Image: 21.9 x 32.4 cm (8 5/8 x 12 3/4 in.) Mount: 44.6 x 58.9 cm (17 9/16 x 23 3/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286928,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.29,false,true,286929,Photographs,Photograph,"Jérusalem, Vallée de Hinnom, Ensemble du flanc droit",,,,,,Printer|Artist,,"Imprimerie photographique de Blanquart-Évrard, à Lille|Auguste Salzmann","French, active 1851–55|French, 1824–1872",,"Imprimerie photographique de Blanquart-Évrard, à Lille|Salzmann, Auguste",French|French,1851 |1824,1855 |1872,1854,1854,1859,Salted paper print from paper negative,Image: 23.4 x 33.3 cm (9 3/16 x 13 1/8 in.) Mount: 44.5 x 59.7 cm (17 1/2 x 23 1/2 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286929,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.30,false,true,286930,Photographs,Photograph,"Jérusalem, Vallée de Hinnom, Détails du flanc droit, 1",,,,,,Printer|Artist,,"Imprimerie photographique de Blanquart-Évrard, à Lille|Auguste Salzmann","French, active 1851–55|French, 1824–1872",,"Imprimerie photographique de Blanquart-Évrard, à Lille|Salzmann, Auguste",French|French,1851 |1824,1855 |1872,1854,1854,1859,Salted paper print from paper negative,Image: 23.1 x 32.3 cm (9 1/8 x 12 11/16 in.) Mount: 44.7 x 60.4 cm (17 5/8 x 23 3/4 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286930,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.31,false,true,286931,Photographs,Photograph,"Jérusalem, Vallée de Hinnom, Détails du flanc droit, 2",,,,,,Printer|Artist,,"Imprimerie photographique de Blanquart-Évrard, à Lille|Auguste Salzmann","French, active 1851–55|French, 1824–1872",,"Imprimerie photographique de Blanquart-Évrard, à Lille|Salzmann, Auguste",French|French,1851 |1824,1855 |1872,1854,1854,1859,Salted paper print from paper negative,Image: 23.5 x 33 cm (9 1/4 x 13 in.) Mount: 44.6 x 49.7 cm (17 9/16 x 19 9/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286931,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.32,false,true,286932,Photographs,Photograph,"Jérusalem, Champ du sang",,,,,,Printer|Artist,,"Imprimerie photographique de Blanquart-Évrard, à Lille|Auguste Salzmann","French, active 1851–55|French, 1824–1872",,"Imprimerie photographique de Blanquart-Évrard, à Lille|Salzmann, Auguste",French|French,1851 |1824,1855 |1872,1854,1854,1859,Salted paper print from paper negative,Image: 23.4 x 33 cm (9 3/16 x 13 in.) Mount: 44.6 x 59.7 cm (17 9/16 x 23 1/2 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286932,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.33,false,true,286933,Photographs,Photograph,"Jérusalem, Vallée de Hinnom, Tombeau antique à fronton triangulaire et à crossettes",,,,,,Printer|Artist,,"Imprimerie photographique de Blanquart-Évrard, à Lille|Auguste Salzmann","French, active 1851–55|French, 1824–1872",,"Imprimerie photographique de Blanquart-Évrard, à Lille|Salzmann, Auguste",French|French,1851 |1824,1855 |1872,1854,1854,1859,Salted paper print from paper negative,Image: 32.6 x 23.4 cm (12 13/16 x 9 3/16 in.) Mount: 59.6 x 44.4 cm (23 7/16 x 17 1/2 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286933,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.34,false,true,286934,Photographs,Photograph,"Jérusalem, Piscine de Siloe, Vue générale",,,,,,Printer|Artist,,"Imprimerie photographique de Blanquart-Évrard, à Lille|Auguste Salzmann","French, active 1851–55|French, 1824–1872",,"Imprimerie photographique de Blanquart-Évrard, à Lille|Salzmann, Auguste",French|French,1851 |1824,1855 |1872,1854,1854,1859,Salted paper print from paper negative,Image: 23.4 x 32.5 cm (9 3/16 x 12 13/16 in.) Mount: 44.6 x 60.1 cm (17 9/16 x 23 11/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286934,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.35,false,true,286935,Photographs,Photograph,"Jérusalem, Piscine de Siloe, Détails",,,,,,Printer|Artist,,"Imprimerie photographique de Blanquart-Évrard, à Lille|Auguste Salzmann","French, active 1851–55|French, 1824–1872",,"Imprimerie photographique de Blanquart-Évrard, à Lille|Salzmann, Auguste",French|French,1851 |1824,1855 |1872,1854,1854,1859,Salted paper print from paper negative,Image: 23.5 x 33.3 cm (9 1/4 x 13 1/8 in.) Mount: 44.7 x 60.2 cm (17 5/8 x 23 11/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286935,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.36,false,true,286936,Photographs,Photograph,"Jérusalem, Piscine de Siloe, Canal taillé dans le roc",,,,,,Printer|Artist,,"Imprimerie photographique de Blanquart-Évrard, à Lille|Auguste Salzmann","French, active 1851–55|French, 1824–1872",,"Imprimerie photographique de Blanquart-Évrard, à Lille|Salzmann, Auguste",French|French,1851 |1824,1855 |1872,1854,1854,1859,Salted paper print from paper negative,Image: 22.7 x 32.3 cm (8 15/16 x 12 11/16 in.) Mount: 44.6 x 60 cm (17 9/16 x 23 5/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286936,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.37,false,true,286937,Photographs,Photograph,"Jérusalem, Village de Siloam, Vue générale",,,,,,Printer|Artist,,"Imprimerie photographique de Blanquart-Évrard, à Lille|Auguste Salzmann","French, active 1851–55|French, 1824–1872",,"Imprimerie photographique de Blanquart-Évrard, à Lille|Salzmann, Auguste",French|French,1851 |1824,1855 |1872,1854,1854,1859,Salted paper print from paper negative,Image: 23.2 x 32.2 cm (9 1/8 x 12 11/16 in.) Mount: 44.5 x 60 cm (17 1/2 x 23 5/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286937,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.38,false,true,286938,Photographs,Photograph,"Jérusalem, Village de Siloam, Monolithe de forme égyptienne, 1",,,,,,Printer|Artist,,"Imprimerie photographique de Blanquart-Évrard, à Lille|Auguste Salzmann","French, active 1851–55|French, 1824–1872",,"Imprimerie photographique de Blanquart-Évrard, à Lille|Salzmann, Auguste",French|French,1851 |1824,1855 |1872,1854,1854,1859,Salted paper print from paper negative,Image: 23.4 x 33.1 cm (9 3/16 x 13 1/16 in.) Mount: 44.6 x 59.5 cm (17 9/16 x 23 7/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286938,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.39,false,true,286939,Photographs,Photograph,"Jérusalem, Village de Siloam, Monolithe de forme égyptienne, 2",,,,,,Printer|Artist,,"Imprimerie photographique de Blanquart-Évrard, à Lille|Auguste Salzmann","French, active 1851–55|French, 1824–1872",,"Imprimerie photographique de Blanquart-Évrard, à Lille|Salzmann, Auguste",French|French,1851 |1824,1855 |1872,1854,1854,1859,Salted paper print from paper negative,Image: 23.1 x 32.4 cm (9 1/8 x 12 3/4 in.) Mount: 44.7 x 59.7 cm (17 5/8 x 23 1/2 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286939,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.40,false,true,286940,Photographs,Photograph,"Jérusalem, Village de Siloam, Monolithe de forme égyptienne, 3",,,,,,Printer|Artist,,"Imprimerie photographique de Blanquart-Évrard, à Lille|Auguste Salzmann","French, active 1851–55|French, 1824–1872",,"Imprimerie photographique de Blanquart-Évrard, à Lille|Salzmann, Auguste",French|French,1851 |1824,1855 |1872,1854,1854,1859,Salted paper print from paper negative,Image: 23 x 32.5 cm (9 1/16 x 12 13/16 in.) Mount: 44.7 x 60.7 cm (17 5/8 x 23 7/8 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286940,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.41,false,true,286941,Photographs,Photograph,"Jérusalem, Vallée de Josaphat, Faces Ouest et Nord, 1",,,,,,Printer|Artist,,"Imprimerie photographique de Blanquart-Évrard, à Lille|Auguste Salzmann","French, active 1851–55|French, 1824–1872",,"Imprimerie photographique de Blanquart-Évrard, à Lille|Salzmann, Auguste",French|French,1851 |1824,1855 |1872,1854,1854,1859,Salted paper print from paper negative,Image: 22.8 x 32.8 cm (9 x 12 15/16 in.) Mount: 44.5 x 60.4 cm (17 1/2 x 23 3/4 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286941,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.42,false,true,286942,Photographs,Photograph,"Jérusalem, Vallée de Josaphat, Face Ouest et Nord, 2",,,,,,Printer|Artist,,"Imprimerie photographique de Blanquart-Évrard, à Lille|Auguste Salzmann","French, active 1851–55|French, 1824–1872",,"Imprimerie photographique de Blanquart-Évrard, à Lille|Salzmann, Auguste",French|French,1851 |1824,1855 |1872,1854,1854,1859,Salted paper print from paper negative,Image: 23.5 x 32.5 cm (9 1/4 x 12 13/16 in.) Mount: 45 x 59.5 cm (17 11/16 x 23 7/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286942,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.43,false,true,286943,Photographs,Photograph,"Jérusalem, Vallée de Josaphat, Vue générale",,,,,,Printer|Artist,,"Imprimerie photographique de Blanquart-Évrard, à Lille|Auguste Salzmann","French, active 1851–55|French, 1824–1872",,"Imprimerie photographique de Blanquart-Évrard, à Lille|Salzmann, Auguste",French|French,1851 |1824,1855 |1872,1854,1854,1859,Salted paper print from paper negative,Image: 22.3 x 32.2 cm (8 3/4 x 12 11/16 in.) Mount: 44.4 x 59.6 cm (17 1/2 x 23 7/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286943,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.44,false,true,286944,Photographs,Photograph,"Jérusalem, Vallée de Josaphat, Tombeau de Zacharie",,,,,,Printer|Artist,,"Imprimerie photographique de Blanquart-Évrard, à Lille|Auguste Salzmann","French, active 1851–55|French, 1824–1872",,"Imprimerie photographique de Blanquart-Évrard, à Lille|Salzmann, Auguste",French|French,1851 |1824,1855 |1872,1854,1854,1859,Salted paper print from paper negative,Image: 23.4 x 31.1 cm (9 3/16 x 12 1/4 in.) Mount: 44.8 x 64.3 cm (17 5/8 x 25 5/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286944,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.45,false,true,286894,Photographs,Photograph,"Jérusalem, Vallée de Josaphat, Tombeau de St. Jacques",,,,,,Printer|Artist,,"Imprimerie photographique de Blanquart-Évrard, à Lille|Auguste Salzmann","French, active 1851–55|French, 1824–1872",,"Imprimerie photographique de Blanquart-Évrard, à Lille|Salzmann, Auguste",French|French,1851 |1824,1855 |1872,1854,1854,1859,Salted paper print from paper negative,Image: 23.3 x 31.7 cm (9 3/16 x 12 1/2 in.) Mount: 44.6 x 59.7 cm (17 9/16 x 23 1/2 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286894,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.82,false,true,286797,Photographs,Photograph,"Jérusalem, Saint Sépulcre, Façade",,,,,,Printer|Artist,,"Imprimerie photographique de Blanquart-Évrard, à Lille|Auguste Salzmann","French, active 1851–55|French, 1824–1872",,"Imprimerie photographique de Blanquart-Évrard, à Lille|Salzmann, Auguste",French|French,1851 |1824,1855 |1872,1854,1854,1859,Salted paper print from paper negative,Image: 33.3 x 23.7 cm (13 1/8 x 9 5/16 in.) Mount: 60.2 x 44.6 cm (23 11/16 x 17 9/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286797,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.86,false,true,286798,Photographs,Photograph,"Jérusalem, Saint Sépulcre, détails des chapiteaux",,,,,,Printer|Artist,,"Imprimerie photographique de Blanquart-Évrard, à Lille|Auguste Salzmann","French, active 1851–55|French, 1824–1872",,"Imprimerie photographique de Blanquart-Évrard, à Lille|Salzmann, Auguste",French|French,1851 |1824,1855 |1872,1854,1854,1859,Salted paper print from paper negative,Image: 23.2 x 32.5 cm (9 1/8 x 12 13/16 in.) Mount: 44.6 x 60.2 cm (17 9/16 x 23 11/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286798,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.90,false,true,286799,Photographs,Photograph,"Jérusalem, Saint Sépulcre, Vue générale de la Chapelle du Calvaire",,,,,,Printer|Artist,,"Imprimerie photographique de Blanquart-Évrard, à Lille|Auguste Salzmann","French, active 1851–55|French, 1824–1872",,"Imprimerie photographique de Blanquart-Évrard, à Lille|Salzmann, Auguste",French|French,1851 |1824,1855 |1872,1854,1854,1859,Salted paper print from paper negative,Image: 33.3 x 23.7 cm (13 1/8 x 9 5/16 in.) Mount: 60.2 x 44.6 cm (23 11/16 x 17 9/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286799,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.96,false,true,286800,Photographs,Photograph,"Jérusalem, Saint Sépulcre, vue générale, 2",,,,,,Printer|Artist,,"Imprimerie photographique de Blanquart-Évrard, à Lille|Auguste Salzmann","French, active 1851–55|French, 1824–1872",,"Imprimerie photographique de Blanquart-Évrard, à Lille|Salzmann, Auguste",French|French,1851 |1824,1855 |1872,1854,1854,1859,Salted paper print from paper negative,Image: 23.2 x 33 cm (9 1/8 x 13 in.) Mount: 44.6 x 60.2 cm (17 9/16 x 23 11/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286800,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.97,false,true,286801,Photographs,Photograph,"Jérusalem, Saint Sépulcre, coupole",,,,,,Printer|Artist,,"Imprimerie photographique de Blanquart-Évrard, à Lille|Auguste Salzmann","French, active 1851–55|French, 1824–1872",,"Imprimerie photographique de Blanquart-Évrard, à Lille|Salzmann, Auguste",French|French,1851 |1824,1855 |1872,1854,1854,1859,Salted paper print from paper negative,Image: 33.2 x 23.5 cm (13 1/16 x 9 1/4 in.) Mount: 60.2 x 44.6 cm (23 11/16 x 17 9/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286801,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.100,false,true,286802,Photographs,Photograph,"Jérusalem, Saint Sépulcre, abside",,,,,,Printer|Artist,,"Imprimerie photographique de Blanquart-Évrard, à Lille|Auguste Salzmann","French, active 1851–55|French, 1824–1872",,"Imprimerie photographique de Blanquart-Évrard, à Lille|Salzmann, Auguste",French|French,1851 |1824,1855 |1872,1854,1854,1859,Salted paper print from paper negative,Image: 23.5 x 31 cm (9 1/4 x 12 3/16 in.) Mount: 44.6 x 60.2 cm (17 9/16 x 23 11/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286802,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.373.105,false,true,286793,Photographs,Photograph,"Jérusalem, croix en argent ciselé, donnée par Louis XIII à l'Eglise du Saint Sépulcre",,,,,,Printer|Artist,,"Imprimerie photographique de Blanquart-Évrard, à Lille|Auguste Salzmann","French, active 1851–55|French, 1824–1872",,"Imprimerie photographique de Blanquart-Évrard, à Lille|Salzmann, Auguste",French|French,1851 |1824,1855 |1872,1854,1854,1859,Salted paper prints from paper negative,Image: 34.8 x 23.5 cm (13 11/16 x 9 1/4 in.) Mount: 60.2 x 44.6 cm (23 11/16 x 17 9/16 in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286793,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.54,false,true,283137,Photographs,Photograph,[The Ascent of Mont Blanc],,,,,,Artist|Photography Studio,,Auguste-Rosalie Bisson|Bisson Frères,"French, 1826–1900|French, active 1852–1863",,"Bisson, Auguste-Rosalie|Bisson Frères",French|French,1826 |1852,1900 |1863,1861,1861,1861,Albumen silver print from glass negative,Image: 39.6 x 23.7 cm (15 9/16 x 9 5/16 in.) Mount: 63 x 45.7 cm (24 13/16 x 18 in.),"Gilman Collection, Purchase, Alfred Stieglitz Society Gifts, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283137,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1249,false,true,286265,Photographs,Photograph,François Flameng,,,,,,Artist|Person in Photograph,Person in photograph,Edmond Bénard|François Flameng,"French, 1838–1907|French, Paris 1856–1923 Paris",,"Bénard, Edmond|Flameng, François",French|French,1838 |1856,1907 |1923,1880s–90s,1880,1899,Albumen silver print from glass negative,Image: 20.1 × 26 cm (7 15/16 × 10 1/4 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286265,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2011.65,false,true,294768,Photographs,Photograph,Charles Delahaye,,,,,,Artist|Person in Photograph,,Charles Marville|Charles Hippolyte Delahaye,"French, Paris 1813–1879 Paris|French, 1835/36– 1878 Paris",,"Marville, Charles|Delahaye, Charles Hippolyte",French|French,1813 |1835,1879 |1878,1852–53,1852,1853,Salted paper print from paper negative,Image: 21.6 x 15.9 cm (8 1/2 x 6 1/4 in.) Mount: 45 x 31.5 cm (17 11/16 x 12 3/8 in.),"Purchase, W. Bruce and Delaney H. Lundberg and Christian Keesee Charitable Trust Gifts, 2011",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/294768,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1998.57,true,true,282190,Photographs,Photograph,Pierrot Laughing,,,,,,Artist|Artist|Person in Photograph,,Adrien Tournachon|Nadar|Jean-Charles Deburau,"French, 1825–1903|French, Paris 1820–1910 Paris|French, 1829–1873",,"Tournachon, Adrien|Nadar|Deburau, Jean-Charles",French|French,1825 |1820 |1829,1903 |1910 |1873,1855,1855,1855,Gelatin-coated salted paper print (vernis-cuir),27.3 x 19.8 cm (10 3/4 x 7 13/16 in.),"Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 1998",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/282190,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.43,false,true,283118,Photographs,Photograph,Pierrot Running,,,,,,Artist|Artist|Person in Photograph,Person in photograph,Adrien Tournachon|Nadar|Jean-Charles Deburau,"French, 1825–1903|French, Paris 1820–1910 Paris|French, 1829–1873",,"Tournachon, Adrien|Nadar|Deburau, Jean-Charles",French|French,1825 |1820 |1829,1903 |1910 |1873,1854–55,1854,1855,Albumen silver print from glass negative,Image: 26.5 x 20.8cm (10 7/16 x 8 3/16in.) Mount: 41.3 x 34.5 cm (16 1/4 x 13 9/16 in.),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283118,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.173.1–.48,false,true,305829,Photographs,Portfolio,Mécanisme de la physionomie humaine ou Analyse électro-physiologique de l’expression des passions applicable à la pratique des arts plastiques,,,,,,Artist|Artist,,Guillaume-Benjamin-Amand Duchenne de Boulogne|Adrien Tournachon,"French, 1806–1875|French, 1825–1903",,"Duchenne de Boulogne, Guillaume-Benjamin-Amand|Tournachon, Adrien",French|French,1806 |1825,1875 |1903,"1854–56, printed 1862",1854,1856,Albumen silver prints from glass negatives,,"Purchase, The Buddy Taub Foundation Gift, Dennis A. Roach and Jill Roach, Directors; Harris Brisbane Dick and William E. Dodge Funds; and W. Bruce and Delaney H. Lundberg Gift, 2013",,,,,,,,,,,,Portfolios,,http://www.metmuseum.org/art/collection/search/305829,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1156,false,true,285664,Photographs,Stereograph; Autochrome,[Tulips],,,,,,Artist|Artist,,Auguste-Marie-Louis-Nicolas Lumière|Louis-Jean Lumière,"French, Besançon 1862–1954 Lyon|French, Besançon 1864–1948 Bandol",and,"Lumière, Auguste-Marie-Louis-Nicolas|Lumière, Louis-Jean",French|French,1862 |1864,1954 |1948,1896–1903,1896,1903,Trichromie,8.5 x 17.8cm,"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Transparencies,,http://www.metmuseum.org/art/collection/search/285664,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1157,false,true,285666,Photographs,Stereograph; Autochrome,[Still Life of Flowers in a Stein],,,,,,Artist|Artist,,Auguste-Marie-Louis-Nicolas Lumière|Louis-Jean Lumière,"French, Besançon 1862–1954 Lyon|French, Besançon 1864–1948 Bandol",and,"Lumière, Auguste-Marie-Louis-Nicolas|Lumière, Louis-Jean",French|French,1862 |1864,1954 |1948,1896–1903,1896,1903,Trichromie,2 13/16 × 6 9/16 in. (7.2 × 16.7 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Transparencies,,http://www.metmuseum.org/art/collection/search/285666,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1155,false,true,285665,Photographs,Autochrome,[Peacock],,,,,,Artist|Artist,,Louis-Jean Lumière|Auguste-Marie-Louis-Nicolas Lumière,"French, Besançon 1864–1948 Bandol|French, Besançon 1862–1954 Lyon",and,"Lumière, Louis-Jean|Lumière, Auguste-Marie-Louis-Nicolas",French|French,1864 |1862,1948 |1954,ca. 1907,1902,1912,Autochrome,13.0 x 17.7 cm (5 1/8 x 7in.),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Transparencies,,http://www.metmuseum.org/art/collection/search/285665,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.631,false,true,286695,Photographs,Photograph,[Empress Eugénie and the Prince Imperial],,,,,,Artist|Artist,Possibly by|Attributed to,Louise Deglane|François-Benjamin-Maria Delessert,"French|French, 1817–1868",,"Deglane, Louise|Delessert, François-Benjamin-Marie",French|French,1817,1868,1862,1862,1862,Albumen silver print from glass negative,21.6 x 16 cm (8 1/2 x 6 5/16 in.),"Gilman Collection, Purchase, Gift of The Howard Gilman Foundation, by exchange, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286695,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1993.248,false,true,266904,Photographs,Photograph,Icono-photographique. Mécanisme de la Physionomie Humaine. Fig. 65,,,,,,Artist|Artist,,Guillaume-Benjamin-Amand Duchenne de Boulogne|Adrien Tournachon,"French, 1806–1875|French, 1825–1903",,"Duchenne de Boulogne, Guillaume-Benjamin-Amand|Tournachon, Adrien",French|French,1806 |1825,1875 |1903,"1854–56, printed 1862",1854,1856,Albumen silver print from glass negative,12.0 x 9.2 cm (4 3/4 x 3 5/8 in.),"Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 1993",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/266904,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.229,false,true,307063,Photographs,Photograph,Faradisation du muscle frontal,,,,,,Artist|Artist,Possibly with,Guillaume-Benjamin-Amand Duchenne de Boulogne|Adrien Tournachon,"French, 1806–1875|French, 1825–1903",,"Duchenne de Boulogne, Guillaume-Benjamin-Amand|Tournachon, Adrien",French|French,1806 |1825,1875 |1903,"1854–56, printed 1862",1854,1856,Albumen silver print from glass negative,Image: 18.6 × 11.7 cm (7 5/16 × 4 5/8 in.) Mount: 27 × 18.6 cm (10 5/8 × 7 5/16 in.),"The Horace W. Goldsmith Foundation Fund, through Joyce and Robert Menschel, 2013",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/307063,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.173.1,false,true,623026,Photographs,Photograph,Figure 3: The face of an old man... photographed in repose.,,,,,,Artist|Artist,,Guillaume-Benjamin-Amand Duchenne de Boulogne|Adrien Tournachon,"French, 1806–1875|French, 1825–1903",,"Duchenne de Boulogne, Guillaume-Benjamin-Amand|Tournachon, Adrien",French|French,1806 |1825,1875 |1903,"1854–56, printed 1862",1854,1856,Albumen silver print from glass negative,Image (Oval): 28.3 × 20.3 cm (11 1/8 × 8 in.) Sheet: 30 × 22.9 cm (11 13/16 × 9 in.) Mount: 40.1 × 28.4 cm (15 13/16 × 11 3/16 in.),"Purchase, The Buddy Taub Foundation Gift, Dennis A. Roach and Jill Roach, Directors; Harris Brisbane Dick and William E. Dodge Funds; and W. Bruce and Delaney H. Lundberg Gift, 2013",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/623026,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.173.2,false,true,623027,Photographs,Photograph,Figure 4: The face in repose of a young man,,,,,,Artist|Artist,,Guillaume-Benjamin-Amand Duchenne de Boulogne|Adrien Tournachon,"French, 1806–1875|French, 1825–1903",,"Duchenne de Boulogne, Guillaume-Benjamin-Amand|Tournachon, Adrien",French|French,1806 |1825,1875 |1903,"1854–56, printed 1862",1854,1856,Albumen silver print from glass negative,Image (Oval): 28.5 × 20.3 cm (11 1/4 × 8 in.) Sheet: 30 × 22.8 cm (11 13/16 × 9 in.) Mount: 40.1 × 28.6 cm (15 13/16 × 11 1/4 in.),"Purchase, The Buddy Taub Foundation Gift, Dennis A. Roach and Jill Roach, Directors; Harris Brisbane Dick and William E. Dodge Funds; and W. Bruce and Delaney H. Lundberg Gift, 2013",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/623027,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.173.3,false,true,623028,Photographs,Photograph,Figure 6: The grimice produced is similar to a tic of the face,,,,,,Artist|Artist,,Guillaume-Benjamin-Amand Duchenne de Boulogne|Adrien Tournachon,"French, 1806–1875|French, 1825–1903",,"Duchenne de Boulogne, Guillaume-Benjamin-Amand|Tournachon, Adrien",French|French,1806 |1825,1875 |1903,"1854–56, printed 1862",1854,1856,Albumen silver print from glass negative,Image (Oval): 28.3 × 20.4 cm (11 1/8 × 8 1/16 in.) Sheet: 29.8 × 22.4 cm (11 3/4 × 8 13/16 in.) Mount: 40.1 × 28.6 cm (15 13/16 × 11 1/4 in.),"Purchase, The Buddy Taub Foundation Gift, Dennis A. Roach and Jill Roach, Directors; Harris Brisbane Dick and William E. Dodge Funds; and W. Bruce and Delaney H. Lundberg Gift, 2013",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/623028,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.173.4,false,true,623029,Photographs,Photograph,Figure 8: Contraction of the right m. frontalis.,,,,,,Artist|Artist,,Guillaume-Benjamin-Amand Duchenne de Boulogne|Adrien Tournachon,"French, 1806–1875|French, 1825–1903",,"Duchenne de Boulogne, Guillaume-Benjamin-Amand|Tournachon, Adrien",French|French,1806 |1825,1875 |1903,"1854–56, printed 1862",1854,1856,Albumen silver print from glass negative,Image (Oval): 28.4 × 20.2 cm (11 3/16 × 7 15/16 in.) Sheet: 29.8 × 22.9 cm (11 3/4 × 9 in.) Mount: 40.1 × 28.6 cm (15 13/16 × 11 1/4 in.),"Purchase, The Buddy Taub Foundation Gift, Dennis A. Roach and Jill Roach, Directors; Harris Brisbane Dick and William E. Dodge Funds; and W. Bruce and Delaney H. Lundberg Gift, 2013",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/623029,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.173.5,false,true,623030,Photographs,Photograph,Figure 9: A study of m. frontalis in maximum contraction,,,,,,Artist|Artist,,Guillaume-Benjamin-Amand Duchenne de Boulogne|Adrien Tournachon,"French, 1806–1875|French, 1825–1903",,"Duchenne de Boulogne, Guillaume-Benjamin-Amand|Tournachon, Adrien",French|French,1806 |1825,1875 |1903,"1854–56, printed 1862",1854,1856,Albumen silver print from glass negative,Image (Oval): 28.4 × 20.3 cm (11 3/16 × 8 in.) Sheet: 29.6 × 22.1 cm (11 5/8 × 8 11/16 in.) Mount: 40.2 × 28.2 cm (15 13/16 × 11 1/8 in.),"Purchase, The Buddy Taub Foundation Gift, Dennis A. Roach and Jill Roach, Directors; Harris Brisbane Dick and William E. Dodge Funds; and W. Bruce and Delaney H. Lundberg Gift, 2013",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/623030,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.173.6,false,true,623031,Photographs,Photograph,Figure 10: Showing the expressive lines of m. frontalis in a young girl,,,,,,Artist|Artist,,Guillaume-Benjamin-Amand Duchenne de Boulogne|Adrien Tournachon,"French, 1806–1875|French, 1825–1903",,"Duchenne de Boulogne, Guillaume-Benjamin-Amand|Tournachon, Adrien",French|French,1806 |1825,1875 |1903,"1854–56, printed 1862",1854,1856,Albumen silver print from glass negative,Image (Oval): 28.4 × 20.4 cm (11 3/16 × 8 1/16 in.) Sheet: 30 × 22.4 cm (11 13/16 × 8 13/16 in.) Mount: 40.2 × 28.4 cm (15 13/16 × 11 3/16 in.),"Purchase, The Buddy Taub Foundation Gift, Dennis A. Roach and Jill Roach, Directors; Harris Brisbane Dick and William E. Dodge Funds; and W. Bruce and Delaney H. Lundberg Gift, 2013",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/623031,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.173.7,false,true,623032,Photographs,Photograph,Figure 12: A study of the contraction of and the expression produced by the superior part of m. orbicularis oculi,,,,,,Artist|Artist,,Guillaume-Benjamin-Amand Duchenne de Boulogne|Adrien Tournachon,"French, 1806–1875|French, 1825–1903",,"Duchenne de Boulogne, Guillaume-Benjamin-Amand|Tournachon, Adrien",French|French,1806 |1825,1875 |1903,"1854–56, printed 1862",1854,1856,Albumen silver print from glass negative,Image (Oval): 28.3 × 20.4 cm (11 1/8 × 8 1/16 in.) Sheet: 29.5 × 22.1 cm (11 5/8 × 8 11/16 in.) Mount: 40.2 × 28.5 cm (15 13/16 × 11 1/4 in.),"Purchase, The Buddy Taub Foundation Gift, Dennis A. Roach and Jill Roach, Directors; Harris Brisbane Dick and William E. Dodge Funds; and W. Bruce and Delaney H. Lundberg Gift, 2013",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/623032,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.173.8,false,true,623033,Photographs,Photograph,"Figure 15: Mediation, mental concentration",,,,,,Artist|Artist,,Guillaume-Benjamin-Amand Duchenne de Boulogne|Adrien Tournachon,"French, 1806–1875|French, 1825–1903",,"Duchenne de Boulogne, Guillaume-Benjamin-Amand|Tournachon, Adrien",French|French,1806 |1825,1875 |1903,"1854–56, printed 1862",1854,1856,Albumen silver print from glass negative,Image (Oval): 28.3 × 20.3 cm (11 1/8 × 8 in.) Sheet: 29.9 × 22.1 cm (11 3/4 × 8 11/16 in.) Mount: 40.1 × 28.5 cm (15 13/16 × 11 1/4 in.),"Purchase, The Buddy Taub Foundation Gift, Dennis A. Roach and Jill Roach, Directors; Harris Brisbane Dick and William E. Dodge Funds; and W. Bruce and Delaney H. Lundberg Gift, 2013",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/623033,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.173.9,false,true,623034,Photographs,Photograph,Figure 16: Expression of severity,,,,,,Artist|Artist,,Guillaume-Benjamin-Amand Duchenne de Boulogne|Adrien Tournachon,"French, 1806–1875|French, 1825–1903",,"Duchenne de Boulogne, Guillaume-Benjamin-Amand|Tournachon, Adrien",French|French,1806 |1825,1875 |1903,"1854–56, printed 1862",1854,1856,Albumen silver print from glass negative,Image (Oval): 28.5 × 20.4 cm (11 1/4 × 8 1/16 in.) Sheet: 29.9 × 21.9 cm (11 3/4 × 8 5/8 in.) Mount: 40.2 × 28.4 cm (15 13/16 × 11 3/16 in.),"Purchase, The Buddy Taub Foundation Gift, Dennis A. Roach and Jill Roach, Directors; Harris Brisbane Dick and William E. Dodge Funds; and W. Bruce and Delaney H. Lundberg Gift, 2013",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/623034,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.173.10,false,true,623035,Photographs,Photograph,"Figure 17: On the right, electrization of m. procerus: severity, aggression. On the left: attention.",,,,,,Artist|Artist,,Guillaume-Benjamin-Amand Duchenne de Boulogne|Adrien Tournachon,"French, 1806–1875|French, 1825–1903",,"Duchenne de Boulogne, Guillaume-Benjamin-Amand|Tournachon, Adrien",French|French,1806 |1825,1875 |1903,"1854–56, printed 1862",1854,1856,Albumen silver print from glass negative,Image (Oval): 28.3 × 20.5 cm (11 1/8 × 8 1/16 in.) Sheet: 29.7 × 22.7 cm (11 11/16 × 8 15/16 in.) Mount: 40.2 × 28.5 cm (15 13/16 × 11 1/4 in.),"Purchase, The Buddy Taub Foundation Gift, Dennis A. Roach and Jill Roach, Directors; Harris Brisbane Dick and William E. Dodge Funds; and W. Bruce and Delaney H. Lundberg Gift, 2013",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/623035,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.173.11,false,true,623036,Photographs,Photograph,"Figure 18: Aggression, wickedness",,,,,,Artist|Artist,,Guillaume-Benjamin-Amand Duchenne de Boulogne|Adrien Tournachon,"French, 1806–1875|French, 1825–1903",,"Duchenne de Boulogne, Guillaume-Benjamin-Amand|Tournachon, Adrien",French|French,1806 |1825,1875 |1903,"1854–56, printed 1862",1854,1856,Albumen silver print from glass negative,Image (Oval): 28.5 × 20.4 cm (11 1/4 × 8 1/16 in.) Sheet: 29.9 × 22.6 cm (11 3/4 × 8 7/8 in.) Mount: 40 × 28.3 cm (15 3/4 × 11 1/8 in.),"Purchase, The Buddy Taub Foundation Gift, Dennis A. Roach and Jill Roach, Directors; Harris Brisbane Dick and William E. Dodge Funds; and W. Bruce and Delaney H. Lundberg Gift, 2013",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/623036,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.173.12,false,true,623037,Photographs,Photograph,Figure 19: Suffering,,,,,,Artist|Artist,,Guillaume-Benjamin-Amand Duchenne de Boulogne|Adrien Tournachon,"French, 1806–1875|French, 1825–1903",,"Duchenne de Boulogne, Guillaume-Benjamin-Amand|Tournachon, Adrien",French|French,1806 |1825,1875 |1903,"1854–56, printed 1862",1854,1856,Albumen silver print from glass negative,"Image (Oval): 28.2 × 20.4 cm (11 1/8 × 8 1/16 in.) Sheet: 29.4 cm, 22.2 gr (11 9/16) Mount: 40.3 × 28 cm (15 7/8 × 11 in.)","Purchase, The Buddy Taub Foundation Gift, Dennis A. Roach and Jill Roach, Directors; Harris Brisbane Dick and William E. Dodge Funds; and W. Bruce and Delaney H. Lundberg Gift, 2013",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/623037,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.173.13,false,true,623038,Photographs,Photograph,"Figure 20: Profound suffering, with resignation",,,,,,Artist|Artist,,Guillaume-Benjamin-Amand Duchenne de Boulogne|Adrien Tournachon,"French, 1806–1875|French, 1825–1903",,"Duchenne de Boulogne, Guillaume-Benjamin-Amand|Tournachon, Adrien",French|French,1806 |1825,1875 |1903,"1854–56, printed 1862",1854,1856,Albumen silver print from glass negative,Image (Oval): 28.5 × 20.4 cm (11 1/4 × 8 1/16 in.) Sheet: 29.8 × 22.4 cm (11 3/4 × 8 13/16 in.) Mount: 40.3 × 28.5 cm (15 7/8 × 11 1/4 in.),"Purchase, The Buddy Taub Foundation Gift, Dennis A. Roach and Jill Roach, Directors; Harris Brisbane Dick and William E. Dodge Funds; and W. Bruce and Delaney H. Lundberg Gift, 2013",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/623038,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.173.14,false,true,623039,Photographs,Photograph,Figure 21: Painful recollection and recollection or calling something to mind,,,,,,Artist|Artist,,Guillaume-Benjamin-Amand Duchenne de Boulogne|Adrien Tournachon,"French, 1806–1875|French, 1825–1903",,"Duchenne de Boulogne, Guillaume-Benjamin-Amand|Tournachon, Adrien",French|French,1806 |1825,1875 |1903,"1854–56, printed 1862",1854,1856,Albumen silver print from glass negative,Image (Oval): 28.4 × 20.3 cm (11 3/16 × 8 in.) Sheet: 29.7 × 22.7 cm (11 11/16 × 8 15/16 in.) Mount: 40.2 × 28.6 cm (15 13/16 × 11 1/4 in.),"Purchase, The Buddy Taub Foundation Gift, Dennis A. Roach and Jill Roach, Directors; Harris Brisbane Dick and William E. Dodge Funds; and W. Bruce and Delaney H. Lundberg Gift, 2013",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/623039,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.173.15,false,true,623040,Photographs,Photograph,Figure 22: No painful expression,,,,,,Artist|Artist,,Guillaume-Benjamin-Amand Duchenne de Boulogne|Adrien Tournachon,"French, 1806–1875|French, 1825–1903",,"Duchenne de Boulogne, Guillaume-Benjamin-Amand|Tournachon, Adrien",French|French,1806 |1825,1875 |1903,"1854–56, printed 1862",1854,1856,Albumen silver print from glass negative,Image (Oval): 28.5 × 20.4 cm (11 1/4 × 8 1/16 in.) Sheet: 29.7 × 22.6 cm (11 11/16 × 8 7/8 in.) Mount: 40.3 × 28.1 cm (15 7/8 × 11 1/16 in.),"Purchase, The Buddy Taub Foundation Gift, Dennis A. Roach and Jill Roach, Directors; Harris Brisbane Dick and William E. Dodge Funds; and W. Bruce and Delaney H. Lundberg Gift, 2013",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/623040,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.173.16,false,true,623041,Photographs,Photograph,"Figure 24: Extreme pain to the point of exhaustion, the head of Christ and memory of love or ecstatic gaze.",,,,,,Artist|Artist,,Guillaume-Benjamin-Amand Duchenne de Boulogne|Adrien Tournachon,"French, 1806–1875|French, 1825–1903",,"Duchenne de Boulogne, Guillaume-Benjamin-Amand|Tournachon, Adrien",French|French,1806 |1825,1875 |1903,"1854–56, printed 1862",1854,1856,Albumen silver print from glass negative,Image (Oval): 28.3 × 20.3 cm (11 1/8 × 8 in.) Sheet: 29.9 × 22 cm (11 3/4 × 8 11/16 in.) Mount: 40.2 × 28.5 cm (15 13/16 × 11 1/4 in.),"Purchase, The Buddy Taub Foundation Gift, Dennis A. Roach and Jill Roach, Directors; Harris Brisbane Dick and William E. Dodge Funds; and W. Bruce and Delaney H. Lundberg Gift, 2013",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/623041,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.173.17,false,true,623042,Photographs,Photograph,Figure 25: Not an expression of pain,,,,,,Artist|Artist,,Guillaume-Benjamin-Amand Duchenne de Boulogne|Adrien Tournachon,"French, 1806–1875|French, 1825–1903",,"Duchenne de Boulogne, Guillaume-Benjamin-Amand|Tournachon, Adrien",French|French,1806 |1825,1875 |1903,"1854–56, printed 1862",1854,1856,Albumen silver print from glass negative,Image (Oval): 28.4 × 20.4 cm (11 3/16 × 8 1/16 in.) Sheet: 29.3 × 22 cm (11 9/16 × 8 11/16 in.) Mount: 40.2 × 28.5 cm (15 13/16 × 11 1/4 in.),"Purchase, The Buddy Taub Foundation Gift, Dennis A. Roach and Jill Roach, Directors; Harris Brisbane Dick and William E. Dodge Funds; and W. Bruce and Delaney H. Lundberg Gift, 2013",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/623042,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.173.18,false,true,623043,Photographs,Photograph,"Figure 26: Expression of painful attention and attention, attentive gaze.",,,,,,Artist|Artist,,Guillaume-Benjamin-Amand Duchenne de Boulogne|Adrien Tournachon,"French, 1806–1875|French, 1825–1903",,"Duchenne de Boulogne, Guillaume-Benjamin-Amand|Tournachon, Adrien",French|French,1806 |1825,1875 |1903,"1854–56, printed 1862",1854,1856,Albumen silver print from glass negative,Image (Oval): 28.5 × 20.3 cm (11 1/4 × 8 in.) Sheet: 29.7 × 22.5 cm (11 11/16 × 8 7/8 in.) Mount: 40.3 × 28 cm (15 7/8 × 11 in.),"Purchase, The Buddy Taub Foundation Gift, Dennis A. Roach and Jill Roach, Directors; Harris Brisbane Dick and William E. Dodge Funds; and W. Bruce and Delaney H. Lundberg Gift, 2013",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/623043,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.173.19,false,true,623044,Photographs,Photograph,Figure 27: Expression proportionally more pained,,,,,,Artist|Artist,,Guillaume-Benjamin-Amand Duchenne de Boulogne|Adrien Tournachon,"French, 1806–1875|French, 1825–1903",,"Duchenne de Boulogne, Guillaume-Benjamin-Amand|Tournachon, Adrien",French|French,1806 |1825,1875 |1903,"1854–56, printed 1862",1854,1856,Albumen silver print from glass negative,Image (Oval): 28.4 × 20.3 cm (11 3/16 × 8 in.) Sheet: 29.9 × 22.2 cm (11 3/4 × 8 3/4 in.) Mount: 40.2 × 28.5 cm (15 13/16 × 11 1/4 in.),"Purchase, The Buddy Taub Foundation Gift, Dennis A. Roach and Jill Roach, Directors; Harris Brisbane Dick and William E. Dodge Funds; and W. Bruce and Delaney H. Lundberg Gift, 2013",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/623044,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.173.20,false,true,623045,Photographs,Photograph,Figure 34: Grimace,,,,,,Artist|Artist,,Guillaume-Benjamin-Amand Duchenne de Boulogne|Adrien Tournachon,"French, 1806–1875|French, 1825–1903",,"Duchenne de Boulogne, Guillaume-Benjamin-Amand|Tournachon, Adrien",French|French,1806 |1825,1875 |1903,"1854–56, printed 1862",1854,1856,Albumen silver print from glass negative,Image (Oval): 28.4 × 20.4 cm (11 3/16 × 8 1/16 in.) Sheet: 29.9 × 22.5 cm (11 3/4 × 8 7/8 in.) Mount: 40.2 × 28.5 cm (15 13/16 × 11 1/4 in.),"Purchase, The Buddy Taub Foundation Gift, Dennis A. Roach and Jill Roach, Directors; Harris Brisbane Dick and William E. Dodge Funds; and W. Bruce and Delaney H. Lundberg Gift, 2013",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/623045,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.173.21,false,true,623046,Photographs,Photograph,Figure 36: Scornful laughter and scornful disgust,,,,,,Artist|Artist,,Guillaume-Benjamin-Amand Duchenne de Boulogne|Adrien Tournachon,"French, 1806–1875|French, 1825–1903",,"Duchenne de Boulogne, Guillaume-Benjamin-Amand|Tournachon, Adrien",French|French,1806 |1825,1875 |1903,"1854–56, printed 1862",1854,1856,Albumen silver print from glass negative,Image (Oval): 28.2 × 20.5 cm (11 1/8 × 8 1/16 in.) Sheet: 29.6 × 22.3 cm (11 5/8 × 8 3/4 in.) Mount: 40.2 × 28.1 cm (15 13/16 × 11 1/16 in.),"Purchase, The Buddy Taub Foundation Gift, Dennis A. Roach and Jill Roach, Directors; Harris Brisbane Dick and William E. Dodge Funds; and W. Bruce and Delaney H. Lundberg Gift, 2013",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/623046,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.173.22,false,true,623047,Photographs,Photograph,Figure 39: The attention attracted by an object that provokes lascivious ideas and desires.,,,,,,Artist|Artist,,Guillaume-Benjamin-Amand Duchenne de Boulogne|Adrien Tournachon,"French, 1806–1875|French, 1825–1903",,"Duchenne de Boulogne, Guillaume-Benjamin-Amand|Tournachon, Adrien",French|French,1806 |1825,1875 |1903,"1854–56, printed 1862",1854,1856,Albumen silver print from glass negative,Image (Oval): 28.5 × 20.4 cm (11 1/4 × 8 1/16 in.) Sheet: 29.8 × 22.8 cm (11 3/4 × 9 in.) Mount: 40.2 × 28.2 cm (15 13/16 × 11 1/8 in.),"Purchase, The Buddy Taub Foundation Gift, Dennis A. Roach and Jill Roach, Directors; Harris Brisbane Dick and William E. Dodge Funds; and W. Bruce and Delaney H. Lundberg Gift, 2013",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/623047,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.173.23,false,true,623048,Photographs,Photograph,"Figure 42: Gaiety expressed by the ideas of lustfulness, cynicism, and lewdness.",,,,,,Artist|Artist,,Guillaume-Benjamin-Amand Duchenne de Boulogne|Adrien Tournachon,"French, 1806–1875|French, 1825–1903",,"Duchenne de Boulogne, Guillaume-Benjamin-Amand|Tournachon, Adrien",French|French,1806 |1825,1875 |1903,"1854–56, printed 1862",1854,1856,Albumen silver print from glass negative,Image (Oval): 28.6 × 20.3 cm (11 1/4 × 8 in.) Sheet: 29.9 × 22.4 cm (11 3/4 × 8 13/16 in.) Mount: 40.2 × 28.4 cm (15 13/16 × 11 3/16 in.),"Purchase, The Buddy Taub Foundation Gift, Dennis A. Roach and Jill Roach, Directors; Harris Brisbane Dick and William E. Dodge Funds; and W. Bruce and Delaney H. Lundberg Gift, 2013",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/623048,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.173.24,false,true,623049,Photographs,Photograph,Figure 45: Pain and despair.,,,,,,Artist|Artist,,Guillaume-Benjamin-Amand Duchenne de Boulogne|Adrien Tournachon,"French, 1806–1875|French, 1825–1903",,"Duchenne de Boulogne, Guillaume-Benjamin-Amand|Tournachon, Adrien",French|French,1806 |1825,1875 |1903,"1854–56, printed 1862",1854,1856,Albumen silver print from glass negative,Image (Oval): 28.6 × 20.4 cm (11 1/4 × 8 1/16 in.) Sheet: 29.8 × 22.9 cm (11 3/4 × 9 in.) Mount: 40.1 × 28.4 cm (15 13/16 × 11 3/16 in.),"Purchase, The Buddy Taub Foundation Gift, Dennis A. Roach and Jill Roach, Directors; Harris Brisbane Dick and William E. Dodge Funds; and W. Bruce and Delaney H. Lundberg Gift, 2013",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/623049,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.173.25,false,true,623050,Photographs,Photograph,Figure 47: A suggestion of this same weeping,,,,,,Artist|Artist,,Guillaume-Benjamin-Amand Duchenne de Boulogne|Adrien Tournachon,"French, 1806–1875|French, 1825–1903",,"Duchenne de Boulogne, Guillaume-Benjamin-Amand|Tournachon, Adrien",French|French,1806 |1825,1875 |1903,"1854–56, printed 1862",1854,1856,Albumen silver print from glass negative,Image (Oval): 27.9 × 20.3 cm (11 × 8 in.) Sheet: 29.6 × 21.8 cm (11 5/8 × 8 9/16 in.) Mount: 40.2 × 28.4 cm (15 13/16 × 11 3/16 in.),"Purchase, The Buddy Taub Foundation Gift, Dennis A. Roach and Jill Roach, Directors; Harris Brisbane Dick and William E. Dodge Funds; and W. Bruce and Delaney H. Lundberg Gift, 2013",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/623050,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.173.26,false,true,623051,Photographs,Photograph,"Figure 48: Mild weeping, pity and feeble false laughter",,,,,,Artist|Artist,,Guillaume-Benjamin-Amand Duchenne de Boulogne|Adrien Tournachon,"French, 1806–1875|French, 1825–1903",,"Duchenne de Boulogne, Guillaume-Benjamin-Amand|Tournachon, Adrien",French|French,1806 |1825,1875 |1903,"1854–56, printed 1862",1854,1856,Albumen silver print from glass negative,Image (Oval): 28.5 × 20.4 cm (11 1/4 × 8 1/16 in.) Sheet: 30.2 × 23.1 cm (11 7/8 × 9 1/8 in.) Mount: 40.2 × 28.5 cm (15 13/16 × 11 1/4 in.),"Purchase, The Buddy Taub Foundation Gift, Dennis A. Roach and Jill Roach, Directors; Harris Brisbane Dick and William E. Dodge Funds; and W. Bruce and Delaney H. Lundberg Gift, 2013",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/623051,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.173.27,false,true,623052,Photographs,Photograph,Figure 49: Painful weeping and forward looking.,,,,,,Artist|Artist,,Guillaume-Benjamin-Amand Duchenne de Boulogne|Adrien Tournachon,"French, 1806–1875|French, 1825–1903",,"Duchenne de Boulogne, Guillaume-Benjamin-Amand|Tournachon, Adrien",French|French,1806 |1825,1875 |1903,"1854–56, printed 1862",1854,1856,Albumen silver print from glass negative,Image (Oval): 28.6 × 20.5 cm (11 1/4 × 8 1/16 in.) Sheet: 29.5 × 22.8 cm (11 5/8 × 9 in.) Mount: 40.1 × 28.7 cm (15 13/16 × 11 5/16 in.),"Purchase, The Buddy Taub Foundation Gift, Dennis A. Roach and Jill Roach, Directors; Harris Brisbane Dick and William E. Dodge Funds; and W. Bruce and Delaney H. Lundberg Gift, 2013",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/623052,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.173.28,false,true,623053,Photographs,Photograph,Figure 50: Affected weeping and face in repose,,,,,,Artist|Artist,,Guillaume-Benjamin-Amand Duchenne de Boulogne|Adrien Tournachon,"French, 1806–1875|French, 1825–1903",,"Duchenne de Boulogne, Guillaume-Benjamin-Amand|Tournachon, Adrien",French|French,1806 |1825,1875 |1903,"1854–56, printed 1862",1854,1856,Albumen silver print from glass negative,Image (Oval): 28.5 × 20.4 cm (11 1/4 × 8 1/16 in.) Sheet: 30.1 × 22.6 cm (11 7/8 × 8 7/8 in.) Mount: 40.3 × 28.4 cm (15 7/8 × 11 3/16 in.),"Purchase, The Buddy Taub Foundation Gift, Dennis A. Roach and Jill Roach, Directors; Harris Brisbane Dick and William E. Dodge Funds; and W. Bruce and Delaney H. Lundberg Gift, 2013",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/623053,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.173.29,false,true,623054,Photographs,Photograph,Figure 52: Voluntary retraction of the lower lip,,,,,,Artist|Artist,,Guillaume-Benjamin-Amand Duchenne de Boulogne|Adrien Tournachon,"French, 1806–1875|French, 1825–1903",,"Duchenne de Boulogne, Guillaume-Benjamin-Amand|Tournachon, Adrien",French|French,1806 |1825,1875 |1903,"1854–56, printed 1862",1854,1856,Albumen silver print from glass negative,Image (Oval): 28.4 × 20.3 cm (11 3/16 × 8 in.) Sheet: 29.2 × 22.2 cm (11 1/2 × 8 3/4 in.) Mount: 40.1 × 28.4 cm (15 13/16 × 11 3/16 in.),"Purchase, The Buddy Taub Foundation Gift, Dennis A. Roach and Jill Roach, Directors; Harris Brisbane Dick and William E. Dodge Funds; and W. Bruce and Delaney H. Lundberg Gift, 2013",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/623054,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.173.30,false,true,623055,Photographs,Photograph,Figure 53: Whimpering and false laughter,,,,,,Artist|Artist,,Guillaume-Benjamin-Amand Duchenne de Boulogne|Adrien Tournachon,"French, 1806–1875|French, 1825–1903",,"Duchenne de Boulogne, Guillaume-Benjamin-Amand|Tournachon, Adrien",French|French,1806 |1825,1875 |1903,"1854–56, printed 1862",1854,1856,Albumen silver print from glass negative,Image (Oval): 28.6 × 20.5 cm (11 1/4 × 8 1/16 in.) Mount: 40.2 × 28.5 cm (15 13/16 × 11 1/4 in.) Sheet: 29.7 × 22.1 cm (11 11/16 × 8 11/16 in.),"Purchase, The Buddy Taub Foundation Gift, Dennis A. Roach and Jill Roach, Directors; Harris Brisbane Dick and William E. Dodge Funds; and W. Bruce and Delaney H. Lundberg Gift, 2013",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/623055,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.173.31,false,true,623056,Photographs,Photograph,Figure 54: Voluntary lowering of the lower jaw,,,,,,Artist|Artist,,Guillaume-Benjamin-Amand Duchenne de Boulogne|Adrien Tournachon,"French, 1806–1875|French, 1825–1903",,"Duchenne de Boulogne, Guillaume-Benjamin-Amand|Tournachon, Adrien",French|French,1806 |1825,1875 |1903,"1854–56, printed 1862",1854,1856,Albumen silver print from glass negative,Image (Oval): 28.6 × 20.6 cm (11 1/4 × 8 1/8 in.) Sheet: 30 × 23.1 cm (11 13/16 × 9 1/8 in.) Mount: 40.3 × 28.6 cm (15 7/8 × 11 1/4 in.),"Purchase, The Buddy Taub Foundation Gift, Dennis A. Roach and Jill Roach, Directors; Harris Brisbane Dick and William E. Dodge Funds; and W. Bruce and Delaney H. Lundberg Gift, 2013",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/623056,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.173.32,false,true,623057,Photographs,Photograph,Figure 55: Astonishment badly rendered by the subject: a ridiculous and inane expression.,,,,,,Artist|Artist,,Guillaume-Benjamin-Amand Duchenne de Boulogne|Adrien Tournachon,"French, 1806–1875|French, 1825–1903",,"Duchenne de Boulogne, Guillaume-Benjamin-Amand|Tournachon, Adrien",French|French,1806 |1825,1875 |1903,"1854–56, printed 1862",1854,1856,Albumen silver print from glass negative,Image (Oval): 28.3 × 20.4 cm (11 1/8 × 8 1/16 in.) Sheet: 29.8 × 23.1 cm (11 3/4 × 9 1/8 in.) Mount: 40.2 × 28.5 cm (15 13/16 × 11 1/4 in.),"Purchase, The Buddy Taub Foundation Gift, Dennis A. Roach and Jill Roach, Directors; Harris Brisbane Dick and William E. Dodge Funds; and W. Bruce and Delaney H. Lundberg Gift, 2013",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/623057,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.173.33,false,true,623058,Photographs,Photograph,Figure 56: Surprise,,,,,,Artist|Artist,,Guillaume-Benjamin-Amand Duchenne de Boulogne|Adrien Tournachon,"French, 1806–1875|French, 1825–1903",,"Duchenne de Boulogne, Guillaume-Benjamin-Amand|Tournachon, Adrien",French|French,1806 |1825,1875 |1903,"1854–56, printed 1862",1854,1856,Albumen silver print from glass negative,Image (Oval): 28.3 × 20.3 cm (11 1/8 × 8 in.) Sheet: 30 × 22.6 cm (11 13/16 × 8 7/8 in.) Mount: 40.2 × 28.5 cm (15 13/16 × 11 1/4 in.),"Purchase, The Buddy Taub Foundation Gift, Dennis A. Roach and Jill Roach, Directors; Harris Brisbane Dick and William E. Dodge Funds; and W. Bruce and Delaney H. Lundberg Gift, 2013",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/623058,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.173.34,false,true,623059,Photographs,Photograph,"Figure 57: Astonishment, stupefaction, amazement",,,,,,Artist|Artist,,Guillaume-Benjamin-Amand Duchenne de Boulogne|Adrien Tournachon,"French, 1806–1875|French, 1825–1903",,"Duchenne de Boulogne, Guillaume-Benjamin-Amand|Tournachon, Adrien",French|French,1806 |1825,1875 |1903,"1854–56, printed 1862",1854,1856,Albumen silver print from glass negative,"Image (Oval): 28.3 × 20.3 cm (11 1/8 × 8 in.) Sheet: 29.9 × 22.7 cm (11 3/4 in., 22.7 kg) Mount: 40.2 × 28.4 cm (15 13/16 × 11 3/16 in.)","Purchase, The Buddy Taub Foundation Gift, Dennis A. Roach and Jill Roach, Directors; Harris Brisbane Dick and William E. Dodge Funds; and W. Bruce and Delaney H. Lundberg Gift, 2013",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/623059,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.173.35,false,true,623060,Photographs,Photograph,Figure 60: Fright,,,,,,Artist|Artist,,Guillaume-Benjamin-Amand Duchenne de Boulogne|Adrien Tournachon,"French, 1806–1875|French, 1825–1903",,"Duchenne de Boulogne, Guillaume-Benjamin-Amand|Tournachon, Adrien",French|French,1806 |1825,1875 |1903,"1854–56, printed 1862",1854,1856,Albumen silver print from glass negative,Image (Oval): 28.3 × 20.3 cm (11 1/8 × 8 in.) Sheet: 29.8 × 23 cm (11 3/4 × 9 1/16 in.) Mount: 40.2 × 28.5 cm (15 13/16 × 11 1/4 in.),"Purchase, The Buddy Taub Foundation Gift, Dennis A. Roach and Jill Roach, Directors; Harris Brisbane Dick and William E. Dodge Funds; and W. Bruce and Delaney H. Lundberg Gift, 2013",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/623060,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.173.36,false,true,623061,Photographs,Photograph,"Figure 62: Terror, semiprofile",,,,,,Artist|Artist,,Guillaume-Benjamin-Amand Duchenne de Boulogne|Adrien Tournachon,"French, 1806–1875|French, 1825–1903",,"Duchenne de Boulogne, Guillaume-Benjamin-Amand|Tournachon, Adrien",French|French,1806 |1825,1875 |1903,"1854–56, printed 1862",1854,1856,Albumen silver print from glass negative,Image: 28.4 × 20.3 cm (11 3/16 × 8 in.) Sheet: 29.7 × 22.3 cm (11 11/16 × 8 3/4 in.) Mount: 40.3 × 28.5 cm (15 7/8 × 11 1/4 in.),"Purchase, The Buddy Taub Foundation Gift, Dennis A. Roach and Jill Roach, Directors; Harris Brisbane Dick and William E. Dodge Funds; and W. Bruce and Delaney H. Lundberg Gift, 2013",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/623061,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.173.37,false,true,623062,Photographs,Photograph,Figure 63: Expression of terror,,,,,,Artist|Artist,,Guillaume-Benjamin-Amand Duchenne de Boulogne|Adrien Tournachon,"French, 1806–1875|French, 1825–1903",,"Duchenne de Boulogne, Guillaume-Benjamin-Amand|Tournachon, Adrien",French|French,1806 |1825,1875 |1903,"1854–56, printed 1862",1854,1856,Albumen silver print from glass negative,Image (Oval): 28.2 × 20.3 cm (11 1/8 × 8 in.) Sheet: 29.5 × 22.2 cm (11 5/8 × 8 3/4 in.) Mount: 40.3 × 28.5 cm (15 7/8 × 11 1/4 in.),"Purchase, The Buddy Taub Foundation Gift, Dennis A. Roach and Jill Roach, Directors; Harris Brisbane Dick and William E. Dodge Funds; and W. Bruce and Delaney H. Lundberg Gift, 2013",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/623062,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.173.38,false,true,623063,Photographs,Photograph,"Figure 66: Head of Arrotino (the spy, the knife grinder, and so on)",,,,,,Artist|Artist,,Guillaume-Benjamin-Amand Duchenne de Boulogne|Adrien Tournachon,"French, 1806–1875|French, 1825–1903",,"Duchenne de Boulogne, Guillaume-Benjamin-Amand|Tournachon, Adrien",French|French,1806 |1825,1875 |1903,"1854–56, printed 1862",1854,1856,Albumen silver print from glass negative,Image (Oval): 28.5 × 20.4 cm (11 1/4 × 8 1/16 in.) Sheet: 30 × 22.7 cm (11 13/16 × 8 15/16 in.) Mount: 40.1 × 28.6 cm (15 13/16 × 11 1/4 in.),"Purchase, The Buddy Taub Foundation Gift, Dennis A. Roach and Jill Roach, Directors; Harris Brisbane Dick and William E. Dodge Funds; and W. Bruce and Delaney H. Lundberg Gift, 2013",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/623063,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.173.39,false,true,623064,Photographs,Photograph,Figure 70: Head of the Laocoön of Rome,,,,,,Artist|Artist,,Guillaume-Benjamin-Amand Duchenne de Boulogne|Adrien Tournachon,"French, 1806–1875|French, 1825–1903",,"Duchenne de Boulogne, Guillaume-Benjamin-Amand|Tournachon, Adrien",French|French,1806 |1825,1875 |1903,"1854–56, printed 1862",1854,1856,Albumen silver print from glass negative,Image (Oval): 28.2 × 20.3 cm (11 1/8 × 8 in.) Sheet: 29.1 × 21.4 cm (11 7/16 × 8 7/16 in.) Mount: 40.3 × 28.6 cm (15 7/8 × 11 1/4 in.),"Purchase, The Buddy Taub Foundation Gift, Dennis A. Roach and Jill Roach, Directors; Harris Brisbane Dick and William E. Dodge Funds; and W. Bruce and Delaney H. Lundberg Gift, 2013",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/623064,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.173.40,false,true,623065,Photographs,Photograph,Figure 71: Same head as in Plate 70,,,,,,Artist|Artist,,Guillaume-Benjamin-Amand Duchenne de Boulogne|Adrien Tournachon,"French, 1806–1875|French, 1825–1903",,"Duchenne de Boulogne, Guillaume-Benjamin-Amand|Tournachon, Adrien",French|French,1806 |1825,1875 |1903,"1854–56, printed 1862",1854,1856,Albumen silver print from glass negative,Image (Oval): 28.3 × 20.4 cm (11 1/8 × 8 1/16 in.) Sheet: 29.7 × 22.7 cm (11 11/16 × 8 15/16 in.) Mount: 40.2 × 28.2 cm (15 13/16 × 11 1/8 in.),"Purchase, The Buddy Taub Foundation Gift, Dennis A. Roach and Jill Roach, Directors; Harris Brisbane Dick and William E. Dodge Funds; and W. Bruce and Delaney H. Lundberg Gift, 2013",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/623065,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.173.41,false,true,623066,Photographs,Photograph,Figure 73: Head of Niobe,,,,,,Artist|Artist,,Guillaume-Benjamin-Amand Duchenne de Boulogne|Adrien Tournachon,"French, 1806–1875|French, 1825–1903",,"Duchenne de Boulogne, Guillaume-Benjamin-Amand|Tournachon, Adrien",French|French,1806 |1825,1875 |1903,"1854–56, printed 1862",1854,1856,Albumen silver print from glass negative,Image (Oval): 28.6 × 20.4 cm (11 1/4 × 8 1/16 in.) Sheet: 29.2 × 21.5 cm (11 1/2 × 8 7/16 in.) Mount: 40.1 × 28.3 cm (15 13/16 × 11 1/8 in.),"Purchase, The Buddy Taub Foundation Gift, Dennis A. Roach and Jill Roach, Directors; Harris Brisbane Dick and William E. Dodge Funds; and W. Bruce and Delaney H. Lundberg Gift, 2013",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/623066,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.173.42,false,true,623067,Photographs,Photograph,Figure 75: Nun saying her prayers,,,,,,Artist|Artist,,Guillaume-Benjamin-Amand Duchenne de Boulogne|Adrien Tournachon,"French, 1806–1875|French, 1825–1903",,"Duchenne de Boulogne, Guillaume-Benjamin-Amand|Tournachon, Adrien",French|French,1806 |1825,1875 |1903,"1854–56, printed 1862",1854,1856,Albumen silver print from glass negative,Image (Oval): 28.8 × 22 cm (11 5/16 × 8 11/16 in.) Sheet: 30.5 × 23.4 cm (12 × 9 3/16 in.) Mount: 40.2 × 28.6 cm (15 13/16 × 11 1/4 in.),"Purchase, The Buddy Taub Foundation Gift, Dennis A. Roach and Jill Roach, Directors; Harris Brisbane Dick and William E. Dodge Funds; and W. Bruce and Delaney H. Lundberg Gift, 2013",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/623067,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.173.43,false,true,623068,Photographs,Photograph,Figure 78: Scene of coquetry,,,,,,Artist|Artist,,Guillaume-Benjamin-Amand Duchenne de Boulogne|Adrien Tournachon,"French, 1806–1875|French, 1825–1903",,"Duchenne de Boulogne, Guillaume-Benjamin-Amand|Tournachon, Adrien",French|French,1806 |1825,1875 |1903,"1854–56, printed 1862",1854,1856,Albumen silver print from glass negative,Image (Oval): 28.5 × 20.3 cm (11 1/4 × 8 in.) Sheet: 30.1 × 21.9 cm (11 7/8 × 8 5/8 in.) Mount: 40.2 × 28.6 cm (15 13/16 × 11 1/4 in.),"Purchase, The Buddy Taub Foundation Gift, Dennis A. Roach and Jill Roach, Directors; Harris Brisbane Dick and William E. Dodge Funds; and W. Bruce and Delaney H. Lundberg Gift, 2013",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/623068,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.173.44,false,true,623069,Photographs,Photograph,"Figure 81: Lady Macbeth, moderate expression of cruelty",,,,,,Artist|Artist,,Guillaume-Benjamin-Amand Duchenne de Boulogne|Adrien Tournachon,"French, 1806–1875|French, 1825–1903",,"Duchenne de Boulogne, Guillaume-Benjamin-Amand|Tournachon, Adrien",French|French,1806 |1825,1875 |1903,"1854–56, printed 1862",1854,1856,Albumen silver print from glass negative,Image: 28.4 × 22.7 cm (11 3/16 × 8 15/16 in.) Sheet: 29.7 × 23.6 cm (11 11/16 × 9 5/16 in.) Mount: 40.2 × 28.6 cm (15 13/16 × 11 1/4 in.),"Purchase, The Buddy Taub Foundation Gift, Dennis A. Roach and Jill Roach, Directors; Harris Brisbane Dick and William E. Dodge Funds; and W. Bruce and Delaney H. Lundberg Gift, 2013",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/623069,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.173.45,false,true,623070,Photographs,Photograph,"Figure 82: Lady Macbeth, strong expression of cruelty",,,,,,Artist|Artist,,Guillaume-Benjamin-Amand Duchenne de Boulogne|Adrien Tournachon,"French, 1806–1875|French, 1825–1903",,"Duchenne de Boulogne, Guillaume-Benjamin-Amand|Tournachon, Adrien",French|French,1806 |1825,1875 |1903,"1854–56, printed 1862",1854,1856,Albumen silver print from glass negative,Image (Oval): 28.4 × 20.3 cm (11 3/16 × 8 in.) Sheet: 29.2 × 21.9 cm (11 1/2 × 8 5/8 in.) Mount: 40.3 × 28.1 cm (15 7/8 × 11 1/16 in.),"Purchase, The Buddy Taub Foundation Gift, Dennis A. Roach and Jill Roach, Directors; Harris Brisbane Dick and William E. Dodge Funds; and W. Bruce and Delaney H. Lundberg Gift, 2013",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/623070,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.173.46,false,true,623071,Photographs,Photograph,"Figure 83: Lady Macbeth, ferocious cruelty",,,,,,Artist|Artist,,Guillaume-Benjamin-Amand Duchenne de Boulogne|Adrien Tournachon,"French, 1806–1875|French, 1825–1903",,"Duchenne de Boulogne, Guillaume-Benjamin-Amand|Tournachon, Adrien",French|French,1806 |1825,1875 |1903,"1854–56, printed 1862",1854,1856,Albumen silver print from glass negative,Image: 28.3 × 21.4 cm (11 1/8 × 8 7/16 in.) Sheet: 30 × 22.9 cm (11 13/16 × 9 in.) Mount: 40.2 × 28.4 cm (15 13/16 × 11 3/16 in.),"Purchase, The Buddy Taub Foundation Gift, Dennis A. Roach and Jill Roach, Directors; Harris Brisbane Dick and William E. Dodge Funds; and W. Bruce and Delaney H. Lundberg Gift, 2013",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/623071,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1994.417,true,true,267087,Photographs,Photograph,[Landscape with Cottage],,,,,,Artist|Artist,,Marie-Charles-Isidore Choiselat|Stanislas Ratel,"French, 1815–1858|French, 1824–1904",,"Choiselat, Marie-Charles-Isidore & Stanislas Ratel|Ratel, Stanislas",French|French,1815 |1824,1858 |1904,1844,1844,1844,Daguerreotype,16.4 x 21.7 cm (6 7/16 x 8 9/16 in.),"Louis V. Bell Fund, 1994",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267087,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.29,false,true,283103,Photographs,Photograph,[The Pavillon de Flore and the Tuileries Gardens],,,,,,Artist|Artist,,Marie-Charles-Isidore Choiselat|Stanislas Ratel,"French, 1815–1858|French, 1824–1904",,"Choiselat, Marie-Charles-Isidore & Stanislas Ratel|Ratel, Stanislas",French|French,1815 |1824,1858 |1904,1849,1849,1849,Daguerreotype,15.2 x 18.7 cm (6 x 7 3/8 in.),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283103,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.185,false,true,286110,Photographs,Photograph,Défilé sur le Pont-Royal,,,,,,Artist|Artist,,Marie-Charles-Isidore Choiselat|Stanislas Ratel,"French, 1815–1858|French, 1824–1904",,"Choiselat, Marie-Charles-Isidore & Stanislas Ratel|Ratel, Stanislas",French|French,1815 |1824,1858 |1904,"May 1, 1844",1844,1844,Daguerreotype,Image: 6 1/16 × 4 7/16 in. (15.4 × 11.3 cm) Frame: 8 3/4 × 7 1/16 in. (22.3 × 18 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286110,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.954,false,true,629811,Photographs,Photograph,"[Pompeii, Pompey's Lane (or Street of Pompeii), Tomb Monument of Mamia]",,,,,,Artist|Artist,,Firmin-Eugène Le Dien|Gustave Le Gray,"French, 1817–1865|French, 1820–1884",,"Le Dien, Firmin-Eugène|Le Gray, Gustave",French|French,1817 |1820,1865 |1884,ca. 1853,1848,1858,Salted paper print from a waxed paper negative,Mount: 13 13/16 in. × 19 3/16 in. (35.1 × 48.8 cm) Image: 8 15/16 × 12 7/16 in. (22.7 × 31.6 cm),"Purchase, Mr. and Mrs. John A. Moran Gift, in memory of Louise Chisholm Moran, 2013",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/629811,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.955,false,true,629831,Photographs,Photograph,"Amalfi, Cathedral",,,,,,Artist|Artist,,Firmin-Eugène Le Dien|Gustave Le Gray,"French, 1817–1865|French, 1820–1884",,"Le Dien, Firmin-Eugène|Le Gray, Gustave",French|French,1817 |1820,1865 |1884,1853,1853,1853,Salted paper print from a waxed paper negative,Mount: 19 5/16 in. × 13 13/16 in. (49.1 × 35.1 cm) Image: 13 1/8 × 9 5/16 in. (33.3 × 23.7 cm),"Purchase, Alfred Stieglitz Society Gifts, 2013",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/629831,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -39.108,false,true,268645,Photographs,Photograph,Maison Élevée Rue St. Georges par M. Renaud,,,,,,Artist|Artist,From a daguerreotype plate by,Armand-Hippolyte-Louis Fizeau|Noël-Marie-Paymal Lerebours,"French, 1819–1896|French, 1807–1873",,"Fizeau, Armand-Hippolyte-Louis|Lerebours, Noël-Marie-Paymal",French|French,1819 |1807,1896 |1873,ca. 1841,1839,1843,Photogravure,,"Gift of the Museum of Modern Art, 1939",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268645,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -47.152,false,true,269123,Photographs,Photograph,Excursions Daguerriennes. Vues et monuments les plus remarquables du globe,,,,,,Artist|Artist,,Armand-Hippolyte-Louis Fizeau|Noël-Marie-Paymal Lerebours,"French, 1819–1896|French, 1807–1873",,"Fizeau, Armand-Hippolyte-Louis|Lerebours, Noël-Marie-Paymal",French|French,1819 |1807,1896 |1873,1840s,1840,1849,"Etchings, aquatints, lithographs, and photogravures after daguerreotypes",,"David Hunter McAlpin Fund, 1947",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/269123,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2012.140,false,true,302241,Photographs,Photograph,"Electro–Physiologie, Figure 64",,,,,,Artist|Artist,,Adrien Tournachon|Guillaume-Benjamin-Amand Duchenne de Boulogne,"French, 1825–1903|French, 1806–1875",,"Tournachon, Adrien|Duchenne de Boulogne, Guillaume-Benjamin-Amand",French|French,1825 |1806,1903 |1875,"1854–56, printed 1862",1854,1856,Albumen silver print from glass negative,Image: 29.8 x 22.3 cm (11 3/4 x 8 3/4 in.) Mount: 40.1 x 28.5 cm (15 13/16 x 11 1/4 in.),"Purchase, The Buddy Taub Foundation Gift, Dennis A. Roach and Jill Roach, Directors, 2012",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/302241,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.558,false,true,286696,Photographs,Photograph,Marine Terrace,,,,,,Artist|Artist,,Charles Victor Hugo|Auguste Vacquerie,"French, 1826–1871|French, 1819–1855",,"Hugo, Charles Victor|Vacquerie, Auguste",French|French,1826 |1819,1871 |1855,"October 9, 1855",1855,1855,Salted paper print from glass negative,Image: 6.4 x 9.5 cm (2 1/2 x 3 3/4 in.) Mount: 5 13/16 × 8 11/16 in. (14.8 × 22.1 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286696,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.819,false,true,286434,Photographs,Photograph,Victor Hugo,,,,,,Artist|Artist,,Charles Victor Hugo|Auguste Vacquerie,"French, 1826–1871|French, 1819–1855",,"Hugo, Charles Victor|Vacquerie, Auguste",French|French,1826 |1819,1871 |1855,1852,1852,1852,Salted paper print from paper negative,Image: 3 15/16 in. × 3 in. (10 × 7.6 cm),"Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/286434,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1987.1054,false,true,265094,Photographs,Photograph,Chronophotograph,,,,,,Artist|Artist,,Etienne-Jules Marey|Charles Fremont,"French, 1830–1904|French, 1855–1930",,"Marey, Etienne-Jules|Fremont, Charles",French|French,1830 |1855,1904 |1930,1894,1894,1894,Gelatin silver print from glass negative,16.3 x 20.2 cm (6 7/16 x 7 15/16 in.),"Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel and Rogers Fund, 1987",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/265094,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1993.219,false,true,266895,Photographs,Photograph,Nebuleuse de la Lyre,,,,,,Artist|Artist,,Paul Henry|Prosper Henry,"French, 1848–1905|French, 1849–1903",,"Henry, Paul|Henry, Prosper",French|French,1848 |1849,1905 |1903,ca. 1885,1883,1887,Albumen silver print from glass negative,Image: 22.8 x 16.4 cm. (9 x 6 7/16 in.),"Gift of Arnold H. Crane, by exchange, and Purchase, The Horace W. Goldsmith Foundation Gift through Joyce and Robert Menschel, 1993",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/266895,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.124,false,true,283255,Photographs,Photograph,"A Section of the Constellation Cygnus (August 13, 1885)",,,,,,Artist|Artist,,Paul Henry|Prosper Henry,"French, 1848–1905|French, 1849–1903",,"Henry, Paul|Henry, Prosper",French|French,1848 |1849,1905 |1903,1885,1885,1885,Albumen silver print from glass negative,25.8 x 21.2cm (10 3/16 x 8 3/8in.) Mount: 31.4 × 23.8 cm (12 3/8 × 9 3/8 in.),"Gilman Collection, Purchase, Robert Rosenkranz Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283255,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2012.59,false,true,299470,Photographs,Photograph,[Male Musculature Study],,,,,,Artist|Artist,,Albert Londe|Paul Marie Louis Pierre Richer,"French, 1858–1917|French, 1849–1933",,"Londe, Albert|Richer, Paul Marie Louis Pierre",French|French,1858 |1849,1917 |1933,ca. 1890,1885,1895,Albumen silver print,Image: 14.9 x 9.6 cm (5 7/8 x 3 3/4 in.) Mount: 14.9 x 9.9 cm (5 7/8 x 3 7/8 in.),"Gift of Charles Isaacs and Carol Nigro, 2012",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/299470,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.34,false,true,283107,Photographs,Photograph,The Ramparts of Carcassonne,,,,,,Artist|Artist,,Gustave Le Gray|Auguste Mestral,"French, 1820–1884|French, Rans 1812–1884 Rans",,"Le Gray, Gustave|Mestral, Auguste",French|French,1820 |1812,1884 |1884,1851,1851,1851,Salted paper print from waxed paper negative,Image: 23.5 x 33.2 cm (9 1/4 x 13 1/16 in.) Mount: 31.2 x 45.2 cm (12 5/16 x 17 13/16 in.),"Gilman Collection, Purchase, Harriette and Noel Levine Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283107,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.799 (1-30),false,true,285904,Photographs,Album,Roma,,,,,,Artist|Publisher,,Eugène Constant|Edouard Mauche et Cie,"French, active Italy, 1848–55",,"Constant, Eugène|Edouard Mauche et Cie",French|French,1848,1855,1848–52,1848,1852,Salted paper print from glass negative,Prints approx. 8 3/4 x 11 1/4,"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Albums,,http://www.metmuseum.org/art/collection/search/285904,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1174.1–.16,false,true,285641,Photographs,Album,The Evacuation of Fort Sumter,,,,,,Publisher|Publisher,,Osborn's Gallery|Edward Anthony,"American, active Charleston, South Carolina, 1850s–1860s|American, 1818–1888",,"Osborn's Gallery|Anthony, Edward",American|American,1850 |1818,1869 |1888,April 1861,1861,1861,Albumen silver prints from glass negatives,Album: 12.6 × 9.4 × 2.5 cm (4 15/16 × 3 11/16 × 1 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Albums,,http://www.metmuseum.org/art/collection/search/285641,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1999.89,false,true,282753,Photographs,Album; Photographs; Locket; Pendant,[Miniature Wedding Album of General Tom Thumb and Lavinia Warren],,,,,,Photography Studio|Person in Photograph|Person in Photograph,,Mathew B. Brady|General Tom Thumb|Lavinia Warren,"American, born Ireland, 1823?–1896 New York|American, 1838–1883|American, 1841–1919",,"Brady, Mathew B.|Thumb, Tom|Warren, Lavinia",American|American,1823 |1838 |1841,1896 |1883 |1919,ca. 1863,1862,1864,"Albumen silver prints, brass","Overall: 1 1/16 × 13/16 × 3/8 in. (2.7 × 2 × 1 cm) Images: 7/8 × 13/16 in. (2.3 × 2 cm), each","Joyce F. Menschel Photography Library Fund, 1999",,,,,,,,,,,,Albums|Jewelry,,http://www.metmuseum.org/art/collection/search/282753,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2011.479,false,true,299298,Photographs,Postcard,"Animas Canyon, Colorado",,,,,,Publisher|Artist,,Detroit Publishing Company|William Henry Jackson,"American|American, 1843–1942",,"Detroit Publishing Company|Jackson, William Henry",American|American,1843,1942,1906,1906,1906,Chromolithograph,Image: 8.1 x 12.3 cm (3 3/16 x 4 13/16 in.) 8.9 x 14 cm (3 1/2 x 5 1/2 in.) Frame: 27.9 x 35.6 cm (11 x 14 in.),"Funds from various donors, 2011",,,,,,,,,,,,Prints,,http://www.metmuseum.org/art/collection/search/299298,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2011.476,false,true,299309,Photographs,Photograph,"Slave Pen, Alexandria, Virginia",,,,,,Photography Studio|Publisher,,Brady & Co.|E. & H. T. Anthony,"American, active 1840s–1880s|American",,Brady & Co.|E. & H. T. Anthony,American|American,1840,1889,1862,1862,1862,Albumen silver print from glass negative,Image: 8 x 16 cm (3 1/8 x 6 5/16 in.) Mount: 8.4 x 17.5 cm (3 5/16 x 6 7/8 in.),"The Horace W. Goldsmith Foundation Fund, through Joyce and Robert Menschel, 2011",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/299309,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1982.1065.14,false,true,263363,Photographs,Photograph,Nude,,,,,,Printer|Artist,,Morris & Bendien|Charles W. Gilhousen,"American, New York|American, 1867–1929",,"Morris & Bendien|Gilhousen, Charles W.",American|American,1867,1929,1917,1917,1917,Gelatin silver print,Image: 9 3/16 × 6 11/16 in. (23.4 × 17 cm) Mount: 9 9/16 in. × 7 5/16 in. (24.3 × 18.5 cm),"Gift of Rita McNamara Pleet, 1982",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/263363,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.457.916,false,true,291117,Photographs,Stereograph,"[Stereographic View of Statue of Simon Bolivar by R. de la Cora, Central Park, New York]",,,,,,Publisher|Publisher,,Edward Anthony|Henry T. Anthony,"American, 1818–1888|American, 1814–1884",,"Anthony, Edward|Anthony, Henry T.",American|American,1818 |1814,1888 |1884,1884–98,1884,1898,Gelatin silver print from glass negative,"Image: 8.3 x 14.5 cm (3 1/4 x 5 11/16 in.), overall Mount: 8.8 x 17.7 cm (3 7/16 x 6 15/16 in.)","Herbert Mitchell Collection, 2007",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/291117,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.100,false,true,283210,Photographs,Photograph,Sky Chief (Tirawahut Resaru),,,,,,Artist|Printer,Attributed to,Edric L. Eaton|William Henry Jackson,"American, 1836–1890|American, 1843–1942",,"Eaton, Edric L.|Jackson, William Henry",American|American,1836 |1843,1890 |1942,ca. 1867,1865,1869,Albumen silver print from glass negative,Image: 18.6 × 13.2 cm (7 5/16 × 5 3/16 in.) Mount: 35.4 × 27.8 cm (13 15/16 × 10 15/16 in.),"Gilman Collection, Purchase, Sam Salz Foundation Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283210,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2012.13,false,true,299482,Photographs,Medal; Button,[Presidential Campaign Medal with Portraits of Abraham Lincoln and Andrew Johnson],,,,,,Maker|Photography Studio|Artist,After,Unknown|Brady & Co.|Thomas Le Mere,"American, active 1840s–1880s|American, active 1860s",,"Unknown|Brady & Co.|Le Mere, Thomas",American|American,1840 |1860,1889 |1869,1864,1864,1864,Tintype,"Image: 1.6 cm (5/8 in.), diameter Overall: 2.5 cm (1 in.), diameter","Purchase, The Overbrook Foundation Gift, 2012",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/299482,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1116,false,true,285876,Photographs,Photograph,Abraham Lincoln,,,,,,Artist|Photography Studio,,Anthony Berger|Brady & Co.,"American, active 1860s|American, active 1840s–1880s",,"Berger, Anthony|Brady & Co.",American|American,1860 |1840,1869 |1889,"February 9, 1864",1864,1864,Albumen silver print from glass negative,Image: 41 × 24.2 cm (16 1/8 × 9 1/2 in.) Mount: 50.8 × 34.4 cm (20 × 13 9/16 in.),"Gilman Collection, Purchase, The Horace W. Goldsmith Foundation Gift, through Joyce and Robert Menschel, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/285876,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1982.1182.2017–.2034,false,true,288317,Photographs,Stereographs,[Group of 18 Stereograph Views of the 1884/1885 New Orleans Centennial International Exhibition],,,,,,Publisher|Artist|Artist,,Centennial Photographic Company|Unknown|Edward Livingston Wilson,"American, founded 1876|American|American, 1838–1903",,"Centennial Photographic Company|Unknown|Wilson, Edward Livingston",American|American,1876 |1838,1903,1850s–1910s,1850,1919,Albumen silver prints,Mounts: 10.8 x 17.8 cm (4 1/4 x 7 in.),"Gift of Weston J. Naef, in memory of Kathleen W. Naef and Weston J. Naef Sr., 1982",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/288317,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.182,false,true,267971,Photographs,Photograph,Yorktown Landing,,,,,,Artist|Former Attribution,Formerly attributed to,James F. Gibson|Mathew B. Brady,"American, born 1828|American, born Ireland, 1823?–1896 New York",,"Gibson, James F.|Brady, Mathew B.",American|American,1828 |1823,1928 |1896,1862,1862,1862,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267971,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.50,false,true,268242,Photographs,Photograph,Water Battery at Yorktown,,,,,,Former Attribution|Artist,Formerly attributed to,Mathew B. Brady|James F. Gibson,"American, born Ireland, 1823?–1896 New York|American, born 1828",,"Brady, Mathew B.|Gibson, James F.",American|American,1823 |1828,1896 |1928,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268242,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.252,false,true,268048,Photographs,Photograph,"Water Battery, Yorktown",,,,,,Former Attribution|Artist,Formerly attributed to,Mathew B. Brady|James F. Gibson,"American, born Ireland, 1823?–1896 New York|American, born 1828",,"Brady, Mathew B.|Gibson, James F.",American|American,1823 |1828,1896 |1928,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268048,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.418,false,true,268232,Photographs,Photograph,"Confederate Fortifications, Yorktown, Virginia",,,,,,Former Attribution|Artist,Formerly attributed to,Mathew B. Brady|James F. Gibson,"American, born Ireland, 1823?–1896 New York|American, born 1828",,"Brady, Mathew B.|Gibson, James F.",American|American,1823 |1828,1896 |1928,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268232,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.52,false,true,268244,Photographs,Photograph,Charleston,,,,,,Artist|Former Attribution,Mathew B. Brady,George N. Barnard|Mathew B. Brady,"American, 1819–1902|American, born Ireland, 1823?–1896 New York",,"Barnard, George N.|Brady, Mathew B.",American|American,1819 |1823,1902 |1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268244,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.40,false,true,268212,Photographs,Photograph,"Laborers at Quartermaster's Wharf, Alexandria, Virginia",,,,,,Former Attribution|Artist,Formerly attributed to|Attributed to,Mathew B. Brady|Andrew Joseph Russell,"American, born Ireland, 1823?–1896 New York|American, 1830–1902",,"Brady, Mathew B.|Russell, Andrew Joseph",American|American,1823 |1830,1896 |1902,1863–65,1863,1865,Albumen silver print from glass negative,Image: 13.2 × 20.2 cm (5 3/16 × 7 15/16 in.),"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268212,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.190,false,true,267980,Photographs,Photograph,Cannon,,,,,,Former Attribution|Artist,Formerly attributed to,Mathew B. Brady|Andrew Joseph Russell,"American, born Ireland, 1823?–1896 New York|American, 1830–1902",,"Brady, Mathew B.|Russell, Andrew Joseph",American|American,1823 |1830,1896 |1902,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267980,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.659.560,false,true,291992,Photographs,Carte-de-visite,[Jervis McEntee],,,,,,Person in Photograph|Artist,,Jervis McEntee|Austin Augustus Turner,"American, Rondout, New York 1828–1891 Rondout, New York|American, ca. 1813–1866",,"McEntee, Jervis|Turner, Austin Augustus",American|American,1828 |1810,1891 |1866,1860s,1860,1869,Albumen silver print from glass negative,Image: 9.4 x 5.1 cm (3 11/16 x 2 in.) Mount: 10.2 x 6.3 cm (4 x 2 1/2 in.),"The Albert Ten Eyck Gardner Collection, Gift of the Centennial Committee, 1970",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/291992,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.281,false,true,268080,Photographs,Photograph,"Confederate Earthworks, Centreville, Virginia",,,,,,Artist|Artist|Former Attribution,Formerly attributed to,George N. Barnard|James F. Gibson|Mathew B. Brady,"American, 1819–1902|American, born 1828|American, born Ireland, 1823?–1896 New York",,"Barnard, George N.|Gibson, James F.|Brady, Mathew B.",American|American,1819 |1828 |1823,1902 |1928 |1896,1862,1862,1862,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268080,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.68,false,true,268261,Photographs,Photograph,[A Union Station on the James River Established for Extracting Gunpowder from Confederate Torpedoes],,,,,,Former Attribution|Artist|Former Attribution,Attributed to|Formerly attributed to,Andrew Joseph Russell|Egbert Guy Fowx|Mathew B. Brady,"American, 1830–1902|American, born 1821|American, born Ireland, 1823?–1896 New York",,"Russell, Andrew Joseph|Fowx, Egbert Guy|Brady, Mathew B.",American|American,1830 |1821 |1823,1902 |1896,1864,1864,1864,Albumen silver print from glass negative,12.9 x 19.6 cm (5 1/16 x 7 11/16 in.),"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268261,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.69,false,true,268262,Photographs,Photograph,[A Union Station on the James River Established for Extracting Gunpowder from Confederate Torpedoes],,,,,,Former Attribution|Artist|Former Attribution,Attributed to|Formerly attributed to,Andrew Joseph Russell|Egbert Guy Fowx|Mathew B. Brady,"American, 1830–1902|American, born 1821|American, born Ireland, 1823?–1896 New York",,"Russell, Andrew Joseph|Fowx, Egbert Guy|Brady, Mathew B.",American|American,1830 |1821 |1823,1902 |1896,1864,1864,1864,Albumen silver print from glass negative,Image: 13.3 x 20.6 cm (5 1/4 x 8 1/8 in.),"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268262,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.172,false,true,267960,Photographs,Photograph,Pontoon Bridge,,,,,,Former Attribution|Artist|Former Attribution,Formerly attributed to,Andrew Joseph Russell|Egbert Guy Fowx|Mathew B. Brady,"American, 1830–1902|American, born 1821|American, born Ireland, 1823?–1896 New York",,"Russell, Andrew Joseph|Fowx, Egbert Guy|Brady, Mathew B.",American|American,1830 |1821 |1823,1902 |1896,1861–65,1861,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267960,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.253,false,true,268049,Photographs,Photograph,Dutch Gap Canal,,,,,,Artist|Artist|Former Attribution,Formerly attributed to,Andrew Joseph Russell|Egbert Guy Fowx|Mathew B. Brady,"American, 1830–1902|American, born 1821|American, born Ireland, 1823?–1896 New York",,"Russell, Andrew Joseph|Fowx, Egbert Guy|Brady, Mathew B.",American|American,1830 |1821 |1823,1902 |1896,1865,1865,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268049,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.254,false,true,268050,Photographs,Photograph,Dutch Gap Canal,,,,,,Artist|Artist|Former Attribution,Formerly attributed to,Andrew Joseph Russell|Egbert Guy Fowx|Mathew B. Brady,"American, 1830–1902|American, born 1821|American, born Ireland, 1823?–1896 New York",,"Russell, Andrew Joseph|Fowx, Egbert Guy|Brady, Mathew B.",American|American,1830 |1821 |1823,1902 |1896,1865,1865,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268050,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.347,false,true,268153,Photographs,Photograph,"Butler's Lookout Tower, Opposite Dutch Gap",,,,,,Artist|Artist|Former Attribution,Mathew B. Brady,Andrew Joseph Russell|Egbert Guy Fowx|Mathew B. Brady,"American, 1830–1902|American, born 1821|American, born Ireland, 1823?–1896 New York",,"Russell, Andrew Joseph|Fowx, Egbert Guy|Brady, Mathew B.",American|American,1830 |1821 |1823,1902 |1896,1865,1865,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268153,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.32,false,true,268123,Photographs,Photograph,Dutch Gap Canal,,,,,,Artist|Artist|Former Attribution,Formerly attributed to,Egbert Guy Fowx|Andrew Joseph Russell|Mathew B. Brady,"American, born 1821|American, 1830–1902|American, born Ireland, 1823?–1896 New York",,"Fowx, Egbert Guy|Russell, Andrew Joseph|Brady, Mathew B.",American|American,1821 |1830 |1823,1902 |1896,1865,1865,1865,Albumen silver print from glass negative,16.4 x 22.5 cm (6 7/16 x 8 7/8 in. ),"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268123,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.74,false,true,268268,Photographs,Photograph,"Dutch Gap Canal, James River",,,,,,Artist|Artist|Former Attribution,Formerly attributed to,Egbert Guy Fowx|Andrew Joseph Russell|Mathew B. Brady,"American, born 1821|American, 1830–1902|American, born Ireland, 1823?–1896 New York",,"Fowx, Egbert Guy|Russell, Andrew Joseph|Brady, Mathew B.",American|American,1821 |1830 |1823,1902 |1896,1864,1864,1864,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268268,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.234,false,true,268028,Photographs,Photograph,Dutch Gap Canal,,,,,,Artist|Artist|Former Attribution,Formerly attributed to,Egbert Guy Fowx|Andrew Joseph Russell|Mathew B. Brady,"American, born 1821|American, 1830–1902|American, born Ireland, 1823?–1896 New York",,"Fowx, Egbert Guy|Russell, Andrew Joseph|Brady, Mathew B.",American|American,1821 |1830 |1823,1902 |1896,1865,1865,1865,Albumen silver print from glass negative,,"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268028,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.65.174,false,true,267962,Photographs,Photograph,"Procession of Troops and Civilians on Way to Dedication of Soldiers' National Cemetery, Gettysburg, Pennsylvania",,,,,,Artist|Artist|Former Attribution,Formerly attributed to,Isaac G. Tyson|Charles J. Tyson|Mathew B. Brady,"American, 1833–1913|American, 1838–1906|American, born Ireland, 1823?–1896 New York",and,"Tyson, Isaac G.|Tyson, Charles J.|Brady, Mathew B.",American|American,1833 |1838 |1823,1913 |1906 |1896,"November 19, 1863",1863,1863,Albumen silver print from glass negative,Image: 17.6 × 20.8 cm (6 15/16 × 8 3/16 in.),"Harris Brisbane Dick Fund, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267962,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.288,false,true,283251,Photographs,Photograph,[Portrait of F. Holland Day with Male Nude],,,,,,Person in Photograph|Artist,,F. Holland Day|Clarence H. White,"American, Norwood, Massachusetts 1864–1933 Norwood, Massachusetts|American, 1871–1925",,"Day, F. Holland|White, Clarence H.",American|American,1864 |1871,1933 |1925,1902,1902,1902,Platinum print,Image: 24.2 x 18.8 cm (9 1/2 x 7 3/8 in.),"Gilman Collection, Purchase, Harriette and Noel Levine Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283251,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.14.8,false,true,268361,Photographs,Photograph,[Students from the Emerson School for Girls],,,,,,Artist|Photography Studio,,Albert Sands Southworth|Southworth and Hawes,"American, West Fairlee, Vermont 1811–1894 Charlestown, Massachusetts|American, active 1843–1863",,"Southworth, Albert Sands|Southworth and Hawes",American|American,1811 |1843,1894 |1863,ca. 1850,1848,1852,Daguerreotype,21.6 x 16.5 cm (8 1/2 x 6 1/2 in.),"Gift of I. N. Phelps Stokes, Edward S. Hawes, Alice Mary Hawes, and Marion Augusta Hawes, 1937",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/268361,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.79,false,true,283178,Photographs,Photograph,[Albert Sands Southworth],,,,,,Artist|Photography Studio,,Albert Sands Southworth|Southworth and Hawes,"American, West Fairlee, Vermont 1811–1894 Charlestown, Massachusetts|American, active 1843–1863",,"Southworth, Albert Sands|Southworth and Hawes",American|American,1811 |1843,1894 |1863,ca. 1845–50,1845,1850,Daguerreotype,"Overall: 28.3 × 23.4 cm (11 1/8 × 9 3/16 in.) Image: 11.8 × 8.5 cm (4 5/8 in., 8.5 cm); visible","Gilman Collection, Gift of The Howard Gilman Foundation, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283178,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.89,false,true,283191,Photographs,Photograph,Abraham Lincoln,,,,,,Artist|Person in Photograph,,William Marsh|Abraham Lincoln,"American, active Springfield, Illinois, 1850s–1860s|American, Hardin County, Kentucky 1809–1865 Washington, D.C.",,"Marsh, William|Lincoln, Abraham",American|American,1850 |1809,1869 |1865,"May 20, 1860",1860,1860,Salted paper print from glass negative,Image: 19.9 x 14.5 cm (7 13/16 x 5 11/16 in.),"Gilman Collection, Purchase, Joyce F. Menschel Gift, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/283191,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1174.1,false,true,302664,Photographs,Carte-de-visite,"The Evacuation of Fort Sumter, April 1861",,,,,,Publisher|Artist,,Osborn's Gallery|J. M. Osborn,"American, active Charleston, South Carolina, 1850s–1860s|American, active Charleston, South Carolina, 1850s–1860s",,"Osborn's Gallery|Osborn, J. M.",American|American,1850 |1850,1869 |1869,April 1861,1861,1861,Albumen silver print from glass negative,Image: 1 15/16 × 3 1/8 in. (5 × 7.9 cm) Mount: 3 3/8 in. × 4 3/4 in. (8.5 × 12 cm),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/302664,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1174.2,false,true,302665,Photographs,Carte-de-visite,"The Evacuation of Fort Sumter, April 1861",,,,,,Publisher|Artist,,Osborn's Gallery|J. M. Osborn,"American, active Charleston, South Carolina, 1850s–1860s|American, active Charleston, South Carolina, 1850s–1860s",,"Osborn's Gallery|Osborn, J. M.",American|American,1850 |1850,1869 |1869,April 1861,1861,1861,Albumen silver print from glass negative,Image: 1 15/16 × 3 1/8 in. (5 × 7.9 cm) Mount: 3 3/8 in. × 4 3/4 in. (8.5 × 12 cm),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/302665,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1174.15,false,true,302678,Photographs,Carte-de-visite,"Salient with North-west Casemates, Fort Sumter",,,,,,Publisher|Artist,,Osborn's Gallery|J. M. Osborn,"American, active Charleston, South Carolina, 1850s–1860s|American, active Charleston, South Carolina, 1850s–1860s",,"Osborn's Gallery|Osborn, J. M.",American|American,1850 |1850,1869 |1869,April 1861,1861,1861,Albumen silver print from glass negative,Image: 6 × 7.3 cm (2 3/8 × 2 7/8 in.) Mount: 8.5 × 12 cm (3 3/8 × 4 3/4 in.),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/302678,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.100.1174.16,false,true,302679,Photographs,Carte-de-visite,"The Evacuation of Fort Sumter, April 1861",,,,,,Publisher|Artist,,Osborn's Gallery|J. M. Osborn,"American, active Charleston, South Carolina, 1850s–1860s|American, active Charleston, South Carolina, 1850s–1860s",,"Osborn's Gallery|Osborn, J. M.",American|American,1850 |1850,1869 |1869,April 1861,1861,1861,Albumen silver print from glass negative,Image: 1 15/16 × 3 1/4 in. (5 × 8.2 cm) Mount: 3 3/8 in. × 4 3/4 in. (8.5 × 12 cm),"Gilman Collection, Museum Purchase, 2005",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/302679,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.400.12,false,true,291765,Photographs,Daguerreotype,[Young Woman Wearing Lace Collar and Brooch],,,,,,Photography Studio|Artist|Artist,Attributed to|Attributed to|Attributed to,Southworth and Hawes|Albert Sands Southworth|Josiah Johnson Hawes,"American, active 1843–1863|American, West Fairlee, Vermont 1811–1894 Charlestown, Massachusetts|American, Wayland, Massachusetts 1808–1901 Crawford Notch, New Hampshire",,"Southworth and Hawes|Southworth, Albert Sands|Hawes, Josiah Johnson",American|American,1843 |1811 |1808,1863 |1894 |1901,1850s,1850,1859,Daguerreotype,Image: 8.9 x 6.6 cm (3 1/2 x 2 5/8 in.) Plate: 10.6 x 8.1 cm (4 3/16 x 3 3/16 in.) Case: 1.9 x 11.7 x 9.4 cm (3/4 x 4 5/8 x 3 11/16 in.),"Bequest of Herbert Mitchell, 2008",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/291765,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.400.15,false,true,291768,Photographs,Daguerreotype,[Young Man in Three-piece Suit and Bow Tie],,,,,,Photography Studio|Artist|Artist,Attributed to|Attributed to,Southworth and Hawes|Albert Sands Southworth|Josiah Johnson Hawes,"American, active 1843–1863|American, West Fairlee, Vermont 1811–1894 Charlestown, Massachusetts|American, Wayland, Massachusetts 1808–1901 Crawford Notch, New Hampshire",,"Southworth and Hawes|Southworth, Albert Sands|Hawes, Josiah Johnson",American|American,1843 |1811 |1808,1863 |1894 |1901,1850s,1850,1859,Daguerreotype,Image: 9.9 x 7.5 cm (3 7/8 x 2 15/16 in.) Plate: 10.8 x 8.3 cm (4 1/4 x 3 1/4 in.) Case: 1.9 x 11.7 x 9.5 cm (3/4 x 4 5/8 x 3 3/4 in.),"Bequest of Herbert Mitchell, 2008",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/291768,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.1189.1–.101,false,true,264895,Photographs,Album,Ambroise Bernard Album,,,,,,Artist|Artist,Attributed to,Carleton E. Watkins|Carleton E. Watkins,"American, 1829–1916|American, 1829–1916",,"Watkins, Carleton E.|Watkins, Carleton E.",American|American,1829 |1829,1916 |1916,1870s,1870,1879,Albumen silver prints,43.5 x 37.5 cm (17 1/8 x 14 3/4 in.),"Gift of Carole and Irwin Lainoff, Ruth P. Lasser and Joseph R. Lasser, Mr. and Mrs. John T. Marvin, Martin E. and Joan Messinger, Richard L. Yett and Sheri and Paul Siegel, 1986",,,,,,,,,,,,Albums,,http://www.metmuseum.org/art/collection/search/264895,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1980.1056.3,false,true,262762,Photographs,Stereograph,"[Broadway, New York City, in the rain]",,,,,,Artist|Artist,,Henry T. Anthony|Edward Anthony,"American, 1814–1884|American, 1818–1888",,"Anthony, Henry T.|Anthony, Edward",American|American,1814 |1818,1884 |1888,ca. 1860s,1858,1862,Albumen silver print,,"Warner Communications Inc. Purchase Fund, 1980",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/262762,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1981.1229.19,false,true,263167,Photographs,Photograph,The Moon,,,,,,Artist|Artist,,John Adams Whipple|James Wallace Black,"American, 1822–1891|American, 1825–1896",,"Whipple, John Adams|Black, James Wallace",American|American,1822 |1825,1891 |1896,1857–60,1857,1860,Salted paper print from glass negative,21 x 15.7 cm (8 1/4 x 6 3/16 in. ),"Robert O. Dougan Collection, Gift of Warner Communications Inc., 1981",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/263167,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1981.1229.55,false,true,263204,Photographs,Photograph,The Moon,,,,,,Artist|Artist,,John Adams Whipple|James Wallace Black,"American, 1822–1891|American, 1825–1896",,"Whipple, John Adams|Black, James Wallace",American|American,1822 |1825,1891 |1896,1857–60,1857,1860,Salted paper print from glass negative,21 x 16.2 cm (8 1/4 x 6 3/8 in. ),"Robert O. Dougan Collection, Gift of Warner Communications Inc., 1981",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/263204,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.43.391,false,true,267805,Photographs,Photograph,Torso,,,,,,Artist|Artist,,Clarence H. White|Alfred Stieglitz,"American, 1871–1925|American, Hoboken, New Jersey 1864–1946 New York",,"White, Clarence H.|Stieglitz, Alfred",American|American,1871 |1864,1925 |1946,"1907, printed 1907–9",1907,1907,Platinum print,24.0 x 18.9 cm. (9 7/16 x 7 7/16 in.),"Alfred Stieglitz Collection, 1933",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/267805,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -17.190.386,false,true,464366,Medieval Art,Statuette,Mourner,French,,,,,Artist|Artist,,Étienne Bobillet|Paul Mosselman,"Franco-Netherlandish, active Bourges, 1453|Franco-Netherlandish, active Bourges, 1453",,Bobillet Étienne|Mosselman Paul,Franco-Netherlandish|Franco-Netherlandish,1453 |1453,1453 |1453,ca. 1453,1453,1453,Alabaster,Overall: 15 3/16 x 5 5/16 x 3 7/8 in. (38.6 x 13.5 x 9.8 cm),"Gift of J. Pierpont Morgan, 1917",,,,,,,,,,,,Sculpture-Stone,,http://www.metmuseum.org/art/collection/search/464366,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -27.18.1,false,true,466663,Medieval Art,sculpture,Griffin and Other Monster,Spanish,,,,,Artist,,Gil de Siloe,"Spanish, active 1475–1505",,"Siloe, Gil de",Spanish,1475,1505,15th century,1400,1499,Alabaster,Overall: 18 5/16 x 9 7/8 x 9 1/4 in. (46.5 x 25.1 x 23.5 cm),"Rogers Fund, 1927",,,,,,,,,,,,Sculpture,,http://www.metmuseum.org/art/collection/search/466663,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -30.4.227,false,true,479678,Medieval Art,Facsimile,"Facsimile of the Apse Painting in Tomb 25, Bagawat Necropolis, Kharga Oasis",Egyptian,,,,,Artist,,Charles K. Wilkinson,,,"Wilkinson, Charles K.",,1897,1986,A.D. 2nd century or later,100,199,Tempera on paper,Overall: 26 3/4 x 18 7/8 in. (67.9 x 47.9 cm) Framed: 27 7/8 x 20 7/16 x 7/8 in. (70.8 x 51.9 x 2.2 cm),"Rogers Fund, 1930",,,,,,,,,,,,Reproductions-Paintings,,http://www.metmuseum.org/art/collection/search/479678,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -30.4.229,false,true,479680,Medieval Art,Facsimile,"Facsimile of Painting in the Chapel of Peace, Bagawat Necropolis, Kharga Oasis",Egyptian,,,,,Artist,,Charles K. Wilkinson,,,"Wilkinson, Charles K.",,1897,1986,A.D. 2nd–6th century,100,599,Tempera on paper,Overall: 22 1/16 x 13 3/4 in. (56 x 34.9 cm) Framed: 23 1/4 x 15 1/8 x 7/8 in. (59.1 x 38.4 x 2.2 cm),"Rogers Fund, 1930",,,,,,,,,,,,Reproductions-Paintings,,http://www.metmuseum.org/art/collection/search/479680,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -48.154.1,false,true,468257,Medieval Art,Relief,Holy Family,South German,,,,,Artist,,Niclaus Weckmann,1481–1528,,WECKMANN NICLAUS,,1481,1528,ca. 1500,1500,1500,Limewood with traces of paint and gilding,Overall: 31 7/8 x 19 11/16 x 7 3/4 in. (81 x 50 x 19.7 cm),"Gift of Alastair Bradley Martin, 1948",,,,,,,,,,,,Sculpture-Wood,,http://www.metmuseum.org/art/collection/search/468257,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"06.163a, b",false,true,462887,Medieval Art,Statuette,Female Saint,South Netherlandish,,,,,Artist,,Jan van Steffesweert,"Maastricht, ca. 1460–1531",,"van Steffesweert, Jan",,1460,1531,ca. 1520,1520,1520,Oak with traces of polychromy,Overall: 32 1/4 x 11 1/16 x 9 7/8 in. (81.9 x 28.1 x 25.1 cm),"Rogers Fund, 1906",,,,,,,,,,,,Sculpture-Wood,,http://www.metmuseum.org/art/collection/search/462887,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"51.28a, b",false,true,468372,Medieval Art,Sculpture,"The Three Magi, from an Adoration Group",South German,,,,,Artist,Workshop of,Hans Thoman,"German, active Memmingen, ca. 1514–25",,"Thoman, Hans",,1514,1525,ca. 1515–20,1515,1520,"Wood, gesso, paint, gilding",Overall (Balthasar and Melchior): 23 1/2 x 12 1/4 x 6 1/2 in. (59.7 x 31.1 x 16.5 cm) Overall (Gaspar): 22 1/2 x 9 1/4 x 5 1/4 in. (57.2 x 23.5 x 13.3 cm) Base: 4 x 20 x 10 in. (10.2 x 50.8 x 25.4 cm),"Purchase, Joseph Pulitzer Bequest, 1951",,,,,,,,,,,,Sculpture-Wood,,http://www.metmuseum.org/art/collection/search/468372,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -31.134.3,false,true,467415,Medieval Art,Choir Book; Manuscript cutting,"Manuscript Illumination with the Visitation in an Initial D, from a Choir Book",French,,,,,Artist,,Spanish Forger,"French, active late 19th–early 20th century",,Spanish Forger,French,1870,1935,late 19th–early 20th century,1875,1925,"Tempera, ink, and gold on parchment",Overall: 6 7/8 x 4 1/2 in. (17.5 x 11.5 cm) Mat size: 19 3/16 x 14 3/16 in. (48.8 x 36.1 cm),"Bequest of Gwynne M. Andrews, 1930",,,,,,,,,,,,Manuscripts and Illuminations,,http://www.metmuseum.org/art/collection/search/467415,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -16.32.213,false,true,463816,Medieval Art,Relief,Meeting of Saints Joachim and Anne at the Golden Gate,North German,,,,,Artist,,Benedikt Dreyer,"German, active Lübeck, ca. 1500–1525",,Dreyer Benedikt,German,1510,1530,ca. 1515–20,1515,1520,Oak with polychromy and gilding,Overall: 23 x 19 1/4 x 4 7/8 in. (58.4 x 48.9 x 12.4 cm),"Gift of J. Pierpont Morgan, 1916",,,,,,,,,,,,Sculpture-Wood,,http://www.metmuseum.org/art/collection/search/463816,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"10.197a, b",false,true,463412,Medieval Art,Beaker and cover,Beaker and Cover,German,,,,,Artist,Probably,Friedrich Hillebrand,"German, 1580–1608",,"Hillebrand, Friedrich",German,1580,1608,19th century (16th century style),1800,1900,"Silver, partially gilt",Overall: 14 15/16 x 5 1/8 in. (37.9 x 13 cm),"Rogers Fund, 1910",,,,,,,,,,,,Metalwork-Silver,,http://www.metmuseum.org/art/collection/search/463412,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -07.65.1,false,true,463042,Medieval Art,Bell,The Bell of Saint Patrick Shrine,Irish,,,,,Maker,,Elkington & Co.,"British, Birmingham, 1829–1963",,Elkington & Co.,British,1829,1963,early 20th century (original dated 1091–1105),1091,1105,"Bronze, gold, silver, gems",Overall: 10 1/2 x 6 3/16 x 4 1/2 in. (26.6 x 15.7 x 11.4 cm),"Rogers Fund, 1906",,,,,,,,,,,,Reproductions-Metalwork,,http://www.metmuseum.org/art/collection/search/463042,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -08.233.4,false,true,463161,Medieval Art,Shrine,Shrine of Saint Patrick's Tooth,Irish,,,,,Maker,,Elkington & Co.,"British, Birmingham, 1829–1963",,Elkington & Co.,British,1829,1963,early 20th century (original dated 1376),1376,1376,"Bronze, gilt, gem stones",12 x 9 1/4 x 2 1/2 in. (30.5 x 23.5 x 6.4 cm),"Rogers Fund, 1908",,,,,,,,,,,,Reproductions-Metalwork,,http://www.metmuseum.org/art/collection/search/463161,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"11.125.15a, b",false,true,463505,Medieval Art,Salt cellar,Salt Cellar,British,,,,,Maker,,Elkington & Co.,"British, Birmingham, 1829–1963",,Elkington & Co.,British,1829,1963,early 20th century (original dated late 15th century),1450,1500,"Silver gilt, glass",Overall: 15 1/16 x 5 1/4 in. (38.2 x 13.4 cm) Lid: 5 9/16 x 4 1/4 in. (14.2 x 10.8 cm) Cellar: 9 1/2 x 5 1/4 in. (24.2 x 13.4 cm),"Dodge Fund, 1911",,,,,,,,,,,,Reproductions-Metalwork,,http://www.metmuseum.org/art/collection/search/463505,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2003.456,false,true,474389,Medieval Art,Statue,Virgin and Child,British,,,,,Artist,Attributed to,Alexander of Abingdon,"British, active 1291–1317",,Alexander,British,1250,1350,ca. 1275–1325,1275,1325,Caen Limestone,Overall: 59 1/4 x 19 3/8 x 11 3/4 in. (150.5 x 49.2 x 29.8 cm),"Purchase, Edward J. Gallagher Jr. Bequest, in memory of his father, Edward Joseph Gallagher, his mother, Ann Hay Gallagher, and his son, Edward Joseph Gallagher III; and Caroline Howard Hyman Gift, 2003",,,,,,,,,,,,Sculpture-Stone,,http://www.metmuseum.org/art/collection/search/474389,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -17.190.489,false,true,464453,Medieval Art,Altarpiece,Altarpiece,North Italian,,,,,Artist,,Baldassare degli Embriachi,"Italian, active 1390–1409",,"Embriachi, Baldassare degli",Italian,1390,1409,ca. 1390–1400,1390,1400,"Bone framed with intarsia and horn, traces of paint and gilding",without wooden base: 50 1/2 x 60 1/2 in. (128.3 x 153.7 cm),"Gift of J. Pierpont Morgan, 1917",,,,,,,,,,,,Ivories-Bone,,http://www.metmuseum.org/art/collection/search/464453,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -27.248.1,false,true,466677,Medieval Art,Book of Hours; Manuscript leaf,"Manuscript Leaf with Adoration of the Holy Name, from a Book of Hours",South Netherlandish,,,,,Artist,Influence of,Simon Bening,"Netherlandish, Ghent (?) 1483/84–1561 Bruges",,"Bening, Simon",Netherlandish,1483,1561,after 1530 (?),1530,1530,"Tempera, ink and shell gold on parchment",5 7/8 x 4 3/16 in. (15 x 10.6 cm) Mat: 12 × 10 in. (30.5 × 25.4 cm),"Gift of Alice M. Dike, 1927",,,,,,,,,,,,Manuscripts and Illuminations,,http://www.metmuseum.org/art/collection/search/466677,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -55.120.2,false,true,471730,The Cloisters,Painting,Saint Michael,North Spanish,,,,,Artist,,Master of Belmonte,"Spanish, Aragon, active ca. 1460–90",,Master of Belmonte,Spanish,1455,1490,1450–1500,1450,1500,Tempera and oil on wood,Overall: 85 1/2 x 47 in. (217.2 x 119.4 cm),"The Cloisters Collection, 1955",,,,,,,,,,,,Paintings-Panels,,http://www.metmuseum.org/art/collection/search/471730,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2000.135,false,true,472854,The Cloisters,Panel,Stained-Glass Panel with a Coat of Arms and a Female Supporter,Swiss,,,,,Artist,Workshop of,Lukas Zeiner,"Swiss, active ca. 1480–1510",,Zeiner Lukas,Swiss,1480,1510,1500–1505,1500,1505,"Pot metal and colorless glass, vitreous paint, and silver stain, lead",14 7/8 x 19 7/8 in. (37.8 x 50.5 cm),"Purchase, Bequest of Jane Hayward, by exchange, 2000",,,,,,,,,,,,Glass-Stained,,http://www.metmuseum.org/art/collection/search/472854,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1988.304.3,false,true,469916,The Cloisters,Roundel,Roundel with Saint Jerome in his Study,South Netherlandish,,,,,Artist,Based on a design by,Pseudo-Ortkens,"South Netherlandish, active Antwerp and Brussels, ca. 1500–30",,Pseudo-Ortkens,South Netherlandish,1500,1530,ca. 1520,1520,1520,"Colorless glass, vitreous paint and silver stain",Overall: 9 in. (22.8 cm),"The Cloisters Collection, 1988",,,,,,,,,,,,Glass-Stained,,http://www.metmuseum.org/art/collection/search/469916,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -25.120.368,false,true,472330,The Cloisters,Statuette,Virgin,French,,,,,Artist,,Joan Avesta,"Spanish, active Catalonia and southwest France, 1355–1390",,"Avesta, Joan",Spanish,1355,1390,ca. 1370–90,1367,1393,"Alabaster, traces of gilt, paint",Overall: 25 9/16 x 9 7/8 x 8 5/8 in. (64.9 x 25.1 x 21.9 cm),"The Cloisters Collection, 1925",,,,,,,,,,,,Sculpture-Stone,,http://www.metmuseum.org/art/collection/search/472330,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2010.22.1,false,true,478972,The Cloisters,Panel,Gathering Manna,German,,,,,Artist|Artist,Workshop of|Based on a design by,Friedrich Brunner|Jan Pollack,"German|Polish (?), active Bavaria, ca. 1479–died 1519",,"Brunner Friedrich|Pollack, Jan",German|Polish (?),1479,1519,1497–99,1497,1499,"Pot-metal glass, vitreous paint, and silver stain",Overall: 19 3/4 x 20 7/8 in. (50.2 x 53 cm),"Purchase, The Cloisters Collection and Gift of The Hearst Foundation, by exchange, 2010",,,,,,,,,,,,Glass-Stained,,http://www.metmuseum.org/art/collection/search/478972,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2010.22.2,false,true,478997,The Cloisters,Panel,Storing up Manna,German,,,,,Artist|Artist,Workshop of|Based on a design by,Friedrich Brunner|Jan Pollack,"German|Polish (?), active Bavaria, ca. 1479–died 1519",,"Brunner Friedrich|Pollack, Jan",German|Polish (?),1479,1519,1497–99,1497,1499,"Pot-metal glass, vitreous paint, and silver stain",Overall: 19 3/4 x 20 7/8 in. (50.2 x 53 cm),"Purchase, The Cloisters Collection and Gift of The Hearst Foundation, by exchange, 2010",,,,,,,,,,,,Glass-Stained,,http://www.metmuseum.org/art/collection/search/478997,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -55.1,false,true,468466,The Cloisters,Shrine,Reliquary Shrine of Saint Barbara,European,,,,,Artist,Workshop of,Louis Marcy (Luigi Parmeggiani),"Italian, 1860–1945",(?),"Marcy, Louis",,1860,1945,ca. 1880–1900 (14th–15th century style),1880,1900,"Silver, Silver-gilt",Overall: 14 13/16 x 11 7/16 x 5 13/16 in. (37.6 x 29 x 14.8 cm),"The Cloisters Collection, 1955",,,,,,,,,,,,Metalwork-Silver,,http://www.metmuseum.org/art/collection/search/468466,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2008.278,true,true,478211,The Cloisters,Dish,Dish with Abraham and Melchizedek,South German,,,,,Artist,,Hans of Landshut,"German, Landshut, active late 15th century",,Hans of Landshut,German,1400,1550,1498,1498,1498,Free-blown glass with paint and metallic foils,Overall: 14 1/2 x 1 5/8 in. (36.9 x 4.2 cm),"The Cloisters Collection, 2008",,,,,,,,,,,,Glass-Miscellany,,http://www.metmuseum.org/art/collection/search/478211,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1985.146,false,true,469897,The Cloisters,Roundel,Roundel with Christ Taking Leave of His Mother,South German,,,,,Artist,After,Hans Schäufelein,"German, Nuremberg ca. 1480–ca. 1540 Nördlingen",,"Schäufelein, Hans",German,1480,1540,1507–15,1507,1515,"Colorless glass, vitreous paint and silver stain",Overall: 6 1/2 in. (16.5 cm),"Gift of Louis R. Slattery, in honor of Ashton Hawkins, 1985",,,,,,,,,,,,Glass-Stained,,http://www.metmuseum.org/art/collection/search/469897,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1979.185,false,true,469840,The Cloisters,Roundel,Roundel with Netting Quail,German,,,,,Artist,After a design by,Augustin Hirschvogel,"German, Nuremberg 1503–1553 Vienna",,"Hirschvolgel, Augustin",German,1503,1553,16th century,1500,1600,"Colorless glass, vitreous paint, silver stain and cold enamel",Overall: 9 1/2 in. (24.1 cm),"The Cloisters Collection, 1979",,,,,,,,,,,,Glass-Stained,,http://www.metmuseum.org/art/collection/search/469840,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -61.266,false,true,471908,The Cloisters,Reliquary bust,Reliquary Bust of Saint Juliana,Italian,,,,,Artist,Circle of,Giovanni di Bartolo,"Italian, active 1364–1404",,"di Bartolo, Giovanni",Italian,1364,1404,ca. 1376,1371,1381,"Copper, gilding, gesso, and tempera paint",Overall: 11 1/16 x 9 x 8 3/8 in. (28.1 x 22.9 x 21.3 cm),"The Cloisters Collection, 1961",,,,,,,,,,,,Metalwork-Copper,,http://www.metmuseum.org/art/collection/search/471908,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -32.24.40,false,true,471103,The Cloisters,Roundel,Roundel with the Hanging of Haman,North Netherlandish (?),,,,,Artist,Style of,Jan Swart van Groningen,"Netherlandish, Groningen ca. 1490/1500–1553 or later Antwerp",(?),"van Groningen, Jan Swart",Netherlandish,1490,1553,ca. 1530–40,1530,1540,"Colorless glass, silver stain, vitreous paint",Overall Diam.: 9 in. (22.9 cm),"The Cloisters Collection, 1932",,,,,,,,,,,,Glass-Stained,,http://www.metmuseum.org/art/collection/search/471103,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -GB1321 .M68 1766,true,true,682011,The Libraries,,Dialogo sobre hua nova obra no Rio Tejo ...,,,,,,Author,,Bento de Moura Portugal,"Portuguese, 1702–1776",,"Moura Portugal, Bento de",Portuguese,1702,1776,1776 (?),1771,1781,,"235 pages, [7] leaves of plates (some folded); Overall: 8 1/16 × 6 1/16 × 1 1/2 in. (20.4 × 15.4 × 3.8 cm)","Gift of Jayne Wrightsman, 2008",,,,,Portugal,,,,,,,||,,http://www.metmuseum.org/art/collection/search/682011,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -PN6231.P6 B63 1669,true,true,681546,The Libraries,,De' ragguagli di Parnaso: Centuria prima-seconda,,,,,,Printer|Author|Binder|Author,,Johannes Blaeu|Girolamo Briani|Simier|Traiano Boccalini,"Dutch|Italian, 1581–1646|Italian, 1556–1613",,"Blaeu, Johannes|Briani, Girolamo|Simier|Boccalini, Traiano",Dutch|Italian|Italian,1581 |0 |1556,1646 |0 |1613,1669,1669,1669,,"2 volumes: [16], 471, [55] pages; 415, [41], 139, [25] pages; Height: 6 1/8 in. (15.5 cm)","Gift of Jayne Wrightsman, 2008",,Amsterdam,,,Netherlands,,,,,,,|,,http://www.metmuseum.org/art/collection/search/681546,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -PQ4272.F5 A34 1697,true,true,681534,The Libraries,,"Contes et nouvelles de Bocace florentin : traduction libre, accommodée au gout de ce temps, & enrichie de figures en taille-douce gravées par Mr. Romain de Hooge",,,,,,Binder|Printer|Author|Printmaker,,Kleihnans|George Gallet|Giovanni Boccaccio|Romeyn de Hooghe,"Italian, Paris 1313–1375 Certaldo, Tuscany|Dutch, 1645–1708",,"Kleihnans|Gallet, George|Boccaccio, Giovanni|Hooghe, Romeyn de",Netherlandish|Italian|Dutch,0 |1313 |1645,0 |1375 |1708,1697,1697,1697,,"2 volumes: illustrations, etchings; Height: 6 5/16 in. (16 cm)","Gift of Jayne Wrightsman, 2008",,Amsterdam,,,Netherlands,,,,,,,|,,http://www.metmuseum.org/art/collection/search/681534,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -BX2010 .A2 1794,true,true,681389,The Libraries,,"Uffizio della Settimana Santa : colle rubriche volgari, argomenti de' Salmi, spiegazione delle cerimonie e misterj, con osservazioni, e riflessioni divote dell'abate Alessandro Mazzinelli",,,,,,Author|Artist|Author|Artist|Artist|Publisher,After|After|After|Presso,Alessandro Mazzinelli|Giovanni Battista Pacetti|Catholic Church|Giuseppe Passeri (Passari)|Annibale Carracci|Luigi Perego Salvioni,"Italian, active ca. 1700|Italian, 1693–1743|Italian, Rome 1654–1714 Rome|Italian, Bologna 1560–1609 Rome",,"Mazzinelli, Alessandro|Pacetti, Giovanni Battista|Catholic Church|Passeri, Giuseppe|Carracci, Annibale|Salvioni",Italian|Italian|Italian|Italian,1650 |1693 |0 |1654 |1560,1750 |1743 |0 |1714 |1609,1794,1794,1794,,3 volumes in 1 (560 pages) : illustrations ; Height: 8 11/16 in. (22 cm),"Gift of Jayne Wrightsman, 2008",,Rome,,,Italy,,,,,,,|,,http://www.metmuseum.org/art/collection/search/681389,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -240.7 T344 F,true,true,591832,The Libraries,,"Academie de l'espee; ou se domonstrent par reigles mathématiques sur le fondement d'un cercle mystérieux, la théorie et pratique des vrais . . .","Anvers, 1628",,,,,Publisher|Artist|Author,Printer:|Engraver:,probably the Elseviers of Leyden|probably A. Boslwert|Girard Thibault,"Flemish, died ca. 1629",,Elseviers|Boslwert A.|Thibault Girard,Flemish,1629,1629,1628,1628,1628,Illustrated book,2 pts. in 1vol.; H: 22 in. (56 cm),Purchased with income from the Jacob S. Rogers Fund,,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/591832,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -216.7 T71,true,true,591860,The Libraries,,The Treatyse of Fysshynge wyth an Angle from the book of Saint Albans,"New York, C. Scribner's Sons, 1903",,,,,Author|Author,Introduction by:,William Loring Andrews|Juliana Berners,"American, 1837–1920|British, b. 1388",,"Andrews, William Loring|Berners Juliana",American|British,1837 |1388,1920 |1388,1903,1903,1903,Illustrated book,H: 7 7/8 in. (20 cm),Presented by Mr. and Mrs. Edward Dean Adams,,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/591860,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -239 B63 Q,true,true,591831,The Libraries,,"Descriptio pbvlicae gratvlationis, spectacvlorvm et lvdorvm, in aventv sereniss: Principis Ernesti Archidvcis Avstriae Dvcis Vrgvndiae","Antwerp: Ex Officina Plantíníana, 1595",,,,,Author|Engraver,,Jean Boch|Peeter van der Borcht,"Belgian, 1545–1608|Netherlandish, Mechelen ca. 1545–1608 Antwerp",,"Boch Jean|Borcht, Peeter van der",Belgian|Netherlandish,1545 |1540,1608 |1608,1595,1595,1595,Illustrated book,174 pp.; 15 x 10 1/4 in. (38 x 26 cm),Presented by Mrs. S. P. Avery,,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/591831,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -920.84 M292,true,true,591865,The Libraries,,Morte d'Arthur,"London: J. M. Dent and Co., 1893",,,,,Author|Illustrator,,Sir Thomas Malory|Aubrey Vincent Beardsley,"British, 1415/18–1471|British, Brighton, Sussex 1872–1898 Menton",,"Malory Thomas Sir|Beardsley, Aubrey Vincent",British|British,1415 |1872,1471 |1898,1893,1893,1893,Illustrated book,12 pts.; H: 10 1/4 in. (26 cm),,,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/591865,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -903.6 R541 F,true,true,591848,The Libraries,,"The Holy Land, Syria, Idumea, Arabia, Egypt & Nubia","London : F. G. Moon, 1842–49",,,,,Author|Author|Author,Lithographed by|Historical descriptions by,Louis Haghe|Rev. George Croly|David Roberts,"Belgian, Tournai 1806–1885 Surrey|1780–1860|British, Stockbridge, Scotland 1796–1864 London",,"Haghe, Louis|Croly George Rev.|Roberts, David","Belgian|British, Scottish",1806 |1780 |1796,1885 |1860 |1864,1842–49,1842,1849,Illustrated books,3 vols.; H: 24 3/4 in. (63 cm),Presented by Charles Lanier,,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/591848,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -146.8 M821 Q,true,true,591863,The Libraries,,"Catalogue of the Collection of Jewels and Precious Works of Art, the Property of J. Pierpont Morgan","London: Chiswick Press, 1910 (deluxe ed.)",,,,,Author|Author,,J. Pierpont Morgan|George Charles Williamson,"British, 1858–1942",,"Morgan, J. Pierpont|Williamson, George Charles",American|British,1837 |1858,1913 |1942,1910,1910,1910,"Illustrated book, fine binding",H: 15 3/4 in. (39 cm),Presented by J. Pierpont Morgan,,,,,,,,,,,,|,,http://www.metmuseum.org/art/collection/search/591863,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -PQ2067.T28 D4 1770,true,true,681573,The Libraries,,"Les oeuvres morales de Mr. Diderot : contenant son traité De l'amitié, et celui Des passions",,,,,,Author|Author|Publisher,Erroneously attributed to|Aux depens de,Marie Geneviève Charlotte Darlus Thiroux d'Arconville|Denis Diderot|La Compagnie des libraires associés,"French, 1720–1805|French, 1713–1784",,"Thiroux d'Arconville, Marie Geneviève Charlotte Darlus|Diderot, Denis|Compagnie des libraires associés",French|French|French,1720 |1713,1805 |1784,1770,1770,1770,,2 volumes bound in 1; Height: 6 11/16 in. (17 cm),"Gift of Jayne Wrightsman, 2008",,,,,,,,,,,,|,,http://www.metmuseum.org/art/collection/search/681573,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -PN1489 .M37 1651,true,true,682013,The Libraries,,L'eschole de Salerne en vers burlesques & duo poemata macaronica: de bello huguenotico: et De gestis magnanimi & prudentissimi Baldi,,,,,,Author|Author|Binder|Author|Author,,Docteur (Louis) Martin|Remy Belleau|Robert Joly|Simon Moynet|Theophilo Folengo,"active 17th century|French, 1527?–1577|French, 1870?–1924|Italian, 1496–1544",,"Martin, Docteur (Louis)|Belleau, Remy|Joly, Robert |Moynet, Simon|Folengo, Theophilo",French|French|Italian,1600 |1527 |1870 |1496,1699 |1577 |1924 |1544,1651,1651,1651,,"139, [1] pages; Height: 5 1/8 in. (13 cm)","Gift of Jayne Wrightsman, 2008",,,,,,,,,,,,|,,http://www.metmuseum.org/art/collection/search/682013,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -BS75 1785b Q,true,true,681149,The Libraries,,Bibliorum Sacrorum Vulgatae versionis,,,,,,Printer|Author,,François Ambroise Didot|Josiah W. (Josiah Willard) Gibbs,"French, 1730–1804|American, 1790–1861",,"Didot, François Ambroise|Gibbs, Josiah W. (Josiah Willard)",French|American,1730 |1790,1804 |1861,1785,1785,1785,,"2 v. (ix [i.e. vii], [1], 596; [4], 548 p.) ; Height: 12 3/16 in. (31 cm)","Gift of Jayne Wrightsman, 2008",,Paris,,,France,,,,,,,|,,http://www.metmuseum.org/art/collection/search/681149,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -DA447.G7 H26 1876,true,true,681556,The Libraries,,Mémoires du Comte de Grammont : histoire amoureuse de la cour d'Angleterre sous Charles II / par Antoine Hamilton ; préface et notes par Benjamin Pifteau,,,,,,Author|Illustrator|Printer|Binder,,Count Anthony Hamilton|Jules Adolphe Chauvet|Leon Lamire|Marius Michel et fils,"Irish, ca. 1646–1720|French|French|French",,"Hamilton, Anthony, Count|Chauvet, Jules Adolphe|Lamire, Leon|Marius Michel et fils",Irish|French|French|French,1646,1720,1876,1876,1876,,20 cm,"Gift of Jayne Wrightsman, 2008",,Paris,,,France,,,,,,,|,,http://www.metmuseum.org/art/collection/search/681556,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -PA4375.M8 D3 1772,true,true,681388,The Libraries,,"Traité de Plutarque, sur la manière de discerner un flatteur d'avec un ami et Le banquet des sept sages: dialogue du même auteur revu & corrigé sur des manuscrits de la Bibliothèque du roi; avec une version françoise & des notes",,,,,,Author|Publisher|Artist,De,Plutarch|L'Imprimerie Royale|François Jean Gabriel de La Porte du Theil,"Greek, ca. A.D. 45–ca. 125|French, 1742–1815",,"Plutarch|L'Imprimerie Royale|La Porte du Theil, François Jean Gabriel de",Greek|French|French,0045 |1742,0125 |1815,1772,1772,1772,,"xii, 335, [1] pages ; Height: 8 1/4 in. (21 cm)","Gift of Jayne Wrightsman, 2008",,Paris,,,France,,,,,,,|,,http://www.metmuseum.org/art/collection/search/681388,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -SB472.32.E54 W48 1771,true,true,680835,The Libraries,,"L'art de former les jardins modernes, ou, L'art des jardins anglois : traduit de l'anglois : à quoi le traducteur a ajouté un discours préliminaire sur l'origine de l'art, des notes sur le texte, & une description détaillée des jardins de Stowe, accompagnée du plan",,,,,,Translator|Author|Publisher,Chez,François de Paule Latapie|Thomas Whately|Charles Antoine Jombert,"French, 1739–1823|British, died 1772|French, 1712–1784",,"Latapie, François de Paule|Whately, Thomas|Jombert, Charles Antoine",French|British|French,1739 |1672 |1712,1823 |1772 |1784,1771,1771,1771,,"lxiv, 406 p., 1 folded leaf of plates, plan, Height: 8 11/16 in. (22 cm)","Gift of Jayne Wrightsman, 2009",,Paris,,,France,,,,,,,|,,http://www.metmuseum.org/art/collection/search/680835,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -BV4823 .D48 1741,true,true,681547,The Libraries,,"De l'imitation de Jesus-Christ, traduction nouvelle ; ornée de figures en taille-douce",,,,,,Designer|Engraver|Engraver,Engravings drawn by,Antoine Humblot|J. B. Guélard|Claude Duflos,"French, died 1758|French, active ca. 1730|French, Coucy-le-Château 1665–1727 Paris",,"Humblot, Antoine|Guélard, J. B.|Duflos, Claude",French|French|French,1658 |1730 |1665,1758 |1730 |1727,1741,1741,1741,,"[8], xx, 608 p., [4] leaves of plates ; ill. (engravings) ; Height: 8 1/4 in. (21 cm)","Gift of Jayne Wrightsman, 2008",,Paris,,,France,,,,,,,|,,http://www.metmuseum.org/art/collection/search/681547,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -PQ1809 .A1 1795,true,true,681545,The Libraries,,Contes et nouvelles en vers par Jean de la Fontaine,,,,,,Author|Author|Draftsman|Printer,,Nicolas Boileau Despréaux|Jean de La Fontaine|Charles Dominique Joseph Eisen|Pierre Didot l'ainé,"French, 1636–1711|French, Château-Thierry 1621–1695 Paris|French, Valenciennes 1720–1778 Brussels|French, 1761–1853",,"Boileau Despréaux, Nicolas|La Fontaine, Jean de|Eisen, Charles Dominique Joseph|Didot, Pierre l'ainé",French|French|French|French,1636 |1621 |1720 |1761,1711 |1695 |1778 |1853,1795,1795,1795,,"2 volumes, portraits, Height: 5 1/2 in. (14 cm)","Gift of Jayne Wrightsman, 2009",,Paris,,,France,,,,,,,|,,http://www.metmuseum.org/art/collection/search/681545,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -NE2049.5.W38 W38 1710,true,true,682014,The Libraries,,Figures françoises et comiques nouvellement inventées par M. Watteau; Figures de modes dessinées et gravées à l'eau forte par Watteau; a terminées au buin par Thomassin le fils,,,,,,Artist|Engraver|Engraver|Engraver|Binder,,Antoine Watteau|Charles Nicolas Cochin I|Louis Desplaces|Henri Simon Thomassin|Hardy-Mennil,"French, Valenciennes 1684–1721 Nogent-sur-Marne|French, Paris 1688–1754 Paris|French, Paris 1682–1739 Paris|French, Paris 1687–1741",,"Watteau, Antoine|Cochin, Charles Nicolas, I|Desplaces, Louis|Thomassin, Henri Simon|Hardy-Mennil",French|French|French|French,1684 |1688 |1682 |1687,1721 |1754 |1739 |1687,1710?–?1720,1705,1725,,"[12], [8] leaves; Height: 10 1/4 in. (26 cm)","Gift of Jayne Wrightsman, 2008",,Paris,,,France,,,,,,,|,,http://www.metmuseum.org/art/collection/search/682014,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -PQ1795 .T5 1790,true,true,681128,The Libraries,,"Les aventures de Télémaque, fils d'Ulysse par M. de Fénélon ; avec figures en taille-douce, dessinées par MM. Cochin et Moreau le jeune",,,,,,Author|Engraver|Engraver|Binder,,François de Salignac de La Mothe-Fénelon|Charles Nicolas Cochin II|Jean Michel Moreau the Younger|Jean Claude Bozerian,"French, Château de Fénelon, Périgord 1651–1715 Cambrai|French, Paris 1715–1790 Paris|French, Paris 1741–1814 Paris|French, 1762–1840",,"Fénelon, François de Salignac de La Mothe-|Cochin, Charles Nicolas, II|Moreau, Jean Michel, the Younger|Bozerian, Jean Claude",French|French|French|French,1651 |1715 |1741 |1762,1715 |1790 |1814 |1840,1790,1790,1790,,2 volumes: illustrations (engravings); Height: 9 13/16 in. (25 cm),"Gift of Jayne Wrightsman, 2008",,Paris,,,France,,,,,,,|,,http://www.metmuseum.org/art/collection/search/681128,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -PA6525.H4 N3 1762,true,true,682012,The Libraries,,Epistole eroiche di P. Ovidio Nasone,,,,,,Author|Illustrator|Publisher|Author|Translator,,Ovid|Carlo Gregori|Durand|Giovan Stefano Conti|Remigio Nannini,"Roman, Sulmo 43 B.C.–A.D. 17 Tomis, Moesia|Italian, Lucca 1702–1759 Florence|French, 18th century|Italian, 1720–1791|Italian, 1521?–?1581",,"Ovid|Gregori, Carlo|Durand|Conti, Giovan Stefano|Nannini, Remigio",Roman|Italian|French|Italian|Italian,-0043 |1702 |1700 |1720 |1521,0017 |1759 |1800 |1791 |1581,1762,1762,1762,,"xii, 323 pages: portraits; Height: 8 1/4 in. (21 cm)","Gift of Jayne Wrightsman, 2008",,Paris,,,France,,,,,,,|,,http://www.metmuseum.org/art/collection/search/682012,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -PQ1821 1773,true,true,681563,The Libraries,,"Oeuvres de Moliere : avec des remarques grammaticales, des avertissemens et des observations sur chaque piéce, par M. Bret",,,,,,Author|Printer|Author|Author|Illustrator|Illustrator|Publisher,De l'imprimerie de|Par,M. (Antoine) Bret|Michel Lambert|Jean-Baptiste Poquelin Molière|Voltaire|Pierre Mignard |Jean Michel Moreau the Younger|La Compagnie des libraires associés,"French, Dijon 1717–1792 Paris|French, 1722?–1787|French, 1622–1673|1694–1778|French, Troyes 1612–1695 Paris|French, Paris 1741–1814 Paris",,"Bret, M. (Antoine)|Lambert, Michel|Molière, Jean-Baptiste Poquelin|Voltaire|Mignard, Pierre|Moreau, Jean Michel, the Younger|Compagnie des libraires associés",French|French|French|French|French|French,1717 |1722 |1622 |1694 |1612 |1741,1792 |1787 |1673 |1778 |1695 |1814,1773,1773,1773,,"6 volumes: illustrations, portraits, engravings; Height: 8 1/4 in. (21 cm)","Gift of Jayne Wrightsman, 2008",,Paris,,,France,,,,,,,|,,http://www.metmuseum.org/art/collection/search/681563,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -AE25 .E533 1765 Q,true,true,716648,The Libraries,,"Planches pour l'Encyclopédie, ou pour le Dictionaire raisonné des sciences, des arts libéraux, et des arts méchaniques, avec leur explication",,,,,,Publisher|Author,,Vincenzo Giuntini|Denis Diderot,"French, 1713–1784",,"Giuntini, Vincenzo|Diderot, Denis",,1713,1784,1765–1776,1765,1776,,"11 volumes, illustrations, height: 16 9/16 in. (42 cm)",Bequest of Marianne Khuner,,Lucca,,,Italy,,,,,,,,,http://www.metmuseum.org/art/collection/search/716648,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -NK7983.A1 W36 1752,true,true,705290,The Libraries,,博古圖錄,,,,,,Author,,Fu Wang,"Chinese, 1079–1126",,"Wang, Fu",,1079,1126,1752,1752,1752,,16 volumes : illustrations ; Height: 11 13/16 in. (30 cm),,,,,,China,,,,,,,,,http://www.metmuseum.org/art/collection/search/705290,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -230.51 N55,true,true,715005,The Libraries,,三禮圖,,,,,,Publisher|Author,,Tong zhi tang|Chongyi Nie,"Chinese, active 10th century",,"Tong zhi tang|Nie, Chongyi",,0900,0999,1676,1676,1676,,4 volumes : illustrations ; height: 11 7/16 in. (29 cm),"Purchased with income from the Jacob S. Rogers Fund, 1940",,Beijing,,,China,,,,,,,,,http://www.metmuseum.org/art/collection/search/715005,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -NK1483 .J7 1867 Q,true,true,739504,The Libraries,,Examples of Chinese ornament selected from objects in the South Kensington Museum and other collections,,,,,,Publisher|Author,,S. & T. Gilbert|Owen Jones,"London|British, London 1809–1874 London",,"Gilbert, S. & T.|Jones, Owen",,1800 |1809,1900 |1874,1867,1867,1867,,"3 pages, leaf, 5-15 pages : illustrations, color plate ; Height: 13 3/4 in. (35 cm)",,,London,,,England,,,,,,,,,http://www.metmuseum.org/art/collection/search/739504,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -GT2050 .U813 1884,true,true,726677,The Libraries,,"The sunshade, the glove, the muff",,,,,,Illustrator|Publisher|Author,,Paul Avril|J. C. Nimmo and Bain|Octave Uzanne,"French, born 1843|London|French, Auxerre 1851–1931",,"Avril, Paul|Nimmo, J. C. and Bain|Uzanne, Octave",,1843 |1875 |1851,1943 |1899 |1931,1884,1884,1884,,"viii, 138 pages : illustrations (some color) ; Height: 11 in. (28 cm)",,,London,,,England,,,,,,,,,http://www.metmuseum.org/art/collection/search/726677,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -AE25 .E532 1758 Q,true,true,716639,The Libraries,,"Encyclopédie, ou Dictionnaire raisonné des sciences, des arts et des métiers, par une société de gens de lettres",,,,,,Author|Publisher|Author|Author,,Ottaviano Diodati|Vincenzo Giuntini|Jean Le Rond d'Alembert|Denis Diderot,"Italian, 1716–1786|French, 1717–1783|French, 1713–1784",,"Diodati, Ottaviano|Giuntini, Vincenzo|Alembert, Jean Le Rond d'|Diderot, Denis",,1716 |1717 |1713,1786 |1783 |1784,1758–1771,1758,1771,,"17 volumes, illustrations, height: 16 9/16 in. (42 cm)",Bequest of Marianne Khuner,,Lucca,,,Italy,,,,,,,,,http://www.metmuseum.org/art/collection/search/716639,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -171.1 C17,true,true,699505,The Libraries,,"Dialogos de la pintvra : sv defensa, origen, essecia, definicion, modos y diferencias",,,,,,Author|Publisher|Engraver|Engraver,,Vicente Carducho|Francisco Martínez|Francisco López|Francisco Fernández,"Italian, 1570/78–1638|Spanish, active 1627–45|Spanish, ca. 1552–1629|Spanish, 1605–1646",,"Carducho, Vincente|Martínez, Francisco|López, Francisco|Fernández, Francisco",,1570 |1625 |1550 |1605,1638 |1645 |1630 |1646,1633,1633,1633,,"[18], 229, [24] pages, illustrations, height: 7 7/8 in. (20 cm)",,,Madrid,,,Spain,,,,,,,,,http://www.metmuseum.org/art/collection/search/699505,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -U820.A9 S34 1603 F,true,true,697348,The Libraries,,Der aller durchleuchtigisten und grosmächtigen Kayser ... Königen ... Herren vom Adel ... Bildtnussen und kurtz Beschreibungen ihrer so wol in Fridts- als Kriegszzeiten verrichten fürnembsten Thaten und Handlungen : deren Waffen und Rüstungen ... auss allen Landen der Welt ... in dem Schloss Ombrass ... zu ewiger Gedächtnuss auffbehalten werden ...,,,,,,Author|Translator|Publisher|Engraver|Illustrator,,Jacob Schrenck von Nozing|Johann Engelbert Noyse von Campenhouten|Daniel Baur|Dominicus Custos|Giovanni Battista Fontana,"died 1612|active 1603|died 1639|German, Antwerp after 1550–1612 Augsburg|Italian, ca. 1524–1587",,"Schrenck von Nozing, Jacob|Noyse von Campenhouten, Johann Engelbert|Baur, Daniel|Custos, Dominicus|Fontana, Giovanni Battista",,1603 |1550 |1524,1612 |1603 |1639 |1612 |1587,1603,1603,1603,,"126 engraved plates, Height: 19 5/16 in. (49 cm)",,,Innsbruck,,,Austria,,,,,,,,,http://www.metmuseum.org/art/collection/search/697348,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -QB7 .C15 1772,true,true,681349,The Libraries,,"Calendrier belgique, curieux et utile, contenant les evenemens historiques sur les jours de l'an, et les travaux à faire dans les jardins en chaque mois de l'année, la description des tableaux remarquables, que l'on trouve dans la ville de Gand, avec les noms des peintres pour l'annėe MDCCLXXII",,,,,,Publisher,,Pierre de Goesin,,,Pierre de Goesin,,0,0,1772,1772,1772,,"108 pages, Height: 4 5/16 in. (11 cm)","Gift of Jayne Wrightsman, 2009",,Ghent,,,Belgium,,,,,,,|,,http://www.metmuseum.org/art/collection/search/681349,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -110.5B75 C44,true,true,591837,The Libraries,,Le pitture e sculture di Brescia che sono esposte al pubblico con un' appendice di alcune private gallerie,"Brescia: [s.n.], 1760",,,,,Author,,Luigi Chizzola,,,"Chizzola, Luigi",,1690,1790,1760,1760,1760,Illustrated book,H: 8 1/4 in. (21 cm),Purchased with income from the Jacob S. Rogers Fund,,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/591837,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -201.9F88 B73,true,true,591862,The Libraries,,"Edwin Davis French, A Memorial: His Life, His Art","New York: De Vinne Press, 1908",,,,,Author,,Ira Hutchinson Brainerd,1862–1935,,Brainerd Ira Hutchinson,,1862,1935,1908,1908,1908,Illustrated book,H: 9 7/ 8 in. (25 cm),Presented by Mr. William L. Andrews,,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/591862,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -911.1. M362,true,true,591836,The Libraries,,Il Ritratto di Venezia,"Venice: Presso Gio. Giacomo Hertz, 1684",,,,,Author,,Domenico Martinelli,active 1663–1669,,Martinellie Domenico,,1663,1669,1684,1684,1684,Printed book,H: 6 3/4 in. (17 cm),Presented by Bobby and Allan Weissglass,,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/591836,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -161 L841,true,true,700238,The Libraries,,"The cabinet-makers' London book of prices, and designs of cabinet work, calculated for the convenience of cabinet makers in general, whereby the price of executing any piece of work may be easily found",,,,,,Printer|Publisher|Author,,W. Brown and A. O'Neil|London Society of Cabinet Makers|London Society of Cabinet Makers,London,,"Brown, W. and O'Neil A.|London Society of Cabinet Makers|London Society of Cabinet Makers",,1700,1850,1793,1793,1793,,"xvi, 266, 24 pages, 29 leaves of plates, height: 10 5/8 in. (27 cm)",,,London,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/700238,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -170.1 R674,true,true,738466,The Libraries,,Modern chromatics : with applications to art and industry,,,,,,Author,,Ogden Nicholas Rood,1831-1902,,"Rood, Ogden Nicholas",,1831,1902,1879,1879,1879,,"3 pages, 1 leaf, [v]-viii, [9]-329 pages : color frontispiece, illustrations, diagrams ; Height: 7 7/8 in. (20 cm)",,,New York,New York,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/738466,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -551 G41,true,true,700032,The Libraries,,Petri Gyllii De Bosporo thracio libri III ; Petri Gyllii De topographia Constantinopoleos,,,,,,Publisher|Author,,"Apvd Gvlielmvm, svb scvto veneto|Pierre Gilles",1490–1555,,"Apvd Gvlielmvm, svb scvto veneto|Gilles, Pierre",,1490,1555,1562,1562,1562,,2 parts in 1 volume ; Height: 9 13/16 in. (25 cm),,,Lvgdvni,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/700032,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -146.9 T34,true,true,700050,The Libraries,,"Traité de l'horlogerie, méchanique et pratique, approuvé par l'Academie royale des sciences",,,,,,Author,,Antoine Thiout l'aîné,1692–1767,,"Thiout, Antoine",,1690,1770,1741,1741,1741,,"2 volumes ([26], 400 pages, 50, 41 folded leaves of plates) : illustrations ; Height: 10 1/4 in. (26 cm)",,,Paris,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/700050,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -170.1 C424,true,true,738463,The Libraries,,"The principles of harmony and contrast of colours, and their applications to the arts",,,,,,Author|Translator,,Michel Eugène Chevreul|Charles Martel,1786-1889|-1865,,"Chevreul, Michel Eugène|Martel, Charles",,1786,1889 |1865,1872,1872,1872,,"xlvi, 465 pages, 3 plates ; Height: 7 1/2 in. (19 cm)",,,London,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/738463,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -106.1 V28 F,true,true,704714,The Libraries,,Mr. Vanderbilt's house and collection,,,,,,Publisher|Author,,George Barrie|Edward Strahan (Earl Shinn),"American, 1838–1886",,"Barrie|Strahan, Edward",,1838,1886,1883–84,1883,1884,,"4 volumes, illustrations, height: 18 7/8 in. (48 cm)",,,Boston,Massachusetts,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/704714,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -161 H41,true,true,700240,The Libraries,,"The cabinet-maker and upholsterer's guide, or, Repository of designs for every article of household furniture, in the newest and most approved taste : displaying a great variety of patterns for chairs, stools ... in the plainest and most enriched styles : with a scale to each, and an explanation in letter press : also the plan of a room, shewing the proper distribution of the furniture ... from drawings",,,,,,Publisher|Author,,I. & J. Taylor|A. Hepplewhite & Co.,"London|British, 18th century",,"Taylor, I. & J.|Hepplewhite, A. & Co.",,1750 |1750,1850 |1800,1788,1788,1788,,"30 pages, 125 leaves of plates, illustrations, height: 14 9/16 in. (37 cm)",,,London,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/700240,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -F595 .R38 1895,true,true,738758,The Libraries,,Pony tracks,,,,,,Publisher|Artist,,Harper & Brothers|Frederic Remington,"American, New York|American, Canton, New York 1861–1909 Ridgefield, Connecticut",,"Harper & Brothers|Remington, Frederic",,1833 |1861,1962 |1909,1895,1895,1895,,"viii pages, 1 leaf, 269 pages including plates : illustrations, frontispiece ; Height: 9 1/16 in. (23 cm)",Gift of Friends of the Thomas J. Watson Library,,New York,New York,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/738758,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -NK2229 .S54 1793,true,true,700232,The Libraries,,"The cabinet-maker and upholsterer's drawing-book, in three parts",,,,,,Publisher|Printer|Author,,Thomas Sheraton|Thomas Bensley|Thomas Sheraton,"British, Stockton-on-Tees 1751–1806 London|London|British, Stockton-on-Tees 1751–1806 London",,"Sheraton, Thomas|Bensley, Thomas|Sheraton, Thomas",,1751 |1759 |1751,1806 |1835 |1806,1793–94,1793,1794,,"2 volumes, illustrations, height: 10 5/8 in. (27 cm)",,,London,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/700232,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -NK1510 .J7 1868 Q,true,true,700091,The Libraries,,The grammar of ornament,,,,,,Author|Artist|Artist|Publisher|Artist,,Owen Jones|J. O. Westwood|Matthew Digby Wyatt|Bernard Quaritch Ltd.|J. B. Waring,"British, London 1809–1874 London|British, Rowde, Wiltshire 1820–1877 Cowbridge, South Glamorgan",,"Jones, Owen|Westwood, J. O.|Wyatt, Matthew Digby Sir|Bernard Quaritch Ltd.|Waring, J. B.",,1809 |1805 |1820,1874 |1893 |1877,1868,1868,1868,,"157 pages : illustrations, plates ; Height: 13 3/4 in. (35 cm)",,,London,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/700091,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -PS2267 .A1 1891,true,true,738765,The Libraries,,The song of Hiawatha,,,,,,Publisher|Illustrator|Author,,Houghton Mifflin Company|Frederic Remington|Henry Wadsworth Longfellow,"American, Canton, New York 1861–1909 Ridgefield, Connecticut|American, Portland, Maine 1807–1882 Cambridge, Massachusetts",,"Houghton Mifflin Company|Remington, Frederic|Longfellow, Henry Wadsworth",,1861 |1807,1909 |1882,1891,1891,1891,,"xviii, 242 pages : frontispiece (portrait), illustrations, plates ; Height: 9 7/16 in. (24 cm)",Gift of Friends of the Thomas J. Watson Library,,New York,New York,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/738765,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -DH188.H67 W47 1568,true,true,682010,The Libraries,,"La dedvction de l'innocence de messire Philippe Baron de Montmorency, Conte de Hornes, franc seigneur de Vveert, admiral & capitaine general de la Mer du pais bas ... contre la malicievse apprehension, indeüe detention, injuste procedure, fausse accusation, iniques sentences et tyrannicque exécution en sa personne à grand tort, par voye de faict perpetrees",,,,,,Author,,Jacques de Wesenbeke,1523?–?1577,,"Wesenbeke, Jacques de",,1523,1577,1568,1568,1568,,"[16], 573 [i.e. 572], [2] pages ; Overall: 6 1/8 × 4 1/8 × 1 3/8 in. (15.6 × 10.5 × 3.5 cm)","Gift of Jayne Wrightsman, 2008",,,,,,,,,,,,|,,http://www.metmuseum.org/art/collection/search/682010,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -M2149.5 .C5 1768,true,true,681259,The Libraries,,"Loffice de Noël, 1768",,,,,,Author|Calligrapher,,Catholic Church|Baudouin,,,Catholic Church|Baudouin,,0 |0,0 |0,1768,1768,1768,,209 pages : music ; Height: 8 1/4 in. (21 cm),"Gift of Jayne Wrightsman, 2008",,Versailles,,,France,,,,,,,||,,http://www.metmuseum.org/art/collection/search/681259,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -GV1801 .L6 1889,true,true,726717,The Libraries,,Les jeux du cirque et la vie foraine,,,,,,Author|Illustrator|Publisher,,"Hugues Le Roux|Jules Garnier|E. Plon, Nourrit et Cie.","French, 1847–1889",,"Le Roux Hugues|Garnier, Jules|Plon E., Nourrit et Cie.",,1847,1889,1889,1889,1889,,"v, 250 pages : illustrations(some color), color portraits ; Height: 11 13/16 in. (30 cm)",,,Paris,,,France,,,,,,,,,http://www.metmuseum.org/art/collection/search/726717,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -AE25 .E531 1762 Q,true,true,707455,The Libraries,,"Recueil de planches, sur les sciences, les arts libéraux, et les arts méchaniques : avec leur explication",,,,,,Author|Publisher|Author|Author,,Pierre Mouchon|Briasson|Jean Le Rond d'Alembert|Denis Diderot,"French, 1733–1797|French, 1717–1783|French, 1713–1784",,"Mouchon, Pierre|Briasson|Alembert, Jean Le Rond d'|Diderot, Denis",,1733 |1717 |1713,1797 |1783 |1784,1762–72,1762,1772,,11 volumes ; H: 15 3/4 in. (40 cm),"Jane E. Andrews Fund, in memory of her husband, William Loring Andrews, 1955",,Paris,,,France,,,,,,,,,http://www.metmuseum.org/art/collection/search/707455,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -100.1 B591,true,true,726673,The Libraries,,"Grammaire des arts du dessin : architecture, sculpture, peinture",,,,,,Illustrator|Author,,Léon Gaucherel|Charles Blanc,"French, Paris 1816–1886|French, Castres 1813–1882 Paris",,"Gaucherel, Léon|Blanc, Charles",,1816 |1813,1886 |1882,1876,1876,1876,,"691 pages : illustrations, color plate, diagrams ; Height: 11 in. (28 cm)",,,Paris,,,France,,,,,,,,,http://www.metmuseum.org/art/collection/search/726673,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -100.1 B59,true,true,726669,The Libraries,,"Grammaire des arts du dessin : architecture, sculpture, peinture",,,,,,Author|Illustrator,,Charles Blanc|David-Pierre Giottino Humbert de Superville,"French, Castres 1813–1882 Paris|Dutch, The Hague 1770–1849 Leiden",,"Blanc, Charles|Humbert, de Superville David-Pierre Giottino",,1813 |1770,1882 |1849,1870,1870,1870,,"743 pages : illustrations, color plate ; Height: 11 in. (28 cm)",,,Paris,,,France,,,,,,,,,http://www.metmuseum.org/art/collection/search/726669,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -BX2016.A5 F7 1792,true,true,681246,The Libraries,,"L'office de l'Église en françois contenant les offices pour toute l'année, plusieurs prières tirées de l'écriture-sainte & des saints pères, les hymnes en vers françois, avec une instruction pour les fidèles",,,,,,Author|Publisher,Chez,Catholic Church|Langlois,,,Catholic Church|Langlois,,0,0,1792,1792,1792,,"[12], 612, [2] pages ; Height: 7 1/16 in. (18 cm)","Gift of Jayne Wrightsman, 2008",,Paris,,,France,,,,,,,|,,http://www.metmuseum.org/art/collection/search/681246,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -AY831 .Z7 1792,true,true,679732,The Libraries,,"Almanach royal, année bissextile M.DCC.LXCII : présenté a sa Majeste pour la premiere fois en 1699 par Laurent d'Houry",,,,,,Editor|Publisher,,Laurent Charles d' Houry|Imprimerie de Testu,"French, 1717?–1786",,"Houry, Laurent Charles d'|Imprimerie de Testu",,1717,1786,1791,1791,1791,,"679 pages, 1 folded map, Height: 7 7/8 in. (20 cm)","Gift of Jayne Wrightsman, 2009",,Paris,,,France,,,,,,,|,,http://www.metmuseum.org/art/collection/search/679732,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -273.4D26 AL2,true,true,726678,The Libraries,,"Honoré Daumier, l'homme et l'œuvre : ouvrage orné d'un portrait à l'ea-forte, de deux héliogravures et de 47 illustrations",,,,,,Subject of book|Author,,Honoré Daumier|Arsène Alexandre,"French, Marseilles 1808–1879 Valmondois",,"Daumier, Honoré|Alexandre, Arsène",,1808,1879,1888,1888,1888,,"4 pages of leaves, 383 pages : 12 plates, 1 portrait, illustrations ; Height: 10 5/8 in. (27 cm)",,,Paris,,,France,,,,,,,,,http://www.metmuseum.org/art/collection/search/726678,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -912.63 D23,true,true,591835,The Libraries,,"Asia; oder, Genaue und grundliche Beschreibung des gantzen Syrien und Palestins, oder belobten Landes . . .","Amsterdam : Jacob von Meursen, 1681",,,,,Author,,Olfert Dapper,"Dutch, 1635–1689",,"Dapper, Olfert",Dutch,1639,1689,1681,1681,1681,Illustrated book,2 pts. in 1 vol.; H: 12 1/4 in. (31 cm),Presented by Mrs. John C. McVoy,,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/591835,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -DC130.L14 L14 1749,true,true,681559,The Libraries,,"Memoires et réflexions sur les principaux évenemens du regne de Louis XIV, & sur le caractere de ceux qui y ont eu la principale part. Par mr. l.m.d.L.F",,,,,,Author,,"Charles Auguste, marquis de La Fare","French, 1644–1712",,"Auguste, Charles, marquis de La Fare",French,1644,1712,1749,1749,1749,,15 cm,"Gift of Jayne Wrightsman, 2008",,Amsterdam,,,Netherlands,,,,,,,|,,http://www.metmuseum.org/art/collection/search/681559,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -250 T34,true,true,591839,The Libraries,,"The Theory and Practice of Gardening : Wherein is Fully Handled all that Relates to Fine Gardens, Commonly called Pleasure-Gardens, as Parterres, Groves, Bowling-Greens &c. ....","London: printed by Geo. James, 1712",,,,,Author,,Antoine Joseph Dézallier d'Argenville,"French, Paris 1680–1765 Paris",,"Dézallier d'Argenville, Antoine Joseph",French,1680,1765,1712,1712,1712,Illustrated book,H: 10 1/4 in. (26 cm),"Presented in memory of Daniel W. Langton, Landscape Architect, by Mrs. Langton",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/591839,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -PQ1954.A54 L4 1741,true,true,681502,The Libraries,,"Lettres cabalistiques, ou, Correspondance philosophique, historique & critique, entre deux cabalistes, divers esprits elementaires, & le seigneur Astaroth",,,,,,Author|Publisher,Chez,"Jean Baptiste de Boyer, marquis d'Argens|Pierre Paupie","French, 1704–1771",,"Argens, Jean-Baptiste de Boyer, marquis d'|Pierre Paupie",French,1704 |0,1771 |0,1741,1741,1741,,"6 volumes: illustrations, portraits; Height: 6 5/16 in. (16 cm)","Gift of Jayne Wrightsman, 2008",,,,,,,,,,,,|,,http://www.metmuseum.org/art/collection/search/681502,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -AY831 .Z7 1783,true,true,680708,The Libraries,,Almanach royal : année bissextile M.DCC.LXXXIV. présenté a Sa Majesté pour la premiere fois en 1699 par Laurent d'Houry ...,,,,,,Publisher,,Laurent Charles d' Houry,"French, 1717?–1786",,"Houry, Laurent Charles d'",French,1717,1786,1783,1783,1783,,683 pages,"Gift of Jayne Wrightsman, 2009",,Paris,,,,,,,,,,|,,http://www.metmuseum.org/art/collection/search/680708,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -ND616 .G35 1812,true,true,682015,The Libraries,,"Galerie Giustiniani, ou, Catalogue figuré des tableaux de cette célèbre galerie, transportée d'Italie en France, accompagné d'observations critiques et historiques, et de soixante-douze planches gravée au trait, contenant environ cent cinquante sujets",,,,,,Editor|Printer|Binder,,Charles-Paul Landon|Chaignieau|Simier,French,,"Landon, Charles-Paul|Chaignieau|Simier",French,0019 |0,0019 |0,1812,1812,1812,,"160 pages, 73 leaves of plates; Height: 8 11/16 in. (22 cm)","Gift of Jayne Wrightsman, 2008",,Paris,,,France,,,,,,,|,,http://www.metmuseum.org/art/collection/search/682015,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -BX9454 .S25 1688,true,true,681386,The Libraries,,"Réponse aux plaintes des protestans touchant la prétendüe persecution de France. : Où l'on expose le sentiment de Calvin, & de tous les plus célebres ministres, sur les peines dûës aux hérétiques. On découves aussi plusieurs particularitez dignes d'être sçûës, touchant la Réformation & les réformateurs",,,,,,Printer|Author,Chez,Arnold Seneuse|Denis de Sainte-Marthe,"French, 1650–1725",,Arnold Seneuse|Denis de Sainte-Marthe,French,1650,1725,1688,1688,1688,,"[60], 10 [i.e. 310], [38] pages","Gift of Jayne Wrightsman, 2008",,Paris,,,France,,,,,,,|,,http://www.metmuseum.org/art/collection/search/681386,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -PA6558 .A5 1726,true,true,679476,The Libraries,,"Histoire secrette de Neron, ov, Le festin de Trimalcion, traduit de Petrone, avec des notes historiques par M. Lavaur ...",,,,,,Author|Author,,M. (Guillaume de) Lavaur|Petronius Arbiter,"French, 1653–1730",,"Lavaur, M. (Guillaume de)|Petronius Arbiter",French,1653,1730,1726,1726,1726,,"2 volumes bound in 1 (2 pages, leaf, lxxij, 192 pages; 1 leaf, 193-447, 3 pages), Height: 6 5/16 in. (16 cm)","Gift of Jayne Wrightsman, 2009",,Paris,,,France,,,,,,,|,,http://www.metmuseum.org/art/collection/search/679476,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -TS1484 .B56 1777,true,true,681551,The Libraries,,Mémoire sur un rouet a filer des deux mains a la fois,,,,,,Author|Publisher,,de Bernieres|Clousier,"died 1783|French, active 18th century",,"Bernieres, de|Clousier",French,1683 |1700,1783 |1800,1777,1777,1777,,"vi, 7-22 pages: illustrated plates; Height: 9 13/16 in. (25 cm)","Gift of Jayne Wrightsman, 2008",,Paris,,,France,,,,,,,|,,http://www.metmuseum.org/art/collection/search/681551,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -AY831 .Z7 1788,true,true,680818,The Libraries,,"Almanach royal, année bissextile M.DCC.LXXXVIII, présenté a sa Majeste pour la premiere fois en 1699",,,,,,Publisher,,Laurent Charles d' Houry,"French, 1717?–1786",,"Houry, Laurent Charles d'",French,1717,1786,1787,1787,1787,,"716 pages, Height: 7 7/8 in. (20 cm)","Gift of Jayne Wrightsman, 2009",,Paris,,,France,,,,,,,|,,http://www.metmuseum.org/art/collection/search/680818,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -AY831 .Z7 1784a,true,true,680752,The Libraries,,"Almanach royal, année bissextile M.DCC.LXXXIV.",,,,,,Publisher,,Laurent Charles d' Houry,"French, 1717?–1786",,"Houry, Laurent Charles d'",French,1717,1786,1783,1783,1783,,"127 pages, Height: 4 5/16 in. (11 cm)","Gift of Jayne Wrightsman, 2009",,Paris,,,France,,,,,,,|,,http://www.metmuseum.org/art/collection/search/680752,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -221.1 K63,true,true,591844,The Libraries,,"Phonurgia nova, sive conjugium mechanico-physicum artis & natvrae paranympha phonosophia concinnatum . . .","Campidonae: Rudolphum Dreherr, 1673",,,,,Author,,Athanasius Kircher,"German, 1602–1680",,Kirchner Athanasius,German,1602,1680,1673,1673,1673,Illustrated book,H: 13 3/8 in. (34 cm),"Purchased with income from the Jacob S. Rogers Fund and Bought with the income from the bequest of Nathaniel I. Bowditch, of Boston, (Class of 1812)",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/591844,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -532 L551,true,true,591849,The Libraries,,Denkmaler aus Aegypten und Aethiopien: nach den Zeichnungen der von Deiner Majestat dem Konige von Preussen Friedrich Wilhelm IV Nach Diesen Landern Gesendeten und in den Jahren 1842–1845 Ausgefuhrten Wissenschaftlichen Expedition . . .,"Leipzig, 1897–1913",,,,,Author|Author|Author,,Richard Lepsius|Édouard F. Naville|Ludwig Borchardt,"German, 1810–1884",,"Lepsius, Richard|Naville, Édouard F.|Borchardt, Ludwig",German,1810 |1844 |1863,1884 |1926 |1938,1913,1913,1913,Illustrated books,5 vols.; H: 25 1/4 in. (64 cm),Purchased with income from the Jacob S. Rogers Fund,,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/591849,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -254 B63,true,true,591830,The Libraries,,"Architectura Curiosa Nova: Das ist, neue ergotzliche Sinn und Kunstreiche auch nutzliche Bau- und Wasser-Kunst . . .","Nuremberg : [s.n.], [1664]",,,,,Author,,Georg Andreas Böckler,"German, Cronheim 1644–1698 Ansbach",,"Böckler, Georg Andreas",German,1644,1698,1664,1664,1664,"Printed book, engraved plates",4 pts. in 1 vol.; H: 13 3/8 in. (34 cm),Purchased with income from the Jacob S. Rogers Fund,,Nuremberg,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/591830,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -125.97 D932,true,true,591824,The Libraries,,"""Alberti Dvreri pictoris et architecti praestantissimi De vrbibvs...""","Paris: Officina Christiani Wecheli, 1535",,,,,Author,,Albrecht Dürer,"German, Nuremberg 1471–1528 Nuremberg",,"Dürer, Albrecht",German,1471,1528,1535,1535,1535,Illustrated book,78 pp.; H: 13 3/4 in. (35 cm),Purchased with income from the Jacob S. Rogers Fund,,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/591824,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -912.1 K63 Q,true,true,591834,The Libraries,,"Athanasii Kircheri e Soc. Jesu China monumentis: qua sacris qua profanis, nec non variis naturae & artis spectaculis, aliarumque rerum memorabilium argumentis illustrata",,,,,,Author,,Athanasius Kircher,"German, 1602–1680",,Kirchner Athanasius,German,1602,1680,1667,1667,1667,Printed book,H: 14 5/8 in. (37 cm),"Jane E. Andrews Fund, in memory of her husband, William Loring Andrews",,Amsterdam: Apud Joannem Janssonium a Waesberge,"& Elizeum Weyerstraet, 1667",,,,,,,,,,,http://www.metmuseum.org/art/collection/search/591834,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -916.3 An4 F,true,true,591854,The Libraries,,The New Zealanders,"London: T. McLean, 1847",,,,,Author,,George French Angas,"British, 1822–1886",,Angas George French,British,1822,1886,1847,1847,1847,Illustrated book,H: 22 1/8 in. ( 56 cm),Purchased with income from the Jacob S. Rogers Fund,,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/591854,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -161.1 C44 Q,true,true,591840,The Libraries,,"The Gentleman and Cabinet-maker's Director: Being a Large Collection of . . . Designs of Household Furniture in the Gothic, Chinese and Modern Taste . . .","London: Thomas Chippendale, 1754",,,,,Author,,Thomas Chippendale,"British, baptised Otley, West Yorkshire 1718–1779 London",,"Chippendale, Thomas",British,1718,1779,1754,1754,1754,"Printed book, engraved plates",17 3/4 x 12 1/4 in. (45 x 31 cm),Library Purchase,,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/591840,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -926.1 M642,true,true,591864,The Libraries,,Paradise Lost,"Hammersmith: Doves Press, 1902",,,,,Author,,John Milton,"British, London 1608–1674 London",,"Milton, John",British,1608,1674,1902,1902,1902,Printed book,386 + [2] pp.; H: 9 1/2 in. (24 cm),Presented by Alice M. Dike,,,,,,,,,,,,|,,http://www.metmuseum.org/art/collection/search/591864,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -143.7W41 W413,true,true,591841,The Libraries,,An Address to the Workmen in the Pottery on the Subject of Entering into the Service of Foreign Manufacturers,"Newcastle: J. Smith, 1783 (1st ed.)",,,,,Artist,,Josiah Wedgwood,"British, Burslem, Stoke-on-Trent 1730–1795 Burslem, Stoke-on-Trent",,"Wedgwood, Josiah",British,1730,1795,1783,1783,1783,Printed book,24 pp.; H: 7 7/8 in. (20 cm),Purchased with income from the Jacob S. Rogers Fund,,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/591841,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -170.9 F46,true,true,591855,The Libraries,,"On Painting in Oil and Water Colours, Landscape and Portraits . . .",,,,,,Author,,Theodore Henry Adolphus Fielding,"British, Yorkshire 1781–1851 Croyden",,"Fielding, Theodore Henry Adolphus",British,1781,1851,1839,1839,1839,Illustrated book,H: 11 in. (28 cm),Purchased with income from the Jacob S. Rogers Fund,,London: published for the author,"by Ackermann and Co., 1839",,,,,,,,,,,http://www.metmuseum.org/art/collection/search/591855,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -920.3 W52 F,true,true,591842,The Libraries,,The letters of Sir Richard Westmacott,,,,,,Author,,Sir Richard Westmacott,"British, 1775–1856",,"Westmacott, Richard, Sir",British,1775,1856,19th century,1800,1899,Collection of letters,"18 vols. (ca. 1,400 items) plus index",Purchased with income from the Jacob S. Rogers Fund,,,,,,,,,,,,|,,http://www.metmuseum.org/art/collection/search/591842,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -KKL273.1.Z94 C4 1815,true,true,681372,The Libraries,,Codice civile generale austriaco,,,,,,Binder,,Luigi Lodigiani,"Italian, 1778–1843",,"Lodigiani, Luigi",Italian,1778,1843,1815,1815,1815,,Length: 9 13/16 in. (25 cm),"Gift of Jayne Wrightsman, 2008",,Milan,,,Italy,,,,,,,|,,http://www.metmuseum.org/art/collection/search/681372,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -226 B641,true,true,591845,The Libraries,,Descrizione degli' istromenti armonici d'ogni genere . . .,Presso Pietro Paolo Montignani-Mirabili,,,,,Author,,Filippo Buonanni,"Italian, 1638–1725",,"Buonanni, Filippo",Italian,1638,1725,1806,1806,1806,"Printed books, engraved plates",2 vols.,Presented by Mr. S.P. Avery,,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/591845,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -532 Eg961,true,true,591847,The Libraries,,"Narrative of the Operations and Recent Discoveries Within the Pyramids, Temples, Tombs and Excavations in Egypt and Nubia; and of a Journey to the Coast of the Red Sea, in Search of the Ancient Berenice; and Another to the Oasis of Jupiter Ammon","London: J. Murray, 1820",,,,,Author,,Giovanni Battista Belzoni,"Italian, 1778–1823",,"Belzoni, Giovanni Battista",Italian,1778,1823,1820,1820,1820,Printed book,xix + 533 pp.; H: 11 3/8 in. (29 cm),Presented by Mr. Theodore M. Davis,,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/591847,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -109A L83,true,true,591829,The Libraries,,"Trattato dell' arte della pittura, scultura et architetettura","Milan: [s.n.], 1585",,,,,Author,,Giovanni Paolo Lomazzo,"Italian, Milan 1538–1600 Milan",,"Lomazzo, Giovanni Paolo",Italian,1538,1600,1585,1585,1585,Printed book,H: 8 5/8 in. (22 cm),Purchased with income from the Library Fund,,Milan,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/591829,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -120.32P17 P17,true,true,591828,The Libraries,,I quattro libri dell'architettura di Andrea Palladio . . .,"Venice: Domenico de'Franceschi, 1570",,,,,Author,,Andrea Palladio,"Italian, Padua 1508–1580 Vicenza",,"Palladio, Andrea",Italian,1508,1580,1570,1570,1570,Illustrated book,"4 pts. in 1 vol., 128 pp.; H: 4 3/4 in. (12 cm)",Library Purchase,,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/591828,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -131.1M58 C75,true,true,591825,The Libraries,,Vita di Michelagnolo Buonarroti raccolta per Ascanio Condivi da la Ripa Transone,"Rome: Antonio Blado, 1553 (1st ed.)",,,,,Author,,Ascanio Condivi,"Italian, Ripatransone 1525–1574 Ripatransone",,"Condivi, Ascanio",Italian,1525,1574,1553,1553,1553,Illustrated book,H: 7 7/8 in. (20 cm),Purchased with income from the Jacob S. Rogers Fund,,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/591825,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -120 Se6,true,true,591827,The Libraries,,"Bononiensis de architectura libri quinque quibus cuncta fere architectonicae facultatis mysteria docte, perspicue . . .",,,,,,Author,,Sebastiano Serlio,"Italian, Bologna 1475–1554 Fontainebleau",,"Serlio, Sebastiano",Italian,1475,1554,1568–69,1568,1569,Illustrated book,5 pts. in 1 vol.; H: 12 5/8 in. (32 cm),Purchased with income from the Jacob S. Rogers Fund,,Venice: Apud Francifcum de Francifcis Senenfem,"& Joanneum Chriegher, 1568–1569",,,,,,,,,,,http://www.metmuseum.org/art/collection/search/591827,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -U101 .M7814 1760,true,true,681554,The Libraries,,"Memoires de Montecuculi, generalissime des troupes de l'empereur : divisés en trois livres : I. De l'art militaire en général, II. De la guerre contre le turc, III. Relation de la campagne de 1664",,,,,,Author,,Prince Raimondo Montecuccoli,"Italian, 1609–1680",,"Montecuccoli, Raimondo, Prince",Italian,1609,1680,1760,1760,1760,,"xl, 510, [4] pages: leaves of plates, illustrations, engravings; Height: 6 11/16 in. (17 cm)","Gift of Jayne Wrightsman, 2008",,Paris,,,France,,,,,,,|,,http://www.metmuseum.org/art/collection/search/681554,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -201.9Av3 Av32,true,true,591857,The Libraries,,"S. P. Avery, Engraver on Wood","New York: [s.n.], [18--]",,,,,Author,,Samuel Putnam Avery Sr.,"American, New York 1822–1904 New York",,"Avery, Samuel Putnam, Sr.",American,1822,1904,1800s,1800,1810,Scrapbook,Height: 11 13/16 in. (30 cm),Presented by Emma Avery Welcher and Amy Ogden Welcher,,,,,,,,,,,,||,,http://www.metmuseum.org/art/collection/search/591857,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -543.1N48 M97 Q,true,true,591850,The Libraries,,"A Descriptive Atlas of the Cesnola Collection of Cypriote Antiquities in the Metropolitan Museum of Art, New York (1885–1903)","Boston: J. R. Osgood, 1885–1903",,,,,Author|Author,Introduction by,Ernst Curtius|Luigi Palma di Cesnola,,,"Curtius Ernst|Cesnola, Luigi Palma di",American,1832,1904,1885–1904,1885,1904,Illustrated book,3 vols.; H: 17 3/8 in. (44 cm),Presented by General L. P. di Cesnola,,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/591850,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -UD S83 1842 v.1,true,true,591852,The Libraries,,"Incidents of Travel in Central America, Chiapas, and Yucatan","London: J. Murray, 1842 (new ed.)",,,,,Author,,John Lloyd Stephens,"American, 1805–1852",,Stephens John Lloyd,American,1805,1852,1842,1842,1842,Printed book,2 vols.; H: 9 in. (23 cm),"Gift of the Dept. of Twentieth Century Art, 1998",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/591852,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -UD S83 1843 v.2,true,true,591853,The Libraries,,Incidents of Travel in Yucatan,"New York: Harper Bros., 1843",,,,,Author,,John Lloyd Stephens,"American, 1805–1852",,Stephens John Lloyd,American,1805,1852,1843,1843,1843,Illustrated book,2 vols.; 9 in. (23 cm),"Gift of the Dept. of Twentieth Century Art, 1998",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/591853,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -272.4 Au8,true,true,591859,The Libraries,,Autographs and Sketches from Artist Friends to Samuel P. Avery,,,,,,Author,,Samuel Putnam Avery Sr.,"American, New York 1822–1904 New York",,"Avery, Samuel Putnam, Sr.",American,1822,1904,1874–80,1874,1880,Letter-book (manuscript),1 vol.; H: 11 3/8 in. (29 cm),Presented by Amy Ogden Welcher and Emma Avery Welcher,,,,,,,,,,,,||,,http://www.metmuseum.org/art/collection/search/591859,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -201.9 Av3,true,true,591858,The Libraries,,Diaries of Samuel P. Avery,,,,,,Author,,Samuel Putnam Avery Sr.,"American, New York 1822–1904 New York",,"Avery, Samuel Putnam, Sr.",American,1822,1904,1871–82,1871,1882,Bound manuscript,5 vols.; H: 7 1/8 in. (18 cm),"Presented by Emma Avery Welcher, Amy Ogden Welcher and Alice Lee (Mrs. C. Telford) Erickson. (Avery's granddaughters)",,,,,,,,,,,,|,,http://www.metmuseum.org/art/collection/search/591858,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -192H221 B61,true,true,591861,The Libraries,,"Scrapbook containing newspaper and magazine clippings, calling cards, sketches, photographs of paintings, and two sales catalogs of Harnett's (William Michael Harnett, 1848–1892) work",,,,,,Author,,William Ignatius Blemly,American,,Blemly William Ignatius,American,0018,0018,,1848,1900,Scrapbook,108 pp.; H: 10 1/4 in. (26 cm),Presented by James Maroney in friendship for H. Barbara Weinberg,,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/591861,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -AE25 .E53 1751 Q,true,true,591843,The Libraries,,"Encyclopédie, ou Dictionnaire raisonné des sciences, des arts et des métiers",,,,,,Publisher|Author|Author,,Briasson|Denis Diderot|Jean Le Rond d'Alembert,"French, 1713–1784|French, 1717–1783",,"Briasson|Diderot, Denis|Alembert, Jean Le Rond d'",French|French,1713 |1717,1784 |1783,1751–65,1751,1765,,17 volumes ; H: 15 3/4 in. (40 cm),Bequest of Marianne Khuner,,Paris,,,France,,,,,,,,,http://www.metmuseum.org/art/collection/search/591843,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -AY831 .Z7 1784,true,true,681357,The Libraries,,"Le calendrier de la cour : tiré des ephémérides, pour l'année bissextile mil sept-cent quatre-vingt-quatre : contenant le lieu du soleil, son lever, son coucher, sa déclinaison: le lever de la lune & son coucher, &c. : avec la naissance des rois, reines, princes & princesses de l'Europe imprimé pour la famille royale et maison de sa Majesté",,,,,,Printer|Author,Chez,La Veuve Hérissant|Jacques Collombat,"French, 1668–1744",,"Hérissant, La Veuve|Collombat, Jacques",French|French,1668,1744,1784,1784,1784,,Height: 4 5/16 in. (11 cm),"Gift of Jayne Wrightsman, 2009",,Paris,,,France,,,,,,,|,,http://www.metmuseum.org/art/collection/search/681357,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -PQ1189 .M66 1765,true,true,680832,The Libraries,,"Anthologie françoise, ou, Chansons choisies, depuis le 13a siècle jusqu'à présent",,,,,,Publisher|Author|Author,,Barbou|Jean Monnet|Anne Gabriel Meusnier de Querlon,"French, 1703–1785|French, 1702–1780",,"Barbou|Monnet, Jean|de Querlon, Anne Gabriel Meusnier",French|French,0 |1703 |1702,0 |1785 |1780,1765,1765,1765,,"3 volumes, frontispiece, portraits, plates, Height: 7 1/2 in. (19 cm)","Gift of Jayne Wrightsman, 2009",,Paris,,,France,,,,,,,|,,http://www.metmuseum.org/art/collection/search/680832,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2000.194.1, .2",false,true,26555,Arms and Armor,Pair of flintlock pistols,Pair of Flintlock Pistols,"Scottish, Doune",,,,,Gunsmith,,Alexander Campbell,"Scottish, Doune, died 1790",,"Campbell, Alexander",Scottish,1690,1790,ca. 1750–70,1725,1795,"Steel, silver",L. 11 3/4 in. (29.8 cm),"Gift of Edward Coe Embury Jr., Philip Aymar Embury, and Dorothy Embury Staats, in memory of Aymar Embury II and his wife, Jane Embury Benepe, 2000",,Doune,Perthshire,,,,,,,,,Firearms-Pistols-Flintlock,,http://www.metmuseum.org/art/collection/search/26555,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -52.208.5,false,true,24824,Arms and Armor,Wheellock pistol,Wheellock Pistol,"Swiss, Zürich",,,,,Gunsmith,,Felix Werder,"Swiss, Zurich, 1591–1673",,"Werder, Felix",Swiss,1591,1673,dated 1640,1640,1640,"Steel, bronze, gold, wood",Cal. .50 in. (12.7 mm); L. 24 3/8 in. (61.9 cm); L. of barrel 17 in. (43.2 cm); L. of lockplate 6 5/8 in. (16.8 cm); Wt. 2 lb. 4 oz. (1021 g),"Gift of Alan Rutherfurd Stuyvesant, 1952",,Zürich,,,,,,,,,,Firearms-Pistols,,http://www.metmuseum.org/art/collection/search/24824,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -10.42,false,true,22115,Arms and Armor,Wheellock pistol,Wheellock Pistol,"Swiss, Zürich",,,,,Gunsmith,,Felix Werder,"Swiss, Zurich, 1591–1673",,"Werder, Felix",Swiss,1591,1673,dated 1630,1630,1630,"Steel, bronze, wood (beech), silver",L. 23 1/4 in. (59.1 cm); L. of barrel 16 in. (40.6 cm); L. of plug 1 3/4 in. (4.4 cm); L. of lock 6 1/2 in. (16.5 cm); Cal. 458 in. (11.6 mm); Wt. 3 lb. 7 oz. (1559 g),"Rogers Fund, 1910",,Zürich,,,,,,,,,,Firearms-Pistols-Wheellock,,http://www.metmuseum.org/art/collection/search/22115,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"14.25.790a, b",false,true,22285,Arms and Armor,Armored skirt (base),Armored Skirt (Base),"Austrian, Innsbruck",,,,,Armorer,Attributed to,Konrad Seusenhofer,"Austrian, Innsbruck, died 1517",,"Seusenhofer, Konrad",Austrian,1417,1517,ca. 1510–15,1485,1540,"Steel, gold",D. 1/16 in. (0.2 cm); Wt. 12 lb. 14 oz. (5840 g),"Gift of William H. Riggs, 1913",,Innsbruck,,,,,,,,,,Armor for Man,,http://www.metmuseum.org/art/collection/search/22285,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.158.121,false,true,22824,Arms and Armor,Left pauldron (shoulder defense) of a boy's costume armor,Left Pauldron (Shoulder Defense) from a Boy's Costume Armor,"Austrian, Innsbruck",,,,,Armorer,,Hans Seusenhofer,"Austrian, Innsbruck, 1470–1555",,"Seusenhofer, Hans",Austrian,1470,1555,ca. 1532,1507,1557,"Steel, copper alloy, gold",H. 4 1/2 in. (11.4 cm); W. 4 1/2 in. (11.4 cm); D. 3 1/2 in. (8.9 cm),"Bashford Dean Memorial Collection, Funds from various donors, 1929",,Innsbruck,,,,,,,,,,Armor Parts,,http://www.metmuseum.org/art/collection/search/22824,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.158.304,false,true,23287,Arms and Armor,Left tasset (thigh defense) from a boy's costume armor,Left Tasset (Thigh Defense) from a Boy's Costume Armor,"Austrian, Innsbruck",,,,,Armorer,,Hans Seusenhofer,"Austrian, Innsbruck, 1470–1555",,"Seusenhofer, Hans",Austrian,1470,1555,ca. 1532,1507,1557,"Steel, copper alloy, gold",H. 8 3/8 in. (21.3 cm); W. 6 in. (15.2 cm); D. 4 1/2 in. (11.4 cm),"Bashford Dean Memorial Collection, Funds from various donors, 1929",,Innsbruck,,,,,,,,,,Armor Parts-Thigh and Leg Defense,,http://www.metmuseum.org/art/collection/search/23287,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"29.157.13a, b",false,true,27958,Arms and Armor,Rapier with scabbard,Rapier with Scabbard,"German; blade, Spanish, Toledo",,,,,Bladesmith,Blade signed by,Juan Martinez,"Spanish, Toledo, active ca. 1600",,"Martinez, Juan",Spanish,1575,1625,ca. 1580,1555,1605,"Steel, leather, gold, copper wire, wood, velvet",L. 48 3/4 in. (123.8 cm); W. 8 1/2 in. (21.6 cm); D. 5 in. (12.7 cm); Wt. 3 lb. 5 oz. (1507 g); Wt. of scabbard 6 oz. (170.1 g),"Bashford Dean Memorial Collection, Gift of Mr. and Mrs. Robert W. de Forest, 1929",,Toledo,,,,,,,,,,Swords,,http://www.metmuseum.org/art/collection/search/27958,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"29.157.13a, b, .14",false,true,690007,Arms and Armor,Rapier and parrying dagger,Rapier and Parrying Dagger,"German; rapier blade, Spanish, Toledo",,,,,Bladesmith,Rapier blade signed by,Juan Martinez,"Spanish, Toledo, active ca. 1600",,"Martinez, Juan",Spanish,1575,1625,ca. 1580,1555,1605,"Steel, gold, copper wire, wood, velvet",L. 48 3/4 in. (123.8 cm); W. 8 1/2 in. (21.6 cm); D. 5 in. (12.7 cm); Wt. 3 lb. 5 oz. (1507 g),"Bashford Dean Memorial Collection, Gift of Mr. and Mrs. Robert W. de Forest, 1929",,Toledo,,,,,,,,,,Swords,,http://www.metmuseum.org/art/collection/search/690007,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1997.356,false,true,26554,Arms and Armor,Snaphaunce hunting rifle,Snaphaunce Hunting Rifle,"Swedish, Stockholm",,,,,Stock maker,Signed by,Jonas Schertiger the Younger,"Swedish, active 1715–died 1748",,"Schertiger, Jonas the Younger",Swedish,1715,1748,dated 1722,1722,1722,"Steel, wood (walnut), brass, horn",L. 43 3/8 in. (117.8 cm),"Purchase, Gifts of Prince Albrecht Radziwill and Charles M. Schott Jr., by exchange, and Rogers Fund, 1997",,Stockholm,,,,,,,,,,Firearms,,http://www.metmuseum.org/art/collection/search/26554,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -60.151,false,true,24849,Arms and Armor,Barbute,Barbute,"Italian, Brescia",,,,,Armorer,,"Jacopo da Cannobio, called Bichignola","Italian, Brescia, active ca. 1460",,"Cannobio, Jacobo de","Italian, Brescia",1435,1485,ca. 1460,1435,1485,Steel,H. 11 3/8 in. (28.9 cm); W. 7 7/8 in. (20 cm); D. 10 5/8 in. (27 cm); Wt. 5 lb. 4 oz. (2381 g),"Gift of Mrs. George A. Douglass, in memory of her husband, 1960",,,Brescia,,,,,,,,,Helmets,,http://www.metmuseum.org/art/collection/search/24849,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -04.3.152,false,true,21962,Arms and Armor,Hunting knife,Hunting Knife,"Austrian, Hall",,,,,Bladesmith,,Hans Sumersperger,"Austrian, Hall, active 1492–1498",,"Sumersperger, Hans",Austrian,1492,1498,ca. 1500,1475,1525,"Steel, copper alloy, wood, bone, mother-of-pearl",L. 18 5/8 in. (47.29 cm); W. 2 1/2 in. (6.35 cm),"Rogers Fund, 1904",,Hall,Tyrol,,,,,,,,,Knives,,http://www.metmuseum.org/art/collection/search/21962,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.158.704,false,true,23342,Arms and Armor,Hunting sword,Hunting Sword,"Austrian, Hall",,,,,Sword maker,Attributed to,Hans Sumersperger,"Austrian, Hall, active 1492–1498",,"Sumersperger, Hans",Austrian,1492,1498,ca. 1500,1475,1525,"Steel, copper alloy, horn, bone",L. 49 1/2 in. (125.7 cm); L. of blade 40 in. (101.6 cm); W. of blade 1 7/16 in. (3.6 cm); Wt. 3 ls. 9 oz. (1615.9 g),"Bashford Dean Memorial Collection, Funds from various donors, 1929",,Hall,Tyrol,,,,,,,,,Swords,,http://www.metmuseum.org/art/collection/search/23342,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.150.5a,false,true,23089,Arms and Armor,Sallet,Sallet,"Austrian, Innsbruck",,,,,Armorer,,Jörg Wagner,"Austrian, Innsbruck, recorded 1485–92",,"Wagner, Jörg",Austrian,1485,1492,ca. 1485–95,1460,1520,Steel,D. of tail 7 1/2 in. (19.1 cm),"Bashford Dean Memorial Collection, Bequest of Bashford Dean, 1928",,Innsbruck,,,,,,,,,,Helmets,,http://www.metmuseum.org/art/collection/search/23089,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1991.4,false,true,24945,Arms and Armor,Elements of a light-cavalry armor,Elements of a Light-Cavalry Armor,"Austrian, Innsbruck or Mühlau",,,,,Armorer,,Christian Schreiner the Younger,"Austrian, Mühlau, recorded 1499–1528",,"Schreiner the Younger, Christian",Austrian,1499,1528,ca. 1505–10,1480,1535,"Steel, leather",H. as mounted approximately 32 in. (81.28 cm); Wt. 21 lb. 7 oz. (9724 g),"Purchase, Mr. and Mrs. Arthur Ochs Sulzberger Gift, in honor of Helmut Nickel, 1991",,Innsbruck,,,,,,,,,,Armor for Man-1/2 Armor,,http://www.metmuseum.org/art/collection/search/24945,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -04.3.229,false,true,21984,Arms and Armor,Sallet,Sallet,"Austrian, Innsbruck",,,,,Armorer,Attributed to,Adrian Treytz the Elder,"Austrian, Innsbruck, active ca. 1473–92",,"Treytz the Elder, Adrian",Austrian,1448,1517,ca. 1480,1455,1505,Steel,H. 9 3/4 in. (24.8 cm); W. 11 1/2 in. (29.2 cm); D. 14 5/8 in. (37.1 cm); Wt. 6 lb. 2 oz. (2778 g),"Rogers Fund, 1904",,Innsbruck,,,,,,,,,,Helmets,,http://www.metmuseum.org/art/collection/search/21984,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.158.35,false,true,23234,Arms and Armor,Close helmet,Close Helmet,"Austrian, Innsbruck",,,,,Armorer,,Hans Maystetter,"Austrian, Innsbruck, documented 1508–33",,"Maystetter, Hans",Austrian,1508,1533,ca. 1505–10,1480,1535,Steel,H. 11 in. (27.9 cm); W. 9 in. (22.9 cm); D. 10 3/8 in. (26.4 cm); Wt. 5 lb. 8 oz. (2495 g),"Bashford Dean Memorial Collection, Funds from various donors, 1929",,Innsbruck,,,,,,,,,,Helmets,,http://www.metmuseum.org/art/collection/search/23234,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -28.74.4,false,true,23040,Arms and Armor,Breastplate,Breastplate,"Austrian, Innsbruck",,,,,Armorer,,Hans Maystetter,"Austrian, Innsbruck, documented 1508–33",,"Maystetter, Hans",Austrian,1508,1533,ca. 1510,1485,1535,Steel,H. 17 1/2 in. (44.45 cm); Wt. 5 lb. 11 oz. (2580 g),"Gift of George D. Pratt, 1928",,Innsbruck,,,,,,,,,,Armor Parts-Breastplates,,http://www.metmuseum.org/art/collection/search/23040,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"14.25.1408a, b",false,true,22382,Arms and Armor,Pair of flintlock pistols,Pair of Flintlock Pistols,"Southern Netherlandish, Aachen",,,,,Gunsmith,,Leonardus Graeff,"Aachen (now Germany), active ca. 1670–80",,"Graeff, Leonardus",Aachen,1645,1705,ca. 1675–85,1650,1710,"Steel, gold, silver, ivory",L. of each 19 3/8 in. (49.2 cm); L. of each barrel 12 3/16 in. (30.9 cm); Cal. of each barrel .52 in. (13.2 mm); Wt. of each 2 lb. 3 oz. (992 g),"Gift of William H. Riggs, 1913",,Aachen,,,,,,,,,,Firearms-Pistols-Flintlock,,http://www.metmuseum.org/art/collection/search/22382,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"50.203.1, .2",false,true,24815,Arms and Armor,Pair of wheellock rifles,Pair of Wheellock Rifles Made for Emperor Leopold I (1640–1705),"Bohemian, Prague",,,,,Gunsmith,,Caspar Neireiter,"Bohemian, Prague, recorded 1667–ca. 1730",,"Neireiter, Caspar",Bohemian,1667,1755,ca. 1670–80,1645,1705,"Steel, silver, wood (walnut)",L. of each 43 3/8 in. (110.2 cm); L. of each barrel 32 1/16 in. (81.4 cm); Cal. of each .544 in. (13.8 mm); Wt. of 50.203.1 9 lb. 4 oz. (4196 g); Wt. of 50.203.2 9 lb. 3 oz. (4167 g),"Purchase, Joseph Pulitzer Bequest, 1950",,Prague,,,,,,,,,,Firearms-Guns-Wheellock,,http://www.metmuseum.org/art/collection/search/24815,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -38.137a–c,false,true,24661,Arms and Armor,"Helmet, breastplate, and backplate","Helmet, Breastplate, and Backplate","Flemish, possibly Antwerp",,,,,Armorer,Signed on the backplate by,D. G. V. Lochorst,"Flemish, possibly Antwerp, active ca. 1575",,Lochorst D. G. V.,Flemish,1550,1600,ca. 1575,1550,1600,"Steel, leather, textile (velvet, wool)",H. as mounted 30 1/4 in. (36.8 cm); Wt. 12 lb. 3 oz. (5529 g); helmet (a): H. 15 1/2 in. (39.37 cm); Wt. 3 lb. 12 oz. (1695 g); breastplate (b): H. 18 1/2 in. (47.0 cm); W. 12 in. (30.5 cm); Wt. 4 lb. 9 oz. (2073 g); backplate (c): H. 15 in. (38.1 cm); W. 12 in. (30.5 cm); Wt. 3 lb. 14 oz. (1765 g),"Gift of Christian A. Zabriskie, 1938",,Antwerp,Antwerpen,,,,,,,,,Armor for Man,,http://www.metmuseum.org/art/collection/search/24661,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -09.210.2,false,true,22110,Arms and Armor,Basket-hilted sword,Basket-hilted Sword,"hilt, British; blade, German",,,,,Bladesmith,Blade by,Johannes Wundes the Younger,"Germany, Solingen, active mid-17th century",,"Wundes the Younger, Johannes",Germany,1625,1675,blade dated 1662,1662,1662,"Steel, wood, silver",L. 39 7/8 in. (101.3 cm); L. of blade 34 1/4 in. (87 cm); W. 5 in. (12.7 cm); Wt. 2 lb. 6 oz. (1077 g),"Rogers Fund, 1909",,,,,,,,,,,,Swords,,http://www.metmuseum.org/art/collection/search/22110,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -04.3.163,false,true,21965,Arms and Armor,Wheellock rifle,Wheellock Rifle,"Silesian, Cieszyn",,,,,Gunsmith,,Johannes Hartel,"Polish, Cieszyn (Silesian), active ca. 1650",,"Hartel, Johannes","Polish, Cieszyn (Silesian)",1625,1675,ca. 1650–60,1625,1685,"Steel, silver, wood (red beech), staghorn, mother of pearl",L. 42 3/4 in. (108.6 cm); Cal. 17/32 in. (13.5 mm); L. of barrel 30 7/8 in. (78.4 cm); L. of trigger 8 1 4/ in. (21 cm); Wt. 7 lb. 11 oz. (3500 g),"Rogers Fund, 1904",,Cieszyn,,,,,,,,,,Firearms-Guns-Wheellock,,http://www.metmuseum.org/art/collection/search/21965,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1987.397,false,true,24930,Arms and Armor,Flintlock gun,Gun with Flintlock a la moda,"Spanish, Madrid",,,,,Gunsmith,,Gabriel de Algora,"Spanish, Madrid, documented 1733–died 1761",,"Algora, Gabriel de",Spanish,1733,1761,dated 1744,1744,1744,"Steel, gold, wood",L. of barrel 38 3/4 in. (98.4 cm),"Purchase, Gifts of George D. Pratt, Charles M. Schott Jr., and Bashford Dean, and Bashford Dean Memorial Collection, Funds from various donors, by exchange, 1987",,Madrid,,,,,,,,,,Firearms,,http://www.metmuseum.org/art/collection/search/24930,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"27.160.12, .13",false,true,22985,Arms and Armor,Pair of pistols,Pair of Pistols with Flintlocks a la moda,"Spanish, Madrid",,,,,Gunsmith,,Gabriel de Algora,"Spanish, Madrid, documented 1733–died 1761",,"Algora, Gabriel de",Spanish,1733,1761,ca. 1735–40,1710,1765,"Steel, wood (walnut), gold, silver, horn",L. of each 19 in. (48.3 cm),"Gift of Archer M. Huntington, 1927",,Madrid,,,,,,,,,,Firearms-Pistols-Flintlock,,http://www.metmuseum.org/art/collection/search/22985,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"1986.265.1, .2",true,true,24927,Arms and Armor,Pair of flintlock pistols,Pair of Flintlock Pistols of Empress Catherine the Great (1729–1796),"Russian, Saint Petersburg",,,,,Gunsmith,,Johan Adolph Grecke,"Russian, Saint Petersburg, recorded 1755–90",,Grecke Johan Adolph,Russian,1755,1790,1786,1786,1786,"Steel, ivory, gold, brass",L. of each 14 1/2 in. (36.8 cm),"Gift of John M. Schiff, in memory of Edith Baker Schiff, 1986",,Saint Petersburg,,,,,,,,,,Firearms-Pistols-Flintlock,,http://www.metmuseum.org/art/collection/search/24927,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.158.52,false,true,22775,Arms and Armor,Armet,Armet,Flemish,,,,,Armorer,Possibly by,Guillem Margot,"Flemish, active in Brussels, recorded 1505–20",,"Margot, Guillem",Flemish,1505,1520,ca. 1505,1480,1530,"Steel, copper alloy",H. 12 in. (30.5 cm); W. 8 7/16 in. (21.4 cm); D. 13 in. (33 cm); Wt. 9 lb. 4 oz. (4192 g),"Bashford Dean Memorial Collection, Funds from various donors, 1929",,,,,,,,,,,,Helmets,,http://www.metmuseum.org/art/collection/search/22775,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"29.158.1b–d, g–m",false,true,23226,Arms and Armor,Composed armor,Composed Armor,"Italian, probably Milan",,,,,Armorer,Left elbow cop (h) marked by,Guillem Margot,"Flemish, active in Brussels, recorded 1505–20",,"Margot, Guillem",Flemish,1505,1520,ca. 1500–1520,1475,1545,"Steel, leather",Wt. 16 lb. 15 oz. (7682.7 g),"Bashford Dean Memorial Collection, Funds from various donors, 1929",,probably Milan,,,,,,,,,,Armor for Man,,http://www.metmuseum.org/art/collection/search/23226,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1993.14,true,true,24953,Arms and Armor,Short sword (Yatagan),Short Sword (Yatagan) from the Court of Süleyman the Magnificent (reigned 1520–66),"Turkish, Istanbul",,,,,Sword maker,Workshop of,Ahmed Tekelü,"possibly Iranian, active Istanbul, ca. 1520–30",,"Tekelü, Ahmed",possibly Iranian,1515,1530,ca. 1525–30,1500,1555,"Steel, gold, ivory (walrus), silver, turquoise, pearls, rubies",L. 23 3/8 in. (59.3 cm); L. of blade 18 3/8 in. (46.7 cm); Wt. 1 lb. 8 oz. (691 g),"Purchase, Lila Acheson Wallace Gift, 1993",,Istanbul,,,,,,,,,,Swords,,http://www.metmuseum.org/art/collection/search/24953,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -42.50.32,false,true,24701,Arms and Armor,Sallet,Sallet,"Austrian, Innsbruck or Mühlau",,,,,Armorer,,Kaspar Riederer,"Austrian, Innsbruck and Mühlau, active 1455–99",,"Riederer, Kaspar",Austrian,1455,1499,ca. 1480,1455,1505,"Steel, textile",H. 9 1/2 in. (24.13 cm); W. 9 in. (22.86 cm); D. 14 7/8 in. (37.77 cm); Wt. 6 lb. 8 oz. (2948 g),"Gift of Stephen V. Grancsay, 1942",,Innsbruck or Mühlau,,,,,,,,,,Helmets,,http://www.metmuseum.org/art/collection/search/24701,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.25.1661b,false,true,22400,Arms and Armor,Rondel,Rondel for a Shaffron (Horse's Head Defense),"Austrian, Innsbruck or Mühlau",,,,,Armorer,,Kaspar Riederer,"Austrian, Innsbruck and Mühlau, active 1455–99",,"Riederer, Kaspar",Austrian,1455,1499,ca. 1485–95,1460,1520,Steel,Diam. 7 1/2 in. (19 cm); Wt. 8 oz. (226 g),"Gift of William H. Riggs, 1913",,Innsbruck or Mühlau,,,,,,,,,,Equestrian Equipment-Shaffrons,,http://www.metmuseum.org/art/collection/search/22400,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"48.26a, b",false,true,24785,Arms and Armor,Hunting sword,Hunting Sword,"Austrian, Vienna",,,,,Decorator|Decorator,,Emanuel Pioté|Jacob H. Köchert,"Austrian, Vienna 1781–1865|Austrian, Vienna 1795–1868",,"Pioté, Emanuel|Köchert, Jacob H.",Austrian|Austrian,1781 |1795,1865 |1868,ca. 1825,1800,1850,"Steel, gold, enamel, agate, wood, leather",L. 26 15/16 in. (68.5 cm),"Rogers Fund, 1948",,Vienna,,,,,,,,,,Swords,,http://www.metmuseum.org/art/collection/search/24785,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.150.6,false,true,23090,Arms and Armor,Half armor,Half Armor,"German, Augsburg",,,,,Armorer|Armorer,Helmet and reinforcing pieces attributed to,Kolman Helmschmid|Kolman Helmschmid,"German, Augsburg 1471–1532|German, Augsburg 1471–1532",,"Helmschmid, Kolman|Helmschmid, Kolman",German|German,1471 |1471,1532 |1532,ca. 1510–20 and later,1485,1545,Steel,"as mounted, H. 37 in. (94 cm)","Bashford Dean Memorial Collection, Bequest of Bashford Dean, 1928",,Augsburg,,,,,,,,,,Armor for Man-1/2 Armor,,http://www.metmuseum.org/art/collection/search/23090,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"29.150.1d, e",false,true,23085,Arms and Armor,Pair of tassets,Pair of Tassets of Emperor Charles V of Austria (1500–1558),"German, Augsburg",,,,,Armorer|Armorer,or,Kolman Helmschmid|Desiderius Helmschmid,"German, Augsburg 1471–1532|German, Augsburg, 1513–1579",,"Helmschmid, Kolman|Helmschmid, Desiderius",German|German,1471 |1513,1532 |1579,ca. 1530–40,1505,1565,"Steel, gold",L. of each 8 3/4 in. (22.2 cm); W. of each 9 3/8 in. (23.8 cm); Wt. of right tasset 1 lb. 1 oz. (482 g); Wt. of left tasset 1 lb. 2 oz. (510 g),"Bashford Dean Memorial Collection, Bequest of Bashford Dean, 1928",,Augsburg,,,,,,,,,,Armor Parts,,http://www.metmuseum.org/art/collection/search/23085,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -38.143a,false,true,35795,Arms and Armor,Armet,Armet,"German, Augsburg",,,,,Armorer|Armorer,Attributed to|Attributed to,Kolman Helmschmid|Desiderius Helmschmid,"German, Augsburg 1471–1532|German, Augsburg, 1513–1579",,"Helmschmid, Kolman|Helmschmid, Desiderius",German|German,1471 |1513,1532 |1579,"ca. 1525; bolts, modern",1500,1850,"Steel, gold",Wt. 8 lb. 8 oz. (3849 g),"Gift of Marshall Field, 1938",,Augsburg,,,,,,,,,,Armor for Man,,http://www.metmuseum.org/art/collection/search/35795,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -25.135.66,false,true,22885,Arms and Armor,Burgonet,Burgonet,"German, Augsburg",,,,,Armorer|Decorator,Attributed to|Embossed decoration attributed to,Desiderius Helmschmid|Jörg Sigman,"German, Augsburg, 1513–1579|German, Augsburg, 1527–1601",,"Helmschmid, Desiderius|Sigman, Jörg",German|German,1513 |1527,1579 |1601,ca. 1550–55,1525,1580,Steel,H. 11 1/4 in. (28.6 cm); W. 8 3/4 in. (22.3 cm); D. 15 in. (38.1 cm); Wt. 4 lb. 11 oz. (2126 g),"Gift of William H. Riggs, 1913",,Augsburg,,,,,,,,,,Helmets,,http://www.metmuseum.org/art/collection/search/22885,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2002.233.2–.26,false,true,25106,Arms and Armor,Twenty-five inked impressions of engraved firearms ornament,"Twenty-Five Inked Impressions (or ""Pulls"") of Engraved Firearms Ornament",American and German,,,,,Decorator,,Gustave Young,"American (born Prussia), 1827–1895 Springfield, Massachusetts",,"Young, Gustave","American, born Prussia",1827,1895,ca. 1845–65,1845,1865,Ink on paper,"various sizes, largest: 1 5/8 x 6 3/4 in. (4.1 x 17.1 cm); smallest: 5/8 x 2 in. (1.6 x 5.1 cm)","Gift of Herbert G. Houze, 2002",,,,,,,,,,,,Works on Paper-Prints,,http://www.metmuseum.org/art/collection/search/25106,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -27.206a–l,false,true,23016,Arms and Armor,Foot-combat armor,Foot-Combat Armor of Prince-Elector Christian I of Saxony (reigned 1586–91),"German, Augsburg",,,,,Armorer|Decorator,Decoration attributed to,Anton Peffenhauser|Jörg Sorg the Younger,"German, Augsburg, 1525–1603|German, Augsburg, ca. 1522–1603",,"Peffenhauser, Anton|Sorg the Younger, Jörg",German|German,1525 |1500,1603 |1625,1591,1591,1591,"Steel, gold, leather, copper alloy",H. 38 11/16 in. (98.2 cm); Wt. 46 lb. 3 oz. (20.96 kg); helmet Wt. 11 lb. 9 oz. (5245 g),"Gift of Henry Walters, 1927",,Augsburg,,,,,,,,,,Armor for Man-1/2 Armor,,http://www.metmuseum.org/art/collection/search/23016,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.155.2,false,true,23207,Arms and Armor,Field and tournament armor,"Field and Tournament Armor of Johann Wilhelm (1530–1573), Duke of Saxe-Weimar","German, Augsburg",,,,,Etcher|Armorer,Etched decoration attributed to|Attributed to,Jörg Sorg the Younger|Anton Peffenhauser,"German, Augsburg, ca. 1522–1603|German, Augsburg, 1525–1603",,"Sorg the Younger, Jörg|Peffenhauser, Anton",German|German,1500 |1525,1625 |1603,ca. 1565,1540,1590,"Steel, gold, brass, textile, leather",Wt. 61 lb. 1 oz. (27.7 kg),"Bashford Dean Memorial Collection, Gift of Helen Fahnestock Hubbard, in memory of her father, Harris C. Fahnestock, 1929",,Augsburg,,,,,,,,,,Armor for Man,,http://www.metmuseum.org/art/collection/search/23207,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"14.25.901a, b",false,true,22339,Arms and Armor,Pair of gauntlets,Pair of Gauntlets from a Garniture of Armor of Philip II of Spain (reigned 1554–58),"German, Augsburg",,,,,Etcher|Armorer,,Ulrich Holzmann|Desiderius Helmschmid,"German, Augsburg, recorded 1535–62|German, Augsburg, 1513–1579",,"Holzmann, Ulrich|Helmschmid, Desiderius",German|German,1535 |1513,1562 |1579,1546,1546,1546,"Steel, leather, gold",Gauntlet (a); H. 10 in. (25.5 cm); W. 4 3/4 in. (12 cm); D. 5 in. (12.7 cm); Wt. 1 lb. 1 oz. (475 g); gauntlet (b); H. 10 in. (25.5 cm); W. 4 3/4 in. (12 cm); D. 5 in. (12.7 cm); Wt. 16 oz. (439 g),"Gift of William H. Riggs, 1913",,Augsburg,,,,,,,,,,Armor Parts-Gauntlets,,http://www.metmuseum.org/art/collection/search/22339,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -28.125,false,true,23045,Arms and Armor,Wheellock rifle,Wheellock Rifle Made for Emperor Leopold I,"German, Schwäbisch Gmünd",,,,,Stock maker|Barrelsmith,,Johann Michael Maucher|I. C. Schefl,"German, Schwäbisch Gmünd, 1645–1701|German, Graz, 17th century",,"Maucher, Johann Michael|Schefl, I. C.",German|German,1670 |1601,1701 |1700,ca. 1685,1660,1710,"Steel, pearwood, ivory, mother-of-pearl",L. 43 1/2 in. (110.5 cm); L. of barrel 30 1/4 in. (76.8 cm); Cal. .50 in. (12.7 mm); L. of lock 8 1/16 in. (20.5 cm); L. of stock 43 9/16 in. (110.6 cm); Wt. 8 lb. 10 oz. (3912 g),"Fletcher Fund, 1928",,Schwäbisch Gmünd,,,,,,,,,,Firearms-Guns-Wheellock,,http://www.metmuseum.org/art/collection/search/23045,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.25.1200,false,true,22369,Arms and Armor,Rapier,Rapier,"probably French, Paris",,,,,Hilt Maker|Bladesmith,Hilt signed,Bouqueton|Johannes Wundes,"French, active early 17th century|German, Solingen, ca. 1560–1610",,"Bouqueton|Wundes, Johannes",French|German,1600 |1535,1650 |1635,ca. 1610–20,1585,1645,"Steel, silver, wood",L. 48 1/8 in. (122.2 cm); L. of blade 41 1/2 in. (105.4 cm); W. 9 1/2 in. (24.1 cm); Wt. 3 lb. 6 oz. (1530.9 g),"Gift of William H. Riggs, 1913",,probably Paris,,,,,,,,,,Swords,,http://www.metmuseum.org/art/collection/search/22369,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -38.143b–d,false,true,24660,Arms and Armor,Cuirass and tassets (torso and hip defense),Cuirass and Tassets (Torso and Hip Defense),"German, Augsburg",,,,,Armorer|Decorator,Attributed to|Etching attributed to,Kolman Helmschmid|Daniel Hopfer,"German, Augsburg 1471–1532|German, Kaufbeuren 1471–1536 Augsburg",,"Helmschmid, Kolman|Hopfer, Daniel",German|German,1471 |1471,1532 |1536,ca. 1510–20,1485,1545,"Steel, leather",H. 41 1/2 in. (105.4 cm); Wt. 19 lb. 8 oz. (8845 g),"Gift of Marshall Field, 1938",,Augsburg,,,,,,,,,,Armor,,http://www.metmuseum.org/art/collection/search/24660,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.158.668,false,true,23339,Arms and Armor,Wheellock rifle,Wheellock Rifle,"German, Munich and Augsburg",,,,,Stock maker|Steel-chiseler,,Elias Becker|Caspar Spät,"German, Augsburg, recorded 1633–74|German, Munich, ca. 1611–1691",,"Becker, Elias|Spät, Caspar",German|German,1633 |1586,1674 |1716,dated 1668,1668,1668,"Steel, gold, wood (ebony), staghorn, bone",L. 30 9/16 in. (77.6 cm); L. of barrel 30 9/16 in. (77.6 cm); Cal. .55 in. (13.9 mm); Wt. 7 lb. 14 oz. (3572 g),"Bashford Dean Memorial Collection, Funds from various donors, 1929",,Munich and Augsburg,,,,,,,,,,Firearms-Guns-Wheellock,,http://www.metmuseum.org/art/collection/search/23339,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -04.3.180,false,true,21968,Arms and Armor,Wheellock rifle,Wheellock Rifle,"German, Munich and Augsburg",,,,,Steel-chiseler|Stock maker,,Caspar Spät|Elias Becker,"German, Munich, ca. 1611–1691|German, Augsburg, recorded 1633–74",,"Spät, Caspar|Becker, Elias",German|German,1586 |1633,1716 |1674,ca. 1640–50,1615,1675,"Steel, gold, fruitwood, staghorn, bone",L. 41 7/8 in. (106.4 cm); L. of barrel 30 9/16 in. (77.6 cm); Cal. .46 in. (11.7 mm); Wt. 7 lb. 6 oz. (3350 g),"Rogers Fund, 1904",,Munich and Augsburg,,,,,,,,,,Firearms-Guns-Wheellock,,http://www.metmuseum.org/art/collection/search/21968,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -16.135,false,true,22626,Arms and Armor,Miquelet flintlock gun,Miquelet Flintlock Gun Made for Charles IV of Spain (reigned 1788–1808),"Spanish, Eibar",,,,,Barrelsmith|Lock maker,,Antonio Guisasola|Juan Navaro,"Spanish, Eibar, recorded 1796–1833|Spanish, Eibar, active ca. 1800",,"Guisasola, Antonio|Navaro, Juan",Spanish|Spanish,1796 |1775,1833 |1825,dated 1796,1796,1796,"Steel, gold, silver, wood (walnut)",L. 48 1/2 in. (123.2 cm); L. of barrel 34 in. (86.4 cm); Cal. .68 in. (17.3 mm); Wt. 5 lb. 6 oz. (2438 g),"Rogers Fund, 1916",,Eibar,,,,,,,,,,Firearms-Guns-Miquelet,,http://www.metmuseum.org/art/collection/search/22626,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -57.118,false,true,27568,Arms and Armor,Fencing book,"Gründtliche Beschreibung der freyen ritterlichen unnd adelichen Kunst des Fechtens in allerley gebreuchlichen Wehren mit vil schönen und nützlichen Figuren gezieret und fürgestellet (A Thorough Description of the Free Knightly and Noble Art of Fencing, in All the Typical Guards, Adorned and Arranged with Many Beautiful and Useful Figures)","German, Strasbourg",,,,,Author|Artist,Woodcuts by,Joachim Meyer|Tobias Stimmer,"German, active 16th–17th century|German, active 16th–17th century",,"Meyer, Joachim|Stimmer, Tobias",German|German,1550 |1550,1650 |1650,dated 1570,1570,1570,"Ink, paper",,"Gift of Christian A. Zabriskie, 1957",,Strassburg,,,,,,,,,,Books & Manuscripts,,http://www.metmuseum.org/art/collection/search/27568,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"19.131.1a–r, t–w, .2a–f, l",false,true,22741,Arms and Armor,Armor garniture,"Armor Garniture, Probably of King Henry VIII of England (reigned 1509–47)","British, Greenwich",,,,,Designer|Armorer,Design of the decoration attributed to|Made in the,Hans Holbein the Younger|Royal Workshops at Greenwich,"German, Augsburg 1497/98–1543 London|British, Greenwich, 1511–1640s",,"Holbein, Hans, the Younger|Royal Workshops at Greenwich",German|British,1497 |1511,1543 |1650,dated 1527,1527,1527,"Steel, gold, leather, copper alloys",H. 73 in. (185.4 cm); Wt. 62 lb. 12 oz. (28.45 kg),"Purchase, William H. Riggs Gift and Rogers Fund, 1919",,Greenwich,,,,,,,,,,Armor for Horse and Man,,http://www.metmuseum.org/art/collection/search/22741,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.77,true,true,24860,Arms and Armor,Rapier,Rapier of Prince-Elector Christian II of Saxony (1583–1611),"hilt, German, Dresden; blade, Spanish, Toledo",,,,,Sword cutler|Bladesmith,Hilt by|Blade by,Israel Schuech|Juan Martinez,"German, Dresden, active ca. 1590–1610|Spanish, Toledo, active ca. 1600",,"Schuech, Israel|Martinez, Juan",German|Spanish,1565 |1575,1635 |1625,dated 1606,1606,1606,"Steel, bronze, gold, enamel, paste jewels, cameos, pearls, wood",L. 48 in. (121.9 cm); L. of blade 41 1/4 in. (104.8 cm); W. 6 3/4 in. (17.2 cm); Wt. 3 lb. 4 oz. (1474 g),"Fletcher Fund, 1970",,Dresden|Toledo,,,,,,,,,,Swords,,http://www.metmuseum.org/art/collection/search/24860,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.25.1397,false,true,22379,Arms and Armor,Wheellock rifle,Wheellock Rifle,"German, Munich",,,,,Steel-chiseler|Stock maker,,Daniel Sadeler|Hieronymus Borstorffer,"German, Munich, recorded 1602–1632|German, Munich, recorded 1597–1637",,"Sadeler, Daniel|Borstorffer, Hieronymus",German|German,1602 |1597,1632 |1637,ca. 1610–30,1585,1655,"Steel, gold, wood (ebony, fruitwood), ivory, horn",L. 42 3/8 in. (107.6 cm); L. of barrel 30 7/8 in. (78.4 cm); Cal. .56 in. (14.2 mm); Wt. 8 lb. 13 oz. (4000 g),"Gift of William H. Riggs, 1913",,Munich,,,,,,,,,,Firearms,,http://www.metmuseum.org/art/collection/search/22379,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.25.1396,false,true,22378,Arms and Armor,Wheellock rifle,Wheellock Rifle,"German, Munich",,,,,Steel-chiseler|Stock maker,,Daniel Sadeler|Hieronymus Borstorffer,"German, Munich, recorded 1602–1632|German, Munich, recorded 1597–1637",,"Sadeler, Daniel|Borstorffer, Hieronymus",German|German,1602 |1597,1632 |1637,ca. 1610–30,1585,1655,"Steel, gold, wood (fruitwood), ivory, horn",L. 43 5/8 in. (110.8 cm); L. of barrel 31 15/16 in. (81.1 cm); Cal..56 in. (14.2 mm); Wt. 7 lb. 10 oz. (3450 g),"Gift of William H. Riggs, 1913",,Munich,,,,,,,,,,Firearms-Guns-Wheellock,,http://www.metmuseum.org/art/collection/search/22378,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -32.109.5,false,true,32216,Arms and Armor,Waist lame,Waist Lame,"German, Landshut",,,,,Armorer|Armorer,Attributed to|Attributed to,Wolfgang Grosschedel|Franz Grosschedel,"German, Landshut, active ca. 1517–62|German, Landshut, recorded 1555–79",,"Grosschedel, Wolfgang|Grosschedel, Franz",German|German,1517 |1555,1562 |1579,ca. 1555–60,1530,1585,Steel,H. 2 1/2 in. (6.35 cm); W. 13 in. (33.0 cm); Wt. 5 oz. (141.7 g),"Rogers Fund, 1932",,Landshut,,,,,,,,,,Armor Parts,,http://www.metmuseum.org/art/collection/search/32216,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -32.109.6,false,true,32217,Arms and Armor,Waist lame,Waist Lame,"German, Landshut",,,,,Armorer|Armorer,Attributed to|Attributed to,Wolfgang Grosschedel|Franz Grosschedel,"German, Landshut, active ca. 1517–62|German, Landshut, recorded 1555–79",,"Grosschedel, Wolfgang|Grosschedel, Franz",German|German,1517 |1555,1562 |1579,ca. 1555–60,1530,1585,Steel,H. 2 1/2 in. (6.4 cm); W. 13 in. (33 cm); Wt. 3 oz. (85 g),"Rogers Fund, 1932",,Landshut,,,,,,,,,,Armor Parts,,http://www.metmuseum.org/art/collection/search/32217,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"32.109.1a, b",false,true,32212,Arms and Armor,Right shoulder and arm defense,Right Shoulder and Arm Defense,"German, Landshut",,,,,Armorer|Armorer,Attributed to|Attributed to,Wolfgang Grosschedel|Franz Grosschedel,"German, Landshut, active ca. 1517–62|German, Landshut, recorded 1555–79",,"Grosschedel, Wolfgang|Grosschedel, Franz",German|German,1517 |1555,1562 |1579,ca. 1555–60,1530,1585,"Steel, leather, copper alloy",Shoulder defense (a); H. 8 1/2 in. (21.6 cm); W. 9 in. (22.9 cm); D. 8 3/4 in. (22.2 cm); Wt. 34.1 oz. (966.7 g); shoulder defense (a) and arm defense (b); H. approx. 28 in. (71.1 cm); W. approx. 10 in. (25.4 cm); Wt. 5 lb. 9 oz. (2523.1 g),"Rogers Fund, 1932",,Landshut,,,,,,,,,,Armor Parts,,http://www.metmuseum.org/art/collection/search/32212,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"32.109.2a, b",false,true,32213,Arms and Armor,Left shoulder and arm defense,Left Shoulder and Arm Defense,"German, Landshut",,,,,Armorer|Armorer,Attributed to|Attributed to,Wolfgang Grosschedel|Franz Grosschedel,"German, Landshut, active ca. 1517–62|German, Landshut, recorded 1555–79",,"Grosschedel, Wolfgang|Grosschedel, Franz",German|German,1517 |1555,1562 |1579,ca. 1555–60,1530,1585,Steel,L. approx. 28 in. (71.1 cm); W. approx. 10 in. (25.4 cm); Wt. 5 lb. 6 oz. (2438.1 g),"Rogers Fund, 1932",,Landshut,,,,,,,,,,Armor Parts,,http://www.metmuseum.org/art/collection/search/32213,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -32.109.1–.6,false,true,690153,Arms and Armor,Elements of an armor garniture,Elements of an Armor Garniture,"German, Landshut",,,,,Armorer|Armorer,Attributed to|Attributed to,Wolfgang Grosschedel|Franz Grosschedel,"German, Landshut, active ca. 1517–62|German, Landshut, recorded 1555–79",,"Grosschedel, Wolfgang|Grosschedel, Franz",German|German,1517 |1555,1562 |1579,ca. 1555–60,1530,1585,Steel,"right shoulder and arm defense (32.109.1a, b): H. approx. 28 in. (71.1 cm); W. approx. 10 in. (25.4 cm); Wt. 5 lb. 9 oz. (2523.1 g); left shoulder and arm defense (32.109.2a, b): L. approx. 28 in. (71.1 cm); W. approx. 10 in. (25.4 cm); Wt. 5 lb. 6 oz. (2438.1 g); right thigh and knee defense (32.109.3a–c): L. 20 in. (50.8 cm); W. 8 1/2 in. (21.6 cm); Wt. 3 lb. 3 oz. (1445.8 g); falling buffe (32.109.4): H. 7 1/2 in. (19.1 cm); W. 6 1/2 in. (16.5 cm); Wt. 1 lb. 1 oz. (481.9 g); waist lame (32.109.5): H. 2 1/2 in. (6.35 cm); W. 13 in. (33.0 cm); Wt. 5 oz. (141.7 g); waist lame (32.109.6): H. 2 1/2 in. (6.4 cm); W. 13 in. (33 cm); Wt. 3 oz. (85 g)","Rogers Fund, 1932",,Landshut,,,,,,,,,,Armor Parts,,http://www.metmuseum.org/art/collection/search/690153,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -04.3.278,false,true,21998,Arms and Armor,Composed armor,Composed Armor,"German, Landshut and Augsburg; gorget, Italian",,,,,Armorer|Armorer,"Helmet, arm defenses, gauntlets and leg defenses by|Helmet, arm defenses, gauntlets and leg defenses by",Wolfgang Grosschedel|Franz Grosschedel,"German, Landshut, active ca. 1517–62|German, Landshut, recorded 1555–79",,"Grosschedel, Wolfgang|Grosschedel, Franz",German|German,1517 |1555,1562 |1579,ca. 1550–80,1525,1605,"Steel, gold, leather, textile, copper alloy",Wt. 51 lb. 4 oz. (23.25 kg),"Rogers Fund, 1904",,Landshut|Augsburg,,,,,,,,,,Armor for Man,,http://www.metmuseum.org/art/collection/search/21998,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -32.109.4,false,true,32215,Arms and Armor,Falling buffe for a helmet,Falling Buffe for a Helmet,"German, Landshut",,,,,Armorer|Armorer,Attributed to|Attributed to,Wolfgang Grosschedel|Franz Grosschedel,"German, Landshut, active ca. 1517–62|German, Landshut, recorded 1555–79",,"Grosschedel, Wolfgang|Grosschedel, Franz",German|German,1517 |1555,1562 |1579,ca. 1555–60,1530,1585,Steel,H. 7 1/2 in. (19.1 cm); W. 6 1/2 in. (16.5 cm); Wt. 1 lb. 1 oz. (481.9 g),"Rogers Fund, 1932",,Landshut,,,,,,,,,,Helmets Parts,,http://www.metmuseum.org/art/collection/search/32215,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -32.109.3a–c,false,true,32214,Arms and Armor,Right thigh and knee defense,Right Thigh and Knee Defense,"German, Landshut",,,,,Armorer|Armorer,Attributed to|Attributed to,Wolfgang Grosschedel|Franz Grosschedel,"German, Landshut, active ca. 1517–62|German, Landshut, recorded 1555–79",,"Grosschedel, Wolfgang|Grosschedel, Franz",German|German,1517 |1555,1562 |1579,ca. 1555–60,1530,1585,Steel,L. 20 in. (50.8 cm); W. 8 1/2 in. (21.6 cm); Wt. 3 lb. 3 oz. (1445.8 g),"Rogers Fund, 1932",,Landshut,,,,,,,,,,Armor Parts-Knee Defenses,,http://www.metmuseum.org/art/collection/search/32214,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"04.3.278; 22.147.6, .7",false,true,35774,Arms and Armor,Composed armor,Composed Armor,"German, Landshut and Augsburg; gorget, Italian",,,,,Armorer|Armorer,"Helmet, arm defenses, gauntlets and leg defenses by|Helmet, arm defenses, gauntlets and leg defenses by",Franz Grosschedel|Wolfgang Grosschedel,"German, Landshut, recorded 1555–79|German, Landshut, active ca. 1517–62",,"Grosschedel, Franz|Grosschedel, Wolfgang",German|German,1555 |1517,1579 |1562,ca. 1550–80,1525,1605,"Steel, gold, leather, textile, copper alloy",Wt. 51 lb. 4 oz. (23.25 kg),"Rogers Fund, 1904; gauntlets: Fletcher Fund, 1922",,Landshut|Augsburg,,,,,,,,,,Armor for Man,,http://www.metmuseum.org/art/collection/search/35774,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.25.1425,true,true,22387,Arms and Armor,Double-barreled wheellock pistol,Double-Barreled Wheellock Pistol Made for Emperor Charles V (reigned 1519–56),"German, Munich",,,,,Etcher|Gunsmith,,Ambrosius Gemlich|Peter Peck,"German, Munich and Landshut, active ca. 1520–50|German, Munich, 1503–1596",,"Gemlich, Ambrosius|Peck, Peter",German|German,1495 |1503,1575 |1596,ca. 1540–45,1515,1570,"Steel, gold, wood (cherry), staghorn",L. 19 3/8 in. (49.2 cm); L. of upper barrel 10 in. (25.4 cm); L. of lower barrel 7 5/8 in. (19.4 cm); Cal. of each barrel .46 in. (11.7 mm); Wt. 5 lb. 10 oz. (2550 g),"Gift of William H. Riggs, 1913",,Munich,,,,,,,,,,Firearms-Pistols-Wheellock,,http://www.metmuseum.org/art/collection/search/22387,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"14.25.1398a, b",false,true,22380,Arms and Armor,Pair of wheellock pistols,Pair of Wheellock Pistols Made for the Bodyguard of the Prince-Elector of Saxony,"German, Dresden",,,,,Stock maker|Gunsmith,,Hans Fleischer|Simon Helbig,"German, Dresden, recorded 1590–ca. 1625|German, Dresden, recorded 1609–1642",,"Fleischer, Hans|Helbig, Simon",German|German,1590 |1609,1650 |1642,ca. 1610,1585,1635,"Steel, gold, brass, staghorn, wood (beech)",L. of each pistol 29 1/8 in. (73.9 cm); L. of each barrel 8 3/4 in. (47.6 cm); Cal. of each pistol .57 in. (14.5 mm); Wt. of each pistol 4 lb. 5 oz. (1956 g),"Gift of William H. Riggs, 1913",,Dresden,,,,,,,,,,Firearms-Pistols-Wheellock,,http://www.metmuseum.org/art/collection/search/22380,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.154.5,false,true,29594,Arms and Armor,Miquelet pistol,Miquelet Pistol,"Spanish, Barcelona",,,,,Gunsmith|Gunsmith,,Llorens Torrens|Jacinto Jaumeandreu,"Spanish, Catalonia, active 1793–1824|Spanish, Catalonia, active about 1750–1807",,"Torrens, Llorens|Jaumeandreu, Jacinto",Spanish|Spanish,1793 |1725,1824 |1832,ca. 1790–1807,1765,1832,"Steel, gold, wood (walnut)","Caliber, .665 in, (16.89 mm) Weight, 1 lb. 12 oz. (800 g) Length, 14 11/16 in. (37.31 cm) Length of barrel, 8 13/16 in. (22.38 cm)","Roger's Fund, 1937",,Barcelona,,,,,,,,,,Firearms-Pistols,,http://www.metmuseum.org/art/collection/search/29594,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.25.621,false,true,26505,Arms and Armor,Helmet,Close Helmet for a Boy,"German, Augsburg",,,,,Armorer|Armorer|Decorator,Possibly attributed to|Possibly attributed to|Etching in the style of,Kolman Helmschmid|Desiderius Helmschmid|Daniel Hopfer,"German, Augsburg 1471–1532|German, Augsburg, 1513–1579|German, Kaufbeuren 1471–1536 Augsburg",,"Helmschmid, Kolman|Helmschmid, Desiderius|Hopfer, Daniel",German|German|German,1471 |1513 |1471,1532 |1579 |1536,ca. 1530–40,1505,1565,"Steel, leather, copper alloy",H. 11 in. (27.9 cm); W. 7 3/4 in. (19.7 cm); D. 11 in. (27.9 cm); Wt. 4 lb. 4 oz. (1921 g),"Gift of William H. Riggs, 1913",,Augsburg,,,,,,,,,,Helmets,,http://www.metmuseum.org/art/collection/search/26505,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -04.3.218,false,true,21980,Arms and Armor,Reinforce for a helmet (Gupfe),Reinforce for a Helmet (Gupfe),"German, Augsburg",,,,,Armorer|Etcher|Armorer,,Mattheus Frauenpreis the Elder|Jörg Sorg the Younger|Mattheus Frauenpreis the Younger,"German, Augsburg, recorded 1510–49|German, Augsburg, ca. 1522–1603|German, Augsburg, 1530–1604",,"Frauenpreis the Elder, Mattheus|Sorg the Younger, Jörg|Frauenpreis the Younger, Mattheus",German|German|German,1529 |1500 |1530,1549 |1625 |1604,1549–50,1549,1550,"Steel, gold",H. 5 5/8 in. (14.3 cm); W. 8 11/16 in. (22.1 cm); D. 9 1/8 in. (23.2 cm); Wt. 1 lb. 5 oz. (595 g),"Rogers Fund, 1904",,Augsburg,,,,,,,,,,Helmets,,http://www.metmuseum.org/art/collection/search/21980,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -68.157.1,false,true,24854,Arms and Armor,Percussion revolver,"Colt Model 1849 Pocket Percussion Revolver, Serial no. 81015","American, Hartford, Connecticut",,,,,Engraver|Manufacturer,,Gustave Young|Samuel Colt,"American (born Prussia), 1827–1895 Springfield, Massachusetts|American, Hartford, Connecticut 1814–1862",,"Young, Gustave|Colt, Samuel","American, born Prussia|American",1827 |1814,1895 |1862,ca. 1853,1828,1878,"Steel, brass, silver, ivory",L. of barrel 6 in. (15.24 cm); Cal. .32 in. (8 mm),"Gift of John E. Parsons, 1968",,Hartford,Connecticut,,,,,,,,,Firearms-Pistols-Revolvers,,http://www.metmuseum.org/art/collection/search/24854,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2010.481a–l,false,true,35690,Arms and Armor,Percussion revolver with case and accessories,"Colt Model 1851 Navy Percussion Revolver, Serial Number 29705, with Case and Accessories","American, Hartford, Connecticut",,,,,Engraver|Manufacturer,Engraved by,Gustave Young|Samuel Colt,"American (born Prussia), 1827–1895 Springfield, Massachusetts|American, Hartford, Connecticut 1814–1862",,"Young, Gustave|Colt, Samuel","American, born Prussia|American",1827 |1814,1895 |1862,ca. 1853–54,1828,1879,"Steel, brass, silver, wood, copper, tin, lead, paper",L. of pistol 13 in. (33 cm); L. of barrel 7 1/2 in. (19 cm); Cal. .36 in. (9 mm); case: 14 1/4 x 6 3/8 x 2 1/4 in. (36.2 x 16.2 x 5.6 cm),"Gift of Jack Sayre, 2010",,Hartford,Connecticut,,,,,,,,,Firearms-Pistols-Percussion,,http://www.metmuseum.org/art/collection/search/35690,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -42.50.46,false,true,24708,Arms and Armor,Smallsword hilt,Smallsword Hilt,"British, possibly Birmingham",,,,,Hilt Maker|Hilt Maker,,Matthew Boulton|Josiah Wedgwood,"British, Birmingham 1728–1809 Birmingham|British, Burslem, Stoke-on-Trent 1730–1795 Burslem, Stoke-on-Trent",,"Boulton, Matthew|Wedgwood, Josiah",British|British,1728 |1730,1809 |1795,ca. 1790,1765,1815,"Steel, Wedgwood jasperware",L. 6 7/8 in. (17.5 cm); W. 4 1/2 in. (11.4 cm),"Gift of Stephen V. Grancsay, 1942",,possibly Birmingham,,,,,,,,,,Swords,,http://www.metmuseum.org/art/collection/search/24708,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1985.264a–r,false,true,24921,Arms and Armor,Revolver with case and accessories,"Cased Colt Model 1862 Police Revolver, Serial no. 9174, with Thuer Conversion for Self-contained Cartridges, and Accessories","American, Hartford, Connecticut and New York",,,,,Manufacturer|Steel-chiseler|Maker,Decoration attributed to|Case made by,"Samuel Colt|Louis D. Nimschke|Schuyler, Hartley and Graham","American, Hartford, Connecticut 1814–1862|American, New York, active ca. 1850–1900|American, New York, 19th century",,"Colt, Samuel|Nimschke, Louis D.|Schuyler, Hartley and Graham",American|American|American,1814 |1825 |1801,1862 |1925 |1900,1862,1862,1862,"Steel, gold, silver, brass, wood (rosewood), textile",Cal. .36 in. (9 mm); case 15 x 9 in. (38 x 23 cm),"Gift of Mr. and Mrs. Jerry D. Berger, 1985",,Hartford|New York,Connecticut|New York,,,,,,,,,Firearms-Pistols-Revolvers,,http://www.metmuseum.org/art/collection/search/24921,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -42.50.7a–n,false,true,24684,Arms and Armor,Double-barreled flintlock shotgun with exchangeable percussion locks and barrels,Double-Barreled Flintlock Shotgun with Exchangeable Percussion Locks and Barrels,"French, Versailles and Paris",,,,,Barrelsmith|Gunsmith|Lock maker|Retailer,Exchangeable percussion barrels by|Exchangeable percussion locks by|Exchangeable percussion locks and barrels originally sold by,Léopold Bernard|Nicolas Noël Boutet|B. Montagnon|G. Zaoué,"French, Paris, active 1832–70|French, Versailles and Paris, 1761–1833|French, active ca. 1850|French, active 1857–61",,"Bernard, Léopold|Boutet, Nicholas-Noël|Montagnon, B.|Zaoué, G.",French|French|French|French,1832 |1761 |1825 |1857,1870 |1833 |1875 |1861,"ca. 1818–20; exchangeable percussion locks and barrels, dated 1860",1793,1860,"Steel, gold, wood (walnut), silver, horn",L. of gun 47 3/8 in. (120.3 cm); L. of double barrel 31 7/8 in. (81.0 cm); L. of percussion locks 5 in. (12.7 cm); L. of bullet pouch 7 1/4 in. (18.4 cm); L. of priming flask 6 5/8 in. (16.8 cm); L. of bullet mold 6 3/4 in. (17.1 cm); L. of ramrod extension 10 3/8 in. (26.4 cm); L. of hammer extractor 2 3/16 in. (5.6 cm); L. of brush for cleaning barrel 2 13/16 in. (7.1 cm); L. of bullet extractor 1 5/16 in. (3.3 cm); L. of screwdriver 6 in. (15.2 cm); L. of bullet extractor 3 1/8 in. (7.9 cm); L. of touch hole cleaner 3 3/16 in. (8.1 cm); Cal. .61 in. (15.5 mm); Wt. of gun 6 lbs. 15 oz. (3150 g); Wt. of double barrel 3 lbs. 8 oz. (1600 g); Wt. of percussion locks 5 oz. (150 g); Wt. of bullet pouch 2.5 oz. (71 g); Wt. of priming flask 4.6 oz. (131 g); Wt. of bullet mold 7 oz. (198 g),"Gift of Stephen V. Grancsay, 1942",,Versailles and Paris,,,,,,,,,,Firearms,,http://www.metmuseum.org/art/collection/search/24684,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1987.274,false,true,24929,Arms and Armor,Flintlock gun,Flintlock Gun,"French, Saint-Etienne",,,,,Steel-chiseler|Barrelsmith|Stock maker,,Louis Jaley|Nicolas Carteron|Joseph Blachon,"French, Saint-Étienne 1696–1773|French, Saint-Étienne, recorded ca. 1733–63|French, Saint-Étienne, recorded ca. 1725–35",,"Jaley, Louis|Carteron, Nicolas|Blachon Joseph",French|French|French,1696 |1708 |1700,1773 |1788 |1760,dated 1735,1735,1735,"Steel, gold, wood, silver",L. 57 1/2 in. (146.1 cm); L. of barrel 41 7/8 in. (106.5 cm); caliber .62 in. (15.5 cm),"Harris Brisbane Dick and Rogers Funds, 1987",,Saint-Etienne,,,,,,,,,,Firearms,,http://www.metmuseum.org/art/collection/search/24929,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"14.25.1661b;29.150.5,.70,.80;29.156.66k...",false,true,35789,Arms and Armor,Composite armor,Composite Armor,"Austrian, Innsbruck and Mühlau",,,,,Armorer|Armorer|Armorer,Rondel by|Sallet by|Breastplate and backplate by,Kaspar Riederer|Jörg Wagner|Hans Prunner,"Austrian, Innsbruck and Mühlau, active 1455–99|Austrian, Innsbruck, recorded 1485–92|Austrian, Innsbruck, recorded 1482–99",,"Riederer, Kaspar|Wagner, Jörg|Prunner, Hans",Austrian|Austrian|Austrian,1455 |1485 |1482,1499 |1492 |1499,comprehensively ca. 1485–95,1460,1520,Steel,,"Rondel (14.25.1661b): Gift of William H. Riggs, 1913; sallet, backplate, breastplate (29.150.5a, .70, .80): Bashford Dean Memorial Collection, Bequest of Bashford Dean, 1928; couter (29.156.66k): Bashford Dean Memorial Collection, Gift of Edward S. Harkness, 1929; gauntlet (29.158.255b): Bashford Dean Memorial Collection, Funds from various donors, 1929",,Innsbruck and Mühlau,,,,,,,,,,Armor for Horse and Man,,http://www.metmuseum.org/art/collection/search/35789,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2012.162,false,true,35833,Arms and Armor,Cup-hilted rapier,Cup-Hilted Rapier,"Italian, Milan",,,,,Hilt Maker|Bladesmith|Bladesmith,Attributed to|Attributed to,Francesco Maria Rivolta|Francisco Ruiz the Elder|Francisco Ruiz the Younger,"Italian, Milan, active second half of 17th century|Spanish, Toledo, died after 1617|Spanish, Toledo, active first half of 17th century",", or his son.|, or his father.","Rivolta, Francesco Maria|Ruiz the Elder, Francisco|Ruiz the Younger, Francisco","Italian, Milan|Spanish, Toledo|Spanish, Toledo",1650 |1550 |1600,1700 |1650 |1650,ca. 1670,1645,1695,"Steel, iron wire, wood, textile (felt)",L. 44 5/16 in. (112.5 cm); W. 10 1/8 in. (25.7 cm); D. 5 in. (12.7 cm); Wt. 1 lb 12 oz. (795 g),"Purchase, Arthur Ochs Sulzberger Gift, 2012",,Milan,,,,,,,,,,Swords,,http://www.metmuseum.org/art/collection/search/35833,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2011.486,false,true,35796,Arms and Armor,Engraving,"Two Armorial Trophies marked by the incorporation of a Broken Amphora on the Left, Plate 2 from the Libro de' Trofei","Italian, Rome",,,,,Artist|Former Attribution|Artist|Publisher,,"Anonymous, Italian|Enea Vico|Polidoro da Caravaggio|Antonio Lafreri","Italian, Parma 1523–1567 Ferrara|Italian, Caravaggio ca. 1499–ca. 1543 Messina|French, Orgelet, Franche-Comte ca. 1512–1577 Rome",", 16th century","Anonymous, Italian|Vico, Enea|Caravaggio, Polidoro da|Lafreri, Antonio",Italian|Italian|French,1400 |1523 |1494 |1507,2050 |1567 |1548 |1577,ca. 1550–53,1545,1555,Engraving,9 3/4 x 6 15/16 in. (24.8 x 17.6 cm),"Bequest of Stephen V. Grancsay, 1980",,Rome|Rome,,,,,,,,,,Works on Paper-Prints,,http://www.metmuseum.org/art/collection/search/35796,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -58.171.1,false,true,24844,Arms and Armor,Percussion revolver,"Colt Walker Percussion Revolver, serial no. 1017","American, Whitneyville, Connecticut",,,,,Designer|Engraver|Designer|Manufacturer,,Samuel Colt|Waterman Lilly Ormsby|Captain Samuel Hamilton Walker|Eli Whitney Jr.,"American, Hartford, Connecticut 1814–1862|American, Hampton, Connecticut 1809–1883 Brooklyn, New York|American, died 1847|American, 1820–1888",,"Colt, Samuel|Ormsby, Waterman Lilly|Walker, Samuel Hamilton Captain|Whitney, Eli Jr.",American|American|American|American,1814 |1809 |1747 |1820,1862 |1883 |1847 |1888,1847,1847,1847,"Steel, brass, wood (walnut)",L. 15 1/2 in. (39.37 cm); L. of barrel 9 in. (22.86 cm); Cal. .44 in. (11 mm),"Gift of John E. Parsons, 1958",,Whitneyville,Connecticut,,,,,,,,,Firearms-Pistols-Revolvers,,http://www.metmuseum.org/art/collection/search/24844,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"26.259.5, .6",false,true,22962,Arms and Armor,Pair of flintlock pistols,"Pair of Flintlock Pistols Made for Ferdinand IV, King of Naples and Sicily (1751–1825)","Italo-Spanish, Naples",,,,,Manufacturer|Gunsmith|Barrelsmith,,Royal Arms Manufactory at Torre Annunziata|Michele Battista|Emanuel Esteva,"Italian, Naples, established 1757|Spanish, active in Naples, Italy, recorded about 1760–90|Spanish, active in Naples, Italy, recorded about 1768–73",,"Royal Arms Manufactory at Torre Annunziata|Battista, Michele|Esteva, Emanuel",Italian|Spanish|Spanish,1757 |1735 |1743,1900 |1815 |1798,ca. 1768,1743,1793,"Steel, gold, wood (walnut), silver",L. of each 17 3/8 in. (44.1 cm); L. of each barrel 11 1/16 in. (28.1 cm); Cal. .63 in. (16.0 mm); Wt. of each 2 lb. 4 oz. (1021 g),"Gift of Henry Walters, 1926",,Naples,,,,,,,,,,Firearms-Pistols,,http://www.metmuseum.org/art/collection/search/22962,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"28.196.3, .4",false,true,23051,Arms and Armor,Pair of pistols,Pair of Pistols with Flintlocks a Las Tres Modas,"Spanish, Eibar",,,,,Gunsmith,,Workshop of the Ybarzabel family,"Spanish, Eibar, recorded 1784–1891",,Workshop of the Ybarzabel family,Spanish,1784,1891,late 18th century,1750,1800,"Steel, gold, wood (walnut)",L. of each 11 in. (27.9 cm); L. of each barrel 6 5/8 in. (16.8 cm); Cal. of each .61 in. (15.5 mm); Wt. of each 1 lb. 5 oz. (600 g),"Rogers Fund, 1928",,Eibar,,,,,,,,,,Firearms-Pistols,,http://www.metmuseum.org/art/collection/search/23051,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -19.53.87,false,true,22736,Arms and Armor,Powder flask,Powder Flask,"Spanish, Madrid",,,,,Maker,,Joseph Cano,"Spanish, Madrid, documented 1733–died 1751",,"Cano, Joseph",Spanish,1733,1751,ca. 1740–50,1715,1775,"Tortoiseshell, steel, gold",L. 9 1/2 in. (24.1 cm),"Gift of Charles M. Schott Jr., 1917",,Madrid,,,,,,,,,,Firearms Accessories-Flasks & Primers,,http://www.metmuseum.org/art/collection/search/22736,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -04.3.36,true,true,21940,Arms and Armor,Crossbow,Crossbow of Count Ulrich V of Württemberg (1413–1480),"German, probably Stuttgart",,,,,Maker,Attributed to,Heinrich Heid von Winterthur,"probably Swiss, active Stuttgart, recorded 1453–1460",,"von Winterthur, Heinrich Heid",probably Swiss,1453,1460,dated 1460,1460,1460,"Wood (European hornbeam), horn, animal sinew, staghorn, birch bark, iron alloy, copper alloy, pigment",L. 28 1/16 in. (71.2 cm); W. 25 5/8 in. (65 cm); Wt. 6 lb. 9 oz. (2972 g),"Rogers Fund, 1904",,probably Stuttgart,,,,,,,,,,Archery Equipment-Crossbows,,http://www.metmuseum.org/art/collection/search/21940,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -22.150,false,true,35654,Arms and Armor,Portrait,"Cosimo II de' Medici (1590–1621), Grand Duke of Tuscany",Flemish,,,,,Artist,Workshop of,Justus Sustermans,"Flemish, Antwerp 1597–1681 Florence",,"Sustermans, Justus",Flemish,1597,1681,1597–1681,1597,1681,"Oil on canvas, transferred from wood",78 x 48 in. (198.1 x 121.9 cm),"Gift of Bashford Dean, 1922",,,,,,,,,,,,Miscellaneous-Paintings & Portraits,,http://www.metmuseum.org/art/collection/search/35654,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.590,false,true,35962,Arms and Armor,Album of bit designs,Album of Bit Designs,Bohemian,,,,,Designer,,Rudolf Franz Ferdinand von Talmberg,"Bohemian, ca. 1645–1702",,"Talmberg, Rudolf Franz Ferdinand von",Bohemian,1620,1727,1674,1674,1674,Ink on paper,Covers: 14 1/2 x 9 3/4 in. (36.8 x 24.8 cm); sheets: 14 1/4 x 9 1/2 in. (36.2 x 24.1 cm); plates: approx. 12 1/4 x 4 in. (31.5 x 10 cm),"Purchase, The Elisha Whittelsey Collection, The Elisha Whittelsey Fund; Joseph M. Scheuner and Kenneth and Vivian Lam Gifts; and Bequest of Stephen V. Grancsay, Rogers Fund, Helmut Nickel Gift, and funds from various donors, by exchange, 2013",,,,,,,,,,,,Works on Paper-Prints,,http://www.metmuseum.org/art/collection/search/35962,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -26.261.14,false,true,27937,Arms and Armor,Presentation coin,Presentation Coin (Doppelguldiner) Showing Maximilian I (1459–1519),"Flemish, Antwerp; dies cut in Hall, Austria",,,,,Designer,,Ulrich Ursentaler,"Austrian, Hall, recorded 1508–35, Master of the Mint (Münz–Weister) at Hall in 1535",,Ursentaler Ulrich,Austrian,1508,1535,"minted, 1517; dies cut and dated, 1509",1509,1517,Silver,Diam. 2 1/8 in. (5.4 cm); thickness 1/8 in. (0.3 cm); Wt. 1.9 oz. (53.9 g),"Gift of George D. Pratt, 1926",,Antwerp|Hall,,,,,,,,,,Miscellaneous-Coins and Medals,,http://www.metmuseum.org/art/collection/search/27937,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -04.3.59,false,true,21945,Arms and Armor,Mace,Mace Made for Henry II of France,French,,,,,Damascener,,Diego de Çaias,"Spanish, recorded 1535–49",,Çaias Diego de,Spanish,1535,1549,ca. 1540,1515,1565,"Steel, gold, silver",L. 24 in. (60.9 cm); W. 4 1/2 in. (11.4 cm); Wt. 3 lb. 8 oz. (1588 g),"Rogers Fund, 1904",,,,,,,,,,,,Shafted Weapons,,http://www.metmuseum.org/art/collection/search/21945,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.514.2,false,true,26835,Arms and Armor,Percussion target pistol,Percussion Exhibition Pistol,French,,,,,Gunsmith|Decorator|Barrelsmith,Signed by|Decorated by|Barrel by,Gilles Michel Louis Moutier-Le Page|Antoine Vechte|Léopold Bernard,"French, 1810–1887|French, 1799–1868|French, Paris, active 1832–70",,"Moutier-Le Page|Vechte, Antoine|Bernard, Léopold",French|French|French,1842 |1799 |1832,1865 |1868 |1870,dated 1851,1851,1851,"Steel, gold",L. 15 7/8 in. (40.3 cm); L. of barrel 10 1/4 in. (26.0 cm); Cal. .46 in. (11.7 mm),"Purchase, Ronald S. Lauder Gift, 2013",,Paris|London,,,,,,,,,,Firearms-Pistols-Percussion,,http://www.metmuseum.org/art/collection/search/26835,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2013.514.1, .2",false,true,35866,Arms and Armor,Two percussion target pistols,Two Percussion Exhibition Pistols,French,,,,,Gunsmith|Decorator|Barrelsmith,Signed by|Decorated by|Barrels by,Gilles Michel Louis Moutier-Le Page|Antoine Vechte|Léopold Bernard,"French, 1810–1887|French, 1799–1868|French, Paris, active 1832–70",,"Moutier-Le Page|Vechte, Antoine|Bernard, Léopold",French|French|French,1842 |1799 |1832,1865 |1868 |1870,dated 1849 and 1851,1849,1851,"Steel, gold",2013.514.1: L. 16 in. (40.7 cm); L. of barrel 10 1/4 in. (26.1 cm); Cal. .45 in. (11.4 mm); 2013.514.2: L. 15 7/8 in. (40.3 cm); L. of barrel 10 1/4 in. (26.0 cm); Cal. .46 in. (11.7 mm),"Purchase, Ronald S. Lauder Gift, 2013",,,,,,,,,,,,Firearms-Pistols-Percussion,,http://www.metmuseum.org/art/collection/search/35866,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -55.218,false,true,27558,Arms and Armor,Double-barreled shotgun,Double-barreled Shotgun,French,,,,,Barrelsmith|Gunsmith|Gunsmith,,Antoine Giraud|Nicolas Bouillet|Jean François Brunon,"French, active ca. 1760–80|French, Paris, recorded 1776–1800|French, St. Etienne, born 1737–recorded until 1784",,"Giraud, Antoine|Bouillet, Nicolas|Brunon, Jean François",French|French|French,1735 |1776 |1737,1805 |1800 |1784,"dated 1784, converted to percussion locks ca. 1840–50",1784,1875,"Steel, wood",L. of barrel 34 in. (86.4 cm); Cal. .58 in. (14.7 mm); Wt. 6 lb. 1 oz. (2750 g),"Rogers Fund, 1955",,,,,,,,,,,,Firearms,,http://www.metmuseum.org/art/collection/search/27558,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1992.305.2,false,true,24951,Arms and Armor,Print,"Print of Designs for a Pommel, Quillons, and Locket",British,,,,,Engraver|Designer,After,Wenceslaus Hollar|Hans Holbein the Younger,"Bohemian, Prague 1607–1677 London|German, Augsburg 1497/98–1543 London",,"Hollar, Wenceslaus|Holbein, Hans, the Younger",Bohemian|German,1607 |1497,1677 |1543,1645,1645,1645,Ink on paper,6 1/8 x 4 1/2 in. (15.5 x 11.4 cm),"Gift of Lois Earl Blumka, 1992",,,,,,,,,,,,Works on Paper-Prints,,http://www.metmuseum.org/art/collection/search/24951,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.99,false,true,679156,Arms and Armor,Drawing,"Design for the Decoration of Two Firearms Accessories, a Ladle and a Screwdriver",Spanish,,,,,Designer,,Eusebio Zuloaga,"Spanish, Madrid and Eibar 1808–1898",,"Zuloaga, Eusebio",Spanish,1808,1898,ca. 1850–51,1825,1876,"Pen, ink, and colored wash on paper",8 7/8 x 9 3/8 in. (22.5 x 24 cm),"Purchase, Arthur Ochs Sulzberger Bequest, 2015",,,,,,,,,,,,Works on Paper-Drawings,,http://www.metmuseum.org/art/collection/search/679156,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.100,false,true,679157,Arms and Armor,Drawing,Design for Two Sides of a Dagger Sheath,Spanish,,,,,Designer,,Eusebio Zuloaga,"Spanish, Madrid and Eibar 1808–1898",,"Zuloaga, Eusebio",Spanish,1808,1898,ca. 1850–55,1825,1880,"Pen, ink, and wash on paper",13 5/8 x 10 1/8 in. (34.5 x 25.7 cm),"Purchase, Arthur Ochs Sulzberger Bequest, 2015",,,,,,,,,,,,Works on Paper-Drawings,,http://www.metmuseum.org/art/collection/search/679157,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.101,false,true,679158,Arms and Armor,Drawing,Designs for the Decoration of a Percussion Pistol,Spanish,,,,,Designer,,Eusebio Zuloaga,"Spanish, Madrid and Eibar 1808–1898",,"Zuloaga, Eusebio",Spanish,1808,1898,ca. 1847,1822,1872,"Pen, ink, colored wash, and silver on paper",13 1/2 x 17 5/8 in. (34.5 x 44.8 cm),"Purchase, Arthur Ochs Sulzberger Bequest, 2015",,,,,,,,,,,,Works on Paper-Drawings,,http://www.metmuseum.org/art/collection/search/679158,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2011.35,false,true,35697,Arms and Armor,Mounting for a short sword (Wakizashi goshirae),Mounting for a Short Sword (Wakizashi Goshirae),Japanese,,,,,Lacquer worker|Fittings maker,,Shibata Zeshin|Tsuchiya Masayoshi Yasuchika,"Japanese, 1807–1891|Japanese, died 1860",,"Shibata Zeshin|Yasuchika, Tsuchiya Masayoshi",Japanese|Japanese,1807 |1760,1891 |1860,"mounting 19th century; grip ornaments 16th century; hilt collar (fuchi), dated 1849",1500,1900,"Wood, lacquer, ray skin (samé), thread, copper-gold alloy (shakudō), brass, iron",L. 26 1/8 in. (66.4 cm),"Purchase, The Howard Mansfield Collection, Gift of Howard Mansfield, by exchange, 2011",,,,,,,,,,,,Sword Fittings,,http://www.metmuseum.org/art/collection/search/35697,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.120.101,false,true,24626,Arms and Armor,Sword guard (Tsuba),Sword Guard (Tsuba),Japanese,,,,,Fittings maker|Decorator,Inscribed by|Inscribed by,Kanō Natsuo|Toshiyoshi,"Japanese, 1828–1898|Japanese, recorded 1865–84",,"Natsuo, Kanō|Toshiyoshi",Japanese|Japanese,1828 |1865,1898 |1884,mid-19th century,1825,1875,"Copper-silver alloy (shibuichi), copper-gold alloy (shakudō), gold, silver, copper",H. 2 3/4 in. (7 cm); W. 2 3/8 in. (6 cm); thickness 3/16 in. (0.5 cm); Wt. 4.3 oz. (121.9 g),"The Howard Mansfield Collection, Gift of Howard Mansfield, 1936",,,,,,,,,,,,Sword Furniture-Tsuba,,http://www.metmuseum.org/art/collection/search/24626,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.120.418a–c,false,true,24631,Arms and Armor,Blade and mounting for a short sword (Wakizashi),Blade and Mounting for a Short Sword (Wakizashi),Japanese,,,,,Swordsmith|Fittings maker,Blade attributed to|Mounting by,Yasumitsu|Iwamoto Konkan,"Japanese, Muromachi period, 15th century|Japanese, Edo period, 1744–1801",,Yasumitsu|Iwamoto Konkan,Japanese|Japanese,1400 |1744,1500 |1801,"blade, 15th century; mounting, 18th century",1401,1900,"Steel, wood, lacquer, ray skin (samé); thread, copper-gold alloy (shakudō), copper-silver alloy (shibuichi)",L. 27 1/16 in. (68.8 cm); L. of blade 24 5/16 in. (61.8 cm); L. of cutting edge 19 7/8 in. (50.5 cm); D. of curvature 9/16 in. (1.45 cm); L. of scabbard 19 3/4 in. (50.2 cm); knife (c); L. of blade 7 11/16 in. (19.5 cm); Wt. 0.4 oz. (11.3 g),"The Howard Mansfield Collection, Gift of Howard Mansfield, 1936",,,,,,,,,,,,Swords,,http://www.metmuseum.org/art/collection/search/24631,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"36.120.417a, b",false,true,24630,Arms and Armor,Blade and mounting for a sword (Katana),Blade and Mounting for a Sword (Katana),Japanese,,,,,Swordsmith|Fittings maker,Blade inscribed by|Mounting by,Sukemitsu of Bizen|Iwamoto Konkan,"Japanese, Bizen, Muromachi period, active ca. 1440|Japanese, Edo period, 1744–1801",,Sukemitsu of Bizen|Iwamoto Konkan,Japanese|Japanese,1400 |1744,1500 |1801,"blade, dated 1440; mounting, 18th century",1440,1900,"Steel, wood, lacquer, ray skin (samé), thread, copper-gold alloy (shakudō), copper-silver alloy (shibuichi)",L. 36 1/2 in. (92.7 cm); L. of blade 30 11/16 in. (77.9 cm); L of cutting edge 24 13/16 in. (63.1 cm); D. of curvature 11/16 in. (1.8 cm),"The Howard Mansfield Collection, Gift of Howard Mansfield, 1936",,,Okayama Prefecture,,,,,,,,,Swords,,http://www.metmuseum.org/art/collection/search/24630,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.100.172,true,true,22521,Arms and Armor,Armor (Gusoku),Armor (Gusoku),Japanese,,,,,Armorer|Armorer,"Helmet bowl signed|Breastplate inscribed inside,",Saotome Iyetada|Myōchin Munesuke,"Japanese, Edo period, active early–mid-19th century|Japanese, Edo period, 1688–1735",,"Iyetada, Saotome|Munesuke, Myōchin",Japanese|Japanese,1800 |1688,1875 |1735,16th and 18th centuries,1501,1800,"Iron, lacquer, silk, gilt copper",H. 67 1/2 in. (171.5 cm),"Gift of Bashford Dean, 1914",,,,,,,,,,,,Armor for Man,,http://www.metmuseum.org/art/collection/search/22521,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.100.172; 14.100.527–.528,false,true,35721,Arms and Armor,Armor (Gusoku),Armor (Gusoku),Japanese,,,,,Armorer|Armorer,"Helmet bowl signed|Breastplate inscribed inside,",Saotome Iyetada|Myōchin Munesuke,"Japanese, Edo period, active early–mid-19th century|Japanese, Edo period, 1688–1735",,"Iyetada, Saotome|Munesuke, Myōchin",Japanese|Japanese,1800 |1688,1875 |1735,16th and 18th centuries,1501,1800,"Iron, lacquer, silk, gilt copper",H. 67 1/2 in. (171.5 cm); L. of each foot defense 9 3/4 in. (24.8 cm); W. of each foot defense 4 1/4 in. (10.8 cm),"Gift of Bashford Dean, 1914",,,,,,,,,,,,Armor for Man,,http://www.metmuseum.org/art/collection/search/35721,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -91.2.84,false,true,21913,Arms and Armor,Blade and mounting for a short sword (Wakizashi),Blade and Mounting for a Short Sword (Wakizashi),Japanese,,,,,Swordsmith|Decorator|Fittings maker,Blade inscribed by|Engraving on blade attributed to|Sword guard (Tsuba) inscribed by,Naotane Taikei|Honjo Yoshitane|Yukinaka,"Japanese, Yamagata 1778–1857 Edo|Japanese, Edo period, 19th century|Japanese, Hagi, active 19th century",,"Naotane Taikei|Yoshitane, Honjo|Yukinaka",Japanese|Japanese|Japanese,1778 |1801 |1801,1857 |1900 |1900,"blade, dated 1839; mounting, early–mid 19th century",1801,1900,"Steel, wood, lacquer, rayskin (samé), thread, iron, copper-silver alloy (shibuichi), gold, silver",L. 26 3/4 in. (67.9 cm); L. of blade 23 3/8 in. (59.4 cm); L. of cutting edge 17 27/32 in. (45.3 cm); D. of curvature 9/64 in. (0.36 cm),"Gift of Brayton Ives and W. T. Walters, 1891",,Hagi,Nagato Province (Yamaguchi Prefecture),,,,,,,,,Swords,,http://www.metmuseum.org/art/collection/search/21913,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"91.2.56, .84",false,true,638804,Arms and Armor,Blades and mountings for a pair of swords (Daishō),Blades and Mountings for a Pair of Swords (Daishō),Japanese,,,,,Swordsmith|Decorator|Fittings maker,Blades inscribed by|Engraving on blades attributed to|Sword guards (Tsuba) inscribed by,Naotane Taikei|Honjo Yoshitane|Yukinaka,"Japanese, Yamagata 1778–1857 Edo|Japanese, Edo period, 19th century|Japanese, Hagi, active 19th century",,"Naotane Taikei|Yoshitane, Honjo|Yukinaka",Japanese|Japanese|Japanese,1778 |1801 |1801,1857 |1900 |1900,"blades, dated 1839; mountings, 19th century",1801,1900,"Steel, wood, lacquer, rayskin, silk, iron, copper-gold alloy (shakudō), copper, gold, silver, copper-silver alloy (shibuichi)",L. of sword (katana) 39 1/4 in. (99.8 cm); L. of sword (katana) blade 36 31/32 in. (93.9 cm); L. of sword (katana) blade cutting edge 28 5/8 in. (72.7 cm); D. of sword (katana) blade curvature 1 7/32 in. (3.1 cm); L. of short sword (wakizashi) 26 3/4 in. (67.9 cm); L. of short sword (wakizashi) blade 17 27/32 in. (45.3 cm); D. of short sword (wakizashi) blade curvature 9/64 in. (0.36 cm),"Gift of Brayton Ives and W. T. Walters, 1891",,Hagi,Nagato Province (Yamaguchi Prefecture),,,,,,,,,Swords,,http://www.metmuseum.org/art/collection/search/638804,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"36.120.417a, b, .418a–c",true,true,27789,Arms and Armor,Blades and mountings for a pair of swords (Daishō),Blades and Mountings for a Pair of Swords (Daishō),Japanese,,,,,Swordsmith|Swordsmith|Fittings maker,Long sword (katana) blade inscribed by|Short sword (wakizashi) blade attributed to|Set of sword mountings by,Sukemitsu of Bizen|Yasumitsu|Iwamoto Konkan,"Japanese, Bizen, Muromachi period, active ca. 1440|Japanese, Muromachi period, 15th century|Japanese, Edo period, 1744–1801",,Sukemitsu of Bizen|Yasumitsu|Iwamoto Konkan,Japanese|Japanese|Japanese,1400 |1400 |1744,1500 |1500 |1801,"sword (katana) blade, dated 1440; short sword (wakizashi) blade, 15th century; mountings, late 18th century",1401,1900,"Steel, wood, lacquer, copper-silver alloy (shibuichi), gold, copper, rayskin, silk",L. of sword (katana) 34 1/8 in. (86.7 cm); L. of sword (katana) blade 25 1/4 in. (64.1 cm); L. of sword (katana) scabbard 28 1/4 in. (71.8 cm); L. of short sword (wakizashi) 26 in. (66.0 cm); L. of short sword (wakizashi) blade 20 3/4 in. (52.7 cm); L. of short sword (wakizashi) scabbard 19 3/4 in. (50.2 cm); L. of knife (kozuka) for short sword (wakizashi) mounting 8 3/4 in. (22.2 cm),"The Howard Mansfield Collection, Gift of Howard Mansfield, 1936",,,,,,,,,,,,Swords,,http://www.metmuseum.org/art/collection/search/27789,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1995.52,false,true,26558,Arms and Armor,Cover for a smallsword hilt,Cover for a Smallsword Hilt,"French, Paris",,,,,Sword maker,,Guillaume Pagés,"French, Paris, recorded 1709–56",,Pagés Guillaume,French (Paris),1709,1756,ca. 1725–50,1700,1775,"Leather, ink",L. 8 1/4 in. (20.9 cm); W. 6 7/8 in. (17.5 cm),"Purchase, Rogers Fund, by exchange, 1995",,Paris,,,,,,,,,,Miscellaneous,,http://www.metmuseum.org/art/collection/search/26558,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.514.1,false,true,26893,Arms and Armor,Percussion target pistol,Percussion Exhibition Pistol,"French, Paris",,,,,Gunsmith|Decorator|Barrelsmith,Signed by|Decorated by|Barrel by,Gilles Michel Louis Moutier-Le Page|Antoine Vechte|Léopold Bernard,"French, 1810–1887|French, 1799–1868|French, Paris, active 1832–70",,"Moutier-Le Page|Vechte, Antoine|Bernard, Léopold",French|French|French,1842 |1799 |1832,1865 |1868 |1870,dated 1849,1849,1849,"Steel, gold",L. 16 in. (40.7 cm); L. of barrel 10 1/4 in. (26.1 cm); Cal. .45 in. (11.4 mm),"Purchase, Ronald S. Lauder Gift, 2013",,Paris,,,,,,,,,,Firearms-Pistols-Percussion,,http://www.metmuseum.org/art/collection/search/26893,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2013.512.1, .2",false,true,26980,Arms and Armor,Pair of percussion target pistols,Pair of Percussion Target Pistols Made for Display at the 1844 Exposition des Produits de l'Industrie in Paris,"French, Paris",,,,,Gunsmith|Designer|Barrelsmith,Signed by|Designed by|Barrels by,Alfred Gauvain|Michel Liénard|Léopold Bernard,"French, Paris 1801–1889 Paris|French, La Bouille 1810–1870 Brussels|French, Paris, active 1832–70",,"Gauvain, Alfred|Liénard, Michel|Bernard, Léopold",French|French|French,1801 |1810 |1832,1889 |1870 |1870,dated 1844,1844,1844,"Steel, wood (ebony)",L. of each pistol 16 5/8 in. (42.3 cm); L. of each barrel 11 in. (27.8 cm); Cal. of each barrel .50 in. (13 mm),"Purchase, Arthur Ochs Sulzberger Bequest, 2013",,Paris,,,,,,,,,,Firearms-Pistols-Percussion,,http://www.metmuseum.org/art/collection/search/26980,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2013.513.1, .2",false,true,26981,Arms and Armor,Pair of percussion target pistols,"Pair of Percussion Target Pistols Made for Display at the Crystal Palace Exhibition in London, 1851","French, Paris",,,,,Gunsmith|Designer|Barrelsmith,Signed by|Designed by|Barrels by,Alfred Gauvain|Michel Liénard|Léopold Bernard,"French, Paris 1801–1889 Paris|French, La Bouille 1810–1870 Brussels|French, Paris, active 1832–70",,"Gauvain, Alfred|Liénard, Michel|Bernard, Léopold",French|French|French,1801 |1810 |1832,1889 |1870 |1870,dated 1851,1851,1851,"Steel, wood (ebony), gold",L. of each pistol 17 1/4 in. (44 cm); L. of each barrel 11 3/8 in. (28.8 cm); Cal. of each barrel .46 in. (12 mm),"Purchase, Arthur Ochs Sulzberger Bequest, 2013",,Paris,,,,,,,,,,Firearms-Pistols-Percussion,,http://www.metmuseum.org/art/collection/search/26981,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2011.310.1–.13,false,true,35701,Arms and Armor,Pattern book of firearms ornament,Plusievrs Pieces et Ornements Darquebuzerie (4th extended edition),"French, Paris",,,,,Engraver|Publisher|Engraver|Engraver,Initial eight-plate edition by|Editions with eight and thirdteen plates issued by|Designs by|Expanded edition produced by,Claude Simonin|Laurent le Languedoc|De Lacollombe|Gilles-Antoine Demarteau,"French, Nantes ca. 1635–1693 Nantes|French, Paris, active ca. 1705|French, Paris, active ca. 1702–ca. 1736|French, Paris, 1756–1802",", published in 1684 and reissued in1685|, included in the 1705 edition","Simonin, Claude|Languedoc, Laurent le|Lacollombe, De|Demarteau, Gilles-Antoine",French|French|French|French,1635 |1680 |1702 |1756,1693 |1730 |1736 |1802,ca. 1776,1751,1801,"Ink, paper",9 1/2 x 12 3/4 in. (24.1 x 32.4 cm) to 9 1/4 x 14 in. (23.5 x 35.6 cm),"Purchase, Kenneth and Vivian Lam Gift, 2011",,Paris,,,,,,,,,,Works on Paper-Prints,,http://www.metmuseum.org/art/collection/search/35701,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1993.415,true,true,24957,Arms and Armor,Double-barrel breech-loading pinfire shotgun,Double-Barrel Breech-Loading Pinfire Shotgun,"French, Paris",,,,,Barrelsmith|Gunsmith|Engraver|Goldsmith|Goldsmith,Designed and steel chiseled by|Designed and steel chiseled by,Léopold Bernard|J. C. A. Brun|Jean-Claude Tissot|François-Auguste Fannière|François-Joseph-Louis Fannière,"French, Paris, active 1832–70|French, Paris, active 1849–72|French, Paris, 1811–1889|French, Paris, 1818–1900|French, Paris, 1822–1897",,"Bernard, Léopold|Brun, J. C. A.|Tissot, Jean-Claude|Fannière, François-Auguste|Fannière, François-Joseph-Louis",French|French|French|French|French,1832 |1849 |1811 |1818 |1822,1870 |1872 |1889 |1900 |1897,dated 1866,1866,1866,"Steel, wood (walnut), gold",L. 44 1/8 in. (112 cm),"Purchase, The Sulzberger Foundation Inc. Gift and Rogers Fund; Bashford Dean Memorial Collection, Funds from various donors, Gift of William H. Riggs, The Collection of Giovanni P. Morosini, presented by his daughter Giulia, and Gift of Charles M. Schott Jr., by exchange; and gifts and funds from various donors, 1993",,Paris,,,,,,,,,,Firearms-Guns-Wheellock,,http://www.metmuseum.org/art/collection/search/24957,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1981.363.1–.3,false,true,24888,Arms and Armor,Smallsword,Smallsword,"British, London",,,,,Enameler|Goldsmith,"Signed AT, probably for|Attributed to",Simon Augustin Toussaint|James Morisset,"British, London, active 1768–85|English, London, active 1768–1800",,"Toussaint, Simon Augustin|Morisset James",British|British,1768 |1768,1785 |1800,ca. 1780–85,1755,1810,"Gold, enamel, steel",Sword (a); L. 39 in. (99.1 cm); box (c); L. 41 in. (104.1 cm); W. 6 3/16 in. (15.7 cm); D. 5 1/4 in. (13.3 cm),"Gift of Dr. and Mrs. John C. Weber, 1981",,London,,,,,,,,,,Swords,,http://www.metmuseum.org/art/collection/search/24888,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"1992.330.1, .2",false,true,24952,Arms and Armor,Pair of flintlock pistols,Pair of Flintlock Pistols,"British, London",,,,,Gunsmith|Silversmith,Attributed to,Samuel Brunn|Michael Barnett,"English, London, recorded 1795–1820|English, London, 1758–ca. 1823",,"Brunn, Samuel|Barnett, Michael",British|British,1795 |1758,1820 |1850,hallmarked for 1800–1801,1800,1801,"Steel, wood (walnut), silver, gold",L. of each pistol 16 in. (40.6 cm); L. of each barrel 10 1/8 in. (25.7 cm); Cal. of each pistol .603 in. (15.3 mm),"Purchase, Harris Brisbane Dick Fund and Gift of George D. Pratt, by exchange, 1992",,London,,,,,,,,,,Firearms,,http://www.metmuseum.org/art/collection/search/24952,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1992.305.1,false,true,24950,Arms and Armor,Print,Print of Designs for Hilt and Sheath Fittings,"British, London",,,,,Engraver|Designer,After,Wenceslaus Hollar|Hans Holbein the Younger,"Bohemian, Prague 1607–1677 London|German, Augsburg 1497/98–1543 London",,"Hollar, Wenceslaus|Holbein, Hans, the Younger",Bohemian|German,1607 |1497,1677 |1543,1644,1644,1644,Ink on paper,5 7/8 x 3 3/4 in. (14.9 x 9.5 cm),"Gift of Lois Earl Blumka, 1992",,London,,,,,,,,,,Works on Paper-Prints,,http://www.metmuseum.org/art/collection/search/24950,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2011.361–.362,false,true,35702,Arms and Armor,Pair of miquelet flintlock pistols,Pair of Miquelet Flintlock Pistols,"Colonial Spanish, probably Mexico",,,,,Gunsmith,Signed by,Francisco Pintan,active mid-18th century,,"Pintan, Francisco",,1725,1775,dated 1757,1757,1757,"Steel, wood (family leguminosae), silver",L. of 2011.361: 10 7/8 in. (27.5 cm); L. of barrel of 2011.361: 6 in. (15.2 cm); L. of 2011.362: 10 5/8 in. (27.2 cm); L. of barrel of 2011.362: 6 in. (15.2 cm),"Purchase, Arthur Ochs Sulzberger Gift, 2011",,,,,,,,,,,,Firearms-Guns-Miquelet,,http://www.metmuseum.org/art/collection/search/35702,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2014.282,false,true,650965,Arms and Armor,Close helmet,Close Helmet,"Italian, Milan",,,,,Armorer,,Gian Giacomo Negroli,"Italian, Milan 1463–1543",,"Negroli, Gian Giacomo",,1463,1543,ca. 1510–20,1485,1545,"Steel, gold",H. 13 1/8 in. (33.3 cm); W. 8 1/2 in. (21.6 cm); D. 12 in. (30.5 cm); Wt. 3 lb. 6 oz. (1530 g),"Purchase, Arthur Ochs Sulzberger Gift, 2014",,Milan,,,,,,,,,,Helmets,,http://www.metmuseum.org/art/collection/search/650965,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -39.38,false,true,26533,Arms and Armor,Close-helmet,Close-Helmet,"German, Nuremberg",,,,,Armorer,Attributed to,Kunz Lochner,"German, Nuremberg, 1510–1567",,"Lochner, Kunz",,1510,1567,1550,1525,1575,"Steel, leather",H. 12 3/4 (32.4 cm); W. 8 1/8 in. (20.6 cm); D. 13 in. (33 cm); Wt. 6 lb. 9 oz. (2975 g),"Gift of Mrs. Theodore Offerman, in memory of her husband, 1939",,Nuremberg,,,,,,,,,,Helmets,,http://www.metmuseum.org/art/collection/search/26533,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -32.130.5a–m,false,true,23937,Arms and Armor,Armor,"Armor of Henry Herbert (1534–1601), Second Earl of Pembroke","British, Greenwich",,,,,Armorer,Made in the,Royal Workshops at Greenwich,"British, Greenwich, 1511–1640s",,Royal Workshops at Greenwich,,1511,1650,ca. 1585–86,1560,1611,"Steel, gold",Wt. 60 lb. 1 oz. (27.24 kg),"Rogers Fund, 1932",,Greenwich,,,,,,,,,,Armor for Man,,http://www.metmuseum.org/art/collection/search/23937,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.25.1191,false,true,662414,Arms and Armor,Rapier,Rapier,"hilt, Italian; blade, German, Solingen",,,,,Bladesmith,Blade by,Clemens Hartkopf,"German, Solingen, active ca. 1625",,"Hartkopf, Clemens",,1600,1650,ca. 1625,1600,1650,"Steel, silver, wood, copper alloy",L. 49 1/2 in. (125.7 cm); L. of blade 43 in. (109.2 cm); W. 8 1/2 in. (21.6 cm); D. 5 in. (12.7 cm); Wt. 2 lb. 10 oz. (1190.7 g),"Gift of William H. Riggs, 1913",,,,,,,,,,,,Swords,,http://www.metmuseum.org/art/collection/search/662414,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.446a–d,false,true,679294,Arms and Armor,Wheellock rifle with spanner and accessories,"Wheellock Rifle with Spanner, Shot Extracting Tool, and Shooting Patch","German, Augsburg",,,,,Gunsmith,,Martin Kammerer,"German, Augsburg, active 1654–67",,"Kammerer, Martin",,1654,1667,ca. 1665,1640,1690,"Steel, iron, gold, wood, antler, copper alloy, enamel, bone, textile",rifle (2015.446a): L. 43 1/2 in. (110.5 cm); L. of barrel 32 1/2 in. (82.6 cm); W. 8 1/4 in. (21 cm); D. 4 1/4 in. (10.8 cm); Wt. 9 lb. 7 oz. (4280.8 g); spanner (2015.446b): L. 7 3/4 in. (19.7 cm); W. 7/8 in. (2.2 cm); D. 1 9/16 in. (4 cm); Wt. 4 oz. (113.4 g); shot extracting tool (2015.446c): L. 1 13/16 in. (4.6 cm); Diam. 3/8 in. (1 cm); Wt. 0.5 oz. (14.2 g); shooting patch (2015.446d): Diam. approx. 1 3/8 in. (3.5 cm),"Purchase, Arthur Ochs Sulzberger Bequest, 2015",,Augsburg,,,,,,,,,,Firearms-Guns-Wheellock,,http://www.metmuseum.org/art/collection/search/679294,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"32.130.4a, b",false,true,23938,Arms and Armor,Rapier,Rapier of Ambrogio Spinola (1569–1630),"Northern European, possibly France",,,,,Hilt Maker,Hilt inscribed,M. I. F.,"northern European, active ca. 1600",,M. I. F.,,1575,1625,ca. 1600,1575,1625,Steel,L. 46 1/8 in. (117.1 cm); L. of blade 39 3/4 in. (101 cm),"Rogers Fund, 1932",,,,,,,,,,,,Swords,,http://www.metmuseum.org/art/collection/search/23938,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.25.1052,false,true,27437,Arms and Armor,Rapier,Rapier,"hilt, probably German; blade, Italian, Milan",,,,,Bladesmith,Blade by,Pietro Caino,"Italian, Milan, active second half 16th century",,"Caino, Pietro",,1550,1600,late 16th century,1550,1600,"Steel, wood, iron",L. 45 5/8 in. (115.9 cm); L. of blade 40 1/2 in. (102.9 cm); W. 7 in. (17.8 cm); D. 5 in. (12.7 cm); Wt. 2 lb. 9 oz. (1162.3 g),"Gift of William H. Riggs, 1913",,Milan,,,,,,,,,,Swords,,http://www.metmuseum.org/art/collection/search/27437,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -19.131.2g,false,true,734140,Arms and Armor,Saddle cloth,"Saddle Cloth Associated with Armor Garniture, Probably of King Henry VIII of England (reigned 1509–47)","British, Greenwich",,,,,Armorer|Designer,Made in the|Design of the decoration attributed to,Royal Workshops at Greenwich|Hans Holbein the Younger,"British, Greenwich, 1511–1640s|German, Augsburg 1497/98–1543 London",,"Royal Workshops at Greenwich|Holbein, Hans, the Younger",,1511 |1497,1650 |1543,dated 1527,1527,1527,Textile,,"Purchase, William H. Riggs Gift and Rogers Fund, 1919",,Greenwich,,,,,,,,,,Equestrian Equipment-Saddles,,http://www.metmuseum.org/art/collection/search/734140,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"22.147.4a, b",false,true,22851,Arms and Armor,Right thigh and knee defense (cuisse and poleyn),Right Thigh and Knee Defense (Cuisse and Poleyn) for the Armor of Sir John Scudamore (1541 or 1542–1623),"British, Greenwich",,,,,Armorer,Made under the direction of,Jacob Halder,"British, master armorer at the royal workshops at Greenwich, documented in England 1558–1608",,"Halder, Jacob",,1558,1608,ca. 1587,1562,1612,"Steel, gold, leather",22.147.4a: H. 4 1/2 in. (11.43 cm); 22.147.4b: H. 7 3/4 in. (19.69 cm); W. 5 in. (12.7 cm),"Fletcher Fund, 1922",,Greenwich,,,,,,,,,,Armor Parts-Knee Defenses,,http://www.metmuseum.org/art/collection/search/22851,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -39.121a–n,true,true,24671,Arms and Armor,Armor,"Armor of Henry II, King of France (reigned 1547–59)","French, possibly Paris",,,,,Designer|Designer|Designer,Part of the decoration design by|Part of the decoration design possibly by|Part of the decoration design possibly by,Jean Cousin the Elder|Étienne Delaune|Baptiste Pellerin,"French, Souci (?) ca. 1490–ca. 1560 Paris (?)|French, Orléans 1518/19–1583 Strasbourg|French, documented in Étampes 1542–75 Paris",,"Cousin, Jean, the Elder|Delaune, Étienne|Pellerin, Baptiste",,1485 |1518 |1542,1565 |1583 |1575,ca. 1555,1530,1580,"Steel, gold, silver, leather, textile",H. 74 in. (187.96 cm); Wt. 53 lb. 4 oz. (24.20 kg),"Harris Brisbane Dick Fund, 1939",,possibly Paris,,,,,,,,,,Armor for Man,,http://www.metmuseum.org/art/collection/search/24671,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2015.295a, b",false,true,681022,Arms and Armor,Cuirass,Cuirass,"French, Klingenthal, Alsace",,,,,Designer|Decorator|Manufactory,Designed by|Etched and gilt by|Manufactured at,"François-Joseph Bisch|François-Xavier Bisch|Coulaux Frères, Manufacture Royale d'Armes de Klingenthal","French, Klingenthal, Alsace 1756–1831|French, active in Klingenthal and Boersch, Alsace 1793–1841|French, Klingenthal, Alsace 1801–1836",,"Bisch, François-Joseph|Bisch, François-Xavier|Coulaux Frères, Manufacture Royale d'Armes de Klingenthal",,1756 |1793 |1801,1831 |1841 |1836,ca. 1825,1820,1830,"Steel, gold, copper alloy, leather",H. 17 1/2 in. (44.5 cm); W. 14 1/8 in. (35.9 cm); D. 12 3/4 in. (32.4 cm); Wt. 12 lb. 2.2 oz. (5505.5 g); breastplate: H. 17 1/2 in. (44.5 cm); W. 14 1/8 in. (35.9 cm); D. 7 in. (17.8 cm); Wt. 8 lb. 0.5 oz. (3642.9 g); backplate: H. 16 15/16 in. (43 cm); W. 14 1/8 in. (35.9 cm); D. 5 3/4 in. (14.6 cm); Wt. 4 lb. 1.7 oz. (1862.6 g),"Purchase, Arthur Ochs Sulzberger Bequest, 2015",,Klingenthal,Alsace,,,,,,,,,Armor Parts-Cuirasses,,http://www.metmuseum.org/art/collection/search/681022,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2016.649,false,true,717696,Arms and Armor,Album of photographs,"Album of Photographs of Views of the Interior of the Ottoman Military Museum in the Former Church of St. Irene, Constantinople",Turkish,,,,,Photographer,,Abdullah Frères,"Ottoman, 1858–1899",,Abdullah Frères,,1858,1899,1891,1891,1891,"Albumen prints, paper, leather, textile, gold",L. 17 3/4 in. (45.1 cm); H. 13 in. (33.0 cm); W. 2 3/8 in. (6.0 cm),"Gift of Howard Ricketts in Memory of Roy and Neil Cole, Collectors, 2016",,,,,,,,,,,,Photographs,,http://www.metmuseum.org/art/collection/search/717696,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -22.122.48,false,true,32997,Arms and Armor,Medal,Medal Showing Prince Charles and Prince Henry,Scottish,,,,,Artist,,Ottone Hamerani,"Italian, Rome 1694–1768",,Hamerani Ottone,,1694,1768,1729,1729,1729,Bronze,Diam. 1 13/16 in. (4.6 cm); thickness 1/4 in. (0.6 cm); Wt. 1.8 oz. (51 g),"Gift of Bashford Dean, 1922",,,,,,,,,,,,Miscellaneous-Coins and Medals,,http://www.metmuseum.org/art/collection/search/32997,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -22.122.60,false,true,33009,Arms and Armor,Medal,"Medal Showing Charles III of Spain, 1778","Spanish, Mexico",,,,,Artist,,Jerónimo Antonio Gil,"Spanish, Zamora 1731–1798 Mexico",,"Gil, Jerónimo Antonio",,1732,1798,1778,1778,1778,Bronze,Diam. 2 5/16 in. (5.9 cm); thickness 1/4 in. (0.6 cm); Wt. 3.9 oz. (110.6 g),"Gift of Bashford Dean, 1922",,,,,,,,,,,,Miscellaneous-Coins and Medals,,http://www.metmuseum.org/art/collection/search/33009,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2016.205a, b",false,true,712927,Arms and Armor,Pair of stirrups (Abumi),Pair of Stirrups (Abumi),"Japanese, Kashu",,,,,Artist,Inscribed by,Sanemitsu,"Japanese, active in Ka shū, possibly early 18th century",,Sanemitsu,,1675,1775,probably 18th century,1675,1825,"Iron, wood, silver, lacquer, paper",left: L. 10.7 in. (27.2 cm); W. 5.03 in. (12.8 cm); H. 9.68 in. (24.6 cm); right: L. 10.86 in. (27.6 cm); W. 5.03 in. (12.8 cm); H. 9.76 in. (24.8 cm),"Purchase, Gift of Morihiro and Sumiko Ogawa, in memory of Charles Baber, 2016",,Kanazawa City,Ishikawa Prefecture,,,,,,,,,Equestrian Equipment-Stirrups,,http://www.metmuseum.org/art/collection/search/712927,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.25.1573,false,true,22397,Arms and Armor,Pellet and bolt crossbow combined with a wheel-lock gun,Pellet and Bolt Crossbow Combined with a Wheel-Lock Gun,Central European; possibly Southern German or Austrian,,,,,Designer,Decoration based on designs by,Jacob Floris,1524–1581,", published in Antwerp in 1564.","Floris, Jacob",,1524,1581,ca. 1570–1600,1545,1625,"Steel, wood (cherry), staghorn, hemp, felt","L. 28 1/2 in. (72.4 cm); W. 26 1/2 in. (67.2 cm); Wt. 11 lb. 7 oz. (5,197 g)","Gift of William H. Riggs, 1913",,,,,,,,,,,,Archery Equipment-Crossbows,,http://www.metmuseum.org/art/collection/search/22397,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.597,false,true,693886,Arms and Armor,Medal,Medal of Kolman Helmschmid (1471–1532),"German, Augsburg",,,,,Designer,After a model by,Hans Kels the Younger,"German, 1508/10–1565",,"Kels, Hans, the Younger",,1508,1565,dated 1532,1532,1532,Lead,Diam. 1 15/16 in. (50 mm); Wt. 1.6 oz. (44.96 g),"Purchase, Kenneth and Vivian Lam Gift, 2015",,Augsburg,,,,,,,,,,Medals,,http://www.metmuseum.org/art/collection/search/693886,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -16.29.1–.2,false,true,22609,Arms and Armor,Miniature Italian-style armor for man and horse,Miniature Italian-Style Armor for Man and Horse,French,,,,,Armorer,Possibly made by,Granger LeBlanc,"French, active ca. 1840–70",,"LeBlanc, Granger",,1815,1895,ca. 1860,1835,1885,Steel,H. as mounted 17 7/8 in. (45.4 cm),"Gift of William Oothout, 1916",,,,,,,,,,,,Armor-Miniatures,,http://www.metmuseum.org/art/collection/search/22609,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2016.311,false,true,716772,Arms and Armor,Engraving,Plate Six from Nouveavx Desseins D’Arquebvseries,French,,,,,Engraver,Signed by,Gilles Demarteau,"French, Liège 1722–1776 Paris",,"Demarteau, Gilles",,1722,1776,dated 1749,1749,1749,Engraving,sheet: 11 7/8 x 9 in. (30.16 x 22.86 cm); plate: 9 1/8 x 6 3/8 in. (23.17 x 16.19 cm),"Purchase, Michael H. Pourfar Gift, 2016",,,,,,,,,,,,Works on Paper-Engravings,,http://www.metmuseum.org/art/collection/search/716772,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2016.312,false,true,716774,Arms and Armor,Engraving,Plate Ten from Nouveavx Desseins D’Arquebvseries,French,,,,,Engraver,Signed by,Gilles Demarteau,"French, Liège 1722–1776 Paris",,"Demarteau, Gilles",,1722,1776,dated 1744,1744,1744,Engraving,sheet: 11 7/8 x 9 1/8 in. (30.16 x 23.17 cm); plate: 8 1/2 x 6 3/8 in. (21.59 x 16.19 cm),"Purchase, Michael H. Pourfar Gift, 2016",,,,,,,,,,,,Works on Paper-Engravings,,http://www.metmuseum.org/art/collection/search/716774,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"26.145.291a, b",false,true,32526,Arms and Armor,Smallsword with scabbard,Smallsword with Scabbard,French,,,,,Sword cutler,,C. Liger,"French, Paris, recorded 1770–93",,"Liger, C.",,1770,1793,ca. 1780,1770,1793,"Steel, silver, gold, wood, textile, fishskin",L. with scabbard 38 5/8 in. (98.1 cm); L. without scabbard 38 1/8 in. (96.8 cm); L. of blade 31 1/2 in. (80 cm); W. 4 1/2 in. (11.4 cm); D. 3 1/4 in. (8.3 cm); Wt. 13 oz. (368.54 g); Wt. of scabbard 2.5 oz. (70.9 g),"Gift of Jean Jacques Reubell, in memory of his mother, Julia C. Coster, and of his wife, Adeline E. Post, both of New York City, 1926",,,,,,,,,,,,Swords,,http://www.metmuseum.org/art/collection/search/32526,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -22.122.19,false,true,32971,Arms and Armor,Medal,"Medal Showing Henry IV of France (b. 1553, r. 1589–1610) and Marie de Médicis (1573–1642)",French,,,,,Artist,,Guillaume Dupré,"French, 1579–1640",,Dupré Guillaume,,1579,1640,dated 1603,1603,1603,Bronze,Diam. 2 9/16 in. (6.5 cm); thickness 1/4 in. (0.6 cm); Wt. 2.7 oz. (76.5 g),"Gift of Bashford Dean, 1922",,,,,,,,,,,,Miscellaneous-Coins and Medals,,http://www.metmuseum.org/art/collection/search/32971,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2016.245,false,true,716365,Arms and Armor,Etching and engraving,Design for a Rapier Hilt and Scabbard Chape,French,,,,,Artist,,Pierre Woeiriot de Bouzey II,"French, Neufchâteau 1532–1599 Damblain",,"de Bouzey, Pierre Woeiriot II",,1532,1599,1555,1555,1555,"Etching, engraving",Diam. 7 1/2 in. (19.1 cm),"Purchase, Gift of Andrew Solomon, 2016",,,,,,,,,,,,Works on Paper-Engravings,,http://www.metmuseum.org/art/collection/search/716365,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2016.697,false,true,739608,Arms and Armor,Engraving,"Design for a Sword-belt, a Knife and a Stylus",French,,,,,Artist,,Pierre Woeiriot de Bouzey II,"French, Neufchâteau 1532–1599 Damblain",,"de Bouzey, Pierre Woeiriot II",,1532,1599,ca. 1555,1530,1580,Engraving,7 3/16 x 9 3/4 in. (18.3 x 24.7 cm),"Purchase, Kenneth and Vivian Lam Gift, 2016",,,,,,,,,,,,Works on Paper-Engravings,,http://www.metmuseum.org/art/collection/search/739608,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2014.627,false,true,665952,Arms and Armor,Drawing,Drawing with Seven Designs for Firearms Ornament,French,,,,,Artist,,Jean-Francois Lucas,"French, 1747–1825",,"Lucas, Jean-Francois",,1747,1825,1806,1806,1806,"Pen and black ink, with gray wash and traces of graphite, on paper",10 15/16 x 7 13/16 in. (27.8 x 19.8 cm),"Purchase, Arthur Ochs Sulzberger Gift, 2014",,,,,,,,,,,,Drawings,,http://www.metmuseum.org/art/collection/search/665952,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -22.122.43,false,true,32992,Arms and Armor,Medal,"Medal Commemorating the Marriage of James III and Princess Clementina, 1719",British,,,,,Artist,Signed on the reverse by,Ottone Hamerani,"Italian, Rome 1694–1768",,Hamerani Ottone,,1694,1768,1719,1719,1719,Bronze,Diam. 1 7/8 in. (4.8 cm); thickness 3/16 in. (0.5 cm); Wt. 1.6 oz. (45.4 g),"Gift of Bashford Dean, 1922",,,,,,,,,,,,Miscellaneous-Coins and Medals,,http://www.metmuseum.org/art/collection/search/32992,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -16.154.2a–l,false,true,22629,Arms and Armor,Infantry armor,Infantry Armor,Italian,,,,,Armorer,"Upper plate of the gorget, skirt lames, and cheek pieces made by",Daniel Tachaux,"French, 1857–1928, active in France and America","in the Metropolitan Museum of Art, Armor Workshop","Tachaux, Daniel",,1857,1928,"dated 1571; upper plate of the gorget, skirt lames, and cheek pieces, 1917",1571,1917,"Steel, leather, brass",Wt. 33 lb. 11 oz. (15.28 kg),"Rogers Fund, 1916",,,,,,,,,,,,Armor for Man-1/2 Armor,,http://www.metmuseum.org/art/collection/search/22629,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2008.314,false,true,35640,Arms and Armor,Letter,"Letter from Henry Knox (1750–1806), Secretary of War, to Colonel Marinus Willet (1740–1830)",American,,,,,Author,,General Henry Knox,"American, Boston, Massachusetts 1750–1806 Thomaston, Maine",,"Knox, Henry General",,1750,1806,"dated May 27, 1786",1786,1786,Ink on paper,12 1/2 x 7 7/8 in. (31.7 x 20 cm),"Purchase, Arthur Ochs Sulzberger Gift, 2008",,,,,,,,,,,,Works on Paper,,http://www.metmuseum.org/art/collection/search/35640,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2016.403.5,false,true,722411,Arms and Armor,Engraving,Plate Eight from Nouveavx Desseins D'Arquebvseries,"French, Paris",,,,,Engraver,,Gilles Demarteau,"French, Liège 1722–1776 Paris",,"Demarteau, Gilles",,1722,1776,dated 1743,1743,1743,Engraving,sheet: 11 7/8 x 9 1/16 in. (30.2 x 23 cm); plate: 7 7/16 x 5 1/4 in. (18.9 x 13.3 cm),"Purchase, Arthur Ochs Sulzberger Gift, 2016",,Paris,,,,,,,,,,Works on Paper-Engravings,,http://www.metmuseum.org/art/collection/search/722411,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2016.403.6,false,true,722414,Arms and Armor,Engraving,Plate Nine from Nouveavx Desseins D'Arquebvseries,"French, Paris",,,,,Engraver,,Gilles Demarteau,"French, Liège 1722–1776 Paris",,"Demarteau, Gilles",,1722,1776,dated 1743,1743,1743,Engraving,sheet: 11 3/4 x 9 1/8 in. (29.8 x 23.2 cm); plate: 7 1/4 x 5 1/4 in. (18.4 x 13.3 cm),"Purchase, Arthur Ochs Sulzberger Gift, 2016",,Paris,,,,,,,,,,Works on Paper-Engravings,,http://www.metmuseum.org/art/collection/search/722414,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2016.403.7,false,true,722417,Arms and Armor,Engraving,Plate Eleven from Nouveavx Desseins D'Arquebvseries,"French, Paris",,,,,Engraver,,Gilles Demarteau,"French, Liège 1722–1776 Paris",,"Demarteau, Gilles",,1722,1776,ca. 1749,1724,1774,Engraving,sheet: 11 1/2 x 9 1/4 in. (29.2 x 23.5 cm); plate: 8 7/16 x 6 7/16 in. (21.4 x 16.3 cm),"Purchase, Arthur Ochs Sulzberger Gift, 2016",,Paris,,,,,,,,,,Works on Paper-Engravings,,http://www.metmuseum.org/art/collection/search/722417,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2016.403.18,false,true,722508,Arms and Armor,Engraving,Plate Nine from Nouveavx Desseins D'Arquebvseries,"French, Paris",,,,,Engraver,,Gilles Demarteau,"French, Liège 1722–1776 Paris",,"Demarteau, Gilles",,1722,1776,dated 1743,1743,1743,Engraving,sheet: 8 1/2 x 6 5/8 in. (21.6 x 16.8 cm); plate: 7 1/4 x 5 1/4 in. (18.4 x 13.3 cm),"Purchase, Arthur Ochs Sulzberger Gift, 2016",,Paris,,,,,,,,,,Works on Paper-Engravings,,http://www.metmuseum.org/art/collection/search/722508,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2016.403.20,false,true,722509,Arms and Armor,Engraving,Plate Eleven from Nouveavx Desseins D'Arquebvseries,"French, Paris",,,,,Engraver,,Gilles Demarteau,"French, Liège 1722–1776 Paris",,"Demarteau, Gilles",,1722,1776,ca. 1749,1724,1774,Engraving,sheet: 9 x 7 in. (22.9 x 17.8 cm); plate: 8 7/16 x 6 7/16 in. (21.4 x 16.3 cm),"Purchase, Arthur Ochs Sulzberger Gift, 2016",,Paris,,,,,,,,,,Works on Paper-Engravings,,http://www.metmuseum.org/art/collection/search/722509,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2016.403.21,false,true,722510,Arms and Armor,Engraving,Plate Eight from Nouveavx Desseins D'Arquebvseries,"French, Paris",,,,,Engraver,,Gilles Demarteau,"French, Liège 1722–1776 Paris",,"Demarteau, Gilles",,1722,1776,dated 1743,1743,1743,Engraving,sheet: 8 5/8 x 6 7/8 (21.9 x 17.5 cm); plate: 7 7/16 x 5 1/4 in. (18.9 x 13.3 cm),"Purchase, Arthur Ochs Sulzberger Gift, 2016",,Paris,,,,,,,,,,Works on Paper-Engravings,,http://www.metmuseum.org/art/collection/search/722510,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2016.403.8,false,true,722420,Arms and Armor,Etching and engraving,"Design of a Flintlock, Side Plate, Butt Plate, and Trigger Guard, unnumbered plate from Nouveaux Desseins d'Arquebuserie Inventez et Gravez par Le Sr. Gillot","French, Paris",,,,,Engraver,,Claude Gillot,"French, Langres 1673–1722 Paris",,"Gillot, Claude",,1673,1722,ca. 1715,1690,1740,"Etching, engraving",Sheet: 9 5/8 x 6 9/16 in. (24.4 x 16.6 cm),"Purchase, Arthur Ochs Sulzberger Gift, 2016",,Paris,,,,,,,,,,Works on Paper-Engravings,,http://www.metmuseum.org/art/collection/search/722420,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2016.403.2,false,true,722405,Arms and Armor,Engraving,Plate Two from Nouveavx Desseins D'Arquebvseries,"French, Paris",,,,,Engraver,,De Lacollombe,"French, Paris, active ca. 1702–ca. 1736",,"Lacollombe, De",,1702,1736,dated 1730,1730,1730,Engraving,sheet: 11 7/8 x 9 in. (30.2 x 22.9 cm); plate: 9 3/8 x 6 5/8 (23.8 x 16.8 cm),"Purchase, Arthur Ochs Sulzberger Gift, 2016",,Paris,,,,,,,,,,Works on Paper-Engravings,,http://www.metmuseum.org/art/collection/search/722405,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2016.403.3,false,true,722406,Arms and Armor,Engraving,Plate Four from Nouveavx Desseins D'Arquebvseries,"French, Paris",,,,,Engraver,,De Lacollombe,"French, Paris, active ca. 1702–ca. 1736",,"Lacollombe, De",,1702,1736,ca. 1730,1705,1755,Engraving,sheet: 11 3/4 x 9 1/8 in. (29.8 x 23.2 cm); plate: 9 1/4 x 6 5/8 in. (23.5 x 16.8 cm),"Purchase, Arthur Ochs Sulzberger Gift, 2016",,Paris,,,,,,,,,,Works on Paper-Engravings,,http://www.metmuseum.org/art/collection/search/722406,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2016.403.4,false,true,722409,Arms and Armor,Engraving,Plate Seven from Nouveavx Desseins D'Arquebvseries,"French, Paris",,,,,Engraver,,De Lacollombe,"French, Paris, active ca. 1702–ca. 1736",,"Lacollombe, De",,1702,1736,ca. 1730,1705,1755,Engraving,sheet: 12 x 9 in. (30.5 x 22.9 cm); plate: 9 3/8 x 6 3/8 in. (23.8 x 16.2 cm),"Purchase, Arthur Ochs Sulzberger Gift, 2016",,Paris,,,,,,,,,,Works on Paper-Engravings,,http://www.metmuseum.org/art/collection/search/722409,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2016.403.19,false,true,716437,Arms and Armor,Engraving,Plate Three from Nouveavx Desseins D'Arquebvseries,"French, Paris",,,,,Engraver,,De Lacollombe,"French, Paris, active ca. 1702–ca. 1736",,"Lacollombe, De",,1702,1736,ca. 1730,1705,1755,Engraving,sheet: 8 x 6 3/8 in. (20.3 x 16.2 cm),"Purchase, Arthur Ochs Sulzberger Gift, 2016",,Paris,,,,,,,,,,Works on Paper-Engravings,,http://www.metmuseum.org/art/collection/search/716437,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2016.403.1,false,true,722401,Arms and Armor,Engraving,Plate One (Title Page) from Nouveavx Desseins D'Arquebvseries,"French, Paris",,,,,Engraver|Engraver,,Gilles Demarteau|De Lacollombe,"French, Liège 1722–1776 Paris|French, Paris, active ca. 1702–ca. 1736",,"Demarteau, Gilles|Lacollombe, De",,1722 |1702,1776 |1736,dated 1730,1730,1730,Engraving,sheet: 11 1/2 x 9 in. (29.2 x 22.9 cm); plate: 9 3/8 x 6 5/8 in. (23.8 x 16.8 cm),"Purchase, Arthur Ochs Sulzberger Gift, 2016",,,,,,,,,,,,Works on Paper-Engravings,,http://www.metmuseum.org/art/collection/search/722401,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2013.905,.906;2016.311,.312,.403.1–.7,.409",false,true,726774,Arms and Armor,Engravings,Nouveavx Desseins D'Arquebvseries,"French, Paris",,,,,Engraver|Engraver,,Gilles Demarteau|De Lacollombe,"French, Liège 1722–1776 Paris|French, Paris, active ca. 1702–ca. 1736",,"Demarteau, Gilles|Lacollombe, De",,1722 |1702,1776 |1736,ca. 1705–49,1680,1774,Engravings,plate 1 (2016.403.1): sheet: 11 1/2 x 9 in. (29.2 x 22.9 cm); plate: 9 3/8 x 6 5/8 in. (23.8 x 16.8 cm); plate 2 (2016.403.2): sheet: 11 7/8 x 9 in. (30.2 x 22.9 cm); plate: 9 3/8 x 6 5/8 (23.8 x 16.8 cm); plate 3 (2013.906): 10 1/2 x 7 1/2 in. (26.7 x 19.1 cm); plate 4 (2016.403.3): sheet: 11 3/4 x 9 1/8 in. (29.8 x 23.2 cm); plate: 9 1/4 x 6 5/8 in. (23.5 x 16.8 cm); plate 5 (2016.409): sheet: 11 1/2 x 9 in. (29.2 x 22.9 cm); plate: 9 3/8 x 6 5/8 in. (23.8 x 16.8 cm); plate 6 (2016.311): sheet: 11 7/8 x 9 in. (30.16 x 22.86 cm); plate: 9 1/8 x 6 3/8 in. (23.17 x 16.19 cm); plate 7 (2016.403.4): sheet: 12 x 9 in. (30.5 x 22.9 cm); plate: 9 3/8 x 6 3/8 in. (23.8 x 16.2 cm); plate 8 (2016.403.5): sheet: 11 7/8 x 9 1/16 in. (30.2 x 23 cm); plate: 7 7/16 x 5 1/4 in. (18.9 x 13.3 cm); plate 9 (2016.403.5): sheet: 11 3/4 x 9 1/8 in. (29.8 x 23.2 cm); plate: 7 1/4 x 5 1/4 in. (18.4 x 13.3 cm); plate 10 (2016.312): sheet: 11 7/8 x 9 1/8 in. (30.16 x 23.17 cm); plate: 8 1/2 x 6 3/8 in. (21.59 x 16.19 cm); plate 11 (2016.403.7): sheet: 11 1/2 x 9 1/4 in. (29.2 x 23.5 cm); plate: 8 7/16 x 6 7/16 in. (21.4 x 16.3 cm); plate 12 (2013.905): 7 1/2 x 9 3/8 in. (19 x 23.8 cm),"2013.905, .906: Purchase, Bequest of Stephen V. Grancsay, Rogers Fund, Helmut Nickel Gift, and funds from various donors, by exchange, 2013; 2016.311, .312: Purchase, Michael H. Pourfar Gift, 2016; 2016.403.1–.7: Purchase, Arthur Ochs Sulzberger Gift, 2016; 2016.409: Purchase, Marica F. Vilcek Gift, 2016",,Paris,,,,,,,,,,Works on Paper-Engravings,,http://www.metmuseum.org/art/collection/search/726774,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2016.403.9,false,true,722489,Arms and Armor,Engraving,Plate One from Plusieurs Models des plus nouuelles manieres qui sont en usage en l'Art de Arquebuzerie,"French, Paris",,,,,Publisher|Publisher|Engraver,,Le Hollandois|Thuraine|C. Jacquinet,"French, Paris, active mid-17th century|French, Paris, active mid-17th century|French, Paris, active mid-17th century",,"Hollandois, Le|Thuraine|Jacquinet, C.",,1625 |1625 |1625,1675 |1675 |1675,ca. 1660,1635,1685,Engraving,sheet: 7 x 5 in. (17.8 x 12.7 cm),"Purchase, Arthur Ochs Sulzberger Gift, 2016",,Paris,,,,,,,,,,Works on Paper-Engravings,,http://www.metmuseum.org/art/collection/search/722489,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2016.403.10,false,true,722495,Arms and Armor,Engraving,Plate Two from Plusieurs Models des plus nouuelles manieres qui sont en usage en l'Art de Arquebuzerie,"French, Paris",,,,,Publisher|Publisher|Engraver,,Le Hollandois|Thuraine|C. Jacquinet,"French, Paris, active mid-17th century|French, Paris, active mid-17th century|French, Paris, active mid-17th century",,"Hollandois, Le|Thuraine|Jacquinet, C.",,1625 |1625 |1625,1675 |1675 |1675,ca. 1660,1635,1685,Engraving,sheet: 7 7/8 x 5 3/4 in. (20 x 14.6 cm); plate: 7 7/16 x 5 5/16 in. (18.9 x 13.5 cm),"Purchase, Arthur Ochs Sulzberger Gift, 2016",,Paris,,,,,,,,,,Works on Paper-Engravings,,http://www.metmuseum.org/art/collection/search/722495,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2016.403.11,false,true,722497,Arms and Armor,Engraving,Plate Three from Plusieurs Models des plus nouuelles manieres qui sont en usage en l'Art de Arquebuzerie,"French, Paris",,,,,Engraver|Publisher|Publisher,,C. Jacquinet|Le Hollandois|Thuraine,"French, Paris, active mid-17th century|French, Paris, active mid-17th century|French, Paris, active mid-17th century",,"Jacquinet, C.|Hollandois, Le|Thuraine",,1625 |1625 |1625,1675 |1675 |1675,ca. 1660,1635,1685,Engraving,sheet: 7 x 5 1/8 in. (17.8 x 13 cm),"Purchase, Arthur Ochs Sulzberger Gift, 2016",,Paris,,,,,,,,,,Works on Paper-Engravings,,http://www.metmuseum.org/art/collection/search/722497,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2016.403.12,false,true,722499,Arms and Armor,Engraving,Plate Five from Plusieurs Models des plus nouuelles manieres qui sont en usage en l'Art de Arquebuzerie,"French, Paris",,,,,Publisher|Engraver|Publisher,,Le Hollandois|C. Jacquinet|Thuraine,"French, Paris, active mid-17th century|French, Paris, active mid-17th century|French, Paris, active mid-17th century",,"Hollandois, Le|Jacquinet, C.|Thuraine",,1625 |1625 |1625,1675 |1675 |1675,ca. 1660,1635,1685,Engraving,sheet: 7 1/4 x 5 1/4 in. (18.4 x 13.3 cm),"Purchase, Arthur Ochs Sulzberger Gift, 2016",,Paris,,,,,,,,,,Works on Paper-Engravings,,http://www.metmuseum.org/art/collection/search/722499,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2016.403.13,false,true,722500,Arms and Armor,Engraving,Plate Six from Plusieurs Models des plus nouuelles manieres qui sont en usage en l'Art de Arquebuzerie,"French, Paris",,,,,Publisher|Publisher|Engraver,,Thuraine|Le Hollandois|C. Jacquinet,"French, Paris, active mid-17th century|French, Paris, active mid-17th century|French, Paris, active mid-17th century",,"Thuraine|Hollandois, Le|Jacquinet, C.",,1625 |1625 |1625,1675 |1675 |1675,ca. 1660,1635,1685,Engraving,sheet: 7 1/16 x 5 3/8 in. (17.9 x 13.7 cm),"Purchase, Arthur Ochs Sulzberger Gift, 2016",,Paris,,,,,,,,,,Works on Paper-Engravings,,http://www.metmuseum.org/art/collection/search/722500,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2016.403.14,false,true,722502,Arms and Armor,Engraving,Plate Seven from Plusieurs Models des plus nouuelles manieres qui sont en usage en l'Art de Arquebuzerie,"French, Paris",,,,,Publisher|Publisher|Engraver,,Thuraine|Le Hollandois|C. Jacquinet,"French, Paris, active mid-17th century|French, Paris, active mid-17th century|French, Paris, active mid-17th century",,"Thuraine|Hollandois, Le|Jacquinet, C.",,1625 |1625 |1625,1675 |1675 |1675,ca. 1660,1635,1685,Engraving,sheet: 7 1/2 x 5 1/2 in. (19.1 x 14 cm),"Purchase, Arthur Ochs Sulzberger Gift, 2016",,Paris,,,,,,,,,,Works on Paper-Engravings,,http://www.metmuseum.org/art/collection/search/722502,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2016.403.15,false,true,722503,Arms and Armor,Engraving,Plate Eight from Plusieurs Models des plus nouuelles manieres qui sont en usage en l'Art de Arquebuzerie,"French, Paris",,,,,Publisher|Publisher|Engraver,,Le Hollandois|Thuraine|C. Jacquinet,"French, Paris, active mid-17th century|French, Paris, active mid-17th century|French, Paris, active mid-17th century",,"Hollandois, Le|Thuraine|Jacquinet, C.",,1625 |1625 |1625,1675 |1675 |1675,ca. 1660,1635,1685,Engraving,sheet: 7 1/16 x 5 1/8 in. (17.9 x 13 cm),"Purchase, Arthur Ochs Sulzberger Gift, 2016",,Paris,,,,,,,,,,Works on Paper-Engravings,,http://www.metmuseum.org/art/collection/search/722503,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2016.403.16,false,true,722505,Arms and Armor,Engraving,Plate Nine from Plusieurs Models des plus nouuelles manieres qui sont en usage en l'Art de Arquebuzerie,"French, Paris",,,,,Publisher|Publisher|Engraver,,Le Hollandois|Thuraine|C. Jacquinet,"French, Paris, active mid-17th century|French, Paris, active mid-17th century|French, Paris, active mid-17th century",,"Hollandois, Le|Thuraine|Jacquinet, C.",,1625 |1625 |1625,1675 |1675 |1675,ca. 1660,1635,1685,Engraving,sheet: 7 1/8 x 5 in. (18.1 x 5 cm),"Purchase, Arthur Ochs Sulzberger Gift, 2016",,Paris,,,,,,,,,,Works on Paper-Engravings,,http://www.metmuseum.org/art/collection/search/722505,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2016.403.17,false,true,722506,Arms and Armor,Engraving,Plate Ten from Plusieurs Models des plus nouuelles manieres qui sont en usage en l'Art de Arquebuzerie,"French, Paris",,,,,Publisher|Engraver|Publisher,,Le Hollandois|C. Jacquinet|Thuraine,"French, Paris, active mid-17th century|French, Paris, active mid-17th century|French, Paris, active mid-17th century",,"Hollandois, Le|Jacquinet, C.|Thuraine",,1625 |1625 |1625,1675 |1675 |1675,ca. 1660,1635,1685,Engraving,sheet: 7 x 5 1/16 in. (17.8 x 12.8 cm),"Purchase, Arthur Ochs Sulzberger Gift, 2016",,Paris,,,,,,,,,,Works on Paper-Engravings,,http://www.metmuseum.org/art/collection/search/722506,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2016.698,false,true,742566,Arms and Armor,"Design for a sword hilt, scabbard, and belt fittings","Design for a Sword Hilt, Scabbard, and Belt Fittings","French, Paris",,,,,Artist,Attributed to,Eugène Julienne,"French, Paris 1808–1875 Paris",,"Julienne, Eugène",,1808,1875,ca. 1840–50,1815,1875,Watercolor and ink on paper,12 3/8 x 18 3/4 in. (32 x 48 cm),"Purchase, Kenneth and Vivian Lam Gift, 2016",,Paris,,,,,,,,,,Works on Paper-Drawings,,http://www.metmuseum.org/art/collection/search/742566,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2016.409,false,true,718296,Arms and Armor,Plate five from Nouveavx Desseins D'Arquebvseries,Plate Five from Nouveavx Desseins D'Arquebvseries,"French, Paris",,,,,Artist,,De Lacollombe,"French, Paris, active ca. 1702–ca. 1736",,"Lacollombe, De",,1702,1736,dated 1730,1730,1730,Engraving,sheet: 11 1/2 x 9 in. (29.2 x 22.9 cm); plate: 9 3/8 x 6 5/8 in. (23.8 x 16.8 cm),"Purchase, Marica F. Vilcek Gift, 2016",,Paris,,,,,,,,,,Works on Paper-Engravings,,http://www.metmuseum.org/art/collection/search/718296,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2004.118.1, .2",false,true,27017,Arms and Armor,Pair of tubelock hammers,Pair of Tubelock Hammers,"British, London",,,,,Gunsmith,,Joseph Manton,"British, Grantham, Lincolnshire 1766–1835 London",,Manton Joseph,,1766,1835,ca. 1819–20,1819,1820,Steel,H. of each 2 5/16 in. (6 cm); Wt. of each 1.1 oz (31 g),"Purchase, Fletcher Fund, by exchange",,London,,,,,,,,,,Firearms Parts,,http://www.metmuseum.org/art/collection/search/27017,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -35.81.3a–f,false,true,32180,Arms and Armor,Repeating flintlock pistol,"Flintlock Repeating Pistol with Lorenzoni Action, bearing the Crests of Vice Admiral Horatio Nelson, with Case and Acccessories","British, London",,,,,Gunsmith,,Harvey Walklate Mortimer,"British, Newcastle-under-Lyme 1753–1819 Hampstead-heath (now London)",,"Mortimer, Harvey Walklate",,1753,1819,ca. 1798–1799,1793,1803,"pistol: steel, wood (walnut), silver; reserve barrel: steel; bullet mould: steel; punch; steel; wrench: steel; case: wood (mahogany), brass, textile, paper",Pistol (a); L. 14 1/2 in. (36.8 cm); L. of barrel 6 in. (15.2 cm); Cal. .55 in. (14 mm); Wt. 3 lb. 15 oz. (1786 g); reserve barrel (b); L. 6 1/8 in. (15.6 cm); Wt. 14.4 oz. (408.2 g); bullet mould (c); L. 5 3/8 in. (13.7 cm); Wt. 2.6 oz. (73.7 g); punch (d); L. 5 1/2 in. (14 cm); Wt. 4.4 oz. (124.7 g); wrench (e); L. 3 3/4 in. (9.5 cm); Wt. 1.6 oz. (45.4 g); case (f); H. 4 1/8 in. (10.5 cm); W. 15 9/16 in. (39.5 cm); D. 7 5/8 in. (19.4 cm); Wt. 6 lb. 7.2 oz. (2925.7 g),"Bequest of Charles N. Daly, 1934",,London,,,,,,,,,,Firearms-Pistols-Flintlock,,http://www.metmuseum.org/art/collection/search/32180,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -26.145.343,false,true,22943,Arms and Armor,Smallsword,Smallsword,"Dutch, Amsterdam",,,,,Sword maker,,Jan Nieuwland,"Dutch, Amsterdam, active 1747–1807",,"Nieuwland, Jan",Dutch,1747,1807,ca. 1750,1725,1775,"Silver, porcelain (Meissen), steel, gold, textile",L. 34 13/16 in. (88.5 cm); L. of blade 28 3/4 in. (73 cm); W. 3 7/8 in. (9.8 cm); D. 2 3/4 in. (7 cm); Wt. 14 oz. (396.9 g),"Gift of Jean Jacques Reubell, in memory of his mother, Julia C. Coster, and of his wife, Adeline E. Post, both of New York City, 1926",,Amsterdam,,,,,,,,,,Swords,,http://www.metmuseum.org/art/collection/search/22943,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"19.53.13, .14",false,true,22732,Arms and Armor,Pair of wheellock pistols,Pair of Wheellock Pistols,"French, Lisieux",,,,,Gunsmith,,Pierre Le Bourgeois,"French, Lisieux, died 1627",,"Le Bourgeois, Pierre",French,1527,1627,ca. 1610–20,1585,1645,"Steel, gold, wood, silver, mother-of-pearl","L. of each pistol 23 5/16 in. (59.2 cm); Cal. of each pistol, .49 in. (12.5 mm); L. of each barrel 15 5/8 in. (39.7 cm); Wt. of 19.53.13, 2 lb. 8 oz. (1134 g); Wt. of 19.56.14, 2 lb. 10 oz. (1191 g)","Gift of Charles M. Schott Jr., 1917",,Lisieux,,,,,,,,,,Firearms-Pistols,,http://www.metmuseum.org/art/collection/search/22732,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.58a–e,false,true,23950,Arms and Armor,Double-barreled flintlock shotgun,Double-Barreled Flintlock Shotgun,"French, Versailles",,,,,Gunsmith,,Nicolas Noël Boutet,"French, Versailles and Paris, 1761–1833",,"Boutet, Nicholas-Noël",French,1761,1833,dated 1801,1801,1801,"Steel, wood (walnut, ebony), gold, silver",L. 48 3/8 in. (122.9 cm); L. of barrel 33 in. (83.8 cm); Cal. .60 in. (15.2 mm); Wt. 7 lb. 6 oz. (3350 g),"Rogers Fund, 1936",,Versailles,,,,,,,,,,Firearms,,http://www.metmuseum.org/art/collection/search/23950,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.179.1a–q,true,true,24861,Arms and Armor,"Cased set of a flintlock rifle, a pair of pistols, and accessories","Cased Set of a Flintlock Rifle, a Pair of Pistols, and Accessories","French, Versailles",,,,,Gunsmith,,Nicolas Noël Boutet,"French, Versailles and Paris, 1761–1833",,"Boutet, Nicholas-Noël",French,1761,1833,ca. 1800,1775,1825,"Steel, wood (walnut, mahogany), silver, gold, horn, velvet",L. of rifle 43 1/2 in. (110.5 cm); L. of barrel of rifle 27 5/8 in. (70.2 cm); Cal. of rifle .64 in. (16.3 mm); Wt. of rifle 6 lb. 7 oz. (2920 g); L. of each pistol 17 in. (43.17 cm); L. of barrel 11 5/8 in. (29.53 cm); Cal. of each pistol .52 in. (13.2 mm); Wt. of each pistol 2 lbs. 2 oz. (963.9 g); Dim. of case 46 7/16 x 15 15/16 x 2 3/16 in. (118 x 40.5 x 5.5 cm),"Fletcher Fund, 1970",,Versailles,,,,,,,,,,Firearms-Guns-Flintlock,,http://www.metmuseum.org/art/collection/search/24861,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2013.581a, b",false,true,35956,Arms and Armor,Prototype for helmet model no. 2,Prototype for Helmet Model No. 2,"American, New York",,,,,Armorer,,Daniel Tachaux,"French, 1857–1928, active in France and America",,"Tachaux, Daniel",French,1857,1928,1917,1917,1917,"Steel, pressed paper or cardboard",H. 8 in. (20.3 cm); W. 10 in. (25.4 cm); D. 13 3/8 in. (34 cm); Wt. 2 lb. 4 oz. (1020 g),"Purchase, Gift of Bashford Dean, by exchange, 2013",,New York,New York,,,,,,,,,Helmets,,http://www.metmuseum.org/art/collection/search/35956,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -11.128.1o,false,true,35902,Arms and Armor,Right thigh and knee defense (cuisse and poleyn),Right Thigh and Knee Defense (Cuisse and Poleyn) for the Armor of Sir John Scudamore (1541 or 1542–1623),"American, New York",,,,,Armorer,,Daniel Tachaux,"French, 1857–1928, active in France and America",,"Tachaux, Daniel",French,1857,1928,dated 1913,1913,1913,"Steel, gold, leather",H. 16 1/2 in. (41.9 cm); W. 7 11/16 in. (19.5 cm); D. 5 in. (12.7 cm); Wt. 2 lb. 12 oz. (1247.4 g),"Frederick C. Hewitt Fund, 1911",,New York,New York,,,,,,,,,Armor Parts,,http://www.metmuseum.org/art/collection/search/35902,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -04.3.291a–q,false,true,35928,Arms and Armor,Jousting armor,Armor for the Joust of Peace,"German; restorations, French, Paris",,,,,Armorer,Helmet (04.3.291a) made by,Daniel Tachaux,"French, 1857–1928, active in France and America",,"Tachaux, Daniel",French,1857,1928,ca. 1500 and later,1475,1960,"Steel, copper alloy, leather, textile, horn","Helmet (a); Wt. 21.5 lb. (9752 g); breastplate (c); Wt. 23 lb. (10.4 kg); tassets (e, f); Wt. of each 6 lb. (2723 g); backplate (i); Wt. 5 lb. (2268 g); rondels (o, p); Wt. of each 5 lb. (2268 g); shield (q); Wt. 6 lb. (2723 g); Wt. overall 85.5 lb. (38.78 kg)","Armor for man and Shaffron: Rogers Fund, 1904; spurs: Bashford Dean Memorial Collection, Funds from various donors, 1929; bit, stirrups, and lance coronel: Bashford Dean Memorial Collection, Bequest of Bashford Dean, 1928; vamplate for the lance: Gift of William H. Riggs, 1913",,Paris,,,,,,,,,,Armor for Man,,http://www.metmuseum.org/art/collection/search/35928,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"04.3.291a–q, .292; etc.",false,true,21926,Arms and Armor,Jousting armor,Jousting Armor,"German; restorations, French, Paris",,,,,Armorer,Helmet made by,Daniel Tachaux,"French, 1857–1928, active in France and America",,"Tachaux, Daniel",French,1857,1928,ca. 1500 and later,1475,1900,"Steel, copper alloy, leather, textile, horn","Helmet (a); Wt. 21.5 lb. (9752 g); breastplate (c); Wt. 23 lb. (10.4 kg); tassets (e, f); Wt. of each 6 lb. (2723 g); backplate (i); Wt. 5 lb. (2268 g); rondels (o, p); Wt. of each 5 lb. (2268 g); shield (q); Wt. 6 lb. (2723 g); Wt. overall 85.5 lb. (38.78 kg)","Rogers Fund, 1904",,Paris,,,,,,,,,,Armor for Horse and Man,,http://www.metmuseum.org/art/collection/search/21926,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2011.354,false,true,35703,Arms and Armor,Flintlock sporting gun,Flintlock Sporting Gun of Empress Margarita Teresa of Spain (1651–1673),"Austrian, Vienna",,,,,Gunsmith,,Jacques Lamarre,"French, recorded Paris 1657–1700 Vienna, Austria",,"Lamarre, Jacques",French,1657,1700,ca. 1670–73,1645,1698,"Steel, wood (burl walnut), silver, copper alloy, gold",L. 52 1/8 in. (132.4 cm); L. of barrel 38 3/8 in. (97.5 cm),"Purchase, Arthur Ochs Sulzberger and Irene Roosevelt Aitken Gifts, 2011",,Vienna,,,,,,,,,,Firearms-Guns-Flintlock,,http://www.metmuseum.org/art/collection/search/35703,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.223,true,true,24865,Arms and Armor,Flintlock gun,"Flintlock Gun of Louis XIII (1601–1643), King of France","French, Lisieux",,,,,Gunsmith|Gunsmith,,Pierre Le Bourgeois|Marin Le Bourgeois,"French, Lisieux, died 1627|French, Lisieux, ca. 1550–1634",,"Le Bourgeois, Pierre|Le Bourgeois, Marin",French,1527 |1550,1627 |1634,ca. 1620,1595,1645,"Steel, brass, silver, gold, wood (walnut), mother-of-pearl",L. 55 5/16 in. (140.5 cm); Cal. .59 in. (55 mm); L. of barrel 41 in. (104.1 cm); L. of lockplate 6 9/16 in. (16.7 cm); Wt. 5 lb. 11 oz. (2580 g),"Rogers Fund and Harris Brisbane Dick Fund, 1972",,Lisieux,,,,,,,,,,Firearms-Guns-Flintlock,,http://www.metmuseum.org/art/collection/search/24865,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2004.101.1,false,true,652955,Arms and Armor,Drawings,Design for the Decoration of the Grip of a Pocket Pistol,"French, Versailles",,,,,Designer,Workshop of,Nicolas Noël Boutet,"French, Versailles and Paris, 1761–1833",,"Boutet, Nicholas-Noël",French,1761,1833,ca. 1795–1805,1770,1830,"Pencil, ink, gray wash on paper",3 3/8 x 7 7/16 in. (8.6 x 18.9 cm),"Purchase, Clay P. Bedford Gift, 2004",,Versailles,,,,,,,,,,Works on Paper-Drawings,,http://www.metmuseum.org/art/collection/search/652955,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2004.101.2,false,true,652958,Arms and Armor,Drawings,Design for the Decoration of the Grip of a Pocket Pistol,"French, Versailles",,,,,Designer,Workshop of,Nicolas Noël Boutet,"French, Versailles and Paris, 1761–1833",,"Boutet, Nicholas-Noël",French,1761,1833,ca. 1797–1805,1772,1830,"Pencil, ink, gray wash on paper",3 5/8 x 2 13/16 in. (9.2 x 7.1 cm),"Purchase, Clay P. Bedford Gift, 2004",,Versailles,,,,,,,,,,Works on Paper-Drawings,,http://www.metmuseum.org/art/collection/search/652958,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2004.101.3,false,true,652959,Arms and Armor,Drawings,Design for the Decoration of the Grip of a Pocket Pistol,"French, Versailles",,,,,Designer,Workshop of,Nicolas Noël Boutet,"French, Versailles and Paris, 1761–1833",,"Boutet, Nicholas-Noël",French,1761,1833,ca. 1797–1805,1772,1830,"Pencil, ink, gray wash on paper",4 x 3 1/2 in. (10.2 x 8.9 cm),"Purchase, Clay P. Bedford Gift, 2004",,Versailles,,,,,,,,,,Works on Paper-Drawings,,http://www.metmuseum.org/art/collection/search/652959,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2004.101.4,false,true,652960,Arms and Armor,Drawings,Design for the Decoration of the Grip of a Pocket Pistol,"French, Versailles",,,,,Designer,Workshop of,Nicolas Noël Boutet,"French, Versailles and Paris, 1761–1833",,"Boutet, Nicholas-Noël",French,1761,1833,ca. 1797–1805,1772,1830,"Pencil, ink, gray wash on paper",5 5/8 x 4 in. (14.3 x 10.2 cm),"Purchase, Clay P. Bedford Gift, 2004",,Versailles,,,,,,,,,,Works on Paper-Drawings,,http://www.metmuseum.org/art/collection/search/652960,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2004.101.5,false,true,652961,Arms and Armor,Drawings,Design for the Decoration of the Side Plate of a Firearm,"French, Versailles",,,,,Designer,Workshop of,Nicolas Noël Boutet,"French, Versailles and Paris, 1761–1833",,"Boutet, Nicholas-Noël",French,1761,1833,ca. 1797–1805,1772,1830,"Pencil, ink, gray wash on paper",2 3/4 x 6 1/4 in. (7 x 15.9 cm),"Purchase, Clay P. Bedford Gift, 2004",,Versailles,,,,,,,,,,Works on Paper-Drawings,,http://www.metmuseum.org/art/collection/search/652961,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2004.101.6,false,true,652962,Arms and Armor,Drawings,Design for the Decoration of the Side Plate of a Firearm,"French, Versailles",,,,,Designer,Workshop of,Nicolas Noël Boutet,"French, Versailles and Paris, 1761–1833",,"Boutet, Nicholas-Noël",French,1761,1833,ca. 1797–1805,1772,1830,"Pencil, ink, gray wash on paper",1 7/16 x 5 in. (3.7 x 12.7 cm),"Purchase, Clay P. Bedford Gift, 2004",,Versailles,,,,,,,,,,Works on Paper-Drawings,,http://www.metmuseum.org/art/collection/search/652962,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2004.101.7,false,true,652963,Arms and Armor,Drawings,Designs for the Decoration of Firearms,"French, Versailles",,,,,Designer,Workshop of,Nicolas Noël Boutet,"French, Versailles and Paris, 1761–1833",,"Boutet, Nicholas-Noël",French,1761,1833,ca. 1797–1805,1772,1830,"Pencil, ink, gray wash on paper",3 3/16 x 7 3/4 in. (8.1 x 19.7 cm),"Purchase, Clay P. Bedford Gift, 2004",,Versailles,,,,,,,,,,Works on Paper-Drawings,,http://www.metmuseum.org/art/collection/search/652963,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2004.101.8,false,true,652964,Arms and Armor,Drawings,Designs for the Decoration of the Frizzen and Jaws of the Cock of a Firearm,"French, Versailles",,,,,Designer,Workshop of,Nicolas Noël Boutet,"French, Versailles and Paris, 1761–1833",,"Boutet, Nicholas-Noël",French,1761,1833,ca. 1797–1805,1772,1830,"Pencil, ink, gray wash on paper",3 1/2 x 1 1/2 in. (8.9 x 3.8 cm),"Purchase, Clay P. Bedford Gift, 2004",,Versailles,,,,,,,,,,Works on Paper-Drawings,,http://www.metmuseum.org/art/collection/search/652964,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2004.101.9,false,true,652965,Arms and Armor,Drawings,Designs for the Decoration of the Jaw and Profile of the Cock of a Firearm,"French, Versailles",,,,,Designer,Workshop of,Nicolas Noël Boutet,"French, Versailles and Paris, 1761–1833",,"Boutet, Nicholas-Noël",French,1761,1833,ca. 1797–1805,1772,1830,"Pencil, ink, gray wash on paper",2 1/4 x 3 in. (5.7 x 7.6 cm),"Purchase, Clay P. Bedford Gift, 2004",,Versailles,,,,,,,,,,Works on Paper-Drawings,,http://www.metmuseum.org/art/collection/search/652965,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2004.101.10,false,true,652966,Arms and Armor,Drawings,Designs for the Decoration of the Jaw and Profile of the Cock of a Firearm,"French, Versailles",,,,,Designer,Workshop of,Nicolas Noël Boutet,"French, Versailles and Paris, 1761–1833",,"Boutet, Nicholas-Noël",French,1761,1833,ca. 1795–1805,1770,1830,"Pencil, ink, gray wash on paper",2 1/4 x 3 1/8 in. (5.7 x 7.9 cm),"Purchase, Clay P. Bedford Gift, 2004",,Versailles,,,,,,,,,,Works on Paper-Drawings,,http://www.metmuseum.org/art/collection/search/652966,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2004.101.11,false,true,652967,Arms and Armor,Drawings,Design for the Decoration of the Jaw of the Cock of a Firearm,"French, Versailles",,,,,Designer,Workshop of,Nicolas Noël Boutet,"French, Versailles and Paris, 1761–1833",,"Boutet, Nicholas-Noël",French,1761,1833,ca. 1795–1805,1770,1830,"Pencil, ink, gray wash on paper",1 x 1 in. (2.5 x 2.5 cm),"Purchase, Clay P. Bedford Gift, 2004",,Versailles,,,,,,,,,,Works on Paper-Drawings,,http://www.metmuseum.org/art/collection/search/652967,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2004.101.12,false,true,652968,Arms and Armor,Drawings,Design for the Decoration of Firearms,"French, Versailles",,,,,Designer,Workshop of,Nicolas Noël Boutet,"French, Versailles and Paris, 1761–1833",,"Boutet, Nicholas-Noël",French,1761,1833,ca. 1795–1805,1770,1830,"Pencil, ink, gray wash on paper",6 3/4 x 3 1/2 in. (17.1 x 8.9 cm),"Purchase, Clay P. Bedford Gift, 2004",,Versailles,,,,,,,,,,Works on Paper-Drawings,,http://www.metmuseum.org/art/collection/search/652968,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2004.101.13,false,true,652969,Arms and Armor,Drawings,Design for the Decoration of Firearms,"French, Versailles",,,,,Designer,Workshop of,Nicolas Noël Boutet,"French, Versailles and Paris, 1761–1833",,"Boutet, Nicholas-Noël",French,1761,1833,ca. 1795–1805,1770,1830,"Pencil, ink, gray wash on paper",6 1/2 x 3 in. (16.5 x 7.6 cm),"Purchase, Clay P. Bedford Gift, 2004",,Versailles,,,,,,,,,,Works on Paper-Drawings,,http://www.metmuseum.org/art/collection/search/652969,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2004.101.14,false,true,652970,Arms and Armor,Drawings,Design for the Decoration of a Gun Stock,"French, Versailles",,,,,Designer,Workshop of,Nicolas Noël Boutet,"French, Versailles and Paris, 1761–1833",,"Boutet, Nicholas-Noël",French,1761,1833,ca. 1795–1805,1770,1830,"Pencil, ink, gray wash on paper",3 1/4 x 7 1/8 in. (8.3 x 18.1 cm),"Purchase, Clay P. Bedford Gift, 2004",,Versailles,,,,,,,,,,Works on Paper-Drawings,,http://www.metmuseum.org/art/collection/search/652970,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2004.101.15,false,true,652971,Arms and Armor,Drawings,Design for the Decoration of Firearms,"French, Versailles",,,,,Designer,Workshop of,Nicolas Noël Boutet,"French, Versailles and Paris, 1761–1833",,"Boutet, Nicholas-Noël",French,1761,1833,ca. 1795–1805,1770,1830,"Pencil, ink, gray wash on paper",2 7/8 x 2 1/2 in. (7.3 x 6.4 cm),"Purchase, Clay P. Bedford Gift, 2004",,Versailles,,,,,,,,,,Works on Paper-Drawings,,http://www.metmuseum.org/art/collection/search/652971,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2004.101.16,false,true,652972,Arms and Armor,Drawings,Design for the Decoration of Firearms,"French, Versailles",,,,,Designer,Workshop of,Nicolas Noël Boutet,"French, Versailles and Paris, 1761–1833",,"Boutet, Nicholas-Noël",French,1761,1833,ca. 1795–1805,1770,1830,"Pencil, ink, gray wash on paper",3 3/8 x 4 5/8 in. (8.6 x 11.7 cm),"Purchase, Clay P. Bedford Gift, 2004",,Versailles,,,,,,,,,,Works on Paper-Drawings,,http://www.metmuseum.org/art/collection/search/652972,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2004.101.17,false,true,652973,Arms and Armor,Drawings,Design for the Decoration of the Surround of the Ramrod Pipe of a Firearm,"French, Versailles",,,,,Designer,Workshop of,Nicolas Noël Boutet,"French, Versailles and Paris, 1761–1833",,"Boutet, Nicholas-Noël",French,1761,1833,ca. 1795–1805,1770,1830,"Pencil, ink, gray wash on paper",2 5/8 x 3 7/8 in. (6.7 x 9.8 cm),"Purchase, Clay P. Bedford Gift, 2004",,Versailles,,,,,,,,,,Works on Paper-Drawings,,http://www.metmuseum.org/art/collection/search/652973,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2004.101.18,false,true,652974,Arms and Armor,Drawings,Design for the Decoration of the Surround of the Ramrod Pipe of a Firearm,"French, Versailles",,,,,Designer,Workshop of,Nicolas Noël Boutet,"French, Versailles and Paris, 1761–1833",,"Boutet, Nicholas-Noël",French,1761,1833,ca. 1795–1805,1770,1830,"Pencil, ink, gray wash on paper",3 1/4 x 1 7/8 in. (8.3 x 4.8 cm),"Purchase, Clay P. Bedford Gift, 2004",,Versailles,,,,,,,,,,Works on Paper-Drawings,,http://www.metmuseum.org/art/collection/search/652974,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2004.101.19,false,true,652975,Arms and Armor,Drawings,Design for the Decoration of the Surround of the Barrel Tang of a Firearm,"French, Versailles",,,,,Designer,Workshop of,Nicolas Noël Boutet,"French, Versailles and Paris, 1761–1833",,"Boutet, Nicholas-Noël",French,1761,1833,ca. 1795–1805,1770,1830,"Pencil, ink, gray wash on paper",5 1/8 x 3 1/8 in. (13 x 7.9 cm),"Purchase, Clay P. Bedford Gift, 2004",,Versailles,,,,,,,,,,Works on Paper-Drawings,,http://www.metmuseum.org/art/collection/search/652975,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2004.101.20,false,true,652978,Arms and Armor,Drawings,Designs for the Decoration of Firearms,"French, Versailles",,,,,Designer,Workshop of,Nicolas Noël Boutet,"French, Versailles and Paris, 1761–1833",,"Boutet, Nicholas-Noël",French,1761,1833,ca. 1797–1805,1772,1830,"Pencil, ink, gray wash on paper",6 1/8 x 8 5/8 in. (15.6 x 21.9 cm),"Purchase, Clay P. Bedford Gift, 2004",,Versailles,,,,,,,,,,Works on Paper-Drawings,,http://www.metmuseum.org/art/collection/search/652978,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2004.101.21,false,true,652980,Arms and Armor,Drawings,Design for the Decoration of Firearms,"French, Versailles",,,,,Designer,Workshop of,Nicolas Noël Boutet,"French, Versailles and Paris, 1761–1833",,"Boutet, Nicholas-Noël",French,1761,1833,ca. 1797–1805,1772,1830,"Pencil, ink, gray wash on paper",3 3/4 x 1 3/4 in. (9.5 x 4.5 cm),"Purchase, Clay P. Bedford Gift, 2004",,Versailles,,,,,,,,,,Works on Paper-Drawings,,http://www.metmuseum.org/art/collection/search/652980,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2004.101.22,false,true,652981,Arms and Armor,Drawings,Designs for the Decoration of Firearms,"French, Versailles",,,,,Designer,Workshop of,Nicolas Noël Boutet,"French, Versailles and Paris, 1761–1833",,"Boutet, Nicholas-Noël",French,1761,1833,ca. 1797–1805,1772,1830,"Pencil, ink, gray wash on paper",4 1/8 x 3 3/4 in. (10.5 x 9.5 cm),"Purchase, Clay P. Bedford Gift, 2004",,Versailles,,,,,,,,,,Works on Paper-Drawings,,http://www.metmuseum.org/art/collection/search/652981,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2004.101.23,false,true,652982,Arms and Armor,Drawings,Design for the Decoration of Firearms,"French, Versailles",,,,,Designer,Workshop of,Nicolas Noël Boutet,"French, Versailles and Paris, 1761–1833",,"Boutet, Nicholas-Noël",French,1761,1833,ca. 1797–1805,1772,1830,"Pencil, ink, gray wash on paper",4 1/8 x 2 1/2 in. (10.5 x 6.4 cm),"Purchase, Clay P. Bedford Gift, 2004",,Versailles,,,,,,,,,,Works on Paper-Drawings,,http://www.metmuseum.org/art/collection/search/652982,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2004.101.24,false,true,652983,Arms and Armor,Drawings,Design for the Decoration of Firearms,"French, Versailles",,,,,Designer,Workshop of,Nicolas Noël Boutet,"French, Versailles and Paris, 1761–1833",,"Boutet, Nicholas-Noël",French,1761,1833,ca. 1797–1805,1772,1830,"Pencil, ink, gray wash on paper",4 1/2 x 1 3/4 in. (11.4 x 4.5 cm),"Purchase, Clay P. Bedford Gift, 2004",,Versailles,,,,,,,,,,Works on Paper-Drawings,,http://www.metmuseum.org/art/collection/search/652983,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2004.101.25,false,true,652984,Arms and Armor,Drawings,Design for the Decoration of Firearms,"French, Versailles",,,,,Designer,Workshop of,Nicolas Noël Boutet,"French, Versailles and Paris, 1761–1833",,"Boutet, Nicholas-Noël",French,1761,1833,ca. 1797–1805,1772,1830,"Pencil, ink, gray wash on paper",2 3/4 x 2 5/8 in. (7 x 6.7 cm),"Purchase, Clay P. Bedford Gift, 2004",,Versailles,,,,,,,,,,Works on Paper-Drawings,,http://www.metmuseum.org/art/collection/search/652984,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2004.101.26,false,true,652985,Arms and Armor,Drawings,Design for the Decoration of Firearms,"French, Versailles",,,,,Designer,Workshop of,Nicolas Noël Boutet,"French, Versailles and Paris, 1761–1833",,"Boutet, Nicholas-Noël",French,1761,1833,ca. 1797–1805,1772,1830,"Pencil, ink, gray wash on paper",1 5/8 x 2 in. (4.1 x 5.1 cm),"Purchase, Clay P. Bedford Gift, 2004",,Versailles,,,,,,,,,,Works on Paper-Drawings,,http://www.metmuseum.org/art/collection/search/652985,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2004.101.27,false,true,652986,Arms and Armor,Drawings,Design for the Decoration of Firearms,"French, Versailles",,,,,Designer,Workshop of,Nicolas Noël Boutet,"French, Versailles and Paris, 1761–1833",,"Boutet, Nicholas-Noël",French,1761,1833,ca. 1797–1805,1772,1830,"Pencil, ink, gray wash on paper",2 1/8 x 1 7/8 in. (5.4 x 4.8 cm),"Purchase, Clay P. Bedford Gift, 2004",,Versailles,,,,,,,,,,Works on Paper-Drawings,,http://www.metmuseum.org/art/collection/search/652986,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2004.101.28,false,true,652987,Arms and Armor,Drawings,Design for the Decoration of Firearms,"French, Versailles",,,,,Designer,Workshop of,Nicolas Noël Boutet,"French, Versailles and Paris, 1761–1833",,"Boutet, Nicholas-Noël",French,1761,1833,ca. 1797–1805,1772,1830,"Pencil, ink, gray wash on paper",1 1/8 x 1 3/4 in. (2.9 x 4.5 cm),"Purchase, Clay P. Bedford Gift, 2004",,Versailles,,,,,,,,,,Works on Paper-Drawings,,http://www.metmuseum.org/art/collection/search/652987,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2004.101.29,false,true,652988,Arms and Armor,Drawings,Design for the Decoration of the Surround of the Barrel Tang of a Firearm,"French, Versailles",,,,,Designer,Workshop of,Nicolas Noël Boutet,"French, Versailles and Paris, 1761–1833",,"Boutet, Nicholas-Noël",French,1761,1833,ca. 1797–1805,1772,1830,"Pencil, ink, gray wash on paper",4 1/8 x 3 3/4 in. (10.5 x 9.5 cm),"Purchase, Clay P. Bedford Gift, 2004",,Versailles,,,,,,,,,,Works on Paper-Drawings,,http://www.metmuseum.org/art/collection/search/652988,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2004.101.30,false,true,652989,Arms and Armor,Drawings,Partial Design for the Decoration of the Surround of the Barrel Tang of a Firearm,"French, Versailles",,,,,Designer,Workshop of,Nicolas Noël Boutet,"French, Versailles and Paris, 1761–1833",,"Boutet, Nicholas-Noël",French,1761,1833,ca. 1797–1805,1772,1830,"Pencil, ink, gray wash on paper",4 1/4 x 4 1/8 in. (10.8 x 10.5 cm),"Purchase, Clay P. Bedford Gift, 2004",,Versailles,,,,,,,,,,Works on Paper-Drawings,,http://www.metmuseum.org/art/collection/search/652989,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2004.101.31,false,true,652990,Arms and Armor,Drawings,Two Designs for the Decoration of Barrel Tang Surrounds of a Firearm,"French, Versailles",,,,,Designer,Workshop of,Nicolas Noël Boutet,"French, Versailles and Paris, 1761–1833",,"Boutet, Nicholas-Noël",French,1761,1833,ca. 1797–1805,1772,1830,"Pencil, ink, gray wash on paper",4 1/8 x 7 7/8 in. (10.5 x 20 cm),"Purchase, Clay P. Bedford Gift, 2004",,Versailles,,,,,,,,,,Works on Paper-Drawings,,http://www.metmuseum.org/art/collection/search/652990,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2004.101.32,false,true,652991,Arms and Armor,Drawings,Thirteen Designs for the Decoration of Firearms,"French, Versailles",,,,,Designer,Workshop of,Nicolas Noël Boutet,"French, Versailles and Paris, 1761–1833",,"Boutet, Nicholas-Noël",French,1761,1833,ca. 1797–1805,1772,1830,"Pencil, ink, gray wash on paper",5 3/4 x 8 1/4 in. (14.6 x 21 cm),"Purchase, Clay P. Bedford Gift, 2004",,Versailles,,,,,,,,,,Works on Paper-Drawings,,http://www.metmuseum.org/art/collection/search/652991,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2004.101.33,false,true,652992,Arms and Armor,Drawings,Four Designs for the Decoration of Firearms,"French, Versailles",,,,,Designer,Workshop of,Nicolas Noël Boutet,"French, Versailles and Paris, 1761–1833",,"Boutet, Nicholas-Noël",French,1761,1833,ca. 1797–1805,1772,1830,"Pencil, ink, gray wash on paper",7 3/4 x 5 7/8 in. (19.7 x 14.9 cm),"Purchase, Clay P. Bedford Gift, 2004",,Versailles,,,,,,,,,,Works on Paper-Drawings,,http://www.metmuseum.org/art/collection/search/652992,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2004.101.34,false,true,652993,Arms and Armor,Drawings,Design for the Decoration of Firearms,"French, Versailles",,,,,Designer,Workshop of,Nicolas Noël Boutet,"French, Versailles and Paris, 1761–1833",,"Boutet, Nicholas-Noël",French,1761,1833,ca. 1797–1805,1772,1830,"Pencil, ink, gray wash on paper",8 1/2 x 3 3/4 in. (21.6 x 9.5 cm),"Purchase, Clay P. Bedford Gift, 2004",,Versailles,,,,,,,,,,Works on Paper-Drawings,,http://www.metmuseum.org/art/collection/search/652993,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2004.101.35,false,true,652994,Arms and Armor,Drawings,Designs for the Decoration of Firearms,"French, Versailles",,,,,Designer,Workshop of,Nicolas Noël Boutet,"French, Versailles and Paris, 1761–1833",,"Boutet, Nicholas-Noël",French,1761,1833,ca. 1797–1805,1772,1830,"Pencil, ink, gray wash on paper",8 1/8 x 3 5/8 in. (20.6 x 9.2 cm),"Purchase, Clay P. Bedford Gift, 2004",,Versailles,,,,,,,,,,Works on Paper-Drawings,,http://www.metmuseum.org/art/collection/search/652994,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2004.101.36,false,true,652995,Arms and Armor,Drawings,Design for the Decoration of Firearms,"French, Versailles",,,,,Designer,Workshop of,Nicolas Noël Boutet,"French, Versailles and Paris, 1761–1833",,"Boutet, Nicholas-Noël",French,1761,1833,ca. 1797–1805,1772,1830,"Pencil, ink, gray wash on paper",2 3/4 x 5 1/4 in. (7 x 13.3 cm),"Purchase, Clay P. Bedford Gift, 2004",,Versailles,,,,,,,,,,Works on Paper-Drawings,,http://www.metmuseum.org/art/collection/search/652995,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2004.101.37,false,true,652996,Arms and Armor,Drawings,Design for the Decoration of Firearms,"French, Versailles",,,,,Designer,Workshop of,Nicolas Noël Boutet,"French, Versailles and Paris, 1761–1833",,"Boutet, Nicholas-Noël",French,1761,1833,ca. 1797–1805,1772,1830,"Pencil, ink, gray wash on paper",2 3/4 x 3 7/8 in. (7 x 9.8 cm),"Purchase, Clay P. Bedford Gift, 2004",,Versailles,,,,,,,,,,Works on Paper-Drawings,,http://www.metmuseum.org/art/collection/search/652996,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2004.101.38,false,true,652997,Arms and Armor,Drawings,Design for the Decoration of Firearms,"French, Versailles",,,,,Designer,Workshop of,Nicolas Noël Boutet,"French, Versailles and Paris, 1761–1833",,"Boutet, Nicholas-Noël",French,1761,1833,ca. 1797–1805,1772,1830,"Pencil, ink, gray wash on paper",3 x 2 3/4 in. (7.6 x 7 cm),"Purchase, Clay P. Bedford Gift, 2004",,Versailles,,,,,,,,,,Works on Paper-Drawings,,http://www.metmuseum.org/art/collection/search/652997,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2004.101.39,false,true,652998,Arms and Armor,Drawings,Partial Design for the Decoration of Firearms,"French, Versailles",,,,,Designer,Workshop of,Nicolas Noël Boutet,"French, Versailles and Paris, 1761–1833",,"Boutet, Nicholas-Noël",French,1761,1833,ca. 1797–1805,1772,1830,"Pencil, ink, gray wash on paper",3 7/8 x 2 3/4 in. (9.8 x 7 cm),"Purchase, Clay P. Bedford Gift, 2004",,Versailles,,,,,,,,,,Works on Paper-Drawings,,http://www.metmuseum.org/art/collection/search/652998,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2004.101.40,false,true,652999,Arms and Armor,Drawings,Design for the Decoration of Firearms,"French, Versailles",,,,,Designer,Workshop of,Nicolas Noël Boutet,"French, Versailles and Paris, 1761–1833",,"Boutet, Nicholas-Noël",French,1761,1833,ca. 1797–1805,1772,1830,"Pencil, ink, gray wash on paper",2 x 4 7/8 in. (5.1 x 12.4 cm),"Purchase, Clay P. Bedford Gift, 2004",,Versailles,,,,,,,,,,Works on Paper-Drawings,,http://www.metmuseum.org/art/collection/search/652999,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2004.101.41,false,true,653000,Arms and Armor,Drawings,Design for the Decoration of the Barrel Tang Surround of a Firearm,"French, Versailles",,,,,Designer,Workshop of,Nicolas Noël Boutet,"French, Versailles and Paris, 1761–1833",,"Boutet, Nicholas-Noël",French,1761,1833,ca. 1797–1805,1772,1830,"Pencil, ink, gray wash on paper",3 7/8 x 1 7/8 in. (9.8 x 4.8 cm),"Purchase, Clay P. Bedford Gift, 2004",,Versailles,,,,,,,,,,Works on Paper-Drawings,,http://www.metmuseum.org/art/collection/search/653000,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2004.101.42,false,true,653001,Arms and Armor,Drawings,Design for the Decoration of the Barrel Tang Surround of a Firearm,"French, Versailles",,,,,Designer,Workshop of,Nicolas Noël Boutet,"French, Versailles and Paris, 1761–1833",,"Boutet, Nicholas-Noël",French,1761,1833,ca. 1797–1805,1772,1830,"Pencil, ink, gray wash on paper",5 1/4 x 2 7/8 in. (13.3 x 7.3 cm),"Purchase, Clay P. Bedford Gift, 2004",,Versailles,,,,,,,,,,Works on Paper-Drawings,,http://www.metmuseum.org/art/collection/search/653001,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2004.101.43,false,true,653002,Arms and Armor,Drawings,Design for the Decoration of the Barrel Tang Surround of a Firearm,"French, Versailles",,,,,Designer,Workshop of,Nicolas Noël Boutet,"French, Versailles and Paris, 1761–1833",,"Boutet, Nicholas-Noël",French,1761,1833,ca. 1797–1805,1772,1830,"Pencil, ink, gray wash on paper",2 7/8 x 3 1/4 in (7.3 x 8.3 cm),"Purchase, Clay P. Bedford Gift, 2004",,Versailles,,,,,,,,,,Works on Paper-Drawings,,http://www.metmuseum.org/art/collection/search/653002,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2004.101.44,false,true,653003,Arms and Armor,Drawings,Design for the Decoration of the Surround of the Barrel Flat and Rear Sight of a Firearm,"French, Versailles",,,,,Designer,Workshop of,Nicolas Noël Boutet,"French, Versailles and Paris, 1761–1833",,"Boutet, Nicholas-Noël",French,1761,1833,ca. 1797–1805,1772,1830,"Pencil, ink, gray wash on paper",4 1/8 x 3 1/2 in. (10.5 x 8.9 cm),"Purchase, Clay P. Bedford Gift, 2004",,Versailles,,,,,,,,,,Works on Paper-Drawings,,http://www.metmuseum.org/art/collection/search/653003,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2004.101.45,false,true,653004,Arms and Armor,Drawings,Design for the Decoration of the Surround of the Rear Sight of a Firearm,"French, Versailles",,,,,Designer,Workshop of,Nicolas Noël Boutet,"French, Versailles and Paris, 1761–1833",,"Boutet, Nicholas-Noël",French,1761,1833,ca. 1797–1805,1772,1830,"Pencil, ink, gray wash on paper",3 3/4 x 3 5/8 in. (9.5 x 9.2 cm),"Purchase, Clay P. Bedford Gift, 2004",,Versailles,,,,,,,,,,Works on Paper-Drawings,,http://www.metmuseum.org/art/collection/search/653004,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2004.101.46,false,true,653005,Arms and Armor,Drawings,Design for the Decoration of Firearms,"French, Versailles",,,,,Designer,Workshop of,Nicolas Noël Boutet,"French, Versailles and Paris, 1761–1833",,"Boutet, Nicholas-Noël",French,1761,1833,ca. 1797–1805,1772,1830,"Pencil, ink, gray wash on paper",2 1/8 x 1 3/4 in. (5.4 x 4.5 cm),"Purchase, Clay P. Bedford Gift, 2004",,Versailles,,,,,,,,,,Works on Paper-Drawings,,http://www.metmuseum.org/art/collection/search/653005,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2004.101.47,false,true,653006,Arms and Armor,Drawings,Design for the Decoration of Firearms,"French, Versailles",,,,,Designer,Workshop of,Nicolas Noël Boutet,"French, Versailles and Paris, 1761–1833",,"Boutet, Nicholas-Noël",French,1761,1833,ca. 1797–1805,1772,1830,"Pencil, ink, gray wash on paper",3 5/8 x 2 7/8 in. (6.7 x 7.3 cm),"Purchase, Clay P. Bedford Gift, 2004",,Versailles,,,,,,,,,,Works on Paper-Drawings,,http://www.metmuseum.org/art/collection/search/653006,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2004.101.48,false,true,653007,Arms and Armor,Drawings,Design for the Decoration of Firearms,"French, Versailles",,,,,Designer,Workshop of,Nicolas Noël Boutet,"French, Versailles and Paris, 1761–1833",,"Boutet, Nicholas-Noël",French,1761,1833,ca. 1797–1805,1772,1830,"Pencil, ink, gray wash on paper",2 7/8 x 1 7/8 in. (7.3 x 4.8 cm),"Purchase, Clay P. Bedford Gift, 2004",,Versailles,,,,,,,,,,Works on Paper-Drawings,,http://www.metmuseum.org/art/collection/search/653007,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2004.101.49,false,true,653008,Arms and Armor,Drawings,Two Designs for the Decoration of Firearms,"French, Versailles",,,,,Designer,Workshop of,Nicolas Noël Boutet,"French, Versailles and Paris, 1761–1833",,"Boutet, Nicholas-Noël",French,1761,1833,ca. 1797–1805,1772,1830,"Pencil, ink, gray wash on paper",1 1/8 x 2 1/2 in. (2.9 x 6.4 cm),"Purchase, Clay P. Bedford Gift, 2004",,Versailles,,,,,,,,,,Works on Paper-Drawings,,http://www.metmuseum.org/art/collection/search/653008,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2004.101.50,false,true,653009,Arms and Armor,Drawings,Design for the Decoration of Firearms,"French, Versailles",,,,,Designer,Workshop of,Nicolas Noël Boutet,"French, Versailles and Paris, 1761–1833",,"Boutet, Nicholas-Noël",French,1761,1833,ca. 1797–1805,1772,1830,"Pencil, ink, gray wash on paper",2 1/8 x 1 1/2 in. (5.4 x 3.8 cm),"Purchase, Clay P. Bedford Gift, 2004",,Versailles,,,,,,,,,,Works on Paper-Drawings,,http://www.metmuseum.org/art/collection/search/653009,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2004.101.51,false,true,653010,Arms and Armor,Drawings,Two Designs for the Decoration of Firearms,"French, Versailles",,,,,Designer,Workshop of,Nicolas Noël Boutet,"French, Versailles and Paris, 1761–1833",,"Boutet, Nicholas-Noël",French,1761,1833,ca. 1797–1805,1772,1830,"Pencil, ink, gray wash on paper",2 1/8 x 2 3/8 in. (5.4 x 6 cm),"Purchase, Clay P. Bedford Gift, 2004",,Versailles,,,,,,,,,,Works on Paper-Drawings,,http://www.metmuseum.org/art/collection/search/653010,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2004.101.52,false,true,653011,Arms and Armor,Drawings,Two Designs for the Decoration of Firearms,"French, Versailles",,,,,Designer,Workshop of,Nicolas Noël Boutet,"French, Versailles and Paris, 1761–1833",,"Boutet, Nicholas-Noël",French,1761,1833,ca. 1797–1805,1772,1830,"Pencil, ink, gray wash on paper",2 1/8 x 2 1/2 in. (5.4 x 6.4 cm),"Purchase, Clay P. Bedford Gift, 2004",,Versailles,,,,,,,,,,Works on Paper-Drawings,,http://www.metmuseum.org/art/collection/search/653011,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2004.101.53,false,true,653012,Arms and Armor,Drawings,Designs for the Decoration of Firearms,"French, Versailles",,,,,Designer,Workshop of,Nicolas Noël Boutet,"French, Versailles and Paris, 1761–1833",,"Boutet, Nicholas-Noël",French,1761,1833,ca. 1797–1805,1772,1830,"Pencil, ink, gray wash on paper",3 5/8 x 2 in. (9.2 x 5.1 cm),"Purchase, Clay P. Bedford Gift, 2004",,Versailles,,,,,,,,,,Works on Paper-Drawings,,http://www.metmuseum.org/art/collection/search/653012,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2004.101.54,false,true,653013,Arms and Armor,Drawings,Design for the Decoration of a Pistol Grip,"French, Versailles",,,,,Designer,Workshop of,Nicolas Noël Boutet,"French, Versailles and Paris, 1761–1833",,"Boutet, Nicholas-Noël",French,1761,1833,ca. 1797–1805,1772,1830,"Pencil, ink, gray wash on paper",4 x 8 1/2 in. (10.2 x 21.6 cm),"Purchase, Clay P. Bedford Gift, 2004",,Versailles,,,,,,,,,,Works on Paper-Drawings,,http://www.metmuseum.org/art/collection/search/653013,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2004.101.55,false,true,653014,Arms and Armor,Drawings,Pair of Designs for the Decoration of the Grips of Pocket Pistols,"French, Versailles",,,,,Designer,Workshop of,Nicolas Noël Boutet,"French, Versailles and Paris, 1761–1833",,"Boutet, Nicholas-Noël",French,1761,1833,ca. 1797–1805,1772,1830,"Pencil, ink, gray wash on paper",3 1/4 x 7 1/4 in. (8.3 x 18.4 cm),"Purchase, Clay P. Bedford Gift, 2004",,Versailles,,,,,,,,,,Works on Paper-Drawings,,http://www.metmuseum.org/art/collection/search/653014,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2004.101.56,false,true,653015,Arms and Armor,Drawings,Pair of Designs for the Decoration of the Grips of Pocket Pistols,"French, Versailles",,,,,Designer,Workshop of,Nicolas Noël Boutet,"French, Versailles and Paris, 1761–1833",,"Boutet, Nicholas-Noël",French,1761,1833,ca. 1797–1805,1772,1830,"Pencil, ink, gray wash on paper",3 1/4 x 7 1/8 in. (8.3 x 18.1 cm),"Purchase, Clay P. Bedford Gift, 2004",,Versailles,,,,,,,,,,Works on Paper-Drawings,,http://www.metmuseum.org/art/collection/search/653015,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2004.101.57,false,true,653016,Arms and Armor,Drawings,Designs for the Decoration of Firearms,"French, Versailles",,,,,Designer,Workshop of,Nicolas Noël Boutet,"French, Versailles and Paris, 1761–1833",,"Boutet, Nicholas-Noël",French,1761,1833,ca. 1797–1805,1772,1830,"Pencil, ink, gray wash on paper",2 1/4 x 2 in. (5.7 x 5.1 cm),"Purchase, Clay P. Bedford Gift, 2004",,Versailles,,,,,,,,,,Works on Paper-Drawings,,http://www.metmuseum.org/art/collection/search/653016,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2004.101.58,false,true,653017,Arms and Armor,Drawings,Designs for the Decoration of Firearms,"French, Versailles",,,,,Designer,Workshop of,Nicolas Noël Boutet,"French, Versailles and Paris, 1761–1833",,"Boutet, Nicholas-Noël",French,1761,1833,ca. 1797–1805,1772,1830,"Pencil, ink, gray wash on paper",2 7/8 x 6 in. (7.3 x 15.2 cm),"Purchase, Clay P. Bedford Gift, 2004",,Versailles,,,,,,,,,,Works on Paper-Drawings,,http://www.metmuseum.org/art/collection/search/653017,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2004.101.59,false,true,653018,Arms and Armor,Drawings,Designs for the Decoration of Firearms,"French, Versailles",,,,,Designer,Workshop of,Nicolas Noël Boutet,"French, Versailles and Paris, 1761–1833",,"Boutet, Nicholas-Noël",French,1761,1833,ca. 1797–1805,1772,1830,"Pencil, ink, gray wash on paper",1 7/8 x 6 1/8 in. (4.8 x 15.6 cm),"Purchase, Clay P. Bedford Gift, 2004",,Versailles,,,,,,,,,,Works on Paper-Drawings,,http://www.metmuseum.org/art/collection/search/653018,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2004.101.60,false,true,653019,Arms and Armor,Drawings,Design for the Decoration of Firearms,"French, Versailles",,,,,Designer,Workshop of,Nicolas Noël Boutet,"French, Versailles and Paris, 1761–1833",,"Boutet, Nicholas-Noël",French,1761,1833,ca. 1797–1805,1772,1830,"Pencil, ink, gray wash on paper",5 1/4 x 2 1/4 in. (13.3 x 5.7 cm),"Purchase, Clay P. Bedford Gift, 2004",,Versailles,,,,,,,,,,Works on Paper-Drawings,,http://www.metmuseum.org/art/collection/search/653019,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2004.101.61,false,true,653020,Arms and Armor,Drawings,Design for the Decoration of Firearms,"French, Versailles",,,,,Designer,Workshop of,Nicolas Noël Boutet,"French, Versailles and Paris, 1761–1833",,"Boutet, Nicholas-Noël",French,1761,1833,ca. 1797–1805,1772,1830,"Pencil, ink, gray wash on paper",5 3/4 x 2 1/4 in. (14.6 x 5.7 cm),"Purchase, Clay P. Bedford Gift, 2004",,Versailles,,,,,,,,,,Works on Paper-Drawings,,http://www.metmuseum.org/art/collection/search/653020,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2004.101.62,false,true,653021,Arms and Armor,Drawings,Designs for the Decoration of Firearms,"French, Versailles",,,,,Designer,Workshop of,Nicolas Noël Boutet,"French, Versailles and Paris, 1761–1833",,"Boutet, Nicholas-Noël",French,1761,1833,ca. 1797–1805,1772,1830,"Pencil, ink, gray wash on paper",5 5/8 x 2 1/4 in. (14.3 x 5.7 cm),"Purchase, Clay P. Bedford Gift, 2004",,Versailles,,,,,,,,,,Works on Paper-Drawings,,http://www.metmuseum.org/art/collection/search/653021,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2004.101.63,false,true,653022,Arms and Armor,Drawings,Design for the Decoration of the Barrel of a Firearm,"French, Versailles",,,,,Designer,Workshop of,Nicolas Noël Boutet,"French, Versailles and Paris, 1761–1833",,"Boutet, Nicholas-Noël",French,1761,1833,ca. 1797–1805,1772,1830,"Pencil, ink, gray wash on paper",11 1/4 x 3 1/4 in. (28.6 x 8.3 cm),"Purchase, Clay P. Bedford Gift, 2004",,Versailles,,,,,,,,,,Works on Paper-Drawings,,http://www.metmuseum.org/art/collection/search/653022,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2004.101.64,false,true,653023,Arms and Armor,Drawings,Design for the Decoration of Firearms,"French, Versailles",,,,,Designer,Workshop of,Nicolas Noël Boutet,"French, Versailles and Paris, 1761–1833",,"Boutet, Nicholas-Noël",French,1761,1833,ca. 1797–1805,1772,1830,"Pencil, ink, gray wash on paper",3 1/8 x 1 7/8 in. (7.9 x 4.8 cm),"Purchase, Clay P. Bedford Gift, 2004",,Versailles,,,,,,,,,,Works on Paper-Drawings,,http://www.metmuseum.org/art/collection/search/653023,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2004.101.65,false,true,653024,Arms and Armor,Drawings,Designs for the Decoration of Firearms,"French, Versailles",,,,,Designer,Workshop of,Nicolas Noël Boutet,"French, Versailles and Paris, 1761–1833",,"Boutet, Nicholas-Noël",French,1761,1833,ca. 1797–1805,1772,1830,"Pencil, ink, gray wash on paper",3 5/8 x 1 3/4 in. (9.2 x 4.5 cm),"Purchase, Clay P. Bedford Gift, 2004",,Versailles,,,,,,,,,,Works on Paper-Drawings,,http://www.metmuseum.org/art/collection/search/653024,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2004.101.66,false,true,653025,Arms and Armor,Drawings,Designs for the Decoration of Firearms,"French, Versailles",,,,,Designer,Workshop of,Nicolas Noël Boutet,"French, Versailles and Paris, 1761–1833",,"Boutet, Nicholas-Noël",French,1761,1833,ca. 1797–1805,1772,1830,"Pencil, ink, gray wash on paper",3 3/4 x 7/8 in. (9.5 x 2.2 cm),"Purchase, Clay P. Bedford Gift, 2004",,Versailles,,,,,,,,,,Works on Paper-Drawings,,http://www.metmuseum.org/art/collection/search/653025,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2004.101.67,false,true,653026,Arms and Armor,Drawings,Design for the Decoration of Firearms,"French, Versailles",,,,,Designer,Workshop of,Nicolas Noël Boutet,"French, Versailles and Paris, 1761–1833",,"Boutet, Nicholas-Noël",French,1761,1833,ca. 1797–1805,1772,1830,"Pencil, ink, gray wash on paper",4 1/4 x 1 1/8 in. (10.8 x 2.9 cm),"Purchase, Clay P. Bedford Gift, 2004",,Versailles,,,,,,,,,,Works on Paper-Drawings,,http://www.metmuseum.org/art/collection/search/653026,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2004.101.68,false,true,653027,Arms and Armor,Drawings,Design for the Decoration of Firearms,"French, Versailles",,,,,Designer,Workshop of,Nicolas Noël Boutet,"French, Versailles and Paris, 1761–1833",,"Boutet, Nicholas-Noël",French,1761,1833,ca. 1797–1805,1772,1830,"Pencil, ink, gray wash on paper",4 x 1 3/4 in. (10.2 x 4.5 cm),"Purchase, Clay P. Bedford Gift, 2004",,Versailles,,,,,,,,,,Works on Paper-Drawings,,http://www.metmuseum.org/art/collection/search/653027,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2004.101.69,false,true,653028,Arms and Armor,Drawings,Design for the Decoration of Firearms,"French, Versailles",,,,,Designer,Workshop of,Nicolas Noël Boutet,"French, Versailles and Paris, 1761–1833",,"Boutet, Nicholas-Noël",French,1761,1833,ca. 1797–1805,1772,1830,"Pencil, ink, gray wash on paper",2 1/8 x 5 1/4 in. (5.4 x 13.3 cm),"Purchase, Clay P. Bedford Gift, 2004",,Versailles,,,,,,,,,,Works on Paper-Drawings,,http://www.metmuseum.org/art/collection/search/653028,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2004.101.70,false,true,653029,Arms and Armor,Drawings,Design for the Decoration of Firearms,"French, Versailles",,,,,Designer,Workshop of,Nicolas Noël Boutet,"French, Versailles and Paris, 1761–1833",,"Boutet, Nicholas-Noël",French,1761,1833,ca. 1797–1805,1772,1830,"Pencil, ink, gray wash on paper",2 1/2 x 6 1/2 in. (6.4 x 16.5 cm),"Purchase, Clay P. Bedford Gift, 2004",,Versailles,,,,,,,,,,Works on Paper-Drawings,,http://www.metmuseum.org/art/collection/search/653029,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2004.101.71,false,true,653030,Arms and Armor,Drawings,Design for the Decoration of Firearms,"French, Versailles",,,,,Designer,Workshop of,Nicolas Noël Boutet,"French, Versailles and Paris, 1761–1833",,"Boutet, Nicholas-Noël",French,1761,1833,ca. 1797–1805,1772,1830,"Pencil, ink, gray wash on paper",1 3/4 x 5 7/8 in. (4.5 x 14.9 cm),"Purchase, Clay P. Bedford Gift, 2004",,Versailles,,,,,,,,,,Works on Paper-Drawings,,http://www.metmuseum.org/art/collection/search/653030,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2004.101.72,false,true,653031,Arms and Armor,Drawings,Design for the Decoration of Firearms,"French, Versailles",,,,,Designer,Workshop of,Nicolas Noël Boutet,"French, Versailles and Paris, 1761–1833",,"Boutet, Nicholas-Noël",French,1761,1833,ca. 1797–1805,1772,1830,"Pencil, ink, gray wash on paper",1 3/4 x 5 7/8 in. (4.5 x 14.9 cm),"Purchase, Clay P. Bedford Gift, 2004",,Versailles,,,,,,,,,,Works on Paper-Drawings,,http://www.metmuseum.org/art/collection/search/653031,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2004.101.1–.72,false,true,26900,Arms and Armor,Drawings,Designs for the Decoration of Firearms,"French, Versailles",,,,,Designer,Workshop of,Nicolas Noël Boutet,"French, Versailles and Paris, 1761–1833",,"Boutet, Nicholas-Noël",French,1761,1833,ca. 1795–1805,1770,1830,"Pencil, ink, gray wash on paper","various sizes, largest: 8 3/8 x 6 1/16 in. (21.2 x 15.3 cm); smallest: 1 x 5/8 in. (2.5 x 1.5 cm)","Purchase, Clay P. Bedford Gift, 2004",,Versailles,,,,,,,,,,Works on Paper-Drawings,,http://www.metmuseum.org/art/collection/search/26900,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -22.122.14,false,true,32967,Arms and Armor,Medal,"François II, King of France, King Consort of Scotland","French, probably Paris",,,,,Designer,After designs of 1559–60 by,Étienne Delaune,"French, Orléans 1518/19–1583 Strasbourg",,"Delaune, Étienne",French,1518,1583,struck ca. 1600–1635,1559,1660,Bronze,Diam. 2 1/16 in. (5.2 cm),"Gift of Bashford Dean, 1922",,,,,,,,,,,,Miscellaneous-Coins and Medals,,http://www.metmuseum.org/art/collection/search/32967,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2004.57,false,true,26920,Arms and Armor,Engraving,Engraving of Firearms Parts,"French, Strasbourg",,,,,Designer,,Perrier,"French, Strasbourg, active mid-18th century",,Perrier,French,1725,1775,ca. 1750,1725,1775,Ink on paper,18 3/4 x 25 1/4 in. (47.5 x 64 cm),"Purchase, Jonathan and Elizabeth Roberts Gift, 2004",,Strasbourg,,,,,,,,,,Works on Paper-Prints,,http://www.metmuseum.org/art/collection/search/26920,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2014.674,false,true,667460,Arms and Armor,Pommel plate,Pommel Plate for a Saddle in the Style of the Late Middles Ages,French,,,,,Maker|Goldsmith,Workshop of,Louis Marcy (Luigi Parmeggiani)|Henri Husson,"Italian, 1860–1945|French, 1851–1914",,"Marcy, Louis|Husson, Henri",French,1860 |1852,1945 |1914,late 19th–early 20th century,1850,1950,"Copper alloy, gold, enamel",H. 13 3/4 in. (34.9 cm); W. 16 1/8 in. (41 cm),"Purchase, Arthur Ochs Sulzberger Gift, 2014",,,,,,,,,,,,Equestrian Equipment-Saddles,,http://www.metmuseum.org/art/collection/search/667460,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -23.149.1,false,true,32850,Arms and Armor,Kidney Dagger,Kidney Dagger in Gothic Style,French,,,,,Maker,,Henri Husson,"French, 1851–1914",,"Husson, Henri",French,1852,1914,ca. 1880–90,1855,1915,"Steel, wood (walnut), copper, black enamel",L. 17 in. (43.2 cm),"Gift of Jacques Reubell, 1923",,Mantes,Île-de-France,,,,,,,,,Forgeries,,http://www.metmuseum.org/art/collection/search/32850,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2013.493.1a, b",false,true,35879,Arms and Armor,Medal with case,Lamarck Medal with Case,French,,,,,Maker,,François-Léon Sicard,"French, 1862–1934",,"Sicard, François-Léon",French,1862,1934,ca. 1910,1885,1935,"Bronze, leather, textile",Diam. 2 3/8 in. (6.0 cm); Diam. of case 2 3/4 in. (7.0 cm); D. of case 1/2 in. (1.3 cm),"Gift of Dean K. Boorman, 2013",,,,,,,,,,,,Miscellaneous-Coins and Medals,,http://www.metmuseum.org/art/collection/search/35879,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.324,false,true,35366,Arms and Armor,Engraving,Engraving of Firearms Ornament,French,,,,,Artist,,Claude Simonin,"French, Nantes ca. 1635–1693 Nantes",,"Simonin, Claude",French,1635,1693,1693 or 1695,1693,1695,Ink on paper,8 1/2 x 6 5/16 in. (21.8 x 16.2 cm),"Purchase, funds from various donors, by exchange, 2007",,,,,,,,,,,,Works on Paper-Prints,,http://www.metmuseum.org/art/collection/search/35366,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"14.25.1433a, b",false,true,22389,Arms and Armor,Pair of wheellock pistols,Pair of Wheellock Pistols,French,,,,,Designer,Decoration on the stocks copied in part from engravings by,Étienne Delaune,"French, Orléans 1518/19–1583 Strasbourg",,"Delaune, Étienne",French,1518,1583,ca. 1570–80,1545,1605,"Steel, gold, silver, brass, wood (walnut), staghorn, pigment",L. of each pistol 20 1/8 in. (51.1 cm); Cal. of 14.25.1433a .444 in (11.2 mm); Cal. of 14.25.1433b .463 in. (11.3 mm); L. of each barrel 12 in. (30.5 cm); L. of plug 7 1/2 in. (19.1 cm); Diam. at muzzle 3/4 in. (19.1 mm); Diam. at breech 1 5/16 in. (3.3 cm); L. of lock 5 5/8 in. (14.3 cm); Wt. of 14.25.1433a 4 lb. 2 oz. (1871 g); Wt. of 14.25.1433b 4 lb. (1814 g),"Gift of William H. Riggs, 1913",,,,,,,,,,,,Firearms-Guns-Wheellock,,http://www.metmuseum.org/art/collection/search/22389,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"14.25.1433a, b; .1523",false,true,35867,Arms and Armor,Pair of wheellock pistols with matching priming flask/spanner,Pair of Wheellock Pistols with Matching Priming Flask/Spanner,French,,,,,Designer,Decoration on the stocks copied in part from engravings by,Étienne Delaune,"French, Orléans 1518/19–1583 Strasbourg",,"Delaune, Étienne",French,1518,1583,ca. 1570–80,1545,1605,"Steel, gold, silver, brass, wood (walnut), staghorn, brass wire, pigment","Cal. of each pistol, .44 in (11.18 mm); Wt. of 14.25.1433a: 4 lb. 2 oz. (1871 g); Wt. of 14.25.1433b: 4 lb. (1814 g); L. of each pistol, 20 1/8 in. (51.1 cm); L. of priming flask/spanner, 5 7/8 in. (14.91 cm)","Gift of William H. Riggs, 1913",,,,,,,,,,,,Firearms-Pistols-Wheellock,,http://www.metmuseum.org/art/collection/search/35867,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1974.4,false,true,24869,Arms and Armor,Flintlock gun,Flintlock Gun,German,,,,,Designer,Decoration follows closely designs of the engraver,De Lacollombe,"French, Paris, active ca. 1702–ca. 1736",,"Lacollombe, De",French,1702,1736,ca. 1730–40,1705,1765,"Steel, silver, wood (walnut)","L. 56 3/8 in. (143.2 cm); L. of barrel 41 1/16 in. (104.3 cm); L. of lockplate 5 7/8 in. (14.9 cm), Cal. .62 in. (15.8 mm); Wt. 6 lb. 10 oz. (3005 g)","Purchase, Bashford Dean Bequest, James Elwood Jones Jr. Gift, and Rogers and Fletcher Funds, 1974",,,,,,,,,,,,Firearms,,http://www.metmuseum.org/art/collection/search/24869,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.511.1–.3a–k,false,true,26891,Arms and Armor,Cased pair of percussion target pistols with loading and cleaning accessories,"Cased Pair of Percussion Target Pistols with Loading and Cleaning Accessories, Made for Henri Charles Ferdinand Marie Dieudonné d'Artois, Duke of Bordeaux, Count of Chambord (1820–1883)","French, Paris",,,,,Gunsmith,,Jean André Prosper Henri Le Page,"French, 1792–1854",,"Le Page, Jean André Prosper Henri",French,1792,1854,dated 1829,1829,1829,"Steel, gold, wood (ebony, walnut, amboyna), silver, velvet, ivory",case L. 15 in. (38 cm); W. 9 7/8 in. (25 cm); H. 3 1/4 in. ( 8 cm); pistol L. (each) 11 1/4 in. (28.6 cm); barrel L. (each) 6 3/8 in. (17 cm); Cal. (each) .43 in. (11 mm),"Purchase, Arthur Ochs Sulzberger Bequest and Irene Roosevelt Aitken Gift, 2013",,,Paris,,,,,,,,,Firearms-Pistols-Percussion,,http://www.metmuseum.org/art/collection/search/26891,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.189a–g,false,true,24936,Arms and Armor,Breech-loading rimfire cartridge pistol with case and accessories,Breech-Loading Rimfire Cartridge Pistol with Case and Accessories,"French, Paris",,,,,Gunsmith,,Louis Nicolas Auguste Flobert,"French, Paris, 1819–1894",,"Flobert, Louis Nicolas Auguste",French,1819,1894,ca. 1855,1830,1880,"Steel, gold, ivory, leather, textile",Pistol (a); L. 13 3/8 in. (33.9 cm); L. of barrel 8 3/8 in. (21.2 cm); Cal. .22 in. (5.6 mm); forked cleaning rod (b); L. 9 9/16 in. (24.3 cm); Wt. 1 oz. (28.3 g); scourging rod (c); L. 9 11/16 in. (24.6 cm); Wt. 1.1 oz. (31.2 g); cartridge box (d); H. 1 1/4 in. (3.2 cm); Diam. 2 in. (5.1 cm); Wt. 1.7 oz. (48.2 g); screwdriver (e); L. 3 1/16 in. (7.8 cm); Wt. 0.5 oz. (14.2 g); case (f); H. 2 3/16 in. (5.6 cm); W. 14 1/2 in. (36.8 cm); D. 8 5/8 in. (21.9 cm); Wt. 1 lb. 14.6 oz. (867.5 g); key (g); L. 1 1/8 in. (2.9 cm),"Purchase, John Stoneacre Ellis Collection, Gift of Mrs. Ellis and Augustus Van Horne Ellis, by exchange, 1989",,Paris,,,,,,,,,,Firearms-Pistols,,http://www.metmuseum.org/art/collection/search/24936,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -35.81.4,false,true,23949,Arms and Armor,Percussion pistol with case,Percussion Pistol with Case,"French, Paris",,,,,Gunsmith,,Alfred Gauvain,"French, Paris 1801–1889 Paris",,"Gauvain, Alfred",French,1801,1889,possibly 1844,1819,1869,"Steel, wood (ebony), gold, wood, textile",L. of pistol 16 7/8 in. (42.9 cm); Cal. .44 in. (11.2 mm); Wt. 3 lb. 4 oz. (1474 g),"Bequest of Charles N. Daly, 1934",,Paris,,,,,,,,,,Firearms-Pistols-Percussion,,http://www.metmuseum.org/art/collection/search/23949,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.318a–m,false,true,27165,Arms and Armor,Cased pair of percussion pistols with accessories,Cased Pair of Percussion Pistols with Accessories,"French, Paris",,,,,Gunsmith,,Louis-Julien Gastinne-Renette,"French, Paris 1812–1885 Paris",,Gastinne-Renette Louis-Julien,French,1812,1885,dated 1856,1856,1856,"Steel, brass, gold, wood (ebony), textile (baizé, wool), copper, leather","Pistols (a, b); L. of each 16 in. (40.7 cm); L. of each barrel 10 5/8 in. (27 cm); Cal. of each .46 in. (12 mm); Wt. of each 2 lb. 3 oz. (992.2 g); ramrod (c); L. 11 1/4 in. (28.6 cm); Wt. 1.5 oz. (42.5 g); cleaning rod (d); L. 12 3/8 in. (31.4 cm); Wt. 1.8 oz. (51 g); mallet (e); L. 7 7/8 in. (20 cm); W. 2 1/2 in. (6.4 cm); Wt. 5.3 oz. (150.3 g); screwdriver (f); L. 6 1/4 in. (15.9 cm); Wt. 2.6 oz. (73.7 g); bullet mould (g); L. 7 7/8 in. (20 cm); Wt. 8.8 oz. (249.5 g); powder flask (h); H. 4 in. (10.2 cm); Wt. 4.2 oz. (119.1 g); patch box (i); H. 1 in. (2.5 cm); Diam. 1 1/4 in. (3.2 cm); Wt. 0.4 oz. (11.3 g); nipple box (j); H. 1 11/16 in. (4.3 cm); Diam. 1 7/8 in. (4.8 cm); Wt. 1.5 oz. (42.5 g); spoon (k); L. 4 in. (10.2 cm); Wt. 0.4 oz. (11.3 g); case (l); H. 2 7/8 in. (7.3 cm); W. 18 7/8 in. (47.9 cm); D. 8 1/4 in. (21 cm); Wt. 7 lb. 4.7 oz. (3308.4 g); leather case (m); H. 3 3/8 in. (8.6 cm); W. 19 3/8 in. (49.2 cm); D. 8 3/4 in. (22.2 cm); Wt. 3 lb. 7.5 oz. (1573.4 g)","Purchase, John Stoneacre Ellis Collection, Gift of Mrs. Ellis and Augustus Van Horne Ellis, and Gift of Charles M. Schott, by exchange, 1989",,Paris,,,,,,,,,,Firearms-Pistols-Percussion,,http://www.metmuseum.org/art/collection/search/27165,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2006.350.1–.19,false,true,34812,Arms and Armor,Engravings,Nouveaux Ornemans D'Arquebuseries,"French, Paris",,,,,Engraver,,Gilles Demarteau,"French, Liège 1722–1776 Paris",,"Demarteau, Gilles",French,1722,1776,ca. 1750–55,1725,1780,Ink on paper,10 1/4 x 6 3/4 in. (26 x 17.1 cm),"Purchase, Gift of Russell B. Aitken, by exchange, 2006",,Paris,,,,,,,,,,Works on Paper-Prints,,http://www.metmuseum.org/art/collection/search/34812,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2002.521.1a, b, .2a, b",false,true,26587,Arms and Armor,Pair of double-barreled flintlock pistols,Pair of Double-Barreled Flintlock Pistols,"French, Paris",,,,,Gunsmith,,François-Alexander Chasteau,"French, Paris, recorded 1741–84",,"Chasteau, François-Alexander",French,1741,1784,1752–53,1752,1753,"Steel, silver, gold, wood (walnut), whalebone",L. of each 14 3/8 in. (36.5 cm); Wt. of each 36 oz. (1030 g),"Gift of Walter A. Eberstadt, 2002",,Paris,,,,,,,,,,Firearms-Pistols-Flintlock,,http://www.metmuseum.org/art/collection/search/26587,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"17.87.3a, b",true,true,22631,Arms and Armor,Smallsword with scabbard,Congressional Presentation Sword with Scabbard of Colonel Marinus Willett (1740–1830),"French, Paris",,,,,Sword maker,,C. Liger,"French, Paris, recorded 1770–93",,"Liger, C.",French,1770,1793,hallmarked for 1785–86,1785,1786,"Steel, silver, gold, fish skin, textile, wood",sword L. 39 5/8 in. (100.6 cm); scabbard L. 33 1/4 in. (84.5 cm),"Bequest of George Willett Van Nest, 1916",,Paris,,,,,,,,,,Swords,,http://www.metmuseum.org/art/collection/search/22631,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -04.3.65,false,true,21949,Arms and Armor,Partisan,"Partisan Carried by the Bodyguard of Louis XIV (1638–1715, reigned from 1643)","French, Paris",,,,,Sword cutler,Inscription probably refers to,Bonaventure Ravoisie,"French, Paris, recorded 1678–1709",,"Ravoisie, Bonaventure",French,1678,1709,ca. 1678–1709,1678,1709,"Steel, gold, wood, textile",L. 94 1/8 in. (239 cm); L. of head 22 9/16 in. (57.3 cm); W. of head 6 1/2 in. (16.5 cm),"Rogers Fund, 1904",,,,,,,,,,,,Shafted Weapons,,http://www.metmuseum.org/art/collection/search/21949,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.3,true,true,24937,Arms and Armor,Armor,"Armor of Infante Luis, Prince of Asturias (1707–1724)","French, Paris",,,,,Armorer,Signature probably refers to,Jean Drouart,"French, Paris, died before October 1715",,"Drouart, Jean",French,1615,1715,dated 1712,1687,1737,"Steel, gold, brass, silk, cotton, metallic yarn, paper",H. 28 in. (71.1 cm),"Purchase, Armand Hammer, Occidental Petroleum Corporation Gift, 1989",,Paris,,,,,,,,,,Armor for Child,,http://www.metmuseum.org/art/collection/search/24937,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1990.114.2,false,true,24943,Arms and Armor,Flintlock pistol,Flintlock Pistol Made for Charles XI of Sweden (1655–1697),"French, Paris",,,,,Gunsmith,,Bertrand Piraube,"French, Paris, recorded ca. 1663–1725",,"Piraube, Bertrand",French,1638,1750,dated 1676,1676,1676,"Steel, gold, wood (walnut)",L. 21 in. (53.3 cm),"Purchase, Annie Laurie Aitken Charitable Trust Gift, 1990",,Paris,,,,,,,,,,Firearms,,http://www.metmuseum.org/art/collection/search/24943,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1988.65.1–.2,true,true,24931,Arms and Armor,Costume armor,Costume Armor in the Classical Style,"French, Paris",,,,,Maker,Helmet includes original paper label of,Hallé,"French, Paris, active ca. 1780–1800",,Hallé,French,1755,1825,ca. 1788–90,1763,1815,"Linen, papier-mâché, bole, gold leaf, graphite (helmet); silk, cotton, metal coils and spangles, metallic yarn (tunic)",armor: H. 26 3/4 (68 cm); W. 22 7/16 (57 cm); D. 11 in. (28 cm); helmet: H. 15 3/4 (40 cm); W. 7 11/16 (19.5 cm); D. 13 3/4 in. (35 cm),"Funds from various donors, 1988",,Paris,,,,,,,,,,Costumes,,http://www.metmuseum.org/art/collection/search/24931,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"1988.65.1–.2; 1995.93a, b",false,true,626019,Arms and Armor,Costume armor and sword in the classical style,Costume Armor and Sword in the Classical Style,"French, Paris",,,,,Maker,Helmet includes original paper label of,Hallé,"French, Paris, active ca. 1780–1800",,Hallé,French,1755,1825,ca. 1788–90,1763,1815,"Linen, papier-mâché, bole, gold leaf, graphite (helmet); silk, cotton, metal coils and spangles, metallic yarn (tunic); steel, wood, gesso, silver, gold leaf (sword)","Helmet, 15 3/4 x 7 11/16 x 13 3/4 in. (40 x 19.5 x 35 cm); Armor, 26 3/4 x 22 7/16 x 11 in. (68 x 57 x 28 cm); Sword L., 31 7/8 in. (81 cm)","Armor: Funds from various donors, 1988; sword: Purchase, Gift of Estate of James Hazen Hyde, by exchange, and Rogers Fund, 1995",,,,,,,,,,,,Costumes,,http://www.metmuseum.org/art/collection/search/626019,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.144.2a–n,false,true,27367,Arms and Armor,Cased pair of percussion pistols,Cased Pair of Percussion Pistols,"French, Paris",,,,,Artist,,Louis-Julien Gastinne-Renette,"French, Paris 1812–1885 Paris",,Gastinne-Renette Louis-Julien,French,1812,1885,mid-19th century,1851,1900,"Steel, wood (ebony), gold, velvet, brass, baize","L. of each pistol (a, b) 16 9/16 in. (42 cm); L. of each barrel 11 1/4 in. (28.6 cm); Cal. of each barrel .46 in. (11.7 mm); Wt. of each 2 lb. 6.7 oz. (1097.1 g); case (c); H. 3 in. (7.6 cm); W. 19 in. (48.3 cm); D. 10 1/2 in. (26.7 cm); Wt. 6 lb. 1.2 oz. (2755.6 g); screwdriver (d); L. 5 3/8 in. (13.7 cm); Wt. 1.7 oz. (48.2 g); nipple wrench (e); L. 5 1/2 in. (14 cm); Wt. 2.2 oz. (62.4 g); bullet mould (f); L. 5 7/8 in. (14.9 cm); Wt. 4.2 oz. (119.1 g); powder measure (g); L. 3 5/8 in. (9.2 cm); Wt. 0.4 oz. (11.3 g); patch box (h); H. 1 1/2 in. (3.8 cm); Diam. 1 1/2 in. (3.8 cm); Wt. 1.1 oz. (31.2 g); percussion cap box with contents (i); H. 1 3/8 in. (3.5 cm); Diam. 2 1/8 in. (5.4 cm); Wt. 2.6 oz. (73.7 g); mallet (j); L. 7 7/8 in. (20 cm); W. 2 1/2 in. (6.4 cm); Wt. 4.7 oz. (133.2 g); ramrod (k); L. 11 1/4 in. (28.6 cm); Wt. 1.5 oz. (42.5 g); cleaning rod (l); L. 11 7/8 in. (30.2 cm); Wt. 1.7 oz. (48.2 g); key (n); L. 1 1/2 in. (3.8 cm); Wt. 0.2 oz. (5.7 cm)","Gift of Mrs. George Henry Warren, in memory of her husband, 1972",,Paris,,,,,,,,,,Firearms-Pistols-Percussion,,http://www.metmuseum.org/art/collection/search/27367,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2012.138,false,true,35854,Arms and Armor,Drawing,Design for a Percussion Pistol,"French, Paris",,,,,Artist,,Martin Riester,"French, Colmar 1819–1883 Paris",,"Riester, Martin",French,1819,1883,dated 1850,1850,1850,"Pen, ink, and pencil on paper",11 x 7 1/8 in. (28 x 18 cm),"Purchase, James C. Meade Gift, 2012",,Paris,,,,,,,,,,Works on Paper-Drawings,,http://www.metmuseum.org/art/collection/search/35854,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -25.135.174,false,true,27290,Arms and Armor,Drawing,Architectural Drawing of the Exterior of the Comte de Nieuwerkerke's House,"French, Paris",,,,,Artist,Studio of,Hector-Martin Lefuel,"French, Versailles 1810–1880 Paris",,"Lefuel, Hector-Martin",French,1810,1880,ca. 1870,1845,1895,Pen on paper,16 1/8 x 9 3/8 in. (40.9 x 23.8 cm),"Gift of William H. Riggs, 1913",,Paris,,,,,,,,,,Works on Paper-Drawings,,http://www.metmuseum.org/art/collection/search/27290,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.25.1878,false,true,436699,Arms and Armor,Painting,William H. Riggs (1837–1924) in Sixteenth-Century Half-Armor,"French, Paris",,,,,Artist,,Ferdinand Humbert,"French, 1842–1934",,"Humbert, Ferdinand",French,1842,1934,dated 1871,1871,1871,Oil on canvas,22 x 15 in. (55.9 x 38.1 cm),"Gift of William H. Riggs, 1913",,Paris,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436699,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2003.174a–gg,false,true,26743,Arms and Armor,"Album of thirty-five original drawings of swords, firearms, and related items",Designs for the Ornament of Swords and Firearms,"French, Paris",,,,,Designer,Workshop of,Louis-François Devisme,"French, Paris, active 1833–1886",,"Devisme, Louis-François",French,1833,1886,ca. 1850–60,1825,1885,"Leather, paper, pencil, ink, colored wash",19 x 12 1/2 in. (48.2 x 31.7 cm),"Purchase, Arthur Ochs Sulzberger Gift, 2003",,Paris,,,,,,,,,,Works on Paper-Drawings,,http://www.metmuseum.org/art/collection/search/26743,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.25.454,false,true,22209,Arms and Armor,Partisan,"Partisan Carried by the Bodyguard of Louis XIV (1638–1715, reigned from 1643)","French, Paris",,,,,Designer,,Jean Berain,"French, Saint-Mihiel 1640–1711 Paris",,"Berain, Jean",French,1640,1711,ca. 1670–80,1645,1705,"Steel, gold, wood, textile",L. 86 11/16 in. (220.2 cm); L. of head 20 9/16 in. (52.2 cm) W. of head 6 1/16 in. (15.4 cm),"Gift of William H. Riggs, 1913",,Paris,,,,,,,,,,Shafted Weapons,,http://www.metmuseum.org/art/collection/search/22209,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1978.498,false,true,24877,Arms and Armor,Drawing,Design for Shield,"French, Paris",,,,,Designer,,Étienne Delaune,"French, Orléans 1518/19–1583 Strasbourg",,"Delaune, Étienne",French,1518,1583,ca. 1550,1550,1550,"Paper, chalk",7 3/4 x 3 3/4 in. (19.7 x 9.6 cm),"Purchase, Rogers Fund, 1978",,Paris,,,,,,,,,,Works on Paper-Drawings,,http://www.metmuseum.org/art/collection/search/24877,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.170,false,true,23348,Arms and Armor,Powder horn,Powder Flask,"German, Nuremberg",,,,,Silversmith,,Jeremias Ritter,"German, recorded 1605–46",,"Ritter, Jeremias",German,1605,1646,ca. 1610,1585,1635,"Staghorn, silver, gold",L. 8 1/4 in. (21 cm); W. 4 in. (10.2 cm),"Rogers Fund, 1929",,Nuremberg,,,,,,,,,,Firearms Accessories-Powder Horns,,http://www.metmuseum.org/art/collection/search/23348,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.153.1,false,true,23201,Arms and Armor,Jousting sallet (Rennhut),"Jousting Sallet Made for Louis II (1506–1526), King of Hungary and Bohemia","German, Augsburg",,,,,Armorer,Attributed to,Kolman Helmschmid,"German, Augsburg 1471–1532",,"Helmschmid, Kolman",German,1471,1532,ca. 1525,1500,1550,"Steel, copper alloy, gold",H. 10 in. (25.4 cm); W. 15 in. (38.1 cm); D. 10 in. (25.4 cm); Wt. 9 lb. (4082 g),"Bashford Dean Memorial Collection, Gift of Mr. and Mrs. Alexander McMillan Welch, 1929",,Augsburg,,,,,,,,,,Helmets,,http://www.metmuseum.org/art/collection/search/23201,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -04.3.286a,false,true,35825,Arms and Armor,Close helmet with mask visor in form of a human face,Close Helmet with Mask Visor in Form of a Human Face,"German, Augsburg",,,,,Armorer,Attributed to,Kolman Helmschmid,"German, Augsburg 1471–1532",,"Helmschmid, Kolman",German,1471,1532,ca. 1515,1490,1540,"Steel, gold",H. 12 in. (30.5 cm); W. 9 3/4 in. (24.8 cm); D. 13 in. (33 cm); Wt. 4 lb. 12 oz. (2146 g),"Rogers Fund, 1904",,Augsburg,,,,,,,,,,Helmets,,http://www.metmuseum.org/art/collection/search/35825,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -27.159.18,false,true,26441,Arms and Armor,Closed burgonet,Closed Burgonet,"German, Augsburg",,,,,Armorer,Attributed to,Kolman Helmschmid,"German, Augsburg 1471–1532",,"Helmschmid, Kolman",German,1471,1532,ca. 1525–30,1500,1555,"Steel, leather",H. 14 5/8 in. (37.1 cm); W. 9 in. (22.9 cm); D. 12 7/8 in. (32.7 cm); Wt. 6 lb. 10 oz. (3004 g),"Gift of George D. Pratt, 1927",,Augsburg,,,,,,,,,,Helmets,,http://www.metmuseum.org/art/collection/search/26441,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"29.150.6a, b",false,true,35826,Arms and Armor,Armet for the tourney,Armet for the Tourney,"German, Augsburg",,,,,Armorer,Attributed to,Kolman Helmschmid,"German, Augsburg 1471–1532",,"Helmschmid, Kolman",German,1471,1532,ca. 1515–20,1490,1545,"Steel, copper alloy",H. 12 1/2 in. (31.8 cm); W. 9 3/4 in. (24.8 cm); D. 11 7/16 in. (29.1 cm); Wt. 8 lb. 8 oz. (3866 g),"Bashford Dean Memorial Collection, Bequest of Bashford Dean, 1928",,Augsburg,,,,,,,,,,Helmets,,http://www.metmuseum.org/art/collection/search/35826,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -24.179,false,true,22875,Arms and Armor,Backplate and hoguine (rump defense) from a costume armor,Backplate and Hoguine (Rump Defense) from a Costume Armor,"German, Augsburg",,,,,Armorer,,Kolman Helmschmid,"German, Augsburg 1471–1532",,"Helmschmid, Kolman",German,1471,1532,ca. 1525,1500,1550,"Steel, gold",H. 27 in. (68.6 cm); W. 18 in. (45.7 cm),"Gift of Bashford Dean, 1924",,Augsburg,,,,,,,,,,Armor Parts,,http://www.metmuseum.org/art/collection/search/22875,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"26.188.1, .2",false,true,22949,Arms and Armor,Pair of Vambraces (arm defenses) from a costume armor,Pair of Vambraces (Arm Defenses) from a Costume Armor,"German, Augsburg",,,,,Armorer,,Kolman Helmschmid,"German, Augsburg 1471–1532",,"Helmschmid, Kolman",German,1471,1532,ca. 1525,1500,1550,"Steel, gold",H. 16 in. (40.6 cm); W. 8 in. (20.3 cm); D. 12 in. (30.5 cm),"Mrs. Stephen V. Harkness Fund, 1926",,Augsburg,,,,,,,,,,Armor Parts,,http://www.metmuseum.org/art/collection/search/22949,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"29.158.363a, b",false,true,23307,Arms and Armor,Top lames of vambraces (arm defenses) from a costume armor,Top Lames of Vambraces (Arm Defenses) from a Costume Armor,"German, Augsburg",,,,,Armorer,,Kolman Helmschmid,"German, Augsburg 1471–1532",,"Helmschmid, Kolman",German,1471,1532,ca. 1525,1500,1550,"Steel, copper alloy, gold",H. 11 1/2 in. (29.2 cm); W. 10 in. (25.4 cm); D. 12 in. (30.5 cm),"Bashford Dean Memorial Collection, Funds from various donors, 1929",,Augsburg,,,,,,,,,,Armor Parts,,http://www.metmuseum.org/art/collection/search/23307,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -04.3.286,false,true,22000,Arms and Armor,Armor,Armor,"German, Augsburg and Landshut",,,,,Armorer,Helmet attributed to,Kolman Helmschmid,"German, Augsburg 1471–1532",,"Helmschmid, Kolman",German,1471,1532,ca. 1515 and later,1490,1900,"Steel, gold, leather",Helmet (04.3.286a); H. 12 in. (30.5 cm); W. 9 3/4 in. (24.8 cm); D. 13 in. (33 cm); Wt. 4 lb. 12 oz. (2146 g),"Rogers Fund, 1904",,Augsburg and Landshut,,,,,,,,,,Armor for Man,,http://www.metmuseum.org/art/collection/search/22000,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"24.179; 26.188.1, .2; 29.158.363a, b",true,true,27790,Arms and Armor,Portions of a costume armor,Portions of a Costume Armor,"German, Augsburg",,,,,Armorer,,Kolman Helmschmid,"German, Augsburg 1471–1532",,"Helmschmid, Kolman",German,1471,1532,ca. 1525,1500,1550,"Steel, gold",H. 27 in. (68.6 cm); W. 18 in. (45.7 cm),"Backplate and rump: Gift of Bashford Dean, 1924; vambraces: Mrs. Stephen V. Harkness Fund, 1926; top lames of vambraces: Bashford Dean Memorial Collection, Funds from various donors, 1929",,Augsburg,,,,,,,,,,Armor for Man,,http://www.metmuseum.org/art/collection/search/27790,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.151.3a–s,false,true,23199,Arms and Armor,Armor,Three-Quarter Armor,"German, Augsburg",,,,,Armorer,Pauldrons and vambraces attributed to,Kolman Helmschmid,"German, Augsburg 1471–1532",,"Helmschmid, Kolman",German,1471,1532,ca. 1525 and later,1500,1925,"Steel, leather",Wt. 48 lb. 7 oz. (21.97 kg); helmet (a); H. 12 in. (30.5 cm); W. 11 in. (27.9 cm); D. 12 1/2 in. (31.8 cm); Wt. 6 lb. 1.2 oz. (2755.6 g); gauntlet (p); H. 10 1/2 in. (26.7 cm); W. 5 in. (12.7 cm); D. 4 3/8 in. (11.1 cm); Wt. 1 lb. 4.5 oz. (581.2 g); gauntlet (q); H. 10 1/2 in. (26.7 cm); W. 5 in. (12.7 cm); D. 4 3/8 in. (11.1 cm); Wt. 1 lb. 5.5 oz. (609.5 g); mail sleeve (r): L. 25 1/2 in. (65.0 cm); H. at shoulder 10 3/16 in. (26.0 cm); Diam. (outside) of chest links 5/16 in. (7.6 mm); Diam. (inside) of chest links 3/16 in. (4.8 mm); Diam. (outside) of sleeve links 5/16 in. (7.7 mm); Diam. (inside) of sleeve links 7/32 in. (5.8 mm); mail sleeve (s): L. 26 13/16 in. (68.0 cm); H. at shoulder 11 in. (28.0 cm); Diam. (outside) of chest links 11/32 in. (8.6 mm); Diam. (inside) of chest links 3/16 in. (4.8 mm); Diam. (outside) of sleeve links 9/32 in. (7.1 mm); Diam. (inside) of sleeve links 7/32 in. (5.6 mm).,"Bashford Dean Memorial Collection, Gift of Mrs. Bashford Dean, 1929",,Augsburg,,,,,,,,,,Armor for Man-3/4 Armor,,http://www.metmuseum.org/art/collection/search/23199,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.25.828,false,true,22302,Arms and Armor,Left pauldron (shoulder defense),Left Shoulder Defense (Pauldron),"German, Augsburg",,,,,Armorer,,Kolman Helmschmid,"German, Augsburg 1471–1532",,"Helmschmid, Kolman",German,1471,1532,ca. 1525,1500,1550,"Steel, copper alloy, leather",H. 10 1/4 in. (26 cm); W. 9 1/4 in. (23.5 cm); D. 12 in. (30.5 cm); Wt. 2 lb. 10.4 oz. (1202 g),"Gift of William H. Riggs, 1913",,Augsburg,,,,,,,,,,Armor Parts-Arms & Shoulders,,http://www.metmuseum.org/art/collection/search/22302,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -20.151.7–.8,false,true,22749,Arms and Armor,Two ear guards,Two Ear Guards from a Shaffron (Horse's Head Defense) of Emperor Charles V (1500–1558),"German, Augsburg",,,,,Armorer,Attributed to,Desiderius Helmschmid,"German, Augsburg, 1513–1579",,"Helmschmid, Desiderius",German,1513,1579,1544,1544,1544,"Steel, gold","20.151.7; H. 5 3/8 in. (13.7 cm); W. 2 3/4 in. (7 cm), Wt. 2 oz. (47 g); 20.151.8; H. 5 1/4 in. (13.3 cm); W. 2 7/8 in. (7.3 cm), Wt. 2 oz. (51 g)","Rogers Fund, 1920",,Augsburg,,,,,,,,,,Equestrian Equipment-Shaffrons,,http://www.metmuseum.org/art/collection/search/22749,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.154.1a–u,false,true,23203,Arms and Armor,Armor for the tilt,Armor for the Tilt,"German, Augsburg",,,,,Armorer,Attributed to,Anton Peffenhauser,"German, Augsburg, 1525–1603",,"Peffenhauser, Anton",German,1525,1603,ca. 1580,1555,1605,"Steel, brass, leather",H. 68 3/4 in. (174.6 cm); W. at shoulders 18 in. (45.72 cm); Wt. 81 lb. (36.8 kg),"Bashford Dean Memorial Collection, Gift of Helen Fahnestock Hubbard, in memory of her father, Harris C. Fahnestock, 1929",,Augsburg,,,,,,,,,,Armor for Man,,http://www.metmuseum.org/art/collection/search/23203,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -38.163.1a–d,false,true,26534,Arms and Armor,Elements of an armor garniture,Elements from a Garniture Made for Christian I of Saxony (1560–1591),"German, Augsburg",,,,,Armorer,,Anton Peffenhauser,"German, Augsburg, 1525–1603",,"Peffenhauser, Anton",German,1525,1603,1582,1582,1582,"Steel, gold, leather, brass",breastplate (a): 19 1/2 x 15 3/8 in. (49.6 x 39 cm); backplate (b): 17 1/2 x 14 3/16 in. (44.5 x 36 cm); right cuisse (c): 16 x 7 3/8 in. (40.6 x 18.7 cm); Wt. 2 lb. 6 oz. (1088 g); left cuisse (d): 16 1/2 x 7 1/2 in. (41.9 x 19 cm); Wt. 2 lb. 7 oz. (1098 g),"Fletcher Fund, 1938",,Augsburg,,,,,,,,,,Armor for Man,,http://www.metmuseum.org/art/collection/search/26534,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.25.689a–p,false,true,22252,Arms and Armor,Jousting armor,Jousting Armor,"German, Augsburg",,,,,Armorer,Left Vambrace attributed to,Anton Peffenhauser,"German, Augsburg, 1525–1603",,"Peffenhauser, Anton",German,1525,1603,ca. 1580,1555,1605,"Steel, leather",Wt. 55 lb. (24.95 kg),"Gift of William H. Riggs, 1913",,Augsburg,,,,,,,,,,Armor for Man,,http://www.metmuseum.org/art/collection/search/22252,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"29.154.1a–u; 14.25.689h, .894; 29.158.204",false,true,35873,Arms and Armor,Armor for the tilt,Armor for the Tilt,"German, Augsburg",,,,,Armorer,Attributed to,Anton Peffenhauser,"German, Augsburg, 1525–1603",,"Peffenhauser, Anton",German,1525,1603,ca. 1580,1555,1605,"Steel, brass, leather",H. 68 3/4 in. (174.6 cm); W. at shoulders 18 in. (45.72 cm); Wt. 81 lb. (36.8 kg),"armor: Bashford Dean Memorial Collection, Gift of Helen Fahnestock Hubbard, in memory of her father, Harris C. Fahnestock, 1929; left vambrace and left gauntlet: Gift of William H. Riggs, 1913; mail brayette: Bashford Dean Memorial Collection, Funds from various donors, 1929",,Augsburg,,,,,,,,,,Armor for Man,,http://www.metmuseum.org/art/collection/search/35873,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1984.72,false,true,24903,Arms and Armor,Gauntlet for the left hand,Gauntlet for the Left Hand,"German, Augsburg",,,,,Goldsmith,Gilt copper ornament attributed to,Jörg Sigman,"German, Augsburg, 1527–1601",,"Sigman, Jörg",German,1527,1601,ca. 1557,1532,1582,"Steel, gold, copper alloy, leather",L. 12 in. (30.5 cm),"Purchase, Bequest of Stephen V. Grancsay, by exchange, 1984",,Augsburg,,,,,,,,,,Armor Parts-Gauntlets,,http://www.metmuseum.org/art/collection/search/24903,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2010.165,false,true,35367,Arms and Armor,Cross hilt sword,Cross Hilt Sword,"hilt, British, London; blade, German, Solingen",,,,,Bladesmith,Blade signed by,Clemens Horn,"German, Solingen, 1580–1630",,"Horn, Clemens",German,1580,1630,1600–1625,1600,1625,"Iron, silver, wood, copper alloy, steel, gold",L. 39 1/4 in. (99.7 cm); L. of blade 30 1/4 in. (76.8 cm); W. 8 3/4 in. (22.2 cm); Wt. 2 lb. 6.5 oz (1093 g),"Purchase, Arthur Ochs Sulzberger Gift, 2010",,London|Solingen,,,,,,,,,,Swords,,http://www.metmuseum.org/art/collection/search/35367,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.164a–x,true,true,23944,Arms and Armor,Armor,Armor of Emperor Ferdinand I (1503–1564),"German, Nuremberg",,,,,Armorer,,Kunz Lochner,"German, Nuremberg, 1510–1567",,"Lochner, Kunz",German,1510,1567,dated 1549,1549,1549,"Steel, brass, leather",H. 67 in. (170.2 cm); Wt. 52 lb. 14 oz. (24 kg),"Purchase, Rogers Fund and George D. Pratt Gift, 1933",,Nuremberg,,,,,,,,,,Armor,,http://www.metmuseum.org/art/collection/search/23944,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.25.628,false,true,26507,Arms and Armor,Burgonet,Burgonet,"German, Nuremberg",,,,,Armorer,Attributed to,Kunz Lochner,"German, Nuremberg, 1510–1567",,"Lochner, Kunz",German,1510,1567,ca. 1540–50,1515,1575,"Steel, leather, textile",H. 12 1/2 in. (31.8 cm); W. 7 7/8 in. (20 cm); D. 12 1/4 in. (31.1 cm); Wt. 3 lb. 15 oz. (1786 g),"Gift of William H. Riggs, 1913",,Nuremberg,,,,,,,,,,Helmets,,http://www.metmuseum.org/art/collection/search/26507,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.25.856,false,true,26581,Arms and Armor,Left pauldron (shoulder defense),"Left Pauldron (Shoulder Defense) Belonging to an Armor for Field and Tournament Made for Duke Nikolaus ""The Black"" Radziwill (1515–1565), Duke of Nesvizh and Olyka, Prince of the Empire, Grand Chancellor and Marshal of Lithuania","German, Nuremberg",,,,,Armorer,,Kunz Lochner,"German, Nuremberg, 1510–1567",,"Lochner, Kunz",German,1510,1567,ca. 1555; probably repainted later,1530,1900,"Steel, gold, paint",H. 8 1/2 in. (12.6 cm); W. 12 1/2 in. (31.7 cm); Wt. 1 lb. 10 oz. (730 g),"Gift of William H. Riggs, 1913",,Nuremberg,,,,,,,,,,Armor Parts,,http://www.metmuseum.org/art/collection/search/26581,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.151.2a–s,false,true,23198,Arms and Armor,Armor,Armor,"German, Nuremberg",,,,,Armorer,,Kunz Lochner,"German, Nuremberg, 1510–1567",,"Lochner, Kunz",German,1510,1567,"dated 1548, with later restorations",1548,1900,"Steel, leather, copper alloy, textile",Wt. approx. 56 lb. (25.4 kg),"Bashford Dean Memorial Collection, Gift of Mrs. Bashford Dean, 1929",,Nuremberg,,,,,,,,,,Armor for Man,,http://www.metmuseum.org/art/collection/search/23198,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.25.854,false,true,26579,Arms and Armor,Crinet plate,"Crinet Plate Belonging to an Armor for Field and Tournament Made for Duke Nikolaus ""The Black"" Radziwill (1515–1565), Duke of Nesvizh and Olyka, Prince of the Empire, Grand Chancellor and Marshal of Lithuania","German, Nuremberg",,,,,Armorer,,Kunz Lochner,"German, Nuremberg, 1510–1567",,"Lochner, Kunz",German,1510,1567,ca. 1555; probably repainted later,1530,1900,"Steel, brass, gold, paint",L. 13 7/8 in. (35.5 cm); H. 3 7/8 in. (9.8 cm); Wt. 9 oz. (261 g),"Gift of William H. Riggs, 1913",,Nuremberg,,,,,,,,,,Armor for Horse,,http://www.metmuseum.org/art/collection/search/26579,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -32.69a–q,false,true,23358,Arms and Armor,Horse armor,"Horse Armor Made for Johann Ernst, Duke of Saxony-Coburg (1521–1553)","German, Nuremberg",,,,,Armorer,,Kunz Lochner,"German, Nuremberg, 1510–1567",,"Lochner, Kunz",German,1510,1567,dated 1548,1548,1548,"Steel, leather, copper alloy, textile",Wt. including saddle 92 lb. (41.73 kg) Bit: H. 6 in (15.2 cm); W. 11 in (27.9 cm),"Rogers Fund, 1932",,Nuremberg,,,,,,,,,,Armor for Horse,,http://www.metmuseum.org/art/collection/search/23358,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.25.854; 21.42,false,true,684263,Arms and Armor,Crinet plate and shaffron,"Crinet Plate and Shaffron Belonging to an Armor for Field and Tournament Made for Duke Nikolaus ""The Black"" Radziwill (1515–1565), Duke of Nesvizh and Olyka, Prince of the Empire, Grand Chancellor and Marshal of Lithuania","German, Nuremberg",,,,,Armorer,,Kunz Lochner,"German, Nuremberg, 1510–1567",,"Lochner, Kunz",German,1510,1567,ca. 1555; probably repainted and eye guards restored later,1530,1900,"Steel, brass, gold, paint",crinet plate: L. 13 7/8 in. (35.5 cm); H. 3 7/8 in. (9.8 cm); Wt. 9 oz. (261 g); shaffron: H. 23 1/4 in. (59 cm); W. 13 1/4 in. (33.6 cm); Wt. 4 lb. 8 oz. (2034 g),"Crinet plate: Gift of William H. Riggs, 1913; shaffron: Rogers Fund, 1921",,Nuremberg,,,,,,,,,,Armor for Horse,,http://www.metmuseum.org/art/collection/search/684263,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"14.25.854–.856, .865, .881a, b; 21.42",false,true,684262,Arms and Armor,Portions of an armor garniture for field and tournament,"Portions of an Armor Garniture for Field and Tournament Made for Duke Nikolaus ""The Black"" Radziwill (1515–1565), Duke of Nesvizh and Olyka, Prince of the Empire, Grand Chancellor and Marshal of Lithuania","German, Nuremberg",,,,,Armorer,,Kunz Lochner,"German, Nuremberg, 1510–1567",,"Lochner, Kunz",German,1510,1567,ca. 1555; probably repainted and shaffron eye guards restored later,1530,1900,"Steel, brass, gold, paint, leather, textile (velvet)",crinet plate (14.25.854): L. 13 7/8 in. (35.5 cm); H. 3 7/8 in. (9.8 cm); Wt. 9 oz. (261 g); vamplate (14.25.855): D. 8 1/2 in. (33.3 cm); H. 4 7/8 in. (12.4 cm); Diam. 13 1/8 in. (33.3 cm); Wt. 3 lb. 3 oz. (1454 g); left pauldron (14.25.856): H. 8 1/2 in. (12.6 cm); W. 12 1/2 in. (31.7 cm); Wt. 1 lb. 10 oz. (730 g); crinet plate (14.25.865): H. 2 3/8 in. (6 cm); W. 10 5/8 in. (27 cm); D. 6 5/16 in. (16 cm); Wt. 8 oz. (236 g); right tasset (14.25.881a): H. 6 in. (15.2 cm); W. 7 3/4 in. (19.7 cm); Wt. 1 lb. 5 oz. (587 g); left tasset (14.25.881b): H. 7 1/2 in. (19 cm); W. 8 1/2 in. (21.6 cm); Wt. 1 lb. 10 oz. (726 g); shaffron (21.42): H. 23 1/4 in. (59 cm); W. 13 1/4 in. (33.6 cm); Wt. 4 lb. 8 oz. (2034 g),"Gift of William H. Riggs, 1913",,Nuremberg,,,,,,,,,,Armor for Horse,,http://www.metmuseum.org/art/collection/search/684262,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.25.855,false,true,26580,Arms and Armor,Vamplate (handguard for the lance) for use in the field and tilt,"Vamplate Belonging to an Armor for Field and Tournament Made for Duke Nikolaus ""The Black"" Radziwill (1515–1565), Duke of Nesvizh and Olyka, Prince of the Empire, Grand Chancellor and Marshal of Lithuania","German, Nuremberg",,,,,Armorer,,Kunz Lochner,"German, Nuremberg, 1510–1567",,"Lochner, Kunz",German,1510,1567,ca. 1555; probably repainted later,1530,1900,"Steel, gold, paint",D. 8 1/2 in. (33.3 cm); H. 4 7/8 in. (12.4 cm); Diam. 13 1/8 in. (33.3 cm); Wt. 3 lb. 3 oz. (1454 g),"Gift of William H. Riggs, 1913",,Nuremberg,,,,,,,,,,Shafted Weapons,,http://www.metmuseum.org/art/collection/search/26580,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.25.865,false,true,22316,Arms and Armor,Crinet plate,"Crinet Plate Belonging to an Armor for Field and Tournament Made for Duke Nikolaus ""The Black"" Radziwill (1515–1565), Duke of Nesvizh and Olyka, Prince of the Empire, Grand Chancellor and Marshal of Lithuaniac","German, Nuremberg",,,,,Armorer,,Kunz Lochner,"German, Nuremberg, 1510–1567",,"Lochner, Kunz",German,1510,1567,ca. 1555,1530,1580,"Steel, gold, leather, textile (velvet)",H. 2 3/8 in. (6 cm); W. 10 5/8 in. (27 cm); D. 6 5/16 in. (16 cm); Wt. 8 oz. (236 g),"Gift of William H. Riggs, 1913",,Nuremberg,,,,,,,,,,Equestrian Equipment,,http://www.metmuseum.org/art/collection/search/22316,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.151.2a–s; 29.158.183–.184; 32.69a–q,false,true,35739,Arms and Armor,Armor for man and horse,Armor for Man and Horse,"German, Nuremberg",,,,,Armorer,,Kunz Lochner,"German, Nuremberg, 1510–1567",,"Lochner, Kunz",German,1510,1567,"dated 1548, with later restorations",1548,1900,"Steel, leather, copper alloy, textile",man's armor: Wt. approx. 56 lb. (25.4 kg); horse armor with saddle: Wt. 92 lb. (41.7 kg),"man's armor: Bashford Dean Memorial Collection, Gift of Mrs. Bashford Dean, 1929; mail sleeves: Bashford Dean Memorial Collection, Funds from various donors, 1929; horse armor: Rogers Fund, 1932",,Nuremberg,,,,,,,,,,Armor for Horse and Man,,http://www.metmuseum.org/art/collection/search/35739,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -21.42,false,true,26583,Arms and Armor,Shaffron (Horse's head defense),"Shaffron (Horse's Head Defense) Belonging to an Armor for Field and Tournament Made for Duke Nikolaus ""The Black"" Radziwill (1515–1565), Duke of Nesvizh and Olyka, Prince of the Empire, Grand Chancellor and Marshal of Lithuania","German, Nuremberg",,,,,Armorer,,Kunz Lochner,"German, Nuremberg, 1510–1567",,"Lochner, Kunz",German,1510,1567,ca. 1555; probably repainted and eye guards restored later,1530,1900,"Steel, brass, gold, paint",H. 23 1/4 in. (59 cm); W. 13 1/4 in. (33.6 cm); D. 7 1/2 in. (19.1 cm); Wt. 4 lb. 8 oz. (2034 g),"Rogers Fund, 1921",,Nuremberg,,,,,,,,,,Equestrian Equipment-Shaffrons,,http://www.metmuseum.org/art/collection/search/26583,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"14.25.881a, b",false,true,26582,Arms and Armor,Pair of tassets (thigh defenses) for the tilt,"Pair of Tassets (Thigh Defenses) Belonging to an Armor for Field and Tournament Made for Duke Nikolaus ""The Black"" Radziwill (1515–1565), Duke of Nesvizh and Olyka, Prince of the Empire, Grand Chancellor and Marshal of Lithuania","German, Nuremberg",,,,,Armorer,,Kunz Lochner,"German, Nuremberg, 1510–1567",,"Lochner, Kunz",German,1510,1567,ca. 1555; probably repainted later,1530,1900,"Steel, gold, paint",right tasset (a): H. 6 in. (15.2 cm); W. 7 3/4 in. (19.7 cm); Wt. 1 lb. 5 oz. (587 g); left tasset (b): H. 7 1/2 in. (19 cm); W. 8 1/2 in. (21.6 cm); Wt. 1 lb. 10 oz. (726 g),"Gift of William H. Riggs, 1913",,Nuremberg,,,,,,,,,,Armor Parts-Thigh and Leg Defense,,http://www.metmuseum.org/art/collection/search/26582,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"26.145.243a, b",true,true,22914,Arms and Armor,Hunting sword with scabbard,Hunting Sword with Scabbard,"German, possibly Munich",,,,,Sword maker,Grip attributed to,Joseph Deutschmann,"German, Imst 1717–1787 Passau",,"Deutschmann, Joseph",German,1717,1787,ca. 1740,1715,1765,"Steel, silver, ivory, wood, leather",L. 29 1/2 in. (74.9 cm); L. of blade 23 1/4 in. (59 cm); W. of blade 1 1/8 in. (2.8 cm); D. of blade 5/16 in. (0.8 cm);Wt. 1 lb. 2 oz. (510.29 g); Wt. of scabbard 1 lb 6 oz. (624 g),"Gift of Jean Jacques Reubell, in memory of his mother, Julia C. Coster, and of his wife, Adeline E. Post, both of New York City, 1926",,possibly Munich,,,,,,,,,,Swords-Hunting,,http://www.metmuseum.org/art/collection/search/22914,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"69.295.1, .2",false,true,24859,Arms and Armor,Pair of flintlock pistols,Pair of Flintlock Pistols,"German, Regensburg",,,,,Gunsmith,,Johann Andreas Kuchenreuter,"German, Regensburg, 1716–1795",,"Kuchenreuter, Johann Andreas",German,1716,1795,ca. 1760–70,1735,1795,"Steel, wood, bronze, gold, silver, horn",L. 16 3/4 in. (42.6 cm); L. of barrel 11 1/4 in. (28.6 cm); L. of lockplate 4 1/2 in. (11.4 cm); Cal. .56 in. (14.2 mm); Wt. 1 lb. 13 oz. (822 g),"Purchase, Bashford Dean Bequest, 1969",,Regensburg,,,,,,,,,,Firearms-Pistols-Flintlock,,http://www.metmuseum.org/art/collection/search/24859,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.156.45,false,true,23213,Arms and Armor,Sallet,Sallet of Emperor Maximilian I (1459–1519),"German, Augsburg",,,,,Armorer,Attributed to,Lorenz Helmschmid,"German, Augsburg, ca. 1445–1516",,"Helmschmid, Lorenz",German,1425,1525,ca. 1490–95,1465,1520,"Steel, copper alloy, gold",H. 12 in. (30.5 cm); W. 9 in. (22.9 cm); D. 12 3/8 in. (31.4 cm); Wt. 4 lb. 15.7 oz. (2261 g),"Bashford Dean Memorial Collection, Gift of Edward S. Harkness, 1929",,Augsburg,,,,,,,,,,Helmets,,http://www.metmuseum.org/art/collection/search/23213,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"29.156.67h, i",false,true,23208,Arms and Armor,Pair of tournament pauldrons (shoulder defenses),Pair of Tournament Pauldrons (Shoulder Defenses),"German, Augsburg",,,,,Armorer,Marked by,Lorenz Helmschmid,"German, Augsburg, ca. 1445–1516",,"Helmschmid, Lorenz",German,1425,1525,ca. 1500,1475,1525,"Steel, copper alloy",Wt. of right pauldron 3 lb. 15 oz. (1786 g); Wt. of left pauldron 4 lb. (1814.4 g),"Bashford Dean Memorial Collection, Gift of Edward S. Harkness, 1929",,Augsburg,,,,,,,,,,Armor Parts-Arms & Shoulders,,http://www.metmuseum.org/art/collection/search/23208,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"04.3.280, .282; 23.58; 26.234.3",false,true,35727,Arms and Armor,Portions of an armor garniture,Portions of an Armor Garniture,"German, Augsburg",,,,,Etcher,,Jörg Sorg the Younger,"German, Augsburg, ca. 1522–1603",,"Sorg the Younger, Jörg",German,1500,1625,"ca. 1550–55, some etched decoration, 19th century",1525,1900,"Steel, gold, leather, velvet",,"04.3.280, .282: Rogers Fund, 1904; 23.58: Anonymous Gift, in memory of Cornelius Stevenson, 1923; 26.234.3: Gift of George D. Pratt, 1926",,Augsburg,,,,,,,,,,Armor for Man,,http://www.metmuseum.org/art/collection/search/35727,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -04.3.282,false,true,32198,Arms and Armor,Portions of an armor garniture,Portions of an Armor Garniture,"German, Augsburg",,,,,Etcher,,Jörg Sorg the Younger,"German, Augsburg, ca. 1522–1603",,"Sorg the Younger, Jörg",German,1500,1625,"ca. 1550–55, some etched decoration, 19th century",1525,1900,"Steel, gold, leather, velvet",,"Rogers Fund, 1904",,Augsburg,,,,,,,,,,Armor for Man-1/2 Armor,,http://www.metmuseum.org/art/collection/search/32198,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -04.3.288,false,true,25419,Arms and Armor,Pommel plate,Pommel Plate,"German, Augsburg",,,,,Etcher,Attributed to,Jörg Sorg the Younger,"German, Augsburg, ca. 1522–1603",,"Sorg the Younger, Jörg",German,1500,1625,ca. 1550,1525,1575,"Steel, gold","9 x 6 in. (22.9 x 15.2 cm), wt. 7 oz. (185 g)","Rogers Fund, 1904",,Augsburg,Bavaria,,,,,,,,,Equestrian Equipment-Saddles,,http://www.metmuseum.org/art/collection/search/25419,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -25.135.88,false,true,22890,Arms and Armor,Wheellock pistol,Wheellock Pistol Made for Maximilian I of Bavaria (1573–1651),"German, Munich",,,,,Steel-chiseler,,Emanuel Sadeler,"German, Munich, active 1594–1610",,"Sadeler, Emanuel",German,1594,1610,ca. 1600–1610,1575,1635,"Steel, gold",L. 14 3/16 in. (36.0 cm); L. of barrel 8 9/16 in. (21.7 cm); Cal. .45 in. (11.4 mm); Wt. 2 lb. 12 oz. (1247 g),"Gift of William H. Riggs, 1913",,Munich,,,,,,,,,,Firearms-Pistols,,http://www.metmuseum.org/art/collection/search/22890,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.25.1419,false,true,22383,Arms and Armor,Wheellock pistol,Wheellock Pistol,"German, Nuremberg",,,,,Gunsmith,,Peter Danner,"German, Nuremberg, ca. 1580–1602",,"Danner, Peter",German,1555,1627,ca. 1580,1555,1605,"Steel, bronze, gold, wood (walnut), staghorn",L. 19 3/4 in. (50.2 cm); L. of barrel 12 in. (30.5 cm); Cal. .528 in. (13.4 mm); L. of lock 7 5/8 in. (19.4 cm); L. of plug 7 3/8 in. (18.7 cm); Wt. 3 lb. 14 oz. (1758 g),"Gift of William H. Riggs, 1913",,Nuremberg,,,,,,,,,,Firearms-Pistols-Wheellock,,http://www.metmuseum.org/art/collection/search/22383,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.158.11,false,true,26445,Arms and Armor,Sallet,Sallet,"German, Basel",,,,,Armorer,Attributed to,Hans Blarer the Younger,"German, Basel, documented 1453–83",,"Blarer, Hans the Younger",German,1453,1483,ca. 1470–80,1445,1505,Steel,H. 9 3/4 (24.8 cm); W. 8 in. (20.2 cm); D. 14 3/4 (37.4 cm); Wt. 7 lb. 2 oz. (3232 g),"Bashford Dean Memorial Collection, Funds from various donors, 1929",,Basel,,,,,,,,,,Helmets,,http://www.metmuseum.org/art/collection/search/26445,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -04.3.183,false,true,21970,Arms and Armor,Wheellock spanner with priming flask and screwdriver,Wheellock Spanner with Priming Flask and Screwdriver,"German, Munich",,,,,Steel-chiseler,Workshop of,Daniel Sadeler,"German, Munich, recorded 1602–1632",,"Sadeler, Daniel",German,1602,1632,ca. 1610–30,1585,1655,"Steel, gold",L. 9 in. (22.9 cm); W. 2 3/4 in. (7 cm),"Rogers Fund, 1904",,Munich,,,,,,,,,,Firearms Accessories-Flasks & Primers,,http://www.metmuseum.org/art/collection/search/21970,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -04.3.267a,false,true,35890,Arms and Armor,Close helmet,Close Helmet,"German, Landshut",,,,,Armorer,Attributed to,Wolfgang Grosschedel,"German, Landshut, active ca. 1517–62",,"Grosschedel, Wolfgang",German,1517,1562,ca. 1560,1535,1585,"Steel, leather, copper alloy",H. 14 3/4 in. (37.5 cm); W. 9 1/8 in. (23.2 cm); D. 13 1/4 in. (33.7 cm); Wt. 11 lb. 3.1 oz. (5077.4 g),"Rogers Fund, 1904",,Landshut,,,,,,,,,,Helmets,,http://www.metmuseum.org/art/collection/search/35890,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -23.261,false,true,22874,Arms and Armor,Armor for man and horse,Armor for Man and Horse Armor Presumably Made for Baron Pankraz von Freyberg (1508–1565),"German, Landshut",,,,,Armorer,,Wolfgang Grosschedel,"German, Landshut, active ca. 1517–62",,"Grosschedel, Wolfgang",German,1517,1562,"man's armor, ca. 1535–40; horse armor, dated 1554; saddle steels, later restorations",1510,1900,"Steel; leather, copper alloy, textile",Wt. of man's armor approx. 55 lb. 11 oz. (25.25 kg); Wt. of horse armor with saddle 65 lb. 7 oz. (29.69 kg),"Fletcher Fund, 1923",,Landshut,,,,,,,,,,Armor for Horse and Man,,http://www.metmuseum.org/art/collection/search/22874,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.25.633,false,true,22234,Arms and Armor,Morion,Morion for the Bodyguard of the Prince-Elector of Saxony,"German, Nuremberg",,,,,Armorer,Probably,Martin Schneider the Younger,"German, Nuremberg, active ca. 1610–20",,"Schneider the Younger, Martin",German,1610,1620,ca. 1570,1545,1595,"Steel, gold, brass, leather",H. 11 9/16 in. (29.4 cm); W. 9 1/4 in. (23.5 cm); D. 13 3/4 in. (34.9 cm); Wt. 3 lb. 5 oz. (1503 g),"Gift of William H. Riggs, 1913",,Nuremberg,,,,,,,,,,Helmets,,http://www.metmuseum.org/art/collection/search/22234,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.25.684a–i,false,true,22251,Arms and Armor,Half armor,Half Armor,"German, Nuremberg",,,,,Armorer,Attributed to,Martin Schneider the Younger,"German, Nuremberg, active ca. 1610–20",,"Schneider the Younger, Martin",German,1610,1620,1610–20,1610,1620,Steel,Wt. 41 lb. 15 oz. (19.01 kg),"Gift of William H. Riggs, 1913",,Nuremberg,,,,,,,,,,Armor for Man-1/2 Armor,,http://www.metmuseum.org/art/collection/search/22251,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.150.8a,false,true,23091,Arms and Armor,Sallet,Sallet,"German, Landshut",,,,,Armorer,,Matthes Deutsch,"German, Landshut, documented 1485–1505",,"Deutsch, Matthes",German,1485,1505,ca. 1490,1465,1515,"Steel, textile",H. 9 3/4 in. (24.8 cm); W. 9 3/4 in. (24.8 cm); D. 15 in. (38.1 cm); Wt. 9 lb. 4 oz. (4190 g),"Bashford Dean Memorial Collection, Bequest of Bashford Dean, 1928",,Landshut,,,,,,,,,,Helmets,,http://www.metmuseum.org/art/collection/search/23091,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -50.160a–x,false,true,24814,Arms and Armor,Composed armor,Composed Armor,"European, Italian and German",,,,,Armorer,Right pauldron (shoulder defense) marked by,Matthes Deutsch,"German, Landshut, documented 1485–1505",,"Deutsch, Matthes",German,1485,1505,15th century and later,1400,1900,"Steel, iron, copper alloy (latten), leather, brass","Wt. approx. 41 lb. 12 oz. (18.94 kg); 50.160i (gauntlet); L. 11 3/4 in. (29.8 cm); W. 5 3/8 in. (13.7 cm); D. 5 in. (12.7 cm); Wt. 1 lb. (453.6 g); 50.160h (elbow reinforce); H. 8 3/8 in. (21.3 cm); W. 4 1/4 in. (10.8 cm); D. 9 1/2 in. (24.1 cm); Wt. 12.2 oz. (345.9 g); 50.160o (greave); H. 14 13/16 in. (37.6 cm); W. 4 5/8 in. (11.7 cm); D. 5 5/16 in. (13.5 cm); Wt. 1 lb. 9.2 oz. (714.4 g); 50.160n (thigh defense); H. 20 3/8 in. (51.8 cm); W. 6 11/16 in. (17 cm); D. 7 1/2 in. (19.1 cm); Wt. 4 lb. 5.5 oz. (1970.3 g); 50.160u (mail collar with integral bevor); H. 15 5/8 in. (37 cm); W. 31 5/16 in. (79.5 cm); Diam. (outside) of collar links 11/32 in. (8.3 mm); Diam. (inside) of collar links 7/32 in. (5.9 mm); Diam. (outside) of shawl links 5/16 in. (7.7 mm); Diam. (inside) of shawl links 1/4 in. (6.3 mm); 50.160v (mail sleeve); L. 29 1/8 in. (74 cm) W. 13 in. (33 cm); Diam. (outside) of links, 9/32 in. (7.2 mm); Diam. (inside) of links, 3/16 in. (5 mm); 50.160w (mail sleeve): L. 27 5/8 in. (70.0 cm); W. 12 3/16 in. (31.0 mm); Diam. (outside) of links 9/32 in. (6.8 mm); Diam. (inside) of links, 3/16 in. (4.6 mm); 50.160x (pair of mail paunces); H. 8 11/16 in. (22 cm); H. of skirt 14 13/16 in. (37.5 cm); W. of bravette 26 in. (66 cm); Diam. of waist 32 1/8 in. (81.5 cm); Diam. (outside) of links 13/32 in. (10.6 mm); Diam. (inside) of links, 7/32 in. (5.6 mm)","Gift of Mrs. Alexander McMillan Welch, in memory of her sister, Mary Alice Dyckman Dean (Mrs. Bashford Dean), 1950",,Landshut,,,,,,,,,,Armor for Man,,http://www.metmuseum.org/art/collection/search/24814,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.25.756,false,true,22282,Arms and Armor,Vamplate (Hand guard for a lance),Vamplate (Hand Guard for a Lance),"German, Landshut",,,,,Armorer,,Matthes Deutsch,"German, Landshut, documented 1485–1505",,"Deutsch, Matthes",German,1485,1505,ca. 1490,1465,1515,Steel,H. 16 3/8 in. (41.5 cm); W. 11 3/8 in. (28.9 cm),"Gift of William H. Riggs, 1913",,Landshut,,,,,,,,,,Shafted Weapons,,http://www.metmuseum.org/art/collection/search/22282,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -25.188.18,false,true,22899,Arms and Armor,Glaive,Glaive of Emperor Rudolf II (reigned 1576–1612),"German, Augsburg",,,,,Etcher,,Hans Stromair,"German, Augsburg, 1524 or 1525–ca. 1583",,"Stromair, Hans",German,1524,1600,dated 1577,1577,1577,"Steel, wood, textile, gold",L. 100 1/2 in. (255.27 cm); blade L. 23 in. (58.42 cm),"Gift of George D. Pratt, 1925",,Augsburg,,,,,,,,,,Shafted Weapons,,http://www.metmuseum.org/art/collection/search/22899,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -04.3.158,false,true,21963,Arms and Armor,Hunting knife combined with wheellock pistol,Hunting Knife Combined with Wheellock Pistol,"German, Munich",,,,,Etcher,,Ambrosius Gemlich,"German, Munich and Landshut, active ca. 1520–50",,"Gemlich, Ambrosius",German,1495,1575,"blade ca. 1528–29, etched with a calendar for the years 1529–34; barrel dated 1540 or 1546",1528,1546,"Steel, gold, staghorn, bronze",L. 18 1/4 in. (46.4 cm); L. of barrel 12 3/8 in. (31.4 cm); L. of blade 13 1/4 in. (33.7 cm); Cal. .28 in. (7.1 mm),"Rogers Fund, 1904",,Munich,,,,,,,,,,Combination Weapons,,http://www.metmuseum.org/art/collection/search/21963,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.25.1655,false,true,27181,Arms and Armor,Crinet,Crinet,"German, Nuremberg",,,,,Armorer,,Valentin Siebenbürger,"German, Nuremberg, ca. 1510–1564, master in 1531",,"Siebenbürger, Valentin",German,1485,1564,ca. 1535,1510,1560,Steel,"L. 37 1/2 in. (95.2 cm); W. 11 3/4 in. (29.9 cm); Wt. 6 lb. 14 oz. (3,070 g)","Gift of William H. Riggs, 1913",,Nuremberg,,,,,,,,,,Armor for Horse,,http://www.metmuseum.org/art/collection/search/27181,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -96.5.115c,false,true,26575,Arms and Armor,Breastplate,Breastplate,"German, Nuremberg",,,,,Armorer,,Valentin Siebenbürger,"German, Nuremberg, ca. 1510–1564, master in 1531",,"Siebenbürger, Valentin",German,1485,1564,ca. 1530–35,1505,1560,Steel,H. 15 in. (38 cm); W. 13 1/2 in. (34.5 cm),"John Stoneacre Ellis Collection, Gift of Mrs. Ellis and Augustus Van Horne Ellis, 1896",,Nuremberg,,,,,,,,,,Armor Parts-Breastplates,,http://www.metmuseum.org/art/collection/search/26575,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"19.131.1a–r, t–w, .2a–c; 27.183.16",true,true,35775,Arms and Armor,Armor garniture,"Armor Garniture, Probably of King Henry VIII of England (reigned 1509–47)","British, Greenwich",,,,,Armorer|Designer,Made at the|Design of the decoration attributed to,Royal Workshops at Greenwich|Hans Holbein the Younger,"British, Greenwich, 1511–1640s|German, Augsburg 1497/98–1543 London",,"Royal Workshops at Greenwich|Holbein, Hans, the Younger",German,1511 |1497,1650 |1543,dated 1527,1527,1527,"Steel, gold, leather, copper alloys",H. 73 in. (185.4 cm); Wt. 62 lb. 12 oz. (28.45 kg),"Armor: Purchase, William H. Riggs Gift and Rogers Fund, 1919; mail brayette: Gift of Prince Albrecht Radziwill, 1927",,Greenwich,,,,,,,,,,Armor for Horse and Man,,http://www.metmuseum.org/art/collection/search/35775,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"1985.248a, b",false,true,24924,Arms and Armor,Crossbow (Halbe rüstung) with winder (cranequin),Crossbow (Halbe Rüstung) with Winder (Cranequin),"German, Dresden",,,,,Maker,Crossbow by,Johann Gottfried Hänisch the Elder,"German, Dresden 1696–1778",,"Hänisch the Elder, Johann Gottfried",German,1696,1778,dated 1742,1742,1742,"Steel, wood, staghorn, copper alloy, hemp, wool, gold, iron alloy","L. of crossbow, 28 13/16 in. (73.2 cm); W. of crossbow, 24 15/16 in. (63.3 cm); Wt. of crossbow, 10 lb. 7 oz. (4,817 g); L. of cranequin (without crank), 14 1/4 in. (36.2 cm); L. of crank, 10 3/4 in. (27.3 cm)","Purchase, Louis V. Bell Fund and Bequest of Stephen V. Grancsay, by exchange, 1985",,Dresden,Saxony,,,,,,,,,Archery Equipment-Crossbows,,http://www.metmuseum.org/art/collection/search/24924,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"29.158.650a, b",false,true,23337,Arms and Armor,Crossbow (Halbe rüstung) with winder (Cranequin),Crossbow (Halbe Rüstung) with Winder (Cranequin),"crossbow, German, probably Dresden; winder, possibly German",,,,,Maker,Crossbow attributed to,Johann Gottfried Hänisch the Elder,"German, Dresden 1696–1778",,"Hänisch the Elder, Johann Gottfried",German,1696,1778,"crossbow, ca. 1720–30; winder, ca. 1575–1600",1550,1755,"Steel, wood (walnut), staghorn, copper alloy, hemp, leather, silk, gold, iron alloy, wool",crossbow: L. 26 15/16 in. (68.4 cm); W. 29 7/8 in. (75.8 cm); Wt. 13 lb. 15 oz. (6329 g); winder: L. 14 in. (35.5 cm); W. 4 1/8 in. (10.4 cm); Wt. 5 lb. 3 oz. (2341 g),"Bashford Dean Memorial Collection, Funds from various donors, 1929",,probably Dresden,Saxony,,,,,,,,,Archery Equipment-Crossbows,,http://www.metmuseum.org/art/collection/search/23337,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2010.315,false,true,35684,Arms and Armor,Light crossbow (Schnepper),Light Crossbow (Schnepper) from the Armory of Sedlitz Palace,"German, Dresden",,,,,Maker,,Johann Gottfried Hänisch the Elder,"German, Dresden, 1696–1778",,"Hänisch the Elder, Johann Gottfried",German,1696,1778,dated 1733,1733,1733,"Steel, wood (walnut), staghorn, hemp, wool, gold","L. 27 11/16 in. (70.3 cm); W. 22 1/2 in. (57.1 cm); Wt. 4 lb. 5 oz. (1,949 g)","Purchase, Arthur Ochs Sulzberger Gift, 2010",,Dresden,,,,,,,,,,Archery Equipment-Crossbows,,http://www.metmuseum.org/art/collection/search/35684,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2011.429,false,true,35706,Arms and Armor,"Small crossbow (Schnepper), probably for a woman or child","Small Crossbow (Schnepper), Probably for a Woman or Child","German, Dresden",,,,,Maker,,Johann Gottfried Hänisch the Elder,"German, Dresden, 1696–1778",,"Hänisch the Elder, Johann Gottfried",German,1696,1778,dated 1738,1738,1738,"Steel, wood (fruitwood, probably plum or cherry), staghorn, hemp, wool, gold, horn",L. 22 7/32 in. (56.4 cm); W. 16 15/16 in. (43 cm); Wt. 1 lb. 14 1/2 oz. (866 g),"Purchase, Arthur Ochs Sulzberger Gift, 2011",,Dresden,Saxony,,,,,,,,,Archery Equipment-Crossbows,,http://www.metmuseum.org/art/collection/search/35706,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -69.199,false,true,24858,Arms and Armor,Box for crossbow bolts (Bolzenkasten),"Box For Crossbow Bolts (Bolzenkasten), Probably Made for William IV, Duke of Bavaria (r. 1508–50)","German, Munich",,,,,Maker,,Hans Wagner the Elder,"German, Munich, recorded 1539–56",,"Wagner the Elder, Hans",German,1539,1556,dated 1539,1539,1539,"Wood (lid and front panel: fruitwood, possibly pear; bottom: walnut; inlay: possibly sycamore; later repairs: mahogany moldings and Indian rosewood veneers on sides), staghorn, iron, gold, paste, paper",H. 3 5/16 in. (8.4 cm); W. 16 in. (40.6 cm); D. 7 31/32 in. (20.2 cm),"Purchase, Bashford Dean Bequest, 1969",,Munich,,,,,,,,,,Archery Equipment,,http://www.metmuseum.org/art/collection/search/24858,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -42.50.8,false,true,24689,Arms and Armor,Wheellock rifle,Wheellock Rifle,"German, Schwäbisch Gmünd",,,,,Maker,,Johann Michael Maucher,"German, Schwäbisch Gmünd, 1645–1701",,"Maucher, Johann Michael",German,1670,1701,ca. 1680–90,1655,1715,"Steel, wood (cherry), ivory, mother-of-pearl",L. 41 5/8 in. (105.7 cm); L. of barrel 30 13/16 in. (78.3 cm); L. of lock 7 1/8 in. (18.1 cm); Cal. .56 in. (14.2 mm); Wt. 7 lb. 11 oz. (3500 g),"Gift of Stephen V. Grancsay, 1942",,Schwäbisch Gmünd,,,,,,,,,,Firearms,,http://www.metmuseum.org/art/collection/search/24689,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.156.47,false,true,23214,Arms and Armor,Close helmet for the tourney,Close Helmet for the Tourney,"Austrian, Innsbruck",,,,,Designer,Ornament copied from a design by,Daniel Hopfer,"German, Kaufbeuren 1471–1536 Augsburg",,"Hopfer, Daniel",German,1471,1536,dated 1552,1552,1552,"Steel, leather, brass",H. 10 1/2 in. (26.7 cm); W. 8 5/16 in. (21.1 cm); D. 12 1/4 in. (31.1 cm); Wt. 6 lb. 3 oz. (2803 g),"Bashford Dean Memorial Collection, Gift of Edward S. Harkness, 1929",,Innsbruck,,,,,,,,,,Helmets,,http://www.metmuseum.org/art/collection/search/23214,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.25.370,false,true,26781,Arms and Armor,Glaive,"Glaive of Maximilian III Joseph, Prince-Elector of Bavaria (reigned 1745–77)",German,,,,,Decorator,Inscribed:,Jungwierth,"German, ca. 1770",,Jungwierth,German,1745,1795,dated 1771,1771,1771,"Steel, wood, gold, copper alloy, textile",L. 8 ft. 5 3/4 in. (258.4 cm); L. of head 27 1/2 in. (69.9 cm); W. 3 3/4 in. (9.5 cm); Wt. 7 lb. 0.9 oz. (3200.7 g),"Gift of William H. Riggs, 1913",,,,,,,,,,,,Shafted Weapons,,http://www.metmuseum.org/art/collection/search/26781,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -49.8,false,true,24797,Arms and Armor,Hunting sword,Hunting Sword,German,,,,,Sword maker,,Johann Georg Klett,"German, 1720–1793",,Klett Johann Georg,German,1720,1793,ca. 1750,1725,1775,"Steel, silver, gold, bloodstone",L. 29 in. (73.7 cm),"Rogers Fund, 1949",,,,,,,,,,,,Swords,,http://www.metmuseum.org/art/collection/search/24797,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.25.702,false,true,22263,Arms and Armor,Armor,Armor,German,,,,,Armorer,Gorget plate of the helmet possibly marked by,Hans Michel,"German, Nuremberg 1539–1599",,"Michel, Hans",German,1539,1599,16th century and later,1500,1900,"Steel, brass, leather",Wt. 41 lb. 9 oz. (18.85 kg); Wt. of helmet 10 lb. 8 oz. (4.75 kg),"Gift of William H. Riggs, 1913",,,,,,,,,,,,Armor for Man,,http://www.metmuseum.org/art/collection/search/22263,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.150.4b–t,false,true,23083,Arms and Armor,Armor,Armor,German,,,,,Etcher,Etched decoration on poleyns (knee defeneses) attributed to,Heilig Jörg,"German, active ca. 1490–1505",,"Jörg, Heilig",German,1465,1530,ca. 1500 and later,1475,1900,"Steel, leather",H. 71 in. (180.3 cm); Wt. 39 lb. 2 oz. (17.75 kg),"Bashford Dean Memorial Collection, Bequest of Bashford Dean, 1928",,,,,,,,,,,,Armor for Man,,http://www.metmuseum.org/art/collection/search/23083,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2014.434,false,true,647195,Arms and Armor,Hand-colored engraving,Ludi equestres. Das Thurnieren (The Tournament),German,,,,,Artist,,Martin Engelbrecht,"German, Augsburg 1684–1756 Augsburg",,"Engelbrecht, Martin",German,1684,1756,ca. 1730,1705,1755,"Paper, ink, polychromy",9 1/2 x 15 3/16 in. (241 x 385 mm),"Purchase, Kenneth and Vivian Lam Gift, 2014",,,,,,,,,,,,Works on Paper-Engravings,,http://www.metmuseum.org/art/collection/search/647195,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2012.204a, b",false,true,35859,Arms and Armor,Sword with scabbard,"Sword with Scabbard of Faustin I (1782–1867), Emperor of Haiti","British, Birmingham",,,,,Sword cutler,,Robert Mole,"British, Birmingham,1800–1856",,"Mole, Robert",British,1800,1856,1850,1825,1875,"Steel, silver, gold, wood, textile (velvet)",L. 39 1/2 in. (100.3 cm),"Bequest of William S. Delafield Sr., 2012",,Birmingham,,,,,,,,,,Swords,,http://www.metmuseum.org/art/collection/search/35859,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2012.15,false,true,35782,Arms and Armor,Helmet for a harquebusier,Helmet for a Harquebusier,"British, London or Greenwich",,,,,Armorer,Probably made in the,Royal Workshops at Greenwich,"British, Greenwich, 1511–1640s",,Royal Workshops at Greenwich,British,1511,1650,ca. 1630–40,1605,1665,"Steel, silver, gold, copper alloy, textile",H. 13 7/8 (35.3 cm); W. 9 3/8 (23.8 cm); D. 16 7/16 in. (41.7 cm); Wt. 4 lb. 7 oz. (2010 g),"Purchase, Arthur Ochs Sulzberger Gift, 2012",,probably Greenwich,,,,,,,,,,Helmets,,http://www.metmuseum.org/art/collection/search/35782,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -21.180a–c,false,true,22768,Arms and Armor,Smallsword hilt with storage case,Smallsword Hilt with Storage Case,"British, Birmingham or London",,,,,Decorator,,"Rundell, Bridge and Rundell","British, London, active 1797–1843",,"Rundell, Bridge and Rundell",British,1797,1843,ca. 1785–1800,1760,1825,"Steel, wood, leather, silk",Hilt (a); L. including tang 8 1/4 in. (21 cm); L. excluding tang 6 15/16 in. (17.7 cm); W. 4 1/2 in. (11.4 cm); D. 3 1/2 in. (8.9 cm); Wt. 10 oz. (284.1 g); case (b); 5.2 oz. (147.4 g); trade card (c); 2 1/2 x 3 9/16 in. (6.4 x 9 cm),"Gift of Richard Hoe Lawrence, 1921",,London or Birmingham,,,,,,,,,,Swords,,http://www.metmuseum.org/art/collection/search/22768,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -26.145.311,false,true,22930,Arms and Armor,Smallsword,Smallsword,"British, probably London",,,,,Sword cutler,Attributed to,John Bland,"British, London, active ca. 1780–85",,"Bland, John",British,1755,1810,ca. 1780–85,1755,1810,"Gold, steel",L. 39 in. (99.1 cm); L. of blade 32 3/16 in. (81.7 cm); Wt. 1 lb. (454 g),"Gift of Jean Jacques Reubell, in memory of his mother, Julia C. Coster, and of his wife, Adeline E. Post, both of New York City, 1926",,London,,,,,,,,,,Swords,,http://www.metmuseum.org/art/collection/search/22930,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -15.113.1–.5; 29.158.885,true,true,27792,Arms and Armor,Harquebusier's armor with buff coat,"Harquebusier's Armor of Pedro II, King of Portugal (reigned 1683–1706) with Buff Coat","British, London; buff coat, European",,,,,Armorer|Armorer,Armor attributed to|Helmet cheek pieces and metal plates on shoulder straps made by,Richard Holden|Daniel Tachaux,"British, London, recorded 1658–1708|French, 1857–1928, active in France and America",,"Holden, Richard|Tachaux, Daniel",British,1658 |1857,1708 |1928,"ca. 1683 and later; buff coat, 17th–18th century",1601,1916,"Steel, gold, leather, textile",armor (15.113.1–.5): Wt. 43 lb. 5 oz. (19.6 kg); helmet: 14 x 11 x 15 1/4 in. (35.6 x 27.9 x 38.7 cm); Wt. 9 lb. 10 oz. (4354 g); breastplate: 18 1/2 x 16 5/16 x 7 13/16 in. (47 x 41.4 x 19.8 cm); Wt. 10 lb. 14 oz. (4944 g); backplate: 17 1/2 x 16 1/4 x 8 1/2 in. (44.5 x 41.3 x 21.6 cm); Wt. 11 lb. 5 oz. (5126 g); reinforcing breastplate: 17 3/8 x 16 3/16 x 6 13/16 in. (44.1 x 41.1 x 17.3 cm); Wt. 9 lb. 6 oz. (4264 g); bridle gauntlet: 5 1/2 x 19 7/16 x 6 3/4 in. (14 x 49.4 x 17.1 cm); Wt. 2 lb. 2 oz. (953 g); buff coat (29.158.885): L. 35 in. (88.9 cm),"Armor: Rogers Fund, 1915; buff coat: Bashford Dean Memorial Collection, Funds from various donors, 1929",,London,,,,,,,,,,Armor for Man,,http://www.metmuseum.org/art/collection/search/27792,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.98.1,false,true,24612,Arms and Armor,Terminal lame of a grand guard,"Terminal Lame of a Grand Guard from the Armor Garniture of George Clifford (1558–1605), Third Earl of Cumberland","British, Greenwich",,,,,Armorer,Made under the direction of,Jacob Halder,"British, master armorer at the royal workshops at Greenwich, documented in England 1558–1608",,"Halder, Jacob",British,1558,1608,1586,1586,1586,"Steel, gold",H. 10 1/4 in. (26.0 cm); W. 8 3/8 in. (21.3 cm),"Rogers Fund, 1936",,Greenwich,,,,,,,,,,Armor Parts,,http://www.metmuseum.org/art/collection/search/24612,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -32.130.6a–y,true,true,23939,Arms and Armor,Armor garniture,"Armor Garniture of George Clifford (1558–1605), Third Earl of Cumberland","British, Greenwich",,,,,Armorer,Made under the direction of,Jacob Halder,"British, master armorer at the royal workshops at Greenwich, documented in England 1558–1608",,"Halder, Jacob",British,1558,1608,1586,1586,1586,"Steel, gold, leather, textile",H. 69 1/2 in. (176.5 cm); Wt. 60 lb. (27.2 kg),"Munsey Fund, 1932",,Greenwich,,,,,,,,,,Armor for Man,,http://www.metmuseum.org/art/collection/search/23939,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -11.128.1a–p,false,true,22138,Arms and Armor,Field armor,Field Armor Probably of Sir John Scudamore (1541 or 1542–1623),"British, Greenwich",,,,,Armorer|Armorer,"Made under the direction of|Helmet, left pauldron, gauntlets, and right sabaton made by",Jacob Halder|Daniel Tachaux,"British, master armorer at the royal workshops at Greenwich, documented in England 1558–1608|French, 1857–1928, active in France and America","in The Metropolitan Museum of Art, Armor Workshop","Halder, Jacob|Tachaux, Daniel",British,1558 |1857,1608 |1928,"ca. 1587, restored and completed 1915",1562,1915,"Steel, gold, leather",Wt. 68 lb. 8 oz. (31.07 kg),"Frederick C. Hewitt Fund, 1911",,Greenwich|New York,New York,,,,,,,,,Armor for Man,,http://www.metmuseum.org/art/collection/search/22138,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -11.128.2a–n,false,true,22139,Arms and Armor,Armor,Armor of Sir James Scudamore (1558–1619),"British, Greenwich",,,,,Armorer|Armorer,"Breastplate, backplate, and gauntlets made by|Made under the direction of",Daniel Tachaux|Jacob Halder,"French, 1857–1928, active in France and America|British, master armorer at the royal workshops at Greenwich, documented in England 1558–1608","in the Metropolitan Museum of Art, Armor Workshop","Tachaux, Daniel|Halder, Jacob",British,1857 |1558,1928 |1608,ca. 1595–96,1570,1621,"Steel, gold, leather",H. 70 1/4 in. (178.5 cm); Wt. 50 lb. 7 oz. (22.88 kg),"Frederick C. Hewitt Fund, 1911",,Greenwich,,,,,,,,,,Armor for Man,,http://www.metmuseum.org/art/collection/search/22139,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"11.128.1a–p; 22.147.4a, b, .11",false,true,35896,Arms and Armor,Field armor,Field Armor Probably of Sir John Scudamore (1541 or 1542–1623),"British, Greenwich",,,,,Armorer|Armorer,"Helmet, left pauldron, gauntlets, and right sabaton made by|Made under the direction of",Daniel Tachaux|Jacob Halder,"French, 1857–1928, active in France and America|British, master armorer at the royal workshops at Greenwich, documented in England 1558–1608","in The Metropolitan Museum of Art, Armor Workshop","Tachaux, Daniel|Halder, Jacob",British,1857 |1558,1928 |1608,"ca. 1587, restored and completed 1915",1562,1915,"Steel, gold, leather",Wt. 68 lb. 8 oz. (31.07 kg),"armor: Frederick C. Hewitt Fund, 1911; right thigh and knee defense, and left foot defense: Fletcher Fund, 1922",,Greenwich|New York,New York,,,,,,,,,Armor for Man,,http://www.metmuseum.org/art/collection/search/35896,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2011.90,false,true,35705,Arms and Armor,Mezzotint,"George Wallis, The Late Celebrated Antiquary and Gunsmith of Hull",British,,,,,Engraver,Engraved by,John Raphael Smith,"British, baptized Derby 1751–1812 Doncaster",after a painting by John Harrison,"Smith, John Raphael",British,1751,1812,"June 20, 1804",1804,1804,Mezzotint on white wove paper,Sheet: 17 3/4 x 14 7/8 in. (45 x 37.7 cm); plate: 15 x 11 in. (38 x 28 cm),"Purchase, Arthur Ochs Sulzberger Gift, 2011",,,,,,,,,,,,Works on Paper-Engravings,,http://www.metmuseum.org/art/collection/search/35705,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2006.427,false,true,35148,Arms and Armor,Sketchbook,Memorandum Book Showing Colored Sketches Mostly of European Helmets,British,,,,,Artist,,William Burges,1827–1881,,"Burges, William",British,1827,1881,ca. 1880,1880,1880,"Paper, leather, silver",H. 3 3/4 in. (9.5 cm); W. 2 3/8 in. (6 cm),"Purchase, Gift of Bashford Dean, by exchange, 2006",,,,,,,,,,,,Books & Manuscripts,,http://www.metmuseum.org/art/collection/search/35148,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.465,false,true,28564,Arms and Armor,Medal,Medal Awarded to the Sword Makers Robert Mole and Sons,British,,,,,Artist,,William Wyon,"British, Birmingham 1795–1851 Brighton",,"Wyon, William",British,1795,1851,1851,1851,1851,Bronze,Diam. 1 3/4 in. (4.4 cm); thickness 5/16 in. (0.8 cm); Wt. 2.2 oz. (62.4 g),"Gift of Herbert G. Houze, 2005",,,,,,,,,,,,Miscellaneous-Coins and Medals,,http://www.metmuseum.org/art/collection/search/28564,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -15.113.1–.5,false,true,22605,Arms and Armor,Harquebusier's armor,"Harquebusier's Armor of Pedro II, King of Portugal (reigned 1683–1706)","British, London",,,,,Armorer|Armorer,Attributed to|Helmet cheek pieces and metal plates on shoulder straps made by,Richard Holden|Daniel Tachaux,"British, London, recorded 1658–1708|French, 1857–1928, active in France and America",,"Holden, Richard|Tachaux, Daniel",British,1658 |1857,1708 |1928,ca. 1683 and later,1655,1916,"Steel, gold, leather, textile",Wt. 43 lb. 5 oz. (19.6 kg); helmet: 14 x 11 x 15 1/4 in. (35.6 x 27.9 x 38.7 cm); Wt. 9 lb. 10 oz. (4354 g); breastplate: 18 1/2 x 16 5/16 x 7 13/16 in. (47 x 41.4 x 19.8 cm); Wt. 10 lb. 14 oz. (4944 g); backplate: 17 1/2 x 16 1/4 x 8 1/2 in. (44.5 x 41.3 x 21.6 cm); Wt. 11 lb. 5 oz. (5126 g); reinforcing breastplate: 17 3/8 x 16 3/16 x 6 13/16 in. (44.1 x 41.1 x 17.3 cm); Wt. 9 lb. 6 oz. (4264 g); bridle gauntlet: 5 1/2 x 19 7/16 x 6 3/4 in. (14 x 49.4 x 17.1 cm); Wt. 2 lb. 2 oz. (953 g),"Rogers Fund, 1915",,London,,,,,,,,,,Armor for Man,,http://www.metmuseum.org/art/collection/search/22605,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2006.471.1, .2",false,true,34183,Arms and Armor,Pair of pistols,Pair of Snaphaunce Pistols,"Italian, Bargi",,,,,Gunsmith,,"Matteo Cecchi, called Acquafresca","Italian, Bargi, 1651–1738",,"Cecchi, Matteo",Italian,1651,1738,ca. 1690,1690,1690,"Steel, silver, wood (ebony)",L. of each pistol 21 1/2 in. (54.7 cm); L. of each barrel 15 in. (38.2 cm); Cal. of each barrel 1/2 in. (13 mm),"Purchase, Arthur Ochs Sulzberger Gift, 2006",,Bargi,,,,,,,,,,Firearms-Pistols-Snaphaunce,,http://www.metmuseum.org/art/collection/search/34183,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -04.3.202,false,true,21974,Arms and Armor,Helmet all'antica,Helmet all'Antica,"Italian, Milan",,,,,Armorer,Attributed to,Filippo Negroli,"Italian, Milan ca. 1510–1579",,"Negroli, Filippo",Italian,1485,1605,ca. 1532–35,1507,1560,Steel,H. 11 1/4 in. (85 cm); W. 8 1/4 in. (20.9 cm); D. 9 in. (22.7 cm); Wt. 2 lb. 2 oz. (964 g),"Rogers Fund, 1904",,Milan,,,,,,,,,,Helmets,,http://www.metmuseum.org/art/collection/search/21974,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -17.190.1720,true,true,22634,Arms and Armor,Burgonet,Burgonet,"Italian, Milan",,,,,Armorer,,Filippo Negroli,"Italian, Milan ca. 1510–1579",,"Negroli, Filippo",Italian,1485,1605,dated 1543,1543,1543,"Steel, gold, textile",H. 9 1/2 in. (24.1 cm); W. 7 5/16 in. (18.6 cm); D. 11 1/2 in. (29.2 cm); Wt. 4 lb. 2 oz. (1871 g),"Gift of J. Pierpont Morgan, 1917",,Milan,,,,,,,,,,Helmets,,http://www.metmuseum.org/art/collection/search/22634,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -26.53,false,true,22903,Arms and Armor,Close helmet,Close Helmet,"Italian, Milan",,,,,Armorer,Attributed to,Giovan Paolo Negroli,"Italian, Milan ca. 1513–1569",,"Negroli, Giovan Paolo",Italian,1488,1594,ca. 1540–45,1515,1570,"Steel, copper alloy, gold",H. 10 3/4 in. (27.3 cm); W. 11 1/2 in. (29.2 cm); D. 15 in. (38.1 cm); Wt. 6 lb. 8 oz. (3068 g),"Purchase, Rogers Fund and George D. Pratt Gift, 1926",,Milan,,,,,,,,,,Helmets,,http://www.metmuseum.org/art/collection/search/22903,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.25.1855,false,true,22408,Arms and Armor,Breastplate,Breastplate,"Italian, Milan",,,,,Armorer,,Giovan Paolo Negroli,"Italian, Milan ca. 1513–1569",,"Negroli, Giovan Paolo",Italian,1488,1594,ca. 1540–45,1515,1570,"Steel, gold",W. 16 3/8 in. (41.6 cm); H. 23 1/4 in. (59.1 cm),"Gift of William H. Riggs, 1913",,Milan,,,,,,,,,,Armor Parts-Breastplates,,http://www.metmuseum.org/art/collection/search/22408,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.25.603,false,true,22230,Arms and Armor,Close helmet with falling buffe,Close Helmet with Falling Buffe,"Italian, Milan",,,,,Armorer,Attributed to,Pompeo della Cesa,"Italian, Milan, ca. 1537–1610",,"Cesa, Pompeo della",Italian,1512,1635,ca. 1590–95,1565,1620,"Steel, gold",H. 11 in. (27.9 cm); W. 8 7/8 in. (22.5 cm); D. 12 1/2 in. (31.8 cm); Wt. 6 lb. 2 oz. (2778 g),"Gift of William H. Riggs, 1913",,Milan,,,,,,,,,,Helmets,,http://www.metmuseum.org/art/collection/search/22230,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.25.656,false,true,22239,Arms and Armor,Close helmet,Close Helmet,"Italian, Milan",,,,,Armorer,Attributed to,Pompeo della Cesa,"Italian, Milan, ca. 1537–1610",,"Cesa, Pompeo della",Italian,1512,1635,ca. 1585,1560,1610,"Steel, gold, brass",H. 11 5/8 in. (29.5 cm); W. 9 1/2 in. (24.1 cm); D. 11 in. (27.9 cm); Wt. 5 lb. 14 oz. (2665 g),"Gift of William H. Riggs, 1913",,Milan,,,,,,,,,,Helmets,,http://www.metmuseum.org/art/collection/search/22239,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2001.72a–d,false,true,25114,Arms and Armor,Portions of an armor,Portions of an Armor for Vincenzo Luigi di Capua (d. 1627),"Italian, Milan",,,,,Armorer,,Pompeo della Cesa,"Italian, Milan, ca. 1537–1610",,"Cesa, Pompeo della",Italian,1512,1635,ca. 1595,1570,1620,"Steel, gold, leather, copper alloy",H. as mounted 19 in. (48 cm),"Purchase, Arthur Ochs Sulzberger Gift, 2001",,Milan,,,,,,,,,,Armor for Man,,http://www.metmuseum.org/art/collection/search/25114,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2001.72; 2013.618,false,true,642980,Arms and Armor,Portions of an armor,Portions of an Armor for Vincenzo Luigi di Capua (d. 1627),"Italian, Milan",,,,,Armorer,,Pompeo della Cesa,"Italian, Milan, ca. 1537–1610",,"Cesa, Pompeo della",Italian,1512,1635,ca. 1595,1570,1620,"Steel, gold, leather, copper alloy",H. as mounted 19 in. (48 cm),"Gorget, pauldrons, and breastplate: Purchase, Arthur Ochs Sulzberger Gift, 2001; Backplate: Purchase, Gift in honor of Maximilian and Alexander Saga, 2013",,Milan,,,,,,,,,,Armor for Man,,http://www.metmuseum.org/art/collection/search/642980,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.618,false,true,35960,Arms and Armor,Backplate,Backplate of an Armor for Vincenzo Luigi di Capua (d. 1627),"Italian, Milan",,,,,Armorer,,Pompeo della Cesa,"Italian, Milan, ca. 1537–1610",,"Cesa, Pompeo della",Italian,1512,1635,ca. 1595,1570,1620,"Steel, gold, leather, copper alloy",H. approx. 15 in. (38 cm); W. approx. 11 in. (28 cm),"Purchase, Gift in honor of Maximilian and Alexander Saga, 2013",,Milan,,,,,,,,,,Armor Parts-Backplates,,http://www.metmuseum.org/art/collection/search/35960,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"28.196.15, .16",false,true,23049,Arms and Armor,Pair of pistols,Pair of Pistols with Flintlocks alla Fiorentina,"Italian, Pistoia",,,,,Gunsmith,Attributed to,Cristiano Leoni,"Italian, Pistoia, active ca. 1780",,"Leoni, Cristiano",Italian,1755,1805,ca. 1750–75,1725,1800,"Steel, wood",L. of each 18 3/8 in. (46.7 cm); L. of each barrel 13 3/8 in. (33.9 cm); Cal. of each .46 in. (11.7 mm); Wt. of each 1 lb. 8 oz. (680 g),"Rogers Fund, 1928",,Pistoia,,,,,,,,,,Firearms-Pistols,,http://www.metmuseum.org/art/collection/search/23049,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -04.3.162,false,true,21964,Arms and Armor,Wheellock gun,Wheellock gun,"Italian, Brescia",,,,,Barrelsmith,,Cominazzo workshop,"Italian, Brescia, mid-17th century",,Cominazzo workshop,Italian,1625,1675,mid-17th century,1625,1675,"Steel, wood (walnut)",L. 45 1/2 in. (115.6 cm); L. of barrel 32 3/4 in. (83.2 cm); Cal. .58 in. (14.7 mm); Wt. 9 lb. 6 oz. (4250 g),"Rogers Fund, 1904",,Brescia,,,,,,,,,,Firearms-Guns-Wheellock,,http://www.metmuseum.org/art/collection/search/21964,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"04.3.23a, b",false,true,21935,Arms and Armor,Rapier with scabbard,Rapier with Scabbard,French or Italian,,,,,Goldsmith,Decoration attributed to,Gasparo Mola,"Italian, Coldre ca. 1580–1640 Rome",,"Mola, Gasparo",Italian,1575,1640,ca. 1620–30,1595,1655,"Steel, gold, silver, leather, wood",L. 49 1/4 in. (125.1 cm); W. 8 3/4 in. (22.2 cm); Wt. of scabbard 7.5 oz. (212.6 g),"Rogers Fund, 1904",,,,,,,,,,,,Swords,,http://www.metmuseum.org/art/collection/search/21935,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -19.128.1–.2,false,true,22740,Arms and Armor,Pair of gauntlets for a child,Pair of Gauntlets for a Child,"Italian, Milan",,,,,Armorer,,Lucio Piccinino,"Italian, Milan, active ca. 1575–90",,"Piccinino, Lucio",Italian,1550,1615,ca. 1585,1560,1610,"Steel, gold, silver",L. of each 7 1/8 in. (18.1 cm); W. of each 4 1/2 in. (12.4 cm); D. of each 4 3/8 in. (11.1 cm); Wt. of each 8 oz. (226.8 g),"Rogers Fund, 1919",,Milan,,,,,,,,,,Armor Parts-Gauntlets,,http://www.metmuseum.org/art/collection/search/22740,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -04.3.270a–o,false,true,21996,Arms and Armor,Half armor,"Half Armor Probably Made for Don Gonzalo Fernández de Córdoba y Fernández de Córdoba, Duke of Sessa (1520/1524–1578)","Italian, Milan",,,,,Armorer,Attributed to,Lucio Piccinino,"Italian, Milan, active ca. 1575–90",,"Piccinino, Lucio",Italian,1550,1615,ca. 1560 and later,1535,1900,"Steel, gold",Wt. approx. 30 lb. 7 oz. (13.8 kg); Wt. of helmet approx. 7 lb. 11 oz. (,"Rogers Fund, 1904",,Milan,,,,,,,,,,Armor for Man-1/2 Armor,,http://www.metmuseum.org/art/collection/search/21996,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"14.25.714a–h, j",false,true,22184,Arms and Armor,Armor,Armor of the Dukes of Alba,"Italian, Milan",,,,,Armorer,,Lucio Piccinino,"Italian, Milan, active ca. 1575–90",,"Piccinino, Lucio",Italian,1550,1615,ca. 1575–85,1550,1610,"Steel, gold, silver","Wt. 25 lbs. 12 oz. (11.68 kg); Helmet (a) H. 12 in. (30.5 cm); W. 8 1/2 in. (21.6 cm); D. 11 1/2 in. (29.2 cm); Wt. 5 lbs. 1 oz. (2,296.31 g); Colletin (b) H. 7 in. (17.8 cm); W. 11 7/16 in. (29.1 cm); Wt. 2 lbs. 2 oz. (963.88 g); Breastplate (c) H. 18 1/8 in. (46.0 cm); W. 15 in. (38.1 cm); D. 7 7/8 in. (19.9 cm); Wt. 5 lbs. 4 oz (2,381.36 g); Backplate (d) H. 14 5/8 in. (37.2 cm); W. 14 5/8 in. (37.2 cm); Wt. 3 lbs. 3 oz. (1,445.83 g); Pauldron right (e) H. 10 1/2 in. (26.7 cm); W. 11 5/8 in. (29.5 cm); Wt. 2 lbs. 9 oz. (1,162.33 g); Pauldron left (f) H. 10 3/8 in. (26.4 cm); W. 11 in. (27.9 cm); Wt. 2 lbs. 12 oz. (1,247.38 g); Arm Defense right (g) L. 18 1/2 in. (46.9 cm); Wt. 2 lbs. 7 oz. (1,105.63 g); Arm Defense left (h) L. 18 1/4 in. (46.4 cm); Wt. 2 lbs. 6 oz. (1,077.28 g)","Gift of William H. Riggs, 1913",,Milan,,,,,,,,,,Armor for Man-1/2 Armor,,http://www.metmuseum.org/art/collection/search/22184,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -11.89.2,false,true,22135,Arms and Armor,Cup-hilted rapier,Cup-Hilted Rapier,"Italian, Milan",,,,,Sword maker,Cup signed,Carlo Piccinino,"Italian, Milan, active ca. 1650–75",,"Piccinino, Carlo",Italian,1625,1700,ca. 1650–75,1625,1700,"Steel, iron wire",L. 44 in. (111.8 cm); L. of blade 37 1/4 in. (94.6 cm); W. 12 1/16 in. (30.6 cm); Wt. 2 lb. 5 oz. (1048.9 g),"Gift of J. Pierpont Morgan, 1911",,Milan,,,,,,,,,,Swords,,http://www.metmuseum.org/art/collection/search/22135,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.150.9aa,false,true,35853,Arms and Armor,Shaffron (Horse's head defense),Shaffron (Horse's Head Defense),"Italian, possibly Brescia",,,,,Armorer,Stamped with marks attributed to,Ambrogio de Osma,"Italian, Brescia, documented 1446–75",,"Osma, Ambrogio de",Italian,1446,1475,ca. 1460–70,1435,1495,Steel,H. 21 1/2 in. (54.6 cm); W. 9 1/4 in. (23.5 cm); Wt. 1 lb. 15 oz. (878.8 g),"Bashford Dean Memorial Collection, Bequest of Bashford Dean, 1928",,possibly Brescia,,,,,,,,,,Equestrian Equipment-Shaffrons,,http://www.metmuseum.org/art/collection/search/35853,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"28.196.17, .18",false,true,23048,Arms and Armor,Pair of flintlock pistols,Pair of Flintlock Pistols,"Italian, Brescia",,,,,Gunsmith,,Girolamo Francino,"Italian, Brescia, recorded 1666–1709",,"Francino, Girolamo",Italian,1666,1709,ca. 1650–60,1625,1685,"Steel, wood (walnut)",L. of each 22 1/2 in. (57.2 cm); L. of each barrel 15 13/16 in. (40.2 cm); Cal. of each .52 in. (13.2 mm); Wt. of each 1 lb. 15 oz. (879 g),"Rogers Fund, 1928",,Brescia,,,,,,,,,,Firearms-Pistols-Flintlock,,http://www.metmuseum.org/art/collection/search/23048,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.158.1e–f,false,true,26584,Arms and Armor,Arm defenses,Arm Defenses,"Italian, Milan",,,,,Armorer,Both arms marked by,Domenico Negroli,"Italian, Milan, active 1492–ca. 1516",,"Negroli, Domenico",Italian,1492,1531,ca. 1510,1485,1535,Steel,Wt. of right arm (e) 2 lb. 10 oz. (1190.7 g); Wt. of left arm (f) 2 lb. 6 oz. (1077.3 g),"Bashford Dean Memorial Collection, Funds from various donors, 1929",,Milan,,,,,,,,,,Armor Parts-Arms & Shoulders,,http://www.metmuseum.org/art/collection/search/26584,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.25.598,false,true,22227,Arms and Armor,Close-helmet,Close-Helmet for the Tournament on Foot,"Italian, Milan",,,,,Armorer,,the Master of the Castle Mark,"Italian, Milan, active ca. 1590–1620",,"Master of the Castle Mark, the",Italian,1565,1645,ca. 1600–1610,1575,1635,"Steel, gold",H. 13 in. (33 cm); W. 9 1/2 in. (24.1 cm); D. 11 9/16 in. (29.4 cm); Wt. 11 lb. 13 oz. (5360 g),"Gift of William H. Riggs, 1913",,Milan,,,,,,,,,,Helmets,,http://www.metmuseum.org/art/collection/search/22227,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.25.605a,false,true,22232,Arms and Armor,Close helmet for foot combat,Close Helmet for Foot Combat,"Italian, Milan",,,,,Armorer,Attributed to,the Master of the Castle Mark,"Italian, Milan, active ca. 1590–1620",,"Master of the Castle Mark, the",Italian,1565,1645,ca. 1600–1610,1575,1635,"Steel, gold, leather",H. 12 1/2 in. (31.8 cm); W. 9 3/8 in. (23.8 cm); D. 12 1/4 in. (31.1 cm); Wt. 14 lb. 7 oz. (6450 g),"Gift of William H. Riggs, 1913",,Milan,,,,,,,,,,Helmets,,http://www.metmuseum.org/art/collection/search/22232,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2014.154,false,true,639897,Arms and Armor,Backplate,Backplate,"Italian, Milan",,,,,Armorer,Probably by,Francesco Negroli,"Italian, Milan, died before December 1519",,"Negroli, Francesco",Italian,1450,1519,ca. 1505–10,1480,1535,Steel,19 3/8 x 15 1/4 x 6 3/4 in. (49.3 x 39 x 17.3 cm); Wt. 5 lb. 6 oz. (2461 g),"Purchase, Arthur Ochs Sulzberger Gift, 2014",,Milan,,,,,,,,,,Armor Parts-Backplates,,http://www.metmuseum.org/art/collection/search/639897,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.25.1662,false,true,27460,Arms and Armor,Crinet,Crinet,Franco-Italian,,,,,Armorer,Attributed to,Romain des Ursins,"Italian, Milan, recorded in Lyon 1493–95",,"des Ursins, Romain",Italian,1493,1495,"ca. 1480–95; bottom lame and mail fringe, restored ca. 1863–1900",1455,1900,Steel,"L. 33 7/8 in. (85.9 cm); W. 6 7/8 in. (17.5 cm); Wt. 9 lb. 10 oz. (4,360 g)","Gift of William H. Riggs, 1913",,,,,,,,,,,,Armor for Horse,,http://www.metmuseum.org/art/collection/search/27460,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -04.3.253,false,true,25403,Arms and Armor,Shaffron (horse's head defense),"Shaffron (Horse's Head Defense) of Henry II of France, When Dauphin",Franco-Italian,,,,,Armorer,Attributed to,Romain des Ursins,"Italian, Milan, recorded in Lyon 1493–95",,"des Ursins, Romain",Italian,1493,1495,"ca. 1490–1500, redecorated 1539",1465,1539,"Steel, gold, brass",H. 27 1/2 in. (69.8 cm); W. 15 in. (38.1 cm); Wt. 5 lb. 3 oz. (2350 g),"Rogers Fund, 1904",,,,,,,,,,,,Equestrian Equipment-Shaffrons,,http://www.metmuseum.org/art/collection/search/25403,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.25.1661a,false,true,26921,Arms and Armor,Shaffron (Horse's head defense),Shaffron (Horse's Head Defense),Franco-Italian,,,,,Armorer,Attributed to,Romain des Ursins,"Italian, Milan, recorded in Lyon 1493–95",,"des Ursins, Romain",Italian,1493,1495,"ca. 1480–95; ear guards, eye guards, and plume holder, 19th century restorations",1455,1900,"Steel, textile",H. 24 7/8 (63.2 cm); W. 13 in. (33 cm); D. 9 1/2 in. (24.1 cm); Wt. 4 lb. 4 oz. (1918 g),"Gift of William H. Riggs, 1913",,,,,,,,,,,,Equestrian Equipment-Shaffrons,,http://www.metmuseum.org/art/collection/search/26921,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"14.25.1661a, .1662",false,true,35725,Arms and Armor,Shaffron and crinet (Horse's head and neck defenses),Shaffron and Crinet (Horse's Head and Neck Defenses),Franco-Italian,,,,,Armorer,Attributed to,Romain des Ursins,"Italian, Milan, recorded in Lyon 1493–95",,"des Ursins, Romain",Italian,1493,1495,"ca. 1480–95, with 19th century restorations",1455,1900,"Steel, textile",14.25.1661a (shaffron); H. 24 7/8 in. (63.2 cm); W 13 in. (33 cm); D. 9 1/2 in. (24.1 cm); Wt. 4 lb. 4 oz. (1918 g); 14.25.1662 (crinet); L. 33 7/8 in. (85.9 cm.); W. 6 7/8 in. (17.5 cm); Wt. 9 lb. 10 oz. (4360 g),"Gift of William H. Riggs, 1913",,,,,,,,,,,,Equestrian Equipment-Shaffrons,,http://www.metmuseum.org/art/collection/search/35725,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -42.50.2,true,true,24686,Arms and Armor,Armet,Armet,"Italian, probably Milan",,,,,Armorer,"Stamped with the armorer's name,",LIONARDO,"Italian, probably active in Milan, ca. 1440",,LIONARDO,Italian,1415,1465,ca. 1440,1415,1465,"Steel, copper alloy",H. 10 3/8 in. (26.3 cm); W. 8 1/4 in. (21 cm); D. 11 in. (27.9 cm); Wt. 9 lb. 4 oz. (4196 g),"Gift of Stephen V. Grancsay, 1942",,probably Milan,,,,,,,,,,Helmets,,http://www.metmuseum.org/art/collection/search/24686,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -56.23,false,true,24837,Arms and Armor,Trigger guard of a gun,Trigger Guard of a Gun made for a Prince of the House of the Medici,"Italian, Reggio Emilia",,,,,Steel-chiseler,,Pietro Ancini,"Italian, Reggio Emilia, recorded 1616–1702",,"Ancini, Pietro",Italian,1616,1702,dated 1643,1643,1643,Steel,L. 8 1/4 in. (21 cm),"Purchase, Bashford Dean Bequest, 1956",,Reggio Emilia,,,,,,,,,,Firearms Parts,,http://www.metmuseum.org/art/collection/search/24837,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -42.50.14,false,true,24692,Arms and Armor,Barbute,Barbute,"Italian, Milan",,,,,Armorer,,Bernardino da Carnago,"Italian, active in Milan and Naples, ca. 1475",,Carnago Bernardino da,Italian,1450,1500,ca. 1475,1450,1500,Steel,H. 12 1/8 in. (30.8 cm); W. 8 1/8 in. (20.6 cm); D. 10 3/4 in. (27.3 cm); Wt. 6 lb. 8 oz. (2948 g),"Gift of Stephen V. Grancsay, 1942",,,Milan,,,,,,,,,Helmets,,http://www.metmuseum.org/art/collection/search/24692,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.25.579,false,true,22223,Arms and Armor,Barbute,Barbute,"Italian, Brescia",,,,,Armorer,,Pietro da Castello,"Italian, Brescia, documented 1469–86, died before 1498",,"Castello, Pietro da",Italian,1469,1498,ca. 1470–80,1445,1505,Steel,H. 10 1/4 in. (26 cm); W. 7 1/2 in. (19.1 cm); D. 10 in. (25.4 cm); Wt. 4 lb. 14 oz. (2211 g),"Gift of William H. Riggs, 1913",,Brescia,,,,,,,,,,Helmets,,http://www.metmuseum.org/art/collection/search/22223,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -42.50.16,true,true,24693,Arms and Armor,Shield,Shield,"Italian, probably Bologna",,,,,Maker,Attributed to,Girolamo da Treviso,"Italian, Treviso ca. 1498–1544 Boulogne-sur-Mer",,"Treviso, Girolamo da",Italian,1498,1544,ca. 1535,1510,1560,"Wood, linen, gesso, gold leaf, polychromy",Diam. 24 5/8 in. (62.53 cm),"Gift of Stephen V. Grancsay, 1942",,probably Bologna,,,,,,,,,,Shell,,http://www.metmuseum.org/art/collection/search/24693,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1991.251,false,true,27136,Arms and Armor,Drawing,Design for the Hilt of a Small-Sword,"Italian, Rome",,,,,Artist,Workshop of,Luigi Valadier,"Italian, Rome 1726–1785 Rome",,"Valadier, Luigi",Italian,1726,1785,ca. 1780–90,1755,1815,"Pen, ink, wash, graphite, vellum",9 5/8 x 6 5/8 in. (24.4 x 16.8 cm),"Rogers Fund, 1991",,Rome,,,,,,,,,,Works on Paper-Drawings,,http://www.metmuseum.org/art/collection/search/27136,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.25.1870,false,true,436343,Arms and Armor,Painting,"Alfonso II d'Este (1533–1597), Duke of Ferrara","Italian, Ferrara",,,,,Artist,,Italian (Ferrarese) Painter,late 16th century,,Italian (Ferrarese) Painter,Italian,1400,1499,late 16th century,1570,1599,Oil on canvas,47 x 35 3/4 in. (119.4 x 90.8 cm),"Gift of William H. Riggs, 1913",,Ferrara,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436343,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.25.1874,false,true,436729,Arms and Armor,Painting,"Alfonso I d'Este (1476–1534), Duke of Ferrara","Italian, Ferrara",,,,,Artist,,Italian (Ferrarese) Painter,second quarter 16th century,,Italian (Ferrarese) Painter,Italian,1400,1499,second quarter 16th century,1525,1550,Oil on canvas,52 7/8 x 38 1/4 in. (134.3 x 97.2 cm),"Gift of William H. Riggs, 1913",,Ferrara,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436729,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1993.234,false,true,24956,Arms and Armor,Design for a saddle plate,Design for the Pommel Plate of a Saddle from a Garniture of Alessandro Farnese (1545–1592),"Italian, Parma",,,,,Designer,,Andrea Casalini,"Italian, Parma, died 1597",,"Casalini, Andrea",Italian,1500,1597,ca. 1575–80,1575,1580,"Pen and brown ink, with color washes, on paper",19 1/2 x 16 3/8 in. (49.5 x 39.2 cm),"Purchase, Fletcher Fund and Gift of William H. Riggs, by exchange, 1993",,Parma,,,,,,,,,,Works on Paper-Drawings,,http://www.metmuseum.org/art/collection/search/24956,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -11.89.5,false,true,22136,Arms and Armor,Sallet,Sallet,Italian,,,,,Armorer,Attributed to,Pietro Giacomo da Castello,"Italian, documented 1485–1525",,"Castello, Pietro Giacomo da",Italian,1485,1525,ca. 1510–20,1485,1545,Steel,"H. without crest, 11 1/2 in. (29.2 cm); W. 7 5/8 in. (19.4 cm); D. 10 1/2 in. (26.7 cm); Wt. 4 lb. 3 oz. (1899 g)","Gift of J. Pierpont Morgan, 1911",,,,,,,,,,,,Helmets,,http://www.metmuseum.org/art/collection/search/22136,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.156.77,false,true,25367,Arms and Armor,Medal,"Medal of Vicenzo II Gonzaga, 7th Duke of Mantua",Italian,,,,,Artist,,Gasparo Morone,"Italian, born Milan (?), died Rome, 1669",,"Morone, Gasparo",Italian,1569,1669,ca. 1625,1625,1625,"Bronze, gold",Diam. 1 11/16 in. (4.3 cm); thickness 1/8 in. (0.3 cm); Wt. 0.8 oz. (22.7 g),"Bashford Dean Memorial Collection, Gift of Edward S. Harkness, 1929",,,,,,,,,,,,Miscellaneous,,http://www.metmuseum.org/art/collection/search/25367,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"22.19a, b",true,true,22769,Arms and Armor,Presentation sword with scabbard,Presentation Sword and Scabbard of Brigadier General Daniel Davis (1777–1814) of the New York Militia,"American, New York",,,,,Silversmith,,John Targee,"American, ca. 1774–1850",,"Targee, John",American,1769,1850,ca. 1815–17,1790,1842,"Steel, gold, silver",L. 37 1/4 in. (94.6 cm),"Gift of Francis P. Garvan, 1922",,New York,New York,,,,,,,,,Swords,,http://www.metmuseum.org/art/collection/search/22769,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.8a–c,true,true,35650,Arms and Armor,Sword with scabbard,Congressional Presentation Sword and Scabbard of Major General John E. Wool (1784–1869),"American, probably Baltimore",,,,,Sword cutler,,Samuel Jackson,"American, Baltimore, active 1833–70",,"Jackson, Samuel",American,1833,1870,1854–55,1854,1855,"Steel, gold, brass, diamonds, rubies",L. with scabbard 39 3/16 in. (99.6 cm); L. without scabbard 38 13/16 in. (98.5 cm); L. of blade 31 7/16 in. (79.9 cm); W. 5 5/8 in. (14.3 cm),"Purchase, Arthur Ochs Sulzberger and Mr. and Mrs. Robert G. Goelet Gifts, 2009",,Baltimore,Maryland,,,,,,,,,Swords,,http://www.metmuseum.org/art/collection/search/35650,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.131.7,false,true,24649,Arms and Armor,Powder horn,Powder Horn,Colonial American,,,,,Engraver,,Jacob Gay,"American, New York, recorded 1758–87",,"Gay, Jacob",American,1758,1887,dated 1759,1759,1759,"Horn (cow), wood",L. 13 in. (33 cm); Diam. 3 1/2 in. (8.9 cm); Wt. 14.8 oz. (419.6 g),"The Collection of J. H. Grenville Gilbert, of Ware, Massachusetts, Gift of Mrs. Gilbert, 1937",,,New York,,,,,,,,,Firearms Accessories-Powder Horns,,http://www.metmuseum.org/art/collection/search/24649,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.127,false,true,679351,Arms and Armor,Sword,Cavalry Officer's Saber,"American, Baltimore",,,,,Silversmith,Hilt by,John Lynch,"American, Baltimore 1761–1848 Baltimore",,"Lynch, John",American,1761,1848,ca. 1810,1785,1835,"Steel, silver, wood, gold",H. 39 7/8 in. (101.3 cm); H. of blade 34 1/4 in. (87 cm); W. 3 3/4 in. (9.5 cm); Wt. 1 lb. 9.6 oz. (725.7 g),"Purchase, Arthur Ochs Sulzberger Bequest, 2015",,Baltimore,Maryland,,,,,,,,,Swords,,http://www.metmuseum.org/art/collection/search/679351,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1995.336,true,true,24960,Arms and Armor,Percussion revolver,"Colt Third Model Dragoon Percussion Revolver, Serial Number 12406","American, Hartford, Connecticut",,,,,Manufacturer,,Samuel Colt,"American, Hartford, Connecticut 1814–1862",,"Colt, Samuel",American,1814,1862,ca. 1853,1828,1878,"Steel, brass, gold, wood (walnut)",L. 14 in. (35.6 cm); L. of barrel 7 1/2 in. (19.1 cm); Cal. .44 in. (11.2 mm); case; H. 3 in. (7.6 cm); W. 16 3/16 in. (41.1 cm); D. 8 1/8 in. (20.6 cm); Wt. 3 lb. 9.8 oz. (224 g),"Gift of George and Butonne Repaire, 1995",,Hartford,Connecticut,,,,,,,,,Firearms-Pistols-Revolvers,,http://www.metmuseum.org/art/collection/search/24960,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -55.206.1,false,true,24835,Arms and Armor,Percussion revolver,"Colt Paterson Percussion Revolver, No. 5, Holster Model, serial no. 528","American, Paterson, New Jersey",,,,,Manufacturer,,Samuel Colt,"American, Hartford, Connecticut 1814–1862",,"Colt, Samuel",American,1814,1862,ca. 1838–40,1813,1865,"Steel, silver, ivory",L. 16 3/4 in. (42.55 cm); L. of barrel 12 in. (30.48 cm); Cal. .40 in. (10.2 mm),"Gift of John E. Parsons, 1955",,Paterson,New Jersey,,,,,,,,,Firearms-Pistols-Revolvers,,http://www.metmuseum.org/art/collection/search/24835,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -55.206.2,false,true,24836,Arms and Armor,Percussion revolver,"Colt Model 1861 Navy Percussion Revolver, serial no. 12240","American, Hartford, Connecticut",,,,,Manufacturer,,Samuel Colt,"American, Hartford, Connecticut 1814–1862",,"Colt, Samuel",American,1814,1862,1863,1863,1863,"Steel, brass, wood (walnut)",L. 12 3/4 in. (32.39 cm); L. of barrel 7 1/2 in. (19.05 cm); Cal. .36 in. (9.1 mm),"Gift of John E. Parsons, 1955",,Hartford,Connecticut,,,,,,,,,Firearms-Pistols-Revolvers,,http://www.metmuseum.org/art/collection/search/24836,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -56.167.2,false,true,24839,Arms and Armor,Percussion revolver,"Colt Dragoon Percussion Revolver, Third Model, serial no. 12403","American, Hartford, Connecticut",,,,,Manufacturer,,Samuel Colt,"American, Hartford, Connecticut 1814–1862",,"Colt, Samuel",American,1814,1862,1852,1852,1852,"Steel, brass, silver, wood (walnut)",L. 14 in. (35.56 cm); L. of barrel 7 1/2 in. (19.05 cm); Cal. .44 in. (11 mm),"Gift of John E. Parsons, 1956",,Hartford,Connecticut,,,,,,,,,Firearms-Pistols-Revolvers,,http://www.metmuseum.org/art/collection/search/24839,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -57.166.1,false,true,24841,Arms and Armor,Percussion revolver,"Colt Paterson Percussion Revolver, No. 5, Holster Model, serial no. 940","American, Paterson, New Jersey",,,,,Manufacturer,,Samuel Colt,"American, Hartford, Connecticut 1814–1862",,"Colt, Samuel",American,1814,1862,ca. 1840,1815,1865,"Steel, silver, mother-of-pearl",L. 14 in. (35.56 cm); L. of barrel 9 in. (22.86 cm); Cal. .40 in. (10 mm),"Gift of John E. Parsons, 1957",,Paterson,New Jersey,,,,,,,,,Firearms-Pistols-Revolvers,,http://www.metmuseum.org/art/collection/search/24841,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -58.171.3,false,true,24840,Arms and Armor,Revolver,"Colt Model 1851 Navy Revolver with Thuer Conversion for Self-Contained Cartridges, Serial no. 27060","American, Hartford, Connecticut",,,,,Manufacturer,,Samuel Colt,"American, Hartford, Connecticut 1814–1862",,"Colt, Samuel",American,1814,1862,"1853; converted for cartidges, ca. 1868–71",1853,1896,"Steel, brass, silver, ivory",L. 13 in. (33.02 cm); L. of barrel 7 1/2 in. (19.05 cm); Cal. .36 in. (9 mm),"Gift of John E. Parsons, 1958",,Hartford,Connecticut,,,,,,,,,Firearms-Pistols-Revolvers,,http://www.metmuseum.org/art/collection/search/24840,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -59.143.4,false,true,24848,Arms and Armor,Revolver,"""Peacemaker"" Colt Single-Action Army Revolver, serial no. 4519","American, Hartford, Connecticut",,,,,Manufacturer,,Samuel Colt,"American, Hartford, Connecticut 1814–1862",,"Colt, Samuel",American,1814,1862,1874,1874,1874,"Steel, iron, wood",L.13 in. (33.02 cm); L. of barrel 7 1/2 in. (19.05 cm); Cal. .45 in. (11 mm),"Gift of John E. Parsons, 1959",,Hartford,Connecticut,,,,,,,,,Firearms-Pistols-Revolvers,,http://www.metmuseum.org/art/collection/search/24848,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -68.157.2,false,true,24855,Arms and Armor,Percussion revolver,"Colt Model 1851 Navy Percussion Revolver, serial no. 2","American, Hartford, Connecticut",,,,,Manufacturer,,Samuel Colt,"American, Hartford, Connecticut 1814–1862",,"Colt, Samuel",American,1814,1862,1850,1850,1850,"Steel, brass, silver, wood (walnut)",L. of barrel 7 1/2 in. (19.05 cm); Cal. .38 in. (10 mm),"Gift of John E. Parsons, 1968",,Hartford,Connecticut,,,,,,,,,Firearms-Pistols-Revolvers,,http://www.metmuseum.org/art/collection/search/24855,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1983.442a–o,false,true,24892,Arms and Armor,Percussion revolver with case and accessories,"Cased Colt Model 1860 Army Percussion Revolver, Serial no. 7569, with Accessories","American, Hartford, Connecticut",,,,,Manufacturer,,Samuel Colt,"American, Hartford, Connecticut 1814–1862",,"Colt, Samuel",American,1814,1862,1861,1861,1861,"Steel, brass, silver, gold, wood (mahogany), textile",L. 14 1/2 in. (36.83 cm); L. of barrel 8 in. (20.32 cm); Cal. .44 in. (11 mm),"Gift of Mr. and Mrs. Jerry D. Berger, 1983",,Hartford,Connecticut,,,,,,,,,Firearms-Pistols-Revolvers,,http://www.metmuseum.org/art/collection/search/24892,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -56.167.1a–g,false,true,24838,Arms and Armor,Percussion revolver with case and accessories,"Colt Paterson Pocket Percussion Revolver, Fourth Ehlers Model, serial no. 152, with Case and Accessories","American, Paterson, New Jersey",,,,,Manufacturer,,Samuel Colt,"American, Hartford, Connecticut 1814–1862",,"Colt, Samuel",American,1814,1862,ca. 1840–43,1815,1868,"Steel, wood (walnut)",Revolver (a); L. 6 1/2 in. (16.5 cm); L. of barrel 3 in. (7.6 cm); Cal. .28 in. (7.1 mm); bullet mould (b); L. 3 3/16 in. (8.1 cm); W. (when closed) 1 3/4 in. (4.5 cm); Wt. 2.2 oz. (62.4 g); primer (c); L. 3 11/16 in. (9.4 cm); Diam. 1 1/8 in. (2.8 cm); Wt. 3.3 oz. (93.6 g); cleaning rod (d); L. 4 5/8 in. (11.7 cm); Wt. 1.2 oz. (34 g); spanner-screwdriver (e); L. 2 5/8 in. (6.7 cm); W. 2 in. (5.1 cm); Wt. 0.9 oz. (25.5 g); case (f); H. 2 in. (5.1 cm); W. 10 in. (25.4 cm); D. 5 7/8 in. (14.9 cm); Wt. 1 lb. 0.8 oz. (476.3 g); key (g); L. 1 1/8 in. (2.8 cm),"Gift of John E. Parsons, 1956",,Paterson,New Jersey,,,,,,,,,Firearms-Pistols-Revolvers,,http://www.metmuseum.org/art/collection/search/24838,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -57.166.2a–e,false,true,24842,Arms and Armor,Percussion revolver with case and accessories,"Colt Model 1855 Pocket Percussion Revolver, Serial no. 4460, with Case and Accessories","American, Hartford, Connecticut",,,,,Manufacturer,,Samuel Colt,"American, Hartford, Connecticut 1814–1862",,"Colt, Samuel",American,1814,1862,1855,1855,1855,"Steel, wood (oak)",L. 7 7/8 in. (19.99 cm); L. of barrel 3 1/2 in. (8.89 cm); Cal. .28 in. (7 mm); case 9 3/8 x 5 3/4 x 1 7/8 in. (23.8 x 14.61 x 4.75 cm); L. of spanner-screwdriver 3 1/4 in. (8.26 cm); greatest W. of spanner-screwdriver 1 3/8 in. (3.48 cm); L. of primer 4 5/8 in. (11.73 cm); greatest W. of primer 2 in. (5.08 cm); L. of bullet mould 4 3/8 in. (11.1 cm),"Gift of John E. Parsons, 1957",,Hartford,Connecticut,,,,,,,,,Firearms-Pistols-Revolvers,,http://www.metmuseum.org/art/collection/search/24842,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -59.143.1a–h,false,true,24846,Arms and Armor,Percussion revolver with case and accessories,"Colt Paterson Percussion Revolver, No. 3, Belt Model, Serial no. 156, with Case and Accessories","American, Paterson, New Jersey",,,,,Manufacturer,,Samuel Colt,"American, Hartford, Connecticut 1814–1862",,"Colt, Samuel",American,1814,1862,ca. 1838,1813,1863,"Steel, silver, brass, wood (walnut), copper, velvet","Revolver (a); L. 9 5/8 in. (24.4 cm); L. of barrel 5 1/2 in. (14 cm); Cal. .34 in. (8 mm); extra cylinder (b); L. 1 1/4 in. (3.2 cm); Diam. 1 1/4 in. (3.2 cm); Wt. 4.2 oz. (119.1 g); combination patch, powder and ball cylinder (c); L. 5 3/8 in. (13.6 cm); Diam. 1 5/16 in. (3.3 cm); Wt. 12.2 oz. (345.9 g); bullet mould (d); L. 3 11/16 in. (9.4 cm); W. 1 1/4 in. (3.2 cm); Wt. 2.2 oz. (62.4 g); combination screwdriver and pricker (e); L. 4 11/16 in. (11.9 cm); Wt. 1.7 oz. (48.2 g); percussion cap box (f); L. 3 5/16 in. (8.4 cm); Diam. 1 13/16 in. (4.6 cm); Wt. 3.1 oz. (87.9 g); cleaning rod (g); L. 6 7/8 in. (17.5 cm); Wt. 1.1 oz. (31.2 g); case (h); H. 2 1/8 in. (5.4 cm); W. 10 3/8 in. (26.3 cm); D. 7 in. (17.8 cm); Wt. 2 lb. 3.3 oz. (1000.7 g)","Gift of John E. Parsons, 1959",,Paterson,New Jersey,,,,,,,,,Firearms-Pistols-Revolvers,,http://www.metmuseum.org/art/collection/search/24846,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -48.125,false,true,24794,Arms and Armor,Percussion revolver,"Colt Dragoon Percussion Revolver, Third Model, serial no. 13096","American, Hartford, Connecticut",,,,,Manufacturer,,Samuel Colt,"American, Hartford, Connecticut 1814–1862",,"Colt, Samuel",American,1814,1862,1853,1853,1853,"Steel, brass, silver, wood (walnut)",L. 14 3/4 in. (37.47 cm); L. of barrel 7 1/2 in. (19.05 cm); Cal. .45 in. (11.4 mm),"Purchase, Friends of Albert Foster Jr. Gifts, in his memory, 1948",,Hartford,Connecticut,,,,,,,,,Firearms-Pistols-Percussion,,http://www.metmuseum.org/art/collection/search/24794,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2012.105a, b",false,true,35845,Arms and Armor,Sword with scabbard,Sword and Scabbard of Captain Richard French,"American, Chicopee, Massachusetts",,,,,Manufacturer,,Ames Manufacturing Company,"American, Chicopee, Massachusetts, 1829–1935",,Ames Manufacturing Company,American,1829,1935,1850,1825,1875,"Gold, brass, steel",L. 40 in. (101.6 cm); L. of blade 32 1/4 in. (81.9 cm),"Purchase, Arthur Ochs Sulzberger Gift, 2012",,Chicopee,Massachusetts,,,,,,,,,Swords,,http://www.metmuseum.org/art/collection/search/35845,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -96.5.36,false,true,21921,Arms and Armor,Flintlock duelling pistol,Flintlock Duelling Pistol,"American, Middletown, Connecticut",,,,,Gunsmith,,Simeon North,"American, Middletown, Connecticut, 1765–1852",,North Simeon,American,1765,1852,ca. 1815–20,1790,1845,"Steel, gold, silver, wood (walnut, hickory), horn",L. 15 3/4 in. (40.01 cm); L. of barrel 10 in. (25.4 cm); Cal. .56 in. (14.2 mm); Wt. 2 lb. 2 oz. (964 g),"John Stoneacre Ellis Collection, Gift of Mrs. Ellis and Augustus Van Horne Ellis, 1896",,Middletown,Connecticut,,,,,,,,,Firearms-Pistols,,http://www.metmuseum.org/art/collection/search/21921,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"96.5.36, .149",false,true,35815,Arms and Armor,Pair of flintlock duelling pistols,Pair of Flintlock Duelling Pistols,"American, Middletown, Connecticut",,,,,Gunsmith,,Simeon North,"American, Middletown, Connecticut, 1765–1852",,North Simeon,American,1765,1852,ca. 1815–20,1790,1845,"Steel, gold, silver, wood (walnut, hickory), horn","L. of each 15 3/4 in. (40 cm); L. of barrel of each 10 in. (25.4 cm); Cal. of each .56 in. (14.2 mm); Wt. of 96.5.36, 2 lb. 2 oz. (964 g); Wt. of 96.5.149, 2 lb. (907 g)","John Stoneacre Ellis Collection, Gift of Mrs. Ellis and Augustus Van Horne Ellis, 1896",,Middletown,Connecticut,,,,,,,,,Firearms-Pistols,,http://www.metmuseum.org/art/collection/search/35815,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -96.5.149,false,true,25035,Arms and Armor,Flintlock duelling pistol,Flintlock Duelling Pistol,"American, Middletown, Connecticut",,,,,Gunsmith,,Simeon North,"American, Middletown, Connecticut, 1765–1852",,North Simeon,American,1765,1852,ca. 1815–20,1790,1845,"Steel, gold, silver, wood (walnut, hickory), horn",L. 15 3/4 in. (40 cm); L. of barrel 10 in. (25.4 cm); Cal. .56 in. (14.2 mm); Wt. 2 lb. (907 g),"John Stoneacre Ellis Collection, Gift of Mrs. Ellis and Augustus Van Horne Ellis, 1896",,Middletown,Connecticut,,,,,,,,,Firearms-Pistols-Flintlock,,http://www.metmuseum.org/art/collection/search/25035,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.126,false,true,679349,Arms and Armor,Sword,Cavalry Officer's Saber,"American, Philadelphia",,,,,Silversmith,Hilt by,Parry & Musgrave,"American, Philadelphia 1792–1796 Philadelphia",,Parry & Musgrave,American,1792,1796,ca. 1793–95,1768,1820,"Steel, silver, wood, textile, copper",H. 41 in. (104.1 cm); H. of blade 34 1/2 in. (87.6 cm); W. 4 5/8 in. (11.7 cm); D. 4 in. (10.2 cm); Wt. 1 lb. 10.3 oz. (745.6 g),"Purchase, Arthur Ochs Sulzberger Bequest, 2015",,Philadelphia,Pennsylvania,,,,,,,,,Swords,,http://www.metmuseum.org/art/collection/search/679349,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -38.116a–k,false,true,24666,Arms and Armor,Flintlock rifle with case and bullet mould,Flintlock Rifle with Case and Bullet Mould Made for Colonel Jacob Bates (1746–1836),"American, Shrewsbury, Massachusetts",,,,,Gunsmith,,Silas Allen Jr.,"American, Shrewsbury, Massachusetts, 1785–1868",,Allen Silas,American,1785,1868,ca. 1820,1795,1845,"Steel, silver, brass, wood, silver wire",Rifle (a); L. 55 3/4 in. (141.61 cm); L. of barrel 40 1/2 in. (102.87 cm); Cal. .51 in. (12.7 mm); Wt. 11 lb. 2 oz. (5046 g); bullet mould (c); L. 6 1/4 in. (15.9 cm); Wt. 5.6 oz. (158.8 g),"Gift of Christian A. Zabriskie, 1938",,Shrewsbury,Massachusetts,,,,,,,,,Firearms,,http://www.metmuseum.org/art/collection/search/24666,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.582,false,true,35957,Arms and Armor,Experimental helmet prototype,American Helmet Model No. 5,"American, Philadelphia, Pennsylvania",,,,,Manufacturer,,Hale & Kilburn Company,"American, Philadelphia, Pennsylvania 1873–1933 Indianapolis, Indiana",,Hale & Kilburn Company,American,1873,1933,1918,1918,1918,"Steel, paint, leather, textile, string",H. 9 1/4 in. (23.5 cm); W. 10 3/4 in. (27.3 cm); D. 12 5/8 in. (32.1 cm); Wt. 2 lb. 8 oz. (1130 g),"Purchase, Gift of Bashford Dean, by exchange, 2013",,Philadelphia,Pennsylvania,,,,,,,,,Helmets,,http://www.metmuseum.org/art/collection/search/35957,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -42.22,true,true,24681,Arms and Armor,Flintlock rifle,Flintlock Rifle,"American, Pennsylvania",,,,,Gunsmith,,Jacob Kuntz,"American, Allentown, Pennsylvania 1780–1876 Philadelphia, Pennsylvania",,"Kuntz, Jacob",American,1780,1876,ca. 1810–15,1785,1840,"Steel, brass, wood (maple), silver, bone, horn",L. 59 1/4 in. (150.5 cm); L. of barrel 43 in. (109.22 cm); Cal. .46 in. (11.7 mm); Wt. 9 lb. 13 oz. (4451 g),"Gift of Wilfrid Wood, 1956",,,Pennsylvania,,,,,,,,,Firearms-Guns-Flintlock,,http://www.metmuseum.org/art/collection/search/24681,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2013.622a, b",false,true,625200,Arms and Armor,Presentation sword and scabbard,Congressional Presentation Sword and Scabbard of Peleg K. Dunham (1794–1822),American,,,,,Bladesmith|Decorator,Possibly decorated by,"William Rose Sr., 1783–1856|John Meer",active ca. 1795–1834,,"Rose, William Sr.|Meer, John",American,1783 |1795,1856 |1834,1817,1817,1817,"Steel, copper alloy (brass), gold, leather",L. with scabbard 39 5/16 in. (99.8 cm); L. without scabbard 38 1/2 in. (97.8 cm); L. of blade 32 5/16 in. (82 cm); W. 4 5/8 in. (11.7 cm); D. 1 7/8 in. (4.7 cm); Wt. 1 lb. 11 oz. (888 g); Wt. of scabbard 1 lb. 1 oz. (481.9 g),"Purchase, Arthur Ochs Sulberger Gift, 2013",,,,,,,,,,,,Swords,,http://www.metmuseum.org/art/collection/search/625200,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -35.52,false,true,32177,Arms and Armor,Letter,Letter from George Washington to New York Governor George Clinton,American,,,,,Author|Maker,In the hand of,George Washington|Tench Tilghman,"American, 1732–1799|1774–1786",,"Washington, George|Tilghman, Tench",American,1732 |1774,1799 |1786,1780,1780,1780,"Ink, paper",13 1/2 x 8 3/8 in. (34.2 x 21.2 cm),"Rogers Fund, 1935",,New Windsor,New York,,,,,,,,,Works on Paper-Miscellaneous,,http://www.metmuseum.org/art/collection/search/32177,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.178,false,true,23349,Arms and Armor,Armor (Gusoku),Armor (Gusoku),Japanese and European,,,,,Armorer,Helmet signed by,Saotome Iyetada,"Japanese, Edo period, active early–mid-19th century",,"Iyetada, Saotome",Japanese,1800,1875,early–mid-19th century,1800,1875,"Iron, silk, copper, gold",,"Gift of Edith McCagg, in memory of her husband, Louis B. McCagg, 1929",,,,,,,,,,,,Armor for Man,,http://www.metmuseum.org/art/collection/search/23349,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1999.270,false,true,26636,Arms and Armor,Sword guard (Tsuba),Sword Guard (Tsuba),"Japanese, Tokyo",,,,,Fittings maker,Inscribed by,Joka,"Japanese, Tokyo, active late 18th–early 19th century",", after Yasuchika",Joka,Japanese,1750,1850,early 19th century,1801,1850,"Iron, lacquer, gold",H. 3 1/4 in. (8.3 cm); W. 3 in. (7.6 cm),"Gift of Ryoichi Iida, 1999",,Tokyo,,,,,,,,,,Sword Furniture-Tsuba,,http://www.metmuseum.org/art/collection/search/26636,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -43.120.723,false,true,28455,Arms and Armor,Sword guard (Tsuba),Sword Guard (Tsuba),Japanese,,,,,Fittings maker,Inscribed by,Ishiguro Masatsune,"Japanese, 1760–1828",,Ishiguro Masatsune,Japanese,1760,1828,late 18th–early 19th century,1750,1850,"Copper-gold alloy (shakudō), copper-silver alloy (shibuichi), gold, copper",H. 2 15/16 in. (7.5 cm); W. 2 3/4 in. (7 cm); thickness 5/16 in. (0.8 cm); Wt. 5.7 oz. (161.6 g),"Gift of Herman A. E. and Paul C. Jaehne, 1943",,,,,,,,,,,,Sword Furniture-Tsuba,,http://www.metmuseum.org/art/collection/search/28455,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -43.120.724,false,true,28456,Arms and Armor,Sword guard (Tsuba),Sword Guard (Tsuba),Japanese,,,,,Fittings maker,Inscribed by,Ishiguro Masatsune,"Japanese, 1760–1828",,Ishiguro Masatsune,Japanese,1760,1828,late 18th–early 19th century,1750,1850,"Copper-gold alloy (shakudō), copper-silver alloy (shibuichi), gold, copper",H. 3 1/8 in. (7.9 cm); W. 2 15/16 in. (7.5 cm); thickness 5/16 in. (0.8 cm); Wt. 7 oz. (198.4 g),"Gift of Herman A. E. and Paul C. Jaehne, 1943",,,,,,,,,,,,Sword Furniture-Tsuba,,http://www.metmuseum.org/art/collection/search/28456,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"43.120.723, .724",false,true,657341,Arms and Armor,Pair of sword guards (daishō tsuba),Pair of Sword Guards (Daishō Tsuba),Japanese,,,,,Fittings maker,Inscribed by,Ishiguro Masatsune,"Japanese, 1760–1828",,Ishiguro Masatsune,Japanese,1760,1828,late 18th–early 19th century,1750,1850,"Copper-gold alloy (shakudō), copper-silver alloy (shibuichi), gold, copper",43.120.723: H. 2 15/16 in. (7.5 cm); W. 2 3/4 in. (7 cm); thickness 5/16 in. (0.8 cm); Wt. 5.7 oz. (161.6 g); 43.120.724: H. 3 1/8 in. (7.9 cm); W. 2 15/16 in. (7.5 cm); thickness 5/16 in. (0.8 cm); Wt. 7 oz. (198.4 g),"Gift of Herman A. E. and Paul C. Jaehne, 1943",,,,,,,,,,,,Sword Furniture-Tsuba,,http://www.metmuseum.org/art/collection/search/657341,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -43.120.467,false,true,25362,Arms and Armor,Knife handle (Kozuka),Knife Handle (Kozuka),Japanese,,,,,Fittings maker,Inscribed by,Iwama Masayoshi (Shoro),"Japanese, 1764–1837",,Iwama Masayoshi (Shoro),Japanese,1764,1837,late 18th–early 19th century,1750,1850,"Copper-silver alloy (shibuichi), copper-gold alloy (shakudō), gold, silver, copper",L. 3 13/16 in. (9.7 cm); W. 9/16 in. (1.4 cm); thickness 1/4 in. (0.6 cm); Wt. 1.4 oz. (39.7 g),"Gift of Herman A. E. and Paul C. Jaehne, 1943",,,,,,,,,,,,Sword Furniture-Kozuka,,http://www.metmuseum.org/art/collection/search/25362,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.120.335,false,true,29721,Arms and Armor,Knife handle (Kozuka),Knife Handle (Kozuka),Japanese,,,,,Fittings maker,Inscribed by,Kanō Natsuo,"Japanese, 1828–1898",,"Natsuo, Kanō",Japanese,1828,1898,mid-19th century,1825,1875,"Copper-silver alloy (shibuichi), silver, copper-gold alloy (shakudō)",L. 3 13/16 in. (9.7 cm); W. 9/16 in. (1.4 cm); thickness 3/16 in. (0.5 cm); Wt. 1.2 oz. (34 g),"The Howard Mansfield Collection, Gift of Howard Mansfield, 1936",,,,,,,,,,,,Sword Furniture-Kozuka,,http://www.metmuseum.org/art/collection/search/29721,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.120.339,false,true,29725,Arms and Armor,Knife handle (Kozuka),Knife Handle (Kozuka),Japanese,,,,,Fittings maker,Inscribed by,Kanō Natsuo,"Japanese, 1828–1898",,"Natsuo, Kanō",Japanese,1828,1898,mid-19th century,1825,1875,"Copper-silver alloy (shibuichi), silver",L. 3 1/4 in. (8.3 cm); W. 9/16 in. (1.4 cm); thickness 3/16 in. (0.5 cm); Wt. 1 oz. (28.3 g),"The Howard Mansfield Collection, Gift of Howard Mansfield, 1936",,,,,,,,,,,,Sword Furniture-Kozuka,,http://www.metmuseum.org/art/collection/search/29725,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -07.109.5,false,true,22102,Arms and Armor,Blade and mounting for a slung sword (Tachi),Blade and Mounting for a Slung Sword (Tachi),Japanese,,,,,Sword maker,,Naganori,"Japanese, active 13th century",,Naganori,Japanese,1201,1300,"blade, late 13th–early 14th century; mounting, 18th–early 19th century",1201,1900,"Steel, wood, lacquer, rayskin (samé), thread, gold, silver",L. 32 7/8 in. (83. 5 cm); L. of blade 25 15/16 in. (65.8 cm); L. of cutting edge 19 5/8 in. (49.8 cm); D. of curvature 5/16 in. (0.75 cm),"Gift of the family of Dr. Francis E. Doughty, 1907",,,,,,,,,,,,Swords,,http://www.metmuseum.org/art/collection/search/22102,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -32.75.321,false,true,23393,Arms and Armor,Ceremonial arrowhead (Yanonē),Ceremonial Arrowhead (Yanonē),Japanese,,,,,Steel-chiseler,,Umetada Motoshige,"Japanese, Edo period, died 1675",,"Motoshige, Umetada",Japanese,1575,1675,dated 1645,1645,1645,Steel,L. 12 5/8 in. (32.1 cm); L. of head 5 3/4 in. (14.6 cm); W. 2 5/8 in. (6.7 cm); Wt. 6.7 oz. (189.9 g),"The Collection of Giovanni P. Morosini, presented by his daughter Giulia, 1932",,,,,,,,,,,,Archery Equipment-Arrowheads,,http://www.metmuseum.org/art/collection/search/23393,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -32.75.327,false,true,23399,Arms and Armor,Ceremonial arrowhead (Yanonē),Ceremonial Arrowhead (Yanonē),Japanese,,,,,Steel-chiseler,,Umetada Motoshige,"Japanese, Edo period, died 1675",,"Motoshige, Umetada",Japanese,1575,1675,dated 1645,1645,1645,"Iron, gold",L. 11 1/4 in. (28.6 cm); L. of head 4 3/4 in. (12.1 cm); W. 5 in. (12.7 cm); Wt. 6.4 oz. (181.4 g),"The Collection of Giovanni P. Morosini, presented by his daughter Giulia, 1932",,,,,,,,,,,,Archery Equipment-Arrowheads,,http://www.metmuseum.org/art/collection/search/23399,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -32.75.330,false,true,23402,Arms and Armor,Ceremonial arrowhead (Yanoné),Ceremonial Arrowhead (Yanoné),Japanese,,,,,Steel-chiseler,,Umetada Motoshige,"Japanese, Edo period, died 1675",,"Motoshige, Umetada",Japanese,1575,1675,dated 1645,1645,1645,Steel,L. 12 1/8 in. (30.8 cm); L. of head 5 7/16 in. (13.8 cm); W. 2 1/2 in. (6.4 cm); Wt. 6.5 oz. (184.3 g),"The Collection of Giovanni P. Morosini, presented by his daughter Giulia, 1932",,,,,,,,,,,,Archery Equipment-Arrowheads,,http://www.metmuseum.org/art/collection/search/23402,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -32.75.334,false,true,23406,Arms and Armor,Ceremonial arrowhead (Yanonē),Ceremonial Arrowhead (Yanonē),Japanese,,,,,Steel-chiseler,,Umetada Motoshige,"Japanese, Edo period, died 1675",,"Motoshige, Umetada",Japanese,1575,1675,dated 1645,1645,1645,Steel,L. 11 3/8 in. (28.9 cm); L. of head 4 1/2 in. (11.4 cm); W. 2 5/16 in. (5.9 cm); Wt. 5.2 oz. (147.4 g),"The Collection of Giovanni P. Morosini, presented by his daughter Giulia, 1932",,,,,,,,,,,,Archery Equipment-Arrowheads,,http://www.metmuseum.org/art/collection/search/23406,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -32.75.337,false,true,23409,Arms and Armor,Ceremonial arrowhead (Yanonē),Ceremonial Arrowhead (Yanonē),Japanese,,,,,Steel-chiseler,,Umetada Motoshige,"Japanese, Edo period, died 1675",,"Motoshige, Umetada",Japanese,1575,1675,dated 1645,1645,1645,Steel,L. 11 in. (27.9 cm); L. of head 4 1/8 in. (10.5 cm); W. 2 1/8 in. (5.4 cm); Wt. 3.9 oz. (110.6 g),"The Collection of Giovanni P. Morosini, presented by his daughter Giulia, 1932",,,,,,,,,,,,Archery Equipment-Arrowheads,,http://www.metmuseum.org/art/collection/search/23409,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -32.75.339,false,true,23411,Arms and Armor,Ceremonial arrowhead (Yanonē),Ceremonial Arrowhead (Yanonē),Japanese,,,,,Steel-chiseler,,Umetada Motoshige,"Japanese, Edo period, died 1675",,"Motoshige, Umetada",Japanese,1575,1675,dated 1645,1645,1645,Steel,L. 11 3/4 in. (29.9 cm); L. of head 3 7/8 in. (9.8 cm); W. 2 1/8 in. (5.4 cm); Wt. 4.3 oz. (121.9 g),"The Collection of Giovanni P. Morosini, presented by his daughter Giulia, 1932",,,,,,,,,,,,Archery Equipment-Arrowheads,,http://www.metmuseum.org/art/collection/search/23411,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -32.75.340,false,true,23412,Arms and Armor,Ceremonial arrowhead (Yanonē),Ceremonial Arrowhead (Yanonē),Japanese,,,,,Steel-chiseler,,Umetada Motoshige,"Japanese, Edo period, died 1675",,"Motoshige, Umetada",Japanese,1575,1675,dated August 1645,1645,1645,Steel,L. 11 3/4 in. (29.9 cm); L. of head 5 1/8 in. (13 cm); W. 2 3/8 in. (6 cm); Wt. 5.3 oz. (150.3 g),"The Collection of Giovanni P. Morosini, presented by his daughter Giulia, 1932",,,,,,,,,,,,Archery Equipment-Arrowheads,,http://www.metmuseum.org/art/collection/search/23412,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -32.75.398,false,true,23469,Arms and Armor,Ceremonial arrowhead (Yanonē),Ceremonial Arrowhead (Yanonē),Japanese,,,,,Steel-chiseler,,Umetada Motoshige,"Japanese, Edo period, died 1675",,"Motoshige, Umetada",Japanese,1575,1675,dated August 1645,1645,1645,Steel,L. 12 3/4 in. (32.4 cm); L. of head 6 in. (15.7 cm); W. 2 3/4 in. (7 cm); Wt. 7.1 oz. (201.3 g),"The Collection of Giovanni P. Morosini, presented by his daughter Giulia, 1932",,,,,,,,,,,,Archery Equipment-Arrowheads,,http://www.metmuseum.org/art/collection/search/23469,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -32.75.399,false,true,23470,Arms and Armor,Ceremonial arrowhead (Yanonē),Ceremonial Arrowhead (Yanonē),Japanese,,,,,Steel-chiseler,,Umetada Motoshige,"Japanese, Edo period, died 1675",,"Motoshige, Umetada",Japanese,1575,1675,dated 1645,1645,1645,Steel,L. 11 1/4 in. (28.6 cm); L. of head 4 3/8 in. (11.1 cm); W. 3 3/8 in. (8.6 cm); Wt. 5.7 oz. (161.6 g),"The Collection of Giovanni P. Morosini, presented by his daughter Giulia, 1932",,,,,,,,,,,,Archery Equipment-Arrowheads,,http://www.metmuseum.org/art/collection/search/23470,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -32.75.403,false,true,23474,Arms and Armor,Ceremonial arrowhead (Yanonē),Ceremonial Arrowhead (Yanonē),Japanese,,,,,Steel-chiseler,,Umetada Motoshige,"Japanese, Edo period, died 1675",,"Motoshige, Umetada",Japanese,1575,1675,dated 1645,1645,1645,Steel,L. 10 5/8 in. (27 cm); L. of head 4 1/8 in. (10.5 cm); W. 1 3/4 in. (4.5 cm); Wt. 3.5 oz. (99.2 g),"The Collection of Giovanni P. Morosini, presented by his daughter Giulia, 1932",,,,,,,,,,,,Archery Equipment-Arrowheads,,http://www.metmuseum.org/art/collection/search/23474,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -32.75.406,false,true,23477,Arms and Armor,Ceremonial arrowhead (Yanonē),Ceremonial Arrowhead (Yanonē),Japanese,,,,,Steel-chiseler,,Umetada Motoshige,"Japanese, Edo period, died 1675",,"Motoshige, Umetada",Japanese,1575,1675,dated 1645,1645,1645,Steel,L. 10 1/2 in. (26.7 cm); L. of head 3 7/8 in. (9.8 cm); W. 2 in. (5.1 cm); Wt. 3.5 oz. (99.2 g),"The Collection of Giovanni P. Morosini, presented by his daughter Giulia, 1932",,,,,,,,,,,,Archery Equipment-Arrowheads,,http://www.metmuseum.org/art/collection/search/23477,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -32.75.409,false,true,23480,Arms and Armor,Ceremonial arrowhead (Yanonē),Ceremonial Arrowhead (Yanonē),Japanese,,,,,Steel-chiseler,,Umetada Motoshige,"Japanese, Edo period, died 1675",,"Motoshige, Umetada",Japanese,1575,1675,dated 1645,1645,1645,Steel,L. 10 5/8 in. (27 cm); L. of head 4 1/16 in. (10.3 cm); W. 1 7/8 in. (4.8 cm); Wt. 3.8 oz. (107.7 g),"The Collection of Giovanni P. Morosini, presented by his daughter Giulia, 1932",,,,,,,,,,,,Archery Equipment-Arrowheads,,http://www.metmuseum.org/art/collection/search/23480,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.112.13,false,true,22161,Arms and Armor,Mask,Mask,Japanese,,,,,Armorer,Inscribed by,Myōchin Muneakira,"Japanese, Edo period, 1673–1745",,"Muneakira, Myōchin",Japanese,1673,1745,dated 1715,1715,1715,"Iron, lacquer",H. 7 3/4 in. (19.7 cm); W. 6 3/4 in. (17.2 cm),"Rogers Fund, 1913",,,,,,,,,,,,Armor Parts-Masks,,http://www.metmuseum.org/art/collection/search/22161,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.100.45,false,true,22469,Arms and Armor,Mask,Mask,Japanese,,,,,Armorer,Inscribed by,Myōchin Muneakira,"Japanese, Edo period, 1673–1745",,"Muneakira, Myōchin",Japanese,1673,1745,dated 1713,1713,1713,"Iron, lacquer",H. 8 11/16 in. (22.2 cm); W. 6 3/4 in. (17.2 cm),"Gift of Bashford Dean, 1914",,,,,,,,,,,,Armor Parts-Masks,,http://www.metmuseum.org/art/collection/search/22469,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.112.4,false,true,22154,Arms and Armor,Armor (Gusoku),Armor (Gusoku),Japanese,,,,,Armorer,Signed by,Myōchin Munesuke,"Japanese, Edo period, 1688–1735",,"Munesuke, Myōchin",Japanese,1688,1735,early 18th century,1700,1750,"Iron, lacquer, silk, copper, gold",,"Rogers Fund, 1913",,,,,,,,,,,,Armor for Man,,http://www.metmuseum.org/art/collection/search/22154,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.60.27,false,true,34931,Arms and Armor,Sword guard (Tsuba),Sword Guard (Tsuba),Japanese,,,,,Fittings maker,Inscribed by,Hisanori,"Japanese, active ca. 17th century",,Hisanori,Japanese,1575,1725,possibly 17th century,1575,1725,"Brass, copper-gold alloy (shakudō)",H. 3 1/4 in. (8.3 cm); W. 3 1/8 in. (7.9 cm); thickness 3/16 in. (0.5 cm); Wt. 4.4 oz. (124.7 g),"Gift of Mrs. Mary E. Larkin Joline, 1914",,,,,,,,,,,,Sword Furniture-Tsuba,,http://www.metmuseum.org/art/collection/search/34931,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2001.574,false,true,24978,Arms and Armor,Blade for a sword (Katana),Blade for a Sword (Katana),Japanese,,,,,Swordsmith,Blade inscribed by,Masazane,"Japanese, Ise, documented 1515–26",,Masazane,Japanese,1515,1526,dated 1526,1526,1526,Steel,L. 36 1/8 in. (91.8 cm); L. of blade edge 29 9/16 in. (75.1 cm); D. of curvature 3/32 in. (2.4 cm),"Purchase, Arthur Ochs Sulzberger Gift, 2001",,,,,,,,,,,,Swords,,http://www.metmuseum.org/art/collection/search/24978,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2007.478.2a, b",false,true,27600,Arms and Armor,Blade for a sword (Katana),Blade for a Sword (Katana),Japanese,,,,,Sword maker,Signed,Etchu no kami Fujiwara Takahira,"Japanese, active early 17th century",,"Takahira, Etchu no kami Fujiwara",Japanese,1600,1650,dated June 1622,1622,1622,Steel,L. 36 1/2 in. (92.8 cm); L. of cutting edge 28 1/16 in. (71.5 cm); D. of curvature 1 in. (1.5 cm),"Gift of Etsuko O. Morris and John H. Morris Jr., in memory of Dr. Frederick M. Pederson, 2007",,,,,,,,,,,,Sword Blades,,http://www.metmuseum.org/art/collection/search/27600,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"1991.373a, b",true,true,24948,Arms and Armor,Blade for a dagger (Tantō),Blade for a Dagger (Tantō),Japanese,,,,,Swordsmith,Blade inscribed by,Rai Kunitoshi,"Japanese, active ca. 1290–ca. 1320",,Kunitoshi Rai,Japanese,1265,1345,ca. 1315–16,1290,1341,Steel,L. 13 5/8 in. (34.6 cm); L. of cutting edge 9 3/8 in. (23.8 cm); W. of blade at hilt 15/16 in. (2.4 cm); Wt. 7 oz. (185 g),"Gift of Mr. and Mrs. Robert Andrews Izard, 1991",,,,,,,,,,,,Daggers,,http://www.metmuseum.org/art/collection/search/24948,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1998.127.2,false,true,26571,Arms and Armor,Blade and mounting for a dagger (Tantō),Blade and Mounting for a Dagger (Tantō),Japanese,,,,,Swordsmith,Blade inscribed by,Uda Kunimitsu,"Japanese, active early–mid-14th century",,"Kunimitsu, Uda",Japanese,1300,1375,"blade, dated November, 1333; mounting, 19th century",1333,1900,"Steel, wood, lacquer, ray skin (samé), leather, copper, gold, silver, gold, shark skin, copper-gold alloy (shakudō)",L. 17 5/8 in. (44.7 cm); L. of cutting edge 12 in. (30.4 cm),"Gift of Peter H. B. Frelinghuysen, 1998",,,,,,,,,,,,Daggers,,http://www.metmuseum.org/art/collection/search/26571,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -09.49.2,false,true,22108,Arms and Armor,Hand cannon,Hand Cannon,Japanese,,,,,Gunsmith,Inscribed by,Kazuki Nobumichi,"Japanese, active late 18th–early 19th century",,"Nobumichi, Kazuki",Japanese,1750,1850,late 18th–early 19th century,1750,1850,"Iron, gold, silver, wood, brass",L. 37 in. (94 cm),"Gift of Marshall C. Lefferts, 1909",,,,,,,,,,,,Firearms-Hand Cannon,,http://www.metmuseum.org/art/collection/search/22108,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -45.24.52a–d,false,true,24727,Arms and Armor,Set of sword fittings (Mitokoromono),Set of Sword Fittings (Mitokoromono),Japanese,,,,,Fittings maker,,Gotō Sōjō,"Japanese, ca. 1461–1538, second-generation Gotō master",,Gotō Sōjō,Japanese,1461,1538,late 15th–early 16th century,1450,1550,"Copper-gold alloy (shakudō), gold","L. of hair dressing tool (kogai) (a) 8 5/16 in. (21.1 cm); L. of each grip ornament (menuki) (b, c) 1 11/32 in. (3.4 cm); L. of knife handle (kozuka) (d) 3 13/16 in. (9.7 cm)","Rogers Fund, 1945",,,,,,,,,,,,"Sword Furniture-Fittings, Sets of",,http://www.metmuseum.org/art/collection/search/24727,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"36.120.407a, b",false,true,29684,Arms and Armor,Helmet (Hoshi kabuto) in the 16th-century style,Helmet (Hoshi- Kabuto) in the 16th-Century Style,Japanese,,,,,Armorer,Inscribed by,Saotame Iesuek,"Japanese, Miyazaki Prefecture, active late 17th–early 18th century",,"Iesuek, Saotame",Japanese,1650,1750,probably late 17th–early 18th century,1625,1775,"Iron, lacquer, silk",H. 11 3/4 in. (29.9 cm); W. 13 in. (33 cm),"The Howard Mansfield Collection, Gift of Howard Mansfield, 1936",,,,,,,,,,,,Helmets,,http://www.metmuseum.org/art/collection/search/29684,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -17.208.65,false,true,35060,Arms and Armor,Sword guard (Tsuba),Sword Guard (Tsuba),Japanese,,,,,Fittings maker,Inscribed by,Nobuiye Myōchin,"Japanese, ca. 1504–1554",,"Myōchin, Nobuiye",Japanese,1479,1579,16th century,1501,1600,Iron,H. 3 1/8 in. (7.9 cm); W. 3 in. (7.6 cm); thickness 3/16 in. (0.5 cm); Wt. 4.4 oz. (124.7 g),"Gift of a Trustee of the Museum, 1917",,,,,,,,,,,,Sword Furniture-Tsuba,,http://www.metmuseum.org/art/collection/search/35060,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -04.4.9a–l,false,true,22022,Arms and Armor,Armor (Gusoku),Armor (Gusoku),Japanese,,,,,Armorer,Inscribed by,Yukinoshita Sadaiyé,"Japanese, active 17th century",,"Sadaiyé, Yukinoshita",Japanese,1601,1700,17th century,1601,1700,"Iron, lacquer, silk, gilt copper",as mounted: H. 53 in. (134.6 cm); W. 32 in. (81.3 cm); D. 20 in. (50.8 cm),"Rogers Fund, 1904",,,,,,,,,,,,Armor for Man,,http://www.metmuseum.org/art/collection/search/22022,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -32.75.318,false,true,23390,Arms and Armor,Ceremonial arrowhead (Yanonē),Ceremonial Arrowhead (Yanonē),Japanese,,,,,Steel-chiseler,,Umetada Yoshinobu,"Japanese, Edo period, 17th century",,"Yoshinobu, Umetada",Japanese,1601,1700,17th century,1601,1700,Steel,L. 18 5/8 in. (47.3 cm); L. of head 6 3/4 in. (17.1 cm); W. 3 1/2 in. (8.9 cm); Wt. 6.9 oz. (195.6 g),"The Collection of Giovanni P. Morosini, presented by his daughter Giulia, 1932",,,,,,,,,,,,Archery Equipment-Arrowheads,,http://www.metmuseum.org/art/collection/search/23390,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -48.63.4,false,true,24790,Arms and Armor,Helmet (Hoshi-kabuto),Helmet (Hoshi-Kabuto),Japanese,,,,,Armorer,Inscribed by,Iyehisa of Nara,"Japanese, Nara, active 17th century",,"Japanese, Nara Iyehisa of Nara",Japanese,1601,1700,17th century,1601,1700,"Iron, lacquer, gilt copper, copper-gold alloy (shakūdo), silver, silk, gilt leather",H. 11 in. (27.9 cm); L. 15 1/2 in. (39.4 cm),"Rogers Fund, 1948",,,,,,,,,,,,Helmets,,http://www.metmuseum.org/art/collection/search/24790,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.120.91,false,true,30090,Arms and Armor,Sword guard (Tsuba),Sword Guard (Tsuba),Japanese,,,,,Fittings maker,Inscribed by,Ichinomiya Nagatsune,"Japanese, 1721–1786",,Ichinomiya Nagatsune,Japanese,1721,1786,18th century,1701,1800,"Iron, silver",H. 3 1/8 in. (7.9 cm); W. 3 in. (7.6 cm); thickness 3/8 in. (1 cm); Wt. 7.6 oz. (215.5 g),"The Howard Mansfield Collection, Gift of Howard Mansfield, 1936",,,,,,,,,,,,Sword Furniture-Tsuba,,http://www.metmuseum.org/art/collection/search/30090,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -19.71.8,false,true,33366,Arms and Armor,Sword guard (Tsuba),Sword Guard (Tsuba),Japanese,,,,,Fittings maker,Inscribed by,Ōmori Teruhide,"Japanese, Edo period, 1730–1798",,"Teruhide, Ōmori",Japanese,1730,1798,18th century,1701,1800,"Copper-gold alloy (shakudō), gold, copper-silver alloy (shibuichi)",H. 2 7/8 in. (7.3 cm); W. 2 3/4 in. (7 cm); thickness 5/16 in. (0.8 cm); Wt. 5.7 oz. (161.6 g),"Charles Stewart Smith Memorial Fund, 1919",,,,,,,,,,,,Sword Furniture-Tsuba,,http://www.metmuseum.org/art/collection/search/33366,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -43.120.728,false,true,24712,Arms and Armor,Sword guard (Tsuba),Sword Guard (Tsuba),Japanese,,,,,Fittings maker,Inscribed by,Gotō Ichijō,"Japanese, 1791–1876",,Gotō Ichijō,Japanese,1791,1876,19th century,1801,1900,"Copper-gold alloy (shakudō), silver, gold, copper",H. 2 11/16 in. (6.8 cm); W. 2 1/2 in. (6.4 cm); thickness 3/16 in. (0.5 cm); Wt. 4 oz. (113.4 g),"Gift of Herman A. E. and Paul C. Jaehne, 1943",,,,,,,,,,,,Sword Furniture-Tsuba,,http://www.metmuseum.org/art/collection/search/24712,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2012.528.3,false,true,35913,Arms and Armor,Sword guard (Tsuba),Sword Guard (Tsuba),Japanese,,,,,Fittings maker,Inscribed by,Gotō Mitsuakira,"Japanese, 1816–1856",,"Mitsuakira, Gotō",Japanese,1816,1856,19th century,1801,1900,"Copper-gold alloy (shakudō), gold, silver, copper",H. 2 3/4 in. (7 cm); W. 2 9/16 in. (6.5 cm); thickness 5/16 in. (0.8 cm); Wt. 4.4 oz. (124.7 g),"Gift of the Baber Family, in loving memory of Charles Chenault Baber, 2012",,,,,,,,,,,,Sword Furniture-Tsuba,,http://www.metmuseum.org/art/collection/search/35913,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -17.208.42,false,true,35038,Arms and Armor,Knife handle (Kozuka),Knife Handle (Kozuka),Japanese,,,,,Fittings maker,Inscribed by,Ginshōtei Tōmei,"Japanese, 1817–1870",,"Tōmei, Ginshōtei",Japanese,1817,1870,19th century,1801,1900,"Iron, gold, copper-silver alloy (shibuichi)",L. 3 13/16 in. (9.7 cm); W. 9/16 in. (1.4 cm); thickness 1/4 in. (0.6 cm); Wt. 1.4 oz. (39.7 g),"Gift of a Trustee of the Museum, 1917",,,,,,,,,,,,Sword Furniture-Kozuka,,http://www.metmuseum.org/art/collection/search/35038,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -17.208.32a–e,false,true,35031,Arms and Armor,Set of sword fittings (Mitokoromono),Set of Sword Fittings (Mitokoromono),Japanese,,,,,Fittings maker,Inscribed by,Ginshōtei Tōmei,"Japanese, 1817–1870",,"Tōmei, Ginshōtei",Japanese,1817,1870,19th century,1801,1900,"Iron, gold, copper-silver alloy (shibuichi)",sword guard (tsuba) 17.208.32a: 2 5/8 x 2 3/8 in. (6.7 x 6.0 cm); cord knob (kurigata) 17.208.32b: 1 1/4 x 3/4 in. (3.2 x 1.9 cm); socket brace for knife (uragawara) 17.208.32c 1 1/4 x 3/4 in. (3.2 x 1.9 cm); pommel cap (kashira) 17.208.32d: 1 3/8 x 3/4 in. (3.5 x 1.9 cm); hilt collar (fuchi) 17.208.32e: 1 1/2 x 3/4 in. (3.8 x 1.9 cm),"Gift of a Trustee of the Museum, 1917",,,,,,,,,,,,"Sword Furniture-Fittings, Sets of",,http://www.metmuseum.org/art/collection/search/35031,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.120.99,false,true,24625,Arms and Armor,Sword guard (Tsuba),Sword Guard (Tsuba),Japanese,,,,,Fittings maker,,Kanō Natsuo,"Japanese, 1828–1898",,"Natsuo, Kanō",Japanese,1828,1898,19th century,1801,1900,"Copper-gold alloy (shakudō), gold, copper",H. 2 1/4 in. (5.7 cm); W. 1 15/16 in. (4.9 cm); thickness 3/16 in. (0.5 cm); Wt. 2.4 oz. (68 g),"The Howard Mansfield Collection, Gift of Howard Mansfield, 1936",,,,,,,,,,,,Sword Furniture-Tsuba,,http://www.metmuseum.org/art/collection/search/24625,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.120.79,true,true,24623,Arms and Armor,Sword guard (Tsuba),Sword Guard (Tsuba),Japanese,,,,,Fittings maker,Inscribed by,Ishiguro Masayoshi,"Japanese, 1772–after 1851",,Ishiguro Masayoshi,Japanese,1772,1851,19th century,1801,1900,"Copper-gold alloy (shakudō), gold, copper-silver alloy (shibuichi), copper",H. 2 7/8 in. (7.3 cm); W. 2 5/8 in. (6.7 cm); thickness 5/16 in. (0.8 cm); Wt. 5.4 oz. (153.1 g),"The Howard Mansfield Collection, Gift of Howard Mansfield, 1936",,,,,,,,,,,,Sword Furniture-Tsuba,,http://www.metmuseum.org/art/collection/search/24623,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -48.63.5a–l,false,true,24786,Arms and Armor,Fittings for a pair of swords (Daishō soroi-mono),Fittings for a Pair of Swords (Daishō Soroi-Mono),Japanese,,,,,Fittings maker,Inscribed by,Masayoshi,"Japanese, born 1784",,Masayoshi,Japanese,1784,1884,early 19th century,1800,1850,"Copper-gold alloy (shakudō), gold, silver, copper-silver alloy (shibuichi), copper","Sword guards (Tsuba) (a, b); H. of each 2 7/8 in. (7.3 cm); W. of each 2 11/16 in. (6.8 cm); thickness of each 3/8 in. (0.5 cm); Wt. of each 5.2 oz. (147.4 g); knife handles (Kozuka) (c, d); 3 7/8 x 9/16 in. (9.8 x 1.4 cm); sword-hilt collar (fuchi) (e); 1 1/2 x 13/16 in. (3.8 x 2.1 cm); pommel (kashira) (f); 1 3/8 x 11/16 in. (3.5 x 1.7 cm); sword-hilt collar (fuchi) (g); 1 1/2 x 7/8 in. (3.8 x 2.2 cm); pommel (kashira) (h); 1 3/8 x 3/4 in. (3.5 x 1.9 cm); each of a pair of menuki (i, j); 1 1/16 x 5/8 in. (2.7 x 1.6 cm); each of a pair of menuki (k, l); 1 x 9/16 in. (2.5 x 1.4 cm)","Rogers Fund, 1948",,,,,,,,,,,,"Sword Furniture-Fittings, Sets of",,http://www.metmuseum.org/art/collection/search/24786,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2006.262.1, .2",false,true,33557,Arms and Armor,Pair of sword guards (Tsuba),Pair of Sword Guards (Tsuba),Japanese,,,,,Fittings maker,Inscribed on the inner face of each tsuba by,Sunagawa Mao-Yoshi,"Japanese, active early–mid-19th century",,Sunagawa Mao-Yoshi,Japanese,1800,1875,early 19th century,1800,1850,"Copper-gold alloy (shakudō), gold, copper",2006.262.1: H. 2 15/16 in. (7.4 cm); W. 2 3/4 in. (7.0 cm); 2006.262.2: H. 2 13/16 in. (7.1 cm); W. 2 9/16 in. (6.5 cm),"Purchase, Gift of Mrs. George A. Crocker (Elizabeth Masten), by exchange, 2006",,,,,,,,,,,,Sword Furniture-Tsuba,,http://www.metmuseum.org/art/collection/search/33557,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -43.120.537,false,true,25355,Arms and Armor,Knife handle (Kozuka),Knife Handle (Kozuka),Japanese,,,,,Maker,,Hamano Toshihiro,"Japanese, died 1861",,Hamano Toshihiro,Japanese,,1861,late 18th–mid-19th century,1775,1870,"Copper-silver alloy (shibuichi), gold, copper, silver, copper-gold alloy (shakudō)",L. 3 13/16 in. (9.7 cm); W. 9/16 in. (1.4 cm); thickness 3/16 in. (0.5 cm); Wt. 1.2 oz. (34 g),"Gift of Herman A. E. and Paul C. Jaehne, 1943",,,,,,,,,,,,Sword Furniture-Kozuka,,http://www.metmuseum.org/art/collection/search/25355,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -43.120.692,false,true,24711,Arms and Armor,Sword guard (Tsuba),Sword Guard (Tsuba),Japanese,,,,,Maker,Attributed to the,Hirata School,"Japanese, Edo period",,Hirata School,Japanese,1615,1868,ca. 1615–1868,1590,1900,"Copper-gold alloy (shakudō), gold, enameled cloisonné (shippō), copper",H. 2 7/8 in. (7.3 cm); W. 2 15/16 in. (7.5 cm); thickness 1/4 in. (0.6 cm); Wt. 5.4 oz. (153.1 g),"Gift of Herman A. E. and Paul C. Jaehne, 1943",,,,,,,,,,,,Sword Furniture-Tsuba,,http://www.metmuseum.org/art/collection/search/24711,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2004.460,false,true,27692,Arms and Armor,Knife handle (Kozuka),Knife Handle (Kozuka),Japanese,,,,,Maker,,Goto Mitsunori,"Japanese, 1646–1712",,Goto Mitsunori,Japanese,1646,1712,early 18th century,1701,1712,"Copper-gold alloy (shakudō), gold",L. 3 13/16 in. (9.7 cm); W. 9/16 in. (1.4 cm); thickness 1/4 in. (0.6 cm); Wt. 1.2 oz. (34 g),"Purchase, Arthur Ochs Sulzberger Gift, 2004",,,,,,,,,,,,Sword Furniture-Kozuka,,http://www.metmuseum.org/art/collection/search/27692,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -43.120.293,false,true,25262,Arms and Armor,Knife handle (Kozuka),Knife Handle (Kozuka),Japanese,,,,,Maker,,Hamano Masayuki,"Japanese, 1696–1769",,Hamano Masayuki,Japanese,1696,1769,early–mid-18th century,1701,1775,"Copper, gold, copper alloy (sentoku), silver",L. 3 13/16 in. (9.7 cm); W. 9/16 in. (1.4 cm); thickness 5/16 in. (0.8 cm); Wt. 1.3 oz. (36.9 g),"Gift of Herman A. E. and Paul C. Jaehne, 1943",,,,,,,,,,,,Sword Furniture-Kozuka,,http://www.metmuseum.org/art/collection/search/25262,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2004.459a–c,false,true,27691,Arms and Armor,Pair of sword-grip ornaments (Menuki),Pair of Sword-Grip Ornaments (Menuki),Japanese,,,,,Maker,,Tsu Jumpo (Jimpo),"Japanese, 1721–1762",,Tsu Jumpo,Japanese,1721,1762,ca. 1750,1750,1750,Gold,L. of each 2 in. (5 cm); W. of each 1/2 in. (1.3 cm); Wt. of each 0.3 oz. (8.5 g),"Purchase, Arthur Ochs Sulzberger Gift, 2004",,,,,,,,,,,,Sword Furniture-Menuki,,http://www.metmuseum.org/art/collection/search/27691,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.40.913,false,true,26912,Arms and Armor,Knife handle (Kozuka),Knife Handle (Kozuka),Japanese,,,,,Maker,,Iwama Masayoshi (Shoro),"Japanese, 1764–1837",,Iwama Masayoshi (Shoro),Japanese,1764,1837,dated 1828,1828,1828,"Copper-silver alloy (shibuichi), gold, copper-gold alloy (shakudō)",L. 3 13/16 in. (9.7 cm); W. 9/16 in. (1.4 cm); thickness 1/4 in. (0.6 cm); Wt. 1.2 oz. (34 g),"Bequest of Benjamin Altman, 1913",,,,,,,,,,,,Sword Furniture-Kozuka,,http://www.metmuseum.org/art/collection/search/26912,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -43.120.300,false,true,25266,Arms and Armor,Knife handle (Kozuka),Knife Handle (Kozuka),Japanese,,,,,Maker,,Iwama Masayoshi (Shoro),"Japanese, 1764–1837",,Iwama Masayoshi (Shoro),Japanese,1764,1837,late 18th–mid-19th century,1775,1837,"Copper, silver, gold, copper-gold alloy (shakudō)",L. 3 13/16 in. (9.7 cm); W. 9/16 in. (1.4 cm); thickness 3/16 in. (0.5 cm); Wt. 1.1 oz. (31.2 g),"Gift of Herman A. E. and Paul C. Jaehne, 1943",,,,,,,,,,,,Sword Furniture-Kozuka,,http://www.metmuseum.org/art/collection/search/25266,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -43.120.456,false,true,25330,Arms and Armor,Knife handle (Kozuka),Knife Handle (Kozuka),Japanese,,,,,Maker,,Iwama Masayoshi (Shoro),"Japanese, 1764–1837",,Iwama Masayoshi (Shoro),Japanese,1764,1837,late 18th–early 19th century,1801,1900,"Copper-gold alloy (shakudō), silver, gold",L. 3 13/16 in. (9.7 cm); W. 9/16 in. (1.4 cm); thickness 1/4 in. (0.6 cm); Wt. 1.3 oz. (36.9 g),"Gift of Herman A. E. and Paul C. Jaehne, 1943",,,,,,,,,,,,Sword Furniture-Kozuka,,http://www.metmuseum.org/art/collection/search/25330,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -43.120.455,false,true,25329,Arms and Armor,Knife handle (Kozuka),Knife Handle (Kozuka),Japanese,,,,,Maker,,Hamano Naochika,"Japanese, 1784–1808",,Hamano Naochika,Japanese,1784,1808,late 18th–early 19th century,1801,1900,"Copper-gold alloy (shakudō), gold, silver, copper",L. 3 13/16 in. (9.7 cm); W. 9/16 in. (1.4 cm); thickness 1/4 in. (0.6 cm); Wt. 1.3 oz. (36.9 g),"Gift of Herman A. E. and Paul C. Jaehne, 1943",,,,,,,,,,,,Sword Furniture-Kozuka,,http://www.metmuseum.org/art/collection/search/25329,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -43.120.459,false,true,25331,Arms and Armor,Knife handle (Kozuka),Knife Handle (Kozuka),Japanese,,,,,Maker,,Furukawa Jochin,"Japanese, died ca.1750",,Furukawa Jochin,Japanese,1750,1750,early 18th century,1701,1760,Copper-silver alloy (shibuichi),L. 3 3/4 in. (9.5 cm); W. 9/16 in. (1.4 cm); thickness 3/16 in. (0.5 cm); Wt. 1.1 oz. (31.2 g),"Gift of Herman A. E. and Paul C. Jaehne, 1943",,,,,,,,,,,,Sword Furniture-Kozuka,,http://www.metmuseum.org/art/collection/search/25331,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -43.120.421,false,true,25311,Arms and Armor,Knife handle (Kozuka),Knife Handle (Kozuka),Japanese,,,,,Maker,,Katsutora,"Japanese, died ca.1825",,Katsutora,Japanese,,1825,late 18th century–early 19th century,1765,1835,"Copper-silver alloy (shibuichi), silver",L. 3 3/4 in. (9.5 cm); W. 9/16 in. (1.4 cm); thickness 3/16 in. (0.5 cm); Wt. 1.1 oz. (31.2 g),"Gift of Herman A. E. and Paul C. Jaehne, 1943",,,,,,,,,,,,Sword Furniture-Kozuka,,http://www.metmuseum.org/art/collection/search/25311,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -43.120.430,false,true,25317,Arms and Armor,Knife handle (Kozuka),Knife Handle (Kozuka),Japanese,,,,,Maker,,Hamano Haruyuki,"Japanese, died ca.1830",,Hamano Haruyuki,Japanese,1830,1830,late 18th–early 19th century,1740,1850,"Copper-silver alloy (shibuichi), gold, silver, copper, copper-gold alloy (shakudō)",L. 3 13/16 in. (9.7 cm); W. 9/16 in. (1.4 cm); thickness 3/16 in. (0.5 cm); Wt. 1.2 oz. (34 g),"Gift of Herman A. E. and Paul C. Jaehne, 1943",,,,,,,,,,,,Sword Furniture-Kozuka,,http://www.metmuseum.org/art/collection/search/25317,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -43.120.477,false,true,25339,Arms and Armor,Knife handle (Kozuka),Knife Handle (Kozuka),Japanese,,,,,Maker,,Hamano Haruchika,"Japanese, died ca.1850",,Hamano Haruchika,Japanese,1850,1850,late 18th–early 19th century,1750,1860,"Copper-silver alloy (shibuichi), gold, silver",L. 3 13/16 in. (9.7 cm); W. 9/16 in. (1.4 cm); thickness 3/16 in. (0.5 cm); Wt. 1.1 oz. (31.2 g),"Gift of Herman A. E. and Paul C. Jaehne, 1943",,,,,,,,,,,,Sword Furniture-Kozuka,,http://www.metmuseum.org/art/collection/search/25339,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -43.120.478,false,true,25340,Arms and Armor,Knife handle (Kozuka),Knife Handle (Kozuka),Japanese,,,,,Maker,,Hamano Naotsune,"Japanese, died ca.1850",,Hamano Naotsune,Japanese,,1850,late 18th–early 19th century,1750,1860,"Copper-silver alloy (shibuichi), gold, silver",L. 3 13/16 in. (9.7 cm); W. 9/16 in. (1.4 cm); thickness 3/16 in. (0.5 cm); Wt. 1.2 oz. (34 g),"Gift of Herman A. E. and Paul C. Jaehne, 1943",,,,,,,,,,,,Sword Furniture-Kozuka,,http://www.metmuseum.org/art/collection/search/25340,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -12.37.143,false,true,25647,Arms and Armor,Knife handle (Kozuka),Knife Handle (Kozuka),Japanese,,,,,Maker,,Akichika Oishi,"Japanese, active ca.1850",,Oishi Akichika,Japanese,1850,1850,late 18th–early 19th century,1800,1825,"Copper-silver alloy (shibuichi), copper-gold alloy (shakudō), gold, copper",L. 3 13/16 in. (9.7 cm); W. 9/16 in. (1.4 cm); thickness 1/4 in. (0.6 cm); Wt. 1.2 oz. (34 g),"Rogers Fund, 1912",,,,,,,,,,,,Sword Furniture-Kozuka,,http://www.metmuseum.org/art/collection/search/25647,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -32.43.1,false,true,23356,Arms and Armor,Saddle (Kura),Saddle (Kura),Japanese,,,,,Maker,Signed by,Chikara,"Japanese, active ca. 1610",,Chikara,Japanese,1585,1635,dated 1610,1610,1610,"Wood, mother-of-pearl, linen, lacquer, gold",H. 12 1/4 in. (31.1 cm); W. 15 1/2 in. (39.4 cm),"Gift of Fredrick C. MacDonell, 1932",,,,,,,,,,,,Equestrian Equipment-Saddles,,http://www.metmuseum.org/art/collection/search/23356,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -43.120.317,false,true,25270,Arms and Armor,Knife handle (Kozuka),Knife Handle (Kozuka),Japanese,,,,,Maker,,Ichijosai Hironaga (Hirotoshi),"Japanese, died ca. 1800–25",,Ichijosai Hironaga (Hirotoshi),Japanese,1800,1825,mid-18th–early 19th century,1725,1825,"Copper-silver alloy (shibuichi), gold, copper",L. 3 13/16 in. (9.7 cm); W. 9/16 in. (1.4 cm); thickness 5/16 in. (0.8 cm); Wt. 1.3 oz. (36.9 g),"Gift of Herman A. E. and Paul C. Jaehne, 1943",,,,,,,,,,,,Sword Furniture-Kozuka,,http://www.metmuseum.org/art/collection/search/25270,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -43.120.512,false,true,25349,Arms and Armor,Knife handle (Kozuka),Knife Handle (Kozuka),Japanese,,,,,Maker,,Ichijosai Hironaga (Hirotoshi),"Japanese, died ca. 1800–25",,Ichijosai Hironaga (Hirotoshi),Japanese,1800,1825,late 17th–early 18th century,1801,1900,"Copper-silver alloy (shibuichi), gold, silver, copper, copper-gold alloy (shakudō)",L. 3 13/16 in. (9.7 cm); W. 9/16 in. (1.4 cm); thickness 1/4 in. (0.6 cm); Wt. 1.1 oz. (31.2 g),"Gift of Herman A. E. and Paul C. Jaehne, 1943",,,,,,,,,,,,Sword Furniture-Kozuka,,http://www.metmuseum.org/art/collection/search/25349,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -19.115.2,true,true,22739,Arms and Armor,Mask,Mask,Japanese,,,,,Maker,Inscribed by,Myōchin Muneakira,"Japanese, Edo period, 1673–1745",,"Muneakira, Myōchin",Japanese,1673,1745,dated 1745,1745,1745,"Iron, lacquer, textile (silk)",L. 9 1/2 in. (24.1 cm); W. 7 in. (17.8 cm),"Rogers Fund, 1919",,,,,,,,,,,,Armor Parts-Masks,,http://www.metmuseum.org/art/collection/search/22739,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.112.20,false,true,22176,Arms and Armor,Okimono in the form of a raven,Okimono in the Form of a Raven,Japanese,,,,,Maker,,Myōchin Munesuke,"Japanese, Edo period, 1688–1735",,"Munesuke, Myōchin",Japanese,1688,1735,early 18th century,1700,1750,Steel,L. 18 in. (45.7 cm),"Rogers Fund, 1913",,,,,,,,,,,,Miscellaneous,,http://www.metmuseum.org/art/collection/search/22176,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -43.120.178,false,true,25218,Arms and Armor,Knife handle (Kozuka),Knife Handle (Kozuka),Japanese,,,,,Maker,,Gotō Eijō,"Japanese, 1577–1617, sixth-generation Gotō master",,Gotō Eijō,Japanese,1577,1617,17th century,1601,1700,"Gold, copper-gold alloy (shakudō), silver",L. 3 13/16 in. (9.7 cm); W. 9/16 in. (1.4 cm); thickness 1/4 in. (0.6 cm); Wt. 1.4 oz. (39.7 g),"Gift of Herman A. E. and Paul C. Jaehne, 1943",,,,,,,,,,,,Sword Furniture-Kozuka,,http://www.metmuseum.org/art/collection/search/25218,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -43.120.111,false,true,25189,Arms and Armor,Knife handle (Kozuka),Knife Handle (Kozuka),Japanese,,,,,Maker,,Gotō Renjō (Mitsutomo),"Japanese, 1628–1708, tenth-generation Gotō master",,"Renjō (Mitsutomo), Gotō",Japanese,1628,1708,17th century,1601,1700,"Copper-gold alloy (shakudō), gold, silver",L. 3 13/16 in. (9.7 cm); W. 9/16 in. (1.4 cm); thickness 1/4 in. (0.6 cm); Wt. 1 oz. (28.3 g),"Gift of Herman A. E. and Paul C. Jaehne, 1943",,,,,,,,,,,,Sword Furniture-Kozuka,,http://www.metmuseum.org/art/collection/search/25189,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -43.120.487,false,true,25345,Arms and Armor,Knife handle (Kozuka),Knife Handle (Kozuka),Japanese,,,,,Maker,,Gotō Renjō (Mitsutomo),"Japanese, 1628–1708, tenth-generation Gotō master",,"Renjō (Mitsutomo), Gotō",Japanese,1628,1708,17th century,1601,1700,"Copper-gold alloy (shakudō), gold",L. 3 13/16 in. (9.7 cm); W. 9/16 in. (1.4 cm); thickness 1/4 in. (0.6 cm); Wt. 1.4 oz. (39.7 g),"Gift of Herman A. E. and Paul C. Jaehne, 1943",,,,,,,,,,,,Sword Furniture-Kozuka,,http://www.metmuseum.org/art/collection/search/25345,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -43.120.341,false,true,25280,Arms and Armor,Knife handle (Kozuka),Knife Handle (Kozuka),Japanese,,,,,Maker,,Genshosai Masaharu,"Japanese, died 1724",,Genshosai Masaharu,Japanese,,1724,19th century,1801,1900,"Copper-silver alloy (shibuichi), gold, copper",L. 3 13/16 in. (9.7 cm); W. 9/16 in. (1.4 cm); thickness 1/4 in. (0.6 cm); Wt. 1.2 oz. (34 g),"Gift of Herman A. E. and Paul C. Jaehne, 1943",,,,,,,,,,,,Sword Furniture-Kozuka,,http://www.metmuseum.org/art/collection/search/25280,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.120.372,false,true,26151,Arms and Armor,Knife handle (Kozuka),Knife Handle (Kozuka),Japanese,,,,,Maker,,Hamano Toshihiro,"Japanese, died 1861",,Hamano Toshihiro,Japanese,,1861,19th century,1801,1900,"Copper-silver alloy (shibuichi), gold, silver, copper-gold alloy (shakudō)",L. 3 13/16 in. (9.7 cm); W. 9/16 in. (1.4 cm); thickness 3/16 in. (0.5 cm); Wt. 1.2 oz. (34 g),"The Howard Mansfield Collection, Gift of Howard Mansfield, 1936",,,,,,,,,,,,Sword Furniture-Kozuka,,http://www.metmuseum.org/art/collection/search/26151,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -43.120.426,false,true,25314,Arms and Armor,Knife handle (Kozuka),Knife Handle (Kozuka),Japanese,,,,,Maker,,Hamano Toshihiro,"Japanese, died 1861",,Hamano Toshihiro,Japanese,,1861,19th century,1801,1900,"Copper-silver alloy (shibuichi), gold, silver, copper",L. 3 13/16 in. (9.7 cm); W. 9/16 in. (1.4 cm); thickness 1/4 in. (0.6 cm); Wt. 1.2 oz. (34 g),"Gift of Herman A. E. and Paul C. Jaehne, 1943",,,,,,,,,,,,Sword Furniture-Kozuka,,http://www.metmuseum.org/art/collection/search/25314,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -43.120.569,false,true,25733,Arms and Armor,Knife handle (Kozuka),Knife Handle (Kozuka),Japanese,,,,,Maker,,Joken Mori,"Japanese, died 1866",,Mori Joken,Japanese,,1866,19th century,1801,1900,"Copper-silver alloy (shibuichi), iron",L. 3 13/16 in. (9.7 cm); W. 9/16 in. (1.4 cm); thickness 3/16 in. (0.5 cm); Wt. 1 oz. (28.3 g),"Gift of Herman A. E. and Paul C. Jaehne, 1943",,,,,,,,,,,,Sword Furniture-Kozuka,,http://www.metmuseum.org/art/collection/search/25733,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -43.120.439,false,true,25319,Arms and Armor,Knife handle (Kozuka),Knife Handle (Kozuka),Japanese,,,,,Maker,,Gotō Ichijō,"Japanese, 1791–1876",,Gotō Ichijō,Japanese,1791,1876,19th century,1801,1900,"Copper-gold alloy (shakudō), gold, silver, copper",L. 3 13/16 in. (9.7 cm); W. 9/16 in. (1.4 cm); thickness 3/16 in. (0.5 cm); Wt. 1.1 oz. (31.2 g),"Gift of Herman A. E. and Paul C. Jaehne, 1943",,,,,,,,,,,,Sword Furniture-Kozuka,,http://www.metmuseum.org/art/collection/search/25319,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -43.120.318,false,true,25271,Arms and Armor,Knife handle (Kozuka),Knife Handle (Kozuka),Japanese,,,,,Maker,,Tanaka Kiyotoshi (Kiyonaga),"Japanese, 1804–1876",,Tanaka Kiyotoshi (Kiyonaga) Ryuso,Japanese,1804,1876,19th century,1801,1876,"Copper-silver alloy (shibuichi), gold, copper-gold alloy (shakudō)",L. 3 13/16 in. (9.7 cm); W. 9/16 in. (1.4 cm); thickness 1/4 in. (0.6 cm); Wt. 1.3 oz. (36.9 g),"Gift of Herman A. E. and Paul C. Jaehne, 1943",,,,,,,,,,,,Sword Furniture-Kozuka,,http://www.metmuseum.org/art/collection/search/25271,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -43.120.182,false,true,25220,Arms and Armor,Knife handle (Kozuka),Knife Handle (Kozuka),Japanese,,,,,Maker,,Iwama Nobuyoshi,"Japanese, 1807–1878",,Iwama Nobuyoshi,Japanese,1807,1878,19th century,1801,1900,"Copper-silver alloy (shibuichi), silver, gold",L. 3 13/16 in. (9.7 cm); W. 9/16 in. (1.4 cm); thickness 1/4 in. (0.6 cm); Wt. 1.2 oz. (34 g),"Gift of Herman A. E. and Paul C. Jaehne, 1943",,,,,,,,,,,,Sword Furniture-Kozuka,,http://www.metmuseum.org/art/collection/search/25220,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -43.120.366,false,true,25286,Arms and Armor,Knife handle (Kozuka),Knife Handle (Kozuka),Japanese,,,,,Maker,,Iwama Nobuyoshi,"Japanese, 1807–1878",,Iwama Nobuyoshi,Japanese,1807,1878,19th century,1801,1900,"Copper-silver alloy (shibuichi), gold, silver",L. 3 13/16 in. (9.7 cm); W. 9/16 in. (1.4 cm); thickness 1/4 in. (0.6 cm); Wt. 1.2 oz. (34 g),"Gift of Herman A. E. and Paul C. Jaehne, 1943",,,,,,,,,,,,Sword Furniture-Kozuka,,http://www.metmuseum.org/art/collection/search/25286,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -43.120.56,false,true,25180,Arms and Armor,Knife handle (Kozuka),Knife Handle (Kozuka),Japanese,,,,,Maker,,Funada Yoshinaga,"Japanese, 1812–1863",,Funada Yoshinaga,Japanese,1812,1863,19th century,1801,1900,"Copper-silver alloy (shibuichi), copper, gold, silver",L. 3 3/4 in. (9.5 cm); W. 9/16 in. (1.4 cm); thickness 1/4 in. (0.6 cm); Wt. 1.2 oz. (34 g),"Gift of Herman A. E. and Paul C. Jaehne, 1943",,,,,,,,,,,,Sword Furniture-Kozuka,,http://www.metmuseum.org/art/collection/search/25180,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.120.237,false,true,26117,Arms and Armor,Knife handle (Kozuka),Knife Handle (Kozuka),Japanese,,,,,Maker,,Hamano Haruchika,"Japanese, died ca.1850",,Hamano Haruchika,Japanese,1850,1850,19th century,1801,1900,"Copper-silver alloy (shibuichi), gold, copper, copper-gold alloy (shakudō)",L. 3 13/16 in. (9.7 cm); W. 9/16 in. (1.4 cm); thickness 1/4 in. (0.6 cm); Wt. 1.2 oz. (34 g),"The Howard Mansfield Collection, Gift of Howard Mansfield, 1936",,,,,,,,,,,,Sword Furniture-Kozuka,,http://www.metmuseum.org/art/collection/search/26117,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -43.120.240,false,true,25244,Arms and Armor,Knife handle (Kozuka),Knife Handle (Kozuka),Japanese,,,,,Maker,,Hamano Haruchika,"Japanese, died ca.1850",,Hamano Haruchika,Japanese,1850,1850,19th century,1801,1900,"Copper-silver alloy (shibuichi), gold, copper-gold alloy (shakudō)",L. 3 13/16 in. (9.7 cm); W. 9/16 in. (1.4 cm); thickness 3/16 in. (0.5 cm); Wt. 1 oz. (28.3 g),"Gift of Herman A. E. and Paul C. Jaehne, 1943",,,,,,,,,,,,Sword Furniture-Kozuka,,http://www.metmuseum.org/art/collection/search/25244,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -43.120.385,false,true,25297,Arms and Armor,Knife handle (Kozuka),Knife Handle (Kozuka),Japanese,,,,,Maker,,Hamano Haruchika,"Japanese, died ca.1850",,Hamano Haruchika,Japanese,1850,1850,19th century,1801,1900,"Copper-gold alloy (shakudō), iron, gold, silver, copper, brass",L. 3 13/16 in. (9.7 cm); W. 9/16 in. (1.4 cm); thickness 3/16 in. (0.5 cm); Wt. 1.1 oz. (31.2 g),"Gift of Herman A. E. and Paul C. Jaehne, 1943",,,,,,,,,,,,Sword Furniture-Kozuka,,http://www.metmuseum.org/art/collection/search/25297,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -43.120.184,false,true,25221,Arms and Armor,Knife handle (Kozuka),Knife Handle (Kozuka),Japanese,,,,,Maker,,Marukawa Hiroyoshi,"Japanese, died 1841 or 1842",,Marukawa Hiroyoshi,Japanese,1841,1841,19th century,1801,1900,"Copper-silver alloy (shibuichi), silver, gold",L. 3 13/16 in. (9.7 cm); W. 9/16 in. (1.4 cm); thickness 1/4 in. (0.6 cm); Wt. 1.1 oz. (31.2 g),"Gift of Herman A. E. and Paul C. Jaehne, 1943",,,,,,,,,,,,Sword Furniture-Kozuka,,http://www.metmuseum.org/art/collection/search/25221,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -43.120.393,false,true,25301,Arms and Armor,Knife handle (Kozuka),Knife Handle (Kozuka),Japanese,,,,,Maker,,Ichijosai Hironaga (Hirotoshi),"Japanese, died ca. 1800–25",,Ichijosai Hironaga (Hirotoshi),Japanese,1800,1825,19th century,1801,1900,"Copper-silver alloy (shibuichi), gold, silver, copper, copper-gold alloy (shakudō)",L. 3 13/16 in. (9.7 cm); W. 9/16 in. (1.4 cm); thickness 3/16 in. (0.5 cm); Wt. 1.2 oz. (34 g),"Gift of Herman A. E. and Paul C. Jaehne, 1943",,,,,,,,,,,,Sword Furniture-Kozuka,,http://www.metmuseum.org/art/collection/search/25301,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -43.120.214,false,true,25235,Arms and Armor,Knife handle (Kozuka),Knife Handle (Kozuka),Japanese,,,,,Maker,,Ichiyosai Hironao,"Japanese, died ca. 1825–50",,Ichiyosai Hironao,Japanese,1825,1850,19th century,1801,1900,"Copper-silver alloy (shibuichi), gold, silver",L. 3 13/16 in. (9.7 cm); W. 9/16 in. (1.4 cm); thickness 1/4 in. (0.6 cm); Wt. 1.2 oz. (34 g),"Gift of Herman A. E. and Paul C. Jaehne, 1943",,,,,,,,,,,,Sword Furniture-Kozuka,,http://www.metmuseum.org/art/collection/search/25235,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -43.120.337,false,true,25279,Arms and Armor,Knife handle (Kozuka),Knife Handle (Kozuka),Japanese,,,,,Maker,,Ichiyosai Hironao,"Japanese, died ca. 1825–50",,Ichiyosai Hironao,Japanese,1825,1850,19th century,1801,1900,"Copper-silver alloy (shibuichi), gold, copper, silver, copper-gold alloy (shakudō)",L. 3 13/16 in. (9.7 cm); W. 9/16 in. (1.4 cm); thickness 1/4 in. (0.6 cm); Wt. 1.2 oz. (34 g),"Gift of Herman A. E. and Paul C. Jaehne, 1943",,,,,,,,,,,,Sword Furniture-Kozuka,,http://www.metmuseum.org/art/collection/search/25279,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -43.120.451,false,true,25325,Arms and Armor,Knife handle (Kozuka),Knife Handle (Kozuka),Japanese,,,,,Maker,,Gotō Mitsuyoshi (Shinjō),"Japanese, 1780–1843, fifteenth-generation Gotō master",,Gotō Mitsuyoshi (Shinjō),Japanese,1780,1843,19th century,1801,1900,"Copper-gold alloy (shakudō), gold",L. 3 13/16 in. (9.7 cm); W. 9/16 in. (1.4 cm); thickness 1/4 in. (0.6 cm); Wt. 1.4 oz. (39.7 g),"Gift of Herman A. E. and Paul C. Jaehne, 1943",,,,,,,,,,,,Sword Furniture-Kozuka,,http://www.metmuseum.org/art/collection/search/25325,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -43.120.529,false,true,25353,Arms and Armor,Knife handle (Kozuka),Knife Handle (Kozuka),Japanese,,,,,Maker,,Hamano Naoyuki,"Japanese, 1754–1795",,Hamano Naoyuki,Japanese,1754,1795,late 18th century,1775,1799,"Copper-silver alloy (shibuichi), gold",L. 3 13/16 in. (9.7 cm); W. 9/16 in. (1.4 cm); thickness 3/16 in. (0.5 cm); Wt. 1.1 oz. (31.2 g),"Gift of Herman A. E. and Paul C. Jaehne, 1943",,,,,,,,,,,,Sword Furniture-Kozuka,,http://www.metmuseum.org/art/collection/search/25353,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -43.120.253,false,true,25251,Arms and Armor,Knife handle (Kozuka),Knife Handle (Kozuka),Japanese,,,,,Maker,,Hamano Haruchika,"Japanese, died ca.1850",,Hamano Haruchika,Japanese,1850,1850,early 19th century,1801,1850,"Copper-silver alloy (shibuichi), gold",L. 3 13/16 in. (9.7 cm); W. 9/16 in. (1.4 cm); thickness 3/16 in. (0.5 cm); Wt. 1.1 oz. (31.2 g),"Gift of Herman A. E. and Paul C. Jaehne, 1943",,,,,,,,,,,,Sword Furniture-Kozuka,,http://www.metmuseum.org/art/collection/search/25251,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -43.120.391,false,true,25300,Arms and Armor,Knife handle (Kozuka),Knife Handle (Kozuka),Japanese,,,,,Maker,,Marukawa Hiroyoshi,"Japanese, died 1841 or 1842",,Marukawa Hiroyoshi,Japanese,1841,1841,early 19th century,1801,1850,"Copper-silver alloy (shibuichi), gold, silver, copper-gold alloy (shakudō)",L. 3 3/4 in. (9.5 cm); W. 9/16 in. (1.4 cm); thickness 1/4 in. (0.6 cm); Wt. 1.1 oz. (31.2 g),"Gift of Herman A. E. and Paul C. Jaehne, 1943",,,,,,,,,,,,Sword Furniture-Kozuka,,http://www.metmuseum.org/art/collection/search/25300,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -43.120.356,false,true,25282,Arms and Armor,Knife handle (Kozuka),Knife Handle (Kozuka),Japanese,,,,,Maker,,Ichijosai Hironaga (Hirotoshi),"Japanese, died ca. 1800–25",,Ichijosai Hironaga (Hirotoshi),Japanese,1800,1825,early 19th century,1801,1850,"Copper-silver alloy (shibuichi), copper, gold",L. 3 13/16 in. (9.7 cm); W. 9/16 in. (1.4 cm); thickness 3/16 in. (0.5 cm); Wt. 1.2 oz. (34 g),"Gift of Herman A. E. and Paul C. Jaehne, 1943",,,,,,,,,,,,Sword Furniture-Kozuka,,http://www.metmuseum.org/art/collection/search/25282,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -11.105,false,true,22137,Arms and Armor,Okimono in the form of an eagle,Eagle with Outstreched Wings,Japanese,,,,,Artist,,Suzuki Chōkichi,"Japanese, 1848–1919",,"Japanese Chōkichi, Suzuki",Japanese,1848,1919,late 19th century,1850,1900,"Iron, pigment, shakudo, shibuichi, wood","H. (without base), 17 in. (43.2 cm); W. of wingspan, 55 in. (139.7 cm)","Gift of James R. Steers, 1911",,,,,,,,,,,,Miscellaneous-Ironwork,,http://www.metmuseum.org/art/collection/search/22137,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"1997.194.1, .2",false,true,24964,Arms and Armor,Stirrups (Abumi),Stirrups (Abumi),Japanese,,,,,Artist,,Ujiyoshi,"Japanese, active 18th century",,Ujiyoshi,Japanese,0018,0018,18th century,1701,1800,"Iron, silver",H. of each 10 in. (25.4 cm); L. of each 11 1/4 in. (28.6 cm); W. of each 5 1/4 in. (13.3 cm); Wt. of each 5 lb. 6 oz. (2438 g),"Purchase, Gift of Estate of James Hazen Hyde, by exchange, 1997",,,,,,,,,,,,Equestrian Equipment-Stirrups,,http://www.metmuseum.org/art/collection/search/24964,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"04.3.195, .196",false,true,21924,Arms and Armor,Pair of flintlock pistols,"Pair of Flintlock Pistols Made for Christian Ernst, Margrave of Brandenburg-Ansbach-Bayreuth-Kulmbach","Franco-German, Erlangen-Neustadt",,,,,Gunsmith|Barrelsmith,,Charles Froment|M. Bréat,"French, active Germany, 1657–1722|French, active Germany, ca. 1680–90",,"Froment, Charles|Bréat, M.",French|French,1657 |1680,1722 |1680,ca. 1686–90,1661,1715,"Steel, wood, silver",L. of each 19 3/4 in. (50.2 cm); L. of each barrel 12 5/8 in. (32.1 cm); L. of each plug 2 3/8 in. (6 cm); Diam. at muzzle of each 5/8 in. (1.6 cm); Diam. at breech of each 1 in. (2.5 cm); L. of each lock 5 in. (12.7 cm); Cal. of each .545 in. (13.8 mm); Wt. of each 2 lb. 2 oz. (950 g),"Rogers Fund, 1904",,Erlangen,,,,,,,,,,Firearms-Pistols-Flintlock,,http://www.metmuseum.org/art/collection/search/21924,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2014.595,false,true,667967,Arms and Armor,Smallsword,Smallsword Presented by the City of Paris to Commandant Ildefonse Favé (1812–1894),French,,,,,Hilt Maker|Manufacturer,,Paul Bled|Lepage-Moutier,"French, Falaise 1807–1881|French 1842–1868",,"Bled, Paul|Lepage-Moutier",French|French,1807 |1842,1881 |1868,dated 1856,1831,1881,"Steel, gold",L. 36 in. (91.5 cm); L. of blade 30 1/8 in. (76.6 cm); W. 3 1/4 in. (8.3 cm); Wt. 1 lb. 0.6 oz. (470.6 g),"Gift of Peter Finer, in honor of Stuart Pyhrr, 2014",,,,,,,,,,,,Swords,,http://www.metmuseum.org/art/collection/search/667967,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1991.5,false,true,24946,Arms and Armor,Double-barreled percussion shotgun,Double-Barreled Percussion Shotgun,"French, Paris",,,,,Barrelsmith|Gunsmith,,Léopold Bernard|Louis Perrin,"French, Paris, active 1832–70|French, Paris, active 1823–65",,"Bernard, Léopold|Perrin Louis",French|French,1832 |1823,1870 |1865,dated 1854,1854,1854,"Steel, wood (walnut), silver",L. 46 3/5 in. (118.4 cm); L. of barrel 29 1/8 in. (73.9 cm),"Purchase, Rogers Fund, The Sulzberger Foundation Inc. Gift, Gifts of William H. Riggs, Bill and Joyce Anderson, Charles M. Schott Jr., Mr. and Mrs. Robert W. de Forest, William B. Osgood Field, Christian A. Zabriskie, Dr. Albert T. Weston, Henry Victor Burgy, and Bequest of Alan Rutherfurd Stuyvesant, by exchange, and The Collection of Giovanni P. Morosini, presented by his daughter Giulia, John Stoneacre Ellis Collection, Gift of Mrs. Ellis and Augustus Van Horne Ellis, and Bashford Dean Memorial Collection, funds from various donors, by exchange, 1991",,Paris,,,,,,,,,,Firearms,,http://www.metmuseum.org/art/collection/search/24946,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1990.114.1,false,true,24942,Arms and Armor,Flintlock gun,Flintlock Gun,"French, Paris",,,,,Barrelsmith|Gunsmith,,Le Faure|Mollier,"French, Paris, active ca. 1750–90|French, Paris, active ca. 1750",,Le Faure|Mollier,French|French,1725 |1725,1815 |1775,ca. 1750,1725,1775,"Steel, gold, wood (walnut), silver",L. 58 in. (147.3 cm),"Purchase, Annie Laurie Aitken Charitable Trust Gift, 1990",,Paris,,,,,,,,,,Firearms,,http://www.metmuseum.org/art/collection/search/24942,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -27.203,false,true,23015,Arms and Armor,Double-barreled flintlock shotgun,Double-Barreled Flintlock Shotgun,"French, Paris",,,,,Gunsmith|Barrelsmith,,François Pirmet|Jean Le Clerc,"French, Paris, recorded 1779–1818|French, Paris, recorded 1807–10",,"Pirmet, François|Le Clerc, Jean",French|French,1779 |1807,1818 |1810,dated 1809,1809,1809,"Steel, wood (walnut), silver, gold",L. 48 in. (121.9 cm); L. of barrel 32 1/2 in. (82.6 cm); L. of lock 5 1/4 in. (13.3 cm); Cal. .59 in. (15.1 mm); Wt. 6 lb. 10 oz. (3000 g),"Rogers Fund, 1927",,Paris,,,,,,,,,,Firearms,,http://www.metmuseum.org/art/collection/search/23015,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1982.136,false,true,24891,Arms and Armor,Hunting sword,Hunting Sword of Prince Camillo Borghese (1775–1832),"French, Paris",,,,,Goldsmith|Maker,,Antoine-Modeste Fournera|François Pirmet,"French, Paris, documented 1806–17|French, Paris, recorded 1779–1818",,"Fournera, Antoine-Modeste|Pirmet, François",French|French,1806 |1779,1817 |1818,1809–13,1809,1813,"Silver-gilt, steel, leather, mother-of-pearl",L. with scabbard 27 in. (68.6 cm); Wt. with scabbard 1 lb. 10 oz. (737 g); L. of sword 25 7/16 in. (64.6 cm); W. of hilt 4 5/8 in. (11.7 cm); L. of blade 19 3/4 in. (50.2 cm); W. of blade 1 3/16 in. (3 cm); L. of scabbard 21 3/8 in. (54.3 cm),"Purchase, The Sulzberger Foundation, Inc. and David G. Alexander Gifts, and Bequest of Stephen V. Grancsay, by exchange, 1982",,Paris,,,,,,,,,,Swords-Hunting,,http://www.metmuseum.org/art/collection/search/24891,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.229,false,true,27164,Arms and Armor,Sword hilt,Sword Hilt,"French, Paris",,,,,Designer|Goldsmith,Designed and modeled by|Executed by,Albert-Ernest Carrier-Belleuse|Lucien Falize,"French, Anizy-le-Château 1824–1887 Sèvres|French, Paris, 1842–1897",,"Carrier-Belleuse, Albert Ernest|Falize, Lucien",French|French,1824 |1842,1887 |1897,1881–82,1881,1882,"Bronze, gold",H. 6 1/2 in. (16.5 cm); W. 5 3/16 in. (13.2 cm); D. 4 1/2 in. (11.4 cm); Wt. 1 lb. 12 oz. (799.5 g),"Purchase, Gift of William H. Riggs, by exchange, 1989",,Paris,,,,,,,,,,Swords,,http://www.metmuseum.org/art/collection/search/27164,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.905,false,true,625352,Arms and Armor,Ornament print from a firearms pattern book,Plate twelve from Nouveavx Desseins D'Arquebvseries,"French, Paris",,,,,Engraver|Publisher,Published by,De Lacollombe|Gilles Demarteau,"French, Paris, active ca. 1702–ca. 1736|French, Liège 1722–1776 Paris",,"Lacollombe, De|Demarteau, Gilles",French|French,1702 |1722,1736 |1776,dated 1736,1736,1736,Engraving,7 1/2 x 9 3/8 in. (19 x 23.8 cm),"Purchase, Mr. and Mrs. Robert G. Goelet Gift, 2013",,Paris,,,,,,,,,,Works on Paper,,http://www.metmuseum.org/art/collection/search/625352,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.906,false,true,625353,Arms and Armor,Ornament print from a firearms pattern book,Plate three from Nouveavx Desseins D'Arquebvseries,"French, Paris",,,,,Engraver|Publisher,,De Lacollombe|Gilles Demarteau,"French, Paris, active ca. 1702–ca. 1736|French, Liège 1722–1776 Paris",,"Lacollombe, De|Demarteau, Gilles",French|French,1702 |1722,1736 |1776,ca. 1705–30,1680,1755,Engraving,10 1/2 x 7 1/2 in. (26.7 x 19.1 cm),"Purchase, Bequest of Stephen V. Grancsay, Rogers Fund, Helmut Nickel Gift, and funds from various donors, by exchange, 2013",,Paris,,,,,,,,,,Works on Paper,,http://www.metmuseum.org/art/collection/search/625353,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.907,false,true,625354,Arms and Armor,Ornament print from a firearms pattern book,Plate seven from Nouveavx Desseins D'Arquebvseries,"French, Paris",,,,,Engraver|Publisher,,De Lacollombe|Gilles Demarteau,"French, Paris, active ca. 1702–ca. 1736|French, Liège 1722–1776 Paris",,"Lacollombe, De|Demarteau, Gilles",French|French,1702 |1722,1736 |1776,ca. 1705–30,1680,1755,Engraving,11 x 8 1/4 in. (27.9 x 20.9 cm),"Purchase, Bequest of Stephen V. Grancsay, Rogers Fund, Helmut Nickel Gift, and funds from various donors, by exchange, 2013",,Paris,,,,,,,,,,Works on Paper,,http://www.metmuseum.org/art/collection/search/625354,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"04.3.64, .65; 14.25.454",false,true,626023,Arms and Armor,Three partisans,"Three Partisans Carried by the Bodyguard of Louis XIV (1638–1715, reigned from 1643)","French, Paris",,,,,Designer|Sword cutler,14.25.454 designed by|Inscription on 04.3.65 probably refers to,Jean Berain|Bonaventure Ravoisie,"French, Saint-Mihiel 1640–1711 Paris|French, Paris, recorded 1678–1709",,"Berain, Jean|Ravoisie, Bonaventure",French|French,1640 |1678,1711 |1709,ca. 1670–80,1645,1705,"Steel, blued and damascened with gold; wood; textile","04.3.64: Head, 20 3/4 x 5 7/8 in. (52.7 x 15 cm); Overall, 94 3/8 in. (239.7 cm); 04.3.65: Head, 22 9/16 x 6 1/2 in. (57.3 x 16.5 cm); Overall, 94 1/8 in. (239 cm); Head, 20 9/16 x 6 1/16 in. (52.2 x 15.4 cm); Overall, 86 11/16 in. (220.2 cm)","04.3.64, .65: Rogers Fund, 1904; 14.25.454: Gift of William H. Riggs, 1913",,,,,,,,,,,,Shafted Weapons,,http://www.metmuseum.org/art/collection/search/626023,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"28.196.1a, b, .2a–l",false,true,23052,Arms and Armor,Cased pair of flintlock pistols with accessories,Cased Pair of Flintlock Pistols with Accessories,"French, Paris",,,,,Lock maker|Mount maker,Silver mounts hallmarked by,François Pirmet|Nicolas Noël Boutet,"French, Paris, recorded 1779–1818|French, Versailles and Paris, 1761–1833",,"Pirmet, François|Boutet, Nicholas-Noël",French|French,1779 |1761,1818 |1833,ca. 1810,1785,1835,"Steel, gold, wood (walnut, mahogany), silver, tortoiseshell, velvet","L. of each pistol 15 1/4 in. (38.7 cm); L. of each barrel 9 3/8 in. (23.8 cm); Cal. of barrel 28.196.1a, .48 in. (12 mm); Cal. of barrel 28.196.1b, .49 in. (12 mm); Wt. of each pistol 2 lb. 5 oz. (1050 g)","Rogers Fund, 1928",,Paris,,,,,,,,,,Firearms-Pistols,,http://www.metmuseum.org/art/collection/search/23052,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"04.3.192, .193",false,true,21927,Arms and Armor,Pair of wheellock pistols,Pair of Wheellock Pistols,"French, Paris",,,,,Gunsmith|Engraver,Gilt foliate decoration attributed to,François Du Clos|Thomas Picquot,"French, Paris, recorded 1636–active ca. 1650|French, Paris, recorded 1636–38",,"Du Clos, François|Picquot, Thomas",French|French,1636 |1636,1675 |1638,ca. 1640,1615,1665,"Steel, gold, brass, wood, silver, mother-of-pearl",04.3.192; L. 23 1/8 in. (58.7 cm); L. of barrel 15 1/2 in. (39.4 cm); Cal. .52 in. (13.2 mm); Wt. 2 lb. 3 oz. (992 g) 04.3.193; L. 23 1/4 in. (59.1 cm); L. of barrel 15 1/2 in. (39.4 cm); Cal. .53 in. (13.5 mm); Wt. 2 lb. 3 oz. (992 g),"Rogers Fund, 1904",,Paris,,,,,,,,,,Firearms-Pistols-Wheellock,,http://www.metmuseum.org/art/collection/search/21927,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -28.196.5–.6a–c,false,true,23050,Arms and Armor,Cased pair of double-barreled turn-off flintlock pistols,Cased Pair of Double-Barreled Turn-Off Flintlock Pistols,"French, Paris",,,,,Gunsmith|Engraver,Possibly,Jean Lepage|Fleury Montagny,"French, Paris, 1746–1834|French, born St. Étienne, February 4, 1760–died 1836, Marseilles",,"Lepage, Jean|Montagny, Fleury",French|French,1746 |1760,1834 |1836,ca. 1800,1775,1825,"Steel, wood (boxwood), brass, velvet",L. of each pistol 8 in. (20.3 cm); L. of each barrel 3 7/8 in. (9.8 cm); Cal. of each .46 in. (11.7 mm); Wt. of each pistol 1 lb. 5 oz. (600 g); bullet mould (28.196.6a); L. 5 3/4 in. (14.6 cm); Wt. 4.3 oz. (121.9 g); wrench (28.196.6b); L. 3 7/8 in. (9.8 cm); Wt. 2.6 oz. (73.7 g); case (28.196.6c); H. 3 7/16 in. (8.7 cm); W. 11 1/2 in. (29.2 cm); D. 6 7/8 in. (17.5 cm); Wt. 2 lb. 15 oz. (1332.4 g),"Rogers Fund, 1928",,Paris,,,,,,,,,,Firearms-Pistols-Flintlock,,http://www.metmuseum.org/art/collection/search/23050,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -54.173,false,true,27552,Arms and Armor,Drawing,Design for the Right Pauldron of a Parade Armor,"French, Paris",,,,,Artist|Artist,or attributed to|Attributed to,Jean Cousin the Elder|Étienne Delaune,"French, Souci (?) ca. 1490–ca. 1560 Paris (?)|French, Orléans 1518/19–1583 Strasbourg",,"Cousin, Jean, the Elder|Delaune, Étienne",French|French,1485 |1518,1565 |1583,ca. 1555,1530,1580,Pen and ink with watercolor wash on paper,10 x 6 9/16 in. (25.4 x 16.6 cm),"Rogers Fund, 1954",,Paris,,,,,,,,,,Works on Paper-Drawings,,http://www.metmuseum.org/art/collection/search/27552,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.104,false,true,23943,Arms and Armor,Detached flintlock,Detached Flintlock alla Fiorentina,"Italian, Bargi",,,,,Gunsmith|Gunsmith,or,"Matteo Cecchi, called Acquafresca|Sebastiano Cecchi, called Acquafresca","Italian, Bargi, 1651–1738|Italian, Bargi, 1619–1692",,"Cecchi, Matteo|Cecchi, Sebastiano",Italian|Italian,1651 |1619,1738 |1692,dated 1679,1679,1679,Steel,H. 2 3/4 in. (7.0 cm); W. 5 3/4 in. (14.6 cm),"Rogers Fund, 1933",,Bargi,,,,,,,,,,Firearms-Pistols-Snaphaunce,,http://www.metmuseum.org/art/collection/search/23943,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.150.9,false,true,26585,Arms and Armor,Composed armor for man and horse,Composed Armor for Man and Horse,"European, Italy, Spain, Flanders and England",,,,,Armorer|Armorer,Left reinforcing elbow stamped with marks belonging to the|Shaffron stamped with marks attributed to,Missaglia workshop|Ambrogio de Osma,"Italian, Milan, recorded 1430–1529|Italian, Brescia, documented 1446–75",,"Missaglia workshop|Osma, Ambrogio de",Italian|Italian,1430 |1446,1529 |1475,ca. 1450–1525 and later,1425,1550,"Steel, brass, leather, textile (velvet, wool), iron",Wt. of armor for man 59 lb. 4 oz. (26.88 kg); Wt. of helmet 3 lb. 12 oz. (1697 g); 29.150.9v (mail shirt): H. 31 1/2 in. (80.0 cm); W. 45 11/16 in. (116.0 cm); W. of chest 24 in. (61.0 cm); Diam. (outside) of solid links 13/32 in. (10.2 mm); Diam. (inside) of solid links 9/32 in. (7.3 mm); Diam. (outside) of riveted links 3/8 in. (9.5 mm); Diam. (inside) of riveted links 9/32 in. (7.0 mm); Diam. (outside) of latten solid links 3/8 in. (9.1 mm); Diam. (inside) of latten solid links 1/4 in. (6.4 mm); Diam. (outside) of latten riveted links 3/8 in. (9.3 mm); Diam. (inside) of lattened riveted links 5/16 in. (7.8 mm); Diam. (outside) of collar links 11/32 in. (8.5 mm); Diam. (inside) of collar links 1/4 in. (6.2 mm).,"Bashford Dean Memorial Collection, Bequest of Bashford Dean, 1928",,,,,,,,,,,,Armor for Horse and Man,,http://www.metmuseum.org/art/collection/search/26585,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -49.163.5,false,true,24811,Arms and Armor,Carbine,Carbine with Flintlock alla Fiorentina,"Italian, Brescia",,,,,Steel-chiseler|Barrelsmith,,Carlo Bottarelli|Giovanni Lazzarino Cominazzo,"Italian, Brescia, active ca. 1660–90|Italian, Brescia, active mid-17th century",,"Bottarelli, Carlo|Cominazzo, Giovanni Lazzarino",Italian|Italian,1635 |1625,1715 |1675,ca. 1660–70,1635,1695,"Steel, wood (walnut)",L. 37 3/4 in. (95.9 cm),"Gift of Alan Rutherfurd Stuyvesant, 1949",,Brescia,,,,,,,,,,Firearms,,http://www.metmuseum.org/art/collection/search/24811,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"47.110.1, .2",false,true,24784,Arms and Armor,Pair of wheellock pistols,Pair of Wheellock Pistols,"Italian, Brescia",,,,,Lock maker|Barrelsmith,Pistols made and decorated by,Giovan Antonio Gavacciolo|Lazarino Cominazzo,"Italian, Brescia, active mid-17th century|Italian, Brescia, active mid-17th century",,"Gavacciolo, Giovan Antonio|Cominazzo, Lazarino",Italian|Italian,1625 |1625,1675 |1675,mid-17th century,1625,1675,"Steel, wood (walnut)",L. of 47.110.1: 22 11/16 in. (57.7 cm); Cal. of 47.110.1: .495 in. (12.6 mm); Wt. of 47.110.1: 2 lb. 3 oz. (997 g); L. of 47.110.2: 22 13/16 in. (57.9 cm); Cal. of 47.110.2: .485 in. (12.3 mm); Wt. of 47.110.2: 2 lb. 3 oz. (1003 g),"Rogers Fund, 1947",,Brescia,,,,,,,,,,Firearms-Pistols-Wheellock,,http://www.metmuseum.org/art/collection/search/24784,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1990.137a–j,false,true,24941,Arms and Armor,Percussion revolver with case,"Cased Six-Barreled Revolving Percussion Pistol (""Pepperbox"")","American, Norwich, Connecticut",,,,,Manufacturer|Manufacturer,,Ethan Allen|Charles T. Thurber,"American, 1808–1871|American, active 1837–55",,"Allen|Thurber, Charles T.",American|American,1808 |1837,1871 |1855,1842–47,1842,1847,"Steel, gold, silver, ivory, wood (rosewood), velvet",L. of pistol 7 in. (17.78 cm); case 12 15/16 x 8 3/16 x 2 5/8 in. (32.84 x 20.78 x 6.65 cm),"Gift of Eric Vaule, 1990",,Norwich,Connecticut,,,,,,,,,Firearms-Pistols-Percussion,,http://www.metmuseum.org/art/collection/search/24941,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1992.375,false,true,26562,Arms and Armor,Percussion target rifle,Percussion Target Rifle,"American, New Bedford, Massachusetts",,,,,Gunsmith|Gunsmith,,Julius Grudchos|Selmar Eggers,"American, active ca. 1856–1860|American, active ca. 1856–1860",,Grudchos Julius|Eggers Selmar,American|American,1856 |1856,1860 |1860,ca. 1855–60,1830,1885,"Wood (walnut), steel, silver, gold, baleen, ivory",L. 49 1/4 in. (125.1 cm),"Purchase, Bashford Dean Memorial Collection, Funds from various donors, by exchange, 1992",,New Bedford,Massachusetts,,,,,,,,,Firearms-Guns-Percussion,,http://www.metmuseum.org/art/collection/search/26562,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -59.143.3,false,true,24847,Arms and Armor,Percussion revolver,"Colt Navy Percussion Revolver, Confederate Model, serial no. 2651","American, Griswoldville, Georgia",,,,,Designer|Manufacturer,,Samuel Colt|The Griswold and Grier Company,"American, Hartford, Connecticut 1814–1862|American, 19th century",,"Colt, Samuel|The Griswold and Grier Company",American|American,1814 |1801,1862 |1900,1862–64,1862,1864,"Steel, brass, wood",L. 13 1/8 in. (33.3 cm); L. of barrel 7 1/2 in. (19.1 cm); Cal. .36 in. (9 mm),"Gift of John E. Parsons, 1959",,Griswoldville,Georgia,,,,,,,,,Firearms-Pistols-Revolvers,,http://www.metmuseum.org/art/collection/search/24847,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2014.699,false,true,675957,Arms and Armor,Revolver,"Colt Model 1862 Police Revolver, Serial No. 38549","American, Hartford, Connecticut and New York",,,,,Manufacturer|Designer,Grip designed by,Colt's Patent Fire Arms Manufacturing Company|John Quincy Adams Ward,"American, Hartford, Connecticut, 1855–present|American, Urbana, Ohio 1830–1910 New York",,"Colt's Patent Fire Arms Manufacturing Company|Ward, John Quincy Adams",American|American,1855 |1830,2014 |1910,ca. 1868,1840,1890,"Steel, gold, copper alloy (brass)",L. of pistol 11 in. (27.9 cm); Cal. .36 in. (9.1 mm),"Gift of W. C. Foxley, 2014",,Hartford|New York,Connecticut|New York,,,,,,,,,Firearms-Pistols,,http://www.metmuseum.org/art/collection/search/675957,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.2028,false,true,156809,Costume Institute,Fan,Fan,Dutch,,,,,Artist,,Carl Gustav Klingstedt,"Swedish, 1657–1734",,Klingstedt Carl Gustav,Swedish,1657,1734,1715–25,1715,1725,"ivory, mother-of-pearl, parchment, gouache, paint",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Millicent V. Hearst, 1962",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/156809,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2004.28a, b",false,true,98556,Costume Institute,Necklace,Necklace,Swiss,,,,,Designer,,Jean Dunand,"French (born Switzerland), Lancy 1877–1942 Paris",,"Dunand, Jean","French, born Switzerland",1877,1942,ca. 1927,1922,1932,"(a,b) metal, lacquer",,"Purchase, Friends of The Costume Institute Gifts, 2004",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/98556,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"C.I.61.7a, b",false,true,100854,Costume Institute,Evening dress,Evening dress,French,,,,,Design House,,Francevramant,"France, founded 1935",,Francevramant,France,1935,1935,1934–36,1934,1936,silk,,"Gift of Mrs. Leonard Feist, 1961",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/100854,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.3041a–c,false,true,157935,Costume Institute,Wedding Corset,Wedding corset,French,,,,,Department Store|Manufacturer,,Stern Brothers|Corset Parisien,"American, founded New York, 1867|French",,Stern Brothers|Corset Parisien,American|French,1867,2001,1881–82,1881,1882,"silk, baleen, cotton, metal",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mrs. M. Disbrow, 1944",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/157935,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1974.95,false,true,81638,Costume Institute,Dinner dress,Dinner dress,French,,,,,Designer,,Vitaldi Babani,"French, born Middle East, active 1895–1940",,"Babani, Vitaldi","French, born Middle East",1895,1940,ca. 1919,1914,1924,"silk, fur, metallic thread",,"Gift of Anita Zahn, 1974",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/81638,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1992.69,false,true,81635,Costume Institute,Dress,Dress,French,,,,,Designer,,Vitaldi Babani,"French, born Middle East, active 1895–1940",,"Babani, Vitaldi","French, born Middle East",1895,1940,ca. 1920,1915,1925,"silk, metal",,"Purchase, Barbara and Gregory Reynolds Gift, 1992",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/81635,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.274,false,true,81637,Costume Institute,Evening dress,Evening dress,French,,,,,Designer,,Vitaldi Babani,"French, born Middle East, active 1895–1940",,"Babani, Vitaldi","French, born Middle East",1895,1940,early 1920s,1920,1925,"silk, metallic thread",,"Gift of Anita Zahn, 1976",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/81637,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2006.465,false,true,135706,Costume Institute,Evening coat,Evening coat,French,,,,,Designer,,Vitaldi Babani,"French, born Middle East, active 1895–1940",,"Babani, Vitaldi","French, born Middle East",1895,1940,early 1920s,1920,1925,"silk, metal",,"Gift of Richard and Judith Webb, 2006",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/135706,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2008.273,false,true,145416,Costume Institute,Robe,Robe,French,,,,,Designer,,Vitaldi Babani,"French, born Middle East, active 1895–1940",,"Babani, Vitaldi","French, born Middle East",1895,1940,1920s,1920,1929,"silk, metallic",,"Gift of Jacqueline Loewe Fowler, 2008",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/145416,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.134.15,false,true,81636,Costume Institute,Evening dress,Evening dress,French,,,,,Designer,,Vitaldi Babani,"French, born Middle East, active 1895–1940",,"Babani, Vitaldi","French, born Middle East",1895,1940,1920–23,1920,1923,"silk, metallic thread",,"Gift of Alice Roosevelt Longworth, 1976",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/81636,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.45.91.2,false,true,81521,Costume Institute,Evening dress,Evening dress,French,,,,,Designer,,Vitaldi Babani,"French, born Middle East, active 1895–1940",,"Babani, Vitaldi","French, born Middle East",1895,1940,ca. 1925,1920,1930,"silk, metal thread",,"Gift of Mrs. Aline Bernstein, 1945",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/81521,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.45.91.3,false,true,109551,Costume Institute,Blouse,Blouse,French,,,,,Designer,,Vitaldi Babani,"French, born Middle East, active 1895–1940",,"Babani, Vitaldi","French, born Middle East",1895,1940,ca. 1925,1925,1930,silk,,"Gift of Mrs. Aline Bernstein, 1945",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/109551,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.45.91.5,false,true,96129,Costume Institute,Dress,Dress,French,,,,,Designer,,Vitaldi Babani,"French, born Middle East, active 1895–1940",,"Babani, Vitaldi","French, born Middle East",1895,1940,1925,1925,1925,[no medium available],,"Gift of Mrs. Aline Bernstein, 1945",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/96129,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.46.16.12,false,true,96130,Costume Institute,Evening dress,Evening dress,French,,,,,Designer,,Vitaldi Babani,"French, born Middle East, active 1895–1940",,"Babani, Vitaldi","French, born Middle East",1895,1940,1924–25,1924,1925,silk,,"Gift of Mrs. Sophie Gimbel, 1946",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/96130,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"1980.443.2a, b",false,true,81639,Costume Institute,Tea gown,Tea gown,French,,,,,Designer,,Vitaldi Babani,"French, born Middle East, active 1895–1940",,"Babani, Vitaldi","French, born Middle East",1895,1940,1917,1917,1917,"silk, metallic thread, cotton",,"Gift of Mrs. Benjamin H. Namm, 1980",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/81639,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2009.300.490a, b",false,true,159027,Costume Institute,Evening dress,Evening dress,French,,,,,Designer,,Vitaldi Babani,"French, born Middle East, active 1895–1940",,"Babani, Vitaldi","French, born Middle East",1895,1940,ca. 1920,1918,1922,"silk, metal",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of George Mangini, 1971",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/159027,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.66.14.2,true,true,81136,Costume Institute,Waistcoat,Waistcoat,British,,,,,Designer|Manufacturer,Textile by|Textile by,Anna Maria Garthwaite|Peter Lekeux,"British, 1690–1763|British, 1716–1768",,"Garthwaite, Anna Maria|Lekeux Peter",British|British,1690 |1716,1763 |1768,1747,1747,1747,"silk, wool, metallic",,"Purchase, Irene Lewisohn Bequest, 1966",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/81136,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.7540a–c,false,true,174593,Costume Institute,Walking suit,Walking suit,British,,,,,Design House|Designer,,House of Lucile|Lucile,"British, founded 1895|British, 1863–1935",,Lucile House of|Lucile,British|British,1895 |1863,1895 |1935,1910–12,1910,1912,silk,,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mrs. Frederick H. Prince, Jr., 1967",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/174593,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1979.116.1,false,true,81634,Costume Institute,Evening coat,Evening coat,Italian,,,,,Designer,Attributed to,Vitaldi Babani,"French, born Middle East, active 1895–1940",,"Babani, Vitaldi","French, born Middle East",1895,1940,early 1920s,1920,1925,silk,,"Gift of Beatrice S. Bartlett, 1979",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/81634,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1995.506.1,false,true,80217,Costume Institute,Dress,Dress,Italian,,,,,Designer,,Vitaldi Babani,"French, born Middle East, active 1895–1940",,"Babani, Vitaldi","French, born Middle East",1895,1940,ca. 1926,1921,1931,"silk, metal",,"Gift of Richard and Judith Webb, 1995",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/80217,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1995.506.2,false,true,80218,Costume Institute,Dress,Dress,Italian,,,,,Designer,,Vitaldi Babani,"French, born Middle East, active 1895–1940",,"Babani, Vitaldi","French, born Middle East",1895,1940,ca. 1926,1921,1931,"silk, metal",,"Gift of Richard and Judith Webb, 1995",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/80218,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1995.506.3,false,true,80219,Costume Institute,Coat,Coat,Italian,,,,,Designer,,Vitaldi Babani,"French, born Middle East, active 1895–1940",,"Babani, Vitaldi","French, born Middle East",1895,1940,ca. 1926,1921,1931,"silk, metal",,"Gift of Richard and Judith Webb, 1995",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/80219,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1978.184.4a–c,false,true,84070,Costume Institute,Costume,Costume,American,,,,,Designer,,Léon Bakst,"Russian, Grodno 1866–1924 Paris",,"Bakst, Léon",Russian,1866,1924,1922–23,1922,1923,"silk, cotton, metallic thread, glass, plastic",,"Purchase, Marcia Sand Bequest, in memory of her daughter, Tiger (Joan) Morse, 1978",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/84070,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2008.190.149,false,true,141787,Costume Institute,Pendant,"""Mobius""",American,,,,,Designer,,Georg Jensen,"Danish, Rådvad 1866–1935 Hellerup",,"Georg, Jensen",Danish,1866,1935,1969,1969,1969,metal,,"Gift of Muriel Kallis Newman, 2008",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/141787,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2010.487.6a, b",false,true,159725,Costume Institute,Evening dress,Evening dress,American,,,,,Designer,,Catherine Donovan,"American (born Ireland), 1826 (?)–1906",,"Donovan, Catherine","American, born Ireland",1826,1906,1890s,1890,1899,"silk, cotton",,"Gift of Christopher Scholz and Ines Elskop, 2010",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/159725,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2009.300.990a, b",false,true,159582,Costume Institute,Evening dress,Evening dress,American,,,,,Designer,,Catherine Donovan,"American (born Ireland), 1826 (?)–1906",,"Donovan, Catherine","American, born Ireland",1826,1906,1900–1903,1900,1903,"silk, linen, metal",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of George R. Cook III, 1980",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/159582,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.375a–c,false,true,158731,Costume Institute,Ensemble,Ensemble,American,,,,,Designer,,Catherine Donovan,"American (born Ireland), 1826 (?)–1906",,"Donovan, Catherine","American, born Ireland",1826,1906,1900–1903,1900,1903,"silk, linen",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mrs. Daniel M. McKeon and Robert Hoguet, Jr., 1965",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/158731,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2009.300.6290a, b",false,true,173337,Costume Institute,Afternoon dress,Afternoon dress,American,,,,,Designer,,Catherine Donovan,"American (born Ireland), 1826 (?)–1906",,"Donovan, Catherine","American, born Ireland",1826,1906,ca. 1883,1881,1885,Silk,,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of the Princess Viggo in accordance with the wishes of the Misses Hewitt, 1931",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/173337,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.7535a–d,false,true,174588,Costume Institute,Dress,Dress,American,,,,,Designer,,Catherine Donovan,"American (born Ireland), 1826 (?)–1906",,"Donovan, Catherine","American, born Ireland",1826,1906,ca. 1885,1883,1887,Cotton,,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mrs. Frederick H. Prince, Jr., 1967",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/174588,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.3120a–c,false,true,158022,Costume Institute,Waist Cincher,Waist cincher,probably French,,,,,Design House,Attributed to,Redfern,1847–1940,,Redfern,,1847,1940,ca. 1900,1898,1902,"silk, bone, metal, elastic, cotton",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of E. A. Meister, 1950",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/158022,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.1240,false,true,155934,Costume Institute,Evening overdress,Evening overdress,probably French,,,,,Design House,Attributed to,Callot Soeurs,"French, active 1895–1937",,Callot Soeurs,,1895,1937,ca. 1920,1918,1922,"silk, linen, metal",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mercedes de Acosta, 1955",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/155934,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.40.130.2a–c,false,true,106752,Costume Institute,Ensemble,Ensemble,American or European,,,,,Design House,Attributed to,Callot Soeurs,"French, active 1895–1937",,Callot Soeurs,,1895,1937,1875–1925,1875,1925,silk,,"Gift of Miss Mercedes de Acosta, 1940",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/106752,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.1771,false,true,156523,Costume Institute,Bonnet,Bonnet,probably French,,,,,Design House,,Redfern,1847–1940,,Redfern,,1847,1940,ca. 1888,1886,1890,silk,,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mrs. James Dowd Lester, 1942",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/156523,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.3278a–c,false,true,158197,Costume Institute,Waist Cincher,Waist cincher,French,,,,,Design House,,Redfern,1847–1940,,Redfern,,1847,1940,1900–1910,1900,1910,"silk, bone, metal",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mrs. George A. Bonaventure in memory of Mrs. James Steel, 1965",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/158197,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.3768a–c,false,true,158740,Costume Institute,Ensemble,Ensemble,French,,,,,Design House,,Redfern,1847–1940,,Redfern,,1847,1940,ca. 1930,1928,1932,"silk, wool, beads, metal, fur",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mrs. Robert S. Kilborne, 1958",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/158740,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2015.732a, b",false,true,693846,Costume Institute,Dress,Dress,French,,,,,Design House|Designer,,House of Lanvin|Jeanne Lanvin,"French, founded 1889|French, 1867–1946",,"Lanvin, House of|Lanvin, Jeanne",,1889 |1867,9999 |1946,winter 1926–27,1926,1927,"silk, metal",,"Gift of Dean L. Merceron, 2015",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/693846,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -49.3.32a–e,false,true,107066,Costume Institute,Ensemble,Ensemble,French,,,,,Designer,,Redfern,1847–1940,,Redfern,,1847,1940,1887–89,1887,1889,"wool, silk, cotton, metallic thread",,"Gift of Orme Wilson and R. Thornton Wilson, in memory of their mother, Mrs. Caroline Schermerhorn Astor Wilson, 1949",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/107066,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.258,false,true,157422,Costume Institute,Dinner dress,Dinner dress,British,,,,,Design House,,Redfern,1847–1940,,Redfern,,1847,1940,1909–11,1909,1911,"silk, metal",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mrs. Robert S. Kilborne, 1958",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/157422,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.3189,false,true,158098,Costume Institute,Evening cape,Evening cape,British,,,,,Design House,,Redfern,1847–1940,,Redfern,,1847,1940,1901,1901,1901,silk,,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mrs. Robert S. Kilborne, 1958",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/158098,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2009.300.107a, b",false,true,155754,Costume Institute,Walking suit,Walking suit,British,,,,,Design House,,Redfern,1847–1940,,Redfern,,1847,1940,ca. 1910,1908,1912,wool,,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mrs. J. W. Post, 1944",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/155754,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2009.300.6575a, b",false,true,173574,Costume Institute,Dinner dress,Dinner dress,American,,,,,Retailer,,"Wechsler, Abraham & Company",,,"Wechsler, Abraham & Company",,1865,1994,1876–78,1876,1878,Silk,,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mrs. James McF. Baker, 1948",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/173574,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"1981.515.1a, b",false,true,92336,Costume Institute,Dress,Dress,American,,,,,Designer,,Redfern,1847–1940,,Redfern,,1847,1940,ca. 1892,1887,1897,"silk, cotton",,"Gift of Mrs. Peter H. B. Frelinghuysen, 1981",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/92336,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.3009a–d,false,true,157899,Costume Institute,Evening dress,Evening dress,American,,,,,Designer,,Mme. Olympe,"American, born France, 1830",,Olympe Mme.,,1830,1930,ca. 1865,1863,1867,"silk, mother-of-pearl",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mrs. H. E. Rifflard, 1932",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/157899,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.69.9.1,false,true,106486,Costume Institute,Jacket,Jacket,probably French,,,,,Designer,,Revillon Frères,"French, founded 1723",,Revillon Frères,French,1723,1850,1902–3,1902,1903,"fur, silk",,"Gift of Mr. Hayward R. Alker, 1969",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/106486,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.38.98.2,false,true,106446,Costume Institute,Evening coat,Evening coat,American or European,,,,,Designer,Attributed to,Callot Soeurs,"French, active 1895–1937",,Callot Soeurs,French,1895,1937,1908–10,1908,1910,"silk, fur",,"Gift of Mrs. Sidney W. Ffoulkes, 1938",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/106446,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.45.36.2,false,true,106461,Costume Institute,Dress,Dress,French,,,,,Design House,,Beer,French,,Beer,French,,1929,late 19th–early 20th century,1875,1925,[no medium available],,"Gift of Mrs. George Kent, 1945",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/106461,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"C.I.64.46.3a, b",false,true,99746,Costume Institute,Dress,Dress,French,,,,,Design House,,Beer,French,,Beer,French,,1929,ca. 1925,1920,1930,"silk, metallic thread, beading",,"Gift of Mrs. William Dubilier, 1964",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/99746,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2009.300.4084a, b",false,true,170488,Costume Institute,Wedding Shoes,Wedding shoes,French,,,,,Maker|Retailer,,Esté|R.W.H. Rogers,"French, 1821–1839",,Esté|Rogers R.W.H.,French,1821,1839,1840–49,1840,1849,Silk,,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mrs. Chauncey E. Low, 1924",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/170488,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.3024,false,true,157916,Costume Institute,Evening coat,Evening coat,French,,,,,Design House,,Rouff,"French, 1844–1914",,Rouff,French,1844,1914,1895–1905,1895,1905,"silk, rhinestones",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mrs. William E. S. Griswold, 1941",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/157916,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2009.300.332a, b",false,true,158254,Costume Institute,Evening dress,Evening dress,French,,,,,Design House,,Rouff,"French, 1844–1914",,Rouff,French,1844,1914,ca. 1897,1895,1899,silk,,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mrs. Jason Westerfield, 1963",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/158254,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2009.300.376a, b",false,true,158742,Costume Institute,Dinner dress,Dinner dress,French,,,,,Design House,,Rouff,"French, 1844–1914",,Rouff,French,1844,1914,1900–1903,1900,1903,"silk, jet beads, rhinestones",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mrs. Daniel M. McKeon and Robert Hoguet, Jr., 1965",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/158742,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2009.300.2339a, b",false,true,157153,Costume Institute,Evening dress,Evening dress,French,,,,,Design House,,Rouff,"French, 1844–1914",,Rouff,French,1844,1914,ca. 1895,1893,1897,"silk, metal",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mrs. William E. S. Griswold, 1941",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/157153,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2009.300.6476a, b",false,true,173496,Costume Institute,Evening dress,Evening dress,French,,,,,Design House,,Rouff,"French, 1844–1914",,Rouff,French,1844,1914,1883–96,1883,1896,Silk,,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mrs. William E. S. Griswold, 1941",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/173496,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2009.300.6477a, b",false,true,173497,Costume Institute,Evening dress,Evening dress,French,,,,,Design House,,Rouff,"French, 1844–1914",,Rouff,French,1844,1914,ca. 1895,1893,1897,Silk,,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mrs. William E. S. Griswold, 1941",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/173497,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2009.300.8354a, b",false,true,175365,Costume Institute,Evening dress,Evening dress,French,,,,,Design House,,Rouff,"French, 1844–1914",,Rouff,French,1844,1914,1896–97,1896,1897,Silk,,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Designated Purchase Fund, 1990",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/175365,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2009.300.7006a, b",false,true,173996,Costume Institute,Dress,Dress,French,,,,,Design House,,Chéruit,"French, 1906–1935",,Chéruit,French,1906,1935,1912,1912,1912,Cotton,,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of the estate of Valerie Dreyfus, 1957",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/173996,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.47.6,false,true,99657,Costume Institute,Evening dress,Evening dress,French,,,,,Design House,,Myrbor,"French, 1922–1936",,Myrbor,French,1922,1936,1923,1923,1923,silk,,"Gift of Miss Ida Brenner, 1947",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/99657,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.3247,false,true,158163,Costume Institute,Evening dress,Evening dress,French,,,,,Design House,,Myrbor,"French, 1922–1936",,Myrbor,French,1922,1936,ca. 1926,1924,1928,"silk, metal",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mrs. V. D. Crisp, 1963",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/158163,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.3248,false,true,158164,Costume Institute,Evening dress,Evening dress,French,,,,,Design House,,Myrbor,"French, 1922–1936",,Myrbor,French,1922,1936,1924,1924,1924,"silk, metal",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mrs. V. D. Crisp, 1963",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/158164,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2009.300.3246a, b",false,true,158162,Costume Institute,Evening ensemble,Evening ensemble,French,,,,,Design House,,Myrbor,"French, 1922–1936",,Myrbor,French,1922,1936,1929,1929,1929,"silk, metal",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mrs. V. D. Crisp, 1963",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/158162,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.56.21,false,true,99596,Costume Institute,Evening coat,Evening coat,French,,,,,Design House,,Revillon Frères,"French, founded 1723",,Revillon Frères,French,1723,1850,1928,1928,1928,"silk, fur, glass, embroidery",,"Gift of Mrs. B. A. Goodman, in memory of Mrs. Gussie A. Matz, 1956",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/99596,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.59.55,false,true,99597,Costume Institute,Evening wrap,Evening wrap,French,,,,,Design House,,Revillon Frères,"French, founded 1723",,Revillon Frères,French,1723,1850,ca. 1923,1918,1928,"silk, fur",,"Gift of Mrs. Gustavus Ober Jr., 1959",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/99597,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2006.74.28,false,true,128422,Costume Institute,Cape,Cape,French,,,,,Design House,,Revillon Frères,"French, founded 1723",,Revillon Frères,French,1723,1850,ca. 1988,1983,1993,"leather, fur",,"Gift of Muriel Kallis Newman, 2006",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/128422,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.8377,false,true,175388,Costume Institute,Stole,Stole,French,,,,,Design House,,Revillon Frères,"French, founded 1723",,Revillon Frères,French,1723,1850,ca. 1935,1933,1937,"Fur, silk",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Lady Emilia Dreher Armstrong, 1993",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/175388,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2003.256.3a, b",false,true,93634,Costume Institute,Accessory Set,Accessory set,French,,,,,Design House,,Revillon Frères,"French, founded 1723",,Revillon Frères,French,1723,1850,ca. 1960,1955,1965,"a) fur, rhinestone, metal, plastic; b) fur, silk",,"Gift of Laura Johnson, 2003",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/93634,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"1973.112a, b",false,true,84498,Costume Institute,Evening shoes,Evening shoes,French,,,,,Manufacturer,,"F. Pinet, Paris","French, founded 1855",,"F. Pinet, Paris",French,1855,1855,1926,1926,1926,silk,,"Gift of Mrs. James A. Cole, 1973",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/84498,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"49.3.5a, b",false,true,105066,Costume Institute,Dress,Dress,French,,,,,Design House,,House of Rouff,"French, founded 1929",,Rouff House of,French,1929,1929,1905–7,1905,1907,silk,,"Gift of Orme Wilson and R. Thornton Wilson, in memory of their mother, Mrs. Caroline Schermerhorn Astor Wilson, 1949",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/105066,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1979.84,false,true,85998,Costume Institute,Evening dress,Evening dress,French,,,,,Design House,,Callot Soeurs,"French, active 1895–1937",,Callot Soeurs,French,1895,1937,fall/winter 1921–22,1921,1922,"silk, cotton, metal, glass",,"Gift of Sandra Grey-Fretty, 1979",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/85998,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1993.50,false,true,94273,Costume Institute,Slip,Slip,French,,,,,Design House,,Callot Soeurs,"French, active 1895–1937",,Callot Soeurs,French,1895,1937,1927,1927,1927,"silk, cotton",,"Isabel Shults Fund, 1993",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/94273,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1993.60,false,true,94277,Costume Institute,Evening dress,Evening dress,French,,,,,Design House,,Callot Soeurs,"French, active 1895–1937",,Callot Soeurs,French,1895,1937,ca. 1927,1922,1932,"silk, metal",,"Isabel Shults Fund, 1993",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/94277,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1993.95,false,true,80789,Costume Institute,Slip,Slip,French,,,,,Design House,,Callot Soeurs,"French, active 1895–1937",,Callot Soeurs,French,1895,1937,ca. 1927,1922,1932,"silk, cotton",,"Gift of Martin M. Kamer, Switzerland, 1993",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/80789,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2014.64,false,true,631535,Costume Institute,Dress,Dress,French,,,,,Design House,,Callot Soeurs,"French, active 1895–1937",,Callot Soeurs,French,1895,1937,winter 1916–17,1916,1917,"silk, glass, metal",,"Millia Davenport and Zipporah Fleisher Fund, 2013",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/631535,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -44.95.3,false,true,86000,Costume Institute,Dress,Dress,French,,,,,Design House,,Callot Soeurs,"French, active 1895–1937",,Callot Soeurs,French,1895,1937,1925–26,1925,1926,"silk, metal thread",,"Gift of Isabel Shults, 1944",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/86000,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -44.95.4,false,true,85995,Costume Institute,Dress,Dress,French,,,,,Design House,,Callot Soeurs,"French, active 1895–1937",,Callot Soeurs,French,1895,1937,1925,1925,1925,silk,,"Gift of Isabel Shults, 1944",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/85995,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -49.3.22,false,true,105415,Costume Institute,Negligée,Negligée,French,,,,,Design House,,Callot Soeurs,"French, active 1895–1937",,Callot Soeurs,French,1895,1937,1898–1902,1898,1902,silk,,"Gift of Orme Wilson and R. Thornton Wilson, in memory of their mother, Mrs. Caroline Schermerhorn Astor Wilson, 1949",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/105415,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -40.182.9,false,true,86024,Costume Institute,Ball gown,Ball gown,French,,,,,Design House,,Callot Soeurs,"French, active 1895–1937",,Callot Soeurs,French,1895,1937,1897–1905,1897,1905,"silk, metal thread",,"Gift of Grace Rainey Rogers, 1940",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/86024,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.40.4,false,true,82547,Costume Institute,Evening dress,Evening dress,French,,,,,Design House,,Callot Soeurs,"French, active 1895–1937",,Callot Soeurs,French,1895,1937,1926–27,1926,1927,"silk, metal thread",,"Gift of Mrs. John Magnin, 1940",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/82547,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.37.2,false,true,106439,Costume Institute,Evening coat,Evening coat,French,,,,,Design House,,Callot Soeurs,"French, active 1895–1937",,Callot Soeurs,French,1895,1937,ca. 1900,1895,1905,"wool, silk, fur",,"Gift of Mrs. G. Macculloch Miller, 1976",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/106439,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -35.134.11,false,true,101637,Costume Institute,Dress,Dress,French,,,,,Design House,,Callot Soeurs,"French, active 1895–1937",,Callot Soeurs,French,1895,1937,1900,1900,1900,"silk, metal thread",,"Gift of Susan Dwight Bliss, 1935",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/101637,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1977.303.1,false,true,84506,Costume Institute,Evening dress,Evening dress,French,,,,,Design House,,Callot Soeurs,"French, active 1895–1937",,Callot Soeurs,French,1895,1937,fall/winter 1920–21,1920,1921,"silk, metallic thread",,"Gift of David Toser, 1977",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/84506,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1977.303.2,false,true,85997,Costume Institute,Evening dress,Evening dress,French,,,,,Design House,,Callot Soeurs,"French, active 1895–1937",,Callot Soeurs,French,1895,1937,1922–23,1922,1923,"silk, glass, metallic threads and cellophane",,"Gift of David Toser, 1977",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/85997,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1978.288.8,false,true,86008,Costume Institute,Evening dress,Evening dress,French,,,,,Design House,,Callot Soeurs,"French, active 1895–1937",,Callot Soeurs,French,1895,1937,ca. 1925,1920,1930,"silk, metallic thread, glass",,"Gift of Julia B. Henry, 1978",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/86008,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1978.288.9,false,true,86009,Costume Institute,Dance dress,Dance dress,French,,,,,Design House,,Callot Soeurs,"French, active 1895–1937",,Callot Soeurs,French,1895,1937,1926–27,1926,1927,"silk, cotton, metallic thread, glass",,"Gift of Julia B. Henry, 1978",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/86009,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1981.380.2,true,true,81139,Costume Institute,Evening dress,Evening dress,French,,,,,Design House,,Callot Soeurs,"French, active 1895–1937",,Callot Soeurs,French,1895,1937,1910–14,1910,1914,"cotton, silk, metal",,"The Jacqueline Loewe Fowler Costume Collection, Gift of Jacqueline Loewe Fowler, 1981",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/81139,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.46.4.8,false,true,82093,Costume Institute,Evening dress,Evening dress,French,,,,,Design House,,Callot Soeurs,"French, active 1895–1937",,Callot Soeurs,French,1895,1937,1922,1922,1922,"silk, beads",,"Gift of Mrs. Harrison Williams, Lady Mendl, and Mrs. Ector Munn, 1946",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/82093,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.51.113,false,true,86002,Costume Institute,Evening dress,Evening dress,French,,,,,Design House,,Callot Soeurs,"French, active 1895–1937",,Callot Soeurs,French,1895,1937,1925–26,1925,1926,"silk, silver thread",,"Gift of Mrs. Nathaniel Bowdich Potter, 1951",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/86002,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.63.2.2,false,true,86019,Costume Institute,Evening dress,Evening dress,French,,,,,Design House,,Callot Soeurs,"French, active 1895–1937",,Callot Soeurs,French,1895,1937,1930,1930,1930,silk,,"Gift of Mrs. Russell W. Davenport, 1963",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/86019,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1978.288.10,false,true,83188,Costume Institute,Evening dress,Evening dress,French,,,,,Design House,,Callot Soeurs,"French, active 1895–1937",,Callot Soeurs,French,1895,1937,ca. 1924,1919,1929,"cotton, metallic thread, glass",,"Gift of Julia B. Henry, 1978",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/83188,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1978.288.12,false,true,86011,Costume Institute,Evening dress,Evening dress,French,,,,,Design House,,Callot Soeurs,"French, active 1895–1937",,Callot Soeurs,French,1895,1937,1924–25,1924,1925,"cotton, plastic, metallic thread",,"Gift of Julia B. Henry, 1978",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/86011,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"44.95.1a, b",false,true,86005,Costume Institute,Dress,Dress,French,,,,,Design House,,Callot Soeurs,"French, active 1895–1937",,Callot Soeurs,French,1895,1937,1925,1925,1925,silk,,"Gift of Isabel Shults, 1944",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/86005,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"44.95.2a, b",false,true,86006,Costume Institute,Dress,Dress,French,,,,,Design House,,Callot Soeurs,"French, active 1895–1937",,Callot Soeurs,French,1895,1937,1925,1925,1925,silk,,"Gift of Isabel Shults, 1944",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/86006,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.40.27.2,false,true,83430,Costume Institute,Evening dress,Evening dress,French,,,,,Design House,,Callot Soeurs,"French, active 1895–1937",,Callot Soeurs,French,1895,1937,1909–10,1909,1910,silk,,"Gift of Miss Agnes Miles Carpenter, 1940",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/83430,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.43.13.5,false,true,85993,Costume Institute,Dress,Dress,French,,,,,Design House,,Callot Soeurs,"French, active 1895–1937",,Callot Soeurs,French,1895,1937,early 1920s,1920,1925,silk,,"Gift of Mrs. John Jay Whitehead, 1943",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/85993,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.51.97.1,false,true,86026,Costume Institute,Evening dress,Evening dress,French,,,,,Design House,,Callot Soeurs,"French, active 1895–1937",,Callot Soeurs,French,1895,1937,1918–19,1918,1919,silk,,"Purchase, Irene Lewisohn Bequest, 1951",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/86026,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.51.97.4,false,true,86028,Costume Institute,Evening dress,Evening dress,French,,,,,Design House,,Callot Soeurs,"French, active 1895–1937",,Callot Soeurs,French,1895,1937,1913,1913,1913,"silk, sequins",,"Purchase, Irene Lewisohn Bequest, 1951",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/86028,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.51.97.6,false,true,106735,Costume Institute,Evening coat,Evening coat,French,,,,,Design House,,Callot Soeurs,"French, active 1895–1937",,Callot Soeurs,French,1895,1937,1916–17,1916,1917,"silk, metallic, fur",,"Purchase, Irene Lewisohn Bequest, 1951",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/106735,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.52.19.2,false,true,86003,Costume Institute,Evening dress,Evening dress,French,,,,,Design House,,Callot Soeurs,"French, active 1895–1937",,Callot Soeurs,French,1895,1937,1920s,1920,1929,"silk, metallic",,"Gift of Madame Veronique Wolf and Madame Frederic Bon, 1952",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/86003,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.52.19.3,false,true,86029,Costume Institute,Evening dress,Evening dress,French,,,,,Design House,,Callot Soeurs,"French, active 1895–1937",,Callot Soeurs,French,1895,1937,1919,1919,1919,"silk, metallic",,"Gift of Madame Veronique Wolf and Madame Frederic Bon, 1952",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/86029,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.65.16.2,false,true,96131,Costume Institute,Dress,Dress,French,,,,,Design House,,Callot Soeurs,"French, active 1895–1937",,Callot Soeurs,French,1895,1937,1908,1908,1908,[no medium available],,"Gift of Mrs. John C. Tomlinson, 1965",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/96131,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.66.43.1,false,true,86031,Costume Institute,Evening dress,Evening dress,French,,,,,Design House,,Callot Soeurs,"French, active 1895–1937",,Callot Soeurs,French,1895,1937,ca. 1910,1905,1915,silk,,"Gift of Mrs. William M. Haupt, 1966",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/86031,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.66.58.3,false,true,99777,Costume Institute,Tea gown,Tea gown,French,,,,,Design House,,Callot Soeurs,"French, active 1895–1937",,Callot Soeurs,French,1895,1937,late 1920s,1925,1929,silk,,"Gift of Mrs. Leon L. Roos, 1966",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/99777,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"35.134.6a, b",false,true,86023,Costume Institute,Dress,Dress,French,,,,,Design House,,Callot Soeurs,"French, active 1895–1937",,Callot Soeurs,French,1895,1937,1899,1899,1899,silk,,"Gift of Susan Dwight Bliss, 1935",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/86023,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.44.64.10,false,true,84585,Costume Institute,Evening dress,Evening dress,French,,,,,Design House,,Callot Soeurs,"French, active 1895–1937",,Callot Soeurs,French,1895,1937,1923–24,1923,1924,silk,,"Gift of Miss Isabel Shults, 1944",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/84585,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.44.64.12,false,true,82550,Costume Institute,Evening dress,Evening dress,French,,,,,Design House,,Callot Soeurs,"French, active 1895–1937",,Callot Soeurs,French,1895,1937,1925–26,1925,1926,silk,,"Gift of Miss Isabel Shults, 1944",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/82550,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.44.64.15,false,true,85994,Costume Institute,Evening dress,Evening dress,French,,,,,Design House,,Callot Soeurs,"French, active 1895–1937",,Callot Soeurs,French,1895,1937,1924–25,1924,1925,silk,,"Gift of Miss Isabel Shults, 1944",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/85994,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.44.64.65,false,true,86018,Costume Institute,Scarf,Scarf,French,,,,,Design House,,Callot Soeurs,"French, active 1895–1937",,Callot Soeurs,French,1895,1937,1925–26,1925,1926,silk,,"Gift of Miss Isabel Shults, 1944",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/86018,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.57.22.11,false,true,86030,Costume Institute,Evening wrap,Evening wrap,French,,,,,Design House,,Callot Soeurs,"French, active 1895–1937",,Callot Soeurs,French,1895,1937,1914,1914,1914,silk,,"Gift of Estate of Valerie Dreyfus, 1957",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/86030,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.58.34.11,false,true,82610,Costume Institute,Afternoon dress,Afternoon dress,French,,,,,Design House,,Callot Soeurs,"French, active 1895–1937",,Callot Soeurs,French,1895,1937,ca. 1926,1921,1931,silk,,"Gift of Mrs. John Chambers Hughes, 1958",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/82610,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"1973.104.3a, b",false,true,110042,Costume Institute,Dress,Dress,French,,,,,Design House,,Callot Soeurs,"French, active 1895–1937",,Callot Soeurs,French,1895,1937,ca. 1922,1917,1927,[no medium available],,"Gift of Mrs. Leon L. Roos, 1973",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/110042,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"1978.184.5a, b",false,true,83224,Costume Institute,Afternoon dress,Afternoon dress,French,,,,,Design House,,Callot Soeurs,"French, active 1895–1937",,Callot Soeurs,French,1895,1937,1915,1915,1915,"cotton, silk",,"Purchase, Marcia Sand Bequest, in memory of her daughter, Tiger (Joan) Morse, 1978",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/83224,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"1978.288.7a, b",true,true,81113,Costume Institute,Dress,Dress,French,,,,,Design House,,Callot Soeurs,"French, active 1895–1937",,Callot Soeurs,French,1895,1937,ca. 1924,1919,1929,"wool, silk, metallic thread",,"Gift of Julia B. Henry, 1978",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/81113,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"1983.290.1a, b",false,true,86021,Costume Institute,Ensemble,Ensemble,French,,,,,Design House,,Callot Soeurs,"French, active 1895–1937",,Callot Soeurs,French,1895,1937,ca. 1929,1924,1934,"silk, fur",,"Gift of Isabel Shults, 1983",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/86021,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"1978.288.11a, b",false,true,86010,Costume Institute,Evening dress,Evening dress,French,,,,,Design House,,Callot Soeurs,"French, active 1895–1937",,Callot Soeurs,French,1895,1937,1925–26,1925,1926,"cotton, silk, plastic, glass",,"Gift of Julia B. Henry, 1978",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/86010,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"C.I.40.37.2a, b",false,true,86014,Costume Institute,Coat,Coat,French,,,,,Design House,,Callot Soeurs,"French, active 1895–1937",,Callot Soeurs,French,1895,1937,1920s,1920,1929,silk,,"Gift of Mrs. William Bamberger, 1940",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/86014,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"C.I.40.90.6a, b",false,true,92226,Costume Institute,Suit,Suit,French,,,,,Design House,,Callot Soeurs,"French, active 1895–1937",,Callot Soeurs,French,1895,1937,1915–17,1915,1917,wool,,"Gift of Mme. Louis Cerlian, 1940",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/92226,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.41.107a–c,false,true,84937,Costume Institute,Cocktail Suit,Cocktail suit,French,,,,,Design House,,Callot Soeurs,"French, active 1895–1937",,Callot Soeurs,French,1895,1937,1935,1935,1935,silk,,"Gift of Mrs. William Bamberger, 1941",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/84937,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"C.I.46.46.8a, b",false,true,86001,Costume Institute,Evening dress,Evening dress,French,,,,,Design House,,Callot Soeurs,"French, active 1895–1937",,Callot Soeurs,French,1895,1937,1920s,1920,1929,[no medium available],,"Gift of Mrs. Harrison Williams, Lady Mendl, and Mrs. Ector Munn, 1946",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/86001,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"C.I.51.97.2a, b",false,true,86027,Costume Institute,Evening dress,Evening dress,French,,,,,Design House,,Callot Soeurs,"French, active 1895–1937",,Callot Soeurs,French,1895,1937,1915–16,1915,1916,"silk, metallic",,"Purchase, Irene Lewisohn Bequest, 1951",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/86027,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"C.I.52.19.1a, b",false,true,86007,Costume Institute,Dress,Dress,French,,,,,Design House,,Callot Soeurs,"French, active 1895–1937",,Callot Soeurs,French,1895,1937,1920s,1920,1929,"silk, metallic",,"Gift of Madame Veronique Wolf and Madame Frederic Bon, 1952",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/86007,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"C.I.60.42.6a, b",false,true,86025,Costume Institute,Dress,Dress,French,,,,,Design House,,Callot Soeurs,"French, active 1895–1937",,Callot Soeurs,French,1895,1937,fall/winter 1910–11,1910,1911,"silk, metal, glass",,"Gift of Mrs. Howard Crosby Brokaw, 1960",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/86025,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1978.288.13a–c,false,true,86012,Costume Institute,Evening dress,Evening dress,French,,,,,Design House,,Callot Soeurs,"French, active 1895–1937",,Callot Soeurs,French,1895,1937,ca. 1923,1918,1928,"silk, cotton, plastic",,"Gift of Julia B. Henry, 1978",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/86012,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"C.I.44.64.11a, b",false,true,86004,Costume Institute,Evening dress,Evening dress,French,,,,,Design House,,Callot Soeurs,"French, active 1895–1937",,Callot Soeurs,French,1895,1937,1925–26,1925,1926,silk,,"Gift of Miss Isabel Shults, 1944",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/86004,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"C.I.44.64.14a, b",false,true,85999,Costume Institute,Evening dress,Evening dress,French,,,,,Design House,,Callot Soeurs,"French, active 1895–1937",,Callot Soeurs,French,1895,1937,1926,1926,1926,silk,,"Gift of Miss Isabel Shults, 1944",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/85999,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"C.I.51.97.25a, b",false,true,106867,Costume Institute,Suit,Suit,French,,,,,Design House,,Callot Soeurs,"French, active 1895–1937",,Callot Soeurs,French,1895,1937,1917,1917,1917,"wool, fur",,"Purchase, Irene Lewisohn Bequest, 1951",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/106867,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.67.64.1a–c,false,true,96117,Costume Institute,Afternoon dress,Afternoon dress,French,,,,,Design House,,Callot Soeurs,"French, active 1895–1937",,Callot Soeurs,French,1895,1937,1920–22,1920,1922,[no medium available],,"Gift of Ms. Ruth T. Constantino, 1967",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/96117,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.67.64.2a–c,false,true,85996,Costume Institute,Dress,Dress,French,,,,,Design House,,Callot Soeurs,"French, active 1895–1937",,Callot Soeurs,French,1895,1937,1920–22,1920,1922,[no medium available],,"Gift of Ruth T. Costantino, 1967",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/85996,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.44.64.13a–c,false,true,84586,Costume Institute,Evening ensemble,Evening ensemble,French,,,,,Design House,,Callot Soeurs,"French, active 1895–1937",,Callot Soeurs,French,1895,1937,1925–26,1925,1926,silk,,"Gift of Miss Isabel Shults, 1944",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/84586,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.44.64.19a–d,false,true,82551,Costume Institute,Pajamas,Pajamas,French,,,,,Design House,,Callot Soeurs,"French, active 1895–1937",,Callot Soeurs,French,1895,1937,1926–27,1926,1927,silk,,"Gift of Miss Isabel Shults, 1944",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/82551,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.51.70.18a–d,false,true,82632,Costume Institute,Evening dress,Evening dress,French,,,,,Design House,,Callot Soeurs,"French, active 1895–1937",,Callot Soeurs,French,1895,1937,1922,1922,1922,"silk, whalebone, steel",,"Gift of Mrs. Robert Lovett, 1951",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/82632,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"C.I.51.97.10a, b",false,true,83256,Costume Institute,Dress,Dress,French,,,,,Design House,,Jacques Doucet,"French, Paris 1853–1929 Paris",,"Doucet, Jacques",French,1853,1929,1918–19,1918,1919,cotton,,"Purchase, Irene Lewisohn Bequest, 1951",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/83256,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.7477,false,true,174537,Costume Institute,Evening bodice,Evening bodice,French,,,,,Designer|Design House,Possibly|Possibly,Charles Frederick Worth|Worth and Bobergh,"French (born England), Bourne 1825–1895 Paris",,"Worth, Charles Frederick|Worth and Bobergh",French,1825,1895,ca. 1865,1863,1867,Silk,,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mrs. Roland A. Goodman, 1966",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/174537,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.1372a–d,false,true,156080,Costume Institute,Evening ensemble,Evening ensemble,French,,,,,Designer|Design House,,Charles Frederick Worth|Worth and Bobergh,"French (born England), Bourne 1825–1895 Paris",,"Worth, Charles Frederick|Worth and Bobergh",French,1825,1895,1862–65,1862,1865,silk,,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Designated Purchase Fund, 1987",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/156080,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2009.300.4249a, b",false,true,168479,Costume Institute,Evening slippers,Evening slippers,French,,,,,Maker,,Esté,"French, 1821–1839",,Esté,French,1821,1839,1860–69,1860,1869,"Silk, metal",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Miriam Storrs Coe, 1934",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/168479,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2002.10a–t,false,true,83332,Costume Institute,Book,"Le bonheur du jour; ou, Les graces a la mode",French,,,,,Artist,,George Barbier,"French, Nantes 1882–1932 Paris",,"Barbier, George",French,1882,1932,1924,1924,1924,paper,,"Purchase, The Paul D. Schurgot Foundation Inc. Gift, 2002",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/83332,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.1254,false,true,155948,Costume Institute,Evening dress,Evening dress,French,,,,,Designer,,Georges Doeuillet,"French, 1865–1929",,"Doeuillet, Georges",French,1865,1929,1926–28,1926,1928,silk,,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mark Mooring, 1957",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/155948,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.1338,false,true,156042,Costume Institute,Evening dress,Evening dress,French,,,,,Designer,,Georges Doeuillet,"French, 1865–1929",,"Doeuillet, Georges",French,1865,1929,1910–13,1910,1913,"silk, rhinestones",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mrs. Frederick H. Prince, Jr., 1967",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/156042,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.4949,false,true,169172,Costume Institute,Evening stole,Evening stole,French,,,,,Designer,,Mme. Jeanne Paquin,"French, 1869–1936",,Paquin Jeanne,French,1869,1936,1920–30,1920,1930,Silk,,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of the estate of Valerie Dreyfus, 1957",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/169172,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1981.328.3,false,true,94862,Costume Institute,Evening dress,Evening dress,French,,,,,Designer,,Jeanne Hallée,"French, 1880–1914",,Hallée Jeanne,French,1880,1914,1911–15,1911,1915,"silk, glass",,"Isabel Shults Fund, 1981",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/94862,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1981.328.4,false,true,94863,Costume Institute,Evening dress,Evening dress,French,,,,,Designer,,Jeanne Hallée,"French, 1880–1914",,Hallée Jeanne,French,1880,1914,1910–14,1910,1914,"silk, glass",,"Isabel Shults Fund, 1981",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/94863,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1981.328.5,false,true,94864,Costume Institute,Evening dress,Evening dress,French,,,,,Designer,,Jeanne Hallée,"French, 1880–1914",,Hallée Jeanne,French,1880,1914,1910–14,1910,1914,"silk, glass",,"Isabel Shults Fund, 1981",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/94864,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1981.328.8,false,true,94865,Costume Institute,Evening dress,Evening dress,French,,,,,Designer,,Jeanne Hallée,"French, 1880–1914",,Hallée Jeanne,French,1880,1914,1913–14,1913,1914,"silk, metal",,"Isabel Shults Fund, 1981",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/94865,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1981.328.9,false,true,94866,Costume Institute,Evening dress,Evening dress,French,,,,,Designer,,Jeanne Hallée,"French, 1880–1914",,Hallée Jeanne,French,1880,1914,1910–14,1910,1914,"silk, glass",,"Isabel Shults Fund, 1981",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/94866,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.49.2.4,false,true,94861,Costume Institute,Afternoon dress,Afternoon dress,French,,,,,Designer,,Jeanne Hallée,"French, 1880–1914",,Hallée Jeanne,French,1880,1914,1912,1912,1912,silk,,"Gift of Howard Sturges, in memory of his mother, Mrs. Howard O. Sturges, 1949",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/94861,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.49.2.5,false,true,84591,Costume Institute,Dress,Dress,French,,,,,Designer,,Jeanne Hallée,"French, 1880–1914",,Hallée Jeanne,French,1880,1914,1907,1907,1907,silk,,"Gift of Howard Sturges, in memory of his mother, Mrs. Howard O. Sturges, 1949",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/84591,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1981.328.10,false,true,94860,Costume Institute,Evening dress,Evening dress,French,,,,,Designer,,Jeanne Hallée,"French, 1880–1914",,Hallée Jeanne,French,1880,1914,1910–14,1910,1914,"silk, metal",,"Isabel Shults Fund, 1981",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/94860,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.37.51.1,false,true,94859,Costume Institute,Opera cape,Opera cape,French,,,,,Designer,,Jeanne Hallée,"French, 1880–1914",,Hallée Jeanne,French,1880,1914,ca. 1905,1900,1910,silk,,"Gift of V. Everett Macy Estate, 1937",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/94859,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"1995.5.1a, b",false,true,80189,Costume Institute,Ball gown,Ball gown,French,,,,,Designer,,Jeanne Hallée,"French, 1880–1914",,Hallée Jeanne,French,1880,1914,1905,1905,1905,"silk, metal",,"Purchase, Irene Lewisohn Trust Gift, 1995",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/80189,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"35.134.10a, b",false,true,94869,Costume Institute,Evening dress,Evening dress,French,,,,,Designer,,Jeanne Hallée,"French, 1880–1914",,Hallée Jeanne,French,1880,1914,1906–7,1906,1907,silk,,"Gift of Susan Dwight Bliss, 1935",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/94869,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"C.I.56.51a, b",false,true,104767,Costume Institute,Riding Habit,Riding habit,French,,,,,Designer,,Jeanne Hallée,"French, 1880–1914",,Hallée Jeanne,French,1880,1914,1910,1910,1910,"wool, silk",,"Gift of Mrs. Albert Ten Eyck Gardner, 1956",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/104767,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"C.I.50.40.4a, b",false,true,94867,Costume Institute,Evening dress,Evening dress,French,,,,,Designer,,Jeanne Hallée,"French, 1880–1914",,Hallée Jeanne,French,1880,1914,1901–5,1901,1905,"silk, metallic, glass",,"Gift of Estate of Annie-May Hegeman, 1950",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/94867,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.64.7.5a–c,false,true,83435,Costume Institute,Evening ensemble,Evening ensemble,French,,,,,Designer,,Jeanne Hallée,"French, 1880–1914",,Hallée Jeanne,French,1880,1914,1913–14,1913,1914,"(a) silk, metallic thread, glass beading; (b, c) silk, leather, metallic thread",,"Gift of Mrs. David J. Colton, 1964",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/83435,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2009.300.374a, b",false,true,158720,Costume Institute,Dinner dress,Dinner dress,French,,,,,Designer,,Jeanne Hallée,"French, 1880–1914",,Hallée Jeanne,French,1880,1914,1894–96,1894,1896,"cotton, silk, metal",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mrs. Daniel M. McKeon and Robert Hoguet, Jr., 1965",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/158720,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.62.36.4a–c,false,true,107166,Costume Institute,Dress,Dress,French,,,,,Designer,,Jeanne Hallée,"French, 1880–1914",,Hallée Jeanne,French,1880,1914,ca. 1892,1887,1897,silk,,"Gift of Mrs. Ogden W. Ross, 1962",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/107166,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2009.300.3098a, b",false,true,157996,Costume Institute,Afternoon dress,Afternoon dress,French,,,,,Designer,,Jeanne Hallée,"French, 1880–1914",,Hallée Jeanne,French,1880,1914,ca. 1900,1898,1902,silk,,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Marion Litchfield, 1950",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/157996,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.430a–c,false,true,158961,Costume Institute,Afternoon dress,Afternoon dress,French,,,,,Designer,,Jeanne Hallée,"French, 1880–1914",,Hallée Jeanne,French,1880,1914,ca. 1903,1901,1905,silk,,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mrs. Frederick H. Prince, Jr., 1967",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/158961,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1978.337.1,false,true,99580,Costume Institute,Evening cape,Evening cape,French,,,,,Designer,,Chéruit,"French, 1906–1935",,Chéruit,French,1906,1935,ca. 1920,1915,1925,silk,,"Gift of the Estate of Mrs. Julia M. Weldon from Mary McDougall, 1978",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/99580,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.51.97.5,false,true,106775,Costume Institute,Evening wrap,Evening wrap,French,,,,,Designer,,Chéruit,"French, 1906–1935",,Chéruit,French,1906,1935,1918–19,1918,1919,silk,,"Purchase, Irene Lewisohn Bequest, 1951",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/106775,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.58.42.3,false,true,106482,Costume Institute,Evening wrap,Evening wrap,French,,,,,Designer,,Chéruit,"French, 1906–1935",,Chéruit,French,1906,1935,1902–4,1902,1904,"silk, metal",,"Gift of Mrs. Lawrence Tibbett, 1958",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/106482,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"C.I.46.4.21a, b",false,true,82106,Costume Institute,Tea gown,Tea gown,French,,,,,Designer,,Chéruit,"French, 1906–1935",,Chéruit,French,1906,1935,1922,1922,1922,silk,,"Gift of Mrs. Harrison Williams, Lady Mendl, and Mrs. Ector Munn, 1946",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/82106,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1979.264,false,true,106763,Costume Institute,Duster,Duster,French,,,,,Designer,,Revillon Frères,"French, founded 1723",,Revillon Frères,French,1723,1850,1914–20,1914,1920,silk,,"Gift of Charles D. Wood in honor of Kathryn Wood, 1979",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/106763,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1993.359,false,true,80823,Costume Institute,Coat,Coat,French,,,,,Designer,,Revillon Frères,"French, founded 1723",,Revillon Frères,French,1723,1850,ca. 1931,1926,1936,"fur, silk",,"Gift of Douglas Dillon, 1993",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/80823,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1999.501,false,true,81868,Costume Institute,Coat,Coat,French,,,,,Designer,,Revillon Frères,"French, founded 1723",,Revillon Frères,French,1723,1850,ca. 1950,1945,1955,fur,,"Gift of Gilbert S. Kahn in memory of Janet Annenberg Hooker, 2000",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/81868,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1977.60.11,false,true,122982,Costume Institute,Cape,Cape,French,,,,,Designer,,Revillon Frères,"French, founded 1723",,Revillon Frères,French,1723,1850,1940s,1940,1949,fur,,"Gift of Roger Goiran, 1977",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/122982,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.297,false,true,157854,Costume Institute,Evening coat,Evening coat,French,,,,,Designer,,Revillon Frères,"French, founded 1723",,Revillon Frères,French,1723,1850,ca. 1926,1924,1928,"fur, silk",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mrs. Edward H. Pflueger in memory of Florence Hazard Murphy, 1961",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/157854,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.455,false,true,158988,Costume Institute,Evening coat,Evening coat,French,,,,,Designer,,Revillon Frères,"French, founded 1723",,Revillon Frères,French,1723,1850,ca. 1930,1928,1932,fur,,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Anonymous gift, 1967",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/158988,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.58.67.14,false,true,106738,Costume Institute,Evening wrap,Evening wrap,French,,,,,Designer,,Revillon Frères,"French, founded 1723",,Revillon Frères,French,1723,1850,ca. 1917,1912,1922,silk,,"Gift of Mrs. Robert S. Kilborne, 1958",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/106738,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.5742,false,true,169949,Costume Institute,Muff,Muff,French,,,,,Designer,,Revillon Frères,"French, founded 1723",,Revillon Frères,French,1723,1850,first quarter 20th century,1900,1925,"Fur, silk",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mrs. Robert G. Olmsted and Constable MacCracken, 1969",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/169949,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"1976.269.13a, b",false,true,106227,Costume Institute,Suit,Suit,French,,,,,Designer,,Revillon Frères,"French, founded 1723",,Revillon Frères,French,1723,1850,1967–70,1967,1970,"fur, leather",,"Gift of Mrs. Morton Jay Seifter, 1976",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/106227,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2009.300.1069a, b",false,true,155742,Costume Institute,Coat,Coat,French,,,,,Designer,,Revillon Frères,"French, founded 1723",,Revillon Frères,French,1723,1850,ca. 1983,1981,1985,"fur, leather",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Anonymous gift, 1994",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/155742,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"1972.30.3a, b",false,true,113590,Costume Institute,Shoes,Shoes,French,,,,,Designer,,"F. Pinet, Paris","French, founded 1855",,"F. Pinet, Paris",French,1855,1855,1920,1920,1920,[no medium available],,"Gift of Madame Lilliana Teruzzi, given in memory of her mother, Mrs. Isak Walker Weiman, 1972",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/113590,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"1973.260.5a, b",false,true,83481,Costume Institute,Evening shoes,Evening shoes,French,,,,,Designer,,"F. Pinet, Paris","French, founded 1855",,"F. Pinet, Paris",French,1855,1855,1930s,1930,1939,"silk, leather",,"Purchase, Irene Lewisohn Bequest, 1973",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/83481,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"C.I.47.58.1a, b",false,true,113471,Costume Institute,Shoes,Shoes,French,,,,,Designer,,"F. Pinet, Paris","French, founded 1855",,"F. Pinet, Paris",French,1855,1855,1920s,1920,1929,leather,,"Gift of Mr. James Stewart Cushman, 1947",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/113471,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"C.I.52.22.1a, b",false,true,113181,Costume Institute,Slippers,Slippers,French,,,,,Designer,,"F. Pinet, Paris","French, founded 1855",,"F. Pinet, Paris",French,1855,1855,1910,1910,1910,silk,,"Gift of Miss Susan W. Street, 1952",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/113181,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"C.I.45.36.17a, b",false,true,113469,Costume Institute,Shoes,Shoes,French,,,,,Designer,,"F. Pinet, Paris","French, founded 1855",,"F. Pinet, Paris",French,1855,1855,ca. 1920s,1915,1935,leather,,"Gift of Mrs. George Kent, 1945",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/113469,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"25.200a, b",false,true,85359,Costume Institute,Visiting dress,Visiting dress,French,,,,,Designer,,Emile Pingat,"French, active 1860–96",,Pingat Emile,French,1860,1896,ca. 1872,1867,1877,silk,,"GIft of Mrs. George D. Cross, 1925",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/85359,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.60.6.7,false,true,107875,Costume Institute,Cloak,Cloak,French,,,,,Designer,,Emile Pingat,"French, active 1860–96",,Pingat Emile,French,1860,1896,1879–80,1879,1880,silk,,"Gift of Chauncey Stillman, 1960",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/107875,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.60.6.8,false,true,107154,Costume Institute,Cape,Cape,French,,,,,Designer,,Emile Pingat,"French, active 1860–96",,Pingat Emile,French,1860,1896,1891–93,1891,1893,silk,,"Gift of Chauncey Stillman, 1960",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/107154,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.60.6.9,false,true,107876,Costume Institute,Dinner dress,Dinner dress,French,,,,,Designer,,Emile Pingat,"French, active 1860–96",,Pingat Emile,French,1860,1896,1877–80,1877,1880,"silk, jet",,"Gift of Chauncey Stillman, 1960",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/107876,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.67,false,true,159226,Costume Institute,Evening coat,Evening coat,French,,,,,Designer,,Emile Pingat,"French, active 1860–96",,Pingat Emile,French,1860,1896,1888–90,1888,1890,"silk, beads",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Lillian E. Glenn Peirce and Mabel Glenn Cooper, 1929",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/159226,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.76,false,true,159326,Costume Institute,Afternoon jacket,Afternoon jacket,French,,,,,Designer,,Emile Pingat,"French, active 1860–96",,Pingat Emile,French,1860,1896,1885–90,1885,1890,silk,,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of the Princess Viggo in accordance with the wishes of the Misses Hewitt, 1931",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/159326,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.47.64.2,false,true,107148,Costume Institute,Cape,Cape,French,,,,,Designer,,Emile Pingat,"French, active 1860–96",,Pingat Emile,French,1860,1896,1890s,1890,1899,"silk, jet",,"Gift of Misses Irene and Emily Braman, 1947",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/107148,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.139,false,true,156099,Costume Institute,Evening jacket,Evening jacket,French,,,,,Designer,,Emile Pingat,"French, active 1860–96",,Pingat Emile,French,1860,1896,ca. 1893,1891,1895,"silk, jet, feathers, metal",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Marion Litchfield, 1950",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/156099,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.140,false,true,156111,Costume Institute,Evening cape,Evening cape,French,,,,,Designer,,Emile Pingat,"French, active 1860–96",,Pingat Emile,French,1860,1896,1885–89,1885,1889,"silk, feathers",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Marion Litchfield, 1950",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/156111,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.141,false,true,156122,Costume Institute,Evening cape,Evening cape,French,,,,,Designer,,Emile Pingat,"French, active 1860–96",,Pingat Emile,French,1860,1896,ca. 1891,1889,1893,"wool, silk, fur, beads",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Marion Litchfield, 1950",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/156122,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.337,false,true,158299,Costume Institute,Mantle,Mantle,French,,,,,Designer,,Emile Pingat,"French, active 1860–96",,Pingat Emile,French,1860,1896,ca. 1891,1889,1893,"wool, silk, metal, feathers",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Cornelia Gracie Henshaw, 1964",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/158299,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.484,false,true,159020,Costume Institute,Evening cloak,Evening cloak,French,,,,,Designer,,Emile Pingat,"French, active 1860–96",,Pingat Emile,French,1860,1896,1885–89,1885,1889,"silk, metal",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Anonymous gift, In memory of Mrs. John Roebling, 1970",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/159020,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.485,false,true,159021,Costume Institute,Cape,Cape,French,,,,,Designer,,Emile Pingat,"French, active 1860–96",,Pingat Emile,French,1860,1896,ca. 1895,1893,1897,"wool, silk, jet",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of the Van Tassell family, 1970",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/159021,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.39.112.3,false,true,106645,Costume Institute,Coat,Coat,French,,,,,Designer,,Emile Pingat,"French, active 1860–96",,Pingat Emile,French,1860,1896,1860–96,1860,1896,silk,,"Gift of Mrs. Roswell Skeel, Jr., 1939",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/106645,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.60.42.13,false,true,84561,Costume Institute,Opera cloak,Opera cloak,French,,,,,Designer,,Emile Pingat,"French, active 1860–96",,Pingat Emile,French,1860,1896,ca. 1882,1877,1887,"silk, fur, feathers, metal",,"Gift of Mrs. Howard Crosby Brokaw, 1960",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/84561,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.6294,false,true,175581,Costume Institute,Evening dolman,Evening dolman,French,,,,,Designer,,Emile Pingat,"French, active 1860–96",,Pingat Emile,French,1860,1896,ca. 1885,1883,1887,"Silk, jet beads",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of the Princess Viggo in accordance with the wishes of the Misses Hewitt, 1931",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/175581,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.7431,false,true,174512,Costume Institute,Evening cape,Evening cape,French,,,,,Designer,,Emile Pingat,"French, active 1860–96",,Pingat Emile,French,1860,1896,1890,1890,1890,"Silk, metallic, beads, stones",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mrs. Robert G. Olmsted, 1965",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/174512,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"1978.295.7a, b",false,true,107064,Costume Institute,Ball gown,Ball gown,French,,,,,Designer,,Emile Pingat,"French, active 1860–96",,Pingat Emile,French,1860,1896,1891–93,1891,1893,"silk, cotton",,"Purchase, Marcia Sand Bequest, in memory of her daughter, Tiger (Joan) Morse, 1978",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/107064,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.43.66a–c,false,true,107765,Costume Institute,Ensemble,Ensemble,French,,,,,Designer,,Emile Pingat,"French, active 1860–96",,Pingat Emile,French,1860,1896,1866–68,1866,1868,silk,,"Gift of Mrs. Price Collier, 1943",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/107765,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"C.I.60.6.4a, b",false,true,107195,Costume Institute,Dress,Dress,French,,,,,Designer,,Emile Pingat,"French, active 1860–96",,Pingat Emile,French,1860,1896,1891–93,1891,1893,silk,,"Gift of Chauncey Stillman, 1960",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/107195,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1978.295.3a–c,false,true,107058,Costume Institute,Evening dress,Evening dress,French,,,,,Designer,,Emile Pingat,"French, active 1860–96",,Pingat Emile,French,1860,1896,1891–93,1891,1893,"silk, metallic thread, glass",,"Purchase, Marcia Sand Bequest, in memory of her daughter, Tiger (Joan) Morse, 1978",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/107058,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"C.I.69.33.1a, b",false,true,82430,Costume Institute,Ball gown,Ball gown,French,,,,,Designer,,Emile Pingat,"French, active 1860–96",,Pingat Emile,French,1860,1896,ca. 1860,1855,1865,silk,,"Gift of Mary Pierrepont Beckwith, 1969",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/82430,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2009.300.628a, b",false,true,159180,Costume Institute,Dress,Dress,French,,,,,Designer,,Emile Pingat,"French, active 1860–96",,Pingat Emile,French,1860,1896,ca. 1885,1883,1887,"silk, cotton",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Lillian E. Glenn Peirce and Mabel Glenn Cooper, 1929",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/159180,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.69.33.12a–c,false,true,82642,Costume Institute,Ball gown,Ball gown,French,,,,,Designer,,Emile Pingat,"French, active 1860–96",,Pingat Emile,French,1860,1896,ca. 1864,1863,1865,silk,,"Gift of Mary Pierrepont Beckwith, 1969",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/82642,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1984.33,false,true,86022,Costume Institute,Evening dress,Evening dress,French,,,,,Designer,Attributed to,Callot Soeurs,"French, active 1895–1937",,Callot Soeurs,French,1895,1937,ca. 1927,1922,1932,silk,,"Purchase, German Fur Industry; Brenner Couture Inc. Leisure Dynamics Foundation, 1983",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/86022,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1984.167,false,true,106443,Costume Institute,Opera coat,Opera coat,French,,,,,Designer,,Callot Soeurs,"French, active 1895–1937",,Callot Soeurs,French,1895,1937,ca. 1907,1902,1912,"silk, metal, feathers",,"Gift of Karen Roston, 1984",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/106443,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1978.184.6,false,true,106848,Costume Institute,Afternoon dress,Afternoon dress,French,,,,,Designer,,Callot Soeurs,"French, active 1895–1937",,Callot Soeurs,French,1895,1937,1915,1915,1915,"cotton, silk",,"Purchase, Marcia Sand Bequest, in memory of her daughter, Tiger (Joan) Morse, 1978",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/106848,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1979.150.1,false,true,109777,Costume Institute,Evening dress,Evening dress,French,,,,,Designer,,Callot Soeurs,"French, active 1895–1937",,Callot Soeurs,French,1895,1937,ca. 1911,1906,1916,"silk, cotton, metallic thread, metal beads",,"Gift of Mrs. W. Allston Flagg, 1979",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/109777,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1982.133.3,false,true,108933,Costume Institute,Tea gown,Tea gown,French,,,,,Designer,,Callot Soeurs,"French, active 1895–1937",,Callot Soeurs,French,1895,1937,1906–8,1906,1908,"silk, cotton",,"Hoechst Fiber Industries Fund, 1982",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/108933,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2000.169.1,false,true,86032,Costume Institute,Dress,Dress,French,,,,,Designer,Attributed to,Callot Soeurs,"French, active 1895–1937",,Callot Soeurs,French,1895,1937,ca. 1918,1913,1923,silk,,"Gift of Richard Martin, 2000",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/86032,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.66.58.4,false,true,81734,Costume Institute,Dress,Dress,French,,,,,Designer,Attributed to,Callot Soeurs,"French, active 1895–1937",,Callot Soeurs,French,1895,1937,1926,1926,1926,silk,,"Gift of Mrs. Leon L. Roos, 1966",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/81734,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"1982.369.2a, b",false,true,109793,Costume Institute,Promenade suit,Promenade suit,French,,,,,Designer,,Callot Soeurs,"French, active 1895–1937",,Callot Soeurs,French,1895,1937,ca. 1911,1906,1916,"silk, cotton",,"Purchase, German Fur Industry Gift, 1982",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/109793,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"C.I.65.39.1a, b",false,true,84006,Costume Institute,Evening dress,Evening dress,French,,,,,Designer,Attributed to,Callot Soeurs,"French, active 1895–1937",,Callot Soeurs,French,1895,1937,ca. 1925,1925,1925,"silk, metallic thread",,"Gift of Mrs. Robin Craven, 1965",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/84006,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.44.64.16a–d,false,true,86015,Costume Institute,Loungewear,Loungewear,French,,,,,Designer,,Callot Soeurs,"French, active 1895–1937",,Callot Soeurs,French,1895,1937,1926–27,1926,1927,silk,,"Gift of Miss Isabel Shults, 1944",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/86015,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.44.64.17a–c,false,true,86016,Costume Institute,Loungewear,Loungewear,French,,,,,Designer,,Callot Soeurs,"French, active 1895–1937",,Callot Soeurs,French,1895,1937,1926–27,1926,1927,silk,,"Gift of Miss Isabel Shults, 1944",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/86016,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.44.64.18a–c,false,true,86017,Costume Institute,Loungewear,Loungewear,French,,,,,Designer,,Callot Soeurs,"French, active 1895–1937",,Callot Soeurs,French,1895,1937,1926–27,1926,1927,silk,,"Gift of Miss Isabel Shults, 1944",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/86017,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.58.30,false,true,119398,Costume Institute,Scarf,Scarf,French,,,,,Designer,Textile by,René-Jules Lalique,"French, Aÿ 1860–1945 Paris",,"Lalique, René-Jules",French,1860,1945,early 20th century,1900,1950,silk,,"Purchase, Irene Lewisohn Bequest, 1958",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/119398,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1983.113.7,false,true,121909,Costume Institute,Belt Buckle,Belt buckle,French,,,,,Designer,attributed to,René-Jules Lalique,"French, Aÿ 1860–1945 Paris",,"Lalique, René-Jules",French,1860,1945,ca. 1932,1927,1937,"glass, metal",,"Gift of Judith Leiber, 1983",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/121909,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -49.3.19,false,true,105413,Costume Institute,Evening dress,Evening dress,French,,,,,Designer,,Jacques Doucet,"French, Paris 1853–1929 Paris",,"Doucet, Jacques",French,1853,1929,1905–8,1905,1908,silk,,"Gift of Orme Wilson and R. Thornton Wilson, in memory of their mother, Mrs. Caroline Schermerhorn Astor Wilson, 1949",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/105413,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -49.3.20,false,true,95195,Costume Institute,Evening dress,Evening dress,French,,,,,Designer,,Jacques Doucet,"French, Paris 1853–1929 Paris",,"Doucet, Jacques",French,1853,1929,1907–8,1907,1908,silk,,"Gift of Orme Wilson and R. Thornton Wilson, in memory of their mother, Mrs. Caroline Schermerhorn Astor Wilson, 1949",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/95195,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1974.242.1,false,true,101303,Costume Institute,Evening coat,Evening coat,French,,,,,Designer,,Jacques Doucet,"French, Paris 1853–1929 Paris",,"Doucet, Jacques",French,1853,1929,ca. 1910,1905,1915,"silk, metallic tread, fur",,"Gift of Olivia Constable, 1974",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/101303,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.390.8,false,true,81749,Costume Institute,Visiting dress,Visiting dress,French,,,,,Designer,,Jacques Doucet,"French, Paris 1853–1929 Paris",,"Doucet, Jacques",French,1853,1929,ca. 1903,1898,1908,"wool, cotton, silk, metallic thread",,"Gift of Irma A. Bloomingdale, 1976",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/81749,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1995.177.2,false,true,79775,Costume Institute,Opera cape,Opera cape,French,,,,,Designer,,Jacques Doucet,"French, Paris 1853–1929 Paris",,"Doucet, Jacques",French,1853,1929,ca. 1890,1885,1895,"silk, fur",,"Purchase, Irene Lewisohn Trust Gift, 1995",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/79775,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1977.329.29,false,true,110065,Costume Institute,Scarf,Scarf,French,,,,,Designer,,Jacques Doucet,"French, Paris 1853–1929 Paris",,"Doucet, Jacques",French,1853,1929,1930s–40s,1930,1949,silk,,"Gift of Col. Edgar W. Garbisch, 1977",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/110065,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"49.3.26a, b",false,true,93763,Costume Institute,Ball gown,Ball gown,French,,,,,Designer,,Jacques Doucet,"French, Paris 1853–1929 Paris",,"Doucet, Jacques",French,1853,1929,1897,1897,1897,silk,,"Gift of Orme Wilson and R. Thornton Wilson, in memory of their mother, Mrs. Caroline Schermerhorn Astor Wilson, 1949",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/93763,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"49.3.27a, b",false,true,84645,Costume Institute,Ball gown,Ball gown,French,,,,,Designer,,Jacques Doucet,"French, Paris 1853–1929 Paris",,"Doucet, Jacques",French,1853,1929,1898–1900,1898,1900,silk,,"Gift of Orme Wilson and R. Thornton Wilson, in memory of their mother, Mrs. Caroline Schermerhorn Astor Wilson, 1949",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/84645,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"49.3.30a, b",false,true,84646,Costume Institute,Ball gown,Ball gown,French,,,,,Designer,,Jacques Doucet,"French, Paris 1853–1929 Paris",,"Doucet, Jacques",French,1853,1929,1898–1900,1898,1900,silk,,"Gift of Orme Wilson and R. Thornton Wilson, in memory of their mother, Mrs. Caroline Schermerhorn Astor Wilson, 1949",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/84646,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"49.3.33a, b",false,true,105047,Costume Institute,Suit,Suit,French,,,,,Designer,,Jacques Doucet,"French, Paris 1853–1929 Paris",,"Doucet, Jacques",French,1853,1929,1903–6,1903,1906,cotton,,"Gift of Orme Wilson and R. Thornton Wilson, in memory of their mother, Mrs. Caroline Schermerhorn Astor Wilson, 1949",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/105047,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.51.13.1,false,true,83429,Costume Institute,Evening dress,Evening dress,French,,,,,Designer,,Jacques Doucet,"French, Paris 1853–1929 Paris",,"Doucet, Jacques",French,1853,1929,1905–7,1905,1907,silk,,"Gift of Miss Marie Louise Constable, 1951",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/83429,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.15a–c,true,true,81138,Costume Institute,Ensemble,Ensemble,French,,,,,Designer,,Jacques Doucet,"French, Paris 1853–1929 Paris",,"Doucet, Jacques",French,1853,1929,1920–23,1920,1923,"wool, silk, glass",,"Gift of Mrs. W.G. Constable, 1975",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/81138,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"1984.337a, b",false,true,107637,Costume Institute,Dress,Dress,French,,,,,Designer,,Jacques Doucet,"French, Paris 1853–1929 Paris",,"Doucet, Jacques",French,1853,1929,ca. 1880,1875,1885,"silk, cotton",,"Purchase, Irene Lewisohn and Alice L. Crowley Bequests, 1984",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/107637,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.128,false,true,155977,Costume Institute,Evening cape,Evening cape,French,,,,,Designer,,Jacques Doucet,"French, Paris 1853–1929 Paris",,"Doucet, Jacques",French,1853,1929,1900–1905,1900,1905,"wool, silk, rhinestones",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Orme and R. Thornton Wilson in memory of Caroline Schermerhorn Astor Wilson, 1949",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/155977,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.431,false,true,158962,Costume Institute,Dress,Dress,French,,,,,Designer,,Jacques Doucet,"French, Paris 1853–1929 Paris",,"Doucet, Jacques",French,1853,1929,ca. 1910,1908,1912,"silk, cotton",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mrs. Frederick H. Prince, Jr., 1967",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/158962,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.471,false,true,159006,Costume Institute,Evening coat,Evening coat,French,,,,,Designer,,Jacques Doucet,"French, Paris 1853–1929 Paris",,"Doucet, Jacques",French,1853,1929,fall/winter 1902,1902,1902,"wool, fur, silk",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mrs. Robert G. Olmsted and Constable MacCracken, 1969",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/159006,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -47.25.1a–c,false,true,102260,Costume Institute,Evening dress,Evening dress,French,,,,,Designer,,Jacques Doucet,"French, Paris 1853–1929 Paris",,"Doucet, Jacques",French,1853,1929,ca. 1904,1899,1909,silk,,"Gift of Marie L. Constable, 1947",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/102260,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.1154,false,true,155837,Costume Institute,Evening dress,Evening dress,French,,,,,Designer,,Jacques Doucet,"French, Paris 1853–1929 Paris",,"Doucet, Jacques",French,1853,1929,ca. 1910,1908,1912,"silk, fur, linen",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Orme and R. Thornton Wilson in memory of Caroline Schermerhorn Astor Wilson, 1949",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/155837,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.3196,false,true,158106,Costume Institute,Tea gown,Tea gown,French,,,,,Designer,,Jacques Doucet,"French, Paris 1853–1929 Paris",,"Doucet, Jacques",French,1853,1929,ca. 1907,1905,1909,"silk, linen",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Rodman A. Heeren, 1959",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/158106,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.3258,false,true,158174,Costume Institute,Afternoon jacket,Afternoon jacket,French,,,,,Designer,,Jacques Doucet,"French, Paris 1853–1929 Paris",,"Doucet, Jacques",French,1853,1929,ca. 1905,1903,1907,"silk, cotton, metal, wood",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mrs. Roland A. Goodman, 1964",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/158174,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.174.1a–d,false,true,109758,Costume Institute,Wedding Ensemble,Wedding ensemble,French,,,,,Designer,(a),Jacques Doucet,"French, Paris 1853–1929 Paris",,"Doucet, Jacques",French,1853,1929,ca. 1907,1902,1912,"cotton, silk, wax",,"Gift of the Alice Langhorne Washburn Estate, 1976",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/109758,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1977.303.3a–c,false,true,110064,Costume Institute,Evening ensemble,Evening ensemble,French,,,,,Designer,,Jacques Doucet,"French, Paris 1853–1929 Paris",,"Doucet, Jacques",French,1853,1929,ca. 1925,1920,1930,"silk, glass, plastic",,"Gift of David Toser, 1977",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/110064,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2009.300.579a, b",false,true,159124,Costume Institute,Afternoon dress,Afternoon dress,French,,,,,Designer,,Jacques Doucet,"French, Paris 1853–1929 Paris",,"Doucet, Jacques",French,1853,1929,1900–1903,1900,1903,"silk, linen, rhinestones",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Designated Purchase Fund, 1990",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/159124,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2009.300.892a, b",false,true,159473,Costume Institute,Visiting dress,Visiting dress,French,,,,,Designer,,Jacques Doucet,"French, Paris 1853–1929 Paris",,"Doucet, Jacques",French,1853,1929,1900–1905,1900,1905,silk,,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mrs. Robert G. Olmsted, 1965",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/159473,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"C.I.44.64.41a, b",false,true,106672,Costume Institute,Evening dress,Evening dress,French,,,,,Designer,,Jacques Doucet,"French, Paris 1853–1929 Paris",,"Doucet, Jacques",French,1853,1929,1893–96,1893,1896,silk,,"Gift of Miss Isabel Shults, 1944",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/106672,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.51.13.2a–c,false,true,106438,Costume Institute,Evening dress,Evening dress,French,,,,,Designer,,Jacques Doucet,"French, Paris 1853–1929 Paris",,"Doucet, Jacques",French,1853,1929,1906–7,1906,1907,silk,,"Gift of Miss Marie Louise Constable, 1951",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/106438,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2009.300.1153a, b",false,true,155836,Costume Institute,Afternoon dress,Afternoon dress,French,,,,,Designer,,Jacques Doucet,"French, Paris 1853–1929 Paris",,"Doucet, Jacques",French,1853,1929,ca. 1903,1901,1905,"cotton, silk",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Orme and R. Thornton Wilson in memory of Caroline Schermerhorn Astor Wilson, 1949",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/155836,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2009.300.1557a, b",false,true,156285,Costume Institute,Afternoon dress,Afternoon dress,French,,,,,Designer,,Jacques Doucet,"French, Paris 1853–1929 Paris",,"Doucet, Jacques",French,1853,1929,ca. 1903,1901,1905,silk,,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mrs. Robert G. Olmsted, 1965",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/156285,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2009.300.2533a, b",false,true,157371,Costume Institute,Evening dress,Evening dress,French,,,,,Designer,,Jacques Doucet,"French, Paris 1853–1929 Paris",,"Doucet, Jacques",French,1853,1929,1898–1900,1898,1900,"silk, metal, plastic",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mrs. Daniel M. McKeon and Robert Hoguet, Jr., 1965",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/157371,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2009.300.3274a, b",false,true,158193,Costume Institute,Ball gown,Ball gown,French,,,,,Designer,,Jacques Doucet,"French, Paris 1853–1929 Paris",,"Doucet, Jacques",French,1853,1929,1898–1902,1898,1902,"silk, metal, linen",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mrs. Daniel M. McKeon and Robert Hoguet, Jr., 1965",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/158193,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2009.300.3309a, b",false,true,158232,Costume Institute,Ball gown,Ball gown,French,,,,,Designer,,Jacques Doucet,"French, Paris 1853–1929 Paris",,"Doucet, Jacques",French,1853,1929,ca. 1902,1900,1904,"silk, metal",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mrs. Frederick H. Prince, Jr., 1967",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/158232,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2009.300.3346a, b",false,true,158273,Costume Institute,Afternoon suit,Afternoon suit,French,,,,,Designer,,Jacques Doucet,"French, Paris 1853–1929 Paris",,"Doucet, Jacques",French,1853,1929,fall/winter 1904,1904,1904,silk,,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mrs. Robert G. Olmsted and Constable MacCracken, 1969",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/158273,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2009.300.7070a, b",false,true,174206,Costume Institute,Evening dress,Evening dress,French,,,,,Designer,,Jacques Doucet,"French, Paris 1853–1929 Paris",,"Doucet, Jacques",French,1853,1929,1905–10,1905,1910,Silk,,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mrs. Alfred Roberts, 1959",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/174206,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2009.300.7409a, b",false,true,174491,Costume Institute,Dinner dress,Dinner dress,French,,,,,Designer,,Jacques Doucet,"French, Paris 1853–1929 Paris",,"Doucet, Jacques",French,1853,1929,1900–1905,1900,1905,"Silk, linen",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mrs. Daniel M. McKeon and Robert Hoguet, Jr., 1965",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/174491,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2009.300.7430a, b",false,true,174511,Costume Institute,Evening dress,Evening dress,French,,,,,Designer,,Jacques Doucet,"French, Paris 1853–1929 Paris",,"Doucet, Jacques",French,1853,1929,ca. 1905,1903,1907,"Silk, sequins, beads, metallic",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mrs. Robert G. Olmsted, 1965",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/174511,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.891a–d,false,true,159472,Costume Institute,Evening dress,Evening dress,French,,,,,Designer,,Jacques Doucet,"French, Paris 1853–1929 Paris",,"Doucet, Jacques",French,1853,1929,ca. 1902,1900,1904,"silk, linen",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mrs. Robert G. Olmsted, 1965",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/159472,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.44.64.56a–c,false,true,85067,Costume Institute,Evening dress,Evening dress,French,,,,,Designer,,Jacques Doucet,"French, Paris 1853–1929 Paris",", Paris","Doucet, Jacques",French,1853,1929,1897–1900,1897,1900,"silk, beads",,"Gift of Miss Isabel Shults, 1944",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/85067,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.3275a–c,false,true,158194,Costume Institute,Ball gown,Ball gown,French,,,,,Designer,,Jacques Doucet,"French, Paris 1853–1929 Paris",,"Doucet, Jacques",French,1853,1929,1898–1900,1898,1900,"silk, metal",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mrs. Daniel M. McKeon and Robert Hoguet, Jr., 1965",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/158194,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -42.148,false,true,83467,Costume Institute,Hat,Hat,French,,,,,Designer,,Paul Poiret,"French, Paris 1879–1944 Paris",,"Poiret, Paul",French,1879,1944,1928,1928,1928,[no medium available],,"Gift of Mrs. Francis Henry Taylor, 1942",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/83467,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1980.86,false,true,81562,Costume Institute,Evening coat,Evening coat,French,,,,,Designer,,Paul Poiret,"French, Paris 1879–1944 Paris",,"Poiret, Paul",French,1879,1944,1913–19,1913,1919,"silk, metallic thread",,"Purchase, the Kyoto Institute: Koichi Tsukamoto, President Fund, 1980",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/81562,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1978.277,false,true,105657,Costume Institute,Evening dress,Evening dress,French,,,,,Designer,,Paul Poiret,"French, Paris 1879–1944 Paris",,"Poiret, Paul",French,1879,1944,ca. 1922,1917,1927,"silk, metallic thread, plastic, glass",,"Purchase, Friends of The Costume Institute Gifts, 1978",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/105657,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1979.428,false,true,84616,Costume Institute,Dress,Dress,French,,,,,Designer,,Paul Poiret,"French, Paris 1879–1944 Paris",,"Poiret, Paul",French,1879,1944,1922,1922,1922,"silk, metallic thread",,"Gift of Leone B. Moats, in memory of Mrs. Wallace Payne Moats, 1979",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/84616,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1982.249,false,true,97118,Costume Institute,Robe de Style,Robe de Style,French,,,,,Designer,,Paul Poiret,"French, Paris 1879–1944 Paris",,"Poiret, Paul",French,1879,1944,1925,1925,1925,silk,,"Gift of Mrs. Ivor Bevan, 1982",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/97118,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.188,false,true,121167,Costume Institute,Dress,"""Rosière""",French,,,,,Designer,,Paul Poiret,"French, Paris 1879–1944 Paris",,"Poiret, Paul",French,1879,1944,1911,1911,1911,linen,,"Catharine Breyer Van Bomel Foundation Fund, 2005",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/121167,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.191,false,true,121170,Costume Institute,Headdress,Flonflon,French,,,,,Designer,,Paul Poiret,"French, Paris 1879–1944 Paris",,"Poiret, Paul",French,1879,1944,ca. 1920,1915,1925,"silk, metallic thread, feathers",,"Purchase, Judith and Gerson Leiber Fund, 2005",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/121170,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.195,false,true,121174,Costume Institute,Tunic,Tunic,French,,,,,Designer,,Paul Poiret,"French, Paris 1879–1944 Paris",,"Poiret, Paul",French,1879,1944,ca. 1920,1915,1925,cotton,,"Catharine Breyer Van Bomel Foundation Fund, 2005",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/121174,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.196,false,true,121175,Costume Institute,Tunic,Tunic,French,,,,,Designer,,Paul Poiret,"French, Paris 1879–1944 Paris",,"Poiret, Paul",French,1879,1944,ca. 1920,1915,1925,cotton,,"Catharine Breyer Van Bomel Foundation Fund, 2005",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/121175,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.200,false,true,121192,Costume Institute,Coat,"""Manteau D'Auto""",French,,,,,Designer,,Paul Poiret,"French, Paris 1879–1944 Paris",,"Poiret, Paul",French,1879,1944,1912,1912,1912,"linen, silk, cellulose",,"Isabel Shults Fund, 2005",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/121192,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.201,false,true,121193,Costume Institute,Coat,Coat,French,,,,,Designer,,Paul Poiret,"French, Paris 1879–1944 Paris",,"Poiret, Paul",French,1879,1944,1918,1918,1918,"wool, rayon",,"Isabel Shults Fund, 2005",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/121193,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.203,false,true,121195,Costume Institute,Jacket,Jacket,French,,,,,Designer,,Paul Poiret,"French, Paris 1879–1944 Paris",,"Poiret, Paul",French,1879,1944,1912,1912,1912,"wool, cotton, rayon",,"Catharine Breyer Van Bomel Foundation Fund, 2005",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/121195,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.205,false,true,121197,Costume Institute,Coat,"""Pré Catelan""",French,,,,,Designer,,Paul Poiret,"French, Paris 1879–1944 Paris",,"Poiret, Paul",French,1879,1944,1918,1918,1918,"silk, metallic thread",,"Millia Davenport and Zipporah Fleisher Fund, 2005",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/121197,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.206,false,true,121198,Costume Institute,Headdress,Headdress,French,,,,,Designer,,Paul Poiret,"French, Paris 1879–1944 Paris",,"Poiret, Paul",French,1879,1944,ca. 1920,1915,1925,"cotton, metal, ceramic",,"Gerson and Judith Leiber Foundation Fund, 2005",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/121198,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.207,false,true,121199,Costume Institute,Coat,"""Paris""",French,,,,,Designer,,Paul Poiret,"French, Paris 1879–1944 Paris",,"Poiret, Paul",French,1879,1944,1919,1919,1919,"silk, wool, metallic thread",,"Purchase, Friends of The Costume Institute Gifts, 2005",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/121199,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.208,false,true,121200,Costume Institute,Headdress,Headdress,French,,,,,Designer,,Paul Poiret,"French, Paris 1879–1944 Paris",,"Poiret, Paul",French,1879,1944,ca. 1910,1905,1915,metal,,"Gerson and Judith Leiber Foundation Fund, 2005",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/121200,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.209,false,true,121201,Costume Institute,Jacket,"""Steppe""",French,,,,,Designer,,Paul Poiret,"French, Paris 1879–1944 Paris",,"Poiret, Paul",French,1879,1944,1912,1912,1912,"silk, wool, fur, cotton",,"Catharine Breyer Van Bomel Foundation Fund, 2005",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/121201,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.210,false,true,121202,Costume Institute,Dress,Mademoiselle,French,,,,,Designer,,Paul Poiret,"French, Paris 1879–1944 Paris",,"Poiret, Paul",French,1879,1944,1923,1923,1923,wool,,"Catharine Breyer Van Bomel Foundation Fund, 2005",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/121202,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.211,false,true,121203,Costume Institute,Hat,"""Bahia""",French,,,,,Designer,,Paul Poiret,"French, Paris 1879–1944 Paris",,"Poiret, Paul",French,1879,1944,1914,1914,1914,"silk, metallic thread, jet, glass, silver, cotton",,"Gerson and Judith Leiber Foundation Fund, 2005",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/121203,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.386,false,true,123612,Costume Institute,Dress,Dress,French,,,,,Designer,,Paul Poiret,"French, Paris 1879–1944 Paris",,"Poiret, Paul",French,1879,1944,1912,1912,1912,silk,,"Purchase, Friends of The Costume Institute Gifts, 2005",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/123612,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.513,false,true,129937,Costume Institute,Dress,Dress,French,,,,,Designer,Attributed to,Paul Poiret,"French, Paris 1879–1944 Paris",,"Poiret, Paul",French,1879,1944,1920s,1920,1929,silk,,"Purchase, Friends of The Costume Institute Gifts, 2005",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/129937,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.146,false,true,136302,Costume Institute,Dress,"""Irudree""",French,,,,,Designer,,Paul Poiret,"French, Paris 1879–1944 Paris",,"Poiret, Paul",French,1879,1944,1922,1922,1922,"metallic, silk",,"Purchase, Friends of The Costume Institute Gifts, 2007",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/136302,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2008.288,false,true,141903,Costume Institute,Opera coat,Opera coat,French,,,,,Designer,,Paul Poiret,"French, Paris 1879–1944 Paris",,"Poiret, Paul",French,1879,1944,1911,1911,1911,silk,,"Alfred Z. Solomon-Janet A. Sloane Endowment Fund, 2008",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/141903,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.51.47,false,true,81560,Costume Institute,Dress,Dress,French,,,,,Designer,,Paul Poiret,"French, Paris 1879–1944 Paris",,"Poiret, Paul",French,1879,1944,ca. 1925,1920,1930,"silk, leather",,"Gift of Mrs. Kenneth Maconochie, 1951",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/81560,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1973.279.1,false,true,81684,Costume Institute,Shawl,Shawl,French,,,,,Designer,,Paul Poiret,"French, Paris 1879–1944 Paris",,"Poiret, Paul",French,1879,1944,1920s,1920,1929,"silk, metal",,"Gift of Henry F. Callahan, 1973",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/81684,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1978.367.2,false,true,97117,Costume Institute,Dance dress,Dance dress,French,,,,,Designer,,Paul Poiret,"French, Paris 1879–1944 Paris",,"Poiret, Paul",French,1879,1944,ca. 1926,1921,1931,"silk, cotton, plastic, glass",,"Gift of Mary Van Rensselaer Thayer, 1978",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/97117,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1982.281.1,false,true,113915,Costume Institute,Picture hat,Picture hat,French,,,,,Designer,,Paul Poiret,"French, Paris 1879–1944 Paris",,"Poiret, Paul",French,1879,1944,ca. 1921,1916,1926,"horse hair, cotton",,"Purchase, Gifts from Various Donors Fund, 1982",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/113915,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1982.350.2,false,true,81563,Costume Institute,Opera coat,Opera coat,French,,,,,Designer,,Paul Poiret,"French, Paris 1879–1944 Paris",,"Poiret, Paul",French,1879,1944,1912,1912,1912,"silk, metal",,"Purchase, Irene Lewisohn Bequest, 1982",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/81563,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1983.115.2,false,true,105663,Costume Institute,Wedding Dress,Wedding dress,French,,,,,Designer,,Paul Poiret,"French, Paris 1879–1944 Paris",,"Poiret, Paul",French,1879,1944,1925,1925,1925,"silk, metal",,"Given by the children of Mrs. Kenneth F. Simpson in memory of her parents, Mr. & Mrs. Nathan T. Porter, 1983",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/105663,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"1983.8a, b",true,true,81781,Costume Institute,Fancy dress costume,Fancy dress costume,French,,,,,Designer,,Paul Poiret,"French, Paris 1879–1944 Paris",,"Poiret, Paul",French,1879,1944,1911,1911,1911,"metal, silk, cotton",,"Purchase, Irene Lewisohn Trust Gift, 1983",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/81781,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.516.7,false,true,109411,Costume Institute,Teddy,Teddy,French,,,,,Designer,,Paul Poiret,"French, Paris 1879–1944 Paris",,"Poiret, Paul",French,1879,1944,1920s,1920,1929,silk,,"Gift of Miriam W. Coletti, 1986",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/109411,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1988.226.2,false,true,97119,Costume Institute,Coat,Coat,French,,,,,Designer,,Paul Poiret,"French, Paris 1879–1944 Paris",,"Poiret, Paul",French,1879,1944,ca. 1925,1920,1930,"silk, fur",,"Gift of Mrs. John Campbell White, 1988",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/97119,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.165.2,false,true,105666,Costume Institute,Dress,Dress,French,,,,,Designer,,Paul Poiret,"French, Paris 1879–1944 Paris",,"Poiret, Paul",French,1879,1944,1928,1928,1928,"silk, glass",,"Gift of Mary C. Hartshorne, 1989",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/105666,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.50.117,false,true,82558,Costume Institute,Dress,Dress,French,,,,,Designer,,Paul Poiret,"French, Paris 1879–1944 Paris",,"Poiret, Paul",French,1879,1944,1925,1925,1925,"wool, silk",,"Gift of Mrs. Alfred Rheinstein, 1950",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/82558,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.64.7.2,false,true,84568,Costume Institute,Coat,Coat,French,,,,,Designer,,Paul Poiret,"French, Paris 1879–1944 Paris",,"Poiret, Paul",French,1879,1944,1923,1923,1923,wool,,"Gift of Mrs. David J. Colton, 1964",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/84568,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.516.16,false,true,116106,Costume Institute,Scarf,Scarf,French,,,,,Designer,,Paul Poiret,"French, Paris 1879–1944 Paris",,"Poiret, Paul",French,1879,1944,1920s,1920,1929,silk,,"Gift of Miriam W. Coletti, 1986",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/116106,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2007.58a, b",false,true,131994,Costume Institute,Boots,"""Favereau""",French,,,,,Designer,,Paul Poiret,"French, Paris 1879–1944 Paris",,"Poiret, Paul",French,1879,1944,1920,1920,1920,"leather, wood",,"Alfred Z. Solomon-Janet A. Sloane Endowment Fund, 2007",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/131994,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.39,false,true,158888,Costume Institute,Evening cape,Evening cape,French,,,,,Designer,,Paul Poiret,"French, Paris 1879–1944 Paris",,"Poiret, Paul",French,1879,1944,ca. 1920,1918,1922,silk,,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Brooklyn Museum Collection",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/158888,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.43.85.1,false,true,97120,Costume Institute,Dinner dress,Dinner dress,French,,,,,Designer,,Paul Poiret,"French, Paris 1879–1944 Paris",,"Poiret, Paul",French,1879,1944,ca. 1923,1923,1923,silk,,"Gift of Mrs. Muriel Draper, 1943",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/97120,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.51.48.3,false,true,105655,Costume Institute,Evening dress,Evening dress,French,,,,,Designer,,Paul Poiret,"French, Paris 1879–1944 Paris",,"Poiret, Paul",French,1879,1944,1924,1924,1924,"silk, metallic",,"Gift of Mrs. Robert L. Dodge, 1951",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/105655,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.51.52.1,false,true,105667,Costume Institute,Fancy dress costume,Fancy dress costume,French,,,,,Designer,,Paul Poiret,"French, Paris 1879–1944 Paris",,"Poiret, Paul",French,1879,1944,1900–1944,1900,1944,"silk, metallic, simulated pearls",,"Gift of Mrs. Mary S. Thomas, 1951",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/105667,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.58.34.8,false,true,105665,Costume Institute,Evening dress,Evening dress,French,,,,,Designer,,Paul Poiret,"French, Paris 1879–1944 Paris",,"Poiret, Paul",French,1879,1944,1927,1927,1927,"silk, metal, plastic",,"Gift of Mrs. John Chambers Hughes, 1958",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/105665,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.61.40.4,true,true,81123,Costume Institute,Coat,Coat,French,,,,,Designer,,Paul Poiret,"French, Paris 1879–1944 Paris",,"Poiret, Paul",French,1879,1944,ca. 1919,1919,1919,"silk, wool, fur, leather",,"Gift of Mrs. David J. Colton, 1961",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/81123,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2005.190a, b",false,true,121169,Costume Institute,Dress,Butard,French,,,,,Designer,,Paul Poiret,"French, Paris 1879–1944 Paris",,"Poiret, Paul",French,1879,1944,1912,1912,1912,"a, b) linen",,"Millia Davenport and Zipporah Fleisher Fund, 2005",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/121169,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2005.367a, b",false,true,123613,Costume Institute,Ensemble,Ensemble,French,,,,,Designer,,Paul Poiret,"French, Paris 1879–1944 Paris",,"Poiret, Paul",French,1879,1944,1912,1912,1912,"a) silk, fur; b) silk",,"Purchase, Friends of The Costume Institute Gifts, 2005",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/123613,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.316,false,true,158066,Costume Institute,Evening cape,Evening cape,French,,,,,Designer,,Paul Poiret,"French, Paris 1879–1944 Paris",,"Poiret, Paul",French,1879,1944,ca. 1920,1918,1922,"wool, metal, silk",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Ogden Goelet, Peter Goelet, and Madison Clews, 1962",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/158066,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.549,false,true,159091,Costume Institute,Evening coat,Evening coat,French,,,,,Designer,,Paul Poiret,"French, Paris 1879–1944 Paris",,"Poiret, Paul",French,1879,1944,spring/summer 1917,1917,1917,silk,,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Designated Purchase Fund, 1983",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/159091,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.560,false,true,159105,Costume Institute,Jacket,Jacket,French,,,,,Designer,Attributed to,Paul Poiret,"French, Paris 1879–1944 Paris",,"Poiret, Paul",French,1879,1944,1924,1924,1924,"wool, cotton",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mary Sefton Thomas, 1986",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/159105,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.193a–g,false,true,121172,Costume Institute,Ensemble,"""Théâtre des Champs-Élysées""",French,,,,,Designer,,Paul Poiret,"French, Paris 1879–1944 Paris",,"Poiret, Paul",French,1879,1944,1913,1913,1913,"a,c-e) silk, rhinestones; b) silk; f, g) silk, leather",,"Purchase, The Paul D. Schurgot Foundation Inc. Gift, 2005",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/121172,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.194a–c,false,true,121173,Costume Institute,Evening ensemble,Evening ensemble,French,,,,,Designer,,Paul Poiret,"French, Paris 1879–1944 Paris",,"Poiret, Paul",French,1879,1944,ca. 1920,1915,1925,"a, b) silk; c) silk, enamel",,"Catharine Breyer Van Bomel Foundation Fund, 2005",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/121173,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.204a–c,false,true,121196,Costume Institute,Ensemble,"""Feuille d'automne""",French,,,,,Designer,,Paul Poiret,"French, Paris 1879–1944 Paris",,"Poiret, Paul",French,1879,1944,1916,1916,1916,"a, b) silk; c) fur, silk",,"Catharine Breyer Van Bomel Foundation Fund, 2005",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/121196,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.1289,false,true,155987,Costume Institute,Evening dress,Evening dress,French,,,,,Designer,,Paul Poiret,"French, Paris 1879–1944 Paris",,"Poiret, Paul",French,1879,1944,1910,1910,1910,"silk, linen",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Ogden Goelet, Peter Goelet and Madison Clews in memory of Mrs. Henry Clews, 1961",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/155987,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.1339,false,true,156043,Costume Institute,Evening coat,Evening coat,French,,,,,Designer,Attributed to,Paul Poiret,"French, Paris 1879–1944 Paris",,"Poiret, Paul",French,1879,1944,ca. 1925,1923,1927,synthetic,,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mrs. John Chapman, 1967",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/156043,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.1368,false,true,156074,Costume Institute,Evening coat,Evening coat,French,,,,,Designer,,Paul Poiret,"French, Paris 1879–1944 Paris",,"Poiret, Paul",French,1879,1944,ca. 1912,1910,1914,"silk, metal",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Purchased with funds given by Mrs. Carl L. Selden, 1985",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/156074,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.2111,false,true,156902,Costume Institute,Cloche,Cloche,French,,,,,Designer,,Paul Poiret,"French, Paris 1879–1944 Paris",,"Poiret, Paul",French,1879,1944,ca. 1923,1921,1925,"wool, leather, silk",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mrs. Frederick H. Prince, Jr., 1967",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/156902,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.2503,false,true,157337,Costume Institute,Evening dress,Evening dress,French,,,,,Designer,,Paul Poiret,"French, Paris 1879–1944 Paris",,"Poiret, Paul",French,1879,1944,1912–14,1912,1914,silk,,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mrs. Andrew J. Love, 1962",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/157337,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.3360,false,true,158288,Costume Institute,Evening dress,"""Robe Sabat""",French,,,,,Designer,,Paul Poiret,"French, Paris 1879–1944 Paris",,"Poiret, Paul",French,1879,1944,1921,1921,1921,"silk, metal",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Augustus Graham School of Design Fund, 1973",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/158288,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.8212,false,true,175230,Costume Institute,Evening coat,Evening coat,French,,,,,Designer,,Paul Poiret,"French, Paris 1879–1944 Paris",,"Poiret, Paul",French,1879,1944,1924,1924,1924,"Silk, synthetic",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mary Sefton Thomas, 1986",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/175230,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"C.I.44.82a, b",false,true,105658,Costume Institute,Dress,Dress,French,,,,,Designer,,Paul Poiret,"French, Paris 1879–1944 Paris",,"Poiret, Paul",French,1879,1944,1922–24,1922,1924,silk,,"Gift of Mrs. Dudley Wadsworth, 1944",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/105658,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"1978.367.3a, b",false,true,105660,Costume Institute,Evening ensemble,Evening ensemble,French,,,,,Designer,,Paul Poiret,"French, Paris 1879–1944 Paris",,"Poiret, Paul",French,1879,1944,1924,1924,1924,"cotton, fur, metallic thread, silk",,"Gift of Mary Van Rensselaer Thayer, 1978",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/105660,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"1978.367.4a, b",false,true,105659,Costume Institute,Evening dress,Evening dress,French,,,,,Designer,,Paul Poiret,"French, Paris 1879–1944 Paris",,"Poiret, Paul",French,1879,1944,1923,1923,1923,"silk, metallic thread",,"Gift of Mary Van Rensselaer Thayer, 1978",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/105659,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"1988.226.1a, b",false,true,105661,Costume Institute,Ensemble,Ensemble,French,,,,,Designer,,Paul Poiret,"French, Paris 1879–1944 Paris",,"Poiret, Paul",French,1879,1944,ca. 1925,1920,1930,"silk, felt, metallic thread",,"Gift of Mrs. John Campbell White, 1988",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/105661,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"1995.588.3a, b",false,true,80338,Costume Institute,Ensemble,Ensemble,French,,,,,Designer,,Paul Poiret,"French, Paris 1879–1944 Paris",,"Poiret, Paul",French,1879,1944,ca. 1922,1922,1922,silk,,"Gift of Miriam K. W. Coletti, 1995",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/80338,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1979.569.6a–c,false,true,105664,Costume Institute,Ensemble,Ensemble,French,,,,,Designer,,Paul Poiret,"French, Paris 1879–1944 Paris",,"Poiret, Paul",French,1879,1944,1925–26,1925,1926,"wool, metal, leather",,"Gift of Mrs. C. O. Kalman, 1979",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/105664,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1983.115.3a–k,false,true,105662,Costume Institute,Evening dress,Evening dress,French,,,,,Designer,,Paul Poiret,"French, Paris 1879–1944 Paris",,"Poiret, Paul",French,1879,1944,ca. 1925,1920,1930,"silk, metal",,"Gift of the children of Mrs. Kenneth F. Simpson, in her memory, 1983",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/105662,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"C.I.43.85.2a, b",false,true,82549,Costume Institute,Evening dress,Evening dress,French,,,,,Designer,,Paul Poiret,"French, Paris 1879–1944 Paris",,"Poiret, Paul",French,1879,1944,1922–23,1922,1923,silk,,"Gift of Mrs. Muriel Draper, 1943",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/82549,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"C.I.50.84.2a, b",false,true,97388,Costume Institute,Evening ensemble,Evening ensemble,French,,,,,Designer,,Paul Poiret,"French, Paris 1879–1944 Paris",,"Poiret, Paul",French,1879,1944,fall/winter 1928–29,1928,1929,silk,,"Gift of Miss Frances McFadden, 1950",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/97388,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"C.I.51.48.1a, b",false,true,105669,Costume Institute,Fancy dress costume,Fancy dress costume,French,,,,,Designer,,Paul Poiret,"French, Paris 1879–1944 Paris",,"Poiret, Paul",French,1879,1944,20th century,1900,1944,"silk, metallic, synthetic gems",,"Gift of Mrs. Robert L. Dodge, 1951",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/105669,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"C.I.51.48.4a, b",false,true,85419,Costume Institute,Evening dress,Evening dress,French,,,,,Designer,,Paul Poiret,"French, Paris 1879–1944 Paris",,"Poiret, Paul",French,1879,1944,ca. 1925,1920,1930,"metallic, simulated pearls",,"Gift of Mrs. Robert L. Dodge, 1951",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/85419,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"C.I.51.48.5a, b",false,true,105656,Costume Institute,Evening dress,Evening dress,French,,,,,Designer,,Paul Poiret,"French, Paris 1879–1944 Paris",,"Poiret, Paul",French,1879,1944,1920s,1920,1929,"sequins, silk, metallic, beads",,"Gift of Mrs. Robert L. Dodge, 1951",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/105656,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.58.34.7a–c,false,true,97121,Costume Institute,Afternoon ensemble,Afternoon ensemble,French,,,,,Designer,,Paul Poiret,"French, Paris 1879–1944 Paris",,"Poiret, Paul",French,1879,1944,1927,1927,1927,silk,,"Gift of Mrs. John Chambers Hughes, 1958",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/97121,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2009.300.1304a, b",false,true,156005,Costume Institute,Evening dress,Evening dress,French,,,,,Designer,,Paul Poiret,"French, Paris 1879–1944 Paris",,"Poiret, Paul",French,1879,1944,ca. 1930,1928,1932,silk,,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mrs. Anthony Wilson, 1963",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/156005,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2009.300.7190a, b",false,true,174318,Costume Institute,Evening dress,Evening dress,French,,,,,Designer,,Paul Poiret,"French, Paris 1879–1944 Paris",,"Poiret, Paul",French,1879,1944,ca. 1928,1926,1930,Silk,,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mrs. Peter W. Lyon, 1962",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/174318,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2009.300.7309a, b",false,true,175768,Costume Institute,Evening dress,Evening dress,French,,,,,Designer,,Paul Poiret,"French, Paris 1879–1944 Paris",,"Poiret, Paul",French,1879,1944,ca. 1932,1930,1934,Silk,,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mrs. Anthony Wilson, 1963",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/175768,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.51.70.19a–c,false,true,81676,Costume Institute,Evening dress,"""Arrow of Gold""",French,,,,,Designer,,Paul Poiret,"French, Paris 1879–1944 Paris",,"Poiret, Paul",French,1879,1944,1925,1925,1925,"silk, metallic thread",,"Gift of Mrs. Robert A. Lovett, 1951",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/81676,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.1094a–g,false,true,155770,Costume Institute,Evening ensemble,Evening ensemble,French,,,,,Designer,,Charles Frederick Worth,"French (born England), Bourne 1825–1895 Paris",,"Worth, Charles Frederick",French,1825,1895,1887,1887,1887,"silk, metal",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Edith Gardiner, 1926",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/155770,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2010.487.1,false,true,159717,Costume Institute,Dolman,Dolman,French,,,,,Designer,,Emile Pingat,"French, active 1860–96",,Pingat Emile,French,1860,1896,1880s,1880,1889,"silk, linen",,"Gift of Christopher Scholz and Ines Elskop, 2010",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/159717,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"C.I.37.59.1a, b",false,true,107660,Costume Institute,Dress,Dress,French,,,,,Designer,,Jacques Doucet,"French, Paris 1853–1929 Paris",,"Doucet, Jacques",French,1853,1929,1880s,1880,1889,silk,,"Gift of Mrs. M. Villone, 1937",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/107660,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2009.300.7758a, b",false,true,174797,Costume Institute,Promenade dress,Promenade dress,French,,,,,Designer,,Emile Pingat,"French, active 1860–96",,Pingat Emile,French,1860,1896,ca. 1888,1886,1890,"Silk, metallic",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Anonymous gift in memory of Mrs. John Roebling, 1970",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/174797,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1977.69,false,true,84504,Costume Institute,Evening dress,Evening dress,British,,,,,Design House,,Callot Soeurs,"French, active 1895–1937",,Callot Soeurs,French,1895,1937,ca. 1930,1925,1935,"silk, metallic thread",,"Gift of Reneé C. Rinaldi and Michelle R. Rinaldi, 1977",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/84504,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.48.38.2,false,true,106772,Costume Institute,Cloak,Cloak,American,,,,,Designer,,Revillon Frères,"French, founded 1723",,Revillon Frères,French,1723,1850,1919,1919,1919,silk,,"Gift of Mrs. Carlo Vicario, 1948",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/106772,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"1979.346.121a, b",false,true,86013,Costume Institute,Dress,Dress,American,,,,,Designer,Attributed to,Callot Soeurs,"French, active 1895–1937",,Callot Soeurs,French,1895,1937,ca. 1924,1919,1929,silk,,"Gift of The New York Historical Society, 1979",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/86013,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.62.36.1a–d,false,true,94892,Costume Institute,Evening ensemble,Evening ensemble,European,,,,,Designer,,Jeanne Hallée,"French, 1880–1914",,Hallée Jeanne,French,1880,1914,1897–98,1897,1898,"(a–d) silk; (c, d) leather",,"Gift of Mrs. Ogden W. Ross, 1962",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/94892,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.303.1,false,true,127572,Costume Institute,Parasol,Parasol,Japanese,,,,,Designer,Attributed to,Paul Poiret,"French, Paris 1879–1944 Paris",,"Poiret, Paul",French,1879,1944,ca. 1910,1905,1915,[no medium available],,"Gift of Elizabeth A. Tilson, 1975",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/127572,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.62.55.1,false,true,86190,Costume Institute,Dress,Dress,Indian,,,,,Department Store,,Liberty & Co.,"British, founded London, 1875",,Liberty & Co.,British,1875,2050,late 18th–early 19th century,1775,1825,[no medium available],,"Gift of Mr. and Mrs. Clarence Stein, 1962",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/86190,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.44.99.4a–c,false,true,106302,Costume Institute,Evening dress,Evening dress,probably British,,,,,Designer,Attributed to,Lucile,"British, 1863–1935",,Lucile,British,1863,1935,ca. 1917,1912,1922,silk,,"Gift of Miss Isabel Shults and Mrs. Bertha Shults Dougherty, 1944",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/106302,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.8103,false,true,175128,Costume Institute,Dress,Dress,French,,,,,Maker,,House of Lucile,"British, founded 1895",,Lucile House of,British,1895,1895,ca. 1925,1923,1927,Silk,,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Designated Purchase Fund, 1984",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/175128,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2001.576a, b",false,true,83042,Costume Institute,Shoes,Shoes,British,,,,,Manufacturer,,E. Pattison,"British, 1800–1850",,E. Pattison,British,1800,1850,1806–15,1806,1815,"(a, b) leather, silk",,"Purchase, Gerson and Judith Leiber Foundation Gift, 2001",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/83042,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.68.53.9,false,true,81527,Costume Institute,Dress,Dress,British,,,,,Design House,,Liberty & Co.,"British, founded London, 1875",,Liberty & Co.,British,1875,2050,1891,1891,1891,silk,,"Gift of Mrs. James G. Flockhart, 1968",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/81527,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1985.155,false,true,81513,Costume Institute,Evening dress,Evening dress,British,,,,,Design House,Attributed to,Liberty & Co.,"British, founded London, 1875",,Liberty & Co.,British,1875,2050,1880s,1880,1889,silk,,"Purchase, Gifts from Various Donors, 1985",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/81513,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1995.294,false,true,80875,Costume Institute,Evening dress,Evening dress,British,,,,,Designer,,Lucile,"British, 1863–1935",,Lucile,British,1863,1935,1910s,1910,1919,metallic thread,,"Purchase, New School for Social Research Fund, 1993",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/80875,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.56.48,false,true,106869,Costume Institute,Afternoon dress,Afternoon dress,British,,,,,Designer,,Lucile,"British, 1863–1935",,Lucile,British,1863,1935,1913,1913,1913,"silk, metal",,"Gift of Mrs. E. Theophilus MacDermott, 1956",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/106869,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.68.75,false,true,94636,Costume Institute,Evening dress,Evening dress,British,,,,,Designer,,Lucile,"British, 1863–1935",,Lucile,British,1863,1935,1915–16,1915,1916,[no medium available],,"Gift of Miss Barbara Jane Pentlarge, 1968",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/94636,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.217.1,false,true,94669,Costume Institute,Evening dress,Evening dress,British,,,,,Designer,,Lucile,"British, 1863–1935",,Lucile,British,1863,1935,ca. 1916,1911,1921,"silk, cotton",,"Gift of the Staten Island Institute of Arts & Sciences, pursuant to the instructions of Mr. and Mrs. Elisha Dyer, 1976",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/94669,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.217.3,false,true,94637,Costume Institute,Evening dress,Evening dress,British,,,,,Designer,,Lucile,"British, 1863–1935",,Lucile,British,1863,1935,ca. 1918,1913,1923,"silk, cotton, metal",,"Gift of the Staten Island Institute of Arts & Sciences, pursuant to the instructions of Mr. and Mrs. Elisha Dyer, 1976",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/94637,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.217.6,false,true,94670,Costume Institute,Evening dress,Evening dress,British,,,,,Designer,,Lucile,"British, 1863–1935",,Lucile,British,1863,1935,1922,1922,1922,"silk, cotton",,"Gift of the Staten Island Institute of Arts & Sciences, pursuant to the instructions of Mr. and Mrs. Elisha Dyer, 1976",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/94670,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1979.369.2,false,true,94672,Costume Institute,Evening dress,Evening dress,British,,,,,Designer,,Lucile,"British, 1863–1935",,Lucile,British,1863,1935,ca. 1913,1908,1918,"silk, cotton, glass, plastic",,"Gift of Mrs. Kingsley Mabon, 1979",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/94672,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1979.569.2,false,true,94663,Costume Institute,Afternoon dress,Afternoon dress,British,,,,,Designer,,Lucile,"British, 1863–1935",,Lucile,British,1863,1935,1917,1917,1917,"silk, cotton",,"Gift of Mrs. C. O. Kalman, 1979",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/94663,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1979.569.4,false,true,94664,Costume Institute,Evening dress,Evening dress,British,,,,,Designer,,Lucile,"British, 1863–1935",,Lucile,British,1863,1935,1919,1919,1919,"silk, cotton",,"Gift of Mrs. C. O. Kalman, 1979",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/94664,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1980.126.32,false,true,107456,Costume Institute,Hat,Hat,British,,,,,Designer,,Lucile,"British, 1863–1935",,Lucile,British,1863,1935,1914–20,1914,1920,"silk, glass, metal, cotton",,"Gift of Mrs. Alan L. Corey Jr., Mrs. William T. Newbold, and Mrs. A.G. Paine, II, 1980",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/107456,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.47.57.1,false,true,94665,Costume Institute,Dance dress,Dance dress,British,,,,,Designer,,Lucile,"British, 1863–1935",,Lucile,British,1863,1935,1939,1939,1939,"silk, fur, metallic thread",,"Gift of Irene Castle (Mrs. George Enzinger), 1947",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/94665,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.47.57.2,false,true,102428,Costume Institute,Muff,Muff,British,,,,,Designer,,Lucile,"British, 1863–1935",,Lucile,British,1863,1935,1914,1914,1914,fur,,"Gift of Irene Castle (Mrs. George Enzinger), 1947",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/102428,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.47.57.4,false,true,108239,Costume Institute,Hat,Hat,British,,,,,Designer,,Lucile,"British, 1863–1935",,Lucile,British,1863,1935,1914–19,1914,1919,"silk, cotton",,"Gift of Irene Castle (Mrs. George Enzinger), 1947",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/108239,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.47.57.5,false,true,94635,Costume Institute,Dance dress,Dance dress,British,,,,,Designer,,Lucile,"British, 1863–1935",,Lucile,British,1863,1935,ca. 1921,1916,1926,"silk, artificial pearls, horsehair",,"Gift of Irene Castle (Mrs. George Enzinger), 1947",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/94635,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.47.57.6,false,true,106877,Costume Institute,Dance dress,Dance dress,British,,,,,Designer,,Lucile,"British, 1863–1935",,Lucile,British,1863,1935,1914–29,1914,1929,"silk, metal thread, glass, horsehair, cotton",,"Gift of Irene Castle (Mrs. George Enzinger), 1947",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/106877,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"1978.112a, b",false,true,95130,Costume Institute,Wedding Dress,Wedding dress,British,,,,,Designer,,Lucile,"British, 1863–1935",,Lucile,British,1863,1935,1915,1915,1915,"silk, cotton, plastic, metal",,"Gift of Charles V. Hickox, 1978",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/95130,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.44.64.33,false,true,94667,Costume Institute,Evening dress,Evening dress,British,,,,,Designer,,Lucile,"British, 1863–1935",,Lucile,British,1863,1935,ca. 1915,1910,1920,silk,,"Gift of Miss Isabel Shults, 1944",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/94667,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.44.64.36,false,true,94668,Costume Institute,Evening dress,Evening dress,British,,,,,Designer,,Lucile,"British, 1863–1935",,Lucile,British,1863,1935,1914–16,1914,1916,silk,,"Gift of Miss Isabel Shults, 1944",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/94668,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.44.64.38,false,true,108238,Costume Institute,Afternoon dress,Afternoon dress,British,,,,,Designer,,Lucile,"British, 1863–1935",,Lucile,British,1863,1935,ca. 1914,1909,1919,silk,,"Gift of Miss Isabel Shults, 1944",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/108238,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.44.64.39,false,true,107905,Costume Institute,Evening dress,Evening dress,British,,,,,Designer,,Lucile,"British, 1863–1935",,Lucile,British,1863,1935,1916–18,1916,1918,[no medium available],,"Gift of Miss Isabel Shults, 1944",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/107905,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.44.64.43,false,true,94662,Costume Institute,Evening dress,Evening dress,British,,,,,Designer,,Lucile,"British, 1863–1935",,Lucile,British,1863,1935,1914,1914,1914,silk,,"Gift of Miss Isabel Shults, 1944",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/94662,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"C.I.39.41a, b",false,true,94666,Costume Institute,Dress,Dress,British,,,,,Designer,,Lucile,"British, 1863–1935",,Lucile,British,1863,1935,1900–1935,1900,1935,silk,,"Gift of Mrs. Edna Woolman Newton, 1939",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/94666,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"1978.288.1a, b",false,true,82580,Costume Institute,Dress,Dress,British,,,,,Designer,,Lucile,"British, 1863–1935",,Lucile,British,1863,1935,1916–17,1916,1917,"silk, cotton",,"Gift of Julia B. Henry, 1978",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/82580,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"1979.569.3a, b",false,true,94638,Costume Institute,Dinner dress,Dinner dress,British,,,,,Designer,,Lucile,"British, 1863–1935",,Lucile,British,1863,1935,1918,1918,1918,"silk, cotton, metal",,"Gift of Mrs. C. O. Kalman, 1979",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/94638,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"C.I.47.57.3a, b",false,true,107906,Costume Institute,Dance dress,Dance dress,British,,,,,Designer,,Lucile,"British, 1863–1935",,Lucile,British,1863,1935,1914,1914,1914,"silk, cotton",,"Gift of Irene Castle (Mrs. George Enzinger), 1947",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/107906,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2009.300.3307a, b",false,true,158230,Costume Institute,Suit,Suit,British,,,,,Designer,,Lucile,"British, 1863–1935",,Lucile,British,1863,1935,1910–12,1910,1912,"wool, silk, metal",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mrs. Frederick H. Prince, Jr., 1967",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/158230,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.44.64.37a–c,false,true,94661,Costume Institute,Evening dress,Evening dress,British,,,,,Designer,,Lucile,"British, 1863–1935",,Lucile,British,1863,1935,1916–18,1916,1918,silk,,"Gift of Miss Isabel Shults, 1944",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/94661,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1978.582.98,false,true,113127,Costume Institute,Boater,Boater,British,,,,,Designer,,James Lock & Co. Ltd,"British, founded 1676",,Lock & Co. Ltd James,British,1676,1676,1950s,1950,1959,straw,,"Gift of Marvin B. Patterson (Mrs. Jefferson Patterson), 1978",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/113127,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.291.11,false,true,112803,Costume Institute,Fedora,Fedora,British,,,,,Designer,,James Lock & Co. Ltd,"British, founded 1676",,Lock & Co. Ltd James,British,1676,1676,1935–49,1935,1949,wool (probably),,"Gift of Jane de Rochemont, 1986",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/112803,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.291.14,false,true,112805,Costume Institute,Derby,Derby,British,,,,,Designer,,James Lock & Co. Ltd,"British, founded 1676",,Lock & Co. Ltd James,British,1676,1676,1930–49,1930,1949,wool (probably),,"Gift of Jane de Rochemont, 1986",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/112805,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2006.271.41,false,true,129921,Costume Institute,Hat,Hat,British,,,,,Designer,,James Lock & Co. Ltd,"British, founded 1676",,Lock & Co. Ltd James,British,1676,1676,1970s,1970,1979,"wool, silk",,"Bequest of Yolande Fielding–Scheftel, 2006",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/129921,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.2214,false,true,157015,Costume Institute,Riding Hat,Riding hat,British,,,,,Designer,,James Lock & Co. Ltd,"British, founded 1676",,Lock & Co. Ltd James,British,1676,1676,ca. 1930,1928,1932,"fur, wool, silk",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mrs. William Randolph Hearst, Jr., 1985",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/157015,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1978.472.2a–j,false,true,84675,Costume Institute,Riding Ensemble,Riding ensemble,British,,,,,Designer,(d) Hat by,James Lock & Co. Ltd,"British, founded 1676",,Lock & Co. Ltd James,British,1676,1676,ca. 1936,1931,1941,a) cotton b) silk c) Linen d) silk e–h) leather i) silk j) leather,,"Gift of Mrs. C. Suydam Cutting, 1978",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/84675,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2003.594.1a–j,false,true,98589,Costume Institute,Ensemble,Ensemble,British,,,,,Designer,"(g, h) Hat by",James Lock & Co. Ltd,"British, founded 1676",,Lock & Co. Ltd James,British,1676,1676,ca. 1950,1945,1955,"a) wool, silk; b-d) wool; e) cotton; f-h) wool; i,j) leather",,"Gift of Mrs. Doreen Simmons, 2003",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/98589,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"1981.301a–i,l–q",false,true,108600,Costume Institute,Hunting ensemble,Hunting ensemble,British,,,,,Designer,(l),James Lock & Co. Ltd,"British, founded 1676",,Lock & Co. Ltd James,British,1676,1676,ca. 1930,1925,1935,"wool, cotton, leather",,"Gift of Thomas A. Bradley, Jr., 1981",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/108600,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.3384,false,true,158314,Costume Institute,Tea gown,Tea gown,British,,,,,Designer,,Liberty & Co.,"British, founded London, 1875",,Liberty & Co.,British,1875,2050,ca. 1885,1883,1887,silk,,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Designated Purchase Fund, 1986",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/158314,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.2104,false,true,156894,Costume Institute,Hat,Hat,British,,,,,Designer,,William Charles Brown,"British, active late 19th century",,"Brown, William Charles",British,1800,1899,ca. 1870,1868,1872,"straw, silk, feathers",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mrs. Frederick H. Prince, Jr., 1967",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/156894,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"C.I.46.46.6a, b",false,true,86742,Costume Institute,Dress,Dress,American,,,,,Designer,,Lucile,"British, 1863–1935",,Lucile,British,1863,1935,1910–12,1910,1912,silk,,"Gift of Mrs. Harrison Williams, Lady Mendl, and Mrs. Ector Munn, 1946",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/86742,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2009.300.4707a, b",false,true,168912,Costume Institute,Evening shoes,Evening shoes,French,,,,,Maker,,Pierre Yantorny,"Italian, 1874–1936",,"Yantorny, Pierre",Italian,1874,1936,1914–19,1914,1919,"Silk, rhinestones",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mercedes de Acosta, 1953",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/168912,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2009.300.5679a, b",false,true,169894,Costume Institute,Shoes,Shoes,French,,,,,Maker,,Pierre Yantorny,"Italian, 1874–1936",,"Yantorny, Pierre",Italian,1874,1936,1925–30,1925,1930,"Leather, metal",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mrs. Edward G. Sparrow, 1969",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/169894,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2009.300.5681a, b",false,true,169895,Costume Institute,Shoes,Shoes,French,,,,,Maker,,Pierre Yantorny,"Italian, 1874–1936",,"Yantorny, Pierre",Italian,1874,1936,1925–30,1925,1930,"Leather, silk, metal",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mrs. Edward G. Sparrow, 1969",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/169895,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2009.300.5683a, b",false,true,169896,Costume Institute,Shoes,Shoes,French,,,,,Maker,,Pierre Yantorny,"Italian, 1874–1936",,"Yantorny, Pierre",Italian,1874,1936,1925–30,1925,1930,Leather,,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mrs. Edward G. Sparrow, 1969",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/169896,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2009.300.5686a, b",false,true,169898,Costume Institute,Shoes,Shoes,French,,,,,Maker,,Pierre Yantorny,"Italian, 1874–1936",,"Yantorny, Pierre",Italian,1874,1936,1925–30,1925,1930,Leather,,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mrs. Edward G. Sparrow, 1969",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/169898,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.4708a–f,false,true,168913,Costume Institute,Shoe Trees,Shoe trees,French,,,,,Maker,,Pierre Yantorny,"Italian, 1874–1936",,"Yantorny, Pierre",Italian,1874,1936,1914–19,1914,1919,"Wood, metal",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mercedes de Acosta, 1953",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/168913,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.5680a–f,false,true,170470,Costume Institute,Shoe Trees,Shoe trees,French,,,,,Maker,,Pierre Yantorny,"Italian, 1874–1936",,"Yantorny, Pierre",Italian,1874,1936,1925–30,1925,1930,"Wood, metal",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mrs. Edward G. Sparrow, 1969",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/170470,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.5682a–f,false,true,170471,Costume Institute,Shoe Trees,Shoe trees,French,,,,,Maker,,Pierre Yantorny,"Italian, 1874–1936",,"Yantorny, Pierre",Italian,1874,1936,1925–30,1925,1930,"Wood, metal",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mrs. Edward G. Sparrow, 1969",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/170471,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.5684a–d,false,true,169897,Costume Institute,Shoes,Shoes,French,,,,,Maker,,Pierre Yantorny,"Italian, 1874–1936",,"Yantorny, Pierre",Italian,1874,1936,1925–30,1925,1930,Leather,,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mrs. Edward G. Sparrow, 1969",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/169897,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.5685a–f,false,true,170472,Costume Institute,Shoe Trees,Shoe trees,French,,,,,Maker,,Pierre Yantorny,"Italian, 1874–1936",,"Yantorny, Pierre",Italian,1874,1936,1925–30,1925,1930,"Wood, metal",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mrs. Edward G. Sparrow, 1969",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/170472,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.5687a–f,false,true,170473,Costume Institute,Shoe Trees,Shoe trees,French,,,,,Maker,,Pierre Yantorny,"Italian, 1874–1936",,"Yantorny, Pierre",Italian,1874,1936,1925–30,1925,1930,"Wood, metal",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mrs. Edward G. Sparrow, 1969",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/170473,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.6176a–f,false,true,170492,Costume Institute,Shoe Trees,Shoe trees,French,,,,,Maker,,Pierre Yantorny,"Italian, 1874–1936",,"Yantorny, Pierre",Italian,1874,1936,1914–19,1914,1919,"Wood, metal",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/170492,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.6177a–f,false,true,170493,Costume Institute,Shoe Trees,Shoe trees,French,,,,,Maker,,Pierre Yantorny,"Italian, 1874–1936",,"Yantorny, Pierre",Italian,1874,1936,1914–19,1914,1919,"Wood, metal",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/170493,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.53.76.25,false,true,172080,Costume Institute,Trunk,Trunk,French,,,,,Designer,,Pierre Yantorny,"Italian, 1874–1936",,"Yantorny, Pierre",Italian,1874,1936,1914–19,1914,1919,"wood, silk",,"Gift of Capezio Inc., 1953",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/172080,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.53.76.26,false,true,175904,Costume Institute,Trunk,Trunk,French,,,,,Designer,,Pierre Yantorny,"Italian, 1874–1936",,"Yantorny, Pierre",Italian,1874,1936,1914–19,1914,1919,"wood, silk",,"Gift of Capezio Inc., 1953",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/175904,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.378a–j,false,true,113560,Costume Institute,Shoes,Shoes,French,,,,,Designer,,Pierre Yantorny,"Italian, 1874–1936",,"Yantorny, Pierre",Italian,1874,1936,1910–20,1910,1920,leather,,"Gift of Mrs. John E. Roosevelt, 1976",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/113560,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"C.I.49.2.9a, b",false,true,113555,Costume Institute,Pumps,Pumps,French,,,,,Designer,,Pierre Yantorny,"Italian, 1874–1936",,"Yantorny, Pierre",Italian,1874,1936,1913–14,1913,1914,"silk, leather",,"Gift of Howard Sturges, in memory of his mother, Mrs. Howard O. Sturges, 1949",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/113555,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"1978.583.31a, b",false,true,84615,Costume Institute,Evening shoes,Evening shoes,French,,,,,Designer,Attributed to,Pierre Yantorny,"Italian, 1874–1936",,"Yantorny, Pierre",Italian,1874,1936,1920–25,1920,1925,"silk, leather",,"Gift of Mrs. John Scholz (Helen Marshall), 1978",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/84615,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.49.2.7a–f,false,true,113564,Costume Institute,Shoe Trees,Shoe trees,French,,,,,Designer,,Pierre Yantorny,"Italian, 1874–1936",,"Yantorny, Pierre",Italian,1874,1936,early 20th century,1900,1950,wood,,"Gift of Howard Sturges, in memory of his mother, Mrs. Howard O. Sturges, 1949",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/113564,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1978.583.27a–h,false,true,113561,Costume Institute,Shoes,Shoes,French,,,,,Designer,,Pierre Yantorny,"Italian, 1874–1936",,"Yantorny, Pierre",Italian,1874,1936,late 1920s,1925,1929,leather,,"Gift of Mrs. John Scholz (Helen Marshall), 1978",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/113561,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1978.583.28a–h,false,true,113557,Costume Institute,Evening shoes,Evening shoes,French,,,,,Designer,,Pierre Yantorny,"Italian, 1874–1936",,"Yantorny, Pierre",Italian,1874,1936,1920–25,1920,1925,"silk, metallic thread, leather, wood",,"Gift of Mrs. John Scholz (Helen Marshall), 1978",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/113557,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1978.583.29a–h,false,true,113558,Costume Institute,Evening shoes,Evening shoes,French,,,,,Designer,,Pierre Yantorny,"Italian, 1874–1936",,"Yantorny, Pierre",Italian,1874,1936,1920–25,1920,1925,"silk, metallic thread, leather",,"Gift of Mrs. John Scholz (Helen Marshall), 1978",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/113558,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1978.583.30a–h,false,true,113559,Costume Institute,Evening shoes,Evening shoes,French,,,,,Designer,,Pierre Yantorny,"Italian, 1874–1936",,"Yantorny, Pierre",Italian,1874,1936,1920–25,1920,1925,"silk, metallic thread, leather, wood",,"Gift of Mrs. John Scholz (Helen Marshall), 1978",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/113559,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.53.76.1a–h,false,true,112919,Costume Institute,Pumps,Pumps,French,,,,,Designer,,Pierre Yantorny,"Italian, 1874–1936",,"Yantorny, Pierre",Italian,1874,1936,1914–19,1914,1919,"leather, silk",,"Gift of Capezio Inc., 1953",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/112919,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.53.76.2a–h,false,true,112920,Costume Institute,Pumps,Pumps,French,,,,,Designer,,Pierre Yantorny,"Italian, 1874–1936",,"Yantorny, Pierre",Italian,1874,1936,1914–19,1914,1919,"leather, silk",,"Gift of Capezio Inc., 1953",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/112920,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.53.76.3a–g,false,true,112921,Costume Institute,Pumps,Pumps,French,,,,,Designer,,Pierre Yantorny,"Italian, 1874–1936",,"Yantorny, Pierre",Italian,1874,1936,1914–19,1914,1919,"leather, silk",,"Gift of Capezio Inc., 1953",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/112921,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.53.76.4a–h,false,true,112922,Costume Institute,Pumps,Pumps,French,,,,,Designer,,Pierre Yantorny,"Italian, 1874–1936",,"Yantorny, Pierre",Italian,1874,1936,1914–19,1914,1919,"leather, silk",,"Gift of Capezio Inc., 1953",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/112922,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.53.76.5a–f,false,true,112923,Costume Institute,Pumps,Pumps,French,,,,,Designer,,Pierre Yantorny,"Italian, 1874–1936",,"Yantorny, Pierre",Italian,1874,1936,1914–19,1914,1919,"leather, silk",,"Gift of Capezio Inc., 1953",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/112923,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.53.76.6a–h,false,true,112924,Costume Institute,Pumps,Pumps,French,,,,,Designer,,Pierre Yantorny,"Italian, 1874–1936",,"Yantorny, Pierre",Italian,1874,1936,1914–19,1914,1919,"leather, silk",,"Gift of Capezio Inc., 1953",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/112924,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.53.76.7a–h,false,true,112925,Costume Institute,Pumps,Pumps,French,,,,,Designer,,Pierre Yantorny,"Italian, 1874–1936",,"Yantorny, Pierre",Italian,1874,1936,1914–19,1914,1919,"leather, silk",,"Gift of Capezio Inc., 1953",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/112925,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.53.76.8a–h,false,true,112926,Costume Institute,Pumps,Pumps,French,,,,,Designer,,Pierre Yantorny,"Italian, 1874–1936",,"Yantorny, Pierre",Italian,1874,1936,1914–19,1914,1919,"leather, silk",,"Gift of Capezio Inc., 1953",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/112926,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.53.76.9a–h,false,true,112927,Costume Institute,Pumps,Pumps,French,,,,,Designer,,Pierre Yantorny,"Italian, 1874–1936",,"Yantorny, Pierre",Italian,1874,1936,1910–19,1910,1919,"leather, silk",,"Gift of Capezio Inc., 1953",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/112927,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.69.12.1a–h,false,true,113563,Costume Institute,Pumps,Pumps,French,,,,,Designer,,Pierre Yantorny,"Italian, 1874–1936",,"Yantorny, Pierre",Italian,1874,1936,1913–17,1913,1917,"(a, b) silk, metallic thread; (c–h) wood",,"Gift of Elizabeth Hudson, 1969",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/113563,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.69.12.2a–h,false,true,105033,Costume Institute,Pumps,Pumps,French,,,,,Designer,,Pierre Yantorny,"Italian, 1874–1936",,"Yantorny, Pierre",Italian,1874,1936,1913–18,1913,1918,"(a, b) silk; (c–h) wood",,"Gift of Elizabeth Hudson, 1969",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/105033,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2009.300.1178a, b",false,true,155863,Costume Institute,Evening shoes,Evening shoes,French,,,,,Designer,,Pierre Yantorny,"Italian, 1874–1936",,"Yantorny, Pierre",Italian,1874,1936,1914–19,1914,1919,"silk, metal, jet",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mercedes de Acosta, 1953",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/155863,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2009.300.1459a, b",false,true,156176,Costume Institute,Mules,Mules,French,,,,,Designer,,Pierre Yantorny,"Italian, 1874–1936",,"Yantorny, Pierre",Italian,1874,1936,1914–19,1914,1919,"silk, metal",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mercedes de Acosta, 1953",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/156176,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2009.300.1592a, b",false,true,156323,Costume Institute,Evening pumps,Evening pumps,French,,,,,Designer,,Pierre Yantorny,"Italian, 1874–1936",,"Yantorny, Pierre",Italian,1874,1936,1925–30,1925,1930,"metal, rhinestones",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mrs. Edward G. Sparrow, 1969",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/156323,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2009.300.1593a, b",false,true,156324,Costume Institute,Evening pumps,Evening pumps,French,,,,,Designer,,Pierre Yantorny,"Italian, 1874–1936",,"Yantorny, Pierre",Italian,1874,1936,1925–30,1925,1930,"silk, rhinestones",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mrs. Edward G. Sparrow, 1969",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/156324,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2009.300.1853a, b",false,true,156614,Costume Institute,Pumps,Pumps,French,,,,,Designer,,Pierre Yantorny,"Italian, 1874–1936",,"Yantorny, Pierre",Italian,1874,1936,1914–19,1914,1919,"leather, metal",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mercedes de Acosta, 1953",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/156614,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.53.76.10a–h,false,true,112928,Costume Institute,Pumps,Pumps,French,,,,,Designer,,Pierre Yantorny,"Italian, 1874–1936",,"Yantorny, Pierre",Italian,1874,1936,1914–19,1914,1919,"leather, silk",,"Gift of Capezio Inc., 1953",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/112928,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.53.76.11a–h,false,true,112929,Costume Institute,Pumps,Pumps,French,,,,,Designer,,Pierre Yantorny,"Italian, 1874–1936",,"Yantorny, Pierre",Italian,1874,1936,1914–19,1914,1919,"leather, silk",,"Gift of Capezio Inc., 1953",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/112929,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.53.76.12a–h,false,true,112930,Costume Institute,Pumps,Pumps,French,,,,,Designer,,Pierre Yantorny,"Italian, 1874–1936",,"Yantorny, Pierre",Italian,1874,1936,1914–19,1914,1919,"leather, silk",,"Gift of Capezio Inc., 1953",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/112930,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.53.76.13a–h,false,true,112931,Costume Institute,Pumps,Pumps,French,,,,,Designer,,Pierre Yantorny,"Italian, 1874–1936",,"Yantorny, Pierre",Italian,1874,1936,1914–19,1914,1919,"leather, silk",,"Gift of Capezio Inc., 1953",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/112931,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.53.76.14a–f,false,true,112932,Costume Institute,Pumps,Pumps,French,,,,,Designer,,Pierre Yantorny,"Italian, 1874–1936",,"Yantorny, Pierre",Italian,1874,1936,1914–19,1914,1919,"leather, silk",,"Gift of Capezio Inc., 1953",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/112932,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.53.76.15a–h,false,true,112933,Costume Institute,Pumps,Pumps,French,,,,,Designer,,Pierre Yantorny,"Italian, 1874–1936",,"Yantorny, Pierre",Italian,1874,1936,1914–19,1914,1919,"leather, silk",,"Gift of Capezio Inc., 1953",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/112933,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.53.76.16a–e,false,true,112934,Costume Institute,Pumps,Pumps,French,,,,,Designer,,Pierre Yantorny,"Italian, 1874–1936",,"Yantorny, Pierre",Italian,1874,1936,1914–19,1914,1919,"leather, silk",,"Gift of Capezio Inc., 1953",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/112934,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.53.76.17a–h,false,true,112935,Costume Institute,Pumps,Pumps,French,,,,,Designer,,Pierre Yantorny,"Italian, 1874–1936",,"Yantorny, Pierre",Italian,1874,1936,1914–19,1914,1919,"leather, silk",,"Gift of Capezio Inc., 1953",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/112935,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.53.76.18a–h,false,true,112936,Costume Institute,Pumps,Pumps,French,,,,,Designer,,Pierre Yantorny,"Italian, 1874–1936",,"Yantorny, Pierre",Italian,1874,1936,1914–19,1914,1919,"leather, silk",,"Gift of Capezio Inc., 1953",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/112936,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.53.76.19a–h,false,true,112937,Costume Institute,Pumps,Pumps,French,,,,,Designer,,Pierre Yantorny,"Italian, 1874–1936",,"Yantorny, Pierre",Italian,1874,1936,1914–19,1914,1919,"leather, silk",,"Gift of Capezio Inc., 1953",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/112937,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.53.76.20a–g,false,true,104759,Costume Institute,Pumps,Pumps,French,,,,,Designer,,Pierre Yantorny,"Italian, 1874–1936",,"Yantorny, Pierre",Italian,1874,1936,1914–19,1914,1919,"leather, silk",,"Gift of Capezio Inc., 1953",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/104759,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.53.76.21a–h,false,true,112938,Costume Institute,Pumps,Pumps,French,,,,,Designer,,Pierre Yantorny,"Italian, 1874–1936",,"Yantorny, Pierre",Italian,1874,1936,1914–19,1914,1919,"leather, silk, metallic",,"Gift of Capezio Inc., 1953",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/112938,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.53.76.22a–h,false,true,112939,Costume Institute,Shoes,Shoes,French,,,,,Designer,,Pierre Yantorny,"Italian, 1874–1936",,"Yantorny, Pierre",Italian,1874,1936,1914–19,1914,1919,"leather, silk",,"Gift of Capezio Inc., 1953",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/112939,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.53.76.23a–h,false,true,112940,Costume Institute,Shoes,Shoes,French,,,,,Designer,,Pierre Yantorny,"Italian, 1874–1936",,"Yantorny, Pierre",Italian,1874,1936,1914–19,1914,1919,"leather, silk",,"Gift of Capezio Inc., 1953",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/112940,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.53.76.24a–g,false,true,112941,Costume Institute,Shoes,Shoes,French,,,,,Designer,,Pierre Yantorny,"Italian, 1874–1936",,"Yantorny, Pierre",Italian,1874,1936,1914–19,1914,1919,"leather, silk",,"Gift of Capezio Inc., 1953",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/112941,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.1179a–f,false,true,155864,Costume Institute,Shoe Trees,Shoe trees,French,,,,,Designer,,Pierre Yantorny,"Italian, 1874–1936",,"Yantorny, Pierre",Italian,1874,1936,1914–19,1914,1919,"wood, metal",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mercedes de Acosta, 1953",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/155864,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.1457a–d,false,true,156174,Costume Institute,Shoes,Shoes,French,,,,,Designer,,Pierre Yantorny,"Italian, 1874–1936",,"Yantorny, Pierre",Italian,1874,1936,1914–19,1914,1919,leather,,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mercedes de Acosta, 1953",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/156174,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.1458a–f,false,true,156175,Costume Institute,Shoe Trees,Shoe trees,French,,,,,Designer,,Pierre Yantorny,"Italian, 1874–1936",,"Yantorny, Pierre",Italian,1874,1936,1914–19,1914,1919,"wood, metal",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mercedes de Acosta, 1953",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/156175,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.1854a–f,false,true,156615,Costume Institute,Shoe Trees,Shoe trees,French,,,,,Designer,,Pierre Yantorny,"Italian, 1874–1936",,"Yantorny, Pierre",Italian,1874,1936,1914–19,1914,1919,"wood, metal",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mercedes de Acosta, 1953",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/156615,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.2144a–d,false,true,156937,Costume Institute,Shoes,Shoes,French,,,,,Designer,,Pierre Yantorny,"Italian, 1874–1936",,"Yantorny, Pierre",Italian,1874,1936,1925–30,1925,1930,leather,,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mrs. Edward G. Sparrow, 1969",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/156937,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.2145a–f,false,true,156938,Costume Institute,Shoe Trees,Shoe trees,French,,,,,Designer,,Pierre Yantorny,"Italian, 1874–1936",,"Yantorny, Pierre",Italian,1874,1936,1925–30,1925,1930,wood,,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mrs. Edward G. Sparrow, 1969",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/156938,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.3408a–f,false,true,158343,Costume Institute,Shoe Trees,Shoe trees,French,,,,,Designer,,Pierre Yantorny,"Italian, 1874–1936",,"Yantorny, Pierre",Italian,1874,1936,1925–30,1925,1930,"wood, metal",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mrs. Edward G. Sparrow, 1969",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/158343,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.3866a–h,false,true,158850,Costume Institute,Shoes,Shoes,French,,,,,Designer,,Pierre Yantorny,"Italian, 1874–1936",,"Yantorny, Pierre",Italian,1874,1936,1925–30,1925,1930,"leather, wood, metal",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mrs. Edward G. Sparrow, 1969",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/158850,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.40.130.12a–h,false,true,104725,Costume Institute,Shoes,Shoes,French,,,,,Designer,,Pierre Yantorny,"Italian, 1874–1936",,"Yantorny, Pierre",Italian,1874,1936,1911–15,1911,1915,"silk, wood",,"Gift of Miss Mercedes de Acosta, 1940",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/104725,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.40.130.13a–h,false,true,110469,Costume Institute,Shoes,Shoes,French,,,,,Designer,,Pierre Yantorny,"Italian, 1874–1936",,"Yantorny, Pierre",Italian,1874,1936,1910–15,1910,1915,"leather, wood",,"Gift of Miss Mercedes de Acosta, 1940",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/110469,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1978.46,false,true,81652,Costume Institute,Evening coat,Evening coat,Italian,,,,,Designer,,Maria Gallenga,"Italian, Rome 1880–1944 Umbria",,Gallenga Maria,Italian,1880,1944,1925–26,1925,1926,"silk, fur",,"Gift of Helen M. Woodruff, 1978",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/81652,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1980.96,false,true,81625,Costume Institute,Evening cape,Evening cape,Italian,,,,,Designer,,Maria Gallenga,"Italian, Rome 1880–1944 Umbria",,Gallenga Maria,Italian,1880,1944,early 1920s,1920,1925,"silk, glass, metallic thread",,"Gift of Leafie Freda, 1980",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/81625,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1978.170,false,true,81651,Costume Institute,Evening cape,Evening cape,Italian,,,,,Designer,,Maria Gallenga,"Italian, Rome 1880–1944 Umbria",,Gallenga Maria,Italian,1880,1944,1923–24,1923,1924,silk,,"Gift of Mrs. A. Winslow Meade, 1978",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/81651,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1982.160,false,true,81593,Costume Institute,Evening dress,Evening dress,Italian,,,,,Designer,,Maria Gallenga,"Italian, Rome 1880–1944 Umbria",,Gallenga Maria,Italian,1880,1944,second quarter 20th century,1925,1950,silk,,"Gift of Mr. and Mrs. James Pinckney Kinard, in memory of her mother, the late Agnes J. Dodds, (Mrs. Robert James Dodds), 1982",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/81593,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2008.191,false,true,141972,Costume Institute,Dress,Dress,Italian,,,,,Designer,,Maria Gallenga,"Italian, Rome 1880–1944 Umbria",,Gallenga Maria,Italian,1880,1944,early 20th century,1900,1950,silk,,"Gift of Anne Parmelee Reed Dean, In Memory of Elizabeth Burd Thompson Reed, 2008",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/141972,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1978.32.1,false,true,81653,Costume Institute,Evening dress,Evening dress,Italian,,,,,Designer,,Maria Gallenga,"Italian, Rome 1880–1944 Umbria",,Gallenga Maria,Italian,1880,1944,1924,1924,1924,silk,,"Gift of Mrs. Douglas Delanoy, 1978",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/81653,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1978.32.2,false,true,81654,Costume Institute,Evening dress,Evening dress,Italian,,,,,Designer,,Maria Gallenga,"Italian, Rome 1880–1944 Umbria",,Gallenga Maria,Italian,1880,1944,1924,1924,1924,silk,,"Gift of Mrs. Douglas Delanoy, 1978",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/81654,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1995.28.5,false,true,80161,Costume Institute,Dress,Dress,Italian,,,,,Designer,,Maria Gallenga,"Italian, Rome 1880–1944 Umbria",,Gallenga Maria,Italian,1880,1944,1920s,1920,1929,silk,,"Gift of Estate of Lillian Gish, 1995",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/80161,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.63.14,false,true,81508,Costume Institute,Tea gown,Tea gown,Italian,,,,,Designer,,Maria Gallenga,"Italian, Rome 1880–1944 Umbria",,Gallenga Maria,Italian,1880,1944,ca. 1930,1925,1935,silk,,"Gift of Louise Rorimer Dushkin, 1963",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/81508,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.165.1,false,true,81650,Costume Institute,Evening coat,Evening coat,Italian,,,,,Designer,,Maria Gallenga,"Italian, Rome 1880–1944 Umbria",,Gallenga Maria,Italian,1880,1944,ca. 1926,1921,1931,silk,,"Gift of Mary C. Hartshorne, 1989",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/81650,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1991.187.1,false,true,81641,Costume Institute,Evening dress,Evening dress,Italian,,,,,Designer,,Maria Gallenga,"Italian, Rome 1880–1944 Umbria",,Gallenga Maria,Italian,1880,1944,first half 20th century,1900,1950,"silk, glass",,"Gift of Mrs. Paxton T. Dunn in memory of her mother, Hildreth Meiere, 1991",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/81641,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1991.187.2,false,true,81642,Costume Institute,Evening dress,Evening dress,Italian,,,,,Designer,,Maria Gallenga,"Italian, Rome 1880–1944 Umbria",,Gallenga Maria,Italian,1880,1944,1920s,1920,1929,"silk, glass",,"Gift of Mrs. Paxton T. Dunn in memory of her mother, Hildreth Meiere, 1991",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/81642,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1991.187.3,false,true,81643,Costume Institute,Evening dress,Evening dress,Italian,,,,,Designer,,Maria Gallenga,"Italian, Rome 1880–1944 Umbria",,Gallenga Maria,Italian,1880,1944,1920s,1920,1929,"silk, glass",,"Gift of Mrs. Paxton T. Dunn in memory of her mother, Hildreth Meiere, 1991",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/81643,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1991.187.4,false,true,81522,Costume Institute,Evening dress,Evening dress,Italian,,,,,Designer,,Maria Gallenga,"Italian, Rome 1880–1944 Umbria",,Gallenga Maria,Italian,1880,1944,early 20th century,1900,1950,"silk, glass",,"Gift of Mrs. Paxton T. Dunn in memory of her mother, Hildreth Meriere, 1991",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/81522,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1991.187.7,false,true,81644,Costume Institute,Evening coat,Evening coat,Italian,,,,,Designer,,Maria Gallenga,"Italian, Rome 1880–1944 Umbria",,Gallenga Maria,Italian,1880,1944,1920s,1920,1929,silk,,"Gift of Mrs. Paxton T. Dunn in memory of her mother, Hildreth Meiere, 1991",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/81644,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1991.187.8,false,true,81645,Costume Institute,Scarf,Scarf,Italian,,,,,Designer,,Maria Gallenga,"Italian, Rome 1880–1944 Umbria",,Gallenga Maria,Italian,1880,1944,first quarter 20th century,1900,1925,silk,,"Gift of Mrs. Paxton T. Dunn in memory of her mother, Hildreth Meiere, 1991",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/81645,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1991.187.9,false,true,81646,Costume Institute,Scarf,Scarf,Italian,,,,,Designer,,Maria Gallenga,"Italian, Rome 1880–1944 Umbria",,Gallenga Maria,Italian,1880,1944,first quarter 20th century,1900,1925,"silk, glass",,"Gift of Mrs. Paxton T. Dunn in memory of her mother, Hildreth Meiere, 1991",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/81646,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1979.569.15,false,true,81597,Costume Institute,Evening dress,Evening dress,Italian,,,,,Designer,Attributed to,Maria Gallenga,"Italian, Rome 1880–1944 Umbria",,Gallenga Maria,Italian,1880,1944,1925–30,1925,1930,"silk, glass",,"Gift of Mrs. C. O. Kalman, 1979",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/81597,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1991.187.10,false,true,81647,Costume Institute,Purse,Purse,Italian,,,,,Designer,,Maria Gallenga,"Italian, Rome 1880–1944 Umbria",,Gallenga Maria,Italian,1880,1944,first quarter 20th century,1900,1925,silk,,"Gift of Mrs. Paxton T. Dunn in memory of her mother, Hildreth Meiere, 1991",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/81647,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1991.187.11,false,true,81648,Costume Institute,Belt,Belt,Italian,,,,,Designer,,Maria Gallenga,"Italian, Rome 1880–1944 Umbria",,Gallenga Maria,Italian,1880,1944,first quarter 20th century,1900,1925,"metal, glass",,"Gift of Mrs. Paxton T. Dunn in memory of her mother, Hildreth Meiere, 1991",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/81648,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1991.187.12,false,true,81649,Costume Institute,Belt,Belt,Italian,,,,,Designer,,Maria Gallenga,"Italian, Rome 1880–1944 Umbria",,Gallenga Maria,Italian,1880,1944,first quarter 20th century,1900,1925,silk,,"Gift of Mrs. Paxton T. Dunn in memory of her mother, Hildreth Meiere, 1991",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/81649,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.342,false,true,158356,Costume Institute,Evening cape,Evening cape,Italian,,,,,Designer,,Maria Gallenga,"Italian, Rome 1880–1944 Umbria",,Gallenga Maria,Italian,1880,1944,1925,1925,1925,"silk, metal",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mrs. Cecil Lubell, 1964",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/158356,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"1995.28.3a, b",false,true,80159,Costume Institute,Dress,Dress,Italian,,,,,Designer,,Maria Gallenga,"Italian, Rome 1880–1944 Umbria",,Gallenga Maria,Italian,1880,1944,1920s,1920,1929,silk,,"Gift of Estate of Lillian Gish, 1995",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/80159,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.6903,false,true,173923,Costume Institute,Evening overdress,Evening overdress,Italian,,,,,Designer,Attributed to,Maria Gallenga,"Italian, Rome 1880–1944 Umbria",,Gallenga Maria,Italian,1880,1944,ca. 1920,1918,1922,"Silk, metallic",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mrs. Thomas Brown Rudd, 1955",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/173923,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"1991.187.6a, b",false,true,81523,Costume Institute,Evening ensemble,Evening ensemble,Italian,,,,,Designer,,Maria Gallenga,"Italian, Rome 1880–1944 Umbria",,Gallenga Maria,Italian,1880,1944,early 20th century,1900,1925,"silk, glass",,"Gift of Mrs. Paxton T. Dunn in memory of her mother, Hildreth Meiere, 1991",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/81523,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1991.187.5a–c,false,true,81590,Costume Institute,Evening ensemble,Evening ensemble,Italian,,,,,Designer,,Maria Gallenga,"Italian, Rome 1880–1944 Umbria",,Gallenga Maria,Italian,1880,1944,1920s,1920,1929,"silk, glass, fur",,"Gift of Mrs. Paxton T. Dunn in memory of her mother, Hildreth Meiere, 1991",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/81590,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.23a–c,false,true,157221,Costume Institute,Evening dress,Evening dress,Italian,,,,,Designer,,Maria Gallenga,"Italian, Rome 1880–1944 Umbria",,Gallenga Maria,Italian,1880,1944,1926,1926,1926,"silk, metal",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mary Cheney Platt, 1966",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/157221,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2009.300.312a, b",false,true,158032,Costume Institute,Evening dress,Evening dress,Italian,,,,,Designer,,Maria Gallenga,"Italian, Rome 1880–1944 Umbria",,Gallenga Maria,Italian,1880,1944,ca. 1920,1918,1922,"silk, metal, glass beads",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Natalie Rector, 1962",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/158032,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2009.300.479a, b",false,true,159014,Costume Institute,Evening dress,Evening dress,Italian,,,,,Designer,,Maria Gallenga,"Italian, Rome 1880–1944 Umbria",,Gallenga Maria,Italian,1880,1944,ca. 1920,1918,1922,"silk, metal, glass",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mrs. Philip J. Roosevelt, 1969",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/159014,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.43.113.1,false,true,109103,Costume Institute,Nightgown,Nightgown,American or European,,,,,Department Store,,B. Altman & Co.,"American, 1865–1990",,B. Altman & Co.,American,1865,1990,1894,1894,1894,[no medium available],,"Gift of Mrs. William Rosenfeld, 1943",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/109103,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.43.113.8,false,true,109109,Costume Institute,Drawers,Drawers,American or European,,,,,Department Store,,B. Altman & Co.,"American, 1865–1990",,B. Altman & Co.,American,1865,1990,1894,1894,1894,[no medium available],,"Gift of Mrs. William Rosenfeld, 1943",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/109109,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.43.113.11,false,true,109104,Costume Institute,Corset Cover,Corset cover,American or European,,,,,Department Store,,B. Altman & Co.,"American, 1865–1990",,B. Altman & Co.,American,1865,1990,1894,1894,1894,[no medium available],,"Gift of Mrs. William Rosenfeld, 1943",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/109104,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.39.24a–c,false,true,109013,Costume Institute,Lingerie,Lingerie,American or European,,,,,Department Store,,B. Altman & Co.,"American, 1865–1990",,B. Altman & Co.,American,1865,1990,1881,1881,1881,cotton,,"Gift of Miss Gertrude M. Oppenheimer, 1939",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/109013,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -49.3.45,false,true,105424,Costume Institute,Slippers,Slippers,French,,,,,Department Store,,B. Altman & Co.,"American, 1865–1990",,B. Altman & Co.,American,1865,1990,1850,1850,1850,"silk, leather, pearls",,"Gift of Orme Wilson and R. Thornton Wilson, in memory of their mother, Mrs. Caroline Schermerhorn Astor Wilson, 1949",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/105424,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -49.3.46,false,true,105428,Costume Institute,Slippers,Slippers,French,,,,,Department Store,,B. Altman & Co.,"American, 1865–1990",,B. Altman & Co.,American,1865,1990,1850,1850,1850,"silk, leather, pearls",,"Gift of Orme Wilson and R. Thornton Wilson, in memory of their mother, Mrs. Caroline Schermerhorn Astor Wilson, 1949",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/105428,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"C.I.42.131.3a, b",false,true,103558,Costume Institute,Dress,Dress,French,,,,,Department Store,,B. Altman & Co.,"American, 1865–1990",,B. Altman & Co.,American,1865,1990,1883,1883,1883,[no medium available],,"Gift of Mrs. R. E. Seeligman, 1942",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/103558,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.246.9a–d,false,true,105006,Costume Institute,Ensemble,Ensemble,American,,,,,Manufacturer,"(c, d)",Alfred J. Cammeyer,1849–1913,,"Cammeyer, Alfred J.",American,1875,1940,1895–98,1895,1898,"wool, leather, canvas",,"Gift of Anne L. Maxwell, in memory of her mother, Julia H. Lawrence, 1989",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/105006,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"C.I.48.39.7a, b",false,true,113195,Costume Institute,Pumps,Pumps,American,,,,,Manufacturer,,Alfred J. Cammeyer,1849–1913,,"Cammeyer, Alfred J.",American,1875,1940,1890s,1890,1899,"silk, leather, glass",,"Gift of Mrs. E. R. Gerkin, 1948",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/113195,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2009.300.2039a, b",false,true,156821,Costume Institute,Stockings,Stockings,American,,,,,Manufacturer,,Alfred J. Cammeyer,1849–1913,,"Cammeyer, Alfred J.",American,1875,1940,1900–1915,1900,1915,silk,,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mrs. Albert Ogden in memory of Sheldon Stewart, 1964",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/156821,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2009.300.4762a, b",false,true,168967,Costume Institute,Carriage boots,Carriage boots,American,,,,,Department Store,,Alfred J. Cammeyer,1849–1913,,"Cammeyer, Alfred J.",American,1875,1940,1895–1915,1895,1915,"Silk, fur",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Charles D. Cords, 1954",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/168967,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2009.300.4945a, b",false,true,169168,Costume Institute,Evening shoes,Evening shoes,American,,,,,Department Store,,Alfred J. Cammeyer,1849–1913,,"Cammeyer, Alfred J.",American,1875,1940,1915–20,1915,1920,"Leather, beads",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Adeline Delbon, 1957",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/169168,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2009.300.5256a, b",false,true,169470,Costume Institute,Slippers,Slippers,American,,,,,Department Store,,Alfred J. Cammeyer,1849–1913,,"Cammeyer, Alfred J.",American,1875,1940,ca. 1891,1889,1893,Leather,,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mrs. Ferris J. Meigs, 1961",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/169470,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2009.300.5597a, b",false,true,169810,Costume Institute,Evening shoes,Evening shoes,American,,,,,Department Store,,Alfred J. Cammeyer,1849–1913,,"Cammeyer, Alfred J.",American,1875,1940,ca. 1925,1923,1927,Leather; metallic,,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mrs. Frederick H. Prince, Jr., 1967",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/169810,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2009.300.5696a, b",false,true,169907,Costume Institute,Slippers,Slippers,American,,,,,Department Store,,Alfred J. Cammeyer,1849–1913,,"Cammeyer, Alfred J.",American,1875,1940,1893–99,1893,1899,Silk,,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mrs. William T. Rose, 1969",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/169907,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2009.300.5954a, b",false,true,170345,Costume Institute,Oxfords,Oxfords,American,,,,,Department Store,,Alfred J. Cammeyer,1849–1913,,"Cammeyer, Alfred J.",American,1875,1940,ca. 1898,1896,1900,"Cotton, leather",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Vera Maxwell, 1985",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/170345,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2009.300.6071a, b",false,true,170286,Costume Institute,Evening pumps,Evening pumps,American,,,,,Department Store,,Alfred J. Cammeyer,1849–1913,,"Cammeyer, Alfred J.",American,1875,1940,1907,1907,1907,Silk,,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Ann Ellis, 1993",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/170286,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.2250,false,true,157055,Costume Institute,Cage crinoline,Cage crinoline,American,,,,,Manufacturer,,Royal Worcester Corset Company,"American, 1864–1950",,Royal Worcester Corset Company,American,1864,1950,1862–63,1862,1863,"linen, metal",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Brooklyn Museum Collection",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/157055,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.6642,false,true,173656,Costume Institute,Corset,Corset,American,,,,,Manufacturer,,Royal Worcester Corset Company,"American, 1864–1950",,Royal Worcester Corset Company,American,1864,1950,ca. 1880,1878,1882,"Cotton, bone",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of E. A. Meister, 1950",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/173656,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.6643,false,true,173657,Costume Institute,Corset,Corset,American,,,,,Manufacturer,,Royal Worcester Corset Company,"American, 1864–1950",,Royal Worcester Corset Company,American,1864,1950,ca. 1885,1883,1887,"Cotton, metal, bone",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of E.A. Meister, 1950",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/173657,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2009.300.3110a, b",false,true,158012,Costume Institute,Corset,Corset,American,,,,,Manufacturer,,Worcester Corset Company,"American, 1864–1950",,Worcester Corset Company,American,1864,1950,ca. 1880,1878,1882,"cotton, metal, bone",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of E. A. Meister, 1950",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/158012,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2009.300.6633a, b",false,true,175650,Costume Institute,Corset,Corset,American,,,,,Manufacturer,,Royal Worcester Corset Company,"American, 1864–1950",,Royal Worcester Corset Company,American,1864,1950,ca. 1862,1860,1864,"Cotton, metal, bone",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of E. A. Meister, 1950",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/175650,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2009.300.6635a, b",false,true,175652,Costume Institute,Corset,Corset,American,,,,,Manufacturer,,Royal Worcester Corset Company,"American, 1864–1950",,Royal Worcester Corset Company,American,1864,1950,ca. 1869,1867,1871,"Cotton, metal",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of E. A. Meister, 1950",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/175652,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2009.300.6636a, b",false,true,175653,Costume Institute,Corset,Corset,American,,,,,Manufacturer,Probably,Royal Worcester Corset Company,"American, 1864–1950",,Royal Worcester Corset Company,American,1864,1950,ca. 1872,1870,1874,"Cotton, bone",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of E. A. Meister, 1950",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/175653,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2009.300.6637a, b",false,true,173651,Costume Institute,Corset,Corset,American,,,,,Manufacturer,,Royal Worcester Corset Company,"American, 1864–1950",,Royal Worcester Corset Company,American,1864,1950,ca. 1870,1868,1872,"Cotton, metal, bone",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of E. A. Meister, 1950",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/173651,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2009.300.6638a, b",false,true,173652,Costume Institute,Corset,Corset,American,,,,,Manufacturer,,Royal Worcester Corset Company,"American, 1864–1950",,Royal Worcester Corset Company,American,1864,1950,ca. 1872,1870,1874,"Cotton, metal, bone",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of E. A. Meister, 1950",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/173652,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2009.300.6650a, b",false,true,175660,Costume Institute,Corset,Corset,American,,,,,Manufacturer,,Royal Worcester Corset Company,"American, 1864–1950",,Royal Worcester Corset Company,American,1864,1950,ca. 1890,1888,1892,"Cotton, metal, bone",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of E.A. Meister, 1950",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/175660,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.3101a–c,false,true,158002,Costume Institute,Corset,Corset,American,,,,,Manufacturer,,Worcester Skirt Company,"American, 1864–1950",,Worcester Skirt Company,American,1864,1950,1861–63,1861,1863,"cotton, bone",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of E. A. Meister, 1950",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/158002,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.3105a–c,false,true,158006,Costume Institute,Corset,Corset,American,,,,,Manufacturer,,Royal Worcester Corset Company,"American, 1864–1950",,Royal Worcester Corset Company,American,1864,1950,1876,1876,1876,"cotton, metal, bone",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of E. A. Meister, 1950",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/158006,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.3106a–c,false,true,158007,Costume Institute,Corset,Corset,American,,,,,Manufacturer,,Royal Worcester Corset Company,"American, 1864–1950",,Royal Worcester Corset Company,American,1864,1950,1876,1876,1876,"silk, cotton, metal, bone",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of E. A. Meister, 1950",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/158007,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.3112a–d,false,true,158014,Costume Institute,Corset,Corset,American,,,,,Manufacturer,,Royal Worcester Corset Company,"American, 1864–1950",,Royal Worcester Corset Company,American,1864,1950,ca. 1885,1883,1887,"cotton, metal, bone",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of E.A. Meister, 1950",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/158014,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.3113a–c,false,true,158015,Costume Institute,Corset,Corset,American,,,,,Manufacturer,,Royal Worcester Corset Company,"American, 1864–1950",,Royal Worcester Corset Company,American,1864,1950,ca. 1890,1888,1892,"cotton, metal, bone",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of E.A. Meister, 1950",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/158015,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.3496a–e,false,true,158439,Costume Institute,Corset,"""Queen Bess""",American,,,,,Manufacturer,,Royal Worcester Corset Company,"American, 1864–1950",,Royal Worcester Corset Company,American,1864,1950,1876,1876,1876,"silk, bone, metal",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of E. A. Meister, 1950",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/158439,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2009.300.6584a, b",false,true,173583,Costume Institute,Spats,Spats,American,,,,,Department Store,,B. Altman & Co.,"American, 1865–1990",,B. Altman & Co.,American,1865,1990,ca. 1890,1888,1892,Wool,,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Bequest of Matilda Alice Shaw, 1948",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/173583,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.223,false,true,157033,Costume Institute,Knickerbockers,Knickerbockers,American,,,,,Department Store,,"Browning, King & Company","American, 1868–1934",,"Browning, King & Company",American,1868,1934,ca. 1925,1923,1927,wool,,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mrs. Wesley Wallace Tillotson, 1956",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/157033,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.257,false,true,157411,Costume Institute,Smoking jacket,Smoking jacket,American,,,,,Department Store,,"Browning, King & Company","American, 1868–1934",,"Browning, King & Company",American,1868,1934,1905–15,1905,1915,wool,,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Simon Spiegal, 1958",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/157411,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.2762a–d,false,true,157625,Costume Institute,Corset,Corset,American,,,,,Manufacturer,,"O'Conner, Moffatt & Company","American, 1868–1945",,"O'Conner, Moffatt & Company",American,1868,1945,1918–19,1918,1919,"cotton, bone, metal, elastic",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of E. A. Meister, 1950",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/157625,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1991.258,false,true,84226,Costume Institute,Fan,Fan,American,,,,,Manufacturer,,H.C.F. Koch,"American, 1890–1930",,Koch H.C.F.,American,1890,1930,ca. 1880,1875,1885,"pasteboard, wood",,"Gift of The Brooklyn Historical Society, 1991",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/84226,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.7532,false,true,174585,Costume Institute,Cravat,Cravat,American,,,,,Retailer,,Saks & Company,"American, 1902–1924",,Saks & Company,American,1902,1924,1920–30,1920,1930,Cotton,,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Albert Moss, 1967",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/174585,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2009.300.5784a, b",false,true,169988,Costume Institute,Evening shoes,Evening shoes,American,,,,,Retailer,,Saks & Company,"American, 1902–1924",,Saks & Company,American,1902,1924,ca. 1933,1931,1935,"Silk, leather",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Helen Gray, 1970",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/169988,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.53.2,false,true,84013,Costume Institute,Opera cloak,Opera cloak,American,,,,,Department Store,,Lord & Taylor,"American, founded 1826",,Lord & Taylor,American,1826,2050,1850s,1850,1859,silk,,"Gift of Mrs. Josephine Mingle Tennant, 1953",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/84013,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1979.34.2a–d,false,true,81661,Costume Institute,Dinner dress,Dinner dress,American,,,,,Department Store,,Lord & Taylor,"American, founded 1826",,Lord & Taylor,American,1826,2050,1877–83,1877,1883,"silk, glass",,"Gift of Elizabeth Kellogg Ammidon, 1979",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/81661,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.6669,false,true,173676,Costume Institute,Ribbon,Ribbon,American,,,,,Manufacturer,,W. B. Conkey Co.,"American, Chicago, Illinois",,"Conkey Co., W. B.",American,1850,1950,1893,1893,1893,"Silk, metallic, pigment",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of E.A. Meister, 1950",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/173676,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.2351,false,true,157167,Costume Institute,Parasol,Parasol,American,,,,,Department Store,,Stern Brothers,"American, founded New York, 1867",,Stern Brothers,American,1867,2001,ca. 1870,1868,1872,"silk, wood, metal",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Lillian E. Glenn Peirce in memory of Mrs. Luther G. Tillotson, 1944",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/157167,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.2528,false,true,157365,Costume Institute,Parasol,Parasol,American,,,,,Department Store,,Stern Brothers,"American, founded New York, 1867",,Stern Brothers,American,1867,2001,1876,1876,1876,"silk, wood, glass, metal",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Genevieve Doherty in memory of Mrs. John Henry, 1964",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/157365,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2009.300.4709a, b",false,true,168914,Costume Institute,Wedding Slippers,Wedding slippers,American,,,,,Department Store,,Stern Brothers,"American, founded New York, 1867",,Stern Brothers,American,1867,2001,1880,1880,1880,Silk,,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Polly Dix, 1954",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/168914,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.6567,false,true,180660,Costume Institute,Dinner shoes,Dinner shoes,American,,,,,Manufacturer,,A.E. Little & Co.,"American, Lynn, Massachusetts 1898–1934",,"Little & Co., A.E.",American,1898,1934,ca. 1916,1911,1921,"Leather, beads",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of the estate of Carrie Chapman Catt, 1947",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/180660,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2009.300.1423a, b",false,true,156137,Costume Institute,Evening shoes,Evening shoes,American,,,,,Manufacturer,,A.E. Little & Co.,"American, Lynn, Massachusetts 1898–1934",,"Little & Co., A.E.",American,1898,1934,ca. 1916,1914,1918,leather,,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Theodora Wilbour, 1932",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/156137,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2009.300.4226a, b",false,true,168459,Costume Institute,Dinner shoes,Dinner shoes,American,,,,,Manufacturer,,A.E. Little & Co.,"American, Lynn, Massachusetts 1898–1934",,"Little & Co., A.E.",American,1898,1934,ca. 1910,1908,1912,"Leather, beads",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Theodora Wilbour, 1932",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/168459,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2009.300.4509a, b",false,true,168717,Costume Institute,Evening pumps,Evening pumps,American,,,,,Manufacturer,,A.E. Little & Co.,"American, Lynn, Massachusetts 1898–1934",,"Little & Co., A.E.",American,1898,1934,1918,1918,1918,Silk,,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Eleanor Curnow, 1946",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/168717,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2009.300.5012a, b",false,true,169234,Costume Institute,Dinner shoes,Dinner shoes,American,,,,,Manufacturer,,A.E. Little & Co.,"American, Lynn, Massachusetts 1898–1934",,"Little & Co., A.E.",American,1898,1934,1915,1915,1915,"Leather, beads",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mrs. Hollis K. Thayer, 1958",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/169234,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2009.300.5340a, b",false,true,169558,Costume Institute,Evening shoes,Evening shoes,American,,,,,Manufacturer,,A.E. Little & Co.,"American, Lynn, Massachusetts 1898–1934",,"Little & Co., A.E.",American,1898,1934,1915–16,1915,1916,"Leather, beads",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mrs. William B. Parker, 1964",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/169558,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.1455a–d,false,true,156172,Costume Institute,Evening oxfords,Evening oxfords,American,,,,,Department Store,,Alfred J. Cammeyer,"American, founded New York, active 1875–1930s",,"Cammeyer, Alfred J.",American,1875,1940,ca. 1891,1889,1893,leather,,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Marion Fisher, 1952",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/156172,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.6950a–c,false,true,173956,Costume Institute,Morning suit,Morning suit,American,,,,,Maker,,"Browning, King & Company","American, 1868–1934",,"Browning, King & Company",American,1868,1934,1913,1913,1913,"Wool, silk",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mrs. Wesley Wallace Tillotson, 1956",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/173956,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"C.I.53.39a, b",false,true,113633,Costume Institute,Shoes,Shoes,American,,,,,Designer,,Alfred J. Cammeyer,1849–1913,,"Cammeyer, Alfred J.",American,1875,1940,1925–30,1925,1930,leather,,"Gift of Mrs. Peter A. Cohn, 1953",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/113633,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"C.I.49.57.2a, b",false,true,113160,Costume Institute,Evening slippers,Evening slippers,American,,,,,Designer,,Alfred J. Cammeyer,1849–1913,,"Cammeyer, Alfred J.",American,1875,1940,1900–1910,1900,1910,leather,,"Gift of Mrs. Robert S. Dixon, 1949",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/113160,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"C.I.51.72.2a, b",false,true,113180,Costume Institute,Wedding Shoes,Wedding shoes,American,,,,,Designer,,Alfred J. Cammeyer,1849–1913,,"Cammeyer, Alfred J.",American,1875,1940,1896,1896,1896,"leather, silk",,"Gift of Miss Mildred Mendelson, 1951",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/113180,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"C.I.56.33.33a, b",false,true,113615,Costume Institute,Shoes,Shoes,American,,,,,Designer,,Alfred J. Cammeyer,1849–1913,,"Cammeyer, Alfred J.",American,1875,1940,1928,1928,1928,"leather, straw",,"Gift of Mrs. Sidney Bernard, 1956",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/113615,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1979.424.1,false,true,96026,Costume Institute,Cocktail Dress,Cocktail dress,American,,,,,Designer,,Ceil Chapman,"American, born 1912",,Chapman Ceil,American,1912,1912,1950–53,1950,1953,rayon,,"Gift of Ann M. Kivlan, 1979",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/96026,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1979.424.4,false,true,89341,Costume Institute,Cocktail Dress,Cocktail dress,American,,,,,Designer,,Ceil Chapman,"American, born 1912",,Chapman Ceil,American,1912,1912,1955–59,1955,1959,silk,,"Gift of Ann M. Kivlan, 1979",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/89341,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2002.196.3,false,true,84293,Costume Institute,Dress,Dress,American,,,,,Designer,,Ceil Chapman,"American, born 1912",,Chapman Ceil,American,1912,1912,late 1940s,1945,1955,synthetic,,"Gift of Mary (Howard) de Liagre, 2002",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/84293,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"1993.185a, b",false,true,80752,Costume Institute,Wedding Dress,Wedding dress,American,,,,,Designer,,Ceil Chapman,"American, born 1912",,Chapman Ceil,American,1912,1912,1948,1948,1948,cotton,,"Gift of Sophie Mitropoulos, 1993",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/80752,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.7196,false,true,174324,Costume Institute,Dinner dress,Dinner dress,American,,,,,Designer,,Ceil Chapman,"American, born 1912",,Chapman Ceil,American,1912,1912,1945,1945,1945,Synthetic,,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of George F. Hoag, 1962",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/174324,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.7204,false,true,174332,Costume Institute,Evening dress,Evening dress,American,,,,,Designer,,Ceil Chapman,"American, born 1912",,Chapman Ceil,American,1912,1912,1953,1953,1953,Silk,,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Hanna T. Rose, 1962",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/174332,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2000.366.1a, b",false,true,81763,Costume Institute,Cocktail Dress,Cocktail dress,American,,,,,Designer,,Ceil Chapman,"American, born 1912",,Chapman Ceil,American,1912,1912,1950s,1950,1959,synthetic,,"Anonymous Gift, 2000",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/81763,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1995.238a–z,false,true,79894,Costume Institute,Vanity Case,Vanity case,American,,,,,Designer,,Richard Hudnut,"American, 1855–1928",,"Hudnut, RIchard",American,1855,1928,1920s,1920,1929,"leather, brass",,"Gift of Lois Small Zabriskie, 1995",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/79894,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.5369,false,true,169584,Costume Institute,Apron,Apron,American,,,,,Designer,,L. S. Plaut & Company,"American, 1870–1923",,Plaut & Company L. S.,American,1870,1923,1930–39,1930,1939,Cotton,,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mrs. Albert Ogden in memory of Sheldon Stewart, 1964",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/169584,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.1315,false,true,156017,Costume Institute,Evening dress,Evening dress,American,,,,,Designer,,Peggy Hoyt,"American, 1893–1937",,Hoyt Peggy,American,1893,1937,spring/summer 1928,1928,1928,"silk, rhinestones",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Anonymous gift, 1964",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/156017,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2009.300.6926a, b",false,true,173946,Costume Institute,Riding Habit,Riding Habit,American,,,,,Designer,,Saks & Company,"American, 1902–1924",,Saks & Company,American,1902,1924,1922,1922,1922,"Wool, leather",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mrs. A. E. Laurancelle, 1956",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/173946,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.68.53.5a–h,true,true,81137,Costume Institute,Wedding Ensemble,Wedding ensemble,American,,,,,Designer,,Herman Rossberg,"American, active 1880s",,Rossberg Herman,American,1880,1880,1887,1887,1887,wool,,"Gift of Mrs. James G. Flockhart, 1968",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/81137,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.104,false,true,155711,Costume Institute,Evening coat,Evening coat,American,,,,,Designer,,C. G. Gunther's Sons,"American, founded 1820",,Gunther,American,1820,1820,ca. 1930,1928,1932,"silk, fur, metal",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mrs. J. W. Post, 1944",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/155711,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.3862,false,true,158846,Costume Institute,Cape (Tippet),Tippet,American,,,,,Designer,,C. G. Gunther's Sons,"American, founded 1820",,Gunther,American,1820,1820,ca. 1865,1863,1867,"fur, silk",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Emma Crampton Trainer, 1968",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/158846,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.6528,false,true,173541,Costume Institute,Evening coat,Evening coat,American,,,,,Designer,,C. G. Gunther's Sons,"American, founded 1820",,Gunther,American,1820,1820,1914,1914,1914,"Silk, metallic",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mrs. J. W. Post, 1944",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/173541,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2009.300.478a, b",false,true,159013,Costume Institute,Accessory Set,Accessory set,American,,,,,Designer,,C. G. Gunther's Sons,"American, founded 1820",,Gunther,American,1820,1820,1890–99,1890,1899,"fur, silk",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Eleanor F. Peck, 1969",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/159013,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.57.38.1,false,true,107859,Costume Institute,Cape,Cape,American,,,,,Designer,,Lord & Taylor,"American, founded 1826",,Lord & Taylor,American,1826,2050,ca. 1885,1880,1890,"jet, silk, cotton",,"Gift of Lord and Taylor, 1957",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/107859,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1979.152.1,false,true,91143,Costume Institute,Hunting coat,Hunting coat,American,,,,,Designer,,John Patterson & Co.,"American, founded 1852",,Patterson John,American,1852,1852,late 19th century,1850,1899,"wool, silk, metal",,"Gift of Jessie Leonard Hill, 1979",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/91143,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.69.24.1,false,true,81727,Costume Institute,Coat,Coat,American,,,,,Designer,,John Patterson & Co.,"American, founded 1852",,Patterson John,American,1852,1852,1890s,1890,1899,wool,,"Gift of Mrs. Edward E. Murray, 1969",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/81727,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.4969,false,true,169192,Costume Institute,Cap,Cap,American,,,,,Designer,,John Patterson & Co.,"American, founded 1852",,Patterson John,American,1852,1852,1929,1929,1929,"Wool, leather, silk",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Anonymous gift, 1957",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/169192,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.7030,false,true,174020,Costume Institute,Coat,Coat,American,,,,,Designer,,John Patterson & Co.,"American, founded 1852",,Patterson John,American,1852,1852,1929,1929,1929,Wool,,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Anonymous gift, 1957",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/174020,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2009.300.820a, b",false,true,159394,Costume Institute,Uniform Coat,Uniform coat,American,,,,,Designer,,John Patterson & Co.,"American, founded 1852",,Patterson John,American,1852,1852,1915,1915,1915,"wool, fur",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Anonymous gift, 1957",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/159394,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.819a–f,false,true,159391,Costume Institute,Uniform,Uniform,American,,,,,Designer,,John Patterson & Co.,"American, founded 1852",,Patterson John,American,1852,1852,1928,1928,1928,"wool, leather",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Anonymous gift, 1957",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/159391,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.7029a–c,false,true,174019,Costume Institute,Uniform,Uniform,American,,,,,Designer,,John Patterson & Co.,"American, founded 1852",,Patterson John,American,1852,1852,1928,1928,1928,"Wool, leather",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Anonymous gift, 1957",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/174019,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"1981.21.2a, b",false,true,92337,Costume Institute,Dinner dress,Dinner dress,American,,,,,Designer,,Alice M. Dunstan,"American, active 1892–1926",,Dunstan Alice M.,American,1892,1926,ca. 1895,1890,1900,silk,,"Purchase, Irene Lewisohn Bequest, 1981",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/92337,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.3289,false,true,158208,Costume Institute,Evening dress,Evening dress,American,,,,,Designer,,Herbert Luey,"American, Northfield, Massachusetts 1860–1916 Brooklyn",,Luey Herbert,American,1860,1916,1912–14,1912,1914,silk,,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Amelia Beard Hollenback, 1966",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/158208,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.6561,false,true,173568,Costume Institute,Evening dress,Evening dress,American,,,,,Designer,,Herbert Luey,"American, Northfield, Massachusetts 1860–1916 Brooklyn",,Luey Herbert,American,1860,1916,1908–11,1908,1911,"Silk, beads, metallic",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Helen Rice, 1946",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/173568,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.7483,false,true,175822,Costume Institute,Dinner dress,Dinner dress,American,,,,,Designer,,Herbert Luey,"American, Northfield, Massachusetts 1860–1916 Brooklyn",,Luey Herbert,American,1860,1916,1908–10,1908,1910,"Silk, cotton",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Amelia Beard Hollenback, 1966",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/175822,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2009.300.3030a, b",false,true,157922,Costume Institute,Evening dress,Evening dress,American,,,,,Designer,,Herbert Luey,"American, Northfield, Massachusetts 1860–1916 Brooklyn",,Luey Herbert,American,1860,1916,ca. 1890,1888,1892,"silk, linen",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mrs. James Dowd Lester, 1942",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/157922,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2009.300.6764a, b",false,true,173779,Costume Institute,Bridesmaid dress,Bridesmaid dress,American,,,,,Designer,,Herbert Luey,"American, Northfield, Massachusetts 1860–1916 Brooklyn",,Luey Herbert,American,1860,1916,1880,1880,1880,Silk,,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Polly Dix, 1954",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/173779,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.8,false,true,96595,Costume Institute,Evening coat,Evening coat,French,,,,,Designer|Design House,,Jeanne Lanvin|House of Lanvin,"French, 1867–1946|French, founded 1889",,"Lanvin, Jeanne|Lanvin, House of",French|French,1867 |1889,1946 |9999,1926,1926,1926,[no medium available],,"Gift of Mrs. Juliet Mason, 1972",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/96595,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1984.7,false,true,104694,Costume Institute,Picture hat,Picture hat,French,,,,,Designer|Design House,Attributed to|Attributed to,Jeanne Lanvin|House of Lanvin,"French, 1867–1946|French, founded 1889",,"Lanvin, Jeanne|Lanvin, House of",French|French,1867 |1889,1946 |9999,ca. 1922,1917,1927,"silk, glass",,"Purchase, Gifts in memory of Elizabeth Lawrence, 1983",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/104694,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1974.91,false,true,96596,Costume Institute,Evening coat,Evening coat,French,,,,,Designer|Design House,,Jeanne Lanvin|House of Lanvin,"French, 1867–1946|French, founded 1889",,"Lanvin, Jeanne|Lanvin, House of",French|French,1867 |1889,1946 |9999,1931–32,1931,1932,[no medium available],,"Gift of Pamela Rankin Smith, 1974",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/96596,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.77,false,true,104257,Costume Institute,Bonnet,Bonnet,French,,,,,Designer|Design House,,Jeanne Lanvin|House of Lanvin,"French, 1867–1946|French, founded 1889",,"Lanvin, Jeanne|Lanvin, House of",French|French,1867 |1889,1946 |9999,ca. 1910,1905,1915,"cotton, metallic thread, silk",,"Purchase, Irene Lewisohn Bequest, 1989",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/104257,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.393,false,true,84806,Costume Institute,Evening dress,Evening dress,French,,,,,Designer|Design House,,Jeanne Lanvin|House of Lanvin,"French, 1867–1946|French, founded 1889",,"Lanvin, Jeanne|Lanvin, House of",French|French,1867 |1889,1946 |9999,1933–37,1933,1937,synthetic,,"Gift of Mrs. Hilda Sutton, 1975",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/84806,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.215,false,true,82597,Costume Institute,Evening coat,Evening coat,French,,,,,Designer|Design House,,Jeanne Lanvin|House of Lanvin,"French, 1867–1946|French, founded 1889",,"Lanvin, Jeanne|Lanvin, House of",French|French,1867 |1889,1946 |9999,spring/summer 1927,1927,1927,"cotton, wool",,"Isabel Shults Fund, 1986",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/82597,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2002.162,false,true,84146,Costume Institute,Dress,Dress,French,,,,,Designer|Design House,,Jeanne Lanvin|House of Lanvin,"French, 1867–1946|French, founded 1889",,"Lanvin, Jeanne|Lanvin, House of",French|French,1867 |1889,1946 |9999,1916,1916,1916,"silk, glass",,"Gift of Charles Kleibacker, 2002",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/84146,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1980.98.4,false,true,84818,Costume Institute,Evening cape,Evening cape,French,,,,,Designer|Design House,,Jeanne Lanvin|House of Lanvin,"French, 1867–1946|French, founded 1889",,"Lanvin, Jeanne|Lanvin, House of",French|French,1867 |1889,1946 |9999,late 1930s,1935,1939,"cotton, plastic",,"Gift of Sven E. Hsia, 1980",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/84818,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.30.13,false,true,96597,Costume Institute,Evening wrap,Evening wrap,French,,,,,Designer|Design House,,Jeanne Lanvin|House of Lanvin,"French, 1867–1946|French, founded 1889",,"Lanvin, Jeanne|Lanvin, House of",French|French,1867 |1889,1946 |9999,1929,1929,1929,fur,,"Gift of Madame Lilliana Teruzzi, 1972",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/96597,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1973.199.1,false,true,84813,Costume Institute,Evening dress,"""Fusée""",French,,,,,Designer|Design House,,Jeanne Lanvin|House of Lanvin,"French, 1867–1946|French, founded 1889",,"Lanvin, Jeanne|Lanvin, House of",French|French,1867 |1889,1946 |9999,1938,1938,1938,silk,,"Gift of Mrs. Lawrence W. Snell, 1973",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/84813,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1979.122.1,false,true,81478,Costume Institute,Robe de Style,Robe de Style,French,,,,,Designer|Design House,,Jeanne Lanvin|House of Lanvin,"French, 1867–1946|French, founded 1889",,"Lanvin, Jeanne|Lanvin, House of",French|French,1867 |1889,1946 |9999,1924–25,1924,1925,"silk, cotton",,"Gift of Mrs. William B. Given Jr., 1979",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/81478,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1979.122.2,false,true,81479,Costume Institute,Coat,Coat,French,,,,,Designer|Design House,,Jeanne Lanvin|House of Lanvin,"French, 1867–1946|French, founded 1889",,"Lanvin, Jeanne|Lanvin, House of",French|French,1867 |1889,1946 |9999,fall/winter 1924–25,1924,1925,"silk, wool, fur, metal",,"Gift of Mrs. William B. Given Jr., 1979",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/81479,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1979.344.8,false,true,84802,Costume Institute,Dinner dress,Dinner dress,French,,,,,Designer|Design House,,Jeanne Lanvin|House of Lanvin,"French, 1867–1946|French, founded 1889",,"Lanvin, Jeanne|Lanvin, House of",French|French,1867 |1889,1946 |9999,spring/summer 1934,1934,1934,silk,,"Gift of Mrs. Anthony Wilson, 1979",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/84802,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1979.344.9,false,true,84803,Costume Institute,Dinner dress,Dinner dress,French,,,,,Designer|Design House,,Jeanne Lanvin|House of Lanvin,"French, 1867–1946|French, founded 1889",,"Lanvin, Jeanne|Lanvin, House of",French|French,1867 |1889,1946 |9999,1934,1934,1934,silk,,"Gift of Mrs. Anthony Wilson, 1979",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/84803,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1979.574.1,false,true,96601,Costume Institute,Coat,Coat,French,,,,,Designer|Design House,,Jeanne Lanvin|House of Lanvin,"French, 1867–1946|French, founded 1889",,"Lanvin, Jeanne|Lanvin, House of",French|French,1867 |1889,1946 |9999,fall/winter 1927–28,1927,1928,"wool, silk",,"Gift of Mrs. Herbert Parsons, 1979",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/96601,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1995.28.20,false,true,80176,Costume Institute,Coat,Coat,French,,,,,Designer|Design House,,Jeanne Lanvin|House of Lanvin,"French, 1867–1946|French, founded 1889",,"Lanvin, Jeanne|Lanvin, House of",French|French,1867 |1889,1946 |9999,fall/winter 1932–33,1932,1933,"silk, fur (possibly ermine)",,"Gift of Estate of Lillian Gish, 1995",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/80176,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1995.588.2,false,true,80337,Costume Institute,Robe de Style,Robe de Style,French,,,,,Designer|Design House,,Jeanne Lanvin|House of Lanvin,"French, 1867–1946|French, founded 1889",,"Lanvin, Jeanne|Lanvin, House of",French|French,1867 |1889,1946 |9999,ca. 1926,1921,1931,silk,,"Gift of Miriam K. W. Coletti, 1995",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/80337,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.62.8.5,false,true,82646,Costume Institute,Robe de Style,Robe de Style,French,,,,,Designer|Design House,,Jeanne Lanvin|House of Lanvin,"French, 1867–1946|French, founded 1889",,"Lanvin, Jeanne|Lanvin, House of",French|French,1867 |1889,1946 |9999,1922,1922,1922,"silk, metal, glass",,"Gift of Mrs. Stephen C. Clark, 1962",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/82646,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.198.10,false,true,103869,Costume Institute,Coat,Coat,French,,,,,Designer|Design House,,Jeanne Lanvin|House of Lanvin,"French, 1867–1946|French, founded 1889",,"Lanvin, Jeanne|Lanvin, House of",French|French,1867 |1889,1946 |9999,fall/winter 1930–31,1930,1931,silk,,"Gift of Mrs. William Rhinelander Stewart, 1976",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/103869,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.198.11,false,true,103870,Costume Institute,Coat,Coat,French,,,,,Designer|Design House,,Jeanne Lanvin|House of Lanvin,"French, 1867–1946|French, founded 1889",,"Lanvin, Jeanne|Lanvin, House of",French|French,1867 |1889,1946 |9999,fall/winter 1930–31,1930,1931,silk,,"Gift of Mrs. William Rhinelander Stewart, 1976",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/103870,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.198.12,false,true,103279,Costume Institute,Dress,Dress,French,,,,,Designer|Design House,,Jeanne Lanvin|House of Lanvin,"French, 1867–1946|French, founded 1889",,"Lanvin, Jeanne|Lanvin, House of",French|French,1867 |1889,1946 |9999,fall/winter 1930–31,1930,1931,"cotton, silk",,"Gift of Mrs. William Rhinelander Stewart, 1976",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/103279,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1978.165.19,false,true,84810,Costume Institute,Evening dress,Evening dress,French,,,,,Designer|Design House,,Jeanne Lanvin|House of Lanvin,"French, 1867–1946|French, founded 1889",,"Lanvin, Jeanne|Lanvin, House of",French|French,1867 |1889,1946 |9999,1937–39,1937,1939,"silk, cotton",,"Gift of Mrs. Stephen M. Kellen, 1978",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/84810,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1978.295.22,false,true,96599,Costume Institute,Evening coat,Evening coat,French,,,,,Designer|Design House,,Jeanne Lanvin|House of Lanvin,"French, 1867–1946|French, founded 1889",,"Lanvin, Jeanne|Lanvin, House of",French|French,1867 |1889,1946 |9999,ca. 1917,1912,1922,silk,,"Purchase, Marcia Sand Bequest, in memory of her daughter, Tiger (Joan) Morse, 1978",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/96599,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1979.344.18,false,true,108910,Costume Institute,Teddy,Teddy,French,,,,,Designer|Design House,,Jeanne Lanvin|House of Lanvin,"French, 1867–1946|French, founded 1889",,"Lanvin, Jeanne|Lanvin, House of",French|French,1867 |1889,1946 |9999,1920s,1920,1929,silk,,"Gift of Mrs. Anthony Wilson, 1979",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/108910,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1979.344.19,false,true,84807,Costume Institute,Teddy,Teddy,French,,,,,Designer|Design House,,Jeanne Lanvin|House of Lanvin,"French, 1867–1946|French, founded 1889",,"Lanvin, Jeanne|Lanvin, House of",French|French,1867 |1889,1946 |9999,mid-1930s,1933,1937,silk,,"Gift of Mrs. Anthony Wilson, 1979",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/84807,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1984.587.17,false,true,96602,Costume Institute,Ski jacket,Ski jacket,French,,,,,Designer|Design House,,Jeanne Lanvin|House of Lanvin,"French, 1867–1946|French, founded 1889",,"Lanvin, Jeanne|Lanvin, House of",French|French,1867 |1889,1946 |9999,fall/winter 1924–25,1924,1925,wool,,"Gift of Miriam Whitney Coletti, 1984",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/96602,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.40.22.1,false,true,102920,Costume Institute,Dress,Dress,French,,,,,Designer|Design House,,Jeanne Lanvin|House of Lanvin,"French, 1867–1946|French, founded 1889",,"Lanvin, Jeanne|Lanvin, House of",French|French,1867 |1889,1946 |9999,1910,1910,1910,silk,,"Gift of Mrs. Whitewright Watson, 1940",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/102920,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.40.22.2,false,true,102921,Costume Institute,Dress,Dress,French,,,,,Designer|Design House,,Jeanne Lanvin|House of Lanvin,"French, 1867–1946|French, founded 1889",,"Lanvin, Jeanne|Lanvin, House of",French|French,1867 |1889,1946 |9999,1910,1910,1910,silk,,"Gift of Mrs. Whitewright Watson, 1940",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/102921,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.56.49.1,false,true,81970,Costume Institute,Robe de Style,Robe de Style,French,,,,,Designer|Design House,,Jeanne Lanvin|House of Lanvin,"French, 1867–1946|French, founded 1889",,"Lanvin, Jeanne|Lanvin, House of",French|French,1867 |1889,1946 |9999,1924–25,1924,1925,silk,,"Gift of Mrs. W.R. Grace, 1956",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/81970,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.62.58.1,false,true,81462,Costume Institute,Robe de Style,Robe de Style,French,,,,,Designer|Design House,,Jeanne Lanvin|House of Lanvin,"French, 1867–1946|French, founded 1889",,"Lanvin, Jeanne|Lanvin, House of",French|French,1867 |1889,1946 |9999,spring/summer 1924,1924,1924,"silk, metallic thread, glass",,"Gift of Mrs. Albert Spalding, 1962",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/81462,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.63.38.3,false,true,109363,Costume Institute,Blouse,Blouse,French,,,,,Designer|Design House,,Jeanne Lanvin|House of Lanvin,"French, 1867–1946|French, founded 1889",,"Lanvin, Jeanne|Lanvin, House of",French|French,1867 |1889,1946 |9999,1946,1946,1946,silk,,"Gift of Mrs. Moore Montgomery, 1963",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/109363,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.66.58.1,false,true,84065,Costume Institute,Evening jacket,Evening jacket,French,,,,,Designer|Design House,,Jeanne Lanvin|House of Lanvin,"French, 1867–1946|French, founded 1889",,"Lanvin, Jeanne|Lanvin, House of",French|French,1867 |1889,1946 |9999,1936–37,1936,1937,"silk, metallic thread, fur",,"Gift of Mrs. Leon L. Roos, 1966",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/84065,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.357,false,true,158521,Costume Institute,Evening dress,Evening dress,French,,,,,Designer|Design House,,Jeanne Lanvin|House of Lanvin,"French, 1867–1946|French, founded 1889",,"Lanvin, Jeanne|Lanvin, House of",French|French,1867 |1889,1946 |9999,spring/summer 1938,1938,1938,"silk, mother-of-pearl, beads",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mrs. Ian B. MacDonald, 1965",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/158521,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.1288,false,true,155986,Costume Institute,Headdress,Headdress,French,,,,,Designer|Design House,,Jeanne Lanvin|House of Lanvin,"French, 1867–1946|French, founded 1889",,"Lanvin, Jeanne|Lanvin, House of",French|French,1867 |1889,1946 |9999,ca. 1925,1923,1927,"cotton, silk",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Rodman A. Heeren, 1961",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/155986,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.1365,false,true,156071,Costume Institute,Evening dress,"""Phèdre""",French,,,,,Designer|Design House,,Jeanne Lanvin|House of Lanvin,"French, 1867–1946|French, founded 1889",,"Lanvin, Jeanne|Lanvin, House of",French|French,1867 |1889,1946 |9999,fall/winter 1933,1933,1933,"silk, metal",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mark Walsh, 1984",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/156071,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.1376,false,true,156084,Costume Institute,Evening dress,Evening dress,French,,,,,Designer|Design House,,Jeanne Lanvin|House of Lanvin,"French, 1867–1946|French, founded 1889",,"Lanvin, Jeanne|Lanvin, House of",French|French,1867 |1889,1946 |9999,fall/winter 1934–35,1934,1935,"silk, metal",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of George Drew, 1988",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/156084,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.2221,false,true,157023,Costume Institute,Evening shawl,Evening shawl,French,,,,,Designer|Design House,Attributed to|Attributed to,Jeanne Lanvin|House of Lanvin,"French, 1867–1946|French, founded 1889",,"Lanvin, Jeanne|Lanvin, House of",French|French,1867 |1889,1946 |9999,ca. 1922,1920,1924,"silk, metal",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Designated Purchase Fund, 1986",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/157023,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.2249,false,true,157053,Costume Institute,Evening belt,Evening belt,French,,,,,Designer|Design House,,Jeanne Lanvin|House of Lanvin,"French, 1867–1946|French, founded 1889",,"Lanvin, Jeanne|Lanvin, House of",French|French,1867 |1889,1946 |9999,ca. 1925,1923,1927,silk,,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Brooklyn Museum Collection",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/157053,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.2401,false,true,157223,Costume Institute,Hat,Hat,French,,,,,Designer|Design House,,Jeanne Lanvin|House of Lanvin,"French, 1867–1946|French, founded 1889",,"Lanvin, Jeanne|Lanvin, House of",French|French,1867 |1889,1946 |9999,1932,1932,1932,silk,,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mrs. Frank L. Babbott, 1954",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/157223,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.2506,false,true,157340,Costume Institute,Robe de Style,Robe de Style,French,,,,,Designer|Design House,,Jeanne Lanvin|House of Lanvin,"French, 1867–1946|French, founded 1889",,"Lanvin, Jeanne|Lanvin, House of",French|French,1867 |1889,1946 |9999,fall/winter 1926,1926,1926,"silk, rhinestones, pearls",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Ogden Goelet, Peter Goelet, and Madison Clews, 1962",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/157340,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.2520,false,true,157356,Costume Institute,Evening dress,Evening dress,French,,,,,Designer|Design House,,Jeanne Lanvin|House of Lanvin,"French, 1867–1946|French, founded 1889",,"Lanvin, Jeanne|Lanvin, House of",French|French,1867 |1889,1946 |9999,ca. 1935,1933,1937,"silk, metal",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Ogden Goelet, Peter Goelet, and Madison Clews, 1963",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/157356,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.2543,false,true,157382,Costume Institute,Evening bag,Evening bag,French,,,,,Designer|Design House,,Jeanne Lanvin|House of Lanvin,"French, 1867–1946|French, founded 1889",,"Lanvin, Jeanne|Lanvin, House of",French|French,1867 |1889,1946 |9999,1925–35,1925,1935,"silk, metal Silk, metallic",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of the executors of the estate of Clara M. Blum in memory of Mr. and Mrs. Albert Blum, 1966",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/157382,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.2563,false,true,157403,Costume Institute,Evening cloche,Evening cloche,French,,,,,Designer|Design House,,Jeanne Lanvin|House of Lanvin,"French, 1867–1946|French, founded 1889",,"Lanvin, Jeanne|Lanvin, House of",French|French,1867 |1889,1946 |9999,ca. 1925,1923,1927,"cotton, metal",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mrs. Frederick H. Prince, Jr., 1967",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/157403,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.2565,false,true,157405,Costume Institute,Evening dress,Evening dress,French,,,,,Designer|Design House,Attributed to|Attributed to,Jeanne Lanvin|House of Lanvin,"French, 1867–1946|French, founded 1889",,"Lanvin, Jeanne|Lanvin, House of",French|French,1867 |1889,1946 |9999,ca. 1922,1920,1924,silk,,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mrs. Frederick H. Prince, Jr., 1967",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/157405,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.2572,false,true,157414,Costume Institute,Evening cloche,Evening cloche,French,,,,,Designer|Design House,,Jeanne Lanvin|House of Lanvin,"French, 1867–1946|French, founded 1889",,"Lanvin, Jeanne|Lanvin, House of",French|French,1867 |1889,1946 |9999,ca. 1925,1923,1927,"silk, metal",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mrs. Frederick H. Prince, Jr., 1967",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/157414,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.2631,false,true,157479,Costume Institute,Blouse,Blouse,French,,,,,Designer|Design House,,Jeanne Lanvin|House of Lanvin,"French, 1867–1946|French, founded 1889",,"Lanvin, Jeanne|Lanvin, House of",French|French,1867 |1889,1946 |9999,ca. 1928,1926,1930,silk,,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Martin Kamer, 1984",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/157479,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.2635,false,true,157483,Costume Institute,Evening dress,"""Jolibois""",French,,,,,Designer|Design House,,Jeanne Lanvin|House of Lanvin,"French, 1867–1946|French, founded 1889",,"Lanvin, Jeanne|Lanvin, House of",French|French,1867 |1889,1946 |9999,fall/winter 1922–23,1922,1923,silk,,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Louise Gross, 1986",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/157483,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.2640,false,true,157490,Costume Institute,Evening coat,Evening coat,French,,,,,Designer|Design House,,Jeanne Lanvin|House of Lanvin,"French, 1867–1946|French, founded 1889",,"Lanvin, Jeanne|Lanvin, House of",French|French,1867 |1889,1946 |9999,spring/summer 1926,1926,1926,silk,,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Gardner and Diana Thoenen in Memory of Meredith Smith Thoenen, 1986",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/157490,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.2782,false,true,157646,Costume Institute,Evening dress,Evening dress,French,,,,,Designer|Design House,,Jeanne Lanvin|House of Lanvin,"French, 1867–1946|French, founded 1889",,"Lanvin, Jeanne|Lanvin, House of",French|French,1867 |1889,1946 |9999,1920,1920,1920,"silk, rhinestones, linen",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Countess Edna E. de Frise, 1953",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/157646,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.2802,false,true,157669,Costume Institute,Robe de Style,Robe de Style,French,,,,,Designer|Design House,,Jeanne Lanvin|House of Lanvin,"French, 1867–1946|French, founded 1889",,"Lanvin, Jeanne|Lanvin, House of",French|French,1867 |1889,1946 |9999,1922,1922,1922,"metal, silk",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mrs. Norman W. Wassman, 1956",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/157669,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.2860,false,true,157733,Costume Institute,Evening dress,Evening dress,French,,,,,Designer|Design House,,Jeanne Lanvin|House of Lanvin,"French, 1867–1946|French, founded 1889",,"Lanvin, Jeanne|Lanvin, House of",French|French,1867 |1889,1946 |9999,1925,1925,1925,"silk, metal",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Helen Appleton Read, 1962",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/157733,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.3225,false,true,158139,Costume Institute,Evening dress,Evening dress,French,,,,,Designer|Design House,,Jeanne Lanvin|House of Lanvin,"French, 1867–1946|French, founded 1889",,"Lanvin, Jeanne|Lanvin, House of",French|French,1867 |1889,1946 |9999,ca. 1923,1921,1925,silk,,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mr. and Mrs. Maxime L. Hermanos, 1961",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/158139,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.3400,false,true,158335,Costume Institute,Evening dress,Evening dress,French,,,,,Designer|Design House,,Jeanne Lanvin|House of Lanvin,"French, 1867–1946|French, founded 1889",,"Lanvin, Jeanne|Lanvin, House of",French|French,1867 |1889,1946 |9999,fall/winter 1938–39,1938,1939,"silk, metal",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Brooklyn Museum Collection",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/158335,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.6756,false,true,173768,Costume Institute,Evening dress,Evening dress,French,,,,,Designer|Design House,Attributed to|Attributed to,Jeanne Lanvin|House of Lanvin,"French, 1867–1946|French, founded 1889",,"Lanvin, Jeanne|Lanvin, House of",French|French,1867 |1889,1946 |9999,ca. 1920,1918,1922,Silk,,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Countess Edna E. de Frise, 1953",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/173768,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.6767,false,true,173782,Costume Institute,Evening coat,Evening coat,French,,,,,Designer|Design House,,Jeanne Lanvin|House of Lanvin,"French, 1867–1946|French, founded 1889",,"Lanvin, Jeanne|Lanvin, House of",French|French,1867 |1889,1946 |9999,winter 1932–33,1932,1933,Silk,,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mrs. Frank L. Babbott, 1954",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/173782,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.6768,false,true,173783,Costume Institute,Hat,Hat,French,,,,,Designer|Design House,,Jeanne Lanvin|House of Lanvin,"French, 1867–1946|French, founded 1889",,"Lanvin, Jeanne|Lanvin, House of",French|French,1867 |1889,1946 |9999,1932,1932,1932,Wool,,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mrs. Frank L. Babbott, 1954",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/173783,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.6850,false,true,173863,Costume Institute,Evening dress,Evening dress,French,,,,,Designer|Design House,,Jeanne Lanvin|House of Lanvin,"French, 1867–1946|French, founded 1889",,"Lanvin, Jeanne|Lanvin, House of",French|French,1867 |1889,1946 |9999,winter 1928,1928,1928,Silk,,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Arturo and Paul Peralta-Ramos, 1955",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/173863,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.7025,false,true,174015,Costume Institute,Evening dress,Evening dress,French,,,,,Designer|Design House,Attributed to|Attributed to,Jeanne Lanvin|House of Lanvin,"French, 1867–1946|French, founded 1889",,"Lanvin, Jeanne|Lanvin, House of",French|French,1867 |1889,1946 |9999,1930,1930,1930,Silk,,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mrs. George B. Wells, 1957",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/174015,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.7328,false,true,175780,Costume Institute,Overcoat,Overcoat,French,,,,,Designer|Design House,,Jeanne Lanvin|House of Lanvin,"French, 1867–1946|French, founded 1889",,"Lanvin, Jeanne|Lanvin, House of",French|French,1867 |1889,1946 |9999,1934,1934,1934,Wool,,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of A. C. Moss, 1964",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/175780,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.7533,false,true,174586,Costume Institute,Hat,Hat,French,,,,,Designer|Design House,,Jeanne Lanvin|House of Lanvin,"French, 1867–1946|French, founded 1889",,"Lanvin, Jeanne|Lanvin, House of",French|French,1867 |1889,1946 |9999,ca. 1918,1916,1920,"Cotton, wire",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mrs. Frederick H. Prince, Jr., 1967",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/174586,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.7534,false,true,174587,Costume Institute,Hat,Hat,French,,,,,Designer|Design House,,Jeanne Lanvin|House of Lanvin,"French, 1867–1946|French, founded 1889",,"Lanvin, Jeanne|Lanvin, House of",French|French,1867 |1889,1946 |9999,ca. 1935,1933,1937,"Straw, silk",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mrs. Frederick H. Prince, Jr., 1967",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/174587,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.8114,false,true,175139,Costume Institute,Evening dress,Evening dress,French,,,,,Designer|Design House,Possibly|Possibly,Jeanne Lanvin|House of Lanvin,"French, 1867–1946|French, founded 1889",,"Lanvin, Jeanne|Lanvin, House of",French|French,1867 |1889,1946 |9999,ca. 1927,1925,1929,"Cotton, beads, metallic",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Rena Gill, 1985",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/175139,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"C.I.60.19a, b",false,true,84022,Costume Institute,Evening ensemble,"""Ko.I.Noor""",French,,,,,Designer|Design House,,Jeanne Lanvin|House of Lanvin,"French, 1867–1946|French, founded 1889",,"Lanvin, Jeanne|Lanvin, House of",French|French,1867 |1889,1946 |9999,spring/summer 1927,1927,1927,"silk, metal b) silk, metal",,"Gift of Mme. Yves Lanvin, 1960",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/84022,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"C.I.61.22a, b",false,true,103601,Costume Institute,Suit,Suit,French,,,,,Designer|Design House,,Jeanne Lanvin|House of Lanvin,"French, 1867–1946|French, founded 1889",,"Lanvin, Jeanne|Lanvin, House of",French|French,1867 |1889,1946 |9999,1927,1927,1927,"wool, silk",,"Gift of Mrs. Dale Scott, 1961",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/103601,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"1993.423.1a, b",false,true,80887,Costume Institute,Evening ensemble,Evening ensemble,French,,,,,Designer|Design House,,Jeanne Lanvin|House of Lanvin,"French, 1867–1946|French, founded 1889",,"Lanvin, Jeanne|Lanvin, House of",French|French,1867 |1889,1946 |9999,ca. 1934,1929,1939,"silk, metal",,"Gift of Miriam W. Coletti, 1993",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/80887,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1982.528.7a–c,false,true,84892,Costume Institute,Evening dress,Evening dress,French,,,,,Designer|Design House,Attributed to|Attributed to,Jeanne Lanvin|House of Lanvin,"French, 1867–1946|French, founded 1889",,"Lanvin, Jeanne|Lanvin, House of",French|French,1867 |1889,1946 |9999,ca. 1935,1930,1940,silk,,"Gift of Mrs. Charles C. Paterson, 1982",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/84892,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.377.1a–c,false,true,81484,Costume Institute,Evening dress,Evening dress,French,,,,,Designer|Design House,,Jeanne Lanvin|House of Lanvin,"French, 1867–1946|French, founded 1889",,"Lanvin, Jeanne|Lanvin, House of",French|French,1867 |1889,1946 |9999,ca. 1930,1925,1935,silk,,"Gift of Mrs. Jill L. Leinbach & Mr. James L. Long in memory of their mother, Mrs. Jane P. Long, 1986",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/81484,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2009.300.96a, b",false,true,159558,Costume Institute,Evening coat,Evening coat,French,,,,,Designer|Design House,,Jeanne Lanvin|House of Lanvin,"French, 1867–1946|French, founded 1889",,"Lanvin, Jeanne|Lanvin, House of",French|French,1867 |1889,1946 |9999,fall/winter 1932–33,1932,1933,"wool, fur",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mrs. Frank L. Babbott, Jr., 1941",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/159558,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"C.I.46.4.18a, b",true,true,82103,Costume Institute,Evening dress,"""Cyclone""",French,,,,,Designer|Design House,,Jeanne Lanvin|House of Lanvin,"French, 1867–1946|French, founded 1889",,"Lanvin, Jeanne|Lanvin, House of",French|French,1867 |1889,1946 |9999,1939,1939,1939,"silk, spangles",,"Gift of Mrs. Harrison Williams, Lady Mendl, and Mrs. Ector Munn, 1946",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/82103,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"C.I.54.49.3a, b",false,true,103584,Costume Institute,Ensemble,Ensemble,French,,,,,Designer|Design House,,Jeanne Lanvin|House of Lanvin,"French, 1867–1946|French, founded 1889",,"Lanvin, Jeanne|Lanvin, House of",French|French,1867 |1889,1946 |9999,ca. 1921,1916,1926,wool,,"Gift of Mrs. Roland L. Redmond, 1954",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/103584,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1978.165.18a–c,false,true,84809,Costume Institute,Ensemble,Ensemble,French,,,,,Designer|Design House,Attributed to,Jeanne Lanvin|House of Lanvin,"French, 1867–1946|French, founded 1889",,"Lanvin, Jeanne|Lanvin, House of",French|French,1867 |1889,1946 |9999,1937–38,1937,1938,a) silk b) cotton c) glass,,"Gift of Mrs. Stephen M. Kellen, 1978",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/84809,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1978.184.12a–c,false,true,94981,Costume Institute,Evening ensemble,Evening ensemble,French,,,,,Designer|Design House,,Jeanne Lanvin|House of Lanvin,"French, 1867–1946|French, founded 1889",,"Lanvin, Jeanne|Lanvin, House of",French|French,1867 |1889,1946 |9999,1930,1930,1930,"silk, metallic thread",,"Purchase, Marcia Sand Bequest, in memory of her daughter, Tiger (Joan) Morse, 1978",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/94981,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2009.300.436a, b",false,true,158967,Costume Institute,Evening dress,Evening dress,French,,,,,Designer|Design House,,Jeanne Lanvin|House of Lanvin,"French, 1867–1946|French, founded 1889",,"Lanvin, Jeanne|Lanvin, House of",French|French,1867 |1889,1946 |9999,spring/summer 1937,1937,1937,"silk, leather",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mrs. Frederick H. Prince, Jr., 1967",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/158967,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.46.4.17a–c,false,true,82102,Costume Institute,Dinner dress,Dinner dress,French,,,,,Designer|Design House,,Jeanne Lanvin|House of Lanvin,"French, 1867–1946|French, founded 1889",,"Lanvin, Jeanne|Lanvin, House of",French|French,1867 |1889,1946 |9999,1939,1939,1939,"silk, spangles",,"Gift of Mrs. Harrison Williams, Lady Mendl, and Mrs. Ector Munn, 1946",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/82102,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"C.I.58.34.19a, b",false,true,84557,Costume Institute,Dress,Dress,French,,,,,Designer|Design House,,Jeanne Lanvin|House of Lanvin,"French, 1867–1946|French, founded 1889",,"Lanvin, Jeanne|Lanvin, House of",French|French,1867 |1889,1946 |9999,1937,1937,1937,"cotton, silk",,"Gift of Mrs. John Chambers Hughes, 1958",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/84557,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"C.I.58.34.24a, b",false,true,84239,Costume Institute,Suit,Suit,French,,,,,Designer|Design House,,Jeanne Lanvin|House of Lanvin,"French, 1867–1946|French, founded 1889",,"Lanvin, Jeanne|Lanvin, House of",French|French,1867 |1889,1946 |9999,1940,1940,1940,silk,,"Gift of Mrs. John Chambers Hughes, 1958",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/84239,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1978.582.199a–e,false,true,97353,Costume Institute,Wedding Ensemble,Wedding ensemble,French,,,,,Designer|Design House,,Jeanne Lanvin|House of Lanvin,"French, 1867–1946|French, founded 1889",,"Lanvin, Jeanne|Lanvin, House of",French|French,1867 |1889,1946 |9999,1940,1940,1940,"linen, silk, cotton",,"Gift of Marvin B. Patterson (Mrs. Jefferson Patterson), 1978",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/97353,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2009.300.1259a, b",false,true,155953,Costume Institute,Evening ensemble,Evening ensemble,French,,,,,Designer|Design House,,Jeanne Lanvin|House of Lanvin,"French, 1867–1946|French, founded 1889",,"Lanvin, Jeanne|Lanvin, House of",French|French,1867 |1889,1946 |9999,spring/summer 1935,1935,1935,silk,,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Ogden Goelet, Peter Goelet and Madison Clews in memory of Mrs. Henry Clews, 1960",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/155953,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2009.300.1318a, b",false,true,156020,Costume Institute,Evening dress,"""Roseraie""",French,,,,,Designer|Design House,,Jeanne Lanvin|House of Lanvin,"French, 1867–1946|French, founded 1889",,"Lanvin, Jeanne|Lanvin, House of",French|French,1867 |1889,1946 |9999,spring/summer 1923,1923,1923,silk,,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Anonymous gift, 1964",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/156020,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2009.300.1335a, b",false,true,156039,Costume Institute,Evening dress,Evening dress,French,,,,,Designer|Design House,,Jeanne Lanvin|House of Lanvin,"French, 1867–1946|French, founded 1889",,"Lanvin, Jeanne|Lanvin, House of",French|French,1867 |1889,1946 |9999,spring/summer 1938,1938,1938,silk,,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mrs. Frederick H. Prince, Jr., 1967",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/156039,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2009.300.2228a, b",false,true,157030,Costume Institute,Evening dress,Evening dress,French,,,,,Designer|Design House,,Jeanne Lanvin|House of Lanvin,"French, 1867–1946|French, founded 1889",,"Lanvin, Jeanne|Lanvin, House of",French|French,1867 |1889,1946 |9999,spring/summer 1923,1923,1923,"silk, metal, feather",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Designated Purchase Fund, 1988",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/157030,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2009.300.2887a, b",false,true,157761,Costume Institute,Evening dress,Evening dress,French,,,,,Designer|Design House,,Jeanne Lanvin|House of Lanvin,"French, 1867–1946|French, founded 1889",,"Lanvin, Jeanne|Lanvin, House of",French|French,1867 |1889,1946 |9999,fall/winter 1925–26,1925,1926,"silk, metal",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mrs. Frederick H. Prince, Jr., 1967",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/157761,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2009.300.3182a, b",false,true,158091,Costume Institute,Evening dress,Evening dress,French,,,,,Designer|Design House,,Jeanne Lanvin|House of Lanvin,"French, 1867–1946|French, founded 1889",,"Lanvin, Jeanne|Lanvin, House of",French|French,1867 |1889,1946 |9999,fall/winter 1930–31,1930,1931,silk,,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mrs. George B. Wells, 1957",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/158091,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2009.300.3310a, b",false,true,158233,Costume Institute,Evening dress,Evening dress,French,,,,,Designer|Design House,,Jeanne Lanvin|House of Lanvin,"French, 1867–1946|French, founded 1889",,"Lanvin, Jeanne|Lanvin, House of",French|French,1867 |1889,1946 |9999,spring/summer 1937,1937,1937,synthetic,,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mrs. Frederick H. Prince, Jr., 1967",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/158233,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2009.300.7310a, b",false,true,175769,Costume Institute,Evening dress,Evening dress,French,,,,,Designer|Design House,,Jeanne Lanvin|House of Lanvin,"French, 1867–1946|French, founded 1889",,"Lanvin, Jeanne|Lanvin, House of",French|French,1867 |1889,1946 |9999,winter 1930–31,1930,1931,Silk,,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mrs. Anthony Wilson, 1963",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/175769,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.1364a–c,false,true,156070,Costume Institute,Evening ensemble,Evening ensemble,French,,,,,Designer|Design House,,Jeanne Lanvin|House of Lanvin,"French, 1867–1946|French, founded 1889",,"Lanvin, Jeanne|Lanvin, House of",French|French,1867 |1889,1946 |9999,spring/summer 1923,1923,1923,"silk, metal, rhinestones",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Designated Purchase Fund, 1984",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/156070,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.2442a–c,false,true,157269,Costume Institute,Evening dress,Evening dress,French,,,,,Designer|Design House,,Jeanne Lanvin|House of Lanvin,"French, 1867–1946|French, founded 1889",,"Lanvin, Jeanne|Lanvin, House of",French|French,1867 |1889,1946 |9999,ca. 1935,1933,1937,"silk, metal",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mrs. Robert E. Blum, 1956",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/157269,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.2564a–d,false,true,157404,Costume Institute,Evening dress,Evening dress,French,,,,,Designer|Design House,,Jeanne Lanvin|House of Lanvin,"French, 1867–1946|French, founded 1889",,"Lanvin, Jeanne|Lanvin, House of",French|French,1867 |1889,1946 |9999,fall/winter 1923–24,1923,1924,"silk, metal",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mrs. Frederick H. Prince, Jr., 1967",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/157404,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.2628a–c,false,true,157476,Costume Institute,Ensemble,Ensemble,French,,,,,Designer|Design House,,Jeanne Lanvin|House of Lanvin,"French, 1867–1946|French, founded 1889",,"Lanvin, Jeanne|Lanvin, House of",French|French,1867 |1889,1946 |9999,ca. 1926,1924,1928,"silk, wool",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Designated Purchase Fund, 1984",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/157476,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.2655a–c,false,true,157506,Costume Institute,Evening dress,Evening dress,French,,,,,Designer|Design House,,Jeanne Lanvin|House of Lanvin,"French, 1867–1946|French, founded 1889",,"Lanvin, Jeanne|Lanvin, House of",French|French,1867 |1889,1946 |9999,fall/winter 1926–27,1926,1927,"silk, metal",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Brooklyn Museum Collection",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/157506,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.7026a–c,false,true,174016,Costume Institute,Evening dress,Evening dress,French,,,,,Designer|Design House,,Jeanne Lanvin|House of Lanvin,"French, 1867–1946|French, founded 1889",,"Lanvin, Jeanne|Lanvin, House of",French|French,1867 |1889,1946 |9999,winter 1930–31,1930,1931,Silk,,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of Mrs. George B. Wells, 1957",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/174016,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1979.90,false,true,110001,Costume Institute,Necktie,Necktie,French,,,,,Design House|Designer,,House of Lanvin|Jeanne Lanvin,"French, founded 1889|French, 1867–1946",,"Lanvin, House of|Lanvin, Jeanne",French|French,1889 |1867,9999 |1946,1974,1974,1974,silk,,"Gift of John Michael Powers, Jr., 1979",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/110001,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1984.54,false,true,109995,Costume Institute,Evening dress,Evening dress,French,,,,,Design House|Designer,Attributed to|Attributed to,House of Lanvin|Jeanne Lanvin,"French, founded 1889|French, 1867–1946",,"Lanvin, House of|Lanvin, Jeanne",French|French,1889 |1867,9999 |1946,ca. 1933,1928,1938,"silk, cotton",,"Gift of Gabriella de Balogh, 1984",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/109995,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.30.2,false,true,94721,Costume Institute,Robe de Style,Robe de Style,French,,,,,Design House|Designer,,House of Lanvin|Jeanne Lanvin,"French, founded 1889|French, 1867–1946",,"Lanvin, House of|Lanvin, Jeanne",French|French,1889 |1867,9999 |1946,fall/winter 1926–27,1926,1927,"silk, plastic, glass",,"Gift of Mrs. Gilbert W. Chapman, 1976",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/94721,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1978.29.3,false,true,94699,Costume Institute,Afternoon dress,Afternoon dress,French,,,,,Design House|Designer,,House of Lanvin|Jeanne Lanvin,"French, founded 1889|French, 1867–1946",,"Lanvin, House of|Lanvin, Jeanne",French|French,1889 |1867,9999 |1946,1928–29,1928,1929,silk,,"Gift of Mr. and Mrs. Charles Abrams, 1978",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/94699,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.303.2,false,true,109980,Costume Institute,Hat,Hat,French,,,,,Design House|Designer,,House of Lanvin|Jeanne Lanvin,"French, founded 1889|French, 1867–1946",,"Lanvin, House of|Lanvin, Jeanne",French|French,1889 |1867,9999 |1946,1914–20,1914,1920,[no medium available],,"Gift of Elizabeth A. Tilson, 1975",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/109980,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.303.3,false,true,83472,Costume Institute,Hat,Hat,French,,,,,Design House|Designer,,House of Lanvin|Jeanne Lanvin,"French, founded 1889|French, 1867–1946",,"Lanvin, House of|Lanvin, Jeanne",French|French,1889 |1867,9999 |1946,1918–25,1918,1925,"horsehair, silk, feathers",,"Gift of Elizabeth A. Tilson, 1975",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/83472,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1978.184.7,false,true,94692,Costume Institute,Robe de Style,Robe de Style,French,,,,,Designer|Designer,,House of Lanvin|Jeanne Lanvin,"French, founded 1889|French, 1867–1946",,"Lanvin, House of|Lanvin, Jeanne",French|French,1889 |1867,9999 |1946,1920–25,1920,1925,"silk, metallic thread, glass, plastic",,"Purchase, Marcia Sand Bequest, in memory of her daughter, Tiger (Joan) Morse, 1978",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/94692,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1978.184.8,false,true,94715,Costume Institute,Robe de Style,Robe de Style,French,,,,,Design House|Designer,,House of Lanvin|Jeanne Lanvin,"French, founded 1889|French, 1867–1946",,"Lanvin, House of|Lanvin, Jeanne",French|French,1889 |1867,9999 |1946,fall/winter 1926–27,1926,1927,silk,,"Purchase, Marcia Sand Bequest, in memory of her daughter, Tiger (Joan) Morse, 1978",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/94715,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1979.87.43,false,true,107363,Costume Institute,Hat,Hat,French,,,,,Design House|Designer,,House of Lanvin|Jeanne Lanvin,"French, founded 1889|French, 1867–1946",,"Lanvin, House of|Lanvin, Jeanne",French|French,1889 |1867,9999 |1946,1967–69,1967,1969,wool,,"Gift of Janet A. Sloane, 1979",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/107363,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1980.487.2,false,true,94722,Costume Institute,Robe de Style,Robe de Style,French,,,,,Design House|Designer,,House of Lanvin|Jeanne Lanvin,"French, founded 1889|French, 1867–1946",,"Lanvin, House of|Lanvin, Jeanne",French|French,1889 |1867,9999 |1946,fall/winter 1924–25,1924,1925,"silk, metallic thread",,"Gift of Mrs. Loretta Hines Howard, 1980",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/94722,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1985.365.1,false,true,109985,Costume Institute,Wedding Dress,Wedding dress,French,,,,,Design House|Designer,,House of Lanvin|Jeanne Lanvin,"French, founded 1889|French, 1867–1946",,"Lanvin, House of|Lanvin, Jeanne",French|French,1889 |1867,9999 |1946,fall/winter 1926–27,1926,1927,silk,,"Gift of Varney Thompson Elliott and Rosemary Thompson Franciscus, in memory of their mother, Margaret Whitney Thompson, 1985",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/109985,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1985.365.5,false,true,94720,Costume Institute,Dress,Dress,French,,,,,Design House|Designer,,House of Lanvin|Jeanne Lanvin,"French, founded 1889|French, 1867–1946",,"Lanvin, House of|Lanvin, Jeanne",French|French,1889 |1867,9999 |1946,1927,1927,1927,silk,,"Gift of Varney Thompson Elliott and Rosemary Thompson Franciscus, in memory of their mother, Margaret Whitney Thompson, 1985",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/94720,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.377.2,false,true,94727,Costume Institute,Evening dress,Evening dress,French,,,,,Design House|Designer,,House of Lanvin|Jeanne Lanvin,"French, founded 1889|French, 1867–1946",,"Lanvin, House of|Lanvin, Jeanne",French|French,1889 |1867,9999 |1946,ca. 1930,1925,1935,silk,,"Gift of Mrs. Jill L. Leinbach & Mr. James L. Long in memory of their mother, Mrs. Jane P. Long, 1986",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/94727,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.516.4,false,true,94703,Costume Institute,Dress,Dress,French,,,,,Design House|Designer,,House of Lanvin|Jeanne Lanvin,"French, founded 1889|French, 1867–1946",,"Lanvin, House of|Lanvin, Jeanne",French|French,1889 |1867,9999 |1946,1925–29,1925,1929,"silk, feathers",,"Gift of Miriam W. Coletti, 1986",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/94703,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.516.9,false,true,91818,Costume Institute,Ski trousers,Ski trousers,French,,,,,Design House|Designer,Attributed to,House of Lanvin|Jeanne Lanvin,"French, founded 1889|French, 1867–1946",,"Lanvin, House of|Lanvin, Jeanne",French|French,1889 |1867,9999 |1946,ca. 1925,1920,1930,"wool, silk",,"Gift of Miriam W. Coletti, 1986",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/91818,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.62.8.2,false,true,94718,Costume Institute,Robe de Style,Robe de Style,French,,,,,Design House|Designer,,House of Lanvin|Jeanne Lanvin,"French, founded 1889|French, 1867–1946",,"Lanvin, House of|Lanvin, Jeanne",French|French,1889 |1867,9999 |1946,1922,1922,1922,silk,,"Gift of Mrs. Stephen C. Clark, 1962",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/94718,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.516.10,false,true,109982,Costume Institute,Belt,Belt,French,,,,,Design House|Designer,Attributed to|Attributed to,House of Lanvin|Jeanne Lanvin,"French, founded 1889|French, 1867–1946",,"Lanvin, House of|Lanvin, Jeanne",French|French,1889 |1867,9999 |1946,1920,1920,1920,"leather, steel",,"Gift of Miriam W. Coletti, 1986",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/109982,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2015.95a, b",false,true,680139,Costume Institute,Dress,Dress,French,,,,,Design House|Designer,,House of Lanvin|Jeanne Lanvin,"French, founded 1889|French, 1867–1946",,"Lanvin, House of|Lanvin, Jeanne",French|French,1889 |1867,9999 |1946,1928,1928,1928,"silk, metal, glass",,"Purchase, Friends of The Costume Institute Gifts, 2015",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/680139,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.46.93.1,false,true,94725,Costume Institute,Evening dress,Evening dress,French,,,,,Design House|Designer,,House of Lanvin|Jeanne Lanvin,"French, founded 1889|French, 1867–1946",,"Lanvin, House of|Lanvin, Jeanne",French|French,1889 |1867,9999 |1946,fall/winter 1930–31,1930,1931,silk,,"Gift of Mrs. Charles Leibman, 1946",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/94725,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.50.84.1,false,true,94726,Costume Institute,Evening dress,Evening dress,French,,,,,Design House|Designer,,House of Lanvin|Jeanne Lanvin,"French, founded 1889|French, 1867–1946",,"Lanvin, House of|Lanvin, Jeanne",French|French,1889 |1867,9999 |1946,spring/summer 1930,1930,1930,silk,,"Gift of Miss Frances McFadden, 1950",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/94726,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.56.49.3,false,true,94719,Costume Institute,Robe de Style,Robe de Style,French,,,,,Design House|Designer,Attributed to|Attributed to,House of Lanvin|Jeanne Lanvin,"French, founded 1889|French, 1867–1946",,"Lanvin, House of|Lanvin, Jeanne",French|French,1889 |1867,9999 |1946,1925,1925,1925,"silk, beading",,"Gift of Mrs. W. R. Grace, 1956",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/94719,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.57.46.2,false,true,94702,Costume Institute,Dress,Dress,French,,,,,Design House|Designer,,House of Lanvin|Jeanne Lanvin,"French, founded 1889|French, 1867–1946",,"Lanvin, House of|Lanvin, Jeanne",French|French,1889 |1867,9999 |1946,ca. 1927,1922,1932,"silk, wool, metal",,"Gift of Mrs. Seaman Schepps, 1957",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/94702,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"1976.28.2a, b",false,true,94705,Costume Institute,Evening dress,Evening dress,French,,,,,Design House|Designer,,House of Lanvin|Jeanne Lanvin,"French, founded 1889|French, 1867–1946",,"Lanvin, House of|Lanvin, Jeanne",French|French,1889 |1867,9999 |1946,spring/summer 1927,1927,1927,"silk, metallic thread",,"Gift of Mrs. Herbert Bayer, 1976",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/94705,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"1976.30.1a, b",false,true,94723,Costume Institute,Robe de Style,Robe de Style,French,,,,,Design House|Designer,,House of Lanvin|Jeanne Lanvin,"French, founded 1889|French, 1867–1946",,"Lanvin, House of|Lanvin, Jeanne",French|French,1889 |1867,9999 |1946,fall/winter 1926–27,1926,1927,"silk, plastic, glass",,"Gift of Mrs. Gilbert W. Chapman, 1976",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/94723,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"1973.199.2a, b",false,true,94694,Costume Institute,Evening dress,Evening dress,French,,,,,Design House|Designer,,House of Lanvin|Jeanne Lanvin,"French, founded 1889|French, 1867–1946",,"Lanvin, House of|Lanvin, Jeanne",French|French,1889 |1867,9999 |1946,1949,1949,1949,silk,,"Gift of Mrs. Lawrence W. Snell, 1973",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/94694,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"C.I.62.8.1a, b",false,true,94717,Costume Institute,Robe de Style,Robe de Style,French,,,,,Design House|Designer,,House of Lanvin|Jeanne Lanvin,"French, founded 1889|French, 1867–1946",,"Lanvin, House of|Lanvin, Jeanne",French|French,1889 |1867,9999 |1946,1922,1922,1922,"silk, glass, metal",,"Gift of Mrs. Stephen C. Clark, 1962",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/94717,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"C.I.62.8.3a, b",false,true,94716,Costume Institute,Robe de Style,Robe de Style,French,,,,,Design House|Designer,,House of Lanvin|Jeanne Lanvin,"French, founded 1889|French, 1867–1946",,"Lanvin, House of|Lanvin, Jeanne",French|French,1889 |1867,9999 |1946,1922,1922,1922,"cotton, silk, glass, metal",,"Gift of Mrs. Stephen C. Clark, 1962",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/94716,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"C.I.62.8.4a, b",false,true,109983,Costume Institute,Robe de Style,Robe de Style,French,,,,,Design House|Designer,,House of Lanvin|Jeanne Lanvin,"French, founded 1889|French, 1867–1946",,"Lanvin, House of|Lanvin, Jeanne",French|French,1889 |1867,9999 |1946,1922,1922,1922,"cotton, metal, silk",,"Gift of Mrs. Stephen C. Clark, 1962",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/109983,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1977.201.9a–c,false,true,94724,Costume Institute,Robe de Style,Robe de Style,French,,,,,Design House|Designer,,House of Lanvin|Jeanne Lanvin,"French, founded 1889|French, 1867–1946",,"Lanvin, House of|Lanvin, Jeanne",French|French,1889 |1867,9999 |1946,1931,1931,1931,"silk, plastic",,"Gift of Esmé O'Brien Hammond, 1977",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/94724,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"1979.344.10a, b",false,true,94706,Costume Institute,Robe de Style,Robe de Style,French,,,,,Design House|Designer,,House of Lanvin|Jeanne Lanvin,"French, founded 1889|French, 1867–1946",,"Lanvin, House of|Lanvin, Jeanne",French|French,1889 |1867,9999 |1946,1922,1922,1922,"silk, glass, metallic thread",,"Gift of Mrs. Anthony Wilson, 1979",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/94706,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1979.59.13a–c,false,true,92035,Costume Institute,Suit,Suit,French,,,,,Design House|Designer,,House of Lanvin|Jeanne Lanvin,"French, founded 1889|French, 1867–1946",,"Lanvin, House of|Lanvin, Jeanne",French|French,1889 |1867,9999 |1946,1931,1931,1931,wool,,"Gift of Stanley F. Waldman, President, The Manhattan Galleries, Inc., 1979",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/92035,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"C.I.62.58.2a, b",false,true,94704,Costume Institute,Evening dress,Evening dress,French,,,,,Design House|Designer,,House of Lanvin|Jeanne Lanvin,"French, founded 1889|French, 1867–1946",,"Lanvin, House of|Lanvin, Jeanne",French|French,1889 |1867,9999 |1946,spring/summer 1923,1923,1923,"silk, metallic thread, glass beads",,"Gift of Mrs. Albert Spaulding, 1962",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/94704,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.58.34.2a–c,false,true,94707,Costume Institute,Afternoon dress,Afternoon dress,French,,,,,Design House|Designer,,House of Lanvin|Jeanne Lanvin,"French, founded 1889|French, 1867–1946",,"Lanvin, House of|Lanvin, Jeanne",French|French,1889 |1867,9999 |1946,1920,1920,1920,silk,,"Gift of Mrs. John Chambers Hughes, 1958",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/94707,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -C.I.61.40.1a–c,false,true,109986,Costume Institute,Robe de Style,Robe de Style,French,,,,,Design House|Designer,,House of Lanvin|Jeanne Lanvin,"French, founded 1889|French, 1867–1946",,"Lanvin, House of|Lanvin, Jeanne",French|French,1889 |1867,9999 |1946,ca. 1927,1922,1932,"silk, metal",,"Gift of Mrs. David J. Colton, 1961",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/109986,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2001.341.1,false,true,82615,Costume Institute,Engraving,The Vanity of Women: Masks and Bustles,Dutch,,,,,Artist,Attributed to,Maerten de Vos,"Netherlandish, Antwerp 1532–1603 Antwerp",,"Vos, Maerten de",Netherlandish,1532,1603,ca. 1600,1595,1605,engraved paper,,"Purchase, Irene Lewisohn Trust Gift, 2001",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/82615,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2001.341.2,false,true,82616,Costume Institute,Engraving,The Pride of Women: Ruffs,Dutch,,,,,Artist,,Maerten de Vos,"Netherlandish, Antwerp 1532–1603 Antwerp",,"Vos, Maerten de",Netherlandish,1532,1603,ca. 1600,1595,1605,engraved paper,,"Purchase, Irene Lewisohn Trust Gift, 2001",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/82616,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.300.3102a–c,false,true,158003,Costume Institute,Corset,Corset,American,,,,,Department Store|Manufacturer,,Day & Horton|Worcester Skirt Company,"American|American, 1864–1950",,Day & Horton|Worcester Skirt Company,American|American,1864,1950,1866–67,1866,1867,"cotton, metal, bone",,"Brooklyn Museum Costume Collection at The Metropolitan Museum of Art, Gift of the Brooklyn Museum, 2009; Gift of E. A. Meister, 1950",,,,,,,,,,,,,,http://www.metmuseum.org/art/collection/search/158003,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -17.134.8,false,true,435608,European Paintings,"Painting, miniature",Portrait of a Man,,,,,,Artist,,Barrois,ca. 1790,,Barrois,French(?),1790,1790,ca. 1790,1785,1795,Ivory,Diameter 2 3/4 in. (67 mm),"Bequest of Ella Church Strobell, 1917",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/435608,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -95.14.72,false,true,437485,European Paintings,"Painting, miniature",Portrait of a Girl,,,,,,Artist,,Sampson Towgood Roch,"Irish, 1759–1847",,"Roch, Sampson Towgood",Irish,1759,1847,ca. 1790,1785,1795,Ivory,"Oval, 2 1/4 x 1 7/8 in. (58 x 48 mm)","The Moses Lazarus Collection, Gift of Josephine and Sarah Lazarus, in memory of their father, 1888–95",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/437485,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -15.43.291,false,true,437141,European Paintings,"Painting, miniature",Portrait of a Man,,,,,,Artist,,Monogrammist IS,"Flemish, dated 1621",,Monogrammist IS,Flemish,1621,1621,1621,1621,1621,Oil on copper,"Oval, 4 1/8 x 3 5/8 in. (105 x 91 mm)","Bequest of Mary Anna Palmer Draper, 1914",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/437141,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.145.10,false,true,437783,European Paintings,"Painting, miniature",Portrait of a Woman,,,,,,Artist,,Mikail Ivanovich Terebenev,"Russian, 1795–1866",,"Terebenev, Mikail Ivanovich",Russian,1795,1866,ca. 1830,1825,1835,Ivory,"Oval, 2 7/8 x 2 1/4 in. (72 x 57 mm)","Gift of Humanities Fund Inc., 1972",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/437783,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -95.14.53,false,true,436610,European Paintings,"Painting, miniature",The Painter Louis Joseph Maurice (1730–1820),,,,,,Artist,,Peter Adolf Hall,"Swedish, 1739–1793",,"Hall, Peter Adolf",Swedish,1739,1793,1772,1772,1772,Ivory,"Oval, 3 3/8 x 2 5/8 in. (84 x 67 mm)","The Moses Lazarus Collection, Gift of Josephine and Sarah Lazarus, in memory of their father, 1888–95",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/436610,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -26.168.45,false,true,436612,European Paintings,"Painting, miniature",Portrait of a Young Woman,,,,,,Artist,,Peter Adolf Hall,"Swedish, 1739–1793",,"Hall, Peter Adolf",Swedish,1739,1793,ca. 1790,1785,1795,Ivory,"Oval, 2 3/4 x 2 1/8 in. (70 x 55 mm)","Bequest of Collis P. Huntington, 1900",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/436612,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -40.186.1,false,true,435685,European Paintings,"Painting, miniature",A Man with the Initials AC,,,,,,Artist,,John Bogle,"British, 1746?–1803",,"Bogle, John","British, Scottish",1746,1803,1774,1774,1774,Ivory,"Oval, 1 3/8 x 1 1/8 in. (36 x 30 mm)","Gift of Mrs. S. M. Breckenridge Long, 1940",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/435685,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -40.186.2,false,true,435686,European Paintings,"Painting, miniature",A Woman with the Initials MCC,,,,,,Artist,,John Bogle,"British, 1746?–1803",,"Bogle, John","British, Scottish",1746,1803,1773,1773,1773,Ivory,"Oval, 1 5/8 x 1 3/8 in. (41 x 34 mm)","Gift of Mrs. S. M. Breckenridge Long, 1940",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/435686,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -62.122.61,false,true,437484,European Paintings,"Painting, miniature",Portrait of a Man,,,,,,Artist,,Charles Robertson,"Irish, ca. 1760–1821",,"Robertson, Charles",Irish,1760,1821,1810,1810,1810,Ivory,"Oval, 2 1/2 x 2 in. (61 x 50 mm)","Bequest of Millie Bruhl Fredrick, 1962",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/437484,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.136.13,false,true,436865,European Paintings,"Painting, miniature",Vertumnus and Pomona,,,,,,Artist,,Thomas Lefebure,"Flemish, ca. 1636–1720",,"Lefebure, Thomas",Flemish,1636,1720,1676,1676,1676,Paper,"Image exclusive of gold rim and dark brown border, 6 3/4 x 5 in. (172 x 126 mm)","Bequest of Margaret Crane Hurlbut, 1933",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/436865,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -38.50.333,false,true,437142,European Paintings,"Painting, miniature",Traveling Players,,,,,,Artist,,Monogrammist JG,"Northern European, ca. 1630",,Monogrammist JG,Northern European,1630,1630,ca. 1630,1625,1635,Vellum laid on wood,2 3/4 x 3 1/2 in. (70 x 90 mm),"Bequest of Kate Read Blacque, in memory of her husband, Valentine Alexander Blacque, 1937",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/437142,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -48.187.740,false,true,437139,European Paintings,"Painting, miniature",An Artist Painting a Heraldic Shield in a Cabinet of Curiosities,,,,,,Artist,,Monogrammist FA,"Northern European, dated 1664",,Monogrammist FA,Northern European,1664,1664,1664,1664,1664,Vellum laid on card,4 1/4 x 6 in. (106 x 152 mm),"Bequest of Catherine D. Wentworth, 1948",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/437139,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -95.14.81,false,true,435821,European Paintings,"Painting, miniature","Portrait of a Woman, Said to Be Emma (1765–1815), Lady Hamilton",,,,,,Artist,,Adam Buck,"Irish, Cork 1759–1833 London",,"Buck, Adam",Irish,1759,1833,1804,1804,1804,Ivory,Diameter 1 1/4 in. (32 mm),"The Moses Lazarus Collection, Gift of Josephine and Sarah Lazarus, in memory of their father, 1888–95",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/435821,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -25.106.27,false,true,436844,European Paintings,"Painting, miniature",Portrait of a Man,,,,,,Artist,,Jean-Baptiste Ponce Lambert,"Swiss (?), active ca. 1801–12",,"Lambert, Jean-Baptiste Ponce",Swiss,1801,1812,1801,1801,1801,Ivory,Diameter 2 3/8 in. (59 mm),"Gift of Mrs. Louis V. Bell, in memory of her husband, 1925",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/436844,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -62.122.27,false,true,436674,European Paintings,"Painting, miniature",Portrait of a Man,,,,,,Artist,Attributed to,Nathaniel Hone,"Irish, Dublin 1718–1784 London",,"Hone, Nathaniel",Irish,1718,1784,ca. 1760,1755,1765,Enamel,"Oval, 1 1/8 x 1 in. (30 x 25 mm)","Bequest of Millie Bruhl Fredrick, 1962",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/436674,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -25.106.1,false,true,435636,European Paintings,"Painting, miniature",Portrait of a Woman,,,,,,Artist,,Rudolphe Bel,"Swiss, active by 1822–died 1849",,"Bel, Rudolphe",Swiss,1822,1849,1822,1822,1822,Paper stretched over metal,"Oval, 5 x 3 1/2 in. (126 x 88 mm)","Gift of Mrs. Louis V. Bell, in memory of her husband, 1925",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/435636,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -49.7.25,false,true,436258,European Paintings,Painting,Self-Portrait,,,,,,Artist,,Anthony van Dyck,"Flemish, Antwerp 1599–1641 London",,"Dyck, Anthony van",Flemish,1599,1641,ca. 1620–21,1620,1621,Oil on canvas,47 1/8 x 34 5/8 in. (119.7 x 87.9 cm),"The Jules Bache Collection, 1949",,,,,,,,,,,,Painted Canvases,,http://www.metmuseum.org/art/collection/search/436258,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -57.37,false,true,436259,European Paintings,"Painting, drawing",Study Head of a Young Woman,,,,,,Artist,,Anthony van Dyck,"Flemish, Antwerp 1599–1641 London",,"Dyck, Anthony van",Flemish,1599,1641,ca. 1618–20,1618,1620,"Oil on paper, laid down on wood",22 1/4 x 16 3/8 in. (56.5 x 41.6 cm),"Gift of Mrs. Ralph J. Hines, 1957",,,,,,,,,,,,Pastels & Oil Sketches on Paper,,http://www.metmuseum.org/art/collection/search/436259,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -45.110.4,false,true,436059,European Paintings,"Painting, miniature",Princess María Francisca de Asis de Borbón and Her Son Infante Carlos Luis María Fernando de Borbón,,,,,,Artist,,Luis de la Cruz y Rios,"Spanish, active by 1815–died 1850",,"Cruz y Rios, Luis de la",Spanish,1815,1850,1818,1818,1818,Ivory,Obverse and reverse each 2 1/4 x 1 3/4 in. (57 x 47 mm),"Gift of Mrs. John LaPorte Given, 1945",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/436059,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -25.106.6,false,true,437614,European Paintings,"Painting, miniature",Portrait of a Young Woman,,,,,,Artist,Attributed to,Piat Joseph Sauvage,"Flemish, Tournai 1744–1818 Tournai",,"Sauvage, Piat Joseph",Flemish,1744,1818,ca. 1790–95,1790,1795,Ivory,Diameter 2 1/8 in. (54 mm),"Gift of Mrs. Louis V. Bell, in memory of her husband, 1925",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/437614,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.390,false,true,437482,European Paintings,"Painting, miniature",Portrait of a Man,,,,,,Artist,,Andrew Robertson,"British, Aberdeen, Scotland 1777–1845 London",,"Robertson, Andrew","British, Scottish",1777,1845,1828,1828,1828,Ivory,"Oval, 3 3/4 x 2 7/8 in. (80 x 63 mm)","Morris K. Jesup Fund, 1986",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/437482,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -24.21,false,true,437483,European Paintings,"Painting, miniature",Sir Joshua Reynolds (1723–1792),,,,,,Artist,,Archibald Robertson,"American, Moneymusk, Scotland 1765–1835 New York",,"Robertson, Archibald","American, Scottish",1765,1835,1786–91,1786,1791,Ivory,"Oval, 3 x 2 3/8 in. (75 x 60 mm)","Bequest of Geraldine Winslow Goddard, 1923",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/437483,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -45.110.3,false,true,436289,European Paintings,"Painting, miniature",Self-Portrait,,,,,,Artist,Attributed to,John Faed,"British, Burley Mill, Scotland 1820–1902 Burley Mill, Scotland",,"Faed, John","British, Scottish",1820,1902,ca. 1850,1845,1855,Ivory,"Oval, 2 5/8 x 2 1/8 in. (68 x 55 mm)","Gift of Mrs. John LaPorte Given, 1945",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/436289,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -61.101.8,false,true,436577,European Paintings,Painting,Saint Andrew,,,,,,Artist,Workshop of,El Greco,"Spanish, ca. 1610",,Greco El,Greek,1540,1614,ca. 1610,1605,1615,Oil on canvas,43 1/4 x 25 1/4 in. (109.9 x 64.1 cm),"Bequest of Stephen C. Clark, 1960",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436577,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1971.115.2,false,true,436816,European Paintings,Painting,The Lacemaker,,,,,,Artist,,Bernhard Keil,"Danish, 1624–1687",,"Keil, Bernhard",Danish,1624,1687,ca. 1665,1660,1670,Oil on canvas,28 1/4 x 38 1/4 in. (71.8 x 97.2 cm),"Bequest of Edward Fowles, 1971",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436816,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -64.294,false,true,436698,European Paintings,Painting,Still Life: A Basket of Grapes and Other Fruit,,,,,,Artist,,Jacob van Hulsdonck,"Flemish, 1582–1647",,"Hulsdonck, Jacob van",Flemish,1582,1647,probably ca. 1635–45,1635,1645,Oil on wood,19 5/8 x 25 1/2 in. (49.8 x 64.8 cm),"The Alfred N. Punnett Endowment Fund, 1964",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436698,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -45.128.14,false,true,436049,European Paintings,Painting,Philip IV (1605–1665) in Parade Armor,,,,,,Artist,,Gaspar de Crayer,"Flemish, 1584–1669",,"Crayer, Gaspar de",Flemish,1584,1669,ca. 1628,1623,1633,Oil on canvas,72 x 46 1/2 in. (182.9 x 118.1 cm),"Bequest of Helen Hay Whitney, 1944",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436049,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -71.45,false,true,436427,European Paintings,Painting,A Partridge and Small Game Birds,,,,,,Artist,,Jan Fyt,"Flemish, 1611–1661",,"Fyt, Jan",Flemish,1611,1661,1650s,1650,1659,Oil on canvas,18 1/4 x 14 1/4 in. (46.4 x 36.2 cm),"Purchase, 1871",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436427,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -81.1.652,false,true,437867,European Paintings,Painting,A Bouquet of Flowers in a Crystal Vase,,,,,,Artist,,Nicolaes van Veerendael,"Flemish, 1640–1691",,"Veerendael, Nicolaes van",Flemish,1640,1691,1662,1662,1662,Oil on canvas,19 1/2 x 15 7/8 in. (49.5 x 40.3 cm),"Bequest of Stephen Whitney Phoenix, 1881",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437867,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -32.75.4,false,true,436693,European Paintings,Painting,A Musical Gathering at the Court of the Elector Karl Albrecht of Bavaria,,,,,,Artist,,Peter Jacob Horemans,"Flemish, 1700–1776",,"Horemans, Peter Jacob",Flemish,1700,1776,1730,1730,1730,Oil on canvas,34 1/2 x 42 in. (87.6 x 106.7 cm),"The Collection of Giovanni P. Morosini, presented by his daughter Giulia, 1932",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436693,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -48.187.737,false,true,436290,European Paintings,Painting,Flowers by a Stone Vase,,,,,,Artist,,Peter Faes,"Flemish, 1750–1814",,"Faes, Peter",Flemish,1750,1814,1786,1786,1786,Oil on wood,20 x 14 7/8 in. (50.8 x 37.8 cm),"Bequest of Catherine D. Wentworth, 1948",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436290,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -48.187.738,false,true,436291,European Paintings,Painting,Flowers in a Stone Vase,,,,,,Artist,,Peter Faes,"Flemish, 1750–1814",,"Faes, Peter",Flemish,1750,1814,1786,1786,1786,Oil on wood,19 1/2 x 15 1/8 in. (49.5 x 38.4 cm),"Bequest of Catherine D. Wentworth, 1948",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436291,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -50.50,false,true,437177,European Paintings,Painting,"Count Giacomo Durazzo (1717–1794) in the Guise of a Huntsman with His Wife (Ernestine Aloisia Ungnad von Weissenwolff, 1732–1794)",,,,,,Artist,,Martin van Meytens the Younger,"Swedish, 1695–1770",,"Meytens, Martin van, the Younger",Swedish,1695,1770,probably early 1760s,1760,1763,Oil on canvas,90 1/8 x 75 in. (228.9 x 190.5 cm),"Gift of Mr. and Mrs. Nate B. Spingold, 1950",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437177,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1971.101,false,true,436776,European Paintings,Painting,The Dead Christ in the Tomb with Two Angels,,,,,,Artist,,Abraham Janssen van Nuyssen,"Flemish, ca. 1575–1632",,"Janssen van Nuyssen, Abraham",Flemish,1575,1632,ca. 1610,1605,1615,Oil on canvas,45 3/8 x 58 in. (115.3 x 147.3 cm),"Gift of James Belden, in memory of Evelyn Berry Belden, 1971",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436776,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -89.15.17,false,true,437729,European Paintings,Painting,Saint Michael the Archangel,,,,,,Artist,,Ignacio de Ries,"Spanish, 1616–after 1665",,"Ries, Ignacio de",Spanish,1616,1665,1640s,1640,1649,Oil on canvas,64 3/4 x 43 1/4 in. (164.5 x 109.9 cm),"Marquand Collection, Gift of Henry G. Marquand, 1889",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437729,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -59.189.2,false,true,436808,European Paintings,Painting,"Edward Smith Stanley (1752–1834), Twelfth Earl of Derby, with His First Wife (Lady Elizabeth Hamilton, 1753–1797) and Their Son (Edward Smith Stanley, 1775–1851)",,,,,,Artist,,Angelica Kauffmann,"Swiss, Chur 1741–1807 Rome",,"Kauffmann, Angelica",Swiss,1741,1807,ca. 1776,1771,1781,Oil on canvas,50 x 40 in. (127 x 101.6 cm),"Gift of Bernard M. Baruch, in memory of his wife, Annie Griffen Baruch, 1959",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436808,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -25.110.187,false,true,436809,European Paintings,Painting,The Sorrow of Telemachus,,,,,,Artist,,Angelica Kauffmann,"Swiss, Chur 1741–1807 Rome",,"Kauffmann, Angelica",Swiss,1741,1807,1783,1783,1783,Oil on canvas,32 3/4 x 45 in. (83.2 x 114.3 cm),"Bequest of Collis P. Huntington, 1900",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436809,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -25.110.188,false,true,436810,European Paintings,Painting,Telemachus and the Nymphs of Calypso,,,,,,Artist,,Angelica Kauffmann,"Swiss, Chur 1741–1807 Rome",,"Kauffmann, Angelica",Swiss,1741,1807,1782,1782,1782,Oil on canvas,32 1/2 x 44 1/4 in. (82.6 x 112.4 cm),"Bequest of Collis P. Huntington, 1900",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436810,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -22.45.10,false,true,437238,European Paintings,Painting,Still Life of Fruit and Game,,,,,,Artist,,Pieter van Overschee,"Flemish, active ca. 1645–61",,"Overschee, Pieter van",Flemish,1645,1661,1645,1645,1645,Oil on wood,32 7/8 x 46 3/4 in. (83.5 x 118.7 cm),"Bequest of Grace Wilkes, 1921",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437238,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -89.22,false,true,436318,European Paintings,Painting,Madame Gaye,,,,,,Artist,,Mariano Fortuny Marsal,"Spanish, Reus 1838–1874 Rome",,"Fortuny Marsal, Mariano",Spanish,1838,1874,1865,1865,1865,Oil on canvas,54 x 39 1/2 in. (137.2 x 100.3 cm),"Gift of Alfred Corning Clark, 1889",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436318,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.1134,false,true,634108,European Paintings,Painting,The Dream of the Shepherd (Der Traum des Hirten),,,,,,Artist,,Ferdinand Hodler,"Swiss, Bern 1853–1918 Geneva",,"Hodler, Ferdinand",Swiss,1853,1918,1896,1896,1896,Oil on canvas,98 1/2 × 51 3/8 in. (250.2 × 130.5 cm),"Purchase, European Paintings Funds, Lila Acheson Wallace Gift, Catharine Lorillard Wolfe Collection, Wolfe Fund, Charles and Jessie Price Gift, funds from various donors, and Bequests of Collis P. Huntington and Isaac D. Fletcher, by exchange, 2013",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/634108,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.145.6,false,true,437198,European Paintings,Painting,The Nightingale Sings,,,,,,Artist,,Mikhail Vasilievich Nesterov,"Russian, Ufa 1862–1942 Moscow",,"Nesterov, Mikhail Vasilievich",Russian,1862,1942,1923,1923,1923,Oil on canvas,31 7/8 x 27 3/8 in. (81 x 69.5 cm),"Gift of Humanities Fund Inc., 1972",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437198,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -06.1198,false,true,436872,European Paintings,Painting,Study for a Portrait of a Woman,,,,,,Artist,,Sir Peter Lely (Pieter van der Faes),"British, Soest 1618–1680 London",,"Lely, Peter, Sir (Pieter van der Faes)","Dutch, British",1618,1680,1670s,1670,1679,Oil on canvas,26 1/2 x 21 1/8 in. (67.3 x 53.7 cm),"Rogers Fund, 1906",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436872,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2001.613,false,true,438407,European Paintings,Painting,Man Holding a Jug,,,,,,Artist,,Michiel Sweerts,"Flemish, Brussels 1618–1664 Goa",,"Sweerts, Michiel",Flemish,1618,1664,ca. 1660,1655,1665,Oil on canvas,19 3/8 x 15 3/8 in. (49.2 x 39.1 cm),"Gift of Herman and Lila Shickman, 2001",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/438407,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1984.459.1,true,true,437769,European Paintings,Painting,Clothing the Naked,,,,,,Artist,,Michiel Sweerts,"Flemish, Brussels 1618–1664 Goa",,"Sweerts, Michiel",Flemish,1618,1664,ca. 1661,1656,1666,Oil on canvas,32 1/4 x 45 in. (81.9 x 114.3 cm),"Gift of Mr. and Mrs. Charles Wrightsman, 1984",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437769,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1980.411,false,true,436423,European Paintings,Painting,The Night-Hag Visiting Lapland Witches,,,,,,Artist,,Henry Fuseli,"Swiss, Zürich 1741–1825 London",,"Fuseli, Henry",Swiss,1741,1825,1796,1796,1796,Oil on canvas,40 x 49 3/4 in. (101.6 x 126.4 cm),"Purchase, Bequest of Lillian S. Timken, by exchange, and Victor Wilbour Memorial, The Alfred N. Punnett Endowment, Marquand and Charles B. Curtis Funds, 1980",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436423,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -93.29,false,true,437084,European Paintings,Painting,Landscape with a Battle between Two Rams,,,,,,Artist,,Jan Miel,"Flemish, Beveren 1599–1664 Turin",,"Miel, Jan",Flemish,1599,1664,ca. 1640,1635,1645,Oil on canvas,68 1/4 x 97 5/8 in. (173.4 x 248 cm),"Gift of Princess Brancaccio, 1893",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437084,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -15.30.48,false,true,437662,European Paintings,Painting,"William Archer Shee (1810–1899), the Artist's Son",,,,,,Artist,,Sir Martin Archer Shee,"Irish, Dublin 1769–1850 Brighton",,"Shee, Martin Archer, Sir",Irish,1769,1850,ca. 1820,1815,1825,Oil on canvas,30 x 24 3/4 in. (76.2 x 62.9 cm),"Bequest of Maria DeWitt Jesup, from the collection of her husband, Morris K. Jesup, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437662,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.145.4,false,true,437631,European Paintings,Painting,Night Scene on the Volga,,,,,,Artist,,Alexei Kondratievich Savrasov,"Russian, Moscow 1830–1897 Moscow",,"Savrasov, Alexei Kondratievich",Russian,1830,1897,1871,1871,1871,Oil on wood,12 7/8 x 21 1/2 in. (32.7 x 54.6 cm),"Gift of Humanities Fund Inc., 1972",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437631,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -87.15.57,false,true,437460,European Paintings,Painting,A Canal in Venice,,,,,,Artist,,Martín Rico y Ortega,"Spanish, Madrid 1833–1908 Venice",,"Rico y Ortega, Martín",Spanish,1833,1908,ca. 1875,1875,1875,Oil on canvas,19 3/4 x 26 3/4 in. (50.2 x 67.9 cm),"Catharine Lorillard Wolfe Collection, Bequest of Catharine Lorillard Wolfe, 1887",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437460,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1982.60.39,false,true,437053,European Paintings,Painting,The Afternoon Meal (La Merienda),,,,,,Artist,,Luis Meléndez,"Spanish, Naples 1716–1780 Madrid",,"Meléndez, Luis",Spanish,1716,1780,ca. 1772,1772,1772,Oil on canvas,41 1/2 x 60 1/2 in. (105.4 x 153.7 cm),"The Jack and Belle Linsky Collection, 1982",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437053,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -83.11,false,true,436879,European Paintings,Painting,"Auction Sale in Clinton Hall, New York, 1876",,,,,,Artist,,Ignacio de León y Escosura,"Spanish, Oviedo 1834–1901 Toledo",,"León y Escosura, Ignacio de",Spanish,1834,1901,1876,1876,1876,Oil on canvas,22 3/8 x 31 5/8 in. (56.8 x 80.3 cm),"Gift of the artist, 1883",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436879,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -44.147.1,false,true,437004,European Paintings,"Painting, part of an altarpiece",Saint Adalbert and Saint Procopius,,,,,,Artist,,Master of Eggenburg,"Austrian, Tirol, active 1490–1500",,Master of Eggenburg,Austrian,1490,1500,ca. 1490–1500,1490,1500,"Oil on spruce, gold ground","Painted surface, including black border, 27 1/8 x 17 in. (68.9 x 43.2 cm)","Gift of William Rosenwald, 1944",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437004,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -44.147.2,false,true,437005,European Paintings,"Painting, part of an altarpiece",The Burial of Saint Wenceslas,,,,,,Artist,,Master of Eggenburg,"Austrian, Tirol, active 1490–1500",,Master of Eggenburg,Austrian,1490,1500,ca. 1490–1500,1490,1500,Oil and gold on spruce,"Painted surface, including black border, 27 1/8 x 17 in. (68.9 x 43.2 cm)","Gift of William Rosenwald, 1944",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437005,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -46.150.1,false,true,437754,European Paintings,Painting,After the Ball,,,,,,Artist,,Alfred Stevens,"Belgian, Brussels 1823–1906 Paris",,"Stevens, Alfred",Belgian,1823,1906,1874,1874,1874,Oil on canvas,37 3/4 x 27 1/8 in. (95.9 x 68.9 cm),"Gift of Estate of Marie L. Russell, 1946",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437754,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -87.15.56,false,true,437756,European Paintings,Painting,The Japanese Robe,,,,,,Artist,,Alfred Stevens,"Belgian, Brussels 1823–1906 Paris",,"Stevens, Alfred",Belgian,1823,1906,ca. 1872,1868,1877,Oil on canvas,36 1/2 x 25 1/8 in. (92.7 x 63.8 cm),"Catharine Lorillard Wolfe Collection, Bequest of Catharine Lorillard Wolfe, 1887",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437756,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -20.33,false,true,437169,European Paintings,Painting,Maude Adams (1872–1953) as Joan of Arc,,,,,,Artist,,Alphonse Mucha,"Czech, Ivančice 1860–1939 Prague",,"Mucha, Alphonse",Czech,1860,1939,1909,1909,1909,Oil on canvas,82 1/4 x 30 in. (208.9 x 76.2 cm),"Gift of A. J. Kobler, 1920",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437169,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -71.41,false,true,436257,European Paintings,Painting,Saint Rosalie Interceding for the Plague-stricken of Palermo,,,,,,Artist,,Anthony van Dyck,"Flemish, Antwerp 1599–1641 London",,"Dyck, Anthony van",Flemish,1599,1641,1624,1624,1624,Oil on canvas,39 1/4 x 29 in. (99.7 x 73.7 cm),"Purchase, 1871",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436257,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -22.221,false,true,436260,European Paintings,Painting,Study Head of an Old Man with a White Beard,,,,,,Artist,,Anthony van Dyck,"Flemish, Antwerp 1599–1641 London",,"Dyck, Anthony van",Flemish,1599,1641,ca. 1617–20,1617,1620,Oil on wood,26 x 20 1/4 in. (66 x 51.4 cm),"Egleston Fund, 1922",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436260,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -49.7.26,false,true,436256,European Paintings,Painting,"Robert Rich (1587–1658), Second Earl of Warwick",,,,,,Artist,,Anthony van Dyck,"Flemish, Antwerp 1599–1641 London",,"Dyck, Anthony van",Flemish,1599,1641,ca. 1632–35,1632,1635,Oil on canvas,"81 7/8 x 50 3/8 in. (208 x 128 cm), with added strip of 2 1/8 in. (5.4 cm) at top","The Jules Bache Collection, 1949",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436256,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -51.33.1,false,true,436261,European Paintings,Painting,Virgin and Child,,,,,,Artist,,Anthony van Dyck,"Flemish, Antwerp 1599–1641 London",,"Dyck, Anthony van",Flemish,1599,1641,ca. 1620,1615,1625,Oil on wood,25 1/4 x 19 1/2 in. (64.1 x 49.5 cm),"Fletcher Fund, 1951",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436261,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -60.71.5,false,true,436262,European Paintings,Painting,Virgin and Child with Saint Catherine of Alexandria,,,,,,Artist,,Anthony van Dyck,"Flemish, Antwerp 1599–1641 London",,"Dyck, Anthony van",Flemish,1599,1641,ca. 1630,1625,1635,Oil on canvas,43 x 35 3/4 in. (109.2 x 90.8 cm); with added strips 44 1/8 x 37 in. (112.1 x 94 cm),"Bequest of Lillian S. Timken, 1959",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436262,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -49.145.1,false,true,436264,European Paintings,"Painting, monochrome",A Man Riding a Horse,,,,,,Artist,Attributed to,Anthony van Dyck,"Flemish, Antwerp 1599–1641 London",,"Dyck, Anthony van",Flemish,1599,1641,ca. 1630,1625,1635,Oil on wood,10 1/8 x 8 7/8 in. (25.7 x 22.5 cm),"Gift of Mr. and Mrs. Siegfried Bieber, 1949",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436264,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -49.145.2,false,true,436263,European Paintings,"Painting, monochrome",A Man Mounting a Horse,,,,,,Artist,Attributed to,Anthony van Dyck,"Flemish, Antwerp 1599–1641 London",,"Dyck, Anthony van",Flemish,1599,1641,ca. 1630,1625,1635,Oil on wood,10 x 8 3/4 in. (25.4 x 22.2 cm),"Gift of Mr. and Mrs. Siegfried Bieber, 1949",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436263,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -89.15.11,false,true,436254,European Paintings,Painting,Portrait of a Man,,,,,,Artist,,Anthony van Dyck,"Flemish, Antwerp 1599–1641 London",,"Dyck, Anthony van",Flemish,1599,1641,ca. 1618,1613,1623,Oil on wood,41 3/4 x 28 5/8 in. (106 x 72.7 cm),"Marquand Collection, Gift of Henry G. Marquand, 1889",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436254,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -89.15.16,false,true,436252,European Paintings,Painting,"James Stuart (1612–1655), Duke of Richmond and Lennox",,,,,,Artist,,Anthony van Dyck,"Flemish, Antwerp 1599–1641 London",,"Dyck, Anthony van",Flemish,1599,1641,ca. 1633–35,1633,1635,Oil on canvas,85 x 50 1/4 in. (215.9 x 127.6 cm),"Marquand Collection, Gift of Henry G. Marquand, 1889",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436252,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.40.615,false,true,436255,European Paintings,Painting,"Portrait of a Woman, Called the Marchesa Durazzo",,,,,,Artist,,Anthony van Dyck,"Flemish, Antwerp 1599–1641 London",,"Dyck, Anthony van",Flemish,1599,1641,probably ca. 1622–25,1622,1625,Oil on canvas,44 5/8 x 37 3/4 in. (113.3 x 95.9 cm),"Bequest of Benjamin Altman, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436255,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.40.619,true,true,436253,European Paintings,Painting,Lucas van Uffel (died 1637),,,,,,Artist,,Anthony van Dyck,"Flemish, Antwerp 1599–1641 London",,"Dyck, Anthony van",Flemish,1599,1641,ca. 1622,1621,1627,Oil on canvas,49 x 39 5/8 in. (124.5 x 100.6 cm),"Bequest of Benjamin Altman, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436253,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2003.42.20,false,true,438644,European Paintings,Painting,"View on the Quirinal Hill, Rome",,,,,,Artist,,Simon Denis,"Flemish, Antwerp 1755–1813 Naples",,"Denis, Simon",Flemish,1755,1813,1800,1800,1800,"Oil on paper, laid down on canvas",11 5/8 x 16 1/8 in. (29.5 x 41 cm),"The Whitney Collection, Gift of Wheelock Whitney III, and Purchase, Gift of Mr. and Mrs. Charles S. McVeigh, by exchange, 2003",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/438644,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.400.40,false,true,439365,European Paintings,Painting,Aniene River at Tivoli,,,,,,Artist,,Simon Denis,"Flemish, Antwerp 1755–1813 Naples",,"Denis, Simon",Flemish,1755,1813,ca. 1786–89,1786,1806,Oil on paper,12 3/4 x 11 1/2 in. (32.4 x 29.2 cm),"Thaw Collection, Jointly Owned by The Metropolitan Museum of Art and The Morgan Library & Museum, Gift of Eugene V. Thaw, 2009",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/439365,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -02.24,false,true,437528,European Paintings,Painting,The Holy Family with Saints Francis and Anne and the Infant Saint John the Baptist,,,,,,Artist,,Peter Paul Rubens,"Flemish, Siegen 1577–1640 Antwerp",,"Rubens, Peter Paul",Flemish,1577,1640,early or mid-1630s,1630,1636,Oil on canvas,69 1/2 x 82 1/2 in. (176.5 x 209.6 cm),"Gift of James Henry Smith, 1902",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437528,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -10.73,false,true,437536,European Paintings,Painting,Wolf and Fox Hunt,,,,,,Artist,,Peter Paul Rubens,"Flemish, Siegen 1577–1640 Antwerp",and Workshop,"Rubens, Peter Paul",Flemish,1577,1640,ca. 1616,1611,1621,Oil on canvas,96 5/8 x 148 1/8 in. (245.4 x 376.2 cm),"John Stewart Kennedy Fund, 1910",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437536,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -44.22,false,true,437523,European Paintings,Painting,Atalanta and Meleager,,,,,,Artist,,Peter Paul Rubens,"Flemish, Siegen 1577–1640 Antwerp",,"Rubens, Peter Paul",Flemish,1577,1640,ca. 1616,1611,1621,Oil on wood,52 1/2 x 42 in. (133.4 x 106.7 cm),"Fletcher Fund, 1944",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437523,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.162,false,true,437535,European Paintings,Painting,Venus and Adonis,,,,,,Artist,,Peter Paul Rubens,"Flemish, Siegen 1577–1640 Antwerp",,"Rubens, Peter Paul",Flemish,1577,1640,probably mid-1630s,1634,1636,Oil on canvas,"With added strips, 77 3/4 x 95 5/8 in. (197.5 x 242.9 cm)","Gift of Harry Payne Bingham, 1937",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437535,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -42.187,false,true,437534,European Paintings,"Painting, sketch",The Triumph of Henry IV,,,,,,Artist,,Peter Paul Rubens,"Flemish, Siegen 1577–1640 Antwerp",,"Rubens, Peter Paul",Flemish,1577,1640,ca. 1630,1625,1635,Oil on wood,19 1/2 x 32 7/8 in. (49.5 x 83.5 cm),"Rogers Fund, 1942",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437534,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.218,false,true,437531,European Paintings,Painting,"Portrait of a Woman, Probably Susanna Lunden (Susanna Fourment, 1599–1628)",,,,,,Artist,,Peter Paul Rubens,"Flemish, Siegen 1577–1640 Antwerp",,"Rubens, Peter Paul",Flemish,1577,1640,ca. 1625–27,1625,1627,Oil on wood,"30 1/4 x 23 5/8 in. (76.8 x 60 cm), including added strip of 3 3/4 in. (9.5 cm) at bottom","Gift of Mr. and Mrs. Charles Wrightsman, 1976",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437531,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1981.238,true,true,437532,European Paintings,Painting,"Rubens, His Wife Helena Fourment (1614–1673), and Their Son Frans (1633–1678)",,,,,,Artist,,Peter Paul Rubens,"Flemish, Siegen 1577–1640 Antwerp",,"Rubens, Peter Paul",Flemish,1577,1640,ca. 1635,1630,1640,Oil on wood,80 1/4 x 62 1/4 in. (203.8 x 158.1 cm),"Gift of Mr. and Mrs. Charles Wrightsman, in honor of Sir John Pope-Hennessy, 1981",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437532,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1990.196,false,true,437526,European Paintings,Painting,A Forest at Dawn with a Deer Hunt,,,,,,Artist,,Peter Paul Rubens,"Flemish, Siegen 1577–1640 Antwerp",,"Rubens, Peter Paul",Flemish,1577,1640,ca. 1635,1630,1640,Oil on wood,24 1/4 x 35 1/2 in. (61.5 x 90.2 cm),"Purchase, The Annenberg Foundation, Mrs. Charles Wrightsman, Michel David-Weill, The Dillon Fund, Henry J. and Drue Heinz Foundation, Lola Kramarsky, Annette de la Renta, Mr. and Mrs. Arthur Ochs Sulzberger, The Vincent Astor Foundation, and Peter J. Sharp Gifts; special funds, gifts, and other gifts and bequests, by exchange, 1990",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437526,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -55.135.1,false,true,437529,European Paintings,Painting,"The Holy Family with Saint Elizabeth, Saint John, and a Dove",,,,,,Artist,,Peter Paul Rubens,"Flemish, Siegen 1577–1640 Antwerp",,"Rubens, Peter Paul",Flemish,1577,1640,ca. 1608–9,1608,1609,Oil on wood,26 x 20 1/4 in. (66 x 51.4 cm),"Bequest of Ada Small Moore, 1955",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437529,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.160.12,false,true,437527,European Paintings,Painting,The Glorification of the Eucharist,,,,,,Artist,,Peter Paul Rubens,"Flemish, Siegen 1577–1640 Antwerp",,"Rubens, Peter Paul",Flemish,1577,1640,ca. 1630–32,1630,1632,Oil on wood,28 x 19 in. (71.1 x 48.3 cm),"Bequest of Ogden Mills, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437527,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -67.187.99,false,true,437533,European Paintings,Painting,Study of Two Heads,,,,,,Artist,,Peter Paul Rubens,"Flemish, Siegen 1577–1640 Antwerp",,"Rubens, Peter Paul",Flemish,1577,1640,ca. 1609,1604,1614,Oil on wood,27 1/2 x 20 1/2 in. (69.9 x 52.1 cm),"Bequest of Miss Adelaide Milton de Groot (1876–1967), 1967",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437533,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1982.60.24,false,true,437530,European Paintings,Painting,"Portrait of a Man, Possibly an Architect or Geographer",,,,,,Artist,,Peter Paul Rubens,"Flemish, Siegen 1577–1640 Antwerp",,"Rubens, Peter Paul",Flemish,1577,1640,1597,1597,1597,Oil on copper,8 1/2 x 5 3/4 in. (21.6 x 14.6 cm),"The Jack and Belle Linsky Collection, 1982",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437530,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1984.433.336,false,true,437524,European Paintings,"Painting, sketch",The Coronation of the Virgin,,,,,,Artist,,Peter Paul Rubens,"Flemish, Siegen 1577–1640 Antwerp",,"Rubens, Peter Paul",Flemish,1577,1640,ca. 1632–33,1632,1633,Oil on wood,19 5/8 x 16 in. (49.8 x 40.6 cm),"Bequest of Scofield Thayer, 1982",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437524,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1984.433.20,false,true,483334,European Paintings,Painting,"Landscape, Kragerø",,,,,,Artist,,Edvard Munch,"Norwegian, Løten 1863–1944 Ekely",,"Munch, Edvard",Norwegian,1863,1944,1912,1912,1912,Oil on canvas,28 1/2 x 39 1/2 in. (72.4 x 100.3 cm),"Bequest of Scofield Thayer, 1982",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/483334,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -34.73,true,true,437455,European Paintings,Painting,The Holy Family with Saints Anne and Catherine of Alexandria,,,,,,Artist,,Jusepe de Ribera (called Lo Spagnoletto),"Spanish, Játiva 1591–1652 Naples",,"Ribera, Jusepe de (called Lo Spagnoletto)",Spanish,1591,1652,1648,1648,1648,Oil on canvas,82 1/2 x 60 3/4 in. (209.6 x 154.3 cm),"Samuel D. Lee Fund, 1934",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437455,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2012.416,false,true,441971,European Paintings,Painting,The Tears of Saint Peter,,,,,,Artist,,Jusepe de Ribera (called Lo Spagnoletto),"Spanish, Játiva 1591–1652 Naples",,"Ribera, Jusepe de (called Lo Spagnoletto)",Spanish,1591,1652,ca. 1612–13,1612,1613,Oil on canvas,63 3/4 x 45 in. (161.9 x 114.3 cm),"Purchase, Gift of Mrs. William M. Haupt, from the collection of Mrs. James B. Haggin, by exchange, and 2011 Benefit Fund, 2012",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/441971,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1971.86,true,true,437869,European Paintings,Painting,Juan de Pareja (1606–1670),,,,,,Artist,,Velázquez (Diego Rodríguez de Silva y Velázquez),"Spanish, Seville 1599–1660 Madrid",,Velázquez (Diego Rodríguez de Silva y Velázquez),Spanish,1599,1660,1650,1650,1650,Oil on canvas,32 x 27 1/2 in. (81.3 x 69.9 cm),"Purchase, Fletcher and Rogers Funds, and Bequest of Miss Adelaide Milton de Groot (1876–1967), by exchange, supplemented by gifts from friends of the Museum, 1971",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437869,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -49.7.42,false,true,437874,European Paintings,Painting,Portrait of a Man,,,,,,Artist,,Velázquez (Diego Rodríguez de Silva y Velázquez),"Spanish, Seville 1599–1660 Madrid",,Velázquez (Diego Rodríguez de Silva y Velázquez),Spanish,1599,1660,ca. 1630–35,1630,1635,Oil on canvas,27 x 21 3/4 in. (68.6 x 55.2 cm),"The Jules Bache Collection, 1949",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437874,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -49.7.43,false,true,437870,European Paintings,Painting,"María Teresa (1638–1683), Infanta of Spain",,,,,,Artist,,Velázquez (Diego Rodríguez de Silva y Velázquez),"Spanish, Seville 1599–1660 Madrid",,Velázquez (Diego Rodríguez de Silva y Velázquez),Spanish,1599,1660,1651–54,1651,1654,Oil on canvas,Overall 13 1/2 x 15 3/4 in. (34.3 x 40 cm); original painted surface 12 7/8 x 15 1/8 in. (32.7 x 38.4 cm),"The Jules Bache Collection, 1949",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437870,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -89.15.29,false,true,437875,European Paintings,Painting,Portrait of a Man,,,,,,Artist,Workshop of,Velázquez,"Spanish, Seville 1599–1660 Madrid",,Velázquez,Spanish,1599,1660,ca. 1650,1645,1655,Oil on canvas,27 1/4 x 22 1/4 in. (69.2 x 56.5 cm),"Marquand Collection, Gift of Henry G. Marquand, 1889",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437875,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.40.631,false,true,437871,European Paintings,Painting,The Supper at Emmaus,,,,,,Artist,,Velázquez (Diego Rodríguez de Silva y Velázquez),"Spanish, Seville 1599–1660 Madrid",,Velázquez (Diego Rodríguez de Silva y Velázquez),Spanish,1599,1660,1622–23,1622,1623,Oil on canvas,48 1/2 x 52 1/4 in. (123.2 x 132.7 cm),"Bequest of Benjamin Altman, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437871,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.40.639,false,true,437873,European Paintings,Painting,"Philip IV (1605–1665), King of Spain",,,,,,Artist,,Velázquez (Diego Rodríguez de Silva y Velázquez),"Spanish, Seville 1599–1660 Madrid",,Velázquez (Diego Rodríguez de Silva y Velázquez),Spanish,1599,1660,probably 1624,1624,1624,Oil on canvas,78 3/4 x 40 1/2 in. (200 x 102.9 cm),"Bequest of Benjamin Altman, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437873,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -87.15.39,false,true,437902,European Paintings,Painting,Examining Antique Arms,,,,,,Artist,,José Villegas y Cordero,"Spanish, Seville 1848–1921 Madrid",,"Villegas y Cordero, José",Spanish,1848,1921,1870,1870,1870,Oil on wood,15 5/8 x 12 1/2 in. (39.7 x 31.8 cm),"Catharine Lorillard Wolfe Collection, Bequest of Catharine Lorillard Wolfe, 1887",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437902,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -71.11,false,true,436799,European Paintings,Painting,The Holy Family with Saint Anne and the Young Baptist and His Parents,,,,,,Artist,,Jacob Jordaens,"Flemish, Antwerp 1593–1678 Antwerp",,"Jordaens, Jacob",Flemish,1593,1678,early 1620s and 1650s,1620,1659,Oil on wood,66 7/8 x 59 in. (169.9 x 149.9 cm),"Purchase, 1871",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436799,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -67.187.76,false,true,436798,European Paintings,Painting,The Holy Family with Shepherds,,,,,,Artist,,Jacob Jordaens,"Flemish, Antwerp 1593–1678 Antwerp",,"Jordaens, Jacob",Flemish,1593,1678,1616,1616,1616,"Oil on canvas, transferred from wood",42 x 30 in. (106.7 x 76.2 cm),"Bequest of Miss Adelaide Milton de Groot (1876–1967), 1967",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436798,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1991.444,false,true,435813,European Paintings,Painting,Aeneas and the Sibyl in the Underworld,,,,,,Artist,,Jan Brueghel the Younger,"Flemish, Antwerp 1601–1678 Antwerp",,"Brueghel, Jan, the Younger",Flemish,1601,1678,1630s,1630,1639,Oil on copper,10 1/2 x 14 1/8 in. (26.7 x 35.9 cm),"Gift of Mrs. Erna S. Blade, in memory of her uncle, Sigmund Herrmann, 1991",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435813,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -67.187.58,false,true,435814,European Paintings,Painting,A Basket of Flowers,,,,,,Artist,,Jan Brueghel the Younger,"Flemish, Antwerp 1601–1678 Antwerp",,"Brueghel, Jan, the Younger",Flemish,1601,1678,probably 1620s,1620,1629,Oil on wood,18 1/2 x 26 7/8 in. (47 x 68.3 cm),"Bequest of Miss Adelaide Milton de Groot (1876–1967), 1967",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435814,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -07.225.272,false,true,437624,European Paintings,"Painting, grisaille overdoor",The Triumph of Bacchus,,,,,,Artist,,Piat Joseph Sauvage,"Flemish, Tournai 1744–1818 Tournai",,"Sauvage, Piat Joseph",Flemish,1744,1818,early 1780s,1780,1783,Oil on canvas,19 1/4 x 46 1/8 in. (48.9 x 117.2 cm),"Gift of J. Pierpont Morgan, 1906",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437624,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2014.228,false,true,652416,European Paintings,Painting,The Crucifixion,,,,,,Artist,,Pedro Orrente,"Spanish, Murcia 1580–1645 Valencia",,"Orrente, Pedro",Spanish,1580,1645,ca. 1625–30,1625,1630,Oil on canvas,48 3/4 × 40 1/2 in. (123.8 × 102.9 cm),"Purchase, Charles and Jessie Price and Fern and George Wachter Gifts, 2014",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/652416,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -43.13,true,true,437175,European Paintings,Painting,Virgin and Child,,,,,,Artist,,Bartolomé Estebán Murillo,"Spanish, Seville 1617–1682 Seville",,"Murillo, Bartolomé Estebán",Spanish,1617,1682,ca. 1670–72,1670,1672,Oil on canvas,65 1/4 x 43 in. (165.7 x 109.2 cm),"Rogers Fund, 1943",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437175,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -27.219,false,true,437173,European Paintings,Painting,Don Andrés de Andrade y la Cal,,,,,,Artist,,Bartolomé Estebán Murillo,"Spanish, Seville 1617–1682 Seville",,"Murillo, Bartolomé Estebán",Spanish,1617,1682,ca. 1665–72,1665,1672,Oil on canvas,79 x 47 in. (200.7 x 119.4 cm),"Bequest of Collis P. Huntington, by exchange, 1927",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437173,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -54.190,false,true,437174,European Paintings,Painting,A Knight of Alcántara or Calatrava,,,,,,Artist,,Bartolomé Estebán Murillo,"Spanish, Seville 1617–1682 Seville",,"Murillo, Bartolomé Estebán",Spanish,1617,1682,ca. 1650–55,1650,1655,Oil on canvas,"Overall, with added strips, 77 x 43 3/4 in. (195.6 x 111.1 cm); original canvas 77 x 38 1/2 in. (195.6 x 97.8 cm)","Gift of Rudolf J. Heinemann, 1954",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437174,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.100.17,false,true,437172,European Paintings,Painting,The Crucifixion,,,,,,Artist,,Bartolomé Estebán Murillo,"Spanish, Seville 1617–1682 Seville",,"Murillo, Bartolomé Estebán",Spanish,1617,1682,ca. 1675,1670,1680,Oil on canvas,20 x 13 in. (50.8 x 33 cm),"Bequest of Harry G. Sperling, 1971",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437172,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -54.168,false,true,437862,European Paintings,Painting,Pietà,,,,,,Artist,,Juan de Valdés Leal,"Spanish, Seville 1622–1690 Seville",,"Valdés Leal, Juan de",Spanish,1622,1690,ca. 1657–60,1657,1660,Oil on canvas,63 1/4 x 56 1/2 in. (160.7 x 143.5 cm),"Victor Wilbour Memorial Fund, 1954",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437862,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.285,false,true,440727,European Paintings,Painting,German Landscape with View towards a Broad Valley,,,,,,Artist,,Fritz Petzholdt,"Danish, Copenhagen 1805–1838 Patras",,"Petzholdt, Fritz",Danish,1805,1838,ca. 1829–30,1824,1834,"Oil on paper, laid down on canvas",Original paper support: 5 3/16 x 9 11/16 in. (13.2 x 24.6 cm) Paper support mounted on stretched canvas: 5 13/16 x 10 1/4 in. (14.8 x 26 cm),"Gift of Wheelock Whitney III, in honor of Eugene V. Thaw, 2009",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/440727,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -72.2,false,true,437778,European Paintings,Painting,Judith with the Head of Holofernes,,,,,,Artist,,David Teniers the Younger,"Flemish, Antwerp 1610–1690 Brussels",,"Teniers, David, the Younger",Flemish,1610,1690,1650s,1650,1659,Oil on copper,14 1/2 x 10 3/8 in. (36.8 x 26.4 cm),"Gift of Gouverneur Kemble, 1872",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437778,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -71.99,false,true,437779,European Paintings,Painting,Peasants Dancing and Feasting,,,,,,Artist,,David Teniers the Younger,"Flemish, Antwerp 1610–1690 Brussels",,"Teniers, David, the Younger",Flemish,1610,1690,ca. 1660,1655,1665,Oil on canvas,25 1/8 x 29 1/2 in. (63.8 x 74.9 cm); with added strip 26 7/8 x 29 1/2 in. (68.3 x 74.9 cm),"Purchase, 1871",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437779,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -64.65.5,false,true,437777,European Paintings,Painting,Guardroom with the Deliverance of Saint Peter,,,,,,Artist,,David Teniers the Younger,"Flemish, Antwerp 1610–1690 Brussels",,"Teniers, David, the Younger",Flemish,1610,1690,ca. 1645–47,1645,1647,Oil on wood,21 3/4 x 29 7/8 in. (55.2 x 75.9 cm),"Gift of Edith Neuman de Végvár, in honor of her husband, Charles Neuman de Végvár, 1964",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437777,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2012.447,false,true,441967,European Paintings,Painting,View over Hallingdal,,,,,,Artist,,Johan Christian Dahl,"Norwegian, Bergen 1788–1857 Dresden",,"Dahl, Johan Christian",Norwegian,1788,1857,1844,1844,1844,Oil on canvas,9 1/2 x 14 3/8 in. (24.1 x 36.5 cm),"Gift of Asbjorn R. Lunde, in memory of his brother, Karl Lunde, 2012",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/441967,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.164.2,false,true,438954,European Paintings,Painting,Mother and Child by the Sea,,,,,,Artist,,Johan Christian Dahl,"Norwegian, Bergen 1788–1857 Dresden",,"Dahl, Johan Christian",Norwegian,1788,1857,1830,1830,1830,Oil on canvas,6 1/4 x 8 1/8 in. (15.9 x 20.6 cm),"Gift of Eugene V. Thaw, 2007",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/438954,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.145.1,false,true,437441,European Paintings,Painting,Portrait of a Boy,,,,,,Artist,,Ilia Efimovich Repin,"Russian, Chuguev 1844–1930 Kuokkala",,"Repin, Ilia Efimovich",Russian,1844,1930,1884,1884,1884,Oil on canvas,22 1/2 x 17 3/8 in. (57.2 x 44.1 cm),"Gift of Humanities Fund Inc., 1972",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437441,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.145.2,false,true,437442,European Paintings,Painting,Vsevolod Mikhailovich Garshin (1855–1888),,,,,,Artist,,Ilia Efimovich Repin,"Russian, Chuguev 1844–1930 Kuokkala",,"Repin, Ilia Efimovich",Russian,1844,1930,1884,1884,1884,Oil on canvas,35 x 27 1/4 in. (88.9 x 69.2 cm),"Gift of Humanities Fund Inc., 1972",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437442,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.280.4,false,true,437440,European Paintings,Painting,Shepherd with a Flock of Sheep,,,,,,Artist,,Ilia Efimovich Repin,"Russian, Chuguev 1844–1930 Kuokkala",,"Repin, Ilia Efimovich",Russian,1844,1930,1870,1870,1870,Oil on canvas board,4 7/8 x 8 7/8 in. (12.4 x 22.5 cm),"Bequest of Mary Jane Dastich, in memory of her husband, General Frank Dastich, 1975",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437440,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.164.7,false,true,438952,European Paintings,Painting,View from the Citadel Ramparts in Copenhagen by Moonlight,,,,,,Artist,,Martinus Rørbye,"Danish, Drammen 1803–1848 Copenhagen",,"Rørbye, Martinus",Danish,1803,1848,1839,1839,1839,Oil on canvas,11 3/8 x 9 5/8 in. (28.9 x 24.4 cm),"Gift of Eugene V. Thaw, 2007",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/438952,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.164.6,false,true,438951,European Paintings,Painting,An Evening beside Lake Arresø,,,,,,Artist,,Johan Thomas Lundbye,"Danish, Kalundborg 1818–1848 Bedsted",,"Lundbye, Johan Thomas",Danish,1818,1848,ca. 1837,1832,1842,Oil on canvas,9 x 11 3/4 in. (22.9 x 29.8 cm),"Gift of Eugene V. Thaw, 2007",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/438951,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.164.4,false,true,438949,European Paintings,Painting,Columns of the Temple of Neptune at Paestum,,,,,,Artist,,Constantin Hansen,"Danish, Rome 1804–1880 Frederiksberg",,"Hansen, Constantin",Danish,1804,1880,1838,1838,1838,Oil on canvas,12 5/8 x 10 in. (32.1 x 25.4 cm),"Gift of Eugene V. Thaw, 2007",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/438949,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -43.101,false,true,437046,European Paintings,Painting,"María Teresa (1638–1683), Infanta of Spain",,,,,,Artist,,Juan Bautista Martínez del Mazo,"Spanish, Cuenca ca. 1612–1667 Madrid",,"Mazo, Juan Bautista Martínez del",Spanish,1612,1667,ca. 1645,1640,1650,Oil on canvas,58 1/4 x 40 1/2 in. (148 x 102.9 cm),"Rogers Fund, 1943",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437046,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -87.15.99,false,true,437923,European Paintings,Painting,"A Day in October, near Waxholm, Sweden",,,,,,Artist,,Alfred Wahlberg,"Swedish, Stockholm 1834–1906 Tranås",,"Wahlberg, Alfred",Swedish,1834,1906,1873,1873,1873,Oil on canvas,41 x 64 1/2 in. (104.1 x 163.8 cm),"Catharine Lorillard Wolfe Collection, Bequest of Catharine Lorillard Wolfe, 1887",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437923,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -64.148,true,true,436819,European Paintings,Painting,Mäda Primavesi (1903–2000),,,,,,Artist,,Gustav Klimt,"Austrian, Baumgarten 1862–1918 Vienna",,"Klimt, Gustav",Austrian,1862,1918,1912–13,1912,1913,Oil on canvas,59 x 43 1/2 in. (149.9 x 110.5 cm),"Gift of André and Clara Mertens, in memory of her mother, Jenny Pulitzer Steiner, 1964",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436819,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1980.412,false,true,436820,European Paintings,Painting,Serena Pulitzer Lederer (1867–1943),,,,,,Artist,,Gustav Klimt,"Austrian, Baumgarten 1862–1918 Vienna",,"Klimt, Gustav",Austrian,1862,1918,1899,1899,1899,Oil on canvas,75 1/8 x 33 5/8 in. (190.8 x 85.4 cm),"Purchase, Wolfe Fund, and Rogers and Munsey Funds, Gift of Henry Walters, and Bequests of Catharine Lorillard Wolfe and Collis P. Huntington, by exchange, 1980",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436820,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2012.25,false,true,441768,European Paintings,Painting,"A Section of the Via Sacra, Rome (The Church of Saints Cosmas and Damian)",,,,,,Artist,,Christoffer Wilhelm Eckersberg,"Danish, Blåkrog 1783–1853 Copenhagen",,"Eckersberg, Christoffer Wilhelm",Danish,1783,1853,ca. 1814–15,1814,1815,Oil on canvas,12 3/8 x 17 1/8 in. (31.4 x 43.5 cm),"Nineteenth-Century, Modern, and Contemporary Funds, 2012",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/441768,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -87.4.7,false,true,435599,European Paintings,Painting,Boatmen of Barcelona,,,,,,Artist,,Dionisio Baixeras y Verdaguer,"Spanish, Barcelona 1862–1943 Barcelona",,"Baixeras y Verdaguer, Dionisio",Spanish,1862,1943,1886,1886,1886,Oil on canvas,59 x 83 in. (149.9 x 210.8 cm),"Gift of George I. Seney, 1886",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435599,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -58.81,false,true,437707,European Paintings,Painting,"Mrs. Winthrop W. Aldrich (Harriet Alexander, 1888–1972)",,,,,,Artist,,Joaquín Sorolla y Bastida,"Spanish, Valencia 1863–1923 Cercedilla",,"Sorolla y Bastida, Joaquín",Spanish,1863,1923,1909,1909,1909,Oil on canvas,40 x 30 3/8 in. (101.6 x 77.2 cm),"Gift of Harriet Alexander Aldrich, 1958",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437707,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -09.71.2,false,true,437704,European Paintings,Painting,"The Bath, Jávea",,,,,,Artist,,Joaquín Sorolla y Bastida,"Spanish, Valencia 1863–1923 Cercedilla",,"Sorolla y Bastida, Joaquín",Spanish,1863,1923,1905,1905,1905,Oil on canvas,35 1/2 x 50 1/2 in. (90.2 x 128.3 cm),"Catharine Lorillard Wolfe Collection, Wolfe Fund, 1909",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437704,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -09.71.3,false,true,437706,European Paintings,Painting,"Señora de Sorolla (Clotilde García del Castillo, 1865–1929) in Black",,,,,,Artist,,Joaquín Sorolla y Bastida,"Spanish, Valencia 1863–1923 Cercedilla",,"Sorolla y Bastida, Joaquín",Spanish,1863,1923,1906,1906,1906,Oil on canvas,73 1/2 x 46 3/4 in. (186.7 x 118.7 cm),"Catharine Lorillard Wolfe Collection, Wolfe Fund, 1909",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437706,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -22.119.1,false,true,437705,European Paintings,Painting,"Castle of San Servando, Toledo",,,,,,Artist,,Joaquín Sorolla y Bastida,"Spanish, Valencia 1863–1923 Cercedilla",,"Sorolla y Bastida, Joaquín",Spanish,1863,1923,1906,1906,1906,Oil on canvas,26 1/4 x 36 1/2 in. (66.7 x 92.7 cm),"Gift of Archer M. Huntington, 1922",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437705,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1990.233,false,true,436825,European Paintings,Painting,"Valdemar Hjartvar Købke (1813–1893), the Artist's Brother",,,,,,Artist,,Christen Købke,"Danish, Copenhagen 1810–1848 Copenhagen",,"Købke, Christen",Danish,1810,1848,ca. 1838,1833,1843,Oil on canvas,21 1/8 x 18 1/4 in. (53.7 x 46.4 cm),"Catharine Lorillard Wolfe Collection, Wolfe Fund, 1990",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436825,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2012.203,false,true,441933,European Paintings,Painting,"Moonlight, Strandgade 30",,,,,,Artist,,Vilhelm Hammershøi,"Danish, Copenhagen 1864–1916 Copenhagen",,"Hammershøi, Vilhelm",Danish,1864,1916,1900–1906,1900,1906,Oil on canvas,16 1/8 x 20 1/8 in. (41 x 51.1 cm),"Purchase, European Paintings Funds, and Annette de la Renta Gift, 2012",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/441933,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -32.100.1,false,true,435806,European Paintings,Painting,A Peasant Woman Picking Fleas off a Dog,,,,,,Artist,,Adriaen Brouwer,"Flemish, Oudenaarde 1605/6–1638 Antwerp",,"Brouwer, Adriaen",Flemish,1605,1638,ca. 1626–27,1626,1627,Oil on wood,Oval 7 1/8 x 5 3/8 in. (18.1 x 13.7 cm); set in rectangular panel 8 x 6 1/4 in. (20.3 x 15.9 cm),"The Friedsam Collection, Bequest of Michael Friedsam, 1931",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435806,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -32.100.21,false,true,435807,European Paintings,Painting,The Smokers,,,,,,Artist,,Adriaen Brouwer,"Flemish, Oudenaarde 1605/6–1638 Antwerp",,"Brouwer, Adriaen",Flemish,1605,1638,ca. 1636,1631,1638,Oil on wood,18 1/4 x 14 1/2 in. (46.4 x 36.8 cm),"The Friedsam Collection, Bequest of Michael Friedsam, 1931",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435807,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -06.289,false,true,436541,European Paintings,Painting,Sebastián Martínez y Pérez (1747–1800),,,,,,Artist,,Goya (Francisco de Goya y Lucientes),"Spanish, Fuendetodos 1746–1828 Bordeaux",,Goya (Francisco de Goya y Lucientes),Spanish,1746,1828,1792,1792,1792,Oil on canvas,36 5/8 x 26 5/8 in. (93 x 67.6 cm),"Rogers Fund, 1906",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436541,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -61.259,false,true,436546,European Paintings,Painting,"José Costa y Bonells (died l870), Called Pepito",,,,,,Artist,,Goya (Francisco de Goya y Lucientes),"Spanish, Fuendetodos 1746–1828 Bordeaux",,Goya (Francisco de Goya y Lucientes),Spanish,1746,1828,ca. 1810,1805,1815,Oil on canvas,41 3/8 x 33 1/4 in. (105.1 x 84.5 cm),"Gift of Countess Bismarck, 1961",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436546,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -49.7.41,true,true,436545,European Paintings,Painting,Manuel Osorio Manrique de Zuñiga (1784–1792),,,,,,Artist,,Goya (Francisco de Goya y Lucientes),"Spanish, Fuendetodos 1746–1828 Bordeaux",,Goya (Francisco de Goya y Lucientes),Spanish,1746,1828,1787–88,1787,1788,Oil on canvas,50 x 40 in. (127 x 101.6 cm),"The Jules Bache Collection, 1949",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436545,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -55.145.1,false,true,436542,European Paintings,Painting,"Ignacio Garcini y Queralt (1752–1825), Brigadier of Engineers",,,,,,Artist,,Goya (Francisco de Goya y Lucientes),"Spanish, Fuendetodos 1746–1828 Bordeaux",,Goya (Francisco de Goya y Lucientes),Spanish,1746,1828,1804,1804,1804,Oil on canvas,41 x 32 3/4 in. (104.1 x 83.2 cm),"Bequest of Harry Payne Bingham, 1955",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436542,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -55.145.2,false,true,436543,European Paintings,Painting,Josefa de Castilla Portugal y van Asbrock de Garcini (1775–about 1850),,,,,,Artist,,Goya (Francisco de Goya y Lucientes),"Spanish, Fuendetodos 1746–1828 Bordeaux",,Goya (Francisco de Goya y Lucientes),Spanish,1746,1828,1804,1804,1804,Oil on canvas,41 x 32 3/8 in. (104.1 x 82.2 cm),"Bequest of Harry Payne Bingham, 1955",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436543,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.10,false,true,436548,European Paintings,Painting,Majas on a Balcony,,,,,,Artist,Attributed to,Goya (Francisco de Goya y Lucientes),"Spanish, Fuendetodos 1746–1828 Bordeaux",,Goya (Francisco de Goya y Lucientes),Spanish,1746,1828,ca. 1800–1810,1800,1810,Oil on canvas,76 3/4 x 49 1/2in. (194.9 x 125.7cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436548,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -30.95.242,false,true,436544,European Paintings,Painting,"Tiburcio Pérez y Cuervo (1785/86–1841), the Architect",,,,,,Artist,,Goya (Francisco de Goya y Lucientes),"Spanish, Fuendetodos 1746–1828 Bordeaux",,Goya (Francisco de Goya y Lucientes),Spanish,1746,1828,1820,1820,1820,Oil on canvas,40 1/4 x 32 in. (102.2 x 81.3 cm),"Theodore M. Davis Collection, Bequest of Theodore M. Davis, 1915",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436544,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1982.60.3,false,true,437298,European Paintings,Painting,The Presentation in the Temple,,,,,,Artist,,Alvaro Pirez,"Portuguese, Évora, active 1411–34 Italy",,"Pirez, Alvaro",Portuguese,1411,1434,probably ca. 1430,1425,1435,Tempera and gold on wood,13 3/8 x 15 7/8 in. (34 x 40.3 cm),"The Jack and Belle Linsky Collection, 1982",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437298,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -45.94.2,false,true,437750,European Paintings,Painting,A Renaissance Portico with Elegant Figures,,,,,,Artist,,Hendrick van Steenwijck II,"Flemish, Antwerp (?) ca. 1580–1649 Leiden",,"Steenwijck, Hendrick van, II",Flemish,1475,1649,ca. 1615,1610,1620,Oil on copper,Diameter 4 3/8 in. (11.1 cm),"Gift of Mrs. James Eads Switzer, in memory of her aunt, Yrene Ceballos de Sanz, 1945",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437750,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1983.452,false,true,436817,European Paintings,Painting,A Mountainous Landscape with a Waterfall,,,,,,Artist,,Kerstiaen de Keuninck,"Flemish, Kortrijk ca. 1560–1632/33 Antwerp",,"Keuninck, Kerstiaen de",Flemish,1560,1633,ca. 1600,1595,1605,Oil on wood,27 1/4 x 48 in. (69.2 x 121.9 cm),"Purchase, Anonymous Gift, L. H. P. Klotz and George T. Delacorte Jr. Gifts; Rogers, Marquand, Charles B. Curtis, and The Alfred N. Punnett Endowment Funds; and Gift of Eugen Boross and Bequest of Collis P. Huntington, by exchange, 1983",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436817,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1974.100,false,true,436833,European Paintings,Painting,Red Sunset on the Dnieper,,,,,,Artist,,Arkhip Ivanovich Kuindzhi,"Russian, Mariupol 1842–1910 St. Petersburg",,"Kuindzhi, Arkhip Ivanovich",Russian,1842,1910,1905–8,1905,1908,Oil on canvas,53 x 74 in. (134.6 x 188 cm),"Rogers Fund, 1974",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436833,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.145.5,false,true,437321,European Paintings,Painting,Christ and the Woman Taken in Adultery,,,,,,Artist,,Vasilii Dmitriviech Polenov,"Russian, St. Petersburg 1844–1927 Polenovo",,"Polenov, Vasilii Dmitrievich",Russian,1844,1927,1884,1884,1884,Oil on canvas,9 1/2 x 17 in. (24.1 x 43.2 cm),"Gift of Humanities Fund Inc., 1972",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437321,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2014.149,false,true,641257,European Paintings,Painting,Head of Christ,,,,,,Artist,,Fernando Yáñez de la Almedina,"Spanish, Almedina, ca. 1475?–1536 Valencia",,"Yáñez de la Almedina, Fernando",Spanish,1470,1536,ca. 1506,1501,1511,Oil on poplar,16 1/2 × 12 in. (41.9 × 30.5 cm),"Purchase, The Morris and Alma Schapiro Fund Gift, and Bequest of George D. Pratt, by exchange, 2014",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/641257,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -20.104,false,true,437969,European Paintings,"Painting, part of an altarpiece",The Battle between Christians and Moors at El Sotillo,,,,,,Artist,,Francisco de Zurbarán,"Spanish, Fuente de Cantos 1598–1664 Madrid",,"Zurbarán, Francisco de",Spanish,1598,1664,ca. 1637–39,1637,1639,Oil on canvas,"Arched top, 131 7/8 x 75 1/4 in. (335 x 191.1 cm)","Kretschmar Fund, 1920",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437969,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -27.137,true,true,437971,European Paintings,Painting,The Young Virgin,,,,,,Artist,,Francisco de Zurbarán,"Spanish, Fuente de Cantos 1598–1664 Madrid",,"Zurbarán, Francisco de",Spanish,1598,1664,ca. 1632–33,1632,1633,Oil on canvas,46 x 37 in. (116.8 x 94 cm),"Fletcher Fund, 1927",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437971,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.100.21,false,true,437970,European Paintings,Painting,Saint Benedict,,,,,,Artist,,Francisco de Zurbarán,"Spanish, Fuente de Cantos 1598–1664 Madrid",,"Zurbarán, Francisco de",Spanish,1598,1664,ca. 1640–45,1640,1645,Oil on canvas,74 x 40 3/4 in. (188 x 103.5 cm),"Bequest of Harry G. Sperling, 1971",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437970,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -26.90,true,true,435683,European Paintings,Painting,Island of the Dead,,,,,,Artist,,Arnold Böcklin,"Swiss, Basel 1827–1901 San Domenico, Italy",,"Böcklin, Arnold",Swiss,1827,1901,1880,1880,1880,Oil on wood,29 x 48 in. (73.7 x 121.9 cm),"Reisinger Fund, 1926",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435683,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -26.100,false,true,435684,European Paintings,Painting,Roman Landscape,,,,,,Artist,Attributed to,Arnold Böcklin,"Swiss, Basel 1827–1901 San Domenico, Italy",,"Böcklin, Arnold",Swiss,1827,1901,ca. 1850–52,1850,1852,"Oil on canvas, several pieces joined",12 1/2 x 18 1/8 in. (31.8 x 46 cm),"Gift of Fearon Galleries Inc., 1926",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435684,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -34.83.2,false,true,436861,European Paintings,Painting,A Masked Ball in Bohemia,,,,,,Artist,Attributed to,Andreas Altomonte,"Austrian, Warsaw or Vienna 1699–1780 Vienna",,"Altomonte, Andreas",Austrian,1699,1780,ca. 1748,1743,1753,Oil on canvas,19 x 38 in. (48.3 x 96.5 cm),"Bequest of Mariana Griswold Van Rensselaer, 1934",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436861,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -15.30.56,false,true,437179,European Paintings,Painting,"Near Penshurst, Kent",,,,,,Artist,,Patrick Nasmyth,"British, Edinburgh, Scotland 1787–1831 London",,"Nasmyth, Patrick","British, Scottish",1787,1831,1828,1828,1828,Oil on wood,27 1/2 x 36 1/4 in. (69.9 x 92.1 cm),"Bequest of Maria DeWitt Jesup, from the collection of her husband, Morris K. Jesup, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437179,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -05.42,false,true,436570,European Paintings,Painting,The Adoration of the Shepherds,,,,,,Artist,,El Greco (Domenikos Theotokopoulos),"Greek, Iráklion (Candia) 1540/41–1614 Toledo",,"Greco, El (Domenikos Theotokopoulos)",Greek,1540,1614,ca. 1605–10,1605,1610,Oil on canvas,56 7/8 x 39 7/8 in. (144.5 x 101.3 cm); with added strips 64 1/2 x 42 in. (163.8 x 106.7 cm),"Rogers Fund, 1905",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436570,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -56.48,false,true,436576,European Paintings,Painting,The Vision of Saint John,,,,,,Artist,,El Greco (Domenikos Theotokopoulos),"Greek, Iráklion (Candia) 1540/41–1614 Toledo",,"Greco, El (Domenikos Theotokopoulos)",Greek,1540,1614,ca. 1609–14,1609,1614,Oil on canvas,87 1/2 x 76in. (222.3 x 193cm); with added strips 88 1/2 x 78 1/2 in. (224.8 x 199.4 cm) [top truncated],"Rogers Fund, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436576,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1978.416,false,true,436572,European Paintings,Painting,Christ Healing the Blind,,,,,,Artist,,El Greco (Domenikos Theotokopoulos),"Greek, Iráklion (Candia) 1540/41–1614 Toledo",,"Greco, El (Domenikos Theotokopoulos)",Greek,1540,1614,ca. 1570,1565,1575,Oil on canvas,47 x 57 1/2 in. (119.4 x 146.1 cm),"Gift of Mr. and Mrs. Charles Wrightsman, 1978",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436572,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -24.197.1,false,true,436574,European Paintings,Painting,Portrait of an Old Man,,,,,,Artist,,El Greco (Domenikos Theotokopoulos),"Greek, Iráklion (Candia) 1540/41–1614 Toledo",,"Greco, El (Domenikos Theotokopoulos)",Greek,1540,1614,ca. 1595–1600,1595,1600,Oil on canvas,20 3/4 x 18 3/8 in. (52.7 x 46.7 cm),"Purchase, Joseph Pulitzer Bequest, 1924",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436574,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.5,true,true,436573,European Paintings,Painting,Cardinal Fernando Niño de Guevara (1541–1609),,,,,,Artist,,El Greco (Domenikos Theotokopoulos),"Greek, Iráklion (Candia) 1540/41–1614 Toledo",,"Greco, El (Domenikos Theotokopoulos)",Greek,1540,1614,ca. 1600,1595,1605,Oil on canvas,67 1/4 x 42 1/2in. (170.8 x 108cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436573,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.6,true,true,436575,European Paintings,Painting,View of Toledo,,,,,,Artist,,El Greco (Domenikos Theotokopoulos),"Greek, Iráklion (Candia) 1540/41–1614 Toledo",,"Greco, El (Domenikos Theotokopoulos)",Greek,1540,1614,ca. 1598–99,1598,1599,Oil on canvas,47 3/4 x 42 3/4 in. (121.3 x 108.6 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436575,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -41.190.17,false,true,436571,European Paintings,Painting,The Adoration of the Shepherds,,,,,,Artist,,El Greco (Domenikos Theotokopoulos) and Workshop,"Greek, Iráklion (Candia) 1540/41–1614 Toledo",,,Greek,1540,1614,ca. 1612–14,1612,1614,Oil on canvas,43 1/2 x 25 5/8 in. (110.5 x 65.1 cm),"Bequest of George Blumenthal, 1941",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436571,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -50.70,false,true,437217,European Paintings,Painting,Moses and Aaron before Pharaoh: An Allegory of the Dinteville Family,,,,,,Artist,,Master of the Dinteville Allegory,"Netherlandish or French, active mid-16th century",,Master of the Dinteville Allegory,Netherlandish/French,1525,1575,1537,1537,1537,Oil on wood,69 1/2 x 75 7/8 in. (176.5 x 192.7 cm),"Wentworth Fund, 1950",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437217,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -15.30.52,false,true,437939,European Paintings,Painting,The Highland Family,,,,,,Artist,,Sir David Wilkie,"British, Cults, Scotland 1785–1841 off Gibraltar",,"Wilkie, David, Sir","British, Scottish",1785,1841,1824,1824,1824,Oil on wood,24 x 36 in. (61 x 91.4 cm),"Bequest of Maria DeWitt Jesup, from the collection of her husband, Morris K. Jesup, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437939,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -91.26.1,false,true,436821,European Paintings,Painting,Girl Building a House of Cards,,,,,,Artist,Attributed to,Thomas Frye,"Irish, Dublin, born ca. 1711–12, died 1762 London",,"Frye, Thomas",Irish,1711,1762,mid-18th century,1731,1762,Oil on canvas,30 1/8 x 25 1/4 in. (76.5 x 64.1 cm),"Marquand Collection, Gift of Henry G. Marquand, 1890",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436821,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.63,false,true,435586,European Paintings,Painting,Ivan Rodin,,,,,,Artist,,Abram Efimovich Arkhipov,"Russian, Egorovo Riazan province 1862–1930 Moscow",,"Arkhipov, Abram Efimovich",Russian,1862,1930,1928,1928,1928,Oil on canvas,44 x 34 1/4 in. (111.8 x 87 cm),"Gift of George D. Pratt, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435586,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.28,false,true,439117,European Paintings,"Painting, grisaille",The Glorification of the Royal Hungarian Saints,,,,,,Artist,,Franz Anton Maulbertsch,"Austrian, Langenargen am Bodensee 1724–1796 Vienna",,"Maulbertsch, Franz Anton",Austrian,1724,1796,ca. 1772–73,1772,1773,Oil on canvas,27 1/2 x 19 7/8 in. (70 x 50.5 cm),"Purchase, Friends of European Paintings Gifts, 2007",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/439117,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2008.420,false,true,439844,European Paintings,Painting,Heroic Landscape with Rainbow,,,,,,Artist,,Joseph Anton Koch,"Austrian, Obergibeln bei Elbigenalp 1768–1839 Rome",,"Koch, Joseph Anton",Austrian,1768,1839,1824,1824,1824,Oil on canvas,42 3/4 x 37 3/4 in. (108.6 x 95.9 cm),"Purchase, Anne Cox Chambers Gift, Gift of Alfred and Katrin Romney, by exchange, and Nineteenth-Century, Modern, and Contemporary Art Funds, 2008",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/439844,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -76.10,false,true,435572,European Paintings,"Painting, part of an altarpiece",Saint Giles with Christ Triumphant over Satan and the Mission of the Apostles,,,,,,Artist,,Miguel Alcañiz (or Miquel Alcanyís),"Spanish, Valencian, active by 1408–died after 1447",,"Alcañiz, Miguel (or Miquel Alcanyís)",Spanish,1408,1447,ca. 1408,1403,1413,"Tempera on wood, gold ground","Overall 59 5/8 x 39 1/2 in. (151.4 x 100.3 cm); upper left panel, painted surface 24 1/8 x 16 7/8 in. (61.3 x 42.9 cm); lower left panel, painted surface 24 5/8 x 16 7/8 in. (62.5 x 42.9 cm); right panel, painted surface 46 1/8 x 16 7/8 in. (117.2 x 42.9 cm)","Gift of J. Bruyn Andrews, 1876",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435572,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -05.32.3,false,true,437941,European Paintings,Painting,Lake Nemi and Genzano from the Terrace of the Capuchin Monastery,,,,,,Artist,,Richard Wilson,"British, Penegoes, Wales 1712/13–1782 Denbighshire, Wales",,"Wilson, Richard","British, Welsh",1712,1782,ca. 1756–57,1756,1757,Oil on canvas,16 7/8 x 21 1/8 in. (42.9 x 53.7 cm),"Gift of George A. Hearn, 1905",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437941,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -49.142,false,true,437355,European Paintings,Painting,George Harley Drummond (1783–1855),,,,,,Artist,,Sir Henry Raeburn,"British, Stockbridge, Scotland 1756–1823 Edinburgh, Scotland",,"Raeburn, Henry, Sir","British, Scottish",1756,1923,ca. 1808–9,1808,1809,Oil on canvas,94 1/4 x 58 in. (239.4 x 147.3 cm),"Gift of Mrs. Guy Fairfax Cary, in memory of her mother, Mrs. Burke Roche, 1949",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437355,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -45.59.2,false,true,437364,European Paintings,Painting,William Scott-Elliot of Arkleton (1811–1901),,,,,,Artist,,Sir Henry Raeburn,"British, Stockbridge, Scotland 1756–1823 Edinburgh, Scotland",,"Raeburn, Henry, Sir","British, Scottish",1756,1923,ca. 1815–16,1815,1816,Oil on canvas,47 3/8 x 36 5/8 in. (120.3 x 93 cm),"Fletcher Fund, 1945",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437364,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -96.30.5,false,true,437363,European Paintings,Painting,William Forsyth (1749–1814),,,,,,Artist,,Sir Henry Raeburn,"British, Stockbridge, Scotland 1756–1823 Edinburgh, Scotland",,"Raeburn, Henry, Sir","British, Scottish",1756,1923,ca. 1800,1795,1805,Oil on canvas,30 x 24 7/8 in. (76.2 x 63.2 cm),"Gift of Arthur H. Hearn, 1896",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437363,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1980.305,false,true,437361,European Paintings,Painting,"Mrs. Richard Alexander Oswald (Louisa Johnston, ?born about 1760, died 1797)",,,,,,Artist,,Sir Henry Raeburn,"British, Stockbridge, Scotland 1756–1823 Edinburgh, Scotland",,"Raeburn, Henry, Sir","British, Scottish",1756,1923,ca. 1794,1789,1798,Oil on canvas,48 1/2 x 40 7/8 in. (123.2 x 103.8 cm),"Gift of Mrs. Paul Moore, 1980",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437361,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -50.145.31,false,true,437354,European Paintings,Painting,The Drummond Children,,,,,,Artist,,Sir Henry Raeburn,"British, Stockbridge, Scotland 1756–1823 Edinburgh, Scotland",,"Raeburn, Henry, Sir","British, Scottish",1756,1923,ca. 1808–9,1808,1809,Oil on canvas,94 1/4 x 60 1/4 in. (239.4 x 153 cm),"Bequest of Mary Stillman Harkness, 1950",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437354,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -50.145.32,false,true,437365,European Paintings,Painting,"William Robertson (1753–1835), Lord Robertson",,,,,,Artist,,Sir Henry Raeburn,"British, Stockbridge, Scotland 1756–1823 Edinburgh, Scotland",,"Raeburn, Henry, Sir","British, Scottish",1756,1923,1805,1805,1805,Oil on canvas,49 1/2 x 39 1/4 in. (125.7 x 99.7 cm),"Bequest of Mary Stillman Harkness, 1950",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437365,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -17.120.119,false,true,436611,European Paintings,"Painting, miniature",Portrait of a Woman,,,,,,Artist,Style of,Peter Adolf Hall,ca. 1780,,"Hall, Peter Adolf",Swedish,1739,1793,,1775,1785,Ivory,Diameter 2 in. (52 mm),"Mr. and Mrs. Isaac D. Fletcher Collection, Bequest of Isaac D. Fletcher, 1917",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/436611,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -62.122.137,false,true,436613,European Paintings,"Painting, miniature",Portrait of a Woman,,,,,,Artist,Style of,Peter Adolf Hall,ca. 1780,,"Hall, Peter Adolf",Swedish,1739,1793,,1775,1785,Ivory,Diameter 2 3/8 in. (62 mm),"Bequest of Millie Bruhl Fredrick, 1962",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/436613,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -24.80.508,false,true,437459,European Paintings,"Painting, miniature","John Churchill (1650–1722), First Duke of Marlborough",,,,,,Artist,,Christian Richter,"Swedish, 1678–1732",,"Richter, Christian",Swedish,1678,1732,,1698,1732,Vellum,"Oval, 3 1/4 x 2 5/8 in. (82 x 67 mm)","Bequest of Mary Clark Thompson, 1923",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/437459,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -62.122.103,false,true,435588,European Paintings,"Painting, miniature","A Young Knight of the Garter, Possibly George Augustus (1683–1760), Later George II of Great Britain and Ireland",,,,,,Artist,Style of,Benjamin Arlaud,"Continental, ca. 1706",,"Arlaud, Benjamin",Swiss,1701,1717,,1701,1711,Vellum,"Oval, 2 x 1 3/4 in. (52 x 45 mm)","Bequest of Millie Bruhl Fredrick, 1962",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/435588,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -62.122.104,false,true,435589,European Paintings,"Painting, miniature","A Woman, Possibly Sophia Dorothea (1687–1757), Later Queen of Prussia",,,,,,Artist,Style of,Benjamin Arlaud,"Continental, ca. 1706",,"Arlaud, Benjamin",Swiss,1701,1717,,1701,1711,Vellum,"Oval, 2 x 1 3/4 in. (52 x 45 mm)","Bequest of Millie Bruhl Fredrick, 1962",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/435589,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -25.106.17,false,true,435758,European Paintings,"Painting, miniature",A Woman Playing a Harp,,,,,,Artist,,Joseph Marie Bouton,"French (?), 1768–1823",,"Bouton, Joseph Marie",French(?),1768,1823,,1788,1823,Ivory,Diameter 3 1/2 in. (90 mm),"Gift of Mrs. Louis V. Bell, in memory of her husband, 1925",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/435758,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -62.122.74,false,true,437626,European Paintings,"Painting, miniature",Putti Harvesting Wheat,,,,,,Artist,Style of,Piat Joseph Sauvage,"French, late 18th century",,"Sauvage, Piat Joseph",Flemish,1744,1818,,1770,1799,Oil on wood,Painted surface diameter 3 in. (76 mm),"Bequest of Millie Bruhl Fredrick, 1962",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/437626,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -55.107.1,false,true,435587,European Paintings,"Painting, miniature","Caroline of Ansbach (1683–1737), Consort of George II of Great Britain and Ireland",,,,,,Artist,Attributed to,Benjamin Arlaud,"Swiss, active ca. 1701–17",,"Arlaud, Benjamin",Swiss,1701,1717,,1701,1717,Ivory,"Oval, 2 5/8 x 2 1/4 in. (68 x 57 mm)","Gift of Estate of Isaac A. Josephi, 1955",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/435587,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -62.122.106,false,true,436234,European Paintings,"Painting, miniature",Portrait of an Officer,,,,,,Artist,,Andrew Dunn,"Irish, active ca. 1800–1820",,"Dunn, Andrew",Irish,1800,1820,,1800,1820,Ivory,"Oval, 2 5/8 x 2 1/8 in. (67 x 55 mm)","Bequest of Millie Bruhl Fredrick, 1962",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/436234,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -18.73,false,true,436242,European Paintings,"Painting, miniature",Margaret Rieche Richard,,,,,,Artist,,Dupuy,"German (?), active ca. 1801–17",,Dupuy,German (?),1801,1817,,1801,1817,Enamel,Diameter 2 1/8 in. (54 mm),"Bequest of Georgiana Emily Reynolds, 1918",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/436242,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -62.122.71,false,true,437278,European Paintings,"Painting, miniature",Portrait of a Woman,,,,,,Artist,Attributed to,Jean Petitot,"Swiss, Geneva 1607–1691 Geneva",,"Petitot, Jean",Swiss,1607,1691,,1627,1691,Vellum,"Oval, 1 3/8 x 1 1/8 in. (36 x 30 mm)","Bequest of Millie Bruhl Fredrick, 1962",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/437278,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -71.80,false,true,436995,European Paintings,Painting,Beggars at a Doorway,,,,,,Artist,,"Master of the Béguins, French or Flemish, active 1650–60 (possibly Abraham Willemsens, Flemish, active by 1627, died 1672)",,,"Master of the Béguins (possibly Abraham Willemsens, Flemish, act",French/Flemish,1627,1672,,1650,1660,Oil on canvas,20 1/4 x 23 3/8 in. (51.4 x 59.4 cm),"Purchase, 1871",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436995,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.145.14,false,true,437554,European Paintings,"Painting, icon",The Annunciation,,,,,,Artist,,"Russian Painter, second half 16th century",,,"Russian Painter, second half 16th century",Russian,1550,1599,,1550,1599,Tempera on wood,13 x 10 3/8 in. (33 x 26.4 cm),"Gift of Humanities Fund Inc., 1972",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437554,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -39.65.9,false,true,436874,European Paintings,Painting,"Barbara Villiers (1640–1709), Duchess of Cleveland",,,,,,Artist,Workshop of,Sir Peter Lely,"British, after 1670",,"Lely, Peter, Sir","Dutch, British",1618,1680,,1670,1727,Oil on canvas,89 x 54 in. (226.1 x 137.2 cm),"Bequest of Jacob Ruppert, 1939",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436874,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.11,false,true,436552,European Paintings,Painting,"María Luisa of Parma (1751–1819), Queen of Spain",,,,,,Artist,Copy after,Goya,"Spanish, after 1800",,Goya,Spanish,1746,1828,,1800,1907,Oil on canvas,43 1/2 x 33 1/2 in. (110.5 x 85.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436552,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -71.1,false,true,436048,European Paintings,Painting,The Meeting of Alexander the Great and Diogenes,,,,,,Artist,,Gaspar de Crayer,"Flemish, 1584–1669",,"Crayer, Gaspar de",Flemish,1584,1669,,1605,1669,Oil on canvas,"88 3/4 x 127 5/8 in. (225.4 x 324.2 cm), including added strips of 13 1/2 in. (34.3 cm) at left and 15 1/2 in. (39.4 cm) at right","Purchase, 1871",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436048,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -71.43,false,true,436424,European Paintings,"Painting, possibly an overdoor",A Basket and Birds,,,,,,Artist,,Jan Fyt,"Flemish, 1611–1661",,"Fyt, Jan",Flemish,1611,1661,,1631,1661,Oil on canvas,23 3/4 x 30 1/4 in. (60.3 x 76.8 cm),"Purchase, 1871",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436424,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -71.44,false,true,436425,European Paintings,"Painting, possibly an overdoor",A Hare and Birds,,,,,,Artist,,Jan Fyt,"Flemish, 1611–1661",,"Fyt, Jan",Flemish,1611,1661,,1631,1661,Oil on canvas,23 7/8 x 31 in. (60.6 x 78.7 cm),"Purchase, 1871",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436425,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -32.100.141,false,true,436426,European Paintings,Painting,"A Hare, Partridges, and Fruit",,,,,,Artist,,Jan Fyt,"Flemish, 1611–1661",,"Fyt, Jan",Flemish,1611,1661,,1611,1661,Oil on canvas,37 1/2 x 43 1/2 in. (95.3 x 110.5 cm),"The Friedsam Collection, Bequest of Michael Friedsam, 1931",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436426,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -71.12,false,true,437592,European Paintings,Painting,The Yard of the Inn at Emmaus,,,,,,Artist,,David Ryckaert III,"Flemish, 1612–1661",,"Ryckaert, David, III",Flemish,1612,1661,,1632,1661,Oil on canvas,35 5/8 x 45 3/8 in. (90.5 x 115.3 cm),"Purchase, 1871",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437592,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -71.13,false,true,437591,European Paintings,Painting,Rustic Interior,,,,,,Artist,,David Ryckaert III,"Flemish, 1612–1661",,"Ryckaert, David, III",Flemish,1612,1661,,1632,1661,Oil on canvas,36 3/8 x 45 5/8 in. (92.4 x 115.9 cm),"Purchase, 1871",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437591,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -71.118,false,true,436752,European Paintings,Painting,Pomegranates and Other Fruit in a Landscape,,,,,,Artist,,Abraham Brueghel,"Flemish, 1631–1697",,"Brueghel, Abraham",Flemish,1631,1697,,1650,1674,Oil on canvas,24 3/8 x 29 1/8 in. (61.9 x 74 cm),"Purchase, 1871",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436752,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -71.93,false,true,436118,European Paintings,Painting,The Forge,,,,,,Artist,,Léonard Defrance,"Flemish, 1735–1805",,"Defrance, Léonard",Flemish,1735,1805,,1755,1805,Oil on wood,12 5/8 x 16 1/2 in. (32.1 x 41.9 cm),"Purchase, 1871",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436118,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -71.105,false,true,436119,European Paintings,Painting,The Rope Dance,,,,,,Artist,,Léonard Defrance,"Flemish, 1735–1805",,"Defrance, Léonard",Flemish,1735,1805,,1755,1805,Oil on wood,19 7/8 x 28 5/8 in. (50.5 x 72.7 cm),"Purchase, 1871",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436119,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -32.75.3,false,true,437697,European Paintings,Painting,Soldiers Bivouacking,,,,,,Artist,,Pieter Snayers,"Flemish, 1592–?1667",,"Snayers, Pieter",Flemish,1592,1667,,1612,1667,Oil on wood,28 5/8 x 41 1/8 in. (72.7 x 104.5 cm),"The Collection of Giovanni P. Morosini, presented by his daughter Giulia, 1932",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437697,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -42.23.1,false,true,436265,European Paintings,"Painting, monochrome",Sir Peter Paul Rubens (1577–1640),,,,,,Artist,Copy after,Anthony van Dyck,"Flemish, 17th century",,"Dyck, Anthony van",Flemish,1599,1641,,1600,1699,Oil on wood,10 x 7 5/8 in. (25.4 x 19.4 cm),"Bequest of Bertha H. Buswell, 1941",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436265,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -32.100.2,false,true,435808,European Paintings,Painting,The Brawl,,,,,,Artist,Copy after,Adriaen Brouwer,"Flemish, 17th century",,"Brouwer, Adriaen",Flemish,1605,1638,,1600,1699,Oil on wood,9 5/8 x 7 1/2 in. (24.4 x 19.1 cm),"The Friedsam Collection, Bequest of Michael Friedsam, 1931",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435808,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -45.128.13,false,true,437766,European Paintings,Painting,Ferdinando II de' Medici (1610–1670) as a Boy,,,,,,Artist,Copy after,Justus Sustermans,"Flemish, 17th century",,"Sustermans, Justus",Flemish,1597,1681,,1600,1699,Oil on canvas,51 7/8 x 40 1/2 in. (131.8 x 102.9 cm),"Bequest of Helen Hay Whitney, 1944",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437766,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.12,false,true,436553,European Paintings,Painting,A City on a Rock,,,,,,Artist,Style of,Goya,"Spanish, 19th century",,Goya,Spanish,1746,1828,,1800,1899,Oil on canvas,33 x 41 in. (83.8 x 104.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436553,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -00.16,false,true,437539,European Paintings,Painting,Cambyses Appointing Otanes Judge,,,,,,Artist,Copy after,Peter Paul Rubens,probably 18th century,,"Rubens, Peter Paul",Flemish,1577,1640,,1700,1799,Oil on wood,18 x 17 1/2 in. (45.7 x 44.5 cm),"Gift of William E. Dodge, 1900",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437539,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1982.60.12,false,true,436992,European Paintings,Painting,The Sense of Sight,,,,,,Artist,,Juan Dò,"Spanish, 1604?–?1656",,"Dò, Juan",Spanish,1604,1656,,1625,1649,Oil on canvas,"Overall, with added strips, 29 7/8 x 24 7/8 in. (75.9 x 63.2 cm); without additions 27 3/4 x 21 3/4 in. (70.5 x 55.2 cm)","The Jack and Belle Linsky Collection, 1982",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436992,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -50.145.19,false,true,436549,European Paintings,Painting,Don Bernardo de Iriarte (1735–1814),,,,,,Artist,Copy after,Goya,"Spanish, 1797 or later",,Goya,Spanish,1746,1828,,1797,1797,Oil on canvas,42 1/2 x 33 1/2 in. (108 x 85.1 cm),"Bequest of Mary Stillman Harkness, 1950",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436549,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.100.18,false,true,437600,European Paintings,Painting,Portrait of a Woman,,,,,,Artist,,Alonzo Sánchez Coello,"Spanish, 1531/32–1588",,"Sánchez Coello, Alonzo",Spanish,1531,1588,,1551,1588,Oil on canvas,38 3/4 x 28 3/8 in. (98.4 x 72.1 cm),"Bequest of Harry G. Sperling, 1971",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437600,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.79.17,false,true,437100,European Paintings,"Painting, icon",The Dormition of the Virgin,,,,,,Artist,,Ioannes Mokos,"Greek, active 1680–1724",,"Mokos, Ioannes",Greek,1680,1724,,1680,1724,"Tempera and oil on wood, gold ground",13 1/2 x 11 1/4 in. (34.3 x 28.6 cm),"Gift of Mrs. Henry Morgenthau, 1933",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437100,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1983.498,false,true,436925,European Paintings,Painting,"Laure de Sade, Comtesse Adhéaume de Chevigné",,,,,,Artist,,Federico de Madrazo y de Ochoa,"Spanish, Paris 1875–1934",,"Madrazo y de Ochoa, Federico de",Spanish,1875,1934,,1895,1934,Oil on canvas,45 1/2 x 29 in. (115.6 x 73.7 cm),"Anonymous Gift, 1983",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436925,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.158.746,false,true,437855,European Paintings,"Painting, icon",Christ Bearing the Cross,,,,,,Artist,,Nicolaos Tzafouris,"Greek, ca. 1455–1500/1501",,"Tzafouris, Nicolaos",Greek,1455,1501,,1489,1500,"Oil and tempera on wood, gold ground",27 1/4 x 21 1/2 in. (69.2 x 54.6 cm),"Bashford Dean Memorial Collection, Funds from various donors, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437855,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1981.365.1,false,true,436990,European Paintings,Painting,The Martyrdom of Saint Lawrence; (reverse) Giving Drink to the Thirsty,,,,,,Artist,,Master of the Acts of Mercy,"Austrian, Salzburg, ca. 1465",,Master of the Acts of Mercy,Austrian,1460,1470,,1460,1470,"Oil on fir, (obverse) gold ground",Painted surface 29 x 18 3/8 in. (73.7 x 46.7 cm),"Gift of The Jack and Belle Linsky Foundation, 1981",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436990,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -71.32,false,true,437817,European Paintings,Painting,Group Portrait: A Wedding Celebration,,,,,,Artist,,Gillis van Tilborgh,"Flemish, ca. 1625–ca. 1678",,"Tilborgh, Gillis van",Flemish,1625,1678,,1645,1678,Oil on canvas,45 1/2 x 63 1/4 in. (115.6 x 160.7 cm),"Purchase, 1871",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437817,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -88.3.82,false,true,437232,European Paintings,Painting,Saint Anne Enthroned with the Virgin and Child,,,,,,Artist,,Osma Master,"Spanish, Castilian, ca. 1500",,Osma Master,Spanish,1500,1500,,1495,1505,Tempera and gold on wood,59 x 32 in. (149.9 x 81.3 cm),"Gift of Coudert Brothers, 1888",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437232,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -58.145.1,false,true,435822,European Paintings,Painting,The Annunciation,,,,,,Artist,,Budapest Master,"Spanish, Castilian, ca. 1500",,Budapest Master,Spanish,1500,1500,,1495,1505,"Oil and gold on canvas, transferred from wood",32 x 20 1/4 in. (81.3 x 51.4 cm),"Bequest of Muriel Stokes, 1958",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435822,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -30.95.243,false,true,436550,European Paintings,Painting,Infanta María Luisa (1782–1824) and Her Son Carlos Luis (1799–1883),,,,,,Artist,Copy after,Goya,"Spanish, 1800 or shortly after",,Goya,Spanish,1746,1828,,1800,1805,Oil on canvas,39 1/8 x 27 in. (99.4 x 68.6 cm),"Theodore M. Davis Collection, Bequest of Theodore M. Davis, 1915",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436550,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -39.65.3,false,true,436871,European Paintings,Painting,"Mary Capel (1630–1715), Later Duchess of Beaufort, and Her Sister Elizabeth (1633–1678), Countess of Carnarvon",,,,,,Artist,,Sir Peter Lely (Pieter van der Faes),"British, Soest 1618–1680 London",,"Lely, Peter, Sir (Pieter van der Faes)","Dutch, British",1618,1680,,1652,1662,Oil on canvas,51 1/4 x 67 in. (130.2 x 170.2 cm),"Bequest of Jacob Ruppert, 1939",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436871,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -39.65.6,false,true,436873,European Paintings,Painting,Sir Henry Capel (1638–1696),,,,,,Artist,,Sir Peter Lely (Pieter van der Faes),"British, Soest 1618–1680 London",,"Lely, Peter, Sir (Pieter van der Faes)","Dutch, British",1618,1680,,1654,1664,Oil on canvas,49 3/4 x 40 1/2 in. (126.4 x 102.9 cm),"Bequest of Jacob Ruppert, 1939",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436873,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.79.14,false,true,437856,European Paintings,"Painting, icon",Head of Christ,,,,,,Artist,,Emmanuel Tzanès,"Greek, active by 1636–died 1690",,"Tzanès, Emmanuel",Greek,1636,1690,,1636,1690,"Tempera on wood, gold ground",8 3/8 x 7 1/8 in. (21.3 x 18.1 cm),"Gift of Mrs. Henry Morgenthau, 1933",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437856,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.79.15,false,true,437858,European Paintings,Painting,Head of the Virgin,,,,,,Artist,,Emmanuel Tzanès,"Greek, active by 1636–died 1690",,"Tzanès, Emmanuel",Greek,1636,1690,,1636,1690,"Tempera on wood, gold ground",8 3/8 x 7 1/8 in. (21.3 x 18.1 cm),"Gift of Mrs. Henry Morgenthau, 1933",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437858,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.79.18,false,true,437857,European Paintings,Painting,Head of Saint John the Baptist,,,,,,Artist,,Emmanuel Tzanès,"Greek, active by 1636–died 1690",,"Tzanès, Emmanuel",Greek,1636,1690,,1636,1690,"Tempera on wood, gold ground",8 3/8 x 7 1/8 in. (21.3 x 18.1 cm),"Gift of Mrs. Henry Morgenthau, 1933",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437857,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -15.30.71,false,true,437461,European Paintings,Painting,On the Seine,,,,,,Artist,,Martín Rico y Ortega,"Spanish, Madrid 1833–1908 Venice",,"Rico y Ortega, Martín",Spanish,1833,1908,,1853,1908,Oil on canvas,15 1/4 x 25 1/2 in. (38.7 x 64.8 cm),"Bequest of Maria DeWitt Jesup, from the collection of her husband, Morris K. Jesup, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437461,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -81.1.666,false,true,437462,European Paintings,Painting,A Spanish Garden,,,,,,Artist,,Martín Rico y Ortega,"Spanish, Madrid 1833–1908 Venice",,"Rico y Ortega, Martín",Spanish,1833,1908,,1853,1908,Oil on canvas,24 x 15 1/4 in. (61 x 38.7 cm),"Bequest of Stephen Whitney Phoenix, 1881",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437462,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -87.15.58,false,true,437045,European Paintings,Painting,The Last Token: A Christian Martyr,,,,,,Artist,,Gabriel Max,"Austrian, Prague 1840–1915 Munich",,"Max, Gabriel",Austrian,1840,1915,,1860,1915,Oil on canvas,67 1/2 x 47 in. (171.5 x 119.4 cm),"Catharine Lorillard Wolfe Collection, Bequest of Catharine Lorillard Wolfe, 1887",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437045,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -23.103.3,false,true,437279,European Paintings,Painting,Market Scene,,,,,,Artist,,August Xaver Karl von Pettenkofen,"Austrian, Vienna 1821–1889 Vienna",,"Pettenkofen, August Xaver Karl von",Austrian,1821,1889,,1841,1889,Oil on wood,4 x 8 1/2 in. (10.2 x 21.6 cm),"The John Hobart Warren Bequest, 1923",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437279,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -71.96,false,true,437074,European Paintings,Painting,A Cavalry Engagement,,,,,,Artist,,Adam Frans van der Meulen,"Flemish, Brussels 1632–1690 Paris",,"Meulen, Adam Frans van der",Flemish,1632,1690,,1652,1690,Oil on wood,8 5/8 x 12 1/2 in. (21.9 x 31.8 cm),"Purchase, 1871",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437074,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -91.26.4,false,true,437543,European Paintings,Painting,Susanna and the Elders,,,,,,Artist,Workshop of,Peter Paul Rubens,"Flemish, Siegen 1577–1640 Antwerp",,"Rubens, Peter Paul",Flemish,1577,1640,,1597,1640,Oil on wood,18 1/4 x 25 3/8 in. (46.4 x 64.5 cm),"Marquand Collection, Gift of Henry G. Marquand, 1890",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437543,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -17.190.19,false,true,437542,European Paintings,Painting,Saint Teresa of Ávila Interceding for Souls in Purgatory,,,,,,Artist,Workshop of,Peter Paul Rubens,"Flemish, Siegen 1577–1640 Antwerp",,"Rubens, Peter Paul",Flemish,1577,1640,,1597,1640,Oil on wood,25 1/4 x 19 1/4 in. (64.1 x 48.9 cm),"Gift of J. Pierpont Morgan, 1917",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437542,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -32.100.37,false,true,437540,European Paintings,Painting,Frans Francken I (1542–1616),,,,,,Artist,Workshop of,Peter Paul Rubens,"Flemish, Siegen 1577–1640 Antwerp",,"Rubens, Peter Paul",Flemish,1577,1640,,1597,1640,Oil on wood,25 1/4 x 19 1/8 in. (64.1 x 48.6 cm),"The Friedsam Collection, Bequest of Michael Friedsam, 1931",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437540,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -32.100.42,false,true,437544,European Paintings,Painting,Virgin and Child,,,,,,Artist,Workshop of,Peter Paul Rubens,"Flemish, Siegen 1577–1640 Antwerp",,"Rubens, Peter Paul",Flemish,1577,1640,,1597,1640,Oil on wood,39 3/4 x 30 3/8 in. (101 x 77.2 cm),"The Friedsam Collection, Bequest of Michael Friedsam, 1931",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437544,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.162.2,false,true,436971,European Paintings,Painting,The Flood Gate,,,,,,Artist,,Émile van Marcke,"French, Sèvres 1827–1890 Hyères",,"Marcke, Émile van",Belgian,1827,1890,,1847,1890,Oil on canvas,24 1/2 x 32 1/2 in. (62.2 x 82.6 cm),"Bequest of Susan P. Colgate, in memory of her husband, Romulus R. Colgate, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436971,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -89.15.18,false,true,437872,European Paintings,Painting,"Mariana of Austria (1634–1696), Queen of Spain",,,,,,Artist,Workshop of,Velázquez,"Spanish, Seville 1599–1660 Madrid",,Velázquez,Spanish,1599,1660,,1619,1660,Oil on canvas,32 1/4 x 39 1/2 in. (81.9 x 100.3 cm),"Marquand Collection, Gift of Henry G. Marquand, 1889",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437872,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -57.104,false,true,437861,European Paintings,Painting,Portrait of a Boy with a Falcon,,,,,,Artist,,Wallerant Vaillant,"Flemish, Lille 1623–1677 Amsterdam",,"Vaillant, Wallerant",Flemish,1623,1677,,1643,1677,Oil on canvas,29 3/4 x 25 in. (75.6 x 63.5 cm),"Purchase, George T. Delacorte Jr. Gift, 1957",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437861,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -07.225.306ab,false,true,437620,European Paintings,"Painting, grisaille",Nymph and Putti in a Vintage Scene; Nymph with a Wreath and Putti with Garlands of Flowers,,,,,,Artist,,Piat Joseph Sauvage,"Flemish, Tournai 1744–1818 Tournai",,"Sauvage, Piat Joseph",Flemish,1744,1818,,1763,1818,Oil on slate,(a) 9 1/2 x 23 1/4 in. (24.1 x 59.1 cm); (b) 9 5/8 x 23 3/4 in. (24.4 x 60.3 cm),"Gift of J. Pierpont Morgan, 1906",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437620,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -87.15.133,false,true,436937,European Paintings,Painting,The Dream after the Ball,,,,,,Artist,,Hans Makart,"Austrian, Salzburg 1840–1884 Vienna",,"Makart, Hans",Austrian,1840,1884,,1860,1884,Oil on canvas,62 3/8 x 37 1/4 in. (158.4 x 94.6 cm),"Catharine Lorillard Wolfe Collection, Bequest of Catharine Lorillard Wolfe, 1887",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436937,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -22.47.3,false,true,437781,European Paintings,Painting,Landscape with Thatched Cottages,,,,,,Artist,Workshop of,David Teniers the Younger,"Flemish, Antwerp 1610–1690 Brussels",,"Teniers, David, the Younger",Flemish,1610,1690,,1630,1690,Oil on wood,5 3/4 x 7 3/4 in. (14.6 x 19.7 cm),"Bequest of John Henry Abegg, 1921",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437781,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -89.15.22,false,true,437780,European Paintings,Painting,Shepherds and Sheep,,,,,,Artist,,David Teniers the Younger,"Flemish, Antwerp 1610–1690 Brussels",,"Teniers, David, the Younger",Flemish,1610,1690,,1630,1690,Oil on wood,6 5/8 x 9 in. (16.8 x 22.9 cm),"Marquand Collection, Gift of Henry G. Marquand, 1889",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437780,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -89.15.25,false,true,437776,European Paintings,Painting,The Good Samaritan,,,,,,Artist,,David Teniers the Younger,"Flemish, Antwerp 1610–1690 Brussels",,"Teniers, David, the Younger",Flemish,1610,1690,,1630,1690,Oil on wood,6 3/4 x 9 in. (17.1 x 22.9 cm),"Marquand Collection, Gift of Henry G. Marquand, 1889",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437776,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -71.46,false,true,437918,European Paintings,Painting,Portrait of a Young Woman,,,,,,Artist,,Cornelis de Vos,"Flemish, Hulst 1584/85–1651 Antwerp",,"Vos, Cornelis de",Flemish,1584,1651,,1603,1651,Oil on canvas,"46 1/2 x 37 1/4 in. (118.1 x 94.6 cm), including added strip of 2 3/4 in. (7 cm) at top","Purchase, 1871",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437918,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -89.15.37,false,true,437917,European Paintings,Painting,Portrait of a Woman,,,,,,Artist,,Cornelis de Vos,"Flemish, Hulst 1584/85–1651 Antwerp",,"Vos, Cornelis de",Flemish,1584,1651,,1603,1651,Oil on wood,49 3/8 x 38 in. (125.4 x 96.5 cm),"Marquand Collection, Gift of Henry G. Marquand, 1889",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437917,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -41.190.28a–d,false,true,437152,European Paintings,"Painting, retable",Virgin and Child Enthroned with Scenes from the Life of the Virgin,,,,,,Artist,,Morata Master,"Spanish, Aragonese, late 15th century",,Morata Master,Spanish,1470,1499,,1470,1499,Tempera and gold on wood,"Central panel, below, 52 x 34 5/8 in. (132.1 x 87.9 cm); central panel, above, 43 3/4 x 34 1/2 in. (111.1 x 87.6 cm); each side panel 84 1/2 x 22 3/4 in. (214.6 x 57.8 cm)","Bequest of George Blumenthal, 1941",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437152,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -56.224.1,false,true,436077,European Paintings,Painting,Portrait of a Woman,,,,,,Artist,,Michael Dahl,"Swedish, Stockholm 1659–1743 London",,"Dahl, Michael",Swedish,1659,1743,,1696,1743,Oil on canvas,77 1/4 x 51 3/4 in. (196.2 x 131.4 cm),"Gift of Margaret Bruguière, in memory of Louis Bruguière, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436077,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -81.1.662,false,true,436829,European Paintings,Painting,Hugo van der Goes Painting the Portrait of Mary of Burgundy,,,,,,Artist,,Guillaume Koller,"Belgian, Vienna 1829–1884 near Nancy",,"Koller, Guillaume",Belgian,1829,1884,,1849,1884,Oil on wood,23 3/8 x 34 in. (59.4 x 86.4 cm),"Bequest of Stephen Whitney Phoenix, 1881",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436829,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -87.15.61,false,true,437637,European Paintings,Painting,Lost: Souvenir of Auvergne,,,,,,Artist,,August Friedrich Albrecht Schenck,"Danish, Glückstadt 1828–1901 Ecouen",,"Schenck, August Friedrich Albrecht",Danish,1828,1901,,1848,1901,Oil on canvas,58 x 97 3/4 in. (147.3 x 248.3 cm),"Catharine Lorillard Wolfe Collection, Bequest of Catharine Lorillard Wolfe, 1887",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437637,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -32.100.3,false,true,435805,European Paintings,Painting,A Peasant with a Bird,,,,,,Artist,,Adriaen Brouwer,"Flemish, Oudenaarde 1605/6–1638 Antwerp",,"Brouwer, Adriaen",Flemish,1605,1638,,1626,1638,Oil on wood,Oval 7 1/8 x 5 1/2 in. (18.1 x 14 cm); set in rectangular panel 8 x 6 1/4 in. (20.3 x 15.9 cm),"The Friedsam Collection, Bequest of Michael Friedsam, 1931",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435805,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -51.70,false,true,436551,European Paintings,Painting,"Ferdinand VII (1784–1833), When Prince of Asturias",,,,,,Artist,,Goya,"Spanish, Fuendetodos 1746–1828 Bordeaux",and Workshop,Goya,Spanish,1746,1828,,1800,1805,Oil on canvas,32 3/4 x 26 1/4 in. (83.2 x 66.7 cm),"Gift of René Fribourg, 1951",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436551,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -22.181,false,true,436554,European Paintings,Painting,Bullfight in a Divided Ring,,,,,,Artist,Attributed to,Goya (Francisco de Goya y Lucientes),"Spanish, Fuendetodos 1746–1828 Bordeaux",,Goya (Francisco de Goya y Lucientes),Spanish,1746,1828,,1800,1829,Oil on canvas,38 3/4 x 49 3/4 in. (98.4 x 126.4 cm),"Catharine Lorillard Wolfe Collection, Wolfe Fund, 1922",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436554,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.180,false,true,436547,European Paintings,Painting,Narcisa Barañana de Goicoechea,,,,,,Artist,Attributed to,Goya (Francisco de Goya y Lucientes),"Spanish, Fuendetodos 1746–1828 Bordeaux",,Goya (Francisco de Goya y Lucientes),Spanish,1746,1828,,1766,1828,Oil on canvas,44 1/4 x 30 3/4 in. (112.4 x 78.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436547,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -65.220.2,false,true,437972,European Paintings,Painting,The Crucifixion,,,,,,Artist,Workshop of,Francisco de Zurbarán,"Spanish, Fuente de Cantos 1598–1664 Madrid",,"Zurbarán, Francisco de",Spanish,1598,1664,,1618,1664,Oil on canvas,"Arched top, 112 x 75 7/8 in. (284.5 x 192.7 cm)","Gift of George R. Hann, 1965",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437972,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.14,false,true,436198,European Paintings,Painting,Saint Cecilia,,,,,,Artist,,Abraham van Diepenbeeck,"Flemish, 's Hertogenbosch 1596–1675 Antwerp",,"Diepenbeeck, Abraham van",Flemish,1596,1675,,1616,1675,Oil on canvas,47 7/8 x 40 3/4 in. (121.6 x 103.5 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436198,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -58.145.2,false,true,435847,European Paintings,Painting,"Saint Vincent, Patron Saint of Lisbon",,,,,,Artist,,Frei Carlos,"Portuguese, active second quarter 16th century",,"Carlos, Frei",Portuguese,1525,1549,,1525,1549,Oil on wood,64 x 20 7/8 in. (162.6 x 53 cm),"Bequest of Muriel Stokes, 1958",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435847,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.280.6,false,true,437270,European Paintings,Painting,Head of a Man,,,,,,Artist,,Vasilii Grigorievich Perov,"Russian, Tobolsk 1834–1882 Kuz'minki, Moscow",,"Perov, Vasilii Grigorievich",Russian,1834,1882,,1854,1882,Oil on canvas board,20 7/8 x 13 7/8 in. (53 x 35.2 cm),"Bequest of Mary Jane Dastich, in memory of her husband, General Frank Dastich, 1975",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437270,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -53.180,false,true,437360,European Paintings,Painting,"Lady Maitland (Catherine Connor, died 1865)",,,,,,Artist,,Sir Henry Raeburn,"British, Stockbridge, Scotland 1756–1823 Edinburgh, Scotland",,"Raeburn, Henry, Sir","British, Scottish",1756,1923,,1776,1823,Oil on canvas,49 3/4 x 39 3/4 in. (126.4 x 101 cm),"Gift of Jessie Woolworth Donahue, 1953",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437360,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -46.13.5,false,true,437358,European Paintings,Painting,Janet Law,,,,,,Artist,,Sir Henry Raeburn,"British, Stockbridge, Scotland 1756–1823 Edinburgh, Scotland",,"Raeburn, Henry, Sir","British, Scottish",1756,1923,,1776,1823,Oil on canvas,35 1/4 x 27 1/4 in. (89.5 x 69.2 cm),"Bequest of Helen Swift Neilson, 1945",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437358,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -60.94.1,false,true,437356,European Paintings,Painting,Alexander Maconochie (1777–1861) of Meadowbank,,,,,,Artist,,Sir Henry Raeburn,"British, Stockbridge, Scotland 1756–1823 Edinburgh, Scotland",,"Raeburn, Henry, Sir","British, Scottish",1756,1923,,1776,1823,Oil on canvas,30 1/4 x 25 in. (76.8 x 63.5 cm),"Gift of William P. Clyde, 1960",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437356,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -60.71.13,false,true,437359,European Paintings,Painting,John Gray (1731–1811) of Newholm,,,,,,Artist,,Sir Henry Raeburn,"British, Stockbridge, Scotland 1756–1823 Edinburgh, Scotland",,"Raeburn, Henry, Sir","British, Scottish",1756,1923,,1776,1823,Oil on canvas,49 3/8 x 40 in. (125.4 x 101.6 cm),"Bequest of Lillian S. Timken, 1959",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437359,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -65.181.13,false,true,437357,European Paintings,Painting,James Johnston of Straiton (died 1841),,,,,,Artist,,Sir Henry Raeburn,"British, Stockbridge, Scotland 1756–1823 Edinburgh, Scotland",,"Raeburn, Henry, Sir","British, Scottish",1756,1923,,1776,1823,Oil on canvas,35 1/4 x 27 1/4 in. (89.5 x 69.2 cm),"Bequest of Adele L. Lehman, in memory of Arthur Lehman, 1965",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437357,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.339.2,false,true,437755,European Paintings,Painting,In the Studio,,,,,,Artist,,Alfred Stevens,"Belgian, Brussels 1823–1906 Paris",,"Stevens, Alfred",Belgian,1823,1906,1888,1888,1888,Oil on canvas,42 x 53 1/2 in. (106.7 x 135.9 cm),"Gift of Mrs. Charles Wrightsman, 1986",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437755,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -30.95.241,false,true,435590,European Paintings,Painting,Family Group in a Landscape,,,,,,Artist|Artist,and,Jacques d'Arthois|Flemish Painter,"Flemish, 1613–ca. 1686|ca. 1645",,"Arthois, Jacques d'|Flemish Painter",Flemish|Flemish,1613,1686,ca. 1645,1640,1650,Oil on canvas,49 1/8 x 60 1/8 in. (124.8 x 152.7 cm),"Theodore M. Davis Collection, Bequest of Theodore M. Davis, 1915",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435590,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -71.50,false,true,437189,European Paintings,Painting,Interior of a Gothic Church at Night,,,,,,Artist|Artist,and,Pieter Neeffs the Younger|Frans Francken III,"Flemish, 1620–after 1675|Flemish, 1607–1667",,"Neeffs, Pieter, the Younger|Francken, Frans, III",Flemish|Flemish,1620 |1607,1675 |1667,ca. 1660,1655,1665,Oil on wood,10 x 7 3/4 in. (25.4 x 19.7 cm),"Purchase, 1871",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437189,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -30.58.20,false,true,437187,European Paintings,Painting,Interior of a Gothic Church by Day,,,,,,Artist|Artist,and,Frans Francken III|Pieter Neeffs the Elder,"Flemish, 1607–1667|Flemish, active 1605–1656/61",,"Francken, Frans, III|Neeffs, Pieter, the Elder",Flemish|Flemish,1607 |1605,1667 |1661,probably ca. 1635–40,1635,1640,Oil on copper,5 1/8 x 6 1/2 in. (13 x 16.5 cm),"Bequest of Edward C. Post, 1915",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437187,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -30.58.21,false,true,437188,European Paintings,Painting,Interior of a Gothic Church at Night,,,,,,Artist|Artist,and,Pieter Neeffs the Elder|Frans Francken III,"Flemish, active 1605–1656/61|Flemish, 1607–1667",,"Neeffs, Pieter, the Elder|Francken, Frans, III",Flemish|Flemish,1605 |1607,1661 |1667,probably ca. 1635–40,1635,1640,Oil on copper,5 1/8 x 6 1/2 in. (13 x 16.5 cm),"Bequest of Edward C. Post, 1915",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437188,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -32.100.52,false,true,436987,European Paintings,Painting,The Rest on the Flight into Egypt,,,,,,Artist|Artist,and|Follower of,Master of the Liège Disciples at Emmaus|Quentin Metsys,"Netherlandish, active mid-16th century|Netherlandish, mid-16th century",,"Master of the Liège Disciples at Emmaus|Metsys, Quentin",Netherlandish|Netherlandish,1530 |1466,1569 |1530,ca. 1540,1535,1545,Oil on wood,37 1/2 x 30 1/4 in. (95.3 x 76.8 cm),"The Friedsam Collection, Bequest of Michael Friedsam, 1931",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436987,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -52.125,false,true,437868,European Paintings,Painting,"Don Gaspar de Guzmán (1587–1645), Count-Duke of Olivares",,,,,,Artist|Artist,Attributed to|and/or,Velázquez (Diego Rodríguez de Silva y Velázquez)|Juan Bautista Martínez del Mazo,"Spanish, Seville 1599–1660 Madrid|Spanish, Cuenca ca. 1612–1667 Madrid",,"Velázquez (Diego Rodríguez de Silva y Velázquez)|Mazo, Juan Bautista Martínez del",Spanish|Spanish,1599 |1612,1660 |1667,ca. 1635,1630,1640,Oil on canvas,50 1/4 x 41 in. (127.6 x 104.1 cm),"Fletcher Fund, 1952",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437868,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -"2011.485a, b",false,true,441672,European Paintings,"Painting, from the wing of an altarpiece",The Dormition of the Virgin; (reverse) Christ Carrying the Cross,,,,,,Artist|Artist,and Attributed to the,Hans Schäufelein|Master of Engerda,"German, Nuremberg ca. 1480–ca. 1540 Nördlingen|German, active ca. 1510–20",,"Schäufelein, Hans|Master of Engerda",German|German,1480 |1510,1540 |1520,ca. 1510,1505,1515,Oil and gold on fir,55 x 53 1/8 in. (139.7 x 134.9 cm),"Purchase, Lila Acheson Wallace, Karen and Mo Zukerman, Kowitz Family Foundation, Anonymous, and Hester Diamond Gifts, 2011",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/441672,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -45.141,false,true,437525,European Paintings,Painting,The Feast of Acheloüs,,,,,,Artist|Artist,and,Peter Paul Rubens|Jan Brueghel the Elder,"Flemish, Siegen 1577–1640 Antwerp|Netherlandish, Brussels 1568–1625 Antwerp",,"Rubens, Peter Paul|Brueghel, Jan, the Elder",Flemish|Netherlandish,1577 |1568,1640 |1625,ca. 1615,1610,1620,Oil on wood,42 1/2 x 64 1/2 in. (108 x 163.8 cm),"Gift of Alvin and Irwin Untermyer, in memory of their parents, 1945",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437525,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.179,false,true,437843,European Paintings,Painting,Portrait of a Man,,,,,,Artist|Artist,Attributed to|Fraudulent Imitation of,Gaspare Traversi|Goya,"Italian, Neapolitan, ca. 1722–1770",,"Traversi, Gaspare|Goya",Italian|Spanish,1722 |1746,1770 |1828,,1742,1770,Oil on canvas,22 x 17 1/2 in. (55.9 x 44.5 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437843,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -06.1039,false,true,437919,European Paintings,Painting,Two Tritons at the Feast of Acheloüs,,,,,,Artist|Artist,Attributed to|and Attributed to,Cornelis de Vos|Frans Snyders,"Flemish, Hulst 1584/85–1651 Antwerp|Flemish, Antwerp 1579–1657 Antwerp",,"Vos, Cornelis de|Snyders, Frans",Flemish|Flemish,1584 |1579,1651 |1657,,1603,1651,Oil on canvas,62 3/4 x 45 7/8 in. (159.4 x 116.5 cm),"Marquand Fund, 1906",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437919,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -39.143,false,true,437324,European Paintings,Painting,Portrait of a Young Woman,,,,,,Artist|Artist,?,Pieter Jansz. Pourbus|Pieter Jansz. Pourbus,"Netherlandish, Gouda? 1524–1584 Bruges|Netherlandish, Gouda? 1524–1584 Bruges",,"Pourbus, Pieter Jansz.|Pourbus, Pieter Jansz.",Netherlandish|Netherlandish,1524 |1524,1584 |1584,,1544,1584,Oil on wood,15 1/2 x 12 1/2 in. (39.4 x 31.8 cm),"Charles B. Curtis Fund, 1939",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437324,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2016.392,false,true,726543,European Paintings,Painting,Monsignor Giuseppe Spina (1756–1828),,,,,,Artist,,Angelica Kauffmann,"Swiss, Chur 1741–1807 Rome",,"Kauffmann, Angelica",,1741,1807,1798,1798,1798,Oil on canvas,37 5/8 × 31 1/2 in. (95.5 × 80 cm),"Gift of Carlo Orsi, 2016",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/726543,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.438,false,true,678013,European Paintings,Painting,Tiger in Repose,,,,,,Artist,,Antoine-Louis Barye,"French, Paris 1796–1875 Paris",,"Barye, Antoine-Louis",,1796,1875,ca. 1850–65,1845,1870,Oil on canvas,10 3/4 × 14 in. (27.3 × 35.6 cm),"Gift of Eugene V. Thaw, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/678013,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2016.306,false,true,712013,European Paintings,Painting,"Grape Vines and Fruit, with Three Wagtails",,,,,,Artist,,Bartolomeo Cavarozzi,"Italian, Viterbo 1587–1625 Rome",,"Cavarozzi, Bartolomeo",,1587,1625,ca. 1615–18,1615,1618,Oil on canvas,40 × 61 3/4 in. (101.6 × 156.8 cm),"Gift of Claire and Giovanni Sarti, in honor of Keith Christiansen, 2016",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/712013,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.645,false,true,702752,European Paintings,Painting,Christ and the Woman of Samaria,,,,,,Artist,,Benedetto Luti,"Italian, Florence 1666–1724 Rome",,"Luti, Benedetto",,1666,1724,1715–20,1715,1720,Oil on copper,15 × 12 1/8 in. (38.2 × 30.9 cm),"Purchase, Rogers Fund, by exchange, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/702752,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2016.485,false,true,712946,European Paintings,Painting,The Calling of Saint Matthew,,,,,,Artist,,Giovanni Battista Caracciolo,"Italian, Naples 1578–1635 Naples",,"Caracciolo, Giovanni Battista",,1578,1635,ca. 1625–30,1625,1630,Oil on canvas,51 3/8 × 61 1/2 in. (130.5 × 156.2 cm),"Purchase, The Morris and Alma Schapiro Fund Gift, 2016",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/712946,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2016.653,false,true,695496,European Paintings,Painting,Bust of a Man (Saint Matthias?),,,,,,Artist,,Giovanni Battista Piazzetta,"Italian, Venice 1682–1754 Venice",,"Piazzetta, Giovanni Battista",,1682,1754,ca. 1715–20,1715,1720,Oil on canvas,17 1/2 × 14 3/4 in. (44.5 × 37.5 cm),"Gift of Dianne Modestini and Eugene V. Thaw, 2016",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/695496,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -07.225.314a,false,true,437625,European Paintings,"Painting, grisaille overdoor",The Triumph of Bacchus,,,,,,Artist,,Piat Joseph Sauvage,"Flemish, Tournai 1744–1818 Tournai",,"Sauvage, Piat Joseph",,1744,1818,1780s,1780,1789,Oil on canvas,14 x 32 7/8 in. (35.6 x 83.5 cm),"Gift of J. Pierpont Morgan, 1906",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437625,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -07.225.314b,false,true,437618,European Paintings,"Painting, grisaille",Infant Bacchanal,,,,,,Artist,,Piat Joseph Sauvage,"Flemish, Tournai 1744–1818 Tournai",,"Sauvage, Piat Joseph",,1744,1818,1780s,1780,1789,Oil on canvas,10 1/4 x 29 5/8 in. (26 x 75.2 cm),"Gift of J. Pierpont Morgan, 1906",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437618,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.761,false,true,440900,European Paintings,Painting,Saint Dominic in Penitence,,,,,,Artist,,Filippo Tarchiani,"Italian, Castello 1576–1645 Florence",,"Tarchiani, Filippo",,1576,1645,ca. 1607,1602,1612,Oil on canvas,52 x 43 in. (132.1 x 109.2 cm),"Gift of Brian J. Brille, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/440900,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2016.507,false,true,712539,European Paintings,Painting,Saint Philip Neri (1515–1595),,,,,,Artist,,Carlo Dolci,"Italian, Florence 1616–1687 Florence",,"Dolci, Carlo",,1616,1687,1645 or 1646,1645,1646,Oil on canvas,17 1/4 × 14 1/4 in. (43.8 × 36.2 cm),"Purchase, George Delacorte Fund Gift, in memory of George T. Delacorte Jr., Ronald S. Lauder, Mr. and Mrs. Richard L. Chilton Jr., and Mr. and Mrs. Frederick W. Beinecke Gifts, 2016",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/712539,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.263,false,true,679686,European Paintings,Painting,Hortensia,,,,,,Artist,,Fernand Khnopff,"Belgian, Grembergen 1858–1921 Brussels",,"Khnopff, Fernand",,1858,1921,1884,1884,1884,Oil on canvas,18 13/16 × 23 1/2 in. (47.8 × 59.7 cm),"Purchase, Bequest of Julia W. Emmons, by exchange, and Catharine Lorillard Wolfe Collection, Wolfe Fund, and Gift of Charles Hack and the Hearn Family Trust, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/679686,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -32.100.55,false,true,435760,European Paintings,Painting,The Man of Sorrows,,,,,,Artist,Workshop of,Aelbert Bouts,"Netherlandish, Leuven ca. 1451/54–1549",,"Bouts, Aelbert",,1451,1549,ca. 1525,1520,1530,Oil on oak,"Arched top, 17 1/2 x 11 1/4 in. (44.5 x 28.6 cm)","The Friedsam Collection, Bequest of Michael Friedsam, 1931",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435760,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2015.398,false,true,687513,European Paintings,Painting,The Lamentation,,,,,,Artist,,Luis de Morales,"Spanish, Plasencia (?) 1510/11–1586 Alcántara",,"Morales, Luis de",,1510,1586,ca. 1560,1555,1565,Oil on walnut,35 × 24 5/8 in. (89 × 62.5 cm),"Purchase, Alejandro Santo Domingo and Annette de la Renta Gifts; Bequests of George D. Pratt and of Annette B. McFadden, and Gifts of Estate of George Quackenbush, in his memory, of Dr. and Mrs. Max A. Goldzieher, of Francis Neilson, of Dr. Foo Chu and Dr. Marguerite Hainje-Chu, of Mr. and Mrs. Harold H. Burns, and of Mr. and Mrs. Joshua Logan, and other gifts and bequests, by exchange; Victor Wilbour Fund; and Hester Diamond Gift, 2015",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/687513,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2016.39,false,true,439977,European Paintings,Painting,Christ Carrying the Cross,,,,,,Artist,,Jan Gossart (called Mabuse),"Netherlandish, Maubeuge ca. 1478–1532 Antwerp (?)",,"Gossart, Jan (called Mabuse)",,1478,1532,ca. 1520–25,1520,1525,Oil on oak,9 7/8 × 7 1/2 in. (25.1 × 19 cm),"Gift of Honorable J. William Middendorf II, and Purchase, Walter and Leonore Annenberg and The Annenberg Foundation Gift, Director's Fund, Gift of George A. Hearn, by exchange, and Marquand and The Alfred N. Punnett Endowment Funds, 2016",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/439977,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2016.63,false,true,670765,European Paintings,Painting,The Death of Cleopatra,,,,,,Artist,,Guido Cagnacci,"Italian, Santarcangelo di Romagna 1601–1663 Vienna",,"Cagnacci, Guido",,1601,1663,ca. 1645–55,1645,1655,Oil on canvas,37 3/8 × 29 1/2 in. (95 × 75 cm),"Purchase, Diane Burke Gift, Gift of J. Pierpont Morgan, by exchange, Friends of European Paintings Gifts, Gwynne Andrews Fund, Lila Acheson Wallace, Charles and Jessie Price, and Álvaro Saieh Bendeck Gifts, Gift and Bequest of George Blumenthal and Fletcher Fund, by exchange, and Michel David-Weill Gift, 2016",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/670765,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -57.54,false,true,436983,European Paintings,"Painting, miniature","Pierre Louis Dubus (1721–1799), Called Préville, of the Comédie-Française",,,,,,Artist,Attributed to,Jean-Baptiste Massé,"French, Paris 1687–1767 Paris",,"Massé, Jean-Baptiste",,1687,1767,,1707,1767,Ivory laid on card,Diameter 1 5/8 in. (42 mm),"Rogers Fund, 1957",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/436983,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -71.156–57,false,true,435763,European Paintings,Painting,The Mourning Virgin; The Man of Sorrows,,,,,,Artist,Posthumous Workshop Copy after,Dieric Bouts,"Netherlandish, Leuven, ca. 1525",,"Bouts, Dieric",,1457,1475,,1520,1530,Oil on oak,Each 16 x 12 1/2 in. (40.6 x 31.8 cm),"Purchase, 1871",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435763,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -47.138,false,true,436689,European Paintings,Painting,"Mrs. Whaley (died 1798, Isle of Man)",,,,,,Artist,Attributed to,George Chinnery,"British, London 1774–1852 Macau",,"Chinnery, George",,1774,1852,,1794,1798,Oil on canvas,93 1/2 x 58 in. (237.5 x 147.3 cm),"Gift of Henry S. Morgan, 1947",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436689,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -07.225.265,false,true,437613,European Paintings,"Painting, grisaille",Venus and Cupid,,,,,,Artist,Attributed to,Piat Joseph Sauvage,"Flemish, Tournai 1744–1818 Tournai",,"Sauvage, Piat Joseph",,1744,1818,,1764,1818,Oil on canvas,49 7/8 x 29 1/4 in. (126.7 x 74.3 cm),"Gift of J. Pierpont Morgan, 1906",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437613,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -21.134.3a–c,false,true,437491,European Paintings,"Painting, parts of a polyptych",Saint Michael; The Mass of Saint Gregory; Saint Jerome,,,,,,Artist,,Master of the Saint Catherine Legend,"Netherlandish, active ca. 1470–1500",,Master of the Saint Catherine Legend,,1470,1500,,1450,1499,Oil on wood,Central panel 6 1/8 x 3 3/4 in. (15.6 x 9.5 cm); left panel 6 1/4 x 3 7/8 in. (15.9 x 9.8 cm); right panel 6 1/4 x 3 3/4 in. (15.9 x 9.5 cm),"Bequest of William H. Herriman, 1920",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437491,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -26.26,false,true,437035,European Paintings,Painting,The Lamentation,,,,,,Artist,Follower of the,Master of the Virgin among Virgins,"Netherlandish, active late 15th century",,Master of the Virgin among Virgins,,1460,1495,,1470,1499,Oil on wood,34 7/8 x 20 1/4 in. (88.6 x 51.4 cm),"Rogers Fund, 1926",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437035,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -26.244,false,true,435725,European Paintings,Painting,Christ's Descent into Hell,,,,,,Artist,Follower of,Hieronymus Bosch,"Netherlandish, second quarter 16th century",,"Bosch, Hieronymus",,1450,1516,,1550,1560,Oil on wood,21 x 46 in. (53.3 x 116.8 cm),"Harris Brisbane Dick Fund, 1926",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435725,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.100.13,false,true,436347,European Paintings,Painting,Trompe l'oeil with Palettes and Miniature,,,,,,Artist,Attributed to,Jean François de Le Motte,"French, born before 1635–died in or after 1685",,"Le Motte, Jean François de",,1635,1685,,1670,1699,Oil on canvas,46 7/8 x 36 1/8 in. (119.1 x 91.8 cm),"Bequest of Harry G. Sperling, 1971",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436347,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -15.133,false,true,436702,European Paintings,Painting,The Temptation of Saint Anthony,,,,,,Artist,Attributed to,Pieter Huys,"Netherlandish, Antwerp, active by 1545–died 1584 Antwerp",,"Huys, Pieter",,1545,1584,,1545,1584,Oil on wood,43 x 59 in. (109.2 x 149.9 cm),"Anonymous Gift, 1915",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436702,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -32.75.16,false,true,435624,European Paintings,"Painting, miniature",Portrait of a Man,,,,,,Artist,,David Baudringhien,"Dutch, ca. 1581–1650",,"Baudringhien, David",Dutch,1581,1650,1627,1627,1627,Oil on copper,"Oval, 3 3/4 x 3 in. (95 x 76 mm)","The Collection of Giovanni P. Morosini, presented by his daughter Giulia, 1932",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/435624,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2004.392,false,true,438740,European Paintings,"Painting, miniature",Portrait of a Woman,,,,,,Artist,,Nicolaes Maes,"Dutch, Dordrecht 1634–1693 Amsterdam",,"Maes, Nicolaes",Dutch,1634,1693,1657,1657,1657,Oil on copper,4 5/8 x 3 3/8 in. (117 x 86 mm),"Gift of Lila and Herman Shickman, 2004",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/438740,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.331.7,false,true,438379,European Paintings,Painting,Young Woman with a Red Necklace,,,,,,Artist,Style of,Rembrandt,"Dutch, ca. 1645",,Rembrandt,Dutch,1606,1669,ca. 1645,1640,1650,Oil on wood,"Overall, with added strips, 8 1/2 x 7 1/4 in. (21.6 x 18.4 cm)","From the Collection of Rita and Frits Markus, Bequest of Rita Markus, 2005",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/438379,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -32.100.8,false,true,436627,European Paintings,Painting,Frans Hals (1582/83–1666),,,,,,Artist,Copy after,Frans Hals,"Dutch, 17th century",,"Hals, Frans",Dutch,1582,1666,probably 1650s,1650,1659,Oil on wood,12 7/8 x 11 in. (32.7 x 27.9 cm),"The Friedsam Collection, Bequest of Michael Friedsam, 1931",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436627,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.40.610,false,true,437414,European Paintings,Painting,Pilate Washing His Hands,,,,,,Artist,Style of,Rembrandt,"Dutch, 17th century",,Rembrandt,Dutch,1606,1669,probably 1660s,1600,1699,Oil on canvas,51 1/4 x 65 3/4 in. (130.2 x 167 cm),"Bequest of Benjamin Altman, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437414,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -11.73,false,true,435769,European Paintings,Painting,The Judgment of Solomon,,,,,,Artist,,Leonaert Bramer,"Dutch, Delft 1596–1674 Delft",,"Bramer, Leonaert",Dutch,1596,1674,1640s,1640,1649,Oil on wood,31 1/8 x 40 1/2 in. (79.1 x 102.9 cm),"Gift of National Surety Company, 1911",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435769,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -89.15.21,true,true,437881,European Paintings,Painting,Young Woman with a Water Pitcher,,,,,,Artist,,Johannes Vermeer,"Dutch, Delft 1632–1675 Delft",,"Vermeer, Johannes",Dutch,1632,1675,ca. 1662,1657,1667,Oil on canvas,18 x 16 in. (45.7 x 40.6 cm),"Marquand Collection, Gift of Henry G. Marquand, 1889",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437881,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.40.611,false,true,437878,European Paintings,Painting,A Maid Asleep,,,,,,Artist,,Johannes Vermeer,"Dutch, Delft 1632–1675 Delft",,"Vermeer, Johannes",Dutch,1632,1675,ca. 1656–57,1656,1657,Oil on canvas,34 1/2 x 30 1/8 in. (87.6 x 76.5 cm),"Bequest of Benjamin Altman, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437878,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -25.110.24,false,true,437880,European Paintings,Painting,Woman with a Lute,,,,,,Artist,,Johannes Vermeer,"Dutch, Delft 1632–1675 Delft",,"Vermeer, Johannes",Dutch,1632,1675,ca. 1662–63,1662,1663,Oil on canvas,20 1/4 x 18 in. (51.4 x 45.7 cm),"Bequest of Collis P. Huntington, 1900",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437880,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -32.100.18,false,true,437877,European Paintings,Painting,Allegory of the Catholic Faith,,,,,,Artist,,Johannes Vermeer,"Dutch, Delft 1632–1675 Delft",,"Vermeer, Johannes",Dutch,1632,1675,ca. 1670–72,1670,1672,Oil on canvas,45 x 35 in. (114.3 x 88.9 cm),"The Friedsam Collection, Bequest of Michael Friedsam, 1931",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437877,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1979.396.1,true,true,437879,European Paintings,Painting,Study of a Young Woman,,,,,,Artist,,Johannes Vermeer,"Dutch, Delft 1632–1675 Delft",,"Vermeer, Johannes",Dutch,1632,1675,ca. 1665–67,1665,1667,Oil on canvas,17 1/2 x 15 3/4 in. (44.5 x 40 cm),"Gift of Mr. and Mrs. Charles Wrightsman, in memory of Theodore Rousseau Jr., 1979",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437879,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1982.60.29,false,true,435598,European Paintings,Painting,"Portrait of a Man, Possibly a Botanist",,,,,,Artist,Attributed to,David Bailly,"Dutch, Leiden 1584–1657 Leiden",,"Bailly, David",Dutch,1584,1657,1641,1641,1641,Oil on wood,33 x 24 1/2 in. (83.8 x 62.2 cm),"The Jack and Belle Linsky Collection, 1982",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435598,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -40.64,false,true,436209,European Paintings,Painting,An Evening School,,,,,,Artist,,Gerrit Dou,"Dutch, Leiden 1613–1675 Leiden",,"Dou, Gerrit",Dutch,1613,1675,ca. 1655–57,1655,1657,Oil on wood,"Arched top, 10 x 9 in. (25.4 x 22.9 cm)","Bequest of Lillian M. Ellis, 1940",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436209,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.40.607,false,true,436210,European Paintings,Painting,Self-Portrait,,,,,,Artist,,Gerrit Dou,"Dutch, Leiden 1613–1675 Leiden",,"Dou, Gerrit",Dutch,1613,1675,ca. 1665,1660,1670,Oil on wood,19 1/4 x 15 3/8 in. (48.9 x 39.1 cm),"Bequest of Benjamin Altman, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436210,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -58.89,true,true,437749,European Paintings,Painting,Merry Company on a Terrace,,,,,,Artist,,Jan Steen,"Dutch, Leiden 1626–1679 Leiden",,"Steen, Jan",Dutch,1626,1679,ca. 1670,1665,1675,Oil on canvas,55 1/2 x 51 3/4 in. (141 x 131.4 cm),"Fletcher Fund, 1958",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437749,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -46.13.2,false,true,437748,European Paintings,Painting,The Lovesick Maiden,,,,,,Artist,,Jan Steen,"Dutch, Leiden 1626–1679 Leiden",,"Steen, Jan",Dutch,1626,1679,ca. 1660,1655,1665,Oil on canvas,34 x 39 in. (86.4 x 99.1 cm),"Bequest of Helen Swift Neilson, 1945",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437748,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1982.60.31,false,true,437747,European Paintings,Painting,The Dissolute Household,,,,,,Artist,,Jan Steen,"Dutch, Leiden 1626–1679 Leiden",,"Steen, Jan",Dutch,1626,1679,ca. 1663–64,1663,1664,Oil on canvas,42 1/2 x 35 1/2 in. (108 x 90.2 cm),"The Jack and Belle Linsky Collection, 1982",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437747,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -20.155.6,false,true,437876,European Paintings,Painting,Entrance to a Dutch Port,,,,,,Artist,,Willem van de Velde II,"Dutch, Leiden 1633–1707 London",,"Velde, Willem van de, II",Dutch,1633,1707,ca. 1665,1660,1670,Oil on canvas,25 7/8 x 30 5/8 in. (65.7 x 77.8 cm),"Bequest of William K. Vanderbilt, 1920",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437876,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -60.71.3,false,true,437088,European Paintings,Painting,The Serenade,,,,,,Artist,,Frans van Mieris the Elder,"Dutch, Leiden 1635–1681 Leiden",,"Mieris, Frans van, the Elder",Dutch,1635,1681,ca. 1678–80,1678,1680,Oil on wood,"Arched top, 5 3/4 x 4 3/8 in. (14.6 x 11.1 cm)","Bequest of Lillian S. Timken, 1959",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437088,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -71.70,false,true,437687,European Paintings,Painting,Johan Hulshout (1623–1687),,,,,,Artist,,Pieter van Slingelandt,"Dutch, Leiden 1640–1691 Leiden",,"Slingelandt, Pieter van",Dutch,1640,1691,ca. 1670 or slightly later,1665,1675,Oil on wood,14 1/2 x 11 3/4 in. (36.8 x 29.8 cm),"Purchase, 1871",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437687,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -60.55.1,false,true,437688,European Paintings,Painting,Portrait of a Man,,,,,,Artist,Attributed to,Pieter van Slingelandt,"Dutch, Leiden 1640–1691 Leiden",,"Slingelandt, Pieter van",Dutch,1640,1691,ca. 1680,1675,1685,Oil on copper,"Oval, 3 3/8 x 2 1/2 in. (8.6 x 6.4 cm)","Bequest of Rupert L. Joseph, 1959",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437688,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.23.2,false,true,437915,European Paintings,Painting,"Interior of the Oude Kerk, Delft",,,,,,Artist,,Hendrick van Vliet,"Dutch, Delft 1611/12–1675 Delft",,"Vliet, Hendrick van",Dutch,1611,1675,1660,1660,1660,Oil on canvas,32 1/2 x 26 in. (82.6 x 66 cm),"Gift of Clarence Dillon, 1976",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437915,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -57.30.1,false,true,436788,European Paintings,Painting,Portrait of a Man,,,,,,Artist,,Cornelis Jonson van Ceulen the Elder,"Dutch, London 1593–1661 Utrecht",,"Jonson van Ceulen, Cornelis, the Elder",Dutch,1593,1661,1648,1648,1648,Oil on canvas,Overall 40 3/4 x 31 1/2 in. (103.5 x 80 cm); painted surface 40 3/4 x 31 1/8 in. (103.5 x 79.1 cm),"Gift of Mrs. J. E. Spingarn, 1957",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436788,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -57.30.2,false,true,436789,European Paintings,Painting,Portrait of a Woman,,,,,,Artist,,Cornelis Jonson van Ceulen the Elder,"Dutch, London 1593–1661 Utrecht",,"Jonson van Ceulen, Cornelis, the Elder",Dutch,1593,1661,1648,1648,1648,Oil on canvas,Overall 40 3/4 x 31 1/2 in. (103.5 x 80 cm); painted surface 40 3/4 x 30 7/8 in. (103.5 x 78.4 cm),"Gift of Mrs. J. E. Spingarn, 1957",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436789,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -95.7,false,true,437102,European Paintings,Painting,Landscape with a Cottage,,,,,,Artist,,Pieter de Molijn,"Dutch, London 1595–1661 Haarlem",,"Molijn, Pieter de",Dutch,1595,1661,1629,1629,1629,Oil on wood,14 3/4 x 21 3/4 in. (37.5 x 55.2 cm),"Gift of Henry G. Marquand, 1895",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437102,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -41.116.3,false,true,436790,European Paintings,Painting,Portrait of a Man with a Watch,,,,,,Artist,,Cornelis Jonson van Ceulen the Younger,"Dutch, London 1634–1715 Utrecht",,"Jonson van Ceulen, Cornelis, the Younger",Dutch,1634,1715,1657,1657,1657,Oil on canvas,33 x 27 3/4 in. (83.8 x 70.5 cm),"Given in memory of Felix M. Warburg by his wife and children, 1941",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436790,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1974.109,false,true,437633,European Paintings,Painting,Cephalus and Procris,,,,,,Artist,,Godfried Schalcken,"Dutch, Made 1643–1706 The Hague",,"Schalcken, Godfried",Dutch,1643,1706,probably 1680s,1680,1689,Oil on canvas,25 1/2 x 31 3/8 in. (64.8 x 79.7 cm),"Rogers Fund, 1974",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437633,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.40.810,false,true,437040,European Paintings,Painting,Changing Pasture,,,,,,Artist,,Anton Mauve,"Dutch, Zaandam 1838–1888 Arnhem",,"Mauve, Anton",Dutch,1838,1888,ca. 1880s,1858,1888,Oil on canvas,24 x 39 5/8 in. (61 x 100.6 cm),"Bequest of Benjamin Altman, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437040,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.40.624,false,true,437406,European Paintings,Painting,"Portrait of a Man (""The Auctioneer"")",,,,,,Artist,Follower of,Rembrandt,"Dutch, third quarter 17th century",,Rembrandt,Dutch,1606,1669,probably ca. 1658–62,1658,1662,Oil on canvas,42 3/4 x 34 in. (108.6 x 86.4 cm),"Bequest of Benjamin Altman, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437406,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -25.110.27,false,true,436309,European Paintings,Painting,Bearded Man with a Velvet Cap,,,,,,Artist,,Govert Flinck,"Dutch, Cleve 1615–1660 Amsterdam",,"Flinck, Govert",Dutch,1615,1660,1645,1645,1645,Oil on wood,23 3/4 x 20 5/8 in. (60.3 x 52.4 cm),"Bequest of Collis P. Huntington, 1900",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436309,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -71.5,false,true,437920,European Paintings,Painting,A Vase with Flowers,,,,,,Artist,,Jacob Vosmaer,"Dutch, Delft ca. 1584–1641 Delft",,"Vosmaer, Jacob",Dutch,1584,1641,probably 1613,1613,1613,Oil on wood,33 1/2 x 24 5/8 in. (85.1 x 62.5 cm),"Purchase, 1871",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437920,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -71.108,false,true,436615,European Paintings,Painting,A Banquet,,,,,,Artist,,Dirck Hals,"Dutch, Haarlem 1591–1656 Haarlem",,"Hals, Dirck",Dutch,1591,1656,1628,1628,1628,Oil on wood,16 x 26 in. (40.6 x 66 cm),"Purchase, 1871",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436615,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1981.318,false,true,437323,European Paintings,Painting,A Brazilian Landscape,,,,,,Artist,,Frans Post,"Dutch, Haarlem 1612–1680 Haarlem",,"Post, Frans",Dutch,1612,1680,1650,1650,1650,Oil on wood,24 x 36 in. (61 x 91.4 cm),"Purchase, Rogers Fund, special funds, James S. Deely Gift, and Gift of Edna H. Sachs and other gifts and bequests, by exchange, 1981",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437323,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1971.48,false,true,437953,European Paintings,Painting,A Man and a Woman on Horseback,,,,,,Artist,,Philips Wouwerman,"Dutch, Haarlem 1619–1668 Haarlem",,"Wouwerman, Philips",Dutch,1619,1668,ca. 1653–54,1653,1654,Oil on wood,12 1/8 x 16 1/4 in. (30.8 x 41.3 cm),"Purchase, Pfeiffer Fund, Joseph Pulitzer Bequest, and Gift of Dr. Ernest G. Stillman, by exchange, 1971",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437953,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -06.288,false,true,437956,European Paintings,Painting,Kitchen Scene,,,,,,Artist,,Peter Wtewael,"Dutch, Utrecht 1596–1660 Utrecht",,"Wtewael, Peter",Dutch,1596,1660,1620s,1620,1629,Oil on canvas,44 3/4 x 63 in. (113.7 x 160 cm),"Rogers Fund, 1906",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437956,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -49.7.38,false,true,435714,European Paintings,Painting,Curiosity,,,,,,Artist,,Gerard ter Borch the Younger,"Dutch, Zwolle 1617–1681 Deventer",,"Borch, Gerard ter, the Younger",Dutch,1617,1681,ca. 1660–62,1660,1662,Oil on canvas,30 x 24 1/2 in. (76.2 x 62.2 cm),"The Jules Bache Collection, 1949",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435714,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -89.15.15,false,true,435715,European Paintings,Painting,Portrait of a Seated Man,,,,,,Artist,,Gerard ter Borch the Younger,"Dutch, Zwolle 1617–1681 Deventer",,"Borch, Gerard ter, the Younger",Dutch,1617,1681,late 1650s or early 1660s,1657,1663,Oil on wood,14 1/8 x 12 in. (35.9 x 30.5 cm),"Marquand Collection, Gift of Henry G. Marquand, 1889",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435715,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.40.617,false,true,435717,European Paintings,Painting,A Woman Playing the Theorbo-Lute and a Cavalier,,,,,,Artist,,Gerard ter Borch the Younger,"Dutch, Zwolle 1617–1681 Deventer",,"Borch, Gerard ter, the Younger",Dutch,1617,1681,ca. 1658,1653,1663,Oil on wood,14 1/2 x 12 3/4 in. (36.8 x 32.4 cm),"Bequest of Benjamin Altman, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435717,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -17.190.10,false,true,435718,European Paintings,Painting,A Young Woman at Her Toilet with a Maid,,,,,,Artist,,Gerard ter Borch the Younger,"Dutch, Zwolle 1617–1681 Deventer",,"Borch, Gerard ter, the Younger",Dutch,1617,1681,ca. 1650–51,1650,1651,Oil on wood,18 3/4 x 13 5/8 in. (47.6 x 34.6 cm),"Gift of J. Pierpont Morgan, 1917",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435718,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1982.60.30,false,true,435716,European Paintings,Painting,The Van Moerkerken Family,,,,,,Artist,,Gerard ter Borch the Younger,"Dutch, Zwolle 1617–1681 Deventer",,"Borch, Gerard ter, the Younger",Dutch,1617,1681,ca. 1653–54,1653,1654,Oil on wood,16 1/4 x 14 in. (41.3 x 35.6 cm),"The Jack and Belle Linsky Collection, 1982",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435716,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -41.116.2,false,true,436211,European Paintings,Painting,Portrait of a Man (Self-Portrait?),,,,,,Artist,,Willem Drost,"Dutch, Amsterdam 1633–1659 Venice",,"Drost, Willem",Dutch,1633,1659,1653 or 1655,1653,1655,Oil on canvas,34 1/8 x 28 1/2 in. (86.7 x 72.4 cm),"Given in memory of Felix M. Warburg by his wife and children, 1941",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436211,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -30.95.268,false,true,437415,European Paintings,Painting,The Sibyl,,,,,,Artist,,Willem Drost,"Dutch, Amsterdam 1633–1659 Venice",,"Drost, Willem",Dutch,1633,1659,ca. 1654,1649,1659,Oil on canvas,38 1/2 x 30 3/4 in. (97.8 x 78.1 cm),"Theodore M. Davis Collection, Bequest of Theodore M. Davis, 1915",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437415,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.331.4,false,true,438376,European Paintings,Painting,"Still Life with Oysters, a Silver Tazza, and Glassware",,,,,,Artist,,Willem Claesz Heda,"Dutch, Haarlem? 1594–1680 Haarlem",,"Heda, Willem Claesz",Dutch,1594,1680,1635,1635,1635,Oil on wood,19 5/8 x 31 3/4 in. (49.8 x 80.6 cm),"From the Collection of Rita and Frits Markus, Bequest of Rita Markus, 2005",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/438376,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -71.62,false,true,436558,European Paintings,Painting,View of Haarlem and the Haarlemmer Meer,,,,,,Artist,,Jan van Goyen,"Dutch, Leiden 1596–1656 The Hague",,"Goyen, Jan van",Dutch,1596,1656,1646,1646,1646,Oil on wood,13 5/8 x 19 7/8 in. (34.6 x 50.5 cm),"Purchase, 1871",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436558,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.25,false,true,436555,European Paintings,Painting,Sandy Road with a Farmhouse,,,,,,Artist,,Jan van Goyen,"Dutch, Leiden 1596–1656 The Hague",,"Goyen, Jan van",Dutch,1596,1656,1627,1627,1627,Oil on wood,12 1/8 x 16 1/4 in. (30.8 x 41.3 cm),"Bequest of Myra Mortimer Pinter, 1972",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436555,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -64.65.1,false,true,436559,European Paintings,Painting,Castle by a River,,,,,,Artist,,Jan van Goyen,"Dutch, Leiden 1596–1656 The Hague",,"Goyen, Jan van",Dutch,1596,1656,1647,1647,1647,Oil on wood,26 x 38 1/4 in. (66 x 97.2 cm),"Gift of Edith Neuman de Végvár, in honor of her husband, Charles Neuman de Végvár, 1964",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436559,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -32.100.6,false,true,436556,European Paintings,Painting,Country House near the Water,,,,,,Artist,,Jan van Goyen,"Dutch, Leiden 1596–1656 The Hague",,"Goyen, Jan van",Dutch,1596,1656,1646,1646,1646,Oil on wood,14 3/8 x 13 in. (36.5 x 33 cm),"The Friedsam Collection, Bequest of Michael Friedsam, 1931",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436556,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -45.146.3,false,true,436557,European Paintings,Painting,The Pelkus Gate near Utrecht,,,,,,Artist,,Jan van Goyen,"Dutch, Leiden 1596–1656 The Hague",,"Goyen, Jan van",Dutch,1596,1656,1646,1646,1646,Oil on wood,"14 1/2 x 22 1/2 in. (36.8 x 57.2 cm) Frame, 23 1/4 x 31 x 2 3/4 in. (59.1 x 78.7 x 7 cm)","Gift of Francis Neilson, 1945",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436557,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.331.2,false,true,438374,European Paintings,Painting,A Beach with Fishing Boats,,,,,,Artist,,Jan van Goyen,"Dutch, Leiden 1596–1656 The Hague",,"Goyen, Jan van",Dutch,1596,1656,probably 1653,1653,1653,Oil on wood,11 x 17 in. (27.9 x 43.2 cm),"From the Collection of Rita and Frits Markus, Bequest of Rita Markus, 2005",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/438374,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.331.3,false,true,438375,European Paintings,Painting,A View of The Hague from the Northwest,,,,,,Artist,,Jan van Goyen,"Dutch, Leiden 1596–1656 The Hague",,"Goyen, Jan van",Dutch,1596,1656,1647,1647,1647,Oil on wood,26 x 37 7/8 in. (66 x 96.2 cm),"From the Collection of Rita and Frits Markus, Bequest of Rita Markus, 2005",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/438375,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -43.125,false,true,437391,European Paintings,Painting,Portrait of a Young Woman with a Fan,,,,,,Artist,,Rembrandt (Rembrandt van Rijn),"Dutch, Leiden 1606–1669 Amsterdam",,Rembrandt (Rembrandt van Rijn),Dutch,1606,1669,1633,1633,1633,Oil on canvas,49 1/2 x 39 3/4 in. (125.7 x 101 cm),"Gift of Helen Swift Neilson, 1943",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437391,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -61.198,true,true,437394,European Paintings,Painting,Aristotle with a Bust of Homer,,,,,,Artist,,Rembrandt (Rembrandt van Rijn),"Dutch, Leiden 1606–1669 Amsterdam",,Rembrandt (Rembrandt van Rijn),Dutch,1606,1669,1653,1653,1653,Oil on canvas,56 1/2 x 53 3/4 in. (143.5 x 136.5 cm),"Purchase, special contributions and funds given or bequeathed by friends of the Museum, 1961",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437394,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -64.126,false,true,437387,European Paintings,Painting,Portrait of a Man,,,,,,Artist,,Rembrandt (Rembrandt van Rijn),"Dutch, Leiden 1606–1669 Amsterdam",,Rembrandt (Rembrandt van Rijn),Dutch,1606,1669,1632,1632,1632,Oil on wood,"Oval, 29 3/4 x 20 1/2 in. (75.6 x 52.1 cm)","Gift of Mrs. Lincoln Ellsworth, in memory of Lincoln Ellsworth, 1964",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437387,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -49.7.35,false,true,437395,European Paintings,Painting,"The Standard Bearer (Floris Soop, 1604–1657)",,,,,,Artist,,Rembrandt (Rembrandt van Rijn),"Dutch, Leiden 1606–1669 Amsterdam",,Rembrandt (Rembrandt van Rijn),Dutch,1606,1669,1654,1654,1654,Oil on canvas,55 1/4 x 45 1/4in. (140.3 x 114.9cm),"The Jules Bache Collection, 1949",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437395,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -91.26.7,false,true,437400,European Paintings,Painting,Portrait of a Man,,,,,,Artist,,Rembrandt (Rembrandt van Rijn),"Dutch, Leiden 1606–1669 Amsterdam",,Rembrandt (Rembrandt van Rijn),Dutch,1606,1669,ca. 1655–60,1655,1660,Oil on canvas,32 7/8 x 25 3/8 in. (83.5 x 64.5 cm),"Marquand Collection, Gift of Henry G. Marquand, 1890",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437400,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -20.155.2,false,true,437385,European Paintings,Painting,"Man in Oriental Costume (""The Noble Slav"")",,,,,,Artist,,Rembrandt (Rembrandt van Rijn),"Dutch, Leiden 1606–1669 Amsterdam",,Rembrandt (Rembrandt van Rijn),Dutch,1606,1669,1632,1632,1632,Oil on canvas,60 1/8 x 43 3/4in. (152.7 x 111.1cm),"Bequest of William K. Vanderbilt, 1920",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437385,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -26.101.9,false,true,437396,European Paintings,Painting,Hendrickje Stoffels (1626–1663),,,,,,Artist,,Rembrandt (Rembrandt van Rijn),"Dutch, Leiden 1606–1669 Amsterdam",,Rembrandt (Rembrandt van Rijn),Dutch,1606,1669,mid-1650s,1654,1656,Oil on canvas,30 7/8 x 27 1/8 in. (78.4 x 68.9 cm),"Gift of Archer M. Huntington, in memory of his father, Collis Potter Huntington, 1926",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437396,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.1,false,true,437392,European Paintings,Painting,"Herman Doomer (born about 1595, died 1650)",,,,,,Artist,,Rembrandt (Rembrandt van Rijn),"Dutch, Leiden 1606–1669 Amsterdam",,Rembrandt (Rembrandt van Rijn),Dutch,1606,1669,1640,1640,1640,Oil on wood,29 5/8 x 21 3/4 in. (75.2 x 55.2 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437392,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.3,false,true,437386,European Paintings,Painting,"Portrait of a Man, probably a Member of the Van Beresteyn Family",,,,,,Artist,,Rembrandt (Rembrandt van Rijn),"Dutch, Leiden 1606–1669 Amsterdam",,Rembrandt (Rembrandt van Rijn),Dutch,1606,1669,1632,1632,1632,Oil on canvas,44 x 35 in. (111.8 x 88.9 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437386,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.4,false,true,437388,European Paintings,Painting,"Portrait of a Woman, probably a Member of the Van Beresteyn Family",,,,,,Artist,,Rembrandt (Rembrandt van Rijn),"Dutch, Leiden 1606–1669 Amsterdam",,Rembrandt (Rembrandt van Rijn),Dutch,1606,1669,1632,1632,1632,Oil on canvas,44 x 35 in. (111.8 x 88.9 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437388,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.40.618,true,true,437397,European Paintings,Painting,Self-Portrait,,,,,,Artist,,Rembrandt (Rembrandt van Rijn),"Dutch, Leiden 1606–1669 Amsterdam",,Rembrandt (Rembrandt van Rijn),Dutch,1606,1669,1660,1660,1660,Oil on canvas,31 5/8 x 26 1/2 in. (80.3 x 67.3 cm),"Bequest of Benjamin Altman, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437397,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.40.620,false,true,437401,European Paintings,Painting,Portrait of a Man Holding Gloves,,,,,,Artist,,Rembrandt (Rembrandt van Rijn),"Dutch, Leiden 1606–1669 Amsterdam",,Rembrandt (Rembrandt van Rijn),Dutch,1606,1669,1648,1648,1648,Oil on wood,31 3/4 x 26 1/2 in. (80.6 x 67.3 cm),"Bequest of Benjamin Altman, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437401,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.40.621,false,true,437399,European Paintings,Painting,Man with a Magnifying Glass,,,,,,Artist,,Rembrandt (Rembrandt van Rijn),"Dutch, Leiden 1606–1669 Amsterdam",,Rembrandt (Rembrandt van Rijn),Dutch,1606,1669,early 1660s,1660,1663,Oil on canvas,36 x 29 1/4 in. (91.4 x 74.3 cm),"Bequest of Benjamin Altman, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437399,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.40.622,false,true,437402,European Paintings,Painting,Woman with a Pink,,,,,,Artist,,Rembrandt (Rembrandt van Rijn),"Dutch, Leiden 1606–1669 Amsterdam",,Rembrandt (Rembrandt van Rijn),Dutch,1606,1669,early 1660s,1660,1663,Oil on canvas,36 1/4 x 29 3/8 in. (92.1 x 74.6 cm),"Bequest of Benjamin Altman, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437402,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.40.625,false,true,437390,European Paintings,Painting,Portrait of a Woman,,,,,,Artist,,Rembrandt (Rembrandt van Rijn),"Dutch, Leiden 1606–1669 Amsterdam",,Rembrandt (Rembrandt van Rijn),Dutch,1606,1669,1633,1633,1633,Oil on wood,"Oval, 26 3/4 x 19 3/4 in. (67.9 x 50.2 cm)","Bequest of Benjamin Altman, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437390,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.40.651,false,true,437393,European Paintings,Painting,The Toilet of Bathsheba,,,,,,Artist,,Rembrandt (Rembrandt van Rijn),"Dutch, Leiden 1606–1669 Amsterdam",,Rembrandt (Rembrandt van Rijn),Dutch,1606,1669,1643,1643,1643,Oil on wood,22 1/2 x 30 in. (57.2 x 76.2 cm),"Bequest of Benjamin Altman, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437393,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -26.101.10,false,true,437398,European Paintings,Painting,Flora,,,,,,Artist,,Rembrandt (Rembrandt van Rijn),"Dutch, Leiden 1606–1669 Amsterdam",,Rembrandt (Rembrandt van Rijn),Dutch,1606,1669,ca. 1654,1649,1659,Oil on canvas,39 3/8 x 36 1/8 in. (100 x 91.8 cm),"Gift of Archer M. Huntington, in memory of his father, Collis Potter Huntington, 1926",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437398,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -32.100.23,false,true,437389,European Paintings,Painting,Bellona,,,,,,Artist,,Rembrandt (Rembrandt van Rijn),"Dutch, Leiden 1606–1669 Amsterdam",,Rembrandt (Rembrandt van Rijn),Dutch,1606,1669,1633,1633,1633,Oil on canvas,50 x 38 3/8 in. (127 x 97.5 cm),"The Friedsam Collection, Bequest of Michael Friedsam, 1931",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437389,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -91.26.11,false,true,437070,European Paintings,Painting,A Musical Party,,,,,,Artist,,Gabriël Metsu,"Dutch, Leiden 1629–1667 Amsterdam",,"Metsu, Gabriël",Dutch,1629,1667,1659,1659,1659,Oil on canvas,24 1/2 x 21 3/8 in. (62.2 x 54.3 cm),"Marquand Collection, Gift of Henry G. Marquand, 1890",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437070,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -17.190.20,false,true,437071,European Paintings,Painting,The Visit to the Nursery,,,,,,Artist,,Gabriël Metsu,"Dutch, Leiden 1629–1667 Amsterdam",,"Metsu, Gabriël",Dutch,1629,1667,1661,1661,1661,Oil on canvas,30 1/2 x 32 in. (77.5 x 81.3 cm),"Gift of J. Pierpont Morgan, 1917",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437071,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1982.60.32,false,true,437073,European Paintings,Painting,A Woman Seated at a Window,,,,,,Artist,,Gabriël Metsu,"Dutch, Leiden 1629–1667 Amsterdam",,"Metsu, Gabriël",Dutch,1629,1667,early 1660s,1660,1663,Oil on wood,10 7/8 x 8 7/8 in. (27.6 x 22.5 cm),"The Jack and Belle Linsky Collection, 1982",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437073,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -71.160,false,true,437178,European Paintings,Painting,The Newborn Baby,,,,,,Artist,,Matthijs Naiveu,"Dutch, Leiden 1647–1726 Amsterdam",,"Naiveu, Matthijs",Dutch,1647,1726,1675,1675,1675,Oil on canvas,25 1/4 x 31 1/2 in. (64.1 x 80 cm),"Purchase, 1871",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437178,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -43.118,false,true,436843,European Paintings,Painting,Apollo and Aurora,,,,,,Artist,,Gerard de Lairesse,"Dutch, Liège 1641–1711 Amsterdam",,"Lairesse, Gerard de",Dutch,1641,1711,1671,1671,1671,Oil on canvas,80 1/2 x 76 1/8 in. (204.5 x 193.4 cm),"Gift of Manuel E. and Ellen G. Rionda, 1943",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436843,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -87.15.30,false,true,436826,European Paintings,Painting,"Winter Landscape, Holland",,,,,,Artist,,Barend Cornelis Koekkoek,"Dutch, Middelburg 1803–1862 Cleve",,"Koekkoek, Barend Cornelis",Dutch,1803,1862,1833,1833,1833,Oil on wood,14 x 17 in. (35.6 x 43.2 cm),"Catharine Lorillard Wolfe Collection, Bequest of Catharine Lorillard Wolfe, 1887",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436826,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -87.15.45,false,true,436827,European Paintings,Painting,Sunset on the Rhine,,,,,,Artist,,Barend Cornelis Koekkoek,"Dutch, Middelburg 1803–1862 Cleve",,"Koekkoek, Barend Cornelis",Dutch,1803,1862,1853,1853,1853,Oil on canvas,32 1/4 x 42 3/8 in. (81.9 x 107.6 cm),"Catharine Lorillard Wolfe Collection, Bequest of Catharine Lorillard Wolfe, 1887",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436827,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -92.1.49,false,true,436975,European Paintings,Painting,Reverie,,,,,,Artist,,Matthys Maris,"Dutch, The Hague 1839–1917 London",,"Maris, Matthys",Dutch,1839,1917,1875,1875,1875,Oil on canvas,12 x 9 1/4 in. (30.5 x 23.5 cm),"Bequest of Elizabeth U. Coles, in memory of her son, William F. Coles, 1892",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436975,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -27.250.1,false,true,436671,European Paintings,Painting,Peacocks,,,,,,Artist,,Melchior d' Hondecoeter,"Dutch, Utrecht 1636–1695 Amsterdam",,"Hondecoeter, Melchior d'",Dutch,1636,1695,1683,1683,1683,Oil on canvas,74 7/8 x 53 in. (190.2 x 134.6 cm),"Gift of Samuel H. Kress, 1927",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436671,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -49.7.33,false,true,436621,European Paintings,Painting,"Portrait of a Man, Possibly Nicolaes Pietersz Duyst van Voorhout (born about 1600, died 1650)",,,,,,Artist,,Frans Hals,"Dutch, Antwerp 1582/83–1666 Haarlem",,"Hals, Frans",Dutch,1582,1666,ca. 1636–38,1636,1638,Oil on canvas,31 3/4 x 26 in. (80.6 x 66 cm),"The Jules Bache Collection, 1949",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436621,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -49.7.34,false,true,436617,European Paintings,Painting,Portrait of a Bearded Man with a Ruff,,,,,,Artist,,Frans Hals,"Dutch, Antwerp 1582/83–1666 Haarlem",,"Hals, Frans",Dutch,1582,1666,1625,1625,1625,Oil on canvas,30 x 25 in. (76.2 x 63.5 cm),"The Jules Bache Collection, 1949",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436617,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -91.26.9,false,true,436623,European Paintings,Painting,Portrait of a Man,,,,,,Artist,,Frans Hals,"Dutch, Antwerp 1582/83–1666 Haarlem",,"Hals, Frans",Dutch,1582,1666,early 1650s,1650,1653,Oil on canvas,43 1/2 x 34 in. (110.5 x 86.4 cm),"Marquand Collection, Gift of Henry G. Marquand, 1890",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436623,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.8,false,true,436619,European Paintings,Painting,Petrus Scriverius (1576–1660),,,,,,Artist,,Frans Hals,"Dutch, Antwerp 1582/83–1666 Haarlem",,"Hals, Frans",Dutch,1582,1666,1626,1626,1626,Oil on wood,8 3/4 x 6 1/2 in. (22.2 x 16.5 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436619,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.9,false,true,436618,European Paintings,Painting,"Anna van der Aar (born 1576/77, died after 1626)",,,,,,Artist,,Frans Hals,"Dutch, Antwerp 1582/83–1666 Haarlem",,"Hals, Frans",Dutch,1582,1666,1626,1626,1626,Oil on wood,8 3/4 x 6 1/2 in. (22.2 x 16.5 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436618,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -89.15.34,false,true,436625,European Paintings,Painting,The Smoker,,,,,,Artist,,Frans Hals,"Dutch, Antwerp 1582/83–1666 Haarlem",,"Hals, Frans",Dutch,1582,1666,ca. 1623–25,1623,1625,Oil on wood,"Octagonal, 18 3/8 x 19 1/2 in. (46.7 x 49.5 cm)","Marquand Collection, Gift of Henry G. Marquand, 1889",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436625,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -91.26.10,false,true,436624,European Paintings,Painting,Portrait of a Woman,,,,,,Artist,,Frans Hals,"Dutch, Antwerp 1582/83–1666 Haarlem",,"Hals, Frans",Dutch,1582,1666,"ca. 1650, reworked probably 18th century",1645,1655,Oil on canvas,39 3/8 x 32 1/4 in. (100 x 81.9 cm),"Marquand Collection, Gift of Henry G. Marquand, 1890",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436624,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.40.602,true,true,436616,European Paintings,Painting,"Young Man and Woman in an Inn (""Yonker Ramp and His Sweetheart"")",,,,,,Artist,,Frans Hals,"Dutch, Antwerp 1582/83–1666 Haarlem",,"Hals, Frans",Dutch,1582,1666,1623,1623,1623,Oil on canvas,41 1/2 x 31 1/4 in. (105.4 x 79.4 cm),"Bequest of Benjamin Altman, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436616,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.40.604,false,true,436626,European Paintings,Painting,Boy with a Lute,,,,,,Artist,,Frans Hals,"Dutch, Antwerp 1582/83–1666 Haarlem",,"Hals, Frans",Dutch,1582,1666,ca. 1625,1620,1630,Oil on canvas,28 3/8 x 23 1/4 in. (72.1 x 59.1 cm),"Bequest of Benjamin Altman, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436626,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.40.605,true,true,436622,European Paintings,Painting,Merrymakers at Shrovetide,,,,,,Artist,,Frans Hals,"Dutch, Antwerp 1582/83–1666 Haarlem",,"Hals, Frans",Dutch,1582,1666,ca. 1616–17,1616,1617,Oil on canvas,51 3/4 x 39 1/4 in. (131.4 x 99.7 cm),"Bequest of Benjamin Altman, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436622,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -26.101.11,false,true,436620,European Paintings,Painting,Paulus Verschuur (1606–1667),,,,,,Artist,,Frans Hals,"Dutch, Antwerp 1582/83–1666 Haarlem",,"Hals, Frans",Dutch,1582,1666,1643,1643,1643,Oil on canvas,46 3/4 x 37 in. (118.7 x 94 cm),"Gift of Archer M. Huntington, in memory of his father, Collis Potter Huntington, 1926",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436620,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1991.305,false,true,435770,European Paintings,Painting,The Preaching of John the Baptist,,,,,,Artist,,Bartholomeus Breenbergh,"Dutch, Deventer 1598–1657 Amsterdam",,"Breenbergh, Bartholomeus",Dutch,1598,1657,1634,1634,1634,Oil on wood,21 1/2 x 29 5/8 in. (54.6 x 75.2 cm),"Purchase, The Annenberg Foundation Gift, 1991",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435770,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -56.228,true,true,435817,European Paintings,Painting,The Crucifixion with the Virgin and Saint John,,,,,,Artist,,Hendrick ter Brugghen,"Dutch, The Hague? 1588–1629 Utrecht",,"Brugghen, Hendrick ter",Dutch,1588,1629,ca. 1624–25,1624,1625,Oil on canvas,61 x 40 1/4 in. (154.9 x 102.2 cm),"Funds from various donors, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435817,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -71.78,false,true,436636,European Paintings,Painting,Still Life with a Glass and Oysters,,,,,,Artist,,Jan Davidsz de Heem,"Dutch, Utrecht 1606–1683/84 Antwerp",,"Heem, Jan Davidsz de",Dutch,1606,1684,ca. 1640,1635,1645,Oil on wood,9 7/8 x 7 1/2 in. (25.1 x 19.1 cm),"Purchase, 1871",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436636,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -12.195,false,true,436637,European Paintings,Painting,Still Life: A Banqueting Scene,,,,,,Artist,,Jan Davidsz de Heem,"Dutch, Utrecht 1606–1683/84 Antwerp",,"Heem, Jan Davidsz de",Dutch,1606,1684,probably ca. 1640–41,1640,1641,Oil on canvas,53 1/4 x 73 in. (135.3 x 185.4 cm),"Charles B. Curtis Fund, 1912",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436637,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -11.144,false,true,436830,European Paintings,Painting,A Panoramic Landscape with a Country Estate,,,,,,Artist,,Philips Koninck,"Dutch, Amsterdam 1619–1688 Amsterdam",,"Koninck, Philips",Dutch,1619,1688,ca. 1649,1644,1654,Oil on canvas,56 3/8 x 68 1/4 in. (143.2 x 173.4 cm),"John Stewart Kennedy Fund, 1911",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436830,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1980.4,false,true,436831,European Paintings,Painting,An Extensive Wooded Landscape,,,,,,Artist,,Philips Koninck,"Dutch, Amsterdam 1619–1688 Amsterdam",,"Koninck, Philips",Dutch,1619,1688,1670s,1670,1679,Oil on canvas,32 3/4 x 44 5/8 in. (83.2 x 113.3 cm),"Purchase, Mr. and Mrs. David T. Schiff and George T. Delacorte Jr. Gifts, special funds, and Bequest of Mary Cushing Fosburgh and other gifts and bequests, by exchange, 1980",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436831,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -63.43.2,false,true,436832,European Paintings,Painting,Wide River Landscape,,,,,,Artist,,Philips Koninck,"Dutch, Amsterdam 1619–1688 Amsterdam",,"Koninck, Philips",Dutch,1619,1688,ca. 1648–49,1648,1649,Oil on canvas,16 1/4 x 22 7/8 in. (41.3 x 58.1 cm),"Gift of Edith Neuman de Végvár, in honor of her husband, Charles Neuman de Végvár, 1963",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436832,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -26.260.8,false,true,436267,European Paintings,Painting,A Musical Party,,,,,,Artist,,Gerbrand van den Eeckhout,"Dutch, Amsterdam 1621–1674 Amsterdam",,"Eeckhout, Gerbrand van den",Dutch,1621,1674,early 1650s,1650,1653,Oil on canvas,20 x 24 1/2 in. (50.8 x 62.2 cm),"Bequest of Annie C. Kane, 1926",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436267,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -25.110.16,false,true,436266,European Paintings,Painting,Isaac Blessing Jacob,,,,,,Artist,,Gerbrand van den Eeckhout,"Dutch, Amsterdam 1621–1674 Amsterdam",,"Eeckhout, Gerbrand van den",Dutch,1621,1674,1642,1642,1642,Oil on canvas,39 5/8 x 50 1/2 in. (100.6 x 128.3 cm),"Bequest of Collis P. Huntington, 1900",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436266,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -12.31,false,true,435842,European Paintings,Painting,A State Yacht and Other Craft in Calm Water,,,,,,Artist,,Jan van de Cappelle,"Dutch, Amsterdam 1626–1679 Amsterdam",,"Cappelle, Jan van de",Dutch,1626,1679,ca. 1660,1655,1665,Oil on wood,27 1/2 x 36 3/8 in. (69.9 x 92.4 cm),"Francis L. Leland Fund, 1912",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435842,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.40.614,false,true,436652,European Paintings,Painting,Entrance to a Village,,,,,,Artist,,Meyndert Hobbema,"Dutch, Amsterdam 1638–1709 Amsterdam",,"Hobbema, Meyndert",Dutch,1638,1709,ca. 1665,1660,1670,Oil on wood,29 1/2 x 43 3/8 in. (74.9 x 110.2 cm),"Bequest of Benjamin Altman, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436652,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -50.145.22,false,true,436653,European Paintings,Painting,Woodland Road,,,,,,Artist,,Meyndert Hobbema,"Dutch, Amsterdam 1638–1709 Amsterdam",,"Hobbema, Meyndert",Dutch,1638,1709,ca. 1670,1665,1675,Oil on canvas,37 1/4 x 51 in. (94.6 x 129.5 cm),"Bequest of Mary Stillman Harkness, 1950",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436653,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -07.225.298,false,true,437946,European Paintings,"Painting, sketch for a ceiling decoration",Allegory of the Arts,,,,,,Artist,,Jacob de Wit,"Dutch, Amsterdam 1695–1754 Amsterdam",,"Wit, Jacob de",Dutch,1695,1754,1742,1742,1742,Oil on canvas,18 7/8 x 23 1/4 in. (47.9 x 59.1 cm),"Gift of J. Pierpont Morgan, 1906",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437946,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -07.225.301,false,true,437947,European Paintings,"Painting, sketch for a ceiling decoration",Flora and Zephyr,,,,,,Artist,,Jacob de Wit,"Dutch, Amsterdam 1695–1754 Amsterdam",,"Wit, Jacob de",Dutch,1695,1754,1743,1743,1743,Oil on canvas,20 7/8 x 24 7/8 in. (53 x 63.2 cm),"Gift of J. Pierpont Morgan, 1906",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437947,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1981.239,false,true,436270,European Paintings,Painting,Conversation Piece (The Sense of Smell),,,,,,Artist,,Jan Ekels the Younger,"Dutch, Amsterdam 1759–1793 Amsterdam",,"Ekels, Jan, the Younger",Dutch,1759,1793,probably 1791,1791,1791,Oil on canvas,25 7/8 x 23 1/2 in. (65.7 x 59.7 cm),"Gift of Mr. and Mrs. Bertram L. Podell, 1981",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436270,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -49.107,false,true,435904,European Paintings,Painting,Still Life with a Skull and a Writing Quill,,,,,,Artist,,Pieter Claesz,"Dutch, Berchem? 1596/97–1660 Haarlem",,"Claesz, Pieter",Dutch,1596,1660,1628,1628,1628,Oil on wood,9 1/2 x 14 1/8 in. (24.1 x 35.9 cm),"Rogers Fund, 1949",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435904,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -57.68,false,true,435690,European Paintings,Painting,Petronella Elias (1648–1667) with a Basket of Fruit,,,,,,Artist,,Ferdinand Bol,"Dutch, Dordrecht 1616–1680 Amsterdam",,"Bol, Ferdinand",Dutch,1616,1680,1657,1657,1657,Oil on canvas,31 5/8 x 26 in. (80.3 x 66 cm),"Purchase, George T. Delacorte Jr. Gift, 1957",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435690,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -30.95.269,false,true,435689,European Paintings,Painting,Portrait of a Woman,,,,,,Artist,,Ferdinand Bol,"Dutch, Dordrecht 1616–1680 Amsterdam",,"Bol, Ferdinand",Dutch,1616,1680,1642,1642,1642,Oil on canvas,34 3/8 x 28 in. (87.3 x 71.1 cm),"Theodore M. Davis Collection, Bequest of Theodore M. Davis, 1915",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435689,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -34.83.1,false,true,436065,European Paintings,Painting,Children and a Cow,,,,,,Artist,Attributed to,Aelbert Cuyp,"Dutch, Dordrecht 1620–1691 Dordrecht",,"Cuyp, Aelbert",Dutch,1620,1691,1635–39,1635,1639,Oil on wood,17 1/4 x 21 1/2 in. (43.8 x 54.6 cm),"Bequest of Mariana Griswold Van Rensselaer, in memory of her father, George Griswold, 1934",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436065,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.40.616,false,true,436064,European Paintings,Painting,Young Herdsmen with Cows,,,,,,Artist,,Aelbert Cuyp,"Dutch, Dordrecht 1620–1691 Dordrecht",,"Cuyp, Aelbert",Dutch,1620,1691,ca. 1655–60,1655,1660,Oil on canvas,44 1/8 x 52 1/8 in. (112.1 x 132.4 cm),"Bequest of Benjamin Altman, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436064,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -25.110.15,false,true,436062,European Paintings,Painting,Piping Shepherds,,,,,,Artist,,Aelbert Cuyp,"Dutch, Dordrecht 1620–1691 Dordrecht",,"Cuyp, Aelbert",Dutch,1620,1691,ca. 1643–44,1643,1644,Oil on canvas,35 3/4 x 47 in. (90.8 x 119.4 cm),"Bequest of Collis P. Huntington, 1900",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436062,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -32.100.20,false,true,436063,European Paintings,Painting,"Equestrian Portrait of Cornelis (1639–1680) and Michiel Pompe van Meerdervoort (1638–1653) with Their Tutor and Coachman (""Starting for the Hunt"")",,,,,,Artist,,Aelbert Cuyp,"Dutch, Dordrecht 1620–1691 Dordrecht",,"Cuyp, Aelbert",Dutch,1620,1691,ca. 1652–53,1652,1653,Oil on canvas,43 1/4 x 61 1/2in. (109.9 x 156.2cm),"The Friedsam Collection, Bequest of Michael Friedsam, 1931",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436063,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1973.155.2,false,true,436061,European Paintings,Painting,Landscape with the Flight into Egypt,,,,,,Artist,,Aelbert Cuyp,"Dutch, Dordrecht 1620–1691 Dordrecht",,"Cuyp, Aelbert",Dutch,1620,1691,ca. 1650,1645,1655,Oil on wood,18 x 22 7/8 in. (45.7 x 58.1 cm),"Bequest of Josephine Bieber, in memory of her husband, Siegfried Bieber, 1970",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436061,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1992.133,false,true,436680,European Paintings,Painting,The Annunciation of the Death of the Virgin,,,,,,Artist,,Samuel van Hoogstraten,"Dutch, Dordrecht 1627–1678 Dordrecht",,"Hoogstraten, Samuel van",Dutch,1627,1678,ca. 1670,1665,1675,Oil on canvas,26 x 20 3/4 in. (66 x 52.7 cm),"Purchase, Rogers Fund and Joseph Pulitzer Bequest, 1992",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436680,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1982.60.33,false,true,435670,European Paintings,Painting,A Young Woman and a Cavalier,,,,,,Artist,,Cornelis Bisschop,"Dutch, Dordrecht 1630–1674 Dordrecht",,"Bisschop, Cornelis",Dutch,1630,1674,early 1660s,1660,1663,Oil on canvas,38 1/2 x 34 3/4 in. (97.8 x 88.3 cm),"The Jack and Belle Linsky Collection, 1982",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435670,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -06.1325,false,true,436933,European Paintings,Painting,Portrait of a Woman,,,,,,Artist,,Nicolaes Maes,"Dutch, Dordrecht 1634–1693 Amsterdam",,"Maes, Nicolaes",Dutch,1634,1693,ca. 1665–70,1665,1670,Oil on canvas,44 x 35 1/4 in. (111.8 x 89.5 cm),"Rogers Fund, 1906",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436933,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1971.73,false,true,436929,European Paintings,Painting,Abraham Dismissing Hagar and Ishmael,,,,,,Artist,,Nicolaes Maes,"Dutch, Dordrecht 1634–1693 Amsterdam",,"Maes, Nicolaes",Dutch,1634,1693,1653,1653,1653,Oil on canvas,34 1/2 x 27 1/2 in. (87.6 x 69.9 cm),"Gift of Mrs. Edward Brayton, 1971",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436929,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -11.149.3,false,true,436930,European Paintings,Painting,"Ingena Rotterdam (died 1704), Betrothed of Admiral Jacob Binkes",,,,,,Artist,,Nicolaes Maes,"Dutch, Dordrecht 1634–1693 Amsterdam",,"Maes, Nicolaes",Dutch,1634,1693,1676,1676,1676,Oil on canvas,17 1/4 x 13 in. (43.8 x 33 cm),"Gift of J. Pierpont Morgan, 1911",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436930,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -32.100.5,false,true,436932,European Paintings,Painting,The Lacemaker,,,,,,Artist,,Nicolaes Maes,"Dutch, Dordrecht 1634–1693 Amsterdam",,"Maes, Nicolaes",Dutch,1634,1693,ca. 1656,1651,1661,Oil on canvas,17 3/4 x 20 3/4 in. (45.1 x 52.7 cm),"The Friedsam Collection, Bequest of Michael Friedsam, 1931",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436932,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.40.612,false,true,436934,European Paintings,Painting,Young Woman Peeling Apples,,,,,,Artist,,Nicolaes Maes,"Dutch, Dordrecht 1634–1693 Amsterdam",,"Maes, Nicolaes",Dutch,1634,1693,ca. 1655,1650,1660,Oil on wood,21 1/2 x 18 in. (54.6 x 45.7 cm),"Bequest of Benjamin Altman, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436934,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -68.190,false,true,437089,European Paintings,Painting,Portrait of a Man,,,,,,Artist,Circle of,Arnold Boonen,"Dutch, Dordrecht 1669–1729 Amsterdam",,"Boonen, Arnold",Dutch,1669,1729,ca. 1720,1715,1725,Oil on canvas,22 1/4 x 18 3/4 in. (56.5 x 47.6 cm),"Gift of Marcel Aubry, 1968",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437089,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -91.26.8,false,true,437760,European Paintings,Painting,Landscape with Cattle,,,,,,Artist,,Jacob van Strij,"Dutch, Dordrecht 1756–1815 Dordrecht",,"Strij, Jacob van",Dutch,1756,1815,probably ca. 1800,1795,1805,Oil on wood,31 1/2 x 42 1/4 in. (80 x 107.3 cm),"Marquand Collection, Gift of Henry G. Marquand, 1890",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437760,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -71.23,false,true,436890,European Paintings,Painting,Battle Scene,,,,,,Artist,,Johannes Lingelbach,"Dutch, Frankfurt 1622–1674 Amsterdam",,"Lingelbach, Johannes",Dutch,1622,1674,1671,1671,1671,Oil on canvas,44 3/8 x 63 1/4 in. (112.7 x 160.7 cm),"Purchase, 1871",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436890,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -71.123,false,true,436889,European Paintings,Painting,Peasants Dancing,,,,,,Artist,,Johannes Lingelbach,"Dutch, Frankfurt 1622–1674 Amsterdam",,"Lingelbach, Johannes",Dutch,1622,1674,1651,1651,1651,Oil on canvas,26 1/2 x 29 1/2 in. (67.3 x 74.9 cm),"Purchase, 1871",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436889,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -64.65.2,false,true,436647,European Paintings,Painting,The Huis ten Bosch at The Hague and Its Formal Garden (View from the South),,,,,,Artist,,Jan van der Heyden,"Dutch, Gorinchem 1637–1712 Amsterdam",,"Heyden, Jan van der",Dutch,1637,1712,ca. 1668–70,1668,1670,Oil on wood,15 3/8 x 21 3/4 in. (39.1 x 55.2 cm),"Gift of Edith Neuman de Végvár, in honor of her husband, Charles Neuman de Végvár, 1964",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436647,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -64.65.3,false,true,436648,European Paintings,Painting,The Huis ten Bosch at The Hague and Its Formal Garden (View from the East),,,,,,Artist,,Jan van der Heyden,"Dutch, Gorinchem 1637–1712 Amsterdam",,"Heyden, Jan van der",Dutch,1637,1712,ca. 1668–70,1668,1670,Oil on wood,15 3/8 x 21 5/8 in. (39.1 x 54.9 cm),"Gift of Edith Neuman de Végvár, in honor of her husband, Charles Neuman de Végvár, 1964",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436648,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -71.69,false,true,436806,European Paintings,Painting,Interior of a Kitchen,,,,,,Artist,,Willem Kalf,"Dutch, Rotterdam 1619–1693 Amsterdam",,"Kalf, Willem",Dutch,1619,1693,ca. 1642–44,1642,1644,Oil on wood,10 1/2 x 12 1/2 in. (26.7 x 31.8 cm),"Purchase, 1871",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436806,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -53.111,false,true,436805,European Paintings,Painting,"Still Life with Fruit, Glassware, and a Wanli Bowl",,,,,,Artist,,Willem Kalf,"Dutch, Rotterdam 1619–1693 Amsterdam",,"Kalf, Willem",Dutch,1619,1693,1659,1659,1659,Oil on canvas,23 x 20 in. (58.4 x 50.8 cm),"Maria DeWitt Jesup Fund, 1953",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436805,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -58.144,false,true,436677,European Paintings,Painting,Paying the Hostess,,,,,,Artist,,Pieter de Hooch,"Dutch, Rotterdam 1629–1684 Amsterdam",,"Hooch, Pieter de",Dutch,1629,1684,ca. 1670,1665,1675,Oil on canvas,37 1/4 x 43 3/4 in. (94.6 x 111.1 cm),"Gift of Stuart Borchard and Evelyn B. Metzger, 1958",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436677,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.7,false,true,436678,European Paintings,Painting,The Visit,,,,,,Artist,,Pieter de Hooch,"Dutch, Rotterdam 1629–1684 Amsterdam",,"Hooch, Pieter de",Dutch,1629,1684,ca. 1657,1652,1662,Oil on wood,26 3/4 x 23 in. (67.9 x 58.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436678,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.40.613,false,true,436675,European Paintings,Painting,Interior with a Young Couple,,,,,,Artist,,Pieter de Hooch,"Dutch, Rotterdam 1629–1684 Amsterdam",,"Hooch, Pieter de",Dutch,1629,1684,probably ca. 1662–65,1662,1665,Oil on canvas,21 5/8 x 24 3/4 in. (54.9 x 62.9 cm),"Bequest of Benjamin Altman, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436675,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -32.100.15,false,true,436676,European Paintings,Painting,"Woman with a Water Pitcher, and a Man by a Bed (""The Maidservant"")",,,,,,Artist,,Pieter de Hooch,"Dutch, Rotterdam 1629–1684 Amsterdam",,"Hooch, Pieter de",Dutch,1629,1684,ca. 1667–70,1667,1670,Oil on canvas,24 1/4 x 20 1/2 in. (61.5 x 52.1 cm),"The Friedsam Collection, Bequest of Michael Friedsam, 1931",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436676,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.100.25,false,true,436679,European Paintings,Painting,A Woman and Two Men in an Arbor,,,,,,Artist,,Pieter de Hooch,"Dutch, Rotterdam 1629–1684 Amsterdam",,"Hooch, Pieter de",Dutch,1629,1684,ca. 1657–58,1657,1658,Oil on wood,Overall 17 3/8 x 14 3/4 in. (44.1 x 37.5 cm); painted surface 17 x 14 3/8 in. (43.2 x 36.5 cm),"Bequest of Harry G. Sperling, 1971",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436679,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1980.203.5,false,true,437227,European Paintings,Painting,The Love Letter,,,,,,Artist,,Jacob Ochtervelt,"Dutch, Rotterdam 1634–1682 Amsterdam",,"Ochtervelt, Jacob",Dutch,1634,1682,early 1670s,1670,1673,Oil on canvas,36 x 25 in. (91.4 x 63.5 cm),"Gift of Mr. and Mrs. Walter Mendelsohn, 1980",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437227,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -25.110.37,false,true,436060,European Paintings,Painting,Bacchus and Nymphs in a Landscape,,,,,,Artist,,Abraham van Cuylenborch,"Dutch, Utrecht ca. 1620–1658 Utrecht",,"Cuylenborch, Abraham van",Dutch,1620,1658,probably 1640s,1640,1649,Oil on wood,22 7/8 x 28 3/8 in. (58.1 x 72.1 cm),"Bequest of Collis P. Huntington, 1900",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436060,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -71.125,false,true,435655,European Paintings,Painting,Rest,,,,,,Artist,,Nicolaes Berchem,"Dutch, Haarlem 1621/22–1683 Amsterdam",,"Berchem, Nicolaes",Dutch,1622,1683,1644,1644,1644,Oil on wood,17 x 13 1/2 in. (43.2 x 34.3 cm),"Purchase, 1871",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435655,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -89.15.4,false,true,437546,European Paintings,Painting,The Forest Stream,,,,,,Artist,,Jacob van Ruisdael,"Dutch, Haarlem 1628/29–1682 Amsterdam",,"Ruisdael, Jacob van",Dutch,1628,1682,ca. 1660,1655,1665,Oil on canvas,39 1/4 x 50 7/8 in. (99.7 x 129.2 cm),"Marquand Collection, Gift of Henry G. Marquand, 1889",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437546,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.40.623,true,true,437549,European Paintings,Painting,Wheat Fields,,,,,,Artist,,Jacob van Ruisdael,"Dutch, Haarlem 1628/29–1682 Amsterdam",,"Ruisdael, Jacob van",Dutch,1628,1682,ca. 1670,1665,1675,Oil on canvas,39 3/8 x 51 1/4 in. (100 x 130.2 cm),"Bequest of Benjamin Altman, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437549,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -25.110.18,false,true,437548,European Paintings,Painting,Mountain Torrent,,,,,,Artist,,Jacob van Ruisdael,"Dutch, Haarlem 1628/29–1682 Amsterdam",,"Ruisdael, Jacob van",Dutch,1628,1682,1670s,1670,1679,Oil on canvas,21 1/4 x 16 1/2 in. (54 x 41.9 cm),"Bequest of Collis P. Huntington, 1900",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437548,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -32.100.14,false,true,437547,European Paintings,Painting,Grainfields,,,,,,Artist,,Jacob van Ruisdael,"Dutch, Haarlem 1628/29–1682 Amsterdam",,"Ruisdael, Jacob van",Dutch,1628,1682,mid- or late 1660s,1664,1669,Oil on canvas,18 1/2 x 22 1/2 in. (47 x 57.2 cm),"The Friedsam Collection, Bequest of Michael Friedsam, 1931",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437547,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -65.181.10,false,true,437545,European Paintings,Painting,Landscape with a Village in the Distance,,,,,,Artist,,Jacob van Ruisdael,"Dutch, Haarlem 1628/29–1682 Amsterdam",,"Ruisdael, Jacob van",Dutch,1628,1682,1646,1646,1646,Oil on wood,30 x 43 in. (76.2 x 109.2 cm),"Bequest of Adele L. Lehman, in memory of Arthur Lehman, 1965",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437545,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1974.368,false,true,436672,European Paintings,Painting,Christ among the Doctors,,,,,,Artist,,Abraham Hondius,"Dutch, Rotterdam ca. 1631–1691 London",,"Hondius, Abraham",Dutch,1631,1691,1668,1668,1668,Oil on wood,15 x 19 1/2 in. (38.1 x 49.5 cm),"Gift of Dr. and Mrs. Carl F. Culicchia, 1974",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436672,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2001.403,false,true,438490,European Paintings,Painting,"Interior of the Oude Kerk, Delft",,,,,,Artist,,Emanuel de Witte,"Dutch, Alkmaar ca. 1616–1692 Amsterdam",,"Witte, Emanuel de",Dutch,1616,1692,probably 1650,1650,1650,Oil on wood,19 x 13 5/8 in. (48.3 x 34.6 cm),"Purchase, Lila Acheson Wallace, Virgilia and Walter C. Klein, The Walter C. Klein Foundation, Edwin Weisl Jr., and Frank E. Richardson Gifts, and Bequest of Theodore Rousseau and Gift of Lincoln Kirstein, by exchange, 2001",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/438490,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -71.60,false,true,437190,European Paintings,Painting,The Farrier,,,,,,Artist,,Aert van der Neer,"Dutch, Gorinchem 1603/4–1677 Amsterdam",,"Neer, Aert van der",Dutch,1603,1677,early or mid-1650s,1650,1656,Oil on wood,19 x 24 1/8 in. (48.3 x 61.3 cm),"Purchase, 1871",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437190,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -17.190.11,false,true,437191,European Paintings,Painting,Landscape at Sunset,,,,,,Artist,,Aert van der Neer,"Dutch, Gorinchem 1603/4–1677 Amsterdam",,"Neer, Aert van der",Dutch,1603,1677,1650s,1650,1659,Oil on canvas,20 x 28 1/8 in. (50.8 x 71.4 cm),"Gift of J. Pierpont Morgan, 1917",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437191,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -32.100.11,false,true,437192,European Paintings,Painting,Sports on a Frozen River,,,,,,Artist,,Aert van der Neer,"Dutch, Gorinchem 1603/4–1677 Amsterdam",,"Neer, Aert van der",Dutch,1603,1677,probably ca. 1660,1655,1665,Oil on wood,9 1/8 x 13 3/4 in. (23.2 x 34.9 cm),"The Friedsam Collection, Bequest of Michael Friedsam, 1931",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437192,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -89.15.6,false,true,437218,European Paintings,Painting,The Card Party,,,,,,Artist,,Caspar Netscher,"Dutch, Heidelberg 1639?–1684 The Hague",,"Netscher, Caspar",Dutch,1639,1684,ca. 1665,1660,1670,Oil on canvas,19 3/4 x 17 3/4 in. (50.2 x 45.1 cm),"Marquand Collection, Gift of Henry G. Marquand, 1889",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437218,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -53.155,false,true,436976,European Paintings,Painting,"Still Life with Poppy, Insects, and Reptiles",,,,,,Artist,,Otto Marseus van Schrieck,"Dutch, Nijmegen 1619/20–1678 Amsterdam",,"Marseus van Schrieck, Otto",Dutch,1619,1678,ca. 1670,1665,1675,Oil on canvas,26 7/8 x 20 3/4 in. (68.3 x 52.7 cm),"Rogers Fund, 1953",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436976,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -89.15.27,false,true,436630,European Paintings,Painting,Portrait of a Woman,,,,,,Artist,,Adriaen Hanneman,"Dutch, The Hague 1603/4–1671 The Hague",,"Hanneman, Adriaen",Dutch,1603,1671,ca. 1653,1648,1658,Oil on canvas,31 1/2 x 25 in. (80 x 63.5 cm),"Marquand Collection, Gift of Henry G. Marquand, 1889",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436630,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.100.22,false,true,437282,European Paintings,Painting,Man with a Celestial Globe,,,,,,Artist,,Nicolaes Eliasz Pickenoy,"Dutch, Amsterdam 1588–1650/56 Amsterdam",,"Pickenoy, Nicolaes Eliasz",Dutch,1588,1656,1624,1624,1624,Oil on wood,41 1/4 x 30 in. (104.8 x 76.2 cm),"Bequest of Harry G. Sperling, 1971",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437282,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -32.100.12,false,true,435723,European Paintings,Painting,Barnyard Scene,,,,,,Artist,,Anthonie van Borssom,"Dutch, Amsterdam 1630/31–1677 Amsterdam",,"Borssom, Anthonie van",Dutch,1631,1677,ca. 1650–55,1650,1655,Oil on canvas,20 x 27 in. (50.8 x 68.6 cm),"The Friedsam Collection, Bequest of Michael Friedsam, 1931",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435723,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -06.1289,false,true,437090,European Paintings,Painting,"Charles I (1600–1649), King of England",,,,,,Artist,,Daniël Mijtens,"Dutch, Delft ca. 1590–1647/48 The Hague",,"Mijtens, Daniël",Dutch,1585,1648,1629,1629,1629,Oil on canvas,78 7/8 x 55 3/8 in. (200.3 x 140.7 cm),"Gift of George A. Hearn, 1906",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437090,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -89.15.7,false,true,437703,European Paintings,Painting,A Kitchen,,,,,,Artist,,Hendrick Sorgh,"Dutch, Rotterdam 1609/11–1670 Rotterdam",,"Sorgh, Hendrick",Dutch,1609,1670,ca. 1643,1638,1648,Oil on wood,20 1/2 x 17 3/8 in. (52.1 x 44.1 cm),"Marquand Collection, Gift of Henry G. Marquand, 1889",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437703,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1971.254,false,true,435663,European Paintings,Painting,Still Life with Lobster and Fruit,,,,,,Artist,,Abraham van Beyeren,"Dutch, The Hague 1620/21–1690 Overschie",,"Beyeren, Abraham van",Dutch,1620,1690,probably early 1650s,1650,1653,Oil on wood,38 x 31 in. (96.5 x 78.7 cm),"Gift of Edith Neuman de Végvár, in honor of her husband, Charles Neuman de Végvár, 1971",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435663,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -20.155.5,false,true,436784,European Paintings,Painting,Scene in a Courtyard,,,,,,Artist,,Ludolf de Jongh,"Dutch, Overschie 1616–1679 Hillegersberg",,"Jongh, Ludolf de",Dutch,1616,1679,early 1660s,1660,1663,Oil on canvas,26 1/2 x 32 3/8 in. (67.3 x 82.2 cm),"Bequest of William K. Vanderbilt, 1920",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436784,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -49.30,true,true,437980,European Paintings,Painting,Cypresses,,,,,,Artist,,Vincent van Gogh,"Dutch, Zundert 1853–1890 Auvers-sur-Oise",,"Gogh, Vincent van",Dutch,1853,1890,1889,1889,1889,Oil on canvas,36 3/4 x 29 1/8 in. (93.4 x 74 cm),"Rogers Fund, 1949",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437980,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -49.41,false,true,436524,European Paintings,Painting,Sunflowers,,,,,,Artist,,Vincent van Gogh,"Dutch, Zundert 1853–1890 Auvers-sur-Oise",,"Gogh, Vincent van",Dutch,1853,1890,1887,1887,1887,Oil on canvas,17 x 24 in. (43.2 x 61 cm),"Rogers Fund, 1949",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436524,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -58.187,false,true,436528,European Paintings,Painting,Irises,,,,,,Artist,,Vincent van Gogh,"Dutch, Zundert 1853–1890 Auvers-sur-Oise",,"Gogh, Vincent van",Dutch,1853,1890,1890,1890,1890,Oil on canvas,29 x 36 1/4 in. (73.7 x 92.1 cm),"Gift of Adele R. Levy, 1958",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436528,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1984.393,false,true,436531,European Paintings,Painting,Peasant Woman Cooking by a Fireplace,,,,,,Artist,,Vincent van Gogh,"Dutch, Zundert 1853–1890 Auvers-sur-Oise",,"Gogh, Vincent van",Dutch,1853,1890,1885,1885,1885,Oil on canvas,17 3/8 x 15 in. (44.1 x 38.1 cm),"Gift of Mr. and Mrs. Mortimer Hays, 1984",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436531,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1993.132,true,true,436535,European Paintings,Painting,Wheat Field with Cypresses,,,,,,Artist,,Vincent van Gogh,"Dutch, Zundert 1853–1890 Auvers-sur-Oise",,"Gogh, Vincent van",Dutch,1853,1890,1889,1889,1889,Oil on canvas,28 7/8 × 36 3/4 in. (73.2 × 93.4 cm),"Purchase, The Annenberg Foundation Gift, 1993",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436535,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1995.535,false,true,436536,European Paintings,Painting,Women Picking Olives,,,,,,Artist,,Vincent van Gogh,"Dutch, Zundert 1853–1890 Auvers-sur-Oise",,"Gogh, Vincent van",Dutch,1853,1890,1889,1889,1889,Oil on canvas,28 5/8 x 36 in. (72.7 x 91.4 cm),"The Walter H. and Leonore Annenberg Collection, Gift of Walter H. and Leonore Annenberg, 1995, Bequest of Walter H. Annenberg, 2002",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436536,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1996.435,false,true,437984,European Paintings,Painting,"La Berceuse (Woman Rocking a Cradle; Augustine-Alix Pellicot Roulin, 1851–1930)",,,,,,Artist,,Vincent van Gogh,"Dutch, Zundert 1853–1890 Auvers-sur-Oise",,"Gogh, Vincent van",Dutch,1853,1890,1889,1889,1889,Oil on canvas,36 1/2 x 29 in. (92.7 x 73.7 cm),"The Walter H. and Leonore Annenberg Collection, Gift of Walter H. and Leonore Annenberg, 1996, Bequest of Walter H. Annenberg, 2002",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437984,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -51.112.3,false,true,436529,European Paintings,Painting,"L'Arlésienne: Madame Joseph-Michel Ginoux (Marie Julien, 1848–1911)",,,,,,Artist,,Vincent van Gogh,"Dutch, Zundert 1853–1890 Auvers-sur-Oise",,"Gogh, Vincent van",Dutch,1853,1890,1888–89,1888,1889,Oil on canvas,36 x 29 in. (91.4 x 73.7 cm),"Bequest of Sam A. Lewisohn, 1951",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436529,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -64.165.2,false,true,436526,European Paintings,Painting,"First Steps, after Millet",,,,,,Artist,,Vincent van Gogh,"Dutch, Zundert 1853–1890 Auvers-sur-Oise",,"Gogh, Vincent van",Dutch,1853,1890,1890,1890,1890,Oil on canvas,28 1/2 x 35 7/8 in. (72.4 x 91.1 cm),"Gift of George N. and Helen M. Richard, 1964",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436526,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1993.400.4,false,true,436525,European Paintings,Painting,Bouquet of Flowers in a Vase,,,,,,Artist,,Vincent van Gogh,"Dutch, Zundert 1853–1890 Auvers-sur-Oise",,"Gogh, Vincent van",Dutch,1853,1890,1890,1890,1890,Oil on canvas,25 5/8 x 21 1/4 in. (65.1 x 54 cm),"The Walter H. and Leonore Annenberg Collection, Gift of Walter H. and Leonore Annenberg, 1993, Bequest of Walter H. Annenberg, 2002",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436525,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1993.400.5,false,true,436534,European Paintings,Painting,Roses,,,,,,Artist,,Vincent van Gogh,"Dutch, Zundert 1853–1890 Auvers-sur-Oise",,"Gogh, Vincent van",Dutch,1853,1890,1890,1890,1890,Oil on canvas,36 5/8 x 29 1/8 in. (93 x 74 cm),"The Walter H. and Leonore Annenberg Collection, Gift of Walter H. and Leonore Annenberg, 1993, Bequest of Walter H. Annenberg, 2002",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436534,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1998.325.1,false,true,437998,European Paintings,Painting,Olive Trees,,,,,,Artist,,Vincent van Gogh,"Dutch, Zundert 1853–1890 Auvers-sur-Oise",,"Gogh, Vincent van",Dutch,1853,1890,1889,1853,1890,Oil on canvas,28 5/8 x 36 1/4 in. (72.7 x 92.1 cm),"The Walter H. and Leonore Annenberg Collection, Gift of Walter H. and Leonore Annenberg, 1998, Bequest of Walter H. Annenberg, 2002",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437998,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -67.187.70a,false,true,436532,European Paintings,Painting,Self-Portrait with a Straw Hat (obverse: The Potato Peeler),,,,,,Artist,,Vincent van Gogh,"Dutch, Zundert 1853–1890 Auvers-sur-Oise",,"Gogh, Vincent van",Dutch,1853,1890,1887,1887,1887,Oil on canvas,16 x 12 1/2 in. (40.6 x 31.8 cm),"Bequest of Miss Adelaide Milton de Groot (1876–1967), 1967",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436532,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -67.187.70b,false,true,438722,European Paintings,Painting,The Potato Peeler (reverse: Self-Portrait with a Straw Hat),,,,,,Artist,,Vincent van Gogh,"Dutch, Zundert 1853–1890 Auvers-sur-Oise",,"Gogh, Vincent van",Dutch,1853,1890,1885,1885,1885,Oil on canvas,16 x 12 1/2 in. (40.6 x 31.8 cm),"Bequest of Miss Adelaide Milton de Groot (1876–1967), 1967",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/438722,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -71.6,false,true,436634,European Paintings,Painting,A Vase of Flowers,,,,,,Artist,,Margareta Haverman,"Dutch, active by 1716–died 1722 or later",,"Haverman, Margareta",Dutch,1716,1722,1716,1716,1716,Oil on wood,31 1/4 x 23 3/4 in. (79.4 x 60.3 cm),"Purchase, 1871",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436634,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -71.170,false,true,437897,European Paintings,Painting,Abraham's Parting from the Family of Lot,,,,,,Artist,,Jan Victors,"Dutch, Amsterdam 1619–1676/77 East Indies",,"Victors, Jan",Dutch,1619,1677,ca. 1655–65,1655,1665,Oil on canvas,58 x 65 1/8 in. (147.3 x 165.4 cm),"Purchase, 1871",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437897,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -50.55,false,true,437937,European Paintings,Painting,"Gamepiece with a Dead Heron (""Falconer's Bag"")",,,,,,Artist,,Jan Weenix,"Dutch, Amsterdam ca. 1641?–1719 Amsterdam",,"Weenix, Jan",Dutch,1641,1719,1695,1695,1695,Oil on canvas,52 3/4 x 43 3/4 in. (134 x 111.1 cm),"Rogers Fund, 1950",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437937,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.100.23,false,true,436288,European Paintings,Painting,Abraham Dismissing Hagar and Ishmael,,,,,,Artist,,Barent Fabritius,"Dutch, Middenbeemster 1624–1673 Amsterdam",,"Fabritius, Barent",Dutch,1624,1673,1658,1658,1658,Oil on wood,19 1/2 x 14 in. (49.5 x 35.6 cm),"Bequest of Harry G. Sperling, 1971",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436288,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.40.609,false,true,437412,European Paintings,Painting,Old Woman Cutting Her Nails,,,,,,Artist,Style of,Rembrandt,"Dutch, second or third quarter 17th century",,Rembrandt,Dutch,1606,1669,ca. 1655–60,1655,1660,Oil on canvas,49 5/8 x 40 1/8 in. (126.1 x 101.9 cm),"Bequest of Benjamin Altman, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437412,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.261,false,true,435713,European Paintings,Painting,"The Disillusioned Medea (""The Enchantress"")",,,,,,Artist,,Paulus Bor,"Dutch, Amersfoort ca. 1601–1669 Amersfoort",,"Bor, Paulus",Dutch,1601,1669,ca. 1640,1635,1645,Oil on canvas,61 1/4 x 44 1/4 in. (155.6 x 112.4 cm),"Gift of Ben Heller, 1972",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435713,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1973.155.1,false,true,437352,European Paintings,Painting,A Party of Merrymakers,,,,,,Artist,,Pieter Jansz. Quast,"Dutch, Amsterdam (?) 1605/6–1647 Amsterdam",,"Quast, Pieter Jansz.",Dutch,1605,1647,ca. 1635–38,1635,1638,Oil on wood,14 3/4 x 19 1/2 in. (37.5 x 49.5 cm),"Bequest of Josephine Bieber, in memory of her husband, Siegfried Bieber, 1970",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437352,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -64.65.4,false,true,436818,European Paintings,Painting,A Musician and His Daughter,,,,,,Artist,,Thomas de Keyser,"Dutch, Amsterdam (?) 1596/97–1667 Amsterdam",,"Keyser, Thomas de",Dutch,1596,1667,1629,1629,1629,Oil on wood,29 1/2 x 20 3/4 in. (74.9 x 52.7 cm),"Gift of Edith Neuman de Végvár, in honor of her husband, Charles Neuman de Végvár, 1964",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436818,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.331.5,false,true,438377,European Paintings,Painting,Portrait of a Man with a Shell,,,,,,Artist,,Thomas de Keyser,"Dutch, Amsterdam (?) 1596/97–1667 Amsterdam",,"Keyser, Thomas de",Dutch,1596,1667,ca. 1625–26,1625,1626,Oil on wood,9 3/8 x 6 3/4 in. (23.8 x 17.1 cm),"From the Collection of Rita and Frits Markus, Bequest of Rita Markus, 2005",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/438377,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.331.6,false,true,438378,European Paintings,Painting,Portrait of a Woman with a Balance,,,,,,Artist,,Thomas de Keyser,"Dutch, Amsterdam (?) 1596/97–1667 Amsterdam",,"Keyser, Thomas de",Dutch,1596,1667,ca. 1625–26,1625,1626,Oil on wood,9 1/8 x 6 7/8 in. (23.2 x 17.5 cm),"From the Collection of Rita and Frits Markus, Bequest of Rita Markus, 2005",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/438378,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -12.202,false,true,437374,European Paintings,Painting,Portrait of a Woman,,,,,,Artist,,Jan van Ravesteyn,"Dutch, Culemborg (?) ca. 1572–1657 The Hague",,"Ravesteyn, Jan van",Dutch,1572,1657,1635,1635,1635,Oil on wood,26 7/8 x 22 7/8 in. (68.3 x 58.1 cm),"Gift of Henry Goldman, 1912",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437374,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -16.39,false,true,436785,European Paintings,Painting,Honfleur,,,,,,Artist,,Johan Barthold Jongkind,"Dutch, Latrop 1819–1891 La-Côte-Saint-André",,"Jongkind, Johan Barthold",Dutch,1819,1891,1865,1865,1865,Oil on canvas,20 1/2 x 32 1/8 in. (52.1 x 81.6 cm),"Catharine Lorillard Wolfe Collection, Wolfe Fund, 1916",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436785,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2001.652,false,true,438381,European Paintings,Painting,View from the Quai d'Orsay,,,,,,Artist,,Johan Barthold Jongkind,"Dutch, Latrop 1819–1891 La-Côte-Saint-André",,"Jongkind, Johan Barthold",Dutch,1819,1891,1854,1854,1854,"Oil on canvas, mounted on wood",17 1/4 x 26 in. (43.8 x 66 cm),"Bequest of Meta Cecile Schwarz, 2001",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/438381,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1980.203.3,false,true,436787,European Paintings,Painting,The Pont Neuf,,,,,,Artist,,Johan Barthold Jongkind,"Dutch, Latrop 1819–1891 La-Côte-Saint-André",,"Jongkind, Johan Barthold",Dutch,1819,1891,1849–50,1849,1850,Oil on canvas,21 1/2 x 32 1/8 in. (54.6 x 81.6 cm),"Gift of Mr. and Mrs. Walter Mendelsohn, 1980",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436787,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -06.1200,false,true,437914,European Paintings,Painting,Calm Sea,,,,,,Artist,,Simon de Vlieger,"Dutch, Rotterdam (?) ca. 1600/1601–1653 Weesp",,"Vlieger, Simon de",Dutch,1600,1653,after 1640,1640,1653,Oil on wood,14 3/4 x 17 1/2 in. (37.5 x 44.5 cm),"Rogers Fund, 1906",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437914,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -39.65.7,false,true,437945,European Paintings,Painting,Portrait of a Woman,,,,,,Artist,,Willem Wissing,"Dutch, Amsterdam or The Hague 1656–1687 Stamford",,"Wissing, Willem",Dutch,1656,1687,ca. 1687,1682,1687,Oil on canvas,49 3/4 x 40 1/4 in. (126.4 x 102.2 cm),"Bequest of Jacob Ruppert, 1939",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437945,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -71.110,false,true,435771,European Paintings,Painting,The Spinner,,,,,,Artist,,Quirijn van Brekelenkam,"Dutch, Zwammerdam (?), after 1622–ca. 1669 Leiden",,"Brekelenkam, Quirijn van",Dutch,1622,1669,1653,1653,1653,Oil on wood,19 x 25 1/4 in. (48.3 x 64.1 cm),"Purchase, 1871",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435771,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -32.100.19,false,true,435772,European Paintings,Painting,Sentimental Conversation,,,,,,Artist,,Quirijn van Brekelenkam,"Dutch, Zwammerdam (?), after 1622–ca. 1669 Leiden",,"Brekelenkam, Quirijn van",Dutch,1622,1669,early 1660s,1660,1663,Oil on wood,16 1/4 x 13 7/8 in. (41.3 x 35.2 cm),"The Friedsam Collection, Bequest of Michael Friedsam, 1931",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435772,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -71.19,false,true,435918,European Paintings,Painting,Vanitas Still Life,,,,,,Artist,,Edwaert Collier,"Dutch, Breda ca. 1640?–after 1707 London or Leiden",,"Collier, Edwaert",Dutch,1640,1707,1662,1662,1662,Oil on wood,37 x 44 1/8 in. (94 x 112.1 cm),"Purchase, 1871",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435918,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.162.1,false,true,437893,European Paintings,Painting,Portrait of a Man,,,,,,Artist,,Johannes Verspronck,"Dutch, Haarlem, born ca. 1601–3, died 1662 Haarlem",,"Verspronck, Johannes",Dutch,1601,1662,1645,1645,1645,Oil on canvas,31 1/4 x 25 1/4 in. (79.4 x 64.1 cm),"Bequest of Susan P. Colgate, in memory of her husband, Romulus R. Colgate, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437893,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -71.63,false,true,437921,European Paintings,Painting,Portrait of a Man,,,,,,Artist,,Abraham de Vries,"Dutch, The Hague (?) ca. 1590–1649/50 The Hague (?)",,"Vries, Abraham de",Dutch,1585,1650,1643,1643,1643,Oil on wood,25 1/4 x 21 in. (64.1 x 53.3 cm),"Purchase, 1871",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437921,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -73.2,false,true,436642,European Paintings,Painting,The Musician,,,,,,Artist,,Bartholomeus van der Helst,"Dutch, Haarlem, born ca. 1612–15, died 1670 Amsterdam",,"Helst, Bartholomeus van der",Dutch,1612,1670,1662,1662,1662,Oil on canvas,54 1/2 x 43 3/4 in. (138.4 x 111.1 cm),"Purchase, 1873",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436642,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -71.73,false,true,436641,European Paintings,Painting,Portrait of a Man,,,,,,Artist,,Bartholomeus van der Helst,"Dutch, Haarlem, born ca. 1612–15, died 1670 Amsterdam",,"Helst, Bartholomeus van der",Dutch,1612,1670,1647,1647,1647,Oil on wood,"Oval, 26 1/4 x 21 5/8 in. (66.7 x 54.9 cm)","Purchase, 1871",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436641,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -71.75,false,true,437588,European Paintings,Painting,Drawing the Eel,,,,,,Artist,,Salomon van Ruysdael,"Dutch, Naarden, born ca. 1600–1603, died 1670 Haarlem",,"Ruysdael, Salomon van",Dutch,1600,1670,early 1650s,1650,1653,Oil on wood,29 1/2 x 41 3/4 in. (74.9 x 106 cm),"Purchase, 1871",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437588,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -71.98,false,true,437587,European Paintings,Painting,Marine,,,,,,Artist,,Salomon van Ruysdael,"Dutch, Naarden, born ca. 1600–1603, died 1670 Haarlem",,"Ruysdael, Salomon van",Dutch,1600,1670,1650,1650,1650,Oil on wood,13 5/8 x 17 1/8 in. (34.6 x 43.5 cm),"Purchase, 1871",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437587,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -06.1201,false,true,437586,European Paintings,Painting,A Country Road,,,,,,Artist,,Salomon van Ruysdael,"Dutch, Naarden, born ca. 1600–1603, died 1670 Haarlem",,"Ruysdael, Salomon van",Dutch,1600,1670,1648,1648,1648,Oil on canvas,38 7/8 x 52 7/8 in. (98.7 x 134.3 cm),"Rogers Fund, 1906",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437586,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -15.30.4,false,true,437589,European Paintings,Painting,Ferry near Gorinchem,,,,,,Artist,,Salomon van Ruysdael,"Dutch, Naarden, born ca. 1600–1603, died 1670 Haarlem",,"Ruysdael, Salomon van",Dutch,1600,1670,1646,1646,1646,Oil on canvas,41 7/8 x 52 1/2 in. (106.4 x 133.4 cm),"Bequest of Maria DeWitt Jesup, from the collection of her husband, Morris K. Jesup, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437589,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -60.55.4,false,true,437585,European Paintings,Painting,Market by the Seashore,,,,,,Artist,,Salomon van Ruysdael,"Dutch, Naarden, born ca. 1600–1603, died 1670 Haarlem",,"Ruysdael, Salomon van",Dutch,1600,1670,1637,1637,1637,Oil on wood,16 x 23 3/8 in. (40.6 x 59.4 cm),"Bequest of Rupert L. Joseph, 1959",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437585,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.331.8,false,true,438380,European Paintings,Painting,Fishing Boats on a River,,,,,,Artist,,Salomon van Ruysdael,"Dutch, Naarden, born ca. 1600–1603, died 1670 Haarlem",,"Ruysdael, Salomon van",Dutch,1600,1670,early 1660s,1660,1663,Oil on wood,"Overall, with added strips, 14 3/4 x 21 7/8 in. (37.5 x 55.6 cm); painted surface 14 1/4 x 21 1/4 in. (36.2 x 54 cm)","From the Collection of Rita and Frits Markus, Bequest of Rita Markus, 2005",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/438380,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1971.102,false,true,436221,European Paintings,Painting,A Couple in an Interior with a Gypsy Fortune-Teller,,,,,,Artist,,Jacob Duck,"Dutch, Utrecht, born ca. 1598–1600, died 1667 Utrecht",,"Duck, Jacob",Dutch,1598,1667,ca. 1632–33,1632,1633,Oil on wood,"Oval, 9 7/8 x 13 in. (25.1 x 33 cm)","Gift of Dr. and Mrs. Richard W. Levy, 1971",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436221,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.331.1,false,true,438373,European Paintings,Painting,A Winter Landscape with Ice Skaters and an Imaginary Castle,,,,,,Artist,,Christoffel van den Berghe,"Dutch, Antwerp ca. 1590–1628 or later, active Middelburg",,"Berghe, Christoffel van den",Dutch,1590,1628,ca. 1615–20,1615,1620,Oil on wood,"Overall, with added strips, 11 x 18 3/8 in. (27.9 x 46.7 cm); painted surface 10 3/4 x 18 in. (27.3 x 45.7 cm)","From the Collection of Rita and Frits Markus, Bequest of Rita Markus, 2005",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/438373,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1981.25,false,true,437757,European Paintings,Painting,Old Woman Praying,,,,,,Artist,,Matthias Stom,"Dutch, Amersfoort?, born ca. 1599–1600, died after 1652 ?Italy",,"Stom, Matthias",Dutch,1599,1652,late 1630s or early 1640s,1637,1643,Oil on canvas,30 5/8 x 25 1/8 in. (77.8 x 63.8 cm),"Gift of Ian Woodner, 1981",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437757,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -32.75.15,false,true,435625,European Paintings,"Painting, miniature",Portrait of a Woman,,,,,,Artist,,David Baudringhien,"Dutch, ca. 1581–1650",,"Baudringhien, David",Dutch,1581,1650,,1601,1650,Oil on copper,"Oval, 3 3/4 x 3 in. (95 x 76 mm)","The Collection of Giovanni P. Morosini, presented by his daughter Giulia, 1932",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/435625,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -17.120.222,false,true,437404,European Paintings,Painting,Head of Christ,,,,,,Artist,Style of,Rembrandt,"Dutch, 1650s",,Rembrandt,Dutch,1606,1669,,1626,1669,Oil on canvas,16 3/4 x 13 1/2 in. (42.5 x 34.3 cm); with added strips 18 5/8 x 14 5/8 in. (47.3 x 37.1 cm),"Mr. and Mrs. Isaac D. Fletcher Collection, Bequest of Isaac D. Fletcher, 1917",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437404,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -49.7.40,false,true,437882,European Paintings,Painting,A Young Woman Reading,,,,,,Artist,Imitator of,Johannes Vermeer,ca. 1925–27,,"Vermeer, Johannes",Dutch,1632,1675,,1900,1924,Oil on canvas,7 3/4 x 5 3/4 in. (19.7 x 14.6 cm),"The Jules Bache Collection, 1949",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437882,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -60.71.14,false,true,437403,European Paintings,Painting,Christ and the Woman of Samaria,,,,,,Artist,Style of,Rembrandt,"Dutch, ca. 1655",,Rembrandt,Dutch,1606,1669,,1650,1660,Oil on wood,25 x 19 1/4 in. (63.5 x 48.9 cm),"Bequest of Lillian S. Timken, 1959",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437403,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -07.225.257,false,true,437949,European Paintings,"Painting, grisaille",Children Playing with a Goat,,,,,,Artist,Style of,Jacob de Wit,"Dutch, 18th century",,"Wit, Jacob de",Dutch,1695,1754,,1715,1754,Oil on canvas,26 3/4 x 41 in. (67.9 x 104.1 cm),"Gift of J. Pierpont Morgan, 1906",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437949,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -32.100.16,false,true,435843,European Paintings,Painting,Winter Scene,,,,,,Artist,Style of,Jan van de Cappelle,18th or 19th century,,"Cappelle, Jan van de",Dutch,1626,1679,,1644,1679,Oil on oak,13 3/8 x 19 1/2 in. (34 x 49.5 cm),"The Friedsam Collection, Bequest of Michael Friedsam, 1931",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435843,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -53.18,false,true,437419,European Paintings,Painting,Rembrandt (1606–1669) as a Young Man,,,,,,Artist,Style of,Rembrandt,"Dutch, ca. 1630–35",,Rembrandt,Dutch,1606,1669,,1600,1699,Oil on wood,8 5/8 x 6 1/2 in. (21.9 x 16.5 cm),"Bequest of Evander B. Schley, 1952",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437419,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -89.15.3,false,true,437418,European Paintings,Painting,Man with a Beard,,,,,,Artist,Style of,Rembrandt,17th century or later,,Rembrandt,Dutch,1606,1669,,1600,1889,Oil on canvas,28 7/8 x 25 1/4 in. (73.3 x 64.1 cm),"Marquand Collection, Gift of Henry G. Marquand, 1889",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437418,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.40.608,false,true,437420,European Paintings,Painting,Rembrandt's Son Titus (1641–1668),,,,,,Artist,Style of,Rembrandt,17th century or later,,Rembrandt,Dutch,1606,1669,,1650,1883,Oil on canvas,31 1/8 x 23 1/4 in. (79.1 x 59.1 cm),"Bequest of Benjamin Altman, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437420,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -65.181.11,false,true,436560,European Paintings,Painting,River View with a Village Church,,,,,,Artist,Style of,Jan van Goyen,"Dutch, mid-17th century",,"Goyen, Jan van",Dutch,1596,1656,,1630,1669,Oil on canvas,25 1/2 x 38 1/2 in. (64.8 x 97.8 cm),"Bequest of Adele L. Lehman, in memory of Arthur Lehman, 1965",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436560,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -21.134.5,false,true,437072,European Paintings,Painting,Tavern Scene,,,,,,Artist,Copy after,Gabriël Metsu,"Dutch, late 17th century",,"Metsu, Gabriël",Dutch,1629,1667,,1649,1667,Oil on wood,14 3/8 x 12 5/8 in. (36.5 x 32.1 cm),"Bequest of William H. Herriman, 1920",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437072,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -60.71.16,false,true,437421,European Paintings,Painting,Study Head of an Old Man,,,,,,Artist,Style of,Rembrandt,"Dutch, mid- to late 1630s",,Rembrandt,Dutch,1606,1669,,1600,1699,Oil on wood,8 1/4 x 6 7/8 in. (21 x 17.5 cm),"Bequest of Lillian S. Timken, 1959",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437421,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.102,false,true,437410,European Paintings,Painting,Portrait of a Man with a Breastplate and Plumed Hat,,,,,,Artist,Style of,Rembrandt,"Dutch, mid- to late 1640s",,Rembrandt,Dutch,1606,1669,,1625,1674,Oil on canvas,47 3/4 x 38 3/4 in. (121.3 x 98.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437410,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.103,false,true,437409,European Paintings,Painting,Portrait of a Woman,,,,,,Artist,Style of,Rembrandt,"Dutch, mid- to late 1640s",,Rembrandt,Dutch,1606,1669,,1625,1674,Oil on canvas,47 5/8 x 38 5/8 in. (121 x 98.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437409,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -49.7.36,false,true,437413,European Paintings,Painting,Man in a Red Cloak,,,,,,Artist,Style of,Rembrandt,"Dutch, 1650s or early 1660s",,Rembrandt,Dutch,1606,1669,,1625,1674,Oil on wood,15 1/8 x 12 1/4 in. (38.4 x 31.1 cm),"The Jules Bache Collection, 1949",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437413,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.198,false,true,437233,European Paintings,Painting,Man with a Tankard,,,,,,Artist,Style of,Adriaen van Ostade,"Dutch, second half 17th century",,"Ostade, Adriaen van",Dutch,1610,1685,,1650,1699,Oil on wood,10 1/8 x 8 1/2 in. (25.7 x 21.6 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437233,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1985.88,false,true,437043,European Paintings,Painting,A Shepherdess and Her Flock,,,,,,Artist,,Anton Mauve,"Dutch, Zaandam 1838–1888 Arnhem",,"Mauve, Anton",Dutch,1838,1888,,1858,1888,Oil on canvas,17 7/8 x 25 1/4 in. (45.4 x 64.1 cm),"Gift of Cole J. Younger, 1985",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437043,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -43.86.8,false,true,437041,European Paintings,Painting,Gathering Wood,,,,,,Artist,,Anton Mauve,"Dutch, Zaandam 1838–1888 Arnhem",,"Mauve, Anton",Dutch,1838,1888,,1858,1888,Oil on canvas,16 1/2 x 13 in. (41.9 x 33 cm),"Bequest of Richard De Wolfe Brixey, 1943",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437041,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.40.812,false,true,437044,European Paintings,Painting,Twilight,,,,,,Artist,,Anton Mauve,"Dutch, Zaandam 1838–1888 Arnhem",,"Mauve, Anton",Dutch,1838,1888,,1858,1888,Oil on canvas,25 7/8 x 17 7/8 in. (65.7 x 45.4 cm),"Bequest of Benjamin Altman, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437044,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.40.816,false,true,437042,European Paintings,Painting,The Return to the Fold,,,,,,Artist,,Anton Mauve,"Dutch, Zaandam 1838–1888 Arnhem",,"Mauve, Anton",Dutch,1838,1888,,1858,1888,Oil on canvas,19 3/4 x 33 7/8 in. (50.2 x 86 cm),"Bequest of Benjamin Altman, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437042,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -49.7.37,false,true,437407,European Paintings,Painting,Christ with a Staff,,,,,,Artist,Follower of,Rembrandt,"Dutch, third quarter 17th century",,Rembrandt,Dutch,1606,1669,,1650,1674,Oil on canvas,37 1/2 x 32 1/2 in. (95.3 x 82.6 cm),"The Jules Bache Collection, 1949",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437407,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -60.71.15,false,true,437405,European Paintings,Painting,"A Young Woman as a Shepherdess (""Saskia as Flora"")",,,,,,Artist,,Govert Flinck,"Dutch, Cleve 1615–1660 Amsterdam",,"Flinck, Govert",Dutch,1615,1660,,1635,1660,"Oil on canvas, transferred from wood","Oval, 26 1/4 x 19 7/8 in. (66.7 x 50.5 cm)","Bequest of Lillian S. Timken, 1959",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437405,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.373,false,true,437408,European Paintings,Painting,Man in a Beret,,,,,,Artist,Style of,Rembrandt,"Dutch, fourth quarter 17th century",,Rembrandt,Dutch,1606,1669,,1675,1699,Oil on canvas,29 7/8 x 24 3/4 in. (75.9 x 62.9 cm),"Gift of Charles S. Payson, 1975",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437408,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -71.76,false,true,436628,European Paintings,Painting,Malle Babbe,,,,,,Artist,Style of,Frans Hals,"Dutch, second quarter 17th century",,"Hals, Frans",Dutch,1582,1666,,1625,1649,Oil on canvas,29 1/2 x 24 in. (74.9 x 61 cm),"Purchase, 1871",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436628,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.2,false,true,435597,European Paintings,Painting,Portrait of an Old Woman,,,,,,Artist,Style of,Jacob Backer,"Dutch, second quarter 17th century",,"Backer, Jacob",Dutch,1608,1651,,1625,1649,Oil on wood,28 x 24 in. (71.1 x 61 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435597,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -71.116,false,true,437922,European Paintings,Painting,The Pigeon House,,,,,,Artist,,Roelof van Vries,"Dutch, Haarlem 1630/31–after 1681",,"Vries, Roelof van",Dutch,1630,1681,,1650,1681,Oil on canvas,14 1/2 x 12 in. (36.8 x 30.5 cm),"Purchase, 1871",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437922,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -50.145.33,false,true,437417,European Paintings,Painting,"Lieven Willemsz van Coppenol (born about 1599, died 1671 or later)",,,,,,Artist,,Rembrandt (Rembrandt van Rijn),"Dutch, Leiden 1606–1669 Amsterdam",,Rembrandt (Rembrandt van Rijn),Dutch,1606,1669,,1626,1669,Oil on wood,14 3/8 x 11 3/8 in. (36.5 x 28.9 cm),"Bequest of Mary Stillman Harkness, 1950",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437417,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.136.3,false,true,436974,European Paintings,Painting,Canal Side,,,,,,Artist,,Jacob Maris,"Dutch, The Hague 1837–1899 Karlsbad",,"Maris, Jacob",Dutch,1837,1899,,1857,1899,Oil on canvas,5 3/8 x 7 in. (13.7 x 17.8 cm),"Bequest of Margaret Crane Hurlbut, 1933",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436974,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -11.92,false,true,435633,European Paintings,Painting,"Skating at Sloten, near Amsterdam",,,,,,Artist,,Johannes Abrahamsz Beerstraten,"Dutch, Amsterdam 1622–1666 Amsterdam",,"Beerstraten, Johannes Abrahamsz",Dutch,1622,1666,,1642,1666,Oil on canvas,36 1/4 x 51 5/8 in. (92.1 x 131.1 cm),"Rogers Fund, 1911",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435633,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -07.225.296,false,true,437948,European Paintings,"Painting, sketch for a ceiling decoration",Allegory of Government: Wisdom Defeating Discord,,,,,,Artist,,Jacob de Wit,"Dutch, Amsterdam 1695–1754 Amsterdam",,"Wit, Jacob de",Dutch,1695,1754,,1715,1754,Oil on canvas,20 1/8 x 15 3/8 in. (51.1 x 39.1 cm),"Gift of J. Pierpont Morgan, 1906",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437948,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -11.149.2,false,true,436931,European Paintings,Painting,"Admiral Jacob Binkes (born about 1640, died 1677)",,,,,,Artist,,Nicolaes Maes,"Dutch, Dordrecht 1634–1693 Amsterdam",,"Maes, Nicolaes",Dutch,1634,1693,,1654,1693,Oil on canvas,17 1/4 x 12 7/8 in. (43.8 x 32.7 cm),"Gift of J. Pierpont Morgan, 1911",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436931,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.40.603,false,true,435596,European Paintings,Painting,Old Woman in an Armchair,,,,,,Artist,Attributed to,Jacob Backer,"Dutch, Harlingen 1608–1651 Amsterdam",,"Backer, Jacob",Dutch,1608,1651,,1629,1639,Oil on canvas,50 3/8 x 39 1/8 in. (128 x 99.4 cm),"Bequest of Benjamin Altman, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435596,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.40.629,false,true,436212,European Paintings,Painting,Young Woman with a Pearl Necklace,,,,,,Artist,Copy after,Willem Drost,"Dutch, late 17th or early 18th century",,"Drost, Willem",Dutch,1633,1659,,1670,1729,Oil on canvas,33 1/8 x 24 1/2 in. (84.1 x 62.2 cm),"Bequest of Benjamin Altman, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436212,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -30.95.260,false,true,437171,European Paintings,Painting,The Old Castle,,,,,,Artist,,Emanuel Murant,"Dutch, Amsterdam 1622–1700 Leeuwarden",,"Murant, Emanuel",Dutch,1622,1700,,1642,1700,Oil on wood,15 5/8 x 21 7/8 in. (39.7 x 55.6 cm),"Theodore M. Davis Collection, Bequest of Theodore M. Davis, 1915",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437171,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -87.8.13,false,true,436724,European Paintings,Painting,Expectation,,,,,,Artist,,Jozef Israëls,"Dutch, Groningen 1824–1911 Scheveningen",,"Israëls, Jozef",Dutch,1824,1911,,1844,1911,Oil on canvas,71 1/2 x 54 in. (181.6 x 137.2 cm),"Gift of George I. Seney, 1887",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436724,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -17.120.227,false,true,436725,European Paintings,Painting,Grandmother's Treasure,,,,,,Artist,,Jozef Israëls,"Dutch, Groningen 1824–1911 Scheveningen",,"Israëls, Jozef",Dutch,1824,1911,,1844,1911,Oil on canvas,27 x 35 5/8 in. (68.6 x 90.5 cm),"Mr. and Mrs. Isaac D. Fletcher Collection, Bequest of Isaac D. Fletcher, 1917",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436725,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -32.100.9,false,true,437193,European Paintings,Painting,The Reader,,,,,,Artist,,Eglon van der Neer,"Dutch, Amsterdam 1635/36–1703 Düsseldorf",,"Neer, Eglon van der",Dutch,1635,1703,,1654,1703,Oil on canvas,15 x 11 in. (38.1 x 27.9 cm),"The Friedsam Collection, Bequest of Michael Friedsam, 1931",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437193,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -71.84,false,true,437416,European Paintings,Painting,Man in Armor (Mars?),,,,,,Artist,Style of,Rembrandt,"Dutch, second or third quarter 17th century",,Rembrandt,Dutch,1606,1669,,1625,1674,Oil on canvas,40 1/8 x 35 5/8 in. (101.9 x 90.5 cm),"Purchase, 1871",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437416,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.40.601,false,true,437411,European Paintings,Painting,Man with a Steel Gorget,,,,,,Artist,Style of,Rembrandt,"Dutch, second or third quarter 17th century",,Rembrandt,Dutch,1606,1669,,1625,1674,Oil on canvas,37 1/8 x 30 5/8 in. (94.3 x 77.8 cm),"Bequest of Benjamin Altman, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437411,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1971.255,false,true,437351,European Paintings,Painting,Paul and Barnabas at Lystra,,,,,,Artist,,Jacob Pynas,"Dutch, Amsterdam 1592/93–after 1650 Amsterdam (?)",,"Pynas, Jacob",Dutch,1592,1650,,1605,1650,Oil on wood,19 x 28 7/8 in. (48.3 x 73.3 cm),"Gift of Emile E. Wolf, 1971",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437351,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -71.135,false,true,437590,European Paintings,Painting,View of the Town of Alkmaar,,,,,,Artist,,Salomon van Ruysdael,"Dutch, Naarden, born ca. 1600–1603, died 1670 Haarlem",,"Ruysdael, Salomon van",Dutch,1600,1670,,1620,1670,Oil on wood,20 1/4 x 33 in. (51.4 x 83.8 cm),"Purchase, 1871",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437590,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -56.13,false,true,436527,European Paintings,Painting,The Flowering Orchard,,,,,,Artist,,Vincent van Gogh,"Dutch, Zundert 1853–1890 Auvers-sur-Oise",,"Gogh, Vincent van",Dutch,1853,1890,1888,1888,1888,Oil on canvas,28 1/2 x 21 in. (72.4 x 53.3 cm),"The Mr. and Mrs. Henry Ittleson Jr. Purchase Fund, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436527,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -62.24,false,true,436530,European Paintings,Painting,Oleanders,,,,,,Artist,,Vincent van Gogh,"Dutch, Zundert 1853–1890 Auvers-sur-Oise",,"Gogh, Vincent van",Dutch,1853,1890,1888,1888,1888,Oil on canvas,23 3/4 x 29 in. (60.3 x 73.7 cm),"Gift of Mr. and Mrs. John L. Loeb, 1962",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436530,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1992.374,false,true,436533,European Paintings,Painting,Shoes,,,,,,Artist,,Vincent van Gogh,"Dutch, Zundert 1853–1890 Auvers-sur-Oise",,"Gogh, Vincent van",Dutch,1853,1890,1888,1888,1888,Oil on canvas,18 x 21 3/4 in. (45.7 x 55.2 cm),"Purchase, The Annenberg Foundation Gift, 1992",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436533,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -25.106.20,false,true,437253,European Paintings,"Painting, miniature",Abbé Charles Bossut (1730–1814),,,,,,Artist,,Pierre Pasquier,"French, 1731–1806",,"Pasquier, Pierre",French,1731,1806,1772,1772,1772,Enamel,"Oval, 2 3/8 x 2 in. (62 x 50 mm)","Gift of Mrs. Louis V. Bell, in memory of her husband, 1925",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/437253,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -25.106.32,false,true,437254,European Paintings,"Painting, miniature",Gérard de Vesme,,,,,,Artist,,Pierre Pasquier,"French, 1731–1806",,"Pasquier, Pierre",French,1731,1806,177(3?),1771,1779,Enamel,"Oval, 2 1/8 x 1 3/4 in. (54 x 44 mm)","Gift of Mrs. Louis V. Bell, in memory of her husband, 1925",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/437254,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -60.14,false,true,436328,European Paintings,"Painting, miniature",Portrait of a Boy,,,,,,Artist,,Marie Anne Gérard Fragonard (Madame Fragonard),"French, 1745–1823",,"Fragonard, Marie Anne Gérard (Madame Fragonard)",French,1745,1823,ca. 1775,1770,1780,Ivory,"Oval, 2 7/8 x 2 3/8 in. (73 x 59 mm)","Rogers Fund, 1960",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/436328,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -61.77,false,true,436605,European Paintings,"Painting, miniature","Alexandre Théodore Victor (1760–1829), Comte de Lameth",,,,,,Artist,,Jean Urbain Guérin,"French, 1761–1836",,"Guérin, Jean Urbain",French,1761,1836,ca. 1789–90,1789,1790,Ivory laid on card,Diameter 3 in. (75 mm),"Rogers Fund, 1961",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/436605,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -62.122.87,false,true,436606,European Paintings,"Painting, miniature",Portrait of a Woman,,,,,,Artist,,Jean Urbain Guérin,"French, 1761–1836",,"Guérin, Jean Urbain",French,1761,1836,ca. 1815,1810,1820,Ivory,"Oval, 2 3/8 x 1 3/4 in. (59 x 46 mm)","Bequest of Millie Bruhl Fredrick, 1962",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/436606,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -26.168.9,false,true,436855,European Paintings,"Painting, miniature",Portrait of a Woman,,,,,,Artist,,Étienne Charles Le Guay,"French, 1762–1846",,"Le Guay, Étienne Charles",French,1762,1846,ca. 1800,1795,1805,Ivory,"Oval, 3 1/2 x 3 in. (90 x 75 mm)","Bequest of Collis P. Huntington, 1900",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/436855,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -25.106.16,false,true,436848,European Paintings,"Painting, miniature",Portrait of a Young Woman,,,,,,Artist,Attributed to,Jean Antoine Laurent,"French, 1763–1832",,"Laurent, Jean Antoine",French,1763,1832,ca. 1795,1790,1800,Ivory,"Octagonal, 2 3/8 x 2 7/8 in. (63 x 73 mm)","Gift of Mrs. Louis V. Bell, in memory of her husband, 1925",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/436848,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.145.11,false,true,436862,European Paintings,"Painting, miniature",Portrait of a Man,,,,,,Artist,,Jeanne Philiberte Ledoux,"French, 1767–1840",,"Ledoux, Jeanne Philiberte",French,1767,1840,ca. 1790,1785,1795,Ivory,Diameter 2 1/4 in. (57 mm),"Gift of Humanities Fund Inc., 1972",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/436862,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -62.122.108,false,true,436924,European Paintings,"Painting, miniature",Portrait of a Man,,,,,,Artist,,Ferdinand Machéra,"French, 1776–1843",,"Machéra, Ferdinand",French,1776,1843,1827,1827,1827,Ivory,"Oval, 2 1/4 x 1 5/8 in. (57 x 43 mm)","Bequest of Millie Bruhl Fredrick, 1962",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/436924,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -48.187.742,false,true,436768,European Paintings,"Painting, miniature",Portrait of a Woman,,,,,,Artist,,Nicolas Henri Jacob,"French, 1782–1871",,"Jacob, Nicolas Henri",French,1782,1871,1817,1817,1817,Card,"Oval, 8 1/4 x 6 3/4 in. (208 x 171 mm)","Bequest of Catherine D. Wentworth, 1948",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/436768,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -95.14.90,false,true,435841,European Paintings,"Painting, miniature",Baron Joseph Dominique Louis (1755–1837),,,,,,Artist,,Pierre Laurent Canon,"French, 1787–1852",,"Canon, Pierre Laurent",French,1787,1852,1844,1844,1844,Ivory,"Oval, 4 1/8 x 3 1/8 in. (105 x 80 mm)","The Moses Lazarus Collection, Gift of Josephine and Sarah Lazarus, in memory of their father, 1888–95",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/435841,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -62.122.90,false,true,437486,European Paintings,"Painting, miniature",Portrait of a Woman,,,,,,Artist,,François-Théodore Rochard,"French, 1798–1858",,"Rochard, François-Théodore",French,1798,1858,1829,1829,1829,Ivory,3 7/8 x 3 1/4 in. (98 x 83 mm),"Bequest of Millie Bruhl Fredrick, 1962",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/437486,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -42.53.1,false,true,437075,European Paintings,"Painting, miniature","Portrait of a Woman, Said to Be Marie de Mautesson",,,,,,Artist,,François Meuret,"French, 1800–1887",,"Meuret, François",French,1800,1887,ca. 1830,1825,1835,Ivory,2 1/4 x 1 7/8 in. (58 x 47 mm),"Gift of Helen O. Brice, 1942",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/437075,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -24.80.533,false,true,435767,European Paintings,"Painting, miniature",Portrait of a Man,,,,,,Artist,Attributed to,Joseph Boze,"French, 1745–1825/26",,"Boze, Joseph",French,1745,1826,probably ca. 1790,1785,1795,Ivory,"Oval, 1 7/8 x 1 1/2 in. (47 x 38 mm)","Bequest of Mary Clark Thompson, 1923",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/435767,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -15.43.298,false,true,437907,European Paintings,"Painting, miniature",Madame Ingouf,,,,,,Artist,,Vincent,"French, active ca. 1790",,Vincent,French,1790,1790,ca. 1790,1785,1795,Ivory,Diameter 4 1/8 in. (105 mm),"Bequest of Mary Anna Palmer Draper, 1914",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/437907,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -68.222.9,false,true,436109,European Paintings,"Painting, miniature","Benjamin Franklin (1706–1790), after a Painting by Greuze of 1777",,,,,,Artist,,Charles Paul Jérôme de Bréa,"French, ca. 1739–1820",,"Bréa, Charles Paul Jérôme de",French,1739,1820,1777,1777,1777,Ivory,"Oval, 3 3/8 x 2 3/4 in. (85 x 68 mm)","Gift of J. William Middendorf II, 1968",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/436109,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -62.122.67,false,true,436110,European Paintings,"Painting, miniature","Marie-Thérèse-Charlotte (1778–1851), Daughter of Louis XVI",,,,,,Artist,,Jacques Joseph de Gault,"French, 1738–after 1812",,"Gault, Jacques Joseph de",French,1738,1812,1795,1795,1795,Ivory,Diameter 2 3/8 in. (60 mm),"Bequest of Millie Bruhl Fredrick, 1962",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/436110,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -95.14.94,false,true,436919,European Paintings,"Painting, miniature","Portrait of a Man, Said to Be James Madison (1751–1836)",,,,,,Artist,,Annibal Christian Loutherbourg,"French, 1765–after 1795",,"Loutherbourg, Annibal Christian",French,1765,1795,1795,1795,1795,Ivory,"Oval, 2 1/2 x 2 1/4 in. (65 x 56 mm)","The Moses Lazarus Collection, Gift of Josephine and Sarah Lazarus, in memory of their father, 1888–95",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/436919,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.136.8,false,true,436181,European Paintings,"Painting, miniature",Portrait of a Woman,,,,,,Artist,,Jacques Delaplace,"French, 1767–after 1831",,"Delaplace, Jacques",French,1767,1831,ca. 1805–10,1805,1810,Ivory extended by card,2 1/2 x 2 1/4 in. (64 x 55 mm),"Bequest of Margaret Crane Hurlbut, 1933",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/436181,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -45.110.1,false,true,435662,European Paintings,"Painting, miniature",Portrait of a Woman,,,,,,Artist,,Vincent Bertrand,"French, 1770–after 1817",,"Bertrand, Vincent",French,1770,1817,ca. 1810,1805,1815,Ivory,4 1/4 x 4 in. (110 x 100 mm),"Gift of Mrs. John LaPorte Given, 1945",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/435662,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -42.150.15,false,true,435661,European Paintings,"Painting, miniature",Portrait of a Woman,,,,,,Artist,,Vincent Bertrand,"French, 1770–after 1817",,"Bertrand, Vincent",French,1770,1817,ca. 1810,1805,1815,Ivory,"Oval, 2 5/8 x 2 in. (58 x 48 mm)","Gift of Mrs. Heyward Cutting, 1942",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/435661,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -95.14.51,false,true,435719,European Paintings,"Painting, miniature",Portrait of a Woman,,,,,,Artist,,Joseph Bordes,"French, 1773–after 1835",,"Bordes, Joseph",French,1773,1835,1808,1808,1808,Ivory,"Octagonal, 4 1/8 x 3 1/8 in. (106 x 80 mm)","The Moses Lazarus Collection, Gift of Josephine and Sarah Lazarus, in memory of their father, 1888–95",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/435719,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -95.14.83,false,true,435721,European Paintings,"Painting, miniature",Portrait of a Man,,,,,,Artist,,Joseph Bordes,"French, 1773–after 1835",,"Bordes, Joseph",French,1773,1835,ca. 1810,1805,1815,Ivory set into card,"Oval, 7 1/8 x 5 7/8 in. (181 x 149 mm)","The Moses Lazarus Collection, Gift of Josephine and Sarah Lazarus, in memory of their father, 1888–95",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/435721,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -95.14.86,false,true,435720,European Paintings,"Painting, miniature",Portrait of a Woman,,,,,,Artist,,Joseph Bordes,"French, 1773–after 1835",,"Bordes, Joseph",French,1773,1835,1812,1812,1812,Ivory,"Oval, 4 x 3 1/8 in. (104 x 82 mm)","The Moses Lazarus Collection, Gift of Josephine and Sarah Lazarus, in memory of their father, 1888–95",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/435720,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -24.80.525,false,true,437627,European Paintings,"Painting, miniature",A River Landscape,,,,,,Artist,,Edmé Charles de Lioux de Savignac,"French, active ca. 1766–72",,"Savignac, Edmé Charles de Lioux de",French,1766,1772,ca. 1766–72,1766,1772,Paper,Diameter 3 in. (77 mm),"Bequest of Mary Clark Thompson, 1923",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/437627,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -24.80.527,false,true,437628,European Paintings,"Painting, miniature",A Picnic,,,,,,Artist,,Edmé Charles de Lioux de Savignac,"French, active ca. 1766–72",,"Savignac, Edmé Charles de Lioux de",French,1766,1772,ca. 1766–72,1766,1772,Paper,Diameter 3 in. (77 mm),"Bequest of Mary Clark Thompson, 1923",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/437628,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -25.106.21,false,true,437905,European Paintings,"Painting, miniature",A Man with the Initials JD,,,,,,Artist,,Villers,"French, active ca. 1781–93",,Villers,French,1781,1793,1790,1790,1790,Ivory,Diameter 2 1/2 in. (62 mm),"Gift of Mrs. Louis V. Bell, in memory of her husband, 1925",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/437905,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -26.168.55,false,true,437904,European Paintings,"Painting, miniature",Portrait of a Woman,,,,,,Artist,,Villers,"French, active ca. 1781–93",,Villers,French,1781,1793,ca. 1790,1785,1793,Ivory,"Oval, 2 3/8 x 1 7/8 in. (60 x 48 mm)","Bequest of Collis P. Huntington, 1900",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/437904,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -25.106.35,false,true,436860,European Paintings,"Painting, miniature",Portrait of a Man,,,,,,Artist,,J. Lecourt,"French, active ca. 1804–30",,"Lecourt, J.",French,1804,1830,ca. 1810,1805,1815,Ivory,Diameter 2 in. (50 mm),"Gift of Mrs. Louis V. Bell, in memory of her husband, 1925",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/436860,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -56.7,false,true,437292,European Paintings,"Painting, drawing",A Shipwreck in a Storm,,,,,,Artist,,Jean Pillement,"French, Lyons 1728–1808 Lyons",,"Pillement, Jean",French,1728,1808,1782,1782,1782,Pastel on gessoed canvas,24 3/4 x 36 in. (62.9 x 91.4 cm),"Gift of Martin Birnbaum, 1956",,,,,,,,,,,,Pastels & Oil Sketches on Paper,,http://www.metmuseum.org/art/collection/search/437292,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -08.60,false,true,436717,European Paintings,"Painting, miniature","Mrs. Rufus Prime (Augusta Temple Palmer, 1807–1840)",,,,,,Artist,,Jean-Baptiste Isabey,"French, Nancy 1767–1855 Paris",,"Isabey, Jean-Baptiste",French,1767,1855,1828,1828,1828,Card,"Oval, 5 3/8 x 4 in. (138 x 102 mm)","Gift of Cornelia Prime, 1908",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/436717,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -42.53.5,false,true,436715,European Paintings,"Painting, miniature",Napoléon I (1769–1821),,,,,,Artist,,Jean-Baptiste Isabey,"French, Nancy 1767–1855 Paris",,"Isabey, Jean-Baptiste",French,1767,1855,1812,1812,1812,Ivory,"Oval, 2 1/4 x 1 3/8 in. (56 x 36 mm)","Gift of Helen O. Brice, 1942",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/436715,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -47.33.3,false,true,436714,European Paintings,"Painting, miniature",Napoléon I (1769–1821),,,,,,Artist,,Jean-Baptiste Isabey,"French, Nancy 1767–1855 Paris",,"Isabey, Jean-Baptiste",French,1767,1855,1810,1810,1810,Ivory,"Oval, 2 x 1 1/8 in. (50 x 30 mm)","Gift of Junius S. and Henry S. Morgan, 1947",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/436714,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -25.106.7,false,true,436710,European Paintings,"Painting, miniature",The Reader,,,,,,Artist,,Jean-Baptiste Isabey,"French, Nancy 1767–1855 Paris",,"Isabey, Jean-Baptiste",French,1767,1855,1790,1790,1790,Ivory,Diameter 3 1/8 in. (79 mm),"Gift of Mrs. Louis V. Bell, in memory of her husband, 1925",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/436710,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -32.75.20,false,true,436713,European Paintings,"Painting, miniature","Madame Jean-Baptiste Isabey (Jeanne Laurice de Salienne, died 1829)",,,,,,Artist,,Jean-Baptiste Isabey,"French, Nancy 1767–1855 Paris",,"Isabey, Jean-Baptiste",French,1767,1855,ca. 1796–1800,1796,1800,Ivory,Diameter 3 3/8 in. (86 mm),"The Collection of Giovanni P. Morosini, presented by his daughter Giulia, 1932",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/436713,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -55.111.2,false,true,436718,European Paintings,"Painting, miniature",Portrait of a Woman,,,,,,Artist,,Jean-Baptiste Isabey,"French, Nancy 1767–1855 Paris",,"Isabey, Jean-Baptiste",French,1767,1855,ca. 1815,1810,1820,Ivory,"Oval, 3 x 2 1/4 in. (76 x 56 mm)","Bequest of Helen Winslow Durkee Mileham, 1954",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/436718,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -62.122.88,false,true,436711,European Paintings,"Painting, miniature",Portrait of a Woman,,,,,,Artist,,Jean-Baptiste Isabey,"French, Nancy 1767–1855 Paris",,"Isabey, Jean-Baptiste",French,1767,1855,ca. 1795,1790,1800,Ivory,Diameter 2 7/8 in. (73 mm),"Bequest of Millie Bruhl Fredrick, 1962",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/436711,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2011.84,false,true,441183,European Paintings,"Painting, drawing","Double Portrait Presumed to Represent François de Jullienne (1722–1754) and His Wife (Marie Élisabeth de Séré de Rieux, 1724–1795)",,,,,,Artist,,Charles Antoine Coypel,"French, Paris 1694–1752 Paris",,"Coypel, Charles Antoine",French,1694,1752,1743,1743,1743,"Pastel, black chalk, watercolor, and traces of black chalk underdrawing on four joined sheets of handmade blue laid paper, mounted on canvas and adhered to a keyed stretcher",39 3/8 x 31 1/2 in. (100 x 80 cm),"Purchase, Mrs. Charles Wrightsman Gift, in honor of Annette de la Renta, 2011",,,,,,,,,,,,Pastels & Oil Sketches on Paper,,http://www.metmuseum.org/art/collection/search/441183,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1977.383,false,true,437596,European Paintings,"Painting, drawing",The Two Sisters,,,,,,Artist,,"Jean Claude Richard, Abbé de Saint-Non","French, Paris 1727–1791 Paris",,"Saint-Non, Jean Claude Richard, Abbé de",French,1727,1791,1770,1770,1770,"Pastel on paper, laid down on canvas",31 5/8 x 25 in. (80.3 x 63.5 cm),"Gift of Daniel Wildenstein, 1977",,,,,,,,,,,,Pastels & Oil Sketches on Paper,,http://www.metmuseum.org/art/collection/search/437596,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -32.75.10,false,true,435834,European Paintings,"Painting, miniature","Louis XVI (1754–1793), King of France",,,,,,Artist,Attributed to,Antoine François Callet,"French, Paris 1741–1823 Paris",,"Callet, Antoine François",French,1741,1823,1787,1787,1787,Ivory,Diameter 2 5/8 in. (67 mm),"The Collection of Giovanni P. Morosini, presented by his daughter Giulia, 1932",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/435834,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.441,false,true,439405,European Paintings,"Painting, drawing",Madame Élisabeth de France (1764–1794),,,,,,Artist,,Adélaïde Labille-Guiard,"French, Paris 1749–1803 Paris",,"Labille-Guiard, Adélaïde",French,1749,1803,ca. 1787,1782,1792,"Pastel on blue paper, seven sheets joined, laid down on canvas","Oval, 31 x 25 3/4 in. (78.7 x 65.4 cm.)","Gift of Mrs. Frederick M. Stafford, 2007",,,,,,,,,,,,Pastels & Oil Sketches on Paper,,http://www.metmuseum.org/art/collection/search/439405,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -25.106.4,false,true,435736,European Paintings,"Painting, miniature",Portrait of a Woman,,,,,,Artist,,Étienne Bouchardy,"French, Paris 1797–1849 Paris",,"Bouchardy, Étienne",French,1797,1849,1832,1832,1832,Ivory laid on card,"Oval, 3 1/4 x 2 5/8 in. (82 x 67 mm)","Gift of Mrs. Louis V. Bell, in memory of her husband, 1925",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/435736,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.136.7,false,true,435735,European Paintings,"Painting, miniature",Portrait of a Woman,,,,,,Artist,,Étienne Bouchardy,"French, Paris 1797–1849 Paris",,"Bouchardy, Étienne",French,1797,1849,1838,1838,1838,Ivory,4 7/8 x 4 1/8 in. (125 x 104 mm),"Bequest of Margaret Crane Hurlbut, 1933",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/435735,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1997.391.1,false,true,437993,European Paintings,"Painting, drawing",At the Milliner's,,,,,,Artist,,Edgar Degas,"French, Paris 1834–1917 Paris",,"Degas, Edgar",French,1834,1917,1881,1881,1881,"Pastel on five pieces of wove paper, backed with paper, and laid down on canvas",27 1/4 x 27 1/4 in. (69.2 x 69.2 cm),"The Walter H. and Leonore Annenberg Collection, Gift of Walter H. and Leonore Annenberg, 1997, Bequest of Walter H. Annenberg, 2002",,,,,,,,,,,,Pastels & Oil Sketches on Paper,,http://www.metmuseum.org/art/collection/search/437993,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -25.106.13,false,true,437668,European Paintings,"Painting, miniature",Portrait of a Man,,,,,,Artist,,Louis Marie Sicardi,"French, Avignon 1743–1825 Paris",,"Sicardi, Louis Marie",French,1743,1825,ca. 1780,1775,1785,Ivory,"Oval, 1 3/8 x 1 1/4 in. (36 x 32 mm)","Gift of Mrs. Louis V. Bell, in memory of her husband, 1925",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/437668,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2003.26,false,true,438615,European Paintings,"Painting, drawing",Olivier Journu (1724–1764),,,,,,Artist,,Jean-Baptiste Perronneau,"French, Paris 1715–1783 Amsterdam",,"Perronneau, Jean-Baptiste",French,1715,1783,1756,1756,1756,"Pastel on blue-gray laid paper, laid down on canvas",22 7/8 x 18 1/2 in. (58.1 x 47 cm),"Wrightsman Fund, 2003",,,,,,,,,,,,Pastels & Oil Sketches on Paper,,http://www.metmuseum.org/art/collection/search/438615,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -26.168.17,false,true,436233,European Paintings,"Painting, miniature","Portrait of a Woman, Said to Be Madame Récamier (1777–1849)",,,,,,Artist,,Nicolas François Dun,"French, Lunéville 1764–1832 Naples",,"Dun, Nicolas François",French,1764,1832,ca. 1812–14,1812,1814,Ivory,"Oval, 2 3/8 x 1 7/8 in. (60 x 48 mm)","Bequest of Collis P. Huntington, 1900",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/436233,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -07.225.287,false,true,436349,European Paintings,"Painting, drawing",Panthers of Bacchus Eating Grapes,,,,,,Artist,,Alexandre François Desportes,"French, Champigneulle 1661–1743 Paris",,"Desportes, Alexandre François",French,1661,1743,ca. 1719–20,1719,1720,"Oil on paper, laid down on card (paste-paper)",13 5/8 x 6 3/4 in. (34.6 x 17.1 cm),"Gift of J. Pierpont Morgan, 1906",,,,,,,,,,,,Pastels & Oil Sketches on Paper,,http://www.metmuseum.org/art/collection/search/436349,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -62.122.69,false,true,437167,European Paintings,"Painting, miniature","Louis XVI (1754–1793), King of France",,,,,,Artist,,Jean Laurent Mosnier,"French, Paris 1743/44–1808 St. Petersburg",,"Mosnier, Jean Laurent",French,1743,1808,1790,1790,1790,Ivory,Diameter 2 3/4 in. (69 mm),"Bequest of Millie Bruhl Fredrick, 1962",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/437167,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -35.89.1,false,true,435915,European Paintings,"Painting, miniature","Charles de Cossé (1506–1563), Count of Brissac",,,,,,Artist,,Jean Clouet,"French, active by 1516–died 1540/41 Paris",,"Clouet, Jean",French,1516,1541,ca. 1535,1530,1540,Vellum,Diameter 1 1/2 in. (37 mm),"Fletcher Fund, 1935",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/435915,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -59.72.1,false,true,437594,European Paintings,"Painting, miniature",Portrait of a Churchman,,,,,,Artist,,E. Jean Saillant,"French, active by 1620–died in or after 1638",,"Saillant, E. Jean",French,1620,1638,1628,1628,1628,Vellum stretched over copper,"Octagonal, 6 1/8 x 4 3/4 in. (157 x 122 mm)","Rogers Fund, 1959",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/437594,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -59.72.2,false,true,437595,European Paintings,"Painting, miniature",Portrait of a Man,,,,,,Artist,,E. Jean Saillant,"French, active by 1620–died in or after 1638",,"Saillant, E. Jean",French,1620,1638,ca. 1628,1623,1633,Vellum stretched over wood,"Octagonal, 6 3/4 x 5 5/8 in. (170 x 142 mm)","Rogers Fund, 1959",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/437595,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -62.122.73,false,true,437353,European Paintings,"Painting, miniature",Portrait of a Man,,,,,,Artist,,Edme Quenedey,"French, Riceys-le-Haut (Aube) 1756–1830 Paris",,"Quenedey, Edme",French,1756,1830,ca. 1780,1775,1785,Ivory,Diameter 2 in. (49 mm),"Bequest of Millie Bruhl Fredrick, 1962",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/437353,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2002.439,false,true,438585,European Paintings,"Painting, drawing",Jean Charles Garnier d'Isle (1697–1755),,,,,,Artist,,Maurice Quentin de La Tour,"French, Saint-Quentin 1704–1788 Saint-Quentin",,"La Tour, Maurice Quentin de",French,1704,1788,ca. 1750,1745,1755,"Pastel and gouache on blue paper, laid down on canvas",25 3/8 x 21 1/4 in. (64.5 x 54 cm),"Purchase, Walter and Leonore Annenberg and The Annenberg Foundation Gift, 2002",,,,,,,,,,,,Pastels & Oil Sketches on Paper,,http://www.metmuseum.org/art/collection/search/438585,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -26.46.1,false,true,437345,European Paintings,Drawing,Cider,,,,,,Artist,,Pierre Puvis de Chavannes,"French, Lyons 1824–1898 Paris",,"Puvis de Chavannes, Pierre",French,1824,1898,ca. 1864,1859,1869,"Oil on paper, laid down on canvas",51 x 99 1/4 in. (129.5 x 252.1 cm),"Catharine Lorillard Wolfe Collection, Wolfe Fund, 1926",,,,,,,,,,,,Drawings,,http://www.metmuseum.org/art/collection/search/437345,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -26.46.2,false,true,437348,European Paintings,Drawing,The River,,,,,,Artist,,Pierre Puvis de Chavannes,"French, Lyons 1824–1898 Paris",,"Puvis de Chavannes, Pierre",French,1824,1898,ca. 1864,1859,1869,"Oil on paper, laid down on canvas",51 x 99 1/4 in. (129.5 x 252.1 cm),"Catharine Lorillard Wolfe Collection, Wolfe Fund, 1926",,,,,,,,,,,,Drawings,,http://www.metmuseum.org/art/collection/search/437348,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -22.27.2,false,true,435974,European Paintings,Drawing,Lake Albano and Castel Gandolfo,,,,,,Artist,,Camille Corot,"French, Paris 1796–1875 Paris",,"Corot, Camille",French,1796,1875,1826–27,1826,1827,"Oil on paper, laid down on wood",9 x 15 1/2 in. (22.9 x 39.4 cm),"Purchase, Dikran G. Kelekian Gift, 1922",,,,,,,,,,,,Drawings,,http://www.metmuseum.org/art/collection/search/435974,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -50.71.2,false,true,435973,European Paintings,Drawing,Italian Landscape,,,,,,Artist,,Camille Corot,"French, Paris 1796–1875 Paris",,"Corot, Camille",French,1796,1875,ca. 1825–28,1825,1828,"Oil on paper, laid down on canvas",5 x 10 5/8 in. (12.7 x 27 cm),"Gift of Mr. and Mrs. William B. Jaffe, 1950",,,,,,,,,,,,Drawings,,http://www.metmuseum.org/art/collection/search/435973,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1979.404,false,true,435970,European Paintings,Drawing,Fontainebleau: Oak Trees at Bas-Bréau,,,,,,Artist,,Camille Corot,"French, Paris 1796–1875 Paris",,"Corot, Camille",French,1796,1875,1832 or 1833,1832,1833,"Oil on paper, laid down on wood",15 5/8 x 19 1/2 in. (39.7 x 49.5 cm),"Catharine Lorillard Wolfe Collection, Wolfe Fund, 1979",,,,,,,,,,,,Drawings,,http://www.metmuseum.org/art/collection/search/435970,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.296,false,true,435660,European Paintings,Drawing,Ravine at Sorrento,,,,,,Artist,,Édouard Bertin,"French, Paris 1797–1871 Paris",,"Bertin, Édouard",French,1797,1871,1821 or later,1821,1871,Oil on paper mounted on board,16 1/8 x 11 5/8 in. (41 x 29.5 cm),"Purchase, Karen B. Cohen Gift, 1986",,,,,,,,,,,,Drawings,,http://www.metmuseum.org/art/collection/search/435660,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.55,false,true,436953,European Paintings,Drawing,George Moore (1852–1933),,,,,,Artist,,Édouard Manet,"French, Paris 1832–1883 Paris",,"Manet, Édouard",French,1832,1883,1879,1879,1879,Pastel on canvas,21 3/4 x 13 7/8 in. (55.2 x 35.2 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Drawings,,http://www.metmuseum.org/art/collection/search/436953,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.56,false,true,436958,European Paintings,Drawing,Mademoiselle Isabelle Lemonnier (1857–1926),,,,,,Artist,,Édouard Manet,"French, Paris 1832–1883 Paris",,"Manet, Édouard",French,1832,1883,1879–82,1879,1882,Pastel on canvas,22 x 18 1/4 in. (55.9 x 46.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Drawings,,http://www.metmuseum.org/art/collection/search/436958,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.561,false,true,436959,European Paintings,Drawing,"Mademoiselle Lucie Delabigne (1859–1910), Called Valtesse de la Bigne",,,,,,Artist,,Édouard Manet,"French, Paris 1832–1883 Paris",,"Manet, Édouard",French,1832,1883,1879,1879,1879,Pastel on canvas,21 3/4 x 14 in. (55.2 x 35.6 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Drawings,,http://www.metmuseum.org/art/collection/search/436959,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -56.231,false,true,436170,European Paintings,Drawing,Woman Combing Her Hair,,,,,,Artist,,Edgar Degas,"French, Paris 1834–1917 Paris",,"Degas, Edgar",French,1834,1917,ca. 1888–90,1888,1890,"Pastel on light green wove paper, now discolored to warm gray, affixed to original pulpboard mount",24 1/8 x 18 1/8 in. (61.3 x 46 cm),"Gift of Mr. and Mrs. Nate B. Spingold, 1956",,,,,,,,,,,,Drawings,,http://www.metmuseum.org/art/collection/search/436170,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -22.27.3,false,true,436151,European Paintings,Drawing,The Milliner,,,,,,Artist,,Edgar Degas,"French, Paris 1834–1917 Paris",,"Degas, Edgar",French,1834,1917,ca. 1882,1877,1887,"Pastel and charcoal on warm gray wove paper, now discolored to buff (watermark MICHALLET), laid down on dark brown wove paper",18 3/4 x 24 1/2 in. (47.6 x 62.2 cm),"Purchase, Rogers Fund and Dikran G. Kelekian Gift, 1922",,,,,,,,,,,,Drawings,,http://www.metmuseum.org/art/collection/search/436151,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1971.185,false,true,436132,European Paintings,Drawing,The Dance Lesson,,,,,,Artist,,Edgar Degas,"French, Paris 1834–1917 Paris",,"Degas, Edgar",French,1834,1917,ca. 1879,1874,1884,"Pastel and black chalk on three pieces of wove paper, joined together",25 3/8 x 22 1/8 in. (64.5 x 56.2 cm),"H. O. Havemeyer Collection, Gift of Adaline Havemeyer Perkins, in memory of her father, Horace Havemeyer, 1971",,,,,,,,,,,,Drawings,,http://www.metmuseum.org/art/collection/search/436132,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -61.101.6,false,true,436158,European Paintings,Drawing,Self-Portrait,,,,,,Artist,,Edgar Degas,"French, Paris 1834–1917 Paris",,"Degas, Edgar",French,1834,1917,ca. 1855–56,1855,1856,"Oil on paper, laid down on canvas",16 x 13 1/2 in. (40.6 x 34.3 cm),"Bequest of Stephen C. Clark, 1960",,,,,,,,,,,,Drawings,,http://www.metmuseum.org/art/collection/search/436158,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -61.101.7,false,true,436159,European Paintings,Drawing,The Singer in Green,,,,,,Artist,,Edgar Degas,"French, Paris 1834–1917 Paris",,"Degas, Edgar",French,1834,1917,ca. 1884,1879,1889,Pastel on light blue laid paper,23 3/4 x 18 1/4 in. (60.3 x 46.4 cm),"Bequest of Stephen C. Clark, 1960",,,,,,,,,,,,Drawings,,http://www.metmuseum.org/art/collection/search/436159,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.35,false,true,436173,European Paintings,Drawing,Woman Having Her Hair Combed,,,,,,Artist,,Edgar Degas,"French, Paris 1834–1917 Paris",,"Degas, Edgar",French,1834,1917,ca. 1886–88,1886,1888,"Pastel on light green wove paper, now discolored to warm gray, affixed to original pulpboard mount",29 1/8 x 23 7/8 in. (74 x 60.6 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Drawings,,http://www.metmuseum.org/art/collection/search/436173,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.36,false,true,436172,European Paintings,Drawing,Woman Drying Her Foot,,,,,,Artist,,Edgar Degas,"French, Paris 1834–1917 Paris",,"Degas, Edgar",French,1834,1917,1885–86,1885,1886,"Pastel on buff wove paper, affixed to original pulpboard mount",19 3/4 x 21 1/4 in. (50.2 x 54 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Drawings,,http://www.metmuseum.org/art/collection/search/436172,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.37,false,true,436128,European Paintings,Drawing,Woman with a Towel,,,,,,Artist,,Edgar Degas,"French, Paris 1834–1917 Paris",,"Degas, Edgar",French,1834,1917,1894 or 1898,1894,1894,Pastel on cream-colored wove paper with red and blue fibers throughout,37 3/4 x 30in. (95.9 x 76.2cm) Frame: 44 1/4 x 36 1/4 x 1 7/8 in. (112.4 x 92.1 x 4.8 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Drawings,,http://www.metmuseum.org/art/collection/search/436128,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.38,false,true,436126,European Paintings,Drawing,At the Milliner's,,,,,,Artist,,Edgar Degas,"French, Paris 1834–1917 Paris",,"Degas, Edgar",French,1834,1917,1882,1882,1882,"Pastel on pale gray wove paper (industrial wrapping paper), laid down on silk bolting",30 x 34 in. (76.2 x 86.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Drawings,,http://www.metmuseum.org/art/collection/search/436126,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.39,false,true,436156,European Paintings,Drawing,The Rehearsal Onstage,,,,,,Artist,,Edgar Degas,"French, Paris 1834–1917 Paris",,"Degas, Edgar",French,1834,1917,ca. 1874,1871,1877,"Pastel over brush-and-ink drawing on thin cream-colored wove paper, laid down on bristol board and mounted on canvas",21 x 28 1/2 in. (53.3 x 72.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Drawings,,http://www.metmuseum.org/art/collection/search/436156,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.40,false,true,436124,European Paintings,Drawing,"The Artist's Cousin, Probably Mrs. William Bell (Mathilde Musson, 1841–1878)",,,,,,Artist,,Edgar Degas,"French, Paris 1834–1917 Paris",,"Degas, Edgar",French,1834,1917,1873,1873,1873,"Pastel on green wove paper, now darkened to brown",18 5/8 x 15 1/8 in. (47.3 x 38.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Drawings,,http://www.metmuseum.org/art/collection/search/436124,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.41,false,true,436127,European Paintings,Drawing,Woman Bathing in a Shallow Tub,,,,,,Artist,,Edgar Degas,"French, Paris 1834–1917 Paris",,"Degas, Edgar",French,1834,1917,1885,1885,1885,"Charcoal and pastel on light green wove paper, now discolored to warm gray, laid down on silk bolting",32 x 22 1/8in. (81.3 x 56.2cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Drawings,,http://www.metmuseum.org/art/collection/search/436127,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.160.26,false,true,436155,European Paintings,Drawing,The Rehearsal of the Ballet Onstage,,,,,,Artist,,Edgar Degas,"French, Paris 1834–1917 Paris",,"Degas, Edgar",French,1834,1917,ca. 1874,1871,1877,"Oil colors freely mixed with turpentine, with traces of watercolor and pastel over pen-and-ink drawing on cream-colored wove paper, laid down on bristol board and mounted on canvas",21 3/8 x 28 3/4 in. (54.3 x 73 cm),"H. O. Havemeyer Collection, Gift of Horace Havemeyer, 1929",,,,,,,,,,,,Drawings,,http://www.metmuseum.org/art/collection/search/436155,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1991.277.1,false,true,436154,European Paintings,Drawing,Portraits at the Stock Exchange,,,,,,Artist,,Edgar Degas,"French, Paris 1834–1917 Paris",,"Degas, Edgar",French,1834,1917,ca. 1878–79,1878,1879,"Pastel on paper, pieced, and laid down on canvas",28 3/8 x 22 7/8 in. (72.1 x 58.1 cm),"Gift of Janice H. Levin, 1991",,,,,,,,,,,,Drawings,,http://www.metmuseum.org/art/collection/search/436154,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1999.288.3,false,true,437994,European Paintings,Drawing,Race Horses,,,,,,Artist,,Edgar Degas,"French, Paris 1834–1917 Paris",,"Degas, Edgar",French,1834,1917,ca. 1885–88,1885,1888,Pastel on wood,11 7/8 x 16 in. (30.2 x 40.6 cm),"The Walter H. and Leonore Annenberg Collection, Gift of Walter H. and Leonore Annenberg, 1999, Bequest of Walter H. Annenberg, 2002",,,,,,,,,,,,Drawings,,http://www.metmuseum.org/art/collection/search/437994,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.332.1,false,true,438518,European Paintings,Drawing,Dancer,,,,,,Artist,,Edgar Degas,"French, Paris 1834–1917 Paris",,"Degas, Edgar",French,1834,1917,1880–85,1880,1885,"Pastel on paper, laid down on board",19 1/4 x 12 3/4 in. (48.9 x 32.4 cm),"Gift of The Philip and Janice Levin Foundation, 2007",,,,,,,,,,,,Drawings,,http://www.metmuseum.org/art/collection/search/438518,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.185,false,true,436125,European Paintings,Drawing,Woman on a Sofa,,,,,,Artist,,Edgar Degas,"French, Paris 1834–1917 Paris",,"Degas, Edgar",French,1834,1917,1875,1875,1875,"Oil colors freely mixed with turpentine, with touches of pastel, over graphite underdrawing, on pink paper",19 1/8 x 16 3/4 in. (48.6 x 42.5 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Drawings,,http://www.metmuseum.org/art/collection/search/436125,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.187,false,true,436165,European Paintings,Drawing,Two Dancers,,,,,,Artist,,Edgar Degas,"French, Paris 1834–1917 Paris",,"Degas, Edgar",French,1834,1917,1873,1873,1873,"Dark brown wash and white gouache on bright pink commercially coated wove paper, now faded to pale pink",24 1/8 x 15 1/2 in. (61.3 x 39.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Drawings,,http://www.metmuseum.org/art/collection/search/436165,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.188,false,true,436135,European Paintings,Drawing,Dancer with a Fan,,,,,,Artist,,Edgar Degas,"French, Paris 1834–1917 Paris",,"Degas, Edgar",French,1834,1917,ca. 1880,1875,1885,Pastel on gray-green laid paper,24 x 16 1/2 in. (61 x 41.9 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Drawings,,http://www.metmuseum.org/art/collection/search/436135,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.189,false,true,436166,European Paintings,Drawing,Two Dancers,,,,,,Artist,,Edgar Degas,"French, Paris 1834–1917 Paris",,"Degas, Edgar",French,1834,1917,ca. 1879,1874,1879,Charcoal and white chalk on green commercially coated wove paper,25 1/8 x 19 1/4 in. (63.8 x 48.9 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Drawings,,http://www.metmuseum.org/art/collection/search/436166,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.190,false,true,436131,European Paintings,Drawing,Bather Stepping into a Tub,,,,,,Artist,,Edgar Degas,"French, Paris 1834–1917 Paris",,"Degas, Edgar",French,1834,1917,ca. 1890,1885,1895,"Pastel and charcoal on blue laid paper, mounted at perimeter on backing board",22 x 18 3/4 in. (55.9 x 47.6 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Drawings,,http://www.metmuseum.org/art/collection/search/436131,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.553,false,true,436171,European Paintings,Drawing,Woman Drying Her Arm,,,,,,Artist,,Edgar Degas,"French, Paris 1834–1917 Paris",,"Degas, Edgar",French,1834,1917,late 1880s–early 1890s,1885,1895,"Pastel and charcoal on light pink wove paper, discolored at the edges",12 x 17 1/2in. (30.5 x 44.5cm) Frame: 17 1/8 x 22 7/8 x 1 7/8 in. (43.5 x 58.1 x 4.8 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Drawings,,http://www.metmuseum.org/art/collection/search/436171,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.554,false,true,436143,European Paintings,Drawing,Fan Mount: The Ballet,,,,,,Artist,,Edgar Degas,"French, Paris 1834–1917 Paris",,"Degas, Edgar",French,1834,1917,1879,1879,1879,"Watercolor, India ink, silver, and gold on silk",6 1/8 x 21 1/4 in. (15.6 x 54 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Drawings,,http://www.metmuseum.org/art/collection/search/436143,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.555,false,true,436142,European Paintings,Drawing,Fan Mount: Ballet Girls,,,,,,Artist,,Edgar Degas,"French, Paris 1834–1917 Paris",,"Degas, Edgar",French,1834,1917,1879,1879,1879,"Watercolor, silver, and gold on silk",7 1/2 x 22 3/4 in. (19.1 x 57.8 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Drawings,,http://www.metmuseum.org/art/collection/search/436142,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.556,false,true,436157,European Paintings,Drawing,Russian Dancer,,,,,,Artist,,Edgar Degas,"French, Paris 1834–1917 Paris",,"Degas, Edgar",French,1834,1917,1899,1899,1899,Pastel over charcoal on tracing paper,24 3/8 x 18 in. (61.9 x 45.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Drawings,,http://www.metmuseum.org/art/collection/search/436157,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.557,false,true,436136,European Paintings,Drawing,Dancer with a Fan,,,,,,Artist,,Edgar Degas,"French, Paris 1834–1917 Paris",,"Degas, Edgar",French,1834,1917,ca. 1890–95,1890,1895,Pastel and charcoal on buff-colored wove tracing paper,21 7/8 x 19 1/4 in. (55.6 x 48.9 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Drawings,,http://www.metmuseum.org/art/collection/search/436136,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.558,false,true,436163,European Paintings,Drawing,Three Dancers Preparing for Class,,,,,,Artist,,Edgar Degas,"French, Paris 1834–1917 Paris",,"Degas, Edgar",French,1834,1917,after 1878,1878,1885,Pastel on buff-colored wove paper,21 1/2 x 20 1/2 in. (54.6 x 52.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Drawings,,http://www.metmuseum.org/art/collection/search/436163,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1974.356.30,false,true,436134,European Paintings,Drawing,Dancer Onstage,,,,,,Artist,,Edgar Degas,"French, Paris 1834–1917 Paris",,"Degas, Edgar",French,1834,1917,ca. 1877,1872,1882,"Gouache over graphite underdrawing on thin wove commercially coated yellow paper, laid down on board",Paper 6 3/4 x 8 3/8 in. (17.1 x 21.3 cm); board 7 x 9 in. (17.8 x 22.9 cm),"The Lesley and Emma Sheafer Collection, Bequest of Emma A. Sheafer, 1973",,,,,,,,,,,,Drawings,,http://www.metmuseum.org/art/collection/search/436134,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -56.50,false,true,437379,European Paintings,Drawing,Bouquet of Flowers,,,,,,Artist,,Odilon Redon,"French, Bordeaux 1840–1916 Paris",,"Redon, Odilon",French,1840,1916,probably ca. 1905,1900,1910,Pastel on paper,31 5/8 x 25 1/4 in. (80.3 x 64.1 cm),"Gift of Mrs. George B. Post, 1956",,,,,,,,,,,,Drawings,,http://www.metmuseum.org/art/collection/search/437379,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -60.54,false,true,437377,European Paintings,Drawing,"Madame Arthur Fontaine (Marie Escudier, born 1865)",,,,,,Artist,,Odilon Redon,"French, Bordeaux 1840–1916 Paris",,"Redon, Odilon",French,1840,1916,1901,1901,1901,Pastel on paper,28 1/2 x 22 1/2 in. (72.4 x 57.2 cm),"The Mr. and Mrs. Henry Ittleson Jr. Purchase Fund, 1960",,,,,,,,,,,,Drawings,,http://www.metmuseum.org/art/collection/search/437377,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -32.100.133,false,true,437521,European Paintings,Drawing,A Village in a Valley,,,,,,Artist,,Théodore Rousseau,"French, Paris 1812–1867 Barbizon",,"Rousseau, Théodore",French,1812,1867,late 1820s,1825,1835,"Oil on paper, mounted on canvas",9 1/8 x 16 in. (23.2 x 40.6 cm),"The Friedsam Collection, Bequest of Michael Friedsam, 1931",,,,,,,,,,,,Drawings,,http://www.metmuseum.org/art/collection/search/437521,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1990.117,false,true,435612,European Paintings,Drawing,"The Artist's Wife (Périe, 1849–1887) Reading",,,,,,Artist,,Albert Bartholomé,"French, Thiverval 1848–1928 Paris",,"Bartholomé, Albert",French,1848,1928,1883,1883,1883,"Pastel and charcoal on wove paper, laid down on blue wove paper, laid down on stretched canvas",19 7/8 x 24 1/8 in. (50.5 x 61.3 cm),"Catharine Lorillard Wolfe Collection, Wolfe Fund, 1990",,,,,,,,,,,,Drawings,,http://www.metmuseum.org/art/collection/search/435612,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1974.356.34,false,true,437435,European Paintings,Drawing,The Milliner,,,,,,Artist,,Auguste Renoir,"French, Limoges 1841–1919 Cagnes-sur-Mer",,"Renoir, Auguste",French,1841,1919,1877,1877,1877,Pastel on paper,21 x 16 1/4 in. (53.3 x 41.3 cm),"The Lesley and Emma Sheafer Collection, Bequest of Emma A. Sheafer, 1973",,,,,,,,,,,,Drawings,,http://www.metmuseum.org/art/collection/search/437435,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -64.153,false,true,437833,European Paintings,Drawing,"Madame Thadée Natanson (Misia Godebska, 1872–1950) at the Theater",,,,,,Artist,,Henri de Toulouse-Lautrec,"French, Albi 1864–1901 Saint-André-du-Bois",,"Toulouse-Lautrec, Henri de",French,1864,1901,1895,1895,1895,Oil on cardboard,24 1/2 x 29 1/2 in. (62.2 x 74.9 cm),"Gift of Mr. and Mrs. Richard Rodgers, 1964",,,,,,,,,,,,Drawings,,http://www.metmuseum.org/art/collection/search/437833,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2003.20.14,false,true,438017,European Paintings,Drawing,Henri-Gabriel Ibels (1867–1936),,,,,,Artist,,Henri de Toulouse-Lautrec,"French, Albi 1864–1901 Saint-André-du-Bois",,"Toulouse-Lautrec, Henri de",French,1864,1901,1892–93,1892,1893,Oil on cardboard,20 1/2 x 15 1/2 in. (52.1 x 39.4 cm),"The Walter H. and Leonore Annenberg Collection, Bequest of Walter H. Annenberg, 2002",,,,,,,,,,,,Drawings,,http://www.metmuseum.org/art/collection/search/438017,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1994.105,false,true,437315,European Paintings,Drawing,Fan Mount: The Cabbage Gatherers,,,,,,Artist,,Camille Pissarro,"French, Charlotte Amalie, Saint Thomas 1830–1903 Paris",,"Pissarro, Camille",French,1830,1903,ca. 1878–79,1850,1903,Gouache on silk,6 1/2 x 20 1/2 in. (16.5 x 52.1 cm),"Purchase, Leonora Brenauer Bequest, in memory of her father, Joseph B. Brenauer, 1994",,,,,,,,,,,,Drawings,,http://www.metmuseum.org/art/collection/search/437315,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -54.195,false,true,436336,European Paintings,"Painting, polyptych",The Pérussis Altarpiece,,,,,,Artist,Circle of,Nicolas Froment,,,"Froment, Nicolas",French,1460,1484,1480,1480,1480,Oil and gold on wood,"Three panels, each 54 1/2 x 23 in. (138.4 x 58.4 cm)","Purchase, Mary Wetmore Shively Bequest, in memory of her husband, Henry L. Shively, M.D., 1954",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436336,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -06.1268,false,true,435672,European Paintings,Painting,Angelica and Medoro,,,,,,Artist,,Jacques Blanchard,"French, 1600–1638",,"Blanchard, Jacques",French,1600,1638,possibly early 1630s,1630,1633,Oil on canvas,With added strip at top 47 7/8 x 69 1/4 in. (121.6 x 175.9 cm),"Gift of George A. Hearn, 1906",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435672,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -43.85.1,false,true,436709,European Paintings,Painting,Ingres (1780–1867) as a Young Man,,,,,,Artist,?,Madame Gustave Héquet,"French, 1845–1865",,"Héquet, Gustave, Madame",French,1845,1865,1850–60,1850,1860,Oil on canvas,34 x 27 1/ 2 in. (86.4 x 69.9 cm),"Bequest of Grace Rainey Rogers, 1943",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436709,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -67.187.101,false,true,489542,European Paintings,Painting,The Large Bouquet,,,,,,Artist,,Séraphine Louis,"French, 1864–1942",,"Louis, Séraphine",French,1864,1942,ca. 1907,1902,1912,Oil on canvas,57 1/2 x 44 3/4 in. (146.1 x 113.7 cm),"Bequest of Miss Adelaide Milton de Groot (1876–1967), 1967",,,,,,,,,,,,Paintings,"© 2016 Artists Rights Society (ARS), New York",http://www.metmuseum.org/art/collection/search/489542,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -27.59,false,true,437082,European Paintings,Painting,The Baker's Cart,,,,,,Artist,,Jean Michelin,"French, ca. 1616–1670",,"Michelin, Jean",French,1616,1670,1656,1656,1656,Oil on canvas,38 3/4 x 49 3/8 in. (98.4 x 125.4 cm),"Fletcher Fund, 1927",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437082,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -32.100.119,false,true,437143,European Paintings,Painting,Portrait of a Man in White,,,,,,Artist,,Monogrammist LAM,"French, active 1568–74",,Monogrammist LAM,French,1568,1574,1574,1574,1574,Oil on wood,16 1/8 x 9 1/2 in. (41 x 24.1 cm),"The Friedsam Collection, Bequest of Michael Friedsam, 1931",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437143,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -53.160,false,true,436190,European Paintings,Painting,Louis-Félix Amiel (1802–1864),,,,,,Artist,,Eugène Devéria,"French, Paris 1805–1865 Pau",,"Devéria, Eugène",French,1805,1865,1837,1837,1837,Oil on canvas,24 x 19 3/4 in. (61 x 50.2 cm),"Rogers Fund, 1953",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436190,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2004.243,false,true,438737,European Paintings,Painting,Faustine Léo (1832–1865),,,,,,Artist,,Henri Lehmann,"French, Kiel 1814–1882 Paris",,"Lehmann, Henri",French,1814,1882,1842,1842,1842,Oil on canvas,39 3/8 x 32 in. (100 x 81.3 cm),"Purchase, Wolfe Fund and Mr. and Mrs. Frank E. Richardson Gift, 2004",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/438737,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1995.317,false,true,437865,European Paintings,Painting,The Hunt Breakfast,,,,,,Artist,,Carle (Charles André) Vanloo,"French, Nice 1705–1765 Paris",,"Vanloo, Carle (Charles André)",French,1705,1765,ca. 1737,1732,1742,Oil on canvas,23 1/4 x 19 1/2 in. (59.1 x 49.5 cm),"Wrightsman Fund, 1995",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437865,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -07.225.285,false,true,437845,European Paintings,"Painting, study for a tapestry cartoon",The Triumph of Mordecai,,,,,,Artist,,Jean François de Troy,"French, Paris 1679–1752 Rome",,"Troy, Jean François de",French,1679,1752,ca. 1736,1731,1741,Oil on canvas,33 7/8 x 59 1/8 in. (86 x 150.2 cm),"Gift of J. Pierpont Morgan, 1906",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437845,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2002.31,false,true,438546,European Paintings,Painting,Madame Charles Maurice de Talleyrand Périgord (1761–1835),,,,,,Artist,,baron François Gérard,"French, Rome 1770–1837 Paris",,"Gérard, François, baron",French,1770,1837,ca. 1804,1799,1809,Oil on canvas,88 7/8 x 64 7/8 in. (225.7 x 164.8 cm),"Wrightsman Fund, 2002",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/438546,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2012.348,false,true,441969,European Paintings,Painting,"Charles Maurice de Talleyrand Périgord (1754–1838), Prince de Bénévent",,,,,,Artist,,baron François Gérard,"French, Rome 1770–1837 Paris",,"Gérard, François, baron",French,1770,1837,1808,1808,1808,Oil on canvas,83 7/8 x 57 7/8 in. (213 x 147 cm),"Purchase, Mrs. Charles Wrightsman Gift, 2012",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/441969,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1994.190,false,true,437340,European Paintings,Painting,"Charles Maurice de Talleyrand Périgord (1754–1838), Prince de Talleyrand",,,,,,Artist,,Pierre Paul Prud'hon,"French, Cluny 1758–1823 Paris",,"Prud'hon, Pierre Paul",French,1758,1823,1817,1817,1817,Oil on canvas,85 x 55 7/8 in. (215.9 x 141.9 cm),"Purchase, Mrs. Charles Wrightsman Gift, in memory of Jacqueline Bouvier Kennedy Onassis, 1994",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437340,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -39.15,false,true,437512,European Paintings,Painting,The Banks of the Bièvre near Bicêtre,,,,,,Artist,,Henri Rousseau (le Douanier),"French, Laval 1844–1910 Paris",,"Rousseau, Henri (le Douanier)",French,1844,1910,ca. 1908–09,1903,1913,Oil on canvas,21 1/2 x 18 in. (54.6 x 45.7 cm),"Gift of Marshall Field, 1939",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437512,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -51.112.5,true,true,438822,European Paintings,Painting,The Repast of the Lion,,,,,,Artist,,Henri Rousseau (le Douanier),"French, Laval 1844–1910 Paris",,"Rousseau, Henri (le Douanier)",French,1844,1910,ca. 1907,1902,1912,Oil on canvas,44 3/4 x 63 in. (113.7 x 160 cm),"Bequest of Sam A. Lewisohn, 1951",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/438822,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1978.493,false,true,435673,European Paintings,Painting,The Outer Harbor of Brest,,,,,,Artist,,Henri Joseph van Blarenberghe,"French, Lille 1750–1826 Lille",,"Blarenberghe, Henri Joseph van",French,1750,1826,1773,1773,1773,Oil on canvas,29 1/4 x 42 1/8 in. (74.3 x 107 cm),"Gift of Mrs. Vincent Astor, 1978",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435673,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -49.4,false,true,435849,European Paintings,Painting,"Mrs. William Astor (Caroline Webster Schermerhorn, 1831–1908)",,,,,,Artist,,Carolus-Duran (Charles-Auguste-Émile Durant),"French, Lille 1837–1917 Paris",,"Carolus-Duran, Charles-Auguste-Émile Durant",French,1837,1917,1890,1890,1890,Oil on canvas,83 1/2 x 42 1/4 in. (212.1 x 107.3 cm),"Gift of R. Thornton Wilson and Orme Wilson, 1949",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435849,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2014.608,false,true,669033,European Paintings,Painting,Henri Fantin-Latour (1836–1904),,,,,,Artist,,Carolus-Duran (Charles-Auguste-Émile Durant),"French, Lille 1837–1917 Paris",,"Carolus-Duran, Charles-Auguste-Émile Durant",French,1837,1917,1861,1861,1861,Oil on canvas,18 × 14 7/8 in. (45.7 × 37.8 cm),"Purchase, Marisa I. Alonso Bequest and Elizabeth and Thomas Easton Gift, in memory of their mother, Joan K. Easton, 2014",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/669033,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -26.260.9,false,true,437265,European Paintings,Painting,"Maria Luisa of Parma (1751–1819), Later Queen of Spain",,,,,,Artist,,Laurent Pécheux,"French, Lyons 1729–1821 Turin",,"Pécheux, Laurent",French,1729,1821,1765,1765,1765,Oil on canvas,90 7/8 x 64 3/4 in. (230.8 x 164.5 cm),"Bequest of Annie C. Kane, 1926",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437265,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2003.42.42,false,true,438665,European Paintings,Painting,Pope Gregory XVI Visiting the Church of San Benedetto at Subiaco,,,,,,Artist,,Jean-François Montessuy,"French, Lyons 1804–1876 Lyons",,"Montessuy, Jean-François",French,1804,1876,1843,1843,1843,Oil on canvas,49 1/4 x 55 3/8 in. (125.1 x 140.7 cm),"The Whitney Collection, Gift of Wheelock Whitney III, and Purchase, Gift of Mr. and Mrs. Charles S. McVeigh, by exchange, 2003",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/438665,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -87.20.1,false,true,437052,European Paintings,Painting,"1807, Friedland",,,,,,Artist,,Ernest Meissonier,"French, Lyons 1815–1891 Paris",,"Meissonier, Ernest",French,1815,1891,ca. 1861–75,1860,1875,Oil on canvas,53 1/2 x 95 1/2 in. (135.9 x 242.6 cm),"Gift of Henry Hilton, 1887",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437052,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -08.136.7,false,true,437050,European Paintings,Painting,Soldier Playing the Theorbo,,,,,,Artist,,Ernest Meissonier,"French, Lyons 1815–1891 Paris",,"Meissonier, Ernest",French,1815,1891,1865,1865,1865,Oil on wood,11 1/2 x 8 5/8 in. (29.2 x 21.9 cm),"Bequest of Martha T. Fiske Collord, in memory of her first husband, Josiah M. Fiske, 1908",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437050,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -87.15.37,false,true,437051,European Paintings,Painting,A General and His Aide-de-camp,,,,,,Artist,,Ernest Meissonier,"French, Lyons 1815–1891 Paris",,"Meissonier, Ernest",French,1815,1891,1869,1869,1869,Oil on wood,7 3/4 x 10 7/8 in. (19.7 x 27.6 cm),"Catharine Lorillard Wolfe Collection, Bequest of Catharine Lorillard Wolfe, 1887",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437051,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -25.110.39,false,true,437049,European Paintings,Painting,The Card Players,,,,,,Artist,,Ernest Meissonier,"French, Lyons 1815–1891 Paris",,"Meissonier, Ernest",French,1815,1891,1863,1863,1863,Oil on wood,13 7/8 x 10 1/2 in. (35.2 x 26.7 cm),"Bequest of Collis P. Huntington, 1900",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437049,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -30.20,false,true,437350,European Paintings,Painting,Tamaris,,,,,,Artist,,Pierre Puvis de Chavannes,"French, Lyons 1824–1898 Paris",,"Puvis de Chavannes, Pierre",French,1824,1898,ca. 1886–87,1886,1887,Oil on canvas,10 x 15 1/2 in. (25.4 x 39.4 cm),"H. O. Havemeyer Collection, Gift of Mrs. J. Watson Webb, 1930",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437350,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -06.177,false,true,437344,European Paintings,Painting,The Shepherd's Song,,,,,,Artist,,Pierre Puvis de Chavannes,"French, Lyons 1824–1898 Paris",,"Puvis de Chavannes, Pierre",French,1824,1898,1891,1891,1891,Oil on canvas,41 1/8 x 43 1/4 in. (104.5 x 109.9 cm),"Rogers Fund, 1906",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437344,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -58.15.1,false,true,437347,European Paintings,Painting,Ludus pro patria (Patriotic Games),,,,,,Artist,,Pierre Puvis de Chavannes,"French, Lyons 1824–1898 Paris",,"Puvis de Chavannes, Pierre",French,1824,1898,ca. 1883–89,1878,1894,Oil on canvas,13 1/8 x 52 7/8 in. (33.3 x 134.3 cm),"Gift of Mrs. Harry Payne Bingham, 1958",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437347,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -58.15.2,false,true,437346,European Paintings,Painting,Inter artes et naturam (Between Art and Nature),,,,,,Artist,,Pierre Puvis de Chavannes,"French, Lyons 1824–1898 Paris",,"Puvis de Chavannes, Pierre",French,1824,1898,ca. 1890–95,1885,1900,Oil on canvas,15 7/8 x 44 3/4 in. (40.3 x 113.7 cm),"Gift of Mrs. Harry Payne Bingham, 1958",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437346,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -30.95.253,false,true,437349,European Paintings,Painting,Sleep,,,,,,Artist,,Pierre Puvis de Chavannes,"French, Lyons 1824–1898 Paris",,"Puvis de Chavannes, Pierre",French,1824,1898,ca. 1867–70,1867,1870,Oil on canvas,26 1/8 x 41 3/4 in. (66.4 x 106 cm),"Theodore M. Davis Collection, Bequest of Theodore M. Davis, 1915",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437349,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.117,false,true,437343,European Paintings,Painting,The Allegory of the Sorbonne,,,,,,Artist,,Pierre Puvis de Chavannes,"French, Lyons 1824–1898 Paris",,"Puvis de Chavannes, Pierre",French,1824,1898,1889,1889,1889,Oil on canvas,32 5/8 x 180 1/4 in. (82.9 x 457.8 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437343,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -50.189,false,true,436836,European Paintings,Painting,Allegory of Music,,,,,,Artist,,Laurent de La Hyre,"French, Paris 1606–1656 Paris",,"La Hyre, Laurent de",French,1606,1656,1649,1649,1649,Oil on canvas,41 5/8 x 56 3/4 in. (105.7 x 144.1 cm),"Charles B. Curtis Fund, 1950",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436836,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1984.342,false,true,436858,European Paintings,Painting,The Rape of Tamar,,,,,,Artist,,Eustache Le Sueur,"French, Paris 1616–1655 Paris",,"Le Sueur, Eustache",French,1616,1655,probably ca. 1640,1635,1645,Oil on canvas,74 1/2 x 63 1/2 in. (189.2 x 161.3 cm),"Purchase, Mr. and Mrs. Charles Wrightsman Gift, 1984",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436858,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.183,false,true,442761,European Paintings,Painting,The Sacrifice of Polyxena,,,,,,Artist,,Charles Le Brun,"French, Paris 1619–1690 Paris",,"Le Brun, Charles",French,1619,1690,1647,1647,1647,Oil on canvas,67 5/16 × 51 9/16 in. (171 × 131 cm),"Purchase, 2012 Benefit Fund, and Bequest of Grace Wilkes and Fletcher Fund, by exchange, 2013",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/442761,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2014.250,false,true,626692,European Paintings,Painting,Everhard Jabach (1618–1695) and His Family,,,,,,Artist,,Charles Le Brun,"French, Paris 1619–1690 Paris",,"Le Brun, Charles",French,1619,1690,ca. 1660,1655,1665,Oil on canvas,110 1/4 × 129 1/8 in. (280 × 328 cm),"Purchase, Mrs. Charles Wrightsman Gift, in honor of Keith Christiansen, 2014",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/626692,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -03.37.2,false,true,436846,European Paintings,Painting,"Portrait of a Woman, Possibly Madame Claude Lambert de Thorigny (Marie Marguerite Bontemps, 1668–1701), and an Enslaved Servant",,,,,,Artist,,Nicolas de Largillierre,"French, Paris 1656–1746 Paris",,"Largillierre, Nicolas de",French,1656,1746,1696,1696,1696,Oil on canvas,55 x 42 in. (139.7 x 106.7 cm),"Rogers Fund, 1903",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436846,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1973.311.4,false,true,436847,European Paintings,Painting,André François Alloys de Theys d'Herculais (1692–1779),,,,,,Artist,,Nicolas de Largillierre,"French, Paris 1656–1746 Paris",,"Largillierre, Nicolas de",French,1656,1746,1727,1727,1727,Oil on canvas,54 1/4 x 41 1/2 in. (137.8 x 105.4 cm),"Gift of Mr. and Mrs. Charles Wrightsman, 1973",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436847,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -45.172,false,true,437181,European Paintings,Painting,Madame Marsollier and Her Daughter,,,,,,Artist,,Jean Marc Nattier,"French, Paris 1685–1766 Paris",,"Nattier, Jean Marc",French,1685,1766,1749,1749,1749,Oil on canvas,57 1/2 x 45 in. (146.1 x 114.3 cm),"Bequest of Florence S. Schuette, 1945",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437181,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -03.37.3,false,true,437183,European Paintings,Painting,Madame de Maison-Rouge as Diana,,,,,,Artist,,Jean Marc Nattier,"French, Paris 1685–1766 Paris",,"Nattier, Jean Marc",French,1685,1766,1756,1756,1756,Oil on canvas,53 3/4 x 41 3/8 in. (136.5 x 105.1 cm),"Rogers Fund, 1903",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437183,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -56.100.2,false,true,437184,European Paintings,Painting,The Spring (La Source),,,,,,Artist,,Jean Marc Nattier,"French, Paris 1685–1766 Paris",,"Nattier, Jean Marc",French,1685,1766,1738,1738,1738,Oil on canvas,31 3/4 x 25 5/8 in. (80.6 x 65.1 cm),"Gift of Jessie Woolworth Donahue, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437184,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -58.102.1,false,true,437185,European Paintings,Painting,"Marie Françoise de La Cropte de St. Abre, Marquise d'Argence",,,,,,Artist,,Jean Marc Nattier,"French, Paris 1685–1766 Paris",,"Nattier, Jean Marc",French,1685,1766,1744,1744,1744,Oil on canvas,32 1/2 x 25 1/2 in. (82.6 x 64.8 cm),"Gift of Jessie Woolworth Donahue, 1958",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437185,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1982.60.42,false,true,437182,European Paintings,Painting,"Portrait of a Woman, Called the Marquise Perrin de Cypierre",,,,,,Artist,,Jean Marc Nattier,"French, Paris 1685–1766 Paris",,"Nattier, Jean Marc",French,1685,1766,1753,1753,1753,Oil on canvas,"31 1/2 x 25 1/4 in. (80 x 64.1 cm), with later additions of 1 1/4 in. (3.2 cm) at bottom, 1 in. (2.5 cm) at left, and 1/2 in. (1.3 cm) at right","The Jack and Belle Linsky Collection, 1982",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437182,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2004.85,false,true,438726,European Paintings,Painting,The Servant Justified,,,,,,Artist,,Nicolas Lancret,"French, Paris 1690–1743 Paris",,"Lancret, Nicolas",French,1690,1743,ca. 1740,1735,1745,Oil on copper,11 x 14 in. (27.9 x 35.6 cm),"Purchase, Walter and Leonore Annenberg and The Annenberg Foundation Gift, 2004",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/438726,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2004.86,false,true,438727,European Paintings,Painting,Brother Philippe's Geese,,,,,,Artist,,Nicolas Lancret,"French, Paris 1690–1743 Paris",,"Lancret, Nicolas",French,1690,1743,ca. 1736,1731,1741,Oil on copper,10 3/4 x 13 7/8 in. (27.3 x 35.2 cm),"Purchase, Walter and Leonore Annenberg and The Annenberg Foundation Gift, 2004",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/438727,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -59.9,false,true,435887,European Paintings,Painting,The Silver Tureen,,,,,,Artist,,Jean Siméon Chardin,"French, Paris 1699–1779 Paris",,"Chardin, Jean Siméon",French,1699,1779,ca. 1728–30,1728,1730,Oil on canvas,30 x 42 1/2 in. (76.2 x 108 cm),"Fletcher Fund, 1959",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435887,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -49.24,true,true,435888,European Paintings,Painting,Soap Bubbles,,,,,,Artist,,Jean Siméon Chardin,"French, Paris 1699–1779 Paris",,"Chardin, Jean Siméon",French,1699,1779,ca. 1733–34,1733,1734,Oil on canvas,24 x 24 7/8 in. (61 x 63.2 cm),"Wentworth Fund, 1949",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435888,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -44.141,false,true,435743,European Paintings,Painting,The Dispatch of the Messenger,,,,,,Artist,,François Boucher,"French, Paris 1703–1770 Paris",,"Boucher, François",French,1703,1770,1765,1765,1765,Oil on canvas,"Oval, 12 5/8 x 10 1/2 in. (32.1 x 26.7 cm)","Gift of Mrs. Joseph Heine, in memory of her husband, I. D. Levy, 1944",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435743,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -66.167,false,true,435744,European Paintings,Painting,Virgin and Child with the Young Saint John the Baptist and Angels,,,,,,Artist,,François Boucher,"French, Paris 1703–1770 Paris",,"Boucher, François",French,1703,1770,1765,1765,1765,Oil on canvas,"Oval, 16 1/8 x 13 5/8 in. (41 x 34.6 cm)","Gift of Adelaide Milton de Groot, in memory of the de Groot and Hawley families, 1966",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435744,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -49.7.46,false,true,435738,European Paintings,Painting,The Interrupted Sleep,,,,,,Artist,,François Boucher,"French, Paris 1703–1770 Paris",,"Boucher, François",French,1703,1770,1750,1750,1750,Oil on canvas,Overall 32 1/4 x 29 5/8 in. (81.9 x 75.2 cm); painted surface (irregular oval) 31 x 27 3/4 in. (78.7 x 70.5 cm),"The Jules Bache Collection, 1949",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435738,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -20.155.9,true,true,435739,European Paintings,"Painting, overdoor",The Toilette of Venus,,,,,,Artist,,François Boucher,"French, Paris 1703–1770 Paris",,"Boucher, François",French,1703,1770,1751,1751,1751,Oil on canvas,42 5/8 x 33 1/2 in. (108.3 x 85.1 cm),"Bequest of William K. Vanderbilt, 1920",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435739,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -53.225.1,false,true,435745,European Paintings,Painting,Shepherd's Idyll,,,,,,Artist,,François Boucher,"French, Paris 1703–1770 Paris",,"Boucher, François",French,1703,1770,1768,1768,1768,Oil on canvas,94 1/2 x 93 1/2 in. (240 x 237.5 cm),"Gift of Julia A. Berwind, 1953",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435745,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -53.225.2,false,true,435746,European Paintings,Painting,Washerwomen,,,,,,Artist,,François Boucher,"French, Paris 1703–1770 Paris",,"Boucher, François",French,1703,1770,1768,1768,1768,Oil on canvas,95 x 93 in. (241.3 x 236.2 cm),"Gift of Julia A. Berwind, 1953",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435746,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -69.155.1,false,true,435740,European Paintings,Painting,Allegory of Autumn,,,,,,Artist,,François Boucher,"French, Paris 1703–1770 Paris",and Workshop,"Boucher, François",French,1703,1770,1753,1753,1753,Oil on canvas,"Irregular, 44 3/4 x 63 3/4 in. (113.7 x 161.9 cm)","Purchase, Mr. and Mrs. Charles Wrightsman Gift, 1969",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435740,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -69.155.2,false,true,435741,European Paintings,Painting,Allegory of Lyric Poetry,,,,,,Artist,,François Boucher,"French, Paris 1703–1770 Paris",and Workshop,"Boucher, François",French,1703,1770,1753,1753,1753,Oil on canvas,"Irregular, 45 1/4 x 62 3/4 in. (114.9 x 159.4 cm)","Purchase, Mr. and Mrs. Charles Wrightsman Gift, 1969",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435741,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1982.60.44,false,true,435737,European Paintings,Painting,Imaginary Landscape with the Palatine Hill from Campo Vaccino,,,,,,Artist,,François Boucher,"French, Paris 1703–1770 Paris",,"Boucher, François",French,1703,1770,1734,1734,1734,Oil on canvas,25 x 31 7/8 in. (63.5 x 81 cm),"The Jack and Belle Linsky Collection, 1982",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435737,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1982.60.45,false,true,435747,European Paintings,Painting,"Jupiter, in the Guise of Diana, and Callisto",,,,,,Artist,,François Boucher,"French, Paris 1703–1770 Paris",,"Boucher, François",French,1703,1770,1763,1763,1763,Oil on canvas,"Oval, 25 1/2 x 21 5/8 in. (64.8 x 54.9 cm)","The Jack and Belle Linsky Collection, 1982",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435747,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1982.60.46,false,true,435742,European Paintings,Painting,Angelica and Medoro,,,,,,Artist,,François Boucher,"French, Paris 1703–1770 Paris",,"Boucher, François",French,1703,1770,1763,1763,1763,Oil on canvas,"Oval, 26 1/4 x 22 1/8 in. (66.7 x 56.2 cm)","The Jack and Belle Linsky Collection, 1982",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435742,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -69.129,false,true,437286,European Paintings,Painting,The Death of Harmonia,,,,,,Artist,,Jean-Baptiste Marie Pierre,"French, Paris 1714–1789 Paris",,"Pierre, Jean-Baptiste Marie",French,1714,1789,ca. 1740–41,1740,1741,Oil on canvas,77 1/2 x 58 1/4 in. (196.9 x 148 cm),"Gift of Mr. and Mrs. Harry N. Abrams, by exchange, 1969",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437286,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -49.7.47,false,true,436214,European Paintings,Painting,"Marie Rinteau, called Mademoiselle de Verrières",,,,,,Artist,,François Hubert Drouais,"French, Paris 1727–1775 Paris",,"Drouais, François Hubert",French,1727,1775,1761,1761,1761,Oil on canvas,45 1/2 x 34 5/8 in. (115.6 x 87.9 cm),"The Jules Bache Collection, 1949",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436214,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -64.159.1,false,true,436215,European Paintings,Painting,Madame Sophie de France (1734–1782),,,,,,Artist,,François Hubert Drouais,"French, Paris 1727–1775 Paris",,"Drouais, François Hubert",French,1727,1775,1762,1762,1762,Oil on canvas,25 5/8 x 20 7/8 in. (65.1 x 53 cm),"Gift of Barbara Lowe Fallass, 1964",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436215,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -65.242.2,false,true,436216,European Paintings,Painting,Portrait of a Young Woman as a Vestal Virgin,,,,,,Artist,,François Hubert Drouais,"French, Paris 1727–1775 Paris",,"Drouais, François Hubert",French,1727,1775,1767,1767,1767,Oil on canvas,31 1/2 x 24 7/8 in. (80 x 63.2 cm),"Gift of Mrs. William M. Haupt, from the collection of Mrs. James B. Haggin, 1965",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436216,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -17.120.210,false,true,436213,European Paintings,Painting,"Portrait of a Woman, Said to be Madame Charles Simon Favart (Marie Justine Benoîte Duronceray, 1727–1772)",,,,,,Artist,,François Hubert Drouais,"French, Paris 1727–1775 Paris",,"Drouais, François Hubert",French,1727,1775,1757,1757,1757,Oil on canvas,31 1/2 x 25 1/2 in. (80 x 64.8 cm),"Mr. and Mrs. Isaac D. Fletcher Collection, Bequest of Isaac D. Fletcher, 1917",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436213,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -35.40.1,false,true,437479,European Paintings,Painting,The Return of the Cattle,,,,,,Artist,,Hubert Robert,"French, Paris 1733–1808 Paris",,"Robert, Hubert",French,1733,1808,ca. 1773–75,1773,1775,Oil on canvas,80 3/4 x 48 in. (205.1 x 121.9 cm),"Bequest of Lucy Work Hewitt, 1934",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437479,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -35.40.2,false,true,437468,European Paintings,Painting,The Portico of a Country Mansion,,,,,,Artist,,Hubert Robert,"French, Paris 1733–1808 Paris",,"Robert, Hubert",French,1733,1808,1773,1773,1773,Oil on canvas,80 3/4 x 48 1/4 in. (205.1 x 122.6 cm),"Bequest of Lucy Work Hewitt, 1934",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437468,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -17.190.25,false,true,437470,European Paintings,Painting,The Mouth of a Cave,,,,,,Artist,,Hubert Robert,"French, Paris 1733–1808 Paris",,"Robert, Hubert",French,1733,1808,1784,1784,1784,Oil on canvas,68 3/4 x 31 1/4 in. (174.6 x 79.4 cm),"Gift of J. Pierpont Morgan, 1917",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437470,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -07.225.504,false,true,437864,European Paintings,Painting,Vase of Flowers and Conch Shell,,,,,,Artist,,Anne Vallayer-Coster,"French, Paris 1744–1818 Paris",,"Vallayer-Coster, Anne",French,1744,1818,1780,1780,1780,Oil on canvas,"Oval, 19 3/4 x 15 in. (50.2 x 38.1 cm)","Gift of J. Pierpont Morgan, 1906",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437864,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -53.225.5,false,true,436840,European Paintings,Painting,"Self-Portrait with Two Pupils, Marie Gabrielle Capet (1761–1818) and Marie Marguerite Carreaux de Rosemond (died 1788)",,,,,,Artist,,Adélaïde Labille-Guiard,"French, Paris 1749–1803 Paris",,"Labille-Guiard, Adélaïde",French,1749,1803,1785,1785,1785,Oil on canvas,83 x 59 1/2 in. (210.8 x 151.1 cm),"Gift of Julia A. Berwind, 1953",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436840,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -57.103,false,true,436875,European Paintings,Painting,The Interior of an Atelier of a Woman Painter,,,,,,Artist,,Marie Victoire Lemoine,"French, Paris 1754–1820 Paris",,"Lemoine, Marie Victoire",French,1754,1820,1789,1789,1789,Oil on canvas,45 7/8 x 35 in. (116.5 x 88.9 cm),"Gift of Mrs. Thorneycroft Ryle, 1957",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436875,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1982.60.49,false,true,437775,European Paintings,Painting,The Billiard Room,,,,,,Artist,,Nicolas Antoine Taunay,"French, Paris 1755–1830 Paris",,"Taunay, Nicolas Antoine",French,1755,1830,ca. 1808,1803,1813,Oil on wood,6 3/8 x 8 5/8 in. (16.2 x 21.9 cm),"The Jack and Belle Linsky Collection, 1982",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437775,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -54.182,false,true,437900,European Paintings,Painting,"Comtesse de la Châtre (Marie Charlotte Louise Perrette Aglaé Bontemps, 1762–1848)",,,,,,Artist,,Élisabeth Louise Vigée Le Brun,"French, Paris 1755–1842 Paris",,"Vigée Le Brun, Élisabeth Louise",French,1755,1842,1789,1789,1789,Oil on canvas,45 x 34 1/2 in. (114.3 x 87.6 cm),"Gift of Jessie Woolworth Donahue, 1954",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437900,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -49.7.53,false,true,437899,European Paintings,Painting,Alexandre Charles Emmanuel de Crussol-Florensac (1743–1815),,,,,,Artist,,Élisabeth Louise Vigée Le Brun,"French, Paris 1755–1842 Paris",,"Vigée Le Brun, Élisabeth Louise",French,1755,1842,1787,1787,1787,Oil on wood,35 3/8 x 25 1/2 in. (89.9 x 64.8 cm),"The Jules Bache Collection, 1949",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437899,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -50.135.2,false,true,437898,European Paintings,Painting,"Madame Grand (Noël Catherine Vorlée, 1761–1835)",,,,,,Artist,,Élisabeth Louise Vigée Le Brun,"French, Paris 1755–1842 Paris",,"Vigée Le Brun, Élisabeth Louise",French,1755,1842,1783,1783,1783,Oil on canvas,"Oval, 36 1/4 x 28 1/2 in. (92.1 x 72.4 cm)","Bequest of Edward S. Harkness, 1940",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437898,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -25.110.8,false,true,437080,European Paintings,Painting,The Mill of Montmartre,,,,,,Artist,,Georges Michel,"French, Paris 1763–1843 Paris",,"Michel, Georges",French,1763,1843,probably ca. 1820,1783,1843,Oil on canvas,29 x 40 in. (73.7 x 101.6 cm),"Bequest of Collis P. Huntington, 1900",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437080,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -53.61.4,false,true,435650,European Paintings,Painting,"Madame Philippe Panon Desbassayns de Richemont (Jeanne Eglé Mourgue, 1778–1855) and Her Son, Eugène (1800–1859)",,,,,,Artist,,Marie Guillelmine Benoist,"French, Paris 1768–1826 Paris",,"Benoist, Marie Guillelmine",French,1768,1826,1802,1802,1802,Oil on canvas,46 x 35 1/4 in. (116.8 x 89.5 cm),"Gift of Julia A. Berwind, 1953",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435650,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -62.254,false,true,437887,European Paintings,Painting,Bertel Thorvaldsen (1768–1844) with the Bust of Horace Vernet,,,,,,Artist,,Horace Vernet,"French, Paris 1789–1863 Paris",,"Vernet, Horace",French,1789,1863,1833 or later,1833,1833,Oil on canvas,38 x 29 1/2 in. (96.5 x 74.9 cm),"Gift of Dr. Rudolf J. Heinemann, 1962",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437887,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1998.84,false,true,438033,European Paintings,Painting,Jean-Louis-André-Théodore Gericault (1791–1824),,,,,,Artist,,Horace Vernet,"French, Paris 1789–1863 Paris",,"Vernet, Horace",French,1789,1863,probably 1822 or 1823,1822,1823,Oil on canvas,18 5/8 x 15 1/8 in. (47.3 x 38.4 cm),"Purchase, Gift of Joanne Toor Cummings, by exchange, 1998",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/438033,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -87.15.47,false,true,437888,European Paintings,Painting,The Start of the Race of the Riderless Horses,,,,,,Artist,,Horace Vernet,"French, Paris 1789–1863 Paris",,"Vernet, Horace",French,1789,1863,1820,1820,1820,Oil on canvas,18 1/8 x 21 1/4 in. (46 x 54 cm),"Catharine Lorillard Wolfe Collection, Bequest of Catharine Lorillard Wolfe, 1887",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437888,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1994.376,false,true,437078,European Paintings,Painting,Waterfall at Mont-Dore,,,,,,Artist,,Achille-Etna Michallon,"French, Paris 1796–1822 Paris",,"Michallon, Achille-Etna",French,1796,1822,1818,1818,1818,Oil on canvas,16 1/4 x 22 1/8 in. (41.3 x 56.2 cm),"Purchase, Wolfe Fund and Nancy Richardson Gift, 1994",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437078,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -28.90,false,true,435991,European Paintings,Painting,A Woman Reading,,,,,,Artist,,Camille Corot,"French, Paris 1796–1875 Paris",,"Corot, Camille",French,1796,1875,1869 and 1870,1869,1870,Oil on canvas,21 3/8 x 14 3/4 in. (54.3 x 37.5 cm),"Gift of Louise Senff Cameron, in memory of her uncle, Charles H. Senff, 1928",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435991,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -30.13,false,true,435977,European Paintings,Painting,Mother and Child,,,,,,Artist,,Camille Corot,"French, Paris 1796–1875 Paris",,"Corot, Camille",French,1796,1875,probably 1860s,1860,1869,Oil on wood,12 3/4 x 8 7/8 in. (32.4 x 22.5 cm),"H. O. Havemeyer Collection, Gift of Mrs. P. H. B. Frelinghuysen, 1930",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435977,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -38.64,false,true,435962,European Paintings,Painting,Hagar in the Wilderness,,,,,,Artist,,Camille Corot,"French, Paris 1796–1875 Paris",,"Corot, Camille",French,1796,1875,1835,1835,1835,Oil on canvas,71 x 106 1/2 in. (180.3 x 270.5 cm),"Rogers Fund, 1938",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435962,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1974.3,false,true,435972,European Paintings,Painting,Honfleur: Calvary,,,,,,Artist,,Camille Corot,"French, Paris 1796–1875 Paris",,"Corot, Camille",French,1796,1875,ca. 1830,1825,1835,Oil on wood,11 3/4 x 16 1/8 in. (29.8 x 41 cm),"Purchase, Mr. and Mrs. Richard J. Bernhard Gift, by exchange, 1974",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435972,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -11.45.4,false,true,435983,European Paintings,Painting,River with a Distant Tower,,,,,,Artist,,Camille Corot,"French, Paris 1796–1875 Paris",,"Corot, Camille",French,1796,1875,1865,1865,1865,Oil on canvas,21 1/2 x 30 7/8 in. (54.6 x 78.4 cm),"Bequest of Robert Graham Dun, 1900",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435983,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1984.75,false,true,435985,European Paintings,Painting,"Study for ""The Destruction of Sodom""",,,,,,Artist,,Camille Corot,"French, Paris 1796–1875 Paris",,"Corot, Camille",French,1796,1875,1843,1843,1843,Oil on canvas,14 1/8 x 19 5/8 in. (35.9 x 49.8 cm),"Catharine Lorillard Wolfe Collection, Wolfe Fund, 1984",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435985,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -21.70.4,false,true,435989,European Paintings,Painting,The Banks of the Seine at Conflans,,,,,,Artist,,Camille Corot,"French, Paris 1796–1875 Paris",,"Corot, Camille",French,1796,1875,ca. 1865–70,1865,1870,Oil on canvas,18 1/4 x 21 7/8 in. (46.4 x 55.6 cm),"Bequest of Eloise Lawrence Breese Norrie, 1921",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435989,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.40.811,false,true,435969,European Paintings,Painting,The Ferryman,,,,,,Artist,,Camille Corot,"French, Paris 1796–1875 Paris",,"Corot, Camille",French,1796,1875,ca. 1865,1860,1870,Oil on canvas,26 1/8 x 19 3/8 in. (66.4 x 49.2 cm),"Bequest of Benjamin Altman, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435969,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.40.813,false,true,435979,European Paintings,Painting,A Pond in Picardy,,,,,,Artist,,Camille Corot,"French, Paris 1796–1875 Paris",,"Corot, Camille",French,1796,1875,ca. 1867,1862,1872,Oil on canvas,17 x 25 in. (43.2 x 63.5 cm),"Bequest of Benjamin Altman, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435979,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.40.817,false,true,435975,European Paintings,Painting,A Lane through the Trees,,,,,,Artist,,Camille Corot,"French, Paris 1796–1875 Paris",,"Corot, Camille",French,1796,1875,ca. 1870–73,1870,1873,Oil on canvas,24 x 18 in. (61 x 45.7 cm),"Bequest of Benjamin Altman, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435975,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -25.110.17,false,true,435987,European Paintings,Painting,A Village Street: Dardagny,,,,,,Artist,,Camille Corot,"French, Paris 1796–1875 Paris",,"Corot, Camille",French,1796,1875,"1852, 1857, or 1863",1852,1863,Oil on canvas,13 1/2 x 9 1/2 in. (34.3 x 24.1 cm),"Bequest of Collis P. Huntington, 1900",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435987,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.18,false,true,435967,European Paintings,Painting,"The Burning of Sodom (formerly ""The Destruction of Sodom"")",,,,,,Artist,,Camille Corot,"French, Paris 1796–1875 Paris",,"Corot, Camille",French,1796,1875,1843 and 1857,1843,1857,Oil on canvas,36 3/8 x 71 3/8 in. (92.4 x 181.3 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435967,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.19,false,true,435963,European Paintings,Painting,Bacchante by the Sea,,,,,,Artist,,Camille Corot,"French, Paris 1796–1875 Paris",,"Corot, Camille",French,1796,1875,1865,1865,1865,Oil on wood,15 1/4 x 23 3/8 in. (38.7 x 59.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435963,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.160.33,false,true,435976,European Paintings,Painting,The Letter,,,,,,Artist,,Camille Corot,"French, Paris 1796–1875 Paris",,"Corot, Camille",French,1796,1875,ca. 1865,1860,1870,Oil on wood,21 1/2 x 14 1/4 in. (54.6 x 36.2 cm),"H. O. Havemeyer Collection, Gift of Horace Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435976,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -30.95.272,false,true,435968,European Paintings,Painting,The Environs of Paris,,,,,,Artist,,Camille Corot,"French, Paris 1796–1875 Paris",,"Corot, Camille",French,1796,1875,1860s,1860,1869,Oil on wood,13 1/2 x 20 1/4 in. (34.3 x 51.4 cm),"Theodore M. Davis Collection, Bequest of Theodore M. Davis, 1915",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435968,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -87.15.141,false,true,435988,European Paintings,Painting,Ville-d'Avray,,,,,,Artist,,Camille Corot,"French, Paris 1796–1875 Paris",,"Corot, Camille",French,1796,1875,1870,1870,1870,Oil on canvas,21 5/8 x 31 1/2 in. (54.9 x 80 cm),"Catharine Lorillard Wolfe Collection, Bequest of Catharine Lorillard Wolfe, 1887",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435988,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -17.120.212,false,true,435964,European Paintings,Painting,The Gypsies,,,,,,Artist,,Camille Corot,"French, Paris 1796–1875 Paris",,"Corot, Camille",French,1796,1875,1872,1872,1872,Oil on canvas,21 3/4 x 31 1/2 in. (55.2 x 80 cm),"Mr. and Mrs. Isaac D. Fletcher Collection, Bequest of Isaac D. Fletcher, 1917",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435964,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -17.120.225,false,true,435990,European Paintings,Painting,A Woman Gathering Faggots at Ville-d'Avray,,,,,,Artist,,Camille Corot,"French, Paris 1796–1875 Paris",,"Corot, Camille",French,1796,1875,ca. 1871–74,1871,1874,Oil on canvas,28 3/8 x 22 1/2 in. (72.1 x 57.2 cm),"Mr. and Mrs. Isaac D. Fletcher Collection, Bequest of Isaac D. Fletcher, 1917",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435990,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1980.203.4,false,true,435986,European Paintings,Painting,View of Lormes,,,,,,Artist,,Camille Corot,"French, Paris 1796–1875 Paris",,"Corot, Camille",French,1796,1875,early 1840s,1840,1844,Oil on canvas,6 1/2 x 21 5/8 in. (16.5 x 54.9 cm),"Gift of Mr. and Mrs. Walter Mendelsohn, 1980",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435986,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1999.288.2,false,true,437991,European Paintings,Painting,The Curious Little Girl,,,,,,Artist,,Camille Corot,"French, Paris 1796–1875 Paris",,"Corot, Camille",French,1796,1875,1860–64,1816,1875,"Oil on cardboard, laid down on wood",16 1/4 x 11 1/4 in. (41.3 x 28.6 cm),"The Walter H. and Leonore Annenberg Collection, Gift of Walter H. and Leonore Annenberg, 1999, Bequest of Walter H. Annenberg, 2002",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437991,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2003.42.13,false,true,438635,European Paintings,Painting,Waterfall at Terni,,,,,,Artist,,Camille Corot,"French, Paris 1796–1875 Paris",,"Corot, Camille",French,1796,1875,1826,1826,1826,"Oil on paper, laid down on wood",10 1/2 x 12 1/8 in. (26.7 x 30.8 cm),"The Whitney Collection, Gift of Wheelock Whitney III, and Purchase, Gift of Mr. and Mrs. Charles S. McVeigh, by exchange, 2003",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/438635,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.193,false,true,435978,European Paintings,Painting,The Muse: History,,,,,,Artist,,Camille Corot,"French, Paris 1796–1875 Paris",,"Corot, Camille",French,1796,1875,ca. 1865,1860,1870,Oil on canvas,18 1/8 x 13 7/8 in. (46 x 35.2 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435978,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.562,false,true,435971,European Paintings,Painting,Girl Weaving a Garland,,,,,,Artist,,Camille Corot,"French, Paris 1796–1875 Paris",,"Corot, Camille",French,1796,1875,1860–65,1860,1865,Oil on canvas,16 1/2 x 11 3/4 in. (41.9 x 29.8 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435971,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.563,false,true,435981,European Paintings,Painting,Reverie,,,,,,Artist,,Camille Corot,"French, Paris 1796–1875 Paris",,"Corot, Camille",French,1796,1875,ca. 1860–65,1855,1870,Oil on wood,19 5/8 x 14 3/8 in. (49.8 x 36.5 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435981,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.564,false,true,435980,European Paintings,Painting,Portrait of a Child,,,,,,Artist,,Camille Corot,"French, Paris 1796–1875 Paris",,"Corot, Camille",French,1796,1875,ca. 1835,1830,1840,Oil on wood,12 5/8 x 9 1/4 in. (32.1 x 23.5 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435980,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.565,true,true,435984,European Paintings,Painting,Sibylle,,,,,,Artist,,Camille Corot,"French, Paris 1796–1875 Paris",,"Corot, Camille",French,1796,1875,ca. 1870,1865,1875,Oil on canvas,32 1/4 x 25 1/2 in. (81.9 x 64.8 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435984,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.598,false,true,435965,European Paintings,Painting,Bacchante in a Landscape,,,,,,Artist,,Camille Corot,"French, Paris 1796–1875 Paris",,"Corot, Camille",French,1796,1875,1865–70,1865,1870,Oil on canvas,12 1/8 x 24 1/4 in. (30.8 x 61.5 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435965,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -32.100.136,false,true,435966,European Paintings,Painting,Boatman among the Reeds,,,,,,Artist,,Camille Corot,"French, Paris 1796–1875 Paris",,"Corot, Camille",French,1796,1875,ca. 1865,1860,1870,Oil on canvas,23 1/2 x 32 in. (59.7 x 81.3 cm),"The Friedsam Collection, Bequest of Michael Friedsam, 1931",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435966,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.201.13,false,true,435961,European Paintings,Painting,Toussaint Lemaistre (1807/8–1888),,,,,,Artist,,Camille Corot,"French, Paris 1796–1875 Paris",,"Corot, Camille",French,1796,1875,1833,1833,1833,Oil on canvas,15 1/8 x 11 5/8 in. (38.4 x 29.5 cm),"Bequest of Joan Whitney Payson, 1975",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435961,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.164.5,false,true,438950,European Paintings,Painting,A Storm off the Normandy Coast,,,,,,Artist,,Eugène Isabey,"French, Paris 1803–1886 Lagny",,"Isabey, Eugène",French,1803,1886,possibly ca. 1850,1840,1860,"Oil on paper, laid down on canvas",13 x 20 in. (33 x 50.8 cm),"Gift of The Eugene Victor Thaw Art Foundation, 2007",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/438950,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -97.40,false,true,436772,European Paintings,Painting,The Sheepfold,,,,,,Artist,,Charles Jacque,"French, Paris 1813–1894 Paris",,"Jacque, Charles",French,1813,1894,1857,1857,1857,Oil on wood,18 1/8 x 36 1/8 in. (46 x 91.8 cm),"Catharine Lorillard Wolfe Collection, Wolfe Fund, 1897",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436772,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -87.15.106,false,true,436418,European Paintings,Painting,Jerusalem from the Mount of Olives,,,,,,Artist,,Charles-Théodore Frère,"French, Paris 1814–1888 Paris",,"Frère, Charles-Théodore",French,1814,1888,by 1880,1870,1880,Oil on canvas,29 1/2 x 43 1/2 in. (74.9 x 110.5 cm),"Catharine Lorillard Wolfe Collection, Bequest of Catharine Lorillard Wolfe, 1887",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436418,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -03.29,false,true,436084,European Paintings,Painting,Boats on the Seacoast at Étaples,,,,,,Artist,,Charles-François Daubigny,"French, Paris 1817–1878 Paris",,"Daubigny, Charles-François",French,1817,1878,1871,1871,1871,Oil on wood,13 1/2 x 22 7/8 in. (34.3 x 58.1 cm),"Catharine Lorillard Wolfe Collection, Wolfe Fund, 1903",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436084,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -11.45.3,false,true,436089,European Paintings,Painting,The Hamlet of Optevoz,,,,,,Artist,,Charles-François Daubigny,"French, Paris 1817–1878 Paris",,"Daubigny, Charles-François",French,1817,1878,ca. 1852,1849,1857,Oil on canvas,22 3/4 x 36 1/2 in. (57.8 x 92.7 cm),"Bequest of Robert Graham Dun, 1900",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436089,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -08.136.4,false,true,436090,European Paintings,Painting,Landscape with a Sunlit Stream,,,,,,Artist,,Charles-François Daubigny,"French, Paris 1817–1878 Paris",,"Daubigny, Charles-François",French,1817,1878,ca. 1877,1877,1877,Oil on canvas,25 1/8 x 18 7/8 in. (63.8 x 47.9 cm),"Bequest of Martha T. Fiske Collord, in memory of her first husband, Josiah M. Fiske, 1908",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436090,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -25.110.3,false,true,436085,European Paintings,Painting,Apple Blossoms,,,,,,Artist,,Charles-François Daubigny,"French, Paris 1817–1878 Paris",,"Daubigny, Charles-François",French,1817,1878,1873,1873,1873,Oil on canvas,23 1/8 x 33 3/8 in. (58.7 x 84.8 cm),"Bequest of Collis P. Huntington, 1900",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436085,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -64.149.7,false,true,436081,European Paintings,Painting,Landscape on a River,,,,,,Artist,,Charles-François Daubigny,"French, Paris 1817–1878 Paris",,"Daubigny, Charles-François",French,1817,1878,1863,1863,1863,Oil on wood,8 1/4 x 15 in. (21 x 38.1 cm),"Gift of Mary V. T. Eberstadt, 1964",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436081,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.40.815,false,true,436080,European Paintings,Painting,The Banks of the Oise,,,,,,Artist,,Charles-François Daubigny,"French, Paris 1817–1878 Paris",,"Daubigny, Charles-François",French,1817,1878,1863,1863,1863,Oil on wood,14 3/4 x 26 3/8 in. (37.5 x 67 cm),"Bequest of Benjamin Altman, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436080,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.40.818,false,true,436083,European Paintings,Painting,A River Landscape with Storks,,,,,,Artist,,Charles-François Daubigny,"French, Paris 1817–1878 Paris",,"Daubigny, Charles-François",French,1817,1878,1864,1864,1864,Oil on wood,9 1/2 x 17 5/8 in. (24.1 x 44.8 cm),"Bequest of Benjamin Altman, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436083,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -30.95.275,false,true,436088,European Paintings,Painting,Portejoie on the Seine,,,,,,Artist,,Charles-François Daubigny,"French, Paris 1817–1878 Paris",,"Daubigny, Charles-François",French,1817,1878,1850–1878,1850,1878,Oil on wood,9 5/8 x 17 3/8 in. (24.4 x 44.1 cm),"Theodore M. Davis Collection, Bequest of Theodore M. Davis, 1915",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436088,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -87.15.120,false,true,436086,European Paintings,Painting,The Seine: Morning,,,,,,Artist,,Charles-François Daubigny,"French, Paris 1817–1878 Paris",,"Daubigny, Charles-François",French,1817,1878,1874,1874,1874,Oil on wood,15 1/4 x 27 1/4 in. (38.7 x 69.2 cm),"Catharine Lorillard Wolfe Collection, Bequest of Catharine Lorillard Wolfe, 1887",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436086,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -17.138.2,false,true,436970,European Paintings,Painting,Penelope,,,,,,Artist,,Charles-François Marchal,"French, Paris 1825–1877 Paris",,"Marchal, Charles-François",French,1825,1877,ca. 1868,1845,1877,Oil on canvas,43 1/2 x 19 1/2 in. (110.5 x 49.5 cm),"Gift of Mrs. Adolf Obrig, in memory of her husband, 1917",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436970,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -21.134.1,false,true,437153,European Paintings,Painting,Oedipus and the Sphinx,,,,,,Artist,,Gustave Moreau,"French, Paris 1826–1898 Paris",,"Moreau, Gustave",French,1826,1898,1864,1864,1864,Oil on canvas,81 1/4 x 41 1/4 in. (206.4 x 104.8 cm),"Bequest of William H. Herriman, 1920",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437153,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -87.15.119,false,true,436184,European Paintings,Painting,Objects of Art from the Louvre,,,,,,Artist,,Blaise-Alexandre Desgoffe,"French, Paris 1830–1901 Paris",,"Desgoffe, Blaise-Alexandre",French,1830,1901,1874,1874,1874,Oil on canvas,28 3/4 x 36 1/4 in. (73 x 92.1 cm),"Catharine Lorillard Wolfe Collection, Bequest of Catharine Lorillard Wolfe, 1887",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436184,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -10.36,false,true,436952,European Paintings,Painting,The Funeral,,,,,,Artist,,Édouard Manet,"French, Paris 1832–1883 Paris",,"Manet, Édouard",French,1832,1883,ca. 1867,1867,1867,Oil on canvas,28 5/8 x 35 5/8 in. (72.7 x 90.5 cm),"Catharine Lorillard Wolfe Collection, Wolfe Fund, 1909",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436952,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -57.10,false,true,436951,European Paintings,Painting,Fishing,,,,,,Artist,,Édouard Manet,"French, Paris 1832–1883 Paris",,"Manet, Édouard",French,1832,1883,ca. 1862–63,1862,1863,Oil on canvas,30 1/4 x 48 1/2 in. (76.8 x 123.2 cm),"Purchase, Mr. and Mrs. Richard J. Bernhard Gift, 1957",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436951,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -55.193,false,true,436954,European Paintings,Painting,George Moore (1852–1933) at the Café,,,,,,Artist,,Édouard Manet,"French, Paris 1832–1883 Paris",,"Manet, Édouard",French,1832,1883,1878 or 1879,1878,1879,Oil on canvas,25 3/4 x 32 in. (65.4 x 81.3 cm),"Gift of Mrs. Ralph J. Hines, 1955",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436954,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -59.129,false,true,436955,European Paintings,Painting,Head of Jean-Baptiste Faure (1830–1914),,,,,,Artist,,Édouard Manet,"French, Paris 1832–1883 Paris",,"Manet, Édouard",French,1832,1883,1882–83,1882,1883,Oil on canvas,18 1/8 x 14 7/8 in. (46 x 37.8 cm),"Gift of Mrs. Ralph J. Hines, 1959",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436955,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -49.58.2,false,true,436944,European Paintings,Painting,The Spanish Singer,,,,,,Artist,,Édouard Manet,"French, Paris 1832–1883 Paris",,"Manet, Édouard",French,1832,1883,1860,1860,1860,Oil on canvas,58 x 45 in. (147.3 x 114.3 cm),"Gift of William Church Osborn, 1949",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436944,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -50.71.1,false,true,436956,European Paintings,Painting,Jean-Baptiste Faure (1830–1914),,,,,,Artist,,Édouard Manet,"French, Paris 1832–1883 Paris",,"Manet, Édouard",French,1832,1883,1882–83,1882,1883,Oil on canvas,23 1/4 x 19 1/2 in. (59.1 x 49.5 cm),"Gift of Mr. and Mrs. William B. Jaffe, 1950",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436956,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -89.21.2,false,true,436948,European Paintings,Painting,Boy with a Sword,,,,,,Artist,,Édouard Manet,"French, Paris 1832–1883 Paris",,"Manet, Édouard",French,1832,1883,1861,1861,1861,Oil on canvas,51 5/8 x 36 3/4 in. (131.1 x 93.4 cm),"Gift of Erwin Davis, 1889",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436948,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -89.21.3,false,true,436964,European Paintings,Painting,Young Lady in 1866,,,,,,Artist,,Édouard Manet,"French, Paris 1832–1883 Paris",,"Manet, Édouard",French,1832,1883,1866,1866,1866,Oil on canvas,72 7/8 x 50 5/8 in. (185.1 x 128.6 cm),"Gift of Erwin Davis, 1889",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436964,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1999.442,false,true,438144,European Paintings,Painting,"The ""Kearsarge"" at Boulogne",,,,,,Artist,,Édouard Manet,"French, Paris 1832–1883 Paris",,"Manet, Édouard",French,1832,1883,1864,1864,1864,Oil on canvas,32 1/8 x 39 3/8 in. (81.6 x 100 cm),"Gift of Peter H. B. Frelinghuysen, and Purchase, Mr. and Mrs. Richard J. Bernhard Gift, by exchange, Gifts of Mr. and Mrs. Richard Rodgers and Joanne Toor Cummings, by exchange, and Drue Heinz Trust, The Dillon Fund, The Vincent Astor Foundation, Mr. and Mrs. Henry R. Kravis, The Charles Engelhard Foundation, and Florence and Herbert Irving Gifts, 1999",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/438144,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -56.230.1,false,true,436963,European Paintings,Painting,Strawberries,,,,,,Artist,,Édouard Manet,"French, Paris 1832–1883 Paris",,"Manet, Édouard",French,1832,1883,ca. 1882,1882,1882,Oil on canvas,8 3/8 x 10 1/2 in. (21.3 x 26.7 cm),"Gift of Mr. and Mrs. Nate B. Spingold, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436963,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.51,false,true,436950,European Paintings,Painting,The Dead Christ with Angels,,,,,,Artist,,Édouard Manet,"French, Paris 1832–1883 Paris",,"Manet, Édouard",French,1832,1883,1864,1864,1864,Oil on canvas,70 5/8 x 59 in. (179.4 x 149.9 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436950,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.52,false,true,436960,European Paintings,Painting,A Matador,,,,,,Artist,,Édouard Manet,"French, Paris 1832–1883 Paris",,"Manet, Édouard",French,1832,1883,1866–67,1866,1867,Oil on canvas,67 3/8 x 44 1/2 in. (171.1 x 113 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436960,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.53,true,true,436945,European Paintings,Painting,Mademoiselle V. . . in the Costume of an Espada,,,,,,Artist,,Édouard Manet,"French, Paris 1832–1883 Paris",,"Manet, Édouard",French,1832,1883,1862,1862,1862,Oil on canvas,65 x 50 1/4 in. (165.1 x 127.6 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436945,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.54,false,true,438819,European Paintings,Painting,Young Man in the Costume of a Majo,,,,,,Artist,,Édouard Manet,"French, Paris 1832–1883 Paris",,"Manet, Édouard",French,1832,1883,1863,1863,1863,Oil on canvas,74 x 49 1/8 in. (188 x 124.8 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/438819,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -67.187.81,false,true,436957,European Paintings,Painting,"Madame Édouard Manet (Suzanne Leenhoff, 1830–1906)",,,,,,Artist,,Édouard Manet,"French, Paris 1832–1883 Paris",,"Manet, Édouard",French,1832,1883,ca. 1873,1870,1876,Oil on canvas,39 1/2 x 30 7/8 in. (100.3 x 78.4 cm),"Bequest of Miss Adelaide Milton de Groot (1876–1967), 1967",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436957,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1997.391.4,false,true,438002,European Paintings,Painting,"Madame Manet (Suzanne Leenhoff, 1830–1906) at Bellevue",,,,,,Artist,,Édouard Manet,"French, Paris 1832–1883 Paris",,"Manet, Édouard",French,1832,1883,1880,1880,1880,Oil on canvas,31 3/4 x 23 3/4 in. (80.6 x 60.3 cm),"The Walter H. and Leonore Annenberg Collection, Gift of Walter H. and Leonore Annenberg, 1997, Bequest of Walter H. Annenberg, 2002",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/438002,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.114,false,true,436949,European Paintings,Painting,"Copy after Delacroix's ""Bark of Dante""",,,,,,Artist,,Édouard Manet,"French, Paris 1832–1883 Paris",,"Manet, Édouard",French,1832,1883,ca. 1859,1854,1864,Oil on canvas,13 x 16 1/8 in. (33 x 41 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436949,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.115,true,true,436947,European Paintings,Painting,Boating,,,,,,Artist,,Édouard Manet,"French, Paris 1832–1883 Paris",,"Manet, Édouard",French,1832,1883,1874,1874,1874,Oil on canvas,38 1/4 x 51 1/4 in. (97.2 x 130.2 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436947,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.201.14,false,true,436965,European Paintings,Painting,The Monet Family in Their Garden at Argenteuil,,,,,,Artist,,Édouard Manet,"French, Paris 1832–1883 Paris",,"Manet, Édouard",French,1832,1883,1874,1874,1874,Oil on canvas,24 x 39 1/4 in. (61 x 99.7 cm),"Bequest of Joan Whitney Payson, 1975",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436965,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.201.16,false,true,436961,European Paintings,Painting,Peonies,,,,,,Artist,,Édouard Manet,"French, Paris 1832–1883 Paris",,"Manet, Édouard",French,1832,1883,1864–65,1864,1865,Oil on canvas,23 3/8 x 13 7/8 in. (59.4 x 35.2 cm),"Bequest of Joan Whitney Payson, 1975",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436961,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -39.161,false,true,436144,European Paintings,Painting,James-Jacques-Joseph Tissot (1836–1902),,,,,,Artist,,Edgar Degas,"French, Paris 1834–1917 Paris",,"Degas, Edgar",French,1834,1917,ca. 1867–68,1867,1868,Oil on canvas,59 5/8 x 44 in. (151.4 x 111.8 cm),"Rogers Fund, 1939",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436144,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -57.171,false,true,436152,European Paintings,Painting,Portrait of a Woman in Gray,,,,,,Artist,,Edgar Degas,"French, Paris 1834–1917 Paris",,"Degas, Edgar",French,1834,1917,ca. 1865,1860,1870,Oil on canvas,36 x 28 1/2 in. (91.4 x 72.4 cm),"Gift of Mr. and Mrs. Edwin C. Vogel, 1957",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436152,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -66.65.2,false,true,436120,European Paintings,Painting,The Old Italian Woman,,,,,,Artist,,Edgar Degas,"French, Paris 1834–1917 Paris",,"Degas, Edgar",French,1834,1917,1857,1857,1857,Oil on canvas,29 1/2 x 24 in. (74.9 x 61 cm),"Bequest of Charles Goldman, 1966",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436120,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1992.380,false,true,436168,European Paintings,Painting,Two Men,,,,,,Artist,,Edgar Degas,"French, Paris 1834–1917 Paris",,"Degas, Edgar",French,1834,1917,ca. 1865–69,1865,1869,Oil on wood,10 5/8 x 8 1/8 in. (27 x 20.6 cm),"Gift of Yvonne Lamon, 1992",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436168,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.529,false,true,438857,European Paintings,Painting,Male Nude,,,,,,Artist,,Edgar Degas,"French, Paris 1834–1917 Paris",,"Degas, Edgar",French,1834,1917,1856,1856,1856,Oil on canvas,13 3/4 x 24 1/4 in. (34.9 x 61.6 cm),"Gift of Philip and Catherine Korsant, 2007",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/438857,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2008.277,false,true,438156,European Paintings,Painting,Young Woman with Ibis,,,,,,Artist,,Edgar Degas,"French, Paris 1834–1917 Paris",,"Degas, Edgar",French,1834,1917,1860–62,1860,1862,Oil on canvas,39 3/8 x 29 1/2 in. (100 x 74.9 cm),"Gift of Stephen Mazoh and Purchase, Bequest of Gioconda King, by exchange, 2008",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/438156,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1987.47.1,true,true,438817,European Paintings,Painting,The Dance Class,,,,,,Artist,,Edgar Degas,"French, Paris 1834–1917 Paris",,"Degas, Edgar",French,1834,1917,1874,1874,1874,Oil on canvas,32 7/8 x 30 3/8 in. (83.5 x 77.2 cm),"Bequest of Mrs. Harry Payne Bingham, 1986",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/438817,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.34,false,true,436139,European Paintings,Painting,Dancers Practicing at the Barre,,,,,,Artist,,Edgar Degas,"French, Paris 1834–1917 Paris",,"Degas, Edgar",French,1834,1917,1877,1877,1877,Mixed media on canvas,29 3/4 x 32 in. (75.6 x 81.3 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436139,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.42,false,true,436140,European Paintings,Painting,"Dancers, Pink and Green",,,,,,Artist,,Edgar Degas,"French, Paris 1834–1917 Paris",,"Degas, Edgar",French,1834,1917,ca. 1890,1885,1895,Oil on canvas,32 3/8 x 29 3/4 in. (82.2 x 75.6 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436140,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.43,false,true,436162,European Paintings,Painting,Sulking,,,,,,Artist,,Edgar Degas,"French, Paris 1834–1917 Paris",,"Degas, Edgar",French,1834,1917,ca. 1870,1870,1870,Oil on canvas,12 3/4 x 18 1/4 in. (32.4 x 46.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436162,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.44,false,true,436122,European Paintings,Painting,The Collector of Prints,,,,,,Artist,,Edgar Degas,"French, Paris 1834–1917 Paris",,"Degas, Edgar",French,1834,1917,1866,1866,1866,Oil on canvas,20 7/8 x 15 3/4 in. (53 x 40 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436122,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.45,false,true,436149,European Paintings,Painting,"Madame Théodore Gobillard (Yves Morisot, 1838–1893)",,,,,,Artist,,Edgar Degas,"French, Paris 1834–1917 Paris",,"Degas, Edgar",French,1834,1917,1869,1869,1869,Oil on canvas,21 3/4 x 25 5/8 in. (55.2 x 65.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436149,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.46,false,true,436174,European Paintings,Painting,A Woman Ironing,,,,,,Artist,,Edgar Degas,"French, Paris 1834–1917 Paris",,"Degas, Edgar",French,1834,1917,1873,1873,1873,Oil on canvas,21 3/8 x 15 1/2 in. (54.3 x 39.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436174,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.127,false,true,436138,European Paintings,Painting,Dancers in the Rehearsal Room with a Double Bass,,,,,,Artist,,Edgar Degas,"French, Paris 1834–1917 Paris",,"Degas, Edgar",French,1834,1917,ca. 1882–85,1882,1885,Oil on canvas,15 3/8 x 35 1/4 in. (39.1 x 89.5 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436138,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.128,false,true,436121,European Paintings,Painting,A Woman Seated beside a Vase of Flowers (Madame Paul Valpinçon?),,,,,,Artist,,Edgar Degas,"French, Paris 1834–1917 Paris",,"Degas, Edgar",French,1834,1917,1865,1865,1865,Oil on canvas,29 x 36 1/2 in. (73.7 x 92.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436121,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.181,false,true,436145,European Paintings,Painting,Joseph-Henri Altès (1826–1895),,,,,,Artist,,Edgar Degas,"French, Paris 1834–1917 Paris",,"Degas, Edgar",French,1834,1917,1868,1868,1868,Oil on canvas,9 7/8 x 7 7/8 in. (25.1 x 20 cm); with added strips 10 5/8 x 8 1/2 in. (27 x 21.6 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436145,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.182,false,true,436150,European Paintings,Painting,Mademoiselle Marie Dihau (1843–1935),,,,,,Artist,,Edgar Degas,"French, Paris 1834–1917 Paris",,"Degas, Edgar",French,1834,1917,1867–68,1867,1868,Oil on canvas,8 3/4 x 10 3/4 in. (22.2 x 27.3 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436150,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.183,false,true,436153,European Paintings,Painting,Portrait of a Young Woman,,,,,,Artist,,Edgar Degas,"French, Paris 1834–1917 Paris",,"Degas, Edgar",French,1834,1917,ca. 1885,1880,1890,Oil on canvas,10 3/4 x 8 3/4 in. (27.3 x 22.2 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436153,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.184,false,true,436141,European Paintings,Painting,The Dancing Class,,,,,,Artist,,Edgar Degas,"French, Paris 1834–1917 Paris",,"Degas, Edgar",French,1834,1917,ca. 1870,1870,1870,Oil on wood,7 3/4 x 10 5/8 in. (19.7 x 27 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436141,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.552,false,true,436123,European Paintings,Painting,"The Ballet from ""Robert le Diable""",,,,,,Artist,,Edgar Degas,"French, Paris 1834–1917 Paris",,"Degas, Edgar",French,1834,1917,1871,1871,1871,Oil on canvas,26 x 21 3/8 in. (66 x 54.3 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436123,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -87.15.101,false,true,437895,European Paintings,Painting,The Reprimand,,,,,,Artist,,Jean-Georges Vibert,"French, Paris 1840–1902 Paris",,"Vibert, Jean-Georges",French,1840,1902,1874,1874,1874,Oil on canvas,20 3/8 x 33 in. (51.8 x 83.8 cm),"Catharine Lorillard Wolfe Collection, Bequest of Catharine Lorillard Wolfe, 1887",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437895,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -25.110.140,false,true,437896,European Paintings,Painting,The Missionary's Adventures,,,,,,Artist,,Jean-Georges Vibert,"French, Paris 1840–1902 Paris",,"Vibert, Jean-Georges",French,1840,1902,ca. 1883,1878,1888,Oil on wood,39 x 53 in. (99.1 x 134.6 cm),"Bequest of Collis P. Huntington, 1900",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437896,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -87.15.74,false,true,436519,European Paintings,Painting,Before the Mirror,,,,,,Artist,,Pierre-Paul-Léon Glaize,"French, Paris 1842–1932 Paris",,"Glaize, Pierre-Paul-Léon",French,1842,1932,1873,1873,1873,Oil on canvas,39 3/4 x 29 7/8 in. (101 x 75.9 cm),"Catharine Lorillard Wolfe Collection, Bequest of Catharine Lorillard Wolfe, 1887",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436519,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -87.15.90,false,true,436870,European Paintings,Painting,Choosing the Dinner,,,,,,Artist,,Alexandre-Louis Leloir,"French, Paris 1843–1884 Paris",,"Leloir, Alexandre-Louis",French,1843,1884,1872,1872,1872,Oil on canvas,12 1/4 x 18 3/8 in. (31.1 x 46.7 cm),"Catharine Lorillard Wolfe Collection, Bequest of Catharine Lorillard Wolfe, 1887",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436870,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -59.185,false,true,435649,European Paintings,Painting,Judith,,,,,,Artist,,Benjamin-Constant (Jean-Joseph-Benjamin Constant),"French, Paris 1845–1902 Paris",,Benjamin-Constant (Jean-Joseph-Benjamin Constant),French,1845,1902,possibly ca. 1886,1883,1889,Oil on canvas,47 1/2 x 31 1/2 in. (120.7 x 80 cm),"Gift of J. E. Gombos, 1959",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435649,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -48.115,false,true,436189,European Paintings,Painting,Gendarmes d'Ordonnance,,,,,,Artist,,Édouard Detaille,"French, Paris 1848–1912 Paris",,"Detaille, Édouard",French,1848,1912,1894,1894,1894,Oil on canvas,22 x 16 5/8 in. (55.9 x 42.2 cm),"Gift of Estate of George Albert Draper, 1948",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436189,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -87.20.2,false,true,436188,European Paintings,Painting,The Defense of Champigny,,,,,,Artist,,Édouard Detaille,"French, Paris 1848–1912 Paris",,"Detaille, Édouard",French,1848,1912,1879,1879,1879,Oil on canvas,48 x 84 3/4 in. (121.9 x 215.3 cm),"Gift of Henry Hilton, 1887",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436188,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -08.136.5,false,true,436187,European Paintings,Painting,A Dragoon on Horseback,,,,,,Artist,,Édouard Detaille,"French, Paris 1848–1912 Paris",,"Detaille, Édouard",French,1848,1912,1876,1876,1876,Oil on wood,9 1/2 x 5 3/8 in. (24.1 x 13.7 cm),"Bequest of Martha T. Fiske Collord, in memory of her first husband, Josiah M. Fiske, 1908",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436187,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -87.8.12,false,true,436880,European Paintings,Painting,The Organ Rehearsal,,,,,,Artist,,Henry Lerolle,"French, Paris 1848–1929 Paris",,"Lerolle, Henry",French,1848,1929,1885,1885,1885,Oil on canvas,93 1/4 x 142 3/4 in. (236.9 x 362.6 cm),"Gift of George I. Seney, 1887",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436880,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -17.120.228,false,true,437366,European Paintings,Painting,"The Fletcher Mansion, New York City",,,,,,Artist,,Jean-François Raffaëlli,"French, Paris 1850–1924 Paris",,"Raffaëlli, Jean-François",French,1850,1924,1899,1899,1899,Oil on canvas,23 3/4 x 32 in. (60.3 x 81.3 cm),"Mr. and Mrs. Isaac D. Fletcher Collection, Bequest of Isaac D. Fletcher, 1917",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437366,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -67.187.80,false,true,436923,European Paintings,Painting,"Morning, Interior",,,,,,Artist,,Maximilien Luce,"French, Paris 1858–1941 Paris",,"Luce, Maximilien",French,1858,1941,1890,1890,1890,Oil on canvas,25 1/2 x 31 7/8 in. (64.8 x 81 cm),"Bequest of Miss Adelaide Milton de Groot (1876–1967), 1967",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436923,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -59.16.5,false,true,437659,European Paintings,Painting,View of the Seine,,,,,,Artist,,Georges Seurat,"French, Paris 1859–1891 Paris",,"Seurat, Georges",French,1859,1891,1882–83,1882,1883,Oil on wood,6 1/4 x 9 3/4 in. (15.9 x 24.8 cm),"Bequest of Mabel Choate, in memory of her father, Joseph Hodges Choate, 1958",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437659,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1980.342,false,true,437657,European Paintings,Painting,Landscape at Saint-Ouen,,,,,,Artist,,Georges Seurat,"French, Paris 1859–1891 Paris",,"Seurat, Georges",French,1859,1891,1878 or 1879,1878,1879,"Oil on wood, mounted on wood",Overall 6 7/8 x 10 3/8 in. (17.5 x 26.4 cm); painted surface 6 5/8 x 10 in. (16.8 x 25.4 cm),"Gift of Bernice Richard, 1980",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437657,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1985.237,false,true,437655,European Paintings,Painting,The Forest at Pontaubert,,,,,,Artist,,Georges Seurat,"French, Paris 1859–1891 Paris",,"Seurat, Georges",French,1859,1891,1881,1881,1881,Oil on canvas,31 1/8 x 24 5/8 in. (79.1 x 62.5 cm),"Purchase, Gift of Raymonde Paul, in memory of her brother, C. Michael Paul, by exchange, 1985",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437655,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -51.112.6,false,true,437658,European Paintings,Painting,"Study for ""A Sunday on La Grande Jatte""",,,,,,Artist,,Georges Seurat,"French, Paris 1859–1891 Paris",,"Seurat, Georges",French,1859,1891,1884,1884,1884,Oil on canvas,27 3/4 x 41 in. (70.5 x 104.1 cm),"Bequest of Sam A. Lewisohn, 1951",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437658,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2002.62.3,false,true,438015,European Paintings,Painting,"Gray Weather, Grande Jatte",,,,,,Artist,,Georges Seurat,"French, Paris 1859–1891 Paris",,"Seurat, Georges",French,1859,1891,ca. 1886–88,1886,1888,Oil on canvas,27 3/4 x 34 in. (70.5 x 86.4 cm),"The Walter H. and Leonore Annenberg Collection, Gift of Walter H. and Leonore Annenberg, 2002, Bequest of Walter H. Annenberg, 2002",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/438015,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -61.101.17,true,true,437654,European Paintings,Painting,Circus Sideshow (Parade de cirque),,,,,,Artist,,Georges Seurat,"French, Paris 1859–1891 Paris",,"Seurat, Georges",French,1859,1891,1887–88,1887,1888,Oil on canvas,39 1/4 x 59 in. (99.7 x 149.9 cm),"Bequest of Stephen C. Clark, 1960",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437654,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -67.187.102,false,true,437656,European Paintings,Painting,The Gardener,,,,,,Artist,,Georges Seurat,"French, Paris 1859–1891 Paris",,"Seurat, Georges",French,1859,1891,1882–83,1882,1883,Oil on wood,6 1/4 x 9 3/4 in. (15.9 x 24.8 cm),"Bequest of Miss Adelaide Milton de Groot (1876–1967), 1967",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437656,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -55.220.1,false,true,437672,European Paintings,Painting,"Notre-Dame-de-la-Garde (La Bonne-Mère), Marseilles",,,,,,Artist,,Paul Signac,"French, Paris 1863–1935 Paris",,"Signac, Paul",French,1863,1935,1905–6,1905,1906,Oil on canvas,35 x 45 3/4 in. (88.9 x 116.2 cm),"Gift of Robert Lehman, 1955",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437672,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.201.19,false,true,437671,European Paintings,Painting,"The Jetty at Cassis, Opus 198",,,,,,Artist,,Paul Signac,"French, Paris 1863–1935 Paris",,"Signac, Paul",French,1863,1935,1889,1889,1889,Oil on canvas,18 1/4 x 25 5/8 in. (46.4 x 65.1 cm),"Bequest of Joan Whitney Payson, 1975",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437671,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -41.17,false,true,436454,European Paintings,Painting,Alfred Dedreux (1810–1860) as a Child,,,,,,Artist,,Théodore Gericault,"French, Rouen 1791–1824 Paris",,"Gericault, Théodore",French,1791,1824,ca. 1819–20,1819,1820,Oil on canvas,18 x 15 in. (45.7 x 38.1 cm),"The Alfred N. Punnett Endowment Fund, 1941",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436454,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2011.5,false,true,441226,European Paintings,Painting,Lions in a Mountainous Landscape,,,,,,Artist,,Théodore Gericault,"French, Rouen 1791–1824 Paris",,"Gericault, Théodore",French,1791,1824,ca. 1818–20,1813,1825,Oil on wood,19 x 23 1/2 in. (48.3 x 59.7 cm),"Purchase, Nineteenth-Century, Modern, and Contemporary Funds and Lila Acheson Wallace Gift, 2011",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/441226,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.183,true,true,436455,European Paintings,Painting,Evening: Landscape with an Aqueduct,,,,,,Artist,,Théodore Gericault,"French, Rouen 1791–1824 Paris",,"Gericault, Théodore",French,1791,1824,1818,1818,1818,Oil on canvas,98 1/2 x 86 1/2 in. (250.2 x 219.7 cm),"Purchase, Gift of James A. Moffett 2nd, in memory of George M. Moffett, by exchange, 1989",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436455,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1974.356.27,false,true,437844,European Paintings,Painting,Comedy,,,,,,Artist,,Pierre Charles Trémolières,"French, Cholet 1703–1739 Paris",,"Trémolières, Pierre Charles",French,1703,1739,ca. 1736,1731,1741,"Oil on canvas, enlarged",18 3/4 x 23 1/2 in. (47.6 x 59.7 cm),"The Lesley and Emma Sheafer Collection, Bequest of Emma A. Sheafer, 1973",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437844,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -46.30,false,true,436321,European Paintings,Painting,Roman Interior,,,,,,Artist,,Jean Honoré Fragonard,"French, Grasse 1732–1806 Paris",,"Fragonard, Jean Honoré",French,1732,1806,ca. 1760,1755,1765,Oil on canvas,19 1/4 x 23 3/8 in. (48.9 x 59.4 cm),"Harris Brisbane Dick Fund, 1946",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436321,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.118,false,true,436323,European Paintings,Painting,A Woman with a Dog,,,,,,Artist,,Jean Honoré Fragonard,"French, Grasse 1732–1806 Paris",,"Fragonard, Jean Honoré",French,1732,1806,ca. 1769,1764,1774,Oil on canvas,32 x 25 3/4 in. (81.3 x 65.4 cm),"Fletcher Fund, 1937",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436323,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -53.161,false,true,436327,European Paintings,Painting,Allegory of Vigilance,,,,,,Artist,,Jean Honoré Fragonard,"French, Grasse 1732–1806 Paris",,"Fragonard, Jean Honoré",French,1732,1806,ca. 1772,1767,1777,Oil on canvas,"Oval, 27 1/8 x 21 5/8 in. (68.9 x 54.9 cm)","Gift of René Fribourg, 1953",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436327,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -65.226,false,true,436320,European Paintings,Painting,Portrait of a Young Woman,,,,,,Artist,,Jean Honoré Fragonard,"French, Grasse 1732–1806 Paris",,"Fragonard, Jean Honoré",French,1732,1806,1770s,1770,1779,Oil on canvas,"Oval, 31 3/4 x 25 in. (80.6 x 63.5 cm)","Bequest of Margaret V. Haggin, 1965",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436320,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -49.7.49,true,true,436322,European Paintings,Painting,The Love Letter,,,,,,Artist,,Jean Honoré Fragonard,"French, Grasse 1732–1806 Paris",,"Fragonard, Jean Honoré",French,1732,1806,early 1770s,1770,1773,Oil on canvas,32 3/4 x 26 3/8 in. (83.2 x 67 cm),"The Jules Bache Collection, 1949",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436322,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -49.7.50,false,true,436319,European Paintings,Painting,The Cascade,,,,,,Artist,,Jean Honoré Fragonard,"French, Grasse 1732–1806 Paris",,"Fragonard, Jean Honoré",French,1732,1806,ca. 1775,1770,1780,Oil on wood,11 1/2 x 9 1/2 in. (29.2 x 24.1 cm),"The Jules Bache Collection, 1949",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436319,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -49.7.51,false,true,436324,European Paintings,Painting,A Shaded Avenue,,,,,,Artist,,Jean Honoré Fragonard,"French, Grasse 1732–1806 Paris",,"Fragonard, Jean Honoré",French,1732,1806,ca. 1775,1770,1780,Oil on wood,11 1/2 x 9 1/2 in. (29.2 x 24.1 cm),"The Jules Bache Collection, 1949",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436324,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -53.61.5,false,true,436326,European Paintings,Painting,The Two Sisters,,,,,,Artist,,Jean Honoré Fragonard,"French, Grasse 1732–1806 Paris",,"Fragonard, Jean Honoré",French,1732,1806,ca. 1769–70,1769,1770,Oil on canvas,28 1/4 x 22 in. (71.8 x 55.9 cm),"Gift of Julia A. Berwind, 1953",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436326,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -56.100.1,false,true,436325,European Paintings,Painting,The Stolen Kiss,,,,,,Artist,,Jean Honoré Fragonard,"French, Grasse 1732–1806 Paris",,"Fragonard, Jean Honoré",French,1732,1806,ca. 1760,1755,1765,Oil on canvas,19 x 25 in. (48.3 x 63.5 cm),"Gift of Jessie Woolworth Donahue, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436325,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2002.441,false,true,438603,European Paintings,Painting,"François Gérard (1770–1837), later Baron Gérard",,,,,,Artist,,baron Antoine Jean Gros,"French, Paris 1771–1835 Meudon",,"Gros, Antoine Jean, baron",French,1771,1835,ca. 1790,1785,1795,Oil on canvas,22 1/8 x 18 5/8 in. (56.2 x 47.3 cm),"Gift of Mrs. Charles Wrightsman, 2002",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/438603,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -27.200,false,true,436483,European Paintings,Painting,Pygmalion and Galatea,,,,,,Artist,,Jean-Léon Gérôme,"French, Vesoul 1824–1904 Paris",,"Gérôme, Jean-Léon",French,1824,1904,ca. 1890,1885,1895,Oil on canvas,35 x 27 in. (88.9 x 68.6 cm),"Gift of Louis C. Raegner, 1927",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436483,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -05.13.4,false,true,436481,European Paintings,Painting,"Cafe House, Cairo (Casting Bullets)",,,,,,Artist,,Jean-Léon Gérôme,"French, Vesoul 1824–1904 Paris",,"Gérôme, Jean-Léon",French,1824,1904,1884 or earlier,1870,1884,Oil on canvas,21 1/2 x 24 3/4 in. (54.6 x 62.9 cm),"Bequest of Henry H. Cook, 1905",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436481,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.162.4,false,true,436484,European Paintings,Painting,Tiger and Cubs,,,,,,Artist,,Jean-Léon Gérôme,"French, Vesoul 1824–1904 Paris",,"Gérôme, Jean-Léon",French,1824,1904,ca. 1884,1879,1889,Oil on canvas,29 x 36 in. (73.7 x 91.4 cm),"Bequest of Susan P. Colgate, in memory of her husband, Romulus R. Colgate, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436484,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -87.15.130,false,true,436482,European Paintings,Painting,Prayer in the Mosque,,,,,,Artist,,Jean-Léon Gérôme,"French, Vesoul 1824–1904 Paris",,"Gérôme, Jean-Léon",French,1824,1904,1871,1871,1871,Oil on canvas,35 x 29 1/2 in. (88.9 x 74.9 cm),"Catharine Lorillard Wolfe Collection, Bequest of Catharine Lorillard Wolfe, 1887",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436482,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2008.547.1,true,true,440723,European Paintings,Painting,Bashi-Bazouk,,,,,,Artist,,Jean-Léon Gérôme,"French, Vesoul 1824–1904 Paris",,"Gérôme, Jean-Léon",French,1824,1904,1868–69,1868,1869,Oil on canvas,31 3/4 x 26 in. (80.6 x 66 cm),"Gift of Mrs. Charles Wrightsman, 2008",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/440723,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.121,false,true,436026,European Paintings,Painting,Spring Flowers,,,,,,Artist,Copy after,Gustave Courbet,"French, second half 19th century",,"Courbet, Gustave",French,1819,1877,ca. 1855–60,1855,1860,Oil on canvas,23 3/4 x 32 1/4 in. (60.3 x 81.9 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436026,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1983.405,false,true,437894,European Paintings,Painting,Eugène Joseph Stanislas Foullon d'Écotier (1753–1821),,,,,,Artist,,Antoine Vestier,"French, Avallon 1740–1824 Paris",,"Vestier, Antoine",French,1740,1824,1785,1785,1785,Oil on canvas,"Oval, 31 5/8 x 25 1/8 in. (80.3 x 63.8 cm)","Gift of Mr. and Mrs. Charles Wrightsman, 1983",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437894,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2002.69,false,true,438559,European Paintings,Painting,The Sack of Jerusalem by the Romans,,,,,,Artist,,François Joseph Heim,"French, Belfort 1787–1865 Paris",,"Heim, François Joseph",French,1787,1865,1824,1824,1824,Oil on canvas,14 x 15 in. (35.6 x 38.1 cm),"Catharine Lorillard Wolfe Collection, Wolfe Fund, 2002",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/438559,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2003.20.8,false,true,438009,European Paintings,Painting,"The Pink Dress (Albertie-Marguerite Carré, later Madame Ferdinand-Henri Himmes, 1854–1935)",,,,,,Artist,,Berthe Morisot,"French, Bourges 1841–1895 Paris",,"Morisot, Berthe",French,1841,1895,ca. 1870,1865,1875,Oil on canvas,21 1/2 x 26 1/2 in. (54.6 x 67.3 cm),"The Walter H. and Leonore Annenberg Collection, Bequest of Walter H. Annenberg, 2002",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/438009,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -67.187.89,false,true,437159,European Paintings,Painting,Young Woman Knitting,,,,,,Artist,,Berthe Morisot,"French, Bourges 1841–1895 Paris",,"Morisot, Berthe",French,1841,1895,ca. 1883,1878,1888,Oil on canvas,19 3/4 x 23 5/8 in. (50.2 x 60 cm),"Bequest of Miss Adelaide Milton de Groot (1876–1967), 1967",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437159,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1979.97,false,true,435856,European Paintings,Painting,Self-Portrait,,,,,,Artist,,Eugène Carrière,"French, Gournay 1849–1906 Paris",,"Carrière, Eugène",French,1849,1906,ca. 1893,1888,1898,Oil on canvas,16 1/4 x 12 7/8 in. (41.3 x 32.7 cm),"Purchase, Albert Otten Foundation Gift, 1979",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435856,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.428,false,true,442849,European Paintings,Painting,Women Sewing at a Table,,,,,,Artist,,Eugène Carrière,"French, Gournay 1849–1906 Paris",,"Carrière, Eugène",French,1849,1906,ca. 1894–96,1894,1896,Oil on canvas,10 1/4 × 15 in. (26 × 38.1 cm),"Gift of Ariane and Alain Kirili, 2013",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/442849,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -46.150.2,false,true,435627,European Paintings,Painting,In the Sun,,,,,,Artist,,Charles-Édouard de Beaumont,"French, Lannion 1821–1888 Paris",,"Beaumont, Charles-Édouard de",French,1821,1888,1875,1875,1875,Oil on canvas,23 1/2 x 37 3/4 in. (59.7 x 95.9 cm),"Gift of Estate of Marie L. Russell, 1946",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435627,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2008.660,false,true,496203,European Paintings,Painting,Woman Standing Beside Railing with Poodle,,,,,,Artist,,Paul Ranson,"French, Limoges 1864–1909 Paris",,"Ranson, Paul",French,1864,1909,ca. 1895,1895,1895,Oil on panel,33 1/2 x 11 5/8 in. (85.1 x 29.5 cm),"Gift of Mrs. Patricia Altschul, 2008",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/496203,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -51.32,false,true,437104,European Paintings,Painting,Dr. Leclenché,,,,,,Artist,,Claude Monet,"French, Paris 1840–1926 Giverny",,"Monet, Claude",French,1840,1926,1864,1864,1864,Oil on canvas,18 x 12 3/4 in. (45.7 x 32.4 cm),"Gift of Mr. and Mrs. Edwin C. Vogel, 1951",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437104,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -57.183,false,true,437130,European Paintings,Painting,Apples and Grapes,,,,,,Artist,,Claude Monet,"French, Paris 1840–1926 Giverny",,"Monet, Claude",French,1840,1926,1879–80,1879,1880,Oil on canvas,26 5/8 x 35 1/4 in. (67.6 x 89.5 cm),"Gift of Henry R. Luce, 1957",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437130,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -59.142,false,true,437108,European Paintings,Painting,The Parc Monceau,,,,,,Artist,,Claude Monet,"French, Paris 1840–1926 Giverny",,"Monet, Claude",French,1840,1926,1878,1878,1878,Oil on canvas,28 5/8 x 21 3/8 in. (72.7 x 54.3 cm),"The Mr. and Mrs. Henry Ittleson Jr. Purchase Fund, 1959",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437108,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -59.206,false,true,437107,European Paintings,Painting,Landscape: The Parc Monceau,,,,,,Artist,,Claude Monet,"French, Paris 1840–1926 Giverny",,"Monet, Claude",French,1840,1926,1876,1876,1876,Oil on canvas,23 1/2 x 32 1/2 in. (59.7 x 82.6 cm),"Bequest of Loula D. Lasker, New York City, 1961",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437107,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -60.154,false,true,437126,European Paintings,Painting,Île aux Orties near Vernon,,,,,,Artist,,Claude Monet,"French, Paris 1840–1926 Giverny",,"Monet, Claude",French,1840,1926,1897,1897,1897,Oil on canvas,28 7/8 x 36 1/2 in. (73.3 x 92.7 cm),"Gift of Mr. and Mrs. Charles S. McVeigh, 1960",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437126,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -64.210,false,true,437131,European Paintings,Painting,"The Bodmer Oak, Fontainebleau Forest",,,,,,Artist,,Claude Monet,"French, Paris 1840–1926 Giverny",,"Monet, Claude",French,1840,1926,1865,1865,1865,Oil on canvas,37 7/8 x 50 7/8 in. (96.2 x 129.2 cm),"Gift of Sam Salz and Bequest of Julia W. Emmons, by exchange, 1964",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437131,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -67.241,true,true,437133,European Paintings,Painting,Garden at Sainte-Adresse,,,,,,Artist,,Claude Monet,"French, Paris 1840–1926 Giverny",,"Monet, Claude",French,1840,1926,1867,1867,1867,Oil on canvas,38 5/8 x 51 1/8 in. (98.1 x 129.9 cm),"Purchase, special contributions and funds given or bequeathed by friends of the Museum, 1967",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437133,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -51.30.3,false,true,437111,European Paintings,Painting,Vétheuil in Summer,,,,,,Artist,,Claude Monet,"French, Paris 1840–1926 Giverny",,"Monet, Claude",French,1840,1926,1880,1880,1880,Oil on canvas,23 5/8 x 39 1/4 in. (60 x 99.7 cm),"Bequest of William Church Osborn, 1951",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437111,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -51.30.4,false,true,437136,European Paintings,Painting,Regatta at Sainte-Adresse,,,,,,Artist,,Claude Monet,"French, Paris 1840–1926 Giverny",,"Monet, Claude",French,1840,1926,1867,1867,1867,Oil on canvas,29 5/8 x 40 in. (75.2 x 101.6 cm),"Bequest of William Church Osborn, 1951",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437136,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -51.30.5,false,true,438823,European Paintings,Painting,The Manneporte (Étretat),,,,,,Artist,,Claude Monet,"French, Paris 1840–1926 Giverny",,"Monet, Claude",French,1840,1926,1883,1883,1883,Oil on canvas,25 3/4 x 32 in. (65.4 x 81.3 cm),"Bequest of William Church Osborn, 1951",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/438823,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1983.532,false,true,437137,European Paintings,Painting,Water Lilies,,,,,,Artist,,Claude Monet,"French, Paris 1840–1926 Giverny",,"Monet, Claude",French,1840,1926,1916–19,1916,1919,Oil on canvas,51 1/4 x 79 in. (130.2 x 200.7 cm),"Gift of Louise Reinhardt Smith, 1983",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437137,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1984.341,false,true,437113,European Paintings,Painting,Cabin of the Customs Watch,,,,,,Artist,,Claude Monet,"French, Paris 1840–1926 Giverny",,"Monet, Claude",French,1840,1926,1882,1882,1882,Oil on canvas,24 x 32 1/4 in. (61 x 81.9 cm),"Bequest of Julia B. Engel, 1984",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437113,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2000.195,false,true,438435,European Paintings,Painting,Jean Monet (1867–1913) on His Hobby Horse,,,,,,Artist,,Claude Monet,"French, Paris 1840–1926 Giverny",,"Monet, Claude",French,1840,1926,1872,1872,1872,Oil on canvas,23 7/8 x 29 1/4 in. (60.6 x 74.3 cm),"Gift of Sara Lee Corporation, 2000",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/438435,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -26.186.1,false,true,437106,European Paintings,Painting,Spring (Fruit Trees in Bloom),,,,,,Artist,,Claude Monet,"French, Paris 1840–1926 Giverny",,"Monet, Claude",French,1840,1926,1873,1873,1873,Oil on canvas,24 1/2 x 39 5/8 in. (62.2 x 100.6 cm),"Bequest of Mary Livingston Willard, 1926",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437106,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -31.67.11,false,true,437119,European Paintings,Painting,The Manneporte near Étretat,,,,,,Artist,,Claude Monet,"French, Paris 1840–1926 Giverny",,"Monet, Claude",French,1840,1926,1886,1886,1886,Oil on canvas,32 x 25 3/4 in. (81.3 x 65.4 cm),"Bequest of Lillie P. Bliss, 1931",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437119,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -56.135.1,false,true,437110,European Paintings,Painting,View of Vétheuil,,,,,,Artist,,Claude Monet,"French, Paris 1840–1926 Giverny",,"Monet, Claude",French,1840,1926,1880,1880,1880,Oil on canvas,31 1/2 x 23 3/4 in. (80 x 60.3 cm),"Bequest of Julia W. Emmons, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437110,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -56.135.4,false,true,437125,European Paintings,Painting,Morning on the Seine near Giverny,,,,,,Artist,,Claude Monet,"French, Paris 1840–1926 Giverny",,"Monet, Claude",French,1840,1926,1897,1897,1897,Oil on canvas,32 1/8 x 36 5/8 in. (81.6 x 93 cm),"Bequest of Julia W. Emmons, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437125,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -56.135.5,false,true,437138,European Paintings,Painting,Île aux Fleurs near Vétheuil,,,,,,Artist,,Claude Monet,"French, Paris 1840–1926 Giverny",,"Monet, Claude",French,1840,1926,1880,1880,1880,Oil on canvas,26 x 32 in. (66 x 81.3 cm),"Bequest of Julia W. Emmons, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437138,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -56.135.6,false,true,437128,European Paintings,Painting,The Houses of Parliament (Effect of Fog),,,,,,Artist,,Claude Monet,"French, Paris 1840–1926 Giverny",,"Monet, Claude",French,1840,1926,1903–4,1903,1904,Oil on canvas,32 x 36 3/8 in. (81.3 x 92.4 cm),"Bequest of Julia W. Emmons, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437128,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -59.188.1,false,true,437129,European Paintings,Painting,The Doge's Palace Seen from San Giorgio Maggiore,,,,,,Artist,,Claude Monet,"French, Paris 1840–1926 Giverny",,"Monet, Claude",French,1840,1926,1908,1908,1908,Oil on canvas,25 3/4 x 36 1/2 in. (65.4 x 92.7 cm),"Gift of Mr. and Mrs. Charles S. McVeigh, 1959",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437129,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2000.93.1,false,true,438005,European Paintings,Painting,Camille Monet (1847–1879) in the Garden at Argenteuil,,,,,,Artist,,Claude Monet,"French, Paris 1840–1926 Giverny",,"Monet, Claude",French,1840,1926,1876,1876,1876,Oil on canvas,32 1/8 x 23 5/8 in. (81.6 x 60 cm),"The Walter H. and Leonore Annenberg Collection, Gift of Walter H. and Leonore Annenberg, 2000, Bequest of Walter H. Annenberg, 2002",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/438005,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2002.62.1,false,true,438003,European Paintings,Painting,Camille Monet (1847–1879) on a Garden Bench,,,,,,Artist,,Claude Monet,"French, Paris 1840–1926 Giverny",,"Monet, Claude",French,1840,1926,1873,1873,1873,Oil on canvas,23 7/8 x 31 5/8 in. (60.6 x 80.3 cm),"The Walter H. and Leonore Annenberg Collection, Gift of Walter H. and Leonore Annenberg, 2002, Bequest of Walter H. Annenberg, 2002",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/438003,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2003.20.7,false,true,438006,European Paintings,Painting,"The Stroller (Suzanne Hoschedé, later Mrs. Theodore Earl Butler, 1868–1899)",,,,,,Artist,,Claude Monet,"French, Paris 1840–1926 Giverny",,"Monet, Claude",French,1840,1926,1887,1887,1887,Oil on canvas,39 5/8 x 27 3/4 in. (100.6 x 70.5 cm),"The Walter H. and Leonore Annenberg Collection, Bequest of Walter H. Annenberg, 2002",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/438006,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -30.95.250,false,true,437124,European Paintings,Painting,Rouen Cathedral: The Portal (Sunlight),,,,,,Artist,,Claude Monet,"French, Paris 1840–1926 Giverny",,"Monet, Claude",French,1840,1926,1894,1894,1894,Oil on canvas,39 1/4 x 25 7/8 in. (99.7 x 65.7 cm),"Theodore M. Davis Collection, Bequest of Theodore M. Davis, 1915",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437124,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -30.95.251,false,true,437118,European Paintings,Painting,The Valley of the Nervia,,,,,,Artist,,Claude Monet,"French, Paris 1840–1926 Giverny",,"Monet, Claude",French,1840,1926,1884,1884,1884,Oil on canvas,26 x 32 in. (66 x 81.3 cm),"Theodore M. Davis Collection, Bequest of Theodore M. Davis, 1915",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437118,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -30.95.271,false,true,437109,European Paintings,Painting,The Seine at Vétheuil,,,,,,Artist,,Claude Monet,"French, Paris 1840–1926 Giverny",,"Monet, Claude",French,1840,1926,1880,1880,1880,Oil on canvas,23 3/4 x 39 1/2 in. (60.3 x 100.3 cm),"Theodore M. Davis Collection, Bequest of Theodore M. Davis, 1915",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437109,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -67.187.87,false,true,437117,European Paintings,Painting,Palm Trees at Bordighera,,,,,,Artist,,Claude Monet,"French, Paris 1840–1926 Giverny",,"Monet, Claude",French,1840,1926,1884,1884,1884,Oil on canvas,25 1/2 x 32in. (64.8 x 81.3cm),"Bequest of Miss Adelaide Milton de Groot (1876–1967), 1967",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437117,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -67.187.88,false,true,437120,European Paintings,Painting,Rapids on the Petite Creuse at Fresselines,,,,,,Artist,,Claude Monet,"French, Paris 1840–1926 Giverny",,"Monet, Claude",French,1840,1926,1889,1889,1889,Oil on canvas,25 3/4 x 36 1/8 in. (65.4 x 91.8 cm),"Bequest of Miss Adelaide Milton de Groot (1876–1967), 1967",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437120,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1998.325.2,false,true,438008,European Paintings,Painting,Water Lilies,,,,,,Artist,,Claude Monet,"French, Paris 1840–1926 Giverny",,"Monet, Claude",French,1840,1926,1919,1919,1919,Oil on canvas,39 3/4 x 78 3/4 in. (101 x 200 cm),"The Walter H. and Leonore Annenberg Collection, Gift of Walter H. and Leonore Annenberg, 1998, Bequest of Walter H. Annenberg, 2002",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/438008,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2001.202.5,false,true,438004,European Paintings,Painting,Poppy Fields near Argenteuil,,,,,,Artist,,Claude Monet,"French, Paris 1840–1926 Giverny",,"Monet, Claude",French,1840,1926,1875,1875,1875,Oil on canvas,21 1/4 x 29 in. (54 x 73.7 cm),"The Walter H. and Leonore Annenberg Collection, Gift of Walter H. and Leonore Annenberg, 2001, Bequest of Walter H. Annenberg, 2002",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/438004,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2001.202.6,false,true,438007,European Paintings,Painting,The Path through the Irises,,,,,,Artist,,Claude Monet,"French, Paris 1840–1926 Giverny",,"Monet, Claude",French,1840,1926,1914–17,1914,1917,Oil on canvas,78 7/8 x 70 7/8 in. (200.3 x 180 cm),"The Walter H. and Leonore Annenberg Collection, Gift of Walter H. and Leonore Annenberg, 2001, Bequest of Walter H. Annenberg, 2002",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/438007,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.106,false,true,437115,European Paintings,Painting,Chrysanthemums,,,,,,Artist,,Claude Monet,"French, Paris 1840–1926 Giverny",,"Monet, Claude",French,1840,1926,1882,1882,1882,Oil on canvas,39 1/2 x 32 1/4 in. (100.3 x 81.9 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437115,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.107,false,true,437112,European Paintings,Painting,Bouquet of Sunflowers,,,,,,Artist,,Claude Monet,"French, Paris 1840–1926 Giverny",,"Monet, Claude",French,1840,1926,1881,1881,1881,Oil on canvas,39 3/4 x 32 in. (101 x 81.3 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437112,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.108,false,true,437123,European Paintings,Painting,Ice Floes,,,,,,Artist,,Claude Monet,"French, Paris 1840–1926 Giverny",,"Monet, Claude",French,1840,1926,1893,1893,1893,Oil on canvas,26 x 39 1/2 in. (66 x 100.3 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437123,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.109,false,true,437122,European Paintings,Painting,Haystacks (Effect of Snow and Sun),,,,,,Artist,,Claude Monet,"French, Paris 1840–1926 Giverny",,"Monet, Claude",French,1840,1926,1891,1891,1891,Oil on canvas,25 3/4 x 36 1/4 in. (65.4 x 92.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437122,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.110,false,true,437121,European Paintings,Painting,The Four Trees,,,,,,Artist,,Claude Monet,"French, Paris 1840–1926 Giverny",,"Monet, Claude",French,1840,1926,1891,1891,1891,Oil on canvas,32 1/4 x 32 1/8 in. (81.9 x 81.6 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437121,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.111,false,true,437105,European Paintings,Painting,The Green Wave,,,,,,Artist,,Claude Monet,"French, Paris 1840–1926 Giverny",,"Monet, Claude",French,1840,1926,ca. 1866–67,1861,1871,Oil on canvas,19 1/8 x 25 1/2 in. (48.6 x 64.8 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437105,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.112,false,true,437135,European Paintings,Painting,La Grenouillère,,,,,,Artist,,Claude Monet,"French, Paris 1840–1926 Giverny",,"Monet, Claude",French,1840,1926,1869,1869,1869,Oil on canvas,29 3/8 x 39 1/4 in. (74.6 x 99.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437135,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.113,false,true,437127,European Paintings,Painting,Bridge over a Pond of Water Lilies,,,,,,Artist,,Claude Monet,"French, Paris 1840–1926 Giverny",,"Monet, Claude",French,1840,1926,1899,1899,1899,Oil on canvas,36 1/2 x 29 in. (92.7 x 73.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437127,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -06.1233.2,false,true,436075,European Paintings,Painting,Madonna of the Rose,,,,,,Artist,,Pascal-Adolphe-Jean Dagnan-Bouveret,"French, Paris 1852–1929 Quincey",,"Dagnan-Bouveret, Pascal-Adolphe-Jean",French,1852,1929,1885,1885,1885,Oil on canvas,33 3/4 x 27 in. (85.7 x 68.6 cm),"Catharine Lorillard Wolfe Collection, Wolfe Fund, 1906",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436075,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -31.132.34,false,true,436076,European Paintings,Painting,The Pardon in Brittany,,,,,,Artist,,Pascal-Adolphe-Jean Dagnan-Bouveret,"French, Paris 1852–1929 Quincey",,"Dagnan-Bouveret, Pascal-Adolphe-Jean",French,1852,1929,1886,1886,1886,Oil on canvas,45 1/8 x 33 3/8 in. (114.6 x 84.8 cm),"Gift of George F. Baker, 1931",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436076,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -17.120.220,false,true,437846,European Paintings,Painting,Going to Market,,,,,,Artist,,Constant Troyon,"French, Sèvres 1810–1865 Paris",,"Troyon, Constant",French,1810,1865,1860,1860,1860,Oil on canvas,16 1/8 x 12 7/8 in. (41 x 32.7 cm),"Mr. and Mrs. Isaac D. Fletcher Collection, Bequest of Isaac D. Fletcher, 1917",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437846,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -87.15.111,false,true,436866,European Paintings,Painting,Graziella,,,,,,Artist,,Jules-Joseph Lefebvre,"French, Tournan 1836–1912 Paris",,"Lefebvre, Jules-Joseph",French,1836,1912,1878,1878,1878,Oil on canvas,78 3/4 x 44 1/4 in. (200 x 112.4 cm),"Catharine Lorillard Wolfe Collection, Bequest of Catharine Lorillard Wolfe, 1887",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436866,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -71.91,false,true,436583,European Paintings,Painting,Study Head of a Woman,,,,,,Artist,,Jean-Baptiste Greuze,"French, Tournus 1725–1805 Paris",,"Greuze, Jean-Baptiste",French,1725,1805,ca. 1780,1775,1785,Oil on wood,18 1/2 x 16 in. (47 x 40.6 cm),"Purchase, 1871",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436583,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -56.55.3,false,true,436587,European Paintings,Painting,Jean Jacques Caffiéri (1725–1792),,,,,,Artist,,Jean-Baptiste Greuze,"French, Tournus 1725–1805 Paris",,"Greuze, Jean-Baptiste",French,1725,1805,ca. 1763,1758,1768,Oil on canvas,"Oval, 25 1/4 x 20 3/4 in. (64.1 x 52.7 cm)","Bequest of Ethel Tod Humphrys, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436587,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -66.28.1,false,true,436581,European Paintings,Painting,"Charles Claude de Flahaut (1730–1809), Comte d'Angiviller",,,,,,Artist,,Jean-Baptiste Greuze,"French, Tournus 1725–1805 Paris",,"Greuze, Jean-Baptiste",French,1725,1805,1763,1763,1763,Oil on canvas,25 1/4 x 21 1/4 in. (64.1 x 54 cm),"Gift of Edith C. Blum (et al.) Executors, in memory of Mr. and Mrs. Albert Blum, 1966",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436581,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1970.295,false,true,436580,European Paintings,Painting,Aegina Visited by Jupiter,,,,,,Artist,,Jean-Baptiste Greuze,"French, Tournus 1725–1805 Paris",,"Greuze, Jean-Baptiste",French,1725,1805,ca. 1767–69,1767,1769,Oil on canvas,57 7/8 x 77 1/8 in. (147 x 195.9 cm),"Gift of Harry N. Abrams and Purchase, Joseph Pulitzer Bequest, Pfeiffer, Fletcher, and Rogers Funds, 1970",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436580,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -20.155.8,true,true,436579,European Paintings,Painting,Broken Eggs,,,,,,Artist,,Jean-Baptiste Greuze,"French, Tournus 1725–1805 Paris",,"Greuze, Jean-Baptiste",French,1725,1805,1756,1756,1756,Oil on canvas,28 3/4 x 37 in. (73 x 94 cm),"Bequest of William K. Vanderbilt, 1920",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436579,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -55.205.2,false,true,436584,European Paintings,Painting,"Madame Jean-Baptiste Nicolet (Anne Antoinette Desmoulins, 1743–1817)",,,,,,Artist,,Jean-Baptiste Greuze,"French, Tournus 1725–1805 Paris",,"Greuze, Jean-Baptiste",French,1725,1805,late 1780s,1787,1789,Oil on wood,25 1/4 x 21 in. (64.1 x 53.3 cm),"Gift of Colonel and Mrs. Jacques Balsan, 1955",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436584,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -65.242.3,false,true,436586,European Paintings,Painting,Princess Varvara Nikolaevna Gagarina (1762–1802),,,,,,Artist,,Jean-Baptiste Greuze,"French, Tournus 1725–1805 Paris",,"Greuze, Jean-Baptiste",French,1725,1805,ca. 1780–82,1780,1782,Oil on canvas,"Oval, 31 1/2 x 25 in. (80 x 63.5 cm)","Gift of Mrs. William M. Haupt, from the collection of Mrs. James B. Haggin, 1965",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436586,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -67.187.72,false,true,436582,European Paintings,Painting,Head of a Young Woman,,,,,,Artist,,Jean-Baptiste Greuze,"French, Tournus 1725–1805 Paris",,"Greuze, Jean-Baptiste",French,1725,1805,possibly 1780s,1780,1789,Oil on canvas,16 1/8 x 12 3/4 in. (41 x 32.4 cm),"Bequest of Miss Adelaide Milton de Groot (1876–1967), 1967",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436582,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -32.100.137,false,true,436588,European Paintings,Painting,Head of a Young Boy,,,,,,Artist,,Jean-Baptiste Greuze,"French, Tournus 1725–1805 Paris",,"Greuze, Jean-Baptiste",French,1725,1805,1763,1763,1763,Oil on canvas,18 7/8 x 15 3/8 in. (47.9 x 39.1 cm),"The Friedsam Collection, Bequest of Michael Friedsam, 1931",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436588,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -06.144,false,true,437885,European Paintings,Painting,The Triumph of Aemilius Paulus,,,,,,Artist,,Carle (Antoine Charles Horace) Vernet,"French, Bordeaux 1758–1836 Paris",,"Vernet, Carle (Antoine Charles Horace)",French,1758,1836,1789,1789,1789,Oil on canvas,51 1/8 x 172 1/2 in. (129.9 x 438.2 cm),"Gift of Darius O. Mills, 1906",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437885,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -27.29,false,true,437380,European Paintings,Painting,The Chariot of Apollo,,,,,,Artist,,Odilon Redon,"French, Bordeaux 1840–1916 Paris",,"Redon, Odilon",French,1840,1916,1905–16,1905,1916,Oil on canvas,26 x 32 in. (66 x 81.3 cm),"Anonymous Gift, 1927",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437380,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -64.266,false,true,437378,European Paintings,Painting,Bouquet in a Chinese Vase,,,,,,Artist,,Odilon Redon,"French, Bordeaux 1840–1916 Paris",,"Redon, Odilon",French,1840,1916,ca. 1912–14,1890,1916,Oil on canvas,25 1/2 x 19 5/8 in. (64.8 x 49.8 cm),"The Mr. and Mrs. Henry Ittleson Jr. Purchase Fund, 1964",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437378,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -59.16.3,false,true,437382,European Paintings,Painting,Vase of Flowers (Pink Background),,,,,,Artist,,Odilon Redon,"French, Bordeaux 1840–1916 Paris",,"Redon, Odilon",French,1840,1916,ca. 1906,1901,1911,Oil on canvas,28 5/8 x 21 1/4 in. (72.7 x 54 cm),"Bequest of Mabel Choate, in memory of her father, Joseph Hodges Choate, 1958",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437382,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -60.19.1,false,true,437383,European Paintings,Painting,Pandora,,,,,,Artist,,Odilon Redon,"French, Bordeaux 1840–1916 Paris",,"Redon, Odilon",French,1840,1916,ca. 1914,1909,1919,Oil on canvas,56 1/2 x 24 1/2 in. (143.5 x 62.2 cm),"Bequest of Alexander M. Bing, 1959",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437383,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -53.140.5,false,true,437381,European Paintings,Painting,Etruscan Vase with Flowers,,,,,,Artist,,Odilon Redon,"French, Bordeaux 1840–1916 Paris",,"Redon, Odilon",French,1840,1916,1900–1910,1900,1910,Tempera on canvas,32 x 23 1/4 in. (81.3 x 59.1 cm),"Maria DeWitt Jesup Fund, 1951; acquired from The Museum of Modern Art, Lillie P. Bliss Collection",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437381,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -51.34,false,true,435886,European Paintings,Painting,Jean-Baptiste Colbert (1619–1683),,,,,,Artist,,Philippe de Champaigne,"French, Brussels 1602–1674 Paris",,"Champaigne, Philippe de",French,1602,1674,1655,1655,1655,Oil on canvas,36 1/4 x 28 1/2 in. (92.1 x 72.4 cm),"Gift of The Wildenstein Foundation Inc., 1951",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435886,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2004.31,false,true,438724,European Paintings,Painting,The Annunciation,,,,,,Artist,,Philippe de Champaigne,"French, Brussels 1602–1674 Paris",,"Champaigne, Philippe de",French,1602,1674,ca. 1644,1639,1649,Oil on oak,"Overall, 28 x 28 3/4 in. (71.1 x 73 cm); painted surface, 27 1/4 x 27 3/4 in. (69.2 x 70.5 cm)","Wrightsman Fund, 2004",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/438724,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -10.41,false,true,436295,European Paintings,Painting,Portrait of a Woman,,,,,,Artist,,Henri Fantin-Latour,"French, Grenoble 1836–1904 Buré",,"Fantin-Latour, Henri",French,1836,1904,1885,1885,1885,Oil on canvas,39 1/2 x 32 in. (100.3 x 81.3 cm),"Catharine Lorillard Wolfe Collection, Wolfe Fund, 1910",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436295,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1980.3,false,true,436293,European Paintings,Painting,Still Life with Flowers and Fruit,,,,,,Artist,,Henri Fantin-Latour,"French, Grenoble 1836–1904 Buré",,"Fantin-Latour, Henri",French,1836,1904,1866,1866,1866,Oil on canvas,28 3/4 x 23 5/8 in. (73 x 60 cm),"Purchase, Mr. and Mrs. Richard J. Bernhard Gift, by exchange, 1980",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436293,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -66.194,false,true,436294,European Paintings,Painting,Still Life with Pansies,,,,,,Artist,,Henri Fantin-Latour,"French, Grenoble 1836–1904 Buré",,"Fantin-Latour, Henri",French,1836,1904,1874,1874,1874,Oil on canvas,18 1/2 x 22 1/4 in. (47 x 56.5 cm),"The Mr. and Mrs. Henry Ittleson Jr. Purchase Fund, 1966",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436294,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1995.91,false,true,436297,European Paintings,Painting,Self-Portrait,,,,,,Artist,,Henri Fantin-Latour,"French, Grenoble 1836–1904 Buré",,"Fantin-Latour, Henri",French,1836,1904,ca. 1858,1856,1904,"Oil on canvas, laid down on canvas",Overall 10 3/8 x 8 3/8 in. (26.4 x 21.3 cm); original canvas 10 x 7 7/8 in. (25.4 x 20 cm),"Catharine Lorillard Wolfe Collection, Wolfe Fund, 1995",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436297,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1987.119,false,true,436292,European Paintings,Painting,Still Life with Roses and Fruit,,,,,,Artist,,Henri Fantin-Latour,"French, Grenoble 1836–1904 Buré",,"Fantin-Latour, Henri",French,1836,1904,1863,1863,1863,Oil on canvas,13 5/8 x 16 3/8 in. (34.6 x 41.6 cm),"Bequest of Alice A. Hay, 1987",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436292,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1996.517,false,true,437985,European Paintings,Painting,Pansies,,,,,,Artist,,Henri Fantin-Latour,"French, Grenoble 1836–1904 Buré",,"Fantin-Latour, Henri",French,1836,1904,1903,1903,1903,Oil on canvas,9 x 11 1/8 in. (22.9 x 28.3 cm),"Gift of Paul O. Fabri, 1996",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437985,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1997.347,false,true,438031,European Paintings,Painting,Summer Flowers,,,,,,Artist,,Henri Fantin-Latour,"French, Grenoble 1836–1904 Buré",,"Fantin-Latour, Henri",French,1836,1904,1880,1880,1880,Oil on canvas,20 x 24 3/8 in. (50.8 x 61.9 cm),"Gift of Susan S. Dillon, 1997",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/438031,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.636,false,true,629928,European Paintings,Painting,Potted Pansies,,,,,,Artist,,Henri Fantin-Latour,"French, Grenoble 1836–1904 Buré",,"Fantin-Latour, Henri",French,1836,1904,1883,1883,1883,Oil on canvas,11 × 13 1/2 in. (27.9 × 34.3 cm),"Gift of Susan S. Dillon, 2013",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/629928,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -23.280.9,false,true,436296,European Paintings,Painting,The Palace of Aurora,,,,,,Artist,,Henri Fantin-Latour,"French, Grenoble 1836–1904 Buré",,"Fantin-Latour, Henri",French,1836,1904,1902,1856,1904,Oil on canvas,18 1/8 x 15 in. (46 x 38.1 cm),"Bequest of Anne D. Thomson, 1923",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436296,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2003.20.6,false,true,437995,European Paintings,Painting,Roses in a Bowl,,,,,,Artist,,Henri Fantin-Latour,"French, Grenoble 1836–1904 Buré",,"Fantin-Latour, Henri",French,1836,1904,1883,1883,1883,Oil on canvas,11 3/4 x 16 3/8 in. (29.8 x 41.6 cm),"The Walter H. and Leonore Annenberg Collection, Bequest of Walter H. Annenberg, 2002",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437995,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2001.202.3,false,true,437997,European Paintings,Painting,Asters and Fruit on a Table,,,,,,Artist,,Henri Fantin-Latour,"French, Grenoble 1836–1904 Buré",,"Fantin-Latour, Henri",French,1836,1904,1868,1868,1868,Oil on canvas,22 3/8 x 21 5/8 in. (56.8 x 54.9 cm),"The Walter H. and Leonore Annenberg Collection, Gift of Walter H. and Leonore Annenberg, 2001, Bequest of Walter H. Annenberg, 2002",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437997,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -49.114,false,true,437647,European Paintings,Painting,Spring in Brittany,,,,,,Artist,,Paul Sébillot,"French, Matignon 1843–1918 Paris",,"Sébillot, Paul",French,1843,1918,1874,1874,1874,Oil on wood,14 x 10 3/4 in. (35.6 x 27.3 cm),"Gift of Paul-Yves Sébillot, 1949",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437647,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -71.57,false,true,437235,European Paintings,Painting,Ducks Resting in Sunshine,,,,,,Artist,,Jean-Baptiste Oudry,"French, Paris 1686–1755 Beauvais",,"Oudry, Jean-Baptiste",French,1686,1755,1753,1753,1753,Oil on canvas,25 1/2 x 31 3/4 in. (64.8 x 80.6 cm),"Purchase, 1871",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437235,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -71.89,false,true,437234,European Paintings,Painting,Dog Guarding Dead Game,,,,,,Artist,,Jean-Baptiste Oudry,"French, Paris 1686–1755 Beauvais",,"Oudry, Jean-Baptiste",French,1686,1755,1753,1753,1753,Oil on canvas,25 1/2 x 31 3/4 in. (64.8 x 80.6 cm),"Purchase, 1871",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437234,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -31.45,true,true,436105,European Paintings,Painting,The Death of Socrates,,,,,,Artist,,Jacques Louis David,"French, Paris 1748–1825 Brussels",,"David, Jacques Louis",French,1748,1825,1787,1787,1787,Oil on canvas,51 x 77 1/4 in. (129.5 x 196.2 cm),"Catharine Lorillard Wolfe Collection, Wolfe Fund, 1931",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436105,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1977.10,true,true,436106,European Paintings,Painting,"Antoine-Laurent Lavoisier (1743–1794) and His Wife (Marie-Anne-Pierrette Paulze, 1758–1836)",,,,,,Artist,,Jacques Louis David,"French, Paris 1748–1825 Brussels",,"David, Jacques Louis",French,1748,1825,1788,1788,1788,Oil on canvas,102 1/4 x 76 5/8 in. (259.7 x 194.6 cm),"Purchase, Mr. and Mrs. Charles Wrightsman Gift, in honor of Everett Fahy, 1977",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436106,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -65.14.5,false,true,436107,European Paintings,Painting,General Étienne-Maurice Gérard (1773–1852),,,,,,Artist,,Jacques Louis David,"French, Paris 1748–1825 Brussels",,"David, Jacques Louis",French,1748,1825,1816,1816,1816,Oil on canvas,77 5/8 x 53 5/8 in. (197.2 x 136.2 cm),"Purchase, Rogers and Fletcher Funds, and Mary Wetmore Shively Bequest, in memory of her husband, Henry L. Shively, M.D., 1965",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436107,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -11.4,false,true,438816,European Paintings,Painting,The Forest in Winter at Sunset,,,,,,Artist,,Théodore Rousseau,"French, Paris 1812–1867 Barbizon",,"Rousseau, Théodore",French,1812,1867,ca. 1846–67,1846,1867,Oil on canvas,64 x 102 3/8 in. (162.6 x 260 cm),"Gift of P. A. B. Widener, 1911",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/438816,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -03.28,false,true,437516,European Paintings,Painting,An Old Chapel in a Valley,,,,,,Artist,,Théodore Rousseau,"French, Paris 1812–1867 Barbizon",,"Rousseau, Théodore",French,1812,1867,ca. 1835,1832,1867,Oil on wood,10 1/2 x 13 7/8 in. (26.7 x 35.2 cm),"Catharine Lorillard Wolfe Collection, Wolfe Fund, 1903",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437516,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -96.27,false,true,437514,European Paintings,Painting,"The Edge of the Woods at Monts-Girard, Fontainebleau Forest",,,,,,Artist,,Théodore Rousseau,"French, Paris 1812–1867 Barbizon",,"Rousseau, Théodore",French,1812,1867,1852–54,1852,1854,Oil on wood,31 1/2 x 48 in. (80 x 121.9 cm),"Catharine Lorillard Wolfe Collection, Wolfe Fund, 1896",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437514,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -11.45.5,false,true,437515,European Paintings,Painting,A Meadow Bordered by Trees,,,,,,Artist,,Théodore Rousseau,"French, Paris 1812–1867 Barbizon",,"Rousseau, Théodore",French,1812,1867,ca. 1845,1840,1850,Oil on wood,16 3/8 x 24 3/8 in. (41.6 x 61.9 cm),"Bequest of Robert Graham Dun, 1900",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437515,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -25.110.4,false,true,437520,European Paintings,Painting,Sunset near Arbonne,,,,,,Artist,,Théodore Rousseau,"French, Paris 1812–1867 Barbizon",,"Rousseau, Théodore",French,1812,1867,ca. 1860–65,1860,1865,Oil on wood,25 1/4 x 39 in. (64.1 x 99.1 cm),"Bequest of Collis P. Huntington, 1900",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437520,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.40.814,false,true,437517,European Paintings,Painting,A Path among the Rocks,,,,,,Artist,,Théodore Rousseau,"French, Paris 1812–1867 Barbizon",,"Rousseau, Théodore",French,1812,1867,probably 1861,1832,1867,Oil on wood,15 x 23 5/8 in. (38.1 x 60 cm),"Bequest of Benjamin Altman, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437517,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -25.110.52,false,true,437518,European Paintings,Painting,A River in a Meadow,,,,,,Artist,,Théodore Rousseau,"French, Paris 1812–1867 Barbizon",,"Rousseau, Théodore",French,1812,1867,probably late 1830s–early 1840s,1835,1845,Oil on wood,16 3/4 x 26 1/8 in. (42.5 x 66.4 cm),"Bequest of Collis P. Huntington, 1900",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437518,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1982.320,false,true,437513,European Paintings,Painting,Still Life with Ham,,,,,,Artist,,Philippe Rousseau,"French, Paris 1816–1887 Acquigny",,"Rousseau, Philippe",French,1816,1887,1870s,1870,1879,Oil on canvas,28 3/4 x 36 1/4 in. (73 x 92.1 cm),"Catharine Lorillard Wolfe Collection, Wolfe Fund, 1982",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437513,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -81.1.656,false,true,435603,European Paintings,Painting,A Footman Sleeping,,,,,,Artist,,Charles Bargue,"French, Paris 1825/26–1883 Paris",,"Bargue, Charles",French,1825,1883,1871,1871,1871,Oil on wood,13 3/4 x 10 1/4 in. (34.9 x 26 cm),"Bequest of Stephen Whitney Phoenix, 1881",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435603,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -87.15.102,false,true,435604,European Paintings,Painting,A Bashi-Bazouk,,,,,,Artist,,Charles Bargue,"French, Paris 1825/26–1883 Paris",,"Bargue, Charles",French,1825,1883,1875,1875,1875,Oil on canvas,18 1/4 x 13 1/8 in. (46.4 x 33.3 cm),"Catharine Lorillard Wolfe Collection, Bequest of Catharine Lorillard Wolfe, 1887",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435604,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -16.95,false,true,437384,European Paintings,Painting,Salomé,,,,,,Artist,,Henri Regnault,"French, Paris 1843–1871 Buzenval",,"Regnault, Henri",French,1843,1871,1870,1870,1870,Oil on canvas,63 x 40 1/2 in. (160 x 102.9 cm),"Gift of George F. Baker, 1916",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437384,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2003.42.54,false,true,438677,European Paintings,Painting,"The Banks of the Rance, Brittany",,,,,,Artist,,Pierre Henri de Valenciennes,"French, Toulouse 1750–1819 Paris",,"Valenciennes, Pierre Henri de",French,1750,1819,possibly 1785,1785,1819,"Oil on paper, laid down on canvas",8 3/8 x 19 3/8 in. (21.3 x 49.2 cm),"The Whitney Collection, Gift of Wheelock Whitney III, and Purchase, Gift of Mr. and Mrs. Charles S. McVeigh, by exchange, 2003",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/438677,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.40.819,false,true,436193,European Paintings,Painting,The Edge of the Woods,,,,,,Artist,,Narcisse-Virgile Diaz de la Peña,"French, Bordeaux 1808–1876 Menton",,"Diaz de la Peña, Narcisse-Virgile",French,1808,1876,1872,1872,1872,Oil on wood,14 7/8 x 18 1/2 in. (37.8 x 47 cm),"Bequest of Benjamin Altman, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436193,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -25.110.30,false,true,436191,European Paintings,Painting,Diana,,,,,,Artist,,Narcisse-Virgile Diaz de la Peña,"French, Bordeaux 1808–1876 Menton",,"Diaz de la Peña, Narcisse-Virgile",French,1808,1876,1849,1849,1849,Oil on canvas,46 1/2 x 27 3/4 in. (118.1 x 70.5 cm),"Bequest of Collis P. Huntington, 1900",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436191,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -25.110.92,false,true,436196,European Paintings,Painting,The Forest of Fontainebleau,,,,,,Artist,,Narcisse-Virgile Diaz de la Peña,"French, Bordeaux 1808–1876 Menton",,"Diaz de la Peña, Narcisse-Virgile",French,1808,1876,1874,1874,1874,Oil on wood,18 5/8 x 23 5/8 in. (47.3 x 60 cm),"Bequest of Collis P. Huntington, 1900",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436196,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -17.120.214,false,true,436192,European Paintings,Painting,Autumn: The Woodland Pond,,,,,,Artist,,Narcisse-Virgile Diaz de la Peña,"French, Bordeaux 1808–1876 Menton",,"Diaz de la Peña, Narcisse-Virgile",French,1808,1876,1867,1867,1867,Oil on canvas,19 3/4 x 26 in. (50.2 x 66 cm),"Mr. and Mrs. Isaac D. Fletcher Collection, Bequest of Isaac D. Fletcher, 1917",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436192,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -17.120.230,false,true,436195,European Paintings,Painting,A Vista through Trees: Fontainebleau,,,,,,Artist,,Narcisse-Virgile Diaz de la Peña,"French, Bordeaux 1808–1876 Menton",,"Diaz de la Peña, Narcisse-Virgile",French,1808,1876,1873,1873,1873,Oil on wood,12 3/4 x 17 1/4 in. (32.4 x 43.8 cm),"Mr. and Mrs. Isaac D. Fletcher Collection, Bequest of Isaac D. Fletcher, 1917",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436195,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -38.75,false,true,437099,European Paintings,Painting,Woman with a Rake,,,,,,Artist,,Jean-François Millet,"French, Gruchy 1814–1875 Barbizon",,"Millet, Jean-François",French,1814,1875,probably 1856–57,1856,1857,Oil on canvas,15 5/8 x 13 1/2 in. (39.7 x 34.3 cm),"Gift of Stephen C. Clark, 1938",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437099,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -50.151,false,true,437095,European Paintings,Painting,Calling the Cows Home,,,,,,Artist,,Jean-François Millet,"French, Gruchy 1814–1875 Barbizon",,"Millet, Jean-François",French,1814,1875,ca. 1872,1872,1872,Oil on wood,37 1/4 x 25 1/2 in. (94.6 x 64.8 cm),"Gift of Mrs. Arthur Whitney, 1950",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437095,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -15.30.24,false,true,437096,European Paintings,Painting,Garden Scene,,,,,,Artist,,Jean-François Millet,"French, Gruchy 1814–1875 Barbizon",,"Millet, Jean-François",French,1814,1875,1854,1854,1854,Oil on canvas,6 3/4 x 8 3/8 in. (17.1 x 21.3 cm),"Bequest of Maria DeWitt Jesup, from the collection of her husband, Morris K. Jesup, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437096,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1983.446,false,true,437098,European Paintings,Painting,Shepherdess Seated on a Rock,,,,,,Artist,,Jean-François Millet,"French, Gruchy 1814–1875 Barbizon",,"Millet, Jean-François",French,1814,1875,1856,1856,1856,Oil on wood,14 1/8 x 11 1/8 in. (35.9 x 28.3 cm),"Gift of Douglas Dillon, 1983",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437098,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2002.613,false,true,438616,European Paintings,Painting,Retreat from the Storm,,,,,,Artist,,Jean-François Millet,"French, Gruchy 1814–1875 Barbizon",,"Millet, Jean-François",French,1814,1875,ca. 1846,1846,1846,Oil on canvas,18 1/4 x 15 in. (46.4 x 38.1 cm),"Gift of Sarina Tang and Peter M. Wood, 2002",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/438616,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -60.71.12,false,true,437097,European Paintings,Painting,Haystacks: Autumn,,,,,,Artist,,Jean-François Millet,"French, Gruchy 1814–1875 Barbizon",,"Millet, Jean-François",French,1814,1875,ca. 1874,1869,1875,Oil on canvas,33 1/2 x 43 3/8 in. (85.1 x 110.2 cm),"Bequest of Lillian S. Timken, 1959",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437097,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -17.120.209,false,true,437094,European Paintings,Painting,Autumn Landscape with a Flock of Turkeys,,,,,,Artist,,Jean-François Millet,"French, Gruchy 1814–1875 Barbizon",,"Millet, Jean-François",French,1814,1875,1872–73,1872,1873,Oil on canvas,31 7/8 x 39 in. (81 x 99.1 cm),"Mr. and Mrs. Isaac D. Fletcher Collection, Bequest of Isaac D. Fletcher, 1917",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437094,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1997.371,false,true,438032,European Paintings,Painting,"Study for ""Portrait of an Indian""",,,,,,Artist,,Anne Louis Girodet-Trioson,"French, Montargis 1767–1824 Paris",,"Girodet-Trioson, Anne Louis",French,1767,1824,ca. 1807,1802,1812,Oil on canvas,16 x 12 7/8 in. (40.6 x 32.7 cm),"Purchase, Gift of Joanne Toor Cummings, by exchange, 1997",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/438032,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1999.101,false,true,438389,European Paintings,Painting,"Madame Jacques-Louis-Étienne Reizet (Colette-Désirée-Thérèse Godefroy, 1782–1850)",,,,,,Artist,,Anne Louis Girodet-Trioson,"French, Montargis 1767–1824 Paris",,"Girodet-Trioson, Anne Louis",French,1767,1824,1823,1823,1823,Oil on canvas,23 3/4 x 19 1/2 in. (60.3 x 49.5 cm),"Purchase, Gifts of Joanne Toor Cummings, Mr. and Mrs. Richard Rodgers, Raymonde Paul, and Estate of Dorothy Lichtensteiger, by exchange, 1999",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/438389,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -38.65,false,true,436708,European Paintings,Painting,Odalisque in Grisaille,,,,,,Artist,,Jean Auguste Dominique Ingres,"French, Montauban 1780–1867 Paris",and Workshop,"Ingres, Jean Auguste Dominique",French,1780,1867,ca. 1824–34,1824,1834,Oil on canvas,32 3/4 x 43 in. (83.2 x 109.2 cm),"Catharine Lorillard Wolfe Collection, Wolfe Fund, 1938",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436708,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -19.77.1,false,true,436706,European Paintings,Painting,Jacques-Louis Leblanc (1774–1846),,,,,,Artist,,Jean Auguste Dominique Ingres,"French, Montauban 1780–1867 Paris",,"Ingres, Jean Auguste Dominique",French,1780,1867,1823,1823,1823,Oil on canvas,47 5/8 x 37 5/8 in. (121 x 95.6 cm),"Catharine Lorillard Wolfe Collection, Wolfe Fund, 1918",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436706,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -19.77.2,true,true,436703,European Paintings,Painting,"Madame Jacques-Louis Leblanc (Françoise Poncelle, 1788–1839)",,,,,,Artist,,Jean Auguste Dominique Ingres,"French, Montauban 1780–1867 Paris",,"Ingres, Jean Auguste Dominique",French,1780,1867,1823,1823,1823,Oil on canvas,47 x 36 1/2 in. (119.4 x 92.7 cm),"Catharine Lorillard Wolfe Collection, Wolfe Fund, 1918",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436703,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -43.85.2,false,true,436704,European Paintings,Painting,Edmond Cavé (1794–1852),,,,,,Artist,,Jean Auguste Dominique Ingres,"French, Montauban 1780–1867 Paris",,"Ingres, Jean Auguste Dominique",French,1780,1867,1844,1844,1844,Oil on canvas,16 x 12 7/8 in. (40.6 x 32.7 cm),"Bequest of Grace Rainey Rogers, 1943",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436704,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -43.85.3,false,true,436707,European Paintings,Painting,"Madame Edmond Cavé (Marie-Élisabeth Blavot, born 1810)",,,,,,Artist,,Jean Auguste Dominique Ingres,"French, Montauban 1780–1867 Paris",,"Ingres, Jean Auguste Dominique",French,1780,1867,ca. 1831–34,1826,1839,Oil on canvas,16 x 12 7/8 in. (40.6 x 32.7 cm),"Bequest of Grace Rainey Rogers, 1943",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436707,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.186,false,true,438434,European Paintings,Painting,The Virgin Adoring the Host,,,,,,Artist,,Jean Auguste Dominique Ingres,"French, Montauban 1780–1867 Paris",,"Ingres, Jean Auguste Dominique",French,1780,1867,1852,1852,1852,Oil on canvas,15 7/8 x 12 7/8 in. (40.3 x 32.7 cm),"Gift of Lila and Herman Shickman, 2005",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/438434,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.23,true,true,438818,European Paintings,Painting,Joseph-Antoine Moltedo (born 1775),,,,,,Artist,,Jean Auguste Dominique Ingres,"French, Montauban 1780–1867 Paris",,"Ingres, Jean Auguste Dominique",French,1780,1867,ca. 1810,1805,1815,Oil on canvas,29 5/8 x 22 7/8 in. (75.2 x 58.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/438818,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.100.6,false,true,436183,European Paintings,Painting,Departure of the Amazons,,,,,,Artist,,Claude Déruet,"French, Nancy ca. 1588–1660 Nancy",,"Déruet, Claude",French,1583,1660,1620s,1620,1629,Oil on canvas,20 x 26 in. (50.8 x 66 cm),"Bequest of Harry G. Sperling, 1971",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436183,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.100.7,false,true,436182,European Paintings,Painting,Triumph of the Amazons,,,,,,Artist,,Claude Déruet,"French, Nancy ca. 1588–1660 Nancy",,"Déruet, Claude",French,1583,1660,1620s,1620,1629,Oil on canvas,20 1/4 x 26 in. (51.4 x 66 cm),"Bequest of Harry G. Sperling, 1971",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436182,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -17.120.204,false,true,437903,European Paintings,Painting,Marie Joséphine Charlotte du Val d'Ognes (died 1868),,,,,,Artist,,Marie Denise Villers,"French, Paris 1774–1821 Paris (?)",,"Villers, Marie Denise",French,1774,1821,1801,1801,1801,Oil on canvas,63 1/2 x 50 5/8in. (161.3 x 128.6cm),"Mr. and Mrs. Isaac D. Fletcher Collection, Bequest of Isaac D. Fletcher, 1917",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437903,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -60.6,false,true,437464,European Paintings,Painting,Louis XV (1710–1774) at the Age of Five in the Costume of the Sacre,,,,,,Artist,,Hyacinthe Rigaud,"French, Perpignan 1659–1743 Paris",and Workshop,"Rigaud, Hyacinthe",French,1659,1743,ca. 1716–24,1716,1724,Oil on canvas,77 x 55 1/2 in. (195.6 x 141 cm),"Purchase, Mary Wetmore Shively Bequest, in memory of her husband, Henry L. Shively, M.D., 1960",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437464,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -59.119,false,true,437463,European Paintings,Painting,Portrait of a Military Officer,,,,,,Artist,,Hyacinthe Rigaud,"French, Perpignan 1659–1743 Paris",,"Rigaud, Hyacinthe",French,1659,1743,ca. 1710,1705,1715,Oil on canvas,54 x 41 3/8 in. (137.2 x 105.1 cm),"The Alfred N. Punnett Endowment Fund, 1959",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437463,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -48.187.733,false,true,437465,European Paintings,Painting,Portrait of a Man,,,,,,Artist,,Hyacinthe Rigaud,"French, Perpignan 1659–1743 Paris",,"Rigaud, Hyacinthe",French,1659,1743,1693,1693,1693,Oil on canvas,"Oval, 32 1/2 x 25 3/4 in. (82.6 x 65.4 cm)","Bequest of Catherine D. Wentworth, 1948",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437465,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -87.15.54,false,true,436644,European Paintings,Painting,A Bather (Echo),,,,,,Artist,,Jean-Jacques Henner,"French, Bernwiller 1829–1905 Paris",,"Henner, Jean-Jacques",French,1829,1905,1881,1881,1881,Oil on canvas,38 1/8 x 27 3/4 in. (96.8 x 70.5 cm),"Catharine Lorillard Wolfe Collection, Bequest of Catharine Lorillard Wolfe, 1887",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436644,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -87.25,true,true,435702,European Paintings,Painting,The Horse Fair,,,,,,Artist,,Rosa Bonheur,"French, Bordeaux 1822–1899 Thomery",,"Bonheur, Rosa",French,1822,1899,1852–55,1852,1855,Oil on canvas,96 1/4 x 199 1/2 in. (244.5 x 506.7 cm),"Gift of Cornelius Vanderbilt, 1887",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435702,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -87.15.77,false,true,435704,European Paintings,Painting,A Limier Briquet Hound,,,,,,Artist,,Rosa Bonheur,"French, Bordeaux 1822–1899 Thomery",,"Bonheur, Rosa",French,1822,1899,ca. 1856,1856,1856,Oil on canvas,14 1/2 x 18 in. (36.8 x 45.7 cm),"Catharine Lorillard Wolfe Collection, Bequest of Catharine Lorillard Wolfe, 1887",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435704,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -87.15.109,false,true,435703,European Paintings,Painting,Weaning the Calves,,,,,,Artist,,Rosa Bonheur,"French, Bordeaux 1822–1899 Thomery",,"Bonheur, Rosa",French,1822,1899,1879,1879,1879,Oil on canvas,25 5/8 x 32 in. (65.1 x 81.3 cm),"Catharine Lorillard Wolfe Collection, Bequest of Catharine Lorillard Wolfe, 1887",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435703,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2012.575,false,true,438158,European Paintings,Painting,Springtime,,,,,,Artist,,Pierre-Auguste Cot,"French, Bédarieux 1837–1883 Paris",,"Cot, Pierre-Auguste",French,1837,1883,1873,1873,1873,Oil on canvas,84 x 50 in. (213.4 x 127 cm),"Gift of Steven and Alexandra Cohen, 2012",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/438158,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -87.15.134,false,true,435997,European Paintings,Painting,The Storm,,,,,,Artist,,Pierre-Auguste Cot,"French, Bédarieux 1837–1883 Paris",,"Cot, Pierre-Auguste",French,1837,1883,1880,1880,1880,Oil on canvas,92 1/4 x 61 3/4 in. (234.3 x 156.8 cm),"Catharine Lorillard Wolfe Collection, Bequest of Catharine Lorillard Wolfe, 1887",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435997,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -47.12,false,true,435907,European Paintings,Painting,Sunrise,,,,,,Artist,,Claude Lorrain (Claude Gellée),"French, Chamagne 1604/5?–1682 Rome",,Claude Lorrain (Claude Gellée),French,1604,1682,possibly 1646–47,1646,1647,Oil on canvas,40 1/2 x 52 3/4 in. (102.9 x 134 cm),"Fletcher Fund, 1947",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435907,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -28.117,false,true,435905,European Paintings,Painting,The Ford,,,,,,Artist,,Claude Lorrain (Claude Gellée),"French, Chamagne 1604/5?–1682 Rome",,Claude Lorrain (Claude Gellée),French,1604,1682,possibly 1636,1636,1636,Oil on canvas,29 1/4 x 39 3/4 in. (74.3 x 101 cm),"Fletcher Fund, 1928",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435905,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -55.119,true,true,435908,European Paintings,Painting,The Trojan Women Setting Fire to Their Fleet,,,,,,Artist,,Claude Lorrain (Claude Gellée),"French, Chamagne 1604/5?–1682 Rome",,Claude Lorrain (Claude Gellée),French,1604,1682,ca. 1643,1643,1643,Oil on canvas,41 3/8 x 59 7/8 in. (105.1 x 152.1 cm),"Fletcher Fund, 1955",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435908,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1978.205,false,true,435909,European Paintings,Painting,View of La Crescenza,,,,,,Artist,,Claude Lorrain (Claude Gellée),"French, Chamagne 1604/5?–1682 Rome",,Claude Lorrain (Claude Gellée),French,1604,1682,1648–50,1648,1650,Oil on canvas,15 1/4 x 22 7/8 in. (38.7 x 58.1 cm),"Purchase, The Annenberg Fund Inc. Gift, 1978",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435909,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -65.181.12,false,true,435906,European Paintings,Painting,Pastoral Landscape: The Roman Campagna,,,,,,Artist,,Claude Lorrain (Claude Gellée),"French, Chamagne 1604/5?–1682 Rome",,Claude Lorrain (Claude Gellée),French,1604,1682,ca. 1639,1634,1644,Oil on canvas,40 x 53 1/2 in. (101.6 x 135.9 cm),"Bequest of Adele L. Lehman, in memory of Arthur Lehman, 1965",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435906,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -89.21.1,true,true,435621,European Paintings,Painting,Joan of Arc,,,,,,Artist,,Jules Bastien-Lepage,"French, Damvillers 1848–1884 Paris",,"Bastien-Lepage, Jules",French,1848,1884,1879,1879,1879,Oil on canvas,100 x 110 in. (254 x 279.4 cm),"Gift of Erwin Davis, 1889",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435621,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2012.156,false,true,438099,European Paintings,Painting,"The Public Viewing David’s ""Coronation"" at the Louvre",,,,,,Artist,,Louis Léopold Boilly,"French, La Bassée 1761–1845 Paris",,"Boilly, Louis Léopold",French,1761,1845,1810,1810,1810,Oil on canvas,24 1/4 x 32 1/2 in. (61.6 x 82.6 cm),"Gift of Mrs. Charles Wrightsman, 2012",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/438099,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -71.56,false,true,437328,European Paintings,Painting,Midas Washing at the Source of the Pactolus,,,,,,Artist,,Nicolas Poussin,"French, Les Andelys 1594–1665 Rome",,"Poussin, Nicolas",French,1594,1665,ca. 1627,1622,1632,Oil on canvas,38 3/8 x 28 5/8 in. (97.5 x 72.7 cm),"Purchase, 1871",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437328,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -46.160,true,true,437329,European Paintings,Painting,The Abduction of the Sabine Women,,,,,,Artist,,Nicolas Poussin,"French, Les Andelys 1594–1665 Rome",,"Poussin, Nicolas",French,1594,1665,probably 1633–34,1633,1634,Oil on canvas,60 7/8 x 82 5/8 in. (154.6 x 209.9 cm),"Harris Brisbane Dick Fund, 1946",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437329,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -24.45.1,true,true,437326,European Paintings,Painting,Blind Orion Searching for the Rising Sun,,,,,,Artist,,Nicolas Poussin,"French, Les Andelys 1594–1665 Rome",,"Poussin, Nicolas",French,1594,1665,1658,1658,1658,Oil on canvas,46 7/8 x 72 in. (119.1 x 182.9 cm),"Fletcher Fund, 1924",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437326,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -24.45.2,false,true,437330,European Paintings,Painting,Saints Peter and John Healing the Lame Man,,,,,,Artist,,Nicolas Poussin,"French, Les Andelys 1594–1665 Rome",,"Poussin, Nicolas",French,1594,1665,1655,1655,1655,Oil on canvas,49 1/2 x 65 in. (125.7 x 165.1 cm),"Marquand Fund, 1924",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437330,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1977.1.2,false,true,437327,European Paintings,Painting,The Companions of Rinaldo,,,,,,Artist,,Nicolas Poussin,"French, Les Andelys 1594–1665 Rome",,"Poussin, Nicolas",French,1594,1665,ca. 1633,1628,1638,Oil on canvas,46 1/2 x 40 1/4 in. (118.1 x 102.2 cm),"Gift of Mr. and Mrs. Charles Wrightsman, 1977",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437327,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1997.117.6,false,true,438025,European Paintings,Painting,The Rest on the Flight into Egypt,,,,,,Artist,,Nicolas Poussin,"French, Les Andelys 1594–1665 Rome",,"Poussin, Nicolas",French,1594,1665,ca. 1627,1622,1632,Oil on canvas,30 x 25 in. (76.2 x 63.5 cm),"Bequest of Lore Heinemann, in memory of her husband, Dr. Rudolf J. Heinemann, 1996",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/438025,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -15.30.20,false,true,437220,European Paintings,Painting,A Cavalryman,,,,,,Artist,,Alphonse-Marie-Adolphe de Neuville,"French, Saint-Omer 1835–1885 Paris",,"Neuville, Alphonse-Marie-Adolphe de",French,1835,1885,1884,1884,1884,Oil on canvas,18 1/8 x 15 in. (46 x 38.1 cm),"Bequest of Maria DeWitt Jesup, from the collection of her husband, Morris K. Jesup, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437220,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -25.110.26,false,true,437219,European Paintings,Painting,The Spy,,,,,,Artist,,Alphonse-Marie-Adolphe de Neuville,"French, Saint-Omer 1835–1885 Paris",,"Neuville, Alphonse-Marie-Adolphe de",French,1835,1885,1880,1880,1880,Oil on canvas,51 1/4 x 84 in. (130.2 x 213.4 cm),"Bequest of Collis P. Huntington, 1900",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437219,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -28.113,false,true,436207,European Paintings,Painting,Don Quixote and Sancho Panza Entertained by Basil and Quiteria,,,,,,Artist,,Gustave Doré,"French, Strasbourg 1832–1883 Paris",,"Doré, Gustave",French,1832,1883,1863?,1863,1863,Oil on canvas,36 1/4 x 28 3/4 in. (92.1 x 73 cm),"Gift of Mrs. William A. McFadden and Mrs. Giles Whiting, 1928",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436207,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -87.15.21,false,true,435774,European Paintings,Painting,A Peasant Girl Knitting,,,,,,Artist,,Jules Breton,"French, Courrières 1827–1906 Paris",,"Breton, Jules",French,1827,1906,ca. 1870,1865,1875,Oil on canvas,22 5/8 x 18 1/2 in. (57.5 x 47 cm),"Catharine Lorillard Wolfe Collection, Bequest of Catharine Lorillard Wolfe, 1887",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435774,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -25.110.66,false,true,435773,European Paintings,Painting,The Weeders,,,,,,Artist,,Jules Breton,"French, Courrières 1827–1906 Paris",,"Breton, Jules",French,1827,1906,1868,1868,1868,Oil on canvas,28 1/8 x 50 1/4 in. (71.4 x 127.6 cm),"Bequest of Collis P. Huntington, 1900",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435773,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1974.2,false,true,435755,European Paintings,Painting,The Baptism of Christ,,,,,,Artist,,Sébastien Bourdon,"French, Montpellier 1616–1671 Paris",,"Bourdon, Sébastien",French,1616,1671,ca. 1650,1645,1655,Oil on canvas,Overall 59 3/4 x 46 1/2 in. (151.8 x 118.1 cm); painted surface (oval) 59 1/8 x 45 1/2 in. (150.2 x 115.6 cm),"Purchase, George T. Delacorte Jr. Gift, 1974",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435755,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1985.90,false,true,435756,European Paintings,Painting,A Classical Landscape,,,,,,Artist,,Sébastien Bourdon,"French, Montpellier 1616–1671 Paris",,"Bourdon, Sébastien",French,1616,1671,probably 1660s,1660,1669,Oil on canvas,27 1/2 x 36 1/4 in. (69.9 x 92.1 cm),"Gift of Atwood A. Allaire, Pamela Askew, and Phoebe A. DesMarais, in memory of their mother, Constance Askew, 1985",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435756,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -94.24.1,false,true,435831,European Paintings,Painting,The Birth of Venus,,,,,,Artist,,Alexandre Cabanel,"French, Montpellier 1823–1889 Paris",,"Cabanel, Alexandre",French,1823,1889,1875,1875,1875,Oil on canvas,41 3/4 x 71 7/8 in. (106 x 182.6 cm),"Gift of John Wolfe, 1893",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435831,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -23.103.1,false,true,435832,European Paintings,Painting,Florentine Poet,,,,,,Artist,,Alexandre Cabanel,"French, Montpellier 1823–1889 Paris",,"Cabanel, Alexandre",French,1823,1889,1861,1861,1861,Oil on wood,12 x 19 7/8 in. (30.5 x 50.5 cm),"The John Hobart Warren Bequest, 1923",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435832,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -65.258.1,false,true,435829,European Paintings,Painting,Echo,,,,,,Artist,,Alexandre Cabanel,"French, Montpellier 1823–1889 Paris",,"Cabanel, Alexandre",French,1823,1889,1874,1874,1874,Oil on canvas,38 1/2 x 26 1/4 in. (97.8 x 66.7 cm),"Gift of Mary Phelps Smith, in memory of her husband, Howard Caswell Smith, 1965",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435829,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -87.15.82,false,true,435830,European Paintings,Painting,Catharine Lorillard Wolfe (1828–1887),,,,,,Artist,,Alexandre Cabanel,"French, Montpellier 1823–1889 Paris",,"Cabanel, Alexandre",French,1823,1889,1876,1876,1876,Oil on canvas,67 1/2 x 42 3/4 in. (171.5 x 108.6 cm),"Catharine Lorillard Wolfe Collection, Bequest of Catharine Lorillard Wolfe, 1887",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435830,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -32.100.102,false,true,435638,European Paintings,"Painting, triptych",The Le Cellier Altarpiece,,,,,,Artist,,Jean Bellegambe,"French, Douai ca. 1470–1535/36 Douai",,"Bellegambe, Jean",French,1470,1536,1509,1509,1509,Oil on wood,Shaped top: central panel 40 x 24 in. (101.6 x 61 cm); left wing 37 3/4 x 10 in. (95.9 x 25.4 cm); right wing 37 1/2 x 9 1/2 in. (95.3 x 24.1 cm),"The Friedsam Collection, Bequest of Michael Friedsam, 1931",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435638,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -32.100.125,false,true,435637,European Paintings,"Painting, wing of a triptych","Charles Coguin, Abbot of Anchin",,,,,,Artist,,Jean Bellegambe,"French, Douai ca. 1470–1535/36 Douai",,"Bellegambe, Jean",French,1470,1536,ca. 1509–13,1509,1513,Oil on wood,"Arched top, 26 3/4 x 11 3/8 in. (67.9 x 28.9 cm)","The Friedsam Collection, Bequest of Michael Friedsam, 1931",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435637,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -59.140,false,true,435751,European Paintings,Painting,Village by a River,,,,,,Artist,,Eugène Boudin,"French, Honfleur 1824–1898 Deauville",,"Boudin, Eugène",French,1824,1898,probably 1867,1867,1867,Oil on wood,14 x 23 in. (35.6 x 58.4 cm),"Gift of Arthur J. Neumark, 1959",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435751,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -07.88.4,false,true,435749,European Paintings,Painting,On the Beach at Trouville,,,,,,Artist,,Eugène Boudin,"French, Honfleur 1824–1898 Deauville",,"Boudin, Eugène",French,1824,1898,1863,1863,1863,Oil on wood,10 x 18 in. (25.4 x 45.7 cm),"Bequest of Amelia B. Lazarus, 1907",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435749,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -39.65.2,false,true,435750,European Paintings,Painting,Beaulieu: The Bay of Fourmis,,,,,,Artist,,Eugène Boudin,"French, Honfleur 1824–1898 Deauville",,"Boudin, Eugène",French,1824,1898,1892,1892,1892,Oil on canvas,21 5/8 x 35 1/2 in. (54.9 x 90.2 cm),"Bequest of Jacob Ruppert, 1939",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435750,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2003.20.1,false,true,437987,European Paintings,Painting,"On the Beach, Dieppe",,,,,,Artist,,Eugène Boudin,"French, Honfleur 1824–1898 Deauville",,"Boudin, Eugène",French,1824,1898,1864,1864,1864,Oil on wood,12 1/2 x 11 1/2 in. (31.8 x 29.2 cm),"The Walter H. and Leonore Annenberg Collection, Bequest of Walter H. Annenberg, 2002",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437987,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2003.20.2,false,true,438551,European Paintings,Painting,"On the Beach, Sunset",,,,,,Artist,,Eugène Boudin,"French, Honfleur 1824–1898 Deauville",,"Boudin, Eugène",French,1824,1898,1865,1865,1865,Oil on wood,15 x 23 in. (38.1 x 58.4 cm),"The Walter H. and Leonore Annenberg Collection, Bequest of Walter H. Annenberg, 2002",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/438551,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1999.288.1,false,true,437988,European Paintings,Painting,Princess Pauline Metternich (1836–1921) on the Beach,,,,,,Artist,,Eugène Boudin,"French, Honfleur 1824–1898 Deauville",,"Boudin, Eugène",French,1824,1898,ca. 1865–67,1844,1898,"Oil on cardboard, laid down on wood",11 5/8 x 9 1/4 in. (29.5 x 23.5 cm),"The Walter H. and Leonore Annenberg Collection, Gift of Walter H. and Leonore Annenberg, 1999, Bequest of Walter H. Annenberg, 2002",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437988,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -67.213,false,true,436241,European Paintings,Painting,Cows Crossing a Ford,,,,,,Artist,,Jules Dupré,"French, Nantes 1811–1889 L'Isle-Adam",,"Dupré, Jules",French,1811,1889,1836,1836,1836,Oil on canvas,14 1/4 x 24 5/8 in. (36.2 x 62.5 cm),"Gift of Mrs. Leon L. Watters, in memory of Leon Laizer Watters, 1967",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436241,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2002.68,false,true,438543,European Paintings,Painting,Still Life with Shells and a Chip-Wood Box,,,,,,Artist,,Sebastian Stoskopff,"French, Strasbourg 1597–1657 Idstein",,"Stoskopff, Sebastian",French,1597,1657,late 1620s,1626,1629,Oil on canvas,18 1/2 x 23 3/8 in. (47 x 59.4 cm),"Wrightsman Fund, 2002",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/438543,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.27,false,true,437256,European Paintings,Painting,Concert Champêtre,,,,,,Artist,,Jean-Baptiste Joseph Pater,"French, Valenciennes 1695–1736 Paris",,"Pater, Jean-Baptiste Joseph",French,1695,1736,ca. 1734,1729,1739,Oil on canvas,20 1/2 x 26 3/4 in. (52.1 x 67.9 cm),"Purchase, Joseph Pulitzer Bequest, 1937",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437256,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -49.7.52,false,true,437257,European Paintings,Painting,The Fair at Bezons,,,,,,Artist,,Jean-Baptiste Joseph Pater,"French, Valenciennes 1695–1736 Paris",,"Pater, Jean-Baptiste Joseph",French,1695,1736,ca. 1733,1728,1738,Oil on canvas,42 x 56 in. (106.7 x 142.2 cm),"The Jules Bache Collection, 1949",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437257,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -56.55.1,false,true,437260,European Paintings,Painting,Troops on the March,,,,,,Artist,,Jean-Baptiste Joseph Pater,"French, Valenciennes 1695–1736 Paris",,"Pater, Jean-Baptiste Joseph",French,1695,1736,ca. 1725,1720,1730,Oil on canvas,21 1/4 x 25 3/4 in. (54 x 65.4 cm),"Bequest of Ethel Tod Humphrys, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437260,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -56.55.2,false,true,437259,European Paintings,Painting,Troops at Rest,,,,,,Artist,,Jean-Baptiste Joseph Pater,"French, Valenciennes 1695–1736 Paris",,"Pater, Jean-Baptiste Joseph",French,1695,1736,ca. 1725,1720,1730,Oil on canvas,21 1/4 x 25 3/4 in. (54 x 65.4 cm),"Bequest of Ethel Tod Humphrys, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437259,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -63.120,false,true,436520,European Paintings,Painting,"Marie Adélaïde de Savoie (1685–1712), Duchesse de Bourgogne",,,,,,Artist,,Pierre Gobert,"French, Fontainebleau 1662–1744 Paris",,"Gobert, Pierre",French,1662,1744,1710,1710,1710,Oil on canvas,"Oval, 28 3/4 x 23 1/4 in. (73 x 59.1 cm)","Gift of the Marquis de La Bégassière, 1963",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436520,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -67.55.1,false,true,436222,European Paintings,Painting,Self-Portrait with a Harp,,,,,,Artist,,Rose Adélaïde Ducreux,"French, Paris 1761–1802 Santo Domingo",,"Ducreux, Rose Adélaïde",French,1761,1802,1791,1791,1791,Oil on canvas,76 x 50 3/4 in. (193 x 128.9 cm),"Bequest of Susan Dwight Bliss, 1966",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436222,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.160.36,false,true,436115,European Paintings,Painting,The Good Samaritan,,,,,,Artist,,Alexandre-Gabriel Decamps,"French, Paris 1803–1860 Fontainebleau",,"Decamps, Alexandre-Gabriel",French,1803,1860,by 1853,1845,1860,Oil on canvas,36 5/8 x 29 1/8 in. (93 x 74 cm),"H. O. Havemeyer Collection, Gift of Horace Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436115,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.196,false,true,436114,European Paintings,Painting,The Experts,,,,,,Artist,,Alexandre-Gabriel Decamps,"French, Paris 1803–1860 Fontainebleau",,"Decamps, Alexandre-Gabriel",French,1803,1860,1837,1837,1837,Oil on canvas,18 1/4 x 25 1/4 in. (46.4 x 64.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436114,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2014.736,false,true,671456,European Paintings,Painting,Chrysanthemums in the Garden at Petit-Gennevilliers,,,,,,Artist,,Gustave Caillebotte,"French, Paris 1848–1894 Gennevilliers",,"Caillebotte, Gustave",French,1848,1894,1893,1893,1893,Oil on canvas,38 5/8 × 23 1/2 in. (98 × 59.8 cm),"Gift of the Honorable John C. Whitehead, 2014",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/671456,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -55.35,false,true,435653,European Paintings,Painting,"Sunday at the Church of Saint-Philippe-du-Roule, Paris",,,,,,Artist,,Jean Béraud,"French, St. Petersburg 1849–1936 Paris",,"Béraud, Jean",French,1849,1936,1877,1877,1877,Oil on canvas,23 3/8 x 31 7/8 in. (59.4 x 81 cm),"Gift of Mr. and Mrs. William B. Jaffe, 1955",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435653,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -52.48.1,false,true,435654,European Paintings,Painting,A Windy Day on the Pont des Arts,,,,,,Artist,,Jean Béraud,"French, St. Petersburg 1849–1936 Paris",,"Béraud, Jean",French,1849,1936,ca. 1880–81,1880,1881,Oil on canvas,15 5/8 x 22 1/4 in. (39.7 x 56.5 cm),"Bequest of Eda K. Loeb, 1951",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435654,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -69.161,false,true,436235,European Paintings,Painting,Madame de Saint-Maurice,,,,,,Artist,,Joseph Siffred Duplessis,"French, Carpentras 1725–1802 Versailles",,"Duplessis, Joseph Siffred",French,1725,1802,1776,1776,1776,Oil on canvas,39 1/2 x 31 7/8 in. (100.3 x 81 cm),"Bequest of James A. Aborn, 1968",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436235,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -32.100.132,false,true,436236,European Paintings,Painting,Benjamin Franklin (1706–1790),,,,,,Artist,,Joseph Siffred Duplessis,"French, Carpentras 1725–1802 Versailles",,"Duplessis, Joseph Siffred",French,1725,1802,1778,1778,1778,Oil on canvas,"Oval, 28 1/2 x 23 in. (72.4 x 58.4 cm)","The Friedsam Collection, Bequest of Michael Friedsam, 1931",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436236,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2002.151,false,true,438510,European Paintings,Painting,The Outskirts of a Village,,,,,,Artist,,Edmond-François Aman-Jean,"French, Chevry-Cossigny 1858–1936 Paris",,"Aman-Jean, Edmond-François",French,1858,1936,ca. 1880,1875,1885,Oil on panel,4 7/8 x 8 9/16 in. (12.4 x 21.7 cm),"Catharine Lorillard Wolfe Collection, Wolfe Fund, 2002",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/438510,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -09.198,false,true,436092,European Paintings,Painting,Don Quixote and the Dead Mule,,,,,,Artist,,Honoré Daumier,"French, Marseilles 1808–1879 Valmondois",,"Daumier, Honoré",French,1808,1879,after 1864,1828,1879,Oil on wood,9 3/4 x 18 1/8 in. (24.8 x 46 cm),"Catharine Lorillard Wolfe Collection, Wolfe Fund, 1909",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436092,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -47.122,false,true,436091,European Paintings,Painting,The Laundress,,,,,,Artist,,Honoré Daumier,"French, Marseilles 1808–1879 Valmondois",,"Daumier, Honoré",French,1808,1879,186[3?],1863,1863,Oil on wood,19 1/4 x 13 in. (48.9 x 33 cm),"Bequest of Lillie P. Bliss, 1931",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436091,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -54.143.1,false,true,436093,European Paintings,Painting,The Drinkers,,,,,,Artist,,Honoré Daumier,"French, Marseilles 1808–1879 Valmondois",,"Daumier, Honoré",French,1808,1879,by 1861,1841,1861,Oil on wood,14 3/8 x 11 in. (36.5 x 27.9 cm),"Bequest of Margaret Seligman Lewisohn, in memory of her husband, Sam A. Lewisohn, 1954",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436093,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.129,true,true,436095,European Paintings,Painting,The Third-Class Carriage,,,,,,Artist,,Honoré Daumier,"French, Marseilles 1808–1879 Valmondois",,"Daumier, Honoré",French,1808,1879,ca. 1862–64,1862,1864,Oil on canvas,25 3/4 x 35 1/2 in. (65.4 x 90.2 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436095,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -56.183,false,true,437148,European Paintings,Painting,Flowers in a Blue Vase,,,,,,Artist,,Adolphe Monticelli,"French, Marseilles 1824–1886 Marseilles",,"Monticelli, Adolphe",French,1824,1886,1879–1883,1879,1883,Oil on wood,"Overall, with added strip at right, 26 1/2 x 19 1/4 in. (67.3 x 48.9 cm)","Gift of Mr. and Mrs. Werner E. Josten, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437148,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -87.15.107,false,true,437069,European Paintings,Painting,"Falling Leaves, Allegory of Autumn",,,,,,Artist,,Hugues Merle,"French, Saint-Marcellin 1823–1881 Paris",,"Merle, Hugues",French,1823,1881,1872,1872,1872,Oil on canvas,68 7/8 x 43 1/4 in. (174.9 x 109.9 cm),"Catharine Lorillard Wolfe Collection, Bequest of Catharine Lorillard Wolfe, 1887",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437069,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -59.21,false,true,437439,European Paintings,Painting,A Young Girl with Daisies,,,,,,Artist,,Auguste Renoir,"French, Limoges 1841–1919 Cagnes-sur-Mer",,"Renoir, Auguste",French,1841,1919,1889,1889,1889,Oil on canvas,25 5/8 x 21 1/4 in. (65.1 x 54 cm),"The Mr. and Mrs. Henry Ittleson Jr. Purchase Fund, 1959",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437439,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -07.122,true,true,438815,European Paintings,Painting,"Madame Georges Charpentier (Marguérite-Louise Lemonnier, 1848–1904) and Her Children, Georgette-Berthe (1872–1945) and Paul-Émile-Charles (1875–1895)",,,,,,Artist,,Auguste Renoir,"French, Limoges 1841–1919 Cagnes-sur-Mer",,"Renoir, Auguste",French,1841,1919,1878,1878,1878,Oil on canvas,60 1/2 x 74 7/8 in. (153.7 x 190.2 cm),"Catharine Lorillard Wolfe Collection, Wolfe Fund, 1907",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/438815,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -51.200,false,true,437424,European Paintings,Painting,"Madame Édouard Bernier (Marie-Octavie-Stéphanie Laurens, 1838–1920)",,,,,,Artist,,Auguste Renoir,"French, Limoges 1841–1919 Cagnes-sur-Mer",,"Renoir, Auguste",French,1841,1919,1871,1871,1871,Oil on canvas,30 3/4 x 24 1/2 in. (78.1 x 62.2 cm),"Gift of Margaret Seligman Lewisohn, in memory of her husband, Sam A. Lewisohn, and of her sister-in-law, Adele Lewisohn Lehman, 1951",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437424,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -56.218,false,true,437428,European Paintings,Painting,Still Life with Peaches and Grapes,,,,,,Artist,,Auguste Renoir,"French, Limoges 1841–1919 Cagnes-sur-Mer",,"Renoir, Auguste",French,1841,1919,1881,1881,1881,Oil on canvas,21 1/4 x 25 5/8 in. (54 x 65.1 cm),"The Mr. and Mrs. Henry Ittleson Jr. Purchase Fund, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437428,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -61.190,false,true,437433,European Paintings,Painting,"The Farm at Les Collettes, Cagnes",,,,,,Artist,,Auguste Renoir,"French, Limoges 1841–1919 Cagnes-sur-Mer",,"Renoir, Auguste",French,1841,1919,1908–14,1908,1914,Oil on canvas,21 1/2 x 25 3/4in. (54.6 x 65.4cm),"Bequest of Charlotte Gina Abrams, in memory of her husband, Lucien Abrams, 1961",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437433,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -64.150,false,true,437438,European Paintings,Painting,Young Girl in a Pink-and-Black Hat,,,,,,Artist,,Auguste Renoir,"French, Limoges 1841–1919 Cagnes-sur-Mer",,"Renoir, Auguste",French,1841,1919,ca. 1891,1886,1896,Oil on canvas,16 x 12 3/4 in. (40.6 x 32.4 cm),"Gift of Kathryn B. Miller, 1964",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437438,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2010.454,false,true,441104,European Paintings,Painting,Still Life with Flowers and Prickly Pears,,,,,,Artist,,Auguste Renoir,"French, Limoges 1841–1919 Cagnes-sur-Mer",,"Renoir, Auguste",French,1841,1919,ca. 1885,1880,1890,Oil on canvas,28 7/8 x 23 3/8 in. (73.3 x 59.4 cm),"Bequest of Catherine Vance Gaisman, 2010",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/441104,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -51.112.4,false,true,437434,European Paintings,Painting,In the Meadow,,,,,,Artist,,Auguste Renoir,"French, Limoges 1841–1919 Cagnes-sur-Mer",,"Renoir, Auguste",French,1841,1919,1888–92,1888,1892,Oil on canvas,32 x 25 3/4 in. (81.3 x 65.4 cm),"Bequest of Sam A. Lewisohn, 1951",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437434,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -56.135.7,false,true,437426,European Paintings,Painting,View of the Seacoast near Wargemont in Normandy,,,,,,Artist,,Auguste Renoir,"French, Limoges 1841–1919 Cagnes-sur-Mer",,"Renoir, Auguste",French,1841,1919,1880,1880,1880,Oil on canvas,19 7/8 x 24 1/2 in. (50.5 x 62.2 cm),"Bequest of Julia W. Emmons, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437426,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -56.135.8,false,true,437427,European Paintings,Painting,The Bay of Naples,,,,,,Artist,,Auguste Renoir,"French, Limoges 1841–1919 Cagnes-sur-Mer",,"Renoir, Auguste",French,1841,1919,1881,1881,1881,Oil on canvas,23 1/2 x 32 in. (59.7 x 81.3 cm),"Bequest of Julia W. Emmons, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437427,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -56.135.9,false,true,437431,European Paintings,Painting,"Hills around the Bay of Moulin Huet, Guernsey",,,,,,Artist,,Auguste Renoir,"French, Limoges 1841–1919 Cagnes-sur-Mer",,"Renoir, Auguste",French,1841,1919,1883,1883,1883,Oil on canvas,18 1/8 x 25 3/4 in. (46 x 65.4 cm),"Bequest of Julia W. Emmons, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437431,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2002.62.2,false,true,438010,European Paintings,Painting,Nini in the Garden (Nini Lopez),,,,,,Artist,,Auguste Renoir,"French, Limoges 1841–1919 Cagnes-sur-Mer",,"Renoir, Auguste",French,1841,1919,1876,1876,1876,Oil on canvas,24 3/8 x 20 in. (61.9 x 50.8 cm),"The Walter H. and Leonore Annenberg Collection, Gift of Walter H. and Leonore Annenberg, 2002, Bequest of Walter H. Annenberg, 2002",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/438010,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2003.20.9,false,true,438011,European Paintings,Painting,"Eugène Murer (Hyacinthe-Eugène Meunier, 1841–1906)",,,,,,Artist,,Auguste Renoir,"French, Limoges 1841–1919 Cagnes-sur-Mer",,"Renoir, Auguste",French,1841,1919,1877,1877,1877,Oil on canvas,18 1/2 x 15 1/2 in. (47 x 39.4 cm),"The Walter H. and Leonore Annenberg Collection, Bequest of Walter H. Annenberg, 2002",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/438011,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -61.101.12,false,true,437429,European Paintings,Painting,Still Life with Peaches,,,,,,Artist,,Auguste Renoir,"French, Limoges 1841–1919 Cagnes-sur-Mer",,"Renoir, Auguste",French,1841,1919,1881,1881,1881,Oil on canvas,21 x 25 1/2 in. (53.3 x 64.8 cm),"Bequest of Stephen C. Clark, 1960",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437429,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -61.101.13,false,true,437432,European Paintings,Painting,"Tilla Durieux (Ottilie Godeffroy, 1880–1971)",,,,,,Artist,,Auguste Renoir,"French, Limoges 1841–1919 Cagnes-sur-Mer",,"Renoir, Auguste",French,1841,1919,1914,1914,1914,Oil on canvas,36 1/4 x 29 in. (92.1 x 73.7 cm),"Bequest of Stephen C. Clark, 1960",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437432,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -61.101.14,false,true,437437,European Paintings,Painting,A Waitress at Duval's Restaurant,,,,,,Artist,,Auguste Renoir,"French, Limoges 1841–1919 Cagnes-sur-Mer",,"Renoir, Auguste",French,1841,1919,ca. 1875,1870,1880,Oil on canvas,39 1/2 x 28 1/8 in. (100.3 x 71.4 cm),"Bequest of Stephen C. Clark, 1960",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437437,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -61.101.15,false,true,437425,European Paintings,Painting,Marguerite-Thérèse (Margot) Berard (1874–1956),,,,,,Artist,,Auguste Renoir,"French, Limoges 1841–1919 Cagnes-sur-Mer",,"Renoir, Auguste",French,1841,1919,1879,1879,1879,Oil on canvas,16 1/8 x 12 3/4 in. (41 x 32.4 cm),"Bequest of Stephen C. Clark, 1960",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437425,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2003.20.10,false,true,438012,European Paintings,Painting,Bouquet of Chrysanthemums,,,,,,Artist,,Auguste Renoir,"French, Limoges 1841–1919 Cagnes-sur-Mer",,"Renoir, Auguste",French,1841,1919,1881,1881,1881,Oil on canvas,26 x 21 7/8 in. (66 x 55.6 cm),"The Walter H. and Leonore Annenberg Collection, Bequest of Walter H. Annenberg, 2002",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/438012,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2003.20.12,false,true,438013,European Paintings,Painting,Reclining Nude,,,,,,Artist,,Auguste Renoir,"French, Limoges 1841–1919 Cagnes-sur-Mer",,"Renoir, Auguste",French,1841,1919,1883,1883,1883,Oil on canvas,25 5/8 x 32 in. (65.1 x 81.3 cm),"The Walter H. and Leonore Annenberg Collection, Bequest of Walter H. Annenberg, 2002",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/438013,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.125,false,true,437430,European Paintings,Painting,By the Seashore,,,,,,Artist,,Auguste Renoir,"French, Limoges 1841–1919 Cagnes-sur-Mer",,"Renoir, Auguste",French,1841,1919,1883,1883,1883,Oil on canvas,36 1/4 x 28 1/2 in. (92.1 x 72.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437430,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1974.356.32,false,true,437436,European Paintings,Painting,A Road in Louveciennes,,,,,,Artist,,Auguste Renoir,"French, Limoges 1841–1919 Cagnes-sur-Mer",,"Renoir, Auguste",French,1841,1919,ca. 1870,1865,1875,Oil on canvas,15 x 18 1/4 in. (38.1 x 46.4 cm),"The Lesley and Emma Sheafer Collection, Bequest of Emma A. Sheafer, 1973",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437436,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1987.279,false,true,437180,European Paintings,Painting,The Rebuke of Adam and Eve,,,,,,Artist,,Charles Joseph Natoire,"French, Nîmes 1700–1777 Castel Gandolfo",,"Natoire, Charles Joseph",French,1700,1777,1740,1740,1740,Oil on copper,26 3/4 x 19 3/4 in. (67.9 x 50.2 cm),"Purchase, Mr. and Mrs. Frank E. Richardson III, George T. Delacorte Jr., and Mr. and Mrs. Henry J. Heinz II Gifts; Victor Wilbour Memorial, Marquand, and The Alfred N. Punnett Endowment Funds; and The Edward Joseph Gallagher III Memorial Collection, Edward J. Gallagher Jr. Bequest, 1987",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437180,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -87.15.22,false,true,436030,European Paintings,Painting,Soap Bubbles,,,,,,Artist,,Thomas Couture,"French, Senlis 1815–1879 Villiers-le-Bel",,"Couture, Thomas",French,1815,1879,ca. 1859,1859,1859,Oil on canvas,51 1/2 x 38 5/8 in. (130.8 x 98.1 cm),"Catharine Lorillard Wolfe Collection, Bequest of Catharine Lorillard Wolfe, 1887",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436030,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -60.122,false,true,435753,European Paintings,Painting,The Proposal,,,,,,Artist,,William Bouguereau,"French, La Rochelle 1825–1905 La Rochelle",,"Bouguereau, William",French,1825,1905,1872,1872,1872,Oil on canvas,64 3/8 x 44 in. (163.5 x 111.8 cm),"Gift of Mrs. Elliot L. Kamen, in memory of her father, Bernard R. Armour, 1960",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435753,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1993.402,false,true,435752,European Paintings,Painting,Young Mother Gazing at Her Child,,,,,,Artist,,William Bouguereau,"French, La Rochelle 1825–1905 La Rochelle",,"Bouguereau, William",French,1825,1905,1871,1871,1871,Oil on canvas,56 x 40 1/2 in. (142.2 x 102.9 cm),"Bequest of Zene Montgomery Pyle, 1993",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435752,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -87.15.32,false,true,435754,European Paintings,Painting,Breton Brother and Sister,,,,,,Artist,,William Bouguereau,"French, La Rochelle 1825–1905 La Rochelle",,"Bouguereau, William",French,1825,1905,1871,1871,1871,Oil on canvas,50 7/8 x 35 1/8 in. (129.2 x 89.2 cm),"Catharine Lorillard Wolfe Collection, Bequest of Catharine Lorillard Wolfe, 1887",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435754,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1998.170,false,true,438098,European Paintings,Painting,Tea,,,,,,Artist,,James Tissot,"French, Nantes 1836–1902 Chenecey-Buillon",,"Tissot, James",French,1836,1902,1872,1872,1872,Oil on wood,26 x 18 7/8 in. (66 x 47.9 cm),"Gift of Mrs. Charles Wrightsman, 1998",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/438098,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2006.278,false,true,438887,European Paintings,Painting,In Full Sunlight (En plein soleil),,,,,,Artist,,James Tissot,"French, Nantes 1836–1902 Chenecey-Buillon",,"Tissot, James",French,1836,1902,ca. 1881,1876,1886,Oil on wood,9 3/4 x 13 7/8 in. (24.8 x 35.2 cm),"Gift of Mrs. Charles Wrightsman, 2006",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/438887,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.359,false,true,440729,European Paintings,Painting,Spring Morning,,,,,,Artist,,James Tissot,"French, Nantes 1836–1902 Chenecey-Buillon",,"Tissot, James",French,1836,1902,ca. 1875,1870,1880,Oil on canvas,22 x 16 3/4 in. (55.9 x 42.5 cm),"Gift of Mrs. Charles Wrightsman, 2009",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/440729,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -19.84,false,true,436015,European Paintings,Painting,Louis Gueymard (1822–1880) as Robert le Diable,,,,,,Artist,,Gustave Courbet,"French, Ornans 1819–1877 La Tour-de-Peilz",,"Courbet, Gustave",French,1819,1877,1857,1857,1857,Oil on canvas,58 1/2 x 42 in. (148.6 x 106.7 cm),"Gift of Elizabeth Milbank Anderson, 1919",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436015,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.77,false,true,436014,European Paintings,Painting,Hunting Dogs with Dead Hare,,,,,,Artist,,Gustave Courbet,"French, Ornans 1819–1877 La Tour-de-Peilz",,"Courbet, Gustave",French,1819,1877,1857,1857,1857,Oil on canvas,36 1/2 x 58 1/2 in. (92.7 x 148.6 cm),"H. O. Havemeyer Collection, Gift of Horace Havemeyer, 1933",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436014,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -40.175,false,true,438820,European Paintings,Painting,Young Ladies of the Village,,,,,,Artist,,Gustave Courbet,"French, Ornans 1819–1877 La Tour-de-Peilz",,"Courbet, Gustave",French,1819,1877,1851–52,1851,1852,Oil on canvas,76 3/4 x 102 3/4 in. (194.9 x 261 cm),"Gift of Harry Payne Bingham, 1940",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/438820,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -22.27.1,false,true,436021,European Paintings,Painting,The Sea,,,,,,Artist,,Gustave Courbet,"French, Ornans 1819–1877 La Tour-de-Peilz",,"Courbet, Gustave",French,1819,1877,1865 or later,1865,1877,Oil on canvas,20 x 24 in. (50.8 x 61 cm),"Purchase, Dikran G. Kelekian Gift, 1922",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436021,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -99.11.3,false,true,436012,European Paintings,Painting,The Fishing Boat,,,,,,Artist,,Gustave Courbet,"French, Ornans 1819–1877 La Tour-de-Peilz",,"Courbet, Gustave",French,1819,1877,1865,1839,1877,Oil on canvas,25 1/2 x 32 in. (64.8 x 81.3 cm),"Gift of Mary Goldenberg, 1899",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436012,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1995.537,false,true,436025,European Paintings,Painting,View of Ornans,,,,,,Artist,,Gustave Courbet,"French, Ornans 1819–1877 La Tour-de-Peilz",,"Courbet, Gustave",French,1819,1877,probably mid-1850s,1839,1877,Oil on canvas,28 3/4 x 36 1/4 in. (73 x 92.1 cm),"Bequest of Alice Tully, 1993",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436025,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -22.16.13,false,true,436013,European Paintings,Painting,The Hidden Brook,,,,,,Artist,,Gustave Courbet,"French, Ornans 1819–1877 La Tour-de-Peilz",,"Courbet, Gustave",French,1819,1877,ca. 1873–77,1839,1877,Oil on canvas,23 3/8 x 29 3/4 in. (59.4 x 75.6 cm),"From the Collection of James Stillman, Gift of Dr. Ernest G. Stillman, 1922",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436013,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -22.16.14,false,true,436020,European Paintings,Painting,River and Rocks,,,,,,Artist,,Gustave Courbet,"French, Ornans 1819–1877 La Tour-de-Peilz",,"Courbet, Gustave",French,1819,1877,1873–77,1873,1877,Oil on canvas,19 5/8 x 23 7/8 in. (49.8 x 60.6 cm),"From the Collection of James Stillman, Gift of Dr. Ernest G. Stillman, 1922",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436020,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -22.16.15,false,true,436009,European Paintings,Painting,"A Brook in a Clearing (possibly ""Brook, Valley of Fontcouverte; Study"")",,,,,,Artist,,Gustave Courbet,"French, Ornans 1819–1877 La Tour-de-Peilz",,"Courbet, Gustave",French,1819,1877,probably 1862,1862,1862,Oil on canvas,20 3/4 x 25 1/2 in. (52.7 x 64.8 cm),"From the Collection of James Stillman, Gift of Dr. Ernest G. Stillman, 1922",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436009,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.57,true,true,436002,European Paintings,Painting,Woman with a Parrot,,,,,,Artist,,Gustave Courbet,"French, Ornans 1819–1877 La Tour-de-Peilz",,"Courbet, Gustave",French,1819,1877,1866,1866,1866,Oil on canvas,51 x 77 in. (129.5 x 195.6 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436002,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.58,false,true,436022,European Paintings,Painting,The Source,,,,,,Artist,,Gustave Courbet,"French, Ornans 1819–1877 La Tour-de-Peilz",,"Courbet, Gustave",French,1819,1877,1862,1862,1862,Oil on canvas,47 1/4 x 29 1/4 in. (120 x 74.3 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436022,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.59,false,true,436024,European Paintings,Painting,Woman in a Riding Habit (L'Amazone),,,,,,Artist,,Gustave Courbet,"French, Ornans 1819–1877 La Tour-de-Peilz",,"Courbet, Gustave",French,1819,1877,1856,1856,1856,Oil on canvas,45 1/2 x 35 1/8 in. (115.6 x 89.2 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436024,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.60,false,true,436018,European Paintings,Painting,Nude with Flowering Branch,,,,,,Artist,,Gustave Courbet,"French, Ornans 1819–1877 La Tour-de-Peilz",,"Courbet, Gustave",French,1819,1877,1863,1863,1863,Oil on canvas,29 1/2 x 24 in. (74.9 x 61 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436018,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.61,false,true,436007,European Paintings,Painting,After the Hunt,,,,,,Artist,,Gustave Courbet,"French, Ornans 1819–1877 La Tour-de-Peilz",,"Courbet, Gustave",French,1819,1877,ca. 1859,1859,1859,Oil on canvas,93 x 73 1/4 in. (236.2 x 186.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436007,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.62,false,true,436004,European Paintings,Painting,The Woman in the Waves,,,,,,Artist,,Gustave Courbet,"French, Ornans 1819–1877 La Tour-de-Peilz",,"Courbet, Gustave",French,1819,1877,1868,1868,1868,Oil on canvas,25 3/4 x 21 1/4 in. (65.4 x 54 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436004,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.63,false,true,436001,European Paintings,Painting,"Jo, La Belle Irlandaise",,,,,,Artist,,Gustave Courbet,"French, Ornans 1819–1877 La Tour-de-Peilz",,"Courbet, Gustave",French,1819,1877,1865–66,1865,1866,Oil on canvas,22 x 26 in. (55.9 x 66 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436001,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.160.34,false,true,436011,European Paintings,Painting,The Deer,,,,,,Artist,,Gustave Courbet,"French, Ornans 1819–1877 La Tour-de-Peilz",,"Courbet, Gustave",French,1819,1877,ca. 1865,1860,1870,Oil on canvas,29 3/8 x 36 3/8 in. (74.6 x 92.4 cm),"H. O. Havemeyer Collection, Gift of Horace Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436011,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.160.35,false,true,436006,European Paintings,Painting,Marine: The Waterspout,,,,,,Artist,,Gustave Courbet,"French, Ornans 1819–1877 La Tour-de-Peilz",,"Courbet, Gustave",French,1819,1877,1870,1870,1870,Oil on canvas,27 1/8 x 39 1/4 in. (68.9 x 99.7 cm),"H. O. Havemeyer Collection, Gift of Horace Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436006,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.118,false,true,436000,European Paintings,Painting,Madame de Brayer,,,,,,Artist,,Gustave Courbet,"French, Ornans 1819–1877 La Tour-de-Peilz",,"Courbet, Gustave",French,1819,1877,1858,1858,1858,Oil on canvas,36 x 28 5/8 in. (91.4 x 72.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436000,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.120,false,true,436017,European Paintings,Painting,Charles Suisse,,,,,,Artist,,Gustave Courbet,"French, Ornans 1819–1877 La Tour-de-Peilz",,"Courbet, Gustave",French,1819,1877,1861,1861,1861,Oil on canvas,23 1/4 x 19 3/8 in. (59.1 x 49.2 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436017,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.122,false,true,436023,European Paintings,Painting,The Source of the Loue,,,,,,Artist,,Gustave Courbet,"French, Ornans 1819–1877 La Tour-de-Peilz",,"Courbet, Gustave",French,1819,1877,1864,1864,1864,Oil on canvas,39 1/4 x 56 in. (99.7 x 142.2 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436023,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.124,false,true,436003,European Paintings,Painting,The Young Bather,,,,,,Artist,,Gustave Courbet,"French, Ornans 1819–1877 La Tour-de-Peilz",,"Courbet, Gustave",French,1819,1877,1866,1866,1866,Oil on canvas,51 1/4 x 38 1/4 in. (130.2 x 97.2 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436003,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.130,false,true,436016,European Paintings,Painting,"Madame Auguste Cuoq (Mathilde Desportes, 1827–1910)",,,,,,Artist,,Gustave Courbet,"French, Ornans 1819–1877 La Tour-de-Peilz",,"Courbet, Gustave",French,1819,1877,ca. 1852–57,1852,1857,Oil on canvas,69 1/2 x 42 1/2 in. (176.5 x 108 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436016,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.132,false,true,436008,European Paintings,Painting,Alphonse Promayet (1822–1872),,,,,,Artist,,Gustave Courbet,"French, Ornans 1819–1877 La Tour-de-Peilz",,"Courbet, Gustave",French,1819,1877,1851,1851,1851,Oil on canvas,42 1/8 x 27 5/8 in. (107 x 70.2 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436008,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.201,false,true,436019,European Paintings,Painting,Portrait of a Man,,,,,,Artist,,Gustave Courbet,"French, Ornans 1819–1877 La Tour-de-Peilz",,"Courbet, Gustave",French,1819,1877,probably ca. 1862,1857,1867,Oil on canvas,16 1/4 x 13 1/8 in. (41.3 x 33.3 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436019,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.566,false,true,436005,European Paintings,Painting,The Calm Sea,,,,,,Artist,,Gustave Courbet,"French, Ornans 1819–1877 La Tour-de-Peilz",,"Courbet, Gustave",French,1819,1877,1869,1869,1869,Oil on canvas,23 1/2 x 28 3/4 in. (59.7 x 73 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436005,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -46.68,false,true,435914,European Paintings,Painting,Guillaume Budé (1467–1540),,,,,,Artist,,Jean Clouet,"French, active by 1516–died 1540/41 Paris",,"Clouet, Jean",French,1516,1541,ca. 1536,1531,1541,Oil on wood,15 5/8 x 13 1/2 in. (39.7 x 34.3 cm),"Maria DeWitt Jesup Fund, 1946",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435914,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2008.459,true,true,439933,European Paintings,Painting,Lute Player,,,,,,Artist,,Valentin de Boulogne,"French, Coulommiers-en-Brie 1591–1632 Rome",,Valentin de Boulogne,French,1591,1632,ca. 1625–26,1625,1626,Oil on canvas,50 1/2 x 39 in. (128.3 x 99.1 cm),"Purchase, Walter and Leonore Annenberg Acquisitions Endowment Fund; Director's Fund; Acquisitions Fund; James and Diane Burke and Mr. and Mrs. Mark Fisch Gifts; Louis V. Bell, Harris Brisbane Dick, Fletcher, and Rogers Funds and Joseph Pulitzer Bequest, 2008",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/439933,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -51.33.2,true,true,437837,European Paintings,Painting,The Sofa,,,,,,Artist,,Henri de Toulouse-Lautrec,"French, Albi 1864–1901 Saint-André-du-Bois",,"Toulouse-Lautrec, Henri de",French,1864,1901,ca. 1894–96,1894,1896,Oil on cardboard,24 3/4 x 31 7/8 in. (62.9 x 81 cm),"Rogers Fund, 1951",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437837,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2003.20.13,false,true,438016,European Paintings,Painting,The Streetwalker,,,,,,Artist,,Henri de Toulouse-Lautrec,"French, Albi 1864–1901 Saint-André-du-Bois",,"Toulouse-Lautrec, Henri de",French,1864,1901,ca. 1890–91,1890,1891,Oil on cardboard,25 1/2 x 21 in. (64.8 x 53.3 cm),"The Walter H. and Leonore Annenberg Collection, Bequest of Walter H. Annenberg, 2002",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/438016,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2003.20.15,false,true,438018,European Paintings,Painting,Woman before a Mirror,,,,,,Artist,,Henri de Toulouse-Lautrec,"French, Albi 1864–1901 Saint-André-du-Bois",,"Toulouse-Lautrec, Henri de",French,1864,1901,1897,1897,1897,Oil on cardboard,24 1/2 x 18 1/2 in. (62.2 x 47 cm),"The Walter H. and Leonore Annenberg Collection, Bequest of Walter H. Annenberg, 2002",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/438018,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -67.187.108,false,true,437835,European Paintings,Painting,"The Englishman (William Tom Warrener, 1861–1934) at the Moulin Rouge",,,,,,Artist,,Henri de Toulouse-Lautrec,"French, Albi 1864–1901 Saint-André-du-Bois",,"Toulouse-Lautrec, Henri de",French,1864,1901,1892,1892,1892,Oil on cardboard,33 3/4 x 26 in. (85.7 x 66 cm),"Bequest of Miss Adelaide Milton de Groot (1876–1967), 1967",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437835,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1974.356.35,false,true,437839,European Paintings,Painting,Émilie,,,,,,Artist,,Henri de Toulouse-Lautrec,"French, Albi 1864–1901 Saint-André-du-Bois",,"Toulouse-Lautrec, Henri de",French,1864,1901,late 1890s,1897,1899,Oil on wood,16 1/4 x 12 3/4 in. (41.3 x 32.4 cm),"The Lesley and Emma Sheafer Collection, Bequest of Emma A. Sheafer, 1973",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437839,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1974.356.36,false,true,437834,European Paintings,Painting,Mademoiselle Nys,,,,,,Artist,,Henri de Toulouse-Lautrec,"French, Albi 1864–1901 Saint-André-du-Bois",,"Toulouse-Lautrec, Henri de",French,1864,1901,1899,1899,1899,Oil on unprimed wood,10 5/8 x 8 5/8 in. (27 x 21.9 cm),"The Lesley and Emma Sheafer Collection, Bequest of Emma A. Sheafer, 1973",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437834,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.201.15,false,true,437838,European Paintings,Painting,Woman in the Garden of Monsieur Forest,,,,,,Artist,,Henri de Toulouse-Lautrec,"French, Albi 1864–1901 Saint-André-du-Bois",,"Toulouse-Lautrec, Henri de",French,1864,1901,1889–91,1889,1891,Oil on canvas,21 7/8 x 18 1/4 in. (55.6 x 46.4 cm),"Bequest of Joan Whitney Payson, 1975",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437838,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1979.135.14,false,true,437836,European Paintings,Painting,Albert (René) Grenier (1858–1925),,,,,,Artist,,Henri de Toulouse-Lautrec,"French, Albi 1864–1901 Saint-André-du-Bois",,"Toulouse-Lautrec, Henri de",French,1864,1901,1887,1887,1887,Oil on wood,13 3/8 x 10 in. (34 x 25.4 cm),"Bequest of Mary Cushing Fosburgh, 1978",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437836,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -80.8,false,true,435709,European Paintings,Painting,John Taylor Johnston (1820–1893),,,,,,Artist,,Léon Bonnat,"French, Bayonne 1833–1922 Monchy-Saint-Eloi",,"Bonnat, Léon",French,1833,1922,1880,1880,1880,Oil on canvas,52 1/2 x 44 in. (133.4 x 111.8 cm),"Gift of the Trustees, 1880",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435709,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -87.15.97,false,true,435711,European Paintings,Painting,An Egyptian Peasant Woman and Her Child,,,,,,Artist,,Léon Bonnat,"French, Bayonne 1833–1922 Monchy-Saint-Eloi",,"Bonnat, Léon",French,1833,1922,1869–70,1869,1870,Oil on canvas,73 1/2 x 41 1/2 in. (186.7 x 105.4 cm),"Catharine Lorillard Wolfe Collection, Bequest of Catharine Lorillard Wolfe, 1887",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435711,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -87.15.137,false,true,435708,European Paintings,Painting,Roman Girl at a Fountain,,,,,,Artist,,Léon Bonnat,"French, Bayonne 1833–1922 Monchy-Saint-Eloi",,"Bonnat, Léon",French,1833,1922,1875,1875,1875,Oil on canvas,67 x 39 1/2 in. (170.2 x 100.3 cm),"Catharine Lorillard Wolfe Collection, Bequest of Catharine Lorillard Wolfe, 1887",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435708,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -23.103.2,false,true,436419,European Paintings,Painting,The Arab Falconer,,,,,,Artist,,Eugène Fromentin,"French, La Rochelle 1820–1876 Saint-Maurice",,"Fromentin, Eugène",French,1820,1876,1864,1864,1864,Oil on canvas,42 3/4 x 28 1/2 in. (108.6 x 72.4 cm),"The John Hobart Warren Bequest, 1923",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436419,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -87.15.64,false,true,436420,European Paintings,Painting,Arabs Crossing a Ford,,,,,,Artist,,Eugène Fromentin,"French, La Rochelle 1820–1876 Saint-Maurice",,"Fromentin, Eugène",French,1820,1876,1873,1873,1873,Oil on wood,20 x 24 1/2 in. (50.8 x 62.2 cm),"Catharine Lorillard Wolfe Collection, Bequest of Catharine Lorillard Wolfe, 1887",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436420,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2012.542,false,true,442356,European Paintings,Painting,"Still Life with Silver and Gold Plate, Shells, and a Sword",,,,,,Artist,,Meiffren Conte,"French, Marseilles ca. 1630–1705 Marseilles",,"Conte, Meiffren",French,1625,1705,fourth quarter 17th century,1675,1699,Oil on canvas,40 × 50 3/4 in. (101.6 × 128.9 cm),"Gift of Mrs. Russell B. Aitken, 2012",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/442356,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.166,false,true,439120,European Paintings,Painting,The Mass of Saint Basil,,,,,,Artist,,Pierre Hubert Subleyras,"French, Saint-Gilles-du-Gard 1699–1749 Rome",,"Subleyras, Pierre Hubert",French,1699,1749,1746,1746,1746,"Oil on canvas, transferred from canvas",54 x 31 1/8 in. (137 x 79 cm),"Wrightsman Fund, 2007",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/439120,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.145,false,true,440464,European Paintings,Painting,"Pope Benedict XIV (Prospero Lambertini, 1675–1758)",,,,,,Artist,,Pierre Hubert Subleyras,"French, Saint-Gilles-du-Gard 1699–1749 Rome",,"Subleyras, Pierre Hubert",French,1699,1749,1746,1746,1746,Oil on canvas,25 1/4 x 19 1/4 in. (64.1 x 48.9 cm),"Purchase, Friends of European Paintings Gifts, Bequest of Joan Whitney Payson, by exchange, Gwynne Andrews Fund, Charles and Jessie Price Gift, and Valerie Delacorte Fund Gift, in memory of George T. Delacorte, 2009",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/440464,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -86.6,false,true,436632,European Paintings,Painting,Moonrise,,,,,,Artist,,Henri-Joseph Harpignies,"French, Valenciennes 1819–1916 Saint-Privé",,"Harpignies, Henri-Joseph",French,1819,1916,1885,1885,1885,Oil on canvas,34 1/2 x 64 1/4 in. (87.6 x 163.2 cm),"Gift of Arnold and Tripp, 1886",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436632,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -60.30,true,true,436838,European Paintings,Painting,The Fortune-Teller,,,,,,Artist,,Georges de La Tour,"French, Vic-sur-Seille 1593–1653 Lunéville",,"La Tour, Georges de",French,1593,1653,probably 1630s,1630,1639,Oil on canvas,40 1/8 x 48 5/8 in. (101.9 x 123.5 cm),"Rogers Fund, 1960",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436838,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1978.517,false,true,436839,European Paintings,Painting,The Penitent Magdalen,,,,,,Artist,,Georges de La Tour,"French, Vic-sur-Seille 1593–1653 Lunéville",,"La Tour, Georges de",French,1593,1653,ca. 1640,1635,1645,Oil on canvas,52 1/2 x 40 1/4 in. (133.4 x 102.2 cm),"Gift of Mr. and Mrs. Charles Wrightsman, 1978",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436839,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2011.597,false,true,441755,European Paintings,Painting,Landscape Study with Clouds,,,,,,Artist,,Émile Loubon,"French, Aix-en-Provence 1809–1863 Marseilles",,"Loubon, Émile",French,1809,1863,ca. 1829–31,1829,1831,Oil on cardboard,5 7/8 x 9 1/16 in. (15 x 23 cm),"Catharine Lorillard Wolfe Collection, Wolfe Fund, 2011",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/441755,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -03.30,true,true,438814,European Paintings,Painting,The Abduction of Rebecca,,,,,,Artist,,Eugène Delacroix,"French, Charenton-Saint-Maurice 1798–1863 Paris",,"Delacroix, Eugène",French,1798,1863,1846,1846,1846,Oil on canvas,39 1/2 x 32 1/4 in. (100.3 x 81.9 cm),"Catharine Lorillard Wolfe Collection, Wolfe Fund, 1903",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/438814,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -22.27.4,false,true,436177,European Paintings,Painting,George Sand's Garden at Nohant,,,,,,Artist,,Eugène Delacroix,"French, Charenton-Saint-Maurice 1798–1863 Paris",,"Delacroix, Eugène",French,1798,1863,1840s,1842,1863,Oil on canvas,17 7/8 x 21 3/4 in. (45.4 x 55.2 cm),"Purchase, Dikran G. Kelekian Gift, 1922",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436177,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.328,false,true,436180,European Paintings,Painting,The Natchez,,,,,,Artist,,Eugène Delacroix,"French, Charenton-Saint-Maurice 1798–1863 Paris",,"Delacroix, Eugène",French,1798,1863,1823–24 and 1835,1823,1835,Oil on canvas,35 1/2 x 46 in. (90.2 x 116.8 cm),"Purchase, Gifts of George N. and Helen M. Richard and Mr. and Mrs. Charles S. McVeigh and Bequest of Emma A. Sheafer, by exchange, 1989",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436180,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1994.430,false,true,436179,European Paintings,Painting,"Madame Henri François Riesener (Félicité Longrois, 1786–1847)",,,,,,Artist,,Eugène Delacroix,"French, Charenton-Saint-Maurice 1798–1863 Paris",,"Delacroix, Eugène",French,1798,1863,1835,1835,1835,Oil on canvas,29 1/4 x 23 3/4 in. (74.3 x 60.3 cm),"Gift of Mrs. Charles Wrightsman, 1994",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436179,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2008.101,false,true,439631,European Paintings,Painting,Ovid among the Scythians,,,,,,Artist,,Eugène Delacroix,"French, Charenton-Saint-Maurice 1798–1863 Paris",,"Delacroix, Eugène",French,1798,1863,1862,1862,1862,"Oil on paper, laid down on wood",12 5/8 x 19 3/4 in. (32.1 x 50.2 cm),"Wrightsman Fund, in honor of Philippe de Montebello, 2008",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/439631,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -67.187.60,false,true,436175,European Paintings,Painting,Basket of Flowers,,,,,,Artist,,Eugène Delacroix,"French, Charenton-Saint-Maurice 1798–1863 Paris",,"Delacroix, Eugène",French,1798,1863,1848–49,1848,1849,Oil on canvas,42 1/4 x 56 in. (107.3 x 142.2 cm),"Bequest of Miss Adelaide Milton de Groot (1876–1967), 1967",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436175,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -67.187.61,false,true,436178,European Paintings,Painting,Hamlet and His Mother,,,,,,Artist,,Eugène Delacroix,"French, Charenton-Saint-Maurice 1798–1863 Paris",,"Delacroix, Eugène",French,1798,1863,1849,1849,1849,Oil on canvas,10 3/4 x 7 1/8 in. (27.3 x 18.1 cm),"Bequest of Miss Adelaide Milton de Groot (1876–1967), 1967",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436178,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.131,false,true,436176,European Paintings,Painting,Christ Asleep during the Tempest,,,,,,Artist,,Eugène Delacroix,"French, Charenton-Saint-Maurice 1798–1863 Paris",,"Delacroix, Eugène",French,1798,1863,ca. 1853,1853,1853,Oil on canvas,20 x 24 in. (50.8 x 61 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436176,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1988.221,false,true,435626,European Paintings,Painting,Porte de la Reine at Aigues-Mortes,,,,,,Artist,,Jean-Frédéric Bazille,"French, Montpellier 1841–1870 Beaune-la-Rolande",,"Bazille, Jean-Frédéric",French,1841,1870,1867,1867,1867,Oil on canvas,31 3/4 x 39 1/4 in. (80.6 x 99.7 cm),"Purchase, Gift of Raymonde Paul, in memory of her brother, C. Michael Paul, by exchange, 1988",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435626,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -34.138,true,true,437926,European Paintings,Painting,Mezzetin,,,,,,Artist,,Antoine Watteau,"French, Valenciennes 1684–1721 Nogent-sur-Marne",,"Watteau, Antoine",French,1684,1721,ca. 1718–20,1718,1720,Oil on canvas,21 3/4 x 17 in. (55.2 x 43.2 cm),"Munsey Fund, 1934",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437926,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -49.7.54,false,true,437925,European Paintings,Painting,The French Comedians,,,,,,Artist,,Antoine Watteau,"French, Valenciennes 1684–1721 Nogent-sur-Marne",,"Watteau, Antoine",French,1684,1721,ca. 1720,1715,1725,Oil on canvas,22 1/2 x 28 3/4 in. (57.2 x 73 cm),"The Jules Bache Collection, 1949",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437925,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -48.187.736,false,true,437456,European Paintings,Painting,Breton Fishermen and Their Families,,,,,,Artist,,Théodule-Augustin Ribot,"French, Saint-Nicolas-d'Attez 1823–1891 Colombes",,"Ribot, Théodule-Augustin",French,1823,1891,possibly ca. 1880–85,1860,1891,Oil on canvas,21 3/4 x 18 1/4 in. (55.2 x 46.4 cm),"Bequest of Catherine D. Wentworth, 1948",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437456,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -80.5.2,false,true,436569,European Paintings,Painting,The Choir of the Capuchin Church in Rome,,,,,,Artist,,François Marius Granet,"French, Aix-en-Provence 1775–1849 Aix-en-Provence",,"Granet, François Marius",French,1775,1849,1814–15,1814,1815,Oil on canvas,77 1/2 x 58 1/4 in. (196.9 x 148 cm),"Gift of P. L. Everard, 1880",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436569,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1996.181,false,true,437973,European Paintings,Painting,"Ponte San Rocco and Waterfalls, Tivoli",,,,,,Artist,,François Marius Granet,"French, Aix-en-Provence 1775–1849 Aix-en-Provence",,"Granet, François Marius",French,1775,1849,ca. 1810–20,1810,1820,Oil on canvas,14 7/8 x 11 1/8 in. (37.8 x 28.3 cm),"Purchase, Leonora Brenauer Bequest, in memory of her father, Joseph B. Brenauer; Wolfe Fund, and Wolfe Fund, by exchange, 1996",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437973,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.164.3,false,true,438948,European Paintings,Painting,"View in the Stables of the Villa of Maecenas, Tivoli",,,,,,Artist,,François Marius Granet,"French, Aix-en-Provence 1775–1849 Aix-en-Provence",,"Granet, François Marius",French,1775,1849,ca. 1805–10,1800,1815,"Oil on paper, laid down on canvas",10 1/2 x 8 3/4 in. (26.7 x 22.2 cm),"Gift of Eugene V. Thaw, 2007",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/438948,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.66,false,true,435885,European Paintings,Painting,View of the Domaine Saint-Joseph,,,,,,Artist,,Paul Cézanne,"French, Aix-en-Provence 1839–1906 Aix-en-Provence",,"Cézanne, Paul",French,1839,1906,late 1880s,1886,1889,Oil on canvas,25 5/8 x 32 in. (65.1 x 81.3 cm),"Catharine Lorillard Wolfe Collection, Wolfe Fund, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435885,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -62.45,false,true,435876,European Paintings,Painting,"Madame Cézanne (Hortense Fiquet, 1850–1922) in a Red Dress",,,,,,Artist,,Paul Cézanne,"French, Aix-en-Provence 1839–1906 Aix-en-Provence",,"Cézanne, Paul",French,1839,1906,1888–90,1888,1890,Oil on canvas,45 7/8 x 35 1/4 in. ( 116.5 x 89.5 cm),"The Mr. and Mrs. Henry Ittleson Jr. Purchase Fund, 1962",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435876,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -57.181,false,true,435871,European Paintings,Painting,Gardanne,,,,,,Artist,,Paul Cézanne,"French, Aix-en-Provence 1839–1906 Aix-en-Provence",,"Cézanne, Paul",French,1839,1906,1885–86,1885,1886,Oil on canvas,31 1/2 x 25 1/4 in. (80 x 64.1 cm),"Gift of Dr. and Mrs. Franz H. Hirschland, 1957",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435871,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -61.103,false,true,435866,European Paintings,Painting,Apples,,,,,,Artist,,Paul Cézanne,"French, Aix-en-Provence 1839–1906 Aix-en-Provence",,"Cézanne, Paul",French,1839,1906,1878–79,1878,1879,Oil on canvas,9 x 13 in. (22.9 x 33 cm),"The Mr. and Mrs. Henry Ittleson Jr. Purchase Fund, 1961",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435866,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1994.420,false,true,435878,European Paintings,Painting,Mont Sainte-Victoire,,,,,,Artist,,Paul Cézanne,"French, Aix-en-Provence 1839–1906 Aix-en-Provence",,"Cézanne, Paul",French,1839,1906,ca. 1902–6,1902,1906,Oil on canvas,22 1/2 x 38 1/4 in. (57.2 x 97.2 cm),"The Walter H. and Leonore Annenberg Collection, Gift of Walter H. and Leonore Annenberg, 1994, Bequest of Walter H. Annenberg, 2002",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435878,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2001.473,false,true,438136,European Paintings,Painting,The Fishermen (Fantastic Scene),,,,,,Artist,,Paul Cézanne,"French, Aix-en-Provence 1839–1906 Aix-en-Provence",,"Cézanne, Paul",French,1839,1906,ca. 1875,1870,1880,Oil on canvas,21 3/4 x 32 1/4 in. (55.2 x 81.9 cm),"Gift of Heather Daniels and Katharine Whild, and Purchase, The Annenberg Foundation Gift, Gift of Joanne Toor Cummings, by exchange, Wolfe Fund, and Ellen Lichtenstein and Joanne Toor Cummings Bequests, Mr. and Mrs. Richard J. Bernhard Gift, Gift of Mr. and Mrs. Richard Rodgers, and Wolfe Fund, by exchange, and funds from various donors, 2001",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/438136,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -51.112.1,true,true,435882,European Paintings,Painting,Still Life with Apples and a Pot of Primroses,,,,,,Artist,,Paul Cézanne,"French, Aix-en-Provence 1839–1906 Aix-en-Provence",,"Cézanne, Paul",French,1839,1906,ca. 1890,1885,1895,Oil on canvas,28 3/4 x 36 3/8 in. (73 x 92.4 cm),"Bequest of Sam A. Lewisohn, 1951",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435882,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -53.140.1,false,true,435870,European Paintings,Painting,"Antoine Dominique Sauveur Aubert (born 1817), the Artist's Uncle",,,,,,Artist,,Paul Cézanne,"French, Aix-en-Provence 1839–1906 Aix-en-Provence",,"Cézanne, Paul",French,1839,1906,1866,1866,1866,Oil on canvas,31 3/8 x 25 1/4 in. (79.7 x 64.1 cm),"Wolfe Fund, 1951; acquired from The Museum of Modern Art, Lillie P. Bliss Collection",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435870,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -61.101.1,true,true,435868,European Paintings,Painting,The Card Players,,,,,,Artist,,Paul Cézanne,"French, Aix-en-Provence 1839–1906 Aix-en-Provence",,"Cézanne, Paul",French,1839,1906,1890–92,1890,1892,Oil on canvas,25 3/4 x 32 1/4 in. (65.4 x 81.9 cm),"Bequest of Stephen C. Clark, 1960",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435868,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -61.101.2,false,true,435875,European Paintings,Painting,"Madame Cézanne (Hortense Fiquet, 1850–1922) in the Conservatory",,,,,,Artist,,Paul Cézanne,"French, Aix-en-Provence 1839–1906 Aix-en-Provence",,"Cézanne, Paul",French,1839,1906,1891,1891,1891,Oil on canvas,36 1/4 x 28 3/4 in. (92.1 x 73 cm),"Bequest of Stephen C. Clark, 1960",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435875,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -61.101.3,false,true,435883,European Paintings,Painting,Still Life with Apples and Pears,,,,,,Artist,,Paul Cézanne,"French, Aix-en-Provence 1839–1906 Aix-en-Provence",,"Cézanne, Paul",French,1839,1906,ca. 1891–92,1891,1892,Oil on canvas,17 5/8 x 23 1/8 in. (44.8 x 58.7 cm),"Bequest of Stephen C. Clark, 1960",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435883,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -61.101.4,false,true,435881,European Paintings,Painting,Still Life with a Ginger Jar and Eggplants,,,,,,Artist,,Paul Cézanne,"French, Aix-en-Provence 1839–1906 Aix-en-Provence",,"Cézanne, Paul",French,1839,1906,1893–94,1893,1894,Oil on canvas,28 1/2 x 36 in. (72.4 x 91.4 cm),"Bequest of Stephen C. Clark, 1960",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435881,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -61.101.5,false,true,435879,European Paintings,Painting,The Pool at the Jas de Bouffan,,,,,,Artist,,Paul Cézanne,"French, Aix-en-Provence 1839–1906 Aix-en-Provence",,"Cézanne, Paul",French,1839,1906,late 1880s,1886,1889,Oil on canvas,25 1/2 x 31 7/8 in. (64.8 x 81 cm),"Bequest of Stephen C. Clark, 1960",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435879,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1997.60.1,false,true,437989,European Paintings,Painting,Dish of Apples,,,,,,Artist,,Paul Cézanne,"French, Aix-en-Provence 1839–1906 Aix-en-Provence",,"Cézanne, Paul",French,1839,1906,ca. 1876–77,1876,1877,Oil on canvas,18 1/8 x 21 3/4 in. (46 x 55.2 cm),"The Walter H. and Leonore Annenberg Collection, Gift of Walter H. and Leonore Annenberg, 1997, Bequest of Walter H. Annenberg, 2002",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437989,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1997.60.2,false,true,437990,European Paintings,Painting,Seated Peasant,,,,,,Artist,,Paul Cézanne,"French, Aix-en-Provence 1839–1906 Aix-en-Provence",,"Cézanne, Paul",French,1839,1906,ca. 1892–96,1892,1896,Oil on canvas,21 1/2 x 17 3/4 in. (54.6 x 45.1 cm),"The Walter H. and Leonore Annenberg Collection, Gift of Walter H. and Leonore Annenberg, 1997, Bequest of Walter H. Annenberg, 2002",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437990,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.64,false,true,435877,European Paintings,Painting,Mont Sainte-Victoire and the Viaduct of the Arc River Valley,,,,,,Artist,,Paul Cézanne,"French, Aix-en-Provence 1839–1906 Aix-en-Provence",,"Cézanne, Paul",French,1839,1906,1882–85,1882,1885,Oil on canvas,25 3/4 x 32 1/8 in. (65.4 x 81.6 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435877,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.65,false,true,435873,European Paintings,Painting,Gustave Boyer (b. 1840) in a Straw Hat,,,,,,Artist,,Paul Cézanne,"French, Aix-en-Provence 1839–1906 Aix-en-Provence",,"Cézanne, Paul",French,1839,1906,1870–71,1870,1871,"Oil on paper, laid down on canvas",21 5/8 x 15 1/4 in. (54.9 x 38.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435873,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.66,true,true,435884,European Paintings,Painting,"Still Life with Jar, Cup, and Apples",,,,,,Artist,,Paul Cézanne,"French, Aix-en-Provence 1839–1906 Aix-en-Provence",,"Cézanne, Paul",French,1839,1906,ca. 1877,1872,1882,Oil on canvas,23 7/8 x 29 in. (60.6 x 73.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435884,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.67,false,true,435872,European Paintings,Painting,The Gulf of Marseilles Seen from L'Estaque,,,,,,Artist,,Paul Cézanne,"French, Aix-en-Provence 1839–1906 Aix-en-Provence",,"Cézanne, Paul",French,1839,1906,ca. 1885,1880,1890,Oil on canvas,28 3/4 x 39 1/2 in. (73 x 100.3 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435872,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1993.400.1,false,true,435869,European Paintings,Painting,"Antoine Dominique Sauveur Aubert (born 1817), the Artist's Uncle, as a Monk",,,,,,Artist,,Paul Cézanne,"French, Aix-en-Provence 1839–1906 Aix-en-Provence",,"Cézanne, Paul",French,1839,1906,1866,1866,1866,Oil on canvas,25 5/8 x 21 1/2 in. (65.1 x 54.6 cm),"The Walter H. and Leonore Annenberg Collection, Gift of Walter H. and Leonore Annenberg, 1993, Bequest of Walter H. Annenberg, 2002",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435869,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1993.400.2,false,true,435874,European Paintings,Painting,The House with the Cracked Walls,,,,,,Artist,,Paul Cézanne,"French, Aix-en-Provence 1839–1906 Aix-en-Provence",,"Cézanne, Paul",French,1839,1906,1892–94,1892,1894,Oil on canvas,31 1/2 x 25 1/4 in. (80 x 64.1 cm),"The Walter H. and Leonore Annenberg Collection, Gift of Walter H. and Leonore Annenberg, 1993, Bequest of Walter H. Annenberg, 2002",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435874,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.194,false,true,435880,European Paintings,Painting,Rocks in the Forest,,,,,,Artist,,Paul Cézanne,"French, Aix-en-Provence 1839–1906 Aix-en-Provence",,"Cézanne, Paul",French,1839,1906,1890s,1890,1899,Oil on canvas,28 7/8 x 36 3/8 in. (73.3 x 92.4 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435880,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.201.12,false,true,435867,European Paintings,Painting,Bathers,,,,,,Artist,,Paul Cézanne,"French, Aix-en-Provence 1839–1906 Aix-en-Provence",,"Cézanne, Paul",French,1839,1906,1874–75,1874,1875,Oil on canvas,15 x 18 1/8 in. (38.1 x 46 cm),"Bequest of Joan Whitney Payson, 1975",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435867,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -60.5,false,true,437311,European Paintings,Painting,"Rue de l'Épicerie, Rouen (Effect of Sunlight)",,,,,,Artist,,Camille Pissarro,"French, Charlotte Amalie, Saint Thomas 1830–1903 Paris",,"Pissarro, Camille",French,1830,1903,1898,1898,1898,Oil on canvas,32 x 25 5/8 in. (81.3 x 65.1 cm),"Purchase, Mr. and Mrs. Richard J. Bernhard Gift, 1960",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437311,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -66.36,false,true,437314,European Paintings,Painting,The Garden of the Tuileries on a Winter Afternoon,,,,,,Artist,,Camille Pissarro,"French, Charlotte Amalie, Saint Thomas 1830–1903 Paris",,"Pissarro, Camille",French,1830,1903,1899,1899,1899,Oil on canvas,29 x 36 1/4 in. (73.7 x 92.1 cm),"Gift of Katrin S. Vietor, in loving memory of Ernest G. Vietor, 1966",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437314,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -56.182,false,true,437300,European Paintings,Painting,"A Cowherd at Valhermeil, Auvers-sur-Oise",,,,,,Artist,,Camille Pissarro,"French, Charlotte Amalie, Saint Thomas 1830–1903 Paris",,"Pissarro, Camille",French,1830,1903,1874,1874,1874,Oil on canvas,21 5/8 x 36 1/4 in. (54.9 x 92.1 cm),"Gift of Edna H. Sachs, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437300,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -58.133,false,true,437309,European Paintings,Painting,Steamboats in the Port of Rouen,,,,,,Artist,,Camille Pissarro,"French, Charlotte Amalie, Saint Thomas 1830–1903 Paris",,"Pissarro, Camille",French,1830,1903,1896,1896,1896,Oil on canvas,18 x 21 1/2 in. (45.7 x 54.6 cm),"Gift of Arthur J. Neumark, 1958",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437309,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -60.174,false,true,437310,European Paintings,Painting,The Boulevard Montmartre on a Winter Morning,,,,,,Artist,,Camille Pissarro,"French, Charlotte Amalie, Saint Thomas 1830–1903 Paris",,"Pissarro, Camille",French,1830,1903,1897,1897,1897,Oil on canvas,25 1/2 x 32 in. (64.8 x 81.3 cm),"Gift of Katrin S. Vietor, in loving memory of Ernest G. Vietor, 1960",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437310,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -64.156,false,true,437301,European Paintings,Painting,The Public Garden at Pontoise,,,,,,Artist,,Camille Pissarro,"French, Charlotte Amalie, Saint Thomas 1830–1903 Paris",,"Pissarro, Camille",French,1830,1903,1874,1874,1874,Oil on canvas,23 5/8 x 28 3/4 in. (60 x 73 cm),"Gift of Mr. and Mrs. Arthur Murray, 1964",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437301,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -51.30.2,false,true,437299,European Paintings,Painting,"Jalais Hill, Pontoise",,,,,,Artist,,Camille Pissarro,"French, Charlotte Amalie, Saint Thomas 1830–1903 Paris",,"Pissarro, Camille",French,1830,1903,1867,1867,1867,Oil on canvas,34 1/4 x 45 1/4 in. (87 x 114.9 cm),"Bequest of William Church Osborn, 1951",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437299,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1979.414,false,true,437312,European Paintings,Painting,The Garden of the Tuileries on a Winter Afternoon,,,,,,Artist,,Camille Pissarro,"French, Charlotte Amalie, Saint Thomas 1830–1903 Paris",,"Pissarro, Camille",French,1830,1903,1899,1899,1899,Oil on canvas,28 7/8 x 36 3/8 in. (73.3 x 92.4 cm),"Gift from the Collection of Marshall Field III, 1979",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437312,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1983.166,false,true,437317,European Paintings,Painting,Still Life with Apples and Pitcher,,,,,,Artist,,Camille Pissarro,"French, Charlotte Amalie, Saint Thomas 1830–1903 Paris",,"Pissarro, Camille",French,1830,1903,1872,1872,1872,Oil on canvas,18 1/4 x 22 1/4 in. (46.4 x 56.5 cm),"Purchase, Mr. and Mrs. Richard J. Bernhard Gift, by exchange, 1983",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437317,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2004.359,false,true,438738,European Paintings,Painting,"Haystacks, Morning, Éragny",,,,,,Artist,,Camille Pissarro,"French, Charlotte Amalie, Saint Thomas 1830–1903 Paris",,"Pissarro, Camille",French,1830,1903,1899,1899,1899,Oil on canvas,25 x 31 1/2 in. (63.5 x 80 cm),"Bequest of Douglas Dillon, 2003",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/438738,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -56.184.1,false,true,437303,European Paintings,Painting,"Washerwoman, Study",,,,,,Artist,,Camille Pissarro,"French, Charlotte Amalie, Saint Thomas 1830–1903 Paris",,"Pissarro, Camille",French,1830,1903,1880,1880,1880,Oil on canvas,28 3/4 x 23 1/4 in. (73 x 59.1 cm),"Gift of Mr. and Mrs. Nate B. Spingold, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437303,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -64.154.1,false,true,437305,European Paintings,Painting,A Washerwoman at Éragny,,,,,,Artist,,Camille Pissarro,"French, Charlotte Amalie, Saint Thomas 1830–1903 Paris",,"Pissarro, Camille",French,1830,1903,1893,1893,1893,Oil on canvas,18 x 15 in. (45.7 x 38.1 cm),"Gift of Mr. and Mrs. Richard Rodgers, 1964",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437305,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1980.21.1,false,true,437308,European Paintings,Painting,"Morning, An Overcast Day, Rouen",,,,,,Artist,,Camille Pissarro,"French, Charlotte Amalie, Saint Thomas 1830–1903 Paris",,"Pissarro, Camille",French,1830,1903,1896,1896,1896,Oil on canvas,21 3/8 x 25 5/8 in. (54.3 x 65.1 cm),"Bequest of Grégoire Tarnopol, 1979, and Gift of Alexander Tarnopol, 1980",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437308,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -67.187.93,false,true,437307,European Paintings,Painting,"Poplars, Éragny",,,,,,Artist,,Camille Pissarro,"French, Charlotte Amalie, Saint Thomas 1830–1903 Paris",,"Pissarro, Camille",French,1830,1903,1895,1895,1895,Oil on canvas,36 1/2 x 25 1/2 in. (92.7 x 64.8 cm),"Bequest of Miss Adelaide Milton de Groot (1876–1967), 1967",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437307,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1973.311.5,false,true,437304,European Paintings,Painting,Two Young Peasant Women,,,,,,Artist,,Camille Pissarro,"French, Charlotte Amalie, Saint Thomas 1830–1903 Paris",,"Pissarro, Camille",French,1830,1903,1891–92,1891,1892,Oil on canvas,35 1/4 x 45 7/8 in. (89.5 x 116.5 cm),"Gift of Mr. and Mrs. Charles Wrightsman, 1973",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437304,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1991.277.2,false,true,437316,European Paintings,Painting,"Côte des Grouettes, near Pontoise",,,,,,Artist,,Camille Pissarro,"French, Charlotte Amalie, Saint Thomas 1830–1903 Paris",,"Pissarro, Camille",French,1830,1903,probably 1878,1878,1878,Oil on canvas,29 1/8 x 23 5/8 in. (74 x 60 cm),"Gift of Janice H. Levin, 1991",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437316,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.126,false,true,437306,European Paintings,Painting,Bather in the Woods,,,,,,Artist,,Camille Pissarro,"French, Charlotte Amalie, Saint Thomas 1830–1903 Paris",,"Pissarro, Camille",French,1830,1903,1895,1895,1895,Oil on canvas,23 3/4 x 28 3/4 in. (60.3 x 73 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437306,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1979.135.16,false,true,437302,European Paintings,Painting,Barges at Pontoise,,,,,,Artist,,Camille Pissarro,"French, Charlotte Amalie, Saint Thomas 1830–1903 Paris",,"Pissarro, Camille",French,1830,1903,1876,1876,1876,Oil on canvas,18 1/8 x 21 5/8 in. (46 x 54.9 cm),"Bequest of Mary Cushing Fosburgh, 1978",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437302,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -39.182,false,true,436451,European Paintings,Painting,Tahitian Landscape,,,,,,Artist,,Paul Gauguin,"French, Paris 1848–1903 Atuona, Hiva Oa, Marquesas Islands",,"Gauguin, Paul",French,1848,1903,1892,1892,1892,Oil on canvas,25 3/8 x 18 5/8 in. (64.5 x 47.3 cm),"Anonymous Gift, 1939",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436451,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -49.58.1,false,true,436446,European Paintings,Painting,Two Tahitian Women,,,,,,Artist,,Paul Gauguin,"French, Paris 1848–1903 Atuona, Hiva Oa, Marquesas Islands",,"Gauguin, Paul",French,1848,1903,1899,1899,1899,Oil on canvas,37 x 28 1/2 in. (94 x 72.4 cm),"Gift of William Church Osborn, 1949",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436446,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -51.112.2,true,true,438821,European Paintings,Painting,Ia Orana Maria (Hail Mary),,,,,,Artist,,Paul Gauguin,"French, Paris 1848–1903 Atuona, Hiva Oa, Marquesas Islands",,"Gauguin, Paul",French,1848,1903,1891,1891,1891,Oil on canvas,44 3/4 x 34 1/2 in. (113.7 x 87.6 cm),"Bequest of Sam A. Lewisohn, 1951",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/438821,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -54.143.2,false,true,436448,European Paintings,Painting,A Farm in Brittany,,,,,,Artist,,Paul Gauguin,"French, Paris 1848–1903 Atuona, Hiva Oa, Marquesas Islands",,"Gauguin, Paul",French,1848,1903,ca. 1894,1894,1894,Oil on canvas,28 1/2 x 35 5/8 in. (72.4 x 90.5 cm),"Bequest of Margaret Seligman Lewisohn, in memory of her husband, Sam A. Lewisohn, 1954",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436448,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1997.60.3,false,true,438000,European Paintings,Painting,Three Tahitian Women,,,,,,Artist,,Paul Gauguin,"French, Paris 1848–1903 Atuona, Hiva Oa, Marquesas Islands",,"Gauguin, Paul",French,1848,1903,1896,1896,1896,Oil on wood,9 5/8 x 17 in. (24.4 x 43.2 cm),"The Walter H. and Leonore Annenberg Collection, Gift of Walter H. and Leonore Annenberg, 1997, Bequest of Walter H. Annenberg, 2002",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/438000,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1993.400.3,false,true,436449,European Paintings,Painting,The Siesta,,,,,,Artist,,Paul Gauguin,"French, Paris 1848–1903 Atuona, Hiva Oa, Marquesas Islands",,"Gauguin, Paul",French,1848,1903,ca. 1892–94,1892,1894,Oil on canvas,35 x 45 3/4 in. (88.9 x 116.2 cm),"The Walter H. and Leonore Annenberg Collection, Gift of Walter H. and Leonore Annenberg, 1993, Bequest of Walter H. Annenberg, 2002",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436449,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1997.391.2,false,true,437999,European Paintings,Painting,Still Life with Teapot and Fruit,,,,,,Artist,,Paul Gauguin,"French, Paris 1848–1903 Atuona, Hiva Oa, Marquesas Islands",,"Gauguin, Paul",French,1848,1903,1896,1896,1896,Oil on canvas,18 3/4 x 26 in. (47.6 x 66 cm),"The Walter H. and Leonore Annenberg Collection, Gift of Walter H. and Leonore Annenberg, 1997, Bequest of Walter H. Annenberg, 2002",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437999,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1997.391.3,false,true,438001,European Paintings,Painting,Two Women,,,,,,Artist,,Paul Gauguin,"French, Paris 1848–1903 Atuona, Hiva Oa, Marquesas Islands",,"Gauguin, Paul",French,1848,1903,1901 or 1902,1901,1902,Oil on canvas,29 x 36 1/4 in. (73.7 x 92.1 cm),"The Walter H. and Leonore Annenberg Collection, Gift of Walter H. and Leonore Annenberg, 1997, Bequest of Walter H. Annenberg, 2002",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/438001,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1996.285,false,true,437974,European Paintings,Painting,Scene in the Jewish Quarter of Constantine,,,,,,Artist,,Théodore Chassériau,"French, Le Limon, Saint-Domingue, West Indies 1819–1856 Paris",,"Chassériau, Théodore",French,1819,1856,1851,1851,1851,Oil on canvas,22 3/8 x 18 1/2 in. (56.8 x 47 cm),"Purchase, The Annenberg Foundation Gift, 1996",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437974,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2001.720,false,true,438541,European Paintings,Painting,"Scene from the Gallic Wars: The Gaul Littavicus, Betraying the Roman Cause, Flees to Gergovie to Support Vercingétorix",,,,,,Artist,,Théodore Chassériau,"French, Le Limon, Saint-Domingue, West Indies 1819–1856 Paris",,"Chassériau, Théodore",French,1819,1856,ca. 1838–40,1838,1840,Oil on canvas,13 1/2 x 17 3/4 in. (34.3 x 45.1 cm),"Gift of Lisa and William O'Reilly, 2001",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/438541,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2002.291,false,true,438590,European Paintings,Painting,"Comtesse de La Tour-Maubourg (Marie-Louise-Charlotte-Gabrielle Thomas de Pange, 1816–1850)",,,,,,Artist,,Théodore Chassériau,"French, Le Limon, Saint-Domingue, West Indies 1819–1856 Paris",,"Chassériau, Théodore",French,1819,1856,1841,1841,1841,Oil on canvas,52 x 37 1/4 in. (132.1 x 94.6 cm),"Wrightsman Fund, 2002",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/438590,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -97.18,false,true,436329,European Paintings,Painting,Gathering Olives at Tivoli,,,,,,Artist,,François-Louis Français,"French, Plombières-les-Bains 1814–1897 Plombières-les-Bains",,"Français, François-Louis",French,1814,1897,1868,1868,1868,Oil on canvas,83 3/4 x 51 5/8 in. (212.7 x 131.1 cm),"Gift of I. Montaignac, 1897",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436329,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -26.168.60,false,true,436720,European Paintings,"Painting, miniature",Napoléon I (1769–1821),,,,,,Artist,Style of,Jean-Baptiste Isabey,19th century,,"Isabey, Jean-Baptiste",French,1767,1855,,1800,1899,Ivory,"Oval, 2 3/4 x 2 in. (70 x 50 mm)","Bequest of Collis P. Huntington, 1900",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/436720,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -24.80.526,false,true,436066,European Paintings,"Painting, miniature",Portrait of an Officer,,,,,,Artist,,D. B.,"French, 1812",,"B., D.",French,1812,1812,,1812,1812,Ivory,"Oval, 2 3/4 x 2 1/4 in. (72 x 58 mm)","Bequest of Mary Clark Thompson, 1923",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/436066,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -95.14.50,false,true,435592,European Paintings,"Painting, miniature",Portrait of a Woman with Tapestry Work,,,,,,Artist,Style of,Jean-Baptiste Jacques Augustin,ca. 1800–1810,,"Augustin, Jean-Baptiste Jacques",French,1759,1832,,1800,1810,Ivory extended by card,5 5/8 x 4 5/8 in. (140 x 117 mm),"The Moses Lazarus Collection, Gift of Josephine and Sarah Lazarus, in memory of their father, 1888–95",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/435592,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -24.80.512,false,true,435913,European Paintings,"Painting, miniature","Henry III (1551–1589), King of France",,,,,,Artist,Style of,François Clouet,"French, 1578 or later",,"Clouet, François",French,1536,1572,,1578,1599,Vellum laid on wood,2 1/4 x 1 3/4 in. (58 x 44 mm),"Bequest of Mary Clark Thompson, 1923",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/435913,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -62.122.63,false,true,435891,European Paintings,"Painting, miniature","Leda and the Swan, after Boucher",,,,,,Artist,Style of,Jacques Charlier,probably 19th century,,"Charlier, Jacques",French,1720,1790,,1800,1899,Ivory,2 x 2 7/8 in. (50 x 72 mm),"Bequest of Millie Bruhl Fredrick, 1962",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/435891,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -62.122.80,false,true,437599,European Paintings,"Painting, miniature",Portrait of a Man,,,,,,Artist,Attributed to,Jean-Baptiste Sambat,"French, ca. 1760–1827",,"Sambat, Jean-Baptiste",French,1760,1827,,1780,1827,Ivory,Diameter 2 5/8 in. (70 mm),"Bequest of Millie Bruhl Fredrick, 1962",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/437599,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -25.106.26,false,true,435903,European Paintings,"Painting, miniature",Portrait of an Officer,,,,,,Artist,,Charles Pierre Cior,"French, 1769–after 1838",,"Cior, Charles Pierre",French,1769,1838,,1789,1838,Ivory,Diameter 3 in. (77 mm),"Gift of Mrs. Louis V. Bell, in memory of her husband, 1925",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/435903,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -62.122.66,false,true,436219,European Paintings,"Painting, miniature",Portrait of a Woman,,,,,,Artist,,Frédéric Dubois,"French, active ca. 1780–1819",,"Dubois, Frédéric",French,1780,1819,,1793,1794,Ivory,Diameter 2 5/8 in. (66 mm),"Bequest of Millie Bruhl Fredrick, 1962",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/436219,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -07.225.291,false,true,435748,European Paintings,"Painting, drawing",Study for a Monument to a Princely Figure,,,,,,Artist,,François Boucher,"French, Paris 1703–1770 Paris",,"Boucher, François",French,1703,1770,,1723,1770,"Oil on paper, laid down on canvas",15 x 12 5/8 in. (38.1 x 32.1 cm),"Gift of J. Pierpont Morgan, 1906",,,,,,,,,,,,Pastels & Oil Sketches on Paper,,http://www.metmuseum.org/art/collection/search/435748,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -67.187.2,false,true,435893,European Paintings,Drawing,Fantasia,,,,,,Artist,,Jules Chéret,"French, Paris 1836–1932 Nice",,"Chéret, Jules",French,1836,1932,,1856,1933,Pastel on canvas,25 5/8 x 18 1/4 in. (65.1 x 46.4 cm),"Bequest of Miss Adelaide Milton de Groot (1876–1967), 1967",,,,,,,,,,,,Drawings,,http://www.metmuseum.org/art/collection/search/435893,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -64.165.1,false,true,436137,European Paintings,Drawing,The Dancers,,,,,,Artist,,Edgar Degas,"French, Paris 1834–1917 Paris",,"Degas, Edgar",French,1834,1917,,1854,1917,Pastel and charcoal on paper,28 x 23 1/4 in. (71.1 x 59.1 cm),"Gift of George N. and Helen M. Richard, 1964",,,,,,,,,,,,Drawings,,http://www.metmuseum.org/art/collection/search/436137,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1974.356.31,false,true,436167,European Paintings,Drawing,"Two Dancers, Half-length",,,,,,Artist,,Edgar Degas,"French, Paris 1834–1917 Paris",,"Degas, Edgar",French,1834,1917,,1854,1917,Pastel on paper,18 3/8 x 21 5/8 in. (46.7 x 54.9 cm),"The Lesley and Emma Sheafer Collection, Bequest of Emma A. Sheafer, 1973",,,,,,,,,,,,Drawings,,http://www.metmuseum.org/art/collection/search/436167,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.21,false,true,437093,European Paintings,Painting,Mercury and Battus,,,,,,Artist,,Francisque Millet,"French, 1642–1679",,"Millet, Francisque",French,1642,1679,,1662,1679,Oil on canvas,47 x 70 in. (119.4 x 177.8 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437093,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.100.11,false,true,436346,European Paintings,Painting,"An Allegory, Probably of the Peace of Utrecht of 1713",,,,,,Artist,,Antoine Rivalz,"French, 1667–1735",,"Rivalz, Antoine",French,1667,1735,,1670,1699,Oil on canvas,18 5/8 x 22 in. (47.3 x 55.9 cm),"Bequest of Harry G. Sperling, 1971",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436346,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -55.205.1,false,true,437831,European Paintings,"Painting, sketch",Jean Marc Nattier (1685–1766),,,,,,Artist,,Louis Tocqué,"French, 1696–1772",,"Tocqué, Louis",French,1696,1772,,1716,1772,Oil on canvas,30 1/2 x 23 1/4 in. (77.5 x 59.1 cm),"Gift of Colonel and Mrs. Jacques Balsan, 1955",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437831,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -60.71.20,false,true,437928,European Paintings,Painting,The Country Dance,,,,,,Artist,Copy after,Antoine Watteau,"French, late 18th century",,"Watteau, Antoine",French,1684,1721,,1704,1721,Oil on wood,Diameter 8 1/2 in. (21.6 cm),"Bequest of Lillian S. Timken, 1959",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437928,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -60.71.21,false,true,437927,European Paintings,Painting,The Cascade,,,,,,Artist,Copy after,Antoine Watteau,"French, late 18th century",,"Watteau, Antoine",French,1684,1721,,1704,1721,Oil on wood,Diameter 8 1/2 in. (21.6 cm),"Bequest of Lillian S. Timken, 1959",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437927,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -48.187.739,false,true,437886,European Paintings,Painting,Harbor Scene with a Grotto and Fishermen Hauling in Nets,,,,,,Artist,Style of,Joseph Vernet,"French, late 18th century",,"Vernet, Joseph",French,1714,1789,,1770,1799,Oil on canvas,22 3/4 x 42 1/8 in. (57.8 x 107 cm),"Bequest of Catherine D. Wentworth, 1948",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437886,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -67.187.69,false,true,436450,European Paintings,Painting,Still Life,,,,,,Artist,Style of,Paul Gauguin,"French, late 19th century",,"Gauguin, Paul",French,1848,1903,,1870,1899,Oil on canvas,15 1/8 x 18 1/4 in. (38.4 x 46.4 cm),"Bequest of Miss Adelaide Milton de Groot (1876–1967), 1967",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436450,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -08.227.1,false,true,436229,European Paintings,Painting,Imaginary Landscape,,,,,,Artist,,Gaspard Dughet,"French, Rome 1615–1675 Rome",,"Dughet, Gaspard",French,1615,1675,,1635,1675,Oil on canvas,37 7/8 x 60 1/2 in. (96.2 x 153.7 cm),"Rogers Fund, 1908",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436229,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -07.225.258,false,true,436837,European Paintings,"Painting, overdoor",Allegory of Winter,,,,,,Artist,,Jacques de La Joue the Younger,"French, Paris 1686–1761 Paris",,"La Joue, Jacques de, the Younger",French,1686,1761,,1706,1761,Oil on canvas,"Irregular, 39 1/4 x 41 5/8 in. (99.7 x 105.7 cm)","Gift of J. Pierpont Morgan, 1906",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436837,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.100.8,false,true,436230,European Paintings,Painting,Card Players in a Drawing Room,,,,,,Artist,,Pierre Louis Dumesnil the Younger,"French, Paris 1698–1781 Paris",,"Dumesnil, Pierre Louis, the Younger",French,1698,1781,,1718,1781,Oil on canvas,31 1/8 x 38 3/4 in. (79.1 x 98.4 cm),"Bequest of Harry G. Sperling, 1971",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436230,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -49.7.48,false,true,436217,European Paintings,Painting,Boy with a Black Spaniel,,,,,,Artist,,François Hubert Drouais,"French, Paris 1727–1775 Paris",,"Drouais, François Hubert",French,1727,1775,,1747,1775,Oil on canvas,"Oval, 25 3/8 x 21 in. (64.5 x 53.3 cm)","The Jules Bache Collection, 1949",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436217,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -65.242.1,false,true,436218,European Paintings,Painting,Boy with a House of Cards,,,,,,Artist,,François Hubert Drouais,"French, Paris 1727–1775 Paris",,"Drouais, François Hubert",French,1727,1775,,1747,1775,Oil on canvas,"Oval, 28 x 23 in. (71.1 x 58.4 cm)","Gift of Mrs. William M. Haupt, from the collection of Mrs. James B. Haggin, 1965",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436218,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -17.190.26,false,true,437477,European Paintings,Painting,The Fountain,,,,,,Artist,,Hubert Robert,"French, Paris 1733–1808 Paris",,"Robert, Hubert",French,1733,1808,,1753,1808,Oil on canvas,68 1/4 x 31 3/8 in. (173.4 x 79.7 cm),"Gift of J. Pierpont Morgan, 1917",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437477,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -17.190.27,false,true,437480,European Paintings,Painting,The Swing,,,,,,Artist,,Hubert Robert,"French, Paris 1733–1808 Paris",,"Robert, Hubert",French,1733,1808,,1753,1808,Oil on canvas,68 1/4 x 34 5/8 in. (173.4 x 87.9 cm),"Gift of J. Pierpont Morgan, 1917",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437480,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -17.190.28,false,true,437476,European Paintings,Painting,The Dance,,,,,,Artist,,Hubert Robert,"French, Paris 1733–1808 Paris",,"Robert, Hubert",French,1733,1808,,1753,1808,Oil on canvas,68 1/4 x 33 5/8 in. (173.4 x 85.4 cm),"Gift of J. Pierpont Morgan, 1917",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437476,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -17.190.29,false,true,437473,European Paintings,Painting,The Bathing Pool,,,,,,Artist,,Hubert Robert,"French, Paris 1733–1808 Paris",,"Robert, Hubert",French,1733,1808,,1753,1808,Oil on canvas,68 3/4 x 48 3/4 in. (174.6 x 123.8 cm),"Gift of J. Pierpont Morgan, 1917",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437473,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -17.190.30,false,true,437481,European Paintings,Painting,Wandering Minstrels,,,,,,Artist,,Hubert Robert,"French, Paris 1733–1808 Paris",,"Robert, Hubert",French,1733,1808,,1753,1808,Oil on canvas,68 3/4 x 48 1/4 in. (174.6 x 122.6 cm),"Gift of J. Pierpont Morgan, 1917",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437481,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -17.190.31,false,true,437472,European Paintings,"Painting, overdoor",Arches in Ruins,,,,,,Artist,,Hubert Robert,"French, Paris 1733–1808 Paris",,"Robert, Hubert",French,1733,1808,,1753,1808,Oil on canvas,23 1/8 x 61 1/4 in. (58.7 x 155.6 cm),"Gift of J. Pierpont Morgan, 1917",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437472,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -17.190.32,false,true,437475,European Paintings,"Painting, overdoor",A Colonnade in Ruins,,,,,,Artist,,Hubert Robert,"French, Paris 1733–1808 Paris",,"Robert, Hubert",French,1733,1808,,1753,1808,Oil on canvas,23 x 61 1/8 in. (58.4 x 155.3 cm),"Gift of J. Pierpont Morgan, 1917",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437475,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -07.225.264a,false,true,437474,European Paintings,"Painting, overdoor",Bridge over a Cascade,,,,,,Artist,,Hubert Robert,"French, Paris 1733–1808 Paris",,"Robert, Hubert",French,1733,1808,,1753,1808,Oil on canvas,32 x 54 1/8 in. (81.3 x 137.5 cm),"Gift of J. Pierpont Morgan, 1906",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437474,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -07.225.264b,false,true,437471,European Paintings,"Painting, overdoor",Aqueduct in Ruins,,,,,,Artist,,Hubert Robert,"French, Paris 1733–1808 Paris",,"Robert, Hubert",French,1733,1808,,1753,1808,Oil on canvas,32 1/8 x 54 1/8 in. (81.6 x 137.5 cm),"Gift of J. Pierpont Morgan, 1906",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437471,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -80.2,false,true,437079,European Paintings,Painting,Landscape with a Plowed Field and a Village,,,,,,Artist,,Georges Michel,"French, Paris 1763–1843 Paris",,"Michel, Georges",French,1763,1843,,1783,1843,Oil on canvas,20 1/8 x 27 5/8 in. (51.1 x 70.2 cm),"Gift of Paul Durand-Ruel, 1880",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437079,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.400.44,false,true,440336,European Paintings,Painting,View in the Roman Campagna,,,,,,Artist,,Alexandre Desgoffe,"French, Paris 1805–1882 Paris",,Desgoffe Alexandre,French,1805,1882,,1834,1837,"Oil on paper, laid down on canvas",5 5/8 x 14 3/8 in. (14.3 x 36.5 cm),"Thaw Collection, Jointly Owned by The Metropolitan Museum of Art and The Morgan Library & Museum, Gift of Eugene V. Thaw, 2009",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/440336,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -60.71.10,false,true,436774,European Paintings,Painting,Springtime,,,,,,Artist,,Charles Jacque,"French, Paris 1813–1894 Paris",,"Jacque, Charles",French,1813,1894,,1833,1894,Oil on wood,16 x 11 1/2 in. (40.6 x 29.2 cm),"Bequest of Lillian S. Timken, 1959",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436774,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -60.155,false,true,436849,European Paintings,Painting,The Village of La Celle-sous-Moret,,,,,,Artist,,Eugène Lavieille,"French, Paris 1820–1889 Paris",,"Lavieille, Eugène",French,1820,1889,,1840,1889,Oil on wood,13 5/8 x 23 in. (34.6 x 58.4 cm),"Gift of Arthur Wiesenberger, 1960",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436849,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -08.123,false,true,437367,European Paintings,Painting,"Place Saint-Germain-des-Prés, Paris",,,,,,Artist,,Jean-François Raffaëlli,"French, Paris 1850–1924 Paris",,"Raffaëlli, Jean-François",French,1850,1924,,1870,1924,Oil on canvas,27 1/2 x 31 1/2 in. (69.9 x 80 cm),"Catharine Lorillard Wolfe Collection, Wolfe Fund, 1908",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437367,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -66.217,false,true,436312,European Paintings,Painting,Recess of the Court,,,,,,Artist,,Jean-Louis Forain,"French, Reims 1852–1931 Paris",,"Forain, Jean-Louis",French,1852,1931,,1872,1931,Oil on canvas,23 7/8 x 28 7/8 in. (60.6 x 73.3 cm),"Gift of Mr. and Mrs. Arthur Wiesenberger, 1966",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436312,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -07.225.461,false,true,435639,European Paintings,"Painting, overdoor",Vase of Flowers in a Niche,,,,,,Artist,Attributed to,Michel Bruno Bellengé,"French, Rouen 1726–1793 Rouen",,"Bellengé, Michel Bruno",French,1726,1793,,1746,1793,Oil on canvas,48 3/8 x 55 in. (122.9 x 139.7 cm),"Gift of J. Pierpont Morgan, 1906",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435639,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.119,false,true,436028,European Paintings,Painting,"Portrait of a Woman, Called Héloïse Abélard",,,,,,Artist,Style of,Gustave Courbet,"French, second half 19th century",,"Courbet, Gustave",French,1819,1877,,1850,1899,Oil on canvas,25 3/8 x 21 1/8 in. (64.5 x 53.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436028,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.123,false,true,436027,European Paintings,Painting,Apples,,,,,,Artist,Style of,Gustave Courbet,"French, second half 19th century",,"Courbet, Gustave",French,1819,1877,,1850,1899,Oil on canvas,13 x 17 3/8 in. (33 x 44.1 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436027,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.138,false,true,435857,European Paintings,Painting,Landscape with a Cave,,,,,,Artist,,Théodore Caruelle d'Aligny,"French, Chaumes 1798–1871 Lyons",,"Aligny, Théodore Caruelle d'",French,1798,1871,,1818,1871,Oil on canvas,24 1/2 x 18 in. (62.2 x 45.7 cm),"Catharine Lorillard Wolfe Collection, Wolfe Fund, 1989",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435857,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -63.138.5,false,true,435855,European Paintings,Painting,The First Communion,,,,,,Artist,,Eugène Carrière,"French, Gournay 1849–1906 Paris",,"Carrière, Eugène",French,1849,1906,,1869,1906,Oil on canvas,25 3/4 x 21 in. (65.4 x 53.3 cm),"Gift of Chester Dale, 1963",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435855,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -25.110.19,false,true,437847,European Paintings,Painting,Road in the Woods,,,,,,Artist,,Constant Troyon,"French, Sèvres 1810–1865 Paris",,"Troyon, Constant",French,1810,1865,,1840,1860,Oil on canvas,22 7/8 x 19 in. (58.1 x 48.3 cm),"Bequest of Collis P. Huntington, 1900",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437847,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -07.233.39,false,true,437319,European Paintings,Painting,Mother and Child,,,,,,Artist,,Antoine-Émile Plassan,"French, Bordeaux 1817–1903 Paris",,"Plassan, Antoine-Émile",French,1817,1903,,1837,1903,Oil on wood,10 5/8 x 8 5/8 in. (27 x 21.9 cm),"Bequest of Margarette A. Jones, 1905",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437319,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -43.86.7,false,true,437519,European Paintings,Painting,A River Landscape,,,,,,Artist,,Théodore Rousseau,"French, Paris 1812–1867 Barbizon",,"Rousseau, Théodore",French,1812,1867,,1832,1867,Oil on wood,16 3/8 x 24 7/8 in. (41.6 x 63.2 cm),"Bequest of Richard De Wolfe Brixey, 1943",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437519,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -15.30.26,false,true,435862,European Paintings,Painting,The Route Nationale at Samer,,,,,,Artist,,Jean-Charles Cazin,"French, Samer 1841–1901 Lavandou",,"Cazin, Jean-Charles",French,1841,1901,,1861,1901,Oil on canvas,41 1/2 x 48 1/4 in. (105.4 x 122.6 cm),"Bequest of Maria DeWitt Jesup, from the collection of her husband, Morris K. Jesup, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435862,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.100.5,false,true,436108,European Paintings,Painting,Head of a Child,,,,,,Artist,Style of,Jacques Louis David,"French, first quarter 19th century",,"David, Jacques Louis",French,1748,1825,,1768,1825,Oil on canvas,15 3/4 x 12 5/8 in. (40 x 32.1 cm),"Bequest of Harry G. Sperling, 1971",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436108,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -32.100.124,false,true,435912,European Paintings,Painting,"Charles IX (1550–1574), King of France",,,,,,Artist,Style of,François Clouet,"French, painted shortly after 1561",,"Clouet, François",French,1536,1572,,1561,1566,Oil on wood,12 3/8 x 9 in. (31.4 x 22.9 cm),"The Friedsam Collection, Bequest of Michael Friedsam, 1931",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435912,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.20,false,true,437331,European Paintings,Painting,Orpheus and Eurydice,,,,,,Artist,Style of,Nicolas Poussin,"French, third quarter 17th century",,"Poussin, Nicolas",French,1594,1665,,1650,1674,Oil on canvas,47 1/2 x 70 3/4 in. (120.7 x 179.7 cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437331,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1985.118,false,true,436705,European Paintings,Painting,Head of Saint John the Evangelist,,,,,,Artist,,Jean Auguste Dominique Ingres,"French, Montauban 1780–1867 Paris",,"Ingres, Jean Auguste Dominique",French,1780,1867,,1818,1856,"Oil on canvas, laid down on wood",15 1/2 x 10 5/8 in. (39.4 x 27 cm),"Catharine Lorillard Wolfe Collection, Purchase, Bequest of Catharine Lorillard Wolfe, by exchange, and Wolfe Fund, 1985",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436705,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.20.2,false,true,436645,European Paintings,Painting,Young Woman Praying,,,,,,Artist,,Jean-Jacques Henner,"French, Bernwiller 1829–1905 Paris",,"Henner, Jean-Jacques",French,1829,1905,,1849,1905,Oil on canvas,24 7/8 x 17 7/8 in. (63.2 x 45.4 cm),"Bequest of Emma T. Gary, 1937",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436645,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.100.2,false,true,435688,European Paintings,Painting,Portrait of a Woman,,,,,,Artist,,Louis Léopold Boilly,"French, La Bassée 1761–1845 Paris",,"Boilly, Louis Léopold",French,1761,1845,,1781,1845,Oil on canvas,8 3/4 x 6 7/8 in. (22.2 x 17.5 cm),"Bequest of Harry G. Sperling, 1971",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435688,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.100.3,false,true,435687,European Paintings,Painting,Portrait of a Man,,,,,,Artist,,Louis Léopold Boilly,"French, La Bassée 1761–1845 Paris",,"Boilly, Louis Léopold",French,1761,1845,,1781,1845,Oil on canvas,8 3/4 x 6 7/8 in. (22.2 x 17.5 cm),"Bequest of Harry G. Sperling, 1971",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435687,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -90.25,false,true,435701,European Paintings,Painting,Environs of Fontainebleau: Woodland and Cattle,,,,,,Artist,,Auguste-François Bonheur,"French, Bordeaux 1824–1884 Bellevue",,"Bonheur, Auguste-François",French,1824,1884,,1844,1884,Oil on canvas,104 1/2 x 157 1/4 in. (265.4 x 399.4 cm),"Gift of James Clinch Smith and his sisters, in memory of their mother, 1890",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435701,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -49.7.39,false,true,435757,European Paintings,Painting,Portrait of a Young Boy,,,,,,Artist,Attributed to,Sébastien Bourdon,"French, Montpellier 1616–1671 Paris",,"Bourdon, Sébastien",French,1616,1671,,1636,1671,Oil on canvas,23 1/4 x 19 3/4 in. (59.1 x 50.2 cm),"The Jules Bache Collection, 1949",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435757,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1997.257,false,true,438030,European Paintings,Painting,Valley of the River Loire,,,,,,Artist,,Jules Dupré,"French, Nantes 1811–1889 L'Isle-Adam",,"Dupré, Jules",French,1811,1889,,1831,1889,Oil on wood,10 3/4 x 19 1/4 in. (27.3 x 48.9 cm),"Bequest of Mr. and Mrs. Richard S. Richards, 1997",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/438030,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1982.60.43,false,true,437258,European Paintings,Painting,The Golden Age,,,,,,Artist,,Jean-Baptiste Joseph Pater,"French, Valenciennes 1695–1736 Paris",,"Pater, Jean-Baptiste Joseph",French,1695,1736,,1715,1736,Oil on wood,6 3/8 x 9 in. (16.2 x 22.9 cm),"The Jack and Belle Linsky Collection, 1982",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437258,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -64.315,false,true,436186,European Paintings,Painting,Still Life with Silver,,,,,,Artist,,Alexandre François Desportes,"French, Champigneulle 1661–1743 Paris",,"Desportes, Alexandre François",French,1661,1743,,1681,1743,Oil on canvas,103 x 73 3/4 in. (261.6 x 187.3 cm),"Purchase, Mary Wetmore Shively Bequest, in memory of her husband, Henry L. Shively, M.D., 1964",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436186,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.100.9,false,true,437039,European Paintings,Painting,Classical Landscape with Figures,,,,,,Artist,,Henri Mauperché,"French, Paris (?) ca. 1602–1686 Paris",,"Mauperché, Henri",French,1602,1686,,1622,1686,Oil on canvas,27 7/8 x 44 1/4 in. (70.8 x 112.4 cm),"Bequest of Harry G. Sperling, 1971",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437039,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -87.15.93,false,true,436116,European Paintings,Painting,The Night Patrol at Smyrna,,,,,,Artist,,Alexandre-Gabriel Decamps,"French, Paris 1803–1860 Fontainebleau",,"Decamps, Alexandre-Gabriel",French,1803,1860,,1823,1860,Oil on canvas,29 1/4 x 36 3/8 in. (74.3 x 92.4 cm),"Catharine Lorillard Wolfe Collection, Bequest of Catharine Lorillard Wolfe, 1887",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436116,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1982.179.12,false,true,436780,European Paintings,Painting,Captain Swaton,,,,,,Artist,Attributed to,Paulin Jénot,"French, active by 1886, died after 1930",,"Jénot, Paulin",French,1886,1930,,1886,1930,Oil on canvas,16 1/8 x 13 in. (41 x 33 cm),"Gift of Raymonde Paul, in memory of her brother, C. Michael Paul, 1982",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436780,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -95.21,false,true,436238,European Paintings,Painting,Benjamin Franklin (1706–1790),,,,,,Artist,Workshop of,Joseph Siffred Duplessis,"French, Carpentras 1725–1802 Versailles",,"Duplessis, Joseph Siffred",French,1725,1802,,1745,1802,Oil on canvas,"Oval, 27 5/8 x 22 1/4 in. (70.2 x 56.5 cm)","Gift of George A. Lucas, 1895",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436238,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -07.267,false,true,437147,European Paintings,Painting,The Court of the Princess,,,,,,Artist,,Adolphe Monticelli,"French, Marseilles 1824–1886 Marseilles",,"Monticelli, Adolphe",French,1824,1886,,1844,1886,Oil on wood,15 x 23 3/8 in. (38.1 x 59.4 cm),"Catharine Lorillard Wolfe Collection, Wolfe Fund, 1907",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437147,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -67.187.198,false,true,437149,European Paintings,Painting,Four Figures,,,,,,Artist,,Adolphe Monticelli,"French, Marseilles 1824–1886 Marseilles",,"Monticelli, Adolphe",French,1824,1886,,1844,1886,Oil on wood,9 3/4 x 7 3/4 in. (24.8 x 19.7 cm),"Bequest of Miss Adelaide Milton de Groot (1876–1967), 1967",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437149,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -87.24,false,true,437269,European Paintings,Painting,"January: Cernay, near Rambouillet",,,,,,Artist,,Léon-Germain Pelouse,"French, Pierrelaye 1838–1891 Pierrelaye",,"Pelouse, Léon-Germain",French,1838,1891,,1858,1891,Oil on canvas,35 3/8 x 46 1/4 in. (89.9 x 117.5 cm),"Gift of Mabel Schaus, 1887",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437269,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -07.225.438a,false,true,436268,European Paintings,Painting,Putti with a Medallion,,,,,,Artist,,Charles Dominique Joseph Eisen,"French, Valenciennes 1720–1778 Brussels",,"Eisen, Charles Dominique Joseph",French,1720,1778,,1740,1778,Oil on wood,"Oval, 25 3/8 x 21 1/4 in. (64.5 x 54 cm)","Gift of J. Pierpont Morgan, 1906",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436268,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -07.225.438b,false,true,436269,European Paintings,Painting,Putti with a Medallion,,,,,,Artist,,Charles Dominique Joseph Eisen,"French, Valenciennes 1720–1778 Brussels",,"Eisen, Charles Dominique Joseph",French,1720,1778,,1740,1778,Oil on wood,"Oval, 25 1/2 x 21 1/4 in. (64.8 x 54 cm)","Gift of J. Pierpont Morgan, 1906",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436269,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -67.212,false,true,436010,European Paintings,Painting,A Brook in the Forest,,,,,,Artist,,Gustave Courbet,"French, Ornans 1819–1877 La Tour-de-Peilz",,"Courbet, Gustave",French,1819,1877,,1839,1877,Oil on canvas,19 7/8 x 24 1/8 in. (50.5 x 61.3 cm),"Gift of Ralph Weiler, 1967",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436010,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -45.128.12,false,true,435916,European Paintings,Painting,"Henry II (1519–1559), King of France",,,,,,Artist,Workshop of,François Clouet,"French, Tours (?), active by 1536–died 1572 Paris",,"Clouet, François",French,1536,1572,,1536,1572,"Oil on canvas, transferred from wood",61 1/2 x 53 in. (156.2 x 134.6 cm),"Bequest of Helen Hay Whitney, 1944",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435916,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2001.202.4,false,true,437996,European Paintings,Painting,Roses and Lilies,,,,,,Artist,,Henri Fantin-Latour,"French, Grenoble 1836–1904 Buré",,"Fantin-Latour, Henri",French,1836,1904,1888,1888,1888,Oil on canvas,23 1/2 x 18 in. (59.7 x 45.7 cm),"The Walter H. and Leonore Annenberg Collection, Gift of Walter H. and Leonore Annenberg, 2001, Bequest of Walter H. Annenberg, 2002",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437996,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1998.325.3,false,true,438014,European Paintings,Painting,"The Daughters of Catulle Mendès, Huguette (1871–1964), Claudine (1876–1937), and Helyonne (1879–1955)",,,,,,Artist,,Auguste Renoir,"French, Limoges 1841–1919 Cagnes-sur-Mer",,"Renoir, Auguste",French,1841,1919,1888,1888,1888,Oil on canvas,63 3/4 x 51 1/8 in. (161.9 x 129.9 cm),"The Walter H. and Leonore Annenberg Collection, Gift of Walter H. and Leonore Annenberg, 1998, Bequest of Walter H. Annenberg, 2002",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/438014,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -95.14.68,false,true,436067,European Paintings,"Painting, miniature","Joseph II (1741–1790), Emperor of Austria",,,,,,Artist,,Adam Ludwig d'Argent,"German, 1748–1829",,"Argent, Adam Ludwig d'",German,1748,1829,ca. 1780,1775,1785,Enamel,"Oval, 1 1/4 x 1 in. (32 x 26 mm)","The Moses Lazarus Collection, Gift of Josephine and Sarah Lazarus, in memory of their father, 1888–95",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/436067,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -24.80.511,false,true,437653,European Paintings,"Painting, miniature",Portrait of a Woman,,,,,,Artist,,Carl August Senff,"German, 1770–1838",,"Senff, Carl August",German,1770,1838,1808,1808,1808,Ivory,"Oval, 2 3/8 x 2 in. (60 x 50 mm)","Bequest of Mary Clark Thompson, 1923",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/437653,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -41.67,false,true,436887,European Paintings,"Painting, miniature",Prince Klemens Wenzel Lothar von Metternich (1773–1859),,,,,,Artist,,Friedrich Johann Gottlieb Lieder,"German, 1780–1859",,"Lieder, Friedrich Johann Gottlieb",German,1780,1859,1822,1822,1822,Card laid on recent support,"Oval, 8 3/4 x 6 3/4 in. (224 x 172 mm)","Fletcher Fund, 1941",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/436887,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -95.14.85,false,true,437632,European Paintings,"Painting, miniature",Joseph and Karl August von Klein,,,,,,Artist,,Heinrich Franz Schalck,"German, 1791–1832",,"Schalk, Heinrich Franz",German,1791,1832,ca. 1810–15,1810,1815,Ivory,4 5/8 x 5 1/8 in. (117 x 131 mm),"The Moses Lazarus Collection, Gift of Josephine and Sarah Lazarus, in memory of their father, 1888–95",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/437632,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -95.14.52,false,true,436633,European Paintings,"Painting, miniature","Ali Pasha (born about 1741, died 1822)",,,,,,Artist,,Jacob Ritter von Hartmann,"German, 1795–1873",,"Hartmann, Jacob Ritter von",German,1795,1873,1822,1822,1822,Ivory,"Oval, 4 1/8 x 3 1/4 in. (105 x 84 mm)","The Moses Lazarus Collection, Gift of Josephine and Sarah Lazarus, in memory of their father, 1888–95",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/436633,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -41.191.3,false,true,436639,European Paintings,"Painting, miniature",Henriette Sontag (1806–1854),,,,,,Artist,,Franz Napoleon Heigel,"German, 1813–1888",,"Heigel, Franz Napoleon",German,1813,1888,ca. 1835,1830,1840,Ivory,"Oval, 2 7/8 x 2 3/8 in. (73 x 60 mm)","Gift of Mrs. Thomas Hunt, 1941",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/436639,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -50.57,false,true,437761,European Paintings,"Painting, miniature",Portrait of a Woman,,,,,,Artist,,Peter Edward Stroely,"German, 1768–after 1826",,"Stroely, Peter Edward",German,1768,1826,ca. 1800,1795,1805,Ivory,"Octagonal, 3 3/8 x 2 5/8 in. (87 x 67 mm)","Rogers Fund, 1950",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/437761,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -24.80.519,false,true,437786,European Paintings,"Painting, miniature",Diana,,,,,,Artist,,Carl Friedrich Thienpondt,"German, Berlin 1730–1796 Warsaw",,"Thienpondt, Carl Friedrich",German,1730,1796,ca. 1760,1755,1765,Enamel,2 7/8 x 2 1/4 in. (74 x 59 mm),"Bequest of Mary Clark Thompson, 1923",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/437786,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -24.80.520,false,true,437785,European Paintings,"Painting, miniature",A Sea Nymph,,,,,,Artist,,Carl Friedrich Thienpondt,"German, Berlin 1730–1796 Warsaw",,"Thienpondt, Carl Friedrich",German,1730,1796,ca. 1760,1755,1765,Enamel,2 7/8 x 2 1/4 in. (74 x 59 mm),"Bequest of Mary Clark Thompson, 1923",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/437785,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -95.14.78,false,true,437076,European Paintings,"Painting, miniature",Hannah Mahady,,,,,,Artist,,Jeremiah Meyer,"German, Tübingen 1735–1789 Kew",,"Meyer, Jeremiah",German,1735,1789,ca. 1760,1755,1765,Ivory,"Oval, 7/8 x 5/8 in. (22 x 16 mm)","The Moses Lazarus Collection, Gift of Josephine and Sarah Lazarus, in memory of their father, 1888–95",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/437076,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -41.68,false,true,436421,European Paintings,"Painting, miniature","Maria Louisa (1745–1792), Empress of Austria",,,,,,Artist,,Heinrich Friedrich Füger,"German, Heilbronn 1751–1818 Vienna",,"Füger, Heinrich Friedrich",German,1751,1818,ca. 1790,1785,1795,Ivory,"Octagonal, 1 1/4 x 7/8 in. (31 x 22 mm)","Fletcher Fund, 1941",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/436421,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -95.14.61,false,true,437960,European Paintings,"Painting, miniature",Richard Abell,,,,,,Artist,,Christian Friedrich Zincke,"German, Dresden 1683/85–1767 London",,"Zincke, Christian Friedrich",German,1683,1767,1724,1724,1724,Enamel,"Oval, 1 3/4 x 1 3/8 in. (45 x 35 mm)","The Moses Lazarus Collection, Gift of Josephine and Sarah Lazarus, in memory of their father, 1888–95",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/437960,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -95.14.99,false,true,437961,European Paintings,"Painting, miniature",Mrs. Vanderbank,,,,,,Artist,,Christian Friedrich Zincke,"German, Dresden 1683/85–1767 London",,"Zincke, Christian Friedrich",German,1683,1767,ca. 1730,1725,1735,Enamel,"Oval, 1 3/8 x 1 1/2 in. (35 x 38 mm)","The Moses Lazarus Collection, Gift of Josephine and Sarah Lazarus, in memory of their father, 1888–95",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/437961,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -25.205,false,true,436663,European Paintings,"Painting, miniature","Thomas Wriothesley (1505–1550), First Earl of Southampton",,,,,,Artist,,Hans Holbein the Younger,"German, Augsburg 1497/98–1543 London",,"Holbein, Hans, the Younger",German,1497,1543,ca. 1535,1530,1540,Vellum laid on card,"Irregular, cut down, 1 1/8 x 1 in. (28 x 25 mm)","Rogers Fund, 1925",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/436663,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -50.69.1,false,true,436661,European Paintings,"Painting, miniature",William Roper (1493/94–1578),,,,,,Artist,,Hans Holbein the Younger,"German, Augsburg 1497/98–1543 London",,"Holbein, Hans, the Younger",German,1497,1543,1535–36,1535,1536,Vellum laid on card,Diameter 1 3/4 in. (45 mm),"Rogers Fund, 1950",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/436661,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -50.69.2,false,true,436662,European Paintings,"Painting, miniature","Margaret More (1505–1544), Wife of William Roper",,,,,,Artist,,Hans Holbein the Younger,"German, Augsburg 1497/98–1543 London",,"Holbein, Hans, the Younger",German,1497,1543,1535–36,1535,1536,Vellum laid on playing card,Diameter 1 3/4 in. (45 mm),"Rogers Fund, 1950",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/436662,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2005.231,false,true,438780,European Paintings,"Painting, drawing",Pleasure,,,,,,Artist,,Anton Raphael Mengs,"German, Ústi nad Labem (Aussig) 1728–1779 Rome",,"Mengs, Anton Raphael",German,1728,1779,ca. 1754,1749,1759,"Pastel on paper, laid down on canvas","Oval, 24 3/8 x 19 1/4 in. (61.9 x 48.9 cm)","Victor Wilbour Memorial, The Alfred N. Punnett Endowment, and Marquand Funds, 2005",,,,,,,,,,,,Pastels & Oil Sketches on Paper,,http://www.metmuseum.org/art/collection/search/438780,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -49.7.31,false,true,436670,European Paintings,Painting,"Edward VI (1537–1553), When Duke of Cornwall",,,,,,Artist,Workshop of,Hans Holbein the Younger,,,"Holbein, Hans, the Younger",German,1497,1543,ca. 1545; reworked 1547 or later,1540,1547,Oil and gold on oak,Diameter 12 3/4 in. (32.4 cm),"The Jules Bache Collection, 1949",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436670,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.40.646,false,true,436669,European Paintings,Painting,"Lady Rich (Elizabeth Jenks, died 1558)",,,,,,Artist,Workshop of,Hans Holbein the Younger,,,"Holbein, Hans, the Younger",German,1497,1543,ca. 1540,1535,1545,Oil and gold on oak,17 1/2 x 13 3/8 in. (44.5 x 34 cm),"Bequest of Benjamin Altman, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436669,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -32.100.61,false,true,436044,European Paintings,Painting,Portrait of a Man,,,,,,Artist,Circle of,Lucas Cranach the Elder,,,"Cranach, Lucas, the Elder",German,1472,1553,1537,1537,1537,Oil on alder,22 x 16 3/4 in. (55.9 x 42.5 cm),"The Friedsam Collection, Bequest of Michael Friedsam, 1931",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436044,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1982.60.37,false,true,436287,European Paintings,Painting,Heinrich(?) vom Rhein zum Mohren (1477–1536),,,,,,Artist,Copy after,Conrad Faber von Creuznach,,,"Faber von Creuznach, Conrad",German,1524,1553,late 1520s,1527,1529,Oil and gold on oak,Overall 21 3/4 x 15 5/8 in. (55.2 x 39.7 cm); painted surface 21 1/2 x 15 in. (54.6 x 38.1 cm),"The Jack and Belle Linsky Collection, 1982",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436287,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -62.267.1,false,true,435818,European Paintings,Painting,Portrait of a Man,,,,,,Artist,,Barthel Bruyn the Elder,"German, 1493–1555",,"Bruyn, Barthel, the Elder",German,1493,1555,1533,1533,1533,Oil on oak,"Overall, with arched top, 12 x 8 7/8 in. (30.5 x 22.5 cm); painted surface 11 3/4 x 8 1/8 in. (29.8 x 20.6 cm)","Gift of James A. Moffett 2nd, 1962",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435818,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -62.267.2,false,true,435819,European Paintings,Painting,Portrait of a Woman,,,,,,Artist,,Barthel Bruyn the Elder,"German, 1493–1555",,"Bruyn, Barthel, the Elder",German,1493,1555,1533,1533,1533,Oil on oak,"Overall, with arched top, 12 x 8 7/8 in. (30.5 x 22.5 cm); painted surface 11 3/4 x 8 1/8 in. (29.8 x 20.6 cm)","Gift of James A. Moffett 2nd, 1962",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435819,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1978.554.2,false,true,436590,European Paintings,Painting,"The Empress Elizabeth of Russia (1709–1762) on Horseback, Attended by a Page",,,,,,Artist,Attributed to,Georg Christoph Grooth,"German, 1716–1749",,"Grooth, Georg Christoph",German,1716,1749,after 1743–49,1743,1749,Oil on canvas,31 3/8 x 24 1/2 in. (79.7 x 62.2 cm),"Gift of Mr. and Mrs. Nathaniel Spear Jr., 1978",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436590,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2006.116,false,true,438849,European Paintings,Painting,Landscape,,,,,,Artist,Circle of,Carl Rottmann,"German, 1797–1850",,"Rottmann, Carl",German,1797,1850,ca. 1835–45,1830,1850,"Oil on paper, laid down on board",8 7/8 x 10 5/8 in. (22.5 x 27 cm),"Purchase, Gift of Joanne Toor Cummings, by exchange, 2006",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/438849,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -32.100.50,false,true,435820,European Paintings,Painting,Portrait of a Woman of the Slosgin Family of Cologne,,,,,,Artist,,Barthel Bruyn the Younger,"German, ca. 1530–before 1610",,"Bruyn, Barthel, the Younger",German,1530,1610,1557,1557,1557,Oil on oak,"Shaped top, 17 3/4 x 14 1/8 in. (45.1 x 35.9 cm)","The Friedsam Collection, Bequest of Michael Friedsam, 1931",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435820,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2002.564,false,true,438617,European Paintings,Painting,"The Children of Martin Anton Heckscher: Johann Gustav Wilhelm Moritz (1797–1865), Carl Martin Adolph (1796–1850), and Leopold (born 1792)",,,,,,Artist,,Johann Heinrich Wilhelm Tischbein,"German, Haina 1751–1829 Eutin",,"Tischbein, Johann Heinrich Wilhelm",German,1751,1829,1805,1805,1805,Oil on canvas,58 x 45 in. (147.3 x 114.3 cm),"Gift of the family of August Heckscher II, in his memory, 2002",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/438617,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -16.148.2,false,true,436886,European Paintings,Painting,The Ropewalk in Edam,,,,,,Artist,,Max Liebermann,"German, Berlin 1847–1935 Berlin",,"Liebermann, Max",German,1847,1935,1904,1904,1904,Oil on canvas,39 3/4 x 28 in. (101 x 71.1 cm),"Reisinger Fund, 1916",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436886,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.70,false,true,439065,European Paintings,Painting,The Family of Mr. Westfal in the Conservatory,,,,,,Artist,,Eduard Gaertner,"German, Berlin 1801–1877 Zechlin",,"Gaertner, Eduard",German,1801,1877,1836,1836,1836,Oil on canvas,9 3/8 x 7 7/8 in. (23.8 x 20 cm),"Purchase, funds from various donors, by exchange, 2007",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/439065,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2006.258,false,true,438848,European Paintings,Painting,Parochialstrasse in Berlin,,,,,,Artist,,Eduard Gaertner,"German, Berlin 1801–1877 Zechlin",,"Gaertner, Eduard",German,1801,1877,1831,1831,1831,Oil on canvas,16 x 11 in. (40.6 x 27.9 cm),"Catharine Lorillard Wolfe Collection, Wolfe Fund, and funds from various donors, by exchange, 2006",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/438848,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.64,false,true,440726,European Paintings,Painting,The Artist's Sitting Room in Ritterstrasse,,,,,,Artist,,Adolph Menzel,"German, Breslau 1815–1905 Berlin",,"Menzel, Adolph",German,1815,1905,1851,1851,1851,Oil on cardboard,12 5/8 x 10 5/8 in. (32.1 x 27 cm),"Purchase, Nineteenth-Century, Modern and Contemporary Funds, Leonora Brenauer Bequest, in memory of her father, Joseph B. Brenauer, Catharine Lorillard Wolfe Collection, Wolfe Fund, and Paul L. and Marlene A. Herring and John D. Herring Gift, 2009",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/440726,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -08.19,false,true,436036,European Paintings,Painting,"Johann (1498–1537), Duke of Saxony",,,,,,Artist,,Lucas Cranach the Elder,"German, Kronach 1472–1553 Weimar",,"Cranach, Lucas, the Elder",German,1472,1553,ca. 1534–37,1534,1537,Oil on beech,25 5/8 x 17 3/8 in. (65.1 x 44.1 cm),"Rogers Fund, 1908",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436036,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -11.15,false,true,436038,European Paintings,Painting,Judith with the Head of Holofernes,,,,,,Artist,,Lucas Cranach the Elder,"German, Kronach 1472–1553 Weimar",,"Cranach, Lucas, the Elder",German,1472,1553,ca. 1530,1525,1535,Oil on linden,35 1/4 x 24 3/8 in. (89.5 x 61.9 cm),"Rogers Fund, 1911",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436038,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -57.22,false,true,436039,European Paintings,Painting,The Martyrdom of Saint Barbara,,,,,,Artist,,Lucas Cranach the Elder,"German, Kronach 1472–1553 Weimar",,"Cranach, Lucas, the Elder",German,1472,1553,ca. 1510,1505,1515,Oil on linden,Overall 60 3/8 x 54 1/4 in. (153.4 x 137.8 cm); painted surface 59 3/8 x 53 1/8 in. (150.8 x 134.9 cm),"Rogers Fund, 1957",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436039,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -28.221,true,true,436037,European Paintings,Painting,The Judgment of Paris,,,,,,Artist,,Lucas Cranach the Elder,"German, Kronach 1472–1553 Weimar",,"Cranach, Lucas, the Elder",German,1472,1553,ca. 1528,1523,1533,Oil on beech,40 1/8 x 28in. (101.9 x 71.1cm),"Rogers Fund, 1928",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436037,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -71.128,false,true,436046,European Paintings,Painting,"Johann I (1468–1532), the Constant, Elector of Saxony",,,,,,Artist,Workshop of,Lucas Cranach the Elder,"German, Kronach 1472–1553 Weimar",,"Cranach, Lucas, the Elder",German,1472,1553,1532–33,1532,1533,"Oil on canvas, transferred from wood, with letterpress-printed paper labels",8 1/4 x 5 7/8 in. (21 x 14.9 cm),"Purchase, 1871",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436046,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2006.469,false,true,439081,European Paintings,Painting,Saint Maurice,,,,,,Artist,,Lucas Cranach the Elder and Workshop,"German, Kronach 1472–1553 Weimar",,"Cranach, Lucas, the Elder, and Workshop",German,1472,1553,ca. 1520–25,1520,1525,Oil on linden,54 x 15 1/2 in. (137.2 x 39.4 cm),"Bequest of Eva F. Kollsman, 2005",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/439081,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -46.179.1,false,true,436043,European Paintings,Painting,"Friedrich III (1463–1525), the Wise, Elector of Saxony",,,,,,Artist,,Lucas Cranach the Elder and Workshop,"German, Kronach 1472–1553 Weimar",,"Cranach, Lucas, the Elder, and Workshop",German,1472,1553,1533,1533,1533,"Oil on beech, with letterpress-printed paper labels",8 x 5 5/8 in. (20.3 x 14.3 cm),"Gift of Robert Lehman, 1946",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436043,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -46.179.2,false,true,436045,European Paintings,Painting,"Johann I (1468–1532), the Constant, Elector of Saxony",,,,,,Artist,,Lucas Cranach the Elder and Workshop,"German, Kronach 1472–1553 Weimar",,"Cranach, Lucas, the Elder, and Workshop",German,1472,1553,1532–33,1532,1533,"Oil on beech, with letterpress-printed paper labels",8 x 5 5/8 in. (20.3 x 14.3 cm),"Gift of Robert Lehman, 1946",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436045,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -55.220.2,false,true,436047,European Paintings,Painting,Martin Luther (1483–1546),,,,,,Artist,Workshop of,Lucas Cranach the Elder,"German, Kronach 1472–1553 Weimar",,"Cranach, Lucas, the Elder",German,1472,1553,probably 1532,1532,1532,Oil on wood,13 1/8 x 9 1/8 in. (33.3 x 23.2 cm),"Gift of Robert Lehman, 1955",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436047,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1981.57.1,false,true,436033,European Paintings,Painting,Lukas Spielhausen,,,,,,Artist,,Lucas Cranach the Elder,"German, Kronach 1472–1553 Weimar",,"Cranach, Lucas, the Elder",German,1472,1553,1532,1532,1532,Oil and gold on beech,20 x 14 3/8 in. (50.8 x 36.5 cm),"Bequest of Gula V. Hirschland, 1980",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436033,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -29.100.24,false,true,436040,European Paintings,Painting,Portrait of a Man with a Rosary,,,,,,Artist,,Lucas Cranach the Elder,"German, Kronach 1472–1553 Weimar",,"Cranach, Lucas, the Elder",German,1472,1553,ca. 1508,1503,1513,Oil on oak,18 3/4 x 13 7/8 in. (47.6 x 35.2cm),"H. O. Havemeyer Collection, Bequest of Mrs. H. O. Havemeyer, 1929",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436040,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1982.60.48,false,true,436042,European Paintings,Painting,Venus and Cupid,,,,,,Artist,,Lucas Cranach the Elder,"German, Kronach 1472–1553 Weimar",,"Cranach, Lucas, the Elder",German,1472,1553,ca. 1525–27,1525,1527,Oil on wood,Diameter 4 3/4 in. (12.1 cm),"The Jack and Belle Linsky Collection, 1982",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436042,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.201.11,false,true,436041,European Paintings,Painting,Samson and Delilah,,,,,,Artist,,Lucas Cranach the Elder,"German, Kronach 1472–1553 Weimar",,"Cranach, Lucas, the Elder",German,1472,1553,ca. 1528–30,1528,1530,Oil on beech,22 1/2 x 14 7/8 in. (57.2 x 37.8cm),"Bequest of Joan Whitney Payson, 1975",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436041,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -39.65.8,false,true,436823,European Paintings,Painting,"Charles Beauclerk (1670–1726), Duke of St. Albans",,,,,,Artist,,Sir Godfrey Kneller,"German, Lübeck 1646–1723 London",,"Kneller, Godfrey, Sir",German,1646,1723,ca. 1690–95,1690,1695,Oil on canvas,49 7/8 x 40 1/2 in. (126.7 x 102.9 cm),"Bequest of Jacob Ruppert, 1939",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436823,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -96.30.6,false,true,436824,European Paintings,Painting,"Lady Mary Berkeley, Wife of Thomas Chambers",,,,,,Artist,,Sir Godfrey Kneller,"German, Lübeck 1646–1723 London",,"Kneller, Godfrey, Sir",German,1646,1723,ca. 1700,1695,1705,Oil on canvas,29 x 25 in. (73.7 x 63.5 cm),"Gift of George A. Hearn, 1896",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436824,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -85.9,false,true,436199,European Paintings,Painting,Christ Healing the Sick,,,,,,Artist,,Christian Wilhelm Ernst Dietrich,"German, Weimar 1712–1774 Dresden",,"Dietrich, Christian Wilhelm Ernst",German,1712,1774,1742,1742,1742,Oil on canvas,35 1/8 x 41 3/8 in. (89.2 x 105.1 cm),"Gift of William H. Webb, 1885",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436199,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -71.162,false,true,436200,European Paintings,Painting,The Adoration of the Shepherds,,,,,,Artist,,Christian Wilhelm Ernst Dietrich,"German, Weimar 1712–1774 Dresden",,"Dietrich, Christian Wilhelm Ernst",German,1712,1774,1760s,1760,1769,Oil on canvas,21 5/8 x 28 3/4 in. (54.9 x 73 cm),"Purchase, 1871",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436200,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.192,false,true,439122,European Paintings,Painting,Gothic Windows in the Ruins of the Monastery at Oybin,,,,,,Artist,,Carl Gustav Carus,"German, Leipzig 1789–1869 Dresden",,"Carus, Carl Gustav",German,1789,1869,ca. 1828,1823,1833,Oil on canvas,17 x 13 1/4 in. (43.2 x 33.7 cm) Frame: 21 3/4 x 17 7/8 x 2 1/4 in. (55.2 x 45.4 x 5.7 cm),"Purchase, 2005 Benefit Fund, and Anna-Maria and Stephen Kellen Foundation and Eugene V. Thaw Gifts, 2007",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/439122,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.164.1,false,true,438947,European Paintings,Painting,An Overgrown Mineshaft,,,,,,Artist,,Carl Gustav Carus,"German, Leipzig 1789–1869 Dresden",,"Carus, Carl Gustav",German,1789,1869,ca. 1824,1819,1829,"Oil on paper, laid down on cardboard",11 1/4 x 8 1/4 in. (28.6 x 21 cm),"Gift of Eugene V. Thaw, 2007",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/438947,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -16.16,false,true,436609,European Paintings,Painting,In the Studio,,,,,,Artist,,Hugo von Habermann,"German, Dillingen 1849–1929 Munich",,"Habermann, Hugo von",German,1849,1929,1885,1885,1885,Oil on canvas,39 5/8 x 37 3/4 in. (100.6 x 95.9 cm),"Reisinger Fund, 1916",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436609,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -56.190,false,true,437648,European Paintings,Painting,Sir James Dashwood (1715–1779),,,,,,Artist,,Enoch Seeman the Younger,"German, Danzig ca. 1690–1744 London",,"Seeman, Enoch, the Younger",German,1685,1744,1737,1737,1737,Oil on canvas,96 x 60 1/4 in. (243.8 x 153 cm),"Victor Wilbour Memorial Fund, 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437648,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2008.6,false,true,439346,European Paintings,Painting,At the Edge of the Forest,,,,,,Artist,,August Heinrich,"German, Dresden 1794–1822 Innsbruck",,"Heinrich, August",German,1794,1822,ca. 1820,1815,1825,Oil on canvas,10 3/4 x 12 3/4 in. (27.3 x 32.4 cm),"Catharine Lorillard Wolfe Collection, Wolfe Fund; and Wolfe Fund and Gift of Frederick Loeser, by exchange, 2008",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/439346,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2010.279,false,true,440888,European Paintings,Painting,Window,,,,,,Artist,,Anton Dieffenbach,"German, Wiesbaden 1831–1904 Hohwald",,"Dieffenbach, Anton",German,1831,1904,1856,1856,1856,"Oil on paper, laid down on canvas",14 3/8 x 9 7/8 in. (36.5 x 25.1 cm),"Purchase, Gifts of Mr. and Mrs. Charles Zadok and William Schaus and Bequest of Mary Jane Dastich, by exchange, 2010",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/440888,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -17.120.203,false,true,437860,European Paintings,Painting,Going Home,,,,,,Artist,,Fritz von Uhde,"German, Wolkenburg 1848–1911 Munich",,"von Uhde, Fritz",German,1848,1911,ca. 1889,1884,1894,Oil on wood,30 7/8 x 39 1/4 in. (78.4 x 99.7 cm),"Mr. and Mrs. Isaac D. Fletcher Collection, Bequest of Isaac D. Fletcher, 1917",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437860,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -06.1038,false,true,436657,European Paintings,Painting,"Benedikt von Hertenstein (born about 1495, died 1522)",,,,,,Artist,,Hans Holbein the Younger,"German, Augsburg 1497/98–1543 London",,"Holbein, Hans, the Younger",German,1497,1543,1517,1517,1517,"Oil and gold on paper, laid down on wood",Overall 20 1/2 x 15 in. (52.4 x 38.1 cm); painted surface 20 3/8 x 14 5/8 in. (51.4 x 37.1 cm),"Rogers Fund, aided by subscribers, 1906",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436657,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -49.7.28,false,true,436668,European Paintings,Painting,Portrait of a Man (Sir Ralph Sadler?),,,,,,Artist,Workshop of,Hans Holbein the Younger,"German, Augsburg 1497/98–1543 London",,"Holbein, Hans, the Younger",German,1497,1543,1535,1535,1535,Oil and gold on oak,Diameter 12 in. (30.5 cm),"The Jules Bache Collection, 1949",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436668,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -49.7.29,false,true,436659,European Paintings,Painting,Derick Berck of Cologne,,,,,,Artist,,Hans Holbein the Younger,"German, Augsburg 1497/98–1543 London",,"Holbein, Hans, the Younger",German,1497,1543,1536,1536,1536,"Oil on canvas, transferred from wood",21 x 16 3/4 in. (53.3 x 42.5 cm),"The Jules Bache Collection, 1949",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436659,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -49.7.30,false,true,436667,European Paintings,Painting,Portrait of a Young Woman,,,,,,Artist,Workshop of,Hans Holbein the Younger,"German, Augsburg 1497/98–1543 London",,"Holbein, Hans, the Younger",German,1497,1543,ca. 1540–45,1540,1545,Oil and gold on oak,11 1/8 x 9 1/8 in. (28.3 x 23.2 cm),"The Jules Bache Collection, 1949",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436667,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -50.135.4,true,true,436658,European Paintings,Painting,Hermann von Wedigh III (died 1560),,,,,,Artist,,Hans Holbein the Younger,"German, Augsburg 1497/98–1543 London",,"Holbein, Hans, the Younger",German,1497,1543,1532,1532,1532,Oil and gold on oak,"16 5/8 x 12 3/4 in. (42.2 x 32.4 cm), with added strip of 1/2 in. (1.3 cm) at bottom","Bequest of Edward S. Harkness, 1940",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436658,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.40.637,false,true,436665,European Paintings,Painting,"Lady Lee (Margaret Wyatt, born about 1509)",,,,,,Artist,Workshop of,Hans Holbein the Younger,"German, Augsburg 1497/98–1543 London",,"Holbein, Hans, the Younger",German,1497,1543,early 1540s,1540,1543,Oil and gold on oak,17 3/8 × 13 3/8 in. (44.1 × 34 cm),"Bequest of Benjamin Altman, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436665,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -50.145.24,false,true,436660,European Paintings,Painting,Portrait of a Man in a Red Cap,,,,,,Artist,,Hans Holbein the Younger,"German, Augsburg 1497/98–1543 London",,"Holbein, Hans, the Younger",German,1497,1543,1532–35,1532,1535,"Oil and gold on parchment, laid down on linden","Overall, with engaged frame, diameter 5 in. (12.7 cm); painted surface diameter 3 3/4 in. (9.5 cm)","Bequest of Mary Stillman Harkness, 1950",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436660,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2000.51,true,true,438417,European Paintings,Painting,Two Men Contemplating the Moon,,,,,,Artist,,Caspar David Friedrich,"German, Greifswald 1774–1840 Dresden",,"Friedrich, Caspar David",German,1774,1840,ca. 1825–30,1825,1830,Oil on canvas,13 3/4 x 17 1/4 in. (34.9 x 43.8 cm),"Wrightsman Fund, 2000",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/438417,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -87.15.23,false,true,437975,European Paintings,Painting,Sunset after a Storm on the Coast of Sicily,,,,,,Artist,,Andreas Achenbach,"German, Kassel 1815–1910 Düsseldorf",,"Achenbach, Andreas",German,1815,1910,1853,1853,1853,Oil on canvas,32 3/4 x 42 1/4 in. (83.2 x 107.3 cm),"Catharine Lorillard Wolfe Collection, Bequest of Catharine Lorillard Wolfe, 1887",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437975,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -71.34,false,true,437759,European Paintings,Painting,Portrait of a Woman,,,,,,Artist,,Bernhard Strigel,"German, Memmingen 1460–1528 Memmingen",,"Strigel, Bernhard",German,1460,1528,ca. 1510–15,1510,1515,Oil on linden,15 1/8 x 10 1/2 in. (38.4 x 26.7 cm),"Purchase, 1871",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437759,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -17.190.5,false,true,436245,European Paintings,Painting,Virgin and Child,,,,,,Artist,,Albrecht Dürer,"German, Nuremberg 1471–1528 Nuremberg",,"Dürer, Albrecht",German,1471,1528,1516,1516,1516,Oil on spruce,11 x 7 3/8 in. (27.9 x 18.7 cm); set in panel 11 x 8 1/4 in. (27.9 x 22.2 cm),"Gift of J. Pierpont Morgan, 1917",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436245,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.40.633,true,true,436244,European Paintings,Painting,Virgin and Child with Saint Anne,,,,,,Artist,,Albrecht Dürer,"German, Nuremberg 1471–1528 Nuremberg",,"Dürer, Albrecht",German,1471,1528,probably 1519,1519,1519,Oil on linden,23 5/8 x 19 5/8 in. (60 x 49.8 cm),"Bequest of Benjamin Altman, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436244,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -32.100.64,false,true,436243,European Paintings,Painting,Salvator Mundi,,,,,,Artist,,Albrecht Dürer,"German, Nuremberg 1471–1528 Nuremberg",,"Dürer, Albrecht",German,1471,1528,ca. 1505,1500,1510,Oil on linden,22 7/8 x 18 1/2in. (58.1 x 47cm),"The Friedsam Collection, Bequest of Michael Friedsam, 1931",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436243,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -12.194,false,true,435635,European Paintings,Painting,Chancellor Leonhard von Eck (1480–1550),,,,,,Artist,,Barthel Beham,"German, Nuremberg ca. 1502–1540 Italy",,"Beham, Barthel",German,1502,1540,1527,1527,1527,Oil on spruce,22 1/8 x 14 7/8 in. (56.2 x 37.8 cm),"John Stewart Kennedy Fund, 1912",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435635,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -16.15,false,true,437848,European Paintings,Painting,Landscape,,,,,,Artist,,Wilhelm Trübner,"German, Heidelberg 1851–1917 Karlsruhe",,"Trübner, Wilhelm",German,1851,1917,1910,1910,1910,Oil on canvas,29 7/8 x 24 1/4 in. (75.9 x 61.5 cm),"Reisinger Fund, 1916",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437848,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -12.115,false,true,435585,European Paintings,Painting,Portrait of a Man and His Wife (Lorenz Kraffter and Honesta Merz?),,,,,,Artist,,Ulrich Apt the Elder,"German, Augsburg ca. 1460–1532 Augsburg",,"Apt, Ulrich, the Elder",German,1460,1532,1512,1512,1512,Oil on linden,13 x 24 7/8 in. (33 x 63.2 cm),"Rogers Fund, 1912",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435585,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -89.15.20,false,true,435776,European Paintings,Painting,Joseph Interpreting the Dreams of Pharaoh,,,,,,Artist,Attributed to,Jörg Breu the Younger,"German, Augsburg ca. 1510–1547 Augsburg",,"Breu, Jörg, the Younger",German,1510,1547,ca. 1534–47,1534,1547,Distemper on linen,67 5/8 x 57 1/4 in. (171.8 x 145.4 cm),"Marquand Collection, Gift of Henry G. Marquand, 1889",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435776,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.164.8,false,true,438953,European Paintings,Painting,The Cemetery at Pronoia near Nauplia,,,,,,Artist,,Carl Rottmann,"German, Handschuhsheim 1797–1850 Munich",,"Rottmann, Carl",German,1797,1850,ca. 1841–47,1836,1847,Oil on canvas,10 x 12 in. (25.4 x 30.5 cm),"Gift of Eugene V. Thaw, 2007",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/438953,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -39.65.4,false,true,436877,European Paintings,Painting,Prince Regent Luitpold of Bavaria (1821–1912),,,,,,Artist,,Franz von Lenbach,"German, Schrobenhausen 1836–1904 Munich",,"Lenbach, Franz von",German,1836,1904,1902,1902,1902,Oil on board,30 x 24 1/4 in. (76.2 x 61.5 cm),"Bequest of Jacob Ruppert, 1939",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436877,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -25.110.46,false,true,436876,European Paintings,Painting,"Marion Lenbach (1892–1947), the Artist's Daughter",,,,,,Artist,,Franz von Lenbach,"German, Schrobenhausen 1836–1904 Munich",,"Lenbach, Franz von",German,1836,1904,1900,1900,1900,Oil on canvas,58 7/8 x 41 1/2 in. (149.5 x 105.4 cm),"Bequest of Collis P. Huntington, 1900",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436876,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -43.161,false,true,436997,European Paintings,"Painting, part of the wing of an altarpiece",The Crucifixion,,,,,,Artist,,Master of the Berswordt Altar,"German, Westphalian, active ca. 1400–35",,Master of the Berswordt Altar,German,1400,1435,ca. 1400,1395,1405,"Oil, egg(?), and gold on plywood, transferred from wood",23 1/2 x 17 in. (59.7 x 43.2 cm),"Rogers Fund, 1943",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436997,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2001.216.2,false,true,438466,European Paintings,Painting,The Flagellation,,,,,,Artist,,Master of the Berswordt Altar,"German, Westphalian, active ca. 1400–35",,Master of the Berswordt Altar,German,1400,1435,ca. 1400,1395,1405,"Oil, egg(?), and gold on plywood, transferred from wood",22 3/4 x 16 7/8 in. (57.8 x 42.9 cm),"Bequest of Hertha Katz, 2000",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/438466,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1982.60.35,false,true,436034,European Paintings,Painting,Christ and the Adulteress,,,,,,Artist,,Lucas Cranach the Younger and Workshop,"German, Wittenberg 1515–1586 Wittenberg",,"Cranach, Lucas, the Younger, and Workshop",German,1515,1586,ca. 1545–50,1545,1550,Oil on beech,6 1/4 x 8 1/2 in. (15.9 x 21.6 cm),"The Jack and Belle Linsky Collection, 1982",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436034,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1982.60.36,false,true,436035,European Paintings,Painting,Christ Blessing the Children,,,,,,Artist,,Lucas Cranach the Younger and Workshop,"German, Wittenberg 1515–1586 Wittenberg",,"Cranach, Lucas, the Younger, and Workshop",German,1515,1586,ca. 1545–50,1545,1550,Oil on beech,6 1/2 x 8 3/4 in. (16.5 x 22.2 cm),"The Jack and Belle Linsky Collection, 1982",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436035,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2006.566,false,true,439118,European Paintings,Painting,The Reverend Philip Cocks (1735–1797),,,,,,Artist,,Johan Joseph Zoffany,"German, near Frankfurt 1733–1810 London",,"Zoffany, Johan Joseph",German,1733,1810,late 1760s,1767,1769,Oil on canvas,35 1/2 x 27 1/4 in. (90.2 x 69.2 cm),"Gift of Mrs. Henry A. Grunwald, 2006",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/439118,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -21.84,false,true,436835,European Paintings,Painting,The Ascension of Christ,,,,,,Artist,,Hans Süss von Kulmbach,"German, Kulmbach ca. 1480–1522 Nuremberg",,"Kulmbach, Hans Süss von",German,1480,1522,1513,1513,1513,Oil on fir,Overall 24 1/4 x 15 in. (61.5 x 38.1 cm); painted surface 24 1/4 x 14 1/8 in. (61.5 x 35.9 cm),"Rogers Fund, 1921",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436835,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -17.190.21,false,true,436834,European Paintings,Painting,Portrait of a Young Man; (reverse) Girl Making a Garland,,,,,,Artist,,Hans Süss von Kulmbach,"German, Kulmbach ca. 1480–1522 Nuremberg",,"Kulmbach, Hans Süss von",German,1480,1522,ca. 1508,1503,1513,Oil on poplar,7 x 5 1/2 in. (17.8 x 14 cm),"Gift of J. Pierpont Morgan, 1917",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436834,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2008.7,false,true,439333,European Paintings,Painting,Wanderer in the Storm,,,,,,Artist,,Julius von Leypold,"German, Dresden 1806–1874 Niederlößnitz",,"Leypold, Julius von",German,1806,1874,1835,1835,1835,Oil on canvas,16 3/4 x 22 1/4 in. (42.5 x 56.5 cm),"Catharine Lorillard Wolfe Collection, Wolfe Fund, 2008",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/439333,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -01.21,false,true,437944,European Paintings,Painting,Florinda,,,,,,Artist,,Franz Xaver Winterhalter,"German, Menzenschwand 1805–1873 Frankfurt",,"Winterhalter, Franz Xaver",German,1805,1873,1853,1853,1853,Oil on canvas,70 1/4 x 96 3/4 in. (178.4 x 245.7 cm),"Bequest of William H. Webb, 1899",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437944,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1978.403,false,true,437942,European Paintings,Painting,"The Empress Eugénie (Eugénie de Montijo, 1826–1920, Condesa de Teba)",,,,,,Artist,,Franz Xaver Winterhalter,"German, Menzenschwand 1805–1873 Frankfurt",,"Winterhalter, Franz Xaver",German,1805,1873,1854,1854,1854,Oil on canvas,36 1/2 x 29 in. (92.7 x 73.7 cm),"Purchase, Mr. and Mrs. Claus von Bülow Gift, 1978",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437942,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -67.187.119,false,true,437943,European Paintings,Painting,"Countess Alexander Nikolaevitch Lamsdorff (Maria Ivanovna Beck, 1835–1866)",,,,,,Artist,,Franz Xaver Winterhalter,"German, Menzenschwand 1805–1873 Frankfurt",,"Winterhalter, Franz Xaver",German,1805,1873,1859,1859,1859,Oil on canvas,57 1/4 x 45 1/4 in. (145.4 x 114.9 cm),"Bequest of Miss Adelaide Milton de Groot (1876–1967), 1967",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437943,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -89.15.28,false,true,437237,European Paintings,Painting,Portrait of a Woman,,,,,,Artist,,Jürgen Ovens,"German, Tönning 1623–1678 Friedrichstadt",,"Ovens, Jürgen",German,1623,1678,1650,1650,1650,Oil on canvas,49 3/8 x 37 3/4 in. (125.4 x 95.9 cm),"Marquand Collection, Gift of Henry G. Marquand, 1889",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437237,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -87.2,false,true,437295,European Paintings,Painting,Thusnelda at the Triumphal Entry of Germanicus into Rome,,,,,,Artist,,Karl Theodor von Piloty,"German, Munich 1826–1886 Ambach bei Munich",,"Piloty, Karl Theodor von",German,1826,1886,ca. 1875,1870,1880,Oil on canvas,53 x 77 1/4 in. (134.6 x 196.2 cm),"Gift of Horace Russell, 1887",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437295,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1999.121,false,true,438390,European Paintings,Painting,Nymph and Shepherd,,,,,,Artist,,Johann Liss,"German, Oldenburg ca. 1595/1600–1631 Verona",,"Liss, Johann",German,1595,1631,ca. 1625,1620,1630,Oil on canvas,41 1/8 x 37 3/8 in. (104.5 x 94.9 cm),"Purchase, Lila Acheson Wallace Gift, Victor Wilbour Memorial Fund, The Alfred N. Punnett Endowment Fund, and Marquand and Curtis Funds, and Bequests of Theodore M. Davis and Helen R. Bleibtreu, by exchange, 1999",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/438390,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -21.152.1,false,true,436305,European Paintings,Painting,Still Life,,,,,,Artist,,Georg Flegel,"German, Olomouc (Olmütz) 1566–1638 Frankfurt",,"Flegel, Georg",German,1566,1638,probably ca. 1625–30,1625,1630,Oil on wood,10 5/8 x 13 3/8 in. (27 x 34 cm),"Gift of Dr. W. Bopp, 1921",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436305,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -48.141,false,true,437067,European Paintings,Painting,Johann Joachim Winckelmann (1717–1768),,,,,,Artist,,Anton Raphael Mengs,"German, Ústi nad Labem (Aussig) 1728–1779 Rome",,"Mengs, Anton Raphael",German,1728,1779,ca. 1777,1772,1782,Oil on canvas,25 x 19 3/8 in. (63.5 x 49.2 cm),"Harris Brisbane Dick Fund, 1948",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437067,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2010.445,false,true,441115,European Paintings,Painting,Self-Portrait,,,,,,Artist,,Anton Raphael Mengs,"German, Ústi nad Labem (Aussig) 1728–1779 Rome",,"Mengs, Anton Raphael",German,1728,1779,1776,1776,1776,Oil on canvas,35 1/2 x 25 7/8 in. (90 x 65.5 cm),"Harris Brisbane Dick Fund, 2010",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/441115,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -09.48,false,true,437787,European Paintings,Painting,At Lake Garda,,,,,,Artist,,Hans Thoma,"German, Bernau im Schwarzwald 1839–1924 Karlsruhe",,"Thoma, Hans",German,1839,1924,1907,1907,1907,Oil on millboard,33 x 26 3/4 in. (83.8 x 67.9 cm),"Gift of Hugo Reisinger, 1909",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437787,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -14.40.630,false,true,436942,European Paintings,Painting,Ulrich Fugger the Younger (1490–1525),,,,,,Artist,,Hans Maler,"German, Ulm, born ca. 1480, died ca. 1526–29 Schwaz (?)",,"Maler, Hans",German,1475,1529,1525,1525,1525,Oil on linden,15 7/8 x 12 3/4 in. (40.3 x 32.4 cm),"Bequest of Benjamin Altman, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436942,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -32.100.33,false,true,436941,European Paintings,Painting,Sebastian Andorfer (1469–1537),,,,,,Artist,,Hans Maler,"German, Ulm, born ca. 1480, died ca. 1526–29 Schwaz (?)",,"Maler, Hans",German,1475,1529,1517,1517,1517,Oil on Swiss stone pine,17 x 14 1/8 in. (43.2 x 35.9 cm),"The Friedsam Collection, Bequest of Michael Friedsam, 1931",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436941,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1997.157,false,true,437983,European Paintings,Painting,A Roman Landscape with Figures,,,,,,Artist,,Goffredo Wals,"German, Cologne, born ca. 1590–95, died 1638–40 Calabria",,"Wals, Goffredo",German,1590,1640,probably 1630s,1630,1639,Oil on copper,Diameter 16 in. (40.6 cm),"Wrightsman Fund, 1997",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437983,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1983.451,true,true,435600,European Paintings,Painting,Saint John on Patmos,,,,,,Artist,,Hans Baldung (called Hans Baldung Grien),"German, Schwäbisch Gmünd (?) 1484/85–1545 Strasbourg (Strassburg)",,"Baldung, Hans (called Hans Baldung Grien)",German,1484,1545,ca. 1511,1506,1516,"Oil, gold, and white metal on spruce",Overall 35 1/4 x 30 1/4 in. (89.5 x 76.8 cm); painted surface 34 3/8 x 29 3/4 in. (87.3 x 75.6 cm),"Purchase, Rogers and Fletcher Funds; The Vincent Astor Foundation, The Dillon Fund, The Charles Engelhard Foundation, Lawrence A. Fleischman, Mrs. Henry J. Heinz II, The Willard T. C. Johnson Foundation Inc., Reliance Group Holdings Inc., Baron H. H. Thyssen-Bornemisza, and Mr. and Mrs. Charles Wrightsman Gifts; Joseph Pulitzer Bequest; special funds; and other gifts and bequests, by exchange, 1983",,,,,,,,,,,,Paintings,"The following credit line may be used for photographs, reproductions, etc.: Contributions from various donors supplemented by Museum purchase funds, 1983",http://www.metmuseum.org/art/collection/search/435600,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -42.53.3,false,true,436640,European Paintings,"Painting, miniature",Lola Montez (1818–1861),,,,,,Artist,Attributed to,Josef Heigel,"German, 1780–1837",,"Heigel, Josef",German,1780,1837,,1800,1837,Ivory,"Oval, 2 1/2 x 2 1/8 in. (65 x 54 mm)","Gift of Helen O. Brice, 1942",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/436640,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -62.122.109,false,true,436666,European Paintings,"Painting, miniature","Portrait of a Man, Said to Be Arnold Franz",,,,,,Artist,Imitator of,Hans Holbein the Younger,17th or early 18th century,,"Holbein, Hans, the Younger",German,1497,1543,,1600,1729,Vellum laid on card,Diameter 2 1/8 in. (53 mm),"Bequest of Millie Bruhl Fredrick, 1962",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/436666,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -62.122.154,false,true,437752,European Paintings,"Painting, miniature",Portrait of a Man,,,,,,Artist,Attributed to,Theodor Friedrich Stein,"German, active ca. 1750–88",,"Stein, Theodor Friedrich",German,1750,1788,,1750,1788,Ivory,1 5/8 x 2 1/4 in. (41 x 62 mm),"Bequest of Millie Bruhl Fredrick, 1962",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/437752,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -95.14.66,false,true,437224,European Paintings,"Painting, miniature",Portrait of a Man,,,,,,Artist,,Johann Esaias Nilson,"German, Augsburg 1721–1788 Augsburg",,"Nilson, Johann Esaias",German,1721,1788,,1741,1788,Ivory,"Oval, 1 7/8 x 1 1/2 in. (49 x 37 mm)","The Moses Lazarus Collection, Gift of Josephine and Sarah Lazarus, in memory of their father, 1888–95",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/437224,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -48.187.495,false,true,437962,European Paintings,"Painting, miniature",Portrait of a Young Man,,,,,,Artist,,Christian Friedrich Zincke,"German, Dresden 1683/85–1767 London",,"Zincke, Christian Friedrich",German,1683,1767,,1703,1767,Enamel,"Oval, 1 3/4 x 1 3/8 in. (45 x 36 mm)","Bequest of Catherine D. Wentworth, 1948",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/437962,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -62.122.114,false,true,437963,European Paintings,"Painting, miniature",Portrait of a Woman,,,,,,Artist,Attributed to,Christian Friedrich Zincke,"German, Dresden 1683/85–1767 London",,"Zincke, Christian Friedrich",German,1683,1767,,1703,1767,Enamel,"Oval, 1 7/8 x 1 1/2 in. (46 x 38 mm)","Bequest of Millie Bruhl Fredrick, 1962",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/437963,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -62.122.115,false,true,437959,European Paintings,"Painting, miniature",Portrait of a Man,,,,,,Artist,,Christian Friedrich Zincke,"German, Dresden 1683/85–1767 London",,"Zincke, Christian Friedrich",German,1683,1767,,1703,1767,Enamel,"Oval, 1 7/8 x 1 1/2 in. (47 x 38 mm)","Bequest of Millie Bruhl Fredrick, 1962",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/437959,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -51.160,false,true,437068,European Paintings,"Painting, miniature",The Vision of Saint Anthony of Padua,,,,,,Artist,,Anton Raphael Mengs,"German, Ústi nad Labem (Aussig) 1728–1779 Rome",,"Mengs, Anton Raphael",German,1728,1779,,1758,1758,Ivory,5 1/8 x 3 3/4 in. (130 x 96 mm),"Gift of Harry G. Friedman, 1951",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/437068,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2006.151.1,false,true,438844,European Paintings,Painting,"Portrait of a Woman, Said to Be Caritas Pirckheimer (1467–1532)",,,,,,Artist,Imitator of,Albrecht Dürer,20th century,,"Dürer, Albrecht",German,1471,1528,,1900,1999,Oil on linen,18 1/2 x 14 1/2 in. (47 x 36.8 cm),"Gift of Julie and Lawrence Salander, 2006",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/438844,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -22.96,false,true,435764,European Paintings,Painting,Virgin and Child,,,,,,Artist,Workshop or Circle of,Hans Traut,"German, ca. 1500",,"Traut, Hans",German,1477,1516,,1495,1505,"Oil, gold, and silver on linden",15 5/8 x 12 1/8 in. (39.7 x 30.8 cm),"Purchase, Joseph Pulitzer Bequest, 1922",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435764,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -07.245.1,false,true,437494,European Paintings,Painting,"Friedrich I (1460–1536), Margrave of Brandenburg-Ansbach",,,,,,Artist,Attributed to,Franz Wolfgang Rohrich,"German, 1787–1834",,"Rohrich, Franz Wolfgang",German,1787,1834,,1807,1834,Oil on canvas,30 1/4 x 22 3/8 in. (76.8 x 56.8 cm),"Gift of Laura Wolcott Lowndes, in memory of her father, Lucius Tuckerman, 1907",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437494,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -07.245.2,false,true,437495,European Paintings,Painting,Sophia (1464–1512) of Poland,,,,,,Artist,,Franz Wolfgang Rohrich,"German, 1787–1834",,"Rohrich, Franz Wolfgang",German,1787,1834,,1807,1834,Oil on canvas,30 1/4 x 22 1/4 in. (76.8 x 56.5 cm),"Gift of Laura Wolcott Lowndes, in memory of her father, Lucius Tuckerman, 1907",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437495,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -22.174,false,true,436591,European Paintings,Painting,Portrait of a Woman,,,,,,Artist,,Johann Nikolaus Grooth,"German, 1723?–1797",,"Grooth, Johann Nikolaus",German,1723,1797,,1743,1797,Oil on canvas,32 x 25 5/8 in. (81.3 x 65.1 cm),"Gift of Édouard Jonas, 1922",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436591,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -20.155.4,false,true,436664,European Paintings,Painting,"Lady Guildford (Mary Wotton, born 1500)",,,,,,Artist,Copy after,Hans Holbein the Younger,"British, 16th century",,"Holbein, Hans, the Younger",German,1497,1543,,1527,1527,Oil and gold on oak,32 1/8 x 26 1/8 in. (81.6 x 66.4 cm),"Bequest of William K. Vanderbilt, 1920",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436664,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1982.60.22,false,true,436781,European Paintings,Painting,The Adoration of the Christ Child,,,,,,Artist,Follower of,Jan Joest of Kalkar,"Netherlandish, active ca. 1515",,"Joest of Kalkar, Jan",German,1510,1520,,1510,1520,Oil on wood,Overall 41 x 28 1/4 in. (104.1 x 71.8 cm); painted surface 41 x 27 5/8 in. (104.1 x 70.2 cm),"The Jack and Belle Linsky Collection, 1982",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436781,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -87.15.110,false,true,436815,European Paintings,Painting,Crusaders before Jerusalem,,,,,,Artist,,Wilhelm von Kaulbach,"German, Arolsen 1804–1874 Munich",,"Kaulbach, Wilhelm von",German,1804,1874,,1825,1874,Oil on canvas,61 5/8 x 74 1/2 in. (156.5 x 189.2 cm),"Catharine Lorillard Wolfe Collection, Bequest of Catharine Lorillard Wolfe, 1887",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436815,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -71.142,false,true,436201,European Paintings,Painting,"Surprised, or Infidelity Found Out",,,,,,Artist,,Christian Wilhelm Ernst Dietrich,"German, Weimar 1712–1774 Dresden",,"Dietrich, Christian Wilhelm Ernst",German,1712,1774,,1732,1774,Oil on canvas,28 3/4 x 28 5/8 in. (73 x 72.7 cm),"Purchase, 1871",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436201,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -94.24.2,false,true,437642,European Paintings,Painting,Battle Scene: Arabs Making a Detour,,,,,,Artist,,Adolf Schreyer,"German, Frankfurt 1828–1899 Kronberg",,"Schreyer, Adolf",German,1828,1899,,1848,1899,Oil on canvas,59 3/8 x 99 1/2 in. (150.8 x 252.7 cm),"Gift of John Wolfe, 1893",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437642,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -87.15.127,false,true,437641,European Paintings,Painting,Arabs on the March,,,,,,Artist,,Adolf Schreyer,"German, Frankfurt 1828–1899 Kronberg",,"Schreyer, Adolf",German,1828,1899,,1848,1899,Oil on canvas,22 5/8 x 37 3/4 in. (57.5 x 95.9 cm),"Catharine Lorillard Wolfe Collection, Bequest of Catharine Lorillard Wolfe, 1887",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437641,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1982.60.38,false,true,435804,European Paintings,Painting,Katharina Merian,,,,,,Artist,Attributed to,Hans Brosamer,"German, active by 1536, probably died 1552",,"Brosamer, Hans",German,1536,1552,,1536,1552,"Oil, gold, and white metal on linden",Overall 18 1/4 x 13 1/8 in. (46.4 x 33.3 cm); painted surface 17 5/8 x 13 1/8 in. (44.8 x 33.3 cm),"The Jack and Belle Linsky Collection, 1982",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435804,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1982.60.34ab,false,true,437639,European Paintings,Painting,Christ before Pilate; The Resurrection,,,,,,Artist,,Ludwig Schongauer,"German, Colmar ca. 1440/55–1493/94 Colmar",,"Schongauer, Ludwig",German,1435,1494,,1479,1494,Oil on fir,"(a) overall 15 1/8 x 8 1/4 in. (38.4 x 21 cm), painted surface 14 3/8 x 7 3/4 in. (36.5 x 19.7 cm); (b) overall 15 1/8 x 8 1/4 in. (38.4 x 21 cm), painted surface 14 1/2 x 7 3/4 in. (36.8 x 19.7 cm)","The Jack and Belle Linsky Collection, 1982",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437639,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -12.75,false,true,436286,European Paintings,Painting,Portrait of a Man with a Moor's Head on His Signet Ring,,,,,,Artist,,Conrad Faber von Creuznach,"German, Kreuznach, active by 1524–died 1552/53 Frankfurt",,"Faber von Creuznach, Conrad",German,1524,1553,,1524,1553,"Oil, gold, and white metal on linden",20 7/8 x 14 1/8 in. (53 x 35.9 cm),"John Stewart Kennedy Fund, 1912",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436286,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -62.122.33,false,true,435999,European Paintings,"Painting, miniature",Portrait of a Woman,,,,,,Artist,,Samuel Cotes,"British, 1734–1818",,"Cotes, Samuel",British,1734,1818,1767,1767,1767,Ivory,"Oval, 1 1/2 x 1 1/8 in. (38 x 30 mm)","Bequest of Millie Bruhl Fredrick, 1962",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/435999,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.136.14,false,true,436058,European Paintings,"Painting, miniature",Portrait of a Woman,,,,,,Artist,,Richard Crosse,"British, 1742–1810",,"Crosse, Richard",British,1742,1810,possibly ca. 1780,1775,1785,Ivory,"Oval, 2 1/4 x 1 7/8 in. (58 x 48 mm)","Bequest of Margaret Crane Hurlbut, 1933",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/436058,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -26.168.59,false,true,437320,European Paintings,"Painting, miniature",Elizabeth Bushby,,,,,,Artist,,Andrew Plimer,"British, 1763–1837",,"Plimer, Andrew",British,1763,1837,1804,1804,1804,Ivory,"Oval, 3 x 2 1/4 in. (75 x 57 mm)","Bequest of Collis P. Huntington, 1900",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/437320,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -95.14.95,false,true,437950,European Paintings,"Painting, miniature",An Interesting Story (Miss Ray),,,,,,Artist,,William Wood,"British, 1769–1810",,"Wood, William",British,1769,1810,1806,1806,1806,Ivory,4 3/4 x 3 7/8 in. (119 x 97 mm),"The Moses Lazarus Collection, Gift of Josephine and Sarah Lazarus, in memory of their father, 1888–95",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/437950,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -39.122,false,true,437225,European Paintings,"Painting, miniature",Portrait of a Young Woman,,,,,,Artist,,James Nixon,"British, ca. 1741–1812",,"Nixon, James",British,1736,1812,ca. 1780–85,1780,1785,Ivory,"Oval, 3 3/4 x 3 in. (97 x 75 mm)","Fletcher Fund, 1939",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/437225,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -62.122.42,false,true,437226,European Paintings,"Painting, miniature",Portrait of a Woman,,,,,,Artist,,James Nixon,"British, ca. 1741–1812",,"Nixon, James",British,1736,1812,ca. 1790,1785,1795,Ivory,"Oval, 2 1/4 x 1 7/8 in. (56 x 47 mm)","Bequest of Millie Bruhl Fredrick, 1962",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/437226,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -95.14.60,false,true,437048,European Paintings,"Painting, miniature","Portrait of a Woman, Said to Be Lady Sophia Boyle",,,,,,Artist,,Anne Foldsone Mee,"British, ca. 1770–1851",,"Mee, Anne Foldsone",British,1765,1851,ca. 1790,1785,1795,Ivory,"Oval, 2 5/8 x 2 1/8 in. (67 x 53 mm)","The Moses Lazarus Collection, Gift of Josephine and Sarah Lazarus, in memory of their father, 1888–95",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/437048,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -17.190.1150,false,true,437047,European Paintings,"Painting, miniature, snuffbox","Portrait of a Woman, Possibly Barbara (1768–1829), Marchioness of Donegall",,,,,,Artist,,Anne Foldsone Mee,"British, ca. 1770–1851",,"Mee, Anne Foldsone",British,1765,1851,ca. 1790,1785,1795,Ivory,"Oval, 2 7/8 x 2 1/8 in. (73 x 53 mm)","Gift of J. Pierpont Morgan, 1917",,,,,,,,,,,,Miniatures|Metalwork-Gold and Platinum,,http://www.metmuseum.org/art/collection/search/437047,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -50.187.44,false,true,435634,European Paintings,"Painting, miniature",Miss Chambers,,,,,,Artist,,Isabella Beetham,"British, 1750–after 1809",,"Beetham, Isabella",British,1750,1809,after 1782,1782,1809,Ivory,"Oval, 2 1/4 x 1 7/8 in. (59 x 48 mm)","The Glenn Tilley Morse Collection, Bequest of Glenn Tilley Morse, 1950",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/435634,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -45.95,false,true,436635,European Paintings,"Painting, miniature",Agnes Sewell,,,,,,Artist,,Thomas Hazlehurst,"British, ca. 1740–ca. 1821",,"Hazlehurst, Thomas",British,1740,1821,possibly ca. 1800,1795,1805,Ivory,"Oval, 3 x 2 3/8 in. (75 x 60 mm)","Gift of Elise Shackelford Black, 1945",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/436635,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -62.122.23,false,true,437140,European Paintings,"Painting, miniature",Portrait of a Man,,,,,,Artist,Attributed to,Monogrammist FS (Franciszek Smiadecki?),"British, active ca. 1650–65",,Monogrammist FS (Franciszek Smiadecki?),British,1650,1665,ca. 1650,1645,1655,Oil on card with gessoed back,"Oval, 2 5/8 x 2 1/4 in. (67 x 56 mm)","Bequest of Millie Bruhl Fredrick, 1962",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/437140,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -24.80.510,false,true,437176,European Paintings,"Painting, miniature",Portrait of a Man,,,,,,Artist,,David Myers,"British, active ca. 1659–76",,"Myers, David",British,1659,1676,1664,1664,1664,Vellum laid on prepared gessoed card,"Oval, 2 1/2 x 2 in. (62 x 51 mm)","Bequest of Mary Clark Thompson, 1923",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/437176,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -15.43.287,false,true,437644,European Paintings,"Painting, miniature",Portrait of a Man,,,,,,Artist,Attributed to,Noah Seaman,"British, active ca. 1724–41",,"Seaman, Noah",British,1724,1741,ca. 1730,1725,1735,Enamel,"Oval, 1 7/8 x 1 1/2 in. (47 x 37 mm)","Bequest of Mary Anna Palmer Draper, 1914",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/437644,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -62.122.35,false,true,436779,European Paintings,"Painting, miniature",Portrait of a Woman,,,,,,Artist,,James Jennings,"British, active ca. 1763–93",,"Jennings, James",British,1763,1793,probably early 1770s,1770,1773,Ivory,"Oval, 1 5/8 x 1 1/4 in. (40 x 34 mm)","Bequest of Millie Bruhl Fredrick, 1962",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/436779,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -24.80.509,false,true,436500,European Paintings,"Painting, miniature","Portrait of a Man, Said to Be John Cecil (1628–1678), Fourth Earl of Exeter",,,,,,Artist,Attributed to,Richard Gibson,"British, 1605/15?–1690 London",,"Gibson, Richard",British,1605,1690,ca. 1670,1665,1675,Vellum laid on card,"Oval, 2 7/8 x 2 3/8 in. (72 x 59 mm)","Bequest of Mary Clark Thompson, 1923",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/436500,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -44.36.1,false,true,436316,European Paintings,"Painting, miniature",Portrait of a Woman,,,,,,Artist,,Thomas Forster,"British, active ca. 1690–1713",,"Forster, Thomas",British,1690,1713,1701,1701,1701,Plumbago on vellum,"Oval, 4 1/4 x 3 1/4 in. (107 x 82 mm)","Rogers Fund, 1944",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/436316,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -44.36.2,false,true,436314,European Paintings,"Painting, miniature",Portrait of a Man,,,,,,Artist,,Thomas Forster,"British, active ca. 1690–1713",,"Forster, Thomas",British,1690,1713,1700,1700,1700,Plumbago on vellum,"Oval, 4 3/8 x 3 5/8 in. (112 x 92 mm)","Rogers Fund, 1944",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/436314,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -44.36.4,false,true,436317,European Paintings,"Painting, miniature",Portrait of a Man,,,,,,Artist,,Thomas Forster,"British, active ca. 1690–1713",,"Forster, Thomas",British,1690,1713,1705,1705,1705,Plumbago on vellum,"Oval, 5 1/8 x 4 in. (130 x 102 mm)","Rogers Fund, 1944",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/436317,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -44.36.5,false,true,436315,European Paintings,"Painting, miniature",Portrait of a Woman,,,,,,Artist,,Thomas Forster,"British, active ca. 1690–1713",,"Forster, Thomas",British,1690,1713,1700,1700,1700,Plumbago on vellum,"Oval, 4 3/8 x 3 1/2 in. (112 x 90 mm)","Rogers Fund, 1944",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/436315,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -30.111.1,false,true,435609,European Paintings,"Painting, miniature",Portrait of a Man,,,,,,Artist,,John Barry,"British, active ca. 1784–1827",,"Barry, John",British,1784,1827,ca. 1790,1785,1795,Ivory,"Oval, 2 1/2 x 2 1/8 in. (64 x 54 mm)","Gift of Mrs. Sherwood Eddy, 1930",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/435609,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -30.111.2,false,true,435610,European Paintings,"Painting, miniature",Portrait of a Woman,,,,,,Artist,,John Barry,"British, active ca. 1784–1827",,"Barry, John",British,1784,1827,ca. 1790,1785,1795,Ivory,"Oval, 2 1/2 x 2 1/8 in. (64 x 54 mm)","Gift of Mrs. Sherwood Eddy, 1930",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/435610,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -62.122.40,false,true,435611,European Paintings,"Painting, miniature","Portrait of a Man, Said to Be John Durham",,,,,,Artist,,John Barry,"British, active ca. 1784–1827",,"Barry, John",British,1784,1827,ca. 1790,1785,1795,Ivory,"Oval, 2 5/8 x 2 1/4 in. (67 x 56 mm)","Bequest of Millie Bruhl Fredrick, 1962",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/435611,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -32.75.8,false,true,437264,European Paintings,"Painting, miniature",Sir Joshua Reynolds (1723–1792),,,,,,Artist,,Thomas Peat,"British, active ca. 1791–1831",,"Peat, Thomas",British,1791,1831,1792,1792,1792,Enamel,"Oval, 4 x 3 1/8 in. (101 x 80 mm)","The Collection of Giovanni P. Morosini, presented by his daughter Giulia, 1932",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/437264,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -44.36.3,false,true,436901,European Paintings,"Painting, miniature",Portrait of a Man,,,,,,Artist,,David Loggan,"British, Gdansk 1634–1692 London",,"Loggan, David",British,1634,1692,1680,1680,1680,Plumbago on vellum,"Oval, 5 1/4 x 4 1/4 in. (132 x 107 mm)","Rogers Fund, 1944",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/436901,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -44.36.6,false,true,437938,European Paintings,"Painting, miniature",Portrait of a Man,,,,,,Artist,,Robert White,"British, London 1645–1703 London",,"White, Robert",British,1645,1703,1690,1690,1690,Plumbago on vellum,"Oval, 4 1/4 x 3 3/8 in. (106 x 85 mm)","Rogers Fund, 1944",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/437938,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -26.168.71,false,true,437663,European Paintings,"Painting, miniature",The Hours,,,,,,Artist,,Samuel Shelley,"British, London 1756–1808 London",,"Shelley, Samuel",British,1756,1808,1801,1801,1801,Ivory,5 1/2 x 4 1/4 in. (140 x 109 mm),"Bequest of Collis P. Huntington, 1900",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/437663,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -62.122.39,false,true,437664,European Paintings,"Painting, miniature",Portrait of a Man,,,,,,Artist,,Samuel Shelley,"British, London 1756–1808 London",,"Shelley, Samuel",British,1756,1808,probably ca. 1800,1795,1805,Ivory,"Oval, 3 x 2 3/8 in. (75 x 60 mm)","Bequest of Millie Bruhl Fredrick, 1962",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/437664,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -67.131,false,true,437582,European Paintings,"Painting, drawing",Robert Shurlock (1772–1847),,,,,,Artist,,John Russell,"British, Guildford 1745–1806 Hull",,"Russell, John",British,1745,1806,1801,1801,1801,"Pastel on paper, laid down on canvas",23 3/4 x 17 3/8 in. (60.3 x 44.1 cm),"Gift of Alan R. Shurlock, 1967",,,,,,,,,,,,Pastels & Oil Sketches on Paper,,http://www.metmuseum.org/art/collection/search/437582,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -67.132,false,true,437580,European Paintings,"Painting, drawing","Mrs. Robert Shurlock (Henrietta Ann Jane Russell, 1775–1849) and Her Daughter Ann",,,,,,Artist,,John Russell,"British, Guildford 1745–1806 Hull",,"Russell, John",British,1745,1806,1801,1801,1801,"Pastel on paper, laid down on canvas",23 7/8 x 17 3/4 in. (60.6 x 45.1 cm),"Gift of Geoffrey Shurlock, 1967",,,,,,,,,,,,Pastels & Oil Sketches on Paper,,http://www.metmuseum.org/art/collection/search/437580,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -61.182.1,false,true,437579,European Paintings,"Painting, drawing",William Man Godschall (1720–1802),,,,,,Artist,,John Russell,"British, Guildford 1745–1806 Hull",,"Russell, John",British,1745,1806,1791,1791,1791,"Pastel on paper, laid down on canvas",23 3/4 x 17 3/4 in. (60.3 x 45.1 cm),"Gift of Mr. and Mrs. Arthur Wiesenberger, 1961",,,,,,,,,,,,Pastels & Oil Sketches on Paper,,http://www.metmuseum.org/art/collection/search/437579,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -61.182.2,false,true,437583,European Paintings,"Painting, drawing","Mrs. William Man Godschall (Sarah Godschall, 1730–1795)",,,,,,Artist,,John Russell,"British, Guildford 1745–1806 Hull",,"Russell, John",British,1745,1806,1791,1791,1791,"Pastel on paper, laid down on canvas",23 3/4 x 17 3/4 in. (60.3 x 45.1 cm),"Gift of Mr. and Mrs. Arthur Wiesenberger, 1961",,,,,,,,,,,,Pastels & Oil Sketches on Paper,,http://www.metmuseum.org/art/collection/search/437583,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.217.2,false,true,437581,European Paintings,"Painting, drawing",Mrs. Robert Shurlock Sr. (Ann Manwaring),,,,,,Artist,,John Russell,"British, Guildford 1745–1806 Hull",,"Russell, John",British,1745,1806,1801,1801,1801,"Pastel on paper, laid down on canvas",24 x 17 7/8 in. (61 x 45.4 cm),"Gift of Olive Shurlock Sjölander, 1975",,,,,,,,,,,,Pastels & Oil Sketches on Paper,,http://www.metmuseum.org/art/collection/search/437581,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -11.218,false,true,436277,European Paintings,"Painting, miniature","Portrait of a Man, Said to Be Mr. De Wolf",,,,,,Artist,,George Engleheart,"British, Kew 1750–1829 Blackheath",,"Engleheart, George",British,1750,1829,ca. 1805,1800,1810,Ivory,"Oval, 3 1/4 x 2 1/2 in. (81 x 62 mm)","Gift of Alfred Ram, 1911",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/436277,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -95.14.55,false,true,436273,European Paintings,"Painting, miniature",Portrait of a Man,,,,,,Artist,,George Engleheart,"British, Kew 1750–1829 Blackheath",,"Engleheart, George",British,1750,1829,ca. 1780,1775,1785,Ivory,"Oval, 1 1/4 x 1 1/8 in. (32 x 29 mm)","The Moses Lazarus Collection, Gift of Josephine and Sarah Lazarus, in memory of their father, 1888–95",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/436273,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -26.168.14,false,true,436275,European Paintings,"Painting, miniature",Colonel Woodford,,,,,,Artist,,George Engleheart,"British, Kew 1750–1829 Blackheath",,"Engleheart, George",British,1750,1829,probably 1788,1788,1788,Ivory,"Oval, 2 1/8 x 1 3/4 in. (55 x 44 mm)","Bequest of Collis P. Huntington, 1900",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/436275,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -38.146.16,false,true,436274,European Paintings,"Painting, miniature","Mrs. Peter De Lancey (Elizabeth Colden, 1720–1784)",,,,,,Artist,,George Engleheart,"British, Kew 1750–1829 Blackheath",,"Engleheart, George",British,1750,1829,1783,1778,1788,Ivory,"Oval, 1 3/8 x 1 1/8 in. (34 x 28 mm)","Fletcher Fund, 1938",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/436274,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -49.122.1,false,true,437690,European Paintings,"Painting, miniature",Mrs. Charlotte Lennox,,,,,,Artist,,John Smart,"British, Norfolk 1741–1811 London",,"Smart, John",British,1741,1811,1777,1777,1777,Pencil and watercolor on card,"Oval, 2 1/2 x 2 in. (62 x 51 mm)","Rogers Fund, 1949",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/437690,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -59.23.75,false,true,437694,European Paintings,"Painting, miniature",Miss Ramus,,,,,,Artist,,John Smart,"British, Norfolk 1741–1811 London",,"Smart, John",British,1741,1811,ca. 1770,1765,1775,Pencil and watercolor on paper,2 1/4 x 2 in. (57 x 51 mm),"Bequest of Alexandrine Sinsheimer, 1958",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/437694,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -59.23.76,false,true,437691,European Paintings,"Painting, miniature",Mrs. Comyns,,,,,,Artist,,John Smart,"British, Norfolk 1741–1811 London",,"Smart, John",British,1741,1811,ca. 1760,1755,1765,Pencil and some watercolor on paper,1 3/4 x 1 5/8 in. (46 x 42 mm),"Bequest of Alexandrine Sinsheimer, 1958",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/437691,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -59.23.77,false,true,437693,European Paintings,"Painting, miniature","Sir George Armytage (1761–1836), Fourth Baronet",,,,,,Artist,,John Smart,"British, Norfolk 1741–1811 London",,"Smart, John",British,1741,1811,possibly ca. 1763,1758,1768,Pencil and watercolor on paper,2 1/8 x 2 in. (54 x 49 mm),"Bequest of Alexandrine Sinsheimer, 1958",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/437693,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -59.23.79,false,true,437692,European Paintings,"Painting, miniature",Mrs. Caroline Deas,,,,,,Artist,,John Smart,"British, Norfolk 1741–1811 London",,"Smart, John",British,1741,1811,ca. 1760,1755,1765,Pencil and watercolor on paper,2 1/4 x 2 1/4 in. (56 x 55 mm),"Bequest of Alexandrine Sinsheimer, 1958",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/437692,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -24.80.493,false,true,437689,European Paintings,"Painting, miniature",Sir William Hood,,,,,,Artist,,John Smart,"British, Norfolk 1741–1811 London",,"Smart, John",British,1741,1811,ca. 1766,1761,1771,Ivory,"Oval, 1 1/2 x 1 1/4 in. (37 x 32 mm)","Bequest of Mary Clark Thompson, 1923",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/437689,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -95.14.47,false,true,435700,European Paintings,"Painting, miniature","Algernon Percy (1602–1668), Tenth Earl of Northumberland, after Van Dyck",,,,,,Artist,,Henry Bone,"British, Truro 1755–1834 Somerstown",,"Bone, Henry",British,1755,1834,1827,1827,1827,Enamel,7 x 5 3/8 in. (178 x 136 mm),"The Moses Lazarus Collection, Gift of Josephine and Sarah Lazarus, in memory of their father, 1888–95",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/435700,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -95.14.82,false,true,435696,European Paintings,"Painting, miniature","Thomas Howard (1585–1646), Second Earl of Arundel, after Rubens",,,,,,Artist,,Henry Bone,"British, Truro 1755–1834 Somerstown",,"Bone, Henry",British,1755,1834,1808,1808,1808,Enamel,7 1/4 x 5 3/4 in. (185 x 147 mm),"The Moses Lazarus Collection, Gift of Josephine and Sarah Lazarus, in memory of their father, 1888–95",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/435696,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -24.80.503,false,true,435698,European Paintings,"Painting, miniature","Matthew Baillie (1761–1823), F.R.S., after Hoppner",,,,,,Artist,,Henry Bone,"British, Truro 1755–1834 Somerstown",,"Bone, Henry",British,1755,1834,1817,1817,1817,Enamel,5 1/8 x 4 in. (130 x 103 mm),"Bequest of Mary Clark Thompson, 1923",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/435698,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -24.80.521,false,true,435695,European Paintings,"Painting, miniature","Henry Hope (1735/36–1811), after Jones",,,,,,Artist,,Henry Bone,"British, Truro 1755–1834 Somerstown",,"Bone, Henry",British,1755,1834,1802,1802,1802,Enamel,"Oval, 2 1/8 x 1 3/4 in. (54 x 43 mm)","Bequest of Mary Clark Thompson, 1923",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/435695,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -24.80.523,false,true,435699,European Paintings,"Painting, miniature","Charles X (1757–1836), King of France, after Gérard",,,,,,Artist,,Henry Bone,"British, Truro 1755–1834 Somerstown",,"Bone, Henry",British,1755,1834,1829,1829,1829,Enamel,14 1/4 x 10 1/4 in. (364 x 260 mm),"Bequest of Mary Clark Thompson, 1923",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/435699,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -26.168.61,false,true,435697,European Paintings,"Painting, miniature","George IV (1762–1830) as Prince Regent, after Lawrence",,,,,,Artist,,Henry Bone,"British, Truro 1755–1834 Somerstown",,"Bone, Henry",British,1755,1834,1816,1816,1816,Enamel,"Oval, 2 1/2 x 2 in. (64 x 49 mm)","Bequest of Collis P. Huntington, 1900",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/435697,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -35.89.2,false,true,436650,European Paintings,"Painting, miniature",Portrait of a Woman,,,,,,Artist,,Nicholas Hilliard,"British, Exeter ca. 1547–1619 London",,"Hilliard, Nicholas",British,1542,1619,1597,1597,1597,Vellum,"Oval, 1 7/8 x 1 1/2 in. (47 x 39 mm)","Fletcher Fund, 1935",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/436650,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -35.89.4,false,true,436649,European Paintings,"Painting, miniature","Portrait of a Young Man, Probably Robert Devereux (1566–1601), Second Earl of Essex",,,,,,Artist,,Nicholas Hilliard,"British, Exeter ca. 1547–1619 London",,"Hilliard, Nicholas",British,1542,1619,1588,1588,1588,Vellum laid on card,"Oval, 1 5/8 x 1 3/8 in. (40 x 33 mm)","Fletcher Fund, 1935",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/436649,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -32.100.311,false,true,436651,European Paintings,"Painting, miniature",Portrait of a Woman,,,,,,Artist,,Nicholas Hilliard,"British, Exeter ca. 1547–1619 London",,"Hilliard, Nicholas",British,1542,1619,ca. 1590,1585,1595,Vellum laid on card,"Oval, 1 x 7/8 in. (27 x 22 mm)","The Friedsam Collection, Bequest of Michael Friedsam, 1931",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/436651,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -49.33,false,true,435941,European Paintings,"Painting, miniature","Henry Carey (1596–1661), Second Earl of Monmouth",,,,,,Artist,,Samuel Cooper,"British, London (?) 1608?–1672 London",,"Cooper, Samuel",British,1608,1672,1649,1649,1649,Vellum on prepared card,"Oval, 2 1/2 x 2 in. (64 x 52 mm)","Rogers Fund, 1949",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/435941,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -24.80.516,false,true,435942,European Paintings,"Painting, miniature","Portrait of a Woman, Said to Be Lucy Percy (1600?–1660), Countess of Carlisle",,,,,,Artist,,Samuel Cooper,"British, London (?) 1608?–1672 London",,"Cooper, Samuel",British,1608,1672,1653,1653,1653,Vellum laid on prepared card,"Oval, 2 1/2 x 2 in. (65 x 50 mm)","Bequest of Mary Clark Thompson, 1923",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/435942,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -35.89.3,false,true,436696,European Paintings,"Painting, miniature","Dr. Brian Walton (born about 1600, died 1661)",,,,,,Artist,,John Hoskins,"British, active by ca. 1615–died 1665",,"Hoskins, John",British,1615,1665,1657,1657,1657,Vellum laid on card,"Oval, 2 3/4 x 2 1/4 in. (72 x 58 mm)","Fletcher Fund, 1935",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/436696,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -24.80.505,false,true,436694,European Paintings,"Painting, miniature",Endymion Porter (1587–1649),,,,,,Artist,,John Hoskins,"British, active by ca. 1615–died 1665",,"Hoskins, John",British,1615,1665,ca. 1630,1625,1635,Vellum,"Oval, 3 1/8 x 2 5/8 in. (80 x 66 mm)","Bequest of Mary Clark Thompson, 1923",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/436694,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -62.122.22,false,true,436695,European Paintings,"Painting, miniature","Portrait of a Man, Said to Be Philip Wharton (1613–1696), Fourth Baron Wharton",,,,,,Artist,,John Hoskins,"British, active by ca. 1615–died 1665",,"Hoskins, John",British,1615,1665,1648,1648,1648,Vellum on prepared card,"Oval, 2 3/4 x 2 1/4 in. (69 x 56 mm)","Bequest of Millie Bruhl Fredrick, 1962",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/436695,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -62.122.28,false,true,436673,European Paintings,"Painting, miniature","Portrait of a Woman, Said to Be Lady Agnes Anne Wrothesley",,,,,,Artist,,Horace Hone,"British, London ca. 1754/56–1825 London",,"Hone, Horace",British,1749,1825,1791,1791,1791,Ivory,"Oval, 2 x 1 5/8 in. (52 x 41 mm)","Bequest of Millie Bruhl Fredrick, 1962",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/436673,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -62.49,false,true,435994,European Paintings,"Painting, miniature",Self-Portrait,,,,,,Artist,,Richard Cosway,"British, Oakford, Devon 1742–1821 London",,"Cosway, Richard",British,1742,1821,ca. 1770–75,1770,1775,Ivory,"Oval, 2 x 1 5/8 in. (50 x 42 mm)","Gift of Charlotte Guilford Muhlhofer, 1962",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/435994,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -24.80.513,false,true,436202,European Paintings,"Painting, miniature",Sir Henry Blount (1602–1682),,,,,,Artist,,Nicholas Dixon,"British, active by ca. 1660–died after 1708",,"Dixon, Nicholas",British,1660,1708,1660s,1660,1669,Vellum laid on card,"Oval, 2 5/8 x 2 1/8 in. (67 x 55 mm)","Bequest of Mary Clark Thompson, 1923",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/436202,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1975.217.1,false,true,437584,European Paintings,Drawing,Robert Shurlock (1772–1847),,,,,,Artist,Attributed to,William Russell,"British, London 1784–1870 Highgate",,"Russell, William",British,1784,1870,ca. 1805,1800,1810,Pastel on paper,23 7/8 x 17 7/8 in. (60.6 x 45.4 cm),"Gift of Olive Shurlock Sjölander, 1975",,,,,,,,,,,,Drawings,,http://www.metmuseum.org/art/collection/search/437584,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -61.233,false,true,436859,European Paintings,Drawing,Catania and Mount Etna,,,,,,Artist,,Edward Lear,"British, London 1812–1888 San Remo",,"Lear, Edward",British,1812,1888,1847,1847,1847,Oil on board,12 1/4 x 19 in. (31.1 x 48.3 cm),"Rogers Fund, 1961",,,,,,,,,,,,Drawings,,http://www.metmuseum.org/art/collection/search/436859,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1990.75,false,true,436845,European Paintings,Painting,"Copy after Rubens's ""Wolf and Fox Hunt""",,,,,,Artist,,Sir Edwin Henry Landseer,,,"Landseer, Edwin Henry, Sir",British,1802,1873,ca. 1824–26,1824,1826,Oil on wood,16 x 23 7/8 in. (40.6 x 60.6 cm),"Catharine Lorillard Wolfe Collection, Wolfe Fund, 1990",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436845,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -92.10.42,false,true,436226,European Paintings,Painting,"Homeward Bound: ""The Great Eastern""",,,,,,Artist,,Robert Charles Dudley,"British, 1826–1909",,"Dudley, Robert Charles",British,1826,1909,ca. 1866,1861,1871,Oil on canvas,44 3/4 x 67 1/4 in. (113.7 x 170.8 cm),"Gift of Cyrus W. Field, 1892",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436226,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -92.10.43,false,true,436224,European Paintings,Painting,Awaiting the Reply,,,,,,Artist,,Robert Charles Dudley,"British, 1826–1909",,"Dudley, Robert Charles",British,1826,1909,ca. 1866,1861,1871,Oil on canvas,23 1/4 x 33 1/2 in. (59.1 x 85.1 cm),"Gift of Cyrus W. Field, 1892",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436224,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -92.10.44,false,true,436223,European Paintings,Painting,Landing the Shore End of the Atlantic Cable,,,,,,Artist,,Robert Charles Dudley,"British, 1826–1909",,"Dudley, Robert Charles",British,1826,1909,1866,1866,1866,Oil on canvas,22 1/2 x 33 in. (57.2 x 83.8 cm),"Gift of Cyrus W. Field, 1892",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436223,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -92.10.45,false,true,436225,European Paintings,Painting,Grappling for the Lost Cable,,,,,,Artist,,Robert Charles Dudley,"British, 1826–1909",,"Dudley, Robert Charles",British,1826,1909,ca. 1866,1861,1871,Oil on canvas,22 3/4 x 33 1/8 in. (57.8 x 84.1 cm),"Gift of Cyrus W. Field, 1892",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436225,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -92.10.46,false,true,436227,European Paintings,Painting,Landing at Newfoundland,,,,,,Artist,,Robert Charles Dudley,"British, 1826–1909",,"Dudley, Robert Charles",British,1826,1909,ca. 1866,1861,1871,Oil on canvas,22 3/4 x 33 1/4 in. (57.8 x 84.5 cm),"Gift of Cyrus W. Field, 1892",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436227,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -92.10.47,false,true,436228,European Paintings,Painting,Making the Splice between the Shore End and the Ocean Cable,,,,,,Artist,,Robert Charles Dudley,"British, 1826–1909",,"Dudley, Robert Charles",British,1826,1909,ca. 1866,1861,1871,Oil on canvas,22 3/4 x 33 1/4 in. (57.8 x 84.5 cm),"Gift of Cyrus W. Field, 1892",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436228,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -15.30.34,false,true,436442,European Paintings,Painting,The Painter's Daughter Mary (1750–1826),,,,,,Artist,Copy after,Thomas Gainsborough,"British, mid-19th century",,"Gainsborough, Thomas",British,1727,1788,mid-19th century,1830,1869,Oil on canvas,17 1/4 x 13 7/8 in. (43.8 x 35.2 cm),"Bequest of Maria DeWitt Jesup, from the collection of her husband, Morris K. Jesup, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436442,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -44.27,false,true,437262,European Paintings,Painting,"Henry Frederick (1594–1612), Prince of Wales, with Sir John Harington (1592–1614), in the Hunting Field",,,,,,Artist,,Robert Peake the Elder,"British, ca. 1551–1619 London",,"Peake, Robert, the Elder",British,1546,1619,1603,1603,1603,Oil on canvas,79 1/2 x 58 in. (201.9 x 147.3 cm),"Purchase, Joseph Pulitzer Bequest, 1944",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437262,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -51.194.1,false,true,437263,European Paintings,Painting,"Princess Elizabeth (1596–1662), Later Queen of Bohemia",,,,,,Artist,,Robert Peake the Elder,"British, ca. 1551–1619 London",,"Peake, Robert, the Elder",British,1546,1619,ca. 1606,1601,1611,Oil on canvas,60 3/4 x 31 1/4 in. (154.3 x 79.4 cm),"Gift of Kate T. Davison, in memory of her husband, Henry Pomeroy Davison, 1951",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437263,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2013.155,false,true,441769,European Paintings,Painting,"Virgil's Tomb by Moonlight, with Silius Italicus Declaiming",,,,,,Artist,,Joseph Wright (Wright of Derby),"British, Derby 1734–1797 Derby",,"Wright, Joseph (Wright of Derby)",British,1734,1797,1779,1779,1779,Oil on canvas,40 x 50 in. (101.6 x 127 cm),"Purchase, Lila Acheson Wallace Gift, Gifts of Mrs. William M. Haupt, Josephine Bay Paul, and Estate of George Quackenbush, in his memory, by exchange, The Morris and Alma Schapiro Fund Gift, and funds from various donors, 2013",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/441769,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.264.6,false,true,437954,European Paintings,Painting,Portrait of a Woman,,,,,,Artist,,Joseph Wright (Wright of Derby),"British, Derby 1734–1797 Derby",,"Wright, Joseph (Wright of Derby)",British,1734,1797,ca. 1770,1765,1775,Oil on canvas,49 7/8 x 40 in. (126.7 x 101.6 cm),"Gift of Heathcote Art Foundation, 1986",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437954,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -43.132.4,false,true,435895,European Paintings,Painting,Self-Portrait,,,,,,Artist,,George Chinnery,"British, London 1774–1852 Macau",,"Chinnery, George",British,1774,1852,1825–28,1825,1828,Oil on canvas,8 5/8 x 7 1/4 in. (21.9 x 18.4 cm),"Rogers Fund, 1943",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435895,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -36.111,false,true,436656,European Paintings,Painting,The Wedding of Stephen Beckingham and Mary Cox,,,,,,Artist,,William Hogarth,"British, London 1697–1764 London",,"Hogarth, William",British,1697,1764,1729,1729,1729,Oil on canvas,50 1/2 x 40 1/2 in. (128.3 x 102.9 cm),"Marquand Fund, 1936",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436656,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -44.159,false,true,437280,European Paintings,Painting,The Strong Family,,,,,,Artist,,Charles Philips,"British, London 1703–1747 London",,"Philips, Charles",British,1703,1747,1732,1732,1732,Oil on canvas,29 5/8 x 37 in. (75.2 x 94 cm),"Gift of Robert Lehman, 1944",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437280,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.357,false,true,440568,European Paintings,Painting,The Saithwaite Family,,,,,,Artist,,Francis Wheatley,"British, London 1747–1801 London",,"Wheatley, Francis",British,1747,1801,ca. 1785,1780,1790,Oil on canvas,38 3/4 x 50 in. (98.4 x 127 cm),"Gift of Mrs. Charles Wrightsman, 2009",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/440568,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -51.30.1,false,true,435671,European Paintings,Painting,The Angel Appearing to Zacharias,,,,,,Artist,,William Blake,"British, London 1757–1827 London",,"Blake, William",British,1757,1827,1799–1800,1799,1800,"Pen and black ink, tempera, and glue size on canvas",10 1/2 x 15 in. (26.7 x 38.1 cm),"Bequest of William Church Osborn, 1951",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435671,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -01.20,false,true,436686,European Paintings,Painting,"Mrs. Richard Bache (Sarah Franklin, 1743–1808)",,,,,,Artist,,John Hoppner,"British, London 1758–1810 London",,"Hoppner, John",British,1758,1810,1793,1793,1793,Oil on canvas,30 1/8 x 24 7/8 in. (76.5 x 63.2 cm),"Catharine Lorillard Wolfe Collection, Wolfe Fund, 1901",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436686,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -06.1242,false,true,436690,European Paintings,Painting,"Portrait of a Woman; (reverse, now covered by relining canvas) Study of a Child's Head",,,,,,Artist,,John Hoppner,"British, London 1758–1810 London",,"Hoppner, John",British,1758,1810,1790s,1790,1799,Oil on canvas,30 x 24 7/8 in. (76.2 x 63.2 cm),"Gift of William T. and Eleanor Blodgett, 1906",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436690,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -46.13.3,false,true,436684,European Paintings,Painting,Major Thomas Pechell (1753–1826),,,,,,Artist,,John Hoppner,"British, London 1758–1810 London",,"Hoppner, John",British,1758,1810,1799,1799,1799,Oil on canvas,30 x 24 7/8 in. (76.2 x 63.2 cm),"Bequest of Helen Swift Neilson, 1945",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436684,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -46.13.4,false,true,436688,European Paintings,Painting,"Mrs. Thomas Pechell (Charlotte Clavering, died 1841)",,,,,,Artist,,John Hoppner,"British, London 1758–1810 London",,"Hoppner, John",British,1758,1810,1799,1799,1799,Oil on canvas,30 x 25 in. (76.2 x 63.5 cm),"Bequest of Helen Swift Neilson, 1945",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436688,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -53.59.3,false,true,436692,European Paintings,Painting,The Sackville Children,,,,,,Artist,,John Hoppner,"British, London 1758–1810 London",,"Hoppner, John",British,1758,1810,1796,1796,1796,Oil on canvas,60 x 49 in. (152.4 x 124.5 cm),"Bequest of Thomas W. Lamont, 1948",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436692,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -15.30.41,false,true,436685,European Paintings,Painting,"Mrs. John Garden (Ann Garden, 1769–1842) and Her Children, John (1796–1854) and Ann Margaret (born 1793)",,,,,,Artist,,John Hoppner,"British, London 1758–1810 London",,"Hoppner, John",British,1758,1810,1796 or 1797,1796,1797,Oil on canvas,50 1/8 x 39 7/8 in. (127.3 x 101.3 cm),"Bequest of Maria DeWitt Jesup, from the collection of her husband, Morris K. Jesup, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436685,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -59.189.3,false,true,436683,European Paintings,Painting,Lady Hester King (died 1873),,,,,,Artist,,John Hoppner,"British, London 1758–1810 London",,"Hoppner, John",British,1758,1810,probably 1805,1804,1810,Oil on canvas,30 x 25 in. (76.2 x 63.5 cm),"Gift of Bernard M. Baruch, in memory of his wife, Annie Griffen Baruch, 1959",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436683,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -25.110.20,false,true,437161,European Paintings,Painting,The Bell Inn,,,,,,Artist,,George Morland,"British, London 1763–1804 London",,"Morland, George",British,1763,1804,late 1780s,1787,1789,Oil on canvas,20 1/2 x 26 1/4 in. (52.1 x 66.7 cm),"Bequest of Collis P. Huntington, 1900",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437161,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -96.29,true,true,437854,European Paintings,Painting,Whalers,,,,,,Artist,,Joseph Mallord William Turner,"British, London 1775–1851 London",,"Turner, Joseph Mallord William",British,1775,1851,ca. 1845,1840,1850,Oil on canvas,36 1/8 x 48 1/4 in. (91.8 x 122.6 cm),"Catharine Lorillard Wolfe Collection, Wolfe Fund, 1896",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437854,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -99.31,true,true,437853,European Paintings,Painting,"Venice, from the Porch of Madonna della Salute",,,,,,Artist,,Joseph Mallord William Turner,"British, London 1775–1851 London",,"Turner, Joseph Mallord William",British,1775,1851,ca. 1835,1830,1835,Oil on canvas,36 x 48 1/8 in. (91.4 x 122.2 cm),"Bequest of Cornelius Vanderbilt, 1899",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437853,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -89.15.9,false,true,437852,European Paintings,Painting,"Saltash with the Water Ferry, Cornwall",,,,,,Artist,,Joseph Mallord William Turner,"British, London 1775–1851 London",,"Turner, Joseph Mallord William",British,1775,1851,1811,1811,1811,Oil on canvas,35 3/8 x 47 1/2 in. (89.9 x 120.7 cm),"Marquand Collection, Gift of Henry G. Marquand, 1889",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437852,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -05.39.1,false,true,437936,European Paintings,Painting,Ariadne,,,,,,Artist,,George Frederic Watts,"British, London 1817–1904 London",,"Watts, George Frederic",British,1817,1904,1894,1894,1894,Oil on canvas,24 x 20 in. (61 x 50.8 cm),"Rogers Fund, 1905",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437936,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -97.41.3,false,true,437935,European Paintings,Painting,"An Old Bridge at Hendon, Middlesex",,,,,,Artist,,Frederick Waters Watts,"British, Bath 1800–1870 Hampstead",,"Watts, Frederick Waters",British,1800,1870,ca. 1828,1820,1833,Oil on canvas,21 3/4 x 32 3/4 in. (55.2 x 83.2 cm),"Gift of George A. Hearn, 1897",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437935,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -55.89,false,true,436853,European Paintings,Painting,Lady Maria Conyngham (died 1843),,,,,,Artist,,Sir Thomas Lawrence,"British, Bristol 1769–1830 London",,"Lawrence, Thomas, Sir",British,1769,1830,ca. 1824–25,1824,1825,Oil on canvas,36 1/4 x 28 1/4 in. (92.1 x 71.8 cm),"Gift of Jessie Woolworth Donahue, 1955",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436853,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -25.110.1,false,true,436850,European Paintings,Painting,"The Calmady Children (Emily, 1818–?1906, and Laura Anne, 1820–1894)",,,,,,Artist,,Sir Thomas Lawrence,"British, Bristol 1769–1830 London",,"Lawrence, Thomas, Sir",British,1769,1830,1823,1789,1830,Oil on canvas,30 7/8 x 30 1/8 in. (78.4 x 76.5 cm),"Bequest of Collis P. Huntington, 1900",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436850,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -50.135.5,true,true,436851,European Paintings,Painting,"Elizabeth Farren (born about 1759, died 1829), Later Countess of Derby",,,,,,Artist,,Sir Thomas Lawrence,"British, Bristol 1769–1830 London",,"Lawrence, Thomas, Sir",British,1769,1830,1790,1790,1790,Oil on canvas,94 x 57 1/2 in. (238.8 x 146.1 cm),"Bequest of Edward S. Harkness, 1940",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436851,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -06.1279,false,true,436441,European Paintings,Painting,Wooded Upland Landscape,,,,,,Artist,,Thomas Gainsborough,"British, Sudbury 1727–1788 London",,"Gainsborough, Thomas",British,1727,1788,probably 1783,1783,1783,Oil on canvas,47 3/8 x 58 1/8 in. (120.3 x 147.6 cm),"Gift of George A. Hearn, 1906",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436441,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -45.59.1,false,true,436437,European Paintings,Painting,"Mrs. William Tennant (Mary Wylde, died 1798)",,,,,,Artist,,Thomas Gainsborough,"British, Sudbury 1727–1788 London",,"Gainsborough, Thomas",British,1727,1788,1780s,1780,1789,Oil on canvas,49 1/2 x 40 in. (125.7 x 101.6 cm),"Fletcher Fund, 1945",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436437,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -89.15.8,false,true,436433,European Paintings,Painting,A Boy with a Cat—Morning,,,,,,Artist,,Thomas Gainsborough,"British, Sudbury 1727–1788 London",,"Gainsborough, Thomas",British,1727,1788,1787,1787,1787,Oil on canvas,59 1/4 x 47 1/2 in. (150.5 x 120.7 cm),"Marquand Collection, Gift of Henry G. Marquand, 1889",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436433,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -20.155.1,true,true,436435,European Paintings,Painting,Mrs. Grace Dalrymple Elliott (1754?–1823),,,,,,Artist,,Thomas Gainsborough,"British, Sudbury 1727–1788 London",,"Gainsborough, Thomas",British,1727,1788,1778,1778,1778,Oil on canvas,92 1/4 x 60 1/2in. (234.3 x 153.7cm),"Bequest of William K. Vanderbilt, 1920",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436435,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -50.145.16,false,true,436431,European Paintings,Painting,Charles Rousseau Burney (1747–1819),,,,,,Artist,,Thomas Gainsborough,"British, Sudbury 1727–1788 London",,"Gainsborough, Thomas",British,1727,1788,ca. 1780,1775,1785,Oil on canvas,30 1/4 x 25 1/8 in. (76.8 x 63.8 cm),"Bequest of Mary Stillman Harkness, 1950",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436431,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -50.145.17,false,true,436440,European Paintings,Painting,Cottage Children (The Wood Gatherers),,,,,,Artist,,Thomas Gainsborough,"British, Sudbury 1727–1788 London",,"Gainsborough, Thomas",British,1727,1788,1787,1787,1787,Oil on canvas,58 1/8 x 47 3/8 in. (147.6 x 120.3 cm),"Bequest of Mary Stillman Harkness, 1950",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436440,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -17.120.224,false,true,436439,European Paintings,Painting,"Portrait of a Young Woman, Called Miss Sparrow",,,,,,Artist,,Thomas Gainsborough,"British, Sudbury 1727–1788 London",,"Gainsborough, Thomas",British,1727,1788,1770s,1770,1779,Oil on canvas,30 1/8 x 24 7/8 in. (76.5 x 63.2 cm),"Mr. and Mrs. Isaac D. Fletcher Collection, Bequest of Isaac D. Fletcher, 1917",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436439,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -89.15.14,false,true,436057,European Paintings,Painting,"Hautbois Common, Norfolk",,,,,,Artist,,John Crome,"British, Norwich 1768–1821 Norwich",,"Crome, John",British,1768,1821,probably ca. 1810,1805,1815,Oil on canvas,22 x 35 in. (55.9 x 88.9 cm),"Marquand Collection, Gift of Henry G. Marquand, 1889",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436057,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -87.16,false,true,437449,European Paintings,Painting,The Honorable Henry Fane (1739–1802) with Inigo Jones and Charles Blair,,,,,,Artist,,Sir Joshua Reynolds,"British, Plympton 1723–1792 London",,"Reynolds, Joshua, Sir",British,1723,1792,1761–66,1761,1766,Oil on canvas,100 1/4 x 142 in. (254.6 x 360.7 cm),"Gift of Junius S. Morgan, 1887",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437449,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -48.181,false,true,437445,European Paintings,Painting,"George Capel, Viscount Malden (1757–1839), and Lady Elizabeth Capel (1755–1834)",,,,,,Artist,,Sir Joshua Reynolds,"British, Plympton 1723–1792 London",,"Reynolds, Joshua, Sir",British,1723,1792,1768,1768,1768,Oil on canvas,71 1/2 x 57 1/4 in. (181.6 x 145.4 cm),"Gift of Henry S. Morgan, 1948",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437445,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -54.192,false,true,437451,European Paintings,Painting,John Barker (1707–1787),,,,,,Artist,,Sir Joshua Reynolds,"British, Plympton 1723–1792 London",,"Reynolds, Joshua, Sir",British,1723,1792,1786,1786,1786,Oil on canvas,68 1/4 x 47 1/2 in. (173.4 x 120.7 cm),"Gift of Ruth Armour, 1954",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437451,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -06.1241,false,true,437454,European Paintings,Painting,"Mrs. George Baldwin (Jane Maltass, 1763–1839)",,,,,,Artist,Workshop of,Sir Joshua Reynolds,"British, Plympton 1723–1792 London",,"Reynolds, Joshua, Sir",British,1723,1792,1782 or later,1782,1792,Oil on canvas,36 1/8 x 29 1/8 in. (91.8 x 74 cm),"Gift of William T. Blodgett and his sister Eleanor Blodgett, in memory of their father, William T. Blodgett, one of the founders of the Museum, 1906",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437454,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -15.30.38,false,true,437448,European Paintings,Painting,"Georgiana Augusta Frederica Elliott (1782–1813), Later Lady Charles Bentinck",,,,,,Artist,,Sir Joshua Reynolds,"British, Plympton 1723–1792 London",and Workshop,"Reynolds, Joshua, Sir",British,1723,1792,1784,1784,1784,Oil on canvas,35 x 30 in. (88.9 x 76.2 cm),"Bequest of Maria DeWitt Jesup, from the collection of her husband, Morris K. Jesup, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437448,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -20.155.3,true,true,437447,European Paintings,Painting,Captain George K. H. Coussmaker (1759–1801),,,,,,Artist,,Sir Joshua Reynolds,"British, Plympton 1723–1792 London",,"Reynolds, Joshua, Sir",British,1723,1792,1782,1782,1782,Oil on canvas,93 3/4 x 57 1/4 in. (238.1 x 145.4 cm),"Bequest of William K. Vanderbilt, 1920",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437447,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -50.238.2,false,true,437444,European Paintings,Painting,"Anne Dashwood (1743–1830), Later Countess of Galloway",,,,,,Artist,,Sir Joshua Reynolds,"British, Plympton 1723–1792 London",,"Reynolds, Joshua, Sir",British,1723,1792,1764,1764,1764,Oil on canvas,"52 1/2 x 46 3/4 in. (133.4 x 118.7 cm), with strip of 7 1/8 in. (18.1 cm) folded over the top of the stretcher","Gift of Lillian S. Timken, 1950",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437444,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1987.47.2,false,true,437450,European Paintings,Painting,"Mrs. Lewis Thomas Watson (Mary Elizabeth Milles, 1767–1818)",,,,,,Artist,,Sir Joshua Reynolds,"British, Plympton 1723–1792 London",,"Reynolds, Joshua, Sir",British,1723,1792,1789,1789,1789,Oil on canvas,50 x 40 in. (127 x 101.6 cm),"Bequest of Mrs. Harry Payne Bingham, 1986",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437450,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -25.110.10,false,true,437452,European Paintings,Painting,"Lady Smith (Charlotte Delaval) and Her Children (George Henry, Louisa, and Charlotte)",,,,,,Artist,,Sir Joshua Reynolds,"British, Plympton 1723–1792 London",,"Reynolds, Joshua, Sir",British,1723,1792,1787,1787,1787,Oil on canvas,55 3/8 x 44 1/8 in. (140.7 x 112.1 cm),"Bequest of Collis P. Huntington, 1900",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437452,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.264.5,false,true,437443,European Paintings,Painting,"Thomas (1740–1825) and Martha Neate (1741–after 1795) with His Tutor, Thomas Needham",,,,,,Artist,,Sir Joshua Reynolds,"British, Plympton 1723–1792 London",,"Reynolds, Joshua, Sir",British,1723,1792,1748,1748,1748,Oil on canvas,66 1/8 x 71 in. (168 x 180.3 cm),"Gift of Heathcote Art Foundation, 1986",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437443,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1980.468,false,true,437764,European Paintings,Painting,The Third Duke of Dorset's Hunter with a Groom and a Dog,,,,,,Artist,,George Stubbs,"British, Liverpool 1724–1806 London",,"Stubbs, George",British,1724,1806,1768,1768,1768,Oil on canvas,40 x 49 3/4 in. (101.6 x 126.4 cm),"Bequest of Mrs. Paul Moore, 1980",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437764,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -09.1.1,false,true,437751,European Paintings,Painting,"Richmond Castle, Yorkshire",,,,,,Artist,,Philip Wilson Steer,"British, Birkenhead 1860–1942 London",,"Steer, Philip Wilson",British,1860,1942,1903,1903,1903,Oil on canvas,29 1/8 x 34 1/2 in. (74 x 87.6 cm),"Catharine Lorillard Wolfe Collection, Wolfe Fund, 1908",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437751,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -47.26,true,true,435826,European Paintings,Painting,The Love Song,,,,,,Artist,,Sir Edward Burne-Jones,"British, Birmingham 1833–1898 Fulham",,"Burne-Jones, Edward, Sir",British,1833,1898,1868–77,1868,1877,Oil on canvas,45 x 61 3/8 in. (114.3 x 155.9 cm),"The Alfred N. Punnett Endowment Fund, 1947",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435826,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -96.28,false,true,436869,European Paintings,Painting,Lachrymae,,,,,,Artist,,"Frederic, Lord Leighton","British, Scarborough 1830–1896 London",,"Leighton, Frederic, Lord",British,1830,1896,ca. 1894–95,1894,1895,Oil on canvas,62 x 24 3/4 in. (157.5 x 62.9 cm),"Catharine Lorillard Wolfe Collection, Wolfe Fund, 1896",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436869,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -06.1328,false,true,437092,European Paintings,Painting,Portia,,,,,,Artist,,Sir John Everett Millais,"British, Southampton 1829–1896 London",,"Millais, John Everett, Sir",British,1829,1896,1886,1886,1886,Oil on canvas,49 1/4 x 33 in. (125.1 x 83.8 cm),"Catharine Lorillard Wolfe Collection, Wolfe Fund, 1906",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437092,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -64.287,false,true,437680,European Paintings,Painting,The Bridge at Villeneuve-la-Garenne,,,,,,Artist,,Alfred Sisley,"British, Paris 1839–1899 Moret-sur-Loing",,"Sisley, Alfred",British,1839,1899,1872,1872,1872,Oil on canvas,19 1/2 x 25 3/4 in. (49.5 x 65.4 cm),"Gift of Mr. and Mrs. Henry Ittleson Jr., 1964",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437680,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1992.366,false,true,437686,European Paintings,Painting,Rue Eugène Moussoir at Moret: Winter,,,,,,Artist,,Alfred Sisley,"British, Paris 1839–1899 Moret-sur-Loing",,"Sisley, Alfred",British,1839,1899,1891,1891,1891,Oil on canvas,18 3/8 x 22 1/4 in. (46.7 x 56.5 cm),"Bequest of Ralph Friedman, 1992",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437686,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -64.154.2,false,true,437685,European Paintings,Painting,The Road from Versailles to Louveciennes,,,,,,Artist,,Alfred Sisley,"British, Paris 1839–1899 Moret-sur-Loing",,"Sisley, Alfred",British,1839,1899,probably 1879,1859,1899,Oil on canvas,18 x 22 in. (45.7 x 55.9 cm),"Gift of Mr. and Mrs. Richard Rodgers, 1964",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437685,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1991.277.3,false,true,437683,European Paintings,Painting,Sahurs Meadows in Morning Sun,,,,,,Artist,,Alfred Sisley,"British, Paris 1839–1899 Moret-sur-Loing",,"Sisley, Alfred",British,1839,1899,1894,1894,1894,Oil on canvas,28 3/4 x 36 1/4 in. (73 x 92.1 cm),"Gift of Janice H. Levin, 1991",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437683,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -67.187.103,false,true,437682,European Paintings,Painting,View of Marly-le-Roi from Coeur-Volant,,,,,,Artist,,Alfred Sisley,"British, Paris 1839–1899 Moret-sur-Loing",,"Sisley, Alfred",British,1839,1899,1876,1876,1876,Oil on canvas,25 3/4 x 36 3/8 in. (65.4 x 92.4 cm),"Bequest of Miss Adelaide Milton de Groot (1876–1967), 1967",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437682,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1976.201.18,false,true,437684,European Paintings,Painting,The Road from Moret to Saint-Mammès,,,,,,Artist,,Alfred Sisley,"British, Paris 1839–1899 Moret-sur-Loing",,"Sisley, Alfred",British,1839,1899,1883–85,1883,1885,Oil on canvas,19 7/8 x 24 1/4 in. (50.5 x 61.5 cm),"Bequest of Joan Whitney Payson, 1975",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437684,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -26.128,false,true,435923,European Paintings,Painting,Stoke-by-Nayland,,,,,,Artist,,John Constable,"British, East Bergholt 1776–1837 Hampstead",,"Constable, John",British,1776,1837,ca. 1810–11,1805,1815,Oil on canvas,11 1/8 x 14 1/4 in. (28.3 x 36.2 cm),"Charles B. Curtis Fund, 1926",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435923,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -06.1272,false,true,435921,European Paintings,Painting,"Mrs. James Pulham Sr. (Frances Amys, ca. 1766–1856)",,,,,,Artist,,John Constable,"British, East Bergholt 1776–1837 Hampstead",,"Constable, John",British,1776,1837,1818,1818,1818,Oil on canvas,29 3/4 x 24 3/4 in. (75.6 x 62.9 cm),"Gift of George A. Hearn, 1906",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435921,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -50.145.8,true,true,435922,European Paintings,Painting,Salisbury Cathedral from the Bishop's Grounds,,,,,,Artist,,John Constable,"British, East Bergholt 1776–1837 Hampstead",,"Constable, John",British,1776,1837,ca. 1825,1820,1830,Oil on canvas,34 5/8 x 44 in. (87.9 x 111.8 cm),"Bequest of Mary Stillman Harkness, 1950",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435922,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -20.40,false,true,436079,European Paintings,Painting,Uvedale Tomkyns Price (1685–1764) and Members of His Family,,,,,,Artist,,Bartholomew Dandridge,"British, London 1691–in or after 1754 London",,"Dandridge, Bartholomew",British,1691,1754,possibly early 1730s,1730,1733,Oil on canvas,40 1/4 x 62 1/2 in. (102.2 x 158.8 cm),"Rogers Fund, 1920",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436079,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2008.547.3,false,true,440725,European Paintings,Painting,"The Two Central Figures in ""Derby Day""",,,,,,Artist,,William Powell Frith,"British, Aldfield, Yorkshire 1819–1909 London",,"Frith, William Powell",British,1819,1909,1860,1860,1860,Oil on canvas,18 x 12 1/2 in. (45.7 x 31.8 cm),"Gift of Mrs. Charles Wrightsman, 2008",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/440725,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1998.451.2,false,true,490260,European Paintings,Painting,"Maple Street, London",,,,,,Artist,,Walter Richard Sickert,"British, Munich 1860–1942 Bathampton, Somerset",,"Sickert, Walter Richard",British,1860,1942,ca. 1915–23,1910,1928,Oil on canvas,30 1/4 × 20 1/8 in. (76.8 × 51.1 cm),"Gift of Emma Swan Hall, 1998",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/490260,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1998.451.3,false,true,490261,European Paintings,Painting,The Antique Shop,,,,,,Artist,,Walter Richard Sickert,"British, Munich 1860–1942 Bathampton, Somerset",,"Sickert, Walter Richard",British,1860,1942,ca. 1906,1901,1911,Oil on cardboard,9 1/2 x 7 1/2 in. (24.1 x 19.1 cm),"Gift of Emma Swan Hall, 1998",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/490261,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1979.135.17,false,true,437670,European Paintings,Painting,The Cigarette (Jeanne Daurmont),,,,,,Artist,,Walter Richard Sickert,"British, Munich 1860–1942 Bathampton, Somerset",,"Sickert, Walter Richard",British,1860,1942,1906,1906,1906,Oil on canvas,20 x 16 in. (50.8 x 40.6 cm),"Bequest of Mary Cushing Fosburgh, 1978",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437670,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1984.433.24,false,true,483377,European Paintings,Painting,Reclining Nude (Thin Adeline),,,,,,Artist,,Walter Richard Sickert,"British, Munich 1860–1942 Bathampton, Somerset",,"Sickert, Walter Richard",British,1860,1942,1906,1906,1906,Oil on canvas,18 1/8 × 15 1/8 in. (46 × 38.4 cm),"Bequest of Scofield Thayer, 1982",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/483377,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2001.45,false,true,438449,European Paintings,Painting,View near Rouen,,,,,,Artist,,Richard Parkes Bonington,"British, Arnold, Nottinghamshire 1802–1828 London",,"Bonington, Richard Parkes",British,1802,1828,ca. 1825,1820,1830,Oil on millboard,11 x 13 in. (27.9 x 33 cm),"Purchase, Gift of Joanne Toor Cummings, by exchange, 2001",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/438449,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -45.146.1,false,true,435707,European Paintings,Painting,Roadside Halt,,,,,,Artist,,Richard Parkes Bonington,"British, Arnold, Nottinghamshire 1802–1828 London",,"Bonington, Richard Parkes",British,1802,1828,1826,1826,1826,Oil on canvas,18 1/4 x 14 7/8 in. (46.4 x 37.8 cm),"Gift of Francis Neilson, 1945",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435707,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -05.32.1,false,true,435632,European Paintings,Painting,Portrait of a Woman,,,,,,Artist,,Sir William Beechey,"British, Burford, Oxfordshire 1753–1839 Hampstead",,"Beechey, William, Sir",British,1753,1839,ca. 1805,1800,1810,Oil on canvas,50 x 40 1/4 in. (127 x 102.2 cm),"Gift of George A. Hearn, 1905",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435632,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -45.59.4,false,true,437501,European Paintings,Painting,"Mrs. Bryan Cooke (Frances Puleston, 1765–1818)",,,,,,Artist,,George Romney,"British, Beckside, Lancashire 1734–1802 Kendal, Cumbria",,"Romney, George",British,1734,1802,ca. 1787–91,1787,1791,Oil on canvas,50 x 39 1/2 in. (127 x 100.3 cm),"Fletcher Fund, 1945",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437501,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -49.7.57,false,true,437500,European Paintings,Painting,"Lady Elizabeth Stanley (1753–1797), Countess of Derby",,,,,,Artist,,George Romney,"British, Beckside, Lancashire 1734–1802 Kendal, Cumbria",,"Romney, George",British,1734,1802,1776–78,1776,1778,Oil on canvas,50 x 40 in. (127 x 101.6 cm),"The Jules Bache Collection, 1949",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437500,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -15.30.37,false,true,437504,European Paintings,Painting,Self-Portrait,,,,,,Artist,,George Romney,"British, Beckside, Lancashire 1734–1802 Kendal, Cumbria",,"Romney, George",British,1734,1802,1795,1795,1795,Oil on canvas,30 x 25 in. (76.2 x 63.5 cm),"Bequest of Maria DeWitt Jesup, from the collection of her husband, Morris K. Jesup, 1914",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437504,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -58.102.2,false,true,437498,European Paintings,Painting,"Portrait of a Woman, Said to Be Emily Bertie Pott (died 1782)",,,,,,Artist,,George Romney,"British, Beckside, Lancashire 1734–1802 Kendal, Cumbria",,"Romney, George",British,1734,1802,1781,1781,1781,Oil on canvas,29 3/4 x 24 7/8 in. (75.6 x 63.2 cm),"Gift of Jessie Woolworth Donahue, 1958",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437498,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1974.159,false,true,436863,European Paintings,Painting,General Garibaldi's Residence at Caprera,,,,,,Artist,,Frederick Richard Lee,"British, Barnstaple 1798–1879 Hermon Station, Malmsbury, Cape Colony, South Africa",,"Lee, Frederick Richard",British,1798,1879,1865,1865,1865,Oil on canvas,34 1/4 x 54 3/8 in. (87 x 138.1 cm),"Gift of Dr. Melvin Goldberg, 1974",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436863,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -62.122.60,false,true,436589,European Paintings,"Painting, miniature",Sir Joshua Reynolds (1723–1792),,,,,,Artist,Style of,William Grimaldi,1773 or later,,"Grimaldi, William",British,1751,1830,,1773,1773,Ivory,"Oval, 1 7/8 x 1 1/2 in. (47 x 37 mm)","Bequest of Millie Bruhl Fredrick, 1962",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/436589,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -41.191.2,false,true,437696,European Paintings,"Painting, miniature",Portrait of an Officer,,,,,,Artist,Imitator of,John Smart,1784 or later?,,"Smart, John",British,1741,1811,,1784,1789,Ivory,"Oval, 1 3/4 x 1 1/4 in. (45 x 32 mm)","Gift of Mrs. Thomas Hunt, 1941",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/437696,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -62.122.32,false,true,437665,European Paintings,"Painting, miniature",Portrait of a Boy,,,,,,Artist,Style of,Samuel Shelley,late 18th century,,"Shelley, Samuel",British,1756,1808,,1770,1799,Ivory,"Oval, 1 1/4 x 1 in. (33 x 27 mm)","Bequest of Millie Bruhl Fredrick, 1962",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/437665,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1984.442,false,true,436878,European Paintings,"Painting, miniature","Copy after ""Rubens, His Wife Helena Fourment (1614–1673), and Their Son Frans (1633–1678)""",,,,,,Artist,,Bernard Lens,"British, 1682–1740",,"Lens, Bernard",British,1682,1740,,1721,1721,Vellum,15 1/2 x 11 7/8 in. (394 x 302 mm),"Purchase, Mr. and Mrs. Charles Wrightsman Gift, 1984",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/436878,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -24.80.524,false,true,436777,European Paintings,"Painting, miniature",Admiral Adam Duncan (1731–1804),,,,,,Artist,Attributed to,Philip Jean,"British, 1755–1802",,"Jean, Philip",British,1755,1802,,1775,1802,Ivory,"Oval, 2 x 1 1/2 in. (50 x 40 mm)","Bequest of Mary Clark Thompson, 1923",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/436777,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -25.106.29,false,true,436778,European Paintings,"Painting, miniature",Portrait of a Man,,,,,,Artist,Attributed to,Philip Jean,"British, 1755–1802",,"Jean, Philip",British,1755,1802,,1775,1802,Ivory,"Oval, 1 3/4 x 1 1/2 in. (46 x 37 mm)","Gift of Mrs. Louis V. Bell, in memory of her husband, 1925",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/436778,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -62.122.36,false,true,435919,European Paintings,"Painting, miniature","George Howard (1773–1848), Lord Morpeth",,,,,,Artist,,Richard Collins,"British, 1755–1831",,"Collins, Richard",British,1755,1831,,1775,1831,Ivory,"Oval, 2 x 1 5/8 in. (52 x 41 mm)","Bequest of Millie Bruhl Fredrick, 1962",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/435919,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -95.14.97,false,true,437951,European Paintings,"Painting, miniature","Portrait of a Man, Said to Be Mr. Fitzgerald",,,,,,Artist,,William Wood,"British, 1769–1810",,"Wood, William",British,1769,1810,,1789,1810,Ivory,"Oval, 3 1/4 x 2 5/8 in. (81 x 66 mm)","The Moses Lazarus Collection, Gift of Josephine and Sarah Lazarus, in memory of their father, 1888–95",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/437951,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -95.14.69,false,true,435920,European Paintings,"Painting, miniature",Portrait of a Man,,,,,,Artist,,Samuel Collins,"British, 1735?–1768",,"Collins, Samuel",British,1735,1768,,1755,1768,Ivory,"Oval, 1 5/8 x 1 1/4 in. (40 x 32 mm)","The Moses Lazarus Collection, Gift of Josephine and Sarah Lazarus, in memory of their father, 1888–95",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/435920,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -62.122.21,false,true,435943,European Paintings,"Painting, miniature","Charles II (1630–1685), King of England",,,,,,Artist,Style of,Samuel Cooper,"British, probably after 1672",,"Cooper, Samuel",British,1608,1672,,1672,1700,Vellum laid on prepared card with gessoed back,"Oval, 1 1/4 x 1 1/8 in. (33 x 27 mm)","Bequest of Millie Bruhl Fredrick, 1962",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/435943,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -33.136.15,false,true,436298,European Paintings,"Painting, miniature",Portrait of a Woman,,,,,,Artist,,Joshua Wilson Faulkner,"British, active ca. 1809–20",,"Faulkner, Joshua Wilson",British,1809,1820,,1809,1820,Ivory,4 x 3 1/8 in. (100 x 80 mm),"Bequest of Margaret Crane Hurlbut, 1933",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/436298,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -59.23.78,false,true,437695,European Paintings,"Painting, miniature","Portrait of a Woman, Said to Be Lady Dering",,,,,,Artist,,John Smart,"British, Norfolk 1741–1811 London",,"Smart, John",British,1741,1811,,1761,1811,Pencil and watercolor on paper,2 1/4 x 2 1/8 in. (56 x 54 mm),"Bequest of Alexandrine Sinsheimer, 1958",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/437695,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -68.213,false,true,437277,European Paintings,"Painting, miniature",A Man with the Initials FM,,,,,,Artist,Attributed to,William Pether,"British, Carlisle ca. 1738–1821 Bristol",,"Pether, William",British,1733,1821,,1751,1821,Ivory,"Oval, 3 x 2 1/4 in. (75 x 56 mm)","Gift of Lilliana Teruzzi, 1968",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/437277,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -24.80.499,false,true,435996,European Paintings,"Painting, miniature",Ensign Lionel Robert Tollemache (1774–1793),,,,,,Artist,,Richard Cosway,"British, Oakford, Devon 1742–1821 London",,"Cosway, Richard",British,1742,1821,,1762,1821,Ivory,"Oval, 3 1/8 x 2 1/2 in. (81 x 65 mm)","Bequest of Mary Clark Thompson, 1923",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/435996,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -05.31,false,true,436279,European Paintings,"Painting, sketch",The Three Graces,,,,,,Artist,,William Etty,"British, York 1787–1849 York",,"Etty, William",British,1787,1849,,1807,1849,Oil on millboard,22 1/2 x 18 3/4 in. (57.2 x 47.6 cm),"Rogers Fund, 1905",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436279,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -59.131,false,true,436278,European Paintings,Painting,Allegory,,,,,,Artist,,William Etty,"British, York 1787–1849 York",,"Etty, William",British,1787,1849,,1807,1849,"Oil on canvas, laid down on wood","Oval, 28 x 34 1/2 in. (71.1 x 87.6 cm)","Gift of Martin Birnbaum, 1959",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436278,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -39.65.1,false,true,437503,European Paintings,Painting,"Mrs. George Horsley (Charlotte Mary Talbot, died 1828)",,,,,,Artist,Attributed to,John Westbrooke Chandler,"British, 1763?–?1807 Edinburgh",,"Chandler, John Westbrooke",British,1763,1807,,1783,1807,Oil on canvas,30 x 24 7/8 in. (76.2 x 63.2 cm),"Bequest of Jacob Ruppert, 1939",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437503,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -60.94.2,false,true,437103,European Paintings,Painting,Harbor Scene: An English Ship with Sails Loosened Firing a Gun,,,,,,Artist,,Peter Monamy,"British, London 1681–1749 London",,"Monamy, Peter",British,1681,1749,,1704,1749,Oil on canvas,48 x 59 in. (121.9 x 149.9 cm),"Gift of William P. Clyde, 1960",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437103,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -53.113,false,true,436691,European Paintings,Painting,"Richard Humphreys, the Boxer",,,,,,Artist,,John Hoppner,"British, London 1758–1810 London",,"Hoppner, John",British,1758,1810,,1778,1810,Oil on canvas,55 3/4 x 44 1/4 in. (141.6 x 112.4 cm),"The Alfred N. Punnett Endowment Fund, 1953",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436691,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -65.203,false,true,436687,European Paintings,Painting,"Mrs. Richard Brinsley Sheridan (Hester Jane Ogle, 1775/76–1817) and Her Son (Charles Brinsley Sheridan, 1796–1843)",,,,,,Artist,,John Hoppner,"British, London 1758–1810 London",,"Hoppner, John",British,1758,1810,,1778,1810,Oil on canvas,93 3/4 x 59 in. (238.1 x 149.9 cm),"Gift of Mrs. Carll Tucker, 1965",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436687,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -95.27.2,false,true,436631,European Paintings,Painting,Self-Portrait,,,,,,Artist,,George Henry Harlow,"British, London 1787–1819 London",,"Harlow, George Henry",British,1787,1819,,1807,1819,Oil on canvas,30 x 25 in. (76.2 x 63.5 cm),"Gift of George A. Hearn, 1895",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436631,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.264.4,false,true,437239,European Paintings,Painting,"The Grandchildren of Sir William Heathcote, 3rd Baronet",,,,,,Artist,,William Owen,"British, Ludlow 1769–1825 London",,"Owen, William",British,1769,1825,,1789,1825,Oil on canvas,55 1/4 x 67 1/2 in. (140.3 x 171.5 cm),"Gift of Heathcote Art Foundation, 1986",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437239,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -65.181.9,false,true,436852,European Paintings,Painting,John Julius Angerstein (1736–1823),,,,,,Artist,,Sir Thomas Lawrence,"British, Bristol 1769–1830 London",and Workshop,"Lawrence, Thomas, Sir",British,1769,1830,,1789,1830,Oil on canvas,36 x 28 in. (91.4 x 71.1 cm),"Bequest of Adele L. Lehman, in memory of Arthur Lehman, 1965",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436852,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -49.7.55,false,true,436432,European Paintings,Painting,Queen Charlotte,,,,,,Artist,,Thomas Gainsborough,"British, Sudbury 1727–1788 London",,"Gainsborough, Thomas",British,1727,1788,,1747,1788,Oil on canvas,23 3/4 x 17 1/2 in. (60.3 x 44.5 cm),"The Jules Bache Collection, 1949",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436432,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -60.71.7,false,true,436438,European Paintings,Painting,"John Hobart (1723–1793), 2nd Earl of Buckinghamshire",,,,,,Artist,,Thomas Gainsborough,"British, Sudbury 1727–1788 London",,"Gainsborough, Thomas",British,1727,1788,,1747,1788,Oil on canvas,29 1/2 x 24 3/4 in. (74.9 x 62.9 cm),"Bequest of Lillian S. Timken, 1959",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436438,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -66.88.1,false,true,436436,European Paintings,Painting,"Mrs. Ralph Izard (Alice De Lancey, 1746/47–1832)",,,,,,Artist,,Thomas Gainsborough,"British, Sudbury 1727–1788 London",,"Gainsborough, Thomas",British,1727,1788,,1747,1788,Oil on canvas,"Oval, 30 1/4 x 25 1/8 in. (76.8 x 63.8 cm)","Bequest of Jeanne King deRham, in memory of her father, David H. King Jr., 1966",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436436,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1990.200,false,true,436434,European Paintings,Painting,Lieutenant Colonel Paul Pechell (1724–1800),,,,,,Artist,,Thomas Gainsborough,"British, Sudbury 1727–1788 London",,"Gainsborough, Thomas",British,1727,1788,,1747,1788,Oil on canvas,30 1/8 x 25 1/8 in. (76.5 x 63.8 cm),"Gift of Mr. and Mrs. Harry Payne Bingham Jr., 1990",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436434,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -39.65.5,false,true,435998,European Paintings,Painting,"Admiral Harry Paulet (1719/20–1794), Sixth Duke of Bolton",,,,,,Artist,,Francis Cotes,"British, London 1726–1770 Richmond",,"Cotes, Francis",British,1726,1770,,1760,1770,Oil on canvas,50 x 40 in. (127 x 101.6 cm),"Bequest of Jacob Ruppert, 1939",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435998,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -44.56,false,true,437643,European Paintings,Painting,The Building of Westminster Bridge,,,,,,Artist,,Samuel Scott,"British, London ca. 1702–1772 Bath",,"Scott, Samuel",British,1697,1772,,1722,1772,Oil on canvas,24 x 44 3/8 in. (61 x 112.7 cm),"Purchase, Charles B. Curtis Fund and Joseph Pulitzer Bequest, 1944",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437643,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -45.59.3,false,true,437446,European Paintings,Painting,"Mrs. Horton, Later Viscountess Maynard (died 1814/15)",,,,,,Artist,,Sir Joshua Reynolds,"British, Plympton 1723–1792 London",,"Reynolds, Joshua, Sir",British,1723,1792,,1767,1769,Oil on canvas,36 1/4 x 28 in. (92.1 x 71.1 cm),"Fletcher Fund, 1945",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437446,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -42.152.1,false,true,437453,European Paintings,Painting,Portrait of a Woman,,,,,,Artist,,Sir Joshua Reynolds,"British, Plympton 1723–1792 London",,"Reynolds, Joshua, Sir",British,1723,1792,,1743,1792,Oil on canvas,29 5/8 x 24 1/2 in. (75.2 x 62.2 cm),"Bequest of George D. Pratt, 1935",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437453,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -50.145.18,false,true,436240,European Paintings,Painting,Mrs. John Puget (Catherine Hawkins),,,,,,Artist,Attributed to,Richard Gainsborough Dupont,"British, Sudbury 1789–1874 Sudbury",,"Dupont, Richard Gainsborough",British,1789,1874,,1774,1797,Oil on copper,6 x 4 3/4 in. (15.2 x 12.1 cm),"Bequest of Mary Stillman Harkness, 1950",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436240,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -87.15.79,false,true,436868,European Paintings,Painting,Lucia,,,,,,Artist,,"Frederic, Lord Leighton","British, Scarborough 1830–1896 London",,"Leighton, Frederic, Lord",British,1830,1896,,1850,1896,Oil on canvas,14 7/8 x 10 in. (37.8 x 25.4 cm),"Catharine Lorillard Wolfe Collection, Bequest of Catharine Lorillard Wolfe, 1887",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436868,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -69.104,false,true,435993,European Paintings,Painting,"Marianne Dorothy Harland (1759–1785), Later Mrs. William Dalrymple",,,,,,Artist,,Richard Cosway,"British, Oakford, Devon 1742–1821 London",,"Cosway, Richard",British,1742,1821,,1762,1821,Oil on canvas,28 x 36 1/8 in. (71.1 x 91.8 cm),"Gift of Mrs. William M. Haupt, from the collection of Mrs. James B. Haggin, 1969",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435993,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -49.7.56,false,true,436239,European Paintings,Painting,"Anne Elizabeth Cholmley (1769–1788), Later Lady Mulgrave",,,,,,Artist,,Gainsborough Dupont,"British, Sudbury, Suffolk 1754–1797 London",,"Dupont, Gainsborough",British,1754,1797,,1774,1797,Oil on wood,Overall 7 1/8 x 5 3/4 in. (18.1 x 14.6 cm); painted surface 6 x 4 3/4 in. (15.2 x 12.1 cm),"The Jules Bache Collection, 1949",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436239,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -56.54.1,false,true,437660,European Paintings,Painting,Portrait of a Horseman,,,,,,Artist,,James Seymour,"British, London ca. 1702–1752 Southwark (London)",,"Seymour, James",British,1697,1752,,1748,1748,Oil on canvas,37 x 51 5/8 in. (94 x 131.1 cm),"Gift of the children of the late Otto H. and Addie W. Kahn (Lady Maud E. Marriott, Mrs. Margaret D. Ryan, Roger W. Kahn, and Gilbert W. Kahn), 1956",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437660,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.264.2,false,true,435630,European Paintings,Painting,Edward Miles (1752–1828),,,,,,Artist,,Sir William Beechey,"British, Burford, Oxfordshire 1753–1839 Hampstead",,"Beechey, William, Sir",British,1753,1839,,1785,1785,Oil on canvas,11 7/8 x 9 7/8 in. (30.2 x 25.1 cm),"Gift of Heathcote Art Foundation, 1986",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435630,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1986.264.3,false,true,435631,European Paintings,Painting,"George IV (1762–1830), When Prince of Wales",,,,,,Artist,,Sir William Beechey,"British, Burford, Oxfordshire 1753–1839 Hampstead",and Workshop,"Beechey, William, Sir",British,1753,1839,,1773,1839,Oil on canvas,56 1/4 x 44 1/2 in. (142.9 x 113 cm),"Gift of Heathcote Art Foundation, 1986",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435631,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -50.169,false,true,437499,European Paintings,Painting,Portrait of a Man,,,,,,Artist,,George Romney,"British, Beckside, Lancashire 1734–1802 Kendal, Cumbria",,"Romney, George",British,1734,1802,,1754,1802,Oil on canvas,30 x 24 3/4 in. (76.2 x 62.9 cm),"Gift of Mr. and Mrs. Edwin C. Vogel, 1950",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437499,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -53.220,false,true,437505,European Paintings,Painting,Admiral Sir Chaloner Ogle (1726–1816),,,,,,Artist,,George Romney,"British, Beckside, Lancashire 1734–1802 Kendal, Cumbria",,"Romney, George",British,1734,1802,,1754,1802,Oil on canvas,30 x 24 5/8 in. (76.2 x 62.5 cm),"Gift of Lennen and Newell Inc., 1953",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437505,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -45.59.5,false,true,437502,European Paintings,Painting,"Mrs. Charles Frederick (Martha Rigden, died 1794)",,,,,,Artist,,George Romney,"British, Beckside, Lancashire 1734–1802 Kendal, Cumbria",,"Romney, George",British,1734,1802,,1754,1802,Oil on canvas,29 3/4 x 24 3/4 in. (75.6 x 62.9 cm),"Fletcher Fund, 1945",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437502,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1974.289.2,false,true,435775,European Paintings,Painting,Kynance,,,,,,Artist,,John Brett,"British, Bletchingly 1831–1902 London",,"Brett, John",British,1831,1902,1888,1888,1888,Oil on canvas,7 x 14 1/8 in. (17.8 x 35.9 cm),"Bequest of Theodore Rousseau Jr., 1973",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435775,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1972.145.7,false,true,435726,European Paintings,"Painting, miniature",Count Alexander Ivanovich Sollogoub (1788–1844),,,,,,Artist,,Domenico Bossi,"Italian, Venetian, 1765–1853",,"Bossi, Domenico",Italian,1765,1853,1810,1810,1810,Ivory,"Oval, 2 3/8 x 1 7/8 in. (60 x 47 mm)","Gift of Humanities Fund Inc., 1972",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/435726,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1992.104,false,true,436977,European Paintings,"Painting, miniature",Napoléon I (1769–1821) on Horseback,,,,,,Artist,,Luigi Marta,"Italian, Neapolitan, 1790–1858",,"Marta, Luigi",Italian,1790,1858,1830,1830,1830,Ivory,5 3/4 x 7 1/2 in. (146 x 191 mm),"Gift of Gloria Zicht, 1992",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/436977,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.360,false,true,439272,European Paintings,"Painting, drawing",Study of a Boy in a Blue Jacket,,,,,,Artist,,Benedetto Luti,"Italian, Florence 1666–1724 Rome",,"Luti, Benedetto",Italian,1666,1724,1717,1717,1717,"Pastel and chalk on blue laid paper, laid down on paste paper",16 x 13 in. (40.6 x 33 cm),"Gwynne Andrews Fund, 2007",,,,,,,,,,,,Pastels & Oil Sketches on Paper,,http://www.metmuseum.org/art/collection/search/439272,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2007.361,false,true,439273,European Paintings,"Painting, drawing",Study of a Girl in Red,,,,,,Artist,,Benedetto Luti,"Italian, Florence 1666–1724 Rome",,"Luti, Benedetto",Italian,1666,1724,1717,1717,1717,"Pastel and chalk on blue laid paper, laid down on paste paper",16 1/2 x 13 3/8 in. (41.9 x 34 cm),"Gwynne Andrews Fund, 2007",,,,,,,,,,,,Pastels & Oil Sketches on Paper,,http://www.metmuseum.org/art/collection/search/439273,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -49.122.2,false,true,435854,European Paintings,"Painting, miniature",Portrait of a Man,,,,,,Artist,,Rosalba Carriera,"Italian, Venice 1673–1757 Venice",,"Carriera, Rosalba",Italian,1673,1757,ca. 1710,1705,1715,Ivory,"Oval, 3 x 2 1/4 in. (76 x 59 mm)","Rogers Fund, 1949",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/435854,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2002.22,false,true,438544,European Paintings,"Painting, drawing","Gustavus Hamilton (1710–1746), Second Viscount Boyne, in Masquerade Costume",,,,,,Artist,,Rosalba Carriera,"Italian, Venice 1673–1757 Venice",,"Carriera, Rosalba",Italian,1673,1757,1730–31,1730,1731,"Pastel on paper, laid down on canvas",22 1/4 x 16 7/8 in. (56.5 x 42.9 cm),"Purchase, George Delacorte Fund Gift, in memory of George T. Delacorte Jr., and Gwynne Andrews, Victor Wilbour Memorial, and Marquand Funds, 2002",,,,,,,,,,,,Pastels & Oil Sketches on Paper,,http://www.metmuseum.org/art/collection/search/438544,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1999.287,false,true,438393,European Paintings,"Painting, miniature",Pope Pius VII (1742–1823),,,,,,Artist,Attributed to,Bianca Boni,"Italian, Florentine, active early 19th century",,"Boni, Bianca",Italian,1800,1829,ca. 1820,1815,1825,Watercolor on ivory,Diameter 2 3/4 in. (70 mm),"Bequest of Francesca Rospigliosi, 1998",,,,,,,,,,,,Miniatures,,http://www.metmuseum.org/art/collection/search/438393,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -11.126.2,false,true,436032,European Paintings,"Painting, cassone panel",The Legend of Cloelia,,,,,,Artist,,Guidoccio di Giovanni Cozzarelli,"Italian, Sienese, 1450–1516",,"Cozzarelli, Guidoccio di Giovanni",Italian,1450,1516,ca. 1480,1475,1485,Tempera and gold on wood,Overall 17 3/4 x 45 1/2 in. (45.1 x 115.6 cm); painted surface 15 1/8 x 43 1/4 in. (38.4 x 109.9 cm),"Frederick C. Hewitt Fund, 1911",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436032,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2010.289,false,true,441024,European Paintings,Painting,Head of an Old Woman,,,,,,Artist,,Orazio Borgianni,"Italian, Rome 1578–1616 Rome",,"Borgianni, Orazio",Italian,1578,1616,after 1610,1611,1616,Oil on canvas,20 7/8 x 15 3/8 in. (53 x 39 cm),"Purchase, Gwynne Andrews Fund and Marco Voena and Luigi Koelliker Gift, 2010",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/441024,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2012.543,false,true,442183,European Paintings,Painting,A Cardinal's Procession,,,,,,Artist,,Ottavio Leoni (Il Padovano),"Italian, Rome 1578–1630 Rome",,"Leoni, Ottavio (Il Padovano)",Italian,1578,1630,1621,1621,1621,Oil on copper,15 1/2 x 14 3/4 in. (39.4 x 37.5 cm),"Gift of Damon Mezzacappa, 2012",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/442183,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2014.277,false,true,643540,European Paintings,Painting,Portrait of a Woman,,,,,,Artist,,Giovanni Battista Gaulli (Il Baciccio),"Italian, Genoa 1639–1709 Rome",,"Gaulli, Giovanni Battista",Italian,1639,1709,ca. 1670s,1670,1679,Oil on canvas,28 5/8 × 23 1/4 in. (72.7 × 59.1 cm),"Gift of Álvaro Saieh Bendeck, Jean-Luc Baroni, and Fabrizio Moretti, in honor of Keith Christiansen, 2014",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/643540,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1987.75,false,true,437784,European Paintings,Painting,Alexander the Great Rescued from the River Cydnus,,,,,,Artist,,Pietro Testa,"Italian, Lucca 1612–1650 Rome",,"Testa, Pietro",Italian,1612,1650,ca. 1650,1645,1655,Oil on canvas,38 x 54 in. (96.5 x 137.2 cm),"Gift of Eula M. Ganz, in memory of Paul H. Ganz, 1987",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437784,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -03.37.1,false,true,435623,European Paintings,Painting,Portrait of a Young Man,,,,,,Artist,,Pompeo Batoni,"Italian, Lucca 1708–1787 Rome",,"Batoni, Pompeo",Italian,1708,1787,ca. 1760–65,1760,1765,Oil on canvas,97 1/8 x 69 1/4 in. (246.7 x 175.9 cm),"Rogers Fund, 1903",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435623,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1982.438,false,true,435622,European Paintings,Painting,Diana and Cupid,,,,,,Artist,,Pompeo Batoni,"Italian, Lucca 1708–1787 Rome",,"Batoni, Pompeo",Italian,1708,1787,1761,1761,1761,Oil on canvas,49 x 68 in. (124.5 x 172.7 cm),"Purchase, The Charles Engelhard Foundation, Robert Lehman Foundation Inc., Mrs. Haebler Frantz, April R. Axton, L. H. P. Klotz, and David Mortimer Gifts; and Gifts of Mr. and Mrs. Charles Wrightsman, George Blumenthal, and J. Pierpont Morgan, Bequests of Millie Bruhl Fredrick and Mary Clark Thompson, and Rogers Fund, by exchange, 1982",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435622,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1971.93,false,true,437607,European Paintings,Painting,Paradise,,,,,,Artist,,Carlo Saraceni,"Italian, Venetian, 1579?–1620",,"Saraceni, Carlo",Italian,1579,1620,ca. 1598,1593,1603,Oil on copper,Overall 21 3/8 x 18 7/8 in. (54.3 x 47.9 cm); painted surface 20 7/8 x 18 3/8 in. (53 x 46.7 cm),"Theodore M. Davis Collection, Bequest of Theodore M. Davis, by exchange, 1971",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437607,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.212,false,true,436908,European Paintings,Painting,Saint Catherine of Alexandria,,,,,,Artist,,Pietro Lorenzetti,"Italian, active Siena 1320–44",,"Lorenzetti, Pietro",Italian,1320,1344,shortly after 1342,1342,1344,"Tempera on wood, gold ground",Overall 26 x 16 1/4 in. (66 x 41.3 cm); painted surface 24 1/2 x 16 1/4 in. (62.2 x 41.3 cm),"Rogers Fund, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436908,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2002.436,true,true,438605,European Paintings,Painting,The Crucifixion,,,,,,Artist,,Pietro Lorenzetti,"Italian, active Siena 1320–44",,"Lorenzetti, Pietro",Italian,1320,1344,1340s,1340,1344,Tempera and gold leaf on wood,Overall 16 1/2 x 12 1/2 in. (41.9 x 31.8 cm); painted surface 14 1/8 x 10 1/8 in. (35.9 x 25.7 cm),"Purchase, Lila Acheson Wallace Gift and Gwynne Andrews Fund, 2002",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/438605,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -92.1.62,false,true,436943,European Paintings,Painting,A Circus Boy,,,,,,Artist,,Antonio Mancini,"Italian, Albano 1852–1930 Rome",,"Mancini, Antonio",Italian,1852,1930,1872,1872,1872,Oil on canvas,59 5/8 x 28 1/2 in. (151.4 x 72.4 cm),"Bequest of Elizabeth U. Coles, in memory of her son, William F. Coles, 1892",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436943,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1984.191,false,true,436935,European Paintings,Painting,The Tame Magpie,,,,,,Artist,,Alessandro Magnasco,"Italian, Genoa 1667–1749 Genoa",,"Magnasco, Alessandro",Italian,1667,1749,ca. 1707–8,1707,1708,Oil on canvas,25 x 29 1/2 in. (63.5 x 74.9 cm),"Purchase, Katherine D. W. Glover Gift, 1984",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436935,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -41.100.13,false,true,435659,European Paintings,Painting,Madonna and Child with Angels,,,,,,Artist,,Bernardino da Genoa,"Italian, Genoese, active in 1515",,Bernardino da Genoa,Italian,1515,1515,1515,1515,1515,Oil on wood,29 3/8 x 22 5/8 in. (74.6 x 57.5 cm),"Gift of George Blumenthal, 1941",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435659,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1973.165,false,true,435833,European Paintings,Painting,Herodias,,,,,,Artist,,Francesco Cairo,"Italian, Milan 1607–1665 Milan",,"Cairo, Francesco",Italian,1607,1665,before 1635,1627,1635,Oil on canvas,29 5/8 x 24 5/8 in. (75.2 x 62.5 cm),"Gift of Paul Ganz, in memory of Rudolf Wittkower, 1973",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435833,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2012.100.2,false,true,441230,European Paintings,Painting,A Female Martyr Saint,,,,,,Artist,,Carlo Francesco Nuvolone,"Italian, Milan 1609–1662 Milan",,"Nuvolone, Carlo Francesco",Italian,1609,1662,ca. 1650,1645,1655,Oil on wood,20 x 16 3/8 in. (50.8 x 41.6 cm),"Bequest of Anna Mont, in memory of Frederick Mont, 2010",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/441230,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -30.15,false,true,435864,European Paintings,Painting,A Woman with a Dog,,,,,,Artist,,Giacomo Ceruti,"Italian, Milan 1698–1767 Milan",,"Ceruti, Giacomo",Italian,1698,1767,1740s,1740,1749,Oil on canvas,38 x 28 1/2 in. (96.5 x 72.4 cm),"Maria DeWitt Jesup Fund, 1930",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435864,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -59.40,false,true,437745,European Paintings,Painting,Judith with the Head of Holofernes,,,,,,Artist,,Massimo Stanzione,"Italian, Neapolitan, 1585–1656",,"Stanzione, Massimo",Italian,1585,1656,ca. 1640,1635,1645,Oil on canvas,78 1/2 x 57 1/2 in. (199.4 x 146.1 cm),"Gift of Edward W. Carter, 1959",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437745,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -71.17,false,true,437376,European Paintings,Painting,A Cat Stealing Fish,,,,,,Artist,,Giuseppe Recco,"Italian, Neapolitan, 1634–1695",,"Recco, Giuseppe",Italian,1634,1695,late 1660s,1667,1669,Oil on canvas,38 x 50 1/2 in. (96.5 x 128.3 cm),"Purchase, 1871",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437376,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -06.1046,false,true,436512,European Paintings,"Painting, predella panel",Paradise,,,,,,Artist,,Giovanni di Paolo (Giovanni di Paolo di Grazia),"Italian, Siena 1398–1482 Siena",,Giovanni di Paolo (Giovanni di Paolo di Grazia),Italian,1398,1482,1445,1445,1445,"Tempera and gold on canvas, transferred from wood",Overall 18 1/2 x 16 in. (47 x 40.6 cm); painted surface 17 1/2 x 15 1/8 in. (44.5 x 38.4 cm),"Rogers Fund, 1906",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436512,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -41.100.4,false,true,436513,European Paintings,"Painting, predella panel",The Presentation of Christ in the Temple,,,,,,Artist,,Giovanni di Paolo (Giovanni di Paolo di Grazia),"Italian, Siena 1398–1482 Siena",,Giovanni di Paolo (Giovanni di Paolo di Grazia),Italian,1398,1482,ca. 1435,1430,1440,Tempera and gold on wood,Overall 15 1/2 x 18 1/8 in. (39.4 x 46 cm); painted surface 15 1/4 x 17 1/4 in. (38.7 x 43.8 cm),"Gift of George Blumenthal, 1941",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436513,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -88.3.111,false,true,436515,European Paintings,Painting,Saints Matthew and Francis,,,,,,Artist,,Giovanni di Paolo (Giovanni di Paolo di Grazia),"Italian, Siena 1398–1482 Siena",,Giovanni di Paolo (Giovanni di Paolo di Grazia),Italian,1398,1482,ca. 1435,1430,1440,"Tempera on wood, gold ground","Overall, with added strips, 54 5/8 x 34 3/4 in. (138.7 x 88.3 cm); painted surface 52 7/8 x 33 1/2 in. (134.3 x 85.1 cm)","Gift of Coudert Brothers, 1888",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436515,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1982.60.4,false,true,436509,European Paintings,Painting,The Adoration of the Magi,,,,,,Artist,,Giovanni di Paolo (Giovanni di Paolo di Grazia),"Italian, Siena 1398–1482 Siena",,Giovanni di Paolo (Giovanni di Paolo di Grazia),Italian,1398,1482,ca. 1460,1455,1465,Tempera and gold on wood,10 5/8 x 9 1/8 in. (27 x 23.2 cm),"The Jack and Belle Linsky Collection, 1982",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436509,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -32.100.76,false,true,436508,European Paintings,"Painting, polyptych",Madonna and Child with Saints,,,,,,Artist,,Giovanni di Paolo (Giovanni di Paolo di Grazia),"Italian, Siena 1398–1482 Siena",,Giovanni di Paolo (Giovanni di Paolo di Grazia),Italian,1398,1482,1454,1454,1454,"Tempera on wood, gold ground","Central panel 82 3/4 x 25 7/8 in. (210.2 x 65.7 cm); left panels 70 7/8 x 16 7/8 in. (180 x 42.9 cm), 70 7/8 x 16 3/4 in. (180 x 42.5 cm); right panels 70 7/8 x 16 7/8 in. (180 x 42.9 cm), 70 7/8 x 16 3/4 in. (180 x 42.5 cm)","The Friedsam Collection, Bequest of Michael Friedsam, 1931",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436508,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -41.190.16,false,true,436510,European Paintings,Painting,Madonna and Child with Two Angels and a Donor,,,,,,Artist,,Giovanni di Paolo (Giovanni di Paolo di Grazia),"Italian, Siena 1398–1482 Siena",,Giovanni di Paolo (Giovanni di Paolo di Grazia),Italian,1398,1482,ca. 1445,1440,1450,"Tempera on wood, gold ground (partly checkered with modern red glazes)",Shaped top: overall 57 1/8 x 32 in. (145.1 x 81.3 cm); painted surface 54 1/4 x 32 in. (137.8 x 81.3 cm),"Bequest of George Blumenthal, 1941",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436510,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -32.100.83a–d,false,true,436514,European Paintings,"Painting, pilasters of an altarpiece","Saints Catherine of Alexandria, Barbara, Agatha, and Margaret",,,,,,Artist,,Giovanni di Paolo (Giovanni di Paolo di Grazia),"Italian, Siena 1398–1482 Siena",,Giovanni di Paolo (Giovanni di Paolo di Grazia),Italian,1398,1482,ca. 1470,1465,1475,"Tempera on wood, gold ground","(a) overall 18 3/4 x 6 in. (47.6 x 15.2 cm), painted surface 18 1/4 x 5 1/2 in. (46.4 x 14 cm); (b) overall 18 3/4 x 6 in. (47.6 x 15.2 cm), painted surface 18 3/8 x 5 5/8 in. (46.7 x 14.3 cm); (c) overall 18 3/4 x 6 in. (47.6 x 15.2 cm), painted surface 18 3/8 x 5 3/8 in. (46.7 x 13.7 cm); (d) overall 18 3/4 x 6 in. (47.6 x 15.2 cm), painted surface 18 1/4 x 5 5/8 in. (46.4 x 14.3 cm)","The Friedsam Collection, Bequest of Michael Friedsam, 1931",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436514,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -58.189.1,false,true,437606,European Paintings,"Painting, predella panel",The Massacre of the Innocents,,,,,,Artist,,Sano di Pietro (Ansano di Pietro di Mencio),"Italian, Siena 1405–1481 Siena",,Sano di Pietro (Ansano di Pietro di Mencio),Italian,1405,1481,ca. 1470,1465,1475,Tempera on wood,11 7/8 x 17 3/8 in. (30.2 x 44.1 cm),"Gift of Irma N. Straus, 1958",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437606,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -58.189.2,false,true,437602,European Paintings,"Painting, predella panel",The Adoration of the Magi,,,,,,Artist,,Sano di Pietro (Ansano di Pietro di Mencio),"Italian, Siena 1405–1481 Siena",,Sano di Pietro (Ansano di Pietro di Mencio),Italian,1405,1481,ca. 1470,1465,1475,Tempera and gold on wood,11 7/8 x 18 3/4 in. (30.2 x 47.6 cm),"Gift of Irma N. Straus, 1958",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437602,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -64.189.4,false,true,437605,European Paintings,"Painting, portable triptych",Madonna and Child; Saint John the Baptist; Saint Jerome,,,,,,Artist,,Sano di Pietro (Ansano di Pietro di Mencio),"Italian, Siena 1405–1481 Siena",,Sano di Pietro (Ansano di Pietro di Mencio),Italian,1405,1481,ca. 1450–55,1450,1455,"Tempera on wood, gold ground","Central panel, overall, with engaged frame, 17 3/8 x 12 5/8 in. (44.1 x 32.1 cm), painted surface 14 3/4 x 10 1/8 in. (37.5 x 25.7 cm); each wing, overall, with engaged frame, 17 3/8 x 6 1/4 in. (44.1x 15.9 cm), painted surface 15 1/2 x 4 5/8 in. (39.4 x 11.7 cm)","Gift of Irma N. Straus, 1964",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437605,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -65.181.7,false,true,437603,European Paintings,"Painting, predella panel",The Burial of Saint Martha,,,,,,Artist,,Sano di Pietro (Ansano di Pietro di Mencio),"Italian, Siena 1405–1481 Siena",,Sano di Pietro (Ansano di Pietro di Mencio),Italian,1405,1481,ca. 1460–70,1455,1465,Tempera and gold on wood,5 1/2 x 11 1/2 in. (14 x 29.2 cm),"Bequest of Adele L. Lehman, in memory of Arthur Lehman, 1965",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437603,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1987.290.2ab,false,true,437604,European Paintings,"Painting, portable altarpiece","Madonna and Child with the Dead Christ, Saints Agnes and Catherine of Alexandria, and Two Angels",,,,,,Artist,,Sano di Pietro (Ansano di Pietro di Mencio),"Italian, Siena 1405–1481 Siena",,Sano di Pietro (Ansano di Pietro di Mencio),Italian,1405,1481,ca. 1470–80,1465,1475,"Tempera on wood, gold ground","Main panel, overall, with engaged (modern) frame, 12 3/4 x 11 3/4 in. (32.4 x 29.8 cm), painted surface 10 7/8 x 9 7/8 in. (27.6 x 25.1 cm); predella, overall, with engaged (modern) frame, 3 5/8 x 12 5/8 in. (9.2 x 32.1 cm), painted surface 2 5/8 x 11 7/8 in. (6.7 x 30.2 cm)","Anonymous Bequest, 1984",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437604,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -20.182,false,true,436330,European Paintings,"Painting, fragment of a cassone panel",Goddess of Chaste Love,,,,,,Artist,,Francesco di Giorgio Martini,"Italian, Siena 1439–1501 Siena",,Francesco di Giorgio Martini,Italian,1439,1501,1468–75,1468,1475,Tempera and gold on wood,15 1/2 x 17 1/4 in. (39.4 x 43.8 cm),"Marquand Fund, 1920",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436330,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1996.341,false,true,438029,European Paintings,"Painting, lunette",The Man of Sorrows with Two Angels,,,,,,Artist,Workshop of,Francesco di Giorgio Martini,"Italian, Siena 1439–1501 Siena",,Francesco di Giorgio Martini,Italian,1439,1501,ca. 1470,1465,1475,Tempera on wood,Frame 53 3/4 x 32 5/8 in. (136.5 x 82.9 cm); painted surface 6 7/8 x 18 1/4 in. (17.5 x 46.4 cm),"Anonymous Gift, in memory of Kurt Cassirer, 1996",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/438029,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -61.43,false,true,437195,European Paintings,Painting,Madonna and Child with Saints Jerome and Mary Magdalen,,,,,,Artist,,Neroccio de' Landi,"Italian, Siena 1447–1500 Siena",,Neroccio de' Landi,Italian,1447,1500,ca. 1490,1485,1495,Tempera on wood,24 x 17 1/4 in. (61 x 43.8 cm),"Gift of Samuel H. Kress Foundation, by exchange, 1961",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437195,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -22.60.61,false,true,437291,European Paintings,Painting,Processional Crucifix,,,,,,Artist,,Pietro di Francesco Orioli,"Italian, Siena 1458–1496 Siena",,Pietro di Francesco Orioli,Italian,1458,1496,ca. 1480s,1478,1496,"Tempera on wood, gold ground",Overall 21 1/4 x 18 1/2 in. (54 x 47 cm); painted surface 18 5/8 x 14 in. (47.3 x 35.6 cm),"The Bequest of Michael Dreicer, 1921",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437291,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -16.30ab,true,true,437372,European Paintings,"Painting, altarpiece",Madonna and Child Enthroned with Saints,,,,,,Artist,,Raphael (Raffaello Sanzio or Santi),"Italian, Urbino 1483–1520 Rome",,Raphael (Raffaello Sanzio or Santi),Italian,1483,1520,ca. 1504,1499,1509,Oil and gold on wood,"Main panel, overall 67 7/8 x 67 7/8 in. (172.4 x 172.4 cm), painted surface 66 3/4 x 66 1/2 in. (169.5 x 168.9 cm); lunette, overall 29 1/2 x 70 7/8 in. (74.9 x 180 cm), painted surface 25 1/2 x 67 1/2 in. (64.8 x 171.5 cm)","Gift of J. Pierpont Morgan, 1916",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437372,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -06.180,false,true,436498,European Paintings,Painting,The Man of Sorrows,,,,,,Artist,,Michele Giambono (Michele Giovanni Bono),"Italian, active Venice 1420–62",,"Giambono, Michele (Michele Giovanni Bono)",Italian,1420,1462,ca. 1430,1425,1435,Tempera and gold on wood,"Overall, with engaged frame, 21 5/8 x 15 1/4 in. (54.9 x 38.7 cm); painted surface 18 1/2 x 12 1/4 in. (47 x 31.1 cm)","Rogers Fund, 1906",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436498,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -50.229.1,false,true,437912,European Paintings,Painting,The Death of the Virgin,,,,,,Artist,,Bartolomeo Vivarini,"Italian, active Venice 1450–91",,"Vivarini, Bartolomeo",Italian,1450,1491,1485,1485,1485,Tempera on wood,"Arched top, 74 3/4 x 59 in. (189.9 x 149.9 cm)","Gift of Robert Lehman, 1950",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437912,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -65.181.1,false,true,437913,European Paintings,Painting fragment,A Saint (Mark?) Reading,,,,,,Artist,,Bartolomeo Vivarini,"Italian, active Venice 1450–91",,"Vivarini, Bartolomeo",Italian,1450,1491,ca. 1470,1465,1475,"Tempera on wood, gold ground",18 5/8 x 14 3/4 in. (47.3 x 37.5 cm),"Bequest of Adele L. Lehman, in memory of Arthur Lehman, 1965",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437913,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1971.155,true,true,435853,European Paintings,Painting,The Coronation of the Virgin,,,,,,Artist,,Annibale Carracci,"Italian, Bologna 1560–1609 Rome",,"Carracci, Annibale",Italian,1560,1609,after 1595,1595,1609,Oil on canvas,46 3/8 x 55 5/8 in. (117.8 x 141.3 cm),"Purchase, Bequest of Miss Adelaide Milton de Groot (1876–1967), by exchange, and Dr. and Mrs. Manuel Porter and sons Gift, in honor of Mrs. Sarah Porter, 1971",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435853,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1998.188,false,true,438338,European Paintings,Painting,The Burial of Christ,,,,,,Artist,,Annibale Carracci,"Italian, Bologna 1560–1609 Rome",,"Carracci, Annibale",Italian,1560,1609,1595,1595,1595,Oil on copper,17 1/4 x 13 3/4 in. (43.8 x 34.9 cm),"Purchase, Edwin L. Weisl Jr. Gift, 1998",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/438338,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2009.252,false,true,438813,European Paintings,Painting,Saint John the Baptist Bearing Witness,,,,,,Artist,,Annibale Carracci,"Italian, Bologna 1560–1609 Rome",,"Carracci, Annibale",Italian,1560,1609,ca. 1600,1595,1605,Oil on copper,21 3/8 x 17 1/8 in. (54.3 x 43.5 cm),"Gift of Fabrizio Moretti and Adam Williams, in honor of Everett Fahy, 2009",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/438813,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -57.23,false,true,437763,European Paintings,Painting,Tobias Curing His Father's Blindness,,,,,,Artist,,Bernardo Strozzi,"Italian, Genoa 1581–1644 Venice",,"Strozzi, Bernardo",Italian,1581,1644,1630–35,1630,1635,Oil on canvas,57 1/2 x 88 in. (146.1 x 223.5 cm),"Purchase, Mary Wetmore Shively Bequest, in memory of her husband, Henry L. Shively, M.D., 1957",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437763,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2014.270,false,true,647338,European Paintings,Painting,Saint Francis in Ecstasy,,,,,,Artist,,Giovanni Benedetto Castiglione (Il Grechetto),"Italian, Genoa 1609–1664 Mantua",,"Castiglione, Giovanni Benedetto (Il Grechetto)",Italian,1609,1664,ca. 1650,1645,1655,Oil on canvas,77 × 53 1/4 in. (195.6 × 135.3 cm),"Purchase, Lila Acheson Wallace Gift; Gwynne Andrews Fund; and Gift in memory of Felix M. Warburg from his wife and children, Bequest and Gift of George Blumenthal, Bequests of Theodore M. Davis, Adele L. Lehman, in memory of Arthur Lehman, Helen Hay Whitney, Jean Fowles, in memory of her first husband, R. Langton Douglas, and Gifts of Coudert Brothers and Harry Payne Bingham Jr., by exchange, 2014",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/647338,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1982.60.1,false,true,437243,European Paintings,Painting,Saint Romuald,,,,,,Artist,,Guido Palmeruccio (Guiduccio Palmerucci),"Italian, Gubbio, active 1315–49",,"Palmeruccio, Guido (Guiduccio Palmerucci)",Italian,1315,1349,possibly 1320s,1315,1349,"Tempera on wood, gold ground","Overall, with engaged frame, 18 1/8 x 10 3/4 in. (46 x 27.3 cm)","The Jack and Belle Linsky Collection, 1982",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437243,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -22.60.59,false,true,435616,European Paintings,Painting,Saint Dominic Resuscitating Napoleone Orsini,,,,,,Artist,,Bartolomeo degli Erri,"Italian, Modena, active 1460–79",,Bartolomeo degli Erri,Italian,1460,1479,1467–74,1467,1474,"Tempera on canvas, transferred from wood",14 x 17 1/2 in. (35.6 x 44.5 cm),"The Bequest of Michael Dreicer, 1921",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435616,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1973.311.3,false,true,436604,European Paintings,Painting,The Vocation of Saint Aloysius (Luigi) Gonzaga,,,,,,Artist,,Guercino (Giovanni Francesco Barbieri),"Italian, Cento 1591–1666 Bologna",,Guercino (Giovanni Francesco Barbieri),Italian,1591,1666,ca. 1650,1645,1655,Oil on canvas,140 x 106 in. (355.6 x 269.2 cm),"Gift of Mr. and Mrs. Charles Wrightsman, 1973",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436604,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1984.459.2,true,true,436603,European Paintings,Painting,Samson Captured by the Philistines,,,,,,Artist,,Guercino (Giovanni Francesco Barbieri),"Italian, Cento 1591–1666 Bologna",,Guercino (Giovanni Francesco Barbieri),Italian,1591,1666,1619,1619,1619,Oil on canvas,75 1/4 x 93 1/4 in. (191.1 x 236.9 cm),"Gift of Mr. and Mrs. Charles Wrightsman, 1984",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436603,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -47.71,false,true,435693,European Paintings,Painting,"Consuelo Vanderbilt (1876–1964), Duchess of Marlborough, and Her Son, Lord Ivor Spencer-Churchill (1898–1956)",,,,,,Artist,,Giovanni Boldini,"Italian, Ferrara 1842–1931 Paris",,"Boldini, Giovanni",Italian,1842,1931,1906,1906,1906,Oil on canvas,87 1/4 x 67 in. (221.6 x 170.2 cm),"Gift of Consuelo Vanderbilt Balsan, 1946",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435693,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -59.78,false,true,435692,European Paintings,Painting,"Mrs. Charles Warren-Cram (Ella Brooks Carter, 1846–1896)",,,,,,Artist,,Giovanni Boldini,"Italian, Ferrara 1842–1931 Paris",,"Boldini, Giovanni",Italian,1842,1931,1885,1885,1885,Oil on canvas,19 3/8 x 14 in. (49.2 x 35.6 cm),"Gift of Mrs. Edward C. Moën, 1959",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435692,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -87.15.81,false,true,435691,European Paintings,Painting,Gossip,,,,,,Artist,,Giovanni Boldini,"Italian, Ferrara 1842–1931 Paris",,"Boldini, Giovanni",Italian,1842,1931,1873,1873,1873,Oil on wood,7 x 9 1/2 in. (17.8 x 24.1 cm),"Catharine Lorillard Wolfe Collection, Bequest of Catharine Lorillard Wolfe, 1887",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435691,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -08.136.12,false,true,435694,European Paintings,Painting,The Dispatch-Bearer,,,,,,Artist,,Giovanni Boldini,"Italian, Ferrara 1842–1931 Paris",,"Boldini, Giovanni",Italian,1842,1931,?1879,1865,1931,Oil on wood,16 3/4 x 13 1/2 in. (42.5 x 34.3 cm),"Bequest of Martha T. Fiske Collord, in memory of her first husband, Josiah M. Fiske, 1908",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435694,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -2011.26,false,true,441227,European Paintings,Painting,The Holy Family with the Infant Saint John the Baptist,,,,,,Artist,,Perino del Vaga (Pietro Buonaccorsi),"Italian, Florence 1501–1547 Rome",,Perino del Vaga (Pietro Buonaccorsi),Italian,1501,1547,ca. 1524–26,1524,1526,Oil on wood,34 3/4 x 25 5/8 in. (88.3 x 65.1 cm),"Purchase, Acquisitions Fund, Mr. and Mrs. Mark Fisch, Denise and Andrew Saul, and Friends of European Paintings Gifts, Gwynne Andrews Fund, Mr. and Mrs. J. Tomilson Hill, Jon and Barbara Landau, Charles and Jessie Price, Hester Diamond, and Fern and George Wachter Gifts, 2011",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/441227,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1989.225,false,true,436891,European Paintings,Painting,Tobit Burying the Dead,,,,,,Artist,,Andrea di Lione,"Italian, Naples 1610–1685 Naples",,"Lione, Andrea di",Italian,1610,1685,1640s,1640,1649,Oil on canvas,50 1/4 x 68 1/2 in. (127.6 x 174 cm),"Gwynne Andrews Fund, 1989",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436891,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1973.311.2,false,true,436502,European Paintings,Painting,The Annunciation,,,,,,Artist,,Luca Giordano,"Italian, Naples 1634–1705 Naples",,"Giordano, Luca",Italian,1634,1705,1672,1672,1672,Oil on canvas,93 1/8 x 66 7/8 in. (236.5 x 169.9 cm),"Gift of Mr. and Mrs. Charles Wrightsman, 1973",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436502,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -71.31,false,true,437246,European Paintings,Painting,"Interior of Saint Peter's, Rome",,,,,,Artist,,Giovanni Paolo Panini,"Italian, Piacenza 1691–1765 Rome",,"Panini, Giovanni Paolo",Italian,1691,1765,after 1754,1754,1765,Oil on canvas,29 1/8 x 39 1/4 in. (74 x 99.7 cm),"Purchase, 1871",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437246,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -52.63.1,false,true,437244,European Paintings,Painting,Ancient Rome,,,,,,Artist,,Giovanni Paolo Panini,"Italian, Piacenza 1691–1765 Rome",,"Panini, Giovanni Paolo",Italian,1691,1765,1757,1757,1757,Oil on canvas,67 3/4 x 90 1/2 in. (172.1 x 229.9 cm),"Gwynne Andrews Fund, 1952",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437244,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -52.63.2,false,true,437245,European Paintings,Painting,Modern Rome,,,,,,Artist,,Giovanni Paolo Panini,"Italian, Piacenza 1691–1765 Rome",,"Panini, Giovanni Paolo",Italian,1691,1765,1757,1757,1757,Oil on canvas,67 3/4 x 91 3/4 in. (172.1 x 233 cm),"Gwynne Andrews Fund, 1952",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437245,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -41.100.34,false,true,436920,European Paintings,Painting,Madonna and Child,,,,,,Artist,,Luca di Tommè di Nuto,"Italian, Sienese, active 1356–89",,Luca di Tommè di Nuto,Italian,1356,1389,ca. 1360–65,1360,1365,"Tempera on wood, transferred from wood, gold ground","Shaped top, 52 7/8 x 23 1/8 in. (134.3 x 58.7 cm)","Gift of George Blumenthal, 1941",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/436920,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -41.100.35–37,false,true,437335,European Paintings,"Painting, triptych",Madonna and Child with Saints,,,,,,Artist,,Priamo della Quercia (Priamo del Pietro),"Italian, Sienese, active 1442–67",,Priamo della Quercia (Priamo del Pietro),Italian,1442,1467,ca. 1442,1442,1467,"Tempera on wood, gold ground",Central panel 43 1/4 x 22 1/2 in. (109.9 x 57.2 cm); left wing 45 1/2 x 22 in. (115.6 x 55.9 cm); right wing 45 1/4 x 22 1/4 in. (114.9 x 56.5 cm),"Gift of George Blumenthal, 1941",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437335,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -13.75,false,true,437821,European Paintings,Painting,The Miracle of the Loaves and Fishes,,,,,,Artist,,Jacopo Tintoretto (Jacopo Robusti),"Italian, Venice 1519–1594 Venice",,"Tintoretto, Jacopo (Jacopo Robusti)",Italian,1519,1594,ca. 1545–50,1545,1550,Oil on canvas,61 x 160 1/2 in. (154.9 x 407.7 cm),"Francis L. Leland Fund, 1913",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437821,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -58.49,false,true,437818,European Paintings,Painting,Portrait of a Young Man,,,,,,Artist,,Jacopo Tintoretto (Jacopo Robusti),"Italian, Venice 1519–1594 Venice",,"Tintoretto, Jacopo (Jacopo Robusti)",Italian,1519,1594,1551,1551,1551,Oil on canvas,54 1/2 x 42 in. (138.4 x 106.7 cm),"Gift of Lionel F. Straus Jr., in memory of his parents, Mr. and Mrs. Lionel F. Straus, 1958",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437818,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -10.206,false,true,437819,European Paintings,Painting,Doge Alvise Mocenigo (1507–1577) Presented to the Redeemer,,,,,,Artist,,Jacopo Tintoretto (Jacopo Robusti),"Italian, Venice 1519–1594 Venice",,"Tintoretto, Jacopo (Jacopo Robusti)",Italian,1519,1594,probably 1577,1577,1577,Oil on canvas,38 1/4 x 78 in. (97.2 x 198.1 cm),"John Stewart Kennedy Fund, 1910",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437819,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -41.100.12,false,true,437822,European Paintings,Painting,Portrait of a Man,,,,,,Artist,,Jacopo Tintoretto (Jacopo Robusti),"Italian, Venice 1519–1594 Venice",,"Tintoretto, Jacopo (Jacopo Robusti)",Italian,1519,1594,ca. 1540,1535,1545,Oil on canvas,44 3/8 x 35 in. (112.7 x 88.9 cm),"Gift of George Blumenthal, 1941",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437822,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1984.458,false,true,437268,European Paintings,Painting,Bacchus and Ariadne,,,,,,Artist,,Giovanni Antonio Pellegrini,"Italian, Venice 1675–1741 Venice",,"Pellegrini, Giovanni Antonio",Italian,1675,1741,1720s,1720,1729,Oil on canvas,46 x 50 1/2 in. (116.8 x 128.3 cm),"Gift of Mr. and Mrs. Eugene Victor Thaw, 1984",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437268,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1985.5,false,true,435573,European Paintings,Painting,Flora and Zephyr,,,,,,Artist,,Jacopo Amigoni,"Italian, Venice 1682–1752 Madrid",,"Amigoni, Jacopo",Italian,1682,1752,1730s,1730,1739,Oil on canvas,84 x 58 in. (213.4 x 147.3 cm),"Purchase, Rudolph and Lentilhon G. von Fluegge Foundation Inc. Gift, 1985",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/435573,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -67.187.90,false,true,437281,European Paintings,Painting,Saint Christopher Carrying the Infant Christ,,,,,,Artist,,Giovanni Battista Piazzetta,"Italian, Venice 1682–1754 Venice",,"Piazzetta, Giovanni Battista",Italian,1682,1754,1730s,1730,1739,Oil on canvas,28 1/4 x 22 1/8 in. (71.8 x 56.2 cm),"Bequest of Miss Adelaide Milton de Groot (1876–1967), 1967",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437281,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -23.128,false,true,437798,European Paintings,"Painting, ceiling decoration",The Glorification of the Barbaro Family,,,,,,Artist,,Giovanni Battista Tiepolo,"Italian, Venice 1696–1770 Madrid",,"Tiepolo, Giovanni Battista",Italian,1696,1770,ca. 1750,1745,1755,Oil on canvas,"Irregular oval, 96 x 183 3/4 in. (243.8 x 466.7 cm)","Anonymous Gift, in memory of Oliver H. Payne, 1923",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437798,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -71.121,false,true,437800,European Paintings,"Painting, sketch",The Investiture of Bishop Harold as Duke of Franconia,,,,,,Artist,,Giovanni Battista Tiepolo,"Italian, Venice 1696–1770 Madrid",,"Tiepolo, Giovanni Battista",Italian,1696,1770,ca. 1751–52,1751,1752,Oil on canvas,28 1/4 x 20 1/4 in. (71.8 x 51.4 cm),"Purchase, 1871",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437800,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1984.49,false,true,437796,European Paintings,"Painting, grisaille",A Female Allegorical Figure,,,,,,Artist,,Giovanni Battista Tiepolo,"Italian, Venice 1696–1770 Madrid",,"Tiepolo, Giovanni Battista",Italian,1696,1770,ca. 1740–50,1740,1750,"Oil on canvas, gold ground","Oval, 32 x 24 7/8 in. (81.3 x 63.2 cm)","Gift of Mr. and Mrs. Charles Wrightsman, 1984",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437796,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -1977.1.3,true,true,437790,European Paintings,"Painting, sketch",Allegory of the Planets and Continents,,,,,,Artist,,Giovanni Battista Tiepolo,"Italian, Venice 1696–1770 Madrid",,"Tiepolo, Giovanni Battista",Italian,1696,1770,1752,1752,1752,Oil on canvas,73 x 54 7/8 in. (185.4 x 139.4 cm),"Gift of Mr. and Mrs. Charles Wrightsman, 1977",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437790,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -37.165.2,false,true,437803,European Paintings,"Painting, sketch",Saint Thecla Praying for the Plague-Stricken,,,,,,Artist,,Giovanni Battista Tiepolo,"Italian, Venice 1696–1770 Madrid",,"Tiepolo, Giovanni Battista",Italian,1696,1770,1758–59,1758,1759,Oil on canvas,32 x 17 5/8 in. (81.3 x 44.8 cm),"Rogers Fund, 1937",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437803,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -43.85.12,false,true,437804,European Paintings,Painting,Virtue and Abundance,,,,,,Artist,,Giovanni Battista Tiepolo,"Italian, Venice 1696–1770 Madrid",and Workshop,"Tiepolo, Giovanni Battista",Italian,1696,1770,1760,1760,1760,"Fresco, transferred to canvas",Diameter 114 in. (289.6 cm),"Bequest of Grace Rainey Rogers, 1943",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437804,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" -43.85.21,false,true,437806,European Paintings,"Painting, grisaille",Prudence,,,,,,Artist,Workshop of,Giovanni Battista Tiepolo,"Italian, Venice 1696–1770 Madrid",,"Tiepolo, Giovanni Battista",Italian,1696,1770,1760,1760,1760,"Fresco, transferred to canvas","Oval, 49 1/8 x 36 1/4 in. (124.8 x 92.1 cm)","Bequest of Grace Rainey Rogers, 1943",,,,,,,,,,,,Paintings,,http://www.metmuseum.org/art/collection/search/437806,2017-02-06 08:00:16.000000 UTC,"Metropolitan Museum of Art, New York, NY" diff --git a/notebooks/geo/geoseries.ipynb b/notebooks/geo/geoseries.ipynb deleted file mode 100644 index 1159b8d31de..00000000000 --- a/notebooks/geo/geoseries.ipynb +++ /dev/null @@ -1,1051 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# Copyright 2025 Google LLC\n", - "#\n", - "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", - "# you may not use this file except in compliance with the License.\n", - "# You may obtain a copy of the License at\n", - "#\n", - "# https://www.apache.org/licenses/LICENSE-2.0\n", - "#\n", - "# Unless required by applicable law or agreed to in writing, software\n", - "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", - "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", - "# See the License for the specific language governing permissions and\n", - "# limitations under the License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Analyzing a GEOGRAPHY column with `bigframes.geopandas.GeoSeries`" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import bigframes\n", - "import bigframes.geopandas\n", - "import bigframes.pandas as bpd\n", - "bpd.options.display.progress_bar = None" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1. Load the Counties table from the Census Bureau US Boundaries dataset" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/google/home/arwas/src1/python-bigquery-dataframes/bigframes/session/_io/bigquery/read_gbq_table.py:280: DefaultIndexWarning: Table 'bigquery-public-data.geo_us_boundaries.counties' is clustered\n", - "and/or partitioned, but BigQuery DataFrames was not able to find a\n", - "suitable index. To avoid this warning, set at least one of:\n", - "`index_col` or `filters`.\n", - " warnings.warn(msg, category=bfe.DefaultIndexWarning)\n" - ] - } - ], - "source": [ - "df = bpd.read_gbq(\"bigquery-public-data.geo_us_boundaries.counties\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2. Create a series from the int_point_geom column" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "point_geom_series = df['int_point_geom']" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## The `GeoSeries` constructor accepts local data or a `bigframes.pandas.Series` object." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 1. Create a GeoSeries from local data with `Peek`" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "18 POINT (-83.91172 42.60253)\n", - "86 POINT (-90.13369 43.00102)\n", - "177 POINT (-117.23219 48.54382)\n", - "208 POINT (-84.50352 36.43523)\n", - "300 POINT (-91.85079 43.29299)\n", - "Name: int_point_geom, dtype: geometry" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "five_geo_points = point_geom_series.peek(n = 5)\n", - "five_geo_points" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2. Convert the five geo points to `GeoSeries`" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 POINT (-83.91172 42.60253)\n", - "1 POINT (-90.13369 43.00102)\n", - "2 POINT (-117.23219 48.54382)\n", - "3 POINT (-84.50352 36.43523)\n", - "4 POINT (-91.85079 43.29299)\n", - "dtype: geometry" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "geo_points = bigframes.geopandas.GeoSeries(\n", - " [point for point in five_geo_points]\n", - ")\n", - "geo_points" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 3. Retrieve the x (longitude) and y (latitude) from the GeoSeries with `.x` and `.y`." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Note: TypeError is raised if `.x` and `.y` are used with a geometry type other than `Point`." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### `.x`" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 -83.911718\n", - "1 -90.133691\n", - "2 -117.232191\n", - "3 -84.50352\n", - "4 -91.850788\n", - "dtype: Float64" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "geo_points.x" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### `.y`" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 42.602532\n", - "1 43.001021\n", - "2 48.543825\n", - "3 36.435234\n", - "4 43.292989\n", - "dtype: Float64" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "geo_points.y" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 4. Alternatively, use the `.geo` accessor to access GeoSeries methods from a `bigframes.pandas.Series` object." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### `geo.x`" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 -101.298265\n", - "1 -99.111085\n", - "2 -66.58687\n", - "3 -102.601791\n", - "4 -71.578625\n", - "5 -88.961529\n", - "6 -87.492986\n", - "7 -82.422666\n", - "8 -100.208166\n", - "9 -85.815939\n", - "10 -101.681133\n", - "11 -119.516659\n", - "12 -89.398306\n", - "13 -107.78848\n", - "14 -91.159306\n", - "15 -113.887042\n", - "16 -83.470416\n", - "17 -98.520146\n", - "18 -83.911718\n", - "19 -87.321865\n", - "20 -91.727626\n", - "21 -93.466093\n", - "22 -101.143324\n", - "23 -78.657634\n", - "24 -94.272323\n", - "dtype: Float64" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "point_geom_series.geo.x" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### `geo.y`" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 46.710819\n", - "1 29.353661\n", - "2 18.211152\n", - "3 38.835646\n", - "4 41.869768\n", - "5 39.860237\n", - "6 36.892059\n", - "7 38.143642\n", - "8 34.524623\n", - "9 30.862007\n", - "10 40.180165\n", - "11 46.228125\n", - "12 36.054196\n", - "13 38.154731\n", - "14 38.761902\n", - "15 44.928506\n", - "16 30.447232\n", - "17 29.448671\n", - "18 42.602532\n", - "19 34.529776\n", - "20 33.957675\n", - "21 42.037538\n", - "22 29.875285\n", - "23 36.299884\n", - "24 44.821657\n", - "dtype: Float64" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "point_geom_series.geo.y" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Retrive the `area` of different geometry shapes. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 1. Create a geometry collection from local data with `Peek`" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "304 POLYGON ((-88.69875 38.56219, -88.69876 38.562...\n", - "288 POLYGON ((-100.55792 46.24588, -100.5579 46.24...\n", - "42 POLYGON ((-98.09779 30.49744, -98.0978 30.4971...\n", - "775 POLYGON ((-90.33573 41.67043, -90.33592 41.669...\n", - "83 POLYGON ((-85.98402 35.6552, -85.98402 35.6551...\n", - "Name: county_geom, dtype: geometry" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "geom_series = df[\"county_geom\"].peek(n = 5)\n", - "geom_series" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2. Convert the geometry collection to `GeoSeries`" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 POLYGON ((-88.69875 38.56219, -88.69876 38.562...\n", - "1 POLYGON ((-100.55792 46.24588, -100.5579 46.24...\n", - "2 POLYGON ((-98.09779 30.49744, -98.0978 30.4971...\n", - "3 POLYGON ((-90.33573 41.67043, -90.33592 41.669...\n", - "4 POLYGON ((-85.98402 35.6552, -85.98402 35.6551...\n", - "dtype: geometry" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "five_geom = bigframes.geopandas.GeoSeries(\n", - " [point for point in geom_series]\n", - ")\n", - "five_geom" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "tags": [ - "raises-exception" - ] - }, - "source": [ - "## Note: `GeoSeries.area` raises NotImplementedError. " - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "tags": [ - "raises-exception" - ] - }, - "outputs": [ - { - "ename": "NotImplementedError", - "evalue": "GeoSeries.area is not supported. Use bigframes.bigquery.st_area(series), instead. Share your usecase with the BigQuery DataFrames team at the https://bit.ly/bigframes-feedback survey. You are currently running BigFrames version 1.41.0.", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNotImplementedError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[13], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mfive_geom\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43marea\u001b[49m\n", - "File \u001b[0;32m~/src1/python-bigquery-dataframes/bigframes/geopandas/geoseries.py:67\u001b[0m, in \u001b[0;36mGeoSeries.area\u001b[0;34m(self, crs)\u001b[0m\n\u001b[1;32m 48\u001b[0m \u001b[38;5;129m@property\u001b[39m\n\u001b[1;32m 49\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21marea\u001b[39m(\u001b[38;5;28mself\u001b[39m, crs\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m bigframes\u001b[38;5;241m.\u001b[39mseries\u001b[38;5;241m.\u001b[39mSeries: \u001b[38;5;66;03m# type: ignore\u001b[39;00m\n\u001b[1;32m 50\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Returns a Series containing the area of each geometry in the GeoSeries\u001b[39;00m\n\u001b[1;32m 51\u001b[0m \u001b[38;5;124;03m expressed in the units of the CRS.\u001b[39;00m\n\u001b[1;32m 52\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 65\u001b[0m \u001b[38;5;124;03m GeoSeries.area is not supported. Use bigframes.bigquery.st_area(series), instead.\u001b[39;00m\n\u001b[1;32m 66\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m---> 67\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mNotImplementedError\u001b[39;00m(\n\u001b[1;32m 68\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mGeoSeries.area is not supported. Use bigframes.bigquery.st_area(series), instead. \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mconstants\u001b[38;5;241m.\u001b[39mFEEDBACK_LINK\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 69\u001b[0m )\n", - "\u001b[0;31mNotImplementedError\u001b[0m: GeoSeries.area is not supported. Use bigframes.bigquery.st_area(series), instead. Share your usecase with the BigQuery DataFrames team at the https://bit.ly/bigframes-feedback survey. You are currently running BigFrames version 1.41.0." - ] - } - ], - "source": [ - "five_geom.area" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 3. Use `bigframes.bigquery.st_area` to retrieve the `area` in square meters instead. See: https://cloud.google.com/bigquery/docs/reference/standard-sql/geography_functions#st_area" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "import bigframes.bigquery as bbq" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 1851741847.416806\n", - "1 4018075889.856168\n", - "2 2652483302.084653\n", - "3 1167209931.07698\n", - "4 1124055521.2818\n", - "dtype: Float64" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "geom_area = bbq.st_area(five_geom)\n", - "geom_area" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Use `GeoSeries.from_xy()` to create a GeoSeries of `Point` geometries. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 1. Reuse the `geo_points.x` and `geo_points.y` results by passing them to `.from_xy()` " - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 POINT (-83.91172 42.60253)\n", - "1 POINT (-90.13369 43.00102)\n", - "2 POINT (-117.23219 48.54382)\n", - "3 POINT (-84.50352 36.43523)\n", - "4 POINT (-91.85079 43.29299)\n", - "dtype: geometry" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bigframes.geopandas.GeoSeries.from_xy(geo_points.x, geo_points.y)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Use `GeoSeries.to_wkt()` to convert geo points from geometry data type to Well-Knonw Text (WKT)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 1. Reuse the `geo_points`" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 POINT(-83.9117183 42.6025316)\n", - "1 POINT(-90.1336915 43.0010208)\n", - "2 POINT(-117.2321913 48.5438247)\n", - "3 POINT(-84.50352 36.435234)\n", - "4 POINT(-91.850788 43.2929889)\n", - "dtype: string" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "geo_to_wkts = bigframes.geopandas.GeoSeries.to_wkt(geo_points)\n", - "geo_to_wkts" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Use `GeoSeries.from_wkt()` to convert geo points from Well-Knonw Text (WKT) to geometry data type." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 1. Reuse `geo_to_wkts` results from `GeoSeries.to_wkts`" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 POINT (-83.91172 42.60253)\n", - "1 POINT (-90.13369 43.00102)\n", - "2 POINT (-117.23219 48.54382)\n", - "3 POINT (-84.50352 36.43523)\n", - "4 POINT (-91.85079 43.29299)\n", - "dtype: geometry" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "wkts_from_geo = bigframes.geopandas.GeoSeries.from_wkt(geo_to_wkts)\n", - "wkts_from_geo" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Discover the set-theoretic boundary of geometry objects with `GeoSeries.boundary`" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 POLYGON ((0 0, 1 1, 0 1, 0 0))\n", - "1 POLYGON ((10 0, 10 5, 0 0, 10 0))\n", - "2 POLYGON ((0 0, 2 2, 2 0, 0 0))\n", - "3 LINESTRING (0 0, 1 1, 0 1)\n", - "4 POINT (0 1)\n", - "dtype: geometry" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from shapely.geometry import Polygon, LineString, Point\n", - "geom_obj = bigframes.geopandas.GeoSeries(\n", - " [\n", - " Polygon([(0, 0), (1, 1), (0, 1)]),\n", - " Polygon([(10, 0), (10, 5), (0, 0)]),\n", - " Polygon([(0, 0), (2, 2), (2, 0)]),\n", - " LineString([(0, 0), (1, 1), (0, 1)]),\n", - " Point(0, 1),\n", - " ]\n", - ")\n", - "geom_obj" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 LINESTRING (0 0, 1 1, 0 1, 0 0)\n", - "1 LINESTRING (10 0, 10 5, 0 0, 10 0)\n", - "2 LINESTRING (0 0, 2 2, 2 0, 0 0)\n", - "3 MULTIPOINT (0 0, 0 1)\n", - "4 GEOMETRYCOLLECTION EMPTY\n", - "dtype: geometry" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "geom_obj.geo.boundary" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Find the `difference` between two `GeoSeries` " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Reuse `five_geom` and `geom_obj` to find the difference between the geometry objects" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": { - "tags": [ - "raises-exception" - ] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "0 POLYGON ((-88.69875 38.56219, -88.69876 38.562...\n", - "1 POLYGON ((-100.55792 46.24588, -100.5579 46.24...\n", - "2 GEOMETRYCOLLECTION EMPTY\n", - "3 POLYGON ((-90.33573 41.67043, -90.33592 41.669...\n", - "4 POLYGON ((-85.98402 35.6552, -85.98402 35.6551...\n", - "dtype: geometry" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "five_geom.difference(geom_obj)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Find the difference between a `GeoSeries` and a single geometry shape." - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 POLYGON ((-88.69875 38.56219, -88.69876 38.562...\n", - "1 None\n", - "2 None\n", - "3 None\n", - "4 None\n", - "dtype: geometry" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "five_geom.difference([Polygon([(0, 0), (10, 0), (10, 10), (0, 0)])])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Find the difference in `GeoSeries` with the same shapes" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 GEOMETRYCOLLECTION EMPTY\n", - "1 GEOMETRYCOLLECTION EMPTY\n", - "2 GEOMETRYCOLLECTION EMPTY\n", - "3 GEOMETRYCOLLECTION EMPTY\n", - "4 GEOMETRYCOLLECTION EMPTY\n", - "dtype: geometry" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "five_geom.difference(five_geom)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## You can also use`BigQuery.st_difference()` to find the difference between two `GeoSeries`. See, https://cloud.google.com/bigquery/docs/reference/standard-sql/geography_functions#st_difference" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 POLYGON ((-88.69875 38.56219, -88.69876 38.562...\n", - "1 POLYGON ((-100.55792 46.24588, -100.5579 46.24...\n", - "2 GEOMETRYCOLLECTION EMPTY\n", - "3 POLYGON ((-90.33573 41.67043, -90.33592 41.669...\n", - "4 POLYGON ((-85.98402 35.6552, -85.98402 35.6551...\n", - "dtype: geometry" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bbq.st_difference(five_geom, geom_obj)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Find the difference between a `GeoSeries` and a single geometry shape." - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 POLYGON ((-88.69875 38.56219, -88.69876 38.562...\n", - "1 None\n", - "2 None\n", - "3 None\n", - "4 None\n", - "dtype: geometry" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bbq.st_difference(five_geom, [Polygon([(0, 0), (10, 0), (10, 10), (0, 0)])])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Find the difference in GeoSeries with the same shapes" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 GEOMETRYCOLLECTION EMPTY\n", - "1 GEOMETRYCOLLECTION EMPTY\n", - "2 GEOMETRYCOLLECTION EMPTY\n", - "3 GEOMETRYCOLLECTION EMPTY\n", - "4 GEOMETRYCOLLECTION EMPTY\n", - "dtype: geometry" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bbq.st_difference(geom_obj, geom_obj)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Use `GeoSeries.intersection()` to find the intersecting points in two geometry shapes " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Reuse `wkts_from_geo` and `geom_obj`" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 GEOMETRYCOLLECTION EMPTY\n", - "1 GEOMETRYCOLLECTION EMPTY\n", - "2 POLYGON ((-98.09779 30.49744, -98.0978 30.4971...\n", - "3 GEOMETRYCOLLECTION EMPTY\n", - "4 GEOMETRYCOLLECTION EMPTY\n", - "dtype: geometry" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "five_geom.intersection(geom_obj)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Find the difference between a `GeoSeries` and a single geometry shape." - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 GEOMETRYCOLLECTION EMPTY\n", - "1 None\n", - "2 None\n", - "3 None\n", - "4 None\n", - "dtype: geometry" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "five_geom.intersection([Polygon([(0, 0), (10, 0), (10, 10), (0, 0)])])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## You can also use`BigQuery.st_intersection()` to find the intersecting points between two `GeoSeries`. See, https://cloud.google.com/bigquery/docs/reference/standard-sql/geography_functions#st_intersection" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 GEOMETRYCOLLECTION EMPTY\n", - "1 GEOMETRYCOLLECTION EMPTY\n", - "2 POLYGON ((-98.09779 30.49744, -98.0978 30.4971...\n", - "3 GEOMETRYCOLLECTION EMPTY\n", - "4 GEOMETRYCOLLECTION EMPTY\n", - "dtype: geometry" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bbq.st_intersection(five_geom, geom_obj)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Find the difference between a `GeoSeries` and a single geometry shape." - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 GEOMETRYCOLLECTION EMPTY\n", - "1 None\n", - "2 None\n", - "3 None\n", - "4 None\n", - "dtype: geometry" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bbq.st_intersection(five_geom, [Polygon([(0, 0), (1, 0), (10, 10), (0, 0)])])" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.19" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/notebooks/getting_started/bq_dataframes_template.ipynb b/notebooks/getting_started/bq_dataframes_template.ipynb deleted file mode 100644 index 664a3a68d33..00000000000 --- a/notebooks/getting_started/bq_dataframes_template.ipynb +++ /dev/null @@ -1,1407 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "id": "ur8xi4C7S06n" - }, - "outputs": [], - "source": [ - "# Copyright 2023 Google LLC\n", - "#\n", - "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", - "# you may not use this file except in compliance with the License.\n", - "# You may obtain a copy of the License at\n", - "#\n", - "# https://www.apache.org/licenses/LICENSE-2.0\n", - "#\n", - "# Unless required by applicable law or agreed to in writing, software\n", - "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", - "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", - "# See the License for the specific language governing permissions and\n", - "# limitations under the License." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "JAPoU8Sm5E6e" - }, - "source": [ - "# Get started with BigQuery DataFrames\n", - "\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - "\n", - "
\n", - " \n", - " \"Colab Run in Colab\n", - " \n", - " \n", - " \n", - " \"GitHub\n", - " View on GitHub\n", - " \n", - " \n", - " \n", - " \"Vertex\n", - " Open in Vertex AI Workbench\n", - " \n", - " \n", - " \n", - " \"BQ\n", - " Open in BQ Studio\n", - " \n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "24743cf4a1e1" - }, - "source": [ - "**_NOTE_**: This notebook has been tested in the following environment:\n", - "\n", - "* Python version = 3.10" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "tvgnzT1CKxrO" - }, - "source": [ - "## Overview\n", - "\n", - "BigQuery DataFrames (also known as BigFrames) provides a Pythonic DataFrame and machine learning (ML) API powered by the BigQuery engine.\n", - "\n", - "* `bigframes.pandas` provides a pandas API for analytics. Many workloads can be\n", - " migrated from pandas to bigframes by just changing a few imports.\n", - "* `bigframes.ml` provides a scikit-learn-like API for ML.\n", - "* `bigframes.ml.llm` provides API for large language models including Gemini.\n", - "\n", - "You can learn more about [BigQuery DataFrames](https://cloud.google.com/bigquery/docs/bigquery-dataframes-introduction) and its [API reference](https://cloud.google.com/python/docs/reference/bigframes/latest).\n", - "\n", - "For any issues or feedback please reach out to bigframes-feedback@google.com." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "BF1j6f9HApxa" - }, - "source": [ - "## Before you begin\n", - "\n", - "Complete the tasks in this section to set up your environment." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Yq7zKYWelRQP" - }, - "source": [ - "### Install the python package\n", - "\n", - "You need the [bigframes](https://pypi.org/project/bigframes/) python package to be installed. If you don't have that, uncomment and run the following cell and *restart the kernel*." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "#%pip install --upgrade bigframes" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "WReHDGG5g0XY" - }, - "source": [ - "### Set your project id and location\n", - "\n", - "Following are some quick references:\n", - "\n", - "* Google Cloud Project: https://cloud.google.com/resource-manager/docs/creating-managing-projects.\n", - "* BigQuery Location: https://cloud.google.com/bigquery/docs/locations." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "id": "oM1iC_MfAts1" - }, - "outputs": [], - "source": [ - "PROJECT_ID = \"bigframes-dev\" # @param {type: \"string\"}\n", - "LOCATION = \"US\" # @param {type: \"string\"}" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "960505627ddf" - }, - "source": [ - "### Import library" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "id": "PyQmSRbKA8r-" - }, - "outputs": [], - "source": [ - "import bigframes.pandas as bpd" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "init_aip:mbsdk,all" - }, - "source": [ - "\n", - "### Set BigQuery DataFrames options" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "id": "NPPMuw2PXGeo" - }, - "outputs": [], - "source": [ - "# Note: The project option is not required in all environments.\n", - "# For example, In BigQuery Studio, the project ID is automatically detected,\n", - "# But in Google Colab it must be set by the user.\n", - "bpd.options.bigquery.project = PROJECT_ID\n", - "\n", - "# Note: The location option is not required.\n", - "# It defaults to the location of the first table or query\n", - "# passed to read_gbq(). For APIs where a location can't be\n", - "# auto-detected, the location defaults to the \"US\" location.\n", - "bpd.options.bigquery.location = LOCATION\n", - "\n", - "# Note: BigQuery DataFrames objects are by default fully ordered like Pandas.\n", - "# If ordering is not important for you, you can uncomment the following\n", - "# expression to run BigQuery DataFrames in partial ordering mode.\n", - "bpd.options.bigquery.ordering_mode = \"partial\"\n", - "\n", - "# Note: By default BigQuery DataFrames emits out BigQuery job metadata via a\n", - "# progress bar. But in this notebook let's disable the progress bar to keep the\n", - "# experience less verbose. If you would like the default behavior, please\n", - "# comment out the following expression. \n", - "bpd.options.display.progress_bar = None" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "pDfrKwMKE_dK" - }, - "source": [ - "If you want to reset the project and/or location of the created DataFrame or Series objects, reset the session by executing `bpd.close_session()`. After that, you can redo the above steps." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "9EMAqR37AfLS" - }, - "source": [ - "## Create a BigQuery DataFrames DataFrame\n", - "\n", - "You can create a BigQuery DataFrames DataFrame by reading data from any of the\n", - "following locations:\n", - "\n", - "* A local data file\n", - "* Data stored in a BigQuery table\n", - "* A data file stored in Cloud Storage\n", - "* An in-memory pandas DataFrame\n", - "\n", - "Note that the DataFrame does not copy the data to the local memory, instead\n", - "keeps the underlying data in a BigQuery table during read and analysis. That's\n", - "how it can handle really large size of data (at BigQuery Scale) independent of\n", - "the local memory.\n", - "\n", - "For simplicity, speed and cost efficiency, this tutorial uses the\n", - "[`penguins`](https://pantheon.corp.google.com/bigquery?ws=!1m5!1m4!4m3!1sbigquery-public-data!2sml_datasets!3spenguins)\n", - "table from BigQuery public data, which contains 27 KB data about a set of\n", - "penguins - species, island of residence, culmen length and depth, flipper length\n", - "and sex. There is a version of this data in the Cloud Storage\n", - "[cloud samples data](https://pantheon.corp.google.com/storage/browser/_details/cloud-samples-data/vertex-ai/bigframe/penguins.csv)\n", - "as well." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "id": "Vyex9BQI-BNa" - }, - "outputs": [], - "source": [ - "# This is how you read a BigQuery table\n", - "df = bpd.read_gbq(\"bigquery-public-data.ml_datasets.penguins\")\n", - "\n", - "# This is how you would read a csv from the Cloud Storage\n", - "#df = bpd.read_csv(\"gs://cloud-samples-data/vertex-ai/bigframe/penguins.csv\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can use `peek` to preview a few rows (selected arbitrarily) from the dataframes:" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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speciesislandculmen_length_mmculmen_depth_mmflipper_length_mmbody_mass_gsex
0Adelie Penguin (Pygoscelis adeliae)Dream36.618.4184.03475.0FEMALE
1Adelie Penguin (Pygoscelis adeliae)Dream39.819.1184.04650.0MALE
2Adelie Penguin (Pygoscelis adeliae)Dream40.918.9184.03900.0MALE
3Chinstrap penguin (Pygoscelis antarctica)Dream46.517.9192.03500.0FEMALE
4Adelie Penguin (Pygoscelis adeliae)Dream37.316.8192.03000.0FEMALE
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" - ], - "text/plain": [ - " species island culmen_length_mm \\\n", - "0 Adelie Penguin (Pygoscelis adeliae) Dream 36.6 \n", - "1 Adelie Penguin (Pygoscelis adeliae) Dream 39.8 \n", - "2 Adelie Penguin (Pygoscelis adeliae) Dream 40.9 \n", - "3 Chinstrap penguin (Pygoscelis antarctica) Dream 46.5 \n", - "4 Adelie Penguin (Pygoscelis adeliae) Dream 37.3 \n", - "\n", - " culmen_depth_mm flipper_length_mm body_mass_g sex \n", - "0 18.4 184.0 3475.0 FEMALE \n", - "1 19.1 184.0 4650.0 MALE \n", - "2 18.9 184.0 3900.0 MALE \n", - "3 17.9 192.0 3500.0 FEMALE \n", - "4 16.8 192.0 3000.0 FEMALE " - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.peek()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "gE6CEALjDZZV" - }, - "source": [ - "We just created a DataFrame, `df`, refering to the entirety of the source table data, without downloading it to the local machine." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "rwPLjqW2Ajzh" - }, - "source": [ - "## Inspect and manipulate data in BigQuery DataFrames" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "bExmYlL_ELtV" - }, - "source": [ - "### Using pandas API\n", - "\n", - "You can use pandas API on the BigQuery DataFrames DataFrame as you normally would in Pandas, but computation happens in the BigQuery query engine instead of your local environment." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "EJIZJaNXFQzh" - }, - "source": [ - "Let's compute the mean of the `body_mass_g` series:" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "id": "YKwCW7Nsavap" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "average_body_mass: 4201.754385964914\n" - ] - } - ], - "source": [ - "average_body_mass = df[\"body_mass_g\"].mean()\n", - "print(f\"average_body_mass: {average_body_mass}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "DSs1cnca-MOU" - }, - "source": [ - "Calculate the mean `body_mass_g` by `species` using the `groupby` operation:" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "id": "4PyKMR61-Mjy" - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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body_mass_g
species
Adelie Penguin (Pygoscelis adeliae)3700.662252
Chinstrap penguin (Pygoscelis antarctica)3733.088235
Gentoo penguin (Pygoscelis papua)5076.01626
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3 rows × 1 columns

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[3 rows x 1 columns in total]" - ], - "text/plain": [ - " body_mass_g\n", - "species \n", - "Adelie Penguin (Pygoscelis adeliae) 3700.662252\n", - "Chinstrap penguin (Pygoscelis antarctica) 3733.088235\n", - "Gentoo penguin (Pygoscelis papua) 5076.01626\n", - "\n", - "[3 rows x 1 columns]" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df[[\"species\", \"body_mass_g\"]].groupby(by=df[\"species\"]).mean(numeric_only=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "6sf9kZ2C9Ixe" - }, - "source": [ - "You can confirm that the calculations were run in BigQuery by clicking \"Open job\" from the previous cells' output. This takes you to the BigQuery console to view the SQL statement and job details." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Using SQL functions\n", - "\n", - "The [bigframes.bigquery module](https://cloud.google.com/python/docs/reference/bigframes/latest/bigframes.bigquery) provides many [BigQuery SQL functions](https://cloud.google.com/bigquery/docs/reference/standard-sql/functions-all) which may not have a pandas-equivalent." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "import bigframes.bigquery" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The `bigframes.bigquery.struct()` function creates a new STRUCT Series with subfields for each column in a DataFrames." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 {'culmen_length_mm': 36.6, 'culmen_depth_mm': ...\n", - "1 {'culmen_length_mm': 39.8, 'culmen_depth_mm': ...\n", - "2 {'culmen_length_mm': 40.9, 'culmen_depth_mm': ...\n", - "3 {'culmen_length_mm': 46.5, 'culmen_depth_mm': ...\n", - "4 {'culmen_length_mm': 37.3, 'culmen_depth_mm': ...\n", - "dtype: struct[pyarrow]" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "lengths = bigframes.bigquery.struct(\n", - " df[[\"culmen_length_mm\", \"culmen_depth_mm\", \"flipper_length_mm\"]]\n", - ")\n", - "lengths.peek()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Use the `bigframes.bigquery.sql_scalar()` function to access arbitrary SQL syntax representing a single column expression." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 18.4\n", - "1 19.1\n", - "2 18.9\n", - "3 17.9\n", - "4 16.8\n", - "dtype: Float64" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "shortest = bigframes.bigquery.sql_scalar(\n", - " \"LEAST({0}, {1}, {2})\",\n", - " columns=[df['culmen_depth_mm'], df['culmen_length_mm'], df['flipper_length_mm']],\n", - ")\n", - "shortest.peek()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Visualize data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### First party visualizations\n", - "\n", - "BigQuery DataFrames provides a number of visualizations via the `plot` method and [accessor](https://cloud.google.com/python/docs/reference/bigframes/latest/bigframes.operations.plotting.PlotAccessor) on the DataFrame and Series objects." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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7775j8eLFREREMHfuXFauXMltt90GwNy5czl79iwtWrTgjTfeAMDf318NVF5++WU++OAD/P39efLJJ3n88cfZsWPHDTnG1sD17QULeOell5jz2We8NH06Dz30EA0aNGDatGmEhoby+OOPM2nSJNauXVvpY3o9JEgRQghRruzsbObOncv8+fN57LHHAGjYsCE9evSwyyxUhtlsZtGiRXh4eNCsWTNuu+02zpw5w5o1a9BqtTRp0oR33nmHzZs307lzZ7Zv387evXtJTk7GyckJgPfff5+VK1eyfPlyJkyYoK53yZIleHh4APDoo4+yadOmSgcpsbGxLF68mNjYWGrXrg3A888/z7p161i8eDFvvfUWYJmg77PPPqNhw4aAJbCxBhsA8+bNY9q0aQwfPhyA+fPns2bNGvV5Ly8vHB0dcXV1JSgoqFQ7Zs2aRe/evQF46aWXGDJkCAUFBRWaJO1qx1gDNAwI4J233mLjihW09vICs9lyzIYN44ExY9B7ezN16lS6du3Kq6++ysCBAwF47rnnGDNmTKWO599xywQpCxYsYMGCBZhMpupuihBC/G0uDjpOvjGw2rZdUadOncJgMNCvX78q235YWJgaSAAEBgai0+nsJgILDAwkOTkZgCNHjpCTk4Ovr6/devLz8+26HkquNzg4WF1HZRw7dgyTyUTjxo3tHjcYDHZtcHV1VQOUktvLzMwkKSmJTp06qc/rdDrat2+PuTgguJZWrVrZrRsgOTmZ0NDQa742LCwMd3d3FKMRxWjE38sLbcOGmJKTMefkYC4owN/Li6RLl1CMRvV1rTt0QOflBVjeA4CWLVuqzwcGBlJQUEBWVhaenp4V2o+/45YJUiZOnMjEiRPJysrCq/gACiHErUqj0VSqy6W6XG0Kf2tQYVvjUpE7PDs4ONj9rtFoynzMejLPyckhODi4zMyN7fDmq62jMnJyctDpdBw4cACdzj6gc3d3v+r2qvLmkbbrt85UbLs/itlsF2CojxcVoQcMZ85ceb6gAJ3ZjDE11bI+nQ6tgwM4OeNYvz6a4v10sbmvlPXfa7XjRqr5fyFCCCGqTaNGjXBxcWHTpk2MGzfO7jl/f38AEhIS8PHxASyFs1WtXbt2JCYmotfrCQsLq/L1l9S2bVtMJhPJycn07Nnzutbh5eVFYGAg+/bto1evXoDlJpMHDx6kTZs26nKOjo5X7SFQFAWlqAizwQBAYUIChTo9oGDOyUEp47XG9HQUk+lKgKLRgIMDGkdH9L6+oNOh8/FB4+SE1s0VnZvbde3jzSBBihBCiHI5OzszdepUXnzxRRwdHenevTspKSmcOHGCUaNGERISwowZM5g1axZnz57lgw8+qPI29O/fn65duzJs2DDeffddGjduzKVLl1i9ejXDhw+nQ4cOVbq9xo0b8/DDDzNq1Cg++OAD2rZtS0pKCps2baJVq1YMGTKkQut55plnePvttwkPD6dJeDjz5s0jPT0dzGbMBQUA1Ktdm93btnF2xw7c3dyo5e1NUVISAIVxcRSmpGA2GCiMiwPAnJWFKTPjykbKuBeURqNBo9PhGBqK1t0dNBp07u5ojUYciruNbhUSpAghhLiqV199Fb1ez2uvvcalS5cIDg7mySefxMHBge+//56nnnqKVq1a0bFjR2bOnMl9991XpdvXaDSsWbOGl19+mTFjxpCSkkJQUBC9evVS6yaq2uLFi5k5cyb//e9/iY+Px8/Pjy5dunDnnXeWubxiMmEuLARQA5AXnnuOhNhYRj36KDqNhsfvvZf+XbqgMxoxnD8PwDP33sv4Q4do078/+QUFnFq3DnNeHmCZs8Ss0YBGg6a4a03n64dDcZGtxskJrbt7qZtW6v390Tg4oLsJNSM3mkapyg60m8Bak5KZmXlTinaEEKIqFBQUEBUVRf369avkFvbi5rEWn155QMGUlY1iKCj+1dL1wjVOp4pGQ5s77+SeQYN4/bnnLA9qteg8PdGUqG9R6XToPDzUmpGa7mqf8+s5f0smRQghxL+WUlRkX+xqNGLKzEIxWgqAFaMRc25uhdal0ensul9i4uPZtHMXvW/rg9HZmU8WLiQ6Pp5Rzz6Lc9OmVbkb/1gSpAghhPhHi4mJoXnz5lcesA1KFIWDv/5KyDVqNUpmMjQuLuiK6z0AtC4uaFxc7LpeXN3c+O6115j2/nsoikKLFi3YuHEjERERf2t/mjdvTkxMTJnPff755zz88MN/a/01iQQpQggh/jEUkwlTZiZKcX2IYjLjm5XF7p9+Kvc1wQGBoLHUfGg0oHX3QOtaPPRao0Hr7o62eBK5yggJCanwDLGVsWbNmnKHet+oGp3qIkGKEEKIGkcxm1GKikBRLIWoRqPlsfwCzPl5Zc4PUh6dRkNDmwnQtG5uaN3c0Oh0V68HqaHq1atX3U24aW6ZIEVmnBVCiFuXYjJZajtsuloUsxmloADFOjGYdWiuyYxiMl6zELU8GicnS1cMWDIhxUEJJSYpEzXfLROkyIyzQghRcyiKgmIwlA4kFDAXGlDy8y0BR/HzisFQ5sRjV6PRakGjReNomYgMjRats5Ol9sPRkTJDDY0GdDoJRP4hbpkgRQghxN+jKIp6Izm7x41GzHl5KPn5V7IaJZnMmAvyrwQdJlOlMx0aBwc0Do42D4DWyRkc9NZf0Tg7o9HrLYGGg4MEG/9yEqQIIcQtTDGbMefno+QXlBE0KJgNBpTCIlDMdpmNqqDRaqGM+Ts0ev2V0S7W53U6tK6uEnSISpEgRQghajjFZMJcYADFkuVQ8vMtI1hMZst8Hn838NBo0bo4W4KI8iYN02jsgw6NxtLlIkGHuIEkSBFCiBtErdsAS0ZDUSzFoiW6VJSCAowZGVBGzYZiKCyeZr38QESj16N1dYXiqdPtnnNwQOPkZLmfi7UrpSSt9oYFG0uWLGHy5MlkZGTckPXfSH369KFNmzbMmTPnhm9Lo9GwYsUKhg0bdsO3dSuRIEUIIapI/rHj5B87ijk3l/wjR8g/cBBTejoA5uBgTK+8jMFoLDOYuBaN3gGNrvh11rvYOjqhcdBL7cYtZMaMGaxcufKG3C36n0iCFCHEv15RUjLmvBJTnysKhshI8vfvJ//ECUtdx1Uo+fkYzp27vgZoNOi8vNCWcU8fjYMDGhcXtI6OZbxQiH+2yofzQghxi1GMRrLWrCHl43mlfmInTOB8795cGHyH/c8dQ4h/5lnSvv6G/P0HKDh69Ko/hnPnQK/HrVdPPO+4g4AXnqfe98tovH8fTQ7sp/7/lqMPCsKpYUOcIyJwbtoU54ah6o9jgA96T5dSPzoXPVqKoDC36n4qWcNiNpt59913CQ8Px8nJidDQUGbNmsWWLVvQaDR2XTmHDx9Go9EQHR1d5rpmzJhBmzZtWLRoEaGhobi7u/P0009jMpl49913CQoKIiAggFmzZtm9LiMjg3HjxuHv74+npyd9+/blyJEjpda7dOlSwsLC8PLy4oEHHiA7O7tC+5ibm8uoUaNwd3cnODiYDz74oNQyBoOB559/njp16uDm5kbnzp3ZsmWL+vySJUvw9vZm5cqVNGrUCGdnZwYOHEhcXJz6/P/93/9x5MgRS/ebRsOSJUvU16empjJ8+HBcXV1p1KgRq1atqlDbre/D+vXradu2LS4uLvTt25fk5GTWrl1LREQEnp6ePPTQQ+QV32EZLN1ZzzzzDJMnT8bHx4fAwEAWLlxIbm4uY8aMwcPDg/DwcNauXVuhdtwIkkkRQtwylMJCDFHRYLKfbdSUnUP6D99jOHmqzNeZcnIwXb5c/oqLpz4vySEoEJf27XFt2xatx7Xv2urcLAKHoKAyn9PqdGi0WjQ6naX4tDAX3gm55jpviOmXwNGtwotPmzaNhQsX8tFHH9GjRw8SEhI4ffr0dW8+MjKStWvXsm7dOiIjI7n33nu5cOECjRs35q+//mLnzp08/vjj9O/fn86dOwNw33334eLiwtq1a/Hy8uLzzz+nX79+nD17llq1aqnrXblyJb///jvp6emMHDmS2bNnlwp4yvLCCy/w119/8euvvxIQEMD06dM5ePAgbdq0UZeZNGkSJ0+e5IcffqB27dqsWLGCQYMGcezYMRo1agRAXl4es2bN4ptvvsHR0ZGnn36aBx54gB07dnD//fdz/Phx1q1bx8aNGwHs5v36v//7P959913ee+895s2bx8MPP0xMTIy6f9cyY8YM5s+fj6urKyNHjmTkyJE4OTmxbNkycnJyGD58OPPmzWPq1Knqa77++mtefPFF9u7dy48//shTTz3FihUrGD58ONOnT+ejjz7i0UcfJTY2FldX1wq1oypJkCKEqHaKopC7Yyd5+/aVfZVvNlFw8hR5hw6h5Odf1zZ0Pj543H57qcJRrYcH3iOG4/gvmmq8MrKzs5k7dy7z58/nscceA6Bhw4b06NHDLotQGWazmUWLFuHh4UGzZs247bbbOHPmDGvWrEGr1dKkSRPeeecdNm/eTOfOndm+fTt79+4lOTkZp+J76Lz//vusXLmS5cuXM2HCBHW9S5YswcPDA4BHH32UTZs2XTNIycnJ4auvvuLbb7+lX79+gOXkXbduXXWZ2NhYFi9eTGxsLLVr1wbg+eefZ926dSxevJi33noLgKKiIubPn68GV19//TURERHs3buXTp064e7ujl6vJ6iMYHb06NE8+OCDALz11lt8/PHH7N27l0GDBlXouM6cOZPu3bsDMHbsWKZNm0ZkZCQNGjQA4N5772Xz5s12QUrr1q155ZVXAEswOnv2bPz8/Bg/fjwAr732Gp9++ilHjx6lS5cuFWpHVZIgRQhRaea8PIouXar4CxQFw/nz5O7aTf6RI+qIF3V9hQaMlxIqtCqth4dlJIstjQbXDh3wvmcEmrJuBKfR4NykSenXVScHV0tGo7q2XUGnTp3CYDCoJ++qEBYWpgYSYLkpnk6nQ2tTUBwYGEhycjIAR44cIScnB19fX7v15OfnExkZWe56g4OD1XVcTWRkJIWFhWpgAVCrVi2aNGmi/n7s2DFMJhONGze2e63BYLBrl16vp2PHjurvTZs2xdvbm1OnTtGpU6ertqNVq1bq/93c3PD09KxQ+8t6fWBgIK6urmqAYn1s79695b5Gp9Ph6+tLy5Yt7V4DVKodVemWCVLk3j1CVC1FUSiMiiZv7x5MmVkVfp0pM5OMn3/GXMG+/orSODnhOWQIOo/S3S4ADnVDcO3cCafwcMskYrc6jaZSXS7VxcXFpdznrEGFYpP9Ku/uvLYcStzQT6PRlPmYuXiodk5ODsHBwWVmbry9va+6XnN5M+hWUk5ODjqdjgMHDqArMZeMexldhdfj77bf9vXXOqZX22bJ9QBVdhwr65YJUuTePUJYKCYTZpvit8ow5+WRt28/uTt3krtrF8aEimUvyqJ1d6/U3WP1AQG4demMa6dO6GxOLFaODRqg9/G57vaIG6NRo0a4uLiwadMmxo0bZ/ecv78/AAkJCfgUv3c3Ymhtu3btSExMRK/XExYWVuXrb9iwIQ4ODuzZs4fQ4rslp6enc/bsWXr37g1A27ZtMZlMJCcn07Nnz3LXZTQa2b9/v5o1OXPmDBkZGURERADg6OgoF9uVcMsEKUL8EykmEwUnT1JUXP1vy5ieTv6Bg3YBiVJoIP+IZR6OqqBxcMClbVscQupee2H1RRrcunTF847B/4yMhrgqZ2dnpk6dyosvvoijoyPdu3cnJSWFEydOMGrUKEJCQpgxYwazZs3i7NmzZY6K+bv69+9P165dGTZsGO+++y6NGzfm0qVLrF69muHDh9OhQ4e/tX53d3fGjh3LCy+8gK+vLwEBAbz88st23U+NGzfm4YcfZtSoUXzwwQe0bduWlJQUNm3aRKtWrRgyZAhgyUw888wzfPzxx+j1eiZNmkSXLl3UoCUsLIyoqCgOHz5M3bp18fDwUOtsRGkSpAhRQYqiUBQTgzE1laKkJEsAUWi49gttGU3kHztGUWysZZ1mc5mzjN5IThERuHXtilu3bri2b4f2Kul8IQBeffVV9Ho9r732GpcuXSI4OJgnn3wSBwcHvv/+e5566ilatWpFx44dmTlzJvfdd1+Vbl+j0bBmzRpefvllxowZQ0pKCkFBQfTq1Uutmfi73nvvPXJychg6dCgeHh7897//JTMz026ZxYsXM3PmTP773/8SHx+Pn58fXbp04c4771SXcXV1ZerUqTz00EPEx8fTs2dPvvrqK/X5e+65h19++YXbbruNjIwMFi9ezOjRo6tkH/6JNIpShXebugms3T2ZmZl4el57SKAQiqJgTE6+ajBgvJxG7o7t5GzfjjExqcxlzAUFmFJTq7x9Wg8PnJo0RqO17+fWODnh0rYNDnZfwhqcmjTBqXGjsm9Tf82NacueFl3ccAUFBURFRVG/fn2cy5i0Tdz6buVbAFSVq33Or+f8Ld9W4pahKAqGU6cw2FTzX3X5IiP5hw6Rs3UrxqSyA4/K0jg44FCnDlo3N1w7dEB3HTUUjmFhuLRsod49Vu/nJ4GDEEKUQb4ZxU1lNhgovHDB0s2hQMGpkxhOneJqCT3jpQTy9u/HbDCA0Vjuclel0101ENA4OeHasSPuPXsWZzXKqLXQanFq2LBmDWMVQlxTbGwszZo1K/f5kydPqgWzNdGTTz7Jt99+W+ZzjzzyCJ999tlNbtHNI909ohRFUa45bbZiNJJ/4ACFMTGYMjLI3bX7mkNSFSxDXq93Mi4AjasrLs2bV3hUiWODBrj37o1rp45opThNVCPp7qk+RqOx3Gn6wVLMqq/B2czk5GSyssqeJsDT05OAgICb3KLySXePqBJFiYkYU0tME64oZK1ZQ/qyZaUm26pKOi8vNMUfXn1QIG4dO6JxKv9LW+vujlvnTuh8fND5+sqN1oQQlaLX6wkPD6/uZly3gICAGhWI3EwSpNzCDBcukH/kKJjLKAhVFPJPnKDgxEkoMQmPOS+PwgsX/vb2dX5+uLRqhdbZGZcO7XEMufZ9SPQBATg1biy3lRdCCHFNEqTUYIbISDJ/XYVSWGj3uFJYSO7u3X8v0NBq0QcEWGa9tKH39cVv4tO42NxUqzw6Ly+ZJ0MIIcQNI0FKNSlKSMBw/jymzCxyt23FWHLImslM3p49KFebYtrBAdc2bdC6lT21tj4oELcuXdG6lOhK0Wpxbt4cfQXvrCmEEEJUBwlSbjDDhQskvfMORdEx6mOKyUTRxYsVer1bjx44RzQt8ahlrgz33r3Q2dxMSwghhPgnkSClCihFRRjT0688YDaTf/AgWevWk7N5c9nZEI3GcqM0VxdcO3TAqUHDUl0vDrVr49q5k9RvCCGE+FeSIOU6mQ0GCiMjMVyIImn27KvOROrWqye+Y8ehcbhyuB1DQtAX35xLCCFqMkVReOKJJ1i+fDnp6el4eXkxevRo5syZA1iG8E6ePJnJkydXazsrQqPRsGLFCoYNG1bdTWHGjBmsXLnyhtyU8Z9CgpRKMuXkkjJnDpkrV2LOybnyhEZjlwlxCKmL58BBeA4aiFNEhGRDhBC3rHXr1rFkyRK2bNlCgwYNuPfee+2e37dvH27l1MYJi5oUHN1KbpkgZcGCBSxYsOCm3eLalJ1N7o6ddl01RQkJZPzwA0WXLgGg9fJC5+6O55Ah+E2aKPN3CCH+kSIjIwkODqZbt24ApSY+868hWeHCwkIc5Xv4H+WWGT86ceJETp48yb59+27YNhRFIfvPP0n7ZikX7rqb+MmTufTCC+pPyocfUnTpEg61axOycCGNd+0kfNNGAqb8RwIUIUSlKIpCXlFetfxUZqLx0aNH88wzzxAbG4tGoyEsLKzUMmFhYWrXD1iyBp9++imDBw/GxcWFBg0asHz5cvX56OhoNBoNP/zwA926dcPZ2ZkWLVrw119/2a33+PHjDB48GHd3dwIDA3n00UdJtela79OnD5MmTWLy5Mn4+fkxcODAir8BxeLi4hg5ciTe3t7UqlWLu+++22522tGjRzNs2DDef/99goOD8fX1ZeLEiRTZXMAmJCQwZMgQXFxcqF+/PsuWLbM7JtZjNnz48DKP4dKlSwkLC8PLy4sHHniA7GvM3m27/8888wyTJ0/Gx8eHwMBAFi5cSG5uLmPGjMHDw4Pw8HDWrl2rvmbLli1oNBrWr19P27ZtcXFxoW/fviQnJ7N27VoiIiLw9PTkoYceIi8vr9LHs6rdMpmUmyF740bin3lW/V1fOxgnmw+TxtkF9z698bxjCDp3SW0KIa5fvjGfzss6V8u29zy0B1eHit2Dau7cuTRs2JAvvviCffv2odPpuO+++675uldffZXZs2czd+5cli5dygMPPMCxY8eIiIhQl3nhhReYM2cOzZo148MPP2To0KFERUXh6+tLRkYGffv2Zdy4cXz00Ufk5+czdepURo4cyZ9//qmu4+uvv+app55ix44dlT4ORUVFDBw4kK5du7Jt2zb0ej0zZ85k0KBBHD16VM3KbN68meDgYDZv3sz58+e5//77adOmDePHjwdg1KhRpKamsmXLFhwcHJgyZQrJycnqdvbt20dAQACLFy9m0KBB6HRX7ngeGRnJypUr+f3330lPT2fkyJHMnj2bWbNmVWgfvv76a1588UX27t3Ljz/+yFNPPcWKFSsYPnw406dP56OPPuLRRx8lNjYWV5v7js2YMYP58+fj6urKyJEjGTlyJE5OTixbtoycnByGDx/OvHnzmDp1aqWPa1WSIMVG2leLAHBu0QK3bt3we2JCuXOQCCHEv4GXlxceHh7odDqCgoIq/Lr77ruPcePGAfDmm2+yYcMG5s2bxyeffKIuM2nSJO655x4APv30U9atW8dXX33Fiy++yPz582nbti1vvfWWuvyiRYsICQnh7NmzNG7cGIBGjRrx7rvvXte+/fjjj5jNZr788ku1bnDx4sV4e3uzZcsWBgwYAICPjw/z589Hp9PRtGlThgwZwqZNmxg/fjynT59m48aN7Nu3jw4dOgDw5Zdf0qhRI3U71u4wb2/vUsfQbDazZMkSPIqnk3j00UfZtGlThYOU1q1b88orrwAwbdo0Zs+ejZ+fnxpAvfbaa3z66accPXqULl26qK+bOXMm3bt3B2Ds2LFMmzaNyMhIGjRoAMC9997L5s2bJUipKfIOHSL/8GE0Dg6EfPqJjLwRQtxQLnoX9jy0p9q2faN17dq11O8lR7HYLqPX6+nQoQOnTp0C4MiRI2zevBl3d/dS646MjFSDlPbt2193G48cOcL58+fVAMGqoKCAyMhI9ffmzZvbZT+Cg4M5duwYAGfOnEGv19OuXTv1+fDwcHx8fCrUhrCwMLvtBwcH22VhrqVVq1bq/3U6Hb6+vrRs2VJ9LDAwEKDUOm1fFxgYiKurqxqgWB/bu3dvhdtxo0iQUixt8RIAPO8aKgGKEOKG02g0Fe5y+TfKyclh6NChvPPOO6WeCw4OVv//d0YV5eTk0L59e7777rtSz9kWAzuUuOu6RqPBXOKeaNfr7667rNfbPmbNEJVcZ8llbuQ+/h23TOHsjeZ9zwhcO3XCd/To6m6KEELc8nbv3l3qd9t6lJLLGI1GDhw4oC7Trl07Tpw4QVhYGOHh4XY/VTXcuV27dpw7d46AgIBS2/Dy8qrQOpo0aYLRaOTQoUPqY+fPnyfddoJPLEHBzRqd+k8iQUox9969qffN1zjZ9CMKIYS4Pj///DOLFi3i7NmzvP766+zdu5dJkybZLbNgwQJWrFjB6dOnmThxIunp6Tz++OOAZURnWloaDz74IPv27SMyMpL169czZsyYKjvZP/zww/j5+XH33Xezbds2oqKi2LJlC88++ywXK3jrkqZNm9K/f38mTJjA3r17OXToEBMmTMDFxcVufqywsDA2bdpEYmJiqQBGlE+CFCGEEFXu//7v//jhhx9o1aoV33zzDd9//z3NmjWzW2b27NnMnj2b1q1bs337dlatWoWfnx8AtWvXZseOHZhMJgYMGEDLli2ZPHky3t7eaKvo7uuurq5s3bqV0NBQRowYQUREBGPHjqWgoABPT88Kr+ebb74hMDCQXr16MXz4cMaPH4+HhwfOzldu7vrBBx+wYcMGQkJCaNu2bZW0/99Ao1RmwHwNkJWVhZeXF5mZmZX6EAkhRHUqKCggKiqK+vXr2528/omuNbtqdHQ09evX59ChQ7Rp0+amtu1muHjxIiEhIWzcuJF+/fpVd3Nuqqt9zq/n/C2Fs0IIIcTf8Oeff5KTk0PLli1JSEjgxRdfJCwsjF69elV302550t0jhBDiH+G7777D3d29zJ/mzZvfsO0WFRUxffp0mjdvzvDhw/H391cndrtesbGx5e6Lu7s7sbGxVbgHNZdkUoQQQlSpa1URhIWFVWpq/oq666676Ny57Fl8/07AcC0DBw68rin5r6Z27dpXvTty7dq1q3R7NZUEKUIIIf4RPDw8Sk3MdqvS6/WEh4dXdzOqnXT3CCGEEKJGkiBFCCGEEDWSBClCCCGEqJEkSBFCCCFEjSRBihBCCCFqJAlShBBClKtPnz5Mnjy5Ste5ZMkSvL29q3Sd4p9JghQhhBBC1EgSpAghhBCiRrplgpQFCxbQrFkzOnbsWN1NEUKIfxWj0cikSZPw8vLCz8+PV199VZ0xNj09nVGjRuHj44OrqyuDBw/m3Llzdq9fsmQJoaGhuLq6Mnz4cC5fvqw+Fx0djVarZf/+/XavmTNnDvXq1cNsNl+1bVu2bEGj0bB+/Xratm2Li4sLffv2JTk5mbVr1xIREYGnpycPPfQQeXl56uvWrVtHjx498Pb2xtfXlzvvvJPIyEj1+cLCQiZNmkRwcDDOzs7Uq1ePt99+G7DMqDtjxgxCQ0NxcnKidu3aPPvssxU6lgkJCQwZMgQXFxfq16/PsmXLCAsLY86cORV6/b/NLTPj7MSJE5k4caJ6F0UhhLiVKYqCkp9fLdvWuLig0WgqvPzXX3/N2LFj2bt3L/v372fChAmEhoYyfvx4Ro8ezblz51i1ahWenp5MnTqVO+64g5MnT+Lg4MCePXsYO3Ysb7/9NsOGDWPdunW8/vrr6rrDwsLo378/ixcvpkOHDurjixcvZvTo0Wi1FbuWnjFjBvPnz8fV1ZWRI0cycuRInJycWLZsGTk5OQwfPpx58+YxdepUAHJzc5kyZQqtWrUiJyeH1157jeHDh3P48GG0Wi0ff/wxq1at4qeffiI0NJS4uDji4uIA+N///sdHH33EDz/8QPPmzUlMTOTIkSMVaueoUaNITU1V7+0zZcoUkpOTK/pW/OvcMkGKEEL8kyj5+Zxp175att3k4AE0rq4VXj4kJISPPvoIjUZDkyZNOHbsGB999BF9+vRh1apV7Nixg27dugGWm/yFhISwcuVK7rvvPubOncugQYN48cUXAWjcuDE7d+5k3bp16vrHjRvHk08+yYcffoiTkxMHDx7k2LFj/PrrrxVu48yZM+nevTsAY8eOZdq0aURGRtKgQQMA7r33XjZv3qwGKffcc4/d6xctWoS/vz8nT56kRYsWxMbG0qhRI3r06IFGo6FevXrqsrGxsQQFBdG/f38cHBwIDQ2lU6dO12zj6dOn2bhxI/v27VMDsi+//JJGjRpVeD//bW6Z7h4hhBDVo0uXLnaZl65du3Lu3DlOnjyJXq+3u6mfr68vTZo04dSpUwCcOnWq1E3/unbtavf7sGHD0Ol0rFixArB0D912222EhYVVuI2tWrVS/x8YGIirq6saoFgfs81YnDt3jgcffJAGDRrg6empbst6d+HRo0dz+PBhmjRpwrPPPssff/yhvva+++4jPz+fBg0aMH78eFasWIHRaLxmG8+cOYNer6ddu3bqY+Hh4fj4+FR4P/9tJJMihBDVQOPiQpODB6pt2zWJo6Mjo0aNYvHixYwYMYJly5Yxd+7cSq3D9i7HGo2m1F2PNRqNXX3L0KFDqVevHgsXLqR27dqYzWZatGhBYWEhAO3atSMqKoq1a9eyceNGRo4cSf/+/Vm+fDkhISGcOXOGjRs3smHDBp5++mnee+89/vrrrxt6t+V/IwlShBCiGmg0mkp1uVSnPXv22P2+e/duGjVqRLNmzTAajezZs0ft7rl8+TJnzpyhWbNmAERERJT5+pLGjRtHixYt+OSTTzAajYwYMeIG7c2VNi5cuJCePXsCsH379lLLeXp6cv/993P//fdz7733MmjQINLS0qhVqxYuLi4MHTqUoUOHMnHiRJo2bcqxY8fssiQlNWnSBKPRyKFDh2jf3tLVd/78edLT02/Mjv4DSJAihBDiqmJjY5kyZQpPPPEEBw8eZN68eXzwwQc0atSIu+++m/Hjx/P555/j4eHBSy+9RJ06dbj77rsBePbZZ+nevTvvv/8+d999N+vXr7erR7GKiIigS5cuTJ06lccffxyXG5jt8fHxwdfXly+++ILg4GBiY2N56aWX7Jb58MMPCQ4Opm3btmi1Wn7++WeCgoLw9vZmyZIlmEwmOnfujKurK99++y0uLi52dStladq0Kf3792fChAl8+umnODg48N///heXShYy/5tITYoQQoirGjVqFPn5+XTq1ImJEyfy3HPPMWHCBMAyCqd9+/bceeeddO3aFUVRWLNmjdrt0aVLFxYuXMjcuXNp3bo1f/zxB6+88kqZ2xk7diyFhYU8/vjjN3R/tFotP/zwAwcOHKBFixb85z//4b333rNbxsPDg3fffZcOHTrQsWNHoqOjWbNmDVqtFm9vbxYuXEj37t1p1aoVGzdu5LfffsPX1/ea2/7mm28IDAykV69eDB8+nPHjx+Ph4YGzs/ON2t1bmkaxDna/RViHIGdmZuLp6VndzRFCiAopKCggKiqK+vXrywmpHG+++SY///wzR48ere6m3DQXL14kJCSEjRs30q9fv+puzt92tc/59Zy/pbtHCCFEtcrJySE6Opr58+czc+bM6m7ODfXnn3+Sk5NDy5YtSUhI4MUXXyQsLIxevXpVd9NqJOnuEUIIUa0mTZpE+/bt6dOnT6munieffBJ3d/cyf5588slqanHZtm3bVm5b3d3dASgqKmL69Ok0b96c4cOH4+/vr07sJkqT7h4hhLgJpLvn+iQnJ5OVlVXmc56engQEBNzkFpUvPz+f+Pj4cp8PDw+/ia2pHtLdI4QQ4l8jICCgRgUiV+Pi4vKvCERuJunuEUKIm+gWS14LUSlV/fmWIEUIIW4CnU4HoM5oKsQ/kfVO01VVYyPdPUIIcRPo9XpcXV1JSUnBwcGhwnf3FeJWoCgKeXl5JCcn4+3trQblf5cEKUIIcRNoNBqCg4OJiooiJiamupsjxA3h7e1NUFBQla1PghQhhLhJHB0dadSokXT5iH8kBweHKsugWEmQIoQQN5FWq5UhyEJUkHSKCiGEEKJGkiBFCCGEEDWSBClCCCGEqJEkSBFCCCFEjSRBihBCCCFqJAlShBBCCFEjSZAihBBCiBpJghQhhBBC1EgSpAghhBCiRpIgRQghhBA1kgQpQgghhKiRJEgRQgghRI0kQYoQQgghaqSbHqRkZGTQoUMH2rRpQ4sWLVi4cOHNboIQQgghbgH6m71BDw8Ptm7diqurK7m5ubRo0YIRI0bg6+t7s5sihBBCiBrspmdSdDodrq6uABgMBhRFQVGUm90MIYQQQtRwlQ5Stm7dytChQ6lduzYajYaVK1eWWmbBggWEhYXh7OxM586d2bt3r93zGRkZtG7dmrp16/LCCy/g5+d33TsghBBCiH+mSgcpubm5tG7dmgULFpT5/I8//siUKVN4/fXXOXjwIK1bt2bgwIEkJyery3h7e3PkyBGioqJYtmwZSUlJ178HQgghhPhHqnSQMnjwYGbOnMnw4cPLfP7DDz9k/PjxjBkzhmbNmvHZZ5/h6urKokWLSi0bGBhI69at2bZtW7nbMxgMZGVl2f0IIYQQ4p+vSmtSCgsLOXDgAP3797+yAa2W/v37s2vXLgCSkpLIzs4GIDMzk61bt9KkSZNy1/n222/j5eWl/oSEhFRlk4UQQghRQ1VpkJKamorJZCIwMNDu8cDAQBITEwGIiYmhZ8+etG7dmp49e/LMM8/QsmXLctc5bdo0MjMz1Z+4uLiqbLIQQgghaqibPgS5U6dOHD58uMLLOzk54eTkdOMaJIQQQogaqUozKX5+fuh0ulKFsElJSQQFBVXlpoQQQgjxD1elQYqjoyPt27dn06ZN6mNms5lNmzbRtWvXqtyUEEIIIf7hKt3dk5OTw/nz59Xfo6KiOHz4MLVq1SI0NJQpU6bw2GOP0aFDBzp16sScOXPIzc1lzJgxf6uhCxYsYMGCBZhMpr+1HiGEEELcGjRKJad73bJlC7fddlupxx977DGWLFkCwPz583nvvfdITEykTZs2fPzxx3Tu3LlKGpyVlYWXlxeZmZl4enpWyTqFEEIIcWNdz/m70kFKdZMgRQghhLj1XM/5+6bfu0cIIYQQoiIkSBFCCCFEjSRBihBCCCFqJAlShBBCCFEj3TJByoIFC2jWrBkdO3as7qYIIYQQ4iaQ0T1CCCGEuOFkdI8QQggh/jEkSBFCCCFEjSRBihBCCCFqJAlShBBCCFEjSZAihBBCiBrplglSZAiyEEII8e8iQ5CFEEIIccPJEGQhhBBC/GNIkCKEEEKIGkmCFCGEEELUSBKkCCGEEKJGkiBFCCGEEDWSBClCCCGEqJFumSBF5kkRQggh/l1knhQhhBBC3HAyT4oQQggh/jEkSBFCCCFEjSRBihBCCCFqJAlShBBCCFEjSZAihBBCiBpJghQhhBBC1EgSpAghhBCiRpIgRQghhBA10i0TpMiMs0IIIcS/i8w4K4QQQogbTmacFUIIIcQ/hgQpQgghhKiRJEgRQgghRI0kQYoQQgghaiQJUoQQQghRI0mQIoQQQogaSYIUIYQQQtRIEqQIIYQQokaSIEUIIYQQNZIEKUIIIYSokW6ZIEXu3SOEEEL8u8i9e4QQQghxw8m9e4QQQgjxjyFBihBCCCFqJAlShBBCCFEjSZAihBBCiBpJghQhhBBC1EgSpAghhBCiRpIgRQghhBA1kgQpQgghhKiRJEgRQgghRI0kQYoQQgghaiQJUoQQQghRI0mQIoQQQogaSYIUIYQQQtRIEqQIIYQQoka6ZYKUBQsW0KxZMzp27FjdTRFCCCHETaBRFEWp7kZURlZWFl5eXmRmZuLp6VndzRFCCCFEBVzP+fuWyaQIIYQQ4t9FghQhhBBC1EgSpAghhBCiRpIgRQghhBA1kgQpQgghhKiRJEgRQgghRI0kQYoQQgghaiQJUoQQQghRI0mQIoQQQogaSYIUIYQQQtRIEqQIIYQQokaSIEUIIYS4yQ4nH2ZDzAbyivIA2J2wm0PJh8pdvsBYwDcnvuHtPW+TW5QLwIXMC2yJ2wLAxpiNnLp8yu41J1JP8NOZn9h6cSs/nfmJ6Mxo9bn4nHiKTEV2yx9KPsTehL1/f+eqkL66GyCEEEL8XUazkdisWOp71Uej0VzXOvKK8lh+djk+zj50Ce6Cv6t/pV7/W+RvzDkwhze6v0Er/1YUGAvKXEemIZNxf4zDYDLg4eDBqOajWHB4AXqtnlV3r6KuR111H/Ym7OW5zc+RU5Sjvj4lP4X3er3H0xufJj4nnocjHua7U9/hoHXgrZ5vMShsEHFZcYxZP4Z8Y776ulCPUH4b/hvHUo/x6JpHaVqrKV8N/AoPRw9ismJ4fN3jmBQTy4Yso4Vfi+s6hlVN7oIshBCiTGbFjAbNdZ/0r0VRFFaeX0k9z3q0C2xX6debFTNajaVDYOrWqayJWkPfkL482+5ZwjzD0Gl1pbZ3JOUI3k7ehHqGqq+1en/f+3x98msAnHROjGo2isdbPE5MdgxatDT0boijzhGzYua5zc9xJPkIA8IGMKX9FFz0Lty18i6is6LxcfLBaDaioPDz0J/JKswiwDUAPxc/ADbFbmLy5sll7lNDr4Yk5yfj5uDGXQ3vYm/CXg6nHAYg0DWQywWXMZqN3F7vdjbEbCj1eg0aPu77MYuOL+JQ8iGC3YJxc3AjPieefGM+n/b/lI0xG/nfuf8BEO4dTp+QPpy6fIodl3YA0My3GcvuWFbq+P1d13P+liBFCHFLsz1R1SQXMi/g6+yLl5PXTd3uycsnOZ56nPsa3/e3goszaWcYs24MOq2OTkGdmNh2Ig28GqAoSrnrLTIVUWQuwkXvUuYyhaZC3trzFl5OXjzd5mlWRa7ijV1v4Kp3ZfWI1VzIuECe0dL9EeQWRNNaTckpzGFDzAaMipF7Gt2DVqPFZDbx7r53WXl+JeNajqNTcCceWfOI3bbCPMP4pP8nhHiEqI8tPr6YDw98CEDTWk0Z22Isy88tp4VvC0Y0GsGjax8lrSDNbj16jR6jYgTARe/CgHoDqOtRlwWHF6jLPBLxCHc2uJMHVj9Qap/ruNchPicenUbHgHoDmN55Op8d/YzvTn3H8PDhxOfEszdxLwEuAaTkp6BQ+pSs1+pZPnQ5YZ5h/HT2J97a81apZXQaHb3q9mJz3Gb1MTcHN/531/+o416Hd/a+w7envqV7ne6cTD1JuiG91Dq0Gi0uehdyi3J5ufPLPNC09P78HRKkCCH+Vb489iULjy7kgz4f0KNOj+pujmp3wm6e2PAE7QLasXjQYvXx/Yn7STekc3u92wFIyUvBpJgIcguqku1mGjLp8YPlOHw14Cs6BXdSn8suzOZ02mlOp50mOS8ZsFx1DwwbSHO/5qXW9eTGJ9kRv0P9Xa/V88XtXzD/0HyyCrOY0W0G3k7efHbkM3QaHXU96vLVsa8oMBXQLqAdSwYtKRWofHnsS+YenAtYgojkvGQ1KPF09CSrMMtu+Vb+rTibdpYCUwEAt4XcRr4xn8TcRKKzotXldBodJsVE9zrdKTQVcjz1OPnGfPxd/Pl68NcsPbmUtII0NsduptBciIPWgSKzfT2Gla+zLxvu28C2i9v46MBHRGdF46p3Ra/Vl2pf1+Cu7ErYhY+TDwPCBvDjmR9p6deSpLwkwr3D2XlpZ6n1B7sFk5qfSpG5iPd7v0+POj1YFbmKXnV78fOZn/nl3C9MajuJDEMG8w7NA+Duhnczs8dMwBKUP7LmEY6lHgMsmZDzGecZHDaYN3u8yaNrHuVU2im8nbx5t9e7dK3dFYDozGiGrhyqtsPbyZsf7vyBnZd2sidhD1svbuWBJg8Q7B7MslPLeKXLK3QO7lzmMbpeEqQIIapNYm4im+M2c2/je3HQOpS5jMFk4H9n/0ff0L5/+8T8W+RvTN8+HYA76t/BO73esXv+QNIBcoty6VW3FwD7EvdRaCqke53ugOXLfnfCbhJyEhgWPqzc1Ha+MZ9fzv3CwLCBfHXsKzbEbOClTi/Rv17/Mpc3mo30+akPmYZMAHY8uIPUvFRqu9em94+9yTPmsXzoclZHrWbpiaW4OriyevhqvJ291XUUmYr45dwvhHqG0jm4M6n5qczeO5v7m9yvnjgURWFt1FrMmKnvVZ89CXs4mnKUTbGbAHiu3XOMazkOgHXR65i+bXqZJ+Zw73BW3L1C/T01P5X10euZvXc2eo2eD/p8wDcnv+FA0gH8XfxJyU+5+htT7Mc7f+RA0gG8nLw4dfkUUZlRHEw+SL4xHxe9i1orEewWTEJuAgDOOmca+TTCrJg5nXYak2ICoJ5nPeKy4zArZnX9eo2ekU1GsvL8SvKMebjoXfj17l8Jdg8mJS+FCRsmcD7jPLWca9llR3rU6cHLnV9m/B/juZhzkcFhg4nLjuP45eMAjGk+hikdpljeB3MRkRmRhHqE4qJ34VDyIRYdX8RfF/+ioVdDvr/ze+745Q5S81PV9c/vO59edXuh0WjULqhRzUZxR/07mLptKjFZMeqyf93/F7Wca9kdN2umSlEUXt/5OlvitvD14K+p71VfXeZE6gkeWfsIoR6hfDP4G1aeX8mw8GF4OXmRmp/Kuqh13F7vdgLdAu3WbduddW/je3m96+ul3jeT2YRJMeGoc6zQ+1wZEqQIIW6KoylHySnKoVvtboDli63PT33IMGTwSudXuL/p/fx4+ke+Pvk1n/X/jFDPUAA+OvARi44vorlvc0a3GM32i9uZ1nkabg5uldr++fTz3P/7/RSaCwHLFfd3d3ynPp9pyKTfz/0wmAz8evev1HKuRd+f+2JWzKy7Zx3+Lv6M3zCefYn7AJjVYxZ3NbyrzG3NPTiXL499qV6x2rqr4V3M6jELgMv5l3F1cOX3C7/zxq431GXaBbTjYPJBuxqChl4NicyMVJd5t9e7DK4/WP193qF5fHH0CwDa+Lehvld9VpxfQbh3OD/e+SPnM86zNmotS04sKfcYDWkwhNk9Z5NTmMOQFUNIK0gj0DWQZr7NCPEIwWg2suz0MnQaHfse3oeDzpJdGPLLEDVouK/xfbzW9TUiMyIZ9uuwMrfTNbgrJsXEsdRjPN/hef6I+YM9CXvsgg9brfxbMa/vPHbE7yA+J557Gt3Dhwc+5EDSAd7p9Q5tA9oClvd456WdtAloQ0u/lmyJ28J3p7+jW+1uhHuH08CrAXU96mIwGTifcR4vRy/qetRVtxObFcuIVSMwmAyAJThx0DrwSpdXCHANIK8oj7jsOJrUakK+MZ8Xt77IoeRDfD/ke7suorJcyLiAn6sfno6efLD/A/V9aBvQlq8GfqUG6da2NavVDI1GQ1pBGn1+7IOCQoBLAJtGbrrqdoByu9fisuJwc3QrFeRcy2+Rv7E6ajXTOk2jnme9Sr3275IgRVS5IlMRf138i5Z+LUtF5TXN/sT9BLkF2X1RiSvMipmdl3bSyr8Vno6eFJoK2XpxK93rdMdF71Lh9ZxJO8ODqx/EaDayatgqwrzC+PX8r7yy4xUAOgV14quBXzHyt5GcSjvFxDYTebL1k6QVpDHof4PsRhsATG43mbEtx7LmwhoOJB3gjgZ30C6gHSbFRG5RLkXmIr45+Q3NfJsxKGwQhaZCHlj9AOfSz6knQle9K7se2qXWpiw7tYy3974NwIRWE6jrXpfXdr4GwGtdX6Oue10mbJigtsE22LBlNBsZsHxAudkDDRp2PLiD5LxkHvj9ARp6N6TAWGAXgFTEsPBhvNn9TcCSkRq6YigFpoIyuyUa+zTmbPpZ9fcA1wDS8tPoWrsruUW55Bblcib9jJohmXNgDl8d/4owzzB+ufsX9QSqKApdv7e8ZsVdKwj3CWdz7Gae3fwsHg4ejGwykgmtJuDq4ArAo2seVQs4fx32K8FuwQDqZ8dkNqHT6uy6dKwG1BtAx6COpOSncE+je6jtXtvueetpqKoLdK01KNZut2vVLl1PfVNsViwjfx9JC78WzL1t7jUD7qjMKN7Z9w4jwkcwIGxApbZ1q7ue87cMQf6HKjIX8Wfsn8RkxTCq2Sic9c7XtZ539r3Dj2d+RKfRMbHNRMa3Gl/FLS19pWBWzBxNOUqTWk0qfPI8dfkUY9aPwdvJm9UjVuPpeGMC2LjsOIxmo13qtTLOpJ3Bz8UPXxffMq+QsgqzWH1hNVq0jGwykjPpZwj1CCUxL5E/Y//kvsb3XbUQ02Q28drO1/B09OTFji/arf/zI5/zyZFPqONeh3l95/HlsS9ZE7WGB5o8wMtdXq5Q+/OK8nhp20vqiXNt1FrGtRqn9p0DnM84j8ls4kLmBcCSmo7MiOT9/e+Tb8wvdeL9+ezPpBWk8c3JbwD46exPTG43mX2J+9hxaYdauKjT6Ah2CyYyI5Jz6eeo5VyLpYOXMuiXQeQZ80jITaCOex0KTYWsPL9SXf+aC2uo53XlinH7xe3qMbQWNR5IOlDm/u68tLNUgPLN4G9o5N2Igf8bSFZhFoeTD/Pbhd8oMBVw4vIJwHLintBqQqmTtS1HrSNvdn+TqdumsjN+J4qicDb9LK/ueFWt6xjSYAhv7n7T7nVn08+i1+ip61GXMS3GMDx8OGbFrHZXJeYmcvvy24nKjCLTkMkPZ34A4D/t/2PXDafRaGjo1ZCjqUe5kHmBcJ9w9biNaDSCye0n2213ZJORHE45TMegjjTwalBqf6zb7xrclblY9tvD0YPNIzfjpHMq9zhY23IjjG4+mma+zWju27xCwcf1FGCHeoay7f5t6LX6Cu1Hfa/6fNb/s0pv59/qlglSFixYwIIFCzCZTNXdlJsm05DJ+uj13NngTvVqpjwHkg6Qb8ynR50eFJmKeGTtI5y8fBKwVNRPajuJ2KxYDiQd4O7wu9U/xuzCbP6M/ZM+IX1KnfxismJYfnY5ACbFxCdHPuGBpg/g4ehht5yiKPx+4Xea1mpKI59G6jbH/TGO2KxYhjcazviW40vtQ74xnzd3vcnOSzuZc9scNsVuwqSYyDJk8WvkrwwPH84b3d9AURQu5lykrnvdUl8Cv0X+xsHkg7jpLVcvGYYMpmyZgruDO8+0fYaG3g3LPF6KorAqchWt/FvZBRz7E/dTaCqkW51upV5zOf8y9/12H7lFuXQJ7sL7vd+v1MiN7fHbeXrj0wS4BnBXw7tYfnY50ztPZ1D9QZjMJpadXsb8Q/PVQsK10Ws5kHSAUI9Q8ox5pOansiFmA18O+LLUe6C2P2k/qyJXATCo/iBa+7cGLGnnZaeXAZZJnEb+PhKj2TJi4bcLv/Gf9v+xe39+OP0DkRmRTOkwRQ0Uk3KTeHrT05zPOI9Wo8WsmFkbvZbGPo1JykvCw9GD7MJs0grS2B6/XU2zH0w+yKNrHiW7KBsNGt7r/R7fnvwWNwc3DiYfJD4nXg1QOgd3Zk/CHuYdmqfWIxgVI+4O7uQU5TB923R1vogxzccQ6BZIA68GnE0/y7n0c/wR/QfzDs2jyFyEXqvHQevAxZyLXMy5qO7bXxf/Uvvbp3WaxnObnyM+J56fzvyEt5M3t4XchoPOcjL/8cyPAAwPH05MVgwhHiFqd0Tf0L6sPL+SX879wp9xf9q9D3fUv4MedXqoQYoGDQoKGjQ08mnE2fSzDAwbSN/QvjjpnEjOT+ZIyhEmbppIVmEW7g7uvNTpJRr5NOK7U99xIfOCemwAnmj9BE+2flLdnk5zpZ4m0DVQLUL96thX5BblUse9Dn1C+pT6vNT3qs/R1KPsTdzL6bTTbL24FbBkdkq6s8GdeDl50dy3dJGtraa1mqqfhSH1h1wzQLmRNBpNlRd/lsX6eRFV75YJUiZOnMjEiRPVdFFV2xCzge9OfcfUjlOJ8I0oc5njqcd5fefr3Nv4Xlr7t2Z99HrGthxLYm4il/Mv08KvRbknj5JS81O5mH2RNgFt7B5PzE1k2rZptPRryfmM82yL38bh5MO81fPKkLM9CXtIyU+hgVcDmvk242L2Rcb9MQ6j2cjCAQtJyElQAxSA709/T5+QPjy18SkyDBkYFSP3Nb6P6MxoJm+eTGRmZKlq/NT8VGbsnIFJMdGzTk/ic+K5kHmB7fHbGVx/MCdSTxDgGoC/qz9/XfyL6dun46h15OvBX9PCrwWfHvlUnT3xy2NfciTlCE46Jxy1jrzf531MZhPj/xjPkZQjgGUkgXUWRasV51fQrXY3PjvyGZGZkdzd8G7e7P6m2sYicxFv7XmLnKIcuysg6xd5WkEaXw/6GpNiQq/VE5cVx/v738ekmBgYNpBXdryCs86Zb+/4lia1mpBRkMETG56g0FzI3Nvm0je0L9mF2cw9OJdjqcdo7d9abePuhN18e+pbBtcfzMnLJ3HRu9Dct7ldMaht6jjTkMnrO15HQSEpL4mFxxYCMGPXDFr7t2bl+ZV8cuQTAGq71eZS7iX16j42O1Zd58nLJxm7fizz+80ntyiXZ/58Ri3Q9HLyorbblTT6d6e+U4OUNRfWkGHIwEXvQtuAtuqoA61GS25RLrP2zOKBJg/Q0r8lv0X+xqw9lq6PPGMeM7vPRKPRMHvvbM6mn8XX2ZfZvWYzadMkojKjeHmHJQtzX+P7OJZ6jH2J+9STO6COiAj1CGV2z9m09G9Jv9B+AOqwSGedM7N6zOL2erfzyJpHOJp6FLAM7by/yf14O3lz72/3qsfCUevI8EbDAdST/sKjC9XXAYxsPJI8Y56aHQj3Die9IJ3LBZfJN+YT4BpAz7o9iagVwfHLx9WMhb+LP893eB6TYmLrxa3oNDpGNx9NA2/77EH7wPasPL+SjbEbAehWuxuxWbEk5ibyYNMHCfcOx8PBg+yibCa1ncT8Q/PpFNSJZ9o9wzcnvmFS20k4653pENiBHZd28OqOV8kqzCLEI4Qlg5YQ4BoAwCf9P2Fvwl4Ghg1k1NpROOmdeLzF45RHo9HQpFYT9iXuY/EJy8iiOxvcWWaWwLpPtu9XG/82hPuEl7leaxHy1ei0Oh5o8gArzq/goYiHrrm8EFcjNSnFXvzrRdZGr+WeRvcwo9sM9fGL2RfJMGQQlx3Hi1tfVB+PqBXBqbRTtAtox9HUoxjNRpx0TrzU6SWcdE74u/rTJbhLqe0k5SZZ/oh/f4CkvCQW9FvAH9F/0MinEQ81fYgx68eoJ25b397xLa39W7Pr0i61L12v0bNy2Eq+PPal+kVc2602jjpHorOiea7dc/x6/leis6LVKzmwfFk39mnM2qi1dmPyX+r0Ev1C++Gid2H4r8NJyU/BUevIsiHLWBu1lq+Of8WgsEH0qtuL6dun4+HowZw+c/jxzI/8EfMHAD5OPnzc92MeW/cYZsXM2BZj+f7092p2AOD1rq9zLPUYv5z7BU9HTxQUsguzAfBz8SOjIEOdl6Aka1EmWEZrPL7+ype1Bg0jGo3gQuYFTl4+icFkoLZbbYrMRTzZ+km1uwEsw+8yDBmA5aT02/Df+CP6D7Vuwd3BncdbPM4PZ35Qh2tatfJvxdGUo3g4eJBrzLUbcTC2xViea/ccG2M3Mn3bdPrV68fUjlNZdHwRS04soY57HdIL0skz5qmjDnrU6cHZtLMk5yfzXLvneLzF48zaPYvl55bzSMQjbIzZiMFkYHrn6czcPZN0Qzp13evSKbgTv5z7pczjZP18vNnjTdz0bry+83XSDelMaT+F0c1Hsyl2E/E58RhMBrWrRoOGObfNYerWqeqQT4AZXWfQvU53Bv5vIGbFzPKhy2lSqwkv/PUC66LXqcutHr6a3y78xmdHyk5lP9/heR5r/pjdY5mGTL4+8TUDwwbSpFYTu/fVw9GDtSPWqtmqqMwoJm6aSFx2HA9HPMxLnV4CKFUD8WDTB5nSfgrOemfyivLYGLuR8xnnGRg2kPXR61l8fDEejh483+F5RjQawbv73mXpyaUAalBh68nWTzKxzcRS+xOXFccdK+5Qf19x1wq8nb3JKMhQT/I7L+3kUs4l7ml0D+cyzhHoGlgq+/Zn7J88t/k59ff/tv8vo1uMLvMYQvmFlLaswZ/V78N/L7NIckvcFp758xn19xc6vMCdDe+sdDGmEBUhhbN/w4GkA4xeNxoXvQvf3vEtDloHUvNTGbt+bJmT65Tk5uBmlwnQoOGhiIfYfWm3ZZKgZo+yLnodL2590a5P3nYoXpfgLuxO2G03eZD1ZNravzVLBy/l5e0v89uF39TtdKvdjd0JuzErZruhdh4OHvxx7x9sjtusDtNs7tucyIxIuxNQjzo9qOteV+231ml0jGo2isUnFhPkFsT8vvNpUqsJR1OO8vCah9XUf8niR1thnmFEZ0XTq24vFvRbwJ6EPUzbNg0PRw8uZF7AWedMgakADRo+v/1zYrNimblnJt1qd+OTfp+QU5TD7xd+Z/be2YClj7ttYFs+OfwJeo2emT1m4u3kzfb47XZfxK38WvHdEMsID9uhdhXxYscX2XVpF9vit5V6LsQjhMTcRIrMReg0Otbds46HVj+k1io08mmETqPjdNppACa2mciKcyu4lHsJsKTU0wrSyDRkMq/vPMI8w7iUc4lg92BG/DpCfa89HD3YMnKL2hWRXZiNh6MHReYijGYjLnoX4rLjGL12NMn5VwKnN7q9Qbh3OE9seILsomy8nbxp4tOEPYl77PajmW8zFg1cZFfYl16Qzqi1o9Q5J6xp+tb+relVtxfzDs3DUetIjzo9+DPuTzoGdWTRwEWApfvrlR2vsD1+O73r9mZ+v/kcSznGQ2uuXD1b56/QarRsuHeDmh24ll2XduHn4qd2H1plGjLZm7iXPnX7qCn2HfE7eHKjpetjcNhgZvWYVW763ayYSStIo5ZzLTWzcDrtNBP+mMCwRsOY1GYSXx3/isXHF5NvzKdDYAe+uP2LMtenKAq3/XQblwsuM77leJ5t92yF9q2s9Ty4+kFOXD6BXqNn430b8XXxva51WcVkxTBr9yyOXz5Or7q9mN1zdpnLxWbFMmTFEMAyCunrwRX/mxGisiRI+RsURWHEqhHqEEO9Rk8djzrEZMXg7eRNsFsw7QPbE5MVo57IHLWOFJoLCfUI5fs7v2fRsUV8dfwrtf/c1uMtHudM2hl12mEnnRNF5iK7q3CwpN/n3jaXPQl7iMqM4qVOL3Hfb/dRYCrg49s+Ztr2aeQW5fJs22f5+NDH6uv6hvTlufbP8cXRLziXfo6HIx5mRKMRgGW4qI+TD3U96vJ/u/6P/537Hw5aBz7p/wldgrtQYCxg8pbJHEk+onadmBUzY1qMYUp7y3wBZsXMwP8NJDE3EbAEVC56F3V2w2C3YO5scKfajQEwu+dshjQYoh5fg8nAkF+GkJyfjFaj5YUOL/BIM8sskcdTjxPuHa4W+GYaMrlr5V246l1ZNmQZ3k7eTN8+nd8v/F7qvbP21b/a5VVGNhkJQEZBBtO2TyPYLZhDyYc4n3Ge9oHtea3La9z9692ApWhybMuxvLHrDcsIiYI0jGYjPwz5gT2Je1hzYQ3d6nTjqdZP8cXRL/jy2Jdq4PXZkc9YcHgBjX0a890d3+Gsd+brE1/z/v731Xb5Ovui0WjUORRqu9VmzYg1dvNxvL7zdTUbUjKLV55vTnzDe/vfAyxB7J8j/8RB68DvF35n+rbpjGkxhnEtx/HtyW9ZG72WAmMBnYI6Mb3z9HJrm6yjOqze6vEWQxoM4bk/n2PLxS3q4x/0/sBuRIKiKJzPOE8d9zrquh9Z84iaDbRmL7rX6X7DigVNZhOLTyymkXcjeof0vq51lFW8rSjKNacFP5B0gFOXT/FA0wfQa6+/93xvwl6e2PAEd4Xfxf91+7/rXk9lmcwm2ixtA1gyZvc0vuembVv8+0iQ8jf9cPoHtS/eylHryNp71qpXgLb3XHij2xtoNVo6B3dWaxFS81PxcvTi1Z2vsiVuC31C+rD6wmrAMmOj0WzkydZP0qduH746bpkYqplvM9IK0kjMTbQ70VpZU7fWVHSwWzDr7lnHg6sf5OTlk4R7h7Nk0JIKFXEm5ibyzt53GNFoBD3r9rR7rmTa+csBX9oVnVmDLHcHd+5scCc6rY4nNzzJ/qT9TG43ma61u3L/7/er+7r1/q2lanT2Juzl57M/80izR9R6ifLkFuWq0zSDZTj0lC1T2Ba/DQVFDfC23m8p9vN28i4zDZ5RkMH2S9vpG9IXVwdXHl//OPsS9zG6+WgmtpnIgOUD1CmiG3g14Ndhv5Zah9FsZG3UWrrW7oqfix9FpiJ+v/A7ver2srvqte16mNpxKkFuQfxny38AeLbts6VGR8Vlx3HXirswKsZSx7s8OYU53L78dnKKcri/yf280uUV9bn0gnS8nLwqPUqh0FRIn5/6kF2Yjavelc0jN+Pq4EpWYRYf7v+QqMwoGno3ZHrn6dc8GW+I2cCULZbgdvXw1Zy4fIJOQZ3+dnbgny7TkImbg9vfCnaux/Kzy4nKjOI/7f9z07ct/l0kSPmbikxFfHn8S+q41+HzI58Tmx1banhmgbGAIb8MwagYWT18Ne6O7uWuz1o4+fTGp9XsSx33OqwdsRaNRkNSbhKLji/ikYhH8HD0ICkvSe2Xt5Wcl8zg/w1WJ66yZjjOpJ1h5fmVjG4+ukrmMDGYDPT+sTe5Rbm46F3Y/sD2a846aDAZOJB0gE5BndBpdAz63yAu5V66YVfOiqJgUkzEZsUyddtUWvm14tWur1ZqHVGZUfx89meeaPUEXk5e/HLuFxYcXkBDr4aMbzWejkEd/1YbN8du5lzGOcY0H4ODzoFZu2dxNPUon/X/DB9nn1LLr4teR0JOAqObj67wUMwfT//It6e+ZW7fuWUOB70e1qyOdVTV9TKZTUzZMgWtRssHfT6okffVEULcfBKkVKG47DjWRq3l4YiHS03Ok1aQhqIoFb4ytO0zr8ycFLa2XdzGzks7cXVwZVSzUTfspmXTtk3j9wu/07NOTz7p/0mlX2/NJMzpM4d+9frdgBaKGyWjIIPl55Zfcy4WIYS4HhKk1FBmxcyIX0cQmRnJwgELyxz1U1PEZMXw3r73eLL1k7Twa1Hp1yuKQoYho8yMgRBCiH8vCVJqsMTcRM5nnK9Rd2oVQgghbhaZFr8GC3ILqrLbsQshhBD/BlLRJoQQQogaSYIUIYQQQtRIEqQIIYQQokaSIEUIIYQQNZIEKUIIIYSokSRIEUIIIUSNJEGKEEIIIWokCVKEEEIIUSNJkCKEEEKIGkmCFCGEEELUSBKkCCGEEKJGkiBFCCGEEDWSBClCCCGEqJEkSBFCCCFEjSRBihBCCCFqJAlShBBCCFEjSZAihBBCiBpJghQhhBBC1EgSpAghhBCiRpIgRQghhBA1kgQpQgghhKiRbnqQEhcXR58+fWjWrBmtWrXi559/vtlNEEIIIcQtQH/TN6jXM2fOHNq0aUNiYiLt27fnjjvuwM3N7WY3RQghhBA12E0PUoKDgwkODgYgKCgIPz8/0tLSJEgRQgghhJ1Kd/ds3bqVoUOHUrt2bTQaDStXriy1zIIFCwgLC8PZ2ZnOnTuzd+/eMtd14MABTCYTISEhlW64EEIIIf7ZKh2k5Obm0rp1axYsWFDm8z/++CNTpkzh9ddf5+DBg7Ru3ZqBAweSnJxst1xaWhqjRo3iiy++uL6WCyGEEOIfTaMoinLdL9ZoWLFiBcOGDVMf69y5Mx07dmT+/PkAmM1mQkJCeOaZZ3jppZcAMBgM3H777YwfP55HH330qtswGAwYDAb196ysLEJCQsjMzMTT0/N6my6EEEKImygrKwsvL69Knb+rdHRPYWEhBw4coH///lc2oNXSv39/du3aBYCiKIwePZq+ffteM0ABePvtt/Hy8lJ/pGtICCGE+Heo0iAlNTUVk8lEYGCg3eOBgYEkJiYCsGPHDn788UdWrlxJmzZtaNOmDceOHSt3ndOmTSMzM1P9iYuLq8omCyGEEKKGuumje3r06IHZbK7w8k5OTjg5Od3AFgkhhBCiJqrSTIqfnx86nY6kpCS7x5OSkggKCqrKTQkhhBDiH65KgxRHR0fat2/Ppk2b1MfMZjObNm2ia9euVbkpIYQQQvzDVbq7Jycnh/Pnz6u/R0VFcfjwYWrVqkVoaChTpkzhscceo0OHDnTq1Ik5c+aQm5vLmDFjqrThQgghhPhnq3SQsn//fm677Tb19ylTpgDw2GOPsWTJEu6//35SUlJ47bXXSExMpE2bNqxbt65UMW1lLViwgAULFmAymf7WeoQQQghxa/hb86RUh+sZZy2EEEKI6lXt86QIIYQQQlQVCVKEEEIIUSNJkCKEEEKIGkmCFCGEEELUSLdMkLJgwQKaNWtGx44dq7spQgghhLgJZHSPEEIIIW44Gd0jhBBCiH8MCVKEEEIIUSNJkCKEEEKIGkmCFCGEEELUSBKkCCGEEKJGumWCFBmCLIQQQvy7yBBkIYQQQtxwMgRZCCGEEP8YEqQIIYQQokaSIEUIIYQQNZIEKUIIIYSokSRIEUIIIUSNJEGKEEIIIWokCVKEEEKIfzlFUXhs0V5Gfr4Lk7nmzExyywQpMpmbuFWcSsjip/1x3GJTEAkh/sWSsgz8dTaFvVFpxKfnV3dzVPrqbkBFTZw4kYkTJ6qTwQhRUw2euw0AP3dH+jYNrObWCCHEtUWm5Kj/v5ieR6ivazW25opbJpMixK3ANntyNinnKkuKxMwC/jydJBknIa7CYDTx/d5Y0nMLb+h2zidf+b6KS8+7oduqDAlShKhCWflG9f9uTrdMorJavPTLUR5fsp990enV3RQhaqylu2KY9ssxZq05dd3rKDSaeeO3k2w+nVzuMvaZlJrT3SNBihBV6FLmlT9uo8lcjS2p+aJTc+3+rcnMZoWXVxzjs78iq7spKIpCXqHx2guKGud6soaH4jIA2Ho2hQWbz3PPpzvJyKtcVmX9iUQW7Yji1V+Pl7uMbSZFghQh/qEuZVz5484uKP9EYjSZOXoxo0ZV0d9sl4vT16m5hmpuybUdvpjBd3timb32NNkFRdXalhmrTtDi9fW89L+jZOZXb1tExRy7mMkdc7dx94Idlb54OXUpC4DkbAMf/HGGAzHprDmWWKl1HIixZCsvpueTnF1Q5jK2mZS4NOnuEZVgvkVPZJ9sOc+MVSf+VTUHlzKvfAHkGMoPUr7YdoG75u9g2d7YSq3/QkoOBqPputtXVU5cymTPhcvX/fpCo1kN4i7nlH1VeCAmnUXboyr1+YlLy2P+n+eq/OSdaPO+Wr/wq8tfZ1MwK/DDvjjeW3+6WttyNVkFRSzYfJ7YyzXnhHej7YtOY/w3++1O8mcSsxnx6Q5OJmRx9GIm55JL16qZzQoLNp/n653Rdo/nFRqJunwl02g9Few4n1qpdh2MvfKZPRybUer5rIIikrKuXCxIJkVUWEq2gU5vbeT1q6TpaiKzWeHDP86yZGe03Yn7Zsg1GFl/IpH8wqo5ma85lsCjX+0hJdvyR3y1gk/7TIr9ibKgyMSEb/bz1fYoLqRYvnjOJGapz+cYjIxZvJef9seV2Y4DMWn0/eAvXllh/1m42UFgQZGJIR9v5/4vdqtXZS8uP8LjS/ZVOIBKt0lXX86xHFejyUzs5TwKjZYrzem/HOON30/afcFarTh0ka93RnPa5vgBzP/zPO//cZZvd8dc176VJ8bmRLsvOq1K111SfqGJUwlZ5T5/2aaAcm/UjW3L3/HTvjjeW3+G9/44U91NqTCTWWHDySTeXnuKTaeSSj1/JC6Dh7/cXeb7YzCa+M+Ph9lwMsmuW3D1sQSKTFf+Rk8lZFFkMnP3gh0MnbedgiIT8/48z3vrz/D6qhPsLA5AkrMLOBCTTll/3jsjUyt88ZpXaOTEpSvttXYf2bJ+H7k46ABIyi6oERdDcAsNQf63OhibTmpOIRtPJfN/d1f8dZ9sOU9BoYn/3N4YjUZT6e3+vD+O0FqudG7gS47ByD2f7KRrQ19m3NW8Qq/PNhgxFv8RpecWUsfbpdJtuF5fbL3A3E3nmH5HUyb0aljucgVFJpz02msenyU7otkbncbm08k0CnRn+Cc7AVg2vjPdGvrZLZtgE6RklejuWbIzmj9OJvHHySQGNrcMTbYGPgC7Ii+z+UwK8Rn5jOwQQn6hiY82nqWhvxvD29blVEI2AGeTstXXFBrNDFuwAxdHHT8/0RWttvx9Sc4q4LO/LvBwl1Aa+rtfdZ/PJWVzKbOA3o39Sz1nm0mIS8vDw8mBn/ZfBCxFfuN6NrjqusE+e2I96U7+8TC/H03AUa9lzv1tiC2+Go25nEf7erXU5Q/HZfCfH48AoNNq2PbibdQu/nxFF191Hr2Ycc02VEZs2pWr2RsdGMxac5Jvd8cyY2gzRnevb/dcXqHRrhsxMiWXgiITzsUnl5rkQnGt0aEygsyqkpCZb5mArEMIA5sH8efpZB7qHIqD7vquv7/bE8Nrv54A4JudMRx89XZcHK8c27sX7ADg9VUnmDmsBQk2fyPf7Y5VMxDrTySRlGUgI69Q/R501GkpNJk5lZCFj6sjR4qDhWe/P8QfJ68ERC8sP4qbk85udKCPqwPpeUVoNOCk15KeV8TJhCzq+7mx+lgC+6PTCPJ0pkNYLfw9nFh/IpEtZ1K4rUkAnerXsutWtr4fCZn51HJzxEmv448Tlu6j9vV8OBCTTn6RiUsZBdT3c7uu41iVJEip4ZKzLFeqKTkGFEWpUMCRYzDy7jrL1UvTYE/uaBlcqW1Gp+bywvKjBHo6sWd6f5bvj+NMUjZnkrIrHKRk2aTbs25yv7n1JB57lX7VCyk5DJ67jQc7hV5zn1KKr/Qvpufx9torFfaxl/PoViIGupRxJWtUsiZln83JzToKyDZISSuuzbCewLeeS+GLrRcAWLQ9msEtgwDsujJ2XbjMyeKrusu5hfi6OfLM94eo5ebIm8Na2G3/p/1xLNoRRX6RkbdHtLrqPk9YeoCo1Fy2vnBbqfkStp5LUf+fmGkg0PPKPny5LYpHutS75kkzzSYbkFq8v/uLR/kUGs38tD+O/CLLldyljHzeX3+GFnU8GdQimNM2V7Ems8LhuAw1SLEWLtteOVYF28/SkbjMCgcGey5cJiGzgGFt61R4W9/utnQBzvjtJMPb1sXL1UF9ztrt5Oaow9lBx+XcQk4nZtMmxPuq60zJNrDlTDIDWwTh6exw1WX/juyCIh79ai+Bnk7kFWcyL6bnk5ZbSC03x+ta3ysrj9O1gS8PdAot9fyqw5c4m5TDD/viWHXkEkcvZhKVmnvNv+mdkal4uTjQvLb9nFu7Iq90YeYXmdh2LoUBzS1/d4dtMhBxaXk8tmgvCZkFvDioCZ3r+/LhhrPq86k5BjaWyMSM6R7G51svcCohm2Sbv3trgHJ/hxD+OJlIfEbprpb7OoRQZDJTx9uFHedT2XwmhT9PJ7PxVBJHL2aWu5+H4zLo0sAS4DcOdOdsUg5H4jL5bk8Mr648zh0tg3ltaDMW74gG4NGu9UjKKuBccg5xaXk1Iki5Zbp7/q0zzlo/zIVGM9lXqXGwZTue/v0/zlS6UMvaZZGUZaCgyESCTXeNyaxQaDTznx8Pl9stAfYn0qybXGhobX9GXvnbnb32NAajmSUl+oDLYg0kDl/MJN1mnRllBF/xV+nuOZ14JQNi7e6wBkAAabmW5dPyCjGZFTVABTiTlK2eeG23u+XMlSGFiZkFxKXnsfpYAkt3x6jdJlbW9zHxGt1vBUUmooqvgmPSSo+82Xr2Sn94Qma+XVYkMauA9SeuXdR32aZY9nKOAaPJbFfQd8im33z1sUTmbz7PyyuOoyiKXR89oKbeTWaFhOIg8WJ6PplXef8ry7a7p9BkVq+CrybHYOT+L3Yz+cfDV+2+KcnDZuj6J3+dt3susfgzEejlTLPanoClPqigyMSKQxfLfG//OJFI17c38cLyo3y25caOTnp33RkOx2Ww/oT9ydOa2TIYTUxdfpQFm8+XswZ7n2yJ5NfDl3h55fFSXXsA+4uzejGXc9XtXe1vutBo5vmfj/DQwj3c8+lOkrLsj5d1HRHBlmNrm+FYvCNK/X9CZoH69/TuujPc8+lOcgxGujbw5a7WtUtt19/DSb1Y3B+Txh8nLOu1vtc9wv2YNbwFnz7Snse61mPeg21ZNq6z+vqIYA9eH9qccT0b0L+ZJQv70cazHL2YiYeznid7N+Te9nXxc3fEUaelf0QAA4qX233BcnH0ZO+G+Lk7kV9k4uUVxzEr8PvRBD7ZHEl+kYk2Id4MaBZISC3LRUlNqUu5ZYKUiRMncvLkSfbt23fDtqEoSo0bNppsU8xke9V9Nbb9/RdScktF9NeSahPkJGQW2J1IM/OLOBibzopD8czdeK7cddhmT272CIT44hPV1bZru09XU1BkUgtgT16yv2JJLzEM0GRW7L70cmwyKVkFRXYBjPX/yVkGtabEuj5Fsfw/tURBaWRxwV1WfpHaH20778GlzHy7gDIj3/711s/P5dxCVh6KZ8pPh8vsd7atq0ktcZySswvsTriJmQV2WRGA6NRrF0raviYtt5CkbAO2Xey2790pm0xRak4hUcX95w2Kr/JOFgdvKdkGNbUOcCKh/CvMyig0mtVj0i7UGyi7X7+k349cUv9/7CpXu7ZyDEa7i5Etp1Psnrd+voI8ndUswIlLWXyzK5r//HiE7u/8yS8HL9q95s3VJ9XjUhVFv0fiMvh0SySf/RVJQdGVz8+JS5YrdCvb99C6/wu3XuDH/ZZaFWsXg8Fo4tvdMXyy5bxdnUVSVoEaGJjMCtN/OYaiKJxJzCa7oAhFUdT9sa35AOwCfFtLd8ew/IDl+BQUmfnUJmhLyTYQn5GPRgP/vb0xAJtOJWE0mYlOzeX3owllrlNX3MXap4k/X43uwPB2lqxZaC1XHHSW57o08KVJkAdajWW7+UUm6vm68s3YTjzZuyELHmqHXqelSwNf/u/uFgxtXZtu4X588nA77m1fl8EtrmTD7+8QQttQb7VWZeqgprw0uCnv39eavdP7c+KNgXz5WEfmPNCGxoHuOOq0vHl3c4a3rcOnj7TD38PJrv3W+q1Jt4Wj0WgY0jKYZ/qG06KOZ5n7e7NJd0+xV1Ye46f9F5k2uCljSvQDV6ckm6vL1GwDDfzcyuzy+Wp7FNkFRUzu39juah/gZEI2g1pUvMvnss2JKSEz324ei7TcQjVTk5xdgNmsqHUQMZdzycgronWIt90X1N8JUiJTcjgQk86wNnVw1F87pjYYTeqJtWQQYavkydfWsj2xrDh0kQc6htIhzMfmNfbry8i136+SJ0nb7p6SJwfrc4biDJmns4PdiftyTqFdtgEgprjLwaxYan4u5xiItrnCT8wssDtppOcWEeDhfKV9xfucmm3gww1niU3LY3CLYG5vZj91v20wlZptv88HS+xHQlaBXSEnXLnavxrbfTWaFbWA2NpvX55zSdlq3ckdLYOZv/m8GsSUTJOfvJRVqmboelzKyMesgLODlgHNgzgYm1HmCImSfrTJNJ4sJ5OyNyqNvEIjfZoEAJS6Z8q55GzyCo24Olq+qhMzLe+hJUixZlKy1AyKyaww7Zdj3NEyGGcHHUUms906T1zKwmA0odVoKlW3kZJt4EJKDq1DvHlw4W61Kyctt5Dpd0QA8NW2KMqr5TxyMZOYy7nM+/NKBmX6imM0CfJgzOJ9av1KfV83zAo0DHBjxcF4CorMNAv2JCo1l4OxlmHgr6w8TpsQb96/r1WpANnqj5NJPNAxhEsZBYT6uqoXAr8ejgdgcIsg1h5P5Ls9MZxLzqaBnzvuzpZjHO7vTp8m/modyK4Ll/n18CVMZoXbmviTW2hS65Ke7deICb0aYDSZ8Xa1dGfd1iSAxWM60izYk/fXn+HnAxfpHxGAs4MOB50WQ3GGc8rtjWkb6kPbUJ+SzVfd0TK4VHe9Xqflo5FtuOfTnTQMcOdBm24wrVaDFsv3saujnlWTelBkMuNR3MXXMawW6yf3Yl90Gj/vv8jGU0kYzQruTnp6Nrb8rdzTvm657akOEqQU02u1FBrNFc5W3Cy2mZQvtl5gwtIDTLotnHE966vBSkGRiVmrT2JWYGSHkFIT/diOf68I2/R9QkaB3ZC59LxCNegoMimk5xXi626JzB9auIekrAJ2vtT3uoKU2Mt56HUatb4A4KX/HWVfdDpfbYti2h1N6VS/lvqFXRbbdHdGXhGLd0RxOiGbt0e0RKvVsDcqjbRcg93J1zbQWrQ9ijd+PwnAvuh0ejYq/yRXMggqOf+AbXdPyZO7rZRsA57ODnbddJdzDaWG5toWv2XlF7GnRAFnQmaBWsdRVvusgZltsHU2KbtUkGKb5i0ZzJ0rLuZzcdCRX2QqzqSUyLZUIEgpGdgcj7ecxJvX8eREfFa5gcqpxGw1MBvUIoj5m89zKbOAjLxCuwwQXKlLuZxj4KlvDxLs7cxTfRri5qjn6e8O0iHMh8aBHsRczmNy/0bl1phYg8PQWq60La79OBR39YzE6cQsuy4ra7Zn5/lULucWMrR1bXZGpvLQwj3otBp2vtSXQE9nLhZPR96ijicp2QaSsgycvJSFl4sDD325R+3CCvRypkUdSyblVEIWzjYBvMFoZlfkZQqKTIT5WU76eq0GnVZDjsFI3/f/IjXHwLP9GvFk74ZqJqA8i3dE8faa0xSazDzVp6EaoIDl72Voq9qE1HLh92OWTEOvxv5sPWvJAGk0lszgnguXGb14Hwajmc71a3E5t5DzyTk8tHCPXXD52qoTpGQbCPJ0Vi9Knukbzg/74vjrbArvF48UOhyXwRu/lz8D66IdUfx6OJ590ek83r0+uy9cJqk4oNZq4M1hLcgqKGLH+cvqj1Wrut7odVrubFWbpbtjmLPxnFqP8lz/xvx6OF4NUjqF1cK9jJmlbysOOmcOb8GDnUPVz82DnUJZsjOa5wc05u42Fa9TKinMz41d0/qh12quWizv7KAr9bmu5ebIwOZBJGUVqFn2Pk38cdLXvOJrkCBFZU2BJde0IMXmxLepOLU/a80psguKmDKgCWA5OVnPX1GpuerJztlBS0GRWR1eVlG2V/BHL2bYZQTScgvtaiKSsgz4ujuRmXelO+P4pUy7OhTbqeLLk19oYsjH23B21LF7Wj90Wg2KoqhTpp9Jymb04n00DfJg3eRe5a7H9gsvM6+ID/44S47ByCNd6tG8ticjP99V6jU5hZZMRn6hidlrLfNOdAzzYV90OtvOlZ6PoI63C/EZ+aVqXqxBRbCXMwmZBeQWmjCZFXRajV1NQ0kp2QYa+ruTllcik1K8PjdHHbklhlNn5BWVWmdiZn6JTMqV9SmKogbgtgGAbZ2M1UWb+3aU7BY7Xxzw9mjkx4aTSSRk5KsBRwM/Ny6k5pbKpJjNCs/8cAgnnZYPRrZGo9GQllMySLF0B9T2diEtt7Dc47X1bAqFRjMOOg1NgzzU9+JUQrYapNRycyQtt1AdurzuRCJ7i4cN/3Eiif8OaMyx+EyOxV/pgmng58bIjiFlbtOaSQyt5UbLul7otBqSsgwkZOYT7FX2qDVrxqChvxuRKbnFc2Rk8NCXe4ofd+eZZYeAK8W/A5sHqQFiXW9XgjxdSMqy1HYcjE23u4AK8nQmzNeVMF9XoouHbTs7aBnUPIiVhy8xZomlW9zaPRVSyxVvVwcOxWaofyPvrT+Dm6Ou1AgiW2eTsvm/306qvy/bYynq7VS/Fp7OejaeSmbo/O3UcnOk0GimaZAHD3QMUYOUzvVrkZVv5GRCFtkGI7W9nPno/jYcjsvg6e8Oqm15rl8j5m46d2WYf/FnyFGvpVdjf84m5fDX2RS7vznrNmyzb70b+3M6MYsLKbnq994im1oSgG4N/fBzd2LhqA4ciEknKcvAb0cu8Vfx+lqHWIK/kR1CWLo7Rs2C9o8IpE2Itzozq16roV0973KPHYCTXkc7m0zJ9DsiGNM9jHq+f78gtSKZ5avp0sBX/f/A4uLgmuiWqUm50WpikGI0mUtdcVr9cihe/b/tFeSF1Fy1u6d9Pcsfx4WUnEpNCGd7pV3yJJ2eW2iXGbEGUbF2kxflVDqTEpuWR7bBSEq2Qb16tz1B3tEyCK3GclJNyCy/oMtudI3BqNaTXEzPI6GcK3xr/UxUai6FJjPerg68e2/rcrfRNMgDKD9TEWbzBWStSyl5lW/L+sVsl0nJMagzsTYu3p6tzPwidcIo68iOS5kFdpkk226/HIORgqLS2YmzZQQp8XaZFPt9tH5BWzNMSdkGtf0Rxd0PJYsR49LzWH00gV8OxXM+OYePNpzlQIlhqdasR7Cn81WHq1tPJKG1XNHrtGqB48mELPUYD20VjIPOEhhGp+baBen5RSY1ILI7Dkmlj4OVtTi5ZR0vXB31NAm0vB+HyunyOZOYzZrirMLcB9riqNeSYzBy32dXAuTlBy7a/W1bC0utAWJdHxda1bWcLI/FZ5bqmgn0dEaj0XBnqytFmq3qenNb0wC75Q4Wt7GOtwst65S+e/zmMymlHrNVstDW+rfcONCdmcNa0q2hL1rNle67h7vUo4nN57WBvzvLn+rKqK71aF/Ph6XjOlPb24VBzYNo6G/5Owmp5cIzfcNpXcYIpe4NfXFz0pcbDLSs48UTva8Mee9UvxafPNweB50GvVajFrFGBHuqc4Dc3cbymKujnp6N/Lm3fV0+f7Q9XRv44qTXqkOKW9TxVP/WnR20vD60GWApcvVw1jOwRdBVs7plcdRrqyRAqQqNAtxpWceLuj4upT43NYkEKcUCrEGKzRes2azwvwMXq+3eIqk5hWVO5AOWjIF1sjLbE2B0aq7a3dOyjjeOxX2gZQ1rK49tTcqFEvuelldodzVj7Y6yDVLOJWVXOEjJzCtiZ2Qq8RlXXm/dH+vJJbSWK5883J6mQZYT0tXqAcoLBuIz8tWCy5KsmSLriJb6fm7U9XFBX04atbEapJTIpOReyaQ4FV/lWDNK1nb5uZcehmlb0Gq7LmsmxXpStJWRX6ge8871LUMMEzML7AIx2yCqZLBhFZmSU2oUkF13j03QbjYr6nvStYEvOq0Gk9lSyAjQrDhgSM0ppMgmWxNl8xma8tMRuytm7+LhtdbPZ5CXs113X3msQyOtJ69t51LUgulGgR50KJ5XZeu5FC6U6O60zjfzTN9wZg1voR6HsqTnFqqB+pBWltqAtsXZif3l3Bjx290xKAoMah5EizpeaoGvwWibwbKvUbGOKrEe+zo+LrQsDlKOXswo1YUb5GWpNbqz9ZV6hfb1fOgeXnb3ZB1vF7vg4f+Kh+gejE23u4ApKDKp9wWKS8vj1+Li3/kPtbVbX5NAD4K8nFk2vguHXhvAZ4+05+0RLXmoUyhhvm7q5z+0liuujnreuLsF/3uqmzo/j1ar4dU7m+Hv4cT0wRHodVqe7RtOgIcT/+nfWN3O7c0sV/htQryxluJ5uThw4JX+/PVCH357pofdXD4N/d1oX8+HdZN7sW5yLz5+sC1//rc3qyZ1Z/WzPXh7REtGtCtdc+HsoOO7cZ05+OrtahCh0Wh4qo9ljoGpg5qqo16CvJw58MrtfPxA21LruZVoNBpWPN2NP//bp8wuq5pCgpRi1gJD25TqjshU/vvzEV5ZWT2zvZa8IgXwdNbj7eqAolz5YrUd0RFtk0nxc3ekXvEcFyWDjZK+3R3D9BXHmLfp3FWzSem5hXYjd6xttMukJGWTadPFc7Ug5bVVx3lo4R41jWy7P9YTYoPiKy7rycHaP5yWW0huiWHZ5WVZLqbncyG17BORdX+sJ7MGfu446LSl5gexsl5dZeYX2s32ag3ufN0d8SguwssxGCkymdX0tfXK31ZKjoEik9muWy0hs8DmqrWMICWvSD3mnWyClESb/bfNzJRXa2U0K3ZBBJRfk3IpM5/8IhMOOg31/dzUwN6aBQkPcFdHMyRnG1h9NIGHFu62m6H1WIksRuMA+30L9nKxC1Ksc2tYPwNW1pPd7RGWepqd5y9zLtkSfNTxdqFX8YnrrzMppfbP+nfj5+6krudsUg6jFu3liaX77d7T1ccSMJoVIoI9CQ+wLGvNIq0/kVhmhtKa7bm3uADR+v54OOkJ9LQcM2uxr7U75lh8JoqiXOnu8XFVMx8XUnNLTaUe5Gn5vmoS6KF+Hrs28MXP3YkO9UoXYtbxcaFPkwCcHbR0DPPh4c6huDjoyC4wql140am5tH1jA81eW8+Qj7fx8srjmMwKPRv5MaRlsPqZBksgaOXl4sCgFkE82CkUXXHtS9Piz/nV5tno0ySAfS/3Z3BxYWi/iED2vtyf5/o3ok8Tf/w9nNRJDz2cHdTPSscwH3zdndRgooHNxITW97Ohv7v6fjXwt/w9N/B3V9tYFq1WU+rO5Xe3qcPpNweVGkzhqNdes5bnVqDXaf92t9GNVrNbdxMFFH95pOVduQq09ouX/JK7WcoKFhoGuBNe/Ido/bK1zR5EXc5Vr6C9XR3VP9rIMu4XYRVzOZdXVh5n2Z5YPthwtszx8YNbWK5o0nKL7Ia2JqndPVeO0fnkHLsrv6vNk2KdVdH6xW67P1HFQYX1i87arXEoNoO4tDx6vbuZBxfutjupxGeU3aUTn5Ffbm1OVolMivWE2KCcL1hr0FBkUricW6gO47VmK/zcndRq+uwCI0lZlpohR522zHWmZBtKdR1ZT0o6rabUCRosV7nWIKZDvVpoNJZaE9v7b9hmeq42msn2qr7QaLYbUXY5t1A9EVu7esJ83dDrtOrVvJWfu6Ma7Een5jJx2UF2Rl5mweby5+ZoFGg/822QlzN1vK+s1/qeRwR50qM4S3BbE39Gdw8DLIFRmK8rhSaz+vdax8dFvbr+62yKWmhrPXlbR2DZBinxGflsPZvC+hNJHIvPpNBoZsQnO9QLlDtbXclY9GkSgJujjviMfA7FWTIRB2LSyDUYiU7NJTYtDwedhq4NLX3+k/s35p17WrJt6m1qQaX1venRyB9HnZaMvCLi0vLtunv83J3wc3dCUUrPWWHNyGk0Gr54tAOfPtxODZ4+e7Q9v03qYRcg1PF2oY63C9un9uXrxzuh12nVY2vNCK0+lqAWXp+4lMXWsyloNTBtcAQajYYWNhOflRU423rjrub89/bG9L3OboRFj3Vk7/R+alE+QO8mlve0ZNeEj6sDtzcLpFP9Wjdk8rGaOJvvv0nNzfHcZLVcHdX09eWcQoK8nNUv9qQs+6G2N8LP++NYezyRZsGejO1RHx83RzVLEeDhpAYsDf3d0Ws17I9JVwMP23vjxF7Ow7m4StvH1UE9wV1thM+1Cmt9XB3o1diftccT7Ub3AGw+ncITS/erozPAkta27fcva8ZZ6+y51n20nefAWldyJZNiOZFYh+odjc9g6e4YcgxGjl7MZH9MOh3DahW/tvxMijXd/mzfcAB2RF7mQEy62r5Ia5BS/EVX3hdePV9XnPSWbrQOMzfi4aRnbM/66r74ujupV53ZBUVcyrC8LtjbGS/Xsrt70ksMZ7bWitRyc8TP5ovaypqR8HN3wsvVAT93p1LZEtvAp6xMilZjGc58KDZDHWmQkJmPoqDun8mssHR3DF0b+qpBivUKtbaXC4fIUNdXy82JQE8n4jPymfdn+XPo2BrYPIjvbLJowV7OaneDVgNDWwez+UwyvRr7MbR1bXIKjAR4XgliNBoN/SIC+Wq7pTiyU1gtGhW3z1rADJbi4+a1PdXJvyzHzhE/d0c8nfV2tzBYezwRg9Gs1nM0r+3JyA5XimqdHXQMaB7EikPx/HYkgRWH4vl2dyzuTnr1qrR9PR/1qryWmyP3d7QME/Ut0d1Xx9uZiGAPjlzM5Otd0aTnFaHTatSuhSZB7qSev/LeLR7dES9XB/Q2NSqhvq52WT9rcNM40F0NvOv4uKjPWbWv58OuC5a/gYc6h7K9uFtrbI/6rDmWQEJmAfd3DFUnjWtRx5NdFy7j5+50zdljW4d4l1ljUlFlfddOub0xtzUJULs3rTQaDQtHdbjubYmaTYKUYlqtBj93R5KyDCRnFxDk5ax+sRvNlivmkpPgVKV31p0mNaeQP08ns+18KsGezqwrnuyoeW1PkosL3Br6X0mpny8jk2I0K+qcDN6ujmradWfk5XKn1S9r+njbivkWdbzUL6W0XPualPiMfLt6F71Wg9Gs2F3FZ+YX2W375KUshn+yg3E965c5z4Fak1L8BdvQ70pmw3pCsU4XD5YbmXUMq6XeoA4sAYZtBiw+PU8dEtyjkT+d6tcictlBwJLpURTlSnePv7vdvwC1vZy5lFmAh5MeV0c9Pq6OahdOtsHIHJuJ7Wy7e7ILjGomqbaXC14uV6Ykd9Rbhr1HpuSUCiKsV7S+1whSQmtZTj7BNp9Xq2sFKXe2qs2qI5dYtjeWx7vXJ9TXVR3tE+brRlJ2ARl5Rby+6gSNAtzVK29rkNIhzIfVx65McFXLzVHNrlhnuSzL8LZ18Pdwwt/diV6N/bmtib9awOnv4YReq8FBp6GBnzvD29ZlQLMg9YRfVqHi0Na1+Wp7FCG1XPj0kXbq52xo69rq56SBv7tdcAPg5+GERqOhYYC7XRHsuuOJ6tTxg5oH8dmj7cvYZnBxcBKjZmZyDEYoPsy9yrjnEYCvm/176efuRO/G/hy5mKkGWne0DFZrBBoHeqjDY31cHSpV4Ngk0IP1xTObllWM3LF+LdhsuYHmfR3qqqNYHu4cypjuYaw7nshDna/MwdGpvi8Lt0Wp3a43m7ODTs1OiX8P6e6xYU1VW4tBbVPk15pK/O/IKihSuwocdBqOxGWoAQpgNyFVeIA7DYtPEueTc1AURb2pnaujfVrS29WBvk0DcHHQqZMhlcWaJrfe4wEsJ1pr8dvIDiFqkFIyk1KS7bA2qyKTYjd/x29HL2Ewmu2uoG0lZOZTZDKrwVP94myQVqtheBn3QFl9LIELKTnEpOVRaDLj4qBTJ7qyyiowqilza3bJ06ZL5nJuIdkFRjQa1Doe20yKtZbEv7hb0Nvmfiol+bk5qScZSybF8tmp4+OCp02/fs9wP7xdHbiYns/cTZb7fviWuEIt76rVWr8SWnzFbe1GsJVRRnePbcDzUOdQuof7Umg0M/LzXUz56TDbiu/L0yHMx27Zc8k56pwK1iGVJVP5ns56Am0CgZLFeNbgpk8Tf6bfEcH4XpZRGR/d34a2od6M7FAXB52WAE9n1jzbk2+LpwUvWSdQUpsQb1Y/24Pfn+lp1z1gOz15oKclKLJl3T/bmy066DREpeaqM6faTuZnq3fjAIa0DFYDlPE96/PR/a3Vv5l+TQPLfJ1fiQsdfw8nxvdqYFdQPb7nlfoH26LpihQU27IWeGs1lOqaA8solZ6N/MgvMvHAF7spLL43jKVw3JVxPRvYBYX9IwL4clQHZpW4J5QQN9ItE6TcjHv3BJQYhmx79XkuOZtt51JKTZtvNJl56tsD/OfHw3a1EZVhvfr3c3diyZhOOOq1hNRy4avHOrBodAdGdatHkKczGo2laNNakxKdmmcpHi0e5VMyDerj6oi7k16tJ/lfiemy1e0XBwMDml0ZK59rMPLrpO7Mf6gtQ1vXxqe4myI121Dqxnm2+keUfaVnO1fK/uJCyvLurXMps4A/TiRhMit4uzqoRYIArw1tzoOdLKn3/97emKZBHuQVmhg0dxtfF9+zIzzAvdx0tKezXg0EPF30xW0rUrsy6ni7qH3QtrUgTYMtX/iBxYGsj023TcmTsSWTUhwAGYxqpqm2twueNpmUOj4uvDSoKYA6H4z1RG67Lke9Vg1urFk0K2uQ8my/RrQuHg1iTbOnlVE4GxF85aQX7OXMa3c2x0mvJTGrgF8OxvP9XsssqZ3q18LZwf7rIT2vCL1WoxaClhxKqdFo7O478/KQCLX408VBx1ePdeC9e1uVureJt6sjK57ubjfsu1GgR6Uyl81re9llqSyPXQlU3Z30dutz1F05ptYgpWmQhxp4WQPa9mUUoYKlVmj+Q22Zc38bJvdvxPMDmzC8bV02/Kc3Pz3R1W4kjS2/MoJQD2cHXhpsmbW1R7gfrep6q8/bDj+v7J3EW9f1RqfV0DjQo8zZZXVaDZ8+0l793Fi3X95NTDUaDf2bBZbKSAlxI90y3T0TJ05k4sSJZGVl4eVVerx/VbAWz1q/0G2HbU75yXJr+Pb1fJhzfxu1z3jt8UTWHrdkPSb0amA3esO2jiUl20BUaq76BW997PO/ItXthvm60j3cj93T+uHhrLf7Ylk4qgMpOQWE1HLFbFbUbo83i2dHreXmSJ8mAWraXKNB/dK+p31dfjkUz29HLvHKkIhSKXNr0WtDmxNkVoGRpkGe6rBf60m/5KRittwcdbQr8aVurW3IzC8iyMuZgiITR+Kufh+TlGwDHxTPLDm6W5jdl6ZOq+HtEa14pm8jgjyd/7+98w6Pqkr/+HcmyUwSUob0BNIDgVACBAihSYnSVEBRVFRERRGwofwUXcXdVXF1LeuKfRU7igIqTSnSNHRCJxB6C4FAek/O7493ztx7p6RAOu/neeaZcsuce+bOPd/7toOxPdrg6QW7sOnoJXyZQne/7QI9YFINWDLWCKDBTO5PWlKyCkrxrxVUxE0dHBjg6Yonk9tDrwN6Rvjg/bVHLHfWakvKLT3aWL5b9pXa3SPrjrQxuWpmoPV0dcbtPUPxw7ZTFitXhG8r7DyVbUkLlv3u52FEbnE5Qn3cNTFEEWZrj5Neh+8fTsKyPefQIcgLI9/dgNziMpRXVMLZSW8RSh2DvSwptYFernB1ccK6mYPx4bojmPfXcUs/JUb64vH5qTa/Tfcwk8ayIWdWlcgB1uCsx+09Q3HgXC72nMlBhF8rhPu2atAaETqdDp/e2xNz16ZjxvWxmgBuPw+D5TwY0z0Eaw6exwP9IxHk7WZxkQCwmSXXev/Wsxtbx4dY42tlzZExKuMS2iLav5XGxQjAEl8D1N6SEurjjl+m97PrLpR4GJ3xw5QkfLzuKNYduoAHVVYchmkKNBtLSkMgzcGyQJm9jIjtJy7jujf+wL2fbcHcP9Lx9IJdlmVrVJO9zVl+AN3/udIyQ+ntH6Xg9o9SsGKv4sZ5fcVBfLrxGF5dRgOkvID7tDLY3Pl0aeuNIWYTsl6vwyzzfBmLU6mOQbC3q8YPLstgA5SaGObjjrzicizeqUx6dvRCPhZsO2Vx94T7uFsuaNbWAW83F6hvsDyMzpa75p8eScI/RnfCt5P72KTYBpvNzNJFtPdMjsOS5+r6IkcvFsDT6IxJfe1fNENMbtDrdWjb2h1/GxWnWdY+0FMToNpK5QZ7flRHy2tp1Vi08wx2nsyGl6uzZR4SyePJ7fDo0HZIivbFzheuxwzzxGPqwEV13QV3A5Whlv2490yOJWg5xKSNSfF0dYFer8MrY7tYfisfDwOmmmszAIr7Rw5m6uwgFyedxs3j6uKEW3q0RXtzxowQ1O+XC0qRZi5WNrJLMHQ6yh6RFqMgb1dMuS4aMlYx3NcdQd6ueG4kWXl6qVwe1nU43hnfHa4uetzXNwIAuYDen9ADfz07BE56ncUyYW3layiS4wKxaGo/hPm6aywpardLsLcbFkzpi+Gdg9Et1GRZz8vVuc7TM9VuHW83F00p8u5hrW2sQZ6uLhYLSm0tKQCJrMBqLB9GZyc8OrQdfnykrya1mGGaAixSVPib/8y/7cvAuZwizRwVku5hJlQKKsn8xm9pmgJNapHy0bqjyCkqw12fbMbbqw5Zgjjf+O0gyisqUVxWYbHASCKquAOz5o5eoRa3h5erM+7pE67ZXp0to9frcG9SOABg3l/HIIRAcVkF7vnfFsz8cTdKyiuh15H74Yv7e6FHmAkf36sNFnTS6zTWCW83Fyye3g8/PJyEhHAf3JsUgfhQE1yc9Jp6CvKim1NUhoU7TmOcquqmNW1MbpoJ+qYOjoF3FbEfkrhgL83Fv12A1pLy2NB20OmogFVnVdVNdXwIAPxtVFyVd8Emd+XuWx2s3FW1T3nO3NiVxMCGwxdx+nIRPI3OiA81WVxM9P3Uxo7BXpg+mDKOEsJa44nk9nhlbGf0DG9tqSERZC6/rh5EhnQIQGs7bi1n1W/w0q/78e2WkxCC4hu6hZrw1f2J+Ow+rds0yNvVInikoJjULxIrnhiAeZN6W9xM1iIlLsQLqS/eYKnGqdfrMLJLsEWkDYoNwKoZA/HsiA4O+7Wh8GllsAjtqqwL303ug96RPnjnjm513gaTu8EiBmvqzpLWV+s4K4a5Fmg27p6GQMY+XMwvRdKcNTbL2wd6YNHUfkjPzMOGwxex8fBFbDqahbE92uDrTSex8+RlXC4ohbtRuTvKKSrDR+sow8DVRY8jFwrw2vKDaB/oaSnZLgmvRY6/TqfDq2O74Ink9vD3MFrcSup0ZTW39wrF2ysP4dD5fPx1JAt7z+RosnICPF3h4qRHpxBvLJzaz+53Rvl7WDIAvN1cEO3vgWg7SQzR/h6WgmvSWrHpaJYlewFQMlsAGmzXHMxEzwgfBHq5Yumec7g3KRxTVOWuq0Kv16F/jJ/FqtQ+0NNS2MunlQEPDojC7b1CNa4WADbva5M50DnEC9tPXIbBWW83XTLctxWGdgjAqgMkXCf1i7D5PrWYe/L69nhwQKQllmVCYjgmJIZblj+R3A7tAzxwS0Jby/TyN1nFdqiR1Yh/3XUWv5qNfbKORn8HkyY+P6ojvNxcMM0smFyc9BZ338tjOuPUpSK7hcKqqyMRE9A07s5dnPTwcTcgq6DUbuVfSUyAB354OKle2uCk18GnlQEX86tug5qXx3TGA/0jWaQw1yQsUlT0i/HFyC5BWJt2wb4VJZQu0DEBnogJ8NRUIdx2/DIOZuRh+nc78PjQ9prt9DpgynXRCPA04qVf9+NT1WCtpjaWFICEirUp953x3XDXp5sxyVzsSuLl6oJxCW3xRcoJ/GfVYRywKsttPSmcPQa199eIFEfc2qMNUk9lw83FydK+eeag1vi23ugT7Qujkx7vmidhG90tBG+M6wqfVgbklZTjoYFR6NrW22EAnz0GtvfH4tSzcHNxspjFnfQ6y/wn1gIBgMaq0drdBW1b19yc/uT17eHh6oyx3R1Paz6pXyRWHciEh9EZ9/enc8XD4GypT+JpZcnxtNNGSbS/Bx4d2g5CCCRG+qCwtMJm9mI13UJNmpoggGNxIony98Db47vZXSbrfDR3/D2NZpFSf+UEqsPPw4iL+aXw96xZAGoro7PGAsgw1xIsUlS4G5zx/oQEvLB4L77aRIGQvq0MljlVulVRH+D5UR3x8Ffb8Wd6Fg6fp/obsYGeuLtPGHpH+iI2yBNCCPh4GPHm72k4kVUINxcn9Ivxtdxth/tcfVBh3xg/bH0+2W6K7L19I/BFygnLjLCd23hhdHwbvLLsgKXAWVUMig3AmyspVdY63VnNhMRw6PU69IrwgV4H/LY3A3kl5dDrgDdv74aYAA+s2KvU1wj0crUEFHq5ulxREajkuEB0DzOhd4QP9OZiWCnPDqnSXaQWLl3bmmolikzuBswcprgwXrgxDv9csh8P9leEa78YP/z3zu5o09oNJnOMjF6vg6erC3KKyqoUJY7Q6ShA1lHNG8nTw2Kxcv95tG3tZpnFNjGSa0z4expxMCOvUUWKjC+qqSWFYa5lWKTYoW+0r0WkdG7jbSnZbm/eFcmAdv6YO6EHJn2+1eJu6RjsiXuSIizr6HQ0K+fN8SHIK6bKkmvTLlhESk3iL2qCI193tL8HBsX6Y23aBTjrdXj91njEhXhhUKx/jbIu1OZm67lE1Oj1Oo2r4oO7E/DIN9txW0KoJcU2TCXIguogpdHL1QWLrNxU1aVKempEytXdqU7qG4GkKF9L0KrEnktmSIcAbD6a5TBNtSZUJ6j6RPmiT5QvhBBwNzghwNMVblUIy2uF23uGIqeoDEMdpMo3BDKNvS7Oe4Zp6bBIsUOiqiCZq4seDw+MwuXCUk2ApD36RfvBzcXJUrjMOp1QjRwgR3QOwsxhsXZnuq0Pnkhuj31nczHlumhLueuaRvTr9Tq0MbnhTHZRrQb1/u38sOvFGzSxGxF+VFpe56DQVEOgdvdY1yepLXq9ztKf1fH2+G71Ps2CRKfTtRhXTV1wU3xIlbE8DcFD10XBw9XZ7my8DMNoYZFiB3UhsMuFZfjono5VrK1gcNajd6SPxfJSk8mudDqdJVCxIegWasLW55OvePufp/fDvD+PY6I55bSmWA/I7gZnfHF/bwCNN4GXm+p7e0Y0bIpsQwgUpmnSIcgL/xjNVVsZpiawSHHAP8d0xj9/3Y9nhtcudbJfjK9FpNibvba54+dhxNPDYutkX/ZK6DckOp0Ov07vj6KyiiuqQcEwDMPULyxSHHBPn3DcnRhWq2BKQKkjodPVzJLCNC5drjIWhWEYhqk/WKRUQW0FCkCFxR4f2g4mdxe7M7YyDMMwDFMzeBStY3Q6HZ68vn31KzIMwzAMUyVcFp9hGIZhmCZJsxEpc+fORVxcHHr16lX9ygzDMAzDNHt0QghR/WpNh9zcXHh7eyMnJwdeXjyXBcMwDMM0B65k/G42lhSGYRiGYa4tWKQwDMMwDNMkYZHCMAzDMEyThEUKwzAMwzBNEhYpDMMwDMM0SVikMAzDMAzTJGGRwjAMwzBMk4RFCsMwDMMwTRIWKQzDMAzDNElYpDAMwzAM0yRhkcIwDMMwTJOERQrDMAzDME0SFikMwzAMwzRJWKQwDMMwDNMkYZHCMAzDMEyThEUKwzDXFhVlwOaPgawjjd0ShmGqgUUKw1wLHFwK/PoEUF7S2C2pP7Z9Bqx6CRBC+Sz3LLD1f0BZsfLZ2teA5TOBr29t8CYCAPLOA9vnAaUF9D73LLVd3UaGYQCwSGGYqiktAEryGrsVV8/yZ4HtnwMHfrW//OJhIH11w7apLiktAJbNBDa+DZzZrny+YBKwdAbw23PKZ5s/oufLx2r3HeteB3Z+ffVtXf868OvjwK7v6P03twNLngTW/LP6bU/8BZzZcfVtaAgqK4B9i4DCS43dEqYZwyKFaVlcPgGc2lo3+yovBd5PAj7s3/h3uXsXAnP7ACtmAfmZtds2PxPIOUmvz+60v878CcDXtwAZe6+unY3Fme1AZTm9Pq36/U9toudt/1M+K62h6CwrpnMAINfQH68AP08Dck5fXVtzz2mfz++h5+1fVL1dcS4wbxTwyWDgwqGq1y0vBQ6vUtrfGCx/BlhwH7D0qcZrA9PsYZHSUtn7E7B7QWO3ouH55jbgf8nAiZSr39flY0D2CeDyceDw71e/P4AsFgVZtd9u0wfAhQPApvdJTFSU13xb9Z332VTb5QUXgYtp9Fo9wF86Cqx5hZbXlOJcYN0bQMYe22V7fyIXi/r41a6ZqsjYW7U4O7lJeX1qi+P1pDAAAKO3/XWKLgOLpwH/Cgc+GkCWtKLLyvLqxER1lOTSc3G29vPqxFPhRUBU0utFD9Nvc8mBNWjrJ8A3twILJl5VUzWUlwDr/11zIbv1E3ret7Du2sBcczQbkTJ37lzExcWhV69ejd2Upk9xDvDTZLqQFec0dmtqz6ktwL8igG2f12479WC76f2rb8fFw8rrvT/Vwf7SyTLz3R3kdnktHDi6tvrtKsqBjN3K+4w9WstAdajdH+d2AZWVVstVIub8Pnq+dBT4fCS5JlbOtt1naYF24AaA0kLg2/HAHy8DPz5gK0DWvQ6krwL2/EDvf/8b8O92inDa+Q1tv/qfiiCprATWvAx82A/4coxjUXNSJUrVQqt1pPI654x2PUds/wJI/RooLwYuHAR+e177P9rxJQXf1oayImDhw+T+kPuq7X9T7XY8u4N+mw3/tr/u1k/pOW2Z7e9tj/xM+r/JOBl7pC0nl9Sql6rfX3Gu8tojsPr1GcYBzUakTJs2Dfv378fWrXVkym8JXEynAcQ6ZiLzICAq6JF9snHaZo9d84Etn2g/K7wEHF2nHXz++i8NgNL/fzYVeK8XsOfHqvevthIcXnn1Ai0rXXl96Lerj005thaoLANOb6E70uJsrfiprLC/3YUDNGAavYCR5kFpzSvkjqisJFfNd3dpXVKXjgFH/qB+VYuU0jzgklVWy1m1SDHfJf94P5BntjrsWQD8cC/w+SgaxIQAPhsGvNebfr8TKSTovr8bOPkXbXMxTSvASguAi2YXxemt9Nts+QQouAAsmkLC9NfHgEMraOCVwmjrp8D6N+h15j77GTmVFVoXX84pxWIiVH16eqtWpJTk2hc9UuTEjgKgA3Z8AZz4U1men1G1tcYex/8Eds8nK5PFkmI+P/Uuynrf3AZ8d6d9EWTv/Ms7b//7groor89WE8OSnwnM7Q0seUKJ17FH7hnz+g6+U82xdcprV1P16zOMA5qNSGkyCEF3wWkrGrslFAz45zvAlo+1n184oLyub5FycjPw2XDgkyHAn+9qL/rFOYppP/csDUbLngaOrVfWWTgZ+PJmYN2/6H1RNg1UAFkPyksoG+PiIeCnB8j1kpdBF9Pcs9q2qOMtyotsrR/5mdqBvLyEAkoXPmw/zkAtUsqL6A6/pu4Je5zeprw+l0rPFw/TgLRkBvBaGHBwme128riC44Ge9wNuPkBJDg3aZ3cCB5cAaUuBFc9Q+5Y8CbzbHfhqDLDwIUWkuHpr9yextqTkX1DW8WsPVJQA+38GTmwEdnxFwiJjD1CQCSyeCnw+HHivJ3BkNeDiDkQNom3VA17GHsVVcWor/YfKzb/FhQMkeirLgYA4+uzgUoqn2PWttq3S7ZaxF/jlMTq+jD0kvgyeyvYrngEyD2gH9tNbrUSOsLUcCKGIlH6PAeF9zdtu065XkEmiec0r9FydtUJanYouKVaG4hz6PqHa9vDvZP1I/dZ2HyX59BzSAxj/tbIPe5QWKq8dBUtLfnpAad+RNY7XK7hg/s7sqvcH0E2CpCbrM4wDWKTUhrIiutP5/m7g+wnVR61XVgDHNtB2R9eS2bo2sQQAmag/TaY7bzXlpcDxjfTaOto/86Dy+sJBYOWLwLnduCoqK+0P0Oteo7vTM9uBlS8AvzxK65UVAR8NBN5LoLu9A0sAmLdf8wqtc/Ewmf8BEiJH/qDBsMIc7FdRSoPlKVW8wbwb6a5v+f8BHw/Wxj7IgdXgSc/qfsk8CLzdiS7IALXvq1uAzR/QHe77SbaDtxzQOtxIz1s+0lqChKCBeN9i237JyyB3xuUTymfWAx0AXEgDFj9C7pvSfBKe8hy5cIjE3y+P0vs2PQC9E4kVgH5T2X8ApbVufJvSWSEAnZ5cK8XZgLMb0OkWbT+V5FFw5eHflH2U5CoxBH6xQP8Z2vamzFWsLQBwaLny2skA3PENMPJN87IVighQW7lyTpK1DABirgcMHjRQtwoA7v2Z3AMlOcDOL6mtOj3Q/0laf++PJCw/7E8Wjt3fK4OwfywQM5Re7/8ZWPq0VqSc20UiQU1JHrVx5Wz6jx1dS5YCvTP1s7sPrWct9s/soHNp/ev0/Oc79HnuOfvByXKgLrykWFKKsun7hR0L2vo3lHTxomyyVsntjJ6K4HQkUtSuuENV3FAJQTcaknO7HQsuGZtUVAMLpRThsi1XI+6ZaxoWKTWhvJQGtdRvgHTzHUJlefVm1N3fA1/cCHw8CPhyNF14ahPbIATFL5zeSr7gomy6a6soA85sA8rsDAAACRPJ+n8Df/6HAgCLc2mgnBNGxazscTGdRNiRP5Q27JoP/DtGGeAlRdmKVaT/k4DOCdj5FV2kd35NVo+iy2SyP/CLst2pTWQO3vYZvXcyAhDAhjeV/tGZT81N7ysXXKM3mfKLc2ib/Axg/l3KRVUODl3G0bM6qDBtGYmetGW0v13zyTJg8ASCutIA8PsL2uOTlpQBTwHJL9FrtdVq/2ISSwsm2t6R//IoDcSr/0HvCy8BWYdhQ9ElcqcANPBcPkbnTWkhuVjUrpqQ7vRsESm7lPNR9pe0SHW4Ebj7JyC8P7ktbv0UaGuO58rYQ/38VicKrgTot/OJptfydwntDXS9HbjhZdqXux8JDGvBDAATfgKmbwWihwB+MYCzKwBBA1vRZdv/ijxHR70JzEwHHlwDTNkAeAQAHW+mZTIrJLwf0G0CvT6znYQlVINeptly6BEADHkRuN6cynvpiJLxA5AlwDqOpiSP/lN/vkP/sa/G0OdBXQAXN8CtNb23trRlWIn+NS+T+H6rA4lnKWoy9ppdj9n0vqJEaVNxjn2RoXeh8zzNbFVb9jRZq6QYU4sUKVysUR/npWOORUJZIbVJUpKjxHVZI0VKSY5j16REHaBcYb5+MswVwCKlOiorSWS814vuztSccZDOKTm2gZ7VouFgNaZXgETI7h9ILKgvhvMnkFDY9AHFcUhyT5MZfuM7JELU31emMvuu/jsJhpIcIOU9unBcPq797tUv0cXwqzF08d7+OQXgFmbRwKYejA//Thdc/w40iLcfrny+8R1lvS0fKT79djfQ8+aPSPQBwPXmgfzkJqoDAQBdx9Oz7PMutwGPpwLjvwFu/xKYcYBiNLJPUoxH9kkg7ywAHdBpDG1z6ajSBrlfUUn1QPYtovcDZgB3fkdWgOMblH4tziGTPgD4xgA9H6B1sg5Tv6x7HVj1d2X/aivJ0bWKWyJ9FV3QpVVHWnms8YsFBjxNrze8SYPehQOASytlnZAe9BzcVfkeKWJufo+epQslegg9Ji0F7vwW6HijEqeQsRtY9n90HniG0Hd0HQ+07UnL5fkT2pssN30fBWKSgR73mPtSFZ8BAKGJQLtkoHWE8pmb2QJxcCnwehQJL0AbRNnjXqB1OImBtgmAZxB9Hjdau/+40fQbBJrb79+BRFH0EG173X0BZwPQfhi9zzun3U9hFlBoR6TknIINUhBKkVJpFSMirWxhfYGud5A1ZP3r5oVCOffm3wV8M04JSFZTnGPfFSKPX1rhpAiTNyNGTzr35T7soRYpFSWOxYy0ButdgIgB9PrUZvvrSncP4Hh/AF2/1Otat4dhagGLlOo4t5N8/zmnFKtBl9vpuTpLinqQlKSvobiIkjwy9Uv2/0IDX0EW8EE/itU4uES77Qmze+fw77ZZIb9MB1bNBj4dantxlmz9n9ntAkqt/WQIxS7IAfTyCa3/+ufptnfN0lpxcrPi+uh4Ez1HD6bnje+QcPIIBLzDzL73SrIAXPcsrZO2jD73iQZ6TwZMYTQQVJYBPlF0B6+mwygyvXe8kS7irXyB2JG0LPVbKtoFAG0SyDICkGgpLTQHVqouvKnfkiABgE5jAe+2QMJ99P6PV+muU1pRPAIBVy96RF5Hn/30ANXMUBcDO/EXiazsU+RmkBRnkyVs93x6HzsC8DAPxp4hynqRAyjexOhNFgCZnXTb58DoucCot2hAB4DgbvR8+Rj1q39H6i95dw0oA7ga/1hyYxTnUDqrk5GE33NngLEf2IqD0ETt++ih2vfyOBKn2H6Xuy89H/5dG3MxyPz7x1xPx2SPiP7AoFl0nG16Ap1vBXQ64J6FwOQ/gKmbSBS18lf6AVDeS2FhTWEWCTN120tylf9L74e0bQBsgz6lOJDCppUvMPo9YMTrSjwMQFaHijL6nwH2U7LLi5QsJqM3He+oNwEv83khB/q8DHqWgatqS0pZoW0tlMpKW/GTbyUaJNL95e6j/N6OgoILVanoRdn21wHMxyToXJPnAYsU5gpxbuwGNEkqK+miqNPZVuF0NQE9J5GvXw7uQtBDr9J8QigBrBN/pbvg93rRwJm2lO6ULx0FbnqXLigrX6R101eTudXNB+j1IN3B7l+sTak9uUm58IcmKgOwVxvlQmZNYGeKJVDXYsjcT897fqR4B1nXIHIg+cNPbab9uZroew7/RhewU5sVNwagmOejzCKl3Gza7fsY4BsNbHiLBsg+j9CF3DdGEQH9n6S79ajBFGMAkLWlbW/AO5RcVElTgbgxtsfUaSwN/nI7VxMw9kO64LqaqF8vHyNzs/rO74j5Nw3uBviYU1T7z6DU0lObaLk07/vGKNt1GKW4V7xD6c7f1USfrXuNHhKvNnTMR9ZQUChALpke9wJ9ppAgPLOdLFoA3cUaPYDudwOb5gIQdM60u4HOQzXqtFqAxI2TC1mydn9PIs/Hah0AcDaSFULGlLTtSZ+pj6/9cCWGwbeddvvQ3hQYK61z478mF4sUT2rczUJBZvQAQO+HgYRJFFhritD+X9TodCRmpKCReATQQyJFifwvtPKjZ2uR0sqfBny1WDKFkruwJE8RAcHxwLQtZE2T55v1vkzhSvE1gAZhJxcg8WF6fH8PuTYLL2nruqhjk9RIt5B/e+BBc3yRjNfJzyQBIsWBjF1RW1IAOred/bTv5bF6h5Kgyj9PbjhrpHhw81Esaed22W+rul5OVcGwUvR5BJGVrDCr+QbP5mWQm77vY/bPc3sIYfufvVqKc6gdfrGKRfMagS0p1pTkAf+7Hni3Gw2Q6sBEAIi6jgY3nRNd5JY+DbwZS3Uc1IG0eRl0Yun0NOAaPYAO5jv/hQ8rVpZfH1MECqAEiQ59ARjyPBCWqFw8JJVldMEK7093pJK7f1IsAlIwAHQBGvWm8t66iNXpLcD5/Uo2Rp9pSgwGACRMJOECkMtICpS40cBtXyjuB99ouijK7+w5iSwHD66ku83ATvTnla4crzbK62hVe2Oup/56dAfFKwx61v6fPnqwciwurYA75wN+5oHVJ4qeP+hL7jrZJ3JgA7TWGq9gcukAFFuw40vzd6gsErEjKdbCyQhMWECDyvUqsSYxeAB3fa8cm2TE62QxaZMAdL5FaSugmNp7PwjAfKwDnrJ/3Hq94rppHUliFqBnZzcSLY5Qp6aG9bFdfuunQPd7gDEf2IoIZ6OS7QIAAR0dX7ilu0eK5qGzgZGv0/H4RDkWKLWhlZ/Ve/Nv6+Sidau5+2oHdVdvxUKiFimeQSQsEx8i4QwAbibtd1gfr7QUWLepMEubqmvtLpJIS4vaYiOPoyBTcTmqMXoCTs7KMVq7fKR1xKUV/cfkvuxRqLKkSHedPfdXWREFdlu+I9v+/gBFpHgGKSKvuVpSNr5NLvKNb9ds/S2fAC8HAodqWfxRCGDtv4AUO/WdLh0F5iaSeF06Q5u5VZcByUKQ5XzdG3W3zzqALSmS9NXk+01fSUGpAMVSyHTEiAHkIuh4M2Bwpwv0+b2K9SH/PAU63rOILpLSiuITBbi40ut+j9Od9aWjAHRmK8gmumj2mEiZE6KCBsLOqsnP2qhEisFTsYb0fpDutrd+CnS7i9o06m1qY3A88G4PMm8Hx9OAFDmQXFbDX6V5TuQd8emt5DuvKKU76fbDaDDpfjetnzjFNnBw6Is0iKrR6cj1s+l9St80tIJdEqfQhazzrRRDAJArxehFcR8R/egzucwRzkbgFnO8S9KjgKcq3sE32tYd12EUCYWTKXShl24qSf8nKUNGbqd3od9F4hkIPPA7tTGgI33m30H1ne2AUf+mgcGvHQm2gE404A2YQZYfNaGJJGJDE8ltAND5cuPbdD5Jd5Y9Rv6b4pYGzVIG/NDewN8yquwyBHZWXocl2S43epKgdETUYBLu3qEkJB1hM3j721/varDep/o73Vsr/xODBw3C0prm5kPHCWhFinQBqbFnSdF8p5VQkm0ozKrZ9AXSwqJ21cnjyr+gtE2NbLurNx2jtZXCYh1prVieqnP3uLVWBE1xDqU7q39f66rDVdUgkm32CtZmKDUnykvo/y+t5TIuqCoqymiQryihLL2YoYrYrY7T24C1r9LrLrcBHqpze83LivCrKKWbSpnmv/gR+j8+kqLd5ko49Jsyf1SXcfatsY0AixTJ/sXK3bNk7WtkNvWLBe76gcyg8u4z4T7KpAiIA9pdT+se3wBs/pACDWW8iXoQM4UBD68H/nqPBrG4MRRnEtKDYh4uH6OYkI43aS9apjBqQ84p4LqZZHnxCKQMDicX4GlVbIter6RhmkKB8zmKpWP815TWGtqLrDvORqpRcvk4fbebD7mf5N376LnKftUDQJuetqmpkqEvkkiyd5cucfWigViNuw/w0FryY7u4Od7WmtgR9LBGWlIAymoZ8gLFGeidyLRuDw9/4OZ3lSymTmNs//gys0ai15NQ2PMjWU98o5VlbiZg6l+O2x7QEXh4gxIwKuk5yfE2krA+VfexIyyWFJ2S7VMbuoyjgGe1iLaHTN2VNIRIUb93a624UoyeAIQSJO6uEimFWYo7xTPY9jusRUp1lhSLSLlIltbqkJYUtcVGCouCTPvxZdIq5OpNsV/q6q6AA5HioABboWpdVy/ad0kuWcD8Y5X1rANhq3LfyPpFnsGKmGlOlpTcs8BH19Hxy1iiCwerd+McWqH85lmHKWsv/g7H68tg+7Y9lZg1gNzpHc1lD7JPKSUOAjpRfOTxjSRSyksomaGilIoo6p3p5kFe7yVC0O9gbRUUgizjOWfI6vvbLG3bWKQ0Mdr2oh9d50R33D89oGRKDJ5F1pNw1Z1n78n0kLh6U9rpujeA+LsU5S3vuCVGT9qfRCpigO7yTeFA0nTtNjodcN8SyqzxDqXA28iBJFCqIrgbWXsiBiptDDUPTHKgbttbuXiP+5/WGqHG2UjtOr6BxI6jP6uLm7afaoN6gL9a1APODS/XfEDvMo4Gjm3zlNoc1WEvfqKmBHWufp26JDSRrIL+HWwvWjXBMwiYWoPS8m5WIuVq7/LsYW3FULt/1N9v9CTrl2VZa0WkyNgovYutsAJsA2dtLCmORMrVWFIClH1YFywEyDIEkKgA7Lh7sunZzaRkUzly90jxII/dqw1wIZcspxqRYmVJqdLdo3KfSddlc4pJ2fiOrautOJuEmjomSlYFdnKhOEbpLpexgcufoeu/vLERggT+0bVkhZ43ij5/fLe2NMWpTYpI2fwhWdcjr6Nr0y+PUkmJjL3k7pY1pfYtVuob3fAK0Nc8hpzcTDe1pzaRG/36v5PbHaD9SDeWnKpCcmYb0PW22vddPcAiRdLjXnpI4u+k9Nu+j9ma6e3RbQLV0MjYQ3EmMjXUWqRUhVcIMOwV+8vUf45Bz9RsfyPfIJeQzAaxR58pFIjb/3H7GSFqHLWtKRIxgASnb4xtlkp1DHjK1pXVUnBxJcFb3zSIu8fa1aIWKSoLiNELMKiCZt18FGuEnJ/JM8i+8LaxpERYfaeVsLGIlEv2XTXWSCuOWgy5+wLQkRXXXuqy2t0D2BEpKuuI2nVkD4u7x3wc3m3IVW0dgF9o7e7Jtr8/QBWTEqJkHtXEklJWTK6Ldjcort7yUmDZU3SjVdNBs7KSLJz7f6HBf9RbijsVqNoikpehBOJbc+EgXYfLiqksw+HfKZB8ykaayuH4BrJm3P0TVUM+vYUm1Hwslfp51UuKGDmRotwEL/8/bf/I4noV5cCu7+h1n6nKjWVFKRVRVBdSVGeC/v483fycTSVLiQyiTl9JbbzxHbKorjaXUAjvR7V0AjtRhelNc7XzXzUyLFIcMfw1iqKWtSmqQ+8EjHiDirfJE8a3nVI7pDEwuCv1HhzRJgF40k56ZHPHLwaYtpkGsrqOtGeqp0HcPSpRYvTWxjC5W1lS1LEBandPlkqk2MPoSWJXZtZ4BZPVRQbC2ggltSWlBnPcSNSWFCdz6m7hRduicbJN6m2qEinVuntUMSmAEpeSYyVSbNw9VcWkqAJnpZipSUzKxrcpQ27gTGDI3+izg0vIDb/jS3OgvBdVto5JJje7NSdSqCq4b5SSpRTUBRhoLgtwejtdowc/R/Vtzu4g97UUMZs/IvHg7KqICMmFNLJgp6+i8ACA4vq+vpXcMNABo9+nG9O7f6Qq1rlnqJr02te0NatyVTF+cl8db6bMsDPbKTj2ZAqdR+6+dLx6J8cZnNKiIvnpQeU363I7pdev+xcJlZ+n0o23qATaDSM3tbxGXjpGIuXcbhJjMp6yEeHsHke4uNIAXpsBLjwJuOVjCoZ0caeiY46CR5n6x6+d45oZTP2idrcYPGsXZ1RTDK2UYnfWYkFjSfHUWnbU7h45EDkSKTqd1i1m8NTu25G7p+Bi7USKtetNigt79VVqKlLcfVTungt0Z772X9r5odR1UgCqGQTYDoRywJNusxpl9wTXLrtH1qE6uFT5TD3X0paPyV2y+UMqkFdRTjF2y5+hCRzLS6heVGmeNo06fRW5r3LP0bZlhTRlxbxRwLe3UzXtlPdpexmXOOxVpYqzrGck4wxlyQdZCiDTbO0a8jcg3pzR5+pNwgKgwNeyQhJLD6y0H6ANHWUKtgogQfxqMPD1LbQobgwJV52OYgb7PkoWD3sMnU3WHPl7Df4bjUmhvSiuMmIAiRNp0ek0RjvGtY4gi2Rlmf1zrxFgkVLXdL6VIq2npgCBcdWvzzAtEbUlw1pA1CVy3zYixcqSohEpKkuKxO7AIdc3D7QGT3IjyPfOrnQzokZ+T2WZdoJKazysYr/UlhRAsTypy/pL1IGzgG31V7V1xOLuySQ3xtpXKd5OrqOukwIolhQbkWJ298iAdHvunvP7gH+3V0STV7DixpLrl+TT/Fu/Pq7dtqJcme8nc78Si6OeLHXzR9p5kRZOBub2IuGx8gXKKsxKp4E+abpSAuBkCs3b9fEg7VxOF9PIKiYqyUXy2/NkvfIMpqy++LvovEh82NwWc2VjWexu4EwlWy6gE2VvqpHxhtLKMfh5ysCTQsYnSumfdtdToKpcpkYdpN4umWLs1FmHamJHkoUIANqPIAuSFCF6vW1ZBCmkJDpVQH0Tcfmwu6c+COhQ/ToM05JRixR1PFVd08qfAp2t3UnWlhS1iHG3I1IcWVIAZSCRgapy3+52XIkGd6XgXVXWA+9QraXFOkC3qj67EndPRYkyN1VZIVklBj2rrZMCKNVuj6wBPh9Jwfy+Mcqsxm170WCdeZCsDm0SKMNRp6P0fXlMAZ1ITMm+OrsT2L2A7vCPb6CHVxuycvh3oMw7tTskfTW529Wpv8XZyjxXgBIoCpB7Qm8ezgY9C/QyZ+id2aG49PIzaM4wNTe/S8X7ds9Xykkk3EeWizFzlbbL54Is5X1YH7JorH8DSJ5tm8gga0sB1Bcy5i/pUUpW6DaBvnvLx0C/J2jZDS/T9Bgn/gQWPUK/h71SAV3GkXssqDNZm3JOUf0m3xhgxL/INRY70vb87DAKWPIEid/gePvnWY97KUO0uhjFBoJFCsMwdY/RiwaNyvL6iUeRyH3buF2qsqSYtMXdAGVwtoccaI3WIsVONpBsS45qwNXp6W7d6K2U5TeFKvWYZJvUtLIaPDyDyY2id1GqBKtFSkU5cHw9metlTSO31uRmM3hQIbayAnLXVJSSVSJpmmLhkMck3T0ADZQL7qMsxsKLJKy63k6TiOadVWbnHvA0uToOmWfTvuk/ZIWwdpUtfFAREgBNLQGQ+8Q6WDV9JaXvysDm6CEknNRCBiBrQfoqslzJlF71oB6WqJ3YU24fHE8JBfF3UsmH/PP0XV7BSnFESVA8xRdmHQaWzyTB5+5HlhDfaGCCVWaMpJUfuXgy9lCZBPm7efhTKABAGTeDZ2lFs5uJxMSM/SR87BU+9AwCntxL58M340ikBMaRuHLyUCZZtcbdx1zraKUyj5o1suhoE4HdPQzD1D06neJCqE93j7wTtL4jtM7usXb3uKpEipPB1uyt2ZeJnm0sKb52V9cKJC9FcPhEKJ+rCzQGdQG8VOLAeh9BXZXpGYweyt2xZR6h0zRQfTWW5uKSMRJScMiUUwC49X8US1F0ieo1ycwPa3ePJOswZZ8A5PawTvsGKJX1yBqyaDkZgM7jlCBmnyiahNE7jN5XlpO4cjIP2GFJ2lpSslL2gSVUrLCyjERW97uVdZwMlJAQHA+MeV8pXSAqaNBWV3LuM822/o2TgeaAutlcE8roCdy7GJixj6pIW5+vej1N6QEosRyhiTWLV0x8hH5bub01er2tVU9i9NBOW2GNixuJEhmfoi7UWBUj36Dsxb6P1Wz9RoYtKQzD1A/uPlRrwtoqUJf0epCsBN3u0n5eVUyKu4+2bkrnW6t2rzi0pDgSKarPvdsC0JGrwRSmBHRGDQLu/43iWoK62t4tq4sRjv+aYi7ksUikJUXOxeRkMFuuAoAb/qlk9t32BcV5tO1J2xReBJY8qcw1ZfBQRIXBnQbgy8fJOrLkSdqnRxC5AdSzoHceRxaI4xuUAogR/bWVap1cgPvNqbJ/vks1O/o/STE5xzdSWYPz+5SaIT3vJ5GwZwFloQAkYqIGUz9CkOi663vlO/xjlXgR/w5at0tgHPDUQZpUdPEjSt/WtBKsJP5OCoCVgcYdRtVsu+4T6FGf9HqA/md9ptZsfZ9IKrrZTGCRwjBM/WCxpNSjuye4KzDuMzvfbRWT4mqigba8iAZI9WSDvSbbbG53X9KSIoucOYo9Uw/kfR8Ddn1Lr919yW2Qc4rqkVSVedbhRppvK3oIVbm1FkqANtjW4EH1b/za0/xNatHjFUwPSfxdwB9zlGJl1oX3Jq0gl4aLG7WjOIf6zOCuFXe9HiRLx/ENShyMIxcCQFNlJNxnTuvWKcIyoj9VsD6znWIhIgfQrOIyeDegIwnL4HgKrrWu+uzfAcDP9FptNVIjZ0YHtJOG1hSDO6UVn95GsSa1qX9V3/jHArfNa+xW1BssUhiGqR+iB9OgciXl+68WNxMsd95Gc1bOQ2tpUJXp0De+Q1aCtglV7yu8L1k85CSQ3e+hgdLRgBg5kOIs/NpTXMVhc6yGqzcwaTmlyFaXGu9s0MZGSCFhz5ICUPpqdTWRJC6uVBByqblgYdR12uV6PaA395G7j9b15OJGabDlxVRyQQi6Kz+9nWqNqN0y9lC72dQkz1a9aUVWphXPUql5WWuq5/3UZuspGdSVcR39Jv6x5GKqKNG6g2pDmwR6MA0KixSGYeqH6/6PTPvVTd9QH+idKA4g9wy5WQCtNQGo2RxJALlmZp1WjkOvB0K6OV4/aRrFfcSNJotBQBywbxGJFg9/AFdgWbJYUlQipXUkDdiuJiChhsci6fUgTWRXWeE4ANgRCar0V52ufqozm0KBO76hirPSFZUwUfvdEnVMiyOR4uRCy87uoHnQmGYDixSGYeqPxhAokuFz6m5ftTkOt9baOIT+T9LM4oFdHG9THWF9yNWizlzR6+27umqKdW2Wpkh1M6ED5L5xaUVZS2q3jjXDX6OKrp1vqbv2MfWOTgghGrsRtSE3Nxfe3t7IycmBl5cD0yHDMExLo4mUKW+SnEiheKMmUtuDsc+VjN9sSWEYhmkOsEBxzJXOvM40ebhOCsMwDMMwTRIWKQzDMAzDNElYpDAMwzAM0yRhkcIwDMMwTJOERQrDMAzDME0SFikMwzAMwzRJWKQwDMMwDNMkaRSRMnbsWLRu3Rrjxo1rjK9nGIZhGKYZ0Cgi5fHHH8eXX37ZGF/NMAzDMEwzoVFEyqBBg+Dp6Vn9igzDMAzDXLPUWqSsX78eN910E0JCQqDT6bB48WKbdebOnYuIiAi4uroiMTERW7ZsqYu2MgzDMAxzDVFrkVJQUID4+HjMnTvX7vLvv/8eM2bMwOzZs7Fjxw7Ex8dj2LBhyMzMvKIGlpSUIDc3V/NgGIZhGKblU2uRMmLECLz88ssYO3as3eVvvfUWJk+ejEmTJiEuLg4ffvgh3N3d8dlnVzal+Jw5c+Dt7W15hIaGXtF+GIZhGIZpXtTpLMilpaXYvn07Zs2aZflMr9cjOTkZKSkpV7TPWbNmYcaMGZb3OTk5CAsLY4sKwzAMwzQj5LgthKjxNnUqUi5evIiKigoEBgZqPg8MDMTBgwct75OTk7Fr1y4UFBSgbdu2WLBgAZKS7E+1bTQaYTQaLe/lQbJFhWEYhmGaH3l5efD29q7RunUqUmrKqlWrrnjbkJAQnDp1Cp6entDpdHXWptzcXISGhuLUqVPw8vKqs/02R7gvCO4HgvuB4H4guB8I7geiNv0ghEBeXh5CQkJqvP86FSl+fn5wcnLC+fPnNZ+fP38eQUFBdfIder0ebdu2rZN92cPLy+uaPuHUcF8Q3A8E9wPB/UBwPxDcD0RN+6GmFhRJndZJMRgMSEhIwOrVqy2fVVZWYvXq1Q7dOQzDMAzDMPaotSUlPz8f6enplvfHjh1DamoqfHx8EBYWhhkzZmDixIno2bMnevfujXfeeQcFBQWYNGlSnTacYRiGYZiWTa1FyrZt2zB48GDLe5l5M3HiRMybNw/jx4/HhQsX8OKLLyIjIwPdunXDihUrbIJpmxpGoxGzZ8/WBOleq3BfENwPBPcDwf1AcD8Q3A9EffeDTtQmF4hhGIZhGKaBaJS5exiGYRiGYaqDRQrDMAzDME0SFikMwzAMwzRJWKQwDMMwDNMkYZHCMAzDMEyThEWKmblz5yIiIgKurq5ITEzEli1bGrtJ9cpLL70EnU6neXTo0MGyvLi4GNOmTYOvry88PDxw66232lQSbo6sX78eN910E0JCQqDT6bB48WLNciEEXnzxRQQHB8PNzQ3Jyck4fPiwZp1Lly5hwoQJ8PLygslkwgMPPID8/PwGPIqrp7p+uO+++2zOj+HDh2vWaQn9MGfOHPTq1Quenp4ICAjAmDFjkJaWplmnJv+FkydPYtSoUXB3d0dAQABmzpyJ8vLyhjyUq6Im/TBo0CCbc2LKlCmadZp7P3zwwQfo2rWrpXpqUlISli9fbll+LZwLQPX90KDngmDE/PnzhcFgEJ999pnYt2+fmDx5sjCZTOL8+fON3bR6Y/bs2aJTp07i3LlzlseFCxcsy6dMmSJCQ0PF6tWrxbZt20SfPn1E3759G7HFdcOyZcvE888/LxYuXCgAiEWLFmmWv/baa8Lb21ssXrxY7Nq1S9x8880iMjJSFBUVWdYZPny4iI+PF5s2bRIbNmwQMTEx4s4772zgI7k6quuHiRMniuHDh2vOj0uXLmnWaQn9MGzYMPH555+LvXv3itTUVDFy5EgRFhYm8vPzLetU918oLy8XnTt3FsnJyWLnzp1i2bJlws/PT8yaNasxDumKqEk/XHfddWLy5MmacyInJ8eyvCX0wy+//CKWLl0qDh06JNLS0sRzzz0nXFxcxN69e4UQ18a5IET1/dCQ5wKLFCFE7969xbRp0yzvKyoqREhIiJgzZ04jtqp+mT17toiPj7e7LDs7W7i4uIgFCxZYPjtw4IAAIFJSUhqohfWP9eBcWVkpgoKCxBtvvGH5LDs7WxiNRvHdd98JIYTYv3+/ACC2bt1qWWf58uVCp9OJM2fONFjb6xJHImX06NEOt2mJ/SCEEJmZmQKAWLdunRCiZv+FZcuWCb1eLzIyMizrfPDBB8LLy0uUlJQ07AHUEdb9IAQNTI8//rjDbVpiPwghROvWrcWnn356zZ4LEtkPQjTsuXDNu3tKS0uxfft2JCcnWz7T6/VITk5GSkpKI7as/jl8+DBCQkIQFRWFCRMm4OTJkwCA7du3o6ysTNMnHTp0QFhYWIvuk2PHjiEjI0Nz3N7e3khMTLQcd0pKCkwmE3r27GlZJzk5GXq9Hps3b27wNtcna9euRUBAAGJjY/HII48gKyvLsqyl9kNOTg4AwMfHB0DN/gspKSno0qWLpqr2sGHDkJubi3379jVg6+sO636QfPPNN/Dz80Pnzp0xa9YsFBYWWpa1tH6oqKjA/PnzUVBQgKSkpGv2XLDuB0lDnQt1Ogtyc+TixYuoqKiwKdsfGBiIgwcPNlKr6p/ExETMmzcPsbGxOHfuHP7+979jwIAB2Lt3LzIyMmAwGGAymTTbBAYGIiMjo3Ea3ADIY7N3LshlGRkZCAgI0Cx3dnaGj49Pi+qb4cOH45ZbbkFkZCSOHDmC5557DiNGjEBKSgqcnJxaZD9UVlbiiSeeQL9+/dC5c2cAqNF/ISMjw+45I5c1N+z1AwDcddddCA8PR0hICHbv3o1nnnkGaWlpWLhwIYCW0w979uxBUlISiouL4eHhgUWLFiEuLg6pqanX1LngqB+Ahj0XrnmRcq0yYsQIy+uuXbsiMTER4eHh+OGHH+Dm5taILWOaAnfccYfldZcuXdC1a1dER0dj7dq1GDp0aCO2rP6YNm0a9u7di40bNzZ2UxoVR/3w0EMPWV536dIFwcHBGDp0KI4cOYLo6OiGbma9ERsbi9TUVOTk5ODHH3/ExIkTsW7dusZuVoPjqB/i4uIa9Fy45t09fn5+cHJysonQPn/+PIKCghqpVQ2PyWRC+/btkZ6ejqCgIJSWliI7O1uzTkvvE3lsVZ0LQUFByMzM1CwvLy/HpUuXWnTfREVFwc/PzzIDekvrh+nTp2PJkiX4448/0LZtW8vnNfkvBAUF2T1n5LLmhKN+sEdiYiIAaM6JltAPBoMBMTExSEhIwJw5cxAfH4///Oc/19y54Kgf7FGf58I1L1IMBgMSEhKwevVqy2eVlZVYvXq1xv/W0snPz8eRI0cQHByMhIQEuLi4aPokLS0NJ0+ebNF9EhkZiaCgIM1x5+bmYvPmzZbjTkpKQnZ2NrZv325ZZ82aNaisrLT8UVsip0+fRlZWFoKDgwG0nH4QQmD69OlYtGgR1qxZg8jISM3ymvwXkpKSsGfPHo1oW7lyJby8vCzm8aZOdf1gj9TUVADQnBPNvR/sUVlZiZKSkmvmXHCE7Ad71Ou5cAVBvi2O+fPnC6PRKObNmyf2798vHnroIWEymTSRyS2Np556Sqxdu1YcO3ZM/PnnnyI5OVn4+fmJzMxMIQSl2oWFhYk1a9aIbdu2iaSkJJGUlNTIrb568vLyxM6dO8XOnTsFAPHWW2+JnTt3ihMnTgghKAXZZDKJn3/+WezevVuMHj3abgpy9+7dxebNm8XGjRtFu3btml3qbVX9kJeXJ55++mmRkpIijh07JlatWiV69Ogh2rVrJ4qLiy37aAn98Mgjjwhvb2+xdu1aTTplYWGhZZ3q/gsy3fKGG24QqampYsWKFcLf379ZpZ1W1w/p6eniH//4h9i2bZs4duyY+Pnnn0VUVJQYOHCgZR8toR+effZZsW7dOnHs2DGxe/du8eyzzwqdTid+//13IcS1cS4IUXU/NPS5wCLFzH//+18RFhYmDAaD6N27t9i0aVNjN6leGT9+vAgODhYGg0G0adNGjB8/XqSnp1uWFxUVialTp4rWrVsLd3d3MXbsWHHu3LlGbHHd8McffwgANo+JEycKISgN+YUXXhCBgYHCaDSKoUOHirS0NM0+srKyxJ133ik8PDyEl5eXmDRpksjLy2uEo7lyquqHwsJCccMNNwh/f3/h4uIiwsPDxeTJk21Ee0voB3t9AEB8/vnnlnVq8l84fvy4GDFihHBzcxN+fn7iqaeeEmVlZQ18NFdOdf1w8uRJMXDgQOHj4yOMRqOIiYkRM2fO1NTGEKL598P9998vwsPDhcFgEP7+/mLo0KEWgSLEtXEuCFF1PzT0uaATQoja2V4YhmEYhmHqn2s+JoVhGIZhmKYJixSGYRiGYZokLFIYhmEYhmmSsEhhGIZhGKZJwiKFYRiGYZgmCYsUhmEYhmGaJCxSGIZhGIZpkrBIYRiGYRimScIihWEYhmGYJgmLFIZhGIZhmiQsUhiGYRiGaZL8P4ghvL23MyAKAAAAAElFTkSuQmCC", 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m5li9ejVev36NIUOGwMDAAI6Ojjh06NBH5SAiquhYYERm8uTJWLBgAaZOnYrr169jy5Yt/2nK+vj4eBw6dAiHDx/G77//jrVr16Jz58548OABTp06hR9//BFTpkzB+fPnFc/p3bu34hdydHQ0PDw80LZtW7x48UJpv3v27MH+/fuxf/9+nDp1CgsWLPioTBMnTsSpU6fw559/4ujRozh58iQuXryotE1AQAAiIiKwdetWXL58Gb1790aHDh1w+/ZtxTaZmZmYO3cuNm7ciLNnzyI1NRV9+/YFAHz55Zf49ttvUatWLSQnJyM5ORlffvml4rkzZ85Enz59cPnyZXTq1AkDBgxQen//ZMaMGVi6dCnCw8ORlJSEPn36IDg4GFu2bMGBAwdw9OhR/PLLL0rP2bBhA6pUqYLIyEiMGjUKI0aMQO/evdG0aVNcvHgR7du3x8CBA5WKDxGRVLHAiMirV6+wePFiBAUFYdCgQahevTqaN28Of3//f73PgoICrFu3Dq6urujatStat26NuLg4BAcHw9nZGUOGDIGzszNOnDgBADhz5gwiIyOxY8cONGjQAE5OTvjpp59gZGSEnTt3Ku03JCQEtWvXRosWLTBw4ECVu4wXJyMjA2vXrsVPP/2Etm3bws3NDRs2bEBeXp5im8TERKxfvx47duxAixYtUL16dUyYMAHNmzfH+vXrFdvl5uZi6dKl8PT0RP369bFhwwaEh4cjMjISurq60NfXh4aGBiwsLGBhYQFdXV3FcwcPHox+/frB0dER8+bNQ0ZGBiIjIz/6+zpnzhw0a9YM7u7u8PPzw6lTp7BixQq4u7ujRYsW+OKLLxTf0yJ169bFlClT4OTkhMmTJ0NHRwdVqlTB0KFD4eTkhGnTpuH58+e4fPnyR+cgIqqoeBWSiNy4cQPZ2dlo27Ztie3Tzs4OBgYGisfm5uZQV1eHmpqa0rInT54AAGJjY5GRkQETExOl/WRlZSE+Pv69+7W0tFTs40Pi4+ORk5ODxo0bK5ZVrlwZzs7OisdXrlxBfn4+atSoofTc7OxspVwaGhpo2LCh4nHNmjVhZGSEGzduoFGjRh/MUadOHcW/K1WqBLlc/lH5i3u+ubk59PT04ODgoLTs74Xo3eeoq6vDxMQEbm5uSs8B8Ek5iIgqKhYYEXn3CMHfFRWOd+/N+b67JL9LU1NT6bFMJit2WUFBAYC3R0gsLS2VxpsUMTIy+uB+i/bxX2VkZEBdXR3R0dFQV1dXWqevr18ir/Ff87/7/H/6nn7oNf++HwAl9n0kIhIznkISEScnJ+jq6hZ7KsbU1BQAkJycrFgWExNT4hk8PDyQkpICDQ0NODo6Kn1VqVLlP++/evXq0NTUVBpz8/LlS9y6dUvx2N3dHfn5+Xjy5IlKBgsLC8V2eXl5iIqKUjyOi4tDamoqXFze3t1VS0sL+fn5/zkzERGVPRYYEdHR0UFgYCAmTZqEjRs3Ij4+HufOncPatWvh6OgIa2trzJgxA7dv38aBAweKvXrnv/Ly8oKnpye6d++Oo0eP4t69ewgPD8cPP/ygVBb+LX19ffj5+WHixIk4fvw4rl69isGDByud0qpRowYGDBgAHx8f7Nq1CwkJCYiMjMT8+fNx4MABxXaampoYNWoUzp8/j+joaAwePBhNmjRRnD6ys7NDQkICYmJi8OzZM2RnZ//n/EREVDZ4CuldZTwz7r8xdepUaGhoYNq0aXj06BEsLS0xfPhwaGpq4vfff8eIESNQp04dNGzYEHPmzEHv3r1L9PVlMhkOHjyIH374AUOGDMHTp09hYWGBli1b/qerod71v//9DxkZGejatSsMDAzw7bffIi1N+b/N+vXrMWfOHHz77bd4+PAhqlSpgiZNmqBLly6KbfT09BAYGIj+/fvj4cOHaNGiBdauXatY36tXL+zatQutW7dGamoq1q9fj8GDB5fIeyAiotIlK3x30EQFkp6eDkNDQ6SlpUEulyute/PmDRISEmBvbw8dHR2BElJpEvNtE0oKP+dE4nejpkuJ79Pl5o0S32dJ+tDv73fxFBIRERGJDgsMlanExETo6+u/9ysxMVHoiB80fPjw92YfPny40PGIiCSDp5B4aL1M5eXlvffWBsDbgbUaGuV3aNaTJ0+Qnp5e7Dq5XA4zM7MyTvR+/JwTiR9PIb3/FFL5/U1BFVLR5ddiZWZmVq5KChGRVPEUEhEREYkOCwwRERGJDgsMERERiQ4LDBEREYkOCwwRERGJDq9CeofbBrcyfb0rg6588nMKCwvx9ddfY+fOnXj58iUMDQ0xePBgBAcHA3h7GfLYsWMxduzYkg1bCmQyGXbv3o3u3bsLHQUzZszAnj17SuUGmEREVPJ4BEZkDh8+jJCQEOzfvx/JycmoXbu20voLFy5g2LBhAqUTB5lMhj179ggdg4iI/gMegRGZ+Ph4WFpaomnTpgCgMumbqampELFU5OTkQEtLS+gYRERUQfEIjIgMHjwYo0aNQmJiImQyGezs7FS2sbOzU5xOAt4ebVixYgU6duwIXV1dODg4YOfOnYr19+7dg0wmw9atW9G0aVPo6Oigdu3aOHXqlNJ+r169io4dO0JfXx/m5uYYOHAgnj17pljfqlUrBAQEYOzYsahSpQq8vb0/+f0lJSWhT58+MDIyQuXKldGtWzelWXsHDx6M7t2746effoKlpSVMTEwwcuRI5ObmKrZJTk5G586doaurC3t7e2zZskXpe1L0PevRo0ex38NNmzbBzs4OhoaG6Nu3L169evVR2Vu1aoVRo0Zh7NixMDY2hrm5OVavXo3Xr19jyJAhMDAwgKOjIw4dOqR4zsmTJyGTyXDkyBG4u7tDV1cXbdq0wZMnT3Do0CG4uLhALpejf//+yMzM/OTvJxFRRcYCIyKLFy/GrFmzUK1aNSQnJ+PChQsf9bypU6eiV69eiI2NxYABA9C3b1/cuKE8lfTEiRPx7bff4tKlS/D09ETXrl3x/PlzAEBqairatGkDd3d3REVF4fDhw3j8+DH69OmjtI8NGzZAS0sLZ8+excqVKz/pveXm5sLb2xsGBgb466+/cPbsWejr66NDhw7IyclRbHfixAnEx8fjxIkT2LBhA0JCQhASEqJY7+Pjg0ePHuHkyZP4448/8Ouvv+LJkyeK9UXfs/Xr16t8D+Pj47Fnzx7s378f+/fvx6lTp7BgwYKPfg8bNmxAlSpVEBkZiVGjRmHEiBHo3bs3mjZtiosXL6J9+/YYOHCgShmZMWMGli5divDwcEWJCw4OxpYtW3DgwAEcPXoUv/zyyyd9P4mIKjoWGBExNDSEgYEB1NXVYWFh8dGni3r37g1/f3/UqFEDs2fPRoMGDVR+IQYEBKBXr15wcXHBihUrYGhoiLVr1wIAli5dCnd3d8ybNw81a9aEu7s71q1bhxMnTuDWrVuKfTg5OSEoKAjOzs5wdnb+pPe2bds2FBQUYM2aNXBzc4OLiwvWr1+PxMREnDx5UrGdsbExli5dipo1a6JLly7o3LkzwsLCAAA3b97EsWPHsHr1ajRu3BgeHh5Ys2YNsrKyFM8v+p4ZGRmpfA8LCgoQEhKC2rVro0WLFhg4cKBi3x+jbt26mDJlCpycnDB58mTo6OigSpUqGDp0KJycnDBt2jQ8f/4cly9fVnrenDlz0KxZM7i7u8PPzw+nTp3CihUr4O7ujhYtWuCLL77AiRMnPun7SURU0XEMjAR4enqqPP771TbvbqOhoYEGDRoojtLExsbixIkT0NfXV9l3fHw8atSoAQCoX7/+v84YGxuLO3fuwMDAQGn5mzdvEB8fr3hcq1YtqKurKx5bWlriypW3V3PFxcVBQ0MDHh4eivWOjo4wNjb+qAx2dnZKr29paal09Oaf1KlTR/FvdXV1mJiYwM3t/65sMzc3BwCVfb77PHNzc+jp6cHBwUFpWWRk5EfnICKSAhYY+kcZGRno2rUrfvzxR5V1lpaWin9XqlTpP71G/fr1sXnzZpV17x4l0dTUVFonk8lQUFDwr1/3Xf9138U9/91lMpkMAFT2+fdtSvM9EhFVFDyFJAHnzp1Teezi4vLebfLy8hAdHa3YxsPDA9euXYOdnR0cHR2Vvv5LaXmXh4cHbt++DTMzM5XXMDQ0/Kh9ODs7Iy8vD5cuXVIsu3PnDl6+fKm0naamJvLz80skNxERCYMFRgJ27NiBdevW4datW5g+fToiIyMREBCgtM2yZcuwe/du3Lx5EyNHjsTLly/h6+sLABg5ciRevHiBfv364cKFC4iPj8eRI0cwZMiQEisCAwYMQJUqVdCtWzf89ddfSEhIwMmTJzF69Gg8ePDgo/ZRs2ZNeHl5YdiwYYiMjMSlS5cwbNgw6OrqKo5+AG9PFYWFhSElJUWl3BARkTjwFNI7/s3MuGIwc+ZMbN26Fd988w0sLS3x+++/w9XVVWmbBQsWYMGCBYiJiYGjoyP27t2LKlWqAACsrKxw9uxZBAYGon379sjOzoatrS06dOgANbWS6cB6eno4ffo0AgMD0bNnT7x69QpVq1ZF27ZtIZfLP3o/GzduhJ+fH1q2bAkLCwvMnz8f165dg46OjmKbhQsXYvz48Vi9ejWqVq2qdKk2ERGJg6ywsLBQ6BClIT09HYaGhkhLS1P5BfjmzRskJCTA3t5e6RdbRfRP0/Xfu3cP9vb2uHTpEurVq1em2crCgwcPYG1tjWPHjqFt27ZCxylTUvqcE1VUN2q6/PNGn8jl5o1/3khAH/r9/S4egaEK5fjx48jIyICbmxuSk5MxadIk2NnZoWXLlkJHIyKiEsQxMFQqNm/eDH19/WK/atWqVWqvm5ubi++//x61atVCjx49YGpqipMnT6pc2fMpEhMT3/te9PX1kZiYWILvgIiIPgaPwFRw/3SG0M7O7h+3+Tc+//xzNG7cuNh1/6VM/BNvb+9/dRuDD7GysvrgXaqtrKxK9PWIiOifscBQqTAwMFCZlE6sNDQ04OjoKHQMIiJ6B08hERERkeiwwBAREZHosMAQERGR6LDAEBERkeiwwBAREZHo8Cqkd5TGjIcf8qmzIbZq1Qr16tVDcHBwiWUICQnB2LFjkZqaWmL7JCIiKm08AkNERESiwwJDREREosMCIzJ5eXkICAiAoaEhqlSpgqlTpypm0n358iV8fHxgbGwMPT09dOzYEbdv31Z6fkhICGxsbKCnp4cePXrg+fPninX37t2DmpoaoqKilJ4THBwMW1tbFBQUfDDbyZMnIZPJcOTIEbi7u0NXVxdt2rTBkydPcOjQIbi4uEAul6N///7IzMxUPO/w4cNo3rw5jIyMYGJigi5duiA+Pl6xPicnBwEBAbC0tISOjg5sbW0xf/58AG9nGp4xYwZsbGygra0NKysrjB49+qO+l8nJyejcuTN0dXVhb2+PLVu2wM7OrkRP0RERUelggRGZDRs2QENDA5GRkVi8eDF+/vlnrFmzBgAwePBgREVFYe/evYiIiEBhYSE6deqE3NxcAMD58+fh5+eHgIAAxMTEoHXr1pgzZ45i33Z2dvDy8sL69euVXnP9+vUYPHgw1NQ+7uMyY8YMLF26FOHh4UhKSkKfPn0QHByMLVu24MCBAzh69Ch++eUXxfavX7/G+PHjERUVhbCwMKipqaFHjx6KwrRkyRLs3bsX27dvR1xcHDZv3gw7OzsAwB9//IFFixZh1apVuH37Nvbs2QM3N7ePyunj44NHjx7h5MmT+OOPP/Drr7/iyZMnH/VcIiISFgfxioy1tTUWLVoEmUwGZ2dnXLlyBYsWLUKrVq2wd+9enD17Fk2bNgXw9oaK1tbW2LNnD3r37o3FixejQ4cOmDRpEgCgRo0aCA8Px+HDhxX79/f3x/Dhw/Hzzz9DW1sbFy9exJUrV/Dnn39+dMY5c+agWbNmAAA/Pz9MnjwZ8fHxcHBwAAB88cUXOHHiBAIDAwEAvXr1Unr+unXrYGpqiuvXr6N27dpITEyEk5MTmjdvDplMBltbW8W2iYmJsLCwgJeXFzQ1NWFjY4NGjRr9Y8abN2/i2LFjuHDhAho0aAAAWLNmDZycnD76fRIRkXB4BEZkmjRpAplMpnjs6emJ27dv4/r169DQ0FC6gaKJiQmcnZ1x48bbq51u3LihcoNFT09Ppcfdu3eHuro6du/eDeDtKafWrVsrjnh8jDp16ij+bW5uDj09PUV5KVr27pGO27dvo1+/fnBwcIBcLle8VtFdngcPHoyYmBg4Oztj9OjROHr0qOK5vXv3RlZWFhwcHDB06FDs3r0beXl5/5gxLi4OGhoa8PDwUCxzdHSEsbHxR79PIiISDgsMKdHS0oKPjw/Wr1+PnJwcbNmyBb6+vp+0j3fvNi2TyVTuPi2TyZTG03Tt2hUvXrzA6tWrcf78eZw/fx7A27EvAODh4YGEhATMnj0bWVlZ6NOnD7744gsAb49IxcXFYfny5dDV1cU333yDli1bKk6bERFRxcQCIzJFv9yLnDt3Dk5OTnB1dUVeXp7S+ufPnyMuLg6urq4AABcXl2Kf/3f+/v44duwYli9fjry8PPTs2bMU3olyxilTpqBt27ZwcXHBy5cvVbaTy+X48ssvsXr1amzbtg1//PEHXrx4AQDQ1dVF165dsWTJEpw8eRIRERG4cuXKB1/X2dkZeXl5uHTpkmLZnTt3in1tIiIqfzgGRmQSExMxfvx4fP3117h48SJ++eUXLFy4EE5OTujWrRuGDh2KVatWwcDAAN999x2qVq2Kbt26AQBGjx6NZs2a4aeffkK3bt1w5MgRpfEvRVxcXNCkSRMEBgbC19cXurq6pfZ+jI2NYWJigl9//RWWlpZITEzEd999p7TNzz//DEtLS7i7u0NNTQ07duyAhYUFjIyMEBISgvz8fDRu3Bh6enr47bffoKurqzROpjg1a9aEl5cXhg0bhhUrVkBTUxPffvstdHV1lU7RERFR+cQC845PnRlXCD4+PsjKykKjRo2grq6OMWPGYNiwYQDeXi00ZswYdOnSBTk5OWjZsiUOHjyoOIXTpEkTrF69GtOnT8e0adPg5eWFKVOmYPbs2Sqv4+fnh/Dw8E8+ffSp1NTUsHXrVowePRq1a9eGs7MzlixZglatWim2MTAwQFBQEG7fvg11dXU0bNgQBw8ehJqaGoyMjLBgwQKMHz8e+fn5cHNzw759+2BiYvKPr71x40b4+fmhZcuWsLCwwPz583Ht2jXo6OiU4jsmIqKSICssmkSkgklPT4ehoSHS0tIgl8uV1r158wYJCQmwt7fnL6v3mD17Nnbs2IHLly8LHaXMPHjwANbW1jh27Bjatm0rdJz/jJ9zIvErjVvclPc/1j/0+/td/2kMzIIFCyCTyTB27FjFsjdv3mDkyJEwMTGBvr4+evXqhcePHys9LzExEZ07d4aenh7MzMwwceJElStHTp48CQ8PD2hra8PR0REhISH/JSp9pIyMDFy9ehVLly7FqFGjhI5Tqo4fP469e/ciISEB4eHh6Nu3L+zs7NCyZUuhoxER0T/41wXmwoULWLVqldIlswAwbtw47Nu3Dzt27MCpU6fw6NEjpUGg+fn56Ny5M3JychAeHo4NGzYgJCQE06ZNU2yTkJCAzp07o3Xr1oiJicHYsWPh7++PI0eO/Nu49JECAgJQv359tGrVSuX00fDhw6Gvr1/s1/DhwwVKXLy//vrrvVn19fUBALm5ufj+++9Rq1Yt9OjRA6ampjh58qTKVVNERFT+/KtTSBkZGfDw8MDy5csxZ84cxR2S09LSYGpqii1btiguc7158yZcXFwQERGBJk2a4NChQ+jSpQsePXoEc3NzAMDKlSsRGBiIp0+fQktLC4GBgThw4ACuXr2qeM2+ffsiNTW12EGnxeEppJL35MkTpKenF7tOLpfDzMysjBO9X1ZWFh4+fPje9Y6OjmWYRhj8nBOJH08hvf8U0r8axDty5Eh07twZXl5eSlPRR0dHIzc3F15eXoplNWvWhI2NjaLAREREwM3NTVFeAMDb2xsjRozAtWvX4O7ujoiICKV9FG3z7qmqv8vOzkZ2drbi8ft+0dK/Z2ZmVq5Kyofo6upKoqQQEUnVJxeYrVu34uLFi7hw4YLKupSUFGhpacHIyEhpubm5OVJSUhTbvFteitYXrfvQNunp6cjKyir2st758+dj5syZn/ReKuj4ZSIA/HwTUcX2SWNgkpKSMGbMGGzevLncHZKePHky0tLSFF9JSUnv3bZojMO7d0QmqmiKPt8c00NEFdEnHYGJjo7GkydPlO4fk5+fj9OnT2Pp0qU4cuQIcnJykJqaqnQU5vHjx7CwsAAAWFhYIDIyUmm/RVcpvbvN369cevz4MeRy+XsnVdPW1oa2tvZHvQ91dXUYGRkp7sejp6fHycuowigsLERmZiaePHkCIyMjqKurCx2JiKjEfVKBadu2rcoU7UOGDEHNmjURGBgIa2traGpqIiwsTHGH4bi4OCQmJipuGujp6Ym5c+fiyZMnivEUoaGhkMvliinvPT09cfDgQaXXCQ0NVbnx4H9RVJbevakgUUViZGSk+JwTEVU0n1RgDAwMULt2baVllSpVgomJiWK5n58fxo8fj8qVK0Mul2PUqFHw9PREkyZNAADt27eHq6srBg4ciKCgIKSkpGDKlCkYOXKk4gjK8OHDsXTpUkyaNAm+vr44fvw4tm/fjgMHDpTEewbw9oaClpaWMDMz443/qMLR1NTkkRciqtBK/FYCixYtgpqaGnr16oXs7Gx4e3tj+fLlivXq6urYv38/RowYAU9PT1SqVAmDBg3CrFmzFNvY29vjwIEDGDduHBYvXoxq1aphzZo18Pb2Lum4UFdX5w96IiIikZHkrQSIiIjEgPPAlNKtBIiIiIiEwAJDREREosMCQ0RERKLDAkNERESiwwJDREREosMCQ0RERKLDAkNERESiwwJDREREosMCQ0RERKLDAkNERESiwwJDREREolPiN3MkIhKrkr7vTHm/5wyRmPEIDBEREYkOj8CQIKR4h1UiIio5PAJDREREosMCQ0RERKLDAkNERESiwwJDREREosMCQ0RERKLDAkNERESiwwJDREREosMCQ0RERKLDAkNERESiwwJDREREosMCQ0RERKLDAkNERESiwwJDREREosMCQ0RERKLDAkNERESiwwJDREREosMCQ0RERKLDAkNERESiwwJDREREosMCQ0RERKLDAkNERESiwwJDREREosMCQ0RERKLDAkNERESiwwJDREREosMCQ0RERKLDAkNERESiwwJDREREosMCQ0RERKLDAkNERESiwwJDREREosMCQ0RERKLDAkNERESiwwJDREREosMCQ0RERKLDAkNERESiwwJDREREosMCQ0RERKLDAkNERESiwwJDREREosMCQ0RERKLDAkNERESiwwJDREREosMCQ0RERKLzSQVmxYoVqFOnDuRyOeRyOTw9PXHo0CHF+jdv3mDkyJEwMTGBvr4+evXqhcePHyvtIzExEZ07d4aenh7MzMwwceJE5OXlKW1z8uRJeHh4QFtbG46OjggJCfn375CIiIgqnE8qMNWqVcOCBQsQHR2NqKgotGnTBt26dcO1a9cAAOPGjcO+ffuwY8cOnDp1Co8ePULPnj0Vz8/Pz0fnzp2Rk5OD8PBwbNiwASEhIZg2bZpim4SEBHTu3BmtW7dGTEwMxo4dC39/fxw5cqSE3jIRERGJnaywsLDwv+ygcuXK+N///ocvvvgCpqam2LJlC7744gsAwM2bN+Hi4oKIiAg0adIEhw4dQpcuXfDo0SOYm5sDAFauXInAwEA8ffoUWlpaCAwMxIEDB3D16lXFa/Tt2xepqak4fPjwR+dKT0+HoaEh0tLSIJfL/8tbpFJwo6ZLie/T5eaNEt8nSUtJfy75maT/Soo/Kz/29/e/HgOTn5+PrVu34vXr1/D09ER0dDRyc3Ph5eWl2KZmzZqwsbFBREQEACAiIgJubm6K8gIA3t7eSE9PVxzFiYiIUNpH0TZF+3if7OxspKenK30RERFRxfTJBebKlSvQ19eHtrY2hg8fjt27d8PV1RUpKSnQ0tKCkZGR0vbm5uZISUkBAKSkpCiVl6L1Res+tE16ejqysrLem2v+/PkwNDRUfFlbW3/qWyMiIiKR+OQC4+zsjJiYGJw/fx4jRozAoEGDcP369dLI9kkmT56MtLQ0xVdSUpLQkYiIiKiUaHzqE7S0tODo6AgAqF+/Pi5cuIDFixfjyy+/RE5ODlJTU5WOwjx+/BgWFhYAAAsLC0RGRirtr+gqpXe3+fuVS48fP4ZcLoeuru57c2lra0NbW/tT3w4RERGJ0H+eB6agoADZ2dmoX78+NDU1ERYWplgXFxeHxMREeHp6AgA8PT1x5coVPHnyRLFNaGgo5HI5XF1dFdu8u4+ibYr2QURERPRJR2AmT56Mjh07wsbGBq9evcKWLVtw8uRJHDlyBIaGhvDz88P48eNRuXJlyOVyjBo1Cp6enmjSpAkAoH379nB1dcXAgQMRFBSElJQUTJkyBSNHjlQcPRk+fDiWLl2KSZMmwdfXF8ePH8f27dtx4MCBkn/3REREJEqfVGCePHkCHx8fJCcnw9DQEHXq1MGRI0fQrl07AMCiRYugpqaGXr16ITs7G97e3li+fLni+erq6ti/fz9GjBgBT09PVKpUCYMGDcKsWbMU29jb2+PAgQMYN24cFi9ejGrVqmHNmjXw9vYuobdMREREYvef54EprzgPTPkmxbkNqPzjPDBU3kjxZ2WpzwNDREREJBQWGCIiIhIdFhgiIiISHRYYIiIiEh0WGCIiIhIdFhgiIiISHRYYIiIiEh0WGCIiIhIdFhgiIiISHRYYIiIiEh0WGCIiIhIdFhgiIiISHRYYIiIiEh0WGCIiIhIdFhgiIiISHRYYIiIiEh0WGCIiIhIdFhgiIiISHRYYIiIiEh0WGCIiIhIdFhgiIiISHRYYIiIiEh0WGCIiIhIdFhgiIiISHRYYIiIiEh0WGCIiIhIdFhgiIiISHRYYIiIiEh0WGCIiIhIdFhgiIiISHRYYIiIiEh0WGCIiIhIdFhgiIiISHRYYIiIiEh0WGCIiIhIdFhgiIiISHRYYIiIiEh0WGCIiIhIdFhgiIiISHRYYIiIiEh0WGCIiIhIdFhgiIiISHRYYIiIiEh0WGCIiIhIdFhgiIiISHRYYIiIiEh0WGCIiIhIdFhgiIiISHRYYIiIiEh0WGCIiIhIdFhgiIiISHRYYIiIiEh0WGCIiIhIdFhgiIiISHRYYIiIiEh0WGCIiIhIdFhgiIiISHRYYIiIiEp1PKjDz589Hw4YNYWBgADMzM3Tv3h1xcXFK27x58wYjR46EiYkJ9PX10atXLzx+/Fhpm8TERHTu3Bl6enowMzPDxIkTkZeXp7TNyZMn4eHhAW1tbTg6OiIkJOTfvUMiIiKqcD6pwJw6dQojR47EuXPnEBoaitzcXLRv3x6vX79WbDNu3Djs27cPO3bswKlTp/Do0SP07NlTsT4/Px+dO3dGTk4OwsPDsWHDBoSEhGDatGmKbRISEtC5c2e0bt0aMTExGDt2LPz9/XHkyJESeMtEREQkdrLCwsLCf/vkp0+fwszMDKdOnULLli2RlpYGU1NTbNmyBV988QUA4ObNm3BxcUFERASaNGmCQ4cOoUuXLnj06BHMzc0BACtXrkRgYCCePn0KLS0tBAYG4sCBA7h69aritfr27YvU1FQcPnz4o7Klp6fD0NAQaWlpkMvl//YtUim5UdOlxPfpcvNGie+TpKWkP5f8TNJ/JcWflR/7+/s/jYFJS0sDAFSuXBkAEB0djdzcXHh5eSm2qVmzJmxsbBAREQEAiIiIgJubm6K8AIC3tzfS09Nx7do1xTbv7qNom6J9FCc7Oxvp6elKX0RERFQx/esCU1BQgLFjx6JZs2aoXbs2ACAlJQVaWlowMjJS2tbc3BwpKSmKbd4tL0Xri9Z9aJv09HRkZWUVm2f+/PkwNDRUfFlbW//bt0ZERETl3L8uMCNHjsTVq1exdevWkszzr02ePBlpaWmKr6SkJKEjERERUSnR+DdPCggIwP79+3H69GlUq1ZNsdzCwgI5OTlITU1VOgrz+PFjWFhYKLaJjIxU2l/RVUrvbvP3K5ceP34MuVwOXV3dYjNpa2tDW1v737wdIiIiEplPOgJTWFiIgIAA7N69G8ePH4e9vb3S+vr160NTUxNhYWGKZXFxcUhMTISnpycAwNPTE1euXMGTJ08U24SGhkIul8PV1VWxzbv7KNqmaB9EREQkbZ90BGbkyJHYsmUL/vzzTxgYGCjGrBgaGkJXVxeGhobw8/PD+PHjUblyZcjlcowaNQqenp5o0qQJAKB9+/ZwdXXFwIEDERQUhJSUFEyZMgUjR45UHEEZPnw4li5dikmTJsHX1xfHjx/H9u3bceDAgRJ++0RERCRGn3QEZsWKFUhLS0OrVq1gaWmp+Nq2bZtim0WLFqFLly7o1asXWrZsCQsLC+zatUuxXl1dHfv374e6ujo8PT3x1VdfwcfHB7NmzVJsY29vjwMHDiA0NBR169bFwoULsWbNGnh7e5fAWyYiIiKx+0/zwJRnnAemfJPi3AZU/nEeGCpvpPizskzmgSEiIiISAgsMERERiQ4LDBEREYkOCwwRERGJDgsMERERiQ4LDBEREYkOCwwRERGJDgsMERERiQ4LDBEREYkOCwwRERGJDgsMERERiQ4LDBEREYkOCwwRERGJDgsMERERiQ4LDBEREYkOCwwRERGJDgsMERERiQ4LDBEREYkOCwwRERGJDgsMERERiQ4LDBEREYkOCwwRERGJDgsMERERiQ4LDBEREYkOCwwRERGJDgsMERERiQ4LDBEREYkOCwwRERGJDgsMERERiQ4LDBEREYkOCwwRERGJDgsMERERiQ4LDBEREYkOCwwRERGJDgsMERERiQ4LDBEREYkOCwwRERGJDgsMERERiQ4LDBEREYkOCwwRERGJDgsMERERiQ4LDBEREYkOCwwRERGJDgsMERERiQ4LDBEREYkOCwwRERGJDgsMERERiQ4LDBEREYkOCwwRERGJDgsMERERiQ4LDBEREYkOCwwRERGJDgsMERERiQ4LDBEREYkOCwwRERGJDgsMERERiQ4LDBEREYkOCwwRERGJDgsMERERic4nF5jTp0+ja9eusLKygkwmw549e5TWFxYWYtq0abC0tISuri68vLxw+/ZtpW1evHiBAQMGQC6Xw8jICH5+fsjIyFDa5vLly2jRogV0dHRgbW2NoKCgT393REREVCF9coF5/fo16tati2XLlhW7PigoCEuWLMHKlStx/vx5VKpUCd7e3njz5o1imwEDBuDatWsIDQ3F/v37cfr0aQwbNkyxPj09He3bt4etrS2io6Pxv//9DzNmzMCvv/76L94iERERVTQan/qEjh07omPHjsWuKywsRHBwMKZMmYJu3boBADZu3Ahzc3Ps2bMHffv2xY0bN3D48GFcuHABDRo0AAD88ssv6NSpE3766SdYWVlh8+bNyMnJwbp166ClpYVatWohJiYGP//8s1LRISIiImkq0TEwCQkJSElJgZeXl2KZoaEhGjdujIiICABAREQEjIyMFOUFALy8vKCmpobz588rtmnZsiW0tLQU23h7eyMuLg4vX74s9rWzs7ORnp6u9EVEREQVU4kWmJSUFACAubm50nJzc3PFupSUFJiZmSmt19DQQOXKlZW2KW4f777G382fPx+GhoaKL2tr6//+hoiIiKhcqjBXIU2ePBlpaWmKr6SkJKEjERERUSkp0QJjYWEBAHj8+LHS8sePHyvWWVhY4MmTJ0rr8/Ly8OLFC6VtitvHu6/xd9ra2pDL5UpfREREVDGVaIGxt7eHhYUFwsLCFMvS09Nx/vx5eHp6AgA8PT2RmpqK6OhoxTbHjx9HQUEBGjdurNjm9OnTyM3NVWwTGhoKZ2dnGBsbl2RkIiIiEqFPLjAZGRmIiYlBTEwMgLcDd2NiYpCYmAiZTIaxY8dizpw52Lt3L65cuQIfHx9YWVmhe/fuAAAXFxd06NABQ4cORWRkJM6ePYuAgAD07dsXVlZWAID+/ftDS0sLfn5+uHbtGrZt24bFixdj/PjxJfbGiYiISLw++TLqqKgotG7dWvG4qFQMGjQIISEhmDRpEl6/fo1hw4YhNTUVzZs3x+HDh6Gjo6N4zubNmxEQEIC2bdtCTU0NvXr1wpIlSxTrDQ0NcfToUYwcORL169dHlSpVMG3aNF5CTURERAAAWWFhYaHQIUpDeno6DA0NkZaWxvEw5dCNmi4lvk+XmzdKfJ8kLSX9ueRnkv4rKf6s/Njf3xXmKiQiIiKSDhYYIiIiEh0WGCIiIhIdFhgiIiISHRYYIiIiEh0WGCIiIhIdFhgiIiISHRYYIiIiEh0WGCIiIhIdFhgiIiISHRYYIiIiEh0WGCIiIhIdFhgiIiISHRYYIiIiEh0WGCIiIhIdFhgiIiISHRYYIiIiEh0WGCIiIhIdFhgiIiISHRYYIiIiEh0WGCIiIhIdFhgiIiISHRYYIiIiEh0WGCIiIhIdFhgiIiISHRYYIiIiEh0WGCIiIhIdFhgiIiISHRYYIiIiEh0WGCIiIhIdFhgiIiISHRYYIiIiEh0WGCIiIhIdFhgiIiISHRYYIiIiEh0WGCIiIhIdFhgiIiISHRYYIiIiEh0WGCIiIhIdFhgiIiISHRYYIiIiEh0WGCIiIhIdFhgiIiISHRYYIiIiEh0WGCIiIhIdFhgiIiISHRYYIiIiEh0NoQMQERFVBG4b3Ep8n9tLfI8VBwsMEYkSf1kQSRsLDH2Ukv5lwV8URET0X3AMDBEREYkOCwwRERGJDgsMERERiQ4LDBEREYkOCwwRERGJDgsMERERiQ4LDBEREYkOCwwRERGJTrkuMMuWLYOdnR10dHTQuHFjREZGCh2JiIiIyoFyOxPvtm3bMH78eKxcuRKNGzdGcHAwvL29ERcXBzMzM6HjlRi77w6U+D7vLehc4vskaSnpzyU/k/Rf8Wcl/V25PQLz888/Y+jQoRgyZAhcXV2xcuVK6OnpYd26dUJHIyIiIoGVyyMwOTk5iI6OxuTJkxXL1NTU4OXlhYiIiGKfk52djezsbMXjtLQ0AEB6enrphv2PCrIzS3yf6ZPlJb7PfNtqJbq/jPz8Et0fUP7/W4tJSX8uxfCZBEr+c8nPZMkRw89KMXwmgfL/uSzKV1hY+MHtymWBefbsGfLz82Fubq603NzcHDdv3iz2OfPnz8fMmTNVlltbW5dKxvLMsFT2eqNE99aoRPf2/xmWzjun/04Mn0mgFD6X/EyWayX/X0cEn0lANJ/LV69ewfADWctlgfk3Jk+ejPHjxyseFxQU4MWLFzAxMYFMJhMwmfilp6fD2toaSUlJkMtL/i9pok/FzySVN/xMlpzCwkK8evUKVlZWH9yuXBaYKlWqQF1dHY8fP1Za/vjxY1hYWBT7HG1tbWhraystMzIyKq2IkiSXy/k/JpUr/ExSecPPZMn40JGXIuVyEK+Wlhbq16+PsLAwxbKCggKEhYXB09NTwGRERERUHpTLIzAAMH78eAwaNAgNGjRAo0aNEBwcjNevX2PIkCFCRyMiIiKBldsC8+WXX+Lp06eYNm0aUlJSUK9ePRw+fFhlYC+VPm1tbUyfPl3lFB2RUPiZpPKGn8myJyv8p+uUiIiIiMqZcjkGhoiIiOhDWGCIiIhIdFhgiIiISHRYYIiIiEh0WGCIiIhIdMrtZdRUPmRnZ/OyQBJcQkIC/vrrL9y/fx+ZmZkwNTWFu7s7PD09oaOjI3Q8kiB+JoXHAkNKDh06hK1bt+Kvv/5CUlISCgoKUKlSJbi7u6N9+/YYMmTIP96fgqikbN68GYsXL0ZUVBTMzc1hZWUFXV1dvHjxAvHx8dDR0cGAAQMQGBgIW1tboeOSBPAzWX5wHhgCAOzevRuBgYF49eoVOnXqhEaNGin9j3n16lX89ddfiIiIwODBgzF79myYmpoKHZsqMHd3d2hpaWHQoEHo2rWryp3ls7OzERERga1bt+KPP/7A8uXL0bt3b4HSkhTwM1m+sMAQAMDT0xNTpkxBx44doab2/qFRDx8+xC+//AJzc3OMGzeuDBOS1Bw5cgTe3t4fte3z589x79491K9fv5RTkZTxM1m+sMAQERGR6HAMDL1XTk4OEhISUL16dWho8KNC5cObN2+Qk5OjtEwulwuUhoifSaHwMmpSkZmZCT8/P+jp6aFWrVpITEwEAIwaNQoLFiwQOB1JUWZmJgICAmBmZoZKlSrB2NhY6YuorPEzKTwWGFIxefJkxMbG4uTJk0qXA3p5eWHbtm0CJiOpmjhxIo4fP44VK1ZAW1sba9aswcyZM2FlZYWNGzcKHY8kiJ9J4XEMDKmwtbXFtm3b0KRJExgYGCA2NhYODg64c+cOPDw8kJ6eLnREkhgbGxts3LgRrVq1glwux8WLF+Ho6IhNmzbh999/x8GDB4WOSBLDz6TweASGVDx9+hRmZmYqy1+/fg2ZTCZAIpK6Fy9ewMHBAcDbsQUvXrwAADRv3hynT58WMhpJFD+TwmOBIRUNGjTAgQMHFI+LSsuaNWvg6ekpVCySMAcHByQkJAAAatasie3btwMA9u3bByMjIwGTkVTxMyk8XlpCKubNm4eOHTvi+vXryMvLw+LFi3H9+nWEh4fj1KlTQscjCRoyZAhiY2Px2Wef4bvvvkPXrl2xdOlS5Obm4ueffxY6HkkQP5PC4xgYKlZ8fDwWLFiA2NhYZGRkwMPDA4GBgXBzcxM6GhHu37+P6OhoODo6ok6dOkLHIeJnUgAsMERERCQ6PIVEH8QJmqg8GD16NBwdHTF69Gil5UuXLsWdO3cQHBwsTDCSrFmzZn1w/bRp08ooiXTxCAypyMzMxKRJk7B9+3Y8f/5cZX1+fr4AqUjKqlatir1796rcV+bixYv4/PPP8eDBA4GSkVS5u7srPc7NzUVCQgI0NDRQvXp1XLx4UaBk0sEjMKRi4sSJOHHiBFasWIGBAwdi2bJlePjwIVatWsWZeEkQz58/h6GhocpyuVyOZ8+eCZCIpO7SpUsqy9LT0zF48GD06NFDgETSw8uoScW+ffuwfPly9OrVCxoaGmjRogWmTJmCefPmYfPmzULHIwlydHTE4cOHVZYfOnRIMRcHkdDkcjlmzpyJqVOnCh1FEngEhlR8aIKmESNGCBmNJGr8+PEICAjA06dP0aZNGwBAWFgYFi5cyPEvVK6kpaUhLS1N6BiSwAJDKoomaLKxsVFM0NSoUSNO0ESC8fX1RXZ2NubOnYvZs2cDAOzs7LBixQr4+PgInI6kaMmSJUqPCwsLkZycjE2bNqFjx44CpZIWDuIlFYsWLYK6ujpGjx6NY8eOoWvXrigsLFRM0DRmzBihI5KEPX36FLq6utDX1xc6CkmYvb290mM1NTWYmpqiTZs2mDx5MgwMDARKJh0sMPSPOEETERGVNywwRFQueXh4ICwsDMbGxnB3d//gjUR5ySoJKSkpCQBgbW0tcBJp4RgYAvD2fO6wYcOgo6Ojcm737/4+mRhRaejWrRu0tbUV/+ad0Kk8ycvLw8yZM7FkyRJkZGQAAPT19TFq1ChMnz4dmpqaAies+HgEhgC8PZ8bFRUFExMTlXO775LJZLh7924ZJiMiKn9GjBiBXbt2YdasWfD09AQAREREYMaMGejevTtWrFghcMKKjwWGiMo9BwcHXLhwASYmJkrLU1NT4eHhwVJNZc7Q0BBbt25VueLo4MGD6NevHy+lLgOcyI6Iyr179+4VewuL7Oxs3kaABKGtrQ07OzuV5fb29tDS0ir7QBLEMTAE4O1EYR/r559/LsUkRP9n7969in8fOXJE6XYC+fn5CAsL++ApT6LSEhAQgNmzZ2P9+vWKsVpFcxUFBAQInE4aeAqJAACtW7f+qO1kMhmOHz9eymmI3lJTe3uQWCaT4e8/qjQ1NWFnZ4eFCxeiS5cuQsQjCevRowfCwsKgra2NunXrAgBiY2ORk5ODtm3bKm27a9cuISJWeDwCQwCAEydOCB2BSEVBQQGAt4flL1y4gCpVqgiciOgtIyMj9OrVS2kZL6MuWzwCQ+91584dxMfHo2XLltDV1UVhYSEvZSUionKBg3hJxfPnz9G2bVvUqFEDnTp1QnJyMgDAz88P3377rcDpSIpGjx5d7PxES5cuxdixY8s+EBEJjgWGVIwbNw6amppITEyEnp6eYvmXX36Jw4cPC5iMpOqPP/5As2bNVJY3bdoUO3fuFCAREbBz50706dMHTZo0gYeHh9IXlT4WGFJx9OhR/Pjjj6hWrZrScicnJ9y/f1+gVCRlz58/V7oCqYhcLsezZ88ESERSt2TJEgwZMgTm5ua4dOkSGjVqBBMTE9y9e5d3oy4jLDCk4vXr10pHXoq8ePFCcbkgUVlydHQs9ujfoUOH4ODgIEAikrrly5fj119/xS+//AItLS1MmjQJoaGhGD16NCexKyO8ColUtGjRAhs3bsTs2bMBvL2EtaCgAEFBQR99uTVRSRo/fjwCAgLw9OlTtGnTBgAQFhaGhQsXIjg4WNhwJEmJiYlo2rQpAEBXVxevXr0CAAwcOBBNmjTB0qVLhYwnCSwwpCIoKAht27ZFVFQUcnJyMGnSJFy7dg0vXrzA2bNnhY5HEuTr66uYJKyoWNvZ2WHFihXw8fEROB1JkYWFBV68eAFbW1vY2Njg3LlzqFu3LhISElTmLKLSwVNIpKJ27dq4desWmjdvjm7duuH169fo2bMnLl26hOrVqwsdjyQmLy8PGzduRM+ePfHgwQM8fvwY6enpuHv3LssLCaZNmzaKmaKHDBmCcePGoV27dvjyyy/Ro0cPgdNJA+eBIaJyT09PDzdu3ICtra3QUYgAvJ1ksaCgABoab09kbN26FeHh4XBycsLXX3/N+yGVARYYAgBcvnz5o7etU6dOKSYhUtWqVSuMHTsW3bt3FzoKEZUTHANDAIB69eop7jfz7my7Rf323WXF3RWYqDR98803+Pbbb/HgwQPUr18flSpVUlrPUk1CePnyJdauXYsbN24AAFxdXTFkyBBUrlxZ4GTSwCMwBABK87tcunQJEyZMwMSJE+Hp6QkAiIiIwMKFCxEUFMS/gqnMFd3U8V3vFm6Waiprp0+fxueffw65XI4GDRoAAKKjo5Gamop9+/ahZcuWAies+FhgSEWjRo0wY8YMdOrUSWn5wYMHMXXqVERHRwuUjKTqnyZQ5NgYKmtubm7w9PTEihUroK6uDuDt0elvvvkG4eHhuHLlisAJKz4WGFKhq6uLixcvwsXFRWn5jRs34OHhgaysLIGSERGVD7q6uoiJiYGzs7PS8ri4ONSrV48/J8sAx8CQChcXF8yfPx9r1qxRjKTPycnB/PnzVUoNUVm6fv06EhMTkZOTo7T8888/FygRSZWHhwdu3LihUmBu3LiBunXrCpRKWlhgSMXKlSvRtWtXVKtWTTE48vLly5DJZNi3b5/A6UiK7t69ix49euDKlSuKsS/A/w0u5xgYKmujR4/GmDFjcOfOHTRp0gQAcO7cOSxbtgwLFixQurKTg8xLB08hUbFev36NzZs34+bNmwDeHpXp37+/ytUfRGWha9euUFdXx5o1a2Bvb4/IyEg8f/4c3377LX766Se0aNFC6IgkMcUNLH8XB5mXPhYYIir3qlSpguPHj6NOnTowNDREZGQknJ2dcfz4cXz77be4dOmS0BFJYv5pYPm7OMi8dPAUEr0XxxtQeZGfnw8DAwMAb8vMo0eP4OzsDFtbW8TFxQmcjqSIpUR4LDCkguMNqLypXbs2YmNjYW9vj8aNGyMoKAhaWlr49ddf4eDgIHQ8IhIAb+ZIKsaMGQN7e3s8efIEenp6uHbtGk6fPo0GDRrg5MmTQscjCZoyZQoKCgoAALNmzUJCQgJatGiBgwcPYvHixQKnIyIhcAwMqeB4AxKDFy9ewNjYWOk2F0QkHTwCQyqKG28AgOMNSDC+vr549eqV0rLKlSsjMzMTvr6+AqUiIiGxwJCKovEGABTjDc6ePYtZs2ZxvAEJYsOGDcXObJqVlYWNGzcKkIikLikpCQ8ePFA8joyMxNixY/Hrr78KmEpaWGBIxYfGGyxZskTgdCQl6enpSEtLQ2FhIV69eoX09HTF18uXL3Hw4EGYmZkJHZMkqH///jhx4gQAICUlBe3atUNkZCR++OEHzJo1S+B00sAxMPRRON6AhKCmpvbBz5xMJsPMmTPxww8/lGEqIsDY2Bjnzp2Ds7MzlixZgm3btuHs2bM4evQohg8fjrt37wodscLjZdT0USpXrix0BJKgEydOoLCwEG3atMEff/yh9DnU0tKCra0trKysBExIUpWbmwttbW0AwLFjxxTzY9WsWRPJyclCRpMMFhgiKrc+++wzAEBCQgKsra3/cfp2orJSq1YtrFy5Ep07d0ZoaChmz54NAHj06BFMTEwETicNPIVERKKQmpqKyMhIPHnyRDFGq4iPj49AqUiqTp48iR49eiA9PR2DBg3CunXrAADff/89bt68iV27dgmcsOJjgSGicm/fvn0YMGAAMjIyIJfLlcbFyGQyvHjxQsB0JFX5+flIT0+HsbGxYtm9e/egp6fHweVlgAWGiMq9GjVqoFOnTpg3bx709PSEjkNE5QALDKnYsGEDqlSpgs6dOwMAJk2ahF9//RWurq74/fffeRMzKnOVKlXClStXOA8RCcrDwwNhYWEwNjaGu7v7B6+Qu3jxYhkmkyYO4iUV8+bNw4oVKwAAERERWLZsGRYtWoT9+/dj3LhxPLdLZc7b2xtRUVEsMCSobt26Ka486t69u7BhiEdgSJWenh5u3rwJGxsbBAYGIjk5GRs3bsS1a9fQqlUrPH36VOiIJDFr167FrFmzMGTIELi5uUFTU1NpfdElrEQkHTwCQyr09fXx/Plz2NjY4OjRoxg/fjwAQEdHp9jp3IlK29ChQwGg2BlOZTIZ8vPzyzoSEQmMBYZUtGvXDv7+/nB3d8etW7fQqVMnAMC1a9dgZ2cnbDiSpL9fNk0khE+ZjZxXxpU+FhhSsWzZMkyZMgVJSUn4448/FJMyRUdHo1+/fgKnIyISRnBwsNAR6B0cA0NEovD69WucOnUKiYmJyMnJUVo3evRogVIRkVBYYAgAcPnyZdSuXRtqamq4fPnyB7etU6dOGaUieuvSpUvo1KkTMjMz8fr1a1SuXBnPnj1TTBjGG+eREOLj47F+/XrEx8dj8eLFMDMzw6FDh2BjY4NatWoJHa/CY4EhAG/v+puSkgIzMzPFHYDf/WgUPeaASRJCq1atUKNGDaxcuRKGhoaIjY2FpqYmvvrqK4wZMwY9e/YUOiJJzKlTp9CxY0c0a9YMp0+fxo0bN+Dg4IAFCxYgKioKO3fuFDpihccCQwCA+/fvw8bGBjKZDPfv3//gtpzIjsqakZERzp8/D2dnZxgZGSEiIgIuLi44f/48Bg0ahJs3bwodkSTG09MTvXv3xvjx42FgYIDY2Fg4ODggMjISPXv2xIMHD4SOWOFxEC8BUC4lLChU3mhqairuRG1mZobExES4uLjA0NAQSUlJAqcjKbpy5Qq2bNmistzMzAzPnj0TIJH0sMAQAGDv3r0fvS0nDaOy5u7ujgsXLsDJyQmfffYZpk2bhmfPnmHTpk2oXbu20PFIgoyMjJCcnAx7e3ul5ZcuXULVqlUFSiUtPIVEAKD46/afcAwMCSEqKgqvXr1C69at8eTJE/j4+CA8PBxOTk5Yt24d6tatK3REkpgJEybg/Pnz2LFjB2rUqIGLFy/i8ePH8PHxgY+PD6ZPny50xAqPBYaIiOgT5eTkYOTIkQgJCUF+fj40NDSQn5+P/v37IyQkBOrq6kJHrPBYYOiD3rx5Ax0dHaFjEBGVS0lJSbhy5QoyMjLg7u4OJycnoSNJBgsMqcjPz8e8efOwcuVKPH78GLdu3YKDgwOmTp0KOzs7+Pn5CR2RiIgk7uMGPpCkzJ07FyEhIQgKCoKWlpZiee3atbFmzRoBkxERlQ+9evXCjz/+qLI8KCgIvXv3FiCR9LDAkIqNGzfi119/xYABA5TO49atW5fzbRARATh9+rTiRrfv6tixI06fPi1AIulhgSEVDx8+hKOjo8rygoIC5ObmCpCISFVqaqrQEUjCMjIylI5QF9HU1ER6eroAiaSHBYZUuLq64q+//lJZvnPnTri7uwuQiKTuxx9/xLZt2xSP+/TpAxMTE1StWhWxsbECJiOpcnNzU/pMFtm6dStcXV0FSCQ9nMiOVEybNg2DBg3Cw4cPUVBQgF27diEuLg4bN27E/v37hY5HErRy5Ups3rwZABAaGorQ0FAcOnQI27dvx8SJE3H06FGBE5LUTJ06FT179kR8fDzatGkDAAgLC8Pvv/+OHTt2CJxOGngVEhXrr7/+wqxZsxAbG4uMjAx4eHhg2rRpaN++vdDRSIJ0dXVx69YtWFtbY8yYMXjz5g1WrVqFW7duoXHjxnj58qXQEUmCDhw4gHnz5iEmJga6urqoU6cOpk+fjs8++0zoaJLAAkNE5Z6VlRV27tyJpk2bwtnZGXPmzEHv3r0RFxeHhg0bcswBkQTxFBKpuHDhAgoKCtC4cWOl5efPn4e6ujoaNGggUDKSqp49e6J///5wcnLC8+fP0bFjRwBv7ztT3IBzotKWlJQEmUyGatWqAQAiIyOxZcsWuLq6YtiwYQKnkwYO4iUVI0eOLPYOvw8fPsTIkSMFSERSt2jRIgQEBMDV1RWhoaHQ19cHACQnJ+Obb74ROB1JUf/+/XHixAkAQEpKCry8vBAZGYkffvgBs2bNEjidNPAUEqnQ19fH5cuX4eDgoLQ8ISEBderUwatXrwRKRkRUPhgbG+PcuXNwdnbGkiVLsG3bNpw9exZHjx7F8OHDcffuXaEjVng8hUQqtLW18fjxY5UCk5ycDA0NfmSobOzduxcdO3aEpqYm9u7d+8FtP//88zJKRfRWbm4utLW1AQDHjh1TfAZr1qyJ5ORkIaNJBo/AkIp+/fohOTkZf/75JwwNDQG8nTSse/fuMDMzw/bt2wVOSFKgpqaGlJQUmJmZQU3t/We7ZTIZ8vPzyzAZEdC4cWO0bt0anTt3Rvv27XHu3DnUrVsX586dwxdffIEHDx4IHbHCY4EhFQ8fPkTLli3x/PlzxcR1MTExMDc3R2hoKKytrQVOSEQkrJMnT6JHjx5IT0/HoEGDsG7dOgDA999/j5s3b2LXrl0CJ6z4WGCoWK9fv8bmzZsRGxurmN+gX79+0NTUFDoaEVG5kJ+fj/T0dBgbGyuW3bt3D3p6ejAzMxMwmTSwwBBRubRkyZKP3nb06NGlmITo/Z4+fYq4uDgAgLOzM0xNTQVOJB0sMKRiw4YNqFKlCjp37gwAmDRpEn799Ve4urri999/h62trcAJSQrs7e0/ajuZTMYrPqjMvX79GqNGjcLGjRtRUFAAAFBXV4ePjw9++eUX6OnpCZyw4mOBIRXOzs5YsWIF2rRpg4iICLRt2xbBwcHYv38/NDQ0eG6XiCTv66+/xrFjx7B06VI0a9YMAHDmzBmMHj0a7dq1w4oVKwROWPGxwJAKPT093Lx5EzY2NggMDERycjI2btyIa9euoVWrVnj69KnQEUmicnJykJCQgOrVq/OSfhJUlSpVsHPnTrRq1Upp+YkTJ9CnTx/+nCwDnImXVOjr6+P58+cAgKNHj6Jdu3YAAB0dHWRlZQkZjSQqMzMTfn5+0NPTQ61atZCYmAgAGDVqFBYsWCBwOpKizMxMmJubqyw3MzNDZmamAImkhwWGVLRr1w7+/v7w9/fHrVu30KlTJwDAtWvXYGdnJ2w4kqTJkycjNjYWJ0+ehI6OjmK5l5cXtm3bJmAykipPT09Mnz4db968USzLysrCzJkz4enpKWAy6eAxWFKxbNkyTJkyBUlJSfjjjz9gYmICAIiOjka/fv0ETkdStGfPHmzbtg1NmjSBTCZTLK9Vqxbi4+MFTEZStXjxYnh7e6NatWqoW7cuACA2NhY6Ojo4cuSIwOmkgWNgiKjc09PTw9WrV+Hg4AADAwPExsbCwcEBsbGxaNmyJdLS0oSOSBKUmZmJzZs34+bNmwAAFxcXDBgwALq6ugInkwYegaFipaamYu3atbhx4waAt3/p+vr6Km4tQFSWGjRogAMHDmDUqFEAoDgKs2bNGh6uJ8Ho6elh6NChQseQLB6BIRVRUVHw9vaGrq4uGjVqBAC4cOECsrKycPToUXh4eAickKTmzJkz6NixI7766iuEhITg66+/xvXr1xEeHo5Tp06hfv36QkckiXnfDUZlMhl0dHTg6Oj40XMZ0b/DAkMqWrRoAUdHR6xevVpxqWpeXh78/f1x9+5dnD59WuCEJEXx8fFYsGABYmNjkZGRAQ8PDwQGBsLNzU3oaCRBampqkMlk+Puv0KJlMpkMzZs3x549e5RuNUAlhwWGVOjq6uLSpUuoWbOm0vLr16+jQYMGvESQiCQvLCwMP/zwA+bOnas4Uh0ZGYmpU6diypQpMDQ0xNdff43GjRtj7dq1AqetmDgGhlTI5XIkJiaqFJikpCQYGBgIlIqk7ODBg1BXV4e3t7fS8iNHjqCgoAAdO3YUKBlJ1ZgxY/Drr7+iadOmimVt27aFjo4Ohg0bhmvXriE4OBi+vr4CpqzYOA8Mqfjyyy/h5+eHbdu2ISkpCUlJSdi6dSv8/f15GTUJ4rvvvkN+fr7K8sLCQnz33XcCJCKpi4+Ph1wuV1kul8sV9+ZycnLCs2fPyjqaZPAIDKn46aefIJPJ4OPjg7y8PACApqYmRowYwVlPSRC3b9+Gq6uryvKaNWvizp07AiQiqatfvz4mTpyIjRs3Ku5A/fTpU0yaNAkNGzYE8PZza21tLWTMCo0FhlRoaWlh8eLFmD9/vmKSsOrVq/PuqiQYQ0ND3L17V2Um6Dt37qBSpUrChCJJW7t2Lbp164Zq1aopSkpSUhIcHBzw559/AgAyMjIwZcoUIWNWaBzES0Tl3tdff42IiAjs3r0b1atXB/C2vPTq1QsNGzbEmjVrBE5IUlRQUICjR4/i1q1bAABnZ2e0a9cOamocnVEWWGBIRY8ePZSmay/y7vwG/fv3h7OzswDpSIrS0tLQoUMHREVFoVq1agCABw8eoEWLFti1axeMjIyEDUiSc/fuXTg4OAgdQ9JYYEjF4MGDsWfPHhgZGSkmCLt48SJSU1PRvn17xMbG4t69ewgLC0OzZs0ETktSUVhYiNDQUMTGxkJXVxd16tRBy5YthY5FEqWmpobPPvsMfn5++OKLL5RuMkplgwWGVHz33XdIT0/H0qVLFYdCCwoKMGbMGBgYGGDu3LkYPnw4rl27hjNnzgiclqQqNTWVR15IMDExMVi/fj1+//135OTk4Msvv4Svry8aN24sdDTJYIEhFaampjh79ixq1KihtPzWrVto2rQpnj17hitXrqBFixZITU0VJiRJyo8//gg7Ozt8+eWXAIA+ffrgjz/+gIWFBQ4ePKi4GzBRWcvLy8PevXsREhKCw4cPo0aNGvD19cXAgQMVVydR6eBII1KRl5enuLvqu27evKmYi0NHR6fYcTJEpWHlypWKKz1CQ0MRGhqKQ4cOoWPHjpg4caLA6UjKNDQ00LNnT+zYsQM//vgj7ty5gwkTJsDa2ho+Pj5ITk4WOmKFxcuoScXAgQPh5+eH77//XjGfwYULFzBv3jz4+PgAAE6dOoVatWoJGZMkJCUlRVFg9u/fjz59+qB9+/aws7PjIXsSVFRUFNatW4etW7eiUqVKmDBhAvz8/PDgwQPMnDkT3bp1Q2RkpNAxKyQWGFKxaNEimJubIygoCI8fPwYAmJubY9y4cQgMDAQAtG/fHh06dBAyJkmIsbExkpKSYG1tjcOHD2POnDkA3g7sLW6GXqLS9vPPP2P9+vWIi4tDp06dsHHjRnTq1EkxbtDe3h4hISEqcxdRyeEYGPqg9PR0ACh2ymyishIQEID9+/fDyckJly5dwr1796Cvr4+tW7ciKCgIFy9eFDoiSYyTkxN8fX0xePBgWFpaFrtNTk4Ofv/9dwwaNKiM00kDCwypmD59Onx9fWFrayt0FCIAQG5uLhYvXoykpCQMHjwY7u7uAN4eLTQwMIC/v7/ACYmorLHAkIp69erh6tWrijkOevXqBW1tbaFjEREJ7vXr15gwYQL27t2LnJwctG3bFr/88guvOBIACwwV69KlS4o5DvLy8tC3b1/4+voqBvUSlbX4+HgEBwfjxo0bAABXV1eMHTuWs6FSmRo/fjx+/fVXDBgwADo6Ovj999/RrFkz7N69W+hoksMCQx+Um5uLffv2Yf369Thy5Ahq1qwJPz8/DB48GIaGhkLHI4k4cuQIPv/8c9SrV08x+/PZs2cRGxuLffv2oV27dgInJKmwt7dHUFAQevfuDQCIjo5GkyZNkJWVBQ0NXhdTllhg6INycnKwe/durFu3DsePH0fTpk3x6NEjPH78GKtXr1ZMLEZUmtzd3eHt7Y0FCxYoLf/uu+9w9OhRDuKlMqOpqYn79+/DyspKsUxPTw83b96EjY2NgMmkhxPZUbGio6MREBAAS0tLjBs3Du7u7rhx4wZOnTqF27dvY+7cuRg9erTQMUkibty4AT8/P5Xlvr6+uH79ugCJSKoKCgqgqamptExDQ4OX8wuAx7tIhZubG27evIn27dtj7dq16Nq1K9TV1ZW26devH8aMGSNQQpIaU1NTxMTEwMnJSWl5TEwMzMzMBEpFUlRYWIi2bdsqnS7KzMxE165doaWlpVjGo4KljwWGVPTp0we+vr6oWrXqe7epUqUKCgoKyjAVSdnQoUMxbNgw3L17F02bNgXwdgzMjz/+iPHjxwucjqRk+vTpKsu6desmQBLiGBhSkp6ejvPnzyMnJweNGjXipYFULhQWFiI4OBgLFy7Eo0ePAABWVlaYOHEiRo8ezftyEUkQCwwpxMTEoFOnTnj8+DEKCwthYGCA7du3w9vbW+hoRAqvXr0CABgYGAichIiExEG8pBAYGAh7e3ucOXMG0dHRaNu2LQICAoSORaTEwMCA5YUE0aFDB5w7d+4ft3v16hV+/PFHLFu2rAxSSRePwJBClSpVcPToUXh4eAAAUlNTUblyZaSmpvJeSCQod3f3Yk8TyWQy6OjowNHREYMHD0br1q0FSEdSsXbtWkybNg2Ghobo2rUrGjRoACsrK+jo6ODly5e4fv06zpw5g4MHD6Jz58743//+x0urSxELDCmoqakhJSVF6aoOAwMDXL58Gfb29gImI6mbPHkyVqxYATc3NzRq1AgAcOHCBVy+fBmDBw/G9evXERYWhl27dnFAJZWq7Oxs7NixA9u2bcOZM2eQlpYG4G2ZdnV1hbe3N/z8/ODi4iJw0oqPBYYU1NTUcPz4cVSuXFmxrGnTpti+fTuqVaumWFanTh0h4pGEDR06FDY2Npg6darS8jlz5uD+/ftYvXo1pk+fjgMHDiAqKkqglCRFaWlpyMrKgomJicr8MFS6WGBIQU1NDTKZDMV9JIqWy2QyTthEZc7Q0BDR0dFwdHRUWn7nzh3Ur18faWlpuHnzJho2bKgY5EtEFRvngSGFhIQEoSMQFUtHRwfh4eEqBSY8PBw6OjoA3s6QWvRvIqr4WGBIwdbWVugIRMUaNWoUhg8fjujoaMUd0S9cuIA1a9bg+++/B/D2ho/16tUTMCURlSWeQiIAQGJi4ieNln/48OEHZ+olKmmbN2/G0qVLERcXBwBwdnbGqFGj0L9/fwBAVlaW4qokIqr4WGAIAGBubo7u3bvD399f8Rfu36WlpWH79u1YvHgxhg0bxps5EhGRYHgKiQAA169fx9y5c9GuXTvo6Oigfv36KvMbXLt2DR4eHggKCkKnTp2EjkwSMmjQIPj5+aFly5ZCRyFSkpOTgydPnqjcG47zv5Q+HoEhJVlZWThw4ADOnDmD+/fvIysrC1WqVIG7uzu8vb1Ru3ZtoSOSBHXv3h0HDx6Era0thgwZgkGDBvEUJgnq9u3b8PX1RXh4uNJyXq1ZdlhgiEgUnj59ik2bNmHDhg24fv06vLy84Ofnh27dunH+DSpzzZo1g4aGBr777jtYWlqqzBRdt25dgZJJBwsMEYnOxYsXsX79eqxZswb6+vr46quv8M0338DJyUnoaCQRlSpVQnR0NGrWrCl0FMnizRyJSFSSk5MRGhqK0NBQqKuro1OnTrhy5QpcXV2xaNEioeORRLi6uuLZs2dCx5A0HoEhonIvNzcXe/fuxfr163H06FHUqVMH/v7+6N+/v+JGo7t374avry9evnwpcFqSguPHj2PKlCmYN28e3NzcVE5j8ga4pY8FhojKvSpVqqCgoAD9+vXD0KFDi52wLjU1Fe7u7pxRmsqEmtrbExh/H/vCQbxlhwWGiMq9TZs2oXfv3pykjsqNU6dOfXD9Z599VkZJpIsFhop1+/ZtnDhxotj5DaZNmyZQKpKie/fuITQ0FLm5ufjss89Qq1YtoSMRUTnAAkMqVq9ejREjRqBKlSqwsLBQOkQqk8lw8eJFAdORlJw4cQJdunRBVlYWAEBDQwPr1q3DV199JXAykqLLly+jdu3aUFNTw+XLlz+4bZ06dcoolXSxwJAKW1tbfPPNNwgMDBQ6Cklc8+bNUaVKFaxYsQI6OjqYMmUKdu/ejUePHgkdjSRITU0NKSkpMDMzg5qaGmQyGYr7FcoxMGWDBYZUyOVyxMTEwMHBQegoJHFGRkYIDw+Hq6srACAzMxNyuRyPHz+GiYmJwOlIau7fvw8bGxvIZDLcv3//g9va2tqWUSrpYoEhFX5+fmjYsCGGDx8udBSSuHf/4i1iYGCA2NhYFmwiiePNHEmFo6Mjpk6dinPnzhU7vwHvQk1l6ciRIzA0NFQ8LigoQFhYGK5evapY9vnnnwsRjSRs48aNH1zv4+NTRkmki0dgSIW9vf1718lkMty9e7cM05CUFc218SEcb0BCMDY2Vnqcm5uLzMxMaGlpQU9PDy9evBAomXTwCAyp4ERgVF78/RJ+ovKiuBmfb9++jREjRmDixIkCJJIeHoEhIiIqIVFRUfjqq69w8+ZNoaNUeDwCQwCA8ePHY/bs2ahUqRLGjx//wW1//vnnMkpFUnbu3Dk0adLko7bNzMxEQkICJ7kjwWloaPAy/zLCAkMAgEuXLiE3N1fx7/f5+30/iErLwIED4eDgAH9/f3Tq1AmVKlVS2eb69ev47bffsH79evz4448sMFRm9u7dq/S4sLAQycnJWLp0KZo1ayZQKmnhKSQiKpdyc3OxYsUKLFu2DHfv3kWNGjVgZWUFHR0dvHz5Ejdv3kRGRgZ69OiB77//Hm5ubkJHJgn5+wBzmUwGU1NTtGnTBgsXLoSlpaVAyaSDBYaIyr2oqCicOXMG9+/fR1ZWFqpUqQJ3d3e0bt0alStXFjoeEQmABYZUtG7d+oOnio4fP16GaYiIiFRxDAypqFevntLj3NxcxMTE4OrVqxg0aJAwoYiIypH3Xewgk8mgo6MDR0dHdOvWjUcISxGPwNBHmzFjBjIyMvDTTz8JHYWISFCtW7fGxYsXkZ+fD2dnZwDArVu3oK6ujpo1ayIuLg4ymQxnzpxR3MuLShYLDH20O3fuoFGjRpxhkogkLzg4GH/99RfWr18PuVwOAEhLS4O/vz+aN2+OoUOHon///sjKysKRI0cETlsxscDQR9u0aRMCAwM5xwERSV7VqlURGhqqcnTl2rVraN++PR4+fIiLFy+iffv2ePbsmUApKzaOgSEVPXv2VHpcNL9BVFQUpk6dKlAqIqLyIy0tDU+ePFEpME+fPkV6ejoAwMjICDk5OULEkwQWGFLx7p1/gbfzHTg7O2PWrFlo3769QKlI6sLCwhAWFoYnT56o3CNp3bp1AqUiqerWrRt8fX2xcOFCNGzYEABw4cIFTJgwAd27dwcAREZGokaNGgKmrNh4ComIyr2ZM2di1qxZaNCgASwtLVUu89+9e7dAyUiqMjIyMG7cOGzcuBF5eXkA3t5GYNCgQVi0aBEqVaqEmJgYAKpXdlLJYIEhonLP0tISQUFBGDhwoNBRiJRkZGTg7t27AAAHBwfo6+sLnEg6WGBIhbGxcbET2b07v8HgwYMxZMgQAdKRFJmYmCAyMhLVq1cXOgoRlRMcA0Mqpk2bhrlz56Jjx45o1KgRgLfncg8fPoyRI0ciISEBI0aMQF5eHoYOHSpwWpICf39/bNmyhYPIqdx4/fo1FixY8N5xWUVHZaj0sMCQijNnzmDOnDkYPny40vJVq1bh6NGj+OOPP1CnTh0sWbKEBYbKxJs3b/Drr7/i2LFjqFOnDjQ1NZXW//zzzwIlI6ny9/fHqVOnMHDgwGLHZVHp4ykkUqGvr4+YmBg4OjoqLb9z5w7q1auHjIwMxMfHo06dOnj9+rVAKUlKWrdu/d51MpmM9+eiMmdkZIQDBw6gWbNmQkeRLB6BIRWVK1fGvn37MG7cOKXl+/btU9zX4/Xr1zAwMBAiHknQiRMnhI5ApMTY2Jj3ORIYCwypmDp1KkaMGIETJ04oxsBcuHABBw8exMqVKwEAoaGh+Oyzz4SMSUQkmNmzZ2PatGnYsGED9PT0hI4jSTyFRMU6e/Ysli5diri4OACAs7MzRo0ahaZNmwqcjKSiZ8+eCAkJgVwuV5kd+u927dpVRqmI3nJ3d0d8fDwKCwthZ2enMi7r4sWLAiWTDh6BoWI1a9aM53ZJUIaGhoqBkX+fHZpIaEWz7ZJweASGilVQUIA7d+4Ue3lgy5YtBUpFRET0Fo/AkIpz586hf//+uH//Pv7eb2UyGfLz8wVKRkRUfqSmpmLnzp2Ij4/HxIkTUblyZVy8eBHm5uaoWrWq0PEqPB6BIRX16tVDjRo1MHPmzGLnN+DhfCpr9vb2H5xng5OGUVm7fPkyvLy8YGhoiHv37iEuLg4ODg6YMmUKEhMTsXHjRqEjVng8AkMqbt++jZ07d6rMA0MklLFjxyo9zs3NxaVLl3D48GFMnDhRmFAkaePHj8fgwYMRFBSkNKVEp06d0L9/fwGTSQcLDKlo3Lgx7ty5wwJD5caYMWOKXb5s2TJERUWVcRqit1NLrFq1SmV51apVkZKSIkAi6WGBIRWjRo3Ct99+i5SUFLi5ualcHlinTh2BkhEp69ixIyZPnoz169cLHYUkRltbG+np6SrLb926BVNTUwESSQ/HwJAKNTU1lWUymQyFhYUcxEvlSlBQEJYvX4579+4JHYUkxt/fH8+fP8f27dtRuXJlXL58Gerq6ujevTtatmyJ4OBgoSNWeCwwpOL+/fsfXG9ra1tGSYjecnd3VxrEW1hYiJSUFDx9+hTLly/HsGHDBExHUpSWloYvvvgCUVFRePXqFaysrJCSkgJPT08cPHgQlSpVEjpihccCQ0Tl3syZM5Ueq6mpwdTUFK1atULNmjUFSkUEnDlzBpcvX0ZGRgY8PDzg5eUldCTJYIGhYm3atAkrV65EQkICIiIiYGtri+DgYNjb26Nbt25CxyMiIolTHexAkrdixQqMHz8enTp1QmpqqmLMi5GREc/rkiDS09OL/Xr16hVycnKEjkcSFRYWhi5duqB69eqoXr06unTpgmPHjgkdSzJYYEjFL7/8gtWrV+OHH36Aurq6YnmDBg1w5coVAZORVBkZGcHY2Fjly8jICLq6urC1tcX06dNVbntBVFqWL1+ODh06wMDAAGPGjMGYMWMgl8vRqVMnLFu2TOh4ksDLqElFQkIC3N3dVZZra2vj9evXAiQiqQsJCcEPP/yAwYMHo1GjRgCAyMhIbNiwAVOmTMHTp0/x008/QVtbG99//73AaUkK5s2bh0WLFiEgIECxbPTo0WjWrBnmzZuHkSNHCphOGlhgSIW9vT1iYmJUrjY6fPgwXFxcBEpFUrZhwwYsXLgQffr0USzr2rUr3NzcsGrVKoSFhcHGxgZz585lgaEykZqaig4dOqgsb9++PQIDAwVIJD08hUQqxo8fj5EjR2Lbtm0oLCxEZGQk5s6di8mTJ2PSpElCxyMJCg8PL/aooLu7OyIiIgAAzZs3R2JiYllHI4n6/PPPsXv3bpXlf/75J7p06SJAIunhERhS4e/vD11dXUyZMgWZmZno378/rKyssHjxYvTt21foeCRB1tbWWLt2LRYsWKC0fO3atbC2tgYAPH/+HMbGxkLEIwlydXXF3LlzcfLkSXh6egIAzp07h7Nnz+Lbb7/FkiVLFNuOHj1aqJgVGi+jJhXZ2dnIy8tDpUqVkJmZiYyMDJiZmQkdiyRs79696N27N2rWrImGDRsCAKKionDz5k3s3LkTXbp0wYoVK3D79m38/PPPAqclKbC3t/+o7WQyGe+WXkpYYEjh6dOn8PHxwbFjx1BQUICGDRti8+bNqF69utDRiJCQkIBVq1bh1q1bAABnZ2d8/fXXsLOzEzYYEQmCBYYUfH19cejQIYwePRo6OjpYtWoVLC0tceLECaGjERERKWGBIQVra2usWbMG3t7eAIDbt2/DxcUFr1+/hra2tsDpSOpSU1MRGRmJJ0+eqMz34uPjI1AqIhIKCwwpqKur4+HDh7CwsFAsq1SpEq5du8bD9CSoffv2YcCAAcjIyIBcLle6saNMJsOLFy8ETEdEQuBl1KTk3Zl3ix6z45LQvv32W/j6+iIjIwOpqal4+fKl4ovlhUiaeASGFNTU1GBoaKj0121qairkcjnU1P6v6/IXBpW1SpUq4cqVK3BwcBA6ChGVE5wHhhTWr18vdASiYnl7eyMqKooFhsqV1NRUrF27Fjdu3AAA1KpVC76+vjA0NBQ4mTTwCAwRlXtr167FrFmzMGTIELi5uUFTU1Np/eeffy5QMpKqqKgoeHt7Q1dXV3F/rgsXLiArKwtHjx6Fh4eHwAkrPhYYIir33j2F+XcymQz5+fllmIYIaNGiBRwdHbF69WpoaLw9mZGXlwd/f3/cvXsXp0+fFjhhxccCQ0RE9Il0dXVx6dIl1KxZU2n59evX0aBBA2RmZgqUTDp4FRIRicqbN2+EjkAEuVxe7M1Dk5KSYGBgIEAi6WGBIaJyLz8/H7Nnz0bVqlWhr6+vuLfM1KlTsXbtWoHTkRR9+eWX8PPzw7Zt25CUlISkpCRs3boV/v7+6Nevn9DxJIEFht4rJycHcXFxyMvLEzoKSdzcuXMREhKCoKAgaGlpKZbXrl0ba9asETAZSdVPP/2Enj17wsfHB3Z2drCzs8PgwYPxxRdf4McffxQ6niRwDAypyMzMxKhRo7BhwwYAwK1bt+Dg4IBRo0ahatWq+O677wROSFLj6OiIVatWoW3btjAwMEBsbCwcHBxw8+ZNeHp64uXLl0JHJInKzMxEfHw8AKB69erQ09MTOJF08AgMqZg8eTJiY2Nx8uRJ6OjoKJZ7eXlh27ZtAiYjqXr48CEcHR1VlhcUFCA3N1eARERv6enpwdjYGMbGxiwvZYwFhlTs2bMHS5cuRfPmzZVm5a1Vq5biLw2isuTq6oq//vpLZfnOnTvh7u4uQCKSuoKCAsyaNQuGhoawtbWFra0tjIyMMHv2bJWbjVLp4Ey8pOLp06cwMzNTWf769WulQkNUVqZNm4ZBgwbh4cOHKCgowK5duxAXF4eNGzdi//79QscjCfrhhx+wdu1aLFiwAM2aNQMAnDlzBjNmzMCbN28wd+5cgRNWfBwDQypatmyJ3r17Y9SoUTAwMMDly5dhb2+PUaNG4fbt2zh8+LDQEUmC/vrrL8yaNQuxsbHIyMiAh4cHpk2bhvbt2wsdjSTIysoKK1euVJkF+s8//8Q333yDhw8fCpRMOngEhlTMmzcPHTt2xPXr15GXl4fFixfj+vXrCA8Px6lTp4SORxLVokULhIaGCh2DCMDbm9r+fRI7AKhZsyZveFtGOAaGVDRv3hwxMTHIy8uDm5sbjh49CjMzM0RERKB+/fpCxyMJi4qKwqZNm7Bp0yZER0cLHYckrG7duli6dKnK8qVLl6Ju3boCJJIenkIionLvwYMH6NevH86ePQsjIyMAb+8E3LRpU2zduhXVqlUTNiBJzqlTp9C5c2fY2NjA09MTABAREYGkpCQcPHgQLVq0EDhhxccjMAQASE9PV/r3h76Iypq/vz9yc3Nx48YNvHjxAi9evMCNGzdQUFAAf39/oeORBH322We4desWevTogdTUVKSmpqJnz56Ii4tjeSkjPAJDAAB1dXUkJyfDzMwMampqxV5tVFhYyDv/kiB0dXURHh6ucsl0dHQ0WrRowRvnUZlLTEyEtbV1sT8rExMTYWNjI0AqaeEgXgIAHD9+HJUrVwYAnDhxQuA0RMqsra2LnbAuPz8fVlZWAiQiqbO3t1f80feu58+fw97enn/olQEWGALw9nBocf8mKg/+97//YdSoUVi2bBkaNGgA4O2A3jFjxuCnn34SOB1JUdER6b/LyMhQmsGcSg9PIREA4PLlyx+9bZ06dUoxCZEqY2NjZGZmIi8vDxoab//uKvp3pUqVlLblJaxUmsaPHw8AWLx4MYYOHap0+4D8/HycP38e6urqOHv2rFARJYNHYAgAUK9ePchkMvxTn+UYGBJCcHCw0BGIAACXLl0C8PYIzJUrV5Tujq6lpYW6detiwoQJQsWTFB6BIQDA/fv3P3pbW1vbUkxCRFT+DRkyBIsXL4ZcLhc6imSxwBAREZHocB4YKtamTZvQrFkzWFlZKY7OBAcH488//xQ4GRGR8F6/fo2pU6eiadOmcHR0hIODg9IXlT6OgSEVK1aswLRp0zB27FjMnTtXMebFyMgIwcHB6Natm8AJiYiE5e/vj1OnTmHgwIGwtLQs9ookKl08hUQqXF1dMW/ePHTv3h0GBgaIjY2Fg4MDrl69ilatWuHZs2dCRyQiEpSRkREOHDiAZs2aCR1FsngKiVQkJCSozHgKANra2nj9+rUAiYj+T1JSEpKSkoSOQRJnbGysmPyThMECQyrs7e0RExOjsvzw4cNwcXEp+0AkeXl5eZg6dSoMDQ1hZ2cHOzs7GBoaYsqUKcXO0EtU2mbPno1p06bxNhYC4hgYUjF+/HiMHDkSb968QWFhISIjI/H7779j/vz5WLNmjdDxSIJGjRqFXbt2ISgoSOnOvzNmzMDz58+xYsUKgROS1CxcuBDx8fEwNzeHnZ0dNDU1ldZfvHhRoGTSwTEwVKzNmzdjxowZiI+PBwBYWVlh5syZ8PPzEzgZSZGhoSG2bt2Kjh07Ki0/ePAg+vXrh7S0NIGSkVTNnDnzg+unT59eRkmkiwWGPigzMxMZGRkqNywjKktmZmY4deqUyinMGzduoGXLlnj69KlAyYhIKBwDQx+kp6fH8kKCCwgIwOzZs5Gdna1Ylp2djblz5yIgIEDAZCRlqampWLNmDSZPnqy4B9fFixfx8OFDgZNJA4/AEADA3d39o+cx4LldKms9evRAWFgYtLW1UbduXQBAbGwscnJy0LZtW6Vtd+3aJUREkpjLly/Dy8sLhoaGuHfvHuLi4uDg4IApU6YgMTERGzduFDpihcdBvAQA6N69u+Lfb968wfLly+Hq6qoYMHnu3Dlcu3YN33zzjUAJScqMjIzQq1cvpWXW1tYCpSF6e7HD4MGDERQUBAMDA8XyTp06oX///gImkw4egSEV/v7+sLS0xOzZs5WWT58+HUlJSVi3bp1AyYiIygdDQ0NcvHgR1atXV5rw8/79+3B2dsabN2+EjljhcQwMqdixYwd8fHxUln/11Vf4448/BEhERFS+aGtrIz09XWX5rVu3YGpqKkAi6eEpJFKhq6uLs2fPwsnJSWn52bNnoaOjI1AqkrqdO3di+/btSExMRE5OjtI6jsuisvb5559j1qxZ2L59OwBAJpMhMTERgYGBKqc7qXTwCAypGDt2LEaMGIHRo0fjt99+w2+//YZRo0Zh5MiRGDdunNDxSIKWLFmCIUOGwNzcHJcuXUKjRo1gYmKCu3fvqswNQ1QWFi5cqJhiIisrC5999hkcHR1hYGCAuXPnCh1PEjgGhoq1fft2LF68GDdu3AAAuLi4YMyYMejTp4/AyUiKatasienTp6Nfv35K4w2mTZuGFy9eYOnSpUJHJIk6c+YMLl++jIyMDHh4eMDLy0voSJLBAkOf5OrVq6hdu7bQMUhi9PT0cOPGDdja2sLMzAyhoaGoW7cubt++jSZNmuD58+dCRySiMsYxMPSPXr16hd9//x1r1qxBdHQ08vPzhY5EEmNhYYEXL17A1tYWNjY2OHfuHOrWrYuEhATwbzAqS1lZWQgLC0OXLl0AAJMnT1aaYFFdXR2zZ8/meMEywAJD73X69GmsWbMGu3btgpWVFXr27Illy5YJHYskqE2bNti7dy/c3d0xZMgQjBs3Djt37kRUVBR69uwpdDySkA0bNuDAgQOKArN06VLUqlULurq6AICbN2/CysqK4wXLAE8hkZKUlBSEhIRg7dq1SE9PR58+fbBy5UrExsbC1dVV6HgkUQUFBSgoKICGxtu/ubZu3Yrw8HA4OTnh66+/hpaWlsAJSSpatGiBSZMmoWvXrgCgNCYLAH777TcsW7YMERERQsaUBBYYUujatStOnz6Nzp07Y8CAAejQoQPU1dWhqanJAkOCycvLw7x58+Dr64tq1aoJHYckztLSEhEREbCzswMAmJqa4sKFC4rHt27dQsOGDXmH9DLAy6hJ4dChQ/Dz88PMmTPRuXNnqKurCx2JCBoaGggKCkJeXp7QUYiQmpqqNObl6dOnivICvD1a+O56Kj0sMKRw5swZvHr1CvXr10fjxo2xdOlSPHv2TOhYRGjbti1OnToldAwiVKtWDVevXn3v+suXL/NIYRnhKSRS8fr1a2zbtg3r1q1DZGQk8vPz8fPPP8PX11fppmVEZWXlypWYOXMmBgwYgPr166NSpUpK6z///HOBkpHUjBkzBseOHUN0dLTKlUZZWVlo0KABvLy8sHjxYoESSgcLDH1QXFwc1q5di02bNiE1NRXt2rXD3r17hY5FEqOm9v6DxTKZjJf2U5l5/Pgx6tWrBy0tLQQEBKBGjRoA3v6sXLp0KfLy8nDp0iWYm5sLnLTiY4Ghj5Kfn499+/Zh3bp1LDBEJGkJCQkYMWIEQkNDFfMQyWQytGvXDsuXL1dckUSliwWGiMq9jRs34ssvv4S2trbS8pycHGzdurXYu6cTlbYXL17gzp07AABHR0dUrlxZ4ETSwgJDROWeuro6kpOTYWZmprT8+fPnMDMz4ykkIgniVUhEVO4VFhZCJpOpLH/w4AEMDQ0FSEREQuOtBIio3HJ3d4dMJoNMJkPbtm0VM/ECb8dlJSQkoEOHDgImJCKhsMAQUbnVvXt3AEBMTAy8vb2hr6+vWKelpQU7Ozv06tVLoHREJCSOgSGicm/Dhg3o27evyiBeIpIuFhgiKveSkpIgk8kUM5xGRkZiy5YtcHV1xbBhwwROR0RC4CBeIir3+vfvjxMnTgB4e8d0Ly8vREZG4ocffsCsWbMETkdEQmCBIaJy7+rVq2jUqBEAYPv27XBzc0N4eDg2b96MkJAQYcMRkSBYYIio3MvNzVWMfzl27Jji3kc1a9ZEcnKykNGISCAsMERU7tWqVQsrV67EX3/9hdDQUMWl048ePYKJiYnA6YhICCwwRFTu/fjjj1i1ahVatWqFfv36oW7dugCAvXv3Kk4tEZG08CokIhKF/Px8pKenw9jYWLHs3r170NPTU7nFABFVfCwwREREJDo8hURE5d7jx48xcOBAWFlZQUNDA+rq6kpfRCQ9vJUAEZV7gwcPRmJiIqZOnQpLS8tib+xIRNLCU0hEVO4ZGBjgr7/+Qr169YSOQkTlBE8hEVG5Z21tDf6tRUTvYoEhonIvODgY3333He7duyd0FCIqJ3gKiYjKPWNjY2RmZiIvLw96enrQ1NRUWv/ixQuBkhGRUDiIl4jKveDgYKEjEFE5wyMwREREJDo8AkNE5VJ6ejrkcrni3x9StB0RSQePwBBRuaSuro7k5GSYmZlBTU2t2LlfCgsLIZPJkJ+fL0BCIhISj8AQUbl0/PhxVK5cGQBw4sQJgdMQUXnDIzBEREQkOjwCQ0SikJqaisjISDx58gQFBQVK63x8fARKRURC4REYIir39u3bhwEDBiAjIwNyuVxpPIxMJuM8MEQSxAJDROVejRo10KlTJ8ybNw96enpCxyGicoAFhojKvUqVKuHKlStwcHAQOgoRlRO8FxIRlXve3t6IiooSOgYRlSMcxEtE5dLevXsV/+7cuTMmTpyI69evw83NTeVeSJ9//nlZxyMigfEUEhGVS2pqH3eAmBPZEUkTCwwRERGJDsfAEBERkeiwwBBRuXX8+HG4uroWezPHtLQ01KpVC6dPnxYgGREJjQWGiMqt4OBgDB06tNi7TRsaGuLrr7/GokWLBEhGREJjgSGicis2NhYdOnR47/r27dsjOjq6DBMRUXnBAkNE5dbjx49VLpl+l4aGBp4+fVqGiYiovGCBIaJyq2rVqrh69ep711++fBmWlpZlmIiIygsWGCIqtzp16oSpU6fizZs3KuuysrIwffp0dOnSRYBkRCQ0zgNDROXW48eP4eHhAXV1dQQEBMDZ2RkAcPPmTSxbtgz5+fm4ePEizM3NBU5KRGWNBYaIyrX79+9jxIgROHLkCIp+XMlkMnh7e2PZsmWwt7cXOCERCYEFhohE4eXLl7hz5w4KCwvh5OQEY2NjoSMRkYBYYIiIiEh0OIiXiIiIRIcFhoiIiESHBYaIiIhEhwWGiIiIRIcFhogqnMGDB6N79+5CxyCiUsSrkIiowklLS0NhYSGMjIyEjkJEpYQFhoiIiESHp5CIqFTs3LkTbm5u0NXVhYmJCby8vPD69WvF6Z2ZM2fC1NQUcrkcw4cPR05OjuK5BQUFmD9/Puzt7aGrq4u6deti586dSvu/du0aunTpArlcDgMDA7Ro0QLx8fEAVE8h/dP+Xr58iQEDBsDU1BS6urpwcnLC+vXrS/cbRET/iYbQAYio4klOTka/fv0QFBSEHj164NWrV/jrr78UtwIICwuDjo4OTp48iXv37mHIkCEwMTHB3LlzAQDz58/Hb7/9hpUrV8LJyQmnT5/GV199BVNTU3z22Wd4+PAhWrZsiVatWuH48eOQy+U4e/Ys8vLyis3zT/ubOnUqrl+/jkOHDqFKlSq4c+cOsrKyyuz7RUSfjqeQiKjEXbx4EfXr18e9e/dga2urtG7w4MHYt28fkpKSoKenBwBYuXIlJk6ciLS0NOTm5qJy5co4duwYPD09Fc/z9/dHZmYmtmzZgu+//x5bt25FXFwcNDU1VV5/8ODBSE1NxZ49e5Cdnf2P+/v8889RpUoVrFu3rpS+I0RU0ngEhohKXN26ddG2bVu4ubnB29sb7du3xxdffKG4f1HdunUV5QUAPD09kZGRgaSkJGRkZCAzMxPt2rVT2mdOTg7c3d0BADExMWjRokWx5eXv7ty584/7GzFiBHr16oWLFy+iffv26N69O5o2bfqfvgdEVLpYYIioxKmrqyM0NBTh4eE4evQofvnlF/zwww84f/78Pz43IyMDAHDgwAFUrVpVaZ22tjYAQFdX96OzfMz+OnbsiPv37+PgwYMIDQ1F27ZtMXLkSPz0008f/TpEVLZYYIioVMhkMjRr1gzNmjXDtGnTYGtri927dwMAYmNjkZWVpSgi586dg76+PqytrVG5cmVoa2sjMTERn332WbH7rlOnDjZs2IDc3Nx/PArj6ur6j/sDAFNTUwwaNAiDBg1CixYtMHHiRBYYonKMBYaIStz58+cRFhaG9u3bw8zMDOfPn8fTp0/h4uKCy5cvIycnB35+fpgyZQru3buH6dOnIyAgAGpqajAwMMCECf+vnftVUS0IADD+gSBYjoKYTSJiEQWL/4LJN9DkC1iENRyDCAaTIPgS2s0GQfApxFfQdorcdtmFC3dZdu9l4Pv1GYZJHzPMvDGdTnm9XrTbbR6PB5fLhSiKGI/HTCYTdrsdw+GQOI7JZrNcr1eazSblcvnDWj4z32KxoNFoUK1WSZKE4/FIpVL5T7sn6TMMGEnfLooizucz2+2W5/NJsVhks9kwGAw4HA70+31KpRLdbpckSRiNRiyXy9/jV6sVhUKB9XrN7XYjl8tRr9eZz+cA5PN5TqcTs9mMXq9HKpWiVqvRarX+uJ6/zZdOp4njmPv9TiaTodPpsN/vf3yfJH2dr5Ak/VPvXwhJ0lf5kZ0kSQqOASNJkoLjFZIkSQqOJzCSJCk4BowkSQqOASNJkoJjwEiSpOAYMJIkKTgGjCRJCo4BI0mSgmPASJKk4PwCwpjOvTnIzUoAAAAASUVORK5CYII=", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "means = df.groupby(\"species\").mean(numeric_only=True)\n", - "means.plot.bar()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Integration with open source visualizations\n", - "\n", - "BigQuery Dataframes is also compatible with several open source visualization packages, such as `matplotlib`." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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d7NixAz/++OMrewg9PT2Rm5uLb7/9Fr169UJwcDC+++47jX1K+jNx6tSpAh8qEFUWzikhIqIy27BhAzp37lzoEK2+ffsiJCQEV69eRf/+/fHpp59ixowZaNq0Ke7du4cxY8Zo7P/RRx/hxx9/xMaNG9GoUSO0b98emzZtQu3atUuUxdfXFydPnsTt27fx5ptvwt/fH3PnztUYVlZaZc0EAG3btsV3332HFStWwM/PDwcPHsTHH3+sMawoMDBQ/fw0b94caWlpGr1K5aVRo0Zo0qRJgU/US8LLywtt2rRBvXr1CgxnmzlzJgYMGIAPPvgArVu3Vs9dePEcFy9ejL59+2Lw4MFo0qQJ7ty5g0OHDqmLIyMjI8yePRu+vr5o164dZDKZeplaIyMjHD58GE5OTujevTsaNWqExYsXq9+4BwYGYv/+/Th8+DCaN2+OVq1a4euvv1b3yrxKccd+mVQqxY4dO3Dp0iU0bNgQH3/8MZYuXaqxj4WFBf744w9cu3YN/v7+mDNnToFhWtWqVUNwcDAUCgW6du2KRo0aYfLkybCxsVH3fBTHz88PK1aswJIlS9CwYUNs3boVX375pcY+JfmZePToEc6cOaNeSY2osklULw9eJSIiokoxYsQI3Lp1S8gyrH/++SemT5+O69evl+jN73MqlQpeXl4YO3bsK3uJlEolfHx80K9fv3K5Cj1VnJkzZyI5OfmV17ghqigcvkVERFRJli1bhi5dusDc3Bx//fUXNm/eXOyFIStSjx49EBkZiUePHsHNza1Ej3n69Cl27NiBuLi4Qj9Rv3fvHg4fPoz27dsjJycHq1evRnR0NAYOHFje8amcOTk5lXgoIlFFYE8JERFRJenXrx9OnDiBtLQ0eHh4YMKECRg9erToWCUmkUjg4OCAlStXFlpoPHjwAO+99x6uX78OlUqFhg0bYvHixeqJ70RERWFRQkREREREQnGiOxERERERCcWihIiIiIiIhGJRQkREREREQnH1LSIiLZadq0BShhxJGXIkZ/7zb4YcSZm5SM2UI0+pglQigVQCSKWSf/8vkUDywv+lkvxJykYGUtibG8HR0hhOliZwtDSGg4URDGT8jIqIiMRhUUJEJEh2rgJ34tMRGZ+GyCfpeJSSpS4+kjNykZQhR1auosJzSCWArVl+ofJiseL0z+1qNqbwcraAlYlhhWchIiL9xNW3iIgqWJY8v/i4/SQNkfHpiPzn34fJmVDq0G9gV2sTeDlboq6TBeq6WMLb2RLeLpYwMZSJjkZERDqORQkRUTlKy87FhegkXIxJxu0nabj9JA2PUrJQVX/TGkgl8HSyQKPq1mhUwxoNq1ujvqsVCxUiIioVFiVERGWQJVcg5F4SzkQl4kxUIq4/SoVCl7o/KoCBVIKG1a3Rrq4j2td1QGM3W8ikEtGxiIhIi7EoISIqBXmeEpfvJ+NsVCLORiUi9EEK5Aql6FhazcrEAG09HdCuriPa1XVEdRtT0ZGIiEjLsCghInqFiLg0HA1/gjNRCbh0LxnZuSxCysLD0RztvBzRvq4jWnnYw9SIQ72IiPQdixIiokI8SMrEvrDH2Bf6GBFP0kTHqbKMDKRo7m6Ljt5OeKtxNThZmoiOREREArAoISL6R0J6Dv68Gou9oY9w+X6K6Dh6RyaVoJ2XA95p6obO9Z1gbMAeFCIifcGihIj0WnpOHg5ej8Pe0Ec4E5Wo95PUtYW1qSF6+bmib5Ma8K9pKzoOERFVMBYlRKR35HlKHL/1BHtDH+P4rXjk5HGOiDar42iOvk1r4D/+NeBizeFdRERVEYsSItIbKZly/HLuHjafvYenaTmi41ApSSVAW08HvNO0BgIbuPBaKEREVQiLEiKq8u4nZmLD6bvYfekhMuUK0XGoHNiaGWJIG3d82KY2rM0MRcchIqIyYlFCRFXWpXvJ+PHUXRy6EQdOFamazI1kGNSqFj56ozacrDi0i4hIV7EoIaIqRalU4fDNOPzwv7tcQUuPGBlI8U7TGhjdrg5q2puJjkNERKXEooSIqoQsuQK7Lz3AT6ejEZOYKToOCSKTStDT1xVjO3jC28VSdBwiIiohFiVEpNNy8hTYfCYG605EITkzV3Qc0hISCRBQzwljO3qiCZcUJiLSeixKiEgnKZUq/HblEVYcuY1HKVmi45AWa+1hjxlB3rzeCRGRFmNRQkQ65++IeCz56xZuxaWJjkI6QiIB/uNfAzO7ecPJkhPiiYi0DYsSItIZt+KeYcH+mwi+kyg6CukoS2MDTAjwxIdta8NQJhUdh4iI/sGihIi0XkqmHMsP38a2C/eh4Nq+VA48HM0xt2d9dPB2Eh2FiIjAooSItJhCqcIv5+7h66O3kcJJ7FQBAuo5YW6v+qhlby46ChGRXmNRQkRaKSQmCXN+u46IJ5w3QhXLyECK4W/UxoROnjAzMhAdh4hIL7EoISKtkpOnwPLDt/Hjqbu8CjtVKhcrE8zqVg+9/auLjkJEpHdYlBCR1rj+KBVTdoXi9pN00VFIjwXUc8KSd3zhYGEsOgoRkd5gUUJEwuUplFjzdxRW/x2JXAV/JZF4DhZG+OodX3Sq5yw6ChGRXmBRQkRC3YlPw5RdYbj6MFV0FKICBreqhTk9fGBiKBMdhYioSmNRQkRCKJUqbDgdjWWHI5CTpxQdh6hInk4WWPleYzSoZi06ChFRlcWihIgq3YOkTEzdHYYL0UmioxCViJFMiqld62LEmx6QSiWi4xARVTksSoioUm2/cB8L999EhlwhOgpRqbX2sMeK/n5wtTYVHYWIqEphUUJElUKep8Sc365h96WHoqMQlYm1qSEW9WmInr7VREchIqoyWJQQUYV7mpaD0b9cwqV7yaKjEJWbAS3c8PnbDWEok4qOQkSk81iUEFGFuvYwFSO3hCA2NVt0FKJy18rDDt+93xQ2ZkaioxAR6TQWJURUYfaGPsLMPVeRncvVtajqqu1gjp+GNkdtB3PRUYiIdBaLEiIqd0qlCksPR2DdiSjRUYgqhY2ZIdYNaorWdexFRyEi0kksSoioXKVl52LyjlAcuxUvOgpRpTKUSbCodyP0a+4mOgoRkc5hUUJE5SYmIQMf/RyCO/HpoqMQCTOqvQdmBdWDRMLrmRARlRSLEiIqF6cjEzBu22WkZuWKjkIkXGADZ3zT3x+mRjLRUYiIdAKLEiIqs7+uxWLijivIVfDXCdFzDatbYcOQ5nC2MhEdhYhI67EoIaIy2Rv6CFN3hSFPyV8lRC9zsTLBxg+bw8fVSnQUIiKtxqKEiF7bnksPMf2/YWA9QlQ0O3MjbBvREvVcWJgQERWFl6Elotey48J9FiREJZCUIceg9edx+0ma6ChERFqLRQkRldqWszGY/ds1FiREJZSYIcfA9ecQycKEiKhQHL5FRKWy4XQ0Fuy/KToGkU5ysDDGjpEt4elkKToKEZFWYVFCRCW27kQUlhy8JToGkU5ztDTG9hGt4OlkIToKEZHWYFFCRCWy8mgkvj56W3QMoirB0dIYO0a2Qh1HFiZERACLEiIqgeWHI/Dt8TuiYxBVKU7/FCYeLEyIiDjRnYiKtzE4mgUJUQWIT8vBgPXnEJ2QIToKEZFwLEqIqEhHbj7hpHaiCvTkWQ4G/HAO9xMzRUchIhKKRQkRFeraw1RM2nGFy/4SVbC4Z9kYuvECUjLloqMQEQnDooSICniUkoVhmy8iU64QHYVIL9xNyMCoLZcgz1OKjkJEJASLEiLSkJadi2EbL+JpWo7oKER65Xx0Emb9elV0DCIiIViUEJFankKJsVsvI4JXnSYS4tfLj7DqWKToGERElY5FCRGpzfntOk5FJoiOQaTXVhy5jb2hj0THICKqVCxKiAgAsObvO9gZ8kB0DCICMHPPVVx/lCo6BhFRpWFRQkTYF/YYyw5HiI5BRP/IzlVi5M8hSEjn3C4i0g+8ojuRngt9kIJ+35/lqj9VROq53Ug5uRmWTd+CXeeRGttUKhXid89DdvQlOPaZA7O6rQttQ6XIQ8qpLciKCkFeahykxuYwqeUHm/ZDYWBpn79PXi4SD65CZuQ5yMxtYdd1LEzdG/+b4/weKJ49hV2X0RV2rvqghbsdto5oCUMZP0MkoqqNv+WI9Fhadi4mbr/CgqSKyIm9jbTQgzB0dC90e1rIXkDy6nZUeTmQx0XBus17cB2yEo69/w+5SY/w9NcF/7YVdhDyuDtweX8ZLPyCkPDHUjz/jCs3JQ7pYYdg0+6D8jgtvXYhJgmf/8ELmBJR1ceihEiPffr7ddxP4pWkqwKlPAsJfyyDfdAESE0sCmyXP7mLZxd+g0O3ya9sS2psDuf3FsLc500Y2teAcfV6sOsyGvK4O8h7Fg8AyE18AFPPljByrAXLJj2gzEyFMusZACDp8FrYdhgKqbFZuZ6jvtpy7h52XrwvOgYRUYViUUKkp369/BC/hz4WHYPKSdKRdTCt01xjCNVzytxsJPyxFHZdx0BmYfta7StzMgFIIDXOL3iMnGoj5+FNKHNzkB19GTILO0hNrZB+429IDIxgVrdNGc6GXjZv301EJ2SIjkFEVGFYlBDpoXuJGZi794boGFROMm6ehDwuCrbthxS6PfnYjzCu7gMzr1av1b4qT46UExthVr+duvfDolEXGDrVxuMNY5F6dhcc3p4JZXY6Uk9vhV3nUUj+3xY8+n4Enuz8FHlpXGa6rLJyFZi8MxR5Cg61JKKqiUUJkZ7JVSgxcfsVpOfkiY5C5SDv2VMkHVsPh17TIDEwKrA9M/I8su+HwTZgxGu1r1Lk4enexQAA+67j1PdLZAaw7zoGNUZvgOuQr2FSowGSj2+AZdNekD+5i6zIs3D98FsYV6uH5KM/vN7JkYawBylY83eU6BhERBWCq28R6ZnFf93Cdyf5xqaqyLx9Fk9/WwRIXviMSaUEIAEkElj6d0fa5T8BiURzu0QK4xr14TJwcZFtPy9I8lLi4DzgC8hMrYrcN/veVSSf3AiX95ch+e+fIJHKYNtxGORP7+HJtllwm7S9HM6WDKQS/Dq2DXxr2IiOQkRUrgxEByCiyhN8JwHf/48FSVViUssPrsNWa9yXeGAlDO1rwKplX8hMrWHROEhje+xP42Hb6SOYerYosl11QZL8GM4Dviy2IFHlyZF0ZF1+b41UBqiU+XURACgVUKk45Ki85ClVmLwzFAcmvgkTQ5noOERE5YbDt4j0RFKGHB/vDAX7RqsWqbEZjBzdNb4khsaQmljCyNEdMgvbAtsBwMDKEYY2Lup2Hq0fjczbZwD8U5D8/iXkcXfg0GsaoFRCkZ4MRXoyVIrcAhlSzuyAqUczGDnXAQAYV6+PzNtnII+PRtrl/TCp7lPxT4Qeufs0A18eCBcdg4ioXLGnhEhPTN8dhvg0Xh2aCpeX9PCfFbYARXoisu6cBwDEbpyosZ/zgC9gUtNXfVv+NAaZt07Bdei36vvM6rVF9oNriNs6E4b21eHQa3olnIF++fncPQT4OKNdXUfRUYiIygXnlBDpgc1nYvDZPq62RVSVOFsZ49DkdrAxK7jAARGRruHwLaIqLjY1C0sO3hIdg4jK2ZNnOfjk9+uiYxARlQsWJURV3MI/w5EpV4iOQUQVYP/VWOwNfSQ6BhFRmbEoIarCgu8k4M+rsaJjEFEFmrv3BpIy5KJjEBGVCYsSoioqV6HE3L0c2kFU1aVm5WLFkQjRMYiIyoRFCVEV9dPpaEQ9zRAdg4gqwfYLDxARlyY6BhHRa2NRQlQFxaVmY9WxSNExiKiSKJQqLNh/U3QMIqLXxqKEqApa+OdNZHByO5FeOX0nAUdvPhEdg4jotbAoIapiztxJwH5ObifSS4sOhCNXoRQdg4io1FiUEFUhuQolL5JIpMeiEzKw+UyM6BhERKXGooSoCtkYHI3I+HTRMYhIoJXHIrlEMBHpHBYlRFVE/LNsrDp2R3QMIhIsLTsPyw9ziWAi0i0sSoiqiLUnopCekyc6BhFpgR0XH+BW3DPRMYiISoxFCVEV8DQtB9sv3Bcdg4i0BJcIJiJdw6KEqApYf+oucvK44g4R/Sv4TiL+jogXHYOIqERYlBDpuKQMOX45d090DCLSQutORImOQERUIixKiHTchtN3kckLJRJRIS5EJ+Hy/WTRMYiIXolFCZEOS83Kxc9n2EtCREX7/iR7S4hI+7EoIdJhm4JjkMYVt4ioGEduPsHdp7x+ERFpNxYlRDoqPScPG89Ei45BRFpOqQJ++N9d0TGIiIrFooRIR205ew8pmbmiYxCRDvj1yiPEp2WLjkFEVCQWJUQ6KEuuwIbT/OSTiEpGnqfET6djRMcgIioSixIiHbTtwn0kpMtFxyAiHbL1/D2kcw4aEWkpFiVEOiZPocSPp9hLQkSlk5adh23nuVofEWknFiVEOubYrXjEpnJsOBGV3k+nYyDPU4qOQURUAIsSIh2z/cJ90RGISEfFPcvG76GPRMcgIiqARQmRDnmYnIn/3X4qOgYR6bCt5/nBBhFpHxYlRDpk58UHUKpEpyAiXRb2IIUXUyQircOihEhHKJQq7Ap5IDoGEVUBv1/hEC4i0i4sSoh0RHrUWQyzvQZTmUJ0FCLScb+FPoJKxW5XItIeLEqIdIT15XUY9WQeblh9jD+8/kRXhyTRkYhIRz1IykLIvWTRMYiI1CQqflRCpP2ykoFldQGF5gUTMx18ccS4CxY/aoTYbCNB4YhIFw1sWRNf9GkkOgYREQAWJUS64eKPwJ9Ti9ysMjDFQ5dO+DnrTfz42A0qlaQSwxGRLrI2NcSFOQEwNpCJjkJExKKESCesDwAehZRo1zwrN1y0DsLS+Ga4nGpZwcGISJd9934TBDV0FR2DiIhFCZHWS4oGVjUu9cNUkCDVpTX2Sjph2YO6SMszKP9sRKTTAhs44/vBzUTHICLiRHcirRdx4LUeJoEKNnFnMCR2Ia6aT8Ahr9/Rxzm+nMMRkS77+9ZTpGTKX70jEVEFY1FCpO0i/ipzE5KcVHg/2IWvUyfjVrUF+M7zPDzMssshHBHpMrlCif1XY0XHICLi8C0irZaVAiytAyjzyr1plcwIT1w6YLu8HdY+qo1cJSfHE+mjZrVs8d8xbUTHICI9x6KESJtd+y+wZ3iFH0Zh7oJQuyB8ndgSp5OsK/x4RKQ9JBIgZE5n2FsYi45CRHqMw7eItNlrzicpLVlGHJo+2IRfMsfgWs0VWOJxFfZGuZVybCISS6UCgqMSRccgIj3HooRIWylygTtHK/2wlvEh6P94MUJMxuK4524MdH1c6RmIqHIFRyaIjkBEeo7Dt4i01d2TwM9viU4BAJDb1EGwZRC+ivVHeLqZ6DhEVM6q25gieFYn0TGISI+xp4RIW5XDqlvlxSglCh0frMEBxSiE1P4Bs2rdhqlMIToWEZWTRylZiE7IEB2DiPQYixIibXVbe4qS5yQqBRxiT2D0k3m4YfUx/vD6E10ckkTHIqJycPoOh3ARkTgcvkWkjRIigdW6c5XlDAc/HDHugiWPGiI220h0HCJ6DUENXPDd4KaiYxCRnjIQHYCICnH/rOgEpWKeEIbeCMPbBqZ46BmAn7PewI+P3aBS8donRLriTFQClEoVpFL+3BJR5ePwLSJt9OC86ASvRZKXBbeH+zEncRYiHWdhm9cJNLFOFx2LiErgWXYerj5KFR2DiPQUixIibfTggugEZWbw7AHaPPgBe+SjccV9DebVDoelQflfmZ6Iyk8w55UQkSCcU0KkbTKTgK88AFS9H02liQ1uOwZi3bPW2PvESXQcInpJKw877BjZWnQMItJDLEqItM3tQ8C2fqJTVLhs+/r427QLljzyQ0yWieg4RATAyECKsLldYWokEx2FiPQMh28RaZv750QnqBQmiTfR7eFK/C0djbN1NmFizbswlPIzEiKR5HlKXHmQLDoGEekhFiVE2qYKzCcpDYlCDtdHhzEl/hPcspuGPV5H0NaWk22JRLkVmyY6AhHpIRYlRNpEkQc8viw6hTCy9Fg0fbARW7PG4GrNr7HY4xrsjXJFxyLSKxFxLEqIqPLxOiVE2iTuKpCbKTqFVrCKv4j3cBH9TSxwt2ZX/JjRBttjq4mORVTl3XrCooSIKh97Soi0ycMQ0Qm0jkSejjoPf8WXydNw22UufvIKRj0LFm5EFSXySRq4Bg4RVTb2lBBpk6e3RCfQakYpd9Ap5Q46Sg2QUPtN7FZ2wLcPPZCl4EpBROUlU67A/aRM1LI3Fx2FiPQIe0qItEnCbdEJdIJEmQfH2L8x9slnuGH9MfZ5HUCAfZLoWERVxi3OKyGiSsaihEibJN4RnUDnSDMT4PvgF2zIGI8bNZbg6zqX4WIsFx2LSKdxsjsRVTYO3yLSFjlpQFqs6BQ6zTwhDH0Qht6Gpnjg1hk/Z72BDY9rQKWSiI5GpFNYlBBRZWNPCZG2SIgUnaDKkORloebDP/BJ4kxEOs7GNq8TaGyVLjoWkc64FfdMdAQi0jMsSoi0BYduVQiDZ/fR5sEP+C13NK64r8Fn7uEwN1CIjkWk1WISM5Gdy58TIqo8LEqItAUnuVcoiUoJ27hgfBi3ANcsJuCg11685RQvOhaRVlIoVbgTz95FIqo8LEqItAWHb1UaaXYK6j3YiVXPJiO8+iKs87wAd9Ns0bGItArnlRBRZeJEdyJtweFbQpgm3kA33ECQzAhxdTpim7wd1j6sBYWKn9mQfnuYnCU6AhHpEf7VJdIGKhWQGCU6hV6TKORwfXQIU5/OwW37Gfiv1xG0tk0VHYtImIT0HNERiEiPsCgh0gaZSUAeP5XUFrL0x2j2YCO2ZY3F1Vrf4AuPa7A1zBMdi6hSJWawKCGiysPhW0TaIDNBdAIqhAQqWD25gIG4gAGmFohyD8SP6W2wI9ZVdDSiCpeQxouQElHlYU8JkTbIYFGi7STydHg+2IPFyVNx22UufvIKRl1z9m5R1ZXAnhIiqkTsKSHSBuwp0SlGKXfQKeUOOkoNkODRDrsU7bHqQR3kKPk5D1UdCWksSoio8vAvKJE2yHgqOgG9BokyD46Pj2Pck88QbvMx9nr9hQD7JNGxiMrFs+w8yPOUomMQkZ5gUUKkDTISRSegMpJmPoXfgy3YkDEeN9y+woo6V+BizDH5pNs42Z2IKguHbxFpAw7fqlLMn4biPwhFHyMz3HfrjM1ZbbHxcQ2oVBLR0YhKJTFdDldrU9ExiEgPsKeESBtwonuVJMnNRK2H+zA3cSZuO/4ftnqdRGOrdNGxiErsKa9VQkSVhD0lRNqAPSVVnuGze2j77Hu0kUiR7N4Gv0k6YsWDusjIk4mORlSkxHQOQSSiysGihEgbcE6J3pColLCLO43hOI0PLWwR4RiINSmtsf+po+hoRAXwqu5EVFk4fItIG2Snik5AAkizk+HzYAdWp01CePVFWOd5ATVNs0XHIlJLymBPCRFVDvaUEGkDZZ7oBCSYaeINdMMNBMmMEVunI7bK2+G7hzWhUPGzIxInO1chOgIR6Qn+tSPSBir+4ad8EkUOqj06iOlP/w8RDjOx2+soWtuyJ43EyFOqREcgIj3BooRIGyhZlFBBBmmP0PzBT9iWNRZhtVbii9rXYGvIXjWqPAoFixIiqhwcvkWkDdhTQsWQQAXrJ+cxEOcxwMwSUU5d8UNaW+yKcxEdjao4hYpFCRFVDvaUEGkDpVJ0AtIRkpw0eD7Yg69SpuC262fY4HUGdc2zRMeiKkrB4VtEVEnYU0KkDdhTQq/BKDkSAcmR6CQ1wFOPdtiV1wHfPvRAjpKfN1H54JwSIqosLEqItAHnlFAZSJR5cHp8HONxHEMda+L9Wj5QgK8pKjsn59YA/EXHICI9wKKESBuoOHyLyodF6n04SOvifOpt0VGoCqjv4Ck6AhHpCfbxE2kDDt+ictQtV3QCqipkEpnoCESkJ1iUEGkD9pRQOeocfQkGUnaEU9nJpCxKiKhysCgh0gaGZqITUBVinZmMVlYcdkNlx54SIqosLEqItIGxlegEVMV0y+aQQCo7qYRvE4iocvC3DZE2MGFRQuWrU/RFGEmNRMcgHWdmwF5cIqocLEqItAF7SqicWWQ/Q1sO4aIysjGxER2BiPQEixIibcCeEqoA3bKyRUcgHWdrbCs6AhHpCRYlRNqAPSVUAdrfvQhTmYnoGKTD2FNCRJWFRQmRNmBPCVUAM3kG3rSqIzoG6TD2lBBRZWFRQqQN2FNCFaRberroCKTD2FNCRJWFRQmRNjCxFp2Aqqg3716EOVdQotfEnhIiqiwsSoi0AXtKqIIY52Wjg6WH6Bikg6QSKayN+YEJEVUOFiVE2sDcQXQCqsK6PUsVHYF0kJWRFS+eSESVhr9tiLSBrbvoBFSFtYm+CEtDC9ExSMfYGNuIjkBEeoRFCZE2YFFCFchQIUeARW3RMUjH2JpwPgkRVR4WJUTawNwBMOIn2VRxuqUkio5AOsbBlMNKiajysCgh0hY2tUQnoCqsRUwIbI04aZlKrpYVfycRUeVhUUKkLWz5BoAqjoEyD53Na4qOQTqktjWH/BFR5WFRQqQtOK+EKli3pHjREUiH1LZiUUJElYdFCZG24PAtqmBN712Co4md6BikI9hTQkSViUUJkbZgTwlVMKlKiS4m1UXHIB3gZOoECy6+QUSViEUJkbbgnBKqBN0SHouOQDqAvSREVNlYlBBpC1t3gFdPpgrm9yAULqaOomOQlnO3dhcdgYj0DN8BEWkLQ1PAzkN0CqriJFAh0NhFdAzScuwpIaLKxqKESJu4+IpOQHqgW/x90RFIy7EoIaLKxqKESJu4siihitfg0TW4mbG3hIrmYc1eWyKqXCxKiLQJe0qokgQacl4JFc7MwAzOZs6iYxCRnmFRQqRNXP1EJyA9ERR3V3QE0lKNHBpBIpGIjkFEeoZFCZE2MXcAbGqKTkF6wDsuHLXNec0SKqiJcxPREYhID7EoIdI21ZuJTkB6IkhmKzoCaSEWJUQkAosSIm1To7noBKQngmJvi45AWsZAYgBfB85tI6LKx6KESNvUYE8JVQ6P+Duoa8HhgvSvenb1YGZoJjoGEekhFiVE2sbVD5AZiU5BeiJIaik6AmkRDt0iIlFYlBBpGwNjoEYL0SlITwQ9vCk6AmkRFiVEJAqLEiJt5BkgOgHpCbfEe2hgxat3EyCBBE2cWJQQkRgsSoi0EYsSqkRBKlPREUgL1LauDVsTrshGRGKwKCHSRi6+gLmT6BSkJ4LuX4cEvFievvN38hcdgYj0GIsSIm0kkQB1OolOQXrCJeUh/Kw8RMcgwZo6NxUdgYj0GIsSIm3FIVxUiYIUXPFNn0klUrSp1kZ0DCLSYyxKiLRVnU4Ah9RQJel6PwxSCf8k6KvGjo1hb2ovOgYR6TH+BSLSVuYO+dcsIaoEjs/i0NSqjugYJEiXWl1ERyAiPceihEibcQgXVaKgXP5J0Feda3UWHYGI9Bz/AhFpM0++UaDK0yXmMgwkBqJjUCVrYN8ALuYuomMQkZ5jUUKkzdxaAhbOolOQnrDNSEQLaw7h0jfsJSEibcCPxIi0mVQGNOwLnFsrOgnpiaAcFc6IDvGCjIgMJBxIQNa9LOSl5KHmhJqwamql3p6Xmoe4XXFIv5EORaYC5nXN4fq+K4xdjIttN+FQApL+TkJuYi5kljJYN7OG8zvOkBrlf1aXciYFcf+NgzJbCds3beE6wFX9WPlTOWKWxaDOvDqQmcoq5sQrUeeaLEqISDz2lBBpu0bvik5AeiQg+iIMpYaiY6gpc5QwqWmCaoOrFdimUqlwb9U9yJ/KUXNiTXjO94ShgyFilsZAmaMsss2Usyl4svsJnN52gtcXXqg+rDpSL6TiyZ4nAIC8tDw82vgIrv1d4T7NHSlnUvAs9Jn68Y+3PIbzu85VoiCpY10H7tbuomMQEbEoIdJ61ZsA9l6iU5CesMpKRRsrT9Ex1Cx9LeHc11mjd+Q5+RM5sqKyUG1INZh5mMHY1RjVPqgGpVyJlHMpRbaZeScTZl5msGltAyNHI1g2tIR1S2tk3c3Kb/epHDJTGaxbWsPMwwzmPubIeZwDAEg5lwKJTALrZtYVcr6VjUO3iEhbsCgh0gW+/UQnID0SmCUXHaFEVLkqAIDE8N/r+UikEkgMJci8nVnk48w8zZAVk4XMu/n7yOPlSL+aDgtfCwCAsbMxlHJl/pCx9DxkRWfBxM0EigwF4n+Nh+v7rkW2rWtYlBCRtuCcEiJd0Ogd4O9FolOQnugUHQJjNxfkKHJERymWsasxDO0N8WT3E1QfWh0SYwkSDyUiLykPeal5RT7OprUNFOkKRC+KhgoqQAHYdbSDUy8nAIDMXIYaI2rg4fqHUMlVsGljA8tGlni44SHsAuyQm5CL+yvvQ6VQwam3E6yb62avSQ2LGqhnV090DCIiACxKiHSDnQdQoznw8KLoJKQHzHPS8KbVGziafEN0lGJJDCSoOaEmHm14hPBx4YAUsKhvkd/joSr6cenh6Xj6x1O4fuAKMw8zyOPliN0ai/i98XB6O78wsWpqpTFkLONWBnIe5qDa+9Vwe+ZtuI12g4G1AaI+j4K5tzkMrHTvz2lvz96iIxARqeneb1EifeXbn0UJVZrA9EwcFR2iBEzdTeG5wBOKTAVUeSoYWOUXCqbupkU+Jv63eNi0sYFdezsAgImbCZQ5Sjza9AiOvRwhkUo09lfmKvH458eoMbIG5PFyqBQqmNczBwAYuxgjMyoTVv4F57xoMwOpAfrW7Ss6BhGRGueUEOmKBv8BpPwcgSpH+5iLMDUo+o29tpGZyWBgZYCcuBxkRWfBsollkfsqc5QF//oV89fw6b6nsGhkAVN3U6iUKuCFhb1UeZq3dUVHt45wMHUQHYOISI1FCZGuMLfnFd6p0pjKM9HBUvyFFBXZCmTdy0LWvX9WxkqQI+teFuSJ+ZPxUy+kIj08HfJ4OZ5dfoaYpTGwamIFy4b/FiUPf3iIuN1x6tuWjS2RdDwJKedSIH8qR/r1dMT/Gg/LxpYFekmyH2Uj9UIqnP+TfxFTY1djQAIknUxCWmgacmJzYOqhO8Xbc/29+4uOQESkgR+7EumSZsOB2wdFpyA9EZj2DH8JzpAVnYWYJTHq23Hb84sLm7Y2qDGiBvJS8xC7IxaKVAUMbAxg08YGjm87arQhT5QDL9QaTm85QSKRIP7XeOQm58LA0gCWjfOXHn6RSqXC402P4TLABVLj/M/wpEZSVP+oOmK3xEKVq4LrYFcY2mrPdV1Kwt3KHS1dW4qOQUSkQaJSqYqZDkhEWkWlAlY3BxIjRScpN+suyrEuRI6YlPwxMA2cZJjbzgjdvP59o3f2QR7mHM/B+UcKyCRAYxcZDr1vBlNDSVHNYs0FOZaeyUFcugp+LlJ8280ULar/e7G7KYeysSlUDnMjCRYHmGCQ77/H230jFz9fzcUfA8wq4Ix1h1xmjA516iAtN110FCpH05tNxwcNPhAdg4hIA4dvEekSiQRoOUp0inJVw0qCxZ2NcWmkOUJGmqOTuwxv78jCjXgFgPyCJGhrJrrWMcCFj8xxcYQ5xrcwgrToegQ7r+diyuFsfNbeGJdHmcPPWYbAXzIQn5Ff+PwRkYtt13JxeLA5vupsgo/+yEJCZv621GwV5hzPwZruJhV+7trOSJGDjha1RcegcmQiM8Hbnm+LjkFEVACLEiJd03ggYGIjOkW56eVtiO5ehvCyl6GuvQyLAkxgYQSce5hflHx8KAcTWxhh1hvGaOAkg7eDDP0aGMLYoOiqZMW5HIxoYogP/Y1Q31GG73qawMxQgp+u5AIAwhOU6OAuQ7NqMgxoZAgrYwmik/M7jWccycaYZoaoac1fjwAQmJokOgKVo67uXWFtrJvXVSGiqo1/dYl0jZE50HSI6BQVQqFUYcf1XGTkAq3dZIjPUOL8IwWczKVosyEDzsvS0H5TBk7fL/rCeHKFCpceK9HZ498pc1KJBJ09DHD2n0LHz1mGkMcKJGepcOmxAlm5KnjaSXH6fh4uxykwsaVRhZ+rrmgdHQJrI91a7paKxgnuRKStWJQQ6aIWI6vU8sDXnihg8cUzGC9Mw+j9WfitvynqO8pwNzl/SNW8k/k9HwcHmaGJiwwBP2ciMlFRaFsJmSooVICzuWZPirO5BHHp+e0FehrgfV9DNF+fjqF7s7C5tynMjYAxf2bjux6mWBeSC+/V6Wj7U4Z6GJm+MlTmorN5LdExqBz42PnA19FXdAwiokKxKCHSRdY1AJ9eolOUG28HKUJHW+D8R+YY08wIQ37Pxs2nCij/WYZjVNP8oVj+rjJ8HWQCb3upeijW65rXwQR3Jlri2hgL9PExxJen5Ohc2wCGMmDh/3Jw+kMzfORviA9+zyqHM9RtQclPRUegcjCg3gDREYiIisSihEhXtRonOkG5MZJJ4GknRdNqMnzZ2QR+zlKsPCeHq0X+r6j6jpq/qnwcpbj/rPAr1jmYSSCTAE8yNBcWfJKhgotF4b/ybiUo8Mu1XCzoZIwTMXloV0sGR3Mp+jUwxOVYJdJy9HuRwuYxl2BnbCs6BpVBdYvq6FWn6nyQQURVD4sSIl3l1hyo3kx0igqhVAE5CsDdRoJqlhJEJGgWILcTlahVxER0I5kETatJcezuv/NOlCoVjt3NQ+sasgL7q1QqjNqfjRVdjWFhJIFCCeT+c7jn/yr0uyaBTKVAF7MaomNQGYzyHQWDKjTkk4iqHhYlRLqs7STRCcps9tFs/O9eHmJSlLj2RIHZR7NxIkaBQY0MIZFIML2NEVZdkOO/N3NxJ0mJT49n41aCEsP9/52MHvBzBlZfkKtvT2lljPWXc7E5VI7wpwqM2Z+NjFwVPmxc8CJ3P17OhaOZBL2887e1rWmA49F5OPcwD1+fzUF9RylsTIpZf1hPdEuIe/VOpJVqWNRgLwkRaT1+bEKky3x6Aa6NgdhQ0UleW3yGCh/8loXYdBWsjSXwdZbi0Ptm6FIn/9fT5FbGyM4DPj6UjaQsFfycZTgy2Ax17P79TCUqSam+zggA9G9oiKeZKsw9kX/xxMYuUhwcZAbnl4ZvPUlXYtGpHJwZbq6+r0V1Gaa2NkaPbVlwMpdgc2/TCn4GdEOT+5fh5OOP+OwE0VGolEb6jmQvCRFpPV7RnUjX3TkK/NJXdArSA0v8e+CXlGuiY1ApuFm6YV/vfSxKiEjrcfgWka7z7AzUekN0CtID3Z4+FB2BSom9JESkK1iUEFUFAXNFJyA94PswDNXNnEXHoBKqaVkTvTw4l4SIdAOLEqKqoGZLoG6Q6BSkB7oaOYmOQCU00nckZNKCK84REWkjFiVEVUWnTwFwlSiqWN3iokVHoBKoZVULPT16io5BRFRiLEqIqgqXhkBDTniniuUTexO1zKuJjkGvMMp3FHtJiEinsCghqko6/h/ASa1UwQIN7EVHoGI0dmzMXhIi0jksSoiqEvs6QJMholNQFdct9o7oCFQEmUSGT1p9AomEQzmJSLewKCGqagI+BcwcRKegKszzSQQ8LdxEx6BC9PfuD287b9ExiIhKjUUJUVVjagsELhKdgqq4QKm16Aj0EgdTB4z3Hy86BhHRa2FRQlQV+b0HuL8pOgVVYd0e3RIdgV4ypekUWBpZio5BRPRaWJQQVVU9vwZkRqJTUBVVK+EufCxriY5B/2jq3BS96vBCiUSku1iUEFVVDl5A28miU1AVFggL0REIgIHEAHNazhEdg4ioTFiUEFVlb04F7DxEp6AqKujhDdERCMBAn4HwsvUSHYOIqExYlBBVZYYmQI/lolNQFVU96T58rVj0iuRk6oSxjceKjkFEVGYsSoiqujqdeKV3qjCBShPREfTajBYzYG5oLjoGEVGZsSgh0geBXwKmdqJTUBUUeP8qJOCF+kToVrsbAt0DRccgIioXLEqI9IGlM/D2GtEpqApyTn0Mf+s6omPoHWczZ3zS6hPRMYiIyg2LEiJ9Ua870Pwj0SmoCgrKMxAdQa9IIMGiNxbByshKdBQionLDooRIn3RdBDj6iE5BVUzXmCuQSWSiY+iNQT6D0NK1pegYRETlikUJkT4xNAHe+Qkw4ORkKj/26U/RjEO4KkVd27qY3HSy6BhEROWORQmRvnGuD3RdKDoFVTFBctEJqj5TA1Msbb8UxjJj0VGIiModixIifdRiBODdXXQKqkK6RF+CgZRzSyrS7Baz4WFdsdeFkUgk+P3334vcfuLECUgkEqSkpFRoDira0KFD0bt37zK3I5fL4enpiTNnzpQ9lA5xd3fHN998o779qte8Ppg3bx4aN25cbu0dPHgQjRs3hlKpLNXjWJQQ6au31wCWrqJTUBVhnZmMVlaeomNUWd1rd0cfrz5laiMuLg4TJkyAh4cHjI2N4ebmhl69euHYsWMlbqNNmzaIjY2FtbV1mbI8V95vhvTBypUrsWnTpjK3891336F27dpo06aN+j6JRKL+sra2Rtu2bXH8+PEyH0ubxcbGolu3bsKOX9mFfmFF2LRp00r1e+BVgoKCYGhoiK1bt5bqcSxKiPSVmR3Q53tAwl8DVD6CshWiI1RJbpZumNt6bpnaiImJQdOmTXH8+HEsXboU165dw8GDB9GxY0eMGzeuxO0YGRnBxcUFEknlXpsmNze3Uo+nzaytrWFjY1OmNlQqFVavXo3hw4cX2LZx40bExsYiODgYDg4O6NmzJ+7evVum42kzFxcXGBtXjSGRr/tzYmFhAXt7+3LNMnToUKxatapUj+G7ESJ95tEeCCjbmx2i5wKiL8JIaiQ6RpViYWiBbzt9W+arto8dOxYSiQQXLlxA3759UbduXTRo0ABTpkzBuXPnNPZNSEhAnz59YGZmBi8vL+zbt0+97eVPdTdt2gQbGxscOnQIPj4+sLCwQFBQEGJjYzUe06JFC5ibm8PGxgZt27bFvXv3sGnTJsyfPx9hYWHqT+ef9wBIJBKsW7cOb731FszNzbFo0SIoFAoMHz4ctWvXhqmpKby9vbFy5UqN7M+HNs2fPx+Ojo6wsrLC6NGjIZcXPenp+Tn8/vvv8PLygomJCQIDA/HgwQON/fbu3YsmTZrAxMQEHh4emD9/PvLy8tTbJRIJfvzxxyKfOwDYt2+f+hgdO3bE5s2bNZ7PwnqOvvnmG7i7uxc4x+c6dOiAiRMnYsaMGbCzs4OLiwvmzZtX5PkCwKVLlxAVFYUePXoU2GZjYwMXFxc0bNgQ69atQ1ZWFo4cOYKff/4Z9vb2yMnJ0di/d+/eGDx4sPr2woUL4eTkBEtLS3z00UeYNWuWxjkplUp8/vnnqFGjBoyNjdG4cWMcPHhQvV0ul2P8+PFwdXWFiYkJatWqhS+//FK9PSUlBaNGjYKzszNMTEzQsGFD7N+/X7399OnTePPNN2Fqago3NzdMnDgRGRkZRT4XL/YcvOrYL7t48SK6dOkCBwcHWFtbo3379rh8+XKB9ot6XcTExKBjx44AAFtbW0gkEgwdOhRA/hCoN954AzY2NrC3t0fPnj0RFRWlbjcmJgYSiQQ7d+5E+/btYWJiou6Z+Omnn9CgQQMYGxvD1dUV48ePBwD166hPnz6QSCTq24W97opqAwBWrFiBRo0awdzcHG5ubhg7dizS09M1Ht+rVy+EhIRoZH4VFiVE+u6NjwG/gaJTUBVgkf0Mba05hKu8yCQyLG2/FHVsyrayWVJSEg4ePIhx48bB3LxgcfPyp+7z589Hv379cPXqVXTv3h2DBg1CUlJSke1nZmZi2bJl2LJlC/73v//h/v37mDZtGgAgLy8PvXv3Rvv27XH16lWcPXsWI0eOhEQiQf/+/TF16lQ0aNAAsbGxiI2NRf/+/dXtzps3D3369MG1a9cwbNgwKJVK1KhRA7t378bNmzcxd+5c/N///R927dqlkefYsWMIDw/HiRMnsH37dvz666+YP39+sc9RZmYmFi1ahJ9//hnBwcFISUnBe++9p95+6tQpfPDBB5g0aRJu3ryJ77//Hps2bcKiRYtK/NxFR0fjnXfeQe/evREWFoZRo0Zhzpw5xeYqqc2bN8Pc3Bznz5/HV199hc8//xxHjhwpcv9Tp06hbt26sLS0LLZdU1NTAPlv1t99910oFAqNQis+Ph5//vknhg0bBgDYunUrFi1ahCVLluDSpUuoWbMm1q1bp9HmypUrsXz5cixbtgxXr15FYGAg3nrrLURGRgIAVq1ahX379mHXrl2IiIjA1q1b1W+elUolunXrhuDgYPzyyy+4efMmFi9eDJksf0nyqKgoBAUFoW/fvrh69Sp27tyJ06dPa7yhLk5xxy5MWloahgwZgtOnT+PcuXPw8vJC9+7dkZaWprFfUa8LNzc37NmzBwAQERGB2NhYdaGdkZGBKVOmICQkBMeOHYNUKkWfPn0KzNOYNWsWJk2ahPDwcAQGBmLdunUYN24cRo4ciWvXrmHfvn3w9Mz/vXzx4kUA//aGPb/9suLaAACpVIpVq1bhxo0b2Lx5M44fP44ZM2ZotFGzZk04Ozvj1KlTJXjm83FWIhEBvVYCSXeBB+devS9RMYIys/G36BBVxPTm0/FG9TfK3M6dO3egUqlQr169Eu0/dOhQDBgwAADwxRdfYNWqVbhw4QKCgoIK3T83Nxffffcd6tTJL57Gjx+Pzz//HADw7NkzpKamomfPnurtPj7/XivJwsICBgYGcHFxKdDuwIED8eGHH2rc92JxUbt2bZw9exa7du1Cv3791PcbGRnhp59+gpmZGRo0aIDPP/8c06dPx4IFCyCVFv5ZbG5uLlavXo2WLfOv/7J582b4+PjgwoULaNGiBebPn49Zs2ZhyJAhAAAPDw8sWLAAM2bMwGeffVai5+7777+Ht7c3li5dCgDw9vbG9evXCxQ2r8PX11edw8vLC6tXr8axY8fQpUuXQve/d+8eqlWrVmybmZmZ+OSTTyCTydC+fXuYmppi4MCB2LhxI959910AwC+//IKaNWuiQ4cOAIBvv/0Ww4cPV3/f5s6di8OHD2t8ir5s2TLMnDlTXfQtWbIEf//9N7755husWbMG9+/fh5eXF9544w1IJBLUqlVL/dijR4/iwoULCA8PR926dQHkfy+e+/LLLzFo0CBMnjxZ/VysWrUK7du3x7p162BiUvxy+MUduzCdOnXSuP3DDz/AxsYGJ0+eRM+ePdX3F/e6sLOzAwA4OTlpfEDQt29fjbZ/+uknODo64ubNm2jYsKH6/smTJ+M///mP+vbChQsxdepUTJo0SX1f8+bNAQCOjo4A/u0NK0pxbTw/5nPu7u5YuHAhRo8ejbVr12q0U61aNdy7d6/I47yMPSVEBBgYAe9tBaxrik5COq7D3YswlfE6OGXVr24/DPIZVC5tqVSqUu3v6+ur/r+5uTmsrKwQHx9f5P5mZmbqggMAXF1d1fvb2dlh6NChCAwMRK9evbBy5UqNoV3FadasWYH71qxZg6ZNm8LR0REWFhb44YcfcP/+fY19/Pz8YGZmpr7dunVrpKenFxiO9SIDAwONN1316tWDjY0NwsPDAQBhYWH4/PPPYWFhof4aMWIEYmNjkZmZqX5ccc9dRESExjEAoEWLFiV5Kl7pxeMCmt+DwmRlZRX5Bn3AgAGwsLCApaUl9uzZgw0bNqjbHzFiBA4fPoxHjx4ByB/6NnToUPUco4iIiALn9OLtZ8+e4fHjx2jbtq3GPm3btlU/10OHDkVoaCi8vb0xceJEHD58WL1faGgoatSooS5IXhYWFoZNmzZpfJ8CAwOhVCoRHR1d5PPxXHHHLsyTJ08wYsQIeHl5wdraGlZWVkhPTy/wmiztzxQAREZGYsCAAfDw8ICVlZW6x+bltl/8OYmPj8fjx48REBDwynMtSknaOHr0KAICAlC9enVYWlpi8ODBSExM1PhZAPJ72l6+rzgsSogon7kDMHAHYFR8dz5RcczkGXjTihdSLItWrq0wu+XscmvPy8sLEokEt27dKtH+hoaGGrclEkmxS3sWtv+LhdDGjRtx9uxZtGnTBjt37kTdunULzGMpzMtDzXbs2IFp06Zh+PDhOHz4MEJDQ/Hhhx8WO1+kvKSnp2P+/PkIDQ1Vf127dg2RkZEab+5L+9y9TCqVFigiSzJ5ubTHdXBwQHJycqHbvv76a4SGhiIuLg5xcXHq3iEA8Pf3h5+fH37++WdcunQJN27cUM+BKC9NmjRBdHQ0FixYgKysLPTr1w/vvPMOgH+HkxUlPT0do0aN0vg+hYWFITIyUqNwfp1jF2bIkCEIDQ3FypUrcebMGYSGhsLe3r7Aa/J1Xhe9evVCUlIS1q9fj/Pnz+P8+fMAUKDtF39OXvX8lMSr2oiJiUHPnj3h6+uLPXv24NKlS1izZk2h2ZKSktS9MyXBooSI/uXcAOj7I1fkojIJemnCI5Wcu5U7lndYXq7XfLGzs0NgYCDWrFlT6ITfyliK1N/fH7Nnz8aZM2fQsGFDbNu2DUD+UCuFomSrtgUHB6NNmzYYO3Ys/P394enpWegk2rCwMGRlZalvnzt3DhYWFnBzcyuy7by8PISEhKhvR0REICUlRT3UrEmTJoiIiICnp2eBr6KGhL3M29tb4xgACozpd3R0RFxcnEZhEhoaWqL2S8Pf3x+3bt0qtBfNxcUFnp6eRb6Z/Oijj7Bp0yZs3LgRnTt31nhevb29C5zTi7etrKxQrVo1BAcHa+wTHByM+vXra+zXv39/rF+/Hjt37sSePXuQlJQEX19fPHz4ELdv3y40W5MmTXDz5s1Cv09GRiVbhKOoYxcmODgYEydORPfu3dWTwhMSEkp0nOee53rx5yAxMRERERH45JNPEBAQAB8fnyKLyBdZWlrC3d292OV9DQ0Ni/2Ze1Ubly5dglKpxPLly9GqVSvUrVsXjx8/LrBfdnY2oqKi4O/v/8rcz/GdBxFp8g4COhc/KZSoOO3uXoS5gdmrdyQN1sbWWBOwBlZGVuXe9po1a6BQKNCiRQvs2bMHkZGRCA8Px6pVq9C6detyP95z0dHRmD17Ns6ePYt79+7h8OHDiIyMVL/Zd3d3R3R0NEJDQ5GQkFBgZacXeXl5ISQkBIcOHcLt27fx6aefFjpRVy6XY/jw4bh58yYOHDiAzz77DOPHjy+2eDA0NMSECRNw/vx5XLp0CUOHDkWrVq3UQ4/mzp2Ln3/+GfPnz8eNGzcQHh6OHTt24JNPPinxczFq1CjcunULM2fOxO3bt7Fr1y6N1caA/JW0nj59iq+++gpRUVFYs2YN/vrrrxIfo6Q6duyI9PR03Lhxo9SPHThwIB4+fIj169erJ7g/N2HCBGzYsAGbN29GZGQkFi5ciKtXr2osIT19+nQsWbIEO3fuREREBGbNmoXQ0FD1/IUVK1Zg+/btuHXrFm7fvo3du3fDxcUFNjY2aN++Pdq1a4e+ffviyJEjiI6Oxl9//aVevWvmzJk4c+YMxo8fj9DQUERGRmLv3r0lnuhe3LEL4+XlhS1btiA8PBznz5/HoEGDSt1bUatWLUgkEuzfvx9Pnz5Feno6bG1tYW9vjx9++AF37tzB8ePHMWXKlBK1N2/ePCxfvhyrVq1CZGQkLl++jG+//Va9/XnBERcXV2ShU1wbnp6eyM3Nxbfffou7d+9iy5Yt+O677wq0ce7cORgbG5fq9wuLEiIqqO1EwP990SlIRxnnZaODZcVeebyqMZAa4OsOX6OmVcXM6/Lw8MDly5fRsWNHTJ06FQ0bNkSXLl1w7NixAqsjlSczMzPcunVLvQzxyJEjMW7cOIwaNQpA/mTeoKAgdOzYEY6Ojti+fXuRbY0aNQr/+c9/0L9/f7Rs2RKJiYkYO3Zsgf0CAgLg5eWFdu3aoX///njrrbdeuUSumZkZZs6ciYEDB6Jt27awsLDAzp071dsDAwOxf/9+HD58GM2bN0erVq3w9ddfv3Ii9Itq166N//73v/j111/h6+uLdevWqVffen6dDB8fH6xduxZr1qyBn58fLly4oF7JrDzZ29ujT58+pb64HZB/nZS+ffvCwsKiwJXlBw0ahNmzZ2PatGnqoVBDhw7VGOI2ceJETJkyBVOnTkWjRo1w8OBB9VLJQP4n9V999RWaNWuG5s2bIyYmBgcOHFAXlXv27EHz5s0xYMAA1K9fHzNmzFB/8u/r64uTJ0/i9u3bePPNN+Hv74+5c+e+clL/c6869ss2bNiA5ORkNGnSBIMHD8bEiRPh5ORUquezevXq6oUUnJ2d1QX0jh07cOnSJTRs2BAff/yxeoGEVxkyZAi++eYbrF27Fg0aNEDPnj3VK5sBwPLly3HkyBG4ubkV2YtRXBt+fn5YsWIFlixZgoYNG2Lr1q2FLpu8fft2DBo0SGN+16tIVKWdAUdE+kGRC+wYCEQWP9GPqDAnPNtigqLoicWk6fM2n5f5iu2UP1E5JSWlwBWri7Np0yZMnjy50q6o/aJFixbhu+++K3YSfkW5evUqunTpgqioKFhYWJTqsQEBAWjQoEGJLo7XpUsXuLi4YMuWLa8blXRMQkKCerhi7dq1S/w4LglMRIWTGQL9fgZ+eQe4d1p0GtIxbaMvwrKOJ9JyOb/kVWa1mMWCRE+sXbsWzZs3h729PYKDg7F06dISDy0qb76+vliyZAmio6PRqFGjEj0mOTkZJ06cwIkTJwos/wrkLyP83XffITAwEDKZDNu3b8fRo0eLvWYKVT0xMTFYu3ZtqQoSgEUJERXH0DR/Ra7NbwGPL796f6J/GCrkCLCojd+Tr4mOotWmNZtWbkv/kvZ7Ps8iKSkJNWvWxNSpUzF7dvmttFZapV05y9/fH8nJyViyZAm8vb0LbJdIJDhw4AAWLVqE7OxseHt7Y8+ePejcuXM5JSZd0KxZs0KX9H4VDt8iolfLTAI29QDib4pOQjok2KMlRqtKdk0KffRx048xrOGwV+9IRKQHONGdiF7NzA74YB/gUPCTMaKitIy5BFsja9ExtNJE/4ksSIiIXsCihIhKxsIRGPIHYO8lOgnpCANlHjqbV8xqUrpsrN9YjPAdIToGEZFWYVFCRCVn6ZxfmNjxit1UMkFJ8aIjaJWRviMxpvEY0TGIiLQOixIiKh0rV2DofsDeU3QS0gHN7l2Co4md6BhaYVjDYZjgP0F0DCIircSiRAfFxMRAIpEgNDS0zG1t2LABXbt2LXsoHbJp0yaNq7POmzcPjRs3FpanMh08eBCNGzeGUqksW0NW1YBhh4HqTcsnGFVZUpUSXUxriI4h3JD6Q/Bx049FxyAi0lqlLkri4uIwadIkeHp6wsTEBM7Ozmjbti3WrVuHzMzMcg3XoUMHTJ48uVzbrArc3NwQGxuLhg0blqmd7OxsfPrpp/jss8/U982bNw8SiQQSiQQGBgZwd3fHxx9/jPT0qnutgWnTpuHYsWOiY1SKoKAgGBoavtZVfAswtweG7Ae89KuopdILevpIdARhJJDg46YfY1rz8r8qNxFRVVKqouTu3bvw9/fH4cOH8cUXX+DKlSs4e/YsZsyYgf379+Po0aMVlZNeIJPJ4OLiAgODsl1m5r///S+srKzQtm1bjfsbNGiA2NhYxMTEYMmSJfjhhx8wderUMh1Lm1lYWMDe3l50jEozdOjQEl2Ft0SMzID3tgONeZ0FKlrjB6FwMXUUHaPSGUmN8FW7r7jKFhFRCZSqKBk7diwMDAwQEhKCfv36wcfHBx4eHnj77bfx559/olevXup9U1JS8NFHH8HR0RFWVlbo1KkTwsLC1NufD5nZsmUL3N3dYW1tjffeew9paWkA8t84nTx5EitXrlR/ch8TEwMAOHnyJFq0aAFjY2O4urpi1qxZyMvLU7edk5ODiRMnwsnJCSYmJnjjjTdw8eLFYs/N3d0dCxYswIABA2Bubo7q1atjzZo1GvuU9ZwAIC0tDYMGDYK5uTlcXV3x9ddfF+gRkkgk+P333zWObWNjg02bNgEoOHzrxIkTkEgkOHbsGJo1awYzMzO0adMGERERxZ7zjh07NL5nzxkYGMDFxQU1atRA//79MWjQIOzbtw8qlQqenp5YtmyZxv6hoaGQSCS4c+cOAODWrVt44403YGJigvr16+Po0aMFzunatWvo1KkTTE1NYW9vj5EjR2r0xpw4cQItWrSAubk5bGxs0LZtW9y7d0+9/Y8//kDz5s1hYmICBwcH9Onz79WQc3JyMG3aNFSvXh3m5uZo2bIlTpw4UeTz8PLwrVcd+0XPvxc7duxAmzZtYGJigoYNG+LkyZPqfRQKBYYPH47atWvD1NQU3t7eWLlypUY7Q4cORe/evTF//nz162v06NGQy+Xqfdzd3fHNN99oPK5x48aYN2+e+vaKFSvQqFEjmJubw83NDWPHji3Qy9WrVy+EhIQgKiqqyOekVGQGQO+1wBtTyqc9qnIkUCHQ2FV0jEplbWyN9V3XI6h2kOgoREQ6ocRFSWJiIg4fPoxx48bB3Ny80H0kEon6/++++y7i4+Px119/4dKlS2jSpAkCAgKQlJSk3icqKgq///479u/fj/379+PkyZNYvHgxAGDlypVo3bo1RowYgdjYWMTGxsLNzQ2PHj1C9+7d0bx5c4SFhWHdunXYsGEDFi5cqG53xowZ2LNnDzZv3ozLly/D09MTgYGBGscuzNKlS+Hn54crV65g1qxZmDRpEo4cOVJu5wQAU6ZMQXBwMPbt24cjR47g1KlTuHy5fK6UPWfOHCxfvhwhISEwMDDAsGHFfzp3+vTpEl1x09TUFHK5HBKJBMOGDcPGjRs1tm/cuBHt2rWDp6cnFAoFevfuDTMzM5w/fx4//PAD5syZo7F/RkYGAgMDYWtri4sXL2L37t04evQoxo8fDwDIy8tD79690b59e1y9ehVnz57FyJEj1a+vP//8E3369EH37t1x5coVHDt2DC1atFC3P378eJw9exY7duzA1atX8e677yIoKAiRkZGvPNdXHbso06dPx9SpU3HlyhW0bt0avXr1QmJiIgBAqVSiRo0a2L17N27evIm5c+fi//7v/7Br1y6NNo4dO4bw8HCcOHEC27dvx6+//or58+e/MvOLpFIpVq1ahRs3bmDz5s04fvw4ZsyYobFPzZo14ezsjFOnTpWq7Vfq/BnQbSkg4VQ1KigovvDCviqqYVEDv3T7BU2cm4iOQkSkM0o8/ufOnTtQqVTw9ta8eJqDgwOys7MBAOPGjcOSJUtw+vRpXLhwAfHx8TA2NgYALFu2DL///jv++9//YuTIkQDy36xt2rQJlpaWAIDBgwfj2LFjWLRoEaytrWFkZAQzMzO4uLioj7d27Vq4ublh9erVkEgkqFevHh4/foyZM2di7ty5yMrKwrp167Bp0yZ069YNALB+/XocOXIEGzZswPTp04s8x7Zt22LWrFkAgLp16yI4OBhff/01unTpUi7nlJaWhs2bN2Pbtm0ICAgAkP+Gvlq1aiX9NhRr0aJFaN++PQBg1qxZ6NGjB7Kzs2FiYlJg35SUFKSmpr7y2JcuXcK2bdvQqVMnAPmf6M+dOxcXLlxAixYtkJubi23btql7T44cOYKoqCicOHFC/X1btGgRunTpom5z27ZtyM7Oxs8//6wucFevXo1evXphyZIlMDQ0RGpqKnr27Ik6dfKXnvXx8dE4z/fee0/jDbufnx8A4P79+9i4cSPu37+vPrdp06bh4MGD2LhxI7744otiz/fZs2fFHrso48ePR9++fQEA69atw8GDB7FhwwbMmDEDhoaGGllr166Ns2fPYteuXejXr5/6fiMjI/z0008wMzNDgwYN8Pnnn2P69OlYsGABpNKSvdF/scfN3d0dCxcuxOjRo7F27VqN/apVq1Zk70+ZtByZfz2TX0cBipzyb590VsNH1+DWoAUeZMaJjlKhfB18sarTKtib6s+QUCKi8lDmjzQvXLiA0NBQNGjQADk5+W9CwsLCkJ6eDnt7e1hYWKi/oqOjNYaMuLu7q9+8A4Crqyvi44tf0z48PBytW7fW+OS6bdu2SE9Px8OHDxEVFYXc3FyNeRKGhoZo0aIFwsPDi227devWBW4/f0x5nNPdu3eRm5ur8am+tbV1gULvdfn6+mocF0CRz2dWVhYAFFqwXLt2DRYWFjA1NUWLFi3QunVrrF69GkD+m9kePXrgp59+ApA/jConJwfvvvsuACAiIgJubm4aheSL5wvkfw/9/Pw0etzatm0LpVKJiIgI2NnZYejQoQgMDESvXr2wcuVKxMbGqvcNDQ1VF3WFZVcoFKhbt67G9+nkyZMlGq70qmMX5cXXjoGBAZo1a6bxeluzZg2aNm0KR0dHWFhY4IcffsD9+/c12vDz84OZmZlGm+np6Xjw4MErj//c0aNHERAQgOrVq8PS0hKDBw9GYmJigUUoTE1Ny31hCrUGfYD39wCmXAaWNAUaVe15JZ3cOmFD4AYWJEREr6HEPSWenp6QSCQF5il4eHgAyH+T81x6ejpcXV0LHcf/4lKshoaGGtskEknZlyqtIJV5ThKJBCqVSuO+3NzcVz7uxWM/L9qKOra9vT0kEgmSk5MLbPP29sa+fftgYGCAatWqwcjISGP7Rx99hMGDB+Prr7/Gxo0b0b9/f4030+Vh48aNmDhxIg4ePIidO3fik08+wZEjR9CqVSuN19rL0tPTIZPJcOnSJchkMo1tFhYWZT7269ixYwemTZuG5cuXo3Xr1rC0tMTSpUtx/vz5UrUjlUqLfV3ExMSgZ8+eGDNmDBYtWgQ7OzucPn0aw4cPh1wu1/geJSUlwdGxAt8g1n4TGHUS2DkYiA2tuOOQTgmKvYsfi/7x1Wnv+7yP6c2nQ8rhi0REr6XEvz3t7e3RpUsXrF69GhkZGcXu26RJE8TFxcHAwACenp4aXw4ODiUOZ2RkBIVCoXGfj48Pzp49q/HmLDg4GJaWlqhRowbq1KkDIyMjBAcHq7fn5ubi4sWLqF+/frHHO3fuXIHbz4fulMc5eXh4wNDQUGPSfWpqKm7fvq2xn6Ojo8an85GRkeX+qbaRkRHq16+PmzdvFrrN09MT7u7uBQoSAOjevTvMzc3Vw5RenLvi7e2NBw8e4MmTJ+r7Xl5kwMfHB2FhYRqvo+DgYEilUo1eI39/f8yePRtnzpxBw4YNsW3bNgD5PUJFLeHr7+8PhUKB+Pj4At+nF3tvXqWoYxflxddOXl4eLl26pH7tBAcHo02bNhg7diz8/f3h6elZaK9NWFiYugfreZsWFhZwc3MDUPB18ezZM0RHR6tvX7p0CUqlEsuXL0erVq1Qt25dPH78uMBxsrOzERUVBX9//xI+G6/JpiYw/DDQ5IOKPQ7pDO+4cNQ2ry46RrkykhphTss5mNliJgsSIqIyKNVv0LVr1yIvLw/NmjXDzp07ER4ejoiICPzyyy+4deuW+pPpzp07o3Xr1ujduzcOHz6MmJgYnDlzBnPmzEFISEiJj+fu7o7z588jJiYGCQkJUCqVGDt2LB48eIAJEybg1q1b2Lt3Lz777DNMmTIFUqkU5ubmGDNmDKZPn46DBw/i5s2bGDFiBDIzMzF8+PBijxccHIyvvvoKt2/fxpo1a7B7925MmjSp3M7J0tISQ4YMwfTp0/H333/jxo0bGD58OKRSqcZwtE6dOmH16tW4cuUKQkJCMHr06AI9MOUhMDAQp0+fLvXjZDIZhg4ditmzZ8PLy0tj6FKXLl1Qp04dDBkyBFevXkVwcDA++eQTAP/23gwaNAgmJiYYMmQIrl+/jr///hsTJkzA4MGD4ezsjOjoaMyePRtnz57FvXv3cPjwYURGRqrf5H/22WfYvn07PvvsM4SHh+PatWtYsmQJgPy5QIMGDcIHH3yAX3/9FdHR0bhw4QK+/PJL/Pnnn688t1cduyhr1qzBb7/9hlu3bmHcuHFITk5WF2teXl4ICQnBoUOHcPv2bXz66aeFrgYnl8sxfPhw3Lx5EwcOHMBnn32G8ePHq+eTdOrUCVu2bMGpU6dw7do1DBkyRKM3yNPTE7m5ufj2229x9+5dbNmyBd99912B45w7dw7GxsYFhitWCANj4K1vgbdWAwYFhwqS/gkyqDrD+mpa1sSW7lvwXr33REchItJ5pSpK6tSpgytXrqBz586YPXs2/Pz80KxZM3z77beYNm0aFixYACD/zeeBAwfQrl07fPjhh6hbty7ee+893Lt3D87OziU+3rRp0yCTyVC/fn04Ojri/v37qF69Og4cOIALFy7Az88Po0ePxvDhw9VvfAFg8eLF6Nu3LwYPHowmTZrgzp07OHToEGxtbYs93tSpUxESEgJ/f38sXLgQK1asQGBgYLme04oVK9C6dWv07NkTnTt3Rtu2beHj46Mxt2P58uVwc3PDm2++iYEDB2LatGnlPjwKAIYPH44DBw4gNTX1tR4rl8vx4Ycfatwvk8nw+++/Iz09Hc2bN8dHH32kXn3r+TmamZnh0KFDSEpKQvPmzfHOO+8gICBAPW/FzMwMt27dQt++fVG3bl2MHDkS48aNw6hRowDkX1Rz9+7d2LdvHxo3boxOnTrhwoUL6gwbN27EBx98gKlTp8Lb2xu9e/fGxYsXUbNmzVee16uOXZTFixdj8eLF8PPzw+nTp7Fv3z51D9qoUaPwn//8B/3790fLli2RmJiIsWPHFmgjICAAXl5eaNeuHfr374+33npLY7nf2bNno3379ujZsyd69OiB3r17qyfjA/lzUlasWIElS5agYcOG2Lp1K7788ssCx9m+fTsGDRpUIa+pIjUZDAw7lN97Qnot6PHtV++kA7q5d8OuXrtQ3774HngiIioZierlQep6yt3dHZMnT670K8hnZGSgevXqWL58+St7cirCu+++iyZNmmD27NmletypU6cQEBCABw8evLIoCw4OxhtvvIE7d+5ovImuCmJiYlC7dm1cuXJF41onpTV06FCkpKQUuD5NeUtISIC3tzdCQkJQu3btCj1WoTKTgF9HAneOvHpfqrL6NnoDt9Pvv3pHLWQiM8HMFjPxTt13REchIqpSynZJcCq1K1eu4NatW2jRogVSU1Px+eefAwDefvttIXmWLl2KP/74o8T75+Tk4OnTp5g3bx7efffdQguS3377DRYWFvDy8sKdO3cwadIktG3btsoVJLooJiYGa9euFVOQAICZHTBwF/C/r4CTSwCVdi5sQRUrSGoFXewvqW1dG8vaL0Nd27qioxARVTmclSfAsmXL4Ofnh86dOyMjIwOnTp0q1QIA5cnd3R0TJkwo8f7bt29HrVq1kJKSgq+++qrQfdLS0jBu3DjUq1cPQ4cORfPmzbF3797yikxl0KxZM/Tv319sCKkU6DALGPIHYOsuNgsJEfSw4AIb2u6tOm9hR48dLEiIiCoIh28RkTjyDODIXODiBgD8VaRP3vNrjxvPol+9o2CmBqaY03IO3vYU05tNRKQv2FNCROIYmQM9lgND9nESvJ4JUlXiQguvqZlzM+zquYsFCRFRJWBPCRFph5x04PAnwKWNopNQJYi1dUOgjRQqLewhsza2xtSmU9Hbs7fGcu1ERFRxWJQQkXaJOg7smwikPhCdhCrYYL+OCH1W8EKiInWv3R0zms+Avam96ChERHqFw7eISLvU6QSMOQM0GQKAn1JXZUFKY9ER1GpY1MB3nb/DknZLWJAQEQnAnhIi0l4PQ4CDs4CHF0UnoQrw1MoFnR1MoBS4NLSBxACDGwzGWL+xMDEwefUDiIioQrAoISLtplIB13YDR+cBzx6JTkPlbFjjAFxMjRRy7EYOjfBZ68/gbect5PhERPQvXjyRiLSbRAL49gPq9QSCV+Z/5WWJTkXlJChPhsruB3MydcLoxqPR16svpBKOYiYi0gbsKSEi3ZL6EDjyGXD9v6KTUDlIMndAgLMV8lR5FX4sG2MbDG84HO/Ve49DtYiItAyLEiLSTQ8uAH/NBB5fFp2EymiUfxecSYmosPbNDMwwuP5gDG0wFBZGFhV2HCIien0sSohId6lUQMQB4H/LWJzosN/qd8bcrNvl3q6R1Aj9vPthhO8I2JnYlXv7RERUfliUEFHVcOdYfnFy/4zoJFRKqaY26FjNHrnK3HJpTyaR4a0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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "\n", - "# plotting a histogram\n", - "species_counts = df[\"species\"].value_counts()\n", - "plt.pie(species_counts, labels=species_counts.index, autopct='%1.1f%%')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Pandas interoperability\n", - "\n", - "BigQuery DataFrames can be converted from and to Pandas DataFrame with `to_pandas` and `read_pandas` respectively.\n", - "This could be handy to take advantage of the capabilities of the two systems.\n", - "\n", - "> Note: `to_pandas` converts the BigQuery DataFrame to Pandas DataFrame by bringing all the data in memory, which would be an issue\n", - "for large data, as your machine may not have enough memory to accommodate that." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "We have a dataframe of \n", - "\n", - "\n", - "We have a dataframe of \n", - "\n", - "\n", - "We have a dataframe of \n", - "\n" - ] - } - ], - "source": [ - "def print_type(df):\n", - " print(f\"\\nWe have a dataframe of {type(df)}\\n\")\n", - "\n", - "# The original bigframes dataframe\n", - "cur_df = df\n", - "print_type(cur_df)\n", - "\n", - "# Convert to pandas dataframe\n", - "cur_df = cur_df.to_pandas()\n", - "print_type(cur_df)\n", - "\n", - "# Convert back to bigframes dataframe\n", - "cur_df = bpd.read_pandas(cur_df)\n", - "print_type(cur_df)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Machine Learning with BigQuery DataFrames" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Clean and prepare data\n", - "\n", - "We're are going to start with supervised learning, where a Linear Regression model will learn to predict the body mass (output variable `y`) using input features such as flipper length, sex, species, and more (features `X`)." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "# Drop any rows that has missing (NA) values\n", - "df = df.dropna()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Part of preparing data for a machine learning task is splitting it into subsets for training and testing to ensure that the solution is not overfitting. By default, BQML will automatically manage splitting the data for you. However, BQML also supports manually splitting out your training data.\n", - "\n", - "Performing a manual data split can be done with `bigframes.ml.model_selection.train_test_split` like so:" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - " df_train shape: (267, 7)\n", - " df_test shape: (67, 7)\n", - "\n" - ] - } - ], - "source": [ - "from bigframes.ml.model_selection import train_test_split\n", - "\n", - "\n", - "# This will split df into test and training sets, with 20% of the rows in the test set,\n", - "# and the rest in the training set\n", - "df_train, df_test = train_test_split(df, test_size=0.2)\n", - "\n", - "# Show the shape of the data after the split\n", - "print(f\"\"\"\n", - " df_train shape: {df_train.shape}\n", - " df_test shape: {df_test.shape}\n", - "\"\"\")" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - " X_train shape: (267, 6)\n", - " X_test shape: (67, 7)\n", - " y_train shape: (267, 1)\n", - " y_test shape: (67, 1)\n", - "\n" - ] - } - ], - "source": [ - "# Isolate input features and output variable into DataFrames\n", - "X_train = df_train[[\n", - " 'island',\n", - " 'culmen_length_mm',\n", - " 'culmen_depth_mm',\n", - " 'flipper_length_mm',\n", - " 'sex',\n", - " 'species',\n", - "]]\n", - "y_train = df_train[['body_mass_g']]\n", - "\n", - "X_test = df_test[[\n", - " 'island',\n", - " 'culmen_length_mm',\n", - " 'culmen_depth_mm',\n", - " 'flipper_length_mm',\n", - " 'sex',\n", - " 'species',\n", - " # Include the actual body_mass_g so that we can compare with the predicted\n", - " # without a join.\n", - " 'body_mass_g'\n", - "]]\n", - "y_test = df_test[['body_mass_g']]\n", - "\n", - "# Print the shapes of features and label\n", - "print(f\"\"\"\n", - " X_train shape: {X_train.shape}\n", - " X_test shape: {X_test.shape}\n", - " y_train shape: {y_train.shape}\n", - " y_test shape: {y_test.shape}\n", - "\"\"\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Define pipeline\n", - "\n", - "This step is subjective to the problem. Although a model can be directly trained on the original data, it is often useful to apply some preprocessing to the original data.\n", - "In this example we want to apply a [`ColumnTransformer`](https://cloud.google.com/python/docs/reference/bigframes/latest/bigframes.ml.compose.ColumnTransformer) in which we apply [`OneHotEncoder`](https://cloud.google.com/python/docs/reference/bigframes/latest/bigframes.ml.preprocessing.OneHotEncoder) to the category features and [`StandardScaler`](https://cloud.google.com/python/docs/reference/bigframes/latest/bigframes.ml.preprocessing.StandardScaler) to the numeric features." - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Pipeline(steps=[('preproc',\n", - " ColumnTransformer(transformers=[('onehot', OneHotEncoder(),\n", - " ['island', 'species', 'sex']),\n", - " ('scaler', StandardScaler(),\n", - " ['culmen_depth_mm',\n", - " 'culmen_length_mm',\n", - " 'flipper_length_mm'])])),\n", - " ('linreg', LinearRegression(fit_intercept=False))])" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from bigframes.ml.linear_model import LinearRegression\n", - "from bigframes.ml.pipeline import Pipeline\n", - "from bigframes.ml.compose import ColumnTransformer\n", - "from bigframes.ml.preprocessing import StandardScaler, OneHotEncoder\n", - "\n", - "preprocessing = ColumnTransformer([\n", - " (\"onehot\", OneHotEncoder(), [\"island\", \"species\", \"sex\"]),\n", - " (\"scaler\", StandardScaler(), [\"culmen_depth_mm\", \"culmen_length_mm\", \"flipper_length_mm\"]),\n", - "])\n", - "\n", - "model = LinearRegression(fit_intercept=False)\n", - "\n", - "pipeline = Pipeline([\n", - " ('preproc', preprocessing),\n", - " ('linreg', model)\n", - "])\n", - "\n", - "# View the pipeline\n", - "pipeline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Train and Predict\n", - "\n", - "Supervised learning is when we train a model on input-output pairs, and then ask it to predict the output for new inputs. An example of such a predictor is `bigframes.ml.linear_models.LinearRegression`." - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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33943.436439Biscoe40.118.9188.0MALEAdelie Penguin (Pygoscelis adeliae)4300.0
43986.662895Biscoe41.418.6191.0MALEAdelie Penguin (Pygoscelis adeliae)3700.0
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" - ], - "text/plain": [ - " predicted_body_mass_g island culmen_length_mm culmen_depth_mm \\\n", - "0 3271.548077 Biscoe 37.9 18.6 \n", - "1 3224.661209 Biscoe 37.7 16.0 \n", - "2 3395.403541 Biscoe 34.5 18.1 \n", - "3 3943.436439 Biscoe 40.1 18.9 \n", - "4 3986.662895 Biscoe 41.4 18.6 \n", - "\n", - " flipper_length_mm sex species body_mass_g \n", - "0 172.0 FEMALE Adelie Penguin (Pygoscelis adeliae) 3150.0 \n", - "1 183.0 FEMALE Adelie Penguin (Pygoscelis adeliae) 3075.0 \n", - "2 187.0 FEMALE Adelie Penguin (Pygoscelis adeliae) 2900.0 \n", - "3 188.0 MALE Adelie Penguin (Pygoscelis adeliae) 4300.0 \n", - "4 191.0 MALE Adelie Penguin (Pygoscelis adeliae) 3700.0 " - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Learn from the training data how to predict output y\n", - "pipeline.fit(X_train, y_train)\n", - "\n", - "# Predict y for the test data\n", - "y_pred = pipeline.predict(X_test)\n", - "\n", - "# View predictions preview\n", - "y_pred.peek()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Evaluate results\n", - "\n", - "Some models include a convenient `.score(X, y)` method for evaulation with a preset accuracy metric:" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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mean_absolute_errormean_squared_errormean_squared_log_errormedian_absolute_errorr2_scoreexplained_variance
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[1 rows x 6 columns in total]" - ], - "text/plain": [ - " mean_absolute_error mean_squared_error mean_squared_log_error \\\n", - " 231.914252 78873.600421 0.005172 \n", - "\n", - " median_absolute_error r2_score explained_variance \n", - " 178.724985 0.890549 0.890566 \n", - "\n", - "[1 rows x 6 columns]" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pipeline.score(X_test.drop(columns=[\"body_mass_g\"]), y_test)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For a more general approach, the library `bigframes.ml.metrics` is provided:" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "np.float64(0.8905492944632485)" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from bigframes.ml.metrics import r2_score\n", - "\n", - "r2_score(y_pred['body_mass_g'], y_pred[\"predicted_body_mass_g\"])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Generative AI with BigQuery DataFrames\n", - "\n", - "BigQuery DataFrames integration with the Large Language Models (LLM) supported by BigQuery ML. Check out the [`bigframes.ml.llm`](https://cloud.google.com/python/docs/reference/bigframes/latest/bigframes.ml.llm) module for all the available models.\n", - "\n", - "To use this feature you would need to have a few additional APIs enabled and IAM roles configured. Please make sure of that by following [this documentation](https://cloud.google.com/bigquery/docs/use-bigquery-dataframes#remote-models) and then uncomment the code in the following cells to try out the integration with Gemini." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create prompts\n", - "\n", - "A \"prompt\" text column can be initialized either directly or via the pandas APIs. For simplicity let's use a direct initialization here." - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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0What is BigQuery?
1What is BQML?
2What is BigQuery DataFrames?
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[3 rows x 1 columns in total]" - ], - "text/plain": [ - " prompt\n", - "0 What is BigQuery?\n", - "1 What is BQML?\n", - "2 What is BigQuery DataFrames?\n", - "\n", - "[3 rows x 1 columns]" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df = bpd.DataFrame(\n", - " {\n", - " \"prompt\": [\"What is BigQuery?\", \"What is BQML?\", \"What is BigQuery DataFrames?\"],\n", - " })\n", - "df" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Generate responses\n", - "\n", - "Here we will use the [`GeminiTextGenerator`](https://cloud.google.com/python/docs/reference/bigframes/latest/bigframes.ml.llm.GeminiTextGenerator) LLM to answer the questions. Read the [GeminiTextGenerator API documentation](https://cloud.google.com/python/docs/reference/bigframes/latest/bigframes.ml.llm.GeminiTextGenerator) for all the model versions supported via the `model_name` param." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# from bigframes.ml.llm import GeminiTextGenerator\n", - "\n", - "# model = GeminiTextGenerator(model_name=\"gemini-2.5-flash\")\n", - "\n", - "# pred = model.predict(df)\n", - "# pred" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's print the full text response for the question \"What is BigQuery DataFrames?\"." - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "# import IPython.display\n", - "\n", - "# IPython.display.Markdown(pred.loc[2][\"ml_generate_text_llm_result\"])" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "TpV-iwP9qw9c" - }, - "source": [ - "## Cleaning up\n", - "\n", - "To clean up all Google Cloud resources used in this project, you can [delete the Google Cloud\n", - "project](https://cloud.google.com/resource-manager/docs/creating-managing-projects#shutting_down_projects) you used for the tutorial.\n", - "\n", - "To remove any temporary cloud artifacts (inclusing BQ tables) created in the current BigQuery DataFrames session, simply call `close_session`." - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "# Delete the temporary cloud artifacts created during the bigframes session \n", - "bpd.close_session()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "wCsmt0IwFkDy" - }, - "source": [ - "## Summary and next steps\n", - "\n", - "1. You created BigQuery DataFrames objects, and inspected and manipulated data with pandas APIs at BigQuery scale and speed.\n", - "\n", - "1. You also created ML model from a DataFrame and used them to run predictions on another DataFrame.\n", - "\n", - "1. You got access to Google's state-of-the-art Gemini LLM through simple pythonic API.\n", - "\n", - "Learn more about BigQuery DataFrames in the documentation [BigQuery DataFrames](https://cloud.google.com/bigquery/docs/bigquery-dataframes-introduction) and its [API reference](https://cloud.google.com/python/docs/reference/bigframes/latest).\n", - "\n", - "Also, find more sample notebooks in the [GitHub repo](https://github.com/googleapis/python-bigquery-dataframes/tree/main/notebooks), including the [pypi.ipynb](https://github.com/googleapis/python-bigquery-dataframes/blob/main/notebooks/dataframes/pypi.ipynb) that processes 400+ TB data at the cost and efficiency close to direct SQL by taking advantage of the [partial ordering](https://cloud.google.com/python/docs/reference/bigframes/latest/bigframes._config.bigquery_options.BigQueryOptions#bigframes__config_bigquery_options_BigQueryOptions_ordering_mode) mode." - ] - } - ], - "metadata": { - "colab": { - "provenance": [], - "toc_visible": true - }, - "kernelspec": { - "display_name": "venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.16" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/notebooks/getting_started/getting_started_bq_dataframes.ipynb b/notebooks/getting_started/getting_started_bq_dataframes.ipynb index f9fb950c534..6cc6acc9935 100644 --- a/notebooks/getting_started/getting_started_bq_dataframes.ipynb +++ b/notebooks/getting_started/getting_started_bq_dataframes.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { "id": "ur8xi4C7S06n" }, @@ -29,18 +29,18 @@ "id": "JAPoU8Sm5E6e" }, "source": [ - "# BigQuery DataFrames Quickstart Guide\n", + "# Get started with BigQuery DataFrames\n", "\n", "\n", "\n", " \n", " \n", @@ -49,13 +49,7 @@ " \"Vertex\n", " Open in Vertex AI Workbench\n", " \n", - " \n", - " \n", + " \n", "
\n", " \n", - " \"Colab Run in Colab\n", + " \"Colab Run in Colab\n", " \n", " \n", " \n", - " \"GitHub\n", + " \"GitHub\n", " View on GitHub\n", " \n", " \n", - " \n", - " \"BQ\n", - " Open in BQ Studio\n", - " \n", - "
" ] }, @@ -67,7 +61,7 @@ "source": [ "**_NOTE_**: This notebook has been tested in the following environment:\n", "\n", - "* Python version = 3.12" + "* Python version = 3.10" ] }, { @@ -78,17 +72,31 @@ "source": [ "## Overview\n", "\n", - "In this guide, you learn how to install BigQuery DataFrames, load data into a BigQuery DataFrames DataFrame, and inspect and manipulate the data using pandas and a custom Python function, running at BigQuery scale.\n", + "Use this notebook to get started with BigQuery DataFrames, including setup, installation, and basic tutorials.\n", + "\n", + "BigQuery DataFrames provides a Pythonic DataFrame and machine learning (ML) API powered by the BigQuery engine.\n", + "\n", + "* `bigframes.pandas` provides a pandas-like API for analytics.\n", + "* `bigframes.ml` provides a scikit-learn-like API for ML.\n", + "\n", + "Learn more about [BigQuery DataFrames](https://cloud.google.com/python/docs/reference/bigframes/latest)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "d975e698c9a4" + }, + "source": [ + "### Objective\n", + "\n", + "In this tutorial, you learn how to install BigQuery DataFrames, load data into a BigQuery DataFrames DataFrame, and inspect and manipulate the data using pandas and a custom Python function, running at BigQuery scale.\n", "\n", "The steps include:\n", "\n", - "- Installing the BigQuery Dataframes package.\n", - "- Setting up the environment.\n", "- Creating a BigQuery DataFrames DataFrame: Access data from a local CSV to create a BigQuery DataFrames DataFrame.\n", "- Inspecting and manipulating data: Use pandas to perform data cleaning and preparation on the DataFrame.\n", - "- Deploying a custom function: Deploy a [remote function ](https://cloud.google.com/bigquery/docs/remote-functions)that runs a scalar Python function at BigQuery scale.\n", - "\n", - "You can learn more about [BigQuery DataFrames](https://cloud.google.com/python/docs/reference/bigframes/latest)." + "- Deploying a custom function: Deploy a [remote function ](https://cloud.google.com/bigquery/docs/remote-functions)that runs a scalar Python function at BigQuery scale." ] }, { @@ -137,112 +145,11 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": { "id": "mfPoOwPLGpSr" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - 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] - } - ], + "outputs": [], "source": [ "!pip install bigframes" ] @@ -260,7 +167,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": { "id": "f200f10a1da3" }, @@ -279,7 +186,7 @@ "id": "BF1j6f9HApxa" }, "source": [ - "## Environment setup\n", + "## Before you begin\n", "\n", "Complete the tasks in this section to set up your environment." ] @@ -333,7 +240,10 @@ }, "outputs": [], "source": [ - "PROJECT_ID = \"\" # @param {type:\"string\"}" + "PROJECT_ID = \"\" # @param {type:\"string\"}\n", + "\n", + "# Set the project id\n", + "! gcloud config set project {PROJECT_ID}" ] }, { @@ -349,7 +259,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": { "id": "eF-Twtc4XGem" }, @@ -393,7 +303,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": { "id": "254614fa0c46" }, @@ -415,7 +325,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": { "id": "603adbbf0532" }, @@ -436,13 +346,13 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": { "id": "PyQmSRbKA8r-" }, "outputs": [], "source": [ - "import bigframes.pandas as bpd" + "import bigframes.pandas as bf" ] }, { @@ -457,27 +367,14 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": { "id": "NPPMuw2PXGeo" }, "outputs": [], "source": [ - "# Note: The project option is not required in all environments.\n", - "# On BigQuery Studio, the project ID is automatically detected.\n", - "bpd.options.bigquery.project = PROJECT_ID\n", - "\n", - "# Note: The location option is not required.\n", - "# It defaults to the location of the first table or query\n", - "# passed to read_gbq(). For APIs where a location can't be\n", - "# auto-detected, the location defaults to the \"US\" location.\n", - "bpd.options.bigquery.location = REGION\n", - "\n", - "# Note: By default BigQuery DataFrames emits out BigQuery job metadata via a\n", - "# progress bar. But in this notebook let's disable the progress bar to keep the\n", - "# experience less verbose. If you would like the default behavior, please\n", - "# comment out the following expression. \n", - "bpd.options.display.progress_bar = None" + "bf.options.bigquery.project = PROJECT_ID\n", + "bf.options.bigquery.location = REGION" ] }, { @@ -486,7 +383,7 @@ "id": "pDfrKwMKE_dK" }, "source": [ - "If you want to reset the location of the created DataFrame or Series objects, reset the session by executing `bpd.close_session()`. After that, you can reuse `bpd.options.bigquery.location` to specify another location." + "If you want to reset the location of the created DataFrame or Series objects, reset the session by executing `bf.close_session()`. After that, you can reuse `bf.options.bigquery.location` to specify another location." ] }, { @@ -530,7 +427,7 @@ }, "outputs": [], "source": [ - "# bq_df_sample = bpd.read_gbq(\"bigquery-samples.wikipedia_pageviews.200809h\")" + "# bq_df_sample = bf.read_gbq(\"bigquery-samples.wikipedia_pageviews.200809h\")" ] }, { @@ -550,7 +447,7 @@ "source": [ "Uncomment and run the following cell to see pandas in action over your new BigQuery DataFrames DataFrame.\n", "\n", - "This code uses regex to filter the DataFrame to include only rows with Wikipedia page titles containing the word \"Google\", sums the total views by page title, and then returns the top 10 results." + "This code uses regex to filter the DataFrame to include only rows with Wikipedia page titles containing the word \"Google\", sums the total views by page title, and then returns the top 100 results." ] }, { @@ -559,114 +456,12 @@ "metadata": { "id": "XfGq5apK-D_e" }, - "outputs": [ - { - "data": { - "text/html": [ - "
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titleviews
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27251Google_Android99450
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31825Google_chrome78399
30204Google_Street_View71580
40798Image:Google_Chrome.png60746
35222Googleplex53848
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[10 rows x 2 columns in total]" - ], - "text/plain": [ - " title views\n", - "21911 Google 1414560\n", - "27669 Google_Chrome 962482\n", - "28394 Google_Earth 383566\n", - "29184 Google_Maps 205089\n", - "27251 Google_Android 99450\n", - "33900 Google_search 97665\n", - "31825 Google_chrome 78399\n", - "30204 Google_Street_View 71580\n", - "40798 Image:Google_Chrome.png 60746\n", - "35222 Googleplex 53848\n", - "\n", - "[10 rows x 2 columns]" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# bq_df_sample[bq_df_sample.title.str.contains(r\"[Gg]oogle\")]\\\n", - "# .groupby(['title'], as_index=False)['views'].sum(numeric_only=True)\\\n", - "# .sort_values('views', ascending=False)\\\n", - "# .head(10)" + "# .groupby(['title'], as_index=False)['views'].sum(numeric_only=True)\\\n", + "# .sort_values('views', ascending=False)\\\n", + "# .head(100)" ] }, { @@ -731,16 +526,12 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": { "id": "SvyXzkRl783u" }, "outputs": [], "source": [ - "# BigQuery DataFrames can read directly from GCS.\n", - "fn = 'gs://cloud-samples-data/vertex-ai/bigframe/penguins.csv'\n", - "\n", - "# Or from a local file.\n", "# fn = 'penguins.csv'" ] }, @@ -757,7 +548,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": { "id": "3QHQYlnoBLpt" }, @@ -783,15 +574,13 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": { "id": "EDAaIwHpQCDZ" }, "outputs": [], "source": [ - "# If order is not important, use the \"bigquery\" engine to\n", - "# allow BigQuery DataFrames to read directly from GCS.\n", - "df_from_local = bpd.read_csv(fn, engine=\"bigquery\")" + "df_from_local = bf.read_csv(fn)" ] }, { @@ -800,124 +589,18 @@ "id": "U-RVfNCu_h_h" }, "source": [ - "Take a look at the rows randomly sampled from the DataFrame:" + "Take a look at the first few rows of the DataFrame:" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": { "id": "_gPD0Zn1Stdb" }, - "outputs": [ - { - "data": { - "text/html": [ - "
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speciesislandculmen_length_mmculmen_depth_mmflipper_length_mmbody_mass_gsex
41Gentoo penguin (Pygoscelis papua)Biscoe49.816.82305700MALE
73Gentoo penguin (Pygoscelis papua)Biscoe46.816.12155500MALE
75Gentoo penguin (Pygoscelis papua)Biscoe49.616.02255700MALE
93Adelie Penguin (Pygoscelis adeliae)Biscoe35.516.21953350FEMALE
299Chinstrap penguin (Pygoscelis antarctica)Dream52.018.12014050MALE
\n", - "
" - ], - "text/plain": [ - " species island culmen_length_mm \\\n", - "41 Gentoo penguin (Pygoscelis papua) Biscoe 49.8 \n", - "73 Gentoo penguin (Pygoscelis papua) Biscoe 46.8 \n", - "75 Gentoo penguin (Pygoscelis papua) Biscoe 49.6 \n", - "93 Adelie Penguin (Pygoscelis adeliae) Biscoe 35.5 \n", - "299 Chinstrap penguin (Pygoscelis antarctica) Dream 52.0 \n", - "\n", - " culmen_depth_mm flipper_length_mm body_mass_g sex \n", - "41 16.8 230 5700 MALE \n", - "73 16.1 215 5500 MALE \n", - "75 16.0 225 5700 MALE \n", - "93 16.2 195 3350 FEMALE \n", - "299 18.1 201 4050 MALE " - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_from_local.peek()" + "outputs": [], + "source": [ + "df_from_local.head()" ] }, { @@ -942,19 +625,11 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": { "id": "ZSP7gt13QrQt" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Dataset birds created.\n" - ] - } - ], + "outputs": [], "source": [ "DATASET_ID = \"birds\" # @param {type:\"string\"}\n", "\n", @@ -977,27 +652,13 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": { "id": "oP1NIAmUBjop" }, - "outputs": [ - { - "data": { - "text/plain": [ - "'bigframes-dev.birds.penguins'" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "df_from_local.to_gbq(\n", - " f\"{PROJECT_ID}.{DATASET_ID}.penguins\",\n", - " if_exists=\"replace\",\n", - ")" + "df_from_local.to_gbq(PROJECT_ID + \".\" + DATASET_ID + \".penguins\")" ] }, { @@ -1021,121 +682,15 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": { "id": "IBuo-d6dWfsA" }, - "outputs": [ - { - "data": { - "text/html": [ - "
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speciesislandculmen_length_mmculmen_depth_mmflipper_length_mmbody_mass_gsex
79Gentoo penguin (Pygoscelis papua)Biscoe43.314.02084575FEMALE
118Adelie Penguin (Pygoscelis adeliae)Biscoe40.618.61833550MALE
213Adelie Penguin (Pygoscelis adeliae)Torgersen42.119.11954000MALE
315Adelie Penguin (Pygoscelis adeliae)Torgersen38.719.01953450FEMALE
338Chinstrap penguin (Pygoscelis antarctica)Dream40.916.61873200FEMALE
\n", - "
" - ], - "text/plain": [ - " species island culmen_length_mm \\\n", - "79 Gentoo penguin (Pygoscelis papua) Biscoe 43.3 \n", - "118 Adelie Penguin (Pygoscelis adeliae) Biscoe 40.6 \n", - "213 Adelie Penguin (Pygoscelis adeliae) Torgersen 42.1 \n", - "315 Adelie Penguin (Pygoscelis adeliae) Torgersen 38.7 \n", - "338 Chinstrap penguin (Pygoscelis antarctica) Dream 40.9 \n", - "\n", - " culmen_depth_mm flipper_length_mm body_mass_g sex \n", - "79 14.0 208 4575 FEMALE \n", - "118 18.6 183 3550 MALE \n", - "213 19.1 195 4000 MALE \n", - "315 19.0 195 3450 FEMALE \n", - "338 16.6 187 3200 FEMALE " - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "query_or_table = f\"{PROJECT_ID}.{DATASET_ID}.penguins\"\n", - "bq_df = bpd.read_gbq(query_or_table)\n", - "bq_df.peek()" + "outputs": [], + "source": [ + "query_or_table = f\"\"\"{PROJECT_ID}.{DATASET_ID}.penguins\"\"\"\n", + "bq_df = bf.read_gbq(query_or_table)\n", + "bq_df.head()" ] }, { @@ -1169,34 +724,13 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": { "id": "6i6HkFJZa8na" }, - "outputs": [ - { - "data": { - "text/plain": [ - "133 \n", - "279 3150\n", - "34 3400\n", - "96 3600\n", - "208 3950\n", - "18 3800\n", - "64 2850\n", - "310 3175\n", - "118 3550\n", - "2 3075\n", - "Name: body_mass_g, dtype: Int64" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bq_df[\"body_mass_g\"].peek(10)" + "outputs": [], + "source": [ + "bq_df[\"body_mass_g\"].head(10)" ] }, { @@ -1210,19 +744,11 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": { "id": "YKwCW7Nsavap" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "average_body_mass: 4201.754385964914\n" - ] - } - ], + "outputs": [], "source": [ "average_body_mass = bq_df[\"body_mass_g\"].mean()\n", "print(f\"average_body_mass: {average_body_mass}\")" @@ -1239,74 +765,13 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "metadata": { "id": "4PyKMR61-Mjy" }, - "outputs": [ - { - "data": { - "text/html": [ - "
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body_mass_g
species
Adelie Penguin (Pygoscelis adeliae)3700.662252
Chinstrap penguin (Pygoscelis antarctica)3733.088235
Gentoo penguin (Pygoscelis papua)5076.01626
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3 rows × 1 columns

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[3 rows x 1 columns in total]" - ], - "text/plain": [ - " body_mass_g\n", - "species \n", - "Adelie Penguin (Pygoscelis adeliae) 3700.662252\n", - "Chinstrap penguin (Pygoscelis antarctica) 3733.088235\n", - "Gentoo penguin (Pygoscelis papua) 5076.01626\n", - "\n", - "[3 rows x 1 columns]" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bq_df[[\"species\", \"body_mass_g\"]].groupby(by=bq_df[\"species\"]).mean(numeric_only=True)" + "outputs": [], + "source": [ + "bq_df[\"species\", \"body_mass_g\"].groupby(by=bq_df[\"species\"]).mean(numeric_only=True).head()" ] }, { @@ -1335,9 +800,9 @@ "id": "zjw8toUbHuRD" }, "source": [ - "Running the cell below creates a custom function using the `remote_function` method. This function categorizes a value into one of two buckets: >= 3500 or <3500.\n", + "Running the cell below creates a custom function using the `remote_function` method. This function categorizes a value into one of two buckets: >= 4000 or <4000.\n", "\n", - "> Note: Creating a function requires a [BigQuery connection](https://cloud.google.com/bigquery/docs/remote-functions#create_a_remote_function). This code assumes a pre-created connection named `bigframes-default-connection`. If\n", + "> Note: Creating a function requires a [BigQuery connection](https://cloud.google.com/bigquery/docs/remote-functions#create_a_remote_function). This code assumes a pre-created connection named `bigframes-rf-conn`. If\n", "the connection is not already created, BigQuery DataFrames attempts to create one assuming the [necessary APIs\n", "and IAM permissions](https://cloud.google.com/python/docs/reference/bigframes/latest/bigframes.pandas#bigframes_pandas_remote_function) are set up in the project.\n", "\n", @@ -1346,17 +811,17 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "metadata": { "id": "rSWTOG-vb2Fc" }, "outputs": [], "source": [ - "@bpd.remote_function(cloud_function_service_account=\"default\")\n", - "def get_bucket(num: float) -> str:\n", - " if not num: return \"NA\"\n", - " boundary = 3500\n", - " return \"at_or_above_3500\" if num >= boundary else \"below_3500\"" + "@bf.remote_function([float], str, bigquery_connection='bigframes-rf-conn')\n", + "def get_bucket(num):\n", + " if not num: return \"NA\"\n", + " boundary = 4000\n", + " return \"at_or_above_4000\" if num >= boundary else \"below_4000\"" ] }, { @@ -1372,20 +837,11 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "metadata": { "id": "6ejPXoyEQpWE" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Cloud Function Name projects/bigframes-dev/locations/us-central1/functions/bigframes-sessioncf7a5d-aa59468b9d6c757c1256e46c9f71ebe3\n", - "Remote Function Name bigframes-dev._63cfa399614a54153cc386c27d6c0c6fdb249f9e.bigframes_sessioncf7a5d_aa59468b9d6c757c1256e46c9f71ebe3\n" - ] - } - ], + "outputs": [], "source": [ "CLOUD_FUNCTION_NAME = format(get_bucket.bigframes_cloud_function)\n", "print(\"Cloud Function Name \" + CLOUD_FUNCTION_NAME)\n", @@ -1404,113 +860,14 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "metadata": { "id": "NxSd9WZFcIji" }, - "outputs": [ - { - "data": { - "text/html": [ - "
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body_mass_gbody_mass_bucket
133<NA>NA
2793150below_3500
343400below_3500
963600at_or_above_3500
2083950at_or_above_3500
183800at_or_above_3500
642850below_3500
3103175below_3500
1183550at_or_above_3500
23075below_3500
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" - ], - "text/plain": [ - " body_mass_g body_mass_bucket\n", - "133 NA\n", - "279 3150 below_3500\n", - "34 3400 below_3500\n", - "96 3600 at_or_above_3500\n", - "208 3950 at_or_above_3500\n", - "18 3800 at_or_above_3500\n", - "64 2850 below_3500\n", - "310 3175 below_3500\n", - "118 3550 at_or_above_3500\n", - "2 3075 below_3500" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "bq_df = bq_df.assign(body_mass_bucket=bq_df['body_mass_g'].apply(get_bucket))\n", - "bq_df[['body_mass_g', 'body_mass_bucket']].peek(10)" + "bq_df[['body_mass_g', 'body_mass_bucket']].head(10)" ] }, { @@ -1542,17 +899,7 @@ }, { "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "# Delete the temporary cloud artifacts created during the bigframes session \n", - "bpd.close_session()" - ] - }, - { - "cell_type": "code", - "execution_count": 27, + "execution_count": null, "metadata": { "id": "sx_vKniMq9ZX" }, @@ -1569,7 +916,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "metadata": { "id": "_dTCXvCxtPw9" }, @@ -1578,14 +925,14 @@ "# # Delete the BigQuery Connection\n", "# from google.cloud import bigquery_connection_v1 as bq_connection\n", "# client = bq_connection.ConnectionServiceClient()\n", - "# CONNECTION_ID = f\"projects/{PROJECT_ID}/locations/{REGION}/connections/bigframes-default-connection\"\n", + "# CONNECTION_ID = f\"projects/{PROJECT_ID}/locations/{REGION}/connections/bigframes-rf-conn\"\n", "# client.delete_connection(name=CONNECTION_ID)\n", "# print(\"Deleted connection '{}'.\".format(CONNECTION_ID))" ] }, { "cell_type": "code", - "execution_count": 29, + "execution_count": null, "metadata": { "id": "EDAIIfcpwNOF" }, @@ -1597,7 +944,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": null, "metadata": { "id": "QwumLUKmVpuH" }, @@ -1615,21 +962,8 @@ "toc_visible": true }, "kernelspec": { - "display_name": "venv", - "language": "python", + "display_name": "Python 3", "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.16" } }, "nbformat": 4, diff --git a/notebooks/getting_started/magics.ipynb b/notebooks/getting_started/magics.ipynb deleted file mode 100644 index 1f2cf7a409b..00000000000 --- a/notebooks/getting_started/magics.ipynb +++ /dev/null @@ -1,406 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "91edcf7b", - "metadata": {}, - "source": [ - "# %%bqsql cell magics\n", - "\n", - "The BigQuery DataFrames (aka BigFrames) package provides a `%%bqsql` cell magics for Jupyter environments.\n", - "\n", - "To use it, first activate the extension:" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "98cd0489", - "metadata": {}, - "outputs": [], - "source": [ - "%load_ext bigframes" - ] - }, - { - "cell_type": "markdown", - "id": "f18fdc63", - "metadata": {}, - "source": [ - "Now, use the magics by including SQL in the body." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "269c5862", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " Query processed 0 Bytes. [Job bigframes-dev:US.job_UVe7FsupxF3CbYuLcLT7fpw9dozg details]\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "1e2fb7b019754d31b11323a054f97f47", - "version_major": 2, - "version_minor": 1 - }, - "text/html": [ - "
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stategenderyearnamenumber
0HIF1999Ariana10
1HIF2002Jordyn10
2HIF2006Mya10
3HIF2010Jordyn10
4HIM1921Nobuo10
5HIM1925Ralph10
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8HIM1933Larry10
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[5552452 rows x 5 columns in total]" - ], - "text/plain": [ - "state gender year name number\n", - " HI F 1999 Ariana 10\n", - " HI F 2002 Jordyn 10\n", - " HI F 2006 Mya 10\n", - " HI F 2010 Jordyn 10\n", - " HI M 1921 Nobuo 10\n", - " HI M 1925 Ralph 10\n", - " HI M 1926 Hisao 10\n", - " HI M 1927 Moses 10\n", - " HI M 1933 Larry 10\n", - " HI M 1933 Alfredo 10\n", - "...\n", - "\n", - "[5552452 rows x 5 columns]" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "%%bqsql\n", - "SELECT * FROM `bigquery-public-data.usa_names.usa_1910_2013`" - ] - }, - { - "cell_type": "markdown", - "id": "8771e10f", - "metadata": {}, - "source": [ - "The output DataFrame can be saved to a variable." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "30bb6327", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " Query processed 0 Bytes. [Job bigframes-dev:US.c142adf3-cd95-42da-bbdc-c176b36b934f details]\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "%%bqsql mydf\n", - "SELECT * FROM `bigquery-public-data.usa_names.usa_1910_2013`" - ] - }, - { - "cell_type": "markdown", - "id": "533e2e9e", - "metadata": {}, - "source": [ - "You can chain cells together using format strings. DataFrame objects are automatically turned into table expressions." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "6a8a8123", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " Query processed 88.1 MB in a moment of slot time.\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "c4889de9296440428de90defb5c58070", - "version_major": 2, - "version_minor": 1 - }, - "text/html": [ - "
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total_countname
0304036Tracy
1293876Travis
2203784Troy
3150127Trevor
496397Tristan
589996Tracey
665546Trinity
750112Traci
849657Trenton
945692Trent
\n", - "

10 rows × 2 columns

\n", - "
[238 rows x 2 columns in total]" - ], - "text/plain": [ - " total_count name\n", - "0 304036 Tracy\n", - "1 293876 Travis\n", - "2 203784 Troy\n", - "3 150127 Trevor\n", - "4 96397 Tristan\n", - "5 89996 Tracey\n", - "6 65546 Trinity\n", - "7 50112 Traci\n", - "8 49657 Trenton\n", - "9 45692 Trent\n", - "...\n", - "\n", - "[238 rows x 2 columns]" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "%%bqsql\n", - "SELECT sum(number) as total_count, name\n", - "FROM {mydf}\n", - "WHERE name LIKE 'Tr%'\n", - "GROUP BY name\n", - "ORDER BY total_count DESC" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d2a17078", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.18" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks/getting_started/ml_fundamentals.ipynb b/notebooks/getting_started/ml_fundamentals.ipynb new file mode 100644 index 00000000000..2f566dd7049 --- /dev/null +++ b/notebooks/getting_started/ml_fundamentals.ipynb @@ -0,0 +1,3408 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Using ML - ML fundamentals\n", + "\n", + "The `bigframes.ml` module implements Scikit-Learn's machine learning API in\n", + "BigQuery DataFrames. It exposes BigQuery's ML capabilities in a simple, popular\n", + "API that works seamlessly with the rest of the BigQuery DataFrames API." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "0c8a8bc0b4d64448aef68d6a98fae666", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HTML(value='Query job 28e903c6-e874-4b99-8f53-0755e0b0c188 is RUNNING. \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
islandculmen_length_mmculmen_depth_mmflipper_length_mmsexspecies
penguin_id
156Biscoe46.214.5209.0FEMALEGentoo penguin (Pygoscelis papua)
189Biscoe35.318.9187.0FEMALEAdelie Penguin (Pygoscelis adeliae)
279Biscoe45.114.5215.0FEMALEGentoo penguin (Pygoscelis papua)
245Biscoe49.516.2229.0MALEGentoo penguin (Pygoscelis papua)
343Torgersen37.320.5199.0MALEAdelie Penguin (Pygoscelis adeliae)
\n", + "

5 rows × 6 columns

\n", + "[5 rows x 6 columns in total]" + ], + "text/plain": [ + " island culmen_length_mm culmen_depth_mm flipper_length_mm \\\n", + "penguin_id \n", + "156 Biscoe 46.2 14.5 209.0 \n", + "189 Biscoe 35.3 18.9 187.0 \n", + "279 Biscoe 45.1 14.5 215.0 \n", + "245 Biscoe 49.5 16.2 229.0 \n", + "343 Torgersen 37.3 20.5 199.0 \n", + "\n", + " sex species \n", + "penguin_id \n", + "156 FEMALE Gentoo penguin (Pygoscelis papua) \n", + "189 FEMALE Adelie Penguin (Pygoscelis adeliae) \n", + "279 FEMALE Gentoo penguin (Pygoscelis papua) \n", + "245 MALE Gentoo penguin (Pygoscelis papua) \n", + "343 MALE Adelie Penguin (Pygoscelis adeliae) \n", + "\n", + "[5 rows x 6 columns]" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# If we look at the data, we can see that random rows were selected for\n", + "# each side of the split\n", + "X_test.head(5)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "d6dd794f89724099950dcc927d63d0f5", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HTML(value='Query job d5a173bd-a7dc-42fa-8468-b088d47ccfe0 is RUNNING. \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
standard_scaled_culmen_length_mmstandard_scaled_culmen_depth_mmstandard_scaled_flipper_length_mm
penguin_id
0-1.3441880.642519-1.193942
1-0.7500471.005876-1.193942
2-0.5458110.90206-1.193942
4-1.214219-0.188011-0.619171
5-0.1187720.694427-0.619171
60.568203-0.291828-0.619171
71.2366110.642519-0.044401
9-0.6757791.524957-0.044401
10-0.5643780.902060.530369
11-0.8985820.798243-1.122096
12-1.26992-0.136103-1.122096
130.586770.071529-1.122096
14-1.826927-0.032287-1.122096
15-1.26992-0.343736-1.122096
160.34540.071529-0.547325
18-0.7686140.382978-0.547325
19-1.1213850.486795-0.547325
200.5125020.33107-0.547325
211.3851461.057784-0.547325
22-0.675779-0.032287-0.547325
241.0695090.538703-0.547325
26-0.434410.6944270.027445
281.9235861.8883140.027445
301.2923120.6944270.027445
31-1.994029-0.551368-1.62502
\n", + "

25 rows × 3 columns

\n", + "[267 rows x 3 columns in total]" + ], + "text/plain": [ + " standard_scaled_culmen_length_mm standard_scaled_culmen_depth_mm \\\n", + "penguin_id \n", + "0 -1.344188 0.642519 \n", + "1 -0.750047 1.005876 \n", + "2 -0.545811 0.90206 \n", + "4 -1.214219 -0.188011 \n", + "5 -0.118772 0.694427 \n", + "6 0.568203 -0.291828 \n", + "7 1.236611 0.642519 \n", + "9 -0.675779 1.524957 \n", + "10 -0.564378 0.90206 \n", + "11 -0.898582 0.798243 \n", + "12 -1.26992 -0.136103 \n", + "13 0.58677 0.071529 \n", + "14 -1.826927 -0.032287 \n", + "15 -1.26992 -0.343736 \n", + "16 0.3454 0.071529 \n", + "18 -0.768614 0.382978 \n", + "19 -1.121385 0.486795 \n", + "20 0.512502 0.33107 \n", + "21 1.385146 1.057784 \n", + "22 -0.675779 -0.032287 \n", + "24 1.069509 0.538703 \n", + "26 -0.43441 0.694427 \n", + "28 1.923586 1.888314 \n", + "30 1.292312 0.694427 \n", + "31 -1.994029 -0.551368 \n", + "\n", + " standard_scaled_flipper_length_mm \n", + "penguin_id \n", + "0 -1.193942 \n", + "1 -1.193942 \n", + "2 -1.193942 \n", + "4 -0.619171 \n", + "5 -0.619171 \n", + "6 -0.619171 \n", + "7 -0.044401 \n", + "9 -0.044401 \n", + "10 0.530369 \n", + "11 -1.122096 \n", + "12 -1.122096 \n", + "13 -1.122096 \n", + "14 -1.122096 \n", + "15 -1.122096 \n", + "16 -0.547325 \n", + "18 -0.547325 \n", + "19 -0.547325 \n", + "20 -0.547325 \n", + "21 -0.547325 \n", + "22 -0.547325 \n", + "24 -0.547325 \n", + "26 0.027445 \n", + "28 0.027445 \n", + "30 0.027445 \n", + "31 -1.62502 \n", + "...\n", + "\n", + "[267 rows x 3 columns]" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from bigframes.ml.preprocessing import StandardScaler\n", + "\n", + "# StandardScaler will only work on numeric columns\n", + "numeric_columns = [\"culmen_length_mm\", \"culmen_depth_mm\", \"flipper_length_mm\"]\n", + "\n", + "scaler = StandardScaler()\n", + "scaler.fit(X_train[numeric_columns])\n", + "\n", + "# Now, standardscaler should transform the numbers to have mean of zero\n", + "# and standard deviation of one:\n", + "scaler.transform(X_train[numeric_columns])" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "74f3c24c0a434e12bf6a56dc4809b501", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HTML(value='Query job c6268b07-0d3d-4fe0-971d-cc99fd98cd7e is RUNNING. \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
standard_scaled_culmen_length_mmstandard_scaled_culmen_depth_mmstandard_scaled_flipper_length_mm
penguin_id
30.4939350.382978-0.619171
81.0509420.953968-0.044401
171.2551781.1616-0.547325
23-1.3070540.694427-0.547325
251.5151140.4867950.027445
271.2366111.2654170.027445
291.4037130.9539680.027445
340.4196680.538703-1.62502
35-1.4555890.694427-1.050249
390.3268331.1616-0.475479
51-1.0656840.227254-0.978403
52-0.2487410.071529-0.978403
600.5310690.382978-0.403633
610.4011010.90206-0.403633
64-1.4555890.33107-0.403633
65-0.5643780.642519-0.403633
671.2737451.3173250.171138
832.6291280.33107-1.409481
85-1.2884870.746335-0.83471
93-0.5086770.4867950.314831
1040.382534-0.032287-0.762864
105-1.0656840.746335-0.762864
1081.1623430.382978-0.762864
1131.4965471.2135090.386677
130-0.3415751.213509-0.044401
\n", + "

25 rows × 3 columns

\n", + "[67 rows x 3 columns in total]" + ], + "text/plain": [ + " standard_scaled_culmen_length_mm standard_scaled_culmen_depth_mm \\\n", + "penguin_id \n", + "3 0.493935 0.382978 \n", + "8 1.050942 0.953968 \n", + "17 1.255178 1.1616 \n", + "23 -1.307054 0.694427 \n", + "25 1.515114 0.486795 \n", + "27 1.236611 1.265417 \n", + "29 1.403713 0.953968 \n", + "34 0.419668 0.538703 \n", + "35 -1.455589 0.694427 \n", + "39 0.326833 1.1616 \n", + "51 -1.065684 0.227254 \n", + "52 -0.248741 0.071529 \n", + "60 0.531069 0.382978 \n", + "61 0.401101 0.90206 \n", + "64 -1.455589 0.33107 \n", + "65 -0.564378 0.642519 \n", + "67 1.273745 1.317325 \n", + "83 2.629128 0.33107 \n", + "85 -1.288487 0.746335 \n", + "93 -0.508677 0.486795 \n", + "104 0.382534 -0.032287 \n", + "105 -1.065684 0.746335 \n", + "108 1.162343 0.382978 \n", + "113 1.496547 1.213509 \n", + "130 -0.341575 1.213509 \n", + "\n", + " standard_scaled_flipper_length_mm \n", + "penguin_id \n", + "3 -0.619171 \n", + "8 -0.044401 \n", + "17 -0.547325 \n", + "23 -0.547325 \n", + "25 0.027445 \n", + "27 0.027445 \n", + "29 0.027445 \n", + "34 -1.62502 \n", + "35 -1.050249 \n", + "39 -0.475479 \n", + "51 -0.978403 \n", + "52 -0.978403 \n", + "60 -0.403633 \n", + "61 -0.403633 \n", + "64 -0.403633 \n", + "65 -0.403633 \n", + "67 0.171138 \n", + "83 -1.409481 \n", + "85 -0.83471 \n", + "93 0.314831 \n", + "104 -0.762864 \n", + "105 -0.762864 \n", + "108 -0.762864 \n", + "113 0.386677 \n", + "130 -0.044401 \n", + "...\n", + "\n", + "[67 rows x 3 columns]" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# We can then repeat this transformation on new data\n", + "scaler.transform(X_test[numeric_columns])" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Composing transformers\n", + "\n", + "To process data where different columns need different preprocessors, `bigframes.composition.ColumnTransformer` can be employed:" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "d642a617d27f4e2493c80dbdd1686193", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HTML(value='Query job a8d8afa4-d91e-487e-8709-8727a73ab453 is RUNNING. \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
onehotencoded_islandstandard_scaled_culmen_length_mmstandard_scaled_culmen_depth_mmstandard_scaled_flipper_length_mmonehotencoded_sexonehotencoded_species
penguin_id
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1[{'index': 2, 'value': 1.0}]-0.7500471.005876-1.193942[{'index': 3, 'value': 1.0}][{'index': 1, 'value': 1.0}]
2[{'index': 2, 'value': 1.0}]-0.5458110.90206-1.193942[{'index': 3, 'value': 1.0}][{'index': 1, 'value': 1.0}]
4[{'index': 2, 'value': 1.0}]-1.214219-0.188011-0.619171[{'index': 2, 'value': 1.0}][{'index': 1, 'value': 1.0}]
5[{'index': 2, 'value': 1.0}]-0.1187720.694427-0.619171[{'index': 3, 'value': 1.0}][{'index': 1, 'value': 1.0}]
6[{'index': 2, 'value': 1.0}]0.568203-0.291828-0.619171[{'index': 2, 'value': 1.0}][{'index': 2, 'value': 1.0}]
7[{'index': 2, 'value': 1.0}]1.2366110.642519-0.044401[{'index': 2, 'value': 1.0}][{'index': 2, 'value': 1.0}]
9[{'index': 2, 'value': 1.0}]-0.6757791.524957-0.044401[{'index': 3, 'value': 1.0}][{'index': 1, 'value': 1.0}]
10[{'index': 2, 'value': 1.0}]-0.5643780.902060.530369[{'index': 3, 'value': 1.0}][{'index': 1, 'value': 1.0}]
11[{'index': 2, 'value': 1.0}]-0.8985820.798243-1.122096[{'index': 3, 'value': 1.0}][{'index': 1, 'value': 1.0}]
12[{'index': 2, 'value': 1.0}]-1.26992-0.136103-1.122096[{'index': 2, 'value': 1.0}][{'index': 1, 'value': 1.0}]
13[{'index': 2, 'value': 1.0}]0.586770.071529-1.122096[{'index': 2, 'value': 1.0}][{'index': 2, 'value': 1.0}]
14[{'index': 2, 'value': 1.0}]-1.826927-0.032287-1.122096[{'index': 2, 'value': 1.0}][{'index': 1, 'value': 1.0}]
15[{'index': 2, 'value': 1.0}]-1.26992-0.343736-1.122096[{'index': 2, 'value': 1.0}][{'index': 1, 'value': 1.0}]
16[{'index': 2, 'value': 1.0}]0.34540.071529-0.547325[{'index': 2, 'value': 1.0}][{'index': 2, 'value': 1.0}]
18[{'index': 2, 'value': 1.0}]-0.7686140.382978-0.547325[{'index': 3, 'value': 1.0}][{'index': 1, 'value': 1.0}]
19[{'index': 2, 'value': 1.0}]-1.1213850.486795-0.547325[{'index': 3, 'value': 1.0}][{'index': 1, 'value': 1.0}]
20[{'index': 2, 'value': 1.0}]0.5125020.33107-0.547325[{'index': 2, 'value': 1.0}][{'index': 2, 'value': 1.0}]
21[{'index': 2, 'value': 1.0}]1.3851461.057784-0.547325[{'index': 3, 'value': 1.0}][{'index': 2, 'value': 1.0}]
22[{'index': 2, 'value': 1.0}]-0.675779-0.032287-0.547325[{'index': 2, 'value': 1.0}][{'index': 1, 'value': 1.0}]
24[{'index': 2, 'value': 1.0}]1.0695090.538703-0.547325[{'index': 3, 'value': 1.0}][{'index': 2, 'value': 1.0}]
26[{'index': 2, 'value': 1.0}]-0.434410.6944270.027445[{'index': 3, 'value': 1.0}][{'index': 1, 'value': 1.0}]
28[{'index': 2, 'value': 1.0}]1.9235861.8883140.027445[{'index': 3, 'value': 1.0}][{'index': 2, 'value': 1.0}]
30[{'index': 2, 'value': 1.0}]1.2923120.6944270.027445[{'index': 3, 'value': 1.0}][{'index': 2, 'value': 1.0}]
31[{'index': 2, 'value': 1.0}]-1.994029-0.551368-1.62502[{'index': 2, 'value': 1.0}][{'index': 1, 'value': 1.0}]
\n", + "

25 rows × 6 columns

\n", + "[267 rows x 6 columns in total]" + ], + "text/plain": [ + " onehotencoded_island standard_scaled_culmen_length_mm \\\n", + "penguin_id \n", + "0 [{'index': 2, 'value': 1.0}] -1.344188 \n", + "1 [{'index': 2, 'value': 1.0}] -0.750047 \n", + "2 [{'index': 2, 'value': 1.0}] -0.545811 \n", + "4 [{'index': 2, 'value': 1.0}] -1.214219 \n", + "5 [{'index': 2, 'value': 1.0}] -0.118772 \n", + "6 [{'index': 2, 'value': 1.0}] 0.568203 \n", + "7 [{'index': 2, 'value': 1.0}] 1.236611 \n", + "9 [{'index': 2, 'value': 1.0}] -0.675779 \n", + "10 [{'index': 2, 'value': 1.0}] -0.564378 \n", + "11 [{'index': 2, 'value': 1.0}] -0.898582 \n", + "12 [{'index': 2, 'value': 1.0}] -1.26992 \n", + "13 [{'index': 2, 'value': 1.0}] 0.58677 \n", + "14 [{'index': 2, 'value': 1.0}] -1.826927 \n", + "15 [{'index': 2, 'value': 1.0}] -1.26992 \n", + "16 [{'index': 2, 'value': 1.0}] 0.3454 \n", + "18 [{'index': 2, 'value': 1.0}] -0.768614 \n", + "19 [{'index': 2, 'value': 1.0}] -1.121385 \n", + "20 [{'index': 2, 'value': 1.0}] 0.512502 \n", + "21 [{'index': 2, 'value': 1.0}] 1.385146 \n", + "22 [{'index': 2, 'value': 1.0}] -0.675779 \n", + "24 [{'index': 2, 'value': 1.0}] 1.069509 \n", + "26 [{'index': 2, 'value': 1.0}] -0.43441 \n", + "28 [{'index': 2, 'value': 1.0}] 1.923586 \n", + "30 [{'index': 2, 'value': 1.0}] 1.292312 \n", + "31 [{'index': 2, 'value': 1.0}] -1.994029 \n", + "\n", + " standard_scaled_culmen_depth_mm \\\n", + "penguin_id \n", + "0 0.642519 \n", + "1 1.005876 \n", + "2 0.90206 \n", + "4 -0.188011 \n", + "5 0.694427 \n", + "6 -0.291828 \n", + "7 0.642519 \n", + "9 1.524957 \n", + "10 0.90206 \n", + "11 0.798243 \n", + "12 -0.136103 \n", + "13 0.071529 \n", + "14 -0.032287 \n", + "15 -0.343736 \n", + "16 0.071529 \n", + "18 0.382978 \n", + "19 0.486795 \n", + "20 0.33107 \n", + "21 1.057784 \n", + "22 -0.032287 \n", + "24 0.538703 \n", + "26 0.694427 \n", + "28 1.888314 \n", + "30 0.694427 \n", + "31 -0.551368 \n", + "\n", + " standard_scaled_flipper_length_mm onehotencoded_sex \\\n", + "penguin_id \n", + "0 -1.193942 [{'index': 2, 'value': 1.0}] \n", + "1 -1.193942 [{'index': 3, 'value': 1.0}] \n", + "2 -1.193942 [{'index': 3, 'value': 1.0}] \n", + "4 -0.619171 [{'index': 2, 'value': 1.0}] \n", + "5 -0.619171 [{'index': 3, 'value': 1.0}] \n", + "6 -0.619171 [{'index': 2, 'value': 1.0}] \n", + "7 -0.044401 [{'index': 2, 'value': 1.0}] \n", + "9 -0.044401 [{'index': 3, 'value': 1.0}] \n", + "10 0.530369 [{'index': 3, 'value': 1.0}] \n", + "11 -1.122096 [{'index': 3, 'value': 1.0}] \n", + "12 -1.122096 [{'index': 2, 'value': 1.0}] \n", + "13 -1.122096 [{'index': 2, 'value': 1.0}] \n", + "14 -1.122096 [{'index': 2, 'value': 1.0}] \n", + "15 -1.122096 [{'index': 2, 'value': 1.0}] \n", + "16 -0.547325 [{'index': 2, 'value': 1.0}] \n", + "18 -0.547325 [{'index': 3, 'value': 1.0}] \n", + "19 -0.547325 [{'index': 3, 'value': 1.0}] \n", + "20 -0.547325 [{'index': 2, 'value': 1.0}] \n", + "21 -0.547325 [{'index': 3, 'value': 1.0}] \n", + "22 -0.547325 [{'index': 2, 'value': 1.0}] \n", + "24 -0.547325 [{'index': 3, 'value': 1.0}] \n", + "26 0.027445 [{'index': 3, 'value': 1.0}] \n", + "28 0.027445 [{'index': 3, 'value': 1.0}] \n", + "30 0.027445 [{'index': 3, 'value': 1.0}] \n", + "31 -1.62502 [{'index': 2, 'value': 1.0}] \n", + "\n", + " onehotencoded_species \n", + "penguin_id \n", + "0 [{'index': 1, 'value': 1.0}] \n", + "1 [{'index': 1, 'value': 1.0}] \n", + "2 [{'index': 1, 'value': 1.0}] \n", + "4 [{'index': 1, 'value': 1.0}] \n", + "5 [{'index': 1, 'value': 1.0}] \n", + "6 [{'index': 2, 'value': 1.0}] \n", + "7 [{'index': 2, 'value': 1.0}] \n", + "9 [{'index': 1, 'value': 1.0}] \n", + "10 [{'index': 1, 'value': 1.0}] \n", + "11 [{'index': 1, 'value': 1.0}] \n", + "12 [{'index': 1, 'value': 1.0}] \n", + "13 [{'index': 2, 'value': 1.0}] \n", + "14 [{'index': 1, 'value': 1.0}] \n", + "15 [{'index': 1, 'value': 1.0}] \n", + "16 [{'index': 2, 'value': 1.0}] \n", + "18 [{'index': 1, 'value': 1.0}] \n", + "19 [{'index': 1, 'value': 1.0}] \n", + "20 [{'index': 2, 'value': 1.0}] \n", + "21 [{'index': 2, 'value': 1.0}] \n", + "22 [{'index': 1, 'value': 1.0}] \n", + "24 [{'index': 2, 'value': 1.0}] \n", + "26 [{'index': 1, 'value': 1.0}] \n", + "28 [{'index': 2, 'value': 1.0}] \n", + "30 [{'index': 2, 'value': 1.0}] \n", + "31 [{'index': 1, 'value': 1.0}] \n", + "...\n", + "\n", + "[267 rows x 6 columns]" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from bigframes.ml.compose import ColumnTransformer\n", + "from bigframes.ml.preprocessing import OneHotEncoder\n", + "\n", + "# Create an aggregate transform that applies StandardScaler to the numeric columns,\n", + "# and OneHotEncoder to the string columns\n", + "preproc = ColumnTransformer([\n", + " (\"scale\", StandardScaler(), [\"culmen_length_mm\", \"culmen_depth_mm\", \"flipper_length_mm\"]),\n", + " (\"encode\", OneHotEncoder(), [\"species\", \"sex\", \"island\"])])\n", + "\n", + "# Now we can fit all columns of the training data\n", + "preproc.fit(X_train)\n", + "\n", + "processed_X_train = preproc.transform(X_train)\n", + "processed_X_test = preproc.transform(X_test)\n", + "\n", + "processed_X_train" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Predictors\n", + "\n", + "Predictors are estimators that learn and make predictions. In addition to `.fit(...)`, the predictor implements a `.predict(...)` method, which will use what was learned during `.fit(...)` to predict some output.\n", + "\n", + "Predictors can be further broken down into two categories:\n", + "\n", + "#### Supervised predictors\n", + "\n", + "Supervised learning is when we train a model on input-output pairs, and then ask it to predict the output for new inputs. An example of such a predictor is `bigframes.ml.linear_models.LinearRegression`." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "5db4c5c80ba4417db151aa561dab5ee7", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HTML(value='Query job ceced0cc-13a7-4b14-b42c-4d5f69e7e49a is RUNNING. \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
predicted_body_mass_g
penguin_id
33394.116212
84048.683645
173976.452358
233541.580346
254032.842027
274118.34983
294087.765797
343183.75379
353418.800633
393519.18471
513398.133564
523223.614107
603445.012713
613505.637004
643515.903779
654028.361259
674159.991956
833348.167212
853485.048557
934172.872284
1043299.300454
1053515.68617
1083405.222757
1134209.13832
1304197.90382
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25 rows × 1 columns

\n", + "[67 rows x 1 columns in total]" + ], + "text/plain": [ + " predicted_body_mass_g\n", + "penguin_id \n", + "3 3394.116212\n", + "8 4048.683645\n", + "17 3976.452358\n", + "23 3541.580346\n", + "25 4032.842027\n", + "27 4118.34983\n", + "29 4087.765797\n", + "34 3183.75379\n", + "35 3418.800633\n", + "39 3519.18471\n", + "51 3398.133564\n", + "52 3223.614107\n", + "60 3445.012713\n", + "61 3505.637004\n", + "64 3515.903779\n", + "65 4028.361259\n", + "67 4159.991956\n", + "83 3348.167212\n", + "85 3485.048557\n", + "93 4172.872284\n", + "104 3299.300454\n", + "105 3515.68617\n", + "108 3405.222757\n", + "113 4209.13832\n", + "130 4197.90382\n", + "...\n", + "\n", + "[67 rows x 1 columns]" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pipeline.fit(X_train, y_train)\n", + "\n", + "predicted_y_test = pipeline.predict(X_test)\n", + "predicted_y_test" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the backend, a pipeline will actually be compiled into a single model with an embedded TRANSFORM step." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Evaluating results\n", + "\n", + "Some models include a convenient `.score(X, y)` method for evaulation with a preset accuracy metric:" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "2d32081be31f44abb8de67e2209d76cd", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HTML(value='Query job 2a043039-670f-4eb8-9cf0-765ee6ed7de6 is RUNNING. \n", - "\n", - " \n", - " \n", - " \"Colab Run in Colab\n", - " \n", - " \n", - " \n", - " \n", - " \"GitHub\n", - " View on GitHub\n", - " \n", - " \n", - " \n", - " \n", - " \"Vertex\n", - " Open in Vertex AI Workbench\n", - " \n", - " \n", - " \n", - " \n", - " \"BQ\n", - " Open in BQ Studio\n", - " \n", - " \n", - "" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "24743cf4a1e1" - }, - "source": [ - "**_NOTE_**: This notebook has been tested in the following environment:\n", - "\n", - "* Python version = 3.10" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "tvgnzT1CKxrO" - }, - "source": [ - "## Overview\n", - "\n", - "The `bigframes.ml` module implements Scikit-Learn's machine learning API in\n", - "BigQuery DataFrames. It exposes BigQuery's ML capabilities in a simple, popular\n", - "API that works seamlessly with the rest of the BigQuery DataFrames API.\n", - "\n", - "Learn more about [BigQuery DataFrames](https://cloud.google.com/python/docs/reference/bigframes/latest)." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "d975e698c9a4" - }, - "source": [ - "### Objective\n", - "\n", - "In this tutorial, you will walk through an end-to-end machine learning workflow using BigQuery DataFrames. You will load data, manipulate and prepare it for model training, build supervised and unsupervised models, and evaluate and save a model for future use; all using built-in BigQuery DataFrames functionality." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "08d289fa873f" - }, - "source": [ - "### Dataset\n", - "\n", - "This tutorial uses the [```penguins``` table](https://console.cloud.google.com/bigquery?p=bigquery-public-data&d=ml_datasets&t=penguins) (a BigQuery public dataset), which contains data on a set of penguins including species, island of residence, weight, culmen length and depth, flipper length, and sex." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "aed92deeb4a0" - }, - "source": [ - "### Costs\n", - "\n", - "This tutorial uses billable components of Google Cloud:\n", - "\n", - "* BigQuery (storage and compute)\n", - "* BigQuery ML\n", - "\n", - "Learn about [BigQuery storage pricing](https://cloud.google.com/bigquery/pricing#storage),\n", - "[BigQuery compute pricing](https://cloud.google.com/bigquery/pricing#analysis_pricing_models),\n", - "and [BigQuery ML pricing](https://cloud.google.com/bigquery/pricing#bqml),\n", - "and use the [Pricing Calculator](https://cloud.google.com/products/calculator/)\n", - "to generate a cost estimate based on your projected usage." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "i7EUnXsZhAGF" - }, - "source": [ - "## Installation\n", - "\n", - "Depending on your Jupyter environment, you might have to install packages." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "NRTcBQPZpKWd" - }, - "source": [ - "**Vertex AI Workbench or Colab**\n", - "\n", - "Do nothing, BigQuery DataFrames package is already installed." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "bdOJtFo1pRnc" - }, - "source": [ - "**Local JupyterLab instance**\n", - "\n", - "Uncomment and run the following cell:" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "id": "mfPoOwPLGpSr" - }, - "outputs": [], - "source": [ - "# !pip install bigframes" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "BF1j6f9HApxa" - }, - "source": [ - "## Before you begin\n", - "\n", - "Complete the tasks in this section to set up your environment." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Yq7zKYWelRQP" - }, - "source": [ - "### Set up your Google Cloud project\n", - "\n", - "**The following steps are required, regardless of your notebook environment.**\n", - "\n", - "1. [Select or create a Google Cloud project](https://console.cloud.google.com/cloud-resource-manager). When you first create an account, you get a $300 credit towards your compute/storage costs.\n", - "\n", - "2. [Make sure that billing is enabled for your project](https://cloud.google.com/billing/docs/how-to/modify-project).\n", - "\n", - "3. [Click here](https://console.cloud.google.com/flows/enableapi?apiid=bigquery.googleapis.com) to enable the BigQuery API.\n", - "\n", - "4. If you are running this notebook locally, install the [Cloud SDK](https://cloud.google.com/sdk)." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "WReHDGG5g0XY" - }, - "source": [ - "#### Set your project ID\n", - "\n", - "If you don't know your project ID, try the following:\n", - "* Run `gcloud config list`.\n", - "* Run `gcloud projects list`.\n", - "* See the support page: [Locate the project ID](https://support.google.com/googleapi/answer/7014113)." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "id": "oM1iC_MfAts1" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Updated property [core/project].\n" - ] - } - ], - "source": [ - "PROJECT_ID = \"\" # @param {type:\"string\"}\n", - "\n", - "# Set the project id\n", - "! gcloud config set project {PROJECT_ID}" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "region" - }, - "source": [ - "#### Set the region\n", - "\n", - "You can also change the `REGION` variable used by BigQuery. Learn more about [BigQuery regions](https://cloud.google.com/bigquery/docs/locations#supported_locations)." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "id": "eF-Twtc4XGem" - }, - "outputs": [], - "source": [ - "REGION = \"US\" # @param {type: \"string\"}" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "XcW9adriUQRc" - }, - "source": [ - "#### Set the dataset ID\n", - "\n", - "As part of this notebook, you will save BigQuery ML models to your Google Cloud project, which requires a dataset. Create the dataset, if needed, and provide the ID here as the `DATASET` variable used by BigQuery. Learn how to create a [BigQuery dataset](https://cloud.google.com/bigquery/docs/datasets)." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "id": "BbMh9JHvUHAn" - }, - "outputs": [], - "source": [ - "DATASET = \"\" # @param {type: \"string\"}" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "NwxfWoR5UGwO" - }, - "source": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "sBCra4QMA2wR" - }, - "source": [ - "### Authenticate your Google Cloud account\n", - "\n", - "Depending on your Jupyter environment, you might have to manually authenticate. Follow the relevant instructions below." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "74ccc9e52986" - }, - "source": [ - "**Vertex AI Workbench**\n", - "\n", - "Do nothing, you are already authenticated." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "de775a3773ba" - }, - "source": [ - "**Local JupyterLab instance**\n", - "\n", - "Uncomment and run the following cell:" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "id": "254614fa0c46" - }, - "outputs": [], - "source": [ - "# ! gcloud auth login" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "ef21552ccea8" - }, - "source": [ - "**Colab**\n", - "\n", - "Uncomment and run the following cell:" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "id": "603adbbf0532" - }, - "outputs": [], - "source": [ - "# from google.colab import auth\n", - "# auth.authenticate_user()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "960505627ddf" - }, - "source": [ - "### Import libraries" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "id": "PyQmSRbKA8r-" - }, - "outputs": [], - "source": [ - "import bigframes.pandas as bpd" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "init_aip:mbsdk,all" - }, - "source": [ - "\n", - "### Set BigQuery DataFrames options" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "id": "NPPMuw2PXGeo" - }, - "outputs": [], - "source": [ - "# Note: The project option is not required in all environments.\n", - "# On BigQuery Studio, the project ID is automatically detected.\n", - "bpd.options.bigquery.project = PROJECT_ID\n", - "\n", - "# Note: The location option is not required.\n", - "# It defaults to the location of the first table or query\n", - "# passed to read_gbq(). For APIs where a location can't be\n", - "# auto-detected, the location defaults to the \"US\" location.\n", - "bpd.options.bigquery.location = REGION" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "pDfrKwMKE_dK" - }, - "source": [ - "If you want to reset the location of the created DataFrame or Series objects, reset the session by executing `bpd.reset_session()`. After that, you can reuse `bpd.options.bigquery.location` to specify another location." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "LjfRpSruzg5j" - }, - "source": [ - "## Import data into BigQuery DataFrames\n", - "\n", - "You can create a DataFrame by reading data from a BigQuery table." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "id": "d86W4hNqzZJb" - }, - "outputs": [], - "source": [ - "df = bpd.read_gbq(\"bigquery-public-data.ml_datasets.penguins\")\n", - "df = df.dropna()\n", - "\n", - "# BigQuery DataFrames creates a default numbered index, which we can give a name\n", - "df.index.name = \"penguin_id\"" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "pDfCJ6-LkRB1" - }, - "source": [ - "Take a look at a few rows of the DataFrame:" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "id": "arGaUZVWkSwT" - }, - "outputs": [ - { - "data": { - "text/html": [ - "Query job d3acda60-1059-4bb0-9912-ed374491c5c3 is DONE. 28.9 kB processed. 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speciesislandculmen_length_mmculmen_depth_mmflipper_length_mmbody_mass_gsex
penguin_id
0Gentoo penguin (Pygoscelis papua)Biscoe50.515.9225.05400.0MALE
1Gentoo penguin (Pygoscelis papua)Biscoe45.114.5215.05000.0FEMALE
2Adelie Penguin (Pygoscelis adeliae)Torgersen41.418.5202.03875.0MALE
3Adelie Penguin (Pygoscelis adeliae)Torgersen38.617.0188.02900.0FEMALE
4Gentoo penguin (Pygoscelis papua)Biscoe46.514.8217.05200.0FEMALE
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[5 rows x 7 columns in total]" - ], - "text/plain": [ - " species island culmen_length_mm \\\n", - "penguin_id \n", - "0 Gentoo penguin (Pygoscelis papua) Biscoe 50.5 \n", - "1 Gentoo penguin (Pygoscelis papua) Biscoe 45.1 \n", - "2 Adelie Penguin (Pygoscelis adeliae) Torgersen 41.4 \n", - "3 Adelie Penguin (Pygoscelis adeliae) Torgersen 38.6 \n", - "4 Gentoo penguin (Pygoscelis papua) Biscoe 46.5 \n", - "\n", - " culmen_depth_mm flipper_length_mm body_mass_g sex \n", - "penguin_id \n", - "0 15.9 225.0 5400.0 MALE \n", - "1 14.5 215.0 5000.0 FEMALE \n", - "2 18.5 202.0 3875.0 MALE \n", - "3 17.0 188.0 2900.0 FEMALE \n", - "4 14.8 217.0 5200.0 FEMALE \n", - "\n", - "[5 rows x 7 columns]" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "WkUIcMXPkahu" - }, - "source": [ - "## Clean and prepare data" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "DScncEoDkiTG" - }, - "source": [ - "We're are going to start with supervised learning, where a Linear Regression model will learn to predict the body mass (output variable `y`) using input features such as flipper length, sex, species, and more (features `X`)." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "id": "B9mW93o9z_-L" - }, - "outputs": [], - "source": [ - "# Isolate input features and output variable into DataFrames\n", - "X = df[['island', 'culmen_length_mm', 'culmen_depth_mm', 'flipper_length_mm', 'sex', 'species']]\n", - "y = df[['body_mass_g']]" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "wkw0Cs62k_cl" - }, - "source": [ - "Part of preparing data for a machine learning task is splitting it into subsets for training and testing to ensure that the solution is not overfitting. By default, BQML will automatically manage splitting the data for you. However, BQML also supports manually splitting out your training data.\n", - "\n", - "Performing a manual data split can be done with `bigframes.ml.model_selection.train_test_split` like so:" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "id": "NysWAWmvlAxB" - }, - "outputs": [ - { - "data": { - "text/html": [ - "Query job 7bd14e04-b3b4-4281-b5be-187f7baad62f is DONE. 28.9 kB processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Query job 240cc7db-19ac-4bd3-8e76-a79f75ded077 is DONE. 28.9 kB processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Query job 91194fee-d9b9-4cb9-a469-e49e9d77c624 is DONE. 28.9 kB processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Query job 84c71647-956b-4385-8dce-c8bc70a917c8 is DONE. 28.9 kB processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Query job 9c94600b-2231-4d04-8e3a-fb46f8892b6a is DONE. 28.9 kB processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "X_train shape: (267, 6)\n", - "X_test shape: (67, 6)\n", - "y_train shape: (267, 1)\n", - "y_test shape: (67, 1)\n" - ] - } - ], - "source": [ - "from bigframes.ml.model_selection import train_test_split\n", - "\n", - "# This will split X and y into test and training sets, with 20% of the rows in the test set,\n", - "# and the rest in the training set\n", - "X_train, X_test, y_train, y_test = train_test_split(\n", - " X, y, test_size=0.2)\n", - "\n", - "# Show the shape of the data after the split\n", - "print(f\"\"\"X_train shape: {X_train.shape}\n", - "X_test shape: {X_test.shape}\n", - "y_train shape: {y_train.shape}\n", - "y_test shape: {y_test.shape}\"\"\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "faFnVnNolydu" - }, - "source": [ - "If we look at the data, we can see that random rows were selected for\n", - "each side of the split:" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "id": "f8bz1HwLlyLP" - }, - "outputs": [ - { - "data": { - "text/html": [ - "Query job 8ad534c1-eb49-4616-b7a6-f7d8b044b8bf is DONE. 28.9 kB processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Query job 3793de66-fb3c-4ca4-a337-aa708c718cc5 is DONE. 0 Bytes processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Query job 66524afb-4509-4927-8902-4a72826e83c4 is DONE. 456 Bytes processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
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islandculmen_length_mmculmen_depth_mmflipper_length_mmsexspecies
penguin_id
188Dream51.518.7187.0MALEChinstrap penguin (Pygoscelis antarctica)
251Biscoe49.516.1224.0MALEGentoo penguin (Pygoscelis papua)
231Biscoe45.713.9214.0FEMALEGentoo penguin (Pygoscelis papua)
271Biscoe59.617.0230.0MALEGentoo penguin (Pygoscelis papua)
128Biscoe38.817.2180.0MALEAdelie Penguin (Pygoscelis adeliae)
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5 rows × 6 columns

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[5 rows x 6 columns in total]" - ], - "text/plain": [ - " island culmen_length_mm culmen_depth_mm flipper_length_mm \\\n", - "penguin_id \n", - "188 Dream 51.5 18.7 187.0 \n", - "251 Biscoe 49.5 16.1 224.0 \n", - "231 Biscoe 45.7 13.9 214.0 \n", - "271 Biscoe 59.6 17.0 230.0 \n", - "128 Biscoe 38.8 17.2 180.0 \n", - "\n", - " sex species \n", - "penguin_id \n", - "188 MALE Chinstrap penguin (Pygoscelis antarctica) \n", - "251 MALE Gentoo penguin (Pygoscelis papua) \n", - "231 FEMALE Gentoo penguin (Pygoscelis papua) \n", - "271 MALE Gentoo penguin (Pygoscelis papua) \n", - "128 MALE Adelie Penguin (Pygoscelis adeliae) \n", - "\n", - "[5 rows x 6 columns]" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X_test.head(5)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "v4ic7GQEl67Y" - }, - "source": [ - "Note that the `y_test` data matches the same rows in `X_test`:" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "id": "PflbhKGkl8v2" - }, - "outputs": [ - { - "data": { - "text/html": [ - "Query job 6a87fcc2-f2d0-44f5-8ab2-08f109c2b70d is DONE. 28.9 kB processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Query job ed8e49f8-0f4c-4ef2-bbc2-b8c5ef9fd064 is DONE. 0 Bytes processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Query job 97fea642-03aa-49fd-943e-f4efa5a87f0f is DONE. 120 Bytes processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
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body_mass_g
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[5 rows x 1 columns in total]" - ], - "text/plain": [ - " body_mass_g\n", - "penguin_id \n", - "188 3250.0\n", - "251 5650.0\n", - "231 4400.0\n", - "271 6050.0\n", - "128 3800.0\n", - "\n", - "[5 rows x 1 columns]" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "y_test.head(5)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Dkf52IdvmSaj" - }, - "source": [ - "## Estimators\n", - "\n", - "Following scikit-learn, all learning components are \"estimators\"; objects that can learn from training data and then apply themselves to new data. Estimators share the following patterns:\n", - "\n", - "- a constructor that takes a list of parameters\n", - "- a standard string representation that shows the class name and all non-default parameters, e.g. `LinearRegression(fit_intercept=False)`\n", - "- a `.fit(..)` method to fit the estimator to training data\n", - "\n", - "There estimators can be further broken down into two main subtypes:\n", - " 1. Transformers\n", - " 2. Predictors\n", - "\n", - "Let's walk through each of these with our example model." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "55oNSWQ2Q5te" - }, - "source": [ - "### Transformers\n", - "\n", - "Transformers are estimators that are used to prepare data for consumption by other estimators ('preprocessing'). In addition to `.fit(...)`, the transformer implements a `.transform(...)` method, which will apply a transformation based on what was computed during `.fit(..)`. With this pattern dynamic preprocessing steps can be applied to both training and test/production data consistently.\n", - "\n", - "An example of a transformer is `bigframes.ml.preprocessing.StandardScaler`, which rescales a dataset to have a mean of zero and a standard deviation of one:" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "id": "yhATDMR-mkdF" - }, - "outputs": [ - { - "data": { - "text/html": [ - "Query job aee64759-42bb-44d6-b8c7-1c737cdd6eed is DONE. 28.9 kB processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Query job acb29d04-a20d-4f1c-8d90-51c7e8ac9922 is DONE. 0 Bytes processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Query job 2bd034db-7d9b-467c-be17-49bca094cceb is DONE. 28.9 kB processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Query job 5dfb583a-1ced-4f2a-94b9-f1282263134d is DONE. 2.1 kB processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Query job 8fe87288-4a95-49f4-9895-7c41c1004901 is DONE. 0 Bytes processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Query job 7ebcecee-beff-402d-ac71-6384014a54da is DONE. 8.5 kB processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
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standard_scaled_culmen_length_mmstandard_scaled_culmen_depth_mmstandard_scaled_flipper_length_mm
penguin_id
01.20778-0.6515311.772656
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standard_scaled_culmen_length_mmstandard_scaled_culmen_depth_mmstandard_scaled_flipper_length_mm
penguin_id
10.220718-1.3592771.045621
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"0 1.772656 [{'index': 3, 'value': 1.0}] \n", - "2 0.100476 [{'index': 3, 'value': 1.0}] \n", - "3 -0.917372 [{'index': 2, 'value': 1.0}] \n", - "4 1.191028 [{'index': 2, 'value': 1.0}] \n", - "5 -0.626559 [{'index': 2, 'value': 1.0}] \n", - "7 0.682104 [{'index': 2, 'value': 1.0}] \n", - "8 1.409139 [{'index': 3, 'value': 1.0}] \n", - "9 -0.263041 [{'index': 3, 'value': 1.0}] \n", - "10 0.827511 [{'index': 3, 'value': 1.0}] \n", - "11 -0.771966 [{'index': 2, 'value': 1.0}] \n", - "12 1.409139 [{'index': 3, 'value': 1.0}] \n", - "13 0.900214 [{'index': 2, 'value': 1.0}] \n", - "14 -0.626559 [{'index': 2, 'value': 1.0}] \n", - "16 1.481842 [{'index': 3, 'value': 1.0}] \n", - "17 -0.190338 [{'index': 2, 'value': 1.0}] \n", - "18 1.263732 [{'index': 3, 'value': 1.0}] \n", - "19 1.191028 [{'index': 2, 'value': 1.0}] \n", - "20 0.609401 [{'index': 2, 'value': 1.0}] \n", - "21 -0.190338 [{'index': 2, 'value': 1.0}] \n", - "22 -0.699262 [{'index': 2, 'value': 1.0}] \n", - "24 -0.699262 [{'index': 2, 'value': 1.0}] \n", - "25 -0.771966 [{'index': 3, 'value': 1.0}] \n", - "26 -0.990076 [{'index': 2, 'value': 1.0}] \n", - "27 -0.408448 [{'index': 2, 'value': 1.0}] \n", - "29 1.990767 [{'index': 3, 'value': 1.0}] \n", - "\n", - " onehotencoded_species \n", - "penguin_id \n", - "0 [{'index': 3, 'value': 1.0}] \n", - "2 [{'index': 1, 'value': 1.0}] \n", - "3 [{'index': 1, 'value': 1.0}] \n", - "4 [{'index': 3, 'value': 1.0}] \n", - "5 [{'index': 1, 'value': 1.0}] \n", - "7 [{'index': 3, 'value': 1.0}] \n", - "8 [{'index': 3, 'value': 1.0}] \n", - "9 [{'index': 1, 'value': 1.0}] \n", - "10 [{'index': 2, 'value': 1.0}] \n", - "11 [{'index': 1, 'value': 1.0}] \n", - "12 [{'index': 3, 'value': 1.0}] \n", - "13 [{'index': 3, 'value': 1.0}] \n", - "14 [{'index': 2, 'value': 1.0}] \n", - "16 [{'index': 3, 'value': 1.0}] \n", - "17 [{'index': 1, 'value': 1.0}] \n", - "18 [{'index': 3, 'value': 1.0}] \n", - "19 [{'index': 3, 'value': 1.0}] \n", - "20 [{'index': 3, 'value': 1.0}] \n", - "21 [{'index': 2, 'value': 1.0}] \n", - "22 [{'index': 1, 'value': 1.0}] \n", - "24 [{'index': 1, 'value': 1.0}] \n", - "25 [{'index': 1, 'value': 1.0}] \n", - "26 [{'index': 1, 'value': 1.0}] \n", - "27 [{'index': 2, 'value': 1.0}] \n", - "29 [{'index': 3, 'value': 1.0}] \n", - "...\n", - "\n", - "[267 rows x 6 columns]" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from bigframes.ml.compose import ColumnTransformer\n", - "from bigframes.ml.preprocessing import OneHotEncoder\n", - "\n", - "# Create an aggregate transform that applies StandardScaler to the numeric columns,\n", - "# and OneHotEncoder to the string columns\n", - "preproc = ColumnTransformer([\n", - " (\"scale\", StandardScaler(), [\"culmen_length_mm\", \"culmen_depth_mm\", \"flipper_length_mm\"]),\n", - " (\"encode\", OneHotEncoder(), [\"species\", \"sex\", \"island\"])])\n", - "\n", - "# Now we can fit all columns of the training data\n", - "preproc.fit(X_train)\n", - "\n", - "processed_X_train = preproc.transform(X_train)\n", - "processed_X_test = preproc.transform(X_test)\n", - "\n", - "# View the processed training data\n", - "processed_X_train" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "JhoO4fctm4Q5" - }, - "source": [ - "### Predictors\n", - "\n", - "Predictors are estimators that learn and make predictions. In addition to `.fit(...)`, the predictor implements a `.predict(...)` method, which will use what was learned during `.fit(...)` to predict some output.\n", - "\n", - "Predictors can be further broken down into two categories:\n", - "* Supervised predictors\n", - "* Unsupervised predictors" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "TqLItVyjslP8" - }, - "source": [ - "#### Supervised predictors\n", - "\n", - "Supervised learning is when we train a model on input-output pairs, and then ask it to predict the output for new inputs. An example of such a predictor is `bigframes.ml.linear_models.LinearRegression`." - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "id": "ZeloMmopm8KI" - }, - "outputs": [ - { - "data": { - "text/html": [ - "Query job a59bf4cc-4c92-4a68-96b1-7465fbcb3ed0 is DONE. 21.4 kB processed. 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penguin_id
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"89 3915.240555 [{'index': 2, 'value': 1.0}] \n", - "92 3425.563946 [{'index': 2, 'value': 1.0}] \n", - "93 4141.497717 [{'index': 1, 'value': 1.0}] \n", - "96 3394.72289 [{'index': 2, 'value': 1.0}] \n", - "100 3507.226918 [{'index': 2, 'value': 1.0}] \n", - "101 4922.286202 [{'index': 1, 'value': 1.0}] \n", - "102 4016.243221 [{'index': 2, 'value': 1.0}] \n", - "107 4933.655362 [{'index': 1, 'value': 1.0}] \n", - "\n", - " standard_scaled_culmen_length_mm standard_scaled_culmen_depth_mm \\\n", - "penguin_id \n", - "1 0.220718 -1.359277 \n", - "15 -0.510439 0.157322 \n", - "28 -1.058807 0.713408 \n", - "32 1.463685 1.168388 \n", - "33 -0.254534 0.056215 \n", - "34 -0.510439 0.460642 \n", - "37 1.354012 0.511195 \n", - "41 -0.674949 -0.095445 \n", - "47 -1.168481 0.662855 \n", - "52 0.458344 0.308982 \n", - "56 -1.040528 0.460642 \n", - "57 -0.967412 0.005662 \n", - "62 0.988433 -0.752638 \n", - "65 1.756148 1.370601 \n", - "67 0.677691 -1.359277 \n", - "75 -1.113644 1.421155 \n", - "81 0.677691 0.561748 \n", - "89 -0.857739 0.713408 \n", - "92 -0.802902 0.308982 \n", - "93 -0.309371 1.168388 \n", - "96 -0.309371 0.662855 \n", - "100 -0.912576 0.814515 \n", - "101 0.549739 -1.308724 \n", - "102 -0.126582 0.662855 \n", - "107 1.20778 -1.005404 \n", - "\n", - " standard_scaled_flipper_length_mm onehotencoded_sex \\\n", - "penguin_id \n", - "1 1.045621 [{'index': 2, 'value': 1.0}] \n", - "15 -0.771966 [{'index': 3, 'value': 1.0}] \n", - "28 -0.771966 [{'index': 2, 'value': 1.0}] \n", - "32 0.39129 [{'index': 3, 'value': 1.0}] \n", - "33 -0.990076 [{'index': 2, 'value': 1.0}] \n", - "34 0.318587 [{'index': 3, 'value': 1.0}] \n", - "37 -0.263041 [{'index': 3, 'value': 1.0}] \n", - "41 -1.789814 [{'index': 2, 'value': 1.0}] \n", - "47 -0.117634 [{'index': 3, 'value': 1.0}] \n", - "52 -0.699262 [{'index': 2, 'value': 1.0}] \n", - "56 -1.135483 [{'index': 3, 'value': 1.0}] \n", - "57 -0.117634 [{'index': 2, 'value': 1.0}] \n", - "62 1.191028 [{'index': 3, 'value': 1.0}] \n", - "65 0.318587 [{'index': 3, 'value': 1.0}] \n", - "67 1.045621 [{'index': 3, 'value': 1.0}] \n", - "75 -0.771966 [{'index': 3, 'value': 1.0}] \n", - "81 -0.408448 [{'index': 2, 'value': 1.0}] \n", - "89 -0.771966 [{'index': 3, 'value': 1.0}] \n", - "92 -0.917372 [{'index': 2, 'value': 1.0}] \n", - "93 -0.263041 [{'index': 3, 'value': 1.0}] \n", - "96 -1.499 [{'index': 2, 'value': 1.0}] \n", - "100 -0.771966 [{'index': 2, 'value': 1.0}] \n", - "101 1.554546 [{'index': 2, 'value': 1.0}] \n", - "102 -0.626559 [{'index': 3, 'value': 1.0}] \n", - "107 1.118325 [{'index': 2, 'value': 1.0}] \n", - "\n", - " onehotencoded_species \n", - "penguin_id \n", - "1 [{'index': 3, 'value': 1.0}] \n", - "15 [{'index': 1, 'value': 1.0}] \n", - "28 [{'index': 1, 'value': 1.0}] \n", - "32 [{'index': 2, 'value': 1.0}] \n", - "33 [{'index': 2, 'value': 1.0}] \n", - "34 [{'index': 1, 'value': 1.0}] \n", - "37 [{'index': 2, 'value': 1.0}] \n", - "41 [{'index': 1, 'value': 1.0}] \n", - "47 [{'index': 1, 'value': 1.0}] \n", - "52 [{'index': 2, 'value': 1.0}] \n", - "56 [{'index': 1, 'value': 1.0}] \n", - "57 [{'index': 1, 'value': 1.0}] \n", - "62 [{'index': 3, 'value': 1.0}] \n", - "65 [{'index': 2, 'value': 1.0}] \n", - "67 [{'index': 3, 'value': 1.0}] \n", - "75 [{'index': 1, 'value': 1.0}] \n", - "81 [{'index': 2, 'value': 1.0}] \n", - "89 [{'index': 1, 'value': 1.0}] \n", - "92 [{'index': 1, 'value': 1.0}] \n", - "93 [{'index': 1, 'value': 1.0}] \n", - "96 [{'index': 1, 'value': 1.0}] \n", - "100 [{'index': 1, 'value': 1.0}] \n", - "101 [{'index': 3, 'value': 1.0}] \n", - "102 [{'index': 1, 'value': 1.0}] \n", - "107 [{'index': 3, 'value': 1.0}] \n", - "\n", - "[67 rows x 7 columns]" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from bigframes.ml.linear_model import LinearRegression\n", - "\n", - "linreg = LinearRegression()\n", - "\n", - "# Learn from the training data how to predict output y\n", - "linreg.fit(processed_X_train, y_train)\n", - "\n", - "# Predict y for the test data\n", - "predicted_y_test = linreg.predict(processed_X_test)\n", - "\n", - "# View predictions\n", - "predicted_y_test" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "z42qesW_nAIf" - }, - "source": [ - "#### Unsupervised predictors\n", - "\n", - "In unsupervised learning, there are no known outputs in the training data, instead the model learns on input data alone and predicts something else. An example of an unsupervised predictor is `bigframes.ml.cluster.KMeans`, which learns how to fit input data to a target number of clusters." - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "id": "M13zd02znCIg" - }, - "outputs": [ - { - "data": { - "text/html": [ - "Query job 728068d3-2349-4636-a030-016b500a9812 is DONE. 23.5 kB processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Query job 37bac685-2afa-4ece-b3a3-e0b84a92c65f is DONE. 0 Bytes processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Query job 38416629-4615-45f5-9e27-d9164124f755 is DONE. 6.2 kB processed. 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CENTROID_IDNEAREST_CENTROIDS_DISTANCEonehotencoded_islandstandard_scaled_culmen_length_mmstandard_scaled_culmen_depth_mmstandard_scaled_flipper_length_mmonehotencoded_sexonehotencoded_species
penguin_id
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322[{'CENTROID_ID': 2, 'DISTANCE': 1.237504384283...[{'index': 2, 'value': 1.0}]1.4636851.1683880.39129[{'index': 3, 'value': 1.0}][{'index': 2, 'value': 1.0}]
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372[{'CENTROID_ID': 2, 'DISTANCE': 0.816670297369...[{'index': 2, 'value': 1.0}]1.3540120.511195-0.263041[{'index': 3, 'value': 1.0}][{'index': 2, 'value': 1.0}]
411[{'CENTROID_ID': 1, 'DISTANCE': 1.317560921596...[{'index': 3, 'value': 1.0}]-0.674949-0.095445-1.789814[{'index': 2, 'value': 1.0}][{'index': 1, 'value': 1.0}]
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522[{'CENTROID_ID': 2, 'DISTANCE': 1.004096945181...[{'index': 2, 'value': 1.0}]0.4583440.308982-0.699262[{'index': 2, 'value': 1.0}][{'index': 2, 'value': 1.0}]
564[{'CENTROID_ID': 4, 'DISTANCE': 1.218648668822...[{'index': 1, 'value': 1.0}]-1.0405280.460642-1.135483[{'index': 3, 'value': 1.0}][{'index': 1, 'value': 1.0}]
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623[{'CENTROID_ID': 3, 'DISTANCE': 0.876984617451...[{'index': 1, 'value': 1.0}]0.988433-0.7526381.191028[{'index': 3, 'value': 1.0}][{'index': 3, 'value': 1.0}]
652[{'CENTROID_ID': 2, 'DISTANCE': 1.439604004538...[{'index': 2, 'value': 1.0}]1.7561481.3706010.318587[{'index': 3, 'value': 1.0}][{'index': 2, 'value': 1.0}]
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754[{'CENTROID_ID': 4, 'DISTANCE': 1.075788925734...[{'index': 1, 'value': 1.0}]-1.1136441.421155-0.771966[{'index': 3, 'value': 1.0}][{'index': 1, 'value': 1.0}]
812[{'CENTROID_ID': 2, 'DISTANCE': 0.777307801541...[{'index': 2, 'value': 1.0}]0.6776910.561748-0.408448[{'index': 2, 'value': 1.0}][{'index': 2, 'value': 1.0}]
894[{'CENTROID_ID': 4, 'DISTANCE': 0.891303183824...[{'index': 2, 'value': 1.0}]-0.8577390.713408-0.771966[{'index': 3, 'value': 1.0}][{'index': 1, 'value': 1.0}]
921[{'CENTROID_ID': 1, 'DISTANCE': 0.934676470689...[{'index': 2, 'value': 1.0}]-0.8029020.308982-0.917372[{'index': 2, 'value': 1.0}][{'index': 1, 'value': 1.0}]
934[{'CENTROID_ID': 4, 'DISTANCE': 0.984620018517...[{'index': 1, 'value': 1.0}]-0.3093711.168388-0.263041[{'index': 3, 'value': 1.0}][{'index': 1, 'value': 1.0}]
961[{'CENTROID_ID': 1, 'DISTANCE': 1.446939975674...[{'index': 2, 'value': 1.0}]-0.3093710.662855-1.499[{'index': 2, 'value': 1.0}][{'index': 1, 'value': 1.0}]
1001[{'CENTROID_ID': 1, 'DISTANCE': 1.101117711572...[{'index': 2, 'value': 1.0}]-0.9125760.814515-0.771966[{'index': 2, 'value': 1.0}][{'index': 1, 'value': 1.0}]
1013[{'CENTROID_ID': 3, 'DISTANCE': 0.823832007899...[{'index': 1, 'value': 1.0}]0.549739-1.3087241.554546[{'index': 2, 'value': 1.0}][{'index': 3, 'value': 1.0}]
1024[{'CENTROID_ID': 4, 'DISTANCE': 0.995348310182...[{'index': 2, 'value': 1.0}]-0.1265820.662855-0.626559[{'index': 3, 'value': 1.0}][{'index': 1, 'value': 1.0}]
1073[{'CENTROID_ID': 3, 'DISTANCE': 0.930021405831...[{'index': 1, 'value': 1.0}]1.20778-1.0054041.118325[{'index': 2, 'value': 1.0}][{'index': 3, 'value': 1.0}]
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25 rows × 8 columns

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[67 rows x 8 columns in total]" - ], - "text/plain": [ - " CENTROID_ID NEAREST_CENTROIDS_DISTANCE \\\n", - "penguin_id \n", - "1 3 [{'CENTROID_ID': 3, 'DISTANCE': 0.857057881337... \n", - "15 4 [{'CENTROID_ID': 4, 'DISTANCE': 1.181613302004... \n", - "28 1 [{'CENTROID_ID': 1, 'DISTANCE': 1.006856853050... \n", - "32 2 [{'CENTROID_ID': 2, 'DISTANCE': 1.237504384283... \n", - "33 2 [{'CENTROID_ID': 2, 'DISTANCE': 1.656439702919... \n", - "34 4 [{'CENTROID_ID': 4, 'DISTANCE': 1.343792119214... \n", - "37 2 [{'CENTROID_ID': 2, 'DISTANCE': 0.816670297369... \n", - "41 1 [{'CENTROID_ID': 1, 'DISTANCE': 1.317560921596... \n", - "47 4 [{'CENTROID_ID': 4, 'DISTANCE': 1.135112005343... \n", - "52 2 [{'CENTROID_ID': 2, 'DISTANCE': 1.004096945181... \n", - "56 4 [{'CENTROID_ID': 4, 'DISTANCE': 1.218648668822... \n", - "57 1 [{'CENTROID_ID': 1, 'DISTANCE': 1.238466630273... \n", - "62 3 [{'CENTROID_ID': 3, 'DISTANCE': 0.876984617451... \n", - "65 2 [{'CENTROID_ID': 2, 'DISTANCE': 1.439604004538... \n", - "67 3 [{'CENTROID_ID': 3, 'DISTANCE': 0.763112987694... \n", - "75 4 [{'CENTROID_ID': 4, 'DISTANCE': 1.075788925734... \n", - "81 2 [{'CENTROID_ID': 2, 'DISTANCE': 0.777307801541... \n", - "89 4 [{'CENTROID_ID': 4, 'DISTANCE': 0.891303183824... \n", - "92 1 [{'CENTROID_ID': 1, 'DISTANCE': 0.934676470689... \n", - "93 4 [{'CENTROID_ID': 4, 'DISTANCE': 0.984620018517... \n", - "96 1 [{'CENTROID_ID': 1, 'DISTANCE': 1.446939975674... \n", - "100 1 [{'CENTROID_ID': 1, 'DISTANCE': 1.101117711572... \n", - "101 3 [{'CENTROID_ID': 3, 'DISTANCE': 0.823832007899... \n", - "102 4 [{'CENTROID_ID': 4, 'DISTANCE': 0.995348310182... \n", - "107 3 [{'CENTROID_ID': 3, 'DISTANCE': 0.930021405831... \n", - "\n", - " onehotencoded_island standard_scaled_culmen_length_mm \\\n", - "penguin_id \n", - "1 [{'index': 1, 'value': 1.0}] 0.220718 \n", - "15 [{'index': 2, 'value': 1.0}] -0.510439 \n", - "28 [{'index': 2, 'value': 1.0}] -1.058807 \n", - "32 [{'index': 2, 'value': 1.0}] 1.463685 \n", - "33 [{'index': 2, 'value': 1.0}] -0.254534 \n", - "34 [{'index': 2, 'value': 1.0}] -0.510439 \n", - "37 [{'index': 2, 'value': 1.0}] 1.354012 \n", - "41 [{'index': 3, 'value': 1.0}] -0.674949 \n", - "47 [{'index': 2, 'value': 1.0}] -1.168481 \n", - "52 [{'index': 2, 'value': 1.0}] 0.458344 \n", - "56 [{'index': 1, 'value': 1.0}] -1.040528 \n", - "57 [{'index': 1, 'value': 1.0}] -0.967412 \n", - "62 [{'index': 1, 'value': 1.0}] 0.988433 \n", - "65 [{'index': 2, 'value': 1.0}] 1.756148 \n", - "67 [{'index': 1, 'value': 1.0}] 0.677691 \n", - "75 [{'index': 1, 'value': 1.0}] -1.113644 \n", - "81 [{'index': 2, 'value': 1.0}] 0.677691 \n", - "89 [{'index': 2, 'value': 1.0}] -0.857739 \n", - "92 [{'index': 2, 'value': 1.0}] -0.802902 \n", - "93 [{'index': 1, 'value': 1.0}] -0.309371 \n", - "96 [{'index': 2, 'value': 1.0}] -0.309371 \n", - "100 [{'index': 2, 'value': 1.0}] -0.912576 \n", - "101 [{'index': 1, 'value': 1.0}] 0.549739 \n", - "102 [{'index': 2, 'value': 1.0}] -0.126582 \n", - "107 [{'index': 1, 'value': 1.0}] 1.20778 \n", - "\n", - " standard_scaled_culmen_depth_mm \\\n", - "penguin_id \n", - "1 -1.359277 \n", - "15 0.157322 \n", - "28 0.713408 \n", - "32 1.168388 \n", - "33 0.056215 \n", - "34 0.460642 \n", - "37 0.511195 \n", - "41 -0.095445 \n", - "47 0.662855 \n", - "52 0.308982 \n", - "56 0.460642 \n", - "57 0.005662 \n", - "62 -0.752638 \n", - "65 1.370601 \n", - "67 -1.359277 \n", - "75 1.421155 \n", - "81 0.561748 \n", - "89 0.713408 \n", - "92 0.308982 \n", - "93 1.168388 \n", - "96 0.662855 \n", - "100 0.814515 \n", - "101 -1.308724 \n", - "102 0.662855 \n", - "107 -1.005404 \n", - "\n", - " standard_scaled_flipper_length_mm onehotencoded_sex \\\n", - "penguin_id \n", - "1 1.045621 [{'index': 2, 'value': 1.0}] \n", - "15 -0.771966 [{'index': 3, 'value': 1.0}] \n", - "28 -0.771966 [{'index': 2, 'value': 1.0}] \n", - "32 0.39129 [{'index': 3, 'value': 1.0}] \n", - "33 -0.990076 [{'index': 2, 'value': 1.0}] \n", - "34 0.318587 [{'index': 3, 'value': 1.0}] \n", - "37 -0.263041 [{'index': 3, 'value': 1.0}] \n", - "41 -1.789814 [{'index': 2, 'value': 1.0}] \n", - "47 -0.117634 [{'index': 3, 'value': 1.0}] \n", - "52 -0.699262 [{'index': 2, 'value': 1.0}] \n", - "56 -1.135483 [{'index': 3, 'value': 1.0}] \n", - "57 -0.117634 [{'index': 2, 'value': 1.0}] \n", - "62 1.191028 [{'index': 3, 'value': 1.0}] \n", - "65 0.318587 [{'index': 3, 'value': 1.0}] \n", - "67 1.045621 [{'index': 3, 'value': 1.0}] \n", - "75 -0.771966 [{'index': 3, 'value': 1.0}] \n", - "81 -0.408448 [{'index': 2, 'value': 1.0}] \n", - "89 -0.771966 [{'index': 3, 'value': 1.0}] \n", - "92 -0.917372 [{'index': 2, 'value': 1.0}] \n", - "93 -0.263041 [{'index': 3, 'value': 1.0}] \n", - "96 -1.499 [{'index': 2, 'value': 1.0}] \n", - "100 -0.771966 [{'index': 2, 'value': 1.0}] \n", - "101 1.554546 [{'index': 2, 'value': 1.0}] \n", - "102 -0.626559 [{'index': 3, 'value': 1.0}] \n", - "107 1.118325 [{'index': 2, 'value': 1.0}] \n", - "\n", - " onehotencoded_species \n", - "penguin_id \n", - "1 [{'index': 3, 'value': 1.0}] \n", - "15 [{'index': 1, 'value': 1.0}] \n", - "28 [{'index': 1, 'value': 1.0}] \n", - "32 [{'index': 2, 'value': 1.0}] \n", - "33 [{'index': 2, 'value': 1.0}] \n", - "34 [{'index': 1, 'value': 1.0}] \n", - "37 [{'index': 2, 'value': 1.0}] \n", - "41 [{'index': 1, 'value': 1.0}] \n", - "47 [{'index': 1, 'value': 1.0}] \n", - "52 [{'index': 2, 'value': 1.0}] \n", - "56 [{'index': 1, 'value': 1.0}] \n", - "57 [{'index': 1, 'value': 1.0}] \n", - "62 [{'index': 3, 'value': 1.0}] \n", - "65 [{'index': 2, 'value': 1.0}] \n", - "67 [{'index': 3, 'value': 1.0}] \n", - "75 [{'index': 1, 'value': 1.0}] \n", - "81 [{'index': 2, 'value': 1.0}] \n", - "89 [{'index': 1, 'value': 1.0}] \n", - "92 [{'index': 1, 'value': 1.0}] \n", - "93 [{'index': 1, 'value': 1.0}] \n", - "96 [{'index': 1, 'value': 1.0}] \n", - "100 [{'index': 1, 'value': 1.0}] \n", - "101 [{'index': 3, 'value': 1.0}] \n", - "102 [{'index': 1, 'value': 1.0}] \n", - "107 [{'index': 3, 'value': 1.0}] \n", - "\n", - "[67 rows x 8 columns]" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from bigframes.ml.cluster import KMeans\n", - "\n", - "# Specify KMeans with four clusters\n", - "kmeans = KMeans(n_clusters=4)\n", - "\n", - "# Fit data\n", - "kmeans.fit(processed_X_train)\n", - "\n", - "# View predictions\n", - "kmeans.predict(processed_X_test)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "DFwsIbscnEvh" - }, - "source": [ - "## Pipelines\n", - "\n", - "Transfomers and predictors can be chained into a single estimator component using `bigframes.ml.pipeline.Pipeline`:" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": { - "id": "Ku2OXqgJnEeR" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Pipeline(steps=[('preproc',\n", - " ColumnTransformer(transformers=[('scale', StandardScaler(),\n", - " ['culmen_length_mm',\n", - " 'culmen_depth_mm',\n", - " 'flipper_length_mm']),\n", - " ('encode', OneHotEncoder(),\n", - " ['species', 'sex',\n", - " 'island'])])),\n", - " ('linreg', LinearRegression())])" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from bigframes.ml.pipeline import Pipeline\n", - "\n", - "pipeline = Pipeline([\n", - " ('preproc', preproc),\n", - " ('linreg', linreg)\n", - "])\n", - "\n", - "# Print our pipeline\n", - "pipeline" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "cCQCY_6wnKz_" - }, - "source": [ - "The pipeline simplifies the workflow by applying each of its component steps automatically:" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": { - "id": "hsF7FYagnMko" - }, - "outputs": [ - { - "data": { - "text/html": [ - "Query job 95b43592-b198-4f9e-a990-4e837b82121f is DONE. 24.8 kB processed. 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predicted_body_mass_gislandculmen_length_mmculmen_depth_mmflipper_length_mmsexspecies
penguin_id
14772.374547Biscoe45.114.5215.0FEMALEGentoo penguin (Pygoscelis papua)
153883.371052Dream41.117.5190.0MALEAdelie Penguin (Pygoscelis adeliae)
283479.706166Dream38.118.6190.0FEMALEAdelie Penguin (Pygoscelis adeliae)
324223.851137Dream51.919.5206.0MALEChinstrap penguin (Pygoscelis antarctica)
333197.620461Dream42.517.3187.0FEMALEChinstrap penguin (Pygoscelis antarctica)
344155.265191Dream41.118.1205.0MALEAdelie Penguin (Pygoscelis adeliae)
373991.311319Dream51.318.2197.0MALEChinstrap penguin (Pygoscelis antarctica)
413232.644783Torgersen40.217.0176.0FEMALEAdelie Penguin (Pygoscelis adeliae)
474017.738303Dream37.518.5199.0MALEAdelie Penguin (Pygoscelis adeliae)
523365.077659Dream46.417.8191.0FEMALEChinstrap penguin (Pygoscelis antarctica)
563791.328893Biscoe38.218.1185.0MALEAdelie Penguin (Pygoscelis adeliae)
573547.890609Biscoe38.617.2199.0FEMALEAdelie Penguin (Pygoscelis adeliae)
625372.086117Biscoe49.315.7217.0MALEGentoo penguin (Pygoscelis papua)
654263.229571Dream53.519.9205.0MALEChinstrap penguin (Pygoscelis antarctica)
675234.457401Biscoe47.614.5215.0MALEGentoo penguin (Pygoscelis papua)
753979.311469Biscoe37.820.0190.0MALEAdelie Penguin (Pygoscelis adeliae)
813481.328573Dream47.618.3195.0FEMALEChinstrap penguin (Pygoscelis antarctica)
893915.237615Dream39.218.6190.0MALEAdelie Penguin (Pygoscelis adeliae)
923425.560982Dream39.517.8188.0FEMALEAdelie Penguin (Pygoscelis adeliae)
934141.494969Biscoe42.219.5197.0MALEAdelie Penguin (Pygoscelis adeliae)
963394.719445Dream42.218.5180.0FEMALEAdelie Penguin (Pygoscelis adeliae)
1003507.223965Dream38.918.8190.0FEMALEAdelie Penguin (Pygoscelis adeliae)
1014922.284991Biscoe46.914.6222.0FEMALEGentoo penguin (Pygoscelis papua)
1024016.240318Dream43.218.5192.0MALEAdelie Penguin (Pygoscelis adeliae)
1074933.653758Biscoe50.515.2216.0FEMALEGentoo penguin (Pygoscelis papua)
\n", - "

25 rows × 7 columns

\n", - "
[67 rows x 7 columns in total]" - ], - "text/plain": [ - " predicted_body_mass_g island culmen_length_mm \\\n", - "penguin_id \n", - "1 4772.374547 Biscoe 45.1 \n", - "15 3883.371052 Dream 41.1 \n", - "28 3479.706166 Dream 38.1 \n", - "32 4223.851137 Dream 51.9 \n", - "33 3197.620461 Dream 42.5 \n", - "34 4155.265191 Dream 41.1 \n", - "37 3991.311319 Dream 51.3 \n", - "41 3232.644783 Torgersen 40.2 \n", - "47 4017.738303 Dream 37.5 \n", - "52 3365.077659 Dream 46.4 \n", - "56 3791.328893 Biscoe 38.2 \n", - "57 3547.890609 Biscoe 38.6 \n", - "62 5372.086117 Biscoe 49.3 \n", - "65 4263.229571 Dream 53.5 \n", - "67 5234.457401 Biscoe 47.6 \n", - "75 3979.311469 Biscoe 37.8 \n", - "81 3481.328573 Dream 47.6 \n", - "89 3915.237615 Dream 39.2 \n", - "92 3425.560982 Dream 39.5 \n", - "93 4141.494969 Biscoe 42.2 \n", - "96 3394.719445 Dream 42.2 \n", - "100 3507.223965 Dream 38.9 \n", - "101 4922.284991 Biscoe 46.9 \n", - "102 4016.240318 Dream 43.2 \n", - "107 4933.653758 Biscoe 50.5 \n", - "\n", - " culmen_depth_mm flipper_length_mm sex \\\n", - "penguin_id \n", - "1 14.5 215.0 FEMALE \n", - "15 17.5 190.0 MALE \n", - "28 18.6 190.0 FEMALE \n", - "32 19.5 206.0 MALE \n", - "33 17.3 187.0 FEMALE \n", - "34 18.1 205.0 MALE \n", - "37 18.2 197.0 MALE \n", - "41 17.0 176.0 FEMALE \n", - "47 18.5 199.0 MALE \n", - "52 17.8 191.0 FEMALE \n", - "56 18.1 185.0 MALE \n", - "57 17.2 199.0 FEMALE \n", - "62 15.7 217.0 MALE \n", - "65 19.9 205.0 MALE \n", - "67 14.5 215.0 MALE \n", - "75 20.0 190.0 MALE \n", - "81 18.3 195.0 FEMALE \n", - "89 18.6 190.0 MALE \n", - "92 17.8 188.0 FEMALE \n", - "93 19.5 197.0 MALE \n", - "96 18.5 180.0 FEMALE \n", - "100 18.8 190.0 FEMALE \n", - "101 14.6 222.0 FEMALE \n", - "102 18.5 192.0 MALE \n", - "107 15.2 216.0 FEMALE \n", - "\n", - " species \n", - "penguin_id \n", - "1 Gentoo penguin (Pygoscelis papua) \n", - "15 Adelie Penguin (Pygoscelis adeliae) \n", - "28 Adelie Penguin (Pygoscelis adeliae) \n", - "32 Chinstrap penguin (Pygoscelis antarctica) \n", - "33 Chinstrap penguin (Pygoscelis antarctica) \n", - "34 Adelie Penguin (Pygoscelis adeliae) \n", - "37 Chinstrap penguin (Pygoscelis antarctica) \n", - "41 Adelie Penguin (Pygoscelis adeliae) \n", - "47 Adelie Penguin (Pygoscelis adeliae) \n", - "52 Chinstrap penguin (Pygoscelis antarctica) \n", - "56 Adelie Penguin (Pygoscelis adeliae) \n", - "57 Adelie Penguin (Pygoscelis adeliae) \n", - "62 Gentoo penguin (Pygoscelis papua) \n", - "65 Chinstrap penguin (Pygoscelis antarctica) \n", - "67 Gentoo penguin (Pygoscelis papua) \n", - "75 Adelie Penguin (Pygoscelis adeliae) \n", - "81 Chinstrap penguin (Pygoscelis antarctica) \n", - "89 Adelie Penguin (Pygoscelis adeliae) \n", - "92 Adelie Penguin (Pygoscelis adeliae) \n", - "93 Adelie Penguin (Pygoscelis adeliae) \n", - "96 Adelie Penguin (Pygoscelis adeliae) \n", - "100 Adelie Penguin (Pygoscelis adeliae) \n", - "101 Gentoo penguin (Pygoscelis papua) \n", - "102 Adelie Penguin (Pygoscelis adeliae) \n", - "107 Gentoo penguin (Pygoscelis papua) \n", - "\n", - "[67 rows x 7 columns]" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pipeline.fit(X_train, y_train)\n", - "\n", - "predicted_y_test = pipeline.predict(X_test)\n", - "predicted_y_test" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "SiLzpsg8nRXn" - }, - "source": [ - "In the backend, a pipeline will actually be compiled into a single model with an embedded TRANSFORM step." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "sTzAxTv1nUKZ" - }, - "source": [ - "## Evaluating results\n", - "\n", - "Some models include a convenient `.score(X, y)` method for evaulation with a preset accuracy metric:" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": { - "id": "Q8nR1ZqznU-B" - }, - "outputs": [ - { - "data": { - "text/html": [ - "Query job c098e1d1-b3ed-4ec5-94c7-6ba3b2b59e3f is DONE. 29.6 kB processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Query job 035234b0-537a-44ce-adff-bb51c40b4ffa is DONE. 0 Bytes processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Query job b4a2a367-3e06-4fa3-9f00-bdbca884cfdd is DONE. 48 Bytes processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
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mean_absolute_errormean_squared_errormean_squared_log_errormedian_absolute_errorr2_scoreexplained_variance
0225.88351277765.9892810.004457179.5480410.8731660.873315
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[1 rows x 6 columns in total]" - ], - "text/plain": [ - " mean_absolute_error mean_squared_error mean_squared_log_error \\\n", - "0 225.883512 77765.989281 0.004457 \n", - "\n", - " median_absolute_error r2_score explained_variance \n", - "0 179.548041 0.873166 0.873315 \n", - "\n", - "[1 rows x 6 columns]" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# In the case of a pipeline, this will be equivalent to calling .score on the contained LinearRegression\n", - "pipeline.score(X_test, y_test)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "UHM7jls6nY8A" - }, - "source": [ - "For a more general approach, the library `bigframes.ml.metrics` is provided:" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": { - "id": "vdEN4Ob9nan4" - }, - "outputs": [ - { - "data": { - "text/html": [ - "Query job 20ec1716-3e8e-4d3f-ba08-1f7b9970ce3f is DONE. 28.9 kB processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Query job 6f628f3b-62df-4a5a-8e05-0b313db0ed07 is DONE. 28.9 kB processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Query job c4eee1e5-146f-4a52-8499-83fe5f701f53 is DONE. 30.0 kB processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "0.8731660699616813" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from bigframes.ml.metrics import r2_score\n", - "\n", - "r2_score(y_test, predicted_y_test[\"predicted_body_mass_g\"])" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "opn4ycPyneVh" - }, - "source": [ - "## Save to BigQuery\n", - "\n", - "Estimators can be saved to BigQuery as BQML models, and loaded again in future.\n", - "\n", - "Saving requires `bigquery.tables.create` permission, and loading requires `bigquery.models.getMetadata` permission.\n", - "These permissions can be at project level or the dataset level.\n", - "\n", - "If you have those permissions, please go ahead and uncomment the code in the following cells and run." - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": { - "id": "fb0HpkdpnigJ" - }, - "outputs": [ - { - "data": { - "text/html": [ - "Copy job 06c2b62d-a7aa-46a5-a04a-2f189bafc5ee is DONE. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "Pipeline(steps=[('transform',\n", - " ColumnTransformer(transformers=[('ont_hot_encoder',\n", - " OneHotEncoder(max_categories=1000001,\n", - " min_frequency=0),\n", - " 'island'),\n", - " ('standard_scaler',\n", - " StandardScaler(),\n", - " 'culmen_length_mm'),\n", - " ('standard_scaler',\n", - " StandardScaler(),\n", - " 'culmen_depth_mm'),\n", - " ('standard_scaler',\n", - " StandardScaler(),\n", - " 'flipper_length_mm'),\n", - " ('ont_hot_encoder',\n", - " OneHotEncoder(max_categories=1000001,\n", - " min_frequency=0),\n", - " 'sex'),\n", - " ('ont_hot_encoder',\n", - " OneHotEncoder(max_categories=1000001,\n", - " min_frequency=0),\n", - " 'species')])),\n", - " ('estimator',\n", - " LinearRegression(optimize_strategy='NORMAL_EQUATION'))])" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "linreg.to_gbq(f\"{DATASET}.penguins_model\", replace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": { - "id": "_zNOBlHdnkII" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Pipeline(steps=[('transform',\n", - " ColumnTransformer(transformers=[('ont_hot_encoder',\n", - " OneHotEncoder(max_categories=1000001,\n", - " min_frequency=0),\n", - " 'island'),\n", - " ('standard_scaler',\n", - " StandardScaler(),\n", - " 'culmen_length_mm'),\n", - " ('standard_scaler',\n", - " StandardScaler(),\n", - " 'culmen_depth_mm'),\n", - " ('standard_scaler',\n", - " StandardScaler(),\n", - " 'flipper_length_mm'),\n", - " ('ont_hot_encoder',\n", - " OneHotEncoder(max_categories=1000001,\n", - " min_frequency=0),\n", - " 'sex'),\n", - " ('ont_hot_encoder',\n", - " OneHotEncoder(max_categories=1000001,\n", - " min_frequency=0),\n", - " 'species')])),\n", - " ('estimator',\n", - " LinearRegression(optimize_strategy='NORMAL_EQUATION'))])" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bpd.read_gbq_model(f\"{DATASET}.penguins_model\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "RfV-du5uTcBB" - }, - "source": [ - "We can also save the pipeline to BigQuery. BigQuery will save this as a single model, with the pre-processing steps embedded in the TRANSFORM property:" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": { - "id": "P76_TQ3IR6nB" - }, - "outputs": [ - { - "data": { - "text/html": [ - "Copy job a0ed8c1b-3a3f-4995-853c-e151d41560d7 is DONE. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "Pipeline(steps=[('transform',\n", - " ColumnTransformer(transformers=[('ont_hot_encoder',\n", - " OneHotEncoder(max_categories=1000001,\n", - " min_frequency=0),\n", - " 'island'),\n", - " ('standard_scaler',\n", - " StandardScaler(),\n", - " 'culmen_length_mm'),\n", - " ('standard_scaler',\n", - " StandardScaler(),\n", - " 'culmen_depth_mm'),\n", - " ('standard_scaler',\n", - " StandardScaler(),\n", - " 'flipper_length_mm'),\n", - " ('ont_hot_encoder',\n", - " OneHotEncoder(max_categories=1000001,\n", - " min_frequency=0),\n", - " 'sex'),\n", - " ('ont_hot_encoder',\n", - " OneHotEncoder(max_categories=1000001,\n", - " min_frequency=0),\n", - " 'species')])),\n", - " ('estimator',\n", - " LinearRegression(optimize_strategy='NORMAL_EQUATION'))])" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pipeline.to_gbq(f\"{DATASET}.penguins_pipeline\", replace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": { - "id": "GKvlKFjAbToJ" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Pipeline(steps=[('transform',\n", - " ColumnTransformer(transformers=[('ont_hot_encoder',\n", - " OneHotEncoder(max_categories=1000001,\n", - " min_frequency=0),\n", - " 'island'),\n", - " ('standard_scaler',\n", - " StandardScaler(),\n", - " 'culmen_length_mm'),\n", - " ('standard_scaler',\n", - " StandardScaler(),\n", - " 'culmen_depth_mm'),\n", - " ('standard_scaler',\n", - " StandardScaler(),\n", - " 'flipper_length_mm'),\n", - " ('ont_hot_encoder',\n", - " OneHotEncoder(max_categories=1000001,\n", - " min_frequency=0),\n", - " 'sex'),\n", - " ('ont_hot_encoder',\n", - " OneHotEncoder(max_categories=1000001,\n", - " min_frequency=0),\n", - " 'species')])),\n", - " ('estimator',\n", - " LinearRegression(optimize_strategy='NORMAL_EQUATION'))])" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bpd.read_gbq_model(f\"{DATASET}.penguins_pipeline\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "wCsmt0IwFkDy" - }, - "source": [ - "## Summary and next steps\n", - "\n", - "You've completed an end-to-end machine learning workflow using the built-in capabilities of BigQuery DataFrames.\n", - "\n", - "Learn more about BigQuery DataFrames in the [documentation](https://cloud.google.com/python/docs/reference/bigframes/latest) and find more sample notebooks in the [GitHub repo](https://github.com/googleapis/python-bigquery-dataframes/tree/main/notebooks)." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "TpV-iwP9qw9c" - }, - "source": [ - "### Cleaning up\n", - "\n", - "To clean up all Google Cloud resources used in this project, you can [delete the Google Cloud\n", - "project](https://cloud.google.com/resource-manager/docs/creating-managing-projects#shutting_down_projects) you used for the tutorial.\n", - "\n", - "Otherwise, you can uncomment the remaining cells and run them to delete the individual resources you created in this tutorial:" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": { - "id": "QwumLUKmVpuH" - }, - "outputs": [], - "source": [ - "# # Delete the BQML models\n", - "# MODEL_NAME = f\"{PROJECT_ID}:{DATASET}.penguins_model\"\n", - "# ! bq rm -f --model {MODEL_NAME}\n", - "# PIPELINE_NAME = f\"{PROJECT_ID}:{DATASET}.penguins_pipeline\"\n", - "# ! bq rm -f --model {PIPELINE_NAME}" - ] - } - ], - "metadata": { - "colab": { - "provenance": [] - }, - "kernelspec": { - "display_name": "Python 3", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.1" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/notebooks/getting_started/pandas_extensions.ipynb b/notebooks/getting_started/pandas_extensions.ipynb deleted file mode 100644 index c511eab9b4a..00000000000 --- a/notebooks/getting_started/pandas_extensions.ipynb +++ /dev/null @@ -1,160 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# BigQuery extension for pandas\n", - "\n", - "BigQuery DataFrames provides a pandas extension to execute BigQuery SQL scalar functions directly on pandas DataFrames." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import bigframes # This import registers the bigquery accessor." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "By default, BigQuery DataFrames selects a location to process data based on the\n", - "data location, but using a pandas object doesn't provide such informat. If\n", - "processing location is important to you, configure the location before using the\n", - "accessor." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import bigframes.pandas as bpd\n", - "\n", - "bpd.reset_session()\n", - "bpd.options.bigquery.location = \"US\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Using `sql_scalar`\n", - "\n", - "The `bigquery.sql_scalar` method allows you to apply a SQL scalar function to a pandas DataFrame by converting it to BigFrames, executing the SQL in BigQuery, and returning the result as a pandas Series." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " Query processed 0 Bytes in a moment of slot time.\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "0 2.0\n", - "1 3.0\n", - "2 4.0\n", - "dtype: Float64" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df = pd.DataFrame({\"a\": [1.5, 2.5, 3.5]})\n", - "result = df.bigquery.sql_scalar(\"ROUND({0}, 0)\")\n", - "result" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can also use multiple columns." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " Query processed 0 Bytes in a moment of slot time.\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "0 11\n", - "1 22\n", - "2 33\n", - "dtype: Int64" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df = pd.DataFrame({\"a\": [1, 2, 3], \"b\": [10, 20, 30]})\n", - "result = df.bigquery.sql_scalar(\"{a} + {b}\")\n", - "result" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.9" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/notebooks/kaggle/bq_dataframes_ai_forecast.ipynb b/notebooks/kaggle/bq_dataframes_ai_forecast.ipynb deleted file mode 100644 index 87ef9f6e96d..00000000000 --- a/notebooks/kaggle/bq_dataframes_ai_forecast.ipynb +++ /dev/null @@ -1,1741 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19", - "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5" - }, - "source": [ - "# BigQuery DataFrames (BigFrames) AI Forecast\n", - "\n", - "This notebook is adapted from https://github.com/googleapis/python-bigquery-dataframes/blob/main/notebooks/generative_ai/bq_dataframes_ai_forecast.ipynb to work in the Kaggle runtime. It introduces forecasting with GenAI Foundation Model with BigFrames AI.\n", - "\n", - "Install the bigframes package and upgrade other packages that are already included in Kaggle but have versions incompatible with bigframes." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "trusted": true - }, - "outputs": [], - "source": [ - "%pip install --upgrade bigframes google-cloud-automl google-cloud-translate google-ai-generativelanguage tensorflow " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Important:** restart the kernel by going to \"Run -> Restart & clear cell outputs\" before continuing.\n", - "\n", - "Configure bigframes to use your GCP project. First, go to \"Add-ons -> Google Cloud SDK\" and click the \"Attach\" button. Then," - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "execution": { - "iopub.execute_input": "2025-08-18T19:16:10.449828Z", - "iopub.status.busy": "2025-08-18T19:16:10.449563Z", - "iopub.status.idle": "2025-08-18T19:16:10.618943Z", - "shell.execute_reply": "2025-08-18T19:16:10.617631Z", - "shell.execute_reply.started": "2025-08-18T19:16:10.449803Z" - }, - "trusted": true - }, - "outputs": [], - "source": [ - "from kaggle_secrets import UserSecretsClient\n", - "user_secrets = UserSecretsClient()\n", - "user_credential = user_secrets.get_gcloud_credential()\n", - "user_secrets.set_tensorflow_credential(user_credential)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": { - "iopub.execute_input": "2025-08-18T19:20:00.851870Z", - "iopub.status.busy": "2025-08-18T19:20:00.851472Z", - "iopub.status.idle": "2025-08-18T19:20:00.858175Z", - "shell.execute_reply": "2025-08-18T19:20:00.857098Z", - "shell.execute_reply.started": "2025-08-18T19:20:00.851842Z" - }, - "trusted": true - }, - "outputs": [], - "source": [ - "PROJECT = \"swast-scratch\" # replace with your project\n", - "\n", - "\n", - "import bigframes.pandas as bpd\n", - "bpd.options.bigquery.project = PROJECT\n", - "bpd.options.bigquery.ordering_mode = \"partial\" # Optional: partial ordering mode can accelerate executions and save costs" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1. Create a BigFrames DataFrames from BigQuery public data." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "execution": { - "iopub.execute_input": "2025-08-18T19:20:02.255184Z", - "iopub.status.busy": "2025-08-18T19:20:02.254706Z", - "iopub.status.idle": "2025-08-18T19:20:04.754064Z", - "shell.execute_reply": "2025-08-18T19:20:04.752940Z", - "shell.execute_reply.started": "2025-08-18T19:20:02.255149Z" - }, - "trusted": true - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/lib/python3.11/dist-packages/bigframes/core/log_adapter.py:175: TimeTravelCacheWarning: Reading cached table from 2025-08-18 19:19:20.590271+00:00 to avoid\n", - "incompatibilies with previous reads of this table. To read the latest\n", - "version, set `use_cache=False` or close the current session with\n", - "Session.close() or bigframes.pandas.close_session().\n", - " return method(*args, **kwargs)\n" - ] - }, - { - "data": { - "text/html": [ - "
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trip_idduration_secstart_datestart_station_namestart_station_idend_dateend_station_nameend_station_idbike_numberzip_code...c_subscription_typestart_station_latitudestart_station_longitudeend_station_latitudeend_station_longitudemember_birth_yearmember_genderbike_share_for_all_tripstart_station_geomend_station_geom
02018020921350835967882018-02-09 21:35:08+00:0010th Ave at E 15th St2222018-02-09 21:48:17+00:0010th Ave at E 15th St2223596<NA>...<NA>37.792714-122.2487837.792714-122.248781984MaleYesPOINT (-122.24878 37.79271)POINT (-122.24878 37.79271)
12017081523574224919652017-08-15 23:57:42+00:0010th St at Fallon St2012017-08-16 00:13:48+00:0010th Ave at E 15th St2222491<NA>...<NA>37.797673-122.26299737.792714-122.24878<NA><NA><NA>POINT (-122.26300 37.79767)POINT (-122.24878 37.79271)
22018022816572536325602018-02-28 16:57:25+00:0010th St at Fallon St2012018-02-28 17:06:46+00:0010th Ave at E 15th St2223632<NA>...<NA>37.797673-122.26299737.792714-122.248781984MaleYesPOINT (-122.26300 37.79767)POINT (-122.24878 37.79271)
32017111700460913374972017-11-17 00:46:09+00:0010th St at Fallon St2012017-11-17 00:54:26+00:0010th Ave at E 15th St2221337<NA>...<NA>37.797673-122.26299737.792714-122.24878<NA><NA><NA>POINT (-122.26300 37.79767)POINT (-122.24878 37.79271)
42018022019132312575962018-02-20 19:13:23+00:0010th St at Fallon St2012018-02-20 19:23:19+00:0010th Ave at E 15th St2221257<NA>...<NA>37.797673-122.26299737.792714-122.248781984MaleYesPOINT (-122.26300 37.79767)POINT (-122.24878 37.79271)
520170824232500127913412017-08-24 23:25:00+00:0010th St at Fallon St2012017-08-24 23:47:22+00:0010th Ave at E 15th St2221279<NA>...<NA>37.797673-122.26299737.792714-122.248781969Male<NA>POINT (-122.26300 37.79767)POINT (-122.24878 37.79271)
62018011618004732914892018-01-16 18:00:47+00:0010th St at Fallon St2012018-01-16 18:08:56+00:0010th Ave at E 15th St2223291<NA>...<NA>37.797673-122.26299737.792714-122.248781984MaleYesPOINT (-122.26300 37.79767)POINT (-122.24878 37.79271)
72018040815560118311052018-04-08 15:56:01+00:0013th St at Franklin St3382018-04-08 16:14:26+00:0010th Ave at E 15th St222183<NA>...<NA>37.803189-122.27057937.792714-122.248781987FemaleNoPOINT (-122.27058 37.80319)POINT (-122.24878 37.79271)
82018031418570322046192018-03-14 18:57:03+00:0013th St at Franklin St3382018-03-14 19:07:23+00:0010th Ave at E 15th St2222204<NA>...<NA>37.803189-122.27057937.792714-122.248781982OtherNoPOINT (-122.27058 37.80319)POINT (-122.24878 37.79271)
92017081920533114907432017-08-19 20:53:31+00:002nd Ave at E 18th St2002017-08-19 21:05:54+00:0010th Ave at E 15th St2221490<NA>...<NA>37.800214-122.2538137.792714-122.24878<NA><NA><NA>POINT (-122.25381 37.80021)POINT (-122.24878 37.79271)
102017111818232819603532017-11-18 18:23:28+00:002nd Ave at E 18th St2002017-11-18 18:29:22+00:0010th Ave at E 15th St2221960<NA>...<NA>37.800214-122.2538137.792714-122.248781988Male<NA>POINT (-122.25381 37.80021)POINT (-122.24878 37.79271)
112017081020445483912562017-08-10 20:44:54+00:002nd Ave at E 18th St2002017-08-10 21:05:50+00:0010th Ave at E 15th St222839<NA>...<NA>37.800214-122.2538137.792714-122.24878<NA><NA><NA>POINT (-122.25381 37.80021)POINT (-122.24878 37.79271)
122018011716565535045002018-01-17 16:56:55+00:00El Embarcadero at Grand Ave1972018-01-17 17:05:16+00:0010th Ave at E 15th St2223504<NA>...<NA>37.808848-122.2496837.792714-122.248781987MaleNoPOINT (-122.24968 37.80885)POINT (-122.24878 37.79271)
132018011116131013058582018-01-11 16:13:10+00:00Frank H Ogawa Plaza72018-01-11 16:27:28+00:0010th Ave at E 15th St2221305<NA>...<NA>37.804562-122.27173837.792714-122.248781984MaleYesPOINT (-122.27174 37.80456)POINT (-122.24878 37.79271)
1420180224182655121512352018-02-24 18:26:55+00:00Frank H Ogawa Plaza72018-02-24 18:47:31+00:0010th Ave at E 15th St2221215<NA>...<NA>37.804562-122.27173837.792714-122.248781969MaleNoPOINT (-122.27174 37.80456)POINT (-122.24878 37.79271)
152018030916214834508572018-03-09 16:21:48+00:00Frank H Ogawa Plaza72018-03-09 16:36:06+00:0010th Ave at E 15th St2223450<NA>...<NA>37.804562-122.27173837.792714-122.248781984MaleYesPOINT (-122.27174 37.80456)POINT (-122.24878 37.79271)
162018010219322327179142018-01-02 19:32:23+00:00Frank H Ogawa Plaza72018-01-02 19:47:38+00:0010th Ave at E 15th St2222717<NA>...<NA>37.804562-122.27173837.792714-122.248781984MaleYesPOINT (-122.27174 37.80456)POINT (-122.24878 37.79271)
172018031619102837515642018-03-16 19:10:28+00:00Frank H Ogawa Plaza72018-03-16 19:19:52+00:0010th Ave at E 15th St2223751<NA>...<NA>37.804562-122.27173837.792714-122.248781987MaleNoPOINT (-122.27174 37.80456)POINT (-122.24878 37.79271)
18201712121524032278542017-12-12 15:24:03+00:00Frank H Ogawa Plaza72017-12-12 15:38:17+00:0010th Ave at E 15th St222227<NA>...<NA>37.804562-122.27173837.792714-122.248781984Male<NA>POINT (-122.27174 37.80456)POINT (-122.24878 37.79271)
192018031314370337249172018-03-13 14:37:03+00:00Grand Ave at Webster St1812018-03-13 14:52:20+00:0010th Ave at E 15th St2223724<NA>...<NA>37.811377-122.26519237.792714-122.248781989MaleNoPOINT (-122.26519 37.81138)POINT (-122.24878 37.79271)
202017120617555934265192017-12-06 17:55:59+00:00Lake Merritt BART Station1632017-12-06 18:04:39+00:0010th Ave at E 15th St2223426<NA>...<NA>37.79732-122.2653237.792714-122.248781986Male<NA>POINT (-122.26532 37.79732)POINT (-122.24878 37.79271)
21201804042100344513662018-04-04 21:00:34+00:00Lake Merritt BART Station1632018-04-04 21:06:41+00:0010th Ave at E 15th St222451<NA>...<NA>37.79732-122.2653237.792714-122.248781987MaleNoPOINT (-122.26532 37.79732)POINT (-122.24878 37.79271)
222018012319071617876262018-01-23 19:07:16+00:00Lake Merritt BART Station1632018-01-23 19:17:43+00:0010th Ave at E 15th St2221787<NA>...<NA>37.79732-122.2653237.792714-122.248781987MaleNoPOINT (-122.26532 37.79732)POINT (-122.24878 37.79271)
232017082710570611579732017-08-27 10:57:06+00:00Lake Merritt BART Station1632017-08-27 11:13:19+00:0010th Ave at E 15th St2221157<NA>...<NA>37.79732-122.2653237.792714-122.24878<NA><NA><NA>POINT (-122.26532 37.79732)POINT (-122.24878 37.79271)
24201709071348372074114342017-09-07 13:48:37+00:00Lake Merritt BART Station1632017-09-07 16:59:12+00:0010th Ave at E 15th St2222074<NA>...<NA>37.79732-122.2653237.792714-122.24878<NA><NA><NA>POINT (-122.26532 37.79732)POINT (-122.24878 37.79271)
\n", - "

25 rows × 21 columns

\n", - "
[1947417 rows x 21 columns in total]" - ], - "text/plain": [ - " trip_id duration_sec start_date \\\n", - "201802092135083596 788 2018-02-09 21:35:08+00:00 \n", - "201708152357422491 965 2017-08-15 23:57:42+00:00 \n", - "201802281657253632 560 2018-02-28 16:57:25+00:00 \n", - "201711170046091337 497 2017-11-17 00:46:09+00:00 \n", - "201802201913231257 596 2018-02-20 19:13:23+00:00 \n", - "201708242325001279 1341 2017-08-24 23:25:00+00:00 \n", - "201801161800473291 489 2018-01-16 18:00:47+00:00 \n", - " 20180408155601183 1105 2018-04-08 15:56:01+00:00 \n", - "201803141857032204 619 2018-03-14 18:57:03+00:00 \n", - "201708192053311490 743 2017-08-19 20:53:31+00:00 \n", - "201711181823281960 353 2017-11-18 18:23:28+00:00 \n", - " 20170810204454839 1256 2017-08-10 20:44:54+00:00 \n", - "201801171656553504 500 2018-01-17 16:56:55+00:00 \n", - "201801111613101305 858 2018-01-11 16:13:10+00:00 \n", - "201802241826551215 1235 2018-02-24 18:26:55+00:00 \n", - "201803091621483450 857 2018-03-09 16:21:48+00:00 \n", - "201801021932232717 914 2018-01-02 19:32:23+00:00 \n", - "201803161910283751 564 2018-03-16 19:10:28+00:00 \n", - " 20171212152403227 854 2017-12-12 15:24:03+00:00 \n", - "201803131437033724 917 2018-03-13 14:37:03+00:00 \n", - "201712061755593426 519 2017-12-06 17:55:59+00:00 \n", - " 20180404210034451 366 2018-04-04 21:00:34+00:00 \n", - "201801231907161787 626 2018-01-23 19:07:16+00:00 \n", - "201708271057061157 973 2017-08-27 10:57:06+00:00 \n", - "201709071348372074 11434 2017-09-07 13:48:37+00:00 \n", - "\n", - " start_station_name start_station_id end_date \\\n", - " 10th Ave at E 15th St 222 2018-02-09 21:48:17+00:00 \n", - " 10th St at Fallon St 201 2017-08-16 00:13:48+00:00 \n", - " 10th St at Fallon St 201 2018-02-28 17:06:46+00:00 \n", - " 10th St at Fallon St 201 2017-11-17 00:54:26+00:00 \n", - " 10th St at Fallon St 201 2018-02-20 19:23:19+00:00 \n", - " 10th St at Fallon St 201 2017-08-24 23:47:22+00:00 \n", - " 10th St at Fallon St 201 2018-01-16 18:08:56+00:00 \n", - " 13th St at Franklin St 338 2018-04-08 16:14:26+00:00 \n", - " 13th St at Franklin St 338 2018-03-14 19:07:23+00:00 \n", - " 2nd Ave at E 18th St 200 2017-08-19 21:05:54+00:00 \n", - " 2nd Ave at E 18th St 200 2017-11-18 18:29:22+00:00 \n", - " 2nd Ave at E 18th St 200 2017-08-10 21:05:50+00:00 \n", - "El Embarcadero at Grand Ave 197 2018-01-17 17:05:16+00:00 \n", - " Frank H Ogawa Plaza 7 2018-01-11 16:27:28+00:00 \n", - " Frank H Ogawa Plaza 7 2018-02-24 18:47:31+00:00 \n", - " Frank H Ogawa Plaza 7 2018-03-09 16:36:06+00:00 \n", - " Frank H Ogawa Plaza 7 2018-01-02 19:47:38+00:00 \n", - " Frank H Ogawa Plaza 7 2018-03-16 19:19:52+00:00 \n", - " Frank H Ogawa Plaza 7 2017-12-12 15:38:17+00:00 \n", - " Grand Ave at Webster St 181 2018-03-13 14:52:20+00:00 \n", - " Lake Merritt BART Station 163 2017-12-06 18:04:39+00:00 \n", - " Lake Merritt BART Station 163 2018-04-04 21:06:41+00:00 \n", - " Lake Merritt BART Station 163 2018-01-23 19:17:43+00:00 \n", - " Lake Merritt BART Station 163 2017-08-27 11:13:19+00:00 \n", - " Lake Merritt BART Station 163 2017-09-07 16:59:12+00:00 \n", - "\n", - " end_station_name end_station_id bike_number zip_code ... \\\n", - "10th Ave at E 15th St 222 3596 ... \n", - "10th Ave at E 15th St 222 2491 ... \n", - "10th Ave at E 15th St 222 3632 ... \n", - "10th Ave at E 15th St 222 1337 ... \n", - "10th Ave at E 15th St 222 1257 ... \n", - "10th Ave at E 15th St 222 1279 ... \n", - "10th Ave at E 15th St 222 3291 ... \n", - "10th Ave at E 15th St 222 183 ... \n", - "10th Ave at E 15th St 222 2204 ... \n", - "10th Ave at E 15th St 222 1490 ... \n", - "10th Ave at E 15th St 222 1960 ... \n", - "10th Ave at E 15th St 222 839 ... \n", - "10th Ave at E 15th St 222 3504 ... \n", - "10th Ave at E 15th St 222 1305 ... \n", - "10th Ave at E 15th St 222 1215 ... \n", - "10th Ave at E 15th St 222 3450 ... \n", - "10th Ave at E 15th St 222 2717 ... \n", - "10th Ave at E 15th St 222 3751 ... \n", - "10th Ave at E 15th St 222 227 ... \n", - "10th Ave at E 15th St 222 3724 ... \n", - "10th Ave at E 15th St 222 3426 ... \n", - "10th Ave at E 15th St 222 451 ... \n", - "10th Ave at E 15th St 222 1787 ... \n", - "10th Ave at E 15th St 222 1157 ... \n", - "10th Ave at E 15th St 222 2074 ... \n", - "\n", - "c_subscription_type start_station_latitude start_station_longitude \\\n", - " 37.792714 -122.24878 \n", - " 37.797673 -122.262997 \n", - " 37.797673 -122.262997 \n", - " 37.797673 -122.262997 \n", - " 37.797673 -122.262997 \n", - " 37.797673 -122.262997 \n", - " 37.797673 -122.262997 \n", - " 37.803189 -122.270579 \n", - " 37.803189 -122.270579 \n", - " 37.800214 -122.25381 \n", - " 37.800214 -122.25381 \n", - " 37.800214 -122.25381 \n", - " 37.808848 -122.24968 \n", - " 37.804562 -122.271738 \n", - " 37.804562 -122.271738 \n", - " 37.804562 -122.271738 \n", - " 37.804562 -122.271738 \n", - " 37.804562 -122.271738 \n", - " 37.804562 -122.271738 \n", - " 37.811377 -122.265192 \n", - " 37.79732 -122.26532 \n", - " 37.79732 -122.26532 \n", - " 37.79732 -122.26532 \n", - " 37.79732 -122.26532 \n", - " 37.79732 -122.26532 \n", - "\n", - " end_station_latitude end_station_longitude member_birth_year \\\n", - " 37.792714 -122.24878 1984 \n", - " 37.792714 -122.24878 \n", - " 37.792714 -122.24878 1984 \n", - " 37.792714 -122.24878 \n", - " 37.792714 -122.24878 1984 \n", - " 37.792714 -122.24878 1969 \n", - " 37.792714 -122.24878 1984 \n", - " 37.792714 -122.24878 1987 \n", - " 37.792714 -122.24878 1982 \n", - " 37.792714 -122.24878 \n", - " 37.792714 -122.24878 1988 \n", - " 37.792714 -122.24878 \n", - " 37.792714 -122.24878 1987 \n", - " 37.792714 -122.24878 1984 \n", - " 37.792714 -122.24878 1969 \n", - " 37.792714 -122.24878 1984 \n", - " 37.792714 -122.24878 1984 \n", - " 37.792714 -122.24878 1987 \n", - " 37.792714 -122.24878 1984 \n", - " 37.792714 -122.24878 1989 \n", - " 37.792714 -122.24878 1986 \n", - " 37.792714 -122.24878 1987 \n", - " 37.792714 -122.24878 1987 \n", - " 37.792714 -122.24878 \n", - " 37.792714 -122.24878 \n", - "\n", - " member_gender bike_share_for_all_trip start_station_geom \\\n", - " Male Yes POINT (-122.24878 37.79271) \n", - " POINT (-122.26300 37.79767) \n", - " Male Yes POINT (-122.26300 37.79767) \n", - " POINT (-122.26300 37.79767) \n", - " Male Yes POINT (-122.26300 37.79767) \n", - " Male POINT (-122.26300 37.79767) \n", - " Male Yes POINT (-122.26300 37.79767) \n", - " Female No POINT (-122.27058 37.80319) \n", - " Other No POINT (-122.27058 37.80319) \n", - " POINT (-122.25381 37.80021) \n", - " Male POINT (-122.25381 37.80021) \n", - " POINT (-122.25381 37.80021) \n", - " Male No POINT (-122.24968 37.80885) \n", - " Male Yes POINT (-122.27174 37.80456) \n", - " Male No POINT (-122.27174 37.80456) \n", - " Male Yes POINT (-122.27174 37.80456) \n", - " Male Yes POINT (-122.27174 37.80456) \n", - " Male No POINT (-122.27174 37.80456) \n", - " Male POINT (-122.27174 37.80456) \n", - " Male No POINT (-122.26519 37.81138) \n", - " Male POINT (-122.26532 37.79732) \n", - " Male No POINT (-122.26532 37.79732) \n", - " Male No POINT (-122.26532 37.79732) \n", - " POINT (-122.26532 37.79732) \n", - " POINT (-122.26532 37.79732) \n", - "\n", - " end_station_geom \n", - "POINT (-122.24878 37.79271) \n", - "POINT (-122.24878 37.79271) \n", - "POINT (-122.24878 37.79271) \n", - "POINT (-122.24878 37.79271) \n", - "POINT (-122.24878 37.79271) \n", - "POINT (-122.24878 37.79271) \n", - "POINT (-122.24878 37.79271) \n", - "POINT (-122.24878 37.79271) \n", - "POINT (-122.24878 37.79271) \n", - "POINT (-122.24878 37.79271) \n", - "POINT (-122.24878 37.79271) \n", - "POINT (-122.24878 37.79271) \n", - "POINT (-122.24878 37.79271) \n", - "POINT (-122.24878 37.79271) \n", - "POINT (-122.24878 37.79271) \n", - "POINT (-122.24878 37.79271) \n", - "POINT (-122.24878 37.79271) \n", - "POINT (-122.24878 37.79271) \n", - "POINT (-122.24878 37.79271) \n", - "POINT (-122.24878 37.79271) \n", - "POINT (-122.24878 37.79271) \n", - "POINT (-122.24878 37.79271) \n", - "POINT (-122.24878 37.79271) \n", - "POINT (-122.24878 37.79271) \n", - "POINT (-122.24878 37.79271) \n", - "...\n", - "\n", - "[1947417 rows x 21 columns]" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df = bpd.read_gbq(\"bigquery-public-data.san_francisco_bikeshare.bikeshare_trips\")\n", - "df" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2. Preprocess Data\n", - "\n", - "Only take the `start_date` after 2018 and the \"Subscriber\" category as input. `start_date` are truncated to each hour." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "execution": { - "iopub.execute_input": "2025-08-18T19:20:44.398876Z", - "iopub.status.busy": "2025-08-18T19:20:44.397712Z", - "iopub.status.idle": "2025-08-18T19:20:44.421504Z", - "shell.execute_reply": "2025-08-18T19:20:44.420509Z", - "shell.execute_reply.started": "2025-08-18T19:20:44.398742Z" - }, - "trusted": true - }, - "outputs": [], - "source": [ - "df = df[df[\"start_date\"] >= \"2018-01-01\"]\n", - "df = df[df[\"subscriber_type\"] == \"Subscriber\"]\n", - "df[\"trip_hour\"] = df[\"start_date\"].dt.floor(\"h\")\n", - "df = df[[\"trip_hour\", \"trip_id\"]]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Group and count each hour's num of trips." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "execution": { - "iopub.execute_input": "2025-08-18T19:20:57.500413Z", - "iopub.status.busy": "2025-08-18T19:20:57.499571Z", - "iopub.status.idle": "2025-08-18T19:21:02.999663Z", - "shell.execute_reply": "2025-08-18T19:21:02.998792Z", - "shell.execute_reply.started": "2025-08-18T19:20:57.500376Z" - }, - "trusted": true - }, - "outputs": [ - { - "data": { - "text/html": [ - "Query job e3df71d2-9248-491a-8e5f-4bb5bfedb686 is DONE. 58.7 MB processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
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trip_hournum_trips
02018-01-01 00:00:00+00:0020
12018-01-01 01:00:00+00:0025
22018-01-01 02:00:00+00:0013
32018-01-01 03:00:00+00:0011
42018-01-01 05:00:00+00:004
52018-01-01 06:00:00+00:008
62018-01-01 07:00:00+00:008
72018-01-01 08:00:00+00:0020
82018-01-01 09:00:00+00:0030
92018-01-01 10:00:00+00:0041
102018-01-01 11:00:00+00:0045
112018-01-01 12:00:00+00:0054
122018-01-01 13:00:00+00:0057
132018-01-01 14:00:00+00:0068
142018-01-01 15:00:00+00:0086
152018-01-01 16:00:00+00:0072
162018-01-01 17:00:00+00:0072
172018-01-01 18:00:00+00:0047
182018-01-01 19:00:00+00:0032
192018-01-01 20:00:00+00:0034
202018-01-01 21:00:00+00:0027
212018-01-01 22:00:00+00:0015
222018-01-01 23:00:00+00:006
232018-01-02 00:00:00+00:002
242018-01-02 01:00:00+00:001
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25 rows × 2 columns

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[2842 rows x 2 columns in total]" - ], - "text/plain": [ - " trip_hour num_trips\n", - "2018-01-01 00:00:00+00:00 20\n", - "2018-01-01 01:00:00+00:00 25\n", - "2018-01-01 02:00:00+00:00 13\n", - "2018-01-01 03:00:00+00:00 11\n", - "2018-01-01 05:00:00+00:00 4\n", - "2018-01-01 06:00:00+00:00 8\n", - "2018-01-01 07:00:00+00:00 8\n", - "2018-01-01 08:00:00+00:00 20\n", - "2018-01-01 09:00:00+00:00 30\n", - "2018-01-01 10:00:00+00:00 41\n", - "2018-01-01 11:00:00+00:00 45\n", - "2018-01-01 12:00:00+00:00 54\n", - "2018-01-01 13:00:00+00:00 57\n", - "2018-01-01 14:00:00+00:00 68\n", - "2018-01-01 15:00:00+00:00 86\n", - "2018-01-01 16:00:00+00:00 72\n", - "2018-01-01 17:00:00+00:00 72\n", - "2018-01-01 18:00:00+00:00 47\n", - "2018-01-01 19:00:00+00:00 32\n", - "2018-01-01 20:00:00+00:00 34\n", - "2018-01-01 21:00:00+00:00 27\n", - "2018-01-01 22:00:00+00:00 15\n", - "2018-01-01 23:00:00+00:00 6\n", - "2018-01-02 00:00:00+00:00 2\n", - "2018-01-02 01:00:00+00:00 1\n", - "...\n", - "\n", - "[2842 rows x 2 columns]" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_grouped = df.groupby(\"trip_hour\").count()\n", - "df_grouped = df_grouped.reset_index().rename(columns={\"trip_id\": \"num_trips\"})\n", - "df_grouped" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3. Make forecastings for next 1 week with DataFrames.ai.forecast API" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "execution": { - "iopub.execute_input": "2025-08-18T19:22:58.944068Z", - "iopub.status.busy": "2025-08-18T19:22:58.943589Z", - "iopub.status.idle": "2025-08-18T19:23:11.364356Z", - "shell.execute_reply": "2025-08-18T19:23:11.363152Z", - "shell.execute_reply.started": "2025-08-18T19:22:58.944036Z" - }, - "trusted": true - }, - "outputs": [ - { - "data": { - "text/html": [ - "Query job 3f1225a8-b80b-4dfa-a7cf-94b93e7c18c2 is DONE. 68.2 kB processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
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forecast_timestampforecast_valueconfidence_levelprediction_interval_lower_boundprediction_interval_upper_boundai_forecast_status
02018-04-24 12:00:00+00:00144.5777280.95120.01921169.136247
12018-04-25 00:00:00+00:0054.2155150.9546.839461.591631
22018-04-26 05:00:00+00:008.1405330.95-14.61327230.894339
32018-04-26 14:00:00+00:00198.7449490.95174.982268222.50763
42018-04-27 02:00:00+00:009.918060.95-26.74994846.586069
52018-04-29 03:00:00+00:0032.0633390.95-35.73097899.857656
62018-04-27 04:00:00+00:0025.7571110.958.17803743.336184
72018-04-30 06:00:00+00:0089.8084560.9515.214961164.401952
82018-04-30 02:00:00+00:00-10.5841750.95-60.77202439.603674
92018-04-30 05:00:00+00:0018.1181110.95-40.90213377.138355
102018-04-24 07:00:00+00:00359.0369570.95250.880334467.193579
112018-04-25 10:00:00+00:00227.2720490.95170.918819283.625279
122018-04-27 15:00:00+00:00208.6313630.95188.977435228.285291
132018-04-25 13:00:00+00:00159.7999110.95150.066363169.53346
142018-04-26 12:00:00+00:00190.2269440.95177.898865202.555023
152018-04-24 04:00:00+00:0011.1623380.95-18.58104140.905717
162018-04-24 14:00:00+00:00136.708160.95134.165413139.250907
172018-04-28 21:00:00+00:0065.3088990.9563.00091567.616883
182018-04-29 20:00:00+00:0071.7888490.95-2.49023146.067928
192018-04-30 15:00:00+00:00142.5609440.9541.495553243.626334
202018-04-26 18:00:00+00:00533.7838130.95412.068752655.498875
212018-04-28 03:00:00+00:0025.3797610.9522.56575228.193769
222018-04-30 12:00:00+00:00158.3133850.9579.466457237.160313
232018-04-25 07:00:00+00:00358.7565920.95276.305603441.207581
242018-04-27 22:00:00+00:00103.5890960.9594.45235112.725842
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25 rows × 6 columns

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[168 rows x 6 columns in total]" - ], - "text/plain": [ - " forecast_timestamp forecast_value confidence_level \\\n", - "2018-04-24 12:00:00+00:00 144.577728 0.95 \n", - "2018-04-25 00:00:00+00:00 54.215515 0.95 \n", - "2018-04-26 05:00:00+00:00 8.140533 0.95 \n", - "2018-04-26 14:00:00+00:00 198.744949 0.95 \n", - "2018-04-27 02:00:00+00:00 9.91806 0.95 \n", - "2018-04-29 03:00:00+00:00 32.063339 0.95 \n", - "2018-04-27 04:00:00+00:00 25.757111 0.95 \n", - "2018-04-30 06:00:00+00:00 89.808456 0.95 \n", - "2018-04-30 02:00:00+00:00 -10.584175 0.95 \n", - "2018-04-30 05:00:00+00:00 18.118111 0.95 \n", - "2018-04-24 07:00:00+00:00 359.036957 0.95 \n", - "2018-04-25 10:00:00+00:00 227.272049 0.95 \n", - "2018-04-27 15:00:00+00:00 208.631363 0.95 \n", - "2018-04-25 13:00:00+00:00 159.799911 0.95 \n", - "2018-04-26 12:00:00+00:00 190.226944 0.95 \n", - "2018-04-24 04:00:00+00:00 11.162338 0.95 \n", - "2018-04-24 14:00:00+00:00 136.70816 0.95 \n", - "2018-04-28 21:00:00+00:00 65.308899 0.95 \n", - "2018-04-29 20:00:00+00:00 71.788849 0.95 \n", - "2018-04-30 15:00:00+00:00 142.560944 0.95 \n", - "2018-04-26 18:00:00+00:00 533.783813 0.95 \n", - "2018-04-28 03:00:00+00:00 25.379761 0.95 \n", - "2018-04-30 12:00:00+00:00 158.313385 0.95 \n", - "2018-04-25 07:00:00+00:00 358.756592 0.95 \n", - "2018-04-27 22:00:00+00:00 103.589096 0.95 \n", - "\n", - " prediction_interval_lower_bound prediction_interval_upper_bound \\\n", - " 120.01921 169.136247 \n", - " 46.8394 61.591631 \n", - " -14.613272 30.894339 \n", - " 174.982268 222.50763 \n", - " -26.749948 46.586069 \n", - " -35.730978 99.857656 \n", - " 8.178037 43.336184 \n", - " 15.214961 164.401952 \n", - " -60.772024 39.603674 \n", - " -40.902133 77.138355 \n", - " 250.880334 467.193579 \n", - " 170.918819 283.625279 \n", - " 188.977435 228.285291 \n", - " 150.066363 169.53346 \n", - " 177.898865 202.555023 \n", - " -18.581041 40.905717 \n", - " 134.165413 139.250907 \n", - " 63.000915 67.616883 \n", - " -2.49023 146.067928 \n", - " 41.495553 243.626334 \n", - " 412.068752 655.498875 \n", - " 22.565752 28.193769 \n", - " 79.466457 237.160313 \n", - " 276.305603 441.207581 \n", - " 94.45235 112.725842 \n", - "\n", - "ai_forecast_status \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "...\n", - "\n", - "[168 rows x 6 columns]" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Using all the data except the last week (2842-168) for training. And predict the last week (168).\n", - "result = df_grouped.head(2842-168).ai.forecast(timestamp_column=\"trip_hour\", data_column=\"num_trips\", horizon=168) \n", - "result" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 4. Process the raw result and draw a line plot along with the training data" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "execution": { - "iopub.execute_input": "2025-08-18T19:27:08.306367Z", - "iopub.status.busy": "2025-08-18T19:27:08.305886Z", - "iopub.status.idle": "2025-08-18T19:27:08.318514Z", - "shell.execute_reply": "2025-08-18T19:27:08.317016Z", - "shell.execute_reply.started": "2025-08-18T19:27:08.306336Z" - }, - "trusted": true - }, - "outputs": [], - "source": [ - "result = result.sort_values(\"forecast_timestamp\")\n", - "result = result[[\"forecast_timestamp\", \"forecast_value\"]]\n", - "result = result.rename(columns={\"forecast_timestamp\": \"trip_hour\", \"forecast_value\": \"num_trips_forecast\"})\n", - "df_all = bpd.concat([df_grouped, result])\n", - "df_all = df_all.tail(672) # 4 weeks" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Plot a line chart and compare with the actual result." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "execution": { - "iopub.execute_input": "2025-08-18T19:27:19.461528Z", - "iopub.status.busy": "2025-08-18T19:27:19.461164Z", - "iopub.status.idle": "2025-08-18T19:27:20.737558Z", - "shell.execute_reply": "2025-08-18T19:27:20.736422Z", - "shell.execute_reply.started": "2025-08-18T19:27:19.461497Z" - }, - "trusted": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "df_all = df_all.set_index(\"trip_hour\")\n", - "df_all.plot.line(figsize=(16, 8))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "trusted": true - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kaggle": { - "accelerator": "none", - "dataSources": [ - { - "databundleVersionId": 13391012, - "sourceId": 110281, - "sourceType": "competition" - } - ], - "dockerImageVersionId": 31089, - "isGpuEnabled": false, - "isInternetEnabled": true, - "language": "python", - "sourceType": "notebook" - }, - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.13" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/notebooks/kaggle/describe-product-images-with-bigframes-multimodal.ipynb b/notebooks/kaggle/describe-product-images-with-bigframes-multimodal.ipynb deleted file mode 100644 index 1a7de9b837f..00000000000 --- a/notebooks/kaggle/describe-product-images-with-bigframes-multimodal.ipynb +++ /dev/null @@ -1,1131 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "876eb80c", - "metadata": { - "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19", - "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5" - }, - "source": [ - "# Describe product images with BigFrames multimodal DataFrames\n", - "\n", - "Based on notebook at https://github.com/googleapis/python-bigquery-dataframes/blob/main/notebooks/multimodal/multimodal_dataframe.ipynb\n", - "\n", - "This notebook is introducing BigFrames Multimodal features:\n", - "\n", - "1. Create Multimodal DataFrame\n", - "2. Combine unstructured data with structured data\n", - "3. Conduct image transformations\n", - "4. Use LLM models to ask questions and generate embeddings on images\n", - "5. PDF chunking function\n", - "\n", - "Install the bigframes package and upgrade other packages that are already included in Kaggle but have versions incompatible with bigframes." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "0506e15e", - "metadata": { - "trusted": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Requirement already satisfied: bigframes in /usr/local/google/home/shuowei/src/google-cloud-python/google-cloud-python/packages/bigframes/.venv/lib/python3.13/site-packages (2.39.0)\n", - "Requirement already satisfied: google-cloud-automl in /usr/local/google/home/shuowei/src/google-cloud-python/google-cloud-python/packages/bigframes/.venv/lib/python3.13/site-packages (2.19.0)\n", - "Requirement already satisfied: google-cloud-translate in /usr/local/google/home/shuowei/src/google-cloud-python/google-cloud-python/packages/bigframes/.venv/lib/python3.13/site-packages (3.26.0)\n", - "Requirement already satisfied: google-ai-generativelanguage in /usr/local/google/home/shuowei/src/google-cloud-python/google-cloud-python/packages/bigframes/.venv/lib/python3.13/site-packages (0.11.0)\n", - 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"Requirement already satisfied: typing-inspection>=0.4.2 in /usr/local/google/home/shuowei/src/google-cloud-python/google-cloud-python/packages/bigframes/.venv/lib/python3.13/site-packages (from pydantic!=2.12.0,!=2.12.1,!=2.4.0,!=2.4.1,<3.0,>=2.0->pyiceberg>=0.7.1->bigframes) (0.4.2)\n", - "Requirement already satisfied: pycparser in /usr/local/google/home/shuowei/src/google-cloud-python/google-cloud-python/packages/bigframes/.venv/lib/python3.13/site-packages (from cffi>=2.0.0->cryptography>=38.0.3->google-auth<3.0,>=2.15.0->bigframes) (3.0)\n", - "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/google/home/shuowei/src/google-cloud-python/google-cloud-python/packages/bigframes/.venv/lib/python3.13/site-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib->gcsfs!=2025.5.0,!=2026.2.0,!=2026.3.0,>=2023.3.0->bigframes) (3.3.1)\n", - "\n", - "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m26.1\u001b[0m\n", - "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n", - "Note: you may need to restart the kernel to use updated packages.\n" - ] - } - ], - "source": [ - "%pip install --upgrade bigframes google-cloud-automl google-cloud-translate google-ai-generativelanguage tensorflow " - ] - }, - { - "cell_type": "markdown", - "id": "c749e07c", - "metadata": {}, - "source": [ - "**Important:** restart the kernel by going to \"Run -> Restart & clear cell outputs\" before continuing.\n", - "\n", - "Configure bigframes to use your GCP project. First, go to \"Add-ons -> Google Cloud SDK\" and click the \"Attach\" button. Then," - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "5e00777d", - "metadata": { - "execution": { - "iopub.execute_input": "2025-08-18T20:17:14.873201Z", - "iopub.status.busy": "2025-08-18T20:17:14.872905Z", - "iopub.status.idle": "2025-08-18T20:17:14.946971Z", - "shell.execute_reply": "2025-08-18T20:17:14.945996Z", - "shell.execute_reply.started": "2025-08-18T20:17:14.873171Z" - }, - "trusted": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Not running on Kaggle, skipping Kaggle secrets initialization.\n" - ] - } - ], - "source": [ - "try:\n", - " from kaggle_secrets import UserSecretsClient\n", - " user_secrets = UserSecretsClient()\n", - " user_credential = user_secrets.get_gcloud_credential()\n", - " user_secrets.set_tensorflow_credential(user_credential)\n", - " print(\"Successfully authenticated using Kaggle secrets.\")\n", - "except ImportError:\n", - " print(\"Not running on Kaggle, skipping Kaggle secrets initialization.\")\n", - "except Exception as e:\n", - " print(f\"Could not initialize Kaggle secrets: {e}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "b2e171de", - "metadata": { - "execution": { - "iopub.execute_input": "2025-08-18T20:17:25.574192Z", - "iopub.status.busy": "2025-08-18T20:17:25.573874Z", - "iopub.status.idle": "2025-08-18T20:17:45.102002Z", - "shell.execute_reply": "2025-08-18T20:17:45.101140Z", - "shell.execute_reply.started": "2025-08-18T20:17:25.574168Z" - }, - "trusted": true - }, - "outputs": [], - "source": [ - "PROJECT = \"bigframes-dev\" # replace with your project. \n", - "# Refer to https://cloud.google.com/bigquery/docs/multimodal-data-dataframes-tutorial#required_roles for your required permissions\n", - "\n", - "LOCATION = \"us\" # replace with your location.\n", - "DATASET_ID = \"bigframes_samples\" # replace with your dataset ID.\n", - "OUTPUT_BUCKET = \"bigframes_blob_test\" # replace with your GCS bucket. \n", - "\n", - "FULL_CONNECTION_ID = f\"{PROJECT}.{LOCATION}.bigframes-default-connection\"\n", - "\n", - "import bigframes\n", - "# Setup project\n", - "bigframes.options.bigquery.project = PROJECT\n", - "bigframes.options.bigquery.location = LOCATION\n", - "\n", - "# Display options\n", - "bigframes.options.display.blob_display_width = 300\n", - "bigframes.options.display.progress_bar = None\n", - "\n", - "import bigframes.pandas as bpd\n", - "import bigframes.bigquery as bbq\n", - "\n", - "def get_runtime_json_str(series, mode=\"R\", with_metadata=False):\n", - " \"\"\"Get runtime JSON from objectref.\"\"\"\n", - " s = bbq.obj.fetch_metadata(series) if with_metadata else series\n", - " runtime = bbq.obj.get_access_url(s, mode=mode)\n", - " return bbq.to_json_string(runtime)\n", - "\n", - "def get_metadata(series):\n", - " metadata_obj = bbq.obj.fetch_metadata(series)\n", - " return bbq.json_query(metadata_obj.struct.field(\"details\"), \"$.gcs_metadata\")\n", - "\n", - "def get_content_type(series):\n", - " return bbq.json_value(get_metadata(series), \"$.content_type\")\n", - "\n", - "def get_size(series):\n", - " return bbq.json_value(get_metadata(series), \"$.size\").astype(\"Int64\")\n", - "\n", - "def get_updated(series):\n", - " return bpd.to_datetime(bbq.json_value(get_metadata(series), \"$.updated\").astype(\"Int64\"), unit=\"us\", utc=True)\n", - "\n", - "from IPython.display import HTML, display\n", - "\n", - "def render_images(df):\n", - " \"\"\"Helper to display BigFrames DataFrame with rendered image previews.\"\"\"\n", - " import bigframes.pandas as bpd\n", - " import bigframes.bigquery as bbq\n", - " import bigframes\n", - " from bigframes import dtypes\n", - " import json\n", - " \n", - " if isinstance(df, bpd.Series):\n", - " df = df.to_frame()\n", - " \n", - " object_cols = [\n", - " col for col, dtype in zip(df.columns, df.dtypes)\n", - " if dtype == dtypes.OBJ_REF_DTYPE\n", - " ]\n", - " \n", - " if not object_cols:\n", - " display(df)\n", - " return\n", - "\n", - " limit = bigframes.options.display.max_rows or 10\n", - " view_df = df.head(limit)\n", - " \n", - " runtime_cols = {\n", - " col: get_runtime_json_str(view_df[col], mode=\"R\", with_metadata=False) \n", - " for col in object_cols\n", - " }\n", - " \n", - " pandas_json_df = bpd.DataFrame(runtime_cols).to_pandas()\n", - " final_pd = view_df.to_pandas()\n", - " \n", - " width = bigframes.options.display.blob_display_width or 300\n", - " IMAGE_EXTENSIONS = (\".png\", \".jpg\", \".jpeg\", \".gif\", \".webp\")\n", - " \n", - " def format_cell_html(raw_json):\n", - " if not raw_json:\n", - " return \"\"\n", - " try:\n", - " obj_rt = json.loads(raw_json)\n", - " if \"access_urls\" not in obj_rt:\n", - " err = obj_rt.get(\"errors\", [{\"message\": \"URL Generation Failed\"}])[0].get(\"message\")\n", - " return f'Error: {err}'\n", - " \n", - " uri = obj_rt.get(\"objectref\", {}).get(\"uri\", \"\")\n", - " url = obj_rt[\"access_urls\"][\"read_url\"]\n", - " \n", - " if uri and str(uri).lower().endswith(IMAGE_EXTENSIONS):\n", - " return f''\n", - " \n", - " return f'{uri if uri else \"view\"}'\n", - " except:\n", - " return \"Format Error\"\n", - "\n", - " for col in object_cols:\n", - " final_pd[col] = pandas_json_df[col].map(format_cell_html)\n", - " \n", - " display(HTML(final_pd.to_html(escape=False)))" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "d17afaf1", - "metadata": { - "execution": { - "iopub.execute_input": "2025-08-18T20:17:45.103530Z", - "iopub.status.busy": "2025-08-18T20:17:45.103249Z", - "iopub.status.idle": "2025-08-18T20:17:47.424586Z", - "shell.execute_reply": "2025-08-18T20:17:47.423762Z", - "shell.execute_reply.started": "2025-08-18T20:17:45.103499Z" - }, - "trusted": true - }, - "outputs": [], - "source": [ - "import gcsfs\n", - "import bigframes.bigquery as bbq\n", - "\n", - "# List files using gcsfs (public bucket)\n", - "fs = gcsfs.GCSFileSystem(anon=True)\n", - "uris = fs.glob(\"gs://cloud-samples-data/bigquery/tutorials/cymbal-pets/images/*\")\n", - "\n", - "# Ensure URIs have gs:// prefix\n", - "uris = [u if u.startswith(\"gs://\") else f\"gs://{u}\" for u in uris]\n", - "\n", - "# Read the URIs into a BigQuery DataFrame using UNNEST\n", - "# We take the first 5 for this example\n", - "df_image = bpd.read_gbq(f\"SELECT uri FROM UNNEST({uris[:5]}) as uri\")\n", - "\n", - "# Create the object reference column\n", - "df_image['image'] = bbq.obj.make_ref(df_image['uri'], authorizer=FULL_CONNECTION_ID)\n", - "df_image = df_image[['image']]" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "3e84b922", - "metadata": { - "execution": { - "iopub.execute_input": "2025-08-18T20:17:47.425873Z", - "iopub.status.busy": "2025-08-18T20:17:47.425578Z", - "iopub.status.idle": "2025-08-18T20:18:07.919961Z", - "shell.execute_reply": "2025-08-18T20:18:07.918942Z", - "shell.execute_reply.started": "2025-08-18T20:17:47.425844Z" - }, - "trusted": true - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/google/home/shuowei/src/google-cloud-python/google-cloud-python/packages/bigframes/bigframes/dtypes.py:1044: JSONDtypeWarning: JSON columns will be represented as pandas.ArrowDtype(pyarrow.json_())\n", - "instead of using `db_dtypes` in the future when available in pandas\n", - "(https://github.com/pandas-dev/pandas/issues/60958) and pyarrow.\n", - " warnings.warn(msg, bigframes.exceptions.JSONDtypeWarning)\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
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" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Take only the 5 images to deal with. Preview the content of the Mutimodal DataFrame\n", - "df_image = df_image.head(5)\n", - "render_images(df_image)" - ] - }, - { - "cell_type": "markdown", - "id": "b0eaa73c", - "metadata": {}, - "source": [ - "# 2. Combine unstructured data with structured data\n", - "\n", - "Now you can put more information into the table to describe the files. Such as author info from inputs, or other metadata from the gcs object itself." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "7d64fb54", - "metadata": { - "execution": { - "iopub.execute_input": "2025-08-18T20:18:07.922593Z", - "iopub.status.busy": "2025-08-18T20:18:07.921884Z", - "iopub.status.idle": "2025-08-18T20:18:35.549725Z", - "shell.execute_reply": "2025-08-18T20:18:35.548942Z", - "shell.execute_reply.started": "2025-08-18T20:18:07.922551Z" - }, - "trusted": true - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/google/home/shuowei/src/google-cloud-python/google-cloud-python/packages/bigframes/bigframes/dtypes.py:1044: JSONDtypeWarning: JSON columns will be represented as pandas.ArrowDtype(pyarrow.json_())\n", - "instead of using `db_dtypes` in the future when available in pandas\n", - "(https://github.com/pandas-dev/pandas/issues/60958) and pyarrow.\n", - " warnings.warn(msg, bigframes.exceptions.JSONDtypeWarning)\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
imageauthorcontent_typesizeupdated
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" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Combine unstructured data with structured data\n", - "df_image[\"author\"] = [\"alice\", \"bob\", \"bob\", \"alice\", \"bob\"] # type: ignore\n", - "df_image[\"content_type\"] = get_content_type(df_image[\"image\"])\n", - "df_image[\"size\"] = get_size(df_image[\"image\"])\n", - "df_image[\"updated\"] = get_updated(df_image[\"image\"])\n", - "render_images(df_image)" - ] - }, - { - "cell_type": "markdown", - "id": "a23ef0e4", - "metadata": {}, - "source": [ - "Then you can filter the rows based on the structured data. And for different content types, you can display them respectively or together." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "ce102df0", - "metadata": { - "execution": { - "iopub.execute_input": "2025-08-18T20:18:55.300314Z", - "iopub.status.busy": "2025-08-18T20:18:55.299993Z", - "iopub.status.idle": "2025-08-18T20:19:09.154492Z", - "shell.execute_reply": "2025-08-18T20:19:09.153315Z", - "shell.execute_reply.started": "2025-08-18T20:18:55.300289Z" - }, - "trusted": true - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/google/home/shuowei/src/google-cloud-python/google-cloud-python/packages/bigframes/bigframes/dtypes.py:1044: JSONDtypeWarning: JSON columns will be represented as pandas.ArrowDtype(pyarrow.json_())\n", - "instead of using `db_dtypes` in the future when available in pandas\n", - "(https://github.com/pandas-dev/pandas/issues/60958) and pyarrow.\n", - " warnings.warn(msg, bigframes.exceptions.JSONDtypeWarning)\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
imageauthorcontent_typesizeupdated
0aliceimage/png7157662025-03-20 17:44:38+00:00
3aliceimage/png17365332025-03-20 17:44:39+00:00
" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# filter images and display, you can also display audio and video types\n", - "filtered_df = df_image[df_image[\"author\"] == \"alice\"]\n", - "render_images(filtered_df)" - ] - }, - { - "cell_type": "markdown", - "id": "db2b3b12", - "metadata": {}, - "source": [ - "# 3. Conduct image transformations\n", - "\n", - "BigFrames Multimodal DataFrame provides image(and other) transformation functions. Such as image_blur, image_resize and image_normalize. The output can be saved to GCS folders or to BQ as bytes." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "283036f5", - "metadata": { - "execution": { - "iopub.execute_input": "2025-08-18T20:19:22.950652Z", - "iopub.status.busy": "2025-08-18T20:19:22.950277Z", - "iopub.status.idle": "2025-08-18T20:31:51.799997Z", - "shell.execute_reply": "2025-08-18T20:31:51.798840Z", - "shell.execute_reply.started": "2025-08-18T20:19:22.950625Z" - }, - "trusted": true - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/google/home/shuowei/src/google-cloud-python/google-cloud-python/packages/bigframes/bigframes/pandas/__init__.py:211: PreviewWarning: udf is in preview.\n", - " return global_session.with_default_session(\n", - "/usr/local/google/home/shuowei/src/google-cloud-python/google-cloud-python/packages/bigframes/bigframes/dataframe.py:4695: FunctionAxisOnePreviewWarning: DataFrame.apply with parameter axis=1 scenario is in preview.\n", - " warnings.warn(msg, category=bfe.FunctionAxisOnePreviewWarning)\n", - "/usr/local/google/home/shuowei/src/google-cloud-python/google-cloud-python/packages/bigframes/bigframes/dtypes.py:1044: JSONDtypeWarning: JSON columns will be represented as pandas.ArrowDtype(pyarrow.json_())\n", - "instead of using `db_dtypes` in the future when available in pandas\n", - "(https://github.com/pandas-dev/pandas/issues/60958) and pyarrow.\n", - " warnings.warn(msg, bigframes.exceptions.JSONDtypeWarning)\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
imageblurred
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" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "@bpd.udf(\n", - " input_types=[str, str, int, int],\n", - " output_type=str,\n", - " dataset=DATASET_ID,\n", - " name=\"image_blur_kaggle\",\n", - " bigquery_connection=FULL_CONNECTION_ID,\n", - " packages=[\"opencv-python-headless\", \"numpy\", \"requests\"],\n", - ")\n", - "def image_blur(src_rt: str, dst_rt: str, kx: int, ky: int) -> str:\n", - " import json\n", - " import cv2 as cv\n", - " import numpy as np\n", - " import requests\n", - " \n", - " src_obj = json.loads(src_rt)\n", - " if \"access_urls\" not in src_obj:\n", - " raise ValueError(f\"Missing 'access_urls' in source object. Response: {src_obj}\")\n", - " src_url = src_obj[\"access_urls\"][\"read_url\"]\n", - " \n", - " response = requests.get(src_url, timeout=30)\n", - " response.raise_for_status()\n", - " \n", - " img = cv.imdecode(np.frombuffer(response.content, np.uint8), cv.IMREAD_UNCHANGED)\n", - " if img is None:\n", - " raise ValueError(\"cv.imdecode failed\")\n", - " \n", - " img_blurred = cv.blur(img, ksize=(int(kx), int(ky)))\n", - " success, encoded = cv.imencode(\".jpeg\", img_blurred)\n", - " \n", - " if not success:\n", - " raise ValueError(\"cv.imencode failed\")\n", - " \n", - " if dst_rt: # GCS Output Mode\n", - " dst_obj = json.loads(dst_rt)\n", - " if \"access_urls\" not in dst_obj:\n", - " raise ValueError(f\"Missing 'access_urls' in destination object. Response: {dst_obj}\")\n", - " dst_url = dst_obj[\"access_urls\"][\"write_url\"]\n", - " \n", - " requests.put(dst_url, data=encoded.tobytes(), headers={\"Content-Type\": \"image/jpeg\"}, timeout=30).raise_for_status()\n", - " return dst_obj[\"objectref\"][\"uri\"]\n", - " return \"\"\n", - "\n", - "def apply_transformation(series, dst_folder, udf, *args, verbose=False):\n", - " import os\n", - " dst_folder = os.path.join(dst_folder, \"\")\n", - " metadata = bbq.obj.fetch_metadata(series)\n", - " current_uri = metadata.struct.field(\"uri\")\n", - " dst_uri = current_uri.str.replace(r\"^.*\\/(.*)$\", rf\"{dst_folder}\\1\", regex=True)\n", - " \n", - " # Bypass synchronous validation via JSON initialization\n", - " dst_blob_df = bpd.DataFrame({\"uri\": dst_uri})\n", - " dst_blob_df[\"authorizer\"] = FULL_CONNECTION_ID\n", - " dst_blob = bbq.obj.make_ref(bbq.to_json(bbq.struct(dst_blob_df)))\n", - "\n", - " df_transform = bpd.DataFrame({\n", - " \"src_rt\": get_runtime_json_str(series, mode=\"R\"),\n", - " \"dst_rt\": get_runtime_json_str(dst_blob, mode=\"RW\"),\n", - " })\n", - " res = df_transform[[\"src_rt\", \"dst_rt\"]].apply(udf, axis=1, args=args)\n", - " \n", - " if verbose:\n", - " return res\n", - " \n", - " res_df = bpd.DataFrame({\"uri\": res})\n", - " res_df[\"authorizer\"] = FULL_CONNECTION_ID\n", - " return bbq.obj.make_ref(bbq.to_json(bbq.struct(res_df)))\n", - "\n", - "# Apply Blur Transformation\n", - "df_image[\"blurred\"] = apply_transformation(\n", - " df_image[\"image\"], f\"gs://{OUTPUT_BUCKET}/image_blur_transformed/\",\n", - " image_blur, 20, 20\n", - ")\n", - "render_images(df_image[[\"image\", \"blurred\"]])" - ] - }, - { - "cell_type": "markdown", - "id": "2d68a468", - "metadata": {}, - "source": [ - "# 4. Use LLM models to ask questions and generate embeddings on images" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "662054a0", - "metadata": { - "execution": { - "iopub.execute_input": "2025-08-18T20:36:13.954686Z", - "iopub.status.busy": "2025-08-18T20:36:13.954340Z", - "iopub.status.idle": "2025-08-18T20:36:43.225449Z", - "shell.execute_reply": "2025-08-18T20:36:43.224579Z", - "shell.execute_reply.started": "2025-08-18T20:36:13.954661Z" - }, - "trusted": true - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/google/home/shuowei/src/google-cloud-python/google-cloud-python/packages/bigframes/bigframes/core/logging/log_adapter.py:183: FutureWarning: Since upgrading the default model can cause unintended breakages, the\n", - "default model will be removed in BigFrames 3.0. Please supply an\n", - "explicit model to avoid this message.\n", - " return method(*args, **kwargs)\n", - "/usr/local/google/home/shuowei/src/google-cloud-python/google-cloud-python/packages/bigframes/bigframes/session/__init__.py:437: FutureWarning: You are using the BigFrames session default connection: bigframes-\n", - "default-connection, which can be different from the\n", - "BigQuery project default connection. This default\n", - "connection may change in the future.\n", - " warnings.warn(msg, category=FutureWarning)\n" - ] - } - ], - "source": [ - "from bigframes.ml import llm\n", - "gemini = llm.GeminiTextGenerator()" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "a31730ff", - "metadata": { - "execution": { - "iopub.execute_input": "2025-08-18T20:36:43.227798Z", - "iopub.status.busy": "2025-08-18T20:36:43.227457Z", - "iopub.status.idle": "2025-08-18T20:37:25.238649Z", - "shell.execute_reply": "2025-08-18T20:37:25.237623Z", - "shell.execute_reply.started": "2025-08-18T20:36:43.227764Z" - }, - "trusted": true - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/google/home/shuowei/src/google-cloud-python/google-cloud-python/packages/bigframes/bigframes/dtypes.py:1044: JSONDtypeWarning: JSON columns will be represented as pandas.ArrowDtype(pyarrow.json_())\n", - "instead of using `db_dtypes` in the future when available in pandas\n", - "(https://github.com/pandas-dev/pandas/issues/60958) and pyarrow.\n", - " warnings.warn(msg, bigframes.exceptions.JSONDtypeWarning)\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
ml_generate_text_llm_resultimage
0Please provide me with the picture! I need to see the image to tell you what the item is and what color the picture is.\\n
1To answer your question accurately, I need you to provide me with the picture you are referring to. Once you provide the picture, I can analyze it and tell you what item is in the picture and what color the picture is.
" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Ask the same question on the images\n", - "df_image = df_image.head(2)\n", - "answer = gemini.predict(df_image, prompt=[\"what item is it?\", \"what color is the picture?\"])\n", - "render_images(answer[[\"ml_generate_text_llm_result\", \"image\"]])" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "f5d2a1ed", - "metadata": { - "execution": { - "iopub.execute_input": "2025-08-18T20:37:25.239875Z", - "iopub.status.busy": "2025-08-18T20:37:25.239607Z", - "iopub.status.idle": "2025-08-18T20:37:25.263034Z", - "shell.execute_reply": "2025-08-18T20:37:25.262002Z", - "shell.execute_reply.started": "2025-08-18T20:37:25.239847Z" - }, - "trusted": true - }, - "outputs": [], - "source": [ - "# Ask different questions\n", - "df_image[\"question\"] = [\"what item is it?\", \"what color is the picture?\"]" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "fb67bf8e", - "metadata": { - "execution": { - "iopub.execute_input": "2025-08-18T20:37:25.264585Z", - "iopub.status.busy": "2025-08-18T20:37:25.264072Z", - "iopub.status.idle": "2025-08-18T20:38:10.129667Z", - "shell.execute_reply": "2025-08-18T20:38:10.128677Z", - "shell.execute_reply.started": "2025-08-18T20:37:25.264518Z" - }, - "trusted": true - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/google/home/shuowei/src/google-cloud-python/google-cloud-python/packages/bigframes/bigframes/dtypes.py:1044: JSONDtypeWarning: JSON columns will be represented as pandas.ArrowDtype(pyarrow.json_())\n", - "instead of using `db_dtypes` in the future when available in pandas\n", - "(https://github.com/pandas-dev/pandas/issues/60958) and pyarrow.\n", - " warnings.warn(msg, bigframes.exceptions.JSONDtypeWarning)\n", - "/usr/local/google/home/shuowei/src/google-cloud-python/google-cloud-python/packages/bigframes/bigframes/dtypes.py:1044: JSONDtypeWarning: JSON columns will be represented as pandas.ArrowDtype(pyarrow.json_())\n", - "instead of using `db_dtypes` in the future when available in pandas\n", - "(https://github.com/pandas-dev/pandas/issues/60958) and pyarrow.\n", - " warnings.warn(msg, bigframes.exceptions.JSONDtypeWarning)\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
ml_generate_text_llm_resultimage
0The item is a glass aquarium.
1Dark brown
" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "answer_alt = gemini.predict(df_image, prompt=[df_image[\"question\"], df_image[\"image\"]])\n", - "render_images(answer_alt[[\"ml_generate_text_llm_result\", \"image\"]])" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "0cf33170", - "metadata": { - "execution": { - "iopub.execute_input": "2025-08-18T20:38:10.130851Z", - "iopub.status.busy": "2025-08-18T20:38:10.130617Z", - "iopub.status.idle": "2025-08-18T20:39:04.790416Z", - "shell.execute_reply": "2025-08-18T20:39:04.789398Z", - "shell.execute_reply.started": "2025-08-18T20:38:10.130833Z" - }, - "trusted": true - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/google/home/shuowei/src/google-cloud-python/google-cloud-python/packages/bigframes/bigframes/core/logging/log_adapter.py:183: FutureWarning: Since upgrading the default model can cause unintended breakages, the\n", - "default model will be removed in BigFrames 3.0. Please supply an\n", - "explicit model to avoid this message.\n", - " return method(*args, **kwargs)\n", - "/usr/local/google/home/shuowei/src/google-cloud-python/google-cloud-python/packages/bigframes/bigframes/session/__init__.py:437: FutureWarning: You are using the BigFrames session default connection: bigframes-\n", - "default-connection, which can be different from the\n", - "BigQuery project default connection. This default\n", - "connection may change in the future.\n", - " warnings.warn(msg, category=FutureWarning)\n", - "/usr/local/google/home/shuowei/src/google-cloud-python/google-cloud-python/packages/bigframes/bigframes/dtypes.py:1044: JSONDtypeWarning: JSON columns will be represented as pandas.ArrowDtype(pyarrow.json_())\n", - "instead of using `db_dtypes` in the future when available in pandas\n", - "(https://github.com/pandas-dev/pandas/issues/60958) and pyarrow.\n", - " warnings.warn(msg, bigframes.exceptions.JSONDtypeWarning)\n" - ] - }, - { - "data": { - "text/html": [ - "
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ml_generate_embedding_resultml_generate_embedding_statusml_generate_embedding_start_secml_generate_embedding_end_seccontent
0[ 0.03416207 0.0419732 -0.0227391 ... -0.03...<NA><NA>{\"access_urls\":{\"expiry_time\":\"2026-05-02T03:3...
1[ 0.01908903 0.0193082 -0.00221754 ... 0.00...<NA><NA>{\"access_urls\":{\"expiry_time\":\"2026-05-02T03:3...
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[2 rows x 5 columns in total]" - ], - "text/plain": [ - " ml_generate_embedding_result \\\n", - "0 [ 0.03416207 0.0419732 -0.0227391 ... -0.03... \n", - "1 [ 0.01908903 0.0193082 -0.00221754 ... 0.00... \n", - "\n", - " ml_generate_embedding_status ml_generate_embedding_start_sec \\\n", - "0 \n", - "1 \n", - "\n", - " ml_generate_embedding_end_sec \\\n", - "0 \n", - "1 \n", - "\n", - " content \n", - "0 {\"access_urls\":{\"expiry_time\":\"2026-05-02T03:3... \n", - "1 {\"access_urls\":{\"expiry_time\":\"2026-05-02T03:3... \n", - "\n", - "[2 rows x 5 columns]" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Generate embeddings.\n", - "embed_model = llm.MultimodalEmbeddingGenerator()\n", - "embeddings = embed_model.predict(df_image[\"image\"])\n", - "embeddings" - ] - } - ], - "metadata": { - "kaggle": { - "accelerator": "none", - "dataSources": [ - { - "databundleVersionId": 13391012, - "sourceId": 110281, - "sourceType": "competition" - } - ], - "dockerImageVersionId": 31089, - "isGpuEnabled": false, - "isInternetEnabled": true, - "language": "python", - "sourceType": "notebook" - }, - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.13" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/notebooks/kaggle/vector-search-with-bigframes-over-national-jukebox.ipynb b/notebooks/kaggle/vector-search-with-bigframes-over-national-jukebox.ipynb deleted file mode 100644 index 317ba0f1adb..00000000000 --- a/notebooks/kaggle/vector-search-with-bigframes-over-national-jukebox.ipynb +++ /dev/null @@ -1,1201 +0,0 @@ -{ - "cells": [ - { - "id": "f4ece66a", - "cell_type": "markdown", - "source": [ - "# Creating a searchable index of the National Jukebox\n", - "\n", - "_Extracting text from audio and indexing it with BigQuery DataFrames_\n", - "\n", - "* Tim Swena (formerly, Swast)\n", - "* swast@google.com\n", - "* https://vis.social/@timswast on Mastodon\n", - "\n", - "This notebook lives in\n", - "\n", - "* https://github.com/tswast/code-snippets\n", - "* at https://github.com/tswast/code-snippets/blob/main/2025/national-jukebox/transcribe_songs.ipynb\n", - "\n", - "To follow along, you'll need a Google Cloud project\n", - "\n", - "* Go to https://cloud.google.com/free to start a free trial." - ], - "metadata": { - "@deathbeds/jupyterlab-fonts": { - "styles": { - "": { - "body[data-jp-deck-mode='presenting'] &": { - "zoom": "194%" - } - } - } - }, - "editable": true, - "slideshow": { - "slide_type": "subslide" - }, - "tags": [] - }, - "execution_count": null - }, - { - "id": "bc01a1d3", - "cell_type": "markdown", - "source": [ - "The National Jukebox is a project of the USA Library of Congress to provide access to thousands of acoustic sound recordings from the very earliest days of the commercial record industry.\n", - "\n", - "* Learn more at https://www.loc.gov/collections/national-jukebox/about-this-collection/\n", - "\n", - "\"recording" - ], - "metadata": { - "@deathbeds/jupyterlab-fonts": { - "styles": { - "": { - "body[data-jp-deck-mode='presenting'] &": { - "z-index": "0", - "zoom": "216%" - } - } - } - }, - "slideshow": { - "slide_type": "slide" - } - }, - "execution_count": null - }, - { - "id": "4fc7c468", - "cell_type": "markdown", - "source": [ - "\n", - "To search the National Jukebox, we combine powerful features of BigQuery:\n", - "\n", - "\"audio\n", - "\n", - "1. Integrations with multi-modal AI models to extract information from unstructured data, in this case audio files.\n", - "\n", - " https://cloud.google.com/bigquery/docs/multimodal-data-dataframes-tutorial\n", - " \n", - "2. Vector search to find similar text using embedding models.\n", - "\n", - " https://cloud.google.com/bigquery/docs/vector-index-text-search-tutorial\n", - "\n", - "3. BigQuery DataFrames to use Python instead of SQL.\n", - "\n", - " https://cloud.google.com/bigquery/docs/bigquery-dataframes-introduction" - ], - "metadata": { - "@deathbeds/jupyterlab-fonts": { - "styles": { - "": { - "body[data-jp-deck-mode='presenting'] &": { - "z-index": "0", - "zoom": "181%" - } - } - } - }, - "slideshow": { - "slide_type": "slide" - } - }, - "execution_count": null - }, - { - "id": "90f2e543", - "cell_type": "markdown", - "source": [ - "## Getting started with BigQuery DataFrames (bigframes)\n", - "\n", - "Install the bigframes package." - ], - "metadata": { - "@deathbeds/jupyterlab-fonts": { - "styles": { - "": { - "body[data-jp-deck-mode='presenting'] &": { - "zoom": "275%" - } - } - } - }, - "slideshow": { - "slide_type": "slide" - } - }, - "execution_count": null - }, - { - "id": "56694cb4", - "cell_type": "code", - "source": [ - "%pip install --upgrade bigframes google-cloud-automl google-cloud-translate google-ai-generativelanguage tensorflow " - ], - "metadata": { - "@deathbeds/jupyterlab-fonts": { - "styles": { - "": { - "body[data-jp-deck-mode='presenting'] &": { - "zoom": "214%" - } - } - } - }, - "execution": { - "iopub.execute_input": "2025-08-14T15:53:02.494188Z", - "iopub.status.busy": "2025-08-14T15:53:02.493469Z", - "iopub.status.idle": "2025-08-14T15:53:08.492291Z", - "shell.execute_reply": "2025-08-14T15:53:08.491183Z", - "shell.execute_reply.started": "2025-08-14T15:53:02.494152Z" - }, - "trusted": true - }, - "execution_count": null, - "outputs": [] - }, - { - "id": "fa84ad03", - "cell_type": "markdown", - "source": [ - "**Important:** restart the kernel by going to \"Run -> Restart & clear cell outputs\" before continuing.\n", - "\n", - "Configure bigframes to use your GCP project. First, go to \"Add-ons -> Google Cloud SDK\" and click the \"Attach\" button. Then," - ], - "metadata": { - "@deathbeds/jupyterlab-fonts": { - "styles": { - "": { - "body[data-jp-deck-mode='presenting'] &": { - "z-index": "4", - "zoom": "236%" - } - } - } - } - }, - "execution_count": null - }, - { - "id": "1fbd4f9e", - "cell_type": "code", - "source": [ - "from kaggle_secrets import UserSecretsClient\n", - "user_secrets = UserSecretsClient()\n", - "user_credential = user_secrets.get_gcloud_credential()\n", - "user_secrets.set_tensorflow_credential(user_credential)" - ], - "metadata": { - "execution": { - "iopub.execute_input": "2025-08-14T15:53:08.494636Z", - "iopub.status.busy": "2025-08-14T15:53:08.494313Z", - "iopub.status.idle": "2025-08-14T15:53:08.609706Z", - "shell.execute_reply": "2025-08-14T15:53:08.608705Z", - "shell.execute_reply.started": "2025-08-14T15:53:08.494604Z" - }, - "trusted": true - }, - "execution_count": null, - "outputs": [] - }, - { - "id": "0b0b1cd8", - "cell_type": "code", - "source": [ - "import bigframes._config\n", - "import bigframes.pandas as bpd\n", - "\n", - "PROJECT_ID = \"your-project-id\" # @param {type:\"string\"}\n", - "bpd.options.bigquery.location = \"US\"\n", - "\n", - "# Set to your GCP project ID.\n", - "bpd.options.bigquery.project = PROJECT_ID" - ], - "metadata": { - "@deathbeds/jupyterlab-fonts": { - "styles": { - "": { - "body[data-jp-deck-mode='presenting'] &": { - "zoom": "193%" - } - } - } - }, - "execution": { - "iopub.execute_input": "2025-08-14T15:53:08.610982Z", - "iopub.status.busy": "2025-08-14T15:53:08.610686Z", - "iopub.status.idle": "2025-08-14T15:53:17.658993Z", - "shell.execute_reply": "2025-08-14T15:53:17.657745Z", - "shell.execute_reply.started": "2025-08-14T15:53:08.610961Z" - }, - "trusted": true - }, - "execution_count": null, - "outputs": [] - }, - { - "id": "32e58a7f", - "cell_type": "markdown", - "source": [ - "## Reading data\n", - "\n", - "BigQuery DataFrames can read data from BigQuery, GCS, or even local sources. With `engine=\"bigquery\"`, BigQuery's distributed processing reads the file without it ever having to reach your local Python environment." - ], - "metadata": { - "@deathbeds/jupyterlab-fonts": { - "styles": { - "": { - "body[data-jp-deck-mode='presenting'] &": { - "zoom": "207%" - } - } - } - }, - "slideshow": { - "slide_type": "slide" - } - }, - "execution_count": null - }, - { - "id": "e52aa9e8", - "cell_type": "code", - "source": [ - "df = bpd.read_json(\n", - " \"gs://cloud-samples-data/third-party/usa-loc-national-jukebox/jukebox.jsonl\",\n", - " engine=\"bigquery\",\n", - " orient=\"records\",\n", - " lines=True,\n", - ")" - ], - "metadata": { - "@deathbeds/jupyterlab-fonts": { - "styles": { - "": { - "body[data-jp-deck-mode='presenting'] &": { - "zoom": "225%" - } - } - } - }, - "execution": { - "iopub.execute_input": "2025-08-14T15:53:17.662234Z", - "iopub.status.busy": "2025-08-14T15:53:17.661901Z", - "iopub.status.idle": "2025-08-14T15:53:34.486799Z", - "shell.execute_reply": "2025-08-14T15:53:34.485777Z", - "shell.execute_reply.started": "2025-08-14T15:53:17.662207Z" - }, - "trusted": true - }, - "execution_count": null, - "outputs": [] - }, - { - "id": "0c1fca97", - "cell_type": "code", - "source": [ - "# Use `peek()` instead of `head()` to see arbitrary rows rather than the \"first\" rows.\n", - "df.peek()" - ], - "metadata": { - "@deathbeds/jupyterlab-fonts": { - "styles": { - "": { - "body[data-jp-deck-mode='presenting'] &": { - "zoom": "122%" - } - } - } - }, - "execution": { - "iopub.execute_input": "2025-08-14T15:53:34.488610Z", - "iopub.status.busy": "2025-08-14T15:53:34.488332Z", - "iopub.status.idle": "2025-08-14T15:53:40.347014Z", - "shell.execute_reply": "2025-08-14T15:53:40.345773Z", - "shell.execute_reply.started": "2025-08-14T15:53:34.488589Z" - }, - "slideshow": { - "slide_type": "slide" - }, - "trusted": true - }, - "execution_count": null, - "outputs": [] - }, - { - "id": "4a13e789", - "cell_type": "code", - "source": [ - "df.shape" - ], - "metadata": { - "@deathbeds/jupyterlab-fonts": { - "styles": { - "": { - "body[data-jp-deck-mode='presenting'] &": { - "zoom": "134%" - } - } - } - }, - "execution": { - "iopub.execute_input": "2025-08-14T15:53:40.348376Z", - "iopub.status.busy": "2025-08-14T15:53:40.348021Z", - "iopub.status.idle": "2025-08-14T15:53:40.364129Z", - "shell.execute_reply": "2025-08-14T15:53:40.363204Z", - "shell.execute_reply.started": "2025-08-14T15:53:40.348351Z" - }, - "trusted": true - }, - "execution_count": null, - "outputs": [] - }, - { - "id": "26b8baba", - "cell_type": "code", - "source": [ - "# For the purposes of a demo, select only a subset of rows.\n", - "df = df.sample(n=250)\n", - "df.cache()\n", - "df.shape" - ], - "metadata": { - "execution": { - "iopub.execute_input": "2025-08-14T15:55:55.448664Z", - "iopub.status.busy": "2025-08-14T15:55:55.448310Z", - "iopub.status.idle": "2025-08-14T15:55:59.440964Z", - "shell.execute_reply": "2025-08-14T15:55:59.439988Z", - "shell.execute_reply.started": "2025-08-14T15:55:55.448637Z" - }, - "trusted": true - }, - "execution_count": null, - "outputs": [] - }, - { - "id": "af84cb21", - "cell_type": "code", - "source": [ - "# As a side effect of how I extracted the song information from the HTML DOM,\n", - "# we ended up with lists in places where we only expect one item.\n", - "#\n", - "# We can \"explode\" to flatten these lists.\n", - "flattened = df.explode([\n", - " \"Recording Repository\",\n", - " \"Recording Label\",\n", - " \"Recording Take Number\",\n", - " \"Recording Date\",\n", - " \"Recording Matrix Number\",\n", - " \"Recording Catalog Number\",\n", - " \"Media Size\",\n", - " \"Recording Location\",\n", - " \"Summary\",\n", - " \"Rights Advisory\",\n", - " \"Title\",\n", - "])\n", - "flattened.peek()" - ], - "metadata": { - "@deathbeds/jupyterlab-fonts": { - "styles": { - "": { - "body[data-jp-deck-mode='presenting'] &": { - "zoom": "161%" - } - } - } - }, - "execution": { - "iopub.execute_input": "2025-08-14T15:56:02.040804Z", - "iopub.status.busy": "2025-08-14T15:56:02.040450Z", - "iopub.status.idle": "2025-08-14T15:56:06.544384Z", - "shell.execute_reply": "2025-08-14T15:56:06.543240Z", - "shell.execute_reply.started": "2025-08-14T15:56:02.040777Z" - }, - "slideshow": { - "slide_type": "slide" - }, - "trusted": true - }, - "execution_count": null, - "outputs": [] - }, - { - "id": "085deffd", - "cell_type": "code", - "source": [ - "flattened.shape" - ], - "metadata": { - "execution": { - "iopub.execute_input": "2025-08-14T15:56:06.546531Z", - "iopub.status.busy": "2025-08-14T15:56:06.546140Z", - "iopub.status.idle": "2025-08-14T15:56:06.566005Z", - "shell.execute_reply": "2025-08-14T15:56:06.564355Z", - "shell.execute_reply.started": "2025-08-14T15:56:06.546494Z" - }, - "trusted": true - }, - "execution_count": null, - "outputs": [] - }, - { - "id": "f8e653ee", - "cell_type": "markdown", - "source": [ - "To access unstructured data from BigQuery, create a URI pointing to a file in Google Cloud Storage (GCS). Then, construct a \"blob\" (also known as an \"Object Ref\" in BigQuery terms) so that BigQuery can read from GCS." - ], - "metadata": { - "@deathbeds/jupyterlab-fonts": { - "styles": { - "": { - "body[data-jp-deck-mode='presenting'] &": { - "zoom": "216%" - } - } - } - }, - "editable": true, - "slideshow": { - "slide_type": "slide" - }, - "tags": [] - }, - "execution_count": null - }, - { - "id": "dbd1a844", - "cell_type": "code", - "source": [ - "flattened = flattened.assign(**{\\n \"GCS Prefix\": \"gs://cloud-samples-data/third-party/usa-loc-national-jukebox/\",\\n \"GCS Stub\": flattened['URL'].str.extract(r'/(jukebox-[0-9]+)/'),\\n})\\nflattened[\"GCS URI\"] = flattened[\"GCS Prefix\"] + flattened[\"GCS Stub\"] + \".mp3\"" - ], - "metadata": { - "@deathbeds/jupyterlab-fonts": { - "styles": { - "": { - "body[data-jp-deck-mode='presenting'] &": { - "zoom": "211%" - } - } - } - }, - "editable": true, - "execution": { - "iopub.execute_input": "2025-08-14T15:56:07.394879Z", - "iopub.status.busy": "2025-08-14T15:56:07.394509Z", - "iopub.status.idle": "2025-08-14T15:56:12.217017Z", - "shell.execute_reply": "2025-08-14T15:56:12.215852Z", - "shell.execute_reply.started": "2025-08-14T15:56:07.394853Z" - }, - "slideshow": { - "slide_type": "" - }, - "tags": [], - "trusted": true - }, - "execution_count": null, - "outputs": [] - }, - { - "id": "fae13ec5", - "cell_type": "markdown", - "source": [ - "BigQuery (and BigQuery DataFrames) provide access to powerful models and multimodal capabilities. Here, we transcribe audio to text." - ], - "metadata": { - "@deathbeds/jupyterlab-fonts": { - "styles": { - "": { - "body[data-jp-deck-mode='presenting'] &": { - "zoom": "317%" - } - } - } - }, - "editable": true, - "slideshow": { - "slide_type": "slide" - }, - "tags": [] - }, - "execution_count": null - }, - { - "id": "f08f92b1", - "cell_type": "code", - "source": [ - "import bigframes.bigquery as bbq\n", - "\n", - "# Replace with your own connection name.\n", - "CONNECTION_ID = 'your-project-id.your-location.your-connection' # @param {type:\"string\"}\n", - "\n", - "# Convert the audio URI to the runtime representation required by the model.\n", - "audio_ref = bbq.obj.make_ref(flattened[\"GCS URI\"], authorizer=CONNECTION_ID)\n", - "audio_metadata = bbq.obj.fetch_metadata(audio_ref)\n", - "audio_runtime = bbq.obj.get_access_url(audio_metadata, mode=\"R\")\n", - "\n", - "# Call GenAI model to perform audio transcription\n", - "raw_results = bbq.ai.generate(\n", - " prompt=(\"Transcribe the provided audio.\", audio_runtime),\n", - " endpoint=\"gemini-2.5-flash\"\n", - ")\n", - "\n", - "# Package result struct to contain 'content' and 'status' expected by downstream cells\n", - "transcription_df = bpd.DataFrame({\n", - " \"content\": raw_results.struct.field(\"result\"),\n", - " \"status\": raw_results.struct.field(\"status\")\n", - "})\n", - "flattened[\"Transcription\"] = bbq.struct(transcription_df)" - ], - "metadata": { - "editable": true, - "execution": { - "iopub.execute_input": "2025-08-14T15:56:20.908198Z", - "iopub.status.busy": "2025-08-14T15:56:20.907791Z", - "iopub.status.idle": "2025-08-14T15:58:45.909086Z", - "shell.execute_reply": "2025-08-14T15:58:45.908060Z", - "shell.execute_reply.started": "2025-08-14T15:56:20.908170Z" - }, - "slideshow": { - "slide_type": "" - }, - "tags": [], - "trusted": true - }, - "execution_count": null, - "outputs": [] - }, - { - "id": "30969ae1", - "cell_type": "markdown", - "source": [ - "Sometimes the model has transient errors. Check the status column to see if there are errors." - ], - "metadata": { - "@deathbeds/jupyterlab-fonts": { - "styles": { - "": { - "body[data-jp-deck-mode='presenting'] &": { - "zoom": "229%" - } - } - } - }, - "slideshow": { - "slide_type": "slide" - } - }, - "execution_count": null - }, - { - "id": "7d0dbc38", - "cell_type": "code", - "source": [ - "print(f\"Successful rows: {(flattened['Transcription'].struct.field('status') == '').sum()}\")\n", - "print(f\"Failed rows: {(flattened['Transcription'].struct.field('status') != '').sum()}\")\n", - "flattened.shape" - ], - "metadata": { - "@deathbeds/jupyterlab-fonts": { - "styles": { - "": { - "body[data-jp-deck-mode='presenting'] &": { - "zoom": "177%" - } - } - } - }, - "editable": true, - "execution": { - "iopub.execute_input": "2025-08-14T15:59:43.609239Z", - "iopub.status.busy": "2025-08-14T15:59:43.607976Z", - "iopub.status.idle": "2025-08-14T15:59:44.515118Z", - "shell.execute_reply": "2025-08-14T15:59:44.514275Z", - "shell.execute_reply.started": "2025-08-14T15:59:43.609201Z" - }, - "slideshow": { - "slide_type": "" - }, - "tags": [], - "trusted": true - }, - "execution_count": null, - "outputs": [] - }, - { - "id": "6cddf53b", - "cell_type": "code", - "source": [ - "# Show transcribed lyrics.\n", - "flattened[\"Transcription\"].struct.field(\"content\")" - ], - "metadata": { - "@deathbeds/jupyterlab-fonts": { - "styles": { - "": { - "body[data-jp-deck-mode='presenting'] &": { - "zoom": "141%" - } - } - } - }, - "execution": { - "iopub.execute_input": "2025-08-14T15:59:44.820256Z", - "iopub.status.busy": "2025-08-14T15:59:44.819926Z", - "iopub.status.idle": "2025-08-14T15:59:53.147159Z", - "shell.execute_reply": "2025-08-14T15:59:53.146281Z", - "shell.execute_reply.started": "2025-08-14T15:59:44.820232Z" - }, - "trusted": true - }, - "execution_count": null, - "outputs": [] - }, - { - "id": "ba0386cc", - "cell_type": "code", - "source": [ - "# Find all instrumentatal songs\n", - "instrumental = flattened[flattened[\"Transcription\"].struct.field(\"content\") == \"\"]\n", - "print(instrumental.shape)\n", - "song = instrumental.peek(1)\n", - "song" - ], - "metadata": { - "@deathbeds/jupyterlab-fonts": { - "styles": { - "": { - "body[data-jp-deck-mode='presenting'] &": { - "zoom": "152%" - } - } - } - }, - "execution": { - "iopub.execute_input": "2025-08-14T15:59:53.149222Z", - "iopub.status.busy": "2025-08-14T15:59:53.148783Z", - "iopub.status.idle": "2025-08-14T15:59:58.868959Z", - "shell.execute_reply": "2025-08-14T15:59:58.867804Z", - "shell.execute_reply.started": "2025-08-14T15:59:53.149198Z" - }, - "slideshow": { - "slide_type": "slide" - }, - "trusted": true - }, - "execution_count": null, - "outputs": [] - }, - { - "id": "61a883b2", - "cell_type": "code", - "source": [ - "import gcsfs\n", - "import IPython.display\n", - "\n", - "fs = gcsfs.GCSFileSystem(project='bigframes-dev')\n", - "with fs.open(song[\"GCS URI\"].iloc[0]) as song_file:\n", - " song_bytes = song_file.read()\n", - "\n", - "IPython.display.Audio(song_bytes)" - ], - "metadata": { - "@deathbeds/jupyterlab-fonts": { - "styles": { - "": { - "body[data-jp-deck-mode='presenting'] &": { - "zoom": "152%" - } - } - } - }, - "editable": true, - "execution": { - "iopub.execute_input": "2025-08-14T15:59:58.870143Z", - "iopub.status.busy": "2025-08-14T15:59:58.869868Z", - "iopub.status.idle": "2025-08-14T16:00:15.502470Z", - "shell.execute_reply": "2025-08-14T16:00:15.500813Z", - "shell.execute_reply.started": "2025-08-14T15:59:58.870123Z" - }, - "slideshow": { - "slide_type": "" - }, - "tags": [], - "trusted": true - }, - "execution_count": null, - "outputs": [] - }, - { - "id": "e8a25c46", - "cell_type": "markdown", - "source": [ - "## Creating a searchable index\n", - "\n", - "To be able to search by semantics rather than just text, generate embeddings and then create an index to efficiently search these.\n", - "\n", - "See also, this example: https://github.com/googleapis/python-bigquery-dataframes/blob/main/notebooks/generative_ai/bq_dataframes_llm_vector_search.ipynb" - ], - "metadata": { - "@deathbeds/jupyterlab-fonts": { - "styles": { - "": { - "body[data-jp-deck-mode='presenting'] &": { - "zoom": "181%" - } - } - } - }, - "slideshow": { - "slide_type": "slide" - } - }, - "execution_count": null - }, - { - "id": "ead0fa8c", - "cell_type": "code", - "source": [ - "from bigframes.ml.llm import TextEmbeddingGenerator\n", - "\n", - "text_model = TextEmbeddingGenerator(model_name=\"text-multilingual-embedding-002\")" - ], - "metadata": { - "@deathbeds/jupyterlab-fonts": { - "styles": { - "": { - "body[data-jp-deck-mode='presenting'] &": { - "zoom": "163%" - } - } - } - }, - "execution": { - "iopub.execute_input": "2025-08-14T16:00:15.506380Z", - "iopub.status.busy": "2025-08-14T16:00:15.505775Z", - "iopub.status.idle": "2025-08-14T16:00:25.134987Z", - "shell.execute_reply": "2025-08-14T16:00:25.134124Z", - "shell.execute_reply.started": "2025-08-14T16:00:15.506337Z" - }, - "trusted": true - }, - "execution_count": null, - "outputs": [] - }, - { - "id": "5ed7776d", - "cell_type": "code", - "source": [ - "df_to_index = (\n", - " flattened\n", - " .assign(content=flattened[\"Transcription\"].struct.field(\"content\"))\n", - " [flattened[\"Transcription\"].struct.field(\"content\") != \"\"]\n", - ")\n", - "embedding = text_model.predict(df_to_index)\n", - "embedding.peek(1)" - ], - "metadata": { - "@deathbeds/jupyterlab-fonts": { - "styles": { - "": { - "body[data-jp-deck-mode='presenting'] &": { - "zoom": "125%" - } - } - } - }, - "execution": { - "iopub.execute_input": "2025-08-14T16:00:25.136017Z", - "iopub.status.busy": "2025-08-14T16:00:25.135744Z", - "iopub.status.idle": "2025-08-14T16:00:34.860878Z", - "shell.execute_reply": "2025-08-14T16:00:34.859925Z", - "shell.execute_reply.started": "2025-08-14T16:00:25.135997Z" - }, - "trusted": true - }, - "execution_count": null, - "outputs": [] - }, - { - "id": "c96e9832", - "cell_type": "code", - "source": [ - "# Check the status column to look for errors.\n", - "print(f\"Successful rows: {(embedding['ml_generate_embedding_status'] == '').sum()}\")\n", - "print(f\"Failed rows: {(embedding['ml_generate_embedding_status'] != '').sum()}\")\n", - "embedding.shape" - ], - "metadata": { - "@deathbeds/jupyterlab-fonts": { - "styles": { - "": { - "body[data-jp-deck-mode='presenting'] &": { - "zoom": "178%" - } - } - } - }, - "editable": true, - "execution": { - "iopub.execute_input": "2025-08-14T16:01:20.816923Z", - "iopub.status.busy": "2025-08-14T16:01:20.816523Z", - "iopub.status.idle": "2025-08-14T16:01:22.480554Z", - "shell.execute_reply": "2025-08-14T16:01:22.479604Z", - "shell.execute_reply.started": "2025-08-14T16:01:20.816894Z" - }, - "slideshow": { - "slide_type": "slide" - }, - "tags": [], - "trusted": true - }, - "execution_count": null, - "outputs": [] - }, - { - "id": "0e2a5d7b", - "cell_type": "markdown", - "source": [ - "We're now ready to save this to a table." - ], - "metadata": { - "@deathbeds/jupyterlab-fonts": { - "styles": { - "": { - "body[data-jp-deck-mode='presenting'] &": { - "zoom": "224%" - } - } - } - } - }, - "execution_count": null - }, - { - "id": "51819a0c", - "cell_type": "code", - "source": [ - "embedding_table_id = f\"{bpd.options.bigquery.project}.kaggle.national_jukebox\"\n", - "embedding.to_gbq(embedding_table_id, if_exists=\"replace\")" - ], - "metadata": { - "@deathbeds/jupyterlab-fonts": { - "styles": { - "": { - "body[data-jp-deck-mode='presenting'] &": { - "zoom": "172%" - } - } - } - }, - "execution": { - "iopub.execute_input": "2025-08-14T16:03:43.611592Z", - "iopub.status.busy": "2025-08-14T16:03:43.611265Z", - "iopub.status.idle": "2025-08-14T16:03:47.459025Z", - "shell.execute_reply": "2025-08-14T16:03:47.458079Z", - "shell.execute_reply.started": "2025-08-14T16:03:43.611568Z" - }, - "trusted": true - }, - "execution_count": null, - "outputs": [] - }, - { - "id": "5e16fb14", - "cell_type": "markdown", - "source": [ - "## Searching the database\n", - "\n", - "To search by semantics, we:\n", - "\n", - "1. Turn our search string into an embedding using the same model as our index.\n", - "2. Find the closest matches to the search string." - ], - "metadata": { - "@deathbeds/jupyterlab-fonts": { - "styles": { - "": { - "body[data-jp-deck-mode='presenting'] &": { - "zoom": "183%" - } - } - } - }, - "slideshow": { - "slide_type": "slide" - } - }, - "execution_count": null - }, - { - "id": "1bad3317", - "cell_type": "code", - "source": [ - "import bigframes.pandas as bpd\n", - "\n", - "df_written = bpd.read_gbq(embedding_table_id)\n", - "df_written.peek(1)" - ], - "metadata": { - "@deathbeds/jupyterlab-fonts": { - "styles": { - "": { - "body[data-jp-deck-mode='presenting'] &": { - "zoom": "92%" - } - } - } - }, - "execution": { - "iopub.execute_input": "2025-08-14T16:03:52.674429Z", - "iopub.status.busy": "2025-08-14T16:03:52.673629Z", - "iopub.status.idle": "2025-08-14T16:03:59.962635Z", - "shell.execute_reply": "2025-08-14T16:03:59.961482Z", - "shell.execute_reply.started": "2025-08-14T16:03:52.674399Z" - }, - "slideshow": { - "slide_type": "skip" - }, - "trusted": true - }, - "execution_count": null, - "outputs": [] - }, - { - "id": "8aaaef1f", - "cell_type": "code", - "source": [ - "from bigframes.ml.llm import TextEmbeddingGenerator\n", - "\n", - "search_string = \"walking home\"\n", - "\n", - "text_model = TextEmbeddingGenerator(model_name=\"text-multilingual-embedding-002\")\n", - "search_df = bpd.DataFrame([search_string], columns=['search_string'])\n", - "search_embedding = text_model.predict(search_df)\n", - "search_embedding" - ], - "metadata": { - "@deathbeds/jupyterlab-fonts": { - "styles": { - "": { - "body[data-jp-deck-mode='presenting'] &": { - "zoom": "127%" - } - } - } - }, - "execution": { - "iopub.execute_input": "2025-08-14T16:03:59.964634Z", - "iopub.status.busy": "2025-08-14T16:03:59.964268Z", - "iopub.status.idle": "2025-08-14T16:04:55.051531Z", - "shell.execute_reply": "2025-08-14T16:04:55.050393Z", - "shell.execute_reply.started": "2025-08-14T16:03:59.964598Z" - }, - "trusted": true - }, - "execution_count": null, - "outputs": [] - }, - { - "id": "908a2340", - "cell_type": "code", - "source": [ - "import bigframes.bigquery as bbq\n", - "\n", - "vector_search_results = bbq.vector_search(\n", - " base_table=embedding_table_id,\n", - " column_to_search=\"ml_generate_embedding_result\",\n", - " query=search_embedding,\n", - " distance_type=\"COSINE\",\n", - " query_column_to_search=\"ml_generate_embedding_result\",\n", - " top_k=5,\n", - ")" - ], - "metadata": { - "@deathbeds/jupyterlab-fonts": { - "styles": { - "": { - "body[data-jp-deck-mode='presenting'] &": { - "zoom": "175%" - } - } - } - }, - "editable": true, - "execution": { - "iopub.execute_input": "2025-08-14T16:05:46.473357Z", - "iopub.status.busy": "2025-08-14T16:05:46.473056Z", - "iopub.status.idle": "2025-08-14T16:05:50.564470Z", - "shell.execute_reply": "2025-08-14T16:05:50.563277Z", - "shell.execute_reply.started": "2025-08-14T16:05:46.473336Z" - }, - "slideshow": { - "slide_type": "slide" - }, - "tags": [], - "trusted": true - }, - "execution_count": null, - "outputs": [] - }, - { - "id": "f84ebe70", - "cell_type": "code", - "source": [ - "vector_search_results.dtypes" - ], - "metadata": { - "execution": { - "iopub.execute_input": "2025-08-14T16:05:50.566930Z", - "iopub.status.busy": "2025-08-14T16:05:50.566422Z", - "iopub.status.idle": "2025-08-14T16:05:50.576293Z", - "shell.execute_reply": "2025-08-14T16:05:50.575186Z", - "shell.execute_reply.started": "2025-08-14T16:05:50.566893Z" - }, - "trusted": true - }, - "execution_count": null, - "outputs": [] - }, - { - "id": "eeff1c72", - "cell_type": "code", - "source": [ - "results = vector_search_results[[\"Title\", \"Summary\", \"Names\", \"GCS URI\", \"Transcription\", \"distance\"]].sort_values(\"distance\").to_pandas()\n", - "results" - ], - "metadata": { - "@deathbeds/jupyterlab-fonts": { - "styles": { - "": { - "body[data-jp-deck-mode='presenting'] &": { - "zoom": "158%" - } - } - } - }, - "execution": { - "iopub.execute_input": "2025-08-14T16:05:54.787080Z", - "iopub.status.busy": "2025-08-14T16:05:54.786649Z", - "iopub.status.idle": "2025-08-14T16:05:55.581285Z", - "shell.execute_reply": "2025-08-14T16:05:55.580012Z", - "shell.execute_reply.started": "2025-08-14T16:05:54.787054Z" - }, - "slideshow": { - "slide_type": "slide" - }, - "trusted": true - }, - "execution_count": null, - "outputs": [] - }, - { - "id": "7ec53675", - "cell_type": "code", - "source": [ - "print(results[\"Transcription\"].struct.field(\"content\").iloc[0])" - ], - "metadata": { - "@deathbeds/jupyterlab-fonts": { - "styles": { - "": { - "body[data-jp-deck-mode='presenting'] &": { - "zoom": "138%" - } - } - } - }, - "execution": { - "iopub.execute_input": "2025-08-14T16:05:56.142373Z", - "iopub.status.busy": "2025-08-14T16:05:56.142038Z", - "iopub.status.idle": "2025-08-14T16:05:56.149020Z", - "shell.execute_reply": "2025-08-14T16:05:56.147966Z", - "shell.execute_reply.started": "2025-08-14T16:05:56.142350Z" - }, - "trusted": true - }, - "execution_count": null, - "outputs": [] - }, - { - "id": "a96552fb", - "cell_type": "code", - "source": [ - "import gcsfs\n", - "import IPython.display\n", - "\n", - "fs = gcsfs.GCSFileSystem(project='bigframes-dev')\n", - "with fs.open(results[\"GCS URI\"].iloc[0]) as song_file:\n", - " song_bytes = song_file.read()\n", - "\n", - "IPython.display.Audio(song_bytes)" - ], - "metadata": { - "editable": true, - "execution": { - "iopub.execute_input": "2025-08-14T16:06:04.542878Z", - "iopub.status.busy": "2025-08-14T16:06:04.542537Z", - "iopub.status.idle": "2025-08-14T16:06:04.843052Z", - "shell.execute_reply": "2025-08-14T16:06:04.841220Z", - "shell.execute_reply.started": "2025-08-14T16:06:04.542854Z" - }, - "scrolled": true, - "slideshow": { - "slide_type": "" - }, - "tags": [], - "trusted": true - }, - "execution_count": null, - "outputs": [] - }, - { - "id": "72af7c7f", - "cell_type": "code", - "source": [], - "metadata": { - "trusted": true - }, - "execution_count": null, - "outputs": [] - } - ], - "metadata": { - "kaggle": { - "accelerator": "none", - "dataSources": [ - { - "databundleVersionId": 13238728, - "sourceId": 110281, - "sourceType": "competition" - } - ], - "dockerImageVersionId": 31089, - "isGpuEnabled": false, - "isInternetEnabled": true, - "language": "python", - "sourceType": "notebook" - }, - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.13" - } - }, - "nbformat_minor": 4, - "nbformat": 4 -} \ No newline at end of file diff --git a/notebooks/location/regionalized.ipynb b/notebooks/location/regionalized.ipynb index b1e9e010d48..a7ff5db84e3 100644 --- a/notebooks/location/regionalized.ipynb +++ b/notebooks/location/regionalized.ipynb @@ -7,9 +7,8 @@ "source": [ "# README\n", "\n", - "This Notebook runs requiring the following environent variable:\n", - "1. GOOGLE_CLOUD_PROJECT - The google cloud project id.\n", - "1. BIGQUERY_LOCATION - can take values as per https://cloud.google.com/bigquery/docs/locations, e.g. `us`, `asia-east1`." + "This Notebook runs differently depending on the following environent variable:\n", + "1. BIGQUERY_LOCATION - can take values as per https://cloud.google.com/bigquery/docs/locations, e.g. `us`, `asia-east1`" ] }, { @@ -17,7 +16,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Infer location and set up data in that location if needed" + "### Infer location and set up data in that location if needed" ] }, { @@ -48,36 +47,32 @@ ], "source": [ "# Take multi-region US as the default BQ location, where most of the BQ data lies including the BQ public datasets\n", - "import os\n", - "\n", - "PROJECT_ID = os.environ.get(\"GOOGLE_CLOUD_PROJECT\")\n", - "BQ_LOCATION = os.environ.get(\"BIGQUERY_LOCATION\")\n", - "\n", - "if not PROJECT_ID:\n", - " raise ValueError(\"Project must be set via environment variable GOOGLE_CLOUD_PROJECT\")\n", - "if not BQ_LOCATION:\n", - " raise ValueError(\"BQ location must be set via environment variable BIGQUERY_LOCATION\")\n", - "\n", + "BQ_LOCATION = \"us\"\n", + "PROJECT = \"bigframes-dev\"\n", "DATASET = \"bigframes_testing\"\n", "PENGUINS_TABLE = \"bigquery-public-data.ml_datasets.penguins\"\n", "\n", "\n", "# Check for a location set in the environment and do location-specific setup if needed\n", "\n", + "import os\n", "import google.api_core.exceptions\n", "from google.cloud import bigquery\n", "import bigframes\n", + " \n", + "env_bq_location = os.getenv(\"BIGQUERY_LOCATION\")\n", + "if env_bq_location and env_bq_location != BQ_LOCATION:\n", + " BQ_LOCATION = env_bq_location.lower()\n", "\n", "client = bigquery.Client()\n", "\n", - "BQ_LOCATION = BQ_LOCATION.lower()\n", "if BQ_LOCATION != \"us\":\n", " bq_location_normalized = BQ_LOCATION.replace('-', '_')\n", "\n", " # Nominate a local penguins table\n", " penguins_table_ref = bigquery.TableReference.from_string(PENGUINS_TABLE)\n", " penguins_local_dataset_name = f\"{DATASET}_{bq_location_normalized}\"\n", - " penguins_local_dataset_ref = bigquery.DatasetReference(project=PROJECT_ID, dataset_id=penguins_local_dataset_name)\n", + " penguins_local_dataset_ref = bigquery.DatasetReference(project=PROJECT, dataset_id=penguins_local_dataset_name)\n", " penguins_local_dataset = bigquery.Dataset(penguins_local_dataset_ref)\n", " penguins_local_dataset.location = BQ_LOCATION\n", " penguins_local_table_ref= bigquery.TableReference(penguins_local_dataset, penguins_table_ref.table_id)\n", @@ -99,13 +94,13 @@ " DATASET = f\"{DATASET}_{bq_location_normalized}\"\n", "\n", "# Create the dataset to store the model if it doesn't exist \n", - "model_local_dataset = bigquery.Dataset(bigquery.DatasetReference(project=PROJECT_ID, dataset_id=DATASET))\n", + "model_local_dataset = bigquery.Dataset(bigquery.DatasetReference(project=PROJECT, dataset_id=DATASET))\n", "model_local_dataset.location = BQ_LOCATION\n", "model_dataset = client.create_dataset(model_local_dataset, exists_ok=True)\n", "\n", "# Finally log the variables driving the core notebook execution\n", "log = ('\\n'.join(f\"{name}: {str(value)}\" for name, value in {\n", - " \"BigQuery project\" : PROJECT_ID,\n", + " \"BigQuery project\" : PROJECT,\n", " \"BigQuery location\" : BQ_LOCATION,\n", " \"Penguins Table\" : PENGUINS_TABLE,\n", " \"ML Model Dataset\" : model_dataset.reference\n", @@ -126,7 +121,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Set BigQuery DataFrames options" + "### Set BigQuery DataFrames options" ] }, { @@ -137,14 +132,7 @@ "source": [ "import bigframes.pandas\n", "\n", - "# Note: The project option is not required in all environments.\n", - "# On BigQuery Studio, the project ID is automatically detected.\n", - "bigframes.pandas.options.bigquery.project = PROJECT_ID\n", - "\n", - "# Note: The location option is not required.\n", - "# It defaults to the location of the first table or query\n", - "# passed to read_gbq(). For APIs where a location can't be\n", - "# auto-detected, the location defaults to the \"US\" location.\n", + "bigframes.pandas.options.bigquery.project = PROJECT\n", "bigframes.pandas.options.bigquery.location = BQ_LOCATION" ] }, @@ -1344,7 +1332,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## BigQuery DataFrames gives you the ability to turn your custom scalar functions into a BigQuery remote function.", + "### BigQuery DataFrames gives you the ability to turn your custom scalar functions into a BigQuery remote function.\n", "\n", "It requires the GCP project to be set up appropriately and the user having sufficient privileges to use them. One can find more details on it via `help` command." ] @@ -1421,8 +1409,8 @@ } ], "source": [ - "import bigframes.pandas as bpd\n", - "help(bpd.remote_function)" + "import bigframes.pandas as pd\n", + "help(pd.remote_function)" ] }, { @@ -1461,8 +1449,8 @@ } ], "source": [ - "@bpd.remote_function(bigquery_connection='bigframes-rf-conn', cloud_function_service_account=\"default\")\n", - "def get_bucket(num: float) -> str:\n", + "@pd.remote_function([float], str, bigquery_connection='bigframes-rf-conn')\n", + "def get_bucket(num):\n", " if not num: return \"NA\"\n", " boundary = 4000\n", " return \"at_or_above_4000\" if num >= boundary else \"below_4000\"" @@ -1643,7 +1631,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Initialize a DataFrame from a BigQuery table" + "### Initialize a DataFrame from a BigQuery table" ] }, { @@ -2785,22 +2773,6 @@ "source": [ "model.to_gbq(f\"{DATASET}.penguins_model\", replace=True)" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Clean Up" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "bpd.close_session()" - ] } ], "metadata": { @@ -2819,7 +2791,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.9" + "version": "3.10.12" }, "orig_nbformat": 4 }, diff --git a/notebooks/ml/bq_dataframes_ml_cross_validation.ipynb b/notebooks/ml/bq_dataframes_ml_cross_validation.ipynb deleted file mode 100644 index 3dc0eabf5a1..00000000000 --- a/notebooks/ml/bq_dataframes_ml_cross_validation.ipynb +++ /dev/null @@ -1,1013 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# BigFrames ML Cross-Vaidation\n", - "\n", - "This demo shows how to do cross validation in bigframes.ml" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1. Prepare Data" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import bigframes.pandas as bpd" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "Query job aa2b9845-0e66-4f42-a360-ffe03215caf6 is DONE. 0 Bytes processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Query job fe2bc354-672e-4d08-b969-bb2ede299fca is DONE. 28.9 kB processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Query job 8d16fa20-391f-4917-86fc-1a595dba3fc6 is DONE. 33.6 kB processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
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speciesislandculmen_length_mmculmen_depth_mmflipper_length_mmbody_mass_gsex
0Gentoo penguin (Pygoscelis papua)Biscoe45.216.4223.05950.0MALE
1Gentoo penguin (Pygoscelis papua)Biscoe46.514.5213.04400.0FEMALE
2Adelie Penguin (Pygoscelis adeliae)Biscoe37.716.0183.03075.0FEMALE
3Gentoo penguin (Pygoscelis papua)Biscoe46.415.6221.05000.0MALE
4Gentoo penguin (Pygoscelis papua)Biscoe46.113.2211.04500.0FEMALE
5Adelie Penguin (Pygoscelis adeliae)Torgersen43.119.2197.03500.0MALE
6Gentoo penguin (Pygoscelis papua)Biscoe45.215.8215.05300.0MALE
7Adelie Penguin (Pygoscelis adeliae)Dream36.217.3187.03300.0FEMALE
8Chinstrap penguin (Pygoscelis antarctica)Dream46.018.9195.04150.0FEMALE
9Gentoo penguin (Pygoscelis papua)Biscoe54.315.7231.05650.0MALE
11Adelie Penguin (Pygoscelis adeliae)Torgersen39.517.4186.03800.0FEMALE
12Gentoo penguin (Pygoscelis papua)Biscoe42.713.7208.03950.0FEMALE
13Adelie Penguin (Pygoscelis adeliae)Biscoe41.020.0203.04725.0MALE
14Gentoo penguin (Pygoscelis papua)Biscoe48.515.0219.04850.0FEMALE
15Chinstrap penguin (Pygoscelis antarctica)Dream49.618.2193.03775.0MALE
16Gentoo penguin (Pygoscelis papua)Biscoe50.817.3228.05600.0MALE
17Gentoo penguin (Pygoscelis papua)Biscoe46.214.1217.04375.0FEMALE
18Adelie Penguin (Pygoscelis adeliae)Biscoe38.817.2180.03800.0MALE
19Chinstrap penguin (Pygoscelis antarctica)Dream51.018.8203.04100.0MALE
20Gentoo penguin (Pygoscelis papua)Biscoe42.913.1215.05000.0FEMALE
21Gentoo penguin (Pygoscelis papua)Biscoe50.415.3224.05550.0MALE
22Gentoo penguin (Pygoscelis papua)Biscoe49.016.1216.05550.0MALE
23Gentoo penguin (Pygoscelis papua)Biscoe43.414.4218.04600.0FEMALE
24Gentoo penguin (Pygoscelis papua)Biscoe45.015.4220.05050.0MALE
25Gentoo penguin (Pygoscelis papua)Biscoe47.514.0212.04875.0FEMALE
\n", - "

25 rows × 7 columns

\n", - "
[334 rows x 7 columns in total]" - ], - "text/plain": [ - " species island culmen_length_mm \\\n", - "0 Gentoo penguin (Pygoscelis papua) Biscoe 45.2 \n", - "1 Gentoo penguin (Pygoscelis papua) Biscoe 46.5 \n", - "2 Adelie Penguin (Pygoscelis adeliae) Biscoe 37.7 \n", - "3 Gentoo penguin (Pygoscelis papua) Biscoe 46.4 \n", - "4 Gentoo penguin (Pygoscelis papua) Biscoe 46.1 \n", - "5 Adelie Penguin (Pygoscelis adeliae) Torgersen 43.1 \n", - "6 Gentoo penguin (Pygoscelis papua) Biscoe 45.2 \n", - "7 Adelie Penguin (Pygoscelis adeliae) Dream 36.2 \n", - "8 Chinstrap penguin (Pygoscelis antarctica) Dream 46.0 \n", - "9 Gentoo penguin (Pygoscelis papua) Biscoe 54.3 \n", - "11 Adelie Penguin (Pygoscelis adeliae) Torgersen 39.5 \n", - "12 Gentoo penguin (Pygoscelis papua) Biscoe 42.7 \n", - "13 Adelie Penguin (Pygoscelis adeliae) Biscoe 41.0 \n", - "14 Gentoo penguin (Pygoscelis papua) Biscoe 48.5 \n", - "15 Chinstrap penguin (Pygoscelis antarctica) Dream 49.6 \n", - "16 Gentoo penguin (Pygoscelis papua) Biscoe 50.8 \n", - "17 Gentoo penguin (Pygoscelis papua) Biscoe 46.2 \n", - "18 Adelie Penguin (Pygoscelis adeliae) Biscoe 38.8 \n", - "19 Chinstrap penguin (Pygoscelis antarctica) Dream 51.0 \n", - "20 Gentoo penguin (Pygoscelis papua) Biscoe 42.9 \n", - "21 Gentoo penguin (Pygoscelis papua) Biscoe 50.4 \n", - "22 Gentoo penguin (Pygoscelis papua) Biscoe 49.0 \n", - "23 Gentoo penguin (Pygoscelis papua) Biscoe 43.4 \n", - "24 Gentoo penguin (Pygoscelis papua) Biscoe 45.0 \n", - "25 Gentoo penguin (Pygoscelis papua) Biscoe 47.5 \n", - "\n", - " culmen_depth_mm flipper_length_mm body_mass_g sex \n", - "0 16.4 223.0 5950.0 MALE \n", - "1 14.5 213.0 4400.0 FEMALE \n", - "2 16.0 183.0 3075.0 FEMALE \n", - "3 15.6 221.0 5000.0 MALE \n", - "4 13.2 211.0 4500.0 FEMALE \n", - "5 19.2 197.0 3500.0 MALE \n", - "6 15.8 215.0 5300.0 MALE \n", - "7 17.3 187.0 3300.0 FEMALE \n", - "8 18.9 195.0 4150.0 FEMALE \n", - "9 15.7 231.0 5650.0 MALE \n", - "11 17.4 186.0 3800.0 FEMALE \n", - "12 13.7 208.0 3950.0 FEMALE \n", - "13 20.0 203.0 4725.0 MALE \n", - "14 15.0 219.0 4850.0 FEMALE \n", - "15 18.2 193.0 3775.0 MALE \n", - "16 17.3 228.0 5600.0 MALE \n", - "17 14.1 217.0 4375.0 FEMALE \n", - "18 17.2 180.0 3800.0 MALE \n", - "19 18.8 203.0 4100.0 MALE \n", - "20 13.1 215.0 5000.0 FEMALE \n", - "21 15.3 224.0 5550.0 MALE \n", - "22 16.1 216.0 5550.0 MALE \n", - "23 14.4 218.0 4600.0 FEMALE \n", - "24 15.4 220.0 5050.0 MALE \n", - "25 14.0 212.0 4875.0 FEMALE \n", - "...\n", - "\n", - "[334 rows x 7 columns]" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# read and filter out unavailable data\n", - "df = bpd.read_gbq(\"bigframes-dev.bqml_tutorial.penguins\")\n", - "df = df.dropna()\n", - "df" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# Select X and y from the dataset\n", - "X = df[\n", - " [\n", - " \"species\",\n", - " \"island\",\n", - " \"culmen_length_mm\",\n", - " ]\n", - " ]\n", - "y = df[\"body_mass_g\"]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2.1 Define KFold class and Train/Test for Each Fold (Manual Approach)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "from bigframes.ml import model_selection, linear_model" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# Create KFold instance, n_splits defines how many folds the data will split. For example, n_split=5 will split the entire dataset into 5 pieces. \n", - "# In each fold, 4 pieces will be used for training, and the other piece will be used for evaluation. \n", - "kf = model_selection.KFold(n_splits=5)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "Query job 9ce9fb43-306d-46e9-bbe5-d98ee55143bd is DONE. 37.0 kB processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Query job 8c86156d-ee97-4f66-9dc1-db15ff3d8e8e is DONE. 16.4 kB processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Query job b8f2b382-b938-4dff-8bdb-129703ade285 is DONE. 37.3 kB processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " mean_absolute_error mean_squared_error mean_squared_log_error \\\n", - "0 297.36838 148892.914876 0.009057 \n", - "\n", - " median_absolute_error r2_score explained_variance \n", - "0 238.424052 0.814613 0.816053 \n", - "\n", - "[1 rows x 6 columns]\n" - ] - }, - { - "data": { - "text/html": [ - "Query job ec2968f3-1713-4617-8a26-6fe4267f8061 is DONE. 37.0 kB processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Query job c7a1b80f-26f5-41b1-bcdc-b276af141671 is DONE. 16.4 kB processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Query job 82054991-c22f-41b3-9802-f16919949e26 is DONE. 37.3 kB processed. 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Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " mean_absolute_error mean_squared_error mean_squared_log_error \\\n", - "0 348.412701 180661.063512 0.01125 \n", - "\n", - " median_absolute_error r2_score explained_variance \n", - "0 313.29406 0.744053 0.74537 \n", - "\n", - "[1 rows x 6 columns]\n" - ] - }, - { - "data": { - "text/html": [ - "Query job d90f5938-2894-4c93-8691-21162a2fca4c is DONE. 37.0 kB processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Query job 4c6328b3-2d3f-42bb-9f83-4f8c84773c95 is DONE. 16.4 kB processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Query job 8a885a6a-d3ad-4569-80ce-4f57d9b86105 is DONE. 37.3 kB processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " mean_absolute_error mean_squared_error mean_squared_log_error \\\n", - "0 309.991882 151820.705254 0.008898 \n", - "\n", - " median_absolute_error r2_score explained_variance \n", - "0 212.758708 0.694001 0.694287 \n", - "\n", - "[1 rows x 6 columns]\n" - ] - }, - { - "data": { - "text/html": [ - "Query job d1e60370-11c8-4f49-a8d5-85417662aa51 is DONE. 37.0 kB processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Query job d8e8712a-6347-4725-a27d-49810d4acc1c is DONE. 16.5 kB processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Query job 6a0ebaa6-5572-404f-a41d-b90e2c65d948 is DONE. 37.3 kB processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " mean_absolute_error mean_squared_error mean_squared_log_error \\\n", - "0 256.569216 103495.042886 0.006605 \n", - "\n", - " median_absolute_error r2_score explained_variance \n", - "0 222.940815 0.818589 0.832344 \n", - "\n", - "[1 rows x 6 columns]\n" - ] - } - ], - "source": [ - "for X_train, X_test, y_train, y_test in kf.split(X, y):\n", - " model = linear_model.LinearRegression()\n", - " model.fit(X_train, y_train)\n", - " score = model.score(X_test, y_test)\n", - "\n", - " print(score)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2.2 Use cross_validate Function to Do Cross Validation (Automatic Approach)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "Query job 5bdcd65d-7d72-4094-be3a-cf67a1787cf4 is DONE. 37.0 kB processed. 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Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "{'test_score': [ mean_absolute_error mean_squared_error mean_squared_log_error \\\n", - " 0 322.341485 157616.627179 0.009137 \n", - " \n", - " median_absolute_error r2_score explained_variance \n", - " 0 269.412639 0.705594 0.724882 \n", - " \n", - " [1 rows x 6 columns],\n", - " mean_absolute_error mean_squared_error mean_squared_log_error \\\n", - " 0 289.682121 136550.318797 0.00878 \n", - " \n", - " median_absolute_error r2_score explained_variance \n", - " 0 212.874686 0.799363 0.81416 \n", - " \n", - " [1 rows x 6 columns],\n", - " mean_absolute_error mean_squared_error mean_squared_log_error \\\n", - " 0 325.358522 155218.752974 0.009606 \n", - " \n", - " median_absolute_error r2_score explained_variance \n", - " 0 267.301671 0.777174 0.7782 \n", - " \n", - " [1 rows x 6 columns],\n", - " mean_absolute_error mean_squared_error mean_squared_log_error \\\n", - " 0 286.874056 120586.575364 0.007484 \n", - " \n", - " median_absolute_error r2_score explained_variance \n", - " 0 247.656578 0.79281 0.796001 \n", - " \n", - " [1 rows x 6 columns],\n", - " mean_absolute_error mean_squared_error mean_squared_log_error \\\n", - " 0 287.989397 145947.465344 0.008447 \n", - " \n", - " median_absolute_error r2_score explained_variance \n", - " 0 186.777549 0.791452 0.798825 \n", - " \n", - " [1 rows x 6 columns]],\n", - " 'fit_time': [18.79181448201416,\n", - " 19.092008439009078,\n", - " 75.7446747609647,\n", - " 17.520530884969048,\n", - " 21.157033596013207],\n", - " 'score_time': [4.247669544012751,\n", - " 6.792615927988663,\n", - " 4.502274781989399,\n", - " 4.484583999030292,\n", - " 4.224339194013737]}" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# By using model_selection.cross_validate, the above 2.1 process is automated. The returned scores contains the evaluation results for each fold.\n", - "model = linear_model.LinearRegression()\n", - "scores = model_selection.cross_validate(model, X, y, cv=5)\n", - "scores" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "venv (3.10.14)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.14" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/notebooks/ml/bq_dataframes_ml_linear_regression.ipynb b/notebooks/ml/bq_dataframes_ml_linear_regression.ipynb deleted file mode 100644 index 210922eab94..00000000000 --- a/notebooks/ml/bq_dataframes_ml_linear_regression.ipynb +++ /dev/null @@ -1,760 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "ur8xi4C7S06n" - }, - "outputs": [], - "source": [ - "# Copyright 2023 Google LLC\n", - "#\n", - "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", - "# you may not use this file except in compliance with the License.\n", - "# You may obtain a copy of the License at\n", - "#\n", - "# https://www.apache.org/licenses/LICENSE-2.0\n", - "#\n", - "# Unless required by applicable law or agreed to in writing, software\n", - "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", - "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", - "# See the License for the specific language governing permissions and\n", - "# limitations under the License." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "JAPoU8Sm5E6e" - }, - "source": [ - "# Train a linear regression model with BigQuery DataFrames ML", - "\n", - "\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - "
\n", - " \n", - " \"Colab Run in Colab\n", - " \n", - " \n", - " \n", - " \"GitHub\n", - " View on GitHub\n", - " \n", - " \n", - " \n", - " \"Vertex\n", - " Open in Vertex AI Workbench\n", - " \n", - " \n", - " \n", - " \"BQ\n", - " Open in BQ Studio\n", - " \n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "24743cf4a1e1" - }, - "source": [ - "**_NOTE_**: This notebook has been tested in the following environment:\n", - "\n", - "* Python version = 3.10" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "tvgnzT1CKxrO" - }, - "source": [ - "## Overview\n", - "\n", - "Use this notebook to learn how to train a linear regression model using BigQuery DataFrames ML. BigQuery DataFrames ML provides a provides a scikit-learn-like API for ML powered by the BigQuery engine.\n", - "\n", - "This example is adapted from the [BQML linear regression tutorial](https://cloud.google.com/bigquery-ml/docs/linear-regression-tutorial).\n", - "\n", - "Learn more about [BigQuery DataFrames](https://cloud.google.com/python/docs/reference/bigframes/latest)." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "d975e698c9a4" - }, - "source": [ - "### Objective\n", - "\n", - "In this tutorial, you use BigQuery DataFrames to create a linear regression model that predicts the weight of an Adelie penguin based on the penguin's island of residence, culmen length and depth, flipper length, and sex.\n", - "\n", - "The steps include:\n", - "\n", - "- Creating a DataFrame from a BigQuery table.\n", - "- Cleaning and preparing data using pandas.\n", - "- Creating a linear regression model using `bigframes.ml`.\n", - "- Saving the ML model to BigQuery for future use." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "08d289fa873f" - }, - "source": [ - "### Dataset\n", - "\n", - "This tutorial uses the [```penguins``` table](https://console.cloud.google.com/bigquery?p=bigquery-public-data&d=ml_datasets&t=penguins) (a BigQuery Public Dataset) which includes data on a set of penguins including species, island of residence, weight, culmen length and depth, flipper length, and sex." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "aed92deeb4a0" - }, - "source": [ - "### Costs\n", - "\n", - "This tutorial uses billable components of Google Cloud:\n", - "\n", - "* BigQuery (compute)\n", - "* BigQuery ML\n", - "\n", - "Learn about [BigQuery compute pricing](https://cloud.google.com/bigquery/pricing#analysis_pricing_models)\n", - "and [BigQuery ML pricing](https://cloud.google.com/bigquery/pricing#bqml),\n", - "and use the [Pricing Calculator](https://cloud.google.com/products/calculator/)\n", - "to generate a cost estimate based on your projected usage." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "i7EUnXsZhAGF" - }, - "source": [ - "## Installation\n", - "\n", - "If you don't have [bigframes](https://pypi.org/project/bigframes/) package already installed, uncomment and execute the following cells to\n", - "\n", - "1. Install the package\n", - "1. Restart the notebook kernel (Jupyter or Colab) to work with the package" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "9O0Ka4W2MNF3" - }, - "outputs": [], - "source": [ - "# !pip install bigframes" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "f200f10a1da3" - }, - "outputs": [], - "source": [ - "# Automatically restart kernel after installs so that your environment can access the new packages\n", - "# import IPython\n", - "\n", - "# app = IPython.Application.instance()\n", - "# app.kernel.do_shutdown(True)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "BF1j6f9HApxa" - }, - "source": [ - "## Before you begin\n", - "\n", - "Complete the tasks in this section to set up your environment." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "oDfTjfACBvJk" - }, - "source": [ - "### Set up your Google Cloud project\n", - "\n", - "**The following steps are required, regardless of your notebook environment.**\n", - "\n", - "1. [Select or create a Google Cloud project](https://console.cloud.google.com/cloud-resource-manager). When you first create an account, you get a $300 credit towards your compute/storage costs.\n", - "\n", - "2. [Make sure that billing is enabled for your project](https://cloud.google.com/billing/docs/how-to/modify-project).\n", - "\n", - "3. [Enable the BigQuery API](https://console.cloud.google.com/flows/enableapi?apiid=bigquery.googleapis.com).\n", - "\n", - "4. If you are running this notebook locally, install the [Cloud SDK](https://cloud.google.com/sdk)." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "WReHDGG5g0XY" - }, - "source": [ - "#### Set your project ID\n", - "\n", - "If you don't know your project ID, try the following:\n", - "* Run `gcloud config list`.\n", - "* Run `gcloud projects list`.\n", - "* See the support page: [Locate the project ID](https://support.google.com/googleapi/answer/7014113)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "oM1iC_MfAts1" - }, - "outputs": [], - "source": [ - "PROJECT_ID = \"\" # @param {type:\"string\"}\n", - "\n", - "# Set the project id\n", - "! gcloud config set project {PROJECT_ID}" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "region" - }, - "source": [ - "#### Set the region\n", - "\n", - "You can also change the `REGION` variable used by BigQuery. Learn more about [BigQuery regions](https://cloud.google.com/bigquery/docs/locations#supported_locations)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "eF-Twtc4XGem" - }, - "outputs": [], - "source": [ - "REGION = \"US\" # @param {type: \"string\"}" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "sBCra4QMA2wR" - }, - "source": [ - "### Authenticate your Google Cloud account\n", - "\n", - "Depending on your Jupyter environment, you might have to manually authenticate. Follow the relevant instructions below." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "74ccc9e52986" - }, - "source": [ - "**Vertex AI Workbench**\n", - "\n", - "Do nothing, you are already authenticated." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "de775a3773ba" - }, - "source": [ - "**Local JupyterLab instance**\n", - "\n", - "Uncomment and run the following cell:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "254614fa0c46" - }, - "outputs": [], - "source": [ - "# ! gcloud auth login" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "ef21552ccea8" - }, - "source": [ - "**Colab**\n", - "\n", - "Uncomment and run the following cell:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "603adbbf0532" - }, - "outputs": [], - "source": [ - "# from google.colab import auth\n", - "# auth.authenticate_user()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "960505627ddf" - }, - "source": [ - "### Import libraries" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "PyQmSRbKA8r-" - }, - "outputs": [], - "source": [ - "import bigframes.pandas as bpd" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "init_aip:mbsdk,all" - }, - "source": [ - "### Set BigQuery DataFrames options" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "NPPMuw2PXGeo" - }, - "outputs": [], - "source": [ - "# Note: The project option is not required in all environments.\n", - "# On BigQuery Studio, the project ID is automatically detected.\n", - "bpd.options.bigquery.project = PROJECT_ID\n", - "\n", - "# Note: The location option is not required.\n", - "# It defaults to the location of the first table or query\n", - "# passed to read_gbq(). For APIs where a location can't be\n", - "# auto-detected, the location defaults to the \"US\" location.\n", - "bpd.options.bigquery.location = REGION" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "D21CoOlfFTYI" - }, - "source": [ - "If you want to reset the location of the created DataFrame or Series objects, reset the session by executing `bpd.close_session()`. After that, you can reuse `bpd.options.bigquery.location` to specify another location." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "9EMAqR37AfLS" - }, - "source": [ - "## Read a BigQuery table into a BigQuery DataFrames DataFrame\n", - "\n", - "Read the [```penguins``` table](https://console.cloud.google.com/bigquery?p=bigquery-public-data&d=ml_datasets&t=penguins) into a BigQuery DataFrames DataFrame:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "EDAaIwHpQCDZ" - }, - "outputs": [], - "source": [ - "df = bpd.read_gbq(\"bigquery-public-data.ml_datasets.penguins\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "DJu837YEXD7B" - }, - "source": [ - "Take a look at the DataFrame:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "_gPD0Zn1Stdb" - }, - "outputs": [], - "source": [ - "df.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "rwPLjqW2Ajzh" - }, - "source": [ - "## Clean and prepare data\n", - "\n", - "You can use pandas as you normally would on the BigQuery DataFrames DataFrame, but calculations happen in the BigQuery query engine instead of your local environment.\n", - "\n", - "Because this model will focus on the Adelie Penguin species, you need to filter the data for only those rows representing Adelie penguins. Then you drop the `species` column because it is no longer needed.\n", - "\n", - "As these functions are applied, only the new DataFrame object `adelie_data` is modified. The source table and the original DataFrame object `df` don't change." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "6i6HkFJZa8na" - }, - "outputs": [], - "source": [ - "# Filter down to the data to the Adelie Penguin species\n", - "adelie_data = df[df.species == \"Adelie Penguin (Pygoscelis adeliae)\"]\n", - "\n", - "# Drop the species column\n", - "adelie_data = adelie_data.drop(columns=[\"species\"])\n", - "\n", - "# Take a look at the filtered DataFrame\n", - "adelie_data" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "jhK2OlyMbY4L" - }, - "source": [ - "Drop rows with `NULL` values in order to create a BigQuery DataFrames DataFrame for the training data:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "0am3hdlXZfxZ" - }, - "outputs": [], - "source": [ - "# Drop rows with nulls to get training data\n", - "training_data = adelie_data.dropna()\n", - "\n", - "# Take a peek at the training data\n", - "training_data" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "M_-0X7NxYK5f" - }, - "source": [ - "Specify your feature (or input) columns and the label (or output) column:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "YKwCW7Nsavap" - }, - "outputs": [], - "source": [ - "feature_columns = training_data[['island', 'culmen_length_mm', 'culmen_depth_mm', 'flipper_length_mm', 'sex']]\n", - "label_columns = training_data[['body_mass_g']]" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "CjyM7vZJZ0sQ" - }, - "source": [ - "There is a row within the `adelie_data` BigQuery DataFrames DataFrame that has a `NULL` value for the `body mass` column. `body mass` is the label column, which is the value that the model you are creating is trying to predict.\n", - "\n", - "Create a new BigQuery DataFrames DataFrame, `test_data`, for this row so that you can use it as test data on which to make a prediction later:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "wej78IDUaRW9" - }, - "outputs": [], - "source": [ - "test_data = adelie_data[adelie_data.body_mass_g.isnull()]\n", - "\n", - "test_data" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Fx4lsNqMorJ-" - }, - "source": [ - "## Create the linear regression model\n", - "\n", - "BigQuery DataFrames ML lets you move from exploring data to creating machine learning models through its scikit-learn-like API, `bigframes.ml`. BigQuery DataFrames ML supports several types of [ML models](https://cloud.google.com/python/docs/reference/bigframes/latest#ml-capabilities).\n", - "\n", - "In this notebook, you create a linear regression model, a type of regression model that generates a continuous value from a linear combination of input features.\n", - "\n", - "When you create a model with BigQuery DataFrames ML, it is saved locally and limited to the BigQuery session. However, as you'll see in the next section, you can use `to_gbq` to save the model permanently to your BigQuery project." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "EloGtMnverFF" - }, - "source": [ - "### Create the model using `bigframes.ml`\n", - "\n", - "When you pass the feature columns without transforms, BigQuery ML uses\n", - "[automatic preprocessing](https://cloud.google.com/bigquery/docs/auto-preprocessing) to encode string values and scale numeric values.\n", - "\n", - "BigQuery ML also [automatically splits the data for training and evaluation](https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-create-glm#data_split_method), although for datasets with less than 500 rows (such as this one), all rows are used for training." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "GskyyUQPowBT" - }, - "outputs": [], - "source": [ - "from bigframes.ml.linear_model import LinearRegression\n", - "\n", - "model = LinearRegression()\n", - "\n", - "model.fit(feature_columns, label_columns)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "UGjeMPC2caKK" - }, - "source": [ - "### Score the model\n", - "\n", - "Check how the model performed by using the `score` method. More information on model scoring can be found [here](https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-evaluate#mlevaluate_output)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "kGBJKafpo0dl" - }, - "outputs": [], - "source": [ - "model.score(feature_columns, label_columns)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "P2lUiZZ_cjri" - }, - "source": [ - "### Predict using the model\n", - "\n", - "Use the model to predict the body mass of the data row you saved earlier to the `test_data` DataFrame:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "bsQ9cmoWo0Ps" - }, - "outputs": [], - "source": [ - "model.predict(test_data)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "GTRdUw-Ro5R1" - }, - "source": [ - "## Save the model in BigQuery\n", - "\n", - "The model is saved locally within this session. You can save the model permanently to BigQuery for use in future sessions, and to make the model sharable with others." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "K0mPaoGpcwwy" - }, - "source": [ - "Create a BigQuery dataset to house the model, adding a name for your dataset as the `DATASET_ID` variable:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "ZSP7gt13QrQt" - }, - "outputs": [], - "source": [ - "DATASET_ID = \"\" # @param {type:\"string\"}\n", - "\n", - "from google.cloud import bigquery\n", - "client = bigquery.Client(project=PROJECT_ID)\n", - "dataset = bigquery.Dataset(PROJECT_ID + \".\" + DATASET_ID)\n", - "dataset.location = REGION\n", - "dataset = client.create_dataset(dataset, exists_ok=True)\n", - "print(f\"Dataset {dataset.dataset_id} created.\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "zqAIWWgJczp-" - }, - "source": [ - "Save the model using the `to_gbq` method:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "QE_GD4Byo_jb" - }, - "outputs": [], - "source": [ - "model.to_gbq(DATASET_ID + \".penguin_weight\" , replace=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "f7uHacAy49rT" - }, - "source": [ - "You can view the saved model in the BigQuery console under the dataset you created in the first step. Run the following cell and follow the link to view your BigQuery console:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "qDBoiA_0488Z" - }, - "outputs": [], - "source": [ - "print(f'https://console.developers.google.com/bigquery?p={PROJECT_ID}')" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "G_wjSfXpWTuy" - }, - "source": [ - "# Summary and next steps\n", - "\n", - "You've created a linear regression model using `bigframes.ml`.\n", - "\n", - "Learn more about BigQuery DataFrames in the [documentation](https://cloud.google.com/python/docs/reference/bigframes/latest) and find more sample notebooks in the [GitHub repo](https://github.com/googleapis/python-bigquery-dataframes/tree/main/notebooks)." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "TpV-iwP9qw9c" - }, - "source": [ - "## Cleaning up\n", - "\n", - "To clean up all Google Cloud resources used in this project, you can [delete the Google Cloud\n", - "project](https://cloud.google.com/resource-manager/docs/creating-managing-projects#shutting_down_projects) you used for the tutorial.\n", - "\n", - "Otherwise, you can uncomment the remaining cells and run them to delete the individual resources you created in this tutorial:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "sx_vKniMq9ZX" - }, - "outputs": [], - "source": [ - "# # Delete the BigQuery dataset and associated ML model\n", - "# from google.cloud import bigquery\n", - "# client = bigquery.Client(project=PROJECT_ID)\n", - "# client.delete_dataset(\n", - "# DATASET_ID, delete_contents=True, not_found_ok=True\n", - "# )\n", - "# print(\"Deleted dataset '{}'.\".format(DATASET_ID))" - ] - } - ], - "metadata": { - "colab": { - "provenance": [], - "toc_visible": true - }, - "kernelspec": { - "display_name": "Python 3", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.0" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/notebooks/ml/bq_dataframes_ml_linear_regression_bbq.ipynb b/notebooks/ml/bq_dataframes_ml_linear_regression_bbq.ipynb deleted file mode 100644 index 396fde5a397..00000000000 --- a/notebooks/ml/bq_dataframes_ml_linear_regression_bbq.ipynb +++ /dev/null @@ -1,2637 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "id": "ur8xi4C7S06n" - }, - "outputs": [], - "source": [ - "# Copyright 2023 Google LLC\n", - "#\n", - "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", - "# you may not use this file except in compliance with the License.\n", - "# You may obtain a copy of the License at\n", - "#\n", - "# https://www.apache.org/licenses/LICENSE-2.0\n", - "#\n", - "# Unless required by applicable law or agreed to in writing, software\n", - "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", - "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", - "# See the License for the specific language governing permissions and\n", - "# limitations under the License." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "JAPoU8Sm5E6e" - }, - "source": [ - "# Train a linear regression model with BigQuery DataFrames ML", - "\n", - "\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - "
\n", - " \n", - " \"Colab Run in Colab\n", - " \n", - " \n", - " \n", - " \"GitHub\n", - " View on GitHub\n", - " \n", - " \n", - " \n", - " \"Vertex\n", - " Open in Vertex AI Workbench\n", - " \n", - " \n", - " \n", - " \"BQ\n", - " Open in BQ Studio\n", - " \n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "24743cf4a1e1" - }, - "source": [ - "**_NOTE_**: This notebook has been tested in the following environment:\n", - "\n", - "* Python version = 3.10" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "tvgnzT1CKxrO" - }, - "source": [ - "## Overview\n", - "\n", - "Use this notebook to learn how to train a linear regression model using BigQuery ML and the `bigframes.bigquery` module.\n", - "\n", - "This example is adapted from the [BQML linear regression tutorial](https://cloud.google.com/bigquery-ml/docs/linear-regression-tutorial).\n", - "\n", - "Learn more about [BigQuery DataFrames](https://dataframes.bigquery.dev/)." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "d975e698c9a4" - }, - "source": [ - "### Objective\n", - "\n", - "In this tutorial, you use BigQuery DataFrames to create a linear regression model that predicts the weight of an Adelie penguin based on the penguin's island of residence, culmen length and depth, flipper length, and sex.\n", - "\n", - "The steps include:\n", - "\n", - "- Creating a DataFrame from a BigQuery table.\n", - "- Cleaning and preparing data using pandas.\n", - "- Creating a linear regression model using `bigframes.ml`.\n", - "- Saving the ML model to BigQuery for future use." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "08d289fa873f" - }, - "source": [ - "### Dataset\n", - "\n", - "This tutorial uses the [```penguins``` table](https://console.cloud.google.com/bigquery?p=bigquery-public-data&d=ml_datasets&t=penguins) (a BigQuery Public Dataset) which includes data on a set of penguins including species, island of residence, weight, culmen length and depth, flipper length, and sex." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "aed92deeb4a0" - }, - "source": [ - "### Costs\n", - "\n", - "This tutorial uses billable components of Google Cloud:\n", - "\n", - "* BigQuery (compute)\n", - "* BigQuery ML\n", - "\n", - "Learn about [BigQuery compute pricing](https://cloud.google.com/bigquery/pricing#analysis_pricing_models)\n", - "and [BigQuery ML pricing](https://cloud.google.com/bigquery/pricing#bqml),\n", - "and use the [Pricing Calculator](https://cloud.google.com/products/calculator/)\n", - "to generate a cost estimate based on your projected usage." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "i7EUnXsZhAGF" - }, - "source": [ - "## Installation\n", - "\n", - "If you don't have [bigframes](https://pypi.org/project/bigframes/) package already installed, uncomment and execute the following cells to\n", - "\n", - "1. Install the package\n", - "1. Restart the notebook kernel (Jupyter or Colab) to work with the package" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "id": "9O0Ka4W2MNF3" - }, - "outputs": [], - "source": [ - "# !pip install bigframes" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "id": "f200f10a1da3" - }, - "outputs": [], - "source": [ - "# Automatically restart kernel after installs so that your environment can access the new packages\n", - "# import IPython\n", - "\n", - "# app = IPython.Application.instance()\n", - "# app.kernel.do_shutdown(True)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "BF1j6f9HApxa" - }, - "source": [ - "## Before you begin\n", - "\n", - "Complete the tasks in this section to set up your environment." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "oDfTjfACBvJk" - }, - "source": [ - "### Set up your Google Cloud project\n", - "\n", - "**The following steps are required, regardless of your notebook environment.**\n", - "\n", - "1. [Select or create a Google Cloud project](https://console.cloud.google.com/cloud-resource-manager). When you first create an account, you get a $300 credit towards your compute/storage costs.\n", - "\n", - "2. [Make sure that billing is enabled for your project](https://cloud.google.com/billing/docs/how-to/modify-project).\n", - "\n", - "3. [Enable the BigQuery API](https://console.cloud.google.com/flows/enableapi?apiid=bigquery.googleapis.com).\n", - "\n", - "4. If you are running this notebook locally, install the [Cloud SDK](https://cloud.google.com/sdk)." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "WReHDGG5g0XY" - }, - "source": [ - "#### Set your project ID\n", - "\n", - "If you don't know your project ID, try the following:\n", - "* Run `gcloud config list`.\n", - "* Run `gcloud projects list`.\n", - "* See the support page: [Locate the project ID](https://support.google.com/googleapi/answer/7014113)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "oM1iC_MfAts1" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Updated property [core/project].\n" - ] - } - ], - "source": [ - "PROJECT_ID = \"\" # @param {type:\"string\"}\n", - "\n", - "# Set the project id\n", - "! gcloud config set project {PROJECT_ID}" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "region" - }, - "source": [ - "#### Set the region\n", - "\n", - "You can also change the `REGION` variable used by BigQuery. Learn more about [BigQuery regions](https://cloud.google.com/bigquery/docs/locations#supported_locations)." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "id": "eF-Twtc4XGem" - }, - "outputs": [], - "source": [ - "REGION = \"US\" # @param {type: \"string\"}" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "sBCra4QMA2wR" - }, - "source": [ - "### Authenticate your Google Cloud account\n", - "\n", - "Depending on your Jupyter environment, you might have to manually authenticate. Follow the relevant instructions below." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "74ccc9e52986" - }, - "source": [ - "**Vertex AI Workbench**\n", - "\n", - "Do nothing, you are already authenticated." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "de775a3773ba" - }, - "source": [ - "**Local JupyterLab instance**\n", - "\n", - "Uncomment and run the following cell:" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "id": "254614fa0c46" - }, - "outputs": [], - "source": [ - "# ! gcloud auth login" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "ef21552ccea8" - }, - "source": [ - "**Colab**\n", - "\n", - "Uncomment and run the following cell:" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "id": "603adbbf0532" - }, - "outputs": [], - "source": [ - "# from google.colab import auth\n", - "# auth.authenticate_user()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "960505627ddf" - }, - "source": [ - "### Import libraries" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "id": "PyQmSRbKA8r-" - }, - "outputs": [], - "source": [ - "import bigframes.pandas as bpd" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "init_aip:mbsdk,all" - }, - "source": [ - "### Set BigQuery DataFrames options" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "id": "NPPMuw2PXGeo" - }, - "outputs": [], - "source": [ - "# Note: The project option is not required in all environments.\n", - "# On BigQuery Studio, the project ID is automatically detected.\n", - "bpd.options.bigquery.project = PROJECT_ID\n", - "\n", - "# Note: The location option is not required.\n", - "# It defaults to the location of the first table or query\n", - "# passed to read_gbq(). For APIs where a location can't be\n", - "# auto-detected, the location defaults to the \"US\" location.\n", - "bpd.options.bigquery.location = REGION\n", - "\n", - "# Recommended for performance. Disables pandas default ordering of all rows.\n", - "bpd.options.bigquery.ordering_mode = \"partial\"" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "D21CoOlfFTYI" - }, - "source": [ - "If you want to reset the location of the created DataFrame or Series objects, reset the session by executing `bpd.close_session()`. After that, you can reuse `bpd.options.bigquery.location` to specify another location." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "9EMAqR37AfLS" - }, - "source": [ - "## Read a BigQuery table into a BigQuery DataFrames DataFrame\n", - "\n", - "Read the [```penguins``` table](https://console.cloud.google.com/bigquery?p=bigquery-public-data&d=ml_datasets&t=penguins) into a BigQuery DataFrames DataFrame:" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "id": "EDAaIwHpQCDZ" - }, - "outputs": [], - "source": [ - "df = bpd.read_gbq(\"bigquery-public-data.ml_datasets.penguins\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "DJu837YEXD7B" - }, - "source": [ - "Take a look at the DataFrame:" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "id": "_gPD0Zn1Stdb" - }, - "outputs": [ - { - "data": { - "text/html": [ - "✅ Completed. 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speciesislandculmen_length_mmculmen_depth_mmflipper_length_mmbody_mass_gsex
0Adelie Penguin (Pygoscelis adeliae)Dream36.618.4184.03475.0FEMALE
1Adelie Penguin (Pygoscelis adeliae)Dream39.819.1184.04650.0MALE
2Adelie Penguin (Pygoscelis adeliae)Dream40.918.9184.03900.0MALE
3Chinstrap penguin (Pygoscelis antarctica)Dream46.517.9192.03500.0FEMALE
4Adelie Penguin (Pygoscelis adeliae)Dream37.316.8192.03000.0FEMALE
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" - ], - "text/plain": [ - " species island culmen_length_mm \\\n", - "0 Adelie Penguin (Pygoscelis adeliae) Dream 36.6 \n", - "1 Adelie Penguin (Pygoscelis adeliae) Dream 39.8 \n", - "2 Adelie Penguin (Pygoscelis adeliae) Dream 40.9 \n", - "3 Chinstrap penguin (Pygoscelis antarctica) Dream 46.5 \n", - "4 Adelie Penguin (Pygoscelis adeliae) Dream 37.3 \n", - "\n", - " culmen_depth_mm flipper_length_mm body_mass_g sex \n", - "0 18.4 184.0 3475.0 FEMALE \n", - "1 19.1 184.0 4650.0 MALE \n", - "2 18.9 184.0 3900.0 MALE \n", - "3 17.9 192.0 3500.0 FEMALE \n", - "4 16.8 192.0 3000.0 FEMALE " - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.peek()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "rwPLjqW2Ajzh" - }, - "source": [ - "## Clean and prepare data\n", - "\n", - "You can use pandas as you normally would on the BigQuery DataFrames DataFrame, but calculations happen in the BigQuery query engine instead of your local environment.\n", - "\n", - "Because this model will focus on the Adelie Penguin species, you need to filter the data for only those rows representing Adelie penguins. Then you drop the `species` column because it is no longer needed.\n", - "\n", - "As these functions are applied, only the new DataFrame object `adelie_data` is modified. The source table and the original DataFrame object `df` don't change." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "id": "6i6HkFJZa8na" - }, - "outputs": [ - { - "data": { - "text/html": [ - "✅ Completed. \n", - " Query processed 28.9 kB in 12 seconds of slot time. [Job bigframes-dev:US.bb256e8c-f2c7-4eff-b5f3-fcc6836110cf details]\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "✅ Completed. \n", - " Query processed 8.4 kB in a moment of slot time.\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "✅ Completed. " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
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islandculmen_length_mmculmen_depth_mmflipper_length_mmbody_mass_gsex
0Dream36.618.4184.03475.0FEMALE
1Dream39.819.1184.04650.0MALE
2Dream40.918.9184.03900.0MALE
3Dream37.316.8192.03000.0FEMALE
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[152 rows x 6 columns in total]" - ], - "text/plain": [ - "island culmen_length_mm culmen_depth_mm flipper_length_mm body_mass_g \\\n", - " Dream 36.6 18.4 184.0 3475.0 \n", - " Dream 39.8 19.1 184.0 4650.0 \n", - " Dream 40.9 18.9 184.0 3900.0 \n", - " Dream 37.3 16.8 192.0 3000.0 \n", - " Dream 43.2 18.5 192.0 4100.0 \n", - " Dream 40.2 20.1 200.0 3975.0 \n", - " Dream 40.8 18.9 208.0 4300.0 \n", - " Dream 39.0 18.7 185.0 3650.0 \n", - " Dream 37.0 16.9 185.0 3000.0 \n", - " Dream 34.0 17.1 185.0 3400.0 \n", - "\n", - " sex \n", - "FEMALE \n", - " MALE \n", - " MALE \n", - "FEMALE \n", - " MALE \n", - " MALE \n", - " MALE \n", - " MALE \n", - "FEMALE \n", - "FEMALE \n", - "...\n", - "\n", - "[152 rows x 6 columns]" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Filter down to the data to the Adelie Penguin species\n", - "adelie_data = df[df.species == \"Adelie Penguin (Pygoscelis adeliae)\"]\n", - "\n", - "# Drop the species column\n", - "adelie_data = adelie_data.drop(columns=[\"species\"])\n", - "\n", - "# Take a look at the filtered DataFrame\n", - "adelie_data" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "jhK2OlyMbY4L" - }, - "source": [ - "Drop rows with `NULL` values in order to create a BigQuery DataFrames DataFrame for the training data:" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "id": "0am3hdlXZfxZ" - }, - "outputs": [ - { - "data": { - "text/html": [ - "Starting." - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "✅ Completed. \n", - " Query processed 8.1 kB in a moment of slot time.\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "✅ Completed. " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
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islandculmen_length_mmculmen_depth_mmflipper_length_mmbody_mass_gsex
0Dream36.618.4184.03475.0FEMALE
1Dream39.819.1184.04650.0MALE
2Dream40.918.9184.03900.0MALE
3Dream37.316.8192.03000.0FEMALE
4Dream43.218.5192.04100.0MALE
5Dream40.220.1200.03975.0MALE
6Dream40.818.9208.04300.0MALE
7Dream39.018.7185.03650.0MALE
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9Dream34.017.1185.03400.0FEMALE
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[146 rows x 6 columns in total]" - ], - "text/plain": [ - "island culmen_length_mm culmen_depth_mm flipper_length_mm body_mass_g \\\n", - " Dream 36.6 18.4 184.0 3475.0 \n", - " Dream 39.8 19.1 184.0 4650.0 \n", - " Dream 40.9 18.9 184.0 3900.0 \n", - " Dream 37.3 16.8 192.0 3000.0 \n", - " Dream 43.2 18.5 192.0 4100.0 \n", - " Dream 40.2 20.1 200.0 3975.0 \n", - " Dream 40.8 18.9 208.0 4300.0 \n", - " Dream 39.0 18.7 185.0 3650.0 \n", - " Dream 37.0 16.9 185.0 3000.0 \n", - " Dream 34.0 17.1 185.0 3400.0 \n", - "\n", - " sex \n", - "FEMALE \n", - " MALE \n", - " MALE \n", - "FEMALE \n", - " MALE \n", - " MALE \n", - " MALE \n", - " MALE \n", - "FEMALE \n", - "FEMALE \n", - "...\n", - "\n", - "[146 rows x 6 columns]" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Drop rows with nulls to get training data\n", - "training_data = adelie_data.dropna()\n", - "\n", - "# Take a peek at the training data\n", - "training_data" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Fx4lsNqMorJ-" - }, - "source": [ - "## Create the linear regression model\n", - "\n", - "In this notebook, you create a linear regression model, a type of regression model that generates a continuous value from a linear combination of input features." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Create a BigQuery dataset to house the model, adding a name for your dataset as the `DATASET_ID` variable:" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Dataset bqml_tutorial created.\n" - ] - } - ], - "source": [ - "DATASET_ID = \"bqml_tutorial\" # @param {type:\"string\"}\n", - "\n", - "from google.cloud import bigquery\n", - "client = bigquery.Client(project=PROJECT_ID)\n", - "dataset = bigquery.Dataset(PROJECT_ID + \".\" + DATASET_ID)\n", - "dataset.location = REGION\n", - "dataset = client.create_dataset(dataset, exists_ok=True)\n", - "print(f\"Dataset {dataset.dataset_id} created.\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "EloGtMnverFF" - }, - "source": [ - "### Create the model using `bigframes.bigquery.ml.create_model`\n", - "\n", - "When you pass the feature columns without transforms, BigQuery ML uses\n", - "[automatic preprocessing](https://cloud.google.com/bigquery/docs/auto-preprocessing) to encode string values and scale numeric values.\n", - "\n", - "BigQuery ML also [automatically splits the data for training and evaluation](https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-create-glm#data_split_method), although for datasets with less than 500 rows (such as this one), all rows are used for training." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "id": "GskyyUQPowBT" - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " Query started with request ID bigframes-dev:US.a33b3628-730b-46e8-ad17-c78bb48619ce.
SQL
CREATE OR REPLACE MODEL `bigframes-dev.bqml_tutorial.penguin_weight`\n",
-       "OPTIONS(model_type = 'LINEAR_REG')\n",
-       "AS SELECT\n",
-       "`bfuid_col_3` AS `island`,\n",
-       "`bfuid_col_4` AS `culmen_length_mm`,\n",
-       "`bfuid_col_5` AS `culmen_depth_mm`,\n",
-       "`bfuid_col_6` AS `flipper_length_mm`,\n",
-       "`bfuid_col_7` AS `label`,\n",
-       "`bfuid_col_8` AS `sex`\n",
-       "FROM\n",
-       "(SELECT\n",
-       "  `t0`.`bfuid_col_3`,\n",
-       "  `t0`.`bfuid_col_4`,\n",
-       "  `t0`.`bfuid_col_5`,\n",
-       "  `t0`.`bfuid_col_6`,\n",
-       "  `t0`.`bfuid_col_7`,\n",
-       "  `t0`.`bfuid_col_8`\n",
-       "FROM `bigframes-dev._63cfa399614a54153cc386c27d6c0c6fdb249f9e._e154f0aa_5b29_492a_b464_a77c5f5a3dbd_bqdf_60fa3196-5a3e-45ae-898e-c2b473bfa1e9` AS `t0`)\n",
-       "
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mean_absolute_errormean_squared_errormean_squared_log_errormedian_absolute_errorr2_scoreexplained_variance
0223.87876378553.6016340.005614181.3309110.6239510.623951
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[1 rows x 6 columns in total]" - ], - "text/plain": [ - " mean_absolute_error mean_squared_error mean_squared_log_error \\\n", - "0 223.878763 78553.601634 0.005614 \n", - "\n", - " median_absolute_error r2_score explained_variance \n", - "0 181.330911 0.623951 0.623951 \n", - "\n", - "[1 rows x 6 columns]" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bbq.ml.evaluate(model_name)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "P2lUiZZ_cjri" - }, - "source": [ - "### Use the model to predict outcomes\n", - "\n", - "Now that you have evaluated your model, the next step is to use it to predict an\n", - "outcome. You can run `bigframes.bigquery.ml.predict` function on the model to\n", - "predict the body mass in grams of all penguins that reside on the Biscoe\n", - "Islands." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "id": "bsQ9cmoWo0Ps" - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/google/home/swast/src/github.com/googleapis/python-bigquery-dataframes/bigframes/core/log_adapter.py:182: TimeTravelCacheWarning: Reading cached table from 2025-12-03 16:30:18.272882+00:00 to avoid\n", - "incompatibilies with previous reads of this table. To read the latest\n", - "version, set `use_cache=False` or close the current session with\n", - "Session.close() or bigframes.pandas.close_session().\n", - " return method(*args, **kwargs)\n" - ] - }, - { - "data": { - "text/html": [ - "✅ Completed. \n", - " Query processed 29.3 kB in a moment of slot time.\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "✅ Completed. " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "✅ Completed. " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
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predicted_labelspeciesislandculmen_length_mmculmen_depth_mmflipper_length_mmbody_mass_gsex
03945.010052Gentoo penguin (Pygoscelis papua)Biscoe<NA><NA><NA><NA><NA>
13914.916297Adelie Penguin (Pygoscelis adeliae)Biscoe39.718.9184.03550.0MALE
23278.611224Adelie Penguin (Pygoscelis adeliae)Biscoe36.417.1184.02850.0FEMALE
34006.367355Adelie Penguin (Pygoscelis adeliae)Biscoe41.618.0192.03950.0MALE
43417.610478Adelie Penguin (Pygoscelis adeliae)Biscoe35.017.9192.03725.0FEMALE
54009.612421Adelie Penguin (Pygoscelis adeliae)Biscoe41.118.2192.04050.0MALE
64231.330911Adelie Penguin (Pygoscelis adeliae)Biscoe42.019.5200.04050.0MALE
73554.308906Gentoo penguin (Pygoscelis papua)Biscoe43.813.9208.04300.0FEMALE
83550.677455Gentoo penguin (Pygoscelis papua)Biscoe43.314.0208.04575.0FEMALE
93537.882543Gentoo penguin (Pygoscelis papua)Biscoe44.013.6208.04350.0FEMALE
\n", - "

10 rows × 8 columns

\n", - "
[168 rows x 8 columns in total]" - ], - "text/plain": [ - " predicted_label species island \\\n", - "0 3945.010052 Gentoo penguin (Pygoscelis papua) Biscoe \n", - "1 3914.916297 Adelie Penguin (Pygoscelis adeliae) Biscoe \n", - "2 3278.611224 Adelie Penguin (Pygoscelis adeliae) Biscoe \n", - "3 4006.367355 Adelie Penguin (Pygoscelis adeliae) Biscoe \n", - "4 3417.610478 Adelie Penguin (Pygoscelis adeliae) Biscoe \n", - "5 4009.612421 Adelie Penguin (Pygoscelis adeliae) Biscoe \n", - "6 4231.330911 Adelie Penguin (Pygoscelis adeliae) Biscoe \n", - "7 3554.308906 Gentoo penguin (Pygoscelis papua) Biscoe \n", - "8 3550.677455 Gentoo penguin (Pygoscelis papua) Biscoe \n", - "9 3537.882543 Gentoo penguin (Pygoscelis papua) Biscoe \n", - "\n", - " culmen_length_mm culmen_depth_mm flipper_length_mm body_mass_g sex \n", - "0 \n", - "1 39.7 18.9 184.0 3550.0 MALE \n", - "2 36.4 17.1 184.0 2850.0 FEMALE \n", - "3 41.6 18.0 192.0 3950.0 MALE \n", - "4 35.0 17.9 192.0 3725.0 FEMALE \n", - "5 41.1 18.2 192.0 4050.0 MALE \n", - "6 42.0 19.5 200.0 4050.0 MALE \n", - "7 43.8 13.9 208.0 4300.0 FEMALE \n", - "8 43.3 14.0 208.0 4575.0 FEMALE \n", - "9 44.0 13.6 208.0 4350.0 FEMALE \n", - "...\n", - "\n", - "[168 rows x 8 columns]" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df = bpd.read_gbq(\"bigquery-public-data.ml_datasets.penguins\")\n", - "biscoe = df[df[\"island\"].str.contains(\"Biscoe\")]\n", - "bbq.ml.predict(model_name, biscoe)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "GTRdUw-Ro5R1" - }, - "source": [ - "### Explain the prediction results\n", - "\n", - "To understand why the model is generating these prediction results, you can use the `explain_predict` function." - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " Query started with request ID bigframes-dev:US.161bba69-c852-4916-a2df-bb5b309be6e4.
SQL
SELECT * FROM ML.EXPLAIN_PREDICT(MODEL `bigframes-dev.bqml_tutorial.penguin_weight`, (SELECT\n",
-       "`bfuid_col_22` AS `species`,\n",
-       "`bfuid_col_23` AS `island`,\n",
-       "`bfuid_col_24` AS `culmen_length_mm`,\n",
-       "`bfuid_col_25` AS `culmen_depth_mm`,\n",
-       "`bfuid_col_26` AS `flipper_length_mm`,\n",
-       "`bfuid_col_27` AS `body_mass_g`,\n",
-       "`bfuid_col_28` AS `sex`\n",
-       "FROM\n",
-       "(SELECT\n",
-       "  `t0`.`species`,\n",
-       "  `t0`.`island`,\n",
-       "  `t0`.`culmen_length_mm`,\n",
-       "  `t0`.`culmen_depth_mm`,\n",
-       "  `t0`.`flipper_length_mm`,\n",
-       "  `t0`.`body_mass_g`,\n",
-       "  `t0`.`sex`,\n",
-       "  `t0`.`species` AS `bfuid_col_22`,\n",
-       "  `t0`.`island` AS `bfuid_col_23`,\n",
-       "  `t0`.`culmen_length_mm` AS `bfuid_col_24`,\n",
-       "  `t0`.`culmen_depth_mm` AS `bfuid_col_25`,\n",
-       "  `t0`.`flipper_length_mm` AS `bfuid_col_26`,\n",
-       "  `t0`.`body_mass_g` AS `bfuid_col_27`,\n",
-       "  `t0`.`sex` AS `bfuid_col_28`,\n",
-       "  regexp_contains(`t0`.`island`, 'Biscoe') AS `bfuid_col_29`\n",
-       "FROM (\n",
-       "  SELECT\n",
-       "    `species`,\n",
-       "    `island`,\n",
-       "    `culmen_length_mm`,\n",
-       "    `culmen_depth_mm`,\n",
-       "    `flipper_length_mm`,\n",
-       "    `body_mass_g`,\n",
-       "    `sex`\n",
-       "  FROM `bigquery-public-data.ml_datasets.penguins` FOR SYSTEM_TIME AS OF TIMESTAMP('2025-12-03T16:30:18.272882+00:00')\n",
-       ") AS `t0`\n",
-       "WHERE\n",
-       "  regexp_contains(`t0`.`island`, 'Biscoe'))), STRUCT(3 AS top_k_features))\n",
-       "
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predicted_labeltop_feature_attributionsbaseline_prediction_valueprediction_valueapproximation_errorspeciesislandculmen_length_mmculmen_depth_mmflipper_length_mmbody_mass_gsex
03945.010052[{'feature': 'island', 'attribution': 0.0}\n", - " {'...3945.0100523945.0100520.0Gentoo penguin (Pygoscelis papua)Biscoe<NA><NA><NA><NA><NA>
13914.916297[{'feature': 'flipper_length_mm', 'attribution...3945.0100523914.9162970.0Adelie Penguin (Pygoscelis adeliae)Biscoe39.718.9184.03550.0MALE
23278.611224[{'feature': 'sex', 'attribution': -443.175184...3945.0100523278.6112240.0Adelie Penguin (Pygoscelis adeliae)Biscoe36.417.1184.02850.0FEMALE
34006.367355[{'feature': 'culmen_length_mm', 'attribution'...3945.0100524006.3673550.0Adelie Penguin (Pygoscelis adeliae)Biscoe41.618.0192.03950.0MALE
43417.610478[{'feature': 'sex', 'attribution': -443.175184...3945.0100523417.6104780.0Adelie Penguin (Pygoscelis adeliae)Biscoe35.017.9192.03725.0FEMALE
54009.612421[{'feature': 'culmen_length_mm', 'attribution'...3945.0100524009.6124210.0Adelie Penguin (Pygoscelis adeliae)Biscoe41.118.2192.04050.0MALE
64231.330911[{'feature': 'flipper_length_mm', 'attribution...3945.0100524231.3309110.0Adelie Penguin (Pygoscelis adeliae)Biscoe42.019.5200.04050.0MALE
73554.308906[{'feature': 'sex', 'attribution': -443.175184...3945.0100523554.3089060.0Gentoo penguin (Pygoscelis papua)Biscoe43.813.9208.04300.0FEMALE
83550.677455[{'feature': 'sex', 'attribution': -443.175184...3945.0100523550.6774550.0Gentoo penguin (Pygoscelis papua)Biscoe43.314.0208.04575.0FEMALE
93537.882543[{'feature': 'sex', 'attribution': -443.175184...3945.0100523537.8825430.0Gentoo penguin (Pygoscelis papua)Biscoe44.013.6208.04350.0FEMALE
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" - ], - "text/plain": [ - " predicted_body_mass_g sex flipper_length_mm culmen_depth_mm \\\n", - "0 3596.332211 MALE 180 15 \n", - "1 3384.699918 FEMALE 190 16 \n", - "2 4049.581796 MALE 200 17 \n", - "3 3837.949503 FEMALE 210 18 \n", - "\n", - " culmen_length_mm island \n", - "0 40 Biscoe \n", - "1 41 Biscoe \n", - "2 42 Dream \n", - "3 43 Dream " - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import pandas as pd\n", - "\n", - "predict_df = pd.DataFrame({\n", - " \"sex\": [\"MALE\", \"FEMALE\", \"MALE\", \"FEMALE\"],\n", - " \"flipper_length_mm\": [180, 190, 200, 210],\n", - " \"culmen_depth_mm\": [15, 16, 17, 18],\n", - " \"culmen_length_mm\": [40, 41, 42, 43],\n", - " \"island\": [\"Biscoe\", \"Biscoe\", \"Dream\", \"Dream\"],\n", - "})\n", - "bbq.ml.predict(model_metadata, predict_df).to_pandas()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Compatibility with `bigframes.ml`\n", - "\n", - "The models created with `bigframes.bigquery.ml` can be used with the scikit-learn-like `bigframes.ml` modules by using the `read_gbq_model` method.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "LinearRegression(enable_global_explain=True,\n", - " optimize_strategy='NORMAL_EQUATION')" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model = bpd.read_gbq_model(model_name)\n", - "model" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "✅ Completed. \n", - " Query processed 7.3 kB in a moment of slot time. [Job bigframes-dev:US.f2f86927-bbd1-431d-b89e-3d6a064268d7 details]\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "✅ Completed. " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "✅ Completed. " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
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[1 rows x 6 columns in total]" - ], - "text/plain": [ - " mean_absolute_error mean_squared_error mean_squared_log_error \\\n", - " 223.878763 78553.601634 0.005614 \n", - "\n", - " median_absolute_error r2_score explained_variance \n", - " 181.330911 0.623951 0.623951 \n", - "\n", - "[1 rows x 6 columns]" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X = training_data[[\"sex\", \"flipper_length_mm\", \"culmen_depth_mm\", \"culmen_length_mm\", \"island\"]]\n", - "y = training_data[[\"body_mass_g\"]]\n", - "model.score(X, y)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "G_wjSfXpWTuy" - }, - "source": [ - "# Summary and next steps\n", - "\n", - "You've created a linear regression model using `bigframes.bigquery.ml`.\n", - "\n", - "Learn more about BigQuery DataFrames in the [documentation](https://dataframes.bigquery.dev/) and find more sample notebooks in the [GitHub repo](https://github.com/googleapis/python-bigquery-dataframes/tree/main/notebooks)." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "TpV-iwP9qw9c" - }, - "source": [ - "## Cleaning up\n", - "\n", - "To clean up all Google Cloud resources used in this project, you can [delete the Google Cloud\n", - "project](https://cloud.google.com/resource-manager/docs/creating-managing-projects#shutting_down_projects) you used for the tutorial.\n", - "\n", - "Otherwise, you can uncomment the remaining cells and run them to delete the individual resources you created in this tutorial:" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": { - "id": "sx_vKniMq9ZX" - }, - "outputs": [], - "source": [ - "# # Delete the BigQuery dataset and associated ML model\n", - "# from google.cloud import bigquery\n", - "# client = bigquery.Client(project=PROJECT_ID)\n", - "# client.delete_dataset(\n", - "# DATASET_ID, delete_contents=True, not_found_ok=True\n", - "# )\n", - "# print(\"Deleted dataset '{}'.\".format(DATASET_ID))" - ] - } - ], - "metadata": { - "colab": { - "provenance": [], - "toc_visible": true - }, - "kernelspec": { - "display_name": "venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.9" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/notebooks/ml/bq_dataframes_ml_linear_regression_big.ipynb b/notebooks/ml/bq_dataframes_ml_linear_regression_big.ipynb deleted file mode 100644 index d286f5ce31d..00000000000 --- a/notebooks/ml/bq_dataframes_ml_linear_regression_big.ipynb +++ /dev/null @@ -1,1064 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "ur8xi4C7S06n" - }, - "outputs": [], - "source": [ - "# Copyright 2025 Google LLC\n", - "#\n", - "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", - "# you may not use this file except in compliance with the License.\n", - "# You may obtain a copy of the License at\n", - "#\n", - "# https://www.apache.org/licenses/LICENSE-2.0\n", - "#\n", - "# Unless required by applicable law or agreed to in writing, software\n", - "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", - "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", - "# See the License for the specific language governing permissions and\n", - "# limitations under the License." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "JAPoU8Sm5E6e" - }, - "source": [ - "# Train a linear regression model with BigQuery DataFrames ML", - "\n", - "\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - "
\n", - " \n", - " \"Colab Run in Colab\n", - " \n", - " \n", - " \n", - " \"GitHub\n", - " View on GitHub\n", - " \n", - " \n", - " \n", - " \"Vertex\n", - " Open in Vertex AI Workbench\n", - " \n", - " \n", - " \n", - " \"BQ\n", - " Open in BQ Studio\n", - " \n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "24743cf4a1e1" - }, - "source": [ - "**_NOTE_**: This notebook has been tested in the following environment:\n", - "\n", - "* Python version = 3.11" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "tvgnzT1CKxrO" - }, - "source": [ - "## Overview\n", - "\n", - "This notebook demonstrates training a linear regression model on Big Data using BigQuery DataFrames ML. BigQuery DataFrames ML provides a provides a scikit-learn-like API for ML powered by the BigQuery engine.\n", - "\n", - "Learn more about [BigQuery DataFrames](https://cloud.google.com/python/docs/reference/bigframes/latest)." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "d975e698c9a4" - }, - "source": [ - "### Objective\n", - "\n", - "In this tutorial, we use BigQuery DataFrames to create a linear regression model that predicts the levels of Ozone in the atmosphere.\n", - "\n", - "The steps include:\n", - "\n", - "- Creating a DataFrame from the BigQuery table.\n", - "- Cleaning and preparing data using `bigframes.pandas` module.\n", - "- Creating a linear regression model using `bigframes.ml` module.\n", - "- Saving the ML model to BigQuery for future use.\n", - "\n", - "\n", - "Let's formally define our problem as: **Train a linear regression model to predict the level of ozone in the atmosphere given the measurements of other constituents and properties of the atmosphere.**" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "08d289fa873f" - }, - "source": [ - "### Dataset\n", - "\n", - "In this tutorial we are going to use the [`bigquery-public-data.epa_historical_air_quality`](https://console.cloud.google.com/marketplace/product/epa/historical-air-quality) dataset. To quote the description of the dataset:\n", - "\n", - "\"The United States Environmental Protection Agency (EPA) protects both public health and the environment by establishing the standards for national air quality. The EPA provides annual summary data as well as hourly and daily data in the categories of criteria gases, particulates, meteorological, and toxics.\"\n", - "\n", - "There are several tables capturing data about the constituents of the atmosphere, see them in the [BigQuery cloud console](https://pantheon.corp.google.com/bigquery?p=bigquery-public-data&d=epa_historical_air_quality&page=dataset). Most tables carry 10's of GBs of data, but that is not an issue with BigQuery DataFrames as the data is efficiently processed at BigQuery without transferring them to the client." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "aed92deeb4a0" - }, - "source": [ - "### Costs\n", - "\n", - "This tutorial uses billable components of Google Cloud:\n", - "\n", - "* BigQuery (compute)\n", - "* BigQuery ML\n", - "\n", - "Learn about [BigQuery compute pricing](https://cloud.google.com/bigquery/pricing#analysis_pricing_models)\n", - "and [BigQuery ML pricing](https://cloud.google.com/bigquery/pricing#bqml),\n", - "and use the [Pricing Calculator](https://cloud.google.com/products/calculator/)\n", - "to generate a cost estimate based on your projected usage." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "i7EUnXsZhAGF" - }, - "source": [ - "## Installation\n", - "\n", - "If you don't have [bigframes](https://pypi.org/project/bigframes/) package already installed, uncomment and execute the following cells to\n", - "\n", - "1. Install the package\n", - "1. Restart the notebook kernel (Jupyter or Colab) to work with the package" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "9O0Ka4W2MNF3" - }, - "outputs": [], - "source": [ - "# !pip install bigframes" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "f200f10a1da3" - }, - "outputs": [], - "source": [ - "# Automatically restart kernel after installs so that your environment can access the new packages\n", - "\n", - "# import IPython\n", - "#\n", - "# app = IPython.Application.instance()\n", - "# app.kernel.do_shutdown(True)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "BF1j6f9HApxa" - }, - "source": [ - "## Before you begin\n", - "\n", - "Complete the tasks in this section to set up your environment." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "oDfTjfACBvJk" - }, - "source": [ - "### Set up your Google Cloud project\n", - "\n", - "**The following steps are required, regardless of your notebook environment.**\n", - "\n", - "1. [Select or create a Google Cloud project](https://console.cloud.google.com/cloud-resource-manager). When you first create an account, you get a $300 credit towards your compute/storage costs.\n", - "\n", - "2. [Make sure that billing is enabled for your project](https://cloud.google.com/billing/docs/how-to/modify-project).\n", - "\n", - "3. [Enable the BigQuery API](https://console.cloud.google.com/flows/enableapi?apiid=bigquery.googleapis.com).\n", - "\n", - "4. If you are running this notebook locally, install the [Cloud SDK](https://cloud.google.com/sdk)." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "WReHDGG5g0XY" - }, - "source": [ - "#### Set your project ID\n", - "\n", - "If you don't know your project ID, try the following:\n", - "* Run `gcloud config list`.\n", - "* Run `gcloud projects list`.\n", - "* See the support page: [Locate the project ID](https://support.google.com/googleapi/answer/7014113)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "oM1iC_MfAts1" - }, - "outputs": [], - "source": [ - "PROJECT_ID = \"\" # @param {type:\"string\"}" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "region" - }, - "source": [ - "#### Set the BigQuery location\n", - "\n", - "You can also change the `LOCATION` variable used by BigQuery. Learn more about [BigQuery locations](https://cloud.google.com/bigquery/docs/locations#supported_locations)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "eF-Twtc4XGem" - }, - "outputs": [], - "source": [ - "LOCATION = \"US\" # @param {type: \"string\"}" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "sBCra4QMA2wR" - }, - "source": [ - "### Set up APIs, IAM permissions and Authentication\n", - "\n", - "Follow the instructions at https://cloud.google.com/bigquery/docs/use-bigquery-dataframes#permissions.\n", - "\n", - "Depending on your notebook environment, you might have to manually authenticate. Follow the relevant instructions below." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "74ccc9e52986" - }, - "source": [ - "**Vertex AI Workbench**\n", - "\n", - "Do nothing, you are already authenticated." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "de775a3773ba" - }, - "source": [ - "**Local JupyterLab instance**\n", - "\n", - "Uncomment and run the following cell:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "254614fa0c46" - }, - "outputs": [], - "source": [ - "# ! gcloud auth login\n", - "# ! gcloud auth application-default login" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "ef21552ccea8" - }, - "source": [ - "**Colab**\n", - "\n", - "Uncomment and run the following cell:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "603adbbf0532" - }, - "outputs": [], - "source": [ - "# from google.colab import auth\n", - "# auth.authenticate_user()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "960505627ddf" - }, - "source": [ - "### Import libraries" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "PyQmSRbKA8r-" - }, - "outputs": [], - "source": [ - "import bigframes.pandas as bpd" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "init_aip:mbsdk,all" - }, - "source": [ - "### Set BigQuery DataFrames options" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "NPPMuw2PXGeo" - }, - "outputs": [], - "source": [ - "# NOTE: The project option is not required in all environments.\n", - "# On BigQuery Studio, the project ID is automatically detected.\n", - "bpd.options.bigquery.project = PROJECT_ID\n", - "\n", - "# NOTE: The location option is not required.\n", - "# It defaults to the location of the first table or query\n", - "# passed to read_gbq(). For APIs where a location can't be\n", - "# auto-detected, the location defaults to the \"US\" location.\n", - "bpd.options.bigquery.location = LOCATION\n", - "\n", - "# NOTE: For a machine learning model the order of the data is\n", - "# not important. So let's relax the ordering_mode to accept\n", - "# partial ordering. This allows BigQuery DataFrames to run cost\n", - "# and performance optimized jobs at the BigQuery engine.\n", - "bpd.options.bigquery.ordering_mode = \"partial\"" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "D21CoOlfFTYI" - }, - "source": [ - "If you want to reset the location of the created DataFrame or Series objects, reset the session by executing `bpd.close_session()`. After that, you can reuse `bpd.options.bigquery.location` to specify another location." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "9EMAqR37AfLS" - }, - "source": [ - "## Read data in BigQuery tables as DataFrame\n", - "\n", - "Let's read the tables in the dataset to construct a BigQuery DataFrames DataFrame. We will combine measurements of various parameters of the atmosphere from multiple tables to represent a consolidated dataframe to use for our model training and prediction. We have daily and hourly versions of the data available, but since we want to create a model that is dynamic so that it can capture the variance throughout the day, we would choose the hourly version.\n", - "\n", - "Note that we would use the pandas APIs as we normally would on the BigQuery DataFrames DataFrame, but calculations happen in the BigQuery query engine instead of the local environment." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dataset = \"bigquery-public-data.epa_historical_air_quality\"\n", - "hourly_summary_tables = [\n", - " \"co_hourly_summary\",\n", - " \"hap_hourly_summary\",\n", - " \"no2_hourly_summary\",\n", - " \"nonoxnoy_hourly_summary\",\n", - " \"o3_hourly_summary\",\n", - " \"pm10_hourly_summary\",\n", - " \"pm25_frm_hourly_summary\",\n", - " \"pm25_nonfrm_hourly_summary\",\n", - " \"pm25_speciation_hourly_summary\",\n", - " \"pressure_hourly_summary\",\n", - " \"rh_and_dp_hourly_summary\",\n", - " \"so2_hourly_summary\",\n", - " \"temperature_hourly_summary\",\n", - " \"voc_hourly_summary\",\n", - " \"wind_hourly_summary\",\n", - "]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's pick index columns - to identify a measurement of the atmospheric parameter, param column - to identify which param the measurement pertains to, and value column - the column containing the measurement itself." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "index_columns = [\"state_name\", \"county_name\", \"site_num\", \"date_local\", \"time_local\"]\n", - "param_column = \"parameter_name\"\n", - "value_column = \"sample_measurement\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's observe how much data each table contains:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for table in hourly_summary_tables:\n", - " # get the bigframes global session\n", - " bigframes_session = bpd.get_global_session()\n", - "\n", - " # get the bigquery table info\n", - " table_info = bigframes_session.bqclient.get_table(f\"{dataset}.{table}\")\n", - "\n", - " # read the table as a dataframe\n", - " df = bpd.read_gbq(f\"{dataset}.{table}\")\n", - "\n", - " # print metadata about the table\n", - " print(\n", - " f\"{table}: \"\n", - " f\"{round(table_info.num_bytes/1_000_000_000, 1)} GB, \"\n", - " f\"{round(table_info.num_rows/1_000_000, 1)} million rows, \"\n", - " f\"{df[param_column].nunique()} params\"\n", - " )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's be mindful that the rows in each table may contain duplicates, which may introdude bias in any model trained on the raw data. We will make sure to drop the duplicates when we use the data for model training.\n", - "\n", - "Since we want to predict ozone level, we obviously pick the `o3` table. Let's also pick the tables about other gases - `co`, `no2` and `so2`. Let's also pick `pressure` and `temperature` tables as they seem fundamental indicators for the atmosphere. Note that each of these tables capture measurements for a single parameter (i.e. the column `parameter_name` has a single unique value).\n", - "\n", - "We are also interested in the nonoxny and wind tables, but they capture multiple parameters (i.e. the column `parameter_name` has a more than one unique values). We will include their measurements in later step, as they require extar processing to separate out the measurements for the individual parameters.\n", - "\n", - "We skip the other tables in this exercise for either they have very little or fragmented data or they seem uninteresting for the purpose of predicting ozone levels. You can take this as a separate exercise to train a linear regression model by including those parameters. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's maintain an array of dtaframes, one for each parameter, and eventually combine them into a single dataframe." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "params_dfs = []" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's process the tables with single parameter measurements first." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "EDAaIwHpQCDZ" - }, - "outputs": [], - "source": [ - "table_param_dict = {\n", - " \"co_hourly_summary\" : \"co\",\n", - " \"no2_hourly_summary\" : \"no2\",\n", - " \"o3_hourly_summary\" : \"o3\",\n", - " \"pressure_hourly_summary\" : \"pressure\",\n", - " \"so2_hourly_summary\" : \"so2\",\n", - " \"temperature_hourly_summary\" : \"temperature\",\n", - "}\n", - "\n", - "for table, param in table_param_dict.items():\n", - " param_df = bpd.read_gbq(\n", - " f\"{dataset}.{table}\",\n", - " columns=index_columns + [value_column]\n", - " )\n", - " param_df = param_df\\\n", - " .sort_values(index_columns)\\\n", - " .drop_duplicates(index_columns)\\\n", - " .set_index(index_columns)\\\n", - " .rename(columns={value_column : param})\n", - " params_dfs.append(param_df)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The nonoxnoy table captures measurements for 3 parameters. Let's analyze how many instances of each parameter it contains." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "nonoxnoy_table = f\"{dataset}.nonoxnoy_hourly_summary\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "bpd.read_gbq(nonoxnoy_table, columns=[param_column]).value_counts()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We see that the NOy data is significantly sparse as compared to NO and NOx, so we skip that and include NO and NOx data." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "no_df = bpd.read_gbq(\n", - " nonoxnoy_table,\n", - " columns=index_columns + [value_column],\n", - " filters=[(param_column, \"==\", \"Nitric oxide (NO)\")]\n", - ")\n", - "no_df = no_df\\\n", - " .sort_values(index_columns)\\\n", - " .drop_duplicates(index_columns)\\\n", - " .set_index(index_columns)\\\n", - " .rename(columns={value_column: \"no_\"})\n", - "params_dfs.append(no_df)\n", - "\n", - "nox_df = bpd.read_gbq(\n", - " nonoxnoy_table,\n", - " columns=index_columns + [value_column],\n", - " filters=[(param_column, \"==\", \"Oxides of nitrogen (NOx)\")]\n", - ")\n", - "nox_df = nox_df\\\n", - " .sort_values(index_columns)\\\n", - " .drop_duplicates(index_columns)\\\n", - " .set_index(index_columns)\\\n", - " .rename(columns={value_column: \"nox\"})\n", - "params_dfs.append(nox_df)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The wind table captures measurements for 2 parameters. Let's analyze how many instances of each parameter it contains." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "wind_table = f\"{dataset}.wind_hourly_summary\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "bpd.read_gbq(wind_table, columns=[param_column]).value_counts()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's include the data for wind speed and wind direction." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "wind_speed_df = bpd.read_gbq(\n", - " wind_table,\n", - " columns=index_columns + [value_column],\n", - " filters=[(param_column, \"==\", \"Wind Speed - Resultant\")]\n", - ")\n", - "wind_speed_df = wind_speed_df\\\n", - " .sort_values(index_columns)\\\n", - " .drop_duplicates(index_columns)\\\n", - " .set_index(index_columns)\\\n", - " .rename(columns={value_column: \"wind_speed\"})\n", - "params_dfs.append(wind_speed_df)\n", - "\n", - "wind_dir_df = bpd.read_gbq(\n", - " wind_table,\n", - " columns=index_columns + [value_column],\n", - " filters=[(param_column, \"==\", \"Wind Direction - Resultant\")]\n", - ")\n", - "wind_dir_df = wind_dir_df\\\n", - " .sort_values(index_columns)\\\n", - " .drop_duplicates(index_columns)\\\n", - " .set_index(index_columns)\\\n", - " .rename(columns={value_column: \"wind_dir\"})\n", - "params_dfs.append(wind_dir_df)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's observe each individual parameter and number of data points for each parameter." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for param_df in params_dfs:\n", - " print(f\"{param_df.columns.values}: {len(param_df)}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's combine data from all parameters into a single DataFrame. The measurements for each parameter may not be available for every (state, county, site, date, time) identifier, we will consider only those identifiers for which measurements of all parameters are available. To achieve this we will combine the measurements via \"inner\" join.\n", - "\n", - "We will also materialize this combined data via `cache` method for efficient reuse in the subsequent steps." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df = bpd.concat(params_dfs, axis=1, join=\"inner\").cache()\n", - "df.shape" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "rwPLjqW2Ajzh" - }, - "source": [ - "## Clean and prepare data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's temporarily bring the index columns as dataframe columns for further processing on the index values for the purpose of data preparation.\n", - "We will reconstruct the index back at the time of the model training." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df = df.reset_index()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Observe the years from which we have consolidated data so far." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df[\"date_local\"].dt.year.value_counts().sort_index().to_pandas()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this tutorial we would train a model from the past data to predict ozone levels for the future data. Let's define the cut-off year as 2020. We will pretend that the data before 2020 has known ozone levels, and the 2020 onwards the ozone levels are unknown, which we will predict using our model.\n", - "\n", - "We should further separate the known data into training and test sets. The model would be trained on the training set and then evaluated on the test set to make sure the model generalizes beyond the training data. We could use [train_test_split](https://cloud.google.com/python/docs/reference/bigframes/latest/bigframes.ml.model_selection#bigframes_ml_model_selection_train_test_split) method to randomly split the training and test data, but we leave that for you to try out. In this exercise, let's split based on another cutoff year 2017 - the known data before 2017 would be training data and 2017 onwards would be the test data. This way we stay with the idea that the model is trained on past data and then used to predict the future values." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "6i6HkFJZa8na" - }, - "outputs": [], - "source": [ - "train_data_filter = (df.date_local.dt.year < 2017)\n", - "test_data_filter = (df.date_local.dt.year >= 2017) & (df.date_local.dt.year < 2020)\n", - "predict_data_filter = (df.date_local.dt.year >= 2020)\n", - "\n", - "df_train = df[train_data_filter].set_index(index_columns)\n", - "df_test = df[test_data_filter].set_index(index_columns)\n", - "df_predict = df[predict_data_filter].set_index(index_columns)\n", - "\n", - "df_train.shape, df_test.shape, df_predict.shape" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "M_-0X7NxYK5f" - }, - "source": [ - "Prepare your feature (or input) columns and the target (or output) column for the purpose of model training and evaluation:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "YKwCW7Nsavap" - }, - "outputs": [], - "source": [ - "X_train = df_train.drop(columns=\"o3\")\n", - "y_train = df_train[\"o3\"]\n", - "\n", - "X_test = df_test.drop(columns=\"o3\")\n", - "y_test = df_test[\"o3\"]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Prepare the unknown data for prediction." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "wej78IDUaRW9" - }, - "outputs": [], - "source": [ - "X_predict = df_predict.drop(columns=\"o3\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Fx4lsNqMorJ-" - }, - "source": [ - "## Create the linear regression model\n", - "\n", - "BigQuery DataFrames ML lets you seamlessly transition from exploring data to creating machine learning models through its scikit-learn-like API, `bigframes.ml`. BigQuery DataFrames ML supports several types of [ML models](https://cloud.google.com/python/docs/reference/bigframes/latest#ml-capabilities).\n", - "\n", - "In this notebook, you create a [`LinearRegression`](https://cloud.google.com/python/docs/reference/bigframes/latest/bigframes.ml.linear_model.LinearRegression) model, a type of regression model that generates a continuous value from a linear combination of input features.\n", - "\n", - "When you create a model with BigQuery DataFrames ML, it is saved in an internal location and limited to the BigQuery DataFrames session. However, as you'll see in the next section, you can use `to_gbq` to save the model permanently to your BigQuery project." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "EloGtMnverFF" - }, - "source": [ - "### Create the model using `bigframes.ml`\n", - "\n", - "Please note that BigQuery DataFrames ML is backed by BigQuery ML, which uses\n", - "[automatic preprocessing](https://cloud.google.com/bigquery/docs/auto-preprocessing) to encode string values and scale numeric values when you pass the feature columns without transforms.\n", - "\n", - "BigQuery ML also [automatically splits the data for training and evaluation](https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-create-glm#data_split_method), although for datasets with less than 500 rows (such as this one), all rows are used for training." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "GskyyUQPowBT" - }, - "outputs": [], - "source": [ - "from bigframes.ml.linear_model import LinearRegression\n", - "\n", - "model = LinearRegression()\n", - "\n", - "model.fit(X_train, y_train)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "UGjeMPC2caKK" - }, - "source": [ - "### Score the model\n", - "\n", - "Check how the model performs by using the [`score`](https://cloud.google.com/python/docs/reference/bigframes/latest/bigframes.ml.linear_model.LinearRegression#bigframes_ml_linear_model_LinearRegression_score) method. More information on BigQuery ML model scoring can be found [here](https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-evaluate#mlevaluate_output)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "kGBJKafpo0dl" - }, - "outputs": [], - "source": [ - "# On the training data\n", - "model.score(X_train, y_train)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# On the test data\n", - "model.score(X_test, y_test)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "P2lUiZZ_cjri" - }, - "source": [ - "### Predict using the model\n", - "\n", - "Use the model to predict the levels of ozone. The predicted levels are returned in the column `predicted_o3`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "bsQ9cmoWo0Ps" - }, - "outputs": [], - "source": [ - "df_pred = model.predict(X_predict)\n", - "df_pred.peek()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "GTRdUw-Ro5R1" - }, - "source": [ - "## Save the model in BigQuery\n", - "\n", - "The model is saved locally within this session. You can save the model permanently to BigQuery for use in future sessions, and to make the model sharable with others." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "K0mPaoGpcwwy" - }, - "source": [ - "Create a BigQuery dataset to house the model, adding a name for your dataset as the `DATASET_ID` variable:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "ZSP7gt13QrQt" - }, - "outputs": [], - "source": [ - "DATASET_ID = \"\" # @param {type:\"string\"}\n", - "\n", - "if not DATASET_ID:\n", - " raise ValueError(\"Please define the DATASET_ID\")\n", - "\n", - "client = bpd.get_global_session().bqclient\n", - "dataset = client.create_dataset(DATASET_ID, exists_ok=True)\n", - "print(f\"Dataset {dataset.dataset_id} created.\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "zqAIWWgJczp-" - }, - "source": [ - "Save the model using the `to_gbq` method:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "QE_GD4Byo_jb" - }, - "outputs": [], - "source": [ - "model.to_gbq(DATASET_ID + \".o3_lr_model\" , replace=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "f7uHacAy49rT" - }, - "source": [ - "You can view the saved model in the BigQuery console under the dataset you created in the first step. Run the following cell and follow the link to view your BigQuery console:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "qDBoiA_0488Z" - }, - "outputs": [], - "source": [ - "print(f'https://console.cloud.google.com/bigquery?ws=!1m5!1m4!5m3!1s{PROJECT_ID}!2s{DATASET_ID}!3so3_lr_model')" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "G_wjSfXpWTuy" - }, - "source": [ - "# Summary and next steps\n", - "\n", - "You've created a linear regression model using `bigframes.ml`.\n", - "\n", - "Learn more about BigQuery DataFrames in the [documentation](https://cloud.google.com/python/docs/reference/bigframes/latest) and find more sample notebooks in the [GitHub repo](https://github.com/googleapis/python-bigquery-dataframes/tree/main/notebooks)." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "TpV-iwP9qw9c" - }, - "source": [ - "## Cleaning up\n", - "\n", - "To clean up all Google Cloud resources used in this project, you can [delete the Google Cloud\n", - "project](https://cloud.google.com/resource-manager/docs/creating-managing-projects#shutting_down_projects) you used for the tutorial.\n", - "\n", - "Otherwise, you can uncomment the remaining cells and run them to delete the individual resources you created in this tutorial:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "sx_vKniMq9ZX" - }, - "outputs": [], - "source": [ - "# # Delete the BigQuery dataset and associated ML model\n", - "# client.delete_dataset(DATASET_ID, delete_contents=True, not_found_ok=True)" - ] - } - ], - "metadata": { - "colab": { - "provenance": [], - "toc_visible": true - }, - "kernelspec": { - "display_name": "Python 3", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.0" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/notebooks/ml/easy_linear_regression.ipynb b/notebooks/ml/easy_linear_regression.ipynb deleted file mode 100644 index 5a7258a182e..00000000000 --- a/notebooks/ml/easy_linear_regression.ipynb +++ /dev/null @@ -1,247 +0,0 @@ -{ - "cells": [ - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Using ML - Easy linear regression\n", - "\n", - "This demo shows BigQuery DataFrames ML providing an SKLearn-like experience for\n", - "training a linear regression model.\n", - "\n", - "In this \"easy\" version of linear regression, we use a couple of BQML features to simplify our code:\n", - "\n", - "- We rely on automatic preprocessing to encode string values and scale numeric values\n", - "- We rely on automatic data split & evaluation to test the model\n", - "\n", - "This example is adapted from the [BQML linear regression tutorial](https://cloud.google.com/bigquery-ml/docs/linear-regression-tutorial)." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1. Init & load data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Import `bigframes.pandas` module and get the default session" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "import bigframes.pandas\n", - "session = bigframes.pandas.get_global_session()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Define a dataset for storing BQML model, and create it if it does not exist." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dataset = f\"{session.bqclient.project}.bqml_tutorial\"\n", - "session.bqclient.create_dataset(dataset, exists_ok=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Define a model path" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ - "penguins_model = f\"{dataset}.penguins_model\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Read the penguins data." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# read a BigQuery table to a BigQuery DataFrame\n", - "df = bigframes.pandas.read_gbq(f\"bigquery-public-data.ml_datasets.penguins\")\n", - "\n", - "# take a peek at the dataframe\n", - "df" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2. Data cleaning / prep" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# filter down to the data we want to analyze\n", - "adelie_data = df[df.species == \"Adelie Penguin (Pygoscelis adeliae)\"]\n", - "\n", - "# drop the columns we don't care about\n", - "adelie_data = adelie_data.drop(columns=[\"species\"])\n", - "\n", - "# drop rows with nulls to get our training data\n", - "training_data = adelie_data.dropna()\n", - "\n", - "# take a peek at the training data\n", - "training_data" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "# pick feature columns and label column\n", - "feature_columns = training_data[['island', 'culmen_length_mm', 'culmen_depth_mm', 'flipper_length_mm', 'sex']]\n", - "label_columns = training_data[['body_mass_g']] \n", - "\n", - "# also get the rows that we want to make predictions for (i.e. where the feature column is null)\n", - "missing_body_mass = adelie_data[adelie_data.body_mass_g.isnull()]" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3. Create, score, fit, predict" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from bigframes.ml.linear_model import LinearRegression\n", - "\n", - "model = LinearRegression()\n", - "\n", - "# Here we pass the feature columns without transforms - BQML will then use\n", - "# automatic preprocessing to encode these columns\n", - "model.fit(feature_columns, label_columns)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# check how the model performed\n", - "model.score(feature_columns, label_columns)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# use the model to predict the missing labels\n", - "model.predict(missing_body_mass)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 4. Save in BigQuery" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# save the model to a permanent location in BigQuery, so we can use it in future sessions (and elsewhere in BQ)\n", - "model.to_gbq(penguins_model, replace=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 5. Reload from BigQuery" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# WARNING - until b/281709360 is fixed & pipeline is updated, pipelines will load as models,\n", - "# and details of their transform steps will be lost (the loaded model will behave the same)\n", - "bigframes.pandas.read_gbq_model(penguins_model)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.12" - }, - "orig_nbformat": 4, - "vscode": { - "interpreter": { - "hash": "a850322d07d9bdc9ec5f301d307e048bcab2390ae395e1cbce9335f4e081e5e2" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/notebooks/ml/sklearn_linear_regression.ipynb b/notebooks/ml/sklearn_linear_regression.ipynb deleted file mode 100644 index 95aa314bb09..00000000000 --- a/notebooks/ml/sklearn_linear_regression.ipynb +++ /dev/null @@ -1,1343 +0,0 @@ -{ - "cells": [ - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Using ML - SKLearn linear regression\n", - "\n", - "This demo shows how we can implement a linear regression in BigQuery DataFrames ML, with API that is exactly compatible with scikit-learn." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1. Init & load data" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "Query job f201b84b-5506-4038-92e6-b4a82318df8f is DONE. 0 Bytes processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Query job 12e0f983-695e-4903-8ff1-2f353d7e8cba is DONE. 28.9 kB processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
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speciesislandculmen_length_mmculmen_depth_mmflipper_length_mmbody_mass_gsex
0Adelie Penguin (Pygoscelis adeliae)Biscoe40.118.9188.04300.0MALE
1Adelie Penguin (Pygoscelis adeliae)Torgersen39.118.7181.03750.0MALE
2Gentoo penguin (Pygoscelis papua)Biscoe47.414.6212.04725.0FEMALE
3Chinstrap penguin (Pygoscelis antarctica)Dream42.516.7187.03350.0FEMALE
4Adelie Penguin (Pygoscelis adeliae)Biscoe43.219.0197.04775.0MALE
5Gentoo penguin (Pygoscelis papua)Biscoe46.715.3219.05200.0MALE
6Adelie Penguin (Pygoscelis adeliae)Biscoe41.321.1195.04400.0MALE
7Gentoo penguin (Pygoscelis papua)Biscoe45.213.8215.04750.0FEMALE
8Gentoo penguin (Pygoscelis papua)Biscoe46.513.5210.04550.0FEMALE
9Gentoo penguin (Pygoscelis papua)Biscoe50.515.2216.05000.0FEMALE
10Gentoo penguin (Pygoscelis papua)Biscoe48.215.6221.05100.0MALE
11Adelie Penguin (Pygoscelis adeliae)Dream38.118.6190.03700.0FEMALE
12Gentoo penguin (Pygoscelis papua)Biscoe50.715.0223.05550.0MALE
13Adelie Penguin (Pygoscelis adeliae)Biscoe37.820.0190.04250.0MALE
14Adelie Penguin (Pygoscelis adeliae)Biscoe35.017.9190.03450.0FEMALE
15Gentoo penguin (Pygoscelis papua)Biscoe48.715.7208.05350.0MALE
16Adelie Penguin (Pygoscelis adeliae)Torgersen34.621.1198.04400.0MALE
17Gentoo penguin (Pygoscelis papua)Biscoe46.815.4215.05150.0MALE
18Chinstrap penguin (Pygoscelis antarctica)Dream50.320.0197.03300.0MALE
19Adelie Penguin (Pygoscelis adeliae)Dream37.218.1178.03900.0MALE
20Chinstrap penguin (Pygoscelis antarctica)Dream51.018.8203.04100.0MALE
21Adelie Penguin (Pygoscelis adeliae)Biscoe40.517.9187.03200.0FEMALE
22Gentoo penguin (Pygoscelis papua)Biscoe45.513.9210.04200.0FEMALE
23Adelie Penguin (Pygoscelis adeliae)Dream42.218.5180.03550.0FEMALE
24Chinstrap penguin (Pygoscelis antarctica)Dream51.720.3194.03775.0MALE
\n", - "

25 rows × 7 columns

\n", - "
[344 rows x 7 columns in total]" - ], - "text/plain": [ - " species island culmen_length_mm \\\n", - "0 Adelie Penguin (Pygoscelis adeliae) Biscoe 40.1 \n", - "1 Adelie Penguin (Pygoscelis adeliae) Torgersen 39.1 \n", - "2 Gentoo penguin (Pygoscelis papua) Biscoe 47.4 \n", - "3 Chinstrap penguin (Pygoscelis antarctica) Dream 42.5 \n", - "4 Adelie Penguin (Pygoscelis adeliae) Biscoe 43.2 \n", - "5 Gentoo penguin (Pygoscelis papua) Biscoe 46.7 \n", - "6 Adelie Penguin (Pygoscelis adeliae) Biscoe 41.3 \n", - "7 Gentoo penguin (Pygoscelis papua) Biscoe 45.2 \n", - "8 Gentoo penguin (Pygoscelis papua) Biscoe 46.5 \n", - "9 Gentoo penguin (Pygoscelis papua) Biscoe 50.5 \n", - "10 Gentoo penguin (Pygoscelis papua) Biscoe 48.2 \n", - "11 Adelie Penguin (Pygoscelis adeliae) Dream 38.1 \n", - "12 Gentoo penguin (Pygoscelis papua) Biscoe 50.7 \n", - "13 Adelie Penguin (Pygoscelis adeliae) Biscoe 37.8 \n", - "14 Adelie Penguin (Pygoscelis adeliae) Biscoe 35.0 \n", - "15 Gentoo penguin (Pygoscelis papua) Biscoe 48.7 \n", - "16 Adelie Penguin (Pygoscelis adeliae) Torgersen 34.6 \n", - "17 Gentoo penguin (Pygoscelis papua) Biscoe 46.8 \n", - "18 Chinstrap penguin (Pygoscelis antarctica) Dream 50.3 \n", - "19 Adelie Penguin (Pygoscelis adeliae) Dream 37.2 \n", - "20 Chinstrap penguin (Pygoscelis antarctica) Dream 51.0 \n", - "21 Adelie Penguin (Pygoscelis adeliae) Biscoe 40.5 \n", - "22 Gentoo penguin (Pygoscelis papua) Biscoe 45.5 \n", - "23 Adelie Penguin (Pygoscelis adeliae) Dream 42.2 \n", - "24 Chinstrap penguin (Pygoscelis antarctica) Dream 51.7 \n", - "\n", - " culmen_depth_mm flipper_length_mm body_mass_g sex \n", - "0 18.9 188.0 4300.0 MALE \n", - "1 18.7 181.0 3750.0 MALE \n", - "2 14.6 212.0 4725.0 FEMALE \n", - "3 16.7 187.0 3350.0 FEMALE \n", - "4 19.0 197.0 4775.0 MALE \n", - "5 15.3 219.0 5200.0 MALE \n", - "6 21.1 195.0 4400.0 MALE \n", - "7 13.8 215.0 4750.0 FEMALE \n", - "8 13.5 210.0 4550.0 FEMALE \n", - "9 15.2 216.0 5000.0 FEMALE \n", - "10 15.6 221.0 5100.0 MALE \n", - "11 18.6 190.0 3700.0 FEMALE \n", - "12 15.0 223.0 5550.0 MALE \n", - "13 20.0 190.0 4250.0 MALE \n", - "14 17.9 190.0 3450.0 FEMALE \n", - "15 15.7 208.0 5350.0 MALE \n", - "16 21.1 198.0 4400.0 MALE \n", - "17 15.4 215.0 5150.0 MALE \n", - "18 20.0 197.0 3300.0 MALE \n", - "19 18.1 178.0 3900.0 MALE \n", - "20 18.8 203.0 4100.0 MALE \n", - "21 17.9 187.0 3200.0 FEMALE \n", - "22 13.9 210.0 4200.0 FEMALE \n", - "23 18.5 180.0 3550.0 FEMALE \n", - "24 20.3 194.0 3775.0 MALE \n", - "...\n", - "\n", - "[344 rows x 7 columns]" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Initialize BigQuery DataFrame\n", - "import bigframes.pandas\n", - "\n", - "# read a BigQuery table to a BigQuery DataFrame\n", - "df = bigframes.pandas.read_gbq(\"bigframes-dev.bqml_tutorial.penguins\")\n", - "\n", - "# take a peek at the dataframe\n", - "df" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2. Data cleaning / prep" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "Query job 81305962-a96a-4c86-949c-471b2ae7c86d is DONE. 28.9 kB processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Query job 2af0b0d6-c11b-499e-8d25-a2c628b2853b is DONE. 28.9 kB processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
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islandculmen_length_mmculmen_depth_mmflipper_length_mmbody_mass_gsex
0Biscoe40.118.9188.04300.0MALE
1Torgersen39.118.7181.03750.0MALE
4Biscoe43.219.0197.04775.0MALE
6Biscoe41.321.1195.04400.0MALE
11Dream38.118.6190.03700.0FEMALE
13Biscoe37.820.0190.04250.0MALE
14Biscoe35.017.9190.03450.0FEMALE
16Torgersen34.621.1198.04400.0MALE
19Dream37.218.1178.03900.0MALE
21Biscoe40.517.9187.03200.0FEMALE
23Dream42.218.5180.03550.0FEMALE
30Dream39.221.1196.04150.0MALE
32Torgersen42.917.6196.04700.0MALE
38Dream41.117.5190.03900.0MALE
40Torgersen38.621.2191.03800.0MALE
42Biscoe35.516.2195.03350.0FEMALE
44Dream39.218.6190.04250.0MALE
45Torgersen35.215.9186.03050.0FEMALE
46Dream43.218.5192.04100.0MALE
49Biscoe39.617.7186.03500.0FEMALE
53Biscoe45.620.3191.04600.0MALE
58Torgersen40.916.8191.03700.0FEMALE
60Torgersen40.318.0195.03250.0FEMALE
62Dream36.018.5186.03100.0FEMALE
63Torgersen39.320.6190.03650.0MALE
\n", - "

25 rows × 6 columns

\n", - "
[146 rows x 6 columns in total]" - ], - "text/plain": [ - " island culmen_length_mm culmen_depth_mm flipper_length_mm \\\n", - "0 Biscoe 40.1 18.9 188.0 \n", - "1 Torgersen 39.1 18.7 181.0 \n", - "4 Biscoe 43.2 19.0 197.0 \n", - "6 Biscoe 41.3 21.1 195.0 \n", - "11 Dream 38.1 18.6 190.0 \n", - "13 Biscoe 37.8 20.0 190.0 \n", - "14 Biscoe 35.0 17.9 190.0 \n", - "16 Torgersen 34.6 21.1 198.0 \n", - "19 Dream 37.2 18.1 178.0 \n", - "21 Biscoe 40.5 17.9 187.0 \n", - "23 Dream 42.2 18.5 180.0 \n", - "30 Dream 39.2 21.1 196.0 \n", - "32 Torgersen 42.9 17.6 196.0 \n", - "38 Dream 41.1 17.5 190.0 \n", - "40 Torgersen 38.6 21.2 191.0 \n", - "42 Biscoe 35.5 16.2 195.0 \n", - "44 Dream 39.2 18.6 190.0 \n", - "45 Torgersen 35.2 15.9 186.0 \n", - "46 Dream 43.2 18.5 192.0 \n", - "49 Biscoe 39.6 17.7 186.0 \n", - "53 Biscoe 45.6 20.3 191.0 \n", - "58 Torgersen 40.9 16.8 191.0 \n", - "60 Torgersen 40.3 18.0 195.0 \n", - "62 Dream 36.0 18.5 186.0 \n", - "63 Torgersen 39.3 20.6 190.0 \n", - "\n", - " body_mass_g sex \n", - "0 4300.0 MALE \n", - "1 3750.0 MALE \n", - "4 4775.0 MALE \n", - "6 4400.0 MALE \n", - "11 3700.0 FEMALE \n", - "13 4250.0 MALE \n", - "14 3450.0 FEMALE \n", - "16 4400.0 MALE \n", - "19 3900.0 MALE \n", - "21 3200.0 FEMALE \n", - "23 3550.0 FEMALE \n", - "30 4150.0 MALE \n", - "32 4700.0 MALE \n", - "38 3900.0 MALE \n", - "40 3800.0 MALE \n", - "42 3350.0 FEMALE \n", - "44 4250.0 MALE \n", - "45 3050.0 FEMALE \n", - "46 4100.0 MALE \n", - "49 3500.0 FEMALE \n", - "53 4600.0 MALE \n", - "58 3700.0 FEMALE \n", - "60 3250.0 FEMALE \n", - "62 3100.0 FEMALE \n", - "63 3650.0 MALE \n", - "...\n", - "\n", - "[146 rows x 6 columns]" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# filter down to the data we want to analyze\n", - "adelie_data = df[df.species == \"Adelie Penguin (Pygoscelis adeliae)\"]\n", - "\n", - "# drop the columns we don't care about\n", - "adelie_data = adelie_data.drop(columns=[\"species\"])\n", - "\n", - "# drop rows with nulls to get our training data\n", - "training_data = adelie_data.dropna()\n", - "\n", - "# take a peek at the training data\n", - "training_data" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3. Use `model_selection.train_test_split` to prepare training data" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "Query job 0808457b-a0df-4a37-b7a5-8885f4a4588c is DONE. 28.9 kB processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from bigframes.ml.model_selection import train_test_split\n", - "\n", - "feature_columns = training_data[['island', 'culmen_length_mm', 'culmen_depth_mm', 'flipper_length_mm', 'sex']]\n", - "label_columns = training_data[['body_mass_g']] \n", - "\n", - "X_train, X_test, y_train, y_test = train_test_split(\n", - " feature_columns, label_columns, test_size=0.2)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 4. Configure a linear regression pipeline with preprocessing" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Pipeline(steps=[('preproc',\n", - " ColumnTransformer(transformers=[('onehot', OneHotEncoder(),\n", - " ['island', 'species', 'sex']),\n", - " ('scaler', StandardScaler(),\n", - " ['culmen_depth_mm',\n", - " 'culmen_length_mm',\n", - " 'flipper_length_mm'])])),\n", - " ('linreg', LinearRegression(fit_intercept=False))])" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from bigframes.ml.linear_model import LinearRegression\n", - "from bigframes.ml.pipeline import Pipeline\n", - "from bigframes.ml.compose import ColumnTransformer\n", - "from bigframes.ml.preprocessing import StandardScaler, OneHotEncoder\n", - "\n", - "preprocessing = ColumnTransformer([\n", - " (\"onehot\", OneHotEncoder(), [\"island\", \"sex\"]),\n", - " (\"scaler\", StandardScaler(), [\"culmen_depth_mm\", \"culmen_length_mm\", \"flipper_length_mm\"]),\n", - "])\n", - "\n", - "model = LinearRegression(fit_intercept=False)\n", - "\n", - "pipeline = Pipeline([\n", - " ('preproc', preprocessing),\n", - " ('linreg', model)\n", - "])\n", - "\n", - "# TODO(bmil): pretty printing for pipelines\n", - "pipeline" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 5. Fit the pipeline to the training data\n", - "\n", - "This will create a temporary BQML model in BigQuery" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "Query job e9bfa6a5-a53f-4d8b-ae8c-cc8cd55d0947 is DONE. 28.9 kB processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Query job d8d553cf-3d36-49aa-b18b-9a05576a1fb0 is DONE. 28.9 kB processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Query job 75ef0083-9a4f-4ffb-a6c6-d82974a1659f is DONE. 0 Bytes processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "Pipeline(steps=[('preproc',\n", - " ColumnTransformer(transformers=[('onehot', OneHotEncoder(),\n", - " ['island', 'species', 'sex']),\n", - " ('scaler', StandardScaler(),\n", - " ['culmen_depth_mm',\n", - " 'culmen_length_mm',\n", - " 'flipper_length_mm'])])),\n", - " ('linreg', LinearRegression(fit_intercept=False))])" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pipeline.fit(X_train, y_train)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 6. Score the pipeline on the test data with `metrics.r2_score`" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "Query job 55c5a9ce-8159-4a1a-99a4-af3a906640ba is DONE. 29.3 kB processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Query job 3e41c470-de70-4f13-89d9-c5564d0b2836 is DONE. 232 Bytes processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Query job ed2f9042-a737-4d13-bd21-8c3d29cd61a2 is DONE. 28.9 kB processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Query job 815d16b5-0a5d-42be-a766-1cff5b8f22f2 is DONE. 28.9 kB processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Query job 37a38dc6-5073-4544-a1e3-da145a843922 is DONE. 29.4 kB processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "0.2655729213572775" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from bigframes.ml.metrics import r2_score\n", - "\n", - "y_pred = pipeline.predict(X_test)[\"predicted_body_mass_g\"]\n", - "\n", - "r2_score(y_test, y_pred)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 5. Inference the model on new data" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "Load job 7b46750c-70b4-468d-87ba-9f84f579f2a6 is DONE. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import pandas\n", - "\n", - "new_penguins = bigframes.pandas.read_pandas(\n", - " pandas.DataFrame(\n", - " {\n", - " \"tag_number\": [1633, 1672, 1690],\n", - " \"species\": [\n", - " \"Adelie Penguin (Pygoscelis adeliae)\",\n", - " \"Adelie Penguin (Pygoscelis adeliae)\",\n", - " \"Adelie Penguin (Pygoscelis adeliae)\",\n", - " ],\n", - " \"island\": [\"Torgersen\", \"Torgersen\", \"Dream\"],\n", - " \"culmen_length_mm\": [39.5, 38.5, 37.9],\n", - " \"culmen_depth_mm\": [18.8, 17.2, 18.1],\n", - " \"flipper_length_mm\": [196.0, 181.0, 188.0],\n", - " \"sex\": [\"MALE\", \"FEMALE\", \"FEMALE\"],\n", - " }\n", - " ).set_index(\"tag_number\")\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "Query job d10dd37d-5e8e-4e15-9c83-a7e9a4c592a8 is DONE. 593 Bytes processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Query job 207cb787-cf8a-43ea-8e73-644d3f58b11a is DONE. 24 Bytes processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Query job c5dc5075-cac0-4947-9e9f-06aa9cc5bd2a is DONE. 0 Bytes processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Query job 2ca4a569-7186-48ed-b3e4-004dca704798 is DONE. 282 Bytes processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
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predicted_body_mass_gspeciesislandculmen_length_mmculmen_depth_mmflipper_length_mmsex
tag_number
16334017.203152Adelie Penguin (Pygoscelis adeliae)Torgersen39.518.8196.0MALE
16723127.601519Adelie Penguin (Pygoscelis adeliae)Torgersen38.517.2181.0FEMALE
16903386.101231Adelie Penguin (Pygoscelis adeliae)Dream37.918.1188.0FEMALE
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3 rows × 7 columns

\n", - "
[3 rows x 7 columns in total]" - ], - "text/plain": [ - " predicted_body_mass_g species \\\n", - "tag_number \n", - "1633 4017.203152 Adelie Penguin (Pygoscelis adeliae) \n", - "1672 3127.601519 Adelie Penguin (Pygoscelis adeliae) \n", - "1690 3386.101231 Adelie Penguin (Pygoscelis adeliae) \n", - "\n", - " island culmen_length_mm culmen_depth_mm flipper_length_mm \\\n", - "tag_number \n", - "1633 Torgersen 39.5 18.8 196.0 \n", - "1672 Torgersen 38.5 17.2 181.0 \n", - "1690 Dream 37.9 18.1 188.0 \n", - "\n", - " sex \n", - "tag_number \n", - "1633 MALE \n", - "1672 FEMALE \n", - "1690 FEMALE \n", - "\n", - "[3 rows x 7 columns]" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pipeline.predict(new_penguins)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 6. Save in BigQuery" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "Copy job d1def4a4-1da1-43a9-8ae5-4459444d993d is DONE. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "Pipeline(steps=[('transform',\n", - " ColumnTransformer(transformers=[('ont_hot_encoder',\n", - " OneHotEncoder(max_categories=1000001,\n", - " min_frequency=0),\n", - " 'island'),\n", - " ('standard_scaler',\n", - " StandardScaler(),\n", - " 'culmen_length_mm'),\n", - " ('standard_scaler',\n", - " StandardScaler(),\n", - " 'culmen_depth_mm'),\n", - " ('standard_scaler',\n", - " StandardScaler(),\n", - " 'flipper_length_mm'),\n", - " ('ont_hot_encoder',\n", - " OneHotEncoder(max_categories=1000001,\n", - " min_frequency=0),\n", - " 'sex')])),\n", - " ('estimator',\n", - " LinearRegression(fit_intercept=False,\n", - " optimize_strategy='NORMAL_EQUATION'))])" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pipeline.to_gbq(\"bigframes-dev.bigframes_demo_us.penguin_model\", replace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.9" - }, - "orig_nbformat": 4, - "vscode": { - "interpreter": { - "hash": "a850322d07d9bdc9ec5f301d307e048bcab2390ae395e1cbce9335f4e081e5e2" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/notebooks/ml/timeseries_analysis.ipynb b/notebooks/ml/timeseries_analysis.ipynb deleted file mode 100644 index 3b227460230..00000000000 --- a/notebooks/ml/timeseries_analysis.ipynb +++ /dev/null @@ -1,1135 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "cf1403ce", - "metadata": {}, - "source": [ - "# Time Series Forecasting with BigFrames\n", - "\n", - "This notebook provides a comprehensive walkthrough of time series forecasting using the BigFrames library. We will explore two powerful models, TimesFM and ARIMAPlus, to predict bikeshare trip demand based on historical data from San Francisco. The process covers data loading, preprocessing, model training, and visualization of the results." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c0b2db75", - "metadata": {}, - "outputs": [], - "source": [ - "import bigframes.pandas as bpd\n", - "from bigframes.ml import forecasting\n", - "bpd.options.display.render_mode = \"anywidget\"" - ] - }, - { - "cell_type": "markdown", - "id": "0eba46b9", - "metadata": {}, - "source": [ - "## 1. Data Loading and Preprocessing", - "\n", - "The first step is to load the San Francisco bikeshare dataset from BigQuery. We then preprocess the data by filtering for trips made by 'Subscriber' type users from 2018 onwards. This ensures we are working with a relevant and consistent subset of the data. Finally, we aggregate the trip data by the hour to create a time series of trip counts." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "83928f4d", - "metadata": {}, - "outputs": [], - "source": [ - "df = bpd.read_gbq(\"bigquery-public-data.san_francisco_bikeshare.bikeshare_trips\")\n", - "df = df[df[\"start_date\"] >= \"2018-01-01\"]\n", - "df = df[df[\"subscriber_type\"] == \"Subscriber\"]\n", - "df[\"trip_hour\"] = df[\"start_date\"].dt.floor(\"h\")\n", - "df_grouped = df[[\"trip_hour\", \"trip_id\"]].groupby(\"trip_hour\").count().reset_index()\n", - "df_grouped = df_grouped.rename(columns={\"trip_id\": \"num_trips\"})" - ] - }, - { - "cell_type": "markdown", - "id": "c43b7e65", - "metadata": {}, - "source": [ - "### 2. Forecasting with TimesFM\n", - "\n", - "In this section, we use the TimesFM (Time Series Foundation Model) to forecast future bikeshare demand. TimesFM is a powerful model designed for a wide range of time series forecasting tasks. We will use it to predict the number of trips for the last week of our dataset." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "1096e154", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/google/home/shuowei/src/python-bigquery-dataframes/bigframes/dataframe.py:5340: FutureWarning: The 'ai' property will be removed. Please use 'bigframes.bigquery.ai'\n", - "instead.\n", - " warnings.warn(msg, category=FutureWarning)\n" - ] - }, - { - "data": { - "text/html": [ - "✅ Completed. \n", - " Query processed 58.7 MB in 19 seconds of slot time. [Job bigframes-dev:US.eb026c28-038a-4ca7-acfa-474ed0be4119 details]\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "✅ Completed. \n", - " Query processed 7.1 kB in a moment of slot time.\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "✅ Completed. \n", - " Query processed 7.1 kB in a moment of slot time.\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "929eda852e564b799cf76e62d9f7b46a", - "version_major": 2, - "version_minor": 1 - }, - "text/html": [ - "
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02018-04-24 14:00:00+00:00126.5192110.9596.837778156.200644
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82018-04-25 16:00:00+00:00329.6537480.95201.918472457.389023
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02018-04-24 00:00:00+00:0052.76833534.874520.95-15.462203120.998872-15.462203120.998872
12018-04-24 01:00:00+00:0067.328148.0752550.95-26.729122161.385322-26.729122161.385322
22018-04-24 02:00:00+00:0075.20557353.9109210.95-30.268884180.68003-30.268884180.68003
32018-04-24 03:00:00+00:0080.07092255.9940760.95-29.479141189.620985-29.479141189.620985
42018-04-24 04:00:00+00:0075.16177956.5839740.95-35.542394185.865952-35.542394185.865952
52018-04-24 05:00:00+00:0081.42843256.850870.95-29.797913192.654778-29.797913192.654778
62018-04-24 06:00:00+00:00116.98144557.1807670.955.109671228.8532185.109671228.853218
72018-04-24 07:00:00+00:00237.22236157.7703070.95124.197176350.247546124.197176350.247546
82018-04-24 08:00:00+00:00323.72257258.6816620.95208.91436438.530784208.91436438.530784
92018-04-24 09:00:00+00:00357.28895259.8069060.95240.279247474.298656240.279247474.298656
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[168 rows x 8 columns in total]" - ], - "text/plain": [ - " forecast_timestamp forecast_value standard_error \\\n", - "0 2018-04-24 00:00:00+00:00 52.768335 34.87452 \n", - "1 2018-04-24 01:00:00+00:00 67.3281 48.075255 \n", - "2 2018-04-24 02:00:00+00:00 75.205573 53.910921 \n", - "3 2018-04-24 03:00:00+00:00 80.070922 55.994076 \n", - "4 2018-04-24 04:00:00+00:00 75.161779 56.583974 \n", - "5 2018-04-24 05:00:00+00:00 81.428432 56.85087 \n", - "6 2018-04-24 06:00:00+00:00 116.981445 57.180767 \n", - "7 2018-04-24 07:00:00+00:00 237.222361 57.770307 \n", - "8 2018-04-24 08:00:00+00:00 323.722572 58.681662 \n", - "9 2018-04-24 09:00:00+00:00 357.288952 59.806906 \n", - "\n", - " confidence_level prediction_interval_lower_bound \\\n", - "0 0.95 -15.462203 \n", - "1 0.95 -26.729122 \n", - "2 0.95 -30.268884 \n", - "3 0.95 -29.479141 \n", - "4 0.95 -35.542394 \n", - "5 0.95 -29.797913 \n", - "6 0.95 5.109671 \n", - "7 0.95 124.197176 \n", - "8 0.95 208.91436 \n", - "9 0.95 240.279247 \n", - "\n", - " prediction_interval_upper_bound confidence_interval_lower_bound \\\n", - "0 120.998872 -15.462203 \n", - "1 161.385322 -26.729122 \n", - "2 180.68003 -30.268884 \n", - "3 189.620985 -29.479141 \n", - "4 185.865952 -35.542394 \n", - "5 192.654778 -29.797913 \n", - "6 228.853218 5.109671 \n", - "7 350.247546 124.197176 \n", - "8 438.530784 208.91436 \n", - "9 474.298656 240.279247 \n", - "\n", - " confidence_interval_upper_bound \n", - "0 120.998872 \n", - "1 161.385322 \n", - "2 180.68003 \n", - "3 189.620985 \n", - "4 185.865952 \n", - "5 192.654778 \n", - "6 228.853218 \n", - "7 350.247546 \n", - "8 438.530784 \n", - "9 474.298656 \n", - "...\n", - "\n", - "[168 rows x 8 columns]" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model = forecasting.ARIMAPlus(\n", - " auto_arima_max_order=5, # Reduce runtime for large datasets\n", - " data_frequency=\"hourly\",\n", - " horizon=168\n", - ")\n", - "X = df_grouped.head(2842-168)[[\"trip_hour\"]]\n", - "y = df_grouped.head(2842-168)[[\"num_trips\"]]\n", - "model.fit(\n", - " X, y\n", - ")\n", - "predictions = model.predict(horizon=168, confidence_level=0.95)\n", - "predictions" - ] - }, - { - "cell_type": "markdown", - "id": "ec5a4513", - "metadata": {}, - "source": [ - "### 4. Compare and Visualize Forecasts\n", - "\n", - "Now we will visualize the forecasts from both TimesFM and ARIMAPlus against the actual historical data. This allows for a direct comparison of the two models' performance." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "7f5b5b1e", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "✅ Completed. \n", - " Query processed 31.7 MB in 11 seconds of slot time.\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "✅ Completed. \n", - " Query processed 58.8 MB in 12 seconds of slot time.\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "timesfm_result = result.sort_values(\"forecast_timestamp\")[[\"forecast_timestamp\", \"forecast_value\"]]\n", - "timesfm_result = timesfm_result.rename(columns={\n", - " \"forecast_timestamp\": \"trip_hour\",\n", - " \"forecast_value\": \"timesfm_forecast\"\n", - "})\n", - "arimaplus_result = predictions.sort_values(\"forecast_timestamp\")[[\"forecast_timestamp\", \"forecast_value\"]]\n", - "arimaplus_result = arimaplus_result.rename(columns={\n", - " \"forecast_timestamp\": \"trip_hour\",\n", - " \"forecast_value\": \"arimaplus_forecast\"\n", - "})\n", - "df_all = df_grouped.merge(timesfm_result, on=\"trip_hour\", how=\"left\")\n", - "df_all = df_all.merge(arimaplus_result, on=\"trip_hour\", how=\"left\")\n", - "df_all.tail(672).plot.line(\n", - " x=\"trip_hour\",\n", - " y=[\"num_trips\", \"timesfm_forecast\", \"arimaplus_forecast\"],\n", - " rot=45,\n", - " title=\"Trip Forecasts Comparison\"\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "015804c3", - "metadata": {}, - "source": [ - "### 5. Multiple Time Series Forecasting\n", - "\n", - "This section demonstrates a more advanced capability of ARIMAPlus: forecasting multiple time series simultaneously. This is useful when you have several independent series that you want to model together, such as trip counts from different bikeshare stations. The `id_col` parameter is key here, as it is used to differentiate between the individual time series." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6dbe6c48", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/google/home/shuowei/src/python-bigquery-dataframes/bigframes/core/log_adapter.py:182: TimeTravelCacheWarning: Reading cached table from 2025-12-12 23:04:48.874384+00:00 to avoid\n", - "incompatibilies with previous reads of this table. To read the latest\n", - "version, set `use_cache=False` or close the current session with\n", - "Session.close() or bigframes.pandas.close_session().\n", - " return method(*args, **kwargs)\n" - ] - }, - { - "data": { - "text/html": [ - "✅ Completed. \n", - " Query processed 69.8 MB in a moment of slot time.\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Number of stations: 41\n" - ] - }, - { - "data": { - "text/html": [ - "✅ Completed. \n", - " Query processed 69.8 MB in a moment of slot time.\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "✅ Completed. \n", - " Query processed 69.8 MB in a moment of slot time.\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Date range: 2013-08-29 to 2018-04-30\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " Query processed 18.8 MB in 2 minutes of slot time. [Job bigframes-dev:US.74ada07a-98ad-4d03-90bb-2b98f1d8b558 details]\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "✅ Completed. \n", - " Query processed 1.4 MB in 4 seconds of slot time. [Job bigframes-dev:US.a292f715-1d9c-406d-a7d5-f99b2ba71660 details]\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "✅ Completed. \n", - " Query processed 4.6 kB in a moment of slot time.\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "✅ Completed. \n", - " Query processed 11.5 kB in a moment of slot time.\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "✅ Completed. \n", - " Query processed 0 Bytes in a moment of slot time.\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "00fc1edbf6fd40dfb949a3e3a30b6c3e", - "version_major": 2, - "version_minor": 1 - }, - "text/html": [ - "
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forecast_timestampstart_station_nameforecast_valuestandard_errorconfidence_levelprediction_interval_lower_boundprediction_interval_upper_boundconfidence_interval_lower_boundconfidence_interval_upper_bound
02016-09-01 00:00:00+00:00Beale at Market27.9114173.4224340.9521.21556834.60726521.21556834.607265
12016-09-01 00:00:00+00:00Civic Center BART (7th at Market)17.094554.2662870.958.7477425.4413618.7477425.441361
22016-09-01 00:00:00+00:00Embarcadero at Bryant22.3436483.3937020.9515.70401228.98328415.70401228.983284
32016-09-01 00:00:00+00:00Embarcadero at Folsom28.253293.3821580.9521.6362434.87033921.6362434.870339
42016-09-01 00:00:00+00:00Embarcadero at Sansome52.5380836.2692910.9540.27247764.80368940.27247764.803689
52016-09-01 00:00:00+00:00Embarcadero at Vallejo16.5132332.9536830.9510.73447622.2919910.73447622.29199
62016-09-01 00:00:00+00:00Market at 10th34.0512746.2057980.9521.9098946.19265821.9098946.192658
72016-09-01 00:00:00+00:00Market at 4th25.7460294.0015920.9517.91708233.57497717.91708233.574977
82016-09-01 00:00:00+00:00Market at Sansome46.1343685.0718520.9536.21150356.05723336.21150356.057233
92016-09-01 00:00:00+00:00Mechanics Plaza (Market at Battery)23.2699413.1946750.9517.01969229.52018917.01969229.520189
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10 rows × 9 columns

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[123 rows x 9 columns in total]" - ], - "text/plain": [ - " forecast_timestamp start_station_name \\\n", - "0 2016-09-01 00:00:00+00:00 Beale at Market \n", - "1 2016-09-01 00:00:00+00:00 Civic Center BART (7th at Market) \n", - "2 2016-09-01 00:00:00+00:00 Embarcadero at Bryant \n", - "3 2016-09-01 00:00:00+00:00 Embarcadero at Folsom \n", - "4 2016-09-01 00:00:00+00:00 Embarcadero at Sansome \n", - "5 2016-09-01 00:00:00+00:00 Embarcadero at Vallejo \n", - "6 2016-09-01 00:00:00+00:00 Market at 10th \n", - "7 2016-09-01 00:00:00+00:00 Market at 4th \n", - "8 2016-09-01 00:00:00+00:00 Market at Sansome \n", - "9 2016-09-01 00:00:00+00:00 Mechanics Plaza (Market at Battery) \n", - "\n", - " forecast_value standard_error confidence_level \\\n", - "0 27.911417 3.422434 0.95 \n", - "1 17.09455 4.266287 0.95 \n", - "2 22.343648 3.393702 0.95 \n", - "3 28.25329 3.382158 0.95 \n", - "4 52.538083 6.269291 0.95 \n", - "5 16.513233 2.953683 0.95 \n", - "6 34.051274 6.205798 0.95 \n", - "7 25.746029 4.001592 0.95 \n", - "8 46.134368 5.071852 0.95 \n", - "9 23.269941 3.194675 0.95 \n", - "\n", - " prediction_interval_lower_bound prediction_interval_upper_bound \\\n", - "0 21.215568 34.607265 \n", - "1 8.74774 25.441361 \n", - "2 15.704012 28.983284 \n", - "3 21.63624 34.870339 \n", - "4 40.272477 64.803689 \n", - "5 10.734476 22.29199 \n", - "6 21.90989 46.192658 \n", - "7 17.917082 33.574977 \n", - "8 36.211503 56.057233 \n", - "9 17.019692 29.520189 \n", - "\n", - " confidence_interval_lower_bound confidence_interval_upper_bound \n", - "0 21.215568 34.607265 \n", - "1 8.74774 25.441361 \n", - "2 15.704012 28.983284 \n", - "3 21.63624 34.870339 \n", - "4 40.272477 64.803689 \n", - "5 10.734476 22.29199 \n", - "6 21.90989 46.192658 \n", - "7 17.917082 33.574977 \n", - "8 36.211503 56.057233 \n", - "9 17.019692 29.520189 \n", - "...\n", - "\n", - "[123 rows x 9 columns]" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_multi = bpd.read_gbq(\"bigquery-public-data.san_francisco_bikeshare.bikeshare_trips\")\n", - "df_multi = df_multi[df_multi[\"start_station_name\"].str.contains(\"Market|Powell|Embarcadero\")]\n", - " \n", - "# Create daily aggregation\n", - "features = bpd.DataFrame({\n", - " \"start_station_name\": df_multi[\"start_station_name\"],\n", - " \"date\": df_multi[\"start_date\"].dt.date,\n", - "})\n", - "\n", - "# Group by station and date\n", - "num_trips = features.groupby(\n", - " [\"start_station_name\", \"date\"], as_index=False\n", - ").size()\n", - "# Rename the size column to \"num_trips\"\n", - "num_trips = num_trips.rename(columns={num_trips.columns[-1]: \"num_trips\"})\n", - "\n", - "# Check data quality\n", - "print(f\"Number of stations: {num_trips['start_station_name'].nunique()}\")\n", - "print(f\"Date range: {num_trips['date'].min()} to {num_trips['date'].max()}\")\n", - "\n", - "# Use daily frequency \n", - "model = forecasting.ARIMAPlus(\n", - " data_frequency=\"daily\",\n", - " horizon=30,\n", - " auto_arima_max_order=3,\n", - " min_time_series_length=10,\n", - " time_series_length_fraction=0.8\n", - ")\n", - "\n", - "model.fit(\n", - " num_trips[[\"date\"]],\n", - " num_trips[[\"num_trips\"]],\n", - " id_col=num_trips[[\"start_station_name\"]]\n", - ")\n", - "\n", - "predictions_multi = model.predict()\n", - "predictions_multi" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "venv (3.13.0)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.13.0" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks/multimodal/multimodal_dataframe.ipynb b/notebooks/multimodal/multimodal_dataframe.ipynb deleted file mode 100644 index cd363db6f36..00000000000 --- a/notebooks/multimodal/multimodal_dataframe.ipynb +++ /dev/null @@ -1,1114 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "id": "9edad7a6", - "metadata": {}, - "outputs": [], - "source": [ - "# Copyright 2025 Google LLC\n", - "#\n", - "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", - "# you may not use this file except in compliance with the License.\n", - "# You may obtain a copy of the License at\n", - "#\n", - "# https://www.apache.org/licenses/LICENSE-2.0\n", - "#\n", - "# Unless required by applicable law or agreed to in writing, software\n", - "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", - "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", - "# See the License for the specific language governing permissions and\n", - "# limitations under the License." - ] - }, - { - "cell_type": "markdown", - "id": "816ab253", - "metadata": { - "id": "YOrUAvz6DMw-" - }, - "source": [ - "# BigFrames Multimodal DataFrame\n", - "\n", - "\n", - "\n", - " \n", - " \n", - " \n", - "
\n", - " \n", - " \"Colab Run in Colab\n", - " \n", - " \n", - " \n", - " \"GitHub\n", - " View on GitHub\n", - " \n", - " \n", - " \n", - " \"BQ\n", - " Open in BQ Studio\n", - " \n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "id": "77d821d4", - "metadata": {}, - "source": [ - "This notebook is introducing BigFrames Multimodal features:\n", - "1. Create Multimodal DataFrame\n", - "2. Combine unstructured data with structured data\n", - "3. Conduct image transformations\n", - "4. Use LLM models to ask questions and generate embeddings on images\n", - "5. PDF chunking function\n", - "6. Transcribe audio\n", - "7. Extract EXIF metadata from images" - ] - }, - { - "cell_type": "markdown", - "id": "75ab1c13", - "metadata": { - "id": "PEAJQQ6AFg-n" - }, - "source": [ - "## Setup" - ] - }, - { - "cell_type": "markdown", - "id": "750954c4", - "metadata": {}, - "source": [ - "Install the latest bigframes package if bigframes version < 2.4.0" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2a6fafb1", - "metadata": {}, - "outputs": [], - "source": [ - "# !pip install bigframes --upgrade" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "df561d04", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "bGyhLnfEeB0X", - "outputId": "83ac8b64-3f44-4d43-d089-28a5026cbb42" - }, - "outputs": [], - "source": [ - "PROJECT = \"bigframes-dev\" # replace with your project. \n", - "# Refer to https://cloud.google.com/bigquery/docs/multimodal-data-dataframes-tutorial#required_roles for your required permissions\n", - "\n", - "LOCATION = \"us\" # replace with your location.\n", - "\n", - "# Dataset where the UDF will be created.\n", - "DATASET_ID = \"bigframes_samples\" # replace with your dataset ID.\n", - "\n", - "OUTPUT_BUCKET = \"bigframes_blob_test\" # replace with your GCS bucket. \n", - "# The connection (or bigframes-default-connection of the project) must have read/write permission to the bucket. \n", - "# Refer to https://cloud.google.com/bigquery/docs/multimodal-data-dataframes-tutorial#grant-permissions for setting up connection service account permissions.\n", - "# In this Notebook it uses bigframes-default-connection by default. You can also bring in your own connections in each method.\n", - "\n", - "FULL_CONNECTION_ID = f\"{PROJECT}.{LOCATION}.bigframes-default-connection\"\n", - "\n", - "import bigframes\n", - "# Setup project\n", - "bigframes.options.bigquery.project = PROJECT\n", - "bigframes.options.bigquery.location = LOCATION\n", - "\n", - "# Display options\n", - "bigframes.options.display.blob_display_width = 300\n", - "bigframes.options.display.progress_bar = None\n", - "\n", - "import bigframes.pandas as bpd\n", - "import bigframes.bigquery as bbq" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "35bd6e6e", - "metadata": {}, - "outputs": [], - "source": [ - "import bigframes.bigquery as bbq\n", - "\n", - "def get_runtime_json_str(series, mode=\"R\", with_metadata=False):\n", - " \"\"\"\n", - " Get the runtime (contains signed URL to access gcs data) and apply the\n", - " ToJSONSTring transformation.\n", - " \n", - " Args:\n", - " series: bigframes.series.Series to operate on.\n", - " mode: \"R\" for read, \"RW\" for read/write.\n", - " with_metadata: Whether to fetch and include blob metadata.\n", - " \"\"\"\n", - " # 1. Optionally fetch metadata\n", - " s = (\n", - " bbq.obj.fetch_metadata(series)\n", - " if with_metadata\n", - " else series\n", - " )\n", - " \n", - " # 2. Retrieve the access URL runtime object\n", - " runtime = bbq.obj.get_access_url(s, mode=mode)\n", - " \n", - " # 3. Convert the runtime object to a JSON string\n", - " return bbq.to_json_string(runtime)\n", - "\n", - "def get_metadata(series):\n", - " # Fetch metadata and extract GCS metadata from the details JSON field\n", - " metadata_obj = bbq.obj.fetch_metadata(series)\n", - " return bbq.json_query(metadata_obj.struct.field(\"details\"), \"$.gcs_metadata\")\n", - "\n", - "def get_content_type(series):\n", - " return bbq.json_value(get_metadata(series), \"$.content_type\")\n", - "\n", - "def get_size(series):\n", - " return bbq.json_value(get_metadata(series), \"$.size\").astype(\"Int64\")\n", - "\n", - "def get_updated(series):\n", - " return bpd.to_datetime(bbq.json_value(get_metadata(series), \"$.updated\").astype(\"Int64\"), unit=\"us\", utc=True)\n", - "\n", - "from IPython.display import HTML, display\n", - "\n", - "def render_images(df):\n", - " \"\"\"Helper to display BigFrames DataFrame with rendered image previews.\"\"\"\n", - " import bigframes.pandas as bpd\n", - " import bigframes.bigquery as bbq\n", - " import bigframes\n", - " from bigframes import dtypes\n", - " import json\n", - " \n", - " if isinstance(df, bpd.Series):\n", - " df = df.to_frame()\n", - " \n", - " # 1. Auto-detect columns holding ObjectRefs\n", - " object_cols = [\n", - " col for col, dtype in zip(df.columns, df.dtypes)\n", - " if dtype == dtypes.OBJ_REF_DTYPE\n", - " ]\n", - " \n", - " if not object_cols:\n", - " display(df)\n", - " return\n", - "\n", - " limit = bigframes.options.display.max_rows or 10\n", - " view_df = df.head(limit)\n", - " \n", - " # 2. Bulk-fetch access runtime URLs ONLY (disable with_metadata to bypass potential \n", - " # race conditions on new files where BigQuery may error before async writes finalize)\n", - " runtime_cols = {\n", - " col: get_runtime_json_str(view_df[col], mode=\"R\", with_metadata=False) \n", - " for col in object_cols\n", - " }\n", - " \n", - " pandas_json_df = bpd.DataFrame(runtime_cols).to_pandas()\n", - " final_pd = view_df.to_pandas()\n", - " \n", - " width = bigframes.options.display.blob_display_width or 300\n", - " IMAGE_EXTENSIONS = (\".png\", \".jpg\", \".jpeg\", \".gif\", \".webp\")\n", - " \n", - " def format_cell_html(raw_json):\n", - " if not raw_json:\n", - " return \"\"\n", - " try:\n", - " obj_rt = json.loads(raw_json)\n", - " \n", - " if \"access_urls\" not in obj_rt:\n", - " err = obj_rt.get(\"errors\", [{\"message\": \"URL Generation Failed\"}])[0].get(\"message\")\n", - " return f'Error: {err}'\n", - " \n", - " uri = obj_rt.get(\"objectref\", {}).get(\"uri\", \"\")\n", - " url = obj_rt[\"access_urls\"][\"read_url\"]\n", - " \n", - " # Safely infer type from extension to guarantee immediate display availability\n", - " if uri and str(uri).lower().endswith(IMAGE_EXTENSIONS):\n", - " return f''\n", - " \n", - " return f'{uri if uri else \"view\"}'\n", - " except:\n", - " return \"Format Error\"\n", - "\n", - " for col in object_cols:\n", - " final_pd[col] = pandas_json_df[col].map(format_cell_html)\n", - " \n", - " display(HTML(final_pd.to_html(escape=False)))" - ] - }, - { - "cell_type": "markdown", - "id": "be9ce892", - "metadata": { - "id": "ifKOq7VZGtZy" - }, - "source": [ - "To create a Multimodal DataFrame, you can use `bigframes.bigquery.obj.make_ref` on a series of URIs. You can get the URIs from a BigQuery table or by listing them from Cloud Storage.\n", - "\n", - "In this example, we use `gcsfs` to list the files from Cloud Storage, and then use `read_gbq` to load them into a BigQuery DataFrame before creating the object reference." - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "id": "871d02f4", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "fx6YcZJbeYru", - "outputId": "d707954a-0dd0-4c50-b7bf-36b140cf76cf" - }, - "outputs": [], - "source": [ - "import gcsfs\n", - "import bigframes.bigquery as bbq\n", - "\n", - "# List files using gcsfs (public bucket)\n", - "fs = gcsfs.GCSFileSystem(anon=True)\n", - "uris = fs.glob(\"gs://cloud-samples-data/bigquery/tutorials/cymbal-pets/images/*\")\n", - "\n", - "# Ensure URIs have gs:// prefix\n", - "uris = [u if u.startswith(\"gs://\") else f\"gs://{u}\" for u in uris]\n", - "\n", - "# Read the URIs into a BigQuery DataFrame using UNNEST\n", - "# We take the first 5 for this example\n", - "df_image = bpd.read_gbq(f\"SELECT uri FROM UNNEST({uris[:5]}) as uri\")\n", - "\n", - "# Create the object reference column\n", - "df_image['image'] = bbq.obj.make_ref(df_image['uri'], authorizer=FULL_CONNECTION_ID)\n", - "df_image = df_image[['image']]" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "id": "2e0436b0", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 487 - }, - "id": "HhCb8jRsLe9B", - "outputId": "03081cf9-3a22-42c9-b38f-649f592fdada" - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/google/home/shuowei/src/google-cloud-python/google-cloud-python/packages/bigframes/bigframes/dtypes.py:1044: JSONDtypeWarning: JSON columns will be represented as pandas.ArrowDtype(pyarrow.json_())\n", - "instead of using `db_dtypes` in the future when available in pandas\n", - "(https://github.com/pandas-dev/pandas/issues/60958) and pyarrow.\n", - " warnings.warn(msg, bigframes.exceptions.JSONDtypeWarning)\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
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" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Take only the 5 images to deal with. Preview the content of the Mutimodal DataFrame\n", - "df_image = df_image.head(5)\n", - "render_images(df_image)" - ] - }, - { - "cell_type": "markdown", - "id": "429b0117", - "metadata": { - "id": "b6RRZb3qPi_T" - }, - "source": [ - "### 2. Combine unstructured data with structured data" - ] - }, - { - "cell_type": "markdown", - "id": "991fa065", - "metadata": { - "id": "4YJCdmLtR-qu" - }, - "source": [ - "Now you can put more information into the table to describe the files. Such as author info from inputs, or other metadata from the gcs object itself." - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "id": "08722ec5", - "metadata": { - "id": "YYYVn7NDH0Me" - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/google/home/shuowei/src/google-cloud-python/google-cloud-python/packages/bigframes/bigframes/dtypes.py:1044: JSONDtypeWarning: JSON columns will be represented as pandas.ArrowDtype(pyarrow.json_())\n", - "instead of using `db_dtypes` in the future when available in pandas\n", - "(https://github.com/pandas-dev/pandas/issues/60958) and pyarrow.\n", - " warnings.warn(msg, bigframes.exceptions.JSONDtypeWarning)\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
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" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Combine unstructured data with structured data\n", - "df_image = df_image.head(5)\n", - "df_image[\"author\"] = [\"alice\", \"bob\", \"bob\", \"alice\", \"bob\"] # type: ignore\n", - "df_image[\"content_type\"] = get_content_type(df_image[\"image\"])\n", - "df_image[\"size\"] = get_size(df_image[\"image\"])\n", - "df_image[\"updated\"] = get_updated(df_image[\"image\"])\n", - "render_images(df_image)" - ] - }, - { - "cell_type": "markdown", - "id": "f90826f6", - "metadata": {}, - "source": [ - "### 3. Conduct image transformations" - ] - }, - { - "cell_type": "markdown", - "id": "e24c9f8c", - "metadata": {}, - "source": [ - "This section demonstrates how to perform image transformations like blur, resize, and normalize using custom BigQuery Python UDFs and the `opencv-python` library." - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "id": "db665049", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 487 - }, - "id": "HhCb8jRsLe9B", - "outputId": "03081cf9-3a22-42c9-b38f-649f592fdada" - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/google/home/shuowei/src/google-cloud-python/google-cloud-python/packages/bigframes/bigframes/pandas/__init__.py:211: PreviewWarning: udf is in preview.\n", - " return global_session.with_default_session(\n", - "/usr/local/google/home/shuowei/src/google-cloud-python/google-cloud-python/packages/bigframes/bigframes/dataframe.py:4695: FunctionAxisOnePreviewWarning: DataFrame.apply with parameter axis=1 scenario is in preview.\n", - " warnings.warn(msg, category=bfe.FunctionAxisOnePreviewWarning)\n", - "/usr/local/google/home/shuowei/src/google-cloud-python/google-cloud-python/packages/bigframes/bigframes/dtypes.py:1044: JSONDtypeWarning: JSON columns will be represented as pandas.ArrowDtype(pyarrow.json_())\n", - "instead of using `db_dtypes` in the future when available in pandas\n", - "(https://github.com/pandas-dev/pandas/issues/60958) and pyarrow.\n", - " warnings.warn(msg, bigframes.exceptions.JSONDtypeWarning)\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
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" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Construct the canonical connection ID\n", - "FULL_CONNECTION_ID = f\"{PROJECT}.{LOCATION}.bigframes-default-connection\"\n", - "\n", - "@bpd.udf(\n", - " input_types=[str, str, int, int],\n", - " output_type=str,\n", - " dataset=DATASET_ID,\n", - " name=\"image_blur_v2\",\n", - " bigquery_connection=FULL_CONNECTION_ID,\n", - " packages=[\"opencv-python-headless\", \"numpy\", \"requests\"],\n", - ")\n", - "def image_blur(src_rt: str, dst_rt: str, kx: int, ky: int) -> str:\n", - " import json\n", - " import cv2 as cv\n", - " import numpy as np\n", - " import requests\n", - " import base64\n", - "\n", - " src_obj = json.loads(src_rt)\n", - " if \"access_urls\" not in src_obj:\n", - " raise ValueError(f\"Missing 'access_urls' in source object. Response: {src_obj}\")\n", - " src_url = src_obj[\"access_urls\"][\"read_url\"]\n", - " \n", - " response = requests.get(src_url, timeout=30)\n", - " response.raise_for_status()\n", - " \n", - " img = cv.imdecode(np.frombuffer(response.content, np.uint8), cv.IMREAD_UNCHANGED)\n", - " if img is None:\n", - " raise ValueError(\"cv.imdecode failed\")\n", - " \n", - " kx, ky = int(kx), int(ky)\n", - " img_blurred = cv.blur(img, ksize=(kx, ky))\n", - " \n", - " success, encoded = cv.imencode(\".jpeg\", img_blurred)\n", - " if not success:\n", - " raise ValueError(\"cv.imencode failed\")\n", - " \n", - " # Handle two output modes\n", - " if dst_rt: # GCS/Series output mode\n", - " dst_obj = json.loads(dst_rt)\n", - " if \"access_urls\" not in dst_obj:\n", - " raise ValueError(f\"Missing 'access_urls' in destination object. Verify authorizer permissions. Response: {dst_obj}\")\n", - " dst_url = dst_obj[\"access_urls\"][\"write_url\"]\n", - " \n", - " requests.put(dst_url, data=encoded.tobytes(), headers={\"Content-Type\": \"image/jpeg\"}, timeout=30).raise_for_status()\n", - " \n", - " uri = dst_obj[\"objectref\"][\"uri\"]\n", - " return uri\n", - " \n", - " else: # BigQuery bytes output mode \n", - " image_bytes = encoded.tobytes()\n", - " return base64.b64encode(image_bytes).decode()\n", - "\n", - "def apply_transformation(series, dst_folder, udf, *args, verbose=False):\n", - " import os\n", - " dst_folder = os.path.join(dst_folder, \"\")\n", - " # Fetch metadata to get the URI\n", - " metadata = bbq.obj.fetch_metadata(series)\n", - " current_uri = metadata.struct.field(\"uri\")\n", - " dst_uri = current_uri.str.replace(r\"^.*\\/(.*)$\", rf\"{dst_folder}\\1\", regex=True)\n", - " \n", - " # To avoid synchronous 404 validation checks on files that don't exist yet, \n", - " # bypass the validator by explicitly constructing an objectref JSON.\n", - " dst_blob_df = bpd.DataFrame({\"uri\": dst_uri})\n", - " dst_blob_df[\"authorizer\"] = FULL_CONNECTION_ID\n", - " dst_blob = bbq.obj.make_ref(bbq.to_json(bbq.struct(dst_blob_df)))\n", - "\n", - " df_transform = bpd.DataFrame({\n", - " \"src_rt\": get_runtime_json_str(series, mode=\"R\"),\n", - " \"dst_rt\": get_runtime_json_str(dst_blob, mode=\"RW\"),\n", - " })\n", - " res = df_transform[[\"src_rt\", \"dst_rt\"]].apply(\n", - " udf, axis=1, args=args\n", - " )\n", - " \n", - " if verbose:\n", - " return res\n", - " \n", - " # Final return MUST also use JSON bypass to eliminate temporary 404 validation \n", - " # errors from embedded ObjectRefs during fused query execution pipelines.\n", - " res_df = bpd.DataFrame({\"uri\": res})\n", - " res_df[\"authorizer\"] = FULL_CONNECTION_ID\n", - " return bbq.obj.make_ref(bbq.to_json(bbq.struct(res_df)))\n", - "\n", - "# Apply transformations\n", - "df_image[\"blurred\"] = apply_transformation(\n", - " df_image[\"image\"], f\"gs://{OUTPUT_BUCKET}/image_blur_transformed/\",\n", - " image_blur, 20, 20\n", - ")\n", - "render_images(df_image[[\"image\", \"blurred\"]])" - ] - }, - { - "cell_type": "markdown", - "id": "11fcc6ec", - "metadata": { - "id": "Euk5saeVVdTP" - }, - "source": [ - "### 4. Use LLM models to ask questions and generate embeddings on images" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "793b2f45", - "metadata": { - "id": "mRUGfcaFVW-3" - }, - "outputs": [], - "source": [ - "from bigframes.ml import llm\n", - "gemini = llm.GeminiTextGenerator()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "13d7cb93", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 657 - }, - "id": "DNFP7CbjWdR9", - "outputId": "3f90a062-0abc-4bce-f53c-db57b06a14b9" - }, - "outputs": [], - "source": [ - "# Ask the same question on the images\n", - "answer = gemini.predict(df_image, prompt=[\"what item is it?\", \"what color is the picture?\"])\n", - "render_images(answer[[\"ml_generate_text_llm_result\", \"image\"]])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "68857305", - "metadata": { - "id": "IG3J3HsKhyBY" - }, - "outputs": [], - "source": [ - "# Ask different questions\n", - "df_image[\"question\"] = [\n", - " \"what item is it?\",\n", - " \"what color is the picture?\",\n", - " \"what is the product name?\",\n", - " \"is it for pets?\",\n", - " \"what is the weight of the product?\",\n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "829afc69", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 657 - }, - "id": "qKOb765IiVuD", - "outputId": "731bafad-ea29-463f-c8c1-cb7acfd70e5d" - }, - "outputs": [], - "source": [ - "answer_alt = gemini.predict(df_image, prompt=[df_image[\"question\"], df_image[\"image\"]])\n", - "render_images(answer_alt[[\"ml_generate_text_llm_result\", \"image\"]])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e75df430", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 300 - }, - "id": "KATVv2CO5RT1", - "outputId": "6ec01f27-70b6-4f69-c545-e5e3c879480c" - }, - "outputs": [], - "source": [ - "# Generate embeddings.\n", - "embed_model = llm.MultimodalEmbeddingGenerator()\n", - "embeddings = embed_model.predict(df_image[\"image\"])\n", - "embeddings" - ] - }, - { - "cell_type": "markdown", - "id": "23892b0e", - "metadata": { - "id": "iRUi8AjG7cIf" - }, - "source": [ - "### 5. PDF extraction and chunking function\n", - "\n", - "This section demonstrates how to extract text and chunk text from PDF files using custom BigQuery Python UDFs and the `pypdf` library." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "136a18b8", - "metadata": {}, - "outputs": [], - "source": [ - "# Construct the canonical connection ID\n", - "FULL_CONNECTION_ID = f\"{PROJECT}.{LOCATION}.bigframes-default-connection\"\n", - "\n", - "@bpd.udf(\n", - " input_types=[str],\n", - " output_type=str,\n", - " dataset=DATASET_ID,\n", - " name=\"pdf_extract\",\n", - " bigquery_connection=FULL_CONNECTION_ID,\n", - " packages=[\"pypdf\", \"requests\", \"cryptography\"],\n", - ")\n", - "def pdf_extract(src_obj_ref_rt: str) -> str:\n", - " import io\n", - " import json\n", - " from pypdf import PdfReader\n", - " import requests\n", - " src_obj_ref_rt_json = json.loads(src_obj_ref_rt)\n", - " src_url = src_obj_ref_rt_json[\"access_urls\"][\"read_url\"]\n", - " response = requests.get(src_url, timeout=30, stream=True)\n", - " response.raise_for_status()\n", - " pdf_bytes = response.content\n", - " pdf_file = io.BytesIO(pdf_bytes)\n", - " reader = PdfReader(pdf_file, strict=False)\n", - " all_text = \"\"\n", - " for page in reader.pages:\n", - " page_extract_text = page.extract_text()\n", - " if page_extract_text:\n", - " all_text += page_extract_text\n", - " return all_text\n", - "\n", - "@bpd.udf(\n", - " input_types=[str, int, int],\n", - " output_type=list[str],\n", - " dataset=DATASET_ID,\n", - " name=\"pdf_chunk\",\n", - " bigquery_connection=FULL_CONNECTION_ID,\n", - " packages=[\"pypdf\", \"requests\", \"cryptography\"],\n", - ")\n", - "def pdf_chunk(src_obj_ref_rt: str, chunk_size: int, overlap_size: int) -> list[str]:\n", - " import io\n", - " import json\n", - " from pypdf import PdfReader\n", - " import requests\n", - " src_obj_ref_rt_json = json.loads(src_obj_ref_rt)\n", - " src_url = src_obj_ref_rt_json[\"access_urls\"][\"read_url\"]\n", - " response = requests.get(src_url, timeout=30, stream=True)\n", - " response.raise_for_status()\n", - " pdf_bytes = response.content\n", - " pdf_file = io.BytesIO(pdf_bytes)\n", - " reader = PdfReader(pdf_file, strict=False)\n", - " all_text_chunks = []\n", - " curr_chunk = \"\"\n", - " for page in reader.pages:\n", - " page_text = page.extract_text()\n", - " if page_text:\n", - " curr_chunk += page_text\n", - " while len(curr_chunk) >= chunk_size:\n", - " split_idx = curr_chunk.rfind(\" \", 0, chunk_size)\n", - " if split_idx == -1:\n", - " split_idx = chunk_size\n", - " actual_chunk = curr_chunk[:split_idx]\n", - " all_text_chunks.append(actual_chunk)\n", - " overlap = curr_chunk[split_idx + 1 : split_idx + 1 + overlap_size]\n", - " curr_chunk = overlap + curr_chunk[split_idx + 1 + overlap_size :]\n", - " if curr_chunk:\n", - " all_text_chunks.append(curr_chunk)\n", - " return all_text_chunks" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "234a5f86", - "metadata": {}, - "outputs": [], - "source": [ - "import gcsfs\n", - "import bigframes.bigquery as bbq\n", - "\n", - "# List files using gcsfs\n", - "fs = gcsfs.GCSFileSystem(anon=True)\n", - "uris = fs.glob(\"gs://cloud-samples-data/bigquery/tutorials/cymbal-pets/documents/*\")\n", - "\n", - "# Ensure URIs have gs:// prefix\n", - "uris = [u if u.startswith(\"gs://\") else f\"gs://{u}\" for u in uris]\n", - "\n", - "# Read the URIs into a BigQuery DataFrame\n", - "df_pdf = bpd.read_gbq(f\"SELECT uri FROM UNNEST({uris[:5]}) as uri\")\n", - "\n", - "# Create the object reference column\n", - "df_pdf['pdf'] = bbq.obj.make_ref(df_pdf['uri'], authorizer=FULL_CONNECTION_ID)\n", - "df_pdf = df_pdf[['pdf']]\n", - "\n", - "# Generate a JSON string containing the runtime information (including signed read URLs)\n", - "access_urls = get_runtime_json_str(df_pdf[\"pdf\"], mode=\"R\")\n", - "\n", - "# Apply PDF extraction\n", - "df_pdf[\"extracted_text\"] = access_urls.apply(pdf_extract)\n", - "\n", - "# Apply PDF chunking\n", - "df_pdf[\"chunked\"] = access_urls.apply(pdf_chunk, args=(2000, 200))\n", - "\n", - "df_pdf[[\"extracted_text\", \"chunked\"]]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d80effbe", - "metadata": {}, - "outputs": [], - "source": [ - "# Explode the chunks to see each chunk as a separate row\n", - "chunked = df_pdf[\"chunked\"].explode()\n", - "chunked" - ] - }, - { - "cell_type": "markdown", - "id": "118cf1c7", - "metadata": {}, - "source": [ - "### 6. Audio transcribe" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1794c54f", - "metadata": {}, - "outputs": [], - "source": [ - "import gcsfs\n", - "import bigframes.bigquery as bbq\n", - "\n", - "audio_gcs_path = \"gs://bigframes_blob_test/audio/*\"\n", - "\n", - "# List files using gcsfs\n", - "fs = gcsfs.GCSFileSystem()\n", - "uris = fs.glob(audio_gcs_path)\n", - "\n", - "# Ensure URIs have gs:// prefix\n", - "uris = [u if u.startswith(\"gs://\") else f\"gs://{u}\" for u in uris]\n", - "\n", - "# Read the URIs into a BigQuery DataFrame\n", - "# If the bucket is empty or doesn't exist, this will result in an empty DataFrame\n", - "if not uris:\n", - " # Fallback to a dummy list or just let it be empty\n", - " uris = [\"gs://bigframes_blob_test/audio/dummy.mp3\"]\n", - "\n", - "df = bpd.read_gbq(f\"SELECT uri FROM UNNEST({uris[:5]}) as uri\")\n", - "\n", - "# Create the object reference column\n", - "df['audio'] = bbq.obj.make_ref(df['uri'], authorizer=FULL_CONNECTION_ID)\n", - "df = df[['audio']]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c9f9d484", - "metadata": {}, - "outputs": [], - "source": [ - "# The audio_transcribe function is a convenience wrapper around bigframes.bigquery.ai.generate.\n", - "# Here's how to perform the same operation directly:\n", - "\n", - "audio_series = df[\"audio\"]\n", - "prompt_text = (\n", - " \"**Task:** Transcribe the provided audio. **Instructions:** - Your response \"\n", - " \"must contain only the verbatim transcription of the audio. - Do not include \"\n", - " \"any introductory text, summaries, or conversational filler in your response. \"\n", - " \"The output should begin directly with the first word of the audio.\"\n", - ")\n", - "\n", - "# Convert the audio series to the runtime representation required by the model.\n", - "# This involves fetching metadata and getting a signed access URL.\n", - "audio_metadata = bbq.obj.fetch_metadata(audio_series)\n", - "audio_runtime = bbq.obj.get_access_url(audio_metadata, mode=\"R\")\n", - "\n", - "transcribed_results = bbq.ai.generate(\n", - " prompt=(prompt_text, audio_runtime),\n", - " endpoint=\"gemini-2.5-flash\",\n", - " model_params={\"generationConfig\": {\"temperature\": 0.0}},\n", - ")\n", - "\n", - "transcribed_series = transcribed_results.struct.field(\"result\").rename(\"transcribed_content\")\n", - "transcribed_series" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7209a62a", - "metadata": {}, - "outputs": [], - "source": [ - "# To get verbose results (including status), we can extract both fields from the result struct.\n", - "transcribed_content_series = transcribed_results.struct.field(\"result\")\n", - "transcribed_status_series = transcribed_results.struct.field(\"status\")\n", - "\n", - "transcribed_series_verbose = bpd.DataFrame(\n", - " {\n", - " \"status\": transcribed_status_series,\n", - " \"content\": transcribed_content_series,\n", - " }\n", - ")\n", - "# Package as a struct for consistent display\n", - "transcribed_series_verbose = bbq.struct(transcribed_series_verbose).rename(\"transcription_results\")\n", - "transcribed_series_verbose" - ] - }, - { - "cell_type": "markdown", - "id": "c8351cc3", - "metadata": {}, - "source": [ - "### 7. Extract EXIF metadata from images" - ] - }, - { - "cell_type": "markdown", - "id": "e59670b9", - "metadata": {}, - "source": [ - "This section demonstrates how to extract EXIF metadata from images using a custom BigQuery Python UDF and the `Pillow` library." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "fda362f4", - "metadata": {}, - "outputs": [], - "source": [ - "# Construct the canonical connection ID\n", - "FULL_CONNECTION_ID = f\"{PROJECT}.{LOCATION}.bigframes-default-connection\"\n", - "\n", - "@bpd.udf(\n", - " input_types=[str],\n", - " output_type=str,\n", - " dataset=DATASET_ID,\n", - " name=\"extract_exif\",\n", - " bigquery_connection=FULL_CONNECTION_ID,\n", - " packages=[\"pillow\", \"requests\"],\n", - " max_batching_rows=8192,\n", - " container_cpu=0.33,\n", - " container_memory=\"512Mi\"\n", - ")\n", - "def extract_exif(src_obj_ref_rt: str) -> str:\n", - " import io\n", - " import json\n", - " from PIL import ExifTags, Image\n", - " import requests\n", - " src_obj_ref_rt_json = json.loads(src_obj_ref_rt)\n", - " src_url = src_obj_ref_rt_json[\"access_urls\"][\"read_url\"]\n", - " response = requests.get(src_url, timeout=30)\n", - " bts = response.content\n", - " image = Image.open(io.BytesIO(bts))\n", - " exif_data = image.getexif()\n", - " exif_dict = {}\n", - " if exif_data:\n", - " for tag, value in exif_data.items():\n", - " tag_name = ExifTags.TAGS.get(tag, tag)\n", - " exif_dict[tag_name] = value\n", - " return json.dumps(exif_dict)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "40bb6bc9", - "metadata": {}, - "outputs": [], - "source": [ - "import gcsfs\n", - "import bigframes.bigquery as bbq\n", - "\n", - "# Create a Multimodal DataFrame from the sample image URIs\n", - "fs = gcsfs.GCSFileSystem()\n", - "uris = fs.glob(\"gs://bigframes_blob_test/images_exif/*\")\n", - "\n", - "# Ensure URIs have gs:// prefix\n", - "uris = [u if u.startswith(\"gs://\") else f\"gs://{u}\" for u in uris]\n", - "\n", - "if not uris:\n", - " uris = [\"gs://bigframes_blob_test/images_exif/dummy.jpg\"]\n", - "\n", - "exif_image_df = bpd.read_gbq(f\"SELECT uri FROM UNNEST({uris[:5]}) as uri\")\n", - "exif_image_df['blob_col'] = bbq.obj.make_ref(exif_image_df['uri'], authorizer=FULL_CONNECTION_ID)\n", - "exif_image_df = exif_image_df[['blob_col']]\n", - "\n", - "# Generate a JSON string containing the runtime information (including signed read URLs)\n", - "# This allows the UDF to download the images from Google Cloud Storage\n", - "access_urls = get_runtime_json_str(exif_image_df[\"blob_col\"], mode=\"R\")\n", - "\n", - "# Apply the BigQuery Python UDF to the runtime JSON strings\n", - "# We cast to string to ensure the input matches the UDF's signature\n", - "exif_json = access_urls.astype(str).apply(extract_exif)\n", - "\n", - "# Parse the resulting JSON strings back into a structured JSON type for easier access\n", - "exif_data = bbq.parse_json(exif_json)\n", - "\n", - "exif_data" - ] - } - ], - "metadata": { - "colab": { - "provenance": [] - }, - "kernelspec": { - "display_name": "venv (3.13.0)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.13.0" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/notebooks/regression/bq_dataframes_ml_linear_regression.ipynb b/notebooks/regression/bq_dataframes_ml_linear_regression.ipynb new file mode 100644 index 00000000000..675416f6ea8 --- /dev/null +++ b/notebooks/regression/bq_dataframes_ml_linear_regression.ipynb @@ -0,0 +1,743 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "ur8xi4C7S06n" + }, + "outputs": [], + "source": [ + "# Copyright 2023 Google LLC\n", + "#\n", + "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", + "# you may not use this file except in compliance with the License.\n", + "# You may obtain a copy of the License at\n", + "#\n", + "# https://www.apache.org/licenses/LICENSE-2.0\n", + "#\n", + "# Unless required by applicable law or agreed to in writing, software\n", + "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", + "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", + "# See the License for the specific language governing permissions and\n", + "# limitations under the License." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "JAPoU8Sm5E6e" + }, + "source": [ + "## Train a linear regression model with BigQuery DataFrames ML\n", + "\n", + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + "
\n", + " \n", + " \"Colab Run in Colab\n", + " \n", + " \n", + " \n", + " \"GitHub\n", + " View on GitHub\n", + " \n", + " \n", + " \n", + " \"Vertex\n", + " Open in Vertex AI Workbench\n", + " \n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "24743cf4a1e1" + }, + "source": [ + "**_NOTE_**: This notebook has been tested in the following environment:\n", + "\n", + "* Python version = 3.10" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "tvgnzT1CKxrO" + }, + "source": [ + "## Overview\n", + "\n", + "Use this notebook to learn how to train a linear regression model by using BigQuery DataFrames ML. BigQuery DataFrames ML provides a provides a scikit-learn-like API for ML powered by the BigQuery engine.\n", + "\n", + "This example is adapted from the [BQML linear regression tutorial](https://cloud.google.com/bigquery-ml/docs/linear-regression-tutorial).\n", + "\n", + "Learn more about [BigQuery DataFrames](https://cloud.google.com/python/docs/reference/bigframes/latest)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "d975e698c9a4" + }, + "source": [ + "### Objective\n", + "\n", + "In this tutorial, you use BigQuery DataFrames to create a linear regression model that predicts the weight of an Adelie penguin based on the penguin's island of residence, culmen length and depth, flipper length, and sex.\n", + "\n", + "The steps include:\n", + "\n", + "- Creating a DataFrame from a BigQuery table.\n", + "- Cleaning and preparing data using pandas.\n", + "- Creating a linear regression model using `bigframes.ml`.\n", + "- Saving the ML model to BigQuery for future use." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "08d289fa873f" + }, + "source": [ + "### Dataset\n", + "\n", + "This tutorial uses the [```penguins``` table](https://console.cloud.google.com/bigquery?p=bigquery-public-data&d=ml_datasets&t=penguins) (a BigQuery Public Dataset) which includes data on a set of penguins including species, island of residence, weight, culmen length and depth, flipper length, and sex." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "aed92deeb4a0" + }, + "source": [ + "### Costs\n", + "\n", + "This tutorial uses billable components of Google Cloud:\n", + "\n", + "* BigQuery (compute)\n", + "* BigQuery ML\n", + "\n", + "Learn about [BigQuery compute pricing](https://cloud.google.com/bigquery/pricing#analysis_pricing_models)\n", + "and [BigQuery ML pricing](https://cloud.google.com/bigquery/pricing#bqml),\n", + "and use the [Pricing Calculator](https://cloud.google.com/products/calculator/)\n", + "to generate a cost estimate based on your projected usage." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "i7EUnXsZhAGF" + }, + "source": [ + "## Installation\n", + "\n", + "Install the following packages, which are required to run this notebook:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "9O0Ka4W2MNF3" + }, + "outputs": [], + "source": [ + "!pip install bigframes" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "58707a750154" + }, + "source": [ + "### Colab only\n", + "\n", + "Uncomment and run the following cell to restart the kernel:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "f200f10a1da3" + }, + "outputs": [], + "source": [ + "# Automatically restart kernel after installs so that your environment can access the new packages\n", + "# import IPython\n", + "\n", + "# app = IPython.Application.instance()\n", + "# app.kernel.do_shutdown(True)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "BF1j6f9HApxa" + }, + "source": [ + "## Before you begin\n", + "\n", + "Complete the tasks in this section to set up your environment." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "oDfTjfACBvJk" + }, + "source": [ + "### Set up your Google Cloud project\n", + "\n", + "**The following steps are required, regardless of your notebook environment.**\n", + "\n", + "1. [Select or create a Google Cloud project](https://console.cloud.google.com/cloud-resource-manager). When you first create an account, you get a $300 credit towards your compute/storage costs.\n", + "\n", + "2. [Make sure that billing is enabled for your project](https://cloud.google.com/billing/docs/how-to/modify-project).\n", + "\n", + "3. [Enable the BigQuery API](https://console.cloud.google.com/flows/enableapi?apiid=bigquery.googleapis.com).\n", + "\n", + "4. If you are running this notebook locally, install the [Cloud SDK](https://cloud.google.com/sdk)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "WReHDGG5g0XY" + }, + "source": [ + "#### Set your project ID\n", + "\n", + "If you don't know your project ID, try the following:\n", + "* Run `gcloud config list`.\n", + "* Run `gcloud projects list`.\n", + "* See the support page: [Locate the project ID](https://support.google.com/googleapi/answer/7014113)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "oM1iC_MfAts1" + }, + "outputs": [], + "source": [ + "PROJECT_ID = \"\" # @param {type:\"string\"}\n", + "\n", + "# Set the project id\n", + "! gcloud config set project {PROJECT_ID}" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "region" + }, + "source": [ + "#### Set the region\n", + "\n", + "You can also change the `REGION` variable used by BigQuery. Learn more about [BigQuery regions](https://cloud.google.com/bigquery/docs/locations#supported_locations)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "eF-Twtc4XGem" + }, + "outputs": [], + "source": [ + "REGION = \"US\" # @param {type: \"string\"}" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "sBCra4QMA2wR" + }, + "source": [ + "### Authenticate your Google Cloud account\n", + "\n", + "Depending on your Jupyter environment, you might have to manually authenticate. Follow the relevant instructions below." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "74ccc9e52986" + }, + "source": [ + "**Vertex AI Workbench**\n", + "\n", + "Do nothing, you are already authenticated." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "de775a3773ba" + }, + "source": [ + "**Local JupyterLab instance**\n", + "\n", + "Uncomment and run the following cell:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "254614fa0c46" + }, + "outputs": [], + "source": [ + "# ! gcloud auth login" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ef21552ccea8" + }, + "source": [ + "**Colab**\n", + "\n", + "Uncomment and run the following cell:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "603adbbf0532" + }, + "outputs": [], + "source": [ + "# from google.colab import auth\n", + "# auth.authenticate_user()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "960505627ddf" + }, + "source": [ + "### Import libraries" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "PyQmSRbKA8r-" + }, + "outputs": [], + "source": [ + "import bigframes.pandas as bf" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "init_aip:mbsdk,all" + }, + "source": [ + "### Set BigQuery DataFrames options" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "NPPMuw2PXGeo" + }, + "outputs": [], + "source": [ + "bf.options.bigquery.project = PROJECT_ID\n", + "bf.options.bigquery.location = REGION" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "D21CoOlfFTYI" + }, + "source": [ + "If you want to reset the location of the created DataFrame or Series objects, reset the session by executing `bf.close_session()`. After that, you can reuse `bf.options.bigquery.location` to specify another location." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "9EMAqR37AfLS" + }, + "source": [ + "## Read a BigQuery table into a BigQuery DataFrames DataFrame\n", + "\n", + "Read the [```penguins``` table](https://console.cloud.google.com/bigquery?p=bigquery-public-data&d=ml_datasets&t=penguins) into a BigQuery DataFrames DataFrame:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "EDAaIwHpQCDZ" + }, + "outputs": [], + "source": [ + "df = bf.read_gbq(\"bigquery-public-data.ml_datasets.penguins\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "DJu837YEXD7B" + }, + "source": [ + "Take a look at the DataFrame:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "_gPD0Zn1Stdb" + }, + "outputs": [], + "source": [ + "df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "rwPLjqW2Ajzh" + }, + "source": [ + "## Clean and prepare data\n", + "\n", + "You can use pandas as you normally would on the BigQuery DataFrames DataFrame, but calculations happen in the BigQuery query engine instead of your local environment.\n", + "\n", + "Because this model will focus on the Adelie Penguin species, you need to filter the data for only those rows representing Adelie penguins. Then you drop the `species` column because it is no longer needed.\n", + "\n", + "As these functions are applied, only the new DataFrame object `adelie_data` is modified. The source table and the original DataFrame object `df` don't change." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "6i6HkFJZa8na" + }, + "outputs": [], + "source": [ + "# Filter down to the data to the Adelie Penguin species\n", + "adelie_data = df[df.species == \"Adelie Penguin (Pygoscelis adeliae)\"]\n", + "\n", + "# Drop the species column\n", + "adelie_data = adelie_data.drop(columns=[\"species\"])\n", + "\n", + "# Take a look at the filtered DataFrame\n", + "adelie_data" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "jhK2OlyMbY4L" + }, + "source": [ + "Drop rows with `NULL` values in order to create a BigQuery DataFrames DataFrame for the training data:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "0am3hdlXZfxZ" + }, + "outputs": [], + "source": [ + "# Drop rows with nulls to get training data\n", + "training_data = adelie_data.dropna()\n", + "\n", + "# Take a peek at the training data\n", + "training_data" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "M_-0X7NxYK5f" + }, + "source": [ + "Specify your feature (or input) columns and the label (or output) column:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "YKwCW7Nsavap" + }, + "outputs": [], + "source": [ + "feature_columns = training_data[['island', 'culmen_length_mm', 'culmen_depth_mm', 'flipper_length_mm', 'sex']]\n", + "label_columns = training_data[['body_mass_g']]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "CjyM7vZJZ0sQ" + }, + "source": [ + "There is a row within the `adelie_data` BigQuery DataFrames DataFrame that has a `NULL` value for the `body mass` column. `body mass` is the label column, which is the value that the model you are creating is trying to predict.\n", + "\n", + "Create a new BigQuery DataFrames DataFrame, `test_data`, for this row so that you can use it as test data on which to make a prediction later:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "wej78IDUaRW9" + }, + "outputs": [], + "source": [ + "test_data = adelie_data[adelie_data.body_mass_g.isnull()]\n", + "\n", + "test_data" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Fx4lsNqMorJ-" + }, + "source": [ + "## Create the linear regression model\n", + "\n", + "BigQuery DataFrames ML lets you move from exploring data to creating machine learning models through its scikit-learn-like API, `bigframes.ml`. BigQuery DataFrames ML supports several types of [ML models](https://cloud.google.com/python/docs/reference/bigframes/latest#ml-capabilities).\n", + "\n", + "In this notebook, you create a linear regression model, a type of regression model that generates a continuous value from a linear combination of input features.\n", + "\n", + "When you create a model with BigQuery DataFrames ML, it is saved locally and limited to the BigQuery session. However, as you'll see in the next section, you can use `to_gbq` to save the model permanently to your BigQuery project." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "EloGtMnverFF" + }, + "source": [ + "### Create the model using `bigframes.ml`\n", + "\n", + "When you pass the feature columns without transforms, BigQuery ML uses\n", + "[automatic preprocessing](https://cloud.google.com/bigquery/docs/auto-preprocessing) to encode string values and scale numeric values.\n", + "\n", + "BigQuery ML also [automatically splits the data for training and evaluation](https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-create-glm#data_split_method), although for datasets with less than 500 rows (such as this one), all rows are used for training." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "GskyyUQPowBT" + }, + "outputs": [], + "source": [ + "from bigframes.ml.linear_model import LinearRegression\n", + "\n", + "model = LinearRegression()\n", + "\n", + "model.fit(feature_columns, label_columns)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "UGjeMPC2caKK" + }, + "source": [ + "### Score the model\n", + "\n", + "Check how the model performed by using the `score` method. More information on model scoring can be found [here](https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-evaluate#mlevaluate_output)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "kGBJKafpo0dl" + }, + "outputs": [], + "source": [ + "model.score(feature_columns, label_columns)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "P2lUiZZ_cjri" + }, + "source": [ + "### Predict using the model\n", + "\n", + "Use the model to predict the body mass of the data row you saved earlier to the `test_data` DataFrame:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "bsQ9cmoWo0Ps" + }, + "outputs": [], + "source": [ + "model.predict(test_data)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "GTRdUw-Ro5R1" + }, + "source": [ + "## Save the model in BigQuery\n", + "\n", + "The model is saved locally within this session. You can save the model permanently to BigQuery for use in future sessions, and to make the model sharable with others." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "K0mPaoGpcwwy" + }, + "source": [ + "Create a BigQuery dataset to house the model, adding a name for your dataset as the `DATASET_ID` variable:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "ZSP7gt13QrQt" + }, + "outputs": [], + "source": [ + "DATASET_ID = \"\" # @param {type:\"string\"}\n", + "\n", + "from google.cloud import bigquery\n", + "client = bigquery.Client(project=PROJECT_ID)\n", + "dataset = bigquery.Dataset(PROJECT_ID + \".\" + DATASET_ID)\n", + "dataset.location = REGION\n", + "dataset = client.create_dataset(dataset, exists_ok=True)\n", + "print(f\"Dataset {dataset.dataset_id} created.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "zqAIWWgJczp-" + }, + "source": [ + "Save the model using the `to_gbq` method:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "QE_GD4Byo_jb" + }, + "outputs": [], + "source": [ + "model.to_gbq(DATASET_ID + \".penguin_weight\" , replace=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "f7uHacAy49rT" + }, + "source": [ + "You can view the saved model in the BigQuery console under the dataset you created in the first step. Run the following cell and follow the link to view your BigQuery console:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "qDBoiA_0488Z" + }, + "outputs": [], + "source": [ + "print(f'https://console.developers.google.com/bigquery?p={PROJECT_ID}')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "G_wjSfXpWTuy" + }, + "source": [ + "# Summary and next steps\n", + "\n", + "You've created a linear regression model using `bigframes.ml`.\n", + "\n", + "Learn more about BigQuery DataFrames in the [documentation](https://cloud.google.com/python/docs/reference/bigframes/latest) and find more sample notebooks in the [GitHub repo](https://github.com/googleapis/python-bigquery-dataframes/tree/main/notebooks)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "TpV-iwP9qw9c" + }, + "source": [ + "## Cleaning up\n", + "\n", + "To clean up all Google Cloud resources used in this project, you can [delete the Google Cloud\n", + "project](https://cloud.google.com/resource-manager/docs/creating-managing-projects#shutting_down_projects) you used for the tutorial.\n", + "\n", + "Otherwise, you can uncomment the remaining cells and run them to delete the individual resources you created in this tutorial:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "sx_vKniMq9ZX" + }, + "outputs": [], + "source": [ + "# # Delete the BigQuery dataset and associated ML model\n", + "# from google.cloud import bigquery\n", + "# client = bigquery.Client(project=PROJECT_ID)\n", + "# client.delete_dataset(\n", + "# DATASET_ID, delete_contents=True, not_found_ok=True\n", + "# )\n", + "# print(\"Deleted dataset '{}'.\".format(DATASET_ID))" + ] + } + ], + "metadata": { + "colab": { + "provenance": [], + "toc_visible": true + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks/regression/easy_linear_regression.ipynb b/notebooks/regression/easy_linear_regression.ipynb new file mode 100644 index 00000000000..c441a966ecf --- /dev/null +++ b/notebooks/regression/easy_linear_regression.ipynb @@ -0,0 +1,1270 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Using ML - Easy linear regression\n", + "\n", + "This demo shows BigQuery DataFrames ML providing an SKLearn-like experience for\n", + "training a linear regression model.\n", + "\n", + "In this \"easy\" version of linear regression, we use a couple of BQML features to simplify our code:\n", + "\n", + "- We rely on automatic preprocessing to encode string values and scale numeric values\n", + "- We rely on automatic data split & evaluation to test the model\n", + "\n", + "This example is adapted from the [BQML linear regression tutorial](https://cloud.google.com/bigquery-ml/docs/linear-regression-tutorial)." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Init & load data" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "ffc6d6c7815a4a92903a08a11af6db11", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HTML(value='Query job d1e085ba-66d8-4631-bb51-50a17d0a6e51 is RUNNING. \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
predicted_body_mass_g
2923459.735118
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speciesislandculmen_length_mmculmen_depth_mmflipper_length_mmbody_mass_gsex
0Adelie Penguin (Pygoscelis adeliae)Dream36.618.4184.03475.0FEMALE
1Adelie Penguin (Pygoscelis adeliae)Dream39.819.1184.04650.0MALE
2Adelie Penguin (Pygoscelis adeliae)Dream40.918.9184.03900.0MALE
3Chinstrap penguin (Pygoscelis antarctica)Dream46.517.9192.03500.0FEMALE
4Adelie Penguin (Pygoscelis adeliae)Dream37.316.8192.03000.0FEMALE
5Adelie Penguin (Pygoscelis adeliae)Dream43.218.5192.04100.0MALE
6Chinstrap penguin (Pygoscelis antarctica)Dream46.916.6192.02700.0FEMALE
7Chinstrap penguin (Pygoscelis antarctica)Dream50.518.4200.03400.0FEMALE
8Chinstrap penguin (Pygoscelis antarctica)Dream49.519.0200.03800.0MALE
9Adelie Penguin (Pygoscelis adeliae)Dream40.220.1200.03975.0MALE
10Adelie Penguin (Pygoscelis adeliae)Dream40.818.9208.04300.0MALE
11Adelie Penguin (Pygoscelis adeliae)Dream39.018.7185.03650.0MALE
12Adelie Penguin (Pygoscelis adeliae)Dream37.016.9185.03000.0FEMALE
13Chinstrap penguin (Pygoscelis antarctica)Dream47.017.3185.03700.0FEMALE
14Adelie Penguin (Pygoscelis adeliae)Dream34.017.1185.03400.0FEMALE
15Adelie Penguin (Pygoscelis adeliae)Dream37.016.5185.03400.0FEMALE
16Chinstrap penguin (Pygoscelis antarctica)Dream45.717.3193.03600.0FEMALE
17Chinstrap penguin (Pygoscelis antarctica)Dream50.619.4193.03800.0MALE
18Adelie Penguin (Pygoscelis adeliae)Dream39.717.9193.04250.0MALE
19Adelie Penguin (Pygoscelis adeliae)Dream37.818.1193.03750.0MALE
20Chinstrap penguin (Pygoscelis antarctica)Dream46.617.8193.03800.0FEMALE
21Chinstrap penguin (Pygoscelis antarctica)Dream51.319.2193.03650.0MALE
22Adelie Penguin (Pygoscelis adeliae)Dream40.217.1193.03400.0FEMALE
23Adelie Penguin (Pygoscelis adeliae)Dream36.818.5193.03500.0FEMALE
24Chinstrap penguin (Pygoscelis antarctica)Dream49.618.2193.03775.0MALE
\n", + "

25 rows × 7 columns

\n", + "[344 rows x 7 columns in total]" + ], + "text/plain": [ + " species island culmen_length_mm \\\n", + "0 Adelie Penguin (Pygoscelis adeliae) Dream 36.6 \n", + "1 Adelie Penguin (Pygoscelis adeliae) Dream 39.8 \n", + "2 Adelie Penguin (Pygoscelis adeliae) Dream 40.9 \n", + "3 Chinstrap penguin (Pygoscelis antarctica) Dream 46.5 \n", + "4 Adelie Penguin (Pygoscelis adeliae) Dream 37.3 \n", + "5 Adelie Penguin (Pygoscelis adeliae) Dream 43.2 \n", + "6 Chinstrap penguin (Pygoscelis antarctica) Dream 46.9 \n", + "7 Chinstrap penguin (Pygoscelis antarctica) Dream 50.5 \n", + "8 Chinstrap penguin (Pygoscelis antarctica) Dream 49.5 \n", + "9 Adelie Penguin (Pygoscelis adeliae) Dream 40.2 \n", + "10 Adelie Penguin (Pygoscelis adeliae) Dream 40.8 \n", + "11 Adelie Penguin (Pygoscelis adeliae) Dream 39.0 \n", + "12 Adelie Penguin (Pygoscelis adeliae) Dream 37.0 \n", + "13 Chinstrap penguin (Pygoscelis antarctica) Dream 47.0 \n", + "14 Adelie Penguin (Pygoscelis adeliae) Dream 34.0 \n", + "15 Adelie Penguin (Pygoscelis adeliae) Dream 37.0 \n", + "16 Chinstrap penguin (Pygoscelis antarctica) Dream 45.7 \n", + "17 Chinstrap penguin (Pygoscelis antarctica) Dream 50.6 \n", + "18 Adelie Penguin (Pygoscelis adeliae) Dream 39.7 \n", + "19 Adelie Penguin (Pygoscelis adeliae) Dream 37.8 \n", + "20 Chinstrap penguin (Pygoscelis antarctica) Dream 46.6 \n", + "21 Chinstrap penguin (Pygoscelis antarctica) Dream 51.3 \n", + "22 Adelie Penguin (Pygoscelis adeliae) Dream 40.2 \n", + "23 Adelie Penguin (Pygoscelis adeliae) Dream 36.8 \n", + "24 Chinstrap penguin (Pygoscelis antarctica) Dream 49.6 \n", + "\n", + " culmen_depth_mm flipper_length_mm body_mass_g sex \n", + "0 18.4 184.0 3475.0 FEMALE \n", + "1 19.1 184.0 4650.0 MALE \n", + "2 18.9 184.0 3900.0 MALE \n", + "3 17.9 192.0 3500.0 FEMALE \n", + "4 16.8 192.0 3000.0 FEMALE \n", + "5 18.5 192.0 4100.0 MALE \n", + "6 16.6 192.0 2700.0 FEMALE \n", + "7 18.4 200.0 3400.0 FEMALE \n", + "8 19.0 200.0 3800.0 MALE \n", + "9 20.1 200.0 3975.0 MALE \n", + "10 18.9 208.0 4300.0 MALE \n", + "11 18.7 185.0 3650.0 MALE \n", + "12 16.9 185.0 3000.0 FEMALE \n", + "13 17.3 185.0 3700.0 FEMALE \n", + "14 17.1 185.0 3400.0 FEMALE \n", + "15 16.5 185.0 3400.0 FEMALE \n", + "16 17.3 193.0 3600.0 FEMALE \n", + "17 19.4 193.0 3800.0 MALE \n", + "18 17.9 193.0 4250.0 MALE \n", + "19 18.1 193.0 3750.0 MALE \n", + "20 17.8 193.0 3800.0 FEMALE \n", + "21 19.2 193.0 3650.0 MALE \n", + "22 17.1 193.0 3400.0 FEMALE \n", + "23 18.5 193.0 3500.0 FEMALE \n", + "24 18.2 193.0 3775.0 MALE \n", + "...\n", + "\n", + "[344 rows x 7 columns]" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Initialize BigQuery DataFrame\n", + "import bigframes.pandas\n", + "\n", + "# read a BigQuery table to a BigQuery DataFrame\n", + "df = bigframes.pandas.read_gbq(\"bigframes-dev.bqml_tutorial.penguins\")\n", + "\n", + "# take a peek at the dataframe\n", + "df" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Data cleaning / prep" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "a9ad907fa6e64a61a9dce420bc7d2beb", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HTML(value='Query job 3537a10a-641a-4d40-ae47-449c641b1bc5 is DONE. 28.9 kB processed. \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
islandculmen_length_mmculmen_depth_mmflipper_length_mmbody_mass_gsex
0Dream36.618.4184.03475.0FEMALE
1Dream39.819.1184.04650.0MALE
2Dream40.918.9184.03900.0MALE
4Dream37.316.8192.03000.0FEMALE
5Dream43.218.5192.04100.0MALE
9Dream40.220.1200.03975.0MALE
10Dream40.818.9208.04300.0MALE
11Dream39.018.7185.03650.0MALE
12Dream37.016.9185.03000.0FEMALE
14Dream34.017.1185.03400.0FEMALE
15Dream37.016.5185.03400.0FEMALE
18Dream39.717.9193.04250.0MALE
19Dream37.818.1193.03750.0MALE
22Dream40.217.1193.03400.0FEMALE
23Dream36.818.5193.03500.0FEMALE
26Dream41.518.5201.04000.0MALE
31Dream33.116.1178.02900.0FEMALE
32Dream37.218.1178.03900.0MALE
33Dream39.516.7178.03250.0FEMALE
35Dream36.018.5186.03100.0FEMALE
36Dream39.618.1186.04450.0MALE
38Dream41.320.3194.03550.0MALE
41Dream35.718.0202.03550.0FEMALE
51Dream38.117.6187.03425.0FEMALE
53Dream36.017.1187.03700.0FEMALE
\n", + "

25 rows × 6 columns

\n", + "[146 rows x 6 columns in total]" + ], + "text/plain": [ + " island culmen_length_mm culmen_depth_mm flipper_length_mm body_mass_g \\\n", + "0 Dream 36.6 18.4 184.0 3475.0 \n", + "1 Dream 39.8 19.1 184.0 4650.0 \n", + "2 Dream 40.9 18.9 184.0 3900.0 \n", + "4 Dream 37.3 16.8 192.0 3000.0 \n", + "5 Dream 43.2 18.5 192.0 4100.0 \n", + "9 Dream 40.2 20.1 200.0 3975.0 \n", + "10 Dream 40.8 18.9 208.0 4300.0 \n", + "11 Dream 39.0 18.7 185.0 3650.0 \n", + "12 Dream 37.0 16.9 185.0 3000.0 \n", + "14 Dream 34.0 17.1 185.0 3400.0 \n", + "15 Dream 37.0 16.5 185.0 3400.0 \n", + "18 Dream 39.7 17.9 193.0 4250.0 \n", + "19 Dream 37.8 18.1 193.0 3750.0 \n", + "22 Dream 40.2 17.1 193.0 3400.0 \n", + "23 Dream 36.8 18.5 193.0 3500.0 \n", + "26 Dream 41.5 18.5 201.0 4000.0 \n", + "31 Dream 33.1 16.1 178.0 2900.0 \n", + "32 Dream 37.2 18.1 178.0 3900.0 \n", + "33 Dream 39.5 16.7 178.0 3250.0 \n", + "35 Dream 36.0 18.5 186.0 3100.0 \n", + "36 Dream 39.6 18.1 186.0 4450.0 \n", + "38 Dream 41.3 20.3 194.0 3550.0 \n", + "41 Dream 35.7 18.0 202.0 3550.0 \n", + "51 Dream 38.1 17.6 187.0 3425.0 \n", + "53 Dream 36.0 17.1 187.0 3700.0 \n", + "\n", + " sex \n", + "0 FEMALE \n", + "1 MALE \n", + "2 MALE \n", + "4 FEMALE \n", + "5 MALE \n", + "9 MALE \n", + "10 MALE \n", + "11 MALE \n", + "12 FEMALE \n", + "14 FEMALE \n", + "15 FEMALE \n", + "18 MALE \n", + "19 MALE \n", + "22 FEMALE \n", + "23 FEMALE \n", + "26 MALE \n", + "31 FEMALE \n", + "32 MALE \n", + "33 FEMALE \n", + "35 FEMALE \n", + "36 MALE \n", + "38 MALE \n", + "41 FEMALE \n", + "51 FEMALE \n", + "53 FEMALE \n", + "...\n", + "\n", + "[146 rows x 6 columns]" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# filter down to the data we want to analyze\n", + "adelie_data = df[df.species == \"Adelie Penguin (Pygoscelis adeliae)\"]\n", + "\n", + "# drop the columns we don't care about\n", + "adelie_data = adelie_data.drop(columns=[\"species\"])\n", + "\n", + "# drop rows with nulls to get our training data\n", + "training_data = adelie_data.dropna()\n", + "\n", + "# take a peek at the training data\n", + "training_data" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Use `model_selection.train_test_split` to prepare training data" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "81f9aa34c7234bd88b6b7a4bc77d4b4e", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HTML(value='Query job 288f0daa-a51e-45b4-86bf-d054467c4a99 is DONE. 28.9 kB processed. int:\n", + "def nth_prime(n):\n", " prime_numbers = [2,3]\n", " i=3\n", " if(0Open Job" - ], + "application/vnd.jupyter.widget-view+json": { + "model_id": "0052d103678f47ffb3777ec3ac4e30f7", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HTML(value='Query job ecaf079e-76ef-47bb-828d-a27e9552b597 is RUNNING. " + "HTML(value='Query job 4c1d9d3e-be25-4818-b74d-6214164d99ab is DONE. 0 Bytes processed. \n", " \n", " 0\n", - " 11231597\n", - " In your update, why are some of the system fun...\n", + " 11012908\n", + " you're welcome! according to the docs it shoul...\n", " 0\n", " \n", " \n", " 1\n", - " 49684807\n", - " what you have tried so far . ??\n", - " 1\n", + " 11013760\n", + " You *should* be concerned with the disk being ...\n", + " 0\n", " \n", " \n", " 2\n", - " 7623925\n", - " @Michael: It should work. Perhaps you looked i...\n", + " 11013784\n", + " have you looked at `Integrate` or `NIntegrate`?\n", " 0\n", " \n", " \n", " 3\n", - " 34046685\n", - " Will it work with SQL compact? Please excuse m...\n", + " 11015512\n", + " sorry, is a typo. The variable name is dist. (...\n", " 0\n", " \n", " \n", " 4\n", - " 6426146\n", - " do you know the equation to your pdf?\n", + " 11016238\n", + " Pfff, I'm having trouble with that formula too...\n", " 0\n", " \n", " \n", " 5\n", - " 60686114\n", - " m sorry but at least you have to think about it.\n", + " 11016276\n", + " Thanks thinksteep! Does this mean that by usin...\n", " 0\n", " \n", " \n", " 6\n", - " 16631986\n", - " i think also making disable this by only jquer...\n", + " 11016551\n", + " Jason, thanks for the reply. I've been workin...\n", " 0\n", " \n", " \n", " 7\n", - " 16498565\n", - " I am including these files on my header of the...\n", + " 11017973\n", + " I assume an `off` of 0.5 would put be exactly ...\n", " 0\n", " \n", " \n", " 8\n", - " 26601001\n", - " wrong answer, you didn't understand the logic\n", + " 11018225\n", + " Thank you very much. I do worry too much abou...\n", " 0\n", " \n", " \n", " 9\n", - " 73255842\n", - " Call the setOnClickListener before return row.\n", + " 11018370\n", + " @IanClelland, I edited my question a bit. The ...\n", " 0\n", " \n", " \n", @@ -463,21 +505,21 @@ ], "text/plain": [ " id text score\n", - "0 11231597 In your update, why are some of the system fun... 0\n", - "1 49684807 what you have tried so far . ?? 1\n", - "2 7623925 @Michael: It should work. Perhaps you looked i... 0\n", - "3 34046685 Will it work with SQL compact? Please excuse m... 0\n", - "4 6426146 do you know the equation to your pdf? 0\n", - "5 60686114 m sorry but at least you have to think about it. 0\n", - "6 16631986 i think also making disable this by only jquer... 0\n", - "7 16498565 I am including these files on my header of the... 0\n", - "8 26601001 wrong answer, you didn't understand the logic 0\n", - "9 73255842 Call the setOnClickListener before return row. 0\n", + "0 11012908 you're welcome! according to the docs it shoul... 0\n", + "1 11013760 You *should* be concerned with the disk being ... 0\n", + "2 11013784 have you looked at `Integrate` or `NIntegrate`? 0\n", + "3 11015512 sorry, is a typo. The variable name is dist. (... 0\n", + "4 11016238 Pfff, I'm having trouble with that formula too... 0\n", + "5 11016276 Thanks thinksteep! Does this mean that by usin... 0\n", + "6 11016551 Jason, thanks for the reply. I've been workin... 0\n", + "7 11017973 I assume an `off` of 0.5 would put be exactly ... 0\n", + "8 11018225 Thank you very much. I do worry too much abou... 0\n", + "9 11018370 @IanClelland, I edited my question a bit. The ... 0\n", "\n", "[10 rows x 3 columns]" ] }, - "execution_count": 23, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -497,7 +539,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 7, "id": "55ed241e", "metadata": {}, "outputs": [ @@ -507,9 +549,8 @@ "text": [ "Help on function remote_function in module bigframes.pandas:\n", "\n", - "remote_function(input_types: 'List[type]', output_type: 'type', dataset: 'Optional[str]' = None, bigquery_connection: 'Optional[str]' = None, reuse: 'bool' = True, name: 'Optional[str]' = None, packages: 'Optional[Sequence[str]]' = None)\n", - " Decorator to turn a user defined function into a BigQuery remote function. Check out\n", - " the code samples at: https://cloud.google.com/bigquery/docs/remote-functions#bigquery-dataframes.\n", + "remote_function(input_types: 'List[type]', output_type: 'type', dataset: 'Optional[str]' = None, bigquery_connection: 'Optional[str]' = None, reuse: 'bool' = True)\n", + " Decorator to turn a user defined function into a BigQuery remote function.\n", " \n", " .. note::\n", " Please make sure following is setup before using this API:\n", @@ -535,7 +576,7 @@ " * BigQuery Data Editor (roles/bigquery.dataEditor)\n", " * BigQuery Connection Admin (roles/bigquery.connectionAdmin)\n", " * Cloud Functions Developer (roles/cloudfunctions.developer)\n", - " * Service Account User (roles/iam.serviceAccountUser) on the service account `PROJECT_NUMBER-compute@developer.gserviceaccount.com`\n", + " * Service Account User (roles/iam.serviceAccountUser)\n", " * Storage Object Viewer (roles/storage.objectViewer)\n", " * Project IAM Admin (roles/resourcemanager.projectIamAdmin) (Only required if the bigquery connection being used is not pre-created and is created dynamically with user credentials.)\n", " \n", @@ -561,25 +602,15 @@ " Name of the BigQuery connection. You should either have the\n", " connection already created in the `location` you have chosen, or\n", " you should have the Project IAM Admin role to enable the service\n", - " to create the connection for you if you need it. If this parameter is\n", + " to create the connection for you if you need it.If this parameter is\n", " not provided then the BigQuery connection from the session is used.\n", " reuse (bool, Optional):\n", " Reuse the remote function if already exists.\n", " `True` by default, which will result in reusing an existing remote\n", - " function and corresponding cloud function (if any) that was\n", - " previously created for the same udf.\n", - " Setting it to `False` would force creating a unique remote function.\n", + " function (if any) that was previously created for the same udf.\n", + " Setting it to false would force creating a unique remote function.\n", " If the required remote function does not exist then it would be\n", " created irrespective of this param.\n", - " name (str, Optional):\n", - " Explicit name of the persisted BigQuery remote function. Use it with\n", - " caution, because two users working in the same project and dataset\n", - " could overwrite each other's remote functions if they use the same\n", - " persistent name.\n", - " packages (str[], Optional):\n", - " Explicit name of the external package dependencies. Each dependency\n", - " is added to the `requirements.txt` as is, and can be of the form\n", - " supported in https://pip.pypa.io/en/stable/reference/requirements-file-format/.\n", " Returns:\n", " callable: A remote function object pointing to the cloud assets created\n", " in the background to support the remote execution. The cloud assets can be\n", @@ -600,16 +631,49 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 8, + "id": "c9a8d03d", + "metadata": {}, + "outputs": [], + "source": [ + "# BigQuery DataFrames user is a data scientist and may not have privileges to\n", + "# create a BQ connector and set it up for invoking a cloud function. They\n", + "# should get such a connector created from their cloud admin and use it with\n", + "# BigQuery DataFrames remote functions. If the provided connection name does not\n", + "# exist, BigQuery DataFrames will try to create it on the fly assuming the user\n", + "# has sufficient privileges.\n", + "bq_connection_name = 'bigframes-rf-conn'" + ] + }, + { + "cell_type": "code", + "execution_count": 9, "id": "fbc27f81", "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[INFO][2023-08-18 21:23:29,687][bigframes.remote_function] Creating new cloud function: gcloud functions deploy bigframes-b0feb1fbaf8188b64d7e70118d93c5d4 --gen2 --runtime=python310 --project=bigframes-dev --region=us-central1 --source=/tmp/tmpl2ewfnue --entry-point=udf_http --trigger-http --no-allow-unauthenticated\n", + "[INFO][2023-08-18 21:24:43,689][bigframes.remote_function] Successfully created cloud function bigframes-b0feb1fbaf8188b64d7e70118d93c5d4 with uri (https://bigframes-b0feb1fbaf8188b64d7e70118d93c5d4-7krlje3eoq-uc.a.run.app)\n", + "[INFO][2023-08-18 21:24:57,348][bigframes.remote_function] Connector bigframes-rf-conn already exists\n", + "[INFO][2023-08-18 21:24:57,351][bigframes.remote_function] Creating BQ remote function: \n", + " CREATE OR REPLACE FUNCTION `bigframes-dev.bigframes_temp_us`.bigframes_b0feb1fbaf8188b64d7e70118d93c5d4(n INT64)\n", + " RETURNS INT64\n", + " REMOTE WITH CONNECTION `bigframes-dev.us.bigframes-rf-conn`\n", + " OPTIONS (\n", + " endpoint = \"https://bigframes-b0feb1fbaf8188b64d7e70118d93c5d4-7krlje3eoq-uc.a.run.app\"\n", + " )\n", + "[INFO][2023-08-18 21:24:58,300][bigframes.remote_function] Created remote function bigframes-dev.bigframes_temp_us.bigframes_b0feb1fbaf8188b64d7e70118d93c5d4\n" + ] + }, { "name": "stdout", "output_type": "stream", "text": [ "\n", - "Wall time: 76.2628 s\n" + "Wall time: 89.0601 s\n" ] } ], @@ -620,8 +684,8 @@ "\n", "# User defined function\n", "# https://www.codespeedy.com/find-nth-prime-number-in-python/\n", - "@pd.remote_function(reuse=False, cloud_function_service_account=\"default\")\n", - "def nth_prime(n: int) -> int:\n", + "@pd.remote_function([int], int, bigquery_connection=bq_connection_name)\n", + "def nth_prime(n):\n", " prime_numbers = [2,3]\n", " i=3\n", " if(0Open Job" - ], + "application/vnd.jupyter.widget-view+json": { + "model_id": "2f840ad27c514ed19c759a004b32de33", + "version_major": 2, + "version_minor": 0 + }, "text/plain": [ - "" + "HTML(value='Query job 0f421233-9d02-4746-bb39-86a3b0880aba is RUNNING. Open Job" - ], + "application/vnd.jupyter.widget-view+json": { + "model_id": "adffcff769be46b1bc6e50f9622cdd30", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HTML(value='Query job 4f8d5734-8070-4630-8a59-c05a31d60476 is RUNNING. " + "HTML(value='Query job ec057f9e-726b-44f0-a5c0-24c05c7ecfeb is RUNNING. \n", " \n", " 0\n", - " 11231597\n", - " In your update, why are some of the system fun...\n", + " 11012908\n", + " you're welcome! according to the docs it shoul...\n", " 0\n", " -1\n", " \n", " \n", " 1\n", - " 49684807\n", - " what you have tried so far . ??\n", - " 1\n", - " 2\n", + " 11013760\n", + " You *should* be concerned with the disk being ...\n", + " 0\n", + " -1\n", " \n", " \n", " 2\n", - " 7623925\n", - " @Michael: It should work. Perhaps you looked i...\n", + " 11013784\n", + " have you looked at `Integrate` or `NIntegrate`?\n", " 0\n", " -1\n", " \n", " \n", " 3\n", - " 34046685\n", - " Will it work with SQL compact? Please excuse m...\n", + " 11015512\n", + " sorry, is a typo. The variable name is dist. (...\n", " 0\n", " -1\n", " \n", " \n", " 4\n", - " 6426146\n", - " do you know the equation to your pdf?\n", + " 11016238\n", + " Pfff, I'm having trouble with that formula too...\n", " 0\n", " -1\n", " \n", " \n", " 5\n", - " 60686114\n", - " m sorry but at least you have to think about it.\n", + " 11016276\n", + " Thanks thinksteep! Does this mean that by usin...\n", " 0\n", " -1\n", " \n", " \n", " 6\n", - " 16631986\n", - " i think also making disable this by only jquer...\n", + " 11016551\n", + " Jason, thanks for the reply. I've been workin...\n", " 0\n", " -1\n", " \n", " \n", " 7\n", - " 16498565\n", - " I am including these files on my header of the...\n", + " 11017973\n", + " I assume an `off` of 0.5 would put be exactly ...\n", " 0\n", " -1\n", " \n", " \n", " 8\n", - " 26601001\n", - " wrong answer, you didn't understand the logic\n", + " 11018225\n", + " Thank you very much. I do worry too much abou...\n", " 0\n", " -1\n", " \n", " \n", " 9\n", - " 73255842\n", - " Call the setOnClickListener before return row.\n", + " 11018370\n", + " @IanClelland, I edited my question a bit. The ...\n", " 0\n", " -1\n", " \n", @@ -789,21 +871,21 @@ ], "text/plain": [ " id text score n_prime\n", - "0 11231597 In your update, why are some of the system fun... 0 -1\n", - "1 49684807 what you have tried so far . ?? 1 2\n", - "2 7623925 @Michael: It should work. Perhaps you looked i... 0 -1\n", - "3 34046685 Will it work with SQL compact? Please excuse m... 0 -1\n", - "4 6426146 do you know the equation to your pdf? 0 -1\n", - "5 60686114 m sorry but at least you have to think about it. 0 -1\n", - "6 16631986 i think also making disable this by only jquer... 0 -1\n", - "7 16498565 I am including these files on my header of the... 0 -1\n", - "8 26601001 wrong answer, you didn't understand the logic 0 -1\n", - "9 73255842 Call the setOnClickListener before return row. 0 -1\n", + "0 11012908 you're welcome! according to the docs it shoul... 0 -1\n", + "1 11013760 You *should* be concerned with the disk being ... 0 -1\n", + "2 11013784 have you looked at `Integrate` or `NIntegrate`? 0 -1\n", + "3 11015512 sorry, is a typo. The variable name is dist. (... 0 -1\n", + "4 11016238 Pfff, I'm having trouble with that formula too... 0 -1\n", + "5 11016276 Thanks thinksteep! Does this mean that by usin... 0 -1\n", + "6 11016551 Jason, thanks for the reply. I've been workin... 0 -1\n", + "7 11017973 I assume an `off` of 0.5 would put be exactly ... 0 -1\n", + "8 11018225 Thank you very much. I do worry too much abou... 0 -1\n", + "9 11018370 @IanClelland, I edited my question a bit. The ... 0 -1\n", "\n", "[10 rows x 4 columns]" ] }, - "execution_count": 26, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -818,7 +900,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 11, "id": "2701cb81", "metadata": {}, "outputs": [ @@ -826,8 +908,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "shobs-test.bigframes_temp_us.bigframes_343b7b4bb93ca8747dae20c22bdaec8b_p27heyce\n", - "projects/shobs-test/locations/us-central1/functions/bigframes-343b7b4bb93ca8747dae20c22bdaec8b-p27heyce\n" + "bigframes-dev.bigframes_temp_us.bigframes_b0feb1fbaf8188b64d7e70118d93c5d4\n", + "projects/bigframes-dev/locations/us-central1/functions/bigframes-b0feb1fbaf8188b64d7e70118d93c5d4\n" ] } ], @@ -840,7 +922,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 12, "id": "920fa18e", "metadata": {}, "outputs": [ @@ -855,42 +937,6 @@ " \n", " Then it can be applied to a DataFrame or Series.\n", " \n", - " .. note::\n", - " The return type of the function must be explicitly specified in the\n", - " function's original definition even if not otherwise required.\n", - " \n", - " BigQuery Utils provides many public functions under the ``bqutil`` project on Google Cloud Platform project\n", - " (See: https://github.com/GoogleCloudPlatform/bigquery-utils/tree/master/udfs#using-the-udfs).\n", - " You can checkout Community UDFs to use community-contributed functions.\n", - " (See: https://github.com/GoogleCloudPlatform/bigquery-utils/tree/master/udfs/community#community-udfs).\n", - " \n", - " **Examples:**\n", - " \n", - " Use the ``cw_lower_case_ascii_only`` function from Community UDFs.\n", - " (https://github.com/GoogleCloudPlatform/bigquery-utils/blob/master/udfs/community/cw_lower_case_ascii_only.sqlx)\n", - " \n", - " >>> import bigframes.pandas as bpd\n", - " >>> bpd.options.display.progress_bar = None\n", - " \n", - " >>> df = bpd.DataFrame({'id': [1, 2, 3], 'name': ['AURÉLIE', 'CÉLESTINE', 'DAPHNÉ']})\n", - " >>> df\n", - " id name\n", - " 0 1 AURÉLIE\n", - " 1 2 CÉLESTINE\n", - " 2 3 DAPHNÉ\n", - " \n", - " [3 rows x 2 columns]\n", - " \n", - " >>> func = bpd.read_gbq_function(\"bqutil.fn.cw_lower_case_ascii_only\")\n", - " >>> df1 = df.assign(new_name=df['name'].apply(func))\n", - " >>> df1\n", - " id name new_name\n", - " 0 1 AURÉLIE aurÉlie\n", - " 1 2 CÉLESTINE cÉlestine\n", - " 2 3 DAPHNÉ daphnÉ\n", - " \n", - " [3 rows x 3 columns]\n", - " \n", " Args:\n", " function_name (str):\n", " the function's name in BigQuery in the format\n", @@ -911,7 +957,7 @@ } ], "source": [ - "# Let's try to simulate a scenario in which user shares this remote function to\n", + "# Let's try to simulate a scenario in which user shares this remote funciton to\n", "# their colleague who simply wants to reuse it. BigFrames provides an API to do\n", "# so via `read_gbq_function`. Usage details are available via `help` command.\n", "help(pd.read_gbq_function)" @@ -919,7 +965,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 14, "id": "a6c9da0a", "metadata": {}, "outputs": [], @@ -932,7 +978,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 15, "id": "d7e7de7f", "metadata": {}, "outputs": [ @@ -940,17 +986,19 @@ "name": "stdout", "output_type": "stream", "text": [ - "CPU times: user 70.8 ms, sys: 3.49 ms, total: 74.3 ms\n", - "Wall time: 75.2 ms\n" + "CPU times: user 10.9 ms, sys: 0 ns, total: 10.9 ms\n", + "Wall time: 11.4 ms\n" ] }, { "data": { - "text/html": [ - "Query job f9a0e979-aeac-4ddd-a4c9-6720a5e91009 is DONE. 17.2 GB processed. Open Job" - ], + "application/vnd.jupyter.widget-view+json": { + "model_id": "73d1a73593cb4115821ab128c221a48d", + "version_major": 2, + "version_minor": 0 + }, "text/plain": [ - "" + "HTML(value='Query job bec5f7d1-3df1-4292-8c68-c396bce7dc5d is RUNNING. Open Job" - ], + "application/vnd.jupyter.widget-view+json": { + "model_id": "827770710c1549cf819fe47a5a1cd70f", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HTML(value='Query job 02e3bf43-a387-41c7-85c7-4a5366251de7 is RUNNING. " + "HTML(value='Query job fa4329e8-2918-44c4-96c5-d8591364abc9 is RUNNING. \n", " \n", " 0\n", - " 11231597\n", - " In your update, why are some of the system fun...\n", + " 11012908\n", + " you're welcome! according to the docs it shoul...\n", " 0\n", " -1\n", " -1\n", " \n", " \n", " 1\n", - " 49684807\n", - " what you have tried so far . ??\n", - " 1\n", - " 2\n", - " 2\n", + " 11013760\n", + " You *should* be concerned with the disk being ...\n", + " 0\n", + " -1\n", + " -1\n", " \n", " \n", " 2\n", - " 7623925\n", - " @Michael: It should work. Perhaps you looked i...\n", + " 11013784\n", + " have you looked at `Integrate` or `NIntegrate`?\n", " 0\n", " -1\n", " -1\n", " \n", " \n", " 3\n", - " 34046685\n", - " Will it work with SQL compact? Please excuse m...\n", + " 11015512\n", + " sorry, is a typo. The variable name is dist. (...\n", " 0\n", " -1\n", " -1\n", " \n", " \n", " 4\n", - " 6426146\n", - " do you know the equation to your pdf?\n", + " 11016238\n", + " Pfff, I'm having trouble with that formula too...\n", " 0\n", " -1\n", " -1\n", " \n", " \n", " 5\n", - " 60686114\n", - " m sorry but at least you have to think about it.\n", + " 11016276\n", + " Thanks thinksteep! Does this mean that by usin...\n", " 0\n", " -1\n", " -1\n", " \n", " \n", " 6\n", - " 16631986\n", - " i think also making disable this by only jquer...\n", + " 11016551\n", + " Jason, thanks for the reply. I've been workin...\n", " 0\n", " -1\n", " -1\n", " \n", " \n", " 7\n", - " 16498565\n", - " I am including these files on my header of the...\n", + " 11017973\n", + " I assume an `off` of 0.5 would put be exactly ...\n", " 0\n", " -1\n", " -1\n", " \n", " \n", " 8\n", - " 26601001\n", - " wrong answer, you didn't understand the logic\n", + " 11018225\n", + " Thank you very much. I do worry too much abou...\n", " 0\n", " -1\n", " -1\n", " \n", " \n", " 9\n", - " 73255842\n", - " Call the setOnClickListener before return row.\n", + " 11018370\n", + " @IanClelland, I edited my question a bit. The ...\n", " 0\n", " -1\n", " -1\n", @@ -1084,20 +1148,20 @@ ], "text/plain": [ " id text score \\\n", - "0 11231597 In your update, why are some of the system fun... 0 \n", - "1 49684807 what you have tried so far . ?? 1 \n", - "2 7623925 @Michael: It should work. Perhaps you looked i... 0 \n", - "3 34046685 Will it work with SQL compact? Please excuse m... 0 \n", - "4 6426146 do you know the equation to your pdf? 0 \n", - "5 60686114 m sorry but at least you have to think about it. 0 \n", - "6 16631986 i think also making disable this by only jquer... 0 \n", - "7 16498565 I am including these files on my header of the... 0 \n", - "8 26601001 wrong answer, you didn't understand the logic 0 \n", - "9 73255842 Call the setOnClickListener before return row. 0 \n", + "0 11012908 you're welcome! according to the docs it shoul... 0 \n", + "1 11013760 You *should* be concerned with the disk being ... 0 \n", + "2 11013784 have you looked at `Integrate` or `NIntegrate`? 0 \n", + "3 11015512 sorry, is a typo. The variable name is dist. (... 0 \n", + "4 11016238 Pfff, I'm having trouble with that formula too... 0 \n", + "5 11016276 Thanks thinksteep! Does this mean that by usin... 0 \n", + "6 11016551 Jason, thanks for the reply. I've been workin... 0 \n", + "7 11017973 I assume an `off` of 0.5 would put be exactly ... 0 \n", + "8 11018225 Thank you very much. I do worry too much abou... 0 \n", + "9 11018370 @IanClelland, I edited my question a bit. The ... 0 \n", "\n", " n_prime n_prime_again \n", "0 -1 -1 \n", - "1 2 2 \n", + "1 -1 -1 \n", "2 -1 -1 \n", "3 -1 -1 \n", "4 -1 -1 \n", @@ -1110,7 +1174,7 @@ "[10 rows x 5 columns]" ] }, - "execution_count": 30, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -1122,38 +1186,6 @@ "df = df.assign(n_prime_again=df['score'].apply(nth_prime_existing))\n", "df.head(10)" ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "bafab950", - "metadata": {}, - "outputs": [], - "source": [ - "# Clean up GCP assets created as part of bigframes remote_function\n", - "def cleanup_remote_function_assets(remote_udf, ignore_failures=False):\n", - " \"\"\"Clean up the GCP assets behind a bigframes remote function.\"\"\"\n", - "\n", - " session = pd.get_global_session()\n", - "\n", - " # Clean up BQ remote function\n", - " try:\n", - " session.bqclient.delete_routine(remote_udf.bigframes_remote_function)\n", - " except Exception:\n", - " # By default don't raise exception in cleanup\n", - " if not ignore_failures:\n", - " raise\n", - "\n", - " # Clean up cloud function\n", - " try:\n", - " session.cloudfunctionsclient.delete_function(name=remote_udf.bigframes_cloud_function)\n", - " except Exception:\n", - " # By default don't raise exception in cleanup\n", - " if not ignore_failures:\n", - " raise\n", - "\n", - "cleanup_remote_function_assets(nth_prime)" - ] } ], "metadata": { @@ -1172,7 +1204,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.4" + "version": "3.10.12" } }, "nbformat": 4, diff --git a/notebooks/remote_functions/remote_function_usecases.ipynb b/notebooks/remote_functions/remote_function_usecases.ipynb deleted file mode 100644 index e3a94160ad9..00000000000 --- a/notebooks/remote_functions/remote_function_usecases.ipynb +++ /dev/null @@ -1,1436 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "# Copyright 2023 Google LLC\n", - "#\n", - "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", - "# you may not use this file except in compliance with the License.\n", - "# You may obtain a copy of the License at\n", - "#\n", - "# https://www.apache.org/licenses/LICENSE-2.0\n", - "#\n", - "# Unless required by applicable law or agreed to in writing, software\n", - "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", - "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", - "# See the License for the specific language governing permissions and\n", - "# limitations under the License." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Set Up" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "id": "Y6QAttCqqMM0" - }, - "outputs": [], - "source": [ - "import bigframes.pandas as bpd" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 296 - }, - "id": "xraJ9RRzsvel", - "outputId": "6e3308cf-8de0-4b89-9128-4c6ddf3598c0" - }, - "outputs": [ - { - "data": { - "text/html": [ - "Query job 1f6094e9-1942-477c-9ce3-87a614d71294 is DONE. 0 Bytes processed. 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" - ], - "text/plain": [ - " homeTeamName awayTeamName duration_minutes\n", - "88 Royals Athletics 176\n", - "106 Dodgers Giants 216\n", - "166 Phillies Royals 162\n", - "247 Rangers Royals 161\n", - "374 Athletics Astros 161" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df = bpd.read_gbq(\"bigquery-public-data.baseball.schedules\")[[\"homeTeamName\", \"awayTeamName\", \"duration_minutes\"]]\n", - "df.peek()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Notes\n", - "\n", - "* The API reference documentation for the `remote_function` can be found at\n", - " https://cloud.google.com/python/docs/reference/bigframes/latest/bigframes.session.Session#bigframes_session_Session_remote_function\n", - "\n", - "* More code samples for `remote_function` can be found in the BigQuery\n", - " DataFrames API reference documentation, e.g.\n", - " * https://cloud.google.com/python/docs/reference/bigframes/latest/bigframes.series.Series#bigframes_series_Series_apply\n", - " * https://cloud.google.com/python/docs/reference/bigframes/latest/bigframes.dataframe.DataFrame#bigframes_dataframe_DataFrame_map\n", - " * https://cloud.google.com/python/docs/reference/bigframes/latest/bigframes.dataframe.DataFrame#bigframes_dataframe_DataFrame_apply\n", - "\n", - "* The following examples are only for the purpose of demonstrating\n", - "`remote_function` usage. They are not necessarily the best way to achieve the\n", - "end result.\n", - "\n", - "* In the examples in this notebook we are using `reuse=False` just as a caution\n", - " to avoid concurrent runs of this notebook in the same google cloud project\n", - " stepping over each other's remote function deployment. It may not be neccesary\n", - " in a simple use case." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Pt4mWYE1p5o8" - }, - "source": [ - "# Self-contained function\n", - "\n", - "Let's consider a scenario where we want to categorize the matches as short,\n", - "medium or long duration based on the `duration_minutes` column." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 52 - }, - "id": "VoCPBJ-ZpyeG", - "outputId": "19351206-116e-4da2-8ff0-f288b7745b27" - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/google/home/arwas/src1/python-bigquery-dataframes/bigframes/functions/_function_session.py:335: UserWarning: You have not explicitly set a user-managed cloud_function_service_account. Using the default compute service account, {cloud_function_service_account}. To use Bigframes 2.0, please set an explicit user-managed cloud_function_service_account or set cloud_function_service_account explicitly to `default`.See, https://cloud.google.com/functions/docs/securing/function-identity.\n", - " warnings.warn(msg, category=UserWarning)\n" - ] - }, - { - "data": { - "text/html": [ - "Query job 7c021760-59c4-4f3a-846c-9693a4d16eef is DONE. 0 Bytes processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Created cloud function 'projects/bigframes-dev/locations/us-central1/functions/bigframes-sessionca6012-ca541a90249f8b62951f38b7aba6a711-49to' and BQ remote function 'bigframes-dev._ed1e4d0f7d41174ba506d34d15dccf040d13f69e.bigframes_sessionca6012_ca541a90249f8b62951f38b7aba6a711_49to'.\n" - ] - } - ], - "source": [ - "@bpd.remote_function(reuse=False, cloud_function_service_account=\"default\")\n", - "def duration_category(duration_minutes: int) -> str:\n", - " if duration_minutes < 90:\n", - " return \"short\"\n", - " elif duration_minutes < 180:\n", - " return \"medium\"\n", - " else:\n", - " return \"long\"\n", - "\n", - "print(f\"Created cloud function '{duration_category.bigframes_cloud_function}' and BQ remote function '{duration_category.bigframes_remote_function}'.\")" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 258 - }, - "id": "oXgDB70Lp5cG", - "outputId": "c08aade0-8b03-425b-fc26-deafd89275a4" - }, - "outputs": [ - { - "data": { - "text/html": [ - "Query job 4b116e3e-d4d3-4eb6-9764-0a29a7c5d036 is DONE. 58.3 kB processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Query job d62ac4f0-47c9-47ae-8611-c9ecf78f20c9 is DONE. 157.2 kB processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Query job 5f876ebb-2d95-4c68-9d84-947e02b37bad is DONE. 98.8 kB processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
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homeTeamNameawayTeamNameduration_minutesduration_cat
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" - ], - "text/plain": [ - " homeTeamName awayTeamName duration_minutes duration_cat\n", - "1911 Dodgers Angels 132 medium\n", - "2365 Athletics Angels 134 medium\n", - "1977 Athletics Angels 139 medium\n", - "554 Cubs Angels 142 medium\n", - "654 Astros Angels 143 medium" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df1 = df.assign(duration_cat=df[\"duration_minutes\"].apply(duration_category))\n", - "df1.peek()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "zTaNSVmuzEkc" - }, - "source": [ - "# Function referring to variables outside the function body\n", - "\n", - "Let's consider a slight variation of the earlier example where the labels for\n", - "the short, medium and long duration matches are defined outside the function\n", - "body. They would be captured at the time of `remote_function` deployment and\n", - "any change in their values in the notebook after the deployment will not\n", - "automatically propagate to the `remote_function`." - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": { - "id": "2UEmTbu4znyS" - }, - "outputs": [], - "source": [ - "DURATION_CATEGORY_SHORT = \"S\"\n", - "DURATION_CATEGORY_MEDIUM = \"M\"\n", - "DURATION_CATEGORY_LONG = \"L\"" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 52 - }, - "id": "G-73kpmrznHn", - "outputId": "b5923b7c-d412-43bf-9a20-3946154df81a" - }, - "outputs": [ - { - "data": { - "text/html": [ - "Query job 1909a652-5735-401b-8a77-674d8539ded0 is DONE. 0 Bytes processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Created cloud function 'projects/bigframes-dev/locations/us-central1/functions/bigframes-session54c8b0-4191f0fce98d46cc09359de47e203236-e009' and BQ remote function 'bigframes-dev._1b6c31ff1bcd5d2f6d86833cf8268317f1b12d57.bigframes_session54c8b0_4191f0fce98d46cc09359de47e203236_e009'.\n" - ] - } - ], - "source": [ - "@bpd.remote_function(reuse=False, cloud_function_service_account=\"default\")\n", - "def duration_category(duration_minutes: int) -> str:\n", - " if duration_minutes < 90:\n", - " return DURATION_CATEGORY_SHORT\n", - " elif duration_minutes < 180:\n", - " return DURATION_CATEGORY_MEDIUM\n", - " else:\n", - " return DURATION_CATEGORY_LONG\n", - "\n", - "print(f\"Created cloud function '{duration_category.bigframes_cloud_function}' and BQ remote function '{duration_category.bigframes_remote_function}'.\")" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 258 - }, - "id": "DWHKsfF-z7rL", - "outputId": "c736b57f-1fcb-464a-f725-eb203265ddc2" - }, - "outputs": [ - { - "data": { - "text/html": [ - "Query job a942bdc5-6a6d-4db8-b2aa-a556197377b3 is DONE. 58.3 kB processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Query job 175ae9d3-604f-495b-a167-8b06c0283bd2 is DONE. 147.7 kB processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Query job d331a785-e574-45c9-86c8-d29ddd79a4d1 is DONE. 89.3 kB processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
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homeTeamNameawayTeamNameduration_minutesduration_cat
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" - ], - "text/plain": [ - " homeTeamName awayTeamName duration_minutes duration_cat\n", - "1911 Dodgers Angels 132 M\n", - "2365 Athletics Angels 134 M\n", - "1977 Athletics Angels 139 M\n", - "554 Cubs Angels 142 M\n", - "654 Astros Angels 143 M" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df1 = df.assign(duration_cat=df[\"duration_minutes\"].apply(duration_category))\n", - "df1.peek()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "J-1BIasNzKil" - }, - "source": [ - "# Function referring to imports (built-in) outside the function body\n", - "\n", - "Let's consider a scenario in which we want to categorize the matches in terms of\n", - "hour buckets. E.g. a match finishing in 0-60 minutes would be in 1h category,\n", - "61-120 minutes in 2h category and so on. The function itself makes use of the\n", - "`math` module (a built-in module in a standard python installation) which\n", - "happens to be imported outside the function body, let's say in one of the\n", - "previous cells. For the demo purpose we have aliased the import to `mymath`, but\n", - "it is not necessary.\n", - "\n", - "Later in the notebook we will see another example with a third-party module." - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": { - "id": "zlQfhcW41uzM" - }, - "outputs": [], - "source": [ - "import math as mymath" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 52 - }, - "id": "ktADchck2mh4", - "outputId": "9aed6aea-b361-4414-a0f6-8873e8291090" - }, - "outputs": [ - { - "data": { - "text/html": [ - "Query job bbc0b78f-bc04-4bd5-b711-399786a51519 is DONE. 0 Bytes processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Created cloud function 'projects/bigframes-dev/locations/us-central1/functions/bigframes-session54c8b0-cf31fc2d2c7fe111afa5526f5a9cdf06-gmmo' and BQ remote function 'bigframes-dev._1b6c31ff1bcd5d2f6d86833cf8268317f1b12d57.bigframes_session54c8b0_cf31fc2d2c7fe111afa5526f5a9cdf06_gmmo'.\n" - ] - } - ], - "source": [ - "@bpd.remote_function(reuse=False, cloud_function_service_account=\"default\")\n", - "def duration_category(duration_minutes: int) -> str:\n", - " duration_hours = mymath.ceil(duration_minutes / 60)\n", - " return f\"{duration_hours}h\"\n", - "\n", - "print(f\"Created cloud function '{duration_category.bigframes_cloud_function}' and BQ remote function '{duration_category.bigframes_remote_function}'.\")" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 258 - }, - "id": "ywAtZlJU3GoB", - "outputId": "d3c93a31-3367-4ccf-bdf7-62d5bbff4461" - }, - "outputs": [ - { - "data": { - "text/html": [ - "Query job 991b54ed-9eaa-450f-9208-3e73404bb112 is DONE. 58.3 kB processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Query job 4e464a58-ac5b-42fd-91e3-92c115bdd273 is DONE. 150.1 kB processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Query job d340f55d-1511-431a-970d-a70ed4356935 is DONE. 91.7 kB processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
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homeTeamNameawayTeamNameduration_minutesduration_cat
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" - ], - "text/plain": [ - " homeTeamName awayTeamName duration_minutes duration_cat\n", - "1911 Dodgers Angels 132 3h\n", - "2365 Athletics Angels 134 3h\n", - "1977 Athletics Angels 139 3h\n", - "554 Cubs Angels 142 3h\n", - "654 Astros Angels 143 3h" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df1 = df.assign(duration_cat=df[\"duration_minutes\"].apply(duration_category))\n", - "df1.peek()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "WO0FH7Bm3OxR" - }, - "source": [ - "# Function referring to another function outside the function body\n", - "\n", - "In this example let's create a `remote_function` from a function\n", - "`duration_category` which depends upon another function `get_hour_ceiling`,\n", - "which further depends on another function `get_minutes_in_hour`. This dependency\n", - "chain could be even longer in a real world example. The behaviors of the\n", - "dependencies would be captured at the time of the remote function\n", - "deployment.\n", - "\n", - "Please ntoe that any changes in those functions in the notebook after the\n", - "deployment would not automatically propagate to the remote function." - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": { - "id": "0G91fWiF3pKg" - }, - "outputs": [], - "source": [ - "import math\n", - "\n", - "def get_minutes_in_hour():\n", - " return 60\n", - "\n", - "def get_hour_ceiling(minutes):\n", - " return math.ceil(minutes / get_minutes_in_hour())" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 52 - }, - "id": "lQrC8T2031EJ", - "outputId": "420e7c3d-54cb-4814-f973-c7678be61caa" - }, - "outputs": [ - { - "data": { - "text/html": [ - "Query job 10d1afa3-349b-49a8-adbd-79a8309ce77c is DONE. 0 Bytes processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Created cloud function 'projects/bigframes-dev/locations/us-central1/functions/bigframes-session54c8b0-3c03836c2044bf625d02e25ccdbfe101-k1m4' and BQ remote function 'bigframes-dev._1b6c31ff1bcd5d2f6d86833cf8268317f1b12d57.bigframes_session54c8b0_3c03836c2044bf625d02e25ccdbfe101_k1m4'.\n" - ] - } - ], - "source": [ - "@bpd.remote_function(reuse=False, cloud_function_service_account=\"default\")\n", - "def duration_category(duration_minutes: int) -> str:\n", - " duration_hours = get_hour_ceiling(duration_minutes)\n", - " return f\"{duration_hours} hrs\"\n", - "\n", - "print(f\"Created cloud function '{duration_category.bigframes_cloud_function}' and BQ remote function '{duration_category.bigframes_remote_function}'.\")" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 258 - }, - "id": "GVyrihii4EFG", - "outputId": "e979b649-4ed4-4b82-e814-54180420e3fc" - }, - "outputs": [ - { - "data": { - "text/html": [ - "Query job 33aff336-48d6-4caa-8cae-f459d21b180e is DONE. 58.3 kB processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Query job 561e0aa7-3962-4ef3-b308-a117a0ac3a7d is DONE. 157.4 kB processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Query job 759dccf8-3d88-40e1-a38a-2a2064e1d269 is DONE. 99.0 kB processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
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654AstrosAngels1433 hrs
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" - ], - "text/plain": [ - " homeTeamName awayTeamName duration_minutes duration_cat\n", - "1911 Dodgers Angels 132 3 hrs\n", - "2365 Athletics Angels 134 3 hrs\n", - "1977 Athletics Angels 139 3 hrs\n", - "554 Cubs Angels 142 3 hrs\n", - "654 Astros Angels 143 3 hrs" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df1 = df.assign(duration_cat=df[\"duration_minutes\"].apply(duration_category))\n", - "df1.peek()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Uu7SOoT94vSP" - }, - "source": [ - "# Function requiring external packages\n", - "\n", - "In this example let's say we want to redact the `homeTeamName` values, and we\n", - "choose to use a third party library `cryptography`. Any third party dependencies\n", - "can be specified in [pip format](https://pip.pypa.io/en/stable/reference/requirements-file-format/)\n", - "(with or without version number) as a list via the `packages` parameter." - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 34 - }, - "id": "3EUEyNcW41_l", - "outputId": "2d09d60f-da1a-4eab-86d3-0e62390a360c" - }, - "outputs": [ - { - "data": { - "text/html": [ - "Query job e2a44878-2564-44a5-8dec-b7ea2f42afd4 is DONE. 0 Bytes processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "@bpd.remote_function(reuse=False, packages=[\"cryptography\"], cloud_function_service_account=\"default\")\n", - "def get_hash(input: str) -> str:\n", - " from cryptography.fernet import Fernet\n", - "\n", - " # handle missing value\n", - " if input is None:\n", - " input = \"\"\n", - "\n", - " key = Fernet.generate_key()\n", - " f = Fernet(key)\n", - " return f.encrypt(input.encode()).decode()" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 258 - }, - "id": "OX1Hl7bR5uyd", - "outputId": "8ac3bf28-d16d-438b-b636-74ef2371715f" - }, - "outputs": [ - { - "data": { - "text/html": [ - "Query job bcfab000-ca19-4633-bf0e-45e7d053f3eb is DONE. 60.5 kB processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Query job 139a6449-c07e-41ff-9aed-c6fdd633740a is DONE. 388.3 kB processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Query job 035fa2fb-0a55-4358-bb50-3ef915f5bf54 is DONE. 330.0 kB processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
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homeTeamNameawayTeamNameduration_minutesduration_cat
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" - ], - "text/plain": [ - " homeTeamName awayTeamName duration_minutes duration_cat\n", - "1911 Dodgers Angels 132 2 hours\n", - "2365 Athletics Angels 134 2 hours\n", - "1977 Athletics Angels 139 2 hours\n", - "554 Cubs Angels 142 2 hours\n", - "654 Astros Angels 143 2 hours" - ] - }, - "execution_count": 38, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df1 = df.assign(duration_cat=df[\"duration_minutes\"].apply(duration_category))\n", - "df1.peek()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Clean Up" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "bpd.close_session()" - ] - } - ], - "metadata": { - "colab": { - "provenance": [], - "toc_visible": true - }, - "kernelspec": { - "display_name": "Python 3", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.4" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/notebooks/remote_functions/remote_function_vertex_claude_model.ipynb b/notebooks/remote_functions/remote_function_vertex_claude_model.ipynb deleted file mode 100644 index dfc993072cf..00000000000 --- a/notebooks/remote_functions/remote_function_vertex_claude_model.ipynb +++ /dev/null @@ -1,482 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Use BigQuery DataFrames to run Anthropic LLM at scale\n", - "\n", - "\n", - "\n", - " \n", - " \n", - " \n", - "
\n", - " \n", - " \"Colab Run in Colab\n", - " \n", - " \n", - " \n", - " \"GitHub\n", - " View on GitHub\n", - " \n", - " \n", - " \n", - " \"BQ\n", - " Open in BQ Studio\n", - " \n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Overview\n", - "\n", - "Anthropic Claude models are available as APIs on Vertex AI ([docs](https://cloud.google.com/vertex-ai/generative-ai/docs/partner-models/use-claude)).\n", - "\n", - "To run the Claude models at large scale data we can utilze the BigQuery\n", - "DataFrames remote functions ([docs](https://cloud.google.com/bigquery/docs/use-bigquery-dataframes#remote-functions)).\n", - "BigQuery DataFrames provides a simple pythonic interface `remote_function` to\n", - "deploy the user code as a BigQuery remote function and then invoke it at scale\n", - "by utilizing the parallel distributed computing architecture of BigQuery and\n", - "Google Cloud Function.\n", - "\n", - "In this notebook we showcase one such example. For the demonstration purpose we\n", - "use a small amount of data, but the example generalizes for large data. Check out\n", - "various IO APIs provided by BigQuery DataFrames [here](https://cloud.google.com/python/docs/reference/bigframes/latest/bigframes.pandas#bigframes_pandas_read_gbq)\n", - "to see how you could create a DataFrame from your Big Data sitting in a BigQuery\n", - "table or GCS bucket." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Set Up" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Set up a claude model in Vertex\n", - "\n", - "https://cloud.google.com/vertex-ai/generative-ai/docs/partner-models/use-claude#before_you_begin" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Install Anthropic with Vertex if needed\n", - "\n", - "Uncomment the following cell and run the cell to install anthropic python\n", - "package with vertex extension if you don't already have it." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# !pip install anthropic[vertex] --quiet" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Define project and location for GCP integration" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "PROJECT_ID = \"bigframes-dev\" # @param {type:\"string\"}\n", - "LOCATION = \"us-east5\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Initialize BigQuery DataFrames dataframe\n", - "\n", - "BigQuery DataFrames is a set of open source Python libraries that let you take\n", - "advantage of BigQuery data processing by using familiar Python APIs.\n", - "See for more details https://cloud.google.com/bigquery/docs/bigquery-dataframes-introduction." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Import BigQuery DataFrames pandas module and initialize it with your project\n", - "# and location\n", - "\n", - "import bigframes.pandas as bpd\n", - "bpd.options.bigquery.project = PROJECT_ID\n", - "bpd.options.bigquery.location = LOCATION" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's use a DataFrame with small amount of inline data for demo purpose.\n", - "You could create a DataFrame from your own data. See APIs like `read_gbq`,\n", - "`read_csv`, `read_json` etc. at https://cloud.google.com/python/docs/reference/bigframes/latest/bigframes.pandas." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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questions
0What is the capital of France?
1Explain the concept of photosynthesis in simpl...
2Write a haiku about artificial intelligence.
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[3 rows x 1 columns in total]" - ], - "text/plain": [ - " questions\n", - "0 What is the capital of France?\n", - "1 Explain the concept of photosynthesis in simpl...\n", - "2 Write a haiku about artificial intelligence.\n", - "\n", - "[3 rows x 1 columns]" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df = bpd.DataFrame({\"questions\": [\n", - " \"What is the capital of France?\",\n", - " \"Explain the concept of photosynthesis in simple terms.\",\n", - " \"Write a haiku about artificial intelligence.\"\n", - " ]})\n", - "df" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Use BigQuery DataFrames `remote_function`" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's create a remote function from a custom python function that takes a prompt\n", - "and returns the output of the claude LLM running in Vertex. We will be using\n", - "`max_batching_rows=1` to control parallelization. This ensures that a single\n", - "prompt is processed per batch in the underlying cloud function so that the batch\n", - "processing does not time out. An ideal value for `max_batching_rows` depends on\n", - "the complexity of the prompts in the real use case and should be discovered\n", - "through offline experimentation. Check out the API for other ways to control\n", - "parallelization https://cloud.google.com/python/docs/reference/bigframes/latest/bigframes.pandas#bigframes_pandas_remote_function." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " Query processed 0 Bytes in a moment of slot time. [Job bigframes-dev:us-east5.9bc70627-6891-44a4-b7d7-8a28e213cdec details]\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "@bpd.remote_function(packages=[\"anthropic[vertex]\", \"google-auth[requests]\"],\n", - " max_batching_rows=1, \n", - " bigquery_connection=\"bigframes-dev.us-east5.bigframes-rf-conn\", # replace with your connection\n", - " cloud_function_service_account=\"default\",\n", - ")\n", - "def anthropic_transformer(message: str) -> str:\n", - " from anthropic import AnthropicVertex\n", - " client = AnthropicVertex(region=LOCATION, project_id=PROJECT_ID)\n", - "\n", - " message = client.messages.create(\n", - " max_tokens=1024,\n", - " messages=[\n", - " {\n", - " \"role\": \"user\",\n", - " \"content\": message,\n", - " }\n", - " ],\n", - " model=\"claude-3-haiku@20240307\",\n", - " )\n", - " content_text = message.content[0].text if message.content else \"\"\n", - " return content_text" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'bigframes-dev._e9a5162ae4daa9f50fda3f95febaa9781131f3b8.bigframes_sessionc10c73_49262141176cbf70037559ae84e834d3'" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Print the BigQuery remote function created\n", - "anthropic_transformer.bigframes_remote_function" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'projects/bigframes-dev/locations/us-east5/functions/bigframes-sessionc10c73-49262141176cbf70037559ae84e834d3'" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Print the cloud function created\n", - "anthropic_transformer.bigframes_cloud_function" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " Query started with request ID bigframes-dev:us-east5.821579f4-63ea-4072-a3ce-318e43768432.
SQL
SELECT\n",
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-       "FROM\n",
-       "(SELECT\n",
-       "  `t1`.`bfuid_col_3`,\n",
-       "  `t1`.`bfuid_col_4`,\n",
-       "  `t1`.`bfuid_col_5`,\n",
-       "  `t1`.`bfuid_col_6` AS `bfuid_col_7`\n",
-       "FROM (\n",
-       "  SELECT\n",
-       "    `t0`.`level_0`,\n",
-       "    `t0`.`column_0`,\n",
-       "    `t0`.`bfuid_col_6`,\n",
-       "    `t0`.`level_0` AS `bfuid_col_3`,\n",
-       "    `t0`.`column_0` AS `bfuid_col_4`,\n",
-       "    `bigframes-dev._e9a5162ae4daa9f50fda3f95febaa9781131f3b8.bigframes_sessionc10c73_49262141176cbf70037559ae84e834d3`(`t0`.`column_0`) AS `bfuid_col_5`\n",
-       "  FROM (\n",
-       "    SELECT\n",
-       "      *\n",
-       "    FROM UNNEST(ARRAY<STRUCT<`level_0` INT64, `column_0` STRING, `bfuid_col_6` INT64>>[STRUCT(0, 'What is the capital of France?', 0), STRUCT(1, 'Explain the concept of photosynthesis in simple terms.', 1), STRUCT(2, 'Write a haiku about artificial intelligence.', 2)]) AS `level_0`\n",
-       "  ) AS `t0`\n",
-       ") AS `t1`)\n",
-       "ORDER BY `bfuid_col_7` ASC NULLS LAST\n",
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questionsanswers
0What is the capital of France?The capital of France is Paris.
1Explain the concept of photosynthesis in simpl...Photosynthesis is the process by which plants ...
2Write a haiku about artificial intelligence.Here is a haiku about artificial intelligence:...
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[3 rows x 2 columns in total]" - ], - "text/plain": [ - " questions \\\n", - "0 What is the capital of France? \n", - "1 Explain the concept of photosynthesis in simpl... \n", - "2 Write a haiku about artificial intelligence. \n", - "\n", - " answers \n", - "0 The capital of France is Paris. \n", - "1 Photosynthesis is the process by which plants ... \n", - "2 Here is a haiku about artificial intelligence:... \n", - "\n", - "[3 rows x 2 columns]" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Apply the remote function on the user data\n", - "df[\"answers\"] = df[\"questions\"].apply(anthropic_transformer)\n", - "df" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Clean Up" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "Session sessionc10c73 closed." - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "bpd.close_session()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "venv (3.14.2)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.14.2" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/notebooks/streaming/streaming_dataframe.ipynb b/notebooks/streaming/streaming_dataframe.ipynb deleted file mode 100644 index e3dafa98195..00000000000 --- a/notebooks/streaming/streaming_dataframe.ipynb +++ /dev/null @@ -1,581 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# BigFrames StreamingDataFrame", - "bigframes.streaming.StreamingDataFrame is a special DataFrame type that allows simple operations and can create streaming jobs to process real-time data and reverse ETL output to Bigtable and Pub/Sub using [BigQuery continuous queries](https://cloud.google.com/bigquery/docs/continuous-queries-introduction).\n", - "\n", - "In this notebook, we will:\n", - "* Create a StreamingDataFrame from a BigQuery table\n", - "* Do some operations like select, filter and preview the content\n", - "* Create and manage streaming jobs to both Bigtable and Pub/Sub" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'1.31.0'" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import bigframes\n", - "# make sure bigframes version >= 1.12.0\n", - "bigframes.__version__" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import bigframes.pandas as bpd\n", - "import bigframes.streaming as bst\n", - "bigframes.options._bigquery_options.project = \"bigframes-load-testing\" # Change to your own project ID\n", - "job_id_prefix = \"test_streaming_\"" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "Query job c72abbec-0dda-49e8-8617-4d8178659ec2 is DONE. 0 Bytes processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Query job d55762e7-d9d4-4a79-84a4-4975e9292158 is DONE. 28.9 kB processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "'birds.penguins_bigtable_streaming'" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Copy a table from the public dataset for streaming jobs. Any changes to the table can be reflected in the streaming destination.\n", - "df = bpd.read_gbq(\"bigquery-public-data.ml_datasets.penguins\")\n", - "df.to_gbq(\"birds.penguins_bigtable_streaming\", if_exists=\"replace\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create, select, filter and preview", - "Create the StreamingDataFrame from a BigQuery table, select certain columns, filter rows and preview the output" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/google/home/chelsealin/src/bigframes1/bigframes/session/__init__.py:604: PreviewWarning: The bigframes.streaming module is a preview feature, and subject to change.\n", - " warnings.warn(msg, stacklevel=1, category=bfe.PreviewWarning)\n", - "/usr/local/google/home/chelsealin/src/bigframes1/bigframes/core/blocks.py:141: NullIndexPreviewWarning: Creating object with Null Index. Null Index is a preview feature.\n", - " warnings.warn(msg, category=bfe.NullIndexPreviewWarning)\n" - ] - } - ], - "source": [ - "sdf = bst.read_gbq_table(\"birds.penguins_bigtable_streaming\")" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/google/home/chelsealin/src/bigframes1/bigframes/core/blocks.py:141: NullIndexPreviewWarning: Creating object with Null Index. Null Index is a preview feature.\n", - " warnings.warn(msg, category=bfe.NullIndexPreviewWarning)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - }, - { - "data": { - "text/html": [ - "Query job 2894a764-5336-492f-98e1-c865fb161ef9 is DONE. 28.9 kB processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Query job f8fb08cb-ba11-4d73-8fff-c36081d98206 is DONE. 10.4 kB processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
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speciesrowkeybody_mass_g
0Adelie Penguin (Pygoscelis adeliae)Torgersen3875.0
1Adelie Penguin (Pygoscelis adeliae)Torgersen2900.0
2Adelie Penguin (Pygoscelis adeliae)Biscoe3725.0
3Adelie Penguin (Pygoscelis adeliae)Dream2975.0
4Adelie Penguin (Pygoscelis adeliae)Torgersen3050.0
5Chinstrap penguin (Pygoscelis antarctica)Dream2700.0
6Adelie Penguin (Pygoscelis adeliae)Dream3900.0
7Adelie Penguin (Pygoscelis adeliae)Biscoe3825.0
8Chinstrap penguin (Pygoscelis antarctica)Dream3775.0
9Adelie Penguin (Pygoscelis adeliae)Dream3350.0
10Adelie Penguin (Pygoscelis adeliae)Biscoe3900.0
11Adelie Penguin (Pygoscelis adeliae)Torgersen3650.0
12Adelie Penguin (Pygoscelis adeliae)Biscoe3200.0
13Chinstrap penguin (Pygoscelis antarctica)Dream3650.0
14Adelie Penguin (Pygoscelis adeliae)Dream3700.0
15Chinstrap penguin (Pygoscelis antarctica)Dream3800.0
16Chinstrap penguin (Pygoscelis antarctica)Dream3950.0
17Chinstrap penguin (Pygoscelis antarctica)Dream3350.0
18Adelie Penguin (Pygoscelis adeliae)Dream3100.0
19Chinstrap penguin (Pygoscelis antarctica)Dream3750.0
20Adelie Penguin (Pygoscelis adeliae)Biscoe3550.0
21Chinstrap penguin (Pygoscelis antarctica)Dream3400.0
22Adelie Penguin (Pygoscelis adeliae)Torgersen3450.0
23Adelie Penguin (Pygoscelis adeliae)Torgersen3600.0
24Chinstrap penguin (Pygoscelis antarctica)Dream3650.0
\n", - "

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[165 rows x 3 columns in total]" - ], - "text/plain": [ - " species rowkey body_mass_g\n", - " Adelie Penguin (Pygoscelis adeliae) Torgersen 3875.0\n", - " Adelie Penguin (Pygoscelis adeliae) Torgersen 2900.0\n", - " Adelie Penguin (Pygoscelis adeliae) Biscoe 3725.0\n", - " Adelie Penguin (Pygoscelis adeliae) Dream 2975.0\n", - " Adelie Penguin (Pygoscelis adeliae) Torgersen 3050.0\n", - "Chinstrap penguin (Pygoscelis antarctica) Dream 2700.0\n", - " Adelie Penguin (Pygoscelis adeliae) Dream 3900.0\n", - " Adelie Penguin (Pygoscelis adeliae) Biscoe 3825.0\n", - "Chinstrap penguin (Pygoscelis antarctica) Dream 3775.0\n", - " Adelie Penguin (Pygoscelis adeliae) Dream 3350.0\n", - " Adelie Penguin (Pygoscelis adeliae) Biscoe 3900.0\n", - " Adelie Penguin (Pygoscelis adeliae) Torgersen 3650.0\n", - " Adelie Penguin (Pygoscelis adeliae) Biscoe 3200.0\n", - "Chinstrap penguin (Pygoscelis antarctica) Dream 3650.0\n", - " Adelie Penguin (Pygoscelis adeliae) Dream 3700.0\n", - "Chinstrap penguin (Pygoscelis antarctica) Dream 3800.0\n", - "Chinstrap penguin (Pygoscelis antarctica) Dream 3950.0\n", - "Chinstrap penguin (Pygoscelis antarctica) Dream 3350.0\n", - " Adelie Penguin (Pygoscelis adeliae) Dream 3100.0\n", - "Chinstrap penguin (Pygoscelis antarctica) Dream 3750.0\n", - " Adelie Penguin (Pygoscelis adeliae) Biscoe 3550.0\n", - "Chinstrap penguin (Pygoscelis antarctica) Dream 3400.0\n", - " Adelie Penguin (Pygoscelis adeliae) Torgersen 3450.0\n", - " Adelie Penguin (Pygoscelis adeliae) Torgersen 3600.0\n", - "Chinstrap penguin (Pygoscelis antarctica) Dream 3650.0\n", - "...\n", - "\n", - "[165 rows x 3 columns]" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sdf = sdf[[\"species\", \"island\", \"body_mass_g\"]]\n", - "sdf = sdf[sdf[\"body_mass_g\"] < 4000]\n", - "# BigTable needs a rowkey column\n", - "sdf = sdf.rename(columns={\"island\": \"rowkey\"})\n", - "print(type(sdf))\n", - "sdf" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### BigTable\n", - "Create BigTable streaming job" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/google/home/chelsealin/src/bigframes1/bigframes/streaming/dataframe.py:352: PreviewWarning: The bigframes.streaming module is a preview feature, and subject to change.\n", - " warnings.warn(msg, stacklevel=1, category=bfe.PreviewWarning)\n" - ] - } - ], - "source": [ - "job = sdf.to_bigtable(instance=\"streaming-testing-instance\", # Change to your own Bigtable instance name\n", - " table=\"garrettwu-no-col-family\", # Change to your own Bigtable table name\n", - " service_account_email=\"streaming-testing-admin@bigframes-load-testing.iam.gserviceaccount.com\", # Change to your own service account\n", - " app_profile=None,\n", - " truncate=True,\n", - " overwrite=True,\n", - " auto_create_column_families=True,\n", - " bigtable_options={},\n", - " job_id=None,\n", - " job_id_prefix=job_id_prefix,)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "True\n", - "None\n" - ] - } - ], - "source": [ - "print(job.running())\n", - "print(job.error_result)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "job.cancel()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Pub/Sub\n", - "Create Pub/Sub streaming job" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/google/home/chelsealin/src/bigframes1/bigframes/core/blocks.py:141: NullIndexPreviewWarning: Creating object with Null Index. Null Index is a preview feature.\n", - " warnings.warn(msg, category=bfe.NullIndexPreviewWarning)\n" - ] - } - ], - "source": [ - "# Pub/Sub requires a single column\n", - "sdf = sdf[[\"rowkey\"]]" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/google/home/chelsealin/src/bigframes1/bigframes/streaming/dataframe.py:464: PreviewWarning: The bigframes.streaming module is a preview feature, and subject to change.\n", - " warnings.warn(msg, stacklevel=1, category=bfe.PreviewWarning)\n" - ] - } - ], - "source": [ - "job = sdf.to_pubsub(\n", - " topic=\"penguins\", # Change to your own Pub/Sub topic ID\n", - " service_account_email=\"streaming-testing@bigframes-load-testing.iam.gserviceaccount.com\", # Change to your own service account\n", - " job_id=None,\n", - " job_id_prefix=job_id_prefix,\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "True\n", - "None\n" - ] - } - ], - "source": [ - "print(job.running())\n", - "print(job.error_result)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "job.cancel()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.1" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/notebooks/vertex_sdk/sdk2_bigframes_pytorch.ipynb b/notebooks/vertex_sdk/sdk2_bigframes_pytorch.ipynb new file mode 100644 index 00000000000..598d958f0c3 --- /dev/null +++ b/notebooks/vertex_sdk/sdk2_bigframes_pytorch.ipynb @@ -0,0 +1,723 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "ur8xi4C7S06n" + }, + "outputs": [], + "source": [ + "# Copyright 2023 Google LLC\n", + "#\n", + "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", + "# you may not use this file except in compliance with the License.\n", + "# You may obtain a copy of the License at\n", + "#\n", + "# https://www.apache.org/licenses/LICENSE-2.0\n", + "#\n", + "# Unless required by applicable law or agreed to in writing, software\n", + "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", + "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", + "# See the License for the specific language governing permissions and\n", + "# limitations under the License." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "JAPoU8Sm5E6e" + }, + "source": [ + "# Train a pytorch model with Vertex AI SDK 2.0 and Bigframes\n", + "\n", + "\n", + " \n", + " \n", + "
\n", + " \n", + " \"Colab Run in Colab\n", + " \n", + " \n", + " \n", + " \"GitHub\n", + " View on GitHub\n", + " \n", + " \n", + " \n", + " \"VertexOpen in Vertex AI Workbench\n", + " \n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "tvgnzT1CKxrO" + }, + "source": [ + "## Overview\n", + "\n", + "This tutorial demonstrates how to train a pytorch model using Vertex AI local-to-remote training with Vertex AI SDK 2.0 and BigQuery Bigframes as the data source.\n", + "\n", + "Learn more about [bigframes](https://cloud.google.com/bigquery/docs/)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "d975e698c9a4" + }, + "source": [ + "### Objective\n", + "\n", + "In this tutorial, you learn to use `Vertex AI SDK 2.0` with Bigframes as input data source.\n", + "\n", + "\n", + "This tutorial uses the following Google Cloud ML services:\n", + "\n", + "- `Vertex AI Training`\n", + "- `Vertex AI Remote Training`\n", + "\n", + "\n", + "The steps performed include:\n", + "\n", + "- Initialize a dataframe from a BigQuery table and split the dataset\n", + "- Perform transformations as a Vertex AI remote training.\n", + "- Train the model remotely and evaluate the model locally\n", + "\n", + "**Local-to-remote training**\n", + "\n", + "```\n", + "import vertexai\n", + "from my_module import MyModelClass\n", + "\n", + "vertexai.preview.init(remote=True, project=\"my-project\", location=\"my-location\", staging_bucket=\"gs://my-bucket\")\n", + "\n", + "# Wrap the model class with `vertex_ai.preview.remote`\n", + "MyModelClass = vertexai.preview.remote(MyModelClass)\n", + "\n", + "# Instantiate the class\n", + "model = MyModelClass(...)\n", + "\n", + "# Optional set remote config\n", + "model.fit.vertex.remote_config.display_name = \"MyModelClass-remote-training\"\n", + "model.fit.vertex.remote_config.staging_bucket = \"gs://my-bucket\"\n", + "\n", + "# This `fit` call will be executed remotely\n", + "model.fit(...)\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "08d289fa873f" + }, + "source": [ + "### Dataset\n", + "\n", + "This tutorial uses the IRIS dataset, which predicts the iris species." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "aed92deeb4a0" + }, + "source": [ + "### Costs\n", + "\n", + "This tutorial uses billable components of Google Cloud:\n", + "\n", + "* Vertex AI\n", + "* BigQuery\n", + "* Cloud Storage\n", + "\n", + "Learn about [Vertex AI pricing](https://cloud.google.com/vertex-ai/pricing),\n", + "[BigQuery pricing](https://cloud.google.com/bigquery/pricing),\n", + "and [Cloud Storage pricing](https://cloud.google.com/storage/pricing), \n", + "and use the [Pricing Calculator](https://cloud.google.com/products/calculator/)\n", + "to generate a cost estimate based on your projected usage." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "i7EUnXsZhAGF" + }, + "source": [ + "## Installation\n", + "\n", + "Install the following packages required to execute this notebook. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "2b4ef9b72d43" + }, + "outputs": [], + "source": [ + "# Install the packages\n", + "! pip3 install --upgrade --quiet google-cloud-aiplatform[preview]\n", + "! pip3 install --upgrade --quiet bigframes\n", + "! pip3 install --upgrade --quiet torch" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "58707a750154" + }, + "source": [ + "### Colab only: Uncomment the following cell to restart the kernel." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "f200f10a1da3" + }, + "outputs": [], + "source": [ + "# Automatically restart kernel after installs so that your environment can access the new packages\n", + "# import IPython\n", + "\n", + "# app = IPython.Application.instance()\n", + "# app.kernel.do_shutdown(True)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "BF1j6f9HApxa" + }, + "source": [ + "## Before you begin\n", + "\n", + "### Set up your Google Cloud project\n", + "\n", + "**The following steps are required, regardless of your notebook environment.**\n", + "\n", + "1. [Select or create a Google Cloud project](https://console.cloud.google.com/cloud-resource-manager). When you first create an account, you get a $300 free credit towards your compute/storage costs.\n", + "\n", + "2. [Make sure that billing is enabled for your project](https://cloud.google.com/billing/docs/how-to/modify-project).\n", + "\n", + "3. [Enable the Vertex AI API](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com).\n", + "\n", + "4. If you are running this notebook locally, you need to install the [Cloud SDK](https://cloud.google.com/sdk)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "WReHDGG5g0XY" + }, + "source": [ + "#### Set your project ID\n", + "\n", + "**If you don't know your project ID**, try the following:\n", + "* Run `gcloud config list`.\n", + "* Run `gcloud projects list`.\n", + "* See the support page: [Locate the project ID](https://support.google.com/googleapi/answer/7014113)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "oM1iC_MfAts1" + }, + "outputs": [], + "source": [ + "PROJECT_ID = \"[your-project-id]\" # @param {type:\"string\"}\n", + "\n", + "# Set the project id\n", + "! gcloud config set project {PROJECT_ID}" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "region" + }, + "source": [ + "#### Region\n", + "\n", + "You can also change the `REGION` variable used by Vertex AI. Learn more about [Vertex AI regions](https://cloud.google.com/vertex-ai/docs/general/locations)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "region" + }, + "outputs": [], + "source": [ + "REGION = \"us-central1\" # @param {type: \"string\"}" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "sBCra4QMA2wR" + }, + "source": [ + "### Authenticate your Google Cloud account\n", + "\n", + "Depending on your Jupyter environment, you may have to manually authenticate. Follow the relevant instructions below." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "74ccc9e52986" + }, + "source": [ + "**1. Vertex AI Workbench**\n", + "* Do nothing as you are already authenticated." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "de775a3773ba" + }, + "source": [ + "**2. Local JupyterLab instance, uncomment and run:**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "254614fa0c46" + }, + "outputs": [], + "source": [ + "# ! gcloud auth login" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ef21552ccea8" + }, + "source": [ + "**3. Colab, uncomment and run:**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "603adbbf0532" + }, + "outputs": [], + "source": [ + "# from google.colab import auth\n", + "# auth.authenticate_user()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "f6b2ccc891ed" + }, + "source": [ + "**4. Service account or other**\n", + "* See how to grant Cloud Storage permissions to your service account at https://cloud.google.com/storage/docs/gsutil/commands/iam#ch-examples." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "zgPO1eR3CYjk" + }, + "source": [ + "### Create a Cloud Storage bucket\n", + "\n", + "Create a storage bucket to store intermediate artifacts such as datasets." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "MzGDU7TWdts_" + }, + "outputs": [], + "source": [ + "BUCKET_URI = f\"gs://your-bucket-name-{PROJECT_ID}-unique\" # @param {type:\"string\"}" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-EcIXiGsCePi" + }, + "source": [ + "**Only if your bucket doesn't already exist**: Run the following cell to create your Cloud Storage bucket." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "NIq7R4HZCfIc" + }, + "outputs": [], + "source": [ + "! gsutil mb -l {REGION} -p {PROJECT_ID} {BUCKET_URI}" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "960505627ddf" + }, + "source": [ + "### Import libraries and define constants" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "PyQmSRbKA8r-" + }, + "outputs": [], + "source": [ + "import bigframes.pandas as bf\n", + "import torch\n", + "import vertexai\n", + "from vertexai.preview import VertexModel\n", + "\n", + "bf.options.bigquery.location = \"us\" # Dataset is in 'us' not 'us-central1'\n", + "bf.options.bigquery.project = PROJECT_ID\n", + "\n", + "from bigframes.ml.model_selection import \\\n", + " train_test_split as bf_train_test_split" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "init_aip:mbsdk,all" + }, + "source": [ + "## Initialize Vertex AI SDK for Python\n", + "\n", + "Initialize the Vertex AI SDK for Python for your project and corresponding bucket." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "init_aip:mbsdk,all" + }, + "outputs": [], + "source": [ + "vertexai.init(\n", + " project=PROJECT_ID,\n", + " location=REGION,\n", + " staging_bucket=BUCKET_URI,\n", + ")\n", + "\n", + "REMOTE_JOB_NAME = \"sdk2-bigframes-pytorch\"\n", + "REMOTE_JOB_BUCKET = f\"{BUCKET_URI}/{REMOTE_JOB_NAME}\"" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "105334524e96" + }, + "source": [ + "## Prepare the dataset\n", + "\n", + "Now load the Iris dataset and split the data into train and test sets." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "b44cdc4e03f1" + }, + "outputs": [], + "source": [ + "df = bf.read_gbq(\"bigquery-public-data.ml_datasets.iris\")\n", + "\n", + "species_categories = {\n", + " \"versicolor\": 0,\n", + " \"virginica\": 1,\n", + " \"setosa\": 2,\n", + "}\n", + "df[\"species\"] = df[\"species\"].map(species_categories)\n", + "\n", + "# Assign an index column name\n", + "index_col = \"index\"\n", + "df.index.name = index_col" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "9cb8616b1997" + }, + "outputs": [], + "source": [ + "feature_columns = df[[\"sepal_length\", \"sepal_width\", \"petal_length\", \"petal_width\"]]\n", + "label_columns = df[[\"species\"]]\n", + "train_X, test_X, train_y, test_y = bf_train_test_split(\n", + " feature_columns, label_columns, test_size=0.2\n", + ")\n", + "\n", + "print(\"X_train size: \", train_X.size)\n", + "print(\"X_test size: \", test_X.size)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "23fe7b734b08" + }, + "outputs": [], + "source": [ + "# Switch to remote mode for training\n", + "vertexai.preview.init(remote=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "5904a0f1bb03" + }, + "source": [ + "## PyTorch remote training with CPU (Custom PyTorch model)\n", + "\n", + "First, train a PyTorch model as a remote training job:\n", + "\n", + "- Reinitialize Vertex AI for remote training.\n", + "- Set TorchLogisticRegression for the remote training job.\n", + "- Invoke TorchLogisticRegression locally which will launch the remote training job." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "2a1b85195a17" + }, + "outputs": [], + "source": [ + "# define the custom model\n", + "class TorchLogisticRegression(VertexModel, torch.nn.Module):\n", + " def __init__(self, input_size: int, output_size: int):\n", + " torch.nn.Module.__init__(self)\n", + " VertexModel.__init__(self)\n", + " self.linear = torch.nn.Linear(input_size, output_size)\n", + " self.softmax = torch.nn.Softmax(dim=1)\n", + "\n", + " def forward(self, x):\n", + " return self.softmax(self.linear(x))\n", + "\n", + " @vertexai.preview.developer.mark.train()\n", + " def train(self, X, y, num_epochs, lr):\n", + " X = X.to(torch.float32)\n", + " y = torch.flatten(y) # necessary to get 1D tensor\n", + " dataloader = torch.utils.data.DataLoader(\n", + " torch.utils.data.TensorDataset(X, y),\n", + " batch_size=10,\n", + " shuffle=True,\n", + " generator=torch.Generator(device=X.device),\n", + " )\n", + "\n", + " criterion = torch.nn.CrossEntropyLoss()\n", + " optimizer = torch.optim.SGD(self.parameters(), lr=lr)\n", + "\n", + " for t in range(num_epochs):\n", + " for batch, (X, y) in enumerate(dataloader):\n", + " optimizer.zero_grad()\n", + " pred = self(X)\n", + " loss = criterion(pred, y)\n", + " loss.backward()\n", + " optimizer.step()\n", + "\n", + " @vertexai.preview.developer.mark.predict()\n", + " def predict(self, X):\n", + " X = torch.tensor(X).to(torch.float32)\n", + " with torch.no_grad():\n", + " pred = torch.argmax(self(X), dim=1)\n", + " return pred" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "4e35593f520a" + }, + "outputs": [], + "source": [ + "# Switch to remote mode for training\n", + "vertexai.preview.init(remote=True)\n", + "\n", + "# Instantiate model\n", + "model = TorchLogisticRegression(4, 3)\n", + "\n", + "# Set training config\n", + "model.train.vertex.remote_config.custom_commands = [\n", + " \"pip install torchdata\",\n", + " \"pip install torcharrow\",\n", + "]\n", + "model.train.vertex.remote_config.display_name = REMOTE_JOB_NAME + \"-torch-model\"\n", + "model.train.vertex.remote_config.staging_bucket = REMOTE_JOB_BUCKET\n", + "\n", + "# Train model on Vertex\n", + "model.train(train_X, train_y, num_epochs=200, lr=0.05)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "edf4d0708f02" + }, + "source": [ + "## Remote prediction\n", + "\n", + "Obtain predictions from the trained model." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "42dfbff0ca15" + }, + "outputs": [], + "source": [ + "vertexai.preview.init(remote=True)\n", + "\n", + "# Set remote config\n", + "model.predict.vertex.remote_config.custom_commands = [\n", + " \"pip install torchdata\",\n", + " \"pip install torcharrow\",\n", + "]\n", + "model.predict.vertex.remote_config.display_name = REMOTE_JOB_NAME + \"-torch-predict\"\n", + "model.predict.vertex.remote_config.staging_bucket = REMOTE_JOB_BUCKET\n", + "\n", + "predictions = model.predict(test_X)\n", + "\n", + "print(f\"Remote predictions: {predictions}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "4340ed8316cd" + }, + "source": [ + "## Local evaluation\n", + "\n", + "Evaluate model results locally." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "eb27a31cec6f" + }, + "outputs": [], + "source": [ + "# User must convert bigframes to torch tensor for local evaluation\n", + "train_X_tensor = torch.from_numpy(\n", + " train_X.to_pandas().reset_index().drop(columns=[\"index\"]).values.astype(float)\n", + ")\n", + "train_y_tensor = torch.from_numpy(\n", + " train_y.to_pandas().reset_index().drop(columns=[\"index\"]).values.astype(float)\n", + ")\n", + "\n", + "test_X_tensor = torch.from_numpy(\n", + " test_X.to_pandas().reset_index().drop(columns=[\"index\"]).values.astype(float)\n", + ")\n", + "test_y_tensor = torch.from_numpy(\n", + " test_y.to_pandas().reset_index().drop(columns=[\"index\"]).values.astype(float)\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "7db44ad81389" + }, + "outputs": [], + "source": [ + "from sklearn.metrics import accuracy_score\n", + "\n", + "# Switch to local mode for evaluation\n", + "vertexai.preview.init(remote=False)\n", + "\n", + "# Evaluate model's accuracy score\n", + "print(\n", + " f\"Train accuracy: {accuracy_score(train_y_tensor, model.predict(train_X_tensor))}\"\n", + ")\n", + "\n", + "print(f\"Test accuracy: {accuracy_score(test_y_tensor, model.predict(test_X_tensor))}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "TpV-iwP9qw9c" + }, + "source": [ + "## Cleaning up\n", + "\n", + "To clean up all Google Cloud resources used in this project, you can [delete the Google Cloud\n", + "project](https://cloud.google.com/resource-manager/docs/creating-managing-projects#shutting_down_projects) you used for the tutorial.\n", + "\n", + "Otherwise, you can delete the individual resources you created in this tutorial:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "sx_vKniMq9ZX" + }, + "outputs": [], + "source": [ + "import os\n", + "\n", + "# Delete Cloud Storage objects that were created\n", + "delete_bucket = False\n", + "if delete_bucket or os.getenv(\"IS_TESTING\"):\n", + " ! gsutil -m rm -r $BUCKET_URI" + ] + } + ], + "metadata": { + "colab": { + "collapsed_sections": [], + "name": "sdk2_bigframes_pytorch.ipynb", + "toc_visible": true + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks/vertex_sdk/sdk2_bigframes_sklearn.ipynb b/notebooks/vertex_sdk/sdk2_bigframes_sklearn.ipynb new file mode 100644 index 00000000000..021c0707535 --- /dev/null +++ b/notebooks/vertex_sdk/sdk2_bigframes_sklearn.ipynb @@ -0,0 +1,727 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "ur8xi4C7S06n" + }, + "outputs": [], + "source": [ + "# Copyright 2023 Google LLC\n", + "#\n", + "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", + "# you may not use this file except in compliance with the License.\n", + "# You may obtain a copy of the License at\n", + "#\n", + "# https://www.apache.org/licenses/LICENSE-2.0\n", + "#\n", + "# Unless required by applicable law or agreed to in writing, software\n", + "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", + "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", + "# See the License for the specific language governing permissions and\n", + "# limitations under the License." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "JAPoU8Sm5E6e" + }, + "source": [ + "# Train a scikit-learn model with Vertex AI SDK 2.0 and Bigframes\n", + "\n", + "\n", + " \n", + " \n", + "
\n", + " \n", + " \"Colab Run in Colab\n", + " \n", + " \n", + " \n", + " \"GitHub\n", + " View on GitHub\n", + " \n", + " \n", + " \n", + " \"VertexOpen in Vertex AI Workbench\n", + " \n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "tvgnzT1CKxrO" + }, + "source": [ + "## Overview\n", + "\n", + "This tutorial demonstrates how to train a scikit-learn model using Vertex AI local-to-remote training with Vertex AI SDK 2.0 and BigQuery Bigframes as the data source.\n", + "\n", + "Learn more about [bigframes](https://cloud.google.com/bigquery/docs/)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "d975e698c9a4" + }, + "source": [ + "### Objective\n", + "\n", + "In this tutorial, you learn to use `Vertex AI SDK 2.0` with Bigframes as input data source.\n", + "\n", + "\n", + "This tutorial uses the following Google Cloud ML services:\n", + "\n", + "- `Vertex AI Training`\n", + "- `Vertex AI Remote Training`\n", + "\n", + "\n", + "The steps performed include:\n", + "\n", + "- Initialize a dataframe from a BigQuery table and split the dataset\n", + "- Perform transformations as a Vertex AI remote training.\n", + "- Train the model remotely and evaluate the model locally\n", + "\n", + "**Local-to-remote training**\n", + "\n", + "```\n", + "import vertexai\n", + "from my_module import MyModelClass\n", + "\n", + "vertexai.preview.init(remote=True, project=\"my-project\", location=\"my-location\", staging_bucket=\"gs://my-bucket\")\n", + "\n", + "# Wrap the model class with `vertex_ai.preview.remote`\n", + "MyModelClass = vertexai.preview.remote(MyModelClass)\n", + "\n", + "# Instantiate the class\n", + "model = MyModelClass(...)\n", + "\n", + "# Optional set remote config\n", + "model.fit.vertex.remote_config.display_name = \"MyModelClass-remote-training\"\n", + "model.fit.vertex.remote_config.staging_bucket = \"gs://my-bucket\"\n", + "\n", + "# This `fit` call will be executed remotely\n", + "model.fit(...)\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "08d289fa873f" + }, + "source": [ + "### Dataset\n", + "\n", + "This tutorial uses the IRIS dataset, which predicts the iris species." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "aed92deeb4a0" + }, + "source": [ + "### Costs\n", + "\n", + "This tutorial uses billable components of Google Cloud:\n", + "\n", + "* Vertex AI\n", + "* BigQuery\n", + "* Cloud Storage\n", + "\n", + "Learn about [Vertex AI pricing](https://cloud.google.com/vertex-ai/pricing),\n", + "[BigQuery pricing](https://cloud.google.com/bigquery/pricing),\n", + "and [Cloud Storage pricing](https://cloud.google.com/storage/pricing), \n", + "and use the [Pricing Calculator](https://cloud.google.com/products/calculator/)\n", + "to generate a cost estimate based on your projected usage." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "i7EUnXsZhAGF" + }, + "source": [ + "## Installation\n", + "\n", + "Install the following packages required to execute this notebook. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "2b4ef9b72d43" + }, + "outputs": [], + "source": [ + "# Install the packages\n", + "! pip3 install --upgrade --quiet google-cloud-aiplatform[preview]\n", + "! pip3 install --upgrade --quiet bigframes" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "58707a750154" + }, + "source": [ + "### Colab only: Uncomment the following cell to restart the kernel." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "f200f10a1da3" + }, + "outputs": [], + "source": [ + "# Automatically restart kernel after installs so that your environment can access the new packages\n", + "# import IPython\n", + "\n", + "# app = IPython.Application.instance()\n", + "# app.kernel.do_shutdown(True)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "BF1j6f9HApxa" + }, + "source": [ + "## Before you begin\n", + "\n", + "### Set up your Google Cloud project\n", + "\n", + "**The following steps are required, regardless of your notebook environment.**\n", + "\n", + "1. [Select or create a Google Cloud project](https://console.cloud.google.com/cloud-resource-manager). When you first create an account, you get a $300 free credit towards your compute/storage costs.\n", + "\n", + "2. [Make sure that billing is enabled for your project](https://cloud.google.com/billing/docs/how-to/modify-project).\n", + "\n", + "3. [Enable the Vertex AI API](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com).\n", + "\n", + "4. If you are running this notebook locally, you need to install the [Cloud SDK](https://cloud.google.com/sdk)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "WReHDGG5g0XY" + }, + "source": [ + "#### Set your project ID\n", + "\n", + "**If you don't know your project ID**, try the following:\n", + "* Run `gcloud config list`.\n", + "* Run `gcloud projects list`.\n", + "* See the support page: [Locate the project ID](https://support.google.com/googleapi/answer/7014113)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "oM1iC_MfAts1" + }, + "outputs": [], + "source": [ + "PROJECT_ID = \"[your-project-id]\" # @param {type:\"string\"}\n", + "\n", + "# Set the project id\n", + "! gcloud config set project {PROJECT_ID}" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "region" + }, + "source": [ + "#### Region\n", + "\n", + "You can also change the `REGION` variable used by Vertex AI. Learn more about [Vertex AI regions](https://cloud.google.com/vertex-ai/docs/general/locations)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "region" + }, + "outputs": [], + "source": [ + "REGION = \"us-central1\" # @param {type: \"string\"}" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "sBCra4QMA2wR" + }, + "source": [ + "### Authenticate your Google Cloud account\n", + "\n", + "Depending on your Jupyter environment, you may have to manually authenticate. Follow the relevant instructions below." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "74ccc9e52986" + }, + "source": [ + "**1. Vertex AI Workbench**\n", + "* Do nothing as you are already authenticated." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "de775a3773ba" + }, + "source": [ + "**2. Local JupyterLab instance, uncomment and run:**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "254614fa0c46" + }, + "outputs": [], + "source": [ + "# ! gcloud auth login" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ef21552ccea8" + }, + "source": [ + "**3. Colab, uncomment and run:**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "603adbbf0532" + }, + "outputs": [], + "source": [ + "# from google.colab import auth\n", + "# auth.authenticate_user()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "f6b2ccc891ed" + }, + "source": [ + "**4. Service account or other**\n", + "* See how to grant Cloud Storage permissions to your service account at https://cloud.google.com/storage/docs/gsutil/commands/iam#ch-examples." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "zgPO1eR3CYjk" + }, + "source": [ + "### Create a Cloud Storage bucket\n", + "\n", + "Create a storage bucket to store intermediate artifacts such as datasets." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "MzGDU7TWdts_" + }, + "outputs": [], + "source": [ + "BUCKET_URI = f\"gs://your-bucket-name-{PROJECT_ID}-unique\" # @param {type:\"string\"}" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-EcIXiGsCePi" + }, + "source": [ + "**Only if your bucket doesn't already exist**: Run the following cell to create your Cloud Storage bucket." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "NIq7R4HZCfIc" + }, + "outputs": [], + "source": [ + "! gsutil mb -l {REGION} -p {PROJECT_ID} {BUCKET_URI}" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "960505627ddf" + }, + "source": [ + "### Import libraries and define constants" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "PyQmSRbKA8r-" + }, + "outputs": [], + "source": [ + "import bigframes.pandas as bf\n", + "import vertexai\n", + "\n", + "bf.options.bigquery.location = \"us\" # Dataset is in 'us' not 'us-central1'\n", + "bf.options.bigquery.project = PROJECT_ID\n", + "\n", + "from bigframes.ml.model_selection import \\\n", + " train_test_split as bf_train_test_split\n", + "\n", + "REMOTE_JOB_NAME = \"sdk2-bigframes-sklearn\"\n", + "REMOTE_JOB_BUCKET = f\"{BUCKET_URI}/{REMOTE_JOB_NAME}\"" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "init_aip:mbsdk,all" + }, + "source": [ + "## Initialize Vertex AI SDK for Python\n", + "\n", + "Initialize the Vertex AI SDK for Python for your project and corresponding bucket." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "init_aip:mbsdk,all" + }, + "outputs": [], + "source": [ + "vertexai.init(\n", + " project=PROJECT_ID,\n", + " location=REGION,\n", + " staging_bucket=BUCKET_URI,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "105334524e96" + }, + "source": [ + "## Prepare the dataset\n", + "\n", + "Now load the Iris dataset and split the data into train and test sets." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "b44cdc4e03f1" + }, + "outputs": [], + "source": [ + "df = bf.read_gbq(\"bigquery-public-data.ml_datasets.iris\")\n", + "\n", + "species_categories = {\n", + " \"versicolor\": 0,\n", + " \"virginica\": 1,\n", + " \"setosa\": 2,\n", + "}\n", + "df[\"species\"] = df[\"species\"].map(species_categories)\n", + "\n", + "# Assign an index column name\n", + "index_col = \"index\"\n", + "df.index.name = index_col" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "9cb8616b1997" + }, + "outputs": [], + "source": [ + "feature_columns = df[[\"sepal_length\", \"sepal_width\", \"petal_length\", \"petal_width\"]]\n", + "label_columns = df[[\"species\"]]\n", + "train_X, test_X, train_y, test_y = bf_train_test_split(\n", + " feature_columns, label_columns, test_size=0.2\n", + ")\n", + "\n", + "print(\"X_train size: \", train_X.size)\n", + "print(\"X_test size: \", test_X.size)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "8306545fcc57" + }, + "source": [ + "## Feature transformation\n", + "\n", + "Next, you do feature transformations on the data using the Vertex AI remote training service.\n", + "\n", + "First, you re-initialize Vertex AI to enable remote training." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "55e701c31036" + }, + "outputs": [], + "source": [ + "# Switch to remote mode for training\n", + "vertexai.preview.init(remote=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "4a0e9d59b273" + }, + "source": [ + "### Execute remote job for fit_transform() on training data\n", + "\n", + "Next, indicate that the `StandardScalar` class is to be executed remotely. Then set up the data transform and call the `fit_transform()` method is executed remotely." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "90333089d362" + }, + "outputs": [], + "source": [ + "from sklearn.preprocessing import StandardScaler\n", + "\n", + "# Wrap classes to enable Vertex remote execution\n", + "StandardScaler = vertexai.preview.remote(StandardScaler)\n", + "\n", + "# Instantiate transformer\n", + "transformer = StandardScaler()\n", + "\n", + "# Set training config\n", + "transformer.fit_transform.vertex.remote_config.display_name = (\n", + " f\"{REMOTE_JOB_NAME}-fit-transformer-bigframes\"\n", + ")\n", + "transformer.fit_transform.vertex.remote_config.staging_bucket = REMOTE_JOB_BUCKET\n", + "\n", + "# Execute transformer on Vertex (train_X is bigframes.dataframe.DataFrame, X_train is np.array)\n", + "X_train = transformer.fit_transform(train_X)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "6bf95574c907" + }, + "source": [ + "### Remote transform on test data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "da6eea22a89a" + }, + "outputs": [], + "source": [ + "# Transform test dataset before calculate test score\n", + "transformer.transform.vertex.remote_config.display_name = (\n", + " REMOTE_JOB_NAME + \"-transformer\"\n", + ")\n", + "transformer.transform.vertex.remote_config.staging_bucket = REMOTE_JOB_BUCKET\n", + "\n", + "# Execute transformer on Vertex (test_X is bigframes.dataframe.DataFrame, X_test is np.array)\n", + "X_test = transformer.transform(test_X)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ddf906c886e4" + }, + "source": [ + "## Remote training\n", + "\n", + "First, train the scikit-learn model as a remote training job:\n", + "\n", + "- Set LogisticRegression for the remote training job.\n", + "- Invoke LogisticRegression locally which will launch the remote training job." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "c7b0116fa60c" + }, + "outputs": [], + "source": [ + "from sklearn.linear_model import LogisticRegression\n", + "\n", + "# Wrap classes to enable Vertex remote execution\n", + "LogisticRegression = vertexai.preview.remote(LogisticRegression)\n", + "\n", + "# Instantiate model, warm_start=True for uptraining\n", + "model = LogisticRegression(warm_start=True)\n", + "\n", + "# Set training config\n", + "model.fit.vertex.remote_config.display_name = REMOTE_JOB_NAME + \"-sklearn-model\"\n", + "model.fit.vertex.remote_config.staging_bucket = REMOTE_JOB_BUCKET\n", + "\n", + "# Train model on Vertex\n", + "model.fit(train_X, train_y)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ffe1d5903bcb" + }, + "source": [ + "## Remote prediction\n", + "\n", + "Obtain predictions from the trained model." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "d00ce35920fa" + }, + "outputs": [], + "source": [ + "# Remote evaluation\n", + "vertexai.preview.init(remote=True)\n", + "\n", + "# Evaluate model's accuracy score\n", + "predictions = model.predict(test_X)\n", + "\n", + "print(f\"Remote predictions: {predictions}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "a8cd6cbd4403" + }, + "source": [ + "## Local evaluation\n", + "\n", + "Score model results locally." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "dc105dafdfb9" + }, + "outputs": [], + "source": [ + "# User must convert bigframes to pandas dataframe for local evaluation\n", + "train_X_pd = train_X.to_pandas().reset_index(drop=True)\n", + "train_y_pd = train_y.to_pandas().reset_index(drop=True)\n", + "\n", + "test_X_pd = test_X.to_pandas().reset_index(drop=True)\n", + "test_y_pd = test_y.to_pandas().reset_index(drop=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "25fec549de69" + }, + "outputs": [], + "source": [ + "# Switch to local mode for testing\n", + "vertexai.preview.init(remote=False)\n", + "\n", + "# Evaluate model's accuracy score\n", + "print(f\"Train accuracy: {model.score(train_X_pd, train_y_pd)}\")\n", + "\n", + "print(f\"Test accuracy: {model.score(test_X_pd, test_y_pd)}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "TpV-iwP9qw9c" + }, + "source": [ + "## Cleaning up\n", + "\n", + "To clean up all Google Cloud resources used in this project, you can [delete the Google Cloud\n", + "project](https://cloud.google.com/resource-manager/docs/creating-managing-projects#shutting_down_projects) you used for the tutorial.\n", + "\n", + "Otherwise, you can delete the individual resources you created in this tutorial:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "sx_vKniMq9ZX" + }, + "outputs": [], + "source": [ + "import os\n", + "\n", + "# Delete Cloud Storage objects that were created\n", + "delete_bucket = False\n", + "if delete_bucket or os.getenv(\"IS_TESTING\"):\n", + " ! gsutil -m rm -r $BUCKET_URI" + ] + } + ], + "metadata": { + "colab": { + "collapsed_sections": [], + "name": "sdk2_bigframes_sklearn.ipynb", + "toc_visible": true + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks/vertex_sdk/sdk2_bigframes_tensorflow.ipynb b/notebooks/vertex_sdk/sdk2_bigframes_tensorflow.ipynb new file mode 100644 index 00000000000..e6843b66b57 --- /dev/null +++ b/notebooks/vertex_sdk/sdk2_bigframes_tensorflow.ipynb @@ -0,0 +1,646 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "ur8xi4C7S06n" + }, + "outputs": [], + "source": [ + "# Copyright 2023 Google LLC\n", + "#\n", + "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", + "# you may not use this file except in compliance with the License.\n", + "# You may obtain a copy of the License at\n", + "#\n", + "# https://www.apache.org/licenses/LICENSE-2.0\n", + "#\n", + "# Unless required by applicable law or agreed to in writing, software\n", + "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", + "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", + "# See the License for the specific language governing permissions and\n", + "# limitations under the License." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "JAPoU8Sm5E6e" + }, + "source": [ + "# Train a Tensorflow Keras model with Vertex AI SDK 2.0 and Bigframes \n", + "\n", + "\n", + " \n", + " \n", + "
\n", + " \n", + " \"Colab Run in Colab\n", + " \n", + " \n", + " \n", + " \"GitHub\n", + " View on GitHub\n", + " \n", + " \n", + " \n", + " \"VertexOpen in Vertex AI Workbench\n", + " \n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "tvgnzT1CKxrO" + }, + "source": [ + "## Overview\n", + "\n", + "This tutorial demonstrates how to train a tensorflow keras model using Vertex AI local-to-remote training with Vertex AI SDK 2.0 and BigQuery Bigframes as the data source.\n", + "\n", + "Learn more about [bigframes](https://cloud.google.com/bigquery/docs/)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "d975e698c9a4" + }, + "source": [ + "### Objective\n", + "\n", + "In this tutorial, you learn to use `Vertex AI SDK 2.0` with Bigframes as input data source.\n", + "\n", + "\n", + "This tutorial uses the following Google Cloud ML services:\n", + "\n", + "- `Vertex AI Training`\n", + "- `Vertex AI Remote Training`\n", + "\n", + "\n", + "The steps performed include:\n", + "\n", + "- Initialize a dataframe from a BigQuery table and split the dataset\n", + "- Perform transformations as a Vertex AI remote training.\n", + "- Train the model remotely and evaluate the model locally\n", + "\n", + "**Local-to-remote training**\n", + "\n", + "```\n", + "import vertexai\n", + "from my_module import MyModelClass\n", + "\n", + "vertexai.preview.init(remote=True, project=\"my-project\", location=\"my-location\", staging_bucket=\"gs://my-bucket\")\n", + "\n", + "# Wrap the model class with `vertex_ai.preview.remote`\n", + "MyModelClass = vertexai.preview.remote(MyModelClass)\n", + "\n", + "# Instantiate the class\n", + "model = MyModelClass(...)\n", + "\n", + "# Optional set remote config\n", + "model.fit.vertex.remote_config.display_name = \"MyModelClass-remote-training\"\n", + "model.fit.vertex.remote_config.staging_bucket = \"gs://my-bucket\"\n", + "\n", + "# This `fit` call will be executed remotely\n", + "model.fit(...)\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "08d289fa873f" + }, + "source": [ + "### Dataset\n", + "\n", + "This tutorial uses the IRIS dataset, which predicts the iris species." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "aed92deeb4a0" + }, + "source": [ + "### Costs\n", + "\n", + "This tutorial uses billable components of Google Cloud:\n", + "\n", + "* Vertex AI\n", + "* BigQuery\n", + "* Cloud Storage\n", + "\n", + "Learn about [Vertex AI pricing](https://cloud.google.com/vertex-ai/pricing),\n", + "[BigQuery pricing](https://cloud.google.com/bigquery/pricing),\n", + "and [Cloud Storage pricing](https://cloud.google.com/storage/pricing), \n", + "and use the [Pricing Calculator](https://cloud.google.com/products/calculator/)\n", + "to generate a cost estimate based on your projected usage." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "i7EUnXsZhAGF" + }, + "source": [ + "## Installation\n", + "\n", + "Install the following packages required to execute this notebook. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "2b4ef9b72d43" + }, + "outputs": [], + "source": [ + "# Install the packages\n", + "! pip3 install --upgrade --quiet google-cloud-aiplatform[preview]\n", + "! pip3 install --upgrade --quiet bigframes\n", + "! pip3 install --upgrade --quiet tensorflow==2.12.0" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "58707a750154" + }, + "source": [ + "### Colab only: Uncomment the following cell to restart the kernel." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "f200f10a1da3" + }, + "outputs": [], + "source": [ + "# Automatically restart kernel after installs so that your environment can access the new packages\n", + "# import IPython\n", + "\n", + "# app = IPython.Application.instance()\n", + "# app.kernel.do_shutdown(True)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "BF1j6f9HApxa" + }, + "source": [ + "## Before you begin\n", + "\n", + "### Set up your Google Cloud project\n", + "\n", + "**The following steps are required, regardless of your notebook environment.**\n", + "\n", + "1. [Select or create a Google Cloud project](https://console.cloud.google.com/cloud-resource-manager). When you first create an account, you get a $300 free credit towards your compute/storage costs.\n", + "\n", + "2. [Make sure that billing is enabled for your project](https://cloud.google.com/billing/docs/how-to/modify-project).\n", + "\n", + "3. [Enable the Vertex AI API](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com).\n", + "\n", + "4. If you are running this notebook locally, you need to install the [Cloud SDK](https://cloud.google.com/sdk)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "WReHDGG5g0XY" + }, + "source": [ + "#### Set your project ID\n", + "\n", + "**If you don't know your project ID**, try the following:\n", + "* Run `gcloud config list`.\n", + "* Run `gcloud projects list`.\n", + "* See the support page: [Locate the project ID](https://support.google.com/googleapi/answer/7014113)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "oM1iC_MfAts1" + }, + "outputs": [], + "source": [ + "PROJECT_ID = \"[your-project-id]\" # @param {type:\"string\"}\n", + "\n", + "# Set the project id\n", + "! gcloud config set project {PROJECT_ID}" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "region" + }, + "source": [ + "#### Region\n", + "\n", + "You can also change the `REGION` variable used by Vertex AI. Learn more about [Vertex AI regions](https://cloud.google.com/vertex-ai/docs/general/locations)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "region" + }, + "outputs": [], + "source": [ + "REGION = \"us-central1\" # @param {type: \"string\"}" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "sBCra4QMA2wR" + }, + "source": [ + "### Authenticate your Google Cloud account\n", + "\n", + "Depending on your Jupyter environment, you may have to manually authenticate. Follow the relevant instructions below." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "74ccc9e52986" + }, + "source": [ + "**1. Vertex AI Workbench**\n", + "* Do nothing as you are already authenticated." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "de775a3773ba" + }, + "source": [ + "**2. Local JupyterLab instance, uncomment and run:**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "254614fa0c46" + }, + "outputs": [], + "source": [ + "# ! gcloud auth login" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ef21552ccea8" + }, + "source": [ + "**3. Colab, uncomment and run:**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "603adbbf0532" + }, + "outputs": [], + "source": [ + "# from google.colab import auth\n", + "# auth.authenticate_user()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "f6b2ccc891ed" + }, + "source": [ + "**4. Service account or other**\n", + "* See how to grant Cloud Storage permissions to your service account at https://cloud.google.com/storage/docs/gsutil/commands/iam#ch-examples." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "zgPO1eR3CYjk" + }, + "source": [ + "### Create a Cloud Storage bucket\n", + "\n", + "Create a storage bucket to store intermediate artifacts such as datasets." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "MzGDU7TWdts_" + }, + "outputs": [], + "source": [ + "BUCKET_URI = f\"gs://your-bucket-name-{PROJECT_ID}-unique\" # @param {type:\"string\"}" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-EcIXiGsCePi" + }, + "source": [ + "**Only if your bucket doesn't already exist**: Run the following cell to create your Cloud Storage bucket." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "NIq7R4HZCfIc" + }, + "outputs": [], + "source": [ + "! gsutil mb -l {REGION} -p {PROJECT_ID} {BUCKET_URI}" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "960505627ddf" + }, + "source": [ + "### Import libraries and define constants" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "PyQmSRbKA8r-" + }, + "outputs": [], + "source": [ + "import bigframes.pandas as bf\n", + "import tensorflow as tf\n", + "import vertexai\n", + "from tensorflow import keras\n", + "\n", + "bf.options.bigquery.location = \"us\" # Dataset is in 'us' not 'us-central1'\n", + "bf.options.bigquery.project = PROJECT_ID\n", + "\n", + "from bigframes.ml.model_selection import \\\n", + " train_test_split as bf_train_test_split" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "init_aip:mbsdk,all" + }, + "source": [ + "## Initialize Vertex AI SDK for Python\n", + "\n", + "Initialize the Vertex AI SDK for Python for your project and corresponding bucket." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "init_aip:mbsdk,all" + }, + "outputs": [], + "source": [ + "vertexai.init(\n", + " project=PROJECT_ID,\n", + " location=REGION,\n", + " staging_bucket=BUCKET_URI,\n", + ")\n", + "\n", + "REMOTE_JOB_NAME = \"sdk2-bigframes-tensorflow\"\n", + "REMOTE_JOB_BUCKET = f\"{BUCKET_URI}/{REMOTE_JOB_NAME}\"" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "105334524e96" + }, + "source": [ + "## Prepare the dataset\n", + "\n", + "Now load the Iris dataset and split the data into train and test sets." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "94576deccd8c" + }, + "outputs": [], + "source": [ + "df = bf.read_gbq(\"bigquery-public-data.ml_datasets.iris\")\n", + "\n", + "species_categories = {\n", + " \"versicolor\": 0,\n", + " \"virginica\": 1,\n", + " \"setosa\": 2,\n", + "}\n", + "df[\"target\"] = df[\"species\"].map(species_categories)\n", + "df = df.drop(columns=[\"species\"])\n", + "\n", + "train, test = bf_train_test_split(df, test_size=0.2)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "cfcbce726efa" + }, + "source": [ + "## Remote training with GPU\n", + "\n", + "First, train a TensorFlow model as a remote training job:\n", + "\n", + "- Reinitialize Vertex AI for remote training.\n", + "- Instantiate the tensorflow keras model for the remote training job.\n", + "- Invoke the tensorflow keras model.fit() locally which will launch the remote training job." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "fd865b0c4e8b" + }, + "outputs": [], + "source": [ + "# Switch to remote mode for training\n", + "vertexai.preview.init(remote=True)\n", + "\n", + "keras.Sequential = vertexai.preview.remote(keras.Sequential)\n", + "\n", + "# Instantiate model\n", + "model = keras.Sequential(\n", + " [keras.layers.Dense(5, input_shape=(4,)), keras.layers.Softmax()]\n", + ")\n", + "\n", + "# Specify optimizer and loss function\n", + "model.compile(optimizer=\"adam\", loss=\"mean_squared_error\")\n", + "\n", + "# Set training config\n", + "model.fit.vertex.remote_config.enable_cuda = True\n", + "model.fit.vertex.remote_config.display_name = REMOTE_JOB_NAME + \"-keras-model-gpu\"\n", + "model.fit.vertex.remote_config.staging_bucket = REMOTE_JOB_BUCKET\n", + "model.fit.vertex.remote_config.custom_commands = [\"pip install tensorflow-io==0.32.0\"]\n", + "\n", + "# Manually set compute resources this time\n", + "model.fit.vertex.remote_config.machine_type = \"n1-highmem-4\"\n", + "model.fit.vertex.remote_config.accelerator_type = \"NVIDIA_TESLA_K80\"\n", + "model.fit.vertex.remote_config.accelerator_count = 4\n", + "\n", + "# Train model on Vertex\n", + "model.fit(train, epochs=10)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "f1af94ac1477" + }, + "source": [ + "## Remote prediction\n", + "\n", + "Obtain predictions from the trained model." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "1d75879948b5" + }, + "outputs": [], + "source": [ + "vertexai.preview.init(remote=True)\n", + "\n", + "# Set remote config\n", + "model.predict.vertex.remote_config.enable_cuda = False\n", + "model.predict.vertex.remote_config.display_name = REMOTE_JOB_NAME + \"-keras-predict-cpu\"\n", + "model.predict.vertex.remote_config.staging_bucket = REMOTE_JOB_BUCKET\n", + "model.predict.vertex.remote_config.custom_commands = [\n", + " \"pip install tensorflow-io==0.32.0\"\n", + "]\n", + "\n", + "predictions = model.predict(train)\n", + "\n", + "print(f\"Remote predictions: {predictions}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "798b77c95067" + }, + "source": [ + "## Local evaluation\n", + "\n", + "Evaluate model results locally." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "88e734e30791" + }, + "outputs": [], + "source": [ + "# User must convert bigframes to pandas dataframe for local evaluation\n", + "feature_columns = [\"sepal_length\", \"sepal_width\", \"petal_length\", \"petal_width\"]\n", + "label_columns = [\"target\"]\n", + "\n", + "train_X_np = train[feature_columns].to_pandas().values.astype(float)\n", + "train_y_np = train[label_columns].to_pandas().values.astype(float)\n", + "train_ds = tf.data.Dataset.from_tensor_slices((train_X_np, train_y_np))\n", + "\n", + "test_X_np = test[feature_columns].to_pandas().values.astype(float)\n", + "test_y_np = test[label_columns].to_pandas().values.astype(float)\n", + "test_ds = tf.data.Dataset.from_tensor_slices((test_X_np, test_y_np))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "cb8637f783ad" + }, + "outputs": [], + "source": [ + "# Switch to local mode for evaluation\n", + "vertexai.preview.init(remote=False)\n", + "\n", + "# Evaluate model's mean square errors\n", + "print(f\"Train loss: {model.evaluate(train_ds.batch(32))}\")\n", + "\n", + "print(f\"Test loss: {model.evaluate(test_ds.batch(32))}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "TpV-iwP9qw9c" + }, + "source": [ + "## Cleaning up\n", + "\n", + "To clean up all Google Cloud resources used in this project, you can [delete the Google Cloud\n", + "project](https://cloud.google.com/resource-manager/docs/creating-managing-projects#shutting_down_projects) you used for the tutorial.\n", + "\n", + "Otherwise, you can delete the individual resources you created in this tutorial:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "sx_vKniMq9ZX" + }, + "outputs": [], + "source": [ + "import os\n", + "\n", + "# Delete Cloud Storage objects that were created\n", + "delete_bucket = False\n", + "if delete_bucket or os.getenv(\"IS_TESTING\"):\n", + " ! gsutil -m rm -r $BUCKET_URI" + ] + } + ], + "metadata": { + "colab": { + "collapsed_sections": [], + "name": "sdk2_bigframes_tensorflow.ipynb", + "toc_visible": true + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks/visualization/bq_dataframes_covid_line_graphs.ipynb b/notebooks/visualization/bq_dataframes_covid_line_graphs.ipynb deleted file mode 100644 index b28df7b0d7d..00000000000 --- a/notebooks/visualization/bq_dataframes_covid_line_graphs.ipynb +++ /dev/null @@ -1,648 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "id": "9GIt_orUtNvA" - }, - "outputs": [], - "source": [ - "# Copyright 2023 Google LLC\n", - "#\n", - "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", - "# you may not use this file except in compliance with the License.\n", - "# You may obtain a copy of the License at\n", - "#\n", - "# https://www.apache.org/licenses/LICENSE-2.0\n", - "#\n", - "# Unless required by applicable law or agreed to in writing, software\n", - "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", - "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", - "# See the License for the specific language governing permissions and\n", - "# limitations under the License." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "h7AT6h2ItNvD" - }, - "source": [ - "# Use BigQuery DataFrames to visualize COVID-19 data", - "\n", - "\n", - "\n", - " \n", - " \n", - " \n", - "
\n", - " \n", - " \"Colab Run in Colab\n", - " \n", - " \n", - " \n", - " \"GitHub\n", - " View on GitHub\n", - " \n", - " \n", - " \n", - " \"BQ\n", - " Open in BQ Studio\n", - " \n", - "
" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "n-MFJQxLtNvE" - }, - "source": [ - "## Overview\n", - "\n", - "The goal of this notebook is to demonstrate creating line graphs from a ~20 million-row BigQuery dataset using BigQuery DataFrames. We will first create a plain line graph using matplotlip, then we will downsample and download our data to create a graph with a line of best fit using seaborn.\n", - "\n", - "If you're like me, during 2020 (and/or later years) you often found yourself looking at charts like [these](https://health.google.com/covid-19/open-data/explorer/statistics) visualizing COVID-19 cases over time. For our first graph, we're going to recreate one of those charts by filtering, summing, and then graphing COVID-19 data from the United States. BigQuery DataFrame's default integration with matplotlib will get us a satisfying result for this first graph.\n", - "\n", - "For our second graph, though, we want to use a scatterplot with a line of best fit, something that matplotlib will not do for us automatically. So, we'll demonstrate how to downsample our data and use seaborn to make our plot. Our second graph will be of symptom-related search trends against new cases of COVID-19, so we'll see if searches for things like \"cough\" and \"fever\" are more common in the places and times where more new cases of COVID-19 occur." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "ffqBzbNztNvF" - }, - "source": [ - "### Dataset\n", - "\n", - "This notebook uses the [BigQuery COVID-19 Open Data](https://pantheon.corp.google.com/marketplace/product/bigquery-public-datasets/covid19-open-data). In this dataset, each row represents a new observation of the COVID-19 situation in a particular time and place. We will use the \"new_confirmed\" column, which contains the number of new COVID-19 cases at each observation, along with the \"search_trends_cough\", \"search_trends_fever\", and \"search_trends_bruise\" columns, which are [Google Trends](https://trends.google.com/trends/) data for searches related to cough, fever, and bruises. In the first section of the notebook, we will also use the \"country_code\" and \"date\" columns to compile one data point per day for a particular country." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Nf__tMR-tNvF" - }, - "source": [ - "### Costs\n", - "\n", - "This tutorial uses billable components of Google Cloud:\n", - "\n", - "* BigQuery (compute)\n", - "\n", - "Learn about [BigQuery compute pricing](https://cloud.google.com/bigquery/pricing#analysis_pricing_models),\n", - "and use the [Pricing Calculator](https://cloud.google.com/products/calculator/)\n", - "to generate a cost estimate based on your projected usage." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "7_rsbkCktNvG" - }, - "source": [ - "## Before you begin\n", - "\n", - "### Set up your Google Cloud project\n", - "\n", - "**The following steps are required, regardless of your notebook environment.**\n", - "\n", - "1. [Select or create a Google Cloud project](https://console.cloud.google.com/cloud-resource-manager). When you first create an account, you get a $300 free credit towards your compute/storage costs.\n", - "\n", - "2. [Make sure that billing is enabled for your project](https://cloud.google.com/billing/docs/how-to/modify-project).\n", - "\n", - "3. [Enable the BigQuery API](https://console.cloud.google.com/flows/enableapi?apiid=bigquery.googleapis.com).\n", - "\n", - "4. If you are running this notebook locally, you need to install the [Cloud SDK](https://cloud.google.com/sdk)." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "XZKC6iMFxmMG" - }, - "source": [ - "#### Set your project ID\n", - "\n", - "**If you don't know your project ID**, try the following:\n", - "* Run `gcloud config list`.\n", - "* Run `gcloud projects list`.\n", - "* See the support page: [Locate the project ID](https://support.google.com/googleapi/answer/7014113)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "id": "4aooKMmnxrWF" - }, - "outputs": [], - "source": [ - "PROJECT_ID = \"\" # @param {type:\"string\"}" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "pv5A8Tm-yC1U" - }, - "source": [ - "#### Set the region\n", - "\n", - "You can also change the `REGION` variable used by BigQuery. Learn more about [BigQuery regions](https://cloud.google.com/bigquery/docs/locations#supported_locations)." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "id": "bk03Rt_HyGx-" - }, - "outputs": [], - "source": [ - "REGION = \"US\" # @param {type: \"string\"}" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "B9RWxD1btNvK" - }, - "source": [ - "Now we are ready to use BigQuery DataFrames!" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "wJ0gXezj2w1t" - }, - "source": [ - "## Visualization #1: Cases over time in the US" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "xckgWno6ouHY" - }, - "source": [ - "### Set up project and filter data" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "-uiY0hh4tNvK" - }, - "source": [ - "First, let's do project setup. We use options to tell BigQuery DataFrames what project and what region to use for our cloud computing." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "id": "R7STCS8xB5d2" - }, - "outputs": [], - "source": [ - "import bigframes.pandas as bpd\n", - "\n", - "# Note: The project option is not required in all environments.\n", - "# On BigQuery Studio, the project ID is automatically detected.\n", - "bpd.options.bigquery.project = PROJECT_ID\n", - "\n", - "# Note: The location option is not required.\n", - "# It defaults to the location of the first table or query\n", - "# passed to read_gbq(). For APIs where a location can't be\n", - "# auto-detected, the location defaults to the \"US\" location.\n", - "bpd.options.bigquery.location = REGION\n", - "# Improves performance by avoiding generating total row ordering\n", - "bpd.options.bigquery.ordering_mode = \"partial\"" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "v6FGschEowht" - }, - "source": [ - "Next, we read the data from a publicly available BigQuery dataset. This will take ~1 minute." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "id": "zDSwoBo1CU3G" - }, - "outputs": [], - "source": [ - "all_data = bpd.read_gbq(\"bigquery-public-data.covid19_open_data.covid19_open_data\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "9qV2y3iHp13y" - }, - "source": [ - "Using pandas syntax, we will select from our all_data input dataframe only those rows where the country_code is US. This is called row filtering." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "id": "UjMT_qhjf8Fu" - }, - "outputs": [], - "source": [ - "usa_data = all_data[all_data[\"country_code\"] == \"US\"]" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "IYCUayWkwq8c" - }, - "source": [ - "We're only concerned with the date and the total number of confirmed cases for now, so select just those two columns as well." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "id": "IaoUf57ZwrJ8" - }, - "outputs": [], - "source": [ - "usa_data = usa_data[[\"date\", \"new_confirmed\"]]" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "94oqNRnDvGkr" - }, - "source": [ - "### Sum data" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "TNCQWZW83U0b" - }, - "source": [ - "`usa_data.groupby(\"date\")` will give us a groupby object that lets us perform operations on groups of rows with the same date. We call sum on that object to get the sum for each day. This process might be familiar to pandas users." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "id": "tYDoaKgJChiq" - }, - "outputs": [], - "source": [ - "# numeric_only = True because we don't want to sum dates\n", - "new_cases_usa = usa_data.groupby(\"date\").sum(numeric_only = True)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "3jcwFPgK5BLh" - }, - "source": [ - "### Line graph" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "8GvJAgnH5Nzi" - }, - "source": [ - "BigQuery DataFrames implements some plotting methods with the matplotlib backend. Use `DataFrame.plot.line()` to draw a simple line graph." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "id": "gFbCgfFC2gHw" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "new_cases_usa.plot.line(\n", - " rot=45,\n", - " ylabel=\"New Cases\",\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "sM5-HFDx70RG" - }, - "source": [ - "## Visualization #2: Symptom-related searches compared to new cases" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "se1b6Vf4XB9_" - }, - "source": [ - "### Filter data" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Wl2o-NYMoygb" - }, - "source": [ - "We're curious if searches for symptoms like \"cough\" and \"fever\" went up in the same times and places that new COVID-19 cases occured, compared to non-symptoms like \"bruise.\" Let's plot searches vs. new cases to see if it looks like there's a correlation." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "olfnCzyg8jYi" - }, - "source": [ - "First, we select the new cases column and the search trends we're interested in." - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "metadata": { - "id": "LqqHzjty8jk0" - }, - "outputs": [], - "source": [ - "regional_data = all_data[all_data[\"aggregation_level\"] == 1] # get only region level data,\n", - "symptom_data = regional_data[[\"location_key\", \"new_confirmed\", \"search_trends_cough\", \"search_trends_fever\", \"search_trends_bruise\", \"population\", \"date\"]]" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "b3DlJX-k9SPk" - }, - "source": [ - "Not all rows have data for all of these columns, so let's select only the rows that do. Finally, lets add a new column capturing new confirmed cases as a percentage of area population." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "g4MeM8Oe9Q6X" - }, - "outputs": [], - "source": [ - "symptom_data = symptom_data.dropna()\n", - "symptom_data = symptom_data[symptom_data[\"new_confirmed\"] > 0]\n", - "symptom_data[\"new_cases_percent_of_pop\"] = (symptom_data[\"new_confirmed\"] / symptom_data[\"population\"]) * 100\n", - "\n", - "\n", - "# remove impossible data points\n", - "symptom_data = symptom_data[(symptom_data[\"new_cases_percent_of_pop\"] >= 0)]\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# group data up by week\n", - "weekly_data = symptom_data.groupby([symptom_data.location_key, symptom_data.date.dt.isocalendar().week]).agg({\"new_cases_percent_of_pop\": \"sum\", \"search_trends_cough\": \"mean\", \"search_trends_fever\": \"mean\", \"search_trends_bruise\": \"mean\"})" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "IlXt__om9QYI" - }, - "source": [ - "We want to use a line of best fit to make the correlation stand out. Matplotlib does not include a feature for lines of best fit, but seaborn, which is built on matplotlib, does.\n", - "\n", - "BigQuery DataFrames does not currently integrate with seaborn by default. So we will demonstrate how to downsample and download a DataFrame, and use seaborn on the downloaded data." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "T9Hub_EAXWvY" - }, - "source": [ - "### Graph with lines of best fit" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "hoQ9TPgUPJnN" - }, - "source": [ - "We will now use seaborn to make the plots with the lines of best fit for cough, fever, and bruise. Note that since we're working with a local pandas dataframe, you could use any other Python library or technique you're familiar with, but we'll stick to seaborn for this notebook.\n", - "\n", - "Seaborn will take a few minutes to calculate the lines. Since cough and fever are symptoms of COVID-19, but bruising isn't, we expect the slope of the line of best fit to be positive in the first two graphs, but not the third, indicating that there is a correlation between new COVID-19 cases and cough- and fever-related searches." - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "metadata": { - "id": "EG7qM3R18bOb" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 59, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import seaborn as sns\n", - "\n", - "# draw the graph. This might take ~30 seconds.\n", - "sns.regplot(x=\"new_cases_percent_of_pop\", y=\"search_trends_cough\", data=weekly_data, scatter_kws={'alpha': 0.2, \"s\" :5})" - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "metadata": { - "id": "5nVy61rEGaM4" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 62, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# similarly, for fever\n", - "sns.regplot(x=\"new_cases_percent_of_pop\", y=\"search_trends_fever\", data=weekly_data, scatter_kws={'alpha': 0.2, \"s\" :5})" - ] - }, - { - "cell_type": "code", - "execution_count": 63, - "metadata": { - "id": "-S1A9E3WGaYH" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 63, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# similarly, for bruise\n", - "sns.regplot(\n", - " x=\"new_cases_percent_of_pop\",\n", - " y=\"search_trends_bruise\",\n", - " data=weekly_data,\n", - " scatter_kws={'alpha': 0.2, \"s\" :5}\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Hd2A8707Uhz2" - }, - "source": [ - "We see that the slope of the line is positive in the graphs for cough and fever, but flat for bruise. That means that in places with increasing new cases of COVID-19, we saw increasing searches for cough and fever, but we didn't see increasing searches for unrelated symptoms like bruises. Interesting!" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Recap" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We used matplotlib to draw a line graph of COVID-19 cases over time in the USA. Then, we used downsampling to download only a portion of the available data, used seaborn to plot lines of best fit to observe corellation between COVID-19 cases and searches for related versus unrelated symptoms.\n", - "\n", - "Thank you for using BigQuery DataFrames!" - ] - } - ], - "metadata": { - "colab": { - "provenance": [] - }, - "kernelspec": { - "display_name": "venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.6" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/notebooks/visualization/tutorial.ipynb b/notebooks/visualization/tutorial.ipynb deleted file mode 100644 index 89a5ed87b8f..00000000000 --- a/notebooks/visualization/tutorial.ipynb +++ /dev/null @@ -1,1448 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "b11d1db5", - "metadata": {}, - "outputs": [], - "source": [ - "# Copyright 2025 Google LLC\n", - "#\n", - "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", - "# you may not use this file except in compliance with the License.\n", - "# You may obtain a copy of the License at\n", - "#\n", - "# https://www.apache.org/licenses/LICENSE-2.0\n", - "#\n", - "# Unless required by applicable law or agreed to in writing, software\n", - "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", - "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", - "# See the License for the specific language governing permissions and\n", - "# limitations under the License." - ] - }, - { - "cell_type": "markdown", - "id": "e661697d", - "metadata": {}, - "source": [ - "# BigQuery DataFrame Visualization Tutorials", - "\n", - "\n", - "\n", - " \n", - " \n", - " \n", - "
\n", - " \n", - " \"Colab Run in Colab\n", - " \n", - " \n", - " \n", - " \"GitHub\n", - " View on GitHub\n", - " \n", - " \n", - " \n", - " \"BQ\n", - " Open in BQ Studio\n", - " \n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "5e93c4c1", - "metadata": {}, - "source": [ - "This notebook provides tutorials for all plotting methods that BigQuery DataFrame offers. You will visualize different datasets with histograms, line charts, area charts, bar charts, and scatter plots." - ] - }, - { - "cell_type": "markdown", - "id": "f96c47f7", - "metadata": {}, - "source": [ - "# Before you begin" - ] - }, - { - "cell_type": "markdown", - "id": "a8dd598a", - "metadata": {}, - "source": [ - "## Set up your project ID and region" - ] - }, - { - "cell_type": "markdown", - "id": "d442ab74", - "metadata": {}, - "source": [ - "This step makes sure that you will access the target dataset with the correct auth profile." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "7cc6237d", - "metadata": {}, - "outputs": [], - "source": [ - "PROJECT_ID = \"bigframes-dev\" # @param {type:\"string\"}\n", - "REGION = \"US\" # @param {type: \"string\"}" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "bf96593a", - "metadata": {}, - "outputs": [], - "source": [ - "import bigframes.pandas as bpd\n", - "\n", - "bpd.options.bigquery.project = PROJECT_ID\n", - "bpd.options.bigquery.location = REGION" - ] - }, - { - "cell_type": "markdown", - "id": "165fedc6", - "metadata": {}, - "source": [ - "You can also turn on the partial ordering mode for faster data processing." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "ac5a1722", - "metadata": {}, - "outputs": [], - "source": [ - "bpd.options.bigquery.ordering_mode = 'partial'" - ] - }, - { - "cell_type": "markdown", - "id": "2ed45ca7", - "metadata": {}, - "source": [ - "# Histogram" - ] - }, - { - "cell_type": "markdown", - "id": "88837be7", - "metadata": {}, - "source": [ - "You will use the penguins public dataset in this example. First, you take a look at the shape of this data:" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "fb595a8f", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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speciesislandculmen_length_mmculmen_depth_mmflipper_length_mmbody_mass_gsex
0Adelie Penguin (Pygoscelis adeliae)Dream36.618.4184.03475.0FEMALE
1Adelie Penguin (Pygoscelis adeliae)Dream39.819.1184.04650.0MALE
2Adelie Penguin (Pygoscelis adeliae)Dream40.918.9184.03900.0MALE
3Chinstrap penguin (Pygoscelis antarctica)Dream46.517.9192.03500.0FEMALE
4Adelie Penguin (Pygoscelis adeliae)Dream37.316.8192.03000.0FEMALE
\n", - "
" - ], - "text/plain": [ - " species island culmen_length_mm \\\n", - "0 Adelie Penguin (Pygoscelis adeliae) Dream 36.6 \n", - "1 Adelie Penguin (Pygoscelis adeliae) Dream 39.8 \n", - "2 Adelie Penguin (Pygoscelis adeliae) Dream 40.9 \n", - "3 Chinstrap penguin (Pygoscelis antarctica) Dream 46.5 \n", - "4 Adelie Penguin (Pygoscelis adeliae) Dream 37.3 \n", - "\n", - " culmen_depth_mm flipper_length_mm body_mass_g sex \n", - "0 18.4 184.0 3475.0 FEMALE \n", - "1 19.1 184.0 4650.0 MALE \n", - "2 18.9 184.0 3900.0 MALE \n", - "3 17.9 192.0 3500.0 FEMALE \n", - "4 16.8 192.0 3000.0 FEMALE " - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "penguins = bpd.read_gbq('bigquery-public-data.ml_datasets.penguins')\n", - "penguins.peek()" - ] - }, - { - "cell_type": "markdown", - "id": "176c12f8", - "metadata": {}, - "source": [ - "You want to draw a histogram about the distribution of culmen lengths:" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "333e88a3", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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BAADGosgAAABjUWQAAICxKDIAAMBYFBkAAGAsigwAADAWRQYAABiLIgMAAIxFkQEAAMaiyAAAAGNZfR0A8LTEORsavG3BgpFeTAIA8DZGZAAAgLEoMgAAwFgUGQAAYCyKDAAAMBZFBgAAGIsiAwAAjEWRAQAAxqLIAAAAY1FkAACAsSgyAADAWBQZAABgLIoMAAAwFkUGAAAYiyIDAACMZfV1AACA5yTO2eCV/RYsGOmV/QJNxYgMAAAwFkUGAAAYiyIDAACMRZEBAADGosgAAABjUWQAAICxKDIAAMBYFBkAAGAsigwAADAWRQYAABiLIgMAAIzl0yKzfft2jRo1SvHx8bJYLFq3bp3L8+np6bJYLC6P4cOH+yYsAADwOz4tMhUVFerVq5eWLl16xW2GDx+uoqIi5+Odd95pxoQAAMCf+fTu1yNGjNCIESPq3SY4OFg2m62ZEgEAAJP4/RyZbdu2KSYmRl27dtXDDz+sc+fO1bt9ZWWlysrKXB4AAKBl8umIzNUMHz5cY8aMUefOnZWXl6cnn3xSI0aM0M6dOxUQEFDna7KysjRv3rxmTgoA3pE4Z4OvIwB+za+LzPjx451/vummm9SzZ0916dJF27Zt0+DBg+t8zdy5c5WZmelcLisrU0JCgtezAgCA5uf3l5Z+6Prrr1e7du107NixK24THBysiIgIlwcAAGiZjCoyJ0+e1Llz5xQXF+frKAAAwA/49NJSeXm5y+hKfn6+9u3bp+joaEVHR2vevHkaO3asbDab8vLyNHv2bCUlJWnYsGE+TA0AAPyFT4vMnj17NGjQIOfy5bktkyZN0ptvvqkDBw5oxYoVKikpUXx8vIYOHaoXXnhBwcHBvooMAAD8iE+LzMCBA+VwOK74/IcfftiMaQAAgGmMmiMDAADwQxQZAABgLIoMAAAwFkUGAAAYiyIDAACM5VaR+fLLLz2dAwAAoNHcKjJJSUkaNGiQ/vznP+vixYuezgQAANAgbhWZzz//XD179lRmZqZsNpv+67/+S7t37/Z0NgAAgHq59YF4vXv31muvvaZFixZp/fr1ysnJUWpqqlJSUvTQQw9pwoQJat++vaezAjBU4pwNvo7gdY35GgsWjPRiEu9o6V8fzNWkyb5Wq1VjxozR6tWr9dJLL+nYsWOaOXOmEhISNHHiRBUVFXkqJwAAQC1NKjJ79uzRI488ori4OC1evFgzZ85UXl6ePv74Y506dUp33323p3ICAADU4talpcWLFys7O1uHDx/WXXfdpZUrV+quu+5Sq1bf96LOnTsrJydHiYmJnswKAADgwq0i8+abb+qhhx5Senq64uLi6twmJiZGy5cvb1I4AACA+rhVZI4ePXrVbYKCgjRp0iR3dg8AANAgbs2Ryc7O1urVq2utX716tVasWNHkUAAAAA3hVpHJyspSu3btaq2PiYnRb3/72yaHAgAAaAi3ikxhYaE6d+5ca32nTp1UWFjY5FAAAAAN4VaRiYmJ0YEDB2qt379/v9q2bdvkUAAAAA3hVpG577779Otf/1pbt25VTU2NampqtGXLFj322GMaP368pzMCAADUya13Lb3wwgsqKCjQ4MGDZbV+vwu73a6JEycyRwYAADQbt4pMUFCQ3n33Xb3wwgvav3+/QkNDddNNN6lTp06ezgcAAHBFbhWZy1JSUpSSkuKpLAAAAI3iVpGpqalRTk6ONm/erOLiYtntdpfnt2zZ4pFwAAAA9XGryDz22GPKycnRyJEj1aNHD1ksFk/ngoES52xo8LYFC0Z6MQkA4FrhVpFZtWqV/vKXv+iuu+7ydB4AAIAGc+vt10FBQUpKSvJ0FgAAgEZxq8jMmDFDr732mhwOh6fzAAAANJhbl5Y+/fRTbd26VRs3btSNN96owMBAl+fXrFnjkXAAAAD1cavIREVFKS0tzdNZAAAAGsWtIpOdne3pHAAAAI3m1hwZSbp06ZI++eQT/eEPf9D58+clSadOnVJ5ebnHwgEAANTHrRGZ48ePa/jw4SosLFRlZaV+/vOfKzw8XC+99JIqKyu1bNkyT+cEAACoxa0Rmccee0y33nqrvv32W4WGhjrXp6WlafPmzR4LBwAAUB+3RmT+/ve/a8eOHQoKCnJZn5iYqK+++sojwQAAAK7GrREZu92umpqaWutPnjyp8PDwJocCAABoCLeKzNChQ7VkyRLnssViUXl5uZ599lluWwAAAJqNW5eWFi1apGHDhql79+66ePGi7r//fh09elTt2rXTO++84+mMAAAAdXKryHTo0EH79+/XqlWrdODAAZWXl2vKlCl64IEHXCb/AgAAeJNbRUaSrFarHnzwQU9mAQAAaBS3iszKlSvrfX7ixIluhQEAAGgMt4rMY4895rJcXV2tCxcuKCgoSGFhYRQZAADQLNx619K3337r8igvL9fhw4eVmprKZF8AANBs3L7X0o8lJydrwYIFtUZrAAAAvMVjRUb6fgLwqVOnPLlLAACAK3Jrjsz69etdlh0Oh4qKivTGG2/o9ttv90gwAACAq3GryIwePdpl2WKxqH379rrzzju1aNEiT+QCAAC4KreKjN1u93QOAACARvPoHBkAAIDm5NaITGZmZoO3Xbx4sTuHAAAAuCq3iswXX3yhL774QtXV1eratask6ciRIwoICNAtt9zi3M5isXgmJQAAQB3cKjKjRo1SeHi4VqxYoeuuu07S9x+SN3nyZA0YMEAzZszwaEgAAIC6uDVHZtGiRcrKynKWGEm67rrrNH/+fN61BAAAmo1bRaasrExnz56ttf7s2bM6f/58k0MBAAA0hFtFJi0tTZMnT9aaNWt08uRJnTx5Uv/93/+tKVOmaMyYMZ7OCAAAUCe35sgsW7ZMM2fO1P3336/q6urvd2S1asqUKXr55Zc9GhAAYJbEORsatX3BgpFeSoJrgVtFJiwsTL///e/18ssvKy8vT5LUpUsXtW7d2qPhAAAA6tOkD8QrKipSUVGRkpOT1bp1azkcDk/lAgAAuCq3isy5c+c0ePBgpaSk6K677lJRUZEkacqUKbz1GgAANBu3iszjjz+uwMBAFRYWKiwszLl+3Lhx2rRpk8fCAQAA1MetOTIfffSRPvzwQ3Xo0MFlfXJyso4fP+6RYAAAAFfj1ohMRUWFy0jMZd98842Cg4ObHAoAAKAh3CoyAwYM0MqVK53LFotFdrtdCxcu1KBBgzwWDgAAoD5uXVpauHChBg8erD179qiqqkqzZ8/W//zP/+ibb77RP/7xD09nBAAAqJNbIzI9evTQkSNHlJqaqrvvvlsVFRUaM2aMvvjiC3Xp0sXTGQEAAOrU6BGZ6upqDR8+XMuWLdNTTz3ljUwAAAAN0ugRmcDAQB04cMAjB9++fbtGjRql+Ph4WSwWrVu3zuV5h8OhZ555RnFxcQoNDdWQIUN09OhRjxwbAACYz61LSw8++KCWL1/e5INXVFSoV69eWrp0aZ3PL1y4UL/73e+0bNkyffbZZ2rdurWGDRumixcvNvnYAADAfG5N9r106ZLeeustffLJJ+rTp0+teywtXry4QfsZMWKERowYUedzDodDS5Ys0W9+8xvdfffdkqSVK1cqNjZW69at0/jx492JDgAAWpBGFZkvv/xSiYmJOnjwoG655RZJ0pEjR1y2sVgsHgmWn5+v06dPa8iQIc51kZGR6tevn3bu3HnFIlNZWanKykrncllZmUfyAAAA/9OoIpOcnKyioiJt3bpV0ve3JPjd736n2NhYjwc7ffq0JNXad2xsrPO5umRlZWnevHkez3OtSpyzwdcRjNWY713BgpFeTNJwJmYGcG1r1ByZH9/deuPGjaqoqPBooKaaO3euSktLnY8TJ074OhIAAPAStyb7XvbjYuNJNptNknTmzBmX9WfOnHE+V5fg4GBFRES4PAAAQMvUqCJjsVhqzYHx1JyYH+vcubNsNps2b97sXFdWVqbPPvtM/fv398oxAQCAWRo1R8bhcCg9Pd15Y8iLFy9q6tSptd61tGbNmgbtr7y8XMeOHXMu5+fna9++fYqOjlbHjh01ffp0zZ8/X8nJyercubOefvppxcfHa/To0Y2JDQAAWqhGFZlJkya5LD/44INNOviePXtcbjKZmZnpPE5OTo5mz56tiooK/ed//qdKSkqUmpqqTZs2KSQkpEnHBQAALUOjikx2drZHDz5w4MB659lYLBY9//zzev755z16XAAA0DI0abIvAACAL1FkAACAsSgyAADAWBQZAABgLIoMAAAwFkUGAAAYiyIDAACMRZEBAADGosgAAABjNeqTfQF4R+KcDV7bd8GCkV7bN9zjzfNtosZ8Pxrz8+yt/cK/MCIDAACMRZEBAADGosgAAABjUWQAAICxKDIAAMBYFBkAAGAsigwAADAWRQYAABiLIgMAAIxFkQEAAMaiyAAAAGNRZAAAgLEoMgAAwFgUGQAAYCyrrwOg6Rpzq3qJ29UDAFoORmQAAICxKDIAAMBYFBkAAGAsigwAADAWRQYAABiLIgMAAIxFkQEAAMaiyAAAAGNRZAAAgLEoMgAAwFgUGQAAYCyKDAAAMBZFBgAAGIsiAwAAjGX1dQDULXHOBl9H8KrGfn0FC0Z6KQlM15ifJX6OgJaHERkAAGAsigwAADAWRQYAABiLIgMAAIxFkQEAAMaiyAAAAGNRZAAAgLEoMgAAwFgUGQAAYCyKDAAAMBZFBgAAGIsiAwAAjEWRAQAAxqLIAAAAY1l9HQDwpcQ5Gxq8bcGCkV5M4j2N+Rr9Yb/eZGJmAPVjRAYAABiLIgMAAIxFkQEAAMaiyAAAAGNRZAAAgLEoMgAAwFgUGQAAYCyKDAAAMBZFBgAAGIsiAwAAjEWRAQAAxvLrIvPcc8/JYrG4PLp16+brWAAAwE/4/U0jb7zxRn3yySfOZavV7yMDAIBm4vetwGq1ymaz+ToGAADwQ359aUmSjh49qvj4eF1//fV64IEHVFhYWO/2lZWVKisrc3kAAICWya9HZPr166e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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "penguins['culmen_depth_mm'].plot.hist(bins=40)" - ] - }, - { - "cell_type": "markdown", - "id": "9e0aa359", - "metadata": {}, - "source": [ - "# Line Chart" - ] - }, - { - "cell_type": "markdown", - "id": "b0f37913", - "metadata": {}, - "source": [ - "In this example you will use the NOAA public dataset." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "49ed2417", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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stnwbandateyearmodatempcount_tempdewpcount_dewp...flag_minprcpflag_prcpsndpfograin_drizzlesnow_ice_pelletshailthundertornado_funnel_cloud
0010030999992021-11-102021111026.4417.94...<NA>0.0I999.9000000
1010030999992021-02-01202102018.940.54...<NA>2.76G999.9000000
2010060999992021-07-222021072234.449999.90...<NA>0.0I999.9000000
3010070999992021-04-052021040517.946.44...<NA>0.0I999.9000000
4010070999992021-02-042021020419.149999.90...<NA>0.0I999.9000000
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" - ], - "text/plain": [ - " stn wban date year mo da temp count_temp dewp \\\n", - "0 010030 99999 2021-11-10 2021 11 10 26.4 4 17.9 \n", - "1 010030 99999 2021-02-01 2021 02 01 8.9 4 0.5 \n", - "2 010060 99999 2021-07-22 2021 07 22 34.4 4 9999.9 \n", - "3 010070 99999 2021-04-05 2021 04 05 17.9 4 6.4 \n", - "4 010070 99999 2021-02-04 2021 02 04 19.1 4 9999.9 \n", - "\n", - " count_dewp ... flag_min prcp flag_prcp sndp fog rain_drizzle \\\n", - "0 4 ... 0.0 I 999.9 0 0 \n", - "1 4 ... 2.76 G 999.9 0 0 \n", - "2 0 ... 0.0 I 999.9 0 0 \n", - "3 4 ... 0.0 I 999.9 0 0 \n", - "4 0 ... 0.0 I 999.9 0 0 \n", - "\n", - " snow_ice_pellets hail thunder tornado_funnel_cloud \n", - "0 0 0 0 0 \n", - "1 0 0 0 0 \n", - "2 0 0 0 0 \n", - "3 0 0 0 0 \n", - "4 0 0 0 0 \n", - "\n", - "[5 rows x 33 columns]" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "noaa_surface = bpd.read_gbq(\"bigquery-public-data.noaa_gsod.gsod2021\")\n", - "noaa_surface.peek()" - ] - }, - { - "cell_type": "markdown", - "id": "239ec3d1", - "metadata": {}, - "source": [ - "You are going to plot a line chart of temperatures by date. The original dataset contains many rows for a single date, and you wan to coalesce them with their median values." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "e06afd00", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "Query job a2cee421-0f51-49a8-918a-68177b3199dc is DONE. 64.4 MB processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
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temp
date
2021-02-1224.6
2021-02-1125.9
2021-02-1330.4
2021-02-1432.1
2021-01-0932.9
\n", - "
" - ], - "text/plain": [ - " temp\n", - "date \n", - "2021-02-12 24.6\n", - "2021-02-11 25.9\n", - "2021-02-13 30.4\n", - "2021-02-14 32.1\n", - "2021-01-09 32.9" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "noaa_surface_median_temps=noaa_surface[['date', 'temp']].groupby('date').median()\n", - "noaa_surface_median_temps.peek()" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "68324aaf", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "noaa_surface_median_temps.plot.line()" - ] - }, - { - "cell_type": "markdown", - "id": "5b1e75df", - "metadata": {}, - "source": [ - "# Area Chart" - ] - }, - { - "cell_type": "markdown", - "id": "544f5605", - "metadata": {}, - "source": [ - "In this example you will use the table that tracks the popularity of names in the USA." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "0c8f9726", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
stategenderyearnamenumber
0ALF1910Sadie40
1ALF1910Mary875
2ARF1910Vera39
3ARF1910Marie78
4ARF1910Lucille66
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" - ], - "text/plain": [ - " state gender year name number\n", - "0 AL F 1910 Sadie 40\n", - "1 AL F 1910 Mary 875\n", - "2 AR F 1910 Vera 39\n", - "3 AR F 1910 Marie 78\n", - "4 AR F 1910 Lucille 66" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "usa_names = bpd.read_gbq(\"bigquery-public-data.usa_names.usa_1910_2013\")\n", - "usa_names.peek()" - ] - }, - { - "cell_type": "markdown", - "id": "be525493", - "metadata": {}, - "source": [ - "You want to visualize the trends of the popularities of three names in US history: Mary, Emily and Lisa." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "a12cd1f5", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "Query job d1fc606b-18b0-4e7e-a669-0e505a95aa5a is DONE. 132.6 MB processed. Open Job" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
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nameEmilyLisaMary
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19331036055769
\n", - "
" - ], - "text/plain": [ - "name Emily Lisa Mary\n", - "year \n", - "1927 1631 0 70864\n", - "1918 2353 0 67492\n", - "1912 1126 0 32375\n", - "1923 2047 0 71799\n", - "1933 1036 0 55769" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "name_counts = usa_names[usa_names['name'].isin(('Mary', 'Emily', 'Lisa'))].groupby(('year', 'name'))['number'].sum()\n", - "name_counts = name_counts.unstack(level=1).fillna(0)\n", - "name_counts.peek()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "4af287bd", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "name_counts.plot.area(stacked=False, alpha=0.5)" - ] - }, - { - "cell_type": "markdown", - "id": "26d14b7e", - "metadata": {}, - "source": [ - "You can also use set `subplots` to `True` to draw separate graphs for each column." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "531e20b5", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([, ,\n", - " ], dtype=object)" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "name_counts.plot.area(subplots=True, alpha=0.5)" - ] - }, - { - "cell_type": "markdown", - "id": "89d576a9", - "metadata": {}, - "source": [ - "# Bar Chart" - ] - }, - { - "cell_type": "markdown", - "id": "9e4c6864", - "metadata": {}, - "source": [ - "Bar Charts are suitable for analyzing categorical data. For example, you are going to check the sex distribution of the penguin data:" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "e4aef1a1", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "penguin_count_by_sex = penguins[penguins['sex'].isin((\"MALE\", \"FEMALE\"))].groupby('sex')['species'].count()\n", - "penguin_count_by_sex.plot.bar()" - ] - }, - { - "cell_type": "markdown", - "id": "41f5f621", - "metadata": {}, - "source": [ - "# Scatter Plot" - ] - }, - { - "cell_type": "markdown", - "id": "d79c527a", - "metadata": {}, - "source": [ - "In this example, you will explore the relationship between NYC taxi fares and trip distances." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "b6bf3f2a", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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vendor_idpickup_datetimedropoff_datetimepassenger_counttrip_distancerate_codestore_and_fwd_flagpayment_typefare_amountextramta_taxtip_amounttolls_amountimp_surchargeairport_feetotal_amountpickup_location_iddropoff_location_iddata_file_yeardata_file_month
022021-09-19 10:25:05+00:002021-09-19 10:25:10+00:0010E-91.0N10E-90E-90E-90E-90E-90E-90E-90E-926426420219
122021-09-20 14:53:02+00:002021-09-20 14:53:23+00:0010E-91.0N10E-90E-90E-90E-90E-90E-90E-90E-919319320219
212021-09-14 12:01:02+00:002021-09-14 12:07:19+00:0010E-91.0N10E-90E-90E-90E-90E-90E-90E-90E-917017020219
322021-09-12 10:40:32+00:002021-09-12 10:41:26+00:0010E-91.0N10E-90E-90E-90E-90E-90E-90E-90E-919319320219
412021-09-25 11:57:21+00:002021-09-25 11:58:32+00:0010E-91.0N10E-90E-90E-90E-90E-90E-90E-90E-9959520219
\n", - "
" - ], - "text/plain": [ - " vendor_id pickup_datetime dropoff_datetime \\\n", - "0 2 2021-09-19 10:25:05+00:00 2021-09-19 10:25:10+00:00 \n", - "1 2 2021-09-20 14:53:02+00:00 2021-09-20 14:53:23+00:00 \n", - "2 1 2021-09-14 12:01:02+00:00 2021-09-14 12:07:19+00:00 \n", - "3 2 2021-09-12 10:40:32+00:00 2021-09-12 10:41:26+00:00 \n", - "4 1 2021-09-25 11:57:21+00:00 2021-09-25 11:58:32+00:00 \n", - "\n", - " passenger_count trip_distance rate_code store_and_fwd_flag payment_type \\\n", - "0 1 0E-9 1.0 N 1 \n", - "1 1 0E-9 1.0 N 1 \n", - "2 1 0E-9 1.0 N 1 \n", - "3 1 0E-9 1.0 N 1 \n", - "4 1 0E-9 1.0 N 1 \n", - "\n", - " fare_amount extra mta_tax tip_amount tolls_amount imp_surcharge \\\n", - "0 0E-9 0E-9 0E-9 0E-9 0E-9 0E-9 \n", - "1 0E-9 0E-9 0E-9 0E-9 0E-9 0E-9 \n", - "2 0E-9 0E-9 0E-9 0E-9 0E-9 0E-9 \n", - "3 0E-9 0E-9 0E-9 0E-9 0E-9 0E-9 \n", - "4 0E-9 0E-9 0E-9 0E-9 0E-9 0E-9 \n", - "\n", - " airport_fee total_amount pickup_location_id dropoff_location_id \\\n", - "0 0E-9 0E-9 264 264 \n", - "1 0E-9 0E-9 193 193 \n", - "2 0E-9 0E-9 170 170 \n", - "3 0E-9 0E-9 193 193 \n", - "4 0E-9 0E-9 95 95 \n", - "\n", - " data_file_year data_file_month \n", - "0 2021 9 \n", - "1 2021 9 \n", - "2 2021 9 \n", - "3 2021 9 \n", - "4 2021 9 " - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "taxi_trips = bpd.read_gbq('bigquery-public-data.new_york_taxi_trips.tlc_yellow_trips_2021').dropna()\n", - "taxi_trips.peek()" - ] - }, - { - "cell_type": "markdown", - "id": "413c0f91", - "metadata": {}, - "source": [ - "First, you santize the data a bit by remove outliers and pathological datapoints:" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "d4876b08", - "metadata": {}, - "outputs": [], - "source": [ - "taxi_trips = taxi_trips[taxi_trips['trip_distance'].between(0, 10, inclusive='right')]\n", - "taxi_trips = taxi_trips[taxi_trips['fare_amount'].between(0, 50, inclusive='right')]" - ] - }, - { - "cell_type": "markdown", - "id": "f1ed53f7", - "metadata": {}, - "source": [ - "You also need to sort the data before plotting if you have turned on the partial ordering mode during the setup stage." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "e9ddad9b", - "metadata": {}, - "outputs": [], - "source": [ - "taxi_trips = taxi_trips.sort_values('pickup_datetime')" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "e34ab06d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "taxi_trips.plot.scatter(x='trip_distance', y='fare_amount', alpha=0.5)" - ] - }, - { - "cell_type": "markdown", - "id": "7ab4ded3", - "metadata": {}, - "source": [ - "# Advacned Plotting with Pandas/Matplotlib Parameters" - ] - }, - { - "cell_type": "markdown", - "id": "51e3b044", - "metadata": {}, - "source": [ - "Because BigQuery DataFrame's plotting library is powered by Matplotlib and Pandas, you are able to pass in more parameters to fine tune your graph like what you do with Pandas. \n", - "\n", - "In the following example, you will resuse the taxi trips dataset, except that you will rename the labels for X-axis and Y-axis, use `passenger_count` for point sizes, color points with `tip_amount`, and resize the figure. " - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "51c4dfc7", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "\n", - "taxi_trips['passenger_count_scaled'] = taxi_trips['passenger_count'] * 30\n", - "\n", - "taxi_trips.plot.scatter(\n", - " x='trip_distance', \n", - " xlabel='trip distance (miles)',\n", - " y='fare_amount', \n", - " ylabel ='fare amount (usd)',\n", - " alpha=0.5, \n", - " s='passenger_count_scaled', \n", - " label='passenger_count',\n", - " c='tip_amount',\n", - " cmap='jet',\n", - " colorbar=True,\n", - " legend=True,\n", - " figsize=(15,7),\n", - " sampling_n=1000)" - ] - }, - { - "cell_type": "markdown", - "id": "6356cdab", - "metadata": {}, - "source": [ - "# Visualize Large Dataset" - ] - }, - { - "cell_type": "markdown", - "id": "fce79ba0", - "metadata": {}, - "source": [ - "BigQuery DataFrame downloads data to your local machine for visualization. The amount of datapoints to be downloaded is capped at 1000 by default. If the amount of datapoints exceeds the cap, BigQuery DataFrame will randomly sample the amount of datapoints equal to the cap.\n", - "\n", - "You can override this cap by setting the `sampling_n` parameter when plotting graphs. For example:" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "3d0ef911", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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rklBabUL/CB3WPhKPCD8PqctyaVqVEg+M6gKgvk29xSKr+xCIJMNgQtTBfX/wLGZ/tg/VRjNGdQvE6odGIMBLK3VZHcLM4VHwdlMh81wVtp4olLocIllgMCHqwD7dlYV5Xx+EySzi5v5h+OS+ofDSqq7+QbILbzc17hnRGQDwwfZMyKx7A5EkGEyIOqgvk07j5Y3HIYrArPjOeO/OQdwtWAL3j+wCjUqBg7ll2JtVInU5RJLj30JEHZDFImJ5w0Zyj43tipdv6QOFQpC4qo4pyFuL6Q09Yj7czs39iBhMiDqg5JxSnCmtgZdWhb9c3x2CwFAipTmjY6AQgMT0czieVy51OUSSYjAh6oDWp9Tfnjqpbyjc1EqJq6HOAZ64qX84AFhnsog6KgYTog7GUGfGpsP5AIDbBnWSuBpq9MiYGADAj4fzkFNcLXE1RNJhMCHqYLalnoO+xoRQHzcMjwmQuhxq0CdchzE9gmARgY9/56wJdVwMJkQdzIaGyzi3DgyHkgteZaVxc7+1+8/g2+Qz2JNZjNPFVTDUmSWujMhx2LCAqAPRV5vwW2oRAOC2wbyMIzcjYvwxMNIXB3PL8OTaQ01eC/TSopOfOx4b2xUJfUIlqpCo/XHGhKgD2XQkH0azBbGh3ogN9ZG6HLqIIAj41+39cceQCFzTNQBdAj2hbegtc77SgEO5ZXhidQqOntVLXClR++GMCVEH0ngZh4te5at7iDfeuH2A9feiKKK02oS8shos/SUdv6YW4bGvDuDHv4yCj5tawkqJ2gdnTIg6iNySavyRXQJBAG4dyGDiLARBgL+nBn076fD2HQPRydcdOSXVePa7w2xhTy6JwYSog/j+YP1syTVdAxCqc5O4GmoNnYcay2YOhlop4KcjBfh8z2mpSyKyOwYTog5AFEVrU7UpnC1xagMjffHspF4AgNc3ncDqvTmoMfKuHXIdDCZEHcDRs+XIPFcFrUqBG/ryjg5n98DIaCT0CYHRbMFz649gxOJfsXjzCZwpZWM2cn4MJkQdwLqUMwCAiX1C4c0Fk05PEAS8e+cgPHdjLCL83KGvMeGjxFMY/cY2PPzFfuzOPM/1J+S0eFcOkYurM1uw8VAeAOC2QeESV0P24qZWYs7orpg9Kga/pRbh091Z2JVRjC3HCrHlWCF6hnhj1jXRmDIoHB4a/lVPzkMQZRary8vLodPpoNfr4ePDPgtEbbU9rQj3rdqHAE8Nkp67HmolJ0pd1cnCCny2JxvfJZ9Fjal+3YmPmwp3DovC/PE94K7hho3Ufuz185t/QxG5uMbeJZMHhDOUuLjuId74x5R+SHruerxwUy9E+XugvLYOH+84hX/+nCp1eUQtwr+liFxYlaEOW44VAgCmsKlah6FzV+PBa2Ow7cmxeOWWPgCAX44Xct0JOQUGEyIXtuVYAWpMZnQJ9MSACJ3U5ZCDKRUC7hgSCY1KgbNlNTh1vkrqkoiuisGEyIVd2LtEELiTcEfkrlFiWLQ/AGBH+jmJqyG6OgYTIhdVVF6LXRnnAXBvnI5udI9AAAwm5BwYTIhc1A+H8mARgbjOfogK8JC6HJLQtd2DAABJp0pgqGOXWJI3BhMiF2W9jMPZkg4vNtQbwd5a1JjM2J9dKnU5RFfEYELkgtILK3AsrxxqpYCb+4VJXQ5JTBAE66wJL+eQ3DGYELmgxtmSsT2D4eepkbgakoPGdSaJDCYkcwwmRC7GYhHxfUMw4aJXanRt9yAIApBaUIGi8lqpyyG6LAYTIhfzR3YJ8vS18HZTYVxssNTlkEz4e2rQr1N9L5sdJ89LXA3R5TGYELmY9QfqZ0tu6hcGNzX3RqE/jeY6E3ICDCZELqTWZMZPR/IB8G4cutToHvXBZGfGeVgsbE9P8sRgQuRCfkstQoWhDp183a3dPokaDYryhZdWhZIqI46c1UtdDlGzGEyIXEjj3Ti3DgyHQsEW9NSUWqmw3p3zn51ZEldD1DwGEyIXUVplxPa0IgC8G4cub+513SAIwMZDeUjJYbM1kh8GEyIX8eORfJjMIvqE+6B7iLfU5ZBM9QnXYdrgCADAop9OQBS51oTkhcGEyEVsYO8SaqG/TewBN7UC+7JLseVYodTlEDXBYELkAk4XVyH5dCkUAnDLgHCpyyGZC9O546FrYwAA//w5FSazReKKiP7EYELkAjak5AEARnYLRLCPm8TVkDN4eExXBHppkHW+Cqv35khdDpEVgwmRkxNFERsO8jIO2cZLq8K88T0AAO9sTUd5rUniiojqMZgQOblDZ/TIOl8Fd7USCX1CpS6HnMidQyPRNcgTpdUmfLAtU+pyiAAwmBA5vfUHzgAAEvqEwFOrkrgaciYqpQLP3dgLAPDJriycKa2WuCIiBhMip2YyW7DxMFvQU+uNiw1GfEwAjHUWvLklTepyiBhMiJzZ7yfPoaTKiEAvLUZ1C5S6HHJCgiDg+ZvqZ002HMzD4TNl0hZEHR6DCZETW9ewk/AtA8KhUvL/ztQ6fTvpMLVhxo1N10hq/JuMyElV1Jrwy/H65li8G4fa6m8JPaFVKZB0qgS/niiSuhzqwBhMiJzUz0cLYKizoGuQJ/p28pG6HHJynXzd8cCoLgCARZtPsOkaSYbBhMhJNfYumTo4AoLAnYSp7R4d2xX+nhqcOleFNftypS6HOigGEyInlK+vwe7MYgBsQU/24+Omxrzx3QEA7/ySjgo2XSMJMJgQOaEfDuZBFIFh0f6I9PeQuhxyIXcNi0JMoCeKq4z4KPGU1OVQB8RgQuSE1jfsJMzeJWRvaqUCz06KBQCs+P0U8vU1EldEHQ2DCZGTOZFfjtSCCmiUCtzUL0zqcsgFTegdgmFd/GGos+DNLelSl0MdDIMJkZPZ0DBbMi42GDoPtcTVkCsSBAHPN7SqX5dyBkfP6iWuiDoSBhMiJ2K2iPj+YB4AXsah9jUg0he3DAiHKAKLN7PpGjkOgwmRE0k6VYyC8lro3NW4LjZI6nLIxT2V0BMapQK7MoqxPf2c1OVQB2FzMDl79izuvvtuBAQEwN3dHf369cP+/futr4uiiBdffBFhYWFwd3fH+PHjcfLkSbsWTdQRpRVU4G/fHAIA3NQ/DFqVUuKKyNVF+nvg/pHRAIBFm06gjk3XyAFsCialpaUYOXIk1Go1Nm/ejOPHj+Ott96Cn5+f9T1vvPEG3nvvPSxfvhx79+6Fp6cnEhISUFtba/fiiTqKfdklmL58NwrKa9Et2Avzru8udUnUQTx2XTf4eqhxsqgSa5PPSF0OdQCCaMOFw2effRa7du3C77//3uzroigiPDwcf/vb3/Dkk08CAPR6PUJCQvDpp5/izjvvvOo5ysvLodPpoNfr4ePDNttEvxwvxOOrD8BQZ0FcZz+snDUEvh4aqcuiDmTVriy8svE4Ar20SHxqLDy1KqlLIhmy189vm2ZMfvjhBwwZMgTTp09HcHAwBg0ahBUrVlhfz8rKQkFBAcaPH299TqfTYfjw4dizZ0+zxzQYDCgvL2/yIKJ6X+/LwcNf7IehzoLrY4Px5ezhDCXkcDOHd0Z0gAfOVxrw0Q42XaP2ZVMwOXXqFD788EN0794dW7ZswaOPPoq//OUv+OyzzwAABQUFAICQkJAmnwsJCbG+drHFixdDp9NZH5GRka35HkQuRRRF/Pu3k3jmuyOwiMD0uAh8dE8c3DVcV0KOp1Fd0HRtxynsyjjPTf6o3dgUTCwWCwYPHoxFixZh0KBBmDNnDh566CEsX7681QUsXLgQer3e+sjN5cZR1LFZLCJe/uEY3vxffWOrx8Z2xRu394dKyZvoSDoJfUIxpLMfakxmzPzPXgx69RfeRkztwqa/6cLCwtC7d+8mz/Xq1Qs5OTkAgNDQUABAYWFhk/cUFhZaX7uYVquFj49PkwdRR2WoM+OJNSn4bM9pCALw0uTeePqGWO4eTJITBAHvzxiEqYM7IcBTg0pDHT5KPIUfDuVJXRq5GJuCyciRI5GWltbkufT0dHTu3BkA0KVLF4SGhuLXX3+1vl5eXo69e/ciPj7eDuUSua6KWhPuX7UPmw7nQ60U8O6dg3D/yC5Sl0VkFaZzx9t3DMS+58fjiXHdAACv/XgcZdVGiSsjV2JTMJk/fz6SkpKwaNEiZGRkYPXq1fj4448xd+5cAPWJet68efjHP/6BH374AUeOHMG9996L8PBwTJkypT3qJ3IJRRW1uPPjJOzOLIanRolV9w3DLQPCpS6LqFkKhYDHx3VDt2AvnK80YsnmVKlLIhdiUzAZOnQo1q9fj//+97/o27cvXnvtNbzzzjuYOXOm9T1PP/00nnjiCcyZMwdDhw5FZWUlfv75Z7i5udm9eCJXcLq4Crd/uAfH8soR6KXBmjnxGNU9UOqyiK5Iq1Ji0W39AABr9uXij6wSiSsiV2FTHxNHYB8T6kiOntXjvlV/4HylEVH+Hvj8gWGIDvSUuiyiFnv2u8NYsy8XXYM88dNfr2VH4g5Mkj4mRGQ/uzLO4/8+2oPzlUb0DvPBt4/GM5SQ01k4qRcCvTTIPFeFjxLZ44TajsGESAIbD+XhvlV/oMpoRnxMAL5+eASCvXm5k5yPzkONv99cf7fmv7dl4NS5SokrImfHYELkYJ/uysJf1qTAZBZxU78wfPrAUHi7qaUui6jVbhkQjtE9gmCss+D59UfZ24TahMGEyEFEUcS/tqTi5Y3HIYrAvfGd8d5dg3hNnpyeIAh4fUpfuKkV2HOqGN9ysz9qAwYTIjvKKa7G46sP4FBuWZPn68wWPPPdYSzblgkAeHJiD7xySx8oFWycRq4h0t8D88b3AAC8/tMJFFcaJK6InBWDCZEd/WPTcfx4OB9zvthvbTpVYzTjkS+T8c3+M1AIwJKp/fD4uO7s5kouZ/aoLogN9UZZtQmvbzohdTnkpBhMiOzk1LlK/HKifjuGwnIDnlt/BGXVRty9ci+2niiCVqXA8rvjcOewKIkrJWofaqUCi6f2gyAA61LOYufJ81KXRE6IwYTITlb8ngVRBHqH+UClEPDTkQJMWLoDyadL4eOmwpcPDsfEPs3vGUXkKgZF+eGeEfXblDy/4QhqTWaJKyJnw2BCZAfnKgz47kD9gr+Xb+mDeeO7W58P9XHD2keuwdBofylLJHKYpxJ6IsRHi9PF1fj3bxlSl0NOhsGEyA4+250NY50FAyN9MTTaD4+O7YYpA8MRHxOA7x67Bj1DvaUukchhvN3UeOWWPgCA5YmZSC+skLgiciYMJkRtVGWowxdJpwEAD4+OgSAIUCoEvHPnIPx3zgh08nWXuEIix0voE4rxvUJQZxGxcN0RWCzsbUItw2BC1Ebf7M+FvsaE6AAPriEhaiAIAl69tQ88NUokny7Ff/flSF0SOQkGE6I2qDNbsHJnFgDgwWtj2JeE6ALhvu7428SeAIAlm1NRVF4rcUXkDBhMiNrgp6MFOFNagwBPDW6Pi5C6HCLZmXVNNPpH6FBRW4dXfjwudTnkBBhMiFpJFEV8lFjfyfXe+Gi4qdlanuhiSoWARbf1g1IhYNPhfGxLLZK6JJI5BhOiVtqdWYxjeeVwUytwT3xnqcshkq2+nXR4YGQ0AOCFDUdRbayTtiCSNQYTolb6aMcpAMAdQyLh76mRuBoieZs3vgc6+brjbFkN3tl6UupySMYYTIha4UR+OXakn4NCAB4cFSN1OUSy56lV4bUp9b1NVu7MwrE8vcQVkVwxmBC1woqG2ZJJ/cIQFeAhcTVEzmFcbAhu6hcGc0NvEzN7m1AzGEyIbJRXVoMfDuUBqG+oRkQt99Lk3vB2U+HwGT0+35MtdTkkQwwmRDZatSsLdRYRI2L80T/CV+pyiJxKsI8bnrkhFgDw5pY05JXVSFwRyQ2DCZEN9DUmrN5b38Hy4dFdJa6GyDnNGBaFuM5+qDKa8dIPx6Quh2SGwYTIBqv35qDKaEaPEC+M7RkkdTlETknR0NtEpRDwy/FC/Hy0QOqSSEYYTIhayFBnxqpd9e3nH7q2frM+ImqdnqHeeHhM/Rqtl384hopak8QVkVwwmBC10PcH81BUYUCIjxa3DuwkdTlETu+Jcd3ROcADBeW1eOt/6VKXQzLBYELUAhaLaL1F+IGRXaBR8f86RG3lplbi9Sn9AACf7cnGwdwyaQsiWeDfrkQtsD29CCeLKuGlVeGu4VFSl0PkMkZ1D8RtgzpBFIGF647AZLZIXRJJjMGEqAWWJ9bPlswYHgUfN7XE1RC5lhdu6gVfDzVO5Jfjk51ZUpdDEmMwIbqKg7ll+COrBCqFgPsbNiIjIvsJ8NLiuRt7AQCWbk1Hbkm1xBWRlBhMiK7i4x2ZAIBbBoYjTOcucTVErml6XARGxPij1mTBCxuOQhTZrr6jYjAhuoLTxVXWHgtz2H6eqN0IgoDXb+sHjVKBxPRz2Hg4X+qSSCIMJkRX8J/fs2ARgbE9gxAb6iN1OUQurWuQF+Ze1w0A8OrG49BXs7dJR8RgQnQZJVVGrE3OBcDZEiJHeWRsDLoGeeJ8pQFLfk6VuhySAIMJ0WV8vicbtSYL+nXSIT4mQOpyiDoErUqJRbfV9zb57x852JddInFF5GgMJkTNqDGa8dnubAD1syVsP0/kOMNjAvB/QyIBAM+tOwJjHXubdCQMJkTN+DY5F6XVJkT4uWNS31CpyyHqcBbeGItALw1OFlXio8RMqcshB2IwIbqI2SLiPw1Nnh4c1QUqJf9vQuRovh4a/P3m3gCA97dl4NS5SokrIkfh37hEF9lyrACni6vh66HGHUMjpS6HqMO6ZUA4ru0eCGOdBc+vZ2+TjoLBhOgCoijio4bN+u4d0RkeGpXEFRF1XIIg4PUp/eCmVmDPqWKsO3BW6pLIARhMiC7wR1YJDuWWQatS4N5roqUuh6jDiwrwwF+v7wEA+Mem4yipMkpcEbU3BhOiC3zcMFsyLS4CgV5aiashIgB48NouiA31Rmm1Ca9vOiF1OdTOGEyIGpwsrMCvqUUQBOCha9lQjUgu1EoFFk/tB0EAvjtwBrszzktdErUjBhOiBo2zJRN7h6BLoKfE1RDRhQZF+eGeEZ0BAM+tP4Jak1niiqi9MJgQASgsr8WGg/UL6+aM7ipxNUTUnKcSeiLYW4vs4mrr5prkehhMiACs2pUNk1nEkM5+iOvsJ3U5RNQMbzc17hoWBQBYl8I7dFwVgwm5DENd66Z2Kw11+GrvaQDAw2M4W0IkZ1MHdwIA7Dx5DoXltRJXQ+2BwYScntkiYvFPJ9D7xS3WgGGLNX/koKK2Dl2DPHF9bHA7VEhE9tI5wBNDOvvBIgLfH+SsiStiMCGnVlFrwkOf78dHO07BbBHxfUqeTZ83mS1Y2dB+/qFrY6BQcLM+IrmbOjgCAPBd8ll2g3VBDCbktHKKqzH1g934LbUImob9bA7mlqHG2PJLOhsP5SFfX4tALy2mDOrUXqUSkR3d1C8MGpUCaYUVOJ5fLnU5ZGcMJuSU9mQW49ZlO3GyqBIhPlp8+2g8Qn3cYDRbcCCntEXHEEXReovw/SOj4aZWtmfJRGQnOg81JvQKAQC2qXdBDCbkdFbvzcE9K/eitNqEARE6/PD4KPSP8MWIGH8AQNKp4hYdZ8fJ80gtqICHRom7h3duz5KJyM4aF8F+f/As6swWiashe2IwIadRZ7bg5R+O4bn1R1BnETF5QDi+fjgeIT5uAID4rgEAgE93ZWPB1wfx/cGzKL3Cvhof78gEANw5NAo6D3X7fwEispvRPYIQ4KnB+Uojfj/JTrCuhFunklPQV5vw+H8PWP8CenJiD8y9rhsE4c/FquNiQxDolY7zlQasSzmLdSlnoRCAAZG+GNsjGGN7BqFfJx0UCgFHz+qxK6MYSoWAB0ZFS/StiKi11EoFbhkYjlW7svHdgTO4jnfUuQwGE5K9U+cq8eBn+3HqfBXc1Uos/b+BuKFv6CXvC/LWYvez47A/uwTb089he1oR0gsrkZJThpScMizdmo4ATw1G9whCXlkNAODm/mGI8PNw9FciIjuYNjgCq3Zl43/HC6GvMUHnzplPV8BgQrL2+8lzmPvVAZTX1iFc54YVs4agT7jusu/XqBS4plsgrukWiOdu7IW8shokNoSUXRnFKK4yYv0FHSPnjOZmfUTOqk+4D3qEeCG9sBKbj+TjzoausOTcGExIlkRRxOd7TuPVH4/DbBExOMoXH90zBEHeWpuOE+7rjruGReGuYVEw1lmQfLoU29OLsCezGMOi/a8YcohI3gRBwNTBEViyORXrDpxlMHERDCYkO6aGRa5f7c0BUL/6fvHUftCq2nY7r0alQHzXAOsiWSJyflMGdsI/f07FH9klyCmuRlQAL806O96VQ7JSaajDvSv/wFd7cyAIwHM3xuKt6QPaHEqIyDWF6twwqlsgADS5TEvOi8GEZOVfP6diz6lieGlV+M+9QzBndNcmd94QEV2ssafJupQzbFHvAhhMSDaOnNHji6T6TfiW3x2H6xs6OxIRXUlCn1B4aJQ4XVzd4s7PJF8MJiQLZouI5zccgUUEbh0YjlHdA6UuiYichIdGhUl9wwAA37FFvdOzKZi8/PLLEAShySM2Ntb6em1tLebOnYuAgAB4eXlh2rRpKCwstHvR5HpW7z2Nw2f08HZT4fmbekldDhE5mWkNl3N+PJSHWlPLN/Ik+bF5xqRPnz7Iz8+3Pnbu3Gl9bf78+di4cSPWrl2LxMRE5OXlYerUqXYtmFxPUUUt3tiSBgB4KqEngr3dJK6IiJzNiJgAhOvcUF5bh99Si6Quh9rA5mCiUqkQGhpqfQQG1k+56/V6rFy5Em+//TbGjRuHuLg4rFq1Crt370ZSUpLdCyfX8fqmE6iorUP/CB1mcjM9ImoFhULAlEENi2APnJG4GmoLm4PJyZMnER4ejpiYGMycORM5OfW9JpKTk2EymTB+/Hjre2NjYxEVFYU9e/Zc9ngGgwHl5eVNHtRx7Mo4j+8P5kEhAK9P6QelgnfgEFHrNN6dsz3tHM5XGiSuhlrLpmAyfPhwfPrpp/j555/x4YcfIisrC9deey0qKipQUFAAjUYDX1/fJp8JCQlBQUHBZY+5ePFi6HQ66yMyMrJVX4Scj6HOjL9vOAoAuGdEZ/SLYBdWImq9bsHeGBChQ51FxMZDeVKXQ61kUzCZNGkSpk+fjv79+yMhIQE//fQTysrK8M0337S6gIULF0Kv11sfubm5rT4WOZePE0/h1PkqBHlr8beEnlKXQ0QuYOrgCADAOt6d47TadLuwr68vevTogYyMDISGhsJoNKKsrKzJewoLCxEaeulOsI20Wi18fHyaPMj15RRX49/bMgAAL9zUCz5u3BWUiNpu8oBwqBQCjpzVI72wQupyqBXaFEwqKyuRmZmJsLAwxMXFQa1W49dff7W+npaWhpycHMTHx7e5UHIdoiji798fhaHOgpHdAnDLgHCpSyIiF+HvqcF1scEAOGvirGwKJk8++SQSExORnZ2N3bt347bbboNSqcRdd90FnU6H2bNnY8GCBdi2bRuSk5Nx//33Iz4+HiNGjGiv+skJbT5agMT0c9AoFXjt1r5sOU9EdtXY02RDylmYLWxR72xs2l34zJkzuOuuu1BcXIygoCCMGjUKSUlJCAoKAgAsXboUCoUC06ZNg8FgQEJCAj744IN2KZycU6WhDq9uPA4AeGRsV8QEeUlcERG5mutig6FzV6OgvBZ7MovZSdrJCKLMdjwqLy+HTqeDXq/nehMX9NqPx7FyZxY6B3hgy7zRcFNz12Aisr8XNhzBl0k5mDqoE97+v4FSl9Mh2OvnN/fKIYfJ19fg093ZAIBXb+3LUEJE7abx7pzNRwtQZaiTuBqyBYMJOUzSqWKYLSIGROgwpkeQ1OUQkQsbFOmLLoGeqDGZ8dORfKnLIRswmJDD7Muu3458eEyAxJUQkasTBAG3x9XPmrz/WwY39nMiDCbkMPuzSwAAcZ39JK6EiDqC+66JRoiPFjkl1Vi5M0vqcqiFGEzIIcqqjUgvrAQADGEwISIH8NSq8NyNvQAA//4tA/n6GokropZgMCGHSD5dfxknJsgTAV5aiashoo7ilgHhGNLZDzUmM5ZsTpW6HGoBBhNyiP0NwWRoZ3+JKyGijkQQBLx8Sx8IAvD9wTzsa7ikTPLFYEIO0bi+ZEg0L+MQkWP17aTDnUOjAAAv/3CM3WBljsGE2l2tyYxDuXoAwNBozpgQkeM9ObEHvN1UOJZXjq/3cRd7OWMwoXZ39KweRrMFgV5adA7wkLocIuqAAry0WDChBwDgX1tSoa82SVwRXQ6DCbW7xv4lQ6P9uGEfEUnm7hGd0SPEC6XVJizdmi51OXQZDCbU7v5cX8LLOEQkHbVSgZcm9wEAfJF0GmkFFRJXRM1hMKF2ZbGI1jty2L+EiKQ2slsgbugTCrNFxCsbj0Fm+9gSGEyonWWcq4S+xgR3tRK9w7lbNBFJ7/mbekGrUmB3ZjF+PlogdTl0EQYTaleNPQMGRflCreQfNyKSXqS/Bx4e0xUA8I9NJ7iPjszwJwW1q/0NC1+5voSI5OTRMV0RrnPD2bIafJR4Supy6AIMJtSu9p+unzEZysZqRCQj7holnrupfh+dD7Zn4ExptcQVUSMGE2o3Bfpa5JbUQCEAg6IYTIhIXm7qF4bhXfxhqLNg8U/cR0cuGEyo3TTOlvQO94GXViVxNURETTXuo6MQgE1H8rEns1jqkggMJtSOrOtLuHEfEclUrzAfzBzeGQDwysZjqDNbJK6IGEyo3TTekcP9cYhIzhZM6AFfDzVSCyqw+o8cqcvp8BhMqF1U1JpwIr8cAHcUJiJ58/PU4G8N++i89b90lFYZJa6oY2MwoXaRklMGiwhE+rsjxMdN6nKIiK7ormFRiA31hr7GhLd+SZO6nA6NwYTaReP+OEO5voSInIBKqcDLt9Tvo7N6bw6O5eklrqjjYjChdrGPjdWIyMmMiAnATf3DYBGBV344zn10JMJgQnZnMluQklsfTNhYjYicyXM39oKbWoE/skvw4+F8qcvpkBhMyO6O55Wj1mSBr4caXYO8pC6HiKjFOvm647Gx3QAAi346gWpjncQV2VeVoQ43vLMD72xNl+0eQQwmZHeNtwkP6ewHhUKQuBoiItvMGR2DCD935Otr8eH2TKnLsaufjxYgtaAC61POQquSZwSQZ1Xk1LhxHxE5Mze1Ei807KPz0Y5TyCmWZh+dSkMdtqcVYdm2DJw6V2mXY65NzgUA3D44AoIgz384sk842ZUoitZW9EM6c30JETmnhD6hGNktALsyivH6T8fx0T1D2v2cFbUm7M8uRdKpYiRlleDoWT3MlvoFuF8mncbGJ0Yh0Evb6uPnllQj6VQJBAGYGhdhr7LtjsGE7Cq7uBrnK43QqBToF6GTuhwiolYRBAEvTe6DSe/+ji3HCvH7yXO4tnuQXc+hrzFhf3YJkk4VY29DELFcdCNQpL87THUi8vW1eGJ1Cr6YPQwqZesudnx34AwA4JquAejk697W8tsNgwnZVeP6kgEROmhVSomrISJqvR4h3rhnRGd8ujsbr2w8js1/vRbqVoYCACirNuKPrBLszSrB3qxiHMsrx8V3JHcO8MCILgEYHuOP4TH1AeJkYQWmLNuFPaeK8caWNDx3Yy+bz22xiNZgMj0ustXfwREYTMiufj95HgDXlxCRa5g/vgd+OJSHjKJKfL7nNGaP6tLiz5ZWGa0hJOlUCVILLg0iXQI9MSLGH8MbwkiY7tKZjO4h3nhz+gA8+tUBfLzjFPpH6HBz/3CbvsferBLkltTAS6tCQp9Qmz7raAwmZDcbUs5i46E8AMC42GCJqyEiajudhxpPJfTEwnVH8M7WdNw6MPyy6zyKKw3WGZGkU8VILai45D0xQZ4YEROA4V38MSImoMVbdkzqF4ZHxnTF8sRMPP3tYXQP9kbPUO8Wf49vk+tnS27uHwZ3jbxnsxlMyC4OnynDM98dBgDMva4rdxQmIpdxx5BIfJl0GsfyyvHmljQsmdYfAHC+0oC9pxpnRIqRXnjpnTPdg73qL8s0zIgEe7d+77AnJ/bA0bN67Mw4j4e/2I/vHx8Fnbv6qp+rMtRh89H6ZnG3y3jRayMGE2qzoopazPk8GYY6C66PDcbfJvSUuiQiIrtRKgS8cksf3L58D77en4s6i4iDuWXIKLo0iPQM8cbwmPrZkGFd/Nt0F83FVEoF3rtrECa/vxPZxdVY8PVBrLh3yFX7Rf10JB/VRjO6BHoizgnulmQwoTYx1JnxyBfJKCivRbdgL7xz50A2VSMilzMk2h9TBoZjw8E862URAIgN9caImACMiPHHsC4B8PfUtGsd/p4aLL87DtOW78avqUV4/7cM/HV89yt+prHe2+Pk27vkQgwm1GqiKOLFDcdwIKcMPm4qrLh3CLzdrj6tSETkjF64uTcAwM9TUz8jEu0Pv3YOIs3pF6HD61P64qlvD+OdX9PRL8IH42JDmn1vTnE19mbV9y65bVAnB1faOgwm1Gqf7c7G1/tzoRCA92cMRpdAT6lLIiJqN4FeWrxz5yCpywAATB8SiUNnyvBlUg7mrTmIHx4fhehm/g7+tuEW4VHdAhEu494lF2JLemqV3Rnn8dqmEwCAhZN6YUwP+zYeIiKiK3vx5j4YFOWL8to6PPJl8iUbDlosIr674DKOs2AwIZvlFFfjsdUHYLaImDqoEx68tuX39RMRkX1oVAosvzsOgV5apBZU4NnvjkC8oFFKUlYxzpbVwNsJepdciMGEbFJlqMNDn+9HWbUJAyJ0WDS1n1MspiIickUhPm74YOZgqBQCfjiUh092ZVtf+3Z/Q++SAeFwU8u7d8mFGEyoxSwWEQu+OYi0wgoEeWvx0T1DnOoPOxGRKxrWxd+6G/Kin04g6VQxKmpN+MmJepdciMGEWuy9305iy7FCaJQKfHRPHEJ1rW8URERE9jPrmmjcNqgTzBYRT/w3BRsO5qHWZEFMkCcGR/lKXZ5NGEyoRX4+mo93tp4EAPzjtr4YHCX/Jj1ERB2FIAhYdFs/BHtrca7CgDc2pwJwnt4lF2IwoatKLSjHgm8OAQDuHxmNO4bIe2dKIqKOyF2jxC0D6jf3qzDUQSEAUwc512UcgMGErqKkyoiHPt+PaqMZI7sF4PlWbLdNRESOcevAP5uoXds9yCkvuTOY0GWZzBbM/eoAcktqEOXvgX/fNRgqJf/IEBHJVd9OPugW7AUAmD7E+WZLAHZ+pSt4fdMJ7DlVDE+NEv+ZNUSS1stERNRygiBg+d1xOHpWj5v6hUldTqswmFCzvt6Xg093ZwMAlv7fQPQI8Za2ICIiapFuwV7WWRNnxHl5ukTy6RK8sOEoAGDBhB6Y6EQdA4mIyLkxmFAT+foaPPzFAZjMIib1DcXj13WTuiQiIupAGEzIymS24OEvknG+0oDYUG+8OX0AFArnuv+diIicG4MJWX29LxeHz+ihc1djxb1D4KnlEiQiInIsBhMCAFQa6vDO1nQA9etKIv09JK6IiIg6IgYTAgB8vOMUzlcaER3ggbuGRUldDhERdVAMJoSi8lqs2HEKAPDMDbHQqPjHgoiIpMGfQBIw1llgsYhSl2G1dGs6akxmDI7yxQ19eWswERFJh8HEwaoMdRj9xjbc88leqUsBAJwsrMDX+3IBAM/d2MvpdqEkIiLXwtsuHCytsAIF5bUoKK9Fea0JPm5qSev558+psIhAQp8QDIn2l7QWIiIizpg4WG5JtfXX6QUVElYCJJ0qxtYTRVAqBDx9Q6yktRAREQFtDCZLliyBIAiYN2+e9bna2lrMnTsXAQEB8PLywrRp01BYWNjWOl3GmdIa669TJQwmFouIRT+dAADMGBaFrkHOu68CERG5jlYHk3379uGjjz5C//79mzw/f/58bNy4EWvXrkViYiLy8vIwderUNhfqKi4MJmkSBpNNR/Jx+Iwenhol/nJ9d8nqICIiulCrgkllZSVmzpyJFStWwM/Pz/q8Xq/HypUr8fbbb2PcuHGIi4vDqlWrsHv3biQlJdmtaGd2pvTPSzlphdIEE0OdGW9sSQUAPDymK4K8tZLUQUREdLFWBZO5c+fipptuwvjx45s8n5ycDJPJ1OT52NhYREVFYc+ePc0ey2AwoLy8vMnDlZ29aMZEFB1/2/CXSTnILalBsLcWD17bxeHnJyIiuhybg8maNWtw4MABLF68+JLXCgoKoNFo4Ovr2+T5kJAQFBQUNHu8xYsXQ6fTWR+RkZG2luQ0LBYRZ8r+DCb6GhMKyw0OrUFfY8L7v50EUN963kPDG7OIiEg+bAomubm5+Otf/4qvvvoKbm5udilg4cKF0Ov11kdubq5djitH5ysNMNZZoFQIiA6o34smtcCxM0QfbM9AWbUJPUK8cHtchEPPTUREdDU2BZPk5GQUFRVh8ODBUKlUUKlUSExMxHvvvQeVSoWQkBAYjUaUlZU1+VxhYSFCQ5vvKKrVauHj49Pk4apyGy7jhPq4oU+4DoBjF8CeLavBql3ZAIBnJ8VCpeTd4kREJC82/WS6/vrrceTIERw8eND6GDJkCGbOnGn9tVqtxq+//mr9TFpaGnJychAfH2/34p1N48LXCD939Az1BuDYYPLW/9JgrLNgRIw/rusZ7LDzEhERtZRNCwy8vb3Rt2/fJs95enoiICDA+vzs2bOxYMEC+Pv7w8fHB0888QTi4+MxYsQI+1XtpBpvFY7w87AGE0f1MjmWp8f6lLMA2HqeiIjky+4rH5cuXQqFQoFp06bBYDAgISEBH3zwgb1P45T+DCbuiG0IJhnnKlFntrT7ZZUlm1MhisAtA8LRP8K3Xc9FRETUWm0OJtu3b2/yezc3NyxbtgzLli1r66FdzoWXciL9POChUaLaaEZ2cRW6BXu323l3pJ/D7yfPQ6NU4KmEnu12HiIiorbi6kcHOnvBpRyFQkD3kPa/nGO+oPX8vfGdEenv0W7nIiIiaisGEwexWMQml3IAIDak/RfArk85i9SCCvi4qfD4uG7tdh4iIiJ7YDBxkHOVBhjN9T1MwnT1PWDaewFsrcmMt/6XBgCYe103+Hpo2uU8RERE9sJg4iCN60tCfdysC10bF8Cmt9OeOZ/sykK+vhadfN0x65rodjkHERGRPTGYOEjjZZxIf3frc40zJjkl1ag21tn1fCVVRny4LRMA8GRCD7iplXY9PhERUXtgMHGQC3uYNArw0iLQSwtRBNILK+16vvd/O4kKQx36hPvg1gGd7HpsIiKi9sId3Nqg1mRGabURxZVGlFTVP4qrjCipMlh/3/hcflktgD8XvjaKDfXGzgwD0grKMTDS1y51nS6uwpdJpwHUN1NTKNhMjYiInAODyVUY6yz4fE82UgsqmgaPSiOqjGabjqVWCoiPCWjyXM9Qb+zMOG/XBbBvbEmDySxiTI8gjOwWaLfjEhERtTcGkysQRREvfn8Ua/ZdfsdjlUKAv6emySPAUwN/Ty38vTTw92h4zkuDEB836NzVTT7f0863DKfklGLT4XwIArDwxli7HJOIiMhRGEyu4Ku9OVizLxcKAXhsbDdE+rvDz6M+ZPh7auHvqYGPm6pN+87YczM/URSx+KdUAMDtgyMQG+q6OzUTEZFrYjC5jD+ySvDyD8cAAE/fEItHxnRtl/P0CPGGIADFVUacqzAgyFvb6mNtPVGEP7JL4KZWYMHEHnaskoiIyDF4V04z8vU1eOyrZNRZRNzcPwwPj45pt3O5a5To3NAmvi2zJnVmC5Zsrm89P3tUF4Tp3K/yCSIiIvlhMLlIrcmMR75IxvlKI2JDvfHG7f3bdKmmJf7sAFve6mN8vT8Xmeeq4O+pwcPtNLtDRETU3hhMLiCKIp5ffxSHzujh66HGinuHwEPT/le7ejasBWntjEmVoQ5LfzkJAPjLuG7wcVNf5RNERETyxGBygc92Z+O7A2egEIBlMwY7bCfextb0aa1sTb/i91M4X2lAdIAHZgzvbM/SiIiIHIrBpMGezGK8tql+jcZzN/ZyaP+PnhfsmWOxiDZ91mIRsfL3LAD1i3Q1Kv4nJSIi58WfYqjfYG/u6gMwW0TcNqgTZo/q4tDzRwd4QqtSoNZkQU5JtU2fLayoRYWhDiqFgIQ+oe1UIRERkWN0+GBSYzTj4S+SUVJlRN9OPlg8tV+7L3a9mFIhoHuIFwDY3AE2t6R+D55wX3co2XqeiIicXIcOJqIoYuG6wziWV44ATw0+umeIZLvw9gxp3QLY3IYZlgt3LSYiInJWHTqYrNyZhQ0H86BSCFg2czA6+Ur3w/3PBbC23TKcW9oQTPwcs1CXiIioPXXYYLLz5Hks+ql+sevfb+6NERdtrudof/Yyad2lHEfdQURERNSeOmQwySmuxuP/PQCLCEyPi8C98dLfYts4Y5J9vgq1ppbvWtw4YxLhx0s5RETk/DpcMKk21mHOF/tRVm3CgEhfvDalr8MXuzYnyFsLPw81LCKQklPW4s+dsa4x4YwJERE5vw4VTERRxFNrDyO1oAKBXlp8dHecZItdLyYIAib0DgEA/PPn1Bb1MzHWWZBfXguAMyZEROQaOlQw+TAxE5uO5EOtFLD87sEI1blJXVITT07sCU+NEgdzy7Au5exV359XVgNRBNzUCgR5tX5XYiIiIrnoMMFk58nz+NeWNADAK7f0xZBof4krulSwjxueuL47AGDJ5lRU1Jqu+P4/15d4yOJyFBERUVt1mGDSt5MPRnULxIzhUZgxPErqci7r/pHR6BLoifOVBixPzLzie6135PAyDhERuYgOE0x8PTRYdd9QvDy5j9SlXJFWpcSjY7sCAPaeKrnie609TLjwlYiIXIRK6gIcSaV0jhzm56EBAJiusgDW2vWVzdWIiMhFOMdP6g5GpaxfL2K2WK74vjOljc3VeCmHiIhcA4OJDKkV9f9Z6sxXnjE5c8HiVyIiIlfAYCJDjTMmJvPlZ0yqjXU4X2kEwDUmRETkOhhMZEilqA8mdVdYY9J4GcfHTQWdu9ohdREREbU3BhMZalyke6VLOblsRU9ERC6IwUSG/pwxufylHN6RQ0RErojBRIbULZkx4R05RETkghhMZKgli195KYeIiFwRg4kMWW8XvsLiV+uMCS/lEBGRC2EwkSFlw4zJ5S7liKKIM9YZE17KISIi18FgIkPqhsWvpsssftXXmFBhqAPA5mpERORaGExkqPF2YVEELM1czmncVTjIWws3tdKhtREREbUnBhMZalz8CjQ/a2LdVdiPl3GIiMi1MJjIUOPiV6D5dSa8I4eIiFwVg4kMXThj0mwwKWVzNSIick0MJjLU2PkVuMylnBI2VyMiItfEYCJDgiBAqbj8LcOcMSEiIlfFYCJTl9svx2IRrTsLc40JERG5GgYTmbrcfjnnKg0w1lmgVAgI07lJURoREVG7YTCRqcYFsBfPmDTekROmc7P2OyEiInIV/MkmU6qGW4ZNF82YNK4viWAPEyIickEMJjKlusziV+sdOVz4SkRELojBRKYaL+VcfLswm6sREZErYzCRqcbFr2ZL85dy2MOEiIhcEYOJTDVeyjGZL54x4aUcIiJyXQwmMqVq5nZhk9mCfD17mBARketiMJEpdTO3C+eX1cIiAhqVAkFeWqlKIyIiajcMJjKltF7K+XPG5MJbhRUX7KdDRETkKhhMZEqtuPRSjvWOHK4vISIiF8VgIlPNdX79c48c3pFDRESuicFEpppb/MpdhYmIyNUxmMiUupndhdlcjYiIXJ1NweTDDz9E//794ePjAx8fH8THx2Pz5s3W12trazF37lwEBATAy8sL06ZNQ2Fhod2L7giaX/zKHiZEROTabAomERERWLJkCZKTk7F//36MGzcOt956K44dOwYAmD9/PjZu3Ii1a9ciMTEReXl5mDp1arsU7urU1ks59TMmtSYzzlUYAHCNCRERuS6VLW+ePHlyk9+//vrr+PDDD5GUlISIiAisXLkSq1evxrhx4wAAq1atQq9evZCUlIQRI0Y0e0yDwQCDwWD9fXl5ua3fwSX9ufi1fsbkTMP6Em+tCjp3tWR1ERERtadWrzExm81Ys2YNqqqqEB8fj+TkZJhMJowfP976ntjYWERFRWHPnj2XPc7ixYuh0+msj8jIyNaW5FJUjbcLNwSTxlb0Ef4eEAT2MCEiItdkczA5cuQIvLy8oNVq8cgjj2D9+vXo3bs3CgoKoNFo4Ovr2+T9ISEhKCgouOzxFi5cCL1eb33k5uba/CVckbXza8OlnD/vyOFlHCIicl02XcoBgJ49e+LgwYPQ6/X49ttvMWvWLCQmJra6AK1WC62W7dUv1ngpp3HxK+/IISKijsDmYKLRaNCtWzcAQFxcHPbt24d3330X//d//wej0YiysrImsyaFhYUIDQ21W8EdxZ+XchpmTKy7CnPGhIiIXFeb+5hYLBYYDAbExcVBrVbj119/tb6WlpaGnJwcxMfHt/U0HY6qsY9J44xJKWdMiIjI9dk0Y7Jw4UJMmjQJUVFRqKiowOrVq7F9+3Zs2bIFOp0Os2fPxoIFC+Dv7w8fHx888cQTiI+Pv+wdOXR51s6vFl7KISKijsOmYFJUVIR7770X+fn50Ol06N+/P7Zs2YIJEyYAAJYuXQqFQoFp06bBYDAgISEBH3zwQbsU7uouXPyqrzGhvLYOQP3OwkRERK7KpmCycuXKK77u5uaGZcuWYdmyZW0qiv5cY2KyiNbZkkAvDTw0Ni8LIiIichrcK0emVBfMmDQ2V4tgK3oiInJxDCYydeHiV+sdOVxfQkRELo7BRKYaF7+aLKL1jhyuLyEiIlfHYCJTjYtfzRbLn3fk8FIOERG5OAYTmbIufjWLyC1tvJTDGRMiInJtDCYy9WdL+j8Xv3LGhIiIXB2DiUw1Xsop0Nei1mSBIADhvpwxISIi18ZgIlPKhks52cVVAIAwHzdoVPzPRUREro0/6WRK3XC7cK2pfhO/CN4qTEREHQCDiUw13i7ciOtLiIioI2AwkanGxa+NeEcOERF1BAwmMqVWcMaEiIg6HgYTmbp0xoTBhIiIXB+DiUw17pXTiJdyiIioI2AwkakLF79qlAqEeLtJWA0REZFjMJjI1IUzJp383KG4aAaFiIjIFTGYyJT6ghkT7ipMREQdBYOJTF24+JULX4mIqKNgMJGpCy/l8FZhIiLqKBhMZOrCxa+8I4eIiDoKBhOZUnPGhIiIOiAGE5lqOmPCYEJERB2DSuoCqHl+HmqM7hEED7USfh5qqcshIiJyCAYTmRIEAZ8/MEzqMoiIiByKl3KIiIhINhhMiIiISDYYTIiIiEg2GEyIiIhINhhMiIiISDYYTIiIiEg2GEyIiIhINhhMiIiISDYYTIiIiEg2GEyIiIhINhhMiIiISDYYTIiIiEg2GEyIiIhINhhMiIiISDZUUhdwMVEUAQDl5eUSV0JEREQt1fhzu/HneGvJLphUVFQAACIjIyWuhIiIiGxVUVEBnU7X6s8LYlujjZ1ZLBbk5eXB29sbgiDY9Nny8nJERkYiNzcXPj4+7VSha+BY2YbjZTuOWetw3GzHMbNde4yZKIqoqKhAeHg4FIrWrxSR3YyJQqFAREREm47h4+PDP5wtxLGyDcfLdhyz1uG42Y5jZjt7j1lbZkoacfErERERyQaDCREREcmGSwUTrVaLl156CVqtVupSZI9jZRuOl+04Zq3DcbMdx8x2ch4z2S1+JSIioo7LpWZMiIiIyLkxmBAREZFsMJgQERGRbDCYEBERkWw4JJgsXrwYQ4cOhbe3N4KDgzFlyhSkpaU1eU9tbS3mzp2LgIAAeHl5Ydq0aSgsLLS+fujQIdx1112IjIyEu7s7evXqhXfffbfJMfLz8zFjxgz06NEDCoUC8+bNa3GNy5YtQ3R0NNzc3DB8+HD88ccfTV7/+OOPMXbsWPj4+EAQBJSVldk8DlfjCuP08MMPo2vXrnB3d0dQUBBuvfVWpKam2j4YLeQKYzZ27FgIgtDk8cgjj9g+GC3k7GOWnZ19yXg1PtauXdu6QWkBZx83AMjMzMRtt92GoKAg+Pj44I477mhSnz3Jfbx27NiByZMnIzw8HIIgYMOGDZe8Z926dZg4cSICAgIgCAIOHjxo6zDYxFFjtm7dOkyYMMH65yA+Ph5btmy5an2iKOLFF19EWFgY3N3dMX78eJw8ebLJe15//XVcc8018PDwgK+vb6vGwSHBJDExEXPnzkVSUhJ++eUXmEwmTJw4EVVVVdb3zJ8/Hxs3bsTatWuRmJiIvLw8TJ061fp6cnIygoOD8eWXX+LYsWN4/vnnsXDhQvz73/+2vsdgMCAoKAgvvPACBgwY0OL6vv76ayxYsAAvvfQSDhw4gAEDBiAhIQFFRUXW91RXV+OGG27Ac88918bRuDxXGKe4uDisWrUKJ06cwJYtWyCKIiZOnAiz2dzG0WmeK4wZADz00EPIz8+3Pt544402jMqVOfuYRUZGNhmr/Px8vPLKK/Dy8sKkSZPsMELNc/Zxq6qqwsSJEyEIAn777Tfs2rULRqMRkydPhsViscMINSX38aqqqsKAAQOwbNmyK75n1KhR+Oc//2njt28dR43Zjh07MGHCBPz0009ITk7Gddddh8mTJyMlJeWK9b3xxht47733sHz5cuzduxeenp5ISEhAbW2t9T1GoxHTp0/Ho48+2vqBECVQVFQkAhATExNFURTFsrIyUa1Wi2vXrrW+58SJEyIAcc+ePZc9zmOPPSZed911zb42ZswY8a9//WuL6hk2bJg4d+5c6+/NZrMYHh4uLl68+JL3btu2TQQglpaWtujYbeHM49To0KFDIgAxIyOjRedoK2ccM1uO1x6cccwuNnDgQPGBBx5o0fHtxdnGbcuWLaJCoRD1er31PWVlZaIgCOIvv/zSonO0hdzG60IAxPXr11/29aysLBGAmJKSYvOx28IRY9aod+/e4iuvvHLZ1y0WixgaGir+61//sj5XVlYmarVa8b///e8l71+1apWo0+mueM7LkWSNiV6vBwD4+/sDqE94JpMJ48ePt74nNjYWUVFR2LNnzxWP03iM1jIajUhOTm5yboVCgfHjx1/x3I7g7ONUVVWFVatWoUuXLg7bLdpZx+yrr75CYGAg+vbti4ULF6K6urpN57aFs45Zo+TkZBw8eBCzZ89u07lt5WzjZjAYIAhCk4Zabm5uUCgU2LlzZ5vO3xJyGi9n4agxs1gsqKiouOJ7srKyUFBQ0OTcOp0Ow4cPt/vPSodv4mexWDBv3jyMHDkSffv2BQAUFBRAo9Fccj0qJCQEBQUFzR5n9+7d+Prrr7Fp06Y21XP+/HmYzWaEhIRccu72XBtxNc48Th988AGefvppVFVVoWfPnvjll1+g0WjadP6WcNYxmzFjBjp37ozw8HAcPnwYzzzzDNLS0rBu3bo2nb8lnHXMLrRy5Ur06tUL11xzTZvObQtnHLcRI0bA09MTzzzzDBYtWgRRFPHss8/CbDYjPz+/Tee/GrmNlzNw5Ji9+eabqKysxB133HHZ9zQev7k/Y5c7d2s5fMZk7ty5OHr0KNasWdPqYxw9ehS33norXnrpJUycOLHFn/v999/h5eVlfXz11VetrqG9OfM4zZw5EykpKUhMTESPHj1wxx13NLkG2V6cdczmzJmDhIQE9OvXDzNnzsTnn3+O9evXIzMzszVfwSbOOmaNampqsHr1aofPljjjuAUFBWHt2rXYuHEjvLy8oNPpUFZWhsGDB7dpi/qWcMbxkpqjxmz16tV45ZVX8M033yA4OBhA/QzuhWP2+++/t7qG1nDojMnjjz+OH3/8ETt27EBERIT1+dDQUBiNRpSVlTVJgoWFhQgNDW1yjOPHj+P666/HnDlz8MILL9h0/iFDhjRZVR0SEgKtVgulUnnJyvTmzu0ozj5OOp0OOp0O3bt3x4gRI+Dn54f169fjrrvusqkOWzj7mF1o+PDhAICMjAx07drVpjps4Qpj9u2336K6uhr33nuvTeduC2cet4kTJyIzMxPnz5+HSqWCr68vQkNDERMTY1MNtpDjeMmdo8ZszZo1ePDBB7F27doml2huueUW699DANCpUyfrrFphYSHCwsKanHvgwIFt+bqXatXKFBtZLBZx7ty5Ynh4uJienn7J640Ler799lvrc6mpqZcs6Dl69KgYHBwsPvXUU1c9p62Lxh5//HHr781ms9ipUyeHL351pXFqVFtbK7q7u4urVq1q0Tls5YpjtnPnThGAeOjQoRadw1auNGZjxowRp02b1qLjtpUrjVujX3/9VRQEQUxNTW3ROWwh9/G6EGSy+NWRY7Z69WrRzc1N3LBhQ4trCw0NFd98803rc3q9vl0WvzokmDz66KOiTqcTt2/fLubn51sf1dXV1vc88sgjYlRUlPjbb7+J+/fvF+Pj48X4+Hjr60eOHBGDgoLEu+++u8kxioqKmpwrJSVFTElJEePi4sQZM2aIKSkp4rFjx65Y35o1a0StVit++umn4vHjx8U5c+aIvr6+YkFBgfU9+fn5YkpKirhixQoRgLhjxw4xJSVFLC4uttMoOf84ZWZmiosWLRL3798vnj59Wty1a5c4efJk0d/fXywsLLTbOF3I2ccsIyNDfPXVV8X9+/eLWVlZ4vfffy/GxMSIo0ePtuMoNeXsY9bo5MmToiAI4ubNm+0wKlfnCuP2ySefiHv27BEzMjLEL774QvT39xcXLFhgpxFqSu7jVVFRYf0cAPHtt98WU1JSxNOnT1vfU1xcLKakpIibNm0SAYhr1qwRU1JSxPz8fDuNUlOOGrOvvvpKVKlU4rJly5q8p6ys7Ir1LVmyRPT19RW///578fDhw+Ktt94qdunSRaypqbG+5/Tp02JKSor4yiuviF5eXtYxrqioaPE4OCSYAGj2ceG/omtqasTHHntM9PPzEz08PMTbbrutyX/8l156qdljdO7c+arnuvg9zXn//ffFqKgoUaPRiMOGDROTkpKavH6589tzJsDZx+ns2bPipEmTxODgYFGtVosRERHijBkz2uVfY1f6Hs40Zjk5OeLo0aNFf39/UavVit26dROfeuqpJrd02puzj1mjhQsXipGRkaLZbG7tUNjEFcbtmWeeEUNCQkS1Wi12795dfOutt0SLxdKWYbksuY9X4+z3xY9Zs2ZZ37Nq1apm3/PSSy+1fYCa4agxGzNmzFW/e3MsFov497//XQwJCRG1Wq14/fXXi2lpaU3eM2vWrGaPvW3bthaPg9AwGERERESS4145REREJBsMJkRERCQbDCZEREQkGwwmREREJBsMJkRERCQbDCZEREQkGwwmREREJBsMJkRERCQbDCZEZDdjx47FvHnzpC6DiJwYgwkRSWL79u0QBAFlZWVSl0JEMsJgQkRERLLBYEJErVJVVYV7770XXl5eCAsLw1tvvdXk9S+++AJDhgyBt7c3QkNDMWPGDBQVFQEAsrOzcd111wEA/Pz8IAgC7rvvPgCAxWLB4sWL0aVLF7i7u2PAgAH49ttvHfrdiEg6DCZE1CpPPfUUEhMT8f333+N///sftm/fjgMHDlhfN5lMeO2113Do0CFs2LAB2dnZ1vARGRmJ7777DgCQlpaG/Px8vPvuuwCAxYsX4/PPP8fy5ctx7NgxzJ8/H3fffTcSExMd/h2JyPG4uzAR2ayyshIBAQH48ssvMX36dABASUkJIiIiMGfOHLzzzjuXfGb//v0YOnQoKioq4OXlhe3bt+O6665DaWkpfH19AQAGgwH+/v7YunUr4uPjrZ998MEHUV1djdWrVzvi6xGRhFRSF0BEziczMxNGoxHDhw+3Pufv74+ePXtaf5+cnIyXX34Zhw4dQmlpKSwWCwAgJycHvXv3bva4GRkZqK6uxoQJE5o8bzQaMWjQoHb4JkQkNwwmRGR3VVVVSEhIQEJCAr766isEBQUhJycHCQkJMBqNl/1cZWUlAGDTpk3o1KlTk9e0Wm271kxE8sBgQkQ269q1K9RqNfbu3YuoqCgAQGlpKdLT0zFmzBikpqaiuLgYS5YsQWRkJID6SzkX0mg0AACz2Wx9rnfv3tBqtcjJycGYMWMc9G2ISE4YTIjIZl5eXpg9ezaeeuopBAQEIDg4GM8//zwUivr19FFRUdBoNHj//ffxyCOP4OjRo3jttdeaHKNz584QBAE//vgjbrzxRri7u8Pb2xtPPvkk5s+fD4vFglGjRkGv12PXrl3w8fHBrFmzpPi6RORAvCuHiFrlX//6F6699lpMnjwZ48ePx6hRoxAXFwcACAoKwqeffoq1a9eid+/eWLJkCd58880mn+/UqRNeeeUVPPvsswgJCcHjjz8OAHjttdfw97//HYsXL0avXr1www03YNOmTejSpYvDvyMROR7vyiEiIiLZ4IwJERERyQaDCREREckGgwkRERHJBoMJERERyQaDCREREckGgwkRERHJBoMJERERyQaDCREREckGgwkRERHJBoMJERERyQaDCREREcnG/wO2QTfrb4+kxgAAAABJRU5ErkJggg==", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "noaa_surface_median_temps.plot.line(sampling_n=40)" - ] - }, - { - "cell_type": "markdown", - "id": "64d6f86d", - "metadata": {}, - "source": [ - "Note: `sampling_n` has no effect on histograms. This is because BigQuery DataFrame bucketizes the data on the server side for histograms. If your amount of bins is very large, you may encounter a \"Query too large\" error instead." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.16" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/noxfile.py b/noxfile.py index 839e6e0e115..34b055de445 100644 --- a/noxfile.py +++ b/noxfile.py @@ -8,7 +8,7 @@ # # https://www.apache.org/licenses/LICENSE-2.0 # -# Unless required by applicable law or agreed to in writing, software/ +# Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and @@ -16,80 +16,46 @@ from __future__ import absolute_import -import argparse -import multiprocessing +from multiprocessing import Process import os import pathlib +from pathlib import Path import re import shutil -import time from typing import Dict, List +import warnings import nox import nox.sessions -PROJECT_ID_OVERRIDE = os.getenv("BIGFRAMES_TEST_PROJECT") -ENV_OVERRIDES = ( - {"GOOGLE_CLOUD_PROJECT": PROJECT_ID_OVERRIDE} if PROJECT_ID_OVERRIDE else {} -) - -RUFF_VERSION = "ruff==0.14.14" -MYPY_VERSION = "mypy==1.15.0" - -# Notebook tests should match colab and BQ Studio. -# Check with import sys; sys.version_info -# on a fresh notebook runtime. -COLAB_AND_BQ_STUDIO_PYTHON_VERSIONS = [ - # BQ Studio - "3.10", - # colab.research.google.com - "3.11", -] - -PYTEST_VERSION = "pytest==8.4.2" +BLACK_VERSION = "black==22.3.0" +ISORT_VERSION = "isort==5.12.0" SPHINX_VERSION = "sphinx==4.5.0" -LINT_PATHS = [ - "docs", - "bigframes", - "scripts", - "tests", - "third_party", - "noxfile.py", - "setup.py", -] +LINT_PATHS = ["docs", "bigframes", "tests", "third_party", "noxfile.py", "setup.py"] -DEFAULT_PYTHON_VERSION = "3.14" +DEFAULT_PYTHON_VERSION = "3.10" -ALL_PYTHON = ["3.10", "3.11", "3.12", "3.13", "3.14", "3.15"] +UNIT_TEST_PYTHON_VERSIONS = ["3.9", "3.10", "3.11"] UNIT_TEST_STANDARD_DEPENDENCIES = [ "mock", - PYTEST_VERSION, + "asyncmock", + "pytest", "pytest-cov", - "pytest-timeout", - "pluggy", + "pytest-asyncio", + "pytest-mock", ] UNIT_TEST_EXTERNAL_DEPENDENCIES: List[str] = [] +UNIT_TEST_LOCAL_DEPENDENCIES: List[str] = [] UNIT_TEST_DEPENDENCIES: List[str] = [] -UNIT_TEST_EXTRAS: List[str] = ["tests"] -UNIT_TEST_EXTRAS_BY_PYTHON: Dict[str, List[str]] = { - "3.10": ["tests", "scikit-learn", "anywidget"], - "3.11": ["tests", "polars", "scikit-learn", "anywidget"], - # Make sure we leave some versions without "extras" so we know those - # dependencies are actually optional. - "3.13": ["tests", "polars", "scikit-learn", "anywidget"], - "3.14": ["tests", "polars", "scikit-learn", "anywidget"], -} - -# 3.11 is used by colab. -# 3.10 is needed for Windows tests as it is the only version installed in the -# bigframes-windows container image. For more information, search -# bigframes/windows-docker, internally. -SYSTEM_TEST_PYTHON_VERSIONS: List[str] = ALL_PYTHON +UNIT_TEST_EXTRAS: List[str] = [] +UNIT_TEST_EXTRAS_BY_PYTHON: Dict[str, List[str]] = {} + +SYSTEM_TEST_PYTHON_VERSIONS = ["3.9", "3.11"] SYSTEM_TEST_STANDARD_DEPENDENCIES = [ "jinja2", "mock", "openpyxl", - PYTEST_VERSION, + "pytest", "pytest-cov", "pytest-retry", "pytest-timeout", @@ -101,32 +67,28 @@ SYSTEM_TEST_EXTERNAL_DEPENDENCIES = [ "google-cloud-bigquery", ] -SYSTEM_TEST_EXTRAS: List[str] = [] -SYSTEM_TEST_EXTRAS_BY_PYTHON: Dict[str, List[str]] = { - # Make sure we leave some versions without "extras" so we know those - # dependencies are actually optional. - "3.10": ["tests", "scikit-learn", "anywidget"], - "3.12": ["tests", "scikit-learn", "polars", "anywidget"], - "3.13": ["tests", "polars", "anywidget"], - "3.14": ["tests", "polars", "anywidget"], -} - -LOGGING_NAME_ENV_VAR = "BIGFRAMES_PERFORMANCE_LOG_NAME" +SYSTEM_TEST_LOCAL_DEPENDENCIES: List[str] = [] +SYSTEM_TEST_DEPENDENCIES: List[str] = [] +SYSTEM_TEST_EXTRAS: List[str] = ["tests"] +SYSTEM_TEST_EXTRAS_BY_PYTHON: Dict[str, List[str]] = {} CURRENT_DIRECTORY = pathlib.Path(__file__).parent.absolute() # Sessions are executed in the order so putting the smaller sessions # ahead to fail fast at presubmit running. +# 'docfx' is excluded since it only needs to run in 'docs-presubmit' nox.options.sessions = [ - # Include unit_noextras to ensure at least some unit tests contribute to - # coverage. - # TODO(tswast): Consider removing this when unit_noextras and cover is run - # from GitHub actions. + "lint", + "lint_setup_py", + "mypy", + "format", + "docs", + "docfx", + "unit", "unit_noextras", - "system-3.12", # No extras. + "system", + "doctest", "cover", - # TODO(b/401609005): remove - "cleanup", ] # Error if a python version is missing @@ -140,46 +102,22 @@ def lint(session): Returns a failure if the linters find linting errors or sufficiently serious code quality issues. """ - session.install(RUFF_VERSION) - - # Check imports + session.install("flake8", BLACK_VERSION) session.run( - "ruff", - "check", - "--select", - "I,F", - f"--target-version=py{ALL_PYTHON[0].replace('.', '')}", - "--line-length=88", # Standard Black line length - *LINT_PATHS, - ) - - # Check formatting - session.run( - "ruff", - "format", + "black", "--check", - f"--target-version=py{ALL_PYTHON[0].replace('.', '')}", - "--line-length=88", *LINT_PATHS, ) + # TODO(tswast): lint all LINT_PATHS + session.run("flake8", "bigframes", "tests") -# Use a python runtime which is available in the owlbot post processor here -# https://github.com/googleapis/synthtool/blob/master/docker/owlbot/python/Dockerfile @nox.session(python=DEFAULT_PYTHON_VERSION) def blacken(session): - """(Deprecated) Legacy session. Please use 'nox -s format'.""" - session.log( - "WARNING: The 'blacken' session is deprecated and will be removed in a future release. Please use 'nox -s format' in the future." - ) - - # Just run the ruff formatter (keeping legacy behavior of only formatting, not sorting imports) - session.install(RUFF_VERSION) + """Run black. Format code to uniform standard.""" + session.install(BLACK_VERSION) session.run( - "ruff", - "format", - f"--target-version=py{ALL_PYTHON[0].replace('.', '')}", - "--line-length=88", + "black", *LINT_PATHS, ) @@ -187,31 +125,18 @@ def blacken(session): @nox.session(python=DEFAULT_PYTHON_VERSION) def format(session): """ - Run ruff to sort imports and format code. + Run isort to sort imports. Then run black + to format code to uniform standard. """ - # 1. Install ruff (skipped automatically if you run with --no-venv) - session.install(RUFF_VERSION) - - # 2. Run Ruff to fix imports - # check --select I: Enables strict import sorting - # --fix: Applies the changes automatically + session.install(BLACK_VERSION, ISORT_VERSION) + # Use the --fss option to sort imports using strict alphabetical order. + # See https://pycqa.github.io/isort/docs/configuration/options.html#force-sort-within-sections session.run( - "ruff", - "check", - "--select", - "I,F", - "--fix", - f"--target-version=py{ALL_PYTHON[0].replace('.', '')}", - "--line-length=88", # Standard Black line length + "isort", *LINT_PATHS, ) - - # 3. Run Ruff to format code session.run( - "ruff", - "format", - f"--target-version=py{ALL_PYTHON[0].replace('.', '')}", - "--line-length=88", # Standard Black line length + "black", *LINT_PATHS, ) @@ -219,33 +144,36 @@ def format(session): @nox.session(python=DEFAULT_PYTHON_VERSION) def lint_setup_py(session): """Verify that setup.py is valid (including RST check).""" - session.install("docutils", "pygments", "setuptools") + session.install("docutils", "pygments") session.run("python", "setup.py", "check", "--restructuredtext", "--strict") - session.install("twine", "wheel") - shutil.rmtree("build", ignore_errors=True) - shutil.rmtree("dist", ignore_errors=True) - session.run("python", "setup.py", "sdist") - session.run( - "python", "-m", "twine", "check", *pathlib.Path("dist").glob("*.tar.gz") - ) - def install_unittest_dependencies(session, install_test_extra, *constraints): - extras = [] - if install_test_extra: - if session.python in UNIT_TEST_EXTRAS_BY_PYTHON: - extras = UNIT_TEST_EXTRAS_BY_PYTHON[session.python] - else: - extras = UNIT_TEST_EXTRAS + standard_deps = UNIT_TEST_STANDARD_DEPENDENCIES + UNIT_TEST_DEPENDENCIES + session.install(*standard_deps, *constraints) + + if UNIT_TEST_EXTERNAL_DEPENDENCIES: + warnings.warn( + "'unit_test_external_dependencies' is deprecated. Instead, please " + "use 'unit_test_dependencies' or 'unit_test_local_dependencies'.", + DeprecationWarning, + ) + session.install(*UNIT_TEST_EXTERNAL_DEPENDENCIES, *constraints) - session.install( - *UNIT_TEST_STANDARD_DEPENDENCIES, - *UNIT_TEST_DEPENDENCIES, - "-e", - f".[{','.join(extras)}]" if extras else ".", - *constraints, - ) + if UNIT_TEST_LOCAL_DEPENDENCIES: + session.install(*UNIT_TEST_LOCAL_DEPENDENCIES, *constraints) + + if install_test_extra and UNIT_TEST_EXTRAS_BY_PYTHON: + extras = UNIT_TEST_EXTRAS_BY_PYTHON.get(session.python, []) + elif install_test_extra and UNIT_TEST_EXTRAS: + extras = UNIT_TEST_EXTRAS + else: + extras = [] + + if extras: + session.install("-e", f".[{','.join(extras)}]", *constraints) + else: + session.install("-e", ".", *constraints) def run_unit(session, install_test_extra): @@ -256,16 +184,11 @@ def run_unit(session, install_test_extra): install_unittest_dependencies(session, install_test_extra, "-c", constraints_path) # Run py.test against the unit tests. - scripts_path = "scripts" tests_path = os.path.join("tests", "unit") third_party_tests_path = os.path.join("third_party", "bigframes_vendored") session.run( "py.test", "--quiet", - # Any individual test taking longer than 1 mins will be terminated. - "--timeout=60", - # Log 20 slowest tests - "--durations=20", f"--junitxml=unit_{session.python}_sponge_log.xml", "--cov=bigframes", f"--cov={tests_path}", @@ -275,30 +198,71 @@ def run_unit(session, install_test_extra): "--cov-fail-under=0", tests_path, third_party_tests_path, - scripts_path, *session.posargs, ) -@nox.session(python=ALL_PYTHON) -@nox.parametrize("test_extra", [True, False]) -def unit(session, test_extra): - if session.python == "3.15": - session.skip( - "Skipping 3.15 until wheels are available for pyarrow. Also pyproj wheels are needed for dependency geopandas." - ) - if test_extra: - run_unit(session, install_test_extra=test_extra) - else: - unit_noextras(session) +@nox.session(python=UNIT_TEST_PYTHON_VERSIONS) +def unit(session): + run_unit(session, install_test_extra=True) -@nox.session(python=ALL_PYTHON[-1]) +@nox.session(python=UNIT_TEST_PYTHON_VERSIONS[-1]) def unit_noextras(session): run_unit(session, install_test_extra=False) +@nox.session(python=DEFAULT_PYTHON_VERSION) +def mypy(session): + """Run type checks with mypy.""" + session.install("-e", ".") + + # Just install the dependencies' type info directly, since "mypy --install-types" + # might require an additional pass. + deps = ( + set( + [ + "mypy", + "pandas-stubs", + "types-protobuf", + "types-python-dateutil", + "types-requests", + "types-setuptools", + ] + ) + | set(SYSTEM_TEST_STANDARD_DEPENDENCIES) + | set(UNIT_TEST_STANDARD_DEPENDENCIES) + ) + + session.install(*deps) + shutil.rmtree(".mypy_cache", ignore_errors=True) + session.run( + "mypy", + "bigframes", + os.path.join("tests", "system"), + os.path.join("tests", "unit"), + "--explicit-package-bases", + '--exclude="^third_party"', + ) + + def install_systemtest_dependencies(session, install_test_extra, *constraints): + # Use pre-release gRPC for system tests. + # Exclude version 1.49.0rc1 which has a known issue. + # See https://github.com/grpc/grpc/pull/30642 + session.install("--pre", "grpcio!=1.49.0rc1") + + session.install(*SYSTEM_TEST_STANDARD_DEPENDENCIES, *constraints) + + if SYSTEM_TEST_EXTERNAL_DEPENDENCIES: + session.install(*SYSTEM_TEST_EXTERNAL_DEPENDENCIES, *constraints) + + if SYSTEM_TEST_LOCAL_DEPENDENCIES: + session.install("-e", *SYSTEM_TEST_LOCAL_DEPENDENCIES, *constraints) + + if SYSTEM_TEST_DEPENDENCIES: + session.install("-e", *SYSTEM_TEST_DEPENDENCIES, *constraints) + if install_test_extra and SYSTEM_TEST_EXTRAS_BY_PYTHON: extras = SYSTEM_TEST_EXTRAS_BY_PYTHON.get(session.python, []) elif install_test_extra and SYSTEM_TEST_EXTRAS: @@ -306,19 +270,10 @@ def install_systemtest_dependencies(session, install_test_extra, *constraints): else: extras = [] - # Use pre-release gRPC for system tests. - # Exclude version 1.49.0rc1 which has a known issue. - # See https://github.com/grpc/grpc/pull/30642 - - session.install( - "--pre", - "grpcio!=1.49.0rc1", - *SYSTEM_TEST_STANDARD_DEPENDENCIES, - *SYSTEM_TEST_EXTERNAL_DEPENDENCIES, - "-e", - f".[{','.join(extras)}]" if extras else ".", - *constraints, - ) + if extras: + session.install("-e", f".[{','.join(extras)}]", *constraints) + else: + session.install("-e", ".", *constraints) def run_system( @@ -330,8 +285,6 @@ def run_system( install_test_extra=True, print_duration=False, extra_pytest_options=(), - timeout_seconds=900, - num_workers=20, ): """Run the system test suite.""" constraints_path = str( @@ -347,17 +300,13 @@ def run_system( install_systemtest_dependencies(session, install_test_extra, "-c", constraints_path) - # Print out package versions for debugging. - session.run("python", "-m", "pip", "freeze") - # Run py.test against the system tests. pytest_cmd = [ "py.test", - "-v", - f"-n={num_workers}", - "--dist=worksteal", + "--quiet", + "-n=20", # Any individual test taking longer than 15 mins will be terminated. - f"--timeout={timeout_seconds}", + "--timeout=900", # Log 20 slowest tests "--durations=20", f"--junitxml={prefix_name}_{session.python}_sponge_log.xml", @@ -381,10 +330,14 @@ def run_system( ) pytest_cmd.extend(extra_pytest_options) - session.run(*pytest_cmd, *session.posargs, test_folder, env=ENV_OVERRIDES) + session.run( + *pytest_cmd, + test_folder, + *session.posargs, + ) -@nox.session(python="3.12") +@nox.session(python=SYSTEM_TEST_PYTHON_VERSIONS) def system(session: nox.sessions.Session): """Run the system test suite.""" run_system( @@ -395,7 +348,7 @@ def system(session: nox.sessions.Session): ) -@nox.session(python=DEFAULT_PYTHON_VERSION) +@nox.session(python=SYSTEM_TEST_PYTHON_VERSIONS[-1]) def system_noextras(session: nox.sessions.Session): """Run the system test suite.""" run_system( @@ -406,46 +359,19 @@ def system_noextras(session: nox.sessions.Session): ) -@nox.session(python="3.12") +@nox.session(python=SYSTEM_TEST_PYTHON_VERSIONS[-1]) def doctest(session: nox.sessions.Session): """Run the system test suite.""" - run_system( session=session, prefix_name="doctest", - extra_pytest_options=( - "--doctest-modules", - "third_party", - "--ignore", - "third_party/bigframes_vendored/ibis", - "--ignore", - "third_party/bigframes_vendored/sqlglot", - "--ignore", - "bigframes/core/compile/polars", - "--ignore", - "bigframes/testing", - "--ignore", - "bigframes/display/anywidget.py", - "--ignore", - "bigframes/bigquery/_operations/ai.py", - "--ignore", - "bigframes/bigquery/ai.py", - "--ignore", - "bigframes/ml", - "--ignore", - "bigframes/operations/ai.py", - "--ignore", - "bigframes/operations/semantics.py", - "--ignore", - "third_party/bigframes_vendored/sklearn", - ), + extra_pytest_options=("--doctest-modules", "third_party"), test_folder="bigframes", check_cov=True, - num_workers=5, ) -@nox.session(python=DEFAULT_PYTHON_VERSION) +@nox.session(python=SYSTEM_TEST_PYTHON_VERSIONS[-1]) def e2e(session: nox.sessions.Session): """Run the large tests in system test suite.""" run_system( @@ -456,15 +382,23 @@ def e2e(session: nox.sessions.Session): ) -@nox.session(python=SYSTEM_TEST_PYTHON_VERSIONS[-1]) -def load(session: nox.sessions.Session): - """Run the very large tests in system test suite.""" - run_system( - session=session, - prefix_name="load", - test_folder=os.path.join("tests", "system", "load"), - print_duration=True, - timeout_seconds=60 * 60 * 12, +@nox.session(python=SYSTEM_TEST_PYTHON_VERSIONS) +def samples(session): + """Run the samples test suite.""" + + constraints_path = str( + CURRENT_DIRECTORY / "testing" / f"constraints-{session.python}.txt" + ) + + # TODO(swast): Use `requirements.txt` files from the samples directories to + # test samples. + install_test_extra = True + install_systemtest_dependencies(session, install_test_extra, "-c", constraints_path) + + session.run( + "py.test", + "samples", + *session.posargs, ) @@ -475,65 +409,34 @@ def cover(session): This outputs the coverage report aggregating coverage from the test runs (including system test runs), and then erases coverage data. """ - # TODO: Remove this skip when the issue is resolved. - # https://github.com/googleapis/google-cloud-python/issues/16635 - session.skip("Temporarily skip coverage session") - session.install("coverage", "pytest-cov") + session.run("coverage", "report", "--show-missing", "--fail-under=90") - # Create a coverage report that includes only the product code. - omitted_paths = [ - # non-prod, unit tested - "bigframes/core/compile/polars/*", - # untested - "bigframes/streaming/*", - # utils - "bigframes/testing/*", - ] - + # Make sure there is no dead code in our test directories. + # TODO(swast): Cleanup dead code in the system tests directory. session.run( "coverage", "report", - "--include=bigframes/*", - # Only unit tested - f"--omit={','.join(omitted_paths)}", "--show-missing", - "--fail-under=84", - ) - - # Make sure there is no dead code in our system test directories. - session.run( - "coverage", - "report", - "--show-missing", - "--include=tests/system/small/*", - # Some tests only run under old pandas, some only under new pandas version - "--fail-under=98", + "--include=tests/unit/*", + "--fail-under=100", ) session.run("coverage", "erase") -@nox.session(python="3.10") +@nox.session(python=DEFAULT_PYTHON_VERSION) def docs(session): """Build the docs for this library.""" - session.install("-e", ".[scikit-learn]") + + session.install("-e", ".") session.install( - "sphinx", - "sphinx-sitemap", - "myst-parser", - "myst-nb", - "pydata-sphinx-theme", + SPHINX_VERSION, + "alabaster", + "recommonmark", ) shutil.rmtree(os.path.join("docs", "_build"), ignore_errors=True) - session.run("python", "-m", "pip", "freeze") - - session.run( - "python", - "scripts/publish_api_coverage.py", - "docs", - ) session.run( "sphinx-build", "-W", # warnings as errors @@ -548,28 +451,19 @@ def docs(session): ) -@nox.session(python="3.10") +@nox.session(python=DEFAULT_PYTHON_VERSION) def docfx(session): """Build the docfx yaml files for this library.""" - session.install("-e", ".[scikit-learn]") + session.install("-e", ".") session.install( SPHINX_VERSION, - "sphinx-sitemap==2.9.0", - "pydata-sphinx-theme==0.13.3", - "myst-parser==0.18.1", - "myst-nb", - "gcp-sphinx-docfx-yaml==3.2.4", - "anywidget", + "alabaster", + "recommonmark", + "gcp-sphinx-docfx-yaml", ) shutil.rmtree(os.path.join("docs", "_build"), ignore_errors=True) - - session.run( - "python", - "scripts/publish_api_coverage.py", - "docs", - ) session.run( "sphinx-build", "-T", # show full traceback on exception @@ -584,7 +478,7 @@ def docfx(session): "sphinx.ext.napoleon," "sphinx.ext.todo," "sphinx.ext.viewcode," - "myst_parser" + "recommonmark" ), "-b", "html", @@ -595,41 +489,74 @@ def docfx(session): ) -def prerelease(session: nox.sessions.Session, tests_path, extra_pytest_options=()): +def prerelease(session: nox.sessions.Session, tests_path): constraints_path = str( CURRENT_DIRECTORY / "testing" / f"constraints-{session.python}.txt" ) - session.install( - *set(UNIT_TEST_STANDARD_DEPENDENCIES + SYSTEM_TEST_STANDARD_DEPENDENCIES), - "-c", - constraints_path, - "-e", - ".", - ) # PyArrow prerelease packages are published to an alternative PyPI host. # https://arrow.apache.org/docs/python/install.html#installing-nightly-packages session.install( - "--no-deps", - "--upgrade", "--extra-index-url", "https://pypi.fury.io/arrow-nightlies/", + "--prefer-binary", + "--pre", + "--upgrade", "pyarrow", - # We exclude each version individually so that we can continue to test - # some prerelease packages. See: - # https://github.com/googleapis/google-cloud-python/pull/268#discussion_r1423205172 - # "pandas!=2.1.4, !=2.2.0rc0, !=2.2.0, !=2.2.1", + ) + session.install( + "--extra-index-url", + "https://pypi.anaconda.org/scipy-wheels-nightly/simple", + "--prefer-binary", + "--pre", + "--upgrade", "pandas", - # Workaround https://github.com/googleapis/python-db-dtypes-pandas/issues/178 - "db-dtypes", - # Ensure we catch breaking changes in the client libraries early. - "git+https://github.com/googleapis/google-cloud-python.git#egg=google-cloud-bigquery&subdirectory=packages/google-cloud-bigquery", + ) + session.install( "--upgrade", - "-e", - "git+https://github.com/googleapis/google-cloud-python.git#egg=google-cloud-bigquery-storage&subdirectory=packages/google-cloud-bigquery-storage", - "git+https://github.com/googleapis/google-cloud-python.git#egg=pandas-gbq&subdirectory=packages/pandas-gbq", + "-e", # Use -e so that py.typed file is included. + "git+https://github.com/ibis-project/ibis.git#egg=ibis-framework", + ) + # Workaround https://github.com/googleapis/python-db-dtypes-pandas/issues/178 + session.install("--no-deps", "db-dtypes") + + # Workaround to install pandas-gbq >=0.15.0, which is required by test only. + session.install("--no-deps", "pandas-gbq") + + session.install( + *set(UNIT_TEST_STANDARD_DEPENDENCIES + SYSTEM_TEST_STANDARD_DEPENDENCIES), + "-c", + constraints_path, ) + # Because we test minimum dependency versions on the minimum Python + # version, the first version we test with in the unit tests sessions has a + # constraints file containing all dependencies and extras. + with open( + CURRENT_DIRECTORY + / "testing" + / f"constraints-{UNIT_TEST_PYTHON_VERSIONS[0]}.txt", + encoding="utf-8", + ) as constraints_file: + constraints_text = constraints_file.read() + + # Ignore leading whitespace and comment lines. + already_installed = frozenset( + ("db-dtypes", "pandas", "pyarrow", "ibis-framework", "pandas-gbq") + ) + deps = [ + match.group(1) + for match in re.finditer( + r"^\s*(\S+)(?===\S+)", constraints_text, flags=re.MULTILINE + ) + if match.group(1) not in already_installed + ] + + # We use --no-deps to ensure that pre-release versions aren't overwritten + # by the version ranges in setup.py. + session.install(*deps) + session.install("--no-deps", "-e", ".") + # Print out prerelease package versions. session.run("python", "-m", "pip", "freeze") @@ -648,13 +575,11 @@ def prerelease(session: nox.sessions.Session, tests_path, extra_pytest_options=( "--cov-report=term-missing", "--cov-fail-under=0", tests_path, - *extra_pytest_options, *session.posargs, - env=ENV_OVERRIDES, ) -@nox.session(python=ALL_PYTHON[-1]) +@nox.session(python=UNIT_TEST_PYTHON_VERSIONS[-1]) def unit_prerelease(session: nox.sessions.Session): """Run the unit test suite with prerelease dependencies.""" prerelease(session, os.path.join("tests", "unit")) @@ -663,101 +588,52 @@ def unit_prerelease(session: nox.sessions.Session): @nox.session(python=SYSTEM_TEST_PYTHON_VERSIONS[-1]) def system_prerelease(session: nox.sessions.Session): """Run the system test suite with prerelease dependencies.""" - small_tests_dir = os.path.join("tests", "system", "small") - - # Let's exclude remote function tests from the prerelease tests, since the - # some of the package dependencies propagate to the cloud run functions' - # requirements.txt, and the prerelease package versions may not be available - # in the standard pip install. - # This would mean that we will only rely on the standard remote function - # tests. - small_remote_function_tests = os.path.join( - small_tests_dir, "functions", "test_remote_function.py" - ) - assert os.path.exists(small_remote_function_tests) + prerelease(session, os.path.join("tests", "system", "small")) - prerelease( - session, - os.path.join("tests", "system", "small"), - (f"--ignore={small_remote_function_tests}",), - ) - - -@nox.session(python=COLAB_AND_BQ_STUDIO_PYTHON_VERSIONS) -def notebook(session: nox.Session): - google_cloud_project = PROJECT_ID_OVERRIDE or os.getenv("GOOGLE_CLOUD_PROJECT") - if not google_cloud_project: - session.error( - "Set GOOGLE_CLOUD_PROJECT environment variable to run notebook session." - ) +@nox.session(python=SYSTEM_TEST_PYTHON_VERSIONS) +def notebook(session): session.install("-e", ".[all]") - session.install( - "pytest", - "pytest-xdist", - "pytest-retry", - "nbmake", - "google-cloud-aiplatform", - "matplotlib", - "seaborn", - "anywidget", - ) + session.install("pytest", "pytest-xdist", "pytest-retry", "nbmake") + + notebooks_list = list(Path("notebooks/").glob("*/*.ipynb")) - notebooks_list = list(pathlib.Path("notebooks/").glob("*/*.ipynb")) denylist = [ # Regionalized testing is manually added later. "notebooks/location/regionalized.ipynb", # These notebooks contain special colab `param {type:"string"}` # comments, which make it easy for customers to fill in their # own information. - # - # With the notebooks_fill_params.py script, we are able to find and - # replace the PROJECT_ID parameter, but not the others. - # - # TODO(b/357904266): Test these notebooks by replacing parameters with + # TODO(ashleyxu): Test these notebooks by replacing parameters with # appropriate values and omitting cleanup logic that may break # our test infrastructure. - "notebooks/getting_started/ml_fundamentals_bq_dataframes.ipynb", # Needs DATASET. - "notebooks/ml/bq_dataframes_ml_linear_regression.ipynb", # Needs DATASET_ID. - "notebooks/ml/bq_dataframes_ml_linear_regression_big.ipynb", # Needs DATASET_ID. - "notebooks/generative_ai/bq_dataframes_ml_drug_name_generation.ipynb", # Needs CONNECTION. - # TODO(b/332737009): investigate why we get 404 errors, even though - # bq_dataframes_llm_code_generation creates a bucket in the sample. - "notebooks/generative_ai/bq_dataframes_llm_code_generation.ipynb", # Needs BUCKET_URI. - "notebooks/generative_ai/sentiment_analysis.ipynb", # Too slow - "notebooks/generative_ai/bq_dataframes_llm_vector_search.ipynb", # Limited quota for vector index ddl statements on table. - "notebooks/generative_ai/bq_dataframes_ml_drug_name_generation.ipynb", # Needs CONNECTION. - "notebooks/generative_ai/ai_movie_poster.ipynb", # Needs CONNECTION. - # TODO(b/366290533): to protect BQML quota - "notebooks/vertex_sdk/sdk2_bigframes_pytorch.ipynb", # Needs BUCKET_URI. - "notebooks/vertex_sdk/sdk2_bigframes_sklearn.ipynb", # Needs BUCKET_URI. - "notebooks/vertex_sdk/sdk2_bigframes_tensorflow.ipynb", # Needs BUCKET_URI. + "notebooks/getting_started/getting_started_bq_dataframes.ipynb", + "notebooks/generative_ai/bq_dataframes_llm_code_generation.ipynb", + "notebooks/regression/bq_dataframes_ml_linear_regression.ipynb", + "notebooks/generative_ai/bq_dataframes_ml_drug_name_generation.ipynb", + "notebooks/vertex_sdk/sdk2_bigframes_pytorch.ipynb", + "notebooks/vertex_sdk/sdk2_bigframes_sklearn.ipynb", + "notebooks/vertex_sdk/sdk2_bigframes_tensorflow.ipynb", # The experimental notebooks imagine features that don't yet # exist or only exist as temporary prototypes. - "notebooks/experimental/ai_operators.ipynb", - "notebooks/experimental/semantic_operators.ipynb", - # The notebooks that are added for more use cases, such as backing a - # blog post, which may take longer to execute and need not be - # continuously tested. - "notebooks/apps/synthetic_data_generation.ipynb", - "notebooks/multimodal/multimodal_dataframe.ipynb", # too slow - # This anywidget notebook uses deferred execution, so it won't - # produce metrics for the performance benchmark script. - "notebooks/dataframes/anywidget_mode.ipynb", - # Needs a connection - "notebooks/remote_functions/remote_function_vertex_claude_model.ipynb", + "notebooks/experimental/longer_ml_demo.ipynb", ] - # Convert each Path notebook object to a string using a list comprehension, - # and remove tests that we choose not to test. + # Convert each Path notebook object to a string using a list comprehension. notebooks = [str(nb) for nb in notebooks_list] - notebooks = [nb for nb in notebooks if nb not in denylist and "/kaggle/" not in nb] + + # Remove tests that we choose not to test. + notebooks = list(filter(lambda nb: nb not in denylist, notebooks)) # Regionalized notebooks notebooks_reg = { "regionalized.ipynb": [ "asia-southeast1", + "eu", + "europe-west4", + "southamerica-west1", "us", + "us-central1", ] } notebooks_reg = { @@ -765,164 +641,49 @@ def notebook(session: nox.Session): for nb, regions in notebooks_reg.items() } - # The pytest --nbmake exits silently with "no tests ran" message if + # For some reason nbmake exits silently with "no tests ran" message if # one of the notebook paths supplied does not exist. Let's make sure that - # each path exists. + # each path exists for nb in notebooks + list(notebooks_reg): assert os.path.exists(nb), nb - # Determine whether to enable multi-process mode based on the environment - # variable. If BENCHMARK_AND_PUBLISH is "true", it indicates we're running - # a benchmark, so we disable multi-process mode. If BENCHMARK_AND_PUBLISH - # is "false", we enable multi-process mode for faster execution. - multi_process_mode = os.getenv("BENCHMARK_AND_PUBLISH", "false") == "false" - - try: - # Populate notebook parameters and make a backup so that the notebooks - # are runnable. - session.run( - "python", - CURRENT_DIRECTORY / "scripts" / "notebooks_fill_params.py", - *notebooks, - env=ENV_OVERRIDES, - ) + # TODO(shobs): For some reason --retries arg masks exceptions occurred in + # notebook failures, and shows unhelpful INTERNALERROR. Investigate that + # and enable retries if we can find a way to surface the real exception + # bacause the notebook is running against real GCP and something may fail + # due to transient issues. + pytest_command = [ + "py.test", + "--nbmake", + "--nbmake-timeout=600", + ] - processes = [] - for notebook in notebooks: - args = ( - "python", - "scripts/run_and_publish_benchmark.py", - "--notebook", - f"--benchmark-path={notebook}", + # Run self-contained notebooks in single session.run + # achieve parallelization via -n + session.run( + *pytest_command, + "-nauto", + *notebooks, + ) + + # Run regionalized notebooks in parallel session.run's, since each notebook + # takes a different region via env param. + processes = [] + for notebook, regions in notebooks_reg.items(): + for region in regions: + process = Process( + target=session.run, + args=(*pytest_command, notebook), + kwargs={"env": {"BIGQUERY_LOCATION": region}}, ) - if multi_process_mode: - process = multiprocessing.Process( - target=session.run, args=args, kwargs={"env": ENV_OVERRIDES} - ) - process.start() - processes.append(process) - # Adding a small delay between starting each - # process to avoid potential race conditions。 - time.sleep(1) - else: - session.run(*args, env=ENV_OVERRIDES) - - for notebook, regions in notebooks_reg.items(): - for region in regions: - region_args = ( - "python", - "scripts/run_and_publish_benchmark.py", - "--notebook", - f"--benchmark-path={notebook}", - f"--region={region}", - ) - if multi_process_mode: - process = multiprocessing.Process( - target=session.run, - args=region_args, - kwargs={"env": ENV_OVERRIDES}, - ) - process.start() - processes.append(process) - # Adding a small delay between starting each - # process to avoid potential race conditions。 - time.sleep(1) - else: - session.run(*region_args, env=ENV_OVERRIDES) - - for process in processes: - process.join() - finally: - # Prevent our notebook changes from getting checked in to git - # accidentally. - session.run( - "python", - CURRENT_DIRECTORY / "scripts" / "notebooks_restore_from_backup.py", - *notebooks, - ) - session.run( - "python", - "scripts/run_and_publish_benchmark.py", - "--notebook", - "--publish-benchmarks=notebooks/", - env=ENV_OVERRIDES, - ) - + process.start() + processes.append(process) -@nox.session(python=DEFAULT_PYTHON_VERSION) -def benchmark(session: nox.Session): - session.install("-e", ".[all]") - base_path = os.path.join("tests", "benchmark") - - parser = argparse.ArgumentParser() - parser.add_argument( - "-i", - "--iterations", - type=int, - default=1, - help="Number of iterations to run each benchmark.", - ) - parser.add_argument( - "-o", - "--output-csv", - nargs="?", - const=True, - default=False, - help=( - "Determines whether to output results to a CSV file. If no location is provided, " - "a temporary location is automatically generated." - ), - ) - parser.add_argument( - "-b", - "--benchmark-filter", - nargs="+", - help=( - "List of file or directory names to include in the benchmarks. If not provided, " - "all benchmarks are run." - ), - ) + for process in processes: + process.join() - args = parser.parse_args(session.posargs) - - benchmark_script_list: List[pathlib.Path] = [] - if args.benchmark_filter: - for filter_item in args.benchmark_filter: - full_path = os.path.join(base_path, filter_item) - if os.path.isdir(full_path): - benchmark_script_list.extend(pathlib.Path(full_path).rglob("*.py")) - elif os.path.isfile(full_path) and full_path.endswith(".py"): - benchmark_script_list.append(pathlib.Path(full_path)) - else: - raise ValueError( - f"Item {filter_item} does not match any valid file or directory" - ) - else: - benchmark_script_list = list(pathlib.Path(base_path).rglob("*.py")) - - try: - for benchmark in benchmark_script_list: - if benchmark.name in ("__init__.py", "utils.py"): - continue - session.run( - "python", - "scripts/run_and_publish_benchmark.py", - f"--benchmark-path={benchmark}", - f"--iterations={args.iterations}", - env=ENV_OVERRIDES, - ) - finally: - session.run( - "python", - "scripts/run_and_publish_benchmark.py", - f"--publish-benchmarks={base_path}", - f"--iterations={args.iterations}", - f"--output-csv={args.output_csv}", - env=ENV_OVERRIDES, - ) - -@nox.session(python=DEFAULT_PYTHON_VERSION) +@nox.session(python="3.10") def release_dry_run(session): env = {} @@ -934,159 +695,3 @@ def release_dry_run(session): ): env["PROJECT_ROOT"] = "." session.run(".kokoro/release-nightly.sh", "--dry-run", env=env) - - -@nox.session(python=DEFAULT_PYTHON_VERSION) -def cleanup(session): - """Clean up stale and/or temporary resources in the test project.""" - google_cloud_project = PROJECT_ID_OVERRIDE or os.getenv("GOOGLE_CLOUD_PROJECT") - cleanup_options = [] - if google_cloud_project: - cleanup_options.append(f"--project-id={google_cloud_project}") - - # Cleanup a few stale (more than 12 hours old) temporary cloud run - # functions created by bigframems. This will help keeping the test GCP - # project within the "Number of functions" quota - # https://cloud.google.com/functions/quotas#resource_limits - recency_cutoff_hours = 12 - cleanup_count_per_location = 40 - cleanup_options.extend( - [ - f"--recency-cutoff={recency_cutoff_hours}", - "cleanup", - f"--number={cleanup_count_per_location}", - ] - ) - - session.install("-e", ".") - - session.run("python", "scripts/manage_cloud_functions.py", *cleanup_options) - - -@nox.session(python=DEFAULT_PYTHON_VERSION) -@nox.parametrize( - "protobuf_implementation", - ["python", "upb"], -) -def core_deps_from_source(session, protobuf_implementation): - """Run all tests with core dependencies installed from source - rather than pulling the dependencies from PyPI. - """ - - # Install all dependencies - session.install("-e", ".") - - # Install dependencies for the unit test environment - unit_deps_all = UNIT_TEST_STANDARD_DEPENDENCIES + UNIT_TEST_EXTERNAL_DEPENDENCIES - session.install(*unit_deps_all) - - # Install dependencies for the system test environment - system_deps_all = ( - SYSTEM_TEST_STANDARD_DEPENDENCIES - + SYSTEM_TEST_EXTERNAL_DEPENDENCIES - + SYSTEM_TEST_EXTRAS - ) - session.install(*system_deps_all) - - # Because we test minimum dependency versions on the minimum Python - # version, the first version we test with in the unit tests sessions has a - # constraints file containing all dependencies and extras. - with open( - CURRENT_DIRECTORY / "testing" / "constraints-3.10.txt", - encoding="utf-8", - ) as constraints_file: - constraints_text = constraints_file.read() - - # Ignore leading whitespace and comment lines. - # Fiona fails to build on GitHub CI because gdal-config is missing and no Python 3.14 wheels are available. - constraints_deps = [ - match.group(1) - for match in re.finditer( - r"^\s*(\S+)(?===\S+)", constraints_text, flags=re.MULTILINE - ) - if match.group(1) != "fiona" - ] - - # Install dependencies specified in `testing/constraints-X.txt`. - session.install(*constraints_deps) - - # TODO(https://github.com/googleapis/gapic-generator-python/issues/2358): `grpcio` and - # `grpcio-status` should be added to the list below so that they are installed from source, - # rather than PyPI. - # TODO(https://github.com/googleapis/gapic-generator-python/issues/2357): `protobuf` should be - # added to the list below so that it is installed from source, rather than PyPI - # Note: If a dependency is added to the `core_dependencies_from_source` list, - # the `prerel_deps` list in the `prerelease_deps` nox session should also be updated. - core_dependencies_from_source = [ - "googleapis-common-protos @ git+https://github.com/googleapis/google-cloud-python#egg=googleapis-common-protos&subdirectory=packages/googleapis-common-protos", - "google-api-core @ git+https://github.com/googleapis/google-cloud-python#egg=google-api-core&subdirectory=packages/google-api-core", - "google-auth @ git+https://github.com/googleapis/google-cloud-python#egg=google-auth&subdirectory=packages/google-auth", - "grpc-google-iam-v1 @ git+https://github.com/googleapis/google-cloud-python#egg=grpc-google-iam-v1&subdirectory=packages/grpc-google-iam-v1", - "proto-plus @ git+https://github.com/googleapis/google-cloud-python#egg=proto-plus&subdirectory=packages/proto-plus", - ] - - for dep in core_dependencies_from_source: - session.install(dep, "--no-deps", "--ignore-installed") - print(f"Installed {dep}") - - session.run( - "py.test", - "tests/unit", - env={ - "PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION": protobuf_implementation, - }, - ) - - -@nox.session(python=ALL_PYTHON[-1]) -def prerelease_deps(session): - """Run all tests with prerelease versions of dependencies installed.""" - # TODO(https://github.com/googleapis/google-cloud-python/issues/16014): - # Add prerelease deps tests - unit_prerelease(session) - system_prerelease(session) - - -# NOTE: this is based on mypy session that came directly from the bigframes split repo -# the split repo used 3.10, the monorepo uses 3.14 -@nox.session(python="3.14") -def mypy(session): - """Run type checks with mypy.""" - # Editable mode is not compatible with mypy when there are multiple - # package directories. See: - # https://github.com/python/mypy/issues/10564#issuecomment-851687749 - session.install("--no-cache-dir", ".") - - # Just install the dependencies' type info directly, since "mypy --install-types" - # might require an additional pass. - deps = ( - set( - [ - MYPY_VERSION, - # TODO: update to latest pandas-stubs once we resolve bigframes issues. - "pandas-stubs<=2.2.3.241126", - "types-protobuf", - "types-python-dateutil", - "types-requests", - "types-setuptools", - "types-tabulate", - "types-PyYAML", - "polars", - "anywidget", - ] - ) - | set(SYSTEM_TEST_STANDARD_DEPENDENCIES) - | set(UNIT_TEST_STANDARD_DEPENDENCIES) - ) - - session.install(*deps) - shutil.rmtree(".mypy_cache", ignore_errors=True) - session.run( - "mypy", - "bigframes", - os.path.join("tests", "system"), - os.path.join("tests", "unit"), - "--check-untyped-defs", - "--explicit-package-bases", - '--exclude="^third_party"', - ) diff --git a/owlbot.py b/owlbot.py new file mode 100644 index 00000000000..be30eea5c2c --- /dev/null +++ b/owlbot.py @@ -0,0 +1,114 @@ +# Copyright 2021 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""This script is used to synthesize generated parts of this library.""" + +import pathlib +import re + +from synthtool import gcp +import synthtool as s +from synthtool.languages import python + +REPO_ROOT = pathlib.Path(__file__).parent.absolute() + +common = gcp.CommonTemplates() + +# ---------------------------------------------------------------------------- +# Add templated files +# ---------------------------------------------------------------------------- +templated_files = common.py_library( + unit_test_python_versions=["3.9", "3.10", "3.11"], + system_test_python_versions=["3.9", "3.11"], + cov_level=35, + intersphinx_dependencies={ + "pandas": "https://pandas.pydata.org/pandas-docs/stable/", + "pydata-google-auth": "https://pydata-google-auth.readthedocs.io/en/latest/", + }, +) +s.move( + templated_files, + excludes=[ + # Multi-processing note isn't relevant, as bigframes is responsible for + # creating clients, not the end user. + "docs/multiprocessing.rst", + "noxfile.py", + ".pre-commit-config.yaml", + "README.rst", + ".github/release-trigger.yml", + # BigQuery DataFrames manages its own Kokoro cluster for presubmit & continuous tests. + ".kokoro/build.sh", + ".kokoro/continuous/common.cfg", + ".kokoro/presubmit/common.cfg", + ], +) + +# ---------------------------------------------------------------------------- +# Fixup files +# ---------------------------------------------------------------------------- + +# Make sure build includes all necessary files. +s.replace( + ["MANIFEST.in"], + re.escape("recursive-include google"), + "recursive-include third_party *\nrecursive-include bigframes", +) + +# Even though BigQuery DataFrames isn't technically a client library, we are +# opting into Cloud RAD for docs hosting. +s.replace( + [".kokoro/docs/common.cfg"], + re.escape('value: "docs-staging-v2-staging"'), + 'value: "docs-staging-v2"', +) + +# Use a custom table of contents since the default one isn't organized well +# enough for the number of classes we have. +s.replace( + [".kokoro/publish-docs.sh"], + ( + re.escape("# upload docs") + + "\n" + + re.escape( + 'python3 -m docuploader upload docs/_build/html/docfx_yaml --metadata-file docs.metadata --destination-prefix docfx --staging-bucket "${V2_STAGING_BUCKET}"' + ) + ), + ( + "# Replace toc.yml template file\n" + + "mv docs/templates/toc.yml docs/_build/html/docfx_yaml/toc.yml\n\n" + + "# upload docs\n" + + 'python3 -m docuploader upload docs/_build/html/docfx_yaml --metadata-file docs.metadata --destination-prefix docfx --staging-bucket "${V2_STAGING_BUCKET}"' + ), +) + +# Fixup the documentation. +s.replace( + ["docs/conf.py"], + re.escape("Google Cloud Client Libraries for bigframes"), + "BigQuery DataFrames provides DataFrame APIs on the BigQuery engine", +) + +# ---------------------------------------------------------------------------- +# Samples templates +# ---------------------------------------------------------------------------- + +python.py_samples(skip_readmes=True) + +# ---------------------------------------------------------------------------- +# Final cleanup +# ---------------------------------------------------------------------------- + +s.shell.run(["nox", "-s", "format"], hide_output=False) +for noxfile in REPO_ROOT.glob("samples/**/noxfile.py"): + s.shell.run(["nox", "-s", "blacken"], cwd=noxfile.parent, hide_output=False) diff --git a/package-lock.json b/package-lock.json deleted file mode 100644 index 064bdaf362d..00000000000 --- a/package-lock.json +++ /dev/null @@ -1,6 +0,0 @@ -{ - "name": "python-bigquery-dataframes", - "lockfileVersion": 3, - "requires": true, - "packages": {} -} diff --git a/pyproject.toml b/pyproject.toml deleted file mode 100644 index e7d9c326a93..00000000000 --- a/pyproject.toml +++ /dev/null @@ -1,6 +0,0 @@ -[build-system] -requires = ["setuptools"] -build-backend = "setuptools.build_meta" - -[tool.ruff.lint.isort] -known-first-party = ["bigframes"] diff --git a/pytest.ini b/pytest.ini index 512fd81a7e6..204c743bbfa 100644 --- a/pytest.ini +++ b/pytest.ini @@ -1,3 +1,4 @@ [pytest] doctest_optionflags = NORMALIZE_WHITESPACE -addopts = "--import-mode=importlib" +filterwarnings = + ignore::pandas.errors.SettingWithCopyWarning diff --git a/release-procedure.md b/release-procedure.md deleted file mode 100644 index aeb87862fe6..00000000000 --- a/release-procedure.md +++ /dev/null @@ -1,51 +0,0 @@ -# BigQuery DataFrames (bigframes) release procedure - -*(Note: bigframes releases are marked with `skip_release: true` in `librarian.yaml` and must be kicked off manually using legacylibrarian.)* - -## Setup (First Time Only) - -* Install `legacylibrarian`: - - go install github.com/googleapis/librarian/cmd/legacylibrarian@latest - -* Authenticate with GitHub CLI: - - gh auth login - -## Release Steps - -* Obtain GitHub token: - - export LIBRARIAN_GITHUB_TOKEN=$(gh auth token) - -* Stash changes (repo must be clean): - - git stash -u - -* Fetch and checkout base: - - git fetch origin main - git fetch origin --tags - git checkout origin/main - -* Check image updates: - - legacylibrarian update-image --push - -* Create release PR: - - # Option A: Push directly - legacylibrarian release stage --repo=https://github.com/googleapis/google-cloud-python --library=bigframes --library-version=X.X.X --push - - # Option B: Manual edit first (omit --push, edit files in /tmp/librarian-*, commit/push from there) - legacylibrarian release stage --repo=https://github.com/googleapis/google-cloud-python --library=bigframes --library-version=X.X.X - # In /tmp repository: - git commit -a -m "chore: create release" --no-verify # keep librarian config pristine - git push origin HEAD - gh pr create --fill --label "release:pending" - -* Post-release restore: - - # Move back any stashed/relocated files (like .vscode) - git checkout main - git stash pop diff --git a/renovate.json b/renovate.json new file mode 100644 index 00000000000..39b2a0ec929 --- /dev/null +++ b/renovate.json @@ -0,0 +1,12 @@ +{ + "extends": [ + "config:base", + "group:all", + ":preserveSemverRanges", + ":disableDependencyDashboard" + ], + "ignorePaths": [".pre-commit-config.yaml", ".kokoro/requirements.txt", "setup.py"], + "pip_requirements": { + "fileMatch": ["requirements-test.txt", "samples/[\\S/]*constraints.txt", "samples/[\\S/]*constraints-test.txt"] + } +} diff --git a/samples/dbt/.dbt.yml b/samples/dbt/.dbt.yml deleted file mode 100644 index a4301a0bab1..00000000000 --- a/samples/dbt/.dbt.yml +++ /dev/null @@ -1,27 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -dbt_sample_project: - outputs: - dev: # The target environment name (e.g., dev, prod) - compute_region: us-central1 # Region used for compute operations - dataset: dbt_sample_dateset # BigQuery dataset where dbt will create models - gcs_bucket: dbt_sample_bucket # GCS bucket to store output files - location: US # BigQuery dataset location - method: oauth # Authentication method - priority: interactive # Job priority: "interactive" or "batch" - project: bigframes-dev # GCP project ID - threads: 1 # Number of threads dbt can use for running models in parallel - type: bigquery # Specifies the dbt adapter - target: dev # The default target environment diff --git a/samples/dbt/README.md b/samples/dbt/README.md deleted file mode 100644 index 986aa2eae32..00000000000 --- a/samples/dbt/README.md +++ /dev/null @@ -1,64 +0,0 @@ -# dbt BigFrames Integration - -This repository provides simple examples of using **dbt Python models** with **BigQuery** in **BigFrames** mode. - -It includes basic configurations and sample models to help you get started quickly in a typical dbt project. - -## Highlights - -- `profiles.yml`: configures your connection to BigQuery. -- `dbt_project.yml`: configures your dbt project - **dbt_sample_project**. -- `dbt_bigframes_code_sample_1.py`: An example to read BigQuery data and perform basic transformation. -- `dbt_bigframes_code_sample_2.py`: An example to build an incremental model that leverages BigFrames UDF capabilities. -- `prepare_table.py`: An ML example to consolidate various data sources into a single, unified table for later usage. -- `prediction.py`: An ML example to train models and then generate predictions using the prepared table. - -## Requirements - -Before using this project, ensure you have: - -- A [Google Cloud account](https://cloud.google.com/free?hl=en) -- A [dbt Cloud account](https://www.getdbt.com/signup) (if using dbt Cloud) -- Python and SQL basics -- Familiarity with dbt concepts and structure - -For more, see: -- https://docs.getdbt.com/guides/dbt-python-bigframes -- https://cloud.google.com/bigquery/docs/dataframes-dbt - -## Run Locally - -Follow these steps to run the Python models using dbt Core. - -1. **Install the dbt BigQuery adapter:** - - ```bash - pip install dbt-bigquery - ``` - -2. **Initialize a dbt project (if not already done):** - - ```bash - dbt init - ``` - - Follow the prompts to complete setup. - -3. **Finish the configuration and add sample code:** - - - Edit `~/.dbt/profiles.yml` to finish the configuration. - - Replace or add code samples in `.../models/example`. - -4. **Run your dbt models:** - - To run all models: - - ```bash - dbt run - ``` - - Or run a specific model: - - ```bash - dbt run --select your_model_name - ``` \ No newline at end of file diff --git a/samples/dbt/dbt_sample_project/dbt_project.yml b/samples/dbt/dbt_sample_project/dbt_project.yml deleted file mode 100644 index 789f4d25496..00000000000 --- a/samples/dbt/dbt_sample_project/dbt_project.yml +++ /dev/null @@ -1,52 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -# Name your project! Project names should contain only lowercase characters -# and underscores. A good package name should reflect your organization's -# name or the intended use of these models -name: 'dbt_sample_project' -version: '1.0.0' - -# This setting configures which "profile" dbt uses for this project. -profile: 'dbt_sample_project' - -# These configurations specify where dbt should look for different types of files. -# The `model-paths` config, for example, states that models in this project can be -# found in the "models/" directory. You probably won't need to change these! -model-paths: ["models"] -analysis-paths: ["analyses"] -test-paths: ["tests"] -seed-paths: ["seeds"] -macro-paths: ["macros"] -snapshot-paths: ["snapshots"] - -clean-targets: # directories to be removed by `dbt clean` - - "target" - - "dbt_packages" - - -# Configuring models -# Full documentation: https://docs.getdbt.com/docs/configuring-models - -# In this example config, we tell dbt to build all models in the example/ -# directory as views. These settings can be overridden in the individual model -# files using the `{{ config(...) }}` macro. -models: - dbt_sample_project: - # Optional: These settings (e.g., submission_method, notebook_template_id, - # etc.) can also be defined directly in the Python model using dbt.config. - submission_method: bigframes - # Config indicated by + and applies to all files under models/example/ - example: - +materialized: view diff --git a/samples/dbt/dbt_sample_project/models/example/dbt_bigframes_code_sample_1.py b/samples/dbt/dbt_sample_project/models/example/dbt_bigframes_code_sample_1.py deleted file mode 100644 index 2e24596b794..00000000000 --- a/samples/dbt/dbt_sample_project/models/example/dbt_bigframes_code_sample_1.py +++ /dev/null @@ -1,80 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -# This example demonstrates one of the most general usages of transforming raw -# BigQuery data into a processed table using a dbt Python model with BigFrames. -# See more from: https://cloud.google.com/bigquery/docs/dataframes-dbt. -# -# Key defaults when using BigFrames in a dbt Python model for BigQuery: -# - The default materialization is 'table' unless specified otherwise. This -# means dbt will create a new BigQuery table from the result of this model. -# - The default timeout for the job is 3600 seconds (60 minutes). This can be -# adjusted if your processing requires more time. -# - If no runtime template is provided, dbt will automatically create and reuse -# a default one for executing the Python code in BigQuery. -# -# BigFrames provides a pandas-like API for BigQuery data, enabling familiar -# data manipulation directly within your dbt project. This code sample -# illustrates a basic pattern for: -# 1. Reading data from an existing BigQuery dataset. -# 2. Processing it using pandas-like DataFrame operations powered by BigFrames. -# 3. Outputting a cleaned and transformed table, managed by dbt. - - -def model(dbt, session): - # Optional: Override settings from your dbt_project.yml file. - # When both are set, dbt.config takes precedence over dbt_project.yml. - # - # Use `dbt.config(submission_method="bigframes")` to tell dbt to execute - # this Python model using BigQuery DataFrames (BigFrames). This allows you - # to write pandas-like code that operates directly on BigQuery data - # without needing to pull all data into memory. - dbt.config(submission_method="bigframes") - - # Define the BigQuery table path from which to read data. - table = "bigquery-public-data.epa_historical_air_quality.temperature_hourly_summary" - - # Define the specific columns to select from the BigQuery table. - columns = [ - "state_name", - "county_name", - "date_local", - "time_local", - "sample_measurement", - ] - - # Read data from the specified BigQuery table into a BigFrames DataFrame. - df = session.read_gbq(table, columns=columns) - - # Sort the DataFrame by the specified columns. This prepares the data for - # `drop_duplicates` to ensure consistent duplicate removal. - df = df.sort_values(columns).drop_duplicates(columns) - - # Group the DataFrame by 'state_name', 'county_name', and 'date_local'. For - # each group, calculate the minimum and maximum of the 'sample_measurement' - # column. The result will be a BigFrames DataFrame with a MultiIndex. - result = df.groupby(["state_name", "county_name", "date_local"])[ - "sample_measurement" - ].agg(["min", "max"]) - - # Rename some columns and convert the MultiIndex of the 'result' DataFrame - # into regular columns. This flattens the DataFrame so 'state_name', - # 'county_name', and 'date_local' become regular columns again. - result = result.rename( - columns={"min": "min_temperature", "max": "max_temperature"} - ).reset_index() - - # Return the processed BigFrames DataFrame. - # In a dbt Python model, this DataFrame will be materialized as a table - return result diff --git a/samples/dbt/dbt_sample_project/models/example/dbt_bigframes_code_sample_2.py b/samples/dbt/dbt_sample_project/models/example/dbt_bigframes_code_sample_2.py deleted file mode 100644 index 1f060cd60bd..00000000000 --- a/samples/dbt/dbt_sample_project/models/example/dbt_bigframes_code_sample_2.py +++ /dev/null @@ -1,79 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -# This example demonstrates how to build an **incremental dbt Python model** -# using BigFrames. -# -# Incremental models are essential for efficiently processing large datasets by -# only transforming new or changed data, rather than reprocessing the entire -# dataset every time. If the target table already exists, dbt will perform a -# merge based on the specified unique keys; otherwise, it will create a new -# table automatically. -# -# This model also showcases the definition and application of a **BigFrames -# User-Defined Function (UDF)** to add a descriptive summary column based on -# temperature data. BigFrames UDFs allow you to execute custom Python logic -# directly within BigQuery, leveraging BigQuery's scalability. - - -def model(dbt, session): - # Optional: override settings from dbt_project.yml. - # When both are set, dbt.config takes precedence over dbt_project.yml. - dbt.config( - # Use BigFrames mode to execute this Python model. This enables - # pandas-like operations directly on BigQuery data. - submission_method="bigframes", - # Materialize this model as an 'incremental' table. This tells dbt to - # only process new or updated data on subsequent runs. - materialized="incremental", - # Use MERGE strategy to update rows during incremental runs. - incremental_strategy="merge", - # Define the composite key that uniquely identifies a row in the - # target table. This key is used by the 'merge' strategy to match - # existing rows for updates during incremental runs. - unique_key=["state_name", "county_name", "date_local"], - ) - - # Reference an upstream dbt model or an existing BigQuery table as a - # BigFrames DataFrame. It allows you to seamlessly use the output of another - # dbt model as input to this one. - df = dbt.ref("dbt_bigframes_code_sample_1") - - # Define a BigFrames UDF to generate a temperature description. - # BigFrames UDFs allow you to define custom Python logic that executes - # directly within BigQuery. This is powerful for complex transformations. - @session.udf(dataset="dbt_sample_dataset", name="describe_udf") - def describe( - max_temperature: float, - min_temperature: float, - ) -> str: - is_hot = max_temperature > 85.0 - is_cold = min_temperature < 50.0 - - if is_hot and is_cold: - return "Expect both hot and cold conditions today." - if is_hot: - return "Overall, it's a hot day." - if is_cold: - return "Overall, it's a cold day." - return "Comfortable throughout the day." - - # Apply the UDF using combine and store the result in a column "describe". - df["describe"] = df["max_temperature"].combine(df["min_temperature"], describe) - - # Return the transformed BigFrames DataFrame. - # This DataFrame will be the final output of your incremental dbt model. - # On subsequent runs, only new or changed rows will be processed and merged - # into the target BigQuery table based on the `unique_key`. - return df diff --git a/samples/dbt/dbt_sample_project/models/ml_example/prediction.py b/samples/dbt/dbt_sample_project/models/ml_example/prediction.py deleted file mode 100644 index d2fb54b3846..00000000000 --- a/samples/dbt/dbt_sample_project/models/ml_example/prediction.py +++ /dev/null @@ -1,67 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -# This DBT Python model prepares and trains a machine learning model to predict -# ozone levels. -# 1. Data Preparation: The model first gets a prepared dataset and splits it -# into three subsets based on the year: training data (before 2017), -# testing data (2017-2019), and prediction data (2020 and later). -# 2. Model Training: It then uses the LinearRegression model from BigFrames -# ML library. The model is trained on the historical data, using other -# atmospheric parameters to predict the 'o3' (ozone) levels. -# 3. Prediction: Finally, the trained model makes predictions on the most -# recent data (from 2020 onwards) and returns the resulting DataFrame of -# predicted ozone values. -# -# See more details from the related blog post: https://docs.getdbt.com/blog/train-linear-dbt-bigframes - - -def model(dbt, session): - dbt.config(submission_method="bigframes", timeout=6000) - - df = dbt.ref("prepare_table") - - # Define the rules for separating the training, test and prediction data. - train_data_filter = (df.date_local.dt.year < 2017) - test_data_filter = ( - (df.date_local.dt.year >= 2017) & (df.date_local.dt.year < 2020) - ) - predict_data_filter = (df.date_local.dt.year >= 2020) - - # Define index_columns again here in prediction. - index_columns = ["state_name", "county_name", "site_num", "date_local", "time_local"] - - # Separate the training, test and prediction data. - df_train = df[train_data_filter].set_index(index_columns) - df_test = df[test_data_filter].set_index(index_columns) - df_predict = df[predict_data_filter].set_index(index_columns) - - # Finalize the training dataframe. - X_train = df_train.drop(columns="o3") - y_train = df_train["o3"] - - # Finalize the prediction dataframe. - X_predict = df_predict.drop(columns="o3") - - # Import the LinearRegression model from bigframes.ml module. - from bigframes.ml.linear_model import LinearRegression - - # Train the model. - model = LinearRegression() - model.fit(X_train, y_train) - - # Make the prediction using the model. - df_pred = model.predict(X_predict) - - return df_pred diff --git a/samples/dbt/dbt_sample_project/models/ml_example/prepare_table.py b/samples/dbt/dbt_sample_project/models/ml_example/prepare_table.py deleted file mode 100644 index 23b54a9122d..00000000000 --- a/samples/dbt/dbt_sample_project/models/ml_example/prepare_table.py +++ /dev/null @@ -1,93 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -# This DBT Python model processes EPA historical air quality data from BigQuery -# using BigFrames. The primary goal is to merge several hourly summary -# tables into a single, unified DataFrame for later prediction. It includes the -# following steps: -# 1. Reading and Cleaning: It reads individual hourly summary tables from -# BigQuery for various atmospheric parameters (like CO, O3, temperature, -# and wind speed). Each table is cleaned by sorting, removing duplicates, -# and renaming columns for clarity. -# 2. Combining Data: It then merges these cleaned tables into a single, -# comprehensive DataFrame. An inner join is used to ensure the final output -# only includes records with complete data across all parameters. -# 3. Final Output: The unified DataFrame is returned as the model's output, -# creating a corresponding BigQuery table for future use. -# -# See more details from the related blog post: https://docs.getdbt.com/blog/train-linear-dbt-bigframes - - -import bigframes.pandas as bpd - -def model(dbt, session): - # Optional: override settings from dbt_project.yml. - # When both are set, dbt.config takes precedence over dbt_project.yml. - dbt.config(submission_method="bigframes", timeout=6000) - - # Define the dataset and the columns of interest representing various parameters - # in the atmosphere. - dataset = "bigquery-public-data.epa_historical_air_quality" - index_columns = ["state_name", "county_name", "site_num", "date_local", "time_local"] - param_column = "parameter_name" - value_column = "sample_measurement" - - # Initialize a list for collecting dataframes from individual parameters. - params_dfs = [] - - # Collect dataframes from tables which contain data for single parameter. - table_param_dict = { - "co_hourly_summary" : "co", - "no2_hourly_summary" : "no2", - "o3_hourly_summary" : "o3", - "pressure_hourly_summary" : "pressure", - "so2_hourly_summary" : "so2", - "temperature_hourly_summary" : "temperature", - } - - for table, param in table_param_dict.items(): - param_df = bpd.read_gbq( - f"{dataset}.{table}", - columns=index_columns + [value_column] - ) - param_df = param_df\ - .sort_values(index_columns)\ - .drop_duplicates(index_columns)\ - .set_index(index_columns)\ - .rename(columns={value_column : param}) - params_dfs.append(param_df) - - # Collect dataframes from the table containing wind speed. - # Optionally: collect dataframes from other tables containing - # wind direction, NO, NOx, and NOy data as needed. - wind_table = f"{dataset}.wind_hourly_summary" - bpd.read_gbq(wind_table, columns=[param_column]).value_counts() - - wind_speed_df = bpd.read_gbq( - wind_table, - columns=index_columns + [value_column], - filters=[(param_column, "==", "Wind Speed - Resultant")] - ) - wind_speed_df = wind_speed_df\ - .sort_values(index_columns)\ - .drop_duplicates(index_columns)\ - .set_index(index_columns)\ - .rename(columns={value_column: "wind_speed"}) - params_dfs.append(wind_speed_df) - - # Combine data for all the selected parameters. - df = bpd.concat(params_dfs, axis=1, join="inner") - df = df.reset_index() - - return df diff --git a/samples/polars/create_polars_df_with_to_arrow_test.py b/samples/polars/create_polars_df_with_to_arrow_test.py deleted file mode 100644 index acb79f23c82..00000000000 --- a/samples/polars/create_polars_df_with_to_arrow_test.py +++ /dev/null @@ -1,40 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -def test_create_polars_df() -> None: - # [START bigquery_dataframes_to_polars] - import polars - - import bigframes.enums - import bigframes.pandas as bpd - - bf_df = bpd.read_gbq_table( - "bigquery-public-data.usa_names.usa_1910_current", - # Setting index_col to either a unique column or NULL will give the - # best performance. - index_col=bigframes.enums.DefaultIndexKind.NULL, - ) - # TODO(developer): Do some analysis using BigQuery DataFrames. - # ... - - # Run the query and download the results as an Arrow table to convert into - # a Polars DataFrame. Use ordered=False if your polars analysis is OK with - # non-deterministic ordering. - arrow_table = bf_df.to_arrow(ordered=False) - polars_df = polars.from_arrow(arrow_table) - # [END bigquery_dataframes_to_polars] - - assert polars_df.shape == bf_df.shape - assert polars_df["number"].sum() == bf_df["number"].sum() diff --git a/samples/polars/noxfile.py b/samples/polars/noxfile.py deleted file mode 100644 index 63e742993f9..00000000000 --- a/samples/polars/noxfile.py +++ /dev/null @@ -1,291 +0,0 @@ -# Copyright 2019 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from __future__ import print_function - -import glob -import os -from pathlib import Path -import sys -from typing import Callable, Dict, Optional - -import nox - -# WARNING - WARNING - WARNING - WARNING - WARNING -# WARNING - WARNING - WARNING - WARNING - WARNING -# DO NOT EDIT THIS FILE EVER! -# WARNING - WARNING - WARNING - WARNING - WARNING -# WARNING - WARNING - WARNING - WARNING - WARNING - -BLACK_VERSION = "black==22.3.0" -ISORT_VERSION = "isort==5.10.1" - -# Copy `noxfile_config.py` to your directory and modify it instead. - -# `TEST_CONFIG` dict is a configuration hook that allows users to -# modify the test configurations. The values here should be in sync -# with `noxfile_config.py`. Users will copy `noxfile_config.py` into -# their directory and modify it. - -TEST_CONFIG = { - # You can opt out from the test for specific Python versions. - "ignored_versions": [], - # Old samples are opted out of enforcing Python type hints - # All new samples should feature them - "enforce_type_hints": False, - # An envvar key for determining the project id to use. Change it - # to 'BUILD_SPECIFIC_GCLOUD_PROJECT' if you want to opt in using a - # build specific Cloud project. You can also use your own string - # to use your own Cloud project. - "gcloud_project_env": "GOOGLE_CLOUD_PROJECT", - # 'gcloud_project_env': 'BUILD_SPECIFIC_GCLOUD_PROJECT', - # If you need to use a specific version of pip, - # change pip_version_override to the string representation - # of the version number, for example, "20.2.4" - "pip_version_override": None, - # A dictionary you want to inject into your test. Don't put any - # secrets here. These values will override predefined values. - "envs": {}, -} - - -try: - # Ensure we can import noxfile_config in the project's directory. - sys.path.append(".") - from noxfile_config import TEST_CONFIG_OVERRIDE -except ImportError as e: - print("No user noxfile_config found: detail: {}".format(e)) - TEST_CONFIG_OVERRIDE = {} - -# Update the TEST_CONFIG with the user supplied values. -TEST_CONFIG.update(TEST_CONFIG_OVERRIDE) - - -def get_pytest_env_vars() -> Dict[str, str]: - """Returns a dict for pytest invocation.""" - ret = {} - - # Override the GCLOUD_PROJECT and the alias. - env_key = TEST_CONFIG["gcloud_project_env"] - # This should error out if not set. - ret["GOOGLE_CLOUD_PROJECT"] = os.environ[env_key] - - # Apply user supplied envs. - ret.update(TEST_CONFIG["envs"]) - return ret - - -# All versions used to test samples. -ALL_VERSIONS = ["3.10", "3.11", "3.12", "3.13", "3.14"] - -# Any default versions that should be ignored. -IGNORED_VERSIONS = TEST_CONFIG["ignored_versions"] - -TESTED_VERSIONS = sorted([v for v in ALL_VERSIONS if v not in IGNORED_VERSIONS]) - -INSTALL_LIBRARY_FROM_SOURCE = os.environ.get("INSTALL_LIBRARY_FROM_SOURCE", False) in ( - "True", - "true", -) - -# Error if a python version is missing -nox.options.error_on_missing_interpreters = True - -# -# Style Checks -# - - -# Linting with flake8. -# -# We ignore the following rules: -# E203: whitespace before ‘:’ -# E266: too many leading ‘#’ for block comment -# E501: line too long -# I202: Additional newline in a section of imports -# -# We also need to specify the rules which are ignored by default: -# ['E226', 'W504', 'E126', 'E123', 'W503', 'E24', 'E704', 'E121'] -FLAKE8_COMMON_ARGS = [ - "--show-source", - "--builtin=gettext", - "--max-complexity=20", - "--exclude=.nox,.cache,env,lib,generated_pb2,*_pb2.py,*_pb2_grpc.py", - "--ignore=E121,E123,E126,E203,E226,E24,E266,E501,E704,W503,W504,I202", - "--max-line-length=88", -] - - -@nox.session -def lint(session: nox.sessions.Session) -> None: - if not TEST_CONFIG["enforce_type_hints"]: - session.install("flake8") - else: - session.install("flake8", "flake8-annotations") - - args = FLAKE8_COMMON_ARGS + [ - ".", - ] - session.run("flake8", *args) - - -# -# Black -# - - -@nox.session -def blacken(session: nox.sessions.Session) -> None: - """Run black. Format code to uniform standard.""" - session.install(BLACK_VERSION) - python_files = [path for path in os.listdir(".") if path.endswith(".py")] - - session.run("black", *python_files) - - -# -# format = isort + black -# - - -@nox.session -def format(session: nox.sessions.Session) -> None: - """ - Run isort to sort imports. Then run black - to format code to uniform standard. - """ - session.install(BLACK_VERSION, ISORT_VERSION) - python_files = [path for path in os.listdir(".") if path.endswith(".py")] - - # Use the --fss option to sort imports using strict alphabetical order. - # See https://pycqa.github.io/isort/docs/configuration/options.html#force-sort-within-sections - session.run("isort", "--fss", *python_files) - session.run("black", *python_files) - - -# -# Sample Tests -# - - -PYTEST_COMMON_ARGS = ["--junitxml=sponge_log.xml"] - - -def _session_tests( - session: nox.sessions.Session, post_install: Callable = None -) -> None: - # check for presence of tests - test_list = glob.glob("**/*_test.py", recursive=True) + glob.glob( - "**/test_*.py", recursive=True - ) - test_list.extend(glob.glob("**/tests", recursive=True)) - - if len(test_list) == 0: - print("No tests found, skipping directory.") - return - - if TEST_CONFIG["pip_version_override"]: - pip_version = TEST_CONFIG["pip_version_override"] - session.install(f"pip=={pip_version}") - """Runs py.test for a particular project.""" - concurrent_args = [] - if os.path.exists("requirements.txt"): - if os.path.exists("constraints.txt"): - session.install("-r", "requirements.txt", "-c", "constraints.txt") - else: - session.install("-r", "requirements.txt") - with open("requirements.txt") as rfile: - packages = rfile.read() - - if os.path.exists("requirements-test.txt"): - if os.path.exists("constraints-test.txt"): - session.install("-r", "requirements-test.txt", "-c", "constraints-test.txt") - else: - session.install("-r", "requirements-test.txt") - with open("requirements-test.txt") as rtfile: - packages += rtfile.read() - - if INSTALL_LIBRARY_FROM_SOURCE: - session.install("-e", _get_repo_root()) - - if post_install: - post_install(session) - - if "pytest-parallel" in packages: - concurrent_args.extend(["--workers", "auto", "--tests-per-worker", "auto"]) - elif "pytest-xdist" in packages: - concurrent_args.extend(["-n", "auto"]) - - session.run( - "pytest", - *(PYTEST_COMMON_ARGS + session.posargs + concurrent_args), - # Pytest will return 5 when no tests are collected. This can happen - # on travis where slow and flaky tests are excluded. - # See http://doc.pytest.org/en/latest/_modules/_pytest/main.html - success_codes=[0, 5], - env=get_pytest_env_vars(), - ) - - -@nox.session(python=ALL_VERSIONS) -def py(session: nox.sessions.Session) -> None: - """Runs py.test for a sample using the specified version of Python.""" - if session.python in TESTED_VERSIONS: - _session_tests(session) - else: - session.skip( - "SKIPPED: {} tests are disabled for this sample.".format(session.python) - ) - - -# -# Readmegen -# - - -def _get_repo_root() -> Optional[str]: - """Returns the root folder of the project.""" - # Get root of this repository. Assume we don't have directories nested deeper than 10 items. - p = Path(os.getcwd()) - for i in range(10): - if p is None: - break - if Path(p / ".git").exists(): - return str(p) - # .git is not available in repos cloned via Cloud Build - # setup.py is always in the library's root, so use that instead - # https://github.com/googleapis/synthtool/issues/792 - if Path(p / "setup.py").exists(): - return str(p) - p = p.parent - raise Exception("Unable to detect repository root.") - - -GENERATED_READMES = sorted([x for x in Path(".").rglob("*.rst.in")]) - - -@nox.session -@nox.parametrize("path", GENERATED_READMES) -def readmegen(session: nox.sessions.Session, path: str) -> None: - """(Re-)generates the readme for a sample.""" - session.install("jinja2", "pyyaml") - dir_ = os.path.dirname(path) - - if os.path.exists(os.path.join(dir_, "requirements.txt")): - session.install("-r", os.path.join(dir_, "requirements.txt")) - - in_file = os.path.join(dir_, "README.rst.in") - session.run( - "python", _get_repo_root() + "/scripts/readme-gen/readme_gen.py", in_file - ) diff --git a/samples/polars/noxfile_config.py b/samples/polars/noxfile_config.py deleted file mode 100644 index 91238e9e2ff..00000000000 --- a/samples/polars/noxfile_config.py +++ /dev/null @@ -1,42 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -# Default TEST_CONFIG_OVERRIDE for python repos. - -# You can copy this file into your directory, then it will be inported from -# the noxfile.py. - -# The source of truth: -# https://github.com/GoogleCloudPlatform/python-docs-samples/blob/master/noxfile_config.py - -TEST_CONFIG_OVERRIDE = { - # You can opt out from the test for specific Python versions. - "ignored_versions": ["2.7", "3.7", "3.8"], - # Old samples are opted out of enforcing Python type hints - # All new samples should feature them - "enforce_type_hints": True, - # An envvar key for determining the project id to use. Change it - # to 'BUILD_SPECIFIC_GCLOUD_PROJECT' if you want to opt in using a - # build specific Cloud project. You can also use your own string - # to use your own Cloud project. - "gcloud_project_env": "GOOGLE_CLOUD_PROJECT", - # "gcloud_project_env": "BUILD_SPECIFIC_GCLOUD_PROJECT", - # If you need to use a specific version of pip, - # change pip_version_override to the string representation - # of the version number, for example, "20.2.4" - "pip_version_override": None, - # A dictionary you want to inject into your test. Don't put any - # secrets here. These values will override predefined values. - "envs": {}, -} diff --git a/samples/polars/requirements-test.txt b/samples/polars/requirements-test.txt deleted file mode 100644 index ce5e1b9e702..00000000000 --- a/samples/polars/requirements-test.txt +++ /dev/null @@ -1,3 +0,0 @@ -# samples/snippets should be runnable with no "extras" -google-cloud-testutils==1.8.0 -pytest==9.0.3 diff --git a/samples/polars/requirements.txt b/samples/polars/requirements.txt deleted file mode 100644 index 218e674b9ed..00000000000 --- a/samples/polars/requirements.txt +++ /dev/null @@ -1,3 +0,0 @@ -bigframes==2.39.0 -polars==1.40.1 -pyarrow==24.0.0 diff --git a/samples/snippets/__init__.py b/samples/snippets/__init__.py new file mode 100644 index 00000000000..1dc90d18483 --- /dev/null +++ b/samples/snippets/__init__.py @@ -0,0 +1,13 @@ +# Copyright 2023 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/samples/snippets/clustering_model_test.py b/samples/snippets/clustering_model_test.py new file mode 100644 index 00000000000..a407fc78058 --- /dev/null +++ b/samples/snippets/clustering_model_test.py @@ -0,0 +1,35 @@ +# Copyright 2023 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +def test_clustering_model(): + # [START bigquery_dataframes_clustering_model] + from bigframes.ml.cluster import KMeans + import bigframes.pandas as bpd + + # Load data from BigQuery + query_or_table = "bigquery-public-data.ml_datasets.penguins" + bq_df = bpd.read_gbq(query_or_table) + + # Create the KMeans model + cluster_model = KMeans(n_clusters=10) + cluster_model.fit(bq_df["culmen_length_mm"], bq_df["sex"]) + + # Predict using the model + result = cluster_model.predict(bq_df) + # Score the model + score = cluster_model.score(bq_df) + # [END bigquery_dataframes_clustering_model] + assert result is not None + assert score is not None diff --git a/samples/snippets/gen_ai_model_test.py b/samples/snippets/gen_ai_model_test.py new file mode 100644 index 00000000000..7cbc90d4c03 --- /dev/null +++ b/samples/snippets/gen_ai_model_test.py @@ -0,0 +1,39 @@ +# Copyright 2023 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +def test_llm_model(): + PROJECT_ID = "bigframes-dev" + REGION = "us" + CONN_NAME = "bigframes-ml" + # [START bigquery_dataframes_gen_ai_model] + from bigframes.ml.llm import PaLM2TextGenerator + import bigframes.pandas as bpd + + # Create the LLM model + session = bpd.get_global_session() + connection = f"{PROJECT_ID}.{REGION}.{CONN_NAME}" + model = PaLM2TextGenerator(session=session, connection_name=connection) + + df_api = bpd.read_csv("gs://cloud-samples-data/vertex-ai/bigframe/df.csv") + + # Prepare the prompts and send them to the LLM model for prediction + df_prompt_prefix = "Generate Pandas sample code for DataFrame." + df_prompt = df_prompt_prefix + df_api["API"] + + # Predict using the model + df_pred = model.predict(df_prompt.to_frame(), max_output_tokens=1024) + # [END bigquery_dataframes_gen_ai_model] + assert df_pred["ml_generate_text_llm_result"] is not None + assert df_pred["ml_generate_text_llm_result"].iloc[0] is not None diff --git a/samples/snippets/load_data_from_bigquery_test.py b/samples/snippets/load_data_from_bigquery_test.py new file mode 100644 index 00000000000..e4c65688bdd --- /dev/null +++ b/samples/snippets/load_data_from_bigquery_test.py @@ -0,0 +1,24 @@ +# Copyright 2023 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +def test_bigquery_dataframes_load_data_from_bigquery(): + # [START bigquery_dataframes_load_data_from_bigquery] + # Create a DataFrame from a BigQuery table: + import bigframes.pandas as bpd + + query_or_table = "bigquery-public-data.ml_datasets.penguins" + bq_df = bpd.read_gbq(query_or_table) + # [END bigquery_dataframes_load_data_from_bigquery] + assert bq_df is not None diff --git a/samples/snippets/load_data_from_biquery_job_test.py b/samples/snippets/load_data_from_biquery_job_test.py new file mode 100644 index 00000000000..5271574a492 --- /dev/null +++ b/samples/snippets/load_data_from_biquery_job_test.py @@ -0,0 +1,51 @@ +# Copyright 2023 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +def test_bigquery_dataframes_load_data_from_bigquery_job(): + from google.cloud import bigquery + + # Construct a BigQuery client object. + client = bigquery.Client(project="bigframes-dev", location="us") + + query = """ + SELECT * + FROM `bigquery-public-data.ml_datasets.penguins` + LIMIT 20 + """ + query_job = client.query(query) + JOB_ID = query_job.job_id + your_project_id = "bigframes-dev" + + # [START bigquery_dataframes_load_data_from_bigquery_job] + from google.cloud import bigquery + + import bigframes.pandas as bpd + + # Project ID inserted based on the query results selected to explore + project = your_project_id + # Location inserted based on the query results selected to explore + location = "us" + client = bigquery.Client(project=project, location=location) + + # Job ID inserted based on the query results selcted to explore + job_id = JOB_ID + job = client.get_job(job_id) + destination = str(job.destination) + + # Load data from a BigQuery table using BigFrames DataFrames: + bq_df = bpd.read_gbq_table(destination) + + # [END bigquery_dataframes_load_data_from_bigquery_job] + assert bq_df is not None diff --git a/samples/snippets/load_data_from_csv_test.py b/samples/snippets/load_data_from_csv_test.py new file mode 100644 index 00000000000..31ab9255bf4 --- /dev/null +++ b/samples/snippets/load_data_from_csv_test.py @@ -0,0 +1,25 @@ +# Copyright 2023 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +def test_bigquery_dataframes_load_data_from_csv(): + # [START bigquery_dataframes_load_data_from_csv] + import bigframes.pandas as bpd + + filepath_or_buffer = "gs://cloud-samples-data/bigquery/us-states/us-states.csv" + df_from_gcs = bpd.read_csv(filepath_or_buffer) + # Display the first few rows of the DataFrame: + df_from_gcs.head() + # [END bigquery_dataframes_load_data_from_csv] + assert df_from_gcs is not None diff --git a/samples/snippets/pandas_methods_test.py b/samples/snippets/pandas_methods_test.py new file mode 100644 index 00000000000..1f472d63466 --- /dev/null +++ b/samples/snippets/pandas_methods_test.py @@ -0,0 +1,34 @@ +# Copyright 2023 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +def test_bigquery_dataframes_pandas_methods(): + # [START bigquery_dataframes_pandas_methods] + import bigframes.pandas as bpd + + # Load data from BigQuery + query_or_table = "bigquery-public-data.ml_datasets.penguins" + bq_df = bpd.read_gbq(query_or_table) + + # Inspect one of the columns (or series) of the DataFrame: + bq_df["body_mass_g"].head(10) + + # Compute the mean of this series: + average_body_mass = bq_df["body_mass_g"].mean() + print(f"average_body_mass: {average_body_mass}") + + # Calculate the mean body_mass_g by species using the groupby operation: + bq_df["body_mass_g"].groupby(by=bq_df["species"]).mean().head() + # [END bigquery_dataframes_pandas_methods] + assert average_body_mass is not None diff --git a/samples/snippets/quickstart.py b/samples/snippets/quickstart.py new file mode 100644 index 00000000000..a15ea168534 --- /dev/null +++ b/samples/snippets/quickstart.py @@ -0,0 +1,71 @@ +# Copyright 2023 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +def run_quickstart(project_id: str): + import bigframes + + session_options = bigframes.BigQueryOptions() + session = bigframes.connect(session_options) + + your_gcp_project_id = project_id + query_or_table = "bigquery-public-data.ml_datasets.penguins" + df_session = session.read_gbq(query_or_table) + average_body_mass = df_session["body_mass_g"].mean() + print(f"average_body_mass (df_session): {average_body_mass}") + + # [START bigquery_bigframes_quickstart] + import bigframes.pandas as bpd + + # Set BigQuery DataFrames options + bpd.options.bigquery.project = your_gcp_project_id + bpd.options.bigquery.location = "us" + + # Create a DataFrame from a BigQuery table + query_or_table = "bigquery-public-data.ml_datasets.penguins" + df = bpd.read_gbq(query_or_table) + + # Use the DataFrame just as you would a pandas DataFrame, but calculations + # happen in the BigQuery query engine instead of the local system. + average_body_mass = df["body_mass_g"].mean() + print(f"average_body_mass: {average_body_mass}") + + # Create the Linear Regression model + from bigframes.ml.linear_model import LinearRegression + + # Filter down to the data we want to analyze + adelie_data = df[df.species == "Adelie Penguin (Pygoscelis adeliae)"] + + # Drop the columns we don't care about + adelie_data = adelie_data.drop(columns=["species"]) + + # Drop rows with nulls to get our training data + training_data = adelie_data.dropna() + + # Pick feature columns and label column + X = training_data[ + [ + "island", + "culmen_length_mm", + "culmen_depth_mm", + "flipper_length_mm", + "sex", + ] + ] + y = training_data[["body_mass_g"]] + + model = LinearRegression(fit_intercept=False) + model.fit(X, y) + model.score(X, y) + # [END bigquery_bigframes_quickstart] diff --git a/samples/snippets/quickstart_test.py b/samples/snippets/quickstart_test.py new file mode 100644 index 00000000000..bbe4a8b3c49 --- /dev/null +++ b/samples/snippets/quickstart_test.py @@ -0,0 +1,31 @@ +# Copyright 2023 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import pytest + +import bigframes.pandas + +from . import quickstart + + +def test_quickstart( + capsys: pytest.CaptureFixture[str], +) -> None: + # We need a fresh session since we're modifying connection options. + bigframes.pandas.close_session() + + # TODO(swast): Get project from environment so contributors can run tests. + quickstart.run_quickstart("bigframes-dev") + out, _ = capsys.readouterr() + assert "average_body_mass (df_session):" in out diff --git a/samples/snippets/regression_model_test.py b/samples/snippets/regression_model_test.py new file mode 100644 index 00000000000..7d1bde689cb --- /dev/null +++ b/samples/snippets/regression_model_test.py @@ -0,0 +1,57 @@ +# Copyright 2023 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +def test_regression_model(): + # [START bigquery_dataframes_regression_model] + from bigframes.ml.linear_model import LinearRegression + import bigframes.pandas as bpd + + # Load data from BigQuery + query_or_table = "bigquery-public-data.ml_datasets.penguins" + bq_df = bpd.read_gbq(query_or_table) + + # Filter down to the data to the Adelie Penguin species + adelie_data = bq_df[bq_df.species == "Adelie Penguin (Pygoscelis adeliae)"] + + # Drop the species column + adelie_data = adelie_data.drop(columns=["species"]) + + # Drop rows with nulls to get training data + training_data = adelie_data.dropna() + + # Specify your feature (or input) columns and the label (or output) column: + feature_columns = training_data[ + ["island", "culmen_length_mm", "culmen_depth_mm", "flipper_length_mm", "sex"] + ] + label_columns = training_data[["body_mass_g"]] + + test_data = adelie_data[adelie_data.body_mass_g.isnull()] + + # Create the linear model + model = LinearRegression() + model.fit(feature_columns, label_columns) + + # Score the model + score = model.score(feature_columns, label_columns) + + # Predict using the model + result = model.predict(test_data) + # [END bigquery_dataframes_regression_model] + assert test_data is not None + assert feature_columns is not None + assert label_columns is not None + assert model is not None + assert score is not None + assert result is not None diff --git a/samples/snippets/remote_function.py b/samples/snippets/remote_function.py new file mode 100644 index 00000000000..646d7b0c307 --- /dev/null +++ b/samples/snippets/remote_function.py @@ -0,0 +1,163 @@ +# Copyright 2023 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +def run_remote_function_and_read_gbq_function(project_id: str): + your_gcp_project_id = project_id + + # [START bigquery_dataframes_remote_function] + import bigframes.pandas as bpd + + # Set BigQuery DataFrames options + bpd.options.bigquery.project = your_gcp_project_id + bpd.options.bigquery.location = "us" + + # BigQuery DataFrames gives you the ability to turn your custom scalar + # functions into a BigQuery remote function. It requires the GCP project to + # be set up appropriately and the user having sufficient privileges to use + # them. One can find more details about the usage and the requirements via + # `help` command. + help(bpd.remote_function) + + # Read a table and inspect the column of interest. + df = bpd.read_gbq("bigquery-public-data.ml_datasets.penguins") + df["body_mass_g"].head(10) + + # Define a custom function, and specify the intent to turn it into a remote + # function. It requires a BigQuery connection. If the connection is not + # already created, BigQuery DataFrames will attempt to create one assuming + # the necessary APIs and IAM permissions are setup in the project. In our + # examples we would be using a pre-created connection named + # `bigframes-rf-conn`. We will also set `reuse=False` to make sure we don't + # step over someone else creating remote function in the same project from + # the exact same source code at the same time. Let's try a `pandas`-like use + # case in which we want to apply a user defined scalar function to every + # value in a `Series`, more specifically bucketize the `body_mass_g` value + # of the penguins, which is a real number, into a category, which is a + # string. + @bpd.remote_function( + [float], + str, + bigquery_connection="bigframes-rf-conn", + reuse=False, + ) + def get_bucket(num): + if not num: + return "NA" + boundary = 4000 + return "at_or_above_4000" if num >= boundary else "below_4000" + + # Then we can apply the remote function on the `Series`` of interest via + # `apply` API and store the result in a new column in the DataFrame. + df = df.assign(body_mass_bucket=df["body_mass_g"].apply(get_bucket)) + + # This will add a new column `body_mass_bucket` in the DataFrame. You can + # preview the original value and the bucketized value side by side. + df[["body_mass_g", "body_mass_bucket"]].head(10) + + # The above operation was possible by doing all the computation on the + # cloud. For that, there is a google cloud function deployed by serializing + # the user code, and a BigQuery remote function created to call the cloud + # function via the latter's http endpoint on the data in the DataFrame. + + # The BigQuery remote function created to support the BigQuery DataFrames + # remote function can be located via a property `bigframes_remote_function` + # set in the remote function object. + print(f"Created BQ remote function: {get_bucket.bigframes_remote_function}") + + # The cloud function can be located via another property + # `bigframes_cloud_function` set in the remote function object. + print(f"Created cloud function: {get_bucket.bigframes_cloud_function}") + + # Warning: The deployed cloud function may be visible to other users with + # sufficient privilege in the project, so the user should be careful about + # having any sensitive data in the code that will be deployed as a remote + # function. + + # Let's continue trying other potential use cases of remote functions. Let's + # say we consider the `species`, `island` and `sex` of the penguins + # sensitive information and want to redact that by replacing with their hash + # code instead. Let's define another scalar custom function and decorate it + # as a remote function. The custom function in this example has external + # package dependency, which can be specified via `packages` parameter. + @bpd.remote_function( + [str], + str, + bigquery_connection="bigframes-rf-conn", + reuse=False, + packages=["cryptography"], + ) + def get_hash(input): + from cryptography.fernet import Fernet + + # handle missing value + if input is None: + input = "" + + key = Fernet.generate_key() + f = Fernet(key) + return f.encrypt(input.encode()).decode() + + # We can use this remote function in another `pandas`-like API `map` that + # can be applied on a DataFrame + df_redacted = df[["species", "island", "sex"]].map(get_hash) + df_redacted.head(10) + + # [END bigquery_dataframes_remote_function] + + existing_get_bucket_bq_udf = get_bucket.bigframes_remote_function + + # [START bigquery_dataframes_read_gbq_function] + + # If you have already defined a custom function in BigQuery, either via the + # BigQuery Google Cloud Console or with the `remote_function` decorator, + # or otherwise, you may use it with BigQuery DataFrames with the + # `read_gbq_function` method. More details are available via the `help` + # command. + import bigframes.pandas as pd + + help(pd.read_gbq_function) + + # Here is an example of using `read_gbq_function` to load an existing + # BigQuery function. + df = pd.read_gbq("bigquery-public-data.ml_datasets.penguins") + get_bucket_function = pd.read_gbq_function(existing_get_bucket_bq_udf) + + df = df.assign(body_mass_bucket=df["body_mass_g"].apply(get_bucket_function)) + df.head(10) + + # It should be noted that if a function is created using the + # `remote_function` decorator, its created BQ remote function is accessible + # immediately afterward via the function's `bigframes_remote_function` + # attribute. The same string can be passed to `read_gbq_function` later in + # another context. + + # [END bigquery_dataframes_read_gbq_function] + + # Clean up cloud artifacts + session = bpd.get_global_session() + for function in (get_bucket, get_hash): + try: + session.bqclient.delete_routine(function.bigframes_remote_function) + except Exception: + # Ignore exception during clean-up + pass + + try: + session.cloudfunctionsclient.delete_function( + name=function.bigframes_cloud_function + ) + except Exception: + # Ignore exception during clean-up + pass diff --git a/samples/snippets/remote_function_test.py b/samples/snippets/remote_function_test.py new file mode 100644 index 00000000000..e1317c6ac08 --- /dev/null +++ b/samples/snippets/remote_function_test.py @@ -0,0 +1,32 @@ +# Copyright 2023 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import pytest + +import bigframes.pandas + +from . import remote_function + + +def test_remote_function_and_read_gbq_function( + capsys: pytest.CaptureFixture[str], +) -> None: + # We need a fresh session since we're modifying connection options. + bigframes.pandas.close_session() + + # TODO(swast): Get project from environment so contributors can run tests. + remote_function.run_remote_function_and_read_gbq_function("bigframes-dev") + out, _ = capsys.readouterr() + assert "Created BQ remote function:" in out + assert "Created cloud function:" in out diff --git a/samples/snippets/set_options_test.py b/samples/snippets/set_options_test.py new file mode 100644 index 00000000000..ef6f41ce541 --- /dev/null +++ b/samples/snippets/set_options_test.py @@ -0,0 +1,34 @@ +# Copyright 2023 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +def test_bigquery_dataframes_set_options(): + # Close the session before resetting the options + import bigframes.pandas as bpd + + bpd.close_session() + + # [START bigquery_dataframes_set_options] + import bigframes.pandas as bpd + + PROJECT_ID = "bigframes-dec" # @param {type:"string"} + REGION = "US" # @param {type:"string"} + + # Set BigQuery DataFrames options + bpd.options.bigquery.project = PROJECT_ID + bpd.options.bigquery.location = REGION + + # [END bigquery_dataframes_set_options] + assert bpd.options.bigquery.project == PROJECT_ID + assert bpd.options.bigquery.location == REGION diff --git a/scratch/.gitignore b/scratch/.gitignore deleted file mode 100644 index b813ccd98e6..00000000000 --- a/scratch/.gitignore +++ /dev/null @@ -1,2 +0,0 @@ -# Ignore all files in this directory. -* diff --git a/scripts/bigquery_generator/__init__.py b/scripts/bigquery_generator/__init__.py deleted file mode 100644 index 58d482ea386..00000000000 --- a/scripts/bigquery_generator/__init__.py +++ /dev/null @@ -1,13 +0,0 @@ -# Copyright 2026 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. diff --git a/scripts/bigquery_generator/constants.py b/scripts/bigquery_generator/constants.py deleted file mode 100644 index 78c3fc60c2b..00000000000 --- a/scripts/bigquery_generator/constants.py +++ /dev/null @@ -1,142 +0,0 @@ -# Copyright 2026 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import pathlib - -SCRIPTS_DIRECTORY = pathlib.Path(__file__).parent.parent.absolute() -PACKAGE_ROOT = SCRIPTS_DIRECTORY.parent -CODE_ROOT = PACKAGE_ROOT / "bigframes" -SCRIPT_PATH_RELATIVE = ( - pathlib.Path(__file__).relative_to(PACKAGE_ROOT).parent.parent - / "generate_bigframes_bigquery.py" -) - - -# Directory containing the YAML files -DATA_DIR = SCRIPTS_DIRECTORY / "data" / "sql-functions" -# Directory where the generated Python files will be placed -OUTPUT_DIR = CODE_ROOT / "operations" / "googlesql" -# Directory where the generated test files will be placed -TEST_OUTPUT_DIR = PACKAGE_ROOT / "tests" / "unit" / "bigquery" / "generated" - -PYTHON_BUILTINS = { - "abs", - "all", - "any", - "ascii", - "bin", - "bool", - "breakpoint", - "bytearray", - "bytes", - "callable", - "chr", - "classmethod", - "compile", - "complex", - "delattr", - "dict", - "dir", - "divmod", - "enumerate", - "eval", - "exec", - "filter", - "float", - "format", - "frozenset", - "getattr", - "globals", - "hasattr", - "hash", - "help", - "hex", - "id", - "input", - "int", - "isinstance", - "issubclass", - "iter", - "len", - "list", - "locals", - "map", - "max", - "memoryview", - "min", - "next", - "object", - "oct", - "open", - "ord", - "pow", - "print", - "property", - "range", - "repr", - "reversed", - "round", - "set", - "setattr", - "slice", - "sorted", - "staticmethod", - "str", - "sum", - "super", - "tuple", - "type", - "vars", - "zip", -} - -DTYPE_MAP = { - "binary": "dtypes.BYTES_DTYPE", - "string": "dtypes.STRING_DTYPE", - "int64": "dtypes.INT_DTYPE", - "i64": "dtypes.INT_DTYPE", - "float64": "dtypes.FLOAT_DTYPE", - "fp64": "dtypes.FLOAT_DTYPE", - "bool": "dtypes.BOOL_DTYPE", - "boolean": "dtypes.BOOL_DTYPE", - "geography": "dtypes.GEO_DTYPE", - "json": "dtypes.JSON_DTYPE", - "date": "dtypes.DATE_DTYPE", - "time": "dtypes.TIME_DTYPE", - "datetime": "dtypes.DATETIME_DTYPE", - "timestamp": "dtypes.TIMESTAMP_DTYPE", - "decimal<38,9>": "dtypes.NUMERIC_DTYPE", - "decimal<76,38>": "dtypes.BIGNUMERIC_DTYPE", -} - -PY_TYPE_MAP = { - "binary": "bytes", - "string": "str", - "int64": "int", - "i64": "int", - "float64": "float", - "fp64": "float", - "bool": "bool", - "boolean": "bool", - "geography": "Any", - "json": "Any", - "date": "datetime.date", - "time": "datetime.time", - "datetime": "datetime.datetime", - "timestamp": "datetime.datetime", - "struct": "dict", - "decimal<38,9>": "decimal.Decimal", - "decimal<76,38>": "decimal.Decimal", - "interval_day": "datetime.timedelta", -} diff --git a/scripts/bigquery_generator/data_models.py b/scripts/bigquery_generator/data_models.py deleted file mode 100644 index e0cffd566a0..00000000000 --- a/scripts/bigquery_generator/data_models.py +++ /dev/null @@ -1,237 +0,0 @@ -# Copyright 2026 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -"""Data models for BigQuery code generator. - -`BQ*` models the Substrait YAML extension structure of BigQuery SQL functions, -while `BigFrames*` models the Jinja template outputs. - -BQ* Class Relations: -==================== -+-------------------+ -| BQModule | -+-------------------+ - | - | functions: list[BQFunc] - v -+-------------------+ -| BQFunc | -+-------------------+ - | - | impls: list[BQFuncImpl] - v -+-------------------+ -| BQFuncImpl | -+-------------------+ - | - | args: list[BQFuncArg] - v -+-------------------+ -| BQFuncArg | -+-------------------+ - -BigFrames* Class Relations: -================================= - +--------------------------------+ - | Accessor |<---+ children: list[Accessor] - +--------------------------------+----+ (nested namespace hierarchy) - | - | functions: list[BigFramesFunc] - v - +--------------------------------+ - | BigFramesFunc | - +--------------------------------+ - | - | args: list[BigFramesFuncArg] - v - +--------------------------------+ - | BigFramesFuncArg | - +--------------------------------+ - - ---------------------------------- - - +--------------------------------+ - | BigFramesOp | (Standalone data model for op defs) - +--------------------------------+ -""" - -from __future__ import annotations - -import dataclasses -import pathlib - -from . import constants - - -@dataclasses.dataclass(frozen=True) -class BQFuncArg: - """ - Represents an argument of a SQL function loaded from a yaml file. - """ - - name: str - value: str # The type of the arg - optional: bool - keyword_only: bool - - -@dataclasses.dataclass(frozen=True) -class BQFuncImpl: - """ - Represents an implementation (i.e. signature) for some SQL function loaded - from a yaml file. - """ - - args: tuple[BQFuncArg, ...] - return_type: str - - @property - def requires_generic_types(self) -> bool: - if "any1" in self.return_type: - return True - - return any("any1" in arg.value for arg in self.args) - - -@dataclasses.dataclass(frozen=True) -class BQFunc: - """ - Represents a SQL function loaded from a yaml file. - """ - - name: str - description: str - impls: tuple[BQFuncImpl, ...] - series_accessor_arg: str | None - - @property - def op_base_name(self) -> str: - return self.name.split(".")[-1] - - -@dataclasses.dataclass(frozen=True) -class BQModule: - """ - Represents the data loaded from a yaml file with SQL functions info. - """ - - yaml_file: pathlib.Path - functions: tuple[BQFunc, ...] - - @property - def module_path(self) -> pathlib.Path: - return self.yaml_file.relative_to(constants.DATA_DIR).with_suffix("") - - @property - def namespace(self) -> tuple[str, ...]: - parts = self.module_path.parts - if "global_namespace" in parts: - return tuple() - return parts - - @property - def is_global(self) -> bool: - return "global_namespace" in self.module_path.parts - - -@dataclasses.dataclass(frozen=True) -class BigFramesOp: - """ - Represents a BigFrames GoogleScalarOp impl to be defined in the code base. - """ - - internal_name: str - sql_name: str - arg_specs: str - signature: str - signature_definition: str | None - - -@dataclasses.dataclass(frozen=True) -class BigFramesFuncArg: - """ - Represents an argument of a BigFrames BigQuery function to be defined in the code base. - """ - - name: str - types: frozenset[str] - optional: bool - keyword_only: bool - - @property - def type_hint(self) -> str: - types = [constants.PY_TYPE_MAP.get(t, "Any") for t in sorted(self.types)] + [ - "Literal[sentinels.Sentinel.ARGUMENT_DEFAULT]" - ] - - if len(types) > 1: - return "Union[" + ", ".join(sorted(set(types))) + "]" - - return types[0] - - @property - def default(self) -> str | None: - if self.optional: - return "sentinels.Sentinel.ARGUMENT_DEFAULT" - return None - - -@dataclasses.dataclass -class BigFramesFuncArgBuilder: - name: str - types: set[str] - optional: bool - keyword_only: bool - - def build(self) -> BigFramesFuncArg: - return BigFramesFuncArg( - name=self.name, - types=frozenset(self.types), - optional=self.optional, - keyword_only=self.keyword_only, - ) - - -@dataclasses.dataclass(frozen=True) -class BigFramesFunc: - """ - Represents a BigFrames BigQuery function to be defined in the codebase. - """ - - name: str - op_name: str - description: str - args: tuple[BigFramesFuncArg, ...] - series_accessor_arg: str | None - import_module: str | None = None - - -@dataclasses.dataclass -class Accessor: - """ - Represents the accessor extensions to be defined for pandas and BigFrames. - It consists of multiple functions bundled under the different namespaces. - - This class is designed to be mutable because it has a recursive data structure. - Mutability makes it easier to build the data structure trees from the top. - """ - - class_name: str - bigframes_class_name: str - pandas_class_name: str - is_root: bool - description: str - children: list[Accessor] - functions: list[BigFramesFunc] - prop_name: str | None = None diff --git a/scripts/bigquery_generator/file_generator.py b/scripts/bigquery_generator/file_generator.py deleted file mode 100644 index 3109afc3f8b..00000000000 --- a/scripts/bigquery_generator/file_generator.py +++ /dev/null @@ -1,132 +0,0 @@ -# Copyright 2026 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import pathlib -import subprocess -import sys -from typing import Sequence - -from . import constants, data_models, template_renderer - - -def _ensure_init_py(directory: pathlib.Path, limit_dir: pathlib.Path) -> None: - """Ensures __init__.py exists in the directory and its parents up to limit_dir.""" - curr = directory - while curr != limit_dir and curr != curr.parent: - init_file = curr / "__init__.py" - if not init_file.exists(): - print(f" Creating {init_file}") - with open(init_file, "w", encoding="utf-8") as f: - f.write(template_renderer.render_license()) - curr = curr.parent - - -def _write_file( - content: str, output_file: pathlib.Path, limit_dir: pathlib.Path -) -> None: - output_file.parent.mkdir(parents=True, exist_ok=True) - _ensure_init_py(output_file.parent, limit_dir) - - with open(output_file, "w", encoding="utf-8") as f: - f.write(content) - print(f" Generated {output_file}") - - -def _run_ruff() -> None: - targets = [ - constants.OUTPUT_DIR, - constants.TEST_OUTPUT_DIR, - constants.CODE_ROOT / "extensions", - ] - ruff_common_args = [ - "--target-version=py310", - "--line-length=88", - ] - - ruff_check_args = [ - "check", - "--select", - "I,F", - "--fix", - ] + ruff_common_args - subprocess.run( - [sys.executable, "-m", "ruff"] + ruff_check_args + targets, - check=True, - ) - - ruff_format_args = [ - "format", - ] + ruff_common_args - subprocess.run( - [sys.executable, "-m", "ruff"] + ruff_format_args + targets, - check=True, - ) - - -def _generate_op_defs(bq_module: data_models.BQModule) -> None: - if not bq_module.functions: - # If there are no function definitions, do not generate file without Python code. - return - - content = template_renderer.render_operation(bq_module) - output_file = constants.OUTPUT_DIR.joinpath(bq_module.module_path).with_suffix( - ".py" - ) - - _write_file(content, output_file, constants.OUTPUT_DIR.parent) - - -def _generate_tests(bq_module: data_models.BQModule) -> None: - if not bq_module.functions: - # If there are no function definitions, do not generate file without Python code. - return - - content = template_renderer.render_tests(bq_module) - output_file = constants.TEST_OUTPUT_DIR.joinpath( - bq_module.module_path.with_name(f"test_{bq_module.module_path.name}") - ).with_suffix(".py") - - _write_file(content, output_file, constants.TEST_OUTPUT_DIR.parent) - - -def _generate_accesor(bq_modules: Sequence[data_models.BQModule]) -> None: - (core_content, pd_content, bf_content) = template_renderer.render_accessor( - bq_modules - ) - - core_output_file = ( - constants.CODE_ROOT / "extensions" / "core" / "series_accessor.py" - ) - _write_file(core_content, core_output_file, constants.CODE_ROOT) - - pd_output_file = ( - constants.CODE_ROOT / "extensions" / "pandas" / "series_accessor.py" - ) - _write_file(pd_content, pd_output_file, constants.CODE_ROOT) - - bf_output_file = ( - constants.CODE_ROOT / "extensions" / "bigframes" / "series_accessor.py" - ) - _write_file(bf_content, bf_output_file, constants.CODE_ROOT) - - -def generate(bq_modules: Sequence[data_models.BQModule]) -> None: - for bq_module in bq_modules: - _generate_op_defs(bq_module) - _generate_tests(bq_module) - - _generate_accesor(bq_modules) - - # Ruff format - _run_ruff() diff --git a/scripts/bigquery_generator/template_renderer.py b/scripts/bigquery_generator/template_renderer.py deleted file mode 100644 index a4e8461286d..00000000000 --- a/scripts/bigquery_generator/template_renderer.py +++ /dev/null @@ -1,334 +0,0 @@ -# Copyright 2026 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -""" -Renders jinja template with module data parsed from yaml. -""" - -from typing import Sequence - -import jinja2 - -from . import constants, data_models - - -def _load_templates() -> dict[str, jinja2.Template]: - env = jinja2.Environment( - loader=jinja2.FileSystemLoader(constants.SCRIPTS_DIRECTORY / "templates"), - trim_blocks=True, - lstrip_blocks=True, - ) - return { - "operation": env.get_template("operation.py.j2"), - "test_operation": env.get_template("test_operation.py.j2"), - "license": env.get_template("license.py.j2"), - "signature_def": env.get_template("signature_def.py.j2"), - "core_series_accessor": env.get_template("core_series_accessor.py.j2"), - "bigframes_series_accessor": env.get_template( - "bigframes_series_accessor.py.j2" - ), - "pandas_series_accessor": env.get_template("pandas_series_accessor.py.j2"), - } - - -TEMPLATES: dict[str, jinja2.Template] = _load_templates() - - -def _unwrap_list_type(yaml_type: str) -> str | None: - if yaml_type.startswith("list<") and yaml_type.endswith(">"): - return yaml_type[5:-1] - return None - - -def _try_get_concrete_type_expr(yaml_type: str) -> str | None: - if yaml_type in constants.DTYPE_MAP: - return constants.DTYPE_MAP[yaml_type] - inner = _unwrap_list_type(yaml_type) - if inner and inner in constants.DTYPE_MAP: - # TODO (b/540011825): Support recursive type parsing - return f"dtypes.list_type({constants.DTYPE_MAP[inner]})" - return None - - -def _get_concrete_type_expr(yaml_type: str) -> str: - expr = _try_get_concrete_type_expr(yaml_type) - if expr is None: - raise ValueError(f"Not a concrete type: {yaml_type}") - return expr - - -def _is_concrete_type(yaml_type: str) -> bool: - return _try_get_concrete_type_expr(yaml_type) is not None - - -def _validate_type(yaml_type: str) -> None: - if yaml_type in ("any1", "struct") or yaml_type in constants.DTYPE_MAP: - return - inner = _unwrap_list_type(yaml_type) - if inner is not None: - if inner == "any1" or inner in constants.DTYPE_MAP: - return - raise ValueError(f"Unsupported inner type: {inner}") - raise ValueError(f"Unsupported type: {yaml_type}") - - -def _validate_types(impls: Sequence[data_models.BQFuncImpl]) -> None: - for impl in impls: - for arg in impl.args: - _validate_type(arg.value) - _validate_type(impl.return_type) - - -def render_signature_def( - bq_func: data_models.BQFunc, -) -> tuple[str, str | None]: - """ - Returns the signature function name and its definition. - If the signature function can be inlined, the first return value is the lambda, - and the second value is None. - - Examples: - Inlined signature function: - ("lambda *args: dtypes.FLOAT64_DTYPE", None) - - Custom signature function definition: - ("_ABS_SIG", "def _ABS_SIG(*args): ...") - """ - return_types = {impl.return_type for impl in bq_func.impls} - # Optimization: if all impls return the same concrete type, - # inline the signature function as a lambda - if len(return_types) == 1: - ret_type = next(iter(return_types)) - if _is_concrete_type(ret_type): - sig_expr = f"lambda *args: {_get_concrete_type_expr(ret_type)}" - return sig_expr, None - - _validate_types(bq_func.impls) - - sig_func_name = f"_{bq_func.op_base_name.upper()}_SIG" - - max_args = max(len(impl.args) for impl in bq_func.impls) - - rendered = TEMPLATES["signature_def"].render( - func_name=sig_func_name, - max_args=max_args, - impls=bq_func.impls, - sql_name=bq_func.name, - dtype_map=constants.DTYPE_MAP, - ) - - return sig_func_name, rendered - - -def _get_bigframes_func_args( - bq_func: data_models.BQFunc, -) -> tuple[data_models.BigFramesFuncArg, ...]: - """ - Coalesces arguments from all the signatures of this function, - and return them in the order of appearance in the yaml file - """ - args_by_name: dict[str, data_models.BigFramesFuncArgBuilder] = {} - arg_order: list[str] = [] - arg_appearances: dict[str, int] = {} - for impl in bq_func.impls: - seen_in_impl = set() - for bq_func_arg in impl.args: - name = bq_func_arg.name - seen_in_impl.add(name) - if name not in args_by_name: - args_by_name[name] = data_models.BigFramesFuncArgBuilder( - name=name, - types=set(), - optional=bq_func_arg.optional, - keyword_only=bq_func_arg.keyword_only, - ) - arg_order.append(name) - else: - # If it was marked optional or keyword_only in any previous impl, keep it. - # Or if this signature marks it as optional/keyword_only, update it. - if bq_func_arg.optional: - args_by_name[name].optional = True - if bq_func_arg.keyword_only: - args_by_name[name].keyword_only = True - args_by_name[name].types.add(bq_func_arg.value) - for name in seen_in_impl: - arg_appearances[name] = arg_appearances.get(name, 0) + 1 - - # If an argument is not in all impls, it must be optional overall - num_impls = len(bq_func.impls) - for name, count in arg_appearances.items(): - if count < num_impls: - args_by_name[name].optional = True - - return tuple(args_by_name[name].build() for name in arg_order) - - -def _to_bigframes_op(bq_func: data_models.BQFunc) -> data_models.BigFramesOp: - arg_specs = [] - for bf_func_arg in _get_bigframes_func_args(bq_func): - spec = "googlesql.ArgSpec(" - if bf_func_arg.keyword_only: - spec += f'arg_name="{bf_func_arg.name}", ' - if bf_func_arg.optional: - spec += "optional=True, " - spec = spec.rstrip(", ") + ")" - arg_specs.append(spec) - - arg_specs_str = ", ".join(arg_specs) - if len(arg_specs) == 1: - arg_specs_str += "," - - (signature, signature_definition) = render_signature_def(bq_func) - - return data_models.BigFramesOp( - internal_name=f"_{bq_func.op_base_name.upper()}_OP", - sql_name=bq_func.name.upper(), - arg_specs=arg_specs_str, - signature=signature, - signature_definition=signature_definition, - ) - - -def _to_bigframes_func( - bq_func: data_models.BQFunc, import_module: str | None = None -) -> data_models.BigFramesFunc: - python_name = bq_func.op_base_name - if python_name in constants.PYTHON_BUILTINS: - python_name = python_name + "_" - - return data_models.BigFramesFunc( - name=python_name, - op_name=f"_{bq_func.op_base_name.upper()}_OP", - description=bq_func.description, - args=_get_bigframes_func_args(bq_func), - series_accessor_arg=bq_func.series_accessor_arg, - import_module=import_module, - ) - - -def render_license() -> str: - return TEMPLATES["license"].render() - - -def render_operation( - bq_module: data_models.BQModule, -) -> str: - ops: list[data_models.BigFramesOp] = [] - functions: list[data_models.BigFramesFunc] = [] - - for bq_func in bq_module.functions: - ops.append(_to_bigframes_op(bq_func)) - functions.append(_to_bigframes_func(bq_func)) - - return TEMPLATES["operation"].render( - yaml_path=bq_module.yaml_file.relative_to(constants.PACKAGE_ROOT), - script_path=constants.SCRIPT_PATH_RELATIVE, - ops=ops, - functions=functions, - ) - - -def render_tests(bq_module: data_models.BQModule) -> str: - import_path = "bigframes.operations.googlesql." + ".".join( - bq_module.module_path.parts - ) - functions: list[data_models.BigFramesFunc] = [] - for bq_func in bq_module.functions: - functions.append(_to_bigframes_func(bq_func)) - - return TEMPLATES["test_operation"].render( - yaml_path=bq_module.yaml_file.relative_to(constants.PACKAGE_ROOT), - script_path=constants.SCRIPT_PATH_RELATIVE, - import_path=import_path, - short_name=bq_module.module_path.name, - is_global=bq_module.is_global, - functions=functions, - ) - - -def _create_accessor_class_name(namespace: tuple[str, ...], prefix: str = "") -> str: - if not namespace: - return f"{prefix}BigQuerySeriesAccessor" - camel_parts = [part.capitalize() for part in namespace] - return f"{prefix}{''.join(camel_parts)}SeriesAccessor" - - -def render_accessor( - bq_modules: Sequence[data_models.BQModule], -) -> tuple[str, str, str]: - """ - Returns the content for core accessor, pandas accessor and BF accessor - """ - - namespaces: set[tuple[str, ...]] = set() - for bq_module in bq_modules: - for i in range(len(bq_module.namespace) + 1): - namespaces.add(bq_module.namespace[:i]) - - sorted_namespaces = sorted(list(namespaces), key=lambda ns: (len(ns), ns)) - - accessors: list[data_models.Accessor] = [] - accessor_lookup_table: dict[tuple[str, ...], data_models.Accessor] = {} - for namespace in sorted_namespaces: - accessor = data_models.Accessor( - class_name=_create_accessor_class_name(namespace), - bigframes_class_name=_create_accessor_class_name( - namespace, prefix="Bigframes" - ), - pandas_class_name=_create_accessor_class_name(namespace, prefix="Pandas"), - is_root=len(namespace) == 0, - description=( - f"Series accessor for BigQuery {'.'.join(namespace)} functions." - if namespace - else "Series accessor for BigQuery functions." - ), - children=[], - functions=[], - ) - accessors.append(accessor) - accessor_lookup_table[namespace] = accessor - - # Establish parent-child relations - if len(namespace) > 0: - accessor.prop_name = namespace[-1] - parent_namespace = namespace[:-1] - accessor_lookup_table[parent_namespace].children.append(accessor) - - # Arrange functions by namespaces - for bq_module in bq_modules: - module_parts = bq_module.module_path.parts - for bq_func in bq_module.functions: - if bq_func.series_accessor_arg is None: - continue - bf_func = _to_bigframes_func( - bq_func, - import_module=f"bigframes.operations.googlesql.{'.'.join(module_parts)}", - ) - accessor_lookup_table[bq_module.namespace].functions.append(bf_func) - - core_content = TEMPLATES["core_series_accessor"].render( - script_path=constants.SCRIPT_PATH_RELATIVE, - namespaces=accessors, - ) - - pandas_content = TEMPLATES["pandas_series_accessor"].render( - script_path=constants.SCRIPT_PATH_RELATIVE, namespaces=accessors - ) - - bigframes_content = TEMPLATES["bigframes_series_accessor"].render( - script_path=constants.SCRIPT_PATH_RELATIVE, namespaces=accessors - ) - - return core_content, pandas_content, bigframes_content diff --git a/scripts/bigquery_generator/yaml_parser.py b/scripts/bigquery_generator/yaml_parser.py deleted file mode 100644 index 6ef1d5d9236..00000000000 --- a/scripts/bigquery_generator/yaml_parser.py +++ /dev/null @@ -1,64 +0,0 @@ -# Copyright 2026 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -import pathlib -from typing import Any - -import yaml - -from . import data_models - - -def _build_func_arg_ir(arg_data: Any) -> data_models.BQFuncArg: - return data_models.BQFuncArg( - name=arg_data["name"], - value=arg_data["value"], - optional=arg_data["optional"], - keyword_only=arg_data["keyword_only"], - ) - - -def _build_func_impl_ir(impl_data: Any) -> data_models.BQFuncImpl: - return data_models.BQFuncImpl( - args=tuple(_build_func_arg_ir(arg) for arg in impl_data["args"]), - return_type=impl_data["return"], - ) - - -def _build_func_ir(func_data: Any) -> data_models.BQFunc: - return data_models.BQFunc( - name=func_data["name"], - description=func_data["description"], - impls=tuple(_build_func_impl_ir(impl) for impl in func_data["impls"]), - series_accessor_arg=func_data.get("series_accessor_arg", None), - ) - - -def parse_yaml(yaml_file: pathlib.Path) -> data_models.BQModule: - print(f"Parsing {yaml_file}...") - - with open(yaml_file, "r", encoding="utf-8") as f: - data = yaml.safe_load(f) - - functions: tuple[data_models.BQFunc, ...] = () - if isinstance(data, dict) and "scalar_functions" in data: - functions = tuple( - _build_func_ir(func_data) for func_data in data["scalar_functions"] - ) - - return data_models.BQModule( - yaml_file=yaml_file, - functions=functions, - ) diff --git a/scripts/conftest.py b/scripts/conftest.py deleted file mode 100644 index 0d55bd4b478..00000000000 --- a/scripts/conftest.py +++ /dev/null @@ -1,8 +0,0 @@ -import sys -from pathlib import Path - -# inserts scripts into path so that tests can import -project_root = Path(__file__).parent.parent -scripts_dir = project_root / "scripts" - -sys.path.insert(0, str(scripts_dir)) diff --git a/scripts/create_gcs.py b/scripts/create_gcs.py deleted file mode 100644 index bdb8a23ddc9..00000000000 --- a/scripts/create_gcs.py +++ /dev/null @@ -1,96 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -# This script create the bigtable resources required for -# bigframes.streaming testing if they don't already exist - -import os -import sys -from pathlib import Path - -import google.cloud.exceptions as exceptions -import google.cloud.storage as gcs -from google.cloud.storage import transfer_manager - -PROJECT_ID = os.getenv("GOOGLE_CLOUD_PROJECT") - -if not PROJECT_ID: - print( - "Please set GOOGLE_CLOUD_PROJECT environment variable before running.", - file=sys.stderr, - ) - sys.exit(1) - - -def create_bucket(client: gcs.Client) -> gcs.Bucket: - bucket_name = "bigframes_blob_test" - - print(f"Creating bucket: {bucket_name}") - try: - bucket = client.create_bucket(bucket_name) - print(f"Bucket {bucket_name} created. ") - - except exceptions.Conflict: - print(f"Bucket {bucket_name} already exists.") - bucket = client.bucket(bucket_name) - - return bucket - - -def upload_data(bucket: gcs.Bucket): - # from https://cloud.google.com/storage/docs/samples/storage-transfer-manager-upload-directory - source_directory = "scripts/data/" - workers = 8 - - # First, recursively get all files in `directory` as Path objects. - directory_as_path_obj = Path(source_directory) - paths = directory_as_path_obj.rglob("*") - - # Filter so the list only includes files, not directories themselves. - file_paths = [path for path in paths if path.is_file()] - - # These paths are relative to the current working directory. Next, make them - # relative to `directory` - relative_paths = [path.relative_to(source_directory) for path in file_paths] - - # Finally, convert them all to strings. - string_paths = [str(path) for path in relative_paths] - - print("Found {} files.".format(len(string_paths))) - - # Start the upload. - results = transfer_manager.upload_many_from_filenames( - bucket, string_paths, source_directory=source_directory, max_workers=workers - ) - - for name, result in zip(string_paths, results): - # The results list is either `None` or an exception for each filename in - # the input list, in order. - - if isinstance(result, Exception): - print("Failed to upload {} due to exception: {}".format(name, result)) - else: - print("Uploaded {} to {}.".format(name, bucket.name)) - - -def main(): - client = gcs.Client(project=PROJECT_ID) - - bucket = create_bucket(client) - - upload_data(bucket) - - -if __name__ == "__main__": - main() diff --git a/scripts/create_load_test_tables.py b/scripts/create_load_test_tables.py deleted file mode 100644 index d94a33aa5cc..00000000000 --- a/scripts/create_load_test_tables.py +++ /dev/null @@ -1,109 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import math -import os -import pathlib -import sys - -import google.cloud.bigquery as bigquery - -REPO_ROOT = pathlib.Path(__file__).parent.parent - -PROJECT_ID = os.getenv("GOOGLE_CLOUD_PROJECT") - -if not PROJECT_ID: - print( - "Please set GOOGLE_CLOUD_PROJECT environment variable before running.", - file=sys.stderr, - ) - sys.exit(1) - -DATASET_ID = f"{PROJECT_ID}.load_testing" -TABLE_ID = f"{DATASET_ID}.scalars" -TABLE_ID_FORMAT = f"{DATASET_ID}.scalars_{{size}}" - -KB_BYTES = 1000 -MB_BYTES = 1000 * KB_BYTES -GB_BYTES = 1000 * MB_BYTES -TB_BYTES = 1000 * GB_BYTES -SIZES = ( - ("1mb", MB_BYTES), - ("10mb", 10 * MB_BYTES), - ("100mb", 100 * MB_BYTES), - ("1gb", GB_BYTES), - ("10gb", 10 * GB_BYTES), - ("100gb", 100 * GB_BYTES), - ("1tb", TB_BYTES), -) -SCHEMA_PATH = REPO_ROOT / "tests" / "data" / "scalars_schema.json" -DATA_PATH = REPO_ROOT / "tests" / "data" / "scalars.jsonl" -BQCLIENT = bigquery.Client() - - -def create_dataset(): - dataset = bigquery.Dataset(DATASET_ID) - BQCLIENT.create_dataset(dataset, exists_ok=True) - - -def load_scalars_table(): - schema = BQCLIENT.schema_from_json(SCHEMA_PATH) - job_config = bigquery.LoadJobConfig() - job_config.schema = schema - job_config.write_disposition = bigquery.WriteDisposition.WRITE_TRUNCATE - job_config.source_format = bigquery.SourceFormat.NEWLINE_DELIMITED_JSON - - print(f"Creating {TABLE_ID}") - with open(DATA_PATH, "rb") as data_file: - BQCLIENT.load_table_from_file( - data_file, - TABLE_ID, - job_config=job_config, - ).result() - - -def multiply_table(previous_table_id, target_table_id, multiplier): - clauses = [f"SELECT * FROM `{previous_table_id}`"] * multiplier - query = " UNION ALL ".join(clauses) - job_config = bigquery.QueryJobConfig() - job_config.destination = target_table_id - job_config.write_disposition = bigquery.WriteDisposition.WRITE_TRUNCATE - print(f"Creating {target_table_id}, {multiplier} x {previous_table_id}") - BQCLIENT.query_and_wait(query, job_config=job_config) - - -def create_tables(): - base_table = BQCLIENT.get_table(TABLE_ID) - previous_bytes = base_table.num_bytes - previous_table_id = TABLE_ID - - for table_suffix, target_bytes in SIZES: - # Make sure we exceed the desired bytes by adding to the multiplier. - multiplier = math.ceil(target_bytes / previous_bytes) + 1 - target_table_id = TABLE_ID_FORMAT.format(size=table_suffix) - multiply_table(previous_table_id, target_table_id, multiplier) - - table = BQCLIENT.get_table(target_table_id) - previous_bytes = table.num_bytes - previous_table_id = target_table_id - - -def main(): - create_dataset() - load_scalars_table() - create_tables() - - -if __name__ == "__main__": - main() diff --git a/scripts/create_read_gbq_colab_benchmark_tables.py b/scripts/create_read_gbq_colab_benchmark_tables.py deleted file mode 100644 index 727a1e116ac..00000000000 --- a/scripts/create_read_gbq_colab_benchmark_tables.py +++ /dev/null @@ -1,541 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from __future__ import annotations - -import argparse -import base64 -import concurrent.futures -import datetime -import json -import math -import time -from typing import Any, Iterable, MutableSequence, Sequence - -import numpy as np -from google.cloud import bigquery - -# --- Input Data --- -# Generated by querying bigquery-magics usage. See internal issue b/420984164. -TABLE_STATS: dict[str, list[float]] = { - "percentile": [9, 19, 29, 39, 49, 59, 69, 79, 89, 99], - "materialized_or_scanned_bytes": [ - 0.0, - 0.0, - 4102.0, - 76901.0, - 351693.0, - 500000.0, - 500000.0, - 1320930.0, - 17486432.0, - 1919625975.0, - ], - "avg_row_bytes": [ - 0.00014346299635435792, - 0.005370969708923197, - 0.3692756731526246, - 4.079344721151818, - 7.5418, - 12.528863516404146, - 22.686258546389798, - 48.69689224091025, - 100.90817356205852, - 2020, - ], - "materialized_mb": [ - 0.0, - 0.0, - 0.004102, - 0.076901, - 0.351693, - 0.5, - 0.5, - 1.32093, - 17.486432, - 1919.625975, - ], -} - -BIGQUERY_DATA_TYPE_SIZES = { - "BOOL": 1, - "DATE": 8, - "FLOAT64": 8, - "INT64": 8, - "DATETIME": 8, - "TIMESTAMP": 8, - "TIME": 8, - "NUMERIC": 16, - # Flexible types. - # JSON base size is its content, BYTES/STRING have 2 byte overhead + content - "JSON": 0, - "BYTES": 2, - "STRING": 2, -} -FIXED_TYPES = [ - "BOOL", - "INT64", - "FLOAT64", - "NUMERIC", - "DATE", - "DATETIME", - "TIMESTAMP", - "TIME", -] -FLEXIBLE_TYPES = ["STRING", "BYTES", "JSON"] - -JSON_CHAR_LIST = list("abcdef") -STRING_CHAR_LIST = list("abcdefghijklmnopqrstuvwxyz0123456789") - -# --- Helper Functions --- - - -def get_bq_schema(target_row_size_bytes: int) -> Sequence[tuple[str, str, int | None]]: - """ - Determines the BigQuery table schema to match the target_row_size_bytes. - Prioritizes fixed-size types for diversity, then uses flexible types. - Returns a list of tuples: (column_name, type_name, length_for_flexible_type). - Length is None for fixed-size types. - """ - schema: MutableSequence[tuple[str, str, int | None]] = [] - current_size = 0 - col_idx = 0 - - for bq_type in FIXED_TYPES: - # For simplicity, we'll allow slight overage if only fixed fields are chosen. - if current_size >= target_row_size_bytes: - break - - type_size = BIGQUERY_DATA_TYPE_SIZES[bq_type] - schema.append((f"col_{bq_type.lower()}_{col_idx}", bq_type, None)) - current_size += type_size - col_idx += 1 - - # Use flexible-size types to fill remaining space - - # Attempt to add one of each flexible type if space allows - if current_size < target_row_size_bytes: - remaining_bytes_for_content = target_row_size_bytes - current_size - - # For simplicity, divide the remaing bytes evenly across the flexible - # columns. - target_size = int(math.ceil(remaining_bytes_for_content / len(FLEXIBLE_TYPES))) - - for bq_type in FLEXIBLE_TYPES: - base_cost = BIGQUERY_DATA_TYPE_SIZES[bq_type] - min_content_size = max(0, target_size - base_cost) - - schema.append( - (f"col_{bq_type.lower()}_{col_idx}", bq_type, min_content_size) - ) - current_size += base_cost + min_content_size - col_idx += 1 - - return schema - - -def generate_bool_batch( - num_rows: int, rng: np.random.Generator, content_length: int | None = None -) -> np.ndarray: - return rng.choice([True, False], size=num_rows) - - -def generate_int64_batch( - num_rows: int, rng: np.random.Generator, content_length: int | None = None -) -> np.ndarray: - return rng.integers(-(10**18), 10**18, size=num_rows, dtype=np.int64) - - -def generate_float64_batch( - num_rows: int, rng: np.random.Generator, content_length: int | None = None -) -> np.ndarray: - return rng.random(size=num_rows) * 2 * 10**10 - 10**10 - - -def generate_numeric_batch( - num_rows: int, rng: np.random.Generator, content_length: int | None = None -) -> np.ndarray: - raw_numerics = rng.random(size=num_rows) * 2 * 10**28 - 10**28 - format_numeric_vectorized = np.vectorize(lambda x: f"{x:.9f}") - return format_numeric_vectorized(raw_numerics) - - -def generate_date_batch( - num_rows: int, rng: np.random.Generator, content_length: int | None = None -) -> np.ndarray: - start_date_ord = datetime.date(1, 1, 1).toordinal() - max_days = (datetime.date(9999, 12, 31) - datetime.date(1, 1, 1)).days - day_offsets = rng.integers(0, max_days + 1, size=num_rows) - date_ordinals = start_date_ord + day_offsets - return np.array( - [ - datetime.date.fromordinal(int(ordinal)).isoformat() - for ordinal in date_ordinals - ] - ) - - -def generate_numpy_datetimes(num_rows: int, rng: np.random.Generator) -> np.ndarray: - # Generate seconds from a broad range (e.g., year 1 to 9999) - # Note: Python's datetime.timestamp() might be limited by system's C mktime. - # For broader range with np.datetime64, it's usually fine. - # Let's generate epoch seconds relative to Unix epoch for np.datetime64 compatibility - min_epoch_seconds = int( - datetime.datetime(1, 1, 1, 0, 0, 0, tzinfo=datetime.timezone.utc).timestamp() - ) - # Max for datetime64[s] is far out, but let's bound it reasonably for BQ. - max_epoch_seconds = int( - datetime.datetime( - 9999, 12, 28, 23, 59, 59, tzinfo=datetime.timezone.utc - ).timestamp() - ) - - epoch_seconds = rng.integers( - min_epoch_seconds, - max_epoch_seconds + 1, - size=num_rows, - dtype=np.int64, - ) - microseconds_offset = rng.integers(0, 1000000, size=num_rows, dtype=np.int64) - - # Create datetime64[s] from epoch seconds and add microseconds as timedelta64[us] - np_timestamps_s = epoch_seconds.astype("datetime64[s]") - np_microseconds_td = microseconds_offset.astype("timedelta64[us]") - return np_timestamps_s + np_microseconds_td - - -def generate_datetime_batch( - num_rows: int, rng: np.random.Generator, content_length: int | None = None -) -> np.ndarray: - np_datetimes = generate_numpy_datetimes(num_rows, rng) - - # np.datetime_as_string produces 'YYYY-MM-DDTHH:MM:SS.ffffff' - # BQ DATETIME typically uses a space separator: 'YYYY-MM-DD HH:MM:SS.ffffff' - datetime_strings = np.datetime_as_string(np_datetimes, unit="us") - return np.array([s.replace("T", " ") for s in datetime_strings]) - - -def generate_timestamp_batch( - num_rows: int, rng: np.random.Generator, content_length: int | None = None -) -> np.ndarray: - np_datetimes = generate_numpy_datetimes(num_rows, rng) - - # Convert to string with UTC timezone indicator - # np.datetime_as_string with timezone='UTC' produces 'YYYY-MM-DDTHH:MM:SS.ffffffZ' - # BigQuery generally accepts this for TIMESTAMP. - return np.datetime_as_string(np_datetimes, unit="us", timezone="UTC") - - -def generate_time_batch( - num_rows: int, rng: np.random.Generator, content_length: int | None = None -) -> np.ndarray: - hours = rng.integers(0, 24, size=num_rows) - minutes = rng.integers(0, 60, size=num_rows) - seconds = rng.integers(0, 60, size=num_rows) - microseconds = rng.integers(0, 1000000, size=num_rows) - time_list = [ - datetime.time(hours[i], minutes[i], seconds[i], microseconds[i]).isoformat() - for i in range(num_rows) - ] - return np.array(time_list) - - -def generate_json_row(content_length: int, rng: np.random.Generator) -> str: - json_val_len = max(0, content_length - 5) - json_val_chars = rng.choice(JSON_CHAR_LIST, size=json_val_len) - json_obj = {"k": "".join(json_val_chars)} - return json.dumps(json_obj) - - -def generate_json_batch( - num_rows: int, rng: np.random.Generator, content_length: int | None = None -) -> np.ndarray: - content_length = content_length if content_length is not None else 10 - json_list = [ - generate_json_row(content_length=content_length, rng=rng) - for _ in range(num_rows) - ] - return np.array(json_list) - - -def generate_string_batch( - num_rows: int, rng: np.random.Generator, content_length: int | None = None -) -> np.ndarray: - content_length = content_length if content_length is not None else 1 - content_length = max(0, content_length) - chars_array = rng.choice(STRING_CHAR_LIST, size=(num_rows, content_length)) - return np.array(["".join(row_chars) for row_chars in chars_array]) - - -def generate_bytes_batch( - num_rows: int, rng: np.random.Generator, content_length: int | None = None -) -> np.ndarray: - content_length = content_length if content_length is not None else 1 - content_length = max(0, content_length) - return np.array( - [ - base64.b64encode(rng.bytes(content_length)).decode("utf-8") - for _ in range(num_rows) - ] - ) - - -BIGQUERY_DATA_TYPE_GENERATORS = { - "BOOL": generate_bool_batch, - "DATE": generate_date_batch, - "FLOAT64": generate_float64_batch, - "INT64": generate_int64_batch, - "DATETIME": generate_datetime_batch, - "TIMESTAMP": generate_timestamp_batch, - "TIME": generate_time_batch, - "NUMERIC": generate_numeric_batch, - "JSON": generate_json_batch, - "BYTES": generate_bytes_batch, - "STRING": generate_string_batch, -} - - -def generate_work_items( - table_id: str, - schema: Sequence[tuple[str, str, int | None]], - num_rows: int, - batch_size: int, -) -> Iterable[tuple[str, Sequence[tuple[str, str, int | None]], int]]: - """ - Generates work items of appropriate batch sizes. - """ - if num_rows == 0: - return - - generated_rows_total = 0 - - while generated_rows_total < num_rows: - current_batch_size = min(batch_size, num_rows - generated_rows_total) - if current_batch_size == 0: - break - - yield (table_id, schema, current_batch_size) - generated_rows_total += current_batch_size - - -def generate_batch( - schema: Sequence[tuple[str, str, int | None]], - num_rows: int, - rng: np.random.Generator, -) -> list[dict[str, Any]]: - col_names_ordered = [s[0] for s in schema] - - columns_data_batch = {} - for col_name, bq_type, length in schema: - generate_batch = BIGQUERY_DATA_TYPE_GENERATORS[bq_type] - columns_data_batch[col_name] = generate_batch( - num_rows, rng, content_length=length - ) - - # Turn numpy objects into Python objects. - # https://stackoverflow.com/a/32850511/101923 - columns_data_batch_json = {} - for column in columns_data_batch: - columns_data_batch_json[column] = columns_data_batch[column].tolist() - - # Assemble batch of rows - batch_data = [] - for i in range(num_rows): - row = { - col_name: columns_data_batch_json[col_name][i] - for col_name in col_names_ordered - } - batch_data.append(row) - - return batch_data - - -def generate_and_load_batch( - client: bigquery.Client, - table_id: str, - schema_def: Sequence[tuple[str, str, int | None]], - num_rows: int, - rng: np.random.Generator, -): - bq_schema = [] - for col_name, type_name, _ in schema_def: - bq_schema.append(bigquery.SchemaField(col_name, type_name)) - table = bigquery.Table(table_id, schema=bq_schema) - - generated_data_chunk = generate_batch(schema_def, num_rows, rng) - errors = client.insert_rows_json(table, generated_data_chunk) - if errors: - raise ValueError(f"Encountered errors while inserting sub-batch: {errors}") - - -def create_and_load_table( - client: bigquery.Client | None, - project_id: str, - dataset_id: str, - table_name: str, - schema_def: Sequence[tuple[str, str, int | None]], - num_rows: int, - executor: concurrent.futures.Executor, -): - """Creates a BigQuery table and loads data into it by consuming a data generator.""" - - if not client: - print(f"Simulating: Generated schema: {schema_def}") - return - - # BQ client library streaming insert batch size (rows per API call) - # This is different from data_gen_batch_size which is for generating data. - # We can make BQ_LOAD_BATCH_SIZE smaller than data_gen_batch_size if needed. - BQ_LOAD_BATCH_SIZE = 500 - - # Actual BigQuery operations occur here because both project_id and dataset_id are provided - print( - f"Attempting BigQuery operations for table {table_name} in project '{project_id}', dataset '{dataset_id}'." - ) - table_id = f"{project_id}.{dataset_id}.{table_name}" - - bq_schema = [] - for col_name, type_name, _ in schema_def: - bq_schema.append(bigquery.SchemaField(col_name, type_name)) - - table = bigquery.Table(table_id, schema=bq_schema) - print(f"(Re)creating table {table_id}...") - table = client.create_table(table, exists_ok=True) - print(f"Table {table_id} created successfully or already exists.") - - # Query in case there's something in the streaming buffer already. - table_rows = next( - iter(client.query_and_wait(f"SELECT COUNT(*) FROM `{table_id}`")) - )[0] - print(f"Table {table_id} has {table_rows} rows.") - num_rows = max(0, num_rows - table_rows) - - if num_rows <= 0: - print(f"No rows to load. Requested {num_rows} rows. Skipping.") - return - - print(f"Starting to load {num_rows} rows into {table_id} in batches...") - - previous_status_time = 0.0 - generated_rows_total = 0 - - for completed_rows in executor.map( - worker_process_item, - generate_work_items( - table_id, - schema_def, - num_rows, - BQ_LOAD_BATCH_SIZE, - ), - ): - generated_rows_total += completed_rows - - current_time = time.monotonic() - if current_time - previous_status_time > 5: - print(f"Wrote {generated_rows_total} out of {num_rows} rows.") - previous_status_time = current_time - - -worker_client: bigquery.Client | None = None -worker_rng: np.random.Generator | None = None - - -def worker_initializer(project_id: str | None): - global worker_client, worker_rng - - # One client per process, since multiprocessing and client connections don't - # play nicely together. - if project_id is not None: - worker_client = bigquery.Client(project=project_id) - - worker_rng = np.random.default_rng() - - -def worker_process_item( - work_item: tuple[str, Sequence[tuple[str, str, int | None]], int], -): - global worker_client, worker_rng - - if worker_client is None or worker_rng is None: - raise ValueError("Worker not initialized.") - - table_id, schema_def, num_rows = work_item - generate_and_load_batch(worker_client, table_id, schema_def, num_rows, worker_rng) - return num_rows - - -# --- Main Script Logic --- -def main(): - """Main function to create and populate BigQuery tables.""" - - parser = argparse.ArgumentParser( - description="Generate and load BigQuery benchmark tables." - ) - parser.add_argument( - "-p", - "--project_id", - type=str, - default=None, - help="Google Cloud Project ID. If not provided, script runs in simulation mode.", - ) - parser.add_argument( - "-d", - "--dataset_id", - type=str, - default=None, - help="BigQuery Dataset ID within the project. If not provided, script runs in simulation mode.", - ) - args = parser.parse_args() - - num_percentiles = len(TABLE_STATS["percentile"]) - client = None - - if args.project_id and args.dataset_id: - client = bigquery.Client(project=args.project_id) - dataset = bigquery.Dataset(f"{args.project_id}.{args.dataset_id}") - client.create_dataset(dataset, exists_ok=True) - - with concurrent.futures.ProcessPoolExecutor( - initializer=worker_initializer, initargs=(args.project_id,) - ) as executor: - for i in range(num_percentiles): - percentile = TABLE_STATS["percentile"][i] - avg_row_bytes_raw = TABLE_STATS["avg_row_bytes"][i] - table_bytes_raw = TABLE_STATS["materialized_or_scanned_bytes"][i] - - target_table_bytes = max(1, int(math.ceil(table_bytes_raw))) - target_row_bytes = max(1, int(math.ceil(avg_row_bytes_raw))) - num_rows = max(1, int(math.ceil(target_table_bytes / target_row_bytes))) - - table_name = f"percentile_{percentile:02d}" - print(f"\n--- Processing Table: {table_name} ---") - print(f"Target average row bytes (rounded up): {target_row_bytes}") - print(f"Number of rows (rounded up): {num_rows}") - - schema_definition = get_bq_schema(target_row_bytes) - print(f"Generated Schema: {schema_definition}") - - create_and_load_table( - client, - args.project_id or "", - args.dataset_id or "", - table_name, - schema_definition, - num_rows, - executor, - ) - - -if __name__ == "__main__": - main() diff --git a/scripts/create_read_gbq_colab_benchmark_tables_test.py b/scripts/create_read_gbq_colab_benchmark_tables_test.py deleted file mode 100644 index 56c9cb2bc56..00000000000 --- a/scripts/create_read_gbq_colab_benchmark_tables_test.py +++ /dev/null @@ -1,334 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from __future__ import annotations - -import base64 -import datetime -import json -import math -import re - -import numpy as np -import pytest - -# Assuming the script to be tested is in the same directory or accessible via PYTHONPATH -from create_read_gbq_colab_benchmark_tables import ( - BIGQUERY_DATA_TYPE_SIZES, - generate_batch, - generate_work_items, - get_bq_schema, -) - - -# Helper function to calculate estimated row size from schema -def _calculate_row_size(schema: list[tuple[str, str, int | None]]) -> int: - """Calculates the estimated byte size of a row based on the schema. - Note: This is a simplified calculation for testing and might not perfectly - match BigQuery's internal storage, especially for complex types or NULLs. - """ - size = 0 - for _, bq_type, length in schema: - if bq_type in ["STRING", "BYTES", "JSON"]: - # Base cost (e.g., 2 bytes) + content length - size += BIGQUERY_DATA_TYPE_SIZES[bq_type] + ( - length if length is not None else 0 - ) - elif bq_type in BIGQUERY_DATA_TYPE_SIZES: - size += BIGQUERY_DATA_TYPE_SIZES[bq_type] - else: - raise AssertionError(f"Got unexpected type {bq_type}") - return size - - -# --- Tests for get_bq_schema --- - - -def test_get_bq_schema_zero_bytes(): - assert get_bq_schema(0) == [] - - -def test_get_bq_schema_one_byte(): - schema = get_bq_schema(1) - - assert len(schema) == 1 - assert schema[0][1] == "BOOL" # ('col_bool_fallback_0', 'BOOL', None) or similar - assert _calculate_row_size(schema) == 1 - - -def test_get_bq_schema_exact_fixed_fit(): - # BOOL (1) + INT64 (8) = 9 bytes - target_size = 9 - schema = get_bq_schema(target_size) - - assert len(schema) == 2 - assert schema[0][1] == "BOOL" - assert schema[1][1] == "INT64" - assert _calculate_row_size(schema) == target_size - - -def test_get_bq_schema_needs_flexible_string(): - # Sum of all fixed types: - # BOOL 1, INT64 8, FLOAT64 8, NUMERIC 16, DATE 8, DATETIME 8, TIMESTAMP 8, TIME 8 - # Total = 1+8+8+16+8+8+8+8 = 65 - target_size = 65 + 1 - schema = get_bq_schema(target_size) - - assert _calculate_row_size(schema) == 65 + 2 + 2 + 1 - - string_cols = [s for s in schema if s[1] == "STRING"] - assert len(string_cols) == 1 - assert string_cols[0][2] == 0 - - bytes_cols = [s for s in schema if s[1] == "BYTES"] - assert len(bytes_cols) == 1 - assert bytes_cols[0][2] == 0 - - json_cols = [s for s in schema if s[1] == "JSON"] - assert len(json_cols) == 1 - assert json_cols[0][2] == 1 - - -def test_get_bq_schema_flexible_expansion(): - # Sum of all fixed types: - # BOOL 1, INT64 8, FLOAT64 8, NUMERIC 16, DATE 8, DATETIME 8, TIMESTAMP 8, TIME 8 - # Total = 1+8+8+16+8+8+8+8 = 65 - target_size = 65 + 3 * 5 - schema = get_bq_schema(target_size) - - assert _calculate_row_size(schema) == target_size - - string_cols = [s for s in schema if s[1] == "STRING"] - assert len(string_cols) == 1 - assert string_cols[0][2] == 3 - - bytes_cols = [s for s in schema if s[1] == "BYTES"] - assert len(bytes_cols) == 1 - assert bytes_cols[0][2] == 3 - - json_cols = [s for s in schema if s[1] == "JSON"] - assert len(json_cols) == 1 - assert json_cols[0][2] == 5 - - -def test_get_bq_schema_all_fixed_types_possible(): - # Sum of all fixed types: - # BOOL 1, INT64 8, FLOAT64 8, NUMERIC 16, DATE 8, DATETIME 8, TIMESTAMP 8, TIME 8 - # Total = 1+8+8+16+8+8+8+8 = 65 - target_size = 65 - schema = get_bq_schema(target_size) - - expected_fixed_types = { - "BOOL", - "INT64", - "FLOAT64", - "NUMERIC", - "DATE", - "DATETIME", - "TIMESTAMP", - "TIME", - } - present_types = {s[1] for s in schema} - - assert expected_fixed_types.issubset(present_types) - - # Check if the size is close to target. - # All fixed (65) - calculated_size = _calculate_row_size(schema) - assert calculated_size == target_size - - -def test_get_bq_schema_uniqueness_of_column_names(): - target_size = 100 # A size that generates multiple columns - schema = get_bq_schema(target_size) - - column_names = [s[0] for s in schema] - assert len(column_names) == len(set(column_names)) - - -# --- Tests for generate_work_items --- - - -def test_generate_work_items_zero_rows(): - schema = [("col_int", "INT64", None)] - data_generator = generate_work_items( - "some_table", schema, num_rows=0, batch_size=10 - ) - - # Expect the generator to be exhausted - with pytest.raises(StopIteration): - next(data_generator) - - -def test_generate_work_items_basic_schema_and_batching(): - schema = [("id", "INT64", None), ("is_active", "BOOL", None)] - num_rows = 25 - batch_size = 10 - - generated_rows_count = 0 - batch_count = 0 - for work_item in generate_work_items("some_table", schema, num_rows, batch_size): - table_id, schema_def, num_rows_in_batch = work_item - assert table_id == "some_table" - assert schema_def == schema - assert num_rows_in_batch <= num_rows - assert num_rows_in_batch <= batch_size - batch_count += 1 - generated_rows_count += num_rows_in_batch - - assert generated_rows_count == num_rows - assert batch_count == math.ceil(num_rows / batch_size) # 25/10 = 2.5 -> 3 batches - - -def test_generate_work_items_batch_size_larger_than_num_rows(): - schema = [("value", "FLOAT64", None)] - num_rows = 5 - batch_size = 100 - - generated_rows_count = 0 - batch_count = 0 - for work_item in generate_work_items("some_table", schema, num_rows, batch_size): - table_id, schema_def, num_rows_in_batch = work_item - assert table_id == "some_table" - assert schema_def == schema - assert num_rows_in_batch == num_rows # Should be one batch with all rows - batch_count += 1 - generated_rows_count += num_rows_in_batch - - assert generated_rows_count == num_rows - assert batch_count == 1 - - -def test_generate_work_items_all_datatypes(rng): - schema = [ - ("c_bool", "BOOL", None), - ("c_int64", "INT64", None), - ("c_float64", "FLOAT64", None), - ("c_numeric", "NUMERIC", None), - ("c_date", "DATE", None), - ("c_datetime", "DATETIME", None), - ("c_timestamp", "TIMESTAMP", None), - ("c_time", "TIME", None), - ("c_string", "STRING", 10), - ("c_bytes", "BYTES", 5), - ("c_json", "JSON", 20), # Length for JSON is content hint - ] - num_rows = 3 - batch_size = 2 # To test multiple batches - - total_rows_processed = 0 - for work_item in generate_work_items("some_table", schema, num_rows, batch_size): - table_id, schema_def, num_rows_in_batch = work_item - assert table_id == "some_table" - assert schema_def == schema - assert num_rows_in_batch <= batch_size - assert num_rows_in_batch <= num_rows - - total_rows_processed += num_rows_in_batch - - assert total_rows_processed == num_rows - - -# --- Pytest Fixture for RNG --- -@pytest.fixture -def rng(): - return np.random.default_rng(seed=42) - - -def test_generate_batch_basic_schema(rng): - schema = [("id", "INT64", None), ("is_active", "BOOL", None)] - batch = generate_batch(schema, 5, rng) - - assert len(batch) == 5 - - for row in batch: - assert isinstance(row, dict) - assert "id" in row - assert "is_active" in row - assert isinstance(row["id"], int) - assert isinstance(row["is_active"], bool) - - -def test_generate_batch_all_datatypes(rng): - schema = [ - ("c_bool", "BOOL", None), - ("c_int64", "INT64", None), - ("c_float64", "FLOAT64", None), - ("c_numeric", "NUMERIC", None), - ("c_date", "DATE", None), - ("c_datetime", "DATETIME", None), - ("c_timestamp", "TIMESTAMP", None), - ("c_time", "TIME", None), - ("c_string", "STRING", 10), - ("c_bytes", "BYTES", 5), - ("c_json", "JSON", 20), # Length for JSON is content hint - ] - num_rows = 3 - - date_pattern = re.compile(r"^\d{4}-\d{2}-\d{2}$") - time_pattern = re.compile(r"^\d{2}:\d{2}:\d{2}(\.\d{1,6})?$") - # BQ DATETIME: YYYY-MM-DD HH:MM:SS.ffffff - datetime_pattern = re.compile(r"^\d{4}-\d{2}-\d{2} \d{2}:\d{2}:\d{2}(\.\d{1,6})?$") - # BQ TIMESTAMP (UTC 'Z'): YYYY-MM-DDTHH:MM:SS.ffffffZ - timestamp_pattern = re.compile( - r"^\d{4}-\d{2}-\d{2}T\d{2}:\d{2}:\d{2}(\.\d{1,6})?Z$" - ) - numeric_pattern = re.compile(r"^-?\d+\.\d{9}$") - - batch = generate_batch(schema, num_rows, rng) - assert len(batch) == num_rows - - for row in batch: - assert isinstance(row["c_bool"], bool) - assert isinstance(row["c_int64"], int) - assert isinstance(row["c_float64"], float) - - assert isinstance(row["c_numeric"], str) - assert numeric_pattern.match(row["c_numeric"]) - - assert isinstance(row["c_date"], str) - assert date_pattern.match(row["c_date"]) - datetime.date.fromisoformat(row["c_date"]) # Check parsable - - assert isinstance(row["c_datetime"], str) - assert datetime_pattern.match(row["c_datetime"]) - datetime.datetime.fromisoformat(row["c_datetime"]) # Check parsable - - assert isinstance(row["c_timestamp"], str) - assert timestamp_pattern.match(row["c_timestamp"]) - # datetime.fromisoformat can parse 'Z' if Python >= 3.11, or needs replace('Z', '+00:00') - dt_obj = datetime.datetime.fromisoformat( - row["c_timestamp"].replace("Z", "+00:00") - ) - assert dt_obj.tzinfo == datetime.timezone.utc - - assert isinstance(row["c_time"], str) - assert time_pattern.match(row["c_time"]) - datetime.time.fromisoformat(row["c_time"]) # Check parsable - - assert isinstance(row["c_string"], str) - assert len(row["c_string"]) == 10 - - c_bytes = base64.b64decode(row["c_bytes"]) - assert isinstance(c_bytes, bytes) - assert len(c_bytes) == 5 - - assert isinstance(row["c_json"], str) - try: - json.loads(row["c_json"]) # Check if it's valid JSON - except json.JSONDecodeError: - pytest.fail(f"Invalid JSON string generated: {row['c_json']}") - # Note: Exact length check for JSON is hard due to content variability and escaping. - # The 'length' parameter for JSON in schema is a hint for content size. - # We are primarily testing that it's valid JSON. diff --git a/scripts/create_test_model_vertex.py b/scripts/create_test_model_vertex.py deleted file mode 100644 index 946e54773e6..00000000000 --- a/scripts/create_test_model_vertex.py +++ /dev/null @@ -1,71 +0,0 @@ -# Copyright 2023 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import argparse -import sys - -import bigframes.ml.linear_model -import bigframes.pandas - - -def create_vertex_model(vertex_model_name): - df = bigframes.pandas.read_gbq("bigquery-public-data.ml_datasets.penguins") - - # filter down to the data we want to analyze - adelie_data = df[df.species == "Adelie Penguin (Pygoscelis adeliae)"] - - # drop the columns we don't care about - adelie_data = adelie_data.drop(columns=["species"]) - - # drop rows with nulls to get our training data - training_data = adelie_data.dropna() - - feature_columns = training_data["culmen_length_mm"] - label_columns = training_data[["body_mass_g"]] - - # create model - model = bigframes.ml.linear_model.LinearRegression() - model.fit(feature_columns, label_columns) - - # register to Vertex Registry - model.register(vertex_model_name) - - -if __name__ == "__main__": - parser = argparse.ArgumentParser( - description="Get top APIs for which there are no code samples in the docstring." - ) - parser.add_argument( - "-m", - "--model-name", - type=str, - required=True, - action="store", - help="Name of the model in Vertex.", - ) - parser.add_argument( - "-p", - "--project-id", - type=str, - required=False, - action="store", - help="Project id in which the model should be created. " - "By default, a project will be resolved as per https://cloud.google.com/python/docs/reference/google-cloud-core/latest/config#overview.", - ) - - args = parser.parse_args(sys.argv[1:]) - if args.project_id: - bigframes.pandas.options.bigquery.project = args.project_id - - create_vertex_model(args.model_name) diff --git a/scripts/data/audio/audio_LJ001-0010.wav b/scripts/data/audio/audio_LJ001-0010.wav deleted file mode 100644 index 01a2e68829a..00000000000 Binary files a/scripts/data/audio/audio_LJ001-0010.wav and /dev/null differ diff --git a/scripts/data/images/img0.jpg b/scripts/data/images/img0.jpg deleted file mode 100644 index 4f9114402b4..00000000000 Binary files a/scripts/data/images/img0.jpg and /dev/null differ diff --git a/scripts/data/images/img1.jpg b/scripts/data/images/img1.jpg deleted file mode 100644 index 15c881bd1af..00000000000 Binary files a/scripts/data/images/img1.jpg and /dev/null differ diff --git a/scripts/data/images_exif/test_image_exif.jpg b/scripts/data/images_exif/test_image_exif.jpg deleted file mode 100644 index fdfdaf9ad08..00000000000 Binary files a/scripts/data/images_exif/test_image_exif.jpg and /dev/null differ diff --git a/scripts/data/pdfs/pdfs_sample-local-pdf.pdf b/scripts/data/pdfs/pdfs_sample-local-pdf.pdf deleted file mode 100644 index d162cd6877e..00000000000 Binary files a/scripts/data/pdfs/pdfs_sample-local-pdf.pdf and /dev/null differ diff --git a/scripts/data/pdfs/test-protected.pdf b/scripts/data/pdfs/test-protected.pdf deleted file mode 100644 index 0d8cd28baa4..00000000000 Binary files a/scripts/data/pdfs/test-protected.pdf and /dev/null differ diff --git a/scripts/data/sql-functions/aead.yaml b/scripts/data/sql-functions/aead.yaml deleted file mode 100644 index 198248782d7..00000000000 --- a/scripts/data/sql-functions/aead.yaml +++ /dev/null @@ -1,134 +0,0 @@ -urn: extension:google:bq_scalar_functions -scalar_functions: - - name: "aead.decrypt_bytes" - description: "Uses the matching key from keyset to decrypt ciphertext and verifies the integrity of the data using additional_data. Returns an error if decryption or verification fails." - series_accessor_arg: keyset - impls: - # Signature: aead.decrypt_bytes:vbin_vbin_vbin - - args: - - name: "keyset" - value: binary - optional: false - keyword_only: false - - name: "ciphertext" - value: binary - optional: false - keyword_only: false - - name: "additional_data" - value: binary - optional: false - keyword_only: false - return: binary - # Signature: aead.decrypt_bytes:struct_vbin_vbin - - args: - - name: "keyset" - value: struct - optional: false - keyword_only: false - - name: "ciphertext" - value: binary - optional: false - keyword_only: false - - name: "additional_data" - value: binary - optional: false - keyword_only: false - return: binary - - name: "aead.decrypt_string" - description: "Like AEAD.DECRYPT_BYTES, but where additional_data is of type STRING." - series_accessor_arg: keyset - impls: - # Signature: aead.decrypt_string:vbin_vbin_str - - args: - - name: "keyset" - value: binary - optional: false - keyword_only: false - - name: "ciphertext" - value: binary - optional: false - keyword_only: false - - name: "additional_data" - value: string - optional: false - keyword_only: false - return: string - # Signature: aead.decrypt_string:struct_vbin_str - - args: - - name: "keyset" - value: struct - optional: false - keyword_only: false - - name: "ciphertext" - value: binary - optional: false - keyword_only: false - - name: "additional_data" - value: string - optional: false - keyword_only: false - return: string - - name: "aead.encrypt" - description: "Encrypts plaintext using the primary cryptographic key in keyset. The algorithm of the primary key must be AEAD_AES_GCM_256. Binds the ciphertext to the context defined by additional_data. Returns NULL if any input is NULL." - series_accessor_arg: keyset - impls: - # Signature: aead.encrypt:vbin_str_str - - args: - - name: "keyset" - value: binary - optional: false - keyword_only: false - - name: "plaintext" - value: string - optional: false - keyword_only: false - - name: "additional_data" - value: string - optional: false - keyword_only: false - return: binary - # Signature: aead.encrypt:vbin_vbin_vbin - - args: - - name: "keyset" - value: binary - optional: false - keyword_only: false - - name: "plaintext" - value: binary - optional: false - keyword_only: false - - name: "additional_data" - value: binary - optional: false - keyword_only: false - return: binary - # Signature: aead.encrypt:struct_str_str - - args: - - name: "keyset" - value: struct - optional: false - keyword_only: false - - name: "plaintext" - value: string - optional: false - keyword_only: false - - name: "additional_data" - value: string - optional: false - keyword_only: false - return: binary - # Signature: aead.encrypt:struct_vbin_vbin - - args: - - name: "keyset" - value: struct - optional: false - keyword_only: false - - name: "plaintext" - value: binary - optional: false - keyword_only: false - - name: "additional_data" - value: binary - optional: false - keyword_only: false - return: binary diff --git a/scripts/data/sql-functions/ai.yaml b/scripts/data/sql-functions/ai.yaml deleted file mode 100644 index f3238c8178b..00000000000 --- a/scripts/data/sql-functions/ai.yaml +++ /dev/null @@ -1 +0,0 @@ -urn: extension:google:bq_scalar_functions diff --git a/scripts/data/sql-functions/global_namespace/aead_encryption.yaml b/scripts/data/sql-functions/global_namespace/aead_encryption.yaml deleted file mode 100644 index 1e62de0f2a6..00000000000 --- a/scripts/data/sql-functions/global_namespace/aead_encryption.yaml +++ /dev/null @@ -1,134 +0,0 @@ -urn: extension:google:bq_scalar_functions -scalar_functions: - - name: "deterministic_decrypt_bytes" - description: "Uses the matching key from `keyset` to decrypt `ciphertext` and verifies the integrity of the data using `additional_data`. Returns an error if decryption fails." - series_accessor_arg: keyset - impls: - # Signature: deterministic_decrypt_bytes:vbin_vbin_vbin - - args: - - name: "keyset" - value: binary - optional: false - keyword_only: false - - name: "ciphertext" - value: binary - optional: false - keyword_only: false - - name: "additional_data" - value: binary - optional: false - keyword_only: false - return: binary - # Signature: deterministic_decrypt_bytes:struct_vbin_vbin - - args: - - name: "keyset" - value: struct - optional: false - keyword_only: false - - name: "ciphertext" - value: binary - optional: false - keyword_only: false - - name: "additional_data" - value: binary - optional: false - keyword_only: false - return: binary - - name: "deterministic_decrypt_string" - description: "Like `DETERMINISTIC_DECRYPT_BYTES`, but where plaintext is of type STRING." - series_accessor_arg: keyset - impls: - # Signature: deterministic_decrypt_string:vbin_vbin_str - - args: - - name: "keyset" - value: binary - optional: false - keyword_only: false - - name: "ciphertext" - value: binary - optional: false - keyword_only: false - - name: "additional_data" - value: string - optional: false - keyword_only: false - return: string - # Signature: deterministic_decrypt_string:struct_vbin_str - - args: - - name: "keyset" - value: struct - optional: false - keyword_only: false - - name: "ciphertext" - value: binary - optional: false - keyword_only: false - - name: "additional_data" - value: string - optional: false - keyword_only: false - return: string - - name: "deterministic_encrypt" - description: "Encrypts `plaintext` using the primary cryptographic key in `keyset` using deterministic AEAD. The algorithm of the primary key must be `DETERMINISTIC_AEAD_AES_SIV_CMAC_256`. Binds the ciphertext to the context defined by `additional_data`. Returns `NULL` if any input is `NULL`." - series_accessor_arg: keyset - impls: - # Signature: deterministic_encrypt:vbin_str_str - - args: - - name: "keyset" - value: binary - optional: false - keyword_only: false - - name: "plaintext" - value: string - optional: false - keyword_only: false - - name: "additional_data" - value: string - optional: false - keyword_only: false - return: binary - # Signature: deterministic_encrypt:vbin_vbin_vbin - - args: - - name: "keyset" - value: binary - optional: false - keyword_only: false - - name: "plaintext" - value: binary - optional: false - keyword_only: false - - name: "additional_data" - value: binary - optional: false - keyword_only: false - return: binary - # Signature: deterministic_encrypt:struct_str_str - - args: - - name: "keyset" - value: struct - optional: false - keyword_only: false - - name: "plaintext" - value: string - optional: false - keyword_only: false - - name: "additional_data" - value: string - optional: false - keyword_only: false - return: binary - # Signature: deterministic_encrypt:struct_vbin_vbin - - args: - - name: "keyset" - value: struct - optional: false - keyword_only: false - - name: "plaintext" - value: binary - optional: false - keyword_only: false - - name: "additional_data" - value: binary - optional: false - keyword_only: false - return: binary diff --git a/scripts/data/sql-functions/global_namespace/array.yaml b/scripts/data/sql-functions/global_namespace/array.yaml deleted file mode 100644 index aa9230c251b..00000000000 --- a/scripts/data/sql-functions/global_namespace/array.yaml +++ /dev/null @@ -1,341 +0,0 @@ -urn: extension:google:bq_scalar_functions -scalar_functions: - - name: "array_concat" - description: "Concatenates one or more arrays with the same element type into a single array." - series_accessor_arg: array_expression_1 - impls: - # Signature: array_concat:list_list - - args: - - name: "array_expression_1" - value: list - optional: false - keyword_only: false - - name: "array_expression_2" - value: list - optional: false - keyword_only: false - return: list - - name: "array_first" - description: "Takes an array and returns the first element in the array." - series_accessor_arg: array_expression - impls: - # Signature: array_first:list - - args: - - name: "array_expression" - value: list - optional: false - keyword_only: false - return: any1 - - name: "array_first_n" - description: "Returns a prefix of `input_array` consisting of the first `n` elements." - series_accessor_arg: input_array - impls: - # Signature: array_first_n:list_i64 - - args: - - name: "input_array" - value: list - optional: false - keyword_only: false - - name: "n" - value: i64 - optional: false - keyword_only: false - return: list - - name: "array_includes" - description: "Takes an array and returns `TRUE` if there is an element in the array that is equal to the search_value." - series_accessor_arg: array_to_search - impls: - # Signature: array_includes:list_any - - args: - - name: "array_to_search" - value: list - optional: false - keyword_only: false - - name: "search_value" - value: any1 - optional: false - keyword_only: false - return: boolean - - name: "array_includes_all" - description: "Takes an array to search and an array of search values. Returns `TRUE` if all search values are in the array to search, otherwise returns `FALSE`." - series_accessor_arg: array_to_search - impls: - # Signature: array_includes_all:list_list - - args: - - name: "array_to_search" - value: list - optional: false - keyword_only: false - - name: "search_values" - value: list - optional: false - keyword_only: false - return: boolean - - name: "array_includes_any" - description: "Takes an array to search and an array of search values. Returns `TRUE` if any search values are in the array to search, otherwise returns `FALSE`." - series_accessor_arg: array_to_search - impls: - # Signature: array_includes_any:list_list - - args: - - name: "array_to_search" - value: list - optional: false - keyword_only: false - - name: "search_values" - value: list - optional: false - keyword_only: false - return: boolean - - name: "array_is_distinct" - description: "Returns `TRUE` if the array contains no repeated elements, using the same equality comparison logic as `SELECT DISTINCT`." - series_accessor_arg: array_expression - impls: - # Signature: array_is_distinct:list - - args: - - name: "array_expression" - value: list - optional: false - keyword_only: false - return: boolean - - name: "array_last" - description: "Takes an array and returns the last element in the array." - series_accessor_arg: array_expression - impls: - # Signature: array_last:list - - args: - - name: "array_expression" - value: list - optional: false - keyword_only: false - return: any1 - - name: "array_length" - description: | - Compute the length of each array element in the Series. - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> import bigframes.bigquery as bbq - - >>> s = bpd.Series([[1, 2, 8, 3], [], [3, 4]]) - >>> bbq.array_length(s) - 0 4 - 1 0 - 2 2 - dtype: Int64 - - You can call this function using the Series `bigquery` accessor. - - >>> s.bigquery.array_length() - 0 4 - 1 0 - 2 2 - dtype: Int64 - - You can also use this accessor on a pandas Series after importing bigframes. - - >>> import bigframes - >>> import pandas as pd - >>> ps = pd.Series([[1, 2, 8, 3], [], [3, 4]]) - >>> ps.bigquery.array_length() - 0 4 - 1 0 - 2 2 - dtype: Int64 - - You can also apply this function directly to Series using `apply`. - - >>> s.apply(bbq.array_length, by_row=False) - 0 4 - 1 0 - 2 2 - dtype: Int64 - - Args: - series (bigframes.series.Series): A Series with array columns. - - Returns: - bigframes.series.Series: A Series of integer values indicating - the length of each element in the Series. - series_accessor_arg: series - impls: - # Signature: array_length:list - - args: - - name: "series" - value: list - optional: false - keyword_only: false - return: i64 - - name: "array_reverse" - description: "Returns the input `ARRAY` with elements in reverse order." - series_accessor_arg: value - impls: - # Signature: array_reverse:list - - args: - - name: "value" - value: list - optional: false - keyword_only: false - return: list - - name: "array_slice" - description: "Returns an array containing zero or more consecutive elements from the input array." - series_accessor_arg: array_to_slice - impls: - # Signature: array_slice:list_i64_i64 - - args: - - name: "array_to_slice" - value: list - optional: false - keyword_only: false - - name: "start_offset" - value: i64 - optional: false - keyword_only: false - - name: "end_offset" - value: i64 - optional: false - keyword_only: false - return: list - - name: "array_to_string" - description: | - Converts array elements within a Series into delimited strings. - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> import bigframes.bigquery as bbq - - >>> s = bpd.Series([["H", "i", "!"], ["Hello", "World"], np.nan, [], ["Hi"]]) - >>> bbq.array_to_string(s, delimiter=", ") - 0 H, i, ! - 1 Hello, World - 2 - 3 - 4 Hi - dtype: string - - You can call this function using the Series `bigquery` accessor. - - >>> s.bigquery.array_to_string(delimiter=", ") - 0 H, i, ! - 1 Hello, World - 2 - 3 - 4 Hi - dtype: string - - You can also use this accessor on a pandas Series after importing bigframes. - - >>> import bigframes - >>> import pandas as pd - >>> ps = pd.Series([["H", "i", "!"], ["Hello", "World"], None, [], ["Hi"]]) - >>> ps.bigquery.array_to_string(delimiter=", ") - 0 H, i, ! - 1 Hello, World - 2 - 3 - 4 Hi - dtype: string - - Args: - series (bigframes.series.Series): A Series containing arrays. - delimiter (str): The string used to separate array elements. - null_text (str, optional): The string to replace any NULL values in the array with. - - Returns: - bigframes.series.Series: A Series containing delimited strings. - series_accessor_arg: series - impls: - # Signature: array_to_string:list_str_str - - args: - - name: "series" - value: list - optional: false - keyword_only: false - - name: "delimiter" - value: string - optional: false - keyword_only: false - - name: "null_text" - value: string - optional: true - keyword_only: false - return: string - # Signature: array_to_string:list_vbin_vbin - - args: - - name: "series" - value: list - optional: false - keyword_only: false - - name: "delimiter" - value: binary - optional: false - keyword_only: false - - name: "null_text" - value: binary - optional: true - keyword_only: false - return: binary - - name: "flatten" - description: "Takes an array of nested data and flattens a specific part of it into a single, flat array with the [array elements field access operator][array-el-field-operator]. Returns `NULL` if the input value is `NULL`." - series_accessor_arg: array_to_flatten - impls: - # Signature: flatten:list_i64 - - args: - - name: "array_to_flatten" - value: list - optional: false - keyword_only: false - - name: "depth" - value: i64 - optional: true - keyword_only: true - return: list - - name: "generate_array" - description: "Returns an array of values. The `start_expression` and `end_expression` parameters determine the inclusive start and end of the array." - impls: - # Signature: generate_array:i64_i64_i64 - - args: - - name: "start_expression" - value: i64 - optional: false - keyword_only: false - - name: "end_expression" - value: i64 - optional: false - keyword_only: false - - name: "step_expression" - value: i64 - optional: true - keyword_only: false - return: list - # Signature: generate_array:dec_dec_dec - - args: - - name: "start_expression" - value: decimal<38,9> - optional: false - keyword_only: false - - name: "end_expression" - value: decimal<38,9> - optional: false - keyword_only: false - - name: "step_expression" - value: decimal<38,9> - optional: true - keyword_only: false - return: list> - # Signature: generate_array:fp64_fp64_fp64 - - args: - - name: "start_expression" - value: fp64 - optional: false - keyword_only: false - - name: "end_expression" - value: fp64 - optional: false - keyword_only: false - - name: "step_expression" - value: fp64 - optional: true - keyword_only: false - return: list diff --git a/scripts/data/sql-functions/global_namespace/bit.yaml b/scripts/data/sql-functions/global_namespace/bit.yaml deleted file mode 100644 index fe14eae7b64..00000000000 --- a/scripts/data/sql-functions/global_namespace/bit.yaml +++ /dev/null @@ -1,27 +0,0 @@ -urn: extension:google:bq_scalar_functions -scalar_functions: - - name: "bit_count" - description: "The input, `expression`, must be an integer or `BYTES`. Returns the number of bits that are set in the input expression. For signed integers, this is the number of bits in two's complement form." - series_accessor_arg: expression - impls: - # Signature: bit_count:i32 - - args: - - name: "expression" - value: i32 - optional: false - keyword_only: false - return: i64 - # Signature: bit_count:i64 - - args: - - name: "expression" - value: i64 - optional: false - keyword_only: false - return: i64 - # Signature: bit_count:vbin - - args: - - name: "expression" - value: binary - optional: false - keyword_only: false - return: i64 diff --git a/scripts/data/sql-functions/global_namespace/conversion.yaml b/scripts/data/sql-functions/global_namespace/conversion.yaml deleted file mode 100644 index c39724427de..00000000000 --- a/scripts/data/sql-functions/global_namespace/conversion.yaml +++ /dev/null @@ -1,119 +0,0 @@ -urn: extension:google:bq_scalar_functions -scalar_functions: - - name: "bool" - description: "Converts a JSON boolean to a SQL BOOL value." - series_accessor_arg: json_string_expression - impls: - # Signature: bool:str - - args: - - name: "json_string_expression" - value: string - optional: false - keyword_only: false - return: boolean - - name: "double" - description: "Converts a JSON number to a SQL FLOAT64 value." - series_accessor_arg: json_string_expression - impls: - # Signature: double:str_str - - args: - - name: "json_string_expression" - value: string - optional: false - keyword_only: false - - name: "wide_number_mode" - value: string - optional: true - keyword_only: true - return: fp64 - - name: "float64" - description: "Converts a JSON number to a SQL FLOAT64 value." - series_accessor_arg: json_string_expression - impls: - # Signature: float64:str_str - - args: - - name: "json_string_expression" - value: string - optional: false - keyword_only: false - - name: "wide_number_mode" - value: string - optional: true - keyword_only: true - return: fp64 - - name: "int64" - description: "Converts a JSON number to a SQL INT64 value." - series_accessor_arg: json_string_expression - impls: - # Signature: int64:str - - args: - - name: "json_string_expression" - value: string - optional: false - keyword_only: false - return: i64 - - name: "parse_bignumeric" - description: "Converts a STRING to a BIGNUMERIC value." - series_accessor_arg: string_expression - impls: - # Signature: parse_bignumeric:str - - args: - - name: "string_expression" - value: string - optional: false - keyword_only: false - return: decimal<76,38> - - name: "parse_numeric" - description: "Converts a STRING to a NUMERIC value." - series_accessor_arg: string_expression - impls: - # Signature: parse_numeric:str - - args: - - name: "string_expression" - value: string - optional: false - keyword_only: false - return: decimal<38,9> - - name: "string" - description: "Converts a value to a STRING value." - series_accessor_arg: expression - impls: - # Signature: string:pts_str - - args: - - name: "expression" - value: timestamp - optional: false - keyword_only: false - - name: "timezone" - value: string - optional: true - keyword_only: false - return: string - # Signature: string:date - - args: - - name: "expression" - value: date - optional: false - keyword_only: false - return: string - # Signature: string:pt - - args: - - name: "expression" - value: time - optional: false - keyword_only: false - return: string - # Signature: string:pts - - args: - - name: "expression" - value: timestamp - optional: false - keyword_only: false - return: string - # Signature: string:str - - args: - - name: "expression" - value: string - optional: false - keyword_only: false - return: string diff --git a/scripts/data/sql-functions/global_namespace/date.yaml b/scripts/data/sql-functions/global_namespace/date.yaml deleted file mode 100644 index 8d1dfc95284..00000000000 --- a/scripts/data/sql-functions/global_namespace/date.yaml +++ /dev/null @@ -1,277 +0,0 @@ -urn: extension:google:bq_scalar_functions -scalar_functions: - - name: "current_date" - description: "Returns the current date as a DATE object. Parentheses are optional when called with no arguments." - impls: - # Signature: current_date:str - - args: - - name: "time_zone_expression" - value: string - optional: true - keyword_only: false - return: date - - name: "date" - description: "Constructs or extracts a date." - series_accessor_arg: expression - impls: - # Signature: date:pts_str - - args: - - name: "expression" - value: timestamp - optional: false - keyword_only: false - - name: "time_zone_expression" - value: string - optional: true - keyword_only: false - return: date - # Signature: date:pts - - args: - - name: "expression" - value: timestamp - optional: false - keyword_only: false - return: date - # Signature: date:i64_i64_i64 - - args: - - name: "year" - value: i64 - optional: false - keyword_only: false - - name: "month" - value: i64 - optional: false - keyword_only: false - - name: "day" - value: i64 - optional: false - keyword_only: false - return: date - # Signature: date:date - - args: - - name: "expression" - value: date - optional: false - keyword_only: false - return: date - # Signature: date:str - - args: - - name: "expression" - value: string - optional: false - keyword_only: false - return: date - - name: "date_add" - description: "Adds a specified time interval to a DATE." - series_accessor_arg: date_expression - impls: - # Signature: date_add:date_i64_any - - args: - - name: "date_expression" - value: date - optional: false - keyword_only: false - - name: "int64_expression" - value: i64 - optional: false - keyword_only: false - - name: "date_part" - value: any1 - optional: false - keyword_only: false - return: date - # TODO(b/527093666): add support for date_bucket when we add an INTERVAL dtype - - name: "date_diff" - description: "Gets the number of unit boundaries between two DATE values (end_date - start_date) at a particular time granularity." - series_accessor_arg: end_date - impls: - # Signature: date_diff:date_date_any - - args: - - name: "end_date" - value: date - optional: false - keyword_only: false - - name: "start_date" - value: date - optional: false - keyword_only: false - - name: "granularity" - value: any1 - optional: false - keyword_only: false - return: i64 - - name: "date_from_unix_date" - description: "Interprets an INT64 expression as the number of days since 1970-01-01." - series_accessor_arg: int64_expression - impls: - # Signature: date_from_unix_date:i64 - - args: - - name: "int64_expression" - value: i64 - optional: false - keyword_only: false - return: date - - name: "date_sub" - description: "Subtracts a specified time interval from a DATE." - series_accessor_arg: date_expression - impls: - # Signature: date_sub:date_i64_any - - args: - - name: "date_expression" - value: date - optional: false - keyword_only: false - - name: "int64_expression" - value: i64 - optional: false - keyword_only: false - - name: "date_part" - value: any1 - optional: false - keyword_only: false - return: date - - name: "date_trunc" - description: "Truncates a DATE, DATETIME, or TIMESTAMP value at a particular granularity." - series_accessor_arg: date_value - impls: - # Signature: date_trunc:date_any - - args: - - name: "date_value" - value: date - optional: false - keyword_only: false - - name: "granularity" - value: any1 - optional: false - keyword_only: false - return: date - - name: "extract" - description: "Returns the value corresponding to the specified date part." - series_accessor_arg: date_expression - impls: - # Signature: extract:date_any - - args: - - name: "date_expression" - value: date - optional: false - keyword_only: false - - name: "part" - value: any1 - optional: false - keyword_only: false - return: i64 - # Signature: extract:pts_any_str - - args: - - name: "date_expression" - value: timestamp - optional: false - keyword_only: false - - name: "part" - value: any1 - optional: false - keyword_only: false - - name: "time_zone" - value: string - optional: true - keyword_only: false - return: i64 - # Signature: extract:pts_any - - args: - - name: "date_expression" - value: timestamp - optional: false - keyword_only: false - - name: "part" - value: any1 - optional: false - keyword_only: false - return: i64 - # Signature: extract:pt_any - - args: - - name: "date_expression" - value: time - optional: false - keyword_only: false - - name: "part" - value: any1 - optional: false - keyword_only: false - return: i64 - - name: "format_date" - description: "Formats a DATE value according to a specified format string." - series_accessor_arg: date_expr - impls: - # Signature: format_date:str_date - - args: - - name: "format_string" - value: string - optional: false - keyword_only: false - - name: "date_expr" - value: date - optional: false - keyword_only: false - return: string - - name: "generate_date_array" - description: "Generates an array of dates in a range." - impls: - # Signature: generate_date_array:date_date_i64_any - - args: - - name: "start_date" - value: date - optional: false - keyword_only: false - - name: "end_date" - value: date - optional: false - keyword_only: false - - name: "int64_expression" - value: i64 - optional: true - keyword_only: false - - name: "date_part" - value: any1 - optional: true - keyword_only: false - return: list - - name: "last_day" - description: "Returns the last day from a date expression. This is commonly used to return the last day of the month." - series_accessor_arg: date_expression - impls: - # Signature: last_day:date_any - - args: - - name: "date_expression" - value: date - optional: false - keyword_only: false - - name: "date_part" - value: any1 - optional: true - keyword_only: false - return: date - - name: "parse_date" - description: "Converts a STRING value to a DATE value." - series_accessor_arg: date_string - impls: - # Signature: parse_date:str_str - - args: - - name: "format_string" - value: string - optional: false - keyword_only: false - - name: "date_string" - value: string - optional: false - keyword_only: false - return: date - - name: "unix_date" - description: "Returns the number of days since 1970-01-01." - series_accessor_arg: date_expression - impls: - # Signature: unix_date:date - - args: - - name: "date_expression" - value: date - optional: false - keyword_only: false - return: i64 diff --git a/scripts/decrypt-secrets.sh b/scripts/decrypt-secrets.sh new file mode 100755 index 00000000000..0018b421ddf --- /dev/null +++ b/scripts/decrypt-secrets.sh @@ -0,0 +1,46 @@ +#!/bin/bash + +# Copyright 2023 Google LLC All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )" +ROOT=$( dirname "$DIR" ) + +# Work from the project root. +cd $ROOT + +# Prevent it from overriding files. +# We recommend that sample authors use their own service account files and cloud project. +# In that case, they are supposed to prepare these files by themselves. +if [[ -f "testing/test-env.sh" ]] || \ + [[ -f "testing/service-account.json" ]] || \ + [[ -f "testing/client-secrets.json" ]]; then + echo "One or more target files exist, aborting." + exit 1 +fi + +# Use SECRET_MANAGER_PROJECT if set, fallback to cloud-devrel-kokoro-resources. +PROJECT_ID="${SECRET_MANAGER_PROJECT:-cloud-devrel-kokoro-resources}" + +gcloud secrets versions access latest --secret="python-docs-samples-test-env" \ + --project="${PROJECT_ID}" \ + > testing/test-env.sh +gcloud secrets versions access latest \ + --secret="python-docs-samples-service-account" \ + --project="${PROJECT_ID}" \ + > testing/service-account.json +gcloud secrets versions access latest \ + --secret="python-docs-samples-client-secrets" \ + --project="${PROJECT_ID}" \ + > testing/client-secrets.json diff --git a/scripts/dev-utils/tpcds_upload_helper.py b/scripts/dev-utils/tpcds_upload_helper.py deleted file mode 100644 index dec5b39768f..00000000000 --- a/scripts/dev-utils/tpcds_upload_helper.py +++ /dev/null @@ -1,596 +0,0 @@ -import argparse -import csv -import os -import sys - -import google.api_core.exceptions -from google.cloud import bigquery - - -def preprocess_csv(input_file_path, output_file_path): - try: - with ( - open(input_file_path, mode="r", newline="", encoding="utf-8") as infile, - open(output_file_path, mode="w", newline="", encoding="utf-8") as outfile, - ): - reader = csv.reader(infile, delimiter="|") - writer = csv.writer(outfile, delimiter="|") - - for row in reader: - writer.writerow(row[:-1]) - except Exception as e: - print(f"An error occurred: {e}") - - -def get_schema(table_name): - schema = { - "customer_address": [ - bigquery.SchemaField("ca_address_sk", "INTEGER", mode="REQUIRED"), - bigquery.SchemaField("ca_address_id", "STRING", mode="REQUIRED"), - bigquery.SchemaField("ca_street_number", "STRING", mode="NULLABLE"), - bigquery.SchemaField("ca_street_name", "STRING", mode="NULLABLE"), - bigquery.SchemaField("ca_street_type", "STRING", mode="NULLABLE"), - bigquery.SchemaField("ca_suite_number", "STRING", mode="NULLABLE"), - bigquery.SchemaField("ca_city", "STRING", mode="NULLABLE"), - bigquery.SchemaField("ca_county", "STRING", mode="NULLABLE"), - bigquery.SchemaField("ca_state", "STRING", mode="NULLABLE"), - bigquery.SchemaField("ca_zip", "STRING", mode="NULLABLE"), - bigquery.SchemaField("ca_country", "STRING", mode="NULLABLE"), - bigquery.SchemaField("ca_gmt_offset", "FLOAT", mode="NULLABLE"), - bigquery.SchemaField("ca_location_type", "STRING", mode="NULLABLE"), - ], - "customer_demographics": [ - bigquery.SchemaField("cd_demo_sk", "INTEGER", mode="REQUIRED"), - bigquery.SchemaField("cd_gender", "STRING", mode="NULLABLE"), - bigquery.SchemaField("cd_marital_status", "STRING", mode="NULLABLE"), - bigquery.SchemaField("cd_education_status", "STRING", mode="NULLABLE"), - bigquery.SchemaField("cd_purchase_estimate", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("cd_credit_rating", "STRING", mode="NULLABLE"), - bigquery.SchemaField("cd_dep_count", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("cd_dep_employed_count", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("cd_dep_college_count", "INTEGER", mode="NULLABLE"), - ], - "date_dim": [ - bigquery.SchemaField("d_date_sk", "INTEGER", mode="REQUIRED"), - bigquery.SchemaField("d_date_id", "STRING", mode="REQUIRED"), - bigquery.SchemaField("d_date", "DATE", mode="NULLABLE"), - bigquery.SchemaField("d_month_seq", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("d_week_seq", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("d_quarter_seq", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("d_year", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("d_dow", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("d_moy", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("d_dom", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("d_qoy", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("d_fy_year", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("d_fy_quarter_seq", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("d_fy_week_seq", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("d_day_name", "STRING", mode="NULLABLE"), - bigquery.SchemaField("d_quarter_name", "STRING", mode="NULLABLE"), - bigquery.SchemaField("d_holiday", "STRING", mode="NULLABLE"), - bigquery.SchemaField("d_weekend", "STRING", mode="NULLABLE"), - bigquery.SchemaField("d_following_holiday", "STRING", mode="NULLABLE"), - bigquery.SchemaField("d_first_dom", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("d_last_dom", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("d_same_day_ly", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("d_same_day_lq", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("d_current_day", "STRING", mode="NULLABLE"), - bigquery.SchemaField("d_current_week", "STRING", mode="NULLABLE"), - bigquery.SchemaField("d_current_month", "STRING", mode="NULLABLE"), - bigquery.SchemaField("d_current_quarter", "STRING", mode="NULLABLE"), - bigquery.SchemaField("d_current_year", "STRING", mode="NULLABLE"), - ], - "warehouse": [ - bigquery.SchemaField("w_warehouse_sk", "INTEGER", mode="REQUIRED"), - bigquery.SchemaField("w_warehouse_id", "STRING", mode="REQUIRED"), - bigquery.SchemaField("w_warehouse_name", "STRING", mode="NULLABLE"), - bigquery.SchemaField("w_warehouse_sq_ft", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("w_street_number", "STRING", mode="NULLABLE"), - bigquery.SchemaField("w_street_name", "STRING", mode="NULLABLE"), - bigquery.SchemaField("w_street_type", "STRING", mode="NULLABLE"), - bigquery.SchemaField("w_suite_number", "STRING", mode="NULLABLE"), - bigquery.SchemaField("w_city", "STRING", mode="NULLABLE"), - bigquery.SchemaField("w_county", "STRING", mode="NULLABLE"), - bigquery.SchemaField("w_state", "STRING", mode="NULLABLE"), - bigquery.SchemaField("w_zip", "STRING", mode="NULLABLE"), - bigquery.SchemaField("w_country", "STRING", mode="NULLABLE"), - bigquery.SchemaField("w_gmt_offset", "FLOAT", mode="NULLABLE"), - ], - "ship_mode": [ - bigquery.SchemaField("sm_ship_mode_sk", "INTEGER", mode="REQUIRED"), - bigquery.SchemaField("sm_ship_mode_id", "STRING", mode="REQUIRED"), - bigquery.SchemaField("sm_type", "STRING", mode="NULLABLE"), - bigquery.SchemaField("sm_code", "STRING", mode="NULLABLE"), - bigquery.SchemaField("sm_carrier", "STRING", mode="NULLABLE"), - bigquery.SchemaField("sm_contract", "STRING", mode="NULLABLE"), - ], - "time_dim": [ - bigquery.SchemaField("t_time_sk", "INTEGER", mode="REQUIRED"), - bigquery.SchemaField("t_time_id", "STRING", mode="REQUIRED"), - bigquery.SchemaField("t_time", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("t_hour", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("t_minute", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("t_second", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("t_am_pm", "STRING", mode="NULLABLE"), - bigquery.SchemaField("t_shift", "STRING", mode="NULLABLE"), - bigquery.SchemaField("t_sub_shift", "STRING", mode="NULLABLE"), - bigquery.SchemaField("t_meal_time", "STRING", mode="NULLABLE"), - ], - "reason": [ - bigquery.SchemaField("r_reason_sk", "INTEGER", mode="REQUIRED"), - bigquery.SchemaField("r_reason_id", "STRING", mode="REQUIRED"), - bigquery.SchemaField("r_reason_desc", "STRING", mode="NULLABLE"), - ], - "income_band": [ - bigquery.SchemaField("ib_income_band_sk", "INTEGER", mode="REQUIRED"), - bigquery.SchemaField("ib_lower_bound", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("ib_upper_bound", "INTEGER", mode="NULLABLE"), - ], - "item": [ - bigquery.SchemaField("i_item_sk", "INTEGER", mode="REQUIRED"), - bigquery.SchemaField("i_item_id", "STRING", mode="REQUIRED"), - bigquery.SchemaField("i_rec_start_date", "DATE", mode="NULLABLE"), - bigquery.SchemaField("i_rec_end_date", "DATE", mode="NULLABLE"), - bigquery.SchemaField("i_item_desc", "STRING", mode="NULLABLE"), - bigquery.SchemaField("i_current_price", "FLOAT", mode="NULLABLE"), - bigquery.SchemaField("i_wholesale_cost", "FLOAT", mode="NULLABLE"), - bigquery.SchemaField("i_brand_id", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("i_brand", "STRING", mode="NULLABLE"), - bigquery.SchemaField("i_class_id", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("i_class", "STRING", mode="NULLABLE"), - bigquery.SchemaField("i_category_id", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("i_category", "STRING", mode="NULLABLE"), - bigquery.SchemaField("i_manufact_id", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("i_manufact", "STRING", mode="NULLABLE"), - bigquery.SchemaField("i_size", "STRING", mode="NULLABLE"), - bigquery.SchemaField("i_formulation", "STRING", mode="NULLABLE"), - bigquery.SchemaField("i_color", "STRING", mode="NULLABLE"), - bigquery.SchemaField("i_units", "STRING", mode="NULLABLE"), - bigquery.SchemaField("i_container", "STRING", mode="NULLABLE"), - bigquery.SchemaField("i_manager_id", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("i_product_name", "STRING", mode="NULLABLE"), - ], - "store": [ - bigquery.SchemaField("s_store_sk", "INTEGER", mode="REQUIRED"), - bigquery.SchemaField("s_store_id", "STRING", mode="REQUIRED"), - bigquery.SchemaField("s_rec_start_date", "DATE", mode="NULLABLE"), - bigquery.SchemaField("s_rec_end_date", "DATE", mode="NULLABLE"), - bigquery.SchemaField("s_closed_date_sk", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("s_store_name", "STRING", mode="NULLABLE"), - bigquery.SchemaField("s_number_employees", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("s_floor_space", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("s_hours", "STRING", mode="NULLABLE"), - bigquery.SchemaField("s_manager", "STRING", mode="NULLABLE"), - bigquery.SchemaField("s_market_id", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("s_geography_class", "STRING", mode="NULLABLE"), - bigquery.SchemaField("s_market_desc", "STRING", mode="NULLABLE"), - bigquery.SchemaField("s_market_manager", "STRING", mode="NULLABLE"), - bigquery.SchemaField("s_division_id", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("s_division_name", "STRING", mode="NULLABLE"), - bigquery.SchemaField("s_company_id", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("s_company_name", "STRING", mode="NULLABLE"), - bigquery.SchemaField("s_street_number", "STRING", mode="NULLABLE"), - bigquery.SchemaField("s_street_name", "STRING", mode="NULLABLE"), - bigquery.SchemaField("s_street_type", "STRING", mode="NULLABLE"), - bigquery.SchemaField("s_suite_number", "STRING", mode="NULLABLE"), - bigquery.SchemaField("s_city", "STRING", mode="NULLABLE"), - bigquery.SchemaField("s_county", "STRING", mode="NULLABLE"), - bigquery.SchemaField("s_state", "STRING", mode="NULLABLE"), - bigquery.SchemaField("s_zip", "STRING", mode="NULLABLE"), - bigquery.SchemaField("s_country", "STRING", mode="NULLABLE"), - bigquery.SchemaField("s_gmt_offset", "FLOAT", mode="NULLABLE"), - bigquery.SchemaField("s_tax_precentage", "FLOAT", mode="NULLABLE"), - ], - "call_center": [ - bigquery.SchemaField("cc_call_center_sk", "INTEGER", mode="REQUIRED"), - bigquery.SchemaField("cc_call_center_id", "STRING", mode="REQUIRED"), - bigquery.SchemaField("cc_rec_start_date", "DATE", mode="NULLABLE"), - bigquery.SchemaField("cc_rec_end_date", "DATE", mode="NULLABLE"), - bigquery.SchemaField("cc_closed_date_sk", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("cc_open_date_sk", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("cc_name", "STRING", mode="NULLABLE"), - bigquery.SchemaField("cc_class", "STRING", mode="NULLABLE"), - bigquery.SchemaField("cc_employees", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("cc_sq_ft", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("cc_hours", "STRING", mode="NULLABLE"), - bigquery.SchemaField("cc_manager", "STRING", mode="NULLABLE"), - bigquery.SchemaField("cc_mkt_id", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("cc_mkt_class", "STRING", mode="NULLABLE"), - bigquery.SchemaField("cc_mkt_desc", "STRING", mode="NULLABLE"), - bigquery.SchemaField("cc_market_manager", "STRING", mode="NULLABLE"), - bigquery.SchemaField("cc_division", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("cc_division_name", "STRING", mode="NULLABLE"), - bigquery.SchemaField("cc_company", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("cc_company_name", "STRING", mode="NULLABLE"), - bigquery.SchemaField("cc_street_number", "STRING", mode="NULLABLE"), - bigquery.SchemaField("cc_street_name", "STRING", mode="NULLABLE"), - bigquery.SchemaField("cc_street_type", "STRING", mode="NULLABLE"), - bigquery.SchemaField("cc_suite_number", "STRING", mode="NULLABLE"), - bigquery.SchemaField("cc_city", "STRING", mode="NULLABLE"), - bigquery.SchemaField("cc_county", "STRING", mode="NULLABLE"), - bigquery.SchemaField("cc_state", "STRING", mode="NULLABLE"), - bigquery.SchemaField("cc_zip", "STRING", mode="NULLABLE"), - bigquery.SchemaField("cc_country", "STRING", mode="NULLABLE"), - bigquery.SchemaField("cc_gmt_offset", "FLOAT", mode="NULLABLE"), - bigquery.SchemaField("cc_tax_percentage", "FLOAT", mode="NULLABLE"), - ], - "customer": [ - bigquery.SchemaField("c_customer_sk", "INTEGER", mode="REQUIRED"), - bigquery.SchemaField("c_customer_id", "STRING", mode="REQUIRED"), - bigquery.SchemaField("c_current_cdemo_sk", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("c_current_hdemo_sk", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("c_current_addr_sk", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("c_first_shipto_date_sk", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("c_first_sales_date_sk", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("c_salutation", "STRING", mode="NULLABLE"), - bigquery.SchemaField("c_first_name", "STRING", mode="NULLABLE"), - bigquery.SchemaField("c_last_name", "STRING", mode="NULLABLE"), - bigquery.SchemaField("c_preferred_cust_flag", "STRING", mode="NULLABLE"), - bigquery.SchemaField("c_birth_day", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("c_birth_month", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("c_birth_year", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("c_birth_country", "STRING", mode="NULLABLE"), - bigquery.SchemaField("c_login", "STRING", mode="NULLABLE"), - bigquery.SchemaField("c_email_address", "STRING", mode="NULLABLE"), - bigquery.SchemaField("c_last_review_date_sk", "STRING", mode="NULLABLE"), - ], - "web_site": [ - bigquery.SchemaField("web_site_sk", "INTEGER", mode="REQUIRED"), - bigquery.SchemaField("web_site_id", "STRING", mode="REQUIRED"), - bigquery.SchemaField("web_rec_start_date", "DATE", mode="NULLABLE"), - bigquery.SchemaField("web_rec_end_date", "DATE", mode="NULLABLE"), - bigquery.SchemaField("web_name", "STRING", mode="NULLABLE"), - bigquery.SchemaField("web_open_date_sk", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("web_close_date_sk", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("web_class", "STRING", mode="NULLABLE"), - bigquery.SchemaField("web_manager", "STRING", mode="NULLABLE"), - bigquery.SchemaField("web_mkt_id", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("web_mkt_class", "STRING", mode="NULLABLE"), - bigquery.SchemaField("web_mkt_desc", "STRING", mode="NULLABLE"), - bigquery.SchemaField("web_market_manager", "STRING", mode="NULLABLE"), - bigquery.SchemaField("web_company_id", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("web_company_name", "STRING", mode="NULLABLE"), - bigquery.SchemaField("web_street_number", "STRING", mode="NULLABLE"), - bigquery.SchemaField("web_street_name", "STRING", mode="NULLABLE"), - bigquery.SchemaField("web_street_type", "STRING", mode="NULLABLE"), - bigquery.SchemaField("web_suite_number", "STRING", mode="NULLABLE"), - bigquery.SchemaField("web_city", "STRING", mode="NULLABLE"), - bigquery.SchemaField("web_county", "STRING", mode="NULLABLE"), - bigquery.SchemaField("web_state", "STRING", mode="NULLABLE"), - bigquery.SchemaField("web_zip", "STRING", mode="NULLABLE"), - bigquery.SchemaField("web_country", "STRING", mode="NULLABLE"), - bigquery.SchemaField("web_gmt_offset", "FLOAT", mode="NULLABLE"), - bigquery.SchemaField("web_tax_percentage", "FLOAT", mode="NULLABLE"), - ], - "store_returns": [ - bigquery.SchemaField("sr_returned_date_sk", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("sr_return_time_sk", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("sr_item_sk", "INTEGER", mode="REQUIRED"), - bigquery.SchemaField("sr_customer_sk", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("sr_cdemo_sk", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("sr_hdemo_sk", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("sr_addr_sk", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("sr_store_sk", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("sr_reason_sk", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("sr_ticket_number", "INTEGER", mode="REQUIRED"), - bigquery.SchemaField("sr_return_quantity", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("sr_return_amt", "FLOAT", mode="NULLABLE"), - bigquery.SchemaField("sr_return_tax", "FLOAT", mode="NULLABLE"), - bigquery.SchemaField("sr_return_amt_inc_tax", "FLOAT", mode="NULLABLE"), - bigquery.SchemaField("sr_fee", "FLOAT", mode="NULLABLE"), - bigquery.SchemaField("sr_return_ship_cost", "FLOAT", mode="NULLABLE"), - bigquery.SchemaField("sr_refunded_cash", "FLOAT", mode="NULLABLE"), - bigquery.SchemaField("sr_reversed_charge", "FLOAT", mode="NULLABLE"), - bigquery.SchemaField("sr_store_credit", "FLOAT", mode="NULLABLE"), - bigquery.SchemaField("sr_net_loss", "FLOAT", mode="NULLABLE"), - ], - "household_demographics": [ - bigquery.SchemaField("hd_demo_sk", "INTEGER", mode="REQUIRED"), - bigquery.SchemaField("hd_income_band_sk", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("hd_buy_potential", "STRING", mode="NULLABLE"), - bigquery.SchemaField("hd_dep_count", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("hd_vehicle_count", "INTEGER", mode="NULLABLE"), - ], - "web_page": [ - bigquery.SchemaField("wp_web_page_sk", "INTEGER", mode="REQUIRED"), - bigquery.SchemaField("wp_web_page_id", "STRING", mode="REQUIRED"), - bigquery.SchemaField("wp_rec_start_date", "DATE", mode="NULLABLE"), - bigquery.SchemaField("wp_rec_end_date", "DATE", mode="NULLABLE"), - bigquery.SchemaField("wp_creation_date_sk", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("wp_access_date_sk", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("wp_autogen_flag", "STRING", mode="NULLABLE"), - bigquery.SchemaField("wp_customer_sk", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("wp_url", "STRING", mode="NULLABLE"), - bigquery.SchemaField("wp_type", "STRING", mode="NULLABLE"), - bigquery.SchemaField("wp_char_count", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("wp_link_count", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("wp_image_count", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("wp_max_ad_count", "INTEGER", mode="NULLABLE"), - ], - "promotion": [ - bigquery.SchemaField("p_promo_sk", "INTEGER", mode="REQUIRED"), - bigquery.SchemaField("p_promo_id", "STRING", mode="REQUIRED"), - bigquery.SchemaField("p_start_date_sk", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("p_end_date_sk", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("p_item_sk", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("p_cost", "FLOAT", mode="NULLABLE"), - bigquery.SchemaField("p_response_target", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("p_promo_name", "STRING", mode="NULLABLE"), - bigquery.SchemaField("p_channel_dmail", "STRING", mode="NULLABLE"), - bigquery.SchemaField("p_channel_email", "STRING", mode="NULLABLE"), - bigquery.SchemaField("p_channel_catalog", "STRING", mode="NULLABLE"), - bigquery.SchemaField("p_channel_tv", "STRING", mode="NULLABLE"), - bigquery.SchemaField("p_channel_radio", "STRING", mode="NULLABLE"), - bigquery.SchemaField("p_channel_press", "STRING", mode="NULLABLE"), - bigquery.SchemaField("p_channel_event", "STRING", mode="NULLABLE"), - bigquery.SchemaField("p_channel_demo", "STRING", mode="NULLABLE"), - bigquery.SchemaField("p_channel_details", "STRING", mode="NULLABLE"), - bigquery.SchemaField("p_purpose", "STRING", mode="NULLABLE"), - bigquery.SchemaField("p_discount_active", "STRING", mode="NULLABLE"), - ], - "catalog_page": [ - bigquery.SchemaField("cp_catalog_page_sk", "INTEGER", mode="REQUIRED"), - bigquery.SchemaField("cp_catalog_page_id", "STRING", mode="REQUIRED"), - bigquery.SchemaField("cp_start_date_sk", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("cp_end_date_sk", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("cp_department", "STRING", mode="NULLABLE"), - bigquery.SchemaField("cp_catalog_number", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("cp_catalog_page_number", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("cp_description", "STRING", mode="NULLABLE"), - bigquery.SchemaField("cp_type", "STRING", mode="NULLABLE"), - ], - "inventory": [ - bigquery.SchemaField("inv_date_sk", "INTEGER", mode="REQUIRED"), - bigquery.SchemaField("inv_item_sk", "INTEGER", mode="REQUIRED"), - bigquery.SchemaField("inv_warehouse_sk", "INTEGER", mode="REQUIRED"), - bigquery.SchemaField("inv_quantity_on_hand", "INTEGER", mode="NULLABLE"), - ], - "catalog_returns": [ - bigquery.SchemaField("cr_returned_date_sk", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("cr_returned_time_sk", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("cr_item_sk", "INTEGER", mode="REQUIRED"), - bigquery.SchemaField("cr_refunded_customer_sk", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("cr_refunded_cdemo_sk", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("cr_refunded_hdemo_sk", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("cr_refunded_addr_sk", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField( - "cr_returning_customer_sk", "INTEGER", mode="NULLABLE" - ), - bigquery.SchemaField("cr_returning_cdemo_sk", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("cr_returning_hdemo_sk", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("cr_returning_addr_sk", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("cr_call_center_sk", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("cr_catalog_page_sk", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("cr_ship_mode_sk", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("cr_warehouse_sk", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("cr_reason_sk", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("cr_order_number", "INTEGER", mode="REQUIRED"), - bigquery.SchemaField("cr_return_quantity", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("cr_return_amount", "FLOAT", mode="NULLABLE"), - bigquery.SchemaField("cr_return_tax", "FLOAT", mode="NULLABLE"), - bigquery.SchemaField("cr_return_amt_inc_tax", "FLOAT", mode="NULLABLE"), - bigquery.SchemaField("cr_fee", "FLOAT", mode="NULLABLE"), - bigquery.SchemaField("cr_return_ship_cost", "FLOAT", mode="NULLABLE"), - bigquery.SchemaField("cr_refunded_cash", "FLOAT", mode="NULLABLE"), - bigquery.SchemaField("cr_reversed_charge", "FLOAT", mode="NULLABLE"), - bigquery.SchemaField("cr_store_credit", "FLOAT", mode="NULLABLE"), - bigquery.SchemaField("cr_net_loss", "FLOAT", mode="NULLABLE"), - ], - "web_returns": [ - bigquery.SchemaField("wr_returned_date_sk", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("wr_returned_time_sk", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("wr_item_sk", "INTEGER", mode="REQUIRED"), - bigquery.SchemaField("wr_refunded_customer_sk", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("wr_refunded_cdemo_sk", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("wr_refunded_hdemo_sk", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("wr_refunded_addr_sk", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField( - "wr_returning_customer_sk", "INTEGER", mode="NULLABLE" - ), - bigquery.SchemaField("wr_returning_cdemo_sk", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("wr_returning_hdemo_sk", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("wr_returning_addr_sk", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("wr_web_page_sk", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("wr_reason_sk", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("wr_order_number", "INTEGER", mode="REQUIRED"), - bigquery.SchemaField("wr_return_quantity", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("wr_return_amt", "FLOAT", mode="NULLABLE"), - bigquery.SchemaField("wr_return_tax", "FLOAT", mode="NULLABLE"), - bigquery.SchemaField("wr_return_amt_inc_tax", "FLOAT", mode="NULLABLE"), - bigquery.SchemaField("wr_fee", "FLOAT", mode="NULLABLE"), - bigquery.SchemaField("wr_return_ship_cost", "FLOAT", mode="NULLABLE"), - bigquery.SchemaField("wr_refunded_cash", "FLOAT", mode="NULLABLE"), - bigquery.SchemaField("wr_reversed_charge", "FLOAT", mode="NULLABLE"), - bigquery.SchemaField("wr_account_credit", "FLOAT", mode="NULLABLE"), - bigquery.SchemaField("wr_net_loss", "FLOAT", mode="NULLABLE"), - ], - "web_sales": [ - bigquery.SchemaField("ws_sold_date_sk", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("ws_sold_time_sk", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("ws_ship_date_sk", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("ws_item_sk", "INTEGER", mode="REQUIRED"), - bigquery.SchemaField("ws_bill_customer_sk", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("ws_bill_cdemo_sk", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("ws_bill_hdemo_sk", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("ws_bill_addr_sk", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("ws_ship_customer_sk", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("ws_ship_cdemo_sk", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("ws_ship_hdemo_sk", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("ws_ship_addr_sk", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("ws_web_page_sk", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("ws_web_site_sk", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("ws_ship_mode_sk", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("ws_warehouse_sk", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("ws_promo_sk", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("ws_order_number", "INTEGER", mode="REQUIRED"), - bigquery.SchemaField("ws_quantity", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("ws_wholesale_cost", "FLOAT", mode="NULLABLE"), - bigquery.SchemaField("ws_list_price", "FLOAT", mode="NULLABLE"), - bigquery.SchemaField("ws_sales_price", "FLOAT", mode="NULLABLE"), - bigquery.SchemaField("ws_ext_discount_amt", "FLOAT", mode="NULLABLE"), - bigquery.SchemaField("ws_ext_sales_price", "FLOAT", mode="NULLABLE"), - bigquery.SchemaField("ws_ext_wholesale_cost", "FLOAT", mode="NULLABLE"), - bigquery.SchemaField("ws_ext_list_price", "FLOAT", mode="NULLABLE"), - bigquery.SchemaField("ws_ext_tax", "FLOAT", mode="NULLABLE"), - bigquery.SchemaField("ws_coupon_amt", "FLOAT", mode="NULLABLE"), - bigquery.SchemaField("ws_ext_ship_cost", "FLOAT", mode="NULLABLE"), - bigquery.SchemaField("ws_net_paid", "FLOAT", mode="NULLABLE"), - bigquery.SchemaField("ws_net_paid_inc_tax", "FLOAT", mode="NULLABLE"), - bigquery.SchemaField("ws_net_paid_inc_ship", "FLOAT", mode="NULLABLE"), - bigquery.SchemaField("ws_net_paid_inc_ship_tax", "FLOAT", mode="NULLABLE"), - bigquery.SchemaField("ws_net_profit", "FLOAT", mode="NULLABLE"), - ], - "catalog_sales": [ - bigquery.SchemaField("cs_sold_date_sk", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("cs_sold_time_sk", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("cs_ship_date_sk", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("cs_bill_customer_sk", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("cs_bill_cdemo_sk", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("cs_bill_hdemo_sk", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("cs_bill_addr_sk", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("cs_ship_customer_sk", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("cs_ship_cdemo_sk", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("cs_ship_hdemo_sk", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("cs_ship_addr_sk", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("cs_call_center_sk", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("cs_catalog_page_sk", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("cs_ship_mode_sk", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("cs_warehouse_sk", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("cs_item_sk", "INTEGER", mode="REQUIRED"), - bigquery.SchemaField("cs_promo_sk", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("cs_order_number", "INTEGER", mode="REQUIRED"), - bigquery.SchemaField("cs_quantity", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("cs_wholesale_cost", "FLOAT", mode="NULLABLE"), - bigquery.SchemaField("cs_list_price", "FLOAT", mode="NULLABLE"), - bigquery.SchemaField("cs_sales_price", "FLOAT", mode="NULLABLE"), - bigquery.SchemaField("cs_ext_discount_amt", "FLOAT", mode="NULLABLE"), - bigquery.SchemaField("cs_ext_sales_price", "FLOAT", mode="NULLABLE"), - bigquery.SchemaField("cs_ext_wholesale_cost", "FLOAT", mode="NULLABLE"), - bigquery.SchemaField("cs_ext_list_price", "FLOAT", mode="NULLABLE"), - bigquery.SchemaField("cs_ext_tax", "FLOAT", mode="NULLABLE"), - bigquery.SchemaField("cs_coupon_amt", "FLOAT", mode="NULLABLE"), - bigquery.SchemaField("cs_ext_ship_cost", "FLOAT", mode="NULLABLE"), - bigquery.SchemaField("cs_net_paid", "FLOAT", mode="NULLABLE"), - bigquery.SchemaField("cs_net_paid_inc_tax", "FLOAT", mode="NULLABLE"), - bigquery.SchemaField("cs_net_paid_inc_ship", "FLOAT", mode="NULLABLE"), - bigquery.SchemaField("cs_net_paid_inc_ship_tax", "FLOAT", mode="NULLABLE"), - bigquery.SchemaField("cs_net_profit", "FLOAT", mode="NULLABLE"), - ], - "store_sales": [ - bigquery.SchemaField("ss_sold_date_sk", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("ss_sold_time_sk", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("ss_item_sk", "INTEGER", mode="REQUIRED"), - bigquery.SchemaField("ss_customer_sk", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("ss_cdemo_sk", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("ss_hdemo_sk", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("ss_addr_sk", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("ss_store_sk", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("ss_promo_sk", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("ss_ticket_number", "INTEGER", mode="REQUIRED"), - bigquery.SchemaField("ss_quantity", "INTEGER", mode="NULLABLE"), - bigquery.SchemaField("ss_wholesale_cost", "FLOAT", mode="NULLABLE"), - bigquery.SchemaField("ss_list_price", "FLOAT", mode="NULLABLE"), - bigquery.SchemaField("ss_sales_price", "FLOAT", mode="NULLABLE"), - bigquery.SchemaField("ss_ext_discount_amt", "FLOAT", mode="NULLABLE"), - bigquery.SchemaField("ss_ext_sales_price", "FLOAT", mode="NULLABLE"), - bigquery.SchemaField("ss_ext_wholesale_cost", "FLOAT", mode="NULLABLE"), - bigquery.SchemaField("ss_ext_list_price", "FLOAT", mode="NULLABLE"), - bigquery.SchemaField("ss_ext_tax", "FLOAT", mode="NULLABLE"), - bigquery.SchemaField("ss_coupon_amt", "FLOAT", mode="NULLABLE"), - bigquery.SchemaField("ss_net_paid", "FLOAT", mode="NULLABLE"), - bigquery.SchemaField("ss_net_paid_inc_tax", "FLOAT", mode="NULLABLE"), - bigquery.SchemaField("ss_net_profit", "FLOAT", mode="NULLABLE"), - ], - } - - return schema[table_name] - - -def load_data_to_bigquery(table_name, file_paths, client, dataset_ref, temp_file): - """Loads data from a list of files into a BigQuery table.""" - job_config = bigquery.LoadJobConfig( - source_format=bigquery.SourceFormat.CSV, - skip_leading_rows=0, # No header in .dat files - field_delimiter="|", - schema=get_schema(table_name), - ) - - table_ref = dataset_ref.table(table_name) - table = bigquery.Table(table_ref) - client.create_table(table) - - # Load data from each file - for file_path in sorted(file_paths): - preprocess_csv(file_path, temp_file) - with open(temp_file, "rb") as source_file: - job = client.load_table_from_file( - source_file, table_ref, job_config=job_config - ) - job.result() - print( - f"Loaded data from {file_path} into table {project_id}:{dataset_id}.{table_name}" - ) - - -if __name__ == "__main__": - """ - Loads TPC-DS data to BigQuery. - - This script loads TPC-DS data generated with source code from - https://www.tpc.org/tpc_documents_current_versions/current_specifications5.asp - into BigQuery. - - Note: If the dataset already exists, the script will exit without uploading data. - - Usage: - python tpcds_upload_helper.py --project_id --dataset_id --ds_path - python tpcds_upload_helper.py -d -p -s - """ - parser = argparse.ArgumentParser(description="Load TPC-DS data to BigQuery") - parser.add_argument( - "--project_id", "-p", required=True, help="Google Cloud project ID" - ) - parser.add_argument("--dataset_id", "-d", required=True, help="BigQuery dataset ID") - parser.add_argument( - "--ds_path", "-s", required=True, help="Path to the TPC-DS data directory" - ) - args = parser.parse_args() - - project_id = args.project_id - dataset_id = args.dataset_id - ds_path = args.ds_path - temp_file = "temp.csv" - - # Initialize BigQuery client - client = bigquery.Client(project=project_id) - dataset_ref = client.dataset(dataset_id) - try: - # Quit if dataset exists - client.get_dataset(dataset_ref) - print(f"Dataset {project_id}:{dataset_id} already exists. Skipping.") - sys.exit(1) - except google.api_core.exceptions.NotFound: - # Create the dataset if it doesn't exist - dataset = bigquery.Dataset(dataset_ref) - client.create_dataset(dataset) - print(f"Created dataset {project_id}:{dataset_id}") - - # Iterate through the folders - for table_name in sorted(os.listdir(ds_path)): - table_path = os.path.join(ds_path, table_name) - table_name = table_name.split(".")[0] - if os.path.isdir(table_path): - file_paths = [ - os.path.join(table_path, f) - for f in os.listdir(table_path) - if f.endswith(".dat") - ] - load_data_to_bigquery( - table_name, file_paths, client, dataset_ref, temp_file - ) - - try: - os.remove(temp_file) - print("Removed temporary file: temp.csv") - except FileNotFoundError: - print("Temporary file not found.") diff --git a/scripts/generate_bigframes_bigquery.py b/scripts/generate_bigframes_bigquery.py deleted file mode 100755 index fe90dc43472..00000000000 --- a/scripts/generate_bigframes_bigquery.py +++ /dev/null @@ -1,45 +0,0 @@ -#!/usr/bin/env -S uv run --active --script -# -# /// script -# dependencies = [ -# "jinja2", -# "pyyaml", -# "ruff==0.14.14", -# ] -# /// -# -# Copyright 2026 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import pathlib -import sys - -scripts_dir = pathlib.Path(__file__).parent -if str(scripts_dir) not in sys.path: - sys.path.insert(0, str(scripts_dir)) - -from bigquery_generator import constants, file_generator, yaml_parser # noqa: E402 - - -def main() -> None: - modules = [] - - for yaml_file in sorted(constants.DATA_DIR.glob("**/*.yaml")): - modules.append(yaml_parser.parse_yaml(yaml_file)) - - file_generator.generate(modules) - - -if __name__ == "__main__": - main() diff --git a/scripts/get_documentation_coverage.py b/scripts/get_documentation_coverage.py deleted file mode 100755 index a6566cafab0..00000000000 --- a/scripts/get_documentation_coverage.py +++ /dev/null @@ -1,172 +0,0 @@ -# Copyright 2023 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import argparse -import importlib -import inspect -import sys -import typing - -import bigframes -import bigframes.pandas as bpd - -PRESENT = "present" -NOT_PRESENT = "not_present" - -CLASSES = [ - bpd.DataFrame, - bpd.Series, - bpd.Index, - bigframes.session.Session, - bigframes.operations.strings.StringMethods, - bigframes.operations.datetimes.DatetimeMethods, - bigframes.operations.structs.StructAccessor, -] - -ML_MODULE_NAMES = [ - "cluster", - "compose", - "decomposition", - "ensemble", - "linear_model", - "metrics", - "model_selection", - "pipeline", - "preprocessing", - "llm", - "forecasting", - "imported", - "remote", -] - -COVERAGE_GENERATORS = { - "documentation": lambda docstr: docstr, - "code samples": lambda docstr: docstr and "**Examples:**" in docstr, -} - -for module_name in ML_MODULE_NAMES: - module = importlib.import_module(f"bigframes.ml.{module_name}") - classes_ = [ - class_ for _, class_ in inspect.getmembers(module, predicate=inspect.isclass) - ] - CLASSES.extend(classes_) - - -def get_coverage_summary( - func: typing.Callable, -) -> typing.Dict[str, typing.Dict[str, typing.List[str]]]: - """Get Summary of the code samples coverage in BigFrames APIs. - - Args: - func (callable): - Function to accept documentation and return whether it satisfies - coverage. - Returns: - Summary: A dictionary of the format - { - class_1: { - "present": [method1, method2, ...], - "not_present": [method3, method4, ...] - }, - class_2: { - ... - } - } - """ - summary: typing.Dict[str, typing.Dict[str, typing.List[str]]] = dict() - - for class_ in CLASSES: - class_key = f"{class_.__module__}.{class_.__name__}" - summary[class_key] = {PRESENT: [], NOT_PRESENT: []} - - members = inspect.getmembers(class_) - - for name, obj in members: - # ignore private methods - if name.startswith("_") and not name.startswith("__"): - continue - - # ignore constructor - if name == "__init__": - continue - - def predicate(impl): - return ( - # This includes class methods like `from_dict`, `from_records` - inspect.ismethod(impl) - # This includes instance methods like `dropna`, join` - or inspect.isfunction(impl) - # This includes properties like `shape`, `values` but not - # generic properties like `__weakref__` - or (inspect.isdatadescriptor(impl) and not name.startswith("__")) - ) - - if not predicate(obj): - continue - - # At this point we have a property or a public method - impl = getattr(class_, name) - - docstr = inspect.getdoc(impl) - coverage_present = func(docstr) - key = PRESENT if coverage_present else NOT_PRESENT - summary[class_key][key].append(name) - - return summary - - -if __name__ == "__main__": - parser = argparse.ArgumentParser( - description="Get a summary of documentation coverage in BigFrames APIs." - ) - parser.add_argument( - "-c", - "--code-samples", - type=bool, - action=argparse.BooleanOptionalAction, - default=False, - help="Whether to calculate code samples coverage. By default the tool" - " calculates the documentation (docstring) coverage.", - ) - parser.add_argument( - "-d", - "--details", - type=bool, - action=argparse.BooleanOptionalAction, - default=False, - help="Whether to print APIs with and without the coverage.", - ) - - args = parser.parse_args(sys.argv[1:]) - - scenario = "code samples" if args.code_samples else "documentation" - summary = get_coverage_summary(COVERAGE_GENERATORS[scenario]) - - total_with_code_samples = 0 - total = 0 - for class_, class_summary in summary.items(): - apis_with_code_samples = len(class_summary[PRESENT]) - total_with_code_samples += apis_with_code_samples - - apis_total = len(class_summary[PRESENT]) + len(class_summary[NOT_PRESENT]) - total += apis_total - - coverage = 100 * apis_with_code_samples / apis_total - print(f"{class_}: {coverage:.1f}% ({apis_with_code_samples}/{apis_total})") - if args.details: - print(f"===> APIs WITH {scenario}: {class_summary[PRESENT]}") - print(f"===> APIs WITHOUT {scenario}: {class_summary[NOT_PRESENT]}") - - coverage = 100 * total_with_code_samples / total - print(f"Total: {coverage:.1f}% ({total_with_code_samples}/{total})") diff --git a/scripts/manage_cloud_functions.py b/scripts/manage_cloud_functions.py deleted file mode 100644 index c92be4ebadb..00000000000 --- a/scripts/manage_cloud_functions.py +++ /dev/null @@ -1,229 +0,0 @@ -# Copyright 2023 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import argparse -import datetime as dt -import sys -import time - -import google.api_core.exceptions -from google.cloud import functions_v2 - -GCF_REGIONS_ALL = [ - "asia-east1", - "asia-east2", - "asia-northeast1", - "asia-northeast2", - "europe-north1", - "europe-southwest1", - "europe-west1", - "europe-west2", - "europe-west4", - "europe-west8", - "europe-west9", - "us-central1", - "us-east1", - "us-east4", - "us-east5", - "us-south1", - "us-west1", - "asia-east2", - "asia-northeast3", - "asia-southeast1", - "asia-southeast2", - "asia-south1", - "asia-south2", - "australia-southeast1", - "australia-southeast2", - "europe-central2", - "europe-west2", - "europe-west3", - "europe-west6", - "northamerica-northeast1", - "northamerica-northeast2", - "southamerica-east1", - "southamerica-west1", - "us-west2", - "us-west3", - "us-west4", -] - -GCF_CLIENT = functions_v2.FunctionServiceClient() - - -def get_bigframes_functions(project, region): - parent = f"projects/{project}/locations/{region}" - functions = GCF_CLIENT.list_functions( - functions_v2.ListFunctionsRequest(parent=parent) - ) - - # Filter bigframes created functions - functions = [ - function - for function in functions - if function.name.startswith( - f"projects/{project}/locations/{region}/functions/bigframes-" - ) - ] - - return functions - - -def summarize_gcfs(args): - """Summarize number of bigframes cloud functions in various regions.""" - - region_counts = {} - for region in args.regions: - functions = get_bigframes_functions(args.project_id, region) - functions_count = len(functions) - - # Exclude reporting regions with 0 bigframes GCFs - if functions_count == 0: - continue - - # Count how many GCFs are newer than a day - recent = 0 - for f in functions: - age = dt.datetime.now() - dt.datetime.fromtimestamp( - f.update_time.timestamp() - ) - if age.total_seconds() < args.recency_cutoff: - recent += 1 - - region_counts[region] = (functions_count, recent) - - for item in sorted( - region_counts.items(), key=lambda item: item[1][0], reverse=True - ): - region = item[0] - count, recent = item[1] - print( - "{}: Total={}, Recent={}, Older={}".format( - region, count, recent, count - recent - ) - ) - - -def cleanup_gcfs(args): - """Clean-up bigframes cloud functions in the given regions.""" - max_delete_per_region = args.number - - for region in args.regions: - functions = get_bigframes_functions(args.project_id, region) - count = 0 - for f in functions: - age = dt.datetime.now() - dt.datetime.fromtimestamp( - f.update_time.timestamp() - ) - if age.total_seconds() >= args.recency_cutoff: - try: - count += 1 - GCF_CLIENT.delete_function(name=f.name) - print( - f"[{region}]: deleted [{count}] {f.name} last updated on {f.update_time}" - ) - if count >= max_delete_per_region: - break - # Mostly there is a 60 mutations per minute quota, we want to use 10% of - # that for this clean-up, i.e. 6 mutations per minute. So wait for - # 60/6 = 10 seconds - time.sleep(10) - except google.api_core.exceptions.NotFound: - # Most likely the function was deleted otherwise - pass - except google.api_core.exceptions.ResourceExhausted: - # Stop deleting in this region for now - print( - f"Failed to delete function in region {region} due to quota exhaustion. Pausing for 2 minutes." - ) - time.sleep(120) - - -def list_str(values): - return [val for val in values.split(",") if val] - - -def get_project_from_environment(): - from google.cloud import bigquery - - return bigquery.Client().project - - -if __name__ == "__main__": - parser = argparse.ArgumentParser( - description="Manage cloud functions created to serve bigframes remote functions." - ) - parser.add_argument( - "-p", - "--project-id", - type=str, - required=False, - action="store", - help="GCP project-id. If not provided, the project-id resolved by the" - " BigQuery client from the user environment would be used.", - ) - parser.add_argument( - "-r", - "--regions", - type=list_str, - required=False, - default=GCF_REGIONS_ALL, - action="store", - help="Cloud functions region(s). If multiple regions, Specify comma separated (e.g. region1,region2)", - ) - - def hours_to_timedelta(hrs): - return dt.timedelta(hours=int(hrs)).total_seconds() - - parser.add_argument( - "-c", - "--recency-cutoff", - type=hours_to_timedelta, - required=False, - default=hours_to_timedelta("24"), - action="store", - help="Number of hours, cloud functions older than which should be considered stale (worthy of cleanup).", - ) - - subparsers = parser.add_subparsers(title="subcommands", required=True) - parser_summary = subparsers.add_parser( - "summary", - help="BigFrames cloud functions summary.", - description="Show the bigframes cloud functions summary.", - ) - parser_summary.set_defaults(func=summarize_gcfs) - parser_cleanup = subparsers.add_parser( - "cleanup", - help="BigFrames cloud functions clean up.", - description="Delete the stale bigframes cloud functions.", - ) - parser_cleanup.add_argument( - "-n", - "--number", - type=int, - required=False, - default=100, - action="store", - help="Number of stale (more than a day old) cloud functions to clean up.", - ) - parser_cleanup.set_defaults(func=cleanup_gcfs) - - args = parser.parse_args(sys.argv[1:]) - if args.project_id is None: - args.project_id = get_project_from_environment() - if args.project_id is None: - raise ValueError( - "Could not resolve a project. Plese set it via --project-id option." - ) - args.func(args) diff --git a/scripts/notebooks_fill_params.py b/scripts/notebooks_fill_params.py deleted file mode 100644 index e0f7c8d687a..00000000000 --- a/scripts/notebooks_fill_params.py +++ /dev/null @@ -1,65 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import json -import os -import re -import shutil -import sys - -GOOGLE_CLOUD_PROJECT = os.environ["GOOGLE_CLOUD_PROJECT"] - - -def make_backup(notebook_path: str): - shutil.copy( - notebook_path, - f"{notebook_path}.backup", - ) - - -def replace_project(line): - """ - Notebooks contain special colab `param {type:"string"}` - comments, which make it easy for customers to fill in their - own information. - """ - # Make sure we're robust to whitespace differences. - cleaned = re.sub(r"\s", "", line) - if cleaned == 'PROJECT_ID=""#@param{type:"string"}': - return f'PROJECT_ID = "{GOOGLE_CLOUD_PROJECT}" # @param {{type:"string"}}\n' - else: - return line - - -def replace_params(notebook_path: str): - with open(notebook_path, "r", encoding="utf-8") as notebook_file: - notebook_json = json.load(notebook_file) - - for cell in notebook_json["cells"]: - lines = cell.get("source", []) - new_lines = [replace_project(line) for line in lines] - cell["source"] = new_lines - - with open(notebook_path, "w", encoding="utf-8") as notebook_file: - json.dump(notebook_json, notebook_file, indent=2, ensure_ascii=False) - - -def main(notebook_paths): - for notebook_path in notebook_paths: - make_backup(notebook_path) - replace_params(notebook_path) - - -if __name__ == "__main__": - main(sys.argv[1:]) diff --git a/scripts/notebooks_restore_from_backup.py b/scripts/notebooks_restore_from_backup.py deleted file mode 100644 index 4d3e0333e39..00000000000 --- a/scripts/notebooks_restore_from_backup.py +++ /dev/null @@ -1,35 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import pathlib -import shutil -import sys - - -def restore_from_backup(notebook_path): - backup_path = pathlib.Path(f"{notebook_path}.backup") - if backup_path.exists(): - shutil.move( - backup_path, - notebook_path, - ) - - -def main(notebook_paths): - for notebook_path in notebook_paths: - restore_from_backup(notebook_path) - - -if __name__ == "__main__": - main(sys.argv[1:]) diff --git a/scripts/publish_api_coverage.py b/scripts/publish_api_coverage.py index f94cd7e6d7f..856307e4407 100644 --- a/scripts/publish_api_coverage.py +++ b/scripts/publish_api_coverage.py @@ -17,112 +17,29 @@ import argparse import inspect -import pathlib -import sys import pandas as pd -import pandas.core.groupby -import pandas.core.indexes.accessors -import pandas.core.strings.accessor -import pandas.core.window.rolling -import bigframes -import bigframes.core.groupby -import bigframes.core.window -import bigframes.operations.datetimes -import bigframes.operations.strings import bigframes.pandas as bpd -REPO_ROOT = pathlib.Path(__file__).parent.parent - -BIGFRAMES_OBJECT = { - "pandas": "bigframes.pandas", - "dataframe": "bigframes.pandas.DataFrame", - "dataframegroupby": "bigframes.pandas.api.typing.DataFrameGroupBy", - "index": "bigframes.pandas.Index", - "series": "bigframes.pandas.Series", - "seriesgroupby": "bigframes.pandas.api.typing.SeriesGroupBy", - "datetimemethods": "bigframes.pandas.api.typing.DatetimeMethods", - "stringmethods": "bigframes.pandas.api.typing.StringMethods", - "window": "bigframes.pandas.api.typing.Window", -} - - -PANDAS_TARGETS = [ - ("pandas", pd, bpd), - ("dataframe", pd.DataFrame, bpd.DataFrame), - ( - "dataframegroupby", - pandas.core.groupby.DataFrameGroupBy, - bigframes.core.groupby.DataFrameGroupBy, - ), - ("series", pd.Series, bpd.Series), - ( - "seriesgroupby", - pandas.core.groupby.DataFrameGroupBy, - bigframes.core.groupby.DataFrameGroupBy, - ), - ( - "datetimemethods", - pandas.core.indexes.accessors.CombinedDatetimelikeProperties, - bigframes.operations.datetimes.DatetimeMethods, - ), - ( - "stringmethods", - pandas.core.strings.accessor.StringMethods, - bigframes.operations.strings.StringMethods, - ), - ( - "window", - pandas.core.window.rolling.Rolling, - bigframes.core.window.Window, - ), - ("index", pd.Index, bpd.Index), -] - - -def names_from_signature(signature): - """Extract the names of parameters from signature - - See: https://docs.python.org/3/library/inspect.html#inspect.signature - """ - return frozenset({parameter for parameter in signature.parameters}) - - -def calculate_missing_parameters(bigframes_function, target_function): - # Some built-in functions can't be inspected. These raise a ValueError. - try: - bigframes_signature = inspect.signature(bigframes_function) - target_signature = inspect.signature(target_function) - except ValueError: - return {} - - bigframes_params = names_from_signature(bigframes_signature) - target_params = names_from_signature(target_signature) - return target_params - bigframes_params - def generate_pandas_api_coverage(): """Inspect all our pandas objects, and compare with the real pandas objects, to see which methods we implement. For each, generate a regex that can be used to check if its present in a notebook""" - header = [ - "api", - "pattern", - "kind", - "is_in_bigframes", - "missing_parameters", - "requires_index", - "requires_ordering", - ] + header = ["api", "pattern", "kind", "is_in_bigframes"] api_patterns = [] + targets = [ + ("pandas", pd, bpd), + ("dataframe", pd.DataFrame, bpd.DataFrame), + ("series", pd.Series, bpd.Series), + ("index", pd.Index, bpd.Index), + ] indexers = ["loc", "iloc", "iat", "ix", "at"] - for name, pandas_obj, bigframes_obj in PANDAS_TARGETS: + for name, pandas_obj, bigframes_obj in targets: for member in dir(pandas_obj): - missing_parameters = "" - # skip private functions and properties - if member[0] == "_": + if member[0] == "_" and member[1] != "_": continue # skip members that are also common python methods @@ -133,17 +50,6 @@ def generate_pandas_api_coverage(): # Function, match .member( token = f"\\.{member}\\(" token_type = "function" - - if hasattr(bigframes_obj, member): - bigframes_function = getattr(bigframes_obj, member) - pandas_function = getattr(pandas_obj, member) - missing_parameters = ", ".join( - sorted( - calculate_missing_parameters( - bigframes_function, pandas_function - ) - ) - ) elif member in indexers: # Indexer, match .indexer[ token = f"\\.{member}\\[" @@ -154,31 +60,9 @@ def generate_pandas_api_coverage(): token_type = "property" is_in_bigframes = hasattr(bigframes_obj, member) - requires_index = "" - requires_ordering = "" - - if is_in_bigframes: - attr = getattr(bigframes_obj, member) - - # TODO(b/361101138): Add check/documentation for partial - # support (e.g. with some parameters). - requires_index = ( - "Y" if hasattr(attr, "_validations_requires_index") else "" - ) - requires_ordering = ( - "Y" if hasattr(attr, "_validations_requires_ordering") else "" - ) api_patterns.append( - [ - f"{name}.{member}", - token, - token_type, - is_in_bigframes, - missing_parameters, - requires_index, - requires_ordering, - ] + [f"{name}.{member}", token, token_type, is_in_bigframes] ) return pd.DataFrame(api_patterns, columns=header) @@ -187,9 +71,6 @@ def generate_pandas_api_coverage(): def generate_sklearn_api_coverage(): """Explore all SKLearn modules, and for each item contained generate a regex to detect it being imported, and record whether we implement it""" - - import sklearn # noqa - sklearn_modules = [ "sklearn", "sklearn.model_selection", @@ -279,131 +160,19 @@ def build_api_coverage_table(bigframes_version: str, release_version: str): sklearn_cov_df["module"] = "bigframes.ml" combined_df = pd.concat([pandas_cov_df, sklearn_cov_df]) combined_df["timestamp"] = pd.Timestamp.now() - # BigQuery only supports microsecond precision timestamps. - combined_df["timestamp"] = combined_df["timestamp"].astype("datetime64[us]") combined_df["bigframes_version"] = bigframes_version combined_df["release_version"] = release_version - combined_df = combined_df.infer_objects().convert_dtypes() - return combined_df - - -def format_api(api_names, is_in_bigframes, api_prefix): - api_names = api_names.str.slice(start=len(f"{api_prefix}.")) - formatted = "" + api_names + "" - bigframes_object = BIGFRAMES_OBJECT.get(api_prefix) - if bigframes_object is None: - return formatted - - linked = ( - '' - + formatted - + "" - ) - return formatted.mask(is_in_bigframes, linked) - - -def generate_api_coverage(df, api_prefix): - dataframe_apis = df.loc[df["api"].str.startswith(f"{api_prefix}.")] - fully_implemented = ( - dataframe_apis["missing_parameters"].str.len() == 0 - ) & dataframe_apis["is_in_bigframes"] - partial_implemented = ( - dataframe_apis["missing_parameters"].str.len() != 0 - ) & dataframe_apis["is_in_bigframes"] - not_implemented = ~dataframe_apis["is_in_bigframes"] - - dataframe_table = pd.DataFrame( - { - "API": format_api( - dataframe_apis["api"], - dataframe_apis["is_in_bigframes"], - api_prefix, - ), - "Implemented": "", - "Requires index": dataframe_apis["requires_index"], - "Requires ordering": dataframe_apis["requires_ordering"], - "Missing parameters": dataframe_apis["missing_parameters"], - } - ) - dataframe_table.loc[fully_implemented, "Implemented"] = "Y" - dataframe_table.loc[partial_implemented, "Implemented"] = "P" - dataframe_table.loc[not_implemented, "Implemented"] = "N" - return dataframe_table - - -def generate_api_coverage_doc(df, api_prefix): - dataframe_table = generate_api_coverage(df, api_prefix) - dataframe_table = dataframe_table.loc[~(dataframe_table["Implemented"] == "N")] - dataframe_table["Implemented"] = dataframe_table["Implemented"].map( - { - "Y": "Y", - "P": "P", - } - ) - - with open( - REPO_ROOT / "docs" / "supported_pandas_apis" / f"bf_{api_prefix}.html", - "w", - ) as html_file: - dataframe_table.to_html( - html_file, index=False, header=True, escape=False, border=0, col_space="8em" - ) - - -def generate_api_coverage_docs(df): - for target in PANDAS_TARGETS: - api_prefix = target[0] - generate_api_coverage_doc(df, api_prefix) - - -def print_api_coverage_summary(df, api_prefix): - dataframe_table = generate_api_coverage(df, api_prefix) - - print(api_prefix) - print(dataframe_table[["Implemented", "API"]].groupby(["Implemented"]).count()) - print(f"{api_prefix} APIs: {dataframe_table.shape[0]}\n") - - -def print_api_coverage_summaries(df): - for target in PANDAS_TARGETS: - api_prefix = target[0] - print_api_coverage_summary(df, api_prefix) - - print(f"\nAll APIs: {len(df.index)}") - fully_implemented = (df["missing_parameters"].str.len() == 0) & df[ - "is_in_bigframes" - ] - print(f"Y: {fully_implemented.sum()}") - partial_implemented = (df["missing_parameters"].str.len() != 0) & df[ - "is_in_bigframes" - ] - print(f"P: {partial_implemented.sum()}") - not_implemented = ~df["is_in_bigframes"] - print(f"N: {not_implemented.sum()}") + return combined_df.infer_objects().convert_dtypes() def main(): parser = argparse.ArgumentParser() - parser.add_argument("output_type") - parser.add_argument("--bigframes_version", default=bigframes.__version__) - parser.add_argument("--release_version", default="") + parser.add_argument("--bigframes_version") + parser.add_argument("--release_version") parser.add_argument("--bigquery_table_name") args = parser.parse_args() df = build_api_coverage_table(args.bigframes_version, args.release_version) - - if args.output_type == "bigquery": - df.to_gbq(args.bigquery_table_name, if_exists="append") - elif args.output_type == "docs": - generate_api_coverage_docs(df) - elif args.output_type == "summary": - print_api_coverage_summaries(df) - else: - print(f"Unexpected output_type {repr(args.output_type)}") - sys.exit(1) + df.to_gbq(args.bigquery_table_name, if_exists="append") if __name__ == "__main__": diff --git a/scripts/readme-gen/readme_gen.py b/scripts/readme-gen/readme_gen.py new file mode 100644 index 00000000000..1acc119835b --- /dev/null +++ b/scripts/readme-gen/readme_gen.py @@ -0,0 +1,69 @@ +#!/usr/bin/env python + +# Copyright 2023 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Generates READMEs using configuration defined in yaml.""" + +import argparse +import io +import os +import subprocess + +import jinja2 +import yaml + + +jinja_env = jinja2.Environment( + trim_blocks=True, + loader=jinja2.FileSystemLoader( + os.path.abspath(os.path.join(os.path.dirname(__file__), "templates")) + ), + autoescape=True, +) + +README_TMPL = jinja_env.get_template("README.tmpl.rst") + + +def get_help(file): + return subprocess.check_output(["python", file, "--help"]).decode() + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("source") + parser.add_argument("--destination", default="README.rst") + + args = parser.parse_args() + + source = os.path.abspath(args.source) + root = os.path.dirname(source) + destination = os.path.join(root, args.destination) + + jinja_env.globals["get_help"] = get_help + + with io.open(source, "r") as f: + config = yaml.load(f) + + # This allows get_help to execute in the right directory. + os.chdir(root) + + output = README_TMPL.render(config) + + with io.open(destination, "w") as f: + f.write(output) + + +if __name__ == "__main__": + main() diff --git a/scripts/readme-gen/templates/README.tmpl.rst b/scripts/readme-gen/templates/README.tmpl.rst new file mode 100644 index 00000000000..4fd239765b0 --- /dev/null +++ b/scripts/readme-gen/templates/README.tmpl.rst @@ -0,0 +1,87 @@ +{# The following line is a lie. BUT! Once jinja2 is done with it, it will + become truth! #} +.. This file is automatically generated. Do not edit this file directly. + +{{product.name}} Python Samples +=============================================================================== + +.. image:: https://gstatic.com/cloudssh/images/open-btn.png + :target: https://console.cloud.google.com/cloudshell/open?git_repo=https://github.com/GoogleCloudPlatform/python-docs-samples&page=editor&open_in_editor={{folder}}/README.rst + + +This directory contains samples for {{product.name}}. {{product.description}} + +{{description}} + +.. _{{product.name}}: {{product.url}} + +{% if required_api_url %} +To run the sample, you need to enable the API at: {{required_api_url}} +{% endif %} + +{% if required_role %} +To run the sample, you need to have `{{required_role}}` role. +{% endif %} + +{{other_required_steps}} + +{% if setup %} +Setup +------------------------------------------------------------------------------- + +{% for section in setup %} + +{% include section + '.tmpl.rst' %} + +{% endfor %} +{% endif %} + +{% if samples %} +Samples +------------------------------------------------------------------------------- + +{% for sample in samples %} +{{sample.name}} ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ + +{% if not sample.hide_cloudshell_button %} +.. image:: https://gstatic.com/cloudssh/images/open-btn.png + :target: https://console.cloud.google.com/cloudshell/open?git_repo=https://github.com/GoogleCloudPlatform/python-docs-samples&page=editor&open_in_editor={{folder}}/{{sample.file}},{{folder}}/README.rst +{% endif %} + + +{{sample.description}} + +To run this sample: + +.. code-block:: bash + + $ python {{sample.file}} +{% if sample.show_help %} + + {{get_help(sample.file)|indent}} +{% endif %} + + +{% endfor %} +{% endif %} + +{% if cloud_client_library %} + +The client library +------------------------------------------------------------------------------- + +This sample uses the `Google Cloud Client Library for Python`_. +You can read the documentation for more details on API usage and use GitHub +to `browse the source`_ and `report issues`_. + +.. _Google Cloud Client Library for Python: + https://googlecloudplatform.github.io/google-cloud-python/ +.. _browse the source: + https://github.com/GoogleCloudPlatform/google-cloud-python +.. _report issues: + https://github.com/GoogleCloudPlatform/google-cloud-python/issues + +{% endif %} + +.. _Google Cloud SDK: https://cloud.google.com/sdk/ \ No newline at end of file diff --git a/scripts/readme-gen/templates/auth.tmpl.rst b/scripts/readme-gen/templates/auth.tmpl.rst new file mode 100644 index 00000000000..1446b94a5e3 --- /dev/null +++ b/scripts/readme-gen/templates/auth.tmpl.rst @@ -0,0 +1,9 @@ +Authentication +++++++++++++++ + +This sample requires you to have authentication setup. Refer to the +`Authentication Getting Started Guide`_ for instructions on setting up +credentials for applications. + +.. _Authentication Getting Started Guide: + https://cloud.google.com/docs/authentication/getting-started diff --git a/scripts/readme-gen/templates/auth_api_key.tmpl.rst b/scripts/readme-gen/templates/auth_api_key.tmpl.rst new file mode 100644 index 00000000000..11957ce2714 --- /dev/null +++ b/scripts/readme-gen/templates/auth_api_key.tmpl.rst @@ -0,0 +1,14 @@ +Authentication +++++++++++++++ + +Authentication for this service is done via an `API Key`_. To obtain an API +Key: + +1. Open the `Cloud Platform Console`_ +2. Make sure that billing is enabled for your project. +3. From the **Credentials** page, create a new **API Key** or use an existing + one for your project. + +.. _API Key: + https://developers.google.com/api-client-library/python/guide/aaa_apikeys +.. _Cloud Console: https://console.cloud.google.com/project?_ diff --git a/scripts/readme-gen/templates/install_deps.tmpl.rst b/scripts/readme-gen/templates/install_deps.tmpl.rst new file mode 100644 index 00000000000..6f069c6c87a --- /dev/null +++ b/scripts/readme-gen/templates/install_deps.tmpl.rst @@ -0,0 +1,29 @@ +Install Dependencies +++++++++++++++++++++ + +#. Clone python-docs-samples and change directory to the sample directory you want to use. + + .. code-block:: bash + + $ git clone https://github.com/GoogleCloudPlatform/python-docs-samples.git + +#. Install `pip`_ and `virtualenv`_ if you do not already have them. You may want to refer to the `Python Development Environment Setup Guide`_ for Google Cloud Platform for instructions. + + .. _Python Development Environment Setup Guide: + https://cloud.google.com/python/setup + +#. Create a virtualenv. Samples are compatible with Python 3.7+. + + .. code-block:: bash + + $ virtualenv env + $ source env/bin/activate + +#. Install the dependencies needed to run the samples. + + .. code-block:: bash + + $ pip install -r requirements.txt + +.. _pip: https://pip.pypa.io/ +.. _virtualenv: https://virtualenv.pypa.io/ diff --git a/scripts/readme-gen/templates/install_portaudio.tmpl.rst b/scripts/readme-gen/templates/install_portaudio.tmpl.rst new file mode 100644 index 00000000000..5ea33d18c00 --- /dev/null +++ b/scripts/readme-gen/templates/install_portaudio.tmpl.rst @@ -0,0 +1,35 @@ +Install PortAudio ++++++++++++++++++ + +Install `PortAudio`_. This is required by the `PyAudio`_ library to stream +audio from your computer's microphone. PyAudio depends on PortAudio for cross-platform compatibility, and is installed differently depending on the +platform. + +* For Mac OS X, you can use `Homebrew`_:: + + brew install portaudio + + **Note**: if you encounter an error when running `pip install` that indicates + it can't find `portaudio.h`, try running `pip install` with the following + flags:: + + pip install --global-option='build_ext' \ + --global-option='-I/usr/local/include' \ + --global-option='-L/usr/local/lib' \ + pyaudio + +* For Debian / Ubuntu Linux:: + + apt-get install portaudio19-dev python-all-dev + +* Windows may work without having to install PortAudio explicitly (it will get + installed with PyAudio). + +For more details, see the `PyAudio installation`_ page. + + +.. _PyAudio: https://people.csail.mit.edu/hubert/pyaudio/ +.. _PortAudio: http://www.portaudio.com/ +.. _PyAudio installation: + https://people.csail.mit.edu/hubert/pyaudio/#downloads +.. _Homebrew: http://brew.sh diff --git a/scripts/run_and_publish_benchmark.py b/scripts/run_and_publish_benchmark.py deleted file mode 100644 index 859d68e60ed..00000000000 --- a/scripts/run_and_publish_benchmark.py +++ /dev/null @@ -1,481 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import argparse -import datetime -import json -import os -import pathlib -import re -import subprocess -import sys -import tempfile -from typing import Dict, List, Tuple, Union - -import numpy as np -import pandas as pd -import pandas_gbq - -LOGGING_NAME_ENV_VAR = "BIGFRAMES_PERFORMANCE_LOG_NAME" -CURRENT_DIRECTORY = pathlib.Path(__file__).parent.absolute() - - -def run_benchmark_subprocess(args, log_env_name_var, file_path=None, region=None): - """ - Runs a benchmark subprocess with configured environment variables. Adjusts PYTHONPATH, - sets region-specific BigQuery location, and logs environment variables. - - This function terminates the benchmark session if the subprocess exits with an error, - due to `check=True` in subprocess.run, which raises CalledProcessError on non-zero - exit status. - """ - env = os.environ.copy() - current_pythonpath = env.get("PYTHONPATH", "") - env["PYTHONPATH"] = ( - os.path.join(os.getcwd(), "tests") + os.pathsep + current_pythonpath - ) - - if region: - env["BIGQUERY_LOCATION"] = region - env[LOGGING_NAME_ENV_VAR] = log_env_name_var - try: - if file_path: # Notebooks - duration_pattern = re.compile(r"(\d+\.\d+)s call") - process = subprocess.Popen(args, env=env, stdout=subprocess.PIPE, text=True) - assert process.stdout is not None - for line in process.stdout: - print(line, end="") - match = duration_pattern.search(line) - if match: - duration = match.group(1) - with open(f"{file_path}.local_exec_time_seconds", "w") as f: - f.write(f"{duration}\n") - process.wait() - if process.returncode != 0: - raise subprocess.CalledProcessError(process.returncode, args) - else: # Benchmarks - file_path = log_env_name_var - subprocess.run(args, env=env, check=True) - except Exception: - directory = pathlib.Path(file_path).parent - for file in directory.glob(f"{pathlib.Path(file_path).name}.*"): - if file.suffix != ".backup": - print(f"Benchmark failed, deleting: {file}") - file.unlink() - error_file = directory / f"{pathlib.Path(file_path).name}.error" - error_file.touch() - - -def collect_benchmark_result( - benchmark_path: str, iterations: int -) -> Tuple[pd.DataFrame, Union[str, None]]: - """Generate a DataFrame report on HTTP queries, bytes processed, slot time and execution time from log files.""" - path = pathlib.Path(benchmark_path) - try: - results_dict: Dict[str, List[Union[int, float, None]]] = {} - # Use local_seconds_files as the baseline - local_seconds_files = sorted(path.rglob("*.local_exec_time_seconds")) - error_files = sorted(path.rglob("*.error")) - benchmarks_with_missing_files = [] - - for local_seconds_file in local_seconds_files: - base_name = local_seconds_file.name.removesuffix(".local_exec_time_seconds") - base_path = local_seconds_file.parent / base_name - filename = base_path.relative_to(path) - - # Construct paths for other metric files - bytes_file = pathlib.Path(f"{base_path}.bytesprocessed") - millis_file = pathlib.Path(f"{base_path}.slotmillis") - bq_seconds_file = pathlib.Path(f"{base_path}.bq_exec_time_seconds") - query_char_count_file = pathlib.Path(f"{base_path}.query_char_count") - - # Check if all corresponding files exist - missing_files = [] - if not bytes_file.exists(): - missing_files.append(bytes_file.name) - if not millis_file.exists(): - missing_files.append(millis_file.name) - if not bq_seconds_file.exists(): - missing_files.append(bq_seconds_file.name) - if not query_char_count_file.exists(): - missing_files.append(query_char_count_file.name) - - if missing_files: - benchmarks_with_missing_files.append((str(filename), missing_files)) - continue - - with open(query_char_count_file, "r") as file: - lines = file.read().splitlines() - query_char_count = sum(int(line) for line in lines) / iterations - query_count = len(lines) / iterations - - with open(local_seconds_file, "r") as file: - lines = file.read().splitlines() - local_seconds = sum(float(line) for line in lines) / iterations - - with open(bytes_file, "r") as file: - lines = file.read().splitlines() - total_bytes = sum(int(line) for line in lines) / iterations - - with open(millis_file, "r") as file: - lines = file.read().splitlines() - total_slot_millis = sum(int(line) for line in lines) / iterations - - with open(bq_seconds_file, "r") as file: - lines = file.read().splitlines() - bq_seconds = sum(float(line) for line in lines) / iterations - - results_dict[str(filename)] = [ - query_count, - total_bytes, - total_slot_millis, - local_seconds, - bq_seconds, - query_char_count, - ] - finally: - for files_to_remove in ( - path.rglob("*.bytesprocessed"), - path.rglob("*.slotmillis"), - path.rglob("*.local_exec_time_seconds"), - path.rglob("*.bq_exec_time_seconds"), - path.rglob("*.query_char_count"), - path.rglob("*.error"), - ): - for log_file in files_to_remove: - log_file.unlink() - - columns = [ - "Query_Count", - "Bytes_Processed", - "Slot_Millis", - "Local_Execution_Time_Sec", - "BigQuery_Execution_Time_Sec", - "Query_Char_Count", - ] - - benchmark_metrics = pd.DataFrame.from_dict( - results_dict, - orient="index", - columns=columns, - ) - - report_title = ( - "---BIGQUERY USAGE REPORT---" - if iterations == 1 - else f"---BIGQUERY USAGE REPORT (Averages over {iterations} Iterations)---" - ) - print(report_title) - for index, row in benchmark_metrics.iterrows(): - formatted_local_exec_time = ( - f"{round(row['Local_Execution_Time_Sec'], 1)} seconds" - if not pd.isna(row["Local_Execution_Time_Sec"]) - else "N/A" - ) - print( - f"{index} - query count: {row['Query_Count']}," - + f" query char count: {row['Query_Char_Count']}," - + f" bytes processed sum: {row['Bytes_Processed']}," - + f" slot millis sum: {row['Slot_Millis']}," - + f" local execution time: {formatted_local_exec_time}" - + f", bigquery execution time: {round(row['BigQuery_Execution_Time_Sec'], 1)} seconds" - ) - - geometric_mean_queries = geometric_mean_excluding_zeros( - benchmark_metrics["Query_Count"] - ) - geometric_mean_query_char_count = geometric_mean_excluding_zeros( - benchmark_metrics["Query_Char_Count"] - ) - geometric_mean_bytes = geometric_mean_excluding_zeros( - benchmark_metrics["Bytes_Processed"] - ) - geometric_mean_slot_millis = geometric_mean_excluding_zeros( - benchmark_metrics["Slot_Millis"] - ) - geometric_mean_local_seconds = geometric_mean_excluding_zeros( - benchmark_metrics["Local_Execution_Time_Sec"] - ) - geometric_mean_bq_seconds = geometric_mean_excluding_zeros( - benchmark_metrics["BigQuery_Execution_Time_Sec"] - ) - - print( - f"---Geometric mean of queries: {geometric_mean_queries}," - + f" Geometric mean of queries char counts: {geometric_mean_query_char_count}," - + f" Geometric mean of bytes processed: {geometric_mean_bytes}," - + f" Geometric mean of slot millis: {geometric_mean_slot_millis}," - + f" Geometric mean of local execution time: {geometric_mean_local_seconds} seconds" - + f", Geometric mean of BigQuery execution time: {geometric_mean_bq_seconds} seconds---" - ) - - all_errors: List[str] = [] - if error_files: - all_errors.extend( - f"Failed: {error_file.relative_to(path).with_suffix('')}" - for error_file in error_files - ) - if ( - benchmarks_with_missing_files - and os.getenv("BENCHMARK_AND_PUBLISH", "false") == "true" - ): - all_errors.extend( - f"Missing files for benchmark '{name}': {files}" - for name, files in benchmarks_with_missing_files - ) - error_message = "\n" + "\n".join(all_errors) if all_errors else None - return ( - benchmark_metrics.reset_index().rename(columns={"index": "Benchmark_Name"}), - error_message, - ) - - -def geometric_mean_excluding_zeros(data): - """ - Calculate the geometric mean of a dataset, excluding any zero values. - Returns NaN if the dataset is empty, contains only NaN values, or if - all non-NaN values are zeros. - - The result is rounded to one decimal place. - """ - data = data.dropna() - data = data[data != 0] - if len(data) == 0: - return np.nan - log_data = np.log(data) - return round(np.exp(log_data.mean()), 1) - - -def get_repository_status(): - current_directory = os.getcwd() - subprocess.run( - ["git", "config", "--global", "--add", "safe.directory", current_directory], - check=True, - ) - - git_hash = subprocess.check_output( - ["git", "rev-parse", "--short", "HEAD"], text=True - ).strip() - bigframes_version = subprocess.check_output( - ["python", "-c", "import bigframes; print(bigframes.__version__)"], text=True - ).strip() - release_version = ( - f"{bigframes_version}dev{datetime.datetime.now().strftime('%Y%m%d')}+{git_hash}" - ) - - return { - "benchmark_start_time": datetime.datetime.now().isoformat(), - "git_hash": git_hash, - "bigframes_version": bigframes_version, - "release_version": release_version, - "python_version": sys.version, - } - - -def find_config(start_path): - """ - Searches for a 'config.jsonl' file starting from the given path and moving up to parent - directories. - - This function ascends from the initial directory specified by `start_path` up to 3 - levels or until it reaches a directory named 'benchmark'. The search moves upwards - because if there are multiple 'config.jsonl' files in the path hierarchy, the closest - configuration to the starting directory (the lowest level) is expected to take effect. - It checks each directory for the presence of 'config.jsonl'. If found, it returns the - path to the configuration file. If not found within the limit or upon reaching - the 'benchmark' directory, it returns None. - """ - target_file = "config.jsonl" - current_path = pathlib.Path(start_path).resolve() - if current_path.is_file(): - current_path = current_path.parent - - levels_checked = 0 - while current_path.name != "benchmark" and levels_checked < 3: - config_path = current_path / target_file - if config_path.exists(): - return config_path - if current_path.parent == current_path: - break - current_path = current_path.parent - levels_checked += 1 - - return None - - -def publish_to_bigquery(dataframe, notebook, project_name="bigframes-metrics"): - bigquery_table = ( - f"{project_name}.benchmark_report.notebook_benchmark" - if notebook - else f"{project_name}.benchmark_report.benchmark" - ) - - repo_status = get_repository_status() - for idx, col in enumerate(repo_status.keys()): - dataframe.insert(idx, col, repo_status[col]) - - pandas_gbq.to_gbq( - dataframe=dataframe, - destination_table=bigquery_table, - if_exists="append", - ) - print(f"Results have been successfully uploaded to {bigquery_table}.") - - -def run_benchmark_from_config(benchmark: str, iterations: int): - print(benchmark) - config_path = find_config(benchmark) - - if config_path: - benchmark_configs = [] - with open(config_path, "r") as f: - for line in f: - if line.strip(): - config = json.loads(line) - python_args = [f"--{key}={value}" for key, value in config.items()] - suffix = ( - config["benchmark_suffix"] - if "benchmark_suffix" in config - else "_".join(f"{key}_{value}" for key, value in config.items()) - ) - benchmark_configs.append((suffix, python_args)) - else: - benchmark_configs = [(None, [])] - - for _ in range(iterations): - for benchmark_config in benchmark_configs: - args = ["python", str(benchmark)] - args.extend(benchmark_config[1]) - log_env_name_var = str(benchmark) - if benchmark_config[0] is not None: - log_env_name_var += f"_{benchmark_config[0]}" - run_benchmark_subprocess(args=args, log_env_name_var=log_env_name_var) - - -def run_notebook_benchmark(benchmark_file: str, region: str): - export_file = f"{benchmark_file}_{region}" if region else benchmark_file - log_env_name_var = os.path.basename(export_file) - # TODO(shobs): For some reason --retries arg masks exceptions occurred in - # notebook failures, and shows unhelpful INTERNALERROR. Investigate that - # and enable retries if we can find a way to surface the real exception - # bacause the notebook is running against real GCP and something may fail - # due to transient issues. - pytest_command = [ - "py.test", - "--nbmake", - "--nbmake-timeout=900", # 15 minutes - "--durations=0", - "--color=yes", - ] - benchmark_args = (*pytest_command, benchmark_file) - - run_benchmark_subprocess( - args=benchmark_args, - log_env_name_var=log_env_name_var, - file_path=export_file, - region=region, - ) - - -def parse_arguments(): - parser = argparse.ArgumentParser( - description="Run benchmarks for different scenarios." - ) - parser.add_argument( - "--notebook", - action="store_true", - help="Set this flag to run the benchmark as a notebook. If not set, it assumes a Python (.py) file.", - ) - - parser.add_argument( - "--benchmark-path", - type=str, - default=None, - help="Specify the file path to the benchmark script, either a Jupyter notebook or a Python script.", - ) - - parser.add_argument( - "--region", - type=str, - default=None, - help="Specify the region where the benchmark will be executed or where the data resides. This parameter is optional.", - ) - - parser.add_argument( - "--publish-benchmarks", - type=str, - default=None, - help="Set the benchmarks to be published to BigQuery.", - ) - - parser.add_argument( - "--iterations", - type=int, - default=1, - help="Number of iterations to run each benchmark.", - ) - parser.add_argument( - "--output-csv", - type=str, - default=None, - help="Determines whether to output results to a CSV file. If no location is provided, a temporary location is automatically generated.", - ) - - return parser.parse_args() - - -def main(): - args = parse_arguments() - - if args.publish_benchmarks: - benchmark_metrics, error_message = collect_benchmark_result( - args.publish_benchmarks, args.iterations - ) - # Output results to CSV without specifying a location - if args.output_csv == "True": - current_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S") - temp_file = tempfile.NamedTemporaryFile( - prefix=f"benchmark_{current_time}_", delete=False, suffix=".csv" - ) - benchmark_metrics.to_csv(temp_file.name, index=False) - print( - f"Benchmark result is saved to a temporary location: {temp_file.name}" - ) - temp_file.close() - # Output results to CSV with specified a custom location - elif args.output_csv != "False": - benchmark_metrics.to_csv(args.output_csv, index=False) - print(f"Benchmark result is saved to: {args.output_csv}") - - # Publish the benchmark metrics to BigQuery under the 'bigframes-metrics' project. - # The 'BENCHMARK_AND_PUBLISH' environment variable should be set to 'true' only - # in specific Kokoro sessions. - if os.getenv("BENCHMARK_AND_PUBLISH", "false") == "true": - publish_to_bigquery(benchmark_metrics, args.notebook) - # If the 'GCLOUD_BENCH_PUBLISH_PROJECT' environment variable is set, publish the - # benchmark metrics to a specified BigQuery table in the provided project. This is - # intended for local testing where the default behavior is not to publish results. - elif project := os.getenv("GCLOUD_BENCH_PUBLISH_PROJECT", ""): - publish_to_bigquery(benchmark_metrics, args.notebook, project) - - if error_message: - raise Exception(error_message) - elif args.notebook: - run_notebook_benchmark(args.benchmark_path, args.region) - else: - run_benchmark_from_config(args.benchmark_path, args.iterations) - - -if __name__ == "__main__": - main() diff --git a/scripts/run_doctest.sh b/scripts/run_doctest.sh deleted file mode 100755 index d5fd7256ece..00000000000 --- a/scripts/run_doctest.sh +++ /dev/null @@ -1,49 +0,0 @@ -#!/bin/bash -set -eo pipefail - -# Disable buffering, so that the logs stream through. -export PYTHONUNBUFFERED=1 - -# Assume we are running from the repo root or we need to find it. -# If this script is in packages/bigframes/scripts/run_doctest.sh, -# then repo root is 3 levels up. -export PROJECT_ROOT=$(realpath "$(dirname "${BASH_SOURCE[0]}")/../../..") -cd "$PROJECT_ROOT" - -git config --global --add safe.directory "$(realpath .)" - -package_name="bigframes" -package_path="packages/${package_name}" -files_to_check="${package_path}" - -# Use the IF block to handle the case where KOKORO vars are missing -# (e.g. local testing) -if [[ -n "${KOKORO_GITHUB_PULL_REQUEST_TARGET_BRANCH}" && -n "${KOKORO_GITHUB_PULL_REQUEST_COMMIT}" ]]; then - echo "checking changes with 'git diff ${KOKORO_GITHUB_PULL_REQUEST_TARGET_BRANCH}...${KOKORO_GITHUB_PULL_REQUEST_COMMIT} -- ${files_to_check}'" - - package_modified=$(git diff "${KOKORO_GITHUB_PULL_REQUEST_TARGET_BRANCH}...${KOKORO_GITHUB_PULL_REQUEST_COMMIT}" -- "${files_to_check}" | wc -l) -else - # If not a PR (like a local run or a different CI trigger), - # we treat it as 0 so it falls through to the "continuous" check. - package_modified=0 -fi - -# Check if modified OR if it's a continuous build -if [[ "${package_modified}" -gt 0 || "$KOKORO_BUILD_ARTIFACTS_SUBDIR" == *"continuous"* ]]; then - echo "------------------------------------------------------------" - echo "Running doctest for: ${package_name}" - echo "------------------------------------------------------------" - - # Ensure credentials are set for system tests in Kokoro - if [[ -z "${GOOGLE_APPLICATION_CREDENTIALS}" && -f "${KOKORO_GFILE_DIR}/service-account.json" ]]; then - export GOOGLE_APPLICATION_CREDENTIALS="${KOKORO_GFILE_DIR}/service-account.json" - fi - - export GOOGLE_CLOUD_PROJECT="bigframes-testing" - NOX_SESSION=("cleanup" "doctest") - - cd "${package_path}" - python3 -m nox -s "${NOX_SESSION[@]}" -else - echo "No changes in ${package_name} and not a continuous build, skipping." -fi \ No newline at end of file diff --git a/scripts/setup-project-for-testing.sh b/scripts/setup-project-for-testing.sh deleted file mode 100755 index df9cea46a4b..00000000000 --- a/scripts/setup-project-for-testing.sh +++ /dev/null @@ -1,259 +0,0 @@ -#!/bin/bash - -# Copyright 2023 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -if [ $# -lt 1 ]; then - echo "USAGE: `basename $0` []" - echo "EXAMPLES:" - echo " `basename $0` my-project" - echo " `basename $0` my-project user:user_id@example.com" - echo " `basename $0` my-project group:group_id@example.com" - echo " `basename $0` my-project serviceAccount:service_account_id@example.com" - exit 1 -fi - -PROJECT_ID=$1 -PRINCIPAL=$2 -BIGFRAMES_DEFAULT_CONNECTION_NAME=bigframes-default-connection -BIGFRAMES_RF_CONNECTION_NAME=bigframes-rf-conn - -if [ "$PRINCIPAL" != "" ]; then - echo $PRINCIPAL | grep -E "(user|group|serviceAccount):" >/dev/null - if [ $? -ne 0 ]; then - echo "principal must have prefix 'user:', 'group:' or 'serviceAccount:'" - exit 1 - fi -fi - -if ! test `which gcloud`; then - echo "gcloud CLI is not installed. Install it from https://cloud.google.com/sdk/docs/install." >&2 - exit 1 -fi - -################################################################################ -# Log and execute a command -################################################################################ -function log_and_execute() { - echo Running command: $* - $* -} - - -################################################################################ -# Enable APIs -################################################################################ -function enable_apis() { - for service in aiplatform.googleapis.com \ - artifactregistry.googleapis.com \ - bigquery.googleapis.com \ - bigqueryconnection.googleapis.com \ - bigquerystorage.googleapis.com \ - cloudbuild.googleapis.com \ - cloudfunctions.googleapis.com \ - cloudresourcemanager.googleapis.com \ - compute.googleapis.com \ - run.googleapis.com \ - ; do - log_and_execute gcloud --project=$PROJECT_ID services enable $service - if [ $? -ne 0 ]; then - echo "Failed to enable service $service, exiting..." - exit 1 - fi - done -} - - -################################################################################ -# Ensure a BQ connection exists with desired IAM rols -################################################################################ -function ensure_bq_connection_with_iam() { - if [ $# -ne 2 ]; then - echo "USAGE: `basename $0` " - echo "EXAMPLES:" - echo " `basename $0` my-project my-connection" - exit 1 - fi - - location=$1 - connection_name=$2 - - log_and_execute bq show \ - --connection \ - --project_id=$PROJECT_ID \ - --location=$location \ - $connection_name 2>&1 >/dev/null - if [ $? -ne 0 ]; then - echo "Connection $connection_name doesn't exists in location \"$location\", creating..." - log_and_execute bq mk \ - --connection \ - --project_id=$PROJECT_ID \ - --location=$location \ - --connection_type=CLOUD_RESOURCE \ - $connection_name - if [ $? -ne 0 ]; then - echo "Failed creating connection, exiting." - exit 1 - fi - else - echo "Connection $connection_name already exists in location $location." - fi - - compact_json_info_cmd="bq show --connection \ - --project_id=$PROJECT_ID \ - --location=$location \ - --format=json \ - $connection_name" - compact_json_info_cmd_output=`$compact_json_info_cmd` - if [ $? -ne 0 ]; then - echo "Failed to fetch connection info: $compact_json_info_cmd_output" - exit 1 - fi - - connection_service_account=`echo $compact_json_info_cmd_output | sed -e 's/.*"cloudResource":{"serviceAccountId":"//' -e 's/".*//'` - - # Configure roles for the service accounts associated with the connection - for role in run.invoker aiplatform.user; do - log_and_execute gcloud projects add-iam-policy-binding $PROJECT_ID \ - --member=serviceAccount:$connection_service_account \ - --role=roles/$role - if [ $? -ne 0 ]; then - echo "Failed to set IAM, exiting..." - exit 1 - fi - done -} - - -################################################################################ -# Create the default BQ connection in US location -################################################################################ -function ensure_bq_connections_with_iam() { - ensure_bq_connection_with_iam "us" "$BIGFRAMES_DEFAULT_CONNECTION_NAME" - - # Create commonly used BQ connection in various locations - for location in asia-southeast1 \ - eu \ - europe-west4 \ - southamerica-west1 \ - us \ - us-central1 \ - us-east5 \ - ; do - ensure_bq_connection_with_iam "$location" "$BIGFRAMES_RF_CONNECTION_NAME" - done -} - - -################################################################################ -# Set up IAM roles for principal -################################################################################ -function setup_iam_roles () { - if [ "$PRINCIPAL" != "" ]; then - for role in aiplatform.user \ - bigquery.user \ - bigquery.connectionAdmin \ - bigquery.dataEditor \ - browser \ - cloudfunctions.developer \ - iam.serviceAccountUser \ - ; do - log_and_execute gcloud projects add-iam-policy-binding $PROJECT_ID \ - --member=$PRINCIPAL \ - --role=roles/$role - if [ $? -ne 0 ]; then - echo "Failed to set IAM, exiting..." - exit 1 - fi - done - fi -} - - -################################################################################ -# Create vertex endpoint for test ML model -################################################################################ -function create_bq_model_vertex_endpoint () { - vertex_region=us-central1 - model_name=bigframes-test-linreg2 - endpoint_name=$model_name-endpoint - - # Create vertex model - log_and_execute python scripts/create_test_model_vertex.py \ - -m $model_name \ - -p $PROJECT_ID - if [ $? -ne 0 ]; then - echo "Failed to create model, exiting..." - exit 1 - fi - - # Create vertex endpoint - log_and_execute gcloud ai endpoints create \ - --project=$PROJECT_ID \ - --region=$vertex_region \ - --display-name=$endpoint_name - if [ $? -ne 0 ]; then - echo "Failed to create vertex endpoint, exiting..." - exit 1 - fi - - # Fetch endpoint id - endpoint_id=`gcloud ai endpoints list \ - --project=$PROJECT_ID \ - --region=$vertex_region \ - --filter=display_name=$endpoint_name 2>/dev/null \ - | tail -n1 | cut -d' ' -f 1` - if [ "$endpoint_id" = "" ]; then - echo "Failed to fetch vertex endpoint id, exiting..." - exit 1 - fi - - # Deploy the model to the vertex endpoint - log_and_execute gcloud ai endpoints deploy-model $endpoint_id \ - --project=$PROJECT_ID \ - --region=$vertex_region \ - --model=$model_name \ - --display-name=$model_name - if [ $? -ne 0 ]; then - echo "Failed to deploy model to vertex endpoint, exiting..." - exit 1 - fi - - # Form the endpoint - endpoint_rel_path=`gcloud ai endpoints describe \ - --project=$PROJECT_ID \ - --region=us-central1 \ - $endpoint_id 2>/dev/null \ - | grep "^name:" | cut -d' ' -f2` - if [ "$endpoint_rel_path" = "" ]; then - echo "Failed to fetch vertex endpoint relativr path, exiting..." - exit 1 - fi - endpoint_path=https://$vertex_region-aiplatform.googleapis.com/v1/$endpoint_rel_path - - # Print the endpoint configuration to be used in tests - echo - echo Run following command to set test model vertex endpoint: - echo export BIGFRAMES_TEST_MODEL_VERTEX_ENDPOINT=$endpoint_path -} - - -################################################################################ -# Set the things up -################################################################################ -enable_apis -ensure_bq_connections_with_iam -setup_iam_roles -create_bq_model_vertex_endpoint diff --git a/scripts/templates/bigframes_series_accessor.py.j2 b/scripts/templates/bigframes_series_accessor.py.j2 deleted file mode 100644 index 8ce37d67321..00000000000 --- a/scripts/templates/bigframes_series_accessor.py.j2 +++ /dev/null @@ -1,44 +0,0 @@ -{% include 'license.py.j2' %} - -# -# DO NOT MODIFY THIS FILE DIRECTLY. -# This file was generated by the script: {{ script_path }} -# - -from __future__ import annotations - -from typing import cast, Optional, TypeVar - -from bigframes.core.logging import log_adapter -from bigframes.extensions.core import series_accessor as core_accessor -from bigframes import series, dataframe, session - -T = TypeVar("T", bound="dataframe.DataFrame") -S = TypeVar("S", bound="series.Series") - - -{% for ns in namespaces %} -@log_adapter.class_logger -class {{ ns.bigframes_class_name }}(core_accessor.{{ ns.class_name }}[T, S]): - def __init__(self, bf_obj: S): - super().__init__(bf_obj) - - def _bf_from_series( - self, session: Optional[session.Session] = None - ) -> series.Series: - return self._obj - - def _to_dataframe(self, bf_df: dataframe.DataFrame) -> T: - return cast(T, bf_df) - - def _to_series(self, bf_series: series.Series) -> S: - return cast(S, bf_series) - - {% for child in ns.children %} - @property - def {{ child.prop_name }}(self) -> {{ child.bigframes_class_name }}[T, S]: - return {{ child.bigframes_class_name }}(self._obj) - - {% endfor %} - -{% endfor %} diff --git a/scripts/templates/core_series_accessor.py.j2 b/scripts/templates/core_series_accessor.py.j2 deleted file mode 100644 index 89decdcbe1e..00000000000 --- a/scripts/templates/core_series_accessor.py.j2 +++ /dev/null @@ -1,81 +0,0 @@ -{% include 'license.py.j2' %} - -# -# DO NOT MODIFY THIS FILE DIRECTLY. -# This file was generated by the script: {{ script_path }} -# - -from __future__ import annotations - -import abc -import datetime -from typing import ( - Any, - Literal, - Optional, - TypeVar, - Union, - cast, -) - -from bigframes import series, session -from bigframes.core import col, sentinels -from bigframes.extensions.core import abstract_series_accessor, series_mixins - -T = TypeVar("T") -S = TypeVar("S") - - -{% for ns in namespaces %} -{% if ns.class_name == "AiSeriesAccessor" %} -class {{ ns.class_name }}(series_mixins.AIMixin[T, S]): -{% else %} -class {{ ns.class_name }}(abstract_series_accessor.AbstractBigQuerySeriesAccessor[T, S]): -{% endif %} - """{{ ns.description }}""" - - {% for child in ns.children %} - @property - @abc.abstractmethod - def {{ child.prop_name }}(self) -> {{ child.class_name }}[T, S]: - """Accessor for BigQuery {{ child.prop_name }} functions.""" - - {% endfor %} - {% for func in ns.functions %} - def {{ func.name }}( - self, - {% for arg in func.args if arg.name != func.series_accessor_arg %} - {{ arg.name }}: Union[series.Series, col.Expression, {{ arg.type_hint }}]{% if arg.default %} = {{ arg.default }}{% endif %}, - {% endfor %} - *, - session: Optional[session.Session] = None, - ) -> S: - """{{ func.description | indent(8) }}""" - from {{ func.import_module }} import {{ func.name }} as {{ func.name }}_impl - {% if func.args | length > 1 %} - - # Resolve session from other arguments if not passed - if session is None: - from bigframes.core import googlesql - session = googlesql._find_session( - {% for arg in func.args if arg.name != func.series_accessor_arg %} - {{ arg.name }}, - {% endfor %} - ) - {% endif %} - - bf_series = self._bf_from_series(session) - result = {{ func.name }}_impl( - {% for arg in func.args %} - {% if arg.name == func.series_accessor_arg %} - bf_series, - {% else %} - {{ arg.name }}, - {% endif %} - {% endfor %} - ) - return self._to_series(cast(series.Series, result)) - - {% endfor %} - -{% endfor %} diff --git a/scripts/templates/license.py.j2 b/scripts/templates/license.py.j2 deleted file mode 100644 index 58d482ea386..00000000000 --- a/scripts/templates/license.py.j2 +++ /dev/null @@ -1,13 +0,0 @@ -# Copyright 2026 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. diff --git a/scripts/templates/operation.py.j2 b/scripts/templates/operation.py.j2 deleted file mode 100644 index 720d867986e..00000000000 --- a/scripts/templates/operation.py.j2 +++ /dev/null @@ -1,50 +0,0 @@ -{% include 'license.py.j2' %} - -# -# DO NOT MODIFY THIS FILE DIRECTLY. -# This file was generated from: {{ yaml_path }} -# by the script: {{ script_path }} - -from __future__ import annotations - -import datetime -import decimal -from typing import Any, Literal, Optional, TypeVar, Union - -from bigframes import dtypes -import bigframes.core.col -import bigframes.core.expression as ex -import bigframes.core.googlesql -import bigframes.core.sentinels as sentinels -from bigframes.operations import googlesql -import bigframes.operations as ops -import bigframes.series as series - -{% for op in ops %} -{% if op.signature_definition %} -{{ op.signature_definition }} - - -{% endif %} -{{ op.internal_name }} = googlesql.GoogleSqlScalarOp( - "{{ op.sql_name }}", - args=({{ op.arg_specs }}), - signature={{ op.signature }}, -) -{% endfor %} -{% for func in functions %} - - -def {{ func.name }}( -{% for arg in func.args %} - {{ arg.name }}: Union[series.Series, bigframes.core.col.Expression, {{ arg.type_hint }}]{% if arg.default %} = {{ arg.default }}{% endif %}, -{% endfor %} -) -> Union[series.Series, bigframes.core.col.Expression]: - """{{ func.description | indent(4) }}""" - return bigframes.core.googlesql.apply_googlesql_scalar_op( - {{ func.op_name }}, -{% for arg in func.args %} - {{ arg.name }}, -{% endfor %} - ) -{% endfor %} diff --git a/scripts/templates/pandas_series_accessor.py.j2 b/scripts/templates/pandas_series_accessor.py.j2 deleted file mode 100644 index 15054665561..00000000000 --- a/scripts/templates/pandas_series_accessor.py.j2 +++ /dev/null @@ -1,53 +0,0 @@ -{% include 'license.py.j2' %} - -# -# DO NOT MODIFY THIS FILE DIRECTLY. -# This file was generated by the script: {{ script_path }} -# - -from __future__ import annotations - -from typing import cast, Optional, TypeVar - -import pandas -import pandas.api.extensions - -from bigframes import dataframe, series, session -from bigframes.core import global_session as bf_session -from bigframes.core.logging import log_adapter -from bigframes.extensions.core import series_accessor as core_accessor - -T = TypeVar("T", bound="pandas.DataFrame") -S = TypeVar("S", bound="pandas.Series") - - -{% for ns in namespaces %} -{% if ns.is_root %} -@pandas.api.extensions.register_series_accessor("bigquery") -{% endif %} -@log_adapter.class_logger -class {{ ns.pandas_class_name }}(core_accessor.{{ ns.class_name }}[T, S]): - def __init__(self, pandas_obj: S): - super().__init__(pandas_obj) - - def _bf_from_series( - self, session: Optional[session.Session] = None - ) -> series.Series: - if session is None: - session = bf_session.get_global_session() - return cast(series.Series, session.read_pandas(self._obj)) - - def _to_dataframe(self, bf_df: dataframe.DataFrame) -> T: - return cast(T, bf_df.to_pandas(ordered=True)) - - def _to_series(self, bf_series: series.Series) -> S: - return cast(S, bf_series.to_pandas(ordered=True)) - - {% for child in ns.children %} - @property - def {{ child.prop_name }}(self) -> {{ child.pandas_class_name }}[T, S]: - return {{ child.pandas_class_name }}(self._obj) - - {% endfor %} - -{% endfor %} diff --git a/scripts/templates/signature_def.py.j2 b/scripts/templates/signature_def.py.j2 deleted file mode 100644 index b00c95e3383..00000000000 --- a/scripts/templates/signature_def.py.j2 +++ /dev/null @@ -1,76 +0,0 @@ -def {{ func_name }}(*args): - # Pad args with None to match max expected args - args = args + (None,) * ({{ max_args }} - len(args)) - {% for impl in impls %} - # Try matching impl {{ loop.index0 }} - {% if impl.requires_generic_types %} - any1_val = None - {% endif %} - match_ok = True - {% for arg in impl.args %} - {% set idx = loop.index0 %} - if match_ok and args[{{ idx }}] is not None: - {% if arg.value == "any1" %} - if any1_val is not None: - try: - any1_val = dtypes.coerce_to_common(any1_val, args[{{ idx }}]) - except TypeError: - match_ok = False - else: - any1_val = args[{{ idx }}] - {% elif arg.value.startswith("list<") and arg.value.endswith(">") %} - {% set inner_type = arg.value[5:-1] %} - if not dtypes.is_array_like(args[{{ idx }}]): - match_ok = False - else: - inner = dtypes.get_array_inner_type(args[{{ idx }}]) - {% if inner_type == "any1" %} - if any1_val is not None: - try: - any1_val = dtypes.coerce_to_common(any1_val, inner) - except TypeError: - match_ok = False - else: - any1_val = inner - {% else %} - {% set dtype_expr = dtype_map[inner_type] %} - try: - if dtypes.coerce_to_common(inner, {{ dtype_expr }}) != {{ dtype_expr }}: - match_ok = False - except TypeError: - match_ok = False - {% endif %} - {% elif arg.value == "struct" %} - if not dtypes.is_struct_like(args[{{ idx }}]): - match_ok = False - {% else %} - {% set dtype_expr = dtype_map[arg.value] %} - try: - if dtypes.coerce_to_common(args[{{ idx }}], {{ dtype_expr }}) != {{ dtype_expr }}: - match_ok = False - except TypeError: - match_ok = False - {% endif %} - {% endfor %} - if match_ok: - {% set return_type_yaml = impl.return_type %} - {% if return_type_yaml == "any1" %} - return any1_val - {% elif return_type_yaml.startswith("list<") and return_type_yaml.endswith(">") %} - {% set inner_type = return_type_yaml[5:-1] %} - {% if inner_type == "any1" %} - if any1_val is not None: - return dtypes.list_type(any1_val) - else: - return None - {% else %} - {% set dtype_expr = dtype_map[inner_type] %} - return dtypes.list_type({{ dtype_expr }}) - {% endif %} - {% else %} - {% set dtype_expr = dtype_map[return_type_yaml] %} - return {{ dtype_expr }} - {% endif %} - - {% endfor %} - raise TypeError(f"Could not find matching signature for {{ sql_name }} with argument types: {[str(t) for t in args]}") diff --git a/scripts/templates/test_operation.py.j2 b/scripts/templates/test_operation.py.j2 deleted file mode 100644 index 6aee365cded..00000000000 --- a/scripts/templates/test_operation.py.j2 +++ /dev/null @@ -1,44 +0,0 @@ -{% include 'license.py.j2' %} - -# -# DO NOT MODIFY THIS FILE DIRECTLY. -# This file was generated from: {{ yaml_path }} -# by the script: {{ script_path }} - -import bigframes.core.col -import bigframes.core.expression as ex -import bigframes.pandas as bpd -import {{ import_path }} as {{ short_name }}_op -import bigframes.bigquery as bbq - - -{% for func in functions %} -def test_{{ func.name }}_expression(): - # Call the function with col() expressions -{% if is_global %} - result = bbq.{{ func.name }}( -{% else %} - result = bbq.{{ short_name }}.{{ func.name }}( -{% endif %} - {% for arg in func.args %} - bpd.col("{{ arg.name }}"), - {% endfor %} - ) - - # Verify result is a col Expression - assert isinstance(result, bigframes.core.col.Expression) - - # Verify the internal expression structure - expr = result._value - assert isinstance(expr, ex.OpExpression) - assert expr.op == {{ short_name }}_op.{{ func.op_name }} - - # Verify arguments are free variables matching the names - assert len(expr.inputs) == {{ func.args | length }} - {% for arg in func.args %} - assert isinstance(expr.inputs[{{ loop.index0 }}], ex.UnboundVariableExpression) - assert expr.inputs[{{ loop.index0 }}].id == "{{ arg.name }}" - {% endfor %} - - -{% endfor %} diff --git a/scripts/test_publish_api_coverage.py b/scripts/test_publish_api_coverage.py index 167cf5917b0..96b2d1bb48d 100644 --- a/scripts/test_publish_api_coverage.py +++ b/scripts/test_publish_api_coverage.py @@ -12,57 +12,34 @@ # See the License for the specific language governing permissions and # limitations under the License. -import sys - import pandas -import pytest -from publish_api_coverage import build_api_coverage_table - -pytest.importorskip("sklearn") - - -@pytest.fixture -def api_coverage_df(): - return build_api_coverage_table("my_bf_ver", "my_release_ver") +import publish_api_coverage -@pytest.mark.skipif( - sys.version_info >= (3, 13), - reason="Issues with installing sklearn for this test in python 3.13", -) -def test_api_coverage_produces_expected_schema(api_coverage_df): - # Older pandas has different timestamp default precision - pytest.importorskip("pandas", minversion="2.0.0") - +def test_api_coverage_produces_expected_schema(): + df = publish_api_coverage.build_api_coverage_table("my_bf_ver", "my_release_ver") pandas.testing.assert_series_equal( - api_coverage_df.dtypes, + df.dtypes, pandas.Series( - data={ - # Note to developer: if you update this test, you will also - # need to update schema of the API coverage BigQuery table in - # the bigframes-metrics project. - "api": "string", - "pattern": "string", - "kind": "string", - "is_in_bigframes": "boolean", - "missing_parameters": "string", - "requires_index": "string", - "requires_ordering": "string", - "module": "string", - "timestamp": "datetime64[us]", - "bigframes_version": "string", - "release_version": "string", - }, + data=[ + "string", + "string", + "string", + "boolean", + "string", + "datetime64[ns]", + "string", + "string", + ], + index=[ + "api", + "pattern", + "kind", + "is_in_bigframes", + "module", + "timestamp", + "bigframes_version", + "release_version", + ], ), - # String dtype behavior not consistent across pandas versions - check_dtype=False, ) - - -@pytest.mark.skipif( - sys.version_info >= (3, 13), - reason="Issues with installing sklearn for this test in python 3.13", -) -def test_api_coverage_produces_missing_parameters(api_coverage_df): - """Make sure at least some functions have reported missing parameters.""" - assert (api_coverage_df["missing_parameters"].str.len() > 0).any() diff --git a/scripts/windows/build.bat b/scripts/windows/build.bat deleted file mode 100644 index d599702c98e..00000000000 --- a/scripts/windows/build.bat +++ /dev/null @@ -1,38 +0,0 @@ -@rem Copyright 2024 Google LLC -@rem -@rem Licensed under the Apache License, Version 2.0 (the "License"); -@rem you may not use this file except in compliance with the License. -@rem You may obtain a copy of the License at -@rem -@rem http://www.apache.org/licenses/LICENSE-2.0 -@rem -@rem Unless required by applicable law or agreed to in writing, software -@rem distributed under the License is distributed on an "AS IS" BASIS, -@rem WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -@rem See the License for the specific language governing permissions and -@rem limitations under the License. - -:; Change directory to repo root. -SET script_dir="%~dp0" -cd "%~dp0"\..\.. - -echo "Listing available Python versions' -py -0 || goto :error - -py -3.10 -m pip install --upgrade pip || goto :error -py -3.10 -m pip install --upgrade pip setuptools wheel || goto :error - -echo "Building Wheel" -py -3.10 -m pip wheel . --wheel-dir wheels || goto :error/ - -echo "Built wheel, now running tests." -call "%script_dir%"/test.bat 3.10 || goto :error - -echo "Windows build has completed successfully" - -:; https://stackoverflow.com/a/46813196/101923 -:; exit 0 -exit /b 0 - -:error -exit /b %errorlevel% diff --git a/scripts/windows/test.bat b/scripts/windows/test.bat deleted file mode 100644 index bcd605bd129..00000000000 --- a/scripts/windows/test.bat +++ /dev/null @@ -1,40 +0,0 @@ -@rem Copyright 2024 Google LLC -@rem -@rem Licensed under the Apache License, Version 2.0 (the "License"); -@rem you may not use this file except in compliance with the License. -@rem You may obtain a copy of the License at -@rem -@rem http://www.apache.org/licenses/LICENSE-2.0 -@rem -@rem Unless required by applicable law or agreed to in writing, software -@rem distributed under the License is distributed on an "AS IS" BASIS, -@rem WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -@rem See the License for the specific language governing permissions and -@rem limitations under the License. - -@rem This test file runs for one Python version at a time, and is intended to -@rem be called from within the build loop. - -:; Change directory to repo root. -SET script_dir="%~dp0" -cd "%~dp0"\..\.. - -set PYTHON_VERSION=%1 -if "%PYTHON_VERSION%"=="" ( - echo "Python version was not provided, using Python 3.10" - set PYTHON_VERSION=3.10 -) - -py -%PYTHON_VERSION%-64 -m pip install nox || goto :error - -py -%PYTHON_VERSION%-64 -m nox -s unit-"%PYTHON_VERSION%" || goto :error - -:; TODO(b/358148440): enable system tests on windows -:; py -%PYTHON_VERSION%-64 -m nox -s system-"%PYTHON_VERSION%" || goto :error - -:; https://stackoverflow.com/a/46813196/101923 -:; exit 0 -exit /b 0 - -:error -exit /b %errorlevel% diff --git a/setup.py b/setup.py index e2717fbe5e4..29eacb74a9a 100644 --- a/setup.py +++ b/setup.py @@ -12,7 +12,6 @@ # See the License for the specific language governing permissions and # limitations under the License. - import io import itertools import os @@ -31,67 +30,36 @@ # 'Development Status :: 3 - Alpha' # 'Development Status :: 4 - Beta' # 'Development Status :: 5 - Production/Stable' -release_status = "Development Status :: 5 - Production/Stable" +release_status = "Development Status :: 3 - Alpha" dependencies = [ - # please keep these in sync with the minimum versions in testing/constraints-3.10.txt "cloudpickle >= 2.0.0", "fsspec >=2023.3.0", - "gcsfs >=2023.3.0, !=2025.5.0, !=2026.2.0, !=2026.3.0", + "gcsfs >=2023.3.0", "geopandas >=0.12.2", - "google-auth[pyopenssl] >=2.15.0,<3.0", - "google-cloud-bigquery[bqstorage,pandas] >=3.36.0", - # 2.30 needed for arrow support. - "google-cloud-bigquery-storage >= 2.30.0, < 3.0.0", - "google-cloud-functions >=1.20.2", - "google-cloud-bigquery-connection >=1.18.2", - "google-cloud-resource-manager >=1.14.2", + "google-auth >2.14.1,<3.0dev", + "google-cloud-bigquery[bqstorage,pandas] >=3.10.0", + "google-cloud-functions >=1.10.1", + "google-cloud-bigquery-connection >=1.12.0", + "google-cloud-iam >=2.12.1", + "google-cloud-resource-manager >=1.10.3", "google-cloud-storage >=2.0.0", - "google-crc32c >=1.0.0,<2.0.0", - "grpc-google-iam-v1 >= 0.14.2", - "numpy >=1.24.0", - "pandas >=1.5.3", - "pandas-gbq >=0.26.1", - "pyarrow >=23.0.1", + # TODO: Relax upper bound once we have fixed `system_prerelease` tests. + "ibis-framework[bigquery] >=6.2.0,<7.0.0dev", + "pandas >=1.5.0", "pydata-google-auth >=1.8.2", "requests >=2.27.1", - "shapely >=1.8.5", - "tabulate >=0.9", - "humanize >=4.6.0", - "matplotlib >=3.7.1", - "db-dtypes >=1.4.2", - "pyiceberg >= 0.7.1", - # For vendored ibis-framework. - "atpublic>=2.3,<6", - "python-dateutil>=2.8.2,<3", - "pytz>=2022.7", - "toolz>=0.11,<2", - "typing-extensions>=4.5.0,<5", - "rich>=12.4.4,<14", + "scikit-learn >=1.2.2", + "sqlalchemy >=1.4,<3.0dev", + "ipywidgets >=7.7.1", + "humanize >= 4.6.0", ] extras = { # Optional test dependencies packages. If they're missed, may skip some tests. "tests": [ - "freezegun", - "pytest-snapshot", - "google-cloud-bigtable >=2.30.0", - "google-cloud-pubsub >=2.29.0", - "tzdata", + "pandas-gbq >=0.19.0", ], - # used for local engine - "polars": ["polars >= 1.21.0"], - "scikit-learn": ["scikit-learn>=1.2.2"], # Packages required for basic development flow. - "dev": [ - "pytest", - "pre-commit", - "nox", - "google-cloud-testutils", - ], - # install anywidget for SQL - "anywidget": [ - "anywidget>=0.9.18", - "traitlets>=5.0.0", - ], + "dev": ["pytest", "pytest-mock", "pre-commit", "nox", "google-cloud-testutils"], } extras["all"] = list(sorted(frozenset(itertools.chain.from_iterable(extras.values())))) @@ -112,53 +80,36 @@ # benchmarks, etc. packages = [ package - for package in setuptools.find_namespace_packages() - if package.startswith("bigframes") -] + [ - package - for package in setuptools.find_namespace_packages("third_party") - if package.startswith("bigframes_vendored") + for package in setuptools.PEP420PackageFinder.find() + if package.startswith("bigframes") or package.startswith("third_party") ] setuptools.setup( name=name, version=version_id, description=description, - download_url="https://github.com/googleapis/google-cloud-python/tree/main/packages/bigframes/releases", long_description=readme, - long_description_content_type="text/x-rst", author="Google LLC", author_email="bigframes-feedback@google.com", license="Apache 2.0", - url="https://dataframes.bigquery.dev", - project_urls={ - "Source": "https://github.com/googleapis/google-cloud-python/tree/main/packages/bigframes", - "Changelog": "https://dataframes.bigquery.dev/changelog.html", - "Issues": "https://github.com/googleapis/google-cloud-python/tree/main/packages/bigframes/issues", - }, + url="https://github.com/googleapis/python-bigquery-dataframes", classifiers=[ release_status, "Intended Audience :: Developers", "License :: OSI Approved :: Apache Software License", "Programming Language :: Python", "Programming Language :: Python :: 3", + "Programming Language :: Python :: 3.9", "Programming Language :: Python :: 3.10", "Programming Language :: Python :: 3.11", - "Programming Language :: Python :: 3.12", - "Programming Language :: Python :: 3.13", - "Programming Language :: Python :: 3.14", "Operating System :: OS Independent", "Topic :: Internet", ], install_requires=dependencies, extras_require=extras, platforms="Posix; MacOS X; Windows", - package_dir={ - "bigframes": "bigframes", - "bigframes_vendored": "third_party/bigframes_vendored", - }, packages=packages, - python_requires=">=3.10", + python_requires=">=3.9", include_package_data=True, zip_safe=False, ) diff --git a/specs/2025-08-04-geoseries-scalars.md b/specs/2025-08-04-geoseries-scalars.md deleted file mode 100644 index e7bc6c61e19..00000000000 --- a/specs/2025-08-04-geoseries-scalars.md +++ /dev/null @@ -1,317 +0,0 @@ -# Implementing GeoSeries scalar operators - -This project is to implement all GeoSeries scalar properties and methods in the -`bigframes.geopandas.GeoSeries` class. Likewise, all BigQuery GEOGRAPHY -functions should be exposed in the `bigframes.bigquery` module. - -## Background - -*Explain the context and why this change is necessary.* -*Include links to relevant issues or documentation.* - -* https://geopandas.org/en/stable/docs/reference/geoseries.html -* https://cloud.google.com/bigquery/docs/reference/standard-sql/geography_functions - -## Acceptance Criteria - -*Define the specific, measurable outcomes that indicate the task is complete.* -*Use a checklist format for clarity.* - -### GeoSeries methods and properties - -- [x] Constructor -- [x] GeoSeries.area -- [x] GeoSeries.boundary -- [ ] GeoSeries.bounds -- [ ] GeoSeries.total_bounds -- [x] GeoSeries.length -- [ ] GeoSeries.geom_type -- [ ] GeoSeries.offset_curve -- [x] GeoSeries.distance -- [ ] GeoSeries.hausdorff_distance -- [ ] GeoSeries.frechet_distance -- [ ] GeoSeries.representative_point -- [ ] GeoSeries.exterior -- [ ] GeoSeries.interiors -- [ ] GeoSeries.minimum_bounding_radius -- [ ] GeoSeries.minimum_clearance -- [x] GeoSeries.x -- [x] GeoSeries.y -- [ ] GeoSeries.z -- [ ] GeoSeries.m -- [ ] GeoSeries.get_coordinates -- [ ] GeoSeries.count_coordinates -- [ ] GeoSeries.count_geometries -- [ ] GeoSeries.count_interior_rings -- [ ] GeoSeries.set_precision -- [ ] GeoSeries.get_precision -- [ ] GeoSeries.get_geometry -- [x] GeoSeries.is_closed -- [ ] GeoSeries.is_empty -- [ ] GeoSeries.is_ring -- [ ] GeoSeries.is_simple -- [ ] GeoSeries.is_valid -- [ ] GeoSeries.is_valid_reason -- [ ] GeoSeries.is_valid_coverage -- [ ] GeoSeries.invalid_coverage_edges -- [ ] GeoSeries.has_m -- [ ] GeoSeries.has_z -- [ ] GeoSeries.is_ccw -- [ ] GeoSeries.contains -- [ ] GeoSeries.contains_properly -- [ ] GeoSeries.crosses -- [ ] GeoSeries.disjoint -- [ ] GeoSeries.dwithin -- [ ] GeoSeries.geom_equals -- [ ] GeoSeries.geom_equals_exact -- [ ] GeoSeries.geom_equals_identical -- [ ] GeoSeries.intersects -- [ ] GeoSeries.overlaps -- [ ] GeoSeries.touches -- [ ] GeoSeries.within -- [ ] GeoSeries.covers -- [ ] GeoSeries.covered_by -- [ ] GeoSeries.relate -- [ ] GeoSeries.relate_pattern -- [ ] GeoSeries.clip_by_rect -- [x] GeoSeries.difference -- [x] GeoSeries.intersection -- [ ] GeoSeries.symmetric_difference -- [ ] GeoSeries.union -- [x] GeoSeries.boundary -- [x] GeoSeries.buffer -- [x] GeoSeries.centroid -- [ ] GeoSeries.concave_hull -- [x] GeoSeries.convex_hull -- [ ] GeoSeries.envelope -- [ ] GeoSeries.extract_unique_points -- [ ] GeoSeries.force_2d -- [ ] GeoSeries.force_3d -- [ ] GeoSeries.make_valid -- [ ] GeoSeries.minimum_bounding_circle -- [ ] GeoSeries.maximum_inscribed_circle -- [ ] GeoSeries.minimum_clearance -- [ ] GeoSeries.minimum_clearance_line -- [ ] GeoSeries.minimum_rotated_rectangle -- [ ] GeoSeries.normalize -- [ ] GeoSeries.orient_polygons -- [ ] GeoSeries.remove_repeated_points -- [ ] GeoSeries.reverse -- [ ] GeoSeries.sample_points -- [ ] GeoSeries.segmentize -- [ ] GeoSeries.shortest_line -- [ ] GeoSeries.simplify -- [ ] GeoSeries.simplify_coverage -- [ ] GeoSeries.snap -- [ ] GeoSeries.transform -- [ ] GeoSeries.affine_transform -- [ ] GeoSeries.rotate -- [ ] GeoSeries.scale -- [ ] GeoSeries.skew -- [ ] GeoSeries.translate -- [ ] GeoSeries.interpolate -- [ ] GeoSeries.line_merge -- [ ] GeoSeries.project -- [ ] GeoSeries.shared_paths -- [ ] GeoSeries.build_area -- [ ] GeoSeries.constrained_delaunay_triangles -- [ ] GeoSeries.delaunay_triangles -- [ ] GeoSeries.explode -- [ ] GeoSeries.intersection_all -- [ ] GeoSeries.polygonize -- [ ] GeoSeries.union_all -- [ ] GeoSeries.voronoi_polygons -- [ ] GeoSeries.from_arrow -- [ ] GeoSeries.from_file -- [ ] GeoSeries.from_wkb -- [x] GeoSeries.from_wkt -- [x] GeoSeries.from_xy -- [ ] GeoSeries.to_arrow -- [ ] GeoSeries.to_file -- [ ] GeoSeries.to_json -- [ ] GeoSeries.to_wkb -- [x] GeoSeries.to_wkt -- [ ] GeoSeries.crs -- [ ] GeoSeries.set_crs -- [ ] GeoSeries.to_crs -- [ ] GeoSeries.estimate_utm_crs -- [ ] GeoSeries.fillna -- [ ] GeoSeries.isna -- [ ] GeoSeries.notna -- [ ] GeoSeries.clip -- [ ] GeoSeries.plot -- [ ] GeoSeries.explore -- [ ] GeoSeries.sindex -- [ ] GeoSeries.has_sindex -- [ ] GeoSeries.cx -- [ ] GeoSeries.__geo_interface__ - -### `bigframes.pandas` methods - -Constructors: Functions that build new geography values from coordinates or -existing geographies. - -- [x] ST_GEOGPOINT -- [ ] ST_MAKELINE -- [ ] ST_MAKEPOLYGON -- [ ] ST_MAKEPOLYGONORIENTED - -Parsers ST_GEOGFROM: Functions that create geographies from an external format -such as WKT and GeoJSON. - -- [ ] ST_GEOGFROMGEOJSON -- [x] ST_GEOGFROMTEXT -- [ ] ST_GEOGFROMWKB -- [ ] ST_GEOGPOINTFROMGEOHASH - -Formatters: Functions that export geographies to an external format such as WKT. - -- [ ] ST_ASBINARY -- [ ] ST_ASGEOJSON -- [x] ST_ASTEXT -- [ ] ST_GEOHASH - -Transformations: Functions that generate a new geography based on input. - -- [x] ST_BOUNDARY -- [x] ST_BUFFER -- [ ] ST_BUFFERWITHTOLERANCE -- [x] ST_CENTROID -- [ ] ST_CENTROID_AGG (Aggregate) -- [ ] ST_CLOSESTPOINT -- [x] ST_CONVEXHULL -- [x] ST_DIFFERENCE -- [ ] ST_EXTERIORRING -- [ ] ST_INTERIORRINGS -- [x] ST_INTERSECTION -- [ ] ST_LINEINTERPOLATEPOINT -- [ ] ST_LINESUBSTRING -- [ ] ST_SIMPLIFY -- [ ] ST_SNAPTOGRID -- [ ] ST_UNION -- [ ] ST_UNION_AGG (Aggregate) - -Accessors: Functions that provide access to properties of a geography without -side-effects. - -- [ ] ST_DIMENSION -- [ ] ST_DUMP -- [ ] ST_ENDPOINT -- [ ] ST_GEOMETRYTYPE -- [x] ST_ISCLOSED -- [ ] ST_ISCOLLECTION -- [ ] ST_ISEMPTY -- [ ] ST_ISRING -- [ ] ST_NPOINTS -- [ ] ST_NUMGEOMETRIES -- [ ] ST_NUMPOINTS -- [ ] ST_POINTN -- [ ] ST_STARTPOINT -- [x] ST_X -- [x] ST_Y - -Predicates: Functions that return TRUE or FALSE for some spatial relationship -between two geographies or some property of a geography. These functions are -commonly used in filter clauses. - -- [ ] ST_CONTAINS -- [ ] ST_COVEREDBY -- [ ] ST_COVERS -- [ ] ST_DISJOINT -- [ ] ST_DWITHIN -- [ ] ST_EQUALS -- [ ] ST_HAUSDORFFDWITHIN -- [ ] ST_INTERSECTS -- [ ] ST_INTERSECTSBOX -- [ ] ST_TOUCHES -- [ ] ST_WITHIN - -Measures: Functions that compute measurements of one or more geographies. - -- [ ] ST_ANGLE -- [x] ST_AREA -- [ ] ST_AZIMUTH -- [ ] ST_BOUNDINGBOX -- [x] ST_DISTANCE -- [ ] ST_EXTENT (Aggregate) -- [ ] ST_HAUSDORFFDISTANCE -- [ ] ST_LINELOCATEPOINT -- [x] ST_LENGTH -- [ ] ST_MAXDISTANCE -- [ ] ST_PERIMETER - -Clustering: Functions that perform clustering on geographies. - -- [ ] ST_CLUSTERDBSCAN - -S2 functions: Functions for working with S2 cell coverings of GEOGRAPHY. - -- [ ] S2_CELLIDFROMPOINT -- [ ] S2_COVERINGCELLIDS - -Raster functions: Functions for analyzing geospatial rasters using geographies. - -- [ ] ST_REGIONSTATS - -## Detailed Steps - -*Break down the implementation into small, actionable steps.* -*This section will guide the development process.* - -### Implementing a new scalar geography operation - -- [ ] **Define the operation dataclass:** - - [ ] In `bigframes/operations/geo_ops.py`, create a new dataclass - inheriting from `base_ops.UnaryOp` or `base_ops.BinaryOp`. Note that - BinaryOp is for methods that take two **columns**. Any literal values can - be passed as parameters to a UnaryOp. - - [ ] Define the `name` of the operation and any parameters it requires. - - [ ] Implement the `output_type` method to specify the data type of the result. -- [ ] **Export the new operation:** - - [ ] In `bigframes/operations/__init__.py`, import your new operation dataclass and add it to the `__all__` list. -- [ ] **Implement the compilation logic:** - - [ ] In `bigframes/core/compile/ibis_compiler/operations/geo_ops.py`: - - [ ] If the BigQuery function has a direct equivalent in Ibis, you can often reuse an existing Ibis method. - - [ ] If not, define a new Ibis UDF using `@ibis_udf.scalar.builtin` to map to the specific BigQuery function signature. - - [ ] Create a new compiler implementation function (e.g., `geo_length_op_impl`). - - [ ] Register this function to your operation dataclass using `@register_unary_op` or `@register_binary_op`. - - [ ] In `bigframes/core/compile/sqlglot/expressions/geo_ops.py`: - - [ ] Create a new compiler implementation function that generates the appropriate `sqlglot.exp` expression. - - [ ] Register this function to your operation dataclass using `@register_unary_op` or `@register_binary_op`. -- [ ] **Implement the user-facing function or property:** - - [ ] For a `bigframes.bigquery` function: - - [ ] In `bigframes/bigquery/_operations/geo.py`, create the user-facing function (e.g., `st_length`). - - [ ] The function should take a `Series` and any other parameters. - - [ ] Inside the function, call `series._apply_unary_op` or `series._apply_binary_op`, passing the operation dataclass you created. - - [ ] Add a comprehensive docstring with examples. - - [ ] In `bigframes/bigquery/__init__.py`, import your new user-facing function and add it to the `__all__` list. - - [ ] For a `GeoSeries` property or method: - - [ ] In `bigframes/geopandas/geoseries.py`, create the property or - method. Omit the docstring. - - [ ] If the operation is not possible to be supported, such as if the - geopandas method returns values in units corresponding to the - coordinate system rather than meters that BigQuery uses, raise a - `NotImplementedError` with a helpful message. Likewise, if a - required parameter takes a value in terms of the coordinate - system, but BigQuery uses meters, raise a `NotImplementedError`. - - [ ] Otherwise, call `series._apply_unary_op` or `series._apply_binary_op`, passing the operation dataclass. - - [ ] Add a comprehensive docstring with examples to the superclass in - `third_party/bigframes_vendored/geopandas/geoseries.py`. -- [ ] **Add Tests:** - - [ ] Add system tests in `tests/system/small/bigquery/test_geo.py` or `tests/system/small/geopandas/test_geoseries.py` to verify the end-to-end functionality. Test various inputs, including edge cases and `NULL` values. - - [ ] If you are overriding a pandas or GeoPandas property and raising `NotImplementedError`, add a unit test to ensure the correct error is raised. - -## Verification - -*Specify the commands to run to verify the changes.* - -- [ ] The `nox -r -s format lint lint_setup_py` linter should pass. -- [ ] The `nox -r -s mypy` static type checker should pass. -- [ ] The `nox -r -s docs docfx` docs should successfully build and include relevant docs in the output. -- [ ] All new and existing unit tests `pytest tests/unit` should pass. -- [ ] Identify all related system tests in the `tests/system` directories. -- [ ] All related system tests `pytest tests/system/small/path_to_relevant_test.py::test_name` should pass. - -## Constraints - -Follow the guidelines listed in GEMINI.md at the root of the repository. diff --git a/specs/2025-08-11-anywidget-align-text.md b/specs/2025-08-11-anywidget-align-text.md deleted file mode 100644 index 03305538dc6..00000000000 --- a/specs/2025-08-11-anywidget-align-text.md +++ /dev/null @@ -1,132 +0,0 @@ -# Anywidget: align text left and numerics right - -The "anywidget" rendering mode outputs an HTML table per page right now, but -the values need to be aligned according to their data type. - -## Background - -Anywidget currently renders pages like the following: - -```html - - - - - - - - - - - - - - - - - - - - - - - - - - - -
stategenderyearnamenumber
VAM1930Pat6
TXM1968Kennith18
-``` - -* This change fixes internal issue b/437697339. -* Numeric data should be right aligned so that it is easier to compare numbers, - especially if they all are rounded to the same precision. -* Text data is better left aligned, since many languages read left to right. - -## Acceptance Criteria - -- [ ] Header cells should align left. -- [ ] Header cells should use the resize CSS property to allow resizing. -- [ ] STRING columns are left-aligned in the output of `TableWidget` in - `bigframes/display/anywidget.py`. -- [ ] Numeric columns (INT64, FLOAT64, NUMERIC, BIGNUMERIC) are right-aligned - in the output of `TableWidget` in `bigframes/display/anywidget.py`. -- [ ] Create option `DisplayOptions.precision` in - `bigframes/_config/display_options.py` that can override the output - precision (defaults to 6, just like `pandas.options.display.precision`). -- [ ] All other non-numeric column types, including BYTES, BOOLEAN, TIMESTAMP, - and more, are left-aligned in the output of `TableWidget` in - `bigframes/display/anywidget.py`. -- [ ] There are parameterized unit tests verifying the alignment is set - correctly. - -## Detailed Steps - -### 1. Create Display Precision Configuration - -- [ ] In `bigframes/_config/display_options.py`, add a new `precision` attribute to the `DisplayOptions` dataclass. -- [ ] Set the default value to `6`. -- [ ] Add precision to the items in `def pandas_repr` that get passed to the pandas options context. -- [ ] Add a docstring explaining that it controls the floating point output precision, similar to `pandas.options.display.precision`. -- [ ] Check these items off with `[x]` as they are completed. - -### 2. Improve the headers - -- [ ] Create `bigframes/display/html.py`. -- [ ] In `bigframes/display/html.py`, create a `def render_html(*, dataframe: pandas.DataFrame, table_id: str)` method. -- [ ] Loop through the column names to create the table head. -- [ ] Apply the `text-align: left` style to the header. -- [ ] Wrap the cell text in a resizable `div`. -- [ ] Check these items off with `[x]` as they are completed. - -### 3. Implement Alignment and Precision Logic in TableWidget - -- [ ] Create a helper function `_is_dtype_numeric(dtype)` that takes a pandas - dtype returns True for types that that should be right-aligned. These - dtypes should correspond to the BigQuery data types: `INT64`, `FLOAT64`, - `NUMERIC`, `BIGNUMERIC`. Use the `bigframes.dtypes` module to map from - pandas type to BigQuery type. -- [ ] In the loop that generates the table rows (`` elements), add a function to determine the style based on the column's `dtype`. -- [ ] If the column's `dtype` is in the numeric set, apply the CSS style `text-align: right`. -- [ ] For all other `dtypes` (including `STRING`, `BYTES`, `BOOLEAN`, `TIMESTAMP`, etc.), apply `text-align: left`. -- [ ] When formatting floating-point numbers for display, use the `bigframes.options.display.precision` value. -- [ ] In `bigframes/display/anywidget.py`, modify the `_set_table_html` method of the `TableWidget` class to call `bigframes.display.html.render_html(...)`. -- [ ] Render the notebook at `notebooks/dataframes/anywidget_mode.ipynb` with - the `jupyter nbconvert --to notebook --execute notebooks/dataframes/anywidget_mode.ipynb` - command and validate that the rendered notebook includes the desired - changes to the HTML tables. -- [ ] Check these items off with `[x]` as they are completed. - -### 4. Add Parameterized Unit Tests - -- [ ] Create a new test file: `tests/unit/display/test_html.py`. -- [ ] Create a parameterized test method, e.g., `test_render_html_alignment_and_precision`. -- [ ] Use `@pytest.mark.parametrize` to test various scenarios. -- [ ] **Scenario 1: Alignment.** - - Create a sample `bigframes.dataframe.DataFrame` with columns of different types: a string, an integer, a float, and a boolean. - - Render the `pandas.DataFrame` to HTML. - - Assert that the integer and float column headers and data cells (`` and ``) have `style="text-align: right;"`. - - Assert that the string and boolean columns have `style="text-align: left;"`. -- [ ] **Scenario 2: Precision.** - - Create a `bigframes.dataframe.DataFrame` with a `FLOAT64` column containing a number with many decimal places (e.g., `3.14159265`). - - Set `bigframes.options.display.precision = 4`. - - Render the `pandas.DataFrame` to HTML. - - Assert that the output string contains the number formatted to 4 decimal places (e.g., `3.1416`). - - Remember to reset the option value after the test to avoid side effects. -- [ ] Check these items off with `[x]` as they are completed. - -## Verification - -*Specify the commands to run to verify the changes.* - -- [ ] The `nox -r -s format lint lint_setup_py` linter should pass. -- [ ] The `nox -r -s mypy` static type checker should pass. -- [ ] The `nox -r -s docs docfx` docs should successfully build and include relevant docs in the output. -- [ ] All new and existing unit tests `pytest tests/unit` should pass. -- [ ] Identify all related system tests in the `tests/system` directories. -- [ ] All related system tests `pytest tests/system/small/path_to_relevant_test.py::test_name` should pass. -- [ ] Check these items off with `[x]` as they are completed. - -## Constraints - -Follow the guidelines listed in GEMINI.md at the root of the repository. diff --git a/specs/TEMPLATE.md b/specs/TEMPLATE.md deleted file mode 100644 index 0d93035dcc2..00000000000 --- a/specs/TEMPLATE.md +++ /dev/null @@ -1,47 +0,0 @@ -# Title of the Specification - -*Provide a brief overview of the feature or bug.* - -## Background - -*Explain the context and why this change is necessary.* -*Include links to relevant issues or documentation.* - -## Acceptance Criteria - -*Define the specific, measurable outcomes that indicate the task is complete.* -*Use a checklist format for clarity.* - -- [ ] Criterion 1 -- [ ] Criterion 2 -- [ ] Criterion 3 - -## Detailed Steps - -*Break down the implementation into small, actionable steps.* -*This section will guide the development process.* - -### 1. Step One - -- [ ] Action 1.1 -- [ ] Action 1.2 - -### 2. Step Two - -- [ ] Action 2.1 -- [ ] Action 2.2 - -## Verification - -*Specify the commands to run to verify the changes.* - -- [ ] The `nox -r -s format lint lint_setup_py` linter should pass. -- [ ] The `nox -r -s mypy` static type checker should pass. -- [ ] The `nox -r -s docs docfx` docs should successfully build and include relevant docs in the output. -- [ ] All new and existing unit tests `pytest tests/unit` should pass. -- [ ] Identify all related system tests in the `tests/system` directories. -- [ ] All related system tests `pytest tests/system/small/path_to_relevant_test.py::test_name` should pass. - -## Constraints - -Follow the guidelines listed in GEMINI.md at the root of the repository. diff --git a/specs/bigframes-bigquery-contributing.md b/specs/bigframes-bigquery-contributing.md deleted file mode 100644 index 10931af0755..00000000000 --- a/specs/bigframes-bigquery-contributing.md +++ /dev/null @@ -1,501 +0,0 @@ -# bigframes.bigquery inputs and outputs policies - -The goal of the [bigframes.bigquery -APIs](https://dataframes.bigquery.dev/reference/api/bigframes.bigquery.html#module-bigframes.bigquery) -is to provide the simplest possible mapping from BigQuery (GoogleSQL) -[functions](https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/functions-all) -and -[operations](https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/query-syntax) -to Python. "Simplest" is somewhat ambiguous though, when it comes to the types -involved and behaviors, so this document aims to expand on that vision with -specific examples. - -## SQL and BigFrames expression types - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
SQL expression type(s) - Python type(s) - Notes - Examples -
Column expression (usable in a SELECT clause) - - - Both Python Series and column expression should be supported as inputs, - with the output reflecting the users input. Use a TypeVar - rather than directly using union types to make type checking easier. -

-Special considerations for Series inputs: -

-If an input and output are both a Series with the same number of rows, make sure -the output Series is implicitly (row identity) alignable with the original -input. In other words, don't generate a table expression. -

-If there are multiple Series inputs, they should be implicitly aligned if -possible so as not to generate unnecessary table expressions. -

Most scalar functions accept one or more column expressions as input. -
Scalar values - - - Theoretically, we could try to get the type system to help the user - disambiguate between this case and the "Column expression" case, but I think - that's more trouble than it it's worth with regards to the expectations of - Python users. - - -
Table expression - bpd.DataFrame -

-All columns are included as normal columns in the input table expression, -including named index columns. If column names aren't unique or contain -characters not compatible with BigQuery flexible column names, raise an error. -

-Outputs are unordered and unindexed to allow for cleaner mapping with SQL. -

Most APIs that take a table expression as input, also output a table - expression with the same number of rows and passing through all unused - columns. - -

This should be used to pass through any index or ordering columns (as well - as all other columns, if that's the SQL behavior), to allow for easy joining - with the original input DataFrame. -

Same number of rows as the input, so we should preserve index and ordering: - - - -

- Different number of rows in output, so no need to preserve index or ordering. - Default index / ordering should be specified with the Session's - configuration: - -

- -

- Possible to have the same number of rows as the input, but joining with the original goes against the purpose of the feature: - -

- -
Table name - string (referring to fully-qualified table ID, e.g. project.dataset.table / project.catalog.namespace.table) - Some SQL APIs do not support or have limitations with arbitrary table expressions, instead taking in a table ID, such as TABLESAMPLE expression. -

-Also, SEARCH and VECTOR_SEARCH, if you want the indexes attached to the table to actually apply. -

-For outputs, it might be preferable to output a table ID instead of a DataFrame, if the user is explicitly creating a table. For example, to_gbq() returns a string with the table name, which is useful for the case where BigFrame generates the table ID for the user. -

All of the items from the "Table expression" row above. APIs that require a table expression, but don't take a table ID can trivially take a table ID through a (SELECT * FROM table) subquery. -

-Some APIs only take a table ID and not an arbitrary table expression:

- -
Aggregated table expression - DataFrameGroupBy - - - -
Analytic table expression -
    - -
  • DataFrameGroupBy - feasibility TBD -
  • Deferred column Expression with a Window applied.
- - 		- - -
Column name (unqualified*) \ - \ -*I've only encountered examples where the table name / table expression is passed in separately. - string, -

-For cases where the column name is used as an alias and we aren't using named Series: -

-dict[str, Expression] -

Often a table expression input is paired with a column name input, as is the case with the CREATE MODEL and VECTOR_SEARCH APIs -

-If SQL expects a column name rather than a column expression, do not attempt to change this in Python. For example, don't allow a Series as a substitute for DataFrames + Column name. \ - \ -If the associated table expression is input as a DataFrame, validate that these map cleanly to SQL and raise a ValueError if not. For example: \ -

    - -
  • Duplicate column names (excluding unnamed index columns). -
  • Column names that are some hashable value other than integer (which maps cleanly to a column name) or string. -
  • Any column name containing a punctuation mark that is not allowed by BigQuery flexible column names, such as ! or $.
- - 		- -
Literal values - corresponding literal Python value (e.g. int, float, string) - For cases where scalar values are also supported, it should be safe to start with this and then expand to support expressions without a breaking change, as is done in https://github.com/googleapis/google-cloud-python/pull/16606. - Most scalar functions accept one or more literal values as input. -
Scalar subqueries - Not supported yet, except implicitly in some aggregation use cases. -

-Would need some sort of bigframes deferred expression that can be tied to a table expression. -

-(Possibly DataFrame with 1 column?) -

- -
- -## Python policies - -### Naming - -Take the SQL function name, keyword name (used as a function name in Python), or argument name and transform them to lower_snake_case to reflect Python conventions. - -### Internal expressions - -Prefer creating deferred BigFrames expression objects where feasible. For -example, all scalar outputting functions should return a -`bigframes.pandas.Series` or `bigframes.core.col.Expression` that wraps a -`bigframes.core.expression.Expression`. - -Prefer returning a `bigframes.pandas.DataFrame` that wraps a -`bigframes.bigframes.core.bigframe_node.BigFrameNode`. See `from_bq_data_source` in -`bigframes.core.array_value.ArrayValue`, as an example. - -Exceptions to this are cases where the output schema is likely to evolve or -differ in ways that are difficult to model, such as the `ML.PREDICT` SQL -function, where output columns differ based on the model type and support for -model types are frequently added to BigQuery. In these exceptional cases, the -generated query should run immediately and the returned value should wrap the -results. - -### Argument syntax details - -Arguments in Python can be one of: - -* Positional - * Supported by `*args` in Python, but not recommended. Positional arguments in SQL should map to named positional or keyword arguments in Python. -* Positional or keyword - * Required positional arguments should be positional, just like they are in SQL. -* Keyword-only - * All other arguments should be keyword-only. Use `, * ,` Python syntax to achieve this. - -For optional parameters, use an optional sentinel (see: ) and omit the value from the generated SQL if the user doesn't explicitly provide one. This ensures that an explicit NULL / None value can be passed in. - -``` - -from enum import Enum - -class Default(Enum): - token = 0 - -DEFAULT = Default.token - -def spam(*, ham: list[str] | None | Default = DEFAULT): - op_kwargs = {} - - if ham is not DEFAULT: - op_kwargs['ham'] = "prosciutto" - - ... - -``` - -### Scalar operations types policies - -Many operations output a table expression. For these, the output type is always a DataFrame, regardless of the input types. - -For scalar operations, there are three cases to consider when determining the output types: - - - - - - - - - - - - - - - - - - -
Scalar ops - Input type(s) - Scalar ops - Output type -
Expression - Expression -
Series / DataFrame - Series / DataFrame -

-Preserve ordering and index(es). Join inputs as needed before applying the operation. -

Mix of Expression and Series / DataFrame - Series / DataFrame -

-Preserve ordering and index(es). Join inputs as needed before applying the operation. -

- -## Examples - -### PIVOT SQL operator - -SQL syntax ([docs](https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/query-syntax#pivot_operator)): - -``` -FROM from_item[, ...] pivot_operator - -pivot_operator: - PIVOT( - aggregate_function_call [as_alias][, ...] - FOR input_column - IN ( pivot_column [as_alias][, ...] ) - ) [AS alias] - -as_alias: - [AS] alias - -``` - -SQL example: - -``` -WITH Produce AS ( - SELECT 'Kale' as product, 51 as sales, 'Q1' as quarter, 2020 as year UNION ALL - SELECT 'Kale', 23, 'Q2', 2020 UNION ALL - SELECT 'Kale', 45, 'Q3', 2020 UNION ALL - SELECT 'Kale', 3, 'Q4', 2020 UNION ALL - SELECT 'Kale', 70, 'Q1', 2021 UNION ALL - SELECT 'Kale', 85, 'Q2', 2021 UNION ALL - SELECT 'Apple', 77, 'Q1', 2020 UNION ALL - SELECT 'Apple', 0, 'Q2', 2020 UNION ALL - SELECT 'Apple', 1, 'Q1', 2021) -SELECT * FROM Produce - -/*---------+-------+---------+------+ - | product | sales | quarter | year | - +---------+-------+---------+------| - | Kale | 51 | Q1 | 2020 | - | Kale | 23 | Q2 | 2020 | - | Kale | 45 | Q3 | 2020 | - | Kale | 3 | Q4 | 2020 | - | Kale | 70 | Q1 | 2021 | - | Kale | 85 | Q2 | 2021 | - | Apple | 77 | Q1 | 2020 | - | Apple | 0 | Q2 | 2020 | - | Apple | 1 | Q1 | 2021 | - +---------+-------+---------+------*/ - - -SELECT * FROM - Produce - PIVOT(SUM(sales) FOR quarter IN ('Q1', 'Q2', 'Q3', 'Q4')) - -/*---------+------+----+------+------+------+ - | product | year | Q1 | Q2 | Q3 | Q4 | - +---------+------+----+------+------+------+ - | Apple | 2020 | 77 | 0 | NULL | NULL | - | Apple | 2021 | 1 | NULL | NULL | NULL | - | Kale | 2020 | 51 | 23 | 45 | 3 | - | Kale | 2021 | 70 | 85 | NULL | NULL | - +---------+------+----+------+------+------*/ - -``` - -Python definition: - -``` -def pivot( - table_expression: bpd.DataFrame, - *, - aggregation: Expression | dict[str, Expression], - input_column: str, - pivot_columns: dict[str, float | str | ...] | Sequence[float | str | ...], -) -> bpd.DataFrame: - ... -``` - -Since pivot creates a table expression, we run immediately. - - \ -Python usage: - -``` -pivotted = bbq.pivot( - my_produce_dataframe, - aggregation=bpd.col("sales").sum(), - input_column="quarter", - pivot_columns=["Q1", "Q2", "Q3", "Q4"], -) -``` - -### UNPIVOT SQL operator - -SQL syntax ([docs](https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/query-syntax#unpivot_operator)): - -``` -FROM from_item[, ...] unpivot_operator - -unpivot_operator: - UNPIVOT [ { INCLUDE NULLS | EXCLUDE NULLS } ] ( - { single_column_unpivot | multi_column_unpivot } - ) [unpivot_alias] - -single_column_unpivot: - values_column - FOR name_column - IN (columns_to_unpivot) - -multi_column_unpivot: - values_column_set - FOR name_column - IN (column_sets_to_unpivot) - -values_column_set: - (values_column[, ...]) - -columns_to_unpivot: - unpivot_column [row_value_alias][, ...] - -column_sets_to_unpivot: - (unpivot_column [row_value_alias][, ...]) - -unpivot_alias and row_value_alias: - [AS] alias -``` - -SQL example: - -``` -WITH Produce AS ( - SELECT 'Kale' as product, 51 as Q1, 23 as Q2, 45 as Q3, 3 as Q4 UNION ALL - SELECT 'Apple', 77, 0, 25, 2) - --- SELECT * FROM Produce -/*---------+----+----+----+----+ - | product | Q1 | Q2 | Q3 | Q4 | - +---------+----+----+----+----+ - | Kale | 51 | 23 | 45 | 3 | - | Apple | 77 | 0 | 25 | 2 | - +---------+----+----+----+----*/ - -SELECT * FROM Produce -UNPIVOT(sales FOR quarter IN (Q1, Q2, Q3, Q4)) -- single_column_unpivot - -/*---------+-------+---------+ - | product | sales | quarter | - +---------+-------+---------+ - | Kale | 51 | Q1 | - | Kale | 23 | Q2 | - | Kale | 45 | Q3 | - | Kale | 3 | Q4 | - | Apple | 77 | Q1 | - | Apple | 0 | Q2 | - | Apple | 25 | Q3 | - | Apple | 2 | Q4 | - +---------+-------+---------*/ -``` - -Python definition: - -``` -def unpivot( - table_expression: bpd.DataFrame, - *, - exclude_nulls: bool = True, - values_column: str | Sequence[str], - name_column: str, - columns_to_unpivot: dict[str, str | int] | Sequence[str], -) -> bpd.DataFrame: - ... -``` - -Since unpivot creates a table expression, we run immediately. - - \ -Python usage: - -``` -unpivotted = bbq.unpivot( - my_produce_dataframe, - values_column="sales", - name_column="quarter", - columns_to_unpivot=["Q1", "Q2", "Q3", "Q4"], -) -``` diff --git a/specs/bigframes-bigquery-generator.md b/specs/bigframes-bigquery-generator.md deleted file mode 100644 index 1078bbd05a3..00000000000 --- a/specs/bigframes-bigquery-generator.md +++ /dev/null @@ -1,100 +0,0 @@ -# Code generation for bigframes.bigquery - -This document describes code generation for the `bigframes.bigquery` modules. -For detailed specifications on input and output types, refer to -[Contributing to bigframes.bigquery](./bigframes-bigquery-contributing.md). - -## Overview - -The script at `packages/bigframes/scripts/generate_bigframes_bigquery.py` -generates python submodules for the `bigframes.bigquery` module. When run -without any arguments, it iterates through all yaml files at -`packages/bigframes/scripts/data/sql-functions/**/*.yaml` to generate the code. - -The script also generates a unit test that verifies that the functions have been -included in the `bigframes.bigquery` module, which is important to check, as the -`__init__.py` file requires manual updates. - -## Running the generator - -Since the dependencies for the script differ from that of bigframes -and its test suite, use the self-contained Python script technique described at -https://docs.astral.sh/uv/guides/scripts/ -to automatically manage dependencies using `uv`. Therefore, the header of the -script will look something like: - -```python -#!/usr/bin/env -S uv run --script -# -# /// script -# dependencies = [ -# "jinja2", -# "pyyaml", -# ] -# /// -# -# Copyright 2026 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# ... -``` - -To run the script: - -```bash -cd packages/bigframes -uv run scripts/generate_bigframes_bigquery.py -``` - -To improve reproducibility, we also check in the uv lock file generated by -running `uv lock --script scripts/generate_bigframes_bigquery.py`. - -## Generated code organization - -The `generate_bigframes_bigquery.py` script generates submodules of -`bigframes.bigquery._operations`, with the full path reflecting the organization -of the YAML files. For example, a YAML file at -`packages/bigframes/scripts/data/sql-functions/aead.yaml` corresponds to a -generated Python module at `bigframes.bigquery._operations.aead`. Likewise, -`packages/bigframes/scripts/data/sql-functions/builtins/bit.yaml` corresponds -to the `bigframes.bigquery._operations.builtins.bit` submodule. - -## Generated module implementation - -Each generated module has all functions defined in the YAML file converted to -the equivalent Python definition, including keyword arguments and docstrings. - -### Code generation - -The code will be templated using the jinja2 template engine. This allows -proposed changes to the templated code to be reviewed more easily. - -### Handling optional arguments - -When the user calls a Python function without specifying the optional -argument, that argument is omitted from the SQL text. To allow for explicit -NULL values to be passed in (None in Python), the default value is specified -to be a default sentinel value enum `bigframes.core.sentinels.DEFAULT`. For -example: - -```python -import bigframes.core.sentinels - -def current_date( - time_zone_expression: str | bigframes.core.sentinels.Default = bigframes.core.sentinels.DEFAULT, -): - ... -``` - -### Input and output types - -Refer to the table in -[Contributing to bigframes.bigquery](./bigframes-bigquery-contributing.md). - -### Internal bigframes operator - -Scalar functions should generate an expression using the `GoogleSqlScalarOp`. -This keeps the implementation as scalar SQL functions consistent. - -Aggregate, analytic, and table-valued functions currently require custom ops. As -such, those functions are currently out of scope for this generator. diff --git a/testing/constraints-3.10.txt b/testing/constraints-3.10.txt index 1dcdd64baa0..9f0786f47e6 100644 --- a/testing/constraints-3.10.txt +++ b/testing/constraints-3.10.txt @@ -1,124 +1,15 @@ -# Please keep these in sync with the minimum versions in setup.py -cloudpickle==2.0.0 -fsspec==2023.3.0 -gcsfs==2023.3.0 -geopandas==0.12.2 -google-auth==2.15.0 -google-cloud-bigtable==2.30.0 -google-cloud-pubsub==2.29.0 -google-cloud-bigquery==3.36.0 -google-cloud-functions==1.20.2 -google-cloud-bigquery-connection==1.18.2 -google-cloud-iam==2.18.2 -google-cloud-resource-manager==1.14.2 -google-cloud-storage==2.0.0 -grpc-google-iam-v1==0.14.2 -numpy==1.24.0 +# Keep in sync with colab/containers/requirements.core.in image +google-auth==2.17.3 +ipykernel==5.5.6 +ipython==7.34.0 +notebook==6.4.8 pandas==1.5.3 -pandas-gbq==0.26.1 -pyarrow==23.0.1 -pydata-google-auth==1.8.2 -pyiceberg==0.7.1 +portpicker==1.3.9 requests==2.27.1 -scikit-learn==1.2.2 -shapely==1.8.5 -tabulate==0.9 -humanize==4.6.0 +tornado==6.3.1 +absl-py==1.4.0 +debugpy==1.6.6 +ipywidgets==7.7.1 matplotlib==3.7.1 -db-dtypes==1.4.2 -# For vendored ibis-framework. -atpublic==2.3 -python-dateutil==2.8.2 -pytz==2022.7 -toolz==0.11 -typing-extensions==4.6.1 -rich==12.4.4 -# For anywidget mode -anywidget>=0.9.18 -traitlets==5.0.0 -# constrained dependencies to give pip a helping hand -aiohappyeyeballs==2.6.1 -aiohttp==3.13.3 -aiosignal==1.4.0 -anywidget==0.9.21 -asttokens==3.0.1 -async-timeout==5.0.1 -attrs==25.4.0 -cachetools==5.5.2 -certifi==2026.1.4 -charset-normalizer==2.0.12 -click==8.3.1 -click-plugins==1.1.1.2 -cligj==0.7.2 -comm==0.2.3 -commonmark==0.9.1 -contourpy==1.3.2 -coverage==7.13.3 -cycler==0.12.1 -db-dtypes==1.4.2 -decorator==5.2.1 -exceptiongroup==1.2.2 -executing==2.2.1 -fiona==1.10.1 -fonttools==4.61.1 -freezegun==1.5.5 -frozenlist==1.8.0 -google-api-core==2.29.0 -google-auth-oauthlib==1.2.4 -google-cloud-bigquery-storage==2.36.0 -google-cloud-core==2.5.0 -google-crc32c==1.8.0 -google-resumable-media==2.8.0 -googleapis-common-protos==1.72.0 -grpc-google-iam-v1==0.14.2 -grpcio==1.74.0 -grpcio-status==1.62.3 -idna==3.11 -iniconfig2.3.0 -ipython==8.21.0 -ipython-genutils==0.2.0 -ipywidgets==8.1.8 -jedi==0.19.2 -joblib==1.5.3 -jupyterlab_widgets==3.0.16 -kiwisolver==1.4.9 -matplotlib-inline==0.2.1 -mock==5.2.0 -moc==5.2.0 -multidict==6.7.1 -oauthlib==3.3.1 -packaging==26.0 -parso==0.8.5 -pexpect==4.9.0 -pillow==12.1.0 -pluggy==1.6.0 -prompt_toolkit==3.0.52 -propcache==0.4.1 -proto-plus==1.27.1 -protobuf==6.33.5 -psygnal==0.15.1 -ptyprocess==0.7.0 -pure_eval==0.2.3 -pyasn1==0.6.2 -pyasn1_modules==0.4.2 -Pygments==2.19.2 -pyparsing==3.3.2 -pyproj==3.7.1 -pytest==8.4.2 -pytest-cov==7.0.0 -pytest-snapshot==0.9.0 -pytest-timeout==2.4.0 -python-dateutil==2.8.2 -requests-oauthlib==2.0.0 -rsa==4.9.1 -scipy==1.15.3 -setuptools==80.9.0 -six==1.17.0 -stack-data==0.6.3 -threadpoolctl==3.6.0 -tomli==2.4.0 -urllib3==1.26.20 -wcwidth==0.6.0 -wheel==0.45.1 -widgetsnbextension==4.0.15 -yarl==1.22.0 +psutil==5.9.5 +traitlets==5.7.1 diff --git a/testing/constraints-3.11.txt b/testing/constraints-3.11.txt index 17854fda96f..e69de29bb2d 100644 --- a/testing/constraints-3.11.txt +++ b/testing/constraints-3.11.txt @@ -1,620 +0,0 @@ -# Keep in sync with %pip freeze in colab. -# Note: These are just constraints, so it's ok to have extra packages we -# aren't installing, except in the version that gets used for prerelease -# tests. -absl-py==1.4.0 -accelerate==1.9.0 -aiofiles==24.1.0 -aiohappyeyeballs==2.6.1 -aiohttp==3.12.15 -aiosignal==1.4.0 -alabaster==1.0.0 -albucore==0.0.24 -albumentations==2.0.8 -ale-py==0.11.2 -altair==5.5.0 -annotated-types==0.7.0 -antlr4-python3-runtime==4.9.3 -anyio==4.10.0 -anywidget==0.9.18 -argon2-cffi==25.1.0 -argon2-cffi-bindings==25.1.0 -array_record==0.7.2 -arviz==0.22.0 -astropy==7.1.0 -astropy-iers-data==0.2025.8.4.0.42.59 -astunparse==1.6.3 -atpublic==5.1 -attrs==25.3.0 -audioread==3.0.1 -autograd==1.8.0 -babel==2.17.0 -backcall==0.2.0 -backports.tarfile==1.2.0 -beautifulsoup4==4.13.4 -betterproto==2.0.0b6 -bigquery-magics==0.10.2 -bleach==6.2.0 -blinker==1.9.0 -blis==1.3.0 -blobfile==3.0.0 -blosc2==3.6.1 -bokeh==3.7.3 -Bottleneck==1.4.2 -bqplot==0.12.45 -branca==0.8.1 -Brotli==1.1.0 -build==1.3.0 -CacheControl==0.14.3 -cachetools==5.5.2 -catalogue==2.0.10 -certifi==2025.8.3 -cffi==1.17.1 -chardet==5.2.0 -charset-normalizer==3.4.2 -chex==0.1.90 -clarabel==0.11.1 -click==8.2.1 -cloudpathlib==0.21.1 -cloudpickle==3.1.1 -cmake==3.31.6 -cmdstanpy==1.2.5 -colorcet==3.1.0 -colorlover==0.3.0 -colour==0.1.5 -community==1.0.0b1 -confection==0.1.5 -cons==0.4.7 -contourpy==1.3.3 -cramjam==2.11.0 -cryptography==43.0.3 -cuda-python==12.6.2.post1 -cudf-polars-cu12==25.6.0 -cufflinks==0.17.3 -cuml-cu12==25.6.0 -cupy-cuda12x==13.3.0 -curl_cffi==0.12.0 -cuvs-cu12==25.6.1 -cvxopt==1.3.2 -cvxpy==1.6.7 -cycler==0.12.1 -cyipopt==1.5.0 -cymem==2.0.11 -Cython==3.0.12 -dask==2025.5.0 -dask-cuda==25.6.0 -dask-cudf-cu12==25.6.0 -dataproc-spark-connect==0.8.3 -datasets==4.0.0 -db-dtypes==1.4.3 -dbus-python==1.2.18 -debugpy==1.8.15 -decorator==4.4.2 -defusedxml==0.7.1 -diffusers==0.34.0 -dill==0.3.8 -distributed==2025.5.0 -distributed-ucxx-cu12==0.44.0 -distro==1.9.0 -dlib==19.24.6 -dm-tree==0.1.9 -docstring_parser==0.17.0 -docutils==0.21.2 -dopamine_rl==4.1.2 -duckdb==1.3.2 -earthengine-api==1.5.24 -easydict==1.13 -editdistance==0.8.1 -eerepr==0.1.2 -einops==0.8.1 -entrypoints==0.4 -et_xmlfile==2.0.0 -etils==1.13.0 -etuples==0.3.10 -Farama-Notifications==0.0.4 -fastai==2.7.19 -fastapi==0.116.1 -fastcore==1.7.29 -fastdownload==0.0.7 -fastjsonschema==2.21.1 -fastprogress==1.0.3 -fastrlock==0.8.3 -ffmpy==0.6.1 -filelock==3.18.0 -firebase-admin==6.9.0 -Flask==3.1.1 -flatbuffers==25.2.10 -flax==0.10.6 -folium==0.20.0 -fonttools==4.59.0 -frozendict==2.4.6 -frozenlist==1.7.0 -fsspec==2025.3.0 -future==1.0.0 -gast==0.6.0 -gcsfs==2025.3.0 -GDAL==3.13.1 -gdown==5.2.0 -geemap==0.35.3 -geocoder==1.38.1 -geographiclib==2.0 -geopandas==1.1.1 -geopy==2.4.1 -gin-config==0.5.0 -gitdb==4.0.12 -GitPython==3.1.45 -glob2==0.7 -google==2.0.3 -google-ai-generativelanguage==0.6.17 -google-api-core==2.25.1 -google-api-python-client==2.177.0 -google-auth==2.38.0 -google-auth-httplib2==0.2.0 -google-auth-oauthlib==1.2.2 -google-cloud-aiplatform==1.106.0 -google-cloud-bigquery==3.36.0 -google-cloud-bigquery-connection==1.18.3 -google-cloud-bigquery-storage==2.32.0 -google-cloud-core==2.4.3 -google-cloud-dataproc==5.21.0 -google-cloud-datastore==2.21.0 -google-cloud-firestore==2.21.0 -google-cloud-functions==1.20.4 -google-cloud-language==2.17.2 -google-cloud-resource-manager==1.14.2 -google-cloud-spanner==3.56.0 -google-cloud-storage==2.19.0 -google-cloud-translate==3.21.1 -google-crc32c==1.7.1 -google-genai==1.28.0 -google-generativeai==0.8.5 -google-pasta==0.2.0 -google-resumable-media==2.7.2 -googleapis-common-protos==1.70.0 -googledrivedownloader==1.1.0 -gradio==6.15.1 -gradio_client==1.11.0 -graphviz==0.21 -greenlet==3.2.3 -groovy==0.1.2 -grpc-google-iam-v1==0.14.2 -grpc-interceptor==0.15.4 -grpcio==1.74.0 -grpcio-status==1.72.1 -grpclib==0.4.8 -gspread==6.2.1 -gspread-dataframe==4.0.0 -gym==0.25.2 -gym-notices==0.1.0 -gymnasium==1.2.0 -h11==0.16.0 -h2==4.2.0 -h5netcdf==1.6.3 -h5py==3.14.0 -hdbscan==0.8.40 -hf-xet==1.1.5 -hf_transfer==0.1.9 -highspy==1.11.0 -holidays==0.78 -holoviews==1.21.0 -hpack==4.1.0 -html5lib==1.1 -httpcore==1.0.9 -httpimport==1.4.1 -httplib2==0.22.0 -httpx==0.28.1 -huggingface-hub==0.34.3 -humanize==4.12.3 -hyperframe==6.1.0 -hyperopt==0.2.7 -ibis-framework==9.5.0 -idna==3.10 -imageio==2.37.0 -imageio-ffmpeg==0.6.0 -imagesize==1.4.1 -imbalanced-learn==0.13.0 -immutabledict==4.2.1 -importlib_metadata==8.7.0 -importlib_resources==6.5.2 -imutils==0.5.4 -inflect==7.5.0 -iniconfig==2.1.0 -intel-cmplr-lib-ur==2025.2.0 -intel-openmp==2025.2.0 -ipyevents==2.0.2 -ipyfilechooser==0.6.0 -ipykernel==6.17.1 -ipyleaflet==0.20.0 -ipyparallel==8.8.0 -ipython==7.34.0 -ipython-genutils==0.2.0 -ipython-sql==0.5.0 -ipytree==0.2.2 -ipywidgets==7.7.1 -itsdangerous==2.2.0 -jaraco.classes==3.4.0 -jaraco.context==6.0.1 -jaraco.functools==4.2.1 -jax==0.5.3 -jax-cuda12-pjrt==0.5.3 -jax-cuda12-plugin==0.5.3 -jaxlib==0.5.3 -jeepney==0.9.0 -jieba==0.42.1 -Jinja2==3.1.6 -jiter==0.10.0 -joblib==1.5.1 -jsonpatch==1.33 -jsonpickle==4.1.1 -jsonpointer==3.0.0 -jsonschema==4.25.0 -jsonschema-specifications==2025.4.1 -jupyter-client==6.1.12 -jupyter-console==6.1.0 -jupyter-leaflet==0.20.0 -jupyter-server==1.16.0 -jupyter_core==5.8.1 -jupyterlab_pygments==0.3.0 -jupyterlab_widgets==3.0.15 -jupytext==1.17.2 -kaggle==1.7.4.5 -kagglehub==0.3.12 -keras==3.10.0 -keras-hub==0.21.1 -keras-nlp==0.21.1 -keyring==25.6.0 -keyrings.google-artifactregistry-auth==1.1.2 -kiwisolver==1.4.8 -langchain==0.3.27 -langchain-core==0.3.72 -langchain-text-splitters==0.3.9 -langcodes==3.5.0 -langsmith==0.8.18 -language_data==1.3.0 -launchpadlib==1.10.16 -lazr.restfulclient==0.14.4 -lazr.uri==1.0.6 -lazy_loader==0.4 -libclang==18.1.1 -libcugraph-cu12==25.6.0 -libcuml-cu12==25.6.0 -libcuvs-cu12==25.6.1 -libkvikio-cu12==25.6.0 -libpysal==4.13.0 -libraft-cu12==25.6.0 -librmm-cu12==25.6.0 -librosa==0.11.0 -libucx-cu12==1.18.1 -libucxx-cu12==0.44.0 -linkify-it-py==2.0.3 -llvmlite==0.43.0 -locket==1.0.0 -logical-unification==0.4.6 -lxml==5.4.0 -Mako==1.1.3 -marisa-trie==1.2.1 -Markdown==3.8.2 -markdown-it-py==3.0.0 -MarkupSafe==3.0.2 -matplotlib==3.10.0 -matplotlib-inline==0.1.7 -matplotlib-venn==1.1.2 -mdit-py-plugins==0.4.2 -mdurl==0.1.2 -miniKanren==1.0.5 -missingno==0.5.2 -mistune==3.3.0 -mizani==0.13.5 -mkl==2025.2.0 -ml_dtypes==0.5.3 -mlxtend==0.23.4 -more-itertools==10.7.0 -moviepy==1.0.3 -mpmath==1.3.0 -msgpack==1.2.1 -multidict==6.6.3 -multipledispatch==1.0.0 -multiprocess==0.70.16 -multitasking==0.0.12 -murmurhash==1.0.13 -music21==9.3.0 -namex==0.1.0 -narwhals==2.0.1 -natsort==8.4.0 -nbclassic==1.3.1 -nbclient==0.10.2 -nbconvert==7.16.6 -nbformat==5.10.4 -ndindex==1.10.0 -nest-asyncio==1.6.0 -networkx==3.5 -nibabel==5.3.2 -nltk==3.9.1 -notebook==6.5.7 -notebook_shim==0.2.4 -numba==0.60.0 -numba-cuda==0.11.0 -numexpr==2.11.0 -numpy==2.0.2 -nvidia-cublas-cu12==12.5.3.2 -nvidia-cuda-cupti-cu12==12.5.82 -nvidia-cuda-nvcc-cu12==12.5.82 -nvidia-cuda-nvrtc-cu12==12.5.82 -nvidia-cuda-runtime-cu12==12.5.82 -nvidia-cudnn-cu12==9.3.0.75 -nvidia-cufft-cu12==11.2.3.61 -nvidia-curand-cu12==10.3.6.82 -nvidia-cusolver-cu12==11.6.3.83 -nvidia-cusparse-cu12==12.5.1.3 -nvidia-cusparselt-cu12==0.6.2 -nvidia-ml-py==12.575.51 -nvidia-nccl-cu12==2.23.4 -nvidia-nvjitlink-cu12==12.5.82 -nvidia-nvtx-cu12==12.4.127 -nvtx==0.2.13 -oauth2client==4.1.3 -oauthlib==3.3.1 -omegaconf==2.3.0 -openai==1.98.0 -opencv-contrib-python==4.12.0.88 -opencv-python==4.12.0.88 -opencv-python-headless==4.12.0.88 -openpyxl==3.1.5 -opt_einsum==3.4.0 -optax==0.2.5 -optree==0.17.0 -orbax-checkpoint==0.11.20 -orjson==3.11.1 -osqp==1.0.4 -packaging==25.0 -pandas==2.2.2 -pandas-datareader==0.10.0 -pandas-gbq==0.29.2 -pandas-stubs==2.2.2.240909 -pandocfilters==1.5.1 -panel==1.7.5 -param==2.2.1 -parso==0.8.4 -parsy==2.1 -partd==1.4.2 -patsy==1.0.1 -peewee==3.18.2 -peft==0.17.0 -pexpect==4.9.0 -pickleshare==0.7.5 -pillow==11.3.0 -platformdirs==4.3.8 -plotly==5.24.1 -plotnine==0.14.5 -pluggy==1.6.0 -ply==3.11 -polars==1.25.2 -pooch==1.8.2 -portpicker==1.5.2 -preshed==3.0.10 -prettytable==3.16.0 -proglog==0.1.12 -progressbar2==4.5.0 -prometheus_client==0.22.1 -promise==2.3 -prompt_toolkit==3.0.51 -propcache==0.3.2 -prophet==1.1.7 -proto-plus==1.26.1 -protobuf==6.33.5 -psutil==5.9.5 -psycopg2==2.9.10 -psygnal==0.14.0 -ptyprocess==0.7.0 -py-cpuinfo==9.0.0 -py4j==0.10.9.7 -pyarrow==23.0.1 -pyasn1==0.6.1 -pyasn1_modules==0.4.2 -pycairo==1.28.0 -pycocotools==2.0.10 -pycparser==2.22 -pycryptodomex==3.23.0 -pydantic==2.11.7 -pydantic_core==2.33.2 -pydata-google-auth==1.9.1 -pydot==3.0.4 -pydotplus==2.0.2 -PyDrive2==1.21.3 -pydub==0.25.1 -pyerfa==2.0.1.5 -pygame==2.6.1 -pygit2==1.18.1 -Pygments==2.19.2 -PyGObject==3.42.0 -PyJWT==2.10.1 -pylibcugraph-cu12==25.6.0 -pylibraft-cu12==25.6.0 -pymc==5.25.1 -pynndescent==0.5.13 -pynvjitlink-cu12==0.7.0 -pynvml==12.0.0 -pyogrio==0.11.1 -pyomo==6.9.2 -PyOpenGL==3.1.9 -pyOpenSSL==24.2.1 -pyparsing==3.2.3 -pyperclip==1.9.0 -pyproj==3.7.1 -pyproject_hooks==1.2.0 -pyshp==2.3.1 -PySocks==1.7.1 -pyspark==3.5.2 -pytensor==2.31.7 -python-apt==0.0.0 -python-box==7.3.2 -python-dateutil==2.9.0.post0 -python-louvain==0.16 -python-multipart==0.0.20 -python-slugify==8.0.4 -python-snappy==0.7.3 -python-utils==3.9.1 -pytz==2025.2 -pyviz_comms==3.0.6 -PyWavelets==1.9.0 -PyYAML==6.0.2 -pyzmq==26.2.1 -raft-dask-cu12==25.6.0 -rapids-dask-dependency==25.6.0 -rapids-logger==0.1.1 -ratelim==0.1.6 -referencing==0.36.2 -regex==2024.11.6 -requests==2.32.3 -requests-oauthlib==2.0.0 -requests-toolbelt==1.0.0 -requirements-parser==0.9.0 -rich==13.9.4 -rmm-cu12==25.6.0 -roman-numerals-py==3.1.0 -rpds-py==0.26.0 -rpy2==3.5.17 -rsa==4.9.1 -ruff==0.12.7 -safehttpx==0.1.6 -safetensors==0.5.3 -scikit-image==0.25.2 -scikit-learn==1.6.1 -scipy==1.16.1 -scooby==0.10.1 -scs==3.2.7.post2 -seaborn==0.13.2 -SecretStorage==3.3.3 -semantic-version==2.10.0 -Send2Trash==1.8.3 -sentence-transformers==4.1.0 -sentencepiece==0.2.0 -sentry-sdk==2.34.1 -shap==0.48.0 -shapely==2.1.1 -shellingham==1.5.4 -simple-parsing==0.1.7 -simplejson==3.20.1 -simsimd==6.5.0 -six==1.17.0 -sklearn-compat==0.1.3 -sklearn-pandas==2.2.0 -slicer==0.0.8 -smart_open==7.3.0.post1 -smmap==5.0.2 -sniffio==1.3.1 -snowballstemmer==3.0.1 -sortedcontainers==2.4.0 -soundfile==0.13.1 -soupsieve==2.8.4 -soxr==0.5.0.post1 -spacy==3.8.7 -spacy-legacy==3.0.12 -spacy-loggers==1.0.5 -spanner-graph-notebook==1.1.7 -Sphinx==8.2.3 -sphinxcontrib-applehelp==2.0.0 -sphinxcontrib-devhelp==2.0.0 -sphinxcontrib-htmlhelp==2.1.0 -sphinxcontrib-jsmath==1.0.1 -sphinxcontrib-qthelp==2.0.0 -sphinxcontrib-serializinghtml==2.0.0 -SQLAlchemy==2.0.42 -sqlparse==0.5.3 -srsly==2.5.1 -stanio==0.5.1 -starlette==0.47.2 -statsmodels==0.14.5 -stringzilla==3.12.5 -stumpy==1.13.0 -sympy==1.13.1 -tables==3.10.2 -tabulate==0.9.0 -tbb==2022.2.0 -tblib==3.1.0 -tcmlib==1.4.0 -tenacity==8.5.0 -tensorboard==2.19.0 -tensorboard-data-server==0.7.2 -tensorflow==2.19.0 -tensorflow-datasets==4.9.9 -tensorflow-hub==0.16.1 -tensorflow-io-gcs-filesystem==0.37.1 -tensorflow-metadata==1.17.2 -tensorflow-probability==0.25.0 -tensorflow-text==2.19.0 -tensorflow_decision_forests==1.12.0 -tensorstore==0.1.76 -termcolor==3.1.0 -terminado==0.18.1 -text-unidecode==1.3 -textblob==0.19.0 -tf-slim==1.1.0 -tf_keras==2.19.0 -thinc==8.3.6 -threadpoolctl==3.6.0 -tifffile==2025.6.11 -tiktoken==0.9.0 -timm==1.0.19 -tinycss2==1.4.0 -tokenizers==0.21.4 -toml==0.10.2 -tomlkit==0.13.3 -toolz==0.12.1 -torchao==0.10.0 -torchdata==0.11.0 -torchsummary==1.5.1 -torchtune==0.6.1 -tornado==6.4.2 -tqdm==4.67.1 -traitlets==5.7.1 -traittypes==0.2.1 -transformers==5.5.0 -treelite==4.4.1 -treescope==0.1.9 -triton==3.2.0 -tsfresh==0.21.0 -tweepy==4.16.0 -typeguard==4.4.4 -typer==0.16.0 -types-pytz==2025.2.0.20250516 -types-setuptools==80.9.0.20250801 -typing-inspection==0.4.1 -typing_extensions==4.14.1 -tzdata==2025.2 -tzlocal==5.3.1 -uc-micro-py==1.0.3 -ucx-py-cu12==0.44.0 -ucxx-cu12==0.44.0 -umap-learn==0.5.9.post2 -umf==0.11.0 -uritemplate==4.2.0 -urllib3==2.5.0 -uvicorn==0.35.0 -vega-datasets==0.9.0 -wadllib==1.3.6 -wandb==0.21.0 -wasabi==1.1.3 -wcwidth==0.2.13 -weasel==0.4.1 -webcolors==24.11.1 -webencodings==0.5.1 -websocket-client==1.8.0 -websockets==15.0.1 -Werkzeug==3.1.3 -widgetsnbextension==3.6.10 -wordcloud==1.9.4 -wrapt==1.17.2 -wurlitzer==3.1.1 -xarray==2025.7.1 -xarray-einstats==0.9.1 -xgboost==3.0.3 -xlrd==2.0.2 -xxhash==3.5.0 -xyzservices==2025.4.0 -yarl==1.20.1 -ydf==0.13.0 -yellowbrick==1.5 -yfinance==0.2.65 -zict==3.0.0 -zipp==3.23.0 diff --git a/testing/constraints-3.12.txt b/testing/constraints-3.12.txt deleted file mode 100644 index e69de29bb2d..00000000000 diff --git a/testing/constraints-3.13.txt b/testing/constraints-3.13.txt deleted file mode 100644 index e69de29bb2d..00000000000 diff --git a/testing/constraints-3.14.txt b/testing/constraints-3.14.txt deleted file mode 100644 index e69de29bb2d..00000000000 diff --git a/testing/constraints-3.15.txt b/testing/constraints-3.15.txt deleted file mode 100644 index e69de29bb2d..00000000000 diff --git a/testing/constraints-3.9.txt b/testing/constraints-3.9.txt new file mode 100644 index 00000000000..f43d3b4ca01 --- /dev/null +++ b/testing/constraints-3.9.txt @@ -0,0 +1,121 @@ +argcomplete==2.1.2 +asyncmock==0.4.2 +atpublic==3.1.1 +attrs==22.2.0 +bidict==0.22.1 +black==23.3.0 +bleach==6.0.0 +cachetools==5.3.0 +certifi==2022.12.7 +cffi==1.15.1 +cfgv==3.3.1 +charset-normalizer==2.0.0 +click==8.1.3 +cloudpickle==2.0.0 +colorlog==6.7.0 +coverage==7.2.2 +cryptography==40.0.1 +distlib==0.3.6 +docstring-inheritance==2.0.0 +docutils==0.19 +exceptiongroup==1.1.1 +execnet==1.9.0 +filelock==3.10.7 +fsspec==2023.3.0 +gcp-docuploader==0.6.5 +gcp-releasetool==1.11.0 +gcsfs==2023.3.0 +geopandas==0.12.2 +google-api-core==2.11.0 +google-auth==2.17.0 +google-auth-oauthlib==1.0.0 +google-cloud-bigquery==3.10.0 +google-cloud-bigquery-connection==1.12.0 +google-cloud-bigquery-storage==2.19.1 +google-cloud-core==2.3.2 +google-cloud-functions==1.10.1 +google-cloud-iam==2.12.1 +google-cloud-resource-manager==1.10.3 +google-cloud-storage==2.0.0 +google-cloud-testutils==1.3.3 +google-crc32c==1.5.0 +google-resumable-media==2.4.1 +googleapis-common-protos==1.59.0 +greenlet==2.0.2 +grpc-google-iam-v1==0.12.6 +grpcio==1.53.0 +grpcio-status==1.48.2 +ibis-framework==6.2.0 +humanize==4.6.0 +identify==2.5.22 +idna==3.4 +importlib-metadata==6.1.0 +iniconfig==2.0.0 +ipywidgets==7.7.1 +jaraco.classes==3.2.3 +jeepney==0.8.0 +Jinja2==3.1.2 +keyring==23.13.1 +markdown-it-py==2.2.0 +MarkupSafe==2.1.2 +mdurl==0.1.2 +mock==5.0.1 +more-itertools==9.1.0 +multipledispatch==0.6.0 +mypy-extensions==1.0.0 +nodeenv==1.7.0 +nox==2022.11.21 +numpy==1.24.2 +oauthlib==3.2.2 +packaging==23.0 +pandas==1.5.0 +pandas-gbq==0.19.0 +parsy==2.1 +pathspec==0.11.1 +pkginfo==1.9.6 +platformdirs==3.2.0 +pluggy==1.0.0 +pooch==1.7.0 +pre-commit==3.2.1 +proto-plus==1.22.2 +protobuf==3.20.3 +pyarrow==11.0.0 +pyasn1==0.4.8 +pyasn1-modules==0.2.8 +pycparser==2.21 +pydata-google-auth==1.8.2 +Pygments==2.14.0 +PyJWT==2.6.0 +pyperclip==1.8.2 +pytest==7.2.2 +pytest-asyncio==0.21.0 +pytest-cov==4.0.0 +pytest-mock==3.11.1 +pytest-retry==1.1.0 +pytest-xdist==3.2.1 +python-dateutil==2.8.2 +pytz==2023.3 +PyYAML==6.0 +readme-renderer==37.3 +requests==2.27.1 +requests-oauthlib==1.3.1 +requests-toolbelt==0.10.1 +rfc3986==2.0.0 +rich==13.3.3 +rsa==4.9 +scikit-learn==1.2.2 +SecretStorage==3.3.3 +six==1.16.0 +SQLAlchemy==1.4.0 +sqlglot==10.6.4 +tomli==2.0.1 +toolz==0.12.0 +tqdm==4.65.0 +twine==4.0.2 +typing_extensions==4.5.0 +tzdata==2023.3 +urllib3==1.26.15 +virtualenv==20.21.0 +webencodings==0.5.1 +xxhash==3.2.0 +zipp==3.15.0 diff --git a/tests/benchmark/.gitignore b/tests/benchmark/.gitignore deleted file mode 100644 index f1bf042bf75..00000000000 --- a/tests/benchmark/.gitignore +++ /dev/null @@ -1,6 +0,0 @@ -*.bytesprocessed -*.bq_exec_time_seconds -*.error -*.local_exec_time_seconds -*.query_char_count -*.slotmillis diff --git a/tests/benchmark/README.md b/tests/benchmark/README.md deleted file mode 100644 index e5b75855143..00000000000 --- a/tests/benchmark/README.md +++ /dev/null @@ -1,83 +0,0 @@ -# BigFrames Benchmarking -## Overview -This directory contains scripts for performance benchmarking of various components of BigFrames. - -## Why Separate Processes? -Each benchmark is executed in a separate process to mitigate the effects of any residual caching or settings that may persist in BigFrames, ensuring that each test is conducted in a clean state. - -## Available Benchmarks -This section lists the benchmarks currently available, with descriptions and links to their sources: -- **DB Benchmark**: This benchmark is adapted from DuckDB Labs and is designed to assess database performance. More information can be found on the [official DB Benchmark GitHub page](https://github.com/duckdblabs/db-benchmark). -- **TPC-H Benchmark**: Based on the TPC-H standards, this benchmark evaluates transaction processing capabilities. It is adapted from code found in the Polars repository, specifically tailored to test and compare these capabilities. Details are available on the [Polars Benchmark GitHub repository](https://github.com/pola-rs/polars-benchmark). -- **Notebooks**: These Jupyter notebooks showcase BigFrames' key features and patterns, and also enable performance benchmarking. Explore them at the [BigFrames Notebooks repository](https://github.com/googleapis/python-bigquery-dataframes/tree/main/notebooks). - -## Benchmark Configuration Using `config.jsonl` Files - -For each benchmark, a corresponding `config.jsonl` file exists in the same folder or its parent folder. These configuration files allow users to control various benchmark parameters without modifying the code directly. By updating the relevant `config.jsonl` file in the specific benchmark's folder, you can easily configure settings such as: -- **benchmark_suffix**: A suffix appended to the benchmark name for identification purposes. -- **ordered**: Controls the mode for BigFrames, specifying whether to use ordered (`true`) or unordered mode (`false`). -- **project_id**: The Google Cloud project ID where the benchmark dataset or table is located. -- **dataset_id**: The dataset ID for querying during the benchmark. -- **table_id**: This is **required** for benchmarks like `dbbenchmark` that target a specific table, but is **not configurable** for benchmarks like `TPC-H`, which use multiple tables with fixed names. - -### Example `config.jsonl` Files - -#### `dbbenchmark` Example -```jsonl -{"benchmark_suffix": "50g_ordered", "project_id": "your-google-cloud-project", "dataset_id": "dbbenchmark", "table_id": "G1_1e9_1e2_5_0", "ordered": true} -{"benchmark_suffix": "50g_unordered", "project_id": "your-google-cloud-project", "dataset_id": "dbbenchmark", "table_id": "G1_1e9_1e2_5_0", "ordered": false} -``` - -#### `TPC-H` Example -```jsonl -{"benchmark_suffix": "10t_unordered", "project_id": "your-google-cloud-project", "dataset_id": "tpch_0010t", "ordered": false} -``` - -## Usage Examples -Our benchmarking process runs internally on a daily basis to continuously monitor the performance of BigFrames. However, there are occasions when you might need to conduct benchmarking locally to test specific changes or new features. - -Here's how you can run benchmarks locally: - -- **Running Notebook Benchmarks**: To execute all notebook benchmarks, use the following command: - ```bash - nox -r -s notebook - ``` - - This command runs all the Jupyter notebooks in the repository as benchmarks. -- **Running Pure Benchmarks**: For executing more traditional benchmarks that do not involve notebooks, use: - ```bash - nox -r -s benchmark - ``` - This will run all the non-notebook benchmarks specified in the repository. - -- **Saving Results**: By default, when run locally, each benchmark concludes by printing a summary of the results, which are not saved automatically. To save the results to a CSV file, you can use the --output-csv or -o option followed by a specific path. If no path is specified, the results will be saved to a temporary location, and the path to this location will be printed at the end of the benchmark. - ```bash - nox -r -s benchmark -- --output-csv path/to/your/results.csv - nox -r -s benchmark -- --output-csv - ``` - -- **Running Multiple Iterations**: To run a benchmark multiple times and obtain an average result, use the -i or --iterations option followed by the number of iterations: - ```bash - nox -r -s benchmark -- --iterations 5 - ``` - -- **Filtering Benchmarks**: If you want to run only specific benchmarks, such as TPC-H, or specific queries within a benchmark, like tpch/q1, you can use the --benchmark-filter or -b option followed by the folder, file name, or both: - ```bash - # Runs all benchmarks in the 'tpch' directory - nox -r -s benchmark -- --benchmark-filter tpch - - # Runs all benchmarks in 'db_benchmark' and specific queries q1 and q2 from TPC-H - nox -r -s benchmark -- --benchmark-filter db_benchmark tpch/q1.py tpch/q2.py - ``` -- **Uploading Results to BigQuery**: To upload benchmark results to BigQuery, set the environment variable GCLOUD_BENCH_PUBLISH_PROJECT to the Google Cloud project where you want to store the results. This enables automatic uploading of the benchmark data to your specified project in BigQuery: - ```bash - export GCLOUD_BENCH_PUBLISH_PROJECT='your-google-cloud-project-id' - - # Run all non-notebook benchmarks and uploads the results to - # your-google-cloud-project-id.benchmark_report.benchmark - nox -r -s benchmark - - # Run all notebook benchmarks and uploads the results to - # your-google-cloud-project-id.benchmark_report.notebook_benchmark - nox -r -s notebook - ``` diff --git a/tests/benchmark/__init__.py b/tests/benchmark/__init__.py deleted file mode 100644 index 6d5e14bcf4a..00000000000 --- a/tests/benchmark/__init__.py +++ /dev/null @@ -1,13 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. diff --git a/tests/benchmark/db_benchmark/groupby/config.jsonl b/tests/benchmark/db_benchmark/groupby/config.jsonl deleted file mode 100644 index b6f23ebbf78..00000000000 --- a/tests/benchmark/db_benchmark/groupby/config.jsonl +++ /dev/null @@ -1,2 +0,0 @@ -{"benchmark_suffix": "50g_ordered", "project_id": "bigframes-dev-perf", "dataset_id": "dbbenchmark", "table_id": "G1_1e9_1e2_5_0", "ordered": true} -{"benchmark_suffix": "50g_unordered", "project_id": "bigframes-dev-perf", "dataset_id": "dbbenchmark", "table_id": "G1_1e9_1e2_5_0", "ordered": false} diff --git a/tests/benchmark/db_benchmark/groupby/q1.py b/tests/benchmark/db_benchmark/groupby/q1.py deleted file mode 100644 index 0051ed5b59c..00000000000 --- a/tests/benchmark/db_benchmark/groupby/q1.py +++ /dev/null @@ -1,32 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import pathlib - -import benchmark.utils as utils -import bigframes_vendored.db_benchmark.groupby_queries as vendored_dbbenchmark_groupby_queries - -if __name__ == "__main__": - config = utils.get_configuration(include_table_id=True) - current_path = pathlib.Path(__file__).absolute() - - utils.get_execution_time( - vendored_dbbenchmark_groupby_queries.q1, - current_path, - config.benchmark_suffix, - config.project_id, - config.dataset_id, - config.table_id, - config.session, - ) diff --git a/tests/benchmark/db_benchmark/groupby/q10.py b/tests/benchmark/db_benchmark/groupby/q10.py deleted file mode 100644 index 08ca9a7fe48..00000000000 --- a/tests/benchmark/db_benchmark/groupby/q10.py +++ /dev/null @@ -1,32 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import pathlib - -import benchmark.utils as utils -import bigframes_vendored.db_benchmark.groupby_queries as vendored_dbbenchmark_groupby_queries - -if __name__ == "__main__": - config = utils.get_configuration(include_table_id=True) - current_path = pathlib.Path(__file__).absolute() - - utils.get_execution_time( - vendored_dbbenchmark_groupby_queries.q10, - current_path, - config.benchmark_suffix, - config.project_id, - config.dataset_id, - config.table_id, - config.session, - ) diff --git a/tests/benchmark/db_benchmark/groupby/q2.py b/tests/benchmark/db_benchmark/groupby/q2.py deleted file mode 100644 index 5b3b6839310..00000000000 --- a/tests/benchmark/db_benchmark/groupby/q2.py +++ /dev/null @@ -1,32 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import pathlib - -import benchmark.utils as utils -import bigframes_vendored.db_benchmark.groupby_queries as vendored_dbbenchmark_groupby_queries - -if __name__ == "__main__": - config = utils.get_configuration(include_table_id=True) - current_path = pathlib.Path(__file__).absolute() - - utils.get_execution_time( - vendored_dbbenchmark_groupby_queries.q2, - current_path, - config.benchmark_suffix, - config.project_id, - config.dataset_id, - config.table_id, - config.session, - ) diff --git a/tests/benchmark/db_benchmark/groupby/q3.py b/tests/benchmark/db_benchmark/groupby/q3.py deleted file mode 100644 index 97d005fbf47..00000000000 --- a/tests/benchmark/db_benchmark/groupby/q3.py +++ /dev/null @@ -1,32 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import pathlib - -import benchmark.utils as utils -import bigframes_vendored.db_benchmark.groupby_queries as vendored_dbbenchmark_groupby_queries - -if __name__ == "__main__": - config = utils.get_configuration(include_table_id=True) - current_path = pathlib.Path(__file__).absolute() - - utils.get_execution_time( - vendored_dbbenchmark_groupby_queries.q3, - current_path, - config.benchmark_suffix, - config.project_id, - config.dataset_id, - config.table_id, - config.session, - ) diff --git a/tests/benchmark/db_benchmark/groupby/q4.py b/tests/benchmark/db_benchmark/groupby/q4.py deleted file mode 100644 index 709b2107d2d..00000000000 --- a/tests/benchmark/db_benchmark/groupby/q4.py +++ /dev/null @@ -1,32 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import pathlib - -import benchmark.utils as utils -import bigframes_vendored.db_benchmark.groupby_queries as vendored_dbbenchmark_groupby_queries - -if __name__ == "__main__": - config = utils.get_configuration(include_table_id=True) - current_path = pathlib.Path(__file__).absolute() - - utils.get_execution_time( - vendored_dbbenchmark_groupby_queries.q4, - current_path, - config.benchmark_suffix, - config.project_id, - config.dataset_id, - config.table_id, - config.session, - ) diff --git a/tests/benchmark/db_benchmark/groupby/q5.py b/tests/benchmark/db_benchmark/groupby/q5.py deleted file mode 100644 index 3d870b0598c..00000000000 --- a/tests/benchmark/db_benchmark/groupby/q5.py +++ /dev/null @@ -1,32 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import pathlib - -import benchmark.utils as utils -import bigframes_vendored.db_benchmark.groupby_queries as vendored_dbbenchmark_groupby_queries - -if __name__ == "__main__": - config = utils.get_configuration(include_table_id=True) - current_path = pathlib.Path(__file__).absolute() - - utils.get_execution_time( - vendored_dbbenchmark_groupby_queries.q5, - current_path, - config.benchmark_suffix, - config.project_id, - config.dataset_id, - config.table_id, - config.session, - ) diff --git a/tests/benchmark/db_benchmark/groupby/q6.py b/tests/benchmark/db_benchmark/groupby/q6.py deleted file mode 100644 index bceb5599b2d..00000000000 --- a/tests/benchmark/db_benchmark/groupby/q6.py +++ /dev/null @@ -1,32 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import pathlib - -import benchmark.utils as utils -import bigframes_vendored.db_benchmark.groupby_queries as vendored_dbbenchmark_groupby_queries - -if __name__ == "__main__": - config = utils.get_configuration(include_table_id=True) - current_path = pathlib.Path(__file__).absolute() - - utils.get_execution_time( - vendored_dbbenchmark_groupby_queries.q6, - current_path, - config.benchmark_suffix, - config.project_id, - config.dataset_id, - config.table_id, - config.session, - ) diff --git a/tests/benchmark/db_benchmark/groupby/q7.py b/tests/benchmark/db_benchmark/groupby/q7.py deleted file mode 100644 index 600e26bf160..00000000000 --- a/tests/benchmark/db_benchmark/groupby/q7.py +++ /dev/null @@ -1,32 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import pathlib - -import benchmark.utils as utils -import bigframes_vendored.db_benchmark.groupby_queries as vendored_dbbenchmark_groupby_queries - -if __name__ == "__main__": - config = utils.get_configuration(include_table_id=True) - current_path = pathlib.Path(__file__).absolute() - - utils.get_execution_time( - vendored_dbbenchmark_groupby_queries.q7, - current_path, - config.benchmark_suffix, - config.project_id, - config.dataset_id, - config.table_id, - config.session, - ) diff --git a/tests/benchmark/db_benchmark/groupby/q8.py b/tests/benchmark/db_benchmark/groupby/q8.py deleted file mode 100644 index 82082bc7e5a..00000000000 --- a/tests/benchmark/db_benchmark/groupby/q8.py +++ /dev/null @@ -1,32 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import pathlib - -import benchmark.utils as utils -import bigframes_vendored.db_benchmark.groupby_queries as vendored_dbbenchmark_groupby_queries - -if __name__ == "__main__": - config = utils.get_configuration(include_table_id=True) - current_path = pathlib.Path(__file__).absolute() - - utils.get_execution_time( - vendored_dbbenchmark_groupby_queries.q8, - current_path, - config.benchmark_suffix, - config.project_id, - config.dataset_id, - config.table_id, - config.session, - ) diff --git a/tests/benchmark/db_benchmark/join/config.jsonl b/tests/benchmark/db_benchmark/join/config.jsonl deleted file mode 100644 index e709281137d..00000000000 --- a/tests/benchmark/db_benchmark/join/config.jsonl +++ /dev/null @@ -1,2 +0,0 @@ -{"benchmark_suffix": "50g_ordered", "project_id": "bigframes-dev-perf", "dataset_id": "dbbenchmark", "table_id": "J1_1e9_NA_0_0", "ordered": true} -{"benchmark_suffix": "50g_unordered", "project_id": "bigframes-dev-perf", "dataset_id": "dbbenchmark", "table_id": "J1_1e9_NA_0_0", "ordered": false} diff --git a/tests/benchmark/db_benchmark/join/q1.py b/tests/benchmark/db_benchmark/join/q1.py deleted file mode 100644 index e9e3c2fad0d..00000000000 --- a/tests/benchmark/db_benchmark/join/q1.py +++ /dev/null @@ -1,33 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import pathlib - -import benchmark.utils as utils -import bigframes_vendored.db_benchmark.join_queries as vendored_dbbenchmark_join_queries - -if __name__ == "__main__": - config = utils.get_configuration(include_table_id=True) - - current_path = pathlib.Path(__file__).absolute() - - utils.get_execution_time( - vendored_dbbenchmark_join_queries.q1, - current_path, - config.benchmark_suffix, - config.project_id, - config.dataset_id, - config.table_id, - config.session, - ) diff --git a/tests/benchmark/db_benchmark/join/q2.py b/tests/benchmark/db_benchmark/join/q2.py deleted file mode 100644 index f4b9f67def4..00000000000 --- a/tests/benchmark/db_benchmark/join/q2.py +++ /dev/null @@ -1,33 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import pathlib - -import benchmark.utils as utils -import bigframes_vendored.db_benchmark.join_queries as vendored_dbbenchmark_join_queries - -if __name__ == "__main__": - config = utils.get_configuration(include_table_id=True) - - current_path = pathlib.Path(__file__).absolute() - - utils.get_execution_time( - vendored_dbbenchmark_join_queries.q2, - current_path, - config.benchmark_suffix, - config.project_id, - config.dataset_id, - config.table_id, - config.session, - ) diff --git a/tests/benchmark/db_benchmark/join/q3.py b/tests/benchmark/db_benchmark/join/q3.py deleted file mode 100644 index 83be831a46e..00000000000 --- a/tests/benchmark/db_benchmark/join/q3.py +++ /dev/null @@ -1,33 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import pathlib - -import benchmark.utils as utils -import bigframes_vendored.db_benchmark.join_queries as vendored_dbbenchmark_join_queries - -if __name__ == "__main__": - config = utils.get_configuration(include_table_id=True) - - current_path = pathlib.Path(__file__).absolute() - - utils.get_execution_time( - vendored_dbbenchmark_join_queries.q3, - current_path, - config.benchmark_suffix, - config.project_id, - config.dataset_id, - config.table_id, - config.session, - ) diff --git a/tests/benchmark/db_benchmark/join/q4.py b/tests/benchmark/db_benchmark/join/q4.py deleted file mode 100644 index 6399683472b..00000000000 --- a/tests/benchmark/db_benchmark/join/q4.py +++ /dev/null @@ -1,33 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import pathlib - -import benchmark.utils as utils -import bigframes_vendored.db_benchmark.join_queries as vendored_dbbenchmark_join_queries - -if __name__ == "__main__": - config = utils.get_configuration(include_table_id=True) - - current_path = pathlib.Path(__file__).absolute() - - utils.get_execution_time( - vendored_dbbenchmark_join_queries.q4, - current_path, - config.benchmark_suffix, - config.project_id, - config.dataset_id, - config.table_id, - config.session, - ) diff --git a/tests/benchmark/db_benchmark/join/q5.py b/tests/benchmark/db_benchmark/join/q5.py deleted file mode 100644 index b0b26f93652..00000000000 --- a/tests/benchmark/db_benchmark/join/q5.py +++ /dev/null @@ -1,33 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import pathlib - -import benchmark.utils as utils -import bigframes_vendored.db_benchmark.join_queries as vendored_dbbenchmark_join_queries - -if __name__ == "__main__": - config = utils.get_configuration(include_table_id=True) - - current_path = pathlib.Path(__file__).absolute() - - utils.get_execution_time( - vendored_dbbenchmark_join_queries.q5, - current_path, - config.benchmark_suffix, - config.project_id, - config.dataset_id, - config.table_id, - config.session, - ) diff --git a/tests/benchmark/db_benchmark/sort/config.jsonl b/tests/benchmark/db_benchmark/sort/config.jsonl deleted file mode 100644 index e709281137d..00000000000 --- a/tests/benchmark/db_benchmark/sort/config.jsonl +++ /dev/null @@ -1,2 +0,0 @@ -{"benchmark_suffix": "50g_ordered", "project_id": "bigframes-dev-perf", "dataset_id": "dbbenchmark", "table_id": "J1_1e9_NA_0_0", "ordered": true} -{"benchmark_suffix": "50g_unordered", "project_id": "bigframes-dev-perf", "dataset_id": "dbbenchmark", "table_id": "J1_1e9_NA_0_0", "ordered": false} diff --git a/tests/benchmark/db_benchmark/sort/q1.py b/tests/benchmark/db_benchmark/sort/q1.py deleted file mode 100644 index d73fe28e30f..00000000000 --- a/tests/benchmark/db_benchmark/sort/q1.py +++ /dev/null @@ -1,32 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import pathlib - -import benchmark.utils as utils -import bigframes_vendored.db_benchmark.sort_queries as vendored_dbbenchmark_sort_queries - -if __name__ == "__main__": - config = utils.get_configuration(include_table_id=True) - current_path = pathlib.Path(__file__).absolute() - - utils.get_execution_time( - vendored_dbbenchmark_sort_queries.q1, - current_path, - config.benchmark_suffix, - config.project_id, - config.dataset_id, - config.table_id, - config.session, - ) diff --git a/tests/benchmark/read_gbq_colab/aggregate_output.py b/tests/benchmark/read_gbq_colab/aggregate_output.py deleted file mode 100644 index e5620d8e16c..00000000000 --- a/tests/benchmark/read_gbq_colab/aggregate_output.py +++ /dev/null @@ -1,63 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -import pathlib - -import benchmark.utils as utils - -import bigframes.pandas as bpd - -PAGE_SIZE = utils.READ_GBQ_COLAB_PAGE_SIZE - - -def aggregate_output(*, project_id, dataset_id, table_id): - # TODO(tswast): Support alternative query if table_id is a local DataFrame, - # e.g. "{local_inline}" or "{local_large}" - df = bpd._read_gbq_colab(f"SELECT * FROM `{project_id}`.{dataset_id}.{table_id}") - - # Simulate getting the first page, since we'll always do that first in the UI. - batches = df._to_pandas_batches(page_size=PAGE_SIZE) - assert (tr := batches.total_rows) is not None and tr >= 0 - next(iter(batches)) - - # To simulate very small rows that can only fit a boolean, - # some tables don't have an integer column. If an integer column is available, - # we prefer to group by that to get a more realistic number of groups. - group_column = "col_int64_1" - if group_column not in df.columns: - group_column = "col_bool_0" - - # Simulate the user aggregating by a column and visualizing those results - df_aggregated = ( - df.assign(rounded=df[group_column].astype("Int64").round(-9)) - .groupby("rounded") - .sum(numeric_only=True) - ) - - batches = df_aggregated._to_pandas_batches(page_size=PAGE_SIZE) - assert (tr := batches.total_rows) is not None and tr >= 0 - next(iter(batches)) - - -if __name__ == "__main__": - config = utils.get_configuration(include_table_id=True, start_session=False) - current_path = pathlib.Path(__file__).absolute() - - utils.get_execution_time( - aggregate_output, - current_path, - config.benchmark_suffix, - project_id=config.project_id, - dataset_id=config.dataset_id, - table_id=config.table_id, - ) diff --git a/tests/benchmark/read_gbq_colab/config.jsonl b/tests/benchmark/read_gbq_colab/config.jsonl deleted file mode 100644 index 6f1ddf4a5f7..00000000000 --- a/tests/benchmark/read_gbq_colab/config.jsonl +++ /dev/null @@ -1,10 +0,0 @@ -{"benchmark_suffix": "percentile_09", "project_id": "bigframes-dev-perf", "dataset_id": "read_gbq_colab_benchmark", "table_id": "percentile_09", "ordered": false} -{"benchmark_suffix": "percentile_19", "project_id": "bigframes-dev-perf", "dataset_id": "read_gbq_colab_benchmark", "table_id": "percentile_19", "ordered": false} -{"benchmark_suffix": "percentile_29", "project_id": "bigframes-dev-perf", "dataset_id": "read_gbq_colab_benchmark", "table_id": "percentile_29", "ordered": false} -{"benchmark_suffix": "percentile_39", "project_id": "bigframes-dev-perf", "dataset_id": "read_gbq_colab_benchmark", "table_id": "percentile_39", "ordered": false} -{"benchmark_suffix": "percentile_49", "project_id": "bigframes-dev-perf", "dataset_id": "read_gbq_colab_benchmark", "table_id": "percentile_49", "ordered": false} -{"benchmark_suffix": "percentile_59", "project_id": "bigframes-dev-perf", "dataset_id": "read_gbq_colab_benchmark", "table_id": "percentile_59", "ordered": false} -{"benchmark_suffix": "percentile_69", "project_id": "bigframes-dev-perf", "dataset_id": "read_gbq_colab_benchmark", "table_id": "percentile_69", "ordered": false} -{"benchmark_suffix": "percentile_79", "project_id": "bigframes-dev-perf", "dataset_id": "read_gbq_colab_benchmark", "table_id": "percentile_79", "ordered": false} -{"benchmark_suffix": "percentile_89", "project_id": "bigframes-dev-perf", "dataset_id": "read_gbq_colab_benchmark", "table_id": "percentile_89", "ordered": false} -{"benchmark_suffix": "percentile_99", "project_id": "bigframes-dev-perf", "dataset_id": "read_gbq_colab_benchmark", "table_id": "percentile_99", "ordered": false} diff --git a/tests/benchmark/read_gbq_colab/dry_run.py b/tests/benchmark/read_gbq_colab/dry_run.py deleted file mode 100644 index 6caf08be72d..00000000000 --- a/tests/benchmark/read_gbq_colab/dry_run.py +++ /dev/null @@ -1,41 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -import pathlib - -import benchmark.utils as utils - -import bigframes.pandas - - -def dry_run(*, project_id, dataset_id, table_id): - # TODO(tswast): Support alternative query if table_id is a local DataFrame, - # e.g. "{local_inline}" or "{local_large}" - bigframes.pandas._read_gbq_colab( - f"SELECT * FROM `{project_id}`.{dataset_id}.{table_id}", - dry_run=True, - ) - - -if __name__ == "__main__": - config = utils.get_configuration(include_table_id=True, start_session=False) - current_path = pathlib.Path(__file__).absolute() - - utils.get_execution_time( - dry_run, - current_path, - config.benchmark_suffix, - project_id=config.project_id, - dataset_id=config.dataset_id, - table_id=config.table_id, - ) diff --git a/tests/benchmark/read_gbq_colab/filter_output.py b/tests/benchmark/read_gbq_colab/filter_output.py deleted file mode 100644 index dc88d313662..00000000000 --- a/tests/benchmark/read_gbq_colab/filter_output.py +++ /dev/null @@ -1,60 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -import pathlib - -import benchmark.utils as utils - -import bigframes.pandas as bpd - -PAGE_SIZE = utils.READ_GBQ_COLAB_PAGE_SIZE - - -def filter_output( - *, - project_id, - dataset_id, - table_id, -): - # TODO(tswast): Support alternative query if table_id is a local DataFrame, - # e.g. "{local_inline}" or "{local_large}" - df = bpd._read_gbq_colab(f"SELECT * FROM `{project_id}`.{dataset_id}.{table_id}") - - # Simulate getting the first page, since we'll always do that first in the UI. - batches = df._to_pandas_batches(page_size=PAGE_SIZE) - assert (tr := batches.total_rows) is not None and tr >= 0 - next(iter(batches)) - - # Simulate the user filtering by a column and visualizing those results - df_filtered = df[df["col_bool_0"]] - batches = df_filtered._to_pandas_batches(page_size=PAGE_SIZE) - assert (tr := batches.total_rows) is not None and tr >= 0 - first_page = next(iter(batches)) - - # It's possible we don't have any pages at all, since we filtered out all - # matching rows. - assert len(first_page.index) <= tr - - -if __name__ == "__main__": - config = utils.get_configuration(include_table_id=True) - current_path = pathlib.Path(__file__).absolute() - - utils.get_execution_time( - filter_output, - current_path, - config.benchmark_suffix, - project_id=config.project_id, - dataset_id=config.dataset_id, - table_id=config.table_id, - ) diff --git a/tests/benchmark/read_gbq_colab/first_page.py b/tests/benchmark/read_gbq_colab/first_page.py deleted file mode 100644 index 33e2a24bd7b..00000000000 --- a/tests/benchmark/read_gbq_colab/first_page.py +++ /dev/null @@ -1,47 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -import pathlib - -import benchmark.utils as utils - -import bigframes.pandas - -PAGE_SIZE = utils.READ_GBQ_COLAB_PAGE_SIZE - - -def first_page(*, project_id, dataset_id, table_id): - # TODO(tswast): Support alternative query if table_id is a local DataFrame, - # e.g. "{local_inline}" or "{local_large}" - df = bigframes.pandas._read_gbq_colab( - f"SELECT * FROM `{project_id}`.{dataset_id}.{table_id}" - ) - - # Get number of rows (to calculate number of pages) and the first page. - batches = df._to_pandas_batches(page_size=PAGE_SIZE) - assert (tr := batches.total_rows) is not None and tr >= 0 - next(iter(batches)) - - -if __name__ == "__main__": - config = utils.get_configuration(include_table_id=True, start_session=False) - current_path = pathlib.Path(__file__).absolute() - - utils.get_execution_time( - first_page, - current_path, - config.benchmark_suffix, - project_id=config.project_id, - dataset_id=config.dataset_id, - table_id=config.table_id, - ) diff --git a/tests/benchmark/read_gbq_colab/last_page.py b/tests/benchmark/read_gbq_colab/last_page.py deleted file mode 100644 index 2e485a070a8..00000000000 --- a/tests/benchmark/read_gbq_colab/last_page.py +++ /dev/null @@ -1,48 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -import pathlib - -import benchmark.utils as utils - -import bigframes.pandas - -PAGE_SIZE = utils.READ_GBQ_COLAB_PAGE_SIZE - - -def last_page(*, project_id, dataset_id, table_id): - # TODO(tswast): Support alternative query if table_id is a local DataFrame, - # e.g. "{local_inline}" or "{local_large}" - df = bigframes.pandas._read_gbq_colab( - f"SELECT * FROM `{project_id}`.{dataset_id}.{table_id}" - ) - - # Get number of rows (to calculate number of pages) and then all pages. - batches = df._to_pandas_batches(page_size=PAGE_SIZE) - assert (tr := batches.total_rows) is not None and tr >= 0 - for _ in batches: - pass - - -if __name__ == "__main__": - config = utils.get_configuration(include_table_id=True, start_session=False) - current_path = pathlib.Path(__file__).absolute() - - utils.get_execution_time( - last_page, - current_path, - config.benchmark_suffix, - project_id=config.project_id, - dataset_id=config.dataset_id, - table_id=config.table_id, - ) diff --git a/tests/benchmark/read_gbq_colab/sort_output.py b/tests/benchmark/read_gbq_colab/sort_output.py deleted file mode 100644 index 3044e0c2a32..00000000000 --- a/tests/benchmark/read_gbq_colab/sort_output.py +++ /dev/null @@ -1,57 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -import pathlib - -import benchmark.utils as utils - -import bigframes.pandas - -PAGE_SIZE = utils.READ_GBQ_COLAB_PAGE_SIZE - - -def sort_output(*, project_id, dataset_id, table_id): - # TODO(tswast): Support alternative query if table_id is a local DataFrame, - # e.g. "{local_inline}" or "{local_large}" - df = bigframes.pandas._read_gbq_colab( - f"SELECT * FROM `{project_id}`.{dataset_id}.{table_id}" - ) - - # Simulate getting the first page, since we'll always do that first in the UI. - batches = df._to_pandas_batches(page_size=PAGE_SIZE) - assert (tr := batches.total_rows) is not None and tr >= 0 - next(iter(batches)) - - # Simulate the user sorting by a column and visualizing those results - sort_column = "col_int64_1" - if sort_column not in df.columns: - sort_column = "col_bool_0" - - df_sorted = df.sort_values(sort_column) - batches = df_sorted._to_pandas_batches(page_size=PAGE_SIZE) - assert (tr := batches.total_rows) is not None and tr >= 0 - next(iter(batches)) - - -if __name__ == "__main__": - config = utils.get_configuration(include_table_id=True, start_session=False) - current_path = pathlib.Path(__file__).absolute() - - utils.get_execution_time( - sort_output, - current_path, - config.benchmark_suffix, - project_id=config.project_id, - dataset_id=config.dataset_id, - table_id=config.table_id, - ) diff --git a/tests/benchmark/tpch/config.jsonl b/tests/benchmark/tpch/config.jsonl deleted file mode 100644 index e6f7a444f65..00000000000 --- a/tests/benchmark/tpch/config.jsonl +++ /dev/null @@ -1,10 +0,0 @@ -{"benchmark_suffix": "1g_ordered", "project_id": "bigframes-dev-perf", "dataset_id": "tpch_0001g", "ordered": true} -{"benchmark_suffix": "1g_unordered", "project_id": "bigframes-dev-perf", "dataset_id": "tpch_0001g", "ordered": false} -{"benchmark_suffix": "10g_ordered", "project_id": "bigframes-dev-perf", "dataset_id": "tpch_0010g", "ordered": true} -{"benchmark_suffix": "10g_unordered", "project_id": "bigframes-dev-perf", "dataset_id": "tpch_0010g", "ordered": false} -{"benchmark_suffix": "100g_ordered", "project_id": "bigframes-dev-perf", "dataset_id": "tpch_0100g", "ordered": true} -{"benchmark_suffix": "100g_unordered", "project_id": "bigframes-dev-perf", "dataset_id": "tpch_0100g", "ordered": false} -{"benchmark_suffix": "1t_ordered", "project_id": "bigframes-dev-perf", "dataset_id": "tpch_0001t", "ordered": true} -{"benchmark_suffix": "1t_unordered", "project_id": "bigframes-dev-perf", "dataset_id": "tpch_0001t", "ordered": false} -{"benchmark_suffix": "10t_ordered", "project_id": "bigframes-dev-perf", "dataset_id": "tpch_0010t", "ordered": true} -{"benchmark_suffix": "10t_unordered", "project_id": "bigframes-dev-perf", "dataset_id": "tpch_0010t", "ordered": false} diff --git a/tests/benchmark/tpch/q1.py b/tests/benchmark/tpch/q1.py deleted file mode 100644 index beacaa436bf..00000000000 --- a/tests/benchmark/tpch/q1.py +++ /dev/null @@ -1,30 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -import pathlib - -import benchmark.utils as utils -import bigframes_vendored.tpch.queries.q1 as vendored_tpch_q1 - -if __name__ == "__main__": - config = utils.get_configuration() - current_path = pathlib.Path(__file__).absolute() - - utils.get_execution_time( - vendored_tpch_q1.q, - current_path, - config.benchmark_suffix, - config.project_id, - config.dataset_id, - config.session, - ) diff --git a/tests/benchmark/tpch/q10.py b/tests/benchmark/tpch/q10.py deleted file mode 100644 index 27262ff2103..00000000000 --- a/tests/benchmark/tpch/q10.py +++ /dev/null @@ -1,30 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -import pathlib - -import benchmark.utils as utils -import bigframes_vendored.tpch.queries.q10 as vendored_tpch_q10 - -if __name__ == "__main__": - config = utils.get_configuration() - current_path = pathlib.Path(__file__).absolute() - - utils.get_execution_time( - vendored_tpch_q10.q, - current_path, - config.benchmark_suffix, - config.project_id, - config.dataset_id, - config.session, - ) diff --git a/tests/benchmark/tpch/q11.py b/tests/benchmark/tpch/q11.py deleted file mode 100644 index 45a0168bb14..00000000000 --- a/tests/benchmark/tpch/q11.py +++ /dev/null @@ -1,30 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -import pathlib - -import benchmark.utils as utils -import bigframes_vendored.tpch.queries.q11 as vendored_tpch_q11 - -if __name__ == "__main__": - config = utils.get_configuration() - current_path = pathlib.Path(__file__).absolute() - - utils.get_execution_time( - vendored_tpch_q11.q, - current_path, - config.benchmark_suffix, - config.project_id, - config.dataset_id, - config.session, - ) diff --git a/tests/benchmark/tpch/q12.py b/tests/benchmark/tpch/q12.py deleted file mode 100644 index d055cd1c0b7..00000000000 --- a/tests/benchmark/tpch/q12.py +++ /dev/null @@ -1,30 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -import pathlib - -import benchmark.utils as utils -import bigframes_vendored.tpch.queries.q12 as vendored_tpch_q12 - -if __name__ == "__main__": - config = utils.get_configuration() - current_path = pathlib.Path(__file__).absolute() - - utils.get_execution_time( - vendored_tpch_q12.q, - current_path, - config.benchmark_suffix, - config.project_id, - config.dataset_id, - config.session, - ) diff --git a/tests/benchmark/tpch/q13.py b/tests/benchmark/tpch/q13.py deleted file mode 100644 index f74ef26448d..00000000000 --- a/tests/benchmark/tpch/q13.py +++ /dev/null @@ -1,30 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -import pathlib - -import benchmark.utils as utils -import bigframes_vendored.tpch.queries.q13 as vendored_tpch_q13 - -if __name__ == "__main__": - config = utils.get_configuration() - current_path = pathlib.Path(__file__).absolute() - - utils.get_execution_time( - vendored_tpch_q13.q, - current_path, - config.benchmark_suffix, - config.project_id, - config.dataset_id, - config.session, - ) diff --git a/tests/benchmark/tpch/q14.py b/tests/benchmark/tpch/q14.py deleted file mode 100644 index 01ee0add398..00000000000 --- a/tests/benchmark/tpch/q14.py +++ /dev/null @@ -1,30 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -import pathlib - -import benchmark.utils as utils -import bigframes_vendored.tpch.queries.q14 as vendored_tpch_q14 - -if __name__ == "__main__": - config = utils.get_configuration() - current_path = pathlib.Path(__file__).absolute() - - utils.get_execution_time( - vendored_tpch_q14.q, - current_path, - config.benchmark_suffix, - config.project_id, - config.dataset_id, - config.session, - ) diff --git a/tests/benchmark/tpch/q15.py b/tests/benchmark/tpch/q15.py deleted file mode 100644 index b19141797ac..00000000000 --- a/tests/benchmark/tpch/q15.py +++ /dev/null @@ -1,30 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -import pathlib - -import benchmark.utils as utils -import bigframes_vendored.tpch.queries.q15 as vendored_tpch_q15 - -if __name__ == "__main__": - config = utils.get_configuration() - current_path = pathlib.Path(__file__).absolute() - - utils.get_execution_time( - vendored_tpch_q15.q, - current_path, - config.benchmark_suffix, - config.project_id, - config.dataset_id, - config.session, - ) diff --git a/tests/benchmark/tpch/q16.py b/tests/benchmark/tpch/q16.py deleted file mode 100644 index 5947bb6ed12..00000000000 --- a/tests/benchmark/tpch/q16.py +++ /dev/null @@ -1,30 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -import pathlib - -import benchmark.utils as utils -import bigframes_vendored.tpch.queries.q16 as vendored_tpch_q16 - -if __name__ == "__main__": - config = utils.get_configuration() - current_path = pathlib.Path(__file__).absolute() - - utils.get_execution_time( - vendored_tpch_q16.q, - current_path, - config.benchmark_suffix, - config.project_id, - config.dataset_id, - config.session, - ) diff --git a/tests/benchmark/tpch/q17.py b/tests/benchmark/tpch/q17.py deleted file mode 100644 index e80f7b23f97..00000000000 --- a/tests/benchmark/tpch/q17.py +++ /dev/null @@ -1,30 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -import pathlib - -import benchmark.utils as utils -import bigframes_vendored.tpch.queries.q17 as vendored_tpch_q17 - -if __name__ == "__main__": - config = utils.get_configuration() - current_path = pathlib.Path(__file__).absolute() - - utils.get_execution_time( - vendored_tpch_q17.q, - current_path, - config.benchmark_suffix, - config.project_id, - config.dataset_id, - config.session, - ) diff --git a/tests/benchmark/tpch/q18.py b/tests/benchmark/tpch/q18.py deleted file mode 100644 index 7e9d6c00c4c..00000000000 --- a/tests/benchmark/tpch/q18.py +++ /dev/null @@ -1,30 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -import pathlib - -import benchmark.utils as utils -import bigframes_vendored.tpch.queries.q18 as vendored_tpch_q18 - -if __name__ == "__main__": - config = utils.get_configuration() - current_path = pathlib.Path(__file__).absolute() - - utils.get_execution_time( - vendored_tpch_q18.q, - current_path, - config.benchmark_suffix, - config.project_id, - config.dataset_id, - config.session, - ) diff --git a/tests/benchmark/tpch/q19.py b/tests/benchmark/tpch/q19.py deleted file mode 100644 index f2c1cfc6237..00000000000 --- a/tests/benchmark/tpch/q19.py +++ /dev/null @@ -1,30 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -import pathlib - -import benchmark.utils as utils -import bigframes_vendored.tpch.queries.q19 as vendored_tpch_q19 - -if __name__ == "__main__": - config = utils.get_configuration() - current_path = pathlib.Path(__file__).absolute() - - utils.get_execution_time( - vendored_tpch_q19.q, - current_path, - config.benchmark_suffix, - config.project_id, - config.dataset_id, - config.session, - ) diff --git a/tests/benchmark/tpch/q2.py b/tests/benchmark/tpch/q2.py deleted file mode 100644 index 64907d0d258..00000000000 --- a/tests/benchmark/tpch/q2.py +++ /dev/null @@ -1,30 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -import pathlib - -import benchmark.utils as utils -import bigframes_vendored.tpch.queries.q2 as vendored_tpch_q2 - -if __name__ == "__main__": - config = utils.get_configuration() - current_path = pathlib.Path(__file__).absolute() - - utils.get_execution_time( - vendored_tpch_q2.q, - current_path, - config.benchmark_suffix, - config.project_id, - config.dataset_id, - config.session, - ) diff --git a/tests/benchmark/tpch/q20.py b/tests/benchmark/tpch/q20.py deleted file mode 100644 index 8a405280ef0..00000000000 --- a/tests/benchmark/tpch/q20.py +++ /dev/null @@ -1,30 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -import pathlib - -import benchmark.utils as utils -import bigframes_vendored.tpch.queries.q20 as vendored_tpch_q20 - -if __name__ == "__main__": - config = utils.get_configuration() - current_path = pathlib.Path(__file__).absolute() - - utils.get_execution_time( - vendored_tpch_q20.q, - current_path, - config.benchmark_suffix, - config.project_id, - config.dataset_id, - config.session, - ) diff --git a/tests/benchmark/tpch/q21.py b/tests/benchmark/tpch/q21.py deleted file mode 100644 index 29b364b3874..00000000000 --- a/tests/benchmark/tpch/q21.py +++ /dev/null @@ -1,30 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -import pathlib - -import benchmark.utils as utils -import bigframes_vendored.tpch.queries.q21 as vendored_tpch_q21 - -if __name__ == "__main__": - config = utils.get_configuration() - current_path = pathlib.Path(__file__).absolute() - - utils.get_execution_time( - vendored_tpch_q21.q, - current_path, - config.benchmark_suffix, - config.project_id, - config.dataset_id, - config.session, - ) diff --git a/tests/benchmark/tpch/q22.py b/tests/benchmark/tpch/q22.py deleted file mode 100644 index 9147115097f..00000000000 --- a/tests/benchmark/tpch/q22.py +++ /dev/null @@ -1,30 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -import pathlib - -import benchmark.utils as utils -import bigframes_vendored.tpch.queries.q22 as vendored_tpch_q22 - -if __name__ == "__main__": - config = utils.get_configuration() - current_path = pathlib.Path(__file__).absolute() - - utils.get_execution_time( - vendored_tpch_q22.q, - current_path, - config.benchmark_suffix, - config.project_id, - config.dataset_id, - config.session, - ) diff --git a/tests/benchmark/tpch/q3.py b/tests/benchmark/tpch/q3.py deleted file mode 100644 index e4eee0630bd..00000000000 --- a/tests/benchmark/tpch/q3.py +++ /dev/null @@ -1,30 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -import pathlib - -import benchmark.utils as utils -import bigframes_vendored.tpch.queries.q3 as vendored_tpch_q3 - -if __name__ == "__main__": - config = utils.get_configuration() - current_path = pathlib.Path(__file__).absolute() - - utils.get_execution_time( - vendored_tpch_q3.q, - current_path, - config.benchmark_suffix, - config.project_id, - config.dataset_id, - config.session, - ) diff --git a/tests/benchmark/tpch/q4.py b/tests/benchmark/tpch/q4.py deleted file mode 100644 index f0aa3b77a0f..00000000000 --- a/tests/benchmark/tpch/q4.py +++ /dev/null @@ -1,30 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -import pathlib - -import benchmark.utils as utils -import bigframes_vendored.tpch.queries.q4 as vendored_tpch_q4 - -if __name__ == "__main__": - config = utils.get_configuration() - current_path = pathlib.Path(__file__).absolute() - - utils.get_execution_time( - vendored_tpch_q4.q, - current_path, - config.benchmark_suffix, - config.project_id, - config.dataset_id, - config.session, - ) diff --git a/tests/benchmark/tpch/q5.py b/tests/benchmark/tpch/q5.py deleted file mode 100644 index 5f82638278d..00000000000 --- a/tests/benchmark/tpch/q5.py +++ /dev/null @@ -1,30 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -import pathlib - -import benchmark.utils as utils -import bigframes_vendored.tpch.queries.q5 as vendored_tpch_q5 - -if __name__ == "__main__": - config = utils.get_configuration() - current_path = pathlib.Path(__file__).absolute() - - utils.get_execution_time( - vendored_tpch_q5.q, - current_path, - config.benchmark_suffix, - config.project_id, - config.dataset_id, - config.session, - ) diff --git a/tests/benchmark/tpch/q6.py b/tests/benchmark/tpch/q6.py deleted file mode 100644 index bf06f8d31c8..00000000000 --- a/tests/benchmark/tpch/q6.py +++ /dev/null @@ -1,30 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -import pathlib - -import benchmark.utils as utils -import bigframes_vendored.tpch.queries.q6 as vendored_tpch_q6 - -if __name__ == "__main__": - config = utils.get_configuration() - current_path = pathlib.Path(__file__).absolute() - - utils.get_execution_time( - vendored_tpch_q6.q, - current_path, - config.benchmark_suffix, - config.project_id, - config.dataset_id, - config.session, - ) diff --git a/tests/benchmark/tpch/q7.py b/tests/benchmark/tpch/q7.py deleted file mode 100644 index f9575dd4d6b..00000000000 --- a/tests/benchmark/tpch/q7.py +++ /dev/null @@ -1,30 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -import pathlib - -import benchmark.utils as utils -import bigframes_vendored.tpch.queries.q7 as vendored_tpch_q7 - -if __name__ == "__main__": - config = utils.get_configuration() - current_path = pathlib.Path(__file__).absolute() - - utils.get_execution_time( - vendored_tpch_q7.q, - current_path, - config.benchmark_suffix, - config.project_id, - config.dataset_id, - config.session, - ) diff --git a/tests/benchmark/tpch/q8.py b/tests/benchmark/tpch/q8.py deleted file mode 100644 index 0af13eaeeb0..00000000000 --- a/tests/benchmark/tpch/q8.py +++ /dev/null @@ -1,30 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -import pathlib - -import benchmark.utils as utils -import bigframes_vendored.tpch.queries.q8 as vendored_tpch_q8 - -if __name__ == "__main__": - config = utils.get_configuration() - current_path = pathlib.Path(__file__).absolute() - - utils.get_execution_time( - vendored_tpch_q8.q, - current_path, - config.benchmark_suffix, - config.project_id, - config.dataset_id, - config.session, - ) diff --git a/tests/benchmark/tpch/q9.py b/tests/benchmark/tpch/q9.py deleted file mode 100644 index 61a319377a0..00000000000 --- a/tests/benchmark/tpch/q9.py +++ /dev/null @@ -1,30 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -import pathlib - -import benchmark.utils as utils -import bigframes_vendored.tpch.queries.q9 as vendored_tpch_q9 - -if __name__ == "__main__": - config = utils.get_configuration() - current_path = pathlib.Path(__file__).absolute() - - utils.get_execution_time( - vendored_tpch_q9.q, - current_path, - config.benchmark_suffix, - config.project_id, - config.dataset_id, - config.session, - ) diff --git a/tests/benchmark/utils.py b/tests/benchmark/utils.py deleted file mode 100644 index 9690e0a3bd5..00000000000 --- a/tests/benchmark/utils.py +++ /dev/null @@ -1,107 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import argparse -import dataclasses -import time - -import bigframes - -READ_GBQ_COLAB_PAGE_SIZE = 100 - - -@dataclasses.dataclass(frozen=True) -class BenchmarkConfig: - project_id: str - dataset_id: str - session: bigframes.Session | None - benchmark_suffix: str | None - table_id: str | None = None - - -def get_configuration(include_table_id=False, start_session=True) -> BenchmarkConfig: - parser = argparse.ArgumentParser() - parser.add_argument( - "--project_id", - type=str, - required=True, - help="The BigQuery project ID.", - ) - parser.add_argument( - "--dataset_id", - type=str, - required=True, - help="The BigQuery dataset ID.", - ) - - if include_table_id: - parser.add_argument( - "--table_id", - type=str, - required=True, - help="The BigQuery table ID to query.", - ) - - parser.add_argument( - "--ordered", - type=str, - help="Set to True (default) to have an ordered session, or False for an unordered session.", - ) - parser.add_argument( - "--benchmark_suffix", - type=str, - help="Suffix to append to benchmark names for identification purposes.", - ) - - args = parser.parse_args() - session = _initialize_session(_str_to_bool(args.ordered)) if start_session else None - - return BenchmarkConfig( - project_id=args.project_id, - dataset_id=args.dataset_id, - table_id=args.table_id if include_table_id else None, - session=session, - benchmark_suffix=args.benchmark_suffix, - ) - - -def get_execution_time(func, current_path, suffix, *args, **kwargs): - start_time = time.perf_counter() - func(*args, **kwargs) - end_time = time.perf_counter() - runtime = end_time - start_time - - clock_time_file_path = f"{current_path}_{suffix}.local_exec_time_seconds" - - with open(clock_time_file_path, "a") as log_file: - log_file.write(f"{runtime}\n") - - -def _str_to_bool(value): - if value == "True": - return True - elif value == "False": - return False - else: - raise argparse.ArgumentTypeError('Only "True" or "False" expected.') - - -def _initialize_session(ordered: bool): - # TODO(tswast): add a flag to enable the polars semi-executor. - context = bigframes.BigQueryOptions( - location="US", ordering_mode="strict" if ordered else "partial" - ) - session = bigframes.Session(context=context) - print(f"Initialized {'ordered' if ordered else 'unordered'} session.") - return session diff --git a/tests/data/json.jsonl b/tests/data/json.jsonl deleted file mode 100644 index 1abdcc9d565..00000000000 --- a/tests/data/json.jsonl +++ /dev/null @@ -1,16 +0,0 @@ -{"rowindex": 0, "json_col": null} -{"rowindex": 1, "json_col": true} -{"rowindex": 2, "json_col": 100} -{"rowindex": 3, "json_col": 0.98} -{"rowindex": 4, "json_col": "a string"} -{"rowindex": 5, "json_col": []} -{"rowindex": 6, "json_col": [1, 2, 3]} -{"rowindex": 7, "json_col": [{"a": 1}, {"a": 2}, {"a": null}, {}]} -{"rowindex": 8, "json_col": "100"} -{"rowindex": 9, "json_col": {"folat_num": 3.14159}} -{"rowindex": 10, "json_col": {"date": "2024-07-16"}} -{"rowindex": 11, "json_col": 100} -{"rowindex": 12, "json_col": {"int_value": 2, "null_filed": null}} -{"rowindex": 13, "json_col": {"list_data": [10, 20, 30]}} -{"rowindex": 14, "json_col": {"person": {"name": "Alice", "age": 35}}} -{"rowindex": 15, "json_col": {"order": {"items": ["book", "pen"], "total": 15.99}}} diff --git a/tests/data/json_schema.json b/tests/data/json_schema.json deleted file mode 100644 index 6bbbf5ca55c..00000000000 --- a/tests/data/json_schema.json +++ /dev/null @@ -1,12 +0,0 @@ -[ - { - "name": "rowindex", - "type": "INTEGER", - "mode": "REQUIRED" - }, - { - "name": "json_col", - "type": "JSON", - "mode": "NULLABLE" - } -] diff --git a/tests/data/nested.jsonl b/tests/data/nested.jsonl deleted file mode 100644 index 751ad0df78b..00000000000 --- a/tests/data/nested.jsonl +++ /dev/null @@ -1,100 +0,0 @@ -{"rowindex":0,"customer_id":"jkl","day":"2023-12-18","flag":1,"label":{"key": "my-key","value":"my-value"},"event_sequence":[{"category":"B","timestamp":"2023-12-18 03:43:58","data":[{"key":"x","value":20.2533015856},{"key":"y","value":42.8363462389}]},{"category":"D","timestamp":"2023-12-18 07:15:37","data":[{"key":"x","value":62.0762664928},{"key":"z","value":83.6655402432}]}],"address":{"street":"123 Test Lane","city":"Testerchon"}} -{"rowindex":1,"customer_id":"def","day":"2023-12-18","flag":2,"event_sequence":[{"category":"D","timestamp":"2023-12-18 23:11:11","data":[{"key":"w","value":36.1388065179}]},{"category":"B","timestamp":"2023-12-18 07:12:50","data":[{"key":"z","value":68.7673488304}]},{"category":"D","timestamp":"2023-12-18 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"mode": "NULLABLE" - }, - { - "name": "age", - "type": "INTEGER", - "mode": "NULLABLE" - }, - { - "name": "address", - "type": "RECORD", - "mode": "NULLABLE", - "fields": [ - { - "name": "city", - "type": "STRING", - "mode": "NULLABLE" - }, - { - "name": "country", - "type": "STRING", - "mode": "NULLABLE" - } - ] - } - ] - }, - { - "name": "bool_col", - "type": "BOOLEAN", - "mode": "NULLABLE" - }, - { - "name": "int64_col", - "type": "INTEGER", - "mode": "NULLABLE" - }, - { - "name": "float64_col", - "type": "FLOAT", - "mode": "NULLABLE" - }, - { - "name": "string_col", - "type": "STRING", - "mode": "NULLABLE" - }, - { - "name": "json_col", - "type": "JSON", - "mode": "NULLABLE" - }, - { - "name": "date_col", - "type": "DATE", - "mode": "NULLABLE" - }, - { - "name": "time_col", - "type": "TIME", - "mode": "NULLABLE" - }, - { - "name": "datetime_col", - "type": "DATETIME", - "mode": "NULLABLE" - }, - { - "name": "timestamp_col", - "type": "TIMESTAMP", - "mode": "NULLABLE" - }, - { - "name": "bytes_col", - "type": "BYTES", - "mode": "NULLABLE" - }, - { - "name": "numeric_col", - "type": "NUMERIC", - "mode": "NULLABLE" - }, - { - "name": "bignumeric_col", - "type": "BIGNUMERIC", - "mode": "NULLABLE" - }, - { - "name": "geography_col", - "type": "GEOGRAPHY", - "mode": "NULLABLE" - }, - { - "name": "duration_col", - "type": "INTEGER", - "mode": "NULLABLE", - "description": "#microseconds" - } -] diff --git a/tests/data/people.csv b/tests/data/people.csv deleted file mode 100644 index f5f9998b82c..00000000000 --- a/tests/data/people.csv +++ /dev/null @@ -1,4 +0,0 @@ -Name,Age,City -Alice,25,New York -Bob,30,London -Charlie,22,Paris diff --git a/tests/data/ratings.jsonl b/tests/data/ratings.jsonl deleted file mode 100644 index b7cd350d085..00000000000 --- a/tests/data/ratings.jsonl +++ /dev/null @@ -1,20 +0,0 @@ -{"user_id": 1, "item_id": 2, "rating": 4.0} -{"user_id": 1, "item_id": 5, "rating": 3.0} -{"user_id": 2, "item_id": 1, "rating": 5.0} -{"user_id": 2, "item_id": 3, "rating": 2.0} -{"user_id": 3, "item_id": 4, "rating": 4.5} -{"user_id": 3, "item_id": 7, "rating": 3.5} -{"user_id": 4, "item_id": 2, "rating": 1.0} -{"user_id": 4, "item_id": 8, "rating": 5.0} -{"user_id": 5, "item_id": 3, "rating": 4.0} -{"user_id": 5, "item_id": 9, "rating": 2.5} -{"user_id": 6, "item_id": 1, "rating": 3.0} -{"user_id": 6, "item_id": 6, "rating": 4.5} -{"user_id": 7, "item_id": 5, "rating": 5.0} -{"user_id": 7, "item_id": 10, "rating": 1.5} -{"user_id": 8, "item_id": 4, "rating": 2.0} -{"user_id": 8, "item_id": 7, "rating": 4.0} -{"user_id": 9, "item_id": 2, "rating": 3.5} -{"user_id": 9, "item_id": 9, "rating": 5.0} -{"user_id": 10, "item_id": 3, "rating": 4.5} -{"user_id": 10, "item_id": 8, "rating": 2.5} diff --git a/tests/data/ratings_schema.json b/tests/data/ratings_schema.json deleted file mode 100644 index 9fd0101ec80..00000000000 --- a/tests/data/ratings_schema.json +++ /dev/null @@ -1,17 +0,0 @@ -[ - { - "mode": "NULLABLE", - "name": "user_id", - "type": "STRING" - }, - { - "mode": "NULLABLE", - "name": "item_id", - "type": "INT64" - }, - { - "mode": "NULLABLE", - "name": "rating", - "type": "FLOAT" - } -] diff --git a/tests/data/repeated.jsonl b/tests/data/repeated.jsonl deleted file mode 100644 index b3c47772f6d..00000000000 --- a/tests/data/repeated.jsonl +++ /dev/null @@ -1,3 +0,0 @@ -{"rowindex": 0, "int_list_col": [1], "bool_list_col": [true], "float_list_col": [1.2, 2.3], "date_list_col": ["2021-07-21"], "date_time_list_col": ["2021-07-21 11:39:45"], "numeric_list_col": [1.2, 2.3, 3.4], "string_list_col": ["abc", "de", "f"]} -{"rowindex": 1, "int_list_col": [1,2], "bool_list_col": [true, false], "float_list_col": [1.1], "date_list_col": ["2021-07-21", "1987-03-28"], "date_time_list_col": ["1999-03-14 17:22:00"], "numeric_list_col": [5.5, 2.3], "string_list_col": ["a", "bc", "de"]} -{"rowindex": 2, "int_list_col": [1,2,3], "bool_list_col": [true], "float_list_col": [0.5, -1.9, 2.3], "date_list_col": ["2017-08-01", "2004-11-22"], "date_time_list_col": ["1979-06-03 03:20:45"], "numeric_list_col": [1.7], "string_list_col": ["", "a"]} diff --git a/tests/data/repeated_schema.json b/tests/data/repeated_schema.json deleted file mode 100644 index 300f32c994c..00000000000 --- a/tests/data/repeated_schema.json +++ /dev/null @@ -1,42 +0,0 @@ -[ - { - "name": "rowindex", - "type": "INTEGER", - "mode": "REQUIRED" - }, - { - "name": "int_list_col", - "type": "INTEGER", - "mode": "REPEATED" - }, - { - "name": "bool_list_col", - "type": "BOOLEAN", - "mode": "REPEATED" - }, - { - "name": "float_list_col", - "type": "FLOAT", - "mode": "REPEATED" - }, - { - "name": "date_list_col", - "type": "DATE", - "mode": "REPEATED" - }, - { - "name": "date_time_list_col", - "type": "DATETIME", - "mode": "REPEATED" - }, - { - "name": "numeric_list_col", - "type": "NUMERIC", - "mode": "REPEATED" - }, - { - "name": "string_list_col", - "type": "STRING", - "mode": "REPEATED" - } -] diff --git a/tests/data/scalars.jsonl b/tests/data/scalars.jsonl index 6e591cfa728..172a55ec11a 100644 --- a/tests/data/scalars.jsonl +++ b/tests/data/scalars.jsonl @@ -1,9 +1,9 @@ -{"bool_col": true, "bytes_col": "SGVsbG8sIFdvcmxkIQ==", "date_col": "2021-07-21", "datetime_col": "2021-07-21 11:39:45", "geography_col": "POINT(-122.0838511 37.3860517)", "int64_col": "123456789", "int64_too": "0", "numeric_col": "1.23456789", "float64_col": "1.25", "rowindex": 0, "rowindex_2": 0, "string_col": "Hello, World!", "time_col": "11:41:43.076160", "timestamp_col": "2021-07-21T17:43:43.945289Z", "duration_col": 4} -{"bool_col": false, "bytes_col": "44GT44KT44Gr44Gh44Gv", "date_col": "1991-02-03", "datetime_col": "1991-01-02 03:45:06", "geography_col": "POINT(-71.104 42.315)", "int64_col": "-987654321", "int64_too": "1", "numeric_col": "1.23456789", "float64_col": "2.51", "rowindex": 1, "rowindex_2": 1, "string_col": "こんにちは", "time_col": "11:14:34.701606", "timestamp_col": "2021-07-21T17:43:43.945289Z", 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UTC","total_visits":"2556"} +{"parsed_date":"2017-06-04 00:00:00 UTC","total_visits":"1534"} +{"parsed_date":"2017-02-12 00:00:00 UTC","total_visits":"1790"} +{"parsed_date":"2017-07-03 00:00:00 UTC","total_visits":"2046"} +{"parsed_date":"2016-09-30 00:00:00 UTC","total_visits":"2303"} +{"parsed_date":"2016-08-08 00:00:00 UTC","total_visits":"2815"} diff --git a/tests/data/time_series_schema.json b/tests/data/time_series_schema.json index 35473dc0e36..857595b9e64 100644 --- a/tests/data/time_series_schema.json +++ b/tests/data/time_series_schema.json @@ -4,11 +4,6 @@ "name": "parsed_date", "type": "TIMESTAMP" }, - { - "mode": "NULLABLE", - "name": "id", - "type": "STRING" - }, { "mode": "NULLABLE", "name": "total_visits", diff --git a/tests/data/urban_areas.jsonl b/tests/data/urban_areas.jsonl deleted file mode 100644 index 3bc2fbf38ec..00000000000 --- a/tests/data/urban_areas.jsonl +++ /dev/null @@ -1,22 +0,0 @@ -{"name":"unknown", "geo_id":"0"} -{"name":"atlantis", "geo_id":"7", 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"name": "name", - "type": "STRING" - }, - { - "mode": "NULLABLE", - "name": "lsad_name", - "type": "STRING" - }, - { - "mode": "NULLABLE", - "name": "area_lsad_code", - "type": "STRING" - }, - { - "mode": "NULLABLE", - "name": "mtfcc_feature_class_code", - "type": "STRING" - }, - { - "mode": "NULLABLE", - "name": "type", - "type": "STRING" - }, - { - "mode": "NULLABLE", - "name": "functional_status", - "type": "STRING" - }, - { - "mode": "NULLABLE", - "name": "area_land_meters", - "type": "FLOAT" - }, - { - "mode": "NULLABLE", - "name": "area_water_meters", - "type": "FLOAT" - }, - { - "mode": "NULLABLE", - "name": "internal_point_lon", - "type": "FLOAT" - }, - { - "mode": "NULLABLE", - "name": "internal_point_lat", - "type": "FLOAT" - }, - { - "mode": "NULLABLE", - "name": "internal_point_geom", - "type": "GEOGRAPHY" - }, - { - "mode": "NULLABLE", - "name": "urban_area_geom", - "type": "GEOGRAPHY" - } -] diff --git a/tests/js/babel.config.cjs b/tests/js/babel.config.cjs deleted file mode 100644 index 549f612a2dd..00000000000 --- a/tests/js/babel.config.cjs +++ /dev/null @@ -1,19 +0,0 @@ -/* - * Copyright 2025 Google LLC - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -module.exports = { - presets: [['@babel/preset-env', {targets: {node: 'current'}}]], -}; diff --git a/tests/js/jest.config.cjs b/tests/js/jest.config.cjs deleted file mode 100644 index ad7dbf97ee3..00000000000 --- a/tests/js/jest.config.cjs +++ /dev/null @@ -1,27 +0,0 @@ -/* - * Copyright 2025 Google LLC - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/** @type {import('jest').Config} */ -const config = { - testEnvironment: 'jsdom', - transform: { - '^.+\.js$': 'babel-jest', - }, - setupFilesAfterEnv: ['./jest.setup.js'], - transformIgnorePatterns: [], -}; - -module.exports = config; diff --git a/tests/js/jest.setup.js b/tests/js/jest.setup.js deleted file mode 100644 index b6b5934d76e..00000000000 --- a/tests/js/jest.setup.js +++ /dev/null @@ -1,20 +0,0 @@ -/* - * Copyright 2025 Google LLC - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -import { TextDecoder, TextEncoder } from "node:util"; - -global.TextEncoder = TextEncoder; -global.TextDecoder = TextDecoder; diff --git a/tests/js/package-lock.json b/tests/js/package-lock.json deleted file mode 100644 index 241ebd2a8d5..00000000000 --- a/tests/js/package-lock.json +++ /dev/null @@ -1,7074 +0,0 @@ -{ - "name": "js-tests", - "version": "1.0.0", - 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* you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -import { jest } from '@jest/globals'; - -describe('TableWidget', () => { - let model; - let el; - let render; - - beforeEach(async () => { - jest.resetModules(); - document.body.innerHTML = '
'; - el = document.body.querySelector('div'); - - const tableWidget = ( - await import('../../bigframes/display/table_widget.js') - ).default; - render = tableWidget.render; - - model = { - get: jest.fn(), - set: jest.fn(), - save_changes: jest.fn(), - on: jest.fn(), - }; - }); - - it('should have a render function', () => { - expect(render).toBeDefined(); - }); - - describe('render', () => { - it('should create the basic structure', () => { - // Mock the initial state - model.get.mockImplementation((property) => { - if (property === 'table_html') { - return ''; - } - if (property === 'row_count') { - return 100; - } - if (property === 'error_message') { - return null; - } - if (property === 'page_size') { - return 10; - } - if (property === 'page') { - return 0; - } - return null; - }); - - render({ model, el }); - - expect(el.classList.contains('bigframes-widget')).toBe(true); - expect(el.querySelector('.error-message')).not.toBeNull(); - expect(el.querySelector('div')).not.toBeNull(); - expect(el.querySelector('div:nth-child(3)')).not.toBeNull(); - }); - - it('should sort when a sortable column is clicked', () => { - // Mock the initial state - model.get.mockImplementation((property) => { - if (property === 'table_html') { - return '
col1
'; - } - if (property === 'orderable_columns') { - return ['col1']; - } - if (property === 'sort_context') { - return []; - } - return null; - }); - - render({ model, el }); - - // Manually trigger the table_html change handler - const tableHtmlChangeHandler = model.on.mock.calls.find( - (call) => call[0] === 'change:table_html', - )[1]; - tableHtmlChangeHandler(); - - const header = el.querySelector('th'); - header.click(); - - expect(model.set).toHaveBeenCalledWith('sort_context', [ - { column: 'col1', ascending: true }, - ]); - expect(model.save_changes).toHaveBeenCalled(); - }); - - it('should reverse sort direction when a sorted column is clicked', () => { - // Mock the initial state - model.get.mockImplementation((property) => { - if (property === 'table_html') { - return '
col1
'; - } - if (property === 'orderable_columns') { - return ['col1']; - } - if (property === 'sort_context') { - return [{ column: 'col1', ascending: true }]; - } - return null; - }); - - render({ model, el }); - - // Manually trigger the table_html change handler - const tableHtmlChangeHandler = model.on.mock.calls.find( - (call) => call[0] === 'change:table_html', - )[1]; - tableHtmlChangeHandler(); - - const header = el.querySelector('th'); - header.click(); - - expect(model.set).toHaveBeenCalledWith('sort_context', [ - { column: 'col1', ascending: false }, - ]); - expect(model.save_changes).toHaveBeenCalled(); - }); - - it('should clear sort when a descending sorted column is clicked', () => { - // Mock the initial state - model.get.mockImplementation((property) => { - if (property === 'table_html') { - return '
col1
'; - } - if (property === 'orderable_columns') { - return ['col1']; - } - if (property === 'sort_context') { - return [{ column: 'col1', ascending: false }]; - } - return null; - }); - - render({ model, el }); - - // Manually trigger the table_html change handler - const tableHtmlChangeHandler = model.on.mock.calls.find( - (call) => call[0] === 'change:table_html', - )[1]; - tableHtmlChangeHandler(); - - const header = el.querySelector('th'); - header.click(); - - expect(model.set).toHaveBeenCalledWith('sort_context', []); - expect(model.save_changes).toHaveBeenCalled(); - }); - - it('should display the correct sort indicator', () => { - // Mock the initial state - model.get.mockImplementation((property) => { - if (property === 'table_html') { - return '
col1
col2
'; - } - if (property === 'orderable_columns') { - return ['col1', 'col2']; - } - if (property === 'sort_context') { - return [{ column: 'col1', ascending: true }]; - } - return null; - }); - - render({ model, el }); - - // Manually trigger the table_html change handler - const tableHtmlChangeHandler = model.on.mock.calls.find( - (call) => call[0] === 'change:table_html', - )[1]; - tableHtmlChangeHandler(); - - const headers = el.querySelectorAll('th'); - const indicator1 = headers[0].querySelector('.sort-indicator'); - const indicator2 = headers[1].querySelector('.sort-indicator'); - - expect(indicator1.textContent).toBe('▲'); - expect(indicator2.textContent).toBe('●'); - }); - - it('should add a column to sort when Shift+Click is used', () => { - // Mock the initial state: already sorted by col1 asc - model.get.mockImplementation((property) => { - if (property === 'table_html') { - return '
col1
col2
'; - } - if (property === 'orderable_columns') { - return ['col1', 'col2']; - } - if (property === 'sort_context') { - return [{ column: 'col1', ascending: true }]; - } - return null; - }); - - render({ model, el }); - - // Manually trigger the table_html change handler - const tableHtmlChangeHandler = model.on.mock.calls.find( - (call) => call[0] === 'change:table_html', - )[1]; - tableHtmlChangeHandler(); - - const headers = el.querySelectorAll('th'); - const header2 = headers[1]; // col2 - - // Simulate Shift+Click - const clickEvent = new MouseEvent('click', { - bubbles: true, - cancelable: true, - shiftKey: true, - }); - header2.dispatchEvent(clickEvent); - - expect(model.set).toHaveBeenCalledWith('sort_context', [ - { column: 'col1', ascending: true }, - { column: 'col2', ascending: true }, - ]); - expect(model.save_changes).toHaveBeenCalled(); - }); - }); - - describe('Theme detection', () => { - beforeEach(() => { - jest.useFakeTimers(); - // Mock the initial state for theme detection tests - model.get.mockImplementation((property) => { - if (property === 'table_html') { - return ''; - } - if (property === 'row_count') { - return 100; - } - if (property === 'error_message') { - return null; - } - if (property === 'page_size') { - return 10; - } - if (property === 'page') { - return 0; - } - return null; - }); - }); - - afterEach(() => { - jest.useRealTimers(); - document.body.classList.remove('vscode-dark'); - }); - - it('should add bigframes-dark-mode class in dark mode', () => { - document.body.classList.add('vscode-dark'); - render({ model, el }); - jest.runAllTimers(); - expect(el.classList.contains('bigframes-dark-mode')).toBe(true); - }); - - it('should not add bigframes-dark-mode class in light mode', () => { - render({ model, el }); - jest.runAllTimers(); - expect(el.classList.contains('bigframes-dark-mode')).toBe(false); - }); - }); - - it('should render the series as a table with an index and one value column', () => { - // Mock the initial state - model.get.mockImplementation((property) => { - if (property === 'table_html') { - return ` -
-
- - - - - - - - - - - - - - - - - -
value
0a
1b
-
-
`; - } - if (property === 'orderable_columns') { - return []; - } - return null; - }); - - render({ model, el }); - - // Manually trigger the table_html change handler - const tableHtmlChangeHandler = model.on.mock.calls.find( - (call) => call[0] === 'change:table_html', - )[1]; - tableHtmlChangeHandler(); - - // Check that the table has two columns - const headers = el.querySelectorAll( - '.paginated-table-container .col-header-name', - ); - expect(headers).toHaveLength(2); - - // Check that the headers are an empty string (for the index) and "value" - expect(headers[0].textContent).toBe(''); - expect(headers[1].textContent).toBe('value'); - }); - - /* - * Tests that the widget correctly renders HTML with truncated columns (ellipsis) - * and ensures that the ellipsis column is not treated as a sortable column. - */ - it('should set height dynamically on first load and remain fixed', () => { - jest.useFakeTimers(); - - // Mock the table's offsetHeight - let mockHeight = 150; - Object.defineProperty(HTMLElement.prototype, 'offsetHeight', { - configurable: true, - get: () => mockHeight, - }); - - // Mock model properties - model.get.mockImplementation((property) => { - if (property === 'table_html') { - return '...
'; - } - return null; - }); - - render({ model, el }); - - const tableContainer = el.querySelector('.table-container'); - - // --- First render --- - const tableHtmlChangeHandler = model.on.mock.calls.find( - (call) => call[0] === 'change:table_html', - )[1]; - tableHtmlChangeHandler(); - jest.runAllTimers(); - - // Height should be set to the mocked offsetHeight + 2px buffer - expect(tableContainer.style.height).toBe('152px'); - - // --- Second render (e.g., page size change) --- - // Simulate the new content being taller - mockHeight = 350; - tableHtmlChangeHandler(); - jest.runAllTimers(); - - // Height should NOT change - expect(tableContainer.style.height).toBe('152px'); - - // Restore original implementation - Object.defineProperty(HTMLElement.prototype, 'offsetHeight', { - value: 0, - }); - jest.useRealTimers(); - }); - - it('should render truncated columns with ellipsis and not make ellipsis sortable', () => { - // Mock HTML with truncated columns - // Use the structure produced by the python backend - const mockHtml = ` - - - - - - - - - - - - - - - -
col1
...
col10
1...10
- `; - - model.get.mockImplementation((property) => { - if (property === 'table_html') { - return mockHtml; - } - if (property === 'orderable_columns') { - // Only actual columns are orderable - return ['col1', 'col10']; - } - if (property === 'sort_context') { - return []; - } - return null; - }); - - render({ model, el }); - - // Manually trigger the table_html change handler - const tableHtmlChangeHandler = model.on.mock.calls.find( - (call) => call[0] === 'change:table_html', - )[1]; - tableHtmlChangeHandler(); - - const headers = el.querySelectorAll('th'); - expect(headers).toHaveLength(3); - - // Check col1 (sortable) - const col1Header = headers[0]; - const col1Indicator = col1Header.querySelector('.sort-indicator'); - expect(col1Indicator).not.toBeNull(); // Should exist (hidden by default) - - // Check ellipsis (not sortable) - const ellipsisHeader = headers[1]; - const ellipsisIndicator = ellipsisHeader.querySelector('.sort-indicator'); - // The render function adds sort indicators only if the column name matches an entry in orderable_columns. - // The ellipsis header content is "..." which is not in ['col1', 'col10']. - expect(ellipsisIndicator).toBeNull(); - - // Check col10 (sortable) - const col10Header = headers[2]; - const col10Indicator = col10Header.querySelector('.sort-indicator'); - expect(col10Indicator).not.toBeNull(); - }); - - describe('Max columns', () => { - /* - * Tests for the max columns dropdown functionality. - */ - - it('should render the max columns dropdown', () => { - // Mock basic state - model.get.mockImplementation((property) => { - if (property === 'max_columns') { - return 20; - } - return null; - }); - - render({ model, el }); - - const maxColumnsContainer = el.querySelector('.max-columns'); - expect(maxColumnsContainer).not.toBeNull(); - const label = maxColumnsContainer.querySelector('label'); - expect(label.textContent).toBe('Max columns:'); - const select = maxColumnsContainer.querySelector('select'); - expect(select).not.toBeNull(); - }); - - it('should select the correct initial value', () => { - const initialMaxColumns = 20; - model.get.mockImplementation((property) => { - if (property === 'max_columns') { - return initialMaxColumns; - } - return null; - }); - - render({ model, el }); - - const select = el.querySelector('.max-columns select'); - expect(Number(select.value)).toBe(initialMaxColumns); - }); - - it('should handle None/null initial value as 0 (All)', () => { - model.get.mockImplementation((property) => { - if (property === 'max_columns') { - return null; // Python None is null in JS - } - return null; - }); - - render({ model, el }); - - const select = el.querySelector('.max-columns select'); - expect(Number(select.value)).toBe(0); - expect(select.options[select.selectedIndex].textContent).toBe('All'); - }); - - it('should update model when value changes', () => { - model.get.mockImplementation((property) => { - if (property === 'max_columns') { - return 20; - } - return null; - }); - - render({ model, el }); - - const select = el.querySelector('.max-columns select'); - - // Change to 10 - select.value = '10'; - const event = new Event('change'); - select.dispatchEvent(event); - - expect(model.set).toHaveBeenCalledWith('max_columns', 10); - expect(model.save_changes).toHaveBeenCalled(); - }); - }); -}); diff --git a/tests/js/table_widget_angular.test.js b/tests/js/table_widget_angular.test.js deleted file mode 100644 index 1e7d0275c5d..00000000000 --- a/tests/js/table_widget_angular.test.js +++ /dev/null @@ -1,178 +0,0 @@ -/* - * Copyright 2026 Google LLC - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -import { jest } from '@jest/globals'; - -describe('TableWidgetAngular', () => { - let render; - - beforeEach(async () => { - jest.resetModules(); - const tableWidgetAngular = ( - await import('../../bigframes/display/table_widget_angular.js') - ).default; - render = tableWidgetAngular.render; - }); - - it('should have a render function', () => { - expect(render).toBeDefined(); - }); - - it( - 'should bootstrap multiple widgets independently ' + - 'on their respective elements', - async () => { - const el1 = document.createElement('div'); - document.body.appendChild(el1); - - const model1 = { - get: jest.fn((prop) => { - if (prop === 'table_html') { - return '
Widget 1 Content
'; - } - if (prop === 'page_size') return 10; - if (prop === 'page') return 0; - if (prop === 'row_count') return 100; - if (prop === 'max_columns') return 20; - return null; - }), - set: jest.fn(), - save_changes: jest.fn(), - on: jest.fn(), - }; - - const el2 = document.createElement('div'); - document.body.appendChild(el2); - - const model2 = { - get: jest.fn((prop) => { - if (prop === 'table_html') { - return '
Widget 2 Content
'; - } - if (prop === 'page_size') return 25; - if (prop === 'page') return 0; - if (prop === 'row_count') return 200; - if (prop === 'max_columns') return 20; - return null; - }), - set: jest.fn(), - save_changes: jest.fn(), - on: jest.fn(), - }; - - render({ model: model1, el: el1 }); - render({ model: model2, el: el2 }); - - // Wait for async angular bootstrap to complete - await new Promise((resolve) => setTimeout(resolve, 200)); - - const appRoot1 = el1.querySelector('.bigframes-widget'); - expect(appRoot1).not.toBeNull(); - expect(el1.textContent).toContain('Widget 1 Content'); - expect(el1.textContent).toContain('100 total rows'); - expect(el1.textContent).toContain('Page 1 of 10'); - - const appRoot2 = el2.querySelector('.bigframes-widget'); - expect(appRoot2).not.toBeNull(); - expect(el2.textContent).toContain('Widget 2 Content'); - expect(el2.textContent).toContain('200 total rows'); - expect(el2.textContent).toContain('Page 1 of 8'); - - document.body.removeChild(el1); - document.body.removeChild(el2); - }); - - it( - 'should render deferred card and trigger execution on click', - async () => { - // Arrange - const el = document.createElement('div'); - document.body.appendChild(el); - - const state = { - is_deferred_mode: true, - dry_run_info: 'Estimated cost: $0.05', - start_execution: false, - table_html: '', - page_size: 10, - page: 0, - row_count: 0, - max_columns: 20, - }; - - const listeners = {}; - const model = { - get: jest.fn((prop) => state[prop]), - set: jest.fn((prop, val) => { - state[prop] = val; - }), - save_changes: jest.fn(), - on: jest.fn((event, callback) => { - listeners[event] = callback; - }), - }; - - // Act - render({ model, el }); - await new Promise((resolve) => setTimeout(resolve, 200)); - - // Assert (Initial state) - const estimate = el.querySelector('.deferred-estimate'); - expect(estimate).not.toBeNull(); - expect(estimate.textContent).toContain('Estimated cost: $0.05'); - - const runButton = el.querySelector('.run-query-button'); - expect(runButton).not.toBeNull(); - expect(runButton.textContent).toContain('Run Query'); - expect(el.querySelector('.table-container')).toBeNull(); - - // Act (Click Run Query) - runButton.click(); - await new Promise((resolve) => setTimeout(resolve, 50)); - - // Assert (Execution requested) - expect(model.set).toHaveBeenCalledWith('start_execution', true); - expect(model.save_changes).toHaveBeenCalled(); - expect(runButton.disabled).toBe(true); - expect(el.querySelector('.spinner')).not.toBeNull(); - - // Act (Simulate Python load completion) - state.is_deferred_mode = false; - state.table_html = '
Data Loaded
'; - state.row_count = 50; - - if (listeners['change:is_deferred_mode']) { - listeners['change:is_deferred_mode'](); - } - if (listeners['change:table_html']) { - listeners['change:table_html'](); - } - if (listeners['change:row_count']) { - listeners['change:row_count'](); - } - await new Promise((resolve) => setTimeout(resolve, 200)); - - // Assert (Transition to loaded state) - expect(el.querySelector('.deferred-container')).toBeNull(); - const tableContainer = el.querySelector('.table-container'); - expect(tableContainer).not.toBeNull(); - expect(el.textContent).toContain('Data Loaded'); - expect(el.textContent).toContain('50 total rows'); - - // Clean up - document.body.removeChild(el); - }); -}); diff --git a/tests/system/conftest.py b/tests/system/conftest.py index 0b30c331a1e..f9f69c6c8ee 100644 --- a/tests/system/conftest.py +++ b/tests/system/conftest.py @@ -12,58 +12,29 @@ # See the License for the specific language governing permissions and # limitations under the License. -import base64 -import datetime -import decimal +from datetime import datetime import hashlib -import json import logging import math import pathlib import textwrap -import traceback import typing -from typing import Dict, Generator, Optional +from typing import Dict, Optional -import db_dtypes # type: ignore[import-untyped] -import fsspec # type: ignore[import-untyped] -import gcsfs # type: ignore[import-untyped] -import geopandas as gpd # type: ignore[import-untyped] -import google.api_core.exceptions import google.cloud.bigquery as bigquery import google.cloud.bigquery_connection_v1 as bigquery_connection_v1 -import google.cloud.bigquery_storage_v1 import google.cloud.exceptions import google.cloud.functions_v2 as functions_v2 import google.cloud.resourcemanager_v3 as resourcemanager_v3 import google.cloud.storage as storage # type: ignore -import numpy as np +import ibis.backends.base import pandas as pd -import pandas.arrays -import pyarrow as pa import pytest import pytz import test_utils.prefixer import bigframes -import bigframes.dataframe -import bigframes.pandas as bpd -import bigframes.series -import bigframes.testing.utils - -# Use this to control the number of cloud functions being deleted in a single -# test session. This should help soften the spike of the number of mutations per -# minute tracked against the quota limit: -# Cloud Functions API -> Per project mutation requests per minute per region -# (default 60, increased to 1000 for the test projects) -# We are running pytest with "-n 20". For a rough estimation, let's say all -# parallel sessions run in parallel. So that allows 1000/20 = 50 mutations per -# minute. One session takes about 1 minute to create a remote function. This -# would allow 50-1 = 49 deletions per session. -# However, because of b/356217175 the service may throw ResourceExhausted("Too -# many operations are currently being executed, try again later."), so we peg -# the cleanup to a more controlled rate. -MAX_NUM_FUNCTIONS_TO_DELETE_PER_SESSION = 15 +from tests.system.utils import convert_pandas_dtypes CURRENT_DIR = pathlib.Path(__file__).parent DATA_DIR = CURRENT_DIR.parent / "data" @@ -79,15 +50,6 @@ def _hash_digest_file(hasher, filepath): hasher.update(chunk) -@pytest.fixture(scope="session", autouse=True) -def configure_gcsfs(): - # gcsfs by default uses a cache that can be stale, causing file loads to - # fail if the file was uploaded indirectly (eg via bq export job) during the - # course of the tests. disable the cache to avoid this. - fsspec.config.conf["gcs"] = {"use_listings_cache": False} - gcsfs.GCSFileSystem.clear_instance_cache() - - @pytest.fixture(scope="session") def tokyo_location() -> str: return TOKYO_LOCATION @@ -108,12 +70,9 @@ def gcs_folder(gcs_client: storage.Client): prefix = prefixer.create_prefix() path = f"gs://{bucket}/{prefix}/" yield path - try: - for blob in gcs_client.list_blobs(bucket, prefix=prefix): - blob = typing.cast(storage.Blob, blob) - blob.delete() - except Exception as exc: - traceback.print_exception(type(exc), exc, None) + for blob in gcs_client.list_blobs(bucket, prefix=prefix): + blob = typing.cast(storage.Blob, blob) + blob.delete() @pytest.fixture(scope="session") @@ -122,15 +81,13 @@ def bigquery_client(session: bigframes.Session) -> bigquery.Client: @pytest.fixture(scope="session") -def bigquery_storage_read_client( - session: bigframes.Session, -) -> google.cloud.bigquery_storage_v1.BigQueryReadClient: - return session.bqstoragereadclient +def bigquery_client_tokyo(session_tokyo: bigframes.Session) -> bigquery.Client: + return session_tokyo.bqclient @pytest.fixture(scope="session") -def bigquery_client_tokyo(session_tokyo: bigframes.Session) -> bigquery.Client: - return session_tokyo.bqclient +def ibis_client(session: bigframes.Session) -> ibis.backends.base.BaseBackend: + return session.ibis_client @pytest.fixture(scope="session") @@ -147,11 +104,6 @@ def cloudfunctions_client( return session.cloudfunctionsclient -@pytest.fixture(scope="session") -def project_id(bigquery_client: bigquery.Client) -> str: - return bigquery_client.project - - @pytest.fixture(scope="session") def resourcemanager_client( session: bigframes.Session, @@ -160,68 +112,17 @@ def resourcemanager_client( @pytest.fixture(scope="session") -def session() -> Generator[bigframes.Session, None, None]: - context = bigframes.BigQueryOptions(location="US") - session = bigframes.Session(context=context) - yield session - session.close() # close generated session at cleanup time - - -@pytest.fixture(scope="session") -def session_load() -> Generator[bigframes.Session, None, None]: - context = bigframes.BigQueryOptions(location="US", project="bigframes-load-testing") - session = bigframes.Session(context=context) - yield session - session.close() # close generated session at cleanup time - - -@pytest.fixture(scope="session", params=["strict", "partial"]) -def maybe_ordered_session(request) -> Generator[bigframes.Session, None, None]: - context = bigframes.BigQueryOptions(location="US", ordering_mode=request.param) - session = bigframes.Session(context=context) - yield session - session.close() # close generated session at cleanup type - - -@pytest.fixture(scope="session") -def unordered_session() -> Generator[bigframes.Session, None, None]: - context = bigframes.BigQueryOptions(location="US", ordering_mode="partial") - session = bigframes.Session(context=context) - yield session - session.close() # close generated session at cleanup type +def session() -> bigframes.Session: + return bigframes.Session() @pytest.fixture(scope="session") -def session_tokyo(tokyo_location: str) -> Generator[bigframes.Session, None, None]: - context = bigframes.BigQueryOptions(location=tokyo_location) - session = bigframes.Session(context=context) - yield session - session.close() # close generated session at cleanup type - - -@pytest.fixture(scope="session") -def test_session() -> Generator[bigframes.Session, None, None]: +def session_tokyo(tokyo_location: str) -> bigframes.Session: context = bigframes.BigQueryOptions( - client_endpoints_override={ - "bqclient": "https://test-bigquery.sandbox.google.com", - "bqconnectionclient": "test-bigqueryconnection.sandbox.googleapis.com", - "bqstoragereadclient": "test-bigquerystorage-grpc.sandbox.googleapis.com", - }, + location=tokyo_location, + use_regional_endpoints=True, ) - session = bigframes.Session(context=context) - yield session - session.close() - - -@pytest.fixture(scope="session") -def bq_connection_name() -> str: - return "bigframes-rf-conn" - - -@pytest.fixture(scope="session") -def bq_connection(bigquery_client: bigquery.Client, bq_connection_name: str) -> str: - # TODO(b/458169181): LOCATION casefold is needed for the mutimodal backend bug. Remove after the bug is fixed. - return f"{bigquery_client.project}.{bigquery_client.location.casefold()}.{bq_connection_name}" + return bigframes.Session(context=context) @pytest.fixture(scope="session", autouse=True) @@ -259,8 +160,9 @@ def dataset_id_not_created(bigquery_client: bigquery.Client): @pytest.fixture(scope="session") -def dataset_id_permanent(bigquery_client: bigquery.Client, project_id: str) -> str: +def dataset_id_permanent(bigquery_client: bigquery.Client) -> str: """Create a dataset if it doesn't exist.""" + project_id = bigquery_client.project dataset_id = f"{project_id}.{PERMANENT_DATASET}" dataset = bigquery.Dataset(dataset_id) bigquery_client.create_dataset(dataset, exists_ok=True) @@ -281,21 +183,6 @@ def dataset_id_permanent_tokyo( return dataset_id -@pytest.fixture(scope="session") -def table_id_not_created(dataset_id: str): - return f"{dataset_id}.{prefixer.create_prefix()}" - - -@pytest.fixture(scope="function") -def table_id_unique(dataset_id: str): - return f"{dataset_id}.{prefixer.create_prefix()}" - - -@pytest.fixture(scope="function") -def routine_id_unique(dataset_id: str): - return f"{dataset_id}.{prefixer.create_prefix()}" - - @pytest.fixture(scope="session") def scalars_schema(bigquery_client: bigquery.Client): # TODO(swast): Add missing scalar data types such as BIGNUMERIC. @@ -345,17 +232,11 @@ def load_test_data_tables( for table_name, schema_filename, data_filename in [ ("scalars", "scalars_schema.json", "scalars.jsonl"), ("scalars_too", "scalars_schema.json", "scalars.jsonl"), - ("nested", "nested_schema.json", "nested.jsonl"), - ("nested_structs", "nested_structs_schema.json", "nested_structs.jsonl"), - ("repeated", "repeated_schema.json", "repeated.jsonl"), - ("json", "json_schema.json", "json.jsonl"), ("penguins", "penguins_schema.json", "penguins.jsonl"), - ("ratings", "ratings_schema.json", "ratings.jsonl"), ("time_series", "time_series_schema.json", "time_series.jsonl"), ("hockey_players", "hockey_players.json", "hockey_players.jsonl"), ("matrix_2by3", "matrix_2by3.json", "matrix_2by3.jsonl"), ("matrix_3by4", "matrix_3by4.json", "matrix_3by4.jsonl"), - ("urban_areas", "urban_areas_schema.json", "urban_areas.jsonl"), ]: test_data_hash = hashlib.md5() _hash_digest_file(test_data_hash, DATA_DIR / schema_filename) @@ -400,13 +281,6 @@ def scalars_table_id(test_data_tables) -> str: return test_data_tables["scalars"] -@pytest.fixture(scope="session") -def baseball_schedules_df(session: bigframes.Session) -> bigframes.dataframe.DataFrame: - """Public BQ table""" - df = session.read_gbq("bigquery-public-data.baseball.schedules") - return df - - @pytest.fixture(scope="session") def hockey_table_id(test_data_tables) -> str: return test_data_tables["hockey_players"] @@ -422,41 +296,11 @@ def scalars_table_tokyo(test_data_tables_tokyo) -> str: return test_data_tables_tokyo["scalars"] -@pytest.fixture(scope="session") -def nested_table_id(test_data_tables) -> str: - return test_data_tables["nested"] - - -@pytest.fixture(scope="session") -def nested_structs_table_id(test_data_tables) -> str: - return test_data_tables["nested_structs"] - - -@pytest.fixture(scope="session") -def repeated_table_id(test_data_tables) -> str: - return test_data_tables["repeated"] - - -@pytest.fixture(scope="session") -def json_table_id(test_data_tables) -> str: - return test_data_tables["json"] - - @pytest.fixture(scope="session") def penguins_table_id(test_data_tables) -> str: return test_data_tables["penguins"] -@pytest.fixture(scope="session") -def ratings_table_id(test_data_tables) -> str: - return test_data_tables["ratings"] - - -@pytest.fixture(scope="session") -def urban_areas_table_id(test_data_tables) -> str: - return test_data_tables["urban_areas"] - - @pytest.fixture(scope="session") def time_series_table_id(test_data_tables) -> str: return test_data_tables["time_series"] @@ -472,302 +316,6 @@ def matrix_3by4_table_id(test_data_tables) -> str: return test_data_tables["matrix_3by4"] -@pytest.fixture(scope="session") -def nested_df( - nested_table_id: str, session: bigframes.Session -) -> bigframes.dataframe.DataFrame: - """DataFrame pointing at test data.""" - return session.read_gbq(nested_table_id, index_col="rowindex") - - -@pytest.fixture(scope="session") -def nested_pandas_df() -> pd.DataFrame: - """pd.DataFrame pointing at test data.""" - - df = pd.read_json( - DATA_DIR / "nested.jsonl", - lines=True, - ) - df = df.set_index("rowindex") - return df - - -@pytest.fixture(scope="session") -def nested_structs_df( - nested_structs_table_id: str, session: bigframes.Session -) -> bigframes.dataframe.DataFrame: - """DataFrame pointing at test data.""" - return session.read_gbq(nested_structs_table_id, index_col="id") - - -@pytest.fixture(scope="session") -def nested_structs_pandas_df(nested_structs_pandas_type: pd.ArrowDtype) -> pd.DataFrame: - """pd.DataFrame pointing at test data. - - Manually parses using json.loads to preserve data types. - """ - with open(DATA_DIR / "nested_structs.jsonl") as f: - raw_rows = [json.loads(line) for line in f] - - ids = [row["id"] for row in raw_rows] - - def get_val(row, col_name): - return row.get(col_name) - - # person - person_struct_schema = nested_structs_pandas_type.pyarrow_dtype - processed_person: list[Optional[dict[str, typing.Any]]] = [] - for row in raw_rows: - x = get_val(row, "person") - if x is None: - processed_person.append(None) - else: - d = dict(x) - if "age" in d and d["age"] is not None: - d["age"] = int(d["age"]) - processed_person.append(d) - person_arr = pa.array(processed_person, type=person_struct_schema) - person_ser = pd.Series(person_arr, index=ids, dtype=nested_structs_pandas_type) - - # bool_col - bool_vals = [ - bool(get_val(row, "bool_col")) if get_val(row, "bool_col") is not None else None - for row in raw_rows - ] - bool_ser = pd.Series(bool_vals, index=ids, dtype=pd.BooleanDtype()) - - # int64_col - int64_vals = [ - int(get_val(row, "int64_col")) - if get_val(row, "int64_col") is not None - else None - for row in raw_rows - ] - int64_ser = pd.Series(int64_vals, index=ids, dtype=pd.Int64Dtype()) - - # float64_col - float64_vals = [ - float(get_val(row, "float64_col")) - if get_val(row, "float64_col") is not None - else None - for row in raw_rows - ] - np_vals = np.array( - [x if x is not None else np.nan for x in float64_vals], dtype=np.float64 - ) - mask = np.array([x is None for x in float64_vals], dtype=bool) - float64_arr = pd.arrays.FloatingArray(np_vals, mask) # type: ignore - float64_ser = pd.Series(float64_arr, index=ids) - - # string_col - string_vals = [ - str(get_val(row, "string_col")) - if get_val(row, "string_col") is not None - else None - for row in raw_rows - ] - string_ser = pd.Series( - string_vals, index=ids, dtype=pd.StringDtype(storage="pyarrow") - ) - - # json_col - json_strs: list[Optional[str]] = [] - for row in raw_rows: - if "json_col" not in row: - json_strs.append(None) - elif row["json_col"] is None: - json_strs.append("null") - else: - json_strs.append( - json.dumps(row["json_col"], sort_keys=True, separators=(",", ":")) - ) - json_arr = pa.array(json_strs, type=db_dtypes.JSONArrowType()) - json_ser = pd.Series( - json_arr, index=ids, dtype=pd.ArrowDtype(db_dtypes.JSONArrowType()) - ) - - # date_col - date_vals = [ - datetime.date.fromisoformat(get_val(row, "date_col")) - if get_val(row, "date_col") is not None - else None - for row in raw_rows - ] - date_arr = pa.array(date_vals, type=pa.date32()) - date_ser = pd.Series(date_arr, index=ids, dtype=pd.ArrowDtype(pa.date32())) - - # time_col - time_vals = [ - datetime.time.fromisoformat(get_val(row, "time_col")) - if get_val(row, "time_col") is not None - else None - for row in raw_rows - ] - time_arr = pa.array(time_vals, type=pa.time64("us")) - time_ser = pd.Series(time_arr, index=ids, dtype=pd.ArrowDtype(pa.time64("us"))) - - # datetime_col - datetime_vals: list[Optional[datetime.datetime]] = [] - for row in raw_rows: - val = get_val(row, "datetime_col") - if val is None: - datetime_vals.append(None) - else: - datetime_vals.append(datetime.datetime.fromisoformat(val.replace(" ", "T"))) - datetime_arr = pa.array(datetime_vals, type=pa.timestamp("us")) - datetime_ser = pd.Series( - datetime_arr, index=ids, dtype=pd.ArrowDtype(pa.timestamp("us")) - ) - - # timestamp_col - timestamp_vals = [ - datetime.datetime.fromisoformat( - get_val(row, "timestamp_col").replace("Z", "+00:00") - ) - if get_val(row, "timestamp_col") is not None - else None - for row in raw_rows - ] - timestamp_arr = pa.array(timestamp_vals, type=pa.timestamp("us", tz="UTC")) - timestamp_ser = pd.Series( - timestamp_arr, index=ids, dtype=pd.ArrowDtype(pa.timestamp("us", tz="UTC")) - ) - - # bytes_col - bytes_vals: list[Optional[bytes]] = [] - for row in raw_rows: - val = get_val(row, "bytes_col") - if val is None: - bytes_vals.append(None) - elif val == "": - bytes_vals.append(b"") - else: - bytes_vals.append(base64.b64decode(val)) - bytes_arr = pa.array(bytes_vals, type=pa.binary()) - bytes_ser = pd.Series(bytes_arr, index=ids, dtype=pd.ArrowDtype(pa.binary())) - - # numeric_col - numeric_vals = [ - decimal.Decimal(str(get_val(row, "numeric_col"))) - if get_val(row, "numeric_col") is not None - else None - for row in raw_rows - ] - numeric_arr = pa.array(numeric_vals, type=pa.decimal128(38, 9)) - numeric_ser = pd.Series( - numeric_arr, index=ids, dtype=pd.ArrowDtype(pa.decimal128(38, 9)) - ) - - # bignumeric_col - bignumeric_vals = [ - decimal.Decimal(str(get_val(row, "bignumeric_col"))) - if get_val(row, "bignumeric_col") is not None - else None - for row in raw_rows - ] - bignumeric_arr = pa.array(bignumeric_vals, type=pa.decimal256(76, 38)) - bignumeric_ser = pd.Series( - bignumeric_arr, index=ids, dtype=pd.ArrowDtype(pa.decimal256(76, 38)) - ) - - # geography_col - geo_vals = [get_val(row, "geography_col") for row in raw_rows] - geo_ser = gpd.GeoSeries.from_wkt(geo_vals) - geo_ser.index = ids - - # duration_col - duration_vals = [ - int(get_val(row, "duration_col")) - if get_val(row, "duration_col") is not None - else None - for row in raw_rows - ] - duration_arr = pa.array(duration_vals, type=pa.duration("us")) - duration_ser = pd.Series( - duration_arr, index=ids, dtype=pd.ArrowDtype(pa.duration("us")) - ) - - df = pd.DataFrame( - { - "person": person_ser, - "bool_col": bool_ser, - "int64_col": int64_ser, - "float64_col": float64_ser, - "string_col": string_ser, - "json_col": json_ser, - "date_col": date_ser, - "time_col": time_ser, - "datetime_col": datetime_ser, - "timestamp_col": timestamp_ser, - "bytes_col": bytes_ser, - "numeric_col": numeric_ser, - "bignumeric_col": bignumeric_ser, - "geography_col": geo_ser, - "duration_col": duration_ser, - }, - index=ids, - ) - df.index.name = "id" - - return df - - -@pytest.fixture(scope="session") -def nested_structs_pandas_type() -> pd.ArrowDtype: - address_struct_schema = pa.struct( - [pa.field("city", pa.string()), pa.field("country", pa.string())] - ) - - person_struct_schema = pa.struct( - [ - pa.field("name", pa.string()), - pa.field("age", pa.int64()), - pa.field("address", address_struct_schema), - ] - ) - - return pd.ArrowDtype(person_struct_schema) - - -@pytest.fixture(scope="session") -def repeated_df( - repeated_table_id: str, session: bigframes.Session -) -> bigframes.dataframe.DataFrame: - """Returns a DataFrame containing columns of list type.""" - return session.read_gbq(repeated_table_id, index_col="rowindex") - - -@pytest.fixture(scope="session") -def repeated_pandas_df() -> pd.DataFrame: - """Returns a DataFrame containing columns of list type.""" - - df = pd.read_json( - DATA_DIR / "repeated.jsonl", - lines=True, - ) - df = df.set_index("rowindex") - return df - - -@pytest.fixture(scope="session") -def json_df( - json_table_id: str, session: bigframes.Session -) -> bigframes.dataframe.DataFrame: - """Returns a DataFrame containing columns of JSON type.""" - return session.read_gbq(json_table_id, index_col="rowindex") - - -@pytest.fixture(scope="session") -def json_pandas_df() -> pd.DataFrame: - """Returns a DataFrame containing columns of JSON type.""" - df = pd.read_json( - DATA_DIR / "json.jsonl", - lines=True, - ) - df = df.set_index("rowindex") - return df - - @pytest.fixture(scope="session") def scalars_df_default_index( scalars_df_index: bigframes.dataframe.DataFrame, @@ -790,38 +338,6 @@ def scalars_df_index( return session.read_gbq(scalars_table_id, index_col="rowindex") -@pytest.fixture(scope="session") -def scalars_df_partial_ordering( - scalars_table_id: str, unordered_session: bigframes.Session -) -> bigframes.dataframe.DataFrame: - """DataFrame pointing at test data.""" - return unordered_session.read_gbq( - scalars_table_id, index_col="rowindex" - ).sort_index() - - -@pytest.fixture(scope="session") -def scalars_df_null_index( - scalars_table_id: str, session: bigframes.Session -) -> bigframes.dataframe.DataFrame: - """DataFrame pointing at test data.""" - return session.read_gbq( - scalars_table_id, index_col=bigframes.enums.DefaultIndexKind.NULL - ).sort_values("rowindex") - - -@pytest.fixture(scope="session") -def scalars_df_unordered( - scalars_table_id: str, unordered_session: bigframes.Session -) -> bigframes.dataframe.DataFrame: - """DataFrame pointing at test data.""" - df = unordered_session.read_gbq( - scalars_table_id, index_col=bigframes.enums.DefaultIndexKind.NULL - ) - assert not df._block.explicitly_ordered - return df - - @pytest.fixture(scope="session") def scalars_df_2_default_index( scalars_df_2_index: bigframes.dataframe.DataFrame, @@ -846,7 +362,7 @@ def scalars_pandas_df_default_index() -> pd.DataFrame: DATA_DIR / "scalars.jsonl", lines=True, ) - bigframes.testing.utils.convert_pandas_dtypes(df, bytes_col=True) + convert_pandas_dtypes(df, bytes_col=True) df = df.set_index("rowindex", drop=False) df.index.name = None @@ -879,39 +395,6 @@ def scalars_dfs( return scalars_df_index, scalars_pandas_df_index -@pytest.fixture(scope="session") -def scalars_dfs_maybe_ordered( - maybe_ordered_session, - scalars_pandas_df_index, -): - return ( - maybe_ordered_session.read_pandas(scalars_pandas_df_index), - scalars_pandas_df_index, - ) - - -@pytest.fixture(scope="session") -def scalars_df_numeric_150_columns_maybe_ordered( - maybe_ordered_session, - scalars_pandas_df_index, -): - """DataFrame pointing at test data.""" - # TODO(b/379911038): After the error fixed, add numeric type. - pandas_df = scalars_pandas_df_index.reset_index(drop=False)[ - [ - "rowindex", - "rowindex_2", - "float64_col", - "int64_col", - "int64_too", - ] - * 30 - ] - - df = maybe_ordered_session.read_pandas(pandas_df) - return (df, pandas_df) - - @pytest.fixture(scope="session") def hockey_df( hockey_table_id: str, session: bigframes.Session @@ -1008,22 +491,6 @@ def penguins_df_default_index( return session.read_gbq(penguins_table_id) -@pytest.fixture(scope="session") -def penguins_df_null_index( - penguins_table_id: str, unordered_session: bigframes.Session -) -> bigframes.dataframe.DataFrame: - """DataFrame pointing at test data.""" - return unordered_session.read_gbq(penguins_table_id) - - -@pytest.fixture(scope="session") -def ratings_df_default_index( - ratings_table_id: str, session: bigframes.Session -) -> bigframes.dataframe.DataFrame: - """DataFrame pointing at test data.""" - return session.read_gbq(ratings_table_id) - - @pytest.fixture(scope="session") def time_series_df_default_index( time_series_table_id: str, session: bigframes.Session @@ -1039,9 +506,9 @@ def new_time_series_pandas_df(): return pd.DataFrame( { "parsed_date": [ - datetime.datetime(2017, 8, 2, tzinfo=utc), - datetime.datetime(2017, 8, 3, tzinfo=utc), - datetime.datetime(2017, 8, 4, tzinfo=utc), + datetime(2017, 8, 2, tzinfo=utc), + datetime(2017, 8, 3, tzinfo=utc), + datetime(2017, 8, 4, tzinfo=utc), ], "total_visits": [2500, 2500, 2500], } @@ -1053,31 +520,6 @@ def new_time_series_df(session, new_time_series_pandas_df): return session.read_pandas(new_time_series_pandas_df) -@pytest.fixture(scope="session") -def new_time_series_pandas_df_w_id(): - """Additional data matching the time series dataset. The values are dummy ones used to basically check the prediction scores.""" - utc = pytz.utc - return pd.DataFrame( - { - "parsed_date": [ - datetime.datetime(2017, 8, 2, tzinfo=utc), - datetime.datetime(2017, 8, 2, tzinfo=utc), - datetime.datetime(2017, 8, 3, tzinfo=utc), - datetime.datetime(2017, 8, 3, tzinfo=utc), - datetime.datetime(2017, 8, 4, tzinfo=utc), - datetime.datetime(2017, 8, 4, tzinfo=utc), - ], - "id": ["1", "2", "1", "2", "1", "2"], - "total_visits": [2500, 2500, 2500, 2500, 2500, 2500], - } - ) - - -@pytest.fixture(scope="session") -def new_time_series_df_w_id(session, new_time_series_pandas_df_w_id): - return session.read_pandas(new_time_series_pandas_df_w_id) - - @pytest.fixture(scope="session") def penguins_pandas_df_default_index() -> pd.DataFrame: """Consistently ordered pandas dataframe for penguins test data""" @@ -1119,42 +561,11 @@ def new_penguins_pandas_df(): ).set_index("tag_number") -@pytest.fixture(scope="session") -def missing_values_penguins_df(): - """Additional data matching the missing values penguins dataset""" - return bpd.DataFrame( - { - "culmen_length_mm": [39.5, 38.5, 37.9], - "culmen_depth_mm": [np.nan, 17.2, 18.1], - "flipper_length_mm": [np.nan, 181.0, 188.0], - } - ) - - @pytest.fixture(scope="session") def new_penguins_df(session, new_penguins_pandas_df): return session.read_pandas(new_penguins_pandas_df) -@pytest.fixture(scope="session") -def llm_text_pandas_df(): - """Additional data matching the penguins dataset, with a new index""" - return pd.DataFrame( - { - "prompt": [ - "What is BigQuery?", - "What is BQML?", - "What is BigQuery DataFrame?", - ], - } - ) - - -@pytest.fixture(scope="session") -def llm_text_df(session, llm_text_pandas_df): - return session.read_pandas(llm_text_pandas_df) - - @pytest.fixture(scope="session") def penguins_linear_model_name( session: bigframes.Session, dataset_id_permanent, penguins_table_id @@ -1321,65 +732,34 @@ def penguins_xgbregressor_model_name( return model_name -def _get_or_create_arima_plus_model( - session: bigframes.Session, dataset_id_permanent, sql -) -> str: - """Internal helper to compute a model name by hasing the given SQL. - attempst to retreive the model, create it if not exist. - retursn the fully qualitifed model""" - - # We use the SQL hash as the name to ensure the model is regenerated if this fixture is edited - model_name = f"{dataset_id_permanent}.time_series_arima_plus_{hashlib.md5(sql.encode()).hexdigest()}" - sql = sql.replace("$model_name", model_name) - try: - session.bqclient.get_model(model_name) - except google.cloud.exceptions.NotFound: - logging.info( - "time_series_arima_plus_model fixture was not found in the permanent dataset, regenerating it..." - ) - session.bqclient.query(sql).result() - finally: - return model_name - - @pytest.fixture(scope="session") def time_series_arima_plus_model_name( session: bigframes.Session, dataset_id_permanent, time_series_table_id ) -> str: """Provides a pretrained model as a test fixture that is cached across test runs. - This lets us run system tests without having to wait for a model.fit(...). - This version does not include time_series_id_col.""" + This lets us run system tests without having to wait for a model.fit(...)""" sql = f""" CREATE OR REPLACE MODEL `$model_name` OPTIONS ( model_type='ARIMA_PLUS', time_series_timestamp_col = 'parsed_date', time_series_data_col = 'total_visits' -) AS SELECT - parsed_date, - total_visits -FROM `{time_series_table_id}`""" - return _get_or_create_arima_plus_model(session, dataset_id_permanent, sql) - - -@pytest.fixture(scope="session") -def time_series_arima_plus_model_name_w_id( - session: bigframes.Session, dataset_id_permanent, time_series_table_id -) -> str: - """Provides a pretrained model as a test fixture that is cached across test runs. - This lets us run system tests without having to wait for a model.fit(...). - This version includes time_series_id_col.""" - sql = f""" -CREATE OR REPLACE MODEL `$model_name` -OPTIONS ( - model_type='ARIMA_PLUS', - time_series_timestamp_col = 'parsed_date', - time_series_data_col = 'total_visits', - time_series_id_col = 'id' ) AS SELECT * FROM `{time_series_table_id}`""" - return _get_or_create_arima_plus_model(session, dataset_id_permanent, sql) + # We use the SQL hash as the name to ensure the model is regenerated if this fixture is edited + model_name = f"{dataset_id_permanent}.time_series_arima_plus_{hashlib.md5(sql.encode()).hexdigest()}" + sql = sql.replace("$model_name", model_name) + + try: + session.bqclient.get_model(model_name) + except google.cloud.exceptions.NotFound: + logging.info( + "time_series_arima_plus_model fixture was not found in the permanent dataset, regenerating it..." + ) + session.bqclient.query(sql).result() + finally: + return model_name @pytest.fixture(scope="session") @@ -1488,18 +868,6 @@ def penguins_randomforest_classifier_model_name( return model_name -@pytest.fixture(scope="session") -def llm_fine_tune_df_default_index( - session: bigframes.Session, -) -> bigframes.dataframe.DataFrame: - training_table_name = "llm_tuning.emotion_classification_train" - df = session.read_gbq(training_table_name).dropna().head(30) - prefix = "Please do sentiment analysis on the following text and only output a number from 0 to 5 where 0 means sadness, 1 means joy, 2 means love, 3 means anger, 4 means fear, and 5 means surprise. Text: " - df["prompt"] = prefix + df["text"] - df["label"] = df["label"].astype("string") - return df - - @pytest.fixture(scope="session") def usa_names_grouped_table( session: bigframes.Session, dataset_id_permanent @@ -1530,14 +898,6 @@ def usa_names_grouped_table( return session.bqclient.get_table(table_id) -@pytest.fixture(scope="session", autouse=True) -def use_sqlglot_compiler(): - original_setting = bigframes.options.experiments.sql_compiler - bigframes.options.experiments.sql_compiler = "experimental" - yield - bigframes.options.experiments.sql_compiler = original_setting - - @pytest.fixture() def restore_sampling_settings(): enable_downsampling = bigframes.options.sampling.enable_downsampling @@ -1547,14 +907,6 @@ def restore_sampling_settings(): bigframes.options.sampling.max_download_size = max_download_size -@pytest.fixture() -def with_multiquery_execution(): - original_setting = bigframes.options.compute.enable_multi_query_execution - bigframes.options.compute.enable_multi_query_execution = True - yield - bigframes.options.compute.enable_multi_query_execution = original_setting - - @pytest.fixture() def weird_strings_pd(): df = pd.DataFrame( @@ -1627,7 +979,7 @@ def floats_pd(): dtype=pd.Float64Dtype(), ) # Index helps debug failed cases - df.index = df.float64_col # type: ignore + df.index = df.float64_col # Upload fails if index name same as column name df.index.name = None return df.float64_col @@ -1637,7 +989,7 @@ def floats_pd(): def floats_product_pd(floats_pd): df = pd.merge(floats_pd, floats_pd, how="cross") # Index helps debug failed cases - df = df.set_index([df.float64_col_x, df.float64_col_y]) # type: ignore + df = df.set_index([df.float64_col_x, df.float64_col_y]) df.index.names = ["left", "right"] return df @@ -1650,91 +1002,3 @@ def floats_bf(session, floats_pd): @pytest.fixture() def floats_product_bf(session, floats_product_pd): return session.read_pandas(floats_product_pd) - - -@pytest.fixture(scope="session", autouse=True) -def use_fast_query_path(): - with bpd.option_context("compute.allow_large_results", False): - yield - - -@pytest.fixture(scope="session", autouse=True) -def cleanup_cloud_functions(session, cloudfunctions_client, dataset_id_permanent): - """Clean up stale cloud functions.""" - permanent_endpoints = bigframes.testing.utils.get_remote_function_endpoints( - session.bqclient, dataset_id_permanent - ) - delete_count = 0 - try: - for cloud_function in bigframes.testing.utils.get_cloud_functions( - cloudfunctions_client, - session.bqclient.project, - session.bqclient.location, - name_prefix="bigframes-", - ): - # Ignore bigframes cloud functions referred by the remote functions in - # the permanent dataset - if cloud_function.service_config.uri in permanent_endpoints: - continue - - # Ignore the functions less than one day old - age = datetime.datetime.now() - datetime.datetime.fromtimestamp( - cloud_function.update_time.timestamp() - ) - if age.days <= 0: - continue - - # Go ahead and delete - try: - bigframes.testing.utils.delete_cloud_function( - cloudfunctions_client, cloud_function.name - ) - delete_count += 1 - if delete_count >= MAX_NUM_FUNCTIONS_TO_DELETE_PER_SESSION: - break - except google.api_core.exceptions.NotFound: - # This can happen when multiple pytest sessions are running in - # parallel. Two or more sessions may discover the same cloud - # function, but only one of them would be able to delete it - # successfully, while the other instance will run into this - # exception. Ignore this exception. - pass - except Exception as exc: - # Don't fail the tests for unknown exceptions. - # - # This can happen if we are hitting GCP limits, e.g. - # google.api_core.exceptions.ResourceExhausted: 429 Quota exceeded - # for quota metric 'Per project mutation requests' and limit - # 'Per project mutation requests per minute per region' of service - # 'cloudfunctions.googleapis.com' for consumer - # 'project_number:1084210331973'. - # [reason: "RATE_LIMIT_EXCEEDED" domain: "googleapis.com" ... - # - # It can also happen occasionally with - # google.api_core.exceptions.ServiceUnavailable when there is some - # backend flakiness. - # - # Let's stop further clean up and leave it to later. - traceback.print_exception(type(exc), exc, None) - - -@pytest.fixture(scope="session") -def images_gcs_path() -> str: - return "gs://bigframes_blob_test/images/*" - - -@pytest.fixture(scope="session") -def images_uris() -> list[str]: - return [ - "gs://bigframes_blob_test/images/img0.jpg", - "gs://bigframes_blob_test/images/img1.jpg", - ] - - -@pytest.fixture() -def reset_default_session_and_location(): - bpd.close_session() - with bpd.option_context("bigquery.location", None): - yield - bpd.close_session() - bpd.options.bigquery.location = None diff --git a/tests/system/large/bigquery/__init__.py b/tests/system/large/bigquery/__init__.py deleted file mode 100644 index 58d482ea386..00000000000 --- a/tests/system/large/bigquery/__init__.py +++ /dev/null @@ -1,13 +0,0 @@ -# Copyright 2026 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. diff --git a/tests/system/large/bigquery/test_ai.py b/tests/system/large/bigquery/test_ai.py deleted file mode 100644 index 504fe5aa389..00000000000 --- a/tests/system/large/bigquery/test_ai.py +++ /dev/null @@ -1,130 +0,0 @@ -# Copyright 2026 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import pytest - -import bigframes.pandas as bpd -from bigframes.bigquery import ai, ml - - -@pytest.fixture(scope="session") -def embedding_model(bq_connection, dataset_id): - model_name = f"{dataset_id}.embedding_model" - return ml.create_model( - model_name=model_name, - options={"endpoint": "gemini-embedding-001"}, - connection_name=bq_connection, - ) - - -@pytest.fixture(scope="session") -def text_model(bq_connection, dataset_id): - model_name = f"{dataset_id}.text_model" - return ml.create_model( - model_name=model_name, - options={"endpoint": "gemini-2.5-flash"}, - connection_name=bq_connection, - ) - - -def test_generate_embedding(embedding_model): - df = bpd.DataFrame( - { - "content": [ - "What is BigQuery?", - "What is BQML?", - ] - } - ) - - result = ai.generate_embedding(embedding_model, df) - - assert len(result) == 2 - assert "embedding" in result.columns - assert "statistics" in result.columns - assert "status" in result.columns - - -def test_generate_embedding_with_options(embedding_model): - df = bpd.DataFrame( - { - "content": [ - "What is BigQuery?", - "What is BQML?", - ] - } - ) - - result = ai.generate_embedding( - embedding_model, df, task_type="RETRIEVAL_DOCUMENT", output_dimensionality=256 - ) - - assert len(result) == 2 - embedding = result["embedding"].to_pandas() - assert len(embedding[0]) == 256 - - -def test_generate_text(text_model): - df = bpd.DataFrame({"prompt": ["Dog", "Cat"]}) - - result = ai.generate_text(text_model, df) - - assert len(result) == 2 - assert "result" in result.columns - assert "statistics" in result.columns - assert "full_response" in result.columns - assert "status" in result.columns - - -def test_generate_text_with_options(text_model): - df = bpd.DataFrame({"prompt": ["Dog", "Cat"]}) - - result = ai.generate_text(text_model, df, max_output_tokens=1) - - # It basically asserts that the results are still returned. - assert len(result) == 2 - - -def test_generate_table(text_model): - df = bpd.DataFrame( - {"prompt": ["Generate a table of 2 programming languages and their creators."]} - ) - - result = ai.generate_table( - text_model, - df, - output_schema="language STRING, creator STRING", - ) - - assert "language" in result.columns - assert "creator" in result.columns - # The model may not always return the exact number of rows requested. - assert len(result) > 0 - - -def test_generate_table_with_mapping_schema(text_model): - df = bpd.DataFrame( - {"prompt": ["Generate a table of 2 programming languages and their creators."]} - ) - - result = ai.generate_table( - text_model, - df, - output_schema={"language": "STRING", "creator": "STRING"}, - ) - - assert "language" in result.columns - assert "creator" in result.columns - # The model may not always return the exact number of rows requested. - assert len(result) > 0 diff --git a/tests/system/large/bigquery/test_io.py b/tests/system/large/bigquery/test_io.py deleted file mode 100644 index 024c6174709..00000000000 --- a/tests/system/large/bigquery/test_io.py +++ /dev/null @@ -1,39 +0,0 @@ -# Copyright 2026 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for for the specific language governing permissions and -# limitations under the License. - -import bigframes.bigquery as bbq - - -def test_load_data(session, dataset_id): - table_name = f"{dataset_id}.test_load_data" - uri = "gs://cloud-samples-data/bigquery/us-states/us-states.csv" - - # Create the external table - table = bbq.load_data( - table_name, - columns={ - "name": "STRING", - "post_abbr": "STRING", - }, - from_files_options={"format": "CSV", "uris": [uri], "skip_leading_rows": 1}, - session=session, - ) - assert table is not None - - # Read the table to verify - import bigframes.pandas as bpd - - bf_df = bpd.read_gbq(table_name) - pd_df = bf_df.to_pandas() - assert len(pd_df) > 0 diff --git a/tests/system/large/bigquery/test_ml.py b/tests/system/large/bigquery/test_ml.py deleted file mode 100644 index f0f7d4f6917..00000000000 --- a/tests/system/large/bigquery/test_ml.py +++ /dev/null @@ -1,117 +0,0 @@ -# Copyright 2026 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import pytest - -import bigframes.bigquery.ml as ml -import bigframes.pandas as bpd - - -@pytest.fixture(scope="session") -def embedding_model(bq_connection, dataset_id): - model_name = f"{dataset_id}.embedding_model" - return ml.create_model( - model_name=model_name, - options={"endpoint": "gemini-embedding-001"}, - connection_name=bq_connection, - ) - - -def test_generate_embedding(embedding_model): - df = bpd.DataFrame( - { - "content": [ - "What is BigQuery?", - "What is BQML?", - ] - } - ) - - result = ml.generate_embedding(embedding_model, df) - assert len(result) == 2 - assert "ml_generate_embedding_result" in result.columns - assert "ml_generate_embedding_status" in result.columns - - -def test_generate_embedding_with_options(embedding_model): - df = bpd.DataFrame( - { - "content": [ - "What is BigQuery?", - "What is BQML?", - ] - } - ) - - result = ml.generate_embedding( - embedding_model, df, task_type="RETRIEVAL_DOCUMENT", output_dimensionality=256 - ) - assert len(result) == 2 - assert "ml_generate_embedding_result" in result.columns - assert "ml_generate_embedding_status" in result.columns - embedding = result["ml_generate_embedding_result"].to_pandas() - assert len(embedding[0]) == 256 - - -def test_get_insights(dataset_id): - df = bpd.DataFrame( - { - "dim1": ["a", "a", "b", "b", "a", "a", "b", "b"], - "dim2": ["x", "y", "x", "y", "x", "y", "x", "y"], - "metric": [10, 20, 30, 40, 12, 25, 35, 45], - "is_test": [False, False, False, False, True, True, True, True], - } - ) - model_name = f"{dataset_id}.contribution_analysis_model" - - ml.create_model( - model_name=model_name, - options={ - "model_type": "CONTRIBUTION_ANALYSIS", - "contribution_metric": "SUM(metric)", - "is_test_col": "is_test", - }, - training_data=df, - ) - - result = ml.get_insights(model_name) - assert len(result) > 0 - assert "contributors" in result.columns - - -def test_create_model_linear_regression(dataset_id): - df = bpd.DataFrame({"x": [1, 2, 3], "y": [2, 4, 6]}) - model_name = f"{dataset_id}.linear_regression_model" - - result = ml.create_model( - model_name=model_name, - options={"model_type": "LINEAR_REG", "input_label_cols": ["y"]}, - training_data=df, - ) - - assert result["modelType"] == "LINEAR_REGRESSION" - - -def test_create_model_with_transform(dataset_id): - df = bpd.DataFrame({"x": [1, 2, 3], "y": [2, 4, 6]}) - model_name = f"{dataset_id}.transform_model" - - result = ml.create_model( - model_name=model_name, - options={"model_type": "LINEAR_REG", "input_label_cols": ["y"]}, - training_data=df, - transform=["x * 2 AS x_doubled", "y"], - ) - - assert result["modelType"] == "LINEAR_REGRESSION" diff --git a/tests/system/large/bigquery/test_table.py b/tests/system/large/bigquery/test_table.py deleted file mode 100644 index dd956b3a040..00000000000 --- a/tests/system/large/bigquery/test_table.py +++ /dev/null @@ -1,36 +0,0 @@ -# Copyright 2026 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import bigframes.bigquery as bbq - - -def test_create_external_table(session, dataset_id, bq_connection): - table_name = f"{dataset_id}.test_object_table" - uri = "gs://cloud-samples-data/bigquery/tutorials/cymbal-pets/images/*" - - # Create the external table - table = bbq.create_external_table( - table_name, - connection_name=bq_connection, - options={"object_metadata": "SIMPLE", "uris": [uri]}, - session=session, - ) - assert table is not None - - # Read the table to verify - import bigframes.pandas as bpd - - bf_df = bpd.read_gbq(table_name) - pd_df = bf_df.to_pandas() - assert len(pd_df) > 0 diff --git a/tests/system/large/functions/__init__.py b/tests/system/large/functions/__init__.py deleted file mode 100644 index 0a2669d7a25..00000000000 --- a/tests/system/large/functions/__init__.py +++ /dev/null @@ -1,13 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. diff --git a/tests/system/large/functions/test_managed_function.py b/tests/system/large/functions/test_managed_function.py deleted file mode 100644 index 888852edd4d..00000000000 --- a/tests/system/large/functions/test_managed_function.py +++ /dev/null @@ -1,1199 +0,0 @@ -# Copyright 2023 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import warnings - -import google.api_core.exceptions -import pandas -import pytest -import test_utils.prefixer - -import bigframes -import bigframes.dataframe -import bigframes.dtypes -import bigframes.exceptions as bfe -import bigframes.pandas as bpd - -prefixer = test_utils.prefixer.Prefixer("bigframes", "") - - -@pytest.fixture -def function_id(dataset_id, session): - name = prefixer.create_prefix() - yield name - try: - session.bqclient.delete_routine(f"{dataset_id}.{name}") - # some tests, like test_managed_function_options_errors, should not actually create the function. - # so we ignore the not found error. - except google.api_core.exceptions.NotFound: - pass - - -def test_managed_function_array_output(session, scalars_dfs, dataset_id, function_id): - with warnings.catch_warnings(record=True) as record: - - @session.udf( - dataset=dataset_id, - name=function_id, - ) - def featurize(x: int) -> list[float]: - return [float(i) for i in [x, x + 1, x + 2]] - - # No following conflict warning when there is no redundant type hints. - input_type_warning = "Conflicting input types detected" - return_type_warning = "Conflicting return type detected" - assert not any(input_type_warning in str(warning.message) for warning in record) - assert not any(return_type_warning in str(warning.message) for warning in record) - - scalars_df, scalars_pandas_df = scalars_dfs - - bf_int64_col = scalars_df["int64_too"] - bf_result = bf_int64_col.apply(featurize).to_pandas() - - pd_int64_col = scalars_pandas_df["int64_too"] - pd_result = pd_int64_col.apply(featurize) - - # Ignore any dtype disparity. - pandas.testing.assert_series_equal(pd_result, bf_result, check_dtype=False) - - # Make sure the read_gbq_function path works for this function. - featurize_ref = session.read_gbq_function(f"{dataset_id}.{function_id}") - - # Test on the function from read_gbq_function. - got = featurize_ref(10) - assert got == [10.0, 11.0, 12.0] - - bf_result_gbq = bf_int64_col.apply(featurize_ref).to_pandas() - pandas.testing.assert_series_equal(bf_result_gbq, pd_result, check_dtype=False) - - -def test_managed_function_series_apply(session, dataset_id, scalars_dfs, function_id): - @session.udf(dataset=dataset_id, name=function_id) - def foo(x: int) -> bytes: - return bytes(abs(x)) - - # Function should still work normally. - assert foo(-2) == bytes(2) - - scalars_df, scalars_pandas_df = scalars_dfs - - bf_result_col = scalars_df["int64_too"].apply(foo) - bf_result = ( - scalars_df["int64_too"].to_frame().assign(result=bf_result_col).to_pandas() - ) - - pd_result_col = scalars_pandas_df["int64_too"].apply(foo) - pd_result = scalars_pandas_df["int64_too"].to_frame().assign(result=pd_result_col) - - pandas.testing.assert_frame_equal(bf_result, pd_result, check_dtype=False) - - # Make sure the read_gbq_function path works for this function. - foo_ref = session.read_gbq_function(f"{dataset_id}.{function_id}") - - bf_result_col_gbq = scalars_df["int64_too"].apply(foo_ref) - bf_result_gbq = ( - scalars_df["int64_too"].to_frame().assign(result=bf_result_col_gbq).to_pandas() - ) - - pandas.testing.assert_frame_equal(bf_result_gbq, pd_result, check_dtype=False) - - -def test_managed_function_series_apply_array_output( - session, - dataset_id, - scalars_dfs, - function_id, -): - with pytest.warns(bfe.PreviewWarning, match="udf is in preview."): - - @session.udf(dataset=dataset_id, name=function_id) - def foo_list(x: int) -> list[float]: - return [float(abs(x)), float(abs(x) + 1)] - - scalars_df, scalars_pandas_df = scalars_dfs - - bf_result_col = scalars_df["int64_too"].apply(foo_list) - bf_result = ( - scalars_df["int64_too"].to_frame().assign(result=bf_result_col).to_pandas() - ) - - pd_result_col = scalars_pandas_df["int64_too"].apply(foo_list) - pd_result = scalars_pandas_df["int64_too"].to_frame().assign(result=pd_result_col) - - # Ignore any dtype difference. - pandas.testing.assert_frame_equal(bf_result, pd_result, check_dtype=False) - - -def test_managed_function_series_combine(session, dataset_id, scalars_dfs, function_id): - # This function is deliberately written to not work with NA input. - def add(x: int, y: int) -> int: - return x + y - - scalars_df, scalars_pandas_df = scalars_dfs - int_col_name_with_nulls = "int64_col" - int_col_name_no_nulls = "int64_too" - bf_df = scalars_df[[int_col_name_with_nulls, int_col_name_no_nulls]] - pd_df = scalars_pandas_df[[int_col_name_with_nulls, int_col_name_no_nulls]] - - # make sure there are NA values in the test column. - assert any([pandas.isna(val) for val in bf_df[int_col_name_with_nulls]]) - - add_managed_func = session.udf(dataset=dataset_id, name=function_id)(add) - - # with nulls in the series the managed function application would fail. - with pytest.raises( - google.api_core.exceptions.BadRequest, match="unsupported operand" - ): - bf_df[int_col_name_with_nulls].combine( - bf_df[int_col_name_no_nulls], add_managed_func - ).to_pandas() - - # after filtering out nulls the managed function application should work - # similar to pandas. - pd_filter = pd_df[int_col_name_with_nulls].notnull() - pd_result = pd_df[pd_filter][int_col_name_with_nulls].combine( - pd_df[pd_filter][int_col_name_no_nulls], add - ) - bf_filter = bf_df[int_col_name_with_nulls].notnull() - bf_result = ( - bf_df[bf_filter][int_col_name_with_nulls] - .combine(bf_df[bf_filter][int_col_name_no_nulls], add_managed_func) - .to_pandas() - ) - - # ignore any dtype difference. - pandas.testing.assert_series_equal(pd_result, bf_result, check_dtype=False) - - # Make sure the read_gbq_function path works for this function. - add_managed_func_ref = session.read_gbq_function(f"{dataset_id}.{function_id}") - bf_result = ( - bf_df[bf_filter][int_col_name_with_nulls] - .combine(bf_df[bf_filter][int_col_name_no_nulls], add_managed_func_ref) - .to_pandas() - ) - pandas.testing.assert_series_equal(bf_result, pd_result, check_dtype=False) - - -def test_managed_function_series_combine_array_output( - session, dataset_id, scalars_dfs, function_id -): - # The type hints in this function's signature has conflicts. The - # `input_types` and `output_type` arguments from udf decorator take - # precedence and will be used instead. - def add_list(x, y: bool) -> list[bool]: - return [x, y] - - scalars_df, scalars_pandas_df = scalars_dfs - int_col_name_with_nulls = "int64_col" - int_col_name_no_nulls = "int64_too" - bf_df = scalars_df[[int_col_name_with_nulls, int_col_name_no_nulls]] - pd_df = scalars_pandas_df[[int_col_name_with_nulls, int_col_name_no_nulls]] - - # Make sure there are NA values in the test column. - assert any([pandas.isna(val) for val in bf_df[int_col_name_with_nulls]]) - - with warnings.catch_warnings(record=True) as record: - add_list_managed_func = session.udf( - input_types=[int, int], - output_type=list[int], - dataset=dataset_id, - name=function_id, - )(add_list) - - input_type_warning = "Conflicting input types detected" - assert any(input_type_warning in str(warning.message) for warning in record) - return_type_warning = "Conflicting return type detected" - assert any(return_type_warning in str(warning.message) for warning in record) - - # After filtering out nulls the managed function application should work - # similar to pandas. - pd_filter = pd_df[int_col_name_with_nulls].notnull() - pd_result = pd_df[pd_filter][int_col_name_with_nulls].combine( - pd_df[pd_filter][int_col_name_no_nulls], add_list - ) - bf_filter = bf_df[int_col_name_with_nulls].notnull() - bf_result = ( - bf_df[bf_filter][int_col_name_with_nulls] - .combine(bf_df[bf_filter][int_col_name_no_nulls], add_list_managed_func) - .to_pandas() - ) - - # Ignore any dtype difference. - pandas.testing.assert_series_equal(pd_result, bf_result, check_dtype=False) - - # Make sure the read_gbq_function path works for this function. - add_list_managed_func_ref = session.read_gbq_function(f"{dataset_id}.{function_id}") - - # Test on the function from read_gbq_function. - got = add_list_managed_func_ref(10, 38) - assert got == [10, 38] - - bf_result_gbq = ( - bf_df[bf_filter][int_col_name_with_nulls] - .combine(bf_df[bf_filter][int_col_name_no_nulls], add_list_managed_func_ref) - .to_pandas() - ) - - pandas.testing.assert_series_equal(bf_result_gbq, pd_result, check_dtype=False) - - -def test_managed_function_dataframe_map(session, dataset_id, scalars_dfs, function_id): - def add_one(x): - return x + 1 - - mf_add_one = session.udf( - input_types=[int], - output_type=int, - dataset=dataset_id, - name=function_id, - )(add_one) - - scalars_df, scalars_pandas_df = scalars_dfs - int64_cols = ["int64_col", "int64_too"] - - bf_int64_df = scalars_df[int64_cols] - bf_int64_df_filtered = bf_int64_df.dropna() - bf_result = bf_int64_df_filtered.map(mf_add_one).to_pandas() - - pd_int64_df = scalars_pandas_df[int64_cols] - pd_int64_df_filtered = pd_int64_df.dropna() - pd_result = pd_int64_df_filtered.map(add_one) - # TODO(shobs): Figure why pandas .map() changes the dtype, i.e. - # pd_int64_df_filtered.dtype is Int64Dtype() - # pd_int64_df_filtered.map(lambda x: x).dtype is int64. - # For this test let's force the pandas dtype to be same as input. - for col in pd_result: - pd_result[col] = pd_result[col].astype(pd_int64_df_filtered[col].dtype) - - pandas.testing.assert_frame_equal(bf_result, pd_result) - - -def test_managed_function_dataframe_map_array_output( - session, scalars_dfs, dataset_id, function_id -): - def add_one_list(x): - return [x + 1] * 3 - - mf_add_one_list = session.udf( - input_types=[int], - output_type=list[int], - dataset=dataset_id, - name=function_id, - )(add_one_list) - - scalars_df, scalars_pandas_df = scalars_dfs - int64_cols = ["int64_col", "int64_too"] - - bf_int64_df = scalars_df[int64_cols] - bf_int64_df_filtered = bf_int64_df.dropna() - bf_result = bf_int64_df_filtered.map(mf_add_one_list).to_pandas() - - pd_int64_df = scalars_pandas_df[int64_cols] - pd_int64_df_filtered = pd_int64_df.dropna() - pd_result = pd_int64_df_filtered.map(add_one_list) - - # Ignore any dtype difference. - pandas.testing.assert_frame_equal(bf_result, pd_result, check_dtype=False) - - # Make sure the read_gbq_function path works for this function. - mf_add_one_list_ref = session.read_gbq_function(f"{dataset_id}.{function_id}") - - bf_result_gbq = bf_int64_df_filtered.map(mf_add_one_list_ref).to_pandas() - pandas.testing.assert_frame_equal(bf_result_gbq, pd_result, check_dtype=False) - - -def test_managed_function_dataframe_apply_axis_1( - session, dataset_id, scalars_dfs, function_id -): - scalars_df, scalars_pandas_df = scalars_dfs - series = scalars_df["int64_too"] - series_pandas = scalars_pandas_df["int64_too"] - - def add_ints(x, y): - return x + y - - add_ints_mf = session.udf( - input_types=[int, int], - output_type=int, - dataset=dataset_id, - name=function_id, - )(add_ints) - - with pytest.warns( - bigframes.exceptions.PreviewWarning, match="axis=1 scenario is in preview." - ): - bf_result = ( - bpd.DataFrame({"x": series, "y": series}) - .apply(add_ints_mf, axis=1) - .to_pandas() - ) - - pd_result = pandas.DataFrame({"x": series_pandas, "y": series_pandas}).apply( - lambda row: add_ints(row["x"], row["y"]), axis=1 - ) - - pandas.testing.assert_series_equal( - pd_result, bf_result, check_dtype=False, check_exact=True - ) - - -def test_managed_function_dataframe_apply_axis_1_array_output( - session, dataset_id, function_id -): - bf_df = bigframes.dataframe.DataFrame( - { - "Id": [1, 2, 3], - "Age": [22.5, 23, 23.5], - "Name": ["alpha", "beta", "gamma"], - } - ) - - expected_dtypes = ( - bigframes.dtypes.INT_DTYPE, - bigframes.dtypes.FLOAT_DTYPE, - bigframes.dtypes.STRING_DTYPE, - ) - - # Assert the dataframe dtypes. - assert tuple(bf_df.dtypes) == expected_dtypes - - @session.udf( - input_types=[int, float, str], - output_type=list[str], - dataset=dataset_id, - name=function_id, - ) - def foo(x, y, z): - return [str(x), str(y), z] - - # Fails to apply on dataframe with incompatible number of columns. - with pytest.raises( - ValueError, - match="^Parameter count mismatch:.* expected 3 parameters but received 2 DataFrame columns.", - ): - bf_df[["Id", "Age"]].apply(foo, axis=1) - - with pytest.raises( - ValueError, - match="^Parameter count mismatch:.* expected 3 parameters but received 4 DataFrame columns.", - ): - bf_df.assign(Country="lalaland").apply(foo, axis=1) - - # Fails to apply on dataframe with incompatible column datatypes. - with pytest.raises( - ValueError, - match="^Data type mismatch for DataFrame columns: Expected .* Received .*", - ): - bf_df.assign(Age=bf_df["Age"].astype("Int64")).apply(foo, axis=1) - - # Successfully applies to dataframe with matching number of columns. - # and their datatypes. - with pytest.warns( - bigframes.exceptions.PreviewWarning, - match="axis=1 scenario is in preview.", - ): - bf_result = bf_df.apply(foo, axis=1).to_pandas() - - # Since this scenario is not pandas-like, let's handcraft the - # expected result. - expected_result = pandas.Series( - [ - ["1", "22.5", "alpha"], - ["2", "23.0", "beta"], - ["3", "23.5", "gamma"], - ] - ) - - pandas.testing.assert_series_equal( - expected_result, bf_result, check_dtype=False, check_index_type=False - ) - - # Make sure the read_gbq_function path works for this function. - foo_ref = session.read_gbq_function(f"{dataset_id}.{function_id}") - - # Test on the function from read_gbq_function. - got = foo_ref(10, 38, "hello") - assert got == ["10", "38.0", "hello"] - - with pytest.warns( - bigframes.exceptions.PreviewWarning, - match="axis=1 scenario is in preview.", - ): - bf_result_gbq = bf_df.apply(foo_ref, axis=1).to_pandas() - - pandas.testing.assert_series_equal( - bf_result_gbq, expected_result, check_dtype=False, check_index_type=False - ) - - -@pytest.mark.parametrize( - "connection_fixture", - [ - "bq_connection_name", - "bq_connection", - ], -) -def test_managed_function_with_connection( - session, scalars_dfs, dataset_id, request, connection_fixture, function_id -): - bigquery_connection = request.getfixturevalue(connection_fixture) - - @session.udf( - bigquery_connection=bigquery_connection, - dataset=dataset_id, - name=function_id, - ) - def foo(x: int) -> int: - return x + 10 - - # Function should still work normally. - assert foo(-2) == 8 - - scalars_df, scalars_pandas_df = scalars_dfs - - bf_result_col = scalars_df["int64_too"].apply(foo) - bf_result = ( - scalars_df["int64_too"].to_frame().assign(result=bf_result_col).to_pandas() - ) - - pd_result_col = scalars_pandas_df["int64_too"].apply(foo) - pd_result = scalars_pandas_df["int64_too"].to_frame().assign(result=pd_result_col) - - pandas.testing.assert_frame_equal(bf_result, pd_result, check_dtype=False) - - -def test_managed_function_options(session, dataset_id, scalars_dfs, function_id): - def multiply_five(x: int) -> int: - return x * 5 - - mf_multiply_five = session.udf( - dataset=dataset_id, - name=function_id, - max_batching_rows=100, - container_cpu=2, - container_memory="2Gi", - )(multiply_five) - - scalars_df, scalars_pandas_df = scalars_dfs - - bf_int64_df = scalars_df["int64_col"] - bf_int64_df_filtered = bf_int64_df.dropna() - bf_result = bf_int64_df_filtered.apply(mf_multiply_five).to_pandas() - - pd_int64_df = scalars_pandas_df["int64_col"] - pd_int64_df_filtered = pd_int64_df.dropna() - pd_result = pd_int64_df_filtered.apply(multiply_five) - - pandas.testing.assert_series_equal(bf_result, pd_result, check_dtype=False) - - # Make sure the read_gbq_function path works for this function. - multiply_five_ref = session.read_gbq_function( - function_name=f"{dataset_id}.{function_id}" # type: ignore - ) - - bf_result = bf_int64_df_filtered.apply(multiply_five_ref).to_pandas() - pandas.testing.assert_series_equal(bf_result, pd_result, check_dtype=False) - - # Retrieve the routine and validate its runtime configuration. - routine = session.bqclient.get_routine(f"{dataset_id}.{function_id}") - - # TODO(jialuo): Use the newly exposed class properties instead of - # accessing the hidden _properties after resolve of this issue: - # https://github.com/googleapis/python-bigquery/issues/2240. - assert routine._properties["externalRuntimeOptions"]["maxBatchingRows"] == "100" - assert routine._properties["externalRuntimeOptions"]["containerCpu"] == 2 - assert routine._properties["externalRuntimeOptions"]["containerMemory"] == "2Gi" - - -def test_managed_function_options_errors(session, dataset_id, function_id): - def foo(x: int) -> int: - return 0 - - with pytest.raises( - google.api_core.exceptions.BadRequest, - # For CPU Value >= 1.0, the value must be one of [1, 2, ...]. - match="Invalid container_cpu function OPTIONS value", - ): - session.udf( - dataset=dataset_id, - name=function_id, - max_batching_rows=100, - container_cpu=2.5, - container_memory="2Gi", - )(foo) - - with pytest.raises( - google.api_core.exceptions.BadRequest, - # For less than 1.0 CPU, the value must be no less than 0.33. - match="Invalid container_cpu function OPTIONS value", - ): - session.udf( - dataset=dataset_id, - name=function_id, - max_batching_rows=100, - container_cpu=0.10, - container_memory="512Mi", - )(foo) - - with pytest.raises( - google.api_core.exceptions.BadRequest, - # For 2.00 CPU, the memory must be in the range of [256Mi, 8Gi]. - match="Invalid container_memory function OPTIONS value", - ): - session.udf( - dataset=dataset_id, - name=function_id, - max_batching_rows=100, - container_cpu=2, - container_memory="64Mi", - )(foo) - - -def test_managed_function_df_apply_axis_1( - session, dataset_id, scalars_dfs, function_id -): - columns = ["bool_col", "int64_col", "int64_too", "float64_col", "string_col"] - scalars_df, scalars_pandas_df = scalars_dfs - - def serialize_row(row): - # TODO(b/435021126): Remove explicit type conversion of the field - # "name" after the issue has been addressed. It is added only to - # accept partial pandas parity for the time being. - custom = { - "name": int(row.name), - "index": [idx for idx in row.index], - "values": [ - val.item() if hasattr(val, "item") else val for val in row.values - ], - } - - return str( - { - "default": row.to_json(), - "split": row.to_json(orient="split"), - "records": row.to_json(orient="records"), - "index": row.to_json(orient="index"), - "table": row.to_json(orient="table"), - "custom": custom, - } - ) - - with pytest.raises( - TypeError, - match="Argument type hint must be Pandas Series, not BigFrames Series.", - ): - serialize_row_mf = session.udf( - input_types=bigframes.series.Series, - output_type=str, - dataset=dataset_id, - name=function_id, - )(serialize_row) - - serialize_row_mf = session.udf( - input_types=pandas.Series, - output_type=str, - dataset=dataset_id, - name=function_id, - )(serialize_row) - - bf_result = scalars_df[columns].apply(serialize_row_mf, axis=1).to_pandas() - pd_result = scalars_pandas_df[columns].apply(serialize_row, axis=1) - - # bf_result.dtype is 'string[pyarrow]' while pd_result.dtype is 'object' - # , ignore this mismatch by using check_dtype=False. - pandas.testing.assert_series_equal(pd_result, bf_result, check_dtype=False) - - # Let's make sure the read_gbq_function path works for this function. - serialize_row_reuse = session.read_gbq_function( - f"{dataset_id}.{function_id}", is_row_processor=True - ) - bf_result = scalars_df[columns].apply(serialize_row_reuse, axis=1).to_pandas() - pandas.testing.assert_series_equal(pd_result, bf_result, check_dtype=False) - - -def test_managed_function_df_apply_axis_1_aggregates( - session, dataset_id, scalars_dfs, function_id -): - columns = ["int64_col", "int64_too", "float64_col"] - scalars_df, scalars_pandas_df = scalars_dfs - - def analyze(row): - # TODO(b/435021126): Remove explicit type conversion of the fields - # after the issue has been addressed. It is added only to accept - # partial pandas parity for the time being. - return str( - { - "dtype": row.dtype, - "count": int(row.count()), - "min": int(row.min()), - "max": int(row.max()), - "mean": float(row.mean()), - "std": float(row.std()), - "var": float(row.var()), - } - ) - - with pytest.warns( - bfe.FunctionPackageVersionWarning, - match=( - "numpy, pandas, and pyarrow versions in the function execution" - "\nenvironment may not precisely match your local environment." - ), - ): - analyze_mf = session.udf( - input_types=pandas.Series, - output_type=str, - dataset=dataset_id, - name=function_id, - )(analyze) - - bf_result = scalars_df[columns].dropna().apply(analyze_mf, axis=1).to_pandas() - pd_result = scalars_pandas_df[columns].dropna().apply(analyze, axis=1) - - # bf_result.dtype is 'string[pyarrow]' while pd_result.dtype is 'object' - # , ignore this mismatch by using check_dtype=False. - pandas.testing.assert_series_equal(pd_result, bf_result, check_dtype=False) - - -@pytest.mark.parametrize( - ("pd_df",), - [ - pytest.param( - pandas.DataFrame( - { - "2": [1, 2, 3], - 2: [1.5, 3.75, 5], - "name, [with. special'- chars\")/\\": [10, 20, 30], - (3, 4): ["pq", "rs", "tu"], - (5.0, "six", 7): [8, 9, 10], - 'raise Exception("hacked!")': [11, 12, 13], - }, - # Default pandas index has non-numpy type, whereas bigframes is - # always numpy-based type, so let's use the index compatible - # with bigframes. See more details in b/369689696. - index=pandas.Index([0, 1, 2], dtype=pandas.Int64Dtype()), - ), - id="all-kinds-of-column-names", - ), - pytest.param( - pandas.DataFrame( - { - "x": [1, 2, 3], - "y": [1.5, 3.75, 5], - "z": ["pq", "rs", "tu"], - }, - index=pandas.MultiIndex.from_frame( - pandas.DataFrame( - { - "idx0": pandas.Series( - ["a", "a", "b"], dtype=pandas.StringDtype() - ), - "idx1": pandas.Series( - [100, 200, 300], dtype=pandas.Int64Dtype() - ), - } - ) - ), - ), - id="multiindex", - marks=pytest.mark.skip( - reason="TODO: revert this skip after this pandas bug is fixed: https://github.com/pandas-dev/pandas/issues/59908" - ), - ), - pytest.param( - pandas.DataFrame( - [ - [10, 1.5, "pq"], - [20, 3.75, "rs"], - [30, 8.0, "tu"], - ], - # Default pandas index has non-numpy type, whereas bigframes is - # always numpy-based type, so let's use the index compatible - # with bigframes. See more details in b/369689696. - index=pandas.Index([0, 1, 2], dtype=pandas.Int64Dtype()), - columns=pandas.MultiIndex.from_arrays( - [ - ["first", "last_two", "last_two"], - [1, 2, 3], - ] - ), - ), - id="column-multiindex", - ), - ], -) -def test_managed_function_df_apply_axis_1_complex( - session, dataset_id, pd_df, function_id -): - bf_df = session.read_pandas(pd_df) - - def serialize_row(row): - # TODO(b/435021126): Remove explicit type conversion of the field - # "name" after the issue has been addressed. It is added only to - # accept partial pandas parity for the time being. - custom = { - "name": int(row.name), - "index": [idx for idx in row.index], - "values": [ - val.item() if hasattr(val, "item") else val for val in row.values - ], - } - return str( - { - "default": row.to_json(), - "split": row.to_json(orient="split"), - "records": row.to_json(orient="records"), - "index": row.to_json(orient="index"), - "custom": custom, - } - ) - - serialize_row_mf = session.udf( - input_types=pandas.Series, - output_type=str, - dataset=dataset_id, - name=function_id, - )(serialize_row) - - bf_result = bf_df.apply(serialize_row_mf, axis=1).to_pandas() - pd_result = pd_df.apply(serialize_row, axis=1) - - # ignore known dtype difference between pandas and bigframes. - pandas.testing.assert_series_equal( - pd_result, bf_result, check_dtype=False, check_index_type=False - ) - - -@pytest.mark.skip(reason="Revert after this bug b/435018880 is fixed.") -def test_managed_function_df_apply_axis_1_na_nan_inf(dataset_id, session, function_id): - """This test is for special cases of float values, to make sure any (nan, - inf, -inf) produced by user code is honored. - """ - bf_df = session.read_gbq( - """\ -SELECT "1" AS text, 1 AS num -UNION ALL -SELECT "2.5" AS text, 2.5 AS num -UNION ALL -SELECT "nan" AS text, IEEE_DIVIDE(0, 0) AS num -UNION ALL -SELECT "inf" AS text, IEEE_DIVIDE(1, 0) AS num -UNION ALL -SELECT "-inf" AS text, IEEE_DIVIDE(-1, 0) AS num -UNION ALL -SELECT "numpy nan" AS text, IEEE_DIVIDE(0, 0) AS num -UNION ALL -SELECT "pandas na" AS text, NULL AS num - """ - ) - - pd_df = bf_df.to_pandas() - - def float_parser(row: pandas.Series): - import numpy as mynp - import pandas as mypd - - if row["text"] == "pandas na": - return mypd.NA - if row["text"] == "numpy nan": - return mynp.nan - return float(row["text"]) - - float_parser_mf = session.udf( - input_types=pandas.Series, - output_type=float, - dataset=dataset_id, - name=function_id, - )(float_parser) - - pd_result = pd_df.apply(float_parser, axis=1) - bf_result = bf_df.apply(float_parser_mf, axis=1).to_pandas() - - # bf_result.dtype is 'Float64' while pd_result.dtype is 'object' - # , ignore this mismatch by using check_dtype=False. - pandas.testing.assert_series_equal(pd_result, bf_result, check_dtype=False) - - # Let's also assert that the data is consistent in this round trip - # (BQ -> BigFrames -> BQ -> GCF -> BQ -> BigFrames) w.r.t. their - # expected values in BQ. - bq_result = bf_df["num"].to_pandas() - bq_result.name = None - pandas.testing.assert_series_equal(bq_result, bf_result) - - -def test_managed_function_df_apply_axis_1_args( - session, dataset_id, scalars_dfs, function_id -): - columns = ["int64_col", "int64_too"] - scalars_df, scalars_pandas_df = scalars_dfs - - def the_sum(s1, s2, x): - return s1 + s2 + x - - the_sum_mf = session.udf( - input_types=[int, int, int], - output_type=int, - dataset=dataset_id, - name=function_id, - )(the_sum) - - args1 = (1,) - - # Fails to apply on dataframe with incompatible number of columns and args. - with pytest.raises( - ValueError, - match="^Parameter count mismatch:.* expected 3 parameters but received 4 values \\(3 DataFrame columns and 1 args\\)", - ): - scalars_df[columns + ["float64_col"]].apply(the_sum_mf, axis=1, args=args1) - - # Fails to apply on dataframe with incompatible column datatypes. - with pytest.raises( - ValueError, - match="^Data type mismatch for DataFrame columns: Expected .* Received .*", - ): - scalars_df[columns].assign( - int64_col=lambda df: df["int64_col"].astype("Float64") - ).apply(the_sum_mf, axis=1, args=args1) - - # Fails to apply on dataframe with incompatible args datatypes. - with pytest.raises( - ValueError, - match="^Data type mismatch for 'args' parameter: Expected .* Received .*", - ): - scalars_df[columns].apply(the_sum_mf, axis=1, args=(1.3,)) - - bf_result = ( - scalars_df[columns].dropna().apply(the_sum_mf, axis=1, args=args1).to_pandas() - ) - pd_result = scalars_pandas_df[columns].dropna().apply(sum, axis=1, args=args1) - - pandas.testing.assert_series_equal(pd_result, bf_result, check_dtype=False) - - -def test_managed_function_df_apply_axis_1_series_args( - session, dataset_id, scalars_dfs, function_id -): - columns = ["int64_col", "float64_col"] - scalars_df, scalars_pandas_df = scalars_dfs - - def analyze(s: pandas.Series, x: bool, y: float) -> str: - value = f"value is {s['int64_col']} and {s['float64_col']}" - if x: - return f"{value}, x is True!" - if y > 0: - return f"{value}, x is False, y is positive!" - return f"{value}, x is False, y is non-positive!" - - analyze_mf = session.udf( - dataset=dataset_id, - name=function_id, - )(analyze) - - args1 = (True, 10.0) - bf_result = ( - scalars_df[columns].dropna().apply(analyze_mf, axis=1, args=args1).to_pandas() - ) - pd_result = scalars_pandas_df[columns].dropna().apply(analyze, axis=1, args=args1) - - pandas.testing.assert_series_equal(pd_result, bf_result, check_dtype=False) - - args2 = (False, -10.0) - analyze_mf_ref = session.read_gbq_function( - f"{dataset_id}.{function_id}", is_row_processor=True - ) - bf_result = ( - scalars_df[columns] - .dropna() - .apply(analyze_mf_ref, axis=1, args=args2) - .to_pandas() - ) - pd_result = scalars_pandas_df[columns].dropna().apply(analyze, axis=1, args=args2) - - pandas.testing.assert_series_equal(pd_result, bf_result, check_dtype=False) - - -def test_managed_function_df_where_mask(session, dataset_id, scalars_dfs, function_id): - # The return type has to be bool type for callable where condition. - def is_sum_positive(a, b): - return a + b > 0 - - is_sum_positive_mf = session.udf( - input_types=[int, int], - output_type=bool, - dataset=dataset_id, - name=function_id, - )(is_sum_positive) - - scalars_df, scalars_pandas_df = scalars_dfs - int64_cols = ["int64_col", "int64_too"] - - bf_int64_df = scalars_df[int64_cols] - bf_int64_df_filtered = bf_int64_df.dropna() - pd_int64_df = scalars_pandas_df[int64_cols] - pd_int64_df_filtered = pd_int64_df.dropna() - - # Test callable condition in dataframe.where method. - bf_result = bf_int64_df_filtered.where(is_sum_positive_mf).to_pandas() - # Pandas doesn't support such case, use following as workaround. - pd_result = pd_int64_df_filtered.where(pd_int64_df_filtered.sum(axis=1) > 0) - - # Ignore any dtype difference. - pandas.testing.assert_frame_equal(bf_result, pd_result, check_dtype=False) - - # Make sure the read_gbq_function path works for dataframe.where method. - is_sum_positive_ref = session.read_gbq_function(f"{dataset_id}.{function_id}") - - bf_result_gbq = bf_int64_df_filtered.where( - is_sum_positive_ref, -bf_int64_df_filtered - ).to_pandas() - pd_result_gbq = pd_int64_df_filtered.where( - pd_int64_df_filtered.sum(axis=1) > 0, -pd_int64_df_filtered - ) - - # Ignore any dtype difference. - pandas.testing.assert_frame_equal(bf_result_gbq, pd_result_gbq, check_dtype=False) - - # Test callable condition in dataframe.mask method. - bf_result_gbq = bf_int64_df_filtered.mask( - is_sum_positive_ref, -bf_int64_df_filtered - ).to_pandas() - pd_result_gbq = pd_int64_df_filtered.mask( - pd_int64_df_filtered.sum(axis=1) > 0, -pd_int64_df_filtered - ) - - # Ignore any dtype difference. - pandas.testing.assert_frame_equal(bf_result_gbq, pd_result_gbq, check_dtype=False) - - -def test_managed_function_df_where_mask_series( - session, dataset_id, scalars_dfs, function_id -): - # The return type has to be bool type for callable where condition. - def is_sum_positive_series(s): - return s["int64_col"] + s["int64_too"] > 0 - - is_sum_positive_series_mf = session.udf( - input_types=pandas.Series, - output_type=bool, - dataset=dataset_id, - name=function_id, - )(is_sum_positive_series) - - scalars_df, scalars_pandas_df = scalars_dfs - int64_cols = ["int64_col", "int64_too"] - - bf_int64_df = scalars_df[int64_cols] - bf_int64_df_filtered = bf_int64_df.dropna() - pd_int64_df = scalars_pandas_df[int64_cols] - pd_int64_df_filtered = pd_int64_df.dropna() - - # Test callable condition in dataframe.where method. - bf_result = bf_int64_df_filtered.where(is_sum_positive_series_mf).to_pandas() - pd_result = pd_int64_df_filtered.where(is_sum_positive_series) - - # Ignore any dtype difference. - pandas.testing.assert_frame_equal(bf_result, pd_result, check_dtype=False) - - # Make sure the read_gbq_function path works for dataframe.where method. - is_sum_positive_series_ref = session.read_gbq_function( - f"{dataset_id}.{function_id}", is_row_processor=True - ) - - # This is for callable `other` arg in dataframe.where method. - def func_for_other(x): - return -x - - bf_result_gbq = bf_int64_df_filtered.where( - is_sum_positive_series_ref, func_for_other - ).to_pandas() - pd_result_gbq = pd_int64_df_filtered.where(is_sum_positive_series, func_for_other) - - # Ignore any dtype difference. - pandas.testing.assert_frame_equal(bf_result_gbq, pd_result_gbq, check_dtype=False) - - # Test callable condition in dataframe.mask method. - bf_result_gbq = bf_int64_df_filtered.mask( - is_sum_positive_series_ref, func_for_other - ).to_pandas() - pd_result_gbq = pd_int64_df_filtered.mask(is_sum_positive_series, func_for_other) - - # Ignore any dtype difference. - pandas.testing.assert_frame_equal(bf_result_gbq, pd_result_gbq, check_dtype=False) - - -def test_managed_function_df_where_other_issue( - session, dataset_id, scalars_df_index, function_id -): - def the_sum(s: pandas.Series) -> int: - return s["int64_col"] + s["int64_too"] - - the_sum_mf = session.udf( - dataset=dataset_id, - name=function_id, - )(the_sum) - - int64_cols = ["int64_col", "int64_too"] - - bf_int64_df = scalars_df_index[int64_cols] - bf_int64_df_filtered = bf_int64_df.dropna() - - with pytest.raises( - ValueError, - match="Seires is not a supported replacement type!", - ): - # The execution of the callable other=the_sum_mf will return a - # Series, which is not a supported replacement type. - bf_int64_df_filtered.where(cond=bf_int64_df_filtered, other=the_sum_mf) - - -def test_managed_function_series_where_mask_map( - session, dataset_id, scalars_dfs, function_id -): - # The return type has to be bool type for callable where condition. - def _is_positive(s): - return s + 1000 > 0 - - is_positive_mf = session.udf( - input_types=int, - output_type=bool, - dataset=dataset_id, - name=function_id, - )(_is_positive) - - scalars, scalars_pandas = scalars_dfs - - bf_int64 = scalars["int64_col"] - bf_int64_filtered = bf_int64.dropna() - pd_int64 = scalars_pandas["int64_col"] - pd_int64_filtered = pd_int64.dropna() - - # Test series.where method: the cond is a callable (managed function) - # and the other is not a callable. - bf_result = bf_int64_filtered.where( - cond=is_positive_mf, other=-bf_int64_filtered - ).to_pandas() - pd_result = pd_int64_filtered.where(cond=_is_positive, other=-pd_int64_filtered) - - # Ignore any dtype difference. - pandas.testing.assert_series_equal(bf_result, pd_result, check_dtype=False) - - # Test series.mask method: the cond is a callable (managed function) - # and the other is not a callable. - bf_result = bf_int64_filtered.mask( - cond=is_positive_mf, other=-bf_int64_filtered - ).to_pandas() - pd_result = pd_int64_filtered.mask(cond=_is_positive, other=-pd_int64_filtered) - - # Ignore any dtype difference. - pandas.testing.assert_series_equal(bf_result, pd_result, check_dtype=False) - - # Test series.map method. - bf_result = bf_int64_filtered.map(is_positive_mf).to_pandas() - pd_result = pd_int64_filtered.map(_is_positive) - - # Ignore any dtype difference. - pandas.testing.assert_series_equal(bf_result, pd_result, check_dtype=False) - - -def test_managed_function_series_apply_args( - session, dataset_id, scalars_dfs, function_id -): - with pytest.warns(bfe.PreviewWarning, match="udf is in preview."): - - @session.udf(dataset=dataset_id, name=function_id) - def foo_list(x: int, y0: float, y1: bytes, y2: bool) -> list[str]: - return [str(x), str(y0), str(y1), str(y2)] - - scalars_df, scalars_pandas_df = scalars_dfs - - bf_result = ( - scalars_df["int64_too"] - .apply(foo_list, args=(12.34, b"hello world", False)) - .to_pandas() - ) - pd_result = scalars_pandas_df["int64_too"].apply( - foo_list, args=(12.34, b"hello world", False) - ) - - # Ignore any dtype difference. - pandas.testing.assert_series_equal(bf_result, pd_result, check_dtype=False) - - -def test_deferred_unnamed_udf_execution(session, scalars_dfs): - import bigframes.functions.udf_def as udf_def - - # Create an unnamed UDF (name=None) - @session.udf() - def unnamed_multiplier(x: int) -> int: - return x * 3 - - assert isinstance(unnamed_multiplier.udf_def, udf_def.PythonUdf) - - scalars_df, scalars_pandas_df = scalars_dfs - bf_series = scalars_df["int64_too"] - pd_series = scalars_pandas_df["int64_too"] - - bf_result = bf_series.apply(unnamed_multiplier).to_pandas() - pd_result = pd_series.apply(lambda x: x * 3) - - pandas.testing.assert_series_equal(bf_result, pd_result, check_dtype=False) - - import bigframes.functions._function_session as functions_sessions - - config = unnamed_multiplier.udf_def.to_managed_function_config() - expected_routine_name = functions_sessions.get_managed_function_name( - config, session.session_id - ) - routine = session.bqclient.get_routine( - f"{session._anonymous_dataset.project}.{session._anonymous_dataset.dataset_id}.{expected_routine_name}" - ) - assert routine is not None - - -def test_deferred_udf_with_runtime_requirements(session, scalars_dfs): - import bigframes.functions.udf_def as udf_def - - # Create an unnamed UDF with custom options - @session.udf( - container_cpu=1, - container_memory="2Gi", - max_batching_rows=25, - ) - def heavy_unnamed_udf(x: int) -> int: - return x + 100 - - assert isinstance(heavy_unnamed_udf.udf_def, udf_def.PythonUdf) - - scalars_df, scalars_pandas_df = scalars_dfs - bf_series = scalars_df["int64_too"] - pd_series = scalars_pandas_df["int64_too"] - - bf_result = bf_series.apply(heavy_unnamed_udf).to_pandas() - pd_result = pd_series.apply(lambda x: x + 100) - - pandas.testing.assert_series_equal(bf_result, pd_result, check_dtype=False) - - # Verify it was deployed with the correct runtime options - import bigframes.functions._function_session as functions_sessions - - config = heavy_unnamed_udf.udf_def.to_managed_function_config() - expected_routine_name = functions_sessions.get_managed_function_name( - config, session.session_id - ) - routine = session.bqclient.get_routine( - f"{session._anonymous_dataset.project}.{session._anonymous_dataset.dataset_id}.{expected_routine_name}" - ) - assert routine._properties["externalRuntimeOptions"]["containerCpu"] == 1 - assert routine._properties["externalRuntimeOptions"]["containerMemory"] == "2Gi" - assert routine._properties["externalRuntimeOptions"]["maxBatchingRows"] == "25" diff --git a/tests/system/large/functions/test_remote_function.py b/tests/system/large/functions/test_remote_function.py deleted file mode 100644 index 69769a1a846..00000000000 --- a/tests/system/large/functions/test_remote_function.py +++ /dev/null @@ -1,3265 +0,0 @@ -# Copyright 2023 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import importlib.util -import inspect -import math # must keep this at top level to test udf referring global import -import os.path -import shutil -import tempfile -import textwrap -import uuid -import warnings -from datetime import datetime - -import google.api_core.exceptions -import pandas -import pytest -import test_utils.prefixer -from google.cloud import bigquery, functions_v2, storage - -import bigframes -import bigframes.dataframe -import bigframes.dtypes -import bigframes.exceptions -import bigframes.pandas as bpd -import bigframes.series -from bigframes.testing.utils import ( - assert_frame_equal, - cleanup_function_assets, - delete_cloud_function, - get_cloud_functions, -) - -# NOTE: Keep this import at the top level to test global var behavior with -# remote functions -_team_pi = "Team Pi" -_team_euler = "Team Euler" - - -def make_uniq_udf(udf): - """Transform a udf to another with same behavior but a unique name. - Use this to test remote functions with reuse=True, in which case parallel - instances of the same tests may evaluate same named cloud functions and BQ - remote functions, therefore interacting with each other and causing unwanted - failures. With this method one can transform a udf into another with the - same behavior but a different name which will remain unique for the - lifetime of one test instance. - """ - - prefixer = test_utils.prefixer.Prefixer(udf.__name__, "") - udf_uniq_name = prefixer.create_prefix() - udf_file_name = f"{udf_uniq_name}.py" - - # We are not using `tempfile.TemporaryDirectory()` because we want to keep - # the temp code around, otherwise `inspect.getsource()` complains. - tmpdir = tempfile.mkdtemp() - udf_file_path = os.path.join(tmpdir, udf_file_name) - with open(udf_file_path, "w") as f: - # TODO(shobs): Find a better way of modifying the udf, maybe regex? - source_key = f"def {udf.__name__}" - target_key = f"def {udf_uniq_name}" - source_code = textwrap.dedent(inspect.getsource(udf)) - target_code = source_code.replace(source_key, target_key, 1) - f.write(target_code) - spec = importlib.util.spec_from_file_location(udf_file_name, udf_file_path) - - assert (spec is not None) and (spec.loader is not None) - module = importlib.util.module_from_spec(spec) - - # exec_module fills the module object with all the functions, classes, and - # variables defined in the module file. - spec.loader.exec_module(module) - udf_uniq = getattr(module, udf_uniq_name) - - return udf_uniq, tmpdir - - -@pytest.fixture(scope="module") -def bq_cf_connection() -> str: - """Pre-created BQ connection in the test project in US location, used to - invoke cloud function. - - $ bq show --connection --location=us --project_id=PROJECT_ID bigframes-rf-conn - """ - return "bigframes-rf-conn" - - -@pytest.mark.flaky(retries=2, delay=120) -def test_remote_function_binop(session, scalars_dfs, dataset_id, bq_cf_connection): - try: - - def func(x, y): - return x * abs(y % 4) - - remote_func = session.remote_function( - # Make sure that the input/output types can be used positionally. - # This avoids the worst of the breaking change from 1.x to 2.x. - [str, int], - str, - dataset_id, - bigquery_connection=bq_cf_connection, - reuse=False, - cloud_function_service_account="default", - )(func) - - scalars_df, scalars_pandas_df = scalars_dfs - - scalars_df = scalars_df.dropna() - scalars_pandas_df = scalars_pandas_df.dropna() - bf_result = ( - scalars_df["string_col"] - .combine(scalars_df["int64_col"], remote_func) - .to_pandas() - ) - pd_result = scalars_pandas_df["string_col"].combine( - scalars_pandas_df["int64_col"], func - ) - pandas.testing.assert_series_equal(bf_result, pd_result) - finally: - # clean up the gcp assets created for the remote function - cleanup_function_assets( - remote_func, session.bqclient, session.cloudfunctionsclient - ) - - -@pytest.mark.flaky(retries=2, delay=120) -def test_remote_function_binop_array_output( - session, scalars_dfs, dataset_id, bq_cf_connection -): - try: - - def func(x, y): - return [len(x), abs(y % 4)] - - remote_func = session.remote_function( - # Make sure that the input/output types can be used positionally. - # This avoids the worst of the breaking change from 1.x to 2.x. - [str, int], - list[int], - dataset_id, - bigquery_connection=bq_cf_connection, - reuse=False, - cloud_function_service_account="default", - )(func) - - scalars_df, scalars_pandas_df = scalars_dfs - - scalars_df = scalars_df.dropna() - scalars_pandas_df = scalars_pandas_df.dropna() - bf_result = ( - scalars_df["string_col"] - .combine(scalars_df["int64_col"], remote_func) - .to_pandas() - ) - pd_result = scalars_pandas_df["string_col"].combine( - scalars_pandas_df["int64_col"], func - ) - pandas.testing.assert_series_equal(bf_result, pd_result, check_dtype=False) - finally: - # clean up the gcp assets created for the remote function - cleanup_function_assets( - remote_func, session.bqclient, session.cloudfunctionsclient - ) - - -@pytest.mark.flaky(retries=2, delay=120) -def test_remote_function_decorator_with_bigframes_series( - session, scalars_dfs, dataset_id, bq_cf_connection -): - try: - - @session.remote_function( - # Make sure that the input/output types can be used positionally. - # This avoids the worst of the breaking change from 1.x to 2.x. - [int], - int, - dataset_id, - bigquery_connection=bq_cf_connection, - reuse=False, - cloud_function_service_account="default", - ) - def square(x): - return x * x - - scalars_df, scalars_pandas_df = scalars_dfs - - bf_int64_col = scalars_df["int64_col"] - bf_int64_col_filter = bf_int64_col.notnull() - bf_int64_col_filtered = bf_int64_col[bf_int64_col_filter] - bf_result_col = bf_int64_col_filtered.apply(square) - bf_result = ( - bf_int64_col_filtered.to_frame().assign(result=bf_result_col).to_pandas() - ) - - pd_int64_col = scalars_pandas_df["int64_col"] - pd_int64_col_filter = pd_int64_col.notnull() - pd_int64_col_filtered = pd_int64_col[pd_int64_col_filter] - pd_result_col = pd_int64_col_filtered.apply(lambda x: x * x) - # TODO(shobs): Figure why pandas .apply() changes the dtype, i.e. - # pd_int64_col_filtered.dtype is Int64Dtype() - # pd_int64_col_filtered.apply(lambda x: x * x).dtype is int64. - # For this test let's force the pandas dtype to be same as bigframes' dtype. - pd_result_col = pd_result_col.astype(pandas.Int64Dtype()) - pd_result = pd_int64_col_filtered.to_frame().assign(result=pd_result_col) - - assert_frame_equal(bf_result, pd_result) - finally: - # clean up the gcp assets created for the remote function - cleanup_function_assets(square, session.bqclient, session.cloudfunctionsclient) - - -@pytest.mark.flaky(retries=2, delay=120) -def test_remote_function_explicit_with_bigframes_series( - session, scalars_dfs, dataset_id, bq_cf_connection -): - try: - - def add_one(x): - return x + 1 - - remote_add_one = session.remote_function( - # Make sure that the input/output types can be used positionally. - # This avoids the worst of the breaking change from 1.x to 2.x. - [int], - int, - dataset_id, - bigquery_connection=bq_cf_connection, - reuse=False, - cloud_function_service_account="default", - )(add_one) - - scalars_df, scalars_pandas_df = scalars_dfs - - bf_int64_col = scalars_df["int64_col"] - bf_int64_col_filter = bf_int64_col.notnull() - bf_int64_col_filtered = bf_int64_col[bf_int64_col_filter] - bf_result_col = bf_int64_col_filtered.apply(remote_add_one) - bf_result = ( - bf_int64_col_filtered.to_frame().assign(result=bf_result_col).to_pandas() - ) - - pd_int64_col = scalars_pandas_df["int64_col"] - pd_int64_col_filter = pd_int64_col.notnull() - pd_int64_col_filtered = pd_int64_col[pd_int64_col_filter] - pd_result_col = pd_int64_col_filtered.apply(add_one) - # TODO(shobs): Figure why pandas .apply() changes the dtype, e.g. - # pd_int64_col_filtered.dtype is Int64Dtype() - # pd_int64_col_filtered.apply(lambda x: x).dtype is int64. - # For this test let's force the pandas dtype to be same as bigframes' dtype. - pd_result_col = pd_result_col.astype(pandas.Int64Dtype()) - pd_result = pd_int64_col_filtered.to_frame().assign(result=pd_result_col) - - assert_frame_equal(bf_result, pd_result) - finally: - # clean up the gcp assets created for the remote function - cleanup_function_assets( - remote_add_one, session.bqclient, session.cloudfunctionsclient - ) - - -@pytest.mark.parametrize( - ("input_types"), - [ - pytest.param([int], id="list-of-int"), - pytest.param(int, id="int"), - ], -) -@pytest.mark.flaky(retries=2, delay=120) -def test_remote_function_input_types(session, scalars_dfs, input_types): - try: - - def add_one(x): - return x + 1 - - remote_add_one = session.remote_function( - # Make sure that the input/output types can be used positionally. - # This avoids the worst of the breaking change from 1.x to 2.x. - input_types, - int, - reuse=False, - cloud_function_service_account="default", - )(add_one) - assert remote_add_one.input_dtypes == (bigframes.dtypes.INT_DTYPE,) - - scalars_df, scalars_pandas_df = scalars_dfs - - bf_result = scalars_df.int64_too.map(remote_add_one).to_pandas() - pd_result = scalars_pandas_df.int64_too.map(add_one) - - pandas.testing.assert_series_equal(bf_result, pd_result, check_dtype=False) - finally: - # clean up the gcp assets created for the remote function - cleanup_function_assets( - remote_add_one, session.bqclient, session.cloudfunctionsclient - ) - - -@pytest.mark.flaky(retries=2, delay=120) -def test_remote_function_explicit_dataset_not_created( - session, - scalars_dfs, - dataset_id_not_created, - bq_cf_connection, -): - try: - - @session.remote_function( - # Make sure that the input/output types can be used positionally. - # This avoids the worst of the breaking change from 1.x to 2.x. - [int], - int, - dataset=dataset_id_not_created, - bigquery_connection=bq_cf_connection, - reuse=False, - cloud_function_service_account="default", - ) - def square(x): - return x * x - - scalars_df, scalars_pandas_df = scalars_dfs - - bf_int64_col = scalars_df["int64_col"] - bf_int64_col_filter = bf_int64_col.notnull() - bf_int64_col_filtered = bf_int64_col[bf_int64_col_filter] - bf_result_col = bf_int64_col_filtered.apply(square) - bf_result = ( - bf_int64_col_filtered.to_frame().assign(result=bf_result_col).to_pandas() - ) - - pd_int64_col = scalars_pandas_df["int64_col"] - pd_int64_col_filter = pd_int64_col.notnull() - pd_int64_col_filtered = pd_int64_col[pd_int64_col_filter] - pd_result_col = pd_int64_col_filtered.apply(lambda x: x * x) - # TODO(shobs): Figure why pandas .apply() changes the dtype, i.e. - # pd_int64_col_filtered.dtype is Int64Dtype() - # pd_int64_col_filtered.apply(lambda x: x * x).dtype is int64. - # For this test let's force the pandas dtype to be same as bigframes' dtype. - pd_result_col = pd_result_col.astype(pandas.Int64Dtype()) - pd_result = pd_int64_col_filtered.to_frame().assign(result=pd_result_col) - - assert_frame_equal(bf_result, pd_result) - finally: - # clean up the gcp assets created for the remote function - cleanup_function_assets(square, session.bqclient, session.cloudfunctionsclient) - - -@pytest.mark.flaky(retries=2, delay=120) -def test_remote_udf_referring_outside_var( - session, scalars_dfs, dataset_id, bq_cf_connection -): - try: - POSITIVE_SIGN = 1 - NEGATIVE_SIGN = -1 - NO_SIGN = 0 - - def sign(num): - if num > 0: - return POSITIVE_SIGN - elif num < 0: - return NEGATIVE_SIGN - return NO_SIGN - - remote_sign = session.remote_function( - # Make sure that the input/output types can be used positionally. - # This avoids the worst of the breaking change from 1.x to 2.x. - [int], - int, - dataset_id, - bigquery_connection=bq_cf_connection, - reuse=False, - cloud_function_service_account="default", - )(sign) - - scalars_df, scalars_pandas_df = scalars_dfs - - bf_int64_col = scalars_df["int64_col"] - bf_int64_col_filter = bf_int64_col.notnull() - bf_int64_col_filtered = bf_int64_col[bf_int64_col_filter] - bf_result_col = bf_int64_col_filtered.apply(remote_sign) - bf_result = ( - bf_int64_col_filtered.to_frame().assign(result=bf_result_col).to_pandas() - ) - - pd_int64_col = scalars_pandas_df["int64_col"] - pd_int64_col_filter = pd_int64_col.notnull() - pd_int64_col_filtered = pd_int64_col[pd_int64_col_filter] - pd_result_col = pd_int64_col_filtered.apply(sign) - # TODO(shobs): Figure why pandas .apply() changes the dtype, e.g. - # pd_int64_col_filtered.dtype is Int64Dtype() - # pd_int64_col_filtered.apply(lambda x: x).dtype is int64. - # For this test let's force the pandas dtype to be same as bigframes' dtype. - pd_result_col = pd_result_col.astype(pandas.Int64Dtype()) - pd_result = pd_int64_col_filtered.to_frame().assign(result=pd_result_col) - - assert_frame_equal(bf_result, pd_result) - finally: - # clean up the gcp assets created for the remote function - cleanup_function_assets( - remote_sign, session.bqclient, session.cloudfunctionsclient - ) - - -@pytest.mark.flaky(retries=2, delay=120) -def test_remote_udf_referring_outside_import( - session, scalars_dfs, dataset_id, bq_cf_connection -): - try: - import math as mymath - - def circumference(radius): - return 2 * mymath.pi * radius - - remote_circumference = session.remote_function( - # Make sure that the input/output types can be used positionally. - # This avoids the worst of the breaking change from 1.x to 2.x. - [float], - float, - dataset_id, - bigquery_connection=bq_cf_connection, - reuse=False, - cloud_function_service_account="default", - )(circumference) - - scalars_df, scalars_pandas_df = scalars_dfs - - bf_float64_col = scalars_df["float64_col"] - bf_float64_col_filter = bf_float64_col.notnull() - bf_float64_col_filtered = bf_float64_col[bf_float64_col_filter] - bf_result_col = bf_float64_col_filtered.apply(remote_circumference) - bf_result = ( - bf_float64_col_filtered.to_frame().assign(result=bf_result_col).to_pandas() - ) - - pd_float64_col = scalars_pandas_df["float64_col"] - pd_float64_col_filter = pd_float64_col.notnull() - pd_float64_col_filtered = pd_float64_col[pd_float64_col_filter] - pd_result_col = pd_float64_col_filtered.apply(circumference) - # TODO(shobs): Figure why pandas .apply() changes the dtype, e.g. - # pd_float64_col_filtered.dtype is Float64Dtype() - # pd_float64_col_filtered.apply(lambda x: x).dtype is float64. - # For this test let's force the pandas dtype to be same as bigframes' dtype. - pd_result_col = pd_result_col.astype(pandas.Float64Dtype()) - pd_result = pd_float64_col_filtered.to_frame().assign(result=pd_result_col) - - assert_frame_equal(bf_result, pd_result) - finally: - # clean up the gcp assets created for the remote function - cleanup_function_assets( - remote_circumference, session.bqclient, session.cloudfunctionsclient - ) - - -@pytest.mark.flaky(retries=2, delay=120) -def test_remote_udf_referring_global_var_and_import( - session, scalars_dfs, dataset_id, bq_cf_connection -): - try: - - def find_team(num): - boundary = (math.pi + math.e) / 2 - if num >= boundary: - return _team_euler - return _team_pi - - remote_find_team = session.remote_function( - input_types=[float], - output_type=str, - dataset=dataset_id, - bigquery_connection=bq_cf_connection, - reuse=False, - cloud_function_service_account="default", - )(find_team) - - scalars_df, scalars_pandas_df = scalars_dfs - - bf_float64_col = scalars_df["float64_col"] - bf_float64_col_filter = bf_float64_col.notnull() - bf_float64_col_filtered = bf_float64_col[bf_float64_col_filter] - bf_result_col = bf_float64_col_filtered.apply(remote_find_team) - bf_result = ( - bf_float64_col_filtered.to_frame().assign(result=bf_result_col).to_pandas() - ) - - pd_float64_col = scalars_pandas_df["float64_col"] - pd_float64_col_filter = pd_float64_col.notnull() - pd_float64_col_filtered = pd_float64_col[pd_float64_col_filter] - pd_result_col = pd_float64_col_filtered.apply(find_team) - # TODO(shobs): Figure if the dtype mismatch is by design: - # bf_result.dtype: string[pyarrow] - # pd_result.dtype: dtype('O'). - # For this test let's force the pandas dtype to be same as bigframes' dtype. - pd_result_col = pd_result_col.astype(pandas.StringDtype(storage="pyarrow")) - pd_result = pd_float64_col_filtered.to_frame().assign(result=pd_result_col) - - assert_frame_equal(bf_result, pd_result) - finally: - # clean up the gcp assets created for the remote function - cleanup_function_assets( - remote_find_team, session.bqclient, session.cloudfunctionsclient - ) - - -@pytest.mark.flaky(retries=2, delay=120) -def test_remote_function_restore_with_bigframes_series( - session, - scalars_dfs, - dataset_id, - bq_cf_connection, -): - try: - - def add_one(x): - return x + 1 - - # Make a unique udf - add_one_uniq, add_one_uniq_dir = make_uniq_udf(add_one) - - # The first time both the cloud function and the bq remote function don't - # exist and would be created - remote_add_one = session.remote_function( - input_types=[int], - output_type=int, - dataset=dataset_id, - bigquery_connection=bq_cf_connection, - reuse=True, - cloud_function_service_account="default", - )(add_one_uniq) - - assert remote_add_one.bigframes_cloud_function is not None - add_one_uniq_cf_name = remote_add_one.bigframes_cloud_function.split("/")[-1] - - # There should have been excactly one cloud function created at this point - cloud_functions = list( - get_cloud_functions( - session.cloudfunctionsclient, - session.bqclient.project, - session.bqclient.location, - name=add_one_uniq_cf_name, - ) - ) - assert len(cloud_functions) == 1 - - # We will test this twice - def inner_test(): - scalars_df, scalars_pandas_df = scalars_dfs - - bf_int64_col = scalars_df["int64_col"] - bf_int64_col_filter = bf_int64_col.notnull() - bf_int64_col_filtered = bf_int64_col[bf_int64_col_filter] - bf_result_col = bf_int64_col_filtered.apply(remote_add_one) - bf_result = ( - bf_int64_col_filtered.to_frame() - .assign(result=bf_result_col) - .to_pandas() - ) - - pd_int64_col = scalars_pandas_df["int64_col"] - pd_int64_col_filter = pd_int64_col.notnull() - pd_int64_col_filtered = pd_int64_col[pd_int64_col_filter] - pd_result_col = pd_int64_col_filtered.apply(add_one_uniq) - # TODO(shobs): Figure why pandas .apply() changes the dtype, i.e. - # pd_int64_col_filtered.dtype is Int64Dtype() - # pd_int64_col_filtered.apply(lambda x: x * x).dtype is int64. - # For this test let's force the pandas dtype to be same as bigframes' dtype. - pd_result_col = pd_result_col.astype(pandas.Int64Dtype()) - pd_result = pd_int64_col_filtered.to_frame().assign(result=pd_result_col) - - assert_frame_equal(bf_result, pd_result) - - # Test that the remote function works as expected - inner_test() - - # Let's delete the cloud function while not touching the bq remote function - delete_operation = delete_cloud_function( - session.cloudfunctionsclient, cloud_functions[0].name - ) - delete_operation.result() - assert delete_operation.done() - - # There should be no cloud functions at this point for the uniq udf - cloud_functions = list( - get_cloud_functions( - session.cloudfunctionsclient, - session.bqclient.project, - session.bqclient.location, - name=add_one_uniq_cf_name, - ) - ) - assert len(cloud_functions) == 0 - - # The second time bigframes detects that the required cloud function doesn't - # exist even though the remote function exists, and goes ahead and recreates - # the cloud function - remote_add_one = session.remote_function( - input_types=[int], - output_type=int, - dataset=dataset_id, - bigquery_connection=bq_cf_connection, - reuse=True, - cloud_function_service_account="default", - )(add_one_uniq) - - # There should be excactly one cloud function again - cloud_functions = list( - get_cloud_functions( - session.cloudfunctionsclient, - session.bqclient.project, - session.bqclient.location, - name=add_one_uniq_cf_name, - ) - ) - assert len(cloud_functions) == 1 - - # Test again after the cloud function is restored that the remote function - # works as expected - inner_test() - - # clean up the temp code - shutil.rmtree(add_one_uniq_dir) - finally: - # clean up the gcp assets created for the remote function - cleanup_function_assets( - remote_add_one, session.bqclient, session.cloudfunctionsclient - ) - - -@pytest.mark.flaky(retries=2, delay=120) -def test_remote_udf_mask_default_value( - session, scalars_dfs, dataset_id, bq_cf_connection -): - try: - - def is_odd(num): - flag = False - try: - flag = num % 2 == 1 - except TypeError: - pass - return flag - - is_odd_remote = session.remote_function( - input_types=[int], - output_type=bool, - dataset=dataset_id, - bigquery_connection=bq_cf_connection, - reuse=False, - cloud_function_service_account="default", - )(is_odd) - - scalars_df, scalars_pandas_df = scalars_dfs - - bf_int64_col = scalars_df["int64_col"] - bf_result_col = bf_int64_col.mask(is_odd_remote) - bf_result = bf_int64_col.to_frame().assign(result=bf_result_col).to_pandas() - - pd_int64_col = scalars_pandas_df["int64_col"] - pd_result_col = pd_int64_col.mask(is_odd) - pd_result = pd_int64_col.to_frame().assign(result=pd_result_col) - - assert_frame_equal(bf_result, pd_result) - finally: - # clean up the gcp assets created for the remote function - cleanup_function_assets( - is_odd_remote, session.bqclient, session.cloudfunctionsclient - ) - - -@pytest.mark.flaky(retries=2, delay=120) -def test_remote_udf_mask_custom_value( - session, scalars_dfs, dataset_id, bq_cf_connection -): - try: - - def is_odd(num): - flag = False - try: - flag = num % 2 == 1 - except TypeError: - pass - return flag - - is_odd_remote = session.remote_function( - input_types=[int], - output_type=bool, - dataset=dataset_id, - bigquery_connection=bq_cf_connection, - reuse=False, - cloud_function_service_account="default", - )(is_odd) - - scalars_df, scalars_pandas_df = scalars_dfs - - # TODO(shobs): Revisit this test when NA handling of pandas' Series.mask is - # fixed https://github.com/pandas-dev/pandas/issues/52955, - # for now filter out the nulls and test the rest - bf_int64_col = scalars_df["int64_col"] - bf_result_col = bf_int64_col[bf_int64_col.notnull()].mask(is_odd_remote, -1) - bf_result = bf_int64_col.to_frame().assign(result=bf_result_col).to_pandas() - - pd_int64_col = scalars_pandas_df["int64_col"] - pd_result_col = pd_int64_col[pd_int64_col.notnull()].mask(is_odd, -1) - pd_result = pd_int64_col.to_frame().assign(result=pd_result_col) - - assert_frame_equal(bf_result, pd_result) - finally: - # clean up the gcp assets created for the remote function - cleanup_function_assets( - is_odd_remote, session.bqclient, session.cloudfunctionsclient - ) - - -@pytest.mark.flaky(retries=2, delay=120) -def test_remote_udf_lambda(session, scalars_dfs, dataset_id, bq_cf_connection): - try: - add_one_lambda = lambda x: x + 1 # noqa: E731 - - add_one_lambda_remote = session.remote_function( - input_types=[int], - output_type=int, - dataset=dataset_id, - bigquery_connection=bq_cf_connection, - reuse=False, - cloud_function_service_account="default", - )(add_one_lambda) - - scalars_df, scalars_pandas_df = scalars_dfs - - bf_int64_col = scalars_df["int64_col"] - bf_int64_col_filter = bf_int64_col.notnull() - bf_int64_col_filtered = bf_int64_col[bf_int64_col_filter] - bf_result_col = bf_int64_col_filtered.apply(add_one_lambda_remote) - bf_result = ( - bf_int64_col_filtered.to_frame().assign(result=bf_result_col).to_pandas() - ) - - pd_int64_col = scalars_pandas_df["int64_col"] - pd_int64_col_filter = pd_int64_col.notnull() - pd_int64_col_filtered = pd_int64_col[pd_int64_col_filter] - pd_result_col = pd_int64_col_filtered.apply(add_one_lambda) - # TODO(shobs): Figure why pandas .apply() changes the dtype, i.e. - # pd_int64_col_filtered.dtype is Int64Dtype() - # pd_int64_col_filtered.apply(lambda x: x).dtype is int64. - # For this test let's force the pandas dtype to be same as bigframes' dtype. - pd_result_col = pd_result_col.astype(pandas.Int64Dtype()) - pd_result = pd_int64_col_filtered.to_frame().assign(result=pd_result_col) - - assert_frame_equal(bf_result, pd_result) - finally: - # clean up the gcp assets created for the remote function - cleanup_function_assets( - add_one_lambda_remote, session.bqclient, session.cloudfunctionsclient - ) - - -@pytest.mark.flaky(retries=2, delay=120) -def test_remote_function_with_explicit_name( - session, scalars_dfs, dataset_id, bq_cf_connection -): - try: - - def square(x): - return x * x - - prefixer = test_utils.prefixer.Prefixer(square.__name__, "") - rf_name = prefixer.create_prefix() - expected_remote_function = f"{dataset_id}.{rf_name}" - - # Initially the expected BQ remote function should not exist - with pytest.raises(google.api_core.exceptions.NotFound): - session.bqclient.get_routine(expected_remote_function) - - # Create the remote function with the name provided explicitly - square_remote = session.remote_function( - input_types=[int], - output_type=int, - dataset=dataset_id, - bigquery_connection=bq_cf_connection, - reuse=False, - name=rf_name, - cloud_function_service_account="default", - )(square) - - # The remote function should reflect the explicitly provided name - assert square_remote.bigframes_remote_function == expected_remote_function - assert square_remote.bigframes_bigquery_function == expected_remote_function - - # Now the expected BQ remote function should exist - session.bqclient.get_routine(expected_remote_function) - - # The behavior of the created remote function should be as expected - scalars_df, scalars_pandas_df = scalars_dfs - - bf_int64_col = scalars_df["int64_too"] - bf_result_col = bf_int64_col.apply(square_remote) - bf_result = bf_int64_col.to_frame().assign(result=bf_result_col).to_pandas() - - pd_int64_col = scalars_pandas_df["int64_too"] - pd_result_col = pd_int64_col.apply(square) - # TODO(shobs): Figure why pandas .apply() changes the dtype, i.e. - # pd_int64_col.dtype is Int64Dtype() - # pd_int64_col.apply(square).dtype is int64. - # For this test let's force the pandas dtype to be same as bigframes' dtype. - pd_result_col = pd_result_col.astype(pandas.Int64Dtype()) - pd_result = pd_int64_col.to_frame().assign(result=pd_result_col) - - assert_frame_equal(bf_result, pd_result) - finally: - # clean up the gcp assets created for the remote function - cleanup_function_assets( - square_remote, session.bqclient, session.cloudfunctionsclient - ) - - -@pytest.mark.flaky(retries=2, delay=120) -def test_remote_function_with_external_package_dependencies( - session, scalars_dfs, dataset_id, bq_cf_connection -): - try: - # The return type hint in this function's signature has conflict. The - # `output_type` argument from remote_function decorator takes precedence - # and will be used instead. - def pd_np_foo(x) -> None: - import numpy as mynp - import pandas as mypd - - return mypd.Series([x, mynp.sqrt(mynp.abs(x))]).sum() - - with warnings.catch_warnings(record=True) as record: - # Create the remote function with the name provided explicitly - pd_np_foo_remote = session.remote_function( - input_types=[int], - output_type=float, - dataset=dataset_id, - bigquery_connection=bq_cf_connection, - reuse=False, - packages=["numpy", "pandas >= 2.0.0"], - cloud_function_service_account="default", - )(pd_np_foo) - - input_type_warning = "Conflicting input types detected" - assert not any(input_type_warning in str(warning.message) for warning in record) - return_type_warning = "Conflicting return type detected" - assert any(return_type_warning in str(warning.message) for warning in record) - - # The behavior of the created remote function should be as expected - scalars_df, scalars_pandas_df = scalars_dfs - - bf_int64_col = scalars_df["int64_too"] - bf_result_col = bf_int64_col.apply(pd_np_foo_remote) - bf_result = bf_int64_col.to_frame().assign(result=bf_result_col).to_pandas() - - pd_int64_col = scalars_pandas_df["int64_too"] - pd_result_col = pd_int64_col.apply(pd_np_foo) - pd_result = pd_int64_col.to_frame().assign(result=pd_result_col) - - # pandas result is non-nullable type float64, make it Float64 before - # comparing for the purpose of this test - pd_result.result = pd_result.result.astype(pandas.Float64Dtype()) - - assert_frame_equal(bf_result, pd_result) - finally: - # clean up the gcp assets created for the remote function - cleanup_function_assets( - pd_np_foo_remote, session.bqclient, session.cloudfunctionsclient - ) - - -@pytest.mark.flaky(retries=2, delay=120) -def test_remote_function_with_explicit_name_reuse( - session, scalars_dfs, dataset_id, bq_cf_connection -): - try: - dirs_to_cleanup = [] - - # Define a user code - def square(x): - return x * x - - # Make it a unique udf - square_uniq, square_uniq_dir = make_uniq_udf(square) - dirs_to_cleanup.append(square_uniq_dir) - - # Define a common routine which accepts a remote function and the - # corresponding user defined function and tests that bigframes bahavior - # on the former is in parity with the pandas behaviour on the latter - def test_internal(rf, udf): - # The behavior of the created remote function should be as expected - scalars_df, scalars_pandas_df = scalars_dfs - - bf_int64_col = scalars_df["int64_too"] - bf_result_col = bf_int64_col.apply(rf) - bf_result = bf_int64_col.to_frame().assign(result=bf_result_col).to_pandas() - - pd_int64_col = scalars_pandas_df["int64_too"] - pd_result_col = pd_int64_col.apply(udf) - # TODO(shobs): Figure why pandas .apply() changes the dtype, i.e. - # pd_int64_col.dtype is Int64Dtype() - # pd_int64_col.apply(square).dtype is int64. - # For this test let's force the pandas dtype to be same as bigframes' dtype. - pd_result_col = pd_result_col.astype(pandas.Int64Dtype()) - pd_result = pd_int64_col.to_frame().assign(result=pd_result_col) - - assert_frame_equal(bf_result, pd_result) - - # Create an explicit name for the remote function - prefixer = test_utils.prefixer.Prefixer("foo", "") - rf_name = prefixer.create_prefix() - expected_remote_function = f"{dataset_id}.{rf_name}" - - # Initially the expected BQ remote function should not exist - with pytest.raises(google.api_core.exceptions.NotFound): - session.bqclient.get_routine(expected_remote_function) - - # Create a new remote function with the name provided explicitly - square_remote1 = session.remote_function( - input_types=[int], - output_type=int, - dataset=dataset_id, - bigquery_connection=bq_cf_connection, - name=rf_name, - cloud_function_service_account="default", - )(square_uniq) - - # The remote function should reflect the explicitly provided name - assert square_remote1.bigframes_remote_function == expected_remote_function - assert square_remote1.bigframes_bigquery_function == expected_remote_function - - # Now the expected BQ remote function should exist - routine = session.bqclient.get_routine(expected_remote_function) - square_remote1_created = routine.created - square_remote1_cf_updated = session.cloudfunctionsclient.get_function( - name=square_remote1.bigframes_cloud_function - ).update_time - - # Test pandas parity with square udf - test_internal(square_remote1, square) - - # Now Create another remote function with the same name provided - # explicitly. Since reuse is True by default, the previously created - # remote function with the same name will be reused. - square_remote2 = session.remote_function( - input_types=[int], - output_type=int, - dataset=dataset_id, - bigquery_connection=bq_cf_connection, - name=rf_name, - cloud_function_service_account="default", - )(square_uniq) - - # The new remote function should still reflect the explicitly provided name - assert square_remote2.bigframes_remote_function == expected_remote_function - assert square_remote2.bigframes_bigquery_function == expected_remote_function - - # The expected BQ remote function should still exist - routine = session.bqclient.get_routine(expected_remote_function) - square_remote2_created = routine.created - square_remote2_cf_updated = session.cloudfunctionsclient.get_function( - name=square_remote2.bigframes_cloud_function - ).update_time - - # The new remote function should reflect that the previous BQ remote - # function and the cloud function were reused instead of creating anew - assert square_remote2_created == square_remote1_created - assert ( - square_remote2.bigframes_cloud_function - == square_remote1.bigframes_cloud_function - ) - assert square_remote2_cf_updated == square_remote1_cf_updated - - # Test again that the new remote function is actually same as the - # previous remote function - test_internal(square_remote2, square) - - # Now define a different user code - def plusone(x): - return x + 1 - - # Make it a unique udf - plusone_uniq, plusone_uniq_dir = make_uniq_udf(plusone) - dirs_to_cleanup.append(plusone_uniq_dir) - - # Now Create a third remote function with the same name provided - # explicitly. Even though reuse is True by default, the previously - # created remote function with the same name should not be reused since - # this time it is a different user code. - plusone_remote = session.remote_function( - input_types=[int], - output_type=int, - dataset=dataset_id, - bigquery_connection=bq_cf_connection, - name=rf_name, - cloud_function_service_account="default", - )(plusone_uniq) - - # The new remote function should still reflect the explicitly provided name - assert plusone_remote.bigframes_remote_function == expected_remote_function - assert plusone_remote.bigframes_bigquery_function == expected_remote_function - - # The expected BQ remote function should still exist - routine = session.bqclient.get_routine(expected_remote_function) - plusone_remote_created = routine.created - plusone_remote_cf_updated = session.cloudfunctionsclient.get_function( - name=plusone_remote.bigframes_cloud_function - ).update_time - - # The new remote function should reflect that the previous BQ remote - # function and the cloud function were NOT reused, instead were created - # anew - assert plusone_remote_created > square_remote2_created - assert ( - plusone_remote.bigframes_cloud_function - != square_remote2.bigframes_cloud_function - ) - assert plusone_remote_cf_updated > square_remote2_cf_updated - - # Test again that the new remote function is equivalent to the new user - # defined function - test_internal(plusone_remote, plusone) - finally: - # clean up the gcp assets created for the remote function - cleanup_function_assets( - square_remote1, session.bqclient, session.cloudfunctionsclient - ) - cleanup_function_assets( - square_remote2, session.bqclient, session.cloudfunctionsclient - ) - cleanup_function_assets( - plusone_remote, session.bqclient, session.cloudfunctionsclient - ) - for dir_ in dirs_to_cleanup: - shutil.rmtree(dir_) - - -@pytest.mark.flaky(retries=2, delay=120) -def test_remote_function_via_session_context_connection_setter( - scalars_dfs, dataset_id, bq_cf_connection -): - # Creating a session scoped only to this test as we would be setting a - # property in it - context = bigframes.BigQueryOptions() - context.bq_connection = bq_cf_connection - session = bigframes.connect(context) - - try: - # Without an explicit bigquery connection, the one present in Session, - # set via context setter would be used. Without an explicit `reuse` the - # default behavior of reuse=True will take effect. Please note that the - # udf is same as the one used in other tests in this file so the underlying - # cloud function would be common with reuse=True. Since we are using a - # unique dataset_id, even though the cloud function would be reused, the bq - # remote function would still be created, making use of the bq connection - # set in the BigQueryOptions above. - @session.remote_function( - input_types=[int], - output_type=int, - dataset=dataset_id, - reuse=False, - cloud_function_service_account="default", - ) - def square(x): - return x * x - - scalars_df, scalars_pandas_df = scalars_dfs - - bf_int64_col = scalars_df["int64_col"] - bf_int64_col_filter = bf_int64_col.notnull() - bf_int64_col_filtered = bf_int64_col[bf_int64_col_filter] - bf_result_col = bf_int64_col_filtered.apply(square) - bf_result = ( - bf_int64_col_filtered.to_frame().assign(result=bf_result_col).to_pandas() - ) - - pd_int64_col = scalars_pandas_df["int64_col"] - pd_int64_col_filter = pd_int64_col.notnull() - pd_int64_col_filtered = pd_int64_col[pd_int64_col_filter] - pd_result_col = pd_int64_col_filtered.apply(lambda x: x * x) - # TODO(shobs): Figure why pandas .apply() changes the dtype, i.e. - # pd_int64_col_filtered.dtype is Int64Dtype() - # pd_int64_col_filtered.apply(lambda x: x * x).dtype is int64. - # For this test let's force the pandas dtype to be same as bigframes' dtype. - pd_result_col = pd_result_col.astype(pandas.Int64Dtype()) - pd_result = pd_int64_col_filtered.to_frame().assign(result=pd_result_col) - - assert_frame_equal(bf_result, pd_result) - finally: - # clean up the gcp assets created for the remote function - cleanup_function_assets(square, session.bqclient, session.cloudfunctionsclient) - - -@pytest.mark.flaky(retries=2, delay=120) -def test_remote_function_default_connection(session, scalars_dfs, dataset_id): - try: - - @session.remote_function( - input_types=[int], - output_type=int, - dataset=dataset_id, - reuse=False, - cloud_function_service_account="default", - ) - def square(x): - return x * x - - scalars_df, scalars_pandas_df = scalars_dfs - - bf_int64_col = scalars_df["int64_col"] - bf_int64_col_filter = bf_int64_col.notnull() - bf_int64_col_filtered = bf_int64_col[bf_int64_col_filter] - bf_result_col = bf_int64_col_filtered.apply(square) - bf_result = ( - bf_int64_col_filtered.to_frame().assign(result=bf_result_col).to_pandas() - ) - - pd_int64_col = scalars_pandas_df["int64_col"] - pd_int64_col_filter = pd_int64_col.notnull() - pd_int64_col_filtered = pd_int64_col[pd_int64_col_filter] - pd_result_col = pd_int64_col_filtered.apply(lambda x: x * x) - # TODO(shobs): Figure why pandas .apply() changes the dtype, i.e. - # pd_int64_col_filtered.dtype is Int64Dtype() - # pd_int64_col_filtered.apply(lambda x: x * x).dtype is int64. - # For this test let's force the pandas dtype to be same as bigframes' dtype. - pd_result_col = pd_result_col.astype(pandas.Int64Dtype()) - pd_result = pd_int64_col_filtered.to_frame().assign(result=pd_result_col) - - assert_frame_equal(bf_result, pd_result) - finally: - # clean up the gcp assets created for the remote function - cleanup_function_assets(square, session.bqclient, session.cloudfunctionsclient) - - -@pytest.mark.flaky(retries=2, delay=120) -def test_remote_function_runtime_error(session, scalars_dfs, dataset_id): - try: - - @session.remote_function( - input_types=[int], - output_type=int, - dataset=dataset_id, - reuse=False, - cloud_function_service_account="default", - ) - def square(x): - return x * x - - scalars_df, _ = scalars_dfs - - with pytest.raises( - google.api_core.exceptions.BadRequest, - match="400.*errorMessage.*unsupported operand type", - ): - # int64_col has nulls which should cause error in square - scalars_df["int64_col"].apply(square).to_pandas() - finally: - # clean up the gcp assets created for the remote function - cleanup_function_assets(square, session.bqclient, session.cloudfunctionsclient) - - -@pytest.mark.flaky(retries=2, delay=120) -def test_remote_function_anonymous_dataset(session, scalars_dfs): - try: - # This usage of remote_function is expected to create the remote - # function in the bigframes session's anonymous dataset. Use reuse=False - # param to make sure parallel instances of the test don't step over each - # other due to the common anonymous dataset. - @session.remote_function( - input_types=[int], - output_type=int, - reuse=False, - cloud_function_service_account="default", - ) - def square(x): - return x * x - - assert ( - bigquery.Routine(square.bigframes_bigquery_function).dataset_id - == session._anonymous_dataset.dataset_id - ) - - scalars_df, scalars_pandas_df = scalars_dfs - - bf_int64_col = scalars_df["int64_col"] - bf_int64_col_filter = bf_int64_col.notnull() - bf_int64_col_filtered = bf_int64_col[bf_int64_col_filter] - bf_result_col = bf_int64_col_filtered.apply(square) - bf_result = ( - bf_int64_col_filtered.to_frame().assign(result=bf_result_col).to_pandas() - ) - - pd_int64_col = scalars_pandas_df["int64_col"] - pd_int64_col_filter = pd_int64_col.notnull() - pd_int64_col_filtered = pd_int64_col[pd_int64_col_filter] - pd_result_col = pd_int64_col_filtered.apply(lambda x: x * x) - # TODO(shobs): Figure why pandas .apply() changes the dtype, i.e. - # pd_int64_col_filtered.dtype is Int64Dtype() - # pd_int64_col_filtered.apply(lambda x: x * x).dtype is int64. - # For this test let's force the pandas dtype to be same as bigframes' dtype. - pd_result_col = pd_result_col.astype(pandas.Int64Dtype()) - pd_result = pd_int64_col_filtered.to_frame().assign(result=pd_result_col) - - assert_frame_equal(bf_result, pd_result) - finally: - # clean up the gcp assets created for the remote function - cleanup_function_assets(square, session.bqclient, session.cloudfunctionsclient) - - -@pytest.mark.flaky(retries=2, delay=120) -def test_remote_function_via_session_custom_sa(scalars_pandas_df_index): - # TODO(shobs): Automate the following set-up during testing in the test project. - # - # For upfront convenience, the following set up has been statically created - # in the project bigfrmames-dev-perf via cloud console: - # - # 1. Create a service account bigframes-dev-perf-1@bigframes-dev-perf.iam.gserviceaccount.com as per - # https://cloud.google.com/iam/docs/service-accounts-create#iam-service-accounts-create-console - # 2. Give necessary roles as per - # https://cloud.google.com/functions/docs/reference/iam/roles#additional-configuration - # - project = "bigframes-dev-perf" - gcf_service_account = ( - "bigframes-dev-perf-1@bigframes-dev-perf.iam.gserviceaccount.com" - ) - - rf_session = bigframes.Session(context=bigframes.BigQueryOptions(project=project)) - - try: - - @rf_session.remote_function( - input_types=[int], - output_type=int, - reuse=False, - cloud_function_service_account=gcf_service_account, - cloud_function_ingress_settings="internal-and-gclb", - ) - def double_num(x): - if x is None: - return x - return x + x - - # assert that the GCF is created with the intended SA - gcf = rf_session.cloudfunctionsclient.get_function( - name=double_num.bigframes_cloud_function - ) - assert gcf.service_config.service_account_email == gcf_service_account - - # assert that the function works as expected on data - - bf_int64_col = rf_session.read_pandas(scalars_pandas_df_index.int64_col) - bf_result_col = bf_int64_col.apply(double_num) - bf_result = bf_int64_col.to_frame().assign(result=bf_result_col).to_pandas() - - pd_int64_col = scalars_pandas_df_index.int64_col - pd_result_col = pd_int64_col.apply(lambda x: x if x is None else x + x) - pd_result = pd_int64_col.to_frame().assign(result=pd_result_col) - - assert_frame_equal(bf_result, pd_result, check_dtype=False) - finally: - # clean up the gcp assets created for the remote function - cleanup_function_assets( - double_num, rf_session.bqclient, rf_session.cloudfunctionsclient - ) - - -@pytest.mark.parametrize( - ("set_build_service_account"), - [ - pytest.param( - "projects/bigframes-dev-perf/serviceAccounts/bigframes-dev-perf-1@bigframes-dev-perf.iam.gserviceaccount.com", - id="fully-qualified-sa", - ), - pytest.param( - "bigframes-dev-perf-1@bigframes-dev-perf.iam.gserviceaccount.com", - id="just-sa-email", - ), - ], -) -@pytest.mark.flaky(retries=2, delay=120) -def test_remote_function_via_session_custom_build_sa( - set_build_service_account, scalars_pandas_df_index -): - # TODO(shobs): Automate the following set-up during testing in the test project. - # - # For upfront convenience, the following set up has been statically created - # in the project bigfrmames-dev-perf via cloud console: - # - # 1. Create a service account bigframes-dev-perf-1@bigframes-dev-perf.iam.gserviceaccount.com as per - # https://cloud.google.com/iam/docs/service-accounts-create#iam-service-accounts-create-console - # 2. Give "Cloud Build Service Account (roles/cloudbuild.builds.builder)" role as per - # https://cloud.google.com/build/docs/cloud-build-service-account#default_permissions_of_the_legacy_service_account - # - project = "bigframes-dev-perf" - expected_build_service_account = "projects/bigframes-dev-perf/serviceAccounts/bigframes-dev-perf-1@bigframes-dev-perf.iam.gserviceaccount.com" - - rf_session = bigframes.Session(context=bigframes.BigQueryOptions(project=project)) - - try: - - @rf_session.remote_function( - input_types=[int], - output_type=int, - reuse=False, - cloud_function_service_account="default", - cloud_build_service_account=set_build_service_account, - cloud_function_ingress_settings="internal-and-gclb", - ) - def double_num(x): - if x is None: - return x - return x + x - - # assert that the GCF is created with the intended SA - gcf = rf_session.cloudfunctionsclient.get_function( - name=double_num.bigframes_cloud_function - ) - assert gcf.build_config.service_account == expected_build_service_account - - bf_int64_col = rf_session.read_pandas(scalars_pandas_df_index.int64_col) - bf_result_col = bf_int64_col.apply(double_num) - bf_result = bf_int64_col.to_frame().assign(result=bf_result_col).to_pandas() - - pd_int64_col = scalars_pandas_df_index.int64_col - pd_result_col = pd_int64_col.apply(lambda x: x if x is None else x + x) - pd_result = pd_int64_col.to_frame().assign(result=pd_result_col) - - assert_frame_equal(bf_result, pd_result, check_dtype=False) - finally: - # clean up the gcp assets created for the remote function - cleanup_function_assets( - double_num, rf_session.bqclient, rf_session.cloudfunctionsclient - ) - - -def test_remote_function_throws_none_cloud_function_service_account(session): - with pytest.raises( - ValueError, - match='^You must provide a user managed cloud_function_service_account, or "default" if you would like to let the default service account be used.$', - ): - session.remote_function(cloud_function_service_account=None) - - -@pytest.mark.flaky(retries=2, delay=120) -def test_remote_function_with_gcf_cmek(): - # TODO(shobs): Automate the following set-up during testing in the test project. - # - # For upfront convenience, the following set up has been statically created - # in the project bigfrmames-dev-perf via cloud console: - # - # 1. Created an encryption key and granting the necessary service accounts - # the required IAM permissions as per https://cloud.google.com/kms/docs/create-key - # 2. Created a docker repository with CMEK (created in step 1) enabled as per - # https://cloud.google.com/artifact-registry/docs/repositories/create-repos#overview - # - project = "bigframes-dev-perf" - cmek = "projects/bigframes-dev-perf/locations/us-central1/keyRings/bigframesKeyRing/cryptoKeys/bigframesKey" - docker_repository = ( - "projects/bigframes-dev-perf/locations/us-central1/repositories/rf-artifacts" - ) - - session = bigframes.Session(context=bigframes.BigQueryOptions(project=project)) - try: - - @session.remote_function( - input_types=[int], - output_type=int, - reuse=False, - cloud_function_service_account="default", - cloud_function_kms_key_name=cmek, - cloud_function_docker_repository=docker_repository, - ) - def square_num(x): - if x is None: - return x - return x * x - - df = pandas.DataFrame({"num": [-1, 0, None, 1]}, dtype="Int64") - bf = session.read_pandas(df) - - bf_result_col = bf["num"].apply(square_num) - bf_result = bf.assign(result=bf_result_col).to_pandas() - - pd_result_col = df["num"].apply(lambda x: x if x is None else x * x) - pd_result = df.assign(result=pd_result_col) - - assert_frame_equal( - bf_result, pd_result, check_dtype=False, check_index_type=False - ) - - # Assert that the GCF is created with the intended SA - gcf = session.cloudfunctionsclient.get_function( - name=square_num.bigframes_cloud_function - ) - assert gcf.kms_key_name == cmek - - # Assert that GCS artifact has CMEK applied - storage_client = storage.Client() - bucket = storage_client.bucket(gcf.build_config.source.storage_source.bucket) - blob = bucket.get_blob(gcf.build_config.source.storage_source.object_) - assert blob.kms_key_name.startswith(cmek) - - finally: - # clean up the gcp assets created for the remote function - cleanup_function_assets( - square_num, session.bqclient, session.cloudfunctionsclient - ) - - -@pytest.mark.flaky(retries=2, delay=120) -def test_remote_function_via_session_vpc(scalars_pandas_df_index): - # TODO(shobs): Automate the following set-up during testing in the test project. - # - # For upfront convenience, the following set up has been statically created - # in the project bigfrmames-dev-perf via cloud console: - # - # 1. Create a vpc connector as per - # https://cloud.google.com/vpc/docs/configure-serverless-vpc-access#gcloud - # - # $ gcloud compute networks vpc-access connectors create bigframes-vpc --project=bigframes-dev-perf --region=us-central1 --range 10.8.0.0/28 - # Create request issued for: [bigframes-vpc] - # Waiting for operation [projects/bigframes-dev-perf/locations/us-central1/operations/f9f90df6-7cf4-4420-8c2f-b3952775dcfb] to complete...done. - # Created connector [bigframes-vpc]. - # - # $ gcloud compute networks vpc-access connectors list --project=bigframes-dev-perf --region=us-central1 - # CONNECTOR_ID REGION NETWORK IP_CIDR_RANGE SUBNET SUBNET_PROJECT MACHINE_TYPE MIN_INSTANCES MAX_INSTANCES MIN_THROUGHPUT MAX_THROUGHPUT STATE - # bigframes-vpc us-central1 default 10.8.0.0/28 e2-micro 2 10 200 1000 READY - - project = "bigframes-dev-perf" - gcf_vpc_connector = "bigframes-vpc" - - rf_session = bigframes.Session(context=bigframes.BigQueryOptions(project=project)) - - try: - - def double_num(x): - if x is None: - return x - return x + x - - double_num_remote = rf_session.remote_function( - input_types=[int], - output_type=int, - reuse=False, - cloud_function_service_account="default", - cloud_function_vpc_connector=gcf_vpc_connector, - cloud_function_vpc_connector_egress_settings="all", - cloud_function_ingress_settings="internal-and-gclb", - )(double_num) - - gcf = rf_session.cloudfunctionsclient.get_function( - name=double_num_remote.bigframes_cloud_function - ) - - # assert that the GCF test_remote_function_via_session_custom_sais created with the intended vpc connector and - # egress settings. - assert gcf.service_config.vpc_connector == gcf_vpc_connector - # The value is since we set - # cloud_function_vpc_connector_egress_settings="all" earlier. - assert gcf.service_config.vpc_connector_egress_settings == 2 - - bf_int64_col = rf_session.read_pandas(scalars_pandas_df_index.int64_col) - bf_result_col = bf_int64_col.apply(double_num_remote) - bf_result = bf_int64_col.to_frame().assign(result=bf_result_col).to_pandas() - - pd_int64_col = scalars_pandas_df_index.int64_col - pd_result_col = pd_int64_col.apply(double_num) - pd_result = pd_int64_col.to_frame().assign(result=pd_result_col) - - assert_frame_equal(bf_result, pd_result, check_dtype=False) - finally: - # clean up the gcp assets created for the remote function - cleanup_function_assets( - double_num_remote, rf_session.bqclient, rf_session.cloudfunctionsclient - ) - - -@pytest.mark.flaky(retries=2, delay=120) -def test_remote_function_no_vpc_connector(session): - def foo(x): - return x - - with pytest.raises( - ValueError, - match="^cloud_function_vpc_connector must be specified before cloud_function_vpc_connector_egress_settings", - ): - session.remote_function( - input_types=[int], - output_type=int, - reuse=False, - cloud_function_service_account="default", - cloud_function_vpc_connector=None, - cloud_function_vpc_connector_egress_settings="all", - cloud_function_ingress_settings="all", - )(foo) - - -@pytest.mark.flaky(retries=2, delay=120) -def test_remote_function_wrong_vpc_egress_value(session): - def foo(x): - return x - - with pytest.raises( - ValueError, - match="^'wrong-egress-value' is not one of the supported vpc egress settings values:", - ): - session.remote_function( - input_types=[int], - output_type=int, - reuse=False, - cloud_function_service_account="default", - cloud_function_vpc_connector="dummy-value", - cloud_function_vpc_connector_egress_settings="wrong-egress-value", - cloud_function_ingress_settings="all", - )(foo) - - -@pytest.mark.parametrize( - ("max_batching_rows"), - [ - 10_000, - None, - ], -) -@pytest.mark.flaky(retries=2, delay=120) -def test_remote_function_max_batching_rows(session, scalars_dfs, max_batching_rows): - try: - - def square(x): - return x * x - - square_remote = session.remote_function( - input_types=[int], - output_type=int, - reuse=False, - max_batching_rows=max_batching_rows, - cloud_function_service_account="default", - )(square) - - bq_routine = session.bqclient.get_routine( - square_remote.bigframes_bigquery_function - ) - assert bq_routine.remote_function_options.max_batching_rows == ( - max_batching_rows or 1000 - ) - - scalars_df, scalars_pandas_df = scalars_dfs - - bf_result = scalars_df["int64_too"].apply(square_remote).to_pandas() - pd_result = scalars_pandas_df["int64_too"].apply(square) - - pandas.testing.assert_series_equal(bf_result, pd_result, check_dtype=False) - finally: - # clean up the gcp assets created for the remote function - cleanup_function_assets( - square_remote, session.bqclient, session.cloudfunctionsclient - ) - - -@pytest.mark.parametrize( - ("timeout_args", "effective_gcf_timeout"), - [ - pytest.param({}, 600, id="no-set"), - pytest.param({"cloud_function_timeout": None}, 60, id="set-None"), - pytest.param({"cloud_function_timeout": 1200}, 1200, id="set-max-allowed"), - ], -) -@pytest.mark.flaky(retries=2, delay=120) -def test_remote_function_gcf_timeout( - session, scalars_dfs, timeout_args, effective_gcf_timeout -): - try: - - def square(x): - return x * x - - square_remote = session.remote_function( - input_types=[int], - output_type=int, - reuse=False, - cloud_function_service_account="default", - **timeout_args, - )(square) - - # Assert that the GCF is created with the intended maximum timeout - gcf = session.cloudfunctionsclient.get_function( - name=square_remote.bigframes_cloud_function - ) - assert gcf.service_config.timeout_seconds == effective_gcf_timeout - - scalars_df, scalars_pandas_df = scalars_dfs - - bf_result = scalars_df["int64_too"].apply(square_remote).to_pandas() - pd_result = scalars_pandas_df["int64_too"].apply(square) - - pandas.testing.assert_series_equal(bf_result, pd_result, check_dtype=False) - finally: - # clean up the gcp assets created for the remote function - cleanup_function_assets( - square_remote, session.bqclient, session.cloudfunctionsclient - ) - - -@pytest.mark.flaky(retries=2, delay=120) -def test_remote_function_gcf_timeout_max_supported_exceeded(session): - with pytest.raises(ValueError): - - @session.remote_function( - input_types=[int], - output_type=int, - reuse=False, - cloud_function_service_account="default", - cloud_function_timeout=1201, - ) - def square(x): - return x * x - - -# Note: Zero represents default, which is 100 instances actually, which is why the remote function still works -# in the df.apply() call here -@pytest.mark.parametrize( - ("max_instances_args", "expected_max_instances"), - [ - pytest.param({}, 0, id="no-set"), - pytest.param({"cloud_function_max_instances": None}, 0, id="set-None"), - pytest.param({"cloud_function_max_instances": 1000}, 1000, id="set-explicit"), - ], -) -@pytest.mark.flaky(retries=2, delay=120) -def test_remote_function_max_instances( - session, scalars_dfs, max_instances_args, expected_max_instances -): - try: - - def square(x): - return x * x - - square_remote = session.remote_function( - input_types=[int], - output_type=int, - reuse=False, - cloud_function_service_account="default", - **max_instances_args, - )(square) - - # Assert that the GCF is created with the intended max instance count - gcf = session.cloudfunctionsclient.get_function( - name=square_remote.bigframes_cloud_function - ) - assert gcf.service_config.max_instance_count == expected_max_instances - - scalars_df, scalars_pandas_df = scalars_dfs - - bf_result = scalars_df["int64_too"].apply(square_remote).to_pandas() - pd_result = scalars_pandas_df["int64_too"].apply(square) - - pandas.testing.assert_series_equal(bf_result, pd_result, check_dtype=False) - finally: - # clean up the gcp assets created for the remote function - cleanup_function_assets( - square_remote, session.bqclient, session.cloudfunctionsclient - ) - - -@pytest.mark.flaky(retries=2, delay=120) -def test_remote_function_reflects_config_change_with_reuse(session): - square_remote = None - square_remote_2 = None - try: - - def square(x): - return x * x - - # random alphanumeric name starting with a letter - deploy_name = "a" + str(uuid.uuid4().hex) - square_remote = session.remote_function( - input_types=[int], - name=deploy_name, - output_type=int, - reuse=True, - cloud_function_service_account="default", - cloud_function_cpus=1, - )(square) - square_remote_2 = session.remote_function( - input_types=[int], - name=deploy_name, - output_type=int, - reuse=True, - cloud_function_service_account="default", - cloud_function_cpus=2, - )(square) - - # Assert that the GCF is created with the intended max instance count - gcf = session.cloudfunctionsclient.get_function( - name=square_remote_2.bigframes_cloud_function - ) - assert float(gcf.service_config.available_cpu) == 2.0 - finally: - # clean up the gcp assets created for the remote function - if square_remote is not None: - cleanup_function_assets( - square_remote, session.bqclient, session.cloudfunctionsclient - ) - if square_remote_2 is not None: - cleanup_function_assets( - square_remote_2, session.bqclient, session.cloudfunctionsclient - ) - - -@pytest.mark.flaky(retries=2, delay=120) -def test_df_apply_axis_1(session, scalars_dfs): - columns = ["bool_col", "int64_col", "int64_too", "float64_col", "string_col"] - scalars_df, scalars_pandas_df = scalars_dfs - try: - - def serialize_row(row): - custom = { - "name": row.name, - "index": [idx for idx in row.index], - "values": [ - val.item() if hasattr(val, "item") else val for val in row.values - ], - } - - return str( - { - "default": row.to_json(), - "split": row.to_json(orient="split"), - "records": row.to_json(orient="records"), - "index": row.to_json(orient="index"), - "table": row.to_json(orient="table"), - "custom": custom, - } - ) - - serialize_row_remote = session.remote_function( - input_types=pandas.Series, - output_type=str, - reuse=False, - cloud_function_service_account="default", - )(serialize_row) - - assert getattr(serialize_row_remote, "is_row_processor") - - bf_result = scalars_df[columns].apply(serialize_row_remote, axis=1).to_pandas() - pd_result = scalars_pandas_df[columns].apply(serialize_row, axis=1) - - # bf_result.dtype is 'string[pyarrow]' while pd_result.dtype is 'object' - # , ignore this mismatch by using check_dtype=False. - pandas.testing.assert_series_equal(pd_result, bf_result, check_dtype=False) - - # Let's make sure the read_gbq_function path works for this function - serialize_row_reuse = session.read_gbq_function( - serialize_row_remote.bigframes_bigquery_function, is_row_processor=True - ) - bf_result = scalars_df[columns].apply(serialize_row_reuse, axis=1).to_pandas() - pandas.testing.assert_series_equal(pd_result, bf_result, check_dtype=False) - finally: - # clean up the gcp assets created for the remote function - cleanup_function_assets( - serialize_row_remote, session.bqclient, session.cloudfunctionsclient - ) - - -@pytest.mark.flaky(retries=2, delay=120) -def test_df_apply_axis_1_aggregates(session, scalars_dfs): - columns = ["int64_col", "int64_too", "float64_col"] - scalars_df, scalars_pandas_df = scalars_dfs - - try: - - def analyze(row): - return str( - { - "dtype": row.dtype, - "count": row.count(), - "min": row.min(), - "max": row.max(), - "mean": row.mean(), - "std": row.std(), - "var": row.var(), - } - ) - - analyze_remote = session.remote_function( - input_types=pandas.Series, - output_type=str, - reuse=False, - cloud_function_service_account="default", - )(analyze) - - assert getattr(analyze_remote, "is_row_processor") - - bf_result = ( - scalars_df[columns].dropna().apply(analyze_remote, axis=1).to_pandas() - ) - pd_result = scalars_pandas_df[columns].dropna().apply(analyze, axis=1) - - # bf_result.dtype is 'string[pyarrow]' while pd_result.dtype is 'object' - # , ignore this mismatch by using check_dtype=False. - pandas.testing.assert_series_equal(pd_result, bf_result, check_dtype=False) - finally: - # clean up the gcp assets created for the remote function - cleanup_function_assets( - analyze_remote, session.bqclient, session.cloudfunctionsclient - ) - - -@pytest.mark.parametrize( - ("pd_df"), - [ - pytest.param( - pandas.DataFrame( - { - "2": [1, 2, 3], - 2: [1.5, 3.75, 5], - "name, [with. special'- chars\")/\\": [10, 20, 30], - (3, 4): ["pq", "rs", "tu"], - (5.0, "six", 7): [8, 9, 10], - 'raise Exception("hacked!")': [11, 12, 13], - }, - # Default pandas index has non-numpy type, whereas bigframes is - # always numpy-based type, so let's use the index compatible - # with bigframes. See more details in b/369689696. - index=pandas.Index([0, 1, 2], dtype=pandas.Int64Dtype()), - ), - id="all-kinds-of-column-names", - ), - pytest.param( - pandas.DataFrame( - { - "x": [1, 2, 3], - "y": [1.5, 3.75, 5], - "z": ["pq", "rs", "tu"], - }, - index=pandas.MultiIndex.from_frame( - pandas.DataFrame( - { - "idx0": pandas.Series( - ["a", "a", "b"], dtype=pandas.StringDtype() - ), - "idx1": pandas.Series( - [100, 200, 300], dtype=pandas.Int64Dtype() - ), - } - ) - ), - ), - id="multiindex", - marks=pytest.mark.skip( - reason="TODO: revert this skip after this pandas bug is fixed: https://github.com/pandas-dev/pandas/issues/59908" - ), - ), - pytest.param( - pandas.DataFrame( - [ - [10, 1.5, "pq"], - [20, 3.75, "rs"], - [30, 8.0, "tu"], - ], - # Default pandas index has non-numpy type, whereas bigframes is - # always numpy-based type, so let's use the index compatible - # with bigframes. See more details in b/369689696. - index=pandas.Index([0, 1, 2], dtype=pandas.Int64Dtype()), - columns=pandas.MultiIndex.from_arrays( - [ - ["first", "last_two", "last_two"], - [1, 2, 3], - ] - ), - ), - id="column-multiindex", - ), - pytest.param( - pandas.DataFrame( - { - datetime.now(): [1, 2, 3], - } - ), - id="column-name-not-supported", - marks=pytest.mark.xfail(raises=NameError), - ), - ], -) -@pytest.mark.flaky(retries=2, delay=120) -def test_df_apply_axis_1_complex(session, pd_df): - bf_df = session.read_pandas(pd_df) - - try: - - def serialize_row(row): - custom = { - "name": row.name, - "index": [idx for idx in row.index], - "values": [ - val.item() if hasattr(val, "item") else val for val in row.values - ], - } - return str( - { - "default": row.to_json(), - "split": row.to_json(orient="split"), - "records": row.to_json(orient="records"), - "index": row.to_json(orient="index"), - "custom": custom, - } - ) - - serialize_row_remote = session.remote_function( - input_types=pandas.Series, - output_type=str, - reuse=False, - cloud_function_service_account="default", - )(serialize_row) - - assert getattr(serialize_row_remote, "is_row_processor") - - bf_result = bf_df.apply(serialize_row_remote, axis=1).to_pandas() - pd_result = pd_df.apply(serialize_row, axis=1) - - # ignore known dtype difference between pandas and bigframes - pandas.testing.assert_series_equal( - pd_result, bf_result, check_dtype=False, check_index_type=False - ) - finally: - # clean up the gcp assets created for the remote function - cleanup_function_assets( - serialize_row_remote, session.bqclient, session.cloudfunctionsclient - ) - - -@pytest.mark.flaky(retries=2, delay=120) -def test_df_apply_axis_1_na_nan_inf(session): - """This test is for special cases of float values, to make sure any (nan, - inf, -inf) produced by user code is honored. - """ - bf_df = session.read_gbq( - """\ -SELECT "1" AS text, 1 AS num -UNION ALL -SELECT "2.5" AS text, 2.5 AS num -UNION ALL -SELECT "nan" AS text, IEEE_DIVIDE(0, 0) AS num -UNION ALL -SELECT "inf" AS text, IEEE_DIVIDE(1, 0) AS num -UNION ALL -SELECT "-inf" AS text, IEEE_DIVIDE(-1, 0) AS num -UNION ALL -SELECT "numpy nan" AS text, IEEE_DIVIDE(0, 0) AS num -UNION ALL -SELECT "pandas na" AS text, NULL AS num - """ - ) - - pd_df = bf_df.to_pandas() - - try: - - def float_parser(row: pandas.Series): - import numpy as mynp - import pandas as mypd - - if row["text"] == "pandas na": - return mypd.NA - if row["text"] == "numpy nan": - return mynp.nan - return float(row["text"]) - - float_parser_remote = session.remote_function( - output_type=float, - reuse=False, - cloud_function_service_account="default", - )(float_parser) - - assert getattr(float_parser_remote, "is_row_processor") - - pd_result = pd_df.apply(float_parser, axis=1) - bf_result = bf_df.apply(float_parser_remote, axis=1).to_pandas() - - # bf_result.dtype is 'Float64' while pd_result.dtype is 'object' - # , ignore this mismatch by using check_dtype=False. - pandas.testing.assert_series_equal(pd_result, bf_result, check_dtype=False) - - # Let's also assert that the data is consistent in this round trip - # (BQ -> BigFrames -> BQ -> GCF -> BQ -> BigFrames) w.r.t. their - # expected values in BQ - bq_result = bf_df["num"].to_pandas() - bq_result.name = None - pandas.testing.assert_series_equal(bq_result, bf_result) - finally: - # clean up the gcp assets created for the remote function - cleanup_function_assets( - float_parser_remote, session.bqclient, session.cloudfunctionsclient - ) - - -@pytest.mark.flaky(retries=2, delay=120) -def test_df_apply_axis_1_args(session, scalars_dfs): - columns = ["int64_col", "int64_too"] - scalars_df, scalars_pandas_df = scalars_dfs - - try: - - def the_sum(s1, s2, x): - return s1 + s2 + x - - the_sum_mf = session.remote_function( - input_types=[int, int, int], - output_type=int, - reuse=False, - cloud_function_service_account="default", - )(the_sum) - - args1 = (1,) - - # Fails to apply on dataframe with incompatible number of columns and args. - with pytest.raises( - ValueError, - match="^Parameter count mismatch:.* expected 3 parameters but received 4 values \\(2 DataFrame columns and 2 args\\)", - ): - scalars_df[columns].apply( - the_sum_mf, - axis=1, - args=( - 1, - 1, - ), - ) - - # Fails to apply on dataframe with incompatible column datatypes. - with pytest.raises( - ValueError, - match="^Data type mismatch for DataFrame columns: Expected .* Received .*", - ): - scalars_df[columns].assign( - int64_col=lambda df: df["int64_col"].astype("Float64") - ).apply(the_sum_mf, axis=1, args=args1) - - # Fails to apply on dataframe with incompatible args datatypes. - with pytest.raises( - ValueError, - match="^Data type mismatch for 'args' parameter: Expected .* Received .*", - ): - scalars_df[columns].apply(the_sum_mf, axis=1, args=("hello world",)) - - bf_result = ( - scalars_df[columns] - .dropna() - .apply(the_sum_mf, axis=1, args=args1) - .to_pandas() - ) - pd_result = scalars_pandas_df[columns].dropna().apply(sum, axis=1, args=args1) - - pandas.testing.assert_series_equal(pd_result, bf_result, check_dtype=False) - - finally: - # clean up the gcp assets created for the remote function. - cleanup_function_assets(the_sum_mf, session.bqclient, ignore_failures=False) - - -@pytest.mark.flaky(retries=2, delay=120) -def test_df_apply_axis_1_series_args(session, scalars_dfs): - columns = ["int64_col", "float64_col"] - scalars_df, scalars_pandas_df = scalars_dfs - - try: - - @session.remote_function( - input_types=[pandas.Series, float, str, bool], - output_type=list[str], - reuse=False, - cloud_function_service_account="default", - ) - def foo_list(x: pandas.Series, y0: float, y1, y2) -> list[str]: - return ( - [str(x["int64_col"]), str(y0), str(y1), str(y2)] - if y2 - else [str(x["float64_col"])] - ) - - args1 = (12.34, "hello world", True) - bf_result = scalars_df[columns].apply(foo_list, axis=1, args=args1).to_pandas() - pd_result = scalars_pandas_df[columns].apply(foo_list, axis=1, args=args1) - - # Ignore any dtype difference. - pandas.testing.assert_series_equal(bf_result, pd_result, check_dtype=False) - - args2 = (43.21, "xxx3yyy", False) - foo_list_ref = session.read_gbq_function( - foo_list.bigframes_bigquery_function, is_row_processor=True - ) - bf_result = ( - scalars_df[columns].apply(foo_list_ref, axis=1, args=args2).to_pandas() - ) - pd_result = scalars_pandas_df[columns].apply(foo_list, axis=1, args=args2) - - # Ignore any dtype difference. - pandas.testing.assert_series_equal(bf_result, pd_result, check_dtype=False) - - finally: - # Clean up the gcp assets created for the remote function. - cleanup_function_assets(foo_list, session.bqclient, ignore_failures=False) - - -@pytest.mark.parametrize( - ( - "memory_mib_args", - "expected_memory", - "expected_cpus", - ), - [ - pytest.param({}, "1024Mi", None, id="no-set"), - pytest.param( - {"cloud_function_memory_mib": None}, "1024Mi", None, id="set-None" - ), - pytest.param({"cloud_function_memory_mib": 128}, "128Mi", None, id="set-128"), - pytest.param( - {"cloud_function_memory_mib": 512, "cloud_function_cpus": 0.6}, - "512Mi", - "0.6", - id="set-512", - ), - pytest.param( - {"cloud_function_memory_mib": 1024}, "1024Mi", None, id="set-1024" - ), - pytest.param( - {"cloud_function_memory_mib": 4096, "cloud_function_cpus": 4}, - "4096Mi", - "4", - id="set-4096", - ), - pytest.param( - {"cloud_function_memory_mib": 32768}, "32768Mi", None, id="set-32768" - ), - ], -) -@pytest.mark.flaky(retries=2, delay=120) -def test_remote_function_gcf_memory( - session, scalars_dfs, memory_mib_args, expected_memory, expected_cpus -): - try: - - def square(x: int) -> int: - return x * x - - square_remote = session.remote_function( - reuse=False, cloud_function_service_account="default", **memory_mib_args - )(square) - - # Assert that the GCF is created with the intended memory - gcf = session.cloudfunctionsclient.get_function( - name=square_remote.bigframes_cloud_function - ) - assert gcf.service_config.available_memory == expected_memory - if expected_cpus is not None: - assert gcf.service_config.available_cpu == expected_cpus - if float(gcf.service_config.available_cpu) >= 1.0: - assert gcf.service_config.max_instance_request_concurrency >= float( - gcf.service_config.available_cpu - ) - - scalars_df, scalars_pandas_df = scalars_dfs - - bf_result = scalars_df["int64_too"].apply(square_remote).to_pandas() - pd_result = scalars_pandas_df["int64_too"].apply(square) - - pandas.testing.assert_series_equal(bf_result, pd_result, check_dtype=False) - finally: - # clean up the gcp assets created for the remote function - cleanup_function_assets( - square_remote, session.bqclient, session.cloudfunctionsclient - ) - - -@pytest.mark.parametrize( - ("memory_mib",), - [ - pytest.param(127, id="127-too-low"), - pytest.param(32769, id="set-32769-too-high"), - ], -) -def test_remote_function_gcf_memory_unsupported(session, memory_mib): - with pytest.raises(ValueError, match="Cloud run supports"): - - @session.remote_function( - reuse=False, - cloud_function_service_account="default", - cloud_function_memory_mib=memory_mib, - ) - def square(x: int) -> int: - return x * x - - -@pytest.mark.flaky(retries=2, delay=120) -def test_remote_function_unnamed_removed_w_session_cleanup(): - # create a clean session - session = bigframes.connect() - - with warnings.catch_warnings(record=True) as record: - # create an unnamed remote function in the session. - # The type hints in this function's signature are redundant. The - # `input_types` and `output_type` arguments from remote_function - # decorator take precedence and will be used instead. - @session.remote_function( - input_types=[int], - output_type=int, - reuse=False, - cloud_function_service_account="default", - ) - def foo(x: int) -> int: - return x + 1 - - # No following warning with only redundant type hints (no conflict). - input_type_warning = "Conflicting input types detected" - assert not any(input_type_warning in str(warning.message) for warning in record) - return_type_warning = "Conflicting return type detected" - assert not any(return_type_warning in str(warning.message) for warning in record) - - # ensure that remote function artifacts are created - assert foo.bigframes_remote_function is not None - session.bqclient.get_routine(foo.bigframes_remote_function) is not None - assert foo.bigframes_bigquery_function is not None - session.bqclient.get_routine(foo.bigframes_bigquery_function) is not None - assert foo.bigframes_cloud_function is not None - session.cloudfunctionsclient.get_function( - name=foo.bigframes_cloud_function - ) is not None - - # explicitly close the session - session.close() - - # ensure that the bq remote function is deleted - with pytest.raises(google.cloud.exceptions.NotFound): - session.bqclient.get_routine(foo.bigframes_bigquery_function) - - # the deletion of cloud function happens in a non-blocking way, ensure that - # it either exists in a being-deleted state, or is already deleted - try: - gcf = session.cloudfunctionsclient.get_function( - name=foo.bigframes_cloud_function - ) - assert gcf.state is functions_v2.Function.State.DELETING - except google.cloud.exceptions.NotFound: - pass - - -@pytest.mark.flaky(retries=2, delay=120) -def test_remote_function_named_perists_w_session_cleanup(): - try: - # create a clean session - session = bigframes.connect() - - # create a name for the remote function - name = test_utils.prefixer.Prefixer("bigframes", "").create_prefix() - - # create an unnamed remote function in the session - @session.remote_function( - reuse=False, name=name, cloud_function_service_account="default" - ) - def foo(x: int) -> int: - return x + 1 - - # ensure that remote function artifacts are created - assert foo.bigframes_remote_function is not None - session.bqclient.get_routine(foo.bigframes_remote_function) is not None - assert foo.bigframes_bigquery_function is not None - session.bqclient.get_routine(foo.bigframes_bigquery_function) is not None - assert foo.bigframes_cloud_function is not None - session.cloudfunctionsclient.get_function( - name=foo.bigframes_cloud_function - ) is not None - - # explicitly close the session - session.close() - - # ensure that the bq remote function still exists - session.bqclient.get_routine(foo.bigframes_bigquery_function) is not None - - # the deletion of cloud function happens in a non-blocking way, ensure - # that it was not deleted and still exists in active state - gcf = session.cloudfunctionsclient.get_function( - name=foo.bigframes_cloud_function - ) - assert gcf.state is functions_v2.Function.State.ACTIVE - finally: - # clean up the gcp assets created for the remote function - cleanup_function_assets(foo, session.bqclient, session.cloudfunctionsclient) - - -@pytest.mark.flaky(retries=2, delay=120) -def test_remote_function_clean_up_by_session_id(): - # Use a brand new session to avoid conflict with other tests - session = bigframes.Session() - session_id = session.session_id - try: - # we will create remote functions, one with explicit name and another - # without it, and later confirm that the former is deleted when the session - # is cleaned up by session id, but the latter remains - ## unnamed - @session.remote_function(reuse=False, cloud_function_service_account="default") - def foo_unnamed(x: int) -> int: - return x + 1 - - ## named - rf_name = test_utils.prefixer.Prefixer("bigframes", "").create_prefix() - - @session.remote_function( - reuse=False, name=rf_name, cloud_function_service_account="default" - ) - def foo_named(x: int) -> int: - return x + 2 - - # check that BQ remote functiosn were created with corresponding cloud - # functions - for foo in [foo_unnamed, foo_named]: - assert foo.bigframes_remote_function is not None - session.bqclient.get_routine(foo.bigframes_remote_function) is not None - assert foo.bigframes_bigquery_function is not None - session.bqclient.get_routine(foo.bigframes_bigquery_function) is not None - assert foo.bigframes_cloud_function is not None - session.cloudfunctionsclient.get_function( - name=foo.bigframes_cloud_function - ) is not None - - # clean up using explicit session id - bpd.clean_up_by_session_id( - session_id, location=session._location, project=session._project - ) - - # ensure that the unnamed bq remote function is deleted along with its - # corresponding cloud function - with pytest.raises(google.cloud.exceptions.NotFound): - session.bqclient.get_routine(foo_unnamed.bigframes_bigquery_function) - try: - gcf = session.cloudfunctionsclient.get_function( - name=foo_unnamed.bigframes_cloud_function - ) - assert gcf.state is functions_v2.Function.State.DELETING - except google.cloud.exceptions.NotFound: - pass - - # ensure that the named bq remote function still exists along with its - # corresponding cloud function - session.bqclient.get_routine(foo_named.bigframes_bigquery_function) is not None - gcf = session.cloudfunctionsclient.get_function( - name=foo_named.bigframes_cloud_function - ) - assert gcf.state is functions_v2.Function.State.ACTIVE - finally: - # clean up the gcp assets created for the remote function - cleanup_function_assets( - foo_named, session.bqclient, session.cloudfunctionsclient - ) - - -def test_df_apply_axis_1_multiple_params(session): - bf_df = bigframes.dataframe.DataFrame( - { - "Id": [1, 2, 3], - "Age": [22.5, 23, 23.5], - "Name": ["alpha", "beta", "gamma"], - } - ) - - expected_dtypes = ( - bigframes.dtypes.INT_DTYPE, - bigframes.dtypes.FLOAT_DTYPE, - bigframes.dtypes.STRING_DTYPE, - ) - - # Assert the dataframe dtypes - assert tuple(bf_df.dtypes) == expected_dtypes - - try: - - @session.remote_function( - input_types=[int, float, str], - output_type=str, - reuse=False, - cloud_function_service_account="default", - ) - def foo(x, y, z): - return f"I got {x}, {y} and {z}" - - assert getattr(foo, "is_row_processor") is False - assert getattr(foo, "input_dtypes") == expected_dtypes - - # Fails to apply on dataframe with incompatible number of columns - with pytest.raises( - ValueError, - match="^Parameter count mismatch:.* expected 3 parameters but received 2 DataFrame columns.", - ): - bf_df[["Id", "Age"]].apply(foo, axis=1) - with pytest.raises( - ValueError, - match="^Parameter count mismatch:.* expected 3 parameters but received 4 DataFrame columns.", - ): - bf_df.assign(Country="lalaland").apply(foo, axis=1) - - # Fails to apply on dataframe with incompatible column datatypes - with pytest.raises( - ValueError, - match="^Data type mismatch for DataFrame columns: Expected .* Received .*", - ): - bf_df.assign(Age=bf_df["Age"].astype("Int64")).apply(foo, axis=1) - - # Successfully applies to dataframe with matching number of columns - # and their datatypes - bf_result = bf_df.apply(foo, axis=1).to_pandas() - - # Since this scenario is not pandas-like, let's handcraft the - # expected result - expected_result = pandas.Series( - [ - "I got 1, 22.5 and alpha", - "I got 2, 23 and beta", - "I got 3, 23.5 and gamma", - ] - ) - - pandas.testing.assert_series_equal( - expected_result, bf_result, check_dtype=False, check_index_type=False - ) - - # Let's make sure the read_gbq_function path works for this function - foo_reuse = session.read_gbq_function(foo.bigframes_bigquery_function) - bf_result = bf_df.apply(foo_reuse, axis=1).to_pandas() - pandas.testing.assert_series_equal( - expected_result, bf_result, check_dtype=False, check_index_type=False - ) - finally: - # clean up the gcp assets created for the remote function - cleanup_function_assets(foo, session.bqclient, session.cloudfunctionsclient) - - -def test_df_apply_axis_1_multiple_params_array_output(session): - bf_df = bigframes.dataframe.DataFrame( - { - "Id": [1, 2, 3], - "Age": [22.5, 23, 23.5], - "Name": ["alpha", "beta", "gamma"], - } - ) - - expected_dtypes = ( - bigframes.dtypes.INT_DTYPE, - bigframes.dtypes.FLOAT_DTYPE, - bigframes.dtypes.STRING_DTYPE, - ) - - # Assert the dataframe dtypes - assert tuple(bf_df.dtypes) == expected_dtypes - - try: - - @session.remote_function( - input_types=[int, float, str], - output_type=list[str], - reuse=False, - cloud_function_service_account="default", - ) - def foo(x, y, z): - return [str(x), str(y), z] - - assert getattr(foo, "is_row_processor") is False - assert getattr(foo, "input_dtypes") == expected_dtypes - assert ( - getattr(foo, "bigframes_bigquery_function_output_dtype") - == bigframes.dtypes.STRING_DTYPE - ) - - # Fails to apply on dataframe with incompatible number of columns - with pytest.raises( - ValueError, - match="^Parameter count mismatch:.* expected 3 parameters but received 2 DataFrame columns.", - ): - bf_df[["Id", "Age"]].apply(foo, axis=1) - with pytest.raises( - ValueError, - match="^Parameter count mismatch:.* expected 3 parameters but received 4 DataFrame columns.", - ): - bf_df.assign(Country="lalaland").apply(foo, axis=1) - - # Fails to apply on dataframe with incompatible column datatypes - with pytest.raises( - ValueError, - match="^Data type mismatch for DataFrame columns: Expected .* Received .*", - ): - bf_df.assign(Age=bf_df["Age"].astype("Int64")).apply(foo, axis=1) - - # Successfully applies to dataframe with matching number of columns - # and their datatypes - bf_result = bf_df.apply(foo, axis=1).to_pandas() - - # Since this scenario is not pandas-like, let's handcraft the - # expected result - expected_result = pandas.Series( - [ - ["1", "22.5", "alpha"], - ["2", "23", "beta"], - ["3", "23.5", "gamma"], - ] - ) - - pandas.testing.assert_series_equal( - expected_result, bf_result, check_dtype=False, check_index_type=False - ) - - # Let's make sure the read_gbq_function path works for this function - foo_reuse = session.read_gbq_function(foo.bigframes_bigquery_function) - bf_result = bf_df.apply(foo_reuse, axis=1).to_pandas() - pandas.testing.assert_series_equal( - expected_result, bf_result, check_dtype=False, check_index_type=False - ) - finally: - # clean up the gcp assets created for the remote function - cleanup_function_assets(foo, session.bqclient, session.cloudfunctionsclient) - - -def test_df_apply_axis_1_single_param_non_series(session): - bf_df = bigframes.dataframe.DataFrame( - { - "Id": [1, 2, 3], - } - ) - - expected_dtypes = (bigframes.dtypes.INT_DTYPE,) - - # Assert the dataframe dtypes - assert tuple(bf_df.dtypes) == expected_dtypes - - try: - - @session.remote_function( - input_types=[int], - output_type=str, - reuse=False, - cloud_function_service_account="default", - ) - def foo(x): - return f"I got {x}" - - assert getattr(foo, "is_row_processor") is False - assert getattr(foo, "input_dtypes") == expected_dtypes - - # Fails to apply on dataframe with incompatible number of columns - with pytest.raises( - ValueError, - match="^Parameter count mismatch:.* expected 1 parameters but received 0 DataFrame.*", - ): - bf_df[[]].apply(foo, axis=1) - with pytest.raises( - ValueError, - match="^Parameter count mismatch:.* expected 1 parameters but received 2 DataFrame.*", - ): - bf_df.assign(Country="lalaland").apply(foo, axis=1) - - # Fails to apply on dataframe with incompatible column datatypes - with pytest.raises( - ValueError, - match="^Data type mismatch for DataFrame columns: Expected .* Received .*", - ): - bf_df.assign(Id=bf_df["Id"].astype("Float64")).apply(foo, axis=1) - - # Successfully applies to dataframe with matching number of columns - # and their datatypes - bf_result = bf_df.apply(foo, axis=1).to_pandas() - - # Since this scenario is not pandas-like, let's handcraft the - # expected result - expected_result = pandas.Series( - [ - "I got 1", - "I got 2", - "I got 3", - ] - ) - - pandas.testing.assert_series_equal( - expected_result, bf_result, check_dtype=False, check_index_type=False - ) - finally: - # clean up the gcp assets created for the remote function - cleanup_function_assets(foo, session.bqclient, session.cloudfunctionsclient) - - -@pytest.mark.flaky(retries=2, delay=120) -def test_df_apply_axis_1_array_output(session, scalars_dfs): - columns = ["int64_col", "int64_too"] - scalars_df, scalars_pandas_df = scalars_dfs - try: - - @session.remote_function(reuse=False, cloud_function_service_account="default") - def generate_stats(row: pandas.Series) -> list[int]: - import pandas as pd - - sum = row["int64_too"] - avg = row["int64_too"] - if pd.notna(row["int64_col"]): - sum += row["int64_col"] - avg = round((avg + row["int64_col"]) / 2) - return [sum, avg] - - assert getattr(generate_stats, "is_row_processor") - - bf_result = scalars_df[columns].apply(generate_stats, axis=1).to_pandas() - pd_result = scalars_pandas_df[columns].apply(generate_stats, axis=1) - - # bf_result.dtype is 'list[pyarrow]' while pd_result.dtype - # is 'object', ignore this mismatch by using check_dtype=False. - pandas.testing.assert_series_equal(pd_result, bf_result, check_dtype=False) - - # Let's make sure the read_gbq_function path works for this function - generate_stats_reuse = session.read_gbq_function( - generate_stats.bigframes_bigquery_function, - is_row_processor=True, - ) - bf_result = scalars_df[columns].apply(generate_stats_reuse, axis=1).to_pandas() - pandas.testing.assert_series_equal(pd_result, bf_result, check_dtype=False) - finally: - # clean up the gcp assets created for the remote function - cleanup_function_assets( - generate_stats, session.bqclient, session.cloudfunctionsclient - ) - - -@pytest.mark.parametrize( - ( - "ingress_settings_args", - "effective_ingress_settings", - "expect_default_ingress_setting_warning", - ), - [ - pytest.param( - {}, - functions_v2.ServiceConfig.IngressSettings.ALLOW_INTERNAL_ONLY, - False, - id="no-set", - ), - pytest.param( - {"cloud_function_ingress_settings": None}, - functions_v2.ServiceConfig.IngressSettings.ALLOW_INTERNAL_ONLY, - True, - id="set-none", - ), - pytest.param( - {"cloud_function_ingress_settings": "internal-only"}, - functions_v2.ServiceConfig.IngressSettings.ALLOW_INTERNAL_ONLY, - False, - id="set-internal-only", - ), - pytest.param( - {"cloud_function_ingress_settings": "internal-and-gclb"}, - functions_v2.ServiceConfig.IngressSettings.ALLOW_INTERNAL_AND_GCLB, - False, - id="set-internal-and-gclb", - ), - ], -) -@pytest.mark.flaky(retries=2, delay=120) -def test_remote_function_ingress_settings( - session, - scalars_dfs, - ingress_settings_args, - effective_ingress_settings, - expect_default_ingress_setting_warning, -): - try: - # Verify the function raises the expected security warning message. - with warnings.catch_warnings(record=True) as record: - - def square(x: int) -> int: - return x * x - - square_remote = session.remote_function( - reuse=False, - cloud_function_service_account="default", - **ingress_settings_args, - )(square) - - default_ingress_setting_warnings = [ - warn - for warn in record - if isinstance(warn.message, UserWarning) - and "The `cloud_function_ingress_settings` is being set to 'internal-only' by default." - ] - assert len(default_ingress_setting_warnings) == ( - 1 if expect_default_ingress_setting_warning else 0 - ) - - # Assert that the GCF is created with the intended maximum timeout - gcf = session.cloudfunctionsclient.get_function( - name=square_remote.bigframes_cloud_function - ) - assert gcf.service_config.ingress_settings == effective_ingress_settings - - scalars_df, scalars_pandas_df = scalars_dfs - - bf_result = scalars_df["int64_too"].apply(square_remote).to_pandas() - pd_result = scalars_pandas_df["int64_too"].apply(square) - - pandas.testing.assert_series_equal(bf_result, pd_result, check_dtype=False) - finally: - # clean up the gcp assets created for the remote function - cleanup_function_assets( - square_remote, session.bqclient, session.cloudfunctionsclient - ) - - -@pytest.mark.flaky(retries=2, delay=120) -def test_remote_function_ingress_settings_unsupported(session): - with pytest.raises( - ValueError, match="'unknown' not one of the supported ingress settings values" - ): - - @session.remote_function( - reuse=False, - cloud_function_service_account="default", - cloud_function_ingress_settings="unknown", - ) - def square(x: int) -> int: - return x * x - - -@pytest.mark.parametrize( - ("session_creator"), - [ - pytest.param(bigframes.Session, id="session-constructor"), - pytest.param(bigframes.connect, id="connect-method"), - ], -) -@pytest.mark.flaky(retries=2, delay=120) -def test_remote_function_w_context_manager_unnamed( - scalars_dfs, dataset_id, bq_cf_connection, session_creator -): - def add_one(x: int) -> int: - return x + 1 - - scalars_df, scalars_pandas_df = scalars_dfs - pd_result = scalars_pandas_df["int64_too"].apply(add_one) - - temporary_bigquery_remote_function = None - temporary_cloud_run_function = None - - try: - with session_creator() as session: - # create a temporary remote function - add_one_remote_temp = session.remote_function( - dataset=dataset_id, - bigquery_connection=bq_cf_connection, - reuse=False, - cloud_function_service_account="default", - )(add_one) - - temporary_bigquery_remote_function = ( - add_one_remote_temp.bigframes_bigquery_function - ) - assert temporary_bigquery_remote_function is not None - assert ( - session.bqclient.get_routine(temporary_bigquery_remote_function) - is not None - ) - - temporary_cloud_run_function = add_one_remote_temp.bigframes_cloud_function - assert temporary_cloud_run_function is not None - assert ( - session.cloudfunctionsclient.get_function( - name=temporary_cloud_run_function - ) - is not None - ) - - bf_result = scalars_df["int64_too"].apply(add_one_remote_temp).to_pandas() - pandas.testing.assert_series_equal(bf_result, pd_result, check_dtype=False) - - # outside the with statement context manager the temporary BQ remote - # function and the underlying cloud run function should have been - # cleaned up - assert temporary_bigquery_remote_function is not None - with pytest.raises(google.api_core.exceptions.NotFound): - session.bqclient.get_routine(temporary_bigquery_remote_function) - # the deletion of cloud function happens in a non-blocking way, ensure that - # it either exists in a being-deleted state, or is already deleted - assert temporary_cloud_run_function is not None - try: - gcf = session.cloudfunctionsclient.get_function( - name=temporary_cloud_run_function - ) - assert gcf.state is functions_v2.Function.State.DELETING - except google.cloud.exceptions.NotFound: - pass - finally: - # clean up the gcp assets created for the temporary remote function, - # just in case it was not explicitly cleaned up in the try clause due - # to assertion failure or exception earlier than that - cleanup_function_assets( - add_one_remote_temp, session.bqclient, session.cloudfunctionsclient - ) - - -@pytest.mark.parametrize( - ("session_creator"), - [ - pytest.param(bigframes.Session, id="session-constructor"), - pytest.param(bigframes.connect, id="connect-method"), - ], -) -@pytest.mark.flaky(retries=2, delay=120) -def test_remote_function_w_context_manager_named( - scalars_dfs, dataset_id, bq_cf_connection, session_creator -): - def add_one(x: int) -> int: - return x + 1 - - scalars_df, scalars_pandas_df = scalars_dfs - pd_result = scalars_pandas_df["int64_too"].apply(add_one) - - persistent_bigquery_remote_function = None - persistent_cloud_run_function = None - - try: - with session_creator() as session: - # create a persistent remote function - name = test_utils.prefixer.Prefixer("bigframes", "").create_prefix() - add_one_remote_persist = session.remote_function( - dataset=dataset_id, - bigquery_connection=bq_cf_connection, - reuse=False, - name=name, - cloud_function_service_account="default", - )(add_one) - - persistent_bigquery_remote_function = ( - add_one_remote_persist.bigframes_bigquery_function - ) - assert persistent_bigquery_remote_function is not None - assert ( - session.bqclient.get_routine(persistent_bigquery_remote_function) - is not None - ) - - persistent_cloud_run_function = ( - add_one_remote_persist.bigframes_cloud_function - ) - assert persistent_cloud_run_function is not None - assert ( - session.cloudfunctionsclient.get_function( - name=persistent_cloud_run_function - ) - is not None - ) - - bf_result = ( - scalars_df["int64_too"].apply(add_one_remote_persist).to_pandas() - ) - pandas.testing.assert_series_equal(bf_result, pd_result, check_dtype=False) - - # outside the with statement context manager the persistent BQ remote - # function and the underlying cloud run function should still exist - assert persistent_bigquery_remote_function is not None - assert ( - session.bqclient.get_routine(persistent_bigquery_remote_function) - is not None - ) - assert persistent_cloud_run_function is not None - assert ( - session.cloudfunctionsclient.get_function( - name=persistent_cloud_run_function - ) - is not None - ) - finally: - # clean up the gcp assets created for the persistent remote function - cleanup_function_assets( - add_one_remote_persist, session.bqclient, session.cloudfunctionsclient - ) - - -@pytest.mark.parametrize( - "array_dtype", - [ - bool, - int, - float, - str, - ], -) -@pytest.mark.flaky(retries=2, delay=120) -def test_remote_function_array_output( - session, scalars_dfs, dataset_id, bq_cf_connection, array_dtype -): - try: - - @session.remote_function( - dataset=dataset_id, - bigquery_connection=bq_cf_connection, - reuse=False, - cloud_function_service_account="default", - ) - def featurize(x: int) -> list[array_dtype]: # type: ignore - return [array_dtype(i) for i in [x, x + 1, x + 2]] - - scalars_df, scalars_pandas_df = scalars_dfs - - bf_int64_col = scalars_df["int64_too"] - bf_result = bf_int64_col.apply(featurize).to_pandas() - - pd_int64_col = scalars_pandas_df["int64_too"] - pd_result = pd_int64_col.apply(featurize) - - # ignore any dtype disparity - pandas.testing.assert_series_equal(pd_result, bf_result, check_dtype=False) - - # Let's make sure the read_gbq_function path works for this function - featurize_reuse = session.read_gbq_function( - featurize.bigframes_bigquery_function # type: ignore - ) - bf_result = scalars_df["int64_too"].apply(featurize_reuse).to_pandas() - pandas.testing.assert_series_equal(pd_result, bf_result, check_dtype=False) - finally: - # clean up the gcp assets created for the remote function - cleanup_function_assets( - featurize, session.bqclient, session.cloudfunctionsclient - ) - - -@pytest.mark.flaky(retries=2, delay=120) -def test_remote_function_array_output_partial_ordering_mode( - unordered_session, scalars_dfs, dataset_id, bq_cf_connection -): - try: - - @unordered_session.remote_function( - dataset=dataset_id, - bigquery_connection=bq_cf_connection, - reuse=False, - cloud_function_service_account="default", - ) - def featurize(x: float) -> list[float]: # type: ignore - return [x, x + 1, x + 2] - - scalars_df, scalars_pandas_df = scalars_dfs - - bf_int64_col = scalars_df["float64_col"].dropna() - bf_result = bf_int64_col.apply(featurize).to_pandas() - - pd_int64_col = scalars_pandas_df["float64_col"].dropna() - pd_result = pd_int64_col.apply(featurize) - - # ignore any dtype disparity - pandas.testing.assert_series_equal(pd_result, bf_result, check_dtype=False) - - # Let's make sure the read_gbq_function path works for this function - featurize_reuse = unordered_session.read_gbq_function( - featurize.bigframes_bigquery_function # type: ignore - ) - bf_int64_col = scalars_df["float64_col"].dropna() - bf_result = bf_int64_col.apply(featurize_reuse).to_pandas() - pandas.testing.assert_series_equal(pd_result, bf_result, check_dtype=False) - finally: - # clean up the gcp assets created for the remote function - cleanup_function_assets( - featurize, - unordered_session.bqclient, - unordered_session.cloudfunctionsclient, - ) - - -@pytest.mark.flaky(retries=2, delay=120) -def test_remote_function_array_output_multiindex( - session, scalars_dfs, dataset_id, bq_cf_connection -): - try: - - @session.remote_function( - dataset=dataset_id, - bigquery_connection=bq_cf_connection, - reuse=False, - cloud_function_service_account="default", - ) - def featurize(x: int) -> list[float]: - return [x, x + 0.5, x + 0.33] - - scalars_df, scalars_pandas_df = scalars_dfs - multiindex_cols = ["rowindex", "string_col"] - scalars_df = scalars_df.reset_index().set_index(multiindex_cols) - scalars_pandas_df = scalars_pandas_df.reset_index().set_index(multiindex_cols) - - bf_int64_col = scalars_df["int64_too"] - bf_result = bf_int64_col.apply(featurize).to_pandas() - - pd_int64_col = scalars_pandas_df["int64_too"] - pd_result = pd_int64_col.apply(featurize) - - # ignore any dtype disparity - pandas.testing.assert_series_equal(pd_result, bf_result, check_dtype=False) - finally: - # clean up the gcp assets created for the remote function - cleanup_function_assets( - featurize, session.bqclient, session.cloudfunctionsclient - ) - - -@pytest.mark.flaky(retries=2, delay=120) -def test_remote_function_connection_path_format( - session, scalars_dfs, dataset_id, bq_cf_connection -): - try: - - @session.remote_function( - dataset=dataset_id, - bigquery_connection=f"projects/{session.bqclient.project}/locations/{session._location}/connections/{bq_cf_connection}", - reuse=False, - cloud_function_service_account="default", - ) - def foo(x: int) -> int: - return x + 1 - - scalars_df, scalars_pandas_df = scalars_dfs - - bf_int64_col = scalars_df["int64_too"] - bf_result = bf_int64_col.apply(foo).to_pandas() - - pd_int64_col = scalars_pandas_df["int64_too"] - pd_result = pd_int64_col.apply(foo) - - # ignore any dtype disparity - pandas.testing.assert_series_equal(pd_result, bf_result, check_dtype=False) - finally: - # clean up the gcp assets created for the remote function - cleanup_function_assets(foo, session.bqclient, session.cloudfunctionsclient) - - -@pytest.mark.flaky(retries=2, delay=120) -def test_remote_function_df_where_mask(session, dataset_id, scalars_dfs): - try: - # The return type has to be bool type for callable where condition. - def is_sum_positive(a, b): - return a + b > 0 - - is_sum_positive_mf = session.remote_function( - input_types=[int, int], - output_type=bool, - dataset=dataset_id, - reuse=False, - cloud_function_service_account="default", - )(is_sum_positive) - - scalars_df, scalars_pandas_df = scalars_dfs - int64_cols = ["int64_col", "int64_too"] - - bf_int64_df = scalars_df[int64_cols] - bf_int64_df_filtered = bf_int64_df.dropna() - pd_int64_df = scalars_pandas_df[int64_cols] - pd_int64_df_filtered = pd_int64_df.dropna() - - # Test callable condition in dataframe.where method. - bf_result = bf_int64_df_filtered.where(is_sum_positive_mf, 0).to_pandas() - # Pandas doesn't support such case, use following as workaround. - pd_result = pd_int64_df_filtered.where(pd_int64_df_filtered.sum(axis=1) > 0, 0) - - # Ignore any dtype difference. - pandas.testing.assert_frame_equal(bf_result, pd_result, check_dtype=False) - - # Test callable condition in dataframe.mask method. - bf_result = bf_int64_df_filtered.mask(is_sum_positive_mf, 0).to_pandas() - # Pandas doesn't support such case, use following as workaround. - pd_result = pd_int64_df_filtered.mask(pd_int64_df_filtered.sum(axis=1) > 0, 0) - - # Ignore any dtype difference. - pandas.testing.assert_frame_equal(bf_result, pd_result, check_dtype=False) - - finally: - # Clean up the gcp assets created for the remote function. - cleanup_function_assets( - is_sum_positive_mf, session.bqclient, ignore_failures=False - ) - - -@pytest.mark.flaky(retries=2, delay=120) -def test_remote_function_df_where_other_issue(session, dataset_id, scalars_df_index): - try: - - def the_sum(a, b): - return a + b - - the_sum_mf = session.remote_function( - input_types=[int, float], - output_type=float, - dataset=dataset_id, - reuse=False, - cloud_function_service_account="default", - )(the_sum) - - int64_cols = ["int64_col", "float64_col"] - bf_int64_df = scalars_df_index[int64_cols] - bf_int64_df_filtered = bf_int64_df.dropna() - - with pytest.raises( - ValueError, - match="Seires is not a supported replacement type!", - ): - # The execution of the callable other=the_sum_mf will return a - # Series, which is not a supported replacement type. - bf_int64_df_filtered.where(cond=bf_int64_df > 100, other=the_sum_mf) - - finally: - # Clean up the gcp assets created for the remote function. - cleanup_function_assets(the_sum_mf, session.bqclient, ignore_failures=False) - - -@pytest.mark.flaky(retries=2, delay=120) -def test_remote_function_df_where_mask_series(session, dataset_id, scalars_dfs): - try: - # The return type has to be bool type for callable where condition. - def is_sum_positive_series(s: pandas.Series) -> bool: - return s["int64_col"] + s["int64_too"] > 0 - - with pytest.raises( - TypeError, - match="Argument type hint must be Pandas Series, not BigFrames Series.", - ): - session.remote_function( - input_types=bigframes.series.Series, - dataset=dataset_id, - reuse=False, - cloud_function_service_account="default", - )(is_sum_positive_series) - - is_sum_positive_series_mf = session.remote_function( - dataset=dataset_id, - reuse=False, - cloud_function_service_account="default", - )(is_sum_positive_series) - - scalars_df, scalars_pandas_df = scalars_dfs - int64_cols = ["int64_col", "int64_too"] - - bf_int64_df = scalars_df[int64_cols] - bf_int64_df_filtered = bf_int64_df.dropna() - pd_int64_df = scalars_pandas_df[int64_cols] - pd_int64_df_filtered = pd_int64_df.dropna() - - # This is for callable `other` arg in dataframe.where method. - def func_for_other(x): - return -x - - # Test callable condition in dataframe.where method. - bf_result = bf_int64_df_filtered.where( - is_sum_positive_series_mf, func_for_other - ).to_pandas() - pd_result = pd_int64_df_filtered.where(is_sum_positive_series, func_for_other) - - # Ignore any dtype difference. - pandas.testing.assert_frame_equal(bf_result, pd_result, check_dtype=False) - - # Test callable condition in dataframe.mask method. - bf_result = bf_int64_df_filtered.mask( - is_sum_positive_series_mf, func_for_other - ).to_pandas() - pd_result = pd_int64_df_filtered.mask(is_sum_positive_series, func_for_other) - - # Ignore any dtype difference. - pandas.testing.assert_frame_equal(bf_result, pd_result, check_dtype=False) - - finally: - # Clean up the gcp assets created for the remote function. - cleanup_function_assets( - is_sum_positive_series_mf, session.bqclient, ignore_failures=False - ) - - -@pytest.mark.flaky(retries=2, delay=120) -def test_remote_function_series_where_mask(session, dataset_id, scalars_dfs): - try: - - def _ten_times(x): - return x * 10 - - ten_times_mf = session.remote_function( - input_types=float, - output_type=float, - dataset=dataset_id, - reuse=False, - cloud_function_service_account="default", - )(_ten_times) - - scalars, scalars_pandas = scalars_dfs - - bf_int64 = scalars["float64_col"] - bf_int64_filtered = bf_int64.dropna() - pd_int64 = scalars_pandas["float64_col"] - pd_int64_filtered = pd_int64.dropna() - - # Test series.where method: the cond is not a callable and the other is - # a callable (remote function). - bf_result = bf_int64_filtered.where( - cond=bf_int64_filtered < 0, other=ten_times_mf - ).to_pandas() - pd_result = pd_int64_filtered.where( - cond=pd_int64_filtered < 0, other=_ten_times - ) - - # Ignore any dtype difference. - pandas.testing.assert_series_equal(bf_result, pd_result, check_dtype=False) - - # Test series.mask method: the cond is not a callable and the other is - # a callable (remote function). - bf_result = bf_int64_filtered.mask( - cond=bf_int64_filtered < 0, other=ten_times_mf - ).to_pandas() - pd_result = pd_int64_filtered.mask(cond=pd_int64_filtered < 0, other=_ten_times) - - # Ignore any dtype difference. - pandas.testing.assert_series_equal(bf_result, pd_result, check_dtype=False) - - finally: - # Clean up the gcp assets created for the remote function. - cleanup_function_assets(ten_times_mf, session.bqclient, ignore_failures=False) - - -@pytest.mark.flaky(retries=2, delay=120) -def test_remote_function_series_apply_args(session, dataset_id, scalars_dfs): - try: - - @session.remote_function( - dataset=dataset_id, - reuse=False, - cloud_function_service_account="default", - ) - def foo(x: int, y: bool, z: float) -> str: - if y: - return f"{x}: y is True." - if z > 0.0: - return f"{x}: y is False and z is positive." - return f"{x}: y is False and z is non-positive." - - scalars_df, scalars_pandas_df = scalars_dfs - - args1 = (True, 10.0) - bf_result = scalars_df["int64_too"].apply(foo, args=args1).to_pandas() - pd_result = scalars_pandas_df["int64_too"].apply(foo, args=args1) - - # Ignore any dtype difference. - pandas.testing.assert_series_equal(bf_result, pd_result, check_dtype=False) - - args2 = (False, -10.0) - foo_ref = session.read_gbq_function(foo.bigframes_bigquery_function) - - bf_result = scalars_df["int64_too"].apply(foo_ref, args=args2).to_pandas() - pd_result = scalars_pandas_df["int64_too"].apply(foo, args=args2) - - # Ignore any dtype difference. - pandas.testing.assert_series_equal(bf_result, pd_result, check_dtype=False) - - finally: - # Clean up the gcp assets created for the remote function. - cleanup_function_assets(foo, session.bqclient, ignore_failures=False) diff --git a/tests/system/large/ml/conftest.py b/tests/system/large/ml/conftest.py deleted file mode 100644 index ffb02e8beb8..00000000000 --- a/tests/system/large/ml/conftest.py +++ /dev/null @@ -1,87 +0,0 @@ -# Copyright 2023 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -import hashlib -import logging - -import google.cloud.exceptions -import pytest -from google.cloud import bigquery - -import bigframes -from bigframes.ml import core, linear_model - -PERMANENT_DATASET = "bigframes_testing" - - -@pytest.fixture(scope="session") -def dataset_id_permanent(bigquery_client: bigquery.Client, project_id: str) -> str: - """Create a dataset if it doesn't exist.""" - dataset_id = f"{project_id}.{PERMANENT_DATASET}" - dataset = bigquery.Dataset(dataset_id) - bigquery_client.create_dataset(dataset, exists_ok=True) - return dataset_id - - -@pytest.fixture(scope="session") -def penguins_bqml_linear_model(session, penguins_linear_model_name) -> core.BqmlModel: - model = session.bqclient.get_model(penguins_linear_model_name) - return core.BqmlModel(session, model) - - -@pytest.fixture(scope="function") -def penguins_linear_model_w_global_explain( - penguins_bqml_linear_model: core.BqmlModel, -) -> linear_model.LinearRegression: - bf_model = linear_model.LinearRegression(enable_global_explain=True) - bf_model._bqml_model = penguins_bqml_linear_model - return bf_model - - -@pytest.fixture(scope="session") -def penguins_table_id(test_data_tables) -> str: - return test_data_tables["penguins"] - - -@pytest.fixture(scope="session") -def penguins_linear_model_name( - session: bigframes.Session, dataset_id_permanent, penguins_table_id -) -> str: - """Provides a pretrained model as a test fixture that is cached across test runs. - This lets us run system tests without having to wait for a model.fit(...)""" - sql = f""" -CREATE OR REPLACE MODEL `$model_name` -OPTIONS ( - model_type='linear_reg', - input_label_cols=['body_mass_g'], - data_split_method='NO_SPLIT' -) AS -SELECT - * -FROM - `{penguins_table_id}` -WHERE - body_mass_g IS NOT NULL""" - # We use the SQL hash as the name to ensure the model is regenerated if this fixture is edited - model_name = f"{dataset_id_permanent}.penguins_linear_reg_{hashlib.md5(sql.encode()).hexdigest()}" - sql = sql.replace("$model_name", model_name) - - try: - session.bqclient.get_model(model_name) - except google.cloud.exceptions.NotFound: - logging.info( - "penguins_linear_model fixture was not found in the permanent dataset, regenerating it..." - ) - session.bqclient.query(sql).result() - finally: - return model_name diff --git a/tests/system/large/ml/test_cluster.py b/tests/system/large/ml/test_cluster.py index 9736199b176..eae6896669b 100644 --- a/tests/system/large/ml/test_cluster.py +++ b/tests/system/large/ml/test_cluster.py @@ -13,15 +13,17 @@ # limitations under the License. import pandas as pd +import pytest from bigframes.ml import cluster -from bigframes.testing import utils +from tests.system.utils import assert_pandas_df_equal_ignore_ordering +@pytest.mark.flaky(retries=2, delay=120) def test_cluster_configure_fit_score_predict( session, penguins_df_default_index, dataset_id ): - model = cluster.KMeans(n_clusters=3, init="random") + model = cluster.KMeans(n_clusters=3) df = penguins_df_default_index.dropna()[ [ @@ -86,81 +88,31 @@ def test_cluster_configure_fit_score_predict( # Check score to ensure the model was fitted score_result = model.score(new_penguins).to_pandas() - - eval_metrics = ["davies_bouldin_index", "mean_squared_distance"] - utils.check_pandas_df_schema_and_index(score_result, columns=eval_metrics, index=1) - - predictions = model.predict(new_penguins).to_pandas() - assert predictions.shape == (4, 9) - utils.check_pandas_df_schema_and_index( - predictions, - columns=["CENTROID_ID"], - index=["test1", "test2", "test3", "test4"], - col_exact=False, + score_expected = pd.DataFrame( + {"davies_bouldin_index": [1.502182], "mean_squared_distance": [1.953408]}, + dtype="Float64", ) + score_expected = score_expected.reindex(index=score_expected.index.astype("Int64")) - # save, load, check n_clusters to ensure configuration was kept - reloaded_model = model.to_gbq( - f"{dataset_id}.temp_configured_cluster_model", replace=True - ) - assert reloaded_model._bqml_model is not None - assert ( - f"{dataset_id}.temp_configured_cluster_model" - in reloaded_model._bqml_model.model_name + pd.testing.assert_frame_equal( + score_result, score_expected, check_exact=False, rtol=0.1 ) - assert reloaded_model.n_clusters == 3 - assert reloaded_model.init == "RANDOM" - assert reloaded_model.distance_type == "EUCLIDEAN" - assert reloaded_model.max_iter == 20 - assert reloaded_model.tol == 0.01 - -def test_cluster_configure_fit_load_params(penguins_df_default_index, dataset_id): - model = cluster.KMeans( - n_clusters=4, - init="random", - distance_type="cosine", - max_iter=30, - tol=0.001, + result = model.predict(new_penguins).to_pandas() + expected = pd.DataFrame( + {"CENTROID_ID": [2, 3, 1, 2]}, + dtype="Int64", + index=pd.Index(["test1", "test2", "test3", "test4"], dtype="string[pyarrow]"), ) - - df = penguins_df_default_index.dropna()[ - [ - "culmen_length_mm", - "culmen_depth_mm", - "flipper_length_mm", - "sex", - ] - ] - - # TODO(swast): How should we handle the default index? Currently, we get: - # "Column bigframes_index_0_z is not found in the input data to the - # EVALUATE function." - df = df.reset_index(drop=True) - - model.fit(df) + expected.index.name = "observation" + assert_pandas_df_equal_ignore_ordering(result, expected) # save, load, check n_clusters to ensure configuration was kept reloaded_model = model.to_gbq( f"{dataset_id}.temp_configured_cluster_model", replace=True ) - assert reloaded_model._bqml_model is not None assert ( f"{dataset_id}.temp_configured_cluster_model" in reloaded_model._bqml_model.model_name ) - assert reloaded_model.n_clusters == 4 - assert reloaded_model.init == "RANDOM" - assert reloaded_model.distance_type == "COSINE" - assert reloaded_model.max_iter == 30 - assert reloaded_model.tol == 0.001 - - -def test_model_centroids_with_custom_index(penguins_df_default_index): - model = cluster.KMeans(n_clusters=3) - penguins = penguins_df_default_index.set_index(["species", "island", "sex"]) - model.fit(penguins) - - assert ( - not model.cluster_centers_["feature"].isin(["species", "island", "sex"]).any() - ) + assert reloaded_model.n_clusters == 3 diff --git a/tests/system/large/ml/test_compose.py b/tests/system/large/ml/test_compose.py index 9279324b3c7..0c280e5d020 100644 --- a/tests/system/large/ml/test_compose.py +++ b/tests/system/large/ml/test_compose.py @@ -12,54 +12,28 @@ # See the License for the specific language governing permissions and # limitations under the License. -from bigframes.ml import compose, preprocessing -from bigframes.testing import utils +import pandas + +import bigframes.ml.cluster +import bigframes.ml.compose +import bigframes.ml.linear_model +import bigframes.ml.pipeline +import bigframes.ml.preprocessing def test_columntransformer_standalone_fit_and_transform( penguins_df_default_index, new_penguins_df ): - transformer = compose.ColumnTransformer( + transformer = bigframes.ml.compose.ColumnTransformer( [ ( "onehot", - preprocessing.OneHotEncoder(), - "species", - ), - ( - "starndard_scale", - preprocessing.StandardScaler(), - ["culmen_length_mm", "flipper_length_mm"], - ), - ( - "min_max_scale", - preprocessing.MinMaxScaler(), - ["culmen_length_mm"], - ), - ( - "increment", - compose.SQLScalarColumnTransformer("{0}+1"), - ["culmen_length_mm", "flipper_length_mm"], - ), - ( - "length", - compose.SQLScalarColumnTransformer( - "CASE WHEN {0} IS NULL THEN -1 ELSE LENGTH({0}) END", - target_column="len_{0}", - ), - "species", - ), - ( - "ohe", - compose.SQLScalarColumnTransformer( - "CASE WHEN {0}='Adelie Penguin (Pygoscelis adeliae)' THEN 1 ELSE 0 END", - target_column="ohe_adelie", - ), + bigframes.ml.preprocessing.OneHotEncoder(), "species", ), ( - "identity", - compose.SQLScalarColumnTransformer("{0}", target_column="{0}"), + "scale", + bigframes.ml.preprocessing.StandardScaler(), ["culmen_length_mm", "flipper_length_mm"], ), ] @@ -70,178 +44,83 @@ def test_columntransformer_standalone_fit_and_transform( ) result = transformer.transform(new_penguins_df).to_pandas() - utils.check_pandas_df_schema_and_index( - result, - columns=[ - "onehotencoded_species", - "standard_scaled_culmen_length_mm", - "min_max_scaled_culmen_length_mm", - "standard_scaled_flipper_length_mm", - "transformed_culmen_length_mm", - "transformed_flipper_length_mm", - "len_species", - "ohe_adelie", - "culmen_length_mm", - "flipper_length_mm", - ], - index=[1633, 1672, 1690], - col_exact=False, + # TODO: bug? feature columns seem to be in nondeterministic random order + # workaround: sort columns by name. Can't repro it in pantheon, so could + # be a bigframes issue... + result = result.reindex(sorted(result.columns), axis=1) + + expected = pandas.DataFrame( + { + "onehotencoded_species": [ + [{"index": 1, "value": 1.0}], + [{"index": 1, "value": 1.0}], + [{"index": 2, "value": 1.0}], + ], + "standard_scaled_culmen_length_mm": [ + -0.811119671289163, + -0.9945520581113803, + -1.104611490204711, + ], + "standard_scaled_flipper_length_mm": [-0.350044, -1.418336, -0.9198], + }, + index=pandas.Index([1633, 1672, 1690], dtype="Int64", name="tag_number"), ) - - -def test_columntransformer_standalone_fit_transform(new_penguins_df): - # rename column to ensure robustness to column names that must be escaped - new_penguins_df = new_penguins_df.rename(columns={"species": "123 'species'"}) - transformer = compose.ColumnTransformer( - [ - ( - "onehot", - preprocessing.OneHotEncoder(), - "123 'species'", - ), - ( - "standard_scale", - preprocessing.StandardScaler(), - ["culmen_length_mm", "flipper_length_mm"], - ), - ( - "length", - compose.SQLScalarColumnTransformer( - "CASE WHEN {0} IS NULL THEN -1 ELSE LENGTH({0}) END", - target_column="len_{0}", - ), - "123 'species'", - ), - ( - "identity", - compose.SQLScalarColumnTransformer("{0}", target_column="{0}"), - ["culmen_length_mm", "flipper_length_mm"], - ), - ] + expected.standard_scaled_culmen_length_mm = ( + expected.standard_scaled_culmen_length_mm.astype("Float64") ) - - result = transformer.fit_transform( - new_penguins_df[["123 'species'", "culmen_length_mm", "flipper_length_mm"]] - ).to_pandas() - - utils.check_pandas_df_schema_and_index( - result, - columns=[ - "onehotencoded_123 'species'", - "standard_scaled_culmen_length_mm", - "standard_scaled_flipper_length_mm", - "len_123 'species'", - "culmen_length_mm", - "flipper_length_mm", - ], - index=[1633, 1672, 1690], - col_exact=False, + expected.standard_scaled_flipper_length_mm = ( + expected.standard_scaled_flipper_length_mm.astype("Float64") ) + pandas.testing.assert_frame_equal(result, expected, rtol=1e-3) + -def test_columntransformer_save_load(new_penguins_df, dataset_id): - transformer = compose.ColumnTransformer( +def test_columntransformer_standalone_fit_transform(new_penguins_df): + transformer = bigframes.ml.compose.ColumnTransformer( [ ( "onehot", - preprocessing.OneHotEncoder(), + bigframes.ml.preprocessing.OneHotEncoder(), "species", ), ( - "standard_scale", - preprocessing.StandardScaler(), + "scale", + bigframes.ml.preprocessing.StandardScaler(), ["culmen_length_mm", "flipper_length_mm"], ), - ( - "length", - compose.SQLScalarColumnTransformer( - "CASE WHEN {0} IS NULL THEN -1 ELSE LENGTH({0}) END", - target_column="len_{0}", - ), - "species", - ), - ( - "identity", - compose.SQLScalarColumnTransformer("{0}", target_column="{0}"), - ["culmen_length_mm", "flipper_length_mm"], - ), - ( - "flexname", - compose.SQLScalarColumnTransformer( - "CASE WHEN {0} IS NULL THEN -1 ELSE LENGTH({0}) END", - target_column="Flex {0} Name", - ), - "species", - ), ] ) - transformer.fit( - new_penguins_df[["species", "culmen_length_mm", "flipper_length_mm"]] - ) - - reloaded_transformer = transformer.to_gbq( - f"{dataset_id}.temp_configured_model", replace=True - ) - - assert isinstance(reloaded_transformer, compose.ColumnTransformer) - - expected = [ - ( - "one_hot_encoder", - preprocessing.OneHotEncoder(max_categories=1000001, min_frequency=0), - "species", - ), - ("standard_scaler", preprocessing.StandardScaler(), "culmen_length_mm"), - ("standard_scaler", preprocessing.StandardScaler(), "flipper_length_mm"), - ( - "sql_scalar_column_transformer", - compose.SQLScalarColumnTransformer( - "CASE WHEN `species` IS NULL THEN -1 ELSE LENGTH(`species`) END", - target_column="len_species", - ), - "?len_species", - ), - ( - "sql_scalar_column_transformer", - compose.SQLScalarColumnTransformer( - "`flipper_length_mm`", target_column="flipper_length_mm" - ), - "?flipper_length_mm", - ), - ( - "sql_scalar_column_transformer", - compose.SQLScalarColumnTransformer( - "`culmen_length_mm`", target_column="culmen_length_mm" - ), - "?culmen_length_mm", - ), - ( - "sql_scalar_column_transformer", - compose.SQLScalarColumnTransformer( - "CASE WHEN `species` IS NULL THEN -1 ELSE LENGTH(`species`) END", - target_column="Flex species Name", - ), - "?Flex species Name", - ), - ] - assert set(reloaded_transformer.transformers) == set(expected) - assert reloaded_transformer._bqml_model is not None result = transformer.fit_transform( new_penguins_df[["species", "culmen_length_mm", "flipper_length_mm"]] ).to_pandas() - utils.check_pandas_df_schema_and_index( - result, - columns=[ - "onehotencoded_species", - "standard_scaled_culmen_length_mm", - "standard_scaled_flipper_length_mm", - "len_species", - "culmen_length_mm", - "flipper_length_mm", - "Flex species Name", - ], - index=[1633, 1672, 1690], - col_exact=False, + # TODO: bug? feature columns seem to be in nondeterministic random order + # workaround: sort columns by name. Can't repro it in pantheon, so could + # be a bigframes issue... + result = result.reindex(sorted(result.columns), axis=1) + + expected = pandas.DataFrame( + { + "onehotencoded_species": [ + [{"index": 1, "value": 1.0}], + [{"index": 1, "value": 1.0}], + [{"index": 2, "value": 1.0}], + ], + "standard_scaled_culmen_length_mm": [ + 1.313249, + -0.20198, + -1.111118, + ], + "standard_scaled_flipper_length_mm": [1.251098, -1.196588, -0.054338], + }, + index=pandas.Index([1633, 1672, 1690], dtype="Int64", name="tag_number"), ) + expected.standard_scaled_culmen_length_mm = ( + expected.standard_scaled_culmen_length_mm.astype("Float64") + ) + expected.standard_scaled_flipper_length_mm = ( + expected.standard_scaled_flipper_length_mm.astype("Float64") + ) + + pandas.testing.assert_frame_equal(result, expected, rtol=1e-3) diff --git a/tests/system/large/ml/test_core.py b/tests/system/large/ml/test_core.py index 6f0551b1efd..3b30d7eb1d9 100644 --- a/tests/system/large/ml/test_core.py +++ b/tests/system/large/ml/test_core.py @@ -12,8 +12,9 @@ # See the License for the specific language governing permissions and # limitations under the License. +import pandas + from bigframes.ml import globals -from bigframes.testing import utils def test_bqml_e2e(session, dataset_id, penguins_df_default_index, new_penguins_df): @@ -34,33 +35,41 @@ def test_bqml_e2e(session, dataset_id, penguins_df_default_index, new_penguins_d X_train, y_train, options={"model_type": "linear_reg"} ) - eval_metrics = [ - "mean_absolute_error", - "mean_squared_error", - "mean_squared_log_error", - "median_absolute_error", - "r2_score", - "explained_variance", - ] # no data - report evaluation from the automatic data split evaluate_result = model.evaluate().to_pandas() - utils.check_pandas_df_schema_and_index( - evaluate_result, columns=eval_metrics, index=1 + evaluate_expected = pandas.DataFrame( + { + "mean_absolute_error": [225.817334], + "mean_squared_error": [80540.705944], + "mean_squared_log_error": [0.004972], + "median_absolute_error": [173.080816], + "r2_score": [0.87529], + "explained_variance": [0.87529], + }, + dtype="Float64", + ) + evaluate_expected = evaluate_expected.reindex( + index=evaluate_expected.index.astype("Int64") + ) + pandas.testing.assert_frame_equal( + evaluate_result, evaluate_expected, check_exact=False, rtol=0.1 ) # evaluate on all training data evaluate_result = model.evaluate(df).to_pandas() - utils.check_pandas_df_schema_and_index( - evaluate_result, columns=eval_metrics, index=1 + pandas.testing.assert_frame_equal( + evaluate_result, evaluate_expected, check_exact=False, rtol=0.1 ) # predict new labels predictions = model.predict(new_penguins_df).to_pandas() - utils.check_pandas_df_schema_and_index( - predictions, - columns=["predicted_body_mass_g"], - index=[1633, 1672, 1690], - col_exact=False, + expected = pandas.DataFrame( + {"predicted_body_mass_g": [4030.1, 3280.8, 3177.9]}, + dtype="Float64", + index=pandas.Index([1633, 1672, 1690], name="tag_number", dtype="Int64"), + ) + pandas.testing.assert_frame_equal( + predictions[["predicted_body_mass_g"]], expected, check_exact=False, rtol=0.1 ) new_name = f"{dataset_id}.my_model" @@ -96,34 +105,42 @@ def test_bqml_manual_preprocessing_e2e( X_train, y_train, transforms=transforms, options=options ) - eval_metrics = [ - "mean_absolute_error", - "mean_squared_error", - "mean_squared_log_error", - "median_absolute_error", - "r2_score", - "explained_variance", - ] - # no data - report evaluation from the automatic data split evaluate_result = model.evaluate().to_pandas() - utils.check_pandas_df_schema_and_index( - evaluate_result, columns=eval_metrics, index=1 + evaluate_expected = pandas.DataFrame( + { + "mean_absolute_error": [309.477334], + "mean_squared_error": [152184.227218], + "mean_squared_log_error": [0.009524], + "median_absolute_error": [257.727777], + "r2_score": [0.764356], + "explained_variance": [0.764356], + }, + dtype="Float64", + ) + evaluate_expected = evaluate_expected.reindex( + index=evaluate_expected.index.astype("Int64") + ) + + pandas.testing.assert_frame_equal( + evaluate_result, evaluate_expected, check_exact=False, rtol=0.1 ) # evaluate on all training data evaluate_result = model.evaluate(df).to_pandas() - utils.check_pandas_df_schema_and_index( - evaluate_result, columns=eval_metrics, index=1 + pandas.testing.assert_frame_equal( + evaluate_result, evaluate_expected, check_exact=False, rtol=0.1 ) # predict new labels predictions = model.predict(new_penguins_df).to_pandas() - utils.check_pandas_df_schema_and_index( - predictions, - columns=["predicted_body_mass_g"], - index=[1633, 1672, 1690], - col_exact=False, + expected = pandas.DataFrame( + {"predicted_body_mass_g": [3968.8, 3176.3, 3545.2]}, + dtype="Float64", + index=pandas.Index([1633, 1672, 1690], name="tag_number", dtype="Int64"), + ) + pandas.testing.assert_frame_equal( + predictions[["predicted_body_mass_g"]], expected, check_exact=False, rtol=0.1 ) new_name = f"{dataset_id}.my_model" @@ -146,16 +163,25 @@ def test_bqml_standalone_transform(penguins_df_default_index, new_penguins_df): "ML.ONE_HOT_ENCODER(species, 'none', 1000000, 0) OVER() AS onehotencoded_species", ], ) - start_execution_count = model.session._metrics.execution_count - - transformed = model.transform(new_penguins_df) - end_execution_count = model.session._metrics.execution_count - assert end_execution_count - start_execution_count == 1 - - utils.check_pandas_df_schema_and_index( - transformed.to_pandas(), - columns=["scaled_culmen_length_mm", "onehotencoded_species"], - index=[1633, 1672, 1690], - col_exact=False, + transformed = model.transform(new_penguins_df).to_pandas() + expected = pandas.DataFrame( + { + "scaled_culmen_length_mm": [-0.8099, -0.9931, -1.103], + "onehotencoded_species": [ + [{"index": 1, "value": 1.0}], + [{"index": 1, "value": 1.0}], + [{"index": 2, "value": 1.0}], + ], + }, + index=pandas.Index([1633, 1672, 1690], name="tag_number", dtype="Int64"), + ) + expected["scaled_culmen_length_mm"] = expected["scaled_culmen_length_mm"].astype( + "Float64" + ) + pandas.testing.assert_frame_equal( + transformed[["scaled_culmen_length_mm", "onehotencoded_species"]], + expected, + check_exact=False, + rtol=0.1, ) diff --git a/tests/system/large/ml/test_decomposition.py b/tests/system/large/ml/test_decomposition.py index c36e8738162..a7049d4c18e 100644 --- a/tests/system/large/ml/test_decomposition.py +++ b/tests/system/large/ml/test_decomposition.py @@ -13,10 +13,8 @@ # limitations under the License. import pandas as pd -import pandas.testing from bigframes.ml import decomposition -from bigframes.testing import utils def test_decomposition_configure_fit_score_predict( @@ -46,176 +44,41 @@ def test_decomposition_configure_fit_score_predict( # Check score to ensure the model was fitted score_result = model.score(new_penguins).to_pandas() - utils.check_pandas_df_schema_and_index( - score_result, columns=["total_explained_variance_ratio"], index=1 + score_expected = pd.DataFrame( + { + "total_explained_variance_ratio": [0.812383], + }, + dtype="Float64", ) + score_expected = score_expected.reindex(index=score_expected.index.astype("Int64")) - result = model.predict(new_penguins).to_pandas() - utils.check_pandas_df_schema_and_index( - result, - columns=[ - "principal_component_1", - "principal_component_2", - "principal_component_3", - ], - index=[1633, 1672, 1690], - ) - - # save, load, check n_components to ensure configuration was kept - reloaded_model = model.to_gbq( - f"{dataset_id}.temp_configured_pca_model", replace=True - ) - assert reloaded_model._bqml_model is not None - assert ( - f"{dataset_id}.temp_configured_pca_model" - in reloaded_model._bqml_model.model_name - ) - assert reloaded_model.n_components == 3 - - -def test_decomposition_configure_fit_score_predict_params( - session, penguins_df_default_index, dataset_id -): - model = decomposition.PCA(n_components=5, svd_solver="randomized") - model.fit(penguins_df_default_index) - - new_penguins = session.read_pandas( - pd.DataFrame( - { - "tag_number": [1633, 1672, 1690], - "species": [ - "Adelie Penguin (Pygoscelis adeliae)", - "Gentoo penguin (Pygoscelis papua)", - "Adelie Penguin (Pygoscelis adeliae)", - ], - "island": ["Dream", "Biscoe", "Torgersen"], - "culmen_length_mm": [37.8, 46.5, 41.1], - "culmen_depth_mm": [18.1, 14.8, 18.6], - "flipper_length_mm": [193.0, 217.0, 189.0], - "body_mass_g": [3750.0, 5200.0, 3325.0], - "sex": ["MALE", "FEMALE", "MALE"], - } - ).set_index("tag_number") - ) - - # Check score to ensure the model was fitted - score_result = model.score(new_penguins).to_pandas() - utils.check_pandas_df_schema_and_index( - score_result, columns=["total_explained_variance_ratio"], index=1 + pd.testing.assert_frame_equal( + score_result, score_expected, check_exact=False, rtol=0.1 ) result = model.predict(new_penguins).to_pandas() - utils.check_pandas_df_schema_and_index( - result, - columns=[ - "principal_component_1", - "principal_component_2", - "principal_component_3", - "principal_component_4", - "principal_component_5", - ], - index=[1633, 1672, 1690], + expected = pd.DataFrame( + { + "principal_component_1": [-1.459, 2.258, -1.685], + "principal_component_2": [-1.120, -1.351, -0.874], + "principal_component_3": [-0.646, 0.443, -0.704], + }, + dtype="Float64", + index=pd.Index([1633, 1672, 1690], name="tag_number", dtype="Int64"), + ) + pd.testing.assert_frame_equal( + abs(result.sort_index()), # results may differ by a minus sign + abs(expected), + check_exact=False, + rtol=0.1, ) # save, load, check n_components to ensure configuration was kept reloaded_model = model.to_gbq( f"{dataset_id}.temp_configured_pca_model", replace=True ) - assert reloaded_model._bqml_model is not None assert ( f"{dataset_id}.temp_configured_pca_model" in reloaded_model._bqml_model.model_name ) - assert reloaded_model.n_components == 5 - assert reloaded_model.svd_solver == "RANDOMIZED" - - -def test_decomposition_configure_fit_load_float_component( - penguins_df_default_index, dataset_id -): - model = decomposition.PCA(n_components=0.2) - model.fit(penguins_df_default_index) - - # save, load, check n_components to ensure configuration was kept - reloaded_model = model.to_gbq( - f"{dataset_id}.temp_configured_pca_model", replace=True - ) - assert reloaded_model._bqml_model is not None - assert ( - f"{dataset_id}.temp_configured_pca_model" - in reloaded_model._bqml_model.model_name - ) - assert reloaded_model.n_components == 0.2 - - -def test_decomposition_configure_fit_load_none_component( - penguins_df_default_index, dataset_id -): - model = decomposition.PCA(n_components=None) - model.fit(penguins_df_default_index) - - # save, load, check n_components. Here n_components is the column size of the training input. - reloaded_model = model.to_gbq( - f"{dataset_id}.temp_configured_pca_model", replace=True - ) - assert reloaded_model._bqml_model is not None - assert ( - f"{dataset_id}.temp_configured_pca_model" - in reloaded_model._bqml_model.model_name - ) - assert reloaded_model.n_components == 7 - - -def test_decomposition_mf_configure_fit_load( - session, ratings_df_default_index, dataset_id -): - model = decomposition.MatrixFactorization( - num_factors=6, - feedback_type="explicit", - user_col="user_id", - item_col="item_id", - rating_col="rating", - l2_reg=9.83, - ) - - model.fit(ratings_df_default_index) - - reloaded_model = model.to_gbq( - f"{dataset_id}.temp_configured_mf_model", replace=True - ) - - new_ratings = session.read_pandas( - pd.DataFrame( - { - "user_id": ["11", "12", "13"], - "item_id": [1, 2, 3], - "rating": [1.0, 2.0, 3.0], - } - ) - ) - - # Make sure the input to score is not ignored. - scores_training_data = reloaded_model.score().to_pandas() - scores_new_ratings = reloaded_model.score(new_ratings).to_pandas() - pandas.testing.assert_index_equal( - scores_training_data.columns, scores_new_ratings.columns - ) - assert ( - scores_training_data["mean_squared_error"].iloc[0] - != scores_new_ratings["mean_squared_error"].iloc[0] - ) - - result = reloaded_model.predict(new_ratings).to_pandas() - - assert reloaded_model._bqml_model is not None - assert ( - f"{dataset_id}.temp_configured_mf_model" - in reloaded_model._bqml_model.model_name - ) - assert result is not None - assert reloaded_model.feedback_type == "explicit" - assert reloaded_model.num_factors == 6 - assert reloaded_model.user_col == "user_id" - assert reloaded_model.item_col == "item_id" - assert reloaded_model.rating_col == "rating" - assert reloaded_model.l2_reg == 9.83 + assert reloaded_model.n_components == 3 diff --git a/tests/system/large/ml/test_ensemble.py b/tests/system/large/ml/test_ensemble.py index eabd36ab387..a8613dfeb9b 100644 --- a/tests/system/large/ml/test_ensemble.py +++ b/tests/system/large/ml/test_ensemble.py @@ -12,13 +12,15 @@ # See the License for the specific language governing permissions and # limitations under the License. +from unittest import TestCase + +import pandas import pytest import bigframes.ml.ensemble -from bigframes.testing import utils -@pytest.mark.flaky(retries=2) +@pytest.mark.flaky(retries=2, delay=120) def test_xgbregressor_default_params(penguins_df_default_index, dataset_id): model = bigframes.ml.ensemble.XGBRegressor() @@ -38,22 +40,31 @@ def test_xgbregressor_default_params(penguins_df_default_index, dataset_id): # Check score to ensure the model was fitted result = model.score(X_train, y_train).to_pandas() - utils.check_pandas_df_schema_and_index( - result, columns=utils.ML_REGRESSION_METRICS, index=1 + expected = pandas.DataFrame( + { + "mean_absolute_error": [97.368139], + "mean_squared_error": [16284.877027], + "mean_squared_log_error": [0.0010189], + "median_absolute_error": [72.158691], + "r2_score": [0.974784], + "explained_variance": [0.974845], + }, + dtype="Float64", ) + expected = expected.reindex(index=expected.index.astype("Int64")) + pandas.testing.assert_frame_equal(result, expected, check_exact=False, rtol=0.1) # save, load, check parameters to ensure configuration was kept reloaded_model = model.to_gbq( f"{dataset_id}.temp_configured_xgbregressor_model", replace=True ) - assert reloaded_model._bqml_model is not None assert ( f"{dataset_id}.temp_configured_xgbregressor_model" in reloaded_model._bqml_model.model_name ) -@pytest.mark.flaky(retries=2) +@pytest.mark.flaky(retries=2, delay=120) def test_xgbregressor_dart_booster_multiple_params( penguins_df_default_index, dataset_id ): @@ -64,14 +75,14 @@ def test_xgbregressor_dart_booster_multiple_params( colsample_bytree=0.95, colsample_bylevel=0.95, colsample_bynode=0.95, - n_estimators=2, + num_parallel_tree=2, max_depth=4, subsample=0.95, reg_alpha=0.0001, reg_lambda=0.0001, learning_rate=0.015, max_iterations=4, - tol=0.02, + min_rel_progress=0.02, ) df = penguins_df_default_index.dropna().sample(n=70) @@ -90,38 +101,45 @@ def test_xgbregressor_dart_booster_multiple_params( # Check score to ensure the model was fitted result = model.score(X_train, y_train).to_pandas() - utils.check_pandas_df_schema_and_index( - result, columns=utils.ML_REGRESSION_METRICS, index=1 - ) + TestCase().assertSequenceEqual(result.shape, (1, 6)) + for col_name in [ + "mean_absolute_error", + "mean_squared_error", + "mean_squared_log_error", + "median_absolute_error", + "r2_score", + "explained_variance", + ]: + assert col_name in result.columns # save, load, check parameters to ensure configuration was kept reloaded_model = model.to_gbq( f"{dataset_id}.temp_configured_xgbregressor_model", replace=True ) - assert reloaded_model._bqml_model is not None assert ( f"{dataset_id}.temp_configured_xgbregressor_model" in reloaded_model._bqml_model.model_name ) assert reloaded_model.booster == "DART" - assert reloaded_model.dart_normalized_type == "TREE" + assert reloaded_model.dart_normalized_type == "tree" assert reloaded_model.tree_method == "AUTO" assert reloaded_model.colsample_bytree == 0.95 assert reloaded_model.colsample_bylevel == 0.95 assert reloaded_model.colsample_bynode == 0.95 + assert reloaded_model.early_stop is True assert reloaded_model.subsample == 0.95 assert reloaded_model.reg_alpha == 0.0001 assert reloaded_model.reg_lambda == 0.0001 assert reloaded_model.learning_rate == 0.015 assert reloaded_model.max_iterations == 4 - assert reloaded_model.tol == 0.02 + assert reloaded_model.min_rel_progress == 0.02 assert reloaded_model.gamma == 0.0 assert reloaded_model.max_depth == 4 assert reloaded_model.min_tree_child_weight == 2 - assert reloaded_model.n_estimators == 2 + assert reloaded_model.num_parallel_tree == 2 -@pytest.mark.flaky(retries=2) +@pytest.mark.flaky(retries=2, delay=120) def test_xgbclassifier_default_params(penguins_df_default_index, dataset_id): model = bigframes.ml.ensemble.XGBClassifier() @@ -140,22 +158,28 @@ def test_xgbclassifier_default_params(penguins_df_default_index, dataset_id): # Check score to ensure the model was fitted result = model.score(X_train, y_train).to_pandas() - utils.check_pandas_df_schema_and_index( - result, columns=utils.ML_CLASSFICATION_METRICS, index=1 - ) + TestCase().assertSequenceEqual(result.shape, (1, 6)) + for col_name in [ + "precision", + "recall", + "accuracy", + "f1_score", + "log_loss", + "roc_auc", + ]: + assert col_name in result.columns # save, load, check parameters to ensure configuration was kept reloaded_model = model.to_gbq( f"{dataset_id}.temp_configured_xgbclassifier_model", replace=True ) - assert reloaded_model._bqml_model is not None assert ( f"{dataset_id}.temp_configured_xgbclassifier_model" in reloaded_model._bqml_model.model_name ) -@pytest.mark.flaky(retries=2) +@pytest.mark.flaky(retries=2, delay=120) def test_xgbclassifier_dart_booster_multiple_params( penguins_df_default_index, dataset_id ): @@ -166,14 +190,14 @@ def test_xgbclassifier_dart_booster_multiple_params( colsample_bytree=0.95, colsample_bylevel=0.95, colsample_bynode=0.95, - n_estimators=2, + num_parallel_tree=2, max_depth=4, subsample=0.95, reg_alpha=0.0001, reg_lambda=0.0001, learning_rate=0.015, max_iterations=4, - tol=0.02, + min_rel_progress=0.02, ) df = penguins_df_default_index.dropna().sample(n=70) @@ -191,38 +215,45 @@ def test_xgbclassifier_dart_booster_multiple_params( # Check score to ensure the model was fitted result = model.score(X_train, y_train).to_pandas() - utils.check_pandas_df_schema_and_index( - result, columns=utils.ML_CLASSFICATION_METRICS, index=1 - ) + TestCase().assertSequenceEqual(result.shape, (1, 6)) + for col_name in [ + "precision", + "recall", + "accuracy", + "f1_score", + "log_loss", + "roc_auc", + ]: + assert col_name in result.columns # save, load, check parameters to ensure configuration was kept reloaded_model = model.to_gbq( f"{dataset_id}.temp_configured_xgbclassifier_model", replace=True ) - assert reloaded_model._bqml_model is not None assert ( f"{dataset_id}.temp_configured_xgbclassifier_model" in reloaded_model._bqml_model.model_name ) assert reloaded_model.booster == "DART" - assert reloaded_model.dart_normalized_type == "TREE" + assert reloaded_model.dart_normalized_type == "tree" assert reloaded_model.tree_method == "AUTO" assert reloaded_model.colsample_bytree == 0.95 assert reloaded_model.colsample_bylevel == 0.95 assert reloaded_model.colsample_bynode == 0.95 + assert reloaded_model.early_stop is True assert reloaded_model.subsample == 0.95 assert reloaded_model.reg_alpha == 0.0001 assert reloaded_model.reg_lambda == 0.0001 assert reloaded_model.learning_rate == 0.015 assert reloaded_model.max_iterations == 4 - assert reloaded_model.tol == 0.02 + assert reloaded_model.min_rel_progress == 0.02 assert reloaded_model.gamma == 0.0 assert reloaded_model.max_depth == 4 assert reloaded_model.min_tree_child_weight == 2 - assert reloaded_model.n_estimators == 2 + assert reloaded_model.num_parallel_tree == 2 -@pytest.mark.flaky(retries=2) +@pytest.mark.flaky(retries=2, delay=120) def test_randomforestregressor_default_params(penguins_df_default_index, dataset_id): model = bigframes.ml.ensemble.RandomForestRegressor() @@ -242,22 +273,28 @@ def test_randomforestregressor_default_params(penguins_df_default_index, dataset # Check score to ensure the model was fitted result = model.score(X_train, y_train).to_pandas() - utils.check_pandas_df_schema_and_index( - result, columns=utils.ML_REGRESSION_METRICS, index=1 - ) + TestCase().assertSequenceEqual(result.shape, (1, 6)) + for col_name in [ + "mean_absolute_error", + "mean_squared_error", + "mean_squared_log_error", + "median_absolute_error", + "r2_score", + "explained_variance", + ]: + assert col_name in result.columns # save, load, check parameters to ensure configuration was kept reloaded_model = model.to_gbq( f"{dataset_id}.temp_configured_randomforestregressor_model", replace=True ) - assert reloaded_model._bqml_model is not None assert ( f"{dataset_id}.temp_configured_randomforestregressor_model" in reloaded_model._bqml_model.model_name ) -@pytest.mark.flaky(retries=2) +@pytest.mark.flaky(retries=2, delay=120) def test_randomforestregressor_multiple_params(penguins_df_default_index, dataset_id): model = bigframes.ml.ensemble.RandomForestRegressor( tree_method="auto", @@ -265,12 +302,12 @@ def test_randomforestregressor_multiple_params(penguins_df_default_index, datase colsample_bytree=0.95, colsample_bylevel=0.95, colsample_bynode=0.95, - n_estimators=90, + num_parallel_tree=90, max_depth=14, subsample=0.95, reg_alpha=0.0001, reg_lambda=0.0001, - tol=0.02, + min_rel_progress=0.02, ) df = penguins_df_default_index.dropna().sample(n=70) @@ -289,15 +326,21 @@ def test_randomforestregressor_multiple_params(penguins_df_default_index, datase # Check score to ensure the model was fitted result = model.score(X_train, y_train).to_pandas() - utils.check_pandas_df_schema_and_index( - result, columns=utils.ML_REGRESSION_METRICS, index=1 - ) + TestCase().assertSequenceEqual(result.shape, (1, 6)) + for col_name in [ + "mean_absolute_error", + "mean_squared_error", + "mean_squared_log_error", + "median_absolute_error", + "r2_score", + "explained_variance", + ]: + assert col_name in result.columns # save, load, check parameters to ensure configuration was kept reloaded_model = model.to_gbq( f"{dataset_id}.temp_configured_randomforestregressor_model", replace=True ) - assert reloaded_model._bqml_model is not None assert ( f"{dataset_id}.temp_configured_randomforestregressor_model" in reloaded_model._bqml_model.model_name @@ -306,18 +349,19 @@ def test_randomforestregressor_multiple_params(penguins_df_default_index, datase assert reloaded_model.colsample_bytree == 0.95 assert reloaded_model.colsample_bylevel == 0.95 assert reloaded_model.colsample_bynode == 0.95 + assert reloaded_model.early_stop is True assert reloaded_model.subsample == 0.95 assert reloaded_model.reg_alpha == 0.0001 assert reloaded_model.reg_lambda == 0.0001 - assert reloaded_model.tol == 0.02 + assert reloaded_model.min_rel_progress == 0.02 assert reloaded_model.gamma == 0.0 assert reloaded_model.max_depth == 14 assert reloaded_model.min_tree_child_weight == 2 - assert reloaded_model.n_estimators == 90 + assert reloaded_model.num_parallel_tree == 90 assert reloaded_model.enable_global_explain is False -@pytest.mark.flaky(retries=2) +@pytest.mark.flaky(retries=2, delay=120) def test_randomforestclassifier_default_params(penguins_df_default_index, dataset_id): model = bigframes.ml.ensemble.RandomForestClassifier() @@ -336,35 +380,41 @@ def test_randomforestclassifier_default_params(penguins_df_default_index, datase # Check score to ensure the model was fitted result = model.score(X_train, y_train).to_pandas() - utils.check_pandas_df_schema_and_index( - result, columns=utils.ML_CLASSFICATION_METRICS, index=1 - ) + TestCase().assertSequenceEqual(result.shape, (1, 6)) + for col_name in [ + "precision", + "recall", + "accuracy", + "f1_score", + "log_loss", + "roc_auc", + ]: + assert col_name in result.columns # save, load, check parameters to ensure configuration was kept reloaded_model = model.to_gbq( f"{dataset_id}.temp_configured_randomforestclassifier_model", replace=True ) - assert reloaded_model._bqml_model is not None assert ( f"{dataset_id}.temp_configured_randomforestclassifier_model" in reloaded_model._bqml_model.model_name ) -@pytest.mark.flaky(retries=2) +@pytest.mark.flaky(retries=2, delay=120) def test_randomforestclassifier_multiple_params(penguins_df_default_index, dataset_id): model = bigframes.ml.ensemble.RandomForestClassifier( - tree_method="auto", + tree_method="AUTO", min_tree_child_weight=2, colsample_bytree=0.95, colsample_bylevel=0.95, colsample_bynode=0.95, - n_estimators=90, + num_parallel_tree=90, max_depth=14, subsample=0.95, reg_alpha=0.0001, reg_lambda=0.0001, - tol=0.02, + min_rel_progress=0.02, ) df = penguins_df_default_index.dropna().sample(n=70) @@ -382,29 +432,36 @@ def test_randomforestclassifier_multiple_params(penguins_df_default_index, datas # Check score to ensure the model was fitted result = model.score(X_train, y_train).to_pandas() - utils.check_pandas_df_schema_and_index( - result, columns=utils.ML_CLASSFICATION_METRICS, index=1 - ) + TestCase().assertSequenceEqual(result.shape, (1, 6)) + for col_name in [ + "precision", + "recall", + "accuracy", + "f1_score", + "log_loss", + "roc_auc", + ]: + assert col_name in result.columns # save, load, check parameters to ensure configuration was kept reloaded_model = model.to_gbq( f"{dataset_id}.temp_configured_randomforestclassifier_model", replace=True ) - assert reloaded_model._bqml_model is not None assert ( f"{dataset_id}.temp_configured_randomforestclassifier_model" in reloaded_model._bqml_model.model_name ) - assert reloaded_model.tree_method.casefold() == "auto" + assert reloaded_model.tree_method == "AUTO" assert reloaded_model.colsample_bytree == 0.95 assert reloaded_model.colsample_bylevel == 0.95 assert reloaded_model.colsample_bynode == 0.95 + assert reloaded_model.early_stop is True assert reloaded_model.subsample == 0.95 assert reloaded_model.reg_alpha == 0.0001 assert reloaded_model.reg_lambda == 0.0001 - assert reloaded_model.tol == 0.02 + assert reloaded_model.min_rel_progress == 0.02 assert reloaded_model.gamma == 0.0 assert reloaded_model.max_depth == 14 assert reloaded_model.min_tree_child_weight == 2 - assert reloaded_model.n_estimators == 90 + assert reloaded_model.num_parallel_tree == 90 assert reloaded_model.enable_global_explain is False diff --git a/tests/system/large/ml/test_forecasting.py b/tests/system/large/ml/test_forecasting.py index 8500ad9d5f1..33b835e8522 100644 --- a/tests/system/large/ml/test_forecasting.py +++ b/tests/system/large/ml/test_forecasting.py @@ -12,182 +12,37 @@ # See the License for the specific language governing permissions and # limitations under the License. -import pytest +import pandas as pd from bigframes.ml import forecasting -from bigframes.testing import utils -ARIMA_EVALUATE_OUTPUT_COL = [ - "non_seasonal_p", - "non_seasonal_d", - "non_seasonal_q", - "has_drift", - "log_likelihood", - "AIC", - "variance", - "seasonal_periods", - "has_holiday_effect", - "has_spikes_and_dips", - "has_step_changes", - "error_message", -] - -def _fit_arima_model(time_series_df_default_index): +def test_arima_plus_model_fit_score( + time_series_df_default_index, dataset_id, new_time_series_df +): model = forecasting.ARIMAPlus() - X_train = time_series_df_default_index["parsed_date"] + X_train = time_series_df_default_index[["parsed_date"]] y_train = time_series_df_default_index[["total_visits"]] - return model, X_train, y_train - - -@pytest.fixture(scope="module") -def arima_model(time_series_df_default_index): - model, X_train, y_train = _fit_arima_model(time_series_df_default_index) model.fit(X_train, y_train) - return model - - -@pytest.fixture(scope="module") -def arima_model_w_id(time_series_df_default_index): - model, X_train, y_train = _fit_arima_model(time_series_df_default_index) - id_cols = time_series_df_default_index[["id"]] - model.fit(X_train, y_train, id_col=id_cols) - return model - -@pytest.mark.parametrize("id_col_name", [None, "id"]) -def test_arima_plus_model_fit_score( - dataset_id, - new_time_series_df, - new_time_series_df_w_id, - arima_model, - arima_model_w_id, - id_col_name, -): - curr_model = arima_model_w_id if id_col_name else arima_model - if id_col_name: - result = curr_model.score( - new_time_series_df_w_id[["parsed_date"]], - new_time_series_df_w_id[["total_visits"]], - id_col=new_time_series_df_w_id[[id_col_name]], - ).to_pandas() - else: - result = curr_model.score( - new_time_series_df[["parsed_date"]], new_time_series_df[["total_visits"]] - ).to_pandas() - expected_columns = [ - "mean_absolute_error", - "mean_squared_error", - "root_mean_squared_error", - "mean_absolute_percentage_error", - "symmetric_mean_absolute_percentage_error", - ] - if id_col_name: - expected_columns.insert(0, id_col_name) - utils.check_pandas_df_schema_and_index( - result, - columns=expected_columns, - index=2 if id_col_name else 1, - col_exact=False, - ) + result = model.score( + new_time_series_df[["parsed_date"]], new_time_series_df[["total_visits"]] + ).to_pandas() + expected = pd.DataFrame( + { + "mean_absolute_error": [154.742547], + "mean_squared_error": [26844.868855], + "root_mean_squared_error": [163.844038], + "mean_absolute_percentage_error": [6.189702], + "symmetric_mean_absolute_percentage_error": [6.097155], + }, + dtype="Float64", + ) + expected = expected.reindex(index=expected.index.astype("Int64")) + pd.testing.assert_frame_equal(result, expected, check_exact=False, rtol=0.1) # save, load to ensure configuration was kept - reloaded_model = curr_model.to_gbq( - f"{dataset_id}.temp_arima_plus_model", replace=True - ) + reloaded_model = model.to_gbq(f"{dataset_id}.temp_configured_model", replace=True) assert ( - f"{dataset_id}.temp_arima_plus_model" in reloaded_model._bqml_model.model_name + f"{dataset_id}.temp_configured_model" in reloaded_model._bqml_model.model_name ) - - -@pytest.mark.parametrize("id_col_name", [None, "id"]) -def test_arima_plus_model_fit_summary( - dataset_id, arima_model, arima_model_w_id, id_col_name -): - curr_model = arima_model_w_id if id_col_name else arima_model - result = curr_model.summary().to_pandas() - expected_columns = ( - [id_col_name] + ARIMA_EVALUATE_OUTPUT_COL - if id_col_name - else ARIMA_EVALUATE_OUTPUT_COL - ) - utils.check_pandas_df_schema_and_index( - result, columns=expected_columns, index=2 if id_col_name else 1 - ) - # save, load to ensure configuration was kept - reloaded_model = curr_model.to_gbq( - f"{dataset_id}.temp_arima_plus_model", replace=True - ) - assert ( - f"{dataset_id}.temp_arima_plus_model" in reloaded_model._bqml_model.model_name - ) - - -@pytest.mark.parametrize("id_col_name", [None, "id"]) -def test_arima_coefficients(arima_model, arima_model_w_id, id_col_name): - result = ( - arima_model_w_id.coef_.to_pandas() - if id_col_name - else arima_model.coef_.to_pandas() - ) - expected_columns = [ - "ar_coefficients", - "ma_coefficients", - "intercept_or_drift", - ] - if id_col_name: - expected_columns.insert(0, id_col_name) - utils.check_pandas_df_schema_and_index( - result, columns=expected_columns, index=2 if id_col_name else 1 - ) - - -@pytest.mark.parametrize("id_col_name", [None, "id"]) -def test_arima_plus_model_fit_params( - time_series_df_default_index, dataset_id, id_col_name -): - model = forecasting.ARIMAPlus( - horizon=100, - auto_arima=True, - auto_arima_max_order=4, - auto_arima_min_order=1, - data_frequency="daily", - holiday_region="US", - clean_spikes_and_dips=False, - adjust_step_changes=False, - forecast_limit_lower_bound=0.0, - time_series_length_fraction=0.5, - min_time_series_length=10, - trend_smoothing_window_size=5, - decompose_time_series=False, - ) - - X_train = time_series_df_default_index[["parsed_date"]] - y_train = time_series_df_default_index["total_visits"] - if id_col_name is None: - model.fit(X_train, y_train) - else: - id_cols = time_series_df_default_index[[id_col_name]] - model.fit(X_train, y_train, id_col=id_cols) - - # save, load to ensure configuration was kept - reloaded_model = model.to_gbq(f"{dataset_id}.temp_arima_plus_model", replace=True) - assert reloaded_model._bqml_model is not None - assert ( - f"{dataset_id}.temp_arima_plus_model" in reloaded_model._bqml_model.model_name - ) - - assert reloaded_model.horizon == 100 - assert reloaded_model.auto_arima is True - assert reloaded_model.auto_arima_max_order == 4 - assert reloaded_model.auto_arima_min_order == 1 - assert reloaded_model.data_frequency == "DAILY" - assert reloaded_model.holiday_region == "US" - assert reloaded_model.clean_spikes_and_dips is False - assert reloaded_model.adjust_step_changes is False - # TODO(b/391399223): API must return forecastLimitLowerBound for the following assertion - # assert reloaded_model.forecast_limit_lower_bound == 0.0 - assert reloaded_model.time_series_length_fraction == 0.5 - assert reloaded_model.min_time_series_length == 10 - assert reloaded_model.trend_smoothing_window_size == 5 - assert reloaded_model.decompose_time_series is False diff --git a/tests/system/large/ml/test_linear_model.py b/tests/system/large/ml/test_linear_model.py index 60edc717a5a..a0f4182e6fb 100644 --- a/tests/system/large/ml/test_linear_model.py +++ b/tests/system/large/ml/test_linear_model.py @@ -13,11 +13,8 @@ # limitations under the License. import pandas as pd -import pytest import bigframes.ml.linear_model -from bigframes.ml import model_selection -from bigframes.testing import utils def test_linear_regression_configure_fit_score(penguins_df_default_index, dataset_id): @@ -39,128 +36,42 @@ def test_linear_regression_configure_fit_score(penguins_df_default_index, datase # Check score to ensure the model was fitted result = model.score(X_train, y_train).to_pandas() - utils.check_pandas_df_schema_and_index( - result, columns=utils.ML_REGRESSION_METRICS, index=1 - ) + expected = pd.DataFrame( + { + "mean_absolute_error": [225.735767], + "mean_squared_error": [80417.461828], + "mean_squared_log_error": [0.004967], + "median_absolute_error": [172.543702], + "r2_score": [0.87548], + "explained_variance": [0.87548], + }, + dtype="Float64", + ) + expected = expected.reindex(index=expected.index.astype("Int64")) + pd.testing.assert_frame_equal(result, expected, check_exact=False, rtol=0.1) # save, load, check parameters to ensure configuration was kept reloaded_model = model.to_gbq(f"{dataset_id}.temp_configured_model", replace=True) - assert reloaded_model._bqml_model is not None - assert ( - f"{dataset_id}.temp_configured_model" in reloaded_model._bqml_model.model_name - ) - assert reloaded_model.optimize_strategy == "NORMAL_EQUATION" - assert reloaded_model.fit_intercept is True - assert reloaded_model.calculate_p_values is False - assert reloaded_model.enable_global_explain is False - assert reloaded_model.l1_reg is None - assert reloaded_model.l2_reg == 0.0 - assert reloaded_model.learning_rate is None - assert reloaded_model.learning_rate_strategy == "line_search" - assert reloaded_model.ls_init_learning_rate is None - assert reloaded_model.max_iterations == 20 - assert reloaded_model.tol == 0.01 - - -@pytest.mark.parametrize( - "df_fixture", - [ - "penguins_df_default_index", - "penguins_df_null_index", - ], -) -def test_linear_regression_configure_fit_with_eval_score( - df_fixture, dataset_id, request -): - df = request.getfixturevalue(df_fixture) - model = bigframes.ml.linear_model.LinearRegression() - - df = df.dropna() - X = df[ - [ - "species", - "island", - "culmen_length_mm", - "culmen_depth_mm", - "flipper_length_mm", - "sex", - ] - ] - y = df[["body_mass_g"]] - - X_train, X_eval, y_train, y_eval = model_selection.train_test_split(X, y) - - model.fit(X_train, y_train, X_eval=X_eval, y_eval=y_eval) - - # Check score to ensure the model was fitted - result = model.score(X_eval, y_eval).to_pandas() - utils.check_pandas_df_schema_and_index( - result, columns=utils.ML_REGRESSION_METRICS, index=1 - ) - - # save, load, check parameters to ensure configuration was kept - bq_model_name = f"{dataset_id}.temp_configured_model" - reloaded_model = model.to_gbq(bq_model_name, replace=True) - assert reloaded_model._bqml_model is not None assert ( f"{dataset_id}.temp_configured_model" in reloaded_model._bqml_model.model_name ) assert reloaded_model.optimize_strategy == "NORMAL_EQUATION" assert reloaded_model.fit_intercept is True assert reloaded_model.calculate_p_values is False + assert reloaded_model.early_stop is True assert reloaded_model.enable_global_explain is False - assert reloaded_model.l1_reg is None assert reloaded_model.l2_reg == 0.0 - assert reloaded_model.learning_rate is None - assert reloaded_model.learning_rate_strategy == "line_search" - assert reloaded_model.ls_init_learning_rate is None + assert reloaded_model.learn_rate_strategy == "line_search" + assert reloaded_model.ls_init_learn_rate == 0.1 assert reloaded_model.max_iterations == 20 - assert reloaded_model.tol == 0.01 - - # make sure the bqml model was internally created with custom split - bq_model = df._session.bqclient.get_model(bq_model_name) - last_fitting = bq_model.training_runs[-1]["trainingOptions"] - assert last_fitting["dataSplitMethod"] == "CUSTOM" - assert "dataSplitColumn" in last_fitting - - # make sure the bqml model has the same evaluation metrics attached as - # returned by model.score() - bq_model_expected_eval_metrics = result[utils.ML_REGRESSION_METRICS[:5]] - bq_model_eval_metrics = bq_model.training_runs[-1]["evaluationMetrics"][ - "regressionMetrics" - ] - bq_model_eval_metrics = pd.DataFrame( - [ - [ - bq_model_eval_metrics["meanAbsoluteError"], - bq_model_eval_metrics["meanSquaredError"], - bq_model_eval_metrics["meanSquaredLogError"], - bq_model_eval_metrics["medianAbsoluteError"], - bq_model_eval_metrics["rSquared"], - ] - ], - columns=utils.ML_REGRESSION_METRICS[:5], - ) - pd.testing.assert_frame_equal( - bq_model_expected_eval_metrics, - bq_model_eval_metrics, - check_dtype=False, - check_index_type=False, - ) + assert reloaded_model.min_rel_progress == 0.01 def test_linear_regression_customized_params_fit_score( penguins_df_default_index, dataset_id ): model = bigframes.ml.linear_model.LinearRegression( - fit_intercept=False, - l2_reg=0.2, - tol=0.02, - l1_reg=0.2, - max_iterations=30, - optimize_strategy="batch_gradient_descent", - learning_rate_strategy="constant", - learning_rate=0.2, + fit_intercept=False, l2_reg=0.1, min_rel_progress=0.01 ) df = penguins_df_default_index.dropna() @@ -179,96 +90,35 @@ def test_linear_regression_customized_params_fit_score( # Check score to ensure the model was fitted result = model.score(X_train, y_train).to_pandas() - utils.check_pandas_df_schema_and_index( - result, columns=utils.ML_REGRESSION_METRICS, index=1 - ) - - # save, load, check parameters to ensure configuration was kept - reloaded_model = model.to_gbq(f"{dataset_id}.temp_configured_model", replace=True) - assert reloaded_model._bqml_model is not None - assert ( - f"{dataset_id}.temp_configured_model" in reloaded_model._bqml_model.model_name - ) - assert reloaded_model.optimize_strategy == "BATCH_GRADIENT_DESCENT" - assert reloaded_model.fit_intercept is False - assert reloaded_model.calculate_p_values is False - assert reloaded_model.enable_global_explain is False - assert reloaded_model.l1_reg == 0.2 - assert reloaded_model.l2_reg == 0.2 - assert reloaded_model.ls_init_learning_rate is None - assert reloaded_model.max_iterations == 30 - assert reloaded_model.tol == 0.02 - assert reloaded_model.learning_rate_strategy == "CONSTANT" - assert reloaded_model.learning_rate == 0.2 - - -def test_unordered_mode_linear_regression_configure_fit_score_predict( - unordered_session, penguins_table_id, dataset_id -): - model = bigframes.ml.linear_model.LinearRegression() - - df = unordered_session.read_gbq(penguins_table_id).dropna() - X_train = df[ - [ - "species", - "island", - "culmen_length_mm", - "culmen_depth_mm", - "flipper_length_mm", - "sex", - ] - ] - y_train = df[["body_mass_g"]] - - start_execution_count = df._block._expr.session._metrics.execution_count - model.fit(X_train, y_train) - end_execution_count = df._block._expr.session._metrics.execution_count - # The fit function initiates two queries: the first generates and caches - # the training data, while the second creates and fits the model. - assert end_execution_count - start_execution_count == 2 - - # Check score to ensure the model was fitted - start_execution_count = end_execution_count - result = model.score(X_train, y_train).to_pandas() - end_execution_count = df._block._expr.session._metrics.execution_count - # The score function and to_pandas reuse same result. - assert end_execution_count - start_execution_count == 1 - - utils.check_pandas_df_schema_and_index( - result, columns=utils.ML_REGRESSION_METRICS, index=1 - ) + expected = pd.DataFrame( + { + "mean_absolute_error": [226.108411], + "mean_squared_error": [80459.668456], + "mean_squared_log_error": [0.00497], + "median_absolute_error": [171.618872], + "r2_score": [0.875415], + "explained_variance": [0.875417], + }, + dtype="Float64", + ) + expected = expected.reindex(index=expected.index.astype("Int64")) + pd.testing.assert_frame_equal(result, expected, check_exact=False, rtol=0.1) # save, load, check parameters to ensure configuration was kept reloaded_model = model.to_gbq(f"{dataset_id}.temp_configured_model", replace=True) - assert reloaded_model._bqml_model is not None assert ( f"{dataset_id}.temp_configured_model" in reloaded_model._bqml_model.model_name ) assert reloaded_model.optimize_strategy == "NORMAL_EQUATION" - assert reloaded_model.fit_intercept is True + assert reloaded_model.fit_intercept is False assert reloaded_model.calculate_p_values is False + assert reloaded_model.early_stop is True assert reloaded_model.enable_global_explain is False - assert reloaded_model.l1_reg is None - assert reloaded_model.l2_reg == 0.0 - assert reloaded_model.learning_rate is None - assert reloaded_model.learning_rate_strategy == "line_search" - assert reloaded_model.ls_init_learning_rate is None + assert reloaded_model.l2_reg == 0.1 + assert reloaded_model.learn_rate_strategy == "line_search" + assert reloaded_model.ls_init_learn_rate == 0.1 assert reloaded_model.max_iterations == 20 - assert reloaded_model.tol == 0.01 - - start_execution_count = df._block._expr.session._metrics.execution_count - pred = reloaded_model.predict(df) - end_execution_count = df._block._expr.session._metrics.execution_count - assert end_execution_count - start_execution_count == 1 - utils.check_pandas_df_schema_and_index( - pred, - columns=("predicted_body_mass_g",), - col_exact=False, - index=334, - ) - - -# TODO(garrettwu): add tests for param warm_start. Requires a trained model. + assert reloaded_model.min_rel_progress == 0.01 def test_logistic_regression_configure_fit_score(penguins_df_default_index, dataset_id): @@ -290,110 +140,37 @@ def test_logistic_regression_configure_fit_score(penguins_df_default_index, data # Check score to ensure the model was fitted result = model.score(X_train, y_train).to_pandas() - utils.check_pandas_df_schema_and_index( - result, columns=utils.ML_CLASSFICATION_METRICS, index=1 - ) + expected = pd.DataFrame( + { + "precision": [0.616753], + "recall": [0.618615], + "accuracy": [0.92515], + "f1_score": [0.617681], + "log_loss": [1.498832], + "roc_auc": [0.975807], + }, + dtype="Float64", + ) + expected = expected.reindex(index=expected.index.astype("Int64")) + pd.testing.assert_frame_equal(result, expected, check_exact=False, rtol=0.1) # save, load, check parameters to ensure configuration was kept reloaded_model = model.to_gbq( f"{dataset_id}.temp_configured_logistic_reg_model", replace=True ) - assert reloaded_model._bqml_model is not None assert ( f"{dataset_id}.temp_configured_logistic_reg_model" in reloaded_model._bqml_model.model_name ) assert reloaded_model.fit_intercept is True - assert reloaded_model.class_weight is None - - -def test_logistic_regression_configure_fit_with_eval_score( - penguins_df_default_index, dataset_id -): - model = bigframes.ml.linear_model.LogisticRegression() - - df = penguins_df_default_index.dropna() - df = df[df["sex"].isin(["MALE", "FEMALE"])] - - X = df[ - [ - "species", - "island", - "culmen_length_mm", - "culmen_depth_mm", - "flipper_length_mm", - "body_mass_g", - ] - ] - y = df[["sex"]] - - X_train, X_eval, y_train, y_eval = model_selection.train_test_split(X, y) - - model.fit(X_train, y_train, X_eval=X_eval, y_eval=y_eval) - - # Check score to ensure the model was fitted - result = model.score(X_eval, y_eval).to_pandas() - utils.check_pandas_df_schema_and_index( - result, columns=utils.ML_CLASSFICATION_METRICS, index=1 - ) - - # save, load, check parameters to ensure configuration was kept - bq_model_name = f"{dataset_id}.temp_configured_logistic_reg_model" - reloaded_model = model.to_gbq(bq_model_name, replace=True) - assert reloaded_model._bqml_model is not None - assert ( - f"{dataset_id}.temp_configured_logistic_reg_model" - in reloaded_model._bqml_model.model_name - ) - assert reloaded_model.fit_intercept is True - assert reloaded_model.class_weight is None - - # make sure the bqml model was internally created with custom split - bq_model = penguins_df_default_index._session.bqclient.get_model(bq_model_name) - last_fitting = bq_model.training_runs[-1]["trainingOptions"] - assert last_fitting["dataSplitMethod"] == "CUSTOM" - assert "dataSplitColumn" in last_fitting - - # make sure the bqml model has the same evaluation metrics attached as - # returned by model.score() - bq_model_expected_eval_metrics = result - bq_model_eval_metrics = bq_model.training_runs[-1]["evaluationMetrics"][ - "binaryClassificationMetrics" - ]["aggregateClassificationMetrics"] - bq_model_eval_metrics = pd.DataFrame( - [ - [ - bq_model_eval_metrics["precision"], - bq_model_eval_metrics["recall"], - bq_model_eval_metrics["accuracy"], - bq_model_eval_metrics["f1Score"], - bq_model_eval_metrics["logLoss"], - bq_model_eval_metrics["rocAuc"], - ] - ], - columns=utils.ML_CLASSFICATION_METRICS, - ) - pd.testing.assert_frame_equal( - bq_model_expected_eval_metrics, - bq_model_eval_metrics, - check_dtype=False, - check_index_type=False, - ) + assert reloaded_model.class_weights is None def test_logistic_regression_customized_params_fit_score( penguins_df_default_index, dataset_id ): model = bigframes.ml.linear_model.LogisticRegression( - fit_intercept=False, - class_weight="balanced", - l2_reg=0.2, - tol=0.02, - l1_reg=0.2, - max_iterations=30, - optimize_strategy="batch_gradient_descent", - learning_rate_strategy="constant", - learning_rate=0.2, + fit_intercept=False, class_weights="balanced" ) df = penguins_df_default_index.dropna() X_train = df[ @@ -410,90 +187,27 @@ def test_logistic_regression_customized_params_fit_score( # Check score to ensure the model was fitted result = model.score(X_train, y_train).to_pandas() - utils.check_pandas_df_schema_and_index( - result, columns=utils.ML_CLASSFICATION_METRICS, index=1 - ) + expected = pd.DataFrame( + { + "precision": [0.58483], + "recall": [0.586616], + "accuracy": [0.877246], + "f1_score": [0.58571], + "log_loss": [1.032699], + "roc_auc": [0.924132], + }, + dtype="Float64", + ) + expected = expected.reindex(index=expected.index.astype("Int64")) + pd.testing.assert_frame_equal(result, expected, check_exact=False, rtol=0.1) # save, load, check parameters to ensure configuration was kept reloaded_model = model.to_gbq( f"{dataset_id}.temp_configured_logistic_reg_model", replace=True ) - assert reloaded_model._bqml_model is not None assert ( f"{dataset_id}.temp_configured_logistic_reg_model" in reloaded_model._bqml_model.model_name ) assert reloaded_model.fit_intercept is False - assert reloaded_model.class_weight == "balanced" - assert reloaded_model.calculate_p_values is False - assert reloaded_model.enable_global_explain is False - assert reloaded_model.l1_reg == 0.2 - assert reloaded_model.l2_reg == 0.2 - assert reloaded_model.ls_init_learning_rate is None - assert reloaded_model.max_iterations == 30 - assert reloaded_model.tol == 0.02 - assert reloaded_model.learning_rate_strategy == "CONSTANT" - assert reloaded_model.learning_rate == 0.2 - - -def test_model_centroids_with_custom_index(penguins_df_default_index): - model = bigframes.ml.linear_model.LogisticRegression( - fit_intercept=False, - class_weight="balanced", - l2_reg=0.2, - tol=0.02, - l1_reg=0.2, - max_iterations=30, - optimize_strategy="batch_gradient_descent", - learning_rate_strategy="constant", - learning_rate=0.2, - ) - df = penguins_df_default_index.dropna().set_index(["species", "island"]) - X_train = df[ - [ - "culmen_length_mm", - "culmen_depth_mm", - "flipper_length_mm", - ] - ] - y_train = df[["sex"]] - model.fit(X_train, y_train) - - # If this line executes without errors, the model has correctly ignored the custom index columns - model.predict(X_train.reset_index(drop=True)) - - -def test_linear_reg_model_global_explain( - penguins_linear_model_w_global_explain, new_penguins_df -): - training_data = new_penguins_df.dropna(subset=["body_mass_g"]) - X = training_data.drop(columns=["body_mass_g"]) - y = training_data[["body_mass_g"]] - penguins_linear_model_w_global_explain.fit(X, y) - global_ex = penguins_linear_model_w_global_explain.global_explain() - assert global_ex.shape == (6, 1) - expected_columns = pd.Index(["attribution"]) - pd.testing.assert_index_equal(global_ex.columns, expected_columns) - result = global_ex.to_pandas().drop(["attribution"], axis=1).sort_index() - expected_feature = ( - pd.DataFrame( - { - "feature": [ - "island", - "species", - "sex", - "flipper_length_mm", - "culmen_depth_mm", - "culmen_length_mm", - ] - }, - ) - .set_index("feature") - .sort_index() - ) - pd.testing.assert_frame_equal( - result, - expected_feature, - check_exact=False, - check_index_type=False, - ) + assert reloaded_model.class_weights == "balanced" diff --git a/tests/system/large/ml/test_llm.py b/tests/system/large/ml/test_llm.py deleted file mode 100644 index 638e151ca14..00000000000 --- a/tests/system/large/ml/test_llm.py +++ /dev/null @@ -1,799 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from typing import Callable -from unittest import mock - -import pandas as pd -import pyarrow as pa -import pytest - -import bigframes.pandas as bpd -from bigframes.ml import core, llm -from bigframes.testing import utils - - -@pytest.mark.parametrize( - "model_name", - ( - "gemini-2.5-pro", - "gemini-2.5-flash", - "gemini-2.5-flash-lite", - ), -) -@pytest.mark.flaky(retries=2) -def test_create_load_gemini_text_generator_model( - dataset_id, model_name, session, bq_connection -): - gemini_text_generator_model = llm.GeminiTextGenerator( - model_name=model_name, connection_name=bq_connection, session=session - ) - assert gemini_text_generator_model is not None - assert gemini_text_generator_model._bqml_model is not None - - # save, load to ensure configuration was kept - reloaded_model = gemini_text_generator_model.to_gbq( - f"{dataset_id}.temp_text_model", replace=True - ) - assert f"{dataset_id}.temp_text_model" == reloaded_model._bqml_model.model_name - assert reloaded_model.connection_name == bq_connection - assert reloaded_model.model_name == model_name - - -@pytest.mark.parametrize( - "model_name", - ( - "gemini-2.5-pro", - "gemini-2.5-flash", - "gemini-2.5-flash-lite", - ), -) -@pytest.mark.flaky(retries=2) -def test_gemini_text_generator_predict_default_params_success( - llm_text_df, model_name, session, bq_connection -): - gemini_text_generator_model = llm.GeminiTextGenerator( - model_name=model_name, connection_name=bq_connection, session=session - ) - df = gemini_text_generator_model.predict(llm_text_df).to_pandas() - utils.check_pandas_df_schema_and_index( - df, columns=utils.ML_GENERATE_TEXT_OUTPUT, index=3, col_exact=False - ) - - -@pytest.mark.parametrize( - "model_name", - ( - "gemini-2.5-pro", - "gemini-2.5-flash", - "gemini-2.5-flash-lite", - ), -) -@pytest.mark.flaky(retries=2) -def test_gemini_text_generator_predict_with_params_success( - llm_text_df, model_name, session, bq_connection -): - gemini_text_generator_model = llm.GeminiTextGenerator( - model_name=model_name, connection_name=bq_connection, session=session - ) - df = gemini_text_generator_model.predict( - llm_text_df, temperature=0.5, max_output_tokens=100, top_k=20, top_p=0.5 - ).to_pandas() - utils.check_pandas_df_schema_and_index( - df, columns=utils.ML_GENERATE_TEXT_OUTPUT, index=3, col_exact=False - ) - - -@pytest.mark.parametrize( - "model_name", - ( - "gemini-2.5-pro", - "gemini-2.5-flash", - "gemini-2.5-flash-lite", - ), -) -@pytest.mark.flaky(retries=2) -def test_gemini_text_generator_multi_cols_predict_success( - llm_text_df: bpd.DataFrame, model_name, session, bq_connection -): - df = llm_text_df.assign(additional_col=1) - gemini_text_generator_model = llm.GeminiTextGenerator( - model_name=model_name, connection_name=bq_connection, session=session - ) - pd_df = gemini_text_generator_model.predict(df).to_pandas() - utils.check_pandas_df_schema_and_index( - pd_df, - columns=utils.ML_GENERATE_TEXT_OUTPUT + ["additional_col"], - index=3, - col_exact=False, - ) - - -@pytest.mark.parametrize( - "model_name", - ( - "gemini-2.5-pro", - "gemini-2.5-flash", - "gemini-2.5-flash-lite", - ), -) -@pytest.mark.flaky(retries=2) -def test_gemini_text_generator_predict_output_schema_success( - llm_text_df: bpd.DataFrame, model_name, session, bq_connection -): - gemini_text_generator_model = llm.GeminiTextGenerator( - model_name=model_name, connection_name=bq_connection, session=session - ) - output_schema = { - "bool_output": "bool", - "int_output": "int64", - "float_output": "float64", - "str_output": "string", - "array_output": "array", - "struct_output": "struct", - } - df = gemini_text_generator_model.predict(llm_text_df, output_schema=output_schema) - assert df["bool_output"].dtype == pd.BooleanDtype() - assert df["int_output"].dtype == pd.Int64Dtype() - assert df["float_output"].dtype == pd.Float64Dtype() - assert df["str_output"].dtype == pd.StringDtype(storage="pyarrow") - assert df["array_output"].dtype == pd.ArrowDtype(pa.list_(pa.int64())) - assert df["struct_output"].dtype == pd.ArrowDtype( - pa.struct([("number", pa.int64())]) - ) - - pd_df = df.to_pandas() - utils.check_pandas_df_schema_and_index( - pd_df, - columns=list(output_schema.keys()) + ["prompt", "full_response", "status"], - index=3, - col_exact=False, - ) - - -@pytest.mark.flaky(retries=2) -@pytest.mark.parametrize( - "model_name", - ( - "gemini-2.5-flash", - "gemini-2.5-flash-lite", - ), -) -def test_llm_gemini_score(llm_fine_tune_df_default_index, model_name): - model = llm.GeminiTextGenerator(model_name=model_name) - - # Check score to ensure the model was fitted - score_result = model.score( - X=llm_fine_tune_df_default_index[["prompt"]], - y=llm_fine_tune_df_default_index[["label"]], - ).to_pandas() - utils.check_pandas_df_schema_and_index( - score_result, - columns=[ - "bleu4_score", - "rouge-l_precision", - "rouge-l_recall", - "rouge-l_f1_score", - "evaluation_status", - ], - index=1, - col_exact=False, - ) - - -@pytest.mark.parametrize( - "model_name", - ( - "gemini-2.5-flash", - "gemini-2.5-flash-lite", - ), -) -def test_llm_gemini_pro_score_params(llm_fine_tune_df_default_index, model_name): - model = llm.GeminiTextGenerator(model_name=model_name) - - # Check score to ensure the model was fitted - score_result = model.score( - X=llm_fine_tune_df_default_index["prompt"], - y=llm_fine_tune_df_default_index["label"], - task_type="classification", - ).to_pandas() - utils.check_pandas_df_schema_and_index( - score_result, - columns=[ - "precision", - "recall", - "f1_score", - "label", - "evaluation_status", - ], - col_exact=False, - ) - - -@pytest.mark.parametrize( - "model_name", - ("text-embedding-005", "text-embedding-004", "text-multilingual-embedding-002"), -) -def test_create_load_text_embedding_generator_model( - dataset_id, model_name, session, bq_connection -): - text_embedding_model = llm.TextEmbeddingGenerator( - model_name=model_name, connection_name=bq_connection, session=session - ) - assert text_embedding_model is not None - assert text_embedding_model._bqml_model is not None - - # save, load to ensure configuration was kept - reloaded_model = text_embedding_model.to_gbq( - f"{dataset_id}.temp_text_model", replace=True - ) - assert f"{dataset_id}.temp_text_model" == reloaded_model._bqml_model.model_name - assert reloaded_model.connection_name == bq_connection - assert reloaded_model.model_name == model_name - - -@pytest.mark.parametrize( - "model_name", - ("text-embedding-005", "text-embedding-004", "text-multilingual-embedding-002"), -) -@pytest.mark.flaky(retries=2) -def test_text_embedding_generator_predict_default_params_success( - llm_text_df, model_name, session, bq_connection -): - text_embedding_model = llm.TextEmbeddingGenerator( - model_name=model_name, connection_name=bq_connection, session=session - ) - df = text_embedding_model.predict(llm_text_df).to_pandas() - utils.check_pandas_df_schema_and_index( - df, columns=utils.ML_GENERATE_EMBEDDING_OUTPUT, index=3, col_exact=False - ) - assert len(df["ml_generate_embedding_result"][0]) == 768 - - -@pytest.mark.parametrize( - "model_name", - ("text-embedding-005", "text-embedding-004", "text-multilingual-embedding-002"), -) -@pytest.mark.flaky(retries=2) -def test_text_embedding_generator_multi_cols_predict_success( - llm_text_df: bpd.DataFrame, model_name, session, bq_connection -): - df = llm_text_df.assign(additional_col=1) - df = df.rename(columns={"prompt": "content"}) - text_embedding_model = llm.TextEmbeddingGenerator( - model_name=model_name, connection_name=bq_connection, session=session - ) - pd_df = text_embedding_model.predict(df).to_pandas() - utils.check_pandas_df_schema_and_index( - pd_df, - columns=utils.ML_GENERATE_EMBEDDING_OUTPUT + ["additional_col"], - index=3, - col_exact=False, - ) - assert len(pd_df["ml_generate_embedding_result"][0]) == 768 - - -def test_create_load_multimodal_embedding_generator_model( - dataset_id, session, bq_connection -): - mm_embedding_model = llm.MultimodalEmbeddingGenerator( - connection_name=bq_connection, session=session - ) - assert mm_embedding_model is not None - assert mm_embedding_model._bqml_model is not None - - # save, load to ensure configuration was kept - reloaded_model = mm_embedding_model.to_gbq( - f"{dataset_id}.temp_mm_model", replace=True - ) - assert f"{dataset_id}.temp_mm_model" == reloaded_model._bqml_model.model_name - assert reloaded_model.connection_name == bq_connection - - -# Overrides __eq__ function for comparing as mock.call parameter -class EqCmpAllDataFrame(bpd.DataFrame): - def __eq__(self, other): - return self.equals(other) - - -@pytest.mark.skip("b/436340035 test failed") -@pytest.mark.parametrize( - ( - "model_class", - "options", - ), - [ - ( - llm.GeminiTextGenerator, - { - "temperature": 0.9, - "max_output_tokens": 8192, - "top_p": 1.0, - "ground_with_google_search": False, - }, - ), - ( - llm.Claude3TextGenerator, - { - "max_output_tokens": 128, - "top_k": 40, - "top_p": 0.95, - }, - ), - ], -) -def test_text_generator_retry_success( - session, - model_class, - options, - bq_connection, -): - # Requests. - df0 = EqCmpAllDataFrame( - { - "prompt": [ - "What is BigQuery?", - "What is BQML?", - "What is BigQuery DataFrame?", - ] - }, - index=[0, 1, 2], - session=session, - ) - df1 = EqCmpAllDataFrame( - { - "ml_generate_text_status": ["error", "error"], - "prompt": [ - "What is BQML?", - "What is BigQuery DataFrame?", - ], - }, - index=[1, 2], - session=session, - ) - df2 = EqCmpAllDataFrame( - { - "ml_generate_text_status": ["error"], - "prompt": [ - "What is BQML?", - ], - }, - index=[1], - session=session, - ) - - mock_generate_text = mock.create_autospec( - Callable[[core.BqmlModel, bpd.DataFrame, dict], bpd.DataFrame] - ) - mock_bqml_model = mock.create_autospec(spec=core.BqmlModel) - type(mock_bqml_model).session = mock.PropertyMock(return_value=session) - generate_text_tvf = core.BqmlModel.TvfDef( - mock_generate_text, "ml_generate_text_status" - ) - # Responses. Retry twice then all succeeded. - mock_generate_text.side_effect = [ - EqCmpAllDataFrame( - { - "ml_generate_text_status": ["", "error", "error"], - "prompt": [ - "What is BigQuery?", - "What is BQML?", - "What is BigQuery DataFrame?", - ], - }, - index=[0, 1, 2], - session=session, - ), - EqCmpAllDataFrame( - { - "ml_generate_text_status": ["error", ""], - "prompt": [ - "What is BQML?", - "What is BigQuery DataFrame?", - ], - }, - index=[1, 2], - session=session, - ), - EqCmpAllDataFrame( - { - "ml_generate_text_status": [""], - "prompt": [ - "What is BQML?", - ], - }, - index=[1], - session=session, - ), - ] - - text_generator_model = model_class(connection_name=bq_connection, session=session) - text_generator_model._bqml_model = mock_bqml_model - - with mock.patch.object(core.BqmlModel, "generate_text_tvf", generate_text_tvf): - # 3rd retry isn't triggered - result = text_generator_model.predict(df0, max_retries=3) - - mock_generate_text.assert_has_calls( - [ - mock.call(mock_bqml_model, df0, options), - mock.call(mock_bqml_model, df1, options), - mock.call(mock_bqml_model, df2, options), - ] - ) - pd.testing.assert_frame_equal( - result.to_pandas(), - pd.DataFrame( - { - "ml_generate_text_status": ["", "", ""], - "prompt": [ - "What is BigQuery?", - "What is BigQuery DataFrame?", - "What is BQML?", - ], - }, - index=[0, 2, 1], - ), - check_dtype=False, - check_index_type=False, - ) - - -@pytest.mark.skip("b/436340035 test failed") -@pytest.mark.parametrize( - ( - "model_class", - "options", - ), - [ - ( - llm.GeminiTextGenerator, - { - "temperature": 0.9, - "max_output_tokens": 8192, - "top_p": 1.0, - "ground_with_google_search": False, - }, - ), - ( - llm.Claude3TextGenerator, - { - "max_output_tokens": 128, - "top_k": 40, - "top_p": 0.95, - }, - ), - ], -) -def test_text_generator_retry_no_progress(session, model_class, options, bq_connection): - # Requests. - df0 = EqCmpAllDataFrame( - { - "prompt": [ - "What is BigQuery?", - "What is BQML?", - "What is BigQuery DataFrame?", - ] - }, - index=[0, 1, 2], - session=session, - ) - df1 = EqCmpAllDataFrame( - { - "ml_generate_text_status": ["error", "error"], - "prompt": [ - "What is BQML?", - "What is BigQuery DataFrame?", - ], - }, - index=[1, 2], - session=session, - ) - - mock_generate_text = mock.create_autospec( - Callable[[core.BqmlModel, bpd.DataFrame, dict], bpd.DataFrame] - ) - mock_bqml_model = mock.create_autospec(spec=core.BqmlModel) - type(mock_bqml_model).session = mock.PropertyMock(return_value=session) - generate_text_tvf = core.BqmlModel.TvfDef( - mock_generate_text, "ml_generate_text_status" - ) - # Responses. Retry once, no progress, just stop. - mock_generate_text.side_effect = [ - EqCmpAllDataFrame( - { - "ml_generate_text_status": ["", "error", "error"], - "prompt": [ - "What is BigQuery?", - "What is BQML?", - "What is BigQuery DataFrame?", - ], - }, - index=[0, 1, 2], - session=session, - ), - EqCmpAllDataFrame( - { - "ml_generate_text_status": ["error", "error"], - "prompt": [ - "What is BQML?", - "What is BigQuery DataFrame?", - ], - }, - index=[1, 2], - session=session, - ), - ] - - text_generator_model = model_class(connection_name=bq_connection, session=session) - text_generator_model._bqml_model = mock_bqml_model - - with mock.patch.object(core.BqmlModel, "generate_text_tvf", generate_text_tvf): - # No progress, only conduct retry once - result = text_generator_model.predict(df0, max_retries=3) - - mock_generate_text.assert_has_calls( - [ - mock.call(mock_bqml_model, df0, options), - mock.call(mock_bqml_model, df1, options), - ] - ) - pd.testing.assert_frame_equal( - result.to_pandas(), - pd.DataFrame( - { - "ml_generate_text_status": ["", "error", "error"], - "prompt": [ - "What is BigQuery?", - "What is BQML?", - "What is BigQuery DataFrame?", - ], - }, - index=[0, 1, 2], - ), - check_dtype=False, - check_index_type=False, - ) - - -@pytest.mark.skip("b/436340035 test failed") -def test_text_embedding_generator_retry_success(session, bq_connection): - # Requests. - df0 = EqCmpAllDataFrame( - { - "content": [ - "What is BigQuery?", - "What is BQML?", - "What is BigQuery DataFrame?", - ] - }, - index=[0, 1, 2], - session=session, - ) - df1 = EqCmpAllDataFrame( - { - "ml_generate_embedding_status": ["error", "error"], - "content": [ - "What is BQML?", - "What is BigQuery DataFrame?", - ], - }, - index=[1, 2], - session=session, - ) - df2 = EqCmpAllDataFrame( - { - "ml_generate_embedding_status": ["error"], - "content": [ - "What is BQML?", - ], - }, - index=[1], - session=session, - ) - - mock_generate_embedding = mock.create_autospec( - Callable[[core.BqmlModel, bpd.DataFrame, dict], bpd.DataFrame] - ) - mock_bqml_model = mock.create_autospec(spec=core.BqmlModel) - type(mock_bqml_model).session = mock.PropertyMock(return_value=session) - generate_embedding_tvf = core.BqmlModel.TvfDef( - mock_generate_embedding, "ml_generate_embedding_status" - ) - - # Responses. Retry twice then all succeeded. - mock_generate_embedding.side_effect = [ - EqCmpAllDataFrame( - { - "ml_generate_embedding_status": ["", "error", "error"], - "content": [ - "What is BigQuery?", - "What is BQML?", - "What is BigQuery DataFrame?", - ], - }, - index=[0, 1, 2], - session=session, - ), - EqCmpAllDataFrame( - { - "ml_generate_embedding_status": ["error", ""], - "content": [ - "What is BQML?", - "What is BigQuery DataFrame?", - ], - }, - index=[1, 2], - session=session, - ), - EqCmpAllDataFrame( - { - "ml_generate_embedding_status": [""], - "content": [ - "What is BQML?", - ], - }, - index=[1], - session=session, - ), - ] - options: dict = {} - - text_embedding_model = llm.TextEmbeddingGenerator( - connection_name=bq_connection, session=session - ) - text_embedding_model._bqml_model = mock_bqml_model - - with mock.patch.object( - core.BqmlModel, "generate_embedding_tvf", generate_embedding_tvf - ): - # 3rd retry isn't triggered - result = text_embedding_model.predict(df0, max_retries=3) - - mock_generate_embedding.assert_has_calls( - [ - mock.call(mock_bqml_model, df0, options), - mock.call(mock_bqml_model, df1, options), - mock.call(mock_bqml_model, df2, options), - ] - ) - pd.testing.assert_frame_equal( - result.to_pandas(), - pd.DataFrame( - { - "ml_generate_embedding_status": ["", "", ""], - "content": [ - "What is BigQuery?", - "What is BigQuery DataFrame?", - "What is BQML?", - ], - }, - index=[0, 2, 1], - ), - check_dtype=False, - check_index_type=False, - ) - - -def test_text_embedding_generator_retry_no_progress(session, bq_connection): - # Requests. - df0 = EqCmpAllDataFrame( - { - "content": [ - "What is BigQuery?", - "What is BQML?", - "What is BigQuery DataFrame?", - ] - }, - index=[0, 1, 2], - session=session, - ) - df1 = EqCmpAllDataFrame( - { - "ml_generate_embedding_status": ["error", "error"], - "content": [ - "What is BQML?", - "What is BigQuery DataFrame?", - ], - }, - index=[1, 2], - session=session, - ) - - mock_generate_embedding = mock.create_autospec( - Callable[[core.BqmlModel, bpd.DataFrame, dict], bpd.DataFrame] - ) - mock_bqml_model = mock.create_autospec(spec=core.BqmlModel) - type(mock_bqml_model).session = mock.PropertyMock(return_value=session) - generate_embedding_tvf = core.BqmlModel.TvfDef( - mock_generate_embedding, "ml_generate_embedding_status" - ) - - # Responses. Retry once, no progress, just stop. - mock_generate_embedding.side_effect = [ - EqCmpAllDataFrame( - { - "ml_generate_embedding_status": ["", "error", "error"], - "content": [ - "What is BigQuery?", - "What is BQML?", - "What is BigQuery DataFrame?", - ], - }, - index=[0, 1, 2], - session=session, - ), - EqCmpAllDataFrame( - { - "ml_generate_embedding_status": ["error", "error"], - "content": [ - "What is BQML?", - "What is BigQuery DataFrame?", - ], - }, - index=[1, 2], - session=session, - ), - ] - options: dict = {} - - text_embedding_model = llm.TextEmbeddingGenerator( - connection_name=bq_connection, session=session - ) - text_embedding_model._bqml_model = mock_bqml_model - - with mock.patch.object( - core.BqmlModel, "generate_embedding_tvf", generate_embedding_tvf - ): - # No progress, only conduct retry once - result = text_embedding_model.predict(df0, max_retries=3) - - mock_generate_embedding.assert_has_calls( - [ - mock.call(mock_bqml_model, df0, options), - mock.call(mock_bqml_model, df1, options), - ] - ) - pd.testing.assert_frame_equal( - result.to_pandas(), - pd.DataFrame( - { - "ml_generate_embedding_status": ["", "error", "error"], - "content": [ - "What is BigQuery?", - "What is BQML?", - "What is BigQuery DataFrame?", - ], - }, - index=[0, 1, 2], - ), - check_dtype=False, - check_index_type=False, - ) - - -# b/436340035 temp disable the test to unblock presumbit -@pytest.mark.parametrize( - "model_class", - [ - llm.TextEmbeddingGenerator, - llm.MultimodalEmbeddingGenerator, - llm.GeminiTextGenerator, - # llm.Claude3TextGenerator, - ], -) -def test_text_embedding_generator_no_default_model_warning(model_class): - message = "Since upgrading the default model can cause unintended breakages, the\ndefault model will be removed in BigFrames 3.0. Please supply an\nexplicit model to avoid this message." - with pytest.warns(FutureWarning, match=message): - model_class(model_name=None) diff --git a/tests/system/large/ml/test_model_selection.py b/tests/system/large/ml/test_model_selection.py deleted file mode 100644 index 26174b7ee98..00000000000 --- a/tests/system/large/ml/test_model_selection.py +++ /dev/null @@ -1,64 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import pytest - -from bigframes.ml import linear_model, model_selection -from bigframes.testing import utils - - -@pytest.mark.parametrize( - ("cv", "n_fold"), - ( - pytest.param( - None, - 5, - ), - pytest.param( - 4, - 4, - ), - pytest.param( - model_selection.KFold(3), - 3, - ), - ), -) -def test_cross_validate(penguins_df_default_index, cv, n_fold): - model = linear_model.LinearRegression() - df = penguins_df_default_index.dropna() - X = df[ - [ - "species", - "island", - "culmen_length_mm", - ] - ] - y = df["body_mass_g"] - - cv_results = model_selection.cross_validate(model, X, y, cv=cv) - - assert "test_score" in cv_results - assert "fit_time" in cv_results - assert "score_time" in cv_results - - assert len(cv_results["test_score"]) == n_fold - assert len(cv_results["fit_time"]) == n_fold - assert len(cv_results["score_time"]) == n_fold - - utils.check_pandas_df_schema_and_index( - cv_results["test_score"][0].to_pandas(), - columns=utils.ML_REGRESSION_METRICS, - index=1, - ) diff --git a/tests/system/large/ml/test_pipeline.py b/tests/system/large/ml/test_pipeline.py index 6c51a11a113..6874a9f301a 100644 --- a/tests/system/large/ml/test_pipeline.py +++ b/tests/system/large/ml/test_pipeline.py @@ -20,12 +20,11 @@ compose, decomposition, ensemble, - impute, linear_model, pipeline, preprocessing, ) -from bigframes.testing import utils +from tests.system.utils import assert_pandas_df_equal_ignore_ordering def test_pipeline_linear_regression_fit_score_predict( @@ -52,8 +51,21 @@ def test_pipeline_linear_regression_fit_score_predict( # Check score to ensure the model was fitted score_result = pl.score(X_train, y_train).to_pandas() - utils.check_pandas_df_schema_and_index( - score_result, columns=utils.ML_REGRESSION_METRICS, index=1 + score_expected = pd.DataFrame( + { + "mean_absolute_error": [309.477331], + "mean_squared_error": [152184.227219], + "mean_squared_log_error": [0.009524], + "median_absolute_error": [257.728263], + "r2_score": [0.764356], + "explained_variance": [0.764356], + }, + dtype="Float64", + ) + score_expected = score_expected.reindex(index=score_expected.index.astype("Int64")) + + pd.testing.assert_frame_equal( + score_result, score_expected, check_exact=False, rtol=0.1 ) # predict new labels @@ -75,11 +87,13 @@ def test_pipeline_linear_regression_fit_score_predict( ).set_index("tag_number") ) predictions = pl.predict(new_penguins).to_pandas() - utils.check_pandas_df_schema_and_index( - predictions, - columns=["predicted_body_mass_g"], - index=[1633, 1672, 1690], - col_exact=False, + expected = pd.DataFrame( + {"predicted_body_mass_g": [3968.8, 3176.3, 3545.2]}, + dtype="Float64", + index=pd.Index([1633, 1672, 1690], name="tag_number", dtype="Int64"), + ) + pd.testing.assert_frame_equal( + predictions[["predicted_body_mass_g"]], expected, check_exact=False, rtol=0.1 ) @@ -101,8 +115,21 @@ def test_pipeline_linear_regression_series_fit_score_predict( # Check score to ensure the model was fitted score_result = pl.score(X_train, y_train).to_pandas() - utils.check_pandas_df_schema_and_index( - score_result, columns=utils.ML_REGRESSION_METRICS, index=1 + score_expected = pd.DataFrame( + { + "mean_absolute_error": [528.495599], + "mean_squared_error": [421722.261808], + "mean_squared_log_error": [0.022963], + "median_absolute_error": [468.895249], + "r2_score": [0.346999], + "explained_variance": [0.346999], + }, + dtype="Float64", + ) + score_expected = score_expected.reindex(index=score_expected.index.astype("Int64")) + + pd.testing.assert_frame_equal( + score_result, score_expected, check_exact=False, rtol=0.1 ) # predict new labels @@ -115,11 +142,13 @@ def test_pipeline_linear_regression_series_fit_score_predict( ).set_index("tag_number") ) predictions = pl.predict(new_penguins["culmen_length_mm"]).to_pandas() - utils.check_pandas_df_schema_and_index( - predictions, - columns=["predicted_body_mass_g"], - index=[1633, 1672, 1690], - col_exact=False, + expected = pd.DataFrame( + {"predicted_body_mass_g": [3818.845703, 3732.022253, 3679.928123]}, + dtype="Float64", + index=pd.Index([1633, 1672, 1690], name="tag_number", dtype="Int64"), + ) + pd.testing.assert_frame_equal( + predictions[["predicted_body_mass_g"]], expected, check_exact=False, rtol=0.1 ) @@ -147,8 +176,21 @@ def test_pipeline_logistic_regression_fit_score_predict( # Check score to ensure the model was fitted score_result = pl.score(X_train, y_train).to_pandas() - utils.check_pandas_df_schema_and_index( - score_result, columns=utils.ML_CLASSFICATION_METRICS, index=1 + score_expected = pd.DataFrame( + { + "precision": [0.537091], + "recall": [0.538636], + "accuracy": [0.805389], + "f1_score": [0.537716], + "log_loss": [1.445433], + "roc_auc": [0.917818], + }, + dtype="Float64", + ) + score_expected = score_expected.reindex(index=score_expected.index.astype("Int64")) + + pd.testing.assert_frame_equal( + score_result, score_expected, check_exact=False, rtol=0.1 ) # predict new labels @@ -169,15 +211,18 @@ def test_pipeline_logistic_regression_fit_score_predict( ).set_index("tag_number") ) predictions = pl.predict(new_penguins).to_pandas() - utils.check_pandas_df_schema_and_index( - predictions, - columns=["predicted_sex"], - index=[1633, 1672, 1690], - col_exact=False, + expected = pd.DataFrame( + {"predicted_sex": ["MALE", "FEMALE", "FEMALE"]}, + dtype=pd.StringDtype(storage="pyarrow"), + index=pd.Index([1633, 1672, 1690], name="tag_number", dtype="Int64"), + ) + pd.testing.assert_frame_equal( + predictions[["predicted_sex"]], + expected, ) -@pytest.mark.flaky(retries=2) +@pytest.mark.flaky(retries=2, delay=120) def test_pipeline_xgbregressor_fit_score_predict(session, penguins_df_default_index): """Test a supervised model with a minimal preprocessing step""" pl = pipeline.Pipeline( @@ -200,8 +245,21 @@ def test_pipeline_xgbregressor_fit_score_predict(session, penguins_df_default_in # Check score to ensure the model was fitted score_result = pl.score(X_train, y_train).to_pandas() - utils.check_pandas_df_schema_and_index( - score_result, columns=utils.ML_REGRESSION_METRICS, index=1 + score_expected = pd.DataFrame( + { + "mean_absolute_error": [202.298434], + "mean_squared_error": [74515.108971], + "mean_squared_log_error": [0.004365], + "median_absolute_error": [142.949219], + "r2_score": [0.88462], + "explained_variance": [0.886454], + }, + dtype="Float64", + ) + score_expected = score_expected.reindex(index=score_expected.index.astype("Int64")) + + pd.testing.assert_frame_equal( + score_result, score_expected, check_exact=False, rtol=0.1 ) # predict new labels @@ -223,15 +281,23 @@ def test_pipeline_xgbregressor_fit_score_predict(session, penguins_df_default_in ).set_index("tag_number") ) predictions = pl.predict(new_penguins).to_pandas() - utils.check_pandas_df_schema_and_index( - predictions, - columns=["predicted_body_mass_g"], - index=[1633, 1672, 1690], - col_exact=False, + expected = pd.DataFrame( + { + "predicted_body_mass_g": [ + 4287.34521484375, + 3198.351806640625, + 3385.34130859375, + ] + }, + dtype="Float64", + index=pd.Index([1633, 1672, 1690], name="tag_number", dtype="Int64"), + ) + pd.testing.assert_frame_equal( + predictions[["predicted_body_mass_g"]], expected, check_exact=False, rtol=0.1 ) -@pytest.mark.flaky(retries=2) +@pytest.mark.flaky(retries=2, delay=120) def test_pipeline_random_forest_classifier_fit_score_predict( session, penguins_df_default_index ): @@ -256,8 +322,21 @@ def test_pipeline_random_forest_classifier_fit_score_predict( # Check score to ensure the model was fitted score_result = pl.score(X_train, y_train).to_pandas() - utils.check_pandas_df_schema_and_index( - score_result, columns=utils.ML_CLASSFICATION_METRICS, index=1 + score_expected = pd.DataFrame( + { + "precision": [0.585505], + "recall": [0.58676], + "accuracy": [0.877246], + "f1_score": [0.585657], + "log_loss": [0.880643], + "roc_auc": [0.970697], + }, + dtype="Float64", + ) + score_expected = score_expected.reindex(index=score_expected.index.astype("Int64")) + + pd.testing.assert_frame_equal( + score_result, score_expected, check_exact=False, rtol=0.1 ) # predict new labels @@ -278,11 +357,14 @@ def test_pipeline_random_forest_classifier_fit_score_predict( ).set_index("tag_number") ) predictions = pl.predict(new_penguins).to_pandas() - utils.check_pandas_df_schema_and_index( - predictions, - columns=["predicted_sex"], - index=[1633, 1672, 1690], - col_exact=False, + expected = pd.DataFrame( + {"predicted_sex": ["MALE", "FEMALE", "FEMALE"]}, + dtype=pd.StringDtype(storage="pyarrow"), + index=pd.Index([1633, 1672, 1690], name="tag_number", dtype="Int64"), + ) + pd.testing.assert_frame_equal( + predictions[["predicted_sex"]], + expected, ) @@ -326,24 +408,45 @@ def test_pipeline_PCA_fit_score_predict(session, penguins_df_default_index): # Check score to ensure the model was fitted score_result = pl.score(new_penguins).to_pandas() - utils.check_pandas_df_schema_and_index( - score_result, columns=["total_explained_variance_ratio"], index=1 + score_expected = pd.DataFrame( + { + "total_explained_variance_ratio": [1.0], + }, + dtype="Float64", + ) + score_expected = score_expected.reindex(index=score_expected.index.astype("Int64")) + + pd.testing.assert_frame_equal( + score_result, score_expected, check_exact=False, rtol=0.1 ) predictions = pl.predict(new_penguins).to_pandas() - utils.check_pandas_df_schema_and_index( - predictions, - columns=[ - "principal_component_1", - "principal_component_2", - "principal_component_3", - ], - index=[1633, 1672, 1690], - col_exact=False, + expected = pd.DataFrame( + { + "principal_component_1": [-1.115259, -1.506141, -1.471173], + "principal_component_2": [-0.074825, 0.69664, 0.406103], + "principal_component_3": [0.500013, -0.544479, 0.075849], + }, + dtype="Float64", + index=pd.Index([1633, 1672, 1690], name="tag_number", dtype="Int64"), + ) + pd.testing.assert_frame_equal( + abs( # results may differ by a minus sign + predictions[ + [ + "principal_component_1", + "principal_component_2", + "principal_component_3", + ] + ] + ), + abs(expected), + check_exact=False, + rtol=0.1, ) -@pytest.mark.flaky(retries=2) +@pytest.mark.flaky(retries=2, delay=120) def test_pipeline_standard_scaler_kmeans_fit_score_predict( session, penguins_pandas_df_default_index ): @@ -432,16 +535,27 @@ def test_pipeline_standard_scaler_kmeans_fit_score_predict( # Check score to ensure the model was fitted score_result = pl.score(new_penguins).to_pandas() - eval_metrics = ["davies_bouldin_index", "mean_squared_distance"] - utils.check_pandas_df_schema_and_index(score_result, columns=eval_metrics, index=1) + score_expected = pd.DataFrame( + {"davies_bouldin_index": [7.542981], "mean_squared_distance": [94.692409]}, + dtype="Float64", + ) + score_expected = score_expected.reindex(index=score_expected.index.astype("Int64")) + + pd.testing.assert_frame_equal( + score_result, score_expected, check_exact=False, rtol=0.1 + ) - predictions = pl.predict(new_penguins).to_pandas().sort_index() - utils.check_pandas_df_schema_and_index( - predictions, - columns=["CENTROID_ID"], - index=["test1", "test2", "test3", "test4", "test5", "test6"], - col_exact=False, + result = pl.predict(new_penguins).to_pandas().sort_index() + expected = pd.DataFrame( + {"CENTROID_ID": [1, 2, 1, 2, 1, 2]}, + dtype="Int64", + index=pd.Index( + ["test1", "test2", "test3", "test4", "test5", "test6"], + dtype="string[pyarrow]", + ), ) + expected.index.name = "observation" + assert_pandas_df_equal_ignore_ordering(result, expected) def test_pipeline_columntransformer_fit_predict(session, penguins_df_default_index): @@ -477,21 +591,11 @@ def test_pipeline_columntransformer_fit_predict(session, penguins_df_default_ind preprocessing.KBinsDiscretizer(strategy="uniform"), ["culmen_length_mm", "flipper_length_mm"], ), - ( - "simple_imputer", - impute.SimpleImputer(strategy="mean"), - ["culmen_length_mm", "flipper_length_mm"], - ), ( "label", preprocessing.LabelEncoder(), "species", ), - ( - "poly_feats", - preprocessing.PolynomialFeatures(), - ["culmen_length_mm", "flipper_length_mm"], - ), ] ), ), @@ -523,11 +627,13 @@ def test_pipeline_columntransformer_fit_predict(session, penguins_df_default_ind ).set_index("tag_number") ) predictions = pl.predict(new_penguins).to_pandas() - utils.check_pandas_df_schema_and_index( - predictions, - columns=["predicted_body_mass_g"], - index=[1633, 1672, 1690], - col_exact=False, + expected = pd.DataFrame( + {"predicted_body_mass_g": [3909.2, 3436.0, 2860.0]}, + dtype="Float64", + index=pd.Index([1633, 1672, 1690], name="tag_number", dtype="Int64"), + ) + pd.testing.assert_frame_equal( + predictions[["predicted_body_mass_g"]], expected, check_exact=False, rtol=0.1 ) @@ -539,7 +645,7 @@ def test_pipeline_columntransformer_to_gbq(penguins_df_default_index, dataset_id compose.ColumnTransformer( [ ( - "one_hot_encoder", + "ont_hot_encoder", preprocessing.OneHotEncoder( drop="most_frequent", min_frequency=5, @@ -567,16 +673,6 @@ def test_pipeline_columntransformer_to_gbq(penguins_df_default_index, dataset_id preprocessing.KBinsDiscretizer(strategy="uniform"), ["culmen_length_mm", "flipper_length_mm"], ), - ( - "simple_imputer", - impute.SimpleImputer(), - ["culmen_length_mm", "flipper_length_mm"], - ), - ( - "polynomial_features", - preprocessing.PolynomialFeatures(), - ["culmen_length_mm", "flipper_length_mm"], - ), ( "label", preprocessing.LabelEncoder(), @@ -599,10 +695,10 @@ def test_pipeline_columntransformer_to_gbq(penguins_df_default_index, dataset_id ) assert isinstance(pl_loaded._transform, compose.ColumnTransformer) - transformers = pl_loaded._transform.transformers + transformers = pl_loaded._transform.transformers_ expected = [ ( - "one_hot_encoder", + "ont_hot_encoder", preprocessing.OneHotEncoder( drop="most_frequent", max_categories=100, min_frequency=5 ), @@ -621,11 +717,6 @@ def test_pipeline_columntransformer_to_gbq(penguins_df_default_index, dataset_id preprocessing.KBinsDiscretizer(strategy="uniform"), "culmen_length_mm", ), - ( - "simple_imputer", - impute.SimpleImputer(), - "culmen_length_mm", - ), ("standard_scaler", preprocessing.StandardScaler(), "flipper_length_mm"), ("max_abs_scaler", preprocessing.MaxAbsScaler(), "flipper_length_mm"), ("min_max_scaler", preprocessing.MinMaxScaler(), "flipper_length_mm"), @@ -634,19 +725,9 @@ def test_pipeline_columntransformer_to_gbq(penguins_df_default_index, dataset_id preprocessing.KBinsDiscretizer(strategy="uniform"), "flipper_length_mm", ), - ( - "simple_imputer", - impute.SimpleImputer(), - "flipper_length_mm", - ), - ( - "polynomial_features", - preprocessing.PolynomialFeatures(), - ("culmen_length_mm", "flipper_length_mm"), - ), ] - assert set(transformers) == set(expected) + assert transformers == expected assert isinstance(pl_loaded._estimator, linear_model.LinearRegression) assert pl_loaded._estimator.fit_intercept is False @@ -831,69 +912,3 @@ def test_pipeline_label_encoder_to_gbq(penguins_df_default_index, dataset_id): assert isinstance(pl_loaded._estimator, linear_model.LinearRegression) assert pl_loaded._estimator.fit_intercept is False - - -def test_pipeline_simple_imputer_to_gbq(penguins_df_default_index, dataset_id): - pl = pipeline.Pipeline( - [ - ( - "transform", - impute.SimpleImputer(strategy="most_frequent"), - ), - ("estimator", linear_model.LinearRegression(fit_intercept=False)), - ] - ) - - df = penguins_df_default_index.dropna() - X_train = df[ - [ - "sex", - "species", - ] - ] - y_train = df[["body_mass_g"]] - pl.fit(X_train, y_train) - - pl_loaded = pl.to_gbq( - f"{dataset_id}.test_penguins_pipeline_simple_imputer", replace=True - ) - assert isinstance(pl_loaded._transform, impute.SimpleImputer) - - simple_imputer = pl_loaded._transform - assert simple_imputer.strategy == "most_frequent" - - assert isinstance(pl_loaded._estimator, linear_model.LinearRegression) - assert pl_loaded._estimator.fit_intercept is False - - -def test_pipeline_poly_features_to_gbq(penguins_df_default_index, dataset_id): - pl = pipeline.Pipeline( - [ - ( - "transform", - preprocessing.PolynomialFeatures(degree=3), - ), - ("estimator", linear_model.LinearRegression(fit_intercept=False)), - ] - ) - - df = penguins_df_default_index.dropna() - X_train = df[ - [ - "culmen_length_mm", - "flipper_length_mm", - ] - ] - y_train = df[["body_mass_g"]] - pl.fit(X_train, y_train) - - pl_loaded = pl.to_gbq( - f"{dataset_id}.test_penguins_pipeline_poly_features", replace=True - ) - assert isinstance(pl_loaded._transform, preprocessing.PolynomialFeatures) - - poly_features = pl_loaded._transform - assert poly_features.degree == 3 - - assert isinstance(pl_loaded._estimator, linear_model.LinearRegression) - assert pl_loaded._estimator.fit_intercept is False diff --git a/tests/system/large/streaming/test_bigtable.py b/tests/system/large/streaming/test_bigtable.py deleted file mode 100644 index f10c534404e..00000000000 --- a/tests/system/large/streaming/test_bigtable.py +++ /dev/null @@ -1,106 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import time -import uuid -from datetime import datetime, timedelta -from typing import Generator - -import pytest - -import bigframes - -pytest.importorskip("google.cloud.bigtable") - -from google.cloud import bigtable # noqa -from google.cloud.bigtable import column_family, instance, table # noqa - - -@pytest.fixture(scope="session") -def bigtable_instance(session_load: bigframes.Session) -> instance.Instance: - client = bigtable.Client(project=session_load._project, admin=True) - - instance_name = "streaming-testing-instance" - bt_instance = instance.Instance( - instance_name, - client, - ) - - if not bt_instance.exists(): - cluster_id = "streaming-testing-instance-c1" - cluster = bt_instance.cluster( - cluster_id, - location_id="us-west1-a", - serve_nodes=1, - ) - operation = bt_instance.create( - clusters=[cluster], - ) - operation.result(timeout=480) - return bt_instance - - -@pytest.fixture(scope="function") -def bigtable_table( - bigtable_instance: instance.Instance, -) -> Generator[table.Table, None, None]: - table_id = "bigframes_test_" + uuid.uuid4().hex - bt_table = table.Table( - table_id, - bigtable_instance, - ) - max_versions_rule = column_family.MaxVersionsGCRule(1) - column_family_id = "body_mass_g" - column_families = {column_family_id: max_versions_rule} - bt_table.create(column_families=column_families) - yield bt_table - bt_table.delete() - - -@pytest.mark.flaky(retries=3, delay=10) -def test_streaming_df_to_bigtable( - session_load: bigframes.Session, bigtable_table: table.Table -): - # launch a continuous query - job_id_prefix = "test_streaming_" - sdf = session_load.read_gbq_table_streaming("birds.penguins_bigtable_streaming") - - sdf = sdf[["species", "island", "body_mass_g"]] - sdf = sdf[sdf["body_mass_g"] < 4000] - sdf = sdf.rename(columns={"island": "rowkey"}) - - try: - query_job = sdf.to_bigtable( - instance="streaming-testing-instance", - table=bigtable_table.table_id, - service_account_email="streaming-testing-admin@bigframes-load-testing.iam.gserviceaccount.com", - app_profile=None, - truncate=True, - overwrite=True, - auto_create_column_families=True, - bigtable_options={}, - job_id=None, - job_id_prefix=job_id_prefix, - start_timestamp=datetime.now() - timedelta(days=1), - ) - - # wait 200 seconds in order to ensure the query doesn't stop - # (i.e. it is continuous) - time.sleep(200) - assert query_job.running() - assert query_job.error_result is None - assert str(query_job.job_id).startswith(job_id_prefix) - assert len(list(bigtable_table.read_rows())) > 0 - finally: - query_job.cancel() diff --git a/tests/system/large/streaming/test_pubsub.py b/tests/system/large/streaming/test_pubsub.py deleted file mode 100644 index cdc27ae65cf..00000000000 --- a/tests/system/large/streaming/test_pubsub.py +++ /dev/null @@ -1,116 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import uuid -from concurrent import futures -from datetime import datetime, timedelta -from typing import Generator - -import pytest - -import bigframes - -pytest.importorskip("google.cloud.pubsub") -from google.cloud import pubsub # type: ignore # noqa - - -def resource_name_full(project_id: str, resource_type: str, resource_id: str): - """Used for bigtable or pubsub resources.""" - return f"projects/{project_id}/{resource_type}/{resource_id}" - - -@pytest.fixture(scope="function") -def pubsub_topic_id(session_load: bigframes.Session) -> Generator[str, None, None]: - publisher = pubsub.PublisherClient() - topic_id = "bigframes_test_topic_" + uuid.uuid4().hex - - topic_name = resource_name_full(session_load._project, "topics", topic_id) - - publisher.create_topic(name=topic_name) - yield topic_id - publisher.delete_topic(topic=topic_name) - - -@pytest.fixture(scope="function") -def pubsub_topic_subscription_ids( - session_load: bigframes.Session, pubsub_topic_id: str -) -> Generator[tuple[str, str], None, None]: - subscriber = pubsub.SubscriberClient() - subscription_id = "bigframes_test_subscription_" + uuid.uuid4().hex - - subscription_name = resource_name_full( - session_load._project, "subscriptions", subscription_id - ) - topic_name = resource_name_full(session_load._project, "topics", pubsub_topic_id) - - subscriber.create_subscription(name=subscription_name, topic=topic_name) - yield (pubsub_topic_id, subscription_id) - subscriber.delete_subscription(subscription=subscription_name) - - -@pytest.mark.flaky(retries=3, delay=10) -def test_streaming_df_to_pubsub( - session_load: bigframes.Session, pubsub_topic_subscription_ids: tuple[str, str] -): - topic_id, subscription_id = pubsub_topic_subscription_ids - - subscriber = pubsub.SubscriberClient() - - subscription_name = "projects/{project_id}/subscriptions/{sub}".format( - project_id=session_load._project, - sub=subscription_id, - ) - - # launch a continuous query - job_id_prefix = "test_streaming_pubsub_" - sdf = session_load.read_gbq_table_streaming("birds.penguins_bigtable_streaming") - - sdf = sdf[sdf["body_mass_g"] < 4000] - sdf = sdf[["island"]] - - try: - - def counter(func): - def wrapper(*args, **kwargs): - wrapper.count += 1 # type: ignore - return func(*args, **kwargs) - - wrapper.count = 0 # type: ignore - return wrapper - - @counter - def callback(message): - message.ack() - - future = subscriber.subscribe(subscription_name, callback) - - query_job = sdf.to_pubsub( - topic=topic_id, - service_account_email="streaming-testing@bigframes-load-testing.iam.gserviceaccount.com", - job_id=None, - job_id_prefix=job_id_prefix, - start_timestamp=datetime.now() - timedelta(days=1), - ) - try: - # wait 200 seconds in order to ensure the query doesn't stop - # (i.e. it is continuous) - future.result(timeout=200) - except futures.TimeoutError: - future.cancel() - assert query_job.running() - assert query_job.error_result is None - assert str(query_job.job_id).startswith(job_id_prefix) - assert callback.count > 0 # type: ignore - finally: - query_job.cancel() diff --git a/tests/system/large/test_dataframe.py b/tests/system/large/test_dataframe.py deleted file mode 100644 index dc7671d18a6..00000000000 --- a/tests/system/large/test_dataframe.py +++ /dev/null @@ -1,66 +0,0 @@ -import sys - -import pandas as pd -import pytest - - -@pytest.mark.skipif( - sys.version_info >= (3, 12), - # See: https://github.com/python/cpython/issues/112282 - reason="setrecursionlimit has no effect on the Python C stack since Python 3.12.", -) -def test_corr_150_columns(scalars_df_numeric_150_columns_maybe_ordered): - scalars_df, scalars_pandas_df = scalars_df_numeric_150_columns_maybe_ordered - bf_result = scalars_df.corr(numeric_only=True).to_pandas() - pd_result = scalars_pandas_df.corr(numeric_only=True) - - pd.testing.assert_frame_equal( - bf_result, - pd_result, - check_dtype=False, - check_index_type=False, - check_column_type=False, - ) - - -@pytest.mark.skipif( - sys.version_info >= (3, 12), - # See: https://github.com/python/cpython/issues/112282 - reason="setrecursionlimit has no effect on the Python C stack since Python 3.12.", -) -def test_cov_150_columns(scalars_df_numeric_150_columns_maybe_ordered): - scalars_df, scalars_pandas_df = scalars_df_numeric_150_columns_maybe_ordered - bf_result = scalars_df.cov(numeric_only=True).to_pandas() - pd_result = scalars_pandas_df.cov(numeric_only=True) - - pd.testing.assert_frame_equal( - bf_result, - pd_result, - check_dtype=False, - check_index_type=False, - check_column_type=False, - ) - - -@pytest.mark.parametrize( - ("keep",), - [ - ("first",), - ("last",), - (False,), - ], -) -def test_drop_duplicates_unordered( - scalars_df_unordered, scalars_pandas_df_default_index, keep -): - uniq_scalar_rows = scalars_df_unordered.drop_duplicates( - subset="bool_col", keep=keep - ) - uniq_pd_rows = scalars_pandas_df_default_index.drop_duplicates( - subset="bool_col", keep=keep - ) - - assert len(uniq_scalar_rows) == len(uniq_pd_rows) - assert len(uniq_scalar_rows.groupby("bool_col")) == len( - uniq_pd_rows.groupby("bool_col") - ) diff --git a/tests/system/large/test_dataframe_io.py b/tests/system/large/test_dataframe_io.py deleted file mode 100644 index c352d618d6a..00000000000 --- a/tests/system/large/test_dataframe_io.py +++ /dev/null @@ -1,68 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import google.api_core.exceptions -import pytest - -import bigframes - -WIKIPEDIA_TABLE = "bigquery-public-data.samples.wikipedia" -LARGE_TABLE_OPTION = "compute.allow_large_results" - - -def test_to_pandas_batches_raise_when_large_result_not_allowed(session): - with ( - bigframes.option_context(LARGE_TABLE_OPTION, False), - pytest.raises(google.api_core.exceptions.Forbidden), - ): - df = session.read_gbq(WIKIPEDIA_TABLE) - next(df.to_pandas_batches(page_size=500, max_results=1500)) - - -def test_large_df_peek_no_job(session): - execution_count_before = session._metrics.execution_count - - # only works with null index, as sequential index requires row_number over full table scan. - df = session.read_gbq( - WIKIPEDIA_TABLE, index_col=bigframes.enums.DefaultIndexKind.NULL - ) - result = df.peek(50) - execution_count_after = session._metrics.execution_count - - assert len(result) == 50 - assert execution_count_after == execution_count_before - - -def test_to_pandas_batches_override_global_option( - session, -): - with bigframes.option_context(LARGE_TABLE_OPTION, False): - df = session.read_gbq(WIKIPEDIA_TABLE) - batches = df.sort_values("id").to_pandas_batches( - page_size=500, max_results=1500, allow_large_results=True - ) - assert batches.total_rows > 0 - assert batches.total_bytes_processed > 0 - pages = list(batches) - assert all((len(page) <= 500) for page in pages) - assert sum(len(page) for page in pages) == 1500 - - -def test_to_pandas_raise_when_large_result_not_allowed(session): - with ( - bigframes.option_context(LARGE_TABLE_OPTION, False), - pytest.raises(google.api_core.exceptions.Forbidden), - ): - df = session.read_gbq(WIKIPEDIA_TABLE) - next(df.to_pandas()) diff --git a/tests/system/large/test_location.py b/tests/system/large/test_location.py deleted file mode 100644 index 3127d5865a9..00000000000 --- a/tests/system/large/test_location.py +++ /dev/null @@ -1,224 +0,0 @@ -# Copyright 2023 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import typing -import unittest.mock as mock - -import google.auth.credentials -import pandas -import pandas.testing -import pytest - -import bigframes -import bigframes.constants -import bigframes.session.clients - - -def _assert_bq_execution_location( - session: bigframes.Session, expected_location: typing.Optional[str] = None -): - df = session.read_gbq( - """ - SELECT "aaa" as name, 111 as number - UNION ALL - SELECT "bbb" as name, 222 as number - UNION ALL - SELECT "aaa" as name, 333 as number - """ - ) - - if expected_location is None: - expected_location = session._location - - query_job = df.query_job - assert query_job is not None - assert query_job.location == expected_location - destination = query_job.destination - assert destination is not None - destination_dataset = session.bqclient.get_dataset( - f"{destination.project}.{destination.dataset_id}" - ) - assert destination_dataset.location == expected_location - - # Ensure operation involving BQ client suceeds - result = ( - df[["name", "number"]] - .groupby("name") - .sum(numeric_only=True) - .sort_values("number", ascending=False) - .head() - ) - - # Use allow_large_results = True to force a job to be created. - result_pd = result.to_pandas(allow_large_results=True) - - query_job = df.query_job - assert query_job is not None - assert query_job.location == expected_location - destination = query_job.destination - assert destination is not None - destination_dataset = session.bqclient.get_dataset( - f"{destination.project}.{destination.dataset_id}" - ) - assert destination_dataset.location == expected_location - - expected_result = pandas.DataFrame( - {"number": [444, 222]}, index=pandas.Index(["aaa", "bbb"], name="name") - ) - pandas.testing.assert_frame_equal( - expected_result, - result_pd, - check_dtype=False, - check_index_type=False, - ) - - -def test_bq_location_default(): - session = bigframes.Session() - - assert session.bqclient.location == "US" - - # by default global endpoint is used - assert ( - session.bqclient._connection.API_BASE_URL == "https://bigquery.googleapis.com" - ) - - # assert that bigframes session honors the location - _assert_bq_execution_location(session) - - -@pytest.mark.parametrize( - "bigquery_location", - # Sort the set to avoid nondeterminism. - sorted(bigframes.constants.ALL_BIGQUERY_LOCATIONS), -) -def test_bq_location(bigquery_location): - session = bigframes.Session( - context=bigframes.BigQueryOptions(location=bigquery_location) - ) - - assert session.bqclient.location == bigquery_location - - # by default global endpoint is used - assert ( - session.bqclient._connection.API_BASE_URL == "https://bigquery.googleapis.com" - ) - - # assert that bigframes session honors the location - _assert_bq_execution_location(session) - - -@pytest.mark.parametrize( - ("set_location", "resolved_location"), - # Sort the set to avoid nondeterminism. - [ - (loc.capitalize(), loc) - for loc in sorted(bigframes.constants.ALL_BIGQUERY_LOCATIONS) - ], -) -def test_bq_location_non_canonical(set_location, resolved_location): - session = bigframes.Session( - context=bigframes.BigQueryOptions(location=set_location) - ) - - assert session.bqclient.location == resolved_location - - # by default global endpoint is used - assert ( - session.bqclient._connection.API_BASE_URL == "https://bigquery.googleapis.com" - ) - - # assert that bigframes session honors the location - _assert_bq_execution_location(session, resolved_location) - - -@pytest.mark.parametrize( - "bigquery_location", - # Sort the set to avoid nondeterminism. - sorted(bigframes.constants.REP_ENABLED_BIGQUERY_LOCATIONS), -) -def test_bq_rep_endpoints(bigquery_location): - session = bigframes.Session( - context=bigframes.BigQueryOptions( - location=bigquery_location, use_regional_endpoints=True - ) - ) - - # Verify that location and endpoint is correctly set for the BigQuery API - # client - assert session.bqclient.location == bigquery_location - assert ( - session.bqclient._connection.API_BASE_URL - == "https://bigquery.{location}.rep.googleapis.com".format( - location=bigquery_location - ) - ) - - # Verify that endpoint is correctly set for the BigQuery Storage API client - # TODO(shobs): Figure out if we can verify that location is set in the - # BigQuery Storage API client. - assert ( - session.bqstoragereadclient.api_endpoint - == f"bigquerystorage.{bigquery_location}.rep.googleapis.com" - ) - - # assert that bigframes session honors the location - _assert_bq_execution_location(session) - - -def test_clients_provider_no_location(): - credentials = mock.create_autospec(google.auth.credentials.Credentials) - - with pytest.raises(ValueError, match="Must set location to use regional endpoints"): - bigframes.session.clients.ClientsProvider( - project="", credentials=credentials, use_regional_endpoints=True - ) - - -@pytest.mark.parametrize( - "bigquery_location", - # Sort the set to avoid nondeterminism. - sorted(bigframes.constants.REP_NOT_ENABLED_BIGQUERY_LOCATIONS), -) -def test_clients_provider_use_regional_endpoints_non_rep_locations(bigquery_location): - credentials = mock.create_autospec(google.auth.credentials.Credentials) - with pytest.raises( - ValueError, - match=f"not .*available in the location {bigquery_location}", - ): - bigframes.session.clients.ClientsProvider( - project="", - credentials=credentials, - location=bigquery_location, - use_regional_endpoints=True, - ) - - -@pytest.mark.parametrize( - "bigquery_location", - # Sort the set to avoid nondeterminism. - sorted(bigframes.constants.REP_NOT_ENABLED_BIGQUERY_LOCATIONS), -) -def test_session_init_fails_to_use_regional_endpoints_non_rep_endpoints( - bigquery_location, -): - with pytest.raises( - ValueError, - match=f"not .*available in the location {bigquery_location}", - ): - bigframes.Session( - context=bigframes.BigQueryOptions( - location=bigquery_location, use_regional_endpoints=True - ) - ) diff --git a/tests/system/large/test_remote_function.py b/tests/system/large/test_remote_function.py new file mode 100644 index 00000000000..c8f8f66ebaa --- /dev/null +++ b/tests/system/large/test_remote_function.py @@ -0,0 +1,1212 @@ +# Copyright 2023 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from datetime import datetime +import importlib.util +import inspect +import math # must keep this at top level to test udf referring global import +import os.path +import shutil +import tempfile +import textwrap + +from google.api_core.exceptions import NotFound, ResourceExhausted +from google.cloud import functions_v2 +import pandas +import pytest +import test_utils.prefixer + +import bigframes +from bigframes.remote_function import ( + get_cloud_function_name, + get_remote_function_locations, +) +from tests.system.utils import assert_pandas_df_equal_ignore_ordering + +# Use this to control the number of cloud functions being deleted in a single +# test session. This should help soften the spike of the number of mutations per +# minute tracked against a quota limit (default 60, increased to 120 for +# bigframes-dev project) by the Cloud Functions API +# We are running pytest with "-n 20". Let's say each session lasts about a +# minute, so we are setting a limit of 120/20 = 6 deletions per session. +_MAX_NUM_FUNCTIONS_TO_DELETE_PER_SESSION = 6 + +# NOTE: Keep this import at the top level to test global var behavior with +# remote functions +_team_pi = "Team Pi" +_team_euler = "Team Euler" + + +def get_remote_function_endpoints(bigquery_client, dataset_id): + """Get endpoints used by the remote functions in a datset""" + endpoints = set() + routines = bigquery_client.list_routines(dataset=dataset_id) + for routine in routines: + rf_options = routine._properties.get("remoteFunctionOptions") + if not rf_options: + continue + rf_endpoint = rf_options.get("endpoint") + if rf_endpoint: + endpoints.add(rf_endpoint) + return endpoints + + +def get_cloud_functions( + functions_client, project, location, name=None, name_prefix=None +): + """Get the cloud functions in the given project and location.""" + + assert ( + not name or not name_prefix + ), f"At most one of the {name.__name__} or {name_prefix.__name__} can be passed." + + _, location = get_remote_function_locations(location) + parent = f"projects/{project}/locations/{location}" + request = functions_v2.ListFunctionsRequest(parent=parent) + page_result = functions_client.list_functions(request=request) + for response in page_result: + # If name is provided and it does not match then skip + if bool(name): + full_name = parent + f"/functions/{name}" + if response.name != full_name: + continue + # If name prefix is provided and it does not match then skip + elif bool(name_prefix): + full_name_prefix = parent + f"/functions/{name_prefix}" + if not response.name.startswith(full_name_prefix): + continue + + yield response + + +def delete_cloud_function(functions_client, full_name): + """Delete a cloud function with the given fully qualified name.""" + request = functions_v2.DeleteFunctionRequest(name=full_name) + operation = functions_client.delete_function(request=request) + return operation + + +def cleanup_remote_function_assets( + bigquery_client, functions_client, remote_udf, ignore_failures=True +): + """Clean up the GCP assets behind a bigframes remote function.""" + + # Clean up BQ remote function + try: + bigquery_client.delete_routine(remote_udf.bigframes_remote_function) + except Exception: + # By default don't raise exception in cleanup + if not ignore_failures: + raise + + # Clean up cloud function + try: + delete_cloud_function(functions_client, remote_udf.bigframes_cloud_function) + except Exception: + # By default don't raise exception in cleanup + if not ignore_failures: + raise + + +def make_uniq_udf(udf): + """Transform a udf to another with same behavior but a unique name. + Use this to test remote functions with reuse=True, in which case parallel + instances of the same tests may evaluate same named cloud functions and BQ + remote functions, therefore interacting with each other and causing unwanted + failures. With this method one can transform a udf into another with the + same behavior but a different name which will remain unique for the + lifetime of one test instance. + """ + + prefixer = test_utils.prefixer.Prefixer(udf.__name__, "") + udf_uniq_name = prefixer.create_prefix() + udf_file_name = f"{udf_uniq_name}.py" + + # We are not using `tempfile.TemporaryDirectory()` because we want to keep + # the temp code around, otherwise `inspect.getsource()` complains. + tmpdir = tempfile.mkdtemp() + udf_file_path = os.path.join(tmpdir, udf_file_name) + with open(udf_file_path, "w") as f: + # TODO(shobs): Find a better way of modifying the udf, maybe regex? + source_key = f"def {udf.__name__}" + target_key = f"def {udf_uniq_name}" + source_code = textwrap.dedent(inspect.getsource(udf)) + target_code = source_code.replace(source_key, target_key, 1) + f.write(target_code) + spec = importlib.util.spec_from_file_location(udf_file_name, udf_file_path) + udf_uniq = getattr(spec.loader.load_module(), udf_uniq_name) + + # This is a bit of a hack but we need to remove the reference to a foreign + # module, otherwise the serialization would keep the foreign module + # reference and deserialization would fail with error like following: + # ModuleNotFoundError: No module named 'add_one_2nxcmd9j' + # TODO(shobs): Figure out if there is a better way of generating the unique + # function object, but for now let's just set it to same module as the + # original udf. + udf_uniq.__module__ = udf.__module__ + + return udf_uniq, tmpdir + + +@pytest.fixture(scope="module") +def bq_cf_connection() -> str: + """Pre-created BQ connection to invoke cloud function for bigframes-dev + $ bq show --connection --location=us --project_id=bigframes-dev bigframes-rf-conn + """ + return "bigframes-rf-conn" + + +@pytest.fixture(scope="module") +def functions_client() -> functions_v2.FunctionServiceClient: + """Cloud Functions client""" + return functions_v2.FunctionServiceClient() + + +@pytest.fixture(scope="module", autouse=True) +def cleanup_cloud_functions(session, functions_client, dataset_id_permanent): + """Clean up stale cloud functions.""" + permanent_endpoints = get_remote_function_endpoints( + session.bqclient, dataset_id_permanent + ) + delete_count = 0 + for cloud_function in get_cloud_functions( + functions_client, + session.bqclient.project, + session.bqclient.location, + name_prefix="bigframes-", + ): + # Ignore bigframes cloud functions referred by the remote functions in + # the permanent dataset + if cloud_function.service_config.uri in permanent_endpoints: + continue + + # Ignore the functions less than one day old + age = datetime.now() - datetime.fromtimestamp( + cloud_function.update_time.timestamp() + ) + if age.days <= 0: + continue + + # Go ahead and delete + try: + delete_cloud_function(functions_client, cloud_function.name) + delete_count += 1 + if delete_count >= _MAX_NUM_FUNCTIONS_TO_DELETE_PER_SESSION: + break + except NotFound: + # This can happen when multiple pytest sessions are running in + # parallel. Two or more sessions may discover the same cloud + # function, but only one of them would be able to delete it + # successfully, while the other instance will run into this + # exception. Ignore this exception. + pass + except ResourceExhausted: + # This can happen if we are hitting GCP limits, e.g. + # google.api_core.exceptions.ResourceExhausted: 429 Quota exceeded + # for quota metric 'Per project mutation requests' and limit + # 'Per project mutation requests per minute per region' of service + # 'cloudfunctions.googleapis.com' for consumer + # 'project_number:1084210331973'. + # [reason: "RATE_LIMIT_EXCEEDED" domain: "googleapis.com" ... + # Let's stop further clean up and leave it to later. + break + + +@pytest.mark.flaky(retries=2, delay=120) +def test_remote_function_multiply_with_ibis( + session, + scalars_table_id, + ibis_client, + dataset_id, + bq_cf_connection, + functions_client, +): + try: + + @session.remote_function( + [int, int], + int, + dataset_id, + bq_cf_connection, + reuse=False, + ) + def multiply(x, y): + return x * y + + project_id, dataset_name, table_name = scalars_table_id.split(".") + if not ibis_client.dataset: + ibis_client.dataset = dataset_name + + col_name = "int64_col" + table = ibis_client.tables[table_name] + table = table.filter(table[col_name].notnull()).order_by("rowindex").head(10) + pandas_df_orig = table.execute() + + col = table[col_name] + col_2x = multiply(col, 2).name("int64_col_2x") + col_square = multiply(col, col).name("int64_col_square") + table = table.mutate([col_2x, col_square]) + pandas_df_new = table.execute() + + pandas.testing.assert_series_equal( + pandas_df_orig[col_name] * 2, + pandas_df_new["int64_col_2x"], + check_names=False, + ) + + pandas.testing.assert_series_equal( + pandas_df_orig[col_name] * pandas_df_orig[col_name], + pandas_df_new["int64_col_square"], + check_names=False, + ) + finally: + # clean up the gcp assets created for the remote function + cleanup_remote_function_assets(session.bqclient, functions_client, multiply) + + +@pytest.mark.flaky(retries=2, delay=120) +def test_remote_function_stringify_with_ibis( + session, + scalars_table_id, + ibis_client, + dataset_id, + bq_cf_connection, + functions_client, +): + try: + + @session.remote_function( + [int], + str, + dataset_id, + bq_cf_connection, + reuse=False, + ) + def stringify(x): + return f"I got {x}" + + project_id, dataset_name, table_name = scalars_table_id.split(".") + if not ibis_client.dataset: + ibis_client.dataset = dataset_name + + col_name = "int64_col" + table = ibis_client.tables[table_name] + table = table.filter(table[col_name].notnull()).order_by("rowindex").head(10) + pandas_df_orig = table.execute() + + col = table[col_name] + col_2x = stringify(col).name("int64_str_col") + table = table.mutate([col_2x]) + pandas_df_new = table.execute() + + pandas.testing.assert_series_equal( + pandas_df_orig[col_name].apply(lambda x: f"I got {x}"), + pandas_df_new["int64_str_col"], + check_names=False, + ) + finally: + # clean up the gcp assets created for the remote function + cleanup_remote_function_assets(session.bqclient, functions_client, stringify) + + +@pytest.mark.flaky(retries=2, delay=120) +def test_remote_function_decorator_with_bigframes_series( + session, scalars_dfs, dataset_id, bq_cf_connection, functions_client +): + try: + + @session.remote_function( + [int], + int, + dataset_id, + bq_cf_connection, + reuse=False, + ) + def square(x): + return x * x + + scalars_df, scalars_pandas_df = scalars_dfs + + bf_int64_col = scalars_df["int64_col"] + bf_int64_col_filter = bf_int64_col.notnull() + bf_int64_col_filtered = bf_int64_col[bf_int64_col_filter] + bf_result_col = bf_int64_col_filtered.apply(square) + bf_result = ( + bf_int64_col_filtered.to_frame().assign(result=bf_result_col).to_pandas() + ) + + pd_int64_col = scalars_pandas_df["int64_col"] + pd_int64_col_filter = pd_int64_col.notnull() + pd_int64_col_filtered = pd_int64_col[pd_int64_col_filter] + pd_result_col = pd_int64_col_filtered.apply(lambda x: x * x) + # TODO(shobs): Figure why pandas .apply() changes the dtype, i.e. + # pd_int64_col_filtered.dtype is Int64Dtype() + # pd_int64_col_filtered.apply(lambda x: x * x).dtype is int64. + # For this test let's force the pandas dtype to be same as bigframes' dtype. + pd_result_col = pd_result_col.astype(pandas.Int64Dtype()) + pd_result = pd_int64_col_filtered.to_frame().assign(result=pd_result_col) + + assert_pandas_df_equal_ignore_ordering(bf_result, pd_result) + finally: + # clean up the gcp assets created for the remote function + cleanup_remote_function_assets(session.bqclient, functions_client, square) + + +@pytest.mark.flaky(retries=2, delay=120) +def test_remote_function_explicit_with_bigframes_series( + session, scalars_dfs, dataset_id, bq_cf_connection, functions_client +): + try: + + def add_one(x): + return x + 1 + + remote_add_one = session.remote_function( + [int], + int, + dataset_id, + bq_cf_connection, + reuse=False, + )(add_one) + + scalars_df, scalars_pandas_df = scalars_dfs + + bf_int64_col = scalars_df["int64_col"] + bf_int64_col_filter = bf_int64_col.notnull() + bf_int64_col_filtered = bf_int64_col[bf_int64_col_filter] + bf_result_col = bf_int64_col_filtered.apply(remote_add_one) + bf_result = ( + bf_int64_col_filtered.to_frame().assign(result=bf_result_col).to_pandas() + ) + + pd_int64_col = scalars_pandas_df["int64_col"] + pd_int64_col_filter = pd_int64_col.notnull() + pd_int64_col_filtered = pd_int64_col[pd_int64_col_filter] + pd_result_col = pd_int64_col_filtered.apply(add_one) + # TODO(shobs): Figure why pandas .apply() changes the dtype, e.g. + # pd_int64_col_filtered.dtype is Int64Dtype() + # pd_int64_col_filtered.apply(lambda x: x).dtype is int64. + # For this test let's force the pandas dtype to be same as bigframes' dtype. + pd_result_col = pd_result_col.astype(pandas.Int64Dtype()) + pd_result = pd_int64_col_filtered.to_frame().assign(result=pd_result_col) + + assert_pandas_df_equal_ignore_ordering(bf_result, pd_result) + finally: + # clean up the gcp assets created for the remote function + cleanup_remote_function_assets( + session.bqclient, functions_client, remote_add_one + ) + + +@pytest.mark.flaky(retries=2, delay=120) +def test_remote_function_explicit_dataset_not_created( + session, scalars_dfs, dataset_id_not_created, bq_cf_connection, functions_client +): + try: + + @session.remote_function( + [int], + int, + dataset_id_not_created, + bq_cf_connection, + reuse=False, + ) + def square(x): + return x * x + + scalars_df, scalars_pandas_df = scalars_dfs + + bf_int64_col = scalars_df["int64_col"] + bf_int64_col_filter = bf_int64_col.notnull() + bf_int64_col_filtered = bf_int64_col[bf_int64_col_filter] + bf_result_col = bf_int64_col_filtered.apply(square) + bf_result = ( + bf_int64_col_filtered.to_frame().assign(result=bf_result_col).to_pandas() + ) + + pd_int64_col = scalars_pandas_df["int64_col"] + pd_int64_col_filter = pd_int64_col.notnull() + pd_int64_col_filtered = pd_int64_col[pd_int64_col_filter] + pd_result_col = pd_int64_col_filtered.apply(lambda x: x * x) + # TODO(shobs): Figure why pandas .apply() changes the dtype, i.e. + # pd_int64_col_filtered.dtype is Int64Dtype() + # pd_int64_col_filtered.apply(lambda x: x * x).dtype is int64. + # For this test let's force the pandas dtype to be same as bigframes' dtype. + pd_result_col = pd_result_col.astype(pandas.Int64Dtype()) + pd_result = pd_int64_col_filtered.to_frame().assign(result=pd_result_col) + + assert_pandas_df_equal_ignore_ordering(bf_result, pd_result) + finally: + # clean up the gcp assets created for the remote function + cleanup_remote_function_assets(session.bqclient, functions_client, square) + + +@pytest.mark.flaky(retries=2, delay=120) +def test_remote_udf_referring_outside_var( + session, scalars_dfs, dataset_id, bq_cf_connection, functions_client +): + try: + POSITIVE_SIGN = 1 + NEGATIVE_SIGN = -1 + NO_SIGN = 0 + + def sign(num): + if num > 0: + return POSITIVE_SIGN + elif num < 0: + return NEGATIVE_SIGN + return NO_SIGN + + remote_sign = session.remote_function( + [int], + int, + dataset_id, + bq_cf_connection, + reuse=False, + )(sign) + + scalars_df, scalars_pandas_df = scalars_dfs + + bf_int64_col = scalars_df["int64_col"] + bf_int64_col_filter = bf_int64_col.notnull() + bf_int64_col_filtered = bf_int64_col[bf_int64_col_filter] + bf_result_col = bf_int64_col_filtered.apply(remote_sign) + bf_result = ( + bf_int64_col_filtered.to_frame().assign(result=bf_result_col).to_pandas() + ) + + pd_int64_col = scalars_pandas_df["int64_col"] + pd_int64_col_filter = pd_int64_col.notnull() + pd_int64_col_filtered = pd_int64_col[pd_int64_col_filter] + pd_result_col = pd_int64_col_filtered.apply(sign) + # TODO(shobs): Figure why pandas .apply() changes the dtype, e.g. + # pd_int64_col_filtered.dtype is Int64Dtype() + # pd_int64_col_filtered.apply(lambda x: x).dtype is int64. + # For this test let's force the pandas dtype to be same as bigframes' dtype. + pd_result_col = pd_result_col.astype(pandas.Int64Dtype()) + pd_result = pd_int64_col_filtered.to_frame().assign(result=pd_result_col) + + assert_pandas_df_equal_ignore_ordering(bf_result, pd_result) + finally: + # clean up the gcp assets created for the remote function + cleanup_remote_function_assets(session.bqclient, functions_client, remote_sign) + + +@pytest.mark.flaky(retries=2, delay=120) +def test_remote_udf_referring_outside_import( + session, scalars_dfs, dataset_id, bq_cf_connection, functions_client +): + try: + import math as mymath + + def circumference(radius): + return 2 * mymath.pi * radius + + remote_circumference = session.remote_function( + [float], + float, + dataset_id, + bq_cf_connection, + reuse=False, + )(circumference) + + scalars_df, scalars_pandas_df = scalars_dfs + + bf_float64_col = scalars_df["float64_col"] + bf_float64_col_filter = bf_float64_col.notnull() + bf_float64_col_filtered = bf_float64_col[bf_float64_col_filter] + bf_result_col = bf_float64_col_filtered.apply(remote_circumference) + bf_result = ( + bf_float64_col_filtered.to_frame().assign(result=bf_result_col).to_pandas() + ) + + pd_float64_col = scalars_pandas_df["float64_col"] + pd_float64_col_filter = pd_float64_col.notnull() + pd_float64_col_filtered = pd_float64_col[pd_float64_col_filter] + pd_result_col = pd_float64_col_filtered.apply(circumference) + # TODO(shobs): Figure why pandas .apply() changes the dtype, e.g. + # pd_float64_col_filtered.dtype is Float64Dtype() + # pd_float64_col_filtered.apply(lambda x: x).dtype is float64. + # For this test let's force the pandas dtype to be same as bigframes' dtype. + pd_result_col = pd_result_col.astype(pandas.Float64Dtype()) + pd_result = pd_float64_col_filtered.to_frame().assign(result=pd_result_col) + + assert_pandas_df_equal_ignore_ordering(bf_result, pd_result) + finally: + # clean up the gcp assets created for the remote function + cleanup_remote_function_assets( + session.bqclient, functions_client, remote_circumference + ) + + +@pytest.mark.flaky(retries=2, delay=120) +def test_remote_udf_referring_global_var_and_import( + session, scalars_dfs, dataset_id, bq_cf_connection, functions_client +): + try: + + def find_team(num): + boundary = (math.pi + math.e) / 2 + if num >= boundary: + return _team_euler + return _team_pi + + remote_find_team = session.remote_function( + [float], + str, + dataset_id, + bq_cf_connection, + reuse=False, + )(find_team) + + scalars_df, scalars_pandas_df = scalars_dfs + + bf_float64_col = scalars_df["float64_col"] + bf_float64_col_filter = bf_float64_col.notnull() + bf_float64_col_filtered = bf_float64_col[bf_float64_col_filter] + bf_result_col = bf_float64_col_filtered.apply(remote_find_team) + bf_result = ( + bf_float64_col_filtered.to_frame().assign(result=bf_result_col).to_pandas() + ) + + pd_float64_col = scalars_pandas_df["float64_col"] + pd_float64_col_filter = pd_float64_col.notnull() + pd_float64_col_filtered = pd_float64_col[pd_float64_col_filter] + pd_result_col = pd_float64_col_filtered.apply(find_team) + # TODO(shobs): Figure if the dtype mismatch is by design: + # bf_result.dtype: string[pyarrow] + # pd_result.dtype: dtype('O'). + # For this test let's force the pandas dtype to be same as bigframes' dtype. + pd_result_col = pd_result_col.astype(pandas.StringDtype(storage="pyarrow")) + pd_result = pd_float64_col_filtered.to_frame().assign(result=pd_result_col) + + assert_pandas_df_equal_ignore_ordering(bf_result, pd_result) + finally: + # clean up the gcp assets created for the remote function + cleanup_remote_function_assets( + session.bqclient, functions_client, remote_find_team + ) + + +@pytest.mark.flaky(retries=2, delay=120) +def test_remote_function_restore_with_bigframes_series( + session, + scalars_dfs, + dataset_id, + bq_cf_connection, + functions_client, +): + try: + + def add_one(x): + return x + 1 + + # Make a unique udf + add_one_uniq, add_one_uniq_dir = make_uniq_udf(add_one) + + # Expected cloud function name for the unique udf + add_one_uniq_cf_name = get_cloud_function_name(add_one_uniq) + + # There should be no cloud function yet for the unique udf + cloud_functions = list( + get_cloud_functions( + functions_client, + session.bqclient.project, + session.bqclient.location, + name=add_one_uniq_cf_name, + ) + ) + assert len(cloud_functions) == 0 + + # The first time both the cloud function and the bq remote function don't + # exist and would be created + remote_add_one = session.remote_function( + [int], + int, + dataset_id, + bq_cf_connection, + reuse=True, + )(add_one_uniq) + + # There should have been excactly one cloud function created at this point + cloud_functions = list( + get_cloud_functions( + functions_client, + session.bqclient.project, + session.bqclient.location, + name=add_one_uniq_cf_name, + ) + ) + assert len(cloud_functions) == 1 + + # We will test this twice + def inner_test(): + scalars_df, scalars_pandas_df = scalars_dfs + + bf_int64_col = scalars_df["int64_col"] + bf_int64_col_filter = bf_int64_col.notnull() + bf_int64_col_filtered = bf_int64_col[bf_int64_col_filter] + bf_result_col = bf_int64_col_filtered.apply(remote_add_one) + bf_result = ( + bf_int64_col_filtered.to_frame() + .assign(result=bf_result_col) + .to_pandas() + ) + + pd_int64_col = scalars_pandas_df["int64_col"] + pd_int64_col_filter = pd_int64_col.notnull() + pd_int64_col_filtered = pd_int64_col[pd_int64_col_filter] + pd_result_col = pd_int64_col_filtered.apply(add_one_uniq) + # TODO(shobs): Figure why pandas .apply() changes the dtype, i.e. + # pd_int64_col_filtered.dtype is Int64Dtype() + # pd_int64_col_filtered.apply(lambda x: x * x).dtype is int64. + # For this test let's force the pandas dtype to be same as bigframes' dtype. + pd_result_col = pd_result_col.astype(pandas.Int64Dtype()) + pd_result = pd_int64_col_filtered.to_frame().assign(result=pd_result_col) + + assert_pandas_df_equal_ignore_ordering(bf_result, pd_result) + + # Test that the remote function works as expected + inner_test() + + # Let's delete the cloud function while not touching the bq remote function + delete_operation = delete_cloud_function( + functions_client, cloud_functions[0].name + ) + delete_operation.result() + assert delete_operation.done() + + # There should be no cloud functions at this point for the uniq udf + cloud_functions = list( + get_cloud_functions( + functions_client, + session.bqclient.project, + session.bqclient.location, + name=add_one_uniq_cf_name, + ) + ) + assert len(cloud_functions) == 0 + + # The second time bigframes detects that the required cloud function doesn't + # exist even though the remote function exists, and goes ahead and recreates + # the cloud function + remote_add_one = session.remote_function( + [int], + int, + dataset_id, + bq_cf_connection, + reuse=True, + )(add_one_uniq) + + # There should be excactly one cloud function again + cloud_functions = list( + get_cloud_functions( + functions_client, + session.bqclient.project, + session.bqclient.location, + name=add_one_uniq_cf_name, + ) + ) + assert len(cloud_functions) == 1 + + # Test again after the cloud function is restored that the remote function + # works as expected + inner_test() + + # clean up the temp code + shutil.rmtree(add_one_uniq_dir) + finally: + # clean up the gcp assets created for the remote function + cleanup_remote_function_assets( + session.bqclient, functions_client, remote_add_one + ) + + +@pytest.mark.flaky(retries=2, delay=120) +def test_remote_udf_mask_default_value( + session, scalars_dfs, dataset_id, bq_cf_connection, functions_client +): + try: + + def is_odd(num): + flag = False + try: + flag = num % 2 == 1 + except TypeError: + pass + return flag + + is_odd_remote = session.remote_function( + [int], + bool, + dataset_id, + bq_cf_connection, + reuse=False, + )(is_odd) + + scalars_df, scalars_pandas_df = scalars_dfs + + bf_int64_col = scalars_df["int64_col"] + bf_result_col = bf_int64_col.mask(is_odd_remote) + bf_result = bf_int64_col.to_frame().assign(result=bf_result_col).to_pandas() + + pd_int64_col = scalars_pandas_df["int64_col"] + pd_result_col = pd_int64_col.mask(is_odd) + pd_result = pd_int64_col.to_frame().assign(result=pd_result_col) + + assert_pandas_df_equal_ignore_ordering(bf_result, pd_result) + finally: + # clean up the gcp assets created for the remote function + cleanup_remote_function_assets( + session.bqclient, functions_client, is_odd_remote + ) + + +@pytest.mark.flaky(retries=2, delay=120) +def test_remote_udf_mask_custom_value( + session, scalars_dfs, dataset_id, bq_cf_connection, functions_client +): + try: + + def is_odd(num): + flag = False + try: + flag = num % 2 == 1 + except TypeError: + pass + return flag + + is_odd_remote = session.remote_function( + [int], + bool, + dataset_id, + bq_cf_connection, + reuse=False, + )(is_odd) + + scalars_df, scalars_pandas_df = scalars_dfs + + # TODO(shobs): Revisit this test when NA handling of pandas' Series.mask is + # fixed https://github.com/pandas-dev/pandas/issues/52955, + # for now filter out the nulls and test the rest + bf_int64_col = scalars_df["int64_col"] + bf_result_col = bf_int64_col[bf_int64_col.notnull()].mask(is_odd_remote, -1) + bf_result = bf_int64_col.to_frame().assign(result=bf_result_col).to_pandas() + + pd_int64_col = scalars_pandas_df["int64_col"] + pd_result_col = pd_int64_col[pd_int64_col.notnull()].mask(is_odd, -1) + pd_result = pd_int64_col.to_frame().assign(result=pd_result_col) + + assert_pandas_df_equal_ignore_ordering(bf_result, pd_result) + finally: + # clean up the gcp assets created for the remote function + cleanup_remote_function_assets( + session.bqclient, functions_client, is_odd_remote + ) + + +@pytest.mark.flaky(retries=2, delay=120) +def test_remote_udf_lambda( + session, scalars_dfs, dataset_id, bq_cf_connection, functions_client +): + try: + add_one_lambda = lambda x: x + 1 # noqa: E731 + + add_one_lambda_remote = session.remote_function( + [int], + int, + dataset_id, + bq_cf_connection, + reuse=False, + )(add_one_lambda) + + scalars_df, scalars_pandas_df = scalars_dfs + + bf_int64_col = scalars_df["int64_col"] + bf_int64_col_filter = bf_int64_col.notnull() + bf_int64_col_filtered = bf_int64_col[bf_int64_col_filter] + bf_result_col = bf_int64_col_filtered.apply(add_one_lambda_remote) + bf_result = ( + bf_int64_col_filtered.to_frame().assign(result=bf_result_col).to_pandas() + ) + + pd_int64_col = scalars_pandas_df["int64_col"] + pd_int64_col_filter = pd_int64_col.notnull() + pd_int64_col_filtered = pd_int64_col[pd_int64_col_filter] + pd_result_col = pd_int64_col_filtered.apply(add_one_lambda) + # TODO(shobs): Figure why pandas .apply() changes the dtype, i.e. + # pd_int64_col_filtered.dtype is Int64Dtype() + # pd_int64_col_filtered.apply(lambda x: x).dtype is int64. + # For this test let's force the pandas dtype to be same as bigframes' dtype. + pd_result_col = pd_result_col.astype(pandas.Int64Dtype()) + pd_result = pd_int64_col_filtered.to_frame().assign(result=pd_result_col) + + assert_pandas_df_equal_ignore_ordering(bf_result, pd_result) + finally: + # clean up the gcp assets created for the remote function + cleanup_remote_function_assets( + session.bqclient, functions_client, add_one_lambda_remote + ) + + +@pytest.mark.flaky(retries=2, delay=120) +def test_remote_function_with_explicit_name( + session, scalars_dfs, dataset_id, bq_cf_connection, functions_client +): + try: + + def square(x): + return x * x + + prefixer = test_utils.prefixer.Prefixer(square.__name__, "") + rf_name = prefixer.create_prefix() + expected_remote_function = f"{dataset_id}.{rf_name}" + + # Initially the expected BQ remote function should not exist + with pytest.raises(NotFound): + session.bqclient.get_routine(expected_remote_function) + + # Create the remote function with the name provided explicitly + square_remote = session.remote_function( + [int], + int, + dataset_id, + bq_cf_connection, + reuse=False, + name=rf_name, + )(square) + + # The remote function should reflect the explicitly provided name + assert square_remote.bigframes_remote_function == expected_remote_function + + # Now the expected BQ remote function should exist + session.bqclient.get_routine(expected_remote_function) + + # The behavior of the created remote function should be as expected + scalars_df, scalars_pandas_df = scalars_dfs + + bf_int64_col = scalars_df["int64_too"] + bf_result_col = bf_int64_col.apply(square_remote) + bf_result = bf_int64_col.to_frame().assign(result=bf_result_col).to_pandas() + + pd_int64_col = scalars_pandas_df["int64_too"] + pd_result_col = pd_int64_col.apply(square) + # TODO(shobs): Figure why pandas .apply() changes the dtype, i.e. + # pd_int64_col.dtype is Int64Dtype() + # pd_int64_col.apply(square).dtype is int64. + # For this test let's force the pandas dtype to be same as bigframes' dtype. + pd_result_col = pd_result_col.astype(pandas.Int64Dtype()) + pd_result = pd_int64_col.to_frame().assign(result=pd_result_col) + + assert_pandas_df_equal_ignore_ordering(bf_result, pd_result) + finally: + # clean up the gcp assets created for the remote function + cleanup_remote_function_assets( + session.bqclient, functions_client, square_remote + ) + + +@pytest.mark.flaky(retries=2, delay=120) +def test_remote_function_with_external_package_dependencies( + session, scalars_dfs, dataset_id, bq_cf_connection, functions_client +): + try: + + def pd_np_foo(x): + import numpy as mynp + import pandas as mypd + + return mypd.Series([x, mynp.sqrt(mynp.abs(x))]).sum() + + # Create the remote function with the name provided explicitly + pd_np_foo_remote = session.remote_function( + [int], + float, + dataset_id, + bq_cf_connection, + reuse=False, + packages=["numpy", "pandas >= 2.0.0"], + )(pd_np_foo) + + # The behavior of the created remote function should be as expected + scalars_df, scalars_pandas_df = scalars_dfs + + bf_int64_col = scalars_df["int64_too"] + bf_result_col = bf_int64_col.apply(pd_np_foo_remote) + bf_result = bf_int64_col.to_frame().assign(result=bf_result_col).to_pandas() + + pd_int64_col = scalars_pandas_df["int64_too"] + pd_result_col = pd_int64_col.apply(pd_np_foo) + pd_result = pd_int64_col.to_frame().assign(result=pd_result_col) + + # pandas result is non-nullable type float64, make it Float64 before + # comparing for the purpose of this test + pd_result.result = pd_result.result.astype(pandas.Float64Dtype()) + + assert_pandas_df_equal_ignore_ordering(bf_result, pd_result) + finally: + # clean up the gcp assets created for the remote function + cleanup_remote_function_assets( + session.bqclient, functions_client, pd_np_foo_remote + ) + + +@pytest.mark.flaky(retries=2, delay=120) +def test_remote_function_with_explicit_name_reuse( + session, scalars_dfs, dataset_id, bq_cf_connection, functions_client +): + try: + + dirs_to_cleanup = [] + + # Define a user code + def square(x): + return x * x + + # Make it a unique udf + square_uniq, square_uniq_dir = make_uniq_udf(square) + dirs_to_cleanup.append(square_uniq_dir) + + # Define a common routine which accepts a remote function and the + # corresponding user defined function and tests that bigframes bahavior + # on the former is in parity with the pandas behaviour on the latter + def test_internal(rf, udf): + # The behavior of the created remote function should be as expected + scalars_df, scalars_pandas_df = scalars_dfs + + bf_int64_col = scalars_df["int64_too"] + bf_result_col = bf_int64_col.apply(rf) + bf_result = bf_int64_col.to_frame().assign(result=bf_result_col).to_pandas() + + pd_int64_col = scalars_pandas_df["int64_too"] + pd_result_col = pd_int64_col.apply(udf) + # TODO(shobs): Figure why pandas .apply() changes the dtype, i.e. + # pd_int64_col.dtype is Int64Dtype() + # pd_int64_col.apply(square).dtype is int64. + # For this test let's force the pandas dtype to be same as bigframes' dtype. + pd_result_col = pd_result_col.astype(pandas.Int64Dtype()) + pd_result = pd_int64_col.to_frame().assign(result=pd_result_col) + + assert_pandas_df_equal_ignore_ordering(bf_result, pd_result) + + # Create an explicit name for the remote function + prefixer = test_utils.prefixer.Prefixer("foo", "") + rf_name = prefixer.create_prefix() + expected_remote_function = f"{dataset_id}.{rf_name}" + + # Initially the expected BQ remote function should not exist + with pytest.raises(NotFound): + session.bqclient.get_routine(expected_remote_function) + + # Create a new remote function with the name provided explicitly + square_remote1 = session.remote_function( + [int], + int, + dataset_id, + bq_cf_connection, + name=rf_name, + )(square_uniq) + + # The remote function should reflect the explicitly provided name + assert square_remote1.bigframes_remote_function == expected_remote_function + + # Now the expected BQ remote function should exist + routine = session.bqclient.get_routine(expected_remote_function) + square_remote1_created = routine.created + square_remote1_cf_updated = session.cloudfunctionsclient.get_function( + name=square_remote1.bigframes_cloud_function + ).update_time + + # Test pandas parity with square udf + test_internal(square_remote1, square) + + # Now Create another remote function with the same name provided + # explicitly. Since reuse is True by default, the previously created + # remote function with the same name will be reused. + square_remote2 = session.remote_function( + [int], + int, + dataset_id, + bq_cf_connection, + name=rf_name, + )(square_uniq) + + # The new remote function should still reflect the explicitly provided name + assert square_remote2.bigframes_remote_function == expected_remote_function + + # The expected BQ remote function should still exist + routine = session.bqclient.get_routine(expected_remote_function) + square_remote2_created = routine.created + square_remote2_cf_updated = session.cloudfunctionsclient.get_function( + name=square_remote2.bigframes_cloud_function + ).update_time + + # The new remote function should reflect that the previous BQ remote + # function and the cloud function were reused instead of creating anew + assert square_remote2_created == square_remote1_created + assert ( + square_remote2.bigframes_cloud_function + == square_remote1.bigframes_cloud_function + ) + assert square_remote2_cf_updated == square_remote1_cf_updated + + # Test again that the new remote function is actually same as the + # previous remote function + test_internal(square_remote2, square) + + # Now define a different user code + def plusone(x): + return x + 1 + + # Make it a unique udf + plusone_uniq, plusone_uniq_dir = make_uniq_udf(plusone) + dirs_to_cleanup.append(plusone_uniq_dir) + + # Now Create a third remote function with the same name provided + # explicitly. Even though reuse is True by default, the previously + # created remote function with the same name should not be reused since + # this time it is a different user code. + plusone_remote = session.remote_function( + [int], + int, + dataset_id, + bq_cf_connection, + name=rf_name, + )(plusone_uniq) + + # The new remote function should still reflect the explicitly provided name + assert plusone_remote.bigframes_remote_function == expected_remote_function + + # The expected BQ remote function should still exist + routine = session.bqclient.get_routine(expected_remote_function) + plusone_remote_created = routine.created + plusone_remote_cf_updated = session.cloudfunctionsclient.get_function( + name=plusone_remote.bigframes_cloud_function + ).update_time + + # The new remote function should reflect that the previous BQ remote + # function and the cloud function were NOT reused, instead were created + # anew + assert plusone_remote_created > square_remote2_created + assert ( + plusone_remote.bigframes_cloud_function + != square_remote2.bigframes_cloud_function + ) + assert plusone_remote_cf_updated > square_remote2_cf_updated + + # Test again that the new remote function is equivalent to the new user + # defined function + test_internal(plusone_remote, plusone) + finally: + # clean up the gcp assets created for the remote function + cleanup_remote_function_assets( + session.bqclient, functions_client, square_remote1 + ) + cleanup_remote_function_assets( + session.bqclient, functions_client, square_remote2 + ) + cleanup_remote_function_assets( + session.bqclient, functions_client, plusone_remote + ) + for dir_ in dirs_to_cleanup: + shutil.rmtree(dir_) + + +@pytest.mark.flaky(retries=2, delay=120) +def test_remote_function_via_session_context_connection_setter( + scalars_dfs, dataset_id, bq_cf_connection +): + # Creating a session scoped only to this test as we would be setting a + # property in it + context = bigframes.BigQueryOptions() + context.bq_connection = bq_cf_connection + session = bigframes.connect(context) + + try: + # Without an explicit bigquery connection, the one present in Session, + # set via context setter would be used. Without an explicit `reuse` the + # default behavior of reuse=True will take effect. Please note that the + # udf is same as the one used in other tests in this file so the underlying + # cloud function would be common with reuse=True. Since we are using a + # unique dataset_id, even though the cloud function would be reused, the bq + # remote function would still be created, making use of the bq connection + # set in the BigQueryOptions above. + @session.remote_function([int], int, dataset=dataset_id) + def square(x): + return x * x + + scalars_df, scalars_pandas_df = scalars_dfs + + bf_int64_col = scalars_df["int64_col"] + bf_int64_col_filter = bf_int64_col.notnull() + bf_int64_col_filtered = bf_int64_col[bf_int64_col_filter] + bf_result_col = bf_int64_col_filtered.apply(square) + bf_result = ( + bf_int64_col_filtered.to_frame().assign(result=bf_result_col).to_pandas() + ) + + pd_int64_col = scalars_pandas_df["int64_col"] + pd_int64_col_filter = pd_int64_col.notnull() + pd_int64_col_filtered = pd_int64_col[pd_int64_col_filter] + pd_result_col = pd_int64_col_filtered.apply(lambda x: x * x) + # TODO(shobs): Figure why pandas .apply() changes the dtype, i.e. + # pd_int64_col_filtered.dtype is Int64Dtype() + # pd_int64_col_filtered.apply(lambda x: x * x).dtype is int64. + # For this test let's force the pandas dtype to be same as bigframes' dtype. + pd_result_col = pd_result_col.astype(pandas.Int64Dtype()) + pd_result = pd_int64_col_filtered.to_frame().assign(result=pd_result_col) + + assert_pandas_df_equal_ignore_ordering(bf_result, pd_result) + finally: + # clean up the gcp assets created for the remote function + cleanup_remote_function_assets( + session.bqclient, session.cloudfunctionsclient, square + ) + + +@pytest.mark.flaky(retries=2, delay=120) +def test_remote_function_default_connection(session, scalars_dfs, dataset_id): + try: + + @session.remote_function([int], int, dataset=dataset_id) + def square(x): + return x * x + + scalars_df, scalars_pandas_df = scalars_dfs + + bf_int64_col = scalars_df["int64_col"] + bf_int64_col_filter = bf_int64_col.notnull() + bf_int64_col_filtered = bf_int64_col[bf_int64_col_filter] + bf_result_col = bf_int64_col_filtered.apply(square) + bf_result = ( + bf_int64_col_filtered.to_frame().assign(result=bf_result_col).to_pandas() + ) + + pd_int64_col = scalars_pandas_df["int64_col"] + pd_int64_col_filter = pd_int64_col.notnull() + pd_int64_col_filtered = pd_int64_col[pd_int64_col_filter] + pd_result_col = pd_int64_col_filtered.apply(lambda x: x * x) + # TODO(shobs): Figure why pandas .apply() changes the dtype, i.e. + # pd_int64_col_filtered.dtype is Int64Dtype() + # pd_int64_col_filtered.apply(lambda x: x * x).dtype is int64. + # For this test let's force the pandas dtype to be same as bigframes' dtype. + pd_result_col = pd_result_col.astype(pandas.Int64Dtype()) + pd_result = pd_int64_col_filtered.to_frame().assign(result=pd_result_col) + + assert_pandas_df_equal_ignore_ordering(bf_result, pd_result) + finally: + # clean up the gcp assets created for the remote function + cleanup_remote_function_assets( + session.bqclient, session.cloudfunctionsclient, square + ) diff --git a/tests/system/large/test_session.py b/tests/system/large/test_session.py index 937b3c9e274..62fa5a83d33 100644 --- a/tests/system/large/test_session.py +++ b/tests/system/large/test_session.py @@ -12,200 +12,41 @@ # See the License for the specific language governing permissions and # limitations under the License. -import datetime -from unittest import mock - -import google.cloud.bigquery as bigquery -import google.cloud.exceptions -import numpy as np -import pandas as pd import pytest -import bigframes -import bigframes.pandas as bpd -import bigframes.session._io.bigquery - - -@pytest.fixture -def large_pd_df(): - nrows = 1000000 - - np_int1 = np.random.randint(0, 1000, size=nrows, dtype=np.int32) - np_int2 = np.random.randint(10000, 20000, size=nrows, dtype=np.int64) - np_bool = np.random.choice([True, False], size=nrows) - np_float1 = np.random.rand(nrows).astype(np.float32) - np_float2 = np.random.normal(loc=50.0, scale=10.0, size=nrows).astype(np.float64) - - return pd.DataFrame( - { - "int_col_1": np_int1, - "int_col_2": np_int2, - "bool_col": np_bool, - "float_col_1": np_float1, - "float_col_2": np_float2, - } - ) - - -@pytest.mark.parametrize( - ("write_engine"), - [ - ("bigquery_load"), - ("bigquery_streaming"), - # TODO(b/502298527): Reenable bigquery_write test - # ("bigquery_write"), - ], -) -def test_read_pandas_large_df(session, large_pd_df, write_engine: str): - df = session.read_pandas(large_pd_df, write_engine=write_engine) - assert len(df.peek(5)) == 5 - assert len(large_pd_df) == 1000000 - - -def test_close(session: bigframes.Session): - # we will create two tables and confirm that they are deleted - # when the session is closed - - bqclient = session.bqclient - - expiration = ( - datetime.datetime.now(datetime.timezone.utc) - + bigframes.constants.DEFAULT_EXPIRATION - ) - full_id_1 = bigframes.session._io.bigquery.create_temp_table( - session.bqclient, - session._anon_dataset_manager.allocate_temp_table(), - expiration, - ) - full_id_2 = bigframes.session._io.bigquery.create_temp_table( - session.bqclient, - session._anon_dataset_manager.allocate_temp_table(), - expiration, - ) - - # check that the tables were actually created - assert bqclient.get_table(full_id_1).created is not None - assert bqclient.get_table(full_id_2).created is not None - - session.close() - - # check that the tables are already deleted - with pytest.raises(google.cloud.exceptions.NotFound): - bqclient.delete_table(full_id_1) - with pytest.raises(google.cloud.exceptions.NotFound): - bqclient.delete_table(full_id_2) - - -def test_clean_up_by_session_id(): - # we do this test in a different region in order to avoid - # overly large amounts of temp tables slowing the test down - option_context = bigframes.BigQueryOptions() - option_context.location = "europe-west10" - session = bigframes.Session(context=option_context) - session_id = session.session_id - - # we will create two tables and confirm that they are deleted - # when the session is cleaned up by id - bqclient = session.bqclient - dataset = session._anonymous_dataset - expiration = ( - datetime.datetime.now(datetime.timezone.utc) - + bigframes.constants.DEFAULT_EXPIRATION - ) - bigframes.session._io.bigquery.create_temp_table( - session.bqclient, - session._anon_dataset_manager.allocate_temp_table(), - expiration, - ) - bigframes.session._io.bigquery.create_temp_table( - session.bqclient, - session._anon_dataset_manager.allocate_temp_table(), - expiration, - ) - - # check that some table exists with the expected session_id - tables_before = bqclient.list_tables( - dataset, - max_results=bigframes.session._io.bigquery._LIST_TABLES_LIMIT, - page_size=bigframes.session._io.bigquery._LIST_TABLES_LIMIT, - ) - assert any([(session.session_id in table.full_table_id) for table in tables_before]) - - bpd.clean_up_by_session_id( - session_id, location=session._location, project=session._project - ) - - # check that no tables with the session_id are left after cleanup - tables_after = bqclient.list_tables( - dataset, - max_results=bigframes.session._io.bigquery._LIST_TABLES_LIMIT, - page_size=bigframes.session._io.bigquery._LIST_TABLES_LIMIT, - ) - assert not any( - [(session.session_id in table.full_table_id) for table in tables_after] - ) +from bigframes import Session @pytest.mark.parametrize( - ("session_creator"), + ("query_or_table", "index_col"), [ - pytest.param(bigframes.Session, id="session-constructor"), - pytest.param(bigframes.connect, id="connect-method"), + pytest.param( + "bigquery-public-data.patents_view.ipcr_201708", + (), + id="1g_table_w_default_index", + ), + pytest.param( + "bigquery-public-data.new_york_taxi_trips.tlc_yellow_trips_2011", + (), + id="30g_table_w_default_index", + ), + # TODO(chelsealin): Disable the long run tests until we have propertily + # ordering support to avoid materializating any data. + # # Adding default index to large tables would take much longer time, + # # e.g. ~5 mins for a 100G table, ~20 mins for a 1T table. + # pytest.param( + # "bigquery-public-data.stackoverflow.post_history", + # ["id"], + # id="100g_table_w_unique_column_index", + # ), + # pytest.param( + # "bigquery-public-data.wise_all_sky_data_release.all_wise", + # ["cntr"], + # id="1t_table_w_unique_column_index", + # ), ], ) -@pytest.mark.flaky(retries=3) -def test_clean_up_via_context_manager(session_creator): - # we will create two tables and confirm that they are deleted - # when the session is closed - with session_creator() as session: - bqclient = session.bqclient - - full_id_1 = session._anon_dataset_manager.create_temp_table( - [bigquery.SchemaField("a", "INT64")], cluster_cols=[] - ) - assert session._session_resource_manager is not None - full_id_2 = session._session_resource_manager.create_temp_table( - [bigquery.SchemaField("b", "STRING")], cluster_cols=["b"] - ) - - # check that the tables were actually created - assert bqclient.get_table(full_id_1).created is not None - assert bqclient.get_table(full_id_2).created is not None - - # check that the tables are already deleted - with pytest.raises(google.cloud.exceptions.NotFound): - bqclient.delete_table(full_id_1) - with pytest.raises(google.cloud.exceptions.NotFound): - bqclient.delete_table(full_id_2) - - -def test_cleanup_old_udfs(session: bigframes.Session): - routine_ref = session._anon_dataset_manager.dataset.routine("test_routine_cleanup") - - # Create a dummy function to be deleted. - create_function_sql = f""" -CREATE OR REPLACE FUNCTION `{routine_ref.project}.{routine_ref.dataset_id}.{routine_ref.routine_id}`(x INT64) -RETURNS INT64 LANGUAGE python -OPTIONS (entry_point='dummy_func', runtime_version='python-3.11') -AS r''' -def dummy_func(x): - return x + 1 -''' - """ - session.bqclient.query(create_function_sql).result() - - assert session.bqclient.get_routine(routine_ref) is not None - - mock_routine = mock.MagicMock(spec=bigquery.Routine) - mock_routine.created = datetime.datetime.now( - datetime.timezone.utc - ) - datetime.timedelta(days=100) - mock_routine.reference = routine_ref - mock_routine._properties = {"routineType": "SCALAR_FUNCTION"} - routines = [mock_routine] - - with mock.patch.object(session.bqclient, "list_routines", return_value=routines): - session._anon_dataset_manager._cleanup_old_udfs() - - with pytest.raises(google.cloud.exceptions.NotFound): - session.bqclient.get_routine(routine_ref) +def test_read_gbq_for_large_tables(session: Session, query_or_table, index_col): + """Verify read_gbq() is able to read large tables.""" + df = session.read_gbq(query_or_table, index_col=index_col) + assert len(df.columns) != 0 diff --git a/tests/system/large/test_tpch.py b/tests/system/large/test_tpch.py deleted file mode 100644 index de630ce0dd4..00000000000 --- a/tests/system/large/test_tpch.py +++ /dev/null @@ -1,101 +0,0 @@ -# Copyright 2026 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import os -import re - -import pandas as pd -import pytest -from google.cloud import bigquery - -TPCH_PATH = "third_party/bigframes_vendored/tpch" -PROJECT_ID = "bigframes-dev-perf" -DATASET_ID = "tpch_0001g" -DATASET = { - "line_item_ds": f"{PROJECT_ID}.{DATASET_ID}.LINEITEM", - "region_ds": f"{PROJECT_ID}.{DATASET_ID}.REGION", - "nation_ds": f"{PROJECT_ID}.{DATASET_ID}.NATION", - "supplier_ds": f"{PROJECT_ID}.{DATASET_ID}.SUPPLIER", - "part_ds": f"{PROJECT_ID}.{DATASET_ID}.PART", - "part_supp_ds": f"{PROJECT_ID}.{DATASET_ID}.PARTSUPP", - "customer_ds": f"{PROJECT_ID}.{DATASET_ID}.CUSTOMER", - "orders_ds": f"{PROJECT_ID}.{DATASET_ID}.ORDERS", -} - - -def _execute_sql_query(bigquery_client, sql_query): - sql_query = sql_query.format(**DATASET) - - job_config = bigquery.QueryJobConfig(use_query_cache=False) - query_job = bigquery_client.query(sql_query, job_config=job_config) - query_job.result() - df = query_job.to_dataframe() - df.columns = df.columns.str.upper() - return df - - -def _execute_bigframes_script(session, bigframes_script): - bigframes_script = re.sub( - r"next\((\w+)\.to_pandas_batches\((.*?)\)\)", - r"return \1.to_pandas()", - bigframes_script, - ) - bigframes_script = re.sub(r"_\s*=\s*(\w+)", r"return \1", bigframes_script) - - bigframes_script = ( - bigframes_script - + f"\nresult = q('{PROJECT_ID}', '{DATASET_ID}', _initialize_session)" - ) - exec_globals = {"_initialize_session": session} - exec(bigframes_script, exec_globals) - bigframes_result = exec_globals.get("result") - return bigframes_result - - -def _verify_result(bigframes_result, sql_result): - if isinstance(bigframes_result, pd.DataFrame): - pd.testing.assert_frame_equal( - sql_result.reset_index(drop=True), - bigframes_result.reset_index(drop=True), - check_dtype=False, - ) - else: - assert sql_result.shape == (1, 1) - sql_scalar = sql_result.iloc[0, 0] - assert sql_scalar == bigframes_result - - -@pytest.mark.parametrize("query_num", range(1, 23)) -@pytest.mark.parametrize("ordered", [True, False]) -def test_tpch_correctness(session, unordered_session, query_num, ordered): - """Runs verification of TPCH benchmark script outputs to ensure correctness.""" - # Execute SQL: - sql_file_path = f"{TPCH_PATH}/sql_queries/q{query_num}.sql" - assert os.path.exists(sql_file_path) - with open(sql_file_path, "r") as f: - sql_query = f.read() - - sql_result = _execute_sql_query(session.bqclient, sql_query) - - # Execute BigFrames: - file_path = f"{TPCH_PATH}/queries/q{query_num}.py" - assert os.path.exists(file_path) - with open(file_path, "r") as file: - bigframes_script = file.read() - - bigframes_result = _execute_bigframes_script( - session if ordered else unordered_session, bigframes_script - ) - - _verify_result(bigframes_result, sql_result) diff --git a/tests/system/load/conftest.py b/tests/system/load/conftest.py deleted file mode 100644 index f15f50c7e73..00000000000 --- a/tests/system/load/conftest.py +++ /dev/null @@ -1,39 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -from typing import Generator - -import pytest - -import bigframes - - -# Override the session to target at bigframes-load-testing at all load tests. That allows to run load tests locally with authentic env. -@pytest.fixture(scope="session") -def session() -> Generator[bigframes.Session, None, None]: - context = bigframes.BigQueryOptions(location="US", project="bigframes-load-testing") - session = bigframes.Session(context=context) - yield session - session.close() # close generated session at cleanup time - - -@pytest.fixture(scope="session") -def session_us_east5() -> Generator[bigframes.Session, None, None]: - context = bigframes.BigQueryOptions( - location="us-east5", project="bigframes-load-testing" - ) - session = bigframes.Session(context=context) - yield session - session.close() # close generated session at cleanup time diff --git a/tests/system/load/test_large_tables.py b/tests/system/load/test_large_tables.py deleted file mode 100644 index ee49c2703ef..00000000000 --- a/tests/system/load/test_large_tables.py +++ /dev/null @@ -1,102 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -"""Load test for query (SQL) inputs with large results sizes.""" - -import pytest - -import bigframes.pandas as bpd - -KB_BYTES = 1000 -MB_BYTES = 1000 * KB_BYTES -GB_BYTES = 1000 * MB_BYTES -TB_BYTES = 1000 * GB_BYTES - - -@pytest.mark.parametrize( - ("sql", "expected_bytes"), - ( - pytest.param( - "SELECT * FROM load_testing.scalars_1gb", - GB_BYTES, - id="1gb", - ), - pytest.param( - "SELECT * FROM load_testing.scalars_10gb", - 10 * GB_BYTES, - id="10gb", - ), - pytest.param( - "SELECT * FROM load_testing.scalars_100gb", - 100 * GB_BYTES, - id="100gb", - ), - pytest.param( - "SELECT * FROM load_testing.scalars_1tb", - TB_BYTES, - id="1tb", - ), - ), -) -def test_read_gbq_sql_large_results(sql, expected_bytes): - df = bpd.read_gbq(sql) - assert df.memory_usage().sum() >= expected_bytes - - -def test_df_repr_large_table(): - df = bpd.read_gbq("load_testing.scalars_100gb") - row_count, column_count = df.shape - expected = f"[{row_count} rows x {column_count} columns]" - actual = repr(df) - assert expected in actual - - -def test_series_repr_large_table(): - df = bpd.read_gbq("load_testing.scalars_1tb") - actual = repr(df["string_col"]) - assert actual is not None - - -def test_index_repr_large_table(): - df = bpd.read_gbq("load_testing.scalars_1tb") - actual = repr(df.index) - assert actual is not None - - -def test_to_pandas_batches_large_table(): - df = bpd.read_gbq("load_testing.scalars_100gb") - _, expected_column_count = df.shape - - # download only a few batches, since 1tb would be too much - iterable = df.to_pandas_batches( - page_size=500, max_results=1500, allow_large_results=True - ) - # use page size since client library doesn't support - # streaming only part of the dataframe via bqstorage - for pdf in iterable: - batch_row_count, batch_column_count = pdf.shape - assert batch_column_count == expected_column_count - assert 0 < batch_row_count <= 500 - - -@pytest.mark.skip(reason="See if it caused kokoro build aborted.") -def test_to_pandas_large_table(): - df = bpd.read_gbq("load_testing.scalars_10gb") - # df will be downloaded locally - expected_row_count, expected_column_count = df.shape - - df_converted = df.to_pandas() - row_count, column_count = df_converted.shape - assert column_count == expected_column_count - assert row_count == expected_row_count diff --git a/tests/system/load/test_llm.py b/tests/system/load/test_llm.py deleted file mode 100644 index eec76cf9b67..00000000000 --- a/tests/system/load/test_llm.py +++ /dev/null @@ -1,160 +0,0 @@ -# Copyright 2023 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import pandas as pd -import pytest - -from bigframes.ml import llm -from bigframes.testing import utils - - -@pytest.fixture(scope="session") -def llm_remote_text_pandas_df(): - """Additional data matching the penguins dataset, with a new index""" - return pd.DataFrame( - { - "prompt": [ - "Please do sentiment analysis on the following text and only output a number from 0 to 5 where 0 means sadness, 1 means joy, 2 means love, 3 means anger, 4 means fear, and 5 means surprise. Text: i feel beautifully emotional knowing that these women of whom i knew just a handful were holding me and my baba on our journey", - "Please do sentiment analysis on the following text and only output a number from 0 to 5 where 0 means sadness, 1 means joy, 2 means love, 3 means anger, 4 means fear, and 5 means surprise. Text: i was feeling a little vain when i did this one", - "Please do sentiment analysis on the following text and only output a number from 0 to 5 where 0 means sadness, 1 means joy, 2 means love, 3 means anger, 4 means fear, and 5 means surprise. Text: a father of children killed in an accident", - ], - } - ) - - -@pytest.fixture(scope="session") -def llm_remote_text_df(session, llm_remote_text_pandas_df): - return session.read_pandas(llm_remote_text_pandas_df) - - -@pytest.mark.parametrize( - "model_name", - ( - "gemini-2.5-flash", - "gemini-2.5-flash-lite", - ), -) -def test_llm_gemini_configure_fit( - session, model_name, llm_fine_tune_df_default_index, llm_remote_text_df -): - model = llm.GeminiTextGenerator( - session=session, model_name=model_name, max_iterations=1 - ) - - X_train = llm_fine_tune_df_default_index[["prompt"]] - y_train = llm_fine_tune_df_default_index[["label"]] - model.fit(X_train, y_train) - - assert model is not None - - df = model.predict( - llm_remote_text_df["prompt"], - temperature=0.5, - max_output_tokens=100, - top_k=20, - top_p=0.5, - ).to_pandas() - utils.check_pandas_df_schema_and_index( - df, - columns=[ - "ml_generate_text_llm_result", - "ml_generate_text_rai_result", - "ml_generate_text_status", - "prompt", - ], - index=3, - ) - - -@pytest.mark.flaky(retries=2) -def test_llm_gemini_w_ground_with_google_search(llm_remote_text_df): - model = llm.GeminiTextGenerator(model_name="gemini-2.5-flash", max_iterations=1) - df = model.predict( - llm_remote_text_df["prompt"], - ground_with_google_search=True, - ).to_pandas() - utils.check_pandas_df_schema_and_index( - df, - columns=[ - "ml_generate_text_llm_result", - "ml_generate_text_rai_result", - "ml_generate_text_grounding_result", - "ml_generate_text_status", - "prompt", - ], - index=3, - ) - - -# (b/366290533): Claude models are of extremely low capacity. The tests should reside in small tests. Moving these here just to protect BQML's shared capacity(as load test only runs once per day.) and make sure we still have minimum coverage. -@pytest.mark.flaky(retries=3, delay=120) -def test_claude3_text_generator_create_load(dataset_id, session, bq_connection): - claude3_text_generator_model = llm.Claude3TextGenerator( - model_name="claude-3-haiku", connection_name=bq_connection, session=session - ) - assert claude3_text_generator_model is not None - assert claude3_text_generator_model._bqml_model is not None - - # save, load to ensure configuration was kept - reloaded_model = claude3_text_generator_model.to_gbq( - f"{dataset_id}.temp_text_model", replace=True - ) - assert f"{dataset_id}.temp_text_model" == reloaded_model._bqml_model.model_name - assert reloaded_model.connection_name == bq_connection - assert reloaded_model.model_name == "claude-3-haiku" - - -@pytest.mark.flaky(retries=3, delay=120) -def test_claude3_text_generator_predict_default_params_success( - llm_text_df, session, bq_connection -): - claude3_text_generator_model = llm.Claude3TextGenerator( - model_name="claude-3-haiku", connection_name=bq_connection, session=session - ) - df = claude3_text_generator_model.predict(llm_text_df).to_pandas() - utils.check_pandas_df_schema_and_index( - df, columns=utils.ML_GENERATE_TEXT_OUTPUT, index=3, col_exact=False - ) - - -@pytest.mark.flaky(retries=3, delay=120) -def test_claude3_text_generator_predict_with_params_success( - llm_text_df, session, bq_connection -): - claude3_text_generator_model = llm.Claude3TextGenerator( - model_name="claude-3-haiku", connection_name=bq_connection, session=session - ) - df = claude3_text_generator_model.predict( - llm_text_df, max_output_tokens=100, top_k=20, top_p=0.5 - ).to_pandas() - utils.check_pandas_df_schema_and_index( - df, columns=utils.ML_GENERATE_TEXT_OUTPUT, index=3, col_exact=False - ) - - -@pytest.mark.flaky(retries=3, delay=120) -def test_claude3_text_generator_predict_multi_col_success( - llm_text_df, session, bq_connection -): - llm_text_df["additional_col"] = 1 - claude3_text_generator_model = llm.Claude3TextGenerator( - model_name="claude-3-haiku", connection_name=bq_connection, session=session - ) - df = claude3_text_generator_model.predict(llm_text_df).to_pandas() - utils.check_pandas_df_schema_and_index( - df, - columns=utils.ML_GENERATE_TEXT_OUTPUT + ["additional_col"], - index=3, - col_exact=False, - ) diff --git a/tests/system/small/bigquery/__init__.py b/tests/system/small/bigquery/__init__.py deleted file mode 100644 index 6d5e14bcf4a..00000000000 --- a/tests/system/small/bigquery/__init__.py +++ /dev/null @@ -1,13 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. diff --git a/tests/system/small/bigquery/test_ai.py b/tests/system/small/bigquery/test_ai.py deleted file mode 100644 index 0b6738dec80..00000000000 --- a/tests/system/small/bigquery/test_ai.py +++ /dev/null @@ -1,590 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from __future__ import annotations - -import uuid -from unittest import mock - -import google.cloud.bigquery -import pandas as pd -import pyarrow as pa -import pytest - -import bigframes.bigquery as bbq -import bigframes.pandas as bpd -from bigframes import dataframe, dtypes, series -from bigframes.testing import utils as test_utils - - -@pytest.fixture -def use_ibis_compiler(): - original_setting = bpd.options.experiments.sql_compiler - bpd.options.experiments.sql_compiler = "legacy" - try: - yield - finally: - bpd.options.experiments.sql_compiler = original_setting - - -def _create_mock_obj_ref_df(session, uris, name="image", connection=None): - df = bpd.DataFrame({name: uris}, session=session) - # Convert string URIs to ObjectRef structs - if connection is None: - connection = "us.bigframes-rf-conn" - df[name] = bbq.obj.make_ref(df[name], authorizer=connection) - - table_id = f"bigframes-dev.bigframes_tests_sys.tmp_obj_ref_{uuid.uuid4().hex}" - df.to_gbq(table_id, if_exists="replace") - - client = session.bqclient - table = client.get_table(table_id) - schema = list(table.schema) - for i, field in enumerate(schema): - if field.name == name: - schema[i] = google.cloud.bigquery.SchemaField( - name=field.name, - field_type=field.field_type, - mode=field.mode, - description="bigframes_dtype: OBJ_REF_DTYPE", - fields=field.fields, - ) - break - table.schema = schema - client.update_table(table, ["schema"]) - - return session.read_gbq(table_id) - - -def test_ai_function_pandas_tuple_input(session): - s1 = pd.Series(["apple", "bear"]) - s2 = bpd.Series(["fruit", "tree"], session=session) - prompt = (s1, " is a ", s2) - - result = bbq.ai.generate_bool(prompt, endpoint="gemini-2.5-flash") - - assert _contains_no_nulls(result) - assert result.dtype == pd.ArrowDtype( - pa.struct( - ( - pa.field("result", pa.bool_()), - pa.field("full_response", dtypes.JSON_ARROW_TYPE), - pa.field("status", pa.string()), - ) - ) - ) - - -def test_ai_function_pandas_series_input(session): - s = pd.Series(["cat", "lavender"]) - - result = bbq.ai.classify( - s, categories=["animal", "plant"], endpoint="gemini-2.5-flash" - ) - - assert len(result) == len(s) - assert result.dtype == dtypes.STRING_DTYPE - - -def test_ai_function_string_input(session): - with mock.patch( - "bigframes.core.global_session.get_global_session" - ) as mock_get_session: - mock_get_session.return_value = session - prompt = "Is apple a fruit?" - - result = bbq.ai.generate_bool(prompt, endpoint="gemini-2.5-flash") - - assert _contains_no_nulls(result) - assert result.dtype == pd.ArrowDtype( - pa.struct( - ( - pa.field("result", pa.bool_()), - pa.field("full_response", dtypes.JSON_ARROW_TYPE), - pa.field("status", pa.string()), - ) - ) - ) - - -def test_ai_function_compile_model_params(session): - s1 = bpd.Series(["apple", "bear"], session=session) - s2 = bpd.Series(["fruit", "tree"], session=session) - prompt = (s1, " is a ", s2) - model_params = {"generation_config": {"thinking_config": {"thinking_budget": 0}}} - - result = bbq.ai.generate_bool( - prompt, endpoint="gemini-2.5-flash", model_params=model_params - ) - - assert _contains_no_nulls(result) - assert result.dtype == pd.ArrowDtype( - pa.struct( - ( - pa.field("result", pa.bool_()), - pa.field("full_response", dtypes.JSON_ARROW_TYPE), - pa.field("status", pa.string()), - ) - ) - ) - - -def test_ai_generate(session): - country = bpd.Series(["Japan", "Canada"], session=session) - prompt = ("What's the capital city of ", country, "? one word only") - - result = bbq.ai.generate(prompt, endpoint="gemini-2.5-flash") - - assert _contains_no_nulls(result) - assert result.dtype == pd.ArrowDtype( - pa.struct( - ( - pa.field("result", pa.string()), - pa.field("full_response", dtypes.JSON_ARROW_TYPE), - pa.field("status", pa.string()), - ) - ) - ) - - -def test_ai_generate_access_full_response_with_ibis(session, use_ibis_compiler): - country = bpd.Series(["Japan", "Canada"], session=session) - prompt = ("What's the capital city of ", country, "? one word only") - - result = ( - bbq.ai.generate(prompt, endpoint="gemini-2.5-flash") - .struct.field("full_response") - .to_pandas() - ) - - assert _contains_no_nulls(result) - - -def test_ai_generate_with_output_schema(session): - country = bpd.Series(["Japan", "Canada"], session=session) - prompt = ("Describe ", country) - - result = bbq.ai.generate( - prompt, - endpoint="gemini-2.5-flash", - output_schema={"population": "INT64", "is_in_north_america": "bool"}, - ) - - assert _contains_no_nulls(result) - assert result.dtype == pd.ArrowDtype( - pa.struct( - ( - pa.field("is_in_north_america", pa.bool_()), - pa.field("population", pa.int64()), - pa.field("full_response", dtypes.JSON_ARROW_TYPE), - pa.field("status", pa.string()), - ) - ) - ) - - -def test_ai_generate_with_invalid_output_schema_raise_error(session): - country = bpd.Series(["Japan", "Canada"], session=session) - prompt = ("Describe ", country) - - with pytest.raises(ValueError): - bbq.ai.generate( - prompt, - endpoint="gemini-2.5-flash", - output_schema={"population": "INT64", "is_in_north_america": "JSON"}, - ) - - -def test_ai_generate_bool(session): - s1 = bpd.Series(["apple", "bear"], session=session) - s2 = bpd.Series(["fruit", "tree"], session=session) - prompt = (s1, " is a ", s2) - - result = bbq.ai.generate_bool(prompt, endpoint="gemini-2.5-flash") - - assert _contains_no_nulls(result) - assert result.dtype == pd.ArrowDtype( - pa.struct( - ( - pa.field("result", pa.bool_()), - pa.field("full_response", dtypes.JSON_ARROW_TYPE), - pa.field("status", pa.string()), - ) - ) - ) - - -def test_ai_generate_bool_access_full_response_with_ibis(session, use_ibis_compiler): - s1 = bpd.Series(["apple", "bear"], session=session) - s2 = bpd.Series(["fruit", "tree"], session=session) - prompt = (s1, " is a ", s2) - - result = ( - bbq.ai.generate_bool(prompt, endpoint="gemini-2.5-flash") - .struct.field("full_response") - .to_pandas() - ) - - assert _contains_no_nulls(result) - - -def test_ai_generate_bool_multi_model(session, bq_connection): - df = _create_mock_obj_ref_df( - session, - ["gs://cloud-samples-data/vision/ocr/sign.jpg"], - name="image", - connection=bq_connection, - ) - - image_runtime = bbq.obj.get_access_url(df["image"], mode="R") - result = bbq.ai.generate_bool((image_runtime, " contains an animal")) - - assert _contains_no_nulls(result) - assert result.dtype == pd.ArrowDtype( - pa.struct( - ( - pa.field("result", pa.bool_()), - pa.field("full_response", dtypes.JSON_ARROW_TYPE), - pa.field("status", pa.string()), - ) - ) - ) - - -def test_ai_generate_int(session): - s = bpd.Series(["Cat"], session=session) - prompt = ("How many legs does a ", s, " have?") - - result = bbq.ai.generate_int(prompt, endpoint="gemini-2.5-flash") - - assert _contains_no_nulls(result) - assert result.dtype == pd.ArrowDtype( - pa.struct( - ( - pa.field("result", pa.int64()), - pa.field("full_response", dtypes.JSON_ARROW_TYPE), - pa.field("status", pa.string()), - ) - ) - ) - - -def test_ai_generate_int_access_full_response_with_ibis(session, use_ibis_compiler): - s = bpd.Series(["Cat"], session=session) - prompt = ("How many legs does a ", s, " have?") - - result = ( - bbq.ai.generate_int(prompt, endpoint="gemini-2.5-flash") - .struct.field("full_response") - .to_pandas() - ) - - assert _contains_no_nulls(result) - - -def test_ai_generate_int_multi_model(session, bq_connection): - df = _create_mock_obj_ref_df( - session, - ["gs://cloud-samples-data/vision/ocr/sign.jpg"], - name="image", - connection=bq_connection, - ) - - image_runtime = bbq.obj.get_access_url(df["image"], mode="R") - result = bbq.ai.generate_int( - ("How many animals are there in the picture ", image_runtime) - ) - - assert _contains_no_nulls(result) - assert result.dtype == pd.ArrowDtype( - pa.struct( - ( - pa.field("result", pa.int64()), - pa.field("full_response", dtypes.JSON_ARROW_TYPE), - pa.field("status", pa.string()), - ) - ) - ) - - -def test_ai_generate_double(session): - s = bpd.Series(["Cat"], session=session) - prompt = ("How many legs does a ", s, " have?") - - result = bbq.ai.generate_double(prompt, endpoint="gemini-2.5-flash") - - assert _contains_no_nulls(result) - assert result.dtype == pd.ArrowDtype( - pa.struct( - ( - pa.field("result", pa.float64()), - pa.field("full_response", dtypes.JSON_ARROW_TYPE), - pa.field("status", pa.string()), - ) - ) - ) - - -def test_ai_generate_double_access_full_response_with_ibis(session, use_ibis_compiler): - s = bpd.Series(["Cat"], session=session) - prompt = ("How many legs does a ", s, " have?") - - result = ( - bbq.ai.generate_double(prompt, endpoint="gemini-2.5-flash") - .struct.field("full_response") - .to_pandas() - ) - - assert _contains_no_nulls(result) - - -def test_ai_generate_double_multi_model(session, bq_connection): - df = _create_mock_obj_ref_df( - session, - ["gs://cloud-samples-data/vision/ocr/sign.jpg"], - name="image", - connection=bq_connection, - ) - - image_runtime = bbq.obj.get_access_url(df["image"], mode="R") - result = bbq.ai.generate_double( - ("How many animals are there in the picture ", image_runtime) - ) - - assert _contains_no_nulls(result) - assert result.dtype == pd.ArrowDtype( - pa.struct( - ( - pa.field("result", pa.float64()), - pa.field("full_response", dtypes.JSON_ARROW_TYPE), - pa.field("status", pa.string()), - ) - ) - ) - - -def test_ai_embed_series_content(session): - content = bpd.Series(["dog"], session=session) - - result = bbq.ai.embed(content, endpoint="text-embedding-005") - - assert _contains_no_nulls(result) - assert result.dtype == pd.ArrowDtype( - pa.struct( - ( - pa.field("result", pa.list_(pa.float64())), - pa.field("status", pa.string()), - ) - ) - ) - - -def test_ai_embed_string_content(session): - with mock.patch( - "bigframes.core.global_session.get_global_session" - ) as mock_get_session: - mock_get_session.return_value = session - - result = bbq.ai.embed("dog", endpoint="text-embedding-005") - - assert _contains_no_nulls(result) - assert result.dtype == pd.ArrowDtype( - pa.struct( - ( - pa.field("result", pa.list_(pa.float64())), - pa.field("status", pa.string()), - ) - ) - ) - - -def test_ai_if(session): - s1 = bpd.Series(["apple", "bear"], session=session) - s2 = bpd.Series(["fruit", "tree"], session=session) - prompt = (s1, " is a ", s2) - - result = bbq.ai.if_( - prompt, - optimization_mode="maximize_quality", - max_error_ratio=0.5, - ) - - assert len(result) == len(s1) - assert result.dtype == dtypes.BOOL_DTYPE - - -def test_ai_if_multi_model(session, bq_connection): - df = _create_mock_obj_ref_df( - session, - ["gs://cloud-samples-data/vision/ocr/sign.jpg"], - name="image", - connection=bq_connection, - ) - - image_runtime = bbq.obj.get_access_url(df["image"], mode="R") - result = bbq.ai.if_((image_runtime, " contains an animal")) - - assert len(result) == len(df) - assert result.dtype == dtypes.BOOL_DTYPE - - -def test_ai_classify(session): - s = bpd.Series(["cat", "orchid"], session=session) - - result = bbq.ai.classify(s, ["animal", "plant"]) - - assert len(result) == len(s) - assert result.dtype == dtypes.STRING_DTYPE - - -def test_ai_classify_with_examples(session): - s = bpd.Series(["cat", "orchid"], session=session) - - result = bbq.ai.classify(s, ["animal", "plant"], examples=[("dog", "animal")]) - - assert len(result) == len(s) - assert result.dtype == dtypes.STRING_DTYPE - - -def test_ai_classify_output_mode(session, bq_connection): - s = bpd.Series(["cat", "orchid"], session=session) - - result = bbq.ai.classify( - s, ["animal", "plant"], output_mode="multi", examples=[("dog", ["animal"])] - ) - - assert len(result) == len(s) - assert result.dtype == dtypes.list_type(dtypes.STRING_DTYPE) - - -def test_ai_classify_multi_model(session, bq_connection): - df = _create_mock_obj_ref_df( - session, - ["gs://cloud-samples-data/vision/ocr/sign.jpg"], - name="image", - connection=bq_connection, - ) - - image_runtime = bbq.obj.get_access_url(df["image"], mode="R") - result = bbq.ai.classify(image_runtime, ["photo", "cartoon"]) - - assert len(result) == len(df) - assert result.dtype == dtypes.STRING_DTYPE - - -def test_ai_score(session): - s = bpd.Series(["Tiger", "Rabbit"], session=session) - prompt = ("Rank the relative weights of ", s, " on the scale from 1 to 3") - - result = bbq.ai.score(prompt) - - assert len(result) == len(s) - assert result.dtype == dtypes.FLOAT_DTYPE - - -def test_ai_score_multi_model(session, bq_connection): - df = _create_mock_obj_ref_df( - session, - ["gs://cloud-samples-data/vision/ocr/sign.jpg"], - name="image", - connection=bq_connection, - ) - image_runtime = bbq.obj.get_access_url(df["image"], mode="R") - prompt = ("Rank the liveliness of ", image_runtime, "on the scale from 1 to 3") - - result = bbq.ai.score(prompt) - - assert len(result) == len(df) - assert result.dtype == dtypes.FLOAT_DTYPE - - -def test_forecast_default_params(time_series_df_default_index: dataframe.DataFrame): - df = time_series_df_default_index[time_series_df_default_index["id"] == "1"] - - result = bbq.ai.forecast(df, timestamp_col="parsed_date", data_col="total_visits") - - expected_columns = [ - "forecast_timestamp", - "forecast_value", - "confidence_level", - "prediction_interval_lower_bound", - "prediction_interval_upper_bound", - "ai_forecast_status", - ] - test_utils.check_pandas_df_schema_and_index( - result, - columns=expected_columns, - index=10, - ) - - -def test_forecast_w_params(time_series_df_default_index: dataframe.DataFrame): - result = bbq.ai.forecast( - time_series_df_default_index, - timestamp_col="parsed_date", - data_col="total_visits", - id_cols=["id"], - horizon=20, - confidence_level=0.98, - context_window=64, - ) - - expected_columns = [ - "id", - "forecast_timestamp", - "forecast_value", - "confidence_level", - "prediction_interval_lower_bound", - "prediction_interval_upper_bound", - "ai_forecast_status", - ] - test_utils.check_pandas_df_schema_and_index( - result, - columns=expected_columns, - index=20 * 2, # 20 for each id - ) - - -def test_ai_similarity(session): - s1 = bpd.Series(["happy", "sad"], session=session) - s2 = pd.Series(["glad", "angry"]) - - result = bbq.ai.similarity(s1, s2, endpoint="text-embedding-005") - - assert _contains_no_nulls(result) - assert result.dtype == dtypes.FLOAT_DTYPE - - -def test_ai_similarity_one_content_is_string_literal(session): - s1 = "happy" - s2 = bpd.Series(["glad", "angry"], session=session) - - result = bbq.ai.similarity(s1, s2, model="embeddinggemma-300m") - - assert _contains_no_nulls(result) - assert result.dtype == dtypes.FLOAT_DTYPE - - -def test_ai_similarity_both_contents_are_string_literals(session): - s1 = "happy" - s2 = "glad" - - result = bbq.ai.similarity(s1, s2, endpoint="text-embedding-005") - - assert _contains_no_nulls(result) - assert result.dtype == dtypes.FLOAT_DTYPE - - -def _contains_no_nulls(s: series.Series | pd.Series) -> bool: - return len(s) == s.count() diff --git a/tests/system/small/bigquery/test_approx_agg.py b/tests/system/small/bigquery/test_approx_agg.py deleted file mode 100644 index c88f5850f80..00000000000 --- a/tests/system/small/bigquery/test_approx_agg.py +++ /dev/null @@ -1,76 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import pytest - -import bigframes.bigquery as bbq -import bigframes.pandas as bpd - - -@pytest.mark.parametrize( - ("data", "expected"), - [ - pytest.param( - [1, 2, 3, 3, 2], [{"value": 3, "count": 2}, {"value": 2, "count": 2}] - ), - pytest.param( - ["apple", "apple", "pear", "pear", "pear", "banana"], - [{"value": "pear", "count": 3}, {"value": "apple", "count": 2}], - ), - pytest.param( - [True, False, True, False, True], - [{"value": True, "count": 3}, {"value": False, "count": 2}], - ), - pytest.param( - [], - [], - ), - pytest.param( - [[1, 2], [1], [1, 2]], - [], - marks=pytest.mark.xfail(raises=TypeError), - ), - ], - ids=["int64", "string", "bool", "null", "array"], -) -def test_approx_top_count_w_dtypes(data, expected): - s = bpd.Series(data) - result = bbq.approx_top_count(s, number=2) - assert result == expected - - -@pytest.mark.parametrize( - ("number", "expected"), - [ - pytest.param( - 0, - [], - marks=pytest.mark.xfail(raises=ValueError), - ), - pytest.param(1, [{"value": 3, "count": 2}]), - pytest.param( - 4, - [ - {"value": 3, "count": 2}, - {"value": 2, "count": 2}, - {"value": 1, "count": 1}, - ], - ), - ], - ids=["zero", "one", "full"], -) -def test_approx_top_count_w_numbers(number, expected): - s = bpd.Series([1, 2, 3, 3, 2]) - result = bbq.approx_top_count(s, number=number) - assert result == expected diff --git a/tests/system/small/bigquery/test_array.py b/tests/system/small/bigquery/test_array.py deleted file mode 100644 index c8c69f7457e..00000000000 --- a/tests/system/small/bigquery/test_array.py +++ /dev/null @@ -1,205 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from typing import cast - -import numpy as np -import pandas as pd -import pytest - -import bigframes.bigquery as bbq -import bigframes.dtypes -import bigframes.pandas as bpd - - -@pytest.mark.parametrize( - ["input_data", "expected"], - [ - pytest.param( - [["A", "AA", "AAA"], ["BB", "B"], np.nan, [], ["C"]], - [ - 3, - 2, - # TODO(b/336880368): Allow for NULL values to be input for ARRAY - # columns. Once we actually store NULL values, this will be - # NULL where the input is NULL. - 0, - 0, - 1, - ], - id="small-string", - ), - pytest.param( - [[1, 2, 3], [4, 5], [], [], [6]], [3, 2, 0, 0, 1], id="small-int64" - ), - pytest.param( - [ - # Regression test for b/414374215 where the Series constructor - # returns empty lists when the lists are too big to embed in - # SQL. - list(np.random.randint(-1_000_000, 1_000_000, size=1000)), - list(np.random.randint(-1_000_000, 1_000_000, size=967)), - list(np.random.randint(-1_000_000, 1_000_000, size=423)), - list(np.random.randint(-1_000_000, 1_000_000, size=5000)), - list(np.random.randint(-1_000_000, 1_000_000, size=1003)), - list(np.random.randint(-1_000_000, 1_000_000, size=9999)), - ], - [ - 1000, - 967, - 423, - 5000, - 1003, - 9999, - ], - id="larger-int64", - ), - ], -) -def test_array_length(input_data, expected): - series = pd.Series(input_data) - expected = pd.Series( - expected, - index=pd.Index(range(len(input_data)), dtype="Int64"), - dtype=bigframes.dtypes.INT_DTYPE, - ) - result = cast(bpd.Series, bbq.array_length(series)) - pd.testing.assert_series_equal( - result.to_pandas(), - expected, - check_index_type=False, - ) - - -@pytest.mark.parametrize( - ("input_data", "output_data"), - [ - pytest.param([1, 2, 3, 4, 5], [[1, 2], [3, 4], [5]], id="ints"), - pytest.param( - ["e", "d", "c", "b", "a"], - [["e", "d"], ["c", "b"], ["a"]], - id="reverse_strings", - ), - pytest.param( - [1.0, 2.0, np.nan, np.nan, np.nan], [[1.0, 2.0], [], []], id="nans" - ), - pytest.param( - [{"A": {"x": 1.0}}, {"A": {"z": 4.0}}, {}, {"B": "b"}, np.nan], - [[{"A": {"x": 1.0}}, {"A": {"z": 4.0}}], [{}, {"B": "b"}], []], - id="structs", - ), - ], -) -def test_array_agg_w_series_groupby(input_data, output_data): - input_index = ["a", "a", "b", "b", "c"] - series = bpd.Series(input_data, index=input_index) - result = bbq.array_agg(series.groupby(level=0)) - - expected = bpd.Series(output_data, index=["a", "b", "c"]) - pd.testing.assert_series_equal( - result.to_pandas(), # type: ignore - expected.to_pandas(), - ) - - -def test_array_agg_w_dataframe_groupby(): - data = { - "a": [1, 1, 2, 1], - "b": [2, None, 1, 2], - "c": [3, 4, 3, 2], - } - df = bpd.DataFrame(data) - result = bbq.array_agg(df.groupby(by=["b"])) - - expected_data = { - "b": [1.0, 2.0], - "a": [[2], [1, 1]], - "c": [[3], [3, 2]], - } - expected = bpd.DataFrame(expected_data).set_index("b") - - pd.testing.assert_frame_equal( - result.to_pandas(), # type: ignore - expected.to_pandas(), - ) - - -def test_array_agg_w_series(): - series = bpd.Series([1, 2, 3, 4, 5], index=["a", "a", "b", "b", "c"]) - # Mypy error expected: array_agg currently incompatible with Series. - # Test for coverage. - with pytest.raises(ValueError): - bbq.array_agg(series) # type: ignore - - -@pytest.mark.parametrize( - ("ascending", "expected_b", "expected_c"), - [ - pytest.param( - True, [["a", "b"], ["e", "d", "c"]], [[4, 5], [1, 2, 3]], id="asc" - ), - pytest.param( - False, [["b", "a"], ["c", "d", "e"]], [[5, 4], [3, 2, 1]], id="des" - ), - ], -) -def test_array_agg_reserve_order(ascending, expected_b, expected_c): - data = { - "a": [1, 1, 2, 2, 2], - "b": ["a", "b", "c", "d", "e"], - "c": [4, 5, 3, 2, 1], - } - df = bpd.DataFrame(data) - - result = bbq.array_agg(df.sort_values("c", ascending=ascending).groupby(by=["a"])) - expected_data = { - "a": [1, 2], - "b": expected_b, - "c": expected_c, - } - expected = bpd.DataFrame(expected_data).set_index("a") - - pd.testing.assert_frame_equal( - result.to_pandas(), # type: ignore - expected.to_pandas(), - ) - - -def test_array_agg_matches_after_explode(): - data = { - "index": np.arange(10), - "a": [np.random.randint(0, 10, 10) for _ in range(10)], - "b": [np.random.randint(0, 10, 10) for _ in range(10)], - } - df = bpd.DataFrame(data).set_index("index") - result = bbq.array_agg(df.explode(["a", "b"]).groupby(level=0)) - result.index.name = "index" - - pd.testing.assert_frame_equal( - result.to_pandas(), # type: ignore - df.to_pandas(), - ) - - -@pytest.mark.parametrize( - ("data"), - [ - pytest.param([[1, 2], [3, 4], [5]], id="int_array"), - pytest.param(["hello", "world"], id="string"), - ], -) -def test_array_to_string_w_type_checks(data): - series = bpd.Series(data) - with pytest.raises(TypeError): - bbq.array_to_string(series, delimiter=", ") diff --git a/tests/system/small/bigquery/test_datetime.py b/tests/system/small/bigquery/test_datetime.py deleted file mode 100644 index 58e07928f0c..00000000000 --- a/tests/system/small/bigquery/test_datetime.py +++ /dev/null @@ -1,144 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import typing - -import pandas as pd -import pyarrow as pa -import pytest - -import bigframes.testing.utils -from bigframes import bigquery - -_TIMESTAMP_DTYPE = pd.ArrowDtype(pa.timestamp("us", tz="UTC")) - - -@pytest.fixture -def int_series(session): - pd_series = pd.Series([1, 2, 3, 4, 5]) - - return session.read_pandas(pd_series), pd_series - - -def test_unix_seconds(scalars_dfs): - bigframes_df, pandas_df = scalars_dfs - - actual_res = bigquery.unix_seconds(bigframes_df["timestamp_col"]).to_pandas() - - expected_res = ( - pandas_df["timestamp_col"] - .apply(lambda ts: _to_unix_epoch(ts, "s")) - .astype("Int64") - ) - bigframes.testing.utils.assert_series_equal(actual_res, expected_res) - - -def test_unix_seconds_after_type_casting(int_series): - bf_series, pd_series = int_series - - actual_res = bigquery.unix_seconds(bf_series.astype(_TIMESTAMP_DTYPE)).to_pandas() - - expected_res = ( - pd_series.astype(_TIMESTAMP_DTYPE) - .apply(lambda ts: _to_unix_epoch(ts, "s")) - .astype("Int64") - ) - bigframes.testing.utils.assert_series_equal( - actual_res, expected_res, check_index_type=False - ) - - -def test_unix_seconds_incorrect_input_type_raise_error(scalars_dfs): - df, _ = scalars_dfs - - with pytest.raises(TypeError): - bigquery.unix_seconds(df["string_col"]) - - -def test_unix_millis(scalars_dfs): - bigframes_df, pandas_df = scalars_dfs - - actual_res = bigquery.unix_millis(bigframes_df["timestamp_col"]).to_pandas() - - expected_res = ( - pandas_df["timestamp_col"] - .apply(lambda ts: _to_unix_epoch(ts, "ms")) - .astype("Int64") - ) - bigframes.testing.utils.assert_series_equal(actual_res, expected_res) - - -def test_unix_millis_after_type_casting(int_series): - bf_series, pd_series = int_series - - actual_res = bigquery.unix_millis(bf_series.astype(_TIMESTAMP_DTYPE)).to_pandas() - - expected_res = ( - pd_series.astype(_TIMESTAMP_DTYPE) - .apply(lambda ts: _to_unix_epoch(ts, "ms")) - .astype("Int64") - ) - bigframes.testing.utils.assert_series_equal( - actual_res, expected_res, check_index_type=False - ) - - -def test_unix_millis_incorrect_input_type_raise_error(scalars_dfs): - df, _ = scalars_dfs - - with pytest.raises(TypeError): - bigquery.unix_millis(df["string_col"]) - - -def test_unix_micros(scalars_dfs): - bigframes_df, pandas_df = scalars_dfs - - actual_res = bigquery.unix_micros(bigframes_df["timestamp_col"]).to_pandas() - - expected_res = ( - pandas_df["timestamp_col"] - .apply(lambda ts: _to_unix_epoch(ts, "us")) - .astype("Int64") - ) - bigframes.testing.utils.assert_series_equal(actual_res, expected_res) - - -def test_unix_micros_after_type_casting(int_series): - bf_series, pd_series = int_series - - actual_res = bigquery.unix_micros(bf_series.astype(_TIMESTAMP_DTYPE)).to_pandas() - - expected_res = ( - pd_series.astype(_TIMESTAMP_DTYPE) - .apply(lambda ts: _to_unix_epoch(ts, "us")) - .astype("Int64") - ) - bigframes.testing.utils.assert_series_equal( - actual_res, expected_res, check_index_type=False - ) - - -def test_unix_micros_incorrect_input_type_raise_error(scalars_dfs): - df, _ = scalars_dfs - - with pytest.raises(TypeError): - bigquery.unix_micros(df["string_col"]) - - -def _to_unix_epoch( - ts: pd.Timestamp, unit: typing.Literal["s", "ms", "us"] -) -> typing.Optional[int]: - if pd.isna(ts): - return None - return (ts - pd.Timestamp("1970-01-01", tz="UTC")) // pd.Timedelta(1, unit) diff --git a/tests/system/small/bigquery/test_geo.py b/tests/system/small/bigquery/test_geo.py deleted file mode 100644 index 16df467d24a..00000000000 --- a/tests/system/small/bigquery/test_geo.py +++ /dev/null @@ -1,490 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from __future__ import annotations - -import geopandas # type: ignore -import pandas as pd -import pandas.testing -import pytest -from shapely.geometry import ( # type: ignore - GeometryCollection, - LineString, - MultiLineString, - MultiPoint, - MultiPolygon, - Point, - Polygon, -) - -import bigframes.bigquery as bbq -import bigframes.geopandas -import bigframes.session -import bigframes.testing.utils -from bigframes.bigquery import st_length - - -def test_geo_st_area(session: bigframes.session.Session): - data = [ - Polygon([(0.000, 0.0), (0.001, 0.001), (0.000, 0.001)]), - Polygon([(0.0010, 0.004), (0.009, 0.005), (0.0010, 0.005)]), - Polygon([(0.001, 0.001), (0.002, 0.001), (0.002, 0.002)]), - LineString([(0, 0), (1, 1), (0, 1)]), - Point(0, 1), - ] - - geopd_s = geopandas.GeoSeries(data=data, crs="EPSG:4326") - geobf_s = bigframes.geopandas.GeoSeries(data=data, session=session) - - # For `geopd_s`, the data was further projected with `geopandas.GeoSeries.to_crs` - # to `to_crs(26393)` to get the area in square meter. See: https://geopandas.org/en/stable/docs/user_guide/projections.html - # and https://spatialreference.org/ref/epsg/26393/. We then rounded both results - # to get them as close to each other as possible. Initially, the area results - # were +ten-millions. We added more zeros after the decimal point to round the - # area results to the nearest thousands. - geopd_s_result = geopd_s.to_crs(26393).area.round(-3) - geobf_s_result = bbq.st_area(geobf_s).to_pandas().round(-3) - assert geobf_s_result.iloc[0] >= 1000 - - bigframes.testing.utils.assert_series_equal( - geobf_s_result, - geopd_s_result, - check_dtype=False, - check_index_type=False, - check_exact=False, - rtol=0.1, - ) - - -# Expected length for 1 degree of longitude at the equator is approx 111195.079734 meters -DEG_LNG_EQUATOR_METERS = 111195.07973400292 - - -def test_st_length_various_geometries(session): - input_geometries = [ - Point(0, 0), - LineString([(0, 0), (1, 0)]), - Polygon([(0, 0), (1, 0), (0, 1), (0, 0)]), - MultiPoint([Point(0, 0), Point(1, 1)]), - MultiLineString([LineString([(0, 0), (1, 0)]), LineString([(0, 0), (0, 1)])]), - MultiPolygon( - [ - Polygon([(0, 0), (1, 0), (0, 1), (0, 0)]), - Polygon([(2, 2), (3, 2), (2, 3), (2, 2)]), - ] - ), - GeometryCollection([Point(0, 0), LineString([(0, 0), (1, 0)])]), - GeometryCollection([]), - None, # Represents NULL geography input - GeometryCollection([Point(1, 1), Point(2, 2)]), - ] - geoseries = bigframes.geopandas.GeoSeries(input_geometries, session=session) - - expected_lengths = pd.Series( - [ - 0.0, # Point - DEG_LNG_EQUATOR_METERS, # LineString - 0.0, # Polygon - 0.0, # MultiPoint - 2 * DEG_LNG_EQUATOR_METERS, # MultiLineString - 0.0, # MultiPolygon - DEG_LNG_EQUATOR_METERS, # GeometryCollection (Point + LineString) - 0.0, # Empty GeometryCollection - pd.NA, # None input for ST_LENGTH(NULL) is NULL - 0.0, # GeometryCollection (Point + Point) - ], - index=pd.Index(range(10), dtype="Int64"), - dtype="Float64", - ) - - # Test default use_spheroid - result_default = st_length(geoseries).to_pandas() - bigframes.testing.utils.assert_series_equal( - result_default, - expected_lengths, - rtol=1e-3, - atol=1e-3, # For comparisons involving 0.0 - ) # type: ignore - - # Test explicit use_spheroid=False - result_explicit_false = st_length(geoseries, use_spheroid=False).to_pandas() - bigframes.testing.utils.assert_series_equal( - result_explicit_false, - expected_lengths, - rtol=1e-3, - atol=1e-3, # For comparisons involving 0.0 - ) # type: ignore - - -def test_geo_st_difference_with_geometry_objects(session: bigframes.session.Session): - data1 = [ - Polygon([(0, 0), (10, 0), (10, 10), (0, 0)]), - Polygon([(0, 0), (1, 1), (0, 1), (0, 0)]), - Point(0, 1), - ] - - data2 = [ - Polygon([(0, 0), (10, 0), (10, 10), (0, 0)]), - Polygon([(0, 0), (1, 1), (0, 1), (0, 0)]), - LineString([(2, 0), (0, 2)]), - ] - - geobf_s1 = bigframes.geopandas.GeoSeries(data=data1, session=session) - geobf_s2 = bigframes.geopandas.GeoSeries(data=data2, session=session) - geobf_s_result = bbq.st_difference(geobf_s1, geobf_s2).to_pandas() - - expected = pd.Series( - [ - GeometryCollection([]), - GeometryCollection([]), - Point(0, 1), - ], - index=[0, 1, 2], - dtype=geopandas.array.GeometryDtype(), - ) - bigframes.testing.utils.assert_series_equal( - geobf_s_result, - expected, - check_index_type=False, - check_exact=False, - rtol=0.1, - ) - - -def test_geo_st_difference_with_single_geometry_object( - session: bigframes.session.Session, -): - pytest.importorskip( - "shapely", - minversion="2.0.0", - reason="shapely objects must be hashable to include in our expression trees", - ) - - data1 = [ - Polygon([(0, 0), (10, 0), (10, 10), (0, 10), (0, 0)]), - Polygon([(0, 1), (10, 1), (10, 9), (0, 9), (0, 1)]), - Point(0, 1), - ] - - geobf_s1 = bigframes.geopandas.GeoSeries(data=data1, session=session) - geobf_s_result = bbq.st_difference( - geobf_s1, - Polygon([(0, 0), (10, 0), (10, 5), (0, 5), (0, 0)]), - ).to_pandas() - - expected = pd.Series( - [ - Polygon([(10, 5), (10, 10), (0, 10), (0, 5), (10, 5)]), - Polygon([(10, 5), (10, 9), (0, 9), (0, 5), (10, 5)]), - GeometryCollection([]), - ], - index=[0, 1, 2], - dtype=geopandas.array.GeometryDtype(), - ) - bigframes.testing.utils.assert_series_equal( - geobf_s_result, - expected, - check_index_type=False, - check_exact=False, - rtol=0.1, - ) - - -def test_geo_st_difference_with_similar_geometry_objects( - session: bigframes.session.Session, -): - data1 = [ - Polygon([(0, 0), (10, 0), (10, 10), (0, 0)]), - Polygon([(0, 0), (1, 1), (0, 1)]), - Point(0, 1), - ] - - geobf_s1 = bigframes.geopandas.GeoSeries(data=data1, session=session) - geobf_s_result = bbq.st_difference(geobf_s1, geobf_s1).to_pandas() - - expected = pd.Series( - [GeometryCollection([]), GeometryCollection([]), GeometryCollection([])], - index=[0, 1, 2], - dtype=geopandas.array.GeometryDtype(), - ) - bigframes.testing.utils.assert_series_equal( - geobf_s_result, - expected, - check_index_type=False, - check_exact=False, - rtol=0.1, - ) - - -def test_geo_st_distance_with_geometry_objects(session: bigframes.session.Session): - data1 = [ - # 0.00001 is approximately 1 meter. - Polygon([(0, 0), (0.00001, 0), (0.00001, 0.00001), (0, 0.00001), (0, 0)]), - Polygon( - [ - (0.00002, 0), - (0.00003, 0), - (0.00003, 0.00001), - (0.00002, 0.00001), - (0.00002, 0), - ] - ), - Point(0, 0.00002), - ] - - data2 = [ - Polygon( - [ - (0.00002, 0), - (0.00003, 0), - (0.00003, 0.00001), - (0.00002, 0.00001), - (0.00002, 0), - ] - ), - Point(0, 0.00002), - Polygon([(0, 0), (0.00001, 0), (0.00001, 0.00001), (0, 0.00001), (0, 0)]), - Point( - 1, 1 - ), # No matching row in data1, so this will be NULL after the call to distance. - ] - - geobf_s1 = bigframes.geopandas.GeoSeries(data=data1, session=session) - geobf_s2 = bigframes.geopandas.GeoSeries(data=data2, session=session) - geobf_s_result = bbq.st_distance(geobf_s1, geobf_s2).to_pandas() - - expected = pd.Series( - [ - 1.112, - 2.486, - 1.112, - None, - ], - index=[0, 1, 2, 3], - dtype="Float64", - ) - bigframes.testing.utils.assert_series_equal( - geobf_s_result, - expected, - check_index_type=False, - check_exact=False, - rtol=0.1, - ) - - -def test_geo_st_distance_with_single_geometry_object( - session: bigframes.session.Session, -): - pytest.importorskip( - "shapely", - minversion="2.0.0", - reason="shapely objects must be hashable to include in our expression trees", - ) - - data1 = [ - # 0.00001 is approximately 1 meter. - Polygon([(0, 0), (0.00001, 0), (0.00001, 0.00001), (0, 0.00001), (0, 0)]), - Polygon( - [ - (0.00001, 0), - (0.00002, 0), - (0.00002, 0.00001), - (0.00001, 0.00001), - (0.00001, 0), - ] - ), - Point(0, 0.00002), - ] - - geobf_s1 = bigframes.geopandas.GeoSeries(data=data1, session=session) - geobf_s_result = bbq.st_distance( - geobf_s1, - Point(0, 0), - ).to_pandas() - - expected = pd.Series( - [ - 0, - 1.112, - 2.224, - ], - dtype="Float64", - ) - bigframes.testing.utils.assert_series_equal( - geobf_s_result, - expected, - check_index_type=False, - check_exact=False, - rtol=0.1, - ) - - -def test_geo_st_intersection_with_geometry_objects(session: bigframes.session.Session): - data1 = [ - Polygon([(0, 0), (10, 0), (10, 10), (0, 0)]), - Polygon([(0, 0), (1, 1), (0, 1), (0, 0)]), - Point(0, 1), - ] - - data2 = [ - Polygon([(0, 0), (10, 0), (10, 10), (0, 0)]), - Polygon([(0, 0), (1, 1), (0, 1), (0, 0)]), - LineString([(2, 0), (0, 2)]), - ] - - geobf_s1 = bigframes.geopandas.GeoSeries(data=data1, session=session) - geobf_s2 = bigframes.geopandas.GeoSeries(data=data2, session=session) - geobf_s_result = bbq.st_intersection(geobf_s1, geobf_s2).to_pandas() - - expected = pd.Series( - [ - Polygon([(0, 0), (10, 0), (10, 10), (0, 0)]), - Polygon([(0, 0), (1, 1), (0, 1), (0, 0)]), - GeometryCollection([]), - ], - index=[0, 1, 2], - dtype=geopandas.array.GeometryDtype(), - ) - bigframes.testing.utils.assert_series_equal( - geobf_s_result, - expected, - check_index_type=False, - check_exact=False, - rtol=0.1, - ) - - -def test_geo_st_intersection_with_single_geometry_object( - session: bigframes.session.Session, -): - pytest.importorskip( - "shapely", - minversion="2.0.0", - reason="shapely objects must be hashable to include in our expression trees", - ) - - data1 = [ - Polygon([(0, 0), (10, 0), (10, 10), (0, 10), (0, 0)]), - Polygon([(0, 1), (10, 1), (10, 9), (0, 9), (0, 1)]), - Point(0, 1), - ] - - geobf_s1 = bigframes.geopandas.GeoSeries(data=data1, session=session) - geobf_s_result = bbq.st_intersection( - geobf_s1, - Polygon([(0, 0), (10, 0), (10, 5), (0, 5), (0, 0)]), - ).to_pandas() - - expected = pd.Series( - [ - Polygon([(0, 0), (10, 0), (10, 5), (0, 5), (0, 0)]), - Polygon([(0, 1), (10, 1), (10, 5), (0, 5), (0, 1)]), - Point(0, 1), - ], - index=[0, 1, 2], - dtype=geopandas.array.GeometryDtype(), - ) - bigframes.testing.utils.assert_series_equal( - geobf_s_result, - expected, - check_index_type=False, - check_exact=False, - rtol=0.1, - ) - - -def test_geo_st_intersection_with_similar_geometry_objects( - session: bigframes.session.Session, -): - data1 = [ - Polygon([(0, 0), (10, 0), (10, 10), (0, 0)]), - Polygon([(0, 0), (1, 1), (0, 1)]), - Point(0, 1), - ] - - geobf_s1 = bigframes.geopandas.GeoSeries(data=data1, session=session) - geobf_s_result = bbq.st_intersection(geobf_s1, geobf_s1).to_pandas() - - expected = pd.Series( - [ - Polygon([(0, 0), (10, 0), (10, 10), (0, 0)]), - Polygon([(0, 0), (1, 1), (0, 1)]), - Point(0, 1), - ], - index=[0, 1, 2], - dtype=geopandas.array.GeometryDtype(), - ) - bigframes.testing.utils.assert_series_equal( - geobf_s_result, - expected, - check_index_type=False, - check_exact=False, - rtol=0.1, - ) - - -def test_geo_st_isclosed(session: bigframes.session.Session): - bf_gs = bigframes.geopandas.GeoSeries( - [ - Point(0, 0), # Point - LineString([(0, 0), (1, 1)]), # Open LineString - LineString([(0, 0), (1, 1), (0, 1), (0, 0)]), # Closed LineString - Polygon([(0, 0), (1, 1), (0, 1)]), # Open polygon - GeometryCollection(), # Empty GeometryCollection - bigframes.geopandas.GeoSeries.from_wkt( - ["GEOMETRYCOLLECTION EMPTY"], session=session - ).iloc[0], # Also empty - None, # Should be filtered out by dropna - ], - index=[0, 1, 2, 3, 4, 5, 6], - session=session, - ) - bf_result = bbq.st_isclosed(bf_gs).to_pandas() - - # Expected results based on ST_ISCLOSED documentation: - expected_data = [ - True, # Point: True - False, # Open LineString: False - True, # Closed LineString: True - False, # Polygon: False (only True if it's a full polygon) - False, # Empty GeometryCollection: False (An empty GEOGRAPHY isn't closed) - False, # GEOMETRYCOLLECTION EMPTY: False - None, - ] - expected_series = pd.Series(data=expected_data, dtype="boolean") - - bigframes.testing.utils.assert_series_equal( - bf_result, - expected_series, - # We default to Int64 (nullable) dtype, but pandas defaults to int64 index. - check_index_type=False, - ) - - -def test_st_buffer(session): - geoseries = bigframes.geopandas.GeoSeries( - [Point(0, 0), LineString([(1, 1), (2, 2)])], session=session - ) - result = bbq.st_buffer(geoseries, 1000).to_pandas() - assert result.iloc[0].geom_type == "Polygon" - assert result.iloc[1].geom_type == "Polygon" - - -def test_st_simplify(session): - geoseries = bigframes.geopandas.GeoSeries( - [LineString([(0, 0), (1, 1), (2, 0)])], session=session - ) - result = bbq.st_simplify(geoseries, 100000).to_pandas() - assert len(result.index) == 1 - assert result.isna().sum() == 0 diff --git a/tests/system/small/bigquery/test_json.py b/tests/system/small/bigquery/test_json.py deleted file mode 100644 index 2d97172e7b5..00000000000 --- a/tests/system/small/bigquery/test_json.py +++ /dev/null @@ -1,487 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -import geopandas as gpd # type: ignore -import pandas as pd -import pyarrow as pa -import pytest - -import bigframes.bigquery as bbq -import bigframes.dtypes as dtypes -import bigframes.pandas as bpd - - -@pytest.mark.parametrize( - ("json_path", "expected_json"), - [ - pytest.param("$.a", ['{"a": 10}'], id="simple"), - pytest.param("$.a.b.c", ['{"a": {"b": {"c": 10, "d": []}}}'], id="nested"), - ], -) -def test_json_set_at_json_path(json_path, expected_json): - original_json = ['{"a": {"b": {"c": "tester", "d": []}}}'] - s = bpd.Series(original_json, dtype=dtypes.JSON_DTYPE) - - actual = bbq.json_set(s, json_path_value_pairs=[(json_path, 10)]) - expected = bpd.Series(expected_json, dtype=dtypes.JSON_DTYPE) - - pd.testing.assert_series_equal(actual.to_pandas(), expected.to_pandas()) - - -@pytest.mark.parametrize( - ("json_value", "expected_json"), - [ - pytest.param(10, ['{"a": {"b": 10}}', '{"a": {"b": 10}}'], id="int"), - pytest.param(0.333, ['{"a": {"b": 0.333}}', '{"a": {"b": 0.333}}'], id="float"), - pytest.param( - "eng", ['{"a": {"b": "eng"}}', '{"a": {"b": "eng"}}'], id="string" - ), - pytest.param([1, 2], ['{"a": {"b": 1}}', '{"a": {"b": 2}}'], id="series"), - ], -) -def test_json_set_at_json_value_type(json_value, expected_json): - original_json = ['{"a": {"b": "dev"}}', '{"a": {"b": [1, 2]}}'] - s = bpd.Series(original_json, dtype=dtypes.JSON_DTYPE) - actual = bbq.json_set(s, json_path_value_pairs=[("$.a.b", json_value)]) - expected = bpd.Series(expected_json, dtype=dtypes.JSON_DTYPE) - - pd.testing.assert_series_equal(actual.to_pandas(), expected.to_pandas()) - - -def test_json_set_w_more_pairs(): - original_json = ['{"a": 2}', '{"b": 5}', '{"c": 1}'] - s = bpd.Series(original_json, dtype=dtypes.JSON_DTYPE) - actual = bbq.json_set( - s, json_path_value_pairs=[("$.a", 1), ("$.b", 2), ("$.a", [3, 4, 5])] - ) - - expected_json = ['{"a": 3,"b":2}', '{"a":4,"b": 2}', '{"a": 5,"b":2,"c":1}'] - expected = bpd.Series(expected_json, dtype=dtypes.JSON_DTYPE) - - pd.testing.assert_series_equal(actual.to_pandas(), expected.to_pandas()) - - -def test_json_set_w_invalid_value_type(): - s = bpd.Series(['{"a": 10}'], dtype=dtypes.JSON_DTYPE) - with pytest.raises(TypeError): - bbq.json_set( - s, - json_path_value_pairs=[ - ( - "$.a", - bpd.read_pandas( - gpd.GeoSeries.from_wkt(["POINT (1 2)", "POINT (2 1)"]) - ), - ) - ], - ) - - -def test_json_set_w_invalid_series_type(): - s = bpd.Series([1, 2]) - with pytest.raises(TypeError): - bbq.json_set(s, json_path_value_pairs=[("$.a", 1)]) - - -def test_json_extract_from_json(): - s = bpd.Series( - ['{"a": {"b": [1, 2]}}', '{"a": {"c": 1}}', '{"a": {"b": 0}}'], - dtype=dtypes.JSON_DTYPE, - ) - with pytest.warns(UserWarning, match="The `json_extract` is deprecated"): - actual = bbq.json_extract(s, "$.a.b") - expected = bpd.Series(["[1, 2]", None, "0"], dtype=dtypes.JSON_DTYPE) - - pd.testing.assert_series_equal(actual.to_pandas(), expected.to_pandas()) - - -def test_json_extract_from_string(): - s = bpd.Series( - ['{"a": {"b": [1, 2]}}', '{"a": {"c": 1}}', '{"a": {"b": 0}}'], - dtype=pd.StringDtype(storage="pyarrow"), - ) - actual = bbq.json_extract(s, "$.a.b") - expected = bpd.Series(["[1,2]", None, "0"], dtype=pd.StringDtype(storage="pyarrow")) - - pd.testing.assert_series_equal(actual.to_pandas(), expected.to_pandas()) - - -def test_json_extract_w_invalid_series_type(): - s = bpd.Series([1, 2]) - with pytest.raises(TypeError): - bbq.json_extract(s, "$.a") - - -def test_json_extract_array_from_json(): - s = bpd.Series( - ['{"a": ["ab", "2", "3 xy"]}', '{"a": []}', '{"a": ["4", "5"]}', "{}"], - dtype=dtypes.JSON_DTYPE, - ) - with pytest.warns(UserWarning, match="The `json_extract_array` is deprecated"): - actual = bbq.json_extract_array(s, "$.a") - - # This code provides a workaround for issue https://github.com/apache/arrow/issues/45262, - # which currently prevents constructing a series using the pa.list_(db_types.JSONArrrowType()) - sql = """ - SELECT 0 AS id, [JSON '"ab"', JSON '"2"', JSON '"3 xy"'] AS data, - UNION ALL - SELECT 1, [], - UNION ALL - SELECT 2, [JSON '"4"', JSON '"5"'], - UNION ALL - SELECT 3, null, - """ - df = bpd.read_gbq(sql).set_index("id").sort_index() - expected = df["data"] - expected.index.name = None - expected.name = None - - pd.testing.assert_series_equal(actual.to_pandas(), expected.to_pandas()) - - -def test_json_extract_array_from_json_strings(): - s = bpd.Series( - ['{"a": ["ab", "2", "3 xy"]}', '{"a": []}', '{"a": ["4","5"]}', "{}"], - dtype=pd.StringDtype(storage="pyarrow"), - ) - actual = bbq.json_extract_array(s, "$.a") - expected = bpd.Series( - [['"ab"', '"2"', '"3 xy"'], [], ['"4"', '"5"'], None], - dtype=pd.ArrowDtype(pa.list_(pa.string())), - ) - - pd.testing.assert_series_equal(actual.to_pandas(), expected.to_pandas()) - - -def test_json_extract_array_from_json_array_strings(): - s = bpd.Series( - ["[1, 2, 3]", "[]", "[4,5]"], - dtype=pd.StringDtype(storage="pyarrow"), - ) - actual = bbq.json_extract_array(s) - expected = bpd.Series( - [["1", "2", "3"], [], ["4", "5"]], - dtype=pd.ArrowDtype(pa.list_(pa.string())), - ) - - pd.testing.assert_series_equal(actual.to_pandas(), expected.to_pandas()) - - -def test_json_extract_array_w_invalid_series_type(): - s = bpd.Series([1, 2]) - with pytest.raises(TypeError): - bbq.json_extract_array(s) - - -def test_json_extract_string_array_from_json_strings(): - s = bpd.Series(['{"a": ["ab", "2", "3 xy"]}', '{"a": []}', '{"a": ["4","5"]}']) - with pytest.warns( - UserWarning, match="The `json_extract_string_array` is deprecated" - ): - actual = bbq.json_extract_string_array(s, "$.a") - expected = bpd.Series([["ab", "2", "3 xy"], [], ["4", "5"]]) - - pd.testing.assert_series_equal(actual.to_pandas(), expected.to_pandas()) - - -def test_json_extract_string_array_from_array_strings(): - s = bpd.Series(["[1, 2, 3]", "[]", "[4,5]"]) - actual = bbq.json_extract_string_array(s) - expected = bpd.Series([["1", "2", "3"], [], ["4", "5"]]) - - pd.testing.assert_series_equal(actual.to_pandas(), expected.to_pandas()) - - -def test_json_extract_string_array_as_float_array_from_array_strings(): - s = bpd.Series(["[1, 2.5, 3]", "[]", "[4,5]"]) - actual = bbq.json_extract_string_array(s, value_dtype=dtypes.FLOAT_DTYPE) - expected = bpd.Series([[1, 2.5, 3], [], [4, 5]]) - - pd.testing.assert_series_equal(actual.to_pandas(), expected.to_pandas()) - - -def test_json_extract_string_array_w_invalid_series_type(): - s = bpd.Series([1, 2]) - with pytest.raises(TypeError): - bbq.json_extract_string_array(s) - - -def test_json_value_array_from_json_strings(): - s = bpd.Series(['{"a": ["ab", "2", "3 xy"]}', '{"a": []}', '{"a": ["4","5"]}']) - actual = bbq.json_value_array(s, "$.a") - expected_data = [["ab", "2", "3 xy"], [], ["4", "5"]] - # Expected dtype after JSON_VALUE_ARRAY is ARRAY - expected = bpd.Series(expected_data, dtype=pd.ArrowDtype(pa.list_(pa.string()))) - pd.testing.assert_series_equal( - actual.to_pandas(), - expected.to_pandas(), - ) - - -def test_json_value_array_from_array_strings(): - s = bpd.Series(["[1, 2, 3]", "[]", "[4,5]"]) - actual = bbq.json_value_array(s) - expected_data = [["1", "2", "3"], [], ["4", "5"]] - expected = bpd.Series(expected_data, dtype=pd.ArrowDtype(pa.list_(pa.string()))) - pd.testing.assert_series_equal( - actual.to_pandas(), - expected.to_pandas(), - ) - - -def test_json_value_array_w_invalid_series_type(): - s = bpd.Series([1, 2], dtype=dtypes.INT_DTYPE) # Not a JSON-like string - with pytest.raises(TypeError): - bbq.json_value_array(s) - - -def test_json_value_array_from_json_native(): - json_data = [ - '{"key": ["hello", "world"]}', - '{"key": ["123", "45.6"]}', - '{"key": []}', - "{}", # case with missing key - ] - s = bpd.Series(json_data, dtype=dtypes.JSON_DTYPE) - actual = bbq.json_value_array(s, json_path="$.key") - - expected_data_pandas = [["hello", "world"], ["123", "45.6"], [], None] - expected = bpd.Series( - expected_data_pandas, dtype=pd.ArrowDtype(pa.list_(pa.string())) - ).fillna(pd.NA) - result_pd = actual.to_pandas().fillna(pd.NA) - pd.testing.assert_series_equal(result_pd, expected.to_pandas()) - - -def test_json_query_from_json(): - s = bpd.Series( - ['{"a": {"b": [1, 2]}}', '{"a": {"c": 1}}', '{"a": {"b": 0}}'], - dtype=dtypes.JSON_DTYPE, - ) - actual = bbq.json_query(s, "$.a.b") - expected = bpd.Series(["[1, 2]", None, "0"], dtype=dtypes.JSON_DTYPE) - - pd.testing.assert_series_equal(actual.to_pandas(), expected.to_pandas()) - - -def test_json_query_from_string(): - s = bpd.Series( - ['{"a": {"b": [1, 2]}}', '{"a": {"c": 1}}', '{"a": {"b": 0}}'], - dtype=pd.StringDtype(storage="pyarrow"), - ) - actual = bbq.json_query(s, "$.a.b") - expected = bpd.Series(["[1,2]", None, "0"], dtype=pd.StringDtype(storage="pyarrow")) - - pd.testing.assert_series_equal(actual.to_pandas(), expected.to_pandas()) - - -def test_json_query_w_invalid_series_type(): - s = bpd.Series([1, 2]) - with pytest.raises(TypeError): - bbq.json_query(s, "$.a") - - -def test_json_query_array_from_json(): - s = bpd.Series( - ['{"a": ["ab", "2", "3 xy"]}', '{"a": []}', '{"a": ["4", "5"]}', "{}"], - dtype=dtypes.JSON_DTYPE, - ) - actual = bbq.json_query_array(s, "$.a") - - # This code provides a workaround for issue https://github.com/apache/arrow/issues/45262, - # which currently prevents constructing a series using the pa.list_(db_types.JSONArrrowType()) - sql = """ - SELECT 0 AS id, [JSON '"ab"', JSON '"2"', JSON '"3 xy"'] AS data, - UNION ALL - SELECT 1, [], - UNION ALL - SELECT 2, [JSON '"4"', JSON '"5"'], - UNION ALL - SELECT 3, null, - """ - df = bpd.read_gbq(sql).set_index("id").sort_index() - expected = df["data"] - expected.index.name = None - expected.name = None - - pd.testing.assert_series_equal(actual.to_pandas(), expected.to_pandas()) - - -def test_json_query_array_from_json_strings(): - s = bpd.Series( - ['{"a": ["ab", "2", "3 xy"]}', '{"a": []}', '{"a": ["4","5"]}', "{}"], - dtype=pd.StringDtype(storage="pyarrow"), - ) - actual = bbq.json_query_array(s, "$.a") - expected = bpd.Series( - [['"ab"', '"2"', '"3 xy"'], [], ['"4"', '"5"'], None], - dtype=pd.ArrowDtype(pa.list_(pa.string())), - ) - - pd.testing.assert_series_equal(actual.to_pandas(), expected.to_pandas()) - - -def test_json_query_array_from_json_array_strings(): - s = bpd.Series( - ["[1, 2, 3]", "[]", "[4,5]"], - dtype=pd.StringDtype(storage="pyarrow"), - ) - actual = bbq.json_query_array(s) - expected = bpd.Series( - [["1", "2", "3"], [], ["4", "5"]], - dtype=pd.ArrowDtype(pa.list_(pa.string())), - ) - - pd.testing.assert_series_equal(actual.to_pandas(), expected.to_pandas()) - - -def test_json_query_array_w_invalid_series_type(): - s = bpd.Series([1, 2]) - with pytest.raises(TypeError): - bbq.json_query_array(s) - - -def test_json_value_from_json(): - s = bpd.Series( - ['{"a": {"b": [1, 2]}}', '{"a": {"c": 1}}', '{"a": {"b": 0}}'], - dtype=dtypes.JSON_DTYPE, - ) - actual = bbq.json_value(s, "$.a.b") - expected = bpd.Series([None, None, "0"], dtype=dtypes.STRING_DTYPE) - - pd.testing.assert_series_equal(actual.to_pandas(), expected.to_pandas()) - - -def test_json_value_from_string(): - s = bpd.Series( - ['{"a": {"b": [1, 2]}}', '{"a": {"c": 1}}', '{"a": {"b": 0}}'], - dtype=pd.StringDtype(storage="pyarrow"), - ) - actual = bbq.json_value(s, "$.a.b") - expected = bpd.Series([None, None, "0"], dtype=dtypes.STRING_DTYPE) - - pd.testing.assert_series_equal(actual.to_pandas(), expected.to_pandas()) - - -def test_json_value_w_invalid_series_type(): - s = bpd.Series([1, 2]) - with pytest.raises(TypeError): - bbq.json_value(s, "$.a") - - -def test_parse_json_w_invalid_series_type(): - s = bpd.Series([1, 2]) - with pytest.raises(TypeError): - bbq.parse_json(s) - - -def test_to_json_from_int(): - s = bpd.Series([1, 2, None, 3]) - actual = bbq.to_json(s) - expected = bpd.Series(["1.0", "2.0", None, "3.0"], dtype=dtypes.JSON_DTYPE) - pd.testing.assert_series_equal(actual.to_pandas(), expected.to_pandas()) - - -def test_to_json_from_struct(): - s = bpd.Series( - [ - {"version": 1, "project": "pandas"}, - {"version": 2, "project": "numpy"}, - ] - ) - assert dtypes.is_struct_like(s.dtype) - - actual = bbq.to_json(s) - expected = bpd.Series( - ['{"version":1,"project":"pandas"}', '{"version":2,"project":"numpy"}'], - dtype=dtypes.JSON_DTYPE, - ) - - pd.testing.assert_series_equal(actual.to_pandas(), expected.to_pandas()) - - -def test_to_json_string_from_int(): - s = bpd.Series([1, 2, None, 3]) - actual = bbq.to_json_string(s) - expected = bpd.Series(["1", "2", "null", "3"], dtype=dtypes.STRING_DTYPE) - pd.testing.assert_series_equal(actual.to_pandas(), expected.to_pandas()) - - -def test_to_json_string_from_struct(): - s = bpd.Series( - [ - {"version": 1, "project": "pandas"}, - {"version": 2, "project": "numpy"}, - ] - ) - assert dtypes.is_struct_like(s.dtype) - - actual = bbq.to_json_string(s) - expected = bpd.Series( - ['{"version":1,"project":"pandas"}', '{"version":2,"project":"numpy"}'], - dtype=dtypes.STRING_DTYPE, - ) - - pd.testing.assert_series_equal(actual.to_pandas(), expected.to_pandas()) - - -def test_json_keys(): - json_data = [ - '{"name": "Alice", "age": 30}', - '{"city": "New York", "country": "USA", "active": true}', - "{}", - '{"items": [1, 2, 3]}', - ] - s = bpd.Series(json_data, dtype=dtypes.JSON_DTYPE) - actual = bbq.json_keys(s) - - expected_data_pandas = [ - ["age", "name"], - [ - "active", - "city", - "country", - ], - [], - ["items"], - ] - expected = bpd.Series( - expected_data_pandas, dtype=pd.ArrowDtype(pa.list_(pa.string())) - ) - pd.testing.assert_series_equal(actual.to_pandas(), expected.to_pandas()) - - -def test_json_keys_with_max_depth(): - json_data = [ - '{"user": {"name": "Bob", "details": {"id": 123, "status": "approved"}}}', - '{"user": {"name": "Charlie"}}', - ] - s = bpd.Series(json_data, dtype=dtypes.JSON_DTYPE) - actual = bbq.json_keys(s, max_depth=2) - - expected_data_pandas = [ - ["user", "user.details", "user.name"], - ["user", "user.name"], - ] - expected = bpd.Series( - expected_data_pandas, dtype=pd.ArrowDtype(pa.list_(pa.string())) - ) - pd.testing.assert_series_equal(actual.to_pandas(), expected.to_pandas()) - - -def test_json_keys_from_string_error(): - s = bpd.Series(['{"a": 1, "b": 2}', '{"c": 3}']) - with pytest.raises(TypeError): - bbq.json_keys(s) diff --git a/tests/system/small/bigquery/test_mathematical.py b/tests/system/small/bigquery/test_mathematical.py deleted file mode 100644 index 66aef96e57d..00000000000 --- a/tests/system/small/bigquery/test_mathematical.py +++ /dev/null @@ -1,37 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import bigframes.bigquery as bbq - - -def test_rand(scalars_df_index): - df = scalars_df_index - - # Apply rand - df = df.assign(random=bbq.rand()) - result = df["random"] - - # Eagerly evaluate - result_pd = result.to_pandas() - - # Check length - assert len(result_pd) == len(df) - - # Check values in [0, 1) - assert (result_pd >= 0).all() - assert (result_pd < 1).all() - - # Check not all values are equal (unlikely collision for random) - if len(result_pd) > 1: - assert result_pd.nunique() > 1 diff --git a/tests/system/small/bigquery/test_sql.py b/tests/system/small/bigquery/test_sql.py deleted file mode 100644 index c0f7eed938e..00000000000 --- a/tests/system/small/bigquery/test_sql.py +++ /dev/null @@ -1,177 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import pytest - -import bigframes.bigquery as bbq -import bigframes.dtypes as dtypes -import bigframes.pandas as bpd -import bigframes.testing.utils - - -def test_sql_scalar_for_all_scalar_types(scalars_df_null_index): - series = bbq.sql_scalar( - """ - CAST({0} AS INT64) - + BYTE_LENGTH({1}) - + UNIX_DATE({2}) - + EXTRACT(YEAR FROM {3}) - + ST_NUMPOINTS({4}) - + LEAST( - {5}, - CAST({6} AS INT64), - CAST({7} AS INT64) - ) + CHAR_LENGTH({8}) - + EXTRACT(SECOND FROM {9}) - + UNIX_SECONDS({10}) - """, - columns=[ - # Try to include all scalar types in a single test. - scalars_df_null_index["bool_col"], - scalars_df_null_index["bytes_col"], - scalars_df_null_index["date_col"], - scalars_df_null_index["datetime_col"], - scalars_df_null_index["geography_col"], - scalars_df_null_index["int64_col"], - scalars_df_null_index["numeric_col"], - scalars_df_null_index["float64_col"], - scalars_df_null_index["string_col"], - scalars_df_null_index["time_col"], - scalars_df_null_index["timestamp_col"], - ], - ) - result = series.to_pandas() - assert len(result) == len(scalars_df_null_index) - - -def test_sql_scalar_for_bool_series(scalars_df_index): - series: bpd.Series = scalars_df_index["bool_col"] - result = bbq.sql_scalar("CAST({0} AS INT64)", [series]) - expected = series.astype(dtypes.INT_DTYPE) - expected.name = result.name - bigframes.testing.utils.assert_series_equal( - result.to_pandas(), expected.to_pandas() - ) - - -@pytest.mark.parametrize( - ("column_name"), - [ - pytest.param("bool_col"), - pytest.param("bytes_col"), - pytest.param("date_col"), - pytest.param("datetime_col"), - pytest.param("geography_col"), - pytest.param("int64_col"), - pytest.param("numeric_col"), - pytest.param("float64_col"), - pytest.param("string_col"), - pytest.param("time_col"), - pytest.param("timestamp_col"), - ], -) -def test_sql_scalar_outputs_all_scalar_types(scalars_df_index, column_name): - series: bpd.Series = scalars_df_index[column_name] - result = bbq.sql_scalar("{0}", [series]) - expected = series - expected.name = result.name - bigframes.testing.utils.assert_series_equal( - result.to_pandas(), expected.to_pandas() - ) - - -def test_sql_scalar_for_array_series(repeated_df): - result = bbq.sql_scalar( - """ - ARRAY_LENGTH({0}) + ARRAY_LENGTH({1}) + ARRAY_LENGTH({2}) - + ARRAY_LENGTH({3}) + ARRAY_LENGTH({4}) + ARRAY_LENGTH({5}) - + ARRAY_LENGTH({6}) - """, - [ - repeated_df["int_list_col"], - repeated_df["bool_list_col"], - repeated_df["float_list_col"], - repeated_df["date_list_col"], - repeated_df["date_time_list_col"], - repeated_df["numeric_list_col"], - repeated_df["string_list_col"], - ], - ) - - expected = ( - repeated_df["int_list_col"].list.len() - + repeated_df["bool_list_col"].list.len() - + repeated_df["float_list_col"].list.len() - + repeated_df["date_list_col"].list.len() - + repeated_df["date_time_list_col"].list.len() - + repeated_df["numeric_list_col"].list.len() - + repeated_df["string_list_col"].list.len() - ) - bigframes.testing.utils.assert_series_equal( - result.to_pandas(), expected.to_pandas() - ) - - -def test_sql_scalar_outputs_array_series(repeated_df): - result = bbq.sql_scalar("{0}", [repeated_df["int_list_col"]]) - expected = repeated_df["int_list_col"] - expected.name = result.name - bigframes.testing.utils.assert_series_equal( - result.to_pandas(), expected.to_pandas() - ) - - -def test_sql_scalar_for_struct_series(nested_structs_df): - result = bbq.sql_scalar( - "CHAR_LENGTH({0}.name) + {0}.age", - [nested_structs_df["person"]], - ) - expected = nested_structs_df["person"].struct.field( - "name" - ).str.len() + nested_structs_df["person"].struct.field("age") - bigframes.testing.utils.assert_series_equal( - result.to_pandas(), expected.to_pandas() - ) - - -def test_sql_scalar_outputs_struct_series(nested_structs_df): - result = bbq.sql_scalar("{0}", [nested_structs_df["person"]]) - expected = nested_structs_df["person"] - expected.name = result.name - bigframes.testing.utils.assert_series_equal( - result.to_pandas(), expected.to_pandas() - ) - - -def test_sql_scalar_for_json_series(json_df): - result = bbq.sql_scalar( - """JSON_VALUE({0}, '$.int_value')""", - [ - json_df["json_col"], - ], - ) - expected = bbq.json_value(json_df["json_col"], "$.int_value") - expected.name = result.name - bigframes.testing.utils.assert_series_equal( - result.to_pandas(), expected.to_pandas() - ) - - -def test_sql_scalar_outputs_json_series(json_df): - result = bbq.sql_scalar("{0}", [json_df["json_col"]]) - expected = json_df["json_col"] - expected.name = result.name - bigframes.testing.utils.assert_series_equal( - result.to_pandas(), expected.to_pandas() - ) diff --git a/tests/system/small/bigquery/test_struct.py b/tests/system/small/bigquery/test_struct.py deleted file mode 100644 index 85404969605..00000000000 --- a/tests/system/small/bigquery/test_struct.py +++ /dev/null @@ -1,62 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import pytest - -import bigframes.bigquery as bbq -import bigframes.series as series -import bigframes.testing.utils - - -@pytest.mark.parametrize( - "columns_arg", - [ - [ - {"version": 1, "project": "pandas"}, - {"version": 2, "project": "pandas"}, - {"version": 1, "project": "numpy"}, - ], - [ - {"version": 1, "project": "pandas"}, - {"version": None, "project": "pandas"}, - {"version": 1, "project": "numpy"}, - ], - [ - {"array": [6, 4, 6], "project": "pandas"}, - {"array": [6, 4, 7, 6], "project": "pandas"}, - {"array": [7, 2, 3], "project": "numpy"}, - ], - [ - {"array": [6, 4, 6], "project": "pandas"}, - {"array": [6, 4, 7, 6], "project": "pandas"}, - {"array": [7, 2, 3], "project": "numpy"}, - ], - [ - {"struct": [{"x": 2, "y": 4}], "project": "pandas"}, - {"struct": [{"x": 9, "y": 3}], "project": "pandas"}, - {"struct": [{"x": 1, "y": 2}], "project": "numpy"}, - ], - ], -) -def test_struct_from_dataframe(columns_arg): - srs = series.Series( - columns_arg, - ) - bigframes.testing.utils.assert_series_equal( - srs.to_pandas(), - bbq.struct(srs.struct.explode()).to_pandas(), - check_index_type=False, - check_dtype=False, - check_names=False, # None vs nan version dependent - ) diff --git a/tests/system/small/bigquery/test_vector_search.py b/tests/system/small/bigquery/test_vector_search.py deleted file mode 100644 index b8ad4c0df22..00000000000 --- a/tests/system/small/bigquery/test_vector_search.py +++ /dev/null @@ -1,221 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import random -from typing import Any, Dict, Iterable, cast - -import google.cloud.bigquery -import numpy as np -import pandas as pd -import pyarrow -import pytest - -import bigframes.bigquery as bbq -import bigframes.pandas as bpd -from bigframes.testing.utils import assert_frame_equal - -# Need at least 5,000 rows to create a vector index. -VECTOR_DF = pd.DataFrame( - { - "rowid": np.arange(9_999), - # 3D values, clustered around the three unit vector axes. - "my_embedding": pd.Series( - [ - [ - 1 + (random.random() - 0.5) if (row % 3) == 0 else 0, - 1 + (random.random() - 0.5) if (row % 3) == 1 else 0, - 1 + (random.random() - 0.5) if (row % 3) == 2 else 0, - ] - for row in range(9_999) - ], - dtype=pd.ArrowDtype(pyarrow.list_(pyarrow.float64())), - ), - # Three groups of animal, vegetable, and mineral, corresponding to - # the embeddings above. - "mystery_word": [ - "aarvark", - "broccoli", - "calcium", - "dog", - "eggplant", - "ferrite", - "gopher", - "huckleberry", - "ice", - ] - * 1_111, - }, -) - - -@pytest.fixture -def vector_table_id( - bigquery_client: google.cloud.bigquery.Client, - # Use non-US location to ensure location autodetection works. - table_id_not_created: str, -): - table = google.cloud.bigquery.Table( - table_id_not_created, - [ - {"name": "rowid", "type": "INT64"}, - {"name": "my_embedding", "type": "FLOAT64", "mode": "REPEATED"}, - {"name": "mystery_word", "type": "STRING"}, - ], - ) - bigquery_client.create_table(table) - bigquery_client.load_table_from_json( - cast(Iterable[Dict[str, Any]], VECTOR_DF.to_dict(orient="records")), - table_id_not_created, - ).result() - yield table_id_not_created - bigquery_client.delete_table(table_id_not_created, not_found_ok=True) - - -def test_create_vector_index_ivf( - session, vector_table_id: str, bigquery_client: google.cloud.bigquery.Client -): - bbq.create_vector_index( - vector_table_id, - "my_embedding", - distance_type="cosine", - stored_column_names=["mystery_word"], - index_type="ivf", - ivf_options={"num_lists": 3}, - session=session, - ) - - # Check that the index was created successfully. - project_id, dataset_id, table_name = vector_table_id.split(".") - indexes = bigquery_client.query_and_wait( - f""" - SELECT index_catalog, index_schema, table_name, index_name, index_column_name - FROM `{project_id}`.`{dataset_id}`.INFORMATION_SCHEMA.VECTOR_INDEX_COLUMNS - WHERE table_name = '{table_name}'; - """ - ).to_dataframe() - - # There should only be one vector index. - assert len(indexes.index) == 1 - assert indexes["index_catalog"].iloc[0] == project_id - assert indexes["index_schema"].iloc[0] == dataset_id - assert indexes["table_name"].iloc[0] == table_name - assert indexes["index_column_name"].iloc[0] == "my_embedding" - - # If no name is specified, use the table name as the index name - assert indexes["index_name"].iloc[0] == table_name - - -def test_vector_search_basic_params_with_df(): - search_query = bpd.DataFrame( - { - "query_id": ["dog", "cat"], - "embedding": [[1.0, 2.0], [3.0, 5.2]], - } - ) - vector_search_result = ( - bbq.vector_search( - base_table="bigframes-dev.bigframes_tests_sys.base_table", - column_to_search="my_embedding", - query=search_query, - top_k=2, - ) - .sort_values("distance") - .sort_index() - .to_pandas() - ) # type:ignore - expected = pd.DataFrame( - { - "query_id": ["cat", "dog", "dog", "cat"], - "embedding": [ - np.array([3.0, 5.2]), - np.array([1.0, 2.0]), - np.array([1.0, 2.0]), - np.array([3.0, 5.2]), - ], - "id": [5, 1, 4, 2], - "my_embedding": [ - np.array([5.0, 5.4]), - np.array([1.0, 2.0]), - np.array([1.0, 3.2]), - np.array([2.0, 4.0]), - ], - "distance": [2.009975, 0.0, 1.2, 1.56205], - }, - index=pd.Index([1, 0, 0, 1], dtype="Int64"), - ) - assert_frame_equal( - expected.sort_values("id"), - vector_search_result.sort_values("id"), - check_dtype=False, - rtol=0.1, - ) - - -def test_vector_search_different_params_with_query(session): - base_df = bpd.DataFrame( - { - "id": [1, 2, 3, 4], - "my_embedding": [ - np.array([0.0, 1.0]), - np.array([1.0, 0.0]), - np.array([0.0, -1.0]), - np.array([-1.0, 0.0]), - ], - }, - session=session, - ) - base_table = base_df.to_gbq() - try: - search_query = bpd.Series([[0.75, 0.25], [-0.25, -0.75]], session=session) - vector_search_result = ( - bbq.vector_search( - base_table=base_table, - column_to_search="my_embedding", - query=search_query, - distance_type="cosine", - top_k=2, - ) - .sort_values("distance") - .sort_index() - .to_pandas() - ) # type:ignore - expected = pd.DataFrame( - { - "0": [ - [0.75, 0.25], - [0.75, 0.25], - [-0.25, -0.75], - [-0.25, -0.75], - ], - "id": [2, 1, 3, 4], - "my_embedding": [ - [1.0, 0.0], - [0.0, 1.0], - [0.0, -1.0], - [-1.0, 0.0], - ], - "distance": [ - 0.051317, - 0.683772, - 0.051317, - 0.683772, - ], - }, - index=pd.Index([0, 0, 1, 1], dtype="Int64"), - ) - pd.testing.assert_frame_equal( - vector_search_result, expected, check_dtype=False, rtol=0.1 - ) - finally: - session.bqclient.delete_table(base_table, not_found_ok=True) diff --git a/tests/system/small/blob/test_properties.py b/tests/system/small/blob/test_properties.py deleted file mode 100644 index c3597b37116..00000000000 --- a/tests/system/small/blob/test_properties.py +++ /dev/null @@ -1,132 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import pandas as pd -import pytest - -import bigframes.bigquery as bbq -import bigframes.dtypes as dtypes -import bigframes.pandas as bpd - -pytest.skip("Skipping blob tests due to b/481790217", allow_module_level=True) - - -def test_blob_uri(images_uris: list[str], images_mm_df: bpd.DataFrame): - actual = images_mm_df["blob_col"].struct.field("uri").to_pandas() - expected = pd.Series(images_uris, name="uri") - - pd.testing.assert_series_equal( - actual, expected, check_dtype=False, check_index_type=False - ) - - -def test_blob_authorizer(images_mm_df: bpd.DataFrame, bq_connection: str): - actual = images_mm_df["blob_col"].struct.field("authorizer").to_pandas() - expected = pd.Series( - [bq_connection.casefold(), bq_connection.casefold()], name="authorizer" - ) - - pd.testing.assert_series_equal( - actual, expected, check_dtype=False, check_index_type=False - ) - - -def test_blob_version(images_mm_df: bpd.DataFrame): - actual = bbq.json_value( - images_mm_df["blob_col"].struct.field("details"), "$.version" - ).to_pandas() - expected = pd.Series(["1753907851152593", "1753907851111538"], name="version") - - pd.testing.assert_series_equal( - actual, expected, check_dtype=False, check_index_type=False - ) - - -def test_blob_metadata(images_mm_df: bpd.DataFrame): - actual = images_mm_df["blob_col"].struct.field("details").to_pandas() - expected = pd.Series( - [ - ( - '{"content_type":"image/jpeg",' - '"md5_hash":"e130ad042261a1883cd2cc06831cf748",' - '"size":338390,' - '"updated":1753907851000000}' - ), - ( - '{"content_type":"image/jpeg",' - '"md5_hash":"e2ae3191ff2b809fd0935f01a537c650",' - '"size":43333,' - '"updated":1753907851000000}' - ), - ], - name="metadata", - dtype=dtypes.JSON_DTYPE, - ) - expected.index = expected.index.astype(dtypes.INT_DTYPE) - pd.testing.assert_series_equal(actual, expected) - - -def test_blob_content_type(images_mm_df: bpd.DataFrame): - actual = bbq.json_value( - images_mm_df["blob_col"].struct.field("details"), "$.content_type" - ).to_pandas() - expected = pd.Series(["image/jpeg", "image/jpeg"], name="content_type") - - pd.testing.assert_series_equal( - actual, expected, check_dtype=False, check_index_type=False - ) - - -def test_blob_md5_hash(images_mm_df: bpd.DataFrame): - actual = bbq.json_value( - images_mm_df["blob_col"].struct.field("details"), "$.md5_hash" - ).to_pandas() - expected = pd.Series( - ["e130ad042261a1883cd2cc06831cf748", "e2ae3191ff2b809fd0935f01a537c650"], - name="md5_hash", - ) - - pd.testing.assert_series_equal( - actual, expected, check_dtype=False, check_index_type=False - ) - - -def test_blob_size(images_mm_df: bpd.DataFrame): - actual = ( - bbq.json_value(images_mm_df["blob_col"].struct.field("details"), "$.size") - .astype("Int64") - .to_pandas() - ) - expected = pd.Series([338390, 43333], name="size") - - pd.testing.assert_series_equal( - actual, expected, check_dtype=False, check_index_type=False - ) - - -def test_blob_updated(images_mm_df: bpd.DataFrame): - actual = bbq.json_value( - images_mm_df["blob_col"].struct.field("details"), "$.updated" - ).to_pandas() - expected = pd.Series( - [ - pd.Timestamp("2025-07-30 20:37:31", tz="UTC"), - pd.Timestamp("2025-07-30 20:37:31", tz="UTC"), - ], - name="updated", - ) - - pd.testing.assert_series_equal( - actual, expected, check_dtype=False, check_index_type=False - ) diff --git a/tests/system/small/core/__init__.py b/tests/system/small/core/__init__.py deleted file mode 100644 index 6d5e14bcf4a..00000000000 --- a/tests/system/small/core/__init__.py +++ /dev/null @@ -1,13 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. diff --git a/tests/system/small/core/indexes/__init__.py b/tests/system/small/core/indexes/__init__.py deleted file mode 100644 index 0a2669d7a25..00000000000 --- a/tests/system/small/core/indexes/__init__.py +++ /dev/null @@ -1,13 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. diff --git a/tests/system/small/core/indexes/test_base.py b/tests/system/small/core/indexes/test_base.py deleted file mode 100644 index 3225f643299..00000000000 --- a/tests/system/small/core/indexes/test_base.py +++ /dev/null @@ -1,35 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import pandas as pd -import pandas.testing -import pytest -from packaging import version - - -@pytest.mark.parametrize("level", [None, 0, 1, "level0", "level1"]) -def test_unique(session, level): - if version.Version(pd.__version__) < version.Version("2.0.0"): - pytest.skip("StringDtype for multi-index not supported until Pandas 2.0") - arrays = [ - pd.Series(["A", "A", "B", "B", "A"], dtype=pd.StringDtype(storage="pyarrow")), - pd.Series([1, 2, 1, 2, 1], dtype=pd.Int64Dtype()), - ] - pd_idx = pd.MultiIndex.from_arrays(arrays, names=["level0", "level1"]) - bf_idx = session.read_pandas(pd_idx) - - actual_result = bf_idx.unique(level).to_pandas() - - expected_result = pd_idx.unique(level) - pandas.testing.assert_index_equal(actual_result, expected_result) diff --git a/tests/system/small/core/indexes/test_datetimes.py b/tests/system/small/core/indexes/test_datetimes.py deleted file mode 100644 index 40ce310b313..00000000000 --- a/tests/system/small/core/indexes/test_datetimes.py +++ /dev/null @@ -1,46 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -import pandas -import pandas.testing -import pytest - - -@pytest.fixture(scope="module") -def datetime_indexes(session): - pd_index = pandas.date_range("2024-12-25", periods=10, freq="d") - bf_index = session.read_pandas(pd_index) - - return bf_index, pd_index - - -@pytest.mark.parametrize( - "access", - [ - pytest.param(lambda x: x.year, id="year"), - pytest.param(lambda x: x.month, id="month"), - pytest.param(lambda x: x.day, id="day"), - pytest.param(lambda x: x.dayofweek, id="dayofweek"), - pytest.param(lambda x: x.day_of_week, id="day_of_week"), - pytest.param(lambda x: x.weekday, id="weekday"), - ], -) -def test_datetime_index_properties(datetime_indexes, access): - bf_index, pd_index = datetime_indexes - - actual_result = access(bf_index).to_pandas() - - expected_result = access(pd_index).astype(pandas.Int64Dtype()) - pandas.testing.assert_index_equal(actual_result, expected_result) diff --git a/tests/system/small/core/logging/__init__.py b/tests/system/small/core/logging/__init__.py deleted file mode 100644 index 58d482ea386..00000000000 --- a/tests/system/small/core/logging/__init__.py +++ /dev/null @@ -1,13 +0,0 @@ -# Copyright 2026 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. diff --git a/tests/system/small/core/logging/test_data_types.py b/tests/system/small/core/logging/test_data_types.py deleted file mode 100644 index d69e17cfff8..00000000000 --- a/tests/system/small/core/logging/test_data_types.py +++ /dev/null @@ -1,113 +0,0 @@ -# Copyright 2026 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from typing import Sequence - -import pandas as pd -import pyarrow as pa - -import bigframes.pandas as bpd -from bigframes import dtypes -from bigframes.core.logging import data_types - - -def encode_types(inputs: Sequence[dtypes.Dtype]) -> str: - encoded_val = 0 - for t in inputs: - encoded_val = encoded_val | data_types._get_dtype_mask(t) - - return f"{encoded_val:x}" - - -def test_get_type_refs_no_op(scalars_df_index): - node = scalars_df_index._block._expr.node - expected_types: list[dtypes.Dtype] = [] - - assert data_types.encode_type_refs(node) == encode_types(expected_types) - - -def test_get_type_refs_projection(scalars_df_index): - node = ( - scalars_df_index["datetime_col"] - scalars_df_index["datetime_col"] - )._block._expr.node - expected_types = [dtypes.DATETIME_DTYPE, dtypes.TIMEDELTA_DTYPE] - - assert data_types.encode_type_refs(node) == encode_types(expected_types) - - -def test_get_type_refs_filter(scalars_df_index): - node = scalars_df_index[scalars_df_index["int64_col"] > 0]._block._expr.node - expected_types = [dtypes.INT_DTYPE, dtypes.BOOL_DTYPE] - - assert data_types.encode_type_refs(node) == encode_types(expected_types) - - -def test_get_type_refs_order_by(scalars_df_index): - node = scalars_df_index.sort_index()._block._expr.node - expected_types = [dtypes.INT_DTYPE] - - assert data_types.encode_type_refs(node) == encode_types(expected_types) - - -def test_get_type_refs_join(scalars_df_index): - node = ( - scalars_df_index[["int64_col"]].merge( - scalars_df_index[["float64_col"]], - left_on="int64_col", - right_on="float64_col", - ) - )._block._expr.node - expected_types = [dtypes.INT_DTYPE, dtypes.FLOAT_DTYPE] - - assert data_types.encode_type_refs(node) == encode_types(expected_types) - - -def test_get_type_refs_isin(scalars_df_index): - node = scalars_df_index["string_col"].isin(["a"])._block._expr.node - expected_types = [dtypes.STRING_DTYPE, dtypes.BOOL_DTYPE] - - assert data_types.encode_type_refs(node) == encode_types(expected_types) - - -def test_get_type_refs_agg(scalars_df_index): - node = scalars_df_index[["bool_col", "string_col"]].count()._block._expr.node - expected_types = [ - dtypes.INT_DTYPE, - dtypes.BOOL_DTYPE, - dtypes.STRING_DTYPE, - dtypes.FLOAT_DTYPE, - ] - - assert data_types.encode_type_refs(node) == encode_types(expected_types) - - -def test_get_type_refs_window(scalars_df_index): - node = ( - scalars_df_index[["string_col", "bool_col"]] - .groupby("string_col") - .rolling(window=3) - .count() - ._block._expr.node - ) - expected_types = [dtypes.STRING_DTYPE, dtypes.BOOL_DTYPE, dtypes.INT_DTYPE] - - assert data_types.encode_type_refs(node) == encode_types(expected_types) - - -def test_get_type_refs_explode(): - df = bpd.DataFrame({"A": ["a", "b"], "B": [[1, 2], [3, 4, 5]]}) - node = df.explode("B")._block._expr.node - expected_types = [pd.ArrowDtype(pa.list_(pa.int64()))] - - assert data_types.encode_type_refs(node) == encode_types(expected_types) diff --git a/tests/system/small/core/test_convert.py b/tests/system/small/core/test_convert.py deleted file mode 100644 index f63f945ad24..00000000000 --- a/tests/system/small/core/test_convert.py +++ /dev/null @@ -1,62 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -import pandas as pd -import pytest -from pandas import testing - -from bigframes import dataframe -from bigframes.core import convert - - -@pytest.mark.parametrize( - ("input", "expected"), - [ - pytest.param(pd.Series([1, 2, 3], name="test"), True, id="pd.Series"), - pytest.param(pd.Index([1, 2, 3], name="test"), True, id="pd.Index"), - pytest.param(pd.DataFrame({"test": [1, 2, 3]}), True, id="pd.DataFrame"), - pytest.param("something", False, id="string"), - ], -) -def test_can_convert_to_dataframe(input, expected): - assert convert.can_convert_to_dataframe(input) is expected - - -@pytest.mark.parametrize( - "input", - [ - pytest.param(pd.Series([1, 2, 3], name="test"), id="pd.Series"), - pytest.param(pd.Index([1, 2, 3], name="test"), id="pd.Index"), - pytest.param(pd.DataFrame({"test": [1, 2, 3]}), id="pd.DataFrame"), - ], -) -def test_to_bf_dataframe(input, session): - result = convert.to_bf_dataframe(input, None, session) - - testing.assert_frame_equal( - result.to_pandas(), - pd.DataFrame({"test": [1, 2, 3]}), - check_dtype=False, - check_index_type=False, - ) - - -def test_to_bf_dataframe_with_bf_dataframe(session): - bf = dataframe.DataFrame({"test": [1, 2, 3]}, session=session) - - testing.assert_frame_equal( - convert.to_bf_dataframe(bf, None, session).to_pandas(), - bf.to_pandas(), - ) diff --git a/tests/system/small/core/test_indexers.py b/tests/system/small/core/test_indexers.py deleted file mode 100644 index 20f1c561853..00000000000 --- a/tests/system/small/core/test_indexers.py +++ /dev/null @@ -1,78 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import warnings - -import pyarrow as pa -import pytest - -import bigframes.exceptions -import bigframes.pandas as bpd - - -@pytest.fixture(scope="module") -def string_indexed_struct_series(session): - return bpd.Series( - [ - {"project": "pandas", "version": 1}, - ], - dtype=bpd.ArrowDtype( - pa.struct([("project", pa.string()), ("version", pa.int64())]) - ), - index=["a"], - session=session, - ) - - -@pytest.fixture(scope="module") -def number_series(session): - return bpd.Series( - [0], - dtype=bpd.Int64Dtype, - session=session, - ) - - -@pytest.fixture(scope="module") -def string_indexed_number_series(session): - return bpd.Series( - [0], - dtype=bpd.Int64Dtype, - index=["a"], - session=session, - ) - - -@pytest.mark.parametrize( - "series", - [ - "string_indexed_struct_series", - "string_indexed_number_series", - ], -) -@pytest.mark.parametrize( - "key", - [ - 0, - "a", - ], -) -def test_struct_series_indexers_should_not_warn(request, series, key): - s = request.getfixturevalue(series) - - with warnings.catch_warnings(): - warnings.simplefilter( - "error", category=bigframes.exceptions.BadIndexerKeyWarning - ) - s[key] diff --git a/tests/system/small/core/test_reshape.py b/tests/system/small/core/test_reshape.py deleted file mode 100644 index aba9bf01859..00000000000 --- a/tests/system/small/core/test_reshape.py +++ /dev/null @@ -1,120 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import pandas as pd -import pytest - -import bigframes.testing.utils -from bigframes import session -from bigframes.core.reshape import merge - - -@pytest.mark.parametrize( - ("left_on", "right_on", "left_index", "right_index"), - [ - ("col_a", None, False, True), - (None, "col_d", True, False), - (None, None, True, True), - ], -) -@pytest.mark.parametrize("how", ["inner", "left", "right", "outer"]) -def test_join_with_index( - session: session.Session, left_on, right_on, left_index, right_index, how -): - df1 = pd.DataFrame({"col_a": [1, 2, 3], "col_b": [2, 3, 4]}, index=[1, 2, 3]) - bf1 = session.read_pandas(df1) - df2 = pd.DataFrame({"col_c": [1, 2, 3], "col_d": [2, 3, 4]}, index=[2, 3, 4]) - bf2 = session.read_pandas(df2) - - bf_result = merge.merge( - bf1, - bf2, - left_on=left_on, - right_on=right_on, - left_index=left_index, - right_index=right_index, - how=how, - ).to_pandas() - pd_result = pd.merge( - df1, - df2, - left_on=left_on, - right_on=right_on, - left_index=left_index, - right_index=right_index, - how=how, - ) - - bigframes.testing.utils.assert_frame_equal( - bf_result, pd_result, check_dtype=False, check_index_type=False - ) - - -@pytest.mark.parametrize( - ("on", "left_on", "right_on", "left_index", "right_index"), - [ - (None, "col_a", None, True, False), - (None, None, "col_c", None, True), - ("col_a", None, None, True, True), - ], -) -def test_join_with_index_invalid_index_arg_raise_error( - session: session.Session, on, left_on, right_on, left_index, right_index -): - df1 = pd.DataFrame({"col_a": [1, 2, 3], "col_b": [2, 3, 4]}, index=[1, 2, 3]) - bf1 = session.read_pandas(df1) - df2 = pd.DataFrame({"col_c": [1, 2, 3], "col_d": [2, 3, 4]}, index=[2, 3, 4]) - bf2 = session.read_pandas(df2) - - with pytest.raises(ValueError): - merge.merge( - bf1, - bf2, - on=on, - left_on=left_on, - right_on=right_on, - left_index=left_index, - right_index=right_index, - ).to_pandas() - - -@pytest.mark.parametrize( - ("left_on", "right_on", "left_index", "right_index"), - [ - (["col_a", "col_b"], None, False, True), - (None, ["col_c", "col_d"], True, False), - (None, None, True, True), - ], -) -@pytest.mark.parametrize("how", ["inner", "left", "right", "outer"]) -def test_join_with_multiindex_raises_error( - session: session.Session, left_on, right_on, left_index, right_index, how -): - multi_idx1 = pd.MultiIndex.from_tuples([(1, 2), (2, 3), (3, 5)]) - df1 = pd.DataFrame({"col_a": [1, 2, 3], "col_b": [2, 3, 4]}, index=multi_idx1) - bf1 = session.read_pandas(df1) - multi_idx2 = pd.MultiIndex.from_tuples([(1, 2), (2, 3), (3, 2)]) - df2 = pd.DataFrame({"col_c": [1, 2, 3], "col_d": [2, 3, 4]}, index=multi_idx2) - bf2 = session.read_pandas(df2) - - with pytest.raises(ValueError): - merge.merge( - bf1, - bf2, - left_on=left_on, - right_on=right_on, - left_index=left_index, - right_index=right_index, - how=how, - ) diff --git a/tests/system/small/engines/__init__.py b/tests/system/small/engines/__init__.py deleted file mode 100644 index 0a2669d7a25..00000000000 --- a/tests/system/small/engines/__init__.py +++ /dev/null @@ -1,13 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. diff --git a/tests/system/small/engines/conftest.py b/tests/system/small/engines/conftest.py deleted file mode 100644 index 823ba9806d5..00000000000 --- a/tests/system/small/engines/conftest.py +++ /dev/null @@ -1,135 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -import pathlib -from typing import Generator - -import google.cloud.bigquery_storage_v1 -import pandas as pd -import pytest -from google.cloud import bigquery - -import bigframes -from bigframes.core import ArrayValue, events, local_data -from bigframes.session import ( - direct_gbq_execution, - local_scan_executor, - polars_executor, - semi_executor, -) - -CURRENT_DIR = pathlib.Path(__file__).parent -DATA_DIR = CURRENT_DIR.parent.parent.parent / "data" - - -@pytest.fixture(scope="module") -def fake_session() -> Generator[bigframes.Session, None, None]: - import bigframes.core.global_session - - # its a "polars session", but we are bypassing session-provided execution - # we just want a minimal placeholder session without expensive setup - from bigframes.testing import polars_session - - session = polars_session.TestSession() - with bigframes.core.global_session._GlobalSessionContext(session): - yield session - - -@pytest.fixture(scope="session") -def pyarrow_engine(): - return local_scan_executor.LocalScanExecutor() - - -@pytest.fixture(scope="session") -def polars_engine(): - return polars_executor.PolarsExecutor() - - -@pytest.fixture(scope="session") -def bq_engine( - bigquery_client: bigquery.Client, - bigquery_storage_read_client: google.cloud.bigquery_storage_v1.BigQueryReadClient, -): - return direct_gbq_execution.DirectGbqExecutor( - bigquery_client, - bqstoragereadclient=bigquery_storage_read_client, - publisher=events.Publisher(), - compiler="ibis", - ) - - -@pytest.fixture(scope="session") -def sqlglot_engine( - bigquery_client: bigquery.Client, - bigquery_storage_read_client: google.cloud.bigquery_storage_v1.BigQueryReadClient, -) -> semi_executor.SemiExecutor: - return direct_gbq_execution.DirectGbqExecutor( - bigquery_client, - bqstoragereadclient=bigquery_storage_read_client, - publisher=events.Publisher(), - ) - - -@pytest.fixture(scope="session", params=["pyarrow", "polars", "bq", "bq-sqlglot"]) -def engine( - request, pyarrow_engine, polars_engine, bq_engine, sqlglot_engine -) -> semi_executor.SemiExecutor: - if request.param == "pyarrow": - return pyarrow_engine - if request.param == "polars": - return polars_engine - if request.param == "bq": - return bq_engine - if request.param == "bq-sqlglot": - return sqlglot_engine - raise ValueError(f"Unrecognized param: {request.param}") - - -@pytest.fixture(scope="module") -def managed_data_source( - scalars_pandas_df_index: pd.DataFrame, -) -> local_data.ManagedArrowTable: - return local_data.ManagedArrowTable.from_pandas(scalars_pandas_df_index) - - -@pytest.fixture(scope="module") -def scalars_array_value( - managed_data_source: local_data.ManagedArrowTable, fake_session: bigframes.Session -): - return ArrayValue.from_managed(managed_data_source, fake_session) - - -@pytest.fixture(scope="module") -def zero_row_source() -> local_data.ManagedArrowTable: - return local_data.ManagedArrowTable.from_pandas(pd.DataFrame({"a": [], "b": []})) - - -@pytest.fixture(scope="module") -def nested_data_source( - nested_pandas_df: pd.DataFrame, -) -> local_data.ManagedArrowTable: - return local_data.ManagedArrowTable.from_pandas(nested_pandas_df) - - -@pytest.fixture(scope="module") -def repeated_data_source( - repeated_pandas_df: pd.DataFrame, -) -> local_data.ManagedArrowTable: - return local_data.ManagedArrowTable.from_pandas(repeated_pandas_df) - - -@pytest.fixture(scope="module") -def arrays_array_value( - repeated_data_source: local_data.ManagedArrowTable, fake_session: bigframes.Session -): - return ArrayValue.from_managed(repeated_data_source, fake_session) diff --git a/tests/system/small/engines/test_aggregation.py b/tests/system/small/engines/test_aggregation.py deleted file mode 100644 index 669eae9ebf7..00000000000 --- a/tests/system/small/engines/test_aggregation.py +++ /dev/null @@ -1,171 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import pytest -from google.cloud import bigquery - -import bigframes.operations.aggregations as agg_ops -from bigframes.core import ( - agg_expressions, - array_value, - expression, - identifiers, - nodes, -) -from bigframes.session import polars_executor -from bigframes.testing.engine_utils import assert_equivalence_execution - -pytest.importorskip("polars") - -# Polars used as reference as its fast and local. Generally though, prefer gbq engine where they disagree. -REFERENCE_ENGINE = polars_executor.PolarsExecutor() - - -def apply_agg_to_all_valid( - array: array_value.ArrayValue, op: agg_ops.UnaryAggregateOp, excluded_cols=[] -) -> array_value.ArrayValue: - """ - Apply the aggregation to every column in the array that has a compatible datatype. - """ - exprs_by_name = [] - for arg in array.column_ids: - if arg in excluded_cols: - continue - try: - _ = op.output_type(array.get_column_type(arg)) - expr = agg_expressions.UnaryAggregation(op, expression.deref(arg)) - name = f"{arg}-{op.name}" - exprs_by_name.append((expr, name)) - except TypeError: - continue - assert len(exprs_by_name) > 0 - new_arr = array.aggregate(exprs_by_name) - return new_arr - - -@pytest.mark.parametrize("engine", ["polars", "bq", "bq-sqlglot"], indirect=True) -def test_engines_aggregate_post_filter_size( - scalars_array_value: array_value.ArrayValue, - engine, -): - w_offsets, offsets_id = ( - scalars_array_value.select_columns(("bool_col", "string_col")) - .filter(expression.deref("bool_col")) - .promote_offsets() - ) - plan = ( - w_offsets.select_columns((offsets_id, "bool_col", "string_col")) - .row_count() - .node - ) - - assert_equivalence_execution(plan, REFERENCE_ENGINE, engine) - - -@pytest.mark.parametrize("engine", ["polars", "bq", "bq-sqlglot"], indirect=True) -def test_engines_aggregate_size( - scalars_array_value: array_value.ArrayValue, - engine, -): - node = nodes.AggregateNode( - scalars_array_value.node, - aggregations=( - ( - agg_expressions.NullaryAggregation(agg_ops.SizeOp()), - identifiers.ColumnId("size_op"), - ), - ( - agg_expressions.UnaryAggregation( - agg_ops.SizeUnaryOp(), expression.deref("string_col") - ), - identifiers.ColumnId("unary_size_op"), - ), - ), - ) - assert_equivalence_execution(node, REFERENCE_ENGINE, engine) - - -@pytest.mark.parametrize("engine", ["polars", "bq", "bq-sqlglot"], indirect=True) -@pytest.mark.parametrize( - "op", - [agg_ops.min_op, agg_ops.max_op, agg_ops.mean_op, agg_ops.sum_op, agg_ops.count_op], -) -def test_engines_unary_aggregates( - scalars_array_value: array_value.ArrayValue, - engine, - op, -): - node = apply_agg_to_all_valid(scalars_array_value, op).node - assert_equivalence_execution(node, REFERENCE_ENGINE, engine) - - -@pytest.mark.parametrize("engine", ["polars", "bq", "bq-sqlglot"], indirect=True) -@pytest.mark.parametrize( - "op", - [agg_ops.std_op, agg_ops.var_op, agg_ops.PopVarOp()], -) -def test_engines_unary_variance_aggregates( - scalars_array_value: array_value.ArrayValue, - engine, - op, -): - node = apply_agg_to_all_valid(scalars_array_value, op).node - assert_equivalence_execution(node, REFERENCE_ENGINE, engine) - - -def test_sql_engines_median_op_aggregates( - scalars_array_value: array_value.ArrayValue, - bigquery_client: bigquery.Client, - bq_engine, - sqlglot_engine, -): - node = apply_agg_to_all_valid( - scalars_array_value, - agg_ops.MedianOp(), - ).node - assert_equivalence_execution(node, bq_engine, sqlglot_engine) - - -@pytest.mark.parametrize("engine", ["polars", "bq", "bq-sqlglot"], indirect=True) -@pytest.mark.parametrize( - "grouping_cols", - [ - ["bool_col"], - ["string_col", "int64_col"], - ["date_col"], - ["datetime_col"], - ["timestamp_col"], - ["bytes_col"], - ], -) -def test_engines_grouped_aggregate( - scalars_array_value: array_value.ArrayValue, engine, grouping_cols -): - node = nodes.AggregateNode( - scalars_array_value.node, - aggregations=( - ( - agg_expressions.NullaryAggregation(agg_ops.SizeOp()), - identifiers.ColumnId("size_op"), - ), - ( - agg_expressions.UnaryAggregation( - agg_ops.SizeUnaryOp(), expression.deref("string_col") - ), - identifiers.ColumnId("unary_size_op"), - ), - ), - by_column_ids=tuple(expression.deref(id) for id in grouping_cols), - ) - assert_equivalence_execution(node, REFERENCE_ENGINE, engine) diff --git a/tests/system/small/engines/test_array_ops.py b/tests/system/small/engines/test_array_ops.py deleted file mode 100644 index 159f23f48d6..00000000000 --- a/tests/system/small/engines/test_array_ops.py +++ /dev/null @@ -1,60 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import pytest - -import bigframes.operations as ops -import bigframes.operations.aggregations as agg_ops -from bigframes.core import array_value, expression -from bigframes.session import polars_executor -from bigframes.testing.engine_utils import assert_equivalence_execution - -pytest.importorskip("polars") - -# Polars used as reference as its fast and local. Generally though, prefer gbq engine where they disagree. -REFERENCE_ENGINE = polars_executor.PolarsExecutor() - - -@pytest.mark.parametrize("engine", ["polars", "bq", "bq-sqlglot"], indirect=True) -def test_engines_to_array_op(scalars_array_value: array_value.ArrayValue, engine): - # Bigquery won't allow you to materialize arrays with null, so use non-nullable - int64_non_null = ops.coalesce_op.as_expr("int64_col", expression.const(0)) - bool_col_non_null = ops.coalesce_op.as_expr("bool_col", expression.const(False)) - float_col_non_null = ops.coalesce_op.as_expr("float64_col", expression.const(0.0)) - string_col_non_null = ops.coalesce_op.as_expr("string_col", expression.const("")) - - arr, _ = scalars_array_value.compute_values( - [ - ops.ToArrayOp().as_expr(int64_non_null), - ops.ToArrayOp().as_expr( - int64_non_null, bool_col_non_null, float_col_non_null - ), - ops.ToArrayOp().as_expr(string_col_non_null, string_col_non_null), - ] - ) - assert_equivalence_execution(arr.node, REFERENCE_ENGINE, engine) - - -@pytest.mark.parametrize("engine", ["polars", "bq", "bq-sqlglot"], indirect=True) -def test_engines_array_reduce_op(arrays_array_value: array_value.ArrayValue, engine): - arr, _ = arrays_array_value.compute_values( - [ - ops.ArrayReduceOp(agg_ops.SumOp()).as_expr("float_list_col"), - ops.ArrayReduceOp(agg_ops.StdOp()).as_expr("float_list_col"), - ops.ArrayReduceOp(agg_ops.MaxOp()).as_expr("date_list_col"), - ops.ArrayReduceOp(agg_ops.CountOp()).as_expr("string_list_col"), - ops.ArrayReduceOp(agg_ops.AnyOp()).as_expr("bool_list_col"), - ] - ) - assert_equivalence_execution(arr.node, REFERENCE_ENGINE, engine) diff --git a/tests/system/small/engines/test_bool_ops.py b/tests/system/small/engines/test_bool_ops.py deleted file mode 100644 index a6ef702885b..00000000000 --- a/tests/system/small/engines/test_bool_ops.py +++ /dev/null @@ -1,64 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import itertools - -import pytest - -import bigframes.operations as ops -from bigframes.core import array_value -from bigframes.session import polars_executor -from bigframes.testing.engine_utils import assert_equivalence_execution - -pytest.importorskip("polars") - -# Polars used as reference as its fast and local. Generally though, prefer gbq engine where they disagree. -REFERENCE_ENGINE = polars_executor.PolarsExecutor() - - -def apply_op_pairwise( - array: array_value.ArrayValue, op: ops.BinaryOp, excluded_cols=[] -) -> array_value.ArrayValue: - exprs = [] - for l_arg, r_arg in itertools.permutations(array.column_ids, 2): - if (l_arg in excluded_cols) or (r_arg in excluded_cols): - continue - try: - _ = op.output_type( - array.get_column_type(l_arg), array.get_column_type(r_arg) - ) - exprs.append(op.as_expr(l_arg, r_arg)) - except TypeError: - continue - assert len(exprs) > 0 - new_arr, _ = array.compute_values(exprs) - return new_arr - - -@pytest.mark.parametrize("engine", ["polars", "bq", "bq-sqlglot"], indirect=True) -@pytest.mark.parametrize( - "op", - [ - ops.and_op, - ops.or_op, - ops.xor_op, - ], -) -def test_engines_project_boolean_op( - scalars_array_value: array_value.ArrayValue, engine, op -): - # exclude string cols as does not contain dates - # bool col actually doesn't work properly for bq engine - arr = apply_op_pairwise(scalars_array_value, op, excluded_cols=["string_col"]) - assert_equivalence_execution(arr.node, REFERENCE_ENGINE, engine) diff --git a/tests/system/small/engines/test_comparison_ops.py b/tests/system/small/engines/test_comparison_ops.py deleted file mode 100644 index cd6ece55863..00000000000 --- a/tests/system/small/engines/test_comparison_ops.py +++ /dev/null @@ -1,84 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import asyncio -import itertools - -import pytest - -import bigframes.operations as ops -from bigframes.core import array_value -from bigframes.session import polars_executor -from bigframes.testing.engine_utils import SPEC, assert_equivalence_execution - -pytest.importorskip("polars") - -# Polars used as reference as its fast and local. Generally though, prefer gbq engine where they disagree. -REFERENCE_ENGINE = polars_executor.PolarsExecutor() - -# numeric domain - - -def apply_op_pairwise( - array: array_value.ArrayValue, op: ops.BinaryOp, excluded_cols=[] -) -> array_value.ArrayValue: - exprs = [] - for l_arg, r_arg in itertools.permutations(array.column_ids, 2): - if (l_arg in excluded_cols) or (r_arg in excluded_cols): - continue - try: - _ = op.output_type( - array.get_column_type(l_arg), array.get_column_type(r_arg) - ) - exprs.append(op.as_expr(l_arg, r_arg)) - except TypeError: - continue - assert len(exprs) > 0 - new_arr, _ = array.compute_values(exprs) - return new_arr - - -@pytest.mark.parametrize("engine", ["polars", "bq", "bq-sqlglot"], indirect=True) -@pytest.mark.parametrize( - "op", - [ - ops.eq_op, - ops.eq_null_match_op, - ops.ne_op, - ops.gt_op, - ops.lt_op, - ops.le_op, - ops.ge_op, - ], -) -def test_engines_project_comparison_op( - scalars_array_value: array_value.ArrayValue, engine, op -): - # exclude string cols as does not contain dates - # bool col actually doesn't work properly for bq engine - arr = apply_op_pairwise(scalars_array_value, op, excluded_cols=["string_col"]) - assert_equivalence_execution(arr.node, REFERENCE_ENGINE, engine) - - -@pytest.mark.parametrize("engine", ["bq-sqlglot"], indirect=True) -def test_engines_precedence_like_and_in( - scalars_array_value: array_value.ArrayValue, engine -): - exprs = [ - ops.eq_op.as_expr("bool_col", ops.StrContainsOp("a").as_expr("string_col")), - ] - arr, _ = scalars_array_value.compute_values(exprs) - res = asyncio.run(engine.execute(arr.node, SPEC)) - assert res is not None - assert len(res.batches().to_pandas()) > 0 diff --git a/tests/system/small/engines/test_concat.py b/tests/system/small/engines/test_concat.py deleted file mode 100644 index 5786cfc4193..00000000000 --- a/tests/system/small/engines/test_concat.py +++ /dev/null @@ -1,51 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import pytest - -from bigframes.core import array_value, ordering -from bigframes.session import polars_executor -from bigframes.testing.engine_utils import assert_equivalence_execution - -pytest.importorskip("polars") - -# Polars used as reference as its fast and local. Generally though, prefer gbq engine where they disagree. -REFERENCE_ENGINE = polars_executor.PolarsExecutor() - - -@pytest.mark.parametrize("engine", ["polars", "bq", "bq-sqlglot"], indirect=True) -def test_engines_concat_self( - scalars_array_value: array_value.ArrayValue, - engine, -): - result = scalars_array_value.concat([scalars_array_value, scalars_array_value]) - - assert_equivalence_execution(result.node, REFERENCE_ENGINE, engine) - - -@pytest.mark.parametrize("engine", ["polars", "bq", "bq-sqlglot"], indirect=True) -def test_engines_concat_filtered_sorted( - scalars_array_value: array_value.ArrayValue, - engine, -): - input_1 = scalars_array_value.select_columns(["float64_col", "int64_col"]).order_by( - [ordering.ascending_over("int64_col")] - ) - input_2 = scalars_array_value.filter_by_id("bool_col").select_columns( - ["float64_col", "int64_too"] - ) - - result = input_1.concat([input_2, input_1, input_2]) - - assert_equivalence_execution(result.node, REFERENCE_ENGINE, engine) diff --git a/tests/system/small/engines/test_filtering.py b/tests/system/small/engines/test_filtering.py deleted file mode 100644 index fcb85aa8859..00000000000 --- a/tests/system/small/engines/test_filtering.py +++ /dev/null @@ -1,67 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -import pytest - -import bigframes.operations as ops -from bigframes.core import array_value, expression, nodes -from bigframes.session import polars_executor -from bigframes.testing.engine_utils import assert_equivalence_execution - -pytest.importorskip("polars") - -# Polars used as reference as its fast and local. Generally though, prefer gbq engine where they disagree. -REFERENCE_ENGINE = polars_executor.PolarsExecutor() - - -@pytest.mark.parametrize("engine", ["polars", "bq", "bq-sqlglot"], indirect=True) -def test_engines_filter_bool_col( - scalars_array_value: array_value.ArrayValue, - engine, -): - node = nodes.FilterNode( - scalars_array_value.node, predicate=expression.deref("bool_col") - ) - assert_equivalence_execution(node, REFERENCE_ENGINE, engine) - - -@pytest.mark.parametrize("engine", ["polars", "bq", "bq-sqlglot"], indirect=True) -def test_engines_filter_expr_cond( - scalars_array_value: array_value.ArrayValue, - engine, -): - predicate = ops.gt_op.as_expr( - expression.deref("float64_col"), expression.deref("int64_col") - ) - node = nodes.FilterNode(scalars_array_value.node, predicate=predicate) - assert_equivalence_execution(node, REFERENCE_ENGINE, engine) - - -@pytest.mark.parametrize("engine", ["polars", "bq", "bq-sqlglot"], indirect=True) -def test_engines_filter_true( - scalars_array_value: array_value.ArrayValue, - engine, -): - predicate = expression.const(True) - node = nodes.FilterNode(scalars_array_value.node, predicate=predicate) - assert_equivalence_execution(node, REFERENCE_ENGINE, engine) - - -@pytest.mark.parametrize("engine", ["polars", "bq", "bq-sqlglot"], indirect=True) -def test_engines_filter_false( - scalars_array_value: array_value.ArrayValue, - engine, -): - predicate = expression.const(False) - node = nodes.FilterNode(scalars_array_value.node, predicate=predicate) - assert_equivalence_execution(node, REFERENCE_ENGINE, engine) diff --git a/tests/system/small/engines/test_generic_ops.py b/tests/system/small/engines/test_generic_ops.py deleted file mode 100644 index 96beb51f99d..00000000000 --- a/tests/system/small/engines/test_generic_ops.py +++ /dev/null @@ -1,535 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import re - -import pytest - -import bigframes.dtypes -import bigframes.operations as ops -from bigframes.core import array_value, expression -from bigframes.session import polars_executor -from bigframes.testing.engine_utils import assert_equivalence_execution - -polars = pytest.importorskip("polars") - -# Polars used as reference as its fast and local. Generally though, prefer gbq engine where they disagree. -REFERENCE_ENGINE = polars_executor.PolarsExecutor() - - -def apply_op( - array: array_value.ArrayValue, op: ops.AsTypeOp, excluded_cols=[] -) -> array_value.ArrayValue: - exprs = [] - labels = [] - for arg in array.column_ids: - if arg in excluded_cols: - continue - try: - _ = op.output_type(array.get_column_type(arg)) - expr = op.as_expr(arg) - exprs.append(expr) - type_string = re.sub(r"[^a-zA-Z\d]", "_", str(op.to_type)) - labels.append(f"{arg}_as_{type_string}") - except TypeError: - continue - assert len(exprs) > 0 - new_arr, ids = array.compute_values(exprs) - new_arr = new_arr.rename_columns( - {new_col: label for new_col, label in zip(ids, labels)} - ) - return new_arr - - -@pytest.mark.parametrize("engine", ["polars", "bq", "bq-sqlglot"], indirect=True) -def test_engines_astype_int(scalars_array_value: array_value.ArrayValue, engine): - polars_version = tuple([int(part) for part in polars.__version__.split(".")]) - if polars_version >= (1, 34, 0): - # TODO(https://github.com/pola-rs/polars/issues/24841): Remove this when - # polars fixes Decimal to Int cast. - scalars_array_value = scalars_array_value.drop_columns(["numeric_col"]) - - arr = apply_op( - scalars_array_value, - ops.AsTypeOp(to_type=bigframes.dtypes.INT_DTYPE), - excluded_cols=["string_col"], - ) - - assert_equivalence_execution(arr.node, REFERENCE_ENGINE, engine) - - -@pytest.mark.parametrize("engine", ["polars", "bq", "bq-sqlglot"], indirect=True) -def test_engines_astype_string_int(scalars_array_value: array_value.ArrayValue, engine): - vals = ["1", "100", "-3"] - arr, _ = scalars_array_value.compute_values( - [ - ops.AsTypeOp(to_type=bigframes.dtypes.INT_DTYPE).as_expr( - expression.const(val) - ) - for val in vals - ] - ) - - assert_equivalence_execution(arr.node, REFERENCE_ENGINE, engine) - - -@pytest.mark.parametrize("engine", ["polars", "bq", "bq-sqlglot"], indirect=True) -def test_engines_astype_float(scalars_array_value: array_value.ArrayValue, engine): - arr = apply_op( - scalars_array_value, - ops.AsTypeOp(to_type=bigframes.dtypes.FLOAT_DTYPE), - excluded_cols=["string_col"], - ) - - assert_equivalence_execution(arr.node, REFERENCE_ENGINE, engine) - - -@pytest.mark.parametrize("engine", ["polars", "bq", "bq-sqlglot"], indirect=True) -def test_engines_astype_string_float( - scalars_array_value: array_value.ArrayValue, engine -): - vals = ["1", "1.1", ".1", "1e3", "1.34235e4", "3.33333e-4"] - arr, _ = scalars_array_value.compute_values( - [ - ops.AsTypeOp(to_type=bigframes.dtypes.FLOAT_DTYPE).as_expr( - expression.const(val) - ) - for val in vals - ] - ) - - assert_equivalence_execution(arr.node, REFERENCE_ENGINE, engine) - - -@pytest.mark.parametrize("engine", ["polars", "bq", "bq-sqlglot"], indirect=True) -def test_engines_astype_bool(scalars_array_value: array_value.ArrayValue, engine): - arr = apply_op( - scalars_array_value, ops.AsTypeOp(to_type=bigframes.dtypes.BOOL_DTYPE) - ) - - assert_equivalence_execution(arr.node, REFERENCE_ENGINE, engine) - - -@pytest.mark.parametrize("engine", ["polars", "bq", "bq-sqlglot"], indirect=True) -def test_engines_astype_string(scalars_array_value: array_value.ArrayValue, engine): - # floats work slightly different with trailing zeroes rn - arr = apply_op( - scalars_array_value, - ops.AsTypeOp(to_type=bigframes.dtypes.STRING_DTYPE), - excluded_cols=["float64_col"], - ) - - assert_equivalence_execution(arr.node, REFERENCE_ENGINE, engine) - - -@pytest.mark.parametrize("engine", ["polars", "bq", "bq-sqlglot"], indirect=True) -def test_engines_astype_numeric(scalars_array_value: array_value.ArrayValue, engine): - arr = apply_op( - scalars_array_value, - ops.AsTypeOp(to_type=bigframes.dtypes.NUMERIC_DTYPE), - excluded_cols=["string_col"], - ) - - assert_equivalence_execution(arr.node, REFERENCE_ENGINE, engine) - - -@pytest.mark.parametrize("engine", ["polars", "bq", "bq-sqlglot"], indirect=True) -def test_engines_astype_string_numeric( - scalars_array_value: array_value.ArrayValue, engine -): - vals = ["1", "1.1", ".1", "23428975070235903.209", "-23428975070235903.209"] - arr, _ = scalars_array_value.compute_values( - [ - ops.AsTypeOp(to_type=bigframes.dtypes.NUMERIC_DTYPE).as_expr( - expression.const(val) - ) - for val in vals - ] - ) - - assert_equivalence_execution(arr.node, REFERENCE_ENGINE, engine) - - -@pytest.mark.parametrize("engine", ["polars", "bq", "bq-sqlglot"], indirect=True) -def test_engines_astype_date(scalars_array_value: array_value.ArrayValue, engine): - arr = apply_op( - scalars_array_value, - ops.AsTypeOp(to_type=bigframes.dtypes.DATE_DTYPE), - excluded_cols=["string_col"], - ) - - assert_equivalence_execution(arr.node, REFERENCE_ENGINE, engine) - - -@pytest.mark.parametrize("engine", ["polars", "bq", "bq-sqlglot"], indirect=True) -def test_engines_astype_string_date( - scalars_array_value: array_value.ArrayValue, engine -): - vals = ["2014-08-15", "2215-08-15", "2016-02-29"] - arr, _ = scalars_array_value.compute_values( - [ - ops.AsTypeOp(to_type=bigframes.dtypes.DATE_DTYPE).as_expr( - expression.const(val) - ) - for val in vals - ] - ) - - assert_equivalence_execution(arr.node, REFERENCE_ENGINE, engine) - - -@pytest.mark.parametrize("engine", ["polars", "bq", "bq-sqlglot"], indirect=True) -def test_engines_astype_datetime(scalars_array_value: array_value.ArrayValue, engine): - arr = apply_op( - scalars_array_value, - ops.AsTypeOp(to_type=bigframes.dtypes.DATETIME_DTYPE), - excluded_cols=["string_col"], - ) - - assert_equivalence_execution(arr.node, REFERENCE_ENGINE, engine) - - -@pytest.mark.parametrize("engine", ["polars", "bq", "bq-sqlglot"], indirect=True) -def test_engines_astype_string_datetime( - scalars_array_value: array_value.ArrayValue, engine -): - vals = ["2014-08-15 08:15:12", "2015-08-15 08:15:12.654754", "2016-02-29 00:00:00"] - arr, _ = scalars_array_value.compute_values( - [ - ops.AsTypeOp(to_type=bigframes.dtypes.DATETIME_DTYPE).as_expr( - expression.const(val) - ) - for val in vals - ] - ) - - assert_equivalence_execution(arr.node, REFERENCE_ENGINE, engine) - - -@pytest.mark.parametrize("engine", ["polars", "bq", "bq-sqlglot"], indirect=True) -def test_engines_astype_timestamp(scalars_array_value: array_value.ArrayValue, engine): - arr = apply_op( - scalars_array_value, - ops.AsTypeOp(to_type=bigframes.dtypes.TIMESTAMP_DTYPE), - excluded_cols=["string_col"], - ) - - assert_equivalence_execution(arr.node, REFERENCE_ENGINE, engine) - - -@pytest.mark.parametrize("engine", ["polars", "bq", "bq-sqlglot"], indirect=True) -def test_engines_astype_string_timestamp( - scalars_array_value: array_value.ArrayValue, engine -): - vals = [ - "2014-08-15 08:15:12+00:00", - "2015-08-15 08:15:12.654754+05:00", - "2016-02-29 00:00:00+08:00", - ] - arr, _ = scalars_array_value.compute_values( - [ - ops.AsTypeOp(to_type=bigframes.dtypes.TIMESTAMP_DTYPE).as_expr( - expression.const(val) - ) - for val in vals - ] - ) - - assert_equivalence_execution(arr.node, REFERENCE_ENGINE, engine) - - -@pytest.mark.parametrize("engine", ["polars", "bq", "bq-sqlglot"], indirect=True) -def test_engines_astype_time(scalars_array_value: array_value.ArrayValue, engine): - arr = apply_op( - scalars_array_value, - ops.AsTypeOp(to_type=bigframes.dtypes.TIME_DTYPE), - excluded_cols=["string_col", "int64_col", "int64_too"], - ) - - assert_equivalence_execution(arr.node, REFERENCE_ENGINE, engine) - - -@pytest.mark.parametrize("engine", ["polars", "bq", "bq-sqlglot"], indirect=True) -def test_engines_astype_from_json(scalars_array_value: array_value.ArrayValue, engine): - exprs = [ - ops.JSONDecode(to_type=bigframes.dtypes.INT_DTYPE).as_expr( - expression.const("5", bigframes.dtypes.JSON_DTYPE) - ), - ops.JSONDecode(to_type=bigframes.dtypes.FLOAT_DTYPE).as_expr( - expression.const("5", bigframes.dtypes.JSON_DTYPE) - ), - ops.JSONDecode(to_type=bigframes.dtypes.BOOL_DTYPE).as_expr( - expression.const("true", bigframes.dtypes.JSON_DTYPE) - ), - ops.JSONDecode(to_type=bigframes.dtypes.STRING_DTYPE).as_expr( - expression.const('"hello world"', bigframes.dtypes.JSON_DTYPE) - ), - ] - arr, _ = scalars_array_value.compute_values(exprs) - - assert_equivalence_execution(arr.node, REFERENCE_ENGINE, engine) - - -@pytest.mark.parametrize("engine", ["polars", "bq", "bq-sqlglot"], indirect=True) -def test_engines_astype_to_json(scalars_array_value: array_value.ArrayValue, engine): - exprs = [ - ops.ToJSON().as_expr(expression.deref("int64_col")), - ops.ToJSON().as_expr( - # Use a const since float to json has precision issues - expression.const(5.2, bigframes.dtypes.FLOAT_DTYPE) - ), - ops.ToJSON().as_expr(expression.deref("bool_col")), - ops.ToJSON().as_expr( - # Use a const since "str_col" has special chars. - expression.const('"hello world"', bigframes.dtypes.STRING_DTYPE) - ), - ] - arr, _ = scalars_array_value.compute_values(exprs) - - assert_equivalence_execution(arr.node, REFERENCE_ENGINE, engine) - - -@pytest.mark.parametrize("engine", ["polars", "bq", "bq-sqlglot"], indirect=True) -def test_engines_to_json_string(scalars_array_value: array_value.ArrayValue, engine): - exprs = [ - ops.ToJSONString().as_expr(expression.deref("int64_col")), - ops.ToJSONString().as_expr( - # Use a const since float to json has precision issues - expression.const(5.2, bigframes.dtypes.FLOAT_DTYPE) - ), - ops.ToJSONString().as_expr(expression.deref("bool_col")), - ops.ToJSONString().as_expr( - # Use a const since "str_col" has special chars. - expression.const('"hello world"', bigframes.dtypes.STRING_DTYPE) - ), - ] - arr, _ = scalars_array_value.compute_values(exprs) - - assert_equivalence_execution(arr.node, REFERENCE_ENGINE, engine) - - -@pytest.mark.parametrize("engine", ["polars", "bq", "bq-sqlglot"], indirect=True) -def test_engines_astype_timedelta(scalars_array_value: array_value.ArrayValue, engine): - arr = apply_op( - scalars_array_value, - ops.AsTypeOp(to_type=bigframes.dtypes.TIMEDELTA_DTYPE), - ) - - assert_equivalence_execution(arr.node, REFERENCE_ENGINE, engine) - - -@pytest.mark.parametrize("engine", ["polars", "bq", "bq-sqlglot"], indirect=True) -def test_engines_where_op(scalars_array_value: array_value.ArrayValue, engine): - arr, _ = scalars_array_value.compute_values( - [ - ops.where_op.as_expr( - expression.deref("int64_col"), - expression.deref("bool_col"), - expression.deref("float64_col"), - ) - ] - ) - - assert_equivalence_execution(arr.node, REFERENCE_ENGINE, engine) - - -@pytest.mark.parametrize("engine", ["polars", "bq", "bq-sqlglot"], indirect=True) -def test_engines_coalesce_op(scalars_array_value: array_value.ArrayValue, engine): - arr, _ = scalars_array_value.compute_values( - [ - ops.coalesce_op.as_expr( - expression.deref("int64_col"), - expression.deref("float64_col"), - ) - ] - ) - - assert_equivalence_execution(arr.node, REFERENCE_ENGINE, engine) - - -@pytest.mark.parametrize("engine", ["polars", "bq", "bq-sqlglot"], indirect=True) -def test_engines_fillna_op(scalars_array_value: array_value.ArrayValue, engine): - arr, _ = scalars_array_value.compute_values( - [ - ops.fillna_op.as_expr( - expression.deref("int64_col"), - expression.deref("float64_col"), - ) - ] - ) - - assert_equivalence_execution(arr.node, REFERENCE_ENGINE, engine) - - -@pytest.mark.parametrize("engine", ["polars", "bq", "bq-sqlglot"], indirect=True) -def test_engines_casewhen_op_single_case( - scalars_array_value: array_value.ArrayValue, engine -): - arr, _ = scalars_array_value.compute_values( - [ - ops.case_when_op.as_expr( - expression.deref("bool_col"), - expression.deref("int64_col"), - ) - ] - ) - - assert_equivalence_execution(arr.node, REFERENCE_ENGINE, engine) - - -@pytest.mark.parametrize("engine", ["polars", "bq", "bq-sqlglot"], indirect=True) -def test_engines_casewhen_op_double_case( - scalars_array_value: array_value.ArrayValue, engine -): - arr, _ = scalars_array_value.compute_values( - [ - ops.case_when_op.as_expr( - ops.gt_op.as_expr(expression.deref("int64_col"), expression.const(3)), - expression.deref("int64_col"), - ops.lt_op.as_expr(expression.deref("int64_col"), expression.const(-3)), - expression.deref("int64_too"), - ) - ] - ) - - assert_equivalence_execution(arr.node, REFERENCE_ENGINE, engine) - - -@pytest.mark.parametrize("engine", ["polars", "bq", "bq-sqlglot"], indirect=True) -def test_engines_isnull_op(scalars_array_value: array_value.ArrayValue, engine): - arr, _ = scalars_array_value.compute_values( - [ops.isnull_op.as_expr(expression.deref("string_col"))] - ) - - assert_equivalence_execution(arr.node, REFERENCE_ENGINE, engine) - - -@pytest.mark.parametrize("engine", ["polars", "bq", "bq-sqlglot"], indirect=True) -def test_engines_notnull_op(scalars_array_value: array_value.ArrayValue, engine): - arr, _ = scalars_array_value.compute_values( - [ops.notnull_op.as_expr(expression.deref("string_col"))] - ) - - assert_equivalence_execution(arr.node, REFERENCE_ENGINE, engine) - - -@pytest.mark.parametrize("engine", ["polars", "bq", "bq-sqlglot"], indirect=True) -def test_engines_coerce_to_bool_op_scalars( - scalars_array_value: array_value.ArrayValue, engine -): - arr, _ = scalars_array_value.compute_values( - [ - ops.coerce_to_bool_op.as_expr(expression.deref("bool_col")), - ops.coerce_to_bool_op.as_expr(expression.deref("int64_col")), - ops.coerce_to_bool_op.as_expr(expression.deref("float64_col")), - ops.coerce_to_bool_op.as_expr(expression.deref("string_col")), - ops.coerce_to_bool_op.as_expr(expression.deref("bytes_col")), - ] - ) - - assert_equivalence_execution(arr.node, REFERENCE_ENGINE, engine) - - -@pytest.mark.parametrize("engine", ["polars", "bq", "bq-sqlglot"], indirect=True) -def test_engines_coerce_to_bool_op_arrays( - arrays_array_value: array_value.ArrayValue, engine -): - arr, _ = arrays_array_value.compute_values( - [ - ops.coerce_to_bool_op.as_expr(expression.deref("int_list_col")), - ops.coerce_to_bool_op.as_expr(expression.deref("bool_list_col")), - ops.coerce_to_bool_op.as_expr(expression.deref("float_list_col")), - ops.coerce_to_bool_op.as_expr(expression.deref("string_list_col")), - ] - ) - - assert_equivalence_execution(arr.node, REFERENCE_ENGINE, engine) - - -@pytest.mark.parametrize("engine", ["polars", "bq", "bq-sqlglot"], indirect=True) -def test_engines_invert_op(scalars_array_value: array_value.ArrayValue, engine): - arr, _ = scalars_array_value.compute_values( - [ - ops.invert_op.as_expr(expression.deref("bytes_col")), - ops.invert_op.as_expr(expression.deref("bool_col")), - ] - ) - - assert_equivalence_execution(arr.node, REFERENCE_ENGINE, engine) - - -@pytest.mark.parametrize("engine", ["polars", "bq", "bq-sqlglot"], indirect=True) -def test_engines_isin_op(scalars_array_value: array_value.ArrayValue, engine): - arr, col_ids = scalars_array_value.compute_values( - [ - ops.IsInOp((1, 2, 3)).as_expr(expression.deref("int64_col")), - ops.IsInOp((None, 123456)).as_expr(expression.deref("int64_col")), - ops.IsInOp((None, 123456), match_nulls=False).as_expr( - expression.deref("int64_col") - ), - ops.IsInOp((1.0, 2.0, 3.0)).as_expr(expression.deref("int64_col")), - ops.IsInOp(("1.0", "2.0")).as_expr(expression.deref("int64_col")), - ops.IsInOp(("1.0", 2.5, 3)).as_expr(expression.deref("int64_col")), - ops.IsInOp(()).as_expr(expression.deref("int64_col")), - ops.IsInOp((1, 2, 3, None)).as_expr(expression.deref("float64_col")), - ] - ) - new_names = ( - "int in ints", - "int in ints w null", - "int in ints w null wo match nulls", - "int in floats", - "int in strings", - "int in mixed", - "int in empty", - "float in ints", - ) - arr = arr.rename_columns( - {old_name: new_names[i] for i, old_name in enumerate(col_ids)} - ) - - assert_equivalence_execution(arr.node, REFERENCE_ENGINE, engine) - - -@pytest.mark.parametrize("engine", ["polars", "bq", "bq-sqlglot"], indirect=True) -def test_engines_isin_op_nested_filter( - scalars_array_value: array_value.ArrayValue, engine -): - isin_clause = ops.IsInOp((1, 2, 3)).as_expr(expression.deref("int64_col")) - filter_clause = ops.invert_op.as_expr( - ops.or_op.as_expr( - expression.deref("bool_col"), ops.invert_op.as_expr(isin_clause) - ) - ) - arr = scalars_array_value.filter(filter_clause) - - assert_equivalence_execution(arr.node, REFERENCE_ENGINE, engine) - - -@pytest.mark.parametrize("engine", ["polars", "bq", "bq-sqlglot"], indirect=True) -def test_engines_getitem_ops(arrays_array_value: array_value.ArrayValue, engine): - arr, _ = arrays_array_value.compute_values( - [ - ops.GetItemOp(0).as_expr(expression.deref("float_list_col")), - ops.DynamicGetItemOp().as_expr( - expression.deref("float_list_col"), expression.const(0) - ), - ops.GetItemOp(0).as_expr(expression.deref("string_list_col")), - ops.DynamicGetItemOp().as_expr( - expression.deref("string_list_col"), expression.const(0) - ), - ] - ) - - assert_equivalence_execution(arr.node, REFERENCE_ENGINE, engine) diff --git a/tests/system/small/engines/test_googlesql_ops.py b/tests/system/small/engines/test_googlesql_ops.py deleted file mode 100644 index e47308fa355..00000000000 --- a/tests/system/small/engines/test_googlesql_ops.py +++ /dev/null @@ -1,36 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -import pytest - -import bigframes.operations.googlesql as gsql_ops -from bigframes.core import array_value -from bigframes.session import polars_executor -from bigframes.testing.engine_utils import assert_equivalence_execution - -polars = pytest.importorskip("polars") - -# Polars used as reference as its fast and local. Generally though, prefer gbq engine where they disagree. -REFERENCE_ENGINE = polars_executor.PolarsExecutor() - - -def test_engines_googlesql_st_area( - scalars_array_value: array_value.ArrayValue, bq_engine, sqlglot_engine -): - expr = gsql_ops.ST_AREA.as_expr("geography_col") - - arr, _ = scalars_array_value.compute_values([expr]) - - assert_equivalence_execution(arr.node, bq_engine, sqlglot_engine) diff --git a/tests/system/small/engines/test_join.py b/tests/system/small/engines/test_join.py deleted file mode 100644 index 15dbfabdac3..00000000000 --- a/tests/system/small/engines/test_join.py +++ /dev/null @@ -1,111 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from typing import Literal - -import pytest - -from bigframes import operations as ops -from bigframes.core import array_value, expression, ordering -from bigframes.session import polars_executor -from bigframes.testing.engine_utils import assert_equivalence_execution - -pytest.importorskip("polars") - -# Polars used as reference as its fast and local. Generally though, prefer gbq engine where they disagree. -REFERENCE_ENGINE = polars_executor.PolarsExecutor() - - -@pytest.mark.parametrize("engine", ["polars", "bq", "bq-sqlglot"], indirect=True) -@pytest.mark.parametrize("join_type", ["left", "inner", "right", "outer"]) -def test_engines_join_on_key( - scalars_array_value: array_value.ArrayValue, - engine, - join_type: Literal["inner", "outer", "left", "right"], -): - result, _ = scalars_array_value.relational_join( - scalars_array_value, conditions=(("int64_col", "int64_col"),), type=join_type - ) - - assert_equivalence_execution(result.node, REFERENCE_ENGINE, engine) - - -@pytest.mark.parametrize("engine", ["polars", "bq", "bq-sqlglot"], indirect=True) -@pytest.mark.parametrize("join_type", ["left", "inner", "right", "outer"]) -def test_engines_join_on_coerced_key( - scalars_array_value: array_value.ArrayValue, - engine, - join_type: Literal["inner", "outer", "left", "right"], -): - result, _ = scalars_array_value.relational_join( - scalars_array_value, conditions=(("int64_col", "float64_col"),), type=join_type - ) - - assert_equivalence_execution(result.node, REFERENCE_ENGINE, engine) - - -@pytest.mark.parametrize("engine", ["polars", "bq", "bq-sqlglot"], indirect=True) -@pytest.mark.parametrize("join_type", ["left", "inner", "right", "outer"]) -def test_engines_join_multi_key( - scalars_array_value: array_value.ArrayValue, - engine, - join_type: Literal["inner", "outer", "left", "right"], -): - l_input = scalars_array_value.order_by([ordering.ascending_over("float64_col")]) - l_input, l_join_cols = scalars_array_value.compute_values( - [ - ops.mod_op.as_expr("int64_col", expression.const(2)), - ops.invert_op.as_expr("bool_col"), - ] - ) - r_input, r_join_cols = scalars_array_value.compute_values( - [ops.mod_op.as_expr("int64_col", expression.const(3)), expression.const(True)] - ) - - conditions = tuple((l_col, r_col) for l_col, r_col in zip(l_join_cols, r_join_cols)) - - result, _ = l_input.relational_join(r_input, conditions=conditions, type=join_type) - - assert_equivalence_execution(result.node, REFERENCE_ENGINE, engine) - - -@pytest.mark.parametrize("engine", ["polars", "bq", "bq-sqlglot"], indirect=True) -def test_engines_cross_join( - scalars_array_value: array_value.ArrayValue, - engine, -): - result, _ = scalars_array_value.relational_join(scalars_array_value, type="cross") - - assert_equivalence_execution(result.node, REFERENCE_ENGINE, engine) - - -@pytest.mark.parametrize("engine", ["polars", "bq", "bq-sqlglot"], indirect=True) -@pytest.mark.parametrize( - ("left_key", "right_key"), - [ - ("int64_col", "float64_col"), - ("float64_col", "int64_col"), - ("int64_too", "int64_col"), - ], -) -def test_engines_isin( - scalars_array_value: array_value.ArrayValue, engine, left_key, right_key -): - other = scalars_array_value.select_columns([right_key]) - result, _ = scalars_array_value.isin( - other, - lcol=left_key, - ) - - assert_equivalence_execution(result.node, REFERENCE_ENGINE, engine) diff --git a/tests/system/small/engines/test_numeric_ops.py b/tests/system/small/engines/test_numeric_ops.py deleted file mode 100644 index c188e37370c..00000000000 --- a/tests/system/small/engines/test_numeric_ops.py +++ /dev/null @@ -1,211 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import datetime -import itertools - -import pytest - -import bigframes.operations as ops -from bigframes.core import array_value, expression -from bigframes.session import polars_executor -from bigframes.testing.engine_utils import assert_equivalence_execution - -pytest.importorskip("polars") - -# Polars used as reference as its fast and local. Generally though, prefer gbq engine where they disagree. -REFERENCE_ENGINE = polars_executor.PolarsExecutor() - - -def apply_op_pairwise( - array: array_value.ArrayValue, op: ops.BinaryOp, excluded_cols=[] -) -> array_value.ArrayValue: - exprs = [] - labels = [] - for l_arg, r_arg in itertools.product(array.column_ids, array.column_ids): - if (l_arg in excluded_cols) or (r_arg in excluded_cols): - continue - try: - _ = op.output_type( - array.get_column_type(l_arg), array.get_column_type(r_arg) - ) - expr = op.as_expr(l_arg, r_arg) - exprs.append(expr) - labels.append(f"{l_arg}_{r_arg}") - except TypeError: - continue - assert len(exprs) > 0 - new_arr, ids = array.compute_values(exprs) - new_arr = new_arr.rename_columns( - {new_col: label for new_col, label in zip(ids, labels)} - ) - return new_arr - - -def apply_op( - array: array_value.ArrayValue, op: ops.UnaryOp, excluded_cols=[] -) -> array_value.ArrayValue: - exprs = [] - labels = [] - for arg in array.column_ids: - if arg in excluded_cols: - continue - try: - _ = op.output_type(array.get_column_type(arg)) - expr = op.as_expr(arg) - exprs.append(expr) - labels.append(f"{arg}_{op.name}") - except TypeError: - continue - assert len(exprs) > 0 - new_arr, ids = array.compute_values(exprs) - new_arr = new_arr.rename_columns( - {new_col: label for new_col, label in zip(ids, labels)} - ) - return new_arr - - -@pytest.mark.parametrize("engine", ["polars", "bq"], indirect=True) -def test_engines_project_ceil( - scalars_array_value: array_value.ArrayValue, - engine, -): - arr = apply_op(scalars_array_value, ops.ceil_op) - assert_equivalence_execution(arr.node, REFERENCE_ENGINE, engine) - - -@pytest.mark.parametrize("engine", ["polars", "bq"], indirect=True) -def test_engines_project_floor( - scalars_array_value: array_value.ArrayValue, - engine, -): - arr = apply_op(scalars_array_value, ops.floor_op) - assert_equivalence_execution(arr.node, REFERENCE_ENGINE, engine) - - -@pytest.mark.parametrize("engine", ["polars", "bq", "bq-sqlglot"], indirect=True) -def test_engines_project_add( - scalars_array_value: array_value.ArrayValue, - engine, -): - arr = apply_op_pairwise(scalars_array_value, ops.add_op) - assert_equivalence_execution(arr.node, REFERENCE_ENGINE, engine) - - -@pytest.mark.parametrize("engine", ["polars", "bq", "bq-sqlglot"], indirect=True) -def test_engines_project_sub( - scalars_array_value: array_value.ArrayValue, - engine, -): - arr = apply_op_pairwise(scalars_array_value, ops.sub_op) - assert_equivalence_execution(arr.node, REFERENCE_ENGINE, engine) - - -@pytest.mark.parametrize("engine", ["polars", "bq", "bq-sqlglot"], indirect=True) -def test_engines_project_mul( - scalars_array_value: array_value.ArrayValue, - engine, -): - arr = apply_op_pairwise(scalars_array_value, ops.mul_op) - assert_equivalence_execution(arr.node, REFERENCE_ENGINE, engine) - - -@pytest.mark.parametrize("engine", ["polars", "bq", "bq-sqlglot"], indirect=True) -def test_engines_project_div(scalars_array_value: array_value.ArrayValue, engine): - # TODO: Duration div is sensitive to zeroes - # TODO: Numeric col is sensitive to scale shifts - arr = apply_op_pairwise( - scalars_array_value, ops.div_op, excluded_cols=["duration_col", "numeric_col"] - ) - assert_equivalence_execution(arr.node, REFERENCE_ENGINE, engine) - - -@pytest.mark.parametrize("engine", ["polars", "bq", "bq-sqlglot"], indirect=True) -def test_engines_project_div_durations( - scalars_array_value: array_value.ArrayValue, engine -): - arr, _ = scalars_array_value.compute_values( - [ - ops.div_op.as_expr( - expression.deref("duration_col"), - expression.const(datetime.timedelta(seconds=3)), - ), - ops.div_op.as_expr( - expression.deref("duration_col"), - expression.const(datetime.timedelta(seconds=-3)), - ), - ops.div_op.as_expr(expression.deref("duration_col"), expression.const(4)), - ops.div_op.as_expr(expression.deref("duration_col"), expression.const(-4)), - ops.div_op.as_expr( - expression.deref("duration_col"), expression.const(55.55) - ), - ops.div_op.as_expr( - expression.deref("duration_col"), expression.const(-55.55) - ), - ] - ) - assert_equivalence_execution(arr.node, REFERENCE_ENGINE, engine) - - -@pytest.mark.parametrize("engine", ["polars", "bq", "bq-sqlglot"], indirect=True) -def test_engines_project_floordiv( - scalars_array_value: array_value.ArrayValue, - engine, -): - arr = apply_op_pairwise( - scalars_array_value, - ops.floordiv_op, - excluded_cols=["duration_col", "numeric_col"], - ) - assert_equivalence_execution(arr.node, REFERENCE_ENGINE, engine) - - -@pytest.mark.parametrize("engine", ["polars", "bq", "bq-sqlglot"], indirect=True) -def test_engines_project_floordiv_durations( - scalars_array_value: array_value.ArrayValue, engine -): - arr, _ = scalars_array_value.compute_values( - [ - ops.floordiv_op.as_expr( - expression.deref("duration_col"), - expression.const(datetime.timedelta(seconds=3)), - ), - ops.floordiv_op.as_expr( - expression.deref("duration_col"), - expression.const(datetime.timedelta(seconds=-3)), - ), - ops.floordiv_op.as_expr( - expression.deref("duration_col"), expression.const(4) - ), - ops.floordiv_op.as_expr( - expression.deref("duration_col"), expression.const(-4) - ), - ops.floordiv_op.as_expr( - expression.deref("duration_col"), expression.const(55.55) - ), - ops.floordiv_op.as_expr( - expression.deref("duration_col"), expression.const(-55.55) - ), - ] - ) - assert_equivalence_execution(arr.node, REFERENCE_ENGINE, engine) - - -@pytest.mark.parametrize("engine", ["polars", "bq", "bq-sqlglot"], indirect=True) -def test_engines_project_mod( - scalars_array_value: array_value.ArrayValue, - engine, -): - arr = apply_op_pairwise(scalars_array_value, ops.mod_op) - assert_equivalence_execution(arr.node, REFERENCE_ENGINE, engine) diff --git a/tests/system/small/engines/test_read_local.py b/tests/system/small/engines/test_read_local.py deleted file mode 100644 index 257bddd9179..00000000000 --- a/tests/system/small/engines/test_read_local.py +++ /dev/null @@ -1,121 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import pytest - -import bigframes -from bigframes.core import identifiers, local_data, nodes -from bigframes.session import polars_executor -from bigframes.testing.engine_utils import assert_equivalence_execution - -pytest.importorskip("polars") - -# Polars used as reference as its fast and local. Generally though, prefer gbq engine where they disagree. -REFERENCE_ENGINE = polars_executor.PolarsExecutor() - - -def test_engines_read_local( - fake_session: bigframes.Session, - managed_data_source: local_data.ManagedArrowTable, - engine, -): - scan_list = nodes.ScanList.from_items( - nodes.ScanItem(identifiers.ColumnId(item.column), item.column) - for item in managed_data_source.schema.items - ) - local_node = nodes.ReadLocalNode( - managed_data_source, scan_list, fake_session, offsets_col=None - ) - assert_equivalence_execution(local_node, REFERENCE_ENGINE, engine) - - -def test_engines_read_local_w_offsets( - fake_session: bigframes.Session, - managed_data_source: local_data.ManagedArrowTable, - engine, -): - scan_list = nodes.ScanList.from_items( - nodes.ScanItem(identifiers.ColumnId(item.column), item.column) - for item in managed_data_source.schema.items - ) - local_node = nodes.ReadLocalNode( - managed_data_source, - scan_list, - fake_session, - offsets_col=identifiers.ColumnId("offsets"), - ) - assert_equivalence_execution(local_node, REFERENCE_ENGINE, engine) - - -def test_engines_read_local_w_col_subset( - fake_session: bigframes.Session, - managed_data_source: local_data.ManagedArrowTable, - engine, -): - scan_list = nodes.ScanList.from_items( - nodes.ScanItem(identifiers.ColumnId(item.column), item.column) - for item in managed_data_source.schema.items[::-2] - ) - local_node = nodes.ReadLocalNode( - managed_data_source, scan_list, fake_session, offsets_col=None - ) - assert_equivalence_execution(local_node, REFERENCE_ENGINE, engine) - - -def test_engines_read_local_w_zero_row_source( - fake_session: bigframes.Session, - zero_row_source: local_data.ManagedArrowTable, - engine, -): - scan_list = nodes.ScanList.from_items( - nodes.ScanItem(identifiers.ColumnId(item.column), item.column) - for item in zero_row_source.schema.items - ) - local_node = nodes.ReadLocalNode( - zero_row_source, scan_list, fake_session, offsets_col=None - ) - assert_equivalence_execution(local_node, REFERENCE_ENGINE, engine) - - -@pytest.mark.parametrize( - "engine", ["polars", "bq", "pyarrow", "bq-sqlglot"], indirect=True -) -def test_engines_read_local_w_nested_source( - fake_session: bigframes.Session, - nested_data_source: local_data.ManagedArrowTable, - engine, -): - scan_list = nodes.ScanList.from_items( - nodes.ScanItem(identifiers.ColumnId(item.column), item.column) - for item in nested_data_source.schema.items - ) - local_node = nodes.ReadLocalNode( - nested_data_source, scan_list, fake_session, offsets_col=None - ) - assert_equivalence_execution(local_node, REFERENCE_ENGINE, engine) - - -def test_engines_read_local_w_repeated_source( - fake_session: bigframes.Session, - repeated_data_source: local_data.ManagedArrowTable, - engine, -): - scan_list = nodes.ScanList.from_items( - nodes.ScanItem(identifiers.ColumnId(item.column), item.column) - for item in repeated_data_source.schema.items - ) - local_node = nodes.ReadLocalNode( - repeated_data_source, scan_list, fake_session, offsets_col=None - ) - assert_equivalence_execution(local_node, REFERENCE_ENGINE, engine) diff --git a/tests/system/small/engines/test_selection.py b/tests/system/small/engines/test_selection.py deleted file mode 100644 index 94c8a6463ca..00000000000 --- a/tests/system/small/engines/test_selection.py +++ /dev/null @@ -1,60 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import pytest - -from bigframes.core import array_value, expression, identifiers, nodes -from bigframes.session import polars_executor -from bigframes.testing.engine_utils import assert_equivalence_execution - -pytest.importorskip("polars") - -# Polars used as reference as its fast and local. Generally though, prefer gbq engine where they disagree. -REFERENCE_ENGINE = polars_executor.PolarsExecutor() - - -def test_engines_select_identity( - scalars_array_value: array_value.ArrayValue, - engine, -): - selection = tuple( - nodes.AliasedRef(expression.deref(col), identifiers.ColumnId(col)) - for col in scalars_array_value.column_ids - ) - node = nodes.SelectionNode(scalars_array_value.node, selection) - assert_equivalence_execution(node, REFERENCE_ENGINE, engine) - - -def test_engines_select_rename( - scalars_array_value: array_value.ArrayValue, - engine, -): - selection = tuple( - nodes.AliasedRef(expression.deref(col), identifiers.ColumnId(f"renamed_{col}")) - for col in scalars_array_value.column_ids - ) - node = nodes.SelectionNode(scalars_array_value.node, selection) - assert_equivalence_execution(node, REFERENCE_ENGINE, engine) - - -def test_engines_select_reorder_rename_drop( - scalars_array_value: array_value.ArrayValue, - engine, -): - selection = tuple( - nodes.AliasedRef(expression.deref(col), identifiers.ColumnId(f"renamed_{col}")) - for col in scalars_array_value.column_ids[::-2] - ) - node = nodes.SelectionNode(scalars_array_value.node, selection) - assert_equivalence_execution(node, REFERENCE_ENGINE, engine) diff --git a/tests/system/small/engines/test_slicing.py b/tests/system/small/engines/test_slicing.py deleted file mode 100644 index 022758893d2..00000000000 --- a/tests/system/small/engines/test_slicing.py +++ /dev/null @@ -1,56 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import pytest - -from bigframes.core import array_value, nodes -from bigframes.session import polars_executor -from bigframes.testing.engine_utils import assert_equivalence_execution - -pytest.importorskip("polars") - -# Polars used as reference as its fast and local. Generally though, prefer gbq engine where they disagree. -REFERENCE_ENGINE = polars_executor.PolarsExecutor() - - -@pytest.mark.parametrize("engine", ["polars", "bq", "bq-sqlglot"], indirect=True) -@pytest.mark.parametrize( - ("start", "stop", "step"), - [ - (1, None, None), - (None, 4, None), - (None, None, 2), - (None, 50_000_000_000, 1), - (5, 4, None), - (3, None, 2), - (1, 7, 2), - (1, 7, 50_000_000_000), - (-1, -7, -2), - (None, -7, -2), - (-1, None, -2), - (-7, -1, 2), - (-7, -1, None), - (-7, 7, None), - (7, -7, -2), - ], -) -def test_engines_slice( - scalars_array_value: array_value.ArrayValue, - engine, - start, - stop, - step, -): - node = nodes.SliceNode(scalars_array_value.node, start, stop, step) - assert_equivalence_execution(node, REFERENCE_ENGINE, engine) diff --git a/tests/system/small/engines/test_sorting.py b/tests/system/small/engines/test_sorting.py deleted file mode 100644 index cbb6215adae..00000000000 --- a/tests/system/small/engines/test_sorting.py +++ /dev/null @@ -1,108 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import asyncio - -import pytest - -import bigframes.operations as bf_ops -from bigframes.core import array_value, nodes, ordering -from bigframes.session import execution_spec, polars_executor -from bigframes.testing.engine_utils import assert_equivalence_execution - -pytest.importorskip("polars") - -# Polars used as reference as its fast and local. Generally though, prefer gbq engine where they disagree. -REFERENCE_ENGINE = polars_executor.PolarsExecutor() - - -@pytest.mark.parametrize("engine", ["polars", "bq", "bq-sqlglot"], indirect=True) -def test_engines_reverse( - scalars_array_value: array_value.ArrayValue, - engine, -): - node = apply_reverse(scalars_array_value.node) - assert_equivalence_execution(node, REFERENCE_ENGINE, engine) - - -@pytest.mark.parametrize("engine", ["polars", "bq", "bq-sqlglot"], indirect=True) -def test_engines_double_reverse( - scalars_array_value: array_value.ArrayValue, - engine, -): - node = apply_reverse(scalars_array_value.node) - assert_equivalence_execution(node, REFERENCE_ENGINE, engine) - - -@pytest.mark.parametrize("engine", ["polars", "bq", "bq-sqlglot"], indirect=True) -@pytest.mark.parametrize( - "sort_col", - [ - "bool_col", - "int64_col", - "bytes_col", - "date_col", - "datetime_col", - "int64_col", - "int64_too", - "numeric_col", - "float64_col", - "string_col", - "time_col", - "timestamp_col", - ], -) -def test_engines_sort_over_column( - scalars_array_value: array_value.ArrayValue, engine, sort_col -): - node = apply_reverse(scalars_array_value.node) - ORDER_EXPRESSIONS = (ordering.descending_over(sort_col, nulls_last=False),) - node = nodes.OrderByNode(node, ORDER_EXPRESSIONS) - assert_equivalence_execution(node, REFERENCE_ENGINE, engine) - - -@pytest.mark.parametrize("engine", ["polars", "bq", "bq-sqlglot"], indirect=True) -def test_engines_sort_multi_column_refs( - scalars_array_value: array_value.ArrayValue, - engine, -): - node = scalars_array_value.node - ORDER_EXPRESSIONS = ( - ordering.ascending_over("bool_col", nulls_last=False), - ordering.descending_over("int64_col"), - ) - node = nodes.OrderByNode(node, ORDER_EXPRESSIONS) - assert_equivalence_execution(node, REFERENCE_ENGINE, engine) - - -@pytest.mark.parametrize("engine", ["polars"], indirect=True) -def test_polars_engines_skips_unrecognized_order_expr( - scalars_array_value: array_value.ArrayValue, - engine, -): - node = scalars_array_value.node - ORDER_EXPRESSIONS = ( - ordering.OrderingExpression( - scalar_expression=bf_ops.sin_op.as_expr("float_col") - ), - ) - node = nodes.OrderByNode(node, ORDER_EXPRESSIONS) - result = asyncio.run( - engine.execute(node, execution_spec.ExecutionSpec(ordered=True)) - ) - assert result is None - - -def apply_reverse(node: nodes.BigFrameNode) -> nodes.BigFrameNode: - return nodes.ReversedNode(node) diff --git a/tests/system/small/engines/test_strings.py b/tests/system/small/engines/test_strings.py deleted file mode 100644 index 32a8c4bcd78..00000000000 --- a/tests/system/small/engines/test_strings.py +++ /dev/null @@ -1,77 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import pytest - -import bigframes.operations as ops -from bigframes.core import array_value -from bigframes.session import polars_executor -from bigframes.testing.engine_utils import assert_equivalence_execution - -pytest.importorskip("polars") - -# Polars used as reference as its fast and local. Generally though, prefer gbq engine where they disagree. -REFERENCE_ENGINE = polars_executor.PolarsExecutor() - - -@pytest.mark.parametrize("engine", ["polars", "bq", "bq-sqlglot"], indirect=True) -def test_engines_str_contains(scalars_array_value: array_value.ArrayValue, engine): - arr, _ = scalars_array_value.compute_values( - [ - ops.StrContainsOp("(?i)hEllo").as_expr("string_col"), - ops.StrContainsOp("Hello").as_expr("string_col"), - ops.StrContainsOp("T").as_expr("string_col"), - ops.StrContainsOp(".*").as_expr("string_col"), - ] - ) - assert_equivalence_execution(arr.node, REFERENCE_ENGINE, engine) - - -@pytest.mark.parametrize("engine", ["polars", "bq", "bq-sqlglot"], indirect=True) -def test_engines_str_contains_regex( - scalars_array_value: array_value.ArrayValue, engine -): - arr, _ = scalars_array_value.compute_values( - [ - ops.StrContainsRegexOp("(?i)hEllo").as_expr("string_col"), - ops.StrContainsRegexOp("Hello").as_expr("string_col"), - ops.StrContainsRegexOp("T").as_expr("string_col"), - ops.StrContainsRegexOp(".*").as_expr("string_col"), - ] - ) - assert_equivalence_execution(arr.node, REFERENCE_ENGINE, engine) - - -@pytest.mark.parametrize("engine", ["polars", "bq", "bq-sqlglot"], indirect=True) -def test_engines_str_startswith(scalars_array_value: array_value.ArrayValue, engine): - arr, _ = scalars_array_value.compute_values( - [ - ops.StartsWithOp("He").as_expr("string_col"), - ops.StartsWithOp("llo").as_expr("string_col"), - ops.StartsWithOp(("He", "T", "ca")).as_expr("string_col"), - ] - ) - assert_equivalence_execution(arr.node, REFERENCE_ENGINE, engine) - - -@pytest.mark.parametrize("engine", ["polars", "bq", "bq-sqlglot"], indirect=True) -def test_engines_str_endswith(scalars_array_value: array_value.ArrayValue, engine): - arr, _ = scalars_array_value.compute_values( - [ - ops.EndsWithOp("!").as_expr("string_col"), - ops.EndsWithOp("llo").as_expr("string_col"), - ops.EndsWithOp(("He", "T", "ca")).as_expr("string_col"), - ] - ) - assert_equivalence_execution(arr.node, REFERENCE_ENGINE, engine) diff --git a/tests/system/small/engines/test_temporal_ops.py b/tests/system/small/engines/test_temporal_ops.py deleted file mode 100644 index 61b1b06b1f2..00000000000 --- a/tests/system/small/engines/test_temporal_ops.py +++ /dev/null @@ -1,113 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import asyncio - -import pandas as pd -import pytest - -import bigframes.operations as ops -from bigframes import dtypes -from bigframes.core import array_value -from bigframes.core import expression as ex -from bigframes.session import polars_executor -from bigframes.testing.engine_utils import SPEC, assert_equivalence_execution - -pytest.importorskip("polars") - -# Polars used as reference as its fast and local. Generally though, prefer gbq engine where they disagree. -REFERENCE_ENGINE = polars_executor.PolarsExecutor() - - -@pytest.mark.parametrize("engine", ["polars", "bq", "bq-sqlglot"], indirect=True) -def test_engines_dt_floor(scalars_array_value: array_value.ArrayValue, engine): - arr, _ = scalars_array_value.compute_values( - [ - ops.FloorDtOp("us").as_expr("timestamp_col"), - ops.FloorDtOp("ms").as_expr("timestamp_col"), - ops.FloorDtOp("s").as_expr("timestamp_col"), - ops.FloorDtOp("min").as_expr("timestamp_col"), - ops.FloorDtOp("h").as_expr("timestamp_col"), - ops.FloorDtOp("D").as_expr("timestamp_col"), - ops.FloorDtOp("W").as_expr("timestamp_col"), - ops.FloorDtOp("M").as_expr("timestamp_col"), - ops.FloorDtOp("Q").as_expr("timestamp_col"), - ops.FloorDtOp("Y").as_expr("timestamp_col"), - ops.FloorDtOp("Q").as_expr("datetime_col"), - ops.FloorDtOp("us").as_expr("datetime_col"), - ] - ) - assert_equivalence_execution(arr.node, REFERENCE_ENGINE, engine) - - -@pytest.mark.parametrize("engine", ["polars", "bq", "bq-sqlglot"], indirect=True) -def test_engines_date_accessors(scalars_array_value: array_value.ArrayValue, engine): - datelike_cols = ["datetime_col", "timestamp_col", "date_col"] - accessors = [ - ops.day_op, - ops.dayofweek_op, - ops.month_op, - ops.quarter_op, - ops.year_op, - ops.iso_day_op, - ops.iso_week_op, - ops.iso_year_op, - ] - - exprs = [acc.as_expr(col) for acc in accessors for col in datelike_cols] - - arr, _ = scalars_array_value.compute_values(exprs) - assert_equivalence_execution(arr.node, REFERENCE_ENGINE, engine) - - -@pytest.mark.parametrize("engine", ["bq", "bq-sqlglot"], indirect=True) -def test_engines_temporal_arithmetic( - scalars_array_value: array_value.ArrayValue, engine -): - exprs = [ - ops.timestamp_add_op.as_expr( - "timestamp_col", ex.const(pd.Timedelta(seconds=1), dtypes.TIMEDELTA_DTYPE) - ), - ops.timestamp_sub_op.as_expr( - "timestamp_col", ex.const(pd.Timedelta(seconds=1), dtypes.TIMEDELTA_DTYPE) - ), - ops.date_add_op.as_expr( - "date_col", ex.const(pd.Timedelta(days=1), dtypes.TIMEDELTA_DTYPE) - ), - ops.date_sub_op.as_expr( - "date_col", ex.const(pd.Timedelta(days=1), dtypes.TIMEDELTA_DTYPE) - ), - ops.timestamp_diff_op.as_expr("timestamp_col", "timestamp_col"), - ops.date_diff_op.as_expr("date_col", "date_col"), - ] - - arr, _ = scalars_array_value.compute_values(exprs) - res = asyncio.run(engine.execute(arr.node, SPEC)) - assert res is not None - assert len(res.batches().to_pandas()) > 0 - - -@pytest.mark.parametrize("engine", ["bq", "bq-sqlglot"], indirect=True) -def test_engines_to_datetime(scalars_array_value: array_value.ArrayValue, engine): - exprs = [ - ops.ToDatetimeOp().as_expr("timestamp_col"), - ] - arr, _ = scalars_array_value.compute_values(exprs) - res = asyncio.run(engine.execute(arr.node, SPEC)) - assert res is not None - df = res.batches().to_pandas() - # The input timestamp was: TIMESTAMP('2021-07-21T17:43:43.945289+00:00') - # The output should be naive DATETIME('2021-07-21T17:43:43.945289') - val = df.iloc[0, -1] - assert pd.Timestamp(val) == pd.Timestamp("2021-07-21T17:43:43.945289") diff --git a/tests/system/small/engines/test_windowing.py b/tests/system/small/engines/test_windowing.py deleted file mode 100644 index 8235fe0ef6b..00000000000 --- a/tests/system/small/engines/test_windowing.py +++ /dev/null @@ -1,64 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import pytest - -import bigframes.operations.aggregations as agg_ops -from bigframes.core import ( - agg_expressions, - array_value, - expression, - identifiers, - nodes, - window_spec, -) -from bigframes.session import polars_executor -from bigframes.testing.engine_utils import assert_equivalence_execution - -pytest.importorskip("polars") - -# Polars used as reference as its fast and local. Generally though, prefer gbq engine where they disagree. -REFERENCE_ENGINE = polars_executor.PolarsExecutor() - - -@pytest.mark.parametrize("engine", ["polars", "bq", "bq-sqlglot"], indirect=True) -def test_engines_with_offsets( - scalars_array_value: array_value.ArrayValue, - engine, -): - result, _ = scalars_array_value.promote_offsets() - assert_equivalence_execution(result.node, REFERENCE_ENGINE, engine) - - -@pytest.mark.parametrize("agg_op", [agg_ops.sum_op, agg_ops.count_op]) -def test_engines_with_rows_window( - scalars_array_value: array_value.ArrayValue, - agg_op, - bq_engine, - sqlglot_engine, -): - window = window_spec.WindowSpec( - bounds=window_spec.RowsWindowBounds.from_window_size(3, "left"), - ) - window_node = nodes.WindowOpNode( - child=scalars_array_value.node, - agg_exprs=( - nodes.ColumnDef( - agg_expressions.UnaryAggregation(agg_op, expression.deref("int64_too")), - identifiers.ColumnId("agg_int64"), - ), - ), - window_spec=window, - ) - assert_equivalence_execution(window_node, bq_engine, sqlglot_engine) diff --git a/tests/system/small/functions/__init__.py b/tests/system/small/functions/__init__.py deleted file mode 100644 index 0a2669d7a25..00000000000 --- a/tests/system/small/functions/__init__.py +++ /dev/null @@ -1,13 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. diff --git a/tests/system/small/functions/test_remote_function.py b/tests/system/small/functions/test_remote_function.py deleted file mode 100644 index 869b26ca38c..00000000000 --- a/tests/system/small/functions/test_remote_function.py +++ /dev/null @@ -1,1616 +0,0 @@ -# Copyright 2023 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import inspect -import re -import textwrap -from typing import Sequence - -import bigframes_vendored.constants as constants -import google.api_core.exceptions -import pandas -import pandas as pd -import pyarrow -import pytest -import test_utils.prefixer -from google.cloud import bigquery - -import bigframes -import bigframes.clients -import bigframes.core.events -import bigframes.dtypes -import bigframes.exceptions -import bigframes.session._io.bigquery -from bigframes.functions import _utils as bff_utils -from bigframes.functions import function as bff -from bigframes.testing.utils import assert_frame_equal, assert_series_equal - -_prefixer = test_utils.prefixer.Prefixer("bigframes", "") - - -def get_function_name(func, package_requirements=None, is_row_processor=False): - """Get a bigframes function name for testing given a udf.""" - # Augment user package requirements with any internal package - # requirements. - package_requirements = bff_utils.get_updated_package_requirements( - package_requirements or [], is_row_processor - ) - - # Compute a unique hash representing the user code. - function_hash = bff_utils.get_hash(func, package_requirements) - - return f"bigframes_{function_hash}" - - -@pytest.fixture(scope="module") -def bq_cf_connection() -> str: - """Pre-created BQ connection in the test project in US location, used to - invoke cloud function. - - $ bq show --connection --location=us --project_id=PROJECT_ID bigframes-rf-conn - """ - return "bigframes-rf-conn" - - -@pytest.fixture(scope="module") -def bq_cf_connection_location() -> str: - """Pre-created BQ connection in the test project in US location, in format - PROJECT_ID.LOCATION.CONNECTION_NAME, used to invoke cloud function. - - $ bq show --connection --location=us --project_id=PROJECT_ID bigframes-rf-conn - """ - return "us.bigframes-rf-conn" - - -@pytest.fixture(scope="module") -def bq_cf_connection_location_mismatched() -> str: - """Pre-created BQ connection in the test project in EU location, in format - LOCATION.CONNECTION_NAME, used to invoke cloud function. - - $ bq show --connection --location=us --project_id=PROJECT_ID bigframes-rf-conn - """ - return "eu.bigframes-rf-conn" - - -@pytest.fixture(scope="module") -def bq_cf_connection_location_project(bigquery_client) -> str: - """Pre-created BQ connection in the test project in US location, in format - PROJECT_ID.LOCATION.CONNECTION_NAME, used to invoke cloud function. - - $ bq show --connection --location=us --project_id=PROJECT_ID bigframes-rf-conn - """ - return f"{bigquery_client.project}.us.bigframes-rf-conn" - - -@pytest.fixture(scope="module") -def bq_cf_connection_location_project_mismatched() -> str: - """Pre-created BQ connection in the bigframes-metrics project in US location, - in format PROJECT_ID.LOCATION.CONNECTION_NAME, used to invoke cloud function. - - $ bq show --connection --location=us --project_id=PROJECT_ID bigframes-rf-conn - """ - return "bigframes-metrics.eu.bigframes-rf-conn" - - -@pytest.fixture(scope="module") -def session_with_bq_connection(bq_cf_connection) -> bigframes.Session: - session = bigframes.Session( - bigframes.BigQueryOptions(bq_connection=bq_cf_connection, location="US") - ) - return session - - -def get_bq_connection_id_path_format(connection_id_dot_format): - fields = connection_id_dot_format.split(".") - return f"projects/{fields[0]}/locations/{fields[1]}/connections/{fields[2]}" - - -@pytest.mark.flaky(retries=2, delay=120) -def test_remote_function_direct_no_session_param( - scalars_dfs, - dataset_id_permanent, - bq_cf_connection, -): - def square(x): - return x * x - - square = bff.remote_function( - input_types=int, - output_type=int, - dataset=dataset_id_permanent, - bigquery_connection=bq_cf_connection, - # See e2e tests for tests that actually deploy the Cloud Function. - reuse=True, - name=get_function_name(square), - cloud_function_service_account="default", - )(square) - - # Function should still work normally. - assert square(2) == 4 - - # Function should have extra metadata attached for remote execution. - assert hasattr(square, "bigframes_remote_function") - assert hasattr(square, "bigframes_bigquery_function") - assert hasattr(square, "bigframes_cloud_function") - - scalars_df, scalars_pandas_df = scalars_dfs - - bf_int64_col = scalars_df["int64_col"] - bf_int64_col_filter = bf_int64_col.notnull() - bf_int64_col_filtered = bf_int64_col[bf_int64_col_filter] - bf_result_col = bf_int64_col_filtered.apply(square) - bf_result = ( - bf_int64_col_filtered.to_frame().assign(result=bf_result_col).to_pandas() - ) - - pd_int64_col = scalars_pandas_df["int64_col"] - pd_int64_col_filter = pd_int64_col.notnull() - pd_int64_col_filtered = pd_int64_col[pd_int64_col_filter] - pd_result_col = pd_int64_col_filtered.apply(lambda x: x * x) - # TODO(shobs): Figure why pandas .apply() changes the dtype, i.e. - # pd_int64_col_filtered.dtype is Int64Dtype() - # pd_int64_col_filtered.apply(lambda x: x * x).dtype is int64. - # For this test let's force the pandas dtype to be same as bigframes' dtype. - pd_result_col = pd_result_col.astype(pd.Int64Dtype()) - pd_result = pd_int64_col_filtered.to_frame().assign(result=pd_result_col) - - assert_frame_equal(bf_result, pd_result) - - -@pytest.mark.flaky(retries=2, delay=120) -def test_remote_function_connection_w_location( - session, - scalars_dfs, - dataset_id_permanent, - bq_cf_connection_location, -): - def square(x): - return x * x - - square = session.remote_function( - input_types=int, - output_type=int, - dataset=dataset_id_permanent, - bigquery_connection=bq_cf_connection_location, - # See e2e tests for tests that actually deploy the Cloud Function. - reuse=True, - name=get_function_name(square), - cloud_function_service_account="default", - )(square) - - # Function should still work normally. - assert square(2) == 4 - - scalars_df, scalars_pandas_df = scalars_dfs - - bf_int64_col = scalars_df["int64_col"] - bf_int64_col_filter = bf_int64_col.notnull() - bf_int64_col_filtered = bf_int64_col[bf_int64_col_filter] - bf_result_col = bf_int64_col_filtered.apply(square) - bf_result = ( - bf_int64_col_filtered.to_frame().assign(result=bf_result_col).to_pandas() - ) - - pd_int64_col = scalars_pandas_df["int64_col"] - pd_int64_col_filter = pd_int64_col.notnull() - pd_int64_col_filtered = pd_int64_col[pd_int64_col_filter] - pd_result_col = pd_int64_col_filtered.apply(lambda x: x * x) - # TODO(shobs): Figure why pandas .apply() changes the dtype, i.e. - # pd_int64_col_filtered.dtype is Int64Dtype() - # pd_int64_col_filtered.apply(lambda x: x * x).dtype is int64. - # For this test let's force the pandas dtype to be same as bigframes' dtype. - pd_result_col = pd_result_col.astype(pd.Int64Dtype()) - pd_result = pd_int64_col_filtered.to_frame().assign(result=pd_result_col) - - assert_frame_equal(bf_result, pd_result) - - -@pytest.mark.flaky(retries=2, delay=120) -def test_remote_function_connection_w_location_mismatched( - session, - dataset_id_permanent, - bq_cf_connection_location_mismatched, -): - def square(x): - # Not expected to reach this code, as the location of the - # connection doesn't match the location of the dataset. - return x * x # pragma: NO COVER - - bq_cf_connection_location_mismatched_path_fmt = get_bq_connection_id_path_format( - bigframes.clients.get_canonical_bq_connection_id( - bq_cf_connection_location_mismatched, - session.bqclient.project, - session._location, - ) - ) - connection_ids = [ - bq_cf_connection_location_mismatched, - bq_cf_connection_location_mismatched_path_fmt, - ] - - for connection_id in connection_ids: - with pytest.raises( - ValueError, - match=re.escape( - "The location does not match BigQuery connection location:" - ), - ): - session.remote_function( - input_types=int, - output_type=int, - dataset=dataset_id_permanent, - bigquery_connection=connection_id, - # See e2e tests for tests that actually deploy the Cloud Function. - reuse=True, - name=get_function_name(square), - cloud_function_service_account="default", - )(square) - - -@pytest.mark.flaky(retries=2, delay=120) -def test_remote_function_connection_w_location_project( - session, - scalars_dfs, - dataset_id_permanent, - bq_cf_connection_location_project, -): - def square(x): - return x * x - - square = session.remote_function( - input_types=int, - output_type=int, - dataset=dataset_id_permanent, - bigquery_connection=bq_cf_connection_location_project, - # See e2e tests for tests that actually deploy the Cloud Function. - reuse=True, - name=get_function_name(square), - cloud_function_service_account="default", - )(square) - - # Function should still work normally. - assert square(2) == 4 - - scalars_df, scalars_pandas_df = scalars_dfs - - bf_int64_col = scalars_df["int64_col"] - bf_int64_col_filter = bf_int64_col.notnull() - bf_int64_col_filtered = bf_int64_col[bf_int64_col_filter] - bf_result_col = bf_int64_col_filtered.apply(square) - bf_result = ( - bf_int64_col_filtered.to_frame().assign(result=bf_result_col).to_pandas() - ) - - pd_int64_col = scalars_pandas_df["int64_col"] - pd_int64_col_filter = pd_int64_col.notnull() - pd_int64_col_filtered = pd_int64_col[pd_int64_col_filter] - pd_result_col = pd_int64_col_filtered.apply(lambda x: x * x) - # TODO(shobs): Figure why pandas .apply() changes the dtype, i.e. - # pd_int64_col_filtered.dtype is Int64Dtype() - # pd_int64_col_filtered.apply(lambda x: x * x).dtype is int64. - # For this test let's force the pandas dtype to be same as bigframes' dtype. - pd_result_col = pd_result_col.astype(pd.Int64Dtype()) - pd_result = pd_int64_col_filtered.to_frame().assign(result=pd_result_col) - - assert_frame_equal(bf_result, pd_result) - - -@pytest.mark.flaky(retries=2, delay=120) -def test_remote_function_connection_w_project_mismatched( - session, - dataset_id_permanent, - bq_cf_connection_location_project_mismatched, -): - def square(x): - # Not expected to reach this code, as the project of the - # connection doesn't match the project of the dataset. - return x * x # pragma: NO COVER - - bq_cf_connection_location_project_mismatched_path_fmt = ( - get_bq_connection_id_path_format( - bigframes.clients.get_canonical_bq_connection_id( - bq_cf_connection_location_project_mismatched, - session.bqclient.project, - session._location, - ) - ) - ) - connection_ids = [ - bq_cf_connection_location_project_mismatched, - bq_cf_connection_location_project_mismatched_path_fmt, - ] - - for connection_id in connection_ids: - with pytest.raises( - ValueError, - match=re.escape( - "The project_id does not match BigQuery connection gcp_project_id:" - ), - ): - session.remote_function( - input_types=int, - output_type=int, - dataset=dataset_id_permanent, - bigquery_connection=connection_id, - # See e2e tests for tests that actually deploy the Cloud Function. - reuse=True, - name=get_function_name(square), - cloud_function_service_account="default", - )(square) - - -@pytest.mark.flaky(retries=2, delay=120) -def test_remote_function_via_session_default( - session_with_bq_connection, scalars_dfs, dataset_id_permanent -): - def square(x): - return x * x - - # Session has bigquery connection initialized via context. Without an - # explicit dataset the default dataset from the session would be used. - # Without an explicit bigquery connection, the one present in Session set - # through the explicit BigQueryOptions would be used. Without an explicit `reuse` - # the default behavior of reuse=True will take effect. Please note that the - # udf is same as the one used in other tests in this file so the underlying - # cloud function would be common and quickly reused. - square = session_with_bq_connection.remote_function( - input_types=int, - output_type=int, - dataset=dataset_id_permanent, - name=get_function_name(square), - cloud_function_service_account="default", - )(square) - - # Function should still work normally. - assert square(2) == 4 - - scalars_df, scalars_pandas_df = scalars_dfs - - bf_int64_col = scalars_df["int64_col"] - bf_int64_col_filter = bf_int64_col.notnull() - bf_int64_col_filtered = bf_int64_col[bf_int64_col_filter] - bf_result_col = bf_int64_col_filtered.apply(square) - bf_result = ( - bf_int64_col_filtered.to_frame().assign(result=bf_result_col).to_pandas() - ) - - pd_int64_col = scalars_pandas_df["int64_col"] - pd_int64_col_filter = pd_int64_col.notnull() - pd_int64_col_filtered = pd_int64_col[pd_int64_col_filter] - pd_result_col = pd_int64_col_filtered.apply(lambda x: x * x) - # TODO(shobs): Figure why pandas .apply() changes the dtype, i.e. - # pd_int64_col_filtered.dtype is Int64Dtype() - # pd_int64_col_filtered.apply(lambda x: x * x).dtype is int64. - # For this test let's force the pandas dtype to be same as bigframes' dtype. - pd_result_col = pd_result_col.astype(pd.Int64Dtype()) - pd_result = pd_int64_col_filtered.to_frame().assign(result=pd_result_col) - - assert_frame_equal(bf_result, pd_result) - - -@pytest.mark.flaky(retries=2, delay=120) -def test_remote_function_via_session_with_overrides( - session, scalars_dfs, dataset_id_permanent, bq_cf_connection -): - def square(x): - return x * x - - square = session.remote_function( - input_types=int, - output_type=int, - dataset=dataset_id_permanent, - bigquery_connection=bq_cf_connection, - # See e2e tests for tests that actually deploy the Cloud Function. - reuse=True, - name=get_function_name(square), - cloud_function_service_account="default", - )(square) - - # Function should still work normally. - assert square(2) == 4 - - scalars_df, scalars_pandas_df = scalars_dfs - - bf_int64_col = scalars_df["int64_col"] - bf_int64_col_filter = bf_int64_col.notnull() - bf_int64_col_filtered = bf_int64_col[bf_int64_col_filter] - bf_result_col = bf_int64_col_filtered.apply(square) - bf_result = ( - bf_int64_col_filtered.to_frame().assign(result=bf_result_col).to_pandas() - ) - - pd_int64_col = scalars_pandas_df["int64_col"] - pd_int64_col_filter = pd_int64_col.notnull() - pd_int64_col_filtered = pd_int64_col[pd_int64_col_filter] - pd_result_col = pd_int64_col_filtered.apply(lambda x: x * x) - # TODO(shobs): Figure why pandas .apply() changes the dtype, i.e. - # pd_int64_col_filtered.dtype is Int64Dtype() - # pd_int64_col_filtered.apply(lambda x: x * x).dtype is int64. - # For this test let's force the pandas dtype to be same as bigframes' dtype. - pd_result_col = pd_result_col.astype(pd.Int64Dtype()) - pd_result = pd_int64_col_filtered.to_frame().assign(result=pd_result_col) - - assert_frame_equal(bf_result, pd_result) - - -@pytest.mark.flaky(retries=2, delay=120) -def test_dataframe_applymap( - session_with_bq_connection, scalars_dfs, dataset_id_permanent -): - def add_one(x): - return x + 1 - - remote_add_one = session_with_bq_connection.remote_function( - input_types=[int], - output_type=int, - dataset=dataset_id_permanent, - name=get_function_name(add_one), - cloud_function_service_account="default", - )(add_one) - - scalars_df, scalars_pandas_df = scalars_dfs - int64_cols = ["int64_col", "int64_too"] - - bf_int64_df = scalars_df[int64_cols] - bf_int64_df_filtered = bf_int64_df.dropna() - bf_result = bf_int64_df_filtered.applymap(remote_add_one).to_pandas() - - pd_int64_df = scalars_pandas_df[int64_cols] - pd_int64_df_filtered = pd_int64_df.dropna() - - # TODO(swast): Remove when pandas 2.1.x+ is the minimum supported. - if hasattr(pd_int64_df_filtered, "map"): - pd_result = pd_int64_df_filtered.map(add_one) - else: - pd_result = pd_int64_df_filtered.applymap(add_one) - # TODO(shobs): Figure why pandas .applymap() changes the dtype, i.e. - # pd_int64_df_filtered.dtype is Int64Dtype() - # pd_int64_df_filtered.applymap(lambda x: x).dtype is int64. - # For this test let's force the pandas dtype to be same as input. - for col in pd_result: - pd_result[col] = pd_result[col].astype(pd_int64_df_filtered[col].dtype) - - assert_frame_equal(bf_result, pd_result) - - -@pytest.mark.flaky(retries=2, delay=120) -def test_dataframe_applymap_explicit_filter( - session_with_bq_connection, scalars_dfs, dataset_id_permanent -): - def add_one(x): - return x + 1 - - remote_add_one = session_with_bq_connection.remote_function( - input_types=[int], - output_type=int, - dataset=dataset_id_permanent, - name=get_function_name(add_one), - cloud_function_service_account="default", - )(add_one) - - scalars_df, scalars_pandas_df = scalars_dfs - int64_cols = ["int64_col", "int64_too"] - - bf_int64_df = scalars_df[int64_cols] - bf_int64_df_filtered = bf_int64_df[bf_int64_df["int64_col"].notnull()] - bf_result = bf_int64_df_filtered.applymap(remote_add_one).to_pandas() - - pd_int64_df = scalars_pandas_df[int64_cols] - pd_int64_df_filtered = pd_int64_df[pd_int64_df["int64_col"].notnull()] - - # TODO(swast): Remove when pandas 2.1.x+ is the minimum supported. - if hasattr(pd_int64_df_filtered, "map"): - pd_result = pd_int64_df_filtered.map(add_one) - else: - pd_result = pd_int64_df_filtered.applymap(add_one) - - # TODO(shobs): Figure why pandas .applymap() changes the dtype, i.e. - # pd_int64_df_filtered.dtype is Int64Dtype() - # pd_int64_df_filtered.applymap(lambda x: x).dtype is int64. - # For this test let's force the pandas dtype to be same as input. - for col in pd_result: - pd_result[col] = pd_result[col].astype(pd_int64_df_filtered[col].dtype) - - assert_frame_equal(bf_result, pd_result) - - -@pytest.mark.flaky(retries=2, delay=120) -def test_dataframe_applymap_na_ignore( - session_with_bq_connection, scalars_dfs, dataset_id_permanent -): - def add_one(x): - return x + 1 - - remote_add_one = session_with_bq_connection.remote_function( - input_types=[int], - output_type=int, - dataset=dataset_id_permanent, - name=get_function_name(add_one), - cloud_function_service_account="default", - )(add_one) - - scalars_df, scalars_pandas_df = scalars_dfs - int64_cols = ["int64_col", "int64_too"] - - bf_int64_df = scalars_df[int64_cols] - bf_result = bf_int64_df.applymap(remote_add_one, na_action="ignore").to_pandas() - - pd_int64_df = scalars_pandas_df[int64_cols] - - # TODO(swast): Remove when pandas 2.1.x+ is the minimum supported. - if hasattr(pd_int64_df, "map"): - pd_result = pd_int64_df.map(add_one, na_action="ignore") - else: - pd_result = pd_int64_df.applymap(add_one, na_action="ignore") - - # TODO(shobs): Figure why pandas .applymap() changes the dtype, i.e. - # pd_int64_df_filtered.dtype is Int64Dtype() - # pd_int64_df_filtered.applymap(lambda x: x).dtype is int64. - # For this test let's force the pandas dtype to be same as input. - for col in pd_result: - pd_result[col] = pd_result[col].astype(pd_int64_df[col].dtype) - - assert_frame_equal(bf_result, pd_result) - - -@pytest.mark.flaky(retries=2, delay=120) -def test_series_map_bytes( - session_with_bq_connection, scalars_dfs, dataset_id_permanent -): - """Check that bytes is support as input and output.""" - scalars_df, scalars_pandas_df = scalars_dfs - - def bytes_to_hex(mybytes: bytes) -> bytes: - import pandas - - return mybytes.hex().encode("utf-8") if pandas.notna(mybytes) else None # type: ignore - - # TODO(b/345516010): the type: ignore is because "Optional" not yet - # supported as a type annotation in @remote_function(). - assert bytes_to_hex(None) is None # type: ignore - assert bytes_to_hex(b"\x00\xdd\xba\x11") == b"00ddba11" - pd_result = scalars_pandas_df.bytes_col.map(bytes_to_hex).astype( - pd.ArrowDtype(pyarrow.binary()) - ) - - packages = ["pandas"] - remote_bytes_to_hex = session_with_bq_connection.remote_function( - dataset=dataset_id_permanent, - name=get_function_name(bytes_to_hex, package_requirements=packages), - packages=packages, - cloud_function_service_account="default", - )(bytes_to_hex) - bf_result = scalars_df.bytes_col.map(remote_bytes_to_hex).to_pandas() - - assert_series_equal( - bf_result, - pd_result, - ) - - -def test_skip_bq_connection_check(dataset_id_permanent): - connection_name = "connection_does_not_exist" - session = bigframes.Session( - context=bigframes.BigQueryOptions( - bq_connection=connection_name, skip_bq_connection_check=True - ) - ) - - # Make sure that the connection does not exist - with pytest.raises(google.api_core.exceptions.NotFound): - session.bqconnectionclient.get_connection( - name=session.bqconnectionclient.connection_path( - session._project, session._location, connection_name - ) - ) - - # Make sure that an attempt to create a remote function routine with - # non-existent connection would result in an exception thrown by the BQ - # service. - # This is different from the exception throw by the BQ Connection service - # if it was not able to create the connection because of lack of permission - # when skip_bq_connection_check was not set to True: - # google.api_core.exceptions.PermissionDenied: 403 Permission 'resourcemanager.projects.setIamPolicy' denied on resource - with pytest.raises( - google.api_core.exceptions.NotFound, - match=f"Not found: Connection {connection_name}", - ): - - def add_one(x): - # Not expected to reach this code, as the connection doesn't exist. - return x + 1 # pragma: NO COVER - - session.remote_function( - input_types=[int], - output_type=int, - dataset=dataset_id_permanent, - name=get_function_name(add_one), - cloud_function_service_account="default", - )(add_one) - - -def test_read_gbq_function_detects_invalid_function(session, dataset_id): - dataset_ref = bigquery.DatasetReference.from_string(dataset_id) - with pytest.raises(ValueError) as e: - session.read_gbq_function( - str(dataset_ref.routine("not_a_function")), - ) - - assert "Unknown function" in str(e.value) - - -@pytest.mark.flaky(retries=2, delay=120) -def test_read_gbq_function_like_original( - session, - scalars_df_index, - dataset_id_permanent, - bq_cf_connection, -): - def square1(x): - return x * x - - square1 = bff.remote_function( - input_types=[int], - output_type=int, - dataset=dataset_id_permanent, - bigquery_connection=bq_cf_connection, - reuse=True, - name=get_function_name(square1), - cloud_function_service_account="default", - )(square1) - - # Function should still work normally. - assert square1(2) == 4 - - square2 = session.read_gbq_function( - function_name=square1.bigframes_bigquery_function, # type: ignore - ) - - # The newly-created function (square1) should have a remote function AND a - # cloud function associated with it, while the read-back version (square2) - # should only have a remote function. - assert square1.bigframes_remote_function # type: ignore - assert square1.bigframes_bigquery_function # type: ignore - assert square1.bigframes_cloud_function # type: ignore - - assert square2.bigframes_remote_function - assert square2.bigframes_bigquery_function - assert square2.bigframes_cloud_function is None - - # They should point to the same function. - assert square1.bigframes_remote_function == square2.bigframes_remote_function # type: ignore - assert square1.bigframes_bigquery_function == square2.bigframes_bigquery_function # type: ignore - assert square2.bigframes_remote_function == square2.bigframes_bigquery_function # type: ignore - - # The result of applying them should be the same. - int64_col = scalars_df_index["int64_col"] - int64_col_filter = int64_col.notnull() - int64_col_filtered = int64_col[int64_col_filter] - - s1_result_col = int64_col_filtered.apply(square1) - s1_result = int64_col_filtered.to_frame().assign(result=s1_result_col) - - s2_result_col = int64_col_filtered.apply(square2) - s2_result = int64_col_filtered.to_frame().assign(result=s2_result_col) - - assert_frame_equal(s1_result.to_pandas(), s2_result.to_pandas()) - - -def test_read_gbq_function_runs_existing_udf(session): - func = session.read_gbq_function("bqutil.fn.cw_lower_case_ascii_only") - got = func("AURÉLIE") - assert got == "aurÉlie" - - -def test_read_gbq_function_runs_existing_udf_4_params(session): - func = session.read_gbq_function("bqutil.fn.cw_instr4") - got = func("TestStr123456Str", "Str", 1, 2) - assert got == 14 - - -def test_read_gbq_function_runs_existing_udf_array_output(session, routine_id_unique): - bigframes.session._io.bigquery.start_query_with_job( - session.bqclient, - textwrap.dedent( - f""" - CREATE OR REPLACE FUNCTION `{routine_id_unique}`(x STRING) - RETURNS ARRAY - AS ( - [x, x] - ) - """ - ), - job_config=bigquery.QueryJobConfig(), - location=None, - project=None, - timeout=None, - metrics=None, - publisher=bigframes.core.events.Publisher(), - ) - func = session.read_gbq_function(routine_id_unique) - - # Test on scalar value - got = func("hello") - assert got == ["hello", "hello"] - - # Test on a series, assert pandas parity - pd_s = pd.Series(["alpha", "beta", "gamma"]) - bf_s = session.read_pandas(pd_s) - pd_result = pd_s.apply(func) - bf_result = bf_s.apply(func) - assert bigframes.dtypes.is_array_string_like(bf_result.dtype) - assert_series_equal( - pd_result, bf_result.to_pandas(), check_dtype=False, check_index_type=False - ) - - -def test_read_gbq_function_runs_existing_udf_2_params_array_output( - session, routine_id_unique -): - bigframes.session._io.bigquery.start_query_with_job( - session.bqclient, - textwrap.dedent( - f""" - CREATE OR REPLACE FUNCTION `{routine_id_unique}`(x STRING, y STRING) - RETURNS ARRAY - AS ( - [x, y] - ) - """ - ), - job_config=bigquery.QueryJobConfig(), - location=None, - project=None, - timeout=None, - metrics=None, - publisher=bigframes.core.events.Publisher(), - ) - func = session.read_gbq_function(routine_id_unique) - - # Test on scalar value - got = func("hello", "world") - assert got == ["hello", "world"] - - # Test on series, assert pandas parity - pd_df = pd.DataFrame( - {"col0": ["alpha", "beta", "gamma"], "col1": ["delta", "theta", "phi"]} - ) - bf_df = session.read_pandas(pd_df) - pd_result = pd_df["col0"].combine(pd_df["col1"], func) - bf_result = bf_df["col0"].combine(bf_df["col1"], func) - assert bigframes.dtypes.is_array_string_like(bf_result.dtype) - assert_series_equal( - pd_result, bf_result.to_pandas(), check_dtype=False, check_index_type=False - ) - - -def test_read_gbq_function_runs_existing_udf_4_params_array_output( - session, routine_id_unique -): - bigframes.session._io.bigquery.start_query_with_job( - session.bqclient, - textwrap.dedent( - f""" - CREATE OR REPLACE FUNCTION `{routine_id_unique}`(x STRING, y BOOL, z INT64, w FLOAT64) - RETURNS ARRAY - AS ( - [x, CAST(y AS STRING), CAST(z AS STRING), CAST(w AS STRING)] - ) - """ - ), - job_config=bigquery.QueryJobConfig(), - location=None, - project=None, - timeout=None, - metrics=None, - publisher=bigframes.core.events.Publisher(), - ) - func = session.read_gbq_function(routine_id_unique) - - # Test on scalar value - got = func("hello", True, 1, 2.3) - assert got == ["hello", "true", "1", "2.3"] - - # Test on a dataframe, assert pandas parity - pd_df = pd.DataFrame( - { - "col0": ["alpha", "beta", "gamma"], - "col1": [True, False, True], - "col2": [1, 2, 3], - "col3": [4.5, 6, 7.75], - } - ) - bf_df = session.read_pandas(pd_df) - # Simulate the result directly, since the function cannot be applied - # directly on a pandas dataframe with axis=1, as this is a special type of - # function with multiple params supported only on bigframes dataframe. - pd_result = pd.Series( - [ - ["alpha", "true", "1", "4.5"], - ["beta", "false", "2", "6"], - ["gamma", "true", "3", "7.75"], - ] - ) - bf_result = bf_df.apply(func, axis=1) - assert bigframes.dtypes.is_array_string_like(bf_result.dtype) - assert_series_equal( - pd_result, bf_result.to_pandas(), check_dtype=False, check_index_type=False - ) - - -def test_read_gbq_function_reads_udfs(session, bigquery_client, dataset_id): - dataset_ref = bigquery.DatasetReference.from_string(dataset_id) - arg = bigquery.RoutineArgument( - name="x", - data_type=bigquery.StandardSqlDataType(bigquery.StandardSqlTypeNames.INT64), - ) - sql_routine = bigquery.Routine( - dataset_ref.routine("square_sql"), - body="x * x", - arguments=[arg], - return_type=bigquery.StandardSqlDataType(bigquery.StandardSqlTypeNames.INT64), - type_=bigquery.RoutineType.SCALAR_FUNCTION, - ) - js_routine = bigquery.Routine( - dataset_ref.routine("square_js"), - body="return x * x", - language="JAVASCRIPT", - arguments=[arg], - return_type=bigquery.StandardSqlDataType(bigquery.StandardSqlTypeNames.INT64), - type_=bigquery.RoutineType.SCALAR_FUNCTION, - ) - - for routine in (sql_routine, js_routine): - # Create the routine in BigQuery and read it back using read_gbq_function. - bigquery_client.create_routine(routine, exists_ok=True) - square = session.read_gbq_function( - str(routine.reference), - ) - - # It should point to the named routine and yield the expected results. - assert square.bigframes_bigquery_function == str(routine.reference) - assert square.input_dtypes == (bigframes.dtypes.INT_DTYPE,) - assert square.output_dtype == bigframes.dtypes.INT_DTYPE - assert ( - square.bigframes_bigquery_function_output_dtype - == bigframes.dtypes.INT_DTYPE - ) - - src = {"x": [-5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5]} - - routine_ref_str = bff_utils.routine_ref_to_string_for_query(routine.reference) - direct_sql = " UNION ALL ".join( - [f"SELECT {x} AS x, {routine_ref_str}({x}) AS y" for x in src["x"]] - ) - direct_df = bigquery_client.query(direct_sql).to_dataframe() - - indirect_df = bigframes.dataframe.DataFrame(src) - indirect_df = indirect_df.assign(y=indirect_df.x.apply(square)) - converted_indirect_df = indirect_df.to_pandas() - - assert_frame_equal( - direct_df, converted_indirect_df, ignore_order=True, check_index_type=False - ) - - -def test_read_gbq_function_requires_explicit_types( - session, bigquery_client, dataset_id -): - dataset_ref = bigquery.DatasetReference.from_string(dataset_id) - typed_arg = bigquery.RoutineArgument( - name="x", - data_type=bigquery.StandardSqlDataType(bigquery.StandardSqlTypeNames.INT64), - ) - untyped_arg = bigquery.RoutineArgument( - name="x", - kind="ANY_TYPE", # With this kind, data_type not required for SQL functions. - ) - - both_types_specified = bigquery.Routine( - dataset_ref.routine("both_types_specified"), - body="x * x", - arguments=[typed_arg], - return_type=bigquery.StandardSqlDataType(bigquery.StandardSqlTypeNames.INT64), - type_=bigquery.RoutineType.SCALAR_FUNCTION, - ) - only_return_type_specified = bigquery.Routine( - dataset_ref.routine("only_return_type_specified"), - body="x * x", - arguments=[untyped_arg], - return_type=bigquery.StandardSqlDataType(bigquery.StandardSqlTypeNames.INT64), - type_=bigquery.RoutineType.SCALAR_FUNCTION, - ) - only_arg_type_specified = bigquery.Routine( - dataset_ref.routine("only_arg_type_specified"), - body="x * x", - arguments=[typed_arg], - type_=bigquery.RoutineType.SCALAR_FUNCTION, - ) - neither_type_specified = bigquery.Routine( - dataset_ref.routine("neither_type_specified"), - body="x * x", - arguments=[untyped_arg], - type_=bigquery.RoutineType.SCALAR_FUNCTION, - ) - - bigquery_client.create_routine(both_types_specified, exists_ok=True) - bigquery_client.create_routine(only_return_type_specified, exists_ok=True) - bigquery_client.create_routine(only_arg_type_specified, exists_ok=True) - bigquery_client.create_routine(neither_type_specified, exists_ok=True) - - session.read_gbq_function( - str(both_types_specified.reference), - ) - with pytest.warns( - bigframes.exceptions.UnknownDataTypeWarning, - match=r"missing input data types[\s\S]*assume default data type", - ): - session.read_gbq_function( - str(only_return_type_specified.reference), - ) - with pytest.raises(ValueError): - session.read_gbq_function( - str(only_arg_type_specified.reference), - ) - with pytest.raises(ValueError): - session.read_gbq_function( - str(neither_type_specified.reference), - ) - - -@pytest.mark.parametrize( - ("session_fixture",), - [ - pytest.param("session"), - pytest.param("unordered_session"), - ], -) -@pytest.mark.parametrize( - ("array_type", "expected_data"), - [ - pytest.param(None, ["[1,2,3]", "[10,11,12]", "[100,101,102]"], id="None"), - pytest.param( - list[str], - [["1", "2", "3"], ["10", "11", "12"], ["100", "101", "102"]], - id="list-str", - ), - pytest.param( - list[int], [[1, 2, 3], [10, 11, 12], [100, 101, 102]], id="list-int" - ), - ], -) -def test_read_gbq_function_respects_python_output_type( - request, session_fixture, bigquery_client, dataset_id, array_type, expected_data -): - session = request.getfixturevalue(session_fixture) - dataset_ref = bigquery.DatasetReference.from_string(dataset_id) - arg = bigquery.RoutineArgument( - name="x", - data_type=bigquery.StandardSqlDataType(bigquery.StandardSqlTypeNames.INT64), - ) - sql_routine = bigquery.Routine( - dataset_ref.routine(_prefixer.create_prefix()), - body="TO_JSON_STRING([x, x+1, x+2])", - arguments=[arg], - return_type=bigquery.StandardSqlDataType(bigquery.StandardSqlTypeNames.STRING), - description=bff_utils.get_bigframes_metadata(python_output_type=array_type), - type_=bigquery.RoutineType.SCALAR_FUNCTION, - ) - - # Create the routine in BigQuery and read it back using read_gbq_function. - bigquery_client.create_routine(sql_routine, exists_ok=True) - func = session.read_gbq_function(str(sql_routine.reference)) - - # test that the function works as expected - s = bigframes.series.Series([1, 10, 100]) - expected = pd.Series(expected_data) - actual = s.apply(func).to_pandas() - - # ignore type disparities, e.g. "int64" in pandas v/s "Int64" in bigframes - assert_series_equal(expected, actual, check_dtype=False, check_index_type=False) - - -@pytest.mark.parametrize( - ("array_type",), - [ - pytest.param(list[bool], id="list-bool"), - pytest.param(list[float], id="list-float"), - pytest.param(list[int], id="list-int"), - pytest.param(list[str], id="list-str"), - ], -) -def test_read_gbq_function_supports_python_output_type_only_for_string_outputs( - session, bigquery_client, dataset_id, array_type -): - dataset_ref = bigquery.DatasetReference.from_string(dataset_id) - arg = bigquery.RoutineArgument( - name="x", - data_type=bigquery.StandardSqlDataType(bigquery.StandardSqlTypeNames.INT64), - ) - sql_routine = bigquery.Routine( - dataset_ref.routine(_prefixer.create_prefix()), - body="x+1", - arguments=[arg], - return_type=bigquery.StandardSqlDataType(bigquery.StandardSqlTypeNames.INT64), - description=bff_utils.get_bigframes_metadata(python_output_type=array_type), - type_=bigquery.RoutineType.SCALAR_FUNCTION, - ) - - # Create the routine in BigQuery and read it back using read_gbq_function. - bigquery_client.create_routine(sql_routine, exists_ok=True) - - # reading back will fail because we currently allow specifying an explicit - # output_type for BQ functions with STRING output - with pytest.raises( - TypeError, - match="An explicit output_type should be provided only for a BigQuery function with STRING output.", - ): - session.read_gbq_function(str(sql_routine.reference)) - - -@pytest.mark.parametrize( - ("array_type",), - [ - pytest.param(list[bool], id="list-bool"), - pytest.param(list[float], id="list-float"), - pytest.param(list[int], id="list-int"), - pytest.param(list[str], id="list-str"), - ], -) -def test_read_gbq_function_supported_python_output_type( - session, bigquery_client, dataset_id, array_type -): - dataset_ref = bigquery.DatasetReference.from_string(dataset_id) - arg = bigquery.RoutineArgument( - name="x", - data_type=bigquery.StandardSqlDataType(bigquery.StandardSqlTypeNames.INT64), - ) - sql_routine = bigquery.Routine( - dataset_ref.routine(_prefixer.create_prefix()), - body="CAST(x AS STRING)", - arguments=[arg], - return_type=bigquery.StandardSqlDataType(bigquery.StandardSqlTypeNames.STRING), - description=bff_utils.get_bigframes_metadata(python_output_type=array_type), - type_=bigquery.RoutineType.SCALAR_FUNCTION, - ) - - # Create the routine in BigQuery and read it back using read_gbq_function. - bigquery_client.create_routine(sql_routine, exists_ok=True) - session.read_gbq_function(str(sql_routine.reference)) - - -@pytest.mark.flaky(retries=2, delay=120) -def test_df_apply_scalar_func(session, scalars_dfs): - scalars_df, _ = scalars_dfs - bdf = bigframes.pandas.DataFrame( - { - "Column1": scalars_df["string_col"], - "Column2": scalars_df["string_col"], - } - ) - - # The "cw_lower_case_ascii_only" is a scalar function. - func_ref = session.read_gbq_function("bqutil.fn.cw_lower_case_ascii_only") - - # DataFrame '.apply()' only supports series level application. - with pytest.raises(NotImplementedError) as context: - bdf.apply(func_ref) - assert str(context.value) == ( - "BigFrames DataFrame '.apply()' does not support BigFrames BigQuery " - "function for column-wise (i.e. with axis=0) operations, please use a " - "regular python function instead. For element-wise operations of the " - "BigFrames BigQuery function, please use '.map()'. " - f"{constants.FEEDBACK_LINK}" - ) - - -@pytest.mark.flaky(retries=2, delay=120) -def test_df_apply_axis_1(session, scalars_dfs, dataset_id_permanent): - columns = [ - "bool_col", - "int64_col", - "int64_too", - "float64_col", - "string_col", - "bytes_col", - ] - scalars_df, scalars_pandas_df = scalars_dfs - - def add_ints(row: pandas.Series) -> int: - return row["int64_col"] + row["int64_too"] - - with pytest.warns( - bigframes.exceptions.PreviewWarning, - match="input_types=Series is in preview.", - ): - add_ints_remote = session.remote_function( - dataset=dataset_id_permanent, - name=get_function_name(add_ints, is_row_processor=True), - cloud_function_service_account="default", - )(add_ints) - assert add_ints_remote.bigframes_remote_function # type: ignore - assert add_ints_remote.bigframes_bigquery_function # type: ignore - assert add_ints_remote.bigframes_cloud_function # type: ignore - - with pytest.warns( - bigframes.exceptions.PreviewWarning, match="axis=1 scenario is in preview." - ): - bf_result = scalars_df[columns].apply(add_ints_remote, axis=1).to_pandas() - - pd_result = scalars_pandas_df[columns].apply(add_ints, axis=1) - - # bf_result.dtype is 'Int64' while pd_result.dtype is 'object', ignore this - # mismatch by using check_dtype=False. - # - # bf_result.to_numpy() produces an array of numpy.float64's - # (in system_prerelease tests), while pd_result.to_numpy() produces an - # array of ints, ignore this mismatch by using check_exact=False. - assert_series_equal(pd_result, bf_result, check_dtype=False, check_exact=False) - - # Read back the deployed BQ remote function using read_gbq_function. - func_ref = session.read_gbq_function( - function_name=add_ints_remote.bigframes_bigquery_function, # type: ignore - is_row_processor=True, - ) - - assert ( - func_ref.bigframes_remote_function == add_ints_remote.bigframes_remote_function - ) # type: ignore - assert ( - func_ref.bigframes_bigquery_function - == add_ints_remote.bigframes_bigquery_function - ) # type: ignore - assert func_ref.bigframes_remote_function == func_ref.bigframes_bigquery_function # type: ignore - - bf_result_gbq = scalars_df[columns].apply(func_ref, axis=1).to_pandas() - assert_series_equal(pd_result, bf_result_gbq, check_dtype=False, check_exact=False) - - -@pytest.mark.flaky(retries=2, delay=120) -def test_df_apply_axis_1_ordering(session, scalars_dfs, dataset_id_permanent): - columns = ["bool_col", "int64_col", "int64_too", "float64_col", "string_col"] - ordering_columns = ["bool_col", "int64_col"] - scalars_df, scalars_pandas_df = scalars_dfs - - def add_ints(row: pandas.Series) -> int: - return row["int64_col"] + row["int64_too"] - - add_ints_remote = session.remote_function( - input_types=pandas.Series, - output_type=int, - dataset=dataset_id_permanent, - name=get_function_name(add_ints, is_row_processor=True), - cloud_function_service_account="default", - )(add_ints) - - bf_result = ( - scalars_df[columns] - .sort_values(ordering_columns) - .apply(add_ints_remote, axis=1) - .to_pandas() - ) - pd_result = ( - scalars_pandas_df[columns].sort_values(ordering_columns).apply(add_ints, axis=1) - ) - - # bf_result.dtype is 'Int64' while pd_result.dtype is 'object', ignore this - # mismatch by using check_dtype=False. - # - # bf_result.to_numpy() produces an array of numpy.float64's - # (in system_prerelease tests), while pd_result.to_numpy() produces an - # array of ints, ignore this mismatch by using check_exact=False. - assert_series_equal(pd_result, bf_result, check_dtype=False, check_exact=False) - - -@pytest.mark.flaky(retries=2, delay=120) -def test_df_apply_axis_1_multiindex(session, dataset_id_permanent): - pd_df = pd.DataFrame( - {"x": [1, 2, 3], "y": [1.5, 3.75, 5], "z": ["pq", "rs", "tu"]}, - index=pd.MultiIndex.from_tuples([("a", 100), ("a", 200), ("b", 300)]), - ) - bf_df = session.read_pandas(pd_df) - - def add_numbers(row): - return row["x"] + row["y"] - - add_numbers_remote = session.remote_function( - input_types=pandas.Series, - output_type=float, - dataset=dataset_id_permanent, - name=get_function_name(add_numbers, is_row_processor=True), - cloud_function_service_account="default", - )(add_numbers) - - bf_result = bf_df.apply(add_numbers_remote, axis=1).to_pandas() - pd_result = pd_df.apply(add_numbers, axis=1) - - # bf_result.dtype is 'Float64' while pd_result.dtype is 'float64', ignore this - # mismatch by using check_dtype=False. - # - # bf_result.index[0].dtype is 'string[pyarrow]' while - # pd_result.index[0].dtype is 'object', ignore this mismatch by using - # check_index_type=False. - assert_series_equal(pd_result, bf_result, check_dtype=False, check_index_type=False) - - -def test_df_apply_axis_1_unsupported_callable(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - columns = ["bool_col", "int64_col", "int64_too", "float64_col", "string_col"] - - def add_ints(row): - return row["int64_col"] + row["int64_too"] - - # pandas works - scalars_pandas_df.apply(add_ints, axis=1) - - with pytest.raises( - ValueError, match="For axis=1 a BigFrames BigQuery function must be used." - ): - scalars_df[columns].apply(add_ints, axis=1) - - -@pytest.mark.flaky(retries=2, delay=120) -def test_df_apply_axis_1_unsupported_dtype(session, scalars_dfs, dataset_id_permanent): - columns_with_not_supported_dtypes = [ - "date_col", - "datetime_col", - "geography_col", - "numeric_col", - "time_col", - "timestamp_col", - ] - - scalars_df, scalars_pandas_df = scalars_dfs - - def echo_len(row): - return len(row) - - echo_len_remote = session.remote_function( - input_types=pandas.Series, - output_type=float, - dataset=dataset_id_permanent, - name=get_function_name(echo_len, is_row_processor=True), - cloud_function_service_account="default", - )(echo_len) - - for column in columns_with_not_supported_dtypes: - # pandas works - scalars_pandas_df[[column]].apply(echo_len, axis=1) - - dtype = scalars_df[column].dtype - - with ( - pytest.raises( - NotImplementedError, - match=re.escape( - f"DataFrame has a column of dtype '{dtype}' which is not supported with axis=1. Supported dtypes are (" - ), - ), - pytest.warns( - bigframes.exceptions.PreviewWarning, - match="axis=1 scenario is in preview.", - ), - ): - scalars_df[[column]].apply(echo_len_remote, axis=1) - - -@pytest.mark.flaky(retries=2, delay=120) -def test_remote_function_application_repr(session, dataset_id_permanent): - # This function deliberately has a param with name "name", this is to test - # a specific ibis' internal handling of object names - def should_mask(name: str) -> bool: - hash = 0 - for char_ in name: - hash += ord(char_) - return hash % 2 == 0 - - assert "name" in inspect.signature(should_mask).parameters - - should_mask = session.remote_function( - dataset=dataset_id_permanent, - name=get_function_name(should_mask), - cloud_function_service_account="default", - )(should_mask) - - s = bigframes.series.Series(["Alice", "Bob", "Caroline"]) - - repr(s.apply(should_mask)) - repr(s.where(s.apply(should_mask))) - repr(s.where(~s.apply(should_mask))) - repr(s.mask(should_mask)) - repr(s.mask(should_mask, "REDACTED")) - - -def test_read_gbq_function_application_repr( - session, routine_id_unique, scalars_df_index -): - # This function deliberately has a param with name "name", this is to test - # a specific ibis' internal handling of object names - session.bqclient.query_and_wait( - f"CREATE OR REPLACE FUNCTION `{routine_id_unique}`(name STRING) RETURNS BOOL AS (MOD(LENGTH(name), 2) = 1)" - ) - routine = session.bqclient.get_routine(routine_id_unique) - assert "name" in [arg.name for arg in routine.arguments] - - # read the function and apply to dataframe - should_mask = session.read_gbq_function(routine_id_unique) - - s = scalars_df_index["string_col"] - - repr(s.apply(should_mask)) - repr(s.where(s.apply(should_mask))) - repr(s.where(~s.apply(should_mask))) - repr(s.mask(should_mask)) - repr(s.mask(should_mask, "REDACTED")) - - -@pytest.mark.parametrize( - ("method",), - [ - pytest.param("apply"), - pytest.param("map"), - pytest.param("mask"), - ], -) -@pytest.mark.flaky(retries=2, delay=120) -def test_remote_function_unary_applied_after_filter( - session, dataset_id_permanent, scalars_dfs, method -): - # This function is deliberately written to not work with NA input - def is_odd(x: int) -> bool: - return x % 2 == 1 - - scalars_df, scalars_pandas_df = scalars_dfs - int_col_name_with_nulls = "int64_col" - - # make sure there are NA values in the test column - assert any([pd.isna(val) for val in scalars_df[int_col_name_with_nulls]]) - - # create a remote function - is_odd_remote = session.remote_function( - dataset=dataset_id_permanent, - name=get_function_name(is_odd), - cloud_function_service_account="default", - )(is_odd) - - # with nulls in the series the remote function application would fail - with pytest.raises( - google.api_core.exceptions.BadRequest, match="unsupported operand" - ): - bf_method = getattr(scalars_df[int_col_name_with_nulls], method) - bf_method(is_odd_remote).to_pandas() - - # after filtering out nulls the remote function application should work - # similar to pandas - pd_method = getattr( - scalars_pandas_df[scalars_pandas_df[int_col_name_with_nulls].notnull()][ - int_col_name_with_nulls - ], - method, - ) - pd_result = pd_method(is_odd) - bf_method = getattr( - scalars_df[scalars_df[int_col_name_with_nulls].notnull()][ - int_col_name_with_nulls - ], - method, - ) - bf_result = bf_method(is_odd_remote).to_pandas() - - # ignore any dtype difference - assert_series_equal(pd_result, bf_result, check_dtype=False) - - -@pytest.mark.flaky(retries=2, delay=120) -def test_remote_function_binary_applied_after_filter( - session, dataset_id_permanent, scalars_dfs -): - # This function is deliberately written to not work with NA input - def add(x: int, y: int) -> int: - return x + y - - scalars_df, scalars_pandas_df = scalars_dfs - int_col_name_with_nulls = "int64_col" - int_col_name_no_nulls = "int64_too" - bf_df = scalars_df[[int_col_name_with_nulls, int_col_name_no_nulls]] - pd_df = scalars_pandas_df[[int_col_name_with_nulls, int_col_name_no_nulls]] - - # make sure there are NA values in the test column - assert any([pd.isna(val) for val in bf_df[int_col_name_with_nulls]]) - - # create a remote function - add_remote = session.remote_function( - dataset=dataset_id_permanent, - name=get_function_name(add), - cloud_function_service_account="default", - )(add) - - # with nulls in the series the remote function application would fail - with pytest.raises( - google.api_core.exceptions.BadRequest, match="unsupported operand" - ): - bf_df[int_col_name_with_nulls].combine( - bf_df[int_col_name_no_nulls], add_remote - ).to_pandas() - - # after filtering out nulls the remote function application should work - # similar to pandas - pd_filter = pd_df[int_col_name_with_nulls].notnull() - pd_result = pd_df[pd_filter][int_col_name_with_nulls].combine( - pd_df[pd_filter][int_col_name_no_nulls], add - ) - bf_filter = bf_df[int_col_name_with_nulls].notnull() - bf_result = ( - bf_df[bf_filter][int_col_name_with_nulls] - .combine(bf_df[bf_filter][int_col_name_no_nulls], add_remote) - .to_pandas() - ) - - # ignore any dtype difference - assert_series_equal(pd_result, bf_result, check_dtype=False) - - -@pytest.mark.flaky(retries=2, delay=120) -def test_remote_function_nary_applied_after_filter( - session, dataset_id_permanent, scalars_dfs -): - # This function is deliberately written to not work with NA input - def add(x: int, y: int, z: float) -> float: - return x + y + z - - scalars_df, scalars_pandas_df = scalars_dfs - int_col_name_with_nulls = "int64_col" - int_col_name_no_nulls = "int64_too" - float_col_name_with_nulls = "float64_col" - bf_df = scalars_df[ - [int_col_name_with_nulls, int_col_name_no_nulls, float_col_name_with_nulls] - ] - pd_df = scalars_pandas_df[ - [int_col_name_with_nulls, int_col_name_no_nulls, float_col_name_with_nulls] - ] - - # make sure there are NA values in the test columns - assert any([pd.isna(val) for val in bf_df[int_col_name_with_nulls]]) - assert any([pd.isna(val) for val in bf_df[float_col_name_with_nulls]]) - - # create a remote function - add_remote = session.remote_function( - dataset=dataset_id_permanent, - name=get_function_name(add), - cloud_function_service_account="default", - )(add) - - # pandas does not support nary functions, so let's create a proxy function - # for testing purpose that takes a series and in turn calls the naray function - def add_pandas(s: pd.Series) -> float: - return add( - s[int_col_name_with_nulls], - s[int_col_name_no_nulls], - s[float_col_name_with_nulls], - ) - - # with nulls in the series the remote function application would fail - with pytest.raises( - google.api_core.exceptions.BadRequest, match="unsupported operand" - ): - bf_df.apply(add_remote, axis=1).to_pandas() - - # after filtering out nulls the remote function application should work - # similar to pandas - pd_filter = ( - pd_df[int_col_name_with_nulls].notnull() - & pd_df[float_col_name_with_nulls].notnull() - ) - pd_result = pd_df[pd_filter].apply(add_pandas, axis=1) - bf_filter = ( - bf_df[int_col_name_with_nulls].notnull() - & bf_df[float_col_name_with_nulls].notnull() - ) - bf_result = bf_df[bf_filter].apply(add_remote, axis=1).to_pandas() - - # ignore any dtype difference - assert_series_equal(pd_result, bf_result, check_dtype=False) - - -@pytest.mark.parametrize( - ("method",), - [ - pytest.param("apply"), - pytest.param("map"), - pytest.param("mask"), - ], -) -def test_remote_function_unary_partial_ordering_mode_assign( - unordered_session, dataset_id_permanent, method -): - df = unordered_session.read_gbq("bigquery-public-data.baseball.schedules")[ - ["duration_minutes"] - ] - - def is_long_duration(minutes: int) -> bool: - return minutes >= 120 - - is_long_duration = unordered_session.remote_function( - dataset=dataset_id_permanent, - name=get_function_name(is_long_duration), - cloud_function_service_account="default", - )(is_long_duration) - - method = getattr(df["duration_minutes"], method) - - df1 = df.assign(duration_meta=method(is_long_duration)) - repr(df1) - - -@pytest.mark.flaky(retries=2, delay=120) -def test_remote_function_binary_partial_ordering_mode_assign( - unordered_session, dataset_id_permanent, scalars_df_index -): - def combiner(x: int, y: int) -> int: - if x is None: - return y - return x - - combiner = unordered_session.remote_function( - dataset=dataset_id_permanent, - name=get_function_name(combiner), - cloud_function_service_account="default", - )(combiner) - - df = scalars_df_index[["int64_col", "int64_too", "float64_col", "string_col"]] - df1 = df.assign(int64_combined=df["int64_col"].combine(df["int64_too"], combiner)) - repr(df1) - - -@pytest.mark.flaky(retries=2, delay=120) -def test_remote_function_nary_partial_ordering_mode_assign( - unordered_session, dataset_id_permanent, scalars_df_index -): - def processor(x: int, y: int, z: float, w: str) -> str: - return f"I got x={x}, y={y}, z={z} and w={w}" - - processor = unordered_session.remote_function( - dataset=dataset_id_permanent, - name=get_function_name(processor), - cloud_function_service_account="default", - )(processor) - - df = scalars_df_index[["int64_col", "int64_too", "float64_col", "string_col"]] - df1 = df.assign(combined=df.apply(processor, axis=1)) - repr(df1) - - -@pytest.mark.flaky(retries=2, delay=120) -def test_remote_function_unsupported_type( - session, - dataset_id_permanent, - bq_cf_connection, -): - # Remote functions do not support tuple return types. - def func_tuple(x): - return (x, x, x) - - with pytest.raises( - ValueError, - match=r"must be one of the supported types", - ): - session.remote_function( - input_types=int, - output_type=Sequence[int], - dataset=dataset_id_permanent, - bigquery_connection=bq_cf_connection, - reuse=True, - name=get_function_name(func_tuple), - cloud_function_service_account="default", - )(func_tuple) diff --git a/tests/system/small/geopandas/test_geoseries.py b/tests/system/small/geopandas/test_geoseries.py deleted file mode 100644 index 9f1f830dc68..00000000000 --- a/tests/system/small/geopandas/test_geoseries.py +++ /dev/null @@ -1,577 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from __future__ import annotations - -import re - -import bigframes_vendored.constants as constants -import geopandas # type: ignore -import geopandas.testing # type:ignore -import google.api_core.exceptions -import pandas as pd -import pytest -from geopandas.array import GeometryDtype # type:ignore -from shapely.geometry import ( # type: ignore - GeometryCollection, - LineString, - Point, - Polygon, -) - -import bigframes.geopandas -import bigframes.pandas -import bigframes.series -import bigframes.session -from bigframes.testing.utils import assert_series_equal - - -@pytest.fixture(scope="session") -def urban_areas_dfs(session, urban_areas_table_id): - bf_ua = session.read_gbq(urban_areas_table_id, index_col="geo_id") - pd_ua = bf_ua.to_pandas() - return (bf_ua, pd_ua) - - -def test_geo_x(urban_areas_dfs): - bf_ua, pd_ua = urban_areas_dfs - bf_series: bigframes.geopandas.GeoSeries = bf_ua["internal_point_geom"].geo - pd_series: geopandas.GeoSeries = geopandas.GeoSeries(pd_ua["internal_point_geom"]) - bf_result = bf_series.x.to_pandas() - pd_result = pd_series.x - - assert_series_equal( - pd_result.astype(pd.Float64Dtype()), - bf_result, - ) - - -def test_geo_x_non_point(urban_areas_dfs): - bf_ua, _ = urban_areas_dfs - bf_series: bigframes.geopandas.GeoSeries = bf_ua["urban_area_geom"].geo - - with pytest.raises(google.api_core.exceptions.BadRequest, match="ST_X"): - bf_series.x.to_pandas() - - -def test_geo_y(urban_areas_dfs): - bf_ua, pd_ua = urban_areas_dfs - bf_series: bigframes.geopandas.GeoSeries = bf_ua["internal_point_geom"].geo - pd_series: geopandas.GeoSeries = geopandas.GeoSeries(pd_ua["internal_point_geom"]) - bf_result = bf_series.y.to_pandas() - pd_result = pd_series.y - - assert_series_equal( - pd_result.astype(pd.Float64Dtype()), - bf_result, - ) - - -def test_geo_area_not_supported(session: bigframes.session.Session): - s = bigframes.pandas.Series( - [ - Polygon([(0, 0), (1, 1), (0, 1)]), - Polygon([(10, 0), (10, 5), (0, 0)]), - Polygon([(0, 0), (2, 2), (2, 0)]), - LineString([(0, 0), (1, 1), (0, 1)]), - Point(0, 1), - ], - dtype=GeometryDtype(), - session=session, - ) - bf_series: bigframes.geopandas.GeoSeries = s.geo - with pytest.raises( - NotImplementedError, - match=re.escape( - f"GeoSeries.area is not supported. Use bigframes.bigquery.st_area(series), instead. {constants.FEEDBACK_LINK}" - ), - ): - bf_series.area - - -def test_geoseries_length_property_not_implemented(session): - gs = bigframes.geopandas.GeoSeries([Point(0, 0)], session=session) - with pytest.raises( - NotImplementedError, - match=re.escape( - "GeoSeries.length is not yet implemented. Please use bigframes.bigquery.st_length(geoseries) instead." - ), - ): - _ = gs.length - - -def test_geo_distance_not_supported(session: bigframes.session.Session): - s1 = bigframes.pandas.Series( - [ - Polygon([(0, 0), (1, 1), (0, 1)]), - Polygon([(10, 0), (10, 5), (0, 0)]), - Polygon([(0, 0), (2, 2), (2, 0)]), - LineString([(0, 0), (1, 1), (0, 1)]), - Point(0, 1), - ], - dtype=GeometryDtype(), - session=session, - ) - s2 = bigframes.geopandas.GeoSeries( - [ - Polygon([(0, 0), (1, 1), (0, 1)]), - Polygon([(10, 0), (10, 5), (0, 0)]), - Polygon([(0, 0), (2, 2), (2, 0)]), - LineString([(0, 0), (1, 1), (0, 1)]), - Point(0, 1), - ], - session=session, - ) - with pytest.raises( - NotImplementedError, - match=re.escape("GeoSeries.distance is not supported."), - ): - s1.geo.distance(s2) - - -def test_geo_from_xy(session: bigframes.session.Session): - x = [2.5, 5, -3.0] - y = [0.5, 1, 1.5] - bf_result = ( - bigframes.geopandas.GeoSeries.from_xy(x, y, session=session) - .astype(geopandas.array.GeometryDtype()) - .to_pandas() - ) - pd_result = geopandas.GeoSeries.from_xy(x, y, crs="EPSG:4326").astype( - geopandas.array.GeometryDtype() - ) - - pd.testing.assert_series_equal( - bf_result, - pd_result, - check_series_type=False, - check_index=False, - ) - - -def test_geo_from_wkt(session: bigframes.session.Session): - wkts = [ - "Point(0 1)", - "Point(2 4)", - "Point(5 3)", - "Point(6 8)", - ] - - bf_result = bigframes.geopandas.GeoSeries.from_wkt( - wkts, session=session - ).to_pandas() - - pd_result = geopandas.GeoSeries.from_wkt(wkts) - - pd.testing.assert_series_equal( - bf_result, - pd_result, - check_series_type=False, - check_index=False, - ) - - -def test_geo_to_wkt(session: bigframes.session.Session): - bf_geo = bigframes.geopandas.GeoSeries( - [ - Point(0, 1), - Point(2, 4), - Point(5, 3), - Point(6, 8), - ], - session=session, - ) - - pd_geo = geopandas.GeoSeries( - [ - Point(0, 1), - Point(2, 4), - Point(5, 3), - Point(6, 8), - ] - ) - - # Test was failing before using str.replace because the pd_result had extra - # whitespace "POINT (0 1)" while bf_result had none "POINT(0 1)". - # str.replace replaces any encountered whitespaces with none. - bf_result = ( - bf_geo.to_wkt().astype("string[pyarrow]").to_pandas().str.replace(" ", "") - ) - - pd_result = pd_geo.to_wkt().astype("string[pyarrow]").str.replace(" ", "") - - pd.testing.assert_series_equal( - bf_result, - pd_result, - check_index=False, - ) - - -def test_geo_boundary(session: bigframes.session.Session): - bf_s = bigframes.series.Series( - [ - Polygon([(0, 0), (1, 1), (0, 1)]), - Polygon([(10, 0), (10, 5), (0, 0)]), - Polygon([(0, 0), (2, 2), (2, 0)]), - LineString([(0, 0), (1, 1), (0, 1)]), - Point(0, 1), - ], - session=session, - ) - - pd_s = geopandas.GeoSeries( - [ - Polygon([(0, 0), (1, 1), (0, 1)]), - Polygon([(10, 0), (10, 5), (0, 0)]), - Polygon([(0, 0), (2, 2), (2, 0)]), - LineString([(0, 0), (1, 1), (0, 1)]), - Point(0, 1), - ], - index=pd.Index([0, 1, 2, 3, 4], dtype="Int64"), - crs="WGS84", - ) - - bf_result = bf_s.geo.boundary.to_pandas() - pd_result = pd_s.boundary - - geopandas.testing.assert_geoseries_equal( - bf_result, - pd_result, - check_series_type=False, - check_index_type=False, - ) - - -# the GeoSeries and GeoPandas results are not always the same. -# For example, when the difference between two polygons is empty, -# GeoPandas returns 'POLYGON EMPTY' while GeoSeries returns 'GeometryCollection([])'. -# This is why we are hard-coding the expected results. -def test_geo_difference_with_geometry_objects(session: bigframes.session.Session): - data1 = [ - Polygon([(0, 0), (10, 0), (10, 10), (0, 0)]), - Polygon([(0, 0), (1, 1), (0, 1), (0, 0)]), - Point(0, 1), - ] - - data2 = [ - Polygon([(0, 0), (10, 0), (10, 10), (0, 0)]), - Polygon([(0, 0), (1, 1), (0, 1), (0, 0)]), - LineString([(2, 0), (0, 2)]), - ] - - bf_s1 = bigframes.geopandas.GeoSeries(data=data1, session=session) - bf_s2 = bigframes.geopandas.GeoSeries(data=data2, session=session) - - bf_result = bf_s1.difference(bf_s2).to_pandas() - - expected = bigframes.geopandas.GeoSeries( - [ - Polygon([]), - Polygon([]), - Point(0, 1), - ], - index=[0, 1, 2], - session=session, - ).to_pandas() - - assert bf_result.dtype == "geometry" - assert expected.iloc[0].equals(bf_result.iloc[0]) - assert expected.iloc[1].equals(bf_result.iloc[1]) - assert expected.iloc[2].equals(bf_result.iloc[2]) - - -def test_geo_difference_with_single_geometry_object(session: bigframes.session.Session): - data1 = [ - Polygon([(0, 0), (10, 0), (10, 10), (0, 0)]), - Polygon([(4, 2), (6, 2), (8, 6), (4, 2)]), - Point(0, 1), - ] - - bf_s1 = bigframes.geopandas.GeoSeries(data=data1, session=session) - bf_result = bf_s1.difference( - bigframes.geopandas.GeoSeries( - [ - Polygon([(0, 0), (10, 0), (10, 10), (0, 0)]), - Polygon([(1, 0), (0, 5), (0, 0), (1, 0)]), - ], - session=session, - ), - ).to_pandas() - - expected = bigframes.geopandas.GeoSeries( - [ - GeometryCollection([]), - Polygon([(4, 2), (6, 2), (8, 6), (4, 2)]), - None, - ], - index=[0, 1, 2], - session=session, - ).to_pandas() - - assert bf_result.dtype == "geometry" - assert (expected.iloc[0]).equals(bf_result.iloc[0]) - assert expected.iloc[1] == bf_result.iloc[1] - assert expected.iloc[2] == bf_result.iloc[2] - - -def test_geo_difference_with_similar_geometry_objects( - session: bigframes.session.Session, -): - data1 = [ - Polygon([(0, 0), (10, 0), (10, 10), (0, 0)]), - Polygon([(0, 0), (1, 1), (0, 1)]), - Point(0, 1), - ] - - bf_s1 = bigframes.geopandas.GeoSeries(data=data1, session=session) - bf_result = bf_s1.difference(bf_s1).to_pandas() - - expected = bigframes.geopandas.GeoSeries( - [GeometryCollection([]), GeometryCollection([]), GeometryCollection([])], - index=[0, 1, 2], - session=session, - ).to_pandas() - - assert bf_result.dtype == "geometry" - assert expected.iloc[0].equals(bf_result.iloc[0]) - assert expected.iloc[1].equals(bf_result.iloc[1]) - assert expected.iloc[2].equals(bf_result.iloc[2]) - - -def test_geo_drop_duplicates(session: bigframes.session.Session): - bf_series = bigframes.geopandas.GeoSeries( - [Point(1, 1), Point(2, 2), Point(3, 3), Point(2, 2)], - session=session, - ) - - pd_series = geopandas.GeoSeries( - [Point(1, 1), Point(2, 2), Point(3, 3), Point(2, 2)] - ) - - bf_result = bf_series.drop_duplicates().to_pandas() - pd_result = pd_series.drop_duplicates() - - pd.testing.assert_series_equal( - geopandas.GeoSeries(bf_result), pd_result, check_index=False - ) - - -# the GeoSeries and GeoPandas results are not always the same. -# For example, when the intersection between two polygons is empty, -# GeoPandas returns 'POLYGON EMPTY' while GeoSeries returns 'GeometryCollection([])'. -# This is why we are hard-coding the expected results. -def test_geo_intersection_with_geometry_objects(session: bigframes.session.Session): - data1 = [ - Polygon([(0, 0), (10, 0), (10, 10), (0, 0)]), - Polygon([(0, 0), (1, 1), (0, 1), (0, 0)]), - Point(0, 1), - ] - - data2 = [ - Polygon([(0, 0), (10, 0), (10, 10), (0, 0)]), - Polygon([(0, 0), (1, 1), (0, 1), (0, 0)]), - LineString([(2, 0), (0, 2)]), - ] - - bf_s1 = bigframes.geopandas.GeoSeries(data=data1, session=session) - bf_s2 = bigframes.geopandas.GeoSeries(data=data2, session=session) - - bf_result = bf_s1.intersection(bf_s2).to_pandas() - - expected = bigframes.geopandas.GeoSeries( - [ - Polygon([(0, 0), (10, 0), (10, 10), (0, 0)]), - Polygon([(0, 0), (1, 1), (0, 1), (0, 0)]), - GeometryCollection([]), - ], - session=session, - ).to_pandas() - - assert bf_result.dtype == "geometry" - assert expected.iloc[0].equals(bf_result.iloc[0]) - assert expected.iloc[1].equals(bf_result.iloc[1]) - assert expected.iloc[2].equals(bf_result.iloc[2]) - - -def test_geo_intersection_with_single_geometry_object( - session: bigframes.session.Session, -): - data1 = [ - Polygon([(0, 0), (10, 0), (10, 10), (0, 0)]), - Polygon([(4, 2), (6, 2), (8, 6), (4, 2)]), - Point(0, 1), - ] - - bf_s1 = bigframes.geopandas.GeoSeries(data=data1, session=session) - bf_result = bf_s1.intersection( - bigframes.geopandas.GeoSeries( - [ - Polygon([(0, 0), (10, 0), (10, 10), (0, 0)]), - Polygon([(1, 0), (0, 5), (0, 0), (1, 0)]), - ], - session=session, - ), - ).to_pandas() - - expected = bigframes.geopandas.GeoSeries( - [ - Polygon([(0, 0), (10, 0), (10, 10), (0, 0)]), - GeometryCollection([]), - None, - ], - index=[0, 1, 2], - session=session, - ).to_pandas() - - assert bf_result.dtype == "geometry" - assert (expected.iloc[0]).equals(bf_result.iloc[0]) - assert expected.iloc[1] == bf_result.iloc[1] - assert expected.iloc[2] == bf_result.iloc[2] - - -def test_geo_intersection_with_similar_geometry_objects( - session: bigframes.session.Session, -): - data1 = [ - Polygon([(0, 0), (10, 0), (10, 10), (0, 0)]), - Polygon([(0, 0), (1, 1), (0, 1)]), - Point(0, 1), - ] - - bf_s1 = bigframes.geopandas.GeoSeries(data=data1, session=session) - bf_result = bf_s1.intersection(bf_s1).to_pandas() - - expected = bigframes.geopandas.GeoSeries( - [ - Polygon([(0, 0), (10, 0), (10, 10), (0, 0)]), - Polygon([(0, 0), (1, 1), (0, 1)]), - Point(0, 1), - ], - index=[0, 1, 2], - session=session, - ).to_pandas() - - assert bf_result.dtype == "geometry" - assert expected.iloc[0].equals(bf_result.iloc[0]) - assert expected.iloc[1].equals(bf_result.iloc[1]) - assert expected.iloc[2].equals(bf_result.iloc[2]) - - -def test_geo_is_closed_not_supported(session: bigframes.session.Session): - s = bigframes.series.Series( - [ - Polygon([(0, 0), (1, 1), (0, 1)]), - Polygon([(10, 0), (10, 5), (0, 0)]), - Polygon([(0, 0), (2, 2), (2, 0)]), - LineString([(0, 0), (1, 1), (0, 1)]), - Point(0, 1), - ], - dtype=GeometryDtype(), - session=session, - ) - bf_series: bigframes.geopandas.GeoSeries = s.geo - with pytest.raises( - NotImplementedError, - match=re.escape( - f"GeoSeries.is_closed is not supported. Use bigframes.bigquery.st_isclosed(series), instead. {constants.FEEDBACK_LINK}" - ), - ): - bf_series.is_closed - - -def test_geo_buffer_raises_notimplemented(session: bigframes.session.Session): - """GeoPandas takes distance in units of the coordinate system, but BigQuery - uses meters. - """ - s = bigframes.geopandas.GeoSeries( - [ - Point(0, 0), - ], - session=session, - ) - with pytest.raises( - NotImplementedError, match=re.escape("bigframes.bigquery.st_buffer") - ): - s.buffer(1000) - - -def test_geo_centroid(session: bigframes.session.Session): - bf_s = bigframes.series.Series( - [ - Polygon([(0, 0), (0.1, 0.1), (0, 0.1)]), - LineString([(10, 10), (10.0001, 10.0001), (10, 10.0001)]), - Point(-10, -10), - ], - session=session, - ) - - pd_s = geopandas.GeoSeries( - [ - Polygon([(0, 0), (0.1, 0.1), (0, 0.1)]), - LineString([(10, 10), (10.0001, 10.0001), (10, 10.0001)]), - Point(-10, -10), - ], - index=pd.Index([0, 1, 2], dtype="Int64"), - crs="WGS84", - ) - - bf_result = bf_s.geo.centroid.to_pandas() - # Avoid warning that centroid is incorrect for geographic CRS. - # https://gis.stackexchange.com/a/401815/275289 - pd_result = pd_s.to_crs("+proj=cea").centroid.to_crs("WGS84") - - geopandas.testing.assert_geoseries_equal( - bf_result, - pd_result, - check_series_type=False, - check_index_type=False, - # BigQuery geography calculations are on a sphere, so results will be - # slightly different. - check_less_precise=True, - ) - - -def test_geo_convex_hull(session: bigframes.session.Session): - bf_s = bigframes.series.Series( - [ - Polygon([(0, 0), (1, 1), (0, 1)]), - Polygon([(10, 0), (10, 5), (0, 0)]), - Polygon([(0, 0), (2, 2), (2, 0)]), - LineString([(0, 0), (1, 1), (0, 1)]), - Point(0, 1), - ], - session=session, - ) - - pd_s = geopandas.GeoSeries( - [ - Polygon([(0, 0), (1, 1), (0, 1)]), - Polygon([(10, 0), (10, 5), (0, 0)]), - Polygon([(0, 0), (2, 2), (2, 0)]), - LineString([(0, 0), (1, 1), (0, 1)]), - Point(0, 1), - ], - index=pd.Index([0, 1, 2, 3, 4], dtype="Int64"), - crs="WGS84", - ) - - bf_result = bf_s.geo.convex_hull.to_pandas() - pd_result = pd_s.convex_hull - - geopandas.testing.assert_geoseries_equal( - bf_result, - pd_result, - check_series_type=False, - check_index_type=False, - ) diff --git a/tests/system/small/ml/conftest.py b/tests/system/small/ml/conftest.py index 2f84b351e04..c11445b79a5 100644 --- a/tests/system/small/ml/conftest.py +++ b/tests/system/small/ml/conftest.py @@ -12,9 +12,8 @@ # See the License for the specific language governing permissions and # limitations under the License. -import os -import uuid from typing import cast +import uuid import pandas as pd import pytest @@ -29,10 +28,15 @@ globals, imported, linear_model, - remote, + llm, ) +@pytest.fixture(scope="session") +def bq_connection() -> str: + return "bigframes-dev.us.bigframes-rf-conn" + + @pytest.fixture(scope="session") def penguins_bqml_linear_model(session, penguins_linear_model_name) -> core.BqmlModel: model = session.bqclient.get_model(penguins_linear_model_name) @@ -41,11 +45,12 @@ def penguins_bqml_linear_model(session, penguins_linear_model_name) -> core.Bqml @pytest.fixture(scope="function") def ephemera_penguins_bqml_linear_model( - session: bigframes.Session, - penguins_bqml_linear_model: core.BqmlModel, + penguins_bqml_linear_model, ) -> core.BqmlModel: model = penguins_bqml_linear_model - return model.copy(f"{session._anonymous_dataset}.{uuid.uuid4().hex}") + return model.copy( + f"{model._model.project}.{model._model.dataset_id}.{uuid.uuid4().hex}" + ) @pytest.fixture(scope="session") @@ -152,25 +157,21 @@ def penguins_pca_model( @pytest.fixture(scope="session") -def onnx_iris_pandas_df(): - """Data matching the iris dataset.""" +def llm_text_pandas_df(): + """Additional data matching the penguins dataset, with a new index""" return pd.DataFrame( { - "sepal_length": [4.9, 5.1, 34.7], - "sepal_width": [3.0, 5.1, 24.7], - "petal_length": [1.4, 1.5, 13.3], - "petal_width": [0.4, 0.2, 18.3], - "species": [ - "setosa", - "setosa", - "virginica", + "prompt": [ + "What is BigQuery?", + "What is BQML?", + "What is BigQuery DataFrame?", ], } ) @pytest.fixture(scope="session") -def xgboost_iris_pandas_df(): +def onnx_iris_pandas_df(): """Data matching the iris dataset.""" return pd.DataFrame( { @@ -178,6 +179,11 @@ def xgboost_iris_pandas_df(): "sepal_width": [3.0, 5.1, 24.7], "petal_length": [1.4, 1.5, 13.3], "petal_width": [0.4, 0.2, 18.3], + "species": [ + "setosa", + "setosa", + "virginica", + ], } ) @@ -188,50 +194,54 @@ def onnx_iris_df(session, onnx_iris_pandas_df): @pytest.fixture(scope="session") -def xgboost_iris_df(session, xgboost_iris_pandas_df): - return session.read_pandas(xgboost_iris_pandas_df) +def llm_text_df(session, llm_text_pandas_df): + return session.read_pandas(llm_text_pandas_df) @pytest.fixture(scope="session") -def linear_remote_model_params() -> dict: - # Pre-deployed endpoint of linear reg model in Vertex. - # bigframes-test-linreg2 -> bigframes-test-linreg-endpoint2 - model_vertex_endpoint = os.environ.get( - "BIGFRAMES_TEST_MODEL_VERTEX_ENDPOINT", - "https://us-central1-aiplatform.googleapis.com/v1/projects/1084210331973/locations/us-central1/endpoints/3193318217619603456", +def bqml_palm2_text_generator_model(session, bq_connection) -> core.BqmlModel: + options = { + "remote_service_type": "CLOUD_AI_LARGE_LANGUAGE_MODEL_V1", + } + return globals.bqml_model_factory().create_remote_model( + session=session, connection_name=bq_connection, options=options ) - return { - "input": {"culmen_length_mm": "float64"}, - "output": {"predicted_body_mass_g": "array"}, - "endpoint": model_vertex_endpoint, - } + +@pytest.fixture(scope="session") +def palm2_text_generator_model(session, bq_connection) -> llm.PaLM2TextGenerator: + return llm.PaLM2TextGenerator(session=session, connection_name=bq_connection) @pytest.fixture(scope="session") -def bqml_linear_remote_model( - session, bq_connection, linear_remote_model_params -) -> core.BqmlModel: - options = { - "endpoint": linear_remote_model_params["endpoint"], - } - return globals.bqml_model_factory().create_remote_model( - session=session, - input=linear_remote_model_params["input"], - output=linear_remote_model_params["output"], - connection_name=bq_connection, - options=options, +def palm2_text_generator_32k_model(session, bq_connection) -> llm.PaLM2TextGenerator: + return llm.PaLM2TextGenerator( + model_name="text-bison-32k", session=session, connection_name=bq_connection ) +@pytest.fixture(scope="function") +def ephemera_palm2_text_generator_model( + session, bq_connection +) -> llm.PaLM2TextGenerator: + return llm.PaLM2TextGenerator(session=session, connection_name=bq_connection) + + @pytest.fixture(scope="session") -def linear_remote_vertex_model( - session, bq_connection, linear_remote_model_params -) -> remote.VertexAIModel: - return remote.VertexAIModel( - endpoint=linear_remote_model_params["endpoint"], - input=linear_remote_model_params["input"], - output=linear_remote_model_params["output"], +def palm2_embedding_generator_model( + session, bq_connection +) -> llm.PaLM2TextEmbeddingGenerator: + return llm.PaLM2TextEmbeddingGenerator( + session=session, connection_name=bq_connection + ) + + +@pytest.fixture(scope="session") +def palm2_embedding_generator_multilingual_model( + session, bq_connection +) -> llm.PaLM2TextEmbeddingGenerator: + return llm.PaLM2TextEmbeddingGenerator( + model_name="textembedding-gecko-multilingual", session=session, connection_name=bq_connection, ) @@ -245,14 +255,6 @@ def time_series_bqml_arima_plus_model( return core.BqmlModel(session, model) -@pytest.fixture(scope="session") -def time_series_bqml_arima_plus_model_w_id( - session, time_series_arima_plus_model_name_w_id -) -> core.BqmlModel: - model = session.bqclient.get_model(time_series_arima_plus_model_name_w_id) - return core.BqmlModel(session, model) - - @pytest.fixture(scope="session") def time_series_arima_plus_model( session, time_series_arima_plus_model_name @@ -263,16 +265,6 @@ def time_series_arima_plus_model( ) -@pytest.fixture(scope="session") -def time_series_arima_plus_model_w_id( - session, time_series_arima_plus_model_name_w_id -) -> forecasting.ARIMAPlus: - return cast( - forecasting.ARIMAPlus, - session.read_gbq_model(time_series_arima_plus_model_name_w_id), - ) - - @pytest.fixture(scope="session") def imported_tensorflow_model_path() -> str: return "gs://cloud-training-demos/txtclass/export/exporter/1549825580/*" @@ -283,11 +275,6 @@ def imported_onnx_model_path() -> str: return "gs://cloud-samples-data/bigquery/ml/onnx/pipeline_rf.onnx" -@pytest.fixture(scope="session") -def imported_xgboost_array_model_path() -> str: - return "gs://bigframes-dev-testing/xgboost-testdata/model.bst" - - @pytest.fixture(scope="session") def imported_tensorflow_model( session, imported_tensorflow_model_path @@ -312,20 +299,3 @@ def imported_onnx_model(session, imported_onnx_model_path) -> imported.ONNXModel session=session, model_path=imported_onnx_model_path, ) - - -@pytest.fixture(scope="session") -def imported_xgboost_model( - session, imported_xgboost_array_model_path -) -> imported.XGBoostModel: - return imported.XGBoostModel( - session=session, - input={ - "petal_length": "float64", - "petal_width": "float64", - "sepal_length": "float64", - "sepal_width": "float64", - }, - output={"predicted_label": "float64"}, - model_path=imported_xgboost_array_model_path, - ) diff --git a/tests/system/small/ml/test_cluster.py b/tests/system/small/ml/test_cluster.py index 2bf334e84df..d95a1e1bc22 100644 --- a/tests/system/small/ml/test_cluster.py +++ b/tests/system/small/ml/test_cluster.py @@ -12,12 +12,10 @@ # See the License for the specific language governing permissions and # limitations under the License. -import numpy as np import pandas as pd -import bigframes.pandas as bpd from bigframes.ml import cluster -from bigframes.testing.utils import assert_frame_equal +from tests.system.utils import assert_pandas_df_equal_ignore_ordering _PD_NEW_PENGUINS = pd.DataFrame.from_dict( { @@ -64,59 +62,13 @@ def test_kmeans_predict(session, penguins_kmeans_model: cluster.KMeans): new_penguins = session.read_pandas(_PD_NEW_PENGUINS) - predictions = penguins_kmeans_model.predict(new_penguins).to_pandas() - assert predictions.shape == (4, 9) - result = predictions[["CENTROID_ID"]] + result = penguins_kmeans_model.predict(new_penguins).to_pandas() expected = pd.DataFrame( {"CENTROID_ID": [2, 3, 1, 2]}, dtype="Int64", index=pd.Index(["test1", "test2", "test3", "test4"], dtype="string[pyarrow]"), ) - assert_frame_equal(result, expected, ignore_order=True) - - -def test_kmeans_detect_anomalies( - penguins_kmeans_model: cluster.KMeans, new_penguins_df: bpd.DataFrame -): - anomalies = penguins_kmeans_model.detect_anomalies(new_penguins_df).to_pandas() - expected = pd.DataFrame( - { - "is_anomaly": [False, False, False], - "normalized_distance": [1.082937, 0.77139, 0.478304], - }, - index=pd.Index([1633, 1672, 1690], name="tag_number", dtype="Int64"), - ) - - pd.testing.assert_frame_equal( - anomalies[["is_anomaly", "normalized_distance"]].sort_index(), - expected, - check_exact=False, - check_dtype=False, - rtol=0.1, - ) - - -def test_kmeans_detect_anomalies_params( - penguins_kmeans_model: cluster.KMeans, new_penguins_df: bpd.DataFrame -): - anomalies = penguins_kmeans_model.detect_anomalies( - new_penguins_df, contamination=0.4 - ).to_pandas() - expected = pd.DataFrame( - { - "is_anomaly": [True, False, False], - "normalized_distance": [1.082937, 0.77139, 0.478304], - }, - index=pd.Index([1633, 1672, 1690], name="tag_number", dtype="Int64"), - ) - - pd.testing.assert_frame_equal( - anomalies[["is_anomaly", "normalized_distance"]].sort_index(), - expected, - check_exact=False, - check_dtype=False, - rtol=0.1, - ) + assert_pandas_df_equal_ignore_ordering(result, expected) def test_kmeans_score(session, penguins_kmeans_model: cluster.KMeans): @@ -137,100 +89,65 @@ def test_kmeans_score(session, penguins_kmeans_model: cluster.KMeans): def test_kmeans_cluster_centers(penguins_kmeans_model: cluster.KMeans): - result = ( - penguins_kmeans_model.cluster_centers_.to_pandas() - .sort_values(["centroid_id", "feature"]) - .reset_index(drop=True) - ) - - # FIX: Helper to ignore row order inside categorical_value lists - # and sign flipping of values inside numerical_value list. - # This prevents the test from failing if BQML returns [MALE, FEMALE] instead of [FEMALE, MALE] - # or 0.197 versus -0.197. - def sort_and_abs_categorical(val): - # Accept BOTH python lists AND numpy arrays - if isinstance(val, (list, np.ndarray)) and len(val) > 0: - # Take abs of value first, then sort - processed = [ - {"category": x["category"], "value": abs(x["value"])} for x in val + result = penguins_kmeans_model.cluster_centers_.to_pandas() + expected = pd.DataFrame( + { + "centroid_id": [1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3], + "feature": [ + "culmen_length_mm", + "culmen_depth_mm", + "flipper_length_mm", + "sex", ] - return sorted(processed, key=lambda x: x["category"]) - return val - - result["numerical_value"] = result["numerical_value"].abs() - result["categorical_value"] = result["categorical_value"].apply( - sort_and_abs_categorical - ) - - expected = ( - pd.DataFrame( - { - "centroid_id": [1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3], - "feature": [ - "culmen_length_mm", - "culmen_depth_mm", - "flipper_length_mm", - "sex", - ] - * 3, - "numerical_value": [ - 47.509677, - 14.993548, - 217.040123, - pd.NA, - 38.207813, - 18.03125, - 187.992188, - pd.NA, - 47.036346, - 18.834808, - 197.1612, - pd.NA, + * 3, + "numerical_value": [ + 47.509677, + 14.993548, + 217.040123, + pd.NA, + 38.207813, + 18.03125, + 187.992188, + pd.NA, + 47.036346, + 18.834808, + 197.1612, + pd.NA, + ], + "categorical_value": [ + [], + [], + [], + [ + {"category": ".", "value": 0.008064516129032258}, + {"category": "MALE", "value": 0.49193548387096775}, + {"category": "FEMALE", "value": 0.47580645161290325}, + {"category": "_null_filler", "value": 0.024193548387096774}, ], - "categorical_value": [ - [], - [], - [], - [ - {"category": ".", "value": 0.008064516129032258}, - {"category": "MALE", "value": 0.49193548387096775}, - {"category": "FEMALE", "value": 0.47580645161290325}, - {"category": "_null_filler", "value": 0.024193548387096774}, - ], - [], - [], - [], - [ - {"category": "MALE", "value": 0.34375}, - {"category": "FEMALE", "value": 0.625}, - {"category": "_null_filler", "value": 0.03125}, - ], - [], - [], - [], - [ - {"category": "MALE", "value": 0.6847826086956522}, - {"category": "FEMALE", "value": 0.2826086956521739}, - {"category": "_null_filler", "value": 0.03260869565217391}, - ], + [], + [], + [], + [ + {"category": "MALE", "value": 0.34375}, + {"category": "FEMALE", "value": 0.625}, + {"category": "_null_filler", "value": 0.03125}, ], - }, - ) - .sort_values(["centroid_id", "feature"]) - .reset_index(drop=True) - ) - - # Sort and sign flip expected values to match the output of the model. - expected["numerical_value"] = expected["numerical_value"].abs() - expected["categorical_value"] = expected["categorical_value"].apply( - sort_and_abs_categorical + [], + [], + [], + [ + {"category": "MALE", "value": 0.6847826086956522}, + {"category": "FEMALE", "value": 0.2826086956521739}, + {"category": "_null_filler", "value": 0.03260869565217391}, + ], + ], + }, ) - pd.testing.assert_frame_equal( result, expected, check_exact=False, - rtol=0.1, # Keep or slightly increase if numerical drift persists + rtol=0.1, # int64 Index by default in pandas versus Int64 (nullable) Index in BigQuery DataFrame check_index_type=False, check_dtype=False, diff --git a/tests/system/small/ml/test_core.py b/tests/system/small/ml/test_core.py index c32ba80b30e..f911dd7eebc 100644 --- a/tests/system/small/ml/test_core.py +++ b/tests/system/small/ml/test_core.py @@ -12,19 +12,18 @@ # See the License for the specific language governing permissions and # limitations under the License. -import typing from datetime import datetime +import typing +from unittest import TestCase -import numpy as np import pandas as pd import pyarrow as pa import pytest import pytz import bigframes -import bigframes.features from bigframes.ml import core -from bigframes.testing import utils +import tests.system.utils def test_model_eval( @@ -79,79 +78,59 @@ def test_model_eval_with_data(penguins_bqml_linear_model, penguins_df_default_in def test_model_centroids(penguins_bqml_kmeans_model: core.BqmlModel): result = penguins_bqml_kmeans_model.centroids().to_pandas() - - # FIX: Helper to ignore row order inside categorical_value lists - # This prevents the test from failing if BQML returns [MALE, FEMALE] instead of [FEMALE, MALE] - def sort_categorical(val): - if isinstance(val, (list, np.ndarray)) and len(val) > 0: - return sorted(val, key=lambda x: x["category"]) - return val - - result["categorical_value"] = result["categorical_value"].apply(sort_categorical) - - expected = ( - pd.DataFrame( - { - "centroid_id": [1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3], - "feature": [ - "culmen_length_mm", - "culmen_depth_mm", - "flipper_length_mm", - "sex", - ] - * 3, - "numerical_value": [ - 47.509677, - 14.993548, - 217.040123, - pd.NA, - 38.207813, - 18.03125, - 187.992188, - pd.NA, - 47.036346, - 18.834808, - 197.1612, - pd.NA, + expected = pd.DataFrame( + { + "centroid_id": [1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3], + "feature": [ + "culmen_length_mm", + "culmen_depth_mm", + "flipper_length_mm", + "sex", + ] + * 3, + "numerical_value": [ + 47.509677, + 14.993548, + 217.040123, + pd.NA, + 38.207813, + 18.03125, + 187.992188, + pd.NA, + 47.036346, + 18.834808, + 197.1612, + pd.NA, + ], + "categorical_value": [ + [], + [], + [], + [ + {"category": ".", "value": 0.008064516129032258}, + {"category": "MALE", "value": 0.49193548387096775}, + {"category": "FEMALE", "value": 0.47580645161290325}, + {"category": "_null_filler", "value": 0.024193548387096774}, ], - "categorical_value": [ - [], - [], - [], - [ - {"category": ".", "value": 0.008064516129032258}, - {"category": "MALE", "value": 0.49193548387096775}, - {"category": "FEMALE", "value": 0.47580645161290325}, - {"category": "_null_filler", "value": 0.024193548387096774}, - ], - [], - [], - [], - [ - {"category": "MALE", "value": 0.34375}, - {"category": "FEMALE", "value": 0.625}, - {"category": "_null_filler", "value": 0.03125}, - ], - [], - [], - [], - [ - {"category": "MALE", "value": 0.6847826086956522}, - {"category": "FEMALE", "value": 0.2826086956521739}, - {"category": "_null_filler", "value": 0.03260869565217391}, - ], + [], + [], + [], + [ + {"category": "MALE", "value": 0.34375}, + {"category": "FEMALE", "value": 0.625}, + {"category": "_null_filler", "value": 0.03125}, ], - }, - ) - .sort_values(["centroid_id", "feature"]) - .reset_index(drop=True) - ) - - # Sort expected values to match the output of the model. - expected["categorical_value"] = expected["categorical_value"].apply( - sort_categorical + [], + [], + [], + [ + {"category": "MALE", "value": 0.6847826086956522}, + {"category": "FEMALE", "value": 0.2826086956521739}, + {"category": "_null_filler", "value": 0.03260869565217391}, + ], + ], + }, ) - pd.testing.assert_frame_equal( result, expected, @@ -169,96 +148,66 @@ def test_pca_model_principal_components(penguins_bqml_pca_model: core.BqmlModel) # result is too long, only check the first principal component here. result = result.head(7) - - # FIX: Helper to ignore row order inside categorical_value lists - # and sign flipping of values inside numerical_value list. - # This prevents the test from failing if BQML returns [MALE, FEMALE] instead of [FEMALE, MALE] - # or 0.197 versus -0.197. - def sort_and_abs_categorical(val): - # Accept BOTH python lists AND numpy arrays - if isinstance(val, (list, np.ndarray)) and len(val) > 0: - # Take abs of value first, then sort - processed = [ - {"category": x["category"], "value": abs(x["value"])} for x in val - ] - return sorted(processed, key=lambda x: x["category"]) - return val - - result["numerical_value"] = result["numerical_value"].abs() - result["categorical_value"] = result["categorical_value"].apply( - sort_and_abs_categorical - ) - - expected = ( - pd.DataFrame( - { - "principal_component_id": [0] * 7, - "feature": [ - "species", - "island", - "culmen_length_mm", - "culmen_depth_mm", - "flipper_length_mm", - "body_mass_g", - "sex", + expected = pd.DataFrame( + { + "principal_component_id": [0] * 7, + "feature": [ + "species", + "island", + "culmen_length_mm", + "culmen_depth_mm", + "flipper_length_mm", + "body_mass_g", + "sex", + ], + "numerical_value": [ + pd.NA, + pd.NA, + 0.401489, + -0.377482, + 0.524052, + 0.501174, + pd.NA, + ], + "categorical_value": [ + [ + { + "category": "Gentoo penguin (Pygoscelis papua)", + "value": 0.25068877125667804, + }, + { + "category": "Adelie Penguin (Pygoscelis adeliae)", + "value": -0.20622291900416198, + }, + { + "category": "Chinstrap penguin (Pygoscelis antarctica)", + "value": -0.030161149275185855, + }, ], - "numerical_value": [ - pd.NA, - pd.NA, - 0.401489, - -0.377482, - 0.524052, - 0.501174, - pd.NA, + [ + {"category": "Biscoe", "value": 0.19761120114410635}, + {"category": "Dream", "value": -0.11264736305259061}, + {"category": "Torgersen", "value": -0.07065913511418596}, ], - "categorical_value": [ - [ - { - "category": "Gentoo penguin (Pygoscelis papua)", - "value": 0.25068877125667804, - }, - { - "category": "Adelie Penguin (Pygoscelis adeliae)", - "value": -0.20622291900416198, - }, - { - "category": "Chinstrap penguin (Pygoscelis antarctica)", - "value": -0.030161149275185855, - }, - ], - [ - {"category": "Biscoe", "value": 0.19761120114410635}, - {"category": "Dream", "value": -0.11264736305259061}, - {"category": "Torgersen", "value": -0.07065913511418596}, - ], - [], - [], - [], - [], - [ - {"category": ".", "value": 0.0015916894448071784}, - {"category": "MALE", "value": 0.06869704739750442}, - {"category": "FEMALE", "value": -0.052521171596813174}, - {"category": "_null_filler", "value": -0.0034628622681684906}, - ], + [], + [], + [], + [], + [ + {"category": ".", "value": 0.0015916894448071784}, + {"category": "MALE", "value": 0.06869704739750442}, + {"category": "FEMALE", "value": -0.052521171596813174}, + {"category": "_null_filler", "value": -0.0034628622681684906}, ], - }, - ) - .sort_values(["principal_component_id", "feature"]) - .reset_index(drop=True) - ) - - # Sort and sign flip expected values to match the output of the model. - expected["numerical_value"] = expected["numerical_value"].abs() - expected["categorical_value"] = expected["categorical_value"].apply( - sort_and_abs_categorical + ], + }, ) - - utils.assert_pandas_df_equal_pca_components( + pd.testing.assert_frame_equal( result, expected, check_exact=False, rtol=0.1, + # int64 Index by default in pandas versus Int64 (nullable) Index in BigQuery DataFrame check_index_type=False, check_dtype=False, ) @@ -276,7 +225,7 @@ def test_pca_model_principal_component_info(penguins_bqml_pca_model: core.BqmlMo "cumulative_explained_variance_ratio": [0.469357, 0.651283, 0.812383], }, ) - utils.assert_frame_equal( + tests.system.utils.assert_pandas_df_equal_ignore_ordering( result, expected, check_exact=False, @@ -284,7 +233,6 @@ def test_pca_model_principal_component_info(penguins_bqml_pca_model: core.BqmlMo # int64 Index by default in pandas versus Int64 (nullable) Index in BigQuery DataFrame check_index_type=False, check_dtype=False, - ignore_order=True, ) @@ -303,32 +251,10 @@ def test_model_predict(penguins_bqml_linear_model: core.BqmlModel, new_penguins_ ) -def test_model_predict_explain( - penguins_bqml_linear_model: core.BqmlModel, new_penguins_df -): - options = {"top_k_features": 3} - predictions = penguins_bqml_linear_model.explain_predict( - new_penguins_df, options - ).to_pandas() - expected = pd.DataFrame( - { - "predicted_body_mass_g": [4030.1, 3280.8, 3177.9], - "approximation_error": [0.0, 0.0, 0.0], - }, - dtype="Float64", - index=pd.Index([1633, 1672, 1690], name="tag_number", dtype="Int64"), - ) - pd.testing.assert_frame_equal( - predictions[["predicted_body_mass_g", "approximation_error"]].sort_index(), - expected, - check_exact=False, - rtol=0.1, - ) - - def test_model_predict_with_unnamed_index( penguins_bqml_linear_model: core.BqmlModel, new_penguins_df ): + # This will result in an index that lacks a name, which the ML library will # need to persist through the call to ML.PREDICT new_penguins_df = new_penguins_df.reset_index() @@ -354,144 +280,50 @@ def test_model_predict_with_unnamed_index( ) -def test_model_predict_explain_with_unnamed_index( - penguins_bqml_linear_model: core.BqmlModel, new_penguins_df +@pytest.mark.flaky(retries=2, delay=120) +def test_model_generate_text( + bqml_palm2_text_generator_model: core.BqmlModel, llm_text_df ): - # This will result in an index that lacks a name, which the ML library will - # need to persist through the call to ML.PREDICT - new_penguins_df = new_penguins_df.reset_index() - - options = {"top_k_features": 3} - # remove the middle tag number to ensure we're really keeping the unnamed index - new_penguins_df = typing.cast( - bigframes.dataframe.DataFrame, - new_penguins_df[new_penguins_df.tag_number != 1672], - ) - - predictions = penguins_bqml_linear_model.explain_predict( - new_penguins_df, options + options = { + "temperature": 0.5, + "max_output_tokens": 100, + "top_k": 20, + "top_p": 0.5, + "flatten_json_output": True, + } + df = bqml_palm2_text_generator_model.generate_text( + llm_text_df, options=options ).to_pandas() - expected = pd.DataFrame( - { - "predicted_body_mass_g": [4030.1, 3177.9], - "approximation_error": [0.0, 0.0], - }, - dtype="Float64", - index=pd.Index([0, 2], dtype="Int64"), - ) - pd.testing.assert_frame_equal( - predictions[["predicted_body_mass_g", "approximation_error"]].sort_index(), - expected, - check_exact=False, - rtol=0.1, + TestCase().assertSequenceEqual(df.shape, (3, 4)) + TestCase().assertSequenceEqual( + [ + "ml_generate_text_llm_result", + "ml_generate_text_rai_result", + "ml_generate_text_status", + "prompt", + ], + df.columns.to_list(), ) + series = df["ml_generate_text_llm_result"] + assert all(series.str.len() > 20) -def test_model_detect_anomalies( - penguins_bqml_pca_model: core.BqmlModel, new_penguins_df -): - options = {"contamination": 0.25} - anomalies = penguins_bqml_pca_model.detect_anomalies( - new_penguins_df, options - ).to_pandas() +def test_model_forecast(time_series_bqml_arima_plus_model: core.BqmlModel): + utc = pytz.utc + forecast = time_series_bqml_arima_plus_model.forecast().to_pandas()[ + ["forecast_timestamp", "forecast_value"] + ] expected = pd.DataFrame( { - "is_anomaly": [True, True, True], - "mean_squared_error": [0.254188, 0.731243, 0.298889], - }, - index=pd.Index([1633, 1672, 1690], name="tag_number", dtype="Int64"), - ) - pd.testing.assert_frame_equal( - anomalies[["is_anomaly", "mean_squared_error"]].sort_index(), - expected, - check_exact=False, - check_dtype=False, - rtol=0.1, - ) - - -@pytest.mark.skip("b/353775058 BQML internal error") -def test_remote_model_predict( - bqml_linear_remote_model: core.BqmlModel, new_penguins_df -): - expected = pd.DataFrame( - {"predicted_body_mass_g": [[3739.54], [3675.79], [3619.54]]}, - index=pd.Index([1633, 1672, 1690], name="tag_number", dtype="Int64"), - dtype=( - pd.ArrowDtype(pa.list_(pa.float64())) - if bigframes.features.PANDAS_VERSIONS.is_arrow_list_dtype_usable - else "object" - ), - ) - predictions = bqml_linear_remote_model.predict(new_penguins_df).to_pandas() - pd.testing.assert_frame_equal( - predictions[["predicted_body_mass_g"]].sort_index(), - expected, - check_exact=False, - rtol=0.1, + "forecast_timestamp": [ + datetime(2017, 8, 2, tzinfo=utc), + datetime(2017, 8, 3, tzinfo=utc), + datetime(2017, 8, 4, tzinfo=utc), + ], + "forecast_value": [2724.472284, 2593.368389, 2353.613034], + } ) - - -@pytest.mark.parametrize("id_col_name", [None, "id"]) -def test_model_forecast( - time_series_bqml_arima_plus_model: core.BqmlModel, - time_series_bqml_arima_plus_model_w_id: core.BqmlModel, - id_col_name, -): - utc = pytz.utc - forecast_cols = ["forecast_timestamp", "forecast_value"] - if id_col_name: - forecast_cols.insert(0, id_col_name) - - forecast = ( - time_series_bqml_arima_plus_model_w_id.forecast( - {"horizon": 4, "confidence_level": 0.8} - ) - if id_col_name - else time_series_bqml_arima_plus_model.forecast( - {"horizon": 4, "confidence_level": 0.8} - ) - ).to_pandas()[forecast_cols] - if id_col_name: - expected = pd.DataFrame( - { - "id": ["1", "2", "1", "2", "1", "2", "1", "2"], - "forecast_timestamp": [ - datetime(2017, 8, 2, tzinfo=utc), - datetime(2017, 8, 2, tzinfo=utc), - datetime(2017, 8, 3, tzinfo=utc), - datetime(2017, 8, 3, tzinfo=utc), - datetime(2017, 8, 4, tzinfo=utc), - datetime(2017, 8, 4, tzinfo=utc), - datetime(2017, 8, 5, tzinfo=utc), - datetime(2017, 8, 5, tzinfo=utc), - ], - "forecast_value": [ - 2634.796023, - 2634.796023, - 2621.332462, - 2621.332462, - 2396.095463, - 2396.095463, - 1742.878278, - 1742.878278, - ], - } - ) - expected["id"] = expected["id"].astype("string[pyarrow]") - else: - expected = pd.DataFrame( - { - "forecast_timestamp": [ - datetime(2017, 8, 2, tzinfo=utc), - datetime(2017, 8, 3, tzinfo=utc), - datetime(2017, 8, 4, tzinfo=utc), - datetime(2017, 8, 5, tzinfo=utc), - ], - "forecast_value": [2634.796023, 2621.332462, 2396.095463, 1742.878278], - } - ) expected["forecast_value"] = expected["forecast_value"].astype(pd.Float64Dtype()) expected["forecast_timestamp"] = expected["forecast_timestamp"].astype( pd.ArrowDtype(pa.timestamp("us", tz="UTC")) @@ -504,25 +336,16 @@ def test_model_forecast( ) -def test_model_register(ephemera_penguins_bqml_linear_model: core.BqmlModel): +def test_model_register(ephemera_penguins_bqml_linear_model): model = ephemera_penguins_bqml_linear_model - - start_execution_count = model.session._metrics.execution_count - model.register() - end_execution_count = model.session._metrics.execution_count - assert end_execution_count - start_execution_count == 1 - - assert model.model.model_id is not None model_name = "bigframes_" + model.model.model_id # Only registered model contains the field, and the field includes project/dataset. Here only check model_id. assert model_name in model.model.training_runs[-1]["vertexAiModelId"] -def test_model_register_with_params( - ephemera_penguins_bqml_linear_model: core.BqmlModel, -): +def test_model_register_with_params(ephemera_penguins_bqml_linear_model): model_name = "bigframes_system_test_model" model = ephemera_penguins_bqml_linear_model model.register(model_name) diff --git a/tests/system/small/ml/test_decomposition.py b/tests/system/small/ml/test_decomposition.py index 36abfe55adf..e31681f4a09 100644 --- a/tests/system/small/ml/test_decomposition.py +++ b/tests/system/small/ml/test_decomposition.py @@ -12,17 +12,13 @@ # See the License for the specific language governing permissions and # limitations under the License. -import numpy as np import pandas as pd -import bigframes.pandas as bpd -import bigframes.testing.utils from bigframes.ml import decomposition +import tests.system.utils -def test_pca_predict( - penguins_pca_model: decomposition.PCA, new_penguins_df: bpd.DataFrame -): +def test_pca_predict(penguins_pca_model, new_penguins_df): predictions = penguins_pca_model.predict(new_penguins_df).to_pandas() expected = pd.DataFrame( { @@ -33,53 +29,11 @@ def test_pca_predict( dtype="Float64", index=pd.Index([1633, 1672, 1690], name="tag_number", dtype="Int64"), ) - - bigframes.testing.utils.assert_pandas_df_equal_pca( - predictions, expected, check_exact=False, rtol=0.2 - ) - - -def test_pca_detect_anomalies( - penguins_pca_model: decomposition.PCA, new_penguins_df: bpd.DataFrame -): - anomalies = penguins_pca_model.detect_anomalies(new_penguins_df).to_pandas() - expected = pd.DataFrame( - { - "is_anomaly": [False, True, False], - "mean_squared_error": [0.254188, 0.731243, 0.298889], - }, - index=pd.Index([1633, 1672, 1690], name="tag_number", dtype="Int64"), - ) - pd.testing.assert_frame_equal( - anomalies[["is_anomaly", "mean_squared_error"]].sort_index(), + predictions.sort_index(), expected, check_exact=False, - check_dtype=False, - rtol=0.2, - ) - - -def test_pca_detect_anomalies_params( - penguins_pca_model: decomposition.PCA, new_penguins_df: bpd.DataFrame -): - anomalies = penguins_pca_model.detect_anomalies( - new_penguins_df, contamination=0.2 - ).to_pandas() - expected = pd.DataFrame( - { - "is_anomaly": [False, True, True], - "mean_squared_error": [0.254188, 0.731243, 0.298889], - }, - index=pd.Index([1633, 1672, 1690], name="tag_number", dtype="Int64"), - ) - - pd.testing.assert_frame_equal( - anomalies[["is_anomaly", "mean_squared_error"]].sort_index(), - expected, - check_exact=False, - check_dtype=False, - rtol=0.2, + rtol=0.1, ) @@ -93,7 +47,7 @@ def test_pca_score(penguins_pca_model: decomposition.PCA): result, expected, check_exact=False, - rtol=0.2, + rtol=0.1, check_index_type=False, ) @@ -103,96 +57,65 @@ def test_pca_components_(penguins_pca_model: decomposition.PCA): # result is too long, only check the first principal component here. result = result.head(7) - - # FIX: Helper to ignore row order inside categorical_value lists - # and sign flipping of values inside numerical_value list. - # This prevents the test from failing if BQML returns [MALE, FEMALE] instead of [FEMALE, MALE] - # or 0.197 versus -0.197. - def sort_and_abs_categorical(val): - # Accept BOTH python lists AND numpy arrays - if isinstance(val, (list, np.ndarray)) and len(val) > 0: - # Take abs of value first, then sort - processed = [ - {"category": x["category"], "value": abs(x["value"])} for x in val - ] - return sorted(processed, key=lambda x: x["category"]) - return val - - result["numerical_value"] = result["numerical_value"].abs() - result["categorical_value"] = result["categorical_value"].apply( - sort_and_abs_categorical - ) - - expected = ( - pd.DataFrame( - { - "principal_component_id": [0] * 7, - "feature": [ - "species", - "island", - "culmen_length_mm", - "culmen_depth_mm", - "flipper_length_mm", - "body_mass_g", - "sex", + expected = pd.DataFrame( + { + "principal_component_id": [0] * 7, + "feature": [ + "species", + "island", + "culmen_length_mm", + "culmen_depth_mm", + "flipper_length_mm", + "body_mass_g", + "sex", + ], + "numerical_value": [ + pd.NA, + pd.NA, + 0.401489, + -0.377482, + 0.524052, + 0.501174, + pd.NA, + ], + "categorical_value": [ + [ + { + "category": "Gentoo penguin (Pygoscelis papua)", + "value": 0.25068877125667804, + }, + { + "category": "Adelie Penguin (Pygoscelis adeliae)", + "value": -0.20622291900416198, + }, + { + "category": "Chinstrap penguin (Pygoscelis antarctica)", + "value": -0.030161149275185855, + }, ], - "numerical_value": [ - pd.NA, - pd.NA, - 0.401489, - -0.377482, - 0.524052, - 0.501174, - pd.NA, + [ + {"category": "Biscoe", "value": 0.19761120114410635}, + {"category": "Dream", "value": -0.11264736305259061}, + {"category": "Torgersen", "value": -0.07065913511418596}, ], - "categorical_value": [ - [ - { - "category": "Gentoo penguin (Pygoscelis papua)", - "value": 0.25068877125667804, - }, - { - "category": "Adelie Penguin (Pygoscelis adeliae)", - "value": -0.20622291900416198, - }, - { - "category": "Chinstrap penguin (Pygoscelis antarctica)", - "value": -0.030161149275185855, - }, - ], - [ - {"category": "Biscoe", "value": 0.19761120114410635}, - {"category": "Dream", "value": -0.11264736305259061}, - {"category": "Torgersen", "value": -0.07065913511418596}, - ], - [], - [], - [], - [], - [ - {"category": ".", "value": 0.0015916894448071784}, - {"category": "MALE", "value": 0.06869704739750442}, - {"category": "FEMALE", "value": -0.052521171596813174}, - {"category": "_null_filler", "value": -0.0034628622681684906}, - ], + [], + [], + [], + [], + [ + {"category": ".", "value": 0.0015916894448071784}, + {"category": "MALE", "value": 0.06869704739750442}, + {"category": "FEMALE", "value": -0.052521171596813174}, + {"category": "_null_filler", "value": -0.0034628622681684906}, ], - }, - ) - .sort_values(["principal_component_id", "feature"]) - .reset_index(drop=True) - ) - - # Sort and sign flip expected values to match the output of the model. - expected["numerical_value"] = expected["numerical_value"].abs() - expected["categorical_value"] = expected["categorical_value"].apply( - sort_and_abs_categorical + ], + }, ) - - bigframes.testing.utils.assert_pandas_df_equal_pca_components( + pd.testing.assert_frame_equal( result, expected, check_exact=False, - rtol=0.2, # FIX: Slightly increased rtol for numerical drift (from 0.1) + rtol=0.1, check_index_type=False, check_dtype=False, ) @@ -207,14 +130,13 @@ def test_pca_explained_variance_(penguins_pca_model: decomposition.PCA): "explained_variance": [3.278657, 1.270829, 1.125354], }, ) - bigframes.testing.utils.assert_frame_equal( + tests.system.utils.assert_pandas_df_equal_ignore_ordering( result, expected, check_exact=False, - rtol=0.2, + rtol=0.1, check_index_type=False, check_dtype=False, - ignore_order=True, ) @@ -227,12 +149,11 @@ def test_pca_explained_variance_ratio_(penguins_pca_model: decomposition.PCA): "explained_variance_ratio": [0.469357, 0.181926, 0.1611], }, ) - bigframes.testing.utils.assert_frame_equal( + tests.system.utils.assert_pandas_df_equal_ignore_ordering( result, expected, check_exact=False, - rtol=0.2, + rtol=0.1, check_index_type=False, check_dtype=False, - ignore_order=True, ) diff --git a/tests/system/small/ml/test_ensemble.py b/tests/system/small/ml/test_ensemble.py index a15e53fb17f..bba083d98d9 100644 --- a/tests/system/small/ml/test_ensemble.py +++ b/tests/system/small/ml/test_ensemble.py @@ -14,7 +14,9 @@ from unittest import TestCase +import google.api_core.exceptions import pandas +import pytest import bigframes.ml.ensemble @@ -96,9 +98,7 @@ def test_xgbregressor_model_score_series( def test_xgbregressor_model_predict( penguins_xgbregressor_model: bigframes.ml.ensemble.XGBRegressor, new_penguins_df ): - predictions = penguins_xgbregressor_model.predict(new_penguins_df).to_pandas() - assert predictions.shape == (3, 8) - result = predictions[["predicted_body_mass_g"]] + result = penguins_xgbregressor_model.predict(new_penguins_df).to_pandas() expected = pandas.DataFrame( {"predicted_body_mass_g": ["4293.1538089", "3410.0271", "3357.944"]}, dtype="Float64", @@ -114,9 +114,11 @@ def test_xgbregressor_model_predict( def test_to_gbq_saved_xgbregressor_model_scores( - penguins_xgbregressor_model, table_id_unique, penguins_df_default_index + penguins_xgbregressor_model, dataset_id, penguins_df_default_index ): - saved_model = penguins_xgbregressor_model.to_gbq(table_id_unique, replace=True) + saved_model = penguins_xgbregressor_model.to_gbq( + f"{dataset_id}.test_penguins_model", replace=True + ) df = penguins_df_default_index.dropna() X_test = df[ [ @@ -151,6 +153,14 @@ def test_to_gbq_saved_xgbregressor_model_scores( ) +def test_to_xgbregressor_model_gbq_replace(penguins_xgbregressor_model, dataset_id): + penguins_xgbregressor_model.to_gbq( + f"{dataset_id}.test_penguins_model", replace=True + ) + with pytest.raises(google.api_core.exceptions.Conflict): + penguins_xgbregressor_model.to_gbq(f"{dataset_id}.test_penguins_model") + + def test_xgbclassifier_model_score( penguins_xgbclassifier_model, penguins_df_default_index ): @@ -210,9 +220,7 @@ def test_xgbclassifier_model_score_series( def test_xgbclassifier_model_predict( penguins_xgbclassifier_model: bigframes.ml.ensemble.XGBClassifier, new_penguins_df ): - predictions = penguins_xgbclassifier_model.predict(new_penguins_df).to_pandas() - assert predictions.shape == (3, 9) - result = predictions[["predicted_sex"]] + result = penguins_xgbclassifier_model.predict(new_penguins_df).to_pandas() expected = pandas.DataFrame( {"predicted_sex": ["MALE", "MALE", "FEMALE"]}, dtype="string[pyarrow]", @@ -228,9 +236,11 @@ def test_xgbclassifier_model_predict( def test_to_gbq_saved_xgbclassifier_model_scores( - penguins_xgbclassifier_model, table_id_unique, penguins_df_default_index + penguins_xgbclassifier_model, dataset_id, penguins_df_default_index ): - saved_model = penguins_xgbclassifier_model.to_gbq(table_id_unique, replace=True) + saved_model = penguins_xgbclassifier_model.to_gbq( + f"{dataset_id}.test_penguins_model", replace=True + ) df = penguins_df_default_index.dropna() X_test = df[ [ @@ -267,6 +277,14 @@ def test_to_gbq_saved_xgbclassifier_model_scores( assert saved_model.max_iterations == 20 +def test_to_xgbclassifier_model_gbq_replace(penguins_xgbclassifier_model, dataset_id): + penguins_xgbclassifier_model.to_gbq( + f"{dataset_id}.test_penguins_model", replace=True + ) + with pytest.raises(google.api_core.exceptions.Conflict): + penguins_xgbclassifier_model.to_gbq(f"{dataset_id}.test_penguins_model") + + def test_randomforestregressor_model_score( penguins_randomforest_regressor_model, penguins_df_default_index ): @@ -345,11 +363,7 @@ def test_randomforestregressor_model_predict( penguins_randomforest_regressor_model: bigframes.ml.ensemble.RandomForestRegressor, new_penguins_df, ): - predictions = penguins_randomforest_regressor_model.predict( - new_penguins_df - ).to_pandas() - assert predictions.shape == (3, 8) - result = predictions[["predicted_body_mass_g"]] + result = penguins_randomforest_regressor_model.predict(new_penguins_df).to_pandas() expected = pandas.DataFrame( {"predicted_body_mass_g": ["3897.341797", "3458.385742", "3458.385742"]}, dtype="Float64", @@ -365,10 +379,10 @@ def test_randomforestregressor_model_predict( def test_to_gbq_saved_randomforestregressor_model_scores( - penguins_randomforest_regressor_model, table_id_unique, penguins_df_default_index + penguins_randomforest_regressor_model, dataset_id, penguins_df_default_index ): saved_model = penguins_randomforest_regressor_model.to_gbq( - table_id_unique, replace=True + f"{dataset_id}.test_penguins_model", replace=True ) df = penguins_df_default_index.dropna() X_test = df[ @@ -404,6 +418,18 @@ def test_to_gbq_saved_randomforestregressor_model_scores( ) +def test_to_randomforestregressor_model_gbq_replace( + penguins_randomforest_regressor_model, dataset_id +): + penguins_randomforest_regressor_model.to_gbq( + f"{dataset_id}.test_penguins_model", replace=True + ) + with pytest.raises(google.api_core.exceptions.Conflict): + penguins_randomforest_regressor_model.to_gbq( + f"{dataset_id}.test_penguins_model" + ) + + def test_randomforestclassifier_model_score( penguins_randomforest_classifier_model, penguins_df_default_index ): @@ -464,11 +490,7 @@ def test_randomforestclassifier_model_predict( penguins_randomforest_classifier_model: bigframes.ml.ensemble.RandomForestClassifier, new_penguins_df, ): - predictions = penguins_randomforest_classifier_model.predict( - new_penguins_df - ).to_pandas() - assert predictions.shape == (3, 9) - result = predictions[["predicted_sex"]] + result = penguins_randomforest_classifier_model.predict(new_penguins_df).to_pandas() expected = pandas.DataFrame( {"predicted_sex": ["MALE", "MALE", "FEMALE"]}, dtype="string[pyarrow]", @@ -484,10 +506,10 @@ def test_randomforestclassifier_model_predict( def test_to_gbq_saved_randomforestclassifier_model_scores( - penguins_randomforest_classifier_model, table_id_unique, penguins_df_default_index + penguins_randomforest_classifier_model, dataset_id, penguins_df_default_index ): saved_model = penguins_randomforest_classifier_model.to_gbq( - table_id_unique, replace=True + f"{dataset_id}.test_penguins_model", replace=True ) df = penguins_df_default_index.dropna() X_test = df[ @@ -521,3 +543,15 @@ def test_to_gbq_saved_randomforestclassifier_model_scores( # int64 Index by default in pandas versus Int64 (nullable) Index in BigQuery DataFrame check_index_type=False, ) + + +def test_to_randomforestclassifier_model_gbq_replace( + penguins_randomforest_classifier_model, dataset_id +): + penguins_randomforest_classifier_model.to_gbq( + f"{dataset_id}.test_penguins_model", replace=True + ) + with pytest.raises(google.api_core.exceptions.Conflict): + penguins_randomforest_classifier_model.to_gbq( + f"{dataset_id}.test_penguins_model" + ) diff --git a/tests/system/small/ml/test_forecasting.py b/tests/system/small/ml/test_forecasting.py index 23487983ee3..cb27dd388c3 100644 --- a/tests/system/small/ml/test_forecasting.py +++ b/tests/system/small/ml/test_forecasting.py @@ -16,169 +16,48 @@ import pandas as pd import pyarrow as pa -import pytest import pytz -from bigframes.ml import forecasting -ARIMA_EVALUATE_OUTPUT_COL = [ - "non_seasonal_p", - "non_seasonal_d", - "non_seasonal_q", - "log_likelihood", - "AIC", - "variance", - "seasonal_periods", - "has_holiday_effect", - "has_spikes_and_dips", - "has_step_changes", - "error_message", -] - - -@pytest.mark.parametrize("id_col_name", [None, "id"]) -def test_arima_plus_predict_default( - time_series_arima_plus_model: forecasting.ARIMAPlus, - time_series_arima_plus_model_w_id: forecasting.ARIMAPlus, - id_col_name, -): +def test_model_predict(time_series_arima_plus_model): utc = pytz.utc - predictions = ( - ( - time_series_arima_plus_model_w_id - if id_col_name - else time_series_arima_plus_model - ) - .predict() - .to_pandas() + predictions = time_series_arima_plus_model.predict().to_pandas() + expected = pd.DataFrame( + { + "forecast_timestamp": [ + datetime(2017, 8, 2, tzinfo=utc), + datetime(2017, 8, 3, tzinfo=utc), + datetime(2017, 8, 4, tzinfo=utc), + ], + "forecast_value": [2724.472284, 2593.368389, 2353.613034], + } ) - assert predictions.shape == ((6, 9) if id_col_name else (3, 8)) - result = predictions[["forecast_timestamp", "forecast_value"]] - if id_col_name: - result["id"] = predictions[["id"]] - result = result[["id", "forecast_timestamp", "forecast_value"]] - - if id_col_name: - expected = pd.DataFrame( - { - "id": ["1", "2", "1", "2", "1", "2"], - "forecast_timestamp": [ - datetime(2017, 8, 2, tzinfo=utc), - datetime(2017, 8, 2, tzinfo=utc), - datetime(2017, 8, 3, tzinfo=utc), - datetime(2017, 8, 3, tzinfo=utc), - datetime(2017, 8, 4, tzinfo=utc), - datetime(2017, 8, 4, tzinfo=utc), - ], - "forecast_value": [ - 2634.796023, - 2634.796023, - 2621.332461, - 2621.332461, - 2396.095462, - 2396.095462, - ], - } - ) - expected["id"] = expected["id"].astype("string[pyarrow]") - else: - expected = pd.DataFrame( - { - "forecast_timestamp": [ - datetime(2017, 8, 2, tzinfo=utc), - datetime(2017, 8, 3, tzinfo=utc), - datetime(2017, 8, 4, tzinfo=utc), - ], - "forecast_value": [ - 2634.796023, - 2621.332461, - 2396.095462, - ], - } - ) expected["forecast_value"] = expected["forecast_value"].astype(pd.Float64Dtype()) expected["forecast_timestamp"] = expected["forecast_timestamp"].astype( pd.ArrowDtype(pa.timestamp("us", tz="UTC")) ) - pd.testing.assert_frame_equal( - result, + predictions, expected, rtol=0.1, check_index_type=False, ) -@pytest.mark.parametrize("id_col_name", [None, "id"]) -def test_arima_plus_predict_explain_default( - time_series_arima_plus_model: forecasting.ARIMAPlus, - time_series_arima_plus_model_w_id: forecasting.ARIMAPlus, - id_col_name, -): - utc = pytz.utc - predictions = ( - ( - time_series_arima_plus_model_w_id - if id_col_name - else time_series_arima_plus_model - ) - .predict_explain() - .to_pandas() +def test_model_score(time_series_arima_plus_model, new_time_series_df): + result = time_series_arima_plus_model.score( + new_time_series_df[["parsed_date"]], new_time_series_df[["total_visits"]] + ).to_pandas() + expected = pd.DataFrame( + { + "mean_absolute_error": [154.742547], + "mean_squared_error": [26844.868855], + "root_mean_squared_error": [163.844038], + "mean_absolute_percentage_error": [6.189702], + "symmetric_mean_absolute_percentage_error": [6.097155], + }, + dtype="Float64", ) - assert predictions.shape[0] == (738 if id_col_name else 369) - predictions = predictions[ - predictions["time_series_type"] == "forecast" - ].reset_index(drop=True) - assert predictions.shape[0] == (6 if id_col_name else 3) - result = predictions[["time_series_timestamp", "time_series_data"]] - if id_col_name: - result["id"] = predictions[["id"]] - result = result[["id", "time_series_timestamp", "time_series_data"]] - if id_col_name: - expected = pd.DataFrame( - { - "id": ["1", "2", "1", "2", "1", "2"], - "time_series_timestamp": [ - datetime(2017, 8, 2, tzinfo=utc), - datetime(2017, 8, 2, tzinfo=utc), - datetime(2017, 8, 3, tzinfo=utc), - datetime(2017, 8, 3, tzinfo=utc), - datetime(2017, 8, 4, tzinfo=utc), - datetime(2017, 8, 4, tzinfo=utc), - ], - "time_series_data": [ - 2634.796023, - 2634.796023, - 2621.332461, - 2621.332461, - 2396.095462, - 2396.095462, - ], - } - ) - expected["id"] = expected["id"].astype("string[pyarrow]") - else: - expected = pd.DataFrame( - { - "time_series_timestamp": [ - datetime(2017, 8, 2, tzinfo=utc), - datetime(2017, 8, 3, tzinfo=utc), - datetime(2017, 8, 4, tzinfo=utc), - ], - "time_series_data": [ - 2634.796023, - 2621.332461, - 2396.095462, - ], - } - ) - expected["time_series_data"] = expected["time_series_data"].astype( - pd.Float64Dtype() - ) - expected["time_series_timestamp"] = expected["time_series_timestamp"].astype( - pd.ArrowDtype(pa.timestamp("us", tz="UTC")) - ) - pd.testing.assert_frame_equal( result, expected, @@ -187,436 +66,23 @@ def test_arima_plus_predict_explain_default( ) -@pytest.mark.parametrize("id_col_name", [None, "id"]) -def test_arima_plus_predict_params( - time_series_arima_plus_model: forecasting.ARIMAPlus, - time_series_arima_plus_model_w_id: forecasting.ARIMAPlus, - id_col_name, -): - utc = pytz.utc - predictions = ( - ( - time_series_arima_plus_model_w_id - if id_col_name - else time_series_arima_plus_model - ) - .predict(horizon=4, confidence_level=0.9) - .to_pandas() - ) - assert predictions.shape == ((8, 9) if id_col_name else (4, 8)) - result = predictions[["forecast_timestamp", "forecast_value"]] - if id_col_name: - result["id"] = predictions[["id"]] - result = result[["id", "forecast_timestamp", "forecast_value"]] - - if id_col_name: - expected = pd.DataFrame( - { - "id": ["1", "2", "1", "2", "1", "2", "1", "2"], - "forecast_timestamp": [ - datetime(2017, 8, 2, tzinfo=utc), - datetime(2017, 8, 2, tzinfo=utc), - datetime(2017, 8, 3, tzinfo=utc), - datetime(2017, 8, 3, tzinfo=utc), - datetime(2017, 8, 4, tzinfo=utc), - datetime(2017, 8, 4, tzinfo=utc), - datetime(2017, 8, 5, tzinfo=utc), - datetime(2017, 8, 5, tzinfo=utc), - ], - "forecast_value": [ - 2634.796023, - 2634.796023, - 2621.332461, - 2621.332461, - 2396.095462, - 2396.095462, - 1781.623071, - 1781.623071, - ], - } - ) - expected["id"] = expected["id"].astype("string[pyarrow]") - else: - expected = pd.DataFrame( - { - "forecast_timestamp": [ - datetime(2017, 8, 2, tzinfo=utc), - datetime(2017, 8, 3, tzinfo=utc), - datetime(2017, 8, 4, tzinfo=utc), - datetime(2017, 8, 5, tzinfo=utc), - ], - "forecast_value": [ - 2634.796023, - 2621.332461, - 2396.095462, - 1781.623071, - ], - } - ) - expected["forecast_value"] = expected["forecast_value"].astype(pd.Float64Dtype()) - expected["forecast_timestamp"] = expected["forecast_timestamp"].astype( - pd.ArrowDtype(pa.timestamp("us", tz="UTC")) +def test_model_score_series(time_series_arima_plus_model, new_time_series_df): + result = time_series_arima_plus_model.score( + new_time_series_df["parsed_date"], new_time_series_df["total_visits"] + ).to_pandas() + expected = pd.DataFrame( + { + "mean_absolute_error": [154.742547], + "mean_squared_error": [26844.868855], + "root_mean_squared_error": [163.844038], + "mean_absolute_percentage_error": [6.189702], + "symmetric_mean_absolute_percentage_error": [6.097155], + }, + dtype="Float64", ) - pd.testing.assert_frame_equal( result, expected, rtol=0.1, check_index_type=False, ) - - -@pytest.mark.parametrize("id_col_name", [None, "id"]) -def test_arima_plus_predict_explain_params( - time_series_arima_plus_model: forecasting.ARIMAPlus, - time_series_arima_plus_model_w_id: forecasting.ARIMAPlus, - id_col_name, -): - predictions = ( - ( - time_series_arima_plus_model_w_id - if id_col_name - else time_series_arima_plus_model - ) - .predict_explain(horizon=4, confidence_level=0.9) - .to_pandas() - ) - assert predictions.shape[0] >= 1 - prediction_columns = set(predictions.columns) - expected_columns = { - "time_series_timestamp", - "time_series_type", - "time_series_data", - "time_series_adjusted_data", - "standard_error", - "confidence_level", - "prediction_interval_lower_bound", - "trend", - "seasonal_period_yearly", - "seasonal_period_quarterly", - "seasonal_period_monthly", - "seasonal_period_weekly", - "seasonal_period_daily", - "holiday_effect", - } - if id_col_name: - expected_columns.add("id") - assert expected_columns <= prediction_columns - - -@pytest.mark.parametrize("id_col_name", [None, "id"]) -def test_arima_plus_detect_anomalies( - time_series_arima_plus_model: forecasting.ARIMAPlus, - time_series_arima_plus_model_w_id: forecasting.ARIMAPlus, - new_time_series_df, - new_time_series_df_w_id, - id_col_name, -): - anomalies = ( - ( - time_series_arima_plus_model_w_id - if id_col_name - else time_series_arima_plus_model - ) - .detect_anomalies( - new_time_series_df_w_id if id_col_name else new_time_series_df - ) - .to_pandas() - ) - - if id_col_name: - expected = pd.DataFrame( - { - "is_anomaly": [False, False, False, False, False, False], - "lower_bound": [ - 2229.930578, - 2229.930578, - 2149.645455, - 2149.645455, - 1892.873256, - 1892.873256, - ], - "upper_bound": [ - 3039.6614686, - 3039.6614686, - 3093.019467, - 3093.019467, - 2899.317669, - 2899.317669, - ], - "anomaly_probability": [ - 0.48545926, - 0.48545926, - 0.3856835, - 0.3856835, - 0.314156, - 0.314156, - ], - }, - ) - else: - expected = pd.DataFrame( - { - "is_anomaly": [False, False, False], - "lower_bound": [2229.930578, 2149.645455, 1892.873256], - "upper_bound": [3039.6614686, 3093.019467, 2899.317669], - "anomaly_probability": [0.48545926, 0.3856835, 0.314156], - }, - ) - pd.testing.assert_frame_equal( - anomalies[["is_anomaly", "lower_bound", "upper_bound", "anomaly_probability"]], - expected, - rtol=0.1, - check_index_type=False, - check_dtype=False, - ) - - -@pytest.mark.parametrize("id_col_name", [None, "id"]) -def test_arima_plus_detect_anomalies_params( - time_series_arima_plus_model: forecasting.ARIMAPlus, - time_series_arima_plus_model_w_id: forecasting.ARIMAPlus, - new_time_series_df, - new_time_series_df_w_id, - id_col_name, -): - anomalies = ( - ( - time_series_arima_plus_model_w_id - if id_col_name - else time_series_arima_plus_model - ) - .detect_anomalies( - new_time_series_df_w_id if id_col_name else new_time_series_df, - anomaly_prob_threshold=0.7, - ) - .to_pandas() - ) - if id_col_name: - expected = pd.DataFrame( - { - "is_anomaly": [False, False, False, False, False, False], - "lower_bound": [ - 2420.11419, - 2420.11419, - 2360.1870, - 2360.1870, - 2086.0609, - 2086.0609, - ], - "upper_bound": [ - 2849.47785, - 2849.47785, - 2826.54981, - 2826.54981, - 2621.165188, - 2621.165188, - ], - "anomaly_probability": [ - 0.485459, - 0.485459, - 0.385683, - 0.385683, - 0.314156, - 0.314156, - ], - }, - ) - else: - expected = pd.DataFrame( - { - "is_anomaly": [False, False, False], - "lower_bound": [2420.11419, 2360.1870, 2086.0609], - "upper_bound": [2849.47785, 2826.54981, 2621.165188], - "anomaly_probability": [0.485459, 0.385683, 0.314156], - }, - ) - pd.testing.assert_frame_equal( - anomalies[["is_anomaly", "lower_bound", "upper_bound", "anomaly_probability"]] - .sort_values("anomaly_probability") - .reset_index(drop=True), - expected.sort_values("anomaly_probability").reset_index(drop=True), - rtol=0.1, - check_index_type=False, - check_dtype=False, - ) - - -@pytest.mark.parametrize("id_col_name", [None, "id"]) -def test_arima_plus_score( - time_series_arima_plus_model: forecasting.ARIMAPlus, - time_series_arima_plus_model_w_id: forecasting.ARIMAPlus, - new_time_series_df, - new_time_series_df_w_id, - id_col_name, -): - if id_col_name: - result = ( - time_series_arima_plus_model_w_id.score( - new_time_series_df_w_id[["parsed_date"]], - new_time_series_df_w_id[["total_visits"]], - new_time_series_df_w_id[["id"]], - ) - .to_pandas() - .sort_values("id") - .reset_index(drop=True) - ) - else: - result = time_series_arima_plus_model.score( - new_time_series_df[["parsed_date"]], new_time_series_df[["total_visits"]] - ).to_pandas() - if id_col_name: - expected = pd.DataFrame( - { - "id": ["2", "1"], - "mean_absolute_error": [120.011007, 120.011007], - "mean_squared_error": [14562.562359, 14562.562359], - "root_mean_squared_error": [120.675442, 120.675442], - "mean_absolute_percentage_error": [4.80044, 4.80044], - "symmetric_mean_absolute_percentage_error": [4.744332, 4.744332], - }, - dtype="Float64", - ) - expected["id"] = expected["id"].astype(str).str.replace(r"\.0$", "", regex=True) - expected["id"] = expected["id"].astype("string[pyarrow]") - expected = expected.sort_values("id") - expected = expected.reset_index(drop=True) - else: - expected = pd.DataFrame( - { - "mean_absolute_error": [120.0110074], - "mean_squared_error": [14562.5623594], - "root_mean_squared_error": [120.675442], - "mean_absolute_percentage_error": [4.80044], - "symmetric_mean_absolute_percentage_error": [4.744332], - }, - dtype="Float64", - ) - pd.testing.assert_frame_equal( - result[expected.columns], - expected, - rtol=0.1, - check_index_type=False, - check_dtype=False, - ) - - -@pytest.mark.parametrize("id_col_name", [None, "id"]) -def test_arima_plus_summary( - time_series_arima_plus_model: forecasting.ARIMAPlus, - time_series_arima_plus_model_w_id: forecasting.ARIMAPlus, - id_col_name, -): - result = ( - time_series_arima_plus_model_w_id - if id_col_name - else time_series_arima_plus_model - ).summary() - assert result.shape == ((2, 13) if id_col_name else (1, 12)) - expected_columns = ( - [id_col_name] + ARIMA_EVALUATE_OUTPUT_COL - if id_col_name - else ARIMA_EVALUATE_OUTPUT_COL - ) - assert all(column in result.columns for column in expected_columns) - - -@pytest.mark.parametrize("id_col_name", [None, "id"]) -def test_arima_plus_summary_show_all_candidates( - time_series_arima_plus_model: forecasting.ARIMAPlus, - time_series_arima_plus_model_w_id: forecasting.ARIMAPlus, - id_col_name, -): - result = ( - time_series_arima_plus_model_w_id - if id_col_name - else time_series_arima_plus_model - ).summary( - show_all_candidate_models=True, - ) - assert result.shape[0] > 1 - expected_columns = ( - [id_col_name] + ARIMA_EVALUATE_OUTPUT_COL - if id_col_name - else ARIMA_EVALUATE_OUTPUT_COL - ) - assert all(column in result.columns for column in expected_columns) - - -@pytest.mark.parametrize("id_col_name", [None, "id"]) -def test_arima_plus_score_series( - time_series_arima_plus_model: forecasting.ARIMAPlus, - time_series_arima_plus_model_w_id: forecasting.ARIMAPlus, - new_time_series_df, - new_time_series_df_w_id, - id_col_name, -): - if id_col_name: - result = ( - time_series_arima_plus_model_w_id.score( - new_time_series_df_w_id["parsed_date"], - new_time_series_df_w_id["total_visits"], - new_time_series_df_w_id["id"], - ) - .to_pandas() - .sort_values("id") - .reset_index(drop=True) - ) - else: - result = time_series_arima_plus_model.score( - new_time_series_df["parsed_date"], new_time_series_df["total_visits"] - ).to_pandas() - if id_col_name: - expected = pd.DataFrame( - { - "id": ["2", "1"], - "mean_absolute_error": [120.011007, 120.011007], - "mean_squared_error": [14562.562359, 14562.562359], - "root_mean_squared_error": [120.675442, 120.675442], - "mean_absolute_percentage_error": [4.80044, 4.80044], - "symmetric_mean_absolute_percentage_error": [4.744332, 4.744332], - }, - dtype="Float64", - ) - expected["id"] = expected["id"].astype(str).str.replace(r"\.0$", "", regex=True) - expected["id"] = expected["id"].astype("string[pyarrow]") - expected = expected.sort_values("id") - expected = expected.reset_index(drop=True) - else: - expected = pd.DataFrame( - { - "mean_absolute_error": [120.0110074], - "mean_squared_error": [14562.5623594], - "root_mean_squared_error": [120.675442], - "mean_absolute_percentage_error": [4.80044], - "symmetric_mean_absolute_percentage_error": [4.744332], - }, - dtype="Float64", - ) - pd.testing.assert_frame_equal( - result[expected.columns], - expected, - rtol=0.1, - check_index_type=False, - check_dtype=False, - ) - - -@pytest.mark.parametrize("id_col_name", [None, "id"]) -def test_arima_plus_summary_series( - time_series_arima_plus_model: forecasting.ARIMAPlus, - time_series_arima_plus_model_w_id: forecasting.ARIMAPlus, - id_col_name, -): - result = ( - time_series_arima_plus_model_w_id - if id_col_name - else time_series_arima_plus_model - ).summary() - assert result.shape == ((2, 13) if id_col_name else (1, 12)) - expected_columns = ( - [id_col_name] + ARIMA_EVALUATE_OUTPUT_COL - if id_col_name - else ARIMA_EVALUATE_OUTPUT_COL - ) - assert all(column in result.columns for column in expected_columns) diff --git a/tests/system/small/ml/test_imported.py b/tests/system/small/ml/test_imported.py index 2b8d04c3aef..d3055670668 100644 --- a/tests/system/small/ml/test_imported.py +++ b/tests/system/small/ml/test_imported.py @@ -32,9 +32,7 @@ def test_tensorflow_create_model_default_session(imported_tensorflow_model_path) def test_tensorflow_model_predict(imported_tensorflow_model, llm_text_df): df = llm_text_df.rename(columns={"prompt": "input"}) - predictions = imported_tensorflow_model.predict(df).to_pandas() - assert predictions.shape == (3, 2) - result = predictions[["dense_1"]] + result = imported_tensorflow_model.predict(df).to_pandas() # The values are non-human-readable. As they are a dense layer of Neural Network. # And since it is pretrained and imported, the model is a opaque-box. # We may want to switch to better test model and cases. @@ -51,7 +49,6 @@ def test_tensorflow_model_predict(imported_tensorflow_model, llm_text_df): result, expected, check_exact=False, - check_dtype=False, atol=0.1, ) @@ -70,14 +67,12 @@ def test_onnx_create_model(imported_onnx_model): def test_onnx_create_model_default_session(imported_onnx_model_path): - model = imported.ONNXModel(model_path=imported_onnx_model_path) + model = imported.TensorFlowModel(model_path=imported_onnx_model_path) assert model is not None def test_onnx_model_predict(imported_onnx_model, onnx_iris_df): - predictions = imported_onnx_model.predict(onnx_iris_df).to_pandas() - assert predictions.shape == (3, 7) - result = predictions[["label", "probabilities"]] + result = imported_onnx_model.predict(onnx_iris_df).to_pandas() value1 = np.array([0.9999993443489075, 0.0, 0.0]) value2 = np.array([0.0, 0.0, 0.9999993443489075]) expected = pd.DataFrame( @@ -91,7 +86,6 @@ def test_onnx_model_predict(imported_onnx_model, onnx_iris_df): result, expected, check_exact=False, - check_dtype=False, atol=0.1, ) @@ -100,43 +94,3 @@ def test_onnx_model_to_gbq(imported_onnx_model: imported.ONNXModel, dataset_id: imported_onnx_model.to_gbq(f"{dataset_id}.test_onnx_model", replace=True) with pytest.raises(google.api_core.exceptions.Conflict): imported_onnx_model.to_gbq(f"{dataset_id}.test_onnx_model") - - -def test_xgboost_create_model(imported_xgboost_model): - # Model creation doesn't return error - assert imported_xgboost_model is not None - - -def test_xgboost_create_model_default_session(imported_xgboost_array_model_path): - model = imported.XGBoostModel(model_path=imported_xgboost_array_model_path) - assert model is not None - - -def test_xgboost_model_predict(imported_xgboost_model, xgboost_iris_df): - predictions = imported_xgboost_model.predict(xgboost_iris_df).to_pandas() - assert predictions.shape == (3, 5) - result = predictions[["predicted_label"]] - value1 = np.array([0.00362173, 0.01580198, 0.98057634]) - value2 = np.array([0.00349651, 0.00999565, 0.98650789]) - value3 = np.array([0.00561748, 0.0108124, 0.98357016]) - expected = pd.DataFrame( - { - "predicted_label": [value1, value2, value3], - }, - index=pd.Index([0, 1, 2], dtype="Int64"), - ) - pd.testing.assert_frame_equal( - result, - expected, - check_exact=False, - check_dtype=False, - atol=0.1, - ) - - -def test_xgboost_model_to_gbq( - imported_xgboost_model: imported.XGBoostModel, dataset_id: str -): - imported_xgboost_model.to_gbq(f"{dataset_id}.test_xgboost_model", replace=True) - with pytest.raises(google.api_core.exceptions.Conflict): - imported_xgboost_model.to_gbq(f"{dataset_id}.test_xgboost_model") diff --git a/tests/system/small/ml/test_impute.py b/tests/system/small/ml/test_impute.py deleted file mode 100644 index 46a614d7033..00000000000 --- a/tests/system/small/ml/test_impute.py +++ /dev/null @@ -1,89 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import pandas as pd - -from bigframes.ml import impute - - -def test_simple_imputer_fit_transform_default_params(missing_values_penguins_df): - imputer = impute.SimpleImputer(strategy="mean") - result = imputer.fit_transform( - missing_values_penguins_df[ - ["culmen_length_mm", "culmen_depth_mm", "flipper_length_mm"] - ] - ).to_pandas() - - expected = pd.DataFrame( - { - "imputer_culmen_length_mm": [39.5, 38.5, 37.9], - "imputer_culmen_depth_mm": [17.65, 17.2, 18.1], - "imputer_flipper_length_mm": [184.5, 181.0, 188.0], - }, - dtype="Float64", - index=pd.Index([0, 1, 2], dtype="Int64"), - ) - - pd.testing.assert_frame_equal(result, expected) - - -def test_simple_imputer_series(missing_values_penguins_df): - imputer = impute.SimpleImputer(strategy="mean") - imputer.fit(missing_values_penguins_df["culmen_depth_mm"]) - - result = imputer.transform( - missing_values_penguins_df["culmen_depth_mm"] - ).to_pandas() - - expected = pd.DataFrame( - { - "imputer_culmen_depth_mm": [17.65, 17.2, 18.1], - }, - dtype="Float64", - index=pd.Index([0, 1, 2], dtype="Int64"), - ) - - pd.testing.assert_frame_equal(result, expected, rtol=0.1) - - -def test_simple_imputer_save_load_mean(missing_values_penguins_df, dataset_id): - transformer = impute.SimpleImputer(strategy="mean") - transformer.fit( - missing_values_penguins_df[ - ["culmen_length_mm", "culmen_depth_mm", "flipper_length_mm"] - ] - ) - - reloaded_transformer = transformer.to_gbq( - f"{dataset_id}.temp_configured_model", replace=True - ) - assert isinstance(reloaded_transformer, impute.SimpleImputer) - assert reloaded_transformer.strategy == transformer.strategy - assert reloaded_transformer._bqml_model is not None - - -def test_simple_imputer_save_load_most_frequent(missing_values_penguins_df, dataset_id): - transformer = impute.SimpleImputer(strategy="most_frequent") - transformer.fit( - missing_values_penguins_df[ - ["culmen_length_mm", "culmen_depth_mm", "flipper_length_mm"] - ] - ) - - reloaded_transformer = transformer.to_gbq( - f"{dataset_id}.temp_configured_model", replace=True - ) - assert isinstance(reloaded_transformer, impute.SimpleImputer) - assert reloaded_transformer.strategy == transformer.strategy - assert reloaded_transformer._bqml_model is not None diff --git a/tests/system/small/ml/test_linear_model.py b/tests/system/small/ml/test_linear_model.py index da9fc8e14f8..3a8232ed9ec 100644 --- a/tests/system/small/ml/test_linear_model.py +++ b/tests/system/small/ml/test_linear_model.py @@ -12,14 +12,10 @@ # See the License for the specific language governing permissions and # limitations under the License. -import re - import google.api_core.exceptions import pandas import pytest -from bigframes.ml import linear_model - def test_linear_reg_model_score(penguins_linear_model, penguins_df_default_index): df = penguins_df_default_index.dropna() @@ -95,105 +91,25 @@ def test_linear_reg_model_score_series( def test_linear_reg_model_predict(penguins_linear_model, new_penguins_df): predictions = penguins_linear_model.predict(new_penguins_df).to_pandas() - assert predictions.shape == (3, 8) - result = predictions[["predicted_body_mass_g"]] expected = pandas.DataFrame( {"predicted_body_mass_g": [4030.1, 3280.8, 3177.9]}, dtype="Float64", index=pandas.Index([1633, 1672, 1690], name="tag_number", dtype="Int64"), ) pandas.testing.assert_frame_equal( - result.sort_index(), + predictions.sort_index(), expected, check_exact=False, rtol=0.1, ) -def test_linear_reg_model_predict_explain(penguins_linear_model, new_penguins_df): - predictions = penguins_linear_model.predict_explain(new_penguins_df).to_pandas() - assert predictions.shape == (3, 12) - result = predictions[["predicted_body_mass_g", "approximation_error"]] - expected = pandas.DataFrame( - { - "predicted_body_mass_g": [4030.1, 3280.8, 3177.9], - "approximation_error": [ - 0.0, - 0.0, - 0.0, - ], - }, - dtype="Float64", - index=pandas.Index([1633, 1672, 1690], name="tag_number", dtype="Int64"), - ) - pandas.testing.assert_frame_equal( - result.sort_index(), - expected, - check_exact=False, - rtol=0.1, - ) - - -def test_linear_model_predict_explain_top_k_features( - penguins_logistic_model: linear_model.LinearRegression, new_penguins_df -): - top_k_features = 0 - - with pytest.raises( - ValueError, - match=re.escape(f"top_k_features must be at least 1, but is {top_k_features}."), - ): - penguins_logistic_model.predict_explain( - new_penguins_df, top_k_features=top_k_features - ).to_pandas() - - -def test_linear_reg_model_predict_params( - penguins_linear_model: linear_model.LinearRegression, new_penguins_df -): - predictions = penguins_linear_model.predict(new_penguins_df).to_pandas() - assert predictions.shape[0] >= 1 - prediction_columns = set(predictions.columns) - expected_columns = { - "predicted_body_mass_g", - "species", - "island", - "culmen_length_mm", - "culmen_depth_mm", - "flipper_length_mm", - "body_mass_g", - "sex", - } - assert expected_columns <= prediction_columns - - -def test_linear_reg_model_predict_explain_params( - penguins_linear_model: linear_model.LinearRegression, new_penguins_df -): - predictions = penguins_linear_model.predict_explain(new_penguins_df).to_pandas() - assert predictions.shape[0] >= 1 - prediction_columns = set(predictions.columns) - expected_columns = { - "predicted_body_mass_g", - "top_feature_attributions", - "baseline_prediction_value", - "prediction_value", - "approximation_error", - "species", - "island", - "culmen_length_mm", - "culmen_depth_mm", - "flipper_length_mm", - "body_mass_g", - "sex", - } - assert expected_columns <= prediction_columns - - def test_to_gbq_saved_linear_reg_model_scores( - penguins_linear_model, table_id_unique, penguins_df_default_index + penguins_linear_model, dataset_id, penguins_df_default_index ): - saved_model = penguins_linear_model.to_gbq(table_id_unique, replace=True) + saved_model = penguins_linear_model.to_gbq( + f"{dataset_id}.test_penguins_model", replace=True + ) df = penguins_df_default_index.dropna() X_test = df[ [ @@ -228,10 +144,10 @@ def test_to_gbq_saved_linear_reg_model_scores( ) -def test_to_gbq_replace(penguins_linear_model, table_id_unique): - penguins_linear_model.to_gbq(table_id_unique, replace=True) +def test_to_gbq_replace(penguins_linear_model, dataset_id): + penguins_linear_model.to_gbq(f"{dataset_id}.test_penguins_model", replace=True) with pytest.raises(google.api_core.exceptions.Conflict): - penguins_linear_model.to_gbq(table_id_unique) + penguins_linear_model.to_gbq(f"{dataset_id}.test_penguins_model") def test_logistic_model_score(penguins_logistic_model, penguins_df_default_index): @@ -306,85 +222,27 @@ def test_logistic_model_score_series( ) -def test_logistic_model_predict(penguins_logistic_model, new_penguins_df): +def test_logsitic_model_predict(penguins_logistic_model, new_penguins_df): predictions = penguins_logistic_model.predict(new_penguins_df).to_pandas() - assert predictions.shape == (3, 9) - result = predictions[["predicted_sex"]] expected = pandas.DataFrame( {"predicted_sex": ["MALE", "MALE", "FEMALE"]}, dtype="string[pyarrow]", index=pandas.Index([1633, 1672, 1690], name="tag_number", dtype="Int64"), ) pandas.testing.assert_frame_equal( - result.sort_index(), + predictions.sort_index(), expected, check_exact=False, rtol=0.1, ) -def test_logistic_model_predict_explain_top_k_features( - penguins_logistic_model: linear_model.LogisticRegression, new_penguins_df +def test_logsitic_model_to_gbq_saved_score( + penguins_logistic_model, dataset_id, penguins_df_default_index ): - top_k_features = 0 - - with pytest.raises( - ValueError, - match=re.escape(f"top_k_features must be at least 1, but is {top_k_features}."), - ): - penguins_logistic_model.predict_explain( - new_penguins_df, top_k_features=top_k_features - ).to_pandas() - - -def test_logistic_model_predict_params( - penguins_logistic_model: linear_model.LogisticRegression, new_penguins_df -): - predictions = penguins_logistic_model.predict(new_penguins_df).to_pandas() - assert predictions.shape[0] >= 1 - prediction_columns = set(predictions.columns) - expected_columns = { - "predicted_sex", - "predicted_sex_probs", - "species", - "island", - "culmen_length_mm", - "culmen_depth_mm", - "flipper_length_mm", - "body_mass_g", - "sex", - } - assert expected_columns <= prediction_columns - - -def test_logistic_model_predict_explain_params( - penguins_logistic_model: linear_model.LogisticRegression, new_penguins_df -): - predictions = penguins_logistic_model.predict_explain(new_penguins_df).to_pandas() - assert predictions.shape[0] >= 1 - prediction_columns = set(predictions.columns) - expected_columns = { - "predicted_sex", - "probability", - "top_feature_attributions", - "baseline_prediction_value", - "prediction_value", - "approximation_error", - "species", - "island", - "culmen_length_mm", - "culmen_depth_mm", - "flipper_length_mm", - "body_mass_g", - "sex", - } - assert expected_columns <= prediction_columns - - -def test_logistic_model_to_gbq_saved_score( - penguins_logistic_model, table_id_unique, penguins_df_default_index -): - saved_model = penguins_logistic_model.to_gbq(table_id_unique, replace=True) + saved_model = penguins_logistic_model.to_gbq( + f"{dataset_id}.test_penguins_model", replace=True + ) df = penguins_df_default_index.dropna() X_test = df[ [ @@ -417,3 +275,9 @@ def test_logistic_model_to_gbq_saved_score( # int64 Index by default in pandas versus Int64 (nullable) Index in BigQuery DataFrame check_index_type=False, ) + + +def test_logistic_model_to_gbq_replace(penguins_logistic_model, dataset_id): + penguins_logistic_model.to_gbq(f"{dataset_id}.test_penguins_model", replace=True) + with pytest.raises(google.api_core.exceptions.Conflict): + penguins_logistic_model.to_gbq(f"{dataset_id}.test_penguins_model") diff --git a/tests/system/small/ml/test_llm.py b/tests/system/small/ml/test_llm.py new file mode 100644 index 00000000000..79d3c40317d --- /dev/null +++ b/tests/system/small/ml/test_llm.py @@ -0,0 +1,230 @@ +# Copyright 2023 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from unittest import TestCase + +import numpy as np +import pytest + +from bigframes.ml import llm + + +def test_create_text_generator_model(palm2_text_generator_model): + # Model creation doesn't return error + assert palm2_text_generator_model is not None + assert palm2_text_generator_model._bqml_model is not None + + +def test_create_text_generator_32k_model(palm2_text_generator_32k_model): + # Model creation doesn't return error + assert palm2_text_generator_32k_model is not None + assert palm2_text_generator_32k_model._bqml_model is not None + + +@pytest.mark.flaky(retries=2, delay=120) +def test_create_text_generator_model_default_session(bq_connection, llm_text_pandas_df): + import bigframes.pandas as bpd + + bpd.close_session() + bpd.options.bigquery.bq_connection = bq_connection + bpd.options.bigquery.location = "us" + + model = llm.PaLM2TextGenerator() + assert model is not None + assert model._bqml_model is not None + assert model.connection_name.casefold() == "bigframes-dev.us.bigframes-rf-conn" + + llm_text_df = bpd.read_pandas(llm_text_pandas_df) + + df = model.predict(llm_text_df).to_pandas() + TestCase().assertSequenceEqual(df.shape, (3, 1)) + assert "ml_generate_text_llm_result" in df.columns + series = df["ml_generate_text_llm_result"] + assert all(series.str.len() > 20) + + +@pytest.mark.flaky(retries=2, delay=120) +def test_create_text_generator_32k_model_default_session( + bq_connection, llm_text_pandas_df +): + import bigframes.pandas as bpd + + bpd.close_session() + bpd.options.bigquery.bq_connection = bq_connection + bpd.options.bigquery.location = "us" + + model = llm.PaLM2TextGenerator(model_name="text-bison-32k") + assert model is not None + assert model._bqml_model is not None + assert model.connection_name.casefold() == "bigframes-dev.us.bigframes-rf-conn" + + llm_text_df = bpd.read_pandas(llm_text_pandas_df) + + df = model.predict(llm_text_df).to_pandas() + TestCase().assertSequenceEqual(df.shape, (3, 1)) + assert "ml_generate_text_llm_result" in df.columns + series = df["ml_generate_text_llm_result"] + assert all(series.str.len() > 20) + + +@pytest.mark.flaky(retries=2, delay=120) +def test_create_text_generator_model_default_connection(llm_text_pandas_df): + from bigframes import _config + import bigframes.pandas as bpd + + bpd.close_session() + _config.options = _config.Options() # reset configs + + llm_text_df = bpd.read_pandas(llm_text_pandas_df) + + model = llm.PaLM2TextGenerator() + assert model is not None + assert model._bqml_model is not None + assert ( + model.connection_name.casefold() + == "bigframes-dev.us.bigframes-default-connection" + ) + + df = model.predict(llm_text_df).to_pandas() + TestCase().assertSequenceEqual(df.shape, (3, 1)) + assert "ml_generate_text_llm_result" in df.columns + series = df["ml_generate_text_llm_result"] + assert all(series.str.len() > 20) + + +# Marked as flaky only because BQML LLM is in preview, the service only has limited capacity, not stable enough. +@pytest.mark.flaky(retries=2, delay=120) +def test_text_generator_predict_default_params_success( + palm2_text_generator_model, llm_text_df +): + df = palm2_text_generator_model.predict(llm_text_df).to_pandas() + TestCase().assertSequenceEqual(df.shape, (3, 1)) + assert "ml_generate_text_llm_result" in df.columns + series = df["ml_generate_text_llm_result"] + assert all(series.str.len() > 20) + + +@pytest.mark.flaky(retries=2, delay=120) +def test_text_generator_predict_series_default_params_success( + palm2_text_generator_model, llm_text_df +): + df = palm2_text_generator_model.predict(llm_text_df["prompt"]).to_pandas() + TestCase().assertSequenceEqual(df.shape, (3, 1)) + assert "ml_generate_text_llm_result" in df.columns + series = df["ml_generate_text_llm_result"] + assert all(series.str.len() > 20) + + +@pytest.mark.flaky(retries=2, delay=120) +def test_text_generator_predict_arbitrary_col_label_success( + palm2_text_generator_model, llm_text_df +): + llm_text_df = llm_text_df.rename(columns={"prompt": "arbitrary"}) + df = palm2_text_generator_model.predict(llm_text_df).to_pandas() + TestCase().assertSequenceEqual(df.shape, (3, 1)) + assert "ml_generate_text_llm_result" in df.columns + series = df["ml_generate_text_llm_result"] + assert all(series.str.len() > 20) + + +@pytest.mark.flaky(retries=2, delay=120) +def test_text_generator_predict_with_params_success( + palm2_text_generator_model, llm_text_df +): + df = palm2_text_generator_model.predict( + llm_text_df, temperature=0.5, max_output_tokens=100, top_k=20, top_p=0.5 + ).to_pandas() + TestCase().assertSequenceEqual(df.shape, (3, 1)) + assert "ml_generate_text_llm_result" in df.columns + series = df["ml_generate_text_llm_result"] + assert all(series.str.len() > 20) + + +def test_create_embedding_generator_model(palm2_embedding_generator_model): + # Model creation doesn't return error + assert palm2_embedding_generator_model is not None + assert palm2_embedding_generator_model._bqml_model is not None + + +def test_create_embedding_generator_multilingual_model( + palm2_embedding_generator_multilingual_model, +): + # Model creation doesn't return error + assert palm2_embedding_generator_multilingual_model is not None + assert palm2_embedding_generator_multilingual_model._bqml_model is not None + + +def test_create_text_embedding_generator_model_defaults(bq_connection): + import bigframes.pandas as bpd + + bpd.close_session() + bpd.options.bigquery.bq_connection = bq_connection + bpd.options.bigquery.location = "us" + + model = llm.PaLM2TextEmbeddingGenerator() + assert model is not None + assert model._bqml_model is not None + + +def test_create_text_embedding_generator_multilingual_model_defaults(bq_connection): + import bigframes.pandas as bpd + + bpd.close_session() + bpd.options.bigquery.bq_connection = bq_connection + bpd.options.bigquery.location = "us" + + model = llm.PaLM2TextEmbeddingGenerator( + model_name="textembedding-gecko-multilingual" + ) + assert model is not None + assert model._bqml_model is not None + + +@pytest.mark.flaky(retries=2, delay=120) +def test_embedding_generator_predict_success( + palm2_embedding_generator_model, llm_text_df +): + df = palm2_embedding_generator_model.predict(llm_text_df).to_pandas() + TestCase().assertSequenceEqual(df.shape, (3, 1)) + assert "text_embedding" in df.columns + series = df["text_embedding"] + value = series[0] + assert isinstance(value, np.ndarray) + assert value.size == 768 + + +@pytest.mark.flaky(retries=2, delay=120) +def test_embedding_generator_multilingual_predict_success( + palm2_embedding_generator_multilingual_model, llm_text_df +): + df = palm2_embedding_generator_multilingual_model.predict(llm_text_df).to_pandas() + TestCase().assertSequenceEqual(df.shape, (3, 1)) + assert "text_embedding" in df.columns + series = df["text_embedding"] + value = series[0] + assert isinstance(value, np.ndarray) + assert value.size == 768 + + +@pytest.mark.flaky(retries=2, delay=120) +def test_embedding_generator_predict_series_success( + palm2_embedding_generator_model, llm_text_df +): + df = palm2_embedding_generator_model.predict(llm_text_df["prompt"]).to_pandas() + TestCase().assertSequenceEqual(df.shape, (3, 1)) + assert "text_embedding" in df.columns + series = df["text_embedding"] + value = series[0] + assert isinstance(value, np.ndarray) + assert value.size == 768 diff --git a/tests/system/small/ml/test_metrics.py b/tests/system/small/ml/test_metrics.py index ab9c3e4552c..b40982e2829 100644 --- a/tests/system/small/ml/test_metrics.py +++ b/tests/system/small/ml/test_metrics.py @@ -17,10 +17,9 @@ import numpy as np import pandas as pd import pytest +import sklearn.metrics as sklearn_metrics # type: ignore -import bigframes -import bigframes.testing.utils -from bigframes.ml import metrics +import bigframes.ml.metrics def test_r2_score_perfect_fit(session): @@ -33,7 +32,9 @@ def test_r2_score_perfect_fit(session): df = session.read_pandas(pd_df) assert ( - metrics.r2_score(df[["y_true_arbitrary_name"]], df[["y_pred_arbitrary_name"]]) + bigframes.ml.metrics.r2_score( + df[["y_true_arbitrary_name"]], df[["y_pred_arbitrary_name"]] + ) == 1.0 ) @@ -42,7 +43,7 @@ def test_r2_score_bad_fit(session): pd_df = pd.DataFrame({"y_true": [1, 2, 3, 4, 5], "y_pred": [5, 4, 3, 2, 1]}) df = session.read_pandas(pd_df) - assert metrics.r2_score(df[["y_true"]], df[["y_pred"]]) == -3.0 + assert bigframes.ml.metrics.r2_score(df[["y_true"]], df[["y_pred"]]) == -3.0 def test_r2_score_force_finite(session): @@ -55,22 +56,23 @@ def test_r2_score_force_finite(session): ) df = session.read_pandas(pd_df) - assert metrics.r2_score( + assert bigframes.ml.metrics.r2_score( df[["y_true"]], df[["y_pred_1"]], force_finite=False ) == float("-inf") - assert metrics.r2_score(df[["y_true"]], df[["y_pred_1"]]) == 0.0 + assert bigframes.ml.metrics.r2_score(df[["y_true"]], df[["y_pred_1"]]) == 0.0 assert math.isnan( - metrics.r2_score(df[["y_true"]], df[["y_pred_2"]], force_finite=False) + bigframes.ml.metrics.r2_score( + df[["y_true"]], df[["y_pred_2"]], force_finite=False + ) ) - assert metrics.r2_score(df[["y_true"]], df[["y_pred_2"]]) == 1.0 + assert bigframes.ml.metrics.r2_score(df[["y_true"]], df[["y_pred_2"]]) == 1.0 def test_r2_score_ok_fit_matches_sklearn(session): - sklearn_metrics = pytest.importorskip("sklearn.metrics") pd_df = pd.DataFrame({"y_true": [1, 2, 3, 4, 5], "y_pred": [2, 3, 4, 3, 6]}) df = session.read_pandas(pd_df) - bf_result = metrics.r2_score(df[["y_true"]], df[["y_pred"]]) + bf_result = bigframes.ml.metrics.r2_score(df[["y_true"]], df[["y_pred"]]) sklearn_result = sklearn_metrics.r2_score(pd_df[["y_true"]], pd_df[["y_pred"]]) assert math.isclose(bf_result, sklearn_result) @@ -79,7 +81,7 @@ def test_r2_score_series(session): pd_df = pd.DataFrame({"y_true": [1, 7, 3, 2, 5], "y_pred": [1, 7, 3, 2, 5]}) df = session.read_pandas(pd_df) - assert metrics.r2_score(df["y_true"], df["y_pred"]) == 1.0 + assert bigframes.ml.metrics.r2_score(df["y_true"], df["y_pred"]) == 1.0 def test_accuracy_score_perfect_fit(session): @@ -92,7 +94,7 @@ def test_accuracy_score_perfect_fit(session): df = session.read_pandas(pd_df) assert ( - metrics.accuracy_score( + bigframes.ml.metrics.accuracy_score( df[["y_true_arbitrary_name"]], df[["y_pred_arbitrary_name"]] ) == 1.0 @@ -103,22 +105,26 @@ def test_accuracy_score_bad_fit(session): pd_df = pd.DataFrame({"y_true": [0, 2, 1, 3, 4], "y_pred": [0, 1, 2, 3, 4]}) df = session.read_pandas(pd_df) - assert metrics.accuracy_score(df[["y_true"]], df[["y_pred"]]) == 0.6 + assert bigframes.ml.metrics.accuracy_score(df[["y_true"]], df[["y_pred"]]) == 0.6 def test_accuracy_score_not_normailze(session): pd_df = pd.DataFrame({"y_true": [0, 2, 1, 3, 4], "y_pred": [0, 1, 2, 3, 4]}) df = session.read_pandas(pd_df) - assert metrics.accuracy_score(df[["y_true"]], df[["y_pred"]], normalize=False) == 3 + assert ( + bigframes.ml.metrics.accuracy_score( + df[["y_true"]], df[["y_pred"]], normalize=False + ) + == 3 + ) def test_accuracy_score_fit_matches_sklearn(session): - sklearn_metrics = pytest.importorskip("sklearn.metrics") pd_df = pd.DataFrame({"y_true": [1, 2, 3, 4, 5], "y_pred": [2, 3, 4, 3, 6]}) df = session.read_pandas(pd_df) - bf_result = metrics.accuracy_score(df[["y_true"]], df[["y_pred"]]) + bf_result = bigframes.ml.metrics.accuracy_score(df[["y_true"]], df[["y_pred"]]) sklearn_result = sklearn_metrics.accuracy_score( pd_df[["y_true"]], pd_df[["y_pred"]] ) @@ -129,7 +135,7 @@ def test_accuracy_score_series(session): pd_df = pd.DataFrame({"y_true": [1, 7, 3, 2, 5], "y_pred": [1, 7, 3, 2, 5]}) df = session.read_pandas(pd_df) - assert metrics.accuracy_score(df["y_true"], df["y_pred"]) == 1.0 + assert bigframes.ml.metrics.accuracy_score(df["y_true"], df["y_pred"]) == 1.0 def test_roc_curve_binary_classification_prediction_returns_expected(session): @@ -152,7 +158,7 @@ def test_roc_curve_binary_classification_prediction_returns_expected(session): ) df = session.read_pandas(pd_df) - fpr, tpr, thresholds = metrics.roc_curve( + fpr, tpr, thresholds = bigframes.ml.metrics.roc_curve( df[["y_true_arbitrary_name"]], df[["y_score_arbitrary_name"]], drop_intermediate=False, @@ -162,7 +168,7 @@ def test_roc_curve_binary_classification_prediction_returns_expected(session): pd_tpr = tpr.to_pandas() pd_thresholds = thresholds.to_pandas() - bigframes.testing.utils.assert_series_equal( + pd.testing.assert_series_equal( # skip testing the first value, as it is redundant and inconsistent across sklearn versions pd_thresholds[1:], pd.Series( @@ -172,7 +178,7 @@ def test_roc_curve_binary_classification_prediction_returns_expected(session): ), check_index=False, ) - bigframes.testing.utils.assert_series_equal( + pd.testing.assert_series_equal( pd_fpr, pd.Series( [0.0, 0.0, 0.0, 0.25, 0.25, 0.5, 0.5, 0.75, 0.75, 0.75, 1.0], @@ -181,7 +187,7 @@ def test_roc_curve_binary_classification_prediction_returns_expected(session): ), check_index_type=False, ) - bigframes.testing.utils.assert_series_equal( + pd.testing.assert_series_equal( pd_tpr, pd.Series( [ @@ -205,7 +211,6 @@ def test_roc_curve_binary_classification_prediction_returns_expected(session): def test_roc_curve_binary_classification_prediction_matches_sklearn(session): - sklearn_metrics = pytest.importorskip("sklearn.metrics") pd_df = pd.DataFrame( { "y_true": [0, 0, 1, 1, 0, 1, 0, 1, 1, 1], @@ -214,7 +219,7 @@ def test_roc_curve_binary_classification_prediction_matches_sklearn(session): ) df = session.read_pandas(pd_df) - fpr, tpr, thresholds = metrics.roc_curve( + fpr, tpr, thresholds = bigframes.ml.metrics.roc_curve( df[["y_true"]], df[["y_score"]], drop_intermediate=False ) expected_fpr, expected_tpr, expected_thresholds = sklearn_metrics.roc_curve( @@ -222,8 +227,8 @@ def test_roc_curve_binary_classification_prediction_matches_sklearn(session): ) # sklearn returns float64 np arrays - np_fpr = fpr.to_pandas().astype("float64").array.to_numpy() - np_tpr = tpr.to_pandas().astype("float64").array.to_numpy() + np_fpr = fpr.to_pandas().astype("float64").array + np_tpr = tpr.to_pandas().astype("float64").array np_thresholds = thresholds.to_pandas().astype("float64").array np.testing.assert_array_equal( @@ -254,7 +259,7 @@ def test_roc_curve_binary_classification_decision_returns_expected(session): ) df = session.read_pandas(pd_df) - fpr, tpr, thresholds = metrics.roc_curve( + fpr, tpr, thresholds = bigframes.ml.metrics.roc_curve( df[["y_true"]], df[["y_score"]], drop_intermediate=False ) @@ -262,7 +267,7 @@ def test_roc_curve_binary_classification_decision_returns_expected(session): pd_tpr = tpr.to_pandas() pd_thresholds = thresholds.to_pandas() - bigframes.testing.utils.assert_series_equal( + pd.testing.assert_series_equal( # skip testing the first value, as it is redundant and inconsistent across sklearn versions pd_thresholds[1:], pd.Series( @@ -272,7 +277,7 @@ def test_roc_curve_binary_classification_decision_returns_expected(session): ), check_index=False, ) - bigframes.testing.utils.assert_series_equal( + pd.testing.assert_series_equal( pd_fpr, pd.Series( [0.0, 0.0, 1.0], @@ -281,7 +286,7 @@ def test_roc_curve_binary_classification_decision_returns_expected(session): ), check_index_type=False, ) - bigframes.testing.utils.assert_series_equal( + pd.testing.assert_series_equal( pd_tpr, pd.Series( [ @@ -297,7 +302,6 @@ def test_roc_curve_binary_classification_decision_returns_expected(session): def test_roc_curve_binary_classification_decision_matches_sklearn(session): - sklearn_metrics = pytest.importorskip("sklearn.metrics") # Instead of operating on probabilities, assume a 70% decision threshold # has been applied, and operate on the final output y_score = [0.1, 0.4, 0.35, 0.8, 0.65, 0.9, 0.5, 0.3, 0.6, 0.45] @@ -310,7 +314,7 @@ def test_roc_curve_binary_classification_decision_matches_sklearn(session): ) df = session.read_pandas(pd_df) - fpr, tpr, thresholds = metrics.roc_curve( + fpr, tpr, thresholds = bigframes.ml.metrics.roc_curve( df[["y_true"]], df[["y_score"]], drop_intermediate=False ) expected_fpr, expected_tpr, expected_thresholds = sklearn_metrics.roc_curve( @@ -318,8 +322,8 @@ def test_roc_curve_binary_classification_decision_matches_sklearn(session): ) # sklearn returns float64 np arrays - np_fpr = fpr.to_pandas().astype("float64").array.to_numpy() - np_tpr = tpr.to_pandas().astype("float64").array.to_numpy() + np_fpr = fpr.to_pandas().astype("float64").array + np_tpr = tpr.to_pandas().astype("float64").array np_thresholds = thresholds.to_pandas().astype("float64").array np.testing.assert_array_equal( @@ -346,7 +350,7 @@ def test_roc_curve_binary_classification_prediction_series(session): ) df = session.read_pandas(pd_df) - fpr, tpr, thresholds = metrics.roc_curve( + fpr, tpr, thresholds = bigframes.ml.metrics.roc_curve( df["y_true"], df["y_score"], drop_intermediate=False ) @@ -354,7 +358,7 @@ def test_roc_curve_binary_classification_prediction_series(session): pd_tpr = tpr.to_pandas() pd_thresholds = thresholds.to_pandas() - bigframes.testing.utils.assert_series_equal( + pd.testing.assert_series_equal( # skip testing the first value, as it is redundant and inconsistent across sklearn versions pd_thresholds[1:], pd.Series( @@ -364,7 +368,7 @@ def test_roc_curve_binary_classification_prediction_series(session): ), check_index=False, ) - bigframes.testing.utils.assert_series_equal( + pd.testing.assert_series_equal( pd_fpr, pd.Series( [0.0, 0.0, 0.0, 0.25, 0.25, 0.5, 0.5, 0.75, 0.75, 0.75, 1.0], @@ -373,7 +377,7 @@ def test_roc_curve_binary_classification_prediction_series(session): ), check_index_type=False, ) - bigframes.testing.utils.assert_series_equal( + pd.testing.assert_series_equal( pd_tpr, pd.Series( [ @@ -416,7 +420,7 @@ def test_roc_auc_score_returns_expected(session): ) df = session.read_pandas(pd_df) - score = metrics.roc_auc_score( + score = bigframes.ml.metrics.roc_auc_score( df[["y_true_arbitrary_name"]], df[["y_score_arbitrary_name"]] ) @@ -424,7 +428,6 @@ def test_roc_auc_score_returns_expected(session): def test_roc_auc_score_returns_matches_sklearn(session): - sklearn_metrics = pytest.importorskip("sklearn.metrics") pd_df = pd.DataFrame( { "y_true": [0, 0, 1, 1, 0, 1, 0, 1, 1, 1], @@ -433,7 +436,7 @@ def test_roc_auc_score_returns_matches_sklearn(session): ) df = session.read_pandas(pd_df) - score = metrics.roc_auc_score(df[["y_true"]], df[["y_score"]]) + score = bigframes.ml.metrics.roc_auc_score(df[["y_true"]], df[["y_score"]]) expected_score = sklearn_metrics.roc_auc_score( pd_df[["y_true"]], pd_df[["y_score"]] ) @@ -450,7 +453,7 @@ def test_roc_auc_score_series(session): ) df = session.read_pandas(pd_df) - score = metrics.roc_auc_score(df["y_true"], df["y_score"]) + score = bigframes.ml.metrics.roc_auc_score(df["y_true"], df["y_score"]) assert score == 0.625 @@ -459,33 +462,33 @@ def test_auc_invalid_x_size(session): pd_df = pd.DataFrame({"x_arbitrary_name": [0], "y_arbitrary_name": [0]}) df = session.read_pandas(pd_df) with pytest.raises(ValueError): - metrics.auc(df[["x_arbitrary_name"]], df[["y_arbitrary_name"]]) + bigframes.ml.metrics.auc(df[["x_arbitrary_name"]], df[["y_arbitrary_name"]]) def test_auc_nondecreasing_x(session): pd_df = pd.DataFrame({"x": [0, 0, 0.5, 0.5, 1], "y": [0, 0.5, 0.5, 1, 1]}) df = session.read_pandas(pd_df) - assert metrics.auc(df[["x"]], df[["y"]]) == 0.75 + assert bigframes.ml.metrics.auc(df[["x"]], df[["y"]]) == 0.75 def test_auc_nonincreasing_x(session): pd_df = pd.DataFrame({"x": [0, 0, -0.5, -0.5, -1], "y": [0, 0.5, 0.5, 1, 1]}) df = session.read_pandas(pd_df) - assert metrics.auc(df[["x"]], df[["y"]]) == 0.75 + assert bigframes.ml.metrics.auc(df[["x"]], df[["y"]]) == 0.75 def test_auc_nonincreasing_x_negative(session): pd_df = pd.DataFrame({"x": [0, 0, -0.5, -0.5, -1], "y": [0, -0.5, -0.5, -1, -1]}) df = session.read_pandas(pd_df) - assert metrics.auc(df[["x"]], df[["y"]]) == -0.75 + assert bigframes.ml.metrics.auc(df[["x"]], df[["y"]]) == -0.75 def test_auc_series(session): pd_df = pd.DataFrame({"x": [0, 0, 0.5, 0.5, 1], "y": [0, 0.5, 0.5, 1, 1]}) df = session.read_pandas(pd_df) - assert metrics.auc(df["x"], df["y"]) == 0.75 + assert bigframes.ml.metrics.auc(df["x"], df["y"]) == 0.75 def test_confusion_matrix(session): @@ -496,7 +499,7 @@ def test_confusion_matrix(session): } ).astype("Int64") df = session.read_pandas(pd_df) - confusion_matrix = metrics.confusion_matrix( + confusion_matrix = bigframes.ml.metrics.confusion_matrix( df[["y_true_arbitrary_name"]], df[["y_pred_arbitrary_name"]] ) expected_pd_df = pd.DataFrame( @@ -506,7 +509,7 @@ def test_confusion_matrix(session): 2: [0, 1, 2], } ).astype("int64") - bigframes.testing.utils.assert_frame_equal( + pd.testing.assert_frame_equal( confusion_matrix, expected_pd_df, check_index_type=False ) @@ -519,18 +522,22 @@ def test_confusion_matrix_column_index(session): } ).astype("Int64") df = session.read_pandas(pd_df) - confusion_matrix = metrics.confusion_matrix(df[["y_true"]], df[["y_pred"]]) - expected_pd_df = pd.DataFrame( - {1: [1, 0, 1, 0], 2: [0, 0, 2, 0], 3: [0, 0, 0, 0], 4: [0, 1, 0, 1]}, - index=[1, 2, 3, 4], - ).astype("int64") - bigframes.testing.utils.assert_frame_equal( + confusion_matrix = bigframes.ml.metrics.confusion_matrix( + df[["y_true"]], df[["y_pred"]] + ) + expected_pd_df = ( + pd.DataFrame( + {1: [1, 0, 1, 0], 2: [0, 0, 2, 0], 3: [0, 0, 0, 0], 4: [0, 1, 0, 1]} + ) + .astype("int64") + .set_index([pd.Index([1, 2, 3, 4])]) + ) + pd.testing.assert_frame_equal( confusion_matrix, expected_pd_df, check_index_type=False ) def test_confusion_matrix_matches_sklearn(session): - sklearn_metrics = pytest.importorskip("sklearn.metrics") pd_df = pd.DataFrame( { "y_true": [2, 3, 3, 3, 4, 1], @@ -538,18 +545,19 @@ def test_confusion_matrix_matches_sklearn(session): } ).astype("Int64") df = session.read_pandas(pd_df) - confusion_matrix = metrics.confusion_matrix(df[["y_true"]], df[["y_pred"]]) + confusion_matrix = bigframes.ml.metrics.confusion_matrix( + df[["y_true"]], df[["y_pred"]] + ) expected_confusion_matrix = sklearn_metrics.confusion_matrix( pd_df[["y_true"]], pd_df[["y_pred"]] ) expected_pd_df = pd.DataFrame(expected_confusion_matrix) - bigframes.testing.utils.assert_frame_equal( + pd.testing.assert_frame_equal( confusion_matrix, expected_pd_df, check_index_type=False ) def test_confusion_matrix_str_matches_sklearn(session): - sklearn_metrics = pytest.importorskip("sklearn.metrics") pd_df = pd.DataFrame( { "y_true": ["cat", "ant", "cat", "cat", "ant", "bird"], @@ -557,15 +565,17 @@ def test_confusion_matrix_str_matches_sklearn(session): } ).astype("str") df = session.read_pandas(pd_df) - confusion_matrix = metrics.confusion_matrix(df[["y_true"]], df[["y_pred"]]) + confusion_matrix = bigframes.ml.metrics.confusion_matrix( + df[["y_true"]], df[["y_pred"]] + ) expected_confusion_matrix = sklearn_metrics.confusion_matrix( pd_df[["y_true"]], pd_df[["y_pred"]] ) - expected_pd_df = pd.DataFrame( - expected_confusion_matrix, index=["ant", "bird", "cat"] + expected_pd_df = pd.DataFrame(expected_confusion_matrix).set_index( + [pd.Index(["ant", "bird", "cat"])] ) expected_pd_df.columns = pd.Index(["ant", "bird", "cat"]) - bigframes.testing.utils.assert_frame_equal( + pd.testing.assert_frame_equal( confusion_matrix, expected_pd_df, check_index_type=False ) @@ -578,7 +588,7 @@ def test_confusion_matrix_series(session): } ).astype("Int64") df = session.read_pandas(pd_df) - confusion_matrix = metrics.confusion_matrix(df["y_true"], df["y_pred"]) + confusion_matrix = bigframes.ml.metrics.confusion_matrix(df["y_true"], df["y_pred"]) expected_pd_df = pd.DataFrame( { 0: [2, 0, 1], @@ -586,7 +596,7 @@ def test_confusion_matrix_series(session): 2: [0, 1, 2], } ).astype("int64") - bigframes.testing.utils.assert_frame_equal( + pd.testing.assert_frame_equal( confusion_matrix, expected_pd_df, check_index_type=False ) @@ -599,20 +609,17 @@ def test_recall_score(session): } ).astype("Int64") df = session.read_pandas(pd_df) - recall = metrics.recall_score( + recall = bigframes.ml.metrics.recall_score( df[["y_true_arbitrary_name"]], df[["y_pred_arbitrary_name"]], average=None ) expected_values = [1.000000, 0.000000, 0.666667] expected_index = [0, 1, 2] expected_recall = pd.Series(expected_values, index=expected_index) - bigframes.testing.utils.assert_series_equal( - recall, expected_recall, check_index_type=False - ) + pd.testing.assert_series_equal(recall, expected_recall, check_index_type=False) def test_recall_score_matches_sklearn(session): - sklearn_metrics = pytest.importorskip("sklearn.metrics") pd_df = pd.DataFrame( { "y_true": [2, 0, 2, 2, 0, 1], @@ -620,19 +627,18 @@ def test_recall_score_matches_sklearn(session): } ).astype("Int64") df = session.read_pandas(pd_df) - recall = metrics.recall_score(df[["y_true"]], df[["y_pred"]], average=None) + recall = bigframes.ml.metrics.recall_score( + df[["y_true"]], df[["y_pred"]], average=None + ) expected_values = sklearn_metrics.recall_score( pd_df[["y_true"]], pd_df[["y_pred"]], average=None ) expected_index = [0, 1, 2] expected_recall = pd.Series(expected_values, index=expected_index) - bigframes.testing.utils.assert_series_equal( - recall, expected_recall, check_index_type=False - ) + pd.testing.assert_series_equal(recall, expected_recall, check_index_type=False) def test_recall_score_str_matches_sklearn(session): - sklearn_metrics = pytest.importorskip("sklearn.metrics") pd_df = pd.DataFrame( { "y_true": ["cat", "ant", "cat", "cat", "ant", "bird"], @@ -640,15 +646,15 @@ def test_recall_score_str_matches_sklearn(session): } ).astype("str") df = session.read_pandas(pd_df) - recall = metrics.recall_score(df[["y_true"]], df[["y_pred"]], average=None) + recall = bigframes.ml.metrics.recall_score( + df[["y_true"]], df[["y_pred"]], average=None + ) expected_values = sklearn_metrics.recall_score( pd_df[["y_true"]], pd_df[["y_pred"]], average=None ) expected_index = ["ant", "bird", "cat"] expected_recall = pd.Series(expected_values, index=expected_index) - bigframes.testing.utils.assert_series_equal( - recall, expected_recall, check_index_type=False - ) + pd.testing.assert_series_equal(recall, expected_recall, check_index_type=False) def test_recall_score_series(session): @@ -659,14 +665,12 @@ def test_recall_score_series(session): } ).astype("Int64") df = session.read_pandas(pd_df) - recall = metrics.recall_score(df["y_true"], df["y_pred"], average=None) + recall = bigframes.ml.metrics.recall_score(df["y_true"], df["y_pred"], average=None) expected_values = [1.000000, 0.000000, 0.666667] expected_index = [0, 1, 2] expected_recall = pd.Series(expected_values, index=expected_index) - bigframes.testing.utils.assert_series_equal( - recall, expected_recall, check_index_type=False - ) + pd.testing.assert_series_equal(recall, expected_recall, check_index_type=False) def test_precision_score(session): @@ -677,20 +681,19 @@ def test_precision_score(session): } ).astype("Int64") df = session.read_pandas(pd_df) - precision_score = metrics.precision_score( + precision_score = bigframes.ml.metrics.precision_score( df[["y_true_arbitrary_name"]], df[["y_pred_arbitrary_name"]], average=None ) expected_values = [0.666667, 0.000000, 0.666667] expected_index = [0, 1, 2] expected_precision = pd.Series(expected_values, index=expected_index) - bigframes.testing.utils.assert_series_equal( + pd.testing.assert_series_equal( precision_score, expected_precision, check_index_type=False ) def test_precision_score_matches_sklearn(session): - sklearn_metrics = pytest.importorskip("sklearn.metrics") pd_df = pd.DataFrame( { "y_true": [2, 0, 2, 2, 0, 1], @@ -698,8 +701,7 @@ def test_precision_score_matches_sklearn(session): } ).astype("Int64") df = session.read_pandas(pd_df) - # TODO(b/340872435): fix type error - precision_score = metrics.precision_score( + precision_score = bigframes.ml.metrics.precision_score( df[["y_true"]], df[["y_pred"]], average=None ) expected_values = sklearn_metrics.precision_score( @@ -707,13 +709,12 @@ def test_precision_score_matches_sklearn(session): ) expected_index = [0, 1, 2] expected_precision = pd.Series(expected_values, index=expected_index) - bigframes.testing.utils.assert_series_equal( + pd.testing.assert_series_equal( precision_score, expected_precision, check_index_type=False ) def test_precision_score_str_matches_sklearn(session): - sklearn_metrics = pytest.importorskip("sklearn.metrics") pd_df = pd.DataFrame( { "y_true": ["cat", "ant", "cat", "cat", "ant", "bird"], @@ -721,7 +722,7 @@ def test_precision_score_str_matches_sklearn(session): } ).astype("str") df = session.read_pandas(pd_df) - precision_score = metrics.precision_score( + precision_score = bigframes.ml.metrics.precision_score( df[["y_true"]], df[["y_pred"]], average=None ) expected_values = sklearn_metrics.precision_score( @@ -729,7 +730,7 @@ def test_precision_score_str_matches_sklearn(session): ) expected_index = ["ant", "bird", "cat"] expected_precision = pd.Series(expected_values, index=expected_index) - bigframes.testing.utils.assert_series_equal( + pd.testing.assert_series_equal( precision_score, expected_precision, check_index_type=False ) @@ -742,80 +743,18 @@ def test_precision_score_series(session): } ).astype("Int64") df = session.read_pandas(pd_df) - precision_score = metrics.precision_score(df["y_true"], df["y_pred"], average=None) + precision_score = bigframes.ml.metrics.precision_score( + df["y_true"], df["y_pred"], average=None + ) expected_values = [0.666667, 0.000000, 0.666667] expected_index = [0, 1, 2] expected_precision = pd.Series(expected_values, index=expected_index) - bigframes.testing.utils.assert_series_equal( + pd.testing.assert_series_equal( precision_score, expected_precision, check_index_type=False ) -@pytest.mark.parametrize( - ("pos_label", "expected_score"), - [ - ("a", 1 / 3), - ("b", 0), - ], -) -def test_precision_score_binary(session, pos_label, expected_score): - pd_df = pd.DataFrame( - { - "y_true": ["a", "a", "a", "b", "b"], - "y_pred": ["b", "b", "a", "a", "a"], - } - ) - df = session.read_pandas(pd_df) - - precision_score = metrics.precision_score( - df["y_true"], df["y_pred"], average="binary", pos_label=pos_label - ) - - assert precision_score == pytest.approx(expected_score) - - -def test_precision_score_binary_default_arguments(session): - pd_df = pd.DataFrame( - { - "y_true": [1, 1, 1, 0, 0], - "y_pred": [0, 0, 1, 1, 1], - } - ) - df = session.read_pandas(pd_df) - - precision_score = metrics.precision_score(df["y_true"], df["y_pred"]) - - assert precision_score == pytest.approx(1 / 3) - - -@pytest.mark.parametrize( - ("y_true", "y_pred", "pos_label"), - [ - pytest.param( - pd.Series([1, 2, 3]), pd.Series([1, 0]), 1, id="y_true-non-binary-label" - ), - pytest.param( - pd.Series([1, 0]), pd.Series([1, 2, 3]), 1, id="y_pred-non-binary-label" - ), - pytest.param( - pd.Series([1, 0]), pd.Series([1, 2]), 1, id="combined-non-binary-label" - ), - pytest.param(pd.Series([1, 0]), pd.Series([1, 0]), 2, id="invalid-pos_label"), - ], -) -def test_precision_score_binary_invalid_input_raise_error( - session, y_true, y_pred, pos_label -): - bf_y_true = session.read_pandas(y_true) - bf_y_pred = session.read_pandas(y_pred) - - with pytest.raises(ValueError): - metrics.precision_score( - bf_y_true, bf_y_pred, average="binary", pos_label=pos_label - ) - - def test_f1_score(session): pd_df = pd.DataFrame( { @@ -824,20 +763,17 @@ def test_f1_score(session): } ).astype("Int64") df = session.read_pandas(pd_df) - f1_score = metrics.f1_score( + f1_score = bigframes.ml.metrics.f1_score( df[["y_true_arbitrary_name"]], df[["y_pred_arbitrary_name"]], average=None ) expected_values = [0.8, 0.000000, 0.666667] expected_index = [0, 1, 2] expected_f1 = pd.Series(expected_values, index=expected_index) - bigframes.testing.utils.assert_series_equal( - f1_score, expected_f1, check_index_type=False - ) + pd.testing.assert_series_equal(f1_score, expected_f1, check_index_type=False) def test_f1_score_matches_sklearn(session): - sklearn_metrics = pytest.importorskip("sklearn.metrics") pd_df = pd.DataFrame( { "y_true": [2, 0, 2, 2, 0, 1], @@ -845,19 +781,18 @@ def test_f1_score_matches_sklearn(session): } ).astype("Int64") df = session.read_pandas(pd_df) - f1_score = metrics.f1_score(df[["y_true"]], df[["y_pred"]], average=None) + f1_score = bigframes.ml.metrics.f1_score( + df[["y_true"]], df[["y_pred"]], average=None + ) expected_values = sklearn_metrics.f1_score( pd_df[["y_true"]], pd_df[["y_pred"]], average=None ) expected_index = [0, 1, 2] expected_f1 = pd.Series(expected_values, index=expected_index) - bigframes.testing.utils.assert_series_equal( - f1_score, expected_f1, check_index_type=False - ) + pd.testing.assert_series_equal(f1_score, expected_f1, check_index_type=False) def test_f1_score_str_matches_sklearn(session): - sklearn_metrics = pytest.importorskip("sklearn.metrics") pd_df = pd.DataFrame( { "y_true": ["cat", "ant", "cat", "cat", "ant", "bird"], @@ -865,15 +800,15 @@ def test_f1_score_str_matches_sklearn(session): } ).astype("str") df = session.read_pandas(pd_df) - f1_score = metrics.f1_score(df[["y_true"]], df[["y_pred"]], average=None) + f1_score = bigframes.ml.metrics.f1_score( + df[["y_true"]], df[["y_pred"]], average=None + ) expected_values = sklearn_metrics.f1_score( pd_df[["y_true"]], pd_df[["y_pred"]], average=None ) expected_index = ["ant", "bird", "cat"] expected_f1 = pd.Series(expected_values, index=expected_index) - bigframes.testing.utils.assert_series_equal( - f1_score, expected_f1, check_index_type=False - ) + pd.testing.assert_series_equal(f1_score, expected_f1, check_index_type=False) def test_f1_score_series(session): @@ -884,25 +819,9 @@ def test_f1_score_series(session): } ).astype("Int64") df = session.read_pandas(pd_df) - f1_score = metrics.f1_score(df["y_true"], df["y_pred"], average=None) + f1_score = bigframes.ml.metrics.f1_score(df["y_true"], df["y_pred"], average=None) expected_values = [0.8, 0.000000, 0.666667] expected_index = [0, 1, 2] expected_f1 = pd.Series(expected_values, index=expected_index) - bigframes.testing.utils.assert_series_equal( - f1_score, expected_f1, check_index_type=False - ) - - -def test_mean_squared_error(session: bigframes.Session): - pd_df = pd.DataFrame({"y_true": [3, -0.5, 2, 7], "y_pred": [2.5, 0.0, 2, 8]}) - df = session.read_pandas(pd_df) - mse = metrics.mean_squared_error(df["y_true"], df["y_pred"]) - assert mse == 0.375 - - -def test_mean_absolute_error(session: bigframes.Session): - pd_df = pd.DataFrame({"y_true": [3, -0.5, 2, 7], "y_pred": [2.5, 0.0, 2, 8]}) - df = session.read_pandas(pd_df) - mse = metrics.mean_absolute_error(df["y_true"], df["y_pred"]) - assert mse == 0.5 + pd.testing.assert_series_equal(f1_score, expected_f1, check_index_type=False) diff --git a/tests/system/small/ml/test_metrics_pairwise.py b/tests/system/small/ml/test_metrics_pairwise.py deleted file mode 100644 index 44f1ed671b7..00000000000 --- a/tests/system/small/ml/test_metrics_pairwise.py +++ /dev/null @@ -1,106 +0,0 @@ -# Copyright 2023 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import numpy as np -import pandas as pd - -import bigframes.pandas as bpd -from bigframes.ml import metrics - - -def test_paired_cosine_distances(): - x_col = [np.array([4.1, 0.5, 1.0])] - y_col = [np.array([3.0, 0.0, 2.5])] - X = bpd.read_pandas(pd.DataFrame({"X": x_col})) - Y = bpd.read_pandas(pd.DataFrame({"Y": y_col})) - - result = metrics.pairwise.paired_cosine_distances(X, Y) - expected_pd_df = pd.DataFrame( - {"X": x_col, "Y": y_col, "cosine_distance": [0.108199]} - ) - - pd.testing.assert_frame_equal( - result.to_pandas(), expected_pd_df, check_dtype=False, check_index_type=False - ) - - -def test_paired_cosine_distances_multiindex(): - x_col = [np.array([4.1, 0.5, 1.0])] - y_col = [np.array([3.0, 0.0, 2.5])] - data = bpd.read_pandas( - pd.DataFrame( - {("DATA", "X"): x_col, ("DATA", "Y"): y_col}, - ) - ) - - result = metrics.pairwise.paired_cosine_distances( - data[("DATA", "X")], data[("DATA", "Y")] - ) - expected_pd_df = pd.DataFrame( - { - ("DATA", "X"): x_col, - ("DATA", "Y"): y_col, - ("cosine_distance", ""): [0.108199], - } - ) - - pd.testing.assert_frame_equal( - result.to_pandas(), expected_pd_df, check_dtype=False, check_index_type=False - ) - - -def test_paired_cosine_distances_single_frame(): - x_col = [np.array([4.1, 0.5, 1.0])] - y_col = [np.array([3.0, 0.0, 2.5])] - input = bpd.read_pandas(pd.DataFrame({"X": x_col})) - input["Y"] = y_col # type: ignore - - result = metrics.pairwise.paired_cosine_distances(input.X, input.Y) - expected_pd_df = pd.DataFrame( - {"X": x_col, "Y": y_col, "cosine_distance": [0.108199]} - ) - - pd.testing.assert_frame_equal( - result.to_pandas(), expected_pd_df, check_dtype=False, check_index_type=False - ) - - -def test_paired_manhattan_distance(): - x_col = [np.array([4.1, 0.5, 1.0])] - y_col = [np.array([3.0, 0.0, 2.5])] - X = bpd.read_pandas(pd.DataFrame({"X": x_col})) - Y = bpd.read_pandas(pd.DataFrame({"Y": y_col})) - - result = metrics.pairwise.paired_manhattan_distance(X, Y) - expected_pd_df = pd.DataFrame({"X": x_col, "Y": y_col, "manhattan_distance": [3.1]}) - - pd.testing.assert_frame_equal( - result.to_pandas(), expected_pd_df, check_dtype=False, check_index_type=False - ) - - -def test_paired_euclidean_distances(): - x_col = [np.array([4.1, 0.5, 1.0])] - y_col = [np.array([3.0, 0.0, 2.5])] - X = bpd.read_pandas(pd.DataFrame({"X": x_col})) - Y = bpd.read_pandas(pd.DataFrame({"Y": y_col})) - - result = metrics.pairwise.paired_euclidean_distances(X, Y) - expected_pd_df = pd.DataFrame( - {"X": x_col, "Y": y_col, "euclidean_distance": [1.926136]} - ) - - pd.testing.assert_frame_equal( - result.to_pandas(), expected_pd_df, check_dtype=False, check_index_type=False - ) diff --git a/tests/system/small/ml/test_model_selection.py b/tests/system/small/ml/test_model_selection.py index b7764a7d916..9eb36455913 100644 --- a/tests/system/small/ml/test_model_selection.py +++ b/tests/system/small/ml/test_model_selection.py @@ -12,31 +12,22 @@ # See the License for the specific language governing permissions and # limitations under the License. -import math -from typing import cast - import pandas as pd import pytest -import bigframes.pandas as bpd -import bigframes.session from bigframes.ml import model_selection +import bigframes.pandas as bpd -@pytest.mark.parametrize( - "df_fixture", - ("penguins_df_default_index", "penguins_df_null_index"), -) -def test_train_test_split_default_correct_shape(df_fixture, request): - df = request.getfixturevalue(df_fixture) - X = df[ +def test_train_test_split_default_correct_shape(penguins_df_default_index): + X = penguins_df_default_index[ [ "species", "island", "culmen_length_mm", ] ] - y = df[["body_mass_g"]] + y = penguins_df_default_index[["body_mass_g"]] X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y) # even though the default seed is random, it should always result in this shape @@ -139,12 +130,12 @@ def test_train_test_split_seeded_correct_rows( X, y, random_state=42 ) - X_train_sorted = X_train.to_pandas().sort_index() - X_test_sorted = X_test.to_pandas().sort_index() - y_train_sorted = y_train.to_pandas().sort_index() - y_test_sorted = y_test.to_pandas().sort_index() + X_train = X_train.to_pandas().sort_index() + X_test = X_test.to_pandas().sort_index() + y_train = y_train.to_pandas().sort_index() + y_test = y_test.to_pandas().sort_index() - train_index: pd.Index = pd.Index( + train_index = pd.Index( [ 144, 146, @@ -171,20 +162,13 @@ def test_train_test_split_seeded_correct_rows( dtype="Int64", name="rowindex", ) - test_index: pd.Index = pd.Index( + test_index = pd.Index( [148, 161, 226, 269, 278, 289, 291], dtype="Int64", name="rowindex" ) all_data.index.name = "_" - - assert ( - isinstance(X_train_sorted, pd.DataFrame) - and isinstance(X_test_sorted, pd.DataFrame) - and isinstance(y_train_sorted, pd.DataFrame) - and isinstance(y_test_sorted, pd.DataFrame) - ) pd.testing.assert_frame_equal( - X_train_sorted, + X_train, all_data[ [ "species", @@ -194,7 +178,7 @@ def test_train_test_split_seeded_correct_rows( ].loc[train_index], ) pd.testing.assert_frame_equal( - X_test_sorted, + X_test, all_data[ [ "species", @@ -204,7 +188,7 @@ def test_train_test_split_seeded_correct_rows( ].loc[test_index], ) pd.testing.assert_frame_equal( - y_train_sorted, + y_train, all_data[ [ "body_mass_g", @@ -212,7 +196,7 @@ def test_train_test_split_seeded_correct_rows( ].loc[train_index], ) pd.testing.assert_frame_equal( - y_test_sorted, + y_test, all_data[ [ "body_mass_g", @@ -221,78 +205,6 @@ def test_train_test_split_seeded_correct_rows( ) -def test_train_test_split_no_shuffle_correct_shape( - penguins_df_default_index: bpd.DataFrame, -): - X = penguins_df_default_index[["species"]] - y = penguins_df_default_index["body_mass_g"] - X_train, X_test, y_train, y_test = model_selection.train_test_split( - X, y, shuffle=False - ) - assert isinstance(X_train, bpd.DataFrame) - assert isinstance(X_test, bpd.DataFrame) - assert isinstance(y_train, bpd.Series) - assert isinstance(y_test, bpd.Series) - - assert X_train.shape == (258, 1) - assert X_test.shape == (86, 1) - assert y_train.shape == (258,) - assert y_test.shape == (86,) - - -def test_train_test_split_no_shuffle_correct_rows( - session: bigframes.session.Session, penguins_pandas_df_default_index: bpd.DataFrame -): - # Note that we're using `penguins_pandas_df_default_index` as this test depends - # on a stable row order being present end to end - # filter down to the chunkiest penguins, to keep our test code a reasonable size - all_data = penguins_pandas_df_default_index[ - penguins_pandas_df_default_index.body_mass_g > 5500 - ].sort_index() - - # Note that bigframes loses the index if it doesn't have a name - all_data.index.name = "rowindex" - - df = session.read_pandas(all_data) - - X = df[ - [ - "species", - "island", - "culmen_length_mm", - ] - ] - y = df["body_mass_g"] - X_train, X_test, y_train, y_test = model_selection.train_test_split( - X, y, shuffle=False - ) - - X_train_pd = cast(bpd.DataFrame, X_train).to_pandas() - X_test_pd = cast(bpd.DataFrame, X_test).to_pandas() - y_train_pd = cast(bpd.Series, y_train).to_pandas() - y_test_pd = cast(bpd.Series, y_test).to_pandas() - - total_rows = len(all_data) - train_size = 0.75 - train_rows = int(total_rows * train_size) - test_rows = total_rows - train_rows - - expected_X_train = all_data.head(train_rows)[ - ["species", "island", "culmen_length_mm"] - ] - expected_y_train = all_data.head(train_rows)["body_mass_g"] - - expected_X_test = all_data.tail(test_rows)[ - ["species", "island", "culmen_length_mm"] - ] - expected_y_test = all_data.tail(test_rows)["body_mass_g"] - - pd.testing.assert_frame_equal(X_train_pd, expected_X_train) - pd.testing.assert_frame_equal(X_test_pd, expected_X_test) - pd.testing.assert_series_equal(y_train_pd, expected_y_train) - pd.testing.assert_series_equal(y_test_pd, expected_y_test) - - @pytest.mark.parametrize( ("train_size", "test_size"), [ @@ -315,245 +227,3 @@ def test_train_test_split_value_error(penguins_df_default_index, train_size, tes model_selection.train_test_split( X, y, train_size=train_size, test_size=test_size ) - - -@pytest.mark.parametrize( - "df_fixture", - ("penguins_df_default_index", "penguins_df_null_index"), -) -def test_train_test_split_stratify(df_fixture, request): - df = request.getfixturevalue(df_fixture) - X = df[ - [ - "species", - "island", - "culmen_length_mm", - ] - ].rename( - columns={"species": "x_species"} - ) # Keep "species" col just for easy checking. Rename to avoid conflicts. - y = df[["species"]] - X_train, X_test, y_train, y_test = model_selection.train_test_split( - X, y, stratify=df["species"] - ) - - # Original distribution is [152, 124, 68]. All the categories follow 75/25 split - train_counts = pd.Series( - [114, 93, 51], - index=pd.Index( - [ - "Adelie Penguin (Pygoscelis adeliae)", - "Gentoo penguin (Pygoscelis papua)", - "Chinstrap penguin (Pygoscelis antarctica)", - ], - name="species", - ), - dtype="Int64", - name="count", - ) - test_counts = pd.Series( - [38, 31, 17], - index=pd.Index( - [ - "Adelie Penguin (Pygoscelis adeliae)", - "Gentoo penguin (Pygoscelis papua)", - "Chinstrap penguin (Pygoscelis antarctica)", - ], - name="species", - ), - dtype="Int64", - name="count", - ) - pd.testing.assert_series_equal( - X_train["x_species"].rename("species").value_counts().to_pandas(), - train_counts, - check_index_type=False, - ) - pd.testing.assert_series_equal( - X_test["x_species"].rename("species").value_counts().to_pandas(), - test_counts, - check_index_type=False, - ) - pd.testing.assert_series_equal( - y_train["species"].value_counts().to_pandas(), - train_counts, - check_index_type=False, - ) - pd.testing.assert_series_equal( - y_test["species"].value_counts().to_pandas(), - test_counts, - check_index_type=False, - ) - - -@pytest.mark.parametrize( - "n_splits", - (3, 5, 10), -) -def test_KFold_get_n_splits(n_splits): - kf = model_selection.KFold(n_splits) - assert kf.get_n_splits() == n_splits - - -@pytest.mark.parametrize( - "df_fixture", - ("penguins_df_default_index", "penguins_df_null_index"), -) -@pytest.mark.parametrize( - "n_splits", - (3, 5), -) -def test_KFold_split(df_fixture, n_splits, request): - df = request.getfixturevalue(df_fixture) - - kf = model_selection.KFold(n_splits=n_splits) - - X = df[ - [ - "species", - "island", - "culmen_length_mm", - ] - ] - y = df["body_mass_g"] - - len_test_upper, len_test_lower = ( - math.ceil(len(df) / n_splits), - math.floor(len(df) / n_splits), - ) - len_train_upper, len_train_lower = ( - len(df) - len_test_lower, - len(df) - len_test_upper, - ) - - for X_train, X_test, y_train, y_test in kf.split(X, y): # type: ignore - assert isinstance(X_train, bpd.DataFrame) - assert isinstance(X_test, bpd.DataFrame) - assert isinstance(y_train, bpd.Series) - assert isinstance(y_test, bpd.Series) - - # Depend on the iteration, train/test can +-1 in size. - assert ( - X_train.shape == (len_train_upper, 3) - and y_train.shape == (len_train_upper,) - and X_test.shape == (len_test_lower, 3) - and y_test.shape == (len_test_lower,) - ) or ( - X_train.shape == (len_train_lower, 3) - and y_train.shape == (len_train_lower,) - and X_test.shape == (len_test_upper, 3) - and y_test.shape == (len_test_upper,) - ) - - -@pytest.mark.parametrize( - "df_fixture", - ("penguins_df_default_index", "penguins_df_null_index"), -) -@pytest.mark.parametrize( - "n_splits", - (3, 5), -) -def test_KFold_split_X_only(df_fixture, n_splits, request): - df = request.getfixturevalue(df_fixture) - - kf = model_selection.KFold(n_splits=n_splits) - - X = df[ - [ - "species", - "island", - "culmen_length_mm", - ] - ] - - len_test_upper, len_test_lower = ( - math.ceil(len(df) / n_splits), - math.floor(len(df) / n_splits), - ) - len_train_upper, len_train_lower = ( - len(df) - len_test_lower, - len(df) - len_test_upper, - ) - - for X_train, X_test, y_train, y_test in kf.split(X, y=None): # type: ignore - assert isinstance(X_train, bpd.DataFrame) - assert isinstance(X_test, bpd.DataFrame) - assert y_train is None - assert y_test is None - - # Depend on the iteration, train/test can +-1 in size. - assert ( - X_train.shape == (len_train_upper, 3) - and X_test.shape == (len_test_lower, 3) - ) or ( - X_train.shape == (len_train_lower, 3) - and X_test.shape == (len_test_upper, 3) - ) - - -def test_KFold_seeded_correct_rows(session, penguins_pandas_df_default_index): - kf = model_selection.KFold(random_state=42) - # Note that we're using `penguins_pandas_df_default_index` as this test depends - # on a stable row order being present end to end - # filter down to the chunkiest penguins, to keep our test code a reasonable size - all_data = penguins_pandas_df_default_index[ - penguins_pandas_df_default_index.body_mass_g > 5500 - ] - - # Note that bigframes loses the index if it doesn't have a name - all_data.index.name = "rowindex" - - df = session.read_pandas(all_data) - - X = df[ - [ - "species", - "island", - "culmen_length_mm", - ] - ] - y = df["body_mass_g"] - X_train, X_test, y_train, y_test = next(kf.split(X, y)) # type: ignore - - X_train_sorted = X_train.to_pandas().sort_index() # type: ignore - X_test_sorted = X_test.to_pandas().sort_index() # type: ignore - y_train_sorted = y_train.to_pandas().sort_index() # type: ignore - y_test_sorted = y_test.to_pandas().sort_index() # type: ignore - - train_index: pd.Index = pd.Index( - [ - 144, - 146, - 148, - 161, - 168, - 183, - 217, - 221, - 225, - 226, - 237, - 244, - 257, - 262, - 264, - 266, - 267, - 269, - 278, - 289, - 290, - 291, - ], - dtype="Int64", - name="rowindex", - ) - test_index: pd.Index = pd.Index( - [186, 240, 245, 260, 263, 268], dtype="Int64", name="rowindex" - ) - - pd.testing.assert_index_equal(X_train_sorted.index, train_index) - pd.testing.assert_index_equal(X_test_sorted.index, test_index) - pd.testing.assert_index_equal(y_train_sorted.index, train_index) - pd.testing.assert_index_equal(y_test_sorted.index, test_index) diff --git a/tests/system/small/ml/test_preprocessing.py b/tests/system/small/ml/test_preprocessing.py index ec63cc94f23..45548acca32 100644 --- a/tests/system/small/ml/test_preprocessing.py +++ b/tests/system/small/ml/test_preprocessing.py @@ -15,23 +15,13 @@ import math import pandas as pd -import pyarrow as pa -import bigframes.features -import bigframes.pandas as bpd -from bigframes.ml import preprocessing -from bigframes.testing import utils - -ONE_HOT_ENCODED_DTYPE = ( - pd.ArrowDtype(pa.list_(pa.struct([("index", pa.int64()), ("value", pa.float64())]))) - if bigframes.features.PANDAS_VERSIONS.is_arrow_list_dtype_usable - else "object" -) +import bigframes.ml.preprocessing def test_standard_scaler_normalizes(penguins_df_default_index, new_penguins_df): # TODO(http://b/292431644): add a second test that compares output to sklearn.preprocessing.StandardScaler, when BQML's change is in prod. - scaler = preprocessing.StandardScaler() + scaler = bigframes.ml.preprocessing.StandardScaler() scaler.fit( penguins_df_default_index[ ["culmen_length_mm", "culmen_depth_mm", "flipper_length_mm"] @@ -50,22 +40,27 @@ def test_standard_scaler_normalizes(penguins_df_default_index, new_penguins_df): result = scaler.transform(new_penguins_df).to_pandas() + # TODO: bug? feature columns seem to be in nondeterministic random order + # workaround: sort columns by name. Can't repro it in pantheon, so could + # be a bigframes issue... + result = result.reindex(sorted(result.columns), axis=1) + expected = pd.DataFrame( { - "standard_scaled_culmen_length_mm": [-0.81112, -0.994552, -1.104611], "standard_scaled_culmen_depth_mm": [0.836148, 0.024748, 0.48116], + "standard_scaled_culmen_length_mm": [-0.81112, -0.994552, -1.104611], "standard_scaled_flipper_length_mm": [-0.350044, -1.418336, -0.9198], }, dtype="Float64", index=pd.Index([1633, 1672, 1690], name="tag_number", dtype="Int64"), ) - pd.testing.assert_frame_equal(result, expected, rtol=0.1) + pd.testing.assert_frame_equal(result, expected, rtol=1e-3) -def test_standard_scaler_normalizes_fit_transform(new_penguins_df): +def test_standard_scaler_normalizeds_fit_transform(new_penguins_df): # TODO(http://b/292431644): add a second test that compares output to sklearn.preprocessing.StandardScaler, when BQML's change is in prod. - scaler = preprocessing.StandardScaler() + scaler = bigframes.ml.preprocessing.StandardScaler() result = scaler.fit_transform( new_penguins_df[["culmen_length_mm", "culmen_depth_mm", "flipper_length_mm"]] ).to_pandas() @@ -74,22 +69,27 @@ def test_standard_scaler_normalizes_fit_transform(new_penguins_df): for column in result.columns: assert math.isclose(result[column].mean(), 0.0, abs_tol=1e-3) + # TODO: bug? feature columns seem to be in nondeterministic random order + # workaround: sort columns by name. Can't repro it in pantheon, so could + # be a bigframes issue... + result = result.reindex(sorted(result.columns), axis=1) + expected = pd.DataFrame( { - "standard_scaled_culmen_length_mm": [1.313249, -0.20198, -1.111118], "standard_scaled_culmen_depth_mm": [1.17072, -1.272416, 0.101848], + "standard_scaled_culmen_length_mm": [1.313249, -0.20198, -1.111118], "standard_scaled_flipper_length_mm": [1.251089, -1.196588, -0.054338], }, dtype="Float64", index=pd.Index([1633, 1672, 1690], name="tag_number", dtype="Int64"), ) - pd.testing.assert_frame_equal(result, expected, rtol=0.1) + pd.testing.assert_frame_equal(result, expected, rtol=1e-3) def test_standard_scaler_series_normalizes(penguins_df_default_index, new_penguins_df): # TODO(http://b/292431644): add a second test that compares output to sklearn.preprocessing.StandardScaler, when BQML's change is in prod. - scaler = preprocessing.StandardScaler() + scaler = bigframes.ml.preprocessing.StandardScaler() scaler.fit(penguins_df_default_index["culmen_length_mm"]) result = scaler.transform(penguins_df_default_index["culmen_length_mm"]).to_pandas() @@ -100,6 +100,11 @@ def test_standard_scaler_series_normalizes(penguins_df_default_index, new_pengui result = scaler.transform(new_penguins_df).to_pandas() + # TODO: bug? feature columns seem to be in nondeterministic random order + # workaround: sort columns by name. Can't repro it in pantheon, so could + # be a bigframes issue... + result = result.reindex(sorted(result.columns), axis=1) + expected = pd.DataFrame( { "standard_scaled_culmen_length_mm": [ @@ -112,72 +117,12 @@ def test_standard_scaler_series_normalizes(penguins_df_default_index, new_pengui index=pd.Index([1633, 1672, 1690], name="tag_number", dtype="Int64"), ) - pd.testing.assert_frame_equal(result, expected, rtol=0.1) - - -def test_standard_scaler_normalizes_non_standard_column_names( - new_penguins_df: bpd.DataFrame, -): - new_penguins_df = new_penguins_df.rename( - columns={ - "culmen_length_mm": "culmen?metric", - "culmen_depth_mm": "culmen/metric", - } - ) - scaler = preprocessing.StandardScaler() - result = scaler.fit_transform( - new_penguins_df[["culmen?metric", "culmen/metric", "flipper_length_mm"]] - ).to_pandas() - - # If standard-scaled correctly, mean should be 0.0 - for column in result.columns: - assert math.isclose(result[column].mean(), 0.0, abs_tol=1e-3) - - expected = pd.DataFrame( - { - "standard_scaled_culmen_metric": [1.313249, -0.20198, -1.111118], - "standard_scaled_culmen_metric_1": [1.17072, -1.272416, 0.101848], - "standard_scaled_flipper_length_mm": [1.251089, -1.196588, -0.054338], - }, - dtype="Float64", - index=pd.Index([1633, 1672, 1690], name="tag_number", dtype="Int64"), - ) - - pd.testing.assert_frame_equal(result, expected, rtol=0.1) - - -def test_standard_scaler_save_load(new_penguins_df, dataset_id): - transformer = preprocessing.StandardScaler() - transformer.fit( - new_penguins_df[["culmen_length_mm", "culmen_depth_mm", "flipper_length_mm"]] - ) - - reloaded_transformer = transformer.to_gbq( - f"{dataset_id}.temp_configured_model", replace=True - ) - assert isinstance(reloaded_transformer, preprocessing.StandardScaler) - assert reloaded_transformer._bqml_model is not None - - result = reloaded_transformer.transform( - new_penguins_df[["culmen_length_mm", "culmen_depth_mm", "flipper_length_mm"]] - ).to_pandas() - - expected = pd.DataFrame( - { - "standard_scaled_culmen_length_mm": [1.313249, -0.20198, -1.111118], - "standard_scaled_culmen_depth_mm": [1.17072, -1.272416, 0.101848], - "standard_scaled_flipper_length_mm": [1.251089, -1.196588, -0.054338], - }, - dtype="Float64", - index=pd.Index([1633, 1672, 1690], name="tag_number", dtype="Int64"), - ) - - pd.testing.assert_frame_equal(result, expected, rtol=0.1) + pd.testing.assert_frame_equal(result, expected, rtol=1e-3) def test_max_abs_scaler_normalizes(penguins_df_default_index, new_penguins_df): # TODO(http://b/292431644): add a second test that compares output to sklearn.preprocessing.MaxAbsScaler, when BQML's change is in prod. - scaler = preprocessing.MaxAbsScaler() + scaler = bigframes.ml.preprocessing.MaxAbsScaler() scaler.fit( penguins_df_default_index[ ["culmen_length_mm", "culmen_depth_mm", "flipper_length_mm"] @@ -196,40 +141,50 @@ def test_max_abs_scaler_normalizes(penguins_df_default_index, new_penguins_df): result = scaler.transform(new_penguins_df).to_pandas() + # TODO: bug? feature columns seem to be in nondeterministic random order + # workaround: sort columns by name. Can't repro it in pantheon, so could + # be a bigframes issue... + result = result.reindex(sorted(result.columns), axis=1) + expected = pd.DataFrame( { - "max_abs_scaled_culmen_length_mm": [0.662752, 0.645973, 0.635906], "max_abs_scaled_culmen_depth_mm": [0.874419, 0.8, 0.84186], + "max_abs_scaled_culmen_length_mm": [0.662752, 0.645973, 0.635906], "max_abs_scaled_flipper_length_mm": [0.848485, 0.78355, 0.813853], }, dtype="Float64", index=pd.Index([1633, 1672, 1690], name="tag_number", dtype="Int64"), ) - pd.testing.assert_frame_equal(result, expected, rtol=0.1) + pd.testing.assert_frame_equal(result, expected, rtol=1e-3) def test_max_abs_scaler_normalizeds_fit_transform(new_penguins_df): - scaler = preprocessing.MaxAbsScaler() + scaler = bigframes.ml.preprocessing.MaxAbsScaler() result = scaler.fit_transform( new_penguins_df[["culmen_length_mm", "culmen_depth_mm", "flipper_length_mm"]] ).to_pandas() + # TODO: bug? feature columns seem to be in nondeterministic random order + # workaround: sort columns by name. Can't repro it in pantheon, so could + # be a bigframes issue... + result = result.reindex(sorted(result.columns), axis=1) + expected = pd.DataFrame( { - "max_abs_scaled_culmen_length_mm": [1.0, 0.974684, 0.959494], "max_abs_scaled_culmen_depth_mm": [1.0, 0.914894, 0.962766], + "max_abs_scaled_culmen_length_mm": [1.0, 0.974684, 0.959494], "max_abs_scaled_flipper_length_mm": [1.0, 0.923469, 0.959184], }, dtype="Float64", index=pd.Index([1633, 1672, 1690], name="tag_number", dtype="Int64"), ) - pd.testing.assert_frame_equal(result, expected, rtol=0.1) + pd.testing.assert_frame_equal(result, expected, rtol=1e-3) def test_max_abs_scaler_series_normalizes(penguins_df_default_index, new_penguins_df): - scaler = preprocessing.MaxAbsScaler() + scaler = bigframes.ml.preprocessing.MaxAbsScaler() scaler.fit(penguins_df_default_index["culmen_length_mm"]) result = scaler.transform(penguins_df_default_index["culmen_length_mm"]).to_pandas() @@ -240,67 +195,48 @@ def test_max_abs_scaler_series_normalizes(penguins_df_default_index, new_penguin result = scaler.transform(new_penguins_df).to_pandas() - expected = pd.DataFrame( - { - "max_abs_scaled_culmen_length_mm": [0.662752, 0.645973, 0.635906], - }, - dtype="Float64", - index=pd.Index([1633, 1672, 1690], name="tag_number", dtype="Int64"), - ) - - pd.testing.assert_frame_equal(result, expected, rtol=0.1) - - -def test_max_abs_scaler_save_load(new_penguins_df, dataset_id): - transformer = preprocessing.MaxAbsScaler() - transformer.fit( - new_penguins_df[["culmen_length_mm", "culmen_depth_mm", "flipper_length_mm"]] - ) - - reloaded_transformer = transformer.to_gbq( - f"{dataset_id}.temp_configured_model", replace=True - ) - assert isinstance(reloaded_transformer, preprocessing.MaxAbsScaler) - assert reloaded_transformer._bqml_model is not None - - result = reloaded_transformer.transform( - new_penguins_df[["culmen_length_mm", "culmen_depth_mm", "flipper_length_mm"]] - ).to_pandas() + # TODO: bug? feature columns seem to be in nondeterministic random order + # workaround: sort columns by name. Can't repro it in pantheon, so could + # be a bigframes issue... + result = result.reindex(sorted(result.columns), axis=1) expected = pd.DataFrame( { - "max_abs_scaled_culmen_length_mm": [1.0, 0.974684, 0.959494], - "max_abs_scaled_culmen_depth_mm": [1.0, 0.914894, 0.962766], - "max_abs_scaled_flipper_length_mm": [1.0, 0.923469, 0.959184], + "max_abs_scaled_culmen_length_mm": [0.662752, 0.645973, 0.635906], }, dtype="Float64", index=pd.Index([1633, 1672, 1690], name="tag_number", dtype="Int64"), ) - pd.testing.assert_frame_equal(result.sort_index(), expected.sort_index(), rtol=0.1) + pd.testing.assert_frame_equal(result, expected, rtol=1e-3) def test_min_max_scaler_normalized_fit_transform(new_penguins_df): - scaler = preprocessing.MinMaxScaler() + scaler = bigframes.ml.preprocessing.MinMaxScaler() result = scaler.fit_transform( new_penguins_df[["culmen_length_mm", "culmen_depth_mm", "flipper_length_mm"]] ).to_pandas() + # TODO: bug? feature columns seem to be in nondeterministic random order + # workaround: sort columns by name. Can't repro it in pantheon, so could + # be a bigframes issue... + result = result.reindex(sorted(result.columns), axis=1) + expected = pd.DataFrame( { - "min_max_scaled_culmen_length_mm": [1.0, 0.375, 0.0], "min_max_scaled_culmen_depth_mm": [1.0, 0.0, 0.5625], + "min_max_scaled_culmen_length_mm": [1.0, 0.375, 0.0], "min_max_scaled_flipper_length_mm": [1.0, 0.0, 0.466667], }, dtype="Float64", index=pd.Index([1633, 1672, 1690], name="tag_number", dtype="Int64"), ) - pd.testing.assert_frame_equal(result, expected, rtol=0.1) + pd.testing.assert_frame_equal(result, expected, rtol=1e-3) def test_min_max_scaler_series_normalizes(penguins_df_default_index, new_penguins_df): - scaler = preprocessing.MinMaxScaler() + scaler = bigframes.ml.preprocessing.MinMaxScaler() scaler.fit(penguins_df_default_index["culmen_length_mm"]) result = scaler.transform(penguins_df_default_index["culmen_length_mm"]).to_pandas() @@ -312,6 +248,11 @@ def test_min_max_scaler_series_normalizes(penguins_df_default_index, new_penguin result = scaler.transform(new_penguins_df).to_pandas() + # TODO: bug? feature columns seem to be in nondeterministic random order + # workaround: sort columns by name. Can't repro it in pantheon, so could + # be a bigframes issue... + result = result.reindex(sorted(result.columns), axis=1) + expected = pd.DataFrame( { "min_max_scaled_culmen_length_mm": [0.269091, 0.232727, 0.210909], @@ -320,12 +261,12 @@ def test_min_max_scaler_series_normalizes(penguins_df_default_index, new_penguin index=pd.Index([1633, 1672, 1690], name="tag_number", dtype="Int64"), ) - pd.testing.assert_frame_equal(result, expected, rtol=0.1) + pd.testing.assert_frame_equal(result, expected, rtol=1e-3) def test_min_max_scaler_normalizes(penguins_df_default_index, new_penguins_df): # TODO(http://b/292431644): add a second test that compares output to sklearn.preprocessing.MinMaxScaler, when BQML's change is in prod. - scaler = preprocessing.MinMaxScaler() + scaler = bigframes.ml.preprocessing.MinMaxScaler() scaler.fit( penguins_df_default_index[ ["culmen_length_mm", "culmen_depth_mm", "flipper_length_mm"] @@ -345,92 +286,52 @@ def test_min_max_scaler_normalizes(penguins_df_default_index, new_penguins_df): result = scaler.transform(new_penguins_df).to_pandas() + # TODO: bug? feature columns seem to be in nondeterministic random order + # workaround: sort columns by name. Can't repro it in pantheon, so could + # be a bigframes issue... + result = result.reindex(sorted(result.columns), axis=1) + expected = pd.DataFrame( { - "min_max_scaled_culmen_length_mm": [0.269091, 0.232727, 0.210909], "min_max_scaled_culmen_depth_mm": [0.678571, 0.4880952, 0.595238], + "min_max_scaled_culmen_length_mm": [0.269091, 0.232727, 0.210909], "min_max_scaled_flipper_length_mm": [0.40678, 0.152542, 0.271186], }, dtype="Float64", index=pd.Index([1633, 1672, 1690], name="tag_number", dtype="Int64"), ) - pd.testing.assert_frame_equal(result, expected, rtol=0.1) - - -def test_min_max_scaler_save_load(new_penguins_df, dataset_id): - transformer = preprocessing.MinMaxScaler() - transformer.fit( - new_penguins_df[["culmen_length_mm", "culmen_depth_mm", "flipper_length_mm"]] - ) - - reloaded_transformer = transformer.to_gbq( - f"{dataset_id}.temp_configured_model", replace=True - ) - assert isinstance(reloaded_transformer, preprocessing.MinMaxScaler) - assert reloaded_transformer._bqml_model is not None - - result = reloaded_transformer.fit_transform( - new_penguins_df[["culmen_length_mm", "culmen_depth_mm", "flipper_length_mm"]] - ).to_pandas() - - expected = pd.DataFrame( - { - "min_max_scaled_culmen_length_mm": [1.0, 0.375, 0.0], - "min_max_scaled_culmen_depth_mm": [1.0, 0.0, 0.5625], - "min_max_scaled_flipper_length_mm": [1.0, 0.0, 0.466667], - }, - dtype="Float64", - index=pd.Index([1633, 1672, 1690], name="tag_number", dtype="Int64"), - ) - - pd.testing.assert_frame_equal(result, expected, rtol=0.1) + pd.testing.assert_frame_equal(result, expected, rtol=1e-3) def test_k_bins_discretizer_normalized_fit_transform_default_params(new_penguins_df): - discretizer = preprocessing.KBinsDiscretizer(strategy="uniform") + discretizer = bigframes.ml.preprocessing.KBinsDiscretizer(strategy="uniform") result = discretizer.fit_transform( new_penguins_df[["culmen_length_mm", "culmen_depth_mm", "flipper_length_mm"]] ).to_pandas() + # TODO: bug? feature columns seem to be in nondeterministic random order + # workaround: sort columns by name. Can't repro it in pantheon, so could + # be a bigframes issue... + result = result.reindex(sorted(result.columns), axis=1) + expected = pd.DataFrame( { - "kbinsdiscretizer_culmen_length_mm": ["bin_5", "bin_3", "bin_2"], "kbinsdiscretizer_culmen_depth_mm": ["bin_5", "bin_2", "bin_4"], + "kbinsdiscretizer_culmen_length_mm": ["bin_5", "bin_3", "bin_2"], "kbinsdiscretizer_flipper_length_mm": ["bin_5", "bin_2", "bin_4"], }, dtype="string[pyarrow]", index=pd.Index([1633, 1672, 1690], name="tag_number", dtype="Int64"), ) - pd.testing.assert_frame_equal(result, expected, rtol=0.1) - - -def test_k_bins_discretizer_normalized_fit_transform_default_params_quantile( - new_penguins_df, -): - discretizer = preprocessing.KBinsDiscretizer(strategy="quantile") - result = discretizer.fit_transform( - new_penguins_df[["culmen_length_mm", "culmen_depth_mm", "flipper_length_mm"]] - ).to_pandas() - - expected = pd.DataFrame( - { - "kbinsdiscretizer_culmen_length_mm": ["bin_2", "bin_2", "bin_1"], - "kbinsdiscretizer_culmen_depth_mm": ["bin_2", "bin_1", "bin_2"], - "kbinsdiscretizer_flipper_length_mm": ["bin_2", "bin_1", "bin_2"], - }, - dtype="string[pyarrow]", - index=pd.Index([1633, 1672, 1690], name="tag_number", dtype="Int64"), - ) - - pd.testing.assert_frame_equal(result, expected, rtol=0.1) + pd.testing.assert_frame_equal(result, expected, rtol=1e-3) def test_k_bins_discretizer_series_normalizes( penguins_df_default_index, new_penguins_df ): - discretizer = preprocessing.KBinsDiscretizer(strategy="uniform") + discretizer = bigframes.ml.preprocessing.KBinsDiscretizer(strategy="uniform") discretizer.fit(penguins_df_default_index["culmen_length_mm"]) result = discretizer.transform( @@ -438,42 +339,25 @@ def test_k_bins_discretizer_series_normalizes( ).to_pandas() result = discretizer.transform(new_penguins_df).to_pandas() - expected = pd.DataFrame( - { - "kbinsdiscretizer_culmen_length_mm": ["bin_3", "bin_3", "bin_3"], - }, - dtype="string[pyarrow]", - index=pd.Index([1633, 1672, 1690], name="tag_number", dtype="Int64"), - ) - - pd.testing.assert_frame_equal(result, expected, rtol=0.1) - - -def test_k_bins_discretizer_series_normalizes_quantile( - penguins_df_default_index, new_penguins_df -): - discretizer = preprocessing.KBinsDiscretizer(strategy="quantile") - discretizer.fit(penguins_df_default_index["culmen_length_mm"]) - - result = discretizer.transform( - penguins_df_default_index["culmen_length_mm"] - ).to_pandas() - result = discretizer.transform(new_penguins_df).to_pandas() + # TODO: bug? feature columns seem to be in nondeterministic random order + # workaround: sort columns by name. Can't repro it in pantheon, so could + # be a bigframes issue... + result = result.reindex(sorted(result.columns), axis=1) expected = pd.DataFrame( { - "kbinsdiscretizer_culmen_length_mm": ["bin_2", "bin_2", "bin_1"], + "kbinsdiscretizer_culmen_length_mm": ["bin_3", "bin_3", "bin_3"], }, dtype="string[pyarrow]", index=pd.Index([1633, 1672, 1690], name="tag_number", dtype="Int64"), ) - pd.testing.assert_frame_equal(result, expected, rtol=0.1) + pd.testing.assert_frame_equal(result, expected, rtol=1e-3) def test_k_bins_discretizer_normalizes(penguins_df_default_index, new_penguins_df): # TODO(http://b/292431644): add a second test that compares output to sklearn.preprocessing.KBinsDiscretizer, when BQML's change is in prod. - discretizer = preprocessing.KBinsDiscretizer(strategy="uniform") + discretizer = bigframes.ml.preprocessing.KBinsDiscretizer(strategy="uniform") discretizer.fit( penguins_df_default_index[ ["culmen_length_mm", "culmen_depth_mm", "flipper_length_mm"] @@ -488,24 +372,31 @@ def test_k_bins_discretizer_normalizes(penguins_df_default_index, new_penguins_d result = discretizer.transform(new_penguins_df).to_pandas() + # TODO: bug? feature columns seem to be in nondeterministic random order + # workaround: sort columns by name. Can't repro it in pantheon, so could + # be a bigframes issue... + result = result.reindex(sorted(result.columns), axis=1) + expected = pd.DataFrame( { - "kbinsdiscretizer_culmen_length_mm": ["bin_3", "bin_3", "bin_3"], "kbinsdiscretizer_culmen_depth_mm": ["bin_5", "bin_4", "bin_4"], + "kbinsdiscretizer_culmen_length_mm": ["bin_3", "bin_3", "bin_3"], "kbinsdiscretizer_flipper_length_mm": ["bin_4", "bin_2", "bin_3"], }, dtype="string[pyarrow]", index=pd.Index([1633, 1672, 1690], name="tag_number", dtype="Int64"), ) - pd.testing.assert_frame_equal(result, expected, rtol=0.1) + pd.testing.assert_frame_equal(result, expected, rtol=1e-3) def test_k_bins_discretizer_normalizes_different_params( penguins_df_default_index, new_penguins_df ): # TODO(http://b/292431644): add a second test that compares output to sklearn.preprocessing.KBinsDiscretizer, when BQML's change is in prod. - discretizer = preprocessing.KBinsDiscretizer(n_bins=6, strategy="uniform") + discretizer = bigframes.ml.preprocessing.KBinsDiscretizer( + n_bins=6, strategy="uniform" + ) discretizer.fit( penguins_df_default_index[ ["culmen_length_mm", "culmen_depth_mm", "flipper_length_mm"] @@ -520,85 +411,48 @@ def test_k_bins_discretizer_normalizes_different_params( result = discretizer.transform(new_penguins_df).to_pandas() + # TODO: bug? feature columns seem to be in nondeterministic random order + # workaround: sort columns by name. Can't repro it in pantheon, so could + # be a bigframes issue... + result = result.reindex(sorted(result.columns), axis=1) + expected = pd.DataFrame( { - "kbinsdiscretizer_culmen_length_mm": ["bin_3", "bin_3", "bin_3"], "kbinsdiscretizer_culmen_depth_mm": ["bin_6", "bin_4", "bin_5"], + "kbinsdiscretizer_culmen_length_mm": ["bin_3", "bin_3", "bin_3"], "kbinsdiscretizer_flipper_length_mm": ["bin_4", "bin_2", "bin_3"], }, dtype="string[pyarrow]", index=pd.Index([1633, 1672, 1690], name="tag_number", dtype="Int64"), ) - pd.testing.assert_frame_equal(result, expected, rtol=0.1) - - -def test_k_bins_discretizer_save_load(new_penguins_df, dataset_id): - transformer = preprocessing.KBinsDiscretizer(n_bins=6, strategy="uniform") - transformer.fit( - new_penguins_df[["culmen_length_mm", "culmen_depth_mm", "flipper_length_mm"]] - ) - - reloaded_transformer = transformer.to_gbq( - f"{dataset_id}.temp_configured_model", replace=True - ) - assert isinstance(reloaded_transformer, preprocessing.KBinsDiscretizer) - assert reloaded_transformer.n_bins == transformer.n_bins - assert reloaded_transformer.strategy == transformer.strategy - assert reloaded_transformer._bqml_model is not None - - result = reloaded_transformer.fit_transform( - new_penguins_df[["culmen_length_mm", "culmen_depth_mm", "flipper_length_mm"]] - ).to_pandas() - - expected = pd.DataFrame( - { - "kbinsdiscretizer_culmen_length_mm": ["bin_6", "bin_4", "bin_2"], - "kbinsdiscretizer_culmen_depth_mm": ["bin_6", "bin_2", "bin_5"], - "kbinsdiscretizer_flipper_length_mm": ["bin_6", "bin_2", "bin_4"], - }, - dtype="string[pyarrow]", - index=pd.Index([1633, 1672, 1690], name="tag_number", dtype="Int64"), - ) - - pd.testing.assert_frame_equal(result, expected, rtol=0.1) - - -def test_k_bins_discretizer_save_load_quantile(new_penguins_df, dataset_id): - transformer = preprocessing.KBinsDiscretizer(n_bins=6, strategy="quantile") - transformer.fit( - new_penguins_df[["culmen_length_mm", "culmen_depth_mm", "flipper_length_mm"]] - ) - - reloaded_transformer = transformer.to_gbq( - f"{dataset_id}.temp_configured_model", replace=True - ) - assert isinstance(reloaded_transformer, preprocessing.KBinsDiscretizer) - assert reloaded_transformer.n_bins == transformer.n_bins - assert reloaded_transformer.strategy == transformer.strategy - assert reloaded_transformer._bqml_model is not None + pd.testing.assert_frame_equal(result, expected, rtol=1e-3) def test_one_hot_encoder_default_params(new_penguins_df): - encoder = preprocessing.OneHotEncoder() + encoder = bigframes.ml.preprocessing.OneHotEncoder() encoder.fit(new_penguins_df[["species", "sex"]]) result = encoder.transform(new_penguins_df).to_pandas() + # TODO: bug? feature columns seem to be in nondeterministic random order + # workaround: sort columns by name. Can't repro it in pantheon, so could + # be a bigframes issue... + result = result.reindex(sorted(result.columns), axis=1) + expected = pd.DataFrame( { - "onehotencoded_species": [ + "onehotencoded_sex": [ + [{"index": 2, "value": 1.0}], [{"index": 1, "value": 1.0}], [{"index": 1, "value": 1.0}], - [{"index": 2, "value": 1.0}], ], - "onehotencoded_sex": [ - [{"index": 2, "value": 1.0}], + "onehotencoded_species": [ [{"index": 1, "value": 1.0}], [{"index": 1, "value": 1.0}], + [{"index": 2, "value": 1.0}], ], }, - dtype=ONE_HOT_ENCODED_DTYPE, index=pd.Index([1633, 1672, 1690], name="tag_number", dtype="Int64"), ) @@ -606,24 +460,28 @@ def test_one_hot_encoder_default_params(new_penguins_df): def test_one_hot_encoder_default_params_fit_transform(new_penguins_df): - encoder = preprocessing.OneHotEncoder() + encoder = bigframes.ml.preprocessing.OneHotEncoder() result = encoder.fit_transform(new_penguins_df[["species", "sex"]]).to_pandas() + # TODO: bug? feature columns seem to be in nondeterministic random order + # workaround: sort columns by name. Can't repro it in pantheon, so could + # be a bigframes issue... + result = result.reindex(sorted(result.columns), axis=1) + expected = pd.DataFrame( { - "onehotencoded_species": [ + "onehotencoded_sex": [ + [{"index": 2, "value": 1.0}], [{"index": 1, "value": 1.0}], [{"index": 1, "value": 1.0}], - [{"index": 2, "value": 1.0}], ], - "onehotencoded_sex": [ - [{"index": 2, "value": 1.0}], + "onehotencoded_species": [ [{"index": 1, "value": 1.0}], [{"index": 1, "value": 1.0}], + [{"index": 2, "value": 1.0}], ], }, - dtype=ONE_HOT_ENCODED_DTYPE, index=pd.Index([1633, 1672, 1690], name="tag_number", dtype="Int64"), ) @@ -631,11 +489,16 @@ def test_one_hot_encoder_default_params_fit_transform(new_penguins_df): def test_one_hot_encoder_series_default_params(new_penguins_df): - encoder = preprocessing.OneHotEncoder() + encoder = bigframes.ml.preprocessing.OneHotEncoder() encoder.fit(new_penguins_df["species"]) result = encoder.transform(new_penguins_df).to_pandas() + # TODO: bug? feature columns seem to be in nondeterministic random order + # workaround: sort columns by name. Can't repro it in pantheon, so could + # be a bigframes issue... + result = result.reindex(sorted(result.columns), axis=1) + expected = pd.DataFrame( { "onehotencoded_species": [ @@ -644,7 +507,6 @@ def test_one_hot_encoder_series_default_params(new_penguins_df): [{"index": 2, "value": 1.0}], ], }, - dtype=ONE_HOT_ENCODED_DTYPE, index=pd.Index([1633, 1672, 1690], name="tag_number", dtype="Int64"), ) @@ -652,25 +514,29 @@ def test_one_hot_encoder_series_default_params(new_penguins_df): def test_one_hot_encoder_params(new_penguins_df): - encoder = preprocessing.OneHotEncoder("most_frequent", 100, 2) + encoder = bigframes.ml.preprocessing.OneHotEncoder("most_frequent", 100, 2) encoder.fit(new_penguins_df[["species", "sex"]]) result = encoder.transform(new_penguins_df).to_pandas() + # TODO: bug? feature columns seem to be in nondeterministic random order + # workaround: sort columns by name. Can't repro it in pantheon, so could + # be a bigframes issue... + result = result.reindex(sorted(result.columns), axis=1) + expected = pd.DataFrame( { - "onehotencoded_species": [ + "onehotencoded_sex": [ [{"index": 0, "value": 1.0}], [{"index": 0, "value": 1.0}], [{"index": 0, "value": 1.0}], ], - "onehotencoded_sex": [ + "onehotencoded_species": [ [{"index": 0, "value": 1.0}], [{"index": 0, "value": 1.0}], [{"index": 0, "value": 1.0}], ], }, - dtype=ONE_HOT_ENCODED_DTYPE, index=pd.Index([1633, 1672, 1690], name="tag_number", dtype="Int64"), ) @@ -678,61 +544,29 @@ def test_one_hot_encoder_params(new_penguins_df): def test_one_hot_encoder_different_data(penguins_df_default_index, new_penguins_df): - encoder = preprocessing.OneHotEncoder() + encoder = bigframes.ml.preprocessing.OneHotEncoder() encoder.fit(penguins_df_default_index[["species", "sex"]]) result = encoder.transform(new_penguins_df).to_pandas() + # TODO: bug? feature columns seem to be in nondeterministic random order + # workaround: sort columns by name. Can't repro it in pantheon, so could + # be a bigframes issue... + result = result.reindex(sorted(result.columns), axis=1) + expected = pd.DataFrame( { - "onehotencoded_species": [ - [{"index": 1, "value": 1.0}], - [{"index": 1, "value": 1.0}], - [{"index": 2, "value": 1.0}], - ], "onehotencoded_sex": [ [{"index": 3, "value": 1.0}], [{"index": 2, "value": 1.0}], [{"index": 2, "value": 1.0}], ], - }, - dtype=ONE_HOT_ENCODED_DTYPE, - index=pd.Index([1633, 1672, 1690], name="tag_number", dtype="Int64"), - ) - - pd.testing.assert_frame_equal(result, expected) - - -def test_one_hot_encoder_save_load(new_penguins_df, dataset_id): - transformer = preprocessing.OneHotEncoder(min_frequency=1, max_categories=10) - transformer.fit(new_penguins_df[["species", "sex"]]) - - reloaded_transformer = transformer.to_gbq( - f"{dataset_id}.temp_configured_model", replace=True - ) - assert isinstance(reloaded_transformer, preprocessing.OneHotEncoder) - assert reloaded_transformer.min_frequency == transformer.min_frequency - assert reloaded_transformer.max_categories == transformer.max_categories - assert reloaded_transformer._bqml_model is not None - - result = reloaded_transformer.fit_transform( - new_penguins_df[["species", "sex"]] - ).to_pandas() - - expected = pd.DataFrame( - { "onehotencoded_species": [ [{"index": 1, "value": 1.0}], [{"index": 1, "value": 1.0}], [{"index": 2, "value": 1.0}], ], - "onehotencoded_sex": [ - [{"index": 2, "value": 1.0}], - [{"index": 1, "value": 1.0}], - [{"index": 1, "value": 1.0}], - ], }, - dtype=ONE_HOT_ENCODED_DTYPE, index=pd.Index([1633, 1672, 1690], name="tag_number", dtype="Int64"), ) @@ -740,11 +574,16 @@ def test_one_hot_encoder_save_load(new_penguins_df, dataset_id): def test_label_encoder_default_params(new_penguins_df): - encoder = preprocessing.LabelEncoder() + encoder = bigframes.ml.preprocessing.LabelEncoder() encoder.fit(new_penguins_df["species"]) result = encoder.transform(new_penguins_df["species"]).to_pandas() + # TODO: bug? feature columns seem to be in nondeterministic random order + # workaround: sort columns by name. Can't repro it in pantheon, so could + # be a bigframes issue... + result = result.reindex(sorted(result.columns), axis=1) + expected = pd.DataFrame( { "labelencoded_species": [ @@ -761,10 +600,15 @@ def test_label_encoder_default_params(new_penguins_df): def test_label_encoder_default_params_fit_transform(new_penguins_df): - encoder = preprocessing.LabelEncoder() + encoder = bigframes.ml.preprocessing.LabelEncoder() result = encoder.fit_transform(new_penguins_df[["species"]]).to_pandas() + # TODO: bug? feature columns seem to be in nondeterministic random order + # workaround: sort columns by name. Can't repro it in pantheon, so could + # be a bigframes issue... + result = result.reindex(sorted(result.columns), axis=1) + expected = pd.DataFrame( { "labelencoded_species": [ @@ -781,11 +625,16 @@ def test_label_encoder_default_params_fit_transform(new_penguins_df): def test_label_encoder_series_default_params(new_penguins_df): - encoder = preprocessing.LabelEncoder() + encoder = bigframes.ml.preprocessing.LabelEncoder() encoder.fit(new_penguins_df["species"]) result = encoder.transform(new_penguins_df).to_pandas() + # TODO: bug? feature columns seem to be in nondeterministic random order + # workaround: sort columns by name. Can't repro it in pantheon, so could + # be a bigframes issue... + result = result.reindex(sorted(result.columns), axis=1) + expected = pd.DataFrame( { "labelencoded_species": [ @@ -802,11 +651,16 @@ def test_label_encoder_series_default_params(new_penguins_df): def test_label_encoder_params(new_penguins_df): - encoder = preprocessing.LabelEncoder(100, 2) + encoder = bigframes.ml.preprocessing.LabelEncoder(100, 2) encoder.fit(new_penguins_df[["species"]]) result = encoder.transform(new_penguins_df).to_pandas() + # TODO: bug? feature columns seem to be in nondeterministic random order + # workaround: sort columns by name. Can't repro it in pantheon, so could + # be a bigframes issue... + result = result.reindex(sorted(result.columns), axis=1) + expected = pd.DataFrame( { "labelencoded_species": [ @@ -823,39 +677,15 @@ def test_label_encoder_params(new_penguins_df): def test_label_encoder_different_data(penguins_df_default_index, new_penguins_df): - encoder = preprocessing.LabelEncoder() + encoder = bigframes.ml.preprocessing.LabelEncoder() encoder.fit(penguins_df_default_index[["species"]]) result = encoder.transform(new_penguins_df).to_pandas() - expected = pd.DataFrame( - { - "labelencoded_species": [ - 1, - 1, - 2, - ], - }, - dtype="Int64", - index=pd.Index([1633, 1672, 1690], name="tag_number", dtype="Int64"), - ) - - pd.testing.assert_frame_equal(result, expected) - - -def test_label_encoder_save_load(new_penguins_df, dataset_id): - transformer = preprocessing.LabelEncoder(min_frequency=1, max_categories=10) - transformer.fit(new_penguins_df[["species"]]) - - reloaded_transformer = transformer.to_gbq( - f"{dataset_id}.temp_configured_model", replace=True - ) - assert isinstance(reloaded_transformer, preprocessing.LabelEncoder) - assert reloaded_transformer.min_frequency == transformer.min_frequency - assert reloaded_transformer.max_categories == transformer.max_categories - assert reloaded_transformer._bqml_model is not None - - result = reloaded_transformer.transform(new_penguins_df).to_pandas() + # TODO: bug? feature columns seem to be in nondeterministic random order + # workaround: sort columns by name. Can't repro it in pantheon, so could + # be a bigframes issue... + result = result.reindex(sorted(result.columns), axis=1) expected = pd.DataFrame( { @@ -873,101 +703,3 @@ def test_label_encoder_save_load(new_penguins_df, dataset_id): # TODO(garrettwu): add OneHotEncoder tests to compare with sklearn. - - -def test_poly_features_default_params(new_penguins_df): - transformer = preprocessing.PolynomialFeatures() - df = new_penguins_df[["culmen_length_mm", "culmen_depth_mm"]] - transformer.fit(df) - - result = transformer.transform(df).to_pandas() - - expected = pd.DataFrame( - { - "poly_feat_culmen_length_mm": [ - 39.5, - 38.5, - 37.9, - ], - "poly_feat_culmen_length_mm_culmen_length_mm": [ - 1560.25, - 1482.25, - 1436.41, - ], - "poly_feat_culmen_length_mm_culmen_depth_mm": [ - 742.6, - 662.2, - 685.99, - ], - "poly_feat_culmen_depth_mm": [ - 18.8, - 17.2, - 18.1, - ], - "poly_feat_culmen_depth_mm_culmen_depth_mm": [ - 353.44, - 295.84, - 327.61, - ], - }, - dtype="Float64", - index=pd.Index([1633, 1672, 1690], name="tag_number", dtype="Int64"), - ) - - pd.testing.assert_frame_equal(result, expected, check_exact=False, rtol=0.1) - - -def test_poly_features_params(new_penguins_df): - transformer = preprocessing.PolynomialFeatures(degree=3) - df = new_penguins_df[["culmen_length_mm", "culmen_depth_mm"]] - transformer.fit(df) - - result = transformer.transform(df).to_pandas() - - utils.check_pandas_df_schema_and_index( - result, - [ - "poly_feat_culmen_length_mm", - "poly_feat_culmen_length_mm_culmen_length_mm", - "poly_feat_culmen_length_mm_culmen_length_mm_culmen_length_mm", - "poly_feat_culmen_length_mm_culmen_length_mm_culmen_depth_mm", - "poly_feat_culmen_length_mm_culmen_depth_mm", - "poly_feat_culmen_length_mm_culmen_depth_mm_culmen_depth_mm", - "poly_feat_culmen_depth_mm", - "poly_feat_culmen_depth_mm_culmen_depth_mm", - "poly_feat_culmen_depth_mm_culmen_depth_mm_culmen_depth_mm", - ], - [1633, 1672, 1690], - ) - - -def test_poly_features_save_load(new_penguins_df, dataset_id): - transformer = preprocessing.PolynomialFeatures(degree=3) - transformer.fit(new_penguins_df[["culmen_length_mm", "culmen_depth_mm"]]) - - reloaded_transformer = transformer.to_gbq( - f"{dataset_id}.temp_configured_model", replace=True - ) - assert isinstance(reloaded_transformer, preprocessing.PolynomialFeatures) - assert reloaded_transformer.degree == 3 - assert reloaded_transformer._bqml_model is not None - - result = reloaded_transformer.transform( - new_penguins_df[["culmen_length_mm", "culmen_depth_mm"]] - ).to_pandas() - - utils.check_pandas_df_schema_and_index( - result, - [ - "poly_feat_culmen_length_mm", - "poly_feat_culmen_length_mm_culmen_length_mm", - "poly_feat_culmen_length_mm_culmen_length_mm_culmen_length_mm", - "poly_feat_culmen_length_mm_culmen_length_mm_culmen_depth_mm", - "poly_feat_culmen_length_mm_culmen_depth_mm", - "poly_feat_culmen_length_mm_culmen_depth_mm_culmen_depth_mm", - "poly_feat_culmen_depth_mm", - "poly_feat_culmen_depth_mm_culmen_depth_mm", - "poly_feat_culmen_depth_mm_culmen_depth_mm_culmen_depth_mm", - ], - [1633, 1672, 1690], - ) diff --git a/tests/system/small/ml/test_register.py b/tests/system/small/ml/test_register.py index f21567da63e..bcf1f4a5b0e 100644 --- a/tests/system/small/ml/test_register.py +++ b/tests/system/small/ml/test_register.py @@ -14,7 +14,7 @@ from typing import cast -from bigframes.ml import core, imported, linear_model +from bigframes.ml import core, imported, linear_model, llm def test_linear_reg_register( @@ -51,6 +51,24 @@ def test_linear_reg_register_with_params( ) +def test_palm2_text_generator_register( + ephemera_palm2_text_generator_model: llm.PaLM2TextGenerator, +): + model = ephemera_palm2_text_generator_model + model.register() + + model_name = "bigframes_" + cast( + str, cast(core.BqmlModel, model._bqml_model).model.model_id + ) + # Only registered model contains the field, and the field includes project/dataset. Here only check model_id. + assert ( + model_name[:63] # truncated + in cast(core.BqmlModel, model._bqml_model).model.training_runs[-1][ + "vertexAiModelId" + ] + ) + + def test_imported_tensorflow_register( ephemera_imported_tensorflow_model: imported.TensorFlowModel, ): diff --git a/tests/system/small/ml/test_remote.py b/tests/system/small/ml/test_remote.py deleted file mode 100644 index c52c4522448..00000000000 --- a/tests/system/small/ml/test_remote.py +++ /dev/null @@ -1,36 +0,0 @@ -# Copyright 2023 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import pandas as pd -import pytest - -from bigframes.ml import remote - - -@pytest.mark.skip("b/353775058 BQML internal error") -def test_remote_linear_vertex_model_predict( - linear_remote_vertex_model: remote.VertexAIModel, new_penguins_df -): - predictions = linear_remote_vertex_model.predict(new_penguins_df).to_pandas() - expected = pd.DataFrame( - {"predicted_body_mass_g": [[3739.54], [3675.79], [3619.54]]}, - index=pd.Index([1633, 1672, 1690], name="tag_number", dtype="Int64"), - ) - pd.testing.assert_frame_equal( - predictions[["predicted_body_mass_g"]].sort_index(), - expected, - check_exact=False, - check_dtype=False, - rtol=0.1, - ) diff --git a/tests/system/small/ml/test_utils.py b/tests/system/small/ml/test_utils.py deleted file mode 100644 index ec3bd315b13..00000000000 --- a/tests/system/small/ml/test_utils.py +++ /dev/null @@ -1,80 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import pandas as pd -import pytest - -import bigframes.ml.utils as utils -import bigframes.testing.utils - -_DATA_FRAME = pd.DataFrame({"column": [1, 2, 3]}) -_SERIES = pd.Series([1, 2, 3], name="column") - - -@pytest.mark.parametrize( - "data", - [pytest.param(_DATA_FRAME, id="dataframe"), pytest.param(_SERIES, id="series")], -) -def test_convert_to_dataframe(session, data): - bf_data = session.read_pandas(data) - - (actual_result,) = utils.batch_convert_to_dataframe(bf_data) - - bigframes.testing.utils.assert_frame_equal( - actual_result.to_pandas(), - _DATA_FRAME, - check_index_type=False, - check_dtype=False, - ) - - -@pytest.mark.parametrize( - "data", - [pytest.param(_DATA_FRAME, id="dataframe"), pytest.param(_SERIES, id="series")], -) -def test_convert_pandas_to_dataframe(data, session): - (actual_result,) = utils.batch_convert_to_dataframe(data, session=session) - - bigframes.testing.utils.assert_frame_equal( - actual_result.to_pandas(), - _DATA_FRAME, - check_index_type=False, - check_dtype=False, - ) - - -@pytest.mark.parametrize( - "data", - [pytest.param(_DATA_FRAME, id="dataframe"), pytest.param(_SERIES, id="series")], -) -def test_convert_to_series(session, data): - bf_data = session.read_pandas(data) - - (actual_result,) = utils.batch_convert_to_series(bf_data) - - bigframes.testing.utils.assert_series_equal( - actual_result.to_pandas(), _SERIES, check_index_type=False, check_dtype=False - ) - - -@pytest.mark.parametrize( - "data", - [pytest.param(_DATA_FRAME, id="dataframe"), pytest.param(_SERIES, id="series")], -) -def test_convert_pandas_to_series(data, session): - (actual_result,) = utils.batch_convert_to_series(data, session=session) - - bigframes.testing.utils.assert_series_equal( - actual_result.to_pandas(), _SERIES, check_index_type=False, check_dtype=False - ) diff --git a/tests/system/small/operations/test_dates.py b/tests/system/small/operations/test_dates.py deleted file mode 100644 index 3554322462b..00000000000 --- a/tests/system/small/operations/test_dates.py +++ /dev/null @@ -1,91 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -import datetime - -import pandas as pd -import pytest -from packaging import version - -import bigframes.testing.utils -from bigframes import dtypes - - -def test_date_diff_between_series(session): - pd_df = pd.DataFrame( - { - "col_1": [datetime.date(2025, 1, 2), datetime.date(2025, 2, 1)], - "col_2": [datetime.date(2024, 1, 2), datetime.date(2026, 1, 30)], - } - ).astype(dtypes.DATE_DTYPE) - bf_df = session.read_pandas(pd_df) - - actual_result = (bf_df["col_1"] - bf_df["col_2"]).to_pandas() - - expected_result = (pd_df["col_1"] - pd_df["col_2"]).astype(dtypes.TIMEDELTA_DTYPE) - bigframes.testing.utils.assert_series_equal( - actual_result, expected_result, check_index_type=False - ) - - -def test_date_diff_literal_sub_series(scalars_dfs): - bf_df, pd_df = scalars_dfs - literal = datetime.date(2030, 5, 20) - - actual_result = (literal - bf_df["date_col"]).to_pandas() - - expected_result = (literal - pd_df["date_col"]).astype(dtypes.TIMEDELTA_DTYPE) - bigframes.testing.utils.assert_series_equal( - actual_result, expected_result, check_index_type=False - ) - - -def test_date_diff_series_sub_literal(scalars_dfs): - bf_df, pd_df = scalars_dfs - literal = datetime.date(1980, 5, 20) - - actual_result = (bf_df["date_col"] - literal).to_pandas() - - expected_result = (pd_df["date_col"] - literal).astype(dtypes.TIMEDELTA_DTYPE) - bigframes.testing.utils.assert_series_equal( - actual_result, expected_result, check_index_type=False - ) - - -def test_date_series_diff_agg(scalars_dfs): - bf_df, pd_df = scalars_dfs - - actual_result = bf_df["date_col"].diff().to_pandas() - - expected_result = pd_df["date_col"].diff().astype(dtypes.TIMEDELTA_DTYPE) - bigframes.testing.utils.assert_series_equal( - actual_result, expected_result, check_index_type=False - ) - - -def test_date_can_cast_after_accessor(scalars_dfs): - if version.Version(pd.__version__) <= version.Version("2.1.0"): - pytest.skip("pd timezone conversion bug") - bf_df, pd_df = scalars_dfs - - actual_result = bf_df["date_col"].dt.isocalendar().week.astype("Int64").to_pandas() - # convert to pd date type rather than arrow, as pandas doesn't handle arrow date well here - expected_result = ( - pd.to_datetime(pd_df["date_col"]).dt.isocalendar().week.astype("Int64") - ) - - bigframes.testing.utils.assert_series_equal( - actual_result, expected_result, check_dtype=False, check_index_type=False - ) diff --git a/tests/system/small/operations/test_datetimes.py b/tests/system/small/operations/test_datetimes.py index ebfe0414de0..7dc55b9367c 100644 --- a/tests/system/small/operations/test_datetimes.py +++ b/tests/system/small/operations/test_datetimes.py @@ -12,47 +12,28 @@ # See the License for the specific language governing permissions and # limitations under the License. -import datetime -import typing - -import numpy import pandas as pd import pytest -from packaging import version -import bigframes.pandas as bpd import bigframes.series -from bigframes.testing.utils import assert_frame_equal, assert_series_equal +from tests.system.utils import assert_series_equal_ignoring_order DATETIME_COL_NAMES = [("datetime_col",), ("timestamp_col",)] -DATE_COLUMNS = [ - ("datetime_col",), - ("timestamp_col",), - ("date_col",), -] - - -@pytest.fixture -def timedelta_series(session): - pd_s = pd.Series(pd.to_timedelta([1.1010101, 2.2020102, 3.3030103], unit="d")) - bf_s = session.read_pandas(pd_s) - - return bf_s, pd_s @pytest.mark.parametrize( ("col_name",), - DATE_COLUMNS, + DATETIME_COL_NAMES, ) -def test_dt_day(scalars_dfs, col_name): - # TODO: supply a reason why this isn't compatible with pandas 1.x - pytest.importorskip("pandas", minversion="2.0.0") +def test_day(scalars_dfs, col_name): + if pd.__version__.startswith("1."): + pytest.skip("Pyarrow datetime objects not support in pandas 1.x.") scalars_df, scalars_pandas_df = scalars_dfs bf_series: bigframes.series.Series = scalars_df[col_name] bf_result = bf_series.dt.day.to_pandas() pd_result = scalars_pandas_df[col_name].dt.day - assert_series_equal( + assert_series_equal_ignoring_order( pd_result.astype(pd.Int64Dtype()), bf_result, ) @@ -62,15 +43,15 @@ def test_dt_day(scalars_dfs, col_name): ("col_name",), DATETIME_COL_NAMES, ) -def test_dt_date(scalars_dfs, col_name): - # TODO: supply a reason why this isn't compatible with pandas 1.x - pytest.importorskip("pandas", minversion="2.0.0") +def test_date(scalars_dfs, col_name): + if pd.__version__.startswith("1."): + pytest.skip("Pyarrow datetime objects not support in pandas 1.x.") scalars_df, scalars_pandas_df = scalars_dfs bf_series: bigframes.series.Series = scalars_df[col_name] bf_result = bf_series.dt.date.to_pandas() pd_result = scalars_pandas_df[col_name].dt.date - assert_series_equal( + assert_series_equal_ignoring_order( pd_result, bf_result, ) @@ -78,108 +59,32 @@ def test_dt_date(scalars_dfs, col_name): @pytest.mark.parametrize( ("col_name",), - DATE_COLUMNS, + DATETIME_COL_NAMES, ) -def test_dt_dayofweek(scalars_dfs, col_name): - # TODO: supply a reason why this isn't compatible with pandas 1.x - pytest.importorskip("pandas", minversion="2.0.0") +def test_dayofweek(scalars_dfs, col_name): + if pd.__version__.startswith("1."): + pytest.skip("Pyarrow datetime objects not support in pandas 1.x.") scalars_df, scalars_pandas_df = scalars_dfs bf_series: bigframes.series.Series = scalars_df[col_name] - bf_result = bf_series.dt.dayofweek.to_pandas() pd_result = scalars_pandas_df[col_name].dt.dayofweek - assert_series_equal(pd_result, bf_result, check_dtype=False) - - -@pytest.mark.parametrize( - ("col_name",), - DATE_COLUMNS, -) -def test_dt_day_of_week(scalars_dfs, col_name): - pytest.importorskip("pandas", minversion="2.0.0") - scalars_df, scalars_pandas_df = scalars_dfs - bf_series: bigframes.series.Series = scalars_df[col_name] - - bf_result = bf_series.dt.day_of_week.to_pandas() - pd_result = scalars_pandas_df[col_name].dt.day_of_week - - assert_series_equal(pd_result, bf_result, check_dtype=False) - - -@pytest.mark.parametrize( - ("col_name",), - DATE_COLUMNS, -) -def test_dt_weekday(scalars_dfs, col_name): - pytest.importorskip("pandas", minversion="2.0.0") - scalars_df, scalars_pandas_df = scalars_dfs - bf_series: bigframes.series.Series = scalars_df[col_name] - - bf_result = bf_series.dt.weekday.to_pandas() - pd_result = scalars_pandas_df[col_name].dt.weekday - - assert_series_equal(pd_result, bf_result, check_dtype=False) - - -@pytest.mark.parametrize( - ("col_name",), - DATE_COLUMNS, -) -def test_dt_dayofyear(scalars_dfs, col_name): - pytest.importorskip("pandas", minversion="2.0.0") - scalars_df, scalars_pandas_df = scalars_dfs - bf_series: bigframes.series.Series = scalars_df[col_name] - - bf_result = bf_series.dt.dayofyear.to_pandas() - pd_result = scalars_pandas_df[col_name].dt.dayofyear - - assert_series_equal(pd_result, bf_result, check_dtype=False) - - -@pytest.mark.parametrize( - ("col_name",), - DATE_COLUMNS, -) -def test_dt_day_name(scalars_dfs, col_name): - pytest.importorskip("pandas", minversion="2.0.0") - scalars_df, scalars_pandas_df = scalars_dfs - bf_series: bigframes.series.Series = scalars_df[col_name] - - bf_result = bf_series.dt.day_name().to_pandas() - pd_result = scalars_pandas_df[col_name].dt.day_name() - - assert_series_equal(pd_result, bf_result, check_dtype=False) - - -@pytest.mark.parametrize( - ("col_name",), - DATE_COLUMNS, -) -def test_dt_day_of_year(scalars_dfs, col_name): - pytest.importorskip("pandas", minversion="2.0.0") - scalars_df, scalars_pandas_df = scalars_dfs - bf_series: bigframes.series.Series = scalars_df[col_name] - - bf_result = bf_series.dt.day_of_year.to_pandas() - pd_result = scalars_pandas_df[col_name].dt.day_of_year - - assert_series_equal(pd_result, bf_result, check_dtype=False) + assert_series_equal_ignoring_order(pd_result, bf_result, check_dtype=False) @pytest.mark.parametrize( ("col_name",), DATETIME_COL_NAMES, ) -def test_dt_hour(scalars_dfs, col_name): - # TODO: supply a reason why this isn't compatible with pandas 1.x - pytest.importorskip("pandas", minversion="2.0.0") +def test_hour(scalars_dfs, col_name): + if pd.__version__.startswith("1."): + pytest.skip("Pyarrow datetime objects not support in pandas 1.x.") scalars_df, scalars_pandas_df = scalars_dfs bf_series: bigframes.series.Series = scalars_df[col_name] bf_result = bf_series.dt.hour.to_pandas() pd_result = scalars_pandas_df[col_name].dt.hour - assert_series_equal( + assert_series_equal_ignoring_order( pd_result.astype(pd.Int64Dtype()), bf_result, ) @@ -189,15 +94,15 @@ def test_dt_hour(scalars_dfs, col_name): ("col_name",), DATETIME_COL_NAMES, ) -def test_dt_minute(scalars_dfs, col_name): - # TODO: supply a reason why this isn't compatible with pandas 1.x - pytest.importorskip("pandas", minversion="2.0.0") +def test_minute(scalars_dfs, col_name): + if pd.__version__.startswith("1."): + pytest.skip("Pyarrow datetime objects not support in pandas 1.x.") scalars_df, scalars_pandas_df = scalars_dfs bf_series: bigframes.series.Series = scalars_df[col_name] bf_result = bf_series.dt.minute.to_pandas() pd_result = scalars_pandas_df[col_name].dt.minute - assert_series_equal( + assert_series_equal_ignoring_order( pd_result.astype(pd.Int64Dtype()), bf_result, ) @@ -205,17 +110,17 @@ def test_dt_minute(scalars_dfs, col_name): @pytest.mark.parametrize( ("col_name",), - DATE_COLUMNS, + DATETIME_COL_NAMES, ) -def test_dt_month(scalars_dfs, col_name): - # TODO: supply a reason why this isn't compatible with pandas 1.x - pytest.importorskip("pandas", minversion="2.0.0") +def test_month(scalars_dfs, col_name): + if pd.__version__.startswith("1."): + pytest.skip("Pyarrow datetime objects not support in pandas 1.x.") scalars_df, scalars_pandas_df = scalars_dfs bf_series: bigframes.series.Series = scalars_df[col_name] bf_result = bf_series.dt.month.to_pandas() pd_result = scalars_pandas_df[col_name].dt.month - assert_series_equal( + assert_series_equal_ignoring_order( pd_result.astype(pd.Int64Dtype()), bf_result, ) @@ -223,17 +128,17 @@ def test_dt_month(scalars_dfs, col_name): @pytest.mark.parametrize( ("col_name",), - DATE_COLUMNS, + DATETIME_COL_NAMES, ) -def test_dt_quarter(scalars_dfs, col_name): - # TODO: supply a reason why this isn't compatible with pandas 1.x - pytest.importorskip("pandas", minversion="2.0.0") +def test_quarter(scalars_dfs, col_name): + if pd.__version__.startswith("1."): + pytest.skip("Pyarrow datetime objects not support in pandas 1.x.") scalars_df, scalars_pandas_df = scalars_dfs bf_series: bigframes.series.Series = scalars_df[col_name] bf_result = bf_series.dt.quarter.to_pandas() pd_result = scalars_pandas_df[col_name].dt.quarter - assert_series_equal( + assert_series_equal_ignoring_order( pd_result.astype(pd.Int64Dtype()), bf_result, ) @@ -243,15 +148,15 @@ def test_dt_quarter(scalars_dfs, col_name): ("col_name",), DATETIME_COL_NAMES, ) -def test_dt_second(scalars_dfs, col_name): - # TODO: supply a reason why this isn't compatible with pandas 1.x - pytest.importorskip("pandas", minversion="2.0.0") +def test_second(scalars_dfs, col_name): + if pd.__version__.startswith("1."): + pytest.skip("Pyarrow datetime objects not support in pandas 1.x.") scalars_df, scalars_pandas_df = scalars_dfs bf_series: bigframes.series.Series = scalars_df[col_name] bf_result = bf_series.dt.second.to_pandas() pd_result = scalars_pandas_df[col_name].dt.second - assert_series_equal( + assert_series_equal_ignoring_order( pd_result.astype(pd.Int64Dtype()), bf_result, ) @@ -261,15 +166,15 @@ def test_dt_second(scalars_dfs, col_name): ("col_name",), DATETIME_COL_NAMES, ) -def test_dt_time(scalars_dfs, col_name): - # TODO: supply a reason why this isn't compatible with pandas 1.x - pytest.importorskip("pandas", minversion="2.0.0") +def test_time(scalars_dfs, col_name): + if pd.__version__.startswith("1."): + pytest.skip("Pyarrow datetime objects not support in pandas 1.x.") scalars_df, scalars_pandas_df = scalars_dfs bf_series: bigframes.series.Series = scalars_df[col_name] bf_result = bf_series.dt.time.to_pandas() pd_result = scalars_pandas_df[col_name].dt.time - assert_series_equal( + assert_series_equal_ignoring_order( pd_result, bf_result, ) @@ -277,404 +182,17 @@ def test_dt_time(scalars_dfs, col_name): @pytest.mark.parametrize( ("col_name",), - DATE_COLUMNS, + DATETIME_COL_NAMES, ) -def test_dt_year(scalars_dfs, col_name): - # TODO: supply a reason why this isn't compatible with pandas 1.x - pytest.importorskip("pandas", minversion="2.0.0") +def test_year(scalars_dfs, col_name): + if pd.__version__.startswith("1."): + pytest.skip("Pyarrow datetime objects not support in pandas 1.x.") scalars_df, scalars_pandas_df = scalars_dfs bf_series: bigframes.series.Series = scalars_df[col_name] bf_result = bf_series.dt.year.to_pandas() pd_result = scalars_pandas_df[col_name].dt.year - assert_series_equal( + assert_series_equal_ignoring_order( pd_result.astype(pd.Int64Dtype()), bf_result, ) - - -def test_dt_isocalendar(session): - # We don't re-use the exisintg scalars_dfs fixture because iso calendar - # get tricky when a new year starts, but the dataset `scalars_dfs` does not cover - # this case. - pd_s = pd.Series(pd.date_range("2009-12-25", "2010-01-07", freq="d")) - bf_s = session.read_pandas(pd_s) - - actual_result = bf_s.dt.isocalendar().to_pandas() - - expected_result = pd_s.dt.isocalendar() - assert_frame_equal( - actual_result, expected_result, check_dtype=False, check_index_type=False - ) - - -@pytest.mark.parametrize( - ("col_name",), - DATETIME_COL_NAMES, -) -def test_dt_tz(scalars_dfs, col_name): - # TODO: supply a reason why this isn't compatible with pandas 1.x - pytest.importorskip("pandas", minversion="2.0.0") - scalars_df, scalars_pandas_df = scalars_dfs - bf_series: bigframes.series.Series = scalars_df[col_name] - bf_result = bf_series.dt.tz - pd_result = scalars_pandas_df[col_name].dt.tz - - assert bf_result == pd_result - - -@pytest.mark.parametrize( - ("col_name", "tz"), - [ - ("datetime_col", None), - ("timestamp_col", None), - ("datetime_col", "UTC"), - ], -) -def test_dt_tz_localize(scalars_dfs, col_name, tz): - pytest.importorskip("pandas", minversion="2.0.0") - scalars_df, scalars_pandas_df = scalars_dfs - bf_series = scalars_df[col_name] - - bf_result = bf_series.dt.tz_localize(tz) - pd_result = scalars_pandas_df[col_name].dt.tz_localize(tz) - - assert_series_equal(bf_result.to_pandas(), pd_result, check_index_type=False) - - -def test_dt_tz_localize_already_localized(scalars_dfs): - pytest.importorskip("pandas", minversion="2.0.0") - scalars_df, _ = scalars_dfs - - with pytest.raises(TypeError): - scalars_df["timestamp_col"].dt.tz_localize("UTC") - - -def test_dt_tz_localize_invalid_timezone(scalars_dfs): - pytest.importorskip("pandas", minversion="2.0.0") - scalars_df, _ = scalars_dfs - - with pytest.raises(ValueError): - scalars_df["datetime_col"].dt.tz_localize("US/Eastern") - - -@pytest.mark.parametrize( - ("col_name",), - DATETIME_COL_NAMES, -) -def test_dt_unit(scalars_dfs, col_name): - # TODO: supply a reason why this isn't compatible with pandas 1.x - pytest.importorskip("pandas", minversion="2.0.0") - scalars_df, scalars_pandas_df = scalars_dfs - bf_series: bigframes.series.Series = scalars_df[col_name] - bf_result = bf_series.dt.unit - pd_result = scalars_pandas_df[col_name].dt.unit - - assert bf_result == pd_result - - -@pytest.mark.parametrize( - ("column", "date_format"), - [ - ("timestamp_col", "%B %d, %Y, %r"), - ("timestamp_col", "%m-%d-%Y %H:%M"), - ("datetime_col", "%m-%d-%Y %H:%M"), - ("datetime_col", "%H:%M"), - ], -) -def test_dt_strftime(scalars_df_index, scalars_pandas_df_index, column, date_format): - # TODO: supply a reason why this isn't compatible with pandas 1.x - pytest.importorskip("pandas", minversion="2.0.0") - bf_result = scalars_df_index[column].dt.strftime(date_format).to_pandas() - pd_result = scalars_pandas_df_index[column].dt.strftime(date_format) - assert_series_equal(bf_result, pd_result, check_dtype=False) - assert bf_result.dtype == "string[pyarrow]" - - -def test_dt_strftime_date(): - bf_series = bigframes.series.Series( - ["2014-08-15", "2215-08-15", "2016-02-29"] - ).astype("date32[day][pyarrow]") - - expected_result = pd.Series(["08/15/2014", "08/15/2215", "02/29/2016"]) - bf_result = bf_series.dt.strftime("%m/%d/%Y").to_pandas() - - assert_series_equal( - bf_result, expected_result, check_index_type=False, check_dtype=False - ) - assert bf_result.dtype == "string[pyarrow]" - - -def test_dt_strftime_time(): - bf_series = bigframes.series.Series( - [143542314, 345234512341, 75543252344, 626546437654754, 8543523452345234] - ).astype("time64[us][pyarrow]") - - expected_result = pd.Series( - ["00:02:23", "23:53:54", "20:59:03", "16:40:37", "08:57:32"] - ) - bf_result = bf_series.dt.strftime("%X").to_pandas() - - assert_series_equal( - bf_result, expected_result, check_index_type=False, check_dtype=False - ) - assert bf_result.dtype == "string[pyarrow]" - - -@pytest.mark.parametrize( - ("col_name",), - DATETIME_COL_NAMES, -) -def test_dt_normalize(scalars_dfs, col_name): - # TODO: supply a reason why this isn't compatible with pandas 1.x - pytest.importorskip("pandas", minversion="2.0.0") - scalars_df, scalars_pandas_df = scalars_dfs - bf_result = scalars_df[col_name].dt.normalize().to_pandas() - pd_result = scalars_pandas_df[col_name].dt.normalize() - - assert_series_equal( - pd_result.astype(scalars_df[col_name].dtype), # normalize preserves type - bf_result, - ) - - -@pytest.mark.parametrize( - ("col_name", "freq"), - [ - ("timestamp_col", "D"), - ("timestamp_col", "min"), - ("datetime_col", "s"), - ("datetime_col", "us"), - ], -) -def test_dt_floor(scalars_dfs, col_name, freq): - # TODO: supply a reason why this isn't compatible with pandas 1.x - pytest.importorskip("pandas", minversion="2.0.0") - scalars_df, scalars_pandas_df = scalars_dfs - bf_result = scalars_df[col_name].dt.floor(freq).to_pandas() - pd_result = scalars_pandas_df[col_name].dt.floor(freq) - - assert_series_equal( - pd_result.astype(scalars_df[col_name].dtype), # floor preserves type - bf_result, - ) - - -def test_dt_compare_coerce_str_datetime(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - bf_series: bigframes.series.Series = scalars_df["datetime_col"] - - bf_result = (bf_series >= "2024-01-01").to_pandas() - pd_result = scalars_pandas_df["datetime_col"] >= pd.to_datetime("2024-01-01") - - # pandas produces pyarrow bool dtype - assert_series_equal(pd_result, bf_result, check_dtype=False) - - -def test_dt_clip_datetime_literals(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - bf_series: bigframes.series.Series = scalars_df["date_col"] - bf_result = bf_series.clip( - datetime.date(2020, 1, 1), datetime.date(2024, 1, 1) - ).to_pandas() - - pd_result = scalars_pandas_df["date_col"].clip( - datetime.date(2020, 1, 1), datetime.date(2024, 1, 1) - ) - - assert_series_equal( - pd_result, - bf_result, - ) - - -def test_dt_clip_coerce_str_date(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - bf_series: bigframes.series.Series = scalars_df["date_col"] - bf_result = bf_series.clip("2020-01-01", "2024-01-01").to_pandas() - - # Pandas can't coerce with pyarrow types so convert first - pd_result = scalars_pandas_df["date_col"].clip( - datetime.date(2020, 1, 1), datetime.date(2024, 1, 1) - ) - - assert_series_equal( - pd_result, - bf_result, - ) - - -def test_dt_clip_coerce_str_timestamp(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - bf_series: bigframes.series.Series = scalars_df["timestamp_col"] - bf_result = bf_series.clip( - "2020-01-01T20:03:50Z", "2024-01-01T20:03:50Z" - ).to_pandas() - - pd_result = scalars_pandas_df["timestamp_col"].clip( - pd.to_datetime("2020-01-01T20:03:50Z", utc=True), - pd.to_datetime("2024-01-01T20:03:50Z", utc=True), - ) - - assert_series_equal( - pd_result, - bf_result, - ) - - -@pytest.mark.parametrize("column", ["timestamp_col", "datetime_col"]) -def test_timestamp_diff_two_series(scalars_dfs, column): - bf_df, pd_df = scalars_dfs - bf_series = bf_df[column] - pd_series = pd_df[column] - - actual_result = (bf_series - bf_series).to_pandas() - - expected_result = pd_series - pd_series - assert_series_equal(actual_result, expected_result) - - -@pytest.mark.parametrize("column", ["timestamp_col", "datetime_col"]) -def test_timestamp_diff_two_series_with_numpy_ops(scalars_dfs, column): - bf_df, pd_df = scalars_dfs - bf_series = bf_df[column] - pd_series = pd_df[column] - - actual_result = numpy.subtract(bf_series, bf_series).to_pandas() - - expected_result = numpy.subtract(pd_series, pd_series) - assert_series_equal(actual_result, expected_result) - - -def test_timestamp_diff_two_dataframes(scalars_dfs): - columns = ["timestamp_col", "datetime_col"] - bf_df, pd_df = scalars_dfs - bf_df = bf_df[columns] - pd_df = pd_df[columns] - - actual_result = (bf_df - bf_df).to_pandas() - - expected_result = pd_df - pd_df - assert_frame_equal(actual_result, expected_result) - - -def test_timestamp_diff_two_series_with_different_types_raise_error(scalars_dfs): - df, _ = scalars_dfs - - with pytest.raises(TypeError): - (df["timestamp_col"] - df["datetime_col"]).to_pandas() - - -@pytest.mark.parametrize( - ("column", "value"), - [ - ("timestamp_col", pd.Timestamp("2025-01-01 00:00:01", tz="America/New_York")), - ("datetime_col", datetime.datetime(2025, 1, 1, 0, 0, 1)), - ], -) -def test_timestamp_diff_series_sub_literal(scalars_dfs, column, value): - bf_df, pd_df = scalars_dfs - bf_series = bf_df[column] - pd_series = pd_df[column] - - # Pandas doesn't handle nulls properly here so we ffill - # overflows for no good reason - # related? https://github.com/apache/arrow/issues/43031 - actual_result = (bf_series.ffill() - value).to_pandas() - - expected_result = pd_series.ffill() - value - assert_series_equal(actual_result, expected_result) - - -@pytest.mark.parametrize( - ("column", "value"), - [ - ("timestamp_col", pd.Timestamp("2025-01-01 00:00:01", tz="America/New_York")), - ("datetime_col", datetime.datetime(2025, 1, 1, 0, 0, 1)), - ], -) -def test_timestamp_diff_literal_sub_series(scalars_dfs, column, value): - bf_df, pd_df = scalars_dfs - bf_series = bf_df[column] - pd_series = pd_df[column] - - # Pandas doesn't handle nulls properly here so we ffill - # overflows for no good reason - # related? https://github.com/apache/arrow/issues/43031 - actual_result = (value - bf_series.ffill()).to_pandas() - - expected_result = value - pd_series.ffill() - assert_series_equal(actual_result, expected_result) - - -@pytest.mark.parametrize("column", ["timestamp_col", "datetime_col"]) -def test_timestamp_series_diff_agg(scalars_dfs, column): - bf_df, pd_df = scalars_dfs - bf_series = bf_df[column] - pd_series = pd_df[column] - - actual_result = bf_series.diff().to_pandas() - - # overflows for no good reason - # related? https://github.com/apache/arrow/issues/43031 - expected_result = pd_series.ffill().diff() - expected_result = expected_result.mask( - pd_series.isnull() | pd_series.shift(1).isnull() - ) - assert_series_equal(actual_result, expected_result) - - -@pytest.mark.parametrize( - "access", - [ - pytest.param(lambda x: x.dt.days, id="dt.days"), - pytest.param(lambda x: x.dt.seconds, id="dt.seconds"), - pytest.param(lambda x: x.dt.microseconds, id="dt.microseconds"), - pytest.param(lambda x: x.dt.total_seconds(), id="dt.total_seconds()"), - ], -) -def test_timedelta_dt_accessors(timedelta_series, access): - bf_s, pd_s = timedelta_series - - actual_result = access(bf_s).to_pandas() - - expected_result = access(pd_s) - assert_series_equal( - actual_result, expected_result, check_dtype=False, check_index_type=False - ) - - -@pytest.mark.parametrize( - "access", - [ - pytest.param(lambda x: x.dt.days, id="dt.days"), - pytest.param(lambda x: x.dt.seconds, id="dt.seconds"), - pytest.param(lambda x: x.dt.microseconds, id="dt.microseconds"), - pytest.param(lambda x: x.dt.total_seconds(), id="dt.total_seconds()"), - ], -) -def test_timedelta_dt_accessors_on_wrong_type_raise_exception(scalars_dfs, access): - bf_df, _ = scalars_dfs - - with pytest.raises(TypeError): - access(bf_df["timestamp_col"]) - - -@pytest.mark.parametrize( - "col", - # TODO(b/431276706) test timestamp_col too. - ["date_col", "datetime_col"], -) -def test_to_datetime(scalars_dfs, col): - if version.Version(pd.__version__) <= version.Version("2.1.0"): - pytest.skip("timezone conversion bug") - bf_df, pd_df = scalars_dfs - - actual_result = typing.cast( - bigframes.series.Series, bpd.to_datetime(bf_df[col]) - ).to_pandas() - - expected_result = pd.Series(pd.to_datetime(pd_df[col])) - assert_series_equal( - actual_result, expected_result, check_dtype=False, check_index_type=False - ) diff --git a/tests/system/small/operations/test_lists.py b/tests/system/small/operations/test_lists.py deleted file mode 100644 index 16a68025721..00000000000 --- a/tests/system/small/operations/test_lists.py +++ /dev/null @@ -1,138 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -import packaging.version -import pandas as pd -import pyarrow as pa -import pytest - -from bigframes.testing.utils import assert_series_equal - - -@pytest.mark.parametrize( - ("key"), - [ - pytest.param(0, id="int"), - pytest.param(slice(None, None, None), id="default_start_slice"), - pytest.param(slice(0, None, 1), id="default_stop_slice"), - pytest.param(slice(0, 2, None), id="default_step_slice"), - ], -) -@pytest.mark.parametrize( - ("column_name", "dtype"), - [ - pytest.param("int_list_col", pd.ArrowDtype(pa.list_(pa.int64()))), - pytest.param("bool_list_col", pd.ArrowDtype(pa.list_(pa.bool_()))), - pytest.param("float_list_col", pd.ArrowDtype(pa.list_(pa.float64()))), - pytest.param("date_list_col", pd.ArrowDtype(pa.list_(pa.date32()))), - pytest.param("date_time_list_col", pd.ArrowDtype(pa.list_(pa.timestamp("us")))), - pytest.param("numeric_list_col", pd.ArrowDtype(pa.list_(pa.decimal128(38, 9)))), - pytest.param("string_list_col", pd.ArrowDtype(pa.list_(pa.string()))), - ], -) -def test_getitem(key, column_name, dtype, repeated_df, repeated_pandas_df): - if packaging.version.Version(pd.__version__) < packaging.version.Version("2.2.0"): - pytest.skip( - "https://pandas.pydata.org/docs/whatsnew/v2.2.0.html#series-list-accessor-for-pyarrow-list-data" - ) - - bf_result = repeated_df[column_name].list[key].to_pandas() - pd_result = repeated_pandas_df[column_name].astype(dtype).list[key] - - assert_series_equal( - pd_result, - bf_result, - check_dtype=False, - check_index_type=False, - check_names=False, - ) - - -@pytest.mark.parametrize( - ("key", "expectation"), - [ - # Negative index - (-1, pytest.raises(NotImplementedError)), - # Slice with negative start - (slice(-1, None, None), pytest.raises(NotImplementedError)), - # Slice with negatiev end - (slice(0, -1, None), pytest.raises(NotImplementedError)), - # Slice with step not equal to 1 - (slice(0, 2, 2), pytest.raises(NotImplementedError)), - ], -) -def test_getitem_notsupported(key, expectation, repeated_df): - with expectation as e: - assert repeated_df["int_list_col"].list[key] == e - - -@pytest.mark.parametrize( - ("column_name", "dtype"), - [ - pytest.param("int_list_col", pd.ArrowDtype(pa.list_(pa.int64()))), - pytest.param("bool_list_col", pd.ArrowDtype(pa.list_(pa.bool_()))), - pytest.param("float_list_col", pd.ArrowDtype(pa.list_(pa.float64()))), - pytest.param("date_list_col", pd.ArrowDtype(pa.list_(pa.date32()))), - pytest.param("date_time_list_col", pd.ArrowDtype(pa.list_(pa.timestamp("us")))), - pytest.param("numeric_list_col", pd.ArrowDtype(pa.list_(pa.decimal128(38, 9)))), - pytest.param("string_list_col", pd.ArrowDtype(pa.list_(pa.string()))), - ], -) -def test_len(column_name, dtype, repeated_df, repeated_pandas_df): - if packaging.version.Version(pd.__version__) < packaging.version.Version("2.2.0"): - pytest.skip( - "https://pandas.pydata.org/docs/whatsnew/v2.2.0.html#series-list-accessor-for-pyarrow-list-data" - ) - - bf_result = repeated_df[column_name].list.len().to_pandas() - pd_result = repeated_pandas_df[column_name].astype(dtype).list.len() - - assert_series_equal( - pd_result, - bf_result, - check_dtype=False, - check_index_type=False, - check_names=False, - ) - - -@pytest.mark.parametrize( - ("column_name", "dtype"), - [ - pytest.param("int_list_col", pd.ArrowDtype(pa.list_(pa.int64()))), - pytest.param("float_list_col", pd.ArrowDtype(pa.list_(pa.float64()))), - ], -) -@pytest.mark.parametrize( - ("func",), - [ - pytest.param(len), - pytest.param(all), - pytest.param(any), - pytest.param(min), - pytest.param(max), - pytest.param(sum), - ], -) -def test_list_apply_callable(column_name, dtype, repeated_df, repeated_pandas_df, func): - bf_result = repeated_df[column_name].apply(func).to_pandas() - pd_result = repeated_pandas_df[column_name].astype(dtype).apply(func) - pd_result.index = pd_result.index.astype("Int64") - - assert_series_equal( - pd_result, - bf_result, - check_dtype=False, - ) diff --git a/tests/system/small/operations/test_plotting.py b/tests/system/small/operations/test_plotting.py deleted file mode 100644 index e579c90b4df..00000000000 --- a/tests/system/small/operations/test_plotting.py +++ /dev/null @@ -1,467 +0,0 @@ -# Copyright 2023 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import numpy as np -import pandas as pd -import pandas._testing as tm -import pytest -from matplotlib.collections import PathCollection - -import bigframes.operations._matplotlib.core as bf_mpl -import bigframes.pandas as bpd - - -def _check_legend_labels(ax, labels): - """ - Check the ax has expected legend label - """ - assert ax.get_legend() is not None - texts = ax.get_legend().get_texts() - actual_labels = [t.get_text() for t in texts] - assert len(actual_labels) == len(labels) - for label, e in zip(actual_labels, labels): - assert label == e - - -@pytest.mark.parametrize( - ("alias"), - [ - pytest.param(True), - pytest.param(False), - ], -) -def test_series_hist_bins(scalars_dfs, alias): - scalars_df, scalars_pandas_df = scalars_dfs - bins = 5 - if alias: - ax = scalars_df["int64_col"].hist(bins=bins) - else: - ax = scalars_df["int64_col"].plot.hist(bins=bins) - pd_ax = scalars_pandas_df["int64_col"].plot.hist(bins=bins) - - # Compares axis values and height between bigframes and pandas histograms. - # Note: Due to potential float rounding by matplotlib, this test may not - # be applied to all cases. - assert len(ax.patches) == len(pd_ax.patches) - for i in range(len(ax.patches)): - assert ax.patches[i].xy == pd_ax.patches[i].xy - assert ax.patches[i]._height == pd_ax.patches[i]._height - - -@pytest.mark.parametrize( - ("alias"), - [ - pytest.param(True), - pytest.param(False), - ], -) -def test_dataframes_hist_bins(scalars_dfs, alias): - scalars_df, scalars_pandas_df = scalars_dfs - bins = 7 - columns = ["int64_col", "int64_too", "float64_col"] - if alias: - ax = scalars_df[columns].hist(bins=bins) - else: - ax = scalars_df[columns].plot.hist(bins=bins) - pd_ax = scalars_pandas_df[columns].plot.hist(bins=bins) - - # Compares axis values and height between bigframes and pandas histograms. - # Note: Due to potential float rounding by matplotlib, this test may not - # be applied to all cases. - assert len(ax.patches) == len(pd_ax.patches) - for i in range(len(ax.patches)): - assert ax.patches[i]._height == pd_ax.patches[i]._height - - -@pytest.mark.parametrize( - ("col_names"), - [ - pytest.param(["int64_col"]), - pytest.param(["float64_col"]), - pytest.param(["int64_too", "bool_col"]), - pytest.param(["bool_col"], marks=pytest.mark.xfail(raises=TypeError)), - pytest.param(["date_col"], marks=pytest.mark.xfail(raises=TypeError)), - pytest.param(["datetime_col"], marks=pytest.mark.xfail(raises=TypeError)), - pytest.param(["time_col"], marks=pytest.mark.xfail(raises=TypeError)), - pytest.param(["timestamp_col"], marks=pytest.mark.xfail(raises=TypeError)), - ], -) -def test_hist_include_types(scalars_dfs, col_names): - scalars_df, _ = scalars_dfs - ax = scalars_df[col_names].plot.hist() - assert len(ax.patches) == 10 - - -@pytest.mark.parametrize( - ("arg_name", "arg_value"), - [ - pytest.param( - "by", ["int64_col"], marks=pytest.mark.xfail(raises=NotImplementedError) - ), - pytest.param( - "bins", [1, 3, 5], marks=pytest.mark.xfail(raises=NotImplementedError) - ), - pytest.param( - "weight", [2, 3], marks=pytest.mark.xfail(raises=NotImplementedError) - ), - pytest.param( - "backend", - "backend.module", - marks=pytest.mark.xfail(raises=NotImplementedError), - ), - ], -) -def test_hist_not_implemented_error(scalars_dfs, arg_name, arg_value): - scalars_df, _ = scalars_dfs - kwargs = {arg_name: arg_value} - scalars_df.plot.hist(**kwargs) - - -def test_hist_kwargs_true_subplots(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - columns = ["int64_col", "int64_too", "float64_col"] - axes = scalars_df[columns].plot.hist(subplots=True) - pd_axes = scalars_pandas_df[columns].plot.hist(subplots=True) - assert len(axes) == len(pd_axes) - - expected_labels = (["int64_col"], ["int64_too"], ["float64_col"]) - for ax, labels in zip(axes, expected_labels): - _check_legend_labels(ax, labels) - - -def test_hist_kwargs_list_subplots(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - columns = ["int64_col", "int64_too", "float64_col"] - subplots = [["int64_col", "int64_too"]] - axes = scalars_df[columns].plot.hist(subplots=subplots) - pd_axes = scalars_pandas_df[columns].plot.hist(subplots=subplots) - assert len(axes) == len(pd_axes) - - expected_labels = (["int64_col", "int64_too"], ["float64_col"]) - for ax, labels in zip(axes, expected_labels): - _check_legend_labels(ax, labels=labels) - - -@pytest.mark.parametrize( - ("orientation"), - [ - pytest.param("horizontal"), - pytest.param("vertical"), - ], -) -def test_hist_kwargs_orientation(scalars_dfs, orientation): - scalars_df, scalars_pandas_df = scalars_dfs - ax = scalars_df["int64_col"].plot.hist(orientation=orientation) - pd_ax = scalars_pandas_df["int64_col"].plot.hist(orientation=orientation) - assert ax.xaxis.get_label().get_text() == pd_ax.xaxis.get_label().get_text() - assert ax.yaxis.get_label().get_text() == pd_ax.yaxis.get_label().get_text() - - -def test_hist_kwargs_ticks_props(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - - xticks = [20, 18] - yticks = [30, 40] - - ax = scalars_df["float64_col"].plot.hist(xticks=xticks, yticks=yticks) - pd_ax = scalars_pandas_df["float64_col"].plot.hist(xticks=xticks, yticks=yticks) - xlabels = ax.get_xticklabels() - pd_xlables = pd_ax.get_xticklabels() - assert len(xlabels) == len(pd_xlables) - for i in range(len(pd_xlables)): - tm.assert_almost_equal(xlabels[i].get_fontsize(), pd_xlables[i].get_fontsize()) - tm.assert_almost_equal(xlabels[i].get_rotation(), pd_xlables[i].get_rotation()) - - ylabels = ax.get_yticklabels() - pd_ylables = pd_ax.get_yticklabels() - assert len(xlabels) == len(pd_xlables) - for i in range(len(pd_xlables)): - tm.assert_almost_equal(ylabels[i].get_fontsize(), pd_ylables[i].get_fontsize()) - tm.assert_almost_equal(ylabels[i].get_rotation(), pd_ylables[i].get_rotation()) - - -@pytest.mark.parametrize( - ("col_names", "alias"), - [ - pytest.param( - ["int64_col", "float64_col", "int64_too", "bool_col"], True, id="df_alias" - ), - pytest.param( - ["int64_col", "float64_col", "int64_too", "bool_col"], False, id="df" - ), - pytest.param(["int64_col"], True, id="series_alias"), - pytest.param(["int64_col"], False, id="series"), - ], -) -def test_line(scalars_dfs, col_names, alias): - scalars_df, scalars_pandas_df = scalars_dfs - if alias: - ax = scalars_df[col_names].line() - else: - ax = scalars_df[col_names].plot.line() - pd_ax = scalars_pandas_df[col_names].plot.line() - tm.assert_almost_equal(ax.get_xticks(), pd_ax.get_xticks()) - tm.assert_almost_equal(ax.get_yticks(), pd_ax.get_yticks()) - for line, pd_line in zip(ax.lines, pd_ax.lines): - # Compare y coordinates between the lines - tm.assert_almost_equal(line.get_data()[1], pd_line.get_data()[1]) - - -@pytest.mark.parametrize( - ("col_names", "alias"), - [ - pytest.param(["int64_col", "float64_col", "int64_too"], True, id="df_alias"), - pytest.param(["int64_col", "float64_col", "int64_too"], False, id="df"), - pytest.param(["int64_col"], True, id="series_alias"), - pytest.param(["int64_col"], False, id="series"), - ], -) -def test_area(scalars_dfs, col_names, alias): - scalars_df, scalars_pandas_df = scalars_dfs - if alias: - ax = scalars_df[col_names].area(stacked=False) - else: - ax = scalars_df[col_names].plot.area(stacked=False) - pd_ax = scalars_pandas_df[col_names].plot.area(stacked=False) - tm.assert_almost_equal(ax.get_xticks(), pd_ax.get_xticks()) - tm.assert_almost_equal(ax.get_yticks(), pd_ax.get_yticks()) - for line, pd_line in zip(ax.lines, pd_ax.lines): - # Compare y coordinates between the lines - tm.assert_almost_equal(line.get_data()[1], pd_line.get_data()[1]) - - -@pytest.mark.parametrize( - ("col_names", "alias"), - [ - pytest.param(["int64_col", "float64_col", "int64_too"], True, id="df_alias"), - pytest.param(["int64_col", "float64_col", "int64_too"], False, id="df"), - pytest.param(["int64_col"], True, id="series_alias"), - pytest.param(["int64_col"], False, id="series"), - ], -) -def test_bar(scalars_dfs, col_names, alias): - scalars_df, scalars_pandas_df = scalars_dfs - if alias: - ax = scalars_df[col_names].bar() - else: - ax = scalars_df[col_names].plot.bar() - pd_ax = scalars_pandas_df[col_names].plot.bar() - tm.assert_almost_equal(ax.get_xticks(), pd_ax.get_xticks()) - tm.assert_almost_equal(ax.get_yticks(), pd_ax.get_yticks()) - for line, pd_line in zip(ax.lines, pd_ax.lines): - # Compare y coordinates between the lines - tm.assert_almost_equal(line.get_data()[1], pd_line.get_data()[1]) - - -@pytest.mark.parametrize( - ("col_names",), - [ - pytest.param(["int64_col", "float64_col", "int64_too"], id="df"), - pytest.param(["int64_col"], id="series"), - ], -) -def test_barh(scalars_dfs, col_names): - scalars_df, scalars_pandas_df = scalars_dfs - ax = scalars_df[col_names].plot.barh() - pd_ax = scalars_pandas_df[col_names].plot.barh() - tm.assert_almost_equal(ax.get_xticks(), pd_ax.get_xticks()) - tm.assert_almost_equal(ax.get_yticks(), pd_ax.get_yticks()) - for line, pd_line in zip(ax.lines, pd_ax.lines): - # Compare y coordinates between the lines - tm.assert_almost_equal(line.get_data()[1], pd_line.get_data()[1]) - - -@pytest.mark.parametrize( - ("col_names",), - [ - pytest.param(["int64_col", "float64_col", "int64_too"], id="df"), - pytest.param(["int64_col"], id="series"), - ], -) -def test_pie(scalars_dfs, col_names): - scalars_df, scalars_pandas_df = scalars_dfs - ax = scalars_df[col_names].abs().plot.pie(y="int64_col") - pd_ax = scalars_pandas_df[col_names].abs().plot.pie(y="int64_col") - tm.assert_almost_equal(ax.get_xticks(), pd_ax.get_xticks()) - tm.assert_almost_equal(ax.get_yticks(), pd_ax.get_yticks()) - for line, pd_line in zip(ax.lines, pd_ax.lines): - # Compare y coordinates between the lines - tm.assert_almost_equal(line.get_data()[1], pd_line.get_data()[1]) - - -@pytest.mark.parametrize( - ("col_names", "alias"), - [ - pytest.param( - ["int64_col", "float64_col", "int64_too", "bool_col"], True, id="df_alias" - ), - pytest.param( - ["int64_col", "float64_col", "int64_too", "bool_col"], False, id="df" - ), - ], -) -def test_scatter(scalars_dfs, col_names, alias): - scalars_df, scalars_pandas_df = scalars_dfs - if alias: - ax = scalars_df[col_names].scatter(x="int64_col", y="float64_col") - else: - ax = scalars_df[col_names].plot.scatter(x="int64_col", y="float64_col") - pd_ax = scalars_pandas_df[col_names].plot.scatter(x="int64_col", y="float64_col") - tm.assert_almost_equal(ax.get_xticks(), pd_ax.get_xticks()) - tm.assert_almost_equal(ax.get_yticks(), pd_ax.get_yticks()) - tm.assert_almost_equal( - ax.collections[0].get_sizes(), pd_ax.collections[0].get_sizes() - ) - - -@pytest.mark.parametrize( - ("c"), - [ - pytest.param("red", id="red"), - pytest.param("c", id="int_column"), - pytest.param("species", id="color_column"), - pytest.param(3, id="column_index"), - ], -) -def test_scatter_args_c(c): - data = { - "a": [1, 2, 3], - "b": [1, 2, 3], - "c": [1, 2, 3], - "species": ["r", "g", "b"], - } - df = bpd.DataFrame(data) - pd_df = pd.DataFrame(data) - - ax = df.plot.scatter(x="a", y="b", c=c) - pd_ax = pd_df.plot.scatter(x="a", y="b", c=c) - assert len(ax.collections[0].get_facecolor()) == len( - pd_ax.collections[0].get_facecolor() - ) - for idx in range(len(ax.collections[0].get_facecolor())): - tm.assert_numpy_array_equal( - ax.collections[0].get_facecolor()[idx], - pd_ax.collections[0].get_facecolor()[idx], - ) - - -@pytest.mark.parametrize( - ("s"), - [ - pytest.param([10, 34, 50], id="int"), - pytest.param([1.0, 3.4, 5.0], id="float"), - pytest.param( - [True, True, False], id="bool", marks=pytest.mark.xfail(raises=ValueError) - ), - ], -) -def test_scatter_args_s(s): - data = { - "a": [1, 2, 3], - "b": [1, 2, 3], - } - data["s"] = s - df = bpd.DataFrame(data) - pd_df = pd.DataFrame(data) - - ax = df.plot.scatter(x="a", y="b", s="s") - pd_ax = pd_df.plot.scatter(x="a", y="b", s="s") - - assert isinstance(pd_ax.collections[0], PathCollection) - tm.assert_numpy_array_equal( - ax.collections[0].get_sizes(), pd_ax.collections[0].get_sizes() - ) - - -@pytest.mark.parametrize( - ("arg_name"), - [ - pytest.param("c", marks=pytest.mark.xfail(raises=NotImplementedError)), - pytest.param("s", marks=pytest.mark.xfail(raises=NotImplementedError)), - ], -) -def test_scatter_sequence_arg(arg_name): - data = { - "a": [1, 2, 3], - "b": [1, 2, 3], - } - arg_value = [3, 3, 1] - bpd.DataFrame(data).plot.scatter(x="a", y="b", **{arg_name: arg_value}) - - -def test_sampling_plot_args_n(): - df = bpd.DataFrame(np.arange(bf_mpl.DEFAULT_SAMPLING_N * 10), columns=["one"]) - ax = df.plot.line() - assert len(ax.lines) == 1 - assert len(ax.lines[0].get_data()[1]) == bf_mpl.DEFAULT_SAMPLING_N - - ax = df.plot.line(sampling_n=2) - assert len(ax.lines) == 1 - assert len(ax.lines[0].get_data()[1]) == 2 - - -def test_sampling_plot_args_random_state(): - df = bpd.DataFrame(np.arange(bf_mpl.DEFAULT_SAMPLING_N * 10), columns=["one"]) - ax_0 = df.plot.line() - ax_1 = df.plot.line() - ax_2 = df.plot.line(sampling_random_state=100) - ax_3 = df.plot.line(sampling_random_state=100) - - # Setting a fixed sampling_random_state guarantees reproducible plotted sampling. - tm.assert_almost_equal(ax_0.lines[0].get_data()[1], ax_1.lines[0].get_data()[1]) - tm.assert_almost_equal(ax_2.lines[0].get_data()[1], ax_3.lines[0].get_data()[1]) - - msg = "numpy array are different" - with pytest.raises(AssertionError, match=msg): - tm.assert_almost_equal(ax_0.lines[0].get_data()[1], ax_2.lines[0].get_data()[1]) - - -def test_sampling_preserve_ordering(): - df = bpd.DataFrame([0.0, 1.0, 2.0, 3.0, 4.0], index=[1, 3, 4, 2, 0]) - pd_df = pd.DataFrame([0.0, 1.0, 2.0, 3.0, 4.0], index=[1, 3, 4, 2, 0]) - ax = df.plot.line() - pd_ax = pd_df.plot.line() - tm.assert_almost_equal(ax.get_xticks(), pd_ax.get_xticks()) - tm.assert_almost_equal(ax.get_yticks(), pd_ax.get_yticks()) - for line, pd_line in zip(ax.lines, pd_ax.lines): - # Compare y coordinates between the lines - tm.assert_almost_equal(line.get_data()[1], pd_line.get_data()[1]) - - -@pytest.mark.parametrize( - ("kind", "col_names", "kwargs"), - [ - pytest.param("hist", ["int64_col", "int64_too"], {}), - pytest.param("line", ["int64_col", "int64_too"], {}), - pytest.param("area", ["int64_col", "int64_too"], {"stacked": False}), - pytest.param( - "scatter", ["int64_col", "int64_too"], {"x": "int64_col", "y": "int64_too"} - ), - pytest.param( - "scatter", - ["int64_col"], - {}, - marks=pytest.mark.xfail(raises=ValueError), - ), - pytest.param( - "bar", - ["int64_col", "int64_too"], - {}, - marks=pytest.mark.xfail(raises=NotImplementedError), - ), - ], -) -def test_plot_call(scalars_dfs, kind, col_names, kwargs): - scalars_df, _ = scalars_dfs - scalars_df[col_names].plot(kind=kind, **kwargs) diff --git a/tests/system/small/operations/test_strings.py b/tests/system/small/operations/test_strings.py index 94285cc7dc4..241cbd576b1 100644 --- a/tests/system/small/operations/test_strings.py +++ b/tests/system/small/operations/test_strings.py @@ -15,24 +15,23 @@ import re import pandas as pd -import pyarrow as pa import pytest -import bigframes.dtypes as dtypes -import bigframes.pandas as bpd -from bigframes.testing.utils import assert_series_equal +import bigframes.series + +from ...utils import assert_series_equal_ignoring_order def test_find(scalars_dfs): scalars_df, scalars_pandas_df = scalars_dfs col_name = "string_col" - bf_series: bpd.Series = scalars_df[col_name] + bf_series: bigframes.series.Series = scalars_df[col_name] bf_result = bf_series.str.find("W").to_pandas() pd_result = scalars_pandas_df[col_name].str.find("W") # One of type mismatches to be documented. Here, the `bf_result.dtype` is `Int64` but # the `pd_result.dtype` is `float64`: https://github.com/pandas-dev/pandas/issues/51948 - assert_series_equal( + assert_series_equal_ignoring_order( pd_result.astype(pd.Int64Dtype()), bf_result, ) @@ -51,7 +50,7 @@ def test_find(scalars_dfs): def test_str_contains(scalars_dfs, pat, case, flags, regex): scalars_df, scalars_pandas_df = scalars_dfs col_name = "string_col" - bf_series: bpd.Series = scalars_df[col_name] + bf_series: bigframes.series.Series = scalars_df[col_name] bf_result = bf_series.str.contains( pat, case=case, flags=flags, regex=regex @@ -73,11 +72,13 @@ def test_str_contains(scalars_dfs, pat, case, flags, regex): def test_str_extract(scalars_dfs, pat): scalars_df, scalars_pandas_df = scalars_dfs col_name = "string_col" - bf_series: bpd.Series = scalars_df[col_name] + bf_series: bigframes.series.Series = scalars_df[col_name] bf_result = bf_series.str.extract(pat).to_pandas() pd_result = scalars_pandas_df[col_name].str.extract(pat) + # Pandas produces int col labels, while bq df only supports str labels at present + pd_result = pd_result.set_axis(pd_result.columns.astype(str), axis=1) pd.testing.assert_frame_equal( pd_result, bf_result, @@ -93,15 +94,12 @@ def test_str_extract(scalars_dfs, pat): (".*", "blah", True, 0, True), ("h.l", "blah", False, 0, True), (re.compile("(?i).e.."), "blah", None, 0, True), - ("H", "h", True, 0, False), - (", ", "__", True, 0, False), - (re.compile(r"hEllo", flags=re.I), "blah", None, 0, True), ], ) def test_str_replace(scalars_dfs, pat, repl, case, flags, regex): scalars_df, scalars_pandas_df = scalars_dfs col_name = "string_col" - bf_series: bpd.Series = scalars_df[col_name] + bf_series: bigframes.series.Series = scalars_df[col_name] bf_result = bf_series.str.replace( pat, repl=repl, case=case, flags=flags, regex=regex @@ -132,7 +130,7 @@ def test_str_replace(scalars_dfs, pat, repl, case, flags, regex): def test_str_startswith(scalars_dfs, pat): scalars_df, scalars_pandas_df = scalars_dfs col_name = "string_col" - bf_series: bpd.Series = scalars_df[col_name] + bf_series: bigframes.series.Series = scalars_df[col_name] pd_series = scalars_pandas_df[col_name].astype("object") bf_result = bf_series.str.startswith(pat).to_pandas() @@ -157,7 +155,7 @@ def test_str_startswith(scalars_dfs, pat): def test_str_endswith(scalars_dfs, pat): scalars_df, scalars_pandas_df = scalars_dfs col_name = "string_col" - bf_series: bpd.Series = scalars_df[col_name] + bf_series: bigframes.series.Series = scalars_df[col_name] pd_series = scalars_pandas_df[col_name].astype("object") bf_result = bf_series.str.endswith(pat).to_pandas() @@ -169,46 +167,26 @@ def test_str_endswith(scalars_dfs, pat): def test_len(scalars_dfs): scalars_df, scalars_pandas_df = scalars_dfs col_name = "string_col" - bf_series: bpd.Series = scalars_df[col_name] + bf_series: bigframes.series.Series = scalars_df[col_name] bf_result = bf_series.str.len().to_pandas() pd_result = scalars_pandas_df[col_name].str.len() # One of dtype mismatches to be documented. Here, the `bf_result.dtype` is `Int64` but # the `pd_result.dtype` is `float64`: https://github.com/pandas-dev/pandas/issues/51948 - assert_series_equal( + assert_series_equal_ignoring_order( pd_result.astype(pd.Int64Dtype()), bf_result, ) -def test_len_with_array_column(nested_df, nested_pandas_df): - """ - Series.str.len() is expected to work on columns containing lists as well as strings. - - See: https://stackoverflow.com/a/41340543/101923 - """ - col_name = "event_sequence" - bf_series: bpd.Series = nested_df[col_name] - bf_result = bf_series.str.len().to_pandas() - pd_result = nested_pandas_df[col_name].str.len() - - # One of dtype mismatches to be documented. Here, the `bf_result.dtype` is `Int64` but - # the `pd_result.dtype` is `float64`: https://github.com/pandas-dev/pandas/issues/51948 - assert_series_equal( - pd_result.astype(pd.Int64Dtype()), - bf_result, - check_index_type=False, - ) - - def test_lower(scalars_dfs): scalars_df, scalars_pandas_df = scalars_dfs col_name = "string_col" - bf_series: bpd.Series = scalars_df[col_name] + bf_series: bigframes.series.Series = scalars_df[col_name] bf_result = bf_series.str.lower().to_pandas() pd_result = scalars_pandas_df[col_name].str.lower() - assert_series_equal( + assert_series_equal_ignoring_order( pd_result, bf_result, ) @@ -217,7 +195,7 @@ def test_lower(scalars_dfs): def test_reverse(scalars_dfs): scalars_df, scalars_pandas_df = scalars_dfs col_name = "string_col" - bf_series: bpd.Series = scalars_df[col_name] + bf_series: bigframes.series.Series = scalars_df[col_name] bf_result = bf_series.str.reverse().to_pandas() pd_result = scalars_pandas_df[col_name].copy() for i in pd_result.index: @@ -227,37 +205,24 @@ def test_reverse(scalars_dfs): else: pd_result.loc[i] = cell[::-1] - assert_series_equal( + assert_series_equal_ignoring_order( pd_result, bf_result, ) @pytest.mark.parametrize( - ["start", "stop"], - [ - (0, 1), - (3, 5), - (100, 101), - (None, 1), - (0, 12), - (0, None), - (None, -1), - (-1, None), - (-5, -1), - (1, -1), - (-10, 10), - ], + ["start", "stop"], [(0, 1), (3, 5), (100, 101), (None, 1), (0, 12), (0, None)] ) def test_slice(scalars_dfs, start, stop): scalars_df, scalars_pandas_df = scalars_dfs col_name = "string_col" - bf_series: bpd.Series = scalars_df[col_name] + bf_series: bigframes.series.Series = scalars_df[col_name] bf_result = bf_series.str.slice(start, stop).to_pandas() pd_series = scalars_pandas_df[col_name] pd_result = pd_series.str.slice(start, stop) - assert_series_equal( + assert_series_equal_ignoring_order( pd_result, bf_result, ) @@ -266,33 +231,11 @@ def test_slice(scalars_dfs, start, stop): def test_strip(scalars_dfs): scalars_df, scalars_pandas_df = scalars_dfs col_name = "string_col" - bf_series: bpd.Series = scalars_df[col_name] + bf_series: bigframes.series.Series = scalars_df[col_name] bf_result = bf_series.str.strip().to_pandas() pd_result = scalars_pandas_df[col_name].str.strip() - assert_series_equal( - pd_result, - bf_result, - ) - - -@pytest.mark.parametrize( - ("to_strip"), - [ - pytest.param(None, id="none"), - pytest.param(" ", id="space"), - pytest.param(" \n", id="space_newline"), - pytest.param("123.!? \n\t", id="multiple_chars"), - ], -) -def test_strip_w_to_strip(to_strip): - s = bpd.Series(["1. Ant. ", "2. Bee!\n", "3. Cat?\t", pd.NA]) - pd_s = s.to_pandas() - - bf_result = s.str.strip(to_strip=to_strip).to_pandas() - pd_result = pd_s.str.strip(to_strip=to_strip) - - assert_series_equal( + assert_series_equal_ignoring_order( pd_result, bf_result, ) @@ -301,11 +244,11 @@ def test_strip_w_to_strip(to_strip): def test_upper(scalars_dfs): scalars_df, scalars_pandas_df = scalars_dfs col_name = "string_col" - bf_series: bpd.Series = scalars_df[col_name] + bf_series: bigframes.series.Series = scalars_df[col_name] bf_result = bf_series.str.upper().to_pandas() pd_result = scalars_pandas_df[col_name].str.upper() - assert_series_equal( + assert_series_equal_ignoring_order( pd_result, bf_result, ) @@ -317,7 +260,7 @@ def test_isnumeric(weird_strings, weird_strings_pd): pd.testing.assert_series_equal( bf_result, - pd_result.astype(pd.BooleanDtype()), + pd_result.astype(pd.BooleanDtype()) # the dtype here is a case of intentional diversion from pandas # see go/bigframes-dtypes ) @@ -329,21 +272,19 @@ def test_isalpha(weird_strings, weird_strings_pd): pd.testing.assert_series_equal( bf_result, - pd_result.astype(pd.BooleanDtype()), + pd_result.astype(pd.BooleanDtype()) # the dtype here is a case of intentional diversion from pandas # see go/bigframes-dtypes ) def test_isdigit(weird_strings, weird_strings_pd): - # check the behavior against normal pandas str, since pyarrow has a bug with superscripts/fractions b/333484335 - # astype object instead of str to support pd.NA - pd_result = weird_strings_pd.astype(object).str.isdigit() + pd_result = weird_strings_pd.str.isdigit() bf_result = weird_strings.str.isdigit().to_pandas() pd.testing.assert_series_equal( bf_result, - pd_result.astype(pd.BooleanDtype()), + pd_result.astype(pd.BooleanDtype()) # the dtype here is a case of intentional diversion from pandas # see go/bigframes-dtypes ) @@ -355,7 +296,7 @@ def test_isdecimal(weird_strings, weird_strings_pd): pd.testing.assert_series_equal( bf_result, - pd_result.astype(pd.BooleanDtype()), + pd_result.astype(pd.BooleanDtype()) # the dtype here is a case of intentional diversion from pandas # see go/bigframes-dtypes ) @@ -367,7 +308,7 @@ def test_isalnum(weird_strings, weird_strings_pd): pd.testing.assert_series_equal( bf_result, - pd_result.astype(pd.BooleanDtype()), + pd_result.astype(pd.BooleanDtype()) # the dtype here is a case of intentional diversion from pandas # see go/bigframes-dtypes ) @@ -379,7 +320,7 @@ def test_isspace(weird_strings, weird_strings_pd): pd.testing.assert_series_equal( bf_result, - pd_result.astype(pd.BooleanDtype()), + pd_result.astype(pd.BooleanDtype()) # the dtype here is a case of intentional diversion from pandas # see go/bigframes-dtypes ) @@ -389,9 +330,9 @@ def test_islower(weird_strings, weird_strings_pd): pd_result = weird_strings_pd.str.islower() bf_result = weird_strings.str.islower().to_pandas() - assert_series_equal( + assert_series_equal_ignoring_order( bf_result, - pd_result.astype(pd.BooleanDtype()), + pd_result.astype(pd.BooleanDtype()) # the dtype here is a case of intentional diversion from pandas # see go/bigframes-dtypes ) @@ -401,9 +342,9 @@ def test_isupper(weird_strings, weird_strings_pd): pd_result = weird_strings_pd.str.isupper() bf_result = weird_strings.str.isupper().to_pandas() - assert_series_equal( + assert_series_equal_ignoring_order( bf_result, - pd_result.astype(pd.BooleanDtype()), + pd_result.astype(pd.BooleanDtype()) # the dtype here is a case of intentional diversion from pandas # see go/bigframes-dtypes ) @@ -412,33 +353,11 @@ def test_isupper(weird_strings, weird_strings_pd): def test_rstrip(scalars_dfs): scalars_df, scalars_pandas_df = scalars_dfs col_name = "string_col" - bf_series: bpd.Series = scalars_df[col_name] + bf_series: bigframes.series.Series = scalars_df[col_name] bf_result = bf_series.str.rstrip().to_pandas() pd_result = scalars_pandas_df[col_name].str.rstrip() - assert_series_equal( - pd_result, - bf_result, - ) - - -@pytest.mark.parametrize( - ("to_strip"), - [ - pytest.param(None, id="none"), - pytest.param(" ", id="space"), - pytest.param(" \n", id="space_newline"), - pytest.param("123.!? \n\t", id="multiple_chars"), - ], -) -def test_rstrip_w_to_strip(to_strip): - s = bpd.Series(["1. Ant. ", "2. Bee!\n", "3. Cat?\t", pd.NA]) - pd_s = s.to_pandas() - - bf_result = s.str.rstrip(to_strip=to_strip).to_pandas() - pd_result = pd_s.str.rstrip(to_strip=to_strip) - - assert_series_equal( + assert_series_equal_ignoring_order( pd_result, bf_result, ) @@ -447,33 +366,11 @@ def test_rstrip_w_to_strip(to_strip): def test_lstrip(scalars_dfs): scalars_df, scalars_pandas_df = scalars_dfs col_name = "string_col" - bf_series: bpd.Series = scalars_df[col_name] + bf_series: bigframes.series.Series = scalars_df[col_name] bf_result = bf_series.str.lstrip().to_pandas() pd_result = scalars_pandas_df[col_name].str.lstrip() - assert_series_equal( - pd_result, - bf_result, - ) - - -@pytest.mark.parametrize( - ("to_strip"), - [ - pytest.param(None, id="none"), - pytest.param(" ", id="space"), - pytest.param(" \n", id="space_newline"), - pytest.param("123.!? \n\t", id="multiple_chars"), - ], -) -def test_lstrip_w_to_strip(to_strip): - s = bpd.Series(["1. Ant. ", "2. Bee!\n", "3. Cat?\t", pd.NA]) - pd_s = s.to_pandas() - - bf_result = s.str.lstrip(to_strip=to_strip).to_pandas() - pd_result = pd_s.str.lstrip(to_strip=to_strip) - - assert_series_equal( + assert_series_equal_ignoring_order( pd_result, bf_result, ) @@ -483,11 +380,11 @@ def test_lstrip_w_to_strip(to_strip): def test_repeat(scalars_dfs, repeats): scalars_df, scalars_pandas_df = scalars_dfs col_name = "string_col" - bf_series: bpd.Series = scalars_df[col_name] + bf_series: bigframes.series.Series = scalars_df[col_name] bf_result = bf_series.str.repeat(repeats).to_pandas() pd_result = scalars_pandas_df[col_name].str.repeat(repeats) - assert_series_equal( + assert_series_equal_ignoring_order( pd_result, bf_result, ) @@ -496,11 +393,11 @@ def test_repeat(scalars_dfs, repeats): def test_capitalize(scalars_dfs): scalars_df, scalars_pandas_df = scalars_dfs col_name = "string_col" - bf_series: bpd.Series = scalars_df[col_name] + bf_series: bigframes.series.Series = scalars_df[col_name] bf_result = bf_series.str.capitalize().to_pandas() pd_result = scalars_pandas_df[col_name].str.capitalize() - assert_series_equal( + assert_series_equal_ignoring_order( pd_result, bf_result, ) @@ -509,16 +406,16 @@ def test_capitalize(scalars_dfs): def test_cat_with_series(scalars_dfs): scalars_df, scalars_pandas_df = scalars_dfs col_name = "string_col" - bf_filter: bpd.Series = scalars_df["bool_col"] - bf_left: bpd.Series = scalars_df[col_name][bf_filter] - bf_right: bpd.Series = scalars_df[col_name] + bf_filter: bigframes.series.Series = scalars_df["bool_col"] + bf_left: bigframes.series.Series = scalars_df[col_name][bf_filter] + bf_right: bigframes.series.Series = scalars_df[col_name] bf_result = bf_left.str.cat(others=bf_right).to_pandas() pd_filter = scalars_pandas_df["bool_col"] pd_left = scalars_pandas_df[col_name][pd_filter] pd_right = scalars_pandas_df[col_name] pd_result = pd_left.str.cat(others=pd_right) - assert_series_equal( + assert_series_equal_ignoring_order( pd_result, bf_result, ) @@ -528,11 +425,11 @@ def test_str_match(scalars_dfs): scalars_df, scalars_pandas_df = scalars_dfs col_name = "string_col" pattern = "[A-Z].*" - bf_series: bpd.Series = scalars_df[col_name] + bf_series: bigframes.series.Series = scalars_df[col_name] bf_result = bf_series.str.match(pattern).to_pandas() pd_result = scalars_pandas_df[col_name].str.match(pattern) - assert_series_equal( + assert_series_equal_ignoring_order( pd_result, bf_result, ) @@ -542,11 +439,11 @@ def test_str_fullmatch(scalars_dfs): scalars_df, scalars_pandas_df = scalars_dfs col_name = "string_col" pattern = "[A-Z].*!" - bf_series: bpd.Series = scalars_df[col_name] + bf_series: bigframes.series.Series = scalars_df[col_name] bf_result = bf_series.str.fullmatch(pattern).to_pandas() pd_result = scalars_pandas_df[col_name].str.fullmatch(pattern) - assert_series_equal( + assert_series_equal_ignoring_order( pd_result, bf_result, ) @@ -555,11 +452,11 @@ def test_str_fullmatch(scalars_dfs): def test_str_get(scalars_dfs): scalars_df, scalars_pandas_df = scalars_dfs col_name = "string_col" - bf_series: bpd.Series = scalars_df[col_name] + bf_series: bigframes.series.Series = scalars_df[col_name] bf_result = bf_series.str.get(8).to_pandas() pd_result = scalars_pandas_df[col_name].str.get(8) - assert_series_equal( + assert_series_equal_ignoring_order( pd_result, bf_result, ) @@ -568,11 +465,11 @@ def test_str_get(scalars_dfs): def test_str_pad(scalars_dfs): scalars_df, scalars_pandas_df = scalars_dfs col_name = "string_col" - bf_series: bpd.Series = scalars_df[col_name] + bf_series: bigframes.series.Series = scalars_df[col_name] bf_result = bf_series.str.pad(8, side="both", fillchar="%").to_pandas() pd_result = scalars_pandas_df[col_name].str.pad(8, side="both", fillchar="%") - assert_series_equal( + assert_series_equal_ignoring_order( pd_result, bf_result, ) @@ -591,11 +488,11 @@ def test_str_zfill(weird_strings, weird_strings_pd): def test_str_ljust(scalars_dfs): scalars_df, scalars_pandas_df = scalars_dfs col_name = "string_col" - bf_series: bpd.Series = scalars_df[col_name] + bf_series: bigframes.series.Series = scalars_df[col_name] bf_result = bf_series.str.ljust(7, fillchar="%").to_pandas() pd_result = scalars_pandas_df[col_name].str.ljust(7, fillchar="%") - assert_series_equal( + assert_series_equal_ignoring_order( pd_result, bf_result, ) @@ -604,157 +501,11 @@ def test_str_ljust(scalars_dfs): def test_str_rjust(scalars_dfs): scalars_df, scalars_pandas_df = scalars_dfs col_name = "string_col" - bf_series: bpd.Series = scalars_df[col_name] + bf_series: bigframes.series.Series = scalars_df[col_name] bf_result = bf_series.str.rjust(9, fillchar="%").to_pandas() pd_result = scalars_pandas_df[col_name].str.rjust(9, fillchar="%") - assert_series_equal( + assert_series_equal_ignoring_order( pd_result, bf_result, ) - - -@pytest.mark.parametrize( - ("pat", "regex"), - [ - pytest.param(" ", None, id="one_char"), - pytest.param("ll", False, id="two_chars"), - pytest.param( - " ", - True, - id="one_char_reg", - marks=pytest.mark.xfail(raises=NotImplementedError), - ), - pytest.param( - "ll", - None, - id="two_chars_reg", - marks=pytest.mark.xfail(raises=NotImplementedError), - ), - ], -) -def test_str_split_raise_errors(scalars_dfs, pat, regex): - scalars_df, scalars_pandas_df = scalars_dfs - col_name = "string_col" - bf_result = scalars_df[col_name].str.split(pat=pat, regex=regex).to_pandas() - pd_result = scalars_pandas_df[col_name].str.split(pat=pat, regex=regex) - - # TODO(b/336880368): Allow for NULL values for ARRAY columns in BigQuery. - pd_result = pd_result.apply(lambda x: [] if pd.isnull(x) is True else x) - - assert_series_equal(pd_result, bf_result, check_dtype=False) - - -@pytest.mark.parametrize( - ("index"), - [ - pytest.param( - "first", id="invalid_type", marks=pytest.mark.xfail(raises=ValueError) - ), - pytest.param( - -1, id="neg_index", marks=pytest.mark.xfail(raises=NotImplementedError) - ), - pytest.param( - slice(0, 2, 2), - id="only_allow_one_step", - marks=pytest.mark.xfail(raises=NotImplementedError), - ), - pytest.param( - slice(-1, None, None), - id="neg_slicing", - marks=pytest.mark.xfail(raises=NotImplementedError), - ), - ], -) -def test_getitem_raise_errors(scalars_dfs, index): - scalars_df, _ = scalars_dfs - col_name = "string_col" - scalars_df[col_name].str[index] - - -@pytest.mark.parametrize( - ("index"), - [ - pytest.param(2, id="int"), - pytest.param(slice(None, None, None), id="default_start_slice"), - pytest.param(slice(0, None, 1), id="default_stop_slice"), - pytest.param(slice(0, 2, None), id="default_step_slice"), - ], -) -def test_getitem_w_string(scalars_dfs, index): - scalars_df, scalars_pandas_df = scalars_dfs - col_name = "string_col" - bf_result = scalars_df[col_name].str[index].to_pandas() - pd_result = scalars_pandas_df[col_name].str[index] - - assert_series_equal(pd_result, bf_result) - - -@pytest.mark.parametrize( - ("index"), - [ - pytest.param(0, id="int"), - pytest.param(slice(None, None, None), id="default_start_slice"), - pytest.param(slice(0, None, 1), id="default_stop_slice"), - pytest.param(slice(0, 2, None), id="default_step_slice"), - pytest.param(slice(0, 0, None), id="single_one_slice"), - ], -) -@pytest.mark.parametrize( - "column_name", - [ - pytest.param("int_list_col"), - pytest.param("bool_list_col"), - pytest.param("float_list_col"), - pytest.param("string_list_col"), - # date, date_time and numeric are excluded because their default types are different - # in Pandas and BigFrames - ], -) -def test_getitem_w_array(index, column_name, repeated_df, repeated_pandas_df): - bf_result = repeated_df[column_name].str[index].to_pandas() - pd_result = repeated_pandas_df[column_name].str[index] - - assert_series_equal(pd_result, bf_result, check_dtype=False, check_index_type=False) - - -def test_getitem_w_struct_array(): - pa_struct = pa.struct( - [ - ("name", pa.string()), - ("age", pa.int64()), - ] - ) - data: list[list[dict]] = [ - [ - {"name": "Alice", "age": 30}, - {"name": "Bob", "age": 25}, - ], - [ - {"name": "Charlie", "age": 35}, - {"name": "David", "age": 40}, - {"name": "Eva", "age": 28}, - ], - [], - [{"name": "Frank", "age": 50}], - ] - s = bpd.Series(data, dtype=bpd.ArrowDtype(pa.list_(pa_struct))) - - result = s.str[1] - assert dtypes.is_struct_like(result.dtype) - - expected_data = [item[1] if len(item) > 1 else None for item in data] - expected = bpd.Series(expected_data, dtype=bpd.ArrowDtype((pa_struct))) - - assert_series_equal(result.to_pandas(), expected.to_pandas()) - - -def test_string_join(session): - pd_series = pd.Series([["a", "b", "c"], ["100"], ["hello", "world"], []]) - bf_series = session.read_pandas(pd_series) - - pd_result = pd_series.str.join("--") - bf_result = bf_series.str.join("--").to_pandas() - - pd_result = pd_result.astype("string[pyarrow]") - assert_series_equal(pd_result, bf_result, check_dtype=False, check_index_type=False) diff --git a/tests/system/small/operations/test_struct.py b/tests/system/small/operations/test_struct.py deleted file mode 100644 index ddb65248d08..00000000000 --- a/tests/system/small/operations/test_struct.py +++ /dev/null @@ -1,40 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -def test_dataframe_struct_explode_multiple_columns(nested_df): - got = nested_df.struct.explode(["label", "address"]) - assert got.columns.to_list() == [ - "customer_id", - "day", - "flag", - "label.key", - "label.value", - "event_sequence", - "address.street", - "address.city", - ] - - -def test_dataframe_struct_explode_separator(nested_df): - got = nested_df.struct.explode("label", separator="__sep__") - assert got.columns.to_list() == [ - "customer_id", - "day", - "flag", - "label__sep__key", - "label__sep__value", - "event_sequence", - "address", - ] diff --git a/tests/system/small/operations/test_timedeltas.py b/tests/system/small/operations/test_timedeltas.py deleted file mode 100644 index 9512950e168..00000000000 --- a/tests/system/small/operations/test_timedeltas.py +++ /dev/null @@ -1,656 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -import datetime -import operator - -import numpy as np -import pandas as pd -import pyarrow as pa -import pytest -from packaging import version - -import bigframes.testing.utils -from bigframes import dtypes - -# Some methods/features used by this test don't exist in pandas 1.x -pytest.importorskip("pandas", minversion="2.0.0") - - -@pytest.fixture(scope="module") -def temporal_dfs(session): - pandas_df = pd.DataFrame( - { - "datetime_col": pd.Series( - [ - pd.Timestamp("2025-02-01 01:00:01"), - pd.Timestamp("2019-01-02 02:00:00"), - pd.Timestamp("1997-01-01 19:00:00"), - ], - dtype=dtypes.DATETIME_DTYPE, - ), - "timestamp_col": pd.Series( - [ - pd.Timestamp("2023-01-01 01:00:01", tz="UTC"), - pd.Timestamp("2024-01-02 02:00:00", tz="UTC"), - pd.Timestamp("2005-03-05 02:00:00", tz="UTC"), - ], - dtype=dtypes.TIMESTAMP_DTYPE, - ), - "date_col": pd.Series( - [ - datetime.date(2000, 1, 1), - datetime.date(2001, 2, 3), - datetime.date(2020, 9, 30), - ], - dtype=dtypes.DATE_DTYPE, - ), - "timedelta_col_1": pd.Series( - [ - pd.Timedelta(5, "s"), - pd.Timedelta(-4, "m"), - pd.Timedelta(5, "h"), - ], - dtype=dtypes.TIMEDELTA_DTYPE, - ), - "timedelta_col_2": pd.Series( - [ - pd.Timedelta(3, "s"), - pd.Timedelta(-4, "m"), - pd.Timedelta(6, "h"), - ], - dtype=dtypes.TIMEDELTA_DTYPE, - ), - "float_col": pd.Series([1.5, 2, -3], dtype=dtypes.FLOAT_DTYPE), - "int_col": pd.Series([1, 2, -3], dtype="Int64"), - "positive_int_col": pd.Series([1, 2, 3], dtype="Int64"), - }, - index=pd.Index(range(3), dtype="Int64"), - ) - - bigframes_df = session.read_pandas(pandas_df) - - return bigframes_df, pandas_df - - -def _assert_series_equal(actual: pd.Series, expected: pd.Series): - """Helper function specifically for timedelta testing. Don't use it outside of this module.""" - bigframes.testing.utils.assert_series_equal( - actual, - expected, - check_index_type=False, - check_dtype=False, - ) - - -@pytest.mark.parametrize( - ("op", "col_1", "col_2", "arrow_supported"), - [ - (operator.add, "timedelta_col_1", "timedelta_col_2", True), - (operator.sub, "timedelta_col_1", "timedelta_col_2", True), - (operator.truediv, "timedelta_col_1", "timedelta_col_2", True), - (operator.floordiv, "timedelta_col_1", "timedelta_col_2", True), - (operator.truediv, "timedelta_col_1", "float_col", False), - (operator.floordiv, "timedelta_col_1", "float_col", False), - (operator.mul, "timedelta_col_1", "float_col", False), - (operator.mul, "float_col", "timedelta_col_1", False), - (operator.mod, "timedelta_col_1", "timedelta_col_2", False), - ], -) -def test_timedelta_binary_ops_between_series( - temporal_dfs, op, col_1, col_2, arrow_supported -): - bf_df, pd_df = temporal_dfs - - actual_result = op(bf_df[col_1], bf_df[col_2]).to_pandas() - - if not arrow_supported: - expected_result = pd_df.apply(lambda x: op(x[col_1], x[col_2]), axis=1) - else: - expected_result = op(pd_df[col_1], pd_df[col_2]) - _assert_series_equal(actual_result, expected_result) - - -@pytest.mark.parametrize( - ("op", "col", "literal", "arrow_supported"), - [ - (operator.add, "timedelta_col_1", pd.Timedelta(2, "s").as_unit("us"), True), - (operator.sub, "timedelta_col_1", pd.Timedelta(2, "s").as_unit("us"), True), - (operator.truediv, "timedelta_col_1", pd.Timedelta(2, "s").as_unit("us"), True), - ( - operator.floordiv, - "timedelta_col_1", - pd.Timedelta(2, "s").as_unit("us"), - False, - ), - (operator.truediv, "timedelta_col_1", 3, True), - (operator.floordiv, "timedelta_col_1", 3, False), - (operator.mul, "timedelta_col_1", 3, True), - (operator.mul, "float_col", pd.Timedelta(1, "s").as_unit("us"), True), - (operator.mod, "timedelta_col_1", pd.Timedelta(7, "s").as_unit("us"), False), - ], -) -def test_timedelta_binary_ops_series_and_literal( - temporal_dfs, op, col, literal, arrow_supported -): - bf_df, pd_df = temporal_dfs - - actual_result = op(bf_df[col], literal).to_pandas() - - if not arrow_supported: - expected_result = pd_df[col].map(lambda x: op(x, literal)) - else: - expected_result = op(pd_df[col], literal) - _assert_series_equal(actual_result, expected_result) - - -@pytest.mark.parametrize( - ("op", "col", "literal", "arrow_supported"), - [ - (operator.add, "timedelta_col_1", pd.Timedelta(2, "s").as_unit("us"), True), - (operator.sub, "timedelta_col_1", pd.Timedelta(2, "s").as_unit("us"), True), - (operator.truediv, "timedelta_col_1", pd.Timedelta(2, "s").as_unit("us"), True), - ( - operator.floordiv, - "timedelta_col_1", - pd.Timedelta(2, "s").as_unit("us"), - True, - ), - (operator.truediv, "float_col", pd.Timedelta(2, "s").as_unit("us"), True), - (operator.floordiv, "float_col", pd.Timedelta(2, "s").as_unit("us"), True), - (operator.mul, "timedelta_col_1", 3, True), - (operator.mul, "float_col", pd.Timedelta(1, "s").as_unit("us"), False), - (operator.mod, "timedelta_col_1", pd.Timedelta(7, "s").as_unit("us"), False), - ], -) -def test_timedelta_binary_ops_literal_and_series( - temporal_dfs, op, col, literal, arrow_supported -): - bf_df, pd_df = temporal_dfs - - actual_result = op(literal, bf_df[col]).to_pandas() - - if not arrow_supported: - expected_result = pd_df[col].map(lambda x: op(literal, x)) - else: - expected_result = op(literal, pd_df[col]) - _assert_series_equal(actual_result, expected_result) - - -@pytest.mark.parametrize("op", [operator.pos, operator.neg, operator.abs]) -def test_timedelta_unary_ops(temporal_dfs, op): - bf_df, pd_df = temporal_dfs - - actual_result = op(bf_df["timedelta_col_1"]).to_pandas() - - expected_result = op(pd_df["timedelta_col_1"]) - _assert_series_equal(actual_result, expected_result) - - -@pytest.mark.parametrize( - ("column", "pd_dtype"), - [ - ("datetime_col", " 0 - - -@pytest.mark.parametrize( - "literal", - [ - pytest.param(pd.Timedelta(1, unit="s").as_unit("us"), id="pandas"), - pytest.param(datetime.timedelta(seconds=1), id="python-datetime"), - pytest.param(np.timedelta64(1, "s"), id="numpy"), - ], -) -def test_timestamp_add__ts_series_plus_td_literal(temporal_dfs, literal): - bf_df, pd_df = temporal_dfs - - actual_result = (bf_df["timestamp_col"] + literal).to_pandas() - - expected_result = pd_df["timestamp_col"] + literal - bigframes.testing.utils.assert_series_equal( - actual_result, expected_result, check_index_type=False - ) - - -@pytest.mark.parametrize( - ("column", "pd_dtype"), - [ - ("datetime_col", " pd.Timedelta(1, "h")) - ].to_pandas() - - expected_result = pd_series[(pd_series - timestamp) > pd.Timedelta(1, "h")] - bigframes.testing.utils.assert_series_equal( - actual_result, expected_result, check_index_type=False - ) - - -def test_timedelta_ordering(session): - pd_df = pd.DataFrame( - { - "col_1": pd.Series( - [ - pd.Timestamp("2025-01-01 01:00:00"), - pd.Timestamp("2025-01-01 02:00:00"), - pd.Timestamp("2025-01-01 03:00:00"), - ], - dtype=dtypes.TIMESTAMP_DTYPE, - ), - "col_2": pd.Series( - [ - pd.Timestamp("2025-01-01 01:00:02"), - pd.Timestamp("2025-01-01 02:00:01"), - pd.Timestamp("2025-01-01 02:59:59"), - ], - dtype=dtypes.TIMESTAMP_DTYPE, - ), - } - ) - bf_df = session.read_pandas(pd_df) - - actual_result = (bf_df["col_2"] - bf_df["col_1"]).sort_values().to_pandas() - - expected_result = (pd_df["col_2"] - pd_df["col_1"]).sort_values() - bigframes.testing.utils.assert_series_equal( - actual_result, expected_result, check_index_type=False - ) - - -def test_timedelta_cumsum(temporal_dfs): - bf_df, pd_df = temporal_dfs - - actual_result = bf_df["timedelta_col_1"].cumsum().to_pandas() - - expected_result = pd_df["timedelta_col_1"].cumsum() - _assert_series_equal(actual_result, expected_result) - - -@pytest.mark.parametrize( - "agg_func", - [ - pytest.param(lambda x: x.min(), id="min"), - pytest.param(lambda x: x.max(), id="max"), - pytest.param(lambda x: x.sum(), id="sum"), - pytest.param(lambda x: x.mean(), id="mean"), - pytest.param(lambda x: x.median(), id="median"), - pytest.param(lambda x: x.quantile(0.5), id="quantile"), - pytest.param(lambda x: x.std(), id="std"), - ], -) -def test_timedelta_agg__timedelta_result(temporal_dfs, agg_func): - bf_df, pd_df = temporal_dfs - - actual_result = agg_func(bf_df["timedelta_col_1"]) - - expected_result = agg_func(pd_df["timedelta_col_1"]) - assert actual_result == expected_result - - -@pytest.mark.parametrize( - "agg_func", - [ - pytest.param(lambda x: x.count(), id="count"), - pytest.param(lambda x: x.nunique(), id="nunique"), - ], -) -def test_timedelta_agg__int_result(temporal_dfs, agg_func): - bf_df, pd_df = temporal_dfs - - actual_result = agg_func(bf_df["timedelta_col_1"]) - - expected_result = agg_func(pd_df["timedelta_col_1"]) - assert actual_result == expected_result - - -def test_timestamp_diff_after_type_casting(temporal_dfs): - if version.Version(pd.__version__) <= version.Version("2.1.0"): - pytest.skip( - "Temporal type casting is not well-supported in older verions of Pandas." - ) - - bf_df, pd_df = temporal_dfs - dtype = pd.ArrowDtype(pa.timestamp("us", tz="UTC")) - - actual_result = ( - bf_df["timestamp_col"] - bf_df["positive_int_col"].astype(dtype) - ).to_pandas() - - expected_result = pd_df["timestamp_col"] - pd_df["positive_int_col"].astype( - "datetime64[us, UTC]" - ) - bigframes.testing.utils.assert_series_equal( - actual_result, expected_result, check_index_type=False, check_dtype=False - ) diff --git a/tests/system/small/pandas/test_describe.py b/tests/system/small/pandas/test_describe.py deleted file mode 100644 index beb7a1968fc..00000000000 --- a/tests/system/small/pandas/test_describe.py +++ /dev/null @@ -1,415 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import pandas.testing -import pytest - -import bigframes.pandas as bpd - - -def test_df_describe_non_temporal(scalars_dfs): - # TODO: supply a reason why this isn't compatible with pandas 1.x - pytest.importorskip("pandas", minversion="2.0.0") - scalars_df, scalars_pandas_df = scalars_dfs - # excluding temporal columns here because BigFrames cannot perform percentiles operations on them - unsupported_columns = [ - "datetime_col", - "timestamp_col", - "time_col", - "date_col", - "duration_col", - ] - bf_result = scalars_df.drop(columns=unsupported_columns).describe().to_pandas() - - modified_pd_df = scalars_pandas_df.drop(columns=unsupported_columns) - pd_result = modified_pd_df.describe() - - # Pandas may produce narrower numeric types, but bigframes always produces Float64 - pd_result = pd_result.astype("Float64") - - # Drop quartiles, as they are approximate - bf_min = bf_result.loc["min", :] - bf_p25 = bf_result.loc["25%", :] - bf_p50 = bf_result.loc["50%", :] - bf_p75 = bf_result.loc["75%", :] - bf_max = bf_result.loc["max", :] - - bf_result = bf_result.drop(labels=["25%", "50%", "75%"]) - pd_result = pd_result.drop(labels=["25%", "50%", "75%"]) - - pandas.testing.assert_frame_equal(pd_result, bf_result, check_index_type=False) - - # Double-check that quantiles are at least plausible. - assert ( - (bf_min <= bf_p25) - & (bf_p25 <= bf_p50) - & (bf_p50 <= bf_p50) - & (bf_p75 <= bf_max) - ).all() - - -@pytest.mark.parametrize("include", [None, "all"]) -def test_df_describe_non_numeric(scalars_dfs, include): - # TODO: supply a reason why this isn't compatible with pandas 1.x - pytest.importorskip("pandas", minversion="2.0.0") - scalars_df, scalars_pandas_df = scalars_dfs - - # Excluding "date_col" here because in BigFrames it is used as PyArrow[date32()], which is - # considered numerical in Pandas - target_columns = ["string_col", "bytes_col", "bool_col", "time_col"] - - modified_bf = scalars_df[target_columns] - bf_result = modified_bf.describe(include=include).to_pandas() - - modified_pd_df = scalars_pandas_df[target_columns] - pd_result = modified_pd_df.describe(include=include) - - # Reindex results with the specified keys and their order, because - # the relative order is not important. - bf_result = bf_result.reindex(["count", "nunique"]) - pd_result = pd_result.reindex( - ["count", "unique"] - # BF counter part of "unique" is called "nunique" - ).rename(index={"unique": "nunique"}) - - pandas.testing.assert_frame_equal( - pd_result.astype("Int64"), - bf_result, - check_index_type=False, - ) - - -def test_df_describe_temporal(scalars_dfs): - # TODO: supply a reason why this isn't compatible with pandas 1.x - pytest.importorskip("pandas", minversion="2.0.0") - scalars_df, scalars_pandas_df = scalars_dfs - - temporal_columns = ["datetime_col", "timestamp_col", "time_col", "date_col"] - - modified_bf = scalars_df[temporal_columns] - bf_result = modified_bf.describe(include="all").to_pandas() - - modified_pd_df = scalars_pandas_df[temporal_columns] - pd_result = modified_pd_df.describe(include="all") - - # Reindex results with the specified keys and their order, because - # the relative order is not important. - bf_result = bf_result.reindex(["count", "nunique"]) - pd_result = pd_result.reindex( - ["count", "unique"] - # BF counter part of "unique" is called "nunique" - ).rename(index={"unique": "nunique"}) - - pandas.testing.assert_frame_equal( - pd_result.astype("Float64"), - bf_result.astype("Float64"), - check_index_type=False, - ) - - -def test_df_describe_mixed_types_include_all(scalars_dfs): - # TODO: supply a reason why this isn't compatible with pandas 1.x - pytest.importorskip("pandas", minversion="2.0.0") - scalars_df, scalars_pandas_df = scalars_dfs - - numeric_columns = [ - "int64_col", - "float64_col", - ] - non_numeric_columns = ["string_col"] - supported_columns = numeric_columns + non_numeric_columns - - modified_bf = scalars_df[supported_columns] - bf_result = modified_bf.describe(include="all").to_pandas() - - modified_pd_df = scalars_pandas_df[supported_columns] - pd_result = modified_pd_df.describe(include="all") - - # Drop quartiles, as they are approximate - bf_min = bf_result.loc["min", :] - bf_p25 = bf_result.loc["25%", :] - bf_p50 = bf_result.loc["50%", :] - bf_p75 = bf_result.loc["75%", :] - bf_max = bf_result.loc["max", :] - - # Reindex results with the specified keys and their order, because - # the relative order is not important. - bf_result = bf_result.reindex(["count", "nunique", "mean", "std", "min", "max"]) - pd_result = pd_result.reindex( - ["count", "unique", "mean", "std", "min", "max"] - # BF counter part of "unique" is called "nunique" - ).rename(index={"unique": "nunique"}) - - pandas.testing.assert_frame_equal( - pd_result[numeric_columns].astype("Float64"), - bf_result[numeric_columns], - check_index_type=False, - ) - - pandas.testing.assert_frame_equal( - pd_result[non_numeric_columns].astype("Int64"), - bf_result[non_numeric_columns], - check_index_type=False, - ) - - # Double-check that quantiles are at least plausible. - assert ( - (bf_min <= bf_p25) - & (bf_p25 <= bf_p50) - & (bf_p50 <= bf_p50) - & (bf_p75 <= bf_max) - ).all() - - -def test_series_describe_numeric(scalars_dfs): - target_col = "int64_col" - bf_df, pd_df = scalars_dfs - bf_s, pd_s = bf_df[target_col], pd_df[target_col] - - bf_result = ( - bf_s.describe() - .to_pandas() - .reindex(["count", "nunique", "mean", "std", "min", "max"]) - ) - pd_result = ( - pd_s.describe() - .reindex(["count", "unique", "mean", "std", "min", "max"]) - .rename(index={"unique": "nunique"}) - ) - - pandas.testing.assert_series_equal( - bf_result, - pd_result, - check_dtype=False, - check_index_type=False, - ) - - -def test_series_describe_non_numeric(scalars_dfs): - target_col = "string_col" - bf_df, pd_df = scalars_dfs - bf_s, pd_s = bf_df[target_col], pd_df[target_col] - - bf_result = bf_s.describe().to_pandas().reindex(["count", "nunique"]) - pd_result = ( - pd_s.describe().reindex(["count", "unique"]).rename(index={"unique": "nunique"}) - ) - - pandas.testing.assert_series_equal( - bf_result, - pd_result, - check_dtype=False, - check_index_type=False, - ) - - -def test_series_describe_temporal(scalars_dfs): - # Pandas returns for unique timestamps only after 2.1.0 - pytest.importorskip("pandas", minversion="2.1.0") - target_col = "timestamp_col" - bf_df, pd_df = scalars_dfs - bf_s, pd_s = bf_df[target_col], pd_df[target_col] - - bf_result = bf_s.describe().to_pandas().reindex(["count", "nunique"]) - pd_result = ( - pd_s.describe().reindex(["count", "unique"]).rename(index={"unique": "nunique"}) - ) - - pandas.testing.assert_series_equal( - bf_result, - pd_result, - check_dtype=False, - check_index_type=False, - ) - - -def test_df_groupby_describe(scalars_dfs): - # TODO: supply a reason why this isn't compatible with pandas 1.x - pytest.importorskip("pandas", minversion="2.0.0") - scalars_df, scalars_pandas_df = scalars_dfs - - numeric_columns = [ - "int64_col", - "float64_col", - ] - non_numeric_columns = ["string_col"] - supported_columns = numeric_columns + non_numeric_columns - - bf_full_result = ( - scalars_df.groupby("bool_col")[supported_columns] - .describe(include="all") - .to_pandas() - ) - - pd_full_result = scalars_pandas_df.groupby("bool_col")[supported_columns].describe( - include="all" - ) - - for col in supported_columns: - pd_result = pd_full_result[col] - bf_result = bf_full_result[col] - - if col in numeric_columns: - # Drop quartiles, as they are approximate - bf_min = bf_result["min"] - bf_p25 = bf_result["25%"] - bf_p50 = bf_result["50%"] - bf_p75 = bf_result["75%"] - bf_max = bf_result["max"] - - # Reindex results with the specified keys and their order, because - # the relative order is not important. - bf_result = bf_result.reindex( - columns=["count", "mean", "std", "min", "max"] - ) - pd_result = pd_result.reindex( - columns=["count", "mean", "std", "min", "max"] - ) - - # Double-check that quantiles are at least plausible. - assert ( - (bf_min <= bf_p25) - & (bf_p25 <= bf_p50) - & (bf_p50 <= bf_p50) - & (bf_p75 <= bf_max) - ).all() - else: - # Reindex results with the specified keys and their order, because - # the relative order is not important. - bf_result = bf_result.reindex(columns=["count", "nunique"]) - pd_result = pd_result.reindex(columns=["count", "unique"]) - pandas.testing.assert_frame_equal( - # BF counter part of "unique" is called "nunique" - pd_result.astype("Float64").rename(columns={"unique": "nunique"}), - bf_result, - check_dtype=False, - check_index_type=False, - ) - - -def test_series_groupby_describe(scalars_dfs): - # TODO: supply a reason why this isn't compatible with pandas 1.x - pytest.importorskip("pandas", minversion="2.0.0") - scalars_df, scalars_pandas_df = scalars_dfs - - numeric_columns = [ - "int64_col", - "float64_col", - ] - non_numeric_columns = ["string_col"] - supported_columns = numeric_columns + non_numeric_columns - - bf_df = scalars_df.groupby("bool_col") - - pd_df = scalars_pandas_df.groupby("bool_col") - - for col in supported_columns: - pd_result = pd_df[col].describe(include="all") - bf_result = bf_df[col].describe(include="all").to_pandas() - - if col in numeric_columns: - # Drop quartiles, as they are approximate - bf_min = bf_result["min"] - bf_p25 = bf_result["25%"] - bf_p50 = bf_result["50%"] - bf_p75 = bf_result["75%"] - bf_max = bf_result["max"] - - # Reindex results with the specified keys and their order, because - # the relative order is not important. - bf_result = bf_result.reindex( - columns=["count", "mean", "std", "min", "max"] - ) - pd_result = pd_result.reindex( - columns=["count", "mean", "std", "min", "max"] - ) - - # Double-check that quantiles are at least plausible. - assert ( - (bf_min <= bf_p25) - & (bf_p25 <= bf_p50) - & (bf_p50 <= bf_p50) - & (bf_p75 <= bf_max) - ).all() - else: - # Reindex results with the specified keys and their order, because - # the relative order is not important. - bf_result = bf_result.reindex(columns=["count", "nunique"]) - pd_result = pd_result.reindex(columns=["count", "unique"]) - pandas.testing.assert_frame_equal( - # BF counter part of "unique" is called "nunique" - pd_result.astype("Float64").rename(columns={"unique": "nunique"}), - bf_result, - check_dtype=False, - check_index_type=False, - ) - - -def test_describe_json_and_obj_ref_returns_count(session): - # Test describe() works on JSON and OBJ_REF types (without nunique, which fails) - import uuid - - import google.cloud.bigquery - - sql = """ - SELECT - PARSE_JSON('{"a": 1}') AS json_col, - 'gs://cloud-samples-data/vision/ocr/sign.jpg' AS uri_col - """ - df_init = session.read_gbq(sql) - - table_id = f"bigframes-dev.bigframes_tests_sys.tmp_obj_ref_{uuid.uuid4().hex}" - df_init.to_gbq(table_id, if_exists="replace") - - client = session.bqclient - table = client.get_table(table_id) - schema = list(table.schema) - for i, field in enumerate(schema): - if field.name == "uri_col": - schema[i] = google.cloud.bigquery.SchemaField( - name=field.name, - field_type=field.field_type, - mode=field.mode, - description="bigframes_dtype: OBJ_REF_DTYPE", - ) - break - table.schema = schema - client.update_table(table, ["schema"]) - - df = session.read_gbq(table_id) - df = df.rename(columns={"uri_col": "obj_ref_col"}) - - res = df.describe(include="all").to_pandas() - - assert "count" in res.index - assert res.loc["count", "json_col"] == 1.0 - assert res.loc["count", "obj_ref_col"] == 1.0 - - -def test_describe_with_unsupported_type_returns_empty_dataframe(session): - df = session.read_gbq("SELECT ST_GEOGPOINT(1.0, 2.0) AS geo_col") - - res = df.describe().to_pandas() - - assert len(res.columns) == 0 - assert len(res.index) == 1 - - -def test_describe_empty_dataframe_returns_empty_dataframe(session): - df = bpd.DataFrame() - - res = df.describe().to_pandas() - - assert len(res.columns) == 0 - assert len(res.index) == 1 diff --git a/tests/system/small/pandas/test_read_gbq_colab.py b/tests/system/small/pandas/test_read_gbq_colab.py deleted file mode 100644 index 6e848ed9eaa..00000000000 --- a/tests/system/small/pandas/test_read_gbq_colab.py +++ /dev/null @@ -1,329 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from __future__ import annotations - -import datetime -import decimal - -import db_dtypes # type: ignore -import geopandas # type: ignore -import numpy -import pandas -import pyarrow -import pytest -import shapely.geometry # type: ignore - -from bigframes.pandas.io import api as module_under_test - - -@pytest.mark.parametrize( - ("df_pd",), - ( - # Regression tests for b/428190014. - # - # Test every BigQuery type we support, especially those where the legacy - # SQL type name differs from the GoogleSQL type name. - # - # See: - # https://cloud.google.com/bigquery/docs/reference/standard-sql/data-types - # and compare to the legacy types at - # https://cloud.google.com/bigquery/docs/data-types - pytest.param( - pandas.DataFrame( - { - "ints": pandas.Series( - [[1], [2], [3]], - dtype=pandas.ArrowDtype(pyarrow.list_(pyarrow.int64())), - ), - "floats": pandas.Series( - [[1.0], [2.0], [3.0]], - dtype=pandas.ArrowDtype(pyarrow.list_(pyarrow.float64())), - ), - } - ), - id="arrays", - ), - pytest.param( - pandas.DataFrame( - { - "bool": pandas.Series([True, False, True], dtype="bool"), - "boolean": pandas.Series([True, None, True], dtype="boolean"), - "object": pandas.Series([True, None, True], dtype="object"), - "arrow": pandas.Series( - [True, None, True], dtype=pandas.ArrowDtype(pyarrow.bool_()) - ), - } - ), - id="bools", - ), - pytest.param( - pandas.DataFrame( - { - "bytes": pandas.Series([b"a", b"b", b"c"], dtype=numpy.bytes_), - "object": pandas.Series([b"a", None, b"c"], dtype="object"), - "arrow": pandas.Series( - [b"a", None, b"c"], dtype=pandas.ArrowDtype(pyarrow.binary()) - ), - } - ), - id="bytes", - ), - pytest.param( - pandas.DataFrame( - { - "object": pandas.Series( - [ - datetime.date(2023, 11, 23), - None, - datetime.date(1970, 1, 1), - ], - dtype="object", - ), - "arrow": pandas.Series( - [ - datetime.date(2023, 11, 23), - None, - datetime.date(1970, 1, 1), - ], - dtype=pandas.ArrowDtype(pyarrow.date32()), - ), - } - ), - id="dates", - ), - pytest.param( - pandas.DataFrame( - { - "object": pandas.Series( - [ - datetime.datetime(2023, 11, 23, 13, 14, 15), - None, - datetime.datetime(1970, 1, 1, 0, 0, 0), - ], - dtype="object", - ), - "datetime64": pandas.Series( - [ - datetime.datetime(2023, 11, 23, 13, 14, 15), - None, - datetime.datetime(1970, 1, 1, 0, 0, 0), - ], - dtype="datetime64[us]", - ), - "arrow": pandas.Series( - [ - datetime.datetime(2023, 11, 23, 13, 14, 15), - None, - datetime.datetime(1970, 1, 1, 0, 0, 0), - ], - dtype=pandas.ArrowDtype(pyarrow.timestamp("us")), - ), - } - ), - id="datetimes", - ), - pytest.param( - pandas.DataFrame( - { - "object": pandas.Series( - [ - shapely.geometry.Point(145.0, -37.8), - None, - shapely.geometry.Point(-122.3, 47.6), - ], - dtype="object", - ), - "geopandas": geopandas.GeoSeries( - [ - shapely.geometry.Point(145.0, -37.8), - None, - shapely.geometry.Point(-122.3, 47.6), - ] - ), - } - ), - id="geographys", - ), - # TODO(tswast): Add INTERVAL once BigFrames supports it. - pytest.param( - pandas.DataFrame( - { - # TODO(tswast): Is there an equivalent object type we can use here? - # TODO(tswast): Add built-in Arrow extension type - "db_dtypes": pandas.Series( - ["{}", None, "123"], - dtype=pandas.ArrowDtype(db_dtypes.JSONArrowType()), - ), - } - ), - id="jsons", - ), - pytest.param( - pandas.DataFrame( - { - "int64": pandas.Series([1, 2, 3], dtype="int64"), - "Int64": pandas.Series([1, None, 3], dtype="Int64"), - "object": pandas.Series([1, None, 3], dtype="object"), - "arrow": pandas.Series( - [1, None, 3], dtype=pandas.ArrowDtype(pyarrow.int64()) - ), - } - ), - id="ints", - ), - pytest.param( - pandas.DataFrame( - { - "object": pandas.Series( - [decimal.Decimal("1.23"), None, decimal.Decimal("4.56")], - dtype="object", - ), - "arrow": pandas.Series( - [decimal.Decimal("1.23"), None, decimal.Decimal("4.56")], - dtype=pandas.ArrowDtype(pyarrow.decimal128(38, 9)), - ), - } - ), - id="numerics", - ), - pytest.param( - pandas.DataFrame( - { - # TODO(tswast): Add object type for BIGNUMERIC. Can bigframes disambiguate? - "arrow": pandas.Series( - [decimal.Decimal("1.23"), None, decimal.Decimal("4.56")], - dtype=pandas.ArrowDtype(pyarrow.decimal256(76, 38)), - ), - } - ), - id="bignumerics", - ), - pytest.param( - pandas.DataFrame( - { - "float64": pandas.Series([1.23, None, 4.56], dtype="float64"), - "Float64": pandas.Series([1.23, None, 4.56], dtype="Float64"), - "object": pandas.Series([1.23, None, 4.56], dtype="object"), - "arrow": pandas.Series( - [1.23, None, 4.56], dtype=pandas.ArrowDtype(pyarrow.float64()) - ), - } - ), - id="floats", - ), - # TODO(tswast): Add RANGE once BigFrames supports it. - pytest.param( - pandas.DataFrame( - { - "string": pandas.Series(["a", "b", "c"], dtype="string[python]"), - "object": pandas.Series(["a", None, "c"], dtype="object"), - "arrow": pandas.Series(["a", None, "c"], dtype="string[pyarrow]"), - } - ), - id="strings", - ), - pytest.param( - pandas.DataFrame( - { - # TODO(tswast): Add object type for STRUCT? How to tell apart from JSON? - "arrow": pandas.Series( - [{"a": 1, "b": 1.0, "c": "c"}], - dtype=pandas.ArrowDtype( - pyarrow.struct( - [ - ("a", pyarrow.int64()), - ("b", pyarrow.float64()), - ("c", pyarrow.string()), - ] - ) - ), - ), - } - ), - id="structs", - ), - pytest.param( - pandas.DataFrame( - { - "object": pandas.Series( - [ - datetime.time(0, 0, 0), - None, - datetime.time(13, 7, 11), - ], - dtype="object", - ), - "arrow": pandas.Series( - [ - datetime.time(0, 0, 0), - None, - datetime.time(13, 7, 11), - ], - dtype=pandas.ArrowDtype(pyarrow.time64("us")), - ), - } - ), - id="times", - ), - pytest.param( - pandas.DataFrame( - { - "object": pandas.Series( - [ - datetime.datetime( - 2023, 11, 23, 13, 14, 15, tzinfo=datetime.timezone.utc - ), - None, - datetime.datetime( - 1970, 1, 1, 0, 0, 0, tzinfo=datetime.timezone.utc - ), - ], - dtype="object", - ), - "datetime64": pandas.Series( - [ - datetime.datetime(2023, 11, 23, 13, 14, 15), - None, - datetime.datetime(1970, 1, 1, 0, 0, 0), - ], - dtype="datetime64[us]", - ).dt.tz_localize("UTC"), - "arrow": pandas.Series( - [ - datetime.datetime( - 2023, 11, 23, 13, 14, 15, tzinfo=datetime.timezone.utc - ), - None, - datetime.datetime( - 1970, 1, 1, 0, 0, 0, tzinfo=datetime.timezone.utc - ), - ], - dtype=pandas.ArrowDtype(pyarrow.timestamp("us", "UTC")), - ), - } - ), - id="timestamps", - ), - ), -) -def test_read_gbq_colab_sessionless_dry_run_generates_valid_sql_for_local_dataframe( - df_pd: pandas.DataFrame, -): - # This method will fail with an exception if it receives invalid SQL. - result = module_under_test._run_read_gbq_colab_sessionless_dry_run( - query="SELECT * FROM {df_pd}", - pyformat_args={"df_pd": df_pd}, - ) - assert isinstance(result, pandas.Series) diff --git a/tests/system/small/pandas/test_read_gbq_information_schema.py b/tests/system/small/pandas/test_read_gbq_information_schema.py deleted file mode 100644 index 32e2dc4712e..00000000000 --- a/tests/system/small/pandas/test_read_gbq_information_schema.py +++ /dev/null @@ -1,50 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import pytest - - -@pytest.mark.parametrize("include_project", [True, False]) -@pytest.mark.parametrize( - "view_id", - [ - # https://cloud.google.com/bigquery/docs/information-schema-intro - "region-US.INFORMATION_SCHEMA.SESSIONS_BY_USER", - "region-US.INFORMATION_SCHEMA.SCHEMATA", - ], -) -def test_read_gbq_jobs_by_user_returns_schema( - unordered_session, view_id: str, include_project: bool -): - if include_project: - table_id = unordered_session.bqclient.project + "." + view_id - else: - table_id = view_id - - df = unordered_session.read_gbq(table_id, max_results=10) - assert df.dtypes is not None - - -def test_read_gbq_schemata_can_be_peeked(unordered_session): - df = unordered_session.read_gbq("region-US.INFORMATION_SCHEMA.SCHEMATA") - result = df.peek() - assert result is not None - - -def test_read_gbq_schemata_four_parts_can_be_peeked(unordered_session): - df = unordered_session.read_gbq( - f"{unordered_session.bqclient.project}.region-US.INFORMATION_SCHEMA.SCHEMATA" - ) - result = df.peek() - assert result is not None diff --git a/tests/system/small/regression/test_issue355_merge_after_filter.py b/tests/system/small/regression/test_issue355_merge_after_filter.py deleted file mode 100644 index d3486810f7c..00000000000 --- a/tests/system/small/regression/test_issue355_merge_after_filter.py +++ /dev/null @@ -1,70 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import pandas as pd -import pytest - -from bigframes.testing.utils import assert_frame_equal - - -@pytest.mark.parametrize( - ("merge_how",), - [ - ("inner",), - ("outer",), - ("left",), - ("right",), - ], -) -def test_merge_after_filter(baseball_schedules_df, merge_how): - on = ["awayTeamName"] - left_columns = [ - "gameId", - "year", - "homeTeamName", - "awayTeamName", - "duration_minutes", - ] - right_columns = [ - "gameId", - "year", - "homeTeamName", - "awayTeamName", - "duration_minutes", - ] - - left = baseball_schedules_df[left_columns] - left = left[left["homeTeamName"] == "Rays"] - # Offset the rows somewhat so that outer join can have an effect. - right = baseball_schedules_df[right_columns] - right = right[right["homeTeamName"] == "White Sox"] - - df = left.merge(right, on=on, how=merge_how) - bf_result = df.to_pandas() - - left_pandas = baseball_schedules_df.to_pandas()[left_columns] - left_pandas = left_pandas[left_pandas["homeTeamName"] == "Rays"] - - right_pandas = baseball_schedules_df.to_pandas()[right_columns] - right_pandas = right_pandas[right_pandas["homeTeamName"] == "White Sox"] - - pd_result = pd.merge( - left_pandas, - right_pandas, - merge_how, - on, - sort=True, - ) - - assert_frame_equal(bf_result, pd_result, ignore_order=True) diff --git a/tests/system/small/session/__init__.py b/tests/system/small/session/__init__.py deleted file mode 100644 index 0a2669d7a25..00000000000 --- a/tests/system/small/session/__init__.py +++ /dev/null @@ -1,13 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. diff --git a/tests/system/small/session/test_read_gbq_colab.py b/tests/system/small/session/test_read_gbq_colab.py deleted file mode 100644 index 6ba9c760847..00000000000 --- a/tests/system/small/session/test_read_gbq_colab.py +++ /dev/null @@ -1,373 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -"""System tests for read_gbq_colab helper functions.""" - -import numpy -import pandas -import pandas.testing -import pytest - -import bigframes -import bigframes.pandas - -pytest.importorskip("polars") - - -def test_read_gbq_colab_to_pandas_batches_preserves_order_by(maybe_ordered_session): - # This query should return enough results to be too big to fit in a single - # page from jobs.query. - executions_before_sql = maybe_ordered_session._metrics.execution_count - df = maybe_ordered_session._read_gbq_colab( - """ - SELECT - name, - state, - gender, - year, - SUM(number) AS total - FROM - `bigquery-public-data.usa_names.usa_1910_2013` - WHERE state LIKE 'W%' - GROUP BY name, state, gender, year - ORDER BY total DESC - """ - ) - executions_before_python = maybe_ordered_session._metrics.execution_count - batches = df.to_pandas_batches( - page_size=100, - ) - assert batches.total_rows > 0 - assert batches.total_bytes_processed is None # No additional query. - - executions_after = maybe_ordered_session._metrics.execution_count - - num_batches = 0 - for batch in batches: - assert batch["total"].is_monotonic_decreasing - assert len(batch.index) == 100 - num_batches += 1 - - # Only test the first few pages to avoid downloading unnecessary data - # and so we can confirm we have full pages in each batch. - if num_batches >= 3: - break - - assert executions_after == executions_before_python == executions_before_sql + 1 - - -def test_read_gbq_colab_fresh_session_is_hybrid(): - bigframes.close_session() - df = bigframes.pandas._read_gbq_colab( - """ - SELECT - name, - SUM(number) AS total - FROM - `bigquery-public-data.usa_names.usa_1910_2013` - WHERE state LIKE 'W%' - GROUP BY name - ORDER BY total DESC - LIMIT 300 - """ - ) - session = df._session - executions_before_python = session._metrics.execution_count - result = df.sort_values("name").peek(100) - executions_after = session._metrics.execution_count - - assert len(result) == 100 - assert session._executor._enable_polars_execution is True # type: ignore - assert executions_before_python == 1 - assert executions_after == 2 - history = session.execution_history().to_dataframe() - assert history.iloc[-1]["job_type"] == "polars" - - -def test_read_gbq_colab_peek_avoids_requery(maybe_ordered_session): - history_before = maybe_ordered_session.execution_history().to_dataframe() - queries_before = ( - len(history_before[history_before["job_type"] == "query"]) - if "job_type" in history_before.columns - else 0 - ) - - df = maybe_ordered_session._read_gbq_colab( - """ - SELECT - name, - SUM(number) AS total - FROM - `bigquery-public-data.usa_names.usa_1910_2013` - WHERE state LIKE 'W%' - GROUP BY name - ORDER BY total DESC - LIMIT 300 - """ - ) - - history_after_read = maybe_ordered_session.execution_history().to_dataframe() - queries_after_read = len( - history_after_read[history_after_read["job_type"] == "query"] - ) - - result = df.peek(100) - - history_after_peek = maybe_ordered_session.execution_history().to_dataframe() - queries_after_peek = len( - history_after_peek[history_after_peek["job_type"] == "query"] - ) - - # Ok, this isn't guaranteed by peek, but should happen with read api based impl - # if starts failing, maybe stopped using read api? - assert result["total"].is_monotonic_decreasing - - assert len(result) == 100 - assert queries_after_read == queries_before + 1 - assert queries_after_peek == queries_after_read - - -def test_read_gbq_colab_repr_avoids_requery(maybe_ordered_session): - history_before = maybe_ordered_session.execution_history().to_dataframe() - queries_before = ( - len(history_before[history_before["job_type"] == "query"]) - if "job_type" in history_before.columns - else 0 - ) - - df = maybe_ordered_session._read_gbq_colab( - """ - SELECT - name, - SUM(number) AS total - FROM - `bigquery-public-data.usa_names.usa_1910_2013` - WHERE state LIKE 'W%' - GROUP BY name - ORDER BY total DESC - LIMIT 300 - """ - ) - - history_after_read = maybe_ordered_session.execution_history().to_dataframe() - queries_after_read = len( - history_after_read[history_after_read["job_type"] == "query"] - ) - - _ = repr(df) - - history_after_repr = maybe_ordered_session.execution_history().to_dataframe() - queries_after_repr = len( - history_after_repr[history_after_repr["job_type"] == "query"] - ) - - assert queries_after_read == queries_before + 1 - assert queries_after_repr == queries_after_read - - -def test_read_gbq_colab_includes_formatted_scalars(session): - pyformat_args = { - "some_integer": 123, - "some_string": "This could be dangerous.", - # This is not a supported type, but ignored if not referenced. - "some_object": object(), - } - - # This query should return few enough results to be small enough to fit in a - # single page from jobs.query. - df = session._read_gbq_colab( - """ - SELECT {some_integer} as some_integer, - '{some_string}' as some_string, - '{{escaped}}' as escaped - """, - pyformat_args=pyformat_args, - ) - result = df.to_pandas() - pandas.testing.assert_frame_equal( - result, - pandas.DataFrame( - { - "some_integer": pandas.Series([123], dtype=pandas.Int64Dtype()), - "some_string": pandas.Series( - ["This could be dangerous."], - dtype="string[pyarrow]", - ), - "escaped": pandas.Series(["{escaped}"], dtype="string[pyarrow]"), - } - ), - check_index_type=False, # int64 vs Int64 - ) - - -@pytest.mark.skipif( - pandas.__version__.startswith("1."), reason="bad left join in pandas 1.x" -) -def test_read_gbq_colab_includes_formatted_dataframes( - session, scalars_df_index, scalars_pandas_df_index -): - pd_df = pandas.DataFrame( - { - "rowindex": [0, 1, 2, 3, 4, 5], - "value": [0, 100, 200, 300, 400, 500], - } - ) - - # Make sure we test with some data that is too large to inline as SQL. - pd_df_large = pandas.DataFrame( - { - "rowindex": numpy.arange(100_000), - "large_value": numpy.arange(100_000), - } - ) - - pyformat_args = { - # Apply some operations to make sure the columns aren't renamed. - "bf_df": scalars_df_index[scalars_df_index["int64_col"] > 0].assign( - int64_col=scalars_df_index["int64_too"] - ), - "pd_df": pd_df, - "pd_df_large": pd_df_large, - # This is not a supported type, but ignored if not referenced. - "some_object": object(), - } - sql = """ - SELECT bf_df.int64_col + pd_df.value + pd_df_large.large_value AS int64_col, - COALESCE(bf_df.rowindex, pd_df.rowindex, pd_df_large.rowindex) AS rowindex - FROM {bf_df} AS bf_df - FULL OUTER JOIN {pd_df} AS pd_df - ON bf_df.rowindex = pd_df.rowindex - LEFT JOIN {pd_df_large} AS pd_df_large - ON bf_df.rowindex = pd_df_large.rowindex - ORDER BY rowindex ASC - """ - - # Do the dry run first so that we don't re-use the uploaded data from the - # real query. - dry_run_output = session._read_gbq_colab( - sql, - pyformat_args=pyformat_args, - dry_run=True, - ) - - df = session._read_gbq_colab( - sql, - pyformat_args=pyformat_args, - ) - - # Confirm that dry_run was accurate. - pandas.testing.assert_series_equal( - pandas.Series(dry_run_output["columnDtypes"]), - df.dtypes, - ) - - result = df.to_pandas() - expected = ( - scalars_pandas_df_index[scalars_pandas_df_index["int64_col"] > 0] - .assign(int64_col=scalars_pandas_df_index["int64_too"]) - .reset_index(drop=False)[["int64_col", "rowindex"]] - .merge( - pd_df, - on="rowindex", - how="outer", - ) - .merge( - pd_df_large, - on="rowindex", - how="left", - ) - .assign( - int64_col=lambda df: ( - df["int64_col"] + df["value"] + df["large_value"] - ).astype("Int64") - ) - .drop(columns=["value", "large_value"]) - .sort_values(by="rowindex") - .reset_index(drop=True) - ) - pandas.testing.assert_frame_equal( - result, - expected, - check_index_type=False, # int64 vs Int64 - ) - - -@pytest.mark.parametrize( - ("pd_df",), - ( - pytest.param( - pandas.DataFrame( - { - "rowindex": [0, 1, 2, 3, 4, 5], - "value": [0, 100, 200, 300, 400, 500], - "value2": [-1, -2, -3, -4, -5, -6], - } - ), - id="inline-df", - ), - pytest.param( - pandas.DataFrame( - { - # Make sure we test with some data that is too large to - # inline as SQL. - "rowindex": numpy.arange(100_000), - "value": numpy.arange(100_000), - "value2": numpy.arange(100_000), - } - ), - id="large-df", - ), - ), -) -def test_read_gbq_colab_with_formatted_dataframe_deduplicates_column_names_just_like_to_gbq( - session, - pd_df, -): - # Create duplicate column names. - pd_df.columns = ["rowindex", "value", "value"] - - pyformat_args = { - "pd_df": pd_df, - } - sql = """ - SELECT rowindex, value, value_1 - FROM {pd_df} - """ - - # Do the dry run first so that we don't re-use the uploaded data from the - # real query. - dry_run_output = session._read_gbq_colab( - sql, - pyformat_args=pyformat_args, - dry_run=True, - ) - - df = session._read_gbq_colab( - sql, - pyformat_args=pyformat_args, - ) - - # Confirm that dry_run was accurate. - pandas.testing.assert_series_equal( - pandas.Series(dry_run_output["columnDtypes"]), - df.dtypes, - ) - - # Make sure the query doesn't fail. - df.to_pandas_batches() - - # Make sure the - table_id = session.read_pandas(pd_df).to_gbq() - table = session.bqclient.get_table(table_id) - assert [field.name for field in table.schema] == ["rowindex", "value", "value_1"] diff --git a/tests/system/small/session/test_read_gbq_query.py b/tests/system/small/session/test_read_gbq_query.py deleted file mode 100644 index bb9026dc705..00000000000 --- a/tests/system/small/session/test_read_gbq_query.py +++ /dev/null @@ -1,113 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import datetime - -import pytest - -import bigframes -import bigframes.core.nodes as nodes - - -def test_read_gbq_query_w_allow_large_results(session: bigframes.Session): - if not hasattr(session.bqclient, "default_job_creation_mode"): - pytest.skip("Jobless query only available on newer google-cloud-bigquery.") - - query = "SELECT 1" - - # Make sure we don't get a cached table. - configuration = {"query": {"useQueryCache": False}} - - # Very small results should wrap a local node. - df_false = session.read_gbq( - query, - configuration=configuration, - allow_large_results=False, - ) - assert df_false.shape == (1, 1) - nodes_false = df_false._get_block().expr.node.unique_nodes() - assert any(isinstance(node, nodes.ReadLocalNode) for node in nodes_false) - assert not any(isinstance(node, nodes.ReadTableNode) for node in nodes_false) - - # Large results allowed should wrap a table. - df_true = session.read_gbq( - query, - configuration=configuration, - allow_large_results=True, - ) - assert df_true.shape == (1, 1) - nodes_true = df_true._get_block().expr.node.unique_nodes() - assert any(isinstance(node, nodes.ReadTableNode) for node in nodes_true) - - -def test_read_gbq_query_w_columns(session: bigframes.Session): - query = """ - SELECT 1 as int_col, - 'a' as str_col, - TIMESTAMP('2025-08-21 10:41:32.123456') as timestamp_col - """ - - result = session.read_gbq( - query, - columns=["timestamp_col", "int_col"], - ) - assert list(result.columns) == ["timestamp_col", "int_col"] - assert result.to_dict(orient="records") == [ - { - "timestamp_col": datetime.datetime( - 2025, 8, 21, 10, 41, 32, 123456, tzinfo=datetime.timezone.utc - ), - "int_col": 1, - } - ] - - -@pytest.mark.parametrize( - ("index_col", "expected_index_names"), - ( - pytest.param( - "my_custom_index", - ("my_custom_index",), - id="string", - ), - pytest.param( - ("my_custom_index",), - ("my_custom_index",), - id="iterable", - ), - pytest.param( - ("my_custom_index", "int_col"), - ("my_custom_index", "int_col"), - id="multiindex", - ), - ), -) -def test_read_gbq_query_w_index_col( - session: bigframes.Session, index_col, expected_index_names -): - query = """ - SELECT 1 as int_col, - 'a' as str_col, - 0 as my_custom_index, - TIMESTAMP('2025-08-21 10:41:32.123456') as timestamp_col - """ - - result = session.read_gbq( - query, - index_col=index_col, - ) - assert tuple(result.index.names) == expected_index_names - assert frozenset(result.columns) == frozenset( - {"int_col", "str_col", "my_custom_index", "timestamp_col"} - ) - frozenset(expected_index_names) diff --git a/tests/system/small/session/test_session_logging.py b/tests/system/small/session/test_session_logging.py deleted file mode 100644 index 4618e110687..00000000000 --- a/tests/system/small/session/test_session_logging.py +++ /dev/null @@ -1,40 +0,0 @@ -# Copyright 2026 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from unittest import mock - -import bigframes.session._io.bigquery as bq_io -from bigframes.core.logging import data_types - - -def test_data_type_logging(scalars_df_index): - s = scalars_df_index["int64_col"] + 1.5 - - # We want to check the job_config passed to _query_and_wait_bigframes - with mock.patch( - "bigframes.session._io.bigquery.start_query_job_optional", - wraps=bq_io.start_query_job_optional, - ) as mock_query: - s.to_pandas() - - # Fetch job labels sent to the BQ client and verify their values - assert mock_query.called - call_args = mock_query.call_args - job_config = call_args.kwargs.get("job_config") - assert job_config is not None - job_labels = job_config.labels - assert "bigframes-dtypes" in job_labels - assert job_labels["bigframes-dtypes"] == data_types.encode_type_refs( - s._block._expr.node - ) diff --git a/tests/system/small/test_anywidget.py b/tests/system/small/test_anywidget.py deleted file mode 100644 index 70106f490b6..00000000000 --- a/tests/system/small/test_anywidget.py +++ /dev/null @@ -1,1158 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -"""System tests for the anywidget-based table widget.""" - -from typing import Any -from unittest import mock - -import pandas as pd -import pytest - -import bigframes as bf -import bigframes.core.blocks -import bigframes.dataframe -import bigframes.display - -pytest.importorskip("anywidget") - -# Test constants to avoid change detector tests -EXPECTED_ROW_COUNT = 6 -EXPECTED_PAGE_SIZE = 2 -EXPECTED_TOTAL_PAGES = 3 - - -@pytest.fixture(scope="module") -def paginated_pandas_df() -> pd.DataFrame: - """Create a minimal test DataFrame with exactly 3 pages of 2 rows each.""" - test_data = pd.DataFrame( - { - "id": [5, 4, 3, 2, 1, 0], - "page_indicator": [ - "page_3_row_2", - "page_3_row_1", - "page_2_row_2", - "page_2_row_1", - "page_1_row_2", - "page_1_row_1", - ], - "value": [5, 4, 3, 2, 1, 0], - } - ) - return test_data - - -@pytest.fixture(scope="module") -def paginated_bf_df( - session: bf.Session, paginated_pandas_df: pd.DataFrame -) -> bigframes.dataframe.DataFrame: - return session.read_pandas(paginated_pandas_df) - - -@pytest.fixture -def table_widget(paginated_bf_df: bigframes.dataframe.DataFrame): - """ - Helper fixture to create a TableWidget instance with a fixed page size. - This reduces duplication across tests that use the same widget configuration. - """ - - from bigframes.display import TableWidget - - with bigframes.option_context( - "display.render_mode", "anywidget", "display.max_rows", 2 - ): - # Delay context manager cleanup of `max_rows` until after tests finish. - yield TableWidget(paginated_bf_df) - - -@pytest.fixture(scope="module") -def small_pandas_df() -> pd.DataFrame: - """Create a DataFrame smaller than the page size for edge case testing.""" - return pd.DataFrame( - { - "id": [0, 1], - "page_indicator": ["small_row_1", "small_row_2"], - "value": [0, 1], - } - ) - - -@pytest.fixture(scope="module") -def small_bf_df( - session: bf.Session, small_pandas_df: pd.DataFrame -) -> bf.dataframe.DataFrame: - return session.read_pandas(small_pandas_df) - - -@pytest.fixture -def small_widget(small_bf_df): - """Helper fixture for tests using a DataFrame smaller than the page size.""" - from bigframes.display import TableWidget - - with bf.option_context("display.render_mode", "anywidget", "display.max_rows", 5): - yield TableWidget(small_bf_df) - - -@pytest.fixture -def unknown_row_count_widget(session): - """Fixture to create a TableWidget with an unknown row count.""" - from bigframes.core import blocks - from bigframes.display import TableWidget - - # Create a small DataFrame with known content - test_data = pd.DataFrame( - { - "id": [0, 1, 2, 3, 4], - "value": ["row_0", "row_1", "row_2", "row_3", "row_4"], - } - ) - bf_df = session.read_pandas(test_data) - - # Simulate a scenario where total_rows is not available from the iterator - with mock.patch.object(bf_df, "_to_pandas_batches") as mock_batches: - # We need to provide an iterator of DataFrames, not Series - batches_iterator = iter([test_data]) - mock_batches.return_value = blocks.PandasBatches( - batches_iterator, total_rows=None - ) - with bf.option_context( - "display.render_mode", "anywidget", "display.max_rows", 2 - ): - widget = TableWidget(bf_df) - yield widget - - -@pytest.fixture(scope="module") -def empty_pandas_df() -> pd.DataFrame: - """Create an empty DataFrame for edge case testing.""" - return pd.DataFrame(columns=["id", "page_indicator", "value"]) - - -@pytest.fixture(scope="module") -def empty_bf_df( - session: bf.Session, empty_pandas_df: pd.DataFrame -) -> bf.dataframe.DataFrame: - return session.read_pandas(empty_pandas_df) - - -def mock_execute_result_with_params( - self, schema, total_rows_val, arrow_batches_val, *args, **kwargs -): - """ - Mocks an execution result with configurable total_rows and arrow_batches. - """ - from bigframes.session.executor import ( - ExecuteResult, - ExecutionMetadata, - ResultsIterator, - ) - - class MockExecuteResult(ExecuteResult): - @property - def execution_metadata(self) -> ExecutionMetadata: - return ExecutionMetadata() - - @property - def schema(self) -> Any: - return schema - - def batches(self, sample_rate=None) -> ResultsIterator: - return ResultsIterator( - arrow_batches_val, - self.schema, - total_rows_val, - None, - ) - - return MockExecuteResult() - - -def _assert_html_matches_pandas_slice( - table_html: str, - expected_pd_slice: pd.DataFrame, - full_pd_df: pd.DataFrame, -): - """ - Assertion helper to verify that the rendered HTML contains exactly the - rows from the expected pandas DataFrame slice and no others. This is - inspired by the pattern of comparing BigFrames output to pandas output. - """ - # Check that the unique indicator from each expected row is present. - for _, row in expected_pd_slice.iterrows(): - assert row["page_indicator"] in table_html - - # Create a DataFrame of all rows that should NOT be present. - unexpected_pd_df = full_pd_df.drop(expected_pd_slice.index) - - # Check that no unique indicators from unexpected rows are present. - for _, row in unexpected_pd_df.iterrows(): - assert row["page_indicator"] not in table_html - - -def test_widget_initialization_should_calculate_total_row_count( - paginated_bf_df: bf.dataframe.DataFrame, -): - """Test that a TableWidget calculates the total row count on creation.""" - """A TableWidget should correctly calculate the total row count on creation.""" - from bigframes.display import TableWidget - - with bigframes.option_context( - "display.render_mode", "anywidget", "display.max_rows", 2 - ): - widget = TableWidget(paginated_bf_df) - - assert widget.row_count == EXPECTED_ROW_COUNT - - -def test_widget_initialization_should_default_to_page_zero( - table_widget, -): - """ - Given a new TableWidget, when it is initialized, - then its page number should default to 0. - """ - # The `table_widget` fixture already creates the widget. - # Assert its state. - assert table_widget.page == 0 - assert table_widget.page_size == EXPECTED_PAGE_SIZE - - -def test_widget_display_should_show_first_page_on_load( - table_widget, paginated_pandas_df: pd.DataFrame -): - """ - Given a widget, when it is first loaded, then it should display - the first page of data. - """ - expected_slice = paginated_pandas_df.iloc[0:2] - - html = table_widget.table_html - - _assert_html_matches_pandas_slice(html, expected_slice, paginated_pandas_df) - - -@pytest.mark.parametrize( - "page_number, start_row, end_row", - [ - (1, 2, 4), # Second page - (2, 4, 6), # Last page - ], - ids=["second_page", "last_page"], -) -def test_widget_navigation_should_display_correct_page( - table_widget, - paginated_pandas_df: pd.DataFrame, - page_number: int, - start_row: int, - end_row: int, -): - """ - Given a widget, when the page is set, then it should display the correct - slice of data. - """ - expected_slice = paginated_pandas_df.iloc[start_row:end_row] - - table_widget.page = page_number - html = table_widget.table_html - - assert table_widget.page == page_number - _assert_html_matches_pandas_slice(html, expected_slice, paginated_pandas_df) - - -def test_setting_negative_page_should_raise_error( - table_widget, -): - """ - Given a widget, when a negative page number is set, - then a ValueError should be raised. - """ - with pytest.raises(ValueError, match="Page number cannot be negative."): - table_widget.page = -1 - - -def test_setting_page_beyond_max_should_clamp_to_last_page( - table_widget, paginated_pandas_df: pd.DataFrame -): - """ - Given a widget, - when a page number greater than the max is set, - then the page number should be clamped to the last valid page. - """ - expected_slice = paginated_pandas_df.iloc[4:6] # Last page data - - table_widget.page = 100 # Set page far beyond the total of 3 pages - html = table_widget.table_html - - assert table_widget.page == 2 # Page is clamped to the last valid page (0-indexed) - _assert_html_matches_pandas_slice(html, expected_slice, paginated_pandas_df) - - -@pytest.mark.parametrize( - "page, start_row, end_row", - [ - (0, 0, 3), # Page 0: rows 0-2 - (1, 3, 6), # Page 1: rows 3-5 - ], - ids=[ - "Page 0 (Rows 0-2)", - "Page 1 (Rows 3-5)", - ], -) -def test_widget_pagination_should_work_with_custom_page_size( - paginated_bf_df: bf.dataframe.DataFrame, - paginated_pandas_df: pd.DataFrame, - page: int, - start_row: int, - end_row: int, -): - """Test that a widget paginates correctly with a custom page size.""" - with bigframes.option_context( - "display.render_mode", "anywidget", "display.max_rows", 3 - ): - from bigframes.display import TableWidget - - widget = TableWidget(paginated_bf_df) - assert widget.page_size == 3 - - expected_slice = paginated_pandas_df.iloc[start_row:end_row] - - widget.page = page - html = widget.table_html - - assert widget.page == page - _assert_html_matches_pandas_slice(html, expected_slice, paginated_pandas_df) - - -def test_widget_with_few_rows_should_display_all_rows(small_widget, small_pandas_df): - """ - Given a DataFrame smaller than the page size, the widget should - display all rows on the first page. - """ - html = small_widget.table_html - - _assert_html_matches_pandas_slice(html, small_pandas_df, small_pandas_df) - - -def test_navigation_beyond_last_page_should_be_clamped(small_widget): - """ - Given a DataFrame smaller than the page size, - when navigating beyond the last page, - then the page should be clamped to the last valid page (page 0). - """ - # For a DataFrame with 2 rows and page_size 5 (from small_widget fixture), - # the frontend should calculate 1 total page. - assert small_widget.row_count == 2 - - # The widget should always be on page 0 for a single-page dataset. - assert small_widget.page == 0 - - # Attempting to navigate to page 1 should be clamped back to page 0, - # confirming that only one page is recognized by the backend. - small_widget.page = 1 - assert small_widget.page == 0 - - -def test_global_options_change_should_not_affect_existing_widget_page_size( - paginated_bf_df: bf.dataframe.DataFrame, -): - """ - Given an existing widget, - when global display options are changed, - then the widget's page size should remain unchanged. - """ - with bigframes.option_context( - "display.render_mode", "anywidget", "display.max_rows", 2 - ): - from bigframes.display import TableWidget - - widget = TableWidget(paginated_bf_df) - initial_page_size = widget.page_size - assert initial_page_size == 2 - widget.page = 1 # a non-default state - assert widget.page == 1 - - bf.options.display.max_rows = 10 # Change global setting - - assert widget.page_size == initial_page_size # Should remain unchanged - assert widget.page == 1 # Page should not be reset - - -def test_widget_with_empty_dataframe_should_have_zero_row_count( - empty_bf_df: bf.dataframe.DataFrame, -): - """ - Given an empty DataFrame, - when a widget is created from it, - then its row_count should be 0. - """ - - with bigframes.option_context("display.render_mode", "anywidget"): - from bigframes.display import TableWidget - - widget = TableWidget(empty_bf_df) - - assert widget.row_count == 0 - - -def test_widget_with_empty_dataframe_should_render_table_headers( - empty_bf_df: bf.dataframe.DataFrame, -): - """ - Given an empty DataFrame, - when a widget is created from it, - then its HTML representation should still render the table headers. - """ - - with bigframes.option_context("display.render_mode", "anywidget"): - from bigframes.display import TableWidget - - widget = TableWidget(empty_bf_df) - html = widget.table_html - assert " 0 - assert ".bigframes-widget .footer" in css_content - - -def test_widget_row_count_should_be_immutable_after_creation( - paginated_bf_df: bf.dataframe.DataFrame, -): - """ - Given a widget created with a specific configuration when global display - options are changed later, the widget's original row_count should remain - unchanged. - """ - from bigframes.display import TableWidget - - # Use a context manager to ensure the option is reset - with bigframes.option_context( - "display.render_mode", "anywidget", "display.max_rows", 2 - ): - widget = TableWidget(paginated_bf_df) - initial_row_count = widget.row_count - - # Change a global option that could influence row count - bf.options.display.max_rows = 10 - - # Verify the row count remains immutable. - assert widget.row_count == initial_row_count - - -class FaultyIterator: - def __iter__(self): - return self - - def __next__(self): - raise ValueError("Simulated read error") - - -def test_widget_should_show_error_on_batch_failure( - paginated_bf_df: bf.dataframe.DataFrame, - monkeypatch: pytest.MonkeyPatch, -): - """ - Given that the internal call to `_to_pandas_batches` fails and returns None, - when the TableWidget is created, its `error_message` should be set and displayed. - """ - # Patch the DataFrame's batch creation method to simulate a failure. - monkeypatch.setattr( - "bigframes.dataframe.DataFrame._to_pandas_batches", - lambda self, *args, **kwargs: None, - ) - - # Create the TableWidget under the error condition. - with bigframes.option_context("display.render_mode", "anywidget"): - from bigframes.display import TableWidget - - # The widget should handle the faulty data from the mock without crashing. - widget = TableWidget(paginated_bf_df) - - # The widget should have an error message and display it in the HTML. - assert widget.row_count is None - assert widget._error_message is not None - assert "Could not retrieve data batches" in widget._error_message - assert widget._error_message in widget.table_html - - -def test_widget_row_count_reflects_actual_data_available( - paginated_bf_df: bf.dataframe.DataFrame, -): - """ - Test that widget row_count reflects the actual data available, - regardless of theoretical limits. - """ - from bigframes.display import TableWidget - - # Set up display options that define a page size. - with bigframes.option_context( - "display.render_mode", "anywidget", "display.max_rows", 2 - ): - widget = TableWidget(paginated_bf_df) - - # The widget should report the total rows in the DataFrame, - # not limited by page_size (which only affects pagination) - assert widget.row_count == EXPECTED_ROW_COUNT - assert widget.page_size == 2 # Respects the display option - - -def test_widget_with_unknown_row_count_should_auto_navigate_to_last_page( - session: bf.Session, -): - """ - Given a widget with unknown row count (row_count=None), when a user - navigates beyond the available data and all data is loaded, then the - widget should automatically navigate back to the last valid page. - """ - from bigframes.display import TableWidget - - # Create a small DataFrame with known content - test_data = pd.DataFrame( - { - "id": [0, 1, 2, 3, 4], - "value": ["row_0", "row_1", "row_2", "row_3", "row_4"], - } - ) - bf_df = session.read_pandas(test_data) - - with bigframes.option_context( - "display.render_mode", "anywidget", "display.max_rows", 2 - ): - widget = TableWidget(bf_df) - - # Manually set row_count to None to simulate unknown total - widget.row_count = None - - # Navigate to a page beyond available data (page 10) - # With page_size=2 and 5 rows, valid pages are 0, 1, 2 - widget.page = 10 - - # Force data loading by accessing table_html - _ = widget.table_html - - # After all data is loaded, widget should auto-navigate to last valid page - # Last valid page = ceil(5 / 2) - 1 = 2 - assert widget.page == 2 - - # Verify the displayed content is the last page - html = widget.table_html - assert "row_4" in html # Last row should be visible - assert "row_0" not in html # First row should not be visible - - -def test_widget_with_unknown_row_count_should_set_none_state_for_frontend( - session: bf.Session, -): - """ - Given a widget with unknown row count, its `row_count` traitlet should be - `None`, which signals the frontend to display 'Page X of many'. - """ - from bigframes.display import TableWidget - - test_data = pd.DataFrame( - { - "id": [0, 1, 2], - "value": ["a", "b", "c"], - } - ) - bf_df = session.read_pandas(test_data) - - with bigframes.option_context( - "display.render_mode", "anywidget", "display.max_rows", 2 - ): - widget = TableWidget(bf_df) - - # Set row_count to None - widget.row_count = None - - # Verify row_count is None (not 0) - assert widget.row_count is None - - # The widget should still function normally - assert widget.page == 0 - assert widget.page_size == 2 - - # Force data loading by accessing table_html. This also ensures that - # rendering does not raise an exception. - _ = widget.table_html - - -def test_widget_with_unknown_row_count_should_allow_forward_navigation( - session: bf.Session, -): - """ - Given a widget with unknown row count, users should be able to navigate - forward until they reach the end of available data. - """ - from bigframes.display import TableWidget - - test_data = pd.DataFrame( - { - "id": [0, 1, 2, 3, 4, 5], - "value": ["p0_r0", "p0_r1", "p1_r0", "p1_r1", "p2_r0", "p2_r1"], - } - ) - bf_df = session.read_pandas(test_data) - - with bigframes.option_context( - "display.render_mode", "anywidget", "display.max_rows", 2 - ): - widget = TableWidget(bf_df) - widget.row_count = None - - # Navigate to page 1 - widget.page = 1 - html = widget.table_html - assert "p1_r0" in html - assert "p1_r1" in html - - # Navigate to page 2 - widget.page = 2 - html = widget.table_html - assert "p2_r0" in html - assert "p2_r1" in html - - # Navigate beyond available data (page 5) - widget.page = 5 - _ = widget.table_html - - # Should auto-navigate back to last valid page (page 2) - assert widget.page == 2 - - -def test_widget_with_unknown_row_count_empty_dataframe( - session: bf.Session, -): - """ - Given an empty DataFrame with unknown row count, the widget should - stay on page 0 and display empty content. - """ - from bigframes.display import TableWidget - - empty_data = pd.DataFrame(columns=["id", "value"]) - bf_df = session.read_pandas(empty_data) - - with bigframes.option_context("display.render_mode", "anywidget"): - widget = TableWidget(bf_df) - widget.row_count = None - - # Attempt to navigate to page 5 - widget.page = 5 - _ = widget.table_html - - # Should stay on page 0 for empty DataFrame - assert widget.page == 0 - - -def test_widget_sort_should_sort_ascending_on_first_click( - table_widget, paginated_pandas_df: pd.DataFrame -): - """ - Given a widget, when a column header is clicked for the first time, - then the data should be sorted by that column in ascending order. - """ - table_widget.sort_context = [{"column": "id", "ascending": True}] - - expected_slice = paginated_pandas_df.sort_values("id", ascending=True).iloc[0:2] - html = table_widget.table_html - - _assert_html_matches_pandas_slice(html, expected_slice, paginated_pandas_df) - - -def test_widget_sort_should_sort_descending_on_second_click( - table_widget, paginated_pandas_df: pd.DataFrame -): - """ - Given a widget sorted by a column, when the same column header is clicked again, - then the data should be sorted by that column in descending order. - """ - table_widget.sort_context = [{"column": "id", "ascending": True}] - - # Second click - table_widget.sort_context = [{"column": "id", "ascending": False}] - - expected_slice = paginated_pandas_df.sort_values("id", ascending=False).iloc[0:2] - html = table_widget.table_html - - _assert_html_matches_pandas_slice(html, expected_slice, paginated_pandas_df) - - -def test_widget_sort_should_switch_column_and_sort_ascending( - table_widget, paginated_pandas_df: pd.DataFrame -): - """ - Given a widget sorted by a column, when a different column header is clicked, - then the data should be sorted by the new column in ascending order. - """ - table_widget.sort_context = [{"column": "id", "ascending": True}] - - # Click on a different column - table_widget.sort_context = [{"column": "value", "ascending": True}] - - expected_slice = paginated_pandas_df.sort_values("value", ascending=True).iloc[0:2] - html = table_widget.table_html - - _assert_html_matches_pandas_slice(html, expected_slice, paginated_pandas_df) - - -def test_widget_sort_should_be_maintained_after_pagination( - table_widget, paginated_pandas_df: pd.DataFrame -): - """ - Given a sorted widget, when the user navigates to the next page, - then the sorting should be maintained. - """ - table_widget.sort_context = [{"column": "id", "ascending": True}] - - # Go to the second page - table_widget.page = 1 - - expected_slice = paginated_pandas_df.sort_values("id", ascending=True).iloc[2:4] - html = table_widget.table_html - - _assert_html_matches_pandas_slice(html, expected_slice, paginated_pandas_df) - - -def test_widget_sort_should_reset_on_page_size_change( - table_widget, paginated_pandas_df: pd.DataFrame -): - """ - Given a sorted widget, when the page size is changed, - then the sorting should be reset. - """ - table_widget.sort_context = [{"column": "id", "ascending": True}] - - table_widget.page_size = 3 - - # Sorting is not reset in the backend, but the view should be of the unsorted df - expected_slice = paginated_pandas_df.iloc[0:3] - html = table_widget.table_html - - _assert_html_matches_pandas_slice(html, expected_slice, paginated_pandas_df) - - -@pytest.fixture(scope="module") -def integer_column_df(session): - """Create a DataFrame with integer column labels.""" - pandas_df = pd.DataFrame([[0, 1], [2, 3]], columns=pd.Index([1, 2])) - return session.read_pandas(pandas_df) - - -@pytest.fixture(scope="module") -def multiindex_column_df(session): - """Create a DataFrame with MultiIndex column labels.""" - pandas_df = pd.DataFrame( - { - "foo": ["one", "one", "one", "two", "two", "two"], - "bar": ["A", "B", "C", "A", "B", "C"], - "baz": [1, 2, 3, 4, 5, 6], - "zoo": ["x", "y", "z", "q", "w", "t"], - } - ) - df = session.read_pandas(pandas_df) - # The session is attached to `df` through the constructor. - # We can pass it to the pivoted DataFrame. - pdf = df.pivot(index="foo", columns="bar", values=["baz", "zoo"]) - return pdf - - -def test_table_widget_integer_columns_disables_sorting(integer_column_df): - """ - Given a DataFrame with integer column labels, the widget should - disable sorting. - """ - from bigframes.display import TableWidget - - widget = TableWidget(integer_column_df) - assert widget.orderable_columns == [] - - -def test_table_widget_multiindex_columns_disables_sorting(multiindex_column_df): - """ - Given a DataFrame with a MultiIndex for columns, the widget should - disable sorting. - """ - from bigframes.display import TableWidget - - widget = TableWidget(multiindex_column_df) - assert widget.orderable_columns == [] - - -def test_repr_mimebundle_should_fallback_to_html_if_anywidget_is_unavailable( - paginated_bf_df: bf.dataframe.DataFrame, -): - """ - Test that _repr_mimebundle_ falls back to static html when anywidget is not available. - """ - with bigframes.option_context( - "display.render_mode", "anywidget", "display.max_rows", 2 - ): - # Mock the ANYWIDGET_INSTALLED flag to simulate absence of anywidget - with mock.patch("bigframes.display.anywidget._ANYWIDGET_INSTALLED", False): - bundle = paginated_bf_df._repr_mimebundle_() - assert "application/vnd.jupyter.widget-view+json" not in bundle - assert "text/html" in bundle - html = bundle["text/html"] - assert "page_3_row_2" in html - assert "page_3_row_1" in html - assert "page_1_row_1" not in html - - -def test_repr_mimebundle_should_return_widget_view_if_anywidget_is_available( - paginated_bf_df: bf.dataframe.DataFrame, -): - """ - Test that _repr_mimebundle_ returns a widget view when anywidget is available. - """ - with bigframes.option_context("display.render_mode", "anywidget"): - bundle = paginated_bf_df._repr_mimebundle_() - assert isinstance(bundle, tuple) - data, metadata = bundle - assert "application/vnd.jupyter.widget-view+json" in data - assert "text/html" in data - assert "text/plain" in data - - -def test_repr_in_anywidget_mode_should_not_be_deferred( - paginated_bf_df: bf.dataframe.DataFrame, -): - """ - Test that repr(df) is not deferred in anywidget mode. - This is to ensure that print(df) works as expected. - """ - with bigframes.option_context("display.render_mode", "anywidget"): - representation = repr(paginated_bf_df) - assert "Computation deferred" not in representation - assert "page_1_row_1" in representation - - -def test_dataframe_repr_mimebundle_should_return_widget_with_metadata_in_anywidget_mode( - monkeypatch: pytest.MonkeyPatch, - session: bigframes.Session, # Add session as a fixture -): - """Test that _repr_mimebundle_ returns a widget view with metadata when anywidget is available.""" - with bigframes.option_context("display.render_mode", "anywidget"): - # Create a real DataFrame object (or a mock that behaves like one minimally) - # for _repr_mimebundle_ to operate on. - test_df = bigframes.dataframe.DataFrame( - pd.DataFrame({"col1": [1, 2], "col2": [3, 4]}), session=session - ) - - mock_get_anywidget_bundle_return_value: tuple[ - dict[str, Any], dict[str, Any] - ] = ( - { - "application/vnd.jupyter.widget-view+json": {"model_id": "123"}, - "text/html": "
My Table HTML
", - "text/plain": "My Table Plain Text", - }, - { - "application/vnd.jupyter.widget-view+json": { - "colab": {"custom_widget_manager": {}} - } - }, - ) - - # Patch the class method directly - with mock.patch( - "bigframes.display.html.get_anywidget_bundle", - return_value=mock_get_anywidget_bundle_return_value, - ): - result = test_df._repr_mimebundle_() - - assert isinstance(result, tuple) - data, metadata = result - assert "application/vnd.jupyter.widget-view+json" in data - assert "text/html" in data - assert "text/plain" in data - assert "application/vnd.jupyter.widget-view+json" in metadata - assert "colab" in metadata["application/vnd.jupyter.widget-view+json"] - - -@pytest.fixture(scope="module") -def custom_index_pandas_df() -> pd.DataFrame: - """Create a DataFrame with a custom named index for testing.""" - test_data = pd.DataFrame( - { - "value_a": [10, 20, 30, 40, 50, 60], - "value_b": ["a", "b", "c", "d", "e", "f"], - } - ) - test_data.index = pd.Index( - ["row_1", "row_2", "row_3", "row_4", "row_5", "row_6"], name="custom_idx" - ) - return test_data - - -@pytest.fixture(scope="module") -def custom_index_bf_df( - session: bf.Session, custom_index_pandas_df: pd.DataFrame -) -> bf.dataframe.DataFrame: - return session.read_pandas(custom_index_pandas_df) - - -@pytest.fixture(scope="module") -def multiindex_pandas_df() -> pd.DataFrame: - """Create a DataFrame with MultiIndex for testing.""" - test_data = pd.DataFrame( - { - "value": [100, 200, 300, 400, 500, 600], - "category": ["X", "Y", "Z", "X", "Y", "Z"], - } - ) - test_data.index = pd.MultiIndex.from_arrays( - [ - ["group_A", "group_A", "group_A", "group_B", "group_B", "group_B"], - [1, 2, 3, 1, 2, 3], - ], - names=["group", "item"], - ) - return test_data - - -@pytest.fixture(scope="module") -def multiindex_bf_df( - session: bf.Session, multiindex_pandas_df: pd.DataFrame -) -> bf.dataframe.DataFrame: - return session.read_pandas(multiindex_pandas_df) - - -def test_widget_with_default_index_should_display_index_column_with_empty_header( - paginated_bf_df: bf.dataframe.DataFrame, -): - """ - Given a DataFrame with a default index, when the TableWidget is rendered, - then an index column should be visible with an empty header. - """ - import re - - from bigframes.display.anywidget import TableWidget - - with bf.option_context("display.render_mode", "anywidget", "display.max_rows", 2): - widget = TableWidget(paginated_bf_df) - html = widget.table_html - - # The header for the index should be present but empty, matching the - # internal rendering logic. - thead = html.split("")[1].split("")[0] - # Find the first header cell and check that its content div is empty. - match = re.search(r"]*>]*>([^<]*)", thead) - assert match is not None, "Could not find table header cell in output." - assert match.group(1) == "", ( - f"Expected empty index header, but found: {match.group(1)}" - ) - - -def test_widget_with_custom_index_should_display_index_column( - custom_index_bf_df: bf.dataframe.DataFrame, -): - """ - Given a DataFrame with a custom named index, when rendered, - then the index column and first page of rows should be visible. - """ - from bigframes.display.anywidget import TableWidget - - with bf.option_context("display.render_mode", "anywidget", "display.max_rows", 2): - widget = TableWidget(custom_index_bf_df) - html = widget.table_html - - assert "custom_idx" in html - assert "row_1" in html - assert "row_2" in html - assert "row_3" not in html # Verify pagination is working - assert "row_4" not in html - - -def test_widget_with_custom_index_pagination_preserves_index( - custom_index_bf_df: bf.dataframe.DataFrame, -): - """ - Given a DataFrame with a custom index, when navigating to the second page, - then the second page's index values should be visible. - """ - from bigframes.display.anywidget import TableWidget - - with bf.option_context("display.render_mode", "anywidget", "display.max_rows", 2): - widget = TableWidget(custom_index_bf_df) - - widget.page = 1 # Navigate to page 2 - html = widget.table_html - - assert "row_3" in html - assert "row_4" in html - assert "row_1" not in html # Verify page 1 content is gone - assert "row_2" not in html - - -def test_widget_with_custom_index_matches_pandas_output( - custom_index_bf_df: bf.dataframe.DataFrame, -): - """ - Given a DataFrame with a custom index and max_rows=3, the widget's HTML - output should contain the first three index values. - """ - from bigframes.display.anywidget import TableWidget - - with bf.option_context("display.render_mode", "anywidget", "display.max_rows", 3): - widget = TableWidget(custom_index_bf_df) - html = widget.table_html - - assert "row_1" in html - assert "row_2" in html - assert "row_3" in html - assert "row_4" not in html # Verify it respects max_rows - - -# TODO(b/438181139): Add tests for custom multiindex -# This may not be necessary for the SQL Cell use case but should be -# considered for completeness. - - -def test_series_anywidget_integration_with_notebook_display( - paginated_bf_df: bf.dataframe.DataFrame, -): - """Test Series display integration in Jupyter-like environment.""" - pytest.importorskip("anywidget") - - with bf.option_context("display.render_mode", "anywidget"): - series = paginated_bf_df["value"] - - # Test the full display pipeline - from IPython.display import display as ipython_display - - # This should work without errors - ipython_display(series) - - -def test_series_different_data_types_anywidget(session: bf.Session): - """Test Series with different data types in anywidget mode.""" - pytest.importorskip("anywidget") - - # Create Series with different types - test_data = pd.DataFrame( - { - "string_col": ["a", "b", "c"], - "int_col": [1, 2, 3], - "float_col": [1.1, 2.2, 3.3], - "bool_col": [True, False, True], - } - ) - bf_df = session.read_pandas(test_data) - - with bf.option_context("display.render_mode", "anywidget"): - for col_name in test_data.columns: - series = bf_df[col_name] - widget = bigframes.display.TableWidget(series.to_frame()) - assert widget.row_count == 3 diff --git a/tests/system/small/test_bq_sessions.py b/tests/system/small/test_bq_sessions.py deleted file mode 100644 index 99d2dfece3b..00000000000 --- a/tests/system/small/test_bq_sessions.py +++ /dev/null @@ -1,87 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import time -from concurrent.futures import ThreadPoolExecutor - -import google -import google.api_core.exceptions -import pytest -from google.cloud import bigquery - -import bigframes.core.events -from bigframes.session import bigquery_session - -TEST_SCHEMA = [ - bigquery.SchemaField("bool field", "BOOLEAN"), - bigquery.SchemaField("string field", "STRING"), - bigquery.SchemaField("float array_field", "FLOAT", mode="REPEATED"), - bigquery.SchemaField( - "struct field", - "RECORD", - fields=(bigquery.SchemaField("int subfield", "INTEGER"),), - ), -] - - -@pytest.fixture -def session_resource_manager( - bigquery_client, -) -> bigquery_session.SessionResourceManager: - return bigquery_session.SessionResourceManager( - bigquery_client, "US", publisher=bigframes.core.events.Publisher() - ) - - -def test_bq_session_create_temp_table_clustered(bigquery_client: bigquery.Client): - session_resource_manager = bigquery_session.SessionResourceManager( - bigquery_client, "US", publisher=bigframes.core.events.Publisher() - ) - cluster_cols = ["string field", "bool field"] - - session_table_ref = session_resource_manager.create_temp_table( - TEST_SCHEMA, cluster_cols=cluster_cols - ) - session_resource_manager._keep_session_alive() - - result_table = bigquery_client.get_table(session_table_ref) - assert result_table.schema == TEST_SCHEMA - assert result_table.clustering_fields == cluster_cols - - session_resource_manager.close() - with pytest.raises(google.api_core.exceptions.NotFound): - # It may take time for the underlying tables to get cleaned up after - # closing the session, so wait at least 1 minute to check. - for _ in range(6): - bigquery_client.get_table(session_table_ref) - time.sleep(10) - - -def test_bq_session_create_multi_temp_tables(bigquery_client: bigquery.Client): - session_resource_manager = bigquery_session.SessionResourceManager( - bigquery_client, "US", publisher=bigframes.core.events.Publisher() - ) - - def create_table(): - return session_resource_manager.create_temp_table(TEST_SCHEMA) - - with ThreadPoolExecutor() as executor: - results = [executor.submit(create_table) for i in range(10)] - - for future in results: - table = future.result() - result_table = bigquery_client.get_table(table) - assert result_table.schema == TEST_SCHEMA - - session_resource_manager.close() diff --git a/tests/system/small/test_dataframe.py b/tests/system/small/test_dataframe.py index a109c33ffa6..3d7ba867ea4 100644 --- a/tests/system/small/test_dataframe.py +++ b/tests/system/small/test_dataframe.py @@ -12,12 +12,10 @@ # See the License for the specific language governing permissions and # limitations under the License. -import io import operator -import sys import tempfile import typing -from typing import Dict, List, Tuple +from typing import Tuple import geopandas as gpd # type: ignore import numpy as np @@ -28,128 +26,30 @@ import bigframes import bigframes._config.display_options as display_options -import bigframes.core.indexes as bf_indexes import bigframes.dataframe as dataframe -import bigframes.dtypes as dtypes -import bigframes.pandas as bpd import bigframes.series as series -import bigframes.testing -from bigframes.testing.utils import ( - assert_dfs_equivalent, - assert_frame_equal, - assert_series_equal, - assert_series_equivalent, +from tests.system.utils import ( + assert_pandas_df_equal_ignore_ordering, + assert_series_equal_ignoring_order, ) def test_df_construct_copy(scalars_dfs): columns = ["int64_col", "string_col", "float64_col"] scalars_df, scalars_pandas_df = scalars_dfs - # Make the mapping from label to col_id non-trivial - bf_df = scalars_df.copy() - bf_df["int64_col"] = bf_df["int64_col"] / 2 - pd_df = scalars_pandas_df.copy() - pd_df["int64_col"] = pd_df["int64_col"] / 2 - - bf_result = dataframe.DataFrame(bf_df, columns=columns).to_pandas() - - pd_result = pd.DataFrame(pd_df, columns=columns) + bf_result = dataframe.DataFrame(scalars_df, columns=columns).to_pandas() + pd_result = pd.DataFrame(scalars_pandas_df, columns=columns) pandas.testing.assert_frame_equal(bf_result, pd_result) -def test_df_construct_pandas_default(scalars_dfs): - # This should trigger the inlined codepath - columns = [ - "int64_too", - "int64_col", - "float64_col", - "bool_col", - "string_col", - "date_col", - "datetime_col", - "numeric_col", - "float64_col", - "time_col", - "timestamp_col", - ] +def test_df_construct_pandas(scalars_dfs): + columns = ["int64_too", "int64_col", "float64_col", "bool_col", "string_col"] _, scalars_pandas_df = scalars_dfs bf_result = dataframe.DataFrame(scalars_pandas_df, columns=columns).to_pandas() pd_result = pd.DataFrame(scalars_pandas_df, columns=columns) pandas.testing.assert_frame_equal(bf_result, pd_result) -@pytest.mark.parametrize( - ("write_engine"), - [ - ("bigquery_inline"), - ("bigquery_load"), - ("bigquery_streaming"), - # TODO(b/502298527): Reenable bigquery_write test - # ("bigquery_write"), - ], -) -def test_read_pandas_all_nice_types( - session: bigframes.Session, scalars_pandas_df_index: pd.DataFrame, write_engine -): - bf_result = session.read_pandas( - scalars_pandas_df_index, write_engine=write_engine - ).to_pandas() - pandas.testing.assert_frame_equal(bf_result, scalars_pandas_df_index) - - -def test_df_construct_large_strings(): - data = [["hello", "w" + "o" * 50000 + "rld"]] - bf_result = dataframe.DataFrame(data).to_pandas() - pd_result = pd.DataFrame(data, dtype=pd.StringDtype(storage="pyarrow")) - pandas.testing.assert_frame_equal(bf_result, pd_result, check_index_type=False) - - -def test_df_construct_pandas_load_job(scalars_dfs_maybe_ordered): - # This should trigger the inlined codepath - columns = [ - "int64_too", - "int64_col", - "float64_col", - "bool_col", - "string_col", - "date_col", - "datetime_col", - "numeric_col", - "float64_col", - "time_col", - "timestamp_col", - "geography_col", - ] - _, scalars_pandas_df = scalars_dfs_maybe_ordered - bf_result = dataframe.DataFrame(scalars_pandas_df, columns=columns) - pd_result = pd.DataFrame(scalars_pandas_df, columns=columns) - assert_dfs_equivalent(pd_result, bf_result) - - -def test_df_construct_structs(session): - pd_frame = pd.Series( - [ - {"version": 1, "project": "pandas"}, - {"version": 2, "project": "pandas"}, - {"version": 1, "project": "numpy"}, - ] - ).to_frame() - bf_series = session.read_pandas(pd_frame) - bigframes.testing.utils.assert_frame_equal( - bf_series.to_pandas(), pd_frame, check_index_type=False, check_dtype=False - ) - - -def test_df_construct_local_concat_pd(scalars_pandas_df_index, session): - pd_df = pd.concat([scalars_pandas_df_index, scalars_pandas_df_index]) - - bf_df = session.read_pandas(pd_df) - - bigframes.testing.utils.assert_frame_equal( - bf_df.to_pandas(), pd_df, check_index_type=False, check_dtype=False - ) - - def test_df_construct_pandas_set_dtype(scalars_dfs): columns = [ "int64_too", @@ -165,17 +65,17 @@ def test_df_construct_pandas_set_dtype(scalars_dfs): pandas.testing.assert_frame_equal(bf_result, pd_result) -def test_df_construct_from_series(scalars_dfs_maybe_ordered): - scalars_df, scalars_pandas_df = scalars_dfs_maybe_ordered +def test_df_construct_from_series(scalars_dfs): + scalars_df, scalars_pandas_df = scalars_dfs bf_result = dataframe.DataFrame( {"a": scalars_df["int64_col"], "b": scalars_df["string_col"]}, dtype="string[pyarrow]", - ) + ).to_pandas() pd_result = pd.DataFrame( {"a": scalars_pandas_df["int64_col"], "b": scalars_pandas_df["string_col"]}, dtype="string[pyarrow]", ) - assert_dfs_equivalent(pd_result, bf_result) + pandas.testing.assert_frame_equal(bf_result, pd_result) def test_df_construct_from_dict(): @@ -192,57 +92,13 @@ def test_df_construct_from_dict(): ) -@pytest.mark.parametrize( - ("json_type"), - [ - pytest.param(dtypes.JSON_DTYPE), - pytest.param("json"), - ], -) -def test_df_construct_w_json_dtype(json_type): - data = [ - "1", - "false", - '["a", {"b": 1}, null]', - None, - ] - df = dataframe.DataFrame({"json_col": data}, dtype=json_type) - - assert df["json_col"].dtype == dtypes.JSON_DTYPE - assert df["json_col"][1] == "false" - - -def test_df_construct_inline_respects_location(reset_default_session_and_location): - # Note: This starts a thread-local session. - with bpd.option_context("bigquery.location", "europe-west1"): - df = bpd.DataFrame([[1, 2, 3], [4, 5, 6]]) - df.to_gbq() - assert df.query_job is not None - table = bpd.get_global_session().bqclient.get_table(df.query_job.destination) - - assert table.location == "europe-west1" - - -def test_df_construct_dtype(): - data = { - "int_col": [1, 2, 3], - "string_col": ["1.1", "2.0", "3.5"], - "float_col": [1.0, 2.0, 3.0], - } - dtype = pd.StringDtype(storage="pyarrow") - bf_result = dataframe.DataFrame(data, dtype=dtype) - pd_result = pd.DataFrame(data, dtype=dtype) - pd_result.index = pd_result.index.astype("Int64") - pandas.testing.assert_frame_equal(bf_result.to_pandas(), pd_result) - - def test_get_column(scalars_dfs): scalars_df, scalars_pandas_df = scalars_dfs col_name = "int64_col" series = scalars_df[col_name] bf_result = series.to_pandas() pd_result = scalars_pandas_df[col_name] - assert_series_equal(bf_result, pd_result) + assert_series_equal_ignoring_order(bf_result, pd_result) def test_get_column_nonstring(scalars_dfs): @@ -250,22 +106,7 @@ def test_get_column_nonstring(scalars_dfs): series = scalars_df.rename(columns={"int64_col": 123.1})[123.1] bf_result = series.to_pandas() pd_result = scalars_pandas_df.rename(columns={"int64_col": 123.1})[123.1] - assert_series_equal(bf_result, pd_result) - - -@pytest.mark.parametrize( - "row_slice", - [ - (slice(1, 7, 2)), - (slice(1, 7, None)), - (slice(None, -3, None)), - ], -) -def test_get_rows_with_slice(scalars_dfs, row_slice): - scalars_df, scalars_pandas_df = scalars_dfs - bf_result = scalars_df[row_slice].to_pandas() - pd_result = scalars_pandas_df[row_slice] - assert_frame_equal(bf_result, pd_result) + assert_series_equal_ignoring_order(bf_result, pd_result) def test_hasattr(scalars_dfs): @@ -275,24 +116,15 @@ def test_hasattr(scalars_dfs): assert not hasattr(scalars_df, "not_exist") -@pytest.mark.parametrize( - ("ordered"), - [ - (True), - (False), - ], -) -def test_head_with_custom_column_labels( - scalars_df_index, scalars_pandas_df_index, ordered -): +def test_head_with_custom_column_labels(scalars_df_index, scalars_pandas_df_index): rename_mapping = { "int64_col": "Integer Column", "string_col": "言語列", } bf_df = scalars_df_index.rename(columns=rename_mapping).head(3) - bf_result = bf_df.to_pandas(ordered=ordered) + bf_result = bf_df.to_pandas() pd_result = scalars_pandas_df_index.rename(columns=rename_mapping).head(3) - assert_frame_equal(bf_result, pd_result, ignore_order=not ordered) + pandas.testing.assert_frame_equal(bf_result, pd_result) def test_tail_with_custom_column_labels(scalars_df_index, scalars_pandas_df_index): @@ -315,13 +147,15 @@ def test_tail_with_custom_column_labels(scalars_df_index, scalars_pandas_df_inde ], ) def test_df_nlargest(scalars_df_index, scalars_pandas_df_index, keep): - bf_result = scalars_df_index.nlargest(3, ["bool_col", "int64_too"], keep=keep) + bf_result = scalars_df_index.nlargest( + 3, ["bool_col", "int64_too"], keep=keep + ).to_pandas() pd_result = scalars_pandas_df_index.nlargest( 3, ["bool_col", "int64_too"], keep=keep ) - bigframes.testing.utils.assert_frame_equal( - bf_result.to_pandas(), + pd.testing.assert_frame_equal( + bf_result, pd_result, ) @@ -335,11 +169,11 @@ def test_df_nlargest(scalars_df_index, scalars_pandas_df_index, keep): ], ) def test_df_nsmallest(scalars_df_index, scalars_pandas_df_index, keep): - bf_result = scalars_df_index.nsmallest(6, ["bool_col"], keep=keep) + bf_result = scalars_df_index.nsmallest(6, ["bool_col"], keep=keep).to_pandas() pd_result = scalars_pandas_df_index.nsmallest(6, ["bool_col"], keep=keep) - bigframes.testing.utils.assert_frame_equal( - bf_result.to_pandas(), + pd.testing.assert_frame_equal( + bf_result, pd_result, ) @@ -349,7 +183,7 @@ def test_get_column_by_attr(scalars_dfs): series = scalars_df.int64_col bf_result = series.to_pandas() pd_result = scalars_pandas_df.int64_col - assert_series_equal(bf_result, pd_result) + assert_series_equal_ignoring_order(bf_result, pd_result) def test_get_columns(scalars_dfs): @@ -357,7 +191,7 @@ def test_get_columns(scalars_dfs): col_names = ["bool_col", "float64_col", "int64_col"] df_subset = scalars_df.get(col_names) df_pandas = df_subset.to_pandas() - bigframes.testing.utils.assert_index_equal( + pd.testing.assert_index_equal( df_pandas.columns, scalars_pandas_df[col_names].columns ) @@ -369,238 +203,11 @@ def test_get_columns_default(scalars_dfs): assert result == "default_val" -@pytest.mark.parametrize( - ("loc", "column", "value", "allow_duplicates"), - [ - (0, 666, 2, False), - (5, "float64_col", 2.2, True), - (13, "rowindex_2", [8, 7, 6, 5, 4, 3, 2, 1, 0], True), - pytest.param( - 14, - "test", - 2, - False, - marks=pytest.mark.xfail( - raises=IndexError, - ), - ), - pytest.param( - 12, - "int64_col", - 2, - False, - marks=pytest.mark.xfail( - raises=ValueError, - ), - ), - ], -) -def test_insert(scalars_dfs, loc, column, value, allow_duplicates): - scalars_df, scalars_pandas_df = scalars_dfs - # insert works inplace, so will influence other tests. - # make a copy to avoid inplace changes. - bf_df = scalars_df.copy() - pd_df = scalars_pandas_df.copy() - bf_df.insert(loc, column, value, allow_duplicates) - pd_df.insert(loc, column, value, allow_duplicates) - - bigframes.testing.utils.assert_frame_equal( - bf_df.to_pandas(), pd_df, check_dtype=False - ) - - -def test_mask_series_cond(scalars_df_index, scalars_pandas_df_index): - cond_bf = scalars_df_index["int64_col"] > 0 - cond_pd = scalars_pandas_df_index["int64_col"] > 0 - - bf_df = scalars_df_index[["int64_too", "int64_col", "float64_col"]] - pd_df = scalars_pandas_df_index[["int64_too", "int64_col", "float64_col"]] - bf_result = bf_df.mask(cond_bf, bf_df + 1).to_pandas() - pd_result = pd_df.mask(cond_pd, pd_df + 1) - pandas.testing.assert_frame_equal(bf_result, pd_result) - - -def test_mask_callable(scalars_df_index, scalars_pandas_df_index): - def is_positive(x): - return x > 0 - - bf_df = scalars_df_index[["int64_too", "int64_col", "float64_col"]] - pd_df = scalars_pandas_df_index[["int64_too", "int64_col", "float64_col"]] - bf_result = bf_df.mask(cond=is_positive, other=lambda x: x + 1).to_pandas() - pd_result = pd_df.mask(cond=is_positive, other=lambda x: x + 1) - - pandas.testing.assert_frame_equal(bf_result, pd_result) - - -def test_where_multi_column(scalars_df_index, scalars_pandas_df_index): - # Test when a dataframe has multi-columns. - columns = ["int64_col", "float64_col"] - dataframe_bf = scalars_df_index[columns] - - dataframe_bf.columns = pd.MultiIndex.from_tuples( - [("str1", 1), ("str2", 2)], names=["STR", "INT"] - ) - cond_bf = dataframe_bf["str1"] > 0 - - with pytest.raises(NotImplementedError) as context: - dataframe_bf.where(cond_bf).to_pandas() - assert ( - str(context.value) - == "The dataframe.where() method does not support multi-column." - ) - - -def test_where_series_cond(scalars_df_index, scalars_pandas_df_index): - # Condition is dataframe, other is None (as default). - cond_bf = scalars_df_index["int64_col"] > 0 - cond_pd = scalars_pandas_df_index["int64_col"] > 0 - bf_result = scalars_df_index.where(cond_bf).to_pandas() - pd_result = scalars_pandas_df_index.where(cond_pd) - pandas.testing.assert_frame_equal(bf_result, pd_result) - - -def test_where_series_cond_const_other(scalars_df_index, scalars_pandas_df_index): - # Condition is a series, other is a constant. - columns = ["int64_col", "float64_col"] - dataframe_bf = scalars_df_index[columns] - dataframe_pd = scalars_pandas_df_index[columns] - dataframe_bf.columns.name = "test_name" - dataframe_pd.columns.name = "test_name" - - cond_bf = dataframe_bf["int64_col"] > 0 - cond_pd = dataframe_pd["int64_col"] > 0 - other = 0 - - bf_result = dataframe_bf.where(cond_bf, other).to_pandas() - pd_result = dataframe_pd.where(cond_pd, other) - pandas.testing.assert_frame_equal(bf_result, pd_result) - - -def test_where_series_cond_dataframe_other(scalars_df_index, scalars_pandas_df_index): - # Condition is a series, other is a dataframe. - columns = ["int64_col", "float64_col"] - dataframe_bf = scalars_df_index[columns] - dataframe_pd = scalars_pandas_df_index[columns] - - cond_bf = dataframe_bf["int64_col"] > 0 - cond_pd = dataframe_pd["int64_col"] > 0 - other_bf = -dataframe_bf - other_pd = -dataframe_pd - - bf_result = dataframe_bf.where(cond_bf, other_bf).to_pandas() - pd_result = dataframe_pd.where(cond_pd, other_pd) - pandas.testing.assert_frame_equal(bf_result, pd_result) - - -def test_where_dataframe_cond(scalars_df_index, scalars_pandas_df_index): - # Condition is a dataframe, other is None. - columns = ["int64_col", "float64_col"] - dataframe_bf = scalars_df_index[columns] - dataframe_pd = scalars_pandas_df_index[columns] - - cond_bf = dataframe_bf > 0 - cond_pd = dataframe_pd > 0 - - bf_result = dataframe_bf.where(cond_bf, None).to_pandas() - pd_result = dataframe_pd.where(cond_pd, None) - pandas.testing.assert_frame_equal(bf_result, pd_result) - - -def test_where_dataframe_cond_const_other(scalars_df_index, scalars_pandas_df_index): - # Condition is a dataframe, other is a constant. - columns = ["int64_col", "float64_col"] - dataframe_bf = scalars_df_index[columns] - dataframe_pd = scalars_pandas_df_index[columns] - - cond_bf = dataframe_bf > 0 - cond_pd = dataframe_pd > 0 - other_bf = 10 - other_pd = 10 - - bf_result = dataframe_bf.where(cond_bf, other_bf).to_pandas() - pd_result = dataframe_pd.where(cond_pd, other_pd) - pandas.testing.assert_frame_equal(bf_result, pd_result) - - -def test_where_dataframe_cond_dataframe_other( - scalars_df_index, scalars_pandas_df_index -): - # Condition is a dataframe, other is a dataframe. - columns = ["int64_col", "float64_col"] - dataframe_bf = scalars_df_index[columns] - dataframe_pd = scalars_pandas_df_index[columns] - - cond_bf = dataframe_bf > 0 - cond_pd = dataframe_pd > 0 - other_bf = dataframe_bf * 2 - other_pd = dataframe_pd * 2 - - bf_result = dataframe_bf.where(cond_bf, other_bf).to_pandas() - pd_result = dataframe_pd.where(cond_pd, other_pd) - pandas.testing.assert_frame_equal(bf_result, pd_result) - - -def test_where_callable_cond_constant_other(scalars_df_index, scalars_pandas_df_index): - # Condition is callable, other is a constant. - columns = ["int64_col", "float64_col"] - dataframe_bf = scalars_df_index[columns] - dataframe_pd = scalars_pandas_df_index[columns] - - other = 10 - - bf_result = dataframe_bf.where(lambda x: x > 0, other).to_pandas() - pd_result = dataframe_pd.where(lambda x: x > 0, other) - pandas.testing.assert_frame_equal(bf_result, pd_result) - - -def test_where_dataframe_cond_callable_other(scalars_df_index, scalars_pandas_df_index): - # Condition is a dataframe, other is callable. - columns = ["int64_col", "float64_col"] - dataframe_bf = scalars_df_index[columns] - dataframe_pd = scalars_pandas_df_index[columns] - - cond_bf = dataframe_bf > 0 - cond_pd = dataframe_pd > 0 - - def func(x): - return x * 2 - - bf_result = dataframe_bf.where(cond_bf, func).to_pandas() - pd_result = dataframe_pd.where(cond_pd, func) - pandas.testing.assert_frame_equal(bf_result, pd_result) - - -def test_where_callable_cond_callable_other(scalars_df_index, scalars_pandas_df_index): - # Condition is callable, other is callable too. - columns = ["int64_col", "float64_col"] - dataframe_bf = scalars_df_index[columns] - dataframe_pd = scalars_pandas_df_index[columns] - - def func(x): - return x["int64_col"] > 0 - - bf_result = dataframe_bf.where(func, lambda x: x * 2).to_pandas() - pd_result = dataframe_pd.where(func, lambda x: x * 2) - pandas.testing.assert_frame_equal(bf_result, pd_result) - - -def test_where_series_other(scalars_df_index): - # When other is a series, throw an error. - columns = ["int64_col", "float64_col"] - dataframe_bf = scalars_df_index[columns] - - with pytest.raises( - ValueError, - match="Seires is not a supported replacement type!", - ): - dataframe_bf.where(dataframe_bf > 0, dataframe_bf["int64_col"]) - - def test_drop_column(scalars_dfs): scalars_df, scalars_pandas_df = scalars_dfs col_name = "int64_col" df_pandas = scalars_df.drop(columns=col_name).to_pandas() - bigframes.testing.utils.assert_index_equal( + pd.testing.assert_index_equal( df_pandas.columns, scalars_pandas_df.drop(columns=col_name).columns ) @@ -609,7 +216,7 @@ def test_drop_columns(scalars_dfs): scalars_df, scalars_pandas_df = scalars_dfs col_names = ["int64_col", "geography_col", "time_col"] df_pandas = scalars_df.drop(columns=col_names).to_pandas() - bigframes.testing.utils.assert_index_equal( + pd.testing.assert_index_equal( df_pandas.columns, scalars_pandas_df.drop(columns=col_names).columns ) @@ -621,7 +228,7 @@ def test_drop_labels_axis_1(scalars_dfs): pd_result = scalars_pandas_df.drop(labels=labels, axis=1) bf_result = scalars_df.drop(labels=labels, axis=1).to_pandas() - bigframes.testing.utils.assert_frame_equal(pd_result, bf_result) + pd.testing.assert_frame_equal(pd_result, bf_result) def test_drop_with_custom_column_labels(scalars_dfs): @@ -639,113 +246,7 @@ def test_drop_with_custom_column_labels(scalars_dfs): pd_result = scalars_pandas_df.rename(columns=rename_mapping).drop( columns=dropped_columns ) - assert_frame_equal(bf_result, pd_result) - - -def test_df_memory_usage(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - - pd_result = scalars_pandas_df.memory_usage() - bf_result = scalars_df.memory_usage() - - bigframes.testing.utils.assert_series_equal(pd_result, bf_result, rtol=1.5) - - -def test_df_info(scalars_dfs): - expected = ( - "\n" - "Index: 9 entries, 0 to 8\n" - "Data columns (total 14 columns):\n" - " # Column Non-Null Count Dtype\n" - "--- ------------- ---------------- ------------------------------\n" - " 0 bool_col 8 non-null boolean\n" - " 1 bytes_col 6 non-null binary[pyarrow]\n" - " 2 date_col 7 non-null date32[day][pyarrow]\n" - " 3 datetime_col 6 non-null timestamp[us][pyarrow]\n" - " 4 geography_col 4 non-null geometry\n" - " 5 int64_col 8 non-null Int64\n" - " 6 int64_too 9 non-null Int64\n" - " 7 numeric_col 6 non-null decimal128(38, 9)[pyarrow]\n" - " 8 float64_col 7 non-null Float64\n" - " 9 rowindex_2 9 non-null Int64\n" - " 10 string_col 8 non-null string\n" - " 11 time_col 6 non-null time64[us][pyarrow]\n" - " 12 timestamp_col 6 non-null timestamp[us, tz=UTC][pyarrow]\n" - " 13 duration_col 7 non-null duration[us][pyarrow]\n" - "dtypes: Float64(1), Int64(3), binary[pyarrow](1), boolean(1), date32[day][pyarrow](1), decimal128(38, 9)[pyarrow](1), duration[us][pyarrow](1), geometry(1), string(1), time64[us][pyarrow](1), timestamp[us, tz=UTC][pyarrow](1), timestamp[us][pyarrow](1)\n" - "memory usage: 1341 bytes\n" - ) - scalars_df, _ = scalars_dfs - - bf_result = io.StringIO() - scalars_df.info(buf=bf_result) - - assert expected == bf_result.getvalue() - - -def test_df_info_no_rows(session): - expected = ( - "\n" - "Index: 0 entries\n" - "Data columns (total 1 columns):\n" - " # Column Non-Null Count Dtype\n" - "--- -------- ---------------- -------\n" - " 0 col 0 non-null Float64\n" - "dtypes: Float64(1)\n" - "memory usage: 0 bytes\n" - ) - df = session.DataFrame({"col": []}) - - bf_result = io.StringIO() - df.info(buf=bf_result) - - assert expected == bf_result.getvalue() - - -def test_df_info_no_cols(session): - expected = ( - "\n" - "Index: 3 entries, 1 to 3\n" - "Empty DataFrame\n" - ) - df = session.DataFrame({}, index=[1, 2, 3]) - - bf_result = io.StringIO() - df.info(buf=bf_result) - - assert expected == bf_result.getvalue() - - -def test_df_info_no_cols_no_rows(session): - expected = ( - "\nIndex: 0 entries\nEmpty DataFrame\n" - ) - df = session.DataFrame({}) - - bf_result = io.StringIO() - df.info(buf=bf_result) - - assert expected == bf_result.getvalue() - - -@pytest.mark.parametrize( - ("include", "exclude"), - [ - ("Int64", None), - (["int"], None), - ("number", None), - ([pd.Int64Dtype(), pd.BooleanDtype()], None), - (None, [pd.Int64Dtype(), pd.BooleanDtype()]), - ("Int64", ["boolean"]), - ], -) -def test_select_dtypes(scalars_dfs, include, exclude): - scalars_df, scalars_pandas_df = scalars_dfs - - pd_result = scalars_pandas_df.select_dtypes(include=include, exclude=exclude) - bf_result = scalars_df.select_dtypes(include=include, exclude=exclude).to_pandas() - - bigframes.testing.utils.assert_frame_equal(pd_result, bf_result) + assert_pandas_df_equal_ignore_ordering(bf_result, pd_result) def test_drop_index(scalars_dfs): @@ -754,7 +255,7 @@ def test_drop_index(scalars_dfs): pd_result = scalars_pandas_df.drop(index=[4, 1, 2]) bf_result = scalars_df.drop(index=[4, 1, 2]).to_pandas() - bigframes.testing.utils.assert_frame_equal(pd_result, bf_result) + pd.testing.assert_frame_equal(pd_result, bf_result) def test_drop_pandas_index(scalars_dfs): @@ -764,7 +265,7 @@ def test_drop_pandas_index(scalars_dfs): pd_result = scalars_pandas_df.drop(index=drop_index) bf_result = scalars_df.drop(index=drop_index).to_pandas() - bigframes.testing.utils.assert_frame_equal(pd_result, bf_result) + pd.testing.assert_frame_equal(pd_result, bf_result) def test_drop_bigframes_index(scalars_dfs): @@ -775,12 +276,10 @@ def test_drop_bigframes_index(scalars_dfs): pd_result = scalars_pandas_df.drop(index=drop_pandas_index) bf_result = scalars_df.drop(index=drop_index).to_pandas() - bigframes.testing.utils.assert_frame_equal(pd_result, bf_result) + pd.testing.assert_frame_equal(pd_result, bf_result) def test_drop_bigframes_index_with_na(scalars_dfs): - if pd.__version__.startswith("3"): - pytest.skip("Pandas 3.0 doesn't doesn't support drop with pd.NA values") scalars_df, scalars_pandas_df = scalars_dfs scalars_df = scalars_df.copy() scalars_pandas_df = scalars_pandas_df.copy() @@ -792,12 +291,10 @@ def test_drop_bigframes_index_with_na(scalars_dfs): pd_result = scalars_pandas_df.drop(index=drop_pandas_index) # drop_pandas_index) bf_result = scalars_df.drop(index=drop_index).to_pandas() - bigframes.testing.utils.assert_frame_equal(pd_result, bf_result) + pd.testing.assert_frame_equal(pd_result, bf_result) def test_drop_bigframes_multiindex(scalars_dfs): - # TODO: supply a reason why this isn't compatible with pandas 1.x - pytest.importorskip("pandas", minversion="2.0.0") scalars_df, scalars_pandas_df = scalars_dfs scalars_df = scalars_df.copy() scalars_pandas_df = scalars_pandas_df.copy() @@ -813,7 +310,7 @@ def test_drop_bigframes_multiindex(scalars_dfs): bf_result = scalars_df.drop(index=drop_index).to_pandas() pd_result = scalars_pandas_df.drop(index=drop_pandas_index) - bigframes.testing.utils.assert_frame_equal(pd_result, bf_result) + pd.testing.assert_frame_equal(pd_result, bf_result) def test_drop_labels_axis_0(scalars_dfs): @@ -822,7 +319,7 @@ def test_drop_labels_axis_0(scalars_dfs): pd_result = scalars_pandas_df.drop(labels=[4, 1, 2], axis=0) bf_result = scalars_df.drop(labels=[4, 1, 2], axis=0).to_pandas() - bigframes.testing.utils.assert_frame_equal(pd_result, bf_result) + pd.testing.assert_frame_equal(pd_result, bf_result) def test_drop_index_and_columns(scalars_dfs): @@ -831,77 +328,18 @@ def test_drop_index_and_columns(scalars_dfs): pd_result = scalars_pandas_df.drop(index=[4, 1, 2], columns="int64_col") bf_result = scalars_df.drop(index=[4, 1, 2], columns="int64_col").to_pandas() - bigframes.testing.utils.assert_frame_equal(pd_result, bf_result) + pd.testing.assert_frame_equal(pd_result, bf_result) def test_rename(scalars_dfs): scalars_df, scalars_pandas_df = scalars_dfs col_name_dict = {"bool_col": 1.2345} df_pandas = scalars_df.rename(columns=col_name_dict).to_pandas() - bigframes.testing.utils.assert_index_equal( + pd.testing.assert_index_equal( df_pandas.columns, scalars_pandas_df.rename(columns=col_name_dict).columns ) -def test_df_peek(scalars_dfs_maybe_ordered): - scalars_df, scalars_pandas_df = scalars_dfs_maybe_ordered - - peek_result = scalars_df.peek(n=3, force=False, allow_large_results=True) - - bigframes.testing.utils.assert_index_equal( - scalars_pandas_df.columns, peek_result.columns - ) - assert len(peek_result) == 3 - - -def test_df_peek_with_large_results_not_allowed(scalars_dfs_maybe_ordered): - scalars_df, scalars_pandas_df = scalars_dfs_maybe_ordered - - peek_result = scalars_df.peek(n=3, force=False, allow_large_results=False) - - bigframes.testing.utils.assert_index_equal( - scalars_pandas_df.columns, peek_result.columns - ) - assert len(peek_result) == 3 - - -def test_df_peek_filtered(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - peek_result = scalars_df[scalars_df.int64_col != 0].peek(n=3, force=False) - bigframes.testing.utils.assert_index_equal( - scalars_pandas_df.columns, peek_result.columns - ) - assert len(peek_result) == 3 - - -def test_df_peek_exception(scalars_dfs): - scalars_df, _ = scalars_dfs - - with pytest.raises(ValueError): - # Window ops aren't compatible with efficient peeking - scalars_df[["int64_col", "int64_too"]].cumsum().peek(n=3, force=False) - - -def test_df_peek_force_default(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - peek_result = scalars_df[["int64_col", "int64_too"]].cumsum().peek(n=3) - bigframes.testing.utils.assert_index_equal( - scalars_pandas_df[["int64_col", "int64_too"]].columns, peek_result.columns - ) - assert len(peek_result) == 3 - - -def test_df_peek_reset_index(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - peek_result = ( - scalars_df[["int64_col", "int64_too"]].reset_index(drop=True).peek(n=3) - ) - bigframes.testing.utils.assert_index_equal( - scalars_pandas_df[["int64_col", "int64_too"]].columns, peek_result.columns - ) - assert len(peek_result) == 3 - - def test_repr_w_all_rows(scalars_dfs): scalars_df, scalars_pandas_df = scalars_dfs @@ -911,6 +349,13 @@ def test_repr_w_all_rows(scalars_dfs): scalars_df = scalars_df.drop(columns=["numeric_col"]) scalars_pandas_df = scalars_pandas_df.drop(columns=["numeric_col"]) + if scalars_pandas_df.index.name is None: + # Note: Not quite the same as no index / default index, but hopefully + # simulates it well enough while being consistent enough for string + # comparison to work. + scalars_df = scalars_df.set_index("rowindex", drop=False).sort_index() + scalars_df.index.name = None + # When there are 10 or fewer rows, the outputs should be identical. actual = repr(scalars_df.head(10)) @@ -920,81 +365,15 @@ def test_repr_w_all_rows(scalars_dfs): assert actual == expected -def test_join_repr(scalars_dfs_maybe_ordered): - scalars_df, scalars_pandas_df = scalars_dfs_maybe_ordered - - scalars_df = ( - scalars_df[["int64_col"]] - .join(scalars_df.set_index("int64_col")[["int64_too"]]) - .sort_index() - ) - scalars_pandas_df = ( - scalars_pandas_df[["int64_col"]] - .join(scalars_pandas_df.set_index("int64_col")[["int64_too"]]) - .sort_index() - ) - # Pandas join result index name seems to depend on the index values in a way that bigframes can't match exactly - scalars_pandas_df.index.name = None - - actual = repr(scalars_df) - - with display_options.pandas_repr(bigframes.options.display): - expected = repr(scalars_pandas_df) - - assert actual == expected - - -def test_repr_w_display_options(scalars_dfs, session): - scalars_df, _ = scalars_dfs - # get a pandas df of the expected format - df, _ = scalars_df._block.to_pandas() - pandas_df = df.set_axis(scalars_df._block.column_labels, axis=1) - pandas_df.index.name = scalars_df.index.name - - history_pre = session.execution_history().to_dataframe() - queries_pre = ( - len(history_pre[history_pre["job_type"] == "query"]) - if "job_type" in history_pre.columns - else 0 - ) - - with bigframes.option_context( - "display.max_rows", 10, "display.max_columns", 5, "display.max_colwidth", 10 - ): - # When there are 10 or fewer rows, the outputs should be identical except for the extra note. - actual = scalars_df.head(10).__repr__() - - history_post = session.execution_history().to_dataframe() - queries_post = len(history_post[history_post["job_type"] == "query"]) - - with display_options.pandas_repr(bigframes.options.display): - pandas_repr = pandas_df.head(10).__repr__() - - assert actual == pandas_repr - assert (queries_post - queries_pre) <= 2 - - -def test_mimebundle_html_repr_w_all_rows(scalars_dfs, session): +def test_repr_html_w_all_rows(scalars_dfs): scalars_df, _ = scalars_dfs # get a pandas df of the expected format df, _ = scalars_df._block.to_pandas() pandas_df = df.set_axis(scalars_df._block.column_labels, axis=1) pandas_df.index.name = scalars_df.index.name - history_pre = session.execution_history().to_dataframe() - queries_pre = ( - len(history_pre[history_pre["job_type"] == "query"]) - if "job_type" in history_pre.columns - else 0 - ) - # When there are 10 or fewer rows, the outputs should be identical except for the extra note. - bundle = scalars_df.head(10)._repr_mimebundle_() - actual = bundle["text/html"] - - history_post = session.execution_history().to_dataframe() - queries_post = len(history_post[history_post["job_type"] == "query"]) - + actual = scalars_df.head(10)._repr_html_() with display_options.pandas_repr(bigframes.options.display): pandas_repr = pandas_df.head(10)._repr_html_() @@ -1003,14 +382,13 @@ def test_mimebundle_html_repr_w_all_rows(scalars_dfs, session): + f"[{len(pandas_df.index)} rows x {len(pandas_df.columns)} columns in total]" ) assert actual == expected - assert (queries_post - queries_pre) <= 2 def test_df_column_name_with_space(scalars_dfs): scalars_df, scalars_pandas_df = scalars_dfs col_name_dict = {"bool_col": "bool col"} df_pandas = scalars_df.rename(columns=col_name_dict).to_pandas() - bigframes.testing.utils.assert_index_equal( + pd.testing.assert_index_equal( df_pandas.columns, scalars_pandas_df.rename(columns=col_name_dict).columns ) @@ -1019,7 +397,7 @@ def test_df_column_name_duplicate(scalars_dfs): scalars_df, scalars_pandas_df = scalars_dfs col_name_dict = {"int64_too": "int64_col"} df_pandas = scalars_df.rename(columns=col_name_dict).to_pandas() - bigframes.testing.utils.assert_index_equal( + pd.testing.assert_index_equal( df_pandas.columns, scalars_pandas_df.rename(columns=col_name_dict).columns ) @@ -1030,25 +408,7 @@ def test_get_df_column_name_duplicate(scalars_dfs): bf_result = scalars_df.rename(columns=col_name_dict)["int64_col"].to_pandas() pd_result = scalars_pandas_df.rename(columns=col_name_dict)["int64_col"] - bigframes.testing.utils.assert_index_equal(bf_result.columns, pd_result.columns) - - -@pytest.mark.parametrize( - ("indices", "axis"), - [ - ([1, 3, 5], 0), - ([2, 4, 6], 1), - ([1, -3, -5, -6], "index"), - ([-2, -4, -6], "columns"), - ], -) -def test_take_df(scalars_dfs, indices, axis): - scalars_df, scalars_pandas_df = scalars_dfs - - bf_result = scalars_df.take(indices, axis=axis).to_pandas() - pd_result = scalars_pandas_df.take(indices, axis=axis) - - assert_frame_equal(bf_result, pd_result) + pd.testing.assert_index_equal(bf_result.columns, pd_result.columns) def test_filter_df(scalars_dfs): @@ -1060,79 +420,20 @@ def test_filter_df(scalars_dfs): pd_bool_series = scalars_pandas_df["bool_col"] pd_result = scalars_pandas_df[pd_bool_series] - assert_frame_equal(bf_result, pd_result) - - -def test_read_gbq_direct_to_batches_row_count(unordered_session): - df = unordered_session.read_gbq("bigquery-public-data.usa_names.usa_1910_2013") - iter = df.to_pandas_batches() - assert iter.total_rows == 5552452 - - -def test_df_to_pandas_batches(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - - capped_unfiltered_batches = scalars_df.to_pandas_batches(page_size=2, max_results=6) - bf_bool_series = scalars_df["bool_col"] - filtered_batches = scalars_df[bf_bool_series].to_pandas_batches() - - pd_bool_series = scalars_pandas_df["bool_col"] - pd_result = scalars_pandas_df[pd_bool_series] - - assert 6 == capped_unfiltered_batches.total_rows - assert len(pd_result) == filtered_batches.total_rows - assert_frame_equal(pd.concat(filtered_batches), pd_result) + assert_pandas_df_equal_ignore_ordering(bf_result, pd_result) -@pytest.mark.parametrize( - ("literal", "expected_dtype"), - ( - pytest.param( - 2, - dtypes.INT_DTYPE, - id="INT64", - ), - # ==================================================================== - # NULL values - # - # These are regression tests for b/428999884. It needs to be possible to - # set a column to NULL with a desired type (not just the pandas default - # of float64). - # ==================================================================== - pytest.param(None, dtypes.FLOAT_DTYPE, id="NULL-None"), - pytest.param( - pa.scalar(None, type=pa.int64()), - dtypes.INT_DTYPE, - id="NULL-pyarrow-TIMESTAMP", - ), - pytest.param( - pa.scalar(None, type=pa.timestamp("us", tz="UTC")), - dtypes.TIMESTAMP_DTYPE, - id="NULL-pyarrow-TIMESTAMP", - ), - pytest.param( - pa.scalar(None, type=pa.timestamp("us")), - dtypes.DATETIME_DTYPE, - id="NULL-pyarrow-DATETIME", - ), - ), -) -def test_assign_new_column_w_literal(scalars_dfs, literal, expected_dtype): +def test_assign_new_column(scalars_dfs): scalars_df, scalars_pandas_df = scalars_dfs - df = scalars_df.assign(new_col=literal) + kwargs = {"new_col": 2} + df = scalars_df.assign(**kwargs) bf_result = df.to_pandas() + pd_result = scalars_pandas_df.assign(**kwargs) - new_col_pd = literal - if isinstance(literal, pa.Scalar): - # PyArrow integer scalars aren't yet supported in pandas Int64Dtype. - new_col_pd = literal.as_py() - - # Pandas might not pick the same dtype as BigFrames, but it should at least - # be castable to it. - pd_result = scalars_pandas_df.assign(new_col=new_col_pd) - pd_result["new_col"] = pd_result["new_col"].astype(expected_dtype) + # Convert default pandas dtypes `int64` to match BigQuery DataFrames dtypes. + pd_result["new_col"] = pd_result["new_col"].astype("Int64") - assert_frame_equal(bf_result, pd_result) + assert_pandas_df_equal_ignore_ordering(bf_result, pd_result) def test_assign_new_column_w_loc(scalars_dfs): @@ -1147,29 +448,22 @@ def test_assign_new_column_w_loc(scalars_dfs): # Convert default pandas dtypes `int64` to match BigQuery DataFrames dtypes. pd_result["new_col"] = pd_result["new_col"].astype("Int64") - bigframes.testing.utils.assert_frame_equal(bf_result, pd_result) + pd.testing.assert_frame_equal(bf_result, pd_result) -@pytest.mark.parametrize( - ("scalar",), - [ - (2.1,), - (None,), - ], -) -def test_assign_new_column_w_setitem(scalars_dfs, scalar): +def test_assign_new_column_w_setitem(scalars_dfs): scalars_df, scalars_pandas_df = scalars_dfs bf_df = scalars_df.copy() pd_df = scalars_pandas_df.copy() - bf_df["new_col"] = scalar - pd_df["new_col"] = scalar + bf_df["new_col"] = 2 + pd_df["new_col"] = 2 bf_result = bf_df.to_pandas() pd_result = pd_df - # Convert default pandas dtypes `float64` to match BigQuery DataFrames dtypes. - pd_result["new_col"] = pd_result["new_col"].astype("Float64") + # Convert default pandas dtypes `int64` to match BigQuery DataFrames dtypes. + pd_result["new_col"] = pd_result["new_col"].astype("Int64") - bigframes.testing.utils.assert_frame_equal(bf_result, pd_result) + pd.testing.assert_frame_equal(bf_result, pd_result) def test_assign_new_column_w_setitem_dataframe(scalars_dfs): @@ -1182,7 +476,7 @@ def test_assign_new_column_w_setitem_dataframe(scalars_dfs): # Convert default pandas dtypes `int64` to match BigQuery DataFrames dtypes. pd_df["int64_col"] = pd_df["int64_col"].astype("Int64") - bigframes.testing.utils.assert_frame_equal(bf_df.to_pandas(), pd_df) + pd.testing.assert_frame_equal(bf_df.to_pandas(), pd_df) def test_assign_new_column_w_setitem_dataframe_error(scalars_dfs): @@ -1208,7 +502,7 @@ def test_assign_new_column_w_setitem_list(scalars_dfs): # Convert default pandas dtypes `int64` to match BigQuery DataFrames dtypes. pd_result["new_col"] = pd_result["new_col"].astype("Int64") - bigframes.testing.utils.assert_frame_equal(bf_result, pd_result) + pd.testing.assert_frame_equal(bf_result, pd_result) def test_assign_new_column_w_setitem_list_repeated(scalars_dfs): @@ -1226,7 +520,7 @@ def test_assign_new_column_w_setitem_list_repeated(scalars_dfs): pd_result["new_col"] = pd_result["new_col"].astype("Int64") pd_result["new_col_2"] = pd_result["new_col_2"].astype("Int64") - bigframes.testing.utils.assert_frame_equal(bf_result, pd_result) + pd.testing.assert_frame_equal(bf_result, pd_result) def test_assign_new_column_w_setitem_list_custom_index(scalars_dfs): @@ -1246,7 +540,7 @@ def test_assign_new_column_w_setitem_list_custom_index(scalars_dfs): # Convert default pandas dtypes `int64` to match BigQuery DataFrames dtypes. pd_result["new_col"] = pd_result["new_col"].astype("Int64") - bigframes.testing.utils.assert_frame_equal(bf_result, pd_result) + pd.testing.assert_frame_equal(bf_result, pd_result) def test_assign_new_column_w_setitem_list_error(scalars_dfs): @@ -1260,76 +554,6 @@ def test_assign_new_column_w_setitem_list_error(scalars_dfs): bf_df["new_col"] = [1, 2, 3] -@pytest.mark.parametrize( - ("key", "value"), - [ - pytest.param(["int64_col", "int64_too"], 1, id="scalar_to_existing_column"), - pytest.param( - ["int64_col", "int64_too"], [1, 2], id="sequence_to_existing_column" - ), - pytest.param( - ["int64_col", "new_col"], [1, 2], id="sequence_to_partial_new_column" - ), - pytest.param( - ["new_col", "new_col_too"], [1, 2], id="sequence_to_full_new_column" - ), - pytest.param( - pd.Index(("new_col", "new_col_too")), - [1, 2], - id="sequence_to_full_new_column_as_index", - ), - ], -) -def test_setitem_multicolumn_with_literals(scalars_dfs, key, value): - scalars_df, scalars_pandas_df = scalars_dfs - bf_result = scalars_df.copy() - pd_result = scalars_pandas_df.copy() - - bf_result[key] = value - pd_result[key] = value - - bigframes.testing.utils.assert_frame_equal( - pd_result, bf_result.to_pandas(), check_dtype=False - ) - - -def test_setitem_multicolumn_with_literals_different_lengths_raise_error(scalars_dfs): - scalars_df, _ = scalars_dfs - bf_result = scalars_df.copy() - - with pytest.raises(ValueError): - bf_result[["int64_col", "int64_too"]] = [1] - - -def test_setitem_multicolumn_with_dataframes(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - bf_result = scalars_df.copy() - pd_result = scalars_pandas_df.copy() - - bf_result[["int64_col", "int64_too"]] = bf_result[["int64_too", "int64_col"]] / 2 - pd_result[["int64_col", "int64_too"]] = pd_result[["int64_too", "int64_col"]] / 2 - - bigframes.testing.utils.assert_frame_equal( - pd_result, bf_result.to_pandas(), check_dtype=False - ) - - -def test_setitem_multicolumn_with_dataframes_series_on_rhs_raise_error(scalars_dfs): - scalars_df, _ = scalars_dfs - bf_result = scalars_df.copy() - - with pytest.raises(ValueError): - bf_result[["int64_col", "int64_too"]] = bf_result["int64_col"] / 2 - - -def test_setitem_multicolumn_with_dataframes_different_lengths_raise_error(scalars_dfs): - scalars_df, _ = scalars_dfs - bf_result = scalars_df.copy() - - with pytest.raises(ValueError): - bf_result[["int64_col"]] = bf_result[["int64_col", "int64_too"]] / 2 - - def test_assign_existing_column(scalars_dfs): scalars_df, scalars_pandas_df = scalars_dfs kwargs = {"int64_col": 2} @@ -1340,7 +564,7 @@ def test_assign_existing_column(scalars_dfs): # Convert default pandas dtypes `int64` to match BigQuery DataFrames dtypes. pd_result["int64_col"] = pd_result["int64_col"].astype("Int64") - assert_frame_equal(bf_result, pd_result) + assert_pandas_df_equal_ignore_ordering(bf_result, pd_result) def test_assign_listlike_to_empty_df(session): @@ -1352,15 +576,14 @@ def test_assign_listlike_to_empty_df(session): pd_result["new_col"] = pd_result["new_col"].astype("Int64") pd_result.index = pd_result.index.astype("Int64") - assert_frame_equal(bf_result.to_pandas(), pd_result) + assert_pandas_df_equal_ignore_ordering(bf_result.to_pandas(), pd_result) def test_assign_to_empty_df_multiindex_error(session): empty_df = dataframe.DataFrame(session=session) empty_pandas_df = pd.DataFrame() - - empty_df["empty_col_1"] = typing.cast(series.Series, []) - empty_df["empty_col_2"] = typing.cast(series.Series, []) + empty_df["empty_col_1"] = [] + empty_df["empty_col_2"] = [] empty_pandas_df["empty_col_1"] = [] empty_pandas_df["empty_col_2"] = [] empty_df = empty_df.set_index(["empty_col_1", "empty_col_2"]) @@ -1372,21 +595,14 @@ def test_assign_to_empty_df_multiindex_error(session): empty_pandas_df.assign(new_col=[1, 2, 3, 4, 5, 6, 7, 8, 9]) -@pytest.mark.parametrize( - ("ordered"), - [ - (True), - (False), - ], -) -def test_assign_series(scalars_dfs, ordered): +def test_assign_series(scalars_dfs): scalars_df, scalars_pandas_df = scalars_dfs column_name = "int64_col" df = scalars_df.assign(new_col=scalars_df[column_name]) - bf_result = df.to_pandas(ordered=ordered) + bf_result = df.to_pandas() pd_result = scalars_pandas_df.assign(new_col=scalars_pandas_df[column_name]) - assert_frame_equal(bf_result, pd_result, ignore_order=not ordered) + assert_pandas_df_equal_ignore_ordering(bf_result, pd_result) def test_assign_series_overwrite(scalars_dfs): @@ -1398,7 +614,7 @@ def test_assign_series_overwrite(scalars_dfs): **{column_name: scalars_pandas_df[column_name] + 3} ) - assert_frame_equal(bf_result, pd_result) + assert_pandas_df_equal_ignore_ordering(bf_result, pd_result) def test_assign_sequential(scalars_dfs): @@ -1413,7 +629,7 @@ def test_assign_sequential(scalars_dfs): pd_result["new_col"] = pd_result["new_col"].astype("Int64") pd_result["new_col2"] = pd_result["new_col2"].astype("Int64") - assert_frame_equal(bf_result, pd_result) + assert_pandas_df_equal_ignore_ordering(bf_result, pd_result) # Require an index so that the self-join is consistent each time. @@ -1447,7 +663,7 @@ def test_assign_different_df( new_col=scalars_pandas_df_index[column_name] ) - assert_frame_equal(bf_result, pd_result) + assert_pandas_df_equal_ignore_ordering(bf_result, pd_result) def test_assign_different_df_w_loc( @@ -1466,7 +682,7 @@ def test_assign_different_df_w_loc( # Convert default pandas dtypes `int64` to match BigQuery DataFrames dtypes. pd_result["int64_col"] = pd_result["int64_col"].astype("Int64") - bigframes.testing.utils.assert_frame_equal(bf_result, pd_result) + pd.testing.assert_frame_equal(bf_result, pd_result) def test_assign_different_df_w_setitem( @@ -1485,7 +701,7 @@ def test_assign_different_df_w_setitem( # Convert default pandas dtypes `int64` to match BigQuery DataFrames dtypes. pd_result["int64_col"] = pd_result["int64_col"].astype("Int64") - bigframes.testing.utils.assert_frame_equal(bf_result, pd_result) + pd.testing.assert_frame_equal(bf_result, pd_result) def test_assign_callable_lambda(scalars_dfs): @@ -1498,79 +714,28 @@ def test_assign_callable_lambda(scalars_dfs): # Convert default pandas dtypes `int64` to match BigQuery DataFrames dtypes. pd_result["new_col"] = pd_result["new_col"].astype("Int64") - assert_frame_equal(bf_result, pd_result) + assert_pandas_df_equal_ignore_ordering(bf_result, pd_result) @pytest.mark.parametrize( - ("axis", "how", "ignore_index", "subset"), + ("axis", "how", "ignore_index"), [ - (0, "any", False, None), - (0, "any", True, None), - (0, "all", False, ["bool_col", "time_col"]), - (0, "any", False, ["bool_col", "time_col"]), - (0, "all", False, "time_col"), - (1, "any", False, None), - (1, "all", False, None), + (0, "any", False), + (0, "any", True), + (1, "any", False), + (1, "all", False), ], ) -def test_df_dropna_by_how(scalars_dfs, axis, how, ignore_index, subset): - # TODO: supply a reason why this isn't compatible with pandas 1.x - pytest.importorskip("pandas", minversion="2.0.0") +def test_df_dropna(scalars_dfs, axis, how, ignore_index): + if pd.__version__.startswith("1."): + pytest.skip("ignore_index parameter not supported in pandas 1.x.") scalars_df, scalars_pandas_df = scalars_dfs - df = scalars_df.dropna(axis=axis, how=how, ignore_index=ignore_index, subset=subset) + df = scalars_df.dropna(axis=axis, how=how, ignore_index=ignore_index) bf_result = df.to_pandas() - pd_result = scalars_pandas_df.dropna( - axis=axis, how=how, ignore_index=ignore_index, subset=subset - ) - - # Pandas uses int64 instead of Int64 (nullable) dtype. - pd_result.index = pd_result.index.astype(pd.Int64Dtype()) - pandas.testing.assert_frame_equal(bf_result, pd_result) - - -@pytest.mark.parametrize( - ("axis", "ignore_index", "subset", "thresh"), - [ - (0, False, None, 2), - (0, True, None, 3), - (1, False, None, 2), - ], -) -def test_df_dropna_by_thresh(scalars_dfs, axis, ignore_index, subset, thresh): - """ - Tests that dropna correctly keeps rows/columns with a minimum number - of non-null values. - """ - # TODO: supply a reason why this isn't compatible with pandas 1.x - pytest.importorskip("pandas", minversion="2.0.0") - scalars_df, scalars_pandas_df = scalars_dfs + pd_result = scalars_pandas_df.dropna(axis=axis, how=how, ignore_index=ignore_index) - df_result = scalars_df.dropna( - axis=axis, thresh=thresh, ignore_index=ignore_index, subset=subset - ) - pd_result = scalars_pandas_df.dropna( - axis=axis, thresh=thresh, ignore_index=ignore_index, subset=subset - ) - - bf_result = df_result.to_pandas() # Pandas uses int64 instead of Int64 (nullable) dtype. pd_result.index = pd_result.index.astype(pd.Int64Dtype()) - bigframes.testing.utils.assert_frame_equal(bf_result, pd_result) - - -def test_df_dropna_range_columns(scalars_dfs): - # TODO: supply a reason why this isn't compatible with pandas 1.x - pytest.importorskip("pandas", minversion="2.0.0") - scalars_df, scalars_pandas_df = scalars_dfs - scalars_df = scalars_df.copy() - scalars_pandas_df = scalars_pandas_df.copy() - scalars_df.columns = pandas.RangeIndex(0, len(scalars_df.columns)) - scalars_pandas_df.columns = pandas.RangeIndex(0, len(scalars_pandas_df.columns)) - - df = scalars_df.dropna() - bf_result = df.to_pandas() - pd_result = scalars_pandas_df.dropna() - pandas.testing.assert_frame_equal(bf_result, pd_result) @@ -1590,64 +755,13 @@ def test_df_interpolate(scalars_dfs): ) -@pytest.mark.parametrize( - "col, fill_value", - [ - (["int64_col", "float64_col"], 3), - (["string_col"], "A"), - (["datetime_col"], pd.Timestamp("2023-01-01")), - ], -) -def test_df_fillna(scalars_dfs, col, fill_value): - scalars_df, scalars_pandas_df = scalars_dfs - bf_result = scalars_df[col].fillna(fill_value).to_pandas() - pd_result = scalars_pandas_df[col].fillna(fill_value) - - bigframes.testing.utils.assert_frame_equal(bf_result, pd_result, check_dtype=False) - - -def test_df_replace_scalar_scalar(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - bf_result = scalars_df.replace(555.555, 3).to_pandas() - pd_result = scalars_pandas_df.replace(555.555, 3) - - # pandas has narrower result types as they are determined dynamically - bigframes.testing.utils.assert_frame_equal(pd_result, bf_result, check_dtype=False) - - -def test_df_replace_regex_scalar(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - bf_result = scalars_df.replace("^H.l", "Howdy, Planet!", regex=True).to_pandas() - pd_result = scalars_pandas_df.replace("^H.l", "Howdy, Planet!", regex=True) - - bigframes.testing.utils.assert_frame_equal( - pd_result, - bf_result, - ) - - -def test_df_replace_list_scalar(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - bf_result = scalars_df.replace([555.555, 3.2], 3).to_pandas() - pd_result = scalars_pandas_df.replace([555.555, 3.2], 3) - - # pandas has narrower result types as they are determined dynamically - bigframes.testing.utils.assert_frame_equal( - pd_result, - bf_result, - check_dtype=False, - ) - - -def test_df_replace_value_dict(scalars_dfs): +def test_df_fillna(scalars_dfs): scalars_df, scalars_pandas_df = scalars_dfs - bf_result = scalars_df.replace(1, {"int64_col": 100, "int64_too": 200}).to_pandas() - pd_result = scalars_pandas_df.replace(1, {"int64_col": 100, "int64_too": 200}) + df = scalars_df[["int64_col", "float64_col"]].fillna(3) + bf_result = df.to_pandas() + pd_result = scalars_pandas_df[["int64_col", "float64_col"]].fillna(3) - bigframes.testing.utils.assert_frame_equal( - pd_result, - bf_result, - ) + pandas.testing.assert_frame_equal(bf_result, pd_result) def test_df_ffill(scalars_dfs): @@ -1717,31 +831,6 @@ def test_apply_series_scalar_callable( pandas.testing.assert_series_equal(bf_result, pd_result) -def test_df_pipe( - scalars_df_index, - scalars_pandas_df_index, -): - columns = ["int64_too", "int64_col"] - - def foo(x: int, y: int, df): - return (df + x) % y - - bf_result = ( - scalars_df_index[columns] - .pipe((foo, "df"), x=7, y=9) - .pipe(lambda x: x**2) - .to_pandas() - ) - - pd_result = ( - scalars_pandas_df_index[columns] - .pipe((foo, "df"), x=7, y=9) - .pipe(lambda x: x**2) - ) - - pandas.testing.assert_frame_equal(bf_result, pd_result) - - def test_df_keys( scalars_df_index, scalars_pandas_df_index, @@ -1763,10 +852,6 @@ def test_iterrows( scalars_df_index, scalars_pandas_df_index, ): - # TODO: supply a reason why this isn't compatible with pandas 1.x - pytest.importorskip("pandas", minversion="2.0.0") - scalars_df_index = scalars_df_index.add_suffix("_suffix", axis=1) - scalars_pandas_df_index = scalars_pandas_df_index.add_suffix("_suffix", axis=1) for (bf_index, bf_series), (pd_index, pd_series) in zip( scalars_df_index.iterrows(), scalars_pandas_df_index.iterrows() ): @@ -1795,7 +880,7 @@ def test_itertuples(scalars_df_index, index, name): assert bf_tuple == pd_tuple -def test_df_isin_list_w_null(scalars_dfs): +def test_df_isin_list(scalars_dfs): scalars_df, scalars_pandas_df = scalars_dfs values = ["Hello, World!", 55555, 2.51, pd.NA, True] bf_result = ( @@ -1810,21 +895,6 @@ def test_df_isin_list_w_null(scalars_dfs): pandas.testing.assert_frame_equal(bf_result, pd_result.astype("boolean")) -def test_df_isin_list_wo_null(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - values = ["Hello, World!", 55555, 2.51, True] - bf_result = ( - scalars_df[["int64_col", "float64_col", "string_col", "bool_col"]] - .isin(values) - .to_pandas() - ) - pd_result = scalars_pandas_df[ - ["int64_col", "float64_col", "string_col", "bool_col"] - ].isin(values) - - pandas.testing.assert_frame_equal(bf_result, pd_result.astype("boolean")) - - def test_df_isin_dict(scalars_dfs): scalars_df, scalars_pandas_df = scalars_dfs values = { @@ -1844,28 +914,6 @@ def test_df_isin_dict(scalars_dfs): pandas.testing.assert_frame_equal(bf_result, pd_result.astype("boolean")) -def test_df_cross_merge(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - left_columns = ["int64_col", "float64_col", "rowindex_2"] - right_columns = ["int64_col", "bool_col", "string_col", "rowindex_2"] - - left = scalars_df[left_columns] - # Offset the rows somewhat so that outer join can have an effect. - right = scalars_df[right_columns].assign(rowindex_2=scalars_df["rowindex_2"] + 2) - - bf_result = left.merge(right, "cross").to_pandas() - - pd_result = scalars_pandas_df[left_columns].merge( - scalars_pandas_df[right_columns].assign( - rowindex_2=scalars_pandas_df["rowindex_2"] + 2 - ), - "cross", - ) - bigframes.testing.utils.assert_frame_equal( - bf_result, pd_result, check_index_type=False - ) - - @pytest.mark.parametrize( ("merge_how",), [ @@ -1897,7 +945,7 @@ def test_df_merge(scalars_dfs, merge_how): sort=True, ) - assert_frame_equal(bf_result, pd_result, ignore_order=True, check_index_type=False) + assert_pandas_df_equal_ignore_ordering(bf_result, pd_result) @pytest.mark.parametrize( @@ -1930,7 +978,7 @@ def test_df_merge_multi_key(scalars_dfs, left_on, right_on): sort=True, ) - assert_frame_equal(bf_result, pd_result, ignore_order=True, check_index_type=False) + assert_pandas_df_equal_ignore_ordering(bf_result, pd_result) @pytest.mark.parametrize( @@ -1960,7 +1008,7 @@ def test_merge_custom_col_name(scalars_dfs, merge_how): pandas_right_df = scalars_pandas_df[right_columns] pd_result = pandas_left_df.merge(pandas_right_df, merge_how, on, sort=True) - assert_frame_equal(bf_result, pd_result, ignore_order=True, check_index_type=False) + assert_pandas_df_equal_ignore_ordering(bf_result, pd_result) @pytest.mark.parametrize( @@ -1993,79 +1041,37 @@ def test_merge_left_on_right_on(scalars_dfs, merge_how): sort=True, ) - assert_frame_equal(bf_result, pd_result, ignore_order=True, check_index_type=False) - - -def test_self_merge_self_w_on_args(): - data = { - "A": pd.Series([1, 2, 3], dtype="Int64"), - "B": pd.Series([1, 2, 3], dtype="Int64"), - "C": pd.Series([100, 200, 300], dtype="Int64"), - "D": pd.Series(["alpha", "beta", "gamma"], dtype="string[pyarrow]"), - } - df = pd.DataFrame(data) - - df1 = df[["A", "C"]] - df2 = df[["B", "C", "D"]] - pd_result = df1.merge(df2, left_on=["A", "C"], right_on=["B", "C"], how="inner") - - bf_df = bpd.DataFrame(data) - - bf_df1 = bf_df[["A", "C"]] - bf_df2 = bf_df[["B", "C", "D"]] - bf_result = bf_df1.merge( - bf_df2, left_on=["A", "C"], right_on=["B", "C"], how="inner" - ).to_pandas() - bigframes.testing.utils.assert_frame_equal( - bf_result, pd_result, check_index_type=False - ) - - -@pytest.mark.parametrize( - ("decimals",), - [ - (2,), - ({"float64_col": 0, "bool_col": 1, "int64_too": -3},), - ({},), - ], -) -def test_dataframe_round(scalars_dfs, decimals): - if pd.__version__.startswith("1."): - pytest.skip("Rounding doesn't work as expected in pandas 1.x") - scalars_df, scalars_pandas_df = scalars_dfs - - bf_result = scalars_df.round(decimals).to_pandas() - pd_result = scalars_pandas_df.round(decimals) - - assert_frame_equal(bf_result, pd_result) + assert_pandas_df_equal_ignore_ordering(bf_result, pd_result) def test_get_dtypes(scalars_df_default_index): dtypes = scalars_df_default_index.dtypes - dtypes_dict: Dict[str, bigframes.dtypes.Dtype] = { - "bool_col": pd.BooleanDtype(), - "bytes_col": pd.ArrowDtype(pa.binary()), - "date_col": pd.ArrowDtype(pa.date32()), - "datetime_col": pd.ArrowDtype(pa.timestamp("us")), - "geography_col": gpd.array.GeometryDtype(), - "int64_col": pd.Int64Dtype(), - "int64_too": pd.Int64Dtype(), - "numeric_col": pd.ArrowDtype(pa.decimal128(38, 9)), - "float64_col": pd.Float64Dtype(), - "rowindex": pd.Int64Dtype(), - "rowindex_2": pd.Int64Dtype(), - "string_col": pd.StringDtype(storage="pyarrow"), - "time_col": pd.ArrowDtype(pa.time64("us")), - "timestamp_col": pd.ArrowDtype(pa.timestamp("us", tz="UTC")), - "duration_col": pd.ArrowDtype(pa.duration("us")), - } - bigframes.testing.utils.assert_series_equal( + pd.testing.assert_series_equal( dtypes, - pd.Series(dtypes_dict), + pd.Series( + { + "bool_col": pd.BooleanDtype(), + "bytes_col": np.dtype("O"), + "date_col": pd.ArrowDtype(pa.date32()), + "datetime_col": pd.ArrowDtype(pa.timestamp("us")), + "geography_col": gpd.array.GeometryDtype(), + "int64_col": pd.Int64Dtype(), + "int64_too": pd.Int64Dtype(), + "numeric_col": np.dtype("O"), + "float64_col": pd.Float64Dtype(), + "rowindex": pd.Int64Dtype(), + "rowindex_2": pd.Int64Dtype(), + "string_col": pd.StringDtype(storage="pyarrow"), + "time_col": pd.ArrowDtype(pa.time64("us")), + "timestamp_col": pd.ArrowDtype(pa.timestamp("us", tz="UTC")), + } + ), ) -def test_get_dtypes_array_struct_query(session): +def test_get_dtypes_array_struct(session): + """We may upgrade struct and array to proper arrow dtype support in future. For now, + we return python objects""" df = session.read_gbq( """SELECT [1, 3, 2] AS array_column, @@ -2075,11 +1081,11 @@ def test_get_dtypes_array_struct_query(session): ) dtypes = df.dtypes - bigframes.testing.utils.assert_series_equal( + pd.testing.assert_series_equal( dtypes, pd.Series( { - "array_column": pd.ArrowDtype(pa.list_(pa.int64())), + "array_column": np.dtype("O"), "struct_column": pd.ArrowDtype( pa.struct( [ @@ -2093,58 +1099,6 @@ def test_get_dtypes_array_struct_query(session): ) -def test_get_dtypes_array_struct_table(nested_df): - dtypes = nested_df.dtypes - bigframes.testing.utils.assert_series_equal( - dtypes, - pd.Series( - { - "customer_id": pd.StringDtype(storage="pyarrow"), - "day": pd.ArrowDtype(pa.date32()), - "flag": pd.Int64Dtype(), - "label": pd.ArrowDtype( - pa.struct( - [ - ("key", pa.string()), - ("value", pa.string()), - ] - ), - ), - "event_sequence": pd.ArrowDtype( - pa.list_( - pa.struct( - [ - pa.field( - "data", - pa.list_( - pa.struct( - [ - ("value", pa.float64()), - ("key", pa.string()), - ], - ), - ), - nullable=False, - ), - ("timestamp", pa.timestamp("us", "UTC")), - ("category", pa.string()), - ] - ), - ), - ), - "address": pd.ArrowDtype( - pa.struct( - [ - ("street", pa.string()), - ("city", pa.string()), - ] - ), - ), - } - ), - ) - - def test_shape(scalars_dfs): scalars_df, scalars_pandas_df = scalars_dfs bf_result = scalars_df.shape @@ -2153,30 +1107,6 @@ def test_shape(scalars_dfs): assert bf_result == pd_result -@pytest.mark.parametrize( - "reference_table, test_table", - [ - ( - "bigframes-dev.bigframes_tests_sys.base_table", - "bigframes-dev.bigframes_tests_sys.base_table_mat_view", - ), - ( - "bigframes-dev.bigframes_tests_sys.base_table", - "bigframes-dev.bigframes_tests_sys.base_table_view", - ), - ( - "bigframes-dev.bigframes_tests_sys.csv_native_table", - "bigframes-dev.bigframes_tests_sys.csv_external_table", - ), - ], -) -def test_view_and_external_table_shape(session, reference_table, test_table): - reference_df = session.read_gbq(reference_table) - test_df = session.read_gbq(test_table) - - assert test_df.shape == reference_df.shape - - def test_len(scalars_dfs): scalars_df, scalars_pandas_df = scalars_dfs bf_result = len(scalars_df) @@ -2185,30 +1115,6 @@ def test_len(scalars_dfs): assert bf_result == pd_result -@pytest.mark.parametrize( - ("n_rows",), - [ - (50,), - (10000,), - ], -) -@pytest.mark.parametrize( - "write_engine", - # TODO(b/502298527): Reenable bigquery_write test - ["bigquery_load", "bigquery_streaming"], -) -def test_df_len_local(session, n_rows, write_engine): - assert ( - len( - session.read_pandas( - pd.DataFrame(np.random.randint(1, 7, n_rows), columns=["one"]), - write_engine=write_engine, - ) - ) - == n_rows - ) - - def test_size(scalars_dfs): scalars_df, scalars_pandas_df = scalars_dfs bf_result = scalars_df.size @@ -2286,52 +1192,6 @@ def test_reset_index(scalars_df_index, scalars_pandas_df_index, drop): pandas.testing.assert_frame_equal(bf_result, pd_result) -def test_reset_index_allow_duplicates(scalars_df_index, scalars_pandas_df_index): - scalars_df_index = scalars_df_index.copy() - scalars_df_index.index.name = "int64_col" - df = scalars_df_index.reset_index(allow_duplicates=True, drop=False) - assert df.index.name is None - - bf_result = df.to_pandas() - - scalars_pandas_df_index = scalars_pandas_df_index.copy() - scalars_pandas_df_index.index.name = "int64_col" - pd_result = scalars_pandas_df_index.reset_index(allow_duplicates=True, drop=False) - - # Pandas uses int64 instead of Int64 (nullable) dtype. - pd_result.index = pd_result.index.astype(pd.Int64Dtype()) - - # reset_index should maintain the original ordering. - pandas.testing.assert_frame_equal(bf_result, pd_result) - - -def test_reset_index_duplicates_error(scalars_df_index): - scalars_df_index = scalars_df_index.copy() - scalars_df_index.index.name = "int64_col" - with pytest.raises(ValueError): - scalars_df_index.reset_index(allow_duplicates=False, drop=False) - - -@pytest.mark.parametrize( - ("drop",), - ((True,), (False,)), -) -def test_reset_index_inplace(scalars_df_index, scalars_pandas_df_index, drop): - df = scalars_df_index.copy() - df.reset_index(drop=drop, inplace=True) - assert df.index.name is None - - bf_result = df.to_pandas() - pd_result = scalars_pandas_df_index.copy() - pd_result.reset_index(drop=drop, inplace=True) - - # Pandas uses int64 instead of Int64 (nullable) dtype. - pd_result.index = pd_result.index.astype(pd.Int64Dtype()) - - # reset_index should maintain the original ordering. - pandas.testing.assert_frame_equal(bf_result, pd_result) - - def test_reset_index_then_filter( scalars_df_index, scalars_pandas_df_index, @@ -2477,78 +1337,25 @@ def test_set_index_key_error(scalars_dfs): ("na_position",), (("first",), ("last",)), ) -@pytest.mark.parametrize( - ("axis",), - ((0,), ("columns",)), -) -def test_sort_index(scalars_dfs, ascending, na_position, axis): +def test_sort_index(scalars_dfs, ascending, na_position): index_column = "int64_col" scalars_df, scalars_pandas_df = scalars_dfs df = scalars_df.set_index(index_column) - bf_result = df.sort_index( - ascending=ascending, na_position=na_position, axis=axis - ).to_pandas() + bf_result = df.sort_index(ascending=ascending, na_position=na_position).to_pandas() pd_result = scalars_pandas_df.set_index(index_column).sort_index( - ascending=ascending, na_position=na_position, axis=axis + ascending=ascending, na_position=na_position ) pandas.testing.assert_frame_equal(bf_result, pd_result) -def test_dataframe_sort_index_inplace(scalars_dfs): - index_column = "int64_col" +def test_df_abs(scalars_dfs): scalars_df, scalars_pandas_df = scalars_dfs - df = scalars_df.copy().set_index(index_column) - df.sort_index(ascending=False, inplace=True) - bf_result = df.to_pandas() - - pd_result = scalars_pandas_df.set_index(index_column).sort_index(ascending=False) - pandas.testing.assert_frame_equal(bf_result, pd_result) - - -def test_df_abs(scalars_dfs_maybe_ordered): - scalars_df, scalars_pandas_df = scalars_dfs_maybe_ordered columns = ["int64_col", "int64_too", "float64_col"] - bf_result = scalars_df[columns].abs() + bf_result = scalars_df[columns].abs().to_pandas() pd_result = scalars_pandas_df[columns].abs() - assert_dfs_equivalent(pd_result, bf_result) - - -def test_df_pos(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - bf_result = (+scalars_df[["int64_col", "numeric_col"]]).to_pandas() - pd_result = +scalars_pandas_df[["int64_col", "numeric_col"]] - - assert_frame_equal(pd_result, bf_result) - - -def test_df_neg(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - bf_result = (-scalars_df[["int64_col", "numeric_col"]]).to_pandas() - pd_result = -scalars_pandas_df[["int64_col", "numeric_col"]] - - assert_frame_equal(pd_result, bf_result) - - -def test_df__abs__(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - bf_result = ( - abs(scalars_df[["int64_col", "numeric_col", "float64_col"]]) - ).to_pandas() - pd_result = abs(scalars_pandas_df[["int64_col", "numeric_col", "float64_col"]]) - - assert_frame_equal(pd_result, bf_result) - - -def test_df_invert(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - columns = ["int64_col", "bool_col"] - - bf_result = (~scalars_df[columns]).to_pandas() - pd_result = ~scalars_pandas_df[columns] - - assert_frame_equal(bf_result, pd_result) + assert_pandas_df_equal_ignore_ordering(bf_result, pd_result) def test_df_isnull(scalars_dfs): @@ -2565,7 +1372,7 @@ def test_df_isnull(scalars_dfs): pd_result["string_col"] = pd_result["string_col"].astype(pd.BooleanDtype()) pd_result["bool_col"] = pd_result["bool_col"].astype(pd.BooleanDtype()) - assert_frame_equal(bf_result, pd_result) + assert_pandas_df_equal_ignore_ordering(bf_result, pd_result) def test_df_notnull(scalars_dfs): @@ -2582,7 +1389,7 @@ def test_df_notnull(scalars_dfs): pd_result["string_col"] = pd_result["string_col"].astype(pd.BooleanDtype()) pd_result["bool_col"] = pd_result["bool_col"].astype(pd.BooleanDtype()) - assert_frame_equal(bf_result, pd_result) + assert_pandas_df_equal_ignore_ordering(bf_result, pd_result) @pytest.mark.parametrize( @@ -2634,7 +1441,7 @@ def test_combine( ) # Some dtype inconsistency for all-NULL columns - bigframes.testing.utils.assert_frame_equal(bf_result, pd_result, check_dtype=False) + pd.testing.assert_frame_equal(bf_result, pd_result, check_dtype=False) @pytest.mark.parametrize( @@ -2653,10 +1460,8 @@ def test_combine( def test_df_update(overwrite, filter_func): if pd.__version__.startswith("1."): pytest.skip("dtype handled differently in pandas 1.x.") - - index1: pandas.Index = pandas.Index([1, 2, 3, 4], dtype="Int64") - - index2: pandas.Index = pandas.Index([1, 2, 4, 5], dtype="Int64") + index1 = pandas.Index([1, 2, 3, 4], dtype="Int64") + index2 = pandas.Index([1, 2, 4, 5], dtype="Int64") pd_df1 = pandas.DataFrame( {"a": [1, None, 3, 4], "b": [5, 6, None, 8]}, dtype="Int64", index=index1 ) @@ -2672,7 +1477,7 @@ def test_df_update(overwrite, filter_func): bf_df1.update(bf_df2, overwrite=overwrite, filter_func=filter_func) pd_df1.update(pd_df2, overwrite=overwrite, filter_func=filter_func) - bigframes.testing.utils.assert_frame_equal(bf_df1.to_pandas(), pd_df1) + pd.testing.assert_frame_equal(bf_df1.to_pandas(), pd_df1) def test_df_idxmin(): @@ -2684,7 +1489,7 @@ def test_df_idxmin(): bf_result = bf_df.idxmin().to_pandas() pd_result = pd_df.idxmin() - bigframes.testing.utils.assert_series_equal( + pd.testing.assert_series_equal( bf_result, pd_result, check_index_type=False, check_dtype=False ) @@ -2698,7 +1503,7 @@ def test_df_idxmax(): bf_result = bf_df.idxmax().to_pandas() pd_result = pd_df.idxmax() - bigframes.testing.utils.assert_series_equal( + pd.testing.assert_series_equal( bf_result, pd_result, check_index_type=False, check_dtype=False ) @@ -2716,9 +1521,8 @@ def test_df_idxmax(): ], ) def test_df_align(join, axis): - index1: pandas.Index = pandas.Index([1, 2, 3, 4], dtype="Int64") - - index2: pandas.Index = pandas.Index([1, 2, 4, 5], dtype="Int64") + index1 = pandas.Index([1, 2, 3, 4], dtype="Int64") + index2 = pandas.Index([1, 2, 4, 5], dtype="Int64") pd_df1 = pandas.DataFrame( {"a": [1, None, 3, 4], "b": [5, 6, None, 8]}, dtype="Int64", index=index1 ) @@ -2735,15 +1539,8 @@ def test_df_align(join, axis): pd_result1, pd_result2 = pd_df1.align(pd_df2, join=join, axis=axis) # Don't check dtype as pandas does unnecessary float conversion - assert isinstance(bf_result1, dataframe.DataFrame) and isinstance( - bf_result2, dataframe.DataFrame - ) - bigframes.testing.utils.assert_frame_equal( - bf_result1.to_pandas(), pd_result1, check_dtype=False - ) - bigframes.testing.utils.assert_frame_equal( - bf_result2.to_pandas(), pd_result2, check_dtype=False - ) + pd.testing.assert_frame_equal(bf_result1.to_pandas(), pd_result1, check_dtype=False) + pd.testing.assert_frame_equal(bf_result2.to_pandas(), pd_result2, check_dtype=False) def test_combine_first( @@ -2768,153 +1565,7 @@ def test_combine_first( pd_result = pd_df_a.combine_first(pd_df_b) # Some dtype inconsistency for all-NULL columns - bigframes.testing.utils.assert_frame_equal(bf_result, pd_result, check_dtype=False) - - -@pytest.mark.parametrize( - ("columns", "numeric_only"), - [ - (["bool_col", "int64_col", "float64_col"], True), - (["bool_col", "int64_col", "float64_col"], False), - (["bool_col", "int64_col", "float64_col", "string_col"], True), - pytest.param( - ["bool_col", "int64_col", "float64_col", "string_col"], - False, - marks=pytest.mark.xfail( - raises=NotImplementedError, - ), - ), - ], -) -def test_df_corr_w_numeric_only(scalars_dfs_maybe_ordered, columns, numeric_only): - scalars_df, scalars_pandas_df = scalars_dfs_maybe_ordered - - bf_result = scalars_df[columns].corr(numeric_only=numeric_only).to_pandas() - pd_result = scalars_pandas_df[columns].corr(numeric_only=numeric_only) - - # BigFrames and Pandas differ in their data type handling: - # - Column types: BigFrames uses Float64, Pandas uses float64. - # - Index types: BigFrames uses strign, Pandas uses object. - bigframes.testing.utils.assert_index_equal(bf_result.columns, pd_result.columns) - # Only check row order in ordered mode. - bigframes.testing.utils.assert_frame_equal( - bf_result, - pd_result, - check_dtype=False, - check_index_type=False, - check_like=~scalars_df._block.session._strictly_ordered, - ) - - -def test_df_corr_w_invalid_parameters(scalars_dfs): - columns = ["int64_too", "int64_col", "float64_col"] - scalars_df, _ = scalars_dfs - - with pytest.raises(NotImplementedError): - scalars_df[columns].corr(method="kendall") - - with pytest.raises(NotImplementedError): - scalars_df[columns].corr(min_periods=1) - - -@pytest.mark.parametrize( - ("columns", "numeric_only"), - [ - (["bool_col", "int64_col", "float64_col"], True), - (["bool_col", "int64_col", "float64_col"], False), - (["bool_col", "int64_col", "float64_col", "string_col"], True), - pytest.param( - ["bool_col", "int64_col", "float64_col", "string_col"], - False, - marks=pytest.mark.xfail( - raises=NotImplementedError, - ), - ), - ], -) -def test_cov_w_numeric_only(scalars_dfs_maybe_ordered, columns, numeric_only): - scalars_df, scalars_pandas_df = scalars_dfs_maybe_ordered - bf_result = scalars_df[columns].cov(numeric_only=numeric_only).to_pandas() - pd_result = scalars_pandas_df[columns].cov(numeric_only=numeric_only) - # BigFrames and Pandas differ in their data type handling: - # - Column types: BigFrames uses Float64, Pandas uses float64. - # - Index types: BigFrames uses strign, Pandas uses object. - bigframes.testing.utils.assert_index_equal(bf_result.columns, pd_result.columns) - # Only check row order in ordered mode. - bigframes.testing.utils.assert_frame_equal( - bf_result, - pd_result, - check_dtype=False, - check_index_type=False, - check_like=~scalars_df._block.session._strictly_ordered, - ) - - -def test_df_corrwith_df(scalars_dfs_maybe_ordered): - scalars_df, scalars_pandas_df = scalars_dfs_maybe_ordered - - l_cols = ["int64_col", "float64_col", "int64_too"] - r_cols = ["int64_too", "float64_col"] - - bf_result = scalars_df[l_cols].corrwith(scalars_df[r_cols]).to_pandas() - pd_result = scalars_pandas_df[l_cols].corrwith(scalars_pandas_df[r_cols]) - - # BigFrames and Pandas differ in their data type handling: - # - Column types: BigFrames uses Float64, Pandas uses float64. - # - Index types: BigFrames uses strign, Pandas uses object. - bigframes.testing.utils.assert_series_equal( - bf_result, pd_result, check_dtype=False, check_index_type=False - ) - - -def test_df_corrwith_df_numeric_only(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - - l_cols = ["int64_col", "float64_col", "int64_too", "string_col"] - r_cols = ["int64_too", "float64_col", "bool_col"] - - bf_result = ( - scalars_df[l_cols].corrwith(scalars_df[r_cols], numeric_only=True).to_pandas() - ) - pd_result = scalars_pandas_df[l_cols].corrwith( - scalars_pandas_df[r_cols], numeric_only=True - ) - - # BigFrames and Pandas differ in their data type handling: - # - Column types: BigFrames uses Float64, Pandas uses float64. - # - Index types: BigFrames uses strign, Pandas uses object. - bigframes.testing.utils.assert_series_equal( - bf_result, pd_result, check_dtype=False, check_index_type=False - ) - - -def test_df_corrwith_df_non_numeric_error(scalars_dfs): - scalars_df, _ = scalars_dfs - - l_cols = ["int64_col", "float64_col", "int64_too", "string_col"] - r_cols = ["int64_too", "float64_col", "bool_col"] - - with pytest.raises(NotImplementedError): - scalars_df[l_cols].corrwith(scalars_df[r_cols], numeric_only=False) - - -def test_df_corrwith_series(scalars_dfs_maybe_ordered): - # TODO: supply a reason why this isn't compatible with pandas 1.x - pytest.importorskip("pandas", minversion="2.0.0") - scalars_df, scalars_pandas_df = scalars_dfs_maybe_ordered - - l_cols = ["int64_col", "float64_col", "int64_too"] - r_col = "float64_col" - - bf_result = scalars_df[l_cols].corrwith(scalars_df[r_col]).to_pandas() - pd_result = scalars_pandas_df[l_cols].corrwith(scalars_pandas_df[r_col]) - - # BigFrames and Pandas differ in their data type handling: - # - Column types: BigFrames uses Float64, Pandas uses float64. - # - Index types: BigFrames uses strign, Pandas uses object. - bigframes.testing.utils.assert_series_equal( - bf_result, pd_result, check_dtype=False, check_index_type=False - ) + pd.testing.assert_frame_equal(bf_result, pd_result, check_dtype=False) @pytest.mark.parametrize( @@ -2958,23 +1609,7 @@ def test_scalar_binop(scalars_dfs, op, other_scalar, reverse_operands): bf_result = maybe_reversed_op(scalars_df[columns], other_scalar).to_pandas() pd_result = maybe_reversed_op(scalars_pandas_df[columns], other_scalar) - assert_frame_equal(bf_result, pd_result) - - -def test_dataframe_string_radd_const(scalars_dfs): - pytest.importorskip( - "pandas", - minversion="2.0.0", - reason="PyArrow string addition requires pandas 2.0+", - ) - - scalars_df, scalars_pandas_df = scalars_dfs - columns = ["string_col", "string_col"] - - bf_result = ("prefix" + scalars_df[columns]).to_pandas() - pd_result = "prefix" + scalars_pandas_df[columns] - - assert_frame_equal(bf_result, pd_result) + assert_pandas_df_equal_ignore_ordering(bf_result, pd_result) @pytest.mark.parametrize(("other_scalar"), [1, -2]) @@ -2986,13 +1621,13 @@ def test_mod(scalars_dfs, other_scalar): bf_result = (scalars_df[["int64_col", "int64_too"]] % other_scalar).to_pandas() pd_result = scalars_pandas_df[["int64_col", "int64_too"]] % other_scalar - assert_frame_equal(bf_result, pd_result) + assert_pandas_df_equal_ignore_ordering(bf_result, pd_result) def test_scalar_binop_str_exception(scalars_dfs): scalars_df, _ = scalars_dfs columns = ["string_col"] - with pytest.raises(TypeError, match="Cannot add dtypes"): + with pytest.raises(TypeError): (scalars_df[columns] + 1).to_pandas() @@ -3042,116 +1677,7 @@ def test_series_binop_axis_index( bf_result = op(scalars_df[df_columns], scalars_df[series_column]).to_pandas() pd_result = op(scalars_pandas_df[df_columns], scalars_pandas_df[series_column]) - assert_frame_equal(bf_result, pd_result) - - -@pytest.mark.parametrize( - ("input"), - [ - ((1000, 2000, 3000)), - (pd.Index([1000, 2000, 3000])), - (pd.Series((1000, 2000), index=["int64_too", "float64_col"])), - ], - ids=[ - "tuple", - "pd_index", - "pd_series", - ], -) -def test_listlike_binop_axis_1_in_memory_data(scalars_dfs, input): - # TODO: supply a reason why this isn't compatible with pandas 1.x - pytest.importorskip("pandas", minversion="2.0.0") - scalars_df, scalars_pandas_df = scalars_dfs - - df_columns = ["int64_col", "float64_col", "int64_too"] - - bf_result = scalars_df[df_columns].add(input, axis=1).to_pandas() - if hasattr(input, "to_pandas"): - input = input.to_pandas() - pd_result = scalars_pandas_df[df_columns].add(input, axis=1) - - assert_frame_equal(bf_result, pd_result, check_dtype=False) - - -def test_df_reverse_binop_pandas(scalars_dfs): - # TODO: supply a reason why this isn't compatible with pandas 1.x - pytest.importorskip("pandas", minversion="2.0.0") - scalars_df, scalars_pandas_df = scalars_dfs - - pd_series = pd.Series([100, 200, 300]) - - df_columns = ["int64_col", "float64_col", "int64_too"] - - bf_result = pd_series + scalars_df[df_columns].to_pandas() - pd_result = pd_series + scalars_pandas_df[df_columns] - - assert_frame_equal(bf_result, pd_result, check_dtype=False) - - -def test_listlike_binop_axis_1_bf_index(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - - df_columns = ["int64_col", "float64_col", "int64_too"] - - bf_result = ( - scalars_df[df_columns] - .add(bf_indexes.Index([1000, 2000, 3000]), axis=1) - .to_pandas() - ) - pd_result = scalars_pandas_df[df_columns].add(pd.Index([1000, 2000, 3000]), axis=1) - - assert_frame_equal(bf_result, pd_result, check_dtype=False) - - -def test_binop_with_self_aggregate(scalars_dfs_maybe_ordered): - scalars_df, scalars_pandas_df = scalars_dfs_maybe_ordered - - df_columns = ["int64_col", "float64_col", "int64_too"] - - history_before = scalars_df._session.execution_history().to_dataframe() - queries_before = ( - len(history_before[history_before["job_type"] == "query"]) - if "job_type" in history_before.columns - else 0 - ) - - bf_df = scalars_df[df_columns] - bf_result = (bf_df - bf_df.mean()).to_pandas() - - history_after = scalars_df._session.execution_history().to_dataframe() - queries_after = len(history_after[history_after["job_type"] == "query"]) - - pd_df = scalars_pandas_df[df_columns] - pd_result = pd_df - pd_df.mean() - - assert (queries_after - queries_before) == 1 - assert_frame_equal(bf_result, pd_result, check_dtype=False) - - -def test_binop_with_self_aggregate_w_index_reset(scalars_dfs_maybe_ordered): - scalars_df, scalars_pandas_df = scalars_dfs_maybe_ordered - - df_columns = ["int64_col", "float64_col", "int64_too"] - - history_before = scalars_df._session.execution_history().to_dataframe() - queries_before = ( - len(history_before[history_before["job_type"] == "query"]) - if "job_type" in history_before.columns - else 0 - ) - - bf_df = scalars_df[df_columns].reset_index(drop=True) - bf_result = (bf_df - bf_df.mean()).to_pandas() - - history_after = scalars_df._session.execution_history().to_dataframe() - queries_after = len(history_after[history_after["job_type"] == "query"]) - - pd_df = scalars_pandas_df[df_columns].reset_index(drop=True) - pd_result = pd_df - pd_df.mean() - - assert (queries_after - queries_before) == 1 - pd_result.index = pd_result.index.astype("Int64") - assert_frame_equal(bf_result, pd_result, check_dtype=False, check_index_type=False) + assert_pandas_df_equal_ignore_ordering(bf_result, pd_result) @pytest.mark.parametrize( @@ -3193,19 +1719,12 @@ def test_binop_df_df_binary_op( pd_result = pd_df_a - pd_df_b # Some dtype inconsistency for all-NULL columns - bigframes.testing.utils.assert_frame_equal(bf_result, pd_result, check_dtype=False) + pd.testing.assert_frame_equal(bf_result, pd_result, check_dtype=False) # Differnt table will only work for explicit index, since default index orders are arbitrary. -@pytest.mark.parametrize( - ("ordered"), - [ - (True), - (False), - ], -) def test_series_binop_add_different_table( - scalars_df_index, scalars_pandas_df_index, scalars_df_2_index, ordered + scalars_df_index, scalars_pandas_df_index, scalars_df_2_index ): df_columns = ["int64_col", "float64_col"] series_column = "int64_too" @@ -3213,61 +1732,39 @@ def test_series_binop_add_different_table( bf_result = ( scalars_df_index[df_columns] .add(scalars_df_2_index[series_column], axis="index") - .to_pandas(ordered=ordered) + .to_pandas() ) pd_result = scalars_pandas_df_index[df_columns].add( scalars_pandas_df_index[series_column], axis="index" ) - assert_frame_equal(bf_result, pd_result, ignore_order=not ordered) + assert_pandas_df_equal_ignore_ordering(bf_result, pd_result) # TODO(garrettwu): Test series binop with different index all_joins = pytest.mark.parametrize( ("how",), - (("outer",), ("left",), ("right",), ("inner",), ("cross",)), + ( + ("outer",), + ("left",), + ("right",), + ("inner",), + ), ) @all_joins -def test_join_same_table(scalars_dfs_maybe_ordered, how): - bf_df, pd_df = scalars_dfs_maybe_ordered +def test_join_same_table(scalars_dfs, how): + bf_df, pd_df = scalars_dfs bf_df_a = bf_df.set_index("int64_too")[["string_col", "int64_col"]] - bf_df_a = bf_df_a.sort_index() - bf_df_b = bf_df.set_index("int64_too")[["float64_col"]] - bf_df_b = bf_df_b[bf_df_b.float64_col > 0] - bf_df_b = bf_df_b.sort_values("float64_col") - bf_result = bf_df_a.join(bf_df_b, how=how).to_pandas() - - pd_df_a = pd_df.set_index("int64_too")[["string_col", "int64_col"]].sort_index() - pd_df_a = pd_df_a.sort_index() - + pd_df_a = pd_df.set_index("int64_too")[["string_col", "int64_col"]] pd_df_b = pd_df.set_index("int64_too")[["float64_col"]] - pd_df_b = pd_df_b[pd_df_b.float64_col > 0] - pd_df_b = pd_df_b.sort_values("float64_col") - pd_result = pd_df_a.join(pd_df_b, how=how) - - assert_frame_equal(bf_result, pd_result, ignore_order=True) - - -def test_join_incompatible_key_type_error(scalars_dfs): - bf_df, _ = scalars_dfs - - bf_df_a = bf_df.set_index("int64_too")[["string_col", "int64_col"]] - bf_df_a = bf_df_a.sort_index() - - bf_df_b = bf_df.set_index("date_col")[["float64_col"]] - bf_df_b = bf_df_b[bf_df_b.float64_col > 0] - bf_df_b = bf_df_b.sort_values("float64_col") - - with pytest.raises(TypeError): - # joining incompatible date, int columns - bf_df_a.join(bf_df_b, how="left") + assert_pandas_df_equal_ignore_ordering(bf_result, pd_result) @all_joins @@ -3280,107 +1777,15 @@ def test_join_different_table( pd_df_a = scalars_pandas_df_index[["string_col", "int64_col"]] pd_df_b = scalars_pandas_df_index.dropna()[["float64_col"]] pd_result = pd_df_a.join(pd_df_b, how=how) - assert_frame_equal(bf_result, pd_result, ignore_order=True) + assert_pandas_df_equal_ignore_ordering(bf_result, pd_result) -@all_joins -def test_join_different_table_with_duplicate_column_name( - scalars_df_index, scalars_pandas_df_index, how -): - bf_df_a = scalars_df_index[["string_col", "int64_col", "int64_too"]].rename( - columns={"int64_too": "int64_col"} - ) - bf_df_b = scalars_df_index.dropna()[ - ["string_col", "int64_col", "int64_too"] - ].rename(columns={"int64_too": "int64_col"}) - bf_result = bf_df_a.join(bf_df_b, how=how, lsuffix="_l", rsuffix="_r").to_pandas() - pd_df_a = scalars_pandas_df_index[["string_col", "int64_col", "int64_too"]].rename( - columns={"int64_too": "int64_col"} - ) - pd_df_b = scalars_pandas_df_index.dropna()[ - ["string_col", "int64_col", "int64_too"] - ].rename(columns={"int64_too": "int64_col"}) - pd_result = pd_df_a.join(pd_df_b, how=how, lsuffix="_l", rsuffix="_r") - - # Ensure no inplace changes - bigframes.testing.utils.assert_index_equal(bf_df_a.columns, pd_df_a.columns) - bigframes.testing.utils.assert_index_equal(bf_df_b.index.to_pandas(), pd_df_b.index) - bigframes.testing.utils.assert_frame_equal( - bf_result, pd_result, check_index_type=False - ) - - -@all_joins -def test_join_param_on_with_duplicate_column_name_not_on_col( - scalars_df_index, scalars_pandas_df_index, how -): - # This test is for duplicate column names, but the 'on' column is not duplicated. - if how == "cross": - return - bf_df_a = scalars_df_index[ - ["string_col", "datetime_col", "timestamp_col", "int64_too"] - ].rename(columns={"timestamp_col": "datetime_col"}) - bf_df_b = scalars_df_index.dropna()[ - ["string_col", "datetime_col", "timestamp_col"] - ].rename(columns={"timestamp_col": "datetime_col"}) - bf_result = bf_df_a.join( - bf_df_b, on="int64_too", how=how, lsuffix="_l", rsuffix="_r" - ).to_pandas() - pd_df_a = scalars_pandas_df_index[ - ["string_col", "datetime_col", "timestamp_col", "int64_too"] - ].rename(columns={"timestamp_col": "datetime_col"}) - pd_df_b = scalars_pandas_df_index.dropna()[ - ["string_col", "datetime_col", "timestamp_col"] - ].rename(columns={"timestamp_col": "datetime_col"}) - pd_result = pd_df_a.join( - pd_df_b, on="int64_too", how=how, lsuffix="_l", rsuffix="_r" - ) - bigframes.testing.utils.assert_frame_equal( - bf_result.sort_index(), - pd_result.sort_index(), - check_like=True, - check_index_type=False, - check_names=False, - ) - bigframes.testing.utils.assert_index_equal(bf_result.columns, pd_result.columns) - - -@pytest.mark.skipif( - pandas.__version__.startswith("1."), reason="bad left join in pandas 1.x" -) -@all_joins -def test_join_param_on_with_duplicate_column_name_on_col( - scalars_df_index, scalars_pandas_df_index, how -): - # This test is for duplicate column names, and the 'on' column is duplicated. - if how == "cross": - return - bf_df_a = scalars_df_index[ - ["string_col", "datetime_col", "timestamp_col", "int64_too"] - ].rename(columns={"timestamp_col": "datetime_col"}) - bf_df_b = scalars_df_index.dropna()[ - ["string_col", "datetime_col", "timestamp_col", "int64_too"] - ].rename(columns={"timestamp_col": "datetime_col"}) - bf_result = bf_df_a.join( - bf_df_b, on="int64_too", how=how, lsuffix="_l", rsuffix="_r" - ).to_pandas() - pd_df_a = scalars_pandas_df_index[ - ["string_col", "datetime_col", "timestamp_col", "int64_too"] - ].rename(columns={"timestamp_col": "datetime_col"}) - pd_df_b = scalars_pandas_df_index.dropna()[ - ["string_col", "datetime_col", "timestamp_col", "int64_too"] - ].rename(columns={"timestamp_col": "datetime_col"}) - pd_result = pd_df_a.join( - pd_df_b, on="int64_too", how=how, lsuffix="_l", rsuffix="_r" - ) - bigframes.testing.utils.assert_frame_equal( - bf_result.sort_index(), - pd_result.sort_index(), - check_like=True, - check_index_type=False, - check_names=False, - ) - bigframes.testing.utils.assert_index_equal(bf_result.columns, pd_result.columns) +def test_join_duplicate_columns_raises_not_implemented(scalars_dfs): + scalars_df, _ = scalars_dfs + df_a = scalars_df[["string_col", "float64_col"]] + df_b = scalars_df[["float64_col"]] + with pytest.raises(NotImplementedError): + df_a.join(df_b, how="outer").to_pandas() @all_joins @@ -3390,39 +1795,13 @@ def test_join_param_on(scalars_dfs, how): bf_df_a = bf_df[["string_col", "int64_col", "rowindex_2"]] bf_df_a = bf_df_a.assign(rowindex_2=bf_df_a["rowindex_2"] + 2) bf_df_b = bf_df[["float64_col"]] + bf_result = bf_df_a.join(bf_df_b, on="rowindex_2", how=how).to_pandas() - if how == "cross": - with pytest.raises(ValueError): - bf_df_a.join(bf_df_b, on="rowindex_2", how=how) - else: - bf_result = bf_df_a.join(bf_df_b, on="rowindex_2", how=how).to_pandas() - - pd_df_a = pd_df[["string_col", "int64_col", "rowindex_2"]] - pd_df_a = pd_df_a.assign(rowindex_2=pd_df_a["rowindex_2"] + 2) - pd_df_b = pd_df[["float64_col"]] - pd_result = pd_df_a.join(pd_df_b, on="rowindex_2", how=how) - assert_frame_equal(bf_result, pd_result, ignore_order=True) - - -@all_joins -def test_df_join_series(scalars_dfs, how): - bf_df, pd_df = scalars_dfs - - bf_df_a = bf_df[["string_col", "int64_col", "rowindex_2"]] - bf_df_a = bf_df_a.assign(rowindex_2=bf_df_a["rowindex_2"] + 2) - bf_series_b = bf_df["float64_col"] - - if how == "cross": - with pytest.raises(ValueError): - bf_df_a.join(bf_series_b, on="rowindex_2", how=how) - else: - bf_result = bf_df_a.join(bf_series_b, on="rowindex_2", how=how).to_pandas() - - pd_df_a = pd_df[["string_col", "int64_col", "rowindex_2"]] - pd_df_a = pd_df_a.assign(rowindex_2=pd_df_a["rowindex_2"] + 2) - pd_series_b = pd_df["float64_col"] - pd_result = pd_df_a.join(pd_series_b, on="rowindex_2", how=how) - assert_frame_equal(bf_result, pd_result, ignore_order=True) + pd_df_a = pd_df[["string_col", "int64_col", "rowindex_2"]] + pd_df_a = pd_df_a.assign(rowindex_2=pd_df_a["rowindex_2"] + 2) + pd_df_b = pd_df[["float64_col"]] + pd_result = pd_df_a.join(pd_df_b, on="rowindex_2", how=how) + assert_pandas_df_equal_ignore_ordering(bf_result, pd_result) @pytest.mark.parametrize( @@ -3452,37 +1831,6 @@ def test_dataframe_sort_values( ) -@pytest.mark.parametrize( - ("by", "ascending", "na_position"), - [ - ("int64_col", True, "first"), - (["bool_col", "int64_col"], True, "last"), - ], -) -def test_dataframe_sort_values_inplace( - scalars_df_index, scalars_pandas_df_index, by, ascending, na_position -): - # Test needs values to be unique - bf_sorted = scalars_df_index.copy() - bf_sorted.sort_values( - by, ascending=ascending, na_position=na_position, inplace=True - ) - bf_result = bf_sorted.to_pandas() - pd_result = scalars_pandas_df_index.sort_values( - by, ascending=ascending, na_position=na_position - ) - - pandas.testing.assert_frame_equal( - bf_result, - pd_result, - ) - - -def test_dataframe_sort_values_invalid_input(scalars_df_index): - with pytest.raises(KeyError): - scalars_df_index.sort_values(by=scalars_df_index["int64_col"]) - - def test_dataframe_sort_values_stable(scalars_df_index, scalars_pandas_df_index): bf_result = ( scalars_df_index.sort_values("int64_col", kind="stable") @@ -3525,7 +1873,7 @@ def test_dataframe_numeric_analytic_op( bf_series = operator(scalars_df_index[columns]) pd_series = operator(scalars_pandas_df_index[columns]) bf_result = bf_series.to_pandas() - bigframes.testing.utils.assert_frame_equal(pd_series, bf_result, check_dtype=False) + pd.testing.assert_frame_equal(pd_series, bf_result, check_dtype=False) @pytest.mark.parametrize( @@ -3550,7 +1898,7 @@ def test_dataframe_general_analytic_op( bf_series = operator(scalars_df_index[col_names]) pd_series = operator(scalars_pandas_df_index[col_names]) bf_result = bf_series.to_pandas() - bigframes.testing.utils.assert_frame_equal( + pd.testing.assert_frame_equal( pd_series, bf_result, ) @@ -3568,7 +1916,7 @@ def test_dataframe_diff(scalars_df_index, scalars_pandas_df_index, periods): col_names = ["int64_too", "float64_col", "int64_col"] bf_result = scalars_df_index[col_names].diff(periods=periods).to_pandas() pd_result = scalars_pandas_df_index[col_names].diff(periods=periods) - bigframes.testing.utils.assert_frame_equal( + pd.testing.assert_frame_equal( pd_result, bf_result, ) @@ -3585,9 +1933,8 @@ def test_dataframe_diff(scalars_df_index, scalars_pandas_df_index, periods): def test_dataframe_pct_change(scalars_df_index, scalars_pandas_df_index, periods): col_names = ["int64_too", "float64_col", "int64_col"] bf_result = scalars_df_index[col_names].pct_change(periods=periods).to_pandas() - # pandas 3.0 does not automatically ffill anymore - pd_result = scalars_pandas_df_index[col_names].ffill().pct_change(periods=periods) - bigframes.testing.utils.assert_frame_equal( + pd_result = scalars_pandas_df_index[col_names].pct_change(periods=periods) + pd.testing.assert_frame_equal( pd_result, bf_result, ) @@ -3596,37 +1943,15 @@ def test_dataframe_pct_change(scalars_df_index, scalars_pandas_df_index, periods def test_dataframe_agg_single_string(scalars_dfs): numeric_cols = ["int64_col", "int64_too", "float64_col"] scalars_df, scalars_pandas_df = scalars_dfs - bf_result = scalars_df[numeric_cols].agg("sum").to_pandas() pd_result = scalars_pandas_df[numeric_cols].agg("sum") - assert bf_result.dtype == "Float64" - bigframes.testing.utils.assert_series_equal( - pd_result, bf_result, check_dtype=False, check_index_type=False - ) - - -@pytest.mark.parametrize( - ("agg",), - ( - ("sum",), - ("size",), - ), -) -def test_dataframe_agg_int_single_string(scalars_dfs, agg): - numeric_cols = ["int64_col", "int64_too", "bool_col"] - scalars_df, scalars_pandas_df = scalars_dfs - - bf_result = scalars_df[numeric_cols].agg(agg).to_pandas() - pd_result = scalars_pandas_df[numeric_cols].agg(agg) - - assert bf_result.dtype == "Int64" - bigframes.testing.utils.assert_series_equal( - pd_result, bf_result, check_dtype=False, check_index_type=False - ) + # Pandas may produce narrower numeric types, but bigframes always produces Float64 + pd_result = pd_result.astype("Float64") + pd.testing.assert_series_equal(pd_result, bf_result, check_index_type=False) -def test_dataframe_agg_multi_string(scalars_dfs_maybe_ordered): +def test_dataframe_agg_multi_string(scalars_dfs): numeric_cols = ["int64_col", "int64_too", "float64_col"] aggregations = [ "sum", @@ -3639,8 +1964,8 @@ def test_dataframe_agg_multi_string(scalars_dfs_maybe_ordered): "nunique", "count", ] - scalars_df, scalars_pandas_df = scalars_dfs_maybe_ordered - bf_result = scalars_df[numeric_cols].agg(aggregations) + scalars_df, scalars_pandas_df = scalars_dfs + bf_result = scalars_df[numeric_cols].agg(aggregations).to_pandas() pd_result = scalars_pandas_df[numeric_cols].agg(aggregations) # Pandas may produce narrower numeric types, but bigframes always produces Float64 @@ -3651,7 +1976,7 @@ def test_dataframe_agg_multi_string(scalars_dfs_maybe_ordered): bf_result = bf_result.drop(labels=["median"]) pd_result = pd_result.drop(labels=["median"]) - assert_dfs_equivalent(pd_result, bf_result, check_index_type=False) + pd.testing.assert_frame_equal(pd_result, bf_result, check_index_type=False) # Double-check that median is at least plausible. assert ( @@ -3659,79 +1984,40 @@ def test_dataframe_agg_multi_string(scalars_dfs_maybe_ordered): ).all() -def test_dataframe_agg_int_multi_string(scalars_dfs): - numeric_cols = ["int64_col", "int64_too", "bool_col"] - aggregations = [ - "sum", - "nunique", - "count", - "size", - ] +def test_df_describe(scalars_dfs): scalars_df, scalars_pandas_df = scalars_dfs - bf_result = scalars_df[numeric_cols].agg(aggregations).to_pandas() - pd_result = scalars_pandas_df[numeric_cols].agg(aggregations) - - for dtype in bf_result.dtypes: - assert dtype == "Int64" - - # Pandas may produce narrower numeric types - # Pandas has object index type - bigframes.testing.utils.assert_frame_equal( - pd_result, bf_result, check_dtype=False, check_index_type=False - ) - - -def test_df_transpose(): - # Include some floats to ensure type coercion - values = [[0, 3.5, True], [1, 4.5, False], [2, 6.5, None]] - # Test complex case of both axes being multi-indices with non-unique elements - - columns: pandas.Index = pd.Index( - ["A", "B", "A"], dtype=pd.StringDtype(storage="pyarrow") - ) - columns_multi = pd.MultiIndex.from_arrays([columns, columns], names=["c1", "c2"]) + # pyarrows time columns fail in pandas + unsupported_columns = ["datetime_col", "timestamp_col", "time_col", "date_col"] + bf_result = scalars_df.describe().to_pandas() - index: pandas.Index = pd.Index( - ["b", "a", "a"], dtype=pd.StringDtype(storage="pyarrow") - ) - rows_multi = pd.MultiIndex.from_arrays([index, index], names=["r1", "r2"]) - - pd_df = pandas.DataFrame(values, index=rows_multi, columns=columns_multi) - bf_df = dataframe.DataFrame(values, index=rows_multi, columns=columns_multi) - - pd_result = pd_df.T - bf_result = bf_df.T.to_pandas() - - assert_frame_equal(pd_result, bf_result, check_dtype=False, nulls_are_nan=True) # type: ignore + modified_pd_df = scalars_pandas_df.drop(columns=unsupported_columns) + pd_result = modified_pd_df.describe() + # Pandas may produce narrower numeric types, but bigframes always produces Float64 + pd_result = pd_result.astype("Float64") -def test_df_transpose_error(): - with pytest.raises(TypeError, match="Cannot coerce.*to a common type."): - dataframe.DataFrame([[1, "hello"], [2, "world"]]).transpose() + # Drop quartiles, as they are approximate + bf_min = bf_result.loc["min", :] + bf_p25 = bf_result.loc["25%", :] + bf_p50 = bf_result.loc["50%", :] + bf_p75 = bf_result.loc["75%", :] + bf_max = bf_result.loc["max", :] + bf_result = bf_result.drop(labels=["25%", "50%", "75%"]) + pd_result = pd_result.drop(labels=["25%", "50%", "75%"]) -def test_df_transpose_repeated_uses_cache(): - bf_df = dataframe.DataFrame([[1, 2.5], [2, 3.5]]) - pd_df = pandas.DataFrame([[1, 2.5], [2, 3.5]]) - # Transposing many times so that operation will fail from complexity if not using cache - for i in range(10): - # Cache still works even with simple scalar binop - bf_df = bf_df.transpose() + i - pd_df = pd_df.transpose() + i + pd.testing.assert_frame_equal(pd_result, bf_result, check_index_type=False) - bigframes.testing.utils.assert_frame_equal( - pd_df, bf_df.to_pandas(), check_dtype=False, check_index_type=False - ) + # Double-check that quantiles are at least plausible. + assert ( + (bf_min <= bf_p25) + & (bf_p25 <= bf_p50) + & (bf_p50 <= bf_p50) + & (bf_p75 <= bf_max) + ).all() -@pytest.mark.parametrize( - ("ordered"), - [ - (True), - (False), - ], -) -def test_df_stack(scalars_dfs, ordered): +def test_df_stack(scalars_dfs): if pandas.__version__.startswith("1.") or pandas.__version__.startswith("2.0"): pytest.skip("pandas <2.1 uses different stack implementation") scalars_df, scalars_pandas_df = scalars_dfs @@ -3741,13 +2027,11 @@ def test_df_stack(scalars_dfs, ordered): # Can only stack identically-typed columns columns = ["int64_col", "int64_too", "rowindex_2"] - bf_result = scalars_df[columns].stack().to_pandas(ordered=ordered) + bf_result = scalars_df[columns].stack().to_pandas() pd_result = scalars_pandas_df[columns].stack(future_stack=True) # Pandas produces NaN, where bq dataframes produces pd.NA - assert_series_equal( - bf_result, pd_result, check_dtype=False, ignore_order=not ordered - ) + pd.testing.assert_series_equal(bf_result, pd_result, check_dtype=False) def test_df_melt_default(scalars_dfs): @@ -3762,11 +2046,8 @@ def test_df_melt_default(scalars_dfs): pd_result = scalars_pandas_df[columns].melt() # Pandas produces int64 index, Bigframes produces Int64 (nullable) - bigframes.testing.utils.assert_frame_equal( - bf_result, - pd_result, - check_index_type=False, - check_dtype=False, + pd.testing.assert_frame_equal( + bf_result, pd_result, check_index_type=False, check_dtype=False ) @@ -3791,19 +2072,12 @@ def test_df_melt_parameterized(scalars_dfs): ) # Pandas produces int64 index, Bigframes produces Int64 (nullable) - bigframes.testing.utils.assert_frame_equal( + pd.testing.assert_frame_equal( bf_result, pd_result, check_index_type=False, check_dtype=False ) -@pytest.mark.parametrize( - ("ordered"), - [ - (True), - (False), - ], -) -def test_df_unstack(scalars_dfs, ordered): +def test_df_unstack(scalars_dfs): scalars_df, scalars_pandas_df = scalars_dfs # To match bigquery dataframes scalars_pandas_df = scalars_pandas_df.copy() @@ -3816,13 +2090,11 @@ def test_df_unstack(scalars_dfs, ordered): ] # unstack on mono-index produces series - bf_result = scalars_df[columns].unstack().to_pandas(ordered=ordered) + bf_result = scalars_df[columns].unstack().to_pandas() pd_result = scalars_pandas_df[columns].unstack() # Pandas produces NaN, where bq dataframes produces pd.NA - assert_series_equal( - bf_result, pd_result, check_dtype=False, ignore_order=not ordered - ) + pd.testing.assert_series_equal(bf_result, pd_result, check_dtype=False) @pytest.mark.parametrize( @@ -3842,9 +2114,7 @@ def test_df_pivot(scalars_dfs, values, index, columns): pd_result = scalars_pandas_df.pivot(values=values, index=index, columns=columns) # Pandas produces NaN, where bq dataframes produces pd.NA - bf_result = bf_result.fillna(float("nan")) - pd_result = pd_result.fillna(float("nan")) - bigframes.testing.utils.assert_frame_equal(bf_result, pd_result, check_dtype=False) + pd.testing.assert_frame_equal(bf_result, pd_result, check_dtype=False) @pytest.mark.parametrize( @@ -3865,50 +2135,7 @@ def test_df_pivot_hockey(hockey_df, hockey_pandas_df, values, index, columns): ) # Pandas produces NaN, where bq dataframes produces pd.NA - bigframes.testing.utils.assert_frame_equal(bf_result, pd_result, check_dtype=False) - - -@pytest.mark.parametrize( - ("values", "index", "columns", "aggfunc", "fill_value"), - [ - (("culmen_length_mm", "body_mass_g"), "species", "sex", "std", 1.0), - ( - ["body_mass_g", "culmen_length_mm"], - ("species", "island"), - "sex", - "sum", - None, - ), - ("body_mass_g", "sex", ["island", "species"], "mean", None), - ("culmen_depth_mm", "island", "species", "max", -1), - ], -) -def test_df_pivot_table( - penguins_df_default_index, - penguins_pandas_df_default_index, - values, - index, - columns, - aggfunc, - fill_value, -): - bf_result = penguins_df_default_index.pivot_table( - values=values, - index=index, - columns=columns, - aggfunc=aggfunc, - fill_value=fill_value, - ).to_pandas() - pd_result = penguins_pandas_df_default_index.pivot_table( - values=values, - index=index, - columns=columns, - aggfunc=aggfunc, - fill_value=fill_value, - ) - bigframes.testing.utils.assert_frame_equal( - bf_result, pd_result, check_dtype=False, check_column_type=False - ) + pd.testing.assert_frame_equal(bf_result, pd_result, check_dtype=False) def test_ipython_key_completions_with_drop(scalars_dfs): @@ -3983,15 +2210,6 @@ def test__dir__with_rename(scalars_dfs): assert "drop" in results -def test_loc_select_columns_w_repeats(scalars_df_index, scalars_pandas_df_index): - bf_result = scalars_df_index[["int64_col", "int64_col", "int64_too"]].to_pandas() - pd_result = scalars_pandas_df_index[["int64_col", "int64_col", "int64_too"]] - bigframes.testing.utils.assert_frame_equal( - bf_result, - pd_result, - ) - - @pytest.mark.parametrize( ("start", "stop", "step"), [ @@ -4010,25 +2228,7 @@ def test_loc_select_columns_w_repeats(scalars_df_index, scalars_pandas_df_index) def test_iloc_slice(scalars_df_index, scalars_pandas_df_index, start, stop, step): bf_result = scalars_df_index.iloc[start:stop:step].to_pandas() pd_result = scalars_pandas_df_index.iloc[start:stop:step] - bigframes.testing.utils.assert_frame_equal( - bf_result, - pd_result, - ) - - -@pytest.mark.parametrize( - ("start", "stop", "step"), - [ - (0, 0, None), - ], -) -def test_iloc_slice_after_cache( - scalars_df_index, scalars_pandas_df_index, start, stop, step -): - scalars_df_index.cache() - bf_result = scalars_df_index.iloc[start:stop:step].to_pandas() - pd_result = scalars_pandas_df_index.iloc[start:stop:step] - bigframes.testing.utils.assert_frame_equal( + pd.testing.assert_frame_equal( bf_result, pd_result, ) @@ -4039,18 +2239,14 @@ def test_iloc_slice_zero_step(scalars_df_index): scalars_df_index.iloc[0:0:0] -@pytest.mark.parametrize( - ("ordered"), - [ - (True), - (False), - ], -) -def test_iloc_slice_nested(scalars_df_index, scalars_pandas_df_index, ordered): - bf_result = scalars_df_index.iloc[1:].iloc[1:].to_pandas(ordered=ordered) +def test_iloc_slice_nested(scalars_df_index, scalars_pandas_df_index): + bf_result = scalars_df_index.iloc[1:].iloc[1:].to_pandas() pd_result = scalars_pandas_df_index.iloc[1:].iloc[1:] - assert_frame_equal(bf_result, pd_result, ignore_order=not ordered) + pd.testing.assert_frame_equal( + bf_result, + pd_result, + ) @pytest.mark.parametrize( @@ -4061,7 +2257,7 @@ def test_iloc_single_integer(scalars_df_index, scalars_pandas_df_index, index): bf_result = scalars_df_index.iloc[index] pd_result = scalars_pandas_df_index.iloc[index] - bigframes.testing.utils.assert_series_equal( + pd.testing.assert_series_equal( bf_result, pd_result, ) @@ -4078,24 +2274,6 @@ def test_iloc_tuple(scalars_df_index, scalars_pandas_df_index, index): assert bf_result == pd_result -@pytest.mark.parametrize( - "index", - [(slice(None), [1, 2, 3]), (slice(1, 7, 2), [2, 5, 3])], -) -def test_iloc_tuple_multi_columns(scalars_df_index, scalars_pandas_df_index, index): - bf_result = scalars_df_index.iloc[index].to_pandas() - pd_result = scalars_pandas_df_index.iloc[index] - - bigframes.testing.utils.assert_frame_equal(bf_result, pd_result) - - -def test_iloc_tuple_multi_columns_single_row(scalars_df_index, scalars_pandas_df_index): - index = (2, [2, 1, 3, -4]) - bf_result = scalars_df_index.iloc[index] - pd_result = scalars_pandas_df_index.iloc[index] - bigframes.testing.utils.assert_series_equal(bf_result, pd_result) - - @pytest.mark.parametrize( ("index", "error"), [ @@ -4138,7 +2316,9 @@ def test_iat_errors(scalars_df_index, scalars_pandas_df_index, index, error): scalars_df_index.iat[index] -def test_iloc_single_integer_out_of_bound_error(scalars_df_index): +def test_iloc_single_integer_out_of_bound_error( + scalars_df_index, scalars_pandas_df_index +): with pytest.raises(IndexError, match="single positional indexer is out-of-bounds"): scalars_df_index.iloc[99] @@ -4147,18 +2327,7 @@ def test_loc_bool_series(scalars_df_index, scalars_pandas_df_index): bf_result = scalars_df_index.loc[scalars_df_index.bool_col].to_pandas() pd_result = scalars_pandas_df_index.loc[scalars_pandas_df_index.bool_col] - bigframes.testing.utils.assert_frame_equal( - bf_result, - pd_result, - ) - - -def test_loc_list_select_rows_and_columns(scalars_df_index, scalars_pandas_df_index): - idx_list = [0, 3, 5] - bf_result = scalars_df_index.loc[idx_list, ["bool_col", "int64_col"]].to_pandas() - pd_result = scalars_pandas_df_index.loc[idx_list, ["bool_col", "int64_col"]] - - bigframes.testing.utils.assert_frame_equal( + pd.testing.assert_frame_equal( bf_result, pd_result, ) @@ -4167,41 +2336,7 @@ def test_loc_list_select_rows_and_columns(scalars_df_index, scalars_pandas_df_in def test_loc_select_column(scalars_df_index, scalars_pandas_df_index): bf_result = scalars_df_index.loc[:, "int64_col"].to_pandas() pd_result = scalars_pandas_df_index.loc[:, "int64_col"] - bigframes.testing.utils.assert_series_equal( - bf_result, - pd_result, - ) - - -def test_loc_select_with_column_condition(scalars_df_index, scalars_pandas_df_index): - bf_result = scalars_df_index.loc[:, scalars_df_index.dtypes == "Int64"].to_pandas() - pd_result = scalars_pandas_df_index.loc[ - :, scalars_pandas_df_index.dtypes == "Int64" - ] - bigframes.testing.utils.assert_frame_equal( - bf_result, - pd_result, - ) - - -def test_loc_select_with_column_condition_bf_series( - scalars_df_index, scalars_pandas_df_index -): - # (b/347072677) GEOGRAPH type doesn't support DISTINCT op - columns = [ - item for item in scalars_pandas_df_index.columns if item != "geography_col" - ] - scalars_df_index = scalars_df_index[columns] - scalars_pandas_df_index = scalars_pandas_df_index[columns] - - size_half = len(scalars_pandas_df_index) / 2 - bf_result = scalars_df_index.loc[ - :, scalars_df_index.nunique() > size_half - ].to_pandas() - pd_result = scalars_pandas_df_index.loc[ - :, scalars_pandas_df_index.nunique() > size_half - ] - bigframes.testing.utils.assert_frame_equal( + pd.testing.assert_series_equal( bf_result, pd_result, ) @@ -4215,7 +2350,7 @@ def test_loc_single_index_with_duplicate(scalars_df_index, scalars_pandas_df_ind index = "Hello, World!" bf_result = scalars_df_index.loc[index] pd_result = scalars_pandas_df_index.loc[index] - bigframes.testing.utils.assert_frame_equal( + pd.testing.assert_frame_equal( bf_result.to_pandas(), pd_result, ) @@ -4227,7 +2362,7 @@ def test_loc_single_index_no_duplicate(scalars_df_index, scalars_pandas_df_index index = -2345 bf_result = scalars_df_index.loc[index] pd_result = scalars_pandas_df_index.loc[index] - bigframes.testing.utils.assert_series_equal( + pd.testing.assert_series_equal( bf_result, pd_result, ) @@ -4241,7 +2376,7 @@ def test_at_with_duplicate(scalars_df_index, scalars_pandas_df_index): index = "Hello, World!" bf_result = scalars_df_index.at[index, "int64_too"] pd_result = scalars_pandas_df_index.at[index, "int64_too"] - bigframes.testing.utils.assert_series_equal( + pd.testing.assert_series_equal( bf_result.to_pandas(), pd_result, ) @@ -4263,40 +2398,32 @@ def test_loc_setitem_bool_series_scalar_new_col(scalars_dfs): bf_df.loc[bf_df["int64_too"] == 0, "new_col"] = 99 pd_df.loc[pd_df["int64_too"] == 0, "new_col"] = 99 - # pandas uses float64 instead + # pandas type difference pd_df["new_col"] = pd_df["new_col"].astype("Float64") - bigframes.testing.utils.assert_frame_equal( + pd.testing.assert_frame_equal( bf_df.to_pandas(), pd_df, ) -@pytest.mark.parametrize( - ("col", "value"), - [ - ("string_col", "hello"), - ("int64_col", 3), - ("float64_col", 3.5), - ], -) -def test_loc_setitem_bool_series_scalar_existing_col(scalars_dfs, col, value): +def test_loc_setitem_bool_series_scalar_existing_col(scalars_dfs): if pd.__version__.startswith("1."): pytest.skip("this loc overload not supported in pandas 1.x.") scalars_df, scalars_pandas_df = scalars_dfs bf_df = scalars_df.copy() pd_df = scalars_pandas_df.copy() - bf_df.loc[bf_df["int64_too"] == 1, col] = value - pd_df.loc[pd_df["int64_too"] == 1, col] = value + bf_df.loc[bf_df["int64_too"] == 1, "string_col"] = "hello" + pd_df.loc[pd_df["int64_too"] == 1, "string_col"] = "hello" - bigframes.testing.utils.assert_frame_equal( + pd.testing.assert_frame_equal( bf_df.to_pandas(), pd_df, ) -def test_loc_setitem_bool_series_scalar_error(scalars_dfs): +def test_loc_setitem_bool_series_scalar_type_error(scalars_dfs): if pd.__version__.startswith("1."): pytest.skip("this loc overload not supported in pandas 1.x.") @@ -4304,98 +2431,36 @@ def test_loc_setitem_bool_series_scalar_error(scalars_dfs): bf_df = scalars_df.copy() pd_df = scalars_pandas_df.copy() - with pytest.raises(Exception): + with pytest.raises(TypeError): bf_df.loc[bf_df["int64_too"] == 1, "string_col"] = 99 - with pytest.raises(Exception): + with pytest.raises(TypeError): pd_df.loc[pd_df["int64_too"] == 1, "string_col"] = 99 @pytest.mark.parametrize( - ("col", "op"), - [ - # Int aggregates - pytest.param("int64_col", lambda x: x.sum(), id="int-sum"), - pytest.param("int64_col", lambda x: x.min(), id="int-min"), - pytest.param("int64_col", lambda x: x.max(), id="int-max"), - pytest.param("int64_col", lambda x: x.count(), id="int-count"), - pytest.param("int64_col", lambda x: x.nunique(), id="int-nunique"), - # Float aggregates - pytest.param("float64_col", lambda x: x.count(), id="float-count"), - pytest.param("float64_col", lambda x: x.nunique(), id="float-nunique"), - # Bool aggregates - pytest.param("bool_col", lambda x: x.sum(), id="bool-sum"), - pytest.param("bool_col", lambda x: x.count(), id="bool-count"), - pytest.param("bool_col", lambda x: x.nunique(), id="bool-nunique"), - # String aggregates - pytest.param("string_col", lambda x: x.count(), id="string-count"), - pytest.param("string_col", lambda x: x.nunique(), id="string-nunique"), - ], -) -def test_dataframe_aggregate_int(scalars_df_index, scalars_pandas_df_index, col, op): - bf_result = op(scalars_df_index[[col]]).to_pandas() - pd_result = op(scalars_pandas_df_index[[col]]) - - # Check dtype separately - assert bf_result.dtype == "Int64" - # Is otherwise "object" dtype - pd_result.index = pd_result.index.astype("string[pyarrow]") - # Pandas may produce narrower numeric types - assert_series_equal(pd_result, bf_result, check_dtype=False, check_index_type=False) - - -@pytest.mark.parametrize( - ("col", "op"), - [ - pytest.param("bool_col", lambda x: x.min(), id="bool-min"), - pytest.param("bool_col", lambda x: x.max(), id="bool-max"), - ], -) -def test_dataframe_aggregate_bool(scalars_df_index, scalars_pandas_df_index, col, op): - bf_result = op(scalars_df_index[[col]]).to_pandas() - pd_result = op(scalars_pandas_df_index[[col]]) - - # Check dtype separately - assert bf_result.dtype == "boolean" - - # Pandas may produce narrower numeric types - # Pandas has object index type - pd_result.index = pd_result.index.astype("string[pyarrow]") - assert_series_equal(pd_result, bf_result, check_dtype=False, check_index_type=False) - - -@pytest.mark.parametrize( - ("op", "bf_dtype"), + ("op"), [ - (lambda x: x.sum(numeric_only=True), "Float64"), - (lambda x: x.mean(numeric_only=True), "Float64"), - (lambda x: x.min(numeric_only=True), "Float64"), - (lambda x: x.max(numeric_only=True), "Float64"), - (lambda x: x.std(numeric_only=True), "Float64"), - (lambda x: x.var(numeric_only=True), "Float64"), - (lambda x: x.count(numeric_only=False), "Int64"), - (lambda x: x.nunique(), "Int64"), + (lambda x: x.sum(numeric_only=True)), + (lambda x: x.mean(numeric_only=True)), + (lambda x: x.min(numeric_only=True)), + (lambda x: x.max(numeric_only=True)), + (lambda x: x.std(numeric_only=True)), + (lambda x: x.var(numeric_only=True)), + (lambda x: x.count(numeric_only=False)), + (lambda x: x.nunique()), ], ids=["sum", "mean", "min", "max", "std", "var", "count", "nunique"], ) -def test_dataframe_aggregates(scalars_dfs_maybe_ordered, op, bf_dtype): - scalars_df_index, scalars_pandas_df_index = scalars_dfs_maybe_ordered +def test_dataframe_aggregates(scalars_df_index, scalars_pandas_df_index, op): col_names = ["int64_too", "float64_col", "string_col", "int64_col", "bool_col"] bf_series = op(scalars_df_index[col_names]) - bf_result = bf_series - pd_result = op(scalars_pandas_df_index[col_names]) - - # Check dtype separately - assert bf_result.dtype == bf_dtype + pd_series = op(scalars_pandas_df_index[col_names]) + bf_result = bf_series.to_pandas() # Pandas may produce narrower numeric types, but bigframes always produces Float64 + pd_series = pd_series.astype("Float64") # Pandas has object index type - pd_result.index = pd_result.index.astype("string[pyarrow]") - assert_series_equivalent( - pd_result, - bf_result, - check_dtype=False, - check_index_type=False, - ) + pd.testing.assert_series_equal(pd_series, bf_result, check_index_type=False) @pytest.mark.parametrize( @@ -4415,8 +2480,10 @@ def test_dataframe_aggregates_axis_1(scalars_df_index, scalars_pandas_df_index, bf_result = op(scalars_df_index[col_names]).to_pandas() pd_result = op(scalars_pandas_df_index[col_names]) + # Pandas may produce narrower numeric types, but bigframes always produces Float64 + pd_result = pd_result.astype("Float64") # Pandas has object index type - assert_series_equal(pd_result, bf_result, check_index_type=False, check_dtype=False) + pd.testing.assert_series_equal(pd_result, bf_result, check_index_type=False) def test_dataframe_aggregates_median(scalars_df_index, scalars_pandas_df_index): @@ -4434,33 +2501,6 @@ def test_dataframe_aggregates_median(scalars_df_index, scalars_pandas_df_index): ) -def test_dataframe_aggregates_quantile_mono(scalars_df_index, scalars_pandas_df_index): - q = 0.45 - col_names = ["int64_too", "int64_col", "float64_col"] - bf_result = scalars_df_index[col_names].quantile(q=q).to_pandas() - pd_result = scalars_pandas_df_index[col_names].quantile(q=q) - - # Pandas may produce narrower numeric types, but bigframes always produces Float64 - pd_result = pd_result.astype("Float64") - - bigframes.testing.utils.assert_series_equal( - bf_result, pd_result, check_index_type=False - ) - - -def test_dataframe_aggregates_quantile_multi(scalars_df_index, scalars_pandas_df_index): - q = [0, 0.33, 0.67, 1.0] - col_names = ["int64_too", "int64_col", "float64_col"] - bf_result = scalars_df_index[col_names].quantile(q=q).to_pandas() - pd_result = scalars_pandas_df_index[col_names].quantile(q=q) - - # Pandas may produce narrower numeric types, but bigframes always produces Float64 - pd_result = pd_result.astype("Float64") - pd_result.index = pd_result.index.astype("Float64") - - bigframes.testing.utils.assert_frame_equal(bf_result, pd_result) - - @pytest.mark.parametrize( ("op"), [ @@ -4483,10 +2523,8 @@ def test_dataframe_bool_aggregates(scalars_df_index, scalars_pandas_df_index, op pd_series = op(scalars_pandas_df_index).astype("boolean") bf_result = bf_series.to_pandas() - pd_series.index = pd_series.index.astype(bf_result.index.dtype) - bigframes.testing.utils.assert_series_equal( - pd_series, bf_result, check_index_type=False - ) + # Pandas has object index type + pd.testing.assert_series_equal(pd_series, bf_result, check_index_type=False) def test_dataframe_prod(scalars_df_index, scalars_pandas_df_index): @@ -4498,9 +2536,7 @@ def test_dataframe_prod(scalars_df_index, scalars_pandas_df_index): # Pandas may produce narrower numeric types, but bigframes always produces Float64 pd_series = pd_series.astype("Float64") # Pandas has object index type - bigframes.testing.utils.assert_series_equal( - pd_series, bf_result, check_index_type=False - ) + pd.testing.assert_series_equal(pd_series, bf_result, check_index_type=False) def test_df_skew_too_few_values(scalars_dfs): @@ -4512,30 +2548,19 @@ def test_df_skew_too_few_values(scalars_dfs): # Pandas may produce narrower numeric types, but bigframes always produces Float64 pd_result = pd_result.astype("Float64") - bigframes.testing.utils.assert_series_equal( - pd_result, bf_result, check_index_type=False - ) + pd.testing.assert_series_equal(pd_result, bf_result, check_index_type=False) -@pytest.mark.parametrize( - ("ordered"), - [ - (True), - (False), - ], -) -def test_df_skew(scalars_dfs, ordered): +def test_df_skew(scalars_dfs): columns = ["float64_col", "int64_col"] scalars_df, scalars_pandas_df = scalars_dfs - bf_result = scalars_df[columns].skew().to_pandas(ordered=ordered) + bf_result = scalars_df[columns].skew().to_pandas() pd_result = scalars_pandas_df[columns].skew() # Pandas may produce narrower numeric types, but bigframes always produces Float64 pd_result = pd_result.astype("Float64") - assert_series_equal( - pd_result, bf_result, check_index_type=False, ignore_order=not ordered - ) + pd.testing.assert_series_equal(pd_result, bf_result, check_index_type=False) def test_df_kurt_too_few_values(scalars_dfs): @@ -4547,9 +2572,7 @@ def test_df_kurt_too_few_values(scalars_dfs): # Pandas may produce narrower numeric types, but bigframes always produces Float64 pd_result = pd_result.astype("Float64") - bigframes.testing.utils.assert_series_equal( - pd_result, bf_result, check_index_type=False - ) + pd.testing.assert_series_equal(pd_result, bf_result, check_index_type=False) def test_df_kurt(scalars_dfs): @@ -4561,9 +2584,7 @@ def test_df_kurt(scalars_dfs): # Pandas may produce narrower numeric types, but bigframes always produces Float64 pd_result = pd_result.astype("Float64") - bigframes.testing.utils.assert_series_equal( - pd_result, bf_result, check_index_type=False - ) + pd.testing.assert_series_equal(pd_result, bf_result, check_index_type=False) @pytest.mark.parametrize( @@ -4583,7 +2604,7 @@ def test_df_kurt(scalars_dfs): "n_default", ], ) -def test_df_to_pandas_sample(scalars_dfs, frac, n, random_state): +def test_sample(scalars_dfs, frac, n, random_state): scalars_df, _ = scalars_dfs df = scalars_df.sample(frac=frac, n=n, random_state=random_state) bf_result = df.to_pandas() @@ -4594,15 +2615,7 @@ def test_df_to_pandas_sample(scalars_dfs, frac, n, random_state): assert bf_result.shape[1] == scalars_df.shape[1] -def test_df_to_pandas_sample_determinism(penguins_df_default_index): - df = penguins_df_default_index.sample(n=100, random_state=12345).head(15) - bf_result = df.to_pandas() - bf_result2 = df.to_pandas() - - pandas.testing.assert_frame_equal(bf_result, bf_result2) - - -def test_df_to_pandas_sample_raises_value_error(scalars_dfs): +def test_sample_raises_value_error(scalars_dfs): scalars_df, _ = scalars_dfs with pytest.raises( ValueError, match="Only one of 'n' or 'frac' parameter can be specified." @@ -4610,28 +2623,6 @@ def test_df_to_pandas_sample_raises_value_error(scalars_dfs): scalars_df.sample(frac=0.5, n=4) -def test_sample_args_sort(scalars_dfs): - scalars_df, _ = scalars_dfs - index = [4, 3, 2, 5, 1, 0] - scalars_df = scalars_df.iloc[index] - - kwargs = {"frac": 1.0, "random_state": 333} - - df = scalars_df.sample(**kwargs).to_pandas() - assert df.index.values != index - assert df.index.values != sorted(index) - - df = scalars_df.sample(sort="random", **kwargs).to_pandas() - assert df.index.values != index - assert df.index.values != sorted(index) - - df = scalars_df.sample(sort=True, **kwargs).to_pandas() - assert df.index.values == sorted(index) - - df = scalars_df.sample(sort=False, **kwargs).to_pandas() - assert df.index.values == index - - @pytest.mark.parametrize( ("axis",), [ @@ -4647,7 +2638,7 @@ def test_df_add_prefix(scalars_df_index, scalars_pandas_df_index, axis): pd_result = scalars_pandas_df_index.add_prefix("prefix_", axis) - bigframes.testing.utils.assert_frame_equal( + pd.testing.assert_frame_equal( bf_result, pd_result, check_index_type=False, @@ -4668,19 +2659,13 @@ def test_df_add_suffix(scalars_df_index, scalars_pandas_df_index, axis): pd_result = scalars_pandas_df_index.add_suffix("_suffix", axis) - bigframes.testing.utils.assert_frame_equal( + pd.testing.assert_frame_equal( bf_result, pd_result, check_index_type=False, ) -def test_df_astype_error_error(session): - input = pd.DataFrame(["hello", "world", "3.11", "4000"]) - with pytest.raises(ValueError): - session.read_pandas(input).astype("Float64", errors="bad_value") - - def test_df_columns_filter_items(scalars_df_index, scalars_pandas_df_index): if pd.__version__.startswith("2.0") or pd.__version__.startswith("1."): pytest.skip("pandas filter items behavior different pre-2.1") @@ -4688,7 +2673,7 @@ def test_df_columns_filter_items(scalars_df_index, scalars_pandas_df_index): pd_result = scalars_pandas_df_index.filter(items=["string_col", "int64_col"]) # Ignore column ordering as pandas order differently depending on version - bigframes.testing.utils.assert_frame_equal( + pd.testing.assert_frame_equal( bf_result.sort_index(axis=1), pd_result.sort_index(axis=1), ) @@ -4699,7 +2684,7 @@ def test_df_columns_filter_like(scalars_df_index, scalars_pandas_df_index): pd_result = scalars_pandas_df_index.filter(like="64_col") - bigframes.testing.utils.assert_frame_equal( + pd.testing.assert_frame_equal( bf_result, pd_result, ) @@ -4710,7 +2695,7 @@ def test_df_columns_filter_regex(scalars_df_index, scalars_pandas_df_index): pd_result = scalars_pandas_df_index.filter(regex="^[^_]+$") - bigframes.testing.utils.assert_frame_equal( + pd.testing.assert_frame_equal( bf_result, pd_result, ) @@ -4726,10 +2711,9 @@ def test_df_rows_filter_items(scalars_df_index, scalars_pandas_df_index): # Pandas uses int64 instead of Int64 (nullable) dtype. pd_result.index = pd_result.index.astype(pd.Int64Dtype()) # Ignore ordering as pandas order differently depending on version - assert_frame_equal( + assert_pandas_df_equal_ignore_ordering( bf_result, pd_result, - ignore_order=True, check_names=False, ) @@ -4742,7 +2726,7 @@ def test_df_rows_filter_like(scalars_df_index, scalars_pandas_df_index): pd_result = scalars_pandas_df_index.filter(like="ello", axis=0) - bigframes.testing.utils.assert_frame_equal( + pd.testing.assert_frame_equal( bf_result, pd_result, ) @@ -4756,23 +2740,22 @@ def test_df_rows_filter_regex(scalars_df_index, scalars_pandas_df_index): pd_result = scalars_pandas_df_index.filter(regex="^[GH].*", axis=0) - bigframes.testing.utils.assert_frame_equal( + pd.testing.assert_frame_equal( bf_result, pd_result, ) -def test_df_reindex_rows_list(scalars_dfs_maybe_ordered): - scalars_df_index, scalars_pandas_df_index = scalars_dfs_maybe_ordered - bf_result = scalars_df_index.reindex(index=[5, 1, 3, 99, 1]) +def test_df_reindex_rows_list(scalars_df_index, scalars_pandas_df_index): + bf_result = scalars_df_index.reindex(index=[5, 1, 3, 99, 1]).to_pandas() pd_result = scalars_pandas_df_index.reindex(index=[5, 1, 3, 99, 1]) # Pandas uses int64 instead of Int64 (nullable) dtype. pd_result.index = pd_result.index.astype(pd.Int64Dtype()) - assert_dfs_equivalent( - pd_result, + pd.testing.assert_frame_equal( bf_result, + pd_result, ) @@ -4787,7 +2770,7 @@ def test_df_reindex_rows_index(scalars_df_index, scalars_pandas_df_index): # Pandas uses int64 instead of Int64 (nullable) dtype. pd_result.index = pd_result.index.astype(pd.Int64Dtype()) - bigframes.testing.utils.assert_frame_equal( + pd.testing.assert_frame_equal( bf_result, pd_result, ) @@ -4812,22 +2795,7 @@ def test_df_reindex_columns(scalars_df_index, scalars_pandas_df_index): # Pandas uses float64 as default for newly created empty column, bf uses Float64 pd_result.not_a_col = pd_result.not_a_col.astype(pandas.Float64Dtype()) - bigframes.testing.utils.assert_frame_equal( - bf_result, - pd_result, - ) - - -def test_df_reindex_columns_with_same_order(scalars_df_index, scalars_pandas_df_index): - # First, make sure the two dataframes have the same columns in order. - columns = ["int64_col", "int64_too"] - bf = scalars_df_index[columns] - pd_df = scalars_pandas_df_index[columns] - - bf_result = bf.reindex(columns=columns).to_pandas() - pd_result = pd_df.reindex(columns=columns) - - bigframes.testing.utils.assert_frame_equal( + pd.testing.assert_frame_equal( bf_result, pd_result, ) @@ -4917,7 +2885,7 @@ def test_df_reindex_like(scalars_df_index, scalars_pandas_df_index): pd_result.index = pd_result.index.astype(pd.Int64Dtype()) # Pandas uses float64 as default for newly created empty column, bf uses Float64 pd_result.not_a_col = pd_result.not_a_col.astype(pandas.Float64Dtype()) - bigframes.testing.utils.assert_frame_equal( + pd.testing.assert_frame_equal( bf_result, pd_result, ) @@ -4928,7 +2896,7 @@ def test_df_values(scalars_df_index, scalars_pandas_df_index): pd_result = scalars_pandas_df_index.values # Numpy isn't equipped to compare non-numeric objects, so convert back to dataframe - bigframes.testing.utils.assert_frame_equal( + pd.testing.assert_frame_equal( pd.DataFrame(bf_result), pd.DataFrame(pd_result), check_dtype=False ) @@ -4938,7 +2906,7 @@ def test_df_to_numpy(scalars_df_index, scalars_pandas_df_index): pd_result = scalars_pandas_df_index.to_numpy() # Numpy isn't equipped to compare non-numeric objects, so convert back to dataframe - bigframes.testing.utils.assert_frame_equal( + pd.testing.assert_frame_equal( pd.DataFrame(bf_result), pd.DataFrame(pd_result), check_dtype=False ) @@ -4948,89 +2916,22 @@ def test_df___array__(scalars_df_index, scalars_pandas_df_index): pd_result = scalars_pandas_df_index.__array__() # Numpy isn't equipped to compare non-numeric objects, so convert back to dataframe - bigframes.testing.utils.assert_frame_equal( + pd.testing.assert_frame_equal( pd.DataFrame(bf_result), pd.DataFrame(pd_result), check_dtype=False ) -@pytest.mark.parametrize( - ("key",), - [ - ("hello",), - (2,), - ("int64_col",), - (None,), - ], -) -def test_df_contains(scalars_df_index, scalars_pandas_df_index, key): - bf_result = key in scalars_df_index - pd_result = key in scalars_pandas_df_index - - assert bf_result == pd_result - - -def test_df_getattr_attribute_error_when_pandas_has(scalars_df_index): - # swapaxes is implemented in pandas but not in bigframes +def test_getattr_attribute_error_when_pandas_has(scalars_df_index): + # asof is implemented in pandas but not in bigframes with pytest.raises(AttributeError): - scalars_df_index.swapaxes() + scalars_df_index.asof() -def test_df_getattr_attribute_error(scalars_df_index): +def test_getattr_attribute_error(scalars_df_index): with pytest.raises(AttributeError): scalars_df_index.not_a_method() -def test_df_getattr_axes(): - df = dataframe.DataFrame( - [[1, 1, 1], [1, 1, 1]], columns=["index", "columns", "my_column"] - ) - assert isinstance(df.index, bigframes.core.indexes.Index) - assert isinstance(df.columns, pandas.Index) - assert isinstance(df.my_column, series.Series) - - -def test_df_setattr_index(): - pd_df = pandas.DataFrame( - [[1, 1, 1], [1, 1, 1]], columns=["index", "columns", "my_column"] - ) - bf_df = dataframe.DataFrame(pd_df) - - pd_df.index = pandas.Index([4, 5]) - bf_df.index = [4, 5] - - assert_frame_equal( - pd_df, bf_df.to_pandas(), check_index_type=False, check_dtype=False - ) - - -def test_df_setattr_columns(): - pd_df = pandas.DataFrame( - [[1, 1, 1], [1, 1, 1]], columns=["index", "columns", "my_column"] - ) - bf_df = dataframe.DataFrame(pd_df) - - pd_df.columns = typing.cast(pandas.Index, pandas.Index([4, 5, 6])) - - bf_df.columns = pandas.Index([4, 5, 6]) - - assert_frame_equal( - pd_df, bf_df.to_pandas(), check_index_type=False, check_dtype=False - ) - - -def test_df_setattr_modify_column(): - pd_df = pandas.DataFrame( - [[1, 1, 1], [1, 1, 1]], columns=["index", "columns", "my_column"] - ) - bf_df = dataframe.DataFrame(pd_df) - pd_df.my_column = [4, 5] - bf_df.my_column = [4, 5] - - assert_frame_equal( - pd_df, bf_df.to_pandas(), check_index_type=False, check_dtype=False - ) - - def test_loc_list_string_index(scalars_df_index, scalars_pandas_df_index): index_list = scalars_pandas_df_index.string_col.iloc[[0, 1, 1, 5]].values @@ -5040,7 +2941,7 @@ def test_loc_list_string_index(scalars_df_index, scalars_pandas_df_index): bf_result = scalars_df_index.loc[index_list].to_pandas() pd_result = scalars_pandas_df_index.loc[index_list] - bigframes.testing.utils.assert_frame_equal( + pd.testing.assert_frame_equal( bf_result, pd_result, ) @@ -5052,14 +2953,13 @@ def test_loc_list_integer_index(scalars_df_index, scalars_pandas_df_index): bf_result = scalars_df_index.loc[index_list] pd_result = scalars_pandas_df_index.loc[index_list] - bigframes.testing.utils.assert_frame_equal( + pd.testing.assert_frame_equal( bf_result.to_pandas(), pd_result, ) -def test_loc_list_multiindex(scalars_dfs_maybe_ordered): - scalars_df_index, scalars_pandas_df_index = scalars_dfs_maybe_ordered +def test_loc_list_multiindex(scalars_df_index, scalars_pandas_df_index): scalars_df_multiindex = scalars_df_index.set_index(["string_col", "int64_col"]) scalars_pandas_df_multiindex = scalars_pandas_df_index.set_index( ["string_col", "int64_col"] @@ -5069,45 +2969,19 @@ def test_loc_list_multiindex(scalars_dfs_maybe_ordered): bf_result = scalars_df_multiindex.loc[index_list] pd_result = scalars_pandas_df_multiindex.loc[index_list] - assert_dfs_equivalent( - pd_result, - bf_result, - ) - - -@pytest.mark.parametrize( - "index_list", - [ - [0, 1, 2, 3, 4, 4], - [0, 0, 0, 5, 4, 7, -2, -5, 3], - [-1, -2, -3, -4, -5, -5], - ], -) -def test_iloc_list(scalars_df_index, scalars_pandas_df_index, index_list): - bf_result = scalars_df_index.iloc[index_list] - pd_result = scalars_pandas_df_index.iloc[index_list] - - bigframes.testing.utils.assert_frame_equal( + pd.testing.assert_frame_equal( bf_result.to_pandas(), pd_result, ) -@pytest.mark.parametrize( - "index_list", - [ - [0, 1, 2, 3, 4, 4], - [0, 0, 0, 5, 4, 7, -2, -5, 3], - [-1, -2, -3, -4, -5, -5], - ], -) -def test_iloc_list_partial_ordering( - scalars_df_partial_ordering, scalars_pandas_df_index, index_list -): - bf_result = scalars_df_partial_ordering.iloc[index_list] +def test_iloc_list(scalars_df_index, scalars_pandas_df_index): + index_list = [0, 0, 0, 5, 4, 7] + + bf_result = scalars_df_index.iloc[index_list] pd_result = scalars_pandas_df_index.iloc[index_list] - bigframes.testing.utils.assert_frame_equal( + pd.testing.assert_frame_equal( bf_result.to_pandas(), pd_result, ) @@ -5125,14 +2999,14 @@ def test_iloc_list_multiindex(scalars_dfs): bf_result = scalars_df.iloc[index_list] pd_result = scalars_pandas_df.iloc[index_list] - bigframes.testing.utils.assert_frame_equal( + pd.testing.assert_frame_equal( bf_result.to_pandas(), pd_result, ) def test_iloc_empty_list(scalars_df_index, scalars_pandas_df_index): - index_list: List[int] = [] + index_list = [] bf_result = scalars_df_index.iloc[index_list] pd_result = scalars_pandas_df_index.iloc[index_list] @@ -5145,7 +3019,7 @@ def test_rename_axis(scalars_df_index, scalars_pandas_df_index): bf_result = scalars_df_index.rename_axis("newindexname") pd_result = scalars_pandas_df_index.rename_axis("newindexname") - bigframes.testing.utils.assert_frame_equal( + pd.testing.assert_frame_equal( bf_result.to_pandas(), pd_result, ) @@ -5155,7 +3029,7 @@ def test_rename_axis_nonstring(scalars_df_index, scalars_pandas_df_index): bf_result = scalars_df_index.rename_axis((4,)) pd_result = scalars_pandas_df_index.rename_axis((4,)) - bigframes.testing.utils.assert_frame_equal( + pd.testing.assert_frame_equal( bf_result.to_pandas(), pd_result, ) @@ -5171,7 +3045,7 @@ def test_loc_bf_series_string_index(scalars_df_index, scalars_pandas_df_index): bf_result = scalars_df_index.loc[bf_string_series] pd_result = scalars_pandas_df_index.loc[pd_string_series] - bigframes.testing.utils.assert_frame_equal( + pd.testing.assert_frame_equal( bf_result.to_pandas(), pd_result, ) @@ -5189,7 +3063,7 @@ def test_loc_bf_series_multiindex(scalars_df_index, scalars_pandas_df_index): bf_result = scalars_df_multiindex.loc[bf_string_series] pd_result = scalars_pandas_df_multiindex.loc[pd_string_series] - bigframes.testing.utils.assert_frame_equal( + pd.testing.assert_frame_equal( bf_result.to_pandas(), pd_result, ) @@ -5202,7 +3076,7 @@ def test_loc_bf_index_integer_index(scalars_df_index, scalars_pandas_df_index): bf_result = scalars_df_index.loc[bf_index] pd_result = scalars_pandas_df_index.loc[pd_index] - bigframes.testing.utils.assert_frame_equal( + pd.testing.assert_frame_equal( bf_result.to_pandas(), pd_result, ) @@ -5222,7 +3096,7 @@ def test_loc_bf_index_integer_index_renamed_col( bf_result = scalars_df_index.loc[bf_index] pd_result = scalars_pandas_df_index.loc[pd_index] - bigframes.testing.utils.assert_frame_equal( + pd.testing.assert_frame_equal( bf_result.to_pandas(), pd_result, ) @@ -5246,38 +3120,11 @@ def test_loc_bf_index_integer_index_renamed_col( ) def test_df_drop_duplicates(scalars_df_index, scalars_pandas_df_index, keep, subset): columns = ["bool_col", "int64_too", "int64_col"] - bf_df = scalars_df_index[columns].drop_duplicates(subset, keep=keep).to_pandas() - pd_df = scalars_pandas_df_index[columns].drop_duplicates(subset, keep=keep) - bigframes.testing.utils.assert_frame_equal( - pd_df, - bf_df, - ) - - -@pytest.mark.parametrize( - ("keep",), - [ - ("first",), - ("last",), - (False,), - ], -) -def test_df_drop_duplicates_w_json(json_df, keep): - bf_df = json_df.drop_duplicates(keep=keep).to_pandas() - - # drop_duplicates relies on pa.compute.dictionary_encode, which is incompatible - # with Arrow string extension types. Temporary conversion to standard Pandas - # strings is required. - json_pandas_df = json_df.to_pandas() - json_pandas_df["json_col"] = json_pandas_df["json_col"].astype( - pd.StringDtype(storage="pyarrow") - ) - - pd_df = json_pandas_df.drop_duplicates(keep=keep) - pd_df["json_col"] = pd_df["json_col"].astype(dtypes.JSON_DTYPE) - bigframes.testing.utils.assert_frame_equal( - pd_df, - bf_df, + bf_series = scalars_df_index[columns].drop_duplicates(subset, keep=keep).to_pandas() + pd_series = scalars_pandas_df_index[columns].drop_duplicates(subset, keep=keep) + pd.testing.assert_frame_equal( + pd_series, + bf_series, ) @@ -5300,47 +3147,7 @@ def test_df_duplicated(scalars_df_index, scalars_pandas_df_index, keep, subset): columns = ["bool_col", "int64_too", "int64_col"] bf_series = scalars_df_index[columns].duplicated(subset, keep=keep).to_pandas() pd_series = scalars_pandas_df_index[columns].duplicated(subset, keep=keep) - bigframes.testing.utils.assert_series_equal(pd_series, bf_series, check_dtype=False) - - -def test_df_from_dict_columns_orient(): - data = {"a": [1, 2], "b": [3.3, 2.4]} - bf_result = dataframe.DataFrame.from_dict(data, orient="columns").to_pandas() - pd_result = pd.DataFrame.from_dict(data, orient="columns") - assert_frame_equal(pd_result, bf_result, check_dtype=False, check_index_type=False) - - -def test_df_from_dict_index_orient(): - data = {"a": [1, 2], "b": [3.3, 2.4]} - bf_result = dataframe.DataFrame.from_dict( - data, orient="index", columns=["col1", "col2"] - ).to_pandas() - pd_result = pd.DataFrame.from_dict(data, orient="index", columns=["col1", "col2"]) - assert_frame_equal(pd_result, bf_result, check_dtype=False, check_index_type=False) - - -def test_df_from_dict_tight_orient(): - data = { - "index": [("i1", "i2"), ("i3", "i4")], - "columns": ["col1", "col2"], - "data": [[1, 2.6], [3, 4.5]], - "index_names": ["in1", "in2"], - "column_names": ["column_axis"], - } - - bf_result = dataframe.DataFrame.from_dict(data, orient="tight").to_pandas() - pd_result = pd.DataFrame.from_dict(data, orient="tight") - assert_frame_equal(pd_result, bf_result, check_dtype=False, check_index_type=False) - - -def test_df_from_records(): - records = ((1, "a"), (2.5, "b"), (3.3, "c"), (4.9, "d")) - - bf_result = dataframe.DataFrame.from_records( - records, columns=["c1", "c2"] - ).to_pandas() - pd_result = pd.DataFrame.from_records(records, columns=["c1", "c2"]) - assert_frame_equal(pd_result, bf_result, check_dtype=False, check_index_type=False) + pd.testing.assert_series_equal(pd_series, bf_series, check_dtype=False) def test_df_to_dict(scalars_df_index, scalars_pandas_df_index): @@ -5353,10 +3160,7 @@ def test_df_to_dict(scalars_df_index, scalars_pandas_df_index): def test_df_to_excel(scalars_df_index, scalars_pandas_df_index): unsupported = ["timestamp_col"] - with ( - tempfile.TemporaryFile() as bf_result_file, - tempfile.TemporaryFile() as pd_result_file, - ): + with tempfile.TemporaryFile() as bf_result_file, tempfile.TemporaryFile() as pd_result_file: scalars_df_index.drop(columns=unsupported).to_excel(bf_result_file) scalars_pandas_df_index.drop(columns=unsupported).to_excel(pd_result_file) bf_result = bf_result_file.read() @@ -5373,88 +3177,6 @@ def test_df_to_latex(scalars_df_index, scalars_pandas_df_index): assert bf_result == pd_result -def test_df_to_json_local_str(scalars_df_index, scalars_pandas_df_index): - # pandas 3.0 bugged for serializing date col - bf_result = scalars_df_index.drop(columns="date_col").to_json() - # default_handler for arrow types that have no default conversion - pd_result = scalars_pandas_df_index.drop(columns="date_col").to_json( - default_handler=str - ) - - assert bf_result == pd_result - - -def test_df_to_json_local_file(scalars_df_index, scalars_pandas_df_index): - # TODO: supply a reason why this isn't compatible with pandas 1.x - pytest.importorskip("pandas", minversion="2.0.0") - # duration not fully supported at pandas level - scalars_df_index = scalars_df_index.drop(columns="duration_col") - scalars_pandas_df_index = scalars_pandas_df_index.drop(columns="duration_col") - with ( - tempfile.TemporaryFile() as bf_result_file, - tempfile.TemporaryFile() as pd_result_file, - ): - scalars_df_index.to_json(bf_result_file, orient="table") - # default_handler for arrow types that have no default conversion - scalars_pandas_df_index.to_json( - pd_result_file, orient="table", default_handler=str - ) - - bf_result = bf_result_file.read() - pd_result = pd_result_file.read() - - assert bf_result == pd_result - - -def test_df_to_csv_local_str(scalars_df_index, scalars_pandas_df_index): - bf_result = scalars_df_index.to_csv() - # default_handler for arrow types that have no default conversion - pd_result = scalars_pandas_df_index.to_csv() - - assert bf_result == pd_result - - -def test_df_to_csv_local_file(scalars_df_index, scalars_pandas_df_index): - with ( - tempfile.TemporaryFile() as bf_result_file, - tempfile.TemporaryFile() as pd_result_file, - ): - scalars_df_index.to_csv(bf_result_file) - scalars_pandas_df_index.to_csv(pd_result_file) - - bf_result = bf_result_file.read() - pd_result = pd_result_file.read() - - assert bf_result == pd_result - - -def test_df_to_parquet_local_bytes(scalars_df_index, scalars_pandas_df_index): - # GEOGRAPHY not supported in parquet export. - unsupported = ["geography_col"] - - bf_result = scalars_df_index.drop(columns=unsupported).to_parquet() - # default_handler for arrow types that have no default conversion - pd_result = scalars_pandas_df_index.drop(columns=unsupported).to_parquet() - - assert bf_result == pd_result - - -def test_df_to_parquet_local_file(scalars_df_index, scalars_pandas_df_index): - # GEOGRAPHY not supported in parquet export. - unsupported = ["geography_col"] - with ( - tempfile.TemporaryFile() as bf_result_file, - tempfile.TemporaryFile() as pd_result_file, - ): - scalars_df_index.drop(columns=unsupported).to_parquet(bf_result_file) - scalars_pandas_df_index.drop(columns=unsupported).to_parquet(pd_result_file) - - bf_result = bf_result_file.read() - pd_result = pd_result_file.read() - - assert bf_result == pd_result - - def test_df_to_records(scalars_df_index, scalars_pandas_df_index): unsupported = ["numeric_col"] bf_result = scalars_df_index.drop(columns=unsupported).to_records() @@ -5474,15 +3196,6 @@ def test_df_to_string(scalars_df_index, scalars_pandas_df_index): assert bf_result == pd_result -def test_df_to_html(scalars_df_index, scalars_pandas_df_index): - unsupported = ["numeric_col"] # formatted differently - - bf_result = scalars_df_index.drop(columns=unsupported).to_html() - pd_result = scalars_pandas_df_index.drop(columns=unsupported).to_html() - - assert bf_result == pd_result - - def test_df_to_markdown(scalars_df_index, scalars_pandas_df_index): # Nulls have bug from tabulate https://github.com/astanin/python-tabulate/issues/231 bf_result = scalars_df_index.dropna().to_markdown() @@ -5492,14 +3205,11 @@ def test_df_to_markdown(scalars_df_index, scalars_pandas_df_index): def test_df_to_pickle(scalars_df_index, scalars_pandas_df_index): - with ( - tempfile.TemporaryFile() as bf_result_file, - tempfile.TemporaryFile() as pd_result_file, - ): + with tempfile.TemporaryFile() as bf_result_file, tempfile.TemporaryFile() as pd_result_file: scalars_df_index.to_pickle(bf_result_file) scalars_pandas_df_index.to_pickle(pd_result_file) bf_result = bf_result_file.read() - pd_result = pd_result_file.read() + pd_result = bf_result_file.read() assert bf_result == pd_result @@ -5513,7 +3223,6 @@ def test_df_to_orc(scalars_df_index, scalars_pandas_df_index): "time_col", "timestamp_col", "geography_col", - "duration_col", ] bf_result_file = tempfile.TemporaryFile() @@ -5528,46 +3237,6 @@ def test_df_to_orc(scalars_df_index, scalars_pandas_df_index): assert bf_result == pd_result -@pytest.mark.parametrize( - ("expr",), - [ - ("new_col = int64_col + int64_too",), - ("new_col = (rowindex > 3) | bool_col",), - ("int64_too = bool_col\nnew_col2 = rowindex",), - ], -) -def test_df_eval(scalars_dfs, expr): - # TODO: supply a reason why this isn't compatible with pandas 1.x - pytest.importorskip("pandas", minversion="2.0.0") - scalars_df, scalars_pandas_df = scalars_dfs - - bf_result = scalars_df.eval(expr).to_pandas() - pd_result = scalars_pandas_df.eval(expr) - - bigframes.testing.utils.assert_frame_equal(bf_result, pd_result) - - -@pytest.mark.parametrize( - ("expr",), - [ - ("int64_col > int64_too",), - ("bool_col",), - ("((int64_col - int64_too) % @local_var) == 0",), - ], -) -def test_df_query(scalars_dfs, expr): - # TODO: supply a reason why this isn't compatible with pandas 1.x - pytest.importorskip("pandas", minversion="2.0.0") - # local_var is referenced in expressions - local_var = 3 # NOQA - scalars_df, scalars_pandas_df = scalars_dfs - - bf_result = scalars_df.query(expr).to_pandas() - pd_result = scalars_pandas_df.query(expr) - - bigframes.testing.utils.assert_frame_equal(bf_result, pd_result) - - @pytest.mark.parametrize( ("subset", "normalize", "ascending", "dropna"), [ @@ -5577,8 +3246,6 @@ def test_df_query(scalars_dfs, expr): ], ) def test_df_value_counts(scalars_dfs, subset, normalize, ascending, dropna): - if pd.__version__.startswith("1."): - pytest.skip("pandas 1.x produces different column labels.") scalars_df, scalars_pandas_df = scalars_dfs bf_result = ( @@ -5590,25 +3257,28 @@ def test_df_value_counts(scalars_dfs, subset, normalize, ascending, dropna): subset, normalize=normalize, ascending=ascending, dropna=dropna ) - bigframes.testing.utils.assert_series_equal( - bf_result, - pd_result, - check_dtype=False, - check_index_type=False, - ignore_order=True, # different pandas versions inconsistent for tie-handling + # Older pandas version may not have these values, bigframes tries to emulate 2.0+ + pd_result.name = "count" + pd_result.index.names = bf_result.index.names + + pd.testing.assert_series_equal( + bf_result, pd_result, check_dtype=False, check_index_type=False ) @pytest.mark.parametrize( - ("na_option", "method", "ascending", "numeric_only", "pct"), + ("na_option", "method", "ascending", "numeric_only"), [ - ("keep", "average", True, True, True), - ("top", "min", False, False, False), - ("bottom", "max", False, False, True), - ("top", "first", False, False, False), - ("bottom", "dense", False, False, True), + ("keep", "average", True, True), + ("top", "min", False, False), + ("bottom", "max", False, False), + ("top", "first", False, False), + ("bottom", "dense", False, False), ], ) +@pytest.mark.skipif( + True, reason="Blocked by possible pandas rank() regression (b/283278923)" +) def test_df_rank_with_nulls( scalars_df_index, scalars_pandas_df_index, @@ -5616,7 +3286,6 @@ def test_df_rank_with_nulls( method, ascending, numeric_only, - pct, ): unsupported_columns = ["geography_col"] bf_result = ( @@ -5626,7 +3295,6 @@ def test_df_rank_with_nulls( method=method, ascending=ascending, numeric_only=numeric_only, - pct=pct, ) .to_pandas() ) @@ -5637,12 +3305,11 @@ def test_df_rank_with_nulls( method=method, ascending=ascending, numeric_only=numeric_only, - pct=pct, ) .astype(pd.Float64Dtype()) ) - bigframes.testing.utils.assert_frame_equal( + pd.testing.assert_frame_equal( bf_result, pd_result, ) @@ -5654,16 +3321,14 @@ def test_df_bool_interpretation_error(scalars_df_index): def test_query_job_setters(scalars_df_default_index: dataframe.DataFrame): - # if allow_large_results=False, might not create query job - with bigframes.option_context("compute.allow_large_results", True): - job_ids = set() - repr(scalars_df_default_index) - assert scalars_df_default_index.query_job is not None - job_ids.add(scalars_df_default_index.query_job.job_id) - scalars_df_default_index.to_pandas(allow_large_results=True) - job_ids.add(scalars_df_default_index.query_job.job_id) + job_ids = set() + repr(scalars_df_default_index) + assert scalars_df_default_index.query_job is not None + job_ids.add(scalars_df_default_index.query_job.job_id) + scalars_df_default_index.to_pandas() + job_ids.add(scalars_df_default_index.query_job.job_id) - assert len(job_ids) == 2 + assert len(job_ids) == 2 def test_df_cached(scalars_df_index): @@ -5672,58 +3337,10 @@ def test_df_cached(scalars_df_index): ) df = df[df["rowindex_2"] % 2 == 0] - df_cached_copy = df.cache() + df_cached_copy = df._cached() pandas.testing.assert_frame_equal(df.to_pandas(), df_cached_copy.to_pandas()) -def test_df_cached_many_index_cols(scalars_df_index): - index_cols = [ - "int64_too", - "time_col", - "int64_col", - "bool_col", - "date_col", - "timestamp_col", - "string_col", - ] - df = scalars_df_index.set_index(index_cols) - df = df[df["rowindex_2"] % 2 == 0] - - df_cached_copy = df.cache() - pandas.testing.assert_frame_equal(df.to_pandas(), df_cached_copy.to_pandas()) - - -def test_assign_after_binop_row_joins(): - pd_df = pd.DataFrame( - { - "idx1": [1, 1, 1, 1, 2, 2, 2, 2], - "idx2": [10, 10, 20, 20, 10, 10, 20, 20], - "metric1": [10, 14, 2, 13, 6, 2, 9, 5], - "metric2": [25, -3, 8, 2, -1, 0, 0, -4], - }, - dtype=pd.Int64Dtype(), - ).set_index(["idx1", "idx2"]) - bf_df = dataframe.DataFrame(pd_df) - - # Expect implicit joiner to be used, preserving input cardinality rather than getting relational join - bf_df["metric_diff"] = bf_df.metric1 - bf_df.metric2 - pd_df["metric_diff"] = pd_df.metric1 - pd_df.metric2 - - assert_frame_equal(bf_df.to_pandas(), pd_df) - - -def test_df_cache_with_implicit_join(scalars_df_index): - """expectation is that cache will be used, but no explicit join will be performed""" - df = scalars_df_index[["int64_col", "int64_too"]].sort_index().reset_index() + 3 - df.cache() - bf_result = df + (df * 2) - sql = bf_result.sql - - # Very crude asserts, want sql to not use join and not use base table, only reference cached table - assert "JOIN" not in sql - assert "bigframes_testing" not in sql - - def test_df_dot_inline(session): df1 = pd.DataFrame([[1, 2, 3], [2, 5, 7]]) df2 = pd.DataFrame([[2, 4, 8], [1, 5, 10], [3, 6, 9]]) @@ -5739,7 +3356,7 @@ def test_df_dot_inline(session): pd_result[name] = pd_result[name].astype(pd.Int64Dtype()) pd_result.index = pd_result.index.astype(pd.Int64Dtype()) - bigframes.testing.utils.assert_frame_equal( + pd.testing.assert_frame_equal( bf_result, pd_result, ) @@ -5756,7 +3373,7 @@ def test_df_dot( for name in pd_result.columns: pd_result[name] = pd_result[name].astype(pd.Int64Dtype()) - bigframes.testing.utils.assert_frame_equal( + pd.testing.assert_frame_equal( bf_result, pd_result, ) @@ -5773,30 +3390,7 @@ def test_df_dot_operator( for name in pd_result.columns: pd_result[name] = pd_result[name].astype(pd.Int64Dtype()) - bigframes.testing.utils.assert_frame_equal( - bf_result, - pd_result, - ) - - -def test_df_dot_series_inline(): - left = [[1, 2, 3], [2, 5, 7]] - right = [2, 1, 3] - - bf1 = dataframe.DataFrame(left) - bf2 = series.Series(right) - bf_result = bf1.dot(bf2).to_pandas() - - df1 = pd.DataFrame(left) - df2 = pd.Series(right) - pd_result = df1.dot(df2) - - # Patch pandas dtypes for testing parity - # Pandas result is int64 instead of Int64 (nullable) dtype. - pd_result = pd_result.astype(pd.Int64Dtype()) - pd_result.index = pd_result.index.astype(pd.Int64Dtype()) - - bigframes.testing.utils.assert_series_equal( + pd.testing.assert_frame_equal( bf_result, pd_result, ) @@ -5812,7 +3406,7 @@ def test_df_dot_series( # Pandas result is object instead of Int64 (nullable) dtype. pd_result = pd_result.astype(pd.Int64Dtype()) - bigframes.testing.utils.assert_series_equal( + pd.testing.assert_series_equal( bf_result, pd_result, ) @@ -5828,559 +3422,7 @@ def test_df_dot_operator_series( # Pandas result is object instead of Int64 (nullable) dtype. pd_result = pd_result.astype(pd.Int64Dtype()) - bigframes.testing.utils.assert_series_equal( + pd.testing.assert_series_equal( bf_result, pd_result, ) - - -def test_recursion_limit(scalars_df_index): - scalars_df_index = scalars_df_index[["int64_too", "int64_col", "float64_col"]] - for i in range(250): - scalars_df_index = scalars_df_index + 4 - scalars_df_index.to_pandas() - - -@pytest.mark.skipif( - reason="b/366477265: Skip until query complexity error can be reliably triggered." -) -def test_query_complexity_error(scalars_df_index): - # This test requires automatic caching/query decomposition to be turned off - bf_df = scalars_df_index - for _ in range(8): - bf_df = bf_df.merge(bf_df, on="int64_col").head(30) - bf_df = bf_df[bf_df.columns[:20]] - - with pytest.raises( - bigframes.exceptions.QueryComplexityError, match=r"Try using DataFrame\.cache" - ): - bf_df.to_pandas() - - -def test_query_complexity_repeated_joins( - scalars_df_index, scalars_pandas_df_index, with_multiquery_execution -): - pd_df = scalars_pandas_df_index - bf_df = scalars_df_index - for _ in range(8): - # recursively join, resuling in 2^8 - 1 = 255 joins - pd_df = pd_df.merge(pd_df, on="int64_col").head(30) - pd_df = pd_df[pd_df.columns[:20]] - bf_df = bf_df.merge(bf_df, on="int64_col").head(30) - bf_df = bf_df[bf_df.columns[:20]] - - bf_result = bf_df.to_pandas() - pd_result = pd_df - assert_frame_equal(bf_result, pd_result, check_index_type=False) - - -def test_query_complexity_repeated_subtrees( - scalars_df_index, scalars_pandas_df_index, with_multiquery_execution -): - # Recursively union the data, if fully inlined has 10^5 identical root tables. - pd_df = scalars_pandas_df_index - bf_df = scalars_df_index - for _ in range(5): - pd_df = pd.concat(10 * [pd_df]).head(5) - bf_df = bpd.concat(10 * [bf_df]).head(5) - bf_result = bf_df.to_pandas() - pd_result = pd_df - assert_frame_equal(bf_result, pd_result) - - -@pytest.mark.skipif( - sys.version_info >= (3, 12), - # See: https://github.com/python/cpython/issues/112282 - reason="setrecursionlimit has no effect on the Python C stack since Python 3.12.", -) -def test_query_complexity_repeated_analytic(scalars_df_index, scalars_pandas_df_index): - bf_df = scalars_df_index[["int64_col", "int64_too"]] - pd_df = scalars_pandas_df_index[["int64_col", "int64_too"]] - # Uses LAG analytic operator, each in a new SELECT - for _ in range(50): - bf_df = bf_df.diff() - pd_df = pd_df.diff() - bf_result = bf_df.to_pandas() - pd_result = pd_df - assert_frame_equal(bf_result, pd_result) - - -def test_to_gbq_and_create_dataset(session, scalars_df_index, dataset_id_not_created): - dataset_id = dataset_id_not_created - destination_table = f"{dataset_id}.scalars_df" - - result_table = scalars_df_index.to_gbq(destination_table) - assert ( - result_table == destination_table - if destination_table - else result_table is not None - ) - - loaded_scalars_df_index = session.read_gbq(result_table) - assert not loaded_scalars_df_index.empty - - -def test_read_gbq_to_pandas_no_exec(unordered_session: bigframes.Session): - metrics = unordered_session._metrics - execs_pre = metrics.execution_count - df = unordered_session.read_gbq("bigquery-public-data.ml_datasets.penguins") - df.to_pandas() - execs_post = metrics.execution_count - assert df.shape == (344, 7) - assert execs_pre == execs_post - - -def test_to_gbq_table_labels(scalars_df_index): - destination_table = "bigframes-dev.bigframes_tests_sys.table_labels" - result_table = scalars_df_index.to_gbq( - destination_table, labels={"test": "labels"}, if_exists="replace" - ) - client = scalars_df_index._session.bqclient - table = client.get_table(result_table) - assert table.labels - assert table.labels["test"] == "labels" - - -def test_to_gbq_obj_ref_persists(session): - # Test that saving and loading an Object Reference retains its dtype - import uuid - - import google.cloud.bigquery - - sql = """ - SELECT STRUCT('gs://cloud-samples-data/vision/ocr/sign.jpg' AS uri, CAST(NULL AS STRING) AS version, CAST(NULL AS STRING) AS authorizer, PARSE_JSON('{}') AS details) AS uris - """ - df_init = session.read_gbq(sql) - - tmp_table_id = f"bigframes-dev.bigframes_tests_sys.tmp_obj_ref_{uuid.uuid4().hex}" - df_init.to_gbq(tmp_table_id, if_exists="replace") - - client = session.bqclient - table = client.get_table(tmp_table_id) - schema = list(table.schema) - for i, field in enumerate(schema): - if field.name == "uris": - schema[i] = google.cloud.bigquery.SchemaField( - name=field.name, - field_type=field.field_type, - mode=field.mode, - description="bigframes_dtype: OBJ_REF_DTYPE", - fields=field.fields, - ) - break - table.schema = schema - client.update_table(table, ["schema"]) - - bdf = session.read_gbq(tmp_table_id) - - destination_table = "bigframes-dev.bigframes_tests_sys.test_obj_ref_persistence" - bdf.to_gbq(destination_table, if_exists="replace") - - loaded_df = session.read_gbq(destination_table) - assert loaded_df["uris"].dtype == dtypes.OBJ_REF_DTYPE - - -@pytest.mark.parametrize( - ("col_names", "ignore_index"), - [ - pytest.param(["A"], False, id="one_array_false"), - pytest.param(["A"], True, id="one_array_true"), - pytest.param(["B"], False, id="one_float_false"), - pytest.param(["B"], True, id="one_float_true"), - pytest.param(["A", "C"], False, id="two_arrays_false"), - pytest.param(["A", "C"], True, id="two_arrays_true"), - ], -) -def test_dataframe_explode(col_names, ignore_index, session): - data = { - "A": [[0, 1, 2], [], [3, 4]], - "B": 3, - "C": [["a", "b", "c"], np.nan, ["d", "e"]], - } - - df = bpd.DataFrame(data, session=session) - pd_df = df.to_pandas() - pd_result = pd_df.explode(col_names, ignore_index=ignore_index) - bf_result = df.explode(col_names, ignore_index=ignore_index) - - history_pre = session.execution_history().to_dataframe() - queries_pre = ( - len(history_pre[history_pre["job_type"] == "query"]) - if "job_type" in history_pre.columns - else 0 - ) - - bf_materialized = bf_result.to_pandas() - - history_post = session.execution_history().to_dataframe() - queries_post = len(history_post[history_post["job_type"] == "query"]) - - bigframes.testing.utils.assert_frame_equal( - bf_materialized, - pd_result, - check_index_type=False, - check_dtype=False, - ) - # we test this property on this method in particular as compilation - # is non-deterministic and won't use the query cache as implemented - assert (queries_post - queries_pre) <= 1 - - -@pytest.mark.parametrize( - ("ignore_index", "ordered"), - [ - pytest.param(True, True, id="include_index_ordered"), - pytest.param(True, False, id="include_index_unordered"), - pytest.param(False, True, id="ignore_index_ordered"), - ], -) -def test_dataframe_explode_reserve_order(ignore_index, ordered): - data = { - "a": [np.random.randint(0, 10, 10) for _ in range(10)], - "b": [np.random.randint(0, 10, 10) for _ in range(10)], - } - df = bpd.DataFrame(data) - pd_df = pd.DataFrame(data) - - res = df.explode(["a", "b"], ignore_index=ignore_index).to_pandas(ordered=ordered) - pd_res = pd_df.explode(["a", "b"], ignore_index=ignore_index).astype( - pd.Int64Dtype() - ) - bigframes.testing.utils.assert_frame_equal( - res if ordered else res.sort_index(), - pd_res, - check_index_type=False, - ) - - -@pytest.mark.parametrize( - ("col_names"), - [ - pytest.param([], id="empty", marks=pytest.mark.xfail(raises=ValueError)), - pytest.param( - ["A", "A"], id="duplicate", marks=pytest.mark.xfail(raises=ValueError) - ), - pytest.param("unknown", id="unknown", marks=pytest.mark.xfail(raises=KeyError)), - ], -) -def test_dataframe_explode_xfail(col_names): - df = bpd.DataFrame({"A": [[0, 1, 2], [], [3, 4]]}) - df.explode(col_names) - - -@pytest.mark.parametrize( - ("on", "rule", "origin"), - [ - pytest.param("datetime_col", "100D", "start"), - pytest.param("datetime_col", "30W", "start"), - pytest.param("datetime_col", "5M", "epoch"), - pytest.param("datetime_col", "3Q", "start_day"), - pytest.param("datetime_col", "3YE", "start"), - ], -) -def test_resample_with_column( - scalars_df_index, scalars_pandas_df_index, on, rule, origin -): - # TODO: supply a reason why this isn't compatible with pandas 1.x - pytest.importorskip("pandas", minversion="2.2.0") - # TODO: supply a reason why this isn't compatible with pandas 1.x - if pandas.__version__.startswith("3"): - pytest.skip( - "pandas 3.0 behavior diverges for day offsets: https://github.com/pandas-dev/pandas/pull/61985" - ) - bf_result = ( - scalars_df_index.resample(rule=rule, on=on, origin=origin)[ - ["int64_col", "int64_too"] - ] - .max() - .to_pandas() - ) - pd_result = scalars_pandas_df_index.resample(rule=rule, on=on, origin=origin)[ - ["int64_col", "int64_too"] - ].max() - # TODO: (b/484364312) - pd_result.index.names = bf_result.index.names - bigframes.testing.utils.assert_frame_equal( - bf_result, pd_result, check_dtype=False, check_index_type=False - ) - - -@pytest.mark.parametrize("index_col", ["timestamp_col", "datetime_col"]) -@pytest.mark.parametrize( - ("index_append", "level"), - [(True, 1), (False, None), (False, 0)], -) -@pytest.mark.parametrize( - "rule", - [ - # TODO(tswast): support timedeltas and dataoffsets. - # TODO(tswast): support bins that default to "right". - "100d", - "1200h", - ], -) -# TODO(tswast): support "right" -@pytest.mark.parametrize("closed", ["left", None]) -# TODO(tswast): support "right" -@pytest.mark.parametrize("label", ["left", None]) -@pytest.mark.parametrize( - "origin", - ["epoch", "start", "start_day"], # TODO(tswast): support end, end_day. -) -def test_resample_with_index( - scalars_df_index, - scalars_pandas_df_index, - index_append, - level, - index_col, - rule, - closed, - origin, - label, -): - # TODO: supply a reason why this isn't compatible with pandas 1.x - if rule == "100d" and pandas.__version__.startswith("3"): - pytest.skip( - "pandas 3.0 behavior diverges for day offsets: https://github.com/pandas-dev/pandas/pull/61985" - ) - pytest.importorskip("pandas", minversion="2.0.0") - scalars_df_index = scalars_df_index.set_index(index_col, append=index_append) - scalars_pandas_df_index = scalars_pandas_df_index.set_index( - index_col, append=index_append - ) - bf_result = ( - scalars_df_index[["int64_col", "int64_too"]] - .resample(rule=rule, level=level, closed=closed, origin=origin, label=label) - .min() - .to_pandas() - ) - pd_result = ( - scalars_pandas_df_index[["int64_col", "int64_too"]] - .resample(rule=rule, level=level, closed=closed, origin=origin, label=label) - .min() - ) - # TODO: (b/484364312) - pd_result.index.names = bf_result.index.names - assert_frame_equal(bf_result, pd_result) - - -@pytest.mark.parametrize( - ("rule", "origin", "data"), - [ - ( - "5h", - "epoch", - { - "timestamp_col": pd.date_range( - start="2021-01-01 13:00:00", periods=30, freq="1h" - ), - "int64_col": range(30), - "int64_too": range(10, 40), - }, - ), - ( - "75min", - "start_day", - { - "timestamp_col": pd.date_range( - start="2021-01-01 13:00:00", periods=30, freq="10min" - ), - "int64_col": range(30), - "int64_too": range(10, 40), - }, - ), - ( - "7s", - "epoch", - { - "timestamp_col": pd.date_range( - start="2021-01-01 13:00:00", periods=30, freq="1s" - ), - "int64_col": range(30), - "int64_too": range(10, 40), - }, - ), - ], -) -def test_resample_start_time(rule, origin, data): - # TODO: supply a reason why this isn't compatible with pandas 1.x - pytest.importorskip("pandas", minversion="2.0.0") - col = "timestamp_col" - scalars_df_index = bpd.DataFrame(data).set_index(col) - scalars_pandas_df_index = pd.DataFrame(data).set_index(col) - scalars_pandas_df_index.index.name = None - - bf_result = scalars_df_index.resample(rule=rule, origin=origin).min().to_pandas() - - pd_result = scalars_pandas_df_index.resample(rule=rule, origin=origin).min() - - # TODO: (b/484364312) - pd_result.index.names = bf_result.index.names - bigframes.testing.utils.assert_frame_equal( - bf_result, pd_result, check_dtype=False, check_index_type=False - ) - - -@pytest.mark.parametrize( - "dtype", - [ - pytest.param("string[pyarrow]", id="type-string"), - pytest.param(pd.StringDtype(storage="pyarrow"), id="type-literal"), - pytest.param( - {"bool_col": "string[pyarrow]", "int64_col": pd.Float64Dtype()}, - id="multiple-types", - ), - ], -) -def test_df_astype(scalars_dfs, dtype): - bf_df, pd_df = scalars_dfs - target_cols = ["bool_col", "int64_col"] - bf_df = bf_df[target_cols] - pd_df = pd_df[target_cols] - - bf_result = bf_df.astype(dtype).to_pandas() - pd_result = pd_df.astype(dtype) - - bigframes.testing.utils.assert_frame_equal( - bf_result, pd_result, check_index_type=False - ) - - -def test_df_astype_python_types(scalars_dfs): - bf_df, pd_df = scalars_dfs - target_cols = ["bool_col", "int64_col"] - bf_df = bf_df[target_cols] - pd_df = pd_df[target_cols] - - bf_result = bf_df.astype({"bool_col": str, "int64_col": float}).to_pandas() - pd_result = pd_df.astype( - {"bool_col": "string[pyarrow]", "int64_col": pd.Float64Dtype()} - ) - - bigframes.testing.utils.assert_frame_equal( - bf_result, pd_result, check_index_type=False - ) - - -def test_astype_invalid_type_fail(scalars_dfs): - bf_df, _ = scalars_dfs - - with pytest.raises(TypeError, match=r".*Share your use case with.*"): - bf_df.astype(123) - - -def test_agg_with_dict_lists_strings(scalars_dfs): - bf_df, pd_df = scalars_dfs - agg_funcs = { - "int64_too": ["min", "max"], - "int64_col": ["min", "count"], - } - - bf_result = bf_df.agg(agg_funcs).to_pandas() - pd_result = pd_df.agg(agg_funcs) - - bigframes.testing.utils.assert_frame_equal( - bf_result, pd_result, check_dtype=False, check_index_type=False - ) - - -@pytest.mark.skipif( - pandas.__version__.startswith("3"), - # See: https://github.com/python/cpython/issues/112282 - reason="pandas 3.0 miscaculates variance", -) -def test_agg_with_dict_lists_callables(scalars_dfs): - bf_df, pd_df = scalars_dfs - agg_funcs = { - "int64_too": [np.min, np.max], - "int64_col": [np.min, np.var], - } - - bf_result = bf_df.agg(agg_funcs).to_pandas() - pd_result = pd_df.agg(agg_funcs) - - bigframes.testing.utils.assert_frame_equal( - bf_result, pd_result, check_dtype=False, check_index_type=False - ) - - -def test_agg_with_dict_list_and_str(scalars_dfs): - bf_df, pd_df = scalars_dfs - agg_funcs = { - "int64_too": ["min", "max"], - "int64_col": "sum", - } - - bf_result = bf_df.agg(agg_funcs).to_pandas() - pd_result = pd_df.agg(agg_funcs) - - bigframes.testing.utils.assert_frame_equal( - bf_result, pd_result, check_dtype=False, check_index_type=False - ) - - -def test_agg_with_dict_strs(scalars_dfs): - bf_df, pd_df = scalars_dfs - agg_funcs = { - "int64_too": "min", - "int64_col": "sum", - "float64_col": "max", - } - - bf_result = bf_df.agg(agg_funcs).to_pandas() - pd_result = pd_df.agg(agg_funcs) - pd_result.index = pd_result.index.astype("string[pyarrow]") - - bigframes.testing.utils.assert_series_equal( - bf_result, pd_result, check_dtype=False, check_index_type=False - ) - - -def test_df_agg_with_builtins(scalars_dfs): - bf_df, pd_df = scalars_dfs - - bf_result = ( - bf_df[["int64_col", "bool_col"]] - .dropna() - .groupby(bf_df.int64_too % 2) - .agg({"int64_col": [len, sum, min, max, list], "bool_col": [all, any, max]}) - .to_pandas() - ) - pd_result = ( - pd_df[["int64_col", "bool_col"]] - .dropna() - .groupby(pd_df.int64_too % 2) - .agg({"int64_col": [len, sum, min, max, list], "bool_col": [all, any, max]}) - ) - - bigframes.testing.utils.assert_frame_equal( - bf_result, pd_result, check_dtype=False, check_index_type=False - ) - - -def test_agg_with_dict_containing_non_existing_col_raise_key_error(scalars_dfs): - bf_df, _ = scalars_dfs - agg_funcs = { - "int64_too": ["min", "max"], - "nonexisting_col": ["count"], - } - - with pytest.raises(KeyError): - bf_df.agg(agg_funcs) - - -def test_empty_agg_projection_succeeds(): - # Tests that the compiler generates a SELECT 1 fallback for empty aggregations, - # protecting against BigQuery syntax errors when both groups and metrics are empty. - import importlib - - bq = importlib.import_module( - "bigframes_vendored.ibis.backends.sql.compilers.bigquery" - ) - sg = importlib.import_module("bigframes_vendored.sqlglot") - - compiler = bq.BigQueryCompiler() - res = compiler.visit_Aggregate( - "op", parent=sg.table("parent_table"), groups=[], metrics=[] - ) - assert "SELECT 1" in res.sql() diff --git a/tests/system/small/test_dataframe_io.py b/tests/system/small/test_dataframe_io.py index ef21e929afa..8f5d706f621 100644 --- a/tests/system/small/test_dataframe_io.py +++ b/tests/system/small/test_dataframe_io.py @@ -12,187 +12,28 @@ # See the License for the specific language governing permissions and # limitations under the License. -import typing from typing import Tuple import google.api_core.exceptions -import numpy -import numpy.testing import pandas as pd import pyarrow as pa import pytest -from google.cloud import bigquery - -import bigframes -import bigframes.dataframe -import bigframes.dtypes as dtypes -import bigframes.enums -import bigframes.features -import bigframes.pandas as bpd -import bigframes.testing -from bigframes.testing import utils - -pandas_gbq = pytest.importorskip("pandas_gbq") - - -def test_sql_executes(scalars_df_default_index, bigquery_client): - """Test that DataFrame.sql returns executable SQL. - - DF.sql is used in public documentation such as - https://cloud.google.com/blog/products/data-analytics/using-bigquery-dataframes-with-carto-geospatial-tools - as a way to pass a DataFrame on to carto without executing the SQL - immediately. - - Make sure that this SQL can be run outside of BigQuery DataFrames (assuming - similar credentials / access to the referenced tables). - """ - # Do some operations to make for more complex SQL. - df = ( - scalars_df_default_index.drop(columns=["geography_col", "duration_col"]) - .groupby("string_col") - .max() - ) - df.index.name = None # Don't include unnamed indexes. - query = df.sql - - bf_result = df.to_pandas().sort_values("rowindex").reset_index(drop=True) - bq_result = ( - bigquery_client.query_and_wait(query) - .to_dataframe() - .sort_values("rowindex") - .reset_index(drop=True) - ) - bq_result["bytes_col"] = bq_result["bytes_col"].astype(dtypes.BYTES_DTYPE) - bigframes.testing.utils.assert_frame_equal(bf_result, bq_result, check_dtype=False) - - -def test_sql_executes_and_includes_named_index( - scalars_df_default_index, bigquery_client -): - """Test that DataFrame.sql returns executable SQL. - - DF.sql is used in public documentation such as - https://cloud.google.com/blog/products/data-analytics/using-bigquery-dataframes-with-carto-geospatial-tools - as a way to pass a DataFrame on to carto without executing the SQL - immediately. - - Make sure that this SQL can be run outside of BigQuery DataFrames (assuming - similar credentials / access to the referenced tables). - """ - # Do some operations to make for more complex SQL. - df = ( - scalars_df_default_index.drop(columns=["geography_col", "duration_col"]) - .groupby("string_col") - .max() - ) - query = df.sql - - bf_result = df.to_pandas().sort_values("rowindex") - bq_result = ( - bigquery_client.query_and_wait(query) - .to_dataframe() - .set_index("string_col") - .sort_values("rowindex") - ) - bq_result["bytes_col"] = bq_result["bytes_col"].astype(dtypes.BYTES_DTYPE) - bigframes.testing.utils.assert_frame_equal( - bf_result, bq_result, check_dtype=False, check_index_type=False - ) - - -def test_sql_executes_and_includes_named_multiindex( - scalars_df_default_index, bigquery_client -): - """Test that DataFrame.sql returns executable SQL. - - DF.sql is used in public documentation such as - https://cloud.google.com/blog/products/data-analytics/using-bigquery-dataframes-with-carto-geospatial-tools - as a way to pass a DataFrame on to carto without executing the SQL - immediately. - - Make sure that this SQL can be run outside of BigQuery DataFrames (assuming - similar credentials / access to the referenced tables). - """ - # Do some operations to make for more complex SQL. - df = ( - scalars_df_default_index.drop(columns=["geography_col", "duration_col"]) - .groupby(["string_col", "bool_col"]) - .max() - ) - query = df.sql - - bf_result = df.to_pandas().sort_values("rowindex") - bq_result = ( - bigquery_client.query_and_wait(query) - .to_dataframe() - .set_index(["string_col", "bool_col"]) - .sort_values("rowindex") - ) - bq_result["bytes_col"] = bq_result["bytes_col"].astype(dtypes.BYTES_DTYPE) - bigframes.testing.utils.assert_frame_equal( - bf_result, bq_result, check_dtype=False, check_index_type=False - ) - - -def test_to_arrow(scalars_df_default_index, scalars_pandas_df_default_index): - """Verify to_arrow() APIs returns the expected data.""" - expected = pa.Table.from_pandas( - scalars_pandas_df_default_index.drop(columns=["geography_col"]) - ) - with pytest.warns( - bigframes.exceptions.PreviewWarning, - match="to_arrow", - ): - actual = scalars_df_default_index.drop(columns=["geography_col"]).to_arrow() - - # Make string_col match type. Otherwise, pa.Table.from_pandas uses - # LargeStringArray. LargeStringArray is unnecessary because our strings are - # less than 2 GB. - expected = expected.set_column( - expected.column_names.index("string_col"), - pa.field("string_col", pa.string()), - expected["string_col"].cast(pa.string()), - ) - - # Note: the final .equals assertion covers all these checks, but these - # finer-grained assertions are easier to debug. - assert actual.column_names == expected.column_names - for column in actual.column_names: - assert actual[column].equals(expected[column]) - assert actual.equals(expected) +from tests.system.utils import ( + assert_pandas_df_equal_ignore_ordering, + convert_pandas_dtypes, +) +try: + import pandas_gbq # type: ignore +except ImportError: + pandas_gbq = None -def test_to_arrow_multiindex(scalars_df_index, scalars_pandas_df_index): - scalars_df_multiindex = scalars_df_index.set_index(["string_col", "int64_col"]) - scalars_pandas_df_multiindex = scalars_pandas_df_index.set_index( - ["string_col", "int64_col"] - ) - expected = pa.Table.from_pandas( - scalars_pandas_df_multiindex.drop(columns=["geography_col"]) - ) - - with pytest.warns( - bigframes.exceptions.PreviewWarning, - match="to_arrow", - ): - actual = scalars_df_multiindex.drop(columns=["geography_col"]).to_arrow() - - # Make string_col match type. Otherwise, pa.Table.from_pandas uses - # LargeStringArray. LargeStringArray is unnecessary because our strings are - # less than 2 GB. - expected = expected.set_column( - expected.column_names.index("string_col"), - pa.field("string_col", pa.string()), - expected["string_col"].cast(pa.string()), - ) +import typing - # Note: the final .equals assertion covers all these checks, but these - # finer-grained assertions are easier to debug. - assert actual.column_names == expected.column_names - for column in actual.column_names: - assert actual[column].equals(expected[column]) - assert actual.equals(expected) +import bigframes +import bigframes.dataframe +import bigframes.pandas as bpd def test_to_pandas_w_correct_dtypes(scalars_df_default_index): @@ -218,14 +59,7 @@ def test_to_pandas_array_struct_correct_result(session): result = df.to_pandas() expected = pd.DataFrame( { - "array_column": pd.Series( - [[1, 3, 2]], - dtype=( - pd.ArrowDtype(pa.list_(pa.int64())) - if bigframes.features.PANDAS_VERSIONS.is_arrow_list_dtype_usable - else "object" - ), - ), + "array_column": [[1, 3, 2]], "struct_column": pd.Series( [{"string_field": "a", "float_field": 1.2}], dtype=pd.ArrowDtype( @@ -249,97 +83,6 @@ def test_to_pandas_array_struct_correct_result(session): ) -def test_to_pandas_override_global_option(scalars_df_index): - # Direct call to_pandas uses global default setting (allow_large_results=True), - # table has 'bqdf' prefix. - with bigframes.option_context("compute.allow_large_results", True): - scalars_df_index.to_pandas() - table_id = scalars_df_index._query_job.destination.table_id - assert table_id is not None - - # When allow_large_results=False, a query_job object should not be created. - # Therefore, the table_id should remain unchanged. - scalars_df_index.to_pandas(allow_large_results=False) - assert scalars_df_index._query_job.destination.table_id == table_id - - -def test_to_pandas_downsampling_option_override(session): - df = session.read_gbq("bigframes-dev.bigframes_tests_sys.batting") - download_size = 1 - - with pytest.warns( - UserWarning, match="The data size .* exceeds the maximum download limit" - ): - # limits only apply for allow_large_result=True - df = df.to_pandas( - max_download_size=download_size, - sampling_method="head", - allow_large_results=True, - ) - - total_memory_bytes = df.memory_usage(deep=True).sum() - total_memory_mb = total_memory_bytes / (1024 * 1024) - assert total_memory_mb == pytest.approx(download_size, rel=0.5) - - -@pytest.mark.parametrize( - ("kwargs", "message"), - [ - pytest.param( - {"sampling_method": "head"}, - r"DEPRECATED[\S\s]*sampling_method[\S\s]*DataFrame.sample", - id="sampling_method", - ), - pytest.param( - {"random_state": 10}, - r"DEPRECATED[\S\s]*random_state[\S\s]*DataFrame.sample", - id="random_state", - ), - pytest.param( - {"max_download_size": 10}, - r"DEPRECATED[\S\s]*max_download_size[\S\s]*DataFrame.to_pandas_batches", - id="max_download_size", - ), - ], -) -def test_to_pandas_warns_deprecated_parameters(scalars_df_index, kwargs, message): - with pytest.warns(FutureWarning, match=message): - scalars_df_index.to_pandas( - # limits only apply for allow_large_result=True - allow_large_results=True, - **kwargs, - ) - - -def test_to_pandas_dry_run(session, scalars_pandas_df_multi_index): - bf_df = session.read_pandas(scalars_pandas_df_multi_index) - - result = bf_df.to_pandas(dry_run=True) - - assert isinstance(result, pd.Series) - assert len(result) > 0 - - -def test_to_arrow_override_global_option(scalars_df_index): - # Direct call to_arrow uses global default setting (allow_large_results=True), - with bigframes.option_context("compute.allow_large_results", True): - scalars_df_index.to_arrow() - table_id = scalars_df_index._query_job.destination.table_id - assert table_id is not None - - # When allow_large_results=False, a query_job object should not be created. - # Therefore, the table_id should remain unchanged. - scalars_df_index.to_arrow(allow_large_results=False) - assert scalars_df_index._query_job.destination.table_id == table_id - - -def test_to_pandas_batches_populates_total_bytes_processed(scalars_df_default_index): - batches = scalars_df_default_index.sort_values( - "int64_col" - ).to_pandas_batches() # Do a sort to force query execution. - assert batches.total_bytes_processed > 0 - - def test_to_pandas_batches_w_correct_dtypes(scalars_df_default_index): """Verify to_pandas_batches() APIs returns the expected dtypes.""" expected = scalars_df_default_index.dtypes @@ -348,141 +91,9 @@ def test_to_pandas_batches_w_correct_dtypes(scalars_df_default_index): pd.testing.assert_series_equal(actual, expected) -def test_to_pandas_batches_w_empty_dataframe(session): - """Verify to_pandas_batches() APIs returns at least one DataFrame. - - See b/428918844 for additional context. - """ - empty = bpd.DataFrame( - { - "idx1": [], - "idx2": [], - "col1": pd.Series([], dtype="string[pyarrow]"), - "col2": pd.Series([], dtype="Int64"), - }, - session=session, - ).set_index(["idx1", "idx2"], drop=True) - - results = list(empty.to_pandas_batches()) - assert len(results) == 1 - assert list(results[0].index.names) == ["idx1", "idx2"] - assert list(results[0].columns) == ["col1", "col2"] - bigframes.testing.utils.assert_series_equal(results[0].dtypes, empty.dtypes) - - -@pytest.mark.skipif( - bigframes.features.PANDAS_VERSIONS.is_arrow_list_dtype_usable, - reason="Test for pandas 1.x behavior only", -) -def test_to_pandas_batches_preserves_dtypes_for_populated_nested_json_pandas1(session): - """Verifies to_pandas_batches() preserves dtypes for nested JSON in pandas 1.x.""" - sql = """ - SELECT - 0 AS id, - [JSON '{"a":1}', JSON '{"b":2}'] AS json_array, - STRUCT(JSON '{"x":1}' AS json_field, 'test' AS str_field) AS json_struct - """ - df = session.read_gbq(sql, index_col="id") - batches = list(df.to_pandas_batches()) - - assert batches[0].dtypes["json_array"] == "object" - assert isinstance(batches[0].dtypes["json_struct"], pd.ArrowDtype) - - -@pytest.mark.skipif( - not bigframes.features.PANDAS_VERSIONS.is_arrow_list_dtype_usable, - reason="Test for pandas 2.x behavior only", -) -def test_to_pandas_batches_preserves_dtypes_for_populated_nested_json_pandas2(session): - """Verifies to_pandas_batches() preserves dtypes for nested JSON in pandas 2.x.""" - sql = """ - SELECT - 0 AS id, - [JSON '{"a":1}', JSON '{"b":2}'] AS json_array, - STRUCT(JSON '{"x":1}' AS json_field, 'test' AS str_field) AS json_struct - """ - df = session.read_gbq(sql, index_col="id") - batches = list(df.to_pandas_batches()) - - assert isinstance(batches[0].dtypes["json_array"], pd.ArrowDtype) - assert isinstance(batches[0].dtypes["json_array"].pyarrow_dtype, pa.ListType) - assert isinstance(batches[0].dtypes["json_struct"], pd.ArrowDtype) - - -@pytest.mark.skipif( - bigframes.features.PANDAS_VERSIONS.is_arrow_list_dtype_usable, - reason="Test for pandas 1.x behavior only", -) -def test_to_pandas_batches_should_not_error_on_empty_nested_json_pandas1(session): - """Verify to_pandas_batches() works with empty nested JSON types in pandas 1.x.""" - - sql = """ - SELECT - 1 AS id, - [] AS json_array, - STRUCT(NULL AS json_field, 'test2' AS str_field) AS json_struct - """ - df = session.read_gbq(sql, index_col="id") - - # The main point: this should not raise an error - batches = list(df.to_pandas_batches()) - assert sum(len(b) for b in batches) == 1 - - assert batches[0].dtypes["json_array"] == "object" - assert isinstance(batches[0].dtypes["json_struct"], pd.ArrowDtype) - - -@pytest.mark.skipif( - not bigframes.features.PANDAS_VERSIONS.is_arrow_list_dtype_usable, - reason="Test for pandas 2.x behavior only", -) -def test_to_pandas_batches_should_not_error_on_empty_nested_json_pandas2(session): - """Verify to_pandas_batches() works with empty nested JSON types in pandas 2.x.""" - - sql = """ - SELECT - 1 AS id, - [] AS json_array, - STRUCT(NULL AS json_field, 'test2' AS str_field) AS json_struct - """ - df = session.read_gbq(sql, index_col="id") - - # The main point: this should not raise an error - batches = list(df.to_pandas_batches()) - assert sum(len(b) for b in batches) == 1 - - assert isinstance(batches[0].dtypes["json_array"], pd.ArrowDtype) - assert isinstance(batches[0].dtypes["json_struct"], pd.ArrowDtype) - assert isinstance(batches[0].dtypes["json_struct"].pyarrow_dtype, pa.StructType) - - -@pytest.mark.parametrize("allow_large_results", (True, False)) -def test_to_pandas_batches_w_page_size_and_max_results(session, allow_large_results): - """Verify to_pandas_batches() APIs returns the expected page size. - - Regression test for b/407521010. - """ - bf_df = session.read_gbq( - "bigquery-public-data.usa_names.usa_1910_2013", - index_col=bigframes.enums.DefaultIndexKind.NULL, - ) - expected_column_count = len(bf_df.columns) - - batch_count = 0 - for pd_df in bf_df.to_pandas_batches( - page_size=42, allow_large_results=allow_large_results, max_results=42 * 3 - ): - batch_row_count, batch_column_count = pd_df.shape - batch_count += 1 - assert batch_column_count == expected_column_count - assert batch_row_count == 42 - - assert batch_count == 3 - - @pytest.mark.parametrize( - ("index",), - [(True,), (False,)], + ("index"), + [True, False], ) def test_to_csv_index( scalars_dfs: Tuple[bigframes.dataframe.DataFrame, pd.DataFrame], @@ -494,9 +105,12 @@ def test_to_csv_index( """Test the `to_csv` API with the `index` parameter.""" scalars_df, scalars_pandas_df = scalars_dfs index_col = None - path = gcs_folder + f"test_index_df_to_csv_index_{index}*.csv" - if index: - index_col = typing.cast(str, scalars_df.index.name) + if scalars_df.index.name is not None: + path = gcs_folder + f"test_index_df_to_csv_index_{index}*.csv" + if index: + index_col = typing.cast(str, scalars_df.index.name) + else: + path = gcs_folder + f"test_default_index_df_to_csv_index_{index}*.csv" # TODO(swast): Support "date_format" parameter and make sure our # DATETIME/TIMESTAMP column export is the same format as pandas by default. @@ -508,20 +122,18 @@ def test_to_csv_index( dtype = scalars_df.reset_index().dtypes.to_dict() dtype.pop("geography_col") dtype.pop("rowindex") - # read_csv will decode into bytes, numeric inproperly, convert_pandas_dtypes will encode properly from string - dtype.pop("bytes_col") - dtype.pop("numeric_col") gcs_df = pd.read_csv( - utils.get_first_file_from_wildcard(path), + path, dtype=dtype, date_format={"timestamp_col": "YYYY-MM-DD HH:MM:SS Z"}, index_col=index_col, ) - utils.convert_pandas_dtypes(gcs_df, bytes_col=True) + convert_pandas_dtypes(gcs_df, bytes_col=True) gcs_df.index.name = scalars_df.index.name scalars_pandas_df = scalars_pandas_df.copy() scalars_pandas_df.index = scalars_pandas_df.index.astype("int64") + # Ordering should be maintained for tables smaller than 1 GB. pd.testing.assert_frame_equal(gcs_df, scalars_pandas_df) @@ -547,17 +159,14 @@ def test_to_csv_tabs( dtype = scalars_df.reset_index().dtypes.to_dict() dtype.pop("geography_col") dtype.pop("rowindex") - # read_csv will decode into bytes, numeric inproperly, convert_pandas_dtypes will encode properly from string - dtype.pop("bytes_col") - dtype.pop("numeric_col") gcs_df = pd.read_csv( - utils.get_first_file_from_wildcard(path), + path, sep="\t", dtype=dtype, date_format={"timestamp_col": "YYYY-MM-DD HH:MM:SS Z"}, index_col=index_col, ) - utils.convert_pandas_dtypes(gcs_df, bytes_col=True) + convert_pandas_dtypes(gcs_df, bytes_col=True) gcs_df.index.name = scalars_df.index.name scalars_pandas_df = scalars_pandas_df.copy() @@ -571,8 +180,8 @@ def test_to_csv_tabs( ("index"), [True, False], ) -@pytest.mark.skipif(pandas_gbq is None, reason="required by pandas_gbq.read_gbq") -def test_to_gbq_w_index(scalars_dfs, dataset_id, index): +@pytest.mark.skipif(pandas_gbq is None, reason="required by pd.read_gbq") +def test_to_gbq_index(scalars_dfs, dataset_id, index): """Test the `to_gbq` API with the `index` parameter.""" scalars_df, scalars_pandas_df = scalars_dfs destination_table = f"{dataset_id}.test_index_df_to_gbq_{index}" @@ -584,356 +193,61 @@ def test_to_gbq_w_index(scalars_dfs, dataset_id, index): index_col = None df_in.to_gbq(destination_table, if_exists="replace", index=index) - df_out = pandas_gbq.read_gbq(destination_table, index_col=index_col) + df_out = pd.read_gbq(destination_table, index_col=index_col) if index: df_out = df_out.sort_index() else: df_out = df_out.sort_values("rowindex_2").reset_index(drop=True) - utils.convert_pandas_dtypes(df_out, bytes_col=False) - # pandas_gbq.read_gbq interprets bytes_col as object, reconvert to pyarrow binary - df_out["bytes_col"] = df_out["bytes_col"].astype(pd.ArrowDtype(pa.binary())) + convert_pandas_dtypes(df_out, bytes_col=False) expected = scalars_pandas_df.copy() expected.index.name = index_col pd.testing.assert_frame_equal(df_out, expected, check_index_type=False) -def test_to_gbq_if_exists_is_fail(scalars_dfs, dataset_id): - scalars_df, scalars_pandas_df = scalars_dfs - destination_table = f"{dataset_id}.test_to_gbq_if_exists_is_fails" - scalars_df.to_gbq(destination_table) - - gcs_df = pandas_gbq.read_gbq(destination_table, index_col="rowindex") - assert len(gcs_df) == len(scalars_pandas_df) - pd.testing.assert_index_equal(gcs_df.columns, scalars_pandas_df.columns) - - # Test default value is "fails" - with pytest.raises(ValueError, match="Table already exists"): - scalars_df.to_gbq(destination_table) - - with pytest.raises(ValueError, match="Table already exists"): - scalars_df.to_gbq(destination_table, if_exists="fail") - - -def test_to_gbq_if_exists_is_replace(scalars_dfs, dataset_id): - scalars_df, scalars_pandas_df = scalars_dfs - destination_table = f"{dataset_id}.test_to_gbq_if_exists_is_replace" - scalars_df.to_gbq(destination_table) - - gcs_df = pandas_gbq.read_gbq(destination_table, index_col="rowindex") - assert len(gcs_df) == len(scalars_pandas_df) - pd.testing.assert_index_equal(gcs_df.columns, scalars_pandas_df.columns) - - # When replacing a table with same schema - scalars_df.to_gbq(destination_table, if_exists="replace") - gcs_df = pandas_gbq.read_gbq(destination_table, index_col="rowindex") - assert len(gcs_df) == len(scalars_pandas_df) - pd.testing.assert_index_equal(gcs_df.columns, scalars_pandas_df.columns) - - # When replacing a table with same schema but different column order - reordered_df = scalars_df[scalars_df.columns[::-1]] - reordered_df.to_gbq(destination_table, if_exists="replace") - gcs_df = pandas_gbq.read_gbq(destination_table, index_col="rowindex") - assert len(gcs_df) == len(scalars_pandas_df) - pd.testing.assert_index_equal(gcs_df.columns, reordered_df.columns) - - # When replacing a table with different schema - partitial_scalars_df = scalars_df.drop(columns=["string_col"]) - partitial_scalars_df.to_gbq(destination_table, if_exists="replace") - gcs_df = pandas_gbq.read_gbq(destination_table, index_col="rowindex") - assert len(gcs_df) == len(partitial_scalars_df) - pd.testing.assert_index_equal(gcs_df.columns, partitial_scalars_df.columns) - - -def test_to_gbq_if_exists_is_append(scalars_dfs, dataset_id): - scalars_df, scalars_pandas_df = scalars_dfs - destination_table = f"{dataset_id}.test_to_gbq_if_exists_is_append" - scalars_df.to_gbq(destination_table) - - gcs_df = pandas_gbq.read_gbq(destination_table, index_col="rowindex") - assert len(gcs_df) == len(scalars_pandas_df) - pd.testing.assert_index_equal(gcs_df.columns, scalars_pandas_df.columns) - - # When appending to a table with same schema - scalars_df.to_gbq(destination_table, if_exists="append") - gcs_df = pandas_gbq.read_gbq(destination_table, index_col="rowindex") - assert len(gcs_df) == 2 * len(scalars_pandas_df) - pd.testing.assert_index_equal(gcs_df.columns, scalars_pandas_df.columns) - - # When appending to a table with different schema - partitial_scalars_df = scalars_df.drop(columns=["string_col"]) - partitial_scalars_df.to_gbq(destination_table, if_exists="append") - gcs_df = pandas_gbq.read_gbq(destination_table, index_col="rowindex") - assert len(gcs_df) == 3 * len(partitial_scalars_df) - pd.testing.assert_index_equal(gcs_df.columns, scalars_df.columns) - - -def test_to_gbq_w_duplicate_column_names( - scalars_df_index, scalars_pandas_df_index, dataset_id -): - """Test the `to_gbq` API when dealing with duplicate column names.""" - destination_table = f"{dataset_id}.test_to_gbq_w_duplicate_column_names" - - # Renaming 'int64_too' to 'int64_col', which will result in 'int64_too' - # becoming 'int64_col_1' after deduplication. - scalars_df_index = scalars_df_index.rename(columns={"int64_too": "int64_col"}) - scalars_df_index.to_gbq(destination_table, if_exists="replace") - - bf_result = bpd.read_gbq(destination_table, index_col="rowindex").to_pandas() - - pd.testing.assert_series_equal( - scalars_pandas_df_index["int64_col"], bf_result["int64_col"] - ) - pd.testing.assert_series_equal( - scalars_pandas_df_index["int64_too"], - bf_result["int64_col_1"], - check_names=False, - ) - - -def test_to_gbq_w_protected_column_names( - scalars_df_index, scalars_pandas_df_index, dataset_id -): - """ - Column names can't use any of the following prefixes: - - * _TABLE_ - * _FILE_ - * _PARTITION - * _ROW_TIMESTAMP - * __ROOT__ - * _COLIDENTIFIER - - See: https://cloud.google.com/bigquery/docs/schemas#column_names - """ - destination_table = f"{dataset_id}.test_to_gbq_w_protected_column_names" - - scalars_df_index = scalars_df_index.rename( - columns={ - "bool_col": "_Table_Suffix", - "bytes_col": "_file_path", - "date_col": "_PARTITIONDATE", - "datetime_col": "_ROW_TIMESTAMP", - "int64_col": "__ROOT__", - "int64_too": "_COLIDENTIFIER", - "numeric_col": "COLIDENTIFIER", # Create a collision at serialization time. - } - )[ - [ - "_Table_Suffix", - "_file_path", - "_PARTITIONDATE", - "_ROW_TIMESTAMP", - "__ROOT__", - "_COLIDENTIFIER", - "COLIDENTIFIER", - ] - ] - scalars_df_index.to_gbq(destination_table, if_exists="replace") - - bf_result = bpd.read_gbq(destination_table, index_col="rowindex").to_pandas() - - # Leading _ characters are removed to make these columns valid in BigQuery. - expected = scalars_pandas_df_index.rename( - columns={ - "bool_col": "Table_Suffix", - "bytes_col": "file_path", - "date_col": "PARTITIONDATE", - "datetime_col": "ROW_TIMESTAMP", - "int64_col": "ROOT__", - "int64_too": "COLIDENTIFIER", - "numeric_col": "COLIDENTIFIER_1", - } - )[ - [ - "Table_Suffix", - "file_path", - "PARTITIONDATE", - "ROW_TIMESTAMP", - "ROOT__", - "COLIDENTIFIER", - "COLIDENTIFIER_1", - ] - ] - - pd.testing.assert_frame_equal(bf_result, expected) - - -def test_to_gbq_w_flexible_column_names( - scalars_df_index, dataset_id: str, bigquery_client -): - """Test the `to_gbq` API when dealing with flexible column names. - - This test is for BigQuery-backed storage nodes. - - See: https://cloud.google.com/bigquery/docs/schemas#flexible-column-names - """ - destination_table = f"{dataset_id}.test_to_gbq_w_flexible_column_names" - renamed_columns = { - # First column in Japanese (tests unicode). - "bool_col": "最初のカラム", - "bytes_col": "col with space", - # Dots aren't allowed in BigQuery column names, so these should be translated - "date_col": "col.with.dots", - "datetime_col": "col-with-hyphens", - "geography_col": "1start_with_number", - "int64_col": "col_with_underscore", - # Just numbers. - "int64_too": "123", - } - bf_df = scalars_df_index[renamed_columns.keys()].rename(columns=renamed_columns) - assert list(bf_df.columns) == list(renamed_columns.values()) - bf_df.to_gbq(destination_table, index=False) - - table = bigquery_client.get_table(destination_table) - columns = [field.name for field in table.schema] - assert columns == [ - "最初のカラム", - "col with space", - # Dots aren't allowed in BigQuery column names, so these should be translated - "col_with_dots", - "col-with-hyphens", - "1start_with_number", - "col_with_underscore", - "123", - ] - - -def test_to_gbq_w_flexible_column_names_local_node( - session, dataset_id: str, bigquery_client -): - """Test the `to_gbq` API when dealing with flexible column names. - - This test is for local nodes, e.g. read_pandas(), since those may go through - a different code path compared to data that starts in BigQuery. - - See: https://cloud.google.com/bigquery/docs/schemas#flexible-column-names - """ - destination_table = f"{dataset_id}.test_to_gbq_w_flexible_column_names_local_node" - - data = { - # First column in Japanese (tests unicode). - "最初のカラム": [1, 2, 3], - "col with space": [4, 5, 6], - # Dots aren't allowed in BigQuery column names, so these should be translated - "col.with.dots": [7, 8, 9], - "col-with-hyphens": [10, 11, 12], - "1start_with_number": [13, 14, 15], - "col_with_underscore": [16, 17, 18], - "123": [19, 20, 21], - } - pd_df = pd.DataFrame(data) - assert list(pd_df.columns) == list(data.keys()) - bf_df = session.read_pandas(pd_df) - assert list(bf_df.columns) == list(data.keys()) - bf_df.to_gbq(destination_table, index=False) - - table = bigquery_client.get_table(destination_table) - columns = [field.name for field in table.schema] - assert columns == [ - "最初のカラム", - "col with space", - # Dots aren't allowed in BigQuery column names, so these should be translated - "col_with_dots", - "col-with-hyphens", - "1start_with_number", - "col_with_underscore", - "123", - ] - - -def test_to_gbq_w_None_column_names( - scalars_df_index, scalars_pandas_df_index, dataset_id -): - """Test the `to_gbq` API with None as a column name.""" - destination_table = f"{dataset_id}.test_to_gbq_w_none_column_names" - - # pandas 3.0 str datatypes produces nan instead of None, so cast to object - # scalars_df_index.columns = scalars_df_index.columns.astype(object) - scalars_df_index = scalars_df_index.rename(columns={"int64_too": None}) - scalars_df_index.to_gbq(destination_table, if_exists="replace") - - bf_result = bpd.read_gbq(destination_table, index_col="rowindex").to_pandas() - - pd.testing.assert_series_equal( - scalars_pandas_df_index["int64_col"], bf_result["int64_col"] - ) - pd.testing.assert_series_equal( - scalars_pandas_df_index["int64_too"], - bf_result["bigframes_unnamed_column"], - check_names=False, - ) - - @pytest.mark.parametrize( - "clustering_columns", + ("if_exists", "expected_index"), [ - pytest.param(["int64_col", "geography_col"]), + pytest.param("replace", 1), + pytest.param("append", 2), pytest.param( - ["float64_col"], - marks=pytest.mark.xfail(raises=google.api_core.exceptions.BadRequest), + "fail", + 0, + marks=pytest.mark.xfail( + raises=google.api_core.exceptions.Conflict, + ), ), pytest.param( - ["int64_col", "int64_col"], - marks=pytest.mark.xfail(raises=ValueError), + "unknown", + 0, + marks=pytest.mark.xfail( + raises=ValueError, + ), ), ], ) -def test_to_gbq_w_clustering( +@pytest.mark.skipif(pandas_gbq is None, reason="required by pd.read_gbq") +def test_to_gbq_if_exists( scalars_df_default_index, + scalars_pandas_df_default_index, dataset_id, - bigquery_client, - clustering_columns, -): - """Test the `to_gbq` API for creating clustered tables.""" - destination_table = ( - f"{dataset_id}.test_to_gbq_clustering_{'_'.join(clustering_columns)}" - ) - - scalars_df_default_index.to_gbq( - destination_table, clustering_columns=clustering_columns - ) - table = bigquery_client.get_table(destination_table) - - assert list(table.clustering_fields) == clustering_columns - assert table.expires is None - - -def test_to_gbq_w_clustering_no_destination( - scalars_df_default_index, - bigquery_client, + if_exists, + expected_index, ): - """Test the `to_gbq` API for creating clustered tables without destination.""" - clustering_columns = ["int64_col", "geography_col"] - destination_table = scalars_df_default_index.to_gbq( - clustering_columns=clustering_columns - ) - table = bigquery_client.get_table(destination_table) - - assert list(table.clustering_fields) == clustering_columns - assert table.expires is not None - + """Test the `to_gbq` API with the `if_exists` parameter.""" + destination_table = f"{dataset_id}.test_to_gbq_if_exists_{if_exists}" -def test_to_gbq_w_clustering_existing_table( - scalars_df_default_index, - dataset_id, - bigquery_client, -): - destination_table = f"{dataset_id}.test_to_gbq_w_clustering_existing_table" scalars_df_default_index.to_gbq(destination_table) + scalars_df_default_index.to_gbq(destination_table, if_exists=if_exists) - table = bigquery_client.get_table(destination_table) - assert table.clustering_fields is None - assert table.expires is None - - with pytest.raises(ValueError, match="Table clustering fields cannot be changed"): - clustering_columns = ["int64_col"] - scalars_df_default_index.to_gbq( - destination_table, - if_exists="replace", - clustering_columns=clustering_columns, - ) + gcs_df = pd.read_gbq(destination_table) + assert len(gcs_df.index) == expected_index * len( + scalars_pandas_df_default_index.index + ) + pd.testing.assert_index_equal( + gcs_df.columns, scalars_pandas_df_default_index.columns + ) def test_to_gbq_w_invalid_destination_table(scalars_df_index): @@ -941,119 +255,6 @@ def test_to_gbq_w_invalid_destination_table(scalars_df_index): scalars_df_index.to_gbq("table_id") -def test_to_gbq_w_json(bigquery_client): - """Test the `to_gbq` API can get a JSON column.""" - s1 = bpd.Series([1, 2, 3, 4]) - s2 = bpd.Series( - ['"a"', "1", "false", '["a", {"b": 1}]', '{"c": [1, 2, 3]}'], - dtype=dtypes.JSON_DTYPE, - ) - - df = bpd.DataFrame({"id": s1, "json_col": s2}) - destination_table = df.to_gbq() - table = bigquery_client.get_table(destination_table) - - assert table.schema[1].name == "json_col" - assert table.schema[1].field_type == "JSON" - - -def test_to_gbq_with_timedelta(bigquery_client, dataset_id): - destination_table = f"{dataset_id}.test_to_gbq_with_timedelta" - s1 = bpd.Series([1, 2, 3, 4]) - s2 = bpd.to_timedelta(bpd.Series([1, 2, 3, 4]), unit="s") - df = bpd.DataFrame({"id": s1, "timedelta_col": s2}) - - df.to_gbq(destination_table) - table = bigquery_client.get_table(destination_table) - - assert table.schema[1].name == "timedelta_col" - assert table.schema[1].field_type == "INTEGER" - assert dtypes.TIMEDELTA_DESCRIPTION_TAG in table.schema[1].description - - -def test_gbq_round_trip_with_timedelta(session, dataset_id): - destination_table = f"{dataset_id}.test_gbq_roundtrip_with_timedelta" - df = pd.DataFrame( - { - "col_1": [1], - "col_2": [pd.Timedelta(1, "s")], - "col_3": [1.1], - } - ) - bpd.DataFrame(df).to_gbq(destination_table) - - result = session.read_gbq(destination_table) - - assert result["col_1"].dtype == dtypes.INT_DTYPE - assert result["col_2"].dtype == dtypes.TIMEDELTA_DTYPE - assert result["col_3"].dtype == dtypes.FLOAT_DTYPE - - -def test_to_gbq_timedelta_tag_ignored_when_appending(bigquery_client, dataset_id): - # First, create a table - destination_table = f"{dataset_id}.test_to_gbq_timedelta_tag_ignored_when_appending" - schema = [bigquery.SchemaField("my_col", "INTEGER")] - bigquery_client.create_table(bigquery.Table(destination_table, schema)) - - # Then, append to that table with timedelta values - df = pd.DataFrame( - { - "my_col": [pd.Timedelta(1, "s")], - } - ) - bpd.DataFrame(df).to_gbq(destination_table, if_exists="append") - - table = bigquery_client.get_table(destination_table) - assert table.schema[0].name == "my_col" - assert table.schema[0].field_type == "INTEGER" - assert table.schema[0].description is None - - -def test_to_gbq_obj_ref(session, dataset_id: str, bigquery_client): - import uuid - - import google.cloud.bigquery - - destination_table = f"{dataset_id}.test_to_gbq_obj_ref" - sql = """ - SELECT STRUCT('gs://cloud-samples-data/vision/ocr/sign.jpg' AS uri, CAST(NULL AS STRING) AS version, CAST(NULL AS STRING) AS authorizer, PARSE_JSON('{}') AS details) AS uri_col - """ - df_init = session.read_gbq(sql) - - tmp_table_id = f"{dataset_id}.tmp_obj_ref_{uuid.uuid4().hex}" - df_init.to_gbq(tmp_table_id, if_exists="replace") - - client = session.bqclient - table = client.get_table(tmp_table_id) - schema = list(table.schema) - for i, field in enumerate(schema): - if field.name == "uri_col": - schema[i] = google.cloud.bigquery.SchemaField( - name=field.name, - field_type=field.field_type, - mode=field.mode, - description="bigframes_dtype: OBJ_REF_DTYPE", - fields=field.fields, - ) - break - table.schema = schema - client.update_table(table, ["schema"]) - - df = session.read_gbq(tmp_table_id) - df = df.rename(columns={"uri_col": "obj_ref_col"}) - - df.to_gbq(destination_table, if_exists="replace") - - table = bigquery_client.get_table(destination_table) - obj_ref_field = next(f for f in table.schema if f.name == "obj_ref_col") - assert obj_ref_field.field_type == "RECORD" - assert obj_ref_field.description == "bigframes_dtype: OBJ_REF_DTYPE" - - reloaded_df = session.read_gbq(destination_table) - assert reloaded_df["obj_ref_col"].dtype == dtypes.OBJ_REF_DTYPE - assert len(reloaded_df) == 1 - - @pytest.mark.parametrize( ("index"), [True, False], @@ -1063,8 +264,11 @@ def test_to_json_index_invalid_orient( gcs_folder: str, index: bool, ): - scalars_df, _ = scalars_dfs - path = gcs_folder + f"test_index_df_to_json_index_{index}*.jsonl" + scalars_df, scalars_pandas_df = scalars_dfs + if scalars_df.index.name is not None: + path = gcs_folder + f"test_index_df_to_json_index_{index}*.jsonl" + else: + path = gcs_folder + f"test_default_index_df_to_json_index_{index}*.jsonl" with pytest.raises(ValueError): scalars_df.to_json(path, index=index, lines=True) @@ -1078,8 +282,11 @@ def test_to_json_index_invalid_lines( gcs_folder: str, index: bool, ): - scalars_df, _ = scalars_dfs - path = gcs_folder + f"test_index_df_to_json_index_{index}.jsonl" + scalars_df, scalars_pandas_df = scalars_dfs + if scalars_df.index.name is not None: + path = gcs_folder + f"test_index_df_to_json_index_{index}.jsonl" + else: + path = gcs_folder + f"test_default_index_df_to_json_index_{index}.jsonl" with pytest.raises(NotImplementedError): scalars_df.to_json(path, index=index) @@ -1093,21 +300,18 @@ def test_to_json_index_records_orient( gcs_folder: str, index: bool, ): - """Test the `to_json` API with the `index` parameter. - - Uses the scalable options orient='records' and lines=True. - """ + """Test the `to_json` API with the `index` parameter.""" scalars_df, scalars_pandas_df = scalars_dfs - path = gcs_folder + f"test_index_df_to_json_index_{index}*.jsonl" + if scalars_df.index.name is not None: + path = gcs_folder + f"test_index_df_to_json_index_{index}*.jsonl" + else: + path = gcs_folder + f"test_default_index_df_to_json_index_{index}*.jsonl" + """ Test the `to_json` API with `orient` is `records` and `lines` is True""" scalars_df.to_json(path, index=index, orient="records", lines=True) - gcs_df = pd.read_json( - utils.get_first_file_from_wildcard(path), - lines=True, - convert_dates=["datetime_col"], - ) - utils.convert_pandas_dtypes(gcs_df, bytes_col=True) + gcs_df = pd.read_json(path, lines=True, convert_dates=["datetime_col"]) + convert_pandas_dtypes(gcs_df, bytes_col=True) if index and scalars_df.index.name is not None: gcs_df = gcs_df.set_index(scalars_df.index.name) @@ -1130,7 +334,11 @@ def test_to_parquet_index(scalars_dfs, gcs_folder, index): """Test the `to_parquet` API with the `index` parameter.""" scalars_df, scalars_pandas_df = scalars_dfs scalars_pandas_df = scalars_pandas_df.copy() - path = gcs_folder + f"test_index_df_to_parquet_{index}*.parquet" + + if scalars_df.index.name is not None: + path = gcs_folder + f"test_index_df_to_parquet_{index}*.parquet" + else: + path = gcs_folder + f"test_default_index_df_to_parquet_{index}*.parquet" # TODO(b/268693993): Type GEOGRAPHY is not currently supported for parquet. scalars_df = scalars_df.drop(columns="geography_col") @@ -1141,8 +349,8 @@ def test_to_parquet_index(scalars_dfs, gcs_folder, index): # table. scalars_df.to_parquet(path, index=index) - gcs_df = pd.read_parquet(utils.get_first_file_from_wildcard(path)) - utils.convert_pandas_dtypes(gcs_df, bytes_col=False) + gcs_df = pd.read_parquet(path.replace("*", "000000000000")) + convert_pandas_dtypes(gcs_df, bytes_col=False) if index and scalars_df.index.name is not None: gcs_df = gcs_df.set_index(scalars_df.index.name) @@ -1154,9 +362,7 @@ def test_to_parquet_index(scalars_dfs, gcs_folder, index): scalars_pandas_df.index = scalars_pandas_df.index.astype("Int64") # Ordering should be maintained for tables smaller than 1 GB. - pd.testing.assert_frame_equal( - gcs_df.drop("bytes_col", axis=1), scalars_pandas_df.drop("bytes_col", axis=1) - ) + pd.testing.assert_frame_equal(gcs_df, scalars_pandas_df) def test_to_sql_query_unnamed_index_included( @@ -1164,19 +370,17 @@ def test_to_sql_query_unnamed_index_included( scalars_df_default_index: bpd.DataFrame, scalars_pandas_df_default_index: pd.DataFrame, ): - bf_df = scalars_df_default_index.reset_index(drop=True).drop(columns="duration_col") + bf_df = scalars_df_default_index.reset_index(drop=True) sql, idx_ids, idx_labels = bf_df._to_sql_query(include_index=True) assert len(idx_labels) == 1 assert len(idx_ids) == 1 assert idx_labels[0] is None assert idx_ids[0].startswith("bigframes") - pd_df = scalars_pandas_df_default_index.reset_index(drop=True).drop( - columns="duration_col" - ) + pd_df = scalars_pandas_df_default_index.reset_index(drop=True) roundtrip = session.read_gbq(sql, index_col=idx_ids) roundtrip.index.names = [None] - utils.assert_frame_equal(roundtrip.to_pandas(), pd_df, check_index_type=False) + assert_pandas_df_equal_ignore_ordering(roundtrip.to_pandas(), pd_df) def test_to_sql_query_named_index_included( @@ -1184,20 +388,16 @@ def test_to_sql_query_named_index_included( scalars_df_default_index: bpd.DataFrame, scalars_pandas_df_default_index: pd.DataFrame, ): - bf_df = scalars_df_default_index.set_index("rowindex_2", drop=True).drop( - columns="duration_col" - ) + bf_df = scalars_df_default_index.set_index("rowindex_2", drop=True) sql, idx_ids, idx_labels = bf_df._to_sql_query(include_index=True) assert len(idx_labels) == 1 assert len(idx_ids) == 1 assert idx_labels[0] == "rowindex_2" assert idx_ids[0] == "rowindex_2" - pd_df = scalars_pandas_df_default_index.set_index("rowindex_2", drop=True).drop( - columns="duration_col" - ) + pd_df = scalars_pandas_df_default_index.set_index("rowindex_2", drop=True) roundtrip = session.read_gbq(sql, index_col=idx_ids) - utils.assert_frame_equal(roundtrip.to_pandas(), pd_df) + assert_pandas_df_equal_ignore_ordering(roundtrip.to_pandas(), pd_df) def test_to_sql_query_unnamed_index_excluded( @@ -1205,18 +405,14 @@ def test_to_sql_query_unnamed_index_excluded( scalars_df_default_index: bpd.DataFrame, scalars_pandas_df_default_index: pd.DataFrame, ): - bf_df = scalars_df_default_index.reset_index(drop=True).drop(columns="duration_col") + bf_df = scalars_df_default_index.reset_index(drop=True) sql, idx_ids, idx_labels = bf_df._to_sql_query(include_index=False) assert len(idx_labels) == 0 assert len(idx_ids) == 0 - pd_df = scalars_pandas_df_default_index.reset_index(drop=True).drop( - columns="duration_col" - ) + pd_df = scalars_pandas_df_default_index.reset_index(drop=True) roundtrip = session.read_gbq(sql) - utils.assert_frame_equal( - roundtrip.to_pandas(), pd_df, check_index_type=False, ignore_order=True - ) + assert_pandas_df_equal_ignore_ordering(roundtrip.to_pandas(), pd_df) def test_to_sql_query_named_index_excluded( @@ -1224,28 +420,13 @@ def test_to_sql_query_named_index_excluded( scalars_df_default_index: bpd.DataFrame, scalars_pandas_df_default_index: pd.DataFrame, ): - bf_df = scalars_df_default_index.set_index("rowindex_2", drop=True).drop( - columns="duration_col" - ) + bf_df = scalars_df_default_index.set_index("rowindex_2", drop=True) sql, idx_ids, idx_labels = bf_df._to_sql_query(include_index=False) assert len(idx_labels) == 0 assert len(idx_ids) == 0 - pd_df = ( - scalars_pandas_df_default_index.set_index("rowindex_2", drop=True) - .reset_index(drop=True) - .drop(columns="duration_col") - ) + pd_df = scalars_pandas_df_default_index.set_index( + "rowindex_2", drop=True + ).reset_index(drop=True) roundtrip = session.read_gbq(sql) - utils.assert_frame_equal( - roundtrip.to_pandas(), pd_df, check_index_type=False, ignore_order=True - ) - - -def test_to_numpy(scalars_dfs): - bf_df, pd_df = scalars_dfs - - bf_result = numpy.array(bf_df[["int64_too"]], dtype="int64") - pd_result = numpy.array(pd_df[["int64_too"]], dtype="int64") - - numpy.testing.assert_array_equal(bf_result, pd_result) + assert_pandas_df_equal_ignore_ordering(roundtrip.to_pandas(), pd_df) diff --git a/tests/system/small/test_encryption.py b/tests/system/small/test_encryption.py deleted file mode 100644 index db87184371b..00000000000 --- a/tests/system/small/test_encryption.py +++ /dev/null @@ -1,267 +0,0 @@ -# Copyright 2023 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -import random - -import pandas -import pytest -from google.cloud import bigquery - -import bigframes -import bigframes.ml.linear_model -from bigframes.testing import utils - - -@pytest.fixture(scope="module") -def bq_cmek() -> str: - """Customer managed encryption key to encrypt BigQuery data at rest. - - This is of the form projects/PROJECT_ID/locations/LOCATION/keyRings/KEY_RING/cryptoKeys/KEY - - See https://cloud.google.com/bigquery/docs/customer-managed-encryption for steps. - """ - - # NOTE: This key is manually set up through the cloud console - # TODO(shobs): Automate the the key creation during the test. This will - # require extra IAM privileges for the test runner. - return "projects/bigframes-dev-perf/locations/us/keyRings/bigframesKeyRing/cryptoKeys/bigframesKey" - - -@pytest.fixture(scope="module") -def session_with_bq_cmek(bq_cmek) -> bigframes.Session: - # allow_large_results = False might not create table, and therefore no encryption config - session = bigframes.Session(bigframes.BigQueryOptions(kms_key_name=bq_cmek)) - - return session - - -def _assert_bq_table_is_encrypted( - df: bigframes.dataframe.DataFrame, - cmek: str, - session: bigframes.Session, -): - # Materialize the data in BQ - df.to_gbq() - - # The df should be backed by a query job with intended encryption on the result table - assert df.query_job is not None - assert df.query_job.destination_encryption_configuration.kms_key_name.startswith( - cmek - ) - - # The result table should exist with the intended encryption - table = session.bqclient.get_table(df.query_job.destination) - assert table.encryption_configuration.kms_key_name == cmek - - -def test_session_query_job(bq_cmek, session_with_bq_cmek): - if not bq_cmek: # pragma: NO COVER - pytest.skip("no cmek set for testing") # pragma: NO COVER - - query_job = session_with_bq_cmek._loader._start_query_with_job( - "SELECT 123", job_config=bigquery.QueryJobConfig(use_query_cache=False) - ) - query_job.result() - - assert query_job.destination_encryption_configuration.kms_key_name.startswith( - bq_cmek - ) - - # The result table should exist with the intended encryption - table = session_with_bq_cmek.bqclient.get_table(query_job.destination) - assert table.encryption_configuration.kms_key_name == bq_cmek - - -def test_read_gbq(bq_cmek, session_with_bq_cmek, scalars_table_id): - if not bq_cmek: # pragma: NO COVER - pytest.skip("no cmek set for testing") # pragma: NO COVER - - # Read the BQ table - df = session_with_bq_cmek.read_gbq(scalars_table_id) - - # Assert encryption - _assert_bq_table_is_encrypted(df, bq_cmek, session_with_bq_cmek) - - -def test_df_apis(bq_cmek, session_with_bq_cmek, scalars_table_id): - if not bq_cmek: # pragma: NO COVER - pytest.skip("no cmek set for testing") # pragma: NO COVER - - # Read a BQ table and assert encryption - df = session_with_bq_cmek.read_gbq(scalars_table_id) - - # Perform a few dataframe operations and assert encryption - df1 = df.dropna() - _assert_bq_table_is_encrypted(df1, bq_cmek, session_with_bq_cmek) - - df2 = df1.head() - _assert_bq_table_is_encrypted(df2, bq_cmek, session_with_bq_cmek) - - -@pytest.mark.parametrize( - "engine", - [ - pytest.param("bigquery", id="bq_engine"), - pytest.param( - None, - id="default_engine", - ), - ], -) -def test_read_csv_gcs( - bq_cmek, session_with_bq_cmek, scalars_df_index, gcs_folder, engine -): - if not bq_cmek: # pragma: NO COVER - pytest.skip("no cmek set for testing") # pragma: NO COVER - - # Let's make the source data non-deterministic so that the test doesn't run - # into a BQ caching path - df = scalars_df_index.copy() - df["int_random"] = random.randint(0, 1_000_000_000) - - # Export the dataframe to a csv in gcs - write_path = gcs_folder + "test_read_csv_gcs_bigquery_engine*.csv" - read_path = ( - utils.get_first_file_from_wildcard(write_path) if engine is None else write_path - ) - df.to_csv(write_path) - - # Read the gcs csv - df = session_with_bq_cmek.read_csv(read_path, engine=engine) - - # Assert encryption - _assert_bq_table_is_encrypted(df, bq_cmek, session_with_bq_cmek) - - -def test_to_gbq(bq_cmek, session_with_bq_cmek, scalars_table_id): - if not bq_cmek: # pragma: NO COVER - pytest.skip("no cmek set for testing") # pragma: NO COVER - - # Read a BQ table and assert encryption - df = session_with_bq_cmek.read_gbq(scalars_table_id) - _assert_bq_table_is_encrypted(df, bq_cmek, session_with_bq_cmek) - - # Modify the dataframe and assert encryption - df = df.dropna().head() - _assert_bq_table_is_encrypted(df, bq_cmek, session_with_bq_cmek) - - # Write the result to BQ and assert encryption - output_table_id = df.to_gbq() - output_table = session_with_bq_cmek.bqclient.get_table(output_table_id) - assert output_table.encryption_configuration.kms_key_name == bq_cmek - - # Write the result to BQ custom table and assert encryption - session_with_bq_cmek.bqclient.get_table(output_table_id) - output_table_ref = session_with_bq_cmek._anon_dataset_manager.allocate_temp_table() - output_table_id = str(output_table_ref) - df.to_gbq(output_table_id) - output_table = session_with_bq_cmek.bqclient.get_table(output_table_id) - assert output_table.encryption_configuration.kms_key_name == bq_cmek - - # Lastly, assert that the encryption is not because of any default set at - # the dataset level - output_table_dataset = session_with_bq_cmek.bqclient.get_dataset( - output_table.dataset_id - ) - assert output_table_dataset.default_encryption_configuration is None - - -def test_read_pandas(bq_cmek, session_with_bq_cmek): - if not bq_cmek: # pragma: NO COVER - pytest.skip("no cmek set for testing") # pragma: NO COVER - - # Read a pandas dataframe - df = session_with_bq_cmek.read_pandas( - pandas.DataFrame([random.randint(0, 1_000_000_000)]) - ) - - # Assert encryption - _assert_bq_table_is_encrypted(df, bq_cmek, session_with_bq_cmek) - - -def test_read_pandas_large(bq_cmek, session_with_bq_cmek): - if not bq_cmek: # pragma: NO COVER - pytest.skip("no cmek set for testing") # pragma: NO COVER - - # Read a pandas dataframe large enough to trigger a BQ load job - df = session_with_bq_cmek.read_pandas(pandas.DataFrame(range(10_000))) - - # Assert encryption - _assert_bq_table_is_encrypted(df, bq_cmek, session_with_bq_cmek) - - -def test_kms_encryption_bqml(bq_cmek, session_with_bq_cmek, penguins_table_id): - if not bq_cmek: # pragma: NO COVER - pytest.skip("no cmek set for testing") # pragma: NO COVER - - model = bigframes.ml.linear_model.LinearRegression() - df = session_with_bq_cmek.read_gbq(penguins_table_id).dropna() - X_train = df[ - [ - "species", - "island", - "culmen_length_mm", - "culmen_depth_mm", - "flipper_length_mm", - "sex", - ] - ] - y_train = df[["body_mass_g"]] - model.fit(X_train, y_train) - - assert model is not None - assert model._bqml_model is not None - assert model._bqml_model.model.encryption_configuration is not None - assert model._bqml_model.model.encryption_configuration.kms_key_name == bq_cmek - - # Assert that model exists in BQ with intended encryption - model_bq = session_with_bq_cmek.bqclient.get_model(model._bqml_model.model_name) - assert model_bq.encryption_configuration.kms_key_name == bq_cmek - - # Explicitly save the model to a destination and assert that encryption holds - model_ref = model._bqml_model_factory._create_model_ref( - session_with_bq_cmek._anonymous_dataset - ) - model_ref_full_name = ( - f"{model_ref.project}.{model_ref.dataset_id}.{model_ref.model_id}" - ) - new_model = model.to_gbq(model_ref_full_name) - assert new_model._bqml_model is not None - assert new_model._bqml_model.model.encryption_configuration is not None - assert new_model._bqml_model.model.encryption_configuration.kms_key_name == bq_cmek - - # Assert that model exists in BQ with intended encryption - model_bq = session_with_bq_cmek.bqclient.get_model(new_model._bqml_model.model_name) - assert model_bq.encryption_configuration.kms_key_name == bq_cmek - - # Assert that model registration keeps the encryption - # Note that model registration only creates an entry (metadata) to be - # included in the Vertex AI Model Registry. See for more details - # https://cloud.google.com/bigquery/docs/update_vertex#add-existing. - # When use deploys the model to an endpoint from the Model Registry then - # they can specify an encryption key to further protect the artifacts at - # rest on the Vertex AI side. See for more details: - # https://cloud.google.com/vertex-ai/docs/general/deployment#deploy_a_model_to_an_endpoint, - # https://cloud.google.com/vertex-ai/docs/general/cmek#create_resources_with_the_kms_key. - # bigframes.ml does not provide any API for the model deployment. - model_registered = new_model.register() - assert model_registered._bqml_model is not None - assert model_registered._bqml_model.model.encryption_configuration is not None - assert ( - model_registered._bqml_model.model.encryption_configuration.kms_key_name - == bq_cmek - ) - model_bq = session_with_bq_cmek.bqclient.get_model(new_model._bqml_model.model_name) - assert model_bq.encryption_configuration.kms_key_name == bq_cmek diff --git a/tests/system/small/test_groupby.py b/tests/system/small/test_groupby.py index 8dde3146434..05154f7ab7d 100644 --- a/tests/system/small/test_groupby.py +++ b/tests/system/small/test_groupby.py @@ -12,16 +12,10 @@ # See the License for the specific language governing permissions and # limitations under the License. -import numpy as np import pandas as pd import pytest import bigframes.pandas as bpd -import bigframes.testing.utils - -# ================= -# DataFrame.groupby -# ================= @pytest.mark.parametrize( @@ -51,25 +45,7 @@ def test_dataframe_groupby_numeric_aggregate( pd_result = operator(scalars_pandas_df_index[col_names].groupby("string_col")) bf_result_computed = bf_result.to_pandas() # Pandas std function produces float64, not matching Float64 from bigframes - bigframes.testing.utils.assert_frame_equal( - pd_result, bf_result_computed, check_dtype=False - ) - - -def test_dataframe_groupby_head(scalars_df_index, scalars_pandas_df_index): - col_names = ["int64_too", "float64_col", "int64_col", "bool_col", "string_col"] - bf_result = scalars_df_index[col_names].groupby("bool_col").head(2).to_pandas() - pd_result = scalars_pandas_df_index[col_names].groupby("bool_col").head(2) - bigframes.testing.utils.assert_frame_equal(pd_result, bf_result, check_dtype=False) - - -def test_dataframe_groupby_len(scalars_df_index, scalars_pandas_df_index): - col_names = ["int64_too", "float64_col", "int64_col", "bool_col", "string_col"] - - bf_result = len(scalars_df_index[col_names].groupby("bool_col")) - pd_result = len(scalars_pandas_df_index[col_names].groupby("bool_col")) - - assert bf_result == pd_result + pd.testing.assert_frame_equal(pd_result, bf_result_computed, check_dtype=False) def test_dataframe_groupby_median(scalars_df_index, scalars_pandas_df_index): @@ -88,75 +64,15 @@ def test_dataframe_groupby_median(scalars_df_index, scalars_pandas_df_index): assert ((pd_min <= bf_result_computed) & (bf_result_computed <= pd_max)).all().all() -@pytest.mark.parametrize( - ("q"), - [ - ([0.2, 0.4, 0.6, 0.8]), - (0.11), - ], -) -def test_dataframe_groupby_quantile(scalars_df_index, scalars_pandas_df_index, q): - col_names = ["int64_too", "float64_col", "int64_col", "string_col"] - bf_result = ( - scalars_df_index[col_names].groupby("string_col").quantile(q) - ).to_pandas() - pd_result = scalars_pandas_df_index[col_names].groupby("string_col").quantile(q) - bigframes.testing.utils.assert_frame_equal( - pd_result, bf_result, check_dtype=False, check_index_type=False - ) - - -@pytest.mark.parametrize( - ("na_option", "method", "ascending", "pct"), - [ - ( - "keep", - "average", - True, - False, - ), - ("top", "min", False, False), - ("bottom", "max", False, False), - ("top", "first", False, True), - ("bottom", "dense", False, True), - ], -) -def test_dataframe_groupby_rank( - scalars_df_index, scalars_pandas_df_index, na_option, method, ascending, pct -): - # TODO: supply a reason why this isn't compatible with pandas 1.x - pytest.importorskip("pandas", minversion="2.2.0") - col_names = ["int64_too", "float64_col", "int64_col", "string_col"] - bf_result = ( - scalars_df_index[col_names] - .groupby("string_col") - .rank(na_option=na_option, method=method, ascending=ascending, pct=pct) - ).to_pandas() - pd_result = ( - ( - scalars_pandas_df_index[col_names] - .groupby("string_col") - .rank(na_option=na_option, method=method, ascending=ascending, pct=pct) - ) - .astype("float64") - .astype("Float64") - ) - bigframes.testing.utils.assert_frame_equal( - pd_result, bf_result, check_dtype=False, check_index_type=False - ) - - @pytest.mark.parametrize( ("operator"), [ (lambda x: x.count()), - (lambda x: x.nunique()), (lambda x: x.any()), (lambda x: x.all()), ], ids=[ "count", - "nunique", "any", "all", ], @@ -169,130 +85,50 @@ def test_dataframe_groupby_aggregate( pd_result = operator(scalars_pandas_df_index[col_names].groupby("string_col")) bf_result_computed = bf_result.to_pandas() - bigframes.testing.utils.assert_frame_equal( - pd_result, bf_result_computed, check_dtype=False - ) - + pd.testing.assert_frame_equal(pd_result, bf_result_computed, check_dtype=False) -def test_dataframe_groupby_corr(scalars_df_index, scalars_pandas_df_index): - col_names = ["int64_too", "float64_col", "int64_col", "bool_col"] - bf_result = scalars_df_index[col_names].groupby("bool_col").corr().to_pandas() - pd_result = scalars_pandas_df_index[col_names].groupby("bool_col").corr() - bigframes.testing.utils.assert_frame_equal( - pd_result, bf_result, check_dtype=False, check_index_type=False - ) - - -def test_dataframe_groupby_cov(scalars_df_index, scalars_pandas_df_index): - col_names = ["int64_too", "float64_col", "int64_col", "bool_col"] - bf_result = scalars_df_index[col_names].groupby("bool_col").cov().to_pandas() - pd_result = scalars_pandas_df_index[col_names].groupby("bool_col").cov() - - bigframes.testing.utils.assert_frame_equal( - pd_result, bf_result, check_dtype=False, check_index_type=False - ) - - -@pytest.mark.parametrize( - ("ordered"), - [ - (True), - (False), - ], -) -def test_dataframe_groupby_agg_string( - scalars_df_index, scalars_pandas_df_index, ordered -): +def test_dataframe_groupby_agg_string(scalars_df_index, scalars_pandas_df_index): col_names = ["int64_too", "float64_col", "int64_col", "bool_col", "string_col"] bf_result = scalars_df_index[col_names].groupby("string_col").agg("count") pd_result = scalars_pandas_df_index[col_names].groupby("string_col").agg("count") - bf_result_computed = bf_result.to_pandas(ordered=ordered) - - bigframes.testing.utils.assert_frame_equal( - pd_result, bf_result_computed, check_dtype=False, ignore_order=not ordered - ) - - -def test_dataframe_groupby_agg_size_string(scalars_df_index, scalars_pandas_df_index): - col_names = ["int64_too", "float64_col", "int64_col", "bool_col", "string_col"] - bf_result = scalars_df_index[col_names].groupby("string_col").agg("size") - pd_result = scalars_pandas_df_index[col_names].groupby("string_col").agg("size") + bf_result_computed = bf_result.to_pandas() - bigframes.testing.utils.assert_series_equal( - pd_result, bf_result.to_pandas(), check_dtype=False + pd.testing.assert_frame_equal( + pd_result, + bf_result_computed, + check_dtype=False, ) def test_dataframe_groupby_agg_list(scalars_df_index, scalars_pandas_df_index): col_names = ["int64_too", "float64_col", "int64_col", "bool_col", "string_col"] - bf_result = ( - scalars_df_index[col_names].groupby("string_col").agg(["count", np.min, "size"]) - ) + bf_result = scalars_df_index[col_names].groupby("string_col").agg(["count", "min"]) pd_result = ( - scalars_pandas_df_index[col_names] - .groupby("string_col") - .agg(["count", np.min, "size"]) + scalars_pandas_df_index[col_names].groupby("string_col").agg(["count", "min"]) ) bf_result_computed = bf_result.to_pandas() - # some inconsistency between versions, so normalize to bigframes behavior - pd_result = pd_result.rename({"amin": "min"}, axis="columns") - bf_result_computed = bf_result_computed.rename({"amin": "min"}, axis="columns") - bigframes.testing.utils.assert_frame_equal( - pd_result, bf_result_computed, check_dtype=False, check_index_type=False - ) - - -def test_dataframe_groupby_agg_list_w_column_multi_index( - scalars_df_index, scalars_pandas_df_index -): - columns = ["int64_too", "string_col", "bool_col"] - multi_columns = pd.MultiIndex.from_tuples(zip(["a", "b", "a"], columns)) - bf_df = scalars_df_index[columns].copy() - bf_df.columns = multi_columns - pd_df = scalars_pandas_df_index[columns].copy() - pd_df.columns = multi_columns - - bf_result = bf_df.groupby(level=0).agg(["count", np.min, "size"]) - pd_result = pd_df.groupby(level=0).agg(["count", np.min, "size"]) - - bf_result_computed = bf_result.to_pandas() - bigframes.testing.utils.assert_frame_equal( - pd_result, bf_result_computed, check_dtype=False - ) + pd.testing.assert_frame_equal(pd_result, bf_result_computed, check_dtype=False) -@pytest.mark.parametrize( - ("as_index"), - [ - (True), - (False), - ], -) def test_dataframe_groupby_agg_dict_with_list( - scalars_df_index, scalars_pandas_df_index, as_index + scalars_df_index, scalars_pandas_df_index ): col_names = ["int64_too", "float64_col", "int64_col", "bool_col", "string_col"] bf_result = ( scalars_df_index[col_names] - .groupby("string_col", as_index=as_index) - .agg( - {"int64_too": [np.mean, np.max], "string_col": "count", "bool_col": "size"} - ) + .groupby("string_col") + .agg({"int64_too": ["mean", "max"], "string_col": "count"}) ) pd_result = ( scalars_pandas_df_index[col_names] - .groupby("string_col", as_index=as_index) - .agg( - {"int64_too": [np.mean, np.max], "string_col": "count", "bool_col": "size"} - ) + .groupby("string_col") + .agg({"int64_too": ["mean", "max"], "string_col": "count"}) ) bf_result_computed = bf_result.to_pandas() - bigframes.testing.utils.assert_frame_equal( - pd_result, bf_result_computed, check_dtype=False, check_index_type=False - ) + pd.testing.assert_frame_equal(pd_result, bf_result_computed, check_dtype=False) def test_dataframe_groupby_agg_dict_no_lists(scalars_df_index, scalars_pandas_df_index): @@ -300,18 +136,16 @@ def test_dataframe_groupby_agg_dict_no_lists(scalars_df_index, scalars_pandas_df bf_result = ( scalars_df_index[col_names] .groupby("string_col") - .agg({"int64_too": np.mean, "string_col": "count"}) + .agg({"int64_too": "mean", "string_col": "count"}) ) pd_result = ( scalars_pandas_df_index[col_names] .groupby("string_col") - .agg({"int64_too": np.mean, "string_col": "count"}) + .agg({"int64_too": "mean", "string_col": "count"}) ) bf_result_computed = bf_result.to_pandas() - bigframes.testing.utils.assert_frame_equal( - pd_result, bf_result_computed, check_dtype=False - ) + pd.testing.assert_frame_equal(pd_result, bf_result_computed, check_dtype=False) def test_dataframe_groupby_agg_named(scalars_df_index, scalars_pandas_df_index): @@ -320,7 +154,7 @@ def test_dataframe_groupby_agg_named(scalars_df_index, scalars_pandas_df_index): scalars_df_index[col_names] .groupby("string_col") .agg( - agg1=bpd.NamedAgg("int64_too", np.sum), + agg1=bpd.NamedAgg("int64_too", "sum"), agg2=bpd.NamedAgg("float64_col", "max"), ) ) @@ -328,52 +162,12 @@ def test_dataframe_groupby_agg_named(scalars_df_index, scalars_pandas_df_index): scalars_pandas_df_index[col_names] .groupby("string_col") .agg( - agg1=pd.NamedAgg("int64_too", np.sum), - agg2=pd.NamedAgg("float64_col", "max"), + agg1=pd.NamedAgg("int64_too", "sum"), agg2=pd.NamedAgg("float64_col", "max") ) ) bf_result_computed = bf_result.to_pandas() - bigframes.testing.utils.assert_frame_equal( - pd_result, bf_result_computed, check_dtype=False - ) - - -def test_dataframe_groupby_agg_kw_tuples(scalars_df_index, scalars_pandas_df_index): - col_names = ["int64_too", "float64_col", "int64_col", "bool_col", "string_col"] - bf_result = ( - scalars_df_index[col_names] - .groupby("string_col") - .agg( - agg1=("int64_too", np.sum), - agg2=("float64_col", "max"), - ) - ) - pd_result = ( - scalars_pandas_df_index[col_names] - .groupby("string_col") - .agg(agg1=("int64_too", np.sum), agg2=("float64_col", "max")) - ) - bf_result_computed = bf_result.to_pandas() - - bigframes.testing.utils.assert_frame_equal( - pd_result, bf_result_computed, check_dtype=False - ) - - -@pytest.mark.parametrize( - ("kwargs"), - [ - ({"hello": "world"}), - ({"too_many_fields": ("one", "two", "three")}), - ], -) -def test_dataframe_groupby_agg_kw_error(scalars_df_index, kwargs): - col_names = ["int64_too", "float64_col", "int64_col", "bool_col", "string_col"] - with pytest.raises( - TypeError, match=r"kwargs values must be 2-tuples of column, aggfunc" - ): - (scalars_df_index[col_names].groupby("string_col").agg(**kwargs)) + pd.testing.assert_frame_equal(pd_result, bf_result_computed, check_dtype=False) @pytest.mark.parametrize( @@ -403,21 +197,20 @@ def test_dataframe_groupby_multi_sum( # BigQuery DataFrames default indices use nullable Int64 always pd_series.index = pd_series.index.astype("Int64") - bigframes.testing.utils.assert_frame_equal( + pd.testing.assert_frame_equal( pd_series, bf_result, ) @pytest.mark.parametrize( - ("operator", "dropna"), + ("operator"), [ - (lambda x: x.cumsum(numeric_only=True), True), - (lambda x: x.cummax(numeric_only=True), True), - (lambda x: x.cummin(numeric_only=True), False), - # Pre-pandas 2.2 doesn't always proeduce float. - (lambda x: x.cumprod().astype("Float64"), False), - (lambda x: x.shift(periods=2), True), + (lambda x: x.cumsum(numeric_only=True)), + (lambda x: x.cummax(numeric_only=True)), + (lambda x: x.cummin(numeric_only=True)), + (lambda x: x.cumprod()), + (lambda x: x.shift(periods=2)), ], ids=[ "cumsum", @@ -428,70 +221,31 @@ def test_dataframe_groupby_multi_sum( ], ) def test_dataframe_groupby_analytic( - scalars_df_index, - scalars_pandas_df_index, - operator, - dropna, + scalars_df_index, scalars_pandas_df_index, operator ): col_names = ["float64_col", "int64_col", "bool_col", "string_col"] - bf_result = operator( - scalars_df_index[col_names].groupby("string_col", dropna=dropna) - ) - pd_result = operator( - scalars_pandas_df_index[col_names].groupby("string_col", dropna=dropna) - ) + bf_result = operator(scalars_df_index[col_names].groupby("string_col")) + pd_result = operator(scalars_pandas_df_index[col_names].groupby("string_col")) bf_result_computed = bf_result.to_pandas() - bigframes.testing.utils.assert_frame_equal( - pd_result, bf_result_computed, check_dtype=False - ) - + pd.testing.assert_frame_equal(pd_result, bf_result_computed, check_dtype=False) -@pytest.mark.parametrize( - ("ascending", "dropna"), - [ - (True, True), - (False, False), - ], -) -def test_dataframe_groupby_cumcount( - scalars_df_index, scalars_pandas_df_index, ascending, dropna -): - bf_result = scalars_df_index.groupby("string_col", dropna=dropna).cumcount( - ascending - ) - pd_result = scalars_pandas_df_index.groupby("string_col", dropna=dropna).cumcount( - ascending - ) - bf_result_computed = bf_result.to_pandas() - bigframes.testing.utils.assert_series_equal( - pd_result, bf_result_computed, check_dtype=False - ) +def test_series_groupby_skew(scalars_df_index, scalars_pandas_df_index): + bf_result = scalars_df_index.groupby("bool_col")["int64_too"].skew().to_pandas() + pd_result = scalars_pandas_df_index.groupby("bool_col")["int64_too"].skew() + pd.testing.assert_series_equal(pd_result, bf_result, check_dtype=False) -def test_dataframe_groupby_size_as_index_false( - scalars_df_index, scalars_pandas_df_index -): - bf_result = scalars_df_index.groupby("string_col", as_index=False).size() - bf_result_computed = bf_result.to_pandas() - pd_result = scalars_pandas_df_index.groupby("string_col", as_index=False).size() - bigframes.testing.utils.assert_frame_equal( - pd_result, bf_result_computed, check_dtype=False, check_index_type=False +def test_series_groupby_kurt(scalars_df_index, scalars_pandas_df_index): + bf_result = scalars_df_index.groupby("bool_col")["int64_too"].kurt().to_pandas() + # Pandas doesn't have groupby.kurt yet: https://github.com/pandas-dev/pandas/issues/40139 + pd_result = scalars_pandas_df_index.groupby("bool_col")["int64_too"].apply( + pd.Series.kurt ) - -def test_dataframe_groupby_size_as_index_true( - scalars_df_index, scalars_pandas_df_index -): - bf_result = scalars_df_index.groupby("string_col", as_index=True).size() - pd_result = scalars_pandas_df_index.groupby("string_col", as_index=True).size() - bf_result_computed = bf_result.to_pandas() - - bigframes.testing.utils.assert_series_equal( - pd_result, bf_result_computed, check_dtype=False - ) + pd.testing.assert_series_equal(pd_result, bf_result, check_dtype=False) def test_dataframe_groupby_skew(scalars_df_index, scalars_pandas_df_index): @@ -499,38 +253,30 @@ def test_dataframe_groupby_skew(scalars_df_index, scalars_pandas_df_index): bf_result = scalars_df_index[col_names].groupby("bool_col").skew().to_pandas() pd_result = scalars_pandas_df_index[col_names].groupby("bool_col").skew() - bigframes.testing.utils.assert_frame_equal(pd_result, bf_result, check_dtype=False) + pd.testing.assert_frame_equal(pd_result, bf_result, check_dtype=False) -@pytest.mark.skipif( - not pd.__version__.startswith("3"), - reason="groupby.kurt not supported on legacy pandas versions", -) def test_dataframe_groupby_kurt(scalars_df_index, scalars_pandas_df_index): col_names = ["float64_col", "int64_col", "bool_col"] bf_result = scalars_df_index[col_names].groupby("bool_col").kurt().to_pandas() # Pandas doesn't have groupby.kurt yet: https://github.com/pandas-dev/pandas/issues/40139 - pd_result = scalars_pandas_df_index[col_names].groupby("bool_col").kurt() + pd_result = ( + scalars_pandas_df_index[col_names] + .groupby("bool_col") + .apply(pd.Series.kurt) + .drop("bool_col", axis=1) + ) - bigframes.testing.utils.assert_frame_equal(pd_result, bf_result, check_dtype=False) + pd.testing.assert_frame_equal(pd_result, bf_result, check_dtype=False) -@pytest.mark.parametrize( - ("ordered"), - [ - (True), - (False), - ], -) -def test_dataframe_groupby_diff(scalars_df_index, scalars_pandas_df_index, ordered): +def test_dataframe_groupby_diff(scalars_df_index, scalars_pandas_df_index): col_names = ["float64_col", "int64_col", "string_col"] bf_result = scalars_df_index[col_names].groupby("string_col").diff(-1) pd_result = scalars_pandas_df_index[col_names].groupby("string_col").diff(-1) - bf_result_computed = bf_result.to_pandas(ordered=ordered) + bf_result_computed = bf_result.to_pandas() - bigframes.testing.utils.assert_frame_equal( - pd_result, bf_result_computed, check_dtype=False, ignore_order=not ordered - ) + pd.testing.assert_frame_equal(pd_result, bf_result_computed, check_dtype=False) def test_dataframe_groupby_getitem( @@ -545,23 +291,7 @@ def test_dataframe_groupby_getitem( scalars_pandas_df_index[col_names].groupby("string_col")["int64_col"].min() ) - bigframes.testing.utils.assert_series_equal(pd_result, bf_result, check_dtype=False) - - -def test_dataframe_groupby_getitem_error( - scalars_df_index, - scalars_pandas_df_index, -): - col_names = ["float64_col", "int64_col", "bool_col", "string_col"] - with pytest.raises( - KeyError, match=r"Columns not found: 'not_in_group'. Did you mean 'string_col'?" - ): - ( - scalars_df_index[col_names] - .groupby("bool_col")["not_in_group"] - .min() - .to_pandas() - ) + pd.testing.assert_series_equal(pd_result, bf_result, check_dtype=False) def test_dataframe_groupby_getitem_list( @@ -576,178 +306,23 @@ def test_dataframe_groupby_getitem_list( scalars_pandas_df_index[col_names].groupby("string_col")[col_names].min() ) - bigframes.testing.utils.assert_frame_equal(pd_result, bf_result, check_dtype=False) - - -def test_dataframe_groupby_getitem_list_error( - scalars_df_index, - scalars_pandas_df_index, -): - col_names = ["float64_col", "int64_col", "bool_col", "string_col"] - with pytest.raises( - KeyError, - match=r"Columns not found: 'col1', 'float'. Did you mean 'bool_col', 'float64_col'?", - ): - ( - scalars_df_index[col_names] - .groupby("string_col")["col1", "float"] - .min() - .to_pandas() - ) - - -def test_dataframe_groupby_nonnumeric_with_mean(): - df = pd.DataFrame( - { - "key1": ["a", "a", "a", "b"], - "key2": ["a", "a", "c", "c"], - "key3": [1, 2, 3, 4], - "key4": [1.6, 2, 3, 4], - } - ) - pd_result = df.groupby(["key1", "key2"]).mean() - - bf_result = bpd.DataFrame(df).groupby(["key1", "key2"]).mean().to_pandas() + pd.testing.assert_frame_equal(pd_result, bf_result, check_dtype=False) - bigframes.testing.utils.assert_frame_equal( - pd_result, bf_result, check_index_type=False, check_dtype=False - ) - -@pytest.mark.skipif( - pd.__version__.startswith("3"), - reason="value_counts behavior change b/485962498", -) -@pytest.mark.parametrize( - ("subset", "normalize", "ascending", "dropna", "as_index"), - [ - (None, True, True, True, True), - (["int64_too", "int64_col"], False, False, False, False), - ], -) -def test_dataframe_groupby_value_counts( - scalars_df_index, - scalars_pandas_df_index, - subset, - normalize, - ascending, - dropna, - as_index, -): - if pd.__version__.startswith("1."): - pytest.skip("pandas 1.x produces different column labels.") - col_names = ["float64_col", "int64_col", "bool_col", "int64_too"] +def test_series_groupby_agg_string(scalars_df_index, scalars_pandas_df_index): bf_result = ( - scalars_df_index[col_names] - .groupby("bool_col", as_index=as_index) - .value_counts( - subset=subset, normalize=normalize, ascending=ascending, dropna=dropna - ) - .to_pandas() - ) - pd_result = ( - scalars_pandas_df_index[col_names] - .groupby("bool_col", as_index=as_index) - .value_counts( - subset=subset, normalize=normalize, ascending=ascending, dropna=dropna - ) - ) - - if as_index: - bigframes.testing.utils.assert_series_equal( - pd_result, bf_result, check_dtype=False - ) - else: - pd_result.index = pd_result.index.astype("Int64") - bigframes.testing.utils.assert_frame_equal( - pd_result, bf_result, check_dtype=False - ) - - -@pytest.mark.parametrize( - ("numeric_only", "min_count"), - [ - (False, 4), - (True, 0), - ], -) -def test_dataframe_groupby_first( - scalars_df_index, scalars_pandas_df_index, numeric_only, min_count -): - # min_count seems to not work properly on older pandas - pytest.importorskip("pandas", minversion="2.0.0") - # bytes, dates not handling min_count properly in pandas - bf_result = ( - scalars_df_index.drop(columns=["bytes_col", "date_col"]) - .groupby(scalars_df_index.int64_col % 2) - .first(numeric_only=numeric_only, min_count=min_count) - ).to_pandas() - pd_result = ( - scalars_pandas_df_index.drop(columns=["bytes_col", "date_col"]) - .groupby(scalars_pandas_df_index.int64_col % 2) - .first(numeric_only=numeric_only, min_count=min_count) - ) - bigframes.testing.utils.assert_frame_equal( - pd_result, - bf_result, - ) - - -@pytest.mark.parametrize( - ("numeric_only", "min_count"), - [ - (True, 2), - (False, -1), - ], -) -def test_dataframe_groupby_last( - scalars_df_index, scalars_pandas_df_index, numeric_only, min_count -): - bf_result = ( - scalars_df_index.groupby(scalars_df_index.int64_col % 2).last( - numeric_only=numeric_only, min_count=min_count - ) - ).to_pandas() - pd_result = scalars_pandas_df_index.groupby( - scalars_pandas_df_index.int64_col % 2 - ).last(numeric_only=numeric_only, min_count=min_count) - bigframes.testing.utils.assert_frame_equal( - pd_result, - bf_result, - ) - - -# ============== -# Series.groupby -# ============== - - -def test_series_groupby_len(scalars_df_index, scalars_pandas_df_index): - bf_result = len(scalars_df_index.groupby("bool_col")["int64_col"]) - pd_result = len(scalars_pandas_df_index.groupby("bool_col")["int64_col"]) - - assert bf_result == pd_result - - -@pytest.mark.parametrize( - ("agg"), - [ - ("count"), - ("size"), - ], -) -def test_series_groupby_agg_string(scalars_df_index, scalars_pandas_df_index, agg): - bf_result = ( - scalars_df_index["int64_col"].groupby(scalars_df_index["string_col"]).agg(agg) + scalars_df_index["int64_col"] + .groupby(scalars_df_index["string_col"]) + .agg("count") ) pd_result = ( scalars_pandas_df_index["int64_col"] .groupby(scalars_pandas_df_index["string_col"]) - .agg(agg) + .agg("count") ) bf_result_computed = bf_result.to_pandas() - bigframes.testing.utils.assert_series_equal( + pd.testing.assert_series_equal( pd_result, bf_result_computed, check_dtype=False, check_names=False ) @@ -756,319 +331,15 @@ def test_series_groupby_agg_list(scalars_df_index, scalars_pandas_df_index): bf_result = ( scalars_df_index["int64_col"] .groupby(scalars_df_index["string_col"]) - .agg(["sum", np.mean, "size"]) + .agg(["sum", "mean"]) ) pd_result = ( scalars_pandas_df_index["int64_col"] .groupby(scalars_pandas_df_index["string_col"]) - .agg(["sum", np.mean, "size"]) + .agg(["sum", "mean"]) ) bf_result_computed = bf_result.to_pandas() - bigframes.testing.utils.assert_frame_equal( + pd.testing.assert_frame_equal( pd_result, bf_result_computed, check_dtype=False, check_names=False ) - - -@pytest.mark.parametrize( - ("na_option", "method", "ascending", "pct"), - [ - ("keep", "average", True, False), - ( - "top", - "min", - False, - True, - ), - ( - "bottom", - "max", - False, - True, - ), - ( - "top", - "first", - False, - True, - ), - ( - "bottom", - "dense", - False, - False, - ), - ], -) -def test_series_groupby_rank( - scalars_df_index, scalars_pandas_df_index, na_option, method, ascending, pct -): - # TODO: supply a reason why this isn't compatible with pandas 1.x - pytest.importorskip("pandas", minversion="2.0.0") - col_names = ["int64_col", "string_col"] - bf_result = ( - scalars_df_index[col_names] - .groupby("string_col")["int64_col"] - .rank(na_option=na_option, method=method, ascending=ascending, pct=pct) - ).to_pandas() - pd_result = ( - ( - scalars_pandas_df_index[col_names] - .groupby("string_col")["int64_col"] - .rank(na_option=na_option, method=method, ascending=ascending, pct=pct) - ) - .astype("float64") - .astype("Float64") - ) - bigframes.testing.utils.assert_series_equal( - pd_result, bf_result, check_dtype=False, check_index_type=False - ) - - -@pytest.mark.parametrize("dropna", [True, False]) -def test_series_groupby_head(scalars_df_index, scalars_pandas_df_index, dropna): - bf_result = ( - scalars_df_index.groupby("bool_col", dropna=dropna)["int64_too"] - .head(1) - .to_pandas() - ) - pd_result = scalars_pandas_df_index.groupby("bool_col", dropna=dropna)[ - "int64_too" - ].head(1) - bigframes.testing.utils.assert_series_equal(pd_result, bf_result, check_dtype=False) - - -def test_series_groupby_kurt(scalars_df_index, scalars_pandas_df_index): - bf_result = ( - scalars_df_index["int64_too"] - .groupby(scalars_df_index["bool_col"]) - .kurt() - .to_pandas() - ) - # Pandas doesn't have groupby.kurt yet: https://github.com/pandas-dev/pandas/issues/40139 - pd_result = scalars_pandas_df_index.groupby("bool_col")["int64_too"].apply( - pd.Series.kurt - ) - - bigframes.testing.utils.assert_series_equal(pd_result, bf_result, check_dtype=False) - - -def test_series_groupby_size(scalars_df_index, scalars_pandas_df_index): - bf_result = ( - scalars_df_index["int64_too"].groupby(scalars_df_index["bool_col"]).size() - ) - pd_result = ( - scalars_pandas_df_index["int64_too"] - .groupby(scalars_pandas_df_index["bool_col"]) - .size() - ) - bf_result_computed = bf_result.to_pandas() - - bigframes.testing.utils.assert_series_equal( - pd_result, bf_result_computed, check_dtype=False - ) - - -def test_series_groupby_skew(scalars_df_index, scalars_pandas_df_index): - bf_result = ( - scalars_df_index["int64_too"] - .groupby(scalars_df_index["bool_col"]) - .skew() - .to_pandas() - ) - pd_result = ( - scalars_pandas_df_index["int64_too"] - .groupby(scalars_pandas_df_index["bool_col"]) - .skew() - ) - - bigframes.testing.utils.assert_series_equal(pd_result, bf_result, check_dtype=False) - - -@pytest.mark.parametrize( - ("q"), - [ - ([0.2, 0.4, 0.6, 0.8]), - (0.11), - ], -) -def test_series_groupby_quantile(scalars_df_index, scalars_pandas_df_index, q): - bf_result = ( - scalars_df_index.groupby("string_col")["int64_col"].quantile(q) - ).to_pandas() - pd_result = scalars_pandas_df_index.groupby("string_col")["int64_col"].quantile(q) - bigframes.testing.utils.assert_series_equal( - pd_result, bf_result, check_dtype=False, check_index_type=False - ) - - -@pytest.mark.skipif( - pd.__version__.startswith("3"), - reason="Pandas 3 change value_counts behavior", -) -@pytest.mark.parametrize( - ("normalize", "ascending", "dropna"), - [ - ( - True, - True, - True, - ), - ( - False, - False, - False, - ), - ], -) -def test_series_groupby_value_counts( - scalars_df_index, - scalars_pandas_df_index, - normalize, - ascending, - dropna, -): - if pd.__version__.startswith("1."): - pytest.skip("pandas 1.x produces different column labels.") - bf_result = ( - scalars_df_index.groupby("bool_col")["string_col"] - .value_counts(normalize=normalize, ascending=ascending, dropna=dropna) - .to_pandas() - ) - pd_result = scalars_pandas_df_index.groupby("bool_col")["string_col"].value_counts( - normalize=normalize, ascending=ascending, dropna=dropna - ) - bigframes.testing.utils.assert_series_equal(pd_result, bf_result, check_dtype=False) - - -@pytest.mark.parametrize( - ("numeric_only", "min_count"), - [ - (True, 2), - (False, -1), - ], -) -def test_series_groupby_first( - scalars_df_index, scalars_pandas_df_index, numeric_only, min_count -): - bf_result = ( - scalars_df_index.groupby("string_col")["int64_col"].first( - numeric_only=numeric_only, min_count=min_count - ) - ).to_pandas() - pd_result = scalars_pandas_df_index.groupby("string_col")["int64_col"].first( - numeric_only=numeric_only, min_count=min_count - ) - bigframes.testing.utils.assert_series_equal( - pd_result, - bf_result, - ) - - -@pytest.mark.parametrize( - ("numeric_only", "min_count"), - [ - (False, 4), - (True, 0), - ], -) -def test_series_groupby_last( - scalars_df_index, scalars_pandas_df_index, numeric_only, min_count -): - bf_result = ( - scalars_df_index.groupby("string_col")["int64_col"].last( - numeric_only=numeric_only, min_count=min_count - ) - ).to_pandas() - pd_result = scalars_pandas_df_index.groupby("string_col")["int64_col"].last( - numeric_only=numeric_only, min_count=min_count - ) - bigframes.testing.utils.assert_series_equal(pd_result, bf_result) - - -def test_series_groupby_agg_transpile_system(scalars_df_index, scalars_pandas_df_index): - def custom_agg(s): - return s.sum() - s.mean() - - bf_df = scalars_df_index.dropna(subset=["int64_col", "bool_col"]) - pd_df = scalars_pandas_df_index.dropna(subset=["int64_col", "bool_col"]) - - with bpd.option_context("experiments.enable_python_transpiler", True): - bf_result = bf_df.groupby("bool_col")["int64_col"].agg(custom_agg).to_pandas() - pd_result = pd_df.groupby("bool_col")["int64_col"].agg(custom_agg) - - bigframes.testing.utils.assert_series_equal(pd_result, bf_result, check_dtype=False) - - -def test_dataframe_groupby_agg_transpile_system( - scalars_df_index, scalars_pandas_df_index -): - def custom_agg(s): - return (s.max() - s.min()) / s.count() - - bf_df = scalars_df_index.dropna(subset=["int64_col", "int64_too", "bool_col"]) - pd_df = scalars_pandas_df_index.dropna( - subset=["int64_col", "int64_too", "bool_col"] - ) - - with bpd.option_context("experiments.enable_python_transpiler", True): - bf_result = ( - bf_df[["int64_col", "int64_too", "bool_col"]] - .groupby("bool_col") - .agg(custom_agg) - .to_pandas() - ) - pd_result = ( - pd_df[["int64_col", "int64_too", "bool_col"]] - .groupby("bool_col") - .agg(custom_agg) - ) - - bigframes.testing.utils.assert_frame_equal(pd_result, bf_result, check_dtype=False) - - -def test_series_groupby_transform_transpile_system( - scalars_df_index, scalars_pandas_df_index -): - def custom_transform(s): - return s - s.mean() - - bf_df = scalars_df_index.dropna(subset=["int64_col", "bool_col"]) - pd_df = scalars_pandas_df_index.dropna(subset=["int64_col", "bool_col"]) - - with bpd.option_context("experiments.enable_python_transpiler", True): - bf_result = ( - bf_df.groupby("bool_col")["int64_col"] - .transform(custom_transform) - .to_pandas() - ) - pd_result = pd_df.groupby("bool_col")["int64_col"].transform(custom_transform) - - bigframes.testing.utils.assert_series_equal(pd_result, bf_result, check_dtype=False) - - -def test_dataframe_groupby_transform_transpile_system( - scalars_df_index, scalars_pandas_df_index -): - def custom_transform(s): - return (s - s.min()) / (s.max() - s.min()) - - bf_df = scalars_df_index.dropna(subset=["int64_col", "int64_too", "bool_col"]) - pd_df = scalars_pandas_df_index.dropna( - subset=["int64_col", "int64_too", "bool_col"] - ) - - with bpd.option_context("experiments.enable_python_transpiler", True): - bf_result = ( - bf_df[["int64_col", "int64_too", "bool_col"]] - .groupby("bool_col") - .transform(custom_transform) - .to_pandas() - ) - pd_result = ( - pd_df[["int64_col", "int64_too", "bool_col"]] - .groupby("bool_col") - .transform(custom_transform) - ) - - bigframes.testing.utils.assert_frame_equal(pd_result, bf_result, check_dtype=False) diff --git a/tests/system/small/test_ibis.py b/tests/system/small/test_ibis.py new file mode 100644 index 00000000000..58b78e00481 --- /dev/null +++ b/tests/system/small/test_ibis.py @@ -0,0 +1,39 @@ +# Copyright 2023 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Tests for monkeypatched ibis code.""" + +import ibis.expr.types as ibis_types + +import bigframes +import third_party.bigframes_vendored.ibis.expr.operations as vendored_ibis_ops + + +def test_approximate_quantiles(session: bigframes.Session, scalars_table_id: str): + num_bins = 3 + ibis_client = session.ibis_client + _, dataset, table_id = scalars_table_id.split(".") + ibis_table: ibis_types.Table = ibis_client.table(table_id, database=dataset) + ibis_column: ibis_types.NumericColumn = ibis_table["int64_col"] + quantiles: ibis_types.ArrayScalar = vendored_ibis_ops.ApproximateMultiQuantile( # type: ignore + ibis_column, num_bins=num_bins + ).to_expr() + value = quantiles[1] + num_edges = quantiles.length() + + sql = ibis_client.compile(value) + num_edges_result = num_edges.to_pandas() + + assert "APPROX_QUANTILES" in sql + assert num_edges_result == num_bins + 1 diff --git a/tests/system/small/test_index.py b/tests/system/small/test_index.py index 26ac609b3c6..f7fa0f0855e 100644 --- a/tests/system/small/test_index.py +++ b/tests/system/small/test_index.py @@ -12,178 +12,11 @@ # See the License for the specific language governing permissions and # limitations under the License. -import re - import numpy import pandas as pd import pytest -import bigframes.pandas as bpd -from bigframes import dtypes -from bigframes.testing.utils import assert_pandas_index_equal_ignore_index_type - - -def test_index_construct_from_list(): - bf_result = bpd.Index( - [3, 14, 159], dtype=pd.Int64Dtype(), name="my_index" - ).to_pandas() - - pd_result: pd.Index = pd.Index([3, 14, 159], dtype=pd.Int64Dtype(), name="my_index") - pd.testing.assert_index_equal(bf_result, pd_result) - - -@pytest.mark.parametrize("key, expected_loc", [("a", 0), ("b", 1), ("c", 2)]) -def test_get_loc_should_return_int_for_unique_index(key, expected_loc): - """Behavior: get_loc on a unique index returns an integer position.""" - # The pandas result is used as the known-correct value. - # We assert our implementation matches it and the expected type. - bf_index = bpd.Index(["a", "b", "c"]) - - result = bf_index.get_loc(key) - - assert result == expected_loc - assert isinstance(result, int) - - -def test_get_loc_should_return_slice_for_monotonic_duplicates(): - """Behavior: get_loc on a monotonic string index with duplicates returns a slice.""" - bf_index = bpd.Index(["a", "b", "b", "c"]) - pd_index = pd.Index(["a", "b", "b", "c"]) - - bf_result = bf_index.get_loc("b") - pd_result = pd_index.get_loc("b") - - assert isinstance(bf_result, slice) - assert bf_result == pd_result # Should be slice(1, 3, None) - - -def test_get_loc_should_return_slice_for_monotonic_numeric_duplicates(): - """Behavior: get_loc on a monotonic numeric index with duplicates returns a slice.""" - bf_index = bpd.Index([1, 2, 2, 3]) - pd_index = pd.Index([1, 2, 2, 3]) - - bf_result = bf_index.get_loc(2) - pd_result = pd_index.get_loc(2) - - assert isinstance(bf_result, slice) - assert bf_result == pd_result # Should be slice(1, 3, None) - - -def test_get_loc_should_return_mask_for_non_monotonic_duplicates(): - """Behavior: get_loc on a non-monotonic string index returns a boolean array.""" - bf_index = bpd.Index(["a", "b", "c", "b"]) - pd_index = pd.Index(["a", "b", "c", "b"]) - - pd_result = pd_index.get_loc("b") - bf_result = bf_index.get_loc("b") - - assert not isinstance(bf_result, (int, slice)) - - if hasattr(bf_result, "to_numpy"): - bf_array = bf_result.to_numpy() - else: - bf_array = bf_result.to_pandas().to_numpy() - numpy.testing.assert_array_equal(bf_array, pd_result) - - -def test_get_loc_should_return_mask_for_non_monotonic_numeric_duplicates(): - """Behavior: get_loc on a non-monotonic numeric index returns a boolean array.""" - bf_index = bpd.Index([1, 2, 3, 2]) - pd_index = pd.Index([1, 2, 3, 2]) - - pd_result = pd_index.get_loc(2) - bf_result = bf_index.get_loc(2) - - assert not isinstance(bf_result, (int, slice)) - - if hasattr(bf_result, "to_numpy"): - bf_array = bf_result.to_numpy() - else: - bf_array = bf_result.to_pandas().to_numpy() - numpy.testing.assert_array_equal(bf_array, pd_result) - - -def test_get_loc_should_raise_error_for_missing_key(): - """Behavior: get_loc raises KeyError when a string key is not found.""" - bf_index = bpd.Index(["a", "b", "c"]) - - with pytest.raises(KeyError): - bf_index.get_loc("d") - - -def test_get_loc_should_raise_error_for_missing_numeric_key(): - """Behavior: get_loc raises KeyError when a numeric key is not found.""" - bf_index = bpd.Index([1, 2, 3]) - - with pytest.raises(KeyError): - bf_index.get_loc(4) - - -def test_get_loc_should_work_for_single_element_index(): - """Behavior: get_loc on a single-element index returns 0.""" - assert bpd.Index(["a"]).get_loc("a") == pd.Index(["a"]).get_loc("a") - - -def test_get_loc_should_return_slice_when_all_elements_are_duplicates(): - """Behavior: get_loc returns a full slice if all elements match the key.""" - bf_index = bpd.Index(["a", "a", "a"]) - pd_index = pd.Index(["a", "a", "a"]) - - bf_result = bf_index.get_loc("a") - pd_result = pd_index.get_loc("a") - - assert isinstance(bf_result, slice) - assert bf_result == pd_result # Should be slice(0, 3, None) - - -def test_index_construct_from_series(): - bf_result = bpd.Index( - bpd.Series([3, 14, 159], dtype=pd.Float64Dtype(), name="series_name"), - name="index_name", - dtype=pd.Int64Dtype(), - ).to_pandas() - pd_result: pd.Index = pd.Index( - pd.Series([3, 14, 159], dtype=pd.Float64Dtype(), name="series_name"), - name="index_name", - dtype=pd.Int64Dtype(), - ) - pd.testing.assert_index_equal(bf_result, pd_result) - - -def test_index_construct_from_index(): - bf_index_input = bpd.Index( - [3, 14, 159], dtype=pd.Float64Dtype(), name="series_name" - ) - bf_result = bpd.Index( - bf_index_input, dtype=pd.Int64Dtype(), name="index_name" - ).to_pandas() - pd_index_input: pd.Index = pd.Index( - [3, 14, 159], dtype=pd.Float64Dtype(), name="series_name" - ) - pd_result: pd.Index = pd.Index( - pd_index_input, dtype=pd.Int64Dtype(), name="index_name" - ) - pd.testing.assert_index_equal(bf_result, pd_result) - - -@pytest.mark.parametrize( - ("json_type"), - [ - pytest.param(dtypes.JSON_DTYPE), - pytest.param("json"), - ], -) -def test_index_construct_w_json_dtype(json_type): - data = [ - "1", - "false", - '["a", {"b": 1}, null]', - None, - ] - index = bpd.Index(data, dtype=json_type) - - assert index.dtype == dtypes.JSON_DTYPE - assert index[1] == "false" +from tests.system.utils import assert_pandas_index_equal_ignore_index_type def test_get_index(scalars_df_index, scalars_pandas_df_index): @@ -200,10 +33,6 @@ def test_index_has_duplicates(scalars_df_index, scalars_pandas_df_index): assert bf_result == pd_result -def test_index_empty_has_duplicates(): - assert not bpd.Index([]).has_duplicates - - def test_index_values(scalars_df_index, scalars_pandas_df_index): bf_result = scalars_df_index.index.values pd_result = scalars_pandas_df_index.index.values @@ -251,18 +80,6 @@ def test_index_astype(scalars_df_index, scalars_pandas_df_index): pd.testing.assert_index_equal(bf_result, pd_result) -def test_index_astype_python(scalars_df_index, scalars_pandas_df_index): - bf_result = scalars_df_index.set_index("int64_col").index.astype(float).to_pandas() - pd_result = scalars_pandas_df_index.set_index("int64_col").index.astype("Float64") - pd.testing.assert_index_equal(bf_result, pd_result) - - -def test_index_astype_error_error(session): - input = pd.Index(["hello", "world", "3.11", "4000"]) - with pytest.raises(ValueError): - session.read_pandas(input).astype("Float64", errors="bad_value") - - def test_index_any(scalars_df_index, scalars_pandas_df_index): bf_result = scalars_df_index.set_index("int64_col").index.any() pd_result = scalars_pandas_df_index.set_index("int64_col").index.any() @@ -423,43 +240,6 @@ def test_index_value_counts(scalars_df_index, scalars_pandas_df_index): pd.testing.assert_series_equal(bf_result, pd_result, check_dtype=False) -@pytest.mark.parametrize( - ("level",), - [ - ("int64_too",), - ("rowindex_2",), - (1,), - ], -) -def test_index_get_level_values(scalars_df_index, scalars_pandas_df_index, level): - bf_result = ( - scalars_df_index.set_index(["int64_too", "rowindex_2"]) - .index.get_level_values(level) - .to_pandas() - ) - pd_result = scalars_pandas_df_index.set_index( - ["int64_too", "rowindex_2"] - ).index.get_level_values(level) - - pd.testing.assert_index_equal(bf_result, pd_result) - - -def test_index_to_series( - scalars_df_index, - scalars_pandas_df_index, -): - bf_result = ( - scalars_df_index.set_index(["int64_too"]) - .index.to_series(index=scalars_df_index["float64_col"], name="new_name") - .to_pandas() - ) - pd_result = scalars_pandas_df_index.set_index(["int64_too"]).index.to_series( - index=scalars_pandas_df_index["float64_col"], name="new_name" - ) - - pd.testing.assert_series_equal(bf_result, pd_result) - - @pytest.mark.parametrize( ("how",), [ @@ -502,222 +282,15 @@ def test_index_drop_duplicates(scalars_df_index, scalars_pandas_df_index, keep): ) -@pytest.mark.parametrize( - ("key",), - [("hello",), (2,), (123123321,), (2.0,), (False,), ((2,),), (pd.NA,)], -) -def test_index_contains(scalars_df_index, scalars_pandas_df_index, key): - col_name = "int64_col" - bf_result = key in scalars_df_index.set_index(col_name).index - pd_result = key in scalars_pandas_df_index.set_index(col_name).index - - assert bf_result == pd_result - - -def test_index_isin_list(scalars_df_index, scalars_pandas_df_index): - col_name = "int64_col" +def test_index_isin(scalars_df_index, scalars_pandas_df_index): bf_series = ( - scalars_df_index.set_index(col_name).index.isin([2, 55555, 4]).to_pandas() + scalars_df_index.set_index("int64_col").index.isin([2, 55555, 4]).to_pandas() ) - pd_result_array = scalars_pandas_df_index.set_index(col_name).index.isin( + pd_result_array = scalars_pandas_df_index.set_index("int64_col").index.isin( [2, 55555, 4] ) pd.testing.assert_index_equal( - pd.Index(pd_result_array).set_names(col_name), + pd.Index(pd_result_array), bf_series, + check_names=False, ) - - -def test_index_isin_bf_series(scalars_df_index, scalars_pandas_df_index, session): - col_name = "int64_col" - bf_series = ( - scalars_df_index.set_index(col_name) - .index.isin(bpd.Series([2, 55555, 4], session=session)) - .to_pandas() - ) - pd_result_array = scalars_pandas_df_index.set_index(col_name).index.isin( - [2, 55555, 4] - ) - pd.testing.assert_index_equal( - pd.Index(pd_result_array).set_names(col_name), - bf_series, - ) - - -def test_index_isin_bf_index(scalars_df_index, scalars_pandas_df_index, session): - col_name = "int64_col" - bf_series = ( - scalars_df_index.set_index(col_name) - .index.isin(bpd.Index([2, 55555, 4], session=session)) - .to_pandas() - ) - pd_result_array = scalars_pandas_df_index.set_index(col_name).index.isin( - [2, 55555, 4] - ) - pd.testing.assert_index_equal( - pd.Index(pd_result_array).set_names(col_name), - bf_series, - ) - - -def test_multiindex_name_is_none(session): - df = pd.DataFrame( - { - "A": [0, 0, 0, 1, 1, 1], - "B": ["x", "y", "z", "x", "y", "z"], - "C": [123, 345, 789, -123, -345, -789], - "D": ["a", "b", "c", "d", "e", "f"], - }, - ) - index = session.read_pandas(df).set_index(["A", "B"]).index - assert index.name is None - - -def test_multiindex_names_not_none(session): - df = pd.DataFrame( - { - "A": [0, 0, 0, 1, 1, 1], - "B": ["x", "y", "z", "x", "y", "z"], - "C": [123, 345, 789, -123, -345, -789], - "D": ["a", "b", "c", "d", "e", "f"], - }, - ) - index = session.read_pandas(df).set_index(["A", "B"]).index - assert tuple(index.names) == ("A", "B") - - -def test_multiindex_repr_includes_all_names(session): - df = pd.DataFrame( - { - "A": [0, 0, 0, 1, 1, 1], - "B": ["x", "y", "z", "x", "y", "z"], - "C": [123, 345, 789, -123, -345, -789], - "D": ["a", "b", "c", "d", "e", "f"], - }, - ) - index = session.read_pandas(df).set_index(["A", "B"]).index - assert "names=['A', 'B']" in repr(index) - - -def test_index_item(session): - # Test with a single item - bf_idx_single = bpd.Index([42], session=session) - pd_idx_single = pd.Index([42]) - assert bf_idx_single.item() == pd_idx_single.item() - - -def test_index_item_with_multiple(session): - # Test with multiple items - bf_idx_multiple = bpd.Index([1, 2, 3], session=session) - pd_idx_multiple = pd.Index([1, 2, 3]) - - try: - pd_idx_multiple.item() - except ValueError as e: - expected_message = str(e) - else: - raise AssertionError("Expected ValueError from pandas, but didn't get one") - - with pytest.raises(ValueError, match=re.escape(expected_message)): - bf_idx_multiple.item() - - -def test_index_item_with_empty(session): - # Test with an empty Index - bf_idx_empty = bpd.Index([], dtype="Int64", session=session) - pd_idx_empty: pd.Index = pd.Index([], dtype="Int64") - - try: - pd_idx_empty.item() - except ValueError as e: - expected_message = str(e) - else: - raise AssertionError("Expected ValueError from pandas, but didn't get one") - - with pytest.raises(ValueError, match=re.escape(expected_message)): - bf_idx_empty.item() - - -def test_index_to_list(scalars_df_index, scalars_pandas_df_index): - bf_result = scalars_df_index.index.to_list() - pd_result = scalars_pandas_df_index.index.to_list() - assert bf_result == pd_result - - -@pytest.mark.parametrize( - ("key", "value"), - [ - (0, "string_value"), - (1, 42), - ("label", None), - (-1, 3.14), - ], -) -def test_index_setitem_different_types(scalars_dfs, key, value): - """Tests that custom Index setitem raises TypeError.""" - scalars_df, _ = scalars_dfs - index = scalars_df.index - - with pytest.raises(TypeError, match="Index does not support mutable operations"): - index[key] = value - - -def test_custom_index_setitem_error(): - """Tests that custom Index setitem raises TypeError.""" - custom_index = bpd.Index([1, 2, 3, 4, 5], name="custom") - - with pytest.raises(TypeError, match="Index does not support mutable operations"): - custom_index[2] = 999 - - -def test_index_eq_const(scalars_df_index, scalars_pandas_df_index): - bf_result = (scalars_df_index.index == 3).to_pandas() - pd_result = scalars_pandas_df_index.index == 3 - assert bf_result == pd.Index(pd_result) - - -def test_index_eq_aligned_index(scalars_df_index, scalars_pandas_df_index): - bf_result = ( - bpd.Index(scalars_df_index.int64_col) - == bpd.Index(scalars_df_index.int64_col.abs()) - ).to_pandas() - pd_result = pd.Index(scalars_pandas_df_index.int64_col) == pd.Index( - scalars_pandas_df_index.int64_col.abs() - ) - assert bf_result == pd.Index(pd_result) - - -def test_index_str_accessor_unary(scalars_df_index, scalars_pandas_df_index): - bf_index = scalars_df_index.set_index("string_col").index - pd_index = scalars_pandas_df_index.set_index("string_col").index - - bf_result = bf_index.str.pad(30, side="both", fillchar="~").to_pandas() - pd_result = pd_index.str.pad(30, side="both", fillchar="~") - - pd.testing.assert_index_equal(bf_result, pd_result) - - -def test_index_str_accessor_binary(scalars_df_index, scalars_pandas_df_index): - if pd.__version__.startswith("1."): - pytest.skip("doesn't work in pandas 1.x.") - bf_index = scalars_df_index.set_index("string_col").index - pd_index = scalars_pandas_df_index.set_index("string_col").index - - bf_result = bf_index.str.cat(bf_index.str[:4]).to_pandas() - pd_result = pd_index.str.cat(pd_index.str[:4]) - - pd.testing.assert_index_equal(bf_result, pd_result) - - -@pytest.mark.parametrize( - ("pat"), - [(r"(ell)(lo)"), (r"(?Ph..)"), (r"(?Pe.*o)([g-l]+)")], -) -def test_index_str_extract(scalars_df_index, scalars_pandas_df_index, pat): - bf_index = scalars_df_index.set_index("string_col").index - pd_index = scalars_pandas_df_index.set_index("string_col").index - - bf_result = bf_index.str.extract(pat).to_pandas() - pd_result = pd_index.str.extract(pat) - - pd.testing.assert_frame_equal(pd_result, bf_result, check_index_type=False) diff --git a/tests/system/small/test_index_io.py b/tests/system/small/test_index_io.py deleted file mode 100644 index b4d7c06da52..00000000000 --- a/tests/system/small/test_index_io.py +++ /dev/null @@ -1,56 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -import pandas as pd - -import bigframes - - -def test_to_pandas_override_global_option(scalars_df_index): - with bigframes.option_context("compute.allow_large_results", True): - bf_index = scalars_df_index.index - - # Direct call to_pandas uses global default setting (allow_large_results=True), - bf_index.to_pandas() - table_id = bf_index._query_job.destination.table_id - assert table_id is not None - - # When allow_large_results=False, a query_job object should not be created. - # Therefore, the table_id should remain unchanged. - bf_index.to_pandas(allow_large_results=False) - assert bf_index._query_job.destination.table_id == table_id - - -def test_to_pandas_dry_run(scalars_df_index): - index = scalars_df_index.index - - result = index.to_pandas(dry_run=True) - - assert isinstance(result, pd.Series) - assert len(result) > 0 - - -def test_to_numpy_override_global_option(scalars_df_index): - with bigframes.option_context("compute.allow_large_results", True): - bf_index = scalars_df_index.index - - # Direct call to_numpy uses global default setting (allow_large_results=True), - # table has 'bqdf' prefix. - bf_index.to_numpy() - table_id = bf_index._query_job.destination.table_id - assert table_id is not None - - # When allow_large_results=False, a query_job object should not be created. - # Therefore, the table_id should remain unchanged. - bf_index.to_numpy(allow_large_results=False) - assert bf_index._query_job.destination.table_id == table_id diff --git a/tests/system/small/test_ipython.py b/tests/system/small/test_ipython.py index 2d233907181..be98ce00674 100644 --- a/tests/system/small/test_ipython.py +++ b/tests/system/small/test_ipython.py @@ -26,4 +26,4 @@ def test_repr_cache(scalars_df_index): results = display_formatter.format(test_df) assert results[0].keys() == {"text/plain", "text/html"} assert test_df._block.retrieve_repr_request_results.cache_info().misses >= 1 - assert test_df._block.retrieve_repr_request_results.cache_info().hits == 0 + assert test_df._block.retrieve_repr_request_results.cache_info().hits >= 1 diff --git a/tests/system/small/test_large_local_data.py b/tests/system/small/test_large_local_data.py deleted file mode 100644 index 39885ea853c..00000000000 --- a/tests/system/small/test_large_local_data.py +++ /dev/null @@ -1,55 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import numpy as np -import pandas as pd -import pytest - -import bigframes -from bigframes.testing.utils import assert_frame_equal - -large_dataframe = pd.DataFrame(np.random.rand(10000, 10), dtype="Float64") -large_dataframe.index = large_dataframe.index.astype("Int64") - - -def test_read_pandas_defer_noop(session: bigframes.Session): - pytest.importorskip("pandas", minversion="2.0.0") - bf_df = session.read_pandas(large_dataframe, write_engine="_deferred") - - assert_frame_equal(large_dataframe, bf_df.to_pandas()) - - -def test_read_pandas_defer_cumsum(session: bigframes.Session): - pytest.importorskip("pandas", minversion="2.0.0") - bf_df = session.read_pandas(large_dataframe, write_engine="_deferred") - bf_df = bf_df.cumsum() - - assert_frame_equal(large_dataframe.cumsum(), bf_df.to_pandas()) - - -def test_read_pandas_defer_cache_cumsum_cumsum(session: bigframes.Session): - pytest.importorskip("pandas", minversion="2.0.0") - bf_df = session.read_pandas(large_dataframe, write_engine="_deferred") - bf_df = bf_df.cumsum().cache().cumsum() - - assert_frame_equal(large_dataframe.cumsum().cumsum(), bf_df.to_pandas()) - - -def test_read_pandas_defer_peek(session: bigframes.Session): - pytest.importorskip("pandas", minversion="2.0.0") - bf_df = session.read_pandas(large_dataframe, write_engine="_deferred") - bf_result = bf_df.peek(15) - - assert len(bf_result) == 15 - assert_frame_equal(large_dataframe.loc[bf_result.index], bf_result) diff --git a/tests/system/small/test_magics.py b/tests/system/small/test_magics.py deleted file mode 100644 index eac0f233f98..00000000000 --- a/tests/system/small/test_magics.py +++ /dev/null @@ -1,100 +0,0 @@ -# Copyright 2026 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -import pandas as pd -import pytest - -import bigframes -import bigframes.pandas as bpd - -IPython = pytest.importorskip("IPython") - - -MAGIC_NAME = "bqsql" - - -@pytest.fixture(scope="module") -def ip(): - """Provides a persistent IPython shell instance for the test session.""" - from IPython.testing.globalipapp import get_ipython - - shell = get_ipython() - shell.extension_manager.load_extension("bigframes") - return shell - - -def test_magic_select_lit_to_var(ip): - bigframes.close_session() - - line = "dst_var" - cell_body = "SELECT 3" - - ip.run_cell_magic(MAGIC_NAME, line, cell_body) - - assert "dst_var" in ip.user_ns - result_df = ip.user_ns["dst_var"] - assert result_df.shape == (1, 1) - assert result_df.to_pandas().iloc[0, 0] == 3 - - -def test_magic_select_lit_dry_run(ip): - bigframes.close_session() - - line = "dst_var --dry_run" - cell_body = "SELECT 3" - - ip.run_cell_magic(MAGIC_NAME, line, cell_body) - - assert "dst_var" in ip.user_ns - result_df = ip.user_ns["dst_var"] - assert result_df.totalBytesProcessed == 0 - - -def test_magic_select_lit_display(ip): - from IPython.utils.capture import capture_output - - bigframes.close_session() - - cell_body = "SELECT 3" - - with capture_output() as io: - ip.run_cell_magic(MAGIC_NAME, "", cell_body) - assert len(io.outputs) > 0 - # Check that the output has data, regardless of the format (html, plain, etc) - available_formats = io.outputs[0].data.keys() - assert len(available_formats) > 0 - - -def test_magic_select_interpolate(ip): - bigframes.close_session() - df = bpd.read_pandas( - pd.DataFrame({"col_a": [1, 2, 3, 4, 5, 6], "col_b": [1, 2, 1, 3, 1, 2]}) - ) - const_val = 1 - - ip.push({"df": df, "const_val": const_val}) - - query = """ - SELECT - SUM(col_a) AS total - FROM - {df} - WHERE col_b={const_val} - """ - - ip.run_cell_magic(MAGIC_NAME, "dst_var", query) - - assert "dst_var" in ip.user_ns - result_df = ip.user_ns["dst_var"] - assert result_df.shape == (1, 1) - assert result_df.loc[0, "total"] == 9 diff --git a/tests/system/small/test_multiindex.py b/tests/system/small/test_multiindex.py index 18368fc5126..bc35f633fdb 100644 --- a/tests/system/small/test_multiindex.py +++ b/tests/system/small/test_multiindex.py @@ -12,85 +12,11 @@ # See the License for the specific language governing permissions and # limitations under the License. -import numpy as np import pandas import pytest import bigframes.pandas as bpd -import bigframes.testing.utils - -# Sample MultiIndex for testing DataFrames where() method. -_MULTI_INDEX = pandas.MultiIndex.from_tuples( - [ - (0, "a"), - (1, "b"), - (2, "c"), - (0, "d"), - (1, "e"), - (2, "f"), - (0, "g"), - (1, "h"), - (2, "i"), - ], - names=["A", "B"], -) - - -def test_multi_index_from_arrays(): - bf_idx = bpd.MultiIndex.from_arrays( - [ - pandas.Index([4, 99], dtype=pandas.Int64Dtype()), - pandas.Index( - [" Hello, World!", "_some_new_string"], - dtype=pandas.StringDtype(storage="pyarrow"), - ), - ], - names=[" 1index 1", "_1index 2"], - ) - pd_idx = pandas.MultiIndex.from_arrays( - [ - pandas.Index([4, 99], dtype=pandas.Int64Dtype()), - pandas.Index( - [" Hello, World!", "_some_new_string"], - dtype=pandas.StringDtype(storage="pyarrow"), - ), - ], - names=[" 1index 1", "_1index 2"], - ) - assert bf_idx.names == pd_idx.names - bigframes.testing.utils.assert_index_equal(bf_idx.to_pandas(), pd_idx) - - -def test_read_pandas_multi_index_axes(): - # TODO: supply a reason why this isn't compatible with pandas 1.x - pytest.importorskip("pandas", minversion="2.0.0") - index = pandas.MultiIndex.from_arrays( - [ - pandas.Index([4, 99], dtype=pandas.Int64Dtype()), - pandas.Index( - [" Hello, World!", "_some_new_string"], - dtype=pandas.StringDtype(storage="pyarrow"), - ), - ], - names=[" 1index 1", "_1index 2"], - ) - columns = pandas.MultiIndex.from_arrays( - [ - pandas.Index([6, 87], dtype=pandas.Int64Dtype()), - pandas.Index( - [" Bonjour le monde!", "_une_chaîne_de_caractères"], - dtype=pandas.StringDtype(storage="pyarrow"), - ), - ], - names=[" 1columns 1", "_1new_index 2"], - ) - pandas_df = pandas.DataFrame( - [[1, 2], [3, 4]], index=index, columns=columns, dtype=pandas.Int64Dtype() - ) - bf_df = bpd.DataFrame(pandas_df) - bf_df_computed = bf_df.to_pandas() - - bigframes.testing.utils.assert_frame_equal(bf_df_computed, pandas_df) +from tests.system.utils import assert_pandas_df_equal_ignore_ordering # Row Multi-index tests @@ -98,71 +24,21 @@ def test_set_multi_index(scalars_df_index, scalars_pandas_df_index): bf_result = scalars_df_index.set_index(["bool_col", "int64_too"]).to_pandas() pd_result = scalars_pandas_df_index.set_index(["bool_col", "int64_too"]) - bigframes.testing.utils.assert_frame_equal(bf_result, pd_result) + pandas.testing.assert_frame_equal(bf_result, pd_result) -@pytest.mark.parametrize( - ("level", "drop"), - [ - (None, True), - (None, False), - (1, True), - ("bool_col", True), - (["float64_col", "int64_too"], True), - ([2, 0], False), - (0, True), - ], -) -def test_df_reset_multi_index(scalars_df_index, scalars_pandas_df_index, level, drop): +def test_reset_multi_index(scalars_df_index, scalars_pandas_df_index): bf_result = ( - scalars_df_index.set_index(["bool_col", "int64_too", "float64_col"]) - .reset_index(level=level, drop=drop) - .to_pandas() + scalars_df_index.set_index(["bool_col", "int64_too"]).reset_index().to_pandas() ) pd_result = scalars_pandas_df_index.set_index( - ["bool_col", "int64_too", "float64_col"] - ).reset_index(level=level, drop=drop) + ["bool_col", "int64_too"] + ).reset_index() # Pandas uses int64 instead of Int64 (nullable) dtype. - if pd_result.index.dtype != bf_result.index.dtype: - pd_result.index = pd_result.index.astype(bf_result.index.dtype) - - bigframes.testing.utils.assert_frame_equal(bf_result, pd_result) - - -@pytest.mark.parametrize( - ("level", "drop"), - [ - (None, True), - (None, False), - (1, True), - ("bool_col", True), - (["float64_col", "int64_too"], True), - ([2, 0], False), - ], -) -def test_series_reset_multi_index( - scalars_df_index, scalars_pandas_df_index, level, drop -): - bf_result = ( - scalars_df_index.set_index(["bool_col", "int64_too", "float64_col"])[ - "string_col" - ] - .reset_index(level=level, drop=drop) - .to_pandas() - ) - pd_result = scalars_pandas_df_index.set_index( - ["bool_col", "int64_too", "float64_col"] - )["string_col"].reset_index(level=level, drop=drop) - - # Pandas uses int64 instead of Int64 (nullable) dtype. - if pd_result.index.dtype != bf_result.index.dtype: - pd_result.index = pd_result.index.astype(pandas.Int64Dtype()) + pd_result.index = pd_result.index.astype(pandas.Int64Dtype()) - if drop: - bigframes.testing.utils.assert_series_equal(bf_result, pd_result) - else: - bigframes.testing.utils.assert_frame_equal(bf_result, pd_result) + pandas.testing.assert_frame_equal(bf_result, pd_result) def test_series_multi_index_idxmin(scalars_df_index, scalars_pandas_df_index): @@ -187,7 +63,7 @@ def test_binop_series_series_matching_multi_indices( bf_result = bf_left["int64_col"] + bf_right["int64_too"] pd_result = pd_left["int64_col"] + pd_right["int64_too"] - bigframes.testing.utils.assert_series_equal( + pandas.testing.assert_series_equal( bf_result.sort_index().to_pandas(), pd_result.sort_index() ) @@ -203,7 +79,7 @@ def test_binop_df_series_matching_multi_indices( bf_result = bf_left[["int64_col", "int64_too"]].add(bf_right["int64_too"], axis=0) pd_result = pd_left[["int64_col", "int64_too"]].add(pd_right["int64_too"], axis=0) - bigframes.testing.utils.assert_frame_equal( + pandas.testing.assert_frame_equal( bf_result.sort_index().to_pandas(), pd_result.sort_index() ) @@ -217,7 +93,7 @@ def test_binop_multi_index_mono_index(scalars_df_index, scalars_pandas_df_index) bf_result = bf_left["int64_col"] + bf_right["int64_too"] pd_result = pd_left["int64_col"] + pd_right["int64_too"] - bigframes.testing.utils.assert_series_equal(bf_result.to_pandas(), pd_result) + pandas.testing.assert_series_equal(bf_result.to_pandas(), pd_result) def test_binop_overlapping_multi_indices(scalars_df_index, scalars_pandas_df_index): @@ -229,7 +105,7 @@ def test_binop_overlapping_multi_indices(scalars_df_index, scalars_pandas_df_ind bf_result = bf_left["int64_col"] + bf_right["int64_too"] pd_result = pd_left["int64_col"] + pd_right["int64_too"] - bigframes.testing.utils.assert_series_equal( + pandas.testing.assert_series_equal( bf_result.sort_index().to_pandas(), pd_result.sort_index() ) @@ -245,7 +121,7 @@ def test_concat_compatible_multi_indices(scalars_df_index, scalars_pandas_df_ind bf_result = bpd.concat([bf_left, bf_right]) pd_result = pandas.concat([pd_left, pd_right]) - bigframes.testing.utils.assert_frame_equal(bf_result.to_pandas(), pd_result) + pandas.testing.assert_frame_equal(bf_result.to_pandas(), pd_result) def test_concat_multi_indices_ignore_index(scalars_df_index, scalars_pandas_df_index): @@ -260,35 +136,16 @@ def test_concat_multi_indices_ignore_index(scalars_df_index, scalars_pandas_df_i # Pandas uses int64 instead of Int64 (nullable) dtype. pd_result.index = pd_result.index.astype(pandas.Int64Dtype()) - bigframes.testing.utils.assert_frame_equal(bf_result.to_pandas(), pd_result) + pandas.testing.assert_frame_equal(bf_result.to_pandas(), pd_result) -@pytest.mark.parametrize( - ("key"), - [ - (2), - ([2, 0]), - ([(2, "capitalize, This "), (-2345, "Hello, World!")]), - ], -) -def test_multi_index_loc_multi_row(scalars_df_index, scalars_pandas_df_index, key): +def test_multi_index_loc(scalars_df_index, scalars_pandas_df_index): bf_result = ( - scalars_df_index.set_index(["int64_too", "string_col"]).loc[key].to_pandas() + scalars_df_index.set_index(["int64_too", "bool_col"]).loc[[2, 0]].to_pandas() ) - pd_result = scalars_pandas_df_index.set_index(["int64_too", "string_col"]).loc[key] - - bigframes.testing.utils.assert_frame_equal(bf_result, pd_result) + pd_result = scalars_pandas_df_index.set_index(["int64_too", "bool_col"]).loc[[2, 0]] - -def test_multi_index_loc_single_row(scalars_df_index, scalars_pandas_df_index): - bf_result = scalars_df_index.set_index(["int64_too", "string_col"]).loc[ - (2, "capitalize, This ") - ] - pd_result = scalars_pandas_df_index.set_index(["int64_too", "string_col"]).loc[ - (2, "capitalize, This ") - ] - - bigframes.testing.utils.assert_series_equal(bf_result, pd_result) + pandas.testing.assert_frame_equal(bf_result, pd_result) def test_multi_index_getitem_bool(scalars_df_index, scalars_pandas_df_index): @@ -298,7 +155,7 @@ def test_multi_index_getitem_bool(scalars_df_index, scalars_pandas_df_index): bf_result = bf_frame[bf_frame["int64_col"] > 0].to_pandas() pd_result = pd_frame[pd_frame["int64_col"] > 0] - bigframes.testing.utils.assert_frame_equal(bf_result, pd_result) + pandas.testing.assert_frame_equal(bf_result, pd_result) @pytest.mark.parametrize( @@ -318,7 +175,7 @@ def test_df_multi_index_droplevel(scalars_df_index, scalars_pandas_df_index, lev bf_result = bf_frame.droplevel(level).to_pandas() pd_result = pd_frame.droplevel(level) - bigframes.testing.utils.assert_frame_equal(bf_result, pd_result) + pandas.testing.assert_frame_equal(bf_result, pd_result) @pytest.mark.parametrize( @@ -338,7 +195,7 @@ def test_series_multi_index_droplevel(scalars_df_index, scalars_pandas_df_index, bf_result = bf_frame["string_col"].droplevel(level).to_pandas() pd_result = pd_frame["string_col"].droplevel(level) - bigframes.testing.utils.assert_series_equal(bf_result, pd_result) + pandas.testing.assert_series_equal(bf_result, pd_result) @pytest.mark.parametrize( @@ -347,7 +204,6 @@ def test_series_multi_index_droplevel(scalars_df_index, scalars_pandas_df_index, (1, 0), ([0, 1], 0), ([True, None], 1), - ((0, True), None), ], ) def test_multi_index_drop(scalars_df_index, scalars_pandas_df_index, labels, level): @@ -357,7 +213,7 @@ def test_multi_index_drop(scalars_df_index, scalars_pandas_df_index, labels, lev bf_result = bf_frame.drop(labels=labels, axis="index", level=level).to_pandas() pd_result = pd_frame.drop(labels=labels, axis="index", level=level) - bigframes.testing.utils.assert_frame_equal(bf_result, pd_result) + pandas.testing.assert_frame_equal(bf_result, pd_result) @pytest.mark.parametrize( @@ -382,7 +238,7 @@ def test_df_multi_index_reorder_levels( bf_result = bf_frame.reorder_levels(order).to_pandas() pd_result = pd_frame.reorder_levels(order) - bigframes.testing.utils.assert_frame_equal(bf_result, pd_result) + pandas.testing.assert_frame_equal(bf_result, pd_result) @pytest.mark.parametrize( @@ -407,7 +263,7 @@ def test_series_multi_index_reorder_levels( bf_result = bf_frame["string_col"].reorder_levels(order).to_pandas() pd_result = pd_frame["string_col"].reorder_levels(order) - bigframes.testing.utils.assert_series_equal(bf_result, pd_result) + pandas.testing.assert_series_equal(bf_result, pd_result) def test_df_multi_index_swaplevel(scalars_df_index, scalars_pandas_df_index): @@ -417,7 +273,7 @@ def test_df_multi_index_swaplevel(scalars_df_index, scalars_pandas_df_index): bf_result = bf_frame.swaplevel().to_pandas() pd_result = pd_frame.swaplevel() - bigframes.testing.utils.assert_frame_equal(bf_result, pd_result) + pandas.testing.assert_frame_equal(bf_result, pd_result) def test_series_multi_index_swaplevel(scalars_df_index, scalars_pandas_df_index): @@ -427,7 +283,7 @@ def test_series_multi_index_swaplevel(scalars_df_index, scalars_pandas_df_index) bf_result = bf_frame["string_col"].swaplevel(0, 2).to_pandas() pd_result = pd_frame["string_col"].swaplevel(0, 2) - bigframes.testing.utils.assert_series_equal(bf_result, pd_result) + pandas.testing.assert_series_equal(bf_result, pd_result) def test_multi_index_series_groupby(scalars_df_index, scalars_pandas_df_index): @@ -443,7 +299,7 @@ def test_multi_index_series_groupby(scalars_df_index, scalars_pandas_df_index): pd_frame["float64_col"].groupby([pd_frame.int64_col % 2, "bool_col"]).mean() ) - bigframes.testing.utils.assert_series_equal(bf_result, pd_result) + pandas.testing.assert_series_equal(bf_result, pd_result) @pytest.mark.parametrize( @@ -470,7 +326,7 @@ def test_multi_index_series_groupby_level( .mean() ) - bigframes.testing.utils.assert_series_equal(bf_result, pd_result) + pandas.testing.assert_series_equal(bf_result, pd_result) def test_multi_index_dataframe_groupby(scalars_df_index, scalars_pandas_df_index): @@ -485,7 +341,7 @@ def test_multi_index_dataframe_groupby(scalars_df_index, scalars_pandas_df_index numeric_only=True ) - bigframes.testing.utils.assert_frame_equal(bf_result, pd_result) + pandas.testing.assert_frame_equal(bf_result, pd_result) @pytest.mark.parametrize( @@ -500,30 +356,20 @@ def test_multi_index_dataframe_groupby(scalars_df_index, scalars_pandas_df_index def test_multi_index_dataframe_groupby_level_aggregate( scalars_df_index, scalars_pandas_df_index, level, as_index ): - index_cols = ["int64_too", "bool_col"] bf_result = ( - scalars_df_index.set_index(index_cols) + scalars_df_index.set_index(["int64_too", "bool_col"]) .groupby(level=level, as_index=as_index) .mean(numeric_only=True) .to_pandas() ) pd_result = ( - scalars_pandas_df_index.set_index(index_cols) + scalars_pandas_df_index.set_index(["int64_too", "bool_col"]) .groupby(level=level, as_index=as_index) .mean(numeric_only=True) ) - # For as_index=False, pandas will drop index levels used as groupings - # In the future, it will include this in the result, bigframes already does this behavior - if not pandas.__version__.startswith("3"): - if not as_index: - for col in index_cols: - if col in bf_result.columns: - bf_result = bf_result.drop(col, axis=1) # Pandas will have int64 index, while bigquery will have Int64 when resetting - bigframes.testing.utils.assert_frame_equal( - bf_result, pd_result, check_index_type=False - ) + pandas.testing.assert_frame_equal(bf_result, pd_result, check_index_type=False) @pytest.mark.parametrize( @@ -541,22 +387,19 @@ def test_multi_index_dataframe_groupby_level_aggregate( def test_multi_index_dataframe_groupby_level_analytic( scalars_df_index, scalars_pandas_df_index, level, as_index ): - # Drop "numeric_col" as pandas doesn't support numerics for grouped window function bf_result = ( - scalars_df_index.drop("numeric_col", axis=1) - .set_index(["int64_too", "bool_col"]) + scalars_df_index.set_index(["int64_too", "bool_col"]) .groupby(level=level, as_index=as_index, dropna=False) .cumsum(numeric_only=True) .to_pandas() ) pd_result = ( - scalars_pandas_df_index.drop("numeric_col", axis=1) - .set_index(["int64_too", "bool_col"]) + scalars_pandas_df_index.set_index(["int64_too", "bool_col"]) .groupby(level=level, as_index=as_index, dropna=False) .cumsum(numeric_only=True) ) - bigframes.testing.utils.assert_frame_equal(bf_result, pd_result, check_dtype=False) + pandas.testing.assert_frame_equal(bf_result, pd_result, check_dtype=False) all_joins = pytest.mark.parametrize( @@ -586,7 +429,7 @@ def test_multi_index_dataframe_join(scalars_dfs, how): (["bool_col", "rowindex_2"]) )[["float64_col"]] pd_result = pd_df_a.join(pd_df_b, how=how) - bigframes.testing.utils.assert_frame_equal(bf_result, pd_result, ignore_order=True) + assert_pandas_df_equal_ignore_ordering(bf_result, pd_result) @all_joins @@ -607,141 +450,7 @@ def test_multi_index_dataframe_join_on(scalars_dfs, how): pd_df_a = pd_df_a.assign(rowindex_2=pd_df_a["rowindex_2"] + 2) pd_df_b = pd_df[["float64_col"]] pd_result = pd_df_a.join(pd_df_b, on="rowindex_2", how=how) - bigframes.testing.utils.assert_frame_equal(bf_result, pd_result, ignore_order=True) - - -def test_multi_index_dataframe_where_series_cond_none_other( - scalars_df_index, scalars_pandas_df_index -): - columns = ["int64_col", "float64_col"] - - # Create multi-index dataframe. - dataframe_bf = bpd.DataFrame( - scalars_df_index[columns].values, - index=_MULTI_INDEX, - columns=scalars_df_index[columns].columns, - ) - dataframe_pd = pandas.DataFrame( - scalars_pandas_df_index[columns].values, - index=_MULTI_INDEX, - columns=scalars_pandas_df_index[columns].columns, - ) - dataframe_bf.columns.name = "test_name" - dataframe_pd.columns.name = "test_name" - - # When condition is series and other is None. - series_cond_bf = dataframe_bf["int64_col"] > 0 - series_cond_pd = dataframe_pd["int64_col"] > 0 - - bf_result = dataframe_bf.where(series_cond_bf).to_pandas() - pd_result = dataframe_pd.where(series_cond_pd) - bigframes.testing.utils.assert_frame_equal( - bf_result, - pd_result, - check_index_type=False, - check_dtype=False, - ) - # Assert the index is still MultiIndex after the operation. - assert isinstance(bf_result.index, pandas.MultiIndex), "Expected a MultiIndex" - assert isinstance(pd_result.index, pandas.MultiIndex), "Expected a MultiIndex" - - -def test_multi_index_dataframe_where_series_cond_dataframe_other( - scalars_df_index, scalars_pandas_df_index -): - columns = ["int64_col", "int64_too"] - - # Create multi-index dataframe. - dataframe_bf = bpd.DataFrame( - scalars_df_index[columns].values, - index=_MULTI_INDEX, - columns=scalars_df_index[columns].columns, - ) - dataframe_pd = pandas.DataFrame( - scalars_pandas_df_index[columns].values, - index=_MULTI_INDEX, - columns=scalars_pandas_df_index[columns].columns, - ) - - # When condition is series and other is dataframe. - series_cond_bf = dataframe_bf["int64_col"] > 1000.0 - series_cond_pd = dataframe_pd["int64_col"] > 1000.0 - dataframe_other_bf = dataframe_bf * 100.0 - dataframe_other_pd = dataframe_pd * 100.0 - - bf_result = dataframe_bf.where(series_cond_bf, dataframe_other_bf).to_pandas() - pd_result = dataframe_pd.where(series_cond_pd, dataframe_other_pd) - bigframes.testing.utils.assert_frame_equal( - bf_result, - pd_result, - check_index_type=False, - check_dtype=False, - ) - - -def test_multi_index_dataframe_where_dataframe_cond_constant_other( - scalars_df_index, scalars_pandas_df_index -): - columns = ["int64_col", "float64_col"] - - # Create multi-index dataframe. - dataframe_bf = bpd.DataFrame( - scalars_df_index[columns].values, - index=_MULTI_INDEX, - columns=scalars_df_index[columns].columns, - ) - dataframe_pd = pandas.DataFrame( - scalars_pandas_df_index[columns].values, - index=_MULTI_INDEX, - columns=scalars_pandas_df_index[columns].columns, - ) - - # When condition is dataframe and other is a constant. - dataframe_cond_bf = dataframe_bf > 0 - dataframe_cond_pd = dataframe_pd > 0 - other = 0 - - bf_result = dataframe_bf.where(dataframe_cond_bf, other).to_pandas() - pd_result = dataframe_pd.where(dataframe_cond_pd, other) - bigframes.testing.utils.assert_frame_equal( - bf_result, - pd_result, - check_index_type=False, - check_dtype=False, - ) - - -def test_multi_index_dataframe_where_dataframe_cond_dataframe_other( - scalars_df_index, scalars_pandas_df_index -): - columns = ["int64_col", "int64_too", "float64_col"] - - # Create multi-index dataframe. - dataframe_bf = bpd.DataFrame( - scalars_df_index[columns].values, - index=_MULTI_INDEX, - columns=scalars_df_index[columns].columns, - ) - dataframe_pd = pandas.DataFrame( - scalars_pandas_df_index[columns].values, - index=_MULTI_INDEX, - columns=scalars_pandas_df_index[columns].columns, - ) - - # When condition is dataframe and other is dataframe. - dataframe_cond_bf = dataframe_bf < 1000.0 - dataframe_cond_pd = dataframe_pd < 1000.0 - dataframe_other_bf = dataframe_bf * -1.0 - dataframe_other_pd = dataframe_pd * -1.0 - - bf_result = dataframe_bf.where(dataframe_cond_bf, dataframe_other_bf).to_pandas() - pd_result = dataframe_pd.where(dataframe_cond_pd, dataframe_other_pd) - bigframes.testing.utils.assert_frame_equal( - bf_result, - pd_result, - check_index_type=False, - check_dtype=False, - ) + assert_pandas_df_equal_ignore_ordering(bf_result, pd_result) @pytest.mark.parametrize( @@ -768,7 +477,7 @@ def test_multi_index_series_groupby_level_aggregate( .mean() ) - bigframes.testing.utils.assert_series_equal(bf_result, pd_result, check_dtype=False) + pandas.testing.assert_series_equal(bf_result, pd_result, check_dtype=False) @pytest.mark.parametrize( @@ -795,7 +504,7 @@ def test_multi_index_series_groupby_level_analytic( .cumsum() ) - bigframes.testing.utils.assert_series_equal(bf_result, pd_result, check_dtype=False) + pandas.testing.assert_series_equal(bf_result, pd_result, check_dtype=False) def test_multi_index_series_rename_dict_same_type( @@ -810,7 +519,7 @@ def test_multi_index_series_rename_dict_same_type( "string_col" ].rename({1: 100, 2: 200}) - bigframes.testing.utils.assert_series_equal( + pandas.testing.assert_series_equal( bf_result, pd_result, check_dtype=False, check_index_type=False ) @@ -828,7 +537,7 @@ def test_multi_index_df_reindex(scalars_df_index, scalars_pandas_df_index): pd_result = scalars_pandas_df_index.set_index(["rowindex_2", "string_col"]).reindex( index=new_index ) - bigframes.testing.utils.assert_frame_equal( + pandas.testing.assert_frame_equal( bf_result, pd_result, check_dtype=False, check_index_type=False ) @@ -846,15 +555,15 @@ def test_column_multi_index_getitem(scalars_df_index, scalars_pandas_df_index): bf_a = bf_df["a"].to_pandas() pd_a = pd_df["a"] - bigframes.testing.utils.assert_frame_equal(bf_a, pd_a) + pandas.testing.assert_frame_equal(bf_a, pd_a) bf_b = bf_df["b"].to_pandas() pd_b = pd_df["b"] - bigframes.testing.utils.assert_frame_equal(bf_b, pd_b) + pandas.testing.assert_frame_equal(bf_b, pd_b) bf_fullkey = bf_df[("a", "int64_too")].to_pandas() pd_fullkey = pd_df[("a", "int64_too")] - bigframes.testing.utils.assert_series_equal(bf_fullkey, pd_fullkey) + pandas.testing.assert_series_equal(bf_fullkey, pd_fullkey) def test_column_multi_index_concat(scalars_df_index, scalars_pandas_df_index): @@ -879,7 +588,7 @@ def test_column_multi_index_concat(scalars_df_index, scalars_pandas_df_index): bf_result = bpd.concat([bf_df1, bf_df2, bf_df1]).to_pandas() pd_result = pandas.concat([pd_df1, pd_df2, pd_df1]) - bigframes.testing.utils.assert_frame_equal(bf_result, pd_result) + pandas.testing.assert_frame_equal(bf_result, pd_result) def test_column_multi_index_drop(scalars_df_index, scalars_pandas_df_index): @@ -892,7 +601,7 @@ def test_column_multi_index_drop(scalars_df_index, scalars_pandas_df_index): bf_a = bf_df.drop(("a", "int64_too"), axis=1).to_pandas() pd_a = pd_df.drop(("a", "int64_too"), axis=1) - bigframes.testing.utils.assert_frame_equal(bf_a, pd_a) + pandas.testing.assert_frame_equal(bf_a, pd_a) @pytest.mark.parametrize( @@ -916,7 +625,7 @@ def test_column_multi_index_assign(scalars_df_index, scalars_pandas_df_index, ke pd_result = pd_df.assign(**kwargs) # Pandas assign results in non-nullable dtype - bigframes.testing.utils.assert_frame_equal(bf_result, pd_result, check_dtype=False) + pandas.testing.assert_frame_equal(bf_result, pd_result, check_dtype=False) def test_column_multi_index_rename(scalars_df_index, scalars_pandas_df_index): @@ -930,37 +639,23 @@ def test_column_multi_index_rename(scalars_df_index, scalars_pandas_df_index): bf_result = bf_df.rename(columns={"b": "c"}).to_pandas() pd_result = pd_df.rename(columns={"b": "c"}) - bigframes.testing.utils.assert_frame_equal(bf_result, pd_result) + pandas.testing.assert_frame_equal(bf_result, pd_result) -@pytest.mark.parametrize( - ("names", "col_fill", "col_level"), - [ - (None, "", "l2"), - (("new_name"), "fill", 1), - ("new_name", "fill", 0), - ], -) -def test_column_multi_index_reset_index( - scalars_df_index, scalars_pandas_df_index, names, col_fill, col_level -): +def test_column_multi_index_reset_index(scalars_df_index, scalars_pandas_df_index): columns = ["int64_too", "int64_col", "float64_col"] - multi_columns = pandas.MultiIndex.from_tuples( - zip(["a", "b", "a"], ["a", "b", "b"]), names=["l1", "l2"] - ) + multi_columns = pandas.MultiIndex.from_tuples(zip(["a", "b", "a"], ["a", "b", "b"])) bf_df = scalars_df_index[columns].copy() bf_df.columns = multi_columns pd_df = scalars_pandas_df_index[columns].copy() pd_df.columns = multi_columns - bf_result = bf_df.reset_index( - names=names, col_fill=col_fill, col_level=col_level - ).to_pandas() - pd_result = pd_df.reset_index(names=names, col_fill=col_fill, col_level=col_level) + bf_result = bf_df.reset_index().to_pandas() + pd_result = pd_df.reset_index() # Pandas uses int64 instead of Int64 (nullable) dtype. pd_result.index = pd_result.index.astype(pandas.Int64Dtype()) - bigframes.testing.utils.assert_frame_equal(bf_result, pd_result) + pandas.testing.assert_frame_equal(bf_result, pd_result) def test_column_multi_index_binary_op(scalars_df_index, scalars_pandas_df_index): @@ -974,28 +669,7 @@ def test_column_multi_index_binary_op(scalars_df_index, scalars_pandas_df_index) bf_result = (bf_df[("a", "a")] + 3).to_pandas() pd_result = pd_df[("a", "a")] + 3 - bigframes.testing.utils.assert_series_equal(bf_result, pd_result) - - -def test_column_multi_index_any(): - # TODO: supply a reason why this isn't compatible with pandas 1.x - pytest.importorskip("pandas", minversion="2.0.0") - columns = pandas.MultiIndex.from_tuples( - [("col0", "col00"), ("col0", "col00"), ("col1", "col11")] - ) - pd_df = pandas.DataFrame( - [[0, 1, 2], [0, 1, 2], [0, 1, 2], [0, 1, 2]], columns=columns - ) - bf_df = bpd.DataFrame(pd_df) - - pd_result = pd_df.isna().any() - bf_result = bf_df.isna().any().to_pandas() - - bigframes.testing.utils.assert_frame_equal( - bf_result.reset_index(drop=False), - pd_result.reset_index(drop=False), - check_dtype=False, - ) + pandas.testing.assert_series_equal(bf_result, pd_result) def test_column_multi_index_agg(scalars_df_index, scalars_pandas_df_index): @@ -1011,9 +685,7 @@ def test_column_multi_index_agg(scalars_df_index, scalars_pandas_df_index): # Pandas may produce narrower numeric types, but bigframes always produces Float64 pd_result = pd_result.astype("Float64") - bigframes.testing.utils.assert_frame_equal( - bf_result, pd_result, check_index_type=False - ) + pandas.testing.assert_frame_equal(bf_result, pd_result, check_index_type=False) def test_column_multi_index_prefix_suffix(scalars_df_index, scalars_pandas_df_index): @@ -1027,7 +699,7 @@ def test_column_multi_index_prefix_suffix(scalars_df_index, scalars_pandas_df_in bf_result = bf_df.add_prefix("prefixed_").add_suffix("_suffixed").to_pandas() pd_result = pd_df.add_prefix("prefixed_").add_suffix("_suffixed") - bigframes.testing.utils.assert_frame_equal(bf_result, pd_result) + pandas.testing.assert_frame_equal(bf_result, pd_result) def test_column_multi_index_cumsum(scalars_df_index, scalars_pandas_df_index): @@ -1043,7 +715,7 @@ def test_column_multi_index_cumsum(scalars_df_index, scalars_pandas_df_index): bf_result = bf_df.cumsum().to_pandas() pd_result = pd_df.cumsum() - bigframes.testing.utils.assert_frame_equal(bf_result, pd_result, check_dtype=False) + pandas.testing.assert_frame_equal(bf_result, pd_result, check_dtype=False) @pytest.mark.parametrize( @@ -1075,8 +747,7 @@ def test_column_multi_index_stack(level): # Pandas produces NaN, where bq dataframes produces pd.NA # Column ordering seems to depend on pandas version - assert isinstance(pd_result, pandas.DataFrame) - bigframes.testing.utils.assert_frame_equal( + pandas.testing.assert_frame_equal( bf_result, pd_result, check_dtype=False, check_index_type=False ) @@ -1104,16 +775,16 @@ def test_column_multi_index_melt(): pd_result = pd_df.melt() # BigFrames uses different string and int types, but values are identical - bigframes.testing.utils.assert_frame_equal( + pandas.testing.assert_frame_equal( bf_result, pd_result, check_index_type=False, check_dtype=False ) def test_column_multi_index_unstack(scalars_df_index, scalars_pandas_df_index): columns = ["int64_too", "int64_col", "rowindex_2"] - level1: pandas.Index = pandas.Index(["b", "a", "b"], dtype="string[pyarrow]") + level1 = pandas.Index(["b", "a", "b"], dtype="string[pyarrow]") # Need resulting column to be pyarrow string rather than object dtype - level2: pandas.Index = pandas.Index(["a", "b", "b"], dtype="string[pyarrow]") + level2 = pandas.Index(["a", "b", "b"], dtype="string[pyarrow]") multi_columns = pandas.MultiIndex.from_arrays([level1, level2]) bf_df = scalars_df_index[columns].copy() bf_df.columns = multi_columns @@ -1126,53 +797,26 @@ def test_column_multi_index_unstack(scalars_df_index, scalars_pandas_df_index): # Pandas produces NaN, where bq dataframes produces pd.NA # Column ordering seems to depend on pandas version - bigframes.testing.utils.assert_series_equal(bf_result, pd_result, check_dtype=False) - - -def test_corr_w_multi_index(scalars_df_index, scalars_pandas_df_index): - columns = ["int64_too", "float64_col", "int64_col"] - multi_columns = pandas.MultiIndex.from_tuples( - zip(["a", "b", "b"], [1, 2, 2]), names=[None, "level_2"] - ) - - bf = scalars_df_index[columns].copy() - bf.columns = multi_columns - - pd_df = scalars_pandas_df_index[columns].copy() - pd_df.columns = multi_columns + pandas.testing.assert_series_equal(bf_result, pd_result, check_dtype=False) - bf_result = bf.corr(numeric_only=True).to_pandas() - pd_result = pd_df.corr(numeric_only=True) - - # BigFrames and Pandas differ in their data type handling: - # - Column types: BigFrames uses Float64, Pandas uses float64. - # - Index types: BigFrames uses strign, Pandas uses object. - bigframes.testing.utils.assert_frame_equal( - bf_result, pd_result, check_dtype=False, check_index_type=False - ) - - -def test_cov_w_multi_index(scalars_df_index, scalars_pandas_df_index): - columns = ["int64_too", "float64_col", "int64_col"] - multi_columns = pandas.MultiIndex.from_tuples( - zip(["a", "b", "b"], [1, 2, 2]), names=["level_1", None] - ) - - bf = scalars_df_index[columns].copy() - bf.columns = multi_columns +@pytest.mark.skip(reason="Pandas fails in newer versions.") +def test_column_multi_index_w_na_stack(scalars_df_index, scalars_pandas_df_index): + columns = ["int64_too", "int64_col", "rowindex_2"] + level1 = pandas.Index(["b", pandas.NA, pandas.NA]) + # Need resulting column to be pyarrow string rather than object dtype + level2 = pandas.Index([pandas.NA, "b", "b"], dtype="string[pyarrow]") + multi_columns = pandas.MultiIndex.from_arrays([level1, level2]) + bf_df = scalars_df_index[columns].copy() + bf_df.columns = multi_columns pd_df = scalars_pandas_df_index[columns].copy() pd_df.columns = multi_columns - bf_result = bf.cov(numeric_only=True).to_pandas() - pd_result = pd_df.cov(numeric_only=True) + bf_result = bf_df.stack().to_pandas() + pd_result = pd_df.stack() - # BigFrames and Pandas differ in their data type handling: - # - Column types: BigFrames uses Float64, Pandas uses float64. - # - Index types: BigFrames uses string, Pandas uses object. - bigframes.testing.utils.assert_frame_equal( - bf_result, pd_result, check_dtype=False, check_index_type=False - ) + # Pandas produces NaN, where bq dataframes produces pd.NA + pandas.testing.assert_frame_equal(bf_result, pd_result, check_dtype=False) @pytest.mark.parametrize( @@ -1249,7 +893,7 @@ def test_column_multi_index_droplevel(scalars_df_index, scalars_pandas_df_index) bf_result = bf_df.droplevel(1, axis=1).to_pandas() pd_result = pd_df.droplevel(1, axis=1) - bigframes.testing.utils.assert_frame_equal(bf_result, pd_result) + pandas.testing.assert_frame_equal(bf_result, pd_result) def test_df_column_multi_index_reindex(scalars_df_index, scalars_pandas_df_index): @@ -1271,7 +915,7 @@ def test_df_column_multi_index_reindex(scalars_df_index, scalars_pandas_df_index # Pandas uses float64 as default for newly created empty column, bf uses Float64 pd_result[("z", "a")] = pd_result[("z", "a")].astype(pandas.Float64Dtype()) - bigframes.testing.utils.assert_frame_equal( + pandas.testing.assert_frame_equal( bf_result, pd_result, ) @@ -1290,7 +934,7 @@ def test_column_multi_index_reorder_levels(scalars_df_index, scalars_pandas_df_i bf_result = bf_df.reorder_levels([-2, -1, 0], axis=1).to_pandas() pd_result = pd_df.reorder_levels([-2, -1, 0], axis=1) - bigframes.testing.utils.assert_frame_equal(bf_result, pd_result) + pandas.testing.assert_frame_equal(bf_result, pd_result) @pytest.mark.parametrize( @@ -1307,7 +951,7 @@ def test_df_multi_index_unstack(hockey_df, hockey_pandas_df, level): ["team_name", "position"], append=True ).unstack(level=level) - bigframes.testing.utils.assert_frame_equal(bf_result, pd_result, check_dtype=False) + pandas.testing.assert_frame_equal(bf_result, pd_result, check_dtype=False) @pytest.mark.parametrize( @@ -1324,7 +968,7 @@ def test_series_multi_index_unstack(hockey_df, hockey_pandas_df, level): "number" ].unstack(level=level) - bigframes.testing.utils.assert_frame_equal(bf_result, pd_result, check_dtype=False) + pandas.testing.assert_frame_equal(bf_result, pd_result, check_dtype=False) def test_column_multi_index_swaplevel(scalars_df_index, scalars_pandas_df_index): @@ -1340,7 +984,7 @@ def test_column_multi_index_swaplevel(scalars_df_index, scalars_pandas_df_index) bf_result = bf_df.swaplevel(-3, -1, axis=1).to_pandas() pd_result = pd_df.swaplevel(-3, -1, axis=1) - bigframes.testing.utils.assert_frame_equal(bf_result, pd_result) + pandas.testing.assert_frame_equal(bf_result, pd_result) def test_df_multi_index_dot_not_supported(): @@ -1401,123 +1045,3 @@ def test_column_multi_index_dot_not_supported(): NotImplementedError, match="Multi-level column input is not supported" ): bf1 @ bf2 - - -def test_explode_w_column_multi_index(): - data = [[[1, 1], np.nan, [3, 3]], [[2], [5], []]] - multi_level_columns = pandas.MultiIndex.from_arrays( - [["col0", "col0", "col1"], ["col00", "col01", "col11"]] - ) - - df = bpd.DataFrame(data, columns=multi_level_columns) - pd_df = df.to_pandas() - - assert isinstance(pd_df, pandas.DataFrame) - assert isinstance(pd_df["col0"], pandas.DataFrame) - bigframes.testing.utils.assert_frame_equal( - df["col0"].explode("col00").to_pandas(), - pd_df["col0"].explode("col00"), - check_dtype=False, - check_index_type=False, - ) - - -def test_explode_w_multi_index(): - data = [[[1, 1], np.nan, [3, 3]], [[2], [5], []]] - columns = ["col00", "col01", "col11"] - multi_index = pandas.MultiIndex.from_frame( - pandas.DataFrame({"idx0": [5, 1], "idx1": ["z", "x"]}) - ) - - df = bpd.DataFrame(data, index=multi_index, columns=columns) - pd_df = df.to_pandas() - - bigframes.testing.utils.assert_frame_equal( - df.explode("col00").to_pandas(), - pd_df.explode("col00"), - check_dtype=False, - check_index_type=False, - ) - - -def test_column_multi_index_w_na_stack(scalars_df_index, scalars_pandas_df_index): - columns = ["int64_too", "int64_col", "rowindex_2"] - level1 = pandas.Index(["b", "c", "d"]) - # Need resulting column to be pyarrow string rather than object dtype - level2: pandas.Index = pandas.Index([None, "b", "b"], dtype="string[pyarrow]") - multi_columns = pandas.MultiIndex.from_arrays([level1, level2]) - bf_df = scalars_df_index[columns].copy() - bf_df.columns = multi_columns - pd_df = scalars_pandas_df_index[columns].copy() - pd_df.columns = multi_columns - - pd_result = pd_df.stack() - bf_result = bf_df.stack().to_pandas() - - # Pandas produces pd.NA, where bq dataframes produces NaN - pd_result["c"] = pd_result["c"].replace(pandas.NA, np.nan) - bigframes.testing.utils.assert_frame_equal(bf_result, pd_result, check_dtype=False) - - -@pytest.mark.parametrize( - ("key",), - [ - ("hello",), - (2,), - (123123321,), - (2.0,), - (pandas.NA,), - (False,), - ((2,),), - ((2, False),), - ((2.0, False),), - ((2, True),), - ], -) -def test_multi_index_contains(scalars_df_index, scalars_pandas_df_index, key): - col_name = ["int64_col", "bool_col"] - bf_result = key in scalars_df_index.set_index(col_name).index - pd_result = key in scalars_pandas_df_index.set_index(col_name).index - - assert bf_result == pd_result - - -def test_multiindex_eq_const(scalars_df_index, scalars_pandas_df_index): - col_name = ["int64_col", "bool_col"] - bf_result = scalars_df_index.set_index(col_name).index == (2, False) - pd_result = scalars_pandas_df_index.set_index(col_name).index == (2, False) - - bigframes.testing.utils.assert_index_equal( - pandas.Index(pd_result, dtype="boolean"), bf_result.to_pandas() - ) - - -def test_count_empty_multiindex_columns(session): - df = pandas.DataFrame( - [], index=[1, 2], columns=pandas.MultiIndex.from_tuples([], names=["a", "b"]) - ) - bdf = session.read_pandas(df) - - # count() operation unpivots columns, triggering the empty MultiIndex bug internally - count_df = bdf.count() - - # The local fix ensures that empty unpivoted columns generate properly typed NULLs - # rather than failing syntax validation downstream in BigQuery. - # We compile to `.sql` to verify it succeeds locally without evaluating on BigQuery natively. - _ = count_df.to_frame().sql - - # Assert structural layout is correct - assert count_df.index.nlevels == 2 - assert list(count_df.index.names) == ["a", "b"] - - -def test_dataframe_melt_multiindex(session): - # Tests that `melt` operations via count do not cause MultiIndex drops in Arrow - df = pandas.DataFrame({"A": [1], "B": ["string"], "C": [3]}) - df.columns = pandas.MultiIndex.from_tuples( - [("Group1", "A"), ("Group2", "B"), ("Group1", "C")] - ) - bdf = session.read_pandas(df) - - count_df = bdf.count().to_pandas() - assert count_df.shape[0] == 3 diff --git a/tests/system/small/test_null_index.py b/tests/system/small/test_null_index.py deleted file mode 100644 index eb9dc114dde..00000000000 --- a/tests/system/small/test_null_index.py +++ /dev/null @@ -1,435 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -import io - -import pandas as pd -import pytest - -import bigframes.exceptions -import bigframes.pandas as bpd - - -def test_null_index_to_gbq(session, scalars_df_null_index, dataset_id_not_created): - dataset_id = dataset_id_not_created - destination_table = f"{dataset_id}.scalars_df_unindexed" - - result_table = scalars_df_null_index.to_gbq( - destination_table, clustering_columns=["int64_col"] - ) - assert ( - result_table == destination_table - if destination_table - else result_table is not None - ) - - loaded_scalars_df_index = session.read_gbq(result_table) - assert not loaded_scalars_df_index.empty - - -def test_null_index_materialize(scalars_df_null_index, scalars_pandas_df_default_index): - bf_result = scalars_df_null_index.to_pandas() - pd.testing.assert_frame_equal( - bf_result, scalars_pandas_df_default_index, check_index_type=False - ) - - -def test_null_index_info(scalars_df_null_index): - expected = ( - "\n" - "NullIndex\n" - "Data columns (total 14 columns):\n" - " # Column Non-Null Count Dtype\n" - "--- ------------- ---------------- ------------------------------\n" - " 0 bool_col 8 non-null boolean\n" - " 1 bytes_col 6 non-null binary[pyarrow]\n" - " 2 date_col 7 non-null date32[day][pyarrow]\n" - " 3 datetime_col 6 non-null timestamp[us][pyarrow]\n" - " 4 geography_col 4 non-null geometry\n" - " 5 int64_col 8 non-null Int64\n" - " 6 int64_too 9 non-null Int64\n" - " 7 numeric_col 6 non-null decimal128(38, 9)[pyarrow]\n" - " 8 float64_col 7 non-null Float64\n" - " 9 rowindex_2 9 non-null Int64\n" - " 10 string_col 8 non-null string\n" - " 11 time_col 6 non-null time64[us][pyarrow]\n" - " 12 timestamp_col 6 non-null timestamp[us, tz=UTC][pyarrow]\n" - " 13 duration_col 7 non-null duration[us][pyarrow]\n" - "dtypes: Float64(1), Int64(3), binary[pyarrow](1), boolean(1), date32[day][pyarrow](1), decimal128(38, 9)[pyarrow](1), duration[us][pyarrow](1), geometry(1), string(1), time64[us][pyarrow](1), timestamp[us, tz=UTC][pyarrow](1), timestamp[us][pyarrow](1)\n" - "memory usage: 1269 bytes\n" - ) - - bf_result = io.StringIO() - - scalars_df_null_index.drop(columns="rowindex").info(buf=bf_result) - - assert expected == bf_result.getvalue() - - -def test_null_index_series_repr(scalars_df_null_index, scalars_pandas_df_default_index): - bf_result = scalars_df_null_index["int64_too"].head(5).__repr__() - pd_result = ( - scalars_pandas_df_default_index["int64_too"] - .head(5) - .to_string(dtype=True, index=False, length=False, name=True) - ) - assert bf_result == pd_result - - -def test_null_index_dataframe_repr( - scalars_df_null_index, scalars_pandas_df_default_index -): - bf_result = scalars_df_null_index[["int64_too", "int64_col"]].head(5).__repr__() - pd_result = ( - scalars_pandas_df_default_index[["int64_too", "int64_col"]] - .head(5) - .to_string(index=False) - ) - assert bf_result == pd_result + "\n\n[5 rows x 2 columns]" - - -def test_null_index_reset_index(scalars_df_null_index, scalars_pandas_df_default_index): - bf_result = scalars_df_null_index.reset_index().to_pandas() - pd_result = scalars_pandas_df_default_index.reset_index(drop=True) - pd.testing.assert_frame_equal(bf_result, pd_result, check_index_type=False) - - -def test_null_index_set_index(scalars_df_null_index, scalars_pandas_df_default_index): - bf_result = scalars_df_null_index.set_index("int64_col").to_pandas() - pd_result = scalars_pandas_df_default_index.set_index("int64_col") - pd.testing.assert_frame_equal(bf_result, pd_result) - - -def test_null_index_concat(scalars_df_null_index, scalars_pandas_df_default_index): - bf_result = bpd.concat( - [scalars_df_null_index, scalars_df_null_index], axis=0 - ).to_pandas() - pd_result = pd.concat( - [scalars_pandas_df_default_index, scalars_pandas_df_default_index], axis=0 - ) - pd.testing.assert_frame_equal(bf_result, pd_result.reset_index(drop=True)) - - -def test_null_index_aggregate(scalars_df_null_index, scalars_pandas_df_default_index): - bf_result = scalars_df_null_index.count().to_pandas() - pd_result = scalars_pandas_df_default_index.count() - - pd_result.index = pd_result.index.astype("string[pyarrow]") - - pd.testing.assert_series_equal( - bf_result, pd_result, check_dtype=False, check_index_type=False - ) - - -def test_null_index_binop_series_axis_0( - scalars_df_null_index, scalars_pandas_df_default_index -): - bf_result = ( - scalars_df_null_index[["int64_col", "int64_too"]] - .add(scalars_df_null_index["int64_col"], axis=0) - .to_pandas() - ) - pd_result = scalars_pandas_df_default_index[["int64_col", "int64_too"]].add( - scalars_pandas_df_default_index.int64_col, axis=0 - ) - - pd.testing.assert_frame_equal( - bf_result, pd_result, check_dtype=False, check_index_type=False - ) - - -def test_null_index_groupby_aggregate( - scalars_df_null_index, scalars_pandas_df_default_index -): - bf_result = scalars_df_null_index.groupby("int64_col").count().to_pandas() - pd_result = scalars_pandas_df_default_index.groupby("int64_col").count() - - pd.testing.assert_frame_equal(bf_result, pd_result, check_dtype=False) - - -def test_null_index_analytic(scalars_df_null_index, scalars_pandas_df_default_index): - # TODO: supply a reason why this isn't compatible with pandas 1.x - pytest.importorskip("pandas", minversion="2.0.0") - bf_result = scalars_df_null_index["int64_col"].cumsum().to_pandas() - pd_result = scalars_pandas_df_default_index["int64_col"].cumsum() - pd.testing.assert_series_equal( - bf_result, pd_result.reset_index(drop=True), check_dtype=False - ) - - -def test_null_index_groupby_analytic( - scalars_df_null_index, scalars_pandas_df_default_index -): - bf_result = ( - scalars_df_null_index.groupby("bool_col")["int64_col"].cummax().to_pandas() - ) - pd_result = scalars_pandas_df_default_index.groupby("bool_col")[ - "int64_col" - ].cummax() - pd.testing.assert_series_equal( - bf_result, pd_result.reset_index(drop=True), check_dtype=False - ) - - -def test_null_index_merge_left_null_index_object( - scalars_df_null_index, scalars_df_default_index, scalars_pandas_df_default_index -): - df1 = scalars_df_null_index[scalars_df_null_index["int64_col"] > 0] - df1_pd = scalars_pandas_df_default_index[ - scalars_pandas_df_default_index["int64_col"] > 0 - ] - assert not df1._has_index - df2 = scalars_df_default_index[scalars_df_default_index["int64_col"] <= 55555] - df2_pd = scalars_pandas_df_default_index[ - scalars_pandas_df_default_index["int64_col"] <= 55555 - ] - assert df2._has_index - - got = df1.merge(df2, how="inner", on="bool_col") - expected = df1_pd.merge(df2_pd, how="inner", on="bool_col") - - # Combining any NULL index object should result in a NULL index. - # This keeps us from generating an index if the user joins a large - # BigQuery table against small local data, for example. - assert not got._has_index - assert got.shape == expected.shape - - -@pytest.mark.parametrize( - ("expr",), - [ - ("new_col = int64_col + int64_too",), - ("new_col = (rowindex > 3) | bool_col",), - ("int64_too = bool_col\nnew_col2 = rowindex",), - ], -) -def test_null_index_df_eval( - scalars_df_null_index, scalars_pandas_df_default_index, expr -): - # TODO: supply a reason why this isn't compatible with pandas 1.x - pytest.importorskip("pandas", minversion="2.0.0") - - bf_result = scalars_df_null_index.eval(expr).to_pandas() - pd_result = scalars_pandas_df_default_index.eval(expr) - - pd.testing.assert_frame_equal(bf_result, pd_result, check_index_type=False) - - -def test_null_index_merge_right_null_index_object( - scalars_df_null_index, scalars_df_default_index, scalars_pandas_df_default_index -): - df1 = scalars_df_default_index[scalars_df_default_index["int64_col"] > 0] - df1_pd = scalars_pandas_df_default_index[ - scalars_pandas_df_default_index["int64_col"] > 0 - ] - assert df1._has_index - df2 = scalars_df_null_index[scalars_df_null_index["int64_col"] <= 55555] - df2_pd = scalars_pandas_df_default_index[ - scalars_pandas_df_default_index["int64_col"] <= 55555 - ] - assert not df2._has_index - - got = df1.merge(df2, how="left", on="bool_col") - expected = df1_pd.merge(df2_pd, how="left", on="bool_col") - - # Combining any NULL index object should result in a NULL index. - # This keeps us from generating an index if the user joins a large - # BigQuery table against small local data, for example. - assert not got._has_index - assert got.shape == expected.shape - - -def test_null_index_merge_two_null_index_objects( - scalars_df_null_index, scalars_pandas_df_default_index -): - df1 = scalars_df_null_index[scalars_df_null_index["int64_col"] > 0] - df1_pd = scalars_pandas_df_default_index[ - scalars_pandas_df_default_index["int64_col"] > 0 - ] - assert not df1._has_index - df2 = scalars_df_null_index[scalars_df_null_index["int64_col"] <= 55555] - df2_pd = scalars_pandas_df_default_index[ - scalars_pandas_df_default_index["int64_col"] <= 55555 - ] - assert not df2._has_index - - got = df1.merge(df2, how="outer", on="bool_col") - expected = df1_pd.merge(df2_pd, how="outer", on="bool_col") - - assert not got._has_index - assert got.shape == expected.shape - - -def test_null_index_stack(scalars_df_null_index, scalars_pandas_df_default_index): - # TODO: supply a reason why this isn't compatible with pandas 1.x - pytest.importorskip("pandas", minversion="2.0.0") - stacking_cols = ["int64_col", "int64_too"] - bf_result = scalars_df_null_index[stacking_cols].stack().to_pandas() - pd_result = ( - scalars_pandas_df_default_index[stacking_cols] - .stack(future_stack=True) - .droplevel(level=0, axis=0) - ) - pd_result.index = pd_result.index.astype(bf_result.index.dtype) - - pd.testing.assert_series_equal( - bf_result, - pd_result, - check_dtype=False, - ) - - -def test_null_index_series_self_join( - scalars_df_null_index, scalars_pandas_df_default_index -): - bf_result = scalars_df_null_index[["int64_col"]].join( - scalars_df_null_index[["int64_too"]] - ) - pd_result = scalars_pandas_df_default_index[["int64_col"]].join( - scalars_pandas_df_default_index[["int64_too"]] - ) - pd.testing.assert_frame_equal( - bf_result.to_pandas(), pd_result.reset_index(drop=True), check_dtype=False - ) - - -def test_null_index_series_self_join_on( - scalars_df_null_index, scalars_pandas_df_default_index -): - # caller doesn't need index, but do need index on arg to join with 'on' - bf_result = scalars_df_null_index[["int64_col", "string_col"]].join( - scalars_df_null_index[["int64_too", "bool_col"]].set_index("int64_too"), - on="int64_col", - ) - pd_result = scalars_pandas_df_default_index[["int64_col", "string_col"]].join( - scalars_pandas_df_default_index[["int64_too", "bool_col"]].set_index( - "int64_too" - ), - on="int64_col", - ) - pd.testing.assert_frame_equal( - bf_result.to_pandas(), pd_result.reset_index(drop=True), check_dtype=False - ) - - -def test_null_index_series_self_aligns( - scalars_df_null_index, scalars_pandas_df_default_index -): - bf_result = scalars_df_null_index["int64_col"] + scalars_df_null_index["int64_too"] - pd_result = ( - scalars_pandas_df_default_index["int64_col"] - + scalars_pandas_df_default_index["int64_too"] - ) - pd.testing.assert_series_equal( - bf_result.to_pandas(), pd_result.reset_index(drop=True), check_dtype=False - ) - - -def test_null_index_df_self_aligns( - scalars_df_null_index, scalars_pandas_df_default_index -): - bf_result = ( - scalars_df_null_index[["int64_col", "float64_col"]] - + scalars_df_null_index[["int64_col", "float64_col"]] - ) - pd_result = ( - scalars_pandas_df_default_index[["int64_col", "float64_col"]] - + scalars_pandas_df_default_index[["int64_col", "float64_col"]] - ) - pd.testing.assert_frame_equal( - bf_result.to_pandas(), pd_result.reset_index(drop=True), check_dtype=False - ) - - -def test_null_index_setitem(scalars_df_null_index, scalars_pandas_df_default_index): - bf_result = scalars_df_null_index.copy() - bf_result["new_col"] = ( - scalars_df_null_index["int64_col"] + scalars_df_null_index["float64_col"] - ) - pd_result = scalars_pandas_df_default_index.copy() - pd_result["new_col"] = ( - scalars_pandas_df_default_index["int64_col"] - + scalars_pandas_df_default_index["float64_col"] - ) - pd.testing.assert_frame_equal( - bf_result.to_pandas(), pd_result.reset_index(drop=True), check_dtype=False - ) - - -def test_null_index_df_concat(scalars_df_null_index, scalars_pandas_df_default_index): - bf_result = bpd.concat([scalars_df_null_index, scalars_df_null_index]) - pd_result = pd.concat( - [scalars_pandas_df_default_index, scalars_pandas_df_default_index] - ) - pd.testing.assert_frame_equal( - bf_result.to_pandas(), pd_result.reset_index(drop=True), check_dtype=False - ) - - -def test_null_index_map_dict_input( - scalars_df_null_index, scalars_pandas_df_default_index -): - local_map = dict() - # construct a local map, incomplete to cover behavior - for s in scalars_pandas_df_default_index.string_col[:-3]: - if isinstance(s, str): - local_map[s] = ord(s[0]) - - pd_result = scalars_pandas_df_default_index.string_col.map(local_map) - pd_result = pd_result.astype("Int64") # pandas type differences - bf_result = scalars_df_null_index.string_col.map(local_map) - - pd.testing.assert_series_equal( - bf_result.to_pandas(), pd_result.reset_index(drop=True), check_dtype=False - ) - - -def test_null_index_align_error(scalars_df_null_index): - with pytest.raises(bigframes.exceptions.NullIndexError): - _ = ( - scalars_df_null_index["int64_col"] - + scalars_df_null_index["int64_col"].cumsum()[ - scalars_df_null_index["int64_col"] > 3 - ] - ) - - -def test_null_index_loc_error(scalars_df_null_index): - with pytest.raises(bigframes.exceptions.NullIndexError): - scalars_df_null_index["int64_col"].loc[1] - - -def test_null_index_at_error(scalars_df_null_index): - with pytest.raises(bigframes.exceptions.NullIndexError): - scalars_df_null_index["int64_col"].at[1] - - -def test_null_index_idxmin_error(scalars_df_null_index): - with pytest.raises(bigframes.exceptions.NullIndexError): - scalars_df_null_index[["int64_col", "int64_too"]].idxmin() - - -def test_null_index_index_property(scalars_df_null_index): - with pytest.raises(bigframes.exceptions.NullIndexError): - _ = scalars_df_null_index.index - - -def test_null_index_transpose(scalars_df_null_index): - with pytest.raises(bigframes.exceptions.NullIndexError): - _ = scalars_df_null_index.T - - -def test_null_index_contains(scalars_df_null_index): - assert 3 not in scalars_df_null_index diff --git a/tests/system/small/test_numpy.py b/tests/system/small/test_numpy.py index 774f72bef4a..5c2a93ec394 100644 --- a/tests/system/small/test_numpy.py +++ b/tests/system/small/test_numpy.py @@ -16,8 +16,6 @@ import pandas as pd import pytest -import bigframes.testing.utils - @pytest.mark.parametrize( ("opname",), @@ -39,17 +37,13 @@ ("log10",), ("sqrt",), ("abs",), - ("isnan",), - ("isfinite",), ], ) def test_series_ufuncs(floats_pd, floats_bf, opname): bf_result = getattr(np, opname)(floats_bf).to_pandas() pd_result = getattr(np, opname)(floats_pd) - bigframes.testing.utils.assert_series_equal( - bf_result, pd_result, nulls_are_nan=True - ) + pd.testing.assert_series_equal(bf_result, pd_result) @pytest.mark.parametrize( @@ -62,10 +56,6 @@ def test_series_ufuncs(floats_pd, floats_bf, opname): ("log10",), ("sqrt",), ("abs",), - ("floor",), - ("ceil",), - ("expm1",), - ("log1p",), ], ) def test_df_ufuncs(scalars_dfs, opname): @@ -76,14 +66,7 @@ def test_df_ufuncs(scalars_dfs, opname): ).to_pandas() pd_result = getattr(np, opname)(scalars_pandas_df[["float64_col", "int64_col"]]) - # In NumPy versions 2 and later, `np.floor` and `np.ceil` now produce integer - # outputs for the "int64_col" column. - if opname in ["floor", "ceil"] and isinstance( - pd_result["int64_col"].dtypes, pd.Int64Dtype - ): - pd_result["int64_col"] = pd_result["int64_col"].astype(pd.Float64Dtype()) - - bigframes.testing.utils.assert_frame_equal(bf_result, pd_result, nulls_are_nan=True) + pd.testing.assert_frame_equal(bf_result, pd_result) @pytest.mark.parametrize( @@ -96,25 +79,16 @@ def test_df_ufuncs(scalars_dfs, opname): ("power",), ], ) -def test_df_binary_ufuncs(scalars_dfs, opname): - scalars_df, scalars_pandas_df = scalars_dfs - op = getattr(np, opname) - - bf_result = op(scalars_df[["float64_col", "int64_col"]], 5.1).to_pandas() - pd_result = op(scalars_pandas_df[["float64_col", "int64_col"]], 5.1) - - bigframes.testing.utils.assert_frame_equal(bf_result, pd_result, nulls_are_nan=True) +def test_series_binary_ufuncs(floats_product_pd, floats_product_bf, opname): + bf_result = getattr(np, opname)( + floats_product_bf.float64_col_x, floats_product_bf.float64_col_y + ).to_pandas() + pd_result = getattr(np, opname)( + floats_product_pd.float64_col_x, floats_product_pd.float64_col_y + ) + pd.testing.assert_series_equal(bf_result, pd_result) -# Operations tested here don't work on full dataframe in numpy+pandas -# Maybe because of nullable dtypes? -@pytest.mark.parametrize( - ("x", "y"), - [ - ("int64_col", "int64_col"), - ("float64_col", "int64_col"), - ], -) @pytest.mark.parametrize( ("opname",), [ @@ -122,23 +96,21 @@ def test_df_binary_ufuncs(scalars_dfs, opname): ("subtract",), ("multiply",), ("divide",), - ("arctan2",), - ("minimum",), - ("maximum",), + ("power",), ], ) -def test_series_binary_ufuncs(scalars_dfs, x, y, opname): +def test_df_binary_ufuncs(scalars_dfs, opname): scalars_df, scalars_pandas_df = scalars_dfs - op = getattr(np, opname) - - bf_result = op(scalars_df[x], scalars_df[y]).to_pandas() - pd_result = op(scalars_pandas_df[x], scalars_pandas_df[y]) - - bigframes.testing.utils.assert_series_equal( - bf_result, pd_result, nulls_are_nan=True + bf_result = getattr(np, opname)( + scalars_df[["float64_col", "int64_col"]], 5.1 + ).to_pandas() + pd_result = getattr(np, opname)( + scalars_pandas_df[["float64_col", "int64_col"]], 5.1 ) + pd.testing.assert_frame_equal(bf_result, pd_result) + def test_series_binary_ufuncs_reverse(scalars_dfs): scalars_df, scalars_pandas_df = scalars_dfs @@ -147,9 +119,7 @@ def test_series_binary_ufuncs_reverse(scalars_dfs): bf_result = np.subtract(5.1, scalars_df["int64_col"]).to_pandas() pd_result = np.subtract(5.1, scalars_pandas_df["int64_col"]) - bigframes.testing.utils.assert_series_equal( - bf_result, pd_result, nulls_are_nan=True - ) + pd.testing.assert_series_equal(bf_result, pd_result) def test_df_binary_ufuncs_reverse(scalars_dfs): @@ -162,4 +132,4 @@ def test_df_binary_ufuncs_reverse(scalars_dfs): scalars_pandas_df[["float64_col", "int64_col"]], ) - bigframes.testing.utils.assert_frame_equal(bf_result, pd_result, nulls_are_nan=True) + pd.testing.assert_frame_equal(bf_result, pd_result) diff --git a/tests/system/small/test_pandas.py b/tests/system/small/test_pandas.py index 356e498021b..0292ebd2069 100644 --- a/tests/system/small/test_pandas.py +++ b/tests/system/small/test_pandas.py @@ -12,123 +12,22 @@ # See the License for the specific language governing permissions and # limitations under the License. -import typing -from datetime import datetime - import pandas as pd -import pyarrow as pa import pytest -import pytz import bigframes.pandas as bpd -import bigframes.testing -from bigframes.testing.utils import assert_frame_equal, assert_series_equal +from tests.system.utils import assert_pandas_df_equal_ignore_ordering -@pytest.mark.parametrize( - ("ordered"), - [ - (True), - (False), - ], -) -def test_concat_dataframe(scalars_dfs, ordered): +def test_concat_dataframe(scalars_dfs): scalars_df, scalars_pandas_df = scalars_dfs bf_result = bpd.concat(11 * [scalars_df]) - bf_result = bf_result.to_pandas(ordered=ordered) + bf_result = bf_result.to_pandas() pd_result = pd.concat(11 * [scalars_pandas_df]) - assert_frame_equal(bf_result, pd_result, ignore_order=not ordered) - - -def test_concat_dataframe_w_struct_cols(nested_structs_df, nested_structs_pandas_df): - """Avoid regressions for internal issue 407107482""" - empty_bf_df = bpd.DataFrame(session=nested_structs_df._block.session) - bf_result = bpd.concat((empty_bf_df, nested_structs_df), ignore_index=True) - bf_result = bf_result.to_pandas() - pd_result = pd.concat((pd.DataFrame(), nested_structs_pandas_df), ignore_index=True) - pd_result.index = pd_result.index.astype("Int64") pd.testing.assert_frame_equal(bf_result, pd_result) -def test_nested_structs_dtypes_and_edge_cases(nested_structs_df): - """Explicitly verify dtypes and edge case values for all supported types.""" - import datetime as dt - import decimal - - import numpy as np - import pandas as pd - - import bigframes.dtypes as bfd - - # 1. Verify BigFrames dtypes - expected_bf_dtypes = { - "person": nested_structs_df["person"].dtype, - "bool_col": bfd.BOOL_DTYPE, - "int64_col": bfd.INT_DTYPE, - "float64_col": bfd.FLOAT_DTYPE, - "string_col": bfd.STRING_DTYPE, - "json_col": bfd.JSON_DTYPE, - "date_col": bfd.DATE_DTYPE, - "time_col": bfd.TIME_DTYPE, - "datetime_col": bfd.DATETIME_DTYPE, - "timestamp_col": bfd.TIMESTAMP_DTYPE, - "bytes_col": bfd.BYTES_DTYPE, - "numeric_col": bfd.NUMERIC_DTYPE, - "bignumeric_col": bfd.BIGNUMERIC_DTYPE, - "geography_col": bfd.GEO_DTYPE, - "duration_col": bfd.TIMEDELTA_DTYPE, - } - - for col_name, expected_dtype in expected_bf_dtypes.items(): - assert nested_structs_df[col_name].dtype == expected_dtype, ( - f"Dtype mismatch for {col_name}" - ) - - # 2. Convert to pandas for value assertions - pd_df = nested_structs_df.to_pandas() - - # Verify we have 6 rows - assert len(pd_df) == 6 - - # Row 1: Normal typical values - assert pd_df.loc[1, "bool_col"] == True - assert pd_df.loc[1, "int64_col"] == 123456789 - assert pd_df.loc[1, "float64_col"] == 1.25 - assert pd_df.loc[1, "string_col"] == "Hello World" - assert pd_df.loc[1, "json_col"] == '{"a":1,"b":[1,2]}' - assert pd_df.loc[1, "date_col"] == dt.date(2026, 6, 24) - - # Row 2: Min bounds / negative infinity - assert pd_df.loc[2, "int64_col"] == -9223372036854775808 - assert pd_df.loc[2, "float64_col"] == float("-inf") - assert pd_df.loc[2, "numeric_col"] == decimal.Decimal( - "-99999999999999999999999999999.999999999" - ) - - # Row 3: Max bounds / infinity - assert pd_df.loc[3, "int64_col"] == 9223372036854775807 - assert pd_df.loc[3, "float64_col"] == float("inf") - - # Row 4: SQL NULLs (omitted keys) - assert pd.isna(pd_df.loc[4, "bool_col"]) - assert pd.isna(pd_df.loc[4, "int64_col"]) - assert pd.isna(pd_df.loc[4, "float64_col"]) - assert pd.isna(pd_df.loc[4, "json_col"]) - assert pd.isna(pd_df.loc[4, "geography_col"]) - - # Row 5: Special edge cases (NaN, empty, multiline) - assert np.isnan(pd_df.loc[5, "float64_col"]) - assert pd_df.loc[5, "float64_col"] is not pd.NA - assert not pd_df["float64_col"].isna().loc[5] - assert pd_df.loc[5, "string_col"] == 'Line 1\nLine 2\n"Quotes"' - assert pd_df.loc[5, "bytes_col"] == b"\x00" - - # Row 6: JSON null literal - assert pd_df.loc[6, "json_col"] == "null" - assert not pd_df["json_col"].isna().loc[6] - - def test_concat_series(scalars_dfs): scalars_df, scalars_pandas_df = scalars_dfs bf_result = bpd.concat( @@ -143,7 +42,7 @@ def test_concat_series(scalars_dfs): ] ) - bigframes.testing.utils.assert_series_equal(bf_result, pd_result) + pd.testing.assert_series_equal(bf_result, pd_result) @pytest.mark.parametrize( @@ -180,7 +79,7 @@ def test_get_dummies_dataframe(scalars_dfs, kwargs): # dtype argument above is needed for pandas v1 only # adjust for expected dtype differences - for column_name, type_name in zip(pd_result.columns, pd_result.dtypes): + for (column_name, type_name) in zip(pd_result.columns, pd_result.dtypes): if type_name == "bool": pd_result[column_name] = pd_result[column_name].astype("boolean") @@ -194,30 +93,22 @@ def test_get_dummies_dataframe_duplicate_labels(scalars_dfs): scalars_df, scalars_pandas_df = scalars_dfs scalars_renamed_df = scalars_df.rename( - columns={ - "int64_too": "int64_col", - "float64_col": "dup_col", - "string_col": "dup_col", - } + columns={"int64_too": "int64_col", "float64_col": None, "string_col": None} ) scalars_renamed_pandas_df = scalars_pandas_df.rename( - columns={ - "int64_too": "int64_col", - "float64_col": "dup_col", - "string_col": "dup_col", - } + columns={"int64_too": "int64_col", "float64_col": None, "string_col": None} ) bf_result = bpd.get_dummies( - scalars_renamed_df, columns=["int64_col", "dup_col"], dtype=bool + scalars_renamed_df, columns=["int64_col", None], dtype=bool ) pd_result = pd.get_dummies( - scalars_renamed_pandas_df, columns=["int64_col", "dup_col"], dtype=bool + scalars_renamed_pandas_df, columns=["int64_col", None], dtype=bool ) # dtype argument above is needed for pandas v1 only # adjust for expected dtype differences - for column_name, type_name in zip(pd_result.columns, pd_result.dtypes): + for (column_name, type_name) in zip(pd_result.columns, pd_result.dtypes): if type_name == "bool": pd_result[column_name] = pd_result[column_name].astype("boolean") @@ -234,8 +125,8 @@ def test_get_dummies_series(scalars_dfs): # dtype argument above is needed for pandas v1 only # adjust for expected dtype differences - for column_name, type_name in zip(pd_result.columns, pd_result.dtypes): - if type_name == "bool": # pragma: NO COVER + for (column_name, type_name) in zip(pd_result.columns, pd_result.dtypes): + if type_name == "bool": pd_result[column_name] = pd_result[column_name].astype("boolean") pd_result.columns = pd_result.columns.astype(object) @@ -255,8 +146,8 @@ def test_get_dummies_series_nameless(scalars_dfs): # dtype argument above is needed for pandas v1 only # adjust for expected dtype differences - for column_name, type_name in zip(pd_result.columns, pd_result.dtypes): - if type_name == "bool": # pragma: NO COVER + for (column_name, type_name) in zip(pd_result.columns, pd_result.dtypes): + if type_name == "bool": pd_result[column_name] = pd_result[column_name].astype("boolean") pd_result.columns = pd_result.columns.astype(object) @@ -287,38 +178,6 @@ def test_concat_dataframe_mismatched_columns(scalars_dfs, how): pd.testing.assert_frame_equal(bf_result, pd_result) -def test_concat_dataframe_upcasting(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - - bf_input1 = scalars_df[["int64_col", "float64_col", "int64_too"]].set_index( - "int64_col", drop=True - ) - bf_input1.columns = ["a", "b"] - bf_input2 = scalars_df[["int64_too", "int64_col", "float64_col"]].set_index( - "float64_col", drop=True - ) - bf_input2.columns = ["a", "b"] - bf_result = bpd.concat([bf_input1, bf_input2], join="outer") - bf_result = bf_result.to_pandas() - - bf_input1 = ( - scalars_pandas_df[["int64_col", "float64_col", "int64_too"]] - .set_index("int64_col", drop=True) - .set_axis(["a", "b"], axis=1) - ) - bf_input2 = ( - scalars_pandas_df[["int64_too", "int64_col", "float64_col"]] - .set_index("float64_col", drop=True) - .set_axis(["a", "b"], axis=1) - ) - pd_result = pd.concat( - [bf_input1, bf_input2], - join="outer", - ) - - pd.testing.assert_frame_equal(bf_result, pd_result) - - @pytest.mark.parametrize( ("how",), [ @@ -393,7 +252,7 @@ def test_merge(scalars_dfs, merge_how): sort=True, ) - assert_frame_equal(bf_result, pd_result, ignore_order=True) + assert_pandas_df_equal_ignore_ordering(bf_result, pd_result) @pytest.mark.parametrize( @@ -427,28 +286,7 @@ def test_merge_left_on_right_on(scalars_dfs, merge_how): sort=True, ) - assert_frame_equal(bf_result, pd_result, ignore_order=True) - - -def test_merge_cross(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - left_columns = ["int64_col", "float64_col", "int64_too"] - right_columns = ["int64_col", "bool_col", "string_col", "rowindex_2"] - - left = scalars_df[left_columns] - right = scalars_df[right_columns] - - df = bpd.merge(left, right, "cross", sort=True) - bf_result = df.to_pandas() - - pd_result = pd.merge( - scalars_pandas_df[left_columns], - scalars_pandas_df[right_columns], - "cross", - sort=True, - ) - - pd.testing.assert_frame_equal(bf_result, pd_result, check_index_type=False) + assert_pandas_df_equal_ignore_ordering(bf_result, pd_result) @pytest.mark.parametrize( @@ -482,365 +320,21 @@ def test_merge_series(scalars_dfs, merge_how): sort=True, ) - assert_frame_equal(bf_result, pd_result, ignore_order=True) - - -def test_merge_w_common_columns(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - left_columns = ["int64_col", "int64_too"] - right_columns = ["int64_col", "bool_col"] - - df = bpd.merge( - scalars_df[left_columns], scalars_df[right_columns], "inner", sort=True - ) - - pd_result = pd.merge( - scalars_pandas_df[left_columns], - scalars_pandas_df[right_columns], - "inner", - sort=True, - ) - assert_frame_equal(df.to_pandas(), pd_result, ignore_order=True) - - -def test_merge_raises_error_when_no_common_columns(scalars_dfs): - scalars_df, _ = scalars_dfs - left_columns = ["float64_col", "int64_too"] - right_columns = ["int64_col", "bool_col"] - - left = scalars_df[left_columns] - right = scalars_df[right_columns] - - with pytest.raises( - ValueError, - match="No common columns to perform merge on.", - ): - bpd.merge(left, right, "inner") - - -def test_merge_raises_error_when_left_right_on_set(scalars_dfs): - scalars_df, _ = scalars_dfs - left_columns = ["int64_col", "int64_too"] - right_columns = ["int64_col", "bool_col"] - - left = scalars_df[left_columns] - right = scalars_df[right_columns] - - with pytest.raises(ValueError): - bpd.merge( - left, - right, - "inner", - left_on="int64_too", - right_on="int64_col", - on="int64_col", - ) - - -def test_crosstab_aligned_series(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - - pd_result = pd.crosstab( - scalars_pandas_df["int64_col"], scalars_pandas_df["int64_too"] - ) - bf_result = bpd.crosstab( - scalars_df["int64_col"], scalars_df["int64_too"] - ).to_pandas() - - assert_frame_equal(bf_result, pd_result, check_dtype=False) - - -def test_crosstab_nondefault_func(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - - pd_result = pd.crosstab( - scalars_pandas_df["int64_col"], - scalars_pandas_df["int64_too"], - values=scalars_pandas_df["float64_col"], - aggfunc="mean", - ) - bf_result = bpd.crosstab( - scalars_df["int64_col"], - scalars_df["int64_too"], - values=scalars_df["float64_col"], - aggfunc="mean", - ).to_pandas() - - assert_frame_equal(bf_result, pd_result, check_dtype=False) - - -def test_crosstab_multi_cols(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - - pd_result = pd.crosstab( - [scalars_pandas_df["int64_col"], scalars_pandas_df["bool_col"]], - [scalars_pandas_df["int64_too"], scalars_pandas_df["string_col"]], - rownames=["a", "b"], - colnames=["c", "d"], - ) - bf_result = bpd.crosstab( - [scalars_df["int64_col"], scalars_df["bool_col"]], - [scalars_df["int64_too"], scalars_df["string_col"]], - rownames=["a", "b"], - colnames=["c", "d"], - ).to_pandas() - - assert_frame_equal(bf_result, pd_result, check_dtype=False) - - -def test_crosstab_unaligned_series(scalars_dfs, session): - scalars_df, scalars_pandas_df = scalars_dfs - other_pd_series = pd.Series( - [10, 20, 10, 30, 10], index=[5, 4, 1, 2, 3], dtype="Int64", name="nums" - ) - other_bf_series = session.Series( - [10, 20, 10, 30, 10], index=[5, 4, 1, 2, 3], name="nums" - ) - - pd_result = pd.crosstab(scalars_pandas_df["int64_col"], other_pd_series) - bf_result = bpd.crosstab(scalars_df["int64_col"], other_bf_series).to_pandas() - - assert_frame_equal(bf_result, pd_result, check_dtype=False) - - -def _convert_pandas_category(pd_s: pd.Series): - """ - Transforms a pandas Series with Categorical dtype into a bigframes-compatible - Series representing intervals." - """ - # When `labels=False` - if pd.api.types.is_integer_dtype(pd_s.dtype) or pd.api.types.is_float_dtype( - pd_s.dtype - ): - return pd_s.astype("Int64") - - if not isinstance(pd_s.dtype, pd.CategoricalDtype): - raise ValueError( - f"Input must be a pandas Series with categorical data: {pd_s.dtype}" - ) - - if pd.api.types.is_object_dtype( - pd_s.cat.categories.dtype - ) or pd.api.types.is_string_dtype(pd_s.cat.categories.dtype): - return pd_s.astype(pd.StringDtype(storage="pyarrow")) - - if not isinstance(pd_s.cat.categories.dtype, pd.IntervalDtype): - raise ValueError( - f"Must be a IntervalDtype with categorical data: {pd_s.cat.categories.dtype}" - ) - - if pd_s.cat.categories.dtype.closed == "left": # type: ignore - left_key = "left_inclusive" - right_key = "right_exclusive" - else: - left_key = "left_exclusive" - right_key = "right_inclusive" - - subtype = pd_s.cat.categories.dtype.subtype # type: ignore - if pd.api.types.is_float_dtype(subtype): # type: ignore - interval_dtype = pa.float64() - elif pd.api.types.is_integer_dtype(subtype): # type: ignore - interval_dtype = pa.int64() - else: - raise ValueError(f"Unknown category type: {subtype}") - - dtype = pd.ArrowDtype( - pa.struct( - [ - pa.field(left_key, interval_dtype, nullable=True), - pa.field(right_key, interval_dtype, nullable=True), - ] - ) - ) - - if len(pd_s.dtype.categories) == 0: - data = [pd.NA] * len(pd_s) - else: - data = [ - {left_key: interval.left, right_key: interval.right} # type: ignore - if pd.notna(val) - else pd.NA - for val, interval in zip(pd_s, pd_s.cat.categories[pd_s.cat.codes]) # type: ignore - ] - - return pd.Series( - data=data, - name=pd_s.name, - dtype=dtype, - index=pd_s.index.astype("Int64"), - ) - - -def test_cut_for_array(): - """Avoid regressions for internal issue 329866195""" - sc = [30, 80, 40, 90, 60, 45, 95, 75, 55, 100, 65, 85] - x = [20, 40, 60, 80, 100] - - pd_result: pd.Series = pd.Series(pd.cut(sc, x)) - bf_result = bpd.cut(sc, x) - - pd_result = _convert_pandas_category(pd_result) - bigframes.testing.utils.assert_series_equal(bf_result.to_pandas(), pd_result) - - -@pytest.mark.parametrize( - ("right", "labels"), - [ - pytest.param(True, None, id="right_w_none_labels"), - pytest.param(True, False, id="right_w_false_labels"), - pytest.param(False, None, id="left_w_none_labels"), - pytest.param(False, False, id="left_w_false_labels"), - ], -) -def test_cut_by_int_bins(scalars_dfs, labels, right): - scalars_df, scalars_pandas_df = scalars_dfs - - pd_result = pd.cut(scalars_pandas_df["float64_col"], 5, labels=labels, right=right) - bf_result = bpd.cut(scalars_df["float64_col"], 5, labels=labels, right=right) - - pd_result = _convert_pandas_category(pd_result) - bigframes.testing.utils.assert_series_equal(bf_result.to_pandas(), pd_result) - - -def test_cut_by_int_bins_w_labels(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - - labels = ["A", "B", "C", "D", "E"] - pd_result = pd.cut(scalars_pandas_df["float64_col"], 5, labels=labels) - bf_result = bpd.cut(scalars_df["float64_col"], 5, labels=labels) - - pd_result = _convert_pandas_category(pd_result) - bigframes.testing.utils.assert_series_equal(bf_result.to_pandas(), pd_result) - - -@pytest.mark.parametrize( - ("breaks", "right", "labels"), - [ - pytest.param( - [0, 5, 10, 15, 20, 100, 1000], - True, - None, - id="int_breaks_w_right_closed_and_none_labels", - ), - pytest.param( - [0, 5, 10, 15, 20, 100, 1000], - False, - False, - id="int_breaks_w_left_closed_and_false_labels", - ), - pytest.param( - [0.5, 10.5, 15.5, 20.5, 100.5, 1000.5], - False, - None, - id="float_breaks_w_left_closed_and_none_labels", - ), - pytest.param( - [0, 5, 10.5, 15.5, 20, 100, 1000.5], - True, - False, - id="mixed_types_breaks_w_right_closed_and_false_labels", - ), - ], -) -def test_cut_by_numeric_breaks(scalars_dfs, breaks, right, labels): - scalars_df, scalars_pandas_df = scalars_dfs - - pd_result = pd.cut( - scalars_pandas_df["float64_col"], breaks, right=right, labels=labels - ) - bf_result = bpd.cut( - scalars_df["float64_col"], breaks, right=right, labels=labels - ).to_pandas() - - pd_result_converted = _convert_pandas_category(pd_result) - bigframes.testing.utils.assert_series_equal(bf_result, pd_result_converted) + assert_pandas_df_equal_ignore_ordering(bf_result, pd_result) -def test_cut_by_numeric_breaks_w_labels(scalars_dfs): +def test_cut(scalars_dfs): scalars_df, scalars_pandas_df = scalars_dfs - bins = [0, 5, 10, 15, 20] - labels = ["A", "B", "C", "D"] - pd_result = pd.cut(scalars_pandas_df["float64_col"], bins, labels=labels) - bf_result = bpd.cut(scalars_df["float64_col"], bins, labels=labels) + pd_result = pd.cut(scalars_pandas_df["float64_col"], 5, labels=False) + bf_result = bpd.cut(scalars_df["float64_col"], 5, labels=False) - pd_result = _convert_pandas_category(pd_result) - bigframes.testing.utils.assert_series_equal(bf_result.to_pandas(), pd_result) + # make sure the result is a supported dtype + assert bf_result.dtype == bpd.Int64Dtype() - -@pytest.mark.parametrize( - ("bins", "right", "labels"), - [ - pytest.param( - [(-5, 2), (2, 3), (-3000, -10)], True, None, id="tuple_right_w_none_labels" - ), - pytest.param( - [(-5, 2), (2, 3), (-3000, -10)], - False, - False, - id="tuple_left_w_false_labels", - ), - pytest.param( - pd.IntervalIndex.from_tuples([(1, 2), (2, 3), (4, 5)]), - True, - False, - id="interval_right_w_none_labels", - ), - pytest.param( - pd.IntervalIndex.from_tuples([(1, 2), (2, 3), (4, 5)]), - False, - None, - id="interval_left_w_false_labels", - ), - ], -) -def test_cut_by_interval_bins(scalars_dfs, bins, right, labels): - scalars_df, scalars_pandas_df = scalars_dfs - bf_result = bpd.cut( - scalars_df["int64_too"], bins, labels=labels, right=right - ).to_pandas() - - if isinstance(bins, list): - bins = pd.IntervalIndex.from_tuples(bins) - pd_result = pd.cut(scalars_pandas_df["int64_too"], bins, labels=labels, right=right) - - pd_result_converted = _convert_pandas_category(pd_result) - bigframes.testing.utils.assert_series_equal(bf_result, pd_result_converted) - - -def test_cut_by_interval_bins_w_labels(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - - bins = pd.IntervalIndex.from_tuples([(1, 2), (2, 3), (4, 5)]) - labels = ["A", "B", "C", "D", "E"] - pd_result = pd.cut(scalars_pandas_df["float64_col"], bins, labels=labels) - bf_result = bpd.cut(scalars_df["float64_col"], bins, labels=labels) - - pd_result = _convert_pandas_category(pd_result) - bigframes.testing.utils.assert_series_equal(bf_result.to_pandas(), pd_result) - - -@pytest.mark.parametrize( - ("bins", "labels"), - [ - pytest.param([], None, id="empty_breaks"), - pytest.param([1], False, id="single_int_breaks"), - pytest.param(pd.IntervalIndex.from_tuples([]), None, id="empty_interval_index"), - ], -) -def test_cut_by_edge_cases_bins(scalars_dfs, bins, labels): - scalars_df, scalars_pandas_df = scalars_dfs - bf_result = bpd.cut(scalars_df["int64_too"], bins, labels=labels).to_pandas() - pd_result = pd.cut(scalars_pandas_df["int64_too"], bins, labels=labels) - - pd_result_converted = _convert_pandas_category(pd_result) - bigframes.testing.utils.assert_series_equal(bf_result, pd_result_converted) - - -def test_cut_empty_array_raises_error(): - bf_df = bpd.Series([]) - with pytest.raises(ValueError, match="Cannot cut empty array"): - bpd.cut(bf_df, bins=5) + bf_result = bf_result.to_pandas() + pd_result = pd_result.astype("Int64") + pd.testing.assert_series_equal(bf_result, pd_result) @pytest.mark.parametrize( @@ -861,339 +355,8 @@ def test_qcut(scalars_dfs, q): scalars_pandas_df["float64_col"], q, labels=False, duplicates="drop" ) bf_result = bpd.qcut(scalars_df["float64_col"], q, labels=False, duplicates="drop") - pd_result = pd_result.astype("Int64") - - bigframes.testing.utils.assert_series_equal(bf_result.to_pandas(), pd_result) - - -@pytest.mark.parametrize( - ("arg", "utc", "unit", "format"), - [ - (173872738, False, None, None), - (32787983.23, True, "s", None), - ("2023-01-01", False, None, "%Y-%m-%d"), - (datetime(2023, 1, 1, 12, 0), False, None, None), - ], -) -def test_to_datetime_scalar(arg, utc, unit, format): - bf_result = bpd.to_datetime(arg, utc=utc, unit=unit, format=format) - pd_result = pd.to_datetime(arg, utc=utc, unit=unit, format=format) - - assert bf_result == pd_result - - -@pytest.mark.parametrize( - ("arg", "utc", "unit", "format"), - [ - ([173872738], False, None, None), - ([32787983.23], True, "s", None), - ( - [datetime(2023, 1, 1, 12, 0, tzinfo=pytz.timezone("America/New_York"))], - True, - None, - None, - ), - (["2023-01-01"], True, None, "%Y-%m-%d"), - (["2023-02-01T15:00:00+07:22"], True, None, None), - (["01-31-2023 14:30 -0800"], True, None, "%m-%d-%Y %H:%M %z"), - (["01-31-2023 14:00", "02-01-2023 15:00"], True, None, "%m-%d-%Y %H:%M"), - ], -) -def test_to_datetime_iterable(arg, utc, unit, format): - bf_result = ( - bpd.to_datetime(arg, utc=utc, unit=unit, format=format) - .to_pandas() - .astype("datetime64[ns, UTC]" if utc else "datetime64[ns]") - ) - pd_result = ( - pd.Series(pd.to_datetime(arg, utc=utc, unit=unit, format=format)) - .dt.floor("us") - .astype("datetime64[ns, UTC]" if utc else "datetime64[ns]") - ) - bigframes.testing.utils.assert_series_equal( - bf_result, pd_result, check_index_type=False, check_names=False - ) - - -def test_to_datetime_series(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - col = "int64_too" - bf_result = ( - bpd.to_datetime(scalars_df[col], unit="s").to_pandas().astype("datetime64[s]") - ) - pd_result = pd.Series(pd.to_datetime(scalars_pandas_df[col], unit="s")).astype( - "datetime64[s]" - ) - bigframes.testing.utils.assert_series_equal( - bf_result, pd_result, check_index_type=False, check_names=False - ) - - -@pytest.mark.parametrize( - ("arg", "unit"), - [ - ([1, 2, 3], "W"), - ([1, 2, 3], "d"), - ([1, 2, 3], "D"), - ([1, 2, 3], "h"), - ([1, 2, 3], "m"), - ([20242330, 25244685, 34324234], "s"), - ([20242330000, 25244685000, 34324234000], "ms"), - ([20242330000000, 25244685000000, 34324234000000], "us"), - ([20242330000000000, 25244685000000000, 34324234000000000], "ns"), - ], -) -def test_to_datetime_unit_param(arg, unit): - bf_result = bpd.to_datetime(arg, unit=unit).to_pandas().astype("datetime64[ns]") - pd_result = ( - pd.Series(pd.to_datetime(arg, unit=unit)) - .dt.floor("us") - .astype("datetime64[ns]") - ) - bigframes.testing.utils.assert_series_equal( - bf_result, pd_result, check_index_type=False, check_names=False - ) + bf_result = bf_result.to_pandas() + pd_result = pd_result.astype("Int64") -@pytest.mark.parametrize( - ("arg", "utc", "format"), - [ - ([20230110, 20230101, 20230101], False, "%Y%m%d"), - ([201301.01], False, "%Y%m.%d"), - (["2023-01-10", "2023-01-20", "2023-01-01"], True, "%Y-%m-%d"), - (["2014-08-15 07:19"], True, "%Y-%m-%d %H:%M"), - ], -) -def test_to_datetime_format_param(arg, utc, format): - bf_result = ( - bpd.to_datetime(arg, utc=utc, format=format) - .to_pandas() - .astype("datetime64[ns, UTC]" if utc else "datetime64[ns]") - ) - pd_result = ( - pd.Series(pd.to_datetime(arg, utc=utc, format=format)) - .dt.floor("us") - .astype("datetime64[ns, UTC]" if utc else "datetime64[ns]") - ) - bigframes.testing.utils.assert_series_equal( - bf_result, pd_result, check_index_type=False, check_names=False - ) - - -@pytest.mark.parametrize( - ("arg", "utc", "output_in_utc", "format"), - [ - ( - ["2014-08-15 08:15:12", "2011-08-15 08:15:12", "2015-08-15 08:15:12"], - False, - False, - None, - ), - ( - [ - "2008-12-25 05:30:00Z", - "2008-12-25 05:30:00-00:00", - "2008-12-25 05:30:00+00:00", - "2008-12-25 05:30:00-0000", - "2008-12-25 05:30:00+0000", - "2008-12-25 05:30:00-00", - "2008-12-25 05:30:00+00", - ], - False, - True, - None, - ), - ( - ["2014-08-15 08:15:12", "2011-08-15 08:15:12", "2015-08-15 08:15:12"], - True, - True, - "%Y-%m-%d %H:%M:%S", - ), - ( - [ - "2014-08-15 08:15:12+05:00", - "2011-08-15 08:15:12+05:00", - "2015-08-15 08:15:12+05:00", - ], - True, - True, - None, - ), - ], -) -def test_to_datetime_string_inputs(arg, utc, output_in_utc, format): - normalized_type = "datetime64[ns, UTC]" if output_in_utc else "datetime64[ns]" - - bf_result = ( - bpd.to_datetime(arg, utc=utc, format=format).to_pandas().astype(normalized_type) - ) - pd_result = ( - pd.Series(pd.to_datetime(arg, utc=utc, format=format)) - .dt.floor("us") - .astype(normalized_type) - ) - - bigframes.testing.utils.assert_series_equal( - bf_result, pd_result, check_index_type=False, check_names=False - ) - - -@pytest.mark.parametrize( - ("arg", "utc", "output_in_utc"), - [ - ( - [datetime(2023, 1, 1, 12, 0), datetime(2023, 2, 1, 12, 0)], - False, - False, - ), - ( - [datetime(2023, 1, 1, 12, 0), datetime(2023, 2, 1, 12, 0)], - True, - True, - ), - ( - [ - datetime(2023, 1, 1, 12, 0, tzinfo=pytz.timezone("UTC")), - datetime(2023, 1, 1, 12, 0, tzinfo=pytz.timezone("UTC")), - ], - True, - True, - ), - ( - [ - datetime(2023, 1, 1, 12, 0, tzinfo=pytz.timezone("America/New_York")), - datetime(2023, 1, 1, 12, 0, tzinfo=pytz.timezone("UTC")), - ], - True, - True, - ), - ], -) -def test_to_datetime_timestamp_inputs(arg, utc, output_in_utc): - normalized_type = "datetime64[ns, UTC]" if output_in_utc else "datetime64[ns]" - - bf_result = bpd.to_datetime(arg, utc=utc).to_pandas().astype(normalized_type) - pd_result = ( - pd.Series(pd.to_datetime(arg, utc=utc)).dt.floor("us").astype(normalized_type) - ) - - bigframes.testing.utils.assert_series_equal( - bf_result, pd_result, check_index_type=False, check_names=False - ) - - -@pytest.mark.parametrize( - "unit", - [ - "W", - "w", - "D", - "d", - "days", - "day", - "hours", - "hour", - "hr", - "h", - "m", - "minute", - "min", - "minutes", - "s", - "seconds", - "sec", - "second", - "ms", - "milliseconds", - "millisecond", - "milli", - "millis", - "us", - "microseconds", - "microsecond", - "µs", - "micro", - "micros", - ], -) -def test_to_timedelta_with_bf_integer_series(session, unit): - bf_series = bpd.Series([1, 2, 3], session=session) - pd_series = pd.Series([1, 2, 3]) - - actual_result = ( - typing.cast(bpd.Series, bpd.to_timedelta(bf_series, unit)) - .to_pandas() - .astype("timedelta64[ns]") - ) - - expected_result = pd.to_timedelta(pd_series, unit).astype("timedelta64[ns]") - assert_series_equal(actual_result, expected_result, check_index_type=False) - - -def test_to_timedelta_with_bf_float_series_value_rounded_down(session): - bf_series = bpd.Series([1.2, 2.9], session=session) - - actual_result = ( - typing.cast(bpd.Series, bpd.to_timedelta(bf_series, "us")) - .to_pandas() - .astype("timedelta64[ns]") - ) - - expected_result = pd.Series([pd.Timedelta(1, "us"), pd.Timedelta(2, "us")]).astype( - "timedelta64[ns]" - ) - bigframes.testing.utils.assert_series_equal( - actual_result, expected_result, check_index_type=False - ) - - -@pytest.mark.parametrize( - "input", - [ - pytest.param([1, 2, 3], id="list"), - pytest.param((1, 2, 3), id="tuple"), - pytest.param(pd.Series([1, 2, 3]), id="pandas-series"), - ], -) -def test_to_timedelta_with_list_like_input(session, input): - actual_result = ( - typing.cast(bpd.Series, bpd.to_timedelta(input, "s", session=session)) - .to_pandas() - .astype("timedelta64[ns]") - ) - - expected_result = pd.Series(pd.to_timedelta(input, "s")).astype("timedelta64[ns]") - bigframes.testing.utils.assert_series_equal( - actual_result, expected_result, check_index_type=False - ) - - -@pytest.mark.parametrize( - "unit", - ["Y", "M", "whatever"], -) -def test_to_timedelta_with_bf_series_invalid_unit(session, unit): - bf_series = bpd.Series([1, 2, 3], session=session) - - with pytest.raises(TypeError): - bpd.to_timedelta(bf_series, unit) - - -@pytest.mark.parametrize("input", [1, 1.2, "1s"]) -def test_to_timedelta_non_bf_series(input): - assert bpd.to_timedelta(input) == pd.to_timedelta(input) - - -def test_to_timedelta_on_timedelta_series__should_be_no_op(scalars_dfs): - bf_df, pd_df = scalars_dfs - bf_series = bpd.to_timedelta(bf_df["int64_too"], unit="us") - pd_series = pd.to_timedelta(pd_df["int64_too"], unit="us") - - actual_result = ( - bpd.to_timedelta(bf_series, unit="s").to_pandas().astype("timedelta64[ns]") - ) - - expected_result = pd.to_timedelta(pd_series, unit="s").astype("timedelta64[ns]") - bigframes.testing.utils.assert_series_equal( - actual_result, expected_result, check_index_type=False - ) + pd.testing.assert_series_equal(bf_result, pd_result) diff --git a/tests/system/small/test_pandas_options.py b/tests/system/small/test_pandas_options.py index a9ec4355f07..ca67710d4ea 100644 --- a/tests/system/small/test_pandas_options.py +++ b/tests/system/small/test_pandas_options.py @@ -13,18 +13,23 @@ # limitations under the License. import datetime -import re -import warnings from unittest import mock import google.api_core.exceptions -import pandas.testing +import google.auth +import google.auth.exceptions import pytest -import bigframes.exceptions +import bigframes.core.global_session import bigframes.pandas as bpd +@pytest.fixture(autouse=True) +def reset_default_session_and_location(): + bpd.close_session() + bpd.options.bigquery.location = None + + @pytest.mark.parametrize( ("read_method", "query_prefix"), [ @@ -48,7 +53,6 @@ def test_read_gbq_start_sets_session_location( dataset_id_permanent, read_method, query_prefix, - reset_default_session_and_location, ): # Form query as a table name or a SQL depending on the test scenario query_tokyo = test_data_tables_tokyo["scalars"] @@ -61,12 +65,8 @@ def test_read_gbq_start_sets_session_location( assert not bpd.options.bigquery.location # Starting user journey with read_gbq* should work for a table in any - # location, in this case tokyo. - with warnings.catch_warnings(): - # Since the query refers to a specific location, no warning should be - # raised. - warnings.simplefilter("error", bigframes.exceptions.DefaultLocationWarning) - df = read_method(query_tokyo) + # location, in this case tokyo + df = read_method(query_tokyo) assert df is not None # Now bigquery options location should be set to tokyo @@ -74,14 +74,12 @@ def test_read_gbq_start_sets_session_location( # Now read_gbq* from another location should fail with pytest.raises( - (google.api_core.exceptions.NotFound, ValueError), + google.api_core.exceptions.NotFound, match=dataset_id_permanent, ): read_method(query) - # Close the global session to start over. - # Note: This is a thread-local operation because of the - # reset_default_session_and_location fixture above. + # Close global session to start over bpd.close_session() # There should still be the previous location set in the bigquery options @@ -101,7 +99,7 @@ def test_read_gbq_start_sets_session_location( # Now read_gbq* from another location should fail with pytest.raises( - (google.api_core.exceptions.NotFound, ValueError), + google.api_core.exceptions.NotFound, match=dataset_id_permanent_tokyo, ): read_method(query_tokyo) @@ -129,7 +127,6 @@ def test_read_gbq_after_session_start_must_comply_with_default_location( dataset_id_permanent_tokyo, read_method, query_prefix, - reset_default_session_and_location, ): # Form query as a table name or a SQL depending on the test scenario query_tokyo = test_data_tables_tokyo["scalars"] @@ -143,16 +140,12 @@ def test_read_gbq_after_session_start_must_comply_with_default_location( # Starting user journey with anything other than read_gbq*, such as # read_pandas would bind the session to default location US - with pytest.warns( - bigframes.exceptions.DefaultLocationWarning, - match=re.escape("using location US for the session"), - ): - df = bpd.read_pandas(scalars_pandas_df_index) + df = bpd.read_pandas(scalars_pandas_df_index) assert df is not None # Doing read_gbq* from a table in another location should fail with pytest.raises( - (google.api_core.exceptions.NotFound, ValueError), + google.api_core.exceptions.NotFound, match=dataset_id_permanent_tokyo, ): read_method(query_tokyo) @@ -183,7 +176,6 @@ def test_read_gbq_must_comply_with_set_location_US( dataset_id_permanent_tokyo, read_method, query_prefix, - reset_default_session_and_location, ): # Form query as a table name or a SQL depending on the test scenario query_tokyo = test_data_tables_tokyo["scalars"] @@ -201,7 +193,7 @@ def test_read_gbq_must_comply_with_set_location_US( # Starting user journey with read_gbq* from another location should fail with pytest.raises( - (google.api_core.exceptions.NotFound, ValueError), + google.api_core.exceptions.NotFound, match=dataset_id_permanent_tokyo, ): read_method(query_tokyo) @@ -234,7 +226,6 @@ def test_read_gbq_must_comply_with_set_location_non_US( dataset_id_permanent, read_method, query_prefix, - reset_default_session_and_location, ): # Form query as a table name or a SQL depending on the test scenario query_tokyo = test_data_tables_tokyo["scalars"] @@ -252,7 +243,7 @@ def test_read_gbq_must_comply_with_set_location_non_US( # Starting user journey with read_gbq* from another location should fail with pytest.raises( - (google.api_core.exceptions.NotFound, ValueError), + google.api_core.exceptions.NotFound, match=dataset_id_permanent, ): read_method(query) @@ -263,43 +254,27 @@ def test_read_gbq_must_comply_with_set_location_non_US( assert df is not None -def test_credentials_need_reauthentication( - monkeypatch, reset_default_session_and_location -): +def test_close_session_after_credentials_need_reauthentication(monkeypatch): # Use a simple test query to verify that default session works to interact - # with BQ. + # with BQ test_query = "SELECT 1" + # Confirm that default session has BQ client with valid credentials + session = bpd.get_global_session() + assert session.bqclient._credentials.valid + # Confirm that default session works as usual df = bpd.read_gbq(test_query) assert df is not None - # Call get_global_session() *after* read_gbq so that our location detection - # has a chance to work. - session = bpd.get_global_session() - assert session.bqclient._http.credentials.valid - - # We look at the thread-local session because of the - # reset_default_session_and_location fixture and that this test mutates - # state that might otherwise be used by tests running in parallel. - current_session = ( - bigframes.core.global_session._global_session_state.thread_local_session - ) - assert current_session is not None - - # Force a temp table to be created, so there is something to cleanup. - current_session._anon_dataset_manager.create_temp_table(schema=()) - with monkeypatch.context() as m: # Simulate expired credentials to trigger the credential refresh flow - m.setattr( - session.bqclient._http.credentials, "expiry", datetime.datetime.utcnow() - ) - assert not session.bqclient._http.credentials.valid + m.setattr(session.bqclient._credentials, "expiry", datetime.datetime.utcnow()) + assert not session.bqclient._credentials.valid # Simulate an exception during the credential refresh flow m.setattr( - session.bqclient._http.credentials, + session.bqclient._credentials, "refresh", mock.Mock(side_effect=google.auth.exceptions.RefreshError()), ) @@ -313,64 +288,10 @@ def test_credentials_need_reauthentication( with pytest.raises(google.auth.exceptions.RefreshError): bpd.read_gbq(test_query) - with warnings.catch_warnings(record=True) as warned: - bpd.close_session() # CleanupFailedWarning: can't clean up - - # The test forces a failure during cleanup and asserts that one or more warning is generated - # when/if multiple temp tables might have been left over. - assert len(warned) >= 1 - assert warned[0].category == bigframes.exceptions.CleanupFailedWarning - - assert ( - bigframes.core.global_session._global_session_state.thread_local_session - is None - ) + # Now verify that closing the session works + bpd.close_session() + assert bigframes.core.global_session._global_session is None # Now verify that use is able to start over df = bpd.read_gbq(test_query) assert df is not None - - -def test_max_rows_normal_execution_within_limit( - scalars_df_index, scalars_pandas_df_index -): - """Test queries execute normally when the number of rows is within the limit.""" - with bpd.option_context("compute.maximum_result_rows", 10): - df = scalars_df_index.head(10) - result = df.to_pandas() - - expected = scalars_pandas_df_index.head(10) - pandas.testing.assert_frame_equal(result, expected) - - with ( - bpd.option_context("compute.maximum_result_rows", 10), - bpd.option_context("display.repr_mode", "head"), - ): - df = scalars_df_index.head(10) - assert repr(df) is not None - - # We should be able to get away with only a single row for shape. - with bpd.option_context("compute.maximum_result_rows", 1): - shape = scalars_df_index.shape - assert shape == scalars_pandas_df_index.shape - - # 0 is not recommended, as it would stop aggregations and many other - # necessary operations, but we shouldn't need even 1 row for to_gbq(). - with bpd.option_context("compute.maximum_result_rows", 0): - destination = scalars_df_index.to_gbq() - assert destination is not None - - -def test_max_rows_exceeds_limit(scalars_df_index): - """Test to_pandas() raises MaximumRowsDownloadedExceeded when the limit is exceeded.""" - with ( - bpd.option_context("compute.maximum_result_rows", 5), - pytest.raises(bigframes.exceptions.MaximumResultRowsExceeded, match="5"), - ): - scalars_df_index.to_pandas() - - with ( - bpd.option_context("compute.maximum_result_rows", 5), - pytest.raises(bigframes.exceptions.MaximumResultRowsExceeded, match="5"), - ): - next(iter(scalars_df_index.to_pandas_batches())) diff --git a/tests/system/small/test_polars_execution.py b/tests/system/small/test_polars_execution.py deleted file mode 100644 index fad8d9dba2f..00000000000 --- a/tests/system/small/test_polars_execution.py +++ /dev/null @@ -1,97 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -import math - -import pytest - -import bigframes -import bigframes.bigquery -from bigframes.testing.utils import assert_frame_equal - -polars = pytest.importorskip("polars") - - -@pytest.fixture(scope="module") -def session_w_polars(): - context = bigframes.BigQueryOptions(location="US", enable_polars_execution=True) - session = bigframes.Session(context=context) - yield session - session.close() # close generated session at cleanup time - - -def test_polar_execution_sorted(session_w_polars, scalars_pandas_df_index): - execution_count_before = session_w_polars._metrics.execution_count - bf_df = session_w_polars.read_pandas(scalars_pandas_df_index) - - pd_result = scalars_pandas_df_index.sort_index(ascending=False)[ - ["int64_too", "bool_col"] - ] - bf_result = bf_df.sort_index(ascending=False)[["int64_too", "bool_col"]].to_pandas() - - assert session_w_polars._metrics.execution_count == execution_count_before + 1 - assert_frame_equal(bf_result, pd_result) - - -def test_polar_execution_sorted_filtered(session_w_polars, scalars_pandas_df_index): - execution_count_before = session_w_polars._metrics.execution_count - bf_df = session_w_polars.read_pandas(scalars_pandas_df_index) - - pd_result = scalars_pandas_df_index.sort_index(ascending=False).dropna( - subset=["int64_col", "string_col"] - ) - bf_result = ( - bf_df.sort_index(ascending=False) - .dropna(subset=["int64_col", "string_col"]) - .to_pandas() - ) - - assert session_w_polars._metrics.execution_count == execution_count_before + 1 - assert_frame_equal(bf_result, pd_result) - - -def test_polar_execution_unsupported_sql_fallback( - session_w_polars, scalars_pandas_df_index -): - execution_count_before = session_w_polars._metrics.execution_count - bf_df = session_w_polars.read_pandas(scalars_pandas_df_index) - - bf_df["geo_area"] = bigframes.bigquery.st_length(bf_df.geography_col) - bf_result = bf_df.to_pandas() - - # geo fns not supported by polar engine yet, so falls back to bq execution - assert session_w_polars._metrics.execution_count == (execution_count_before + 2) - assert math.isclose(bf_result.geo_area.sum(), 70.52332050, rel_tol=0.00001) - - -def test_polars_execution_history(session_w_polars): - import pandas as pd - - # Create a small local DataFrame - pdf = pd.DataFrame({"col_a": [1, 2, 3], "col_b": ["x", "y", "z"]}) - - # Read simple local data - df = session_w_polars.read_pandas(pdf) - - # Trigger execution - _ = df.to_pandas() - - # Verify the execution history captured the local job - history = session_w_polars.execution_history().to_dataframe() - - # Verify we have at least one job and logged as polars - assert len(history) > 0 - last_job = history.iloc[-1] - - assert last_job["job_type"] == "polars" - assert last_job["status"] == "DONE" diff --git a/tests/system/small/test_progress_bar.py b/tests/system/small/test_progress_bar.py index a179e18332a..30ea63b483a 100644 --- a/tests/system/small/test_progress_bar.py +++ b/tests/system/small/test_progress_bar.py @@ -15,59 +15,47 @@ import re import tempfile -import numpy as np import pandas as pd -import pytest import bigframes as bf import bigframes.formatting_helpers as formatting_helpers -from bigframes.session import MAX_INLINE_DF_BYTES -job_load_message_regex = r"Query" -EXPECTED_DRY_RUN_MESSAGE = "Computation deferred. Computation will process" +job_load_message_regex = r"\w+ job [\w-]+ is \w+\." def test_progress_bar_dataframe( penguins_df_default_index: bf.dataframe.DataFrame, capsys ): + bf.options.display.progress_bar = "terminal" capsys.readouterr() # clear output - - with bf.option_context("display.progress_bar", "terminal"): - penguins_df_default_index.to_pandas(allow_large_results=True) + penguins_df_default_index.to_pandas() assert_loading_msg_exist(capsys.readouterr().out) assert penguins_df_default_index.query_job is not None def test_progress_bar_series(penguins_df_default_index: bf.dataframe.DataFrame, capsys): + bf.options.display.progress_bar = "terminal" series = penguins_df_default_index["body_mass_g"].head(10) capsys.readouterr() # clear output - - with bf.option_context("display.progress_bar", "terminal"): - series.to_pandas(allow_large_results=True) + series.to_pandas() assert_loading_msg_exist(capsys.readouterr().out) assert series.query_job is not None def test_progress_bar_scalar(penguins_df_default_index: bf.dataframe.DataFrame, capsys): + bf.options.display.progress_bar = "terminal" capsys.readouterr() # clear output - - with bf.option_context("display.progress_bar", "terminal"): - penguins_df_default_index["body_mass_g"].head(10).mean() + penguins_df_default_index["body_mass_g"].head(10).mean() assert_loading_msg_exist(capsys.readouterr().out) -def test_progress_bar_scalar_allow_large_results( - penguins_df_default_index: bf.dataframe.DataFrame, capsys -): +def test_progress_bar_read_gbq(session: bf.Session, penguins_table_id: str, capsys): + bf.options.display.progress_bar = "terminal" capsys.readouterr() # clear output - - with bf.option_context( - "display.progress_bar", "terminal", "compute.allow_large_results", "True" - ): - penguins_df_default_index["body_mass_g"].head(10).mean() + session.read_gbq(penguins_table_id) assert_loading_msg_exist(capsys.readouterr().out) @@ -75,11 +63,10 @@ def test_progress_bar_scalar_allow_large_results( def test_progress_bar_extract_jobs( penguins_df_default_index: bf.dataframe.DataFrame, gcs_folder, capsys ): + bf.options.display.progress_bar = "terminal" path = gcs_folder + "test_read_csv_progress_bar*.csv" capsys.readouterr() # clear output - - with bf.option_context("display.progress_bar", "terminal"): - penguins_df_default_index.to_csv(path) + penguins_df_default_index.to_csv(path) assert_loading_msg_exist(capsys.readouterr().out) @@ -87,54 +74,53 @@ def test_progress_bar_extract_jobs( def test_progress_bar_load_jobs( session: bf.Session, penguins_pandas_df_default_index: pd.DataFrame, capsys ): - # repeat the DF to be big enough to trigger the load job. - df = penguins_pandas_df_default_index - while len(df) < MAX_INLINE_DF_BYTES: - df = pd.DataFrame(np.repeat(df.values, 2, axis=0)) - - with ( - bf.option_context("display.progress_bar", "terminal"), - tempfile.TemporaryDirectory() as dir, - ): + bf.options.display.progress_bar = "terminal" + with tempfile.TemporaryDirectory() as dir: path = dir + "/test_read_csv_progress_bar*.csv" - df.to_csv(path, index=False) + penguins_pandas_df_default_index.to_csv(path, index=False) capsys.readouterr() # clear output session.read_csv(path) - assert_loading_msg_exist(capsys.readouterr().out, pattern="Load") - - -def test_progress_bar_uniqueness_check(session: bf.Session, capsys): - # Ensure strictly_ordered is True (default) to trigger uniqueness check - assert session._strictly_ordered - - capsys.readouterr() # clear output - - with bf.option_context("display.progress_bar", "terminal"): - # Read a table and specify a non-unique index_col to trigger the check. - # We use a public table to make it a "real" test. - session.read_gbq_table( - "bigquery-public-data.ml_datasets.penguins", - index_col="island", - ) - assert_loading_msg_exist(capsys.readouterr().out) -def assert_loading_msg_exist(capstdout: str, pattern=job_load_message_regex): - num_loading_msg = 0 - lines = capstdout.split("\n") +def assert_loading_msg_exist(capystOut: str, pattern=job_load_message_regex): + numLoadingMsg = 0 + lines = capystOut.split("\n") lines = [line for line in lines if len(line) > 0] assert len(lines) > 0 for line in lines: - if re.search(pattern, line) is not None: - num_loading_msg += 1 - assert num_loading_msg > 0 + if re.match(pattern, line) is not None: + numLoadingMsg += 1 + assert numLoadingMsg > 0 + + +def test_query_job_repr_html(penguins_df_default_index: bf.dataframe.DataFrame): + bf.options.display.progress_bar = "terminal" + penguins_df_default_index._block._expr.session.bqclient.default_query_job_config.use_query_cache = ( + False + ) + penguins_df_default_index.to_pandas() + query_job_repr = formatting_helpers.repr_query_job_html( + penguins_df_default_index.query_job + ).value + string_checks = [ + "Job Id", + "Destination Table", + "Slot Time", + "Bytes Processed", + "Cache hit", + ] + for string in string_checks: + assert string in query_job_repr def test_query_job_repr(penguins_df_default_index: bf.dataframe.DataFrame): - penguins_df_default_index.to_pandas(allow_large_results=True) + penguins_df_default_index._block._expr.session.bqclient.default_query_job_config.use_query_cache = ( + False + ) + penguins_df_default_index.to_pandas() query_job_repr = formatting_helpers.repr_query_job( penguins_df_default_index.query_job ) @@ -149,39 +135,11 @@ def test_query_job_repr(penguins_df_default_index: bf.dataframe.DataFrame): assert string in query_job_repr -def test_query_job_dry_run_dataframe(penguins_df_default_index: bf.dataframe.DataFrame): - with bf.option_context("display.repr_mode", "deferred"): - df_result = repr(penguins_df_default_index) - assert EXPECTED_DRY_RUN_MESSAGE in df_result - - -def test_query_job_dry_run_index(penguins_df_default_index: bf.dataframe.DataFrame): - with bf.option_context("display.repr_mode", "deferred"): - index_result = repr(penguins_df_default_index.index) - assert EXPECTED_DRY_RUN_MESSAGE in index_result - - -def test_query_job_dry_run_series(penguins_df_default_index: bf.dataframe.DataFrame): +def test_query_job_dry_run(penguins_df_default_index: bf.dataframe.DataFrame, capsys): with bf.option_context("display.repr_mode", "deferred"): - series_result = repr(penguins_df_default_index["body_mass_g"]) - assert EXPECTED_DRY_RUN_MESSAGE in series_result - - -def test_repr_anywidget_dataframe(penguins_df_default_index: bf.dataframe.DataFrame): - pytest.importorskip("anywidget") - with bf.option_context("display.render_mode", "anywidget"): - actual_repr = repr(penguins_df_default_index) - assert "species" in actual_repr - assert "island" in actual_repr - assert "[344 rows x 7 columns]" in actual_repr - - -def test_repr_anywidget_index(penguins_df_default_index: bf.dataframe.DataFrame): - pytest.importorskip("anywidget") - with bf.option_context("display.render_mode", "anywidget"): - index = penguins_df_default_index.index - actual_repr = repr(index) - # In non-interactive environments, should still get a useful summary. - assert "Index" in actual_repr - assert "0, 1, 2, 3, 4" in actual_repr - assert "dtype='Int64'" in actual_repr + repr(penguins_df_default_index) + repr(penguins_df_default_index["body_mass_g"]) + lines = capsys.readouterr().out.split("\n") + lines = filter(None, lines) + for line in lines: + assert "Computation deferred. Computation will process" in line diff --git a/tests/system/small/test_remote_function.py b/tests/system/small/test_remote_function.py new file mode 100644 index 00000000000..89907a53dfc --- /dev/null +++ b/tests/system/small/test_remote_function.py @@ -0,0 +1,646 @@ +# Copyright 2023 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from google.cloud import bigquery +import pandas as pd +import pytest + +import bigframes +from bigframes import remote_function as rf +from tests.system.utils import assert_pandas_df_equal_ignore_ordering + + +@pytest.fixture(scope="module") +def bq_cf_connection() -> str: + """Pre-created BQ connection to invoke cloud function for bigframes-dev + $ bq show --connection --location=us --project_id=bigframes-dev bigframes-rf-conn + """ + return "bigframes-rf-conn" + + +@pytest.fixture(scope="module") +def bq_cf_connection_location() -> str: + """Pre-created BQ connection to invoke cloud function for bigframes-dev + $ bq show --connection --location=us --project_id=bigframes-dev bigframes-rf-conn + """ + return "us.bigframes-rf-conn" + + +@pytest.fixture(scope="module") +def bq_cf_connection_location_mismatched() -> str: + """Pre-created BQ connection to invoke cloud function for bigframes-dev + $ bq show --connection --location=eu --project_id=bigframes-dev bigframes-rf-conn + """ + return "eu.bigframes-rf-conn" + + +@pytest.fixture(scope="module") +def bq_cf_connection_location_project() -> str: + """Pre-created BQ connection to invoke cloud function for bigframes-dev + $ bq show --connection --location=us --project_id=bigframes-dev bigframes-rf-conn + """ + return "bigframes-dev.us.bigframes-rf-conn" + + +@pytest.fixture(scope="module") +def bq_cf_connection_location_project_mismatched() -> str: + """Pre-created BQ connection to invoke cloud function for bigframes-dev + $ bq show --connection --location=eu --project_id=bigframes-metrics bigframes-rf-conn + """ + return "bigframes-metrics.eu.bigframes-rf-conn" + + +@pytest.fixture(scope="module") +def session_with_bq_connection_and_permanent_dataset( + bq_cf_connection, dataset_id_permanent +) -> bigframes.Session: + session = bigframes.Session( + bigframes.BigQueryOptions(bq_connection=bq_cf_connection) + ) + session._session_dataset = bigquery.Dataset(dataset_id_permanent) + return session + + +@pytest.mark.flaky(retries=2, delay=120) +def test_remote_function_direct_no_session_param( + bigquery_client, + bigqueryconnection_client, + cloudfunctions_client, + resourcemanager_client, + scalars_dfs, + dataset_id_permanent, + bq_cf_connection, +): + @rf.remote_function( + [int], + int, + bigquery_client=bigquery_client, + bigquery_connection_client=bigqueryconnection_client, + cloud_functions_client=cloudfunctions_client, + resource_manager_client=resourcemanager_client, + dataset=dataset_id_permanent, + bigquery_connection=bq_cf_connection, + # See e2e tests for tests that actually deploy the Cloud Function. + reuse=True, + ) + def square(x): + return x * x + + assert square.bigframes_remote_function + assert square.bigframes_cloud_function + + scalars_df, scalars_pandas_df = scalars_dfs + + bf_int64_col = scalars_df["int64_col"] + bf_int64_col_filter = bf_int64_col.notnull() + bf_int64_col_filtered = bf_int64_col[bf_int64_col_filter] + bf_result_col = bf_int64_col_filtered.apply(square) + bf_result = ( + bf_int64_col_filtered.to_frame().assign(result=bf_result_col).to_pandas() + ) + + pd_int64_col = scalars_pandas_df["int64_col"] + pd_int64_col_filter = pd_int64_col.notnull() + pd_int64_col_filtered = pd_int64_col[pd_int64_col_filter] + pd_result_col = pd_int64_col_filtered.apply(lambda x: x * x) + # TODO(shobs): Figure why pandas .apply() changes the dtype, i.e. + # pd_int64_col_filtered.dtype is Int64Dtype() + # pd_int64_col_filtered.apply(lambda x: x * x).dtype is int64. + # For this test let's force the pandas dtype to be same as bigframes' dtype. + pd_result_col = pd_result_col.astype(pd.Int64Dtype()) + pd_result = pd_int64_col_filtered.to_frame().assign(result=pd_result_col) + + assert_pandas_df_equal_ignore_ordering(bf_result, pd_result) + + +@pytest.mark.flaky(retries=2, delay=120) +def test_remote_function_direct_no_session_param_location_specified( + bigquery_client, + bigqueryconnection_client, + cloudfunctions_client, + resourcemanager_client, + scalars_dfs, + dataset_id_permanent, + bq_cf_connection_location, +): + @rf.remote_function( + [int], + int, + bigquery_client=bigquery_client, + bigquery_connection_client=bigqueryconnection_client, + cloud_functions_client=cloudfunctions_client, + resource_manager_client=resourcemanager_client, + dataset=dataset_id_permanent, + bigquery_connection=bq_cf_connection_location, + # See e2e tests for tests that actually deploy the Cloud Function. + reuse=True, + ) + def square(x): + return x * x + + scalars_df, scalars_pandas_df = scalars_dfs + + bf_int64_col = scalars_df["int64_col"] + bf_int64_col_filter = bf_int64_col.notnull() + bf_int64_col_filtered = bf_int64_col[bf_int64_col_filter] + bf_result_col = bf_int64_col_filtered.apply(square) + bf_result = ( + bf_int64_col_filtered.to_frame().assign(result=bf_result_col).to_pandas() + ) + + pd_int64_col = scalars_pandas_df["int64_col"] + pd_int64_col_filter = pd_int64_col.notnull() + pd_int64_col_filtered = pd_int64_col[pd_int64_col_filter] + pd_result_col = pd_int64_col_filtered.apply(lambda x: x * x) + # TODO(shobs): Figure why pandas .apply() changes the dtype, i.e. + # pd_int64_col_filtered.dtype is Int64Dtype() + # pd_int64_col_filtered.apply(lambda x: x * x).dtype is int64. + # For this test let's force the pandas dtype to be same as bigframes' dtype. + pd_result_col = pd_result_col.astype(pd.Int64Dtype()) + pd_result = pd_int64_col_filtered.to_frame().assign(result=pd_result_col) + + assert_pandas_df_equal_ignore_ordering(bf_result, pd_result) + + +@pytest.mark.flaky(retries=2, delay=120) +def test_remote_function_direct_no_session_param_location_mismatched( + bigquery_client, + bigqueryconnection_client, + cloudfunctions_client, + resourcemanager_client, + dataset_id_permanent, + bq_cf_connection_location_mismatched, +): + with pytest.raises(ValueError): + + @rf.remote_function( + [int], + int, + bigquery_client=bigquery_client, + bigquery_connection_client=bigqueryconnection_client, + cloud_functions_client=cloudfunctions_client, + resource_manager_client=resourcemanager_client, + dataset=dataset_id_permanent, + bigquery_connection=bq_cf_connection_location_mismatched, + # See e2e tests for tests that actually deploy the Cloud Function. + reuse=True, + ) + def square(x): + return x * x + + +@pytest.mark.flaky(retries=2, delay=120) +def test_remote_function_direct_no_session_param_location_project_specified( + bigquery_client, + bigqueryconnection_client, + cloudfunctions_client, + resourcemanager_client, + scalars_dfs, + dataset_id_permanent, + bq_cf_connection_location_project, +): + @rf.remote_function( + [int], + int, + bigquery_client=bigquery_client, + bigquery_connection_client=bigqueryconnection_client, + cloud_functions_client=cloudfunctions_client, + resource_manager_client=resourcemanager_client, + dataset=dataset_id_permanent, + bigquery_connection=bq_cf_connection_location_project, + # See e2e tests for tests that actually deploy the Cloud Function. + reuse=True, + ) + def square(x): + return x * x + + scalars_df, scalars_pandas_df = scalars_dfs + + bf_int64_col = scalars_df["int64_col"] + bf_int64_col_filter = bf_int64_col.notnull() + bf_int64_col_filtered = bf_int64_col[bf_int64_col_filter] + bf_result_col = bf_int64_col_filtered.apply(square) + bf_result = ( + bf_int64_col_filtered.to_frame().assign(result=bf_result_col).to_pandas() + ) + + pd_int64_col = scalars_pandas_df["int64_col"] + pd_int64_col_filter = pd_int64_col.notnull() + pd_int64_col_filtered = pd_int64_col[pd_int64_col_filter] + pd_result_col = pd_int64_col_filtered.apply(lambda x: x * x) + # TODO(shobs): Figure why pandas .apply() changes the dtype, i.e. + # pd_int64_col_filtered.dtype is Int64Dtype() + # pd_int64_col_filtered.apply(lambda x: x * x).dtype is int64. + # For this test let's force the pandas dtype to be same as bigframes' dtype. + pd_result_col = pd_result_col.astype(pd.Int64Dtype()) + pd_result = pd_int64_col_filtered.to_frame().assign(result=pd_result_col) + + assert_pandas_df_equal_ignore_ordering(bf_result, pd_result) + + +@pytest.mark.flaky(retries=2, delay=120) +def test_remote_function_direct_no_session_param_project_mismatched( + bigquery_client, + bigqueryconnection_client, + cloudfunctions_client, + resourcemanager_client, + dataset_id_permanent, + bq_cf_connection_location_project_mismatched, +): + with pytest.raises(ValueError): + + @rf.remote_function( + [int], + int, + bigquery_client=bigquery_client, + bigquery_connection_client=bigqueryconnection_client, + cloud_functions_client=cloudfunctions_client, + resource_manager_client=resourcemanager_client, + dataset=dataset_id_permanent, + bigquery_connection=bq_cf_connection_location_project_mismatched, + # See e2e tests for tests that actually deploy the Cloud Function. + reuse=True, + ) + def square(x): + return x * x + + +@pytest.mark.flaky(retries=2, delay=120) +def test_remote_function_direct_session_param( + session_with_bq_connection_and_permanent_dataset, scalars_dfs +): + @rf.remote_function( + [int], + int, + session=session_with_bq_connection_and_permanent_dataset, + ) + def square(x): + return x * x + + scalars_df, scalars_pandas_df = scalars_dfs + + bf_int64_col = scalars_df["int64_col"] + bf_int64_col_filter = bf_int64_col.notnull() + bf_int64_col_filtered = bf_int64_col[bf_int64_col_filter] + bf_result_col = bf_int64_col_filtered.apply(square) + bf_result = ( + bf_int64_col_filtered.to_frame().assign(result=bf_result_col).to_pandas() + ) + + pd_int64_col = scalars_pandas_df["int64_col"] + pd_int64_col_filter = pd_int64_col.notnull() + pd_int64_col_filtered = pd_int64_col[pd_int64_col_filter] + pd_result_col = pd_int64_col_filtered.apply(lambda x: x * x) + # TODO(shobs): Figure why pandas .apply() changes the dtype, i.e. + # pd_int64_col_filtered.dtype is Int64Dtype() + # pd_int64_col_filtered.apply(lambda x: x * x).dtype is int64. + # For this test let's force the pandas dtype to be same as bigframes' dtype. + pd_result_col = pd_result_col.astype(pd.Int64Dtype()) + pd_result = pd_int64_col_filtered.to_frame().assign(result=pd_result_col) + + assert_pandas_df_equal_ignore_ordering(bf_result, pd_result) + + +@pytest.mark.flaky(retries=2, delay=120) +def test_remote_function_via_session_default( + session_with_bq_connection_and_permanent_dataset, scalars_dfs +): + # Session has bigquery connection initialized via context. Without an + # explicit dataset the default dataset from the session would be used. + # Without an explicit bigquery connection, the one present in Session set + # through the explicit BigQueryOptions would be used. Without an explicit `reuse` + # the default behavior of reuse=True will take effect. Please note that the + # udf is same as the one used in other tests in this file so the underlying + # cloud function would be common and quickly reused. + @session_with_bq_connection_and_permanent_dataset.remote_function([int], int) + def square(x): + return x * x + + scalars_df, scalars_pandas_df = scalars_dfs + + bf_int64_col = scalars_df["int64_col"] + bf_int64_col_filter = bf_int64_col.notnull() + bf_int64_col_filtered = bf_int64_col[bf_int64_col_filter] + bf_result_col = bf_int64_col_filtered.apply(square) + bf_result = ( + bf_int64_col_filtered.to_frame().assign(result=bf_result_col).to_pandas() + ) + + pd_int64_col = scalars_pandas_df["int64_col"] + pd_int64_col_filter = pd_int64_col.notnull() + pd_int64_col_filtered = pd_int64_col[pd_int64_col_filter] + pd_result_col = pd_int64_col_filtered.apply(lambda x: x * x) + # TODO(shobs): Figure why pandas .apply() changes the dtype, i.e. + # pd_int64_col_filtered.dtype is Int64Dtype() + # pd_int64_col_filtered.apply(lambda x: x * x).dtype is int64. + # For this test let's force the pandas dtype to be same as bigframes' dtype. + pd_result_col = pd_result_col.astype(pd.Int64Dtype()) + pd_result = pd_int64_col_filtered.to_frame().assign(result=pd_result_col) + + assert_pandas_df_equal_ignore_ordering(bf_result, pd_result) + + +@pytest.mark.flaky(retries=2, delay=120) +def test_remote_function_via_session_with_overrides( + session, scalars_dfs, dataset_id_permanent, bq_cf_connection +): + @session.remote_function( + [int], + int, + dataset_id_permanent, + bq_cf_connection, + # See e2e tests for tests that actually deploy the Cloud Function. + reuse=True, + ) + def square(x): + return x * x + + scalars_df, scalars_pandas_df = scalars_dfs + + bf_int64_col = scalars_df["int64_col"] + bf_int64_col_filter = bf_int64_col.notnull() + bf_int64_col_filtered = bf_int64_col[bf_int64_col_filter] + bf_result_col = bf_int64_col_filtered.apply(square) + bf_result = ( + bf_int64_col_filtered.to_frame().assign(result=bf_result_col).to_pandas() + ) + + pd_int64_col = scalars_pandas_df["int64_col"] + pd_int64_col_filter = pd_int64_col.notnull() + pd_int64_col_filtered = pd_int64_col[pd_int64_col_filter] + pd_result_col = pd_int64_col_filtered.apply(lambda x: x * x) + # TODO(shobs): Figure why pandas .apply() changes the dtype, i.e. + # pd_int64_col_filtered.dtype is Int64Dtype() + # pd_int64_col_filtered.apply(lambda x: x * x).dtype is int64. + # For this test let's force the pandas dtype to be same as bigframes' dtype. + pd_result_col = pd_result_col.astype(pd.Int64Dtype()) + pd_result = pd_int64_col_filtered.to_frame().assign(result=pd_result_col) + + assert_pandas_df_equal_ignore_ordering(bf_result, pd_result) + + +@pytest.mark.flaky(retries=2, delay=120) +def test_dataframe_applymap( + session_with_bq_connection_and_permanent_dataset, scalars_dfs +): + def add_one(x): + return x + 1 + + remote_add_one = session_with_bq_connection_and_permanent_dataset.remote_function( + [int], int + )(add_one) + + scalars_df, scalars_pandas_df = scalars_dfs + int64_cols = ["int64_col", "int64_too"] + + bf_int64_df = scalars_df[int64_cols] + bf_int64_df_filtered = bf_int64_df.dropna() + bf_result = bf_int64_df_filtered.applymap(remote_add_one).to_pandas() + + pd_int64_df = scalars_pandas_df[int64_cols] + pd_int64_df_filtered = pd_int64_df.dropna() + pd_result = pd_int64_df_filtered.applymap(add_one) + # TODO(shobs): Figure why pandas .applymap() changes the dtype, i.e. + # pd_int64_df_filtered.dtype is Int64Dtype() + # pd_int64_df_filtered.applymap(lambda x: x).dtype is int64. + # For this test let's force the pandas dtype to be same as input. + for col in pd_result: + pd_result[col] = pd_result[col].astype(pd_int64_df_filtered[col].dtype) + + assert_pandas_df_equal_ignore_ordering(bf_result, pd_result) + + +@pytest.mark.flaky(retries=2, delay=120) +def test_dataframe_applymap_na_ignore( + session_with_bq_connection_and_permanent_dataset, scalars_dfs +): + def add_one(x): + return x + 1 + + remote_add_one = session_with_bq_connection_and_permanent_dataset.remote_function( + [int], int + )(add_one) + + scalars_df, scalars_pandas_df = scalars_dfs + int64_cols = ["int64_col", "int64_too"] + + bf_int64_df = scalars_df[int64_cols] + bf_result = bf_int64_df.applymap(remote_add_one, na_action="ignore").to_pandas() + + pd_int64_df = scalars_pandas_df[int64_cols] + pd_result = pd_int64_df.applymap(add_one, na_action="ignore") + # TODO(shobs): Figure why pandas .applymap() changes the dtype, i.e. + # pd_int64_df_filtered.dtype is Int64Dtype() + # pd_int64_df_filtered.applymap(lambda x: x).dtype is int64. + # For this test let's force the pandas dtype to be same as input. + for col in pd_result: + pd_result[col] = pd_result[col].astype(pd_int64_df[col].dtype) + + assert_pandas_df_equal_ignore_ordering(bf_result, pd_result) + + +@pytest.mark.flaky(retries=2, delay=120) +def test_series_map(session_with_bq_connection_and_permanent_dataset, scalars_dfs): + def add_one(x): + return x + 1 + + remote_add_one = session_with_bq_connection_and_permanent_dataset.remote_function( + [int], int + )(add_one) + + scalars_df, scalars_pandas_df = scalars_dfs + + bf_result = scalars_df.int64_too.map(remote_add_one).to_pandas() + pd_result = scalars_pandas_df.int64_too.map(add_one) + pd_result = pd_result.astype("Int64") # pandas type differences + + pd.testing.assert_series_equal( + bf_result, + pd_result, + ) + + +@pytest.mark.flaky(retries=2, delay=120) +def test_read_gbq_function_detects_invalid_function(bigquery_client, dataset_id): + dataset_ref = bigquery.DatasetReference.from_string(dataset_id) + with pytest.raises(ValueError) as e: + rf.read_gbq_function( + str(dataset_ref.routine("not_a_function")), + bigquery_client=bigquery_client, + ) + + assert "Unknown function" in str(e.value) + + +@pytest.mark.flaky(retries=2, delay=120) +def test_read_gbq_function_like_original( + bigquery_client, + bigqueryconnection_client, + cloudfunctions_client, + resourcemanager_client, + scalars_df_index, + dataset_id_permanent, + bq_cf_connection, +): + @rf.remote_function( + [int], + int, + bigquery_client=bigquery_client, + bigquery_connection_client=bigqueryconnection_client, + dataset=dataset_id_permanent, + cloud_functions_client=cloudfunctions_client, + resource_manager_client=resourcemanager_client, + bigquery_connection=bq_cf_connection, + reuse=True, + ) + def square1(x): + return x * x + + square2 = rf.read_gbq_function( + function_name=square1.bigframes_remote_function, + bigquery_client=bigquery_client, + ) + + # The newly-created function (square1) should have a remote function AND a + # cloud function associated with it, while the read-back version (square2) + # should only have a remote function. + assert square1.bigframes_remote_function + assert square1.bigframes_cloud_function + + assert square2.bigframes_remote_function + assert not hasattr(square2, "bigframes_cloud_function") + + # They should point to the same function. + assert square1.bigframes_remote_function == square2.bigframes_remote_function + + # The result of applying them should be the same. + int64_col = scalars_df_index["int64_col"] + int64_col_filter = int64_col.notnull() + int64_col_filtered = int64_col[int64_col_filter] + + s1_result_col = int64_col_filtered.apply(square1) + s1_result = int64_col_filtered.to_frame().assign(result=s1_result_col) + + s2_result_col = int64_col_filtered.apply(square2) + s2_result = int64_col_filtered.to_frame().assign(result=s2_result_col) + + assert_pandas_df_equal_ignore_ordering(s1_result.to_pandas(), s2_result.to_pandas()) + + +@pytest.mark.flaky(retries=2, delay=120) +def test_read_gbq_function_reads_udfs(bigquery_client, dataset_id): + dataset_ref = bigquery.DatasetReference.from_string(dataset_id) + arg = bigquery.RoutineArgument( + name="x", + data_type=bigquery.StandardSqlDataType(bigquery.StandardSqlTypeNames.INT64), + ) + sql_routine = bigquery.Routine( + dataset_ref.routine("square_sql"), + body="x * x", + arguments=[arg], + return_type=bigquery.StandardSqlDataType(bigquery.StandardSqlTypeNames.INT64), + type_=bigquery.RoutineType.SCALAR_FUNCTION, + ) + js_routine = bigquery.Routine( + dataset_ref.routine("square_js"), + body="return x * x", + language="JAVASCRIPT", + arguments=[arg], + return_type=bigquery.StandardSqlDataType(bigquery.StandardSqlTypeNames.INT64), + type_=bigquery.RoutineType.SCALAR_FUNCTION, + ) + + for routine in (sql_routine, js_routine): + # Create the routine in BigQuery and read it back using read_gbq_function. + bigquery_client.create_routine(routine, exists_ok=True) + square = rf.read_gbq_function( + str(routine.reference), bigquery_client=bigquery_client + ) + + # It should point to the named routine and yield the expected results. + assert square.bigframes_remote_function == str(routine.reference) + + src = {"x": [-5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5]} + + routine_ref_str = rf.routine_ref_to_string_for_query(routine.reference) + direct_sql = " UNION ALL ".join( + [f"SELECT {x} AS x, {routine_ref_str}({x}) AS y" for x in src["x"]] + ) + direct_df = bigquery_client.query(direct_sql).to_dataframe() + + indirect_df = bigframes.dataframe.DataFrame(src) + indirect_df = indirect_df.assign(y=indirect_df.x.apply(square)) + indirect_df = indirect_df.to_pandas() + + assert_pandas_df_equal_ignore_ordering(direct_df, indirect_df) + + +@pytest.mark.flaky(retries=2, delay=120) +def test_read_gbq_function_enforces_explicit_types(bigquery_client, dataset_id): + dataset_ref = bigquery.DatasetReference.from_string(dataset_id) + typed_arg = bigquery.RoutineArgument( + name="x", + data_type=bigquery.StandardSqlDataType(bigquery.StandardSqlTypeNames.INT64), + ) + untyped_arg = bigquery.RoutineArgument( + name="x", + kind="ANY_TYPE", # With this kind, data_type not required for SQL functions. + ) + + both_types_specified = bigquery.Routine( + dataset_ref.routine("both_types_specified"), + body="x * x", + arguments=[typed_arg], + return_type=bigquery.StandardSqlDataType(bigquery.StandardSqlTypeNames.INT64), + type_=bigquery.RoutineType.SCALAR_FUNCTION, + ) + only_return_type_specified = bigquery.Routine( + dataset_ref.routine("only_return_type_specified"), + body="x * x", + arguments=[untyped_arg], + return_type=bigquery.StandardSqlDataType(bigquery.StandardSqlTypeNames.INT64), + type_=bigquery.RoutineType.SCALAR_FUNCTION, + ) + only_arg_type_specified = bigquery.Routine( + dataset_ref.routine("only_arg_type_specified"), + body="x * x", + arguments=[typed_arg], + type_=bigquery.RoutineType.SCALAR_FUNCTION, + ) + neither_type_specified = bigquery.Routine( + dataset_ref.routine("neither_type_specified"), + body="x * x", + arguments=[untyped_arg], + type_=bigquery.RoutineType.SCALAR_FUNCTION, + ) + + bigquery_client.create_routine(both_types_specified, exists_ok=True) + bigquery_client.create_routine(only_return_type_specified, exists_ok=True) + bigquery_client.create_routine(only_arg_type_specified, exists_ok=True) + bigquery_client.create_routine(neither_type_specified, exists_ok=True) + + rf.read_gbq_function( + str(both_types_specified.reference), bigquery_client=bigquery_client + ) + rf.read_gbq_function( + str(only_return_type_specified.reference), bigquery_client=bigquery_client + ) + with pytest.raises(ValueError): + rf.read_gbq_function( + str(only_arg_type_specified.reference), bigquery_client=bigquery_client + ) + with pytest.raises(ValueError): + rf.read_gbq_function( + str(neither_type_specified.reference), bigquery_client=bigquery_client + ) diff --git a/tests/system/small/test_series.py b/tests/system/small/test_series.py index 2e80b75c0b4..6bb5c4755ca 100644 --- a/tests/system/small/test_series.py +++ b/tests/system/small/test_series.py @@ -12,32 +12,21 @@ # See the License for the specific language governing permissions and # limitations under the License. -import datetime as dt -import json import math import re import tempfile -import db_dtypes # type: ignore import geopandas as gpd # type: ignore -import google.api_core.exceptions import numpy import pandas as pd import pyarrow as pa # type: ignore import pytest -import shapely.geometry # type: ignore -from packaging.version import Version -import bigframes.dtypes as dtypes -import bigframes.features import bigframes.pandas import bigframes.series as series -import bigframes.testing -import bigframes.testing.utils -from bigframes.testing.utils import ( - assert_frame_equal, - assert_series_equal, - get_first_file_from_wildcard, +from tests.system.utils import ( + assert_pandas_df_equal_ignore_ordering, + assert_series_equal_ignoring_order, ) @@ -49,70 +38,7 @@ def test_series_construct_copy(scalars_dfs): pd_result = pd.Series( scalars_pandas_df["int64_col"], name="test_series", dtype="Float64" ) - bigframes.testing.utils.assert_series_equal(bf_result, pd_result) - - -def test_series_construct_nullable_ints(): - bf_result = series.Series( - [1, 3, bigframes.pandas.NA], index=[0, 4, bigframes.pandas.NA] - ).to_pandas() - - # TODO(b/340885567): fix type error - expected_index = pd.Index( # type: ignore - [0, 4, None], - dtype=pd.Int64Dtype(), - ) - expected = pd.Series([1, 3, pd.NA], dtype=pd.Int64Dtype(), index=expected_index) - - bigframes.testing.utils.assert_series_equal(bf_result, expected) - - -def test_series_construct_timestamps(): - datetimes = [ - dt.datetime(2020, 1, 20, 20, 20, 20, 20), - dt.datetime(2019, 1, 20, 20, 20, 20, 20), - None, - ] - bf_result = series.Series(datetimes).to_pandas() - pd_result = pd.Series(datetimes, dtype=pd.ArrowDtype(pa.timestamp("us"))) - - bigframes.testing.utils.assert_series_equal( - bf_result, pd_result, check_index_type=False - ) - - -def test_series_construct_copy_with_index(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - bf_result = series.Series( - scalars_df["int64_col"], - name="test_series", - dtype="Float64", - index=scalars_df["int64_too"], - ).to_pandas() - pd_result = pd.Series( - scalars_pandas_df["int64_col"], - name="test_series", - dtype="Float64", - index=scalars_pandas_df["int64_too"], - ) - bigframes.testing.utils.assert_series_equal(bf_result, pd_result) - - -def test_series_construct_copy_index(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - bf_result = series.Series( - scalars_df.index, - name="test_series", - dtype="Float64", - index=scalars_df["int64_too"], - ).to_pandas() - pd_result = pd.Series( - scalars_pandas_df.index, - name="test_series", - dtype="Float64", - index=scalars_pandas_df["int64_too"], - ) - bigframes.testing.utils.assert_series_equal(bf_result, pd_result) + pd.testing.assert_series_equal(bf_result, pd_result) def test_series_construct_pandas(scalars_dfs): @@ -124,7 +50,7 @@ def test_series_construct_pandas(scalars_dfs): scalars_pandas_df["int64_col"], name="test_series", dtype="Float64" ) assert bf_result.shape == pd_result.shape - bigframes.testing.utils.assert_series_equal(bf_result.to_pandas(), pd_result) + pd.testing.assert_series_equal(bf_result.to_pandas(), pd_result) def test_series_construct_from_list(): @@ -134,86 +60,7 @@ def test_series_construct_from_list(): # BigQuery DataFrame default indices use nullable Int64 always pd_result.index = pd_result.index.astype("Int64") - bigframes.testing.utils.assert_series_equal(bf_result, pd_result) - - -def test_series_construct_reindex(): - bf_result = series.Series( - series.Series({1: 10, 2: 30, 3: 30}), index=[3, 2], dtype="Int64" - ).to_pandas() - pd_result = pd.Series(pd.Series({1: 10, 2: 30, 3: 30}), index=[3, 2], dtype="Int64") - - # BigQuery DataFrame default indices use nullable Int64 always - pd_result.index = pd_result.index.astype("Int64") - bigframes.testing.utils.assert_series_equal(bf_result, pd_result) - - -def test_series_construct_from_list_w_index(): - bf_result = series.Series( - [1, 1, 2, 3, 5, 8, 13], index=[10, 20, 30, 40, 50, 60, 70], dtype="Int64" - ).to_pandas() - pd_result = pd.Series( - [1, 1, 2, 3, 5, 8, 13], index=[10, 20, 30, 40, 50, 60, 70], dtype="Int64" - ) - - # BigQuery DataFrame default indices use nullable Int64 always - pd_result.index = pd_result.index.astype("Int64") - - bigframes.testing.utils.assert_series_equal(bf_result, pd_result) - - -def test_series_construct_empty(session: bigframes.Session): - bf_series: series.Series = series.Series(session=session) - pd_series: pd.Series = pd.Series() - - bf_result = bf_series.empty - pd_result = pd_series.empty - - assert pd_result - assert bf_result == pd_result - - -def test_series_construct_scalar_no_index(): - bf_result = series.Series("hello world", dtype="string[pyarrow]").to_pandas() - pd_result = pd.Series("hello world", dtype="string[pyarrow]") - - # BigQuery DataFrame default indices use nullable Int64 always - pd_result.index = pd_result.index.astype("Int64") - - bigframes.testing.utils.assert_series_equal(bf_result, pd_result) - - -def test_series_construct_scalar_w_index(): - bf_result = series.Series( - "hello world", dtype="string[pyarrow]", index=[0, 2, 1] - ).to_pandas() - pd_result = pd.Series("hello world", dtype="string[pyarrow]", index=[0, 2, 1]) - - # BigQuery DataFrame default indices use nullable Int64 always - pd_result.index = pd_result.index.astype("Int64") - - bigframes.testing.utils.assert_series_equal(bf_result, pd_result) - - -def test_series_construct_nan(): - bf_result = series.Series(numpy.nan).to_pandas() - pd_result = pd.Series(numpy.nan) - - pd_result.index = pd_result.index.astype("Int64") - pd_result = pd_result.astype("Float64") - - bigframes.testing.utils.assert_series_equal(bf_result, pd_result) - - -def test_series_construct_scalar_w_bf_index(): - bf_result = series.Series( - "hello", index=bigframes.pandas.Index([1, 2, 3]) - ).to_pandas() - pd_result = pd.Series("hello", index=pd.Index([1, 2, 3], dtype="Int64")) - - pd_result = pd_result.astype("string[pyarrow]") - - bigframes.testing.utils.assert_series_equal(bf_result, pd_result) + pd.testing.assert_series_equal(bf_result, pd_result) def test_series_construct_from_list_escaped_strings(): @@ -229,198 +76,7 @@ def test_series_construct_from_list_escaped_strings(): # BigQuery DataFrame default indices use nullable Int64 always pd_result.index = pd_result.index.astype("Int64") - bigframes.testing.utils.assert_series_equal(bf_result.to_pandas(), pd_result) - - -def test_series_construct_geodata(): - pd_series = pd.Series( - [ - shapely.geometry.Point(1, 1), - shapely.geometry.Point(2, 2), - shapely.geometry.Point(3, 3), - ], - dtype=gpd.array.GeometryDtype(), - ) - - series = bigframes.pandas.Series(pd_series) - - bigframes.testing.utils.assert_series_equal( - pd_series, series.to_pandas(), check_index_type=False - ) - - -@pytest.mark.parametrize( - ("dtype"), - [ - pytest.param(pd.Int64Dtype(), id="int"), - pytest.param(pd.Float64Dtype(), id="float"), - pytest.param(pd.StringDtype(storage="pyarrow"), id="string"), - ], -) -def test_series_construct_w_dtype(dtype): - data = [1, 2, 3] - expected = pd.Series(data, dtype=dtype) - expected.index = expected.index.astype("Int64") - series = bigframes.pandas.Series(data, dtype=dtype) - bigframes.testing.utils.assert_series_equal(series.to_pandas(), expected) - - -def test_series_construct_w_dtype_for_struct(): - # The data shows the struct fields are disordered and correctly handled during - # construction. - data = [ - {"a": 1, "c": "pandas", "b": dt.datetime(2020, 1, 20, 20, 20, 20, 20)}, - {"a": 2, "c": "pandas", "b": dt.datetime(2019, 1, 20, 20, 20, 20, 20)}, - {"a": 1, "c": "numpy", "b": None}, - ] - dtype = pd.ArrowDtype( - pa.struct([("a", pa.int64()), ("c", pa.string()), ("b", pa.timestamp("us"))]) - ) - series = bigframes.pandas.Series(data, dtype=dtype) - expected = pd.Series(data, dtype=dtype) - expected.index = expected.index.astype("Int64") - bigframes.testing.utils.assert_series_equal(series.to_pandas(), expected) - - -def test_series_construct_w_dtype_for_array_string(): - data = [["1", "2", "3"], [], ["4", "5"]] - dtype = pd.ArrowDtype(pa.list_(pa.string())) - series = bigframes.pandas.Series(data, dtype=dtype) - expected = pd.Series(data, dtype=dtype) - expected.index = expected.index.astype("Int64") - - # Skip dtype check due to internal issue b/321013333. This issue causes array types - # to be converted to the `object` dtype when calling `to_pandas()`, resulting in - # a mismatch with the expected Pandas type. - if bigframes.features.PANDAS_VERSIONS.is_arrow_list_dtype_usable: - check_dtype = True - else: - check_dtype = False - - bigframes.testing.utils.assert_series_equal( - series.to_pandas(), expected, check_dtype=check_dtype - ) - - -def test_series_construct_w_dtype_for_array_struct(): - data = [[{"a": 1, "c": "aa"}, {"a": 2, "c": "bb"}], [], [{"a": 3, "c": "cc"}]] - dtype = pd.ArrowDtype(pa.list_(pa.struct([("a", pa.int64()), ("c", pa.string())]))) - series = bigframes.pandas.Series(data, dtype=dtype) - expected = pd.Series(data, dtype=dtype) - expected.index = expected.index.astype("Int64") - - # Skip dtype check due to internal issue b/321013333. This issue causes array types - # to be converted to the `object` dtype when calling `to_pandas()`, resulting in - # a mismatch with the expected Pandas type. - if bigframes.features.PANDAS_VERSIONS.is_arrow_list_dtype_usable: - check_dtype = True - else: - check_dtype = False - - bigframes.testing.utils.assert_series_equal( - series.to_pandas(), expected, check_dtype=check_dtype - ) - - -def test_series_construct_local_unordered_has_sequential_index(unordered_session): - series = bigframes.pandas.Series( - ["Sun", "Mon", "Tues", "Wed", "Thurs", "Fri", "Sat"], session=unordered_session - ) - expected: pd.Index = pd.Index([0, 1, 2, 3, 4, 5, 6], dtype=pd.Int64Dtype()) - bigframes.testing.utils.assert_index_equal(series.index.to_pandas(), expected) - - -@pytest.mark.parametrize( - ("json_type"), - [ - pytest.param(dtypes.JSON_DTYPE), - pytest.param("json"), - ], -) -def test_series_construct_w_json_dtype(json_type): - data = [ - "1", - '"str"', - "false", - '["a", {"b": 1}, null]', - None, - '{"a": {"b": [1, 2, 3], "c": true}}', - ] - s = bigframes.pandas.Series(data, dtype=json_type) - - assert s.dtype == dtypes.JSON_DTYPE - assert s[0] == "1" - assert s[1] == '"str"' - assert s[2] == "false" - assert s[3] == '["a",{"b":1},null]' - assert pd.isna(s[4]) - assert s[5] == '{"a":{"b":[1,2,3],"c":true}}' - - -def test_series_construct_w_nested_json_dtype(): - list_data = [ - [{"key": "1"}], - [{"key": None}], - [{"key": '["1","3","5"]'}], - [{"key": '{"a":1,"b":["x","y"],"c":{"x":[],"z":false}}'}], - ] - pa_array = pa.array(list_data, type=pa.list_(pa.struct([("key", pa.string())]))) - - db_json_arrow_dtype = db_dtypes.JSONArrowType() - s = bigframes.pandas.Series( - pd.arrays.ArrowExtensionArray(pa_array), # type: ignore - dtype=pd.ArrowDtype( - pa.list_(pa.struct([("key", db_json_arrow_dtype)])), - ), - ) - - assert s[0][0]["key"] == "1" - assert not s[1][0]["key"] - assert s[2][0]["key"] == '["1","3","5"]' - assert s[3][0]["key"] == '{"a":1,"b":["x","y"],"c":{"x":[],"z":false}}' - - # Test with pyarrow.json_(pa.string()) if available. - if hasattr(pa, "JsonType"): - pyarrow_json_dtype = pa.json_(pa.string()) - s2 = bigframes.pandas.Series( - pd.arrays.ArrowExtensionArray(pa_array), # type: ignore - dtype=pd.ArrowDtype( - pa.list_(pa.struct([("key", pyarrow_json_dtype)])), - ), - ) - - bigframes.testing.utils.assert_series_equal(s.to_pandas(), s2.to_pandas()) - - -def test_series_keys(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - bf_result = scalars_df["int64_col"].keys().to_pandas() - pd_result = scalars_pandas_df["int64_col"].keys() - bigframes.testing.utils.assert_index_equal(bf_result, pd_result) - - -@pytest.mark.parametrize( - ["data", "index"], - [ - (["a", "b", "c"], None), - ([1, 2, 3], ["a", "b", "c"]), - ([1, 2, None], ["a", "b", "c"]), - ([1, 2, 3], [pd.NA, "b", "c"]), - ([numpy.nan, 2, 3], ["a", "b", "c"]), - ], -) -def test_series_items(data, index): - bf_series = series.Series(data, index=index) - pd_series = pd.Series(data, index=index) - - for (bf_index, bf_value), (pd_index, pd_value) in zip( - bf_series.items(), pd_series.items() - ): - # TODO(jialuo): Remove the if conditions after b/373699458 is addressed. - if not pd.isna(bf_index) or not pd.isna(pd_index): - assert bf_index == pd_index - if not pd.isna(bf_value) or not pd.isna(pd_value): - assert bf_value == pd_value + pd.testing.assert_series_equal(bf_result.to_pandas(), pd_result) @pytest.mark.parametrize( @@ -428,14 +84,14 @@ def test_series_items(data, index): [ ("bool_col", pd.BooleanDtype()), # TODO(swast): Use a more efficient type. - ("bytes_col", pd.ArrowDtype(pa.binary())), + ("bytes_col", numpy.dtype("object")), ("date_col", pd.ArrowDtype(pa.date32())), ("datetime_col", pd.ArrowDtype(pa.timestamp("us"))), ("float64_col", pd.Float64Dtype()), ("geography_col", gpd.array.GeometryDtype()), ("int64_col", pd.Int64Dtype()), # TODO(swast): Use a more efficient type. - ("numeric_col", pd.ArrowDtype(pa.decimal128(38, 9))), + ("numeric_col", numpy.dtype("object")), ("int64_too", pd.Int64Dtype()), ("string_col", pd.StringDtype(storage="pyarrow")), ("time_col", pd.ArrowDtype(pa.time64("us"))), @@ -450,35 +106,12 @@ def test_get_column(scalars_dfs, col_name, expected_dtype): assert series_pandas.shape[0] == scalars_pandas_df.shape[0] -def test_get_column_w_json(json_df, json_pandas_df): - series = json_df["json_col"] - series_pandas = series.to_pandas() - assert series.dtype == pd.ArrowDtype(db_dtypes.JSONArrowType()) - assert series_pandas.shape[0] == json_pandas_df.shape[0] - - def test_series_get_column_default(scalars_dfs): scalars_df, _ = scalars_dfs result = scalars_df.get(123123123123123, "default_val") assert result == "default_val" -@pytest.mark.parametrize( - ("key",), - [ - ("hello",), - (2,), - ("int64_col",), - (None,), - ], -) -def test_series_contains(scalars_df_index, scalars_pandas_df_index, key): - bf_result = key in scalars_df_index["int64_col"] - pd_result = key in scalars_pandas_df_index["int64_col"] - - assert bf_result == pd_result - - def test_series_equals_identical(scalars_df_index, scalars_pandas_df_index): bf_result = scalars_df_index.int64_col.equals(scalars_df_index.int64_col) pd_result = scalars_pandas_df_index.int64_col.equals( @@ -541,23 +174,13 @@ def test_series___getitem__(scalars_dfs, index_col, key): scalars_pandas_df = scalars_pandas_df.set_index(index_col, drop=False) bf_result = scalars_df[col_name][key] pd_result = scalars_pandas_df[col_name][key] - bigframes.testing.utils.assert_series_equal(bf_result.to_pandas(), pd_result) + pd.testing.assert_series_equal(bf_result.to_pandas(), pd_result) -@pytest.mark.parametrize( - ("key",), - ( - (-2,), - (-1,), - (0,), - (1,), - ), -) -def test_series___getitem___with_int_key(scalars_dfs, key): - if pd.__version__.startswith("3."): - pytest.skip("pandas 3.0 dropped getitem with int key") +def test_series___getitem___with_int_key(scalars_dfs): col_name = "int64_too" index_col = "string_col" + key = 2 scalars_df, scalars_pandas_df = scalars_dfs scalars_df = scalars_df.set_index(index_col, drop=False) scalars_pandas_df = scalars_pandas_df.set_index(index_col, drop=False) @@ -575,69 +198,6 @@ def test_series___getitem___with_default_index(scalars_dfs): assert bf_result == pd_result -@pytest.mark.parametrize( - ("index_col", "key", "value"), - ( - ("int64_too", 2, "new_string_value"), - ("string_col", "Hello, World!", "updated_value"), - ("int64_too", 0, None), - ), -) -def test_series___setitem__(scalars_dfs, index_col, key, value): - col_name = "string_col" - scalars_df, scalars_pandas_df = scalars_dfs - scalars_df = scalars_df.set_index(index_col, drop=False) - scalars_pandas_df = scalars_pandas_df.set_index(index_col, drop=False) - - bf_series = scalars_df[col_name] - pd_series = scalars_pandas_df[col_name].copy() - - bf_series[key] = value - pd_series[key] = value - - bigframes.testing.utils.assert_series_equal(bf_series.to_pandas(), pd_series) - - -@pytest.mark.parametrize( - ("key", "value"), - ( - (0, 999), - (1, 888), - (0, None), - (-2345, 777), - ), -) -def test_series___setitem___with_int_key_numeric(scalars_dfs, key, value): - col_name = "int64_col" - index_col = "int64_too" - scalars_df, scalars_pandas_df = scalars_dfs - scalars_df = scalars_df.set_index(index_col, drop=False) - scalars_pandas_df = scalars_pandas_df.set_index(index_col, drop=False) - - bf_series = scalars_df[col_name] - pd_series = scalars_pandas_df[col_name].copy() - - bf_series[key] = value - pd_series[key] = value - - bigframes.testing.utils.assert_series_equal(bf_series.to_pandas(), pd_series) - - -def test_series___setitem___with_default_index(scalars_dfs): - col_name = "float64_col" - key = 2 - value = 123.456 - scalars_df, scalars_pandas_df = scalars_dfs - - bf_series = scalars_df[col_name] - pd_series = scalars_pandas_df[col_name].copy() - - bf_series[key] = value - pd_series[key] = value - - assert bf_series.to_pandas().iloc[key] == pd_series.iloc[key] - - @pytest.mark.parametrize( ("col_name",), ( @@ -650,52 +210,7 @@ def test_abs(scalars_dfs, col_name): bf_result = scalars_df[col_name].abs().to_pandas() pd_result = scalars_pandas_df[col_name].abs() - assert_series_equal(pd_result, bf_result) - - -@pytest.mark.parametrize( - ("col_name",), - ( - ("float64_col",), - ("int64_too",), - ), -) -def test_series_pos(scalars_dfs, col_name): - scalars_df, scalars_pandas_df = scalars_dfs - bf_result = (+scalars_df[col_name]).to_pandas() - pd_result = +scalars_pandas_df[col_name] - - assert_series_equal(pd_result, bf_result) - - -@pytest.mark.parametrize( - ("col_name",), - ( - ("float64_col",), - ("int64_too",), - ), -) -def test_series_neg(scalars_dfs, col_name): - scalars_df, scalars_pandas_df = scalars_dfs - bf_result = (-scalars_df[col_name]).to_pandas() - pd_result = -scalars_pandas_df[col_name] - - assert_series_equal(pd_result, bf_result) - - -@pytest.mark.parametrize( - ("col_name",), - ( - ("bool_col",), - ("int64_col",), - ), -) -def test_series_invert(scalars_dfs, col_name): - scalars_df, scalars_pandas_df = scalars_dfs - bf_result = (~scalars_df[col_name]).to_pandas() - pd_result = ~scalars_pandas_df[col_name] - - assert_series_equal(pd_result, bf_result) + assert_series_equal_ignoring_order(pd_result, bf_result) def test_fillna(scalars_dfs): @@ -703,7 +218,7 @@ def test_fillna(scalars_dfs): col_name = "string_col" bf_result = scalars_df[col_name].fillna("Missing").to_pandas() pd_result = scalars_pandas_df[col_name].fillna("Missing") - assert_series_equal( + assert_series_equal_ignoring_order( pd_result, bf_result, ) @@ -717,7 +232,7 @@ def test_series_replace_scalar_scalar(scalars_dfs): ) pd_result = scalars_pandas_df[col_name].replace("Hello, World!", "Howdy, Planet!") - bigframes.testing.utils.assert_series_equal( + pd.testing.assert_series_equal( pd_result, bf_result, ) @@ -733,7 +248,7 @@ def test_series_replace_regex_scalar(scalars_dfs): "^H.l", "Howdy, Planet!", regex=True ) - bigframes.testing.utils.assert_series_equal( + pd.testing.assert_series_equal( pd_result, bf_result, ) @@ -751,82 +266,35 @@ def test_series_replace_list_scalar(scalars_dfs): ["Hello, World!", "T"], "Howdy, Planet!" ) - bigframes.testing.utils.assert_series_equal( - pd_result, - bf_result, - ) - - -def test_series_replace_nans_with_pd_na(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - col_name = "string_col" - bf_result = scalars_df[col_name].replace({pd.NA: "UNKNOWN"}).to_pandas() - pd_result = scalars_pandas_df[col_name].replace({pd.NA: "UNKNOWN"}) - - bigframes.testing.utils.assert_series_equal( - pd_result, - bf_result, - ) - - -@pytest.mark.parametrize( - ("replacement_dict",), - ( - ({"Hello, World!": "Howdy, Planet!", "T": "R"},), - ({},), - ({0: "Hello, World!"},), - ), - ids=[ - "non-empty", - "empty", - "off-type", - ], -) -def test_series_replace_dict(scalars_dfs, replacement_dict): - scalars_df, scalars_pandas_df = scalars_dfs - col_name = "string_col" - bf_result = scalars_df[col_name].replace(replacement_dict).to_pandas() - pd_result = scalars_pandas_df[col_name].replace(replacement_dict) - - bigframes.testing.utils.assert_series_equal( + pd.testing.assert_series_equal( pd_result, bf_result, ) @pytest.mark.parametrize( - ("method",), + ("values",), ( - ("linear",), - ("values",), - ("slinear",), - ("nearest",), - ("zero",), - ("pad",), + ([None, 1, 2, None, None, 16, None],), + ([None, None, 3.6, None],), + ([403.2, None, 352.1, None, None, 111.9],), ), ) -def test_series_interpolate(method): - pytest.importorskip("scipy") - if method == "pad" and pd.__version__.startswith("3."): - pytest.skip("pandas 3.0 dropped method='pad'") - - values = [None, 1, 2, None, None, 16, None] - index = [-3.2, 11.4, 3.56, 4, 4.32, 5.55, 76.8] - pd_series = pd.Series(values, index) +def test_series_interpolate(values): + pd_series = pd.Series(values) bf_series = series.Series(pd_series) # Pandas can only interpolate on "float64" columns # https://github.com/pandas-dev/pandas/issues/40252 - pd_result = pd_series.astype("float64").interpolate(method=method) - bf_result = bf_series.interpolate(method=method).to_pandas() + pd_result = pd_series.astype("float64").interpolate() + bf_result = bf_series.interpolate().to_pandas() # pd uses non-null types, while bf uses nullable types - assert_series_equal( + pd.testing.assert_series_equal( pd_result, bf_result, check_index_type=False, check_dtype=False, - nulls_are_nan=True, ) @@ -844,37 +312,18 @@ def test_series_dropna(scalars_dfs, ignore_index): col_name = "string_col" bf_result = scalars_df[col_name].dropna(ignore_index=ignore_index).to_pandas() pd_result = scalars_pandas_df[col_name].dropna(ignore_index=ignore_index) - bigframes.testing.utils.assert_series_equal( - pd_result, bf_result, check_index_type=False - ) + pd.testing.assert_series_equal(pd_result, bf_result, check_index_type=False) -@pytest.mark.parametrize( - ("agg",), - ( - ("sum",), - ("size",), - ), -) -def test_series_agg_single_string(scalars_dfs, agg): +def test_series_agg_single_string(scalars_dfs): scalars_df, scalars_pandas_df = scalars_dfs - bf_result = scalars_df["int64_col"].agg(agg) - pd_result = scalars_pandas_df["int64_col"].agg(agg) + bf_result = scalars_df["int64_col"].agg("sum") + pd_result = scalars_pandas_df["int64_col"].agg("sum") assert math.isclose(pd_result, bf_result) def test_series_agg_multi_string(scalars_dfs): - aggregations = [ - "sum", - "mean", - "std", - "var", - "min", - "max", - "nunique", - "count", - "size", - ] + aggregations = ["sum", "mean", "std", "var", "min", "max", "nunique", "count"] scalars_df, scalars_pandas_df = scalars_dfs bf_result = scalars_df["int64_col"].agg(aggregations).to_pandas() pd_result = scalars_pandas_df["int64_col"].agg(aggregations) @@ -882,9 +331,7 @@ def test_series_agg_multi_string(scalars_dfs): # Pandas may produce narrower numeric types, but bigframes always produces Float64 pd_result = pd_result.astype("Float64") - bigframes.testing.utils.assert_series_equal( - pd_result, bf_result, check_index_type=False - ) + pd.testing.assert_series_equal(pd_result, bf_result, check_index_type=False) @pytest.mark.parametrize( @@ -1001,7 +448,7 @@ def test_mode_stat(scalars_df_index, scalars_pandas_df_index, col_name): ## Mode implicitly resets index, and bigframes default indices use nullable Int64 pd_result.index = pd_result.index.astype("Int64") - bigframes.testing.utils.assert_series_equal( + pd.testing.assert_series_equal( bf_result, pd_result, ) @@ -1044,8 +491,7 @@ def test_series_int_int_operators_scalar( bf_result = maybe_reversed_op(scalars_df["int64_col"], other_scalar).to_pandas() pd_result = maybe_reversed_op(scalars_pandas_df["int64_col"], other_scalar) - # don't check dtype, as pandas is a bit unstable here across versions, esp floordiv - assert_series_equal(pd_result, bf_result, check_dtype=False) + assert_series_equal_ignoring_order(pd_result, bf_result) def test_series_pow_scalar(scalars_dfs): @@ -1054,7 +500,7 @@ def test_series_pow_scalar(scalars_dfs): bf_result = (scalars_df["int64_col"] ** 2).to_pandas() pd_result = scalars_pandas_df["int64_col"] ** 2 - assert_series_equal(pd_result, bf_result) + assert_series_equal_ignoring_order(pd_result, bf_result) def test_series_pow_scalar_reverse(scalars_dfs): @@ -1063,7 +509,7 @@ def test_series_pow_scalar_reverse(scalars_dfs): bf_result = (0.8 ** scalars_df["int64_col"]).to_pandas() pd_result = 0.8 ** scalars_pandas_df["int64_col"] - assert_series_equal(pd_result, bf_result) + assert_series_equal_ignoring_order(pd_result, bf_result) @pytest.mark.parametrize( @@ -1071,12 +517,10 @@ def test_series_pow_scalar_reverse(scalars_dfs): [ (lambda x, y: x & y), (lambda x, y: x | y), - (lambda x, y: x ^ y), ], ids=[ "and", "or", - "xor", ], ) @pytest.mark.parametrize(("other_scalar"), [True, False, pd.NA]) @@ -1091,7 +535,7 @@ def test_series_bool_bool_operators_scalar( bf_result = maybe_reversed_op(scalars_df["bool_col"], other_scalar).to_pandas() pd_result = maybe_reversed_op(scalars_pandas_df["bool_col"], other_scalar) - assert_series_equal(pd_result.astype(pd.BooleanDtype()), bf_result) + assert_series_equal_ignoring_order(pd_result.astype(pd.BooleanDtype()), bf_result) @pytest.mark.parametrize( @@ -1109,7 +553,6 @@ def test_series_bool_bool_operators_scalar( (lambda x, y: x // y), (lambda x, y: x & y), (lambda x, y: x | y), - (lambda x, y: x ^ y), ], ids=[ "add", @@ -1124,14 +567,13 @@ def test_series_bool_bool_operators_scalar( "floordivide", "bitwise_and", "bitwise_or", - "bitwise_xor", ], ) def test_series_int_int_operators_series(scalars_dfs, operator): scalars_df, scalars_pandas_df = scalars_dfs bf_result = operator(scalars_df["int64_col"], scalars_df["int64_too"]).to_pandas() pd_result = operator(scalars_pandas_df["int64_col"], scalars_pandas_df["int64_too"]) - assert_series_equal(pd_result, bf_result) + assert_series_equal_ignoring_order(pd_result, bf_result) @pytest.mark.parametrize( @@ -1169,12 +611,12 @@ def test_mods(scalars_dfs, col_x, col_y, method): else: bf_result = bf_series.astype("Float64").to_pandas() pd_result = getattr(scalars_pandas_df[col_x], method)(scalars_pandas_df[col_y]) - bigframes.testing.utils.assert_series_equal(pd_result, bf_result) + pd.testing.assert_series_equal(pd_result, bf_result) # We work around a pandas bug that doesn't handle correlating nullable dtypes by doing this # manually with dumb self-correlation instead of parameterized as test_mods is above. -def test_series_corr(scalars_dfs): +def test_corr(scalars_dfs): scalars_df, scalars_pandas_df = scalars_dfs bf_result = scalars_df["int64_too"].corr(scalars_df["int64_too"]) pd_result = ( @@ -1185,26 +627,6 @@ def test_series_corr(scalars_dfs): assert math.isclose(pd_result, bf_result) -def test_series_autocorr(scalars_dfs): - # TODO: supply a reason why this isn't compatible with pandas 1.x - pytest.importorskip("pandas", minversion="2.0.0") - scalars_df, scalars_pandas_df = scalars_dfs - bf_result = scalars_df["float64_col"].autocorr(2) - pd_result = scalars_pandas_df["float64_col"].autocorr(2) - assert math.isclose(pd_result, bf_result) - - -def test_series_cov(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - bf_result = scalars_df["int64_too"].cov(scalars_df["int64_too"]) - pd_result = ( - scalars_pandas_df["int64_too"] - .astype("int64") - .cov(scalars_pandas_df["int64_too"].astype("int64")) - ) - assert math.isclose(pd_result, bf_result) - - @pytest.mark.parametrize( ("col_x",), [ @@ -1233,12 +655,19 @@ def test_divmods_series(scalars_dfs, col_x, col_y, method): scalars_pandas_df[col_y] ) # BigQuery's mod functions return NUMERIC values for non-INT64 inputs. - bigframes.testing.utils.assert_series_equal( - pd_div_result, bf_div_result.to_pandas(), check_dtype=False - ) - bigframes.testing.utils.assert_series_equal( - pd_mod_result, bf_mod_result.to_pandas(), check_dtype=False - ) + if bf_div_result.dtype == pd.Int64Dtype(): + pd.testing.assert_series_equal(pd_div_result, bf_div_result.to_pandas()) + else: + pd.testing.assert_series_equal( + pd_div_result, bf_div_result.astype("Float64").to_pandas() + ) + + if bf_mod_result.dtype == pd.Int64Dtype(): + pd.testing.assert_series_equal(pd_mod_result, bf_mod_result.to_pandas()) + else: + pd.testing.assert_series_equal( + pd_mod_result, bf_mod_result.astype("Float64").to_pandas() + ) @pytest.mark.parametrize( @@ -1268,20 +697,16 @@ def test_divmods_scalars(scalars_dfs, col_x, other, method): pd_div_result, pd_mod_result = getattr(scalars_pandas_df[col_x], method)(other) # BigQuery's mod functions return NUMERIC values for non-INT64 inputs. if bf_div_result.dtype == pd.Int64Dtype(): - bigframes.testing.utils.assert_series_equal( - pd_div_result, bf_div_result.to_pandas(), check_dtype=False - ) + pd.testing.assert_series_equal(pd_div_result, bf_div_result.to_pandas()) else: - bigframes.testing.utils.assert_series_equal( + pd.testing.assert_series_equal( pd_div_result, bf_div_result.astype("Float64").to_pandas() ) if bf_mod_result.dtype == pd.Int64Dtype(): - bigframes.testing.utils.assert_series_equal( - pd_div_result, bf_div_result.to_pandas(), check_dtype=False - ) + pd.testing.assert_series_equal(pd_mod_result, bf_mod_result.to_pandas()) else: - bigframes.testing.utils.assert_series_equal( + pd.testing.assert_series_equal( pd_mod_result, bf_mod_result.astype("Float64").to_pandas() ) @@ -1298,7 +723,7 @@ def test_series_add_scalar(scalars_dfs, other): bf_result = (scalars_df["float64_col"] + other).to_pandas() pd_result = scalars_pandas_df["float64_col"] + other - assert_series_equal(pd_result, bf_result) + assert_series_equal_ignoring_order(pd_result, bf_result) @pytest.mark.parametrize( @@ -1314,7 +739,7 @@ def test_series_add_bigframes_series(scalars_dfs, left_col, right_col): bf_result = (scalars_df[left_col] + scalars_df[right_col]).to_pandas() pd_result = scalars_pandas_df[left_col] + scalars_pandas_df[right_col] - assert_series_equal(pd_result, bf_result) + assert_series_equal_ignoring_order(pd_result, bf_result) @pytest.mark.parametrize( @@ -1336,7 +761,7 @@ def test_series_add_bigframes_series_nested( scalars_pandas_df[left_col] + scalars_pandas_df[right_col] ) + scalars_pandas_df[righter_col] - assert_series_equal(pd_result, bf_result) + assert_series_equal_ignoring_order(pd_result, bf_result) def test_series_add_different_table_default_index( @@ -1354,9 +779,7 @@ def test_series_add_different_table_default_index( + scalars_df_2_default_index["float64_col"].to_pandas() ) # TODO(swast): Can remove sort_index() when there's default ordering. - bigframes.testing.utils.assert_series_equal( - bf_result.sort_index(), pd_result.sort_index() - ) + pd.testing.assert_series_equal(bf_result.sort_index(), pd_result.sort_index()) def test_series_add_different_table_with_index( @@ -1367,7 +790,7 @@ def test_series_add_different_table_with_index( # When index values are unique, we can emulate with values from the same # DataFrame. pd_result = scalars_pandas_df["float64_col"] + scalars_pandas_df["int64_col"] - bigframes.testing.utils.assert_series_equal(bf_result.to_pandas(), pd_result) + pd.testing.assert_series_equal(bf_result.to_pandas(), pd_result) def test_reset_index_drop(scalars_df_index, scalars_pandas_df_index): @@ -1386,45 +809,7 @@ def test_reset_index_drop(scalars_df_index, scalars_pandas_df_index): # BigQuery DataFrames default indices use nullable Int64 always pd_result.index = pd_result.index.astype("Int64") - bigframes.testing.utils.assert_series_equal(bf_result.to_pandas(), pd_result) - - -def test_series_reset_index_allow_duplicates(scalars_df_index, scalars_pandas_df_index): - bf_series = scalars_df_index["int64_col"].copy() - bf_series.index.name = "int64_col" - df = bf_series.reset_index(allow_duplicates=True, drop=False) - assert df.index.name is None - - bf_result = df.to_pandas() - - pd_series = scalars_pandas_df_index["int64_col"].copy() - pd_series.index.name = "int64_col" - pd_result = pd_series.reset_index(allow_duplicates=True, drop=False) - - # Pandas uses int64 instead of Int64 (nullable) dtype. - pd_result.index = pd_result.index.astype(pd.Int64Dtype()) - - # reset_index should maintain the original ordering. - bigframes.testing.utils.assert_frame_equal(bf_result, pd_result) - - -def test_series_reset_index_duplicates_error(scalars_df_index): - scalars_df_index = scalars_df_index["int64_col"].copy() - scalars_df_index.index.name = "int64_col" - with pytest.raises(ValueError): - scalars_df_index.reset_index(allow_duplicates=False, drop=False) - - -def test_series_reset_index_inplace(scalars_df_index, scalars_pandas_df_index): - bf_result = scalars_df_index.sort_index(ascending=False)["float64_col"] - bf_result.reset_index(drop=True, inplace=True) - pd_result = scalars_pandas_df_index.sort_index(ascending=False)["float64_col"] - pd_result.reset_index(drop=True, inplace=True) - - # BigQuery DataFrames default indices use nullable Int64 always - pd_result.index = pd_result.index.astype("Int64") - - bigframes.testing.utils.assert_series_equal(bf_result.to_pandas(), pd_result) + pd.testing.assert_series_equal(bf_result.to_pandas(), pd_result) @pytest.mark.parametrize( @@ -1451,7 +836,18 @@ def test_reset_index_no_drop(scalars_df_index, scalars_pandas_df_index, name): # BigQuery DataFrames default indices use nullable Int64 always pd_result.index = pd_result.index.astype("Int64") - bigframes.testing.utils.assert_frame_equal(bf_result.to_pandas(), pd_result) + pd.testing.assert_frame_equal(bf_result.to_pandas(), pd_result) + + +def test_series_add_pandas_series_not_implemented(scalars_dfs): + scalars_df, _ = scalars_dfs + with pytest.raises(NotImplementedError): + ( + scalars_df["float64_col"] + + pd.Series( + [1, 1, 1, 1], + ) + ).to_pandas() def test_copy(scalars_df_index, scalars_pandas_df_index): @@ -1468,7 +864,7 @@ def test_copy(scalars_df_index, scalars_pandas_df_index): pd_series.loc[0] = 3.4 assert bf_copy.to_pandas().loc[0] != bf_series.to_pandas().loc[0] - bigframes.testing.utils.assert_series_equal(bf_copy.to_pandas(), pd_copy) + pd.testing.assert_series_equal(bf_copy.to_pandas(), pd_copy) def test_isin_raise_error(scalars_df_index, scalars_pandas_df_index): @@ -1509,119 +905,12 @@ def test_isin(scalars_dfs, col_name, test_set): scalars_df, scalars_pandas_df = scalars_dfs bf_result = scalars_df[col_name].isin(test_set).to_pandas() pd_result = scalars_pandas_df[col_name].isin(test_set).astype("boolean") - bigframes.testing.utils.assert_series_equal( - pd_result, - bf_result, - ) - - -@pytest.mark.parametrize( - ( - "col_name", - "test_set", - ), - [ - ( - "int64_col", - [314159, 2.0, 3, pd.NA], - ), - ( - "int64_col", - [2, 55555, 4], - ), - ( - "float64_col", - [-123.456, 1.25, pd.NA], - ), - ( - "int64_too", - [1, 2, pd.NA], - ), - ( - "string_col", - ["Hello, World!", "Hi", "こんにちは"], - ), - ], -) -def test_isin_bigframes_values(scalars_dfs, col_name, test_set, session): - scalars_df, scalars_pandas_df = scalars_dfs - bf_result = ( - scalars_df[col_name].isin(series.Series(test_set, session=session)).to_pandas() - ) - pd_result = scalars_pandas_df[col_name].isin(test_set).astype("boolean") - bigframes.testing.utils.assert_series_equal( + pd.testing.assert_series_equal( pd_result, bf_result, ) -def test_isin_bigframes_index(scalars_dfs, session): - scalars_df, scalars_pandas_df = scalars_dfs - bf_result = ( - scalars_df["string_col"] - .isin( - bigframes.pandas.Index( - ["Hello, World!", "Hi", "こんにちは"], session=session - ) - ) - .to_pandas() - ) - pd_result = ( - scalars_pandas_df["string_col"] - .isin(pd.Index(["Hello, World!", "Hi", "こんにちは"])) - .astype("boolean") - ) - bigframes.testing.utils.assert_series_equal( - pd_result, - bf_result, - ) - - -@pytest.mark.parametrize( - ( - "col_name", - "test_set", - ), - [ - ( - "int64_col", - [314159, 2.0, 3, pd.NA], - ), - ( - "int64_col", - [2, 55555, 4], - ), - ( - "float64_col", - [-123.456, 1.25, pd.NA], - ), - ( - "int64_too", - [1, 2, pd.NA], - ), - ( - "string_col", - ["Hello, World!", "Hi", "こんにちは"], - ), - ], -) -def test_isin_bigframes_values_as_predicate( - scalars_dfs_maybe_ordered, col_name, test_set -): - scalars_df, scalars_pandas_df = scalars_dfs_maybe_ordered - bf_predicate = scalars_df[col_name].isin( - series.Series(test_set, session=scalars_df._session) - ) - bf_result = scalars_df[bf_predicate].to_pandas() - pd_predicate = scalars_pandas_df[col_name].isin(test_set) - pd_result = scalars_pandas_df[pd_predicate] - - bigframes.testing.utils.assert_frame_equal( - pd_result.reset_index(), - bf_result.reset_index(), - ) - - def test_isnull(scalars_dfs): scalars_df, scalars_pandas_df = scalars_dfs col_name = "float64_col" @@ -1630,7 +919,7 @@ def test_isnull(scalars_dfs): # One of dtype mismatches to be documented. Here, the `bf_series.dtype` is `BooleanDtype` but # the `pd_series.dtype` is `bool`. - assert_series_equal(pd_series.astype(pd.BooleanDtype()), bf_series) + assert_series_equal_ignoring_order(pd_series.astype(pd.BooleanDtype()), bf_series) def test_notnull(scalars_dfs): @@ -1641,7 +930,7 @@ def test_notnull(scalars_dfs): # One of dtype mismatches to be documented. Here, the `bf_series.dtype` is `BooleanDtype` but # the `pd_series.dtype` is `bool`. - assert_series_equal(pd_series.astype(pd.BooleanDtype()), bf_series) + assert_series_equal_ignoring_order(pd_series.astype(pd.BooleanDtype()), bf_series) def test_round(scalars_dfs): @@ -1650,7 +939,7 @@ def test_round(scalars_dfs): bf_result = scalars_df[col_name].round().to_pandas() pd_result = scalars_pandas_df[col_name].round() - assert_series_equal(pd_result, bf_result) + assert_series_equal_ignoring_order(pd_result, bf_result) def test_eq_scalar(scalars_dfs): @@ -1659,7 +948,7 @@ def test_eq_scalar(scalars_dfs): bf_result = scalars_df[col_name].eq(0).to_pandas() pd_result = scalars_pandas_df[col_name].eq(0) - assert_series_equal(pd_result, bf_result) + assert_series_equal_ignoring_order(pd_result, bf_result) def test_eq_wider_type_scalar(scalars_dfs): @@ -1668,7 +957,7 @@ def test_eq_wider_type_scalar(scalars_dfs): bf_result = scalars_df[col_name].eq(1.0).to_pandas() pd_result = scalars_pandas_df[col_name].eq(1.0) - assert_series_equal(pd_result, bf_result) + assert_series_equal_ignoring_order(pd_result, bf_result) def test_ne_scalar(scalars_dfs): @@ -1677,7 +966,7 @@ def test_ne_scalar(scalars_dfs): bf_result = (scalars_df[col_name] != 0).to_pandas() pd_result = scalars_pandas_df[col_name] != 0 - assert_series_equal(pd_result, bf_result) + assert_series_equal_ignoring_order(pd_result, bf_result) def test_eq_int_scalar(scalars_dfs): @@ -1686,7 +975,7 @@ def test_eq_int_scalar(scalars_dfs): bf_result = (scalars_df[col_name] == 0).to_pandas() pd_result = scalars_pandas_df[col_name] == 0 - assert_series_equal(pd_result, bf_result) + assert_series_equal_ignoring_order(pd_result, bf_result) @pytest.mark.parametrize( @@ -1705,7 +994,7 @@ def test_eq_same_type_series(scalars_dfs, col_name): # One of dtype mismatches to be documented. Here, the `bf_series.dtype` is `BooleanDtype` but # the `pd_series.dtype` is `bool`. - assert_series_equal(pd_result.astype(pd.BooleanDtype()), bf_result) + assert_series_equal_ignoring_order(pd_result.astype(pd.BooleanDtype()), bf_result) def test_loc_setitem_cell(scalars_df_index, scalars_pandas_df_index): @@ -1717,10 +1006,10 @@ def test_loc_setitem_cell(scalars_df_index, scalars_pandas_df_index): pd_series.loc[2] = "This value isn't in the test data." bf_result = bf_series.to_pandas() pd_result = pd_series - bigframes.testing.utils.assert_series_equal(bf_result, pd_result) + pd.testing.assert_series_equal(bf_result, pd_result) # Per Copy-on-Write semantics, other references to the original DataFrame # should remain unchanged. - bigframes.testing.utils.assert_series_equal(bf_original.to_pandas(), pd_original) + pd.testing.assert_series_equal(bf_original.to_pandas(), pd_original) def test_at_setitem_row_label_scalar(scalars_dfs): @@ -1731,7 +1020,7 @@ def test_at_setitem_row_label_scalar(scalars_dfs): pd_series.at[1] = 1000 bf_result = bf_series.to_pandas() pd_result = pd_series.astype("Int64") - bigframes.testing.utils.assert_series_equal(bf_result, pd_result) + pd.testing.assert_series_equal(bf_result, pd_result) def test_ne_obj_series(scalars_dfs): @@ -1742,7 +1031,7 @@ def test_ne_obj_series(scalars_dfs): # One of dtype mismatches to be documented. Here, the `bf_series.dtype` is `BooleanDtype` but # the `pd_series.dtype` is `bool`. - assert_series_equal(pd_result.astype(pd.BooleanDtype()), bf_result) + assert_series_equal_ignoring_order(pd_result.astype(pd.BooleanDtype()), bf_result) def test_indexing_using_unselected_series(scalars_dfs): @@ -1751,7 +1040,7 @@ def test_indexing_using_unselected_series(scalars_dfs): bf_result = scalars_df[col_name][scalars_df["int64_too"].eq(0)].to_pandas() pd_result = scalars_pandas_df[col_name][scalars_pandas_df["int64_too"].eq(0)] - assert_series_equal( + assert_series_equal_ignoring_order( pd_result, bf_result, ) @@ -1767,29 +1056,12 @@ def test_indexing_using_selected_series(scalars_dfs): scalars_pandas_df["string_col"].eq("Hello, World!") ] - assert_series_equal( + assert_series_equal_ignoring_order( pd_result, bf_result, ) -@pytest.mark.parametrize( - ("indices"), - [ - ([1, 3, 5]), - ([5, -3, -5, -6]), - ([-2, -4, -6]), - ], -) -def test_take(scalars_dfs, indices): - scalars_df, scalars_pandas_df = scalars_dfs - - bf_result = scalars_df.take(indices).to_pandas() - pd_result = scalars_pandas_df.take(indices) - - assert_frame_equal(bf_result, pd_result) - - def test_nested_filter(scalars_dfs): scalars_df, scalars_pandas_df = scalars_dfs string_col = scalars_df["string_col"] @@ -1806,7 +1078,7 @@ def test_nested_filter(scalars_dfs): ) # Convert from nullable bool to nonnullable bool usable as indexer pd_result = pd_string_col[pd_int64_too == 0][~pd_bool_col] - assert_series_equal( + assert_series_equal_ignoring_order( pd_result, bf_result, ) @@ -1825,7 +1097,7 @@ def test_binop_repeated_application_does_row_identity_joins(scalars_dfs): bf_result = bf_series.to_pandas() pd_result = pd_series - assert_series_equal( + assert_series_equal_ignoring_order( bf_result, pd_result, ) @@ -1847,9 +1119,10 @@ def test_binop_opposite_filters(scalars_dfs): pd_bool_col = scalars_pandas_df["bool_col"] pd_result = pd_int64_col1[pd_bool_col] + pd_int64_col2[pd_bool_col.__invert__()] - # Passes with ignore_order=False only with some dependency sets - # TODO: Determine desired behavior and make test more strict - assert_series_equal(bf_result, pd_result, ignore_order=True) + assert_series_equal_ignoring_order( + bf_result, + pd_result, + ) def test_binop_left_filtered(scalars_dfs): @@ -1864,9 +1137,10 @@ def test_binop_left_filtered(scalars_dfs): pd_bool_col = scalars_pandas_df["bool_col"] pd_result = pd_int64_col[pd_bool_col] + pd_float64_col - # Passes with ignore_order=False only with some dependency sets - # TODO: Determine desired behavior and make test more strict - assert_series_equal(bf_result, pd_result, ignore_order=True) + assert_series_equal_ignoring_order( + bf_result, + pd_result, + ) def test_binop_right_filtered(scalars_dfs): @@ -1881,94 +1155,12 @@ def test_binop_right_filtered(scalars_dfs): pd_bool_col = scalars_pandas_df["bool_col"] pd_result = pd_float64_col + pd_int64_col[pd_bool_col] - assert_series_equal( - bf_result, - pd_result, - ) - - -@pytest.mark.parametrize( - ("other",), - [ - ([-1.4, 2.3, None],), - (pd.Index([-1.4, 2.3, None]),), - (pd.Series([-1.4, 2.3, None], index=[44, 2, 1]),), - ], -) -def test_series_binop_w_other_types(scalars_dfs, other): - # TODO: supply a reason why this isn't compatible with pandas 1.x - pytest.importorskip("pandas", minversion="2.0.0") - scalars_df, scalars_pandas_df = scalars_dfs - - bf_result = (scalars_df["int64_col"].head(3) + other).to_pandas() - pd_result = scalars_pandas_df["int64_col"].head(3) + other - - if isinstance(other, pd.Series): - # pandas 3.0 preserves series name, bigframe, earlier pandas do not - pd_result.index.name = bf_result.index.name - - assert_series_equal( - bf_result, - pd_result, - ) - - -@pytest.mark.parametrize( - ("other",), - [ - ([-1.4, 2.3, None],), - (pd.Index([-1.4, 2.3, None]),), - (pd.Series([-1.4, 2.3, None], index=[44, 2, 1]),), - ], -) -def test_series_reverse_binop_w_other_types(scalars_dfs, other): - # TODO: supply a reason why this isn't compatible with pandas 1.x - pytest.importorskip("pandas", minversion="2.0.0") - scalars_df, scalars_pandas_df = scalars_dfs - - bf_result = (other + scalars_df["int64_col"].head(3)).to_pandas() - pd_result = other + scalars_pandas_df["int64_col"].head(3) - - assert_series_equal( - bf_result, - pd_result, - ) - - -def test_series_combine_first(scalars_dfs): - # TODO: supply a reason why this isn't compatible with pandas 1.x - pytest.importorskip("pandas", minversion="2.0.0") - scalars_df, scalars_pandas_df = scalars_dfs - int64_col = scalars_df["int64_col"].head(7) - float64_col = scalars_df["float64_col"].tail(7) - bf_result = int64_col.combine_first(float64_col).to_pandas() - - pd_int64_col = scalars_pandas_df["int64_col"].head(7) - pd_float64_col = scalars_pandas_df["float64_col"].tail(7) - pd_result = pd_int64_col.combine_first(pd_float64_col) - - assert_series_equal( + assert_series_equal_ignoring_order( bf_result, pd_result, ) -def test_series_update(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - int64_col = scalars_df["int64_col"].head(7) - float64_col = scalars_df["float64_col"].tail(7).copy() - float64_col.update(int64_col) - - pd_int64_col = scalars_pandas_df["int64_col"].head(7) - pd_float64_col = scalars_pandas_df["float64_col"].tail(7).copy() - pd_float64_col.update(pd_int64_col) - - assert_series_equal( - float64_col.to_pandas(), - pd_float64_col, - ) - - def test_mean(scalars_dfs): scalars_df, scalars_pandas_df = scalars_dfs col_name = "int64_col" @@ -1977,68 +1169,25 @@ def test_mean(scalars_dfs): assert math.isclose(pd_result, bf_result) -@pytest.mark.parametrize( - ("col_name"), - [ - "int64_col", - # Non-numeric column - "bytes_col", - "date_col", - "datetime_col", - "time_col", - "timestamp_col", - "string_col", - ], -) -def test_median(scalars_dfs, col_name): +def test_median(scalars_dfs): scalars_df, scalars_pandas_df = scalars_dfs - bf_result = scalars_df[col_name].median(exact=False) + col_name = "int64_col" + bf_result = scalars_df[col_name].median() pd_max = scalars_pandas_df[col_name].max() pd_min = scalars_pandas_df[col_name].min() # Median is approximate, so just check for plausibility. assert pd_min < bf_result < pd_max -def test_median_exact(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - col_name = "int64_col" - bf_result = scalars_df[col_name].median() - pd_result = scalars_pandas_df[col_name].median() - assert math.isclose(pd_result, bf_result) - - -def test_series_quantile(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - col_name = "int64_col" - bf_series = scalars_df[col_name] - pd_series = scalars_pandas_df[col_name] - - pd_result = pd_series.quantile([0.0, 0.4, 0.6, 1.0]) - bf_result = bf_series.quantile([0.0, 0.4, 0.6, 1.0]) - bigframes.testing.utils.assert_series_equal( - pd_result, bf_result.to_pandas(), check_dtype=False, check_index_type=False - ) - - -def test_numeric_literal(scalars_dfs): - scalars_df, _ = scalars_dfs - col_name = "numeric_col" - assert scalars_df[col_name].dtype == pd.ArrowDtype(pa.decimal128(38, 9)) - bf_result = scalars_df[col_name] + 42 - assert bf_result.size == scalars_df[col_name].size - assert bf_result.dtype == pd.ArrowDtype(pa.decimal128(38, 9)) - - -def test_series_small_repr(scalars_dfs): +def test_repr(scalars_dfs): scalars_df, scalars_pandas_df = scalars_dfs + if scalars_pandas_df.index.name != "rowindex": + pytest.skip("Require index & ordering for consistent repr.") col_name = "int64_col" bf_series = scalars_df[col_name] pd_series = scalars_pandas_df[col_name] - with bigframes.pandas.option_context("display.repr_mode", "head"): - assert repr(bf_series) == pd_series.to_string( - length=False, dtype=True, name=True - ) + assert repr(bf_series) == repr(pd_series) def test_sum(scalars_dfs): @@ -2064,7 +1213,7 @@ def test_cumprod(scalars_dfs): col_name = "float64_col" bf_result = scalars_df[col_name].cumprod() pd_result = scalars_pandas_df[col_name].cumprod() - bigframes.testing.utils.assert_series_equal( + pd.testing.assert_series_equal( pd_result, bf_result.to_pandas(), ) @@ -2105,19 +1254,13 @@ def test_any(scalars_dfs): def test_groupby_sum(scalars_dfs): scalars_df, scalars_pandas_df = scalars_dfs col_name = "int64_too" - bf_series = ( - scalars_df[col_name] - .groupby([scalars_df["bool_col"], ~scalars_df["bool_col"]]) - .sum() - ) + bf_series = scalars_df[col_name].groupby(scalars_df["string_col"]).sum() pd_series = ( - scalars_pandas_df[col_name] - .groupby([scalars_pandas_df["bool_col"], ~scalars_pandas_df["bool_col"]]) - .sum() + scalars_pandas_df[col_name].groupby(scalars_pandas_df["string_col"]).sum() ) # TODO(swast): Update groupby to use index based on group by key(s). bf_result = bf_series.to_pandas() - assert_series_equal( + assert_series_equal_ignoring_order( pd_series, bf_result, check_exact=False, @@ -2135,7 +1278,7 @@ def test_groupby_std(scalars_dfs): .astype(pd.Float64Dtype()) ) bf_result = bf_series.to_pandas() - assert_series_equal( + assert_series_equal_ignoring_order( pd_series, bf_result, check_exact=False, @@ -2150,7 +1293,7 @@ def test_groupby_var(scalars_dfs): scalars_pandas_df[col_name].groupby(scalars_pandas_df["string_col"]).var() ) bf_result = bf_series.to_pandas() - assert_series_equal( + assert_series_equal_ignoring_order( pd_series, bf_result, check_exact=False, @@ -2161,11 +1304,13 @@ def test_groupby_level_sum(scalars_dfs): # TODO(tbergeron): Use a non-unique index once that becomes possible in tests scalars_df, scalars_pandas_df = scalars_dfs col_name = "int64_too" + if scalars_pandas_df.index.name != "rowindex": + pytest.skip("Require index for groupby level.") bf_series = scalars_df[col_name].groupby(level=0).sum() pd_series = scalars_pandas_df[col_name].groupby(level=0).sum() # TODO(swast): Update groupby to use index based on group by key(s). - bigframes.testing.utils.assert_series_equal( + pd.testing.assert_series_equal( pd_series.sort_index(), bf_series.to_pandas().sort_index(), ) @@ -2175,11 +1320,13 @@ def test_groupby_level_list_sum(scalars_dfs): # TODO(tbergeron): Use a non-unique index once that becomes possible in tests scalars_df, scalars_pandas_df = scalars_dfs col_name = "int64_too" + if scalars_pandas_df.index.name != "rowindex": + pytest.skip("Require index for groupby level.") bf_series = scalars_df[col_name].groupby(level=["rowindex"]).sum() pd_series = scalars_pandas_df[col_name].groupby(level=["rowindex"]).sum() # TODO(swast): Update groupby to use index based on group by key(s). - bigframes.testing.utils.assert_series_equal( + pd.testing.assert_series_equal( pd_series.sort_index(), bf_series.to_pandas().sort_index(), ) @@ -2198,37 +1345,17 @@ def test_groupby_mean(scalars_dfs): ) # TODO(swast): Update groupby to use index based on group by key(s). bf_result = bf_series.to_pandas() - assert_series_equal( + assert_series_equal_ignoring_order( pd_series, bf_result, ) -def test_groupby_median_exact(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - col_name = "int64_too" - bf_result = ( - scalars_df[col_name].groupby(scalars_df["string_col"], dropna=False).median() - ) - pd_result = ( - scalars_pandas_df[col_name] - .groupby(scalars_pandas_df["string_col"], dropna=False) - .median() - ) - - assert_series_equal( - pd_result, - bf_result.to_pandas(), - ) - - -def test_groupby_median_inexact(scalars_dfs): +def test_groupby_median(scalars_dfs): scalars_df, scalars_pandas_df = scalars_dfs col_name = "int64_too" bf_series = ( - scalars_df[col_name] - .groupby(scalars_df["string_col"], dropna=False) - .median(exact=False) + scalars_df[col_name].groupby(scalars_df["string_col"], dropna=False).median() ) pd_max = ( scalars_pandas_df[col_name] @@ -2253,10 +1380,10 @@ def test_groupby_prod(scalars_dfs): bf_series = scalars_df[col_name].groupby(scalars_df["int64_col"]).prod() pd_series = ( scalars_pandas_df[col_name].groupby(scalars_pandas_df["int64_col"]).prod() - ).astype(pd.Float64Dtype()) + ) # TODO(swast): Update groupby to use index based on group by key(s). bf_result = bf_series.to_pandas() - assert_series_equal( + assert_series_equal_ignoring_order( pd_series, bf_result, ) @@ -2269,8 +1396,7 @@ def test_groupby_prod(scalars_dfs): (lambda x: x.cumcount()), (lambda x: x.cummin()), (lambda x: x.cummax()), - # Pandas 2.2 casts to cumprod to float. - (lambda x: x.cumprod().astype("Float64")), + (lambda x: x.cumprod()), (lambda x: x.diff()), (lambda x: x.shift(2)), (lambda x: x.shift(-2)), @@ -2294,25 +1420,18 @@ def test_groupby_window_ops(scalars_df_index, scalars_pandas_df_index, operator) ).to_pandas() pd_series = operator( scalars_pandas_df_index[col_name].groupby(scalars_pandas_df_index[group_key]) - ).astype(bf_series.dtype) - - bigframes.testing.utils.assert_series_equal( + ).astype(pd.Int64Dtype()) + pd.testing.assert_series_equal( pd_series, bf_series, ) -@pytest.mark.parametrize( - ("label", "col_name"), - [ - (0, "bool_col"), - (1, "int64_col"), - ], -) -def test_drop_label(scalars_df_index, scalars_pandas_df_index, label, col_name): - bf_series = scalars_df_index[col_name].drop(label).to_pandas() - pd_series = scalars_pandas_df_index[col_name].drop(label) - bigframes.testing.utils.assert_series_equal( +def test_drop_label(scalars_df_index, scalars_pandas_df_index): + col_name = "int64_col" + bf_series = scalars_df_index[col_name].drop(1).to_pandas() + pd_series = scalars_pandas_df_index[col_name].drop(1) + pd.testing.assert_series_equal( pd_series, bf_series, ) @@ -2322,7 +1441,7 @@ def test_drop_label_list(scalars_df_index, scalars_pandas_df_index): col_name = "int64_col" bf_series = scalars_df_index[col_name].drop([1, 3]).to_pandas() pd_series = scalars_pandas_df_index[col_name].drop([1, 3]) - bigframes.testing.utils.assert_series_equal( + pd.testing.assert_series_equal( pd_series, bf_series, ) @@ -2346,7 +1465,7 @@ def test_drop_label_list(scalars_df_index, scalars_pandas_df_index): def test_drop_duplicates(scalars_df_index, scalars_pandas_df_index, keep, col_name): bf_series = scalars_df_index[col_name].drop_duplicates(keep=keep).to_pandas() pd_series = scalars_pandas_df_index[col_name].drop_duplicates(keep=keep) - bigframes.testing.utils.assert_series_equal( + pd.testing.assert_series_equal( pd_series, bf_series, ) @@ -2360,7 +1479,7 @@ def test_drop_duplicates(scalars_df_index, scalars_pandas_df_index, keep, col_na ], ) def test_unique(scalars_df_index, scalars_pandas_df_index, col_name): - bf_uniq = scalars_df_index[col_name].unique().to_numpy(na_value=None) + bf_uniq = scalars_df_index[col_name].unique().to_numpy() pd_uniq = scalars_pandas_df_index[col_name].unique() numpy.array_equal(pd_uniq, bf_uniq) @@ -2383,7 +1502,7 @@ def test_unique(scalars_df_index, scalars_pandas_df_index, col_name): def test_duplicated(scalars_df_index, scalars_pandas_df_index, keep, col_name): bf_series = scalars_df_index[col_name].duplicated(keep=keep).to_pandas() pd_series = scalars_pandas_df_index[col_name].duplicated(keep=keep) - bigframes.testing.utils.assert_series_equal(pd_series, bf_series, check_dtype=False) + pd.testing.assert_series_equal(pd_series, bf_series, check_dtype=False) def test_shape(scalars_dfs): @@ -2413,24 +1532,6 @@ def test_size(scalars_dfs): assert pd_result == bf_result -def test_series_hasnans_true(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - - bf_result = scalars_df["string_col"].hasnans - pd_result = scalars_pandas_df["string_col"].hasnans - - assert pd_result == bf_result - - -def test_series_hasnans_false(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - - bf_result = scalars_df["string_col"].dropna().hasnans - pd_result = scalars_pandas_df["string_col"].dropna().hasnans - - assert pd_result == bf_result - - def test_empty_false(scalars_dfs): scalars_df, scalars_pandas_df = scalars_dfs @@ -2454,19 +1555,15 @@ def test_empty_true_row_filter(scalars_dfs): assert pd_result == bf_result -def test_series_names(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - - bf_result = scalars_df["string_col"].copy() - bf_result.index.name = "new index name" - bf_result.name = "new series name" +def test_empty_true_memtable(session: bigframes.Session): + bf_series: series.Series = series.Series(session=session) + pd_series: pd.Series = pd.Series() - pd_result = scalars_pandas_df["string_col"].copy() - pd_result.index.name = "new index name" - pd_result.name = "new series name" + bf_result = bf_series.empty + pd_result = pd_series.empty - assert pd_result.name == bf_result.name - assert pd_result.index.name == bf_result.index.name + assert pd_result + assert bf_result == pd_result def test_dtype(scalars_dfs): @@ -2490,10 +1587,13 @@ def test_dtypes(scalars_dfs): def test_head(scalars_dfs): scalars_df, scalars_pandas_df = scalars_dfs + if scalars_df.index.name is None: + pytest.skip("Require explicit index for offset ops.") + bf_result = scalars_df["string_col"].head(2).to_pandas() pd_result = scalars_pandas_df["string_col"].head(2) - assert_series_equal( + assert_series_equal_ignoring_order( pd_result, bf_result, ) @@ -2502,10 +1602,13 @@ def test_head(scalars_dfs): def test_tail(scalars_dfs): scalars_df, scalars_pandas_df = scalars_dfs + if scalars_df.index.name is None: + pytest.skip("Require explicit index for offset ops.") + bf_result = scalars_df["string_col"].tail(2).to_pandas() pd_result = scalars_pandas_df["string_col"].tail(2) - assert_series_equal( + assert_series_equal_ignoring_order( pd_result, bf_result, ) @@ -2514,10 +1617,13 @@ def test_tail(scalars_dfs): def test_head_then_scalar_operation(scalars_dfs): scalars_df, scalars_pandas_df = scalars_dfs + if scalars_df.index.name is None: + pytest.skip("Require explicit index for offset ops.") + bf_result = (scalars_df["float64_col"].head(1) + 4).to_pandas() pd_result = scalars_pandas_df["float64_col"].head(1) + 4 - bigframes.testing.utils.assert_series_equal( + pd.testing.assert_series_equal( bf_result, pd_result, ) @@ -2526,6 +1632,9 @@ def test_head_then_scalar_operation(scalars_dfs): def test_head_then_series_operation(scalars_dfs): scalars_df, scalars_pandas_df = scalars_dfs + if scalars_df.index.name is None: + pytest.skip("Require explicit index for offset ops.") + bf_result = ( scalars_df["float64_col"].head(4) + scalars_df["float64_col"].head(2) ).to_pandas() @@ -2533,107 +1642,19 @@ def test_head_then_series_operation(scalars_dfs): "float64_col" ].head(2) - bigframes.testing.utils.assert_series_equal( + pd.testing.assert_series_equal( bf_result, pd_result, ) -def test_series_peek(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - - peek_result = scalars_df["float64_col"].peek(n=3, force=False) - - bigframes.testing.utils.assert_series_equal( - peek_result, - scalars_pandas_df["float64_col"].reindex_like(peek_result), - ) - assert len(peek_result) == 3 - - -def test_series_peek_with_large_results_not_allowed(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - - session = scalars_df._block.session - slot_millis_sum = session.slot_millis_sum - peek_result = scalars_df["float64_col"].peek( - n=3, force=False, allow_large_results=False - ) - - # The metrics won't be fully updated when we call query_and_wait. - print(session.slot_millis_sum - slot_millis_sum) - assert session.slot_millis_sum - slot_millis_sum < 500 - bigframes.testing.utils.assert_series_equal( - peek_result, - scalars_pandas_df["float64_col"].reindex_like(peek_result), - ) - assert len(peek_result) == 3 - - -def test_series_peek_multi_index(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - bf_series = scalars_df.set_index(["string_col", "bool_col"])["float64_col"] - bf_series.name = ("2-part", "name") - pd_series = scalars_pandas_df.set_index(["string_col", "bool_col"])["float64_col"] - pd_series.name = ("2-part", "name") - peek_result = bf_series.peek(n=3, force=False) - bigframes.testing.utils.assert_series_equal( - peek_result, - pd_series.reindex_like(peek_result), - ) - - -def test_series_peek_filtered(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - peek_result = scalars_df[scalars_df.int64_col > 0]["float64_col"].peek( - n=3, force=False - ) - pd_result = scalars_pandas_df[scalars_pandas_df.int64_col > 0]["float64_col"] - bigframes.testing.utils.assert_series_equal( - peek_result, - pd_result.reindex_like(peek_result), - ) - - -def test_series_peek_force(scalars_dfs): - # TODO: supply a reason why this isn't compatible with pandas 1.x - pytest.importorskip("pandas", minversion="2.0.0") - scalars_df, scalars_pandas_df = scalars_dfs - - cumsum_df = scalars_df[["int64_col", "int64_too"]].cumsum() - df_filtered = cumsum_df[cumsum_df.int64_col > 0]["int64_too"] - peek_result = df_filtered.peek(n=3, force=True) - pd_cumsum_df = scalars_pandas_df[["int64_col", "int64_too"]].cumsum() - pd_result = pd_cumsum_df[pd_cumsum_df.int64_col > 0]["int64_too"] - bigframes.testing.utils.assert_series_equal( - peek_result, - pd_result.reindex_like(peek_result), - ) - - -def test_series_peek_force_float(scalars_dfs): - # TODO: supply a reason why this isn't compatible with pandas 1.x - pytest.importorskip("pandas", minversion="2.0.0") - scalars_df, scalars_pandas_df = scalars_dfs - - cumsum_df = scalars_df[["int64_col", "float64_col"]].cumsum() - df_filtered = cumsum_df[cumsum_df.float64_col > 0]["float64_col"] - peek_result = df_filtered.peek(n=3, force=True) - pd_cumsum_df = scalars_pandas_df[["int64_col", "float64_col"]].cumsum() - pd_result = pd_cumsum_df[pd_cumsum_df.float64_col > 0]["float64_col"] - bigframes.testing.utils.assert_series_equal( - peek_result, - pd_result.reindex_like(peek_result), - ) - - def test_shift(scalars_df_index, scalars_pandas_df_index): col_name = "int64_col" bf_result = scalars_df_index[col_name].shift().to_pandas() # cumsum does not behave well on nullable ints in pandas, produces object type and never ignores NA pd_result = scalars_pandas_df_index[col_name].shift().astype(pd.Int64Dtype()) - bigframes.testing.utils.assert_series_equal( + pd.testing.assert_series_equal( bf_result, pd_result, ) @@ -2644,7 +1665,7 @@ def test_series_ffill(scalars_df_index, scalars_pandas_df_index): bf_result = scalars_df_index[col_name].ffill(limit=1).to_pandas() pd_result = scalars_pandas_df_index[col_name].ffill(limit=1) - bigframes.testing.utils.assert_series_equal( + pd.testing.assert_series_equal( bf_result, pd_result, ) @@ -2655,7 +1676,7 @@ def test_series_bfill(scalars_df_index, scalars_pandas_df_index): bf_result = scalars_df_index[col_name].bfill(limit=2).to_pandas() pd_result = scalars_pandas_df_index[col_name].bfill(limit=2) - bigframes.testing.utils.assert_series_equal( + pd.testing.assert_series_equal( bf_result, pd_result, ) @@ -2670,7 +1691,7 @@ def test_cumsum_int(scalars_df_index, scalars_pandas_df_index): # cumsum does not behave well on nullable ints in pandas, produces object type and never ignores NA pd_result = scalars_pandas_df_index[col_name].cumsum().astype(pd.Int64Dtype()) - bigframes.testing.utils.assert_series_equal( + pd.testing.assert_series_equal( bf_result, pd_result, ) @@ -2691,7 +1712,45 @@ def test_cumsum_int_ordered(scalars_df_index, scalars_pandas_df_index): .astype(pd.Int64Dtype()) ) - bigframes.testing.utils.assert_series_equal( + pd.testing.assert_series_equal( + bf_result, + pd_result, + ) + + +@pytest.mark.parametrize( + ("na_option",), + [ + ("keep",), + ("top",), + ("bottom",), + ], +) +@pytest.mark.parametrize( + ("method",), + [ + ("average",), + ("min",), + ("max",), + ("first",), + ("dense",), + ], +) +@pytest.mark.skipif( + True, reason="Blocked by possible pandas rank() regression (b/283278923)" +) +def test_rank_with_nulls(scalars_df_index, scalars_pandas_df_index, na_option, method): + col_name = "bool_col" + bf_result = ( + scalars_df_index[col_name].rank(na_option=na_option, method=method).to_pandas() + ) + pd_result = ( + scalars_pandas_df_index[col_name] + .rank(na_option=na_option, method=method) + .astype(pd.Float64Dtype()) + ) + + pd.testing.assert_series_equal( bf_result, pd_result, ) @@ -2710,7 +1769,7 @@ def test_series_nlargest(scalars_df_index, scalars_pandas_df_index, keep): bf_result = scalars_df_index[col_name].nlargest(4, keep=keep).to_pandas() pd_result = scalars_pandas_df_index[col_name].nlargest(4, keep=keep) - bigframes.testing.utils.assert_series_equal( + pd.testing.assert_series_equal( bf_result, pd_result, ) @@ -2733,7 +1792,7 @@ def test_diff(scalars_df_index, scalars_pandas_df_index, periods): .astype(pd.Int64Dtype()) ) - bigframes.testing.utils.assert_series_equal( + pd.testing.assert_series_equal( bf_result, pd_result, ) @@ -2750,9 +1809,9 @@ def test_diff(scalars_df_index, scalars_pandas_df_index, periods): def test_series_pct_change(scalars_df_index, scalars_pandas_df_index, periods): bf_result = scalars_df_index["int64_col"].pct_change(periods=periods).to_pandas() # cumsum does not behave well on nullable ints in pandas, produces object type and never ignores NA - pd_result = scalars_pandas_df_index["int64_col"].ffill().pct_change(periods=periods) + pd_result = scalars_pandas_df_index["int64_col"].pct_change(periods=periods) - bigframes.testing.utils.assert_series_equal( + pd.testing.assert_series_equal( bf_result, pd_result, ) @@ -2771,56 +1830,18 @@ def test_series_nsmallest(scalars_df_index, scalars_pandas_df_index, keep): bf_result = scalars_df_index[col_name].nsmallest(2, keep=keep).to_pandas() pd_result = scalars_pandas_df_index[col_name].nsmallest(2, keep=keep) - bigframes.testing.utils.assert_series_equal( + pd.testing.assert_series_equal( bf_result, pd_result, ) -@pytest.mark.parametrize( - ("na_option", "method", "ascending", "numeric_only", "pct"), - [ - ("keep", "average", True, True, False), - ("top", "min", False, False, True), - ("bottom", "max", False, False, False), - ("top", "first", False, False, True), - ("bottom", "dense", False, False, False), - ], -) -def test_series_rank( - scalars_df_index, - scalars_pandas_df_index, - na_option, - method, - ascending, - numeric_only, - pct, -): +def test_rank_ints(scalars_df_index, scalars_pandas_df_index): col_name = "int64_too" - bf_result = ( - scalars_df_index[col_name] - .rank( - na_option=na_option, - method=method, - ascending=ascending, - numeric_only=numeric_only, - pct=pct, - ) - .to_pandas() - ) - pd_result = ( - scalars_pandas_df_index[col_name] - .rank( - na_option=na_option, - method=method, - ascending=ascending, - numeric_only=numeric_only, - pct=pct, - ) - .astype(pd.Float64Dtype()) - ) + bf_result = scalars_df_index[col_name].rank().to_pandas() + pd_result = scalars_pandas_df_index[col_name].rank().astype(pd.Float64Dtype()) - bigframes.testing.utils.assert_series_equal( + pd.testing.assert_series_equal( bf_result, pd_result, ) @@ -2832,7 +1853,7 @@ def test_cast_float_to_int(scalars_df_index, scalars_pandas_df_index): # cumsum does not behave well on nullable floats in pandas, produces object type and never ignores NA pd_result = scalars_pandas_df_index[col_name].astype(pd.Int64Dtype()) - bigframes.testing.utils.assert_series_equal( + pd.testing.assert_series_equal( bf_result, pd_result, ) @@ -2844,7 +1865,7 @@ def test_cast_float_to_bool(scalars_df_index, scalars_pandas_df_index): # cumsum does not behave well on nullable floats in pandas, produces object type and never ignores NA pd_result = scalars_pandas_df_index[col_name].astype(pd.BooleanDtype()) - bigframes.testing.utils.assert_series_equal( + pd.testing.assert_series_equal( bf_result, pd_result, ) @@ -2862,36 +1883,7 @@ def test_cumsum_nested(scalars_df_index, scalars_pandas_df_index): .astype(pd.Float64Dtype()) ) - bigframes.testing.utils.assert_series_equal( - bf_result, - pd_result, - ) - - -def test_nested_analytic_ops_align(scalars_df_index, scalars_pandas_df_index): - # TODO: supply a reason why this isn't compatible with pandas 1.x - pytest.importorskip("pandas", minversion="2.0.0") - col_name = "float64_col" - # set non-unique index to check implicit alignment - bf_series = scalars_df_index.set_index("bool_col")[col_name].fillna(0.0) - pd_series = scalars_pandas_df_index.set_index("bool_col")[col_name].fillna(0.0) - - bf_result = ( - (bf_series + 5) - + (bf_series.cumsum().cumsum().cumsum() + bf_series.rolling(window=3).mean()) - + bf_series.expanding().max() - ).to_pandas() - # cumsum does not behave well on nullable ints in pandas, produces object type and never ignores NA - pd_result = ( - (pd_series + 5) - + ( - pd_series.cumsum().cumsum().cumsum().astype(pd.Float64Dtype()) - + pd_series.rolling(window=3).mean() - ) - + pd_series.expanding().max() - ) - - bigframes.testing.utils.assert_series_equal( + pd.testing.assert_series_equal( bf_result, pd_result, ) @@ -2907,7 +1899,7 @@ def test_cumsum_int_filtered(scalars_df_index, scalars_pandas_df_index): # cumsum does not behave well on nullable ints in pandas, produces object type and never ignores NA pd_result = pd_col[pd_col > -2].cumsum().astype(pd.Int64Dtype()) - bigframes.testing.utils.assert_series_equal( + pd.testing.assert_series_equal( bf_result, pd_result, ) @@ -2919,7 +1911,7 @@ def test_cumsum_float(scalars_df_index, scalars_pandas_df_index): # cumsum does not behave well on nullable floats in pandas, produces object type and never ignores NA pd_result = scalars_pandas_df_index[col_name].cumsum().astype(pd.Float64Dtype()) - bigframes.testing.utils.assert_series_equal( + pd.testing.assert_series_equal( bf_result, pd_result, ) @@ -2930,7 +1922,7 @@ def test_cummin_int(scalars_df_index, scalars_pandas_df_index): bf_result = scalars_df_index[col_name].cummin().to_pandas() pd_result = scalars_pandas_df_index[col_name].cummin() - bigframes.testing.utils.assert_series_equal( + pd.testing.assert_series_equal( bf_result, pd_result, ) @@ -2941,67 +1933,30 @@ def test_cummax_int(scalars_df_index, scalars_pandas_df_index): bf_result = scalars_df_index[col_name].cummax().to_pandas() pd_result = scalars_pandas_df_index[col_name].cummax() - bigframes.testing.utils.assert_series_equal( + pd.testing.assert_series_equal( bf_result, pd_result, ) -@pytest.mark.parametrize( - ("kwargs"), - [ - {}, - {"normalize": True}, - {"ascending": True}, - ], - ids=[ - "default", - "normalize", - "ascending", - ], -) -def test_value_counts(scalars_dfs, kwargs): - if pd.__version__.startswith("1."): - pytest.skip("pandas 1.x produces different column labels.") +def test_value_counts(scalars_dfs): scalars_df, scalars_pandas_df = scalars_dfs col_name = "int64_too" - # Pandas `value_counts` can produce non-deterministic results with tied counts. - # Remove duplicates to enforce a consistent output. - s = scalars_df[col_name].drop(0) - pd_s = scalars_pandas_df[col_name].drop(0) - - bf_result = s.value_counts(**kwargs).to_pandas() - pd_result = pd_s.value_counts(**kwargs) - - bigframes.testing.utils.assert_series_equal( - bf_result, - pd_result, - ) - - -def test_value_counts_with_na(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - col_name = "int64_col" - - bf_result = scalars_df[col_name].value_counts(dropna=False).to_pandas() - pd_result = scalars_pandas_df[col_name].value_counts(dropna=False) + bf_result = scalars_df[col_name].value_counts().to_pandas() + pd_result = scalars_pandas_df[col_name].value_counts() # Older pandas version may not have these values, bigframes tries to emulate 2.0+ pd_result.name = "count" pd_result.index.name = col_name - assert_series_equal( + pd.testing.assert_series_equal( bf_result, pd_result, - # bigframes values_counts does not honor ordering in the original data - ignore_order=True, ) def test_value_counts_w_cut(scalars_dfs): - if pd.__version__.startswith("1."): - pytest.skip("value_counts results different in pandas 1.x.") scalars_df, scalars_pandas_df = scalars_dfs col_name = "int64_col" @@ -3010,19 +1965,23 @@ def test_value_counts_w_cut(scalars_dfs): bf_result = bf_cut.value_counts().to_pandas() pd_result = pd_cut.value_counts() + # Older pandas version may not have these values, bigframes tries to emulate 2.0+ + pd_result.name = "count" + pd_result.index.name = col_name pd_result.index = pd_result.index.astype(pd.Int64Dtype()) - bigframes.testing.utils.assert_series_equal( + pd.testing.assert_series_equal( bf_result, pd_result.astype(pd.Int64Dtype()), ) def test_iloc_nested(scalars_df_index, scalars_pandas_df_index): + bf_result = scalars_df_index["string_col"].iloc[1:].iloc[1:].to_pandas() pd_result = scalars_pandas_df_index["string_col"].iloc[1:].iloc[1:] - bigframes.testing.utils.assert_series_equal( + pd.testing.assert_series_equal( bf_result, pd_result, ) @@ -3051,7 +2010,7 @@ def test_iloc_nested(scalars_df_index, scalars_pandas_df_index): def test_series_iloc(scalars_df_index, scalars_pandas_df_index, start, stop, step): bf_result = scalars_df_index["string_col"].iloc[start:stop:step].to_pandas() pd_result = scalars_pandas_df_index["string_col"].iloc[start:stop:step] - bigframes.testing.utils.assert_series_equal( + pd.testing.assert_series_equal( bf_result, pd_result, ) @@ -3087,7 +2046,7 @@ def test_series_add_prefix(scalars_df_index, scalars_pandas_df_index): pd_result = scalars_pandas_df_index["int64_too"].add_prefix("prefix_") # Index will be object type in pandas, string type in bigframes, but same values - bigframes.testing.utils.assert_series_equal( + pd.testing.assert_series_equal( bf_result, pd_result, check_index_type=False, @@ -3100,7 +2059,7 @@ def test_series_add_suffix(scalars_df_index, scalars_pandas_df_index): pd_result = scalars_pandas_df_index["int64_too"].add_suffix("_suffix") # Index will be object type in pandas, string type in bigframes, but same values - bigframes.testing.utils.assert_series_equal( + pd.testing.assert_series_equal( bf_result, pd_result, check_index_type=False, @@ -3117,7 +2076,11 @@ def test_series_filter_items(scalars_df_index, scalars_pandas_df_index): # Pandas uses int64 instead of Int64 (nullable) dtype. pd_result.index = pd_result.index.astype(pd.Int64Dtype()) # Ignore ordering as pandas order differently depending on version - assert_series_equal(bf_result, pd_result, check_names=False, ignore_order=True) + assert_series_equal_ignoring_order( + bf_result, + pd_result, + check_names=False, + ) def test_series_filter_like(scalars_df_index, scalars_pandas_df_index): @@ -3128,7 +2091,7 @@ def test_series_filter_like(scalars_df_index, scalars_pandas_df_index): pd_result = scalars_pandas_df_index["float64_col"].filter(like="ello") - bigframes.testing.utils.assert_series_equal( + pd.testing.assert_series_equal( bf_result, pd_result, ) @@ -3142,7 +2105,7 @@ def test_series_filter_regex(scalars_df_index, scalars_pandas_df_index): pd_result = scalars_pandas_df_index["float64_col"].filter(regex="^[GH].*") - bigframes.testing.utils.assert_series_equal( + pd.testing.assert_series_equal( bf_result, pd_result, ) @@ -3157,7 +2120,7 @@ def test_series_reindex(scalars_df_index, scalars_pandas_df_index): # Pandas uses int64 instead of Int64 (nullable) dtype. pd_result.index = pd_result.index.astype(pd.Int64Dtype()) - bigframes.testing.utils.assert_series_equal( + pd.testing.assert_series_equal( bf_result, pd_result, ) @@ -3184,7 +2147,7 @@ def test_series_reindex_like(scalars_df_index, scalars_pandas_df_index): # Pandas uses int64 instead of Int64 (nullable) dtype. pd_result.index = pd_result.index.astype(pd.Int64Dtype()) - bigframes.testing.utils.assert_series_equal( + pd.testing.assert_series_equal( bf_result, pd_result, ) @@ -3200,7 +2163,7 @@ def test_where_with_series(scalars_df_index, scalars_pandas_df_index): scalars_pandas_df_index["bool_col"], scalars_pandas_df_index["int64_too"] ) - bigframes.testing.utils.assert_series_equal( + pd.testing.assert_series_equal( bf_result, pd_result, ) @@ -3225,7 +2188,7 @@ def test_where_with_different_indices(scalars_df_index, scalars_pandas_df_index) ) ) - bigframes.testing.utils.assert_series_equal( + pd.testing.assert_series_equal( bf_result, pd_result, ) @@ -3239,62 +2202,27 @@ def test_where_with_default(scalars_df_index, scalars_pandas_df_index): scalars_pandas_df_index["bool_col"] ) - bigframes.testing.utils.assert_series_equal( - bf_result, - pd_result, - ) - - -def test_where_with_callable(scalars_df_index, scalars_pandas_df_index): - def _is_positive(x): - return x > 0 - - # Both cond and other are callable. - bf_result = ( - scalars_df_index["int64_col"] - .where(cond=_is_positive, other=lambda x: x * 10) - .to_pandas() - ) - pd_result = scalars_pandas_df_index["int64_col"].where( - cond=_is_positive, other=lambda x: x * 10 - ) - - bigframes.testing.utils.assert_series_equal( + pd.testing.assert_series_equal( bf_result, pd_result, ) -@pytest.mark.parametrize( - ("ordered"), - [ - (True), - (False), - ], -) -def test_clip(scalars_df_index, scalars_pandas_df_index, ordered): +def test_clip(scalars_df_index, scalars_pandas_df_index): col_bf = scalars_df_index["int64_col"] lower_bf = scalars_df_index["int64_too"] - 1 upper_bf = scalars_df_index["int64_too"] + 1 - bf_result = col_bf.clip(lower_bf, upper_bf).to_pandas(ordered=ordered) + bf_result = col_bf.clip(lower_bf, upper_bf).to_pandas() col_pd = scalars_pandas_df_index["int64_col"] lower_pd = scalars_pandas_df_index["int64_too"] - 1 upper_pd = scalars_pandas_df_index["int64_too"] + 1 pd_result = col_pd.clip(lower_pd, upper_pd) - assert_series_equal(bf_result, pd_result, ignore_order=not ordered) - - -def test_clip_int_with_float_bounds(scalars_df_index, scalars_pandas_df_index): - col_bf = scalars_df_index["int64_too"] - bf_result = col_bf.clip(-100, 3.14151593).to_pandas() - - col_pd = scalars_pandas_df_index["int64_too"] - # pandas doesn't work with Int64 and clip with floats - pd_result = col_pd.astype("int64").clip(-100, 3.14151593).astype("Float64") - - assert_series_equal(bf_result, pd_result) + pd.testing.assert_series_equal( + bf_result, + pd_result, + ) def test_clip_filtered_two_sided(scalars_df_index, scalars_pandas_df_index): @@ -3308,7 +2236,7 @@ def test_clip_filtered_two_sided(scalars_df_index, scalars_pandas_df_index): upper_pd = scalars_pandas_df_index["int64_too"].iloc[:5] + 1 pd_result = col_pd.clip(lower_pd, upper_pd) - bigframes.testing.utils.assert_series_equal( + pd.testing.assert_series_equal( bf_result, pd_result, ) @@ -3323,7 +2251,7 @@ def test_clip_filtered_one_sided(scalars_df_index, scalars_pandas_df_index): lower_pd = scalars_pandas_df_index["int64_too"].iloc[2:] - 1 pd_result = col_pd.clip(lower_pd, None) - bigframes.testing.utils.assert_series_equal( + pd.testing.assert_series_equal( bf_result, pd_result, ) @@ -3353,128 +2281,33 @@ def test_between(scalars_df_index, scalars_pandas_df_index, left, right, inclusi ) pd_result = scalars_pandas_df_index["int64_col"].between(left, right, inclusive) - bigframes.testing.utils.assert_series_equal( + pd.testing.assert_series_equal( bf_result, pd_result.astype(pd.BooleanDtype()), ) -def test_series_case_when(scalars_dfs_maybe_ordered): - pytest.importorskip( - "pandas", - minversion="2.2.0", - reason="case_when added in pandas 2.2.0", - ) - scalars_df, scalars_pandas_df = scalars_dfs_maybe_ordered - - bf_series = scalars_df["int64_col"] - pd_series = scalars_pandas_df["int64_col"] - - # TODO(tswast): pandas case_when appears to assume True when a value is - # null. I suspect this should be considered a bug in pandas. - - # Generate 150 conditions to test case_when with a large number of conditions - bf_conditions = ( - [((bf_series > 645).fillna(True), bf_series - 1)] - + [((bf_series > (-100 + i * 5)).fillna(True), i) for i in range(148, 0, -1)] - + [((bf_series <= -100).fillna(True), pd.NA)] - ) - - pd_conditions = ( - [((pd_series > 645), pd_series - 1)] - + [((pd_series > (-100 + i * 5)), i) for i in range(148, 0, -1)] - + [(pd_series <= -100, pd.NA)] - ) - - assert len(bf_conditions) == 150 - - bf_result = bf_series.case_when(bf_conditions).to_pandas() - pd_result = pd_series.case_when(pd_conditions) - - bigframes.testing.utils.assert_series_equal( - bf_result, - pd_result.astype(pd.Int64Dtype()), - ) - - -def test_series_case_when_change_type(scalars_dfs_maybe_ordered): - pytest.importorskip( - "pandas", - minversion="2.2.0", - reason="case_when added in pandas 2.2.0", - ) - scalars_df, scalars_pandas_df = scalars_dfs_maybe_ordered - - bf_series = scalars_df["int64_col"] - pd_series = scalars_pandas_df["int64_col"] - - # TODO(tswast): pandas case_when appears to assume True when a value is - # null. I suspect this should be considered a bug in pandas. - - bf_conditions = [ - ((bf_series > 645).fillna(True), scalars_df["string_col"]), - ((bf_series <= -100).fillna(True), pd.NA), - (True, "not_found"), - ] - - pd_conditions = [ - ((pd_series > 645).fillna(True), scalars_pandas_df["string_col"]), - ((pd_series <= -100).fillna(True), pd.NA), - # pandas currently fails if both the condition and the value are literals. - ([True] * len(pd_series), ["not_found"] * len(pd_series)), - ] - - bf_result = bf_series.case_when(bf_conditions).to_pandas() - pd_result = pd_series.case_when(pd_conditions) - - bigframes.testing.utils.assert_series_equal( - bf_result, - pd_result.astype("string[pyarrow]"), - ) - - def test_to_frame(scalars_dfs): scalars_df, scalars_pandas_df = scalars_dfs bf_result = scalars_df["int64_col"].to_frame().to_pandas() pd_result = scalars_pandas_df["int64_col"].to_frame() - assert_frame_equal(bf_result, pd_result) - - -def test_to_frame_no_name(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - - bf_result = scalars_df["int64_col"].rename(None).to_frame().to_pandas() - pd_result = scalars_pandas_df["int64_col"].rename(None).to_frame() + assert_pandas_df_equal_ignore_ordering(bf_result, pd_result) - assert_frame_equal(bf_result, pd_result) +def test_to_json(scalars_df_index, scalars_pandas_df_index): + bf_result = scalars_df_index["int64_col"].to_json() + pd_result = scalars_pandas_df_index["int64_col"].to_json() -def test_to_json(gcs_folder, scalars_df_index, scalars_pandas_df_index): - path = gcs_folder + "test_series_to_json*.jsonl" - scalars_df_index["int64_col"].to_json(path, lines=True, orient="records") - gcs_df = pd.read_json(get_first_file_from_wildcard(path), lines=True) - - bigframes.testing.utils.assert_series_equal( - gcs_df["int64_col"].astype(pd.Int64Dtype()), - scalars_pandas_df_index["int64_col"], - check_dtype=False, - check_index=False, - ) + assert bf_result == pd_result -def test_to_csv(gcs_folder, scalars_df_index, scalars_pandas_df_index): - path = gcs_folder + "test_series_to_csv*.csv" - scalars_df_index["int64_col"].to_csv(path) - gcs_df = pd.read_csv(get_first_file_from_wildcard(path)) +def test_to_csv(scalars_df_index, scalars_pandas_df_index): + bf_result = scalars_df_index["int64_col"].to_csv() + pd_result = scalars_pandas_df_index["int64_col"].to_csv() - bigframes.testing.utils.assert_series_equal( - gcs_df["int64_col"].astype(pd.Int64Dtype()), - scalars_pandas_df_index["int64_col"], - check_dtype=False, - check_index=False, - ) + assert bf_result == pd_result def test_to_latex(scalars_df_index, scalars_pandas_df_index): @@ -3484,51 +2317,6 @@ def test_to_latex(scalars_df_index, scalars_pandas_df_index): assert bf_result == pd_result -def test_series_to_json_local_str(scalars_df_index, scalars_pandas_df_index): - bf_result = scalars_df_index.int64_col.to_json() - pd_result = scalars_pandas_df_index.int64_col.to_json() - - assert bf_result == pd_result - - -def test_series_to_json_local_file(scalars_df_index, scalars_pandas_df_index): - # TODO: supply a reason why this isn't compatible with pandas 1.x - pytest.importorskip("pandas", minversion="2.0.0") - with ( - tempfile.TemporaryFile() as bf_result_file, - tempfile.TemporaryFile() as pd_result_file, - ): - scalars_df_index.int64_col.to_json(bf_result_file) - scalars_pandas_df_index.int64_col.to_json(pd_result_file) - - bf_result = bf_result_file.read() - pd_result = pd_result_file.read() - - assert bf_result == pd_result - - -def test_series_to_csv_local_str(scalars_df_index, scalars_pandas_df_index): - bf_result = scalars_df_index.int64_col.to_csv() - # default_handler for arrow types that have no default conversion - pd_result = scalars_pandas_df_index.int64_col.to_csv() - - assert bf_result == pd_result - - -def test_series_to_csv_local_file(scalars_df_index, scalars_pandas_df_index): - with ( - tempfile.TemporaryFile() as bf_result_file, - tempfile.TemporaryFile() as pd_result_file, - ): - scalars_df_index.int64_col.to_csv(bf_result_file) - scalars_pandas_df_index.int64_col.to_csv(pd_result_file) - - bf_result = bf_result_file.read() - pd_result = pd_result_file.read() - - assert bf_result == pd_result - - def test_to_dict(scalars_df_index, scalars_pandas_df_index): bf_result = scalars_df_index["int64_too"].to_dict() @@ -3604,7 +2392,7 @@ def test_series_values(scalars_df_index, scalars_pandas_df_index): pd_result = scalars_pandas_df_index["int64_too"].values # Numpy isn't equipped to compare non-numeric objects, so convert back to dataframe - bigframes.testing.utils.assert_series_equal( + pd.testing.assert_series_equal( pd.Series(bf_result), pd.Series(pd_result), check_dtype=False ) @@ -3637,20 +2425,7 @@ def test_sort_values(scalars_df_index, scalars_pandas_df_index, ascending, na_po ascending=ascending, na_position=na_position ) - bigframes.testing.utils.assert_series_equal( - bf_result, - pd_result, - ) - - -def test_series_sort_values_inplace(scalars_df_index, scalars_pandas_df_index): - # Test needs values to be unique - bf_series = scalars_df_index["int64_col"].copy() - bf_series.sort_values(ascending=False, inplace=True) - bf_result = bf_series.to_pandas() - pd_result = scalars_pandas_df_index["int64_col"].sort_values(ascending=False) - - bigframes.testing.utils.assert_series_equal( + pd.testing.assert_series_equal( bf_result, pd_result, ) @@ -3669,19 +2444,7 @@ def test_sort_index(scalars_df_index, scalars_pandas_df_index, ascending): ) pd_result = scalars_pandas_df_index["int64_too"].sort_index(ascending=ascending) - bigframes.testing.utils.assert_series_equal( - bf_result, - pd_result, - ) - - -def test_series_sort_index_inplace(scalars_df_index, scalars_pandas_df_index): - bf_series = scalars_df_index["int64_too"].copy() - bf_series.sort_index(ascending=False, inplace=True) - bf_result = bf_series.to_pandas() - pd_result = scalars_pandas_df_index["int64_too"].sort_index(ascending=False) - - bigframes.testing.utils.assert_series_equal( + pd.testing.assert_series_equal( bf_result, pd_result, ) @@ -3698,7 +2461,7 @@ def test_mask_default_value(scalars_dfs): pd_col_masked = pd_col.mask(pd_col % 2 == 1) pd_result = pd_col.to_frame().assign(int64_col_masked=pd_col_masked) - assert_frame_equal(bf_result, pd_result) + assert_pandas_df_equal_ignore_ordering(bf_result, pd_result) def test_mask_custom_value(scalars_dfs): @@ -3716,75 +2479,9 @@ def test_mask_custom_value(scalars_dfs): # odd so should be left as is, but it is being masked in pandas. # Accidentally the bigframes bahavior matches, but it should be updated # after the resolution of https://github.com/pandas-dev/pandas/issues/52955 - assert_frame_equal(bf_result, pd_result) - - -def test_mask_with_callable(scalars_df_index, scalars_pandas_df_index): - def _ten_times(x): - return x * 10 - - # Both cond and other are callable. - bf_result = ( - scalars_df_index["int64_col"] - .mask(cond=lambda x: x > 0, other=_ten_times) - .to_pandas() - ) - pd_result = scalars_pandas_df_index["int64_col"].mask( - cond=lambda x: x > 0, other=_ten_times - ) - - bigframes.testing.utils.assert_series_equal( - bf_result, - pd_result, - ) - - -@pytest.mark.parametrize( - ("lambda_",), - [ - pytest.param(lambda x: x > 0), - pytest.param( - lambda x: True if x > 0 else False, - marks=pytest.mark.xfail( - raises=ValueError, - ), - ), - ], - ids=[ - "lambda_arithmatic", - "lambda_arbitrary", - ], -) -def test_mask_lambda(scalars_dfs, lambda_): - scalars_df, scalars_pandas_df = scalars_dfs - - bf_col = scalars_df["int64_col"] - bf_result = bf_col.mask(lambda_).to_pandas() - - pd_col = scalars_pandas_df["int64_col"] - pd_result = pd_col.mask(lambda_) + assert_pandas_df_equal_ignore_ordering(bf_result, pd_result) - # ignore dtype check, which are Int64 and object respectively - assert_series_equal(bf_result, pd_result, check_dtype=False) - -def test_mask_simple_udf(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - - def foo(x): - return x < 1000000 - - bf_col = scalars_df["int64_col"] - bf_result = bf_col.mask(foo).to_pandas() - - pd_col = scalars_pandas_df["int64_col"] - pd_result = pd_col.mask(foo) - - # ignore dtype check, which are Int64 and object respectively - assert_series_equal(bf_result, pd_result, check_dtype=False) - - -@pytest.mark.parametrize("errors", ["raise", "null"]) @pytest.mark.parametrize( ("column", "to_type"), [ @@ -3793,40 +2490,15 @@ def foo(x): ("int64_col", pd.Float64Dtype()), ("int64_col", "string[pyarrow]"), ("int64_col", "boolean"), - ("int64_col", pd.ArrowDtype(pa.decimal128(38, 9))), - ("int64_col", pd.ArrowDtype(pa.decimal256(76, 38))), - ("int64_col", pd.ArrowDtype(pa.timestamp("us"))), - ("int64_col", pd.ArrowDtype(pa.timestamp("us", tz="UTC"))), - ("int64_col", "time64[us][pyarrow]"), - ("int64_col", pd.ArrowDtype(db_dtypes.JSONArrowType())), ("bool_col", "Int64"), ("bool_col", "string[pyarrow]"), - ("bool_col", "Float64"), - ("bool_col", pd.ArrowDtype(db_dtypes.JSONArrowType())), - ("string_col", "binary[pyarrow]"), - ("bytes_col", "string[pyarrow]"), # pandas actually doesn't let folks convert to/from naive timestamp and # raises a deprecation warning to use tz_localize/tz_convert instead, # but BigQuery always stores values as UTC and doesn't have to deal # with timezone conversions, so we'll allow it. - ("timestamp_col", "date32[day][pyarrow]"), - ("timestamp_col", "time64[us][pyarrow]"), ("timestamp_col", pd.ArrowDtype(pa.timestamp("us"))), - ("datetime_col", "date32[day][pyarrow]"), - pytest.param( - "datetime_col", - "string[pyarrow]", - marks=pytest.mark.skipif( - pd.__version__.startswith("2.2"), - reason="pandas 2.2 uses T as date/time separator whereas earlier versions use space", - ), - ), - ("datetime_col", "time64[us][pyarrow]"), ("datetime_col", pd.ArrowDtype(pa.timestamp("us", tz="UTC"))), ("date_col", "string[pyarrow]"), - ("date_col", pd.ArrowDtype(pa.timestamp("us"))), - ("date_col", pd.ArrowDtype(pa.timestamp("us", tz="UTC"))), - ("time_col", "string[pyarrow]"), # TODO(bmil): fix Ibis bug: BigQuery backend rounds to nearest int # ("float64_col", "Int64"), # TODO(bmil): decide whether to fix Ibis bug: BigQuery backend @@ -3837,312 +2509,33 @@ def foo(x): # https://cloud.google.com/bigquery/docs/reference/standard-sql/conversion_functions ], ) -def test_astype(scalars_df_index, scalars_pandas_df_index, column, to_type, errors): - # TODO: supply a reason why this isn't compatible with pandas 1.x - pytest.importorskip("pandas", minversion="2.0.0") - bf_result = scalars_df_index[column].astype(to_type, errors=errors).to_pandas() +def test_astype(scalars_df_index, scalars_pandas_df_index, column, to_type): + bf_result = scalars_df_index[column].astype(to_type).to_pandas() pd_result = scalars_pandas_df_index[column].astype(to_type) - bigframes.testing.utils.assert_series_equal(bf_result, pd_result) + pd.testing.assert_series_equal(bf_result, pd_result) -def test_series_astype_python(session): - input = pd.Series(["hello", "world", "3.11", "4000"]) - exepcted = pd.Series( - [None, None, 3.11, 4000], - dtype="Float64", - index=pd.Index([0, 1, 2, 3], dtype="Int64"), - ) - result = session.read_pandas(input).astype(float, errors="null").to_pandas() - bigframes.testing.utils.assert_series_equal(result, exepcted) +def test_string_astype_int(): + pd_series = pd.Series(["4", "-7", "0", " -03"]) + bf_series = series.Series(pd_series) + pd_result = pd_series.astype("Int64") + bf_result = bf_series.astype("Int64").to_pandas() -def test_astype_safe(session): - input = pd.Series(["hello", "world", "3.11", "4000"]) - exepcted = pd.Series( - [None, None, 3.11, 4000], - dtype="Float64", - index=pd.Index([0, 1, 2, 3], dtype="Int64"), - ) - result = session.read_pandas(input).astype("Float64", errors="null").to_pandas() - bigframes.testing.utils.assert_series_equal(result, exepcted) - - -def test_series_astype_w_invalid_error(session): - input = pd.Series(["hello", "world", "3.11", "4000"]) - with pytest.raises(ValueError): - session.read_pandas(input).astype("Float64", errors="bad_value") - - -def test_astype_numeric_to_int(scalars_df_index, scalars_pandas_df_index): - # TODO: supply a reason why this isn't compatible with pandas 1.x - pytest.importorskip("pandas", minversion="2.0.0") - column = "numeric_col" - to_type = "Int64" - bf_result = scalars_df_index[column].astype(to_type).to_pandas() - # Truncate to int to avoid TypeError - pd_result = ( - scalars_pandas_df_index[column] - .apply(lambda x: None if pd.isna(x) else math.trunc(x)) - .astype(to_type) - ) - bigframes.testing.utils.assert_series_equal(bf_result, pd_result) - - -@pytest.mark.parametrize( - ("column", "to_type"), - [ - ("timestamp_col", "int64[pyarrow]"), - ("datetime_col", "int64[pyarrow]"), - ("time_col", "int64[pyarrow]"), - ], -) -def test_date_time_astype_int( - scalars_df_index, scalars_pandas_df_index, column, to_type -): - # TODO: supply a reason why this isn't compatible with pandas 1.x - pytest.importorskip("pandas", minversion="2.0.0") - bf_result = scalars_df_index[column].astype(to_type).to_pandas() - pd_result = scalars_pandas_df_index[column].astype(to_type) - bigframes.testing.utils.assert_series_equal(bf_result, pd_result, check_dtype=False) - assert bf_result.dtype == "Int64" + pd.testing.assert_series_equal(bf_result, pd_result, check_index_type=False) -def test_string_astype_int(session): - pd_series = pd.Series(["4", "-7", "0", "-03"]) - bf_series = series.Series(pd_series, session=session) - - pd_result = pd_series.astype("Int64") - bf_result = bf_series.astype("Int64").to_pandas() - - bigframes.testing.utils.assert_series_equal( - bf_result, pd_result, check_index_type=False - ) - - -def test_string_astype_float(session): +def test_string_astype_float(): pd_series = pd.Series( - ["1", "-1", "-0", "000", "-03.235", "naN", "-inf", "INf", ".33", "7.235e-8"] + ["1", "-1", "-0", "000", " -03.235", "naN", "-inf", "INf", ".33", "7.235e-8"] ) - bf_series = series.Series(pd_series, session=session) + bf_series = series.Series(pd_series) pd_result = pd_series.astype("Float64") bf_result = bf_series.astype("Float64").to_pandas() - bigframes.testing.utils.assert_series_equal( - bf_result, pd_result, check_index_type=False - ) - - -def test_string_astype_date(session): - if int(pa.__version__.split(".")[0]) < 15: - pytest.skip( - "Avoid pyarrow.lib.ArrowNotImplementedError: " - "Unsupported cast from string to date32 using function cast_date32." - ) - - pd_series = pd.Series(["2014-08-15", "2215-08-15", "2016-02-29"]).astype( - pd.ArrowDtype(pa.string()) - ) - - bf_series = series.Series(pd_series, session=session) - - # TODO(b/340885567): fix type error - pd_result = pd_series.astype("date32[day][pyarrow]") # type: ignore - bf_result = bf_series.astype("date32[day][pyarrow]").to_pandas() - - bigframes.testing.utils.assert_series_equal( - bf_result, pd_result, check_index_type=False - ) - - -def test_string_astype_datetime(session): - pd_series = pd.Series( - ["2014-08-15 08:15:12", "2015-08-15 08:15:12.654754", "2016-02-29 00:00:00"] - ).astype(pd.ArrowDtype(pa.string())) - - bf_series = series.Series(pd_series, session=session) - - pd_result = pd_series.astype(pd.ArrowDtype(pa.timestamp("us"))) - bf_result = bf_series.astype(pd.ArrowDtype(pa.timestamp("us"))).to_pandas() - - bigframes.testing.utils.assert_series_equal( - bf_result, pd_result, check_index_type=False - ) - - -def test_string_astype_timestamp(session): - pd_series = pd.Series( - [ - "2014-08-15 08:15:12+00:00", - "2015-08-15 08:15:12.654754+05:00", - "2016-02-29 00:00:00+08:00", - ] - ).astype(pd.ArrowDtype(pa.string())) - - bf_series = series.Series(pd_series, session=session) - - pd_result = pd_series.astype(pd.ArrowDtype(pa.timestamp("us", tz="UTC"))) - bf_result = bf_series.astype( - pd.ArrowDtype(pa.timestamp("us", tz="UTC")) - ).to_pandas() - - bigframes.testing.utils.assert_series_equal( - bf_result, pd_result, check_index_type=False - ) - - -def test_timestamp_astype_string(session): - bf_series = series.Series( - [ - "2014-08-15 08:15:12+00:00", - "2015-08-15 08:15:12.654754+05:00", - "2016-02-29 00:00:00+08:00", - ], - session=session, - ).astype(pd.ArrowDtype(pa.timestamp("us", tz="UTC"))) - - expected_result = pd.Series( - [ - "2014-08-15 08:15:12+00", - "2015-08-15 03:15:12.654754+00", - "2016-02-28 16:00:00+00", - ] - ) - bf_result = bf_series.astype(pa.string()).to_pandas() - - bigframes.testing.utils.assert_series_equal( - bf_result, expected_result, check_index_type=False, check_dtype=False - ) - assert bf_result.dtype == "string[pyarrow]" - - -@pytest.mark.parametrize("errors", ["raise", "null"]) -def test_float_astype_json(errors, session): - data = ["1.25", "2500000000.1", None, "-12323.24"] - bf_series = series.Series(data, dtype=dtypes.FLOAT_DTYPE, session=session) - - bf_result = bf_series.astype(dtypes.JSON_DTYPE, errors=errors) - assert bf_result.dtype == dtypes.JSON_DTYPE - bf_result_pandas = bf_result.to_pandas() - - expected_data = [float(x) if x is not None else None for x in data] - expected_result = pd.Series(expected_data, dtype=dtypes.JSON_DTYPE) - expected_result.index = expected_result.index.astype("Int64") - bigframes.testing.utils.assert_series_equal(bf_result_pandas, expected_result) - - -def test_float_astype_json_str(session): - data = ["1.25", "2500000000.1", None, "-12323.24"] - bf_series = series.Series(data, dtype=dtypes.FLOAT_DTYPE, session=session) - - bf_result = bf_series.astype("json") - assert bf_result.dtype == dtypes.JSON_DTYPE - - expected_data = [float(x) if x is not None else None for x in data] - expected_result = pd.Series(expected_data, dtype=dtypes.JSON_DTYPE) - expected_result.index = expected_result.index.astype("Int64") - bigframes.testing.utils.assert_series_equal(bf_result.to_pandas(), expected_result) - - -@pytest.mark.parametrize("errors", ["raise", "null"]) -def test_string_astype_json(errors, session): - data = [ - "1", - None, - '["1","3","5"]', - '{"a":1,"b":["x","y"],"c":{"x":[],"z":false}}', - ] - bf_series = series.Series(data, dtype=dtypes.STRING_DTYPE, session=session) - - bf_result = bf_series.astype(dtypes.JSON_DTYPE, errors=errors) - assert bf_result.dtype == dtypes.JSON_DTYPE - - pd_result = bf_series.to_pandas().astype(dtypes.JSON_DTYPE) - bigframes.testing.utils.assert_series_equal(bf_result.to_pandas(), pd_result) - - -def test_string_astype_json_in_safe_mode(session): - data = ["this is not a valid json string"] - bf_series = series.Series(data, dtype=dtypes.STRING_DTYPE, session=session) - bf_result = bf_series.astype(dtypes.JSON_DTYPE, errors="null") - assert bf_result.dtype == dtypes.JSON_DTYPE - - expected = pd.Series([None], dtype=dtypes.JSON_DTYPE) - expected.index = expected.index.astype("Int64") - bigframes.testing.utils.assert_series_equal(bf_result.to_pandas(), expected) - - -def test_string_astype_json_raise_error(session): - data = ["this is not a valid json string"] - bf_series = series.Series(data, dtype=dtypes.STRING_DTYPE, session=session) - with pytest.raises( - google.api_core.exceptions.BadRequest, - match="syntax error while parsing value", - ): - bf_series.astype(dtypes.JSON_DTYPE, errors="raise").to_pandas() - - -@pytest.mark.parametrize("errors", ["raise", "null"]) -@pytest.mark.parametrize( - ("data", "to_type"), - [ - pytest.param(["1", "10.0", None], dtypes.INT_DTYPE, id="to_int"), - pytest.param(["0.0001", "2500000000", None], dtypes.FLOAT_DTYPE, id="to_float"), - pytest.param(["true", "false", None], dtypes.BOOL_DTYPE, id="to_bool"), - pytest.param(['"str"', None], dtypes.STRING_DTYPE, id="to_string"), - pytest.param( - ['"str"', None], - dtypes.TIME_DTYPE, - id="invalid", - marks=pytest.mark.xfail(raises=TypeError), - ), - ], -) -def test_json_astype_others(data, to_type, errors, session): - bf_series = series.Series(data, dtype=dtypes.JSON_DTYPE, session=session) - - bf_result = bf_series.astype(to_type, errors=errors) - assert bf_result.dtype == to_type - - load_data = [json.loads(item) if item is not None else None for item in data] - expected = pd.Series(load_data, dtype=to_type) - expected.index = expected.index.astype("Int64") - bigframes.testing.utils.assert_series_equal(bf_result.to_pandas(), expected) - - -@pytest.mark.parametrize( - ("data", "to_type"), - [ - pytest.param(["10.2", None], dtypes.INT_DTYPE, id="to_int"), - pytest.param(["false", None], dtypes.FLOAT_DTYPE, id="to_float"), - pytest.param(["10.2", None], dtypes.BOOL_DTYPE, id="to_bool"), - pytest.param(["true", None], dtypes.STRING_DTYPE, id="to_string"), - ], -) -def test_json_astype_others_raise_error(data, to_type, session): - bf_series = series.Series(data, dtype=dtypes.JSON_DTYPE, session=session) - with pytest.raises(google.api_core.exceptions.BadRequest): - bf_series.astype(to_type, errors="raise").to_pandas() - - -@pytest.mark.parametrize( - ("data", "to_type"), - [ - pytest.param(["10.2", None], dtypes.INT_DTYPE, id="to_int"), - pytest.param(["false", None], dtypes.FLOAT_DTYPE, id="to_float"), - pytest.param(["10.2", None], dtypes.BOOL_DTYPE, id="to_bool"), - pytest.param(["true", None], dtypes.STRING_DTYPE, id="to_string"), - ], -) -def test_json_astype_others_in_safe_mode(data, to_type, session): - bf_series = series.Series(data, dtype=dtypes.JSON_DTYPE, session=session) - bf_result = bf_series.astype(to_type, errors="null") - assert bf_result.dtype == to_type - - expected = pd.Series([None, None], dtype=to_type) - expected.index = expected.index.astype("Int64") - bigframes.testing.utils.assert_series_equal(bf_result.to_pandas(), expected) + pd.testing.assert_series_equal(bf_result, pd_result, check_index_type=False) @pytest.mark.parametrize( @@ -4156,7 +2549,9 @@ def test_iloc_single_integer(scalars_df_index, scalars_pandas_df_index, index): assert bf_result == pd_result -def test_iloc_single_integer_out_of_bound_error(scalars_df_index): +def test_iloc_single_integer_out_of_bound_error( + scalars_df_index, scalars_pandas_df_index +): with pytest.raises(IndexError, match="single positional indexer is out-of-bounds"): scalars_df_index.string_col.iloc[99] @@ -4165,7 +2560,7 @@ def test_loc_bool_series_explicit_index(scalars_df_index, scalars_pandas_df_inde bf_result = scalars_df_index.string_col.loc[scalars_df_index.bool_col].to_pandas() pd_result = scalars_pandas_df_index.string_col.loc[scalars_pandas_df_index.bool_col] - bigframes.testing.utils.assert_series_equal( + pd.testing.assert_series_equal( bf_result, pd_result, ) @@ -4181,7 +2576,7 @@ def test_loc_bool_series_default_index( scalars_pandas_df_default_index.bool_col ] - assert_frame_equal( + assert_pandas_df_equal_ignore_ordering( bf_result.to_frame(), pd_result.to_frame(), ) @@ -4226,7 +2621,7 @@ def test_rename(scalars_df_index, scalars_pandas_df_index): bf_result = scalars_df_index.string_col.rename("newname") pd_result = scalars_pandas_df_index.string_col.rename("newname") - bigframes.testing.utils.assert_series_equal( + pd.testing.assert_series_equal( bf_result.to_pandas(), pd_result, ) @@ -4236,7 +2631,7 @@ def test_rename_nonstring(scalars_df_index, scalars_pandas_df_index): bf_result = scalars_df_index.string_col.rename((4, 2)) pd_result = scalars_pandas_df_index.string_col.rename((4, 2)) - bigframes.testing.utils.assert_series_equal( + pd.testing.assert_series_equal( bf_result.to_pandas(), pd_result, ) @@ -4248,7 +2643,7 @@ def test_rename_dict_same_type(scalars_df_index, scalars_pandas_df_index): pd_result.index = pd_result.index.astype("Int64") - bigframes.testing.utils.assert_series_equal( + pd.testing.assert_series_equal( bf_result.to_pandas(), pd_result, ) @@ -4258,7 +2653,7 @@ def test_rename_axis(scalars_df_index, scalars_pandas_df_index): bf_result = scalars_df_index.string_col.rename_axis("newindexname") pd_result = scalars_pandas_df_index.string_col.rename_axis("newindexname") - bigframes.testing.utils.assert_series_equal( + pd.testing.assert_series_equal( bf_result.to_pandas(), pd_result, ) @@ -4275,7 +2670,7 @@ def test_loc_list_string_index(scalars_df_index, scalars_pandas_df_index): bf_result = scalars_df_index.string_col.loc[index_list] pd_result = scalars_pandas_df_index.string_col.loc[index_list] - bigframes.testing.utils.assert_series_equal( + pd.testing.assert_series_equal( bf_result.to_pandas(), pd_result, ) @@ -4287,7 +2682,7 @@ def test_loc_list_integer_index(scalars_df_index, scalars_pandas_df_index): bf_result = scalars_df_index.bool_col.loc[index_list] pd_result = scalars_pandas_df_index.bool_col.loc[index_list] - bigframes.testing.utils.assert_series_equal( + pd.testing.assert_series_equal( bf_result.to_pandas(), pd_result, ) @@ -4303,7 +2698,7 @@ def test_loc_list_multiindex(scalars_df_index, scalars_pandas_df_index): bf_result = scalars_df_multiindex.int64_too.loc[index_list] pd_result = scalars_pandas_df_multiindex.int64_too.loc[index_list] - bigframes.testing.utils.assert_series_equal( + pd.testing.assert_series_equal( bf_result.to_pandas(), pd_result, ) @@ -4315,7 +2710,7 @@ def test_iloc_list(scalars_df_index, scalars_pandas_df_index): bf_result = scalars_df_index.string_col.iloc[index_list] pd_result = scalars_pandas_df_index.string_col.iloc[index_list] - bigframes.testing.utils.assert_series_equal( + pd.testing.assert_series_equal( bf_result.to_pandas(), pd_result, ) @@ -4329,7 +2724,7 @@ def test_iloc_list_nameless(scalars_df_index, scalars_pandas_df_index): pd_series = scalars_pandas_df_index.string_col.rename(None) pd_result = pd_series.iloc[index_list] - bigframes.testing.utils.assert_series_equal( + pd.testing.assert_series_equal( bf_result.to_pandas(), pd_result, ) @@ -4344,7 +2739,7 @@ def test_loc_list_nameless(scalars_df_index, scalars_pandas_df_index): pd_series = scalars_pandas_df_index.string_col.rename(None) pd_result = pd_series.loc[index_list] - bigframes.testing.utils.assert_series_equal( + pd.testing.assert_series_equal( bf_result.to_pandas(), pd_result, ) @@ -4360,7 +2755,7 @@ def test_loc_bf_series_string_index(scalars_df_index, scalars_pandas_df_index): bf_result = scalars_df_index.date_col.loc[bf_string_series] pd_result = scalars_pandas_df_index.date_col.loc[pd_string_series] - bigframes.testing.utils.assert_series_equal( + pd.testing.assert_series_equal( bf_result.to_pandas(), pd_result, ) @@ -4378,7 +2773,7 @@ def test_loc_bf_series_multiindex(scalars_df_index, scalars_pandas_df_index): bf_result = scalars_df_multiindex.int64_too.loc[bf_string_series] pd_result = scalars_pandas_df_multiindex.int64_too.loc[pd_string_series] - bigframes.testing.utils.assert_series_equal( + pd.testing.assert_series_equal( bf_result.to_pandas(), pd_result, ) @@ -4391,7 +2786,7 @@ def test_loc_bf_index_integer_index(scalars_df_index, scalars_pandas_df_index): bf_result = scalars_df_index.date_col.loc[bf_index] pd_result = scalars_pandas_df_index.date_col.loc[pd_index] - bigframes.testing.utils.assert_series_equal( + pd.testing.assert_series_equal( bf_result.to_pandas(), pd_result, ) @@ -4405,7 +2800,7 @@ def test_loc_single_index_with_duplicate(scalars_df_index, scalars_pandas_df_ind index = "Hello, World!" bf_result = scalars_df_index.date_col.loc[index] pd_result = scalars_pandas_df_index.date_col.loc[index] - bigframes.testing.utils.assert_series_equal( + pd.testing.assert_series_equal( bf_result.to_pandas(), pd_result, ) @@ -4426,17 +2821,15 @@ def test_series_bool_interpretation_error(scalars_df_index): def test_query_job_setters(scalars_dfs): - # if allow_large_results=False, might not create query job - with bigframes.option_context("compute.allow_large_results", True): - job_ids = set() - df, _ = scalars_dfs - series = df["int64_col"] - assert series.query_job is not None - repr(series) - job_ids.add(series.query_job.job_id) - series.to_pandas() - job_ids.add(series.query_job.job_id) - assert len(job_ids) == 2 + job_ids = set() + df, _ = scalars_dfs + series = df["int64_col"] + assert series.query_job is not None + repr(series) + job_ids.add(series.query_job.job_id) + series.to_pandas() + job_ids.add(series.query_job.job_id) + assert len(job_ids) == 2 @pytest.mark.parametrize( @@ -4450,9 +2843,9 @@ def test_query_job_setters(scalars_dfs): ([1, 1, 1, 1, 1],), ], ) -def test_is_monotonic_increasing(series_input, session): - scalars_df = series.Series(series_input, dtype=pd.Int64Dtype(), session=session) - scalars_pandas_df = pd.Series(series_input, dtype=pd.Int64Dtype()) +def test_is_monotonic_increasing(series_input): + scalars_df = series.Series(series_input) + scalars_pandas_df = pd.Series(series_input) assert ( scalars_df.is_monotonic_increasing == scalars_pandas_df.is_monotonic_increasing ) @@ -4469,8 +2862,8 @@ def test_is_monotonic_increasing(series_input, session): ([1, 1, 1, 1, 1],), ], ) -def test_is_monotonic_decreasing(series_input, session): - scalars_df = series.Series(series_input, session=session) +def test_is_monotonic_decreasing(series_input): + scalars_df = series.Series(series_input) scalars_pandas_df = pd.Series(series_input) assert ( scalars_df.is_monotonic_decreasing == scalars_pandas_df.is_monotonic_decreasing @@ -4490,7 +2883,7 @@ def test_map_dict_input(scalars_dfs): pd_result = pd_result.astype("Int64") # pandas type differences bf_result = scalars_df.string_col.map(local_map) - bigframes.testing.utils.assert_series_equal( + pd.testing.assert_series_equal( bf_result.to_pandas(), pd_result, ) @@ -4509,7 +2902,7 @@ def test_map_series_input(scalars_dfs): pd_result = scalars_pandas_df.int64_too.map(pd_map_series) bf_result = scalars_df.int64_too.map(bf_map_series) - bigframes.testing.utils.assert_series_equal( + pd.testing.assert_series_equal( bf_result.to_pandas(), pd_result, ) @@ -4529,472 +2922,3 @@ def test_map_series_input_duplicates_error(scalars_dfs): scalars_pandas_df.int64_too.map(pd_map_series) with pytest.raises(pd.errors.InvalidIndexError): scalars_df.int64_too.map(bf_map_series, verify_integrity=True) - - -@pytest.mark.parametrize( - ("frac", "n", "random_state"), - [ - (None, 4, None), - (0.5, None, None), - (None, 4, 10), - (0.5, None, 10), - (None, None, None), - ], - ids=[ - "n_wo_random_state", - "frac_wo_random_state", - "n_w_random_state", - "frac_w_random_state", - "n_default", - ], -) -def test_sample(scalars_dfs, frac, n, random_state): - scalars_df, _ = scalars_dfs - df = scalars_df.int64_col.sample(frac=frac, n=n, random_state=random_state) - bf_result = df.to_pandas() - - n = 1 if n is None else n - expected_sample_size = round(frac * scalars_df.shape[0]) if frac is not None else n - assert bf_result.shape[0] == expected_sample_size - - -def test_series_iter( - scalars_df_index, - scalars_pandas_df_index, -): - for bf_i, pd_i in zip( - scalars_df_index["int64_too"], scalars_pandas_df_index["int64_too"] - ): - assert bf_i == pd_i - - -@pytest.mark.parametrize( - ( - "col", - "lambda_", - ), - [ - pytest.param("int64_col", lambda x: x * x + x + 1), - pytest.param("int64_col", lambda x: x % 2 == 1), - pytest.param("string_col", lambda x: x + "_suffix"), - ], - ids=[ - "lambda_int_int", - "lambda_int_bool", - "lambda_str_str", - ], -) -def test_apply_lambda(scalars_dfs, col, lambda_): - scalars_df, scalars_pandas_df = scalars_dfs - - bf_col = scalars_df[col] - - # Can't be applied to BigFrames Series without by_row=False - with pytest.raises(ValueError, match="by_row=False"): - bf_col.apply(lambda_) - - bf_result = bf_col.apply(lambda_, by_row=False).to_pandas() - - pd_col = scalars_pandas_df[col] - if pd.__version__[:3] in ("2.2", "2.3", "3.0"): - pd_result = pd_col.apply(lambda_, by_row=False) - else: - pd_result = pd_col.apply(lambda_) - - # ignore dtype check, which are Int64 and object respectively - # Some columns implicitly convert to floating point. Use check_exact=False to ensure we're "close enough" - assert_series_equal( - bf_result, pd_result, check_dtype=False, check_exact=False, rtol=0.001 - ) - - -@pytest.mark.parametrize( - ("ufunc",), - [ - pytest.param(numpy.log), - pytest.param(numpy.sqrt), - pytest.param(numpy.sin), - ], - ids=[ - "log", - "sqrt", - "sin", - ], -) -def test_apply_numpy_ufunc(scalars_dfs, ufunc): - scalars_df, scalars_pandas_df = scalars_dfs - - bf_col = scalars_df["int64_col"] - - # Can't be applied to BigFrames Series without by_row=False - with pytest.raises(ValueError, match="by_row=False"): - bf_col.apply(ufunc) - - bf_result = bf_col.apply(ufunc, by_row=False).to_pandas() - - pd_col = scalars_pandas_df["int64_col"] - pd_result = pd_col.apply(ufunc) - - assert_series_equal(bf_result, pd_result) - - -@pytest.mark.parametrize( - ("ufunc",), - [ - pytest.param(numpy.add), - pytest.param(numpy.divide), - ], - ids=[ - "add", - "divide", - ], -) -def test_combine_series_ufunc(scalars_dfs, ufunc): - scalars_df, scalars_pandas_df = scalars_dfs - - bf_col = scalars_df["int64_col"].dropna() - bf_result = bf_col.combine(bf_col, ufunc).to_pandas() - - pd_col = scalars_pandas_df["int64_col"].dropna() - pd_result = pd_col.combine(pd_col, ufunc) - - assert_series_equal(bf_result, pd_result, check_dtype=False) - - -def test_combine_scalar_ufunc(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - - bf_col = scalars_df["int64_col"].dropna() - bf_result = bf_col.combine(2.5, numpy.add).to_pandas() - - pd_col = scalars_pandas_df["int64_col"].dropna() - pd_result = pd_col.combine(2.5, numpy.add) - - assert_series_equal(bf_result, pd_result, check_dtype=False) - - -def test_apply_simple_udf(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - - def foo(x): - return x * x + 2 * x + 3 - - bf_col = scalars_df["int64_col"] - - # Can't be applied to BigFrames Series without by_row=False - with pytest.raises(ValueError, match="by_row=False"): - bf_col.apply(foo) - - bf_result = bf_col.apply(foo, by_row=False).to_pandas() - - pd_col = scalars_pandas_df["int64_col"] - - if pd.__version__[:3] in ("2.2", "2.3", "3.0"): - pd_result = pd_col.apply(foo, by_row=False) - else: - pd_result = pd_col.apply(foo) - - # ignore dtype check, which are Int64 and object respectively - # Some columns implicitly convert to floating point. Use check_exact=False to ensure we're "close enough" - assert_series_equal( - bf_result, pd_result, check_dtype=False, check_exact=False, rtol=0.001 - ) - - -@pytest.mark.parametrize( - ("col", "lambda_", "exception"), - [ - pytest.param("int64_col", {1: 2, 3: 4}, ValueError), - pytest.param("int64_col", numpy.square, TypeError), - pytest.param("string_col", lambda x: x.capitalize(), AttributeError), - ], - ids=[ - "not_callable", - "numpy_ufunc", - "custom_lambda", - ], -) -def test_apply_not_supported(scalars_dfs, col, lambda_, exception): - scalars_df, _ = scalars_dfs - - bf_col = scalars_df[col] - with pytest.raises(exception): - bf_col.apply(lambda_, by_row=False) - - -def test_series_pipe( - scalars_df_index, - scalars_pandas_df_index, -): - column = "int64_too" - - def foo(x: int, y: int, df): - return (df + x) % y - - bf_result = ( - scalars_df_index[column] - .pipe((foo, "df"), x=7, y=9) - .pipe(lambda x: x**2) - .to_pandas() - ) - - pd_result = ( - scalars_pandas_df_index[column].pipe((foo, "df"), x=7, y=9).pipe(lambda x: x**2) - ) - - assert_series_equal(bf_result, pd_result) - - -@pytest.mark.parametrize( - ("data"), - [ - pytest.param([1, 2, 3], id="int"), - pytest.param([[1, 2, 3], [], numpy.nan, [3, 4]], id="int_array"), - pytest.param( - [["A", "AA", "AAA"], ["BB", "B"], numpy.nan, [], ["C"]], id="string_array" - ), - pytest.param( - [ - {"A": {"x": 1.0}, "B": "b"}, - {"A": {"y": 2.0}, "B": "bb"}, - {"A": {"z": 4.0}}, - {}, - numpy.nan, - ], - id="struct_array", - ), - ], -) -def test_series_explode(data): - s = bigframes.pandas.Series(data) - pd_s = s.to_pandas() - bigframes.testing.utils.assert_series_equal( - s.explode().to_pandas(), - pd_s.explode(), - check_index_type=False, - check_dtype=False, - ) - - -@pytest.mark.parametrize( - ("index", "ignore_index"), - [ - pytest.param(None, True, id="default_index"), - pytest.param(None, False, id="ignore_default_index"), - pytest.param([5, 1, 3, 2], True, id="unordered_index"), - pytest.param([5, 1, 3, 2], False, id="ignore_unordered_index"), - pytest.param(["z", "x", "a", "b"], True, id="str_index"), - pytest.param(["z", "x", "a", "b"], False, id="ignore_str_index"), - pytest.param( - pd.Index(["z", "x", "a", "b"], name="idx"), True, id="str_named_index" - ), - pytest.param( - pd.Index(["z", "x", "a", "b"], name="idx"), - False, - id="ignore_str_named_index", - ), - pytest.param( - pd.MultiIndex.from_frame( - pd.DataFrame({"idx0": [5, 1, 3, 2], "idx1": ["z", "x", "a", "b"]}) - ), - True, - id="multi_index", - ), - pytest.param( - pd.MultiIndex.from_frame( - pd.DataFrame({"idx0": [5, 1, 3, 2], "idx1": ["z", "x", "a", "b"]}) - ), - False, - id="ignore_multi_index", - ), - ], -) -def test_series_explode_w_index(index, ignore_index): - data = [[], [200.0, 23.12], [4.5, -9.0], [1.0]] - s = bigframes.pandas.Series(data, index=index) - pd_s = pd.Series(data, index=index) - # TODO(b/340885567): fix type error - bigframes.testing.utils.assert_series_equal( - s.explode(ignore_index=ignore_index).to_pandas(), # type: ignore - pd_s.explode(ignore_index=ignore_index).astype(pd.Float64Dtype()), # type: ignore - check_index_type=False, - ) - - -@pytest.mark.parametrize( - ("ignore_index", "ordered"), - [ - pytest.param(True, True, id="include_index_ordered"), - pytest.param(True, False, id="include_index_unordered"), - pytest.param(False, True, id="ignore_index_ordered"), - ], -) -def test_series_explode_reserve_order(ignore_index, ordered): - data = [numpy.random.randint(0, 10, 10) for _ in range(10)] - s = bigframes.pandas.Series(data) - pd_s = pd.Series(data) - - # TODO(b/340885567): fix type error - res = s.explode(ignore_index=ignore_index).to_pandas(ordered=ordered) # type: ignore - # TODO(b/340885567): fix type error - pd_res = pd_s.explode(ignore_index=ignore_index).astype(pd.Int64Dtype()) # type: ignore - pd_res.index = pd_res.index.astype(pd.Int64Dtype()) - bigframes.testing.utils.assert_series_equal( - res if ordered else res.sort_index(), - pd_res, - ) - - -def test_series_explode_w_aggregate(): - data = [[1, 2, 3], [], numpy.nan, [3, 4]] - s = bigframes.pandas.Series(data) - pd_s = pd.Series(data) - assert s.explode().sum() == pd_s.explode().sum() - - -def test_series_construct_empty_array(): - # TODO: supply a reason why this isn't compatible with pandas 1.x - pytest.importorskip("pandas", minversion="2.0.0") - s = bigframes.pandas.Series([[]]) - expected = pd.Series( - [[]], - dtype=pd.ArrowDtype(pa.list_(pa.float64())), - index=pd.Index([0], dtype=pd.Int64Dtype()), - ) - bigframes.testing.utils.assert_series_equal( - expected, - s.to_pandas(), - ) - - -@pytest.mark.parametrize( - ("data"), - [ - pytest.param(numpy.nan, id="null"), - pytest.param([numpy.nan], id="null_array"), - pytest.param([[]], id="empty_array"), - pytest.param([numpy.nan, []], id="null_and_empty_array"), - ], -) -def test_series_explode_null(data): - s = bigframes.pandas.Series(data) - bigframes.testing.utils.assert_series_equal( - s.explode().to_pandas(), - s.to_pandas().explode(), - check_dtype=False, - ) - - -@pytest.mark.parametrize( - ("append", "level", "col", "rule"), - [ - pytest.param(False, None, "timestamp_col", "75D"), - pytest.param(True, 1, "timestamp_col", "25W"), - pytest.param(False, None, "datetime_col", "3ME"), - pytest.param(True, "timestamp_col", "timestamp_col", "1YE"), - ], -) -def test_resample(scalars_df_index, scalars_pandas_df_index, append, level, col, rule): - # TODO: supply a reason why this isn't compatible with pandas 1.x - pytest.importorskip("pandas", minversion="2.0.0") - scalars_df_index = scalars_df_index.set_index(col, append=append)["int64_col"] - scalars_pandas_df_index = scalars_pandas_df_index.set_index(col, append=append)[ - "int64_col" - ] - bf_result = scalars_df_index.resample(rule=rule, level=level).min().to_pandas() - pd_result = scalars_pandas_df_index.resample(rule=rule, level=level).min() - # TODO: (b/484364312) - pd_result.index.names = bf_result.index.names - bigframes.testing.utils.assert_series_equal(bf_result, pd_result) - - -def test_series_struct_get_field_by_attribute( - nested_structs_df, nested_structs_pandas_df -): - if Version(pd.__version__) < Version("2.2.0"): - pytest.skip("struct accessor is not supported before pandas 2.2") - - bf_series = nested_structs_df["person"] - df_series = nested_structs_pandas_df["person"] - - bigframes.testing.utils.assert_series_equal( - bf_series.address.city.to_pandas(), - df_series.struct.field("address").struct.field("city"), - check_dtype=False, - check_index=False, - ) - bigframes.testing.utils.assert_series_equal( - bf_series.address.country.to_pandas(), - df_series.struct.field("address").struct.field("country"), - check_dtype=False, - check_index=False, - ) - - -def test_series_struct_fields_in_dir(nested_structs_df): - series = nested_structs_df["person"] - - assert "age" in dir(series) - assert "address" in dir(series) - assert "city" in dir(series.address) - assert "country" in dir(series.address) - - -def test_series_struct_class_attributes_shadow_struct_fields(nested_structs_df): - series = nested_structs_df["person"] - - assert series.name == "person" - - -def test_series_to_pandas_dry_run(scalars_df_index): - bf_series = scalars_df_index["int64_col"] - - result = bf_series.to_pandas(dry_run=True) - - assert isinstance(result, pd.Series) - assert len(result) > 0 - - -def test_series_item(session): - # Test with a single item - bf_s_single = bigframes.pandas.Series([42], session=session) - pd_s_single = pd.Series([42]) - assert bf_s_single.item() == pd_s_single.item() - - -def test_series_item_with_multiple(session): - # Test with multiple items - bf_s_multiple = bigframes.pandas.Series([1, 2, 3], session=session) - pd_s_multiple = pd.Series([1, 2, 3]) - - try: - pd_s_multiple.item() - except ValueError as e: - expected_message = str(e) - else: - raise AssertionError("Expected ValueError from pandas, but didn't get one") - - with pytest.raises(ValueError, match=re.escape(expected_message)): - bf_s_multiple.item() - - -def test_series_item_with_empty(session): - # Test with an empty Series - bf_s_empty = bigframes.pandas.Series([], dtype="Int64", session=session) - pd_s_empty = pd.Series([], dtype="Int64") - - try: - pd_s_empty.item() - except ValueError as e: - expected_message = str(e) - else: - raise AssertionError("Expected ValueError from pandas, but didn't get one") - - with pytest.raises(ValueError, match=re.escape(expected_message)): - bf_s_empty.item() - - -def test_series_sql(session): - s = bigframes.pandas.Series([], session=session) - - assert len(s.sql) > 0 diff --git a/tests/system/small/test_series_io.py b/tests/system/small/test_series_io.py deleted file mode 100644 index 83c2de70cae..00000000000 --- a/tests/system/small/test_series_io.py +++ /dev/null @@ -1,136 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -import numpy -import numpy.testing -import pandas as pd -import pytest - -import bigframes -import bigframes.series - - -def test_to_pandas_override_global_option(scalars_df_index): - with bigframes.option_context("compute.allow_large_results", True): - bf_series = scalars_df_index["int64_col"] - - # Direct call to_pandas uses global default setting (allow_large_results=True) - bf_series.to_pandas() - table_id = bf_series._query_job.destination.table_id - assert table_id is not None - - session = bf_series._block.session - - history_before = session.execution_history().to_dataframe() - queries_before = ( - len(history_before[history_before["job_type"] == "query"]) - if "job_type" in history_before.columns - else 0 - ) - - # When allow_large_results=False, a query_job object should not be created. - # Therefore, the table_id should remain unchanged. - bf_series.to_pandas(allow_large_results=False) - assert bf_series._query_job.destination.table_id == table_id - - history_after = session.execution_history().to_dataframe() - queries_after = len(history_after[history_after["job_type"] == "query"]) - - assert (queries_after - queries_before) == 1 - - -@pytest.mark.parametrize( - ("kwargs", "message"), - [ - pytest.param( - {"sampling_method": "head"}, - r"DEPRECATED[\S\s]*sampling_method[\S\s]*Series.sample", - id="sampling_method", - ), - pytest.param( - {"random_state": 10}, - r"DEPRECATED[\S\s]*random_state[\S\s]*Series.sample", - id="random_state", - ), - pytest.param( - {"max_download_size": 10}, - r"DEPRECATED[\S\s]*max_download_size[\S\s]*Series.to_pandas_batches", - id="max_download_size", - ), - ], -) -def test_to_pandas_warns_deprecated_parameters(scalars_df_index, kwargs, message): - s: bigframes.series.Series = scalars_df_index["int64_col"] - with pytest.warns(FutureWarning, match=message): - s.to_pandas( - # limits only apply for allow_large_result=True - allow_large_results=True, - **kwargs, - ) - - -@pytest.mark.parametrize( - ("page_size", "max_results", "allow_large_results"), - [ - pytest.param(None, None, True), - pytest.param(2, None, False), - pytest.param(None, 1, True), - pytest.param(2, 5, False), - pytest.param(3, 6, True), - pytest.param(3, 100, False), - pytest.param(100, 100, True), - ], -) -def test_to_pandas_batches(scalars_dfs, page_size, max_results, allow_large_results): - scalars_df, scalars_pandas_df = scalars_dfs - bf_series = scalars_df["int64_col"] - pd_series = scalars_pandas_df["int64_col"] - - total_rows = 0 - expected_total_rows = ( - min(max_results, len(pd_series)) if max_results else len(pd_series) - ) - - hit_last_page = False - for s in bf_series.to_pandas_batches( - page_size=page_size, - max_results=max_results, - allow_large_results=allow_large_results, - ): - assert not hit_last_page - - actual_rows = s.shape[0] - expected_rows = ( - min(page_size, expected_total_rows) if page_size else expected_total_rows - ) - - assert actual_rows <= expected_rows - if actual_rows < expected_rows: - assert page_size - hit_last_page = True - - pd.testing.assert_series_equal( - s, pd_series[total_rows : total_rows + actual_rows] - ) - total_rows += actual_rows - - assert total_rows == expected_total_rows - - -def test_to_numpy(scalars_dfs): - bf_df, pd_df = scalars_dfs - - bf_result = numpy.array(bf_df["int64_too"], dtype="int64") - pd_result = numpy.array(pd_df["int64_too"], dtype="int64") - - numpy.testing.assert_array_equal(bf_result, pd_result) diff --git a/tests/system/small/test_session.py b/tests/system/small/test_session.py index 76788da8a11..bf72e444eba 100644 --- a/tests/system/small/test_session.py +++ b/tests/system/small/test_session.py @@ -11,97 +11,27 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. + import io -import json import random -import re import tempfile import textwrap -import time import typing -import warnings -from typing import List, Optional, Sequence +from typing import List -import bigframes_vendored.pandas.io.gbq as vendored_pandas_gbq -import db_dtypes # type:ignore -import google +import google.api_core.exceptions import google.cloud.bigquery as bigquery import numpy as np import pandas as pd -import pandas.arrays as arrays -import pyarrow as pa import pytest import bigframes +import bigframes.core.indexes.index import bigframes.dataframe import bigframes.dtypes import bigframes.ml.linear_model -import bigframes.session.execution_spec -import bigframes.testing -from bigframes.testing import utils - -all_write_engines = pytest.mark.parametrize( - "write_engine", - [ - "default", - "bigquery_inline", - "bigquery_load", - "bigquery_streaming", - # TODO(b/502298527): Reenable bigquery_write test - # "bigquery_write", - ], -) - - -@pytest.fixture(scope="module") -def df_and_local_csv(scalars_df_index): - # The auto detects of BigQuery load job have restrictions to detect the bytes, - # datetime, numeric and geometry types, so they're skipped here. - drop_columns = [ - "bytes_col", - "datetime_col", - "numeric_col", - "geography_col", - "duration_col", - ] - scalars_df_index = scalars_df_index.drop(columns=drop_columns) - - with tempfile.TemporaryDirectory() as dir: - # Prepares local CSV file for reading - path = dir + "/test_read_csv_w_local_csv.csv" - scalars_df_index.to_csv(path, index=True) - yield scalars_df_index, path - - -@pytest.fixture(scope="module") -def df_and_gcs_csv(scalars_df_index, gcs_folder): - # The auto detects of BigQuery load job have restrictions to detect the bytes, - # datetime, numeric and geometry types, so they're skipped here. - drop_columns = [ - "bytes_col", - "datetime_col", - "numeric_col", - "geography_col", - "duration_col", - ] - scalars_df_index = scalars_df_index.drop(columns=drop_columns) - - path = gcs_folder + "test_read_csv_w_gcs_csv*.csv" - read_path = utils.get_first_file_from_wildcard(path) - scalars_df_index.to_csv(path, index=True) - return scalars_df_index, read_path - -@pytest.fixture(scope="module") -def df_and_gcs_csv_for_two_columns(scalars_df_index, gcs_folder): - # Some tests require only two columns to be present in the CSV file. - selected_cols = ["bool_col", "int64_col"] - scalars_df_index = scalars_df_index[selected_cols] - - path = gcs_folder + "df_and_gcs_csv_for_two_columns*.csv" - read_path = utils.get_first_file_from_wildcard(path) - scalars_df_index.to_csv(path, index=True) - return scalars_df_index, read_path +FIRST_FILE = "000000000000" def test_read_gbq_tokyo( @@ -111,23 +41,17 @@ def test_read_gbq_tokyo( tokyo_location: str, ): df = session_tokyo.read_gbq(scalars_table_tokyo, index_col=["rowindex"]) - df.sort_index(inplace=True) + result = df.sort_index().to_pandas() expected = scalars_pandas_df_index - exec_result = session_tokyo._executor.execute( - df._block.expr, - bigframes.session.execution_spec.ExecutionSpec( - destination_spec=bigframes.session.execution_spec.EphemeralTableSpec() - ), - ) - assert exec_result.query_job is not None - assert exec_result.query_job.location == tokyo_location + _, query_job = df._block.expr.start_query() + assert query_job.location == tokyo_location - assert len(expected) == exec_result.batches().approx_total_rows + pd.testing.assert_frame_equal(result, expected) @pytest.mark.parametrize( - ("query_or_table", "columns"), + ("query_or_table", "col_order"), [ pytest.param( "{scalars_table_id}", ["bool_col", "int64_col"], id="two_cols_in_table" @@ -143,48 +67,27 @@ def test_read_gbq_tokyo( ["my_strings"], id="one_cols_in_query", ), + pytest.param( + "{scalars_table_id}", + ["unknown"], + marks=pytest.mark.xfail( + raises=ValueError, + reason="Column `unknown` not found in this table.", + ), + id="unknown_col", + ), ], ) -def test_read_gbq_w_columns( +def test_read_gbq_w_col_order( session: bigframes.Session, scalars_table_id: str, query_or_table: str, - columns: List[str], + col_order: List[str], ): df = session.read_gbq( - query_or_table.format(scalars_table_id=scalars_table_id), columns=columns + query_or_table.format(scalars_table_id=scalars_table_id), col_order=col_order ) - assert df.columns.tolist() == columns - - -def test_read_gbq_w_unknown_column( - session: bigframes.Session, - scalars_table_id: str, -): - with pytest.raises( - ValueError, - match=re.escape("Column 'int63_col' is not found. Did you mean 'int64_col'?"), - ): - session.read_gbq( - scalars_table_id, - columns=["string_col", "int63_col", "bool_col"], - ) - - -def test_read_gbq_w_unknown_index_col( - session: bigframes.Session, - scalars_table_id: str, -): - with pytest.raises( - ValueError, - match=re.escape( - "Column 'int64_two' of `index_col` not found in this table. Did you mean 'int64_too'?" - ), - ): - session.read_gbq( - scalars_table_id, - index_col=["int64_col", "int64_two"], - ) + assert df.columns.tolist() == col_order @pytest.mark.parametrize( @@ -201,10 +104,9 @@ def test_read_gbq_w_unknown_index_col( CONCAT(t.string_col, "_2") AS my_strings, t.int64_col > 0 AS my_bools, FROM `{scalars_table_id}` AS t - ORDER BY my_strings """, ["my_strings"], - id="string_index_w_order_by", + id="string_index", ), pytest.param( "SELECT GENERATE_UUID() AS uuid, 0 AS my_value FROM UNNEST(GENERATE_ARRAY(1, 20))", @@ -327,69 +229,31 @@ def test_read_gbq_w_anonymous_query_results_table(session: bigframes.Session): df = session.read_gbq(destination, index_col="name") result = df.to_pandas() expected.index = expected.index.astype(result.index.dtype) - bigframes.testing.utils.assert_frame_equal(result, expected, check_dtype=False) + pd.testing.assert_frame_equal(result, expected, check_dtype=False) def test_read_gbq_w_primary_keys_table( session: bigframes.Session, usa_names_grouped_table: bigquery.Table ): - # Validate that the table we're querying has a primary key. table = usa_names_grouped_table - table_constraints = table.table_constraints - assert table_constraints is not None - primary_key = table_constraints.primary_key - assert primary_key is not None - primary_keys = primary_key.columns + # TODO(b/305264153): Use public properties to fetch primary keys once + # added to google-cloud-bigquery. + primary_keys = ( + table._properties.get("tableConstraints", {}) + .get("primaryKey", {}) + .get("columns") + ) assert len(primary_keys) != 0 df = session.read_gbq(f"{table.project}.{table.dataset_id}.{table.table_id}") result = df.head(100).to_pandas() - # Verify that primary keys are used as the index. - assert list(result.index.names) == list(primary_keys) - # Verify that the DataFrame is already sorted by primary keys. sorted_result = result.sort_values(primary_keys) - bigframes.testing.utils.assert_frame_equal(result, sorted_result) + pd.testing.assert_frame_equal(result, sorted_result) # Verify that we're working from a snapshot rather than a copy of the table. - assert "FOR SYSTEM_TIME AS OF" in df.sql - - -def test_read_gbq_w_primary_keys_table_and_filters( - session: bigframes.Session, usa_names_grouped_table: bigquery.Table -): - """ - Verify fix for internal issue 338039517, where using filters didn't use the - primary keys for indexing / ordering. - """ - # Validate that the table we're querying has a primary key. - table = usa_names_grouped_table - table_constraints = table.table_constraints - assert table_constraints is not None - primary_key = table_constraints.primary_key - assert primary_key is not None - primary_keys = primary_key.columns - assert len(primary_keys) != 0 - - df = session.read_gbq( - f"{table.project}.{table.dataset_id}.{table.table_id}", - filters=typing.cast( - vendored_pandas_gbq.FiltersType, - [ - ("name", "LIKE", "W%"), - ("total_people", ">", 100), - ], - ), - ) - result = df.to_pandas() - - # Verify that primary keys are used as the index. - assert list(result.index.names) == list(primary_keys) - - # Verify that the DataFrame is already sorted by primary keys. - sorted_result = result.sort_values(primary_keys) - bigframes.testing.utils.assert_frame_equal(result, sorted_result) + assert "FOR SYSTEM_TIME AS OF TIMESTAMP" in df.sql @pytest.mark.parametrize( @@ -431,432 +295,18 @@ def test_read_gbq_w_max_results( assert bf_result.shape[0] == max_results -@pytest.mark.parametrize( - ("sql_template", "expected_statement_type"), - ( - pytest.param( - """ - CREATE OR REPLACE TABLE `{dataset_id}.test_read_gbq_w_ddl` ( - `col_a` INT64, - `col_b` STRING - ); - """, - "CREATE_TABLE", - id="ddl-create-table", - ), - pytest.param( - # From https://cloud.google.com/bigquery/docs/boosted-tree-classifier-tutorial - """ - CREATE OR REPLACE VIEW `{dataset_id}.test_read_gbq_w_create_view` - AS - SELECT - age, - workclass, - marital_status, - education_num, - occupation, - hours_per_week, - income_bracket, - CASE - WHEN MOD(functional_weight, 10) < 8 THEN 'training' - WHEN MOD(functional_weight, 10) = 8 THEN 'evaluation' - WHEN MOD(functional_weight, 10) = 9 THEN 'prediction' - END AS dataframe - FROM - `bigquery-public-data.ml_datasets.census_adult_income`; - """, - "CREATE_VIEW", - id="ddl-create-view", - ), - pytest.param( - """ - CREATE OR REPLACE TABLE `{dataset_id}.test_read_gbq_w_dml` ( - `col_a` INT64, - `col_b` STRING - ); - - INSERT INTO `{dataset_id}.test_read_gbq_w_dml` - VALUES (123, 'hello world'); - """, - "SCRIPT", - id="dml", - ), - ), -) -def test_read_gbq_w_script_no_select( - session, dataset_id: str, sql_template: str, expected_statement_type: str -): - df = session.read_gbq(sql_template.format(dataset_id=dataset_id)).to_pandas() - assert df["statement_type"][0] == expected_statement_type - - -def test_read_gbq_twice_with_same_timestamp(session, penguins_table_id): - df1 = session.read_gbq(penguins_table_id) - time.sleep(1) - df2 = session.read_gbq(penguins_table_id) - df1.columns = [ - "species1", - "island1", - "culmen_length_mm1", - "culmen_depth_mm1", - "flipper_length_mm1", - "body_mass_g1", - "sex1", - ] - df3 = df1.join(df2) - assert df3 is not None - - -@pytest.mark.parametrize( - "source_table", - [ - # Wildcard tables - "bigquery-public-data.noaa_gsod.gsod194*", - # Materialized views - "bigframes-dev.bigframes_tests_sys.base_table_mat_view", - ], -) -def test_read_gbq_warns_time_travel_disabled(session, source_table): - with warnings.catch_warnings(record=True) as warned: - session.read_gbq(source_table, use_cache=False) - assert len(warned) == 1 - assert warned[0].category == bigframes.exceptions.TimeTravelDisabledWarning - - -def test_read_gbq_w_ambigous_name( - session: bigframes.Session, -): - # Ensure read_gbq works when table and column share a name - df = ( - session.read_gbq("bigframes-dev.bigframes_tests_sys.ambiguous_name") - .sort_values("x", ascending=False) - .reset_index(drop=True) - .to_pandas() - ) - pd_df = pd.DataFrame({"x": [2, 1], "ambiguous_name": [20, 10]}) - bigframes.testing.utils.assert_frame_equal( - df, pd_df, check_dtype=False, check_index_type=False - ) - - -def test_read_gbq_table_clustered_with_filter(session: bigframes.Session): - df = session.read_gbq_table( - "bigquery-public-data.cloud_storage_geo_index.landsat_index", - filters=typing.cast( - vendored_pandas_gbq.FiltersType, - [[("sensor_id", "LIKE", "OLI%")], [("sensor_id", "LIKE", "%TIRS")]], - ), - columns=["sensor_id"], - ) - sensors = df.groupby(["sensor_id"]).agg("count").to_pandas(ordered=False) - assert "OLI" in sensors.index - assert "TIRS" in sensors.index - assert "OLI_TIRS" in sensors.index - - -_GSOD_ALL_TABLES = "bigquery-public-data.noaa_gsod.gsod*" -_GSOD_1930S = "bigquery-public-data.noaa_gsod.gsod193*" - - -@pytest.mark.parametrize( - "api_method", - # Test that both methods work as there's a risk that read_gbq / - # read_gbq_table makes for an infinite loop. Table reads can convert to - # queries and read_gbq reads from tables. - ["read_gbq", "read_gbq_table"], -) -@pytest.mark.parametrize( - ("filters", "table_id", "index_col", "columns", "max_results"), - [ - pytest.param( - [("_table_suffix", ">=", "1930"), ("_table_suffix", "<=", "1939")], - _GSOD_ALL_TABLES, - ["stn", "wban", "year", "mo", "da"], - ["temp", "max", "min"], - 100, - id="all", - ), - pytest.param( - (), # filters - _GSOD_1930S, - (), # index_col - ["temp", "max", "min"], - None, # max_results - id="columns", - ), - pytest.param( - [("_table_suffix", ">=", "1930"), ("_table_suffix", "<=", "1939")], - _GSOD_ALL_TABLES, - (), # index_col, - (), # columns - None, # max_results - id="filters", - ), - pytest.param( - (), # filters - _GSOD_1930S, - ["stn", "wban", "year", "mo", "da"], - (), # columns - None, # max_results - id="index_col", - ), - pytest.param( - (), # filters - _GSOD_1930S, - (), # index_col - (), # columns - 100, # max_results - id="max_results", - ), - ], -) -def test_read_gbq_wildcard( - session: bigframes.Session, - api_method: str, - filters, - table_id: str, - index_col: Sequence[str], - columns: Sequence[str], - max_results: Optional[int], -): - table_metadata = session.bqclient.get_table(table_id) - method = getattr(session, api_method) - df = method( - table_id, - filters=filters, - index_col=index_col, - columns=columns, - max_results=max_results, - ) - num_rows, num_columns = df.shape - - if index_col: - assert list(df.index.names) == list(index_col) - else: - assert df.index.name is None - - expected_columns = ( - columns - if columns - else [ - field.name - for field in table_metadata.schema - if field.name not in index_col and field.name not in columns - ] - ) - assert list(df.columns) == expected_columns - assert num_rows > 0 - assert num_columns == len(expected_columns) - - -@pytest.mark.parametrize( - ("config"), - [ - { - "query": { - "useQueryCache": True, - "maximumBytesBilled": "1000000000", - "timeoutMs": 120_000, - } - }, - pytest.param( - {"query": {"useQueryCache": True, "timeoutMs": 50}}, - marks=pytest.mark.xfail( - raises=google.api_core.exceptions.BadRequest, - reason="Expected failure due to timeout being set too short.", - ), - ), - pytest.param( - {"query": {"useQueryCache": False, "maximumBytesBilled": "100"}}, - marks=pytest.mark.xfail( - raises=google.api_core.exceptions.BadRequest, - reason="Expected failure when the query exceeds the maximum bytes billed limit.", - ), - ), - ], -) -def test_read_gbq_with_configuration( - session: bigframes.Session, scalars_table_id: str, config: dict -): - query = f"""SELECT - t.float64_col * 2 AS my_floats, - CONCAT(t.string_col, "_2") AS my_strings, - t.int64_col > 0 AS my_bools, - FROM `{scalars_table_id}` AS t - """ - - df = session.read_gbq(query, configuration=config) - - assert df.shape == (9, 3) - - -def test_read_gbq_with_custom_global_labels( - session: bigframes.Session, scalars_table_id: str -): - # Ensure we use thread-local variables to avoid conflicts with parallel tests. - with bigframes.option_context("compute.extra_query_labels", {}): - bigframes.options.compute.assign_extra_query_labels(test1=1, test2="abc") - bigframes.options.compute.extra_query_labels["test3"] = False - - query_job = session.read_gbq(scalars_table_id).query_job - - # No real job created from read_gbq, so we should expect 0 labels - assert query_job is not None - assert query_job.labels == {} - # No labels outside of the option_context. - assert len(bigframes.options.compute.extra_query_labels) == 0 - - -def test_read_gbq_external_table(session: bigframes.Session): - # Verify the table is external to ensure it hasn't been altered - external_table_id = "bigframes-dev.bigframes_tests_sys.parquet_external_table" - external_table = session.bqclient.get_table(external_table_id) - assert external_table.table_type == "EXTERNAL" - - df = session.read_gbq(external_table_id) - - assert list(df.columns) == ["idx", "s1", "s2", "s3", "s4", "i1", "f1", "i2", "f2"] - assert df["i1"].max() == 99 - - -def test_read_gbq_w_json(session): - sql = """ - SELECT 0 AS id, JSON_OBJECT('boolean', True) AS json_col, - UNION ALL - SELECT 1, JSON_OBJECT('int', 100), - UNION ALL - SELECT 2, JSON_OBJECT('float', 0.98), - UNION ALL - SELECT 3, JSON_OBJECT('string', 'hello world'), - UNION ALL - SELECT 4, JSON_OBJECT('array', [8, 9, 10]), - UNION ALL - SELECT 5, JSON_OBJECT('null', null), - UNION ALL - SELECT 6, JSON_OBJECT('b', 2, 'a', 1), - UNION ALL - SELECT - 7, - JSON_OBJECT( - 'dict', - JSON_OBJECT( - 'int', 1, - 'array', [JSON_OBJECT('foo', 1), JSON_OBJECT('bar', 'hello')] - ) - ), - """ - df = session.read_gbq(sql, index_col="id") - - assert df.dtypes["json_col"] == pd.ArrowDtype(db_dtypes.JSONArrowType()) - - assert df["json_col"][0] == '{"boolean":true}' - assert df["json_col"][1] == '{"int":100}' - assert df["json_col"][2] == '{"float":0.98}' - assert df["json_col"][3] == '{"string":"hello world"}' - assert df["json_col"][4] == '{"array":[8,9,10]}' - assert df["json_col"][5] == '{"null":null}' - - # Verifies JSON strings preserve array order, regardless of dictionary key order. - assert df["json_col"][6] == '{"a":1,"b":2}' - assert df["json_col"][7] == '{"dict":{"array":[{"foo":1},{"bar":"hello"}],"int":1}}' - - -def test_read_gbq_w_json_and_compare_w_pandas_json(session): - df = session.read_gbq("SELECT JSON_OBJECT('foo', 10, 'bar', TRUE) AS json_col") - assert df.dtypes["json_col"] == pd.ArrowDtype(db_dtypes.JSONArrowType()) +def test_read_gbq_w_script_no_select(session, dataset_id: str): + ddl = f""" + CREATE TABLE `{dataset_id}.test_read_gbq_w_ddl` ( + `col_a` INT64, + `col_b` STRING + ); - # These JSON strings are compatible with BigQuery's JSON storage, - pd_df = pd.DataFrame( - {"json_col": ['{"bar":true,"foo":10}']}, - dtype=pd.ArrowDtype(db_dtypes.JSONArrowType()), - ) - pd_df.index = pd_df.index.astype("Int64") - bigframes.testing.utils.assert_series_equal(df.dtypes, pd_df.dtypes) - bigframes.testing.utils.assert_series_equal( - df["json_col"].to_pandas(), pd_df["json_col"] - ) - - -def test_read_gbq_w_json_in_struct(session): - """Avoid regressions for internal issue 381148539.""" - sql = """ - SELECT 0 AS id, STRUCT(JSON_OBJECT('boolean', True) AS data, 1 AS number) AS struct_col - UNION ALL - SELECT 1, STRUCT(JSON_OBJECT('int', 100), 2), - UNION ALL - SELECT 2, STRUCT(JSON_OBJECT('float', 0.98), 3), - UNION ALL - SELECT 3, STRUCT(JSON_OBJECT('string', 'hello world'), 4), - UNION ALL - SELECT 4, STRUCT(JSON_OBJECT('array', [8, 9, 10]), 5), - UNION ALL - SELECT 5, STRUCT(JSON_OBJECT('null', null), 6), - UNION ALL - SELECT - 6, - STRUCT(JSON_OBJECT( - 'dict', - JSON_OBJECT( - 'int', 1, - 'array', [JSON_OBJECT('foo', 1), JSON_OBJECT('bar', 'hello')] - ) - ), 7), - """ - df = session.read_gbq(sql, index_col="id") - - assert isinstance(df.dtypes["struct_col"], pd.ArrowDtype) - assert isinstance(df.dtypes["struct_col"].pyarrow_dtype, pa.StructType) - - data = df["struct_col"].struct.field("data") - assert data.dtype == pd.ArrowDtype(db_dtypes.JSONArrowType()) - - assert data[0] == '{"boolean":true}' - assert data[1] == '{"int":100}' - assert data[2] == '{"float":0.98}' - assert data[3] == '{"string":"hello world"}' - assert data[4] == '{"array":[8,9,10]}' - assert data[5] == '{"null":null}' - assert data[6] == '{"dict":{"array":[{"foo":1},{"bar":"hello"}],"int":1}}' - - -def test_read_gbq_w_json_in_array(session): - sql = """ - SELECT - 0 AS id, - [ - JSON_OBJECT('boolean', True), - JSON_OBJECT('int', 100), - JSON_OBJECT('float', 0.98), - JSON_OBJECT('string', 'hello world'), - JSON_OBJECT('array', [8, 9, 10]), - JSON_OBJECT('null', null), - JSON_OBJECT( - 'dict', - JSON_OBJECT( - 'int', 1, - 'array', [JSON_OBJECT('bar', 'hello'), JSON_OBJECT('foo', 1)] - ) - ) - ] AS array_col, + INSERT INTO `{dataset_id}.test_read_gbq_w_ddl` + VALUES (123, 'hello world'); """ - df = session.read_gbq(sql, index_col="id") - - assert isinstance(df.dtypes["array_col"], pd.ArrowDtype) - assert isinstance(df.dtypes["array_col"].pyarrow_dtype, pa.ListType) - - data = df["array_col"] - assert data.list.len()[0] == 7 - assert data.list[0].dtype == pd.ArrowDtype(db_dtypes.JSONArrowType()) - - assert data[0] == [ - '{"boolean":true}', - '{"int":100}', - '{"float":0.98}', - '{"string":"hello world"}', - '{"array":[8,9,10]}', - '{"null":null}', - '{"dict":{"array":[{"bar":"hello"},{"foo":1}],"int":1}}', - ] + df = session.read_gbq(ddl).to_pandas() + assert df["statement_type"][0] == "SCRIPT" def test_read_gbq_model(session, penguins_linear_model_name): @@ -872,40 +322,7 @@ def test_read_pandas(session, scalars_dfs): result = df.to_pandas() expected = scalars_pandas_df - bigframes.testing.utils.assert_frame_equal(result, expected) - - -def test_read_pandas_series(session): - idx: pd.Index = pd.Index([2, 7, 1, 2, 8], dtype=pd.Int64Dtype()) - pd_series = pd.Series([3, 1, 4, 1, 5], dtype=pd.Int64Dtype(), index=idx) - bf_series = session.read_pandas(pd_series) - - bigframes.testing.utils.assert_series_equal(bf_series.to_pandas(), pd_series) - - -def test_read_pandas_index(session): - pd_idx: pd.Index = pd.Index([2, 7, 1, 2, 8], dtype=pd.Int64Dtype()) - bf_idx = session.read_pandas(pd_idx) - - bigframes.testing.utils.assert_index_equal(bf_idx.to_pandas(), pd_idx) - - -def test_read_pandas_w_unsupported_mixed_dtype(session): - with pytest.raises(ValueError, match="Could not convert"): - session.read_pandas(pd.DataFrame({"a": [1, "hello"]})) - - -def test_read_pandas_inline_respects_location(): - options = bigframes.BigQueryOptions(location="europe-west1") - session = bigframes.Session(options) - - df = session.read_pandas(pd.DataFrame([[1, 2, 3], [4, 5, 6]])) - df.to_gbq() - - assert df.query_job is not None - - table = session.bqclient.get_table(df.query_job.destination) - assert table.location == "europe-west1" + pd.testing.assert_frame_equal(result, expected) def test_read_pandas_col_label_w_space(session: bigframes.Session): @@ -917,7 +334,7 @@ def test_read_pandas_col_label_w_space(session: bigframes.Session): ) result = session.read_pandas(expected).to_pandas() - bigframes.testing.utils.assert_frame_equal( + pd.testing.assert_frame_equal( result, expected, check_index_type=False, check_dtype=False ) @@ -925,7 +342,7 @@ def test_read_pandas_col_label_w_space(session: bigframes.Session): def test_read_pandas_multi_index(session, scalars_pandas_df_multi_index): df = session.read_pandas(scalars_pandas_df_multi_index) result = df.to_pandas() - bigframes.testing.utils.assert_frame_equal(result, scalars_pandas_df_multi_index) + pd.testing.assert_frame_equal(result, scalars_pandas_df_multi_index) def test_read_pandas_rowid_exists_adds_suffix(session, scalars_pandas_df_default_index): @@ -933,9 +350,7 @@ def test_read_pandas_rowid_exists_adds_suffix(session, scalars_pandas_df_default pandas_df["rowid"] = np.arange(pandas_df.shape[0]) df_roundtrip = session.read_pandas(pandas_df).to_pandas() - bigframes.testing.utils.assert_frame_equal( - df_roundtrip, pandas_df, check_dtype=False - ) + pd.testing.assert_frame_equal(df_roundtrip, pandas_df, check_dtype=False) def test_read_pandas_tokyo( @@ -944,877 +359,418 @@ def test_read_pandas_tokyo( tokyo_location: str, ): df = session_tokyo.read_pandas(scalars_pandas_df_index) - df.to_gbq() + result = df.to_pandas() expected = scalars_pandas_df_index - result = session_tokyo._executor.execute( - df._block.expr, - bigframes.session.execution_spec.ExecutionSpec( - destination_spec=bigframes.session.execution_spec.EphemeralTableSpec() - ), - ) - assert result.query_job is not None - assert result.query_job.location == tokyo_location + _, query_job = df._block.expr.start_query() + assert query_job.location == tokyo_location - assert len(expected) == result.batches().approx_total_rows + pd.testing.assert_frame_equal(result, expected) -@all_write_engines -def test_read_pandas_timedelta_dataframes(session, write_engine): - pytest.importorskip( - "pandas", - minversion="2.0.0", - reason="old versions don't support local casting to arrow duration", +def test_read_csv_gcs_default_engine(session, scalars_dfs, gcs_folder): + scalars_df, _ = scalars_dfs + if scalars_df.index.name is not None: + path = gcs_folder + "test_read_csv_gcs_default_engine_w_index*.csv" + else: + path = gcs_folder + "test_read_csv_gcs_default_engine_wo_index*.csv" + read_path = path.replace("*", FIRST_FILE) + scalars_df.to_csv(path, index=False) + dtype = scalars_df.dtypes.to_dict() + dtype.pop("geography_col") + df = session.read_csv( + read_path, + # Convert default pandas dtypes to match BigQuery DataFrames dtypes. + dtype=dtype, ) - pandas_df = pd.DataFrame({"my_col": pd.to_timedelta([1, 2, 3], unit="d")}) - actual_result = session.read_pandas( - pandas_df, write_engine=write_engine - ).to_pandas() - expected_result = pandas_df.astype(bigframes.dtypes.TIMEDELTA_DTYPE) - expected_result.index = expected_result.index.astype(bigframes.dtypes.INT_DTYPE) + # TODO(chelsealin): If we serialize the index, can more easily compare values. + pd.testing.assert_index_equal(df.columns, scalars_df.columns) - bigframes.testing.utils.assert_frame_equal(actual_result, expected_result) - - -@all_write_engines -def test_read_pandas_timedelta_series(session, write_engine): - expected_series = pd.Series(pd.to_timedelta([1, 2, 3], unit="d")).astype( - "timedelta64[ns]" - ) - - actual_result = ( - session.read_pandas(expected_series, write_engine=write_engine) - .to_pandas() - .astype("timedelta64[ns]") - ) + # The auto detects of BigQuery load job have restrictions to detect the bytes, + # numeric and geometry types, so they're skipped here. + df = df.drop(columns=["bytes_col", "numeric_col", "geography_col"]) + scalars_df = scalars_df.drop(columns=["bytes_col", "numeric_col", "geography_col"]) + assert df.shape[0] == scalars_df.shape[0] + pd.testing.assert_series_equal(df.dtypes, scalars_df.dtypes) - bigframes.testing.utils.assert_series_equal( - actual_result, expected_series, check_index_type=False - ) +def test_read_csv_gcs_bq_engine(session, scalars_dfs, gcs_folder): + scalars_df, _ = scalars_dfs + if scalars_df.index.name is not None: + path = gcs_folder + "test_read_csv_gcs_bq_engine_w_index*.csv" + else: + path = gcs_folder + "test_read_csv_gcs_bq_engine_wo_index*.csv" + scalars_df.to_csv(path, index=False) + df = session.read_csv(path, engine="bigquery") -@all_write_engines -def test_read_pandas_timedelta_index(session, write_engine): - expected_index = pd.to_timedelta([1, 2, 3], unit="d").astype( - "timedelta64[ns]" - ) # to_timedelta returns an index + # TODO(chelsealin): If we serialize the index, can more easily compare values. + pd.testing.assert_index_equal(df.columns, scalars_df.columns) - actual_result = ( - session.read_pandas(expected_index, write_engine=write_engine) - .to_pandas() - .astype("timedelta64[ns]") + # The auto detects of BigQuery load job have restrictions to detect the bytes, + # datetime, numeric and geometry types, so they're skipped here. + df = df.drop(columns=["bytes_col", "datetime_col", "numeric_col", "geography_col"]) + scalars_df = scalars_df.drop( + columns=["bytes_col", "datetime_col", "numeric_col", "geography_col"] ) + assert df.shape[0] == scalars_df.shape[0] + pd.testing.assert_series_equal(df.dtypes, scalars_df.dtypes) - bigframes.testing.utils.assert_index_equal(actual_result, expected_index) +@pytest.mark.parametrize( + "sep", + [ + pytest.param(",", id="default_sep"), + pytest.param("\t", id="custom_sep"), + ], +) +def test_read_csv_local_default_engine(session, scalars_dfs, sep): + scalars_df, scalars_pandas_df = scalars_dfs + with tempfile.TemporaryDirectory() as dir: + path = dir + "/test_read_csv_local_default_engine.csv" + # Using the pandas to_csv method because the BQ one does not support local write. + scalars_pandas_df.to_csv(path, index=False, sep=sep) + dtype = scalars_df.dtypes.to_dict() + dtype.pop("geography_col") + df = session.read_csv( + path, + sep=sep, + # Convert default pandas dtypes to match BigQuery DataFrames dtypes. + dtype=dtype, + ) -@all_write_engines -def test_read_pandas_json_dataframes(session, write_engine): - json_data = [ - "1", - None, - '["1","3","5"]', - '{"a":1,"b":["x","y"],"c":{"x":[],"z":false}}', - ] - expected_df = pd.DataFrame( - {"my_col": pd.Series(json_data, dtype=bigframes.dtypes.JSON_DTYPE)} - ) + # TODO(chelsealin): If we serialize the index, can more easily compare values. + pd.testing.assert_index_equal(df.columns, scalars_df.columns) - actual_result = session.read_pandas( - expected_df, write_engine=write_engine - ).to_pandas() + # The auto detects of BigQuery load job have restrictions to detect the bytes, + # numeric and geometry types, so they're skipped here. + df = df.drop(columns=["bytes_col", "numeric_col", "geography_col"]) + scalars_df = scalars_df.drop( + columns=["bytes_col", "numeric_col", "geography_col"] + ) + assert df.shape[0] == scalars_df.shape[0] + pd.testing.assert_series_equal(df.dtypes, scalars_df.dtypes) - bigframes.testing.utils.assert_frame_equal( - actual_result, expected_df, check_index_type=False - ) +@pytest.mark.parametrize( + "sep", + [ + pytest.param(",", id="default_sep"), + pytest.param("\t", id="custom_sep"), + ], +) +def test_read_csv_local_bq_engine(session, scalars_dfs, sep): + scalars_df, scalars_pandas_df = scalars_dfs + with tempfile.TemporaryDirectory() as dir: + path = dir + "/test_read_csv_local_bq_engine.csv" + # Using the pandas to_csv method because the BQ one does not support local write. + scalars_pandas_df.to_csv(path, index=False, sep=sep) + df = session.read_csv(path, engine="bigquery", sep=sep) -@all_write_engines -def test_read_pandas_json_series(session, write_engine): - json_data = [ - "1", - None, - '[1,"3",null,{"a":null}]', - '{"a":1,"b":["x","y"],"c":{"x":[],"y":null,"z":false}}', - ] - expected_series = pd.Series(json_data, dtype=bigframes.dtypes.JSON_DTYPE) - - actual_result = session.read_pandas( - expected_series, write_engine=write_engine - ).to_pandas() - bigframes.testing.utils.assert_series_equal( - actual_result, expected_series, check_index_type=False - ) + # TODO(chelsealin): If we serialize the index, can more easily compare values. + pd.testing.assert_index_equal(df.columns, scalars_df.columns) + # The auto detects of BigQuery load job have restrictions to detect the bytes, + # datetime, numeric and geometry types, so they're skipped here. + df = df.drop( + columns=["bytes_col", "datetime_col", "numeric_col", "geography_col"] + ) + scalars_df = scalars_df.drop( + columns=["bytes_col", "datetime_col", "numeric_col", "geography_col"] + ) + assert df.shape[0] == scalars_df.shape[0] + pd.testing.assert_series_equal(df.dtypes, scalars_df.dtypes) -@all_write_engines -def test_read_pandas_json_series_w_invalid_json(session, write_engine): - json_data = [ - "False", # Should be "false" - ] - pd_s = pd.Series(json_data, dtype=bigframes.dtypes.JSON_DTYPE) - with pytest.raises(json.JSONDecodeError): - session.read_pandas(pd_s, write_engine=write_engine) +def test_read_csv_localbuffer_bq_engine(session, scalars_dfs): + scalars_df, scalars_pandas_df = scalars_dfs + with tempfile.TemporaryDirectory() as dir: + path = dir + "/test_read_csv_local_bq_engine.csv" + # Using the pandas to_csv method because the BQ one does not support local write. + scalars_pandas_df.to_csv(path, index=False) + with open(path, "rb") as buffer: + df = session.read_csv(buffer, engine="bigquery") + # TODO(chelsealin): If we serialize the index, can more easily compare values. + pd.testing.assert_index_equal(df.columns, scalars_df.columns) -@all_write_engines -def test_read_pandas_json_index(session, write_engine): - json_data = [ - "1", - None, - '["1","3","5"]', - '{"a":1,"b":["x","y"],"c":{"x":[],"z":false}}', - ] - expected_index: pd.Index = pd.Index(json_data, dtype=bigframes.dtypes.JSON_DTYPE) - actual_result = session.read_pandas( - expected_index, write_engine=write_engine - ).to_pandas() - bigframes.testing.utils.assert_index_equal(actual_result, expected_index) + # The auto detects of BigQuery load job have restrictions to detect the bytes, + # datetime, numeric and geometry types, so they're skipped here. + df = df.drop( + columns=["bytes_col", "datetime_col", "numeric_col", "geography_col"] + ) + scalars_df = scalars_df.drop( + columns=["bytes_col", "datetime_col", "numeric_col", "geography_col"] + ) + assert df.shape[0] == scalars_df.shape[0] + pd.testing.assert_series_equal(df.dtypes, scalars_df.dtypes) @pytest.mark.parametrize( - ("write_engine"), + ("kwargs", "match"), [ - pytest.param("bigquery_load"), - ], -) -def test_read_pandas_w_nested_json_fails(session, write_engine): - data = [ - [{"json_field": "1"}], - [{"json_field": None}], - [{"json_field": '["1","3","5"]'}], - [{"json_field": '{"a":1,"b":["x","y"],"c":{"x":[],"z":false}}'}], - ] - # PyArrow currently lacks support for creating structs or lists containing extension types. - # See issue: https://github.com/apache/arrow/issues/45262 - pa_array = pa.array(data, type=pa.list_(pa.struct([("json_field", pa.string())]))) - pd_s = pd.Series( - arrays.ArrowExtensionArray(pa_array), # type: ignore - dtype=pd.ArrowDtype( - pa.list_(pa.struct([("json_field", bigframes.dtypes.JSON_ARROW_TYPE)])) + pytest.param( + {"engine": "bigquery", "names": []}, + "BigQuery engine does not support these arguments", + id="with_names", ), - ) - with pytest.raises( - NotImplementedError, match="Nested JSON types are currently unsupported" - ): - session.read_pandas(pd_s, write_engine=write_engine) - - -@pytest.mark.parametrize( - ("write_engine"), - [ - pytest.param("default"), - pytest.param("bigquery_inline"), - pytest.param("bigquery_streaming"), - # TODO(b/502298527): Reenable bigquery_write test - # pytest.param("bigquery_write"), - ], -) -def test_read_pandas_w_nested_json(session, write_engine): - # TODO: supply a reason why this isn't compatible with pandas 1.x - pytest.importorskip("pandas", minversion="2.0.0") - data = [ - [{"json_field": "1"}], - [{"json_field": None}], - [{"json_field": '["1","3","5"]'}], - [{"json_field": '{"a":1,"b":["x","y"],"c":{"x":[],"z":false}}'}], - ] - pa_array = pa.array(data, type=pa.list_(pa.struct([("json_field", pa.string())]))) - pd_s = pd.Series( - arrays.ArrowExtensionArray(pa_array), # type: ignore - dtype=pd.ArrowDtype( - pa.list_(pa.struct([("json_field", bigframes.dtypes.JSON_ARROW_TYPE)])) + pytest.param( + {"engine": "bigquery", "dtype": {}}, + "BigQuery engine does not support these arguments", + id="with_dtype", ), - ) - bq_s = ( - session.read_pandas(pd_s, write_engine=write_engine) - .to_pandas() - .reset_index(drop=True) - ) - bigframes.testing.utils.assert_series_equal(bq_s, pd_s) - - -@pytest.mark.parametrize( - ("write_engine"), - [ - pytest.param("default"), - pytest.param("bigquery_inline"), - pytest.param("bigquery_load"), - pytest.param("bigquery_streaming"), - ], -) -def test_read_pandas_w_nested_invalid_json(session, write_engine): - # TODO: supply a reason why this isn't compatible with pandas 1.x - pytest.importorskip("pandas", minversion="2.0.0") - data = [ - [{"json_field": "NULL"}], # Should be "null" - ] - pa_array = pa.array(data, type=pa.list_(pa.struct([("json_field", pa.string())]))) - pd_s = pd.Series( - arrays.ArrowExtensionArray(pa_array), # type: ignore - dtype=pd.ArrowDtype( - pa.list_(pa.struct([("json_field", bigframes.dtypes.JSON_ARROW_TYPE)])) + pytest.param( + {"engine": "bigquery", "index_col": False}, + "BigQuery engine only supports a single column name for `index_col`.", + id="with_index_col_false", ), - ) - - with pytest.raises(json.JSONDecodeError): - session.read_pandas(pd_s, write_engine=write_engine) - - -@pytest.mark.parametrize( - ("write_engine"), - [ - pytest.param("bigquery_load"), - ], -) -def test_read_pandas_w_nested_json_index_fails(session, write_engine): - data = [ - [{"json_field": "1"}], - [{"json_field": None}], - [{"json_field": '["1","3","5"]'}], - [{"json_field": '{"a":1,"b":["x","y"],"c":{"x":[],"z":false}}'}], - ] - # PyArrow currently lacks support for creating structs or lists containing extension types. - # See issue: https://github.com/apache/arrow/issues/45262 - pa_array = pa.array(data, type=pa.list_(pa.struct([("json_field", pa.string())]))) - pd_idx: pd.Index = pd.Index( - arrays.ArrowExtensionArray(pa_array), # type: ignore - dtype=pd.ArrowDtype( - pa.list_(pa.struct([("json_field", bigframes.dtypes.JSON_ARROW_TYPE)])) + pytest.param( + {"engine": "bigquery", "index_col": 5}, + "BigQuery engine only supports a single column name for `index_col`.", + id="with_index_col_not_str", ), - ) - with pytest.raises( - NotImplementedError, match="Nested JSON types are currently unsupported" - ): - session.read_pandas(pd_idx, write_engine=write_engine) - - -@pytest.mark.parametrize( - ("write_engine"), - [ - pytest.param("default"), - pytest.param("bigquery_inline"), - pytest.param("bigquery_streaming"), - # TODO(b/502298527): Reenable bigquery_write test - # pytest.param("bigquery_write"), - ], -) -def test_read_pandas_w_nested_json_index(session, write_engine): - # TODO: supply a reason why this isn't compatible with pandas 1.x - pytest.importorskip("pandas", minversion="2.0.0") - data = [ - [{"json_field": "1"}], - [{"json_field": None}], - [{"json_field": '["1","3","5"]'}], - [{"json_field": '{"a":1,"b":["x","y"],"c":{"x":[],"z":false}}'}], - ] - pa_array = pa.array(data, type=pa.list_(pa.struct([("name", pa.string())]))) - pd_idx: pd.Index = pd.Index( - arrays.ArrowExtensionArray(pa_array), # type: ignore - dtype=pd.ArrowDtype( - pa.list_(pa.struct([("name", bigframes.dtypes.JSON_ARROW_TYPE)])) + pytest.param( + {"engine": "bigquery", "usecols": [1, 2]}, + "BigQuery engine only supports an iterable of strings for `usecols`.", + id="with_usecols_invalid", + ), + pytest.param( + {"engine": "bigquery", "encoding": "ASCII"}, + "BigQuery engine only supports the following encodings", + id="with_encoding_invalid", ), - ) - bq_idx = session.read_pandas(pd_idx, write_engine=write_engine).to_pandas() - bigframes.testing.utils.assert_index_equal(bq_idx, pd_idx) - - -@all_write_engines -def test_read_csv_for_gcs_file_w_write_engine(session, df_and_gcs_csv, write_engine): - scalars_df, path = df_and_gcs_csv - - # Compares results for pandas and bigframes engines - pd_df = session.read_csv( - path, - index_col="rowindex", - write_engine=write_engine, - dtype=scalars_df.dtypes.to_dict(), - ) - bigframes.testing.utils.assert_frame_equal( - pd_df.to_pandas(), scalars_df.to_pandas() - ) - - if write_engine in ("default", "bigquery_load"): - bf_df = session.read_csv( - path, engine="bigquery", index_col="rowindex", write_engine=write_engine - ) - bigframes.testing.utils.assert_frame_equal(bf_df.to_pandas(), pd_df.to_pandas()) - - -@pytest.mark.parametrize( - "sep", - [ - pytest.param(",", id="default_sep"), - pytest.param("\t", id="custom_sep"), - ], -) -def test_read_csv_for_local_file_w_sep(session, df_and_local_csv, sep): - scalars_df, _ = df_and_local_csv - - with tempfile.TemporaryDirectory() as dir: - # Prepares local CSV file for reading - path = dir + "/test_read_csv_for_local_file_w_sep.csv" - scalars_df.to_csv(path, index=True, sep=sep) - - # Compares results for pandas and bigframes engines - with open(path, "rb") as buffer: - bf_df = session.read_csv( - buffer, engine="bigquery", index_col="rowindex", sep=sep - ) - with open(path, "rb") as buffer: - # Convert default pandas dtypes to match BigQuery DataFrames dtypes. - pd_df = session.read_csv( - buffer, index_col="rowindex", sep=sep, dtype=scalars_df.dtypes.to_dict() - ) - bigframes.testing.utils.assert_frame_equal( - bf_df.to_pandas(), scalars_df.to_pandas() - ) - bigframes.testing.utils.assert_frame_equal(bf_df.to_pandas(), pd_df.to_pandas()) - - -@pytest.mark.parametrize( - "index_col", - [ - pytest.param(None, id="none"), - pytest.param(False, id="false"), - pytest.param([], id="empty_list"), - ], -) -def test_read_csv_for_index_col_w_false(session, df_and_local_csv, index_col): - # Compares results for pandas and bigframes engines - scalars_df, path = df_and_local_csv - with open(path, "rb") as buffer: - bf_df = session.read_csv( - buffer, - engine="bigquery", - index_col=index_col, - ) - with open(path, "rb") as buffer: - # Convert default pandas dtypes to match BigQuery DataFrames dtypes. - pd_df = session.read_csv( - buffer, index_col=index_col, dtype=scalars_df.dtypes.to_dict() - ) - - assert bf_df.shape == pd_df.shape - - # BigFrames requires `sort_index()` because BigQuery doesn't preserve row IDs - # (b/280889935) or guarantee row ordering. - bf_df = bf_df.set_index("rowindex").sort_index() - pd_df = pd_df.set_index("rowindex") - bigframes.testing.utils.assert_frame_equal(bf_df.to_pandas(), pd_df.to_pandas()) - - -@pytest.mark.parametrize( - "index_col", - [ - pytest.param("rowindex", id="single_str"), - pytest.param(["rowindex", "bool_col"], id="multi_str"), - pytest.param(0, id="single_int"), - pytest.param([0, 2], id="multi_int"), - pytest.param([0, "bool_col"], id="mix_types"), ], ) -def test_read_csv_for_index_col(session, df_and_gcs_csv, index_col): - scalars_pandas_df, path = df_and_gcs_csv - bf_df = session.read_csv(path, engine="bigquery", index_col=index_col) - - # Convert default pandas dtypes to match BigQuery DataFrames dtypes. - pd_df = session.read_csv( - path, index_col=index_col, dtype=scalars_pandas_df.dtypes.to_dict() - ) - - assert bf_df.shape == pd_df.shape - bigframes.testing.utils.assert_frame_equal(bf_df.to_pandas(), pd_df.to_pandas()) +def test_read_csv_bq_engine_throws_not_implemented_error(session, kwargs, match): + with pytest.raises(NotImplementedError, match=match): + session.read_csv("", **kwargs) @pytest.mark.parametrize( - ("index_col", "error_type", "error_msg"), + ("kwargs", "match"), [ pytest.param( - True, ValueError, "The value of index_col couldn't be 'True'", id="true" - ), - pytest.param(100, ValueError, "out of bounds", id="single_int"), - pytest.param([0, 200], ValueError, "out of bounds", id="multi_int"), - pytest.param( - [0.1], TypeError, "it must contain either strings", id="invalid_iterable" + {"chunksize": 5}, + "'chunksize' and 'iterator' arguments are not supported.", + id="with_chunksize", ), pytest.param( - 3.14, TypeError, "Unsupported type for index_col", id="unsupported_type" + {"iterator": True}, + "'chunksize' and 'iterator' arguments are not supported.", + id="with_iterator", ), ], ) -def test_read_csv_raises_error_for_invalid_index_col( - session, df_and_gcs_csv, index_col, error_type, error_msg +def test_read_csv_default_engine_throws_not_implemented_error( + session, + scalars_df_index, + gcs_folder, + kwargs, + match, ): - _, path = df_and_gcs_csv - with pytest.raises( - error_type, - match=error_msg, - ): - session.read_csv(path, engine="bigquery", index_col=index_col) - - -def test_read_csv_for_gcs_wildcard_path(session, df_and_gcs_csv): - scalars_pandas_df, path = df_and_gcs_csv - path = path.replace(".csv", "*.csv") - - index_col = "rowindex" - bf_df = session.read_csv(path, engine="bigquery", index_col=index_col) - - # Convert default pandas dtypes to match BigQuery DataFrames dtypes. - # Also, `expand=True` is needed to read from wildcard paths. See details: - # https://github.com/fsspec/gcsfs/issues/616, - if not pd.__version__.startswith("1."): - storage_options = {"expand": True} - else: - storage_options = None - pd_df = session.read_csv( - path, - index_col=index_col, - dtype=scalars_pandas_df.dtypes.to_dict(), - storage_options=storage_options, + path = ( + gcs_folder + + "test_read_csv_gcs_default_engine_throws_not_implemented_error*.csv" ) + read_path = path.replace("*", FIRST_FILE) + scalars_df_index.to_csv(path) + with pytest.raises(NotImplementedError, match=match): + session.read_csv(read_path, **kwargs) - assert bf_df.shape == pd_df.shape - assert bf_df.columns.tolist() == pd_df.columns.tolist() - bigframes.testing.utils.assert_frame_equal(bf_df.to_pandas(), pd_df.to_pandas()) - - -def test_read_csv_for_names(session, df_and_gcs_csv_for_two_columns): - _, path = df_and_gcs_csv_for_two_columns - - names = ["a", "b", "c"] - bf_df = session.read_csv(path, engine="bigquery", names=names) - # Convert default pandas dtypes to match BigQuery DataFrames dtypes. - pd_df = session.read_csv(path, names=names, dtype=bf_df.dtypes.to_dict()) +def test_read_csv_gcs_default_engine_w_header(session, scalars_df_index, gcs_folder): + path = gcs_folder + "test_read_csv_gcs_default_engine_w_header*.csv" + read_path = path.replace("*", FIRST_FILE) + scalars_df_index.to_csv(path) - assert bf_df.shape == pd_df.shape - assert bf_df.columns.tolist() == pd_df.columns.tolist() + # Skips header=N rows, normally considers the N+1th row as the header, but overridden by + # passing the `names` argument. In this case, pandas will skip the N+1th row too, take + # the column names from `names`, and begin reading data from the N+2th row. + df = session.read_csv( + read_path, + header=2, + names=scalars_df_index.columns.to_list(), + ) + assert df.shape[0] == scalars_df_index.shape[0] - 2 + assert len(df.columns) == len(scalars_df_index.columns) - # BigFrames requires `sort_index()` because BigQuery doesn't preserve row IDs - # (b/280889935) or guarantee row ordering. - bf_df = bf_df.set_index(names[0]).sort_index() - pd_df = pd_df.set_index(names[0]) - bigframes.testing.utils.assert_frame_equal(bf_df.to_pandas(), pd_df.to_pandas()) +def test_read_csv_gcs_bq_engine_w_header(session, scalars_df_index, gcs_folder): + path = gcs_folder + "test_read_csv_gcs_bq_engine_w_header*.csv" + scalars_df_index.to_csv(path, index=False) -def test_read_csv_for_names_more_than_columns_can_raise_error( - session, df_and_gcs_csv_for_two_columns -): - _, path = df_and_gcs_csv_for_two_columns - names = ["a", "b", "c", "d"] - with pytest.raises( - ValueError, - match="Too many columns specified: expected 3 and found 4", - ): - session.read_csv(path, engine="bigquery", names=names) + # Skip the header and the first 2 data rows. Note that one line of header + # also got added while writing the csv through `to_csv`, so we would have to + # pass headers=3 in the `read_csv` to skip reading the header and two rows. + # Without provided schema, the column names would be like `bool_field_0`, + # `string_field_1` and etc. + df = session.read_csv(path, header=3, engine="bigquery") + assert df.shape[0] == scalars_df_index.shape[0] - 2 + assert len(df.columns) == len(scalars_df_index.columns) -def test_read_csv_for_names_less_than_columns(session, df_and_gcs_csv_for_two_columns): - _, path = df_and_gcs_csv_for_two_columns +def test_read_csv_local_default_engine_w_header(session, scalars_pandas_df_index): + with tempfile.TemporaryDirectory() as dir: + path = dir + "/test_read_csv_local_default_engine_w_header.csv" + # Using the pandas to_csv method because the BQ one does not support local write. + scalars_pandas_df_index.to_csv(path, index=False) - names = ["b", "c"] - bf_df = session.read_csv(path, engine="bigquery", names=names) + # Skips header=N rows. Normally row N+1 would be the header now, but overridden by + # passing the `names` argument. In this case, pandas will skip row N+1 too, infer + # the column names from `names`, and begin reading data from row N+2. + df = session.read_csv( + path, + header=2, + names=scalars_pandas_df_index.columns.to_list(), + ) + assert df.shape[0] == scalars_pandas_df_index.shape[0] - 2 + assert len(df.columns) == len(scalars_pandas_df_index.columns) - # Convert default pandas dtypes to match BigQuery DataFrames dtypes. - pd_df = session.read_csv(path, names=names, dtype=bf_df.dtypes.to_dict()) - assert bf_df.shape == pd_df.shape - assert bf_df.columns.tolist() == pd_df.columns.tolist() +def test_read_csv_local_bq_engine_w_header(session, scalars_pandas_df_index): + with tempfile.TemporaryDirectory() as dir: + path = dir + "/test_read_csv_local_bq_engine_w_header.csv" + # Using the pandas to_csv method because the BQ one does not support local write. + scalars_pandas_df_index.to_csv(path, index=False) - # Pandas's index name is None, while BigFrames's index name is "rowindex". - pd_df.index.name = "rowindex" - bigframes.testing.utils.assert_frame_equal(bf_df.to_pandas(), pd_df.to_pandas()) + # Skip the header and the first 2 data rows. Note that one line of + # header also got added while writing the csv through `to_csv`, so we + # would have to pass headers=3 in the `read_csv` to skip reading the + # header and two rows. Without provided schema, the column names would + # be like `bool_field_0`, `string_field_1` and etc. + df = session.read_csv(path, header=3, engine="bigquery") + assert df.shape[0] == scalars_pandas_df_index.shape[0] - 2 + assert len(df.columns) == len(scalars_pandas_df_index.columns) -def test_read_csv_for_names_less_than_columns_raise_error_when_index_col_set( - session, df_and_gcs_csv_for_two_columns +def test_read_csv_gcs_default_engine_w_index_col_name( + session, scalars_df_default_index, gcs_folder ): - _, path = df_and_gcs_csv_for_two_columns + path = gcs_folder + "test_read_csv_gcs_default_engine_w_index_col_name*.csv" + read_path = path.replace("*", FIRST_FILE) + scalars_df_default_index.to_csv(path) - names = ["b", "c"] - with pytest.raises( - KeyError, - match="ensure the number of `names` matches the number of columns in your data.", - ): - session.read_csv(path, engine="bigquery", names=names, index_col="rowindex") + df = session.read_csv(read_path, index_col="rowindex") + scalars_df_default_index = scalars_df_default_index.set_index( + "rowindex" + ).sort_index() + pd.testing.assert_index_equal(df.columns, scalars_df_default_index.columns) + assert df.index.name == "rowindex" -@pytest.mark.parametrize( - "index_col", - [ - pytest.param("a", id="single_str"), - pytest.param(["a", "b"], id="multi_str"), - pytest.param(0, id="single_int"), - ], -) -def test_read_csv_for_names_and_index_col( - session, df_and_gcs_csv_for_two_columns, index_col +def test_read_csv_gcs_default_engine_w_index_col_index( + session, scalars_df_default_index, gcs_folder ): - _, path = df_and_gcs_csv_for_two_columns - names = ["a", "b", "c"] - bf_df = session.read_csv(path, engine="bigquery", index_col=index_col, names=names) - - # Convert default pandas dtypes to match BigQuery DataFrames dtypes. - pd_df = session.read_csv( - path, index_col=index_col, names=names, dtype=bf_df.dtypes.to_dict() - ) + path = gcs_folder + "test_read_csv_gcs_default_engine_w_index_col_index*.csv" + read_path = path.replace("*", FIRST_FILE) + scalars_df_default_index.to_csv(path) - assert bf_df.shape == pd_df.shape - assert bf_df.columns.tolist() == pd_df.columns.tolist() - bigframes.testing.utils.assert_frame_equal( - bf_df.to_pandas(), pd_df.to_pandas(), check_index_type=False - ) + index_col = scalars_df_default_index.columns.to_list().index("rowindex") + df = session.read_csv(read_path, index_col=index_col) + scalars_df_default_index = scalars_df_default_index.set_index( + "rowindex" + ).sort_index() + pd.testing.assert_index_equal(df.columns, scalars_df_default_index.columns) + assert df.index.name == "rowindex" -@pytest.mark.parametrize( - "usecols", - [ - pytest.param(["a", "b", "c"], id="same"), - pytest.param(["a", "c"], id="less_than_names"), - ], -) -def test_read_csv_for_names_and_usecols( - session, usecols, df_and_gcs_csv_for_two_columns +def test_read_csv_local_default_engine_w_index_col_name( + session, scalars_pandas_df_default_index ): - _, path = df_and_gcs_csv_for_two_columns - - names = ["a", "b", "c"] - bf_df = session.read_csv(path, engine="bigquery", names=names, usecols=usecols) - - # Convert default pandas dtypes to match BigQuery DataFrames dtypes. - pd_df = session.read_csv( - path, names=names, usecols=usecols, dtype=bf_df.dtypes.to_dict() - ) - - assert bf_df.shape == pd_df.shape - assert bf_df.columns.tolist() == pd_df.columns.tolist() - - # BigFrames requires `sort_index()` because BigQuery doesn't preserve row IDs - # (b/280889935) or guarantee row ordering. - bf_df = bf_df.set_index(names[0]).sort_index() - pd_df = pd_df.set_index(names[0]) - bigframes.testing.utils.assert_frame_equal(bf_df.to_pandas(), pd_df.to_pandas()) + with tempfile.TemporaryDirectory() as dir: + path = dir + "/test_read_csv_local_default_engine_w_index_col_name" + # Using the pandas to_csv method because the BQ one does not support local write. + scalars_pandas_df_default_index.to_csv(path, index=False) + + df = session.read_csv(path, index_col="rowindex") + scalars_pandas_df_default_index = scalars_pandas_df_default_index.set_index( + "rowindex" + ).sort_index() + pd.testing.assert_index_equal( + df.columns, scalars_pandas_df_default_index.columns + ) + assert df.index.name == "rowindex" -def test_read_csv_for_names_and_invalid_usecols( - session, df_and_gcs_csv_for_two_columns +def test_read_csv_local_default_engine_w_index_col_index( + session, scalars_pandas_df_default_index ): - _, path = df_and_gcs_csv_for_two_columns - - names = ["a", "b", "c"] - usecols = ["a", "X"] - with pytest.raises( - ValueError, - match=re.escape("Column 'X' is not found. "), - ): - session.read_csv(path, engine="bigquery", names=names, usecols=usecols) + with tempfile.TemporaryDirectory() as dir: + path = dir + "/test_read_csv_local_default_engine_w_index_col_index" + # Using the pandas to_csv method because the BQ one does not support local write. + scalars_pandas_df_default_index.to_csv(path, index=False) + + index_col = scalars_pandas_df_default_index.columns.to_list().index("rowindex") + df = session.read_csv(path, index_col=index_col) + scalars_pandas_df_default_index = scalars_pandas_df_default_index.set_index( + "rowindex" + ).sort_index() + pd.testing.assert_index_equal( + df.columns, scalars_pandas_df_default_index.columns + ) + assert df.index.name == "rowindex" @pytest.mark.parametrize( - ("usecols", "index_col"), + "engine", [ - pytest.param(["a", "b", "c"], "a", id="same"), - pytest.param(["a", "b", "c"], ["a", "b"], id="same_two_index"), - pytest.param(["a", "c"], 0, id="less_than_names"), + pytest.param("bigquery", id="bq_engine"), + pytest.param(None, id="default_engine"), ], ) -def test_read_csv_for_names_and_usecols_and_indexcol( - session, usecols, index_col, df_and_gcs_csv_for_two_columns -): - _, path = df_and_gcs_csv_for_two_columns - - names = ["a", "b", "c"] - bf_df = session.read_csv( - path, engine="bigquery", names=names, usecols=usecols, index_col=index_col - ) - - # Convert default pandas dtypes to match BigQuery DataFrames dtypes. - pd_df = session.read_csv( - path, - names=names, - usecols=usecols, - index_col=index_col, - dtype=bf_df.reset_index().dtypes.to_dict(), - ) - - assert bf_df.shape == pd_df.shape - assert bf_df.columns.tolist() == pd_df.columns.tolist() - - bigframes.testing.utils.assert_frame_equal(bf_df.to_pandas(), pd_df.to_pandas()) - - -def test_read_csv_for_names_less_than_columns_and_same_usecols( - session, df_and_gcs_csv_for_two_columns -): - _, path = df_and_gcs_csv_for_two_columns - names = ["a", "c"] - usecols = ["a", "c"] - bf_df = session.read_csv(path, engine="bigquery", names=names, usecols=usecols) - - # Convert default pandas dtypes to match BigQuery DataFrames dtypes. - pd_df = session.read_csv( - path, names=names, usecols=usecols, dtype=bf_df.dtypes.to_dict() - ) - - assert bf_df.shape == pd_df.shape - assert bf_df.columns.tolist() == pd_df.columns.tolist() - - # BigFrames requires `sort_index()` because BigQuery doesn't preserve row IDs - # (b/280889935) or guarantee row ordering. - bf_df = bf_df.set_index(names[0]).sort_index() - pd_df = pd_df.set_index(names[0]) - bigframes.testing.utils.assert_frame_equal(bf_df.to_pandas(), pd_df.to_pandas()) - - -def test_read_csv_for_names_less_than_columns_and_mismatched_usecols( - session, df_and_gcs_csv_for_two_columns -): - _, path = df_and_gcs_csv_for_two_columns - names = ["a", "b"] - usecols = ["a"] - with pytest.raises( - ValueError, - match=re.escape("Number of passed names did not match number"), - ): - session.read_csv(path, engine="bigquery", names=names, usecols=usecols) - - -def test_read_csv_for_names_less_than_columns_and_different_usecols( - session, df_and_gcs_csv_for_two_columns -): - _, path = df_and_gcs_csv_for_two_columns - names = ["a", "b"] - usecols = ["a", "c"] - with pytest.raises( - ValueError, - match=re.escape("Usecols do not match columns"), - ): - session.read_csv(path, engine="bigquery", names=names, usecols=usecols) - - -def test_read_csv_for_dtype(session, df_and_gcs_csv_for_two_columns): - _, path = df_and_gcs_csv_for_two_columns - - dtype = {"bool_col": pd.BooleanDtype(), "int64_col": pd.Float64Dtype()} - bf_df = session.read_csv(path, engine="bigquery", dtype=dtype) - - # Convert default pandas dtypes to match BigQuery DataFrames dtypes. - pd_df = session.read_csv(path, dtype=dtype) - - assert bf_df.shape == pd_df.shape - assert bf_df.columns.tolist() == pd_df.columns.tolist() - - # BigFrames requires `sort_index()` because BigQuery doesn't preserve row IDs - # (b/280889935) or guarantee row ordering. - bf_df = bf_df.set_index("rowindex").sort_index() - pd_df = pd_df.set_index("rowindex") - bigframes.testing.utils.assert_frame_equal(bf_df.to_pandas(), pd_df.to_pandas()) - - -def test_read_csv_for_dtype_w_names(session, df_and_gcs_csv_for_two_columns): - _, path = df_and_gcs_csv_for_two_columns - - names = ["a", "b", "c"] - dtype = {"b": pd.BooleanDtype(), "c": pd.Float64Dtype()} - bf_df = session.read_csv(path, engine="bigquery", names=names, dtype=dtype) - - # Convert default pandas dtypes to match BigQuery DataFrames dtypes. - pd_df = session.read_csv(path, names=names, dtype=dtype) - - assert bf_df.shape == pd_df.shape - assert bf_df.columns.tolist() == pd_df.columns.tolist() +def test_read_csv_gcs_w_usecols(session, scalars_df_index, gcs_folder, engine): + path = gcs_folder + "test_read_csv_gcs_w_usecols" + path = path + "_default_engine*.csv" if engine is None else path + "_bq_engine*.csv" + read_path = path.replace("*", FIRST_FILE) if engine is None else path + scalars_df_index.to_csv(path) - # BigFrames requires `sort_index()` because BigQuery doesn't preserve row IDs - # (b/280889935) or guarantee row ordering. - bf_df = bf_df.set_index("a").sort_index() - pd_df = pd_df.set_index("a") - bigframes.testing.utils.assert_frame_equal(bf_df.to_pandas(), pd_df.to_pandas()) + # df should only have 1 column which is bool_col. + df = session.read_csv(read_path, usecols=["bool_col"], engine=engine) + assert len(df.columns) == 1 @pytest.mark.parametrize( - ("kwargs", "match"), + "engine", [ - pytest.param( - {"chunksize": 5}, - "'chunksize' and 'iterator' arguments are not supported.", - id="with_chunksize", - ), - pytest.param( - {"iterator": True}, - "'chunksize' and 'iterator' arguments are not supported.", - id="with_iterator", - ), + pytest.param("bigquery", id="bq_engine"), + pytest.param(None, id="default_engine"), ], ) -def test_read_csv_default_engine_throws_not_implemented_error( - session, - scalars_df_index, - gcs_folder, - kwargs, - match, -): - path = ( - gcs_folder - + "test_read_csv_gcs_default_engine_throws_not_implemented_error*.csv" - ) - read_path = utils.get_first_file_from_wildcard(path) - scalars_df_index.to_csv(path) - with pytest.raises(NotImplementedError, match=match): - session.read_csv(read_path, **kwargs) - - -@pytest.mark.parametrize( - "header", - [0, 1, 5], -) -def test_read_csv_for_gcs_file_w_header(session, df_and_gcs_csv, header): - # Compares results for pandas and bigframes engines - scalars_df, path = df_and_gcs_csv - bf_df = session.read_csv(path, engine="bigquery", index_col=False, header=header) - pd_df = session.read_csv( - path, index_col=False, header=header, dtype=scalars_df.dtypes.to_dict() - ) - - # b/408461403: workaround the issue where the slice does not work for DataFrame. - expected_df = session.read_pandas(scalars_df.to_pandas()[header:]) - - assert pd_df.shape[0] == expected_df.shape[0] - assert bf_df.shape[0] == pd_df.shape[0] - - # We use a default index because of index_col=False, so the previous index - # column is just loaded as a column. - assert len(pd_df.columns) == len(expected_df.columns) + 1 - assert len(bf_df.columns) == len(pd_df.columns) - - # When `header > 0`, pandas and BigFrames may handle column naming differently. - # Pandas uses the literal content of the specified header row for column names, - # regardless of what it is. BigQuery, however, might generate default names based - # on data type (e.g.,bool_field_0,string_field_1, etc.). - if header == 0: - # BigFrames requires `sort_index()` because BigQuery doesn't preserve row IDs - # (b/280889935) or guarantee row ordering. - bf_df = bf_df.set_index("rowindex").sort_index() - pd_df = pd_df.set_index("rowindex") - bigframes.testing.utils.assert_frame_equal( - bf_df.to_pandas(), scalars_df.to_pandas() - ) - bigframes.testing.utils.assert_frame_equal(bf_df.to_pandas(), pd_df.to_pandas()) - - -def test_read_csv_w_usecols(session, df_and_local_csv): - # Compares results for pandas and bigframes engines - scalars_df, path = df_and_local_csv - usecols = ["rowindex", "bool_col"] - with open(path, "rb") as buffer: - bf_df = session.read_csv( - buffer, - engine="bigquery", - usecols=usecols, - ) - with open(path, "rb") as buffer: - # Convert default pandas dtypes to match BigQuery DataFrames dtypes. - pd_df = session.read_csv( - buffer, - usecols=usecols, - dtype=scalars_df[["bool_col"]].dtypes.to_dict(), - ) - - assert bf_df.shape == pd_df.shape - assert bf_df.columns.tolist() == pd_df.columns.tolist() - - # BigFrames requires `sort_index()` because BigQuery doesn't preserve row IDs - # (b/280889935) or guarantee row ordering. - bf_df = bf_df.set_index("rowindex").sort_index() - pd_df = pd_df.set_index("rowindex") - bigframes.testing.utils.assert_frame_equal(bf_df.to_pandas(), pd_df.to_pandas()) - - -def test_read_csv_w_usecols_and_indexcol(session, df_and_local_csv): - # Compares results for pandas and bigframes engines - scalars_df, path = df_and_local_csv - usecols = ["rowindex", "bool_col"] - with open(path, "rb") as buffer: - bf_df = session.read_csv( - buffer, - engine="bigquery", - usecols=usecols, - index_col="rowindex", - ) - with open(path, "rb") as buffer: - # Convert default pandas dtypes to match BigQuery DataFrames dtypes. - pd_df = session.read_csv( - buffer, - usecols=usecols, - index_col="rowindex", - dtype=scalars_df[["bool_col"]].dtypes.to_dict(), - ) - - assert bf_df.shape == pd_df.shape - assert bf_df.columns.tolist() == pd_df.columns.tolist() - - bigframes.testing.utils.assert_frame_equal(bf_df.to_pandas(), pd_df.to_pandas()) - +def test_read_csv_local_w_usecols(session, scalars_pandas_df_index, engine): + with tempfile.TemporaryDirectory() as dir: + path = dir + "/test_read_csv_local_w_usecols.csv" + # Using the pandas to_csv method because the BQ one does not support local write. + scalars_pandas_df_index.to_csv(path, index=False) -def test_read_csv_w_indexcol_not_in_usecols(session, df_and_local_csv): - _, path = df_and_local_csv - with open(path, "rb") as buffer: - with pytest.raises( - ValueError, - match=re.escape("The specified index column(s) were not found"), - ): - session.read_csv( - buffer, - engine="bigquery", - usecols=["bool_col"], - index_col="rowindex", - ) + # df should only have 1 column which is bool_col. + df = session.read_csv(path, usecols=["bool_col"], engine=engine) + assert len(df.columns) == 1 @pytest.mark.parametrize( "engine", [ - pytest.param( - "bigquery", - id="bq_engine", - marks=pytest.mark.xfail( - raises=NotImplementedError, - ), - ), + pytest.param("bigquery", id="bq_engine"), pytest.param(None, id="default_engine"), ], ) -def test_read_csv_for_others_files(session, engine): - uri = "https://raw.githubusercontent.com/googleapis/python-bigquery-dataframes/main/tests/data/people.csv" - df = session.read_csv(uri, engine=engine) - assert len(df.columns) == 3 - - -def test_read_csv_local_w_encoding(session, penguins_pandas_df_default_index): +def test_read_csv_local_w_encoding(session, penguins_pandas_df_default_index, engine): with tempfile.TemporaryDirectory() as dir: path = dir + "/test_read_csv_local_w_encoding.csv" # Using the pandas to_csv method because the BQ one does not support local write. - penguins_pandas_df_default_index.index.name = "rowindex" - penguins_pandas_df_default_index.to_csv(path, index=True, encoding="ISO-8859-1") + penguins_pandas_df_default_index.to_csv( + path, index=False, encoding="ISO-8859-1" + ) # File can only be read using the same character encoding as when written. - pd_df = session.read_csv( - path, - index_col="rowindex", - encoding="ISO-8859-1", - dtype=penguins_pandas_df_default_index.dtypes.to_dict(), - ) + df = session.read_csv(path, engine=engine, encoding="ISO-8859-1") - bf_df = session.read_csv( - path, engine="bigquery", index_col="rowindex", encoding="ISO-8859-1" + # TODO(chelsealin): If we serialize the index, can more easily compare values. + pd.testing.assert_index_equal( + df.columns, penguins_pandas_df_default_index.columns ) - bigframes.testing.utils.assert_frame_equal( - bf_df.to_pandas(), penguins_pandas_df_default_index - ) - bigframes.testing.utils.assert_frame_equal(bf_df.to_pandas(), pd_df.to_pandas()) + + assert df.shape[0] == penguins_pandas_df_default_index.shape[0] def test_read_pickle_local(session, penguins_pandas_df_default_index, tmp_path): @@ -1823,9 +779,7 @@ def test_read_pickle_local(session, penguins_pandas_df_default_index, tmp_path): penguins_pandas_df_default_index.to_pickle(path) df = session.read_pickle(path) - bigframes.testing.utils.assert_frame_equal( - penguins_pandas_df_default_index, df.to_pandas() - ) + pd.testing.assert_frame_equal(penguins_pandas_df_default_index, df.to_pandas()) def test_read_pickle_buffer(session, penguins_pandas_df_default_index): @@ -1834,9 +788,7 @@ def test_read_pickle_buffer(session, penguins_pandas_df_default_index): buffer.seek(0) df = session.read_pickle(buffer) - bigframes.testing.utils.assert_frame_equal( - penguins_pandas_df_default_index, df.to_pandas() - ) + pd.testing.assert_frame_equal(penguins_pandas_df_default_index, df.to_pandas()) def test_read_pickle_series_buffer(session): @@ -1855,66 +807,26 @@ def test_read_pickle_gcs(session, penguins_pandas_df_default_index, gcs_folder): penguins_pandas_df_default_index.to_pickle(path) df = session.read_pickle(path) - bigframes.testing.utils.assert_frame_equal( - penguins_pandas_df_default_index, df.to_pandas() - ) + pd.testing.assert_frame_equal(penguins_pandas_df_default_index, df.to_pandas()) -@pytest.mark.parametrize( - ("engine", "filename"), - ( - pytest.param( - "auto", - "000000000000.parquet", - id="auto", - ), - pytest.param( - "pyarrow", - "000000000000.parquet", - id="pyarrow", - ), - pytest.param( - "bigquery", - "000000000000.parquet", - id="bigquery", - ), - pytest.param( - "bigquery", - "*.parquet", - id="bigquery_wildcard", - ), - pytest.param( - "auto", - "*.parquet", - id="auto_wildcard", - marks=pytest.mark.xfail( - raises=ValueError, - ), - ), - ), -) -def test_read_parquet_gcs( - session: bigframes.Session, scalars_dfs, gcs_folder, engine, filename -): +def test_read_parquet_gcs(session: bigframes.Session, scalars_dfs, gcs_folder): scalars_df, _ = scalars_dfs # Include wildcard so that multiple files can be written/read if > 1 GB. # https://cloud.google.com/bigquery/docs/exporting-data#exporting_data_into_one_or_more_files - write_path = gcs_folder + test_read_parquet_gcs.__name__ + "*.parquet" - read_path = gcs_folder + test_read_parquet_gcs.__name__ + filename - + path = gcs_folder + test_read_parquet_gcs.__name__ + "*.parquet" df_in: bigframes.dataframe.DataFrame = scalars_df.copy() # GEOGRAPHY not supported in parquet export. df_in = df_in.drop(columns="geography_col") # Make sure we can also serialize the order. df_write = df_in.reset_index(drop=False) df_write.index.name = f"ordering_id_{random.randrange(1_000_000)}" - df_write.to_parquet(write_path, index=True) + df_write.to_parquet(path, index=True) df_out = ( - session.read_parquet(read_path, engine=engine) + session.read_parquet(path) # Restore order. - .set_index(df_write.index.name) - .sort_index() + .set_index(df_write.index.name).sort_index() # Restore index. .set_index(typing.cast(str, df_in.index.name)) ) @@ -1922,141 +834,14 @@ def test_read_parquet_gcs( # DATETIME gets loaded as TIMESTAMP in parquet. See: # https://cloud.google.com/bigquery/docs/exporting-data#parquet_export_details df_out = df_out.assign( - datetime_col=df_out["datetime_col"].astype("timestamp[us][pyarrow]"), - timestamp_col=df_out["timestamp_col"].astype("timestamp[us, tz=UTC][pyarrow]"), - duration_col=df_out["duration_col"].astype("duration[us][pyarrow]"), + datetime_col=df_out["datetime_col"].astype("timestamp[us][pyarrow]") ) # Make sure we actually have at least some values before comparing. assert df_out.size != 0 pd_df_in = df_in.to_pandas() pd_df_out = df_out.to_pandas() - bigframes.testing.utils.assert_frame_equal(pd_df_in, pd_df_out) - - -@pytest.mark.parametrize( - ("engine", "filename"), - ( - pytest.param( - "bigquery", - "000000000000.orc", - id="bigquery", - ), - pytest.param( - "auto", - "000000000000.orc", - id="auto", - ), - pytest.param( - "pyarrow", - "000000000000.orc", - id="pyarrow", - ), - pytest.param( - "bigquery", - "*.orc", - id="bigquery_wildcard", - ), - pytest.param( - "auto", - "*.orc", - id="auto_wildcard", - marks=pytest.mark.xfail( - raises=ValueError, - ), - ), - ), -) -def test_read_orc_gcs( - session: bigframes.Session, scalars_dfs, gcs_folder, engine, filename -): - pytest.importorskip( - "pandas", - minversion="2.0.0", - reason="pandas<2 does not handle nullable int columns well", - ) - scalars_df, _ = scalars_dfs - write_path = gcs_folder + test_read_orc_gcs.__name__ + "000000000000.orc" - read_path = gcs_folder + test_read_orc_gcs.__name__ + filename - - df_in: bigframes.dataframe.DataFrame = scalars_df.copy() - df_in = df_in.drop( - columns=[ - "geography_col", - "time_col", - "datetime_col", - "duration_col", - "timestamp_col", - ] - ) - df_write = df_in.reset_index(drop=False) - df_write.index.name = f"ordering_id_{random.randrange(1_000_000)}" - df_write.to_orc(write_path) - - df_out = ( - session.read_orc(read_path, engine=engine) - .set_index(df_write.index.name) - .sort_index() - .set_index(typing.cast(str, df_in.index.name)) - ) - - assert df_out.size != 0 - pd_df_in = df_in.to_pandas() - pd_df_out = df_out.to_pandas() - bigframes.testing.utils.assert_frame_equal(pd_df_in, pd_df_out) - - -@pytest.mark.parametrize( - ("engine", "filename"), - ( - pytest.param( - "bigquery", - "000000000000.avro", - id="bigquery", - ), - pytest.param( - "bigquery", - "*.avro", - id="bigquery_wildcard", - ), - ), -) -def test_read_avro_gcs( - session: bigframes.Session, scalars_dfs, gcs_folder, engine, filename -): - scalars_df, _ = scalars_dfs - write_uri = gcs_folder + test_read_avro_gcs.__name__ + "*.avro" - read_uri = gcs_folder + test_read_avro_gcs.__name__ + filename - - df_in: bigframes.dataframe.DataFrame = scalars_df.copy() - # datetime round-trips back as str in avro - df_in = df_in.drop(columns=["geography_col", "duration_col", "datetime_col"]) - df_write = df_in.reset_index(drop=False) - index_name = f"ordering_id_{random.randrange(1_000_000)}" - df_write.index.name = index_name - - # Create a BigQuery table - table_id = df_write.to_gbq() - - # Extract to GCS as Avro - client = session.bqclient - extract_job_config = bigquery.ExtractJobConfig() - extract_job_config.destination_format = "AVRO" - extract_job_config.use_avro_logical_types = True - - client.extract_table(table_id, write_uri, job_config=extract_job_config).result() - - df_out = ( - session.read_avro(read_uri, engine=engine) - .set_index(index_name) - .sort_index() - .set_index(typing.cast(str, df_in.index.name)) - ) - - assert df_out.size != 0 - pd_df_in = df_in.to_pandas() - pd_df_out = df_out.to_pandas() - bigframes.testing.utils.assert_frame_equal(pd_df_in, pd_df_out) + pd.testing.assert_frame_equal(pd_df_in, pd_df_out) @pytest.mark.parametrize( @@ -2088,10 +873,9 @@ def test_read_parquet_gcs_compressed( df_write.to_parquet(path, compression=compression, index=True) df_out = ( - session.read_parquet(path, engine="bigquery") + session.read_parquet(path) # Restore order. - .set_index(df_write.index.name) - .sort_index() + .set_index(df_write.index.name).sort_index() # Restore index. .set_index(typing.cast(str, df_in.index.name)) ) @@ -2099,15 +883,14 @@ def test_read_parquet_gcs_compressed( # DATETIME gets loaded as TIMESTAMP in parquet. See: # https://cloud.google.com/bigquery/docs/exporting-data#parquet_export_details df_out = df_out.assign( - datetime_col=df_out["datetime_col"].astype("timestamp[us][pyarrow]"), - duration_col=df_out["duration_col"].astype("duration[us][pyarrow]"), + datetime_col=df_out["datetime_col"].astype("timestamp[us][pyarrow]") ) # Make sure we actually have at least some values before comparing. assert df_out.size != 0 pd_df_in = df_in.to_pandas() pd_df_out = df_out.to_pandas() - bigframes.testing.utils.assert_frame_equal(pd_df_in, pd_df_out) + pd.testing.assert_frame_equal(pd_df_in, pd_df_out) @pytest.mark.parametrize( @@ -2147,37 +930,23 @@ def test_read_parquet_gcs_compression_not_supported( def test_read_json_gcs_bq_engine(session, scalars_dfs, gcs_folder): scalars_df, _ = scalars_dfs path = gcs_folder + "test_read_json_gcs_bq_engine_w_index*.json" - read_path = utils.get_first_file_from_wildcard(path) + read_path = path.replace("*", FIRST_FILE) scalars_df.to_json(path, index=False, lines=True, orient="records") df = session.read_json(read_path, lines=True, orient="records", engine="bigquery") # The auto detects of BigQuery load job does not preserve any ordering of columns for json. - bigframes.testing.utils.assert_index_equal( + pd.testing.assert_index_equal( df.columns.sort_values(), scalars_df.columns.sort_values() ) # The auto detects of BigQuery load job have restrictions to detect the bytes, # datetime, numeric and geometry types, so they're skipped here. - df = df.drop( - columns=[ - "bytes_col", - "datetime_col", - "numeric_col", - "geography_col", - "duration_col", - ] - ) + df = df.drop(columns=["bytes_col", "datetime_col", "numeric_col", "geography_col"]) scalars_df = scalars_df.drop( - columns=[ - "bytes_col", - "datetime_col", - "numeric_col", - "geography_col", - "duration_col", - ] + columns=["bytes_col", "datetime_col", "numeric_col", "geography_col"] ) assert df.shape[0] == scalars_df.shape[0] - bigframes.testing.utils.assert_series_equal( + pd.testing.assert_series_equal( df.dtypes.sort_index(), scalars_df.dtypes.sort_index() ) @@ -2185,7 +954,7 @@ def test_read_json_gcs_bq_engine(session, scalars_dfs, gcs_folder): def test_read_json_gcs_default_engine(session, scalars_dfs, gcs_folder): scalars_df, _ = scalars_dfs path = gcs_folder + "test_read_json_gcs_default_engine_w_index*.json" - read_path = utils.get_first_file_from_wildcard(path) + read_path = path.replace("*", FIRST_FILE) scalars_df.to_json( path, index=False, @@ -2203,187 +972,39 @@ def test_read_json_gcs_default_engine(session, scalars_dfs, gcs_folder): orient="records", ) - bigframes.testing.utils.assert_index_equal(df.columns, scalars_df.columns) + pd.testing.assert_index_equal(df.columns, scalars_df.columns) # The auto detects of BigQuery load job have restrictions to detect the bytes, # numeric and geometry types, so they're skipped here. - df = df.drop(columns=["bytes_col", "numeric_col", "geography_col", "duration_col"]) - scalars_df = scalars_df.drop( - columns=["bytes_col", "numeric_col", "geography_col", "duration_col"] - ) + df = df.drop(columns=["bytes_col", "numeric_col", "geography_col"]) + scalars_df = scalars_df.drop(columns=["bytes_col", "numeric_col", "geography_col"]) # pandas read_json does not respect the dtype overrides for these columns df = df.drop(columns=["date_col", "datetime_col", "time_col"]) scalars_df = scalars_df.drop(columns=["date_col", "datetime_col", "time_col"]) assert df.shape[0] == scalars_df.shape[0] - bigframes.testing.utils.assert_series_equal(df.dtypes, scalars_df.dtypes) - - -@pytest.mark.parametrize( - ("query_or_table", "index_col", "columns"), - [ - pytest.param( - "{scalars_table_id}", - ("int64_col", "string_col", "int64_col"), - ("float64_col", "bool_col"), - id="table_input_index_col_dup", - marks=pytest.mark.xfail( - raises=ValueError, - reason="ValueError: Duplicate names within 'index_col'.", - strict=True, - ), - ), - pytest.param( - """SELECT int64_col, string_col, float64_col, bool_col - FROM `{scalars_table_id}`""", - ("int64_col",), - ("string_col", "float64_col", "string_col"), - id="query_input_columns_dup", - marks=pytest.mark.xfail( - raises=ValueError, - reason="ValueError: Duplicate names within 'columns'.", - strict=True, - ), - ), - pytest.param( - "{scalars_table_id}", - ("int64_col", "string_col"), - ("float64_col", "string_col", "bool_col"), - id="table_input_cross_dup", - marks=pytest.mark.xfail( - raises=ValueError, - reason="ValueError: Overlap between 'index_col' and 'columns'.", - strict=True, - ), - ), - ], -) -def test_read_gbq_duplicate_columns_xfail( - session: bigframes.Session, - scalars_table_id: str, - query_or_table: str, - index_col: tuple, - columns: tuple, -): - session.read_gbq( - query_or_table.format(scalars_table_id=scalars_table_id), - index_col=index_col, - columns=columns, - ) - - -def test_read_gbq_with_table_ref_dry_run(scalars_table_id, session): - result = session.read_gbq(scalars_table_id, dry_run=True) - - assert isinstance(result, pd.Series) - _assert_table_dry_run_stats_are_valid(result) - - -def test_read_gbq_with_query_dry_run(scalars_table_id, session): - query = f"SELECT * FROM {scalars_table_id} LIMIT 10;" - result = session.read_gbq(query, dry_run=True) - - assert isinstance(result, pd.Series) - _assert_query_dry_run_stats_are_valid(result) - - -def test_read_gbq_dry_run_with_column_and_index(scalars_table_id, session): - query = f"SELECT * FROM {scalars_table_id} LIMIT 10;" - result = session.read_gbq( - query, dry_run=True, columns=["int64_col", "float64_col"], index_col="int64_too" - ) - - assert isinstance(result, pd.Series) - _assert_query_dry_run_stats_are_valid(result) - assert result["columnCount"] == 2 - assert result["columnDtypes"] == { - "int64_col": pd.Int64Dtype(), - "float64_col": pd.Float64Dtype(), - } - assert result["indexLevel"] == 1 - assert result["indexDtypes"] == [pd.Int64Dtype()] - + pd.testing.assert_series_equal(df.dtypes, scalars_df.dtypes) -def test_read_gbq_table_dry_run(scalars_table_id, session): - result = session.read_gbq_table(scalars_table_id, dry_run=True) - assert isinstance(result, pd.Series) - _assert_table_dry_run_stats_are_valid(result) +def test_session_id(session): + assert session._session_id is not None + # BQ client always runs query within the opened session. + query_job = session.bqclient.query("SELECT 1") + assert query_job.session_info.session_id == session._session_id -def test_read_gbq_table_dry_run_with_max_results(scalars_table_id, session): - result = session.read_gbq_table(scalars_table_id, dry_run=True, max_results=100) + # TODO(chelsealin): Verify the session id can be binded with a load job. - assert isinstance(result, pd.Series) - _assert_query_dry_run_stats_are_valid(result) +@pytest.mark.flaky(retries=2) +def test_to_close_session(): + session = bigframes.Session() + assert session._session_id is not None + session.close() + assert session._session_id is None -def test_read_gbq_query_dry_run(scalars_table_id, session): - query = f"SELECT * FROM {scalars_table_id} LIMIT 10;" - result = session.read_gbq_query(query, dry_run=True) - - assert isinstance(result, pd.Series) - _assert_query_dry_run_stats_are_valid(result) - - -def test_block_dry_run_includes_local_data(session): - df1 = bigframes.dataframe.DataFrame({"col_1": [1, 2, 3]}, session=session) - df2 = bigframes.dataframe.DataFrame({"col_2": [1, 2, 3]}, session=session) - - result = df1.merge(df2, how="cross").to_pandas(dry_run=True) - - assert isinstance(result, pd.Series) - _assert_query_dry_run_stats_are_valid(result) - assert result["totalBytesProcessed"] > 0 - assert ( - df1.to_pandas(dry_run=True)["totalBytesProcessed"] - + df2.to_pandas(dry_run=True)["totalBytesProcessed"] - == result["totalBytesProcessed"] - ) - - -def _assert_query_dry_run_stats_are_valid(result: pd.Series): - expected_index = pd.Index( - [ - "columnCount", - "columnDtypes", - "indexLevel", - "indexDtypes", - "bigquerySchema", - "projectId", - "location", - "jobType", - "dispatchedSql", - "destinationTable", - "useLegacySql", - "referencedTables", - "totalBytesProcessed", - "cacheHit", - "statementType", - "creationTime", - ] - ) - - bigframes.testing.utils.assert_index_equal(result.index, expected_index) - assert result["columnCount"] + result["indexLevel"] > 0 - - -def _assert_table_dry_run_stats_are_valid(result: pd.Series): - expected_index = pd.Index( - [ - "isQuery", - "columnCount", - "columnDtypes", - "bigquerySchema", - "numBytes", - "numRows", - "location", - "type", - "creationTime", - "lastModifiedTime", - ] - ) - - bigframes.testing.utils.assert_index_equal(result.index, expected_index) - assert result["columnCount"] == len(result["columnDtypes"]) + # Session has expired and is no longer available. + with pytest.raises(google.api_core.exceptions.BadRequest): + query_job = session.bqclient.query("SELECT 1") + query_job.result() # blocks until finished diff --git a/tests/system/small/test_session_as_bpd.py b/tests/system/small/test_session_as_bpd.py deleted file mode 100644 index e280c551cbd..00000000000 --- a/tests/system/small/test_session_as_bpd.py +++ /dev/null @@ -1,154 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -"""Check that bpd and Session can be used interchangablely.""" - -from __future__ import annotations - -from typing import cast - -import numpy as np -import pandas.testing - -import bigframes.pandas as bpd -import bigframes.session - - -def test_cut(session: bigframes.session.Session): - sc = [30, 80, 40, 90, 60, 45, 95, 75, 55, 100, 65, 85] - x = [20, 40, 60, 80, 100] - - bpd_result = bpd.cut(sc, x) - session_result = session.cut(sc, x) - - global_session = bpd.get_global_session() - assert global_session is not session - assert bpd_result._session is global_session - assert session_result._session is session - - bpd_pd = bpd_result.to_pandas() - session_pd = session_result.to_pandas() - pandas.testing.assert_series_equal(bpd_pd, session_pd) - - -def test_dataframe(session: bigframes.session.Session): - data = {"col": ["local", None, "data"]} - - bpd_result = bpd.DataFrame(data) - session_result = session.DataFrame(data) - - global_session = bpd.get_global_session() - assert global_session is not session - assert bpd_result._session is global_session - assert session_result._session is session - - bpd_pd = bpd_result.to_pandas() - session_pd = session_result.to_pandas() - pandas.testing.assert_frame_equal(bpd_pd, session_pd) - - -def test_multiindex_from_arrays(session: bigframes.session.Session): - arrays = [[1, 1, 2, 2], ["red", "blue", "red", "blue"]] - - bpd_result = bpd.MultiIndex.from_arrays(arrays, names=("number", "color")) - session_result = session.MultiIndex.from_arrays(arrays, names=("number", "color")) - - global_session = bpd.get_global_session() - assert global_session is not session - assert bpd_result._session is global_session - assert session_result._session is session - - bpd_pd = bpd_result.to_pandas() - session_pd = session_result.to_pandas() - pandas.testing.assert_index_equal(bpd_pd, session_pd) - - -def test_multiindex_from_tuples(session: bigframes.session.Session): - tuples = [(1, "red"), (1, "blue"), (2, "red"), (2, "blue")] - - bpd_result = bpd.MultiIndex.from_tuples(tuples, names=("number", "color")) - session_result = session.MultiIndex.from_tuples(tuples, names=("number", "color")) - - global_session = bpd.get_global_session() - assert global_session is not session - assert bpd_result._session is global_session - assert session_result._session is session - - bpd_pd = bpd_result.to_pandas() - session_pd = session_result.to_pandas() - pandas.testing.assert_index_equal(bpd_pd, session_pd) - - -def test_index(session: bigframes.session.Session): - index = [1, 2, 3] - - bpd_result = bpd.Index(index) - session_result = session.Index(index) - - global_session = bpd.get_global_session() - assert global_session is not session - assert bpd_result._session is global_session - assert session_result._session is session - - bpd_pd = bpd_result.to_pandas() - session_pd = session_result.to_pandas() - pandas.testing.assert_index_equal(bpd_pd, session_pd) - - -def test_series(session: bigframes.session.Session): - series = [1, 2, 3] - - bpd_result = bpd.Series(series) - session_result = session.Series(series) - - global_session = bpd.get_global_session() - assert global_session is not session - assert bpd_result._session is global_session - assert session_result._session is session - - bpd_pd = bpd_result.to_pandas() - session_pd = session_result.to_pandas() - pandas.testing.assert_series_equal(bpd_pd, session_pd) - - -def test_to_datetime(session: bigframes.session.Session): - datetimes = ["2018-10-26 12:00:00", "2018-10-26 13:00:15"] - - bpd_result = bpd.to_datetime(datetimes) - session_result = cast(bpd.Series, session.to_datetime(datetimes)) - - global_session = bpd.get_global_session() - assert global_session is not session - assert bpd_result._session is global_session - assert session_result._session is session - - bpd_pd = bpd_result.to_pandas() - session_pd = session_result.to_pandas() - pandas.testing.assert_series_equal(bpd_pd, session_pd) - - -def test_to_timedelta(session: bigframes.session.Session): - offsets = np.arange(5) - - bpd_result = bpd.to_timedelta(offsets, unit="s") - session_result = session.to_timedelta(offsets, unit="s") - - global_session = bpd.get_global_session() - assert global_session is not session - assert bpd_result._session is global_session - assert session_result._session is session - - bpd_pd = bpd_result.to_pandas() - session_pd = session_result.to_pandas() - pandas.testing.assert_series_equal(bpd_pd, session_pd) diff --git a/tests/system/small/test_unordered.py b/tests/system/small/test_unordered.py deleted file mode 100644 index c8db041fec2..00000000000 --- a/tests/system/small/test_unordered.py +++ /dev/null @@ -1,295 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -import warnings - -import pandas as pd -import pyarrow as pa -import pytest - -import bigframes.exceptions -import bigframes.pandas as bpd -from bigframes.testing.utils import assert_frame_equal, assert_series_equal - - -def test_unordered_mode_sql_no_hash(unordered_session): - bf_df = unordered_session.read_gbq( - "bigquery-public-data.ethereum_blockchain.blocks" - ) - sql = bf_df.sql - assert "ORDER BY".casefold() not in sql.casefold() - assert "farm_fingerprint".casefold() not in sql.casefold() - - -def test_unordered_mode_job_label(unordered_session): - pd_df = pd.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]}, dtype=pd.Int64Dtype()) - df = bpd.DataFrame(pd_df, session=unordered_session) - df.to_gbq() - job_labels = df.query_job.labels # type:ignore - assert "bigframes-mode" in job_labels - assert job_labels["bigframes-mode"] == "unordered" - - -def test_unordered_mode_cache_aggregate(unordered_session): - pd_df = pd.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]}, dtype=pd.Int64Dtype()) - df = bpd.DataFrame(pd_df, session=unordered_session) - mean_diff = df - df.mean() - mean_diff.cache() - bf_result = mean_diff.to_pandas(ordered=False) - pd_result = pd_df - pd_df.mean() - - assert_frame_equal(bf_result, pd_result, ignore_order=True) # type: ignore - - -def test_unordered_mode_series_peek(unordered_session): - pd_series = pd.Series([1, 2, 3, 4, 5, 6], dtype=pd.Int64Dtype()) - bf_series = bpd.Series(pd_series, session=unordered_session) - pd_result = pd_series.groupby(pd_series % 4).sum() - bf_peek = bf_series.groupby(bf_series % 4).sum().peek(2) - - assert_series_equal(bf_peek, pd_result.reindex(bf_peek.index)) - - -def test_unordered_mode_single_aggregate(unordered_session): - pd_df = pd.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]}, dtype=pd.Int64Dtype()) - bf_df = bpd.DataFrame(pd_df, session=unordered_session) - - assert bf_df.a.mean() == pd_df.a.mean() - - -def test_unordered_mode_print(unordered_session): - pd_df = pd.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]}, dtype=pd.Int64Dtype()) - df = bpd.DataFrame(pd_df, session=unordered_session).cache() - print(df) - - -def test_unordered_mode_read_gbq(unordered_session): - # TODO: supply a reason why this isn't compatible with pandas 1.x - pytest.importorskip("pandas", minversion="2.0.0") - df = unordered_session.read_gbq( - """SELECT - [1, 3, 2] AS array_column, - STRUCT( - "a" AS string_field, - 1.2 AS float_field) AS struct_column""" - ) - expected = pd.DataFrame( - { - "array_column": pd.Series( - [[1, 3, 2]], - dtype=(pd.ArrowDtype(pa.list_(pa.int64()))), - ), - "struct_column": pd.Series( - [{"string_field": "a", "float_field": 1.2}], - dtype=pd.ArrowDtype( - pa.struct( - [ - ("string_field", pa.string()), - ("float_field", pa.float64()), - ] - ) - ), - ), - } - ) - # Don't need ignore_order as there is only 1 row - assert_frame_equal(df.to_pandas(), expected, check_index_type=False) - - -@pytest.mark.parametrize( - ("keep"), - [ - pytest.param( - "first", - ), - pytest.param( - False, - ), - ], -) -def test_unordered_drop_duplicates(unordered_session, keep): - pd_df = pd.DataFrame({"a": [1, 1, 3], "b": [4, 4, 6]}, dtype=pd.Int64Dtype()) - bf_df = bpd.DataFrame(pd_df, session=unordered_session) - - bf_result = bf_df.drop_duplicates(keep=keep) - pd_result = pd_df.drop_duplicates(keep=keep) - - assert_frame_equal(bf_result.to_pandas(), pd_result, ignore_order=True) - - -def test_unordered_reset_index(unordered_session): - pd_df = pd.DataFrame({"a": [1, 1, 3], "b": [4, 4, 6]}, dtype=pd.Int64Dtype()) - bf_df = bpd.DataFrame(pd_df, session=unordered_session) - - bf_result = bf_df.set_index("b").reset_index(drop=False) - pd_result = pd_df.set_index("b").reset_index(drop=False) - - assert_frame_equal(bf_result.to_pandas(), pd_result) - - -def test_unordered_merge(unordered_session): - pd_df = pd.DataFrame( - {"a": [1, 1, 3], "b": [4, 4, 6], "c": [1, 2, 3]}, dtype=pd.Int64Dtype() - ) - bf_df = bpd.DataFrame(pd_df, session=unordered_session) - - bf_result = bf_df.merge(bf_df, left_on="a", right_on="c") - pd_result = pd_df.merge(pd_df, left_on="a", right_on="c") - - assert_frame_equal(bf_result.to_pandas(), pd_result, ignore_order=True) - - -def test_unordered_drop_duplicates_ambiguous(unordered_session): - pd_df = pd.DataFrame( - {"a": [1, 1, 1], "b": [4, 4, 6], "c": [1, 1, 3]}, dtype=pd.Int64Dtype() - ) - bf_df = bpd.DataFrame(pd_df, session=unordered_session) - - # merge first to discard original ordering - bf_result = ( - bf_df.merge(bf_df, left_on="a", right_on="c") - .sort_values("c_y") - .drop_duplicates() - ) - pd_result = ( - pd_df.merge(pd_df, left_on="a", right_on="c") - .sort_values("c_y") - .drop_duplicates() - ) - - assert_frame_equal(bf_result.to_pandas(), pd_result, ignore_order=True) - - -def test_unordered_mode_cache_preserves_order(unordered_session): - pd_df = pd.DataFrame( - {"a": [1, 2, 3, 4, 5, 6], "b": [4, 5, 9, 3, 1, 6]}, dtype=pd.Int64Dtype() - ) - pd_df.index = pd_df.index.astype(pd.Int64Dtype()) - df = bpd.DataFrame(pd_df, session=unordered_session) - sorted_df = df.sort_values("b").cache() - bf_result = sorted_df.to_pandas() - pd_result = pd_df.sort_values("b") - - # B is unique so unstrict order mode result here should be equivalent to strictly ordered - assert_frame_equal(bf_result, pd_result, ignore_order=False) - - -def test_unordered_mode_no_ordering_error(unordered_session): - pd_df = pd.DataFrame( - {"a": [1, 2, 3, 4, 5, 1], "b": [4, 5, 9, 3, 1, 6]}, dtype=pd.Int64Dtype() - ) - pd_df.index = pd_df.index.astype(pd.Int64Dtype()) - df = bpd.DataFrame(pd_df, session=unordered_session) - - with pytest.raises(bigframes.exceptions.OrderRequiredError): - df.merge(df, on="a").head(3) - - -def test_unordered_mode_allows_ambiguity(unordered_session): - pd_df = pd.DataFrame( - {"a": [1, 2, 3, 4, 5, 1], "b": [4, 5, 9, 3, 1, 6]}, dtype=pd.Int64Dtype() - ) - pd_df.index = pd_df.index.astype(pd.Int64Dtype()) - df = bpd.DataFrame(pd_df, session=unordered_session) - df.merge(df, on="a").sort_values("b_x").head(3) - - -def test_unordered_mode_no_ambiguity_warning(unordered_session): - pd_df = pd.DataFrame( - {"a": [1, 2, 3, 4, 5, 1], "b": [4, 5, 9, 3, 1, 6]}, dtype=pd.Int64Dtype() - ) - pd_df.index = pd_df.index.astype(pd.Int64Dtype()) - df = bpd.DataFrame(pd_df, session=unordered_session) - - with warnings.catch_warnings(): - warnings.simplefilter("error") - df.groupby("a").head(3) - - -@pytest.mark.parametrize( - ("rule", "origin", "data"), - [ - ( - "5h", - "epoch", - { - "timestamp_col": pd.date_range( - start="2021-01-01 13:00:00", periods=30, freq="1h" - ), - "int64_col": range(30), - "int64_too": range(10, 40), - }, - ), - ( - "5h", - "epoch", - { - "timestamp_col": pd.DatetimeIndex( - pd.date_range( - start="2021-01-01 13:00:00", periods=15, freq="1h" - ).tolist() - + pd.date_range( - start="2021-01-01 13:00:00", periods=15, freq="1h" - ).tolist() - ), - "int64_col": range(30), - "int64_too": range(10, 40), - }, - ), - ], -) -def test_resample_with_index(unordered_session, rule, origin, data): - # TODO: supply a reason why this isn't compatible with pandas 1.x - pytest.importorskip("pandas", minversion="2.0.0") - col = "timestamp_col" - scalars_df_index = bpd.DataFrame(data, session=unordered_session).set_index(col) - scalars_pandas_df_index = pd.DataFrame(data).set_index(col) - scalars_pandas_df_index.index.name = None - - bf_result = scalars_df_index.resample(rule=rule, origin=origin).min() - pd_result = scalars_pandas_df_index.resample(rule=rule, origin=origin).min() - - assert isinstance(bf_result.index, bpd.DatetimeIndex) - assert isinstance(pd_result.index, pd.DatetimeIndex) - # TODO: (b/484364312) - pd_result.index.name = bf_result.index.name - assert_frame_equal( - bf_result.to_pandas(), - pd_result, - check_index_type=False, - check_dtype=False, - ) - - -@pytest.mark.parametrize( - ("values", "index", "columns"), - [ - ("int64_col", "int64_too", ["string_col"]), - (["int64_col"], "int64_too", ["string_col"]), - (["int64_col", "float64_col"], "int64_too", ["string_col"]), - ], -) -def test_unordered_df_pivot( - scalars_df_unordered, scalars_pandas_df_index, values, index, columns -): - bf_result = scalars_df_unordered.pivot( - values=values, index=index, columns=columns - ).to_pandas() - pd_result = scalars_pandas_df_index.pivot( - values=values, index=index, columns=columns - ) - - # Pandas produces NaN, where bq dataframes produces pd.NA - bf_result = bf_result.fillna(float("nan")) - pd_result = pd_result.fillna(float("nan")) - assert_frame_equal(bf_result, pd_result, check_dtype=False) diff --git a/tests/system/small/test_window.py b/tests/system/small/test_window.py index a70a676e84d..2b9ec1a3c0f 100644 --- a/tests/system/small/test_window.py +++ b/tests/system/small/test_window.py @@ -12,157 +12,9 @@ # See the License for the specific language governing permissions and # limitations under the License. -import datetime - -import numpy as np import pandas as pd import pytest -import bigframes.testing.utils -from bigframes import dtypes - - -@pytest.fixture(scope="module") -def rows_rolling_dfs(scalars_dfs): - bf_df, pd_df = scalars_dfs - - target_cols = ["int64_too", "float64_col", "int64_col"] - - return bf_df[target_cols], pd_df[target_cols] - - -@pytest.fixture(scope="module") -def range_rolling_dfs(session): - values = np.arange(20) - pd_df = pd.DataFrame( - { - "ts_col": pd.Timestamp("20250101", tz="UTC") + pd.to_timedelta(values, "s"), - "int_col": values % 4, - "float_col": values / 2, - } - ) - - bf_df = session.read_pandas(pd_df) - - return bf_df, pd_df - - -@pytest.fixture(scope="module") -def rows_rolling_series(scalars_dfs): - bf_df, pd_df = scalars_dfs - target_col = "int64_too" - - return bf_df[target_col], pd_df[target_col] - - -@pytest.mark.parametrize("closed", ["left", "right", "both", "neither"]) -def test_dataframe_rolling_closed_param(rows_rolling_dfs, closed): - bf_df, pd_df = rows_rolling_dfs - - actual_result = bf_df.rolling(window=3, closed=closed).sum().to_pandas() - - expected_result = pd_df.rolling(window=3, closed=closed).sum() - bigframes.testing.utils.assert_frame_equal( - actual_result, expected_result, check_dtype=False - ) - - -@pytest.mark.parametrize("closed", ["left", "right", "both", "neither"]) -def test_dataframe_groupby_rolling_closed_param(rows_rolling_dfs, closed): - bf_df, pd_df = rows_rolling_dfs - # Need to specify column subset for comparison due to b/406841327 - check_columns = ["float64_col", "int64_col"] - - actual_result = ( - bf_df.groupby(bf_df["int64_too"] % 2) - .rolling(window=3, closed=closed) - .sum() - .to_pandas() - ) - - expected_result = ( - pd_df.groupby(pd_df["int64_too"] % 2).rolling(window=3, closed=closed).sum() - ) - bigframes.testing.utils.assert_frame_equal( - actual_result[check_columns], expected_result, check_dtype=False - ) - - -def test_dataframe_rolling_on(rows_rolling_dfs): - bf_df, pd_df = rows_rolling_dfs - - actual_result = bf_df.rolling(window=3, on="int64_too").sum().to_pandas() - - expected_result = pd_df.rolling(window=3, on="int64_too").sum() - bigframes.testing.utils.assert_frame_equal( - actual_result, expected_result, check_dtype=False - ) - - -def test_dataframe_rolling_on_invalid_column_raise_error(rows_rolling_dfs): - bf_df, _ = rows_rolling_dfs - - with pytest.raises(ValueError): - bf_df.rolling(window=3, on="whatever").sum() - - -def test_dataframe_groupby_rolling_on(rows_rolling_dfs): - bf_df, pd_df = rows_rolling_dfs - # Need to specify column subset for comparison due to b/406841327 - check_columns = ["float64_col", "int64_col"] - - actual_result = ( - bf_df.groupby(bf_df["int64_too"] % 2) - .rolling(window=3, on="float64_col") - .sum() - .to_pandas() - ) - - expected_result = ( - pd_df.groupby(pd_df["int64_too"] % 2).rolling(window=3, on="float64_col").sum() - ) - bigframes.testing.utils.assert_frame_equal( - actual_result[check_columns], expected_result, check_dtype=False - ) - - -def test_dataframe_groupby_rolling_on_invalid_column_raise_error(rows_rolling_dfs): - bf_df, _ = rows_rolling_dfs - - with pytest.raises(ValueError): - bf_df.groupby(level=0).rolling(window=3, on="whatever").sum() - - -@pytest.mark.parametrize("closed", ["left", "right", "both", "neither"]) -def test_series_rolling_closed_param(rows_rolling_series, closed): - bf_series, df_series = rows_rolling_series - - actual_result = bf_series.rolling(window=3, closed=closed).sum().to_pandas() - - expected_result = df_series.rolling(window=3, closed=closed).sum() - bigframes.testing.utils.assert_series_equal( - actual_result, expected_result, check_dtype=False - ) - - -@pytest.mark.parametrize("closed", ["left", "right", "both", "neither"]) -def test_series_groupby_rolling_closed_param(rows_rolling_series, closed): - bf_series, df_series = rows_rolling_series - - actual_result = ( - bf_series.groupby(bf_series % 2) - .rolling(window=3, closed=closed) - .sum() - .to_pandas() - ) - - expected_result = ( - df_series.groupby(df_series % 2).rolling(window=3, closed=closed).sum() - ) - bigframes.testing.utils.assert_series_equal( - actual_result, expected_result, check_dtype=False - ) - @pytest.mark.parametrize( ("windowing"), @@ -189,14 +41,19 @@ def test_series_groupby_rolling_closed_param(rows_rolling_series, closed): pytest.param(lambda x: x.var(), id="var"), ], ) -def test_series_window_agg_ops(rows_rolling_series, windowing, agg_op): - bf_series, pd_series = rows_rolling_series +def test_series_window_agg_ops( + scalars_df_index, scalars_pandas_df_index, windowing, agg_op +): + col_name = "int64_too" + bf_series = agg_op(windowing(scalars_df_index[col_name])).to_pandas() + pd_series = agg_op(windowing(scalars_pandas_df_index[col_name])) - actual_result = agg_op(windowing(bf_series)).to_pandas() + # Pandas always converts to float64, even for min/max/count, which is not desired + pd_series = pd_series.astype(bf_series.dtype) - expected_result = agg_op(windowing(pd_series)) - bigframes.testing.utils.assert_series_equal( - expected_result, actual_result, check_dtype=False + pd.testing.assert_series_equal( + pd_series, + bf_series, ) @@ -226,256 +83,13 @@ def test_series_window_agg_ops(rows_rolling_series, windowing, agg_op): pytest.param(lambda x: x.var(), id="var"), ], ) -def test_dataframe_window_agg_ops(scalars_dfs, windowing, agg_op): - bf_df, pd_df = scalars_dfs - target_columns = ["int64_too", "float64_col", "bool_col"] - index_column = "bool_col" - bf_df = bf_df[target_columns].set_index(index_column) - pd_df = pd_df[target_columns].set_index(index_column) - - bf_result = agg_op(windowing(bf_df)).to_pandas() - - pd_result = agg_op(windowing(pd_df)) - bigframes.testing.utils.assert_frame_equal(pd_result, bf_result, check_dtype=False) - - -@pytest.mark.parametrize( - ("windowing"), - [ - pytest.param(lambda x: x.expanding(), id="expanding"), - pytest.param(lambda x: x.rolling(3, min_periods=3), id="rolling"), - pytest.param( - lambda x: x.groupby(level=0).rolling(3, min_periods=3), id="rollinggroupby" - ), - pytest.param( - lambda x: x.groupby("int64_too").expanding(min_periods=2), - id="expandinggroupby", - ), - ], -) -@pytest.mark.parametrize( - ("func"), - [ - pytest.param("sum", id="sum_by_name"), - pytest.param(np.sum, id="sum_by_by_np"), - pytest.param([np.sum, np.mean], id="list_of_funcs"), - pytest.param( - {"int64_col": np.sum, "float64_col": "mean"}, id="dict_of_single_funcs" - ), - pytest.param( - {"int64_col": np.sum, "float64_col": ["mean", np.max]}, - id="dict_of_lists_and_single_funcs", - ), - ], -) -def test_dataframe_window_agg_func(scalars_dfs, windowing, func): - if pd.__version__.startswith("3"): - pytest.skip( - "pandas 3.0 bugged for this case 'Length of values (8) does not match length of index (9)'" - ) - bf_df, pd_df = scalars_dfs - target_columns = ["int64_too", "float64_col", "bool_col", "int64_col"] - index_column = "bool_col" - bf_df = bf_df[target_columns].set_index(index_column) - pd_df = pd_df[target_columns].set_index(index_column) - - bf_result = windowing(bf_df).agg(func).to_pandas() - - pd_result = windowing(pd_df).agg(func) - - bigframes.testing.utils.assert_frame_equal(pd_result, bf_result, check_dtype=False) - - -def test_series_window_agg_single_func(scalars_dfs): - bf_df, pd_df = scalars_dfs - index_column = "bool_col" - bf_series = bf_df.set_index(index_column).int64_too - pd_series = pd_df.set_index(index_column).int64_too - - bf_result = bf_series.expanding().agg("sum").to_pandas() - - pd_result = pd_series.expanding().agg("sum") - - bigframes.testing.utils.assert_series_equal(pd_result, bf_result, check_dtype=False) - - -def test_series_window_agg_multi_func(scalars_dfs): - bf_df, pd_df = scalars_dfs - index_column = "bool_col" - bf_series = bf_df.set_index(index_column).int64_too - pd_series = pd_df.set_index(index_column).int64_too - - bf_result = bf_series.expanding().agg(["sum", np.mean]).to_pandas() - - pd_result = pd_series.expanding().agg(["sum", np.mean]) - - bigframes.testing.utils.assert_frame_equal(pd_result, bf_result, check_dtype=False) - - -@pytest.mark.parametrize("closed", ["left", "right", "both", "neither"]) -@pytest.mark.parametrize( - "window", # skipped numpy timedelta because Pandas does not support it. - [pd.Timedelta("3s"), datetime.timedelta(seconds=3), "3s"], -) -@pytest.mark.parametrize("ascending", [True, False]) -def test_series_range_rolling(range_rolling_dfs, window, closed, ascending): - bf_df, pd_df = range_rolling_dfs - bf_series = bf_df.set_index("ts_col")["int_col"] - pd_series = pd_df.set_index("ts_col")["int_col"] - - actual_result = ( - bf_series.sort_index(ascending=ascending) - .rolling(window=window, closed=closed) - .min() - .to_pandas() - ) - - expected_result = ( - pd_series.sort_index(ascending=ascending) - .rolling(window=window, closed=closed) - .min() - ) - bigframes.testing.utils.assert_series_equal( - actual_result, expected_result, check_dtype=False, check_index=False - ) - - -def test_series_groupby_range_rolling(range_rolling_dfs): - bf_df, pd_df = range_rolling_dfs - bf_series = bf_df.set_index("ts_col")["int_col"] - pd_series = pd_df.set_index("ts_col")["int_col"] - - actual_result = ( - bf_series.sort_index() - .groupby(bf_series % 2 == 0) - .rolling(window="3s") - .min() - .to_pandas() - ) - - expected_result = ( - pd_series.sort_index().groupby(pd_series % 2 == 0).rolling(window="3s").min() - ) - bigframes.testing.utils.assert_series_equal( - actual_result, expected_result, check_dtype=False, check_index=False - ) - - -@pytest.mark.parametrize("closed", ["left", "right", "both", "neither"]) -@pytest.mark.parametrize( - "window", # skipped numpy timedelta because Pandas does not support it. - [pd.Timedelta("3s"), datetime.timedelta(seconds=3), "3s"], -) -@pytest.mark.parametrize("ascending", [True, False]) -def test_dataframe_range_rolling(range_rolling_dfs, window, closed, ascending): - bf_df, pd_df = range_rolling_dfs - bf_df = bf_df.set_index("ts_col") - pd_df = pd_df.set_index("ts_col") - - actual_result = ( - bf_df.sort_index(ascending=ascending) - .rolling(window=window, closed=closed) - .min() - .to_pandas() - ) - - expected_result = ( - pd_df.sort_index(ascending=ascending) - .rolling(window=window, closed=closed) - .min() - ) - # Need to cast Pandas index type. Otherwise it uses DatetimeIndex that - # does not exist in BigFrame - expected_result.index = expected_result.index.astype(dtypes.TIMESTAMP_DTYPE) - bigframes.testing.utils.assert_frame_equal( - actual_result, - expected_result, - check_dtype=False, - ) - - -def test_dataframe_range_rolling_on(range_rolling_dfs): - bf_df, pd_df = range_rolling_dfs - on = "ts_col" - - actual_result = bf_df.sort_values(on).rolling(window="3s", on=on).min().to_pandas() - - expected_result = pd_df.sort_values(on).rolling(window="3s", on=on).min() - # Need to specify the column order because Pandas (seemingly) - # re-arranges columns alphabetically - cols = ["ts_col", "int_col", "float_col"] - bigframes.testing.utils.assert_frame_equal( - actual_result[cols], - expected_result[cols], - check_dtype=False, - check_index_type=False, - ) - - -def test_dataframe_groupby_range_rolling(range_rolling_dfs): - bf_df, pd_df = range_rolling_dfs - on = "ts_col" - - actual_result = ( - bf_df.sort_values(on) - .groupby("int_col") - .rolling(window="3s", on=on) - .min() - .to_pandas() - ) - - expected_result = ( - pd_df.sort_values(on).groupby("int_col").rolling(window="3s", on=on).min() - ) - expected_result.index = expected_result.index.set_names("index", level=1) - bigframes.testing.utils.assert_frame_equal( - actual_result, - expected_result, - check_dtype=False, - check_index_type=False, - ) - - -def test_range_rolling_order_info_lookup(range_rolling_dfs): - bf_df, pd_df = range_rolling_dfs - - actual_result = ( - bf_df.set_index("ts_col") - .sort_index(ascending=False)["int_col"] - .isin(bf_df["int_col"]) - .rolling(window="3s") - .count() - .to_pandas() - ) - - expected_result = ( - pd_df.set_index("ts_col") - .sort_index(ascending=False)["int_col"] - .isin(pd_df["int_col"]) - .rolling(window="3s") - .count() - ) - bigframes.testing.utils.assert_series_equal( - actual_result, expected_result, check_dtype=False, check_index=False - ) - - -def test_range_rolling_unsupported_index_type_raise_error(range_rolling_dfs): - bf_df, _ = range_rolling_dfs - - with pytest.raises(ValueError): - bf_df["int_col"].sort_index().rolling(window="3s") - - -def test_range_rolling_unsorted_index_raise_error(range_rolling_dfs): - bf_df, _ = range_rolling_dfs - - with pytest.raises(ValueError): - bf_df.set_index("ts_col")["int_col"].rolling(window="3s") - - -def test_range_rolling_unsorted_column_raise_error(range_rolling_dfs): - bf_df, _ = range_rolling_dfs - - with pytest.raises(ValueError): - bf_df.rolling(window="3s", on="ts_col") +def test_dataframe_window_agg_ops( + scalars_df_index, scalars_pandas_df_index, windowing, agg_op +): + scalars_df_index = scalars_df_index.set_index("bool_col") + scalars_pandas_df_index = scalars_pandas_df_index.set_index("bool_col") + col_names = ["int64_too", "float64_col"] + bf_result = agg_op(windowing(scalars_df_index[col_names])).to_pandas() + pd_result = agg_op(windowing(scalars_pandas_df_index[col_names])) + + pd.testing.assert_frame_equal(pd_result, bf_result, check_dtype=False) diff --git a/tests/system/utils.py b/tests/system/utils.py new file mode 100644 index 00000000000..e2daf3b8bf0 --- /dev/null +++ b/tests/system/utils.py @@ -0,0 +1,141 @@ +# Copyright 2023 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import base64 +import decimal + +import geopandas as gpd # type: ignore +import numpy as np +import pandas as pd +import pyarrow as pa # type: ignore + + +def assert_pandas_df_equal_ignore_ordering(df0, df1, **kwargs): + # Sort by a column to get consistent results. + if df0.index.name != "rowindex": + df0 = df0.sort_values( + list(df0.columns.drop("geography_col", errors="ignore")) + ).reset_index(drop=True) + df1 = df1.sort_values( + list(df1.columns.drop("geography_col", errors="ignore")) + ).reset_index(drop=True) + else: + df0 = df0.sort_index() + df1 = df1.sort_index() + + pd.testing.assert_frame_equal(df0, df1, **kwargs) + + +def assert_series_equal_ignoring_order(left: pd.Series, right: pd.Series, **kwargs): + if left.index.name is None: + left = left.sort_values().reset_index(drop=True) + right = right.sort_values().reset_index(drop=True) + else: + left = left.sort_index() + right = right.sort_index() + + pd.testing.assert_series_equal(left, right, **kwargs) + + +def _standardize_index(idx): + return pd.Index(list(idx), name=idx.name) + + +def assert_pandas_index_equal_ignore_index_type(idx0, idx1): + idx0 = _standardize_index(idx0) + idx1 = _standardize_index(idx1) + + pd.testing.assert_index_equal(idx0, idx1) + + +def convert_pandas_dtypes(df: pd.DataFrame, bytes_col: bool): + """Convert pandas dataframe dtypes compatible with bigframes dataframe.""" + + # TODO(chelsealin): updates the function to accept dtypes as input rather than + # hard-code the column names here. + + # Convert basic types columns + df["bool_col"] = df["bool_col"].astype(pd.BooleanDtype()) + df["int64_col"] = df["int64_col"].astype(pd.Int64Dtype()) + df["int64_too"] = df["int64_too"].astype(pd.Int64Dtype()) + df["float64_col"] = df["float64_col"].astype(pd.Float64Dtype()) + df["string_col"] = df["string_col"].astype(pd.StringDtype(storage="pyarrow")) + + if "rowindex" in df.columns: + df["rowindex"] = df["rowindex"].astype(pd.Int64Dtype()) + if "rowindex_2" in df.columns: + df["rowindex_2"] = df["rowindex_2"].astype(pd.Int64Dtype()) + + # Convert time types columns. The `astype` works for Pandas 2.0 but hits an assert + # error at Pandas 1.5. Hence, we have to convert to arrow table and convert back + # to pandas dataframe. + if not isinstance(df["date_col"].dtype, pd.ArrowDtype): + df["date_col"] = pd.to_datetime(df["date_col"], format="%Y-%m-%d") + arrow_table = pa.Table.from_pandas( + pd.DataFrame(df, columns=["date_col"]), + schema=pa.schema([("date_col", pa.date32())]), + ) + df["date_col"] = arrow_table.to_pandas(types_mapper=pd.ArrowDtype)["date_col"] + + if not isinstance(df["datetime_col"].dtype, pd.ArrowDtype): + df["datetime_col"] = pd.to_datetime( + df["datetime_col"], format="%Y-%m-%d %H:%M:%S" + ) + arrow_table = pa.Table.from_pandas( + pd.DataFrame(df, columns=["datetime_col"]), + schema=pa.schema([("datetime_col", pa.timestamp("us"))]), + ) + df["datetime_col"] = arrow_table.to_pandas(types_mapper=pd.ArrowDtype)[ + "datetime_col" + ] + + if not isinstance(df["time_col"].dtype, pd.ArrowDtype): + df["time_col"] = pd.to_datetime(df["time_col"], format="%H:%M:%S.%f") + arrow_table = pa.Table.from_pandas( + pd.DataFrame(df, columns=["time_col"]), + schema=pa.schema([("time_col", pa.time64("us"))]), + ) + df["time_col"] = arrow_table.to_pandas(types_mapper=pd.ArrowDtype)["time_col"] + + if not isinstance(df["timestamp_col"].dtype, pd.ArrowDtype): + df["timestamp_col"] = pd.to_datetime( + df["timestamp_col"], format="%Y-%m-%d %H:%M:%S.%f%Z" + ) + arrow_table = pa.Table.from_pandas( + pd.DataFrame(df, columns=["timestamp_col"]), + schema=pa.schema([("timestamp_col", pa.timestamp("us", tz="UTC"))]), + ) + df["timestamp_col"] = arrow_table.to_pandas(types_mapper=pd.ArrowDtype)[ + "timestamp_col" + ] + + # Convert geography types columns. + if "geography_col" in df.columns: + df["geography_col"] = df["geography_col"].astype( + pd.StringDtype(storage="pyarrow") + ) + df["geography_col"] = gpd.GeoSeries.from_wkt( + df["geography_col"].replace({np.nan: None}) + ) + + # Convert bytes types column. + if bytes_col: + df["bytes_col"] = df["bytes_col"].apply( + lambda value: base64.b64decode(value) if not pd.isnull(value) else value + ) + + # Convert numeric types column. + df["numeric_col"] = df["numeric_col"].apply( + lambda value: decimal.Decimal(str(value)) if value else None # type: ignore + ) diff --git a/tests/unit/_config/test_bigquery_options.py b/tests/unit/_config/test_bigquery_options.py index 0c51abfd95c..e5b6cfe2f1b 100644 --- a/tests/unit/_config/test_bigquery_options.py +++ b/tests/unit/_config/test_bigquery_options.py @@ -13,15 +13,10 @@ # limitations under the License. import re -import warnings -from unittest import mock -import google.auth.credentials import pytest -import bigframes import bigframes._config.bigquery_options as bigquery_options -import bigframes.exceptions @pytest.mark.parametrize( @@ -34,11 +29,6 @@ ("project", "my-project", "my-other-project"), ("bq_connection", "path/to/connection/1", "path/to/connection/2"), ("use_regional_endpoints", False, True), - ("kms_key_name", "kms/key/name/1", "kms/key/name/2"), - ("skip_bq_connection_check", False, True), - ("client_endpoints_override", {}, {"bqclient": "endpoint_address"}), - ("ordering_mode", "strict", "partial"), - ("requests_transport_adapters", object(), object()), ], ) def test_setter_raises_if_session_started(attribute, original_value, new_value): @@ -58,153 +48,30 @@ def test_setter_raises_if_session_started(attribute, original_value, new_value): assert getattr(options, attribute) is not new_value -def test_location_set_us_twice(): - """This test ensures the fix for b/423220936 is working as expected.""" - options = bigquery_options.BigQueryOptions() - setattr(options, "location", "us") - assert getattr(options, "location") == "US" - - options._session_started = True - - setattr(options, "location", "us") - assert getattr(options, "location") == "US" - - @pytest.mark.parametrize( [ "attribute", - "original_value", ], [ - ("application_name", "test-partner"), - ("location", "us-east1"), - ("project", "my-project"), - ("bq_connection", "path/to/connection/1"), - ("use_regional_endpoints", True), - ("kms_key_name", "kms/key/name/1"), - ("skip_bq_connection_check", True), - ("client_endpoints_override", {"bqclient": "endpoint_address"}), - ("ordering_mode", "partial"), + (attribute,) + for attribute in [ + "application_name", + "credentials", + "location", + "project", + "bq_connection", + "use_regional_endpoints", + ] ], ) -def test_setter_if_session_started_but_setting_the_same_value( - attribute, original_value -): +def test_setter_if_session_started_but_setting_the_same_value(attribute): options = bigquery_options.BigQueryOptions() - setattr(options, attribute, original_value) - assert getattr(options, attribute) == original_value + original_object = object() + setattr(options, attribute, original_object) + assert getattr(options, attribute) is original_object # This should work fine since we're setting the same value as before. options._session_started = True - setattr(options, attribute, original_value) - - assert getattr(options, attribute) == original_value - - -def test_setter_if_session_started_but_setting_the_same_credentials_object(): - options = bigquery_options.BigQueryOptions() - original_object = mock.create_autospec( - google.auth.credentials.Credentials, instance=True - ) - options.credentials = original_object - assert options.credentials is original_object - - # This should work fine since we're setting the same value as before. - options._session_started = True - options.credentials = original_object - assert options.credentials is original_object - - -@pytest.mark.parametrize( - [ - "valid_location", - ], - [ - (None,), - ("us-central1",), - ("us-Central1",), - ("US-CENTRAL1",), - ("US",), - ("us",), - ], -) -def test_location_set_to_valid_no_warning(valid_location): - # test setting location through constructor - def set_location_in_constructor(): - bigquery_options.BigQueryOptions(location=valid_location) - - # test setting location property - def set_location_property(): - options = bigquery_options.BigQueryOptions() - options.location = valid_location - - for op in [set_location_in_constructor, set_location_property]: - # Ensure that no warnings are emitted. - # https://docs.pytest.org/en/7.0.x/how-to/capture-warnings.html#additional-use-cases-of-warnings-in-tests - with warnings.catch_warnings(): - # Turn matching UnknownLocationWarning into exceptions. - # https://docs.python.org/3/library/warnings.html#warning-filter - warnings.simplefilter( - "error", category=bigframes.exceptions.UnknownLocationWarning - ) - op() - - -@pytest.mark.parametrize( - [ - "invalid_location", - "possibility", - ], - [ - # Test with common mistakes, see article. - # https://en.wikipedia.org/wiki/Edit_distance#Formal_definition_and_properties - # Substitution - ("us-wist3", "us-west3"), - # Insertion - ("us-central-1", "us-central1"), - # Deletion - ("asia-suth2", "asia-south2"), - ], -) -def test_location_set_to_invalid_warning(invalid_location, possibility): - # test setting location through constructor - def set_location_in_constructor(): - bigquery_options.BigQueryOptions(location=invalid_location) - - # test setting location property - def set_location_property(): - options = bigquery_options.BigQueryOptions() - options.location = invalid_location - - for op in [set_location_in_constructor, set_location_property]: - with warnings.catch_warnings(record=True) as w: - op() - - assert issubclass( - w[0].category, bigframes.exceptions.UnknownLocationWarning - ) - assert ( - f"The location '{invalid_location}' is set to an unknown value. " - in str(w[0].message) - ) - # The message might contain newlines added by textwrap.fill. - assert possibility in str(w[0].message).replace("\n", "") - - -def test_client_endpoints_override_set_shows_warning(): - options = bigquery_options.BigQueryOptions() - - with pytest.warns(UserWarning): - options.client_endpoints_override = {"bqclient": "endpoint_address"} - - -def test_default_options(): - options = bigquery_options.BigQueryOptions() - - assert options.allow_large_results is False - assert options.ordering_mode == "strict" + setattr(options, attribute, original_object) - # We should default to None as an indicator that the user hasn't set these - # explicitly. See internal issue b/445731915. - assert options.credentials is None - assert options.project is None + assert getattr(options, attribute) is original_object diff --git a/tests/unit/_config/test_compute_options.py b/tests/unit/_config/test_compute_options.py deleted file mode 100644 index e06eb76c374..00000000000 --- a/tests/unit/_config/test_compute_options.py +++ /dev/null @@ -1,22 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import bigframes._config as config - - -def test_default_options(): - options = config.compute_options.ComputeOptions() - - assert options.allow_large_results is None - assert config.options._allow_large_results is False diff --git a/tests/unit/_config/test_experiment_options.py b/tests/unit/_config/test_experiment_options.py deleted file mode 100644 index 0d66b2156ab..00000000000 --- a/tests/unit/_config/test_experiment_options.py +++ /dev/null @@ -1,32 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import pytest - -import bigframes._config.experiment_options as experiment_options - - -def test_sql_compiler_default_stable(): - options = experiment_options.ExperimentOptions() - - assert options.sql_compiler == "stable" - - -def test_sql_compiler_set_experimental_shows_warning(): - options = experiment_options.ExperimentOptions() - - with pytest.warns(FutureWarning): - options.sql_compiler = "experimental" - - assert options.sql_compiler == "experimental" diff --git a/tests/unit/_config/test_threaded_options.py b/tests/unit/_config/test_threaded_options.py deleted file mode 100644 index b16a3550bc0..00000000000 --- a/tests/unit/_config/test_threaded_options.py +++ /dev/null @@ -1,41 +0,0 @@ -# Copyright 2023 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import threading - -import bigframes._config - - -def test_mutate_options_threaded(): - options = bigframes._config.Options() - options.display.max_rows = 50 - result_dict = {"this_before": options.display.max_rows} - - def mutate_options_threaded(options, result_dict): - result_dict["other_before"] = options.display.max_rows - - options.display.max_rows = 100 - result_dict["other_after"] = options.display.max_rows - - thread = threading.Thread( - target=(lambda: mutate_options_threaded(options, result_dict)) - ) - thread.start() - thread.join(1) - result_dict["this_after"] = options.display.max_rows - - assert result_dict["this_before"] == 50 - assert result_dict["this_after"] == 50 - assert result_dict["other_before"] == 10 - assert result_dict["other_after"] == 100 diff --git a/tests/unit/_tools/__init__.py b/tests/unit/_tools/__init__.py deleted file mode 100644 index 378d15c4be6..00000000000 --- a/tests/unit/_tools/__init__.py +++ /dev/null @@ -1,19 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -"""Tests for helper methods for processing Python objects with minimal dependencies. - -Please keep the dependencies used in this subpackage to a minimum to avoid the -risk of circular dependencies. -""" diff --git a/tests/unit/_tools/test_strings.py b/tests/unit/_tools/test_strings.py deleted file mode 100644 index 9c83df25568..00000000000 --- a/tests/unit/_tools/test_strings.py +++ /dev/null @@ -1,149 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -"""Tests for helper methods for processing strings with minimal dependencies. - -Please keep the dependencies used in this subpackage to a minimum to avoid the -risk of circular dependencies. -""" - -import base64 -import random -import sys -import uuid - -import pytest - -from bigframes._tools import strings - -# To stress test some unicode comparisons. -# https://stackoverflow.com/a/39682429/101923 -ALL_UNICODE_CHARS = "".join(chr(i) for i in range(32, 0x110000) if chr(i).isprintable()) -RANDOM_STRINGS = ( - pytest.param(str(uuid.uuid4()), id="uuid4"), - pytest.param(hex(random.randint(0, sys.maxsize)), id="hex"), - pytest.param( - base64.b64encode( - "".join(random.choice(ALL_UNICODE_CHARS) for _ in range(100)).encode( - "utf-8" - ) - ).decode("utf-8"), - id="base64", - ), - pytest.param( - "".join(random.choice(ALL_UNICODE_CHARS) for _ in range(8)), id="unicode8" - ), - pytest.param( - "".join(random.choice(ALL_UNICODE_CHARS) for _ in range(64)), id="unicode64" - ), -) - - -def random_char_not_equal(avoid: str): - random_char = avoid - while random_char == avoid: - random_char = random.choice(ALL_UNICODE_CHARS) - return random_char - - -def random_deletion(original: str): - """original string with one character removed""" - char_index = random.randrange(len(original)) - return original[:char_index] + original[char_index + 1 :] - - -def random_insertion(original: str): - char_index = random.randrange(len(original)) - random_char = random.choice(ALL_UNICODE_CHARS) - return original[: char_index + 1] + random_char + original[char_index + 1 :] - - -@pytest.mark.parametrize( - ("left", "right", "expected"), - ( - ("", "", 0), - ("abc", "abc", 0), - # Deletions - ("abcxyz", "abc", 3), - ("xyzabc", "abc", 3), - ("AXYZBC", "ABC", 3), - ("AXYZBC", "XYZ", 3), - # Insertions - ("abc", "abcxyz", 3), - ("abc", "xyzabc", 3), - # Substitutions - ("abc", "aBc", 1), - ("abcxyz", "aBcXyZ", 3), - # Combinations - ("abcdefxyz", "abcExyzα", 4), - ), -) -def test_levenshtein_distance(left: str, right: str, expected: int): - assert strings.levenshtein_distance(left, right) == expected - - -@pytest.mark.parametrize(("random_string",), RANDOM_STRINGS) -def test_levenshtein_distance_equal_strings(random_string: str): - """Mini fuzz test with different strings.""" - assert strings.levenshtein_distance(random_string, random_string) == 0 - - -@pytest.mark.parametrize(("random_string",), RANDOM_STRINGS) -def test_levenshtein_distance_random_deletion(random_string: str): - """Mini fuzz test with different strings.""" - - num_deleted = random.randrange(1, min(10, len(random_string))) - assert 1 <= num_deleted < len(random_string) - - deleted = random_string - for _ in range(num_deleted): - deleted = random_deletion(deleted) - - assert deleted != random_string - assert len(deleted) == len(random_string) - num_deleted - assert strings.levenshtein_distance(random_string, deleted) == num_deleted - - -@pytest.mark.parametrize(("random_string",), RANDOM_STRINGS) -def test_levenshtein_distance_random_insertion(random_string: str): - """Mini fuzz test with different strings.""" - - num_inserted = random.randrange(1, min(10, len(random_string))) - assert 1 <= num_inserted < len(random_string) - - inserted = random_string - for _ in range(num_inserted): - inserted = random_insertion(inserted) - - assert inserted != random_string - assert len(inserted) == len(random_string) + num_inserted - assert strings.levenshtein_distance(random_string, inserted) == num_inserted - - -@pytest.mark.parametrize(("random_string",), RANDOM_STRINGS) -def test_levenshtein_distance_random_substitution(random_string: str): - """Mini fuzz test with different strings. - - Note: we don't do multiple substitutions here to avoid accidentally - substituting the same character twice. - """ - char_index = random.randrange(len(random_string)) - replaced_char = random_string[char_index] - random_char = random_char_not_equal(replaced_char) - substituted = ( - random_string[:char_index] + random_char + random_string[char_index + 1 :] - ) - assert substituted != random_string - assert len(substituted) == len(random_string) - assert strings.levenshtein_distance(random_string, substituted) == 1 diff --git a/tests/unit/bigquery/__init__.py b/tests/unit/bigquery/__init__.py deleted file mode 100644 index 0a2669d7a25..00000000000 --- a/tests/unit/bigquery/__init__.py +++ /dev/null @@ -1,13 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. diff --git a/tests/unit/bigquery/generated/__init__.py b/tests/unit/bigquery/generated/__init__.py deleted file mode 100644 index 58d482ea386..00000000000 --- a/tests/unit/bigquery/generated/__init__.py +++ /dev/null @@ -1,13 +0,0 @@ -# Copyright 2026 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. diff --git a/tests/unit/bigquery/generated/global_namespace/__init__.py b/tests/unit/bigquery/generated/global_namespace/__init__.py deleted file mode 100644 index 58d482ea386..00000000000 --- a/tests/unit/bigquery/generated/global_namespace/__init__.py +++ /dev/null @@ -1,13 +0,0 @@ -# Copyright 2026 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. diff --git a/tests/unit/bigquery/generated/global_namespace/test_aead_encryption.py b/tests/unit/bigquery/generated/global_namespace/test_aead_encryption.py deleted file mode 100644 index 818151952ff..00000000000 --- a/tests/unit/bigquery/generated/global_namespace/test_aead_encryption.py +++ /dev/null @@ -1,101 +0,0 @@ -# Copyright 2026 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# -# DO NOT MODIFY THIS FILE DIRECTLY. -# This file was generated from: scripts/data/sql-functions/global_namespace/aead_encryption.yaml -# by the script: scripts/generate_bigframes_bigquery.py - -import bigframes.bigquery as bbq -import bigframes.core.col -import bigframes.core.expression as ex -import bigframes.operations.googlesql.global_namespace.aead_encryption as aead_encryption_op -import bigframes.pandas as bpd - - -def test_deterministic_decrypt_bytes_expression(): - # Call the function with col() expressions - result = bbq.deterministic_decrypt_bytes( - bpd.col("keyset"), - bpd.col("ciphertext"), - bpd.col("additional_data"), - ) - - # Verify result is a col Expression - assert isinstance(result, bigframes.core.col.Expression) - - # Verify the internal expression structure - expr = result._value - assert isinstance(expr, ex.OpExpression) - assert expr.op == aead_encryption_op._DETERMINISTIC_DECRYPT_BYTES_OP - - # Verify arguments are free variables matching the names - assert len(expr.inputs) == 3 - assert isinstance(expr.inputs[0], ex.UnboundVariableExpression) - assert expr.inputs[0].id == "keyset" - assert isinstance(expr.inputs[1], ex.UnboundVariableExpression) - assert expr.inputs[1].id == "ciphertext" - assert isinstance(expr.inputs[2], ex.UnboundVariableExpression) - assert expr.inputs[2].id == "additional_data" - - -def test_deterministic_decrypt_string_expression(): - # Call the function with col() expressions - result = bbq.deterministic_decrypt_string( - bpd.col("keyset"), - bpd.col("ciphertext"), - bpd.col("additional_data"), - ) - - # Verify result is a col Expression - assert isinstance(result, bigframes.core.col.Expression) - - # Verify the internal expression structure - expr = result._value - assert isinstance(expr, ex.OpExpression) - assert expr.op == aead_encryption_op._DETERMINISTIC_DECRYPT_STRING_OP - - # Verify arguments are free variables matching the names - assert len(expr.inputs) == 3 - assert isinstance(expr.inputs[0], ex.UnboundVariableExpression) - assert expr.inputs[0].id == "keyset" - assert isinstance(expr.inputs[1], ex.UnboundVariableExpression) - assert expr.inputs[1].id == "ciphertext" - assert isinstance(expr.inputs[2], ex.UnboundVariableExpression) - assert expr.inputs[2].id == "additional_data" - - -def test_deterministic_encrypt_expression(): - # Call the function with col() expressions - result = bbq.deterministic_encrypt( - bpd.col("keyset"), - bpd.col("plaintext"), - bpd.col("additional_data"), - ) - - # Verify result is a col Expression - assert isinstance(result, bigframes.core.col.Expression) - - # Verify the internal expression structure - expr = result._value - assert isinstance(expr, ex.OpExpression) - assert expr.op == aead_encryption_op._DETERMINISTIC_ENCRYPT_OP - - # Verify arguments are free variables matching the names - assert len(expr.inputs) == 3 - assert isinstance(expr.inputs[0], ex.UnboundVariableExpression) - assert expr.inputs[0].id == "keyset" - assert isinstance(expr.inputs[1], ex.UnboundVariableExpression) - assert expr.inputs[1].id == "plaintext" - assert isinstance(expr.inputs[2], ex.UnboundVariableExpression) - assert expr.inputs[2].id == "additional_data" diff --git a/tests/unit/bigquery/generated/global_namespace/test_array.py b/tests/unit/bigquery/generated/global_namespace/test_array.py deleted file mode 100644 index 56b85386902..00000000000 --- a/tests/unit/bigquery/generated/global_namespace/test_array.py +++ /dev/null @@ -1,339 +0,0 @@ -# Copyright 2026 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# -# DO NOT MODIFY THIS FILE DIRECTLY. -# This file was generated from: scripts/data/sql-functions/global_namespace/array.yaml -# by the script: scripts/generate_bigframes_bigquery.py - -import bigframes.bigquery as bbq -import bigframes.core.col -import bigframes.core.expression as ex -import bigframes.operations.googlesql.global_namespace.array as array_op -import bigframes.pandas as bpd - - -def test_array_concat_expression(): - # Call the function with col() expressions - result = bbq.array_concat( - bpd.col("array_expression_1"), - bpd.col("array_expression_2"), - ) - - # Verify result is a col Expression - assert isinstance(result, bigframes.core.col.Expression) - - # Verify the internal expression structure - expr = result._value - assert isinstance(expr, ex.OpExpression) - assert expr.op == array_op._ARRAY_CONCAT_OP - - # Verify arguments are free variables matching the names - assert len(expr.inputs) == 2 - assert isinstance(expr.inputs[0], ex.UnboundVariableExpression) - assert expr.inputs[0].id == "array_expression_1" - assert isinstance(expr.inputs[1], ex.UnboundVariableExpression) - assert expr.inputs[1].id == "array_expression_2" - - -def test_array_first_expression(): - # Call the function with col() expressions - result = bbq.array_first( - bpd.col("array_expression"), - ) - - # Verify result is a col Expression - assert isinstance(result, bigframes.core.col.Expression) - - # Verify the internal expression structure - expr = result._value - assert isinstance(expr, ex.OpExpression) - assert expr.op == array_op._ARRAY_FIRST_OP - - # Verify arguments are free variables matching the names - assert len(expr.inputs) == 1 - assert isinstance(expr.inputs[0], ex.UnboundVariableExpression) - assert expr.inputs[0].id == "array_expression" - - -def test_array_first_n_expression(): - # Call the function with col() expressions - result = bbq.array_first_n( - bpd.col("input_array"), - bpd.col("n"), - ) - - # Verify result is a col Expression - assert isinstance(result, bigframes.core.col.Expression) - - # Verify the internal expression structure - expr = result._value - assert isinstance(expr, ex.OpExpression) - assert expr.op == array_op._ARRAY_FIRST_N_OP - - # Verify arguments are free variables matching the names - assert len(expr.inputs) == 2 - assert isinstance(expr.inputs[0], ex.UnboundVariableExpression) - assert expr.inputs[0].id == "input_array" - assert isinstance(expr.inputs[1], ex.UnboundVariableExpression) - assert expr.inputs[1].id == "n" - - -def test_array_includes_expression(): - # Call the function with col() expressions - result = bbq.array_includes( - bpd.col("array_to_search"), - bpd.col("search_value"), - ) - - # Verify result is a col Expression - assert isinstance(result, bigframes.core.col.Expression) - - # Verify the internal expression structure - expr = result._value - assert isinstance(expr, ex.OpExpression) - assert expr.op == array_op._ARRAY_INCLUDES_OP - - # Verify arguments are free variables matching the names - assert len(expr.inputs) == 2 - assert isinstance(expr.inputs[0], ex.UnboundVariableExpression) - assert expr.inputs[0].id == "array_to_search" - assert isinstance(expr.inputs[1], ex.UnboundVariableExpression) - assert expr.inputs[1].id == "search_value" - - -def test_array_includes_all_expression(): - # Call the function with col() expressions - result = bbq.array_includes_all( - bpd.col("array_to_search"), - bpd.col("search_values"), - ) - - # Verify result is a col Expression - assert isinstance(result, bigframes.core.col.Expression) - - # Verify the internal expression structure - expr = result._value - assert isinstance(expr, ex.OpExpression) - assert expr.op == array_op._ARRAY_INCLUDES_ALL_OP - - # Verify arguments are free variables matching the names - assert len(expr.inputs) == 2 - assert isinstance(expr.inputs[0], ex.UnboundVariableExpression) - assert expr.inputs[0].id == "array_to_search" - assert isinstance(expr.inputs[1], ex.UnboundVariableExpression) - assert expr.inputs[1].id == "search_values" - - -def test_array_includes_any_expression(): - # Call the function with col() expressions - result = bbq.array_includes_any( - bpd.col("array_to_search"), - bpd.col("search_values"), - ) - - # Verify result is a col Expression - assert isinstance(result, bigframes.core.col.Expression) - - # Verify the internal expression structure - expr = result._value - assert isinstance(expr, ex.OpExpression) - assert expr.op == array_op._ARRAY_INCLUDES_ANY_OP - - # Verify arguments are free variables matching the names - assert len(expr.inputs) == 2 - assert isinstance(expr.inputs[0], ex.UnboundVariableExpression) - assert expr.inputs[0].id == "array_to_search" - assert isinstance(expr.inputs[1], ex.UnboundVariableExpression) - assert expr.inputs[1].id == "search_values" - - -def test_array_is_distinct_expression(): - # Call the function with col() expressions - result = bbq.array_is_distinct( - bpd.col("array_expression"), - ) - - # Verify result is a col Expression - assert isinstance(result, bigframes.core.col.Expression) - - # Verify the internal expression structure - expr = result._value - assert isinstance(expr, ex.OpExpression) - assert expr.op == array_op._ARRAY_IS_DISTINCT_OP - - # Verify arguments are free variables matching the names - assert len(expr.inputs) == 1 - assert isinstance(expr.inputs[0], ex.UnboundVariableExpression) - assert expr.inputs[0].id == "array_expression" - - -def test_array_last_expression(): - # Call the function with col() expressions - result = bbq.array_last( - bpd.col("array_expression"), - ) - - # Verify result is a col Expression - assert isinstance(result, bigframes.core.col.Expression) - - # Verify the internal expression structure - expr = result._value - assert isinstance(expr, ex.OpExpression) - assert expr.op == array_op._ARRAY_LAST_OP - - # Verify arguments are free variables matching the names - assert len(expr.inputs) == 1 - assert isinstance(expr.inputs[0], ex.UnboundVariableExpression) - assert expr.inputs[0].id == "array_expression" - - -def test_array_length_expression(): - # Call the function with col() expressions - result = bbq.array_length( - bpd.col("series"), - ) - - # Verify result is a col Expression - assert isinstance(result, bigframes.core.col.Expression) - - # Verify the internal expression structure - expr = result._value - assert isinstance(expr, ex.OpExpression) - assert expr.op == array_op._ARRAY_LENGTH_OP - - # Verify arguments are free variables matching the names - assert len(expr.inputs) == 1 - assert isinstance(expr.inputs[0], ex.UnboundVariableExpression) - assert expr.inputs[0].id == "series" - - -def test_array_reverse_expression(): - # Call the function with col() expressions - result = bbq.array_reverse( - bpd.col("value"), - ) - - # Verify result is a col Expression - assert isinstance(result, bigframes.core.col.Expression) - - # Verify the internal expression structure - expr = result._value - assert isinstance(expr, ex.OpExpression) - assert expr.op == array_op._ARRAY_REVERSE_OP - - # Verify arguments are free variables matching the names - assert len(expr.inputs) == 1 - assert isinstance(expr.inputs[0], ex.UnboundVariableExpression) - assert expr.inputs[0].id == "value" - - -def test_array_slice_expression(): - # Call the function with col() expressions - result = bbq.array_slice( - bpd.col("array_to_slice"), - bpd.col("start_offset"), - bpd.col("end_offset"), - ) - - # Verify result is a col Expression - assert isinstance(result, bigframes.core.col.Expression) - - # Verify the internal expression structure - expr = result._value - assert isinstance(expr, ex.OpExpression) - assert expr.op == array_op._ARRAY_SLICE_OP - - # Verify arguments are free variables matching the names - assert len(expr.inputs) == 3 - assert isinstance(expr.inputs[0], ex.UnboundVariableExpression) - assert expr.inputs[0].id == "array_to_slice" - assert isinstance(expr.inputs[1], ex.UnboundVariableExpression) - assert expr.inputs[1].id == "start_offset" - assert isinstance(expr.inputs[2], ex.UnboundVariableExpression) - assert expr.inputs[2].id == "end_offset" - - -def test_array_to_string_expression(): - # Call the function with col() expressions - result = bbq.array_to_string( - bpd.col("series"), - bpd.col("delimiter"), - bpd.col("null_text"), - ) - - # Verify result is a col Expression - assert isinstance(result, bigframes.core.col.Expression) - - # Verify the internal expression structure - expr = result._value - assert isinstance(expr, ex.OpExpression) - assert expr.op == array_op._ARRAY_TO_STRING_OP - - # Verify arguments are free variables matching the names - assert len(expr.inputs) == 3 - assert isinstance(expr.inputs[0], ex.UnboundVariableExpression) - assert expr.inputs[0].id == "series" - assert isinstance(expr.inputs[1], ex.UnboundVariableExpression) - assert expr.inputs[1].id == "delimiter" - assert isinstance(expr.inputs[2], ex.UnboundVariableExpression) - assert expr.inputs[2].id == "null_text" - - -def test_flatten_expression(): - # Call the function with col() expressions - result = bbq.flatten( - bpd.col("array_to_flatten"), - bpd.col("depth"), - ) - - # Verify result is a col Expression - assert isinstance(result, bigframes.core.col.Expression) - - # Verify the internal expression structure - expr = result._value - assert isinstance(expr, ex.OpExpression) - assert expr.op == array_op._FLATTEN_OP - - # Verify arguments are free variables matching the names - assert len(expr.inputs) == 2 - assert isinstance(expr.inputs[0], ex.UnboundVariableExpression) - assert expr.inputs[0].id == "array_to_flatten" - assert isinstance(expr.inputs[1], ex.UnboundVariableExpression) - assert expr.inputs[1].id == "depth" - - -def test_generate_array_expression(): - # Call the function with col() expressions - result = bbq.generate_array( - bpd.col("start_expression"), - bpd.col("end_expression"), - bpd.col("step_expression"), - ) - - # Verify result is a col Expression - assert isinstance(result, bigframes.core.col.Expression) - - # Verify the internal expression structure - expr = result._value - assert isinstance(expr, ex.OpExpression) - assert expr.op == array_op._GENERATE_ARRAY_OP - - # Verify arguments are free variables matching the names - assert len(expr.inputs) == 3 - assert isinstance(expr.inputs[0], ex.UnboundVariableExpression) - assert expr.inputs[0].id == "start_expression" - assert isinstance(expr.inputs[1], ex.UnboundVariableExpression) - assert expr.inputs[1].id == "end_expression" - assert isinstance(expr.inputs[2], ex.UnboundVariableExpression) - assert expr.inputs[2].id == "step_expression" diff --git a/tests/unit/bigquery/generated/global_namespace/test_bit.py b/tests/unit/bigquery/generated/global_namespace/test_bit.py deleted file mode 100644 index 2cccafc0643..00000000000 --- a/tests/unit/bigquery/generated/global_namespace/test_bit.py +++ /dev/null @@ -1,43 +0,0 @@ -# Copyright 2026 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# -# DO NOT MODIFY THIS FILE DIRECTLY. -# This file was generated from: scripts/data/sql-functions/global_namespace/bit.yaml -# by the script: scripts/generate_bigframes_bigquery.py - -import bigframes.bigquery as bbq -import bigframes.core.col -import bigframes.core.expression as ex -import bigframes.operations.googlesql.global_namespace.bit as bit_op -import bigframes.pandas as bpd - - -def test_bit_count_expression(): - # Call the function with col() expressions - result = bbq.bit_count( - bpd.col("expression"), - ) - - # Verify result is a col Expression - assert isinstance(result, bigframes.core.col.Expression) - - # Verify the internal expression structure - expr = result._value - assert isinstance(expr, ex.OpExpression) - assert expr.op == bit_op._BIT_COUNT_OP - - # Verify arguments are free variables matching the names - assert len(expr.inputs) == 1 - assert isinstance(expr.inputs[0], ex.UnboundVariableExpression) - assert expr.inputs[0].id == "expression" diff --git a/tests/unit/bigquery/generated/global_namespace/test_conversion.py b/tests/unit/bigquery/generated/global_namespace/test_conversion.py deleted file mode 100644 index 84dfc02465c..00000000000 --- a/tests/unit/bigquery/generated/global_namespace/test_conversion.py +++ /dev/null @@ -1,172 +0,0 @@ -# Copyright 2026 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# -# DO NOT MODIFY THIS FILE DIRECTLY. -# This file was generated from: scripts/data/sql-functions/global_namespace/conversion.yaml -# by the script: scripts/generate_bigframes_bigquery.py - -import bigframes.bigquery as bbq -import bigframes.core.col -import bigframes.core.expression as ex -import bigframes.operations.googlesql.global_namespace.conversion as conversion_op -import bigframes.pandas as bpd - - -def test_bool__expression(): - # Call the function with col() expressions - result = bbq.bool_( - bpd.col("json_string_expression"), - ) - - # Verify result is a col Expression - assert isinstance(result, bigframes.core.col.Expression) - - # Verify the internal expression structure - expr = result._value - assert isinstance(expr, ex.OpExpression) - assert expr.op == conversion_op._BOOL_OP - - # Verify arguments are free variables matching the names - assert len(expr.inputs) == 1 - assert isinstance(expr.inputs[0], ex.UnboundVariableExpression) - assert expr.inputs[0].id == "json_string_expression" - - -def test_double_expression(): - # Call the function with col() expressions - result = bbq.double( - bpd.col("json_string_expression"), - bpd.col("wide_number_mode"), - ) - - # Verify result is a col Expression - assert isinstance(result, bigframes.core.col.Expression) - - # Verify the internal expression structure - expr = result._value - assert isinstance(expr, ex.OpExpression) - assert expr.op == conversion_op._DOUBLE_OP - - # Verify arguments are free variables matching the names - assert len(expr.inputs) == 2 - assert isinstance(expr.inputs[0], ex.UnboundVariableExpression) - assert expr.inputs[0].id == "json_string_expression" - assert isinstance(expr.inputs[1], ex.UnboundVariableExpression) - assert expr.inputs[1].id == "wide_number_mode" - - -def test_float64_expression(): - # Call the function with col() expressions - result = bbq.float64( - bpd.col("json_string_expression"), - bpd.col("wide_number_mode"), - ) - - # Verify result is a col Expression - assert isinstance(result, bigframes.core.col.Expression) - - # Verify the internal expression structure - expr = result._value - assert isinstance(expr, ex.OpExpression) - assert expr.op == conversion_op._FLOAT64_OP - - # Verify arguments are free variables matching the names - assert len(expr.inputs) == 2 - assert isinstance(expr.inputs[0], ex.UnboundVariableExpression) - assert expr.inputs[0].id == "json_string_expression" - assert isinstance(expr.inputs[1], ex.UnboundVariableExpression) - assert expr.inputs[1].id == "wide_number_mode" - - -def test_int64_expression(): - # Call the function with col() expressions - result = bbq.int64( - bpd.col("json_string_expression"), - ) - - # Verify result is a col Expression - assert isinstance(result, bigframes.core.col.Expression) - - # Verify the internal expression structure - expr = result._value - assert isinstance(expr, ex.OpExpression) - assert expr.op == conversion_op._INT64_OP - - # Verify arguments are free variables matching the names - assert len(expr.inputs) == 1 - assert isinstance(expr.inputs[0], ex.UnboundVariableExpression) - assert expr.inputs[0].id == "json_string_expression" - - -def test_parse_bignumeric_expression(): - # Call the function with col() expressions - result = bbq.parse_bignumeric( - bpd.col("string_expression"), - ) - - # Verify result is a col Expression - assert isinstance(result, bigframes.core.col.Expression) - - # Verify the internal expression structure - expr = result._value - assert isinstance(expr, ex.OpExpression) - assert expr.op == conversion_op._PARSE_BIGNUMERIC_OP - - # Verify arguments are free variables matching the names - assert len(expr.inputs) == 1 - assert isinstance(expr.inputs[0], ex.UnboundVariableExpression) - assert expr.inputs[0].id == "string_expression" - - -def test_parse_numeric_expression(): - # Call the function with col() expressions - result = bbq.parse_numeric( - bpd.col("string_expression"), - ) - - # Verify result is a col Expression - assert isinstance(result, bigframes.core.col.Expression) - - # Verify the internal expression structure - expr = result._value - assert isinstance(expr, ex.OpExpression) - assert expr.op == conversion_op._PARSE_NUMERIC_OP - - # Verify arguments are free variables matching the names - assert len(expr.inputs) == 1 - assert isinstance(expr.inputs[0], ex.UnboundVariableExpression) - assert expr.inputs[0].id == "string_expression" - - -def test_string_expression(): - # Call the function with col() expressions - result = bbq.string( - bpd.col("expression"), - bpd.col("timezone"), - ) - - # Verify result is a col Expression - assert isinstance(result, bigframes.core.col.Expression) - - # Verify the internal expression structure - expr = result._value - assert isinstance(expr, ex.OpExpression) - assert expr.op == conversion_op._STRING_OP - - # Verify arguments are free variables matching the names - assert len(expr.inputs) == 2 - assert isinstance(expr.inputs[0], ex.UnboundVariableExpression) - assert expr.inputs[0].id == "expression" - assert isinstance(expr.inputs[1], ex.UnboundVariableExpression) - assert expr.inputs[1].id == "timezone" diff --git a/tests/unit/bigquery/generated/global_namespace/test_date.py b/tests/unit/bigquery/generated/global_namespace/test_date.py deleted file mode 100644 index 6484208584f..00000000000 --- a/tests/unit/bigquery/generated/global_namespace/test_date.py +++ /dev/null @@ -1,340 +0,0 @@ -# Copyright 2026 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# -# DO NOT MODIFY THIS FILE DIRECTLY. -# This file was generated from: scripts/data/sql-functions/global_namespace/date.yaml -# by the script: scripts/generate_bigframes_bigquery.py - -import bigframes.bigquery as bbq -import bigframes.core.col -import bigframes.core.expression as ex -import bigframes.operations.googlesql.global_namespace.date as date_op -import bigframes.pandas as bpd - - -def test_current_date_expression(): - # Call the function with col() expressions - result = bbq.current_date( - bpd.col("time_zone_expression"), - ) - - # Verify result is a col Expression - assert isinstance(result, bigframes.core.col.Expression) - - # Verify the internal expression structure - expr = result._value - assert isinstance(expr, ex.OpExpression) - assert expr.op == date_op._CURRENT_DATE_OP - - # Verify arguments are free variables matching the names - assert len(expr.inputs) == 1 - assert isinstance(expr.inputs[0], ex.UnboundVariableExpression) - assert expr.inputs[0].id == "time_zone_expression" - - -def test_date_expression(): - # Call the function with col() expressions - result = bbq.date( - bpd.col("expression"), - bpd.col("time_zone_expression"), - bpd.col("year"), - bpd.col("month"), - bpd.col("day"), - ) - - # Verify result is a col Expression - assert isinstance(result, bigframes.core.col.Expression) - - # Verify the internal expression structure - expr = result._value - assert isinstance(expr, ex.OpExpression) - assert expr.op == date_op._DATE_OP - - # Verify arguments are free variables matching the names - assert len(expr.inputs) == 5 - assert isinstance(expr.inputs[0], ex.UnboundVariableExpression) - assert expr.inputs[0].id == "expression" - assert isinstance(expr.inputs[1], ex.UnboundVariableExpression) - assert expr.inputs[1].id == "time_zone_expression" - assert isinstance(expr.inputs[2], ex.UnboundVariableExpression) - assert expr.inputs[2].id == "year" - assert isinstance(expr.inputs[3], ex.UnboundVariableExpression) - assert expr.inputs[3].id == "month" - assert isinstance(expr.inputs[4], ex.UnboundVariableExpression) - assert expr.inputs[4].id == "day" - - -def test_date_add_expression(): - # Call the function with col() expressions - result = bbq.date_add( - bpd.col("date_expression"), - bpd.col("int64_expression"), - bpd.col("date_part"), - ) - - # Verify result is a col Expression - assert isinstance(result, bigframes.core.col.Expression) - - # Verify the internal expression structure - expr = result._value - assert isinstance(expr, ex.OpExpression) - assert expr.op == date_op._DATE_ADD_OP - - # Verify arguments are free variables matching the names - assert len(expr.inputs) == 3 - assert isinstance(expr.inputs[0], ex.UnboundVariableExpression) - assert expr.inputs[0].id == "date_expression" - assert isinstance(expr.inputs[1], ex.UnboundVariableExpression) - assert expr.inputs[1].id == "int64_expression" - assert isinstance(expr.inputs[2], ex.UnboundVariableExpression) - assert expr.inputs[2].id == "date_part" - - -def test_date_diff_expression(): - # Call the function with col() expressions - result = bbq.date_diff( - bpd.col("end_date"), - bpd.col("start_date"), - bpd.col("granularity"), - ) - - # Verify result is a col Expression - assert isinstance(result, bigframes.core.col.Expression) - - # Verify the internal expression structure - expr = result._value - assert isinstance(expr, ex.OpExpression) - assert expr.op == date_op._DATE_DIFF_OP - - # Verify arguments are free variables matching the names - assert len(expr.inputs) == 3 - assert isinstance(expr.inputs[0], ex.UnboundVariableExpression) - assert expr.inputs[0].id == "end_date" - assert isinstance(expr.inputs[1], ex.UnboundVariableExpression) - assert expr.inputs[1].id == "start_date" - assert isinstance(expr.inputs[2], ex.UnboundVariableExpression) - assert expr.inputs[2].id == "granularity" - - -def test_date_from_unix_date_expression(): - # Call the function with col() expressions - result = bbq.date_from_unix_date( - bpd.col("int64_expression"), - ) - - # Verify result is a col Expression - assert isinstance(result, bigframes.core.col.Expression) - - # Verify the internal expression structure - expr = result._value - assert isinstance(expr, ex.OpExpression) - assert expr.op == date_op._DATE_FROM_UNIX_DATE_OP - - # Verify arguments are free variables matching the names - assert len(expr.inputs) == 1 - assert isinstance(expr.inputs[0], ex.UnboundVariableExpression) - assert expr.inputs[0].id == "int64_expression" - - -def test_date_sub_expression(): - # Call the function with col() expressions - result = bbq.date_sub( - bpd.col("date_expression"), - bpd.col("int64_expression"), - bpd.col("date_part"), - ) - - # Verify result is a col Expression - assert isinstance(result, bigframes.core.col.Expression) - - # Verify the internal expression structure - expr = result._value - assert isinstance(expr, ex.OpExpression) - assert expr.op == date_op._DATE_SUB_OP - - # Verify arguments are free variables matching the names - assert len(expr.inputs) == 3 - assert isinstance(expr.inputs[0], ex.UnboundVariableExpression) - assert expr.inputs[0].id == "date_expression" - assert isinstance(expr.inputs[1], ex.UnboundVariableExpression) - assert expr.inputs[1].id == "int64_expression" - assert isinstance(expr.inputs[2], ex.UnboundVariableExpression) - assert expr.inputs[2].id == "date_part" - - -def test_date_trunc_expression(): - # Call the function with col() expressions - result = bbq.date_trunc( - bpd.col("date_value"), - bpd.col("granularity"), - ) - - # Verify result is a col Expression - assert isinstance(result, bigframes.core.col.Expression) - - # Verify the internal expression structure - expr = result._value - assert isinstance(expr, ex.OpExpression) - assert expr.op == date_op._DATE_TRUNC_OP - - # Verify arguments are free variables matching the names - assert len(expr.inputs) == 2 - assert isinstance(expr.inputs[0], ex.UnboundVariableExpression) - assert expr.inputs[0].id == "date_value" - assert isinstance(expr.inputs[1], ex.UnboundVariableExpression) - assert expr.inputs[1].id == "granularity" - - -def test_extract_expression(): - # Call the function with col() expressions - result = bbq.extract( - bpd.col("date_expression"), - bpd.col("part"), - bpd.col("time_zone"), - ) - - # Verify result is a col Expression - assert isinstance(result, bigframes.core.col.Expression) - - # Verify the internal expression structure - expr = result._value - assert isinstance(expr, ex.OpExpression) - assert expr.op == date_op._EXTRACT_OP - - # Verify arguments are free variables matching the names - assert len(expr.inputs) == 3 - assert isinstance(expr.inputs[0], ex.UnboundVariableExpression) - assert expr.inputs[0].id == "date_expression" - assert isinstance(expr.inputs[1], ex.UnboundVariableExpression) - assert expr.inputs[1].id == "part" - assert isinstance(expr.inputs[2], ex.UnboundVariableExpression) - assert expr.inputs[2].id == "time_zone" - - -def test_format_date_expression(): - # Call the function with col() expressions - result = bbq.format_date( - bpd.col("format_string"), - bpd.col("date_expr"), - ) - - # Verify result is a col Expression - assert isinstance(result, bigframes.core.col.Expression) - - # Verify the internal expression structure - expr = result._value - assert isinstance(expr, ex.OpExpression) - assert expr.op == date_op._FORMAT_DATE_OP - - # Verify arguments are free variables matching the names - assert len(expr.inputs) == 2 - assert isinstance(expr.inputs[0], ex.UnboundVariableExpression) - assert expr.inputs[0].id == "format_string" - assert isinstance(expr.inputs[1], ex.UnboundVariableExpression) - assert expr.inputs[1].id == "date_expr" - - -def test_generate_date_array_expression(): - # Call the function with col() expressions - result = bbq.generate_date_array( - bpd.col("start_date"), - bpd.col("end_date"), - bpd.col("int64_expression"), - bpd.col("date_part"), - ) - - # Verify result is a col Expression - assert isinstance(result, bigframes.core.col.Expression) - - # Verify the internal expression structure - expr = result._value - assert isinstance(expr, ex.OpExpression) - assert expr.op == date_op._GENERATE_DATE_ARRAY_OP - - # Verify arguments are free variables matching the names - assert len(expr.inputs) == 4 - assert isinstance(expr.inputs[0], ex.UnboundVariableExpression) - assert expr.inputs[0].id == "start_date" - assert isinstance(expr.inputs[1], ex.UnboundVariableExpression) - assert expr.inputs[1].id == "end_date" - assert isinstance(expr.inputs[2], ex.UnboundVariableExpression) - assert expr.inputs[2].id == "int64_expression" - assert isinstance(expr.inputs[3], ex.UnboundVariableExpression) - assert expr.inputs[3].id == "date_part" - - -def test_last_day_expression(): - # Call the function with col() expressions - result = bbq.last_day( - bpd.col("date_expression"), - bpd.col("date_part"), - ) - - # Verify result is a col Expression - assert isinstance(result, bigframes.core.col.Expression) - - # Verify the internal expression structure - expr = result._value - assert isinstance(expr, ex.OpExpression) - assert expr.op == date_op._LAST_DAY_OP - - # Verify arguments are free variables matching the names - assert len(expr.inputs) == 2 - assert isinstance(expr.inputs[0], ex.UnboundVariableExpression) - assert expr.inputs[0].id == "date_expression" - assert isinstance(expr.inputs[1], ex.UnboundVariableExpression) - assert expr.inputs[1].id == "date_part" - - -def test_parse_date_expression(): - # Call the function with col() expressions - result = bbq.parse_date( - bpd.col("format_string"), - bpd.col("date_string"), - ) - - # Verify result is a col Expression - assert isinstance(result, bigframes.core.col.Expression) - - # Verify the internal expression structure - expr = result._value - assert isinstance(expr, ex.OpExpression) - assert expr.op == date_op._PARSE_DATE_OP - - # Verify arguments are free variables matching the names - assert len(expr.inputs) == 2 - assert isinstance(expr.inputs[0], ex.UnboundVariableExpression) - assert expr.inputs[0].id == "format_string" - assert isinstance(expr.inputs[1], ex.UnboundVariableExpression) - assert expr.inputs[1].id == "date_string" - - -def test_unix_date_expression(): - # Call the function with col() expressions - result = bbq.unix_date( - bpd.col("date_expression"), - ) - - # Verify result is a col Expression - assert isinstance(result, bigframes.core.col.Expression) - - # Verify the internal expression structure - expr = result._value - assert isinstance(expr, ex.OpExpression) - assert expr.op == date_op._UNIX_DATE_OP - - # Verify arguments are free variables matching the names - assert len(expr.inputs) == 1 - assert isinstance(expr.inputs[0], ex.UnboundVariableExpression) - assert expr.inputs[0].id == "date_expression" diff --git a/tests/unit/bigquery/generated/test_aead.py b/tests/unit/bigquery/generated/test_aead.py deleted file mode 100644 index ce728b41899..00000000000 --- a/tests/unit/bigquery/generated/test_aead.py +++ /dev/null @@ -1,101 +0,0 @@ -# Copyright 2026 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# -# DO NOT MODIFY THIS FILE DIRECTLY. -# This file was generated from: scripts/data/sql-functions/aead.yaml -# by the script: scripts/generate_bigframes_bigquery.py - -import bigframes.bigquery as bbq -import bigframes.core.col -import bigframes.core.expression as ex -import bigframes.operations.googlesql.aead as aead_op -import bigframes.pandas as bpd - - -def test_decrypt_bytes_expression(): - # Call the function with col() expressions - result = bbq.aead.decrypt_bytes( - bpd.col("keyset"), - bpd.col("ciphertext"), - bpd.col("additional_data"), - ) - - # Verify result is a col Expression - assert isinstance(result, bigframes.core.col.Expression) - - # Verify the internal expression structure - expr = result._value - assert isinstance(expr, ex.OpExpression) - assert expr.op == aead_op._DECRYPT_BYTES_OP - - # Verify arguments are free variables matching the names - assert len(expr.inputs) == 3 - assert isinstance(expr.inputs[0], ex.UnboundVariableExpression) - assert expr.inputs[0].id == "keyset" - assert isinstance(expr.inputs[1], ex.UnboundVariableExpression) - assert expr.inputs[1].id == "ciphertext" - assert isinstance(expr.inputs[2], ex.UnboundVariableExpression) - assert expr.inputs[2].id == "additional_data" - - -def test_decrypt_string_expression(): - # Call the function with col() expressions - result = bbq.aead.decrypt_string( - bpd.col("keyset"), - bpd.col("ciphertext"), - bpd.col("additional_data"), - ) - - # Verify result is a col Expression - assert isinstance(result, bigframes.core.col.Expression) - - # Verify the internal expression structure - expr = result._value - assert isinstance(expr, ex.OpExpression) - assert expr.op == aead_op._DECRYPT_STRING_OP - - # Verify arguments are free variables matching the names - assert len(expr.inputs) == 3 - assert isinstance(expr.inputs[0], ex.UnboundVariableExpression) - assert expr.inputs[0].id == "keyset" - assert isinstance(expr.inputs[1], ex.UnboundVariableExpression) - assert expr.inputs[1].id == "ciphertext" - assert isinstance(expr.inputs[2], ex.UnboundVariableExpression) - assert expr.inputs[2].id == "additional_data" - - -def test_encrypt_expression(): - # Call the function with col() expressions - result = bbq.aead.encrypt( - bpd.col("keyset"), - bpd.col("plaintext"), - bpd.col("additional_data"), - ) - - # Verify result is a col Expression - assert isinstance(result, bigframes.core.col.Expression) - - # Verify the internal expression structure - expr = result._value - assert isinstance(expr, ex.OpExpression) - assert expr.op == aead_op._ENCRYPT_OP - - # Verify arguments are free variables matching the names - assert len(expr.inputs) == 3 - assert isinstance(expr.inputs[0], ex.UnboundVariableExpression) - assert expr.inputs[0].id == "keyset" - assert isinstance(expr.inputs[1], ex.UnboundVariableExpression) - assert expr.inputs[1].id == "plaintext" - assert isinstance(expr.inputs[2], ex.UnboundVariableExpression) - assert expr.inputs[2].id == "additional_data" diff --git a/tests/unit/bigquery/test_ai.py b/tests/unit/bigquery/test_ai.py deleted file mode 100644 index 2cb876d39a5..00000000000 --- a/tests/unit/bigquery/test_ai.py +++ /dev/null @@ -1,319 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from unittest import mock - -import pandas as pd -import pytest - -import bigframes.bigquery as bbq -import bigframes.dataframe -import bigframes.series -import bigframes.session - - -@pytest.fixture -def mock_session(): - return mock.create_autospec(spec=bigframes.session.Session) - - -@pytest.fixture -def mock_dataframe(mock_session): - df = mock.create_autospec(spec=bigframes.dataframe.DataFrame) - df._session = mock_session - df.sql = "SELECT * FROM my_table" - df._to_sql_query.return_value = ("SELECT * FROM my_table", None, None) - return df - - -@pytest.fixture -def mock_embedding_series(mock_session): - series = mock.create_autospec(spec=bigframes.series.Series) - series._session = mock_session - # Mock to_frame to return a mock dataframe - df = mock.create_autospec(spec=bigframes.dataframe.DataFrame) - df._session = mock_session - df.sql = "SELECT my_col AS content FROM my_table" - df._to_sql_query.return_value = ( - "SELECT my_col AS content FROM my_table", - None, - None, - ) - series.copy.return_value = series - series.to_frame.return_value = df - return series - - -@pytest.fixture -def mock_text_series(mock_session): - series = mock.create_autospec(spec=bigframes.series.Series) - series._session = mock_session - # Mock to_frame to return a mock dataframe - df = mock.create_autospec(spec=bigframes.dataframe.DataFrame) - df._session = mock_session - df.sql = "SELECT my_col AS prompt FROM my_table" - df._to_sql_query.return_value = ( - "SELECT my_col AS prompt FROM my_table", - None, - None, - ) - series.copy.return_value = series - series.to_frame.return_value = df - return series - - -def test_generate_embedding_with_dataframe(mock_dataframe, mock_session): - model_name = "project.dataset.model" - - bbq.ai.generate_embedding( - model_name, - mock_dataframe, - output_dimensionality=256, - ) - - mock_session.read_gbq_query.assert_called_once() - query = mock_session.read_gbq_query.call_args[0][0] - - # Normalize whitespace for comparison - query = " ".join(query.split()) - - expected_part_1 = "SELECT * FROM AI.GENERATE_EMBEDDING(" - expected_part_2 = f"MODEL `{model_name}`," - expected_part_3 = "(SELECT * FROM my_table)," - expected_part_4 = "STRUCT(256 AS `OUTPUT_DIMENSIONALITY`)" - - assert expected_part_1 in query - assert expected_part_2 in query - assert expected_part_3 in query - assert expected_part_4 in query - - -def test_generate_embedding_with_series(mock_embedding_series, mock_session): - model_name = "project.dataset.model" - - bbq.ai.generate_embedding( - model_name, - mock_embedding_series, - start_second=0.0, - end_second=10.0, - interval_seconds=5.0, - ) - - mock_session.read_gbq_query.assert_called_once() - query = mock_session.read_gbq_query.call_args[0][0] - query = " ".join(query.split()) - - assert f"MODEL `{model_name}`" in query - assert "(SELECT my_col AS content FROM my_table)" in query - assert ( - "STRUCT(0.0 AS `START_SECOND`, 10.0 AS `END_SECOND`, 5.0 AS `INTERVAL_SECONDS`)" - in query - ) - - -def test_generate_embedding_defaults(mock_dataframe, mock_session): - model_name = "project.dataset.model" - - bbq.ai.generate_embedding( - model_name, - mock_dataframe, - ) - - mock_session.read_gbq_query.assert_called_once() - query = mock_session.read_gbq_query.call_args[0][0] - query = " ".join(query.split()) - - assert f"MODEL `{model_name}`" in query - assert "STRUCT()" in query - - -@mock.patch("bigframes.pandas.read_pandas") -def test_generate_embedding_with_pandas_dataframe( - read_pandas_mock, mock_dataframe, mock_session -): - # This tests that pandas input path works and calls read_pandas - model_name = "project.dataset.model" - - # Mock return value of read_pandas to be a BigFrames DataFrame - read_pandas_mock.return_value = mock_dataframe - - pandas_df = pd.DataFrame({"content": ["test"]}) - - bbq.ai.generate_embedding( - model_name, - pandas_df, - ) - - read_pandas_mock.assert_called_once() - # Check that read_pandas was called with something (the pandas df) - assert read_pandas_mock.call_args[0][0] is pandas_df - - mock_session.read_gbq_query.assert_called_once() - - -def test_generate_text_with_dataframe(mock_dataframe, mock_session): - model_name = "project.dataset.model" - - bbq.ai.generate_text( - model_name, - mock_dataframe, - max_output_tokens=256, - ) - - mock_session.read_gbq_query.assert_called_once() - query = mock_session.read_gbq_query.call_args[0][0] - - # Normalize whitespace for comparison - query = " ".join(query.split()) - - expected_part_1 = "SELECT * FROM AI.GENERATE_TEXT(" - expected_part_2 = f"MODEL `{model_name}`," - expected_part_3 = "(SELECT * FROM my_table)," - expected_part_4 = "STRUCT(256 AS `MAX_OUTPUT_TOKENS`)" - - assert expected_part_1 in query - assert expected_part_2 in query - assert expected_part_3 in query - assert expected_part_4 in query - - -def test_generate_text_with_series(mock_text_series, mock_session): - model_name = "project.dataset.model" - - bbq.ai.generate_text( - model_name, - mock_text_series, - ) - - mock_session.read_gbq_query.assert_called_once() - query = mock_session.read_gbq_query.call_args[0][0] - query = " ".join(query.split()) - - assert f"MODEL `{model_name}`" in query - assert "(SELECT my_col AS prompt FROM my_table)" in query - - -def test_generate_text_defaults(mock_dataframe, mock_session): - model_name = "project.dataset.model" - - bbq.ai.generate_text( - model_name, - mock_dataframe, - ) - - mock_session.read_gbq_query.assert_called_once() - query = mock_session.read_gbq_query.call_args[0][0] - query = " ".join(query.split()) - - assert f"MODEL `{model_name}`" in query - assert "STRUCT()" in query - - -def test_generate_table_with_dataframe(mock_dataframe, mock_session): - model_name = "project.dataset.model" - - bbq.ai.generate_table( - model_name, - mock_dataframe, - output_schema="col1 STRING, col2 INT64", - ) - - mock_session.read_gbq_query.assert_called_once() - query = mock_session.read_gbq_query.call_args[0][0] - - # Normalize whitespace for comparison - query = " ".join(query.split()) - - expected_part_1 = "SELECT * FROM AI.GENERATE_TABLE(" - expected_part_2 = f"MODEL `{model_name}`," - expected_part_3 = "(SELECT * FROM my_table)," - expected_part_4 = "STRUCT('col1 STRING, col2 INT64' AS `output_schema`)" - - assert expected_part_1 in query - assert expected_part_2 in query - assert expected_part_3 in query - assert expected_part_4 in query - - -def test_generate_table_with_options(mock_dataframe, mock_session): - model_name = "project.dataset.model" - - bbq.ai.generate_table( - model_name, - mock_dataframe, - output_schema="col1 STRING", - temperature=0.5, - max_output_tokens=100, - ) - - mock_session.read_gbq_query.assert_called_once() - query = mock_session.read_gbq_query.call_args[0][0] - query = " ".join(query.split()) - - assert f"MODEL `{model_name}`" in query - assert "(SELECT * FROM my_table)" in query - assert ( - "STRUCT('col1 STRING' AS `output_schema`, 0.5 AS `temperature`, 100 AS `max_output_tokens`)" - in query - ) - - -def test_generate_table_with_mapping_schema(mock_dataframe, mock_session): - model_name = "project.dataset.model" - - bbq.ai.generate_table( - model_name, - mock_dataframe, - output_schema={"col1": "STRING", "col2": "INT64"}, - ) - - mock_session.read_gbq_query.assert_called_once() - query = mock_session.read_gbq_query.call_args[0][0] - - # Normalize whitespace for comparison - query = " ".join(query.split()) - - expected_part_1 = "SELECT * FROM AI.GENERATE_TABLE(" - expected_part_2 = f"MODEL `{model_name}`," - expected_part_3 = "(SELECT * FROM my_table)," - expected_part_4 = "STRUCT('col1 STRING, col2 INT64' AS `output_schema`)" - - assert expected_part_1 in query - assert expected_part_2 in query - assert expected_part_3 in query - assert expected_part_4 in query - - -@mock.patch("bigframes.pandas.read_pandas") -def test_generate_text_with_pandas_dataframe( - read_pandas_mock, mock_dataframe, mock_session -): - # This tests that pandas input path works and calls read_pandas - model_name = "project.dataset.model" - - # Mock return value of read_pandas to be a BigFrames DataFrame - read_pandas_mock.return_value = mock_dataframe - - pandas_df = pd.DataFrame({"content": ["test"]}) - - bbq.ai.generate_text( - model_name, - pandas_df, - ) - - read_pandas_mock.assert_called_once() - # Check that read_pandas was called with something (the pandas df) - assert read_pandas_mock.call_args[0][0] is pandas_df - - mock_session.read_gbq_query.assert_called_once() diff --git a/tests/unit/bigquery/test_json.py b/tests/unit/bigquery/test_json.py deleted file mode 100644 index d9beea26db4..00000000000 --- a/tests/unit/bigquery/test_json.py +++ /dev/null @@ -1,26 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import unittest.mock as mock - -import pytest - -import bigframes.bigquery as bbq -import bigframes.pandas as bpd - - -def test_json_set_w_invalid_json_path_value_pairs(): - mock_series = mock.create_autospec(bpd.pandas.Series, instance=True) - with pytest.raises(ValueError, match="Incorrect format"): - bbq.json_set(mock_series, json_path_value_pairs=[("$.a", 1, 100)]) # type: ignore diff --git a/tests/unit/bigquery/test_mathematical.py b/tests/unit/bigquery/test_mathematical.py deleted file mode 100644 index f0cb16ae145..00000000000 --- a/tests/unit/bigquery/test_mathematical.py +++ /dev/null @@ -1,33 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import bigframes.bigquery as bbq -import bigframes.core.col as col -import bigframes.core.expression as ex -import bigframes.dtypes as dtypes -import bigframes.operations as ops - - -def test_rand_returns_expression(): - expr = bbq.rand() - - assert isinstance(expr, col.Expression) - node = expr._value - assert isinstance(node, ex.OpExpression) - op = node.op - assert isinstance(op, ops.GoogleSqlScalarOp) - assert op.sql_name == "RAND" - assert op.output_type() == dtypes.FLOAT_DTYPE - assert not op.is_deterministic - assert len(node.inputs) == 0 diff --git a/tests/unit/bigquery/test_ml.py b/tests/unit/bigquery/test_ml.py deleted file mode 100644 index a68133225d4..00000000000 --- a/tests/unit/bigquery/test_ml.py +++ /dev/null @@ -1,215 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -from __future__ import annotations - -from unittest import mock - -import pandas as pd -import pytest - -import bigframes.bigquery._operations.ml as ml_ops -import bigframes.session - - -@pytest.fixture -def mock_session(): - return mock.create_autospec(spec=bigframes.session.Session) - - -MODEL_SERIES = pd.Series( - { - "modelReference": { - "projectId": "test-project", - "datasetId": "test-dataset", - "modelId": "test-model", - } - } -) - -MODEL_NAME = "test-project.test-dataset.test-model" - - -@mock.patch("bigframes.bigquery._operations.ml._get_model_metadata") -@mock.patch("bigframes.pandas.read_pandas") -def test_create_model_with_pandas_dataframe( - read_pandas_mock, _get_model_metadata_mock, mock_session -): - df = pd.DataFrame({"col1": [1, 2, 3]}) - read_pandas_mock.return_value._to_sql_query.return_value = ( - "SELECT * FROM `pandas_df`", - [], - [], - ) - ml_ops.create_model("model_name", training_data=df, session=mock_session) - read_pandas_mock.assert_called_once() - mock_session.read_gbq_query.assert_called_once() - generated_sql = mock_session.read_gbq_query.call_args[0][0] - assert "CREATE MODEL `model_name`" in generated_sql - assert "AS SELECT * FROM `pandas_df`" in generated_sql - - -@mock.patch("bigframes.pandas.read_gbq_query") -@mock.patch("bigframes.pandas.read_pandas") -def test_evaluate_with_pandas_dataframe(read_pandas_mock, read_gbq_query_mock): - df = pd.DataFrame({"col1": [1, 2, 3]}) - read_pandas_mock.return_value._to_sql_query.return_value = ( - "SELECT * FROM `pandas_df`", - [], - [], - ) - ml_ops.evaluate(MODEL_SERIES, input_=df) - read_pandas_mock.assert_called_once() - read_gbq_query_mock.assert_called_once() - generated_sql = read_gbq_query_mock.call_args[0][0] - assert "ML.EVALUATE" in generated_sql - assert f"MODEL `{MODEL_NAME}`" in generated_sql - assert "(SELECT * FROM `pandas_df`)" in generated_sql - - -@mock.patch("bigframes.pandas.read_gbq_query") -@mock.patch("bigframes.pandas.read_pandas") -def test_predict_with_pandas_dataframe(read_pandas_mock, read_gbq_query_mock): - df = pd.DataFrame({"col1": [1, 2, 3]}) - read_pandas_mock.return_value._to_sql_query.return_value = ( - "SELECT * FROM `pandas_df`", - [], - [], - ) - ml_ops.predict(MODEL_SERIES, input_=df) - read_pandas_mock.assert_called_once() - read_gbq_query_mock.assert_called_once() - generated_sql = read_gbq_query_mock.call_args[0][0] - assert "ML.PREDICT" in generated_sql - assert f"MODEL `{MODEL_NAME}`" in generated_sql - assert "(SELECT * FROM `pandas_df`)" in generated_sql - - -@mock.patch("bigframes.pandas.read_gbq_query") -@mock.patch("bigframes.pandas.read_pandas") -def test_explain_predict_with_pandas_dataframe(read_pandas_mock, read_gbq_query_mock): - df = pd.DataFrame({"col1": [1, 2, 3]}) - read_pandas_mock.return_value._to_sql_query.return_value = ( - "SELECT * FROM `pandas_df`", - [], - [], - ) - ml_ops.explain_predict(MODEL_SERIES, input_=df) - read_pandas_mock.assert_called_once() - read_gbq_query_mock.assert_called_once() - generated_sql = read_gbq_query_mock.call_args[0][0] - assert "ML.EXPLAIN_PREDICT" in generated_sql - assert f"MODEL `{MODEL_NAME}`" in generated_sql - assert "(SELECT * FROM `pandas_df`)" in generated_sql - - -@mock.patch("bigframes.pandas.read_gbq_query") -def test_global_explain_with_pandas_series_model(read_gbq_query_mock): - ml_ops.global_explain(MODEL_SERIES) - read_gbq_query_mock.assert_called_once() - generated_sql = read_gbq_query_mock.call_args[0][0] - assert "ML.GLOBAL_EXPLAIN" in generated_sql - assert f"MODEL `{MODEL_NAME}`" in generated_sql - - -@mock.patch("bigframes.pandas.read_gbq_query") -@mock.patch("bigframes.pandas.read_pandas") -def test_transform_with_pandas_dataframe(read_pandas_mock, read_gbq_query_mock): - df = pd.DataFrame({"col1": [1, 2, 3]}) - read_pandas_mock.return_value._to_sql_query.return_value = ( - "SELECT * FROM `pandas_df`", - [], - [], - ) - ml_ops.transform(MODEL_SERIES, input_=df) - read_pandas_mock.assert_called_once() - read_gbq_query_mock.assert_called_once() - generated_sql = read_gbq_query_mock.call_args[0][0] - assert "ML.TRANSFORM" in generated_sql - assert f"MODEL `{MODEL_NAME}`" in generated_sql - assert "(SELECT * FROM `pandas_df`)" in generated_sql - - -@mock.patch("bigframes.pandas.read_gbq_query") -@mock.patch("bigframes.pandas.read_pandas") -def test_generate_text_with_pandas_dataframe(read_pandas_mock, read_gbq_query_mock): - df = pd.DataFrame({"col1": [1, 2, 3]}) - read_pandas_mock.return_value._to_sql_query.return_value = ( - "SELECT * FROM `pandas_df`", - [], - [], - ) - ml_ops.generate_text( - MODEL_SERIES, - input_=df, - temperature=0.5, - max_output_tokens=128, - top_k=20, - top_p=0.9, - flatten_json_output=True, - stop_sequences=["a", "b"], - ground_with_google_search=True, - request_type="TYPE", - ) - read_pandas_mock.assert_called_once() - read_gbq_query_mock.assert_called_once() - generated_sql = read_gbq_query_mock.call_args[0][0] - assert "ML.GENERATE_TEXT" in generated_sql - assert f"MODEL `{MODEL_NAME}`" in generated_sql - assert "(SELECT * FROM `pandas_df`)" in generated_sql - assert "STRUCT(\n 0.5 AS `temperature`" in generated_sql - assert "128 AS `max_output_tokens`" in generated_sql - assert "20 AS `top_k`" in generated_sql - assert "0.9 AS `top_p`" in generated_sql - assert "TRUE AS `flatten_json_output`" in generated_sql - assert "['a', 'b'] AS `stop_sequences`" in generated_sql - assert "TRUE AS `ground_with_google_search`" in generated_sql - assert "'TYPE' AS `request_type`" in generated_sql - - -@mock.patch("bigframes.pandas.read_gbq_query") -def test_get_insights(read_gbq_query_mock): - ml_ops.get_insights(MODEL_SERIES) - read_gbq_query_mock.assert_called_once() - generated_sql = read_gbq_query_mock.call_args[0][0] - assert "ML.GET_INSIGHTS" in generated_sql - assert f"MODEL `{MODEL_NAME}`" in generated_sql - - -@mock.patch("bigframes.pandas.read_gbq_query") -@mock.patch("bigframes.pandas.read_pandas") -def test_generate_embedding_with_pandas_dataframe( - read_pandas_mock, read_gbq_query_mock -): - df = pd.DataFrame({"col1": [1, 2, 3]}) - read_pandas_mock.return_value._to_sql_query.return_value = ( - "SELECT * FROM `pandas_df`", - [], - [], - ) - ml_ops.generate_embedding( - MODEL_SERIES, - input_=df, - flatten_json_output=True, - task_type="RETRIEVAL_DOCUMENT", - output_dimensionality=256, - ) - read_pandas_mock.assert_called_once() - read_gbq_query_mock.assert_called_once() - generated_sql = read_gbq_query_mock.call_args[0][0] - assert "ML.GENERATE_EMBEDDING" in generated_sql - assert f"MODEL `{MODEL_NAME}`" in generated_sql - assert "(SELECT * FROM `pandas_df`)" in generated_sql - assert "STRUCT(\n TRUE AS `flatten_json_output`" in generated_sql - assert "'RETRIEVAL_DOCUMENT' AS `task_type`" in generated_sql - assert "256 AS `output_dimensionality`" in generated_sql diff --git a/tests/unit/bigquery/test_obj.py b/tests/unit/bigquery/test_obj.py deleted file mode 100644 index 9eac234b8bc..00000000000 --- a/tests/unit/bigquery/test_obj.py +++ /dev/null @@ -1,125 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import datetime -from unittest import mock - -import bigframes.bigquery.obj as obj -import bigframes.operations as ops -import bigframes.series - - -def create_mock_series(): - result = mock.create_autospec(bigframes.series.Series, instance=True) - result.copy.return_value = result - return result - - -def test_fetch_metadata_op_structure(): - op = ops.obj_fetch_metadata_op - assert op.name == "obj_fetch_metadata" - - -def test_get_access_url_op_structure(): - op = ops.ObjGetAccessUrl(mode="r") - assert op.name == "obj_get_access_url" - assert op.mode == "r" - assert op.duration is None - - -def test_get_access_url_with_duration_op_structure(): - op = ops.ObjGetAccessUrl(mode="rw", duration=3600000000) - assert op.name == "obj_get_access_url" - assert op.mode == "rw" - assert op.duration == 3600000000 - - -def test_make_ref_op_structure(): - op = ops.obj_make_ref_op - assert op.name == "obj_make_ref" - - -def test_make_ref_json_op_structure(): - op = ops.obj_make_ref_json_op - assert op.name == "obj_make_ref_json" - - -def test_fetch_metadata_calls_apply_unary_op(): - series = create_mock_series() - - obj.fetch_metadata(series) - - series._apply_unary_op.assert_called_once() - args, _ = series._apply_unary_op.call_args - assert args[0] == ops.obj_fetch_metadata_op - - -def test_get_access_url_calls_apply_unary_op_without_duration(): - series = create_mock_series() - - obj.get_access_url(series, mode="r") - - series._apply_unary_op.assert_called_once() - args, _ = series._apply_unary_op.call_args - assert isinstance(args[0], ops.ObjGetAccessUrl) - assert args[0].mode == "r" - assert args[0].duration is None - - -def test_get_access_url_calls_apply_unary_op_with_duration(): - series = create_mock_series() - duration = datetime.timedelta(hours=1) - - obj.get_access_url(series, mode="rw", duration=duration) - - series._apply_unary_op.assert_called_once() - args, _ = series._apply_unary_op.call_args - assert isinstance(args[0], ops.ObjGetAccessUrl) - assert args[0].mode == "rw" - # 1 hour = 3600 seconds = 3600 * 1000 * 1000 microseconds - assert args[0].duration == 3600000000 - - -def test_make_ref_calls_apply_binary_op_with_authorizer(): - uri = create_mock_series() - auth = create_mock_series() - - obj.make_ref(uri, authorizer=auth) - - uri._apply_binary_op.assert_called_once() - args, _ = uri._apply_binary_op.call_args - assert args[0] == auth - assert args[1] == ops.obj_make_ref_op - - -def test_make_ref_calls_apply_binary_op_with_authorizer_string(): - uri = create_mock_series() - auth = "us.bigframes-test-connection" - - obj.make_ref(uri, authorizer=auth) - - uri._apply_binary_op.assert_called_once() - args, _ = uri._apply_binary_op.call_args - assert args[0] == auth - assert args[1] == ops.obj_make_ref_op - - -def test_make_ref_calls_apply_unary_op_without_authorizer(): - json_val = create_mock_series() - - obj.make_ref(json_val) - - json_val._apply_unary_op.assert_called_once() - args, _ = json_val._apply_unary_op.call_args - assert args[0] == ops.obj_make_ref_json_op diff --git a/tests/unit/conftest.py b/tests/unit/conftest.py deleted file mode 100644 index d880fe54242..00000000000 --- a/tests/unit/conftest.py +++ /dev/null @@ -1,316 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import datetime -import json -import pathlib -import typing - -import numpy as np -import pandas as pd -import pyarrow as pa -import pytest -from google.cloud import bigquery - -import bigframes.core as core -import bigframes.pandas as bpd -import bigframes.testing.mocks as mocks -import bigframes.testing.utils -from bigframes import dtypes - -CURRENT_DIR = pathlib.Path(__file__).parent -DATA_DIR = CURRENT_DIR.parent / "data" - - -@pytest.fixture(scope="session") -def polars_session(): - pytest.importorskip("polars") - - from bigframes.testing import polars_session - - return polars_session.TestSession() - - -def _create_compiler_session(table_name, table_schema): - """Helper function to create a compiler session.""" - from bigframes.testing import compiler_session - - anonymous_dataset = bigquery.DatasetReference.from_string( - "bigframes-dev.sqlglot_test" - ) - session = mocks.create_bigquery_session( - table_name=table_name, - table_schema=table_schema, - anonymous_dataset=anonymous_dataset, - ) - session._executor = compiler_session.SQLCompilerExecutor() - return session - - -@pytest.fixture(scope="session") -def compiler_session(scalar_types_table_schema): - """Compiler session for scalar types.""" - return _create_compiler_session("scalar_types", scalar_types_table_schema) - - -@pytest.fixture(scope="session") -def compiler_session_w_repeated_types(repeated_types_table_schema): - """Compiler session for repeated data types.""" - return _create_compiler_session("repeated_types", repeated_types_table_schema) - - -@pytest.fixture(scope="session") -def compiler_session_w_nested_structs_types(nested_structs_types_table_schema): - """Compiler session for nested STRUCT data types.""" - return _create_compiler_session( - "nested_structs_types", nested_structs_types_table_schema - ) - - -@pytest.fixture(scope="session") -def compiler_session_w_json_types(json_types_table_schema): - """Compiler session for JSON data types.""" - return _create_compiler_session("json_types", json_types_table_schema) - - -@pytest.fixture(scope="session") -def scalar_types_table_schema() -> typing.Sequence[bigquery.SchemaField]: - return [ - bigquery.SchemaField("bool_col", "BOOLEAN"), - bigquery.SchemaField("bytes_col", "BYTES"), - bigquery.SchemaField("date_col", "DATE"), - bigquery.SchemaField("datetime_col", "DATETIME"), - bigquery.SchemaField("geography_col", "GEOGRAPHY"), - bigquery.SchemaField("int64_col", "INTEGER"), - bigquery.SchemaField("int64_too", "INTEGER"), - bigquery.SchemaField("numeric_col", "NUMERIC"), - bigquery.SchemaField("float64_col", "FLOAT"), - bigquery.SchemaField("rowindex", "INTEGER"), - bigquery.SchemaField("rowindex_2", "INTEGER", mode="REQUIRED"), - bigquery.SchemaField("string_col", "STRING"), - bigquery.SchemaField("time_col", "TIME"), - bigquery.SchemaField("timestamp_col", "TIMESTAMP"), - bigquery.SchemaField("duration_col", "INTEGER"), - ] - - -@pytest.fixture(scope="session") -def scalar_types_df(compiler_session) -> bpd.DataFrame: - """Returns a BigFrames DataFrame containing all scalar types and using the `rowindex` - column as the index.""" - bf_df = compiler_session._loader.read_gbq_table( - "bigframes-dev.sqlglot_test.scalar_types", - enable_snapshot=False, - ) - bf_df = bf_df.set_index("rowindex", drop=False) - return bf_df - - -@pytest.fixture(scope="session") -def scalar_types_pandas_df() -> pd.DataFrame: - """Returns a pandas DataFrame containing all scalar types and using the `rowindex` - column as the index.""" - # TODO: add tests for empty dataframes - df = pd.read_json( - DATA_DIR / "scalars.jsonl", - lines=True, - ) - bigframes.testing.utils.convert_pandas_dtypes(df, bytes_col=True) - - df = df.set_index("rowindex", drop=False) - return df - - -@pytest.fixture(scope="module") -def scalar_types_array_value( - scalar_types_pandas_df: pd.DataFrame, compiler_session: bigframes.Session -) -> core.ArrayValue: - managed_data_source = core.local_data.ManagedArrowTable.from_pandas( - scalar_types_pandas_df - ) - return core.ArrayValue.from_managed(managed_data_source, compiler_session) - - -@pytest.fixture(scope="session") -def nested_structs_types_table_schema() -> typing.Sequence[bigquery.SchemaField]: - return [ - bigquery.SchemaField("id", "INTEGER"), - bigquery.SchemaField( - "people", - "RECORD", - fields=[ - bigquery.SchemaField("name", "STRING"), - bigquery.SchemaField("age", "INTEGER"), - bigquery.SchemaField( - "address", - "RECORD", - fields=[ - bigquery.SchemaField("city", "STRING"), - bigquery.SchemaField("country", "STRING"), - ], - ), - ], - ), - ] - - -@pytest.fixture(scope="session") -def nested_structs_types_df(compiler_session_w_nested_structs_types) -> bpd.DataFrame: - """Returns a BigFrames DataFrame containing all scalar types and using the `rowindex` - column as the index.""" - bf_df = compiler_session_w_nested_structs_types._loader.read_gbq_table( - "bigframes-dev.sqlglot_test.nested_structs_types", - enable_snapshot=False, - ) - bf_df = bf_df.set_index("id", drop=False) - return bf_df - - -@pytest.fixture(scope="session") -def nested_structs_pandas_df() -> pd.DataFrame: - """Returns a pandas DataFrame containing STRUCT types and using the `id` - column as the index.""" - - df = pd.read_json( - DATA_DIR / "nested_structs.jsonl", - lines=True, - ) - df = df.set_index("id") - - address_struct_schema = pa.struct( - [pa.field("city", pa.string()), pa.field("country", pa.string())] - ) - person_struct_schema = pa.struct( - [ - pa.field("name", pa.string()), - pa.field("age", pa.int64()), - pa.field("address", address_struct_schema), - ] - ) - df["person"] = df["person"].astype(pd.ArrowDtype(person_struct_schema)) - - def to_json_str(val): - if val is None or (isinstance(val, float) and np.isnan(val)): - return None - return json.dumps(val) - - df["json_col"] = df["json_col"].apply(to_json_str).astype(dtypes.JSON_DTYPE) - - # timestamp_col - def parse_timestamp(val): - if pd.isna(val): - return None - if isinstance(val, str): - return datetime.datetime.fromisoformat(val.replace("Z", "+00:00")) - if hasattr(val, "to_pydatetime"): - return val.to_pydatetime() - return val - - timestamp_vals = [parse_timestamp(x) for x in df["timestamp_col"]] - timestamp_arr = pa.array(timestamp_vals, type=dtypes.TIMESTAMP_DTYPE.pyarrow_dtype) - df["timestamp_col"] = pd.Series( - timestamp_arr, index=df.index, dtype=dtypes.TIMESTAMP_DTYPE - ) - - return df - - -@pytest.fixture(scope="session") -def repeated_types_table_schema() -> typing.Sequence[bigquery.SchemaField]: - return [ - bigquery.SchemaField("rowindex", "INTEGER"), - bigquery.SchemaField("int_list_col", "INTEGER", "REPEATED"), - bigquery.SchemaField("bool_list_col", "BOOLEAN", "REPEATED"), - bigquery.SchemaField("float_list_col", "FLOAT", "REPEATED"), - bigquery.SchemaField("date_list_col", "DATE", "REPEATED"), - bigquery.SchemaField("date_time_list_col", "DATETIME", "REPEATED"), - bigquery.SchemaField("numeric_list_col", "NUMERIC", "REPEATED"), - bigquery.SchemaField("string_list_col", "STRING", "REPEATED"), - ] - - -@pytest.fixture(scope="session") -def repeated_types_df(compiler_session_w_repeated_types) -> bpd.DataFrame: - """Returns a BigFrames DataFrame containing all scalar types and using the `rowindex` - column as the index.""" - bf_df = compiler_session_w_repeated_types._loader.read_gbq_table( - "bigframes-dev.sqlglot_test.repeated_types", - enable_snapshot=False, - ) - bf_df = bf_df.set_index("rowindex", drop=False) - return bf_df - - -@pytest.fixture(scope="session") -def repeated_types_pandas_df() -> pd.DataFrame: - """Returns a pandas DataFrame containing LIST types and using the `rowindex` - column as the index.""" - - df = pd.read_json( - DATA_DIR / "repeated.jsonl", - lines=True, - ) - # TODO: add dtype conversion here if needed. - df = df.set_index("rowindex") - return df - - -@pytest.fixture(scope="session") -def json_types_table_schema() -> typing.Sequence[bigquery.SchemaField]: - return [ - bigquery.SchemaField("rowindex", "INTEGER"), - bigquery.SchemaField("json_col", "JSON"), - ] - - -@pytest.fixture(scope="session") -def json_types_df(compiler_session_w_json_types) -> bpd.DataFrame: - """Returns a BigFrames DataFrame containing JSON types and using the `rowindex` - column as the index.""" - bf_df = compiler_session_w_json_types._loader.read_gbq_table( - "bigframes-dev.sqlglot_test.json_types", - enable_snapshot=False, - ) - # TODO(b/427305807): Why `drop=False` will produce two "rowindex" columns? - bf_df = bf_df.set_index("rowindex", drop=True) - return bf_df - - -@pytest.fixture(scope="session") -def json_pandas_df() -> pd.DataFrame: - """Returns a pandas DataFrame containing JSON types and using the `rowindex` - column as the index.""" - json_data = [ - "null", - "true", - "100", - "0.98", - '"a string"', - "[]", - "[1, 2, 3]", - '[{"a": 1}, {"a": 2}, {"a": null}, {}]', - '"100"', - '{"date": "2024-07-16"}', - '{"int_value": 2, "null_filed": null}', - '{"list_data": [10, 20, 30]}', - ] - df = pd.DataFrame( - { - "rowindex": pd.Series(range(len(json_data)), dtype=dtypes.INT_DTYPE), - "json_col": pd.Series(json_data, dtype=dtypes.JSON_DTYPE), - }, - ) - # TODO(b/427305807): Why `drop=False` will produce two "rowindex" columns? - df = df.set_index("rowindex", drop=True) - return df diff --git a/tests/unit/core/compile/__init__.py b/tests/unit/core/compile/__init__.py deleted file mode 100644 index 6d5e14bcf4a..00000000000 --- a/tests/unit/core/compile/__init__.py +++ /dev/null @@ -1,13 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. diff --git a/tests/unit/core/compile/sqlglot/__init__.py b/tests/unit/core/compile/sqlglot/__init__.py deleted file mode 100644 index 0a2669d7a25..00000000000 --- a/tests/unit/core/compile/sqlglot/__init__.py +++ /dev/null @@ -1,13 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. diff --git a/tests/unit/core/compile/sqlglot/aggregations/__init__.py b/tests/unit/core/compile/sqlglot/aggregations/__init__.py deleted file mode 100644 index 0a2669d7a25..00000000000 --- a/tests/unit/core/compile/sqlglot/aggregations/__init__.py +++ /dev/null @@ -1,13 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. diff --git a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_binary_compiler/test_corr/out.sql b/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_binary_compiler/test_corr/out.sql deleted file mode 100644 index fb930323dbd..00000000000 --- a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_binary_compiler/test_corr/out.sql +++ /dev/null @@ -1,13 +0,0 @@ -WITH `bfcte_0` AS ( - SELECT - `int64_col`, - `float64_col` - FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` -), `bfcte_1` AS ( - SELECT - CORR(`int64_col`, `float64_col`) AS `bfcol_2` - FROM `bfcte_0` -) -SELECT - `bfcol_2` AS `corr_col` -FROM `bfcte_1` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_binary_compiler/test_cov/out.sql b/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_binary_compiler/test_cov/out.sql deleted file mode 100644 index 92b8ea4d3ab..00000000000 --- a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_binary_compiler/test_cov/out.sql +++ /dev/null @@ -1,13 +0,0 @@ -WITH `bfcte_0` AS ( - SELECT - `int64_col`, - `float64_col` - FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` -), `bfcte_1` AS ( - SELECT - COVAR_SAMP(`int64_col`, `float64_col`) AS `bfcol_2` - FROM `bfcte_0` -) -SELECT - `bfcol_2` AS `cov_col` -FROM `bfcte_1` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_nullary_compiler/test_row_number/out.sql b/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_nullary_compiler/test_row_number/out.sql deleted file mode 100644 index 7056c8b0af3..00000000000 --- a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_nullary_compiler/test_row_number/out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - ROW_NUMBER() OVER () - 1 AS `row_number` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_nullary_compiler/test_row_number_with_window/out.sql b/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_nullary_compiler/test_row_number_with_window/out.sql deleted file mode 100644 index 8efea4b51bc..00000000000 --- a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_nullary_compiler/test_row_number_with_window/out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - ROW_NUMBER() OVER (ORDER BY `int64_col` ASC NULLS LAST) - 1 AS `row_number` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_nullary_compiler/test_size/out.sql b/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_nullary_compiler/test_size/out.sql deleted file mode 100644 index 4d67203ecc6..00000000000 --- a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_nullary_compiler/test_size/out.sql +++ /dev/null @@ -1,12 +0,0 @@ -WITH `bfcte_0` AS ( - SELECT - * - FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` -), `bfcte_1` AS ( - SELECT - COUNT(1) AS `bfcol_32` - FROM `bfcte_0` -) -SELECT - `bfcol_32` AS `size` -FROM `bfcte_1` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_ordered_unary_compiler/test_array_agg/out.sql b/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_ordered_unary_compiler/test_array_agg/out.sql deleted file mode 100644 index f929970a227..00000000000 --- a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_ordered_unary_compiler/test_array_agg/out.sql +++ /dev/null @@ -1,12 +0,0 @@ -WITH `bfcte_0` AS ( - SELECT - `int64_col` - FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` -), `bfcte_1` AS ( - SELECT - ARRAY_AGG(`int64_col` IGNORE NULLS ORDER BY `int64_col` IS NULL ASC, `int64_col` ASC) AS `bfcol_1` - FROM `bfcte_0` -) -SELECT - `bfcol_1` AS `int64_col` -FROM `bfcte_1` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_ordered_unary_compiler/test_string_agg/out.sql b/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_ordered_unary_compiler/test_string_agg/out.sql deleted file mode 100644 index 7e697719b36..00000000000 --- a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_ordered_unary_compiler/test_string_agg/out.sql +++ /dev/null @@ -1,18 +0,0 @@ -WITH `bfcte_0` AS ( - SELECT - `string_col` - FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` -), `bfcte_1` AS ( - SELECT - COALESCE( - STRING_AGG(`string_col`, ',' - ORDER BY - `string_col` IS NULL ASC, - `string_col` ASC), - '' - ) AS `bfcol_1` - FROM `bfcte_0` -) -SELECT - `bfcol_1` AS `string_col` -FROM `bfcte_1` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_all/out.sql b/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_all/out.sql deleted file mode 100644 index dc1f6fb4f79..00000000000 --- a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_all/out.sql +++ /dev/null @@ -1,15 +0,0 @@ -WITH `bfcte_0` AS ( - SELECT - `bool_col`, - `int64_col` - FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` -), `bfcte_1` AS ( - SELECT - COALESCE(LOGICAL_AND(`bool_col`), TRUE) AS `bfcol_2`, - COALESCE(LOGICAL_AND(`int64_col` <> 0), TRUE) AS `bfcol_3` - FROM `bfcte_0` -) -SELECT - `bfcol_2` AS `bool_col`, - `bfcol_3` AS `int64_col` -FROM `bfcte_1` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_all_w_window/out.sql b/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_all_w_window/out.sql deleted file mode 100644 index 7e4c9d6c3c9..00000000000 --- a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_all_w_window/out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - COALESCE(LOGICAL_AND(`bool_col`) OVER (), TRUE) AS `agg_bool` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_any/out.sql b/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_any/out.sql deleted file mode 100644 index 8ae589fb09f..00000000000 --- a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_any/out.sql +++ /dev/null @@ -1,15 +0,0 @@ -WITH `bfcte_0` AS ( - SELECT - `bool_col`, - `int64_col` - FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` -), `bfcte_1` AS ( - SELECT - COALESCE(LOGICAL_OR(`bool_col`), FALSE) AS `bfcol_2`, - COALESCE(LOGICAL_OR(`int64_col` <> 0), FALSE) AS `bfcol_3` - FROM `bfcte_0` -) -SELECT - `bfcol_2` AS `bool_col`, - `bfcol_3` AS `int64_col` -FROM `bfcte_1` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_any_value/out.sql b/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_any_value/out.sql deleted file mode 100644 index e8556018852..00000000000 --- a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_any_value/out.sql +++ /dev/null @@ -1,12 +0,0 @@ -WITH `bfcte_0` AS ( - SELECT - `int64_col` - FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` -), `bfcte_1` AS ( - SELECT - ANY_VALUE(`int64_col`) AS `bfcol_1` - FROM `bfcte_0` -) -SELECT - `bfcol_1` AS `int64_col` -FROM `bfcte_1` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_any_value/window_out.sql b/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_any_value/window_out.sql deleted file mode 100644 index 020d7603b98..00000000000 --- a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_any_value/window_out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - ANY_VALUE(`int64_col`) OVER () AS `agg_int64` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_any_value/window_partition_out.sql b/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_any_value/window_partition_out.sql deleted file mode 100644 index 577c5929b91..00000000000 --- a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_any_value/window_partition_out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - ANY_VALUE(`int64_col`) OVER (PARTITION BY `string_col`) AS `agg_int64` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_any_w_window/out.sql b/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_any_w_window/out.sql deleted file mode 100644 index 33045c4b70d..00000000000 --- a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_any_w_window/out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - COALESCE(LOGICAL_OR(`bool_col`) OVER (), FALSE) AS `agg_bool` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_approx_quartiles/out.sql b/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_approx_quartiles/out.sql deleted file mode 100644 index e2a119499f2..00000000000 --- a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_approx_quartiles/out.sql +++ /dev/null @@ -1,16 +0,0 @@ -WITH `bfcte_0` AS ( - SELECT - `int64_col` - FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` -), `bfcte_1` AS ( - SELECT - APPROX_QUANTILES(`int64_col`, 4)[OFFSET(1)] AS `bfcol_1`, - APPROX_QUANTILES(`int64_col`, 4)[OFFSET(2)] AS `bfcol_2`, - APPROX_QUANTILES(`int64_col`, 4)[OFFSET(3)] AS `bfcol_3` - FROM `bfcte_0` -) -SELECT - `bfcol_1` AS `q1`, - `bfcol_2` AS `q2`, - `bfcol_3` AS `q3` -FROM `bfcte_1` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_approx_top_count/out.sql b/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_approx_top_count/out.sql deleted file mode 100644 index 1c391c6691f..00000000000 --- a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_approx_top_count/out.sql +++ /dev/null @@ -1,12 +0,0 @@ -WITH `bfcte_0` AS ( - SELECT - `int64_col` - FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` -), `bfcte_1` AS ( - SELECT - APPROX_TOP_COUNT(`int64_col`, 10) AS `bfcol_1` - FROM `bfcte_0` -) -SELECT - `bfcol_1` AS `int64_col` -FROM `bfcte_1` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_count/out.sql b/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_count/out.sql deleted file mode 100644 index 61f073b7dc8..00000000000 --- a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_count/out.sql +++ /dev/null @@ -1,12 +0,0 @@ -WITH `bfcte_0` AS ( - SELECT - `int64_col` - FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` -), `bfcte_1` AS ( - SELECT - COUNT(`int64_col`) AS `bfcol_1` - FROM `bfcte_0` -) -SELECT - `bfcol_1` AS `int64_col` -FROM `bfcte_1` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_count/window_out.sql b/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_count/window_out.sql deleted file mode 100644 index e46b49e7e48..00000000000 --- a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_count/window_out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - COUNT(`int64_col`) OVER () AS `agg_int64` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_count/window_partition_out.sql b/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_count/window_partition_out.sql deleted file mode 100644 index 98088d97dfc..00000000000 --- a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_count/window_partition_out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - COUNT(`int64_col`) OVER (PARTITION BY `string_col`) AS `agg_int64` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_cut/int_bins.sql b/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_cut/int_bins.sql deleted file mode 100644 index ac5525fe63f..00000000000 --- a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_cut/int_bins.sql +++ /dev/null @@ -1,47 +0,0 @@ -SELECT - CASE - WHEN `int64_col` <= MIN(`int64_col`) OVER () + ( - 1 * IEEE_DIVIDE(MAX(`int64_col`) OVER () - MIN(`int64_col`) OVER (), 3) - ) - THEN STRUCT( - ( - MIN(`int64_col`) OVER () + ( - 0 * IEEE_DIVIDE(MAX(`int64_col`) OVER () - MIN(`int64_col`) OVER (), 3) - ) - ) - ( - ( - MAX(`int64_col`) OVER () - MIN(`int64_col`) OVER () - ) * 0.001 - ) AS `left_exclusive`, - MIN(`int64_col`) OVER () + ( - 1 * IEEE_DIVIDE(MAX(`int64_col`) OVER () - MIN(`int64_col`) OVER (), 3) - ) + 0 AS `right_inclusive` - ) - WHEN `int64_col` <= MIN(`int64_col`) OVER () + ( - 2 * IEEE_DIVIDE(MAX(`int64_col`) OVER () - MIN(`int64_col`) OVER (), 3) - ) - THEN STRUCT( - ( - MIN(`int64_col`) OVER () + ( - 1 * IEEE_DIVIDE(MAX(`int64_col`) OVER () - MIN(`int64_col`) OVER (), 3) - ) - ) - 0 AS `left_exclusive`, - MIN(`int64_col`) OVER () + ( - 2 * IEEE_DIVIDE(MAX(`int64_col`) OVER () - MIN(`int64_col`) OVER (), 3) - ) + 0 AS `right_inclusive` - ) - WHEN ( - `int64_col` - ) IS NOT NULL - THEN STRUCT( - ( - MIN(`int64_col`) OVER () + ( - 2 * IEEE_DIVIDE(MAX(`int64_col`) OVER () - MIN(`int64_col`) OVER (), 3) - ) - ) - 0 AS `left_exclusive`, - MIN(`int64_col`) OVER () + ( - 3 * IEEE_DIVIDE(MAX(`int64_col`) OVER () - MIN(`int64_col`) OVER (), 3) - ) + 0 AS `right_inclusive` - ) - END AS `int_bins` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_cut/int_bins_labels.sql b/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_cut/int_bins_labels.sql deleted file mode 100644 index 94e9f57b28e..00000000000 --- a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_cut/int_bins_labels.sql +++ /dev/null @@ -1,16 +0,0 @@ -SELECT - CASE - WHEN `int64_col` < MIN(`int64_col`) OVER () + ( - 1 * IEEE_DIVIDE(MAX(`int64_col`) OVER () - MIN(`int64_col`) OVER (), 3) - ) - THEN 'a' - WHEN `int64_col` < MIN(`int64_col`) OVER () + ( - 2 * IEEE_DIVIDE(MAX(`int64_col`) OVER () - MIN(`int64_col`) OVER (), 3) - ) - THEN 'b' - WHEN ( - `int64_col` - ) IS NOT NULL - THEN 'c' - END AS `int_bins_labels` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_cut/interval_bins.sql b/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_cut/interval_bins.sql deleted file mode 100644 index 10f9778f55e..00000000000 --- a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_cut/interval_bins.sql +++ /dev/null @@ -1,8 +0,0 @@ -SELECT - CASE - WHEN `int64_col` > 0 AND `int64_col` <= 1 - THEN STRUCT(0 AS `left_exclusive`, 1 AS `right_inclusive`) - WHEN `int64_col` > 1 AND `int64_col` <= 2 - THEN STRUCT(1 AS `left_exclusive`, 2 AS `right_inclusive`) - END AS `interval_bins` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_cut/interval_bins_labels.sql b/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_cut/interval_bins_labels.sql deleted file mode 100644 index 247c71a6349..00000000000 --- a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_cut/interval_bins_labels.sql +++ /dev/null @@ -1,8 +0,0 @@ -SELECT - CASE - WHEN `int64_col` > 0 AND `int64_col` <= 1 - THEN 0 - WHEN `int64_col` > 1 AND `int64_col` <= 2 - THEN 1 - END AS `interval_bins_labels` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_dense_rank/out.sql b/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_dense_rank/out.sql deleted file mode 100644 index 95f53752c34..00000000000 --- a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_dense_rank/out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - DENSE_RANK() OVER (ORDER BY `int64_col` DESC) AS `agg_int64` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_diff_w_bool/out.sql b/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_diff_w_bool/out.sql deleted file mode 100644 index 592f3e240a4..00000000000 --- a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_diff_w_bool/out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - `bool_col` <> LAG(`bool_col`, 1) OVER (ORDER BY `bool_col` DESC) AS `diff_bool` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_diff_w_date/out.sql b/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_diff_w_date/out.sql deleted file mode 100644 index 4b41355d948..00000000000 --- a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_diff_w_date/out.sql +++ /dev/null @@ -1,5 +0,0 @@ -SELECT - CAST(FLOOR( - DATE_DIFF(`date_col`, LAG(`date_col`, 1) OVER (ORDER BY `date_col` ASC NULLS LAST), DAY) * 86400000000 - ) AS INT64) AS `diff_date` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_diff_w_datetime/out.sql b/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_diff_w_datetime/out.sql deleted file mode 100644 index 866f49b1ed4..00000000000 --- a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_diff_w_datetime/out.sql +++ /dev/null @@ -1,7 +0,0 @@ -SELECT - DATETIME_DIFF( - `datetime_col`, - LAG(`datetime_col`, 1) OVER (ORDER BY `datetime_col` ASC NULLS LAST), - MICROSECOND - ) AS `diff_datetime` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_diff_w_int/out.sql b/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_diff_w_int/out.sql deleted file mode 100644 index 4c8a0880f3b..00000000000 --- a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_diff_w_int/out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - `int64_col` - LAG(`int64_col`, 1) OVER (ORDER BY `int64_col` ASC NULLS LAST) AS `diff_int` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_diff_w_timestamp/out.sql b/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_diff_w_timestamp/out.sql deleted file mode 100644 index 364f6b69d84..00000000000 --- a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_diff_w_timestamp/out.sql +++ /dev/null @@ -1,7 +0,0 @@ -SELECT - TIMESTAMP_DIFF( - `timestamp_col`, - LAG(`timestamp_col`, 1) OVER (ORDER BY `timestamp_col` DESC), - MICROSECOND - ) AS `diff_timestamp` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_first/out.sql b/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_first/out.sql deleted file mode 100644 index 86aedff91d1..00000000000 --- a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_first/out.sql +++ /dev/null @@ -1,6 +0,0 @@ -SELECT - FIRST_VALUE(`int64_col`) OVER ( - ORDER BY `int64_col` DESC - ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING - ) AS `agg_int64` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_first_non_null/out.sql b/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_first_non_null/out.sql deleted file mode 100644 index b7851a350ed..00000000000 --- a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_first_non_null/out.sql +++ /dev/null @@ -1,6 +0,0 @@ -SELECT - FIRST_VALUE(`int64_col` IGNORE NULLS) OVER ( - ORDER BY `int64_col` ASC NULLS LAST - ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING - ) AS `agg_int64` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_last/out.sql b/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_last/out.sql deleted file mode 100644 index d0bb802c333..00000000000 --- a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_last/out.sql +++ /dev/null @@ -1,6 +0,0 @@ -SELECT - LAST_VALUE(`int64_col`) OVER ( - ORDER BY `int64_col` DESC - ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING - ) AS `agg_int64` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_last_non_null/out.sql b/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_last_non_null/out.sql deleted file mode 100644 index 39d063a3c99..00000000000 --- a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_last_non_null/out.sql +++ /dev/null @@ -1,6 +0,0 @@ -SELECT - LAST_VALUE(`int64_col` IGNORE NULLS) OVER ( - ORDER BY `int64_col` ASC NULLS LAST - ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING - ) AS `agg_int64` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_max/out.sql b/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_max/out.sql deleted file mode 100644 index 7e01c2c7187..00000000000 --- a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_max/out.sql +++ /dev/null @@ -1,12 +0,0 @@ -WITH `bfcte_0` AS ( - SELECT - `int64_col` - FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` -), `bfcte_1` AS ( - SELECT - MAX(`int64_col`) AS `bfcol_1` - FROM `bfcte_0` -) -SELECT - `bfcol_1` AS `int64_col` -FROM `bfcte_1` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_max/window_out.sql b/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_max/window_out.sql deleted file mode 100644 index d6dec51cdb4..00000000000 --- a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_max/window_out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - MAX(`int64_col`) OVER () AS `agg_int64` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_max/window_partition_out.sql b/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_max/window_partition_out.sql deleted file mode 100644 index a35a64a8e5f..00000000000 --- a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_max/window_partition_out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - MAX(`int64_col`) OVER (PARTITION BY `string_col`) AS `agg_int64` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_mean/out.sql b/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_mean/out.sql deleted file mode 100644 index 94287fc432b..00000000000 --- a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_mean/out.sql +++ /dev/null @@ -1,23 +0,0 @@ -WITH `bfcte_0` AS ( - SELECT - `bool_col`, - `int64_col`, - `duration_col`, - `int64_col` AS `bfcol_6`, - `bool_col` AS `bfcol_7`, - `duration_col` AS `bfcol_8` - FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` -), `bfcte_1` AS ( - SELECT - AVG(`bfcol_6`) AS `bfcol_12`, - AVG(CAST(`bfcol_7` AS INT64)) AS `bfcol_13`, - CAST(FLOOR(AVG(`bfcol_8`)) AS INT64) AS `bfcol_14`, - CAST(FLOOR(AVG(`bfcol_6`)) AS INT64) AS `bfcol_15` - FROM `bfcte_0` -) -SELECT - `bfcol_12` AS `int64_col`, - `bfcol_13` AS `bool_col`, - `bfcol_14` AS `duration_col`, - `bfcol_15` AS `int64_col_w_floor` -FROM `bfcte_1` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_mean/window_out.sql b/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_mean/window_out.sql deleted file mode 100644 index 3443cd2a680..00000000000 --- a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_mean/window_out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - AVG(`int64_col`) OVER () AS `agg_int64` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_mean/window_partition_out.sql b/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_mean/window_partition_out.sql deleted file mode 100644 index b94b84ddb81..00000000000 --- a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_mean/window_partition_out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - AVG(`int64_col`) OVER (PARTITION BY `string_col`) AS `agg_int64` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_median/out.sql b/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_median/out.sql deleted file mode 100644 index 7d1215163f8..00000000000 --- a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_median/out.sql +++ /dev/null @@ -1,18 +0,0 @@ -WITH `bfcte_0` AS ( - SELECT - `date_col`, - `int64_col`, - `string_col` - FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` -), `bfcte_1` AS ( - SELECT - APPROX_QUANTILES(`int64_col`, 2)[OFFSET(1)] AS `bfcol_3`, - APPROX_QUANTILES(`date_col`, 2)[OFFSET(1)] AS `bfcol_4`, - APPROX_QUANTILES(`string_col`, 2)[OFFSET(1)] AS `bfcol_5` - FROM `bfcte_0` -) -SELECT - `bfcol_3` AS `int64_col`, - `bfcol_4` AS `date_col`, - `bfcol_5` AS `string_col` -FROM `bfcte_1` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_min/out.sql b/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_min/out.sql deleted file mode 100644 index 144c07d7010..00000000000 --- a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_min/out.sql +++ /dev/null @@ -1,12 +0,0 @@ -WITH `bfcte_0` AS ( - SELECT - `int64_col` - FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` -), `bfcte_1` AS ( - SELECT - MIN(`int64_col`) AS `bfcol_1` - FROM `bfcte_0` -) -SELECT - `bfcol_1` AS `int64_col` -FROM `bfcte_1` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_min/window_out.sql b/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_min/window_out.sql deleted file mode 100644 index 031c19eff16..00000000000 --- a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_min/window_out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - MIN(`int64_col`) OVER () AS `agg_int64` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_min/window_partition_out.sql b/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_min/window_partition_out.sql deleted file mode 100644 index 2de5bd5f717..00000000000 --- a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_min/window_partition_out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - MIN(`int64_col`) OVER (PARTITION BY `string_col`) AS `agg_int64` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_nunique/out.sql b/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_nunique/out.sql deleted file mode 100644 index e0cc1a2eac5..00000000000 --- a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_nunique/out.sql +++ /dev/null @@ -1,12 +0,0 @@ -WITH `bfcte_0` AS ( - SELECT - `int64_col` - FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` -), `bfcte_1` AS ( - SELECT - COUNT(DISTINCT `int64_col`) AS `bfcol_1` - FROM `bfcte_0` -) -SELECT - `bfcol_1` AS `int64_col` -FROM `bfcte_1` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_pop_var/out.sql b/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_pop_var/out.sql deleted file mode 100644 index b855c791182..00000000000 --- a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_pop_var/out.sql +++ /dev/null @@ -1,15 +0,0 @@ -WITH `bfcte_0` AS ( - SELECT - `bool_col`, - `int64_col` - FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` -), `bfcte_1` AS ( - SELECT - VAR_POP(`int64_col`) AS `bfcol_4`, - VAR_POP(CAST(`bool_col` AS INT64)) AS `bfcol_5` - FROM `bfcte_0` -) -SELECT - `bfcol_4` AS `int64_col`, - `bfcol_5` AS `bool_col` -FROM `bfcte_1` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_pop_var/window_out.sql b/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_pop_var/window_out.sql deleted file mode 100644 index 3bfaedd3953..00000000000 --- a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_pop_var/window_out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - VAR_POP(`int64_col`) OVER () AS `agg_int64` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_product/out.sql b/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_product/out.sql deleted file mode 100644 index 33204f2ff56..00000000000 --- a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_product/out.sql +++ /dev/null @@ -1,16 +0,0 @@ -WITH `bfcte_0` AS ( - SELECT - `int64_col` - FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` -), `bfcte_1` AS ( - SELECT - CASE - WHEN LOGICAL_OR(`int64_col` = 0) - THEN 0 - ELSE POWER(2, SUM(IF(`int64_col` = 0, 0, LOG(ABS(`int64_col`), 2)))) * POWER(-1, MOD(SUM(CASE WHEN SIGN(`int64_col`) = -1 THEN 1 ELSE 0 END), 2)) - END AS `bfcol_1` - FROM `bfcte_0` -) -SELECT - `bfcol_1` AS `int64_col` -FROM `bfcte_1` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_product/window_partition_out.sql b/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_product/window_partition_out.sql deleted file mode 100644 index 532349d3599..00000000000 --- a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_product/window_partition_out.sql +++ /dev/null @@ -1,16 +0,0 @@ -SELECT - CASE - WHEN LOGICAL_OR(`int64_col` = 0) OVER (PARTITION BY `string_col`) - THEN 0 - ELSE POWER( - 2, - SUM(IF(`int64_col` = 0, 0, LOG(ABS(`int64_col`), 2))) OVER (PARTITION BY `string_col`) - ) * POWER( - -1, - MOD( - SUM(CASE WHEN SIGN(`int64_col`) = -1 THEN 1 ELSE 0 END) OVER (PARTITION BY `string_col`), - 2 - ) - ) - END AS `agg_int64` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_qcut/out.sql b/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_qcut/out.sql deleted file mode 100644 index cb1541d083b..00000000000 --- a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_qcut/out.sql +++ /dev/null @@ -1,51 +0,0 @@ -SELECT - `rowindex`, - `int64_col`, - IF( - ( - `int64_col` - ) IS NOT NULL, - IF( - `int64_col` IS NULL, - NULL, - CAST(GREATEST( - CEIL( - PERCENT_RANK() OVER (PARTITION BY ( - `int64_col` - ) IS NOT NULL ORDER BY `int64_col` ASC) * 4 - ) - 1, - 0 - ) AS INT64) - ), - NULL - ) AS `qcut_w_int`, - IF( - ( - `int64_col` - ) IS NOT NULL, - CASE - WHEN PERCENT_RANK() OVER (PARTITION BY ( - `int64_col` - ) IS NOT NULL ORDER BY `int64_col` ASC) < 0 - THEN NULL - WHEN PERCENT_RANK() OVER (PARTITION BY ( - `int64_col` - ) IS NOT NULL ORDER BY `int64_col` ASC) <= 0.25 - THEN 0 - WHEN PERCENT_RANK() OVER (PARTITION BY ( - `int64_col` - ) IS NOT NULL ORDER BY `int64_col` ASC) <= 0.5 - THEN 1 - WHEN PERCENT_RANK() OVER (PARTITION BY ( - `int64_col` - ) IS NOT NULL ORDER BY `int64_col` ASC) <= 0.75 - THEN 2 - WHEN PERCENT_RANK() OVER (PARTITION BY ( - `int64_col` - ) IS NOT NULL ORDER BY `int64_col` ASC) <= 1 - THEN 3 - ELSE NULL - END, - NULL - ) AS `qcut_w_list` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_quantile/out.sql b/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_quantile/out.sql deleted file mode 100644 index 656d01ea2e5..00000000000 --- a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_quantile/out.sql +++ /dev/null @@ -1,17 +0,0 @@ -WITH `bfcte_0` AS ( - SELECT - `bool_col`, - `int64_col` - FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` -), `bfcte_1` AS ( - SELECT - PERCENTILE_CONT(`int64_col`, 0.5) OVER () AS `bfcol_4`, - PERCENTILE_CONT(CAST(`bool_col` AS INT64), 0.5) OVER () AS `bfcol_5`, - CAST(FLOOR(PERCENTILE_CONT(`int64_col`, 0.5) OVER ()) AS INT64) AS `bfcol_6` - FROM `bfcte_0` -) -SELECT - `bfcol_4` AS `int64`, - `bfcol_5` AS `bool`, - `bfcol_6` AS `int64_w_floor` -FROM `bfcte_1` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_rank/out.sql b/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_rank/out.sql deleted file mode 100644 index 2170d6cdcf7..00000000000 --- a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_rank/out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - RANK() OVER (ORDER BY `int64_col` DESC NULLS FIRST) AS `agg_int64` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_shift/lag.sql b/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_shift/lag.sql deleted file mode 100644 index 2bea343497f..00000000000 --- a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_shift/lag.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - LAG(`int64_col`, 1) OVER (ORDER BY `int64_col` ASC) AS `lag` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_shift/lead.sql b/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_shift/lead.sql deleted file mode 100644 index 5055f443718..00000000000 --- a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_shift/lead.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - LEAD(`int64_col`, 1) OVER (ORDER BY `int64_col` ASC) AS `lead` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_shift/noop.sql b/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_shift/noop.sql deleted file mode 100644 index 65af6af7c79..00000000000 --- a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_shift/noop.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - `int64_col` AS `noop` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_size_unary/out.sql b/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_size_unary/out.sql deleted file mode 100644 index fffb4831b95..00000000000 --- a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_size_unary/out.sql +++ /dev/null @@ -1,12 +0,0 @@ -WITH `bfcte_0` AS ( - SELECT - `float64_col` AS `bfcol_0` - FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` -), `bfcte_1` AS ( - SELECT - COUNT(1) AS `bfcol_1` - FROM `bfcte_0` -) -SELECT - `bfcol_1` AS `float64_col` -FROM `bfcte_1` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_std/out.sql b/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_std/out.sql deleted file mode 100644 index e3c3d7b5253..00000000000 --- a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_std/out.sql +++ /dev/null @@ -1,23 +0,0 @@ -WITH `bfcte_0` AS ( - SELECT - `bool_col`, - `int64_col`, - `duration_col`, - `int64_col` AS `bfcol_6`, - `bool_col` AS `bfcol_7`, - `duration_col` AS `bfcol_8` - FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` -), `bfcte_1` AS ( - SELECT - STDDEV(`bfcol_6`) AS `bfcol_12`, - STDDEV(CAST(`bfcol_7` AS INT64)) AS `bfcol_13`, - CAST(FLOOR(STDDEV(`bfcol_8`)) AS INT64) AS `bfcol_14`, - CAST(FLOOR(STDDEV(`bfcol_6`)) AS INT64) AS `bfcol_15` - FROM `bfcte_0` -) -SELECT - `bfcol_12` AS `int64_col`, - `bfcol_13` AS `bool_col`, - `bfcol_14` AS `duration_col`, - `bfcol_15` AS `int64_col_w_floor` -FROM `bfcte_1` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_std/window_out.sql b/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_std/window_out.sql deleted file mode 100644 index 225dd5acf66..00000000000 --- a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_std/window_out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - STDDEV(`int64_col`) OVER () AS `agg_int64` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_sum/out.sql b/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_sum/out.sql deleted file mode 100644 index c67eef9da34..00000000000 --- a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_sum/out.sql +++ /dev/null @@ -1,15 +0,0 @@ -WITH `bfcte_0` AS ( - SELECT - `bool_col`, - `int64_col` - FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` -), `bfcte_1` AS ( - SELECT - COALESCE(SUM(`int64_col`), 0) AS `bfcol_4`, - COALESCE(SUM(CAST(`bool_col` AS INT64)), 0) AS `bfcol_5` - FROM `bfcte_0` -) -SELECT - `bfcol_4` AS `int64_col`, - `bfcol_5` AS `bool_col` -FROM `bfcte_1` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_sum/window_out.sql b/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_sum/window_out.sql deleted file mode 100644 index ea5a12edfb5..00000000000 --- a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_sum/window_out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - COALESCE(SUM(`int64_col`) OVER (), 0) AS `agg_int64` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_sum/window_partition_out.sql b/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_sum/window_partition_out.sql deleted file mode 100644 index ec6083b1a9d..00000000000 --- a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_sum/window_partition_out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - COALESCE(SUM(`int64_col`) OVER (PARTITION BY `string_col`), 0) AS `agg_int64` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_var/out.sql b/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_var/out.sql deleted file mode 100644 index b35d67c1ce1..00000000000 --- a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_var/out.sql +++ /dev/null @@ -1,15 +0,0 @@ -WITH `bfcte_0` AS ( - SELECT - `bool_col`, - `int64_col` - FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` -), `bfcte_1` AS ( - SELECT - VARIANCE(`int64_col`) AS `bfcol_4`, - VARIANCE(CAST(`bool_col` AS INT64)) AS `bfcol_5` - FROM `bfcte_0` -) -SELECT - `bfcol_4` AS `int64_col`, - `bfcol_5` AS `bool_col` -FROM `bfcte_1` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_var/window_out.sql b/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_var/window_out.sql deleted file mode 100644 index e33797d02fb..00000000000 --- a/tests/unit/core/compile/sqlglot/aggregations/snapshots/test_unary_compiler/test_var/window_out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - VARIANCE(`int64_col`) OVER () AS `agg_int64` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/aggregations/test_binary_compiler.py b/tests/unit/core/compile/sqlglot/aggregations/test_binary_compiler.py deleted file mode 100644 index 11f5cd6bad8..00000000000 --- a/tests/unit/core/compile/sqlglot/aggregations/test_binary_compiler.py +++ /dev/null @@ -1,54 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import typing - -import pytest - -import bigframes.pandas as bpd -from bigframes.core import agg_expressions as agg_exprs -from bigframes.core import array_value, identifiers, nodes -from bigframes.operations import aggregations as agg_ops - -pytest.importorskip("pytest_snapshot") - - -def _apply_binary_agg_ops( - obj: bpd.DataFrame, - ops_list: typing.Sequence[agg_exprs.BinaryAggregation], - new_names: typing.Sequence[str], -) -> str: - aggs = [(op, identifiers.ColumnId(name)) for op, name in zip(ops_list, new_names)] - - agg_node = nodes.AggregateNode(obj._block.expr.node, aggregations=tuple(aggs)) - result = array_value.ArrayValue(agg_node) - - sql = result.session._executor.to_sql(result, enable_cache=False) - return sql - - -def test_corr(scalar_types_df: bpd.DataFrame, snapshot): - bf_df = scalar_types_df[["int64_col", "float64_col"]] - agg_expr = agg_ops.CorrOp().as_expr("int64_col", "float64_col") - sql = _apply_binary_agg_ops(bf_df, [agg_expr], ["corr_col"]) - - snapshot.assert_match(sql, "out.sql") - - -def test_cov(scalar_types_df: bpd.DataFrame, snapshot): - bf_df = scalar_types_df[["int64_col", "float64_col"]] - agg_expr = agg_ops.CovOp().as_expr("int64_col", "float64_col") - sql = _apply_binary_agg_ops(bf_df, [agg_expr], ["cov_col"]) - - snapshot.assert_match(sql, "out.sql") diff --git a/tests/unit/core/compile/sqlglot/aggregations/test_nullary_compiler.py b/tests/unit/core/compile/sqlglot/aggregations/test_nullary_compiler.py deleted file mode 100644 index 0ce8437b904..00000000000 --- a/tests/unit/core/compile/sqlglot/aggregations/test_nullary_compiler.py +++ /dev/null @@ -1,84 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import typing - -import pytest - -import bigframes.pandas as bpd -from bigframes.core import agg_expressions as agg_exprs -from bigframes.core import array_value, identifiers, nodes, ordering, window_spec -from bigframes.operations import aggregations as agg_ops - -pytest.importorskip("pytest_snapshot") - - -def _apply_nullary_agg_ops( - obj: bpd.DataFrame, - ops_list: typing.Sequence[agg_exprs.NullaryAggregation], - new_names: typing.Sequence[str], -) -> str: - aggs = [(op, identifiers.ColumnId(name)) for op, name in zip(ops_list, new_names)] - - agg_node = nodes.AggregateNode(obj._block.expr.node, aggregations=tuple(aggs)) - result = array_value.ArrayValue(agg_node) - - sql = result.session._executor.to_sql(result, enable_cache=False) - return sql - - -def _apply_nullary_window_op( - obj: bpd.DataFrame, - op: agg_exprs.NullaryAggregation, - window_spec: window_spec.WindowSpec, - new_name: str, -) -> str: - win_node = nodes.WindowOpNode( - obj._block.expr.node, - agg_exprs=(nodes.ColumnDef(op, identifiers.ColumnId(new_name)),), - window_spec=window_spec, - ) - result = array_value.ArrayValue(win_node).select_columns([new_name]) - - sql = result.session._executor.to_sql(result, enable_cache=False) - return sql - - -def test_size(scalar_types_df: bpd.DataFrame, snapshot): - bf_df = scalar_types_df - agg_expr = agg_ops.SizeOp().as_expr() - sql = _apply_nullary_agg_ops(bf_df, [agg_expr], ["size"]) - - snapshot.assert_match(sql, "out.sql") - - -def test_row_number(scalar_types_df: bpd.DataFrame, snapshot): - bf_df = scalar_types_df - agg_expr = agg_exprs.NullaryAggregation(agg_ops.RowNumberOp()) - window = window_spec.WindowSpec() - sql = _apply_nullary_window_op(bf_df, agg_expr, window, "row_number") - - snapshot.assert_match(sql, "out.sql") - - -def test_row_number_with_window(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "int64_col" - bf_df = scalar_types_df[[col_name, "int64_too"]] - agg_expr = agg_exprs.NullaryAggregation(agg_ops.RowNumberOp()) - - window = window_spec.WindowSpec(ordering=(ordering.ascending_over(col_name),)) - # window = window_spec.unbound(ordering=(ordering.ascending_over(col_name),ordering.ascending_over("int64_too"))) - sql = _apply_nullary_window_op(bf_df, agg_expr, window, "row_number") - - snapshot.assert_match(sql, "out.sql") diff --git a/tests/unit/core/compile/sqlglot/aggregations/test_op_registration.py b/tests/unit/core/compile/sqlglot/aggregations/test_op_registration.py deleted file mode 100644 index 9306bbf6559..00000000000 --- a/tests/unit/core/compile/sqlglot/aggregations/test_op_registration.py +++ /dev/null @@ -1,64 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import pytest -from bigframes_vendored.sqlglot import expressions as sge - -from bigframes.core.compile.sqlglot.aggregations import op_registration -from bigframes.operations import aggregations as agg_ops - - -def test_register_then_get(): - reg = op_registration.OpRegistration() - input = sge.to_identifier("A") - op = agg_ops.SizeOp() - - @reg.register(agg_ops.SizeOp) - def test_func(op: agg_ops.SizeOp, input: sge.Expression) -> sge.Expression: - return input - - assert reg[agg_ops.SizeOp()](op, input) == test_func(op, input) - - -def test_register_function_first_argument_is_not_agg_op_raise_error(): - reg = op_registration.OpRegistration() - - @reg.register(agg_ops.SizeOp) - def test_func(input: sge.Expression) -> sge.Expression: - return input - - with pytest.raises( - ValueError, match=r".*first parameter must be a window operator.*" - ): - test_func(sge.to_identifier("A")) - - -def test_register_already_registered_raise_error(): - reg = op_registration.OpRegistration() - - @reg.register(agg_ops.SizeOp) - def test_func1(op, input): - return input - - with pytest.raises(ValueError, match=r".*is already registered.*"): - - @reg.register(agg_ops.SizeOp) - def test_func2(op, input): - return input - - -def test_getitem_not_registered_raise_error(): - reg = op_registration.OpRegistration() - with pytest.raises(ValueError, match=r".*is not registered.*"): - _ = reg[agg_ops.SizeOp()] diff --git a/tests/unit/core/compile/sqlglot/aggregations/test_ordered_unary_compiler.py b/tests/unit/core/compile/sqlglot/aggregations/test_ordered_unary_compiler.py deleted file mode 100644 index dd8912a452b..00000000000 --- a/tests/unit/core/compile/sqlglot/aggregations/test_ordered_unary_compiler.py +++ /dev/null @@ -1,67 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import typing - -import pytest - -import bigframes.pandas as bpd -from bigframes.core import agg_expressions as agg_exprs -from bigframes.core import array_value, identifiers, nodes, ordering -from bigframes.operations import aggregations as agg_ops - -pytest.importorskip("pytest_snapshot") - - -def _apply_ordered_unary_agg_ops( - obj: bpd.DataFrame, - ops_list: typing.Sequence[agg_exprs.UnaryAggregation], - new_names: typing.Sequence[str], - ordering_args: typing.Sequence[str], -) -> str: - ordering_exprs = tuple(ordering.ascending_over(arg) for arg in ordering_args) - aggs = [(op, identifiers.ColumnId(name)) for op, name in zip(ops_list, new_names)] - - agg_node = nodes.AggregateNode( - obj._block.expr.node, - aggregations=tuple(aggs), - by_column_ids=(), - order_by=ordering_exprs, - ) - result = array_value.ArrayValue(agg_node) - - sql = result.session._executor.to_sql(result, enable_cache=False) - return sql - - -def test_array_agg(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "int64_col" - bf_df = scalar_types_df[[col_name]] - agg_expr = agg_ops.ArrayAggOp().as_expr(col_name) - sql = _apply_ordered_unary_agg_ops( - bf_df, [agg_expr], [col_name], ordering_args=[col_name] - ) - - snapshot.assert_match(sql, "out.sql") - - -def test_string_agg(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "string_col" - bf_df = scalar_types_df[[col_name]] - agg_expr = agg_ops.StringAggOp(sep=",").as_expr(col_name) - sql = _apply_ordered_unary_agg_ops( - bf_df, [agg_expr], [col_name], ordering_args=[col_name] - ) - - snapshot.assert_match(sql, "out.sql") diff --git a/tests/unit/core/compile/sqlglot/aggregations/test_unary_compiler.py b/tests/unit/core/compile/sqlglot/aggregations/test_unary_compiler.py deleted file mode 100644 index 7c827cd6dc1..00000000000 --- a/tests/unit/core/compile/sqlglot/aggregations/test_unary_compiler.py +++ /dev/null @@ -1,635 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import typing - -import pytest - -import bigframes.pandas as bpd -from bigframes.core import agg_expressions as agg_exprs -from bigframes.core import ( - array_value, - expression, - identifiers, - nodes, - ordering, - window_spec, -) -from bigframes.operations import aggregations as agg_ops - -pytest.importorskip("pytest_snapshot") - - -def _apply_unary_agg_ops( - obj: bpd.DataFrame, - ops_list: typing.Sequence[agg_exprs.UnaryAggregation], - new_names: typing.Sequence[str], -) -> str: - aggs = [(op, identifiers.ColumnId(name)) for op, name in zip(ops_list, new_names)] - - agg_node = nodes.AggregateNode(obj._block.expr.node, aggregations=tuple(aggs)) - result = array_value.ArrayValue(agg_node) - - sql = result.session._executor.to_sql(result, enable_cache=False) - return sql - - -def _apply_unary_window_op( - obj: bpd.DataFrame, - op: agg_exprs.UnaryAggregation, - window_spec: window_spec.WindowSpec, - new_name: str, -) -> str: - win_node = nodes.WindowOpNode( - obj._block.expr.node, - agg_exprs=(nodes.ColumnDef(op, identifiers.ColumnId(new_name)),), - window_spec=window_spec, - ) - result = array_value.ArrayValue(win_node).select_columns([new_name]) - - sql = result.session._executor.to_sql(result, enable_cache=False) - return sql - - -def test_all(scalar_types_df: bpd.DataFrame, snapshot): - bf_df = scalar_types_df[["bool_col", "int64_col"]] - ops_map = { - "bool_col": agg_ops.AllOp().as_expr("bool_col"), - "int64_col": agg_ops.AllOp().as_expr("int64_col"), - } - sql = _apply_unary_agg_ops(bf_df, list(ops_map.values()), list(ops_map.keys())) - - snapshot.assert_match(sql, "out.sql") - - -def test_all_w_window(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "bool_col" - bf_df = scalar_types_df[[col_name]] - agg_expr = agg_ops.AllOp().as_expr(col_name) - - # Window tests - window = window_spec.WindowSpec(ordering=(ordering.ascending_over(col_name),)) - sql_window = _apply_unary_window_op(bf_df, agg_expr, window, "agg_bool") - snapshot.assert_match(sql_window, "out.sql") - - -def test_any(scalar_types_df: bpd.DataFrame, snapshot): - bf_df = scalar_types_df[["bool_col", "int64_col"]] - ops_map = { - "bool_col": agg_ops.AnyOp().as_expr("bool_col"), - "int64_col": agg_ops.AnyOp().as_expr("int64_col"), - } - sql = _apply_unary_agg_ops(bf_df, list(ops_map.values()), list(ops_map.keys())) - - snapshot.assert_match(sql, "out.sql") - - -def test_any_w_window(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "bool_col" - bf_df = scalar_types_df[[col_name]] - agg_expr = agg_ops.AnyOp().as_expr(col_name) - - # Window tests - window = window_spec.WindowSpec(ordering=(ordering.ascending_over(col_name),)) - sql_window = _apply_unary_window_op(bf_df, agg_expr, window, "agg_bool") - snapshot.assert_match(sql_window, "out.sql") - - -def test_approx_quartiles(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "int64_col" - bf_df = scalar_types_df[[col_name]] - agg_ops_map = { - "q1": agg_ops.ApproxQuartilesOp(quartile=1).as_expr(col_name), - "q2": agg_ops.ApproxQuartilesOp(quartile=2).as_expr(col_name), - "q3": agg_ops.ApproxQuartilesOp(quartile=3).as_expr(col_name), - } - sql = _apply_unary_agg_ops( - bf_df, list(agg_ops_map.values()), list(agg_ops_map.keys()) - ) - - snapshot.assert_match(sql, "out.sql") - - -def test_approx_top_count(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "int64_col" - bf_df = scalar_types_df[[col_name]] - agg_expr = agg_ops.ApproxTopCountOp(number=10).as_expr(col_name) - sql = _apply_unary_agg_ops(bf_df, [agg_expr], [col_name]) - - snapshot.assert_match(sql, "out.sql") - - -def test_any_value(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "int64_col" - bf_df = scalar_types_df[[col_name]] - agg_expr = agg_ops.AnyValueOp().as_expr(col_name) - sql = _apply_unary_agg_ops(bf_df, [agg_expr], [col_name]) - - snapshot.assert_match(sql, "out.sql") - - # Window tests - window = window_spec.WindowSpec(ordering=(ordering.descending_over(col_name),)) - sql_window = _apply_unary_window_op(bf_df, agg_expr, window, "agg_int64") - snapshot.assert_match(sql_window, "window_out.sql") - - bf_df_str = scalar_types_df[[col_name, "string_col"]] - window_partition = window_spec.WindowSpec( - grouping_keys=(expression.deref("string_col"),), - ordering=(ordering.ascending_over(col_name),), - ) - sql_window_partition = _apply_unary_window_op( - bf_df_str, agg_expr, window_partition, "agg_int64" - ) - snapshot.assert_match(sql_window_partition, "window_partition_out.sql") - - -def test_count(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "int64_col" - bf_df = scalar_types_df[[col_name]] - agg_expr = agg_ops.CountOp().as_expr(col_name) - sql = _apply_unary_agg_ops(bf_df, [agg_expr], [col_name]) - - snapshot.assert_match(sql, "out.sql") - - # Window tests - window = window_spec.WindowSpec(ordering=(ordering.ascending_over(col_name),)) - sql_window = _apply_unary_window_op(bf_df, agg_expr, window, "agg_int64") - snapshot.assert_match(sql_window, "window_out.sql") - - bf_df_str = scalar_types_df[[col_name, "string_col"]] - window_partition = window_spec.WindowSpec( - grouping_keys=(expression.deref("string_col"),), - ordering=(ordering.descending_over(col_name),), - ) - sql_window_partition = _apply_unary_window_op( - bf_df_str, agg_expr, window_partition, "agg_int64" - ) - snapshot.assert_match(sql_window_partition, "window_partition_out.sql") - - -def test_cut(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "int64_col" - bf_df = scalar_types_df[[col_name]] - agg_ops_map = { - "int_bins": agg_exprs.UnaryAggregation( - agg_ops.CutOp(bins=3, right=True, labels=None), expression.deref(col_name) - ), - "interval_bins": agg_exprs.UnaryAggregation( - agg_ops.CutOp(bins=((0, 1), (1, 2)), right=True, labels=None), - expression.deref(col_name), - ), - "int_bins_labels": agg_exprs.UnaryAggregation( - agg_ops.CutOp(bins=3, labels=("a", "b", "c"), right=False), - expression.deref(col_name), - ), - "interval_bins_labels": agg_exprs.UnaryAggregation( - agg_ops.CutOp(bins=((0, 1), (1, 2)), labels=False, right=True), - expression.deref(col_name), - ), - } - window = window_spec.WindowSpec() - - # Loop through the aggregation map items - for test_name, agg_expr in agg_ops_map.items(): - sql = _apply_unary_window_op(bf_df, agg_expr, window, test_name) - - snapshot.assert_match(sql, f"{test_name}.sql") - - -def test_dense_rank(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "int64_col" - bf_df = scalar_types_df[[col_name]] - agg_expr = agg_exprs.UnaryAggregation( - agg_ops.DenseRankOp(), expression.deref(col_name) - ) - window = window_spec.WindowSpec(ordering=(ordering.descending_over(col_name),)) - sql = _apply_unary_window_op(bf_df, agg_expr, window, "agg_int64") - - snapshot.assert_match(sql, "out.sql") - - -def test_diff_w_int(scalar_types_df: bpd.DataFrame, snapshot): - # Test integer - int_col = "int64_col" - bf_df_int = scalar_types_df[[int_col]] - window = window_spec.WindowSpec(ordering=(ordering.ascending_over(int_col),)) - int_op = agg_exprs.UnaryAggregation( - agg_ops.DiffOp(periods=1), expression.deref(int_col) - ) - int_sql = _apply_unary_window_op(bf_df_int, int_op, window, "diff_int") - snapshot.assert_match(int_sql, "out.sql") - - -def test_diff_w_bool(scalar_types_df: bpd.DataFrame, snapshot): - bool_col = "bool_col" - bf_df_bool = scalar_types_df[[bool_col]] - window = window_spec.WindowSpec(ordering=(ordering.descending_over(bool_col),)) - bool_op = agg_exprs.UnaryAggregation( - agg_ops.DiffOp(periods=1), expression.deref(bool_col) - ) - bool_sql = _apply_unary_window_op(bf_df_bool, bool_op, window, "diff_bool") - snapshot.assert_match(bool_sql, "out.sql") - - -def test_diff_w_datetime(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "datetime_col" - bf_df_date = scalar_types_df[[col_name]] - window = window_spec.WindowSpec(ordering=(ordering.ascending_over(col_name),)) - op = agg_exprs.UnaryAggregation( - agg_ops.DiffOp(periods=1), expression.deref(col_name) - ) - sql = _apply_unary_window_op(bf_df_date, op, window, "diff_datetime") - snapshot.assert_match(sql, "out.sql") - - -def test_diff_w_date(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "date_col" - bf_df_date = scalar_types_df[[col_name]] - window = window_spec.WindowSpec(ordering=(ordering.ascending_over(col_name),)) - op = agg_exprs.UnaryAggregation( - agg_ops.DiffOp(periods=1), expression.deref(col_name) - ) - sql = _apply_unary_window_op(bf_df_date, op, window, "diff_date") - snapshot.assert_match(sql, "out.sql") - - -def test_diff_w_timestamp(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "timestamp_col" - bf_df_timestamp = scalar_types_df[[col_name]] - window = window_spec.WindowSpec(ordering=(ordering.descending_over(col_name),)) - op = agg_exprs.UnaryAggregation( - agg_ops.DiffOp(periods=1), expression.deref(col_name) - ) - sql = _apply_unary_window_op(bf_df_timestamp, op, window, "diff_timestamp") - snapshot.assert_match(sql, "out.sql") - - -def test_first(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "int64_col" - bf_df = scalar_types_df[[col_name]] - agg_expr = agg_exprs.UnaryAggregation(agg_ops.FirstOp(), expression.deref(col_name)) - window = window_spec.WindowSpec(ordering=(ordering.descending_over(col_name),)) - sql = _apply_unary_window_op(bf_df, agg_expr, window, "agg_int64") - - snapshot.assert_match(sql, "out.sql") - - -def test_first_non_null(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "int64_col" - bf_df = scalar_types_df[[col_name]] - agg_expr = agg_exprs.UnaryAggregation( - agg_ops.FirstNonNullOp(), expression.deref(col_name) - ) - window = window_spec.WindowSpec(ordering=(ordering.ascending_over(col_name),)) - sql = _apply_unary_window_op(bf_df, agg_expr, window, "agg_int64") - - snapshot.assert_match(sql, "out.sql") - - -def test_last(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "int64_col" - bf_df = scalar_types_df[[col_name]] - agg_expr = agg_exprs.UnaryAggregation(agg_ops.LastOp(), expression.deref(col_name)) - window = window_spec.WindowSpec(ordering=(ordering.descending_over(col_name),)) - sql = _apply_unary_window_op(bf_df, agg_expr, window, "agg_int64") - - snapshot.assert_match(sql, "out.sql") - - -def test_last_non_null(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "int64_col" - bf_df = scalar_types_df[[col_name]] - agg_expr = agg_exprs.UnaryAggregation( - agg_ops.LastNonNullOp(), expression.deref(col_name) - ) - window = window_spec.WindowSpec(ordering=(ordering.ascending_over(col_name),)) - sql = _apply_unary_window_op(bf_df, agg_expr, window, "agg_int64") - - snapshot.assert_match(sql, "out.sql") - - -def test_max(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "int64_col" - bf_df = scalar_types_df[[col_name]] - agg_expr = agg_ops.MaxOp().as_expr(col_name) - sql = _apply_unary_agg_ops(bf_df, [agg_expr], [col_name]) - - snapshot.assert_match(sql, "out.sql") - - # Window tests - window = window_spec.WindowSpec(ordering=(ordering.ascending_over(col_name),)) - sql_window = _apply_unary_window_op(bf_df, agg_expr, window, "agg_int64") - snapshot.assert_match(sql_window, "window_out.sql") - - bf_df_str = scalar_types_df[[col_name, "string_col"]] - window_partition = window_spec.WindowSpec( - grouping_keys=(expression.deref("string_col"),), - ordering=(ordering.descending_over(col_name),), - ) - sql_window_partition = _apply_unary_window_op( - bf_df_str, agg_expr, window_partition, "agg_int64" - ) - snapshot.assert_match(sql_window_partition, "window_partition_out.sql") - - -def test_mean(scalar_types_df: bpd.DataFrame, snapshot): - col_names = ["int64_col", "bool_col", "duration_col"] - bf_df = scalar_types_df[col_names] - bf_df["duration_col"] = bpd.to_timedelta(bf_df["duration_col"], unit="us") - - # The `to_timedelta` creates a new mapping for the column id. - col_names.insert(0, "rowindex") - name2id = { - col_name: col_id - for col_name, col_id in zip(col_names, bf_df._block.expr.column_ids) - } - - agg_ops_map = { - "int64_col": agg_ops.MeanOp().as_expr(name2id["int64_col"]), - "bool_col": agg_ops.MeanOp().as_expr(name2id["bool_col"]), - "duration_col": agg_ops.MeanOp().as_expr(name2id["duration_col"]), - "int64_col_w_floor": agg_ops.MeanOp(should_floor_result=True).as_expr( - name2id["int64_col"] - ), - } - sql = _apply_unary_agg_ops( - bf_df, list(agg_ops_map.values()), list(agg_ops_map.keys()) - ) - - snapshot.assert_match(sql, "out.sql") - - # Window tests - col_name = "int64_col" - bf_df_int = scalar_types_df[[col_name]] - agg_expr = agg_ops.MeanOp().as_expr(col_name) - window = window_spec.WindowSpec(ordering=(ordering.descending_over(col_name),)) - sql_window = _apply_unary_window_op(bf_df_int, agg_expr, window, "agg_int64") - snapshot.assert_match(sql_window, "window_out.sql") - - bf_df_str = scalar_types_df[[col_name, "string_col"]] - window_partition = window_spec.WindowSpec( - grouping_keys=(expression.deref("string_col"),), - ordering=(ordering.ascending_over(col_name),), - ) - sql_window_partition = _apply_unary_window_op( - bf_df_str, agg_expr, window_partition, "agg_int64" - ) - snapshot.assert_match(sql_window_partition, "window_partition_out.sql") - - -def test_median(scalar_types_df: bpd.DataFrame, snapshot): - bf_df = scalar_types_df - ops_map = { - "int64_col": agg_ops.MedianOp().as_expr("int64_col"), - "date_col": agg_ops.MedianOp().as_expr("date_col"), - "string_col": agg_ops.MedianOp().as_expr("string_col"), - } - sql = _apply_unary_agg_ops(bf_df, list(ops_map.values()), list(ops_map.keys())) - - snapshot.assert_match(sql, "out.sql") - - -def test_min(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "int64_col" - bf_df = scalar_types_df[[col_name]] - agg_expr = agg_ops.MinOp().as_expr(col_name) - sql = _apply_unary_agg_ops(bf_df, [agg_expr], [col_name]) - - snapshot.assert_match(sql, "out.sql") - - # Window tests - window = window_spec.WindowSpec(ordering=(ordering.ascending_over(col_name),)) - sql_window = _apply_unary_window_op(bf_df, agg_expr, window, "agg_int64") - snapshot.assert_match(sql_window, "window_out.sql") - - bf_df_str = scalar_types_df[[col_name, "string_col"]] - window_partition = window_spec.WindowSpec( - grouping_keys=(expression.deref("string_col"),), - ordering=(ordering.descending_over(col_name),), - ) - sql_window_partition = _apply_unary_window_op( - bf_df_str, agg_expr, window_partition, "agg_int64" - ) - snapshot.assert_match(sql_window_partition, "window_partition_out.sql") - - -def test_nunique(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "int64_col" - bf_df = scalar_types_df[[col_name]] - agg_expr = agg_ops.NuniqueOp().as_expr(col_name) - sql = _apply_unary_agg_ops(bf_df, [agg_expr], [col_name]) - - snapshot.assert_match(sql, "out.sql") - - -def test_pop_var(scalar_types_df: bpd.DataFrame, snapshot): - col_names = ["int64_col", "bool_col"] - bf_df = scalar_types_df[col_names] - - agg_ops_map = { - "int64_col": agg_ops.PopVarOp().as_expr("int64_col"), - "bool_col": agg_ops.PopVarOp().as_expr("bool_col"), - } - sql = _apply_unary_agg_ops( - bf_df, list(agg_ops_map.values()), list(agg_ops_map.keys()) - ) - snapshot.assert_match(sql, "out.sql") - - # Window tests - col_name = "int64_col" - bf_df_int = scalar_types_df[[col_name]] - agg_expr = agg_ops.PopVarOp().as_expr(col_name) - window = window_spec.WindowSpec(ordering=(ordering.descending_over(col_name),)) - sql_window = _apply_unary_window_op(bf_df_int, agg_expr, window, "agg_int64") - snapshot.assert_match(sql_window, "window_out.sql") - - -def test_product(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "int64_col" - bf_df = scalar_types_df[[col_name]] - agg_expr = agg_ops.ProductOp().as_expr(col_name) - sql = _apply_unary_agg_ops(bf_df, [agg_expr], [col_name]) - - snapshot.assert_match(sql, "out.sql") - - bf_df_str = scalar_types_df[[col_name, "string_col"]] - window_partition = window_spec.WindowSpec( - grouping_keys=(expression.deref("string_col"),), - ) - sql_window_partition = _apply_unary_window_op( - bf_df_str, agg_expr, window_partition, "agg_int64" - ) - - snapshot.assert_match(sql_window_partition, "window_partition_out.sql") - - -def test_qcut(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "int64_col" - bf = scalar_types_df[[col_name]] - bf["qcut_w_int"] = bpd.qcut(bf[col_name], q=4, labels=False, duplicates="drop") - - q_list = tuple([0, 0.25, 0.5, 0.75, 1]) - bf["qcut_w_list"] = bpd.qcut( - scalar_types_df[col_name], - q=q_list, - labels=False, - duplicates="drop", - ) - - snapshot.assert_match(bf.sql, "out.sql") - - -def test_quantile(scalar_types_df: bpd.DataFrame, snapshot): - bf_df = scalar_types_df[["int64_col", "bool_col"]] - agg_ops_map = { - "int64": agg_ops.QuantileOp(q=0.5).as_expr("int64_col"), - "bool": agg_ops.QuantileOp(q=0.5).as_expr("bool_col"), - "int64_w_floor": agg_ops.QuantileOp(q=0.5, should_floor_result=True).as_expr( - "int64_col" - ), - } - sql = _apply_unary_agg_ops( - bf_df, list(agg_ops_map.values()), list(agg_ops_map.keys()) - ) - - snapshot.assert_match(sql, "out.sql") - - -def test_rank(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "int64_col" - bf_df = scalar_types_df[[col_name]] - agg_expr = agg_exprs.UnaryAggregation(agg_ops.RankOp(), expression.deref(col_name)) - - window = window_spec.WindowSpec( - ordering=(ordering.descending_over(col_name, nulls_last=False),) - ) - sql = _apply_unary_window_op(bf_df, agg_expr, window, "agg_int64") - - snapshot.assert_match(sql, "out.sql") - - -def test_shift(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "int64_col" - bf_df = scalar_types_df[[col_name]] - window = window_spec.WindowSpec( - ordering=(ordering.ascending_over(col_name, nulls_last=False),) - ) - - # Test lag - lag_op = agg_exprs.UnaryAggregation( - agg_ops.ShiftOp(periods=1), expression.deref(col_name) - ) - lag_sql = _apply_unary_window_op(bf_df, lag_op, window, "lag") - snapshot.assert_match(lag_sql, "lag.sql") - - # Test lead - lead_op = agg_exprs.UnaryAggregation( - agg_ops.ShiftOp(periods=-1), expression.deref(col_name) - ) - lead_sql = _apply_unary_window_op(bf_df, lead_op, window, "lead") - snapshot.assert_match(lead_sql, "lead.sql") - - # Test no-op - noop_op = agg_exprs.UnaryAggregation( - agg_ops.ShiftOp(periods=0), expression.deref(col_name) - ) - noop_sql = _apply_unary_window_op(bf_df, noop_op, window, "noop") - snapshot.assert_match(noop_sql, "noop.sql") - - -def test_std(scalar_types_df: bpd.DataFrame, snapshot): - col_names = ["int64_col", "bool_col", "duration_col"] - bf_df = scalar_types_df[col_names] - bf_df["duration_col"] = bpd.to_timedelta(bf_df["duration_col"], unit="us") - - # The `to_timedelta` creates a new mapping for the column id. - col_names.insert(0, "rowindex") - name2id = { - col_name: col_id - for col_name, col_id in zip(col_names, bf_df._block.expr.column_ids) - } - - agg_ops_map = { - "int64_col": agg_ops.StdOp().as_expr(name2id["int64_col"]), - "bool_col": agg_ops.StdOp().as_expr(name2id["bool_col"]), - "duration_col": agg_ops.StdOp().as_expr(name2id["duration_col"]), - "int64_col_w_floor": agg_ops.StdOp(should_floor_result=True).as_expr( - name2id["int64_col"] - ), - } - sql = _apply_unary_agg_ops( - bf_df, list(agg_ops_map.values()), list(agg_ops_map.keys()) - ) - snapshot.assert_match(sql, "out.sql") - - # Window tests - col_name = "int64_col" - bf_df_int = scalar_types_df[[col_name]] - agg_expr = agg_ops.StdOp().as_expr(col_name) - window = window_spec.WindowSpec(ordering=(ordering.descending_over(col_name),)) - sql_window = _apply_unary_window_op(bf_df_int, agg_expr, window, "agg_int64") - snapshot.assert_match(sql_window, "window_out.sql") - - -def test_sum(scalar_types_df: bpd.DataFrame, snapshot): - bf_df = scalar_types_df[["int64_col", "bool_col"]] - agg_ops_map = { - "int64_col": agg_ops.SumOp().as_expr("int64_col"), - "bool_col": agg_ops.SumOp().as_expr("bool_col"), - } - sql = _apply_unary_agg_ops( - bf_df, list(agg_ops_map.values()), list(agg_ops_map.keys()) - ) - - snapshot.assert_match(sql, "out.sql") - - # Window tests - col_name = "int64_col" - bf_df_int = scalar_types_df[[col_name]] - agg_expr = agg_ops.SumOp().as_expr(col_name) - window = window_spec.WindowSpec(ordering=(ordering.descending_over(col_name),)) - sql_window = _apply_unary_window_op(bf_df_int, agg_expr, window, "agg_int64") - snapshot.assert_match(sql_window, "window_out.sql") - - bf_df_str = scalar_types_df[[col_name, "string_col"]] - window_partition = window_spec.WindowSpec( - grouping_keys=(expression.deref("string_col"),), - ordering=(ordering.ascending_over(col_name),), - ) - sql_window_partition = _apply_unary_window_op( - bf_df_str, agg_expr, window_partition, "agg_int64" - ) - snapshot.assert_match(sql_window_partition, "window_partition_out.sql") - - -def test_var(scalar_types_df: bpd.DataFrame, snapshot): - col_names = ["int64_col", "bool_col"] - bf_df = scalar_types_df[col_names] - - agg_ops_map = { - "int64_col": agg_ops.VarOp().as_expr("int64_col"), - "bool_col": agg_ops.VarOp().as_expr("bool_col"), - } - sql = _apply_unary_agg_ops( - bf_df, list(agg_ops_map.values()), list(agg_ops_map.keys()) - ) - snapshot.assert_match(sql, "out.sql") - - # Window tests - col_name = "int64_col" - bf_df_int = scalar_types_df[[col_name]] - agg_expr = agg_ops.VarOp().as_expr(col_name) - window = window_spec.WindowSpec(ordering=(ordering.descending_over(col_name),)) - sql_window = _apply_unary_window_op(bf_df_int, agg_expr, window, "agg_int64") - snapshot.assert_match(sql_window, "window_out.sql") diff --git a/tests/unit/core/compile/sqlglot/aggregations/test_windows.py b/tests/unit/core/compile/sqlglot/aggregations/test_windows.py deleted file mode 100644 index 98d0452c9a7..00000000000 --- a/tests/unit/core/compile/sqlglot/aggregations/test_windows.py +++ /dev/null @@ -1,178 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import unittest - -import bigframes_vendored.sqlglot.expressions as sge -import pandas as pd -import pytest - -import bigframes.core.expression as ex -import bigframes.core.identifiers as ids -import bigframes.core.ordering as ordering -from bigframes import dtypes -from bigframes.core import window_spec -from bigframes.core.compile.sqlglot.aggregations.windows import ( - apply_window_if_present, - get_window_order_by, -) - - -class WindowsTest(unittest.TestCase): - def test_get_window_order_by_empty(self): - self.assertIsNone(get_window_order_by(tuple())) - - def test_get_window_order_by(self): - result = get_window_order_by((ordering.OrderingExpression(ex.deref("col1")),)) - self.assertEqual( - sge.Order(expressions=result).sql(dialect="bigquery"), - "ORDER BY `col1` ASC NULLS LAST", - ) - - def test_get_window_order_by_override_nulls(self): - result = get_window_order_by( - (ordering.OrderingExpression(ex.deref("col1")),), - override_null_order=True, - ) - self.assertEqual( - sge.Order(expressions=result).sql(dialect="bigquery"), - "ORDER BY `col1` IS NULL ASC NULLS LAST, `col1` ASC NULLS LAST", - ) - - def test_get_window_order_by_override_nulls_desc(self): - result = get_window_order_by( - ( - ordering.OrderingExpression( - ex.deref("col1"), - direction=ordering.OrderingDirection.DESC, - na_last=False, - ), - ), - override_null_order=True, - ) - self.assertEqual( - sge.Order(expressions=result).sql(dialect="bigquery"), - "ORDER BY `col1` IS NULL DESC NULLS FIRST, `col1` DESC NULLS FIRST", - ) - - def test_apply_window_if_present_no_window(self): - value = sge.func( - "SUM", sge.Column(this=sge.to_identifier("col_0", quoted=True)) - ) - result = apply_window_if_present(value) - self.assertEqual(result, value) - - def test_apply_window_if_present_row_bounded_no_ordering_raises(self): - with pytest.raises( - ValueError, match="No ordering provided for ordered analytic function" - ): - apply_window_if_present( - sge.Var(this="value"), - window_spec.WindowSpec( - bounds=window_spec.RowsWindowBounds(start=-1, end=1) - ), - ) - - def test_apply_window_if_present_grouping_no_ordering(self): - result = apply_window_if_present( - sge.Var(this="value"), - window_spec.WindowSpec( - grouping_keys=( - ex.ResolvedDerefOp( - ids.ColumnId("col1"), - dtype=dtypes.STRING_DTYPE, - is_nullable=True, - ), - ex.ResolvedDerefOp( - ids.ColumnId("col2"), - dtype=dtypes.FLOAT_DTYPE, - is_nullable=True, - ), - ex.ResolvedDerefOp( - ids.ColumnId("col3"), - dtype=dtypes.JSON_DTYPE, - is_nullable=True, - ), - ex.ResolvedDerefOp( - ids.ColumnId("col4"), - dtype=dtypes.GEO_DTYPE, - is_nullable=True, - ), - ), - ), - ) - self.assertEqual( - result.sql(dialect="bigquery"), - "value OVER (PARTITION BY `col1`, CAST(`col2` AS STRING), TO_JSON_STRING(`col3`), ST_ASBINARY(`col4`))", - ) - - def test_apply_window_if_present_range_bounded(self): - result = apply_window_if_present( - sge.Var(this="value"), - window_spec.WindowSpec( - ordering=(ordering.OrderingExpression(ex.deref("col1")),), - bounds=window_spec.RangeWindowBounds(start=None, end=pd.Timedelta(0)), - ), - ) - self.assertEqual( - result.sql(dialect="bigquery"), - "value OVER (ORDER BY `col1` ASC RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW)", - ) - - def test_apply_window_if_present_range_bounded_timedelta(self): - result = apply_window_if_present( - sge.Var(this="value"), - window_spec.WindowSpec( - ordering=(ordering.OrderingExpression(ex.deref("col1")),), - bounds=window_spec.RangeWindowBounds( - start=pd.Timedelta(days=-1), end=pd.Timedelta(hours=12) - ), - ), - ) - self.assertEqual( - result.sql(dialect="bigquery"), - "value OVER (ORDER BY `col1` ASC RANGE BETWEEN 86400000000 PRECEDING AND 43200000000 FOLLOWING)", - ) - - def test_apply_window_if_present_all_params(self): - result = apply_window_if_present( - sge.Var(this="value"), - window_spec.WindowSpec( - grouping_keys=( - ex.ResolvedDerefOp( - ids.ColumnId("col1"), - dtype=dtypes.STRING_DTYPE, - is_nullable=True, - ), - ), - ordering=( - ordering.OrderingExpression( - ex.ResolvedDerefOp( - ids.ColumnId("col2"), - dtype=dtypes.STRING_DTYPE, - is_nullable=True, - ) - ), - ), - bounds=window_spec.RowsWindowBounds(start=-1, end=0), - ), - ) - self.assertEqual( - result.sql(dialect="bigquery"), - "value OVER (PARTITION BY `col1` ORDER BY `col2` ASC NULLS LAST ROWS BETWEEN 1 PRECEDING AND CURRENT ROW)", - ) - - -if __name__ == "__main__": - unittest.main() diff --git a/tests/unit/core/compile/sqlglot/expressions/__init__.py b/tests/unit/core/compile/sqlglot/expressions/__init__.py deleted file mode 100644 index 0a2669d7a25..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/__init__.py +++ /dev/null @@ -1,13 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_ai_ops/test_ai_classify/None/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_ai_ops/test_ai_classify/None/out.sql deleted file mode 100644 index fc29d96cc1a..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_ai_ops/test_ai_classify/None/out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - AI.CLASSIFY(input => STRUCT(`string_col`), categories => ['greeting', 'rejection']) AS `result` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_ai_ops/test_ai_classify/bigframes-dev.us.bigframes-default-connection/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_ai_ops/test_ai_classify/bigframes-dev.us.bigframes-default-connection/out.sql deleted file mode 100644 index 969b946725b..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_ai_ops/test_ai_classify/bigframes-dev.us.bigframes-default-connection/out.sql +++ /dev/null @@ -1,7 +0,0 @@ -SELECT - AI.CLASSIFY( - input => STRUCT(`string_col`), - categories => ['greeting', 'rejection'], - connection_id => 'bigframes-dev.us.bigframes-default-connection' - ) AS `result` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_ai_ops/test_ai_classify_multi_with_list_examples/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_ai_ops/test_ai_classify_multi_with_list_examples/out.sql deleted file mode 100644 index 74078e98606..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_ai_ops/test_ai_classify_multi_with_list_examples/out.sql +++ /dev/null @@ -1,8 +0,0 @@ -SELECT - AI.CLASSIFY( - input => STRUCT(`string_col`), - categories => ['greeting', 'rejection'], - examples => [('hi', ['greeting', 'positive']), ('bye', ['rejection', 'negative'])], - output_mode => 'multi' - ) AS `result` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_ai_ops/test_ai_classify_with_output_mode/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_ai_ops/test_ai_classify_with_output_mode/out.sql deleted file mode 100644 index 08d7476d77f..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_ai_ops/test_ai_classify_with_output_mode/out.sql +++ /dev/null @@ -1,7 +0,0 @@ -SELECT - AI.CLASSIFY( - input => STRUCT(`string_col`), - categories => ['greeting', 'rejection'], - output_mode => 'multi' - ) AS `result` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_ai_ops/test_ai_classify_with_params/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_ai_ops/test_ai_classify_with_params/out.sql deleted file mode 100644 index 30542740a2d..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_ai_ops/test_ai_classify_with_params/out.sql +++ /dev/null @@ -1,9 +0,0 @@ -SELECT - AI.CLASSIFY( - input => STRUCT(`string_col`), - categories => ['greeting', 'rejection'], - examples => [('hi', 'greeting'), ('bye', 'rejection')], - endpoint => 'gemini-2.5-flash', - max_error_ratio => 0.1 - ) AS `result` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_ai_ops/test_ai_embed/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_ai_ops/test_ai_embed/out.sql deleted file mode 100644 index 9c18a7cd532..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_ai_ops/test_ai_embed/out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - AI.EMBED(`string_col`, endpoint => 'text-embedding-005') AS `result` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_ai_ops/test_ai_embed_with_connection_id/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_ai_ops/test_ai_embed_with_connection_id/out.sql deleted file mode 100644 index 0968a101b22..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_ai_ops/test_ai_embed_with_connection_id/out.sql +++ /dev/null @@ -1,7 +0,0 @@ -SELECT - AI.EMBED( - `string_col`, - endpoint => 'text-embedding-005', - connection_id => 'bigframes-dev.us.bigframes-default-connection' - ) AS `result` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_ai_ops/test_ai_embed_with_model/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_ai_ops/test_ai_embed_with_model/out.sql deleted file mode 100644 index 4c3c76f87b6..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_ai_ops/test_ai_embed_with_model/out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - AI.EMBED(`string_col`, model => 'embeddinggemma-300m') AS `result` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_ai_ops/test_ai_embed_with_model_param_and_title/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_ai_ops/test_ai_embed_with_model_param_and_title/out.sql deleted file mode 100644 index 873db838682..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_ai_ops/test_ai_embed_with_model_param_and_title/out.sql +++ /dev/null @@ -1,9 +0,0 @@ -SELECT - AI.EMBED( - `string_col`, - endpoint => 'text-embedding-005', - task_type => 'retrieval_document', - title => 'My Document', - model_params => JSON '{"outputDimensionality": 256}' - ) AS `result` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_ai_ops/test_ai_embed_with_task_type_and_title/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_ai_ops/test_ai_embed_with_task_type_and_title/out.sql deleted file mode 100644 index 9e4db995871..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_ai_ops/test_ai_embed_with_task_type_and_title/out.sql +++ /dev/null @@ -1,9 +0,0 @@ -SELECT - AI.EMBED( - `string_col`, - endpoint => 'text-embedding-005', - task_type => 'RETRIEVAL_DOCUMENT', - title => 'My Document', - model_params => JSON '{"outputDimensionality": 256}' - ) AS `result` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_ai_ops/test_ai_generate/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_ai_ops/test_ai_generate/out.sql deleted file mode 100644 index 622782fa7d6..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_ai_ops/test_ai_generate/out.sql +++ /dev/null @@ -1,7 +0,0 @@ -SELECT - AI.GENERATE( - prompt => STRUCT(`string_col`, ' is the same as ', `string_col`), - endpoint => 'gemini-2.5-flash', - request_type => 'SHARED' - ) AS `result` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_ai_ops/test_ai_generate_bool/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_ai_ops/test_ai_generate_bool/out.sql deleted file mode 100644 index a71bce037a5..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_ai_ops/test_ai_generate_bool/out.sql +++ /dev/null @@ -1,6 +0,0 @@ -SELECT - AI.GENERATE_BOOL( - prompt => STRUCT(`string_col`, ' is the same as ', `string_col`), - endpoint => 'gemini-2.5-flash' - ) AS `result` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_ai_ops/test_ai_generate_bool_with_connection_id/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_ai_ops/test_ai_generate_bool_with_connection_id/out.sql deleted file mode 100644 index db1ec378aaf..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_ai_ops/test_ai_generate_bool_with_connection_id/out.sql +++ /dev/null @@ -1,7 +0,0 @@ -SELECT - AI.GENERATE_BOOL( - prompt => STRUCT(`string_col`, ' is the same as ', `string_col`), - connection_id => 'bigframes-dev.us.bigframes-default-connection', - endpoint => 'gemini-2.5-flash' - ) AS `result` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_ai_ops/test_ai_generate_bool_with_model_param/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_ai_ops/test_ai_generate_bool_with_model_param/out.sql deleted file mode 100644 index 76af8833e63..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_ai_ops/test_ai_generate_bool_with_model_param/out.sql +++ /dev/null @@ -1,6 +0,0 @@ -SELECT - AI.GENERATE_BOOL( - prompt => STRUCT(`string_col`, ' is the same as ', `string_col`), - model_params => JSON '{}' - ) AS `result` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_ai_ops/test_ai_generate_double/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_ai_ops/test_ai_generate_double/out.sql deleted file mode 100644 index 1cef7568798..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_ai_ops/test_ai_generate_double/out.sql +++ /dev/null @@ -1,6 +0,0 @@ -SELECT - AI.GENERATE_DOUBLE( - prompt => STRUCT(`string_col`, ' is the same as ', `string_col`), - endpoint => 'gemini-2.5-flash' - ) AS `result` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_ai_ops/test_ai_generate_double_with_connection_id/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_ai_ops/test_ai_generate_double_with_connection_id/out.sql deleted file mode 100644 index d0088721e38..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_ai_ops/test_ai_generate_double_with_connection_id/out.sql +++ /dev/null @@ -1,7 +0,0 @@ -SELECT - AI.GENERATE_DOUBLE( - prompt => STRUCT(`string_col`, ' is the same as ', `string_col`), - connection_id => 'bigframes-dev.us.bigframes-default-connection', - endpoint => 'gemini-2.5-flash' - ) AS `result` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_ai_ops/test_ai_generate_double_with_model_param/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_ai_ops/test_ai_generate_double_with_model_param/out.sql deleted file mode 100644 index 2b50e05b7fe..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_ai_ops/test_ai_generate_double_with_model_param/out.sql +++ /dev/null @@ -1,6 +0,0 @@ -SELECT - AI.GENERATE_DOUBLE( - prompt => STRUCT(`string_col`, ' is the same as ', `string_col`), - model_params => JSON '{}' - ) AS `result` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_ai_ops/test_ai_generate_int/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_ai_ops/test_ai_generate_int/out.sql deleted file mode 100644 index 9ef143c8b9e..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_ai_ops/test_ai_generate_int/out.sql +++ /dev/null @@ -1,6 +0,0 @@ -SELECT - AI.GENERATE_INT( - prompt => STRUCT(`string_col`, ' is the same as ', `string_col`), - endpoint => 'gemini-2.5-flash' - ) AS `result` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_ai_ops/test_ai_generate_int_with_connection_id/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_ai_ops/test_ai_generate_int_with_connection_id/out.sql deleted file mode 100644 index 3fa3e8cc05e..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_ai_ops/test_ai_generate_int_with_connection_id/out.sql +++ /dev/null @@ -1,7 +0,0 @@ -SELECT - AI.GENERATE_INT( - prompt => STRUCT(`string_col`, ' is the same as ', `string_col`), - connection_id => 'bigframes-dev.us.bigframes-default-connection', - endpoint => 'gemini-2.5-flash' - ) AS `result` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_ai_ops/test_ai_generate_int_with_model_param/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_ai_ops/test_ai_generate_int_with_model_param/out.sql deleted file mode 100644 index 18adea8a062..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_ai_ops/test_ai_generate_int_with_model_param/out.sql +++ /dev/null @@ -1,6 +0,0 @@ -SELECT - AI.GENERATE_INT( - prompt => STRUCT(`string_col`, ' is the same as ', `string_col`), - model_params => JSON '{}' - ) AS `result` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_ai_ops/test_ai_generate_with_connection_id/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_ai_ops/test_ai_generate_with_connection_id/out.sql deleted file mode 100644 index 14604cfc8df..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_ai_ops/test_ai_generate_with_connection_id/out.sql +++ /dev/null @@ -1,7 +0,0 @@ -SELECT - AI.GENERATE( - prompt => STRUCT(`string_col`, ' is the same as ', `string_col`), - connection_id => 'bigframes-dev.us.bigframes-default-connection', - endpoint => 'gemini-2.5-flash' - ) AS `result` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_ai_ops/test_ai_generate_with_model_param/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_ai_ops/test_ai_generate_with_model_param/out.sql deleted file mode 100644 index 090a42d889f..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_ai_ops/test_ai_generate_with_model_param/out.sql +++ /dev/null @@ -1,6 +0,0 @@ -SELECT - AI.GENERATE( - prompt => STRUCT(`string_col`, ' is the same as ', `string_col`), - model_params => JSON '{}' - ) AS `result` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_ai_ops/test_ai_generate_with_output_schema/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_ai_ops/test_ai_generate_with_output_schema/out.sql deleted file mode 100644 index 31c179e7b01..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_ai_ops/test_ai_generate_with_output_schema/out.sql +++ /dev/null @@ -1,7 +0,0 @@ -SELECT - AI.GENERATE( - prompt => STRUCT(`string_col`, ' is the same as ', `string_col`), - endpoint => 'gemini-2.5-flash', - output_schema => 'x INT64, y FLOAT64' - ) AS `result` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_ai_ops/test_ai_if/None/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_ai_ops/test_ai_if/None/out.sql deleted file mode 100644 index 59cf1c02a35..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_ai_ops/test_ai_if/None/out.sql +++ /dev/null @@ -1,7 +0,0 @@ -SELECT - AI.IF( - prompt => STRUCT(`string_col`, ' is the same as ', `string_col`), - optimization_mode => 'MINIMIZE_COST', - max_error_ratio => 0.5 - ) AS `result` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_ai_ops/test_ai_if/bigframes-dev.us.bigframes-default-connection/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_ai_ops/test_ai_if/bigframes-dev.us.bigframes-default-connection/out.sql deleted file mode 100644 index 0f26ab3c6ea..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_ai_ops/test_ai_if/bigframes-dev.us.bigframes-default-connection/out.sql +++ /dev/null @@ -1,8 +0,0 @@ -SELECT - AI.IF( - prompt => STRUCT(`string_col`, ' is the same as ', `string_col`), - connection_id => 'bigframes-dev.us.bigframes-default-connection', - optimization_mode => 'MINIMIZE_COST', - max_error_ratio => 0.5 - ) AS `result` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_ai_ops/test_ai_if_with_endpoint/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_ai_ops/test_ai_if_with_endpoint/out.sql deleted file mode 100644 index 4dd910528a4..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_ai_ops/test_ai_if_with_endpoint/out.sql +++ /dev/null @@ -1,6 +0,0 @@ -SELECT - AI.IF( - prompt => STRUCT(`string_col`, ' is the same as ', `string_col`), - endpoint => 'gemini-2.5-flash' - ) AS `result` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_ai_ops/test_ai_score/None/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_ai_ops/test_ai_score/None/out.sql deleted file mode 100644 index 37590eec4f0..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_ai_ops/test_ai_score/None/out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - AI.SCORE(prompt => STRUCT(`string_col`, ' is the same as ', `string_col`)) AS `result` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_ai_ops/test_ai_score/bigframes-dev.us.bigframes-default-connection/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_ai_ops/test_ai_score/bigframes-dev.us.bigframes-default-connection/out.sql deleted file mode 100644 index 696c7e9f318..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_ai_ops/test_ai_score/bigframes-dev.us.bigframes-default-connection/out.sql +++ /dev/null @@ -1,6 +0,0 @@ -SELECT - AI.SCORE( - prompt => STRUCT(`string_col`, ' is the same as ', `string_col`), - connection_id => 'bigframes-dev.us.bigframes-default-connection' - ) AS `result` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_ai_ops/test_ai_score_with_endpoint_and_max_error_ratio/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_ai_ops/test_ai_score_with_endpoint_and_max_error_ratio/out.sql deleted file mode 100644 index a802e5a396b..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_ai_ops/test_ai_score_with_endpoint_and_max_error_ratio/out.sql +++ /dev/null @@ -1,7 +0,0 @@ -SELECT - AI.SCORE( - prompt => STRUCT(`string_col`, ' is the same as ', `string_col`), - endpoint => 'gemini-2.5-flash', - max_error_ratio => 0.5 - ) AS `result` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_ai_ops/test_ai_similarity/None/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_ai_ops/test_ai_similarity/None/out.sql deleted file mode 100644 index 1df70aaf18e..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_ai_ops/test_ai_similarity/None/out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - AI.SIMILARITY(content1 => `string_col`, content2 => `string_col`, endpoint => 'text-embedding-005') AS `result` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_ai_ops/test_ai_similarity/bigframes-dev.us.bigframes-default-connection/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_ai_ops/test_ai_similarity/bigframes-dev.us.bigframes-default-connection/out.sql deleted file mode 100644 index db57188ffa0..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_ai_ops/test_ai_similarity/bigframes-dev.us.bigframes-default-connection/out.sql +++ /dev/null @@ -1,8 +0,0 @@ -SELECT - AI.SIMILARITY( - content1 => `string_col`, - content2 => `string_col`, - endpoint => 'text-embedding-005', - connection_id => 'bigframes-dev.us.bigframes-default-connection' - ) AS `result` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_ai_ops/test_ai_similarity_with_model/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_ai_ops/test_ai_similarity_with_model/out.sql deleted file mode 100644 index 704f9f94491..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_ai_ops/test_ai_similarity_with_model/out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - AI.SIMILARITY(content1 => `string_col`, content2 => `string_col`, model => 'embeddinggemma-300m') AS `result` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_ai_ops/test_ai_similarity_with_model_param/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_ai_ops/test_ai_similarity_with_model_param/out.sql deleted file mode 100644 index 5173ac43bd9..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_ai_ops/test_ai_similarity_with_model_param/out.sql +++ /dev/null @@ -1,8 +0,0 @@ -SELECT - AI.SIMILARITY( - content1 => `string_col`, - content2 => `string_col`, - endpoint => 'text-embedding-005', - model_params => JSON '{"outputDimensionality": 256}' - ) AS `result` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_array_ops/test_array_index/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_array_ops/test_array_index/out.sql deleted file mode 100644 index a1f089424a1..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_array_ops/test_array_index/out.sql +++ /dev/null @@ -1,4 +0,0 @@ -SELECT - IF(SUBSTRING(`string_col`, 2, 1) <> '', SUBSTRING(`string_col`, 2, 1), NULL) AS `string_index`, - [`int64_col`, `int64_too`][SAFE_OFFSET(1)] AS `array_index` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_array_ops/test_array_reduce_op/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_array_ops/test_array_reduce_op/out.sql deleted file mode 100644 index 1053ec1c2c6..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_array_ops/test_array_reduce_op/out.sql +++ /dev/null @@ -1,27 +0,0 @@ -SELECT - ( - SELECT - COALESCE(SUM(bf_arr_reduce_uid), 0) - FROM UNNEST(`float_list_col`) AS bf_arr_reduce_uid - ) AS `sum_float`, - ( - SELECT - STDDEV(bf_arr_reduce_uid) - FROM UNNEST(`float_list_col`) AS bf_arr_reduce_uid - ) AS `std_float`, - ( - SELECT - COUNT(bf_arr_reduce_uid) - FROM UNNEST(`string_list_col`) AS bf_arr_reduce_uid - ) AS `count_str`, - ( - SELECT - COALESCE(LOGICAL_OR(bf_arr_reduce_uid), FALSE) - FROM UNNEST(`bool_list_col`) AS bf_arr_reduce_uid - ) AS `any_bool`, - ( - SELECT - ARRAY_AGG(bf_arr_reduce_uid IGNORE NULLS) - FROM UNNEST(`string_list_col`) AS bf_arr_reduce_uid - ) AS `array_agg_str` -FROM `bigframes-dev`.`sqlglot_test`.`repeated_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_array_ops/test_array_slice/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_array_ops/test_array_slice/out.sql deleted file mode 100644 index ffec3b8e934..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_array_ops/test_array_slice/out.sql +++ /dev/null @@ -1,17 +0,0 @@ -SELECT - SUBSTRING(`string_col`, 2, 4) AS `string_slice`, - ARRAY( - SELECT - el - FROM UNNEST([`int64_col`, `int64_too`]) AS el WITH OFFSET AS slice_idx - WHERE - slice_idx >= 1 - ) AS `slice_only_start`, - ARRAY( - SELECT - el - FROM UNNEST([`int64_col`, `int64_too`]) AS el WITH OFFSET AS slice_idx - WHERE - slice_idx >= 1 AND slice_idx < 5 - ) AS `slice_start_stop` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_array_ops/test_array_to_string/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_array_ops/test_array_to_string/out.sql deleted file mode 100644 index 27587771506..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_array_ops/test_array_to_string/out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - ARRAY_TO_STRING(`string_list_col`, '.') AS `string_list_col` -FROM `bigframes-dev`.`sqlglot_test`.`repeated_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_array_ops/test_to_array_op/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_array_ops/test_to_array_op/out.sql deleted file mode 100644 index f7d8d748b4a..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_array_ops/test_to_array_op/out.sql +++ /dev/null @@ -1,10 +0,0 @@ -SELECT - [COALESCE(`bool_col`, FALSE)] AS `bool_col`, - [COALESCE(`int64_col`, 0)] AS `int64_col`, - [COALESCE(`string_col`, ''), COALESCE(`string_col`, '')] AS `strs_col`, - [ - COALESCE(`int64_col`, 0), - CAST(COALESCE(`bool_col`, FALSE) AS INT64), - COALESCE(`float64_col`, 0.0) - ] AS `numeric_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_array_ops/test_to_array_with_subquery_expression/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_array_ops/test_to_array_with_subquery_expression/out.sql deleted file mode 100644 index 63dfcec026b..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_array_ops/test_to_array_with_subquery_expression/out.sql +++ /dev/null @@ -1,12 +0,0 @@ -SELECT - [ - COALESCE( - ( - SELECT - COALESCE(SUM(bf_arr_reduce_uid), 0) - FROM UNNEST(`float_list_col`) AS bf_arr_reduce_uid - ), - 0.0 - ) - ] AS `arr_subquery_coalesce` -FROM `bigframes-dev`.`sqlglot_test`.`repeated_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_bool_ops/test_and_op/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_bool_ops/test_and_op/out.sql deleted file mode 100644 index d6f6587ead9..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_bool_ops/test_and_op/out.sql +++ /dev/null @@ -1,9 +0,0 @@ -SELECT - `rowindex`, - `bool_col`, - `int64_col`, - `int64_col` & `int64_col` AS `int_and_int`, - `bool_col` AND `bool_col` AS `bool_and_bool`, - IF(`bool_col` = FALSE, `bool_col`, NULL) AS `bool_and_null`, - IF(`bool_col` = FALSE, `bool_col`, NULL) AS `null_and_bool` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_bool_ops/test_or_op/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_bool_ops/test_or_op/out.sql deleted file mode 100644 index dad4cee9d0b..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_bool_ops/test_or_op/out.sql +++ /dev/null @@ -1,9 +0,0 @@ -SELECT - `rowindex`, - `bool_col`, - `int64_col`, - `int64_col` | `int64_col` AS `int_and_int`, - `bool_col` OR `bool_col` AS `bool_and_bool`, - IF(`bool_col` = TRUE, `bool_col`, NULL) AS `bool_and_null`, - IF(`bool_col` = TRUE, `bool_col`, NULL) AS `null_and_bool` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_bool_ops/test_xor_op/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_bool_ops/test_xor_op/out.sql deleted file mode 100644 index 4be3b9f94ad..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_bool_ops/test_xor_op/out.sql +++ /dev/null @@ -1,23 +0,0 @@ -SELECT - `rowindex`, - `bool_col`, - `int64_col`, - `int64_col` ^ `int64_col` AS `int_and_int`, - ( - `bool_col` AND NOT `bool_col` - ) OR ( - NOT `bool_col` AND `bool_col` - ) AS `bool_and_bool`, - ( - `bool_col` AND NOT CAST(NULL AS BOOLEAN) - ) - OR ( - NOT `bool_col` AND CAST(NULL AS BOOLEAN) - ) AS `bool_and_null`, - ( - `bool_col` AND NOT CAST(NULL AS BOOLEAN) - ) - OR ( - NOT `bool_col` AND CAST(NULL AS BOOLEAN) - ) AS `null_and_bool` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_comparison_ops/test_eq_null_match/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_comparison_ops/test_eq_null_match/out.sql deleted file mode 100644 index 3d23b8576ec..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_comparison_ops/test_eq_null_match/out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - COALESCE(CAST(`int64_col` AS STRING), '$NULL_SENTINEL$') = COALESCE(CAST(CAST(`bool_col` AS INT64) AS STRING), '$NULL_SENTINEL$') AS `int64_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_comparison_ops/test_eq_numeric/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_comparison_ops/test_eq_numeric/out.sql deleted file mode 100644 index 7827731881e..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_comparison_ops/test_eq_numeric/out.sql +++ /dev/null @@ -1,11 +0,0 @@ -SELECT - `rowindex`, - `int64_col`, - `bool_col`, - `int64_col` = `int64_col` AS `int_eq_int`, - `int64_col` = 1 AS `int_eq_1`, - `int64_col` IS NULL AS `int_eq_null`, - `int64_col` IS NULL AS `null_eq_int`, - `int64_col` = CAST(`bool_col` AS INT64) AS `int_eq_bool`, - CAST(`bool_col` AS INT64) = `int64_col` AS `bool_eq_int` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_comparison_ops/test_ge_numeric/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_comparison_ops/test_ge_numeric/out.sql deleted file mode 100644 index 5903cf03699..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_comparison_ops/test_ge_numeric/out.sql +++ /dev/null @@ -1,10 +0,0 @@ -SELECT - `rowindex`, - `int64_col`, - `bool_col`, - `int64_col` >= `int64_col` AS `int_ge_int`, - `int64_col` >= 1 AS `int_ge_1`, - NULL AS `null_ge_int`, - `int64_col` >= CAST(`bool_col` AS INT64) AS `int_ge_bool`, - CAST(`bool_col` AS INT64) >= `int64_col` AS `bool_ge_int` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_comparison_ops/test_gt_numeric/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_comparison_ops/test_gt_numeric/out.sql deleted file mode 100644 index 42bf029240f..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_comparison_ops/test_gt_numeric/out.sql +++ /dev/null @@ -1,10 +0,0 @@ -SELECT - `rowindex`, - `int64_col`, - `bool_col`, - `int64_col` > `int64_col` AS `int_gt_int`, - `int64_col` > 1 AS `int_gt_1`, - NULL AS `null_gt_int`, - `int64_col` > CAST(`bool_col` AS INT64) AS `int_gt_bool`, - CAST(`bool_col` AS INT64) > `int64_col` AS `bool_gt_int` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_comparison_ops/test_is_in/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_comparison_ops/test_is_in/out.sql deleted file mode 100644 index 308e6f9cbd7..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_comparison_ops/test_is_in/out.sql +++ /dev/null @@ -1,19 +0,0 @@ -SELECT - COALESCE(`bool_col` IN (TRUE, FALSE), FALSE) AS `bools`, - COALESCE(`int64_col` IN (1, 2, 3), FALSE) AS `ints`, - `int64_col` IS NULL AS `ints_w_null`, - COALESCE(`int64_col` IN (1.0, 2.0, 3.0), FALSE) AS `floats`, - FALSE AS `strings`, - COALESCE(`int64_col` IN (2.5, 3, 1e-10, CAST('Infinity' AS FLOAT64), NULL, 0), FALSE) AS `mixed`, - FALSE AS `empty`, - FALSE AS `empty_wo_match_nulls`, - COALESCE(`int64_col` IN (123456), FALSE) AS `ints_wo_match_nulls`, - ( - `float64_col` IS NULL - ) OR `float64_col` IN (1, 2, 3) AS `float_in_ints`, - ( - `int64_col` IS NULL - ) OR `int64_col` IN (2) AS `mixed_with_null`, - COALESCE(CAST(`bool_col` AS INT64) IN (1, 2.5), FALSE) AS `bool_in_mixed`, - `int64_col` IS NULL AS `only_null_match` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_comparison_ops/test_le_numeric/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_comparison_ops/test_le_numeric/out.sql deleted file mode 100644 index c6c86510102..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_comparison_ops/test_le_numeric/out.sql +++ /dev/null @@ -1,10 +0,0 @@ -SELECT - `rowindex`, - `int64_col`, - `bool_col`, - `int64_col` <= `int64_col` AS `int_le_int`, - `int64_col` <= 1 AS `int_le_1`, - NULL AS `null_le_int`, - `int64_col` <= CAST(`bool_col` AS INT64) AS `int_le_bool`, - CAST(`bool_col` AS INT64) <= `int64_col` AS `bool_le_int` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_comparison_ops/test_lt_numeric/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_comparison_ops/test_lt_numeric/out.sql deleted file mode 100644 index ec5c317a8e5..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_comparison_ops/test_lt_numeric/out.sql +++ /dev/null @@ -1,10 +0,0 @@ -SELECT - `rowindex`, - `int64_col`, - `bool_col`, - `int64_col` < `int64_col` AS `int_lt_int`, - `int64_col` < 1 AS `int_lt_1`, - NULL AS `null_lt_int`, - `int64_col` < CAST(`bool_col` AS INT64) AS `int_lt_bool`, - CAST(`bool_col` AS INT64) < `int64_col` AS `bool_lt_int` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_comparison_ops/test_maximum_op/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_comparison_ops/test_maximum_op/out.sql deleted file mode 100644 index a469fa47cf1..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_comparison_ops/test_maximum_op/out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - GREATEST(`int64_col`, `float64_col`) AS `int64_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_comparison_ops/test_minimum_op/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_comparison_ops/test_minimum_op/out.sql deleted file mode 100644 index ea82af979a3..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_comparison_ops/test_minimum_op/out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - LEAST(`int64_col`, `float64_col`) AS `int64_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_comparison_ops/test_ne_numeric/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_comparison_ops/test_ne_numeric/out.sql deleted file mode 100644 index 448a6146294..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_comparison_ops/test_ne_numeric/out.sql +++ /dev/null @@ -1,15 +0,0 @@ -SELECT - `rowindex`, - `int64_col`, - `bool_col`, - `int64_col` <> `int64_col` AS `int_ne_int`, - `int64_col` <> 1 AS `int_ne_1`, - ( - `int64_col` - ) IS NOT NULL AS `int_ne_null`, - ( - `int64_col` - ) IS NOT NULL AS `null_ne_int`, - `int64_col` <> CAST(`bool_col` AS INT64) AS `int_ne_bool`, - CAST(`bool_col` AS INT64) <> `int64_col` AS `bool_ne_int` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_datetime_ops/test_add_timedelta/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_datetime_ops/test_add_timedelta/out.sql deleted file mode 100644 index b1ccf096cfa..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_datetime_ops/test_add_timedelta/out.sql +++ /dev/null @@ -1,10 +0,0 @@ -SELECT - `rowindex`, - `timestamp_col`, - `date_col`, - TIMESTAMP_ADD(CAST(`date_col` AS DATETIME), INTERVAL 86400000000 MICROSECOND) AS `date_add_timedelta`, - TIMESTAMP_ADD(`timestamp_col`, INTERVAL 86400000000 MICROSECOND) AS `timestamp_add_timedelta`, - TIMESTAMP_ADD(CAST(`date_col` AS DATETIME), INTERVAL 86400000000 MICROSECOND) AS `timedelta_add_date`, - TIMESTAMP_ADD(`timestamp_col`, INTERVAL 86400000000 MICROSECOND) AS `timedelta_add_timestamp`, - 172800000000 AS `timedelta_add_timedelta` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_datetime_ops/test_date/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_datetime_ops/test_date/out.sql deleted file mode 100644 index eb0d2f11049..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_datetime_ops/test_date/out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - DATE(`timestamp_col`) AS `timestamp_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_datetime_ops/test_datetime_to_integer_label/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_datetime_ops/test_datetime_to_integer_label/out.sql deleted file mode 100644 index 4b0696386c1..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_datetime_ops/test_datetime_to_integer_label/out.sql +++ /dev/null @@ -1,76 +0,0 @@ -SELECT - CAST(FLOOR( - IEEE_DIVIDE( - UNIX_MICROS(CAST(`datetime_col` AS TIMESTAMP)) - UNIX_MICROS(CAST(`timestamp_col` AS TIMESTAMP)), - 86400000000 - ) - ) AS INT64) AS `fixed_freq`, - CAST(FLOOR(IEEE_DIVIDE(UNIX_MICROS(CAST(`datetime_col` AS TIMESTAMP)) - 0, 86400000000)) AS INT64) AS `origin_epoch`, - CAST(FLOOR( - IEEE_DIVIDE( - UNIX_MICROS(CAST(`datetime_col` AS TIMESTAMP)) - UNIX_MICROS(CAST(CAST(`timestamp_col` AS DATE) AS TIMESTAMP)), - 86400000000 - ) - ) AS INT64) AS `origin_start_day`, - CASE - WHEN UNIX_MICROS( - CAST(TIMESTAMP_TRUNC(`datetime_col`, WEEK(MONDAY)) + INTERVAL 6 DAY AS TIMESTAMP) - ) = UNIX_MICROS( - CAST(TIMESTAMP_TRUNC(`timestamp_col`, WEEK(MONDAY)) + INTERVAL 6 DAY AS TIMESTAMP) - ) - THEN 0 - ELSE CAST(FLOOR( - IEEE_DIVIDE( - UNIX_MICROS( - CAST(TIMESTAMP_TRUNC(`datetime_col`, WEEK(MONDAY)) + INTERVAL 6 DAY AS TIMESTAMP) - ) - UNIX_MICROS( - CAST(TIMESTAMP_TRUNC(`timestamp_col`, WEEK(MONDAY)) + INTERVAL 6 DAY AS TIMESTAMP) - ) - 1, - 604800000000 - ) - ) AS INT64) + 1 - END AS `non_fixed_freq_weekly`, - CASE - WHEN ( - EXTRACT(YEAR FROM `datetime_col`) * 12 + EXTRACT(MONTH FROM `datetime_col`) - 1 - ) = ( - EXTRACT(YEAR FROM `timestamp_col`) * 12 + EXTRACT(MONTH FROM `timestamp_col`) - 1 - ) - THEN 0 - ELSE CAST(FLOOR( - IEEE_DIVIDE( - ( - EXTRACT(YEAR FROM `datetime_col`) * 12 + EXTRACT(MONTH FROM `datetime_col`) - 1 - ) - ( - EXTRACT(YEAR FROM `timestamp_col`) * 12 + EXTRACT(MONTH FROM `timestamp_col`) - 1 - ) - 1, - 1 - ) - ) AS INT64) + 1 - END AS `non_fixed_freq_monthly`, - CASE - WHEN ( - EXTRACT(YEAR FROM `datetime_col`) * 4 + EXTRACT(QUARTER FROM `datetime_col`) - 1 - ) = ( - EXTRACT(YEAR FROM `timestamp_col`) * 4 + EXTRACT(QUARTER FROM `timestamp_col`) - 1 - ) - THEN 0 - ELSE CAST(FLOOR( - IEEE_DIVIDE( - ( - EXTRACT(YEAR FROM `datetime_col`) * 4 + EXTRACT(QUARTER FROM `datetime_col`) - 1 - ) - ( - EXTRACT(YEAR FROM `timestamp_col`) * 4 + EXTRACT(QUARTER FROM `timestamp_col`) - 1 - ) - 1, - 1 - ) - ) AS INT64) + 1 - END AS `non_fixed_freq_quarterly`, - CASE - WHEN EXTRACT(YEAR FROM `datetime_col`) = EXTRACT(YEAR FROM `timestamp_col`) - THEN 0 - ELSE CAST(FLOOR( - IEEE_DIVIDE(EXTRACT(YEAR FROM `datetime_col`) - EXTRACT(YEAR FROM `timestamp_col`) - 1, 1) - ) AS INT64) + 1 - END AS `non_fixed_freq_yearly` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_datetime_ops/test_day/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_datetime_ops/test_day/out.sql deleted file mode 100644 index b9c030cb53e..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_datetime_ops/test_day/out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - EXTRACT(DAY FROM `timestamp_col`) AS `timestamp_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_datetime_ops/test_dayofweek/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_datetime_ops/test_dayofweek/out.sql deleted file mode 100644 index a25d520d804..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_datetime_ops/test_dayofweek/out.sql +++ /dev/null @@ -1,5 +0,0 @@ -SELECT - CAST(MOD(EXTRACT(DAYOFWEEK FROM `datetime_col`) + 5, 7) AS INT64) AS `datetime_col`, - CAST(MOD(EXTRACT(DAYOFWEEK FROM `timestamp_col`) + 5, 7) AS INT64) AS `timestamp_col`, - CAST(MOD(EXTRACT(DAYOFWEEK FROM `date_col`) + 5, 7) AS INT64) AS `date_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_datetime_ops/test_dayofyear/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_datetime_ops/test_dayofyear/out.sql deleted file mode 100644 index 87a410911b5..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_datetime_ops/test_dayofyear/out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - EXTRACT(DAYOFYEAR FROM `timestamp_col`) AS `timestamp_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_datetime_ops/test_floor_dt/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_datetime_ops/test_floor_dt/out.sql deleted file mode 100644 index 49fb8fe5749..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_datetime_ops/test_floor_dt/out.sql +++ /dev/null @@ -1,14 +0,0 @@ -SELECT - TIMESTAMP_TRUNC(`timestamp_col`, MICROSECOND) AS `timestamp_col_us`, - TIMESTAMP_TRUNC(`timestamp_col`, MILLISECOND) AS `timestamp_col_ms`, - TIMESTAMP_TRUNC(`timestamp_col`, SECOND) AS `timestamp_col_s`, - TIMESTAMP_TRUNC(`timestamp_col`, MINUTE) AS `timestamp_col_min`, - TIMESTAMP_TRUNC(`timestamp_col`, HOUR) AS `timestamp_col_h`, - TIMESTAMP_TRUNC(`timestamp_col`, DAY) AS `timestamp_col_D`, - TIMESTAMP_TRUNC(`timestamp_col`, WEEK(MONDAY)) AS `timestamp_col_W`, - TIMESTAMP_TRUNC(`timestamp_col`, MONTH) AS `timestamp_col_M`, - TIMESTAMP_TRUNC(`timestamp_col`, QUARTER) AS `timestamp_col_Q`, - TIMESTAMP_TRUNC(`timestamp_col`, YEAR) AS `timestamp_col_Y`, - TIMESTAMP_TRUNC(`datetime_col`, MICROSECOND) AS `datetime_col_q`, - TIMESTAMP_TRUNC(`datetime_col`, MICROSECOND) AS `datetime_col_us` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_datetime_ops/test_hour/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_datetime_ops/test_hour/out.sql deleted file mode 100644 index e971057f527..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_datetime_ops/test_hour/out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - EXTRACT(HOUR FROM `timestamp_col`) AS `timestamp_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_datetime_ops/test_integer_label_to_datetime/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_datetime_ops/test_integer_label_to_datetime/out.sql deleted file mode 100644 index 2a1bd0e2e21..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_datetime_ops/test_integer_label_to_datetime/out.sql +++ /dev/null @@ -1,58 +0,0 @@ -WITH `bfcte_0` AS ( - SELECT - `rowindex`, - `timestamp_col` - FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` -), `bfcte_1` AS ( - SELECT - *, - CAST(TIMESTAMP_MICROS( - CAST(CAST(`rowindex` AS BIGNUMERIC) * 86400000000 + CAST(UNIX_MICROS(CAST(`timestamp_col` AS TIMESTAMP)) AS BIGNUMERIC) AS INT64) - ) AS TIMESTAMP) AS `bfcol_2`, - CAST(DATETIME( - CASE - WHEN ( - MOD( - `rowindex` * 1 + EXTRACT(YEAR FROM `timestamp_col`) * 4 + EXTRACT(QUARTER FROM `timestamp_col`) - 1, - 4 - ) + 1 - ) * 3 = 12 - THEN CAST(FLOOR( - IEEE_DIVIDE( - `rowindex` * 1 + EXTRACT(YEAR FROM `timestamp_col`) * 4 + EXTRACT(QUARTER FROM `timestamp_col`) - 1, - 4 - ) - ) AS INT64) + 1 - ELSE CAST(FLOOR( - IEEE_DIVIDE( - `rowindex` * 1 + EXTRACT(YEAR FROM `timestamp_col`) * 4 + EXTRACT(QUARTER FROM `timestamp_col`) - 1, - 4 - ) - ) AS INT64) - END, - CASE - WHEN ( - MOD( - `rowindex` * 1 + EXTRACT(YEAR FROM `timestamp_col`) * 4 + EXTRACT(QUARTER FROM `timestamp_col`) - 1, - 4 - ) + 1 - ) * 3 = 12 - THEN 1 - ELSE ( - MOD( - `rowindex` * 1 + EXTRACT(YEAR FROM `timestamp_col`) * 4 + EXTRACT(QUARTER FROM `timestamp_col`) - 1, - 4 - ) + 1 - ) * 3 + 1 - END, - 1, - 0, - 0, - 0 - ) - INTERVAL 1 DAY AS TIMESTAMP) AS `bfcol_3` - FROM `bfcte_0` -) -SELECT - `bfcol_2` AS `fixed_freq`, - `bfcol_3` AS `non_fixed_freq` -FROM `bfcte_1` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_datetime_ops/test_integer_label_to_datetime_fixed/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_datetime_ops/test_integer_label_to_datetime_fixed/out.sql deleted file mode 100644 index 244bd88deb7..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_datetime_ops/test_integer_label_to_datetime_fixed/out.sql +++ /dev/null @@ -1,5 +0,0 @@ -SELECT - CAST(TIMESTAMP_MICROS( - CAST(CAST(`rowindex` AS BIGNUMERIC) * 86400000000 + CAST(UNIX_MICROS(CAST(`timestamp_col` AS TIMESTAMP)) AS BIGNUMERIC) AS INT64) - ) AS TIMESTAMP) AS `fixed_freq` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_datetime_ops/test_integer_label_to_datetime_month/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_datetime_ops/test_integer_label_to_datetime_month/out.sql deleted file mode 100644 index 1ece688b91f..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_datetime_ops/test_integer_label_to_datetime_month/out.sql +++ /dev/null @@ -1,39 +0,0 @@ -SELECT - CAST(TIMESTAMP( - DATETIME( - CASE - WHEN MOD( - `rowindex` * 1 + EXTRACT(YEAR FROM `timestamp_col`) * 12 + EXTRACT(MONTH FROM `timestamp_col`) - 1, - 12 - ) + 1 = 12 - THEN CAST(FLOOR( - IEEE_DIVIDE( - `rowindex` * 1 + EXTRACT(YEAR FROM `timestamp_col`) * 12 + EXTRACT(MONTH FROM `timestamp_col`) - 1, - 12 - ) - ) AS INT64) + 1 - ELSE CAST(FLOOR( - IEEE_DIVIDE( - `rowindex` * 1 + EXTRACT(YEAR FROM `timestamp_col`) * 12 + EXTRACT(MONTH FROM `timestamp_col`) - 1, - 12 - ) - ) AS INT64) - END, - CASE - WHEN MOD( - `rowindex` * 1 + EXTRACT(YEAR FROM `timestamp_col`) * 12 + EXTRACT(MONTH FROM `timestamp_col`) - 1, - 12 - ) + 1 = 12 - THEN 1 - ELSE MOD( - `rowindex` * 1 + EXTRACT(YEAR FROM `timestamp_col`) * 12 + EXTRACT(MONTH FROM `timestamp_col`) - 1, - 12 - ) + 1 + 1 - END, - 1, - 0, - 0, - 0 - ) - ) - INTERVAL 1 DAY AS TIMESTAMP) AS `non_fixed_freq_monthly` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_datetime_ops/test_integer_label_to_datetime_quarter/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_datetime_ops/test_integer_label_to_datetime_quarter/out.sql deleted file mode 100644 index 683b26be91b..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_datetime_ops/test_integer_label_to_datetime_quarter/out.sql +++ /dev/null @@ -1,43 +0,0 @@ -SELECT - CAST(DATETIME( - CASE - WHEN ( - MOD( - `rowindex` * 1 + EXTRACT(YEAR FROM `timestamp_col`) * 4 + EXTRACT(QUARTER FROM `timestamp_col`) - 1, - 4 - ) + 1 - ) * 3 = 12 - THEN CAST(FLOOR( - IEEE_DIVIDE( - `rowindex` * 1 + EXTRACT(YEAR FROM `timestamp_col`) * 4 + EXTRACT(QUARTER FROM `timestamp_col`) - 1, - 4 - ) - ) AS INT64) + 1 - ELSE CAST(FLOOR( - IEEE_DIVIDE( - `rowindex` * 1 + EXTRACT(YEAR FROM `timestamp_col`) * 4 + EXTRACT(QUARTER FROM `timestamp_col`) - 1, - 4 - ) - ) AS INT64) - END, - CASE - WHEN ( - MOD( - `rowindex` * 1 + EXTRACT(YEAR FROM `timestamp_col`) * 4 + EXTRACT(QUARTER FROM `timestamp_col`) - 1, - 4 - ) + 1 - ) * 3 = 12 - THEN 1 - ELSE ( - MOD( - `rowindex` * 1 + EXTRACT(YEAR FROM `timestamp_col`) * 4 + EXTRACT(QUARTER FROM `timestamp_col`) - 1, - 4 - ) + 1 - ) * 3 + 1 - END, - 1, - 0, - 0, - 0 - ) - INTERVAL 1 DAY AS TIMESTAMP) AS `non_fixed_freq` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_datetime_ops/test_integer_label_to_datetime_week/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_datetime_ops/test_integer_label_to_datetime_week/out.sql deleted file mode 100644 index 6196e6976b0..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_datetime_ops/test_integer_label_to_datetime_week/out.sql +++ /dev/null @@ -1,7 +0,0 @@ -SELECT - CAST(TIMESTAMP_MICROS( - CAST(CAST(`rowindex` AS BIGNUMERIC) * 604800000000 + CAST(UNIX_MICROS( - TIMESTAMP_TRUNC(CAST(`timestamp_col` AS TIMESTAMP), WEEK(MONDAY)) + INTERVAL 6 DAY - ) AS BIGNUMERIC) AS INT64) - ) AS TIMESTAMP) AS `non_fixed_freq_weekly` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_datetime_ops/test_integer_label_to_datetime_year/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_datetime_ops/test_integer_label_to_datetime_year/out.sql deleted file mode 100644 index e0d05ec5b4b..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_datetime_ops/test_integer_label_to_datetime_year/out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - CAST(TIMESTAMP(DATETIME(`rowindex` * 1 + EXTRACT(YEAR FROM `timestamp_col`) + 1, 1, 1, 0, 0, 0)) - INTERVAL 1 DAY AS TIMESTAMP) AS `non_fixed_freq_yearly` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_datetime_ops/test_iso_day/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_datetime_ops/test_iso_day/out.sql deleted file mode 100644 index bf7dfea7378..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_datetime_ops/test_iso_day/out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - CAST(MOD(EXTRACT(DAYOFWEEK FROM `timestamp_col`) + 5, 7) AS INT64) + 1 AS `timestamp_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_datetime_ops/test_iso_week/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_datetime_ops/test_iso_week/out.sql deleted file mode 100644 index ce231592164..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_datetime_ops/test_iso_week/out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - EXTRACT(ISOWEEK FROM `timestamp_col`) AS `timestamp_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_datetime_ops/test_iso_year/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_datetime_ops/test_iso_year/out.sql deleted file mode 100644 index aea4bec4371..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_datetime_ops/test_iso_year/out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - EXTRACT(ISOYEAR FROM `timestamp_col`) AS `timestamp_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_datetime_ops/test_minute/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_datetime_ops/test_minute/out.sql deleted file mode 100644 index ed1ffcee104..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_datetime_ops/test_minute/out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - EXTRACT(MINUTE FROM `timestamp_col`) AS `timestamp_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_datetime_ops/test_month/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_datetime_ops/test_month/out.sql deleted file mode 100644 index 8defb0312e9..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_datetime_ops/test_month/out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - EXTRACT(MONTH FROM `timestamp_col`) AS `timestamp_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_datetime_ops/test_normalize/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_datetime_ops/test_normalize/out.sql deleted file mode 100644 index 0ae08c77ad0..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_datetime_ops/test_normalize/out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - TIMESTAMP_TRUNC(`timestamp_col`, DAY) AS `timestamp_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_datetime_ops/test_quarter/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_datetime_ops/test_quarter/out.sql deleted file mode 100644 index 9426f685855..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_datetime_ops/test_quarter/out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - EXTRACT(QUARTER FROM `timestamp_col`) AS `timestamp_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_datetime_ops/test_second/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_datetime_ops/test_second/out.sql deleted file mode 100644 index 953a0ff762a..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_datetime_ops/test_second/out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - EXTRACT(SECOND FROM `timestamp_col`) AS `timestamp_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_datetime_ops/test_strftime/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_datetime_ops/test_strftime/out.sql deleted file mode 100644 index 308c040640d..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_datetime_ops/test_strftime/out.sql +++ /dev/null @@ -1,6 +0,0 @@ -SELECT - FORMAT_DATE('%Y-%m-%d', `date_col`) AS `date_col`, - FORMAT_DATETIME('%Y-%m-%d', `datetime_col`) AS `datetime_col`, - FORMAT_TIME('%Y-%m-%d', `time_col`) AS `time_col`, - FORMAT_TIMESTAMP('%Y-%m-%d', `timestamp_col`) AS `timestamp_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_datetime_ops/test_sub_timedelta/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_datetime_ops/test_sub_timedelta/out.sql deleted file mode 100644 index 5c8b130d59d..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_datetime_ops/test_sub_timedelta/out.sql +++ /dev/null @@ -1,11 +0,0 @@ -SELECT - `rowindex`, - `timestamp_col`, - `duration_col`, - `date_col`, - TIMESTAMP_SUB(CAST(`date_col` AS DATETIME), INTERVAL `duration_col` MICROSECOND) AS `date_sub_timedelta`, - TIMESTAMP_SUB(`timestamp_col`, INTERVAL `duration_col` MICROSECOND) AS `timestamp_sub_timedelta`, - TIMESTAMP_DIFF(CAST(`date_col` AS DATETIME), CAST(`date_col` AS DATETIME), MICROSECOND) AS `timestamp_sub_date`, - TIMESTAMP_DIFF(`timestamp_col`, `timestamp_col`, MICROSECOND) AS `date_sub_timestamp`, - `duration_col` - `duration_col` AS `timedelta_sub_timedelta` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_datetime_ops/test_time/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_datetime_ops/test_time/out.sql deleted file mode 100644 index e46ca373909..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_datetime_ops/test_time/out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - TIME(`timestamp_col`) AS `timestamp_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_datetime_ops/test_to_datetime/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_datetime_ops/test_to_datetime/out.sql deleted file mode 100644 index 50142f20ba5..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_datetime_ops/test_to_datetime/out.sql +++ /dev/null @@ -1,7 +0,0 @@ -SELECT - DATETIME(TIMESTAMP_MICROS(CAST(TRUNC(`int64_col` * 0.001) AS INT64)), 'UTC') AS `int64_col`, - SAFE_CAST(`string_col` AS DATETIME) AS `string_col`, - DATETIME(TIMESTAMP_MICROS(CAST(TRUNC(`float64_col` * 0.001) AS INT64)), 'UTC') AS `float64_col`, - DATETIME(`timestamp_col`, 'UTC') AS `timestamp_col`, - SAFE_CAST(`string_col` AS DATETIME) AS `string_col_fmt` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_datetime_ops/test_to_timestamp/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_datetime_ops/test_to_timestamp/out.sql deleted file mode 100644 index e0fb530cc6d..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_datetime_ops/test_to_timestamp/out.sql +++ /dev/null @@ -1,10 +0,0 @@ -SELECT - CAST(TIMESTAMP_MICROS(CAST(TRUNC(`int64_col` * 0.001) AS INT64)) AS TIMESTAMP) AS `int64_col`, - CAST(TIMESTAMP_MICROS(CAST(TRUNC(`float64_col` * 0.001) AS INT64)) AS TIMESTAMP) AS `float64_col`, - CAST(TIMESTAMP_MICROS(CAST(TRUNC(`int64_col` * 1000000) AS INT64)) AS TIMESTAMP) AS `int64_col_s`, - CAST(TIMESTAMP_MICROS(CAST(TRUNC(`int64_col` * 1000) AS INT64)) AS TIMESTAMP) AS `int64_col_ms`, - CAST(TIMESTAMP_MICROS(CAST(TRUNC(`int64_col`) AS INT64)) AS TIMESTAMP) AS `int64_col_us`, - CAST(TIMESTAMP_MICROS(CAST(TRUNC(`int64_col` * 0.001) AS INT64)) AS TIMESTAMP) AS `int64_col_ns`, - TIMESTAMP(`datetime_col`) AS `datetime_col`, - PARSE_TIMESTAMP('%Y-%m-%d', `string_col`, 'UTC') AS `string_col_fmt` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_datetime_ops/test_unix_micros/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_datetime_ops/test_unix_micros/out.sql deleted file mode 100644 index a212164e6ce..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_datetime_ops/test_unix_micros/out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - UNIX_MICROS(`timestamp_col`) AS `timestamp_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_datetime_ops/test_unix_millis/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_datetime_ops/test_unix_millis/out.sql deleted file mode 100644 index 8df5ad956a3..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_datetime_ops/test_unix_millis/out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - UNIX_MILLIS(`timestamp_col`) AS `timestamp_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_datetime_ops/test_unix_seconds/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_datetime_ops/test_unix_seconds/out.sql deleted file mode 100644 index 7344ca82949..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_datetime_ops/test_unix_seconds/out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - UNIX_SECONDS(`timestamp_col`) AS `timestamp_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_datetime_ops/test_year/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_datetime_ops/test_year/out.sql deleted file mode 100644 index f1a1d7085ef..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_datetime_ops/test_year/out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - EXTRACT(YEAR FROM `timestamp_col`) AS `timestamp_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_generic_ops/test_astype_bool/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_generic_ops/test_astype_bool/out.sql deleted file mode 100644 index 2f75cf4cf7f..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_generic_ops/test_astype_bool/out.sql +++ /dev/null @@ -1,5 +0,0 @@ -SELECT - `bool_col`, - `float64_col` <> 0 AS `float64_col`, - `float64_col` <> 0 AS `float64_w_safe` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_generic_ops/test_astype_float/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_generic_ops/test_astype_float/out.sql deleted file mode 100644 index 7f7bd86084e..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_generic_ops/test_astype_float/out.sql +++ /dev/null @@ -1,5 +0,0 @@ -SELECT - CAST(CAST(`bool_col` AS INT64) AS FLOAT64) AS `bool_col`, - CAST('1.34235e4' AS FLOAT64) AS `str_const`, - SAFE_CAST(SAFE_CAST(`bool_col` AS INT64) AS FLOAT64) AS `bool_w_safe` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_generic_ops/test_astype_from_json/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_generic_ops/test_astype_from_json/out.sql deleted file mode 100644 index c9450a92800..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_generic_ops/test_astype_from_json/out.sql +++ /dev/null @@ -1,7 +0,0 @@ -SELECT - SAFE.INT64(`json_col`) AS `int64_col`, - SAFE.FLOAT64(`json_col`) AS `float64_col`, - SAFE.BOOL(`json_col`) AS `bool_col`, - SAFE.STRING(`json_col`) AS `string_col`, - SAFE.INT64(`json_col`) AS `int64_w_safe` -FROM `bigframes-dev`.`sqlglot_test`.`json_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_generic_ops/test_astype_int/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_generic_ops/test_astype_int/out.sql deleted file mode 100644 index 8d44c674dc9..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_generic_ops/test_astype_int/out.sql +++ /dev/null @@ -1,11 +0,0 @@ -SELECT - UNIX_MICROS(CAST(`datetime_col` AS TIMESTAMP)) AS `datetime_col`, - UNIX_MICROS(SAFE_CAST(`datetime_col` AS TIMESTAMP)) AS `datetime_w_safe`, - TIME_DIFF(CAST(`time_col` AS TIME), '00:00:00', MICROSECOND) AS `time_col`, - TIME_DIFF(SAFE_CAST(`time_col` AS TIME), '00:00:00', MICROSECOND) AS `time_w_safe`, - UNIX_MICROS(`timestamp_col`) AS `timestamp_col`, - CAST(TRUNC(`numeric_col`) AS INT64) AS `numeric_col`, - CAST(TRUNC(`float64_col`) AS INT64) AS `float64_col`, - SAFE_CAST(TRUNC(`float64_col`) AS INT64) AS `float64_w_safe`, - CAST('100' AS INT64) AS `str_const` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_generic_ops/test_astype_json/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_generic_ops/test_astype_json/out.sql deleted file mode 100644 index b62cee83a91..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_generic_ops/test_astype_json/out.sql +++ /dev/null @@ -1,8 +0,0 @@ -SELECT - PARSE_JSON(CAST(`int64_col` AS STRING)) AS `int64_col`, - PARSE_JSON(CAST(`float64_col` AS STRING)) AS `float64_col`, - PARSE_JSON(CAST(`bool_col` AS STRING)) AS `bool_col`, - PARSE_JSON(`string_col`) AS `string_col`, - PARSE_JSON(CAST(`bool_col` AS STRING)) AS `bool_w_safe`, - SAFE.PARSE_JSON(`string_col`) AS `string_w_safe` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_generic_ops/test_astype_string/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_generic_ops/test_astype_string/out.sql deleted file mode 100644 index 174f18d9823..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_generic_ops/test_astype_string/out.sql +++ /dev/null @@ -1,5 +0,0 @@ -SELECT - CAST(`int64_col` AS STRING) AS `int64_col`, - INITCAP(CAST(`bool_col` AS STRING)) AS `bool_col`, - INITCAP(SAFE_CAST(`bool_col` AS STRING)) AS `bool_w_safe` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_generic_ops/test_astype_time_like/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_generic_ops/test_astype_time_like/out.sql deleted file mode 100644 index f50505592bb..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_generic_ops/test_astype_time_like/out.sql +++ /dev/null @@ -1,6 +0,0 @@ -SELECT - CAST(TIMESTAMP_MICROS(`int64_col`) AS DATETIME) AS `int64_to_datetime`, - CAST(TIMESTAMP_MICROS(`int64_col`) AS TIME) AS `int64_to_time`, - CAST(TIMESTAMP_MICROS(`int64_col`) AS TIMESTAMP) AS `int64_to_timestamp`, - SAFE_CAST(TIMESTAMP_MICROS(`int64_col`) AS TIME) AS `int64_to_time_safe` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_generic_ops/test_binary_remote_function_op/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_generic_ops/test_binary_remote_function_op/out.sql deleted file mode 100644 index 29f9d69cb25..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_generic_ops/test_binary_remote_function_op/out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - `my_project`.`my_dataset`.`my_routine`(`int64_col`, `float64_col`) AS `int64_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_generic_ops/test_case_when_op/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_generic_ops/test_case_when_op/out.sql deleted file mode 100644 index 58e901fecc0..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_generic_ops/test_case_when_op/out.sql +++ /dev/null @@ -1,13 +0,0 @@ -SELECT - CASE WHEN `bool_col` THEN `int64_col` END AS `single_case`, - CASE WHEN `bool_col` THEN `int64_col` WHEN `bool_col` THEN `int64_too` END AS `double_case`, - CASE WHEN `bool_col` THEN `bool_col` WHEN `bool_col` THEN `bool_col` END AS `bool_types_case`, - CASE - WHEN `bool_col` - THEN `int64_col` - WHEN `bool_col` - THEN CAST(`bool_col` AS INT64) - WHEN `bool_col` - THEN `float64_col` - END AS `mixed_types_cast` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_generic_ops/test_clip/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_generic_ops/test_clip/out.sql deleted file mode 100644 index bbfeb304181..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_generic_ops/test_clip/out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - GREATEST(LEAST(`rowindex`, `int64_too`), `int64_col`) AS `result_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_generic_ops/test_coalesce/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_generic_ops/test_coalesce/out.sql deleted file mode 100644 index 4f88ec71d88..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_generic_ops/test_coalesce/out.sql +++ /dev/null @@ -1,4 +0,0 @@ -SELECT - `int64_col`, - COALESCE(`int64_too`, `int64_col`) AS `int64_too` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_generic_ops/test_fillna/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_generic_ops/test_fillna/out.sql deleted file mode 100644 index ae6f975da5a..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_generic_ops/test_fillna/out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - COALESCE(`int64_col`, `float64_col`) AS `int64_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_generic_ops/test_hash/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_generic_ops/test_hash/out.sql deleted file mode 100644 index b1afe9db39b..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_generic_ops/test_hash/out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - FARM_FINGERPRINT(`string_col`) AS `string_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_generic_ops/test_invert/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_generic_ops/test_invert/out.sql deleted file mode 100644 index 5cd1b15a776..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_generic_ops/test_invert/out.sql +++ /dev/null @@ -1,11 +0,0 @@ -SELECT - ~( - `int64_col` - ) AS `int64_col`, - ~( - `bytes_col` - ) AS `bytes_col`, - NOT ( - `bool_col` - ) AS `bool_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_generic_ops/test_isnull/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_generic_ops/test_isnull/out.sql deleted file mode 100644 index cfe38ae3600..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_generic_ops/test_isnull/out.sql +++ /dev/null @@ -1,5 +0,0 @@ -SELECT - ( - `float64_col` - ) IS NULL AS `float64_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_generic_ops/test_map/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_generic_ops/test_map/out.sql deleted file mode 100644 index 3b1d0446b3b..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_generic_ops/test_map/out.sql +++ /dev/null @@ -1,9 +0,0 @@ -SELECT - CASE - WHEN `string_col` = 'value1' - THEN 'mapped1' - WHEN `string_col` IS NULL - THEN 'UNKNOWN' - ELSE `string_col` - END AS `string_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_generic_ops/test_nary_remote_function_op/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_generic_ops/test_nary_remote_function_op/out.sql deleted file mode 100644 index a1977d809f7..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_generic_ops/test_nary_remote_function_op/out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - `my_project`.`my_dataset`.`my_routine`(`int64_col`, `float64_col`, `string_col`) AS `int64_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_generic_ops/test_notnull/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_generic_ops/test_notnull/out.sql deleted file mode 100644 index 97b9f54f429..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_generic_ops/test_notnull/out.sql +++ /dev/null @@ -1,5 +0,0 @@ -SELECT - ( - `float64_col` - ) IS NOT NULL AS `float64_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_generic_ops/test_remote_function_op/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_generic_ops/test_remote_function_op/out.sql deleted file mode 100644 index a1977d809f7..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_generic_ops/test_remote_function_op/out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - `my_project`.`my_dataset`.`my_routine`(`int64_col`, `float64_col`, `string_col`) AS `int64_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_generic_ops/test_row_key/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_generic_ops/test_row_key/out.sql deleted file mode 100644 index f5bf9b3b6ee..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_generic_ops/test_row_key/out.sql +++ /dev/null @@ -1,46 +0,0 @@ -SELECT - CONCAT( - CAST(FARM_FINGERPRINT( - CONCAT( - CONCAT('\\', REPLACE(COALESCE(CAST(`rowindex` AS STRING), ''), '\\', '\\\\')), - CONCAT('\\', REPLACE(COALESCE(CAST(`bool_col` AS STRING), ''), '\\', '\\\\')), - CONCAT('\\', REPLACE(COALESCE(CAST(`bytes_col` AS STRING), ''), '\\', '\\\\')), - CONCAT('\\', REPLACE(COALESCE(CAST(`date_col` AS STRING), ''), '\\', '\\\\')), - CONCAT('\\', REPLACE(COALESCE(CAST(`datetime_col` AS STRING), ''), '\\', '\\\\')), - CONCAT('\\', REPLACE(COALESCE(ST_ASTEXT(`geography_col`), ''), '\\', '\\\\')), - CONCAT('\\', REPLACE(COALESCE(CAST(`int64_col` AS STRING), ''), '\\', '\\\\')), - CONCAT('\\', REPLACE(COALESCE(CAST(`int64_too` AS STRING), ''), '\\', '\\\\')), - CONCAT('\\', REPLACE(COALESCE(CAST(`numeric_col` AS STRING), ''), '\\', '\\\\')), - CONCAT('\\', REPLACE(COALESCE(CAST(`float64_col` AS STRING), ''), '\\', '\\\\')), - CONCAT('\\', REPLACE(COALESCE(CAST(`rowindex` AS STRING), ''), '\\', '\\\\')), - CONCAT('\\', REPLACE(COALESCE(CAST(`rowindex_2` AS STRING), ''), '\\', '\\\\')), - CONCAT('\\', REPLACE(COALESCE(`string_col`, ''), '\\', '\\\\')), - CONCAT('\\', REPLACE(COALESCE(CAST(`time_col` AS STRING), ''), '\\', '\\\\')), - CONCAT('\\', REPLACE(COALESCE(CAST(`timestamp_col` AS STRING), ''), '\\', '\\\\')), - CONCAT('\\', REPLACE(COALESCE(CAST(`duration_col` AS STRING), ''), '\\', '\\\\')) - ) - ) AS STRING), - CAST(FARM_FINGERPRINT( - CONCAT( - CONCAT('\\', REPLACE(COALESCE(CAST(`rowindex` AS STRING), ''), '\\', '\\\\')), - CONCAT('\\', REPLACE(COALESCE(CAST(`bool_col` AS STRING), ''), '\\', '\\\\')), - CONCAT('\\', REPLACE(COALESCE(CAST(`bytes_col` AS STRING), ''), '\\', '\\\\')), - CONCAT('\\', REPLACE(COALESCE(CAST(`date_col` AS STRING), ''), '\\', '\\\\')), - CONCAT('\\', REPLACE(COALESCE(CAST(`datetime_col` AS STRING), ''), '\\', '\\\\')), - CONCAT('\\', REPLACE(COALESCE(ST_ASTEXT(`geography_col`), ''), '\\', '\\\\')), - CONCAT('\\', REPLACE(COALESCE(CAST(`int64_col` AS STRING), ''), '\\', '\\\\')), - CONCAT('\\', REPLACE(COALESCE(CAST(`int64_too` AS STRING), ''), '\\', '\\\\')), - CONCAT('\\', REPLACE(COALESCE(CAST(`numeric_col` AS STRING), ''), '\\', '\\\\')), - CONCAT('\\', REPLACE(COALESCE(CAST(`float64_col` AS STRING), ''), '\\', '\\\\')), - CONCAT('\\', REPLACE(COALESCE(CAST(`rowindex` AS STRING), ''), '\\', '\\\\')), - CONCAT('\\', REPLACE(COALESCE(CAST(`rowindex_2` AS STRING), ''), '\\', '\\\\')), - CONCAT('\\', REPLACE(COALESCE(`string_col`, ''), '\\', '\\\\')), - CONCAT('\\', REPLACE(COALESCE(CAST(`time_col` AS STRING), ''), '\\', '\\\\')), - CONCAT('\\', REPLACE(COALESCE(CAST(`timestamp_col` AS STRING), ''), '\\', '\\\\')), - CONCAT('\\', REPLACE(COALESCE(CAST(`duration_col` AS STRING), ''), '\\', '\\\\')), - '_' - ) - ) AS STRING), - CAST(RAND() AS STRING) - ) AS `row_key` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_generic_ops/test_sql_scalar_op/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_generic_ops/test_sql_scalar_op/out.sql deleted file mode 100644 index 8f50ff28ca4..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_generic_ops/test_sql_scalar_op/out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - CAST(`bool_col` AS INT64) + BYTE_LENGTH(`bytes_col`) AS `bool_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_generic_ops/test_to_json/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_generic_ops/test_to_json/out.sql deleted file mode 100644 index 86d6f0e9fbb..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_generic_ops/test_to_json/out.sql +++ /dev/null @@ -1,8 +0,0 @@ -SELECT - IF(`int64_col` IS NULL, NULL, TO_JSON(`int64_col`)) AS `int64_col`, - IF(`float64_col` IS NULL, NULL, TO_JSON(`float64_col`)) AS `float64_col`, - IF(`bool_col` IS NULL, NULL, TO_JSON(`bool_col`)) AS `bool_col`, - SAFE.PARSE_JSON(`string_col`) AS `string_col`, - IF(`bool_col` IS NULL, NULL, TO_JSON(`bool_col`)) AS `bool_w_safe`, - SAFE.PARSE_JSON(`string_col`) AS `string_w_safe` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_generic_ops/test_where/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_generic_ops/test_where/out.sql deleted file mode 100644 index 1ca3b009898..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_generic_ops/test_where/out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - IF(`bool_col`, `int64_col`, `float64_col`) AS `result_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_geo_ops/test_geo_area/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_geo_ops/test_geo_area/out.sql deleted file mode 100644 index 78c786b036e..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_geo_ops/test_geo_area/out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - ST_AREA(`geography_col`) AS `geography_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_geo_ops/test_geo_st_astext/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_geo_ops/test_geo_st_astext/out.sql deleted file mode 100644 index 526c0c37d7e..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_geo_ops/test_geo_st_astext/out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - ST_ASTEXT(`geography_col`) AS `geography_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_geo_ops/test_geo_st_boundary/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_geo_ops/test_geo_st_boundary/out.sql deleted file mode 100644 index 4bf43469cf5..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_geo_ops/test_geo_st_boundary/out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - ST_BOUNDARY(`geography_col`) AS `geography_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_geo_ops/test_geo_st_buffer/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_geo_ops/test_geo_st_buffer/out.sql deleted file mode 100644 index 40669569fbb..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_geo_ops/test_geo_st_buffer/out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - ST_BUFFER(`geography_col`, 1.0, 8.0, FALSE) AS `geography_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_geo_ops/test_geo_st_centroid/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_geo_ops/test_geo_st_centroid/out.sql deleted file mode 100644 index accd33bd627..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_geo_ops/test_geo_st_centroid/out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - ST_CENTROID(`geography_col`) AS `geography_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_geo_ops/test_geo_st_convexhull/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_geo_ops/test_geo_st_convexhull/out.sql deleted file mode 100644 index e4a718d42a9..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_geo_ops/test_geo_st_convexhull/out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - ST_CONVEXHULL(`geography_col`) AS `geography_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_geo_ops/test_geo_st_difference/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_geo_ops/test_geo_st_difference/out.sql deleted file mode 100644 index 2a17ef1c7c8..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_geo_ops/test_geo_st_difference/out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - ST_DIFFERENCE(`geography_col`, `geography_col`) AS `geography_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_geo_ops/test_geo_st_distance/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_geo_ops/test_geo_st_distance/out.sql deleted file mode 100644 index 4c55ddd0824..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_geo_ops/test_geo_st_distance/out.sql +++ /dev/null @@ -1,4 +0,0 @@ -SELECT - ST_DISTANCE(`geography_col`, `geography_col`, TRUE) AS `spheroid`, - ST_DISTANCE(`geography_col`, `geography_col`, FALSE) AS `no_spheroid` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_geo_ops/test_geo_st_geogfromtext/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_geo_ops/test_geo_st_geogfromtext/out.sql deleted file mode 100644 index db62766d4c9..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_geo_ops/test_geo_st_geogfromtext/out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - SAFE.ST_GEOGFROMTEXT(`string_col`) AS `string_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_geo_ops/test_geo_st_geogpoint/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_geo_ops/test_geo_st_geogpoint/out.sql deleted file mode 100644 index 3299ef0bd2d..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_geo_ops/test_geo_st_geogpoint/out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - ST_GEOGPOINT(`rowindex`, `rowindex_2`) AS `rowindex` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_geo_ops/test_geo_st_intersection/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_geo_ops/test_geo_st_intersection/out.sql deleted file mode 100644 index a615ddf042e..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_geo_ops/test_geo_st_intersection/out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - ST_INTERSECTION(`geography_col`, `geography_col`) AS `geography_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_geo_ops/test_geo_st_isclosed/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_geo_ops/test_geo_st_isclosed/out.sql deleted file mode 100644 index 4f04e70b569..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_geo_ops/test_geo_st_isclosed/out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - ST_ISCLOSED(`geography_col`) AS `geography_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_geo_ops/test_geo_st_length/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_geo_ops/test_geo_st_length/out.sql deleted file mode 100644 index ee64b20ca46..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_geo_ops/test_geo_st_length/out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - ST_LENGTH(`geography_col`) AS `geography_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_geo_ops/test_geo_x/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_geo_ops/test_geo_x/out.sql deleted file mode 100644 index c1ab623d9a1..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_geo_ops/test_geo_x/out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - ST_X(`geography_col`) AS `geography_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_geo_ops/test_geo_y/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_geo_ops/test_geo_y/out.sql deleted file mode 100644 index e7575606e6e..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_geo_ops/test_geo_y/out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - ST_Y(`geography_col`) AS `geography_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_json_ops/test_json_extract/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_json_ops/test_json_extract/out.sql deleted file mode 100644 index 7a7ad2f394d..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_json_ops/test_json_extract/out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - JSON_EXTRACT(`json_col`, '$') AS `json_col` -FROM `bigframes-dev`.`sqlglot_test`.`json_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_json_ops/test_json_extract_array/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_json_ops/test_json_extract_array/out.sql deleted file mode 100644 index f2c4cd72985..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_json_ops/test_json_extract_array/out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - JSON_EXTRACT_ARRAY(`json_col`, '$') AS `json_col` -FROM `bigframes-dev`.`sqlglot_test`.`json_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_json_ops/test_json_extract_string_array/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_json_ops/test_json_extract_string_array/out.sql deleted file mode 100644 index 61e8bae8a32..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_json_ops/test_json_extract_string_array/out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - JSON_EXTRACT_STRING_ARRAY(`json_col`, '$') AS `json_col` -FROM `bigframes-dev`.`sqlglot_test`.`json_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_json_ops/test_json_keys/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_json_ops/test_json_keys/out.sql deleted file mode 100644 index 78004c1180c..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_json_ops/test_json_keys/out.sql +++ /dev/null @@ -1,4 +0,0 @@ -SELECT - JSON_KEYS(`json_col`, NULL) AS `json_keys`, - JSON_KEYS(`json_col`, 2) AS `json_keys_w_max_depth` -FROM `bigframes-dev`.`sqlglot_test`.`json_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_json_ops/test_json_query/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_json_ops/test_json_query/out.sql deleted file mode 100644 index 8aa312e9d75..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_json_ops/test_json_query/out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - JSON_QUERY(`json_col`, '$') AS `json_col` -FROM `bigframes-dev`.`sqlglot_test`.`json_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_json_ops/test_json_query_array/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_json_ops/test_json_query_array/out.sql deleted file mode 100644 index 898068fe595..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_json_ops/test_json_query_array/out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - JSON_QUERY_ARRAY(`json_col`, '$') AS `json_col` -FROM `bigframes-dev`.`sqlglot_test`.`json_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_json_ops/test_json_set/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_json_ops/test_json_set/out.sql deleted file mode 100644 index e515d5fdc3b..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_json_ops/test_json_set/out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - JSON_SET(`json_col`, '$.a', 100) AS `json_col` -FROM `bigframes-dev`.`sqlglot_test`.`json_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_json_ops/test_json_value/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_json_ops/test_json_value/out.sql deleted file mode 100644 index c9a73ae1942..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_json_ops/test_json_value/out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - JSON_VALUE(`json_col`, '$') AS `json_col` -FROM `bigframes-dev`.`sqlglot_test`.`json_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_json_ops/test_json_value_array/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_json_ops/test_json_value_array/out.sql deleted file mode 100644 index 8250c02934e..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_json_ops/test_json_value_array/out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - JSON_VALUE_ARRAY(`json_col`, '$') AS `json_col` -FROM `bigframes-dev`.`sqlglot_test`.`json_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_json_ops/test_parse_json/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_json_ops/test_parse_json/out.sql deleted file mode 100644 index 55a195edf20..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_json_ops/test_parse_json/out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - PARSE_JSON(`string_col`) AS `string_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_json_ops/test_to_json/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_json_ops/test_to_json/out.sql deleted file mode 100644 index 0545577e27f..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_json_ops/test_to_json/out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - SAFE.PARSE_JSON(`string_col`) AS `string_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_json_ops/test_to_json_string/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_json_ops/test_to_json_string/out.sql deleted file mode 100644 index 62886c26ed9..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_json_ops/test_to_json_string/out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - TO_JSON_STRING(`json_col`) AS `json_col` -FROM `bigframes-dev`.`sqlglot_test`.`json_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_literals/test_float_literals/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_literals/test_float_literals/out.sql deleted file mode 100644 index 030e733edd7..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_literals/test_float_literals/out.sql +++ /dev/null @@ -1,8 +0,0 @@ -SELECT - CAST('Infinity' AS FLOAT64) AS `inf`, - CAST('-Infinity' AS FLOAT64) AS `ninf`, - NULL AS `nan`, - -0.0 AS `neg_zero`, - 1e-05 AS `0.00001`, - 1e-10 AS `1E-10` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_abs/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_abs/out.sql deleted file mode 100644 index bc53c60895a..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_abs/out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - ABS(`float64_col`) AS `float64_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_add_numeric/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_add_numeric/out.sql deleted file mode 100644 index 3aa06fe16e3..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_add_numeric/out.sql +++ /dev/null @@ -1,10 +0,0 @@ -SELECT - `rowindex`, - `int64_col`, - `bool_col`, - `int64_col` + `int64_col` AS `int_add_int`, - `int64_col` + 1 AS `int_add_1`, - NULL AS `int_add_null`, - `int64_col` + CAST(`bool_col` AS INT64) AS `int_add_bool`, - CAST(`bool_col` AS INT64) + `int64_col` AS `bool_add_int` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_add_string/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_add_string/out.sql deleted file mode 100644 index cf4051464b7..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_add_string/out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - CONCAT(`string_col`, 'a') AS `string_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_add_timedelta/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_add_timedelta/out.sql deleted file mode 100644 index b1ccf096cfa..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_add_timedelta/out.sql +++ /dev/null @@ -1,10 +0,0 @@ -SELECT - `rowindex`, - `timestamp_col`, - `date_col`, - TIMESTAMP_ADD(CAST(`date_col` AS DATETIME), INTERVAL 86400000000 MICROSECOND) AS `date_add_timedelta`, - TIMESTAMP_ADD(`timestamp_col`, INTERVAL 86400000000 MICROSECOND) AS `timestamp_add_timedelta`, - TIMESTAMP_ADD(CAST(`date_col` AS DATETIME), INTERVAL 86400000000 MICROSECOND) AS `timedelta_add_date`, - TIMESTAMP_ADD(`timestamp_col`, INTERVAL 86400000000 MICROSECOND) AS `timedelta_add_timestamp`, - 172800000000 AS `timedelta_add_timedelta` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_arccos/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_arccos/out.sql deleted file mode 100644 index d00086dfde8..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_arccos/out.sql +++ /dev/null @@ -1,7 +0,0 @@ -SELECT - CASE - WHEN ABS(`float64_col`) > 1 - THEN CAST('NaN' AS FLOAT64) - ELSE ACOS(`float64_col`) - END AS `float64_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_arccosh/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_arccosh/out.sql deleted file mode 100644 index f1a04757a0a..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_arccosh/out.sql +++ /dev/null @@ -1,7 +0,0 @@ -SELECT - CASE - WHEN `float64_col` < 1 - THEN CAST('NaN' AS FLOAT64) - ELSE ACOSH(`float64_col`) - END AS `float64_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_arcsin/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_arcsin/out.sql deleted file mode 100644 index eff8f1f5007..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_arcsin/out.sql +++ /dev/null @@ -1,7 +0,0 @@ -SELECT - CASE - WHEN ABS(`float64_col`) > 1 - THEN CAST('NaN' AS FLOAT64) - ELSE ASIN(`float64_col`) - END AS `float64_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_arcsinh/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_arcsinh/out.sql deleted file mode 100644 index 557407f09ee..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_arcsinh/out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - ASINH(`float64_col`) AS `float64_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_arctan/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_arctan/out.sql deleted file mode 100644 index d99b62f2cdb..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_arctan/out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - ATAN(`float64_col`) AS `float64_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_arctan2/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_arctan2/out.sql deleted file mode 100644 index 463896e981f..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_arctan2/out.sql +++ /dev/null @@ -1,4 +0,0 @@ -SELECT - ATAN2(`int64_col`, `float64_col`) AS `int64_col`, - ATAN2(CAST(`bool_col` AS INT64), `float64_col`) AS `bool_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_arctanh/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_arctanh/out.sql deleted file mode 100644 index 9b016071480..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_arctanh/out.sql +++ /dev/null @@ -1,9 +0,0 @@ -SELECT - CASE - WHEN ABS(`float64_col`) < 1 - THEN ATANH(`float64_col`) - WHEN ABS(`float64_col`) > 1 - THEN CAST('NaN' AS FLOAT64) - ELSE CAST('Infinity' AS FLOAT64) * `float64_col` - END AS `float64_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_ceil/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_ceil/out.sql deleted file mode 100644 index f69ae7f2760..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_ceil/out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - CEIL(`float64_col`) AS `float64_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_cos/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_cos/out.sql deleted file mode 100644 index 427dfbb9a93..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_cos/out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - COS(`float64_col`) AS `float64_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_cosh/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_cosh/out.sql deleted file mode 100644 index 0f119c254f0..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_cosh/out.sql +++ /dev/null @@ -1,7 +0,0 @@ -SELECT - CASE - WHEN ABS(`float64_col`) > 709.78 - THEN CAST('Infinity' AS FLOAT64) - ELSE COSH(`float64_col`) - END AS `float64_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_cosine_distance/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_cosine_distance/out.sql deleted file mode 100644 index 1c482fc8a78..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_cosine_distance/out.sql +++ /dev/null @@ -1,4 +0,0 @@ -SELECT - ML.DISTANCE(`int_list_col`, `int_list_col`, 'COSINE') AS `int_list_col`, - ML.DISTANCE(`float_list_col`, `float_list_col`, 'COSINE') AS `float_list_col` -FROM `bigframes-dev`.`sqlglot_test`.`repeated_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_div_numeric/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_div_numeric/out.sql deleted file mode 100644 index e2ccf96410a..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_div_numeric/out.sql +++ /dev/null @@ -1,15 +0,0 @@ -SELECT - `rowindex`, - `int64_col`, - `bool_col`, - `float64_col`, - IEEE_DIVIDE(`int64_col`, `int64_col`) AS `int_div_int`, - IEEE_DIVIDE(`int64_col`, 1) AS `int_div_1`, - IEEE_DIVIDE(`int64_col`, 0.0) AS `int_div_0`, - NULL AS `int_div_null`, - IEEE_DIVIDE(`int64_col`, `float64_col`) AS `int_div_float`, - IEEE_DIVIDE(`float64_col`, `int64_col`) AS `float_div_int`, - IEEE_DIVIDE(`float64_col`, 0.0) AS `float_div_0`, - IEEE_DIVIDE(`int64_col`, CAST(`bool_col` AS INT64)) AS `int_div_bool`, - IEEE_DIVIDE(CAST(`bool_col` AS INT64), `int64_col`) AS `bool_div_int` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_div_timedelta/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_div_timedelta/out.sql deleted file mode 100644 index a733ed81278..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_div_timedelta/out.sql +++ /dev/null @@ -1,10 +0,0 @@ -SELECT - `rowindex`, - `timestamp_col`, - `int64_col`, - CAST(IF( - IEEE_DIVIDE(86400000000, `int64_col`) > 0, - FLOOR(IEEE_DIVIDE(86400000000, `int64_col`)), - CEIL(IEEE_DIVIDE(86400000000, `int64_col`)) - ) AS INT64) AS `timedelta_div_numeric` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_euclidean_distance/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_euclidean_distance/out.sql deleted file mode 100644 index 349d78584a9..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_euclidean_distance/out.sql +++ /dev/null @@ -1,4 +0,0 @@ -SELECT - ML.DISTANCE(`int_list_col`, `int_list_col`, 'EUCLIDEAN') AS `int_list_col`, - ML.DISTANCE(`numeric_list_col`, `numeric_list_col`, 'EUCLIDEAN') AS `numeric_list_col` -FROM `bigframes-dev`.`sqlglot_test`.`repeated_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_exp/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_exp/out.sql deleted file mode 100644 index 178282ca087..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_exp/out.sql +++ /dev/null @@ -1,7 +0,0 @@ -SELECT - CASE - WHEN `float64_col` > 709.78 - THEN CAST('Infinity' AS FLOAT64) - ELSE EXP(`float64_col`) - END AS `float64_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_expm1/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_expm1/out.sql deleted file mode 100644 index 6c896448f24..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_expm1/out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - IF(`float64_col` > 709.78, CAST('Infinity' AS FLOAT64), EXP(`float64_col`) - 1) AS `float64_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_floor/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_floor/out.sql deleted file mode 100644 index 31b715623cb..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_floor/out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - FLOOR(`float64_col`) AS `float64_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_floordiv_numeric/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_floordiv_numeric/out.sql deleted file mode 100644 index 8307b1b8ada..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_floordiv_numeric/out.sql +++ /dev/null @@ -1,48 +0,0 @@ -SELECT - `rowindex`, - `int64_col`, - `bool_col`, - `float64_col`, - CASE - WHEN `int64_col` = CAST(0 AS INT64) - THEN CAST(0 AS INT64) * `int64_col` - ELSE CAST(FLOOR(IEEE_DIVIDE(`int64_col`, `int64_col`)) AS INT64) - END AS `int_div_int`, - CASE - WHEN 1 = CAST(0 AS INT64) - THEN CAST(0 AS INT64) * `int64_col` - ELSE CAST(FLOOR(IEEE_DIVIDE(`int64_col`, 1)) AS INT64) - END AS `int_div_1`, - CASE - WHEN 0.0 = CAST(0 AS INT64) - THEN CAST('Infinity' AS FLOAT64) * `int64_col` - ELSE CAST(FLOOR(IEEE_DIVIDE(`int64_col`, 0.0)) AS INT64) - END AS `int_div_0`, - NULL AS `int_div_null`, - CASE - WHEN `float64_col` = CAST(0 AS INT64) - THEN CAST('Infinity' AS FLOAT64) * `int64_col` - ELSE CAST(FLOOR(IEEE_DIVIDE(`int64_col`, `float64_col`)) AS INT64) - END AS `int_div_float`, - CASE - WHEN `int64_col` = CAST(0 AS INT64) - THEN CAST('Infinity' AS FLOAT64) * `float64_col` - ELSE CAST(FLOOR(IEEE_DIVIDE(`float64_col`, `int64_col`)) AS INT64) - END AS `float_div_int`, - CASE - WHEN 0.0 = CAST(0 AS INT64) - THEN CAST('Infinity' AS FLOAT64) * `float64_col` - ELSE CAST(FLOOR(IEEE_DIVIDE(`float64_col`, 0.0)) AS INT64) - END AS `float_div_0`, - NULL AS `float_div_null`, - CASE - WHEN CAST(`bool_col` AS INT64) = CAST(0 AS INT64) - THEN CAST(0 AS INT64) * `int64_col` - ELSE CAST(FLOOR(IEEE_DIVIDE(`int64_col`, CAST(`bool_col` AS INT64))) AS INT64) - END AS `int_div_bool`, - CASE - WHEN `int64_col` = CAST(0 AS INT64) - THEN CAST(0 AS INT64) * CAST(`bool_col` AS INT64) - ELSE CAST(FLOOR(IEEE_DIVIDE(CAST(`bool_col` AS INT64), `int64_col`)) AS INT64) - END AS `bool_div_int` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_floordiv_timedelta/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_floordiv_timedelta/out.sql deleted file mode 100644 index 4d978991eb5..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_floordiv_timedelta/out.sql +++ /dev/null @@ -1,6 +0,0 @@ -SELECT - `rowindex`, - `timestamp_col`, - `date_col`, - 43200000000 AS `timedelta_div_numeric` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_isfinite/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_isfinite/out.sql deleted file mode 100644 index 54cbe2dd689..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_isfinite/out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - NOT IS_INF(`float64_col`) OR IS_NAN(`float64_col`) AS `float64_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_ln/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_ln/out.sql deleted file mode 100644 index 53ab88b7fc3..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_ln/out.sql +++ /dev/null @@ -1,11 +0,0 @@ -SELECT - CASE - WHEN `float64_col` IS NULL - THEN NULL - WHEN `float64_col` > 0 - THEN LN(`float64_col`) - WHEN `float64_col` < 0 - THEN CAST('NaN' AS FLOAT64) - ELSE CAST('-Infinity' AS FLOAT64) - END AS `float64_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_log10/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_log10/out.sql deleted file mode 100644 index 2037649332f..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_log10/out.sql +++ /dev/null @@ -1,11 +0,0 @@ -SELECT - CASE - WHEN `float64_col` IS NULL - THEN NULL - WHEN `float64_col` > 0 - THEN LOG(`float64_col`, 10) - WHEN `float64_col` < 0 - THEN CAST('NaN' AS FLOAT64) - ELSE CAST('-Infinity' AS FLOAT64) - END AS `float64_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_log1p/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_log1p/out.sql deleted file mode 100644 index f7ddf4c223f..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_log1p/out.sql +++ /dev/null @@ -1,11 +0,0 @@ -SELECT - CASE - WHEN `float64_col` IS NULL - THEN NULL - WHEN `float64_col` > -1 - THEN LN(1 + `float64_col`) - WHEN `float64_col` < -1 - THEN CAST('NaN' AS FLOAT64) - ELSE CAST('-Infinity' AS FLOAT64) - END AS `float64_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_manhattan_distance/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_manhattan_distance/out.sql deleted file mode 100644 index b6132a9fd6e..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_manhattan_distance/out.sql +++ /dev/null @@ -1,4 +0,0 @@ -SELECT - ML.DISTANCE(`float_list_col`, `float_list_col`, 'MANHATTAN') AS `float_list_col`, - ML.DISTANCE(`numeric_list_col`, `numeric_list_col`, 'MANHATTAN') AS `numeric_list_col` -FROM `bigframes-dev`.`sqlglot_test`.`repeated_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_mod_numeric/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_mod_numeric/out.sql deleted file mode 100644 index 78107415b43..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_mod_numeric/out.sql +++ /dev/null @@ -1,194 +0,0 @@ -SELECT - `rowindex`, - `int64_col`, - `float64_col`, - CASE - WHEN `int64_col` = CAST(0 AS INT64) - THEN CAST(0 AS INT64) * `int64_col` - WHEN `int64_col` < CAST(0 AS INT64) - AND ( - MOD(`int64_col`, `int64_col`) - ) > CAST(0 AS INT64) - THEN `int64_col` + ( - MOD(`int64_col`, `int64_col`) - ) - WHEN `int64_col` > CAST(0 AS INT64) - AND ( - MOD(`int64_col`, `int64_col`) - ) < CAST(0 AS INT64) - THEN `int64_col` + ( - MOD(`int64_col`, `int64_col`) - ) - ELSE MOD(`int64_col`, `int64_col`) - END AS `int_mod_int`, - CASE - WHEN -( - `int64_col` - ) = CAST(0 AS INT64) - THEN CAST(0 AS INT64) * `int64_col` - WHEN -( - `int64_col` - ) < CAST(0 AS INT64) - AND ( - MOD(`int64_col`, -( - `int64_col` - )) - ) > CAST(0 AS INT64) - THEN -( - `int64_col` - ) + ( - MOD(`int64_col`, -( - `int64_col` - )) - ) - WHEN -( - `int64_col` - ) > CAST(0 AS INT64) - AND ( - MOD(`int64_col`, -( - `int64_col` - )) - ) < CAST(0 AS INT64) - THEN -( - `int64_col` - ) + ( - MOD(`int64_col`, -( - `int64_col` - )) - ) - ELSE MOD(`int64_col`, -( - `int64_col` - )) - END AS `int_mod_int_neg`, - CASE - WHEN 1 = CAST(0 AS INT64) - THEN CAST(0 AS INT64) * `int64_col` - WHEN 1 < CAST(0 AS INT64) AND ( - MOD(`int64_col`, 1) - ) > CAST(0 AS INT64) - THEN 1 + ( - MOD(`int64_col`, 1) - ) - WHEN 1 > CAST(0 AS INT64) AND ( - MOD(`int64_col`, 1) - ) < CAST(0 AS INT64) - THEN 1 + ( - MOD(`int64_col`, 1) - ) - ELSE MOD(`int64_col`, 1) - END AS `int_mod_1`, - CASE - WHEN 0 = CAST(0 AS INT64) - THEN CAST(0 AS INT64) * `int64_col` - WHEN 0 < CAST(0 AS INT64) AND ( - MOD(`int64_col`, 0) - ) > CAST(0 AS INT64) - THEN 0 + ( - MOD(`int64_col`, 0) - ) - WHEN 0 > CAST(0 AS INT64) AND ( - MOD(`int64_col`, 0) - ) < CAST(0 AS INT64) - THEN 0 + ( - MOD(`int64_col`, 0) - ) - ELSE MOD(`int64_col`, 0) - END AS `int_mod_0`, - CASE - WHEN CAST(`float64_col` AS BIGNUMERIC) = CAST(0 AS INT64) - THEN CAST('NaN' AS FLOAT64) * CAST(`float64_col` AS BIGNUMERIC) - WHEN CAST(`float64_col` AS BIGNUMERIC) < CAST(0 AS INT64) - AND ( - MOD(CAST(`float64_col` AS BIGNUMERIC), CAST(`float64_col` AS BIGNUMERIC)) - ) > CAST(0 AS INT64) - THEN CAST(`float64_col` AS BIGNUMERIC) + ( - MOD(CAST(`float64_col` AS BIGNUMERIC), CAST(`float64_col` AS BIGNUMERIC)) - ) - WHEN CAST(`float64_col` AS BIGNUMERIC) > CAST(0 AS INT64) - AND ( - MOD(CAST(`float64_col` AS BIGNUMERIC), CAST(`float64_col` AS BIGNUMERIC)) - ) < CAST(0 AS INT64) - THEN CAST(`float64_col` AS BIGNUMERIC) + ( - MOD(CAST(`float64_col` AS BIGNUMERIC), CAST(`float64_col` AS BIGNUMERIC)) - ) - ELSE MOD(CAST(`float64_col` AS BIGNUMERIC), CAST(`float64_col` AS BIGNUMERIC)) - END AS `float_mod_float`, - CASE - WHEN CAST(-( - `float64_col` - ) AS BIGNUMERIC) = CAST(0 AS INT64) - THEN CAST('NaN' AS FLOAT64) * CAST(`float64_col` AS BIGNUMERIC) - WHEN CAST(-( - `float64_col` - ) AS BIGNUMERIC) < CAST(0 AS INT64) - AND ( - MOD(CAST(`float64_col` AS BIGNUMERIC), CAST(-( - `float64_col` - ) AS BIGNUMERIC)) - ) > CAST(0 AS INT64) - THEN CAST(-( - `float64_col` - ) AS BIGNUMERIC) + ( - MOD(CAST(`float64_col` AS BIGNUMERIC), CAST(-( - `float64_col` - ) AS BIGNUMERIC)) - ) - WHEN CAST(-( - `float64_col` - ) AS BIGNUMERIC) > CAST(0 AS INT64) - AND ( - MOD(CAST(`float64_col` AS BIGNUMERIC), CAST(-( - `float64_col` - ) AS BIGNUMERIC)) - ) < CAST(0 AS INT64) - THEN CAST(-( - `float64_col` - ) AS BIGNUMERIC) + ( - MOD(CAST(`float64_col` AS BIGNUMERIC), CAST(-( - `float64_col` - ) AS BIGNUMERIC)) - ) - ELSE MOD(CAST(`float64_col` AS BIGNUMERIC), CAST(-( - `float64_col` - ) AS BIGNUMERIC)) - END AS `float_mod_float_neg`, - CASE - WHEN CAST(1 AS BIGNUMERIC) = CAST(0 AS INT64) - THEN CAST('NaN' AS FLOAT64) * CAST(`float64_col` AS BIGNUMERIC) - WHEN CAST(1 AS BIGNUMERIC) < CAST(0 AS INT64) - AND ( - MOD(CAST(`float64_col` AS BIGNUMERIC), CAST(1 AS BIGNUMERIC)) - ) > CAST(0 AS INT64) - THEN CAST(1 AS BIGNUMERIC) + ( - MOD(CAST(`float64_col` AS BIGNUMERIC), CAST(1 AS BIGNUMERIC)) - ) - WHEN CAST(1 AS BIGNUMERIC) > CAST(0 AS INT64) - AND ( - MOD(CAST(`float64_col` AS BIGNUMERIC), CAST(1 AS BIGNUMERIC)) - ) < CAST(0 AS INT64) - THEN CAST(1 AS BIGNUMERIC) + ( - MOD(CAST(`float64_col` AS BIGNUMERIC), CAST(1 AS BIGNUMERIC)) - ) - ELSE MOD(CAST(`float64_col` AS BIGNUMERIC), CAST(1 AS BIGNUMERIC)) - END AS `float_mod_1`, - CASE - WHEN CAST(0 AS BIGNUMERIC) = CAST(0 AS INT64) - THEN CAST('NaN' AS FLOAT64) * CAST(`float64_col` AS BIGNUMERIC) - WHEN CAST(0 AS BIGNUMERIC) < CAST(0 AS INT64) - AND ( - MOD(CAST(`float64_col` AS BIGNUMERIC), CAST(0 AS BIGNUMERIC)) - ) > CAST(0 AS INT64) - THEN CAST(0 AS BIGNUMERIC) + ( - MOD(CAST(`float64_col` AS BIGNUMERIC), CAST(0 AS BIGNUMERIC)) - ) - WHEN CAST(0 AS BIGNUMERIC) > CAST(0 AS INT64) - AND ( - MOD(CAST(`float64_col` AS BIGNUMERIC), CAST(0 AS BIGNUMERIC)) - ) < CAST(0 AS INT64) - THEN CAST(0 AS BIGNUMERIC) + ( - MOD(CAST(`float64_col` AS BIGNUMERIC), CAST(0 AS BIGNUMERIC)) - ) - ELSE MOD(CAST(`float64_col` AS BIGNUMERIC), CAST(0 AS BIGNUMERIC)) - END AS `float_mod_0`, - NULL AS `float_mod_null` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_mul_numeric/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_mul_numeric/out.sql deleted file mode 100644 index ebe8d571d65..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_mul_numeric/out.sql +++ /dev/null @@ -1,10 +0,0 @@ -SELECT - `rowindex`, - `int64_col`, - `bool_col`, - `int64_col` * `int64_col` AS `int_mul_int`, - `int64_col` * 1 AS `int_mul_1`, - NULL AS `int_mul_null`, - `int64_col` * CAST(`bool_col` AS INT64) AS `int_mul_bool`, - CAST(`bool_col` AS INT64) * `int64_col` AS `bool_mul_int` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_mul_timedelta/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_mul_timedelta/out.sql deleted file mode 100644 index 8285d1e7d4a..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_mul_timedelta/out.sql +++ /dev/null @@ -1,16 +0,0 @@ -SELECT - `rowindex`, - `timestamp_col`, - `int64_col`, - `duration_col`, - CAST(IF( - `duration_col` * `int64_col` > 0, - FLOOR(`duration_col` * `int64_col`), - CEIL(`duration_col` * `int64_col`) - ) AS INT64) AS `timedelta_mul_numeric`, - CAST(IF( - `int64_col` * `duration_col` > 0, - FLOOR(`int64_col` * `duration_col`), - CEIL(`int64_col` * `duration_col`) - ) AS INT64) AS `numeric_mul_timedelta` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_neg/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_neg/out.sql deleted file mode 100644 index 13a9f3f6734..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_neg/out.sql +++ /dev/null @@ -1,5 +0,0 @@ -SELECT - -( - `float64_col` - ) AS `float64_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_pos/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_pos/out.sql deleted file mode 100644 index 1890218bdd9..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_pos/out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - `float64_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_pow/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_pow/out.sql deleted file mode 100644 index 7202903ebe3..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_pow/out.sql +++ /dev/null @@ -1,247 +0,0 @@ -SELECT - `rowindex`, - `int64_col`, - `float64_col`, - CASE - WHEN `int64_col` <> 0 AND `int64_col` * LN(ABS(`int64_col`)) > 43.66827237527655 - THEN NULL - ELSE CAST(POWER(CAST(`int64_col` AS NUMERIC), `int64_col`) AS INT64) - END AS `int_pow_int`, - CASE - WHEN `float64_col` = CAST(0 AS INT64) - THEN 1 - WHEN `int64_col` = 1 - THEN 1 - WHEN `int64_col` = CAST(0 AS INT64) AND `float64_col` < CAST(0 AS INT64) - THEN CAST('Infinity' AS FLOAT64) - WHEN ABS(`int64_col`) = CAST('Infinity' AS FLOAT64) - THEN POWER( - `int64_col`, - CASE - WHEN ABS(`float64_col`) > 9007199254740992 - THEN CAST('Infinity' AS FLOAT64) * SIGN(`float64_col`) - ELSE `float64_col` - END - ) - WHEN ABS(`float64_col`) > 9007199254740992 - THEN POWER( - `int64_col`, - CASE - WHEN ABS(`float64_col`) > 9007199254740992 - THEN CAST('Infinity' AS FLOAT64) * SIGN(`float64_col`) - ELSE `float64_col` - END - ) - WHEN `int64_col` < CAST(0 AS INT64) - AND NOT ( - CAST(`float64_col` AS INT64) = `float64_col` - ) - THEN CAST('NaN' AS FLOAT64) - WHEN `int64_col` <> CAST(0 AS INT64) AND `float64_col` * LN(ABS(`int64_col`)) > 709.78 - THEN CAST('Infinity' AS FLOAT64) * CASE - WHEN `int64_col` < CAST(0 AS INT64) AND MOD(CAST(`float64_col` AS INT64), 2) = 1 - THEN -1 - ELSE 1 - END - ELSE POWER( - `int64_col`, - CASE - WHEN ABS(`float64_col`) > 9007199254740992 - THEN CAST('Infinity' AS FLOAT64) * SIGN(`float64_col`) - ELSE `float64_col` - END - ) - END AS `int_pow_float`, - CASE - WHEN `int64_col` = CAST(0 AS INT64) - THEN 1 - WHEN `float64_col` = 1 - THEN 1 - WHEN `float64_col` = CAST(0 AS INT64) AND `int64_col` < CAST(0 AS INT64) - THEN CAST('Infinity' AS FLOAT64) - WHEN ABS(`float64_col`) = CAST('Infinity' AS FLOAT64) - THEN POWER( - `float64_col`, - CASE - WHEN ABS(`int64_col`) > 9007199254740992 - THEN CAST('Infinity' AS FLOAT64) * SIGN(`int64_col`) - ELSE `int64_col` - END - ) - WHEN ABS(`int64_col`) > 9007199254740992 - THEN POWER( - `float64_col`, - CASE - WHEN ABS(`int64_col`) > 9007199254740992 - THEN CAST('Infinity' AS FLOAT64) * SIGN(`int64_col`) - ELSE `int64_col` - END - ) - WHEN `float64_col` < CAST(0 AS INT64) - AND NOT ( - CAST(`int64_col` AS INT64) = `int64_col` - ) - THEN CAST('NaN' AS FLOAT64) - WHEN `float64_col` <> CAST(0 AS INT64) - AND `int64_col` * LN(ABS(`float64_col`)) > 709.78 - THEN CAST('Infinity' AS FLOAT64) * CASE - WHEN `float64_col` < CAST(0 AS INT64) AND MOD(CAST(`int64_col` AS INT64), 2) = 1 - THEN -1 - ELSE 1 - END - ELSE POWER( - `float64_col`, - CASE - WHEN ABS(`int64_col`) > 9007199254740992 - THEN CAST('Infinity' AS FLOAT64) * SIGN(`int64_col`) - ELSE `int64_col` - END - ) - END AS `float_pow_int`, - CASE - WHEN `float64_col` = CAST(0 AS INT64) - THEN 1 - WHEN `float64_col` = 1 - THEN 1 - WHEN `float64_col` = CAST(0 AS INT64) AND `float64_col` < CAST(0 AS INT64) - THEN CAST('Infinity' AS FLOAT64) - WHEN ABS(`float64_col`) = CAST('Infinity' AS FLOAT64) - THEN POWER( - `float64_col`, - CASE - WHEN ABS(`float64_col`) > 9007199254740992 - THEN CAST('Infinity' AS FLOAT64) * SIGN(`float64_col`) - ELSE `float64_col` - END - ) - WHEN ABS(`float64_col`) > 9007199254740992 - THEN POWER( - `float64_col`, - CASE - WHEN ABS(`float64_col`) > 9007199254740992 - THEN CAST('Infinity' AS FLOAT64) * SIGN(`float64_col`) - ELSE `float64_col` - END - ) - WHEN `float64_col` < CAST(0 AS INT64) - AND NOT ( - CAST(`float64_col` AS INT64) = `float64_col` - ) - THEN CAST('NaN' AS FLOAT64) - WHEN `float64_col` <> CAST(0 AS INT64) - AND `float64_col` * LN(ABS(`float64_col`)) > 709.78 - THEN CAST('Infinity' AS FLOAT64) * CASE - WHEN `float64_col` < CAST(0 AS INT64) AND MOD(CAST(`float64_col` AS INT64), 2) = 1 - THEN -1 - ELSE 1 - END - ELSE POWER( - `float64_col`, - CASE - WHEN ABS(`float64_col`) > 9007199254740992 - THEN CAST('Infinity' AS FLOAT64) * SIGN(`float64_col`) - ELSE `float64_col` - END - ) - END AS `float_pow_float`, - CASE - WHEN `int64_col` <> 0 AND 0 * LN(ABS(`int64_col`)) > 43.66827237527655 - THEN NULL - ELSE CAST(POWER(CAST(`int64_col` AS NUMERIC), 0) AS INT64) - END AS `int_pow_0`, - CASE - WHEN 0 = CAST(0 AS INT64) - THEN 1 - WHEN `float64_col` = 1 - THEN 1 - WHEN `float64_col` = CAST(0 AS INT64) AND 0 < CAST(0 AS INT64) - THEN CAST('Infinity' AS FLOAT64) - WHEN ABS(`float64_col`) = CAST('Infinity' AS FLOAT64) - THEN POWER( - `float64_col`, - CASE - WHEN ABS(0) > 9007199254740992 - THEN CAST('Infinity' AS FLOAT64) * SIGN(0) - ELSE 0 - END - ) - WHEN ABS(0) > 9007199254740992 - THEN POWER( - `float64_col`, - CASE - WHEN ABS(0) > 9007199254740992 - THEN CAST('Infinity' AS FLOAT64) * SIGN(0) - ELSE 0 - END - ) - WHEN `float64_col` < CAST(0 AS INT64) AND NOT ( - CAST(0 AS INT64) = 0 - ) - THEN CAST('NaN' AS FLOAT64) - WHEN `float64_col` <> CAST(0 AS INT64) AND 0 * LN(ABS(`float64_col`)) > 709.78 - THEN CAST('Infinity' AS FLOAT64) * CASE - WHEN `float64_col` < CAST(0 AS INT64) AND MOD(CAST(0 AS INT64), 2) = 1 - THEN -1 - ELSE 1 - END - ELSE POWER( - `float64_col`, - CASE - WHEN ABS(0) > 9007199254740992 - THEN CAST('Infinity' AS FLOAT64) * SIGN(0) - ELSE 0 - END - ) - END AS `float_pow_0`, - CASE - WHEN `int64_col` <> 0 AND 1 * LN(ABS(`int64_col`)) > 43.66827237527655 - THEN NULL - ELSE CAST(POWER(CAST(`int64_col` AS NUMERIC), 1) AS INT64) - END AS `int_pow_1`, - CASE - WHEN 1 = CAST(0 AS INT64) - THEN 1 - WHEN `float64_col` = 1 - THEN 1 - WHEN `float64_col` = CAST(0 AS INT64) AND 1 < CAST(0 AS INT64) - THEN CAST('Infinity' AS FLOAT64) - WHEN ABS(`float64_col`) = CAST('Infinity' AS FLOAT64) - THEN POWER( - `float64_col`, - CASE - WHEN ABS(1) > 9007199254740992 - THEN CAST('Infinity' AS FLOAT64) * SIGN(1) - ELSE 1 - END - ) - WHEN ABS(1) > 9007199254740992 - THEN POWER( - `float64_col`, - CASE - WHEN ABS(1) > 9007199254740992 - THEN CAST('Infinity' AS FLOAT64) * SIGN(1) - ELSE 1 - END - ) - WHEN `float64_col` < CAST(0 AS INT64) AND NOT ( - CAST(1 AS INT64) = 1 - ) - THEN CAST('NaN' AS FLOAT64) - WHEN `float64_col` <> CAST(0 AS INT64) AND 1 * LN(ABS(`float64_col`)) > 709.78 - THEN CAST('Infinity' AS FLOAT64) * CASE - WHEN `float64_col` < CAST(0 AS INT64) AND MOD(CAST(1 AS INT64), 2) = 1 - THEN -1 - ELSE 1 - END - ELSE POWER( - `float64_col`, - CASE - WHEN ABS(1) > 9007199254740992 - THEN CAST('Infinity' AS FLOAT64) * SIGN(1) - ELSE 1 - END - ) - END AS `float_pow_1`, - NULL AS `float_pow_null`, - NULL AS `null_pow_float` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_round/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_round/out.sql deleted file mode 100644 index 9ac8e1065b5..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_round/out.sql +++ /dev/null @@ -1,11 +0,0 @@ -SELECT - `rowindex`, - `int64_col`, - `float64_col`, - CAST(ROUND(`int64_col`, 0) AS INT64) AS `int_round_0`, - CAST(ROUND(`int64_col`, 1) AS INT64) AS `int_round_1`, - CAST(ROUND(`int64_col`, -1) AS INT64) AS `int_round_m1`, - ROUND(`float64_col`, 0) AS `float_round_0`, - ROUND(`float64_col`, 1) AS `float_round_1`, - ROUND(`float64_col`, -1) AS `float_round_m1` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_sin/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_sin/out.sql deleted file mode 100644 index ddc7cfab6ce..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_sin/out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - SIN(`float64_col`) AS `float64_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_sinh/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_sinh/out.sql deleted file mode 100644 index a1d71a7a065..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_sinh/out.sql +++ /dev/null @@ -1,7 +0,0 @@ -SELECT - CASE - WHEN ABS(`float64_col`) > 709.78 - THEN SIGN(`float64_col`) * CAST('Infinity' AS FLOAT64) - ELSE SINH(`float64_col`) - END AS `float64_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_sqrt/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_sqrt/out.sql deleted file mode 100644 index 6162a69d571..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_sqrt/out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - CASE WHEN `float64_col` < 0 THEN CAST('NaN' AS FLOAT64) ELSE SQRT(`float64_col`) END AS `float64_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_sub_numeric/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_sub_numeric/out.sql deleted file mode 100644 index c1d0350a664..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_sub_numeric/out.sql +++ /dev/null @@ -1,10 +0,0 @@ -SELECT - `rowindex`, - `int64_col`, - `bool_col`, - `int64_col` - `int64_col` AS `int_sub_int`, - `int64_col` - 1 AS `int_sub_1`, - NULL AS `int_sub_null`, - `int64_col` - CAST(`bool_col` AS INT64) AS `int_sub_bool`, - CAST(`bool_col` AS INT64) - `int64_col` AS `bool_sub_int` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_sub_timedelta/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_sub_timedelta/out.sql deleted file mode 100644 index 5c8b130d59d..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_sub_timedelta/out.sql +++ /dev/null @@ -1,11 +0,0 @@ -SELECT - `rowindex`, - `timestamp_col`, - `duration_col`, - `date_col`, - TIMESTAMP_SUB(CAST(`date_col` AS DATETIME), INTERVAL `duration_col` MICROSECOND) AS `date_sub_timedelta`, - TIMESTAMP_SUB(`timestamp_col`, INTERVAL `duration_col` MICROSECOND) AS `timestamp_sub_timedelta`, - TIMESTAMP_DIFF(CAST(`date_col` AS DATETIME), CAST(`date_col` AS DATETIME), MICROSECOND) AS `timestamp_sub_date`, - TIMESTAMP_DIFF(`timestamp_col`, `timestamp_col`, MICROSECOND) AS `date_sub_timestamp`, - `duration_col` - `duration_col` AS `timedelta_sub_timedelta` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_tan/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_tan/out.sql deleted file mode 100644 index 138b5f84a3b..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_tan/out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - TAN(`float64_col`) AS `float64_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_tanh/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_tanh/out.sql deleted file mode 100644 index c5db31c9579..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_tanh/out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - TANH(`float64_col`) AS `float64_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_unsafe_pow_op/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_unsafe_pow_op/out.sql deleted file mode 100644 index 0795b64a209..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_numeric_ops/test_unsafe_pow_op/out.sql +++ /dev/null @@ -1,14 +0,0 @@ -SELECT - POWER(`int64_col`, `int64_col`) AS `int_pow_int`, - POWER(`int64_col`, `float64_col`) AS `int_pow_float`, - POWER(`float64_col`, `int64_col`) AS `float_pow_int`, - POWER(`float64_col`, `float64_col`) AS `float_pow_float`, - POWER(`int64_col`, CAST(`bool_col` AS INT64)) AS `int_pow_bool`, - POWER(CAST(`bool_col` AS INT64), `int64_col`) AS `bool_pow_int` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` -WHERE - ( - `int64_col` >= 0 - ) AND ( - `int64_col` <= 10 - ) \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_string_ops/test_add_string/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_string_ops/test_add_string/out.sql deleted file mode 100644 index cf4051464b7..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_string_ops/test_add_string/out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - CONCAT(`string_col`, 'a') AS `string_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_string_ops/test_capitalize/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_string_ops/test_capitalize/out.sql deleted file mode 100644 index d11ce9b9934..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_string_ops/test_capitalize/out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - INITCAP(`string_col`, '') AS `string_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_string_ops/test_endswith/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_string_ops/test_endswith/out.sql deleted file mode 100644 index 0295af27992..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_string_ops/test_endswith/out.sql +++ /dev/null @@ -1,5 +0,0 @@ -SELECT - ENDS_WITH(`string_col`, 'ab') AS `single`, - ENDS_WITH(`string_col`, 'ab') OR ENDS_WITH(`string_col`, 'cd') AS `double`, - FALSE AS `empty` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_string_ops/test_isalnum/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_string_ops/test_isalnum/out.sql deleted file mode 100644 index 7654299c79e..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_string_ops/test_isalnum/out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - REGEXP_CONTAINS(`string_col`, '^(\\p{N}|\\p{L})+$') AS `string_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_string_ops/test_isalpha/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_string_ops/test_isalpha/out.sql deleted file mode 100644 index 33a08ff054d..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_string_ops/test_isalpha/out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - REGEXP_CONTAINS(`string_col`, '^\\p{L}+$') AS `string_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_string_ops/test_isdecimal/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_string_ops/test_isdecimal/out.sql deleted file mode 100644 index 7f266cbf604..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_string_ops/test_isdecimal/out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - REGEXP_CONTAINS(`string_col`, '^(\\p{Nd})+$') AS `string_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_string_ops/test_isdigit/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_string_ops/test_isdigit/out.sql deleted file mode 100644 index 9134d035153..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_string_ops/test_isdigit/out.sql +++ /dev/null @@ -1,6 +0,0 @@ -SELECT - REGEXP_CONTAINS( - `string_col`, - '^[\\p{Nd}\\x{00B9}\\x{00B2}\\x{00B3}\\x{2070}\\x{2074}-\\x{2079}\\x{2080}-\\x{2089}]+$' - ) AS `string_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_string_ops/test_islower/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_string_ops/test_islower/out.sql deleted file mode 100644 index bce92035e5c..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_string_ops/test_islower/out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - LOWER(`string_col`) = `string_col` AND UPPER(`string_col`) <> `string_col` AS `string_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_string_ops/test_isnumeric/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_string_ops/test_isnumeric/out.sql deleted file mode 100644 index 82baa081f54..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_string_ops/test_isnumeric/out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - REGEXP_CONTAINS(`string_col`, '^\\pN+$') AS `string_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_string_ops/test_isspace/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_string_ops/test_isspace/out.sql deleted file mode 100644 index 2b44a592d93..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_string_ops/test_isspace/out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - REGEXP_CONTAINS(`string_col`, '^\\s+$') AS `string_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_string_ops/test_isupper/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_string_ops/test_isupper/out.sql deleted file mode 100644 index 17ac14ac53f..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_string_ops/test_isupper/out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - UPPER(`string_col`) = `string_col` AND LOWER(`string_col`) <> `string_col` AS `string_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_string_ops/test_len/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_string_ops/test_len/out.sql deleted file mode 100644 index cff109d09dc..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_string_ops/test_len/out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - LENGTH(`string_col`) AS `string_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_string_ops/test_len_w_array/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_string_ops/test_len_w_array/out.sql deleted file mode 100644 index 1862deb6015..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_string_ops/test_len_w_array/out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - ARRAY_LENGTH(`int_list_col`) AS `int_list_col` -FROM `bigframes-dev`.`sqlglot_test`.`repeated_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_string_ops/test_lower/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_string_ops/test_lower/out.sql deleted file mode 100644 index 851de35ecb0..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_string_ops/test_lower/out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - LOWER(`string_col`) AS `string_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_string_ops/test_lstrip/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_string_ops/test_lstrip/out.sql deleted file mode 100644 index 4023605f8c9..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_string_ops/test_lstrip/out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - LTRIM(`string_col`, ' ') AS `string_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_string_ops/test_regex_replace_str/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_string_ops/test_regex_replace_str/out.sql deleted file mode 100644 index 4728c961ab3..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_string_ops/test_regex_replace_str/out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - REGEXP_REPLACE(`string_col`, 'e', 'a') AS `string_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_string_ops/test_replace_str/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_string_ops/test_replace_str/out.sql deleted file mode 100644 index 154af44b500..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_string_ops/test_replace_str/out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - REPLACE(`string_col`, 'e', 'a') AS `string_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_string_ops/test_reverse/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_string_ops/test_reverse/out.sql deleted file mode 100644 index 97bf57f79e1..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_string_ops/test_reverse/out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - REVERSE(`string_col`) AS `string_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_string_ops/test_rstrip/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_string_ops/test_rstrip/out.sql deleted file mode 100644 index c3d25b56e24..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_string_ops/test_rstrip/out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - RTRIM(`string_col`, ' ') AS `string_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_string_ops/test_startswith/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_string_ops/test_startswith/out.sql deleted file mode 100644 index 760d3db7745..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_string_ops/test_startswith/out.sql +++ /dev/null @@ -1,5 +0,0 @@ -SELECT - STARTS_WITH(`string_col`, 'ab') AS `single`, - STARTS_WITH(`string_col`, 'ab') OR STARTS_WITH(`string_col`, 'cd') AS `double`, - FALSE AS `empty` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_string_ops/test_str_contains/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_string_ops/test_str_contains/out.sql deleted file mode 100644 index 9071a252bc1..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_string_ops/test_str_contains/out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - `string_col` LIKE '%e%' AS `string_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_string_ops/test_str_contains_regex/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_string_ops/test_str_contains_regex/out.sql deleted file mode 100644 index 958f9af6f39..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_string_ops/test_str_contains_regex/out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - REGEXP_CONTAINS(`string_col`, 'e') AS `string_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_string_ops/test_str_extract/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_string_ops/test_str_extract/out.sql deleted file mode 100644 index a87f5d9836d..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_string_ops/test_str_extract/out.sql +++ /dev/null @@ -1,12 +0,0 @@ -SELECT - IF( - REGEXP_CONTAINS(`string_col`, '([a-z]*)'), - REGEXP_REPLACE(`string_col`, CONCAT('.*?(', '([a-z]*)', ').*'), '\\1'), - NULL - ) AS `zero`, - IF( - REGEXP_CONTAINS(`string_col`, '([a-z]*)'), - REGEXP_REPLACE(`string_col`, CONCAT('.*?', '([a-z]*)', '.*'), '\\1'), - NULL - ) AS `one` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_string_ops/test_str_find/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_string_ops/test_str_find/out.sql deleted file mode 100644 index cf21fb8234a..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_string_ops/test_str_find/out.sql +++ /dev/null @@ -1,6 +0,0 @@ -SELECT - INSTR(`string_col`, 'e', 1) - 1 AS `none_none`, - INSTR(`string_col`, 'e', 3) - 1 AS `start_none`, - INSTR(SUBSTRING(`string_col`, 1, 5), 'e') - 1 AS `none_end`, - INSTR(SUBSTRING(`string_col`, 3, 3), 'e') - 1 AS `start_end` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_string_ops/test_str_get/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_string_ops/test_str_get/out.sql deleted file mode 100644 index b4c7c504fc9..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_string_ops/test_str_get/out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - IF(SUBSTRING(`string_col`, 2, 1) <> '', SUBSTRING(`string_col`, 2, 1), NULL) AS `string_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_string_ops/test_str_pad/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_string_ops/test_str_pad/out.sql deleted file mode 100644 index 29766cca6c1..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_string_ops/test_str_pad/out.sql +++ /dev/null @@ -1,13 +0,0 @@ -SELECT - LPAD(`string_col`, GREATEST(LENGTH(`string_col`), 10), '-') AS `left`, - RPAD(`string_col`, GREATEST(LENGTH(`string_col`), 10), '-') AS `right`, - RPAD( - LPAD( - `string_col`, - CAST(FLOOR(SAFE_DIVIDE(GREATEST(LENGTH(`string_col`), 10) - LENGTH(`string_col`), 2)) AS INT64) + LENGTH(`string_col`), - '-' - ), - GREATEST(LENGTH(`string_col`), 10), - '-' - ) AS `both` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_string_ops/test_str_repeat/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_string_ops/test_str_repeat/out.sql deleted file mode 100644 index ed3d06ed35a..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_string_ops/test_str_repeat/out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - REPEAT(`string_col`, 2) AS `string_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_string_ops/test_str_slice/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_string_ops/test_str_slice/out.sql deleted file mode 100644 index f011480ad30..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_string_ops/test_str_slice/out.sql +++ /dev/null @@ -1,18 +0,0 @@ -SELECT - SUBSTRING(`string_col`, 2, 2) AS `1_3`, - SUBSTRING(`string_col`, 1, 3) AS `none_3`, - SUBSTRING(`string_col`, 2) AS `1_none`, - SUBSTRING(`string_col`, -3) AS `m3_none`, - SUBSTRING(`string_col`, 1, GREATEST(0, LENGTH(`string_col`) + -3)) AS `none_m3`, - SUBSTRING( - `string_col`, - GREATEST(1, LENGTH(`string_col`) + -4), - GREATEST(0, LENGTH(`string_col`) + -3) - GREATEST(0, LENGTH(`string_col`) + -5) - ) AS `m5_m3`, - SUBSTRING(`string_col`, 2, GREATEST(0, LENGTH(`string_col`) + -4)) AS `1_m3`, - SUBSTRING( - `string_col`, - GREATEST(1, LENGTH(`string_col`) + -2), - GREATEST(0, 5 - GREATEST(0, LENGTH(`string_col`) + -3)) - ) AS `m3_5` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_string_ops/test_strconcat/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_string_ops/test_strconcat/out.sql deleted file mode 100644 index cf4051464b7..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_string_ops/test_strconcat/out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - CONCAT(`string_col`, 'a') AS `string_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_string_ops/test_string_split/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_string_ops/test_string_split/out.sql deleted file mode 100644 index 5145d6686a8..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_string_ops/test_string_split/out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - SPLIT(`string_col`, ',') AS `string_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_string_ops/test_strip/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_string_ops/test_strip/out.sql deleted file mode 100644 index e07185292bb..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_string_ops/test_strip/out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - TRIM(`string_col`, ' ') AS `string_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_string_ops/test_upper/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_string_ops/test_upper/out.sql deleted file mode 100644 index 88bd78bd095..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_string_ops/test_upper/out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - UPPER(`string_col`) AS `string_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_string_ops/test_zfill/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_string_ops/test_zfill/out.sql deleted file mode 100644 index 818d2907add..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_string_ops/test_zfill/out.sql +++ /dev/null @@ -1,7 +0,0 @@ -SELECT - CASE - WHEN STARTS_WITH(`string_col`, '-') - THEN CONCAT('-', LPAD(SUBSTRING(`string_col`, 2), GREATEST(LENGTH(`string_col`), 10) - 1, '0')) - ELSE LPAD(`string_col`, GREATEST(LENGTH(`string_col`), 10), '0') - END AS `string_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_struct_ops/test_struct_field/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_struct_ops/test_struct_field/out.sql deleted file mode 100644 index 6c3760aa36e..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_struct_ops/test_struct_field/out.sql +++ /dev/null @@ -1,4 +0,0 @@ -SELECT - `people`.`name` AS `string`, - `people`.`name` AS `int` -FROM `bigframes-dev`.`sqlglot_test`.`nested_structs_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_struct_ops/test_struct_op/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_struct_ops/test_struct_op/out.sql deleted file mode 100644 index 3549149609a..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_struct_ops/test_struct_op/out.sql +++ /dev/null @@ -1,8 +0,0 @@ -SELECT - STRUCT( - `bool_col` AS bool_col, - `int64_col` AS int64_col, - `float64_col` AS float64_col, - `string_col` AS string_col - ) AS `result_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_timedelta_ops/test_timedelta_floor/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_timedelta_ops/test_timedelta_floor/out.sql deleted file mode 100644 index 6eb6f8e989d..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_timedelta_ops/test_timedelta_floor/out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - FLOOR(`int64_col`) AS `int64_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_timedelta_ops/test_to_timedelta/out.sql b/tests/unit/core/compile/sqlglot/expressions/snapshots/test_timedelta_ops/test_to_timedelta/out.sql deleted file mode 100644 index 59aa3d9b0b3..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/snapshots/test_timedelta_ops/test_to_timedelta/out.sql +++ /dev/null @@ -1,9 +0,0 @@ -SELECT - `rowindex`, - `int64_col`, - `float64_col`, - `int64_col` AS `duration_us`, - CAST(FLOOR(`float64_col` * 1000000) AS INT64) AS `duration_s`, - `int64_col` * 3600000000 AS `duration_w`, - `int64_col` AS `duration_on_duration` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/expressions/test_ai_ops.py b/tests/unit/core/compile/sqlglot/expressions/test_ai_ops.py deleted file mode 100644 index 57c52490860..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/test_ai_ops.py +++ /dev/null @@ -1,477 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import json - -import pytest - -from bigframes import dataframe -from bigframes import operations as ops -from bigframes.testing import utils - -pytest.importorskip("pytest_snapshot") - -CONNECTION_ID = "bigframes-dev.us.bigframes-default-connection" - - -def test_ai_generate(scalar_types_df: dataframe.DataFrame, snapshot): - col_name = "string_col" - - op = ops.AIGenerate( - prompt_context=(None, " is the same as ", None), - endpoint="gemini-2.5-flash", - request_type="SHARED", - ) - - sql = utils._apply_ops_to_sql( - scalar_types_df, [op.as_expr(col_name, col_name)], ["result"] - ) - - snapshot.assert_match(sql, "out.sql") - - -def test_ai_generate_with_connection_id(scalar_types_df: dataframe.DataFrame, snapshot): - col_name = "string_col" - - op = ops.AIGenerate( - prompt_context=(None, " is the same as ", None), - connection_id=CONNECTION_ID, - endpoint="gemini-2.5-flash", - ) - - sql = utils._apply_ops_to_sql( - scalar_types_df, [op.as_expr(col_name, col_name)], ["result"] - ) - - snapshot.assert_match(sql, "out.sql") - - -def test_ai_generate_with_output_schema(scalar_types_df: dataframe.DataFrame, snapshot): - col_name = "string_col" - - op = ops.AIGenerate( - prompt_context=(None, " is the same as ", None), - connection_id=None, - endpoint="gemini-2.5-flash", - output_schema="x INT64, y FLOAT64", - ) - - sql = utils._apply_ops_to_sql( - scalar_types_df, [op.as_expr(col_name, col_name)], ["result"] - ) - - snapshot.assert_match(sql, "out.sql") - - -def test_ai_generate_with_model_param(scalar_types_df: dataframe.DataFrame, snapshot): - col_name = "string_col" - - op = ops.AIGenerate( - prompt_context=(None, " is the same as ", None), - model_params=json.dumps(dict()), - ) - - sql = utils._apply_ops_to_sql( - scalar_types_df, [op.as_expr(col_name, col_name)], ["result"] - ) - - snapshot.assert_match(sql, "out.sql") - - -def test_ai_generate_bool(scalar_types_df: dataframe.DataFrame, snapshot): - col_name = "string_col" - - op = ops.AIGenerateBool( - prompt_context=(None, " is the same as ", None), - endpoint="gemini-2.5-flash", - ) - - sql = utils._apply_ops_to_sql( - scalar_types_df, [op.as_expr(col_name, col_name)], ["result"] - ) - - snapshot.assert_match(sql, "out.sql") - - -def test_ai_generate_bool_with_connection_id( - scalar_types_df: dataframe.DataFrame, snapshot -): - col_name = "string_col" - - op = ops.AIGenerateBool( - prompt_context=(None, " is the same as ", None), - connection_id=CONNECTION_ID, - endpoint="gemini-2.5-flash", - ) - - sql = utils._apply_ops_to_sql( - scalar_types_df, [op.as_expr(col_name, col_name)], ["result"] - ) - - snapshot.assert_match(sql, "out.sql") - - -def test_ai_generate_bool_with_model_param( - scalar_types_df: dataframe.DataFrame, snapshot -): - col_name = "string_col" - - op = ops.AIGenerateBool( - prompt_context=(None, " is the same as ", None), - model_params=json.dumps(dict()), - ) - - sql = utils._apply_ops_to_sql( - scalar_types_df, [op.as_expr(col_name, col_name)], ["result"] - ) - - snapshot.assert_match(sql, "out.sql") - - -def test_ai_generate_int(scalar_types_df: dataframe.DataFrame, snapshot): - col_name = "string_col" - - op = ops.AIGenerateInt( - # The prompt does not make semantic sense but we only care about syntax correctness. - prompt_context=(None, " is the same as ", None), - endpoint="gemini-2.5-flash", - ) - - sql = utils._apply_ops_to_sql( - scalar_types_df, [op.as_expr(col_name, col_name)], ["result"] - ) - - snapshot.assert_match(sql, "out.sql") - - -def test_ai_generate_int_with_connection_id( - scalar_types_df: dataframe.DataFrame, snapshot -): - col_name = "string_col" - - op = ops.AIGenerateInt( - # The prompt does not make semantic sense but we only care about syntax correctness. - prompt_context=(None, " is the same as ", None), - connection_id=CONNECTION_ID, - endpoint="gemini-2.5-flash", - ) - - sql = utils._apply_ops_to_sql( - scalar_types_df, [op.as_expr(col_name, col_name)], ["result"] - ) - - snapshot.assert_match(sql, "out.sql") - - -def test_ai_generate_int_with_model_param( - scalar_types_df: dataframe.DataFrame, snapshot -): - col_name = "string_col" - - op = ops.AIGenerateInt( - # The prompt does not make semantic sense but we only care about syntax correctness. - prompt_context=(None, " is the same as ", None), - model_params=json.dumps(dict()), - ) - - sql = utils._apply_ops_to_sql( - scalar_types_df, [op.as_expr(col_name, col_name)], ["result"] - ) - - snapshot.assert_match(sql, "out.sql") - - -def test_ai_generate_double(scalar_types_df: dataframe.DataFrame, snapshot): - col_name = "string_col" - - op = ops.AIGenerateDouble( - # The prompt does not make semantic sense but we only care about syntax correctness. - prompt_context=(None, " is the same as ", None), - endpoint="gemini-2.5-flash", - ) - - sql = utils._apply_ops_to_sql( - scalar_types_df, [op.as_expr(col_name, col_name)], ["result"] - ) - - snapshot.assert_match(sql, "out.sql") - - -def test_ai_generate_double_with_connection_id( - scalar_types_df: dataframe.DataFrame, snapshot -): - col_name = "string_col" - - op = ops.AIGenerateDouble( - # The prompt does not make semantic sense but we only care about syntax correctness. - prompt_context=(None, " is the same as ", None), - connection_id=CONNECTION_ID, - endpoint="gemini-2.5-flash", - ) - - sql = utils._apply_ops_to_sql( - scalar_types_df, [op.as_expr(col_name, col_name)], ["result"] - ) - - snapshot.assert_match(sql, "out.sql") - - -def test_ai_generate_double_with_model_param( - scalar_types_df: dataframe.DataFrame, snapshot -): - col_name = "string_col" - - op = ops.AIGenerateDouble( - # The prompt does not make semantic sense but we only care about syntax correctness. - prompt_context=(None, " is the same as ", None), - model_params=json.dumps(dict()), - ) - - sql = utils._apply_ops_to_sql( - scalar_types_df, [op.as_expr(col_name, col_name)], ["result"] - ) - - snapshot.assert_match(sql, "out.sql") - - -def test_ai_embed(scalar_types_df: dataframe.DataFrame, snapshot): - col_name = "string_col" - - op = ops.AIEmbed( - endpoint="text-embedding-005", - ) - - sql = utils._apply_ops_to_sql(scalar_types_df, [op.as_expr(col_name)], ["result"]) - - snapshot.assert_match(sql, "out.sql") - - -def test_ai_embed_with_connection_id(scalar_types_df: dataframe.DataFrame, snapshot): - col_name = "string_col" - - op = ops.AIEmbed( - endpoint="text-embedding-005", - connection_id=CONNECTION_ID, - ) - - sql = utils._apply_ops_to_sql(scalar_types_df, [op.as_expr(col_name)], ["result"]) - - snapshot.assert_match(sql, "out.sql") - - -def test_ai_embed_with_model(scalar_types_df: dataframe.DataFrame, snapshot): - col_name = "string_col" - - op = ops.AIEmbed( - model="embeddinggemma-300m", - ) - - sql = utils._apply_ops_to_sql(scalar_types_df, [op.as_expr(col_name)], ["result"]) - - snapshot.assert_match(sql, "out.sql") - - -def test_ai_embed_with_task_type_and_title( - scalar_types_df: dataframe.DataFrame, snapshot -): - col_name = "string_col" - - op = ops.AIEmbed( - endpoint="text-embedding-005", - task_type="RETRIEVAL_DOCUMENT", - title="My Document", - model_params=json.dumps({"outputDimensionality": 256}), - ) - - sql = utils._apply_ops_to_sql(scalar_types_df, [op.as_expr(col_name)], ["result"]) - - snapshot.assert_match(sql, "out.sql") - - -@pytest.mark.parametrize("connection_id", [None, CONNECTION_ID]) -def test_ai_if(scalar_types_df: dataframe.DataFrame, snapshot, connection_id): - col_name = "string_col" - - op = ops.AIIf( - prompt_context=(None, " is the same as ", None), - connection_id=connection_id, - optimization_mode="MINIMIZE_COST", - max_error_ratio=0.5, - ) - - sql = utils._apply_ops_to_sql( - scalar_types_df, [op.as_expr(col_name, col_name)], ["result"] - ) - - snapshot.assert_match(sql, "out.sql") - - -def test_ai_if_with_endpoint(scalar_types_df: dataframe.DataFrame, snapshot): - col_name = "string_col" - - op = ops.AIIf( - prompt_context=(None, " is the same as ", None), - endpoint="gemini-2.5-flash", - ) - - sql = utils._apply_ops_to_sql( - scalar_types_df, [op.as_expr(col_name, col_name)], ["result"] - ) - - snapshot.assert_match(sql, "out.sql") - - -@pytest.mark.parametrize("connection_id", [None, CONNECTION_ID]) -def test_ai_classify(scalar_types_df: dataframe.DataFrame, snapshot, connection_id): - col_name = "string_col" - - op = ops.AIClassify( - prompt_context=(None,), - categories=("greeting", "rejection"), - connection_id=connection_id, - ) - - sql = utils._apply_ops_to_sql(scalar_types_df, [op.as_expr(col_name)], ["result"]) - - snapshot.assert_match(sql, "out.sql") - - -def test_ai_classify_with_params(scalar_types_df: dataframe.DataFrame, snapshot): - col_name = "string_col" - - op = ops.AIClassify( - prompt_context=(None,), - categories=("greeting", "rejection"), - examples=(("hi", "greeting"), ("bye", "rejection")), - endpoint="gemini-2.5-flash", - max_error_ratio=0.1, - ) - - sql = utils._apply_ops_to_sql(scalar_types_df, [op.as_expr(col_name)], ["result"]) - - snapshot.assert_match(sql, "out.sql") - - -def test_ai_classify_with_output_mode(scalar_types_df: dataframe.DataFrame, snapshot): - col_name = "string_col" - - op = ops.AIClassify( - prompt_context=(None,), - categories=("greeting", "rejection"), - output_mode="multi", - ) - - sql = utils._apply_ops_to_sql(scalar_types_df, [op.as_expr(col_name)], ["result"]) - - snapshot.assert_match(sql, "out.sql") - - -def test_ai_classify_multi_with_list_examples( - scalar_types_df: dataframe.DataFrame, snapshot -): - col_name = "string_col" - - examples = ( - ("hi", ("greeting", "positive")), - ("bye", ("rejection", "negative")), - ) - op = ops.AIClassify( - prompt_context=(None,), - categories=("greeting", "rejection"), - examples=examples, - output_mode="multi", - ) - - sql = utils._apply_ops_to_sql(scalar_types_df, [op.as_expr(col_name)], ["result"]) - - snapshot.assert_match(sql, "out.sql") - - -@pytest.mark.parametrize("connection_id", [None, CONNECTION_ID]) -def test_ai_score(scalar_types_df: dataframe.DataFrame, snapshot, connection_id): - col_name = "string_col" - - op = ops.AIScore( - prompt_context=(None, " is the same as ", None), - connection_id=connection_id, - ) - - sql = utils._apply_ops_to_sql( - scalar_types_df, [op.as_expr(col_name, col_name)], ["result"] - ) - - snapshot.assert_match(sql, "out.sql") - - -def test_ai_score_with_endpoint_and_max_error_ratio( - scalar_types_df: dataframe.DataFrame, snapshot -): - col_name = "string_col" - - op = ops.AIScore( - prompt_context=(None, " is the same as ", None), - endpoint="gemini-2.5-flash", - max_error_ratio=0.5, - ) - - sql = utils._apply_ops_to_sql( - scalar_types_df, [op.as_expr(col_name, col_name)], ["result"] - ) - - snapshot.assert_match(sql, "out.sql") - - -@pytest.mark.parametrize("connection_id", [None, CONNECTION_ID]) -def test_ai_similarity(scalar_types_df: dataframe.DataFrame, snapshot, connection_id): - col_name = "string_col" - - op = ops.AISimilarity( - endpoint="text-embedding-005", - connection_id=connection_id, - ) - - sql = utils._apply_ops_to_sql( - scalar_types_df, [op.as_expr(col_name, col_name)], ["result"] - ) - - snapshot.assert_match(sql, "out.sql") - - -def test_ai_similarity_with_model(scalar_types_df: dataframe.DataFrame, snapshot): - col_name = "string_col" - - op = ops.AISimilarity( - model="embeddinggemma-300m", - ) - - sql = utils._apply_ops_to_sql( - scalar_types_df, [op.as_expr(col_name, col_name)], ["result"] - ) - - snapshot.assert_match(sql, "out.sql") - - -def test_ai_similarity_with_model_param(scalar_types_df: dataframe.DataFrame, snapshot): - col_name = "string_col" - - op = ops.AISimilarity( - endpoint="text-embedding-005", - model_params=json.dumps({"outputDimensionality": 256}), - ) - - sql = utils._apply_ops_to_sql( - scalar_types_df, [op.as_expr(col_name, col_name)], ["result"] - ) - - snapshot.assert_match(sql, "out.sql") diff --git a/tests/unit/core/compile/sqlglot/expressions/test_array_ops.py b/tests/unit/core/compile/sqlglot/expressions/test_array_ops.py deleted file mode 100644 index 1b358b3a3b1..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/test_array_ops.py +++ /dev/null @@ -1,117 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the \"License\"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an \"AS IS\" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import pytest - -import bigframes.operations.aggregations as agg_ops -import bigframes.pandas as bpd -from bigframes import operations as ops -from bigframes.core import expression -from bigframes.testing import utils - -pytest.importorskip("pytest_snapshot") - - -def test_array_to_string(repeated_types_df: bpd.DataFrame, snapshot): - col_name = "string_list_col" - bf_df = repeated_types_df[[col_name]] - sql = utils._apply_ops_to_sql( - bf_df, [ops.ArrayToStringOp(delimiter=".").as_expr(col_name)], [col_name] - ) - - snapshot.assert_match(sql, "out.sql") - - -def test_array_index(scalar_types_df: bpd.DataFrame, snapshot): - ops_map = { - "string_index": ops.GetItemOp(key=1).as_expr("string_col"), - "array_index": expression.OpExpression( - ops.GetItemOp(key=1), - (ops.ToArrayOp().as_expr("int64_col", "int64_too"),), - ), - } - - sql = utils._apply_ops_to_sql( - scalar_types_df, list(ops_map.values()), list(ops_map.keys()) - ) - snapshot.assert_match(sql, "out.sql") - - -def test_array_reduce_op(repeated_types_df: bpd.DataFrame, snapshot): - ops_map = { - "sum_float": ops.ArrayReduceOp(agg_ops.SumOp()).as_expr("float_list_col"), - "std_float": ops.ArrayReduceOp(agg_ops.StdOp()).as_expr("float_list_col"), - "count_str": ops.ArrayReduceOp(agg_ops.CountOp()).as_expr("string_list_col"), - "any_bool": ops.ArrayReduceOp(agg_ops.AnyOp()).as_expr("bool_list_col"), - "array_agg_str": ops.ArrayReduceOp(agg_ops.ArrayAggOp()).as_expr( - "string_list_col" - ), - } - - sql = utils._apply_ops_to_sql( - repeated_types_df, list(ops_map.values()), list(ops_map.keys()) - ) - snapshot.assert_match(sql, "out.sql") - - -def test_array_slice(scalar_types_df: bpd.DataFrame, snapshot): - array_expr = ops.ToArrayOp().as_expr("int64_col", "int64_too") - ops_map = { - "string_slice": ops.ArraySliceOp(start=1, stop=5).as_expr("string_col"), - "slice_only_start": expression.OpExpression( - ops.ArraySliceOp(start=1, stop=None), - (array_expr,), - ), - "slice_start_stop": expression.OpExpression( - ops.ArraySliceOp(start=1, stop=5), - (array_expr,), - ), - } - - sql = utils._apply_ops_to_sql( - scalar_types_df, list(ops_map.values()), list(ops_map.keys()) - ) - snapshot.assert_match(sql, "out.sql") - - -def test_to_array_op(scalar_types_df: bpd.DataFrame, snapshot): - bf_df = scalar_types_df[["int64_col", "bool_col", "float64_col", "string_col"]] - # Bigquery won't allow you to materialize arrays with null, so use non-nullable - int64_non_null = ops.coalesce_op.as_expr("int64_col", expression.const(0)) - bool_col_non_null = ops.coalesce_op.as_expr("bool_col", expression.const(False)) - float_col_non_null = ops.coalesce_op.as_expr("float64_col", expression.const(0.0)) - string_col_non_null = ops.coalesce_op.as_expr("string_col", expression.const("")) - - ops_map = { - "bool_col": ops.ToArrayOp().as_expr(bool_col_non_null), - "int64_col": ops.ToArrayOp().as_expr(int64_non_null), - "strs_col": ops.ToArrayOp().as_expr(string_col_non_null, string_col_non_null), - "numeric_col": ops.ToArrayOp().as_expr( - int64_non_null, bool_col_non_null, float_col_non_null - ), - } - - sql = utils._apply_ops_to_sql(bf_df, list(ops_map.values()), list(ops_map.keys())) - snapshot.assert_match(sql, "out.sql") - - -def test_to_array_with_subquery_expression(repeated_types_df: bpd.DataFrame, snapshot): - reduced = ops.ArrayReduceOp(agg_ops.SumOp()).as_expr("float_list_col") - coalesced_reduced = ops.coalesce_op.as_expr(reduced, expression.const(0.0)) - array_expr = ops.ToArrayOp().as_expr(coalesced_reduced) - - sql = utils._apply_ops_to_sql( - repeated_types_df, [array_expr], ["arr_subquery_coalesce"] - ) - snapshot.assert_match(sql, "out.sql") diff --git a/tests/unit/core/compile/sqlglot/expressions/test_bool_ops.py b/tests/unit/core/compile/sqlglot/expressions/test_bool_ops.py deleted file mode 100644 index bd51ea905a2..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/test_bool_ops.py +++ /dev/null @@ -1,50 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import pandas as pd -import pytest - -import bigframes.pandas as bpd - -pytest.importorskip("pytest_snapshot") - - -def test_and_op(scalar_types_df: bpd.DataFrame, snapshot): - bf_df = scalar_types_df[["bool_col", "int64_col"]] - - bf_df["int_and_int"] = bf_df["int64_col"] & bf_df["int64_col"] - bf_df["bool_and_bool"] = bf_df["bool_col"] & bf_df["bool_col"] - bf_df["bool_and_null"] = bf_df["bool_col"] & pd.NA # type: ignore - bf_df["null_and_bool"] = pd.NA & bf_df["bool_col"] # type: ignore - snapshot.assert_match(bf_df.sql, "out.sql") - - -def test_or_op(scalar_types_df: bpd.DataFrame, snapshot): - bf_df = scalar_types_df[["bool_col", "int64_col"]] - - bf_df["int_and_int"] = bf_df["int64_col"] | bf_df["int64_col"] - bf_df["bool_and_bool"] = bf_df["bool_col"] | bf_df["bool_col"] - bf_df["bool_and_null"] = bf_df["bool_col"] | pd.NA # type: ignore - bf_df["null_and_bool"] = pd.NA | bf_df["bool_col"] # type: ignore - snapshot.assert_match(bf_df.sql, "out.sql") - - -def test_xor_op(scalar_types_df: bpd.DataFrame, snapshot): - bf_df = scalar_types_df[["bool_col", "int64_col"]] - - bf_df["int_and_int"] = bf_df["int64_col"] ^ bf_df["int64_col"] - bf_df["bool_and_bool"] = bf_df["bool_col"] ^ bf_df["bool_col"] - bf_df["bool_and_null"] = bf_df["bool_col"] ^ pd.NA # type: ignore - bf_df["null_and_bool"] = pd.NA ^ bf_df["bool_col"] # type: ignore - snapshot.assert_match(bf_df.sql, "out.sql") diff --git a/tests/unit/core/compile/sqlglot/expressions/test_comparison_ops.py b/tests/unit/core/compile/sqlglot/expressions/test_comparison_ops.py deleted file mode 100644 index 73aceaedeeb..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/test_comparison_ops.py +++ /dev/null @@ -1,167 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import pandas as pd -import pytest - -import bigframes.pandas as bpd -from bigframes import operations as ops -from bigframes.testing import utils - -pytest.importorskip("pytest_snapshot") - - -def test_is_in(scalar_types_df: bpd.DataFrame, snapshot): - bool_col = "bool_col" - int_col = "int64_col" - float_col = "float64_col" - bf_df = scalar_types_df[[bool_col, int_col, float_col]] - ops_map = { - "bools": ops.IsInOp(values=(True, False)).as_expr(bool_col), - "ints": ops.IsInOp(values=(1, 2, 3)).as_expr(int_col), - "ints_w_null": ops.IsInOp(values=(None, pd.NA)).as_expr(int_col), - "floats": ops.IsInOp(values=(1.0, 2.0, 3.0), match_nulls=False).as_expr( - int_col - ), - "strings": ops.IsInOp(values=("1.0", "2.0")).as_expr(int_col), - "mixed": ops.IsInOp( - values=( - "1.0", - 2.5, - 3, - 1e-10, - float("inf"), - float("nan"), - 0, - ) - ).as_expr(int_col), - "empty": ops.IsInOp(values=()).as_expr(int_col), - "empty_wo_match_nulls": ops.IsInOp(values=(), match_nulls=False).as_expr( - int_col - ), - "ints_wo_match_nulls": ops.IsInOp( - values=(None, 123456), match_nulls=False - ).as_expr(int_col), - "float_in_ints": ops.IsInOp(values=(1, 2, 3, None)).as_expr(float_col), - "mixed_with_null": ops.IsInOp( - values=("1.0", 2, None), match_nulls=True - ).as_expr(int_col), - "bool_in_mixed": ops.IsInOp(values=(1, 2.5)).as_expr(bool_col), - "only_null_match": ops.IsInOp(values=(None,), match_nulls=True).as_expr( - int_col - ), - } - - sql = utils._apply_ops_to_sql(bf_df, list(ops_map.values()), list(ops_map.keys())) - snapshot.assert_match(sql, "out.sql") - - -def test_eq_null_match(scalar_types_df: bpd.DataFrame, snapshot): - bf_df = scalar_types_df[["int64_col", "bool_col"]] - sql = utils._apply_binary_op(bf_df, ops.eq_null_match_op, "int64_col", "bool_col") - snapshot.assert_match(sql, "out.sql") - - -def test_eq_numeric(scalar_types_df: bpd.DataFrame, snapshot): - bf_df = scalar_types_df[["int64_col", "bool_col"]] - - bf_df["int_eq_int"] = bf_df["int64_col"] == bf_df["int64_col"] - bf_df["int_eq_1"] = bf_df["int64_col"] == 1 - bf_df["int_eq_null"] = bf_df["int64_col"] == pd.NA - bf_df["null_eq_int"] = pd.NA == bf_df["int64_col"] - - bf_df["int_eq_bool"] = bf_df["int64_col"] == bf_df["bool_col"] - bf_df["bool_eq_int"] = bf_df["bool_col"] == bf_df["int64_col"] - - snapshot.assert_match(bf_df.sql, "out.sql") - - -def test_gt_numeric(scalar_types_df: bpd.DataFrame, snapshot): - bf_df = scalar_types_df[["int64_col", "bool_col"]] - - bf_df["int_gt_int"] = bf_df["int64_col"] > bf_df["int64_col"] - bf_df["int_gt_1"] = bf_df["int64_col"] > 1 - bf_df["null_gt_int"] = pd.NA > bf_df["int64_col"] - - bf_df["int_gt_bool"] = bf_df["int64_col"] > bf_df["bool_col"] - bf_df["bool_gt_int"] = bf_df["bool_col"] > bf_df["int64_col"] - - snapshot.assert_match(bf_df.sql, "out.sql") - - -def test_ge_numeric(scalar_types_df: bpd.DataFrame, snapshot): - bf_df = scalar_types_df[["int64_col", "bool_col"]] - - bf_df["int_ge_int"] = bf_df["int64_col"] >= bf_df["int64_col"] - bf_df["int_ge_1"] = bf_df["int64_col"] >= 1 - bf_df["null_ge_int"] = pd.NA >= bf_df["int64_col"] - - bf_df["int_ge_bool"] = bf_df["int64_col"] >= bf_df["bool_col"] - bf_df["bool_ge_int"] = bf_df["bool_col"] >= bf_df["int64_col"] - - snapshot.assert_match(bf_df.sql, "out.sql") - - -def test_lt_numeric(scalar_types_df: bpd.DataFrame, snapshot): - bf_df = scalar_types_df[["int64_col", "bool_col"]] - - bf_df["int_lt_int"] = bf_df["int64_col"] < bf_df["int64_col"] - bf_df["int_lt_1"] = bf_df["int64_col"] < 1 - bf_df["null_lt_int"] = pd.NA < bf_df["int64_col"] - - bf_df["int_lt_bool"] = bf_df["int64_col"] < bf_df["bool_col"] - bf_df["bool_lt_int"] = bf_df["bool_col"] < bf_df["int64_col"] - - snapshot.assert_match(bf_df.sql, "out.sql") - - -def test_le_numeric(scalar_types_df: bpd.DataFrame, snapshot): - bf_df = scalar_types_df[["int64_col", "bool_col"]] - - bf_df["int_le_int"] = bf_df["int64_col"] <= bf_df["int64_col"] - bf_df["int_le_1"] = bf_df["int64_col"] <= 1 - bf_df["null_le_int"] = pd.NA <= bf_df["int64_col"] - - bf_df["int_le_bool"] = bf_df["int64_col"] <= bf_df["bool_col"] - bf_df["bool_le_int"] = bf_df["bool_col"] <= bf_df["int64_col"] - - snapshot.assert_match(bf_df.sql, "out.sql") - - -def test_maximum_op(scalar_types_df: bpd.DataFrame, snapshot): - bf_df = scalar_types_df[["int64_col", "float64_col"]] - sql = utils._apply_binary_op(bf_df, ops.maximum_op, "int64_col", "float64_col") - - snapshot.assert_match(sql, "out.sql") - - -def test_minimum_op(scalar_types_df: bpd.DataFrame, snapshot): - bf_df = scalar_types_df[["int64_col", "float64_col"]] - sql = utils._apply_binary_op(bf_df, ops.minimum_op, "int64_col", "float64_col") - - snapshot.assert_match(sql, "out.sql") - - -def test_ne_numeric(scalar_types_df: bpd.DataFrame, snapshot): - bf_df = scalar_types_df[["int64_col", "bool_col"]] - - bf_df["int_ne_int"] = bf_df["int64_col"] != bf_df["int64_col"] - bf_df["int_ne_1"] = bf_df["int64_col"] != 1 - bf_df["int_ne_null"] = bf_df["int64_col"] != pd.NA - bf_df["null_ne_int"] = pd.NA != bf_df["int64_col"] - - bf_df["int_ne_bool"] = bf_df["int64_col"] != bf_df["bool_col"] - bf_df["bool_ne_int"] = bf_df["bool_col"] != bf_df["int64_col"] - - snapshot.assert_match(bf_df.sql, "out.sql") diff --git a/tests/unit/core/compile/sqlglot/expressions/test_datetime_ops.py b/tests/unit/core/compile/sqlglot/expressions/test_datetime_ops.py deleted file mode 100644 index e86059b160a..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/test_datetime_ops.py +++ /dev/null @@ -1,404 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import pandas as pd -import pytest - -import bigframes.pandas as bpd -from bigframes import operations as ops -from bigframes.testing import utils - -pytest.importorskip("pytest_snapshot") - - -def test_date(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "timestamp_col" - bf_df = scalar_types_df[[col_name]] - sql = utils._apply_ops_to_sql(bf_df, [ops.date_op.as_expr(col_name)], [col_name]) - - snapshot.assert_match(sql, "out.sql") - - -def test_day(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "timestamp_col" - bf_df = scalar_types_df[[col_name]] - sql = utils._apply_ops_to_sql(bf_df, [ops.day_op.as_expr(col_name)], [col_name]) - - snapshot.assert_match(sql, "out.sql") - - -def test_dayofweek(scalar_types_df: bpd.DataFrame, snapshot): - col_names = ["datetime_col", "timestamp_col", "date_col"] - bf_df = scalar_types_df[col_names] - ops_map = {col_name: ops.dayofweek_op.as_expr(col_name) for col_name in col_names} - - sql = utils._apply_ops_to_sql(bf_df, list(ops_map.values()), list(ops_map.keys())) - snapshot.assert_match(sql, "out.sql") - - -def test_dayofyear(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "timestamp_col" - bf_df = scalar_types_df[[col_name]] - sql = utils._apply_ops_to_sql( - bf_df, [ops.dayofyear_op.as_expr(col_name)], [col_name] - ) - - snapshot.assert_match(sql, "out.sql") - - -def test_datetime_to_integer_label(scalar_types_df: bpd.DataFrame, snapshot): - col_names = ["datetime_col", "timestamp_col"] - bf_df = scalar_types_df[col_names] - ops_map = { - "fixed_freq": ops.DatetimeToIntegerLabelOp( - freq=pd.tseries.offsets.Day(), # type: ignore[arg-type] - origin="start", - closed="left", # type: ignore - ).as_expr("datetime_col", "timestamp_col"), - "origin_epoch": ops.DatetimeToIntegerLabelOp( - freq=pd.tseries.offsets.Day(), # type: ignore[arg-type] - origin="epoch", - closed="left", # type: ignore - ).as_expr("datetime_col", "timestamp_col"), - "origin_start_day": ops.DatetimeToIntegerLabelOp( - freq=pd.tseries.offsets.Day(), # type: ignore[arg-type] - origin="start_day", - closed="left", # type: ignore - ).as_expr("datetime_col", "timestamp_col"), - "non_fixed_freq_weekly": ops.DatetimeToIntegerLabelOp( - freq=pd.tseries.offsets.Week(weekday=6), # type: ignore[arg-type] - origin="start", - closed="left", # type: ignore - ).as_expr("datetime_col", "timestamp_col"), - "non_fixed_freq_monthly": ops.DatetimeToIntegerLabelOp( - freq=pd.tseries.offsets.MonthEnd(), # type: ignore[arg-type] - origin="start", - closed="left", # type: ignore - ).as_expr("datetime_col", "timestamp_col"), - "non_fixed_freq_quarterly": ops.DatetimeToIntegerLabelOp( - freq=pd.tseries.offsets.QuarterEnd(startingMonth=12), # type: ignore[arg-type] - origin="start", - closed="left", # type: ignore - ).as_expr("datetime_col", "timestamp_col"), - "non_fixed_freq_yearly": ops.DatetimeToIntegerLabelOp( - freq=pd.tseries.offsets.YearEnd(), # type: ignore[arg-type] - origin="start", - closed="left", # type: ignore - ).as_expr("datetime_col", "timestamp_col"), - } - - sql = utils._apply_ops_to_sql(bf_df, list(ops_map.values()), list(ops_map.keys())) - snapshot.assert_match(sql, "out.sql") - - -def test_floor_dt(scalar_types_df: bpd.DataFrame, snapshot): - col_names = ["datetime_col", "timestamp_col", "date_col"] - bf_df = scalar_types_df[col_names] - ops_map = { - "timestamp_col_us": ops.FloorDtOp("us").as_expr("timestamp_col"), - "timestamp_col_ms": ops.FloorDtOp("ms").as_expr("timestamp_col"), - "timestamp_col_s": ops.FloorDtOp("s").as_expr("timestamp_col"), - "timestamp_col_min": ops.FloorDtOp("min").as_expr("timestamp_col"), - "timestamp_col_h": ops.FloorDtOp("h").as_expr("timestamp_col"), - "timestamp_col_D": ops.FloorDtOp("D").as_expr("timestamp_col"), - "timestamp_col_W": ops.FloorDtOp("W").as_expr("timestamp_col"), - "timestamp_col_M": ops.FloorDtOp("M").as_expr("timestamp_col"), - "timestamp_col_Q": ops.FloorDtOp("Q").as_expr("timestamp_col"), - "timestamp_col_Y": ops.FloorDtOp("Y").as_expr("timestamp_col"), - "datetime_col_q": ops.FloorDtOp("us").as_expr("datetime_col"), - "datetime_col_us": ops.FloorDtOp("us").as_expr("datetime_col"), - } - - sql = utils._apply_ops_to_sql(bf_df, list(ops_map.values()), list(ops_map.keys())) - snapshot.assert_match(sql, "out.sql") - - -def test_floor_dt_op_invalid_freq(scalar_types_df: bpd.DataFrame): - col_name = "timestamp_col" - bf_df = scalar_types_df[[col_name]] - with pytest.raises( - NotImplementedError, match="Unsupported freq paramater: invalid" - ): - utils._apply_ops_to_sql( - bf_df, - [ops.FloorDtOp(freq="invalid").as_expr(col_name)], # type:ignore - [col_name], - ) - - -def test_hour(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "timestamp_col" - bf_df = scalar_types_df[[col_name]] - sql = utils._apply_ops_to_sql(bf_df, [ops.hour_op.as_expr(col_name)], [col_name]) - - snapshot.assert_match(sql, "out.sql") - - -def test_minute(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "timestamp_col" - bf_df = scalar_types_df[[col_name]] - sql = utils._apply_ops_to_sql(bf_df, [ops.minute_op.as_expr(col_name)], [col_name]) - - snapshot.assert_match(sql, "out.sql") - - -def test_month(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "timestamp_col" - bf_df = scalar_types_df[[col_name]] - sql = utils._apply_ops_to_sql(bf_df, [ops.month_op.as_expr(col_name)], [col_name]) - - snapshot.assert_match(sql, "out.sql") - - -def test_normalize(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "timestamp_col" - bf_df = scalar_types_df[[col_name]] - sql = utils._apply_ops_to_sql( - bf_df, [ops.normalize_op.as_expr(col_name)], [col_name] - ) - - snapshot.assert_match(sql, "out.sql") - - -def test_quarter(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "timestamp_col" - bf_df = scalar_types_df[[col_name]] - sql = utils._apply_ops_to_sql(bf_df, [ops.quarter_op.as_expr(col_name)], [col_name]) - - snapshot.assert_match(sql, "out.sql") - - -def test_second(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "timestamp_col" - bf_df = scalar_types_df[[col_name]] - sql = utils._apply_ops_to_sql(bf_df, [ops.second_op.as_expr(col_name)], [col_name]) - - snapshot.assert_match(sql, "out.sql") - - -def test_strftime(scalar_types_df: bpd.DataFrame, snapshot): - bf_df = scalar_types_df[["timestamp_col", "datetime_col", "date_col", "time_col"]] - ops_map = { - "date_col": ops.StrftimeOp("%Y-%m-%d").as_expr("date_col"), - "datetime_col": ops.StrftimeOp("%Y-%m-%d").as_expr("datetime_col"), - "time_col": ops.StrftimeOp("%Y-%m-%d").as_expr("time_col"), - "timestamp_col": ops.StrftimeOp("%Y-%m-%d").as_expr("timestamp_col"), - } - - sql = utils._apply_ops_to_sql(bf_df, list(ops_map.values()), list(ops_map.keys())) - snapshot.assert_match(sql, "out.sql") - - -def test_time(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "timestamp_col" - bf_df = scalar_types_df[[col_name]] - sql = utils._apply_ops_to_sql(bf_df, [ops.time_op.as_expr(col_name)], [col_name]) - - snapshot.assert_match(sql, "out.sql") - - -def test_to_datetime(scalar_types_df: bpd.DataFrame, snapshot): - col_names = ["int64_col", "string_col", "float64_col", "timestamp_col"] - bf_df = scalar_types_df[col_names] - ops_map = {col_name: ops.ToDatetimeOp().as_expr(col_name) for col_name in col_names} - ops_map["string_col_fmt"] = ops.ToDatetimeOp(format="%Y-%m-%d").as_expr( - "string_col" - ) - - sql = utils._apply_ops_to_sql(bf_df, list(ops_map.values()), list(ops_map.keys())) - snapshot.assert_match(sql + "\n", "out.sql") - - -def test_to_timestamp(scalar_types_df: bpd.DataFrame, snapshot): - bf_df = scalar_types_df[["int64_col", "string_col", "float64_col", "datetime_col"]] - ops_map = { - "int64_col": ops.ToTimestampOp().as_expr("int64_col"), - "float64_col": ops.ToTimestampOp().as_expr("float64_col"), - "int64_col_s": ops.ToTimestampOp(unit="s").as_expr("int64_col"), - "int64_col_ms": ops.ToTimestampOp(unit="ms").as_expr("int64_col"), - "int64_col_us": ops.ToTimestampOp(unit="us").as_expr("int64_col"), - "int64_col_ns": ops.ToTimestampOp(unit="ns").as_expr("int64_col"), - "datetime_col": ops.ToTimestampOp().as_expr("datetime_col"), - "string_col_fmt": ops.ToTimestampOp(format="%Y-%m-%d").as_expr("string_col"), - } - - sql = utils._apply_ops_to_sql(bf_df, list(ops_map.values()), list(ops_map.keys())) - snapshot.assert_match(sql, "out.sql") - - -def test_unix_micros(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "timestamp_col" - bf_df = scalar_types_df[[col_name]] - sql = utils._apply_ops_to_sql( - bf_df, [ops.UnixMicros().as_expr(col_name)], [col_name] - ) - - snapshot.assert_match(sql, "out.sql") - - -def test_unix_millis(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "timestamp_col" - bf_df = scalar_types_df[[col_name]] - sql = utils._apply_ops_to_sql( - bf_df, [ops.UnixMillis().as_expr(col_name)], [col_name] - ) - - snapshot.assert_match(sql, "out.sql") - - -def test_unix_seconds(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "timestamp_col" - bf_df = scalar_types_df[[col_name]] - sql = utils._apply_ops_to_sql( - bf_df, [ops.UnixSeconds().as_expr(col_name)], [col_name] - ) - - snapshot.assert_match(sql, "out.sql") - - -def test_year(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "timestamp_col" - bf_df = scalar_types_df[[col_name]] - sql = utils._apply_ops_to_sql(bf_df, [ops.year_op.as_expr(col_name)], [col_name]) - - snapshot.assert_match(sql, "out.sql") - - -def test_iso_day(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "timestamp_col" - bf_df = scalar_types_df[[col_name]] - sql = utils._apply_ops_to_sql(bf_df, [ops.iso_day_op.as_expr(col_name)], [col_name]) - - snapshot.assert_match(sql, "out.sql") - - -def test_iso_week(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "timestamp_col" - bf_df = scalar_types_df[[col_name]] - sql = utils._apply_ops_to_sql( - bf_df, [ops.iso_week_op.as_expr(col_name)], [col_name] - ) - - snapshot.assert_match(sql, "out.sql") - - -def test_iso_year(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "timestamp_col" - bf_df = scalar_types_df[[col_name]] - sql = utils._apply_ops_to_sql( - bf_df, [ops.iso_year_op.as_expr(col_name)], [col_name] - ) - - snapshot.assert_match(sql, "out.sql") - - -def test_add_timedelta(scalar_types_df: bpd.DataFrame, snapshot): - bf_df = scalar_types_df[["timestamp_col", "date_col"]] - timedelta = pd.Timedelta(1, unit="d") - - bf_df["date_add_timedelta"] = bf_df["date_col"] + timedelta - bf_df["timestamp_add_timedelta"] = bf_df["timestamp_col"] + timedelta - bf_df["timedelta_add_date"] = timedelta + bf_df["date_col"] - bf_df["timedelta_add_timestamp"] = timedelta + bf_df["timestamp_col"] - bf_df["timedelta_add_timedelta"] = timedelta + timedelta - - snapshot.assert_match(bf_df.sql, "out.sql") - - -def test_sub_timedelta(scalar_types_df: bpd.DataFrame, snapshot): - bf_df = scalar_types_df[["timestamp_col", "duration_col", "date_col"]] - bf_df["duration_col"] = bpd.to_timedelta(bf_df["duration_col"], unit="us") - - bf_df["date_sub_timedelta"] = bf_df["date_col"] - bf_df["duration_col"] - bf_df["timestamp_sub_timedelta"] = bf_df["timestamp_col"] - bf_df["duration_col"] - bf_df["timestamp_sub_date"] = bf_df["date_col"] - bf_df["date_col"] - bf_df["date_sub_timestamp"] = bf_df["timestamp_col"] - bf_df["timestamp_col"] - bf_df["timedelta_sub_timedelta"] = bf_df["duration_col"] - bf_df["duration_col"] - - snapshot.assert_match(bf_df.sql, "out.sql") - - -def test_integer_label_to_datetime_fixed(scalar_types_df: bpd.DataFrame, snapshot): - col_names = ["rowindex", "timestamp_col"] - bf_df = scalar_types_df[col_names] - ops_map = { - "fixed_freq": ops.IntegerLabelToDatetimeOp( - freq=pd.tseries.offsets.Day(), # type: ignore[arg-type] - origin="start", - label="left", # type: ignore - ).as_expr("rowindex", "timestamp_col"), - } - - sql = utils._apply_ops_to_sql(bf_df, list(ops_map.values()), list(ops_map.keys())) - snapshot.assert_match(sql, "out.sql") - - -def test_integer_label_to_datetime_week(scalar_types_df: bpd.DataFrame, snapshot): - col_names = ["rowindex", "timestamp_col"] - bf_df = scalar_types_df[col_names] - ops_map = { - "non_fixed_freq_weekly": ops.IntegerLabelToDatetimeOp( - freq=pd.tseries.offsets.Week(weekday=6), # type: ignore[arg-type] - origin="start", - label="left", # type: ignore - ).as_expr("rowindex", "timestamp_col"), - } - - sql = utils._apply_ops_to_sql(bf_df, list(ops_map.values()), list(ops_map.keys())) - snapshot.assert_match(sql, "out.sql") - - -def test_integer_label_to_datetime_month(scalar_types_df: bpd.DataFrame, snapshot): - col_names = ["rowindex", "timestamp_col"] - bf_df = scalar_types_df[col_names] - ops_map = { - "non_fixed_freq_monthly": ops.IntegerLabelToDatetimeOp( - freq=pd.tseries.offsets.MonthEnd(), # type: ignore - origin="start", - label="left", - ).as_expr("rowindex", "timestamp_col"), - } - - sql = utils._apply_ops_to_sql(bf_df, list(ops_map.values()), list(ops_map.keys())) - snapshot.assert_match(sql, "out.sql") - - -def test_integer_label_to_datetime_quarter(scalar_types_df: bpd.DataFrame, snapshot): - col_names = ["rowindex", "timestamp_col"] - bf_df = scalar_types_df[col_names] - ops_map = { - "non_fixed_freq": ops.IntegerLabelToDatetimeOp( - freq=pd.tseries.offsets.QuarterEnd(startingMonth=12), # type: ignore - origin="start", - label="left", - ).as_expr("rowindex", "timestamp_col"), - } - - sql = utils._apply_ops_to_sql(bf_df, list(ops_map.values()), list(ops_map.keys())) - snapshot.assert_match(sql, "out.sql") - - -def test_integer_label_to_datetime_year(scalar_types_df: bpd.DataFrame, snapshot): - col_names = ["rowindex", "timestamp_col"] - bf_df = scalar_types_df[col_names] - ops_map = { - "non_fixed_freq_yearly": ops.IntegerLabelToDatetimeOp( - freq=pd.tseries.offsets.YearEnd(month=12), # type: ignore - origin="start", - label="left", - ).as_expr("rowindex", "timestamp_col"), - } - - sql = utils._apply_ops_to_sql(bf_df, list(ops_map.values()), list(ops_map.keys())) - snapshot.assert_match(sql, "out.sql") diff --git a/tests/unit/core/compile/sqlglot/expressions/test_generic_ops.py b/tests/unit/core/compile/sqlglot/expressions/test_generic_ops.py deleted file mode 100644 index e3669e1b0ed..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/test_generic_ops.py +++ /dev/null @@ -1,357 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import pandas as pd -import pytest -from google.cloud import bigquery - -import bigframes.pandas as bpd -from bigframes import dtypes -from bigframes import operations as ops -from bigframes.core import expression as ex -from bigframes.functions import udf_def -from bigframes.testing import utils - -pytest.importorskip("pytest_snapshot") - - -def test_astype_int(scalar_types_df: bpd.DataFrame, snapshot): - bf_df = scalar_types_df - to_type = dtypes.INT_DTYPE - - ops_map = { - "datetime_col": ops.AsTypeOp(to_type=to_type).as_expr("datetime_col"), - "datetime_w_safe": ops.AsTypeOp(to_type=to_type, safe=True).as_expr( - "datetime_col" - ), - "time_col": ops.AsTypeOp(to_type=to_type).as_expr("time_col"), - "time_w_safe": ops.AsTypeOp(to_type=to_type, safe=True).as_expr("time_col"), - "timestamp_col": ops.AsTypeOp(to_type=to_type).as_expr("timestamp_col"), - "numeric_col": ops.AsTypeOp(to_type=to_type).as_expr("numeric_col"), - "float64_col": ops.AsTypeOp(to_type=to_type).as_expr("float64_col"), - "float64_w_safe": ops.AsTypeOp(to_type=to_type, safe=True).as_expr( - "float64_col" - ), - "str_const": ops.AsTypeOp(to_type=to_type).as_expr(ex.const("100")), - } - - sql = utils._apply_ops_to_sql(bf_df, list(ops_map.values()), list(ops_map.keys())) - snapshot.assert_match(sql, "out.sql") - - -def test_astype_float(scalar_types_df: bpd.DataFrame, snapshot): - bf_df = scalar_types_df - to_type = dtypes.FLOAT_DTYPE - - ops_map = { - "bool_col": ops.AsTypeOp(to_type=to_type).as_expr("bool_col"), - "str_const": ops.AsTypeOp(to_type=to_type).as_expr(ex.const("1.34235e4")), - "bool_w_safe": ops.AsTypeOp(to_type=to_type, safe=True).as_expr("bool_col"), - } - sql = utils._apply_ops_to_sql(bf_df, list(ops_map.values()), list(ops_map.keys())) - snapshot.assert_match(sql + "\n", "out.sql") - - -def test_astype_bool(scalar_types_df: bpd.DataFrame, snapshot): - bf_df = scalar_types_df - to_type = dtypes.BOOL_DTYPE - - ops_map = { - "bool_col": ops.AsTypeOp(to_type=to_type).as_expr("bool_col"), - "float64_col": ops.AsTypeOp(to_type=to_type).as_expr("float64_col"), - "float64_w_safe": ops.AsTypeOp(to_type=to_type, safe=True).as_expr( - "float64_col" - ), - } - sql = utils._apply_ops_to_sql(bf_df, list(ops_map.values()), list(ops_map.keys())) - snapshot.assert_match(sql, "out.sql") - - -def test_astype_time_like(scalar_types_df: bpd.DataFrame, snapshot): - bf_df = scalar_types_df - - ops_map = { - "int64_to_datetime": ops.AsTypeOp(to_type=dtypes.DATETIME_DTYPE).as_expr( - "int64_col" - ), - "int64_to_time": ops.AsTypeOp(to_type=dtypes.TIME_DTYPE).as_expr("int64_col"), - "int64_to_timestamp": ops.AsTypeOp(to_type=dtypes.TIMESTAMP_DTYPE).as_expr( - "int64_col" - ), - "int64_to_time_safe": ops.AsTypeOp( - to_type=dtypes.TIME_DTYPE, safe=True - ).as_expr("int64_col"), - } - sql = utils._apply_ops_to_sql(bf_df, list(ops_map.values()), list(ops_map.keys())) - snapshot.assert_match(sql, "out.sql") - - -def test_astype_string(scalar_types_df: bpd.DataFrame, snapshot): - bf_df = scalar_types_df - to_type = dtypes.STRING_DTYPE - - ops_map = { - "int64_col": ops.AsTypeOp(to_type=to_type).as_expr("int64_col"), - "bool_col": ops.AsTypeOp(to_type=to_type).as_expr("bool_col"), - "bool_w_safe": ops.AsTypeOp(to_type=to_type, safe=True).as_expr("bool_col"), - } - sql = utils._apply_ops_to_sql(bf_df, list(ops_map.values()), list(ops_map.keys())) - snapshot.assert_match(sql + "\n", "out.sql") - - -def test_to_json(scalar_types_df: bpd.DataFrame, snapshot): - bf_df = scalar_types_df - - ops_map = { - "int64_col": ops.ToJSON().as_expr("int64_col"), - "float64_col": ops.ToJSON().as_expr("float64_col"), - "bool_col": ops.ToJSON().as_expr("bool_col"), - "string_col": ops.ToJSON().as_expr("string_col"), - "bool_w_safe": ops.ToJSON(safe=True).as_expr("bool_col"), - "string_w_safe": ops.ToJSON(safe=True).as_expr("string_col"), - } - sql = utils._apply_ops_to_sql(bf_df, list(ops_map.values()), list(ops_map.keys())) - snapshot.assert_match(sql, "out.sql") - - -def test_astype_from_json(json_types_df: bpd.DataFrame, snapshot): - bf_df = json_types_df - - ops_map = { - "int64_col": ops.JSONDecode(to_type=dtypes.INT_DTYPE).as_expr("json_col"), - "float64_col": ops.JSONDecode(to_type=dtypes.FLOAT_DTYPE).as_expr("json_col"), - "bool_col": ops.JSONDecode(to_type=dtypes.BOOL_DTYPE).as_expr("json_col"), - "string_col": ops.JSONDecode(to_type=dtypes.STRING_DTYPE).as_expr("json_col"), - "int64_w_safe": ops.JSONDecode(to_type=dtypes.INT_DTYPE, safe=True).as_expr( - "json_col" - ), - } - sql = utils._apply_ops_to_sql(bf_df, list(ops_map.values()), list(ops_map.keys())) - snapshot.assert_match(sql, "out.sql") - - -def test_tojson_invalid(scalar_types_df: bpd.DataFrame, json_types_df: bpd.DataFrame): - # Test invalid cast to JSON - with pytest.raises(TypeError): - ops_map_to = { - "datetime_to_json": ops.ToJSON().as_expr("datetime_col"), - } - utils._apply_ops_to_sql( - scalar_types_df, list(ops_map_to.values()), list(ops_map_to.keys()) - ) - - # Test invalid cast from JSON - with pytest.raises(TypeError): - ops_map_from = { - "json_to_datetime": ops.JSONDecode(to_type=dtypes.DATETIME_DTYPE).as_expr( - "json_col" - ), - } - utils._apply_ops_to_sql( - json_types_df, list(ops_map_from.values()), list(ops_map_from.keys()) - ) - - -def test_remote_function_op(scalar_types_df: bpd.DataFrame, snapshot): - bf_df = scalar_types_df[["int64_col", "float64_col", "string_col"]] - op = ops.RemoteFunctionOp( - function_def=udf_def.BigqueryUdf( - routine_ref=bigquery.RoutineReference.from_string( - "my_project.my_dataset.my_routine" - ), - signature=udf_def.UdfSignature( - inputs=( - udf_def.UdfArg( - "x", - udf_def.DirectScalarType(int), - ), - udf_def.UdfArg( - "y", - udf_def.DirectScalarType(float), - ), - udf_def.UdfArg( - "z", - udf_def.DirectScalarType(str), - ), - ), - output=udf_def.DirectScalarType(float), - ), - ) - ) - sql = utils._apply_nary_op(bf_df, op, "int64_col", "float64_col", "string_col") - snapshot.assert_match(sql, "out.sql") - - -def test_case_when_op(scalar_types_df: bpd.DataFrame, snapshot): - ops_map = { - "single_case": ops.case_when_op.as_expr( - "bool_col", - "int64_col", - ), - "double_case": ops.case_when_op.as_expr( - "bool_col", - "int64_col", - "bool_col", - "int64_too", - ), - "bool_types_case": ops.case_when_op.as_expr( - "bool_col", - "bool_col", - "bool_col", - "bool_col", - ), - "mixed_types_cast": ops.case_when_op.as_expr( - "bool_col", - "int64_col", - "bool_col", - "bool_col", - "bool_col", - "float64_col", - ), - } - - array_value = scalar_types_df._block.expr - result, col_ids = array_value.compute_values(list(ops_map.values())) - - # Rename columns for deterministic golden SQL results. - assert len(col_ids) == len(ops_map.keys()) - result = result.rename_columns( - {col_id: key for col_id, key in zip(col_ids, ops_map.keys())} - ).select_columns(list(ops_map.keys())) - - sql = result.session._executor.to_sql(result, enable_cache=False) - snapshot.assert_match(sql, "out.sql") - - -def test_coalesce(scalar_types_df: bpd.DataFrame, snapshot): - bf_df = scalar_types_df[["int64_col", "int64_too"]] - - sql = utils._apply_ops_to_sql( - bf_df, - [ - ops.coalesce_op.as_expr("int64_col", "int64_col"), - ops.coalesce_op.as_expr("int64_too", "int64_col"), - ], - ["int64_col", "int64_too"], - ) - snapshot.assert_match(sql, "out.sql") - - -def test_clip(scalar_types_df: bpd.DataFrame, snapshot): - op_expr = ops.clip_op.as_expr("rowindex", "int64_col", "int64_too") - - array_value = scalar_types_df._block.expr - result, col_ids = array_value.compute_values([op_expr]) - - # Rename columns for deterministic golden SQL results. - assert len(col_ids) == 1 - result = result.rename_columns({col_ids[0]: "result_col"}).select_columns( - ["result_col"] - ) - - sql = result.session._executor.to_sql(result, enable_cache=False) - snapshot.assert_match(sql, "out.sql") - - -def test_fillna(scalar_types_df: bpd.DataFrame, snapshot): - bf_df = scalar_types_df[["int64_col", "float64_col"]] - sql = utils._apply_binary_op(bf_df, ops.fillna_op, "int64_col", "float64_col") - snapshot.assert_match(sql, "out.sql") - - -def test_hash(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "string_col" - bf_df = scalar_types_df[[col_name]] - sql = utils._apply_ops_to_sql(bf_df, [ops.hash_op.as_expr(col_name)], [col_name]) - - snapshot.assert_match(sql, "out.sql") - - -def test_invert(scalar_types_df: bpd.DataFrame, snapshot): - bf_df = scalar_types_df[["int64_col", "bytes_col", "bool_col"]] - ops_map = { - "int64_col": ops.invert_op.as_expr("int64_col"), - "bytes_col": ops.invert_op.as_expr("bytes_col"), - "bool_col": ops.invert_op.as_expr("bool_col"), - } - sql = utils._apply_ops_to_sql(bf_df, list(ops_map.values()), list(ops_map.keys())) - - snapshot.assert_match(sql, "out.sql") - - -def test_isnull(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "float64_col" - bf_df = scalar_types_df[[col_name]] - sql = utils._apply_ops_to_sql(bf_df, [ops.isnull_op.as_expr(col_name)], [col_name]) - - snapshot.assert_match(sql, "out.sql") - - -def test_notnull(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "float64_col" - bf_df = scalar_types_df[[col_name]] - sql = utils._apply_ops_to_sql(bf_df, [ops.notnull_op.as_expr(col_name)], [col_name]) - - snapshot.assert_match(sql, "out.sql") - - -def test_row_key(scalar_types_df: bpd.DataFrame, snapshot): - column_ids = (col for col in scalar_types_df._block.expr.column_ids) - sql = utils._apply_ops_to_sql( - scalar_types_df, [ops.RowKey().as_expr(*column_ids)], ["row_key"] - ) - snapshot.assert_match(sql, "out.sql") - - -def test_sql_scalar_op(scalar_types_df: bpd.DataFrame, snapshot): - bf_df = scalar_types_df[["bool_col", "bytes_col"]] - sql = utils._apply_nary_op( - bf_df, - ops.SqlScalarOp(dtypes.INT_DTYPE, "CAST({0} AS INT64) + BYTE_LENGTH({1})"), - "bool_col", - "bytes_col", - ) - snapshot.assert_match(sql, "out.sql") - - -def test_map(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "string_col" - bf_df = scalar_types_df[[col_name]] - sql = utils._apply_ops_to_sql( - bf_df, - [ - ops.MapOp(mappings=(("value1", "mapped1"), (pd.NA, "UNKNOWN"))).as_expr( - col_name - ) - ], - [col_name], - ) - - snapshot.assert_match(sql, "out.sql") - - -def test_where(scalar_types_df: bpd.DataFrame, snapshot): - op_expr = ops.where_op.as_expr("int64_col", "bool_col", "float64_col") - - array_value = scalar_types_df._block.expr - result, col_ids = array_value.compute_values([op_expr]) - - # Rename columns for deterministic golden SQL results. - assert len(col_ids) == 1 - result = result.rename_columns({col_ids[0]: "result_col"}).select_columns( - ["result_col"] - ) - - sql = result.session._executor.to_sql(result, enable_cache=False) - snapshot.assert_match(sql, "out.sql") diff --git a/tests/unit/core/compile/sqlglot/expressions/test_geo_ops.py b/tests/unit/core/compile/sqlglot/expressions/test_geo_ops.py deleted file mode 100644 index 85e374c76db..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/test_geo_ops.py +++ /dev/null @@ -1,148 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import pytest - -import bigframes.pandas as bpd -from bigframes import operations as ops -from bigframes.testing import utils - -pytest.importorskip("pytest_snapshot") - - -def test_geo_st_astext(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "geography_col" - bf_df = scalar_types_df[[col_name]] - sql = utils._apply_ops_to_sql( - bf_df, [ops.geo_st_astext_op.as_expr(col_name)], [col_name] - ) - - snapshot.assert_match(sql, "out.sql") - - -def test_geo_st_boundary(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "geography_col" - bf_df = scalar_types_df[[col_name]] - sql = utils._apply_ops_to_sql( - bf_df, [ops.geo_st_boundary_op.as_expr(col_name)], [col_name] - ) - - snapshot.assert_match(sql, "out.sql") - - -def test_geo_st_buffer(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "geography_col" - bf_df = scalar_types_df[[col_name]] - sql = utils._apply_ops_to_sql( - bf_df, [ops.GeoStBufferOp(1.0, 8.0, False).as_expr(col_name)], [col_name] - ) - - snapshot.assert_match(sql, "out.sql") - - -def test_geo_st_convexhull(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "geography_col" - bf_df = scalar_types_df[[col_name]] - sql = utils._apply_ops_to_sql( - bf_df, [ops.geo_st_convexhull_op.as_expr(col_name)], [col_name] - ) - - snapshot.assert_match(sql, "out.sql") - - -def test_geo_st_distance(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "geography_col" - bf_df = scalar_types_df[[col_name]] - - sql = utils._apply_ops_to_sql( - bf_df, - [ - ops.GeoStDistanceOp(use_spheroid=True).as_expr(col_name, col_name), - ops.GeoStDistanceOp(use_spheroid=False).as_expr(col_name, col_name), - ], - ["spheroid", "no_spheroid"], - ) - snapshot.assert_match(sql, "out.sql") - - -def test_geo_st_difference(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "geography_col" - bf_df = scalar_types_df[[col_name]] - sql = utils._apply_binary_op(bf_df, ops.geo_st_difference_op, col_name, col_name) - - snapshot.assert_match(sql, "out.sql") - - -def test_geo_st_geogfromtext(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "string_col" - bf_df = scalar_types_df[[col_name]] - sql = utils._apply_ops_to_sql( - bf_df, [ops.geo_st_geogfromtext_op.as_expr(col_name)], [col_name] - ) - - snapshot.assert_match(sql, "out.sql") - - -def test_geo_st_geogpoint(scalar_types_df: bpd.DataFrame, snapshot): - col_names = ["rowindex", "rowindex_2"] - bf_df = scalar_types_df[col_names] - sql = utils._apply_binary_op( - bf_df, ops.geo_st_geogpoint_op, col_names[0], col_names[1] - ) - - snapshot.assert_match(sql, "out.sql") - - -def test_geo_st_intersection(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "geography_col" - bf_df = scalar_types_df[[col_name]] - sql = utils._apply_binary_op(bf_df, ops.geo_st_intersection_op, col_name, col_name) - - snapshot.assert_match(sql, "out.sql") - - -def test_geo_st_isclosed(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "geography_col" - bf_df = scalar_types_df[[col_name]] - sql = utils._apply_ops_to_sql( - bf_df, [ops.geo_st_isclosed_op.as_expr(col_name)], [col_name] - ) - - snapshot.assert_match(sql, "out.sql") - - -def test_geo_st_length(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "geography_col" - bf_df = scalar_types_df[[col_name]] - sql = utils._apply_ops_to_sql( - bf_df, [ops.GeoStLengthOp(True).as_expr(col_name)], [col_name] - ) - - snapshot.assert_match(sql, "out.sql") - - -def test_geo_x(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "geography_col" - bf_df = scalar_types_df[[col_name]] - sql = utils._apply_ops_to_sql(bf_df, [ops.geo_x_op.as_expr(col_name)], [col_name]) - - snapshot.assert_match(sql, "out.sql") - - -def test_geo_y(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "geography_col" - bf_df = scalar_types_df[[col_name]] - sql = utils._apply_ops_to_sql(bf_df, [ops.geo_y_op.as_expr(col_name)], [col_name]) - - snapshot.assert_match(sql, "out.sql") diff --git a/tests/unit/core/compile/sqlglot/expressions/test_json_ops.py b/tests/unit/core/compile/sqlglot/expressions/test_json_ops.py deleted file mode 100644 index 69eb8681abc..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/test_json_ops.py +++ /dev/null @@ -1,142 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import pytest - -import bigframes.core.expression as ex -import bigframes.pandas as bpd -from bigframes import operations as ops -from bigframes.testing import utils - -pytest.importorskip("pytest_snapshot") - - -def test_json_extract(json_types_df: bpd.DataFrame, snapshot): - col_name = "json_col" - bf_df = json_types_df[[col_name]] - sql = utils._apply_ops_to_sql( - bf_df, [ops.JSONExtract(json_path="$").as_expr(col_name)], [col_name] - ) - - snapshot.assert_match(sql, "out.sql") - - -def test_json_extract_array(json_types_df: bpd.DataFrame, snapshot): - col_name = "json_col" - bf_df = json_types_df[[col_name]] - sql = utils._apply_ops_to_sql( - bf_df, [ops.JSONExtractArray(json_path="$").as_expr(col_name)], [col_name] - ) - - snapshot.assert_match(sql, "out.sql") - - -def test_json_extract_string_array(json_types_df: bpd.DataFrame, snapshot): - col_name = "json_col" - bf_df = json_types_df[[col_name]] - sql = utils._apply_ops_to_sql( - bf_df, [ops.JSONExtractStringArray(json_path="$").as_expr(col_name)], [col_name] - ) - - snapshot.assert_match(sql, "out.sql") - - -def test_json_keys(json_types_df: bpd.DataFrame, snapshot): - col_name = "json_col" - bf_df = json_types_df[[col_name]] - - ops_map = { - "json_keys": ops.JSONKeys().as_expr(col_name), - "json_keys_w_max_depth": ops.JSONKeys(max_depth=2).as_expr(col_name), - } - - sql = utils._apply_ops_to_sql(bf_df, list(ops_map.values()), list(ops_map.keys())) - snapshot.assert_match(sql, "out.sql") - - -def test_json_query(json_types_df: bpd.DataFrame, snapshot): - col_name = "json_col" - bf_df = json_types_df[[col_name]] - sql = utils._apply_ops_to_sql( - bf_df, [ops.JSONQuery(json_path="$").as_expr(col_name)], [col_name] - ) - - snapshot.assert_match(sql, "out.sql") - - -def test_json_query_array(json_types_df: bpd.DataFrame, snapshot): - col_name = "json_col" - bf_df = json_types_df[[col_name]] - sql = utils._apply_ops_to_sql( - bf_df, [ops.JSONQueryArray(json_path="$").as_expr(col_name)], [col_name] - ) - - snapshot.assert_match(sql, "out.sql") - - -def test_json_value(json_types_df: bpd.DataFrame, snapshot): - col_name = "json_col" - bf_df = json_types_df[[col_name]] - sql = utils._apply_ops_to_sql( - bf_df, [ops.JSONValue(json_path="$").as_expr(col_name)], [col_name] - ) - - snapshot.assert_match(sql, "out.sql") - - -def test_json_value_array(json_types_df: bpd.DataFrame, snapshot): - col_name = "json_col" - bf_df = json_types_df[[col_name]] - sql = utils._apply_ops_to_sql( - bf_df, [ops.JSONValueArray(json_path="$").as_expr(col_name)], [col_name] - ) - - snapshot.assert_match(sql, "out.sql") - - -def test_parse_json(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "string_col" - bf_df = scalar_types_df[[col_name]] - sql = utils._apply_ops_to_sql( - bf_df, [ops.ParseJSON().as_expr(col_name)], [col_name] - ) - - snapshot.assert_match(sql, "out.sql") - - -def test_to_json(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "string_col" - bf_df = scalar_types_df[[col_name]] - sql = utils._apply_ops_to_sql(bf_df, [ops.ToJSON().as_expr(col_name)], [col_name]) - - snapshot.assert_match(sql, "out.sql") - - -def test_to_json_string(json_types_df: bpd.DataFrame, snapshot): - col_name = "json_col" - bf_df = json_types_df[[col_name]] - sql = utils._apply_ops_to_sql( - bf_df, [ops.ToJSONString().as_expr(col_name)], [col_name] - ) - - snapshot.assert_match(sql, "out.sql") - - -def test_json_set(json_types_df: bpd.DataFrame, snapshot): - bf_df = json_types_df[["json_col"]] - sql = utils._apply_binary_op( - bf_df, ops.JSONSet(json_path="$.a"), "json_col", ex.const(100) - ) - - snapshot.assert_match(sql, "out.sql") diff --git a/tests/unit/core/compile/sqlglot/expressions/test_literals.py b/tests/unit/core/compile/sqlglot/expressions/test_literals.py deleted file mode 100644 index aa0d7a1e5b1..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/test_literals.py +++ /dev/null @@ -1,35 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import pytest - -import bigframes.core.expression as ex -import bigframes.pandas as bpd -from bigframes.testing import utils - -pytest.importorskip("pytest_snapshot") - - -def test_float_literals(scalar_types_df: bpd.DataFrame, snapshot): - bf_df = scalar_types_df[["float64_col"]] - ops_map = { - "inf": ex.const(float("inf")), - "ninf": ex.const(float("-inf")), - "nan": ex.const(float("nan")), - "neg_zero": ex.const(-0.0), - "0.00001": ex.const(0.00001), - "1E-10": ex.const(1e-10), - } - sql = utils._apply_ops_to_sql(bf_df, list(ops_map.values()), list(ops_map.keys())) - snapshot.assert_match(sql, "out.sql") diff --git a/tests/unit/core/compile/sqlglot/expressions/test_numeric_ops.py b/tests/unit/core/compile/sqlglot/expressions/test_numeric_ops.py deleted file mode 100644 index b0442f6992e..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/test_numeric_ops.py +++ /dev/null @@ -1,513 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import pandas as pd -import pytest - -import bigframes.core.expression as ex -import bigframes.pandas as bpd -from bigframes import operations as ops -from bigframes.operations import numeric_ops -from bigframes.testing import utils - -pytest.importorskip("pytest_snapshot") - - -def test_arccosh(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "float64_col" - bf_df = scalar_types_df[[col_name]] - sql = utils._apply_ops_to_sql(bf_df, [ops.arccosh_op.as_expr(col_name)], [col_name]) - - snapshot.assert_match(sql, "out.sql") - - -def test_arccos(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "float64_col" - bf_df = scalar_types_df[[col_name]] - sql = utils._apply_ops_to_sql(bf_df, [ops.arccos_op.as_expr(col_name)], [col_name]) - - snapshot.assert_match(sql, "out.sql") - - -def test_arcsin(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "float64_col" - bf_df = scalar_types_df[[col_name]] - sql = utils._apply_ops_to_sql(bf_df, [ops.arcsin_op.as_expr(col_name)], [col_name]) - - snapshot.assert_match(sql, "out.sql") - - -def test_arcsinh(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "float64_col" - bf_df = scalar_types_df[[col_name]] - sql = utils._apply_ops_to_sql(bf_df, [ops.arcsinh_op.as_expr(col_name)], [col_name]) - - snapshot.assert_match(sql, "out.sql") - - -def test_arctan2(scalar_types_df: bpd.DataFrame, snapshot): - bf_df = scalar_types_df[["int64_col", "float64_col", "bool_col"]] - - sql = utils._apply_ops_to_sql( - bf_df, - [ - ops.arctan2_op.as_expr("int64_col", "float64_col"), - ops.arctan2_op.as_expr("bool_col", "float64_col"), - ], - ["int64_col", "bool_col"], - ) - snapshot.assert_match(sql, "out.sql") - - -def test_arctan(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "float64_col" - bf_df = scalar_types_df[[col_name]] - sql = utils._apply_ops_to_sql(bf_df, [ops.arctan_op.as_expr(col_name)], [col_name]) - - snapshot.assert_match(sql, "out.sql") - - -def test_arctanh(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "float64_col" - bf_df = scalar_types_df[[col_name]] - sql = utils._apply_ops_to_sql(bf_df, [ops.arctanh_op.as_expr(col_name)], [col_name]) - - snapshot.assert_match(sql, "out.sql") - - -def test_abs(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "float64_col" - bf_df = scalar_types_df[[col_name]] - sql = utils._apply_ops_to_sql(bf_df, [ops.abs_op.as_expr(col_name)], [col_name]) - - snapshot.assert_match(sql, "out.sql") - - -def test_ceil(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "float64_col" - bf_df = scalar_types_df[[col_name]] - sql = utils._apply_ops_to_sql(bf_df, [ops.ceil_op.as_expr(col_name)], [col_name]) - - snapshot.assert_match(sql, "out.sql") - - -def test_cos(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "float64_col" - bf_df = scalar_types_df[[col_name]] - sql = utils._apply_ops_to_sql(bf_df, [ops.cos_op.as_expr(col_name)], [col_name]) - - snapshot.assert_match(sql, "out.sql") - - -def test_cosh(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "float64_col" - bf_df = scalar_types_df[[col_name]] - sql = utils._apply_ops_to_sql(bf_df, [ops.cosh_op.as_expr(col_name)], [col_name]) - - snapshot.assert_match(sql, "out.sql") - - -def test_cosine_distance(repeated_types_df: bpd.DataFrame, snapshot): - col_names = ["int_list_col", "float_list_col"] - bf_df = repeated_types_df[col_names] - - sql = utils._apply_ops_to_sql( - bf_df, - [ - ops.cosine_distance_op.as_expr("int_list_col", "int_list_col"), - ops.cosine_distance_op.as_expr("float_list_col", "float_list_col"), - ], - ["int_list_col", "float_list_col"], - ) - snapshot.assert_match(sql, "out.sql") - - -def test_exp(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "float64_col" - bf_df = scalar_types_df[[col_name]] - sql = utils._apply_ops_to_sql(bf_df, [ops.exp_op.as_expr(col_name)], [col_name]) - - snapshot.assert_match(sql, "out.sql") - - -def test_expm1(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "float64_col" - bf_df = scalar_types_df[[col_name]] - sql = utils._apply_ops_to_sql(bf_df, [ops.expm1_op.as_expr(col_name)], [col_name]) - - snapshot.assert_match(sql, "out.sql") - - -def test_floor(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "float64_col" - bf_df = scalar_types_df[[col_name]] - sql = utils._apply_ops_to_sql(bf_df, [ops.floor_op.as_expr(col_name)], [col_name]) - - snapshot.assert_match(sql, "out.sql") - - -def test_isfinite(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "float64_col" - bf_df = scalar_types_df[[col_name]] - sql = utils._apply_ops_to_sql( - bf_df, [numeric_ops.isfinite_op.as_expr(col_name)], [col_name] - ) - - snapshot.assert_match(sql, "out.sql") - - -def test_ln(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "float64_col" - bf_df = scalar_types_df[[col_name]] - sql = utils._apply_ops_to_sql(bf_df, [ops.ln_op.as_expr(col_name)], [col_name]) - - snapshot.assert_match(sql, "out.sql") - - -def test_log10(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "float64_col" - bf_df = scalar_types_df[[col_name]] - sql = utils._apply_ops_to_sql(bf_df, [ops.log10_op.as_expr(col_name)], [col_name]) - - snapshot.assert_match(sql, "out.sql") - - -def test_log1p(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "float64_col" - bf_df = scalar_types_df[[col_name]] - sql = utils._apply_ops_to_sql(bf_df, [ops.log1p_op.as_expr(col_name)], [col_name]) - - snapshot.assert_match(sql, "out.sql") - - -def test_neg(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "float64_col" - bf_df = scalar_types_df[[col_name]] - sql = utils._apply_ops_to_sql(bf_df, [ops.neg_op.as_expr(col_name)], [col_name]) - - snapshot.assert_match(sql, "out.sql") - - -def test_pos(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "float64_col" - bf_df = scalar_types_df[[col_name]] - sql = utils._apply_ops_to_sql(bf_df, [ops.pos_op.as_expr(col_name)], [col_name]) - - snapshot.assert_match(sql, "out.sql") - - -def test_pow(scalar_types_df: bpd.DataFrame, snapshot): - bf_df = scalar_types_df[["int64_col", "float64_col"]] - - bf_df["int_pow_int"] = bf_df["int64_col"] ** bf_df["int64_col"] - bf_df["int_pow_float"] = bf_df["int64_col"] ** bf_df["float64_col"] - bf_df["float_pow_int"] = bf_df["float64_col"] ** bf_df["int64_col"] - bf_df["float_pow_float"] = bf_df["float64_col"] ** bf_df["float64_col"] - - bf_df["int_pow_0"] = bf_df["int64_col"] ** 0 - bf_df["float_pow_0"] = bf_df["float64_col"] ** 0 - bf_df["int_pow_1"] = bf_df["int64_col"] ** 1 - bf_df["float_pow_1"] = bf_df["float64_col"] ** 1 - - bf_df["float_pow_null"] = bf_df["float64_col"] ** pd.NA - bf_df["null_pow_float"] = pd.NA ** bf_df["float64_col"] - - snapshot.assert_match(bf_df.sql, "out.sql") - - -def test_round(scalar_types_df: bpd.DataFrame, snapshot): - bf_df = scalar_types_df[["int64_col", "float64_col"]] - - bf_df["int_round_0"] = bf_df["int64_col"].round(0) - bf_df["int_round_1"] = bf_df["int64_col"].round(1) - bf_df["int_round_m1"] = bf_df["int64_col"].round(-1) - - bf_df["float_round_0"] = bf_df["float64_col"].round(0) - bf_df["float_round_1"] = bf_df["float64_col"].round(1) - bf_df["float_round_m1"] = bf_df["float64_col"].round(-1) - - snapshot.assert_match(bf_df.sql, "out.sql") - - -def test_sqrt(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "float64_col" - bf_df = scalar_types_df[[col_name]] - sql = utils._apply_ops_to_sql(bf_df, [ops.sqrt_op.as_expr(col_name)], [col_name]) - - snapshot.assert_match(sql, "out.sql") - - -def test_sin(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "float64_col" - bf_df = scalar_types_df[[col_name]] - sql = utils._apply_ops_to_sql(bf_df, [ops.sin_op.as_expr(col_name)], [col_name]) - - snapshot.assert_match(sql, "out.sql") - - -def test_sinh(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "float64_col" - bf_df = scalar_types_df[[col_name]] - sql = utils._apply_ops_to_sql(bf_df, [ops.sinh_op.as_expr(col_name)], [col_name]) - - snapshot.assert_match(sql, "out.sql") - - -def test_tan(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "float64_col" - bf_df = scalar_types_df[[col_name]] - sql = utils._apply_ops_to_sql(bf_df, [ops.tan_op.as_expr(col_name)], [col_name]) - - snapshot.assert_match(sql, "out.sql") - - -def test_tanh(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "float64_col" - bf_df = scalar_types_df[[col_name]] - sql = utils._apply_ops_to_sql(bf_df, [ops.tanh_op.as_expr(col_name)], [col_name]) - - snapshot.assert_match(sql, "out.sql") - - -def test_add_numeric(scalar_types_df: bpd.DataFrame, snapshot): - bf_df = scalar_types_df[["int64_col", "bool_col"]] - - bf_df["int_add_int"] = bf_df["int64_col"] + bf_df["int64_col"] - bf_df["int_add_1"] = bf_df["int64_col"] + 1 - bf_df["int_add_null"] = bf_df["int64_col"] + pd.NA - - bf_df["int_add_bool"] = bf_df["int64_col"] + bf_df["bool_col"] - bf_df["bool_add_int"] = bf_df["bool_col"] + bf_df["int64_col"] - - snapshot.assert_match(bf_df.sql, "out.sql") - - -def test_add_string(scalar_types_df: bpd.DataFrame, snapshot): - bf_df = scalar_types_df[["string_col"]] - sql = utils._apply_binary_op(bf_df, ops.add_op, "string_col", ex.const("a")) - - snapshot.assert_match(sql, "out.sql") - - -def test_add_timedelta(scalar_types_df: bpd.DataFrame, snapshot): - bf_df = scalar_types_df[["timestamp_col", "date_col"]] - timedelta = pd.Timedelta(1, unit="d") - - bf_df["date_add_timedelta"] = bf_df["date_col"] + timedelta - bf_df["timestamp_add_timedelta"] = bf_df["timestamp_col"] + timedelta - bf_df["timedelta_add_date"] = timedelta + bf_df["date_col"] - bf_df["timedelta_add_timestamp"] = timedelta + bf_df["timestamp_col"] - bf_df["timedelta_add_timedelta"] = timedelta + timedelta - - snapshot.assert_match(bf_df.sql, "out.sql") - - -def test_add_unsupported_raises(scalar_types_df: bpd.DataFrame): - with pytest.raises(TypeError): - utils._apply_binary_op(scalar_types_df, ops.add_op, "timestamp_col", "date_col") - - with pytest.raises(TypeError): - utils._apply_binary_op(scalar_types_df, ops.add_op, "int64_col", "string_col") - - -def test_div_numeric(scalar_types_df: bpd.DataFrame, snapshot): - bf_df = scalar_types_df[["int64_col", "bool_col", "float64_col"]] - - bf_df["int_div_int"] = bf_df["int64_col"] / bf_df["int64_col"] - bf_df["int_div_1"] = bf_df["int64_col"] / 1 - bf_df["int_div_0"] = bf_df["int64_col"] / 0.0 - bf_df["int_div_null"] = bf_df["int64_col"] / pd.NA - - bf_df["int_div_float"] = bf_df["int64_col"] / bf_df["float64_col"] - bf_df["float_div_int"] = bf_df["float64_col"] / bf_df["int64_col"] - bf_df["float_div_0"] = bf_df["float64_col"] / 0.0 - - bf_df["int_div_bool"] = bf_df["int64_col"] / bf_df["bool_col"] - bf_df["bool_div_int"] = bf_df["bool_col"] / bf_df["int64_col"] - - snapshot.assert_match(bf_df.sql, "out.sql") - - -def test_div_timedelta(scalar_types_df: bpd.DataFrame, snapshot): - bf_df = scalar_types_df[["timestamp_col", "int64_col"]] - timedelta = pd.Timedelta(1, unit="d") - bf_df["timedelta_div_numeric"] = timedelta / bf_df["int64_col"] - - snapshot.assert_match(bf_df.sql, "out.sql") - - -def test_euclidean_distance(repeated_types_df: bpd.DataFrame, snapshot): - col_names = ["int_list_col", "numeric_list_col"] - bf_df = repeated_types_df[col_names] - - sql = utils._apply_ops_to_sql( - bf_df, - [ - ops.euclidean_distance_op.as_expr("int_list_col", "int_list_col"), - ops.euclidean_distance_op.as_expr("numeric_list_col", "numeric_list_col"), - ], - ["int_list_col", "numeric_list_col"], - ) - snapshot.assert_match(sql, "out.sql") - - -def test_floordiv_numeric(scalar_types_df: bpd.DataFrame, snapshot): - bf_df = scalar_types_df[["int64_col", "bool_col", "float64_col"]] - - bf_df["int_div_int"] = bf_df["int64_col"] // bf_df["int64_col"] - bf_df["int_div_1"] = bf_df["int64_col"] // 1 - bf_df["int_div_0"] = bf_df["int64_col"] // 0.0 - bf_df["int_div_null"] = bf_df["int64_col"] // pd.NA - - bf_df["int_div_float"] = bf_df["int64_col"] // bf_df["float64_col"] - bf_df["float_div_int"] = bf_df["float64_col"] // bf_df["int64_col"] - bf_df["float_div_0"] = bf_df["float64_col"] // 0.0 - bf_df["float_div_null"] = bf_df["float64_col"] // pd.NA - - bf_df["int_div_bool"] = bf_df["int64_col"] // bf_df["bool_col"] - bf_df["bool_div_int"] = bf_df["bool_col"] // bf_df["int64_col"] - - snapshot.assert_match(bf_df.sql, "out.sql") - - -def test_floordiv_timedelta(scalar_types_df: bpd.DataFrame, snapshot): - bf_df = scalar_types_df[["timestamp_col", "date_col"]] - timedelta = pd.Timedelta(1, unit="d") - - bf_df["timedelta_div_numeric"] = timedelta // 2 - - snapshot.assert_match(bf_df.sql, "out.sql") - - -def test_manhattan_distance(repeated_types_df: bpd.DataFrame, snapshot): - col_names = ["float_list_col", "numeric_list_col"] - bf_df = repeated_types_df[col_names] - - sql = utils._apply_ops_to_sql( - bf_df, - [ - ops.manhattan_distance_op.as_expr("float_list_col", "float_list_col"), - ops.manhattan_distance_op.as_expr("numeric_list_col", "numeric_list_col"), - ], - ["float_list_col", "numeric_list_col"], - ) - snapshot.assert_match(sql, "out.sql") - - -def test_mul_numeric(scalar_types_df: bpd.DataFrame, snapshot): - bf_df = scalar_types_df[["int64_col", "bool_col"]] - - bf_df["int_mul_int"] = bf_df["int64_col"] * bf_df["int64_col"] - bf_df["int_mul_1"] = bf_df["int64_col"] * 1 - bf_df["int_mul_null"] = bf_df["int64_col"] * pd.NA - - bf_df["int_mul_bool"] = bf_df["int64_col"] * bf_df["bool_col"] - bf_df["bool_mul_int"] = bf_df["bool_col"] * bf_df["int64_col"] - - snapshot.assert_match(bf_df.sql, "out.sql") - - -def test_mul_timedelta(scalar_types_df: bpd.DataFrame, snapshot): - bf_df = scalar_types_df[["timestamp_col", "int64_col", "duration_col"]] - bf_df["duration_col"] = bpd.to_timedelta(bf_df["duration_col"], unit="us") - - bf_df["timedelta_mul_numeric"] = bf_df["duration_col"] * bf_df["int64_col"] - bf_df["numeric_mul_timedelta"] = bf_df["int64_col"] * bf_df["duration_col"] - - snapshot.assert_match(bf_df.sql, "out.sql") - - -def test_mod_numeric(scalar_types_df: bpd.DataFrame, snapshot): - bf_df = scalar_types_df[["int64_col", "float64_col"]] - - bf_df["int_mod_int"] = bf_df["int64_col"] % bf_df["int64_col"] - bf_df["int_mod_int_neg"] = bf_df["int64_col"] % -bf_df["int64_col"] - bf_df["int_mod_1"] = bf_df["int64_col"] % 1 - bf_df["int_mod_0"] = bf_df["int64_col"] % 0 - - bf_df["float_mod_float"] = bf_df["float64_col"] % bf_df["float64_col"] - bf_df["float_mod_float_neg"] = bf_df["float64_col"] % -bf_df["float64_col"] - bf_df["float_mod_1"] = bf_df["float64_col"] % 1 - bf_df["float_mod_0"] = bf_df["float64_col"] % 0 - - bf_df["float_mod_null"] = bf_df["float64_col"] % pd.NA - - snapshot.assert_match(bf_df.sql, "out.sql") - - -def test_sub_numeric(scalar_types_df: bpd.DataFrame, snapshot): - bf_df = scalar_types_df[["int64_col", "bool_col"]] - - bf_df["int_sub_int"] = bf_df["int64_col"] - bf_df["int64_col"] - bf_df["int_sub_1"] = bf_df["int64_col"] - 1 - bf_df["int_sub_null"] = bf_df["int64_col"] - pd.NA - - bf_df["int_sub_bool"] = bf_df["int64_col"] - bf_df["bool_col"] - bf_df["bool_sub_int"] = bf_df["bool_col"] - bf_df["int64_col"] - - snapshot.assert_match(bf_df.sql, "out.sql") - - -def test_sub_timedelta(scalar_types_df: bpd.DataFrame, snapshot): - bf_df = scalar_types_df[["timestamp_col", "duration_col", "date_col"]] - bf_df["duration_col"] = bpd.to_timedelta(bf_df["duration_col"], unit="us") - - bf_df["date_sub_timedelta"] = bf_df["date_col"] - bf_df["duration_col"] - bf_df["timestamp_sub_timedelta"] = bf_df["timestamp_col"] - bf_df["duration_col"] - bf_df["timestamp_sub_date"] = bf_df["date_col"] - bf_df["date_col"] - bf_df["date_sub_timestamp"] = bf_df["timestamp_col"] - bf_df["timestamp_col"] - bf_df["timedelta_sub_timedelta"] = bf_df["duration_col"] - bf_df["duration_col"] - - snapshot.assert_match(bf_df.sql, "out.sql") - - -def test_sub_unsupported_raises(scalar_types_df: bpd.DataFrame): - with pytest.raises(TypeError): - utils._apply_binary_op(scalar_types_df, ops.sub_op, "string_col", "string_col") - - with pytest.raises(TypeError): - utils._apply_binary_op(scalar_types_df, ops.sub_op, "int64_col", "string_col") - - -def test_unsafe_pow_op(scalar_types_df: bpd.DataFrame, snapshot): - # Choose certain row so the sql execution won't fail even with unsafe_pow_op. - bf_df = scalar_types_df[ - (scalar_types_df["int64_col"] >= 0) & (scalar_types_df["int64_col"] <= 10) - ] - bf_df = bf_df[["int64_col", "float64_col", "bool_col"]] - - int64_col_id = bf_df["int64_col"]._value_column - float64_col_id = bf_df["float64_col"]._value_column - bool_col_id = bf_df["bool_col"]._value_column - - sql = utils._apply_ops_to_sql( - bf_df, - [ - ops.unsafe_pow_op.as_expr(int64_col_id, int64_col_id), - ops.unsafe_pow_op.as_expr(int64_col_id, float64_col_id), - ops.unsafe_pow_op.as_expr(float64_col_id, int64_col_id), - ops.unsafe_pow_op.as_expr(float64_col_id, float64_col_id), - ops.unsafe_pow_op.as_expr(int64_col_id, bool_col_id), - ops.unsafe_pow_op.as_expr(bool_col_id, int64_col_id), - ], - [ - "int_pow_int", - "int_pow_float", - "float_pow_int", - "float_pow_float", - "int_pow_bool", - "bool_pow_int", - ], - ) - snapshot.assert_match(sql, "out.sql") diff --git a/tests/unit/core/compile/sqlglot/expressions/test_string_ops.py b/tests/unit/core/compile/sqlglot/expressions/test_string_ops.py deleted file mode 100644 index 67efcfb08ca..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/test_string_ops.py +++ /dev/null @@ -1,339 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import pytest - -import bigframes.core.expression as ex -import bigframes.pandas as bpd -from bigframes import operations as ops -from bigframes.testing import utils - -pytest.importorskip("pytest_snapshot") - - -def test_capitalize(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "string_col" - bf_df = scalar_types_df[[col_name]] - sql = utils._apply_ops_to_sql( - bf_df, [ops.capitalize_op.as_expr(col_name)], [col_name] - ) - - snapshot.assert_match(sql, "out.sql") - - -def test_endswith(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "string_col" - bf_df = scalar_types_df[[col_name]] - ops_map = { - "single": ops.EndsWithOp(pat=("ab",)).as_expr(col_name), - "double": ops.EndsWithOp(pat=("ab", "cd")).as_expr(col_name), - "empty": ops.EndsWithOp(pat=()).as_expr(col_name), - } - sql = utils._apply_ops_to_sql(bf_df, list(ops_map.values()), list(ops_map.keys())) - snapshot.assert_match(sql, "out.sql") - - -def test_isalnum(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "string_col" - bf_df = scalar_types_df[[col_name]] - sql = utils._apply_ops_to_sql(bf_df, [ops.isalnum_op.as_expr(col_name)], [col_name]) - - snapshot.assert_match(sql, "out.sql") - - -def test_isalpha(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "string_col" - bf_df = scalar_types_df[[col_name]] - sql = utils._apply_ops_to_sql(bf_df, [ops.isalpha_op.as_expr(col_name)], [col_name]) - - snapshot.assert_match(sql, "out.sql") - - -def test_isdecimal(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "string_col" - bf_df = scalar_types_df[[col_name]] - sql = utils._apply_ops_to_sql( - bf_df, [ops.isdecimal_op.as_expr(col_name)], [col_name] - ) - - snapshot.assert_match(sql, "out.sql") - - -def test_isdigit(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "string_col" - bf_df = scalar_types_df[[col_name]] - sql = utils._apply_ops_to_sql(bf_df, [ops.isdigit_op.as_expr(col_name)], [col_name]) - - snapshot.assert_match(sql, "out.sql") - - -def test_islower(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "string_col" - bf_df = scalar_types_df[[col_name]] - sql = utils._apply_ops_to_sql(bf_df, [ops.islower_op.as_expr(col_name)], [col_name]) - - snapshot.assert_match(sql, "out.sql") - - -def test_isnumeric(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "string_col" - bf_df = scalar_types_df[[col_name]] - sql = utils._apply_ops_to_sql( - bf_df, [ops.isnumeric_op.as_expr(col_name)], [col_name] - ) - - snapshot.assert_match(sql, "out.sql") - - -def test_isspace(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "string_col" - bf_df = scalar_types_df[[col_name]] - sql = utils._apply_ops_to_sql(bf_df, [ops.isspace_op.as_expr(col_name)], [col_name]) - - snapshot.assert_match(sql, "out.sql") - - -def test_isupper(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "string_col" - bf_df = scalar_types_df[[col_name]] - sql = utils._apply_ops_to_sql(bf_df, [ops.isupper_op.as_expr(col_name)], [col_name]) - - snapshot.assert_match(sql, "out.sql") - - -def test_len(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "string_col" - bf_df = scalar_types_df[[col_name]] - sql = utils._apply_ops_to_sql(bf_df, [ops.len_op.as_expr(col_name)], [col_name]) - - snapshot.assert_match(sql, "out.sql") - - -def test_len_w_array(repeated_types_df: bpd.DataFrame, snapshot): - col_name = "int_list_col" - bf_df = repeated_types_df[[col_name]] - sql = utils._apply_ops_to_sql(bf_df, [ops.len_op.as_expr(col_name)], [col_name]) - - snapshot.assert_match(sql, "out.sql") - - -def test_lower(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "string_col" - bf_df = scalar_types_df[[col_name]] - sql = utils._apply_ops_to_sql(bf_df, [ops.lower_op.as_expr(col_name)], [col_name]) - - snapshot.assert_match(sql, "out.sql") - - -def test_lstrip(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "string_col" - bf_df = scalar_types_df[[col_name]] - sql = utils._apply_ops_to_sql( - bf_df, [ops.StrLstripOp(" ").as_expr(col_name)], [col_name] - ) - - snapshot.assert_match(sql, "out.sql") - - -def test_replace_str(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "string_col" - bf_df = scalar_types_df[[col_name]] - sql = utils._apply_ops_to_sql( - bf_df, [ops.ReplaceStrOp("e", "a").as_expr(col_name)], [col_name] - ) - snapshot.assert_match(sql, "out.sql") - - -def test_regex_replace_str(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "string_col" - bf_df = scalar_types_df[[col_name]] - sql = utils._apply_ops_to_sql( - bf_df, [ops.RegexReplaceStrOp(r"e", "a").as_expr(col_name)], [col_name] - ) - snapshot.assert_match(sql, "out.sql") - - -def test_reverse(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "string_col" - bf_df = scalar_types_df[[col_name]] - sql = utils._apply_ops_to_sql(bf_df, [ops.reverse_op.as_expr(col_name)], [col_name]) - - snapshot.assert_match(sql, "out.sql") - - -def test_rstrip(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "string_col" - bf_df = scalar_types_df[[col_name]] - sql = utils._apply_ops_to_sql( - bf_df, [ops.StrRstripOp(" ").as_expr(col_name)], [col_name] - ) - - snapshot.assert_match(sql, "out.sql") - - -def test_startswith(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "string_col" - bf_df = scalar_types_df[[col_name]] - ops_map = { - "single": ops.StartsWithOp(pat=("ab",)).as_expr(col_name), - "double": ops.StartsWithOp(pat=("ab", "cd")).as_expr(col_name), - "empty": ops.StartsWithOp(pat=()).as_expr(col_name), - } - sql = utils._apply_ops_to_sql(bf_df, list(ops_map.values()), list(ops_map.keys())) - snapshot.assert_match(sql, "out.sql") - - -def test_str_get(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "string_col" - bf_df = scalar_types_df[[col_name]] - sql = utils._apply_ops_to_sql( - bf_df, [ops.GetItemOp(1).as_expr(col_name)], [col_name] - ) - - snapshot.assert_match(sql, "out.sql") - - -def test_str_pad(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "string_col" - bf_df = scalar_types_df[[col_name]] - ops_map = { - "left": ops.StrPadOp(length=10, fillchar="-", side="left").as_expr(col_name), - "right": ops.StrPadOp(length=10, fillchar="-", side="right").as_expr(col_name), - "both": ops.StrPadOp(length=10, fillchar="-", side="both").as_expr(col_name), - } - sql = utils._apply_ops_to_sql(bf_df, list(ops_map.values()), list(ops_map.keys())) - snapshot.assert_match(sql, "out.sql") - - -def test_str_slice(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "string_col" - bf_df = scalar_types_df[[col_name]] - ops_map = { - "1_3": ops.StrSliceOp(1, 3).as_expr(col_name), - "none_3": ops.StrSliceOp(None, 3).as_expr(col_name), - "1_none": ops.StrSliceOp(1, None).as_expr(col_name), - "m3_none": ops.StrSliceOp(-3, None).as_expr(col_name), - "none_m3": ops.StrSliceOp(None, -3).as_expr(col_name), - "m5_m3": ops.StrSliceOp(-5, -3).as_expr(col_name), - "1_m3": ops.StrSliceOp(1, -3).as_expr(col_name), - "m3_5": ops.StrSliceOp(-3, 5).as_expr(col_name), - } - sql = utils._apply_ops_to_sql(bf_df, list(ops_map.values()), list(ops_map.keys())) - - snapshot.assert_match(sql, "out.sql") - - -def test_strip(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "string_col" - bf_df = scalar_types_df[[col_name]] - sql = utils._apply_ops_to_sql( - bf_df, [ops.StrStripOp(" ").as_expr(col_name)], [col_name] - ) - - snapshot.assert_match(sql, "out.sql") - - -def test_str_contains(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "string_col" - bf_df = scalar_types_df[[col_name]] - sql = utils._apply_ops_to_sql( - bf_df, [ops.StrContainsOp("e").as_expr(col_name)], [col_name] - ) - - snapshot.assert_match(sql, "out.sql") - - -def test_str_contains_regex(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "string_col" - bf_df = scalar_types_df[[col_name]] - sql = utils._apply_ops_to_sql( - bf_df, [ops.StrContainsRegexOp("e").as_expr(col_name)], [col_name] - ) - - snapshot.assert_match(sql, "out.sql") - - -def test_str_extract(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "string_col" - bf_df = scalar_types_df[[col_name]] - ops_map = { - "zero": ops.StrExtractOp(r"([a-z]*)", 0).as_expr(col_name), - "one": ops.StrExtractOp(r"([a-z]*)", 1).as_expr(col_name), - } - sql = utils._apply_ops_to_sql(bf_df, list(ops_map.values()), list(ops_map.keys())) - - snapshot.assert_match(sql, "out.sql") - - -def test_str_repeat(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "string_col" - bf_df = scalar_types_df[[col_name]] - sql = utils._apply_ops_to_sql( - bf_df, [ops.StrRepeatOp(2).as_expr(col_name)], [col_name] - ) - snapshot.assert_match(sql, "out.sql") - - -def test_str_find(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "string_col" - bf_df = scalar_types_df[[col_name]] - ops_map = { - "none_none": ops.StrFindOp("e", start=None, end=None).as_expr(col_name), - "start_none": ops.StrFindOp("e", start=2, end=None).as_expr(col_name), - "none_end": ops.StrFindOp("e", start=None, end=5).as_expr(col_name), - "start_end": ops.StrFindOp("e", start=2, end=5).as_expr(col_name), - } - sql = utils._apply_ops_to_sql(bf_df, list(ops_map.values()), list(ops_map.keys())) - - snapshot.assert_match(sql, "out.sql") - - -def test_string_split(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "string_col" - bf_df = scalar_types_df[[col_name]] - sql = utils._apply_ops_to_sql( - bf_df, [ops.StringSplitOp(pat=",").as_expr(col_name)], [col_name] - ) - snapshot.assert_match(sql, "out.sql") - - -def test_upper(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "string_col" - bf_df = scalar_types_df[[col_name]] - sql = utils._apply_ops_to_sql(bf_df, [ops.upper_op.as_expr(col_name)], [col_name]) - - snapshot.assert_match(sql, "out.sql") - - -def test_zfill(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "string_col" - bf_df = scalar_types_df[[col_name]] - sql = utils._apply_ops_to_sql( - bf_df, [ops.ZfillOp(width=10).as_expr(col_name)], [col_name] - ) - snapshot.assert_match(sql, "out.sql") - - -def test_add_string(scalar_types_df: bpd.DataFrame, snapshot): - bf_df = scalar_types_df[["string_col"]] - sql = utils._apply_binary_op(bf_df, ops.add_op, "string_col", ex.const("a")) - - snapshot.assert_match(sql, "out.sql") - - -def test_strconcat(scalar_types_df: bpd.DataFrame, snapshot): - bf_df = scalar_types_df[["string_col"]] - sql = utils._apply_binary_op(bf_df, ops.strconcat_op, "string_col", ex.const("a")) - - snapshot.assert_match(sql, "out.sql") diff --git a/tests/unit/core/compile/sqlglot/expressions/test_struct_ops.py b/tests/unit/core/compile/sqlglot/expressions/test_struct_ops.py deleted file mode 100644 index 5e1f3d505cb..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/test_struct_ops.py +++ /dev/null @@ -1,68 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import typing - -import pytest - -import bigframes.pandas as bpd -from bigframes import operations as ops -from bigframes.core import expression as ex -from bigframes.testing import utils - -pytest.importorskip("pytest_snapshot") - - -def _apply_nary_op( - obj: bpd.DataFrame, - op: ops.NaryOp, - *args: typing.Union[str, ex.Expression], -) -> str: - """Applies a nary op to the given DataFrame and return the SQL representing - the resulting DataFrame.""" - array_value = obj._block.expr - op_expr = op.as_expr(*args) - result, col_ids = array_value.compute_values([op_expr]) - - # Rename columns for deterministic golden SQL results. - assert len(col_ids) == 1 - result = result.rename_columns({col_ids[0]: "result_col"}).select_columns( - ["result_col"] - ) - - sql = result.session._executor.to_sql(result, enable_cache=False) - return sql - - -def test_struct_field(nested_structs_types_df: bpd.DataFrame, snapshot): - col_name = "people" - bf_df = nested_structs_types_df[[col_name]] - - ops_map = { - # When a name string is provided. - "string": ops.StructFieldOp("name").as_expr(col_name), - # When an index integer is provided. - "int": ops.StructFieldOp(0).as_expr(col_name), - } - sql = utils._apply_ops_to_sql(bf_df, list(ops_map.values()), list(ops_map.keys())) - - snapshot.assert_match(sql, "out.sql") - - -def test_struct_op(scalar_types_df: bpd.DataFrame, snapshot): - bf_df = scalar_types_df[["bool_col", "int64_col", "float64_col", "string_col"]] - op = ops.StructOp(column_names=tuple(bf_df.columns.tolist())) - sql = _apply_nary_op(bf_df, op, *bf_df.columns.tolist()) - - snapshot.assert_match(sql, "out.sql") diff --git a/tests/unit/core/compile/sqlglot/expressions/test_timedelta_ops.py b/tests/unit/core/compile/sqlglot/expressions/test_timedelta_ops.py deleted file mode 100644 index ae1f6d017c4..00000000000 --- a/tests/unit/core/compile/sqlglot/expressions/test_timedelta_ops.py +++ /dev/null @@ -1,41 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import pytest - -import bigframes.pandas as bpd -from bigframes import operations as ops -from bigframes.testing import utils - -pytest.importorskip("pytest_snapshot") - - -def test_to_timedelta(scalar_types_df: bpd.DataFrame, snapshot): - bf_df = scalar_types_df[["int64_col", "float64_col"]] - bf_df["duration_us"] = bpd.to_timedelta(bf_df["int64_col"], "us") - bf_df["duration_s"] = bpd.to_timedelta(bf_df["float64_col"], "s") - bf_df["duration_w"] = bpd.to_timedelta(bf_df["int64_col"], "h") - bf_df["duration_on_duration"] = bpd.to_timedelta(bf_df["duration_us"], "ms") - - snapshot.assert_match(bf_df.sql, "out.sql") - - -def test_timedelta_floor(scalar_types_df: bpd.DataFrame, snapshot): - col_name = "int64_col" - bf_df = scalar_types_df[[col_name]] - sql = utils._apply_ops_to_sql( - bf_df, [ops.timedelta_floor_op.as_expr(col_name)], [col_name] - ) - - snapshot.assert_match(sql, "out.sql") diff --git a/tests/unit/core/compile/sqlglot/snapshots/test_compile_aggregate/test_compile_aggregate/out.sql b/tests/unit/core/compile/sqlglot/snapshots/test_compile_aggregate/test_compile_aggregate/out.sql deleted file mode 100644 index cfd9c7c87f0..00000000000 --- a/tests/unit/core/compile/sqlglot/snapshots/test_compile_aggregate/test_compile_aggregate/out.sql +++ /dev/null @@ -1,23 +0,0 @@ -WITH `bfcte_0` AS ( - SELECT - `bool_col`, - `int64_too`, - `int64_too` AS `bfcol_2`, - `bool_col` AS `bfcol_3` - FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` -), `bfcte_1` AS ( - SELECT - `bfcol_3`, - COALESCE(SUM(`bfcol_2`), 0) AS `bfcol_6` - FROM `bfcte_0` - WHERE - NOT `bfcol_3` IS NULL - GROUP BY - `bfcol_3` -) -SELECT - `bfcol_3` AS `bool_col`, - `bfcol_6` AS `int64_too` -FROM `bfcte_1` -ORDER BY - `bfcol_3` ASC NULLS LAST \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/snapshots/test_compile_aggregate/test_compile_aggregate_wo_dropna/out.sql b/tests/unit/core/compile/sqlglot/snapshots/test_compile_aggregate/test_compile_aggregate_wo_dropna/out.sql deleted file mode 100644 index e71099d82e6..00000000000 --- a/tests/unit/core/compile/sqlglot/snapshots/test_compile_aggregate/test_compile_aggregate_wo_dropna/out.sql +++ /dev/null @@ -1,21 +0,0 @@ -WITH `bfcte_0` AS ( - SELECT - `bool_col`, - `int64_too`, - `int64_too` AS `bfcol_2`, - `bool_col` AS `bfcol_3` - FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` -), `bfcte_1` AS ( - SELECT - `bfcol_3`, - COALESCE(SUM(`bfcol_2`), 0) AS `bfcol_6` - FROM `bfcte_0` - GROUP BY - `bfcol_3` -) -SELECT - `bfcol_3` AS `bool_col`, - `bfcol_6` AS `int64_too` -FROM `bfcte_1` -ORDER BY - `bfcol_3` ASC NULLS LAST \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/snapshots/test_compile_concat/test_compile_concat/out.sql b/tests/unit/core/compile/sqlglot/snapshots/test_compile_concat/test_compile_concat/out.sql deleted file mode 100644 index 48614357865..00000000000 --- a/tests/unit/core/compile/sqlglot/snapshots/test_compile_concat/test_compile_concat/out.sql +++ /dev/null @@ -1,48 +0,0 @@ -WITH `bfcte_0` AS ( - SELECT - `rowindex` AS `bfcol_3`, - `rowindex` AS `bfcol_4`, - `int64_col` AS `bfcol_5`, - `string_col` AS `bfcol_6` - FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` -), `bfcte_1` AS ( - SELECT - `bfcol_17` AS `bfcol_23`, - `bfcol_18` AS `bfcol_24`, - `bfcol_19` AS `bfcol_25`, - `bfcol_20` AS `bfcol_26`, - `bfcol_21` AS `bfcol_27`, - `bfcol_22` AS `bfcol_28` - FROM ( - ( - SELECT - `bfcol_3` AS `bfcol_17`, - `bfcol_4` AS `bfcol_18`, - `bfcol_5` AS `bfcol_19`, - `bfcol_6` AS `bfcol_20`, - 0 AS `bfcol_21`, - ROW_NUMBER() OVER () - 1 AS `bfcol_22` - FROM `bfcte_0` - ) - UNION ALL - ( - SELECT - `bfcol_3` AS `bfcol_11`, - `bfcol_4` AS `bfcol_12`, - `bfcol_5` AS `bfcol_13`, - `bfcol_6` AS `bfcol_14`, - 1 AS `bfcol_15`, - ROW_NUMBER() OVER () - 1 AS `bfcol_16` - FROM `bfcte_0` - ) - ) -) -SELECT - `bfcol_23` AS `rowindex`, - `bfcol_24` AS `rowindex_1`, - `bfcol_25` AS `int64_col`, - `bfcol_26` AS `string_col` -FROM `bfcte_1` -ORDER BY - `bfcol_27` ASC NULLS LAST, - `bfcol_28` ASC NULLS LAST \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/snapshots/test_compile_concat/test_compile_concat_filter_sorted/out.sql b/tests/unit/core/compile/sqlglot/snapshots/test_compile_concat/test_compile_concat_filter_sorted/out.sql deleted file mode 100644 index 477a47036ae..00000000000 --- a/tests/unit/core/compile/sqlglot/snapshots/test_compile_concat/test_compile_concat_filter_sorted/out.sql +++ /dev/null @@ -1,63 +0,0 @@ -WITH `bfcte_0` AS ( - SELECT - `float64_col` AS `bfcol_7`, - `int64_too` AS `bfcol_8` - FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` - WHERE - `bool_col` -), `bfcte_1` AS ( - SELECT - `float64_col` AS `bfcol_5`, - `int64_col` AS `bfcol_6` - FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` -), `bfcte_2` AS ( - SELECT - `bfcol_21` AS `bfcol_33`, - `bfcol_22` AS `bfcol_34`, - `bfcol_23` AS `bfcol_35`, - `bfcol_24` AS `bfcol_36` - FROM ( - ( - SELECT - `bfcol_5` AS `bfcol_21`, - `bfcol_6` AS `bfcol_22`, - 0 AS `bfcol_23`, - ROW_NUMBER() OVER (ORDER BY `bfcol_6` ASC NULLS LAST) - 1 AS `bfcol_24` - FROM `bfcte_1` - ) - UNION ALL - ( - SELECT - `bfcol_7` AS `bfcol_29`, - `bfcol_8` AS `bfcol_30`, - 1 AS `bfcol_31`, - ROW_NUMBER() OVER () - 1 AS `bfcol_32` - FROM `bfcte_0` - ) - UNION ALL - ( - SELECT - `bfcol_5` AS `bfcol_17`, - `bfcol_6` AS `bfcol_18`, - 2 AS `bfcol_19`, - ROW_NUMBER() OVER (ORDER BY `bfcol_6` ASC NULLS LAST) - 1 AS `bfcol_20` - FROM `bfcte_1` - ) - UNION ALL - ( - SELECT - `bfcol_7` AS `bfcol_25`, - `bfcol_8` AS `bfcol_26`, - 3 AS `bfcol_27`, - ROW_NUMBER() OVER () - 1 AS `bfcol_28` - FROM `bfcte_0` - ) - ) -) -SELECT - `bfcol_33` AS `float64_col`, - `bfcol_34` AS `int64_col` -FROM `bfcte_2` -ORDER BY - `bfcol_35` ASC NULLS LAST, - `bfcol_36` ASC NULLS LAST \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/snapshots/test_compile_explode/test_compile_explode_dataframe/out.sql b/tests/unit/core/compile/sqlglot/snapshots/test_compile_explode/test_compile_explode_dataframe/out.sql deleted file mode 100644 index e2a80e201bb..00000000000 --- a/tests/unit/core/compile/sqlglot/snapshots/test_compile_explode/test_compile_explode_dataframe/out.sql +++ /dev/null @@ -1,21 +0,0 @@ -WITH `bfcte_0` AS ( - SELECT - `rowindex`, - `int_list_col`, - `string_list_col` - FROM `bigframes-dev`.`sqlglot_test`.`repeated_types` AS `bft_0` -), `bfcte_1` AS ( - SELECT - * - REPLACE (`int_list_col`[SAFE_OFFSET(`bfcol_13`)] AS `int_list_col`, `string_list_col`[SAFE_OFFSET(`bfcol_13`)] AS `string_list_col`) - FROM `bfcte_0` - LEFT JOIN UNNEST(GENERATE_ARRAY(0, LEAST(ARRAY_LENGTH(`int_list_col`) - 1, ARRAY_LENGTH(`string_list_col`) - 1))) AS `bfcol_13` WITH OFFSET AS `bfcol_7` -) -SELECT - `rowindex`, - `rowindex` AS `rowindex_1`, - `int_list_col`, - `string_list_col` -FROM `bfcte_1` -ORDER BY - `bfcol_7` ASC NULLS LAST \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/snapshots/test_compile_explode/test_compile_explode_series/out.sql b/tests/unit/core/compile/sqlglot/snapshots/test_compile_explode/test_compile_explode_series/out.sql deleted file mode 100644 index 03ac4d0e03a..00000000000 --- a/tests/unit/core/compile/sqlglot/snapshots/test_compile_explode/test_compile_explode_series/out.sql +++ /dev/null @@ -1,18 +0,0 @@ -WITH `bfcte_0` AS ( - SELECT - `rowindex`, - `int_list_col` - FROM `bigframes-dev`.`sqlglot_test`.`repeated_types` AS `bft_0` -), `bfcte_1` AS ( - SELECT - * - REPLACE (`bfcol_8` AS `int_list_col`) - FROM `bfcte_0` - LEFT JOIN UNNEST(`int_list_col`) AS `bfcol_8` WITH OFFSET AS `bfcol_4` -) -SELECT - `rowindex`, - `int_list_col` -FROM `bfcte_1` -ORDER BY - `bfcol_4` ASC NULLS LAST \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/snapshots/test_compile_filter/test_compile_filter/out.sql b/tests/unit/core/compile/sqlglot/snapshots/test_compile_filter/test_compile_filter/out.sql deleted file mode 100644 index 3e367c1e1e2..00000000000 --- a/tests/unit/core/compile/sqlglot/snapshots/test_compile_filter/test_compile_filter/out.sql +++ /dev/null @@ -1,7 +0,0 @@ -SELECT - `rowindex`, - `rowindex` AS `rowindex_1`, - `int64_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` -WHERE - `rowindex` >= 1 \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/snapshots/test_compile_fromrange/test_compile_fromrange/out.sql b/tests/unit/core/compile/sqlglot/snapshots/test_compile_fromrange/test_compile_fromrange/out.sql deleted file mode 100644 index 4f4e2496498..00000000000 --- a/tests/unit/core/compile/sqlglot/snapshots/test_compile_fromrange/test_compile_fromrange/out.sql +++ /dev/null @@ -1,75 +0,0 @@ -WITH `bfcte_0` AS ( - SELECT - * - FROM UNNEST(ARRAY>[STRUCT(CAST('2021-01-01T13:00:00' AS DATETIME)), STRUCT(CAST('2021-01-01T13:00:01' AS DATETIME)), STRUCT(CAST('2021-01-01T13:00:02' AS DATETIME)), STRUCT(CAST('2021-01-01T13:00:03' AS DATETIME)), STRUCT(CAST('2021-01-01T13:00:04' AS DATETIME)), STRUCT(CAST('2021-01-01T13:00:05' AS DATETIME)), STRUCT(CAST('2021-01-01T13:00:06' AS DATETIME)), STRUCT(CAST('2021-01-01T13:00:07' AS DATETIME)), STRUCT(CAST('2021-01-01T13:00:08' AS DATETIME)), STRUCT(CAST('2021-01-01T13:00:09' AS DATETIME)), STRUCT(CAST('2021-01-01T13:00:10' AS DATETIME)), STRUCT(CAST('2021-01-01T13:00:11' AS DATETIME)), STRUCT(CAST('2021-01-01T13:00:12' AS DATETIME)), STRUCT(CAST('2021-01-01T13:00:13' AS DATETIME)), STRUCT(CAST('2021-01-01T13:00:14' AS DATETIME)), STRUCT(CAST('2021-01-01T13:00:15' AS DATETIME)), STRUCT(CAST('2021-01-01T13:00:16' AS DATETIME)), STRUCT(CAST('2021-01-01T13:00:17' AS DATETIME)), STRUCT(CAST('2021-01-01T13:00:18' AS DATETIME)), STRUCT(CAST('2021-01-01T13:00:19' AS DATETIME)), STRUCT(CAST('2021-01-01T13:00:20' AS DATETIME)), STRUCT(CAST('2021-01-01T13:00:21' AS DATETIME)), STRUCT(CAST('2021-01-01T13:00:22' AS DATETIME)), STRUCT(CAST('2021-01-01T13:00:23' AS DATETIME)), STRUCT(CAST('2021-01-01T13:00:24' AS DATETIME)), STRUCT(CAST('2021-01-01T13:00:25' AS DATETIME)), STRUCT(CAST('2021-01-01T13:00:26' AS DATETIME)), STRUCT(CAST('2021-01-01T13:00:27' AS DATETIME)), STRUCT(CAST('2021-01-01T13:00:28' AS DATETIME)), STRUCT(CAST('2021-01-01T13:00:29' AS DATETIME))]) -), `bfcte_1` AS ( - SELECT - * - FROM UNNEST(ARRAY>[STRUCT(CAST('2021-01-01T13:00:00' AS DATETIME), 0, 10), STRUCT(CAST('2021-01-01T13:00:01' AS DATETIME), 1, 11), STRUCT(CAST('2021-01-01T13:00:02' AS DATETIME), 2, 12), STRUCT(CAST('2021-01-01T13:00:03' AS DATETIME), 3, 13), STRUCT(CAST('2021-01-01T13:00:04' AS DATETIME), 4, 14), STRUCT(CAST('2021-01-01T13:00:05' AS DATETIME), 5, 15), STRUCT(CAST('2021-01-01T13:00:06' AS DATETIME), 6, 16), STRUCT(CAST('2021-01-01T13:00:07' AS DATETIME), 7, 17), STRUCT(CAST('2021-01-01T13:00:08' AS DATETIME), 8, 18), STRUCT(CAST('2021-01-01T13:00:09' AS DATETIME), 9, 19), STRUCT(CAST('2021-01-01T13:00:10' AS DATETIME), 10, 20), STRUCT(CAST('2021-01-01T13:00:11' AS DATETIME), 11, 21), STRUCT(CAST('2021-01-01T13:00:12' AS DATETIME), 12, 22), STRUCT(CAST('2021-01-01T13:00:13' AS DATETIME), 13, 23), STRUCT(CAST('2021-01-01T13:00:14' AS DATETIME), 14, 24), STRUCT(CAST('2021-01-01T13:00:15' AS DATETIME), 15, 25), STRUCT(CAST('2021-01-01T13:00:16' AS DATETIME), 16, 26), STRUCT(CAST('2021-01-01T13:00:17' AS DATETIME), 17, 27), STRUCT(CAST('2021-01-01T13:00:18' AS DATETIME), 18, 28), STRUCT(CAST('2021-01-01T13:00:19' AS DATETIME), 19, 29), STRUCT(CAST('2021-01-01T13:00:20' AS DATETIME), 20, 30), STRUCT(CAST('2021-01-01T13:00:21' AS DATETIME), 21, 31), STRUCT(CAST('2021-01-01T13:00:22' AS DATETIME), 22, 32), STRUCT(CAST('2021-01-01T13:00:23' AS DATETIME), 23, 33), STRUCT(CAST('2021-01-01T13:00:24' AS DATETIME), 24, 34), STRUCT(CAST('2021-01-01T13:00:25' AS DATETIME), 25, 35), STRUCT(CAST('2021-01-01T13:00:26' AS DATETIME), 26, 36), STRUCT(CAST('2021-01-01T13:00:27' AS DATETIME), 27, 37), STRUCT(CAST('2021-01-01T13:00:28' AS DATETIME), 28, 38), STRUCT(CAST('2021-01-01T13:00:29' AS DATETIME), 29, 39)]) -), `bfcte_2` AS ( - SELECT - `bfcol_0` AS `bfcol_4` - FROM `bfcte_0` -), `bfcte_3` AS ( - SELECT - `bfcol_1` AS `bfcol_5`, - `bfcol_2` AS `bfcol_6`, - `bfcol_3` AS `bfcol_7` - FROM `bfcte_1` -), `bfcte_4` AS ( - SELECT - MIN(`bfcol_4`) AS `bfcol_8` - FROM `bfcte_2` -), `bfcte_5` AS ( - SELECT - `bfcol_6` AS `bfcol_11`, - `bfcol_7` AS `bfcol_12`, - CAST(FLOOR( - IEEE_DIVIDE( - UNIX_MICROS(CAST(`bfcol_5` AS TIMESTAMP)) - UNIX_MICROS(CAST(CAST(`bfcol_8` AS DATE) AS TIMESTAMP)), - 7000000 - ) - ) AS INT64) AS `bfcol_13` - FROM `bfcte_3` - CROSS JOIN `bfcte_4` -), `bfcte_6` AS ( - SELECT - CAST(FLOOR( - IEEE_DIVIDE( - UNIX_MICROS(CAST(`bfcol_4` AS TIMESTAMP)) - UNIX_MICROS(CAST(CAST(`bfcol_8` AS DATE) AS TIMESTAMP)), - 7000000 - ) - ) AS INT64) AS `bfcol_14` - FROM `bfcte_2` - CROSS JOIN `bfcte_4` -), `bfcte_7` AS ( - SELECT - MAX(`bfcol_14`) AS `bfcol_15` - FROM `bfcte_6` -), `bfcte_8` AS ( - SELECT - MIN(`bfcol_14`) AS `bfcol_16` - FROM `bfcte_6` -), `bfcte_9` AS ( - SELECT - `bfcol_27` AS `bfcol_17` - FROM `bfcte_8` - CROSS JOIN `bfcte_7` - CROSS JOIN UNNEST(GENERATE_ARRAY(`bfcol_16`, `bfcol_15`, 1)) AS `bfcol_27` -) -SELECT - CAST(TIMESTAMP_MICROS( - CAST(CAST(`bfcol_17` AS BIGNUMERIC) * 7000000 + CAST(UNIX_MICROS(CAST(CAST(`bfcol_8` AS DATE) AS TIMESTAMP)) AS BIGNUMERIC) AS INT64) - ) AS DATETIME) AS `timestamp_col`, - `bfcol_11` AS `int64_col`, - `bfcol_12` AS `int64_too` -FROM ( - SELECT - * - FROM `bfcte_9` - CROSS JOIN `bfcte_4` -) -LEFT JOIN `bfcte_5` - ON `bfcol_17` = `bfcol_13` -ORDER BY - `bfcol_17` ASC NULLS LAST diff --git a/tests/unit/core/compile/sqlglot/snapshots/test_compile_geo/test_st_regionstats/out.sql b/tests/unit/core/compile/sqlglot/snapshots/test_compile_geo/test_st_regionstats/out.sql deleted file mode 100644 index 457436e98c4..00000000000 --- a/tests/unit/core/compile/sqlglot/snapshots/test_compile_geo/test_st_regionstats/out.sql +++ /dev/null @@ -1,51 +0,0 @@ -WITH `bfcte_0` AS ( - SELECT - * - FROM UNNEST(ARRAY>[STRUCT('POINT(1 1)', 0)]) -) -SELECT - ST_REGIONSTATS( - `bfcol_0`, - 'ee://some/raster/uri', - band => 'band1', - include => 'some equation', - options => JSON '{"scale": 100}' - ).`min`, - ST_REGIONSTATS( - `bfcol_0`, - 'ee://some/raster/uri', - band => 'band1', - include => 'some equation', - options => JSON '{"scale": 100}' - ).`max`, - ST_REGIONSTATS( - `bfcol_0`, - 'ee://some/raster/uri', - band => 'band1', - include => 'some equation', - options => JSON '{"scale": 100}' - ).`sum`, - ST_REGIONSTATS( - `bfcol_0`, - 'ee://some/raster/uri', - band => 'band1', - include => 'some equation', - options => JSON '{"scale": 100}' - ).`count`, - ST_REGIONSTATS( - `bfcol_0`, - 'ee://some/raster/uri', - band => 'band1', - include => 'some equation', - options => JSON '{"scale": 100}' - ).`mean`, - ST_REGIONSTATS( - `bfcol_0`, - 'ee://some/raster/uri', - band => 'band1', - include => 'some equation', - options => JSON '{"scale": 100}' - ).`area` -FROM `bfcte_0` -ORDER BY - `bfcol_1` ASC NULLS LAST \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/snapshots/test_compile_geo/test_st_regionstats_without_optional_args/out.sql b/tests/unit/core/compile/sqlglot/snapshots/test_compile_geo/test_st_regionstats_without_optional_args/out.sql deleted file mode 100644 index 410909d80c5..00000000000 --- a/tests/unit/core/compile/sqlglot/snapshots/test_compile_geo/test_st_regionstats_without_optional_args/out.sql +++ /dev/null @@ -1,15 +0,0 @@ -WITH `bfcte_0` AS ( - SELECT - * - FROM UNNEST(ARRAY>[STRUCT('POINT(1 1)', 0)]) -) -SELECT - ST_REGIONSTATS(`bfcol_0`, 'ee://some/raster/uri').`min`, - ST_REGIONSTATS(`bfcol_0`, 'ee://some/raster/uri').`max`, - ST_REGIONSTATS(`bfcol_0`, 'ee://some/raster/uri').`sum`, - ST_REGIONSTATS(`bfcol_0`, 'ee://some/raster/uri').`count`, - ST_REGIONSTATS(`bfcol_0`, 'ee://some/raster/uri').`mean`, - ST_REGIONSTATS(`bfcol_0`, 'ee://some/raster/uri').`area` -FROM `bfcte_0` -ORDER BY - `bfcol_1` ASC NULLS LAST \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/snapshots/test_compile_geo/test_st_simplify/out.sql b/tests/unit/core/compile/sqlglot/snapshots/test_compile_geo/test_st_simplify/out.sql deleted file mode 100644 index 177cb5292b3..00000000000 --- a/tests/unit/core/compile/sqlglot/snapshots/test_compile_geo/test_st_simplify/out.sql +++ /dev/null @@ -1,10 +0,0 @@ -WITH `bfcte_0` AS ( - SELECT - * - FROM UNNEST(ARRAY>[STRUCT(ST_GEOGFROMTEXT('LINESTRING(0 0, 1 1, 2 0)'), 0)]) -) -SELECT - ST_SIMPLIFY(`bfcol_0`, 123.125) AS `0` -FROM `bfcte_0` -ORDER BY - `bfcol_1` ASC NULLS LAST \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/snapshots/test_compile_isin/test_compile_isin/out.sql b/tests/unit/core/compile/sqlglot/snapshots/test_compile_isin/test_compile_isin/out.sql deleted file mode 100644 index a062ec30e44..00000000000 --- a/tests/unit/core/compile/sqlglot/snapshots/test_compile_isin/test_compile_isin/out.sql +++ /dev/null @@ -1,36 +0,0 @@ -WITH `bfcte_0` AS ( - SELECT - `int64_too` - FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` -), `bfcte_1` AS ( - SELECT - `rowindex` AS `bfcol_3`, - `int64_col` AS `bfcol_4` - FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` -), `bfcte_2` AS ( - SELECT - `int64_too` - FROM `bfcte_0` - GROUP BY - `int64_too` -), `bfcte_3` AS ( - SELECT - `int64_too` AS `bfcol_0` - FROM `bfcte_2` -), `bfcte_4` AS ( - SELECT - *, - EXISTS( - SELECT - 1 - FROM `bfcte_3` - WHERE - COALESCE(`bfcol_4`, 0) = COALESCE(`bfcol_0`, 0) - AND COALESCE(`bfcol_4`, 1) = COALESCE(`bfcol_0`, 1) - ) AS `bfcol_5` - FROM `bfcte_1` -) -SELECT - `bfcol_3` AS `rowindex`, - `bfcol_5` AS `int64_col` -FROM `bfcte_4` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/snapshots/test_compile_isin/test_compile_isin_not_nullable/out.sql b/tests/unit/core/compile/sqlglot/snapshots/test_compile_isin/test_compile_isin_not_nullable/out.sql deleted file mode 100644 index 81c83dee6c9..00000000000 --- a/tests/unit/core/compile/sqlglot/snapshots/test_compile_isin/test_compile_isin_not_nullable/out.sql +++ /dev/null @@ -1,33 +0,0 @@ -WITH `bfcte_0` AS ( - SELECT - `rowindex_2` - FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` -), `bfcte_1` AS ( - SELECT - `rowindex` AS `bfcol_3`, - `rowindex_2` AS `bfcol_4` - FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` -), `bfcte_2` AS ( - SELECT - `rowindex_2` - FROM `bfcte_0` - GROUP BY - `rowindex_2` -), `bfcte_3` AS ( - SELECT - `rowindex_2` AS `bfcol_0` - FROM `bfcte_2` -), `bfcte_4` AS ( - SELECT - *, - COALESCE(`bfcol_4` IN (( - SELECT - * - FROM `bfcte_3` - )), FALSE) AS `bfcol_5` - FROM `bfcte_1` -) -SELECT - `bfcol_3` AS `rowindex`, - `bfcol_5` AS `rowindex_2` -FROM `bfcte_4` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/snapshots/test_compile_join/test_compile_join/out.sql b/tests/unit/core/compile/sqlglot/snapshots/test_compile_join/test_compile_join/out.sql deleted file mode 100644 index cac57d0c8c8..00000000000 --- a/tests/unit/core/compile/sqlglot/snapshots/test_compile_join/test_compile_join/out.sql +++ /dev/null @@ -1,18 +0,0 @@ -WITH `bfcte_0` AS ( - SELECT - `int64_col` AS `bfcol_4`, - `int64_too` AS `bfcol_5` - FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` -), `bfcte_1` AS ( - SELECT - `rowindex` AS `bfcol_6`, - `int64_col` AS `bfcol_7` - FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` -) -SELECT - `bfcol_7` AS `int64_col`, - `bfcol_5` AS `int64_too` -FROM `bfcte_1` -LEFT JOIN `bfcte_0` - ON COALESCE(`bfcol_6`, 0) = COALESCE(`bfcol_4`, 0) - AND COALESCE(`bfcol_6`, 1) = COALESCE(`bfcol_4`, 1) \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/snapshots/test_compile_join/test_compile_join_w_on/bool_col/out.sql b/tests/unit/core/compile/sqlglot/snapshots/test_compile_join/test_compile_join_w_on/bool_col/out.sql deleted file mode 100644 index 5042f91cd95..00000000000 --- a/tests/unit/core/compile/sqlglot/snapshots/test_compile_join/test_compile_join_w_on/bool_col/out.sql +++ /dev/null @@ -1,24 +0,0 @@ -WITH `bfcte_0` AS ( - SELECT - `bool_col` AS `bfcol_0`, - `rowindex` AS `bfcol_1` - FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` -), `bfcte_1` AS ( - SELECT - `bfcol_1` AS `bfcol_2`, - `bfcol_0` AS `bfcol_3` - FROM `bfcte_0` -), `bfcte_2` AS ( - SELECT - `bfcol_1` AS `bfcol_4`, - `bfcol_0` AS `bfcol_5` - FROM `bfcte_0` -) -SELECT - `bfcol_4` AS `rowindex_x`, - `bfcol_5` AS `bool_col`, - `bfcol_2` AS `rowindex_y` -FROM `bfcte_2` -INNER JOIN `bfcte_1` - ON COALESCE(CAST(`bfcol_5` AS STRING), '0') = COALESCE(CAST(`bfcol_3` AS STRING), '0') - AND COALESCE(CAST(`bfcol_5` AS STRING), '1') = COALESCE(CAST(`bfcol_3` AS STRING), '1') \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/snapshots/test_compile_join/test_compile_join_w_on/float64_col/out.sql b/tests/unit/core/compile/sqlglot/snapshots/test_compile_join/test_compile_join_w_on/float64_col/out.sql deleted file mode 100644 index 544fedadc5b..00000000000 --- a/tests/unit/core/compile/sqlglot/snapshots/test_compile_join/test_compile_join_w_on/float64_col/out.sql +++ /dev/null @@ -1,24 +0,0 @@ -WITH `bfcte_0` AS ( - SELECT - `float64_col` AS `bfcol_0`, - `rowindex` AS `bfcol_1` - FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` -), `bfcte_1` AS ( - SELECT - `bfcol_1` AS `bfcol_2`, - `bfcol_0` AS `bfcol_3` - FROM `bfcte_0` -), `bfcte_2` AS ( - SELECT - `bfcol_1` AS `bfcol_4`, - `bfcol_0` AS `bfcol_5` - FROM `bfcte_0` -) -SELECT - `bfcol_4` AS `rowindex_x`, - `bfcol_5` AS `float64_col`, - `bfcol_2` AS `rowindex_y` -FROM `bfcte_2` -INNER JOIN `bfcte_1` - ON IF(IS_NAN(`bfcol_5`), 2.0, COALESCE(`bfcol_5`, 0.0)) = IF(IS_NAN(`bfcol_3`), 2.0, COALESCE(`bfcol_3`, 0.0)) - AND IF(IS_NAN(`bfcol_5`), 3, COALESCE(`bfcol_5`, 1.0)) = IF(IS_NAN(`bfcol_3`), 3, COALESCE(`bfcol_3`, 1.0)) \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/snapshots/test_compile_join/test_compile_join_w_on/int64_col/out.sql b/tests/unit/core/compile/sqlglot/snapshots/test_compile_join/test_compile_join_w_on/int64_col/out.sql deleted file mode 100644 index 05b9ceec4de..00000000000 --- a/tests/unit/core/compile/sqlglot/snapshots/test_compile_join/test_compile_join_w_on/int64_col/out.sql +++ /dev/null @@ -1,24 +0,0 @@ -WITH `bfcte_0` AS ( - SELECT - `int64_col` AS `bfcol_0`, - `rowindex` AS `bfcol_1` - FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` -), `bfcte_1` AS ( - SELECT - `bfcol_1` AS `bfcol_2`, - `bfcol_0` AS `bfcol_3` - FROM `bfcte_0` -), `bfcte_2` AS ( - SELECT - `bfcol_1` AS `bfcol_4`, - `bfcol_0` AS `bfcol_5` - FROM `bfcte_0` -) -SELECT - `bfcol_4` AS `rowindex_x`, - `bfcol_5` AS `int64_col`, - `bfcol_2` AS `rowindex_y` -FROM `bfcte_2` -INNER JOIN `bfcte_1` - ON COALESCE(`bfcol_5`, 0) = COALESCE(`bfcol_3`, 0) - AND COALESCE(`bfcol_5`, 1) = COALESCE(`bfcol_3`, 1) \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/snapshots/test_compile_join/test_compile_join_w_on/numeric_col/out.sql b/tests/unit/core/compile/sqlglot/snapshots/test_compile_join/test_compile_join_w_on/numeric_col/out.sql deleted file mode 100644 index 2e0114593e7..00000000000 --- a/tests/unit/core/compile/sqlglot/snapshots/test_compile_join/test_compile_join_w_on/numeric_col/out.sql +++ /dev/null @@ -1,24 +0,0 @@ -WITH `bfcte_0` AS ( - SELECT - `numeric_col` AS `bfcol_0`, - `rowindex` AS `bfcol_1` - FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` -), `bfcte_1` AS ( - SELECT - `bfcol_1` AS `bfcol_2`, - `bfcol_0` AS `bfcol_3` - FROM `bfcte_0` -), `bfcte_2` AS ( - SELECT - `bfcol_1` AS `bfcol_4`, - `bfcol_0` AS `bfcol_5` - FROM `bfcte_0` -) -SELECT - `bfcol_4` AS `rowindex_x`, - `bfcol_5` AS `numeric_col`, - `bfcol_2` AS `rowindex_y` -FROM `bfcte_2` -INNER JOIN `bfcte_1` - ON COALESCE(`bfcol_5`, CAST(0 AS NUMERIC)) = COALESCE(`bfcol_3`, CAST(0 AS NUMERIC)) - AND COALESCE(`bfcol_5`, CAST(1 AS NUMERIC)) = COALESCE(`bfcol_3`, CAST(1 AS NUMERIC)) \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/snapshots/test_compile_join/test_compile_join_w_on/string_col/out.sql b/tests/unit/core/compile/sqlglot/snapshots/test_compile_join/test_compile_join_w_on/string_col/out.sql deleted file mode 100644 index 36aad503435..00000000000 --- a/tests/unit/core/compile/sqlglot/snapshots/test_compile_join/test_compile_join_w_on/string_col/out.sql +++ /dev/null @@ -1,19 +0,0 @@ -WITH `bfcte_0` AS ( - SELECT - `rowindex` AS `bfcol_0`, - `string_col` AS `bfcol_1` - FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` -), `bfcte_1` AS ( - SELECT - `bfcol_0` AS `bfcol_2`, - `bfcol_1` AS `bfcol_3` - FROM `bfcte_0` -) -SELECT - `bfcol_0` AS `rowindex_x`, - `bfcol_1` AS `string_col`, - `bfcol_2` AS `rowindex_y` -FROM `bfcte_0` -INNER JOIN `bfcte_1` - ON COALESCE(CAST(`bfcol_1` AS STRING), '0') = COALESCE(CAST(`bfcol_3` AS STRING), '0') - AND COALESCE(CAST(`bfcol_1` AS STRING), '1') = COALESCE(CAST(`bfcol_3` AS STRING), '1') \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/snapshots/test_compile_join/test_compile_join_w_on/time_col/out.sql b/tests/unit/core/compile/sqlglot/snapshots/test_compile_join/test_compile_join_w_on/time_col/out.sql deleted file mode 100644 index b945a1cbf38..00000000000 --- a/tests/unit/core/compile/sqlglot/snapshots/test_compile_join/test_compile_join_w_on/time_col/out.sql +++ /dev/null @@ -1,19 +0,0 @@ -WITH `bfcte_0` AS ( - SELECT - `rowindex` AS `bfcol_0`, - `time_col` AS `bfcol_1` - FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` -), `bfcte_1` AS ( - SELECT - `bfcol_0` AS `bfcol_2`, - `bfcol_1` AS `bfcol_3` - FROM `bfcte_0` -) -SELECT - `bfcol_0` AS `rowindex_x`, - `bfcol_1` AS `time_col`, - `bfcol_2` AS `rowindex_y` -FROM `bfcte_0` -INNER JOIN `bfcte_1` - ON COALESCE(CAST(`bfcol_1` AS STRING), '0') = COALESCE(CAST(`bfcol_3` AS STRING), '0') - AND COALESCE(CAST(`bfcol_1` AS STRING), '1') = COALESCE(CAST(`bfcol_3` AS STRING), '1') \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/snapshots/test_compile_projection/test_compile_projection/out.sql b/tests/unit/core/compile/sqlglot/snapshots/test_compile_projection/test_compile_projection/out.sql deleted file mode 100644 index 3f819800e51..00000000000 --- a/tests/unit/core/compile/sqlglot/snapshots/test_compile_projection/test_compile_projection/out.sql +++ /dev/null @@ -1,26 +0,0 @@ -WITH `bfcte_0` AS ( - SELECT - `int64_col` AS `bfcol_0`, - `rowindex` AS `bfcol_1` - FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` -), `bfcte_1` AS ( - SELECT - `bfcol_1` AS `bfcol_2`, - `bfcol_0` AS `bfcol_3` - FROM `bfcte_0` -), `bfcte_2` AS ( - SELECT - *, - `bfcol_2` AS `bfcol_4`, - `bfcol_3` + 1 AS `bfcol_5` - FROM `bfcte_1` -), `bfcte_3` AS ( - SELECT - `bfcol_4` AS `bfcol_6`, - `bfcol_5` AS `bfcol_7` - FROM `bfcte_2` -) -SELECT - `bfcol_6` AS `rowindex`, - `bfcol_7` AS `int64_col` -FROM `bfcte_3` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/snapshots/test_compile_random_sample/test_compile_random_sample/out.sql b/tests/unit/core/compile/sqlglot/snapshots/test_compile_random_sample/test_compile_random_sample/out.sql deleted file mode 100644 index 73879aa65df..00000000000 --- a/tests/unit/core/compile/sqlglot/snapshots/test_compile_random_sample/test_compile_random_sample/out.sql +++ /dev/null @@ -1,183 +0,0 @@ -WITH `bfcte_0` AS ( - SELECT - * - FROM UNNEST(ARRAY>[STRUCT( - TRUE, - CAST(b'Hello, World!' AS BYTES), - CAST('2021-07-21' AS DATE), - CAST('2021-07-21T11:39:45' AS DATETIME), - ST_GEOGFROMTEXT('POINT(-122.0838511 37.3860517)'), - 123456789, - 0, - CAST(1.234567890 AS NUMERIC), - 1.25, - 0, - 0, - 'Hello, World!', - CAST('11:41:43.076160' AS TIME), - CAST('2021-07-21T17:43:43.945289+00:00' AS TIMESTAMP), - 4, - 0 - ), STRUCT( - FALSE, - CAST(b'\xe3\x81\x93\xe3\x82\x93\xe3\x81\xab\xe3\x81\xa1\xe3\x81\xaf' AS BYTES), - CAST('1991-02-03' AS DATE), - CAST('1991-01-02T03:45:06' AS DATETIME), - ST_GEOGFROMTEXT('POINT(-71.104 42.315)'), - -987654321, - 1, - CAST(1.234567890 AS NUMERIC), - 2.51, - 1, - 1, - 'こんにちは', - CAST('11:14:34.701606' AS TIME), - CAST('2021-07-21T17:43:43.945289+00:00' AS TIMESTAMP), - -1000000, - 1 - ), STRUCT( - TRUE, - CAST(b'\xc2\xa1Hola Mundo!' AS BYTES), - CAST('2023-03-01' AS DATE), - CAST('2023-03-01T10:55:13' AS DATETIME), - ST_GEOGFROMTEXT('POINT(-0.124474760143016 51.5007826749545)'), - 314159, - 0, - CAST(101.101010100 AS NUMERIC), - 25000000000.0, - 2, - 2, - ' ¡Hola Mundo! ', - CAST('23:59:59.999999' AS TIME), - CAST('2023-03-01T10:55:13.250125+00:00' AS TIMESTAMP), - 0, - 2 - ), STRUCT( - CAST(NULL AS BOOLEAN), - CAST(NULL AS BYTES), - CAST(NULL AS DATE), - CAST(NULL AS DATETIME), - CAST(NULL AS GEOGRAPHY), - CAST(NULL AS INT64), - 1, - CAST(NULL AS NUMERIC), - CAST(NULL AS FLOAT64), - 3, - 3, - CAST(NULL AS STRING), - CAST(NULL AS TIME), - CAST(NULL AS TIMESTAMP), - CAST(NULL AS INT64), - 3 - ), STRUCT( - FALSE, - CAST(b'\xe3\x81\x93\xe3\x82\x93\xe3\x81\xab\xe3\x81\xa1\xe3\x81\xaf' AS BYTES), - CAST('2021-07-21' AS DATE), - CAST(NULL AS DATETIME), - CAST(NULL AS GEOGRAPHY), - -234892, - -2345, - CAST(NULL AS NUMERIC), - CAST(NULL AS FLOAT64), - 4, - 4, - 'Hello, World!', - CAST(NULL AS TIME), - CAST(NULL AS TIMESTAMP), - 31540000000000, - 4 - ), STRUCT( - FALSE, - CAST(b'G\xc3\xbcten Tag' AS BYTES), - CAST('1980-03-14' AS DATE), - CAST('1980-03-14T15:16:17' AS DATETIME), - CAST(NULL AS GEOGRAPHY), - 55555, - 0, - CAST(5.555555000 AS NUMERIC), - 555.555, - 5, - 5, - 'Güten Tag!', - CAST('15:16:17.181921' AS TIME), - CAST('1980-03-14T15:16:17.181921+00:00' AS TIMESTAMP), - 4, - 5 - ), STRUCT( - TRUE, - CAST(b'Hello\tBigFrames!\x07' AS BYTES), - CAST('2023-05-23' AS DATE), - CAST('2023-05-23T11:37:01' AS DATETIME), - ST_GEOGFROMTEXT('LINESTRING(-0.127959 51.507728, -0.127026 51.507473)'), - 101202303, - 2, - CAST(-10.090807000 AS NUMERIC), - -123.456, - 6, - 6, - 'capitalize, This ', - CAST('01:02:03.456789' AS TIME), - CAST('2023-05-23T11:42:55.000001+00:00' AS TIMESTAMP), - CAST(NULL AS INT64), - 6 - ), STRUCT( - TRUE, - CAST(NULL AS BYTES), - CAST('2038-01-20' AS DATE), - CAST('2038-01-19T03:14:08' AS DATETIME), - CAST(NULL AS GEOGRAPHY), - -214748367, - 2, - CAST(11111111.100000000 AS NUMERIC), - 42.42, - 7, - 7, - ' سلام', - CAST('12:00:00.000001' AS TIME), - CAST('2038-01-19T03:14:17.999999+00:00' AS TIMESTAMP), - 4, - 7 - ), STRUCT( - FALSE, - CAST(NULL AS BYTES), - CAST(NULL AS DATE), - CAST(NULL AS DATETIME), - CAST(NULL AS GEOGRAPHY), - 2, - 1, - CAST(NULL AS NUMERIC), - 6.87, - 8, - 8, - 'T', - CAST(NULL AS TIME), - CAST(NULL AS TIMESTAMP), - 432000000000, - 8 - )]) -) -SELECT - `bfcol_0` AS `bool_col`, - `bfcol_1` AS `bytes_col`, - `bfcol_2` AS `date_col`, - `bfcol_3` AS `datetime_col`, - `bfcol_4` AS `geography_col`, - `bfcol_5` AS `int64_col`, - `bfcol_6` AS `int64_too`, - `bfcol_7` AS `numeric_col`, - `bfcol_8` AS `float64_col`, - `bfcol_9` AS `rowindex`, - `bfcol_10` AS `rowindex_2`, - `bfcol_11` AS `string_col`, - `bfcol_12` AS `time_col`, - `bfcol_13` AS `timestamp_col`, - `bfcol_14` AS `duration_col` -FROM ( - SELECT - * - FROM `bfcte_0` - WHERE - RAND() < 0.1 -) -ORDER BY - `bfcol_15` ASC NULLS LAST \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/snapshots/test_compile_readlocal/test_compile_readlocal/out.sql b/tests/unit/core/compile/sqlglot/snapshots/test_compile_readlocal/test_compile_readlocal/out.sql deleted file mode 100644 index 2b080b0b7ce..00000000000 --- a/tests/unit/core/compile/sqlglot/snapshots/test_compile_readlocal/test_compile_readlocal/out.sql +++ /dev/null @@ -1,187 +0,0 @@ -WITH `bfcte_0` AS ( - SELECT - * - FROM UNNEST(ARRAY>[STRUCT( - 0, - TRUE, - CAST(b'Hello, World!' AS BYTES), - CAST('2021-07-21' AS DATE), - CAST('2021-07-21T11:39:45' AS DATETIME), - ST_GEOGFROMTEXT('POINT(-122.0838511 37.3860517)'), - 123456789, - 0, - CAST(1.234567890 AS NUMERIC), - 1.25, - 0, - 0, - 'Hello, World!', - CAST('11:41:43.076160' AS TIME), - CAST('2021-07-21T17:43:43.945289+00:00' AS TIMESTAMP), - 4, - 0 - ), STRUCT( - 1, - FALSE, - CAST(b'\xe3\x81\x93\xe3\x82\x93\xe3\x81\xab\xe3\x81\xa1\xe3\x81\xaf' AS BYTES), - CAST('1991-02-03' AS DATE), - CAST('1991-01-02T03:45:06' AS DATETIME), - ST_GEOGFROMTEXT('POINT(-71.104 42.315)'), - -987654321, - 1, - CAST(1.234567890 AS NUMERIC), - 2.51, - 1, - 1, - 'こんにちは', - CAST('11:14:34.701606' AS TIME), - CAST('2021-07-21T17:43:43.945289+00:00' AS TIMESTAMP), - -1000000, - 1 - ), STRUCT( - 2, - TRUE, - CAST(b'\xc2\xa1Hola Mundo!' AS BYTES), - CAST('2023-03-01' AS DATE), - CAST('2023-03-01T10:55:13' AS DATETIME), - ST_GEOGFROMTEXT('POINT(-0.124474760143016 51.5007826749545)'), - 314159, - 0, - CAST(101.101010100 AS NUMERIC), - 25000000000.0, - 2, - 2, - ' ¡Hola Mundo! ', - CAST('23:59:59.999999' AS TIME), - CAST('2023-03-01T10:55:13.250125+00:00' AS TIMESTAMP), - 0, - 2 - ), STRUCT( - 3, - CAST(NULL AS BOOLEAN), - CAST(NULL AS BYTES), - CAST(NULL AS DATE), - CAST(NULL AS DATETIME), - CAST(NULL AS GEOGRAPHY), - CAST(NULL AS INT64), - 1, - CAST(NULL AS NUMERIC), - CAST(NULL AS FLOAT64), - 3, - 3, - CAST(NULL AS STRING), - CAST(NULL AS TIME), - CAST(NULL AS TIMESTAMP), - CAST(NULL AS INT64), - 3 - ), STRUCT( - 4, - FALSE, - CAST(b'\xe3\x81\x93\xe3\x82\x93\xe3\x81\xab\xe3\x81\xa1\xe3\x81\xaf' AS BYTES), - CAST('2021-07-21' AS DATE), - CAST(NULL AS DATETIME), - CAST(NULL AS GEOGRAPHY), - -234892, - -2345, - CAST(NULL AS NUMERIC), - CAST(NULL AS FLOAT64), - 4, - 4, - 'Hello, World!', - CAST(NULL AS TIME), - CAST(NULL AS TIMESTAMP), - 31540000000000, - 4 - ), STRUCT( - 5, - FALSE, - CAST(b'G\xc3\xbcten Tag' AS BYTES), - CAST('1980-03-14' AS DATE), - CAST('1980-03-14T15:16:17' AS DATETIME), - CAST(NULL AS GEOGRAPHY), - 55555, - 0, - CAST(5.555555000 AS NUMERIC), - 555.555, - 5, - 5, - 'Güten Tag!', - CAST('15:16:17.181921' AS TIME), - CAST('1980-03-14T15:16:17.181921+00:00' AS TIMESTAMP), - 4, - 5 - ), STRUCT( - 6, - TRUE, - CAST(b'Hello\tBigFrames!\x07' AS BYTES), - CAST('2023-05-23' AS DATE), - CAST('2023-05-23T11:37:01' AS DATETIME), - ST_GEOGFROMTEXT('LINESTRING(-0.127959 51.507728, -0.127026 51.507473)'), - 101202303, - 2, - CAST(-10.090807000 AS NUMERIC), - -123.456, - 6, - 6, - 'capitalize, This ', - CAST('01:02:03.456789' AS TIME), - CAST('2023-05-23T11:42:55.000001+00:00' AS TIMESTAMP), - CAST(NULL AS INT64), - 6 - ), STRUCT( - 7, - TRUE, - CAST(NULL AS BYTES), - CAST('2038-01-20' AS DATE), - CAST('2038-01-19T03:14:08' AS DATETIME), - CAST(NULL AS GEOGRAPHY), - -214748367, - 2, - CAST(11111111.100000000 AS NUMERIC), - 42.42, - 7, - 7, - ' سلام', - CAST('12:00:00.000001' AS TIME), - CAST('2038-01-19T03:14:17.999999+00:00' AS TIMESTAMP), - 4, - 7 - ), STRUCT( - 8, - FALSE, - CAST(NULL AS BYTES), - CAST(NULL AS DATE), - CAST(NULL AS DATETIME), - CAST(NULL AS GEOGRAPHY), - 2, - 1, - CAST(NULL AS NUMERIC), - 6.87, - 8, - 8, - 'T', - CAST(NULL AS TIME), - CAST(NULL AS TIMESTAMP), - 432000000000, - 8 - )]) -) -SELECT - `bfcol_0` AS `rowindex`, - `bfcol_1` AS `bool_col`, - `bfcol_2` AS `bytes_col`, - `bfcol_3` AS `date_col`, - `bfcol_4` AS `datetime_col`, - `bfcol_5` AS `geography_col`, - `bfcol_6` AS `int64_col`, - `bfcol_7` AS `int64_too`, - `bfcol_8` AS `numeric_col`, - `bfcol_9` AS `float64_col`, - `bfcol_10` AS `rowindex_1`, - `bfcol_11` AS `rowindex_2`, - `bfcol_12` AS `string_col`, - `bfcol_13` AS `time_col`, - `bfcol_14` AS `timestamp_col`, - `bfcol_15` AS `duration_col` -FROM `bfcte_0` -ORDER BY - `bfcol_16` ASC NULLS LAST \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/snapshots/test_compile_readlocal/test_compile_readlocal_w_json_df/out.sql b/tests/unit/core/compile/sqlglot/snapshots/test_compile_readlocal/test_compile_readlocal_w_json_df/out.sql deleted file mode 100644 index 4e21266b87b..00000000000 --- a/tests/unit/core/compile/sqlglot/snapshots/test_compile_readlocal/test_compile_readlocal_w_json_df/out.sql +++ /dev/null @@ -1,11 +0,0 @@ -WITH `bfcte_0` AS ( - SELECT - * - FROM UNNEST(ARRAY>[STRUCT(0, PARSE_JSON('null'), 0), STRUCT(1, PARSE_JSON('true'), 1), STRUCT(2, PARSE_JSON('100'), 2), STRUCT(3, PARSE_JSON('0.98'), 3), STRUCT(4, PARSE_JSON('"a string"'), 4), STRUCT(5, PARSE_JSON('[]'), 5), STRUCT(6, PARSE_JSON('[1,2,3]'), 6), STRUCT(7, PARSE_JSON('[{"a":1},{"a":2},{"a":null},{}]'), 7), STRUCT(8, PARSE_JSON('"100"'), 8), STRUCT(9, PARSE_JSON('{"date":"2024-07-16"}'), 9), STRUCT(10, PARSE_JSON('{"int_value":2,"null_filed":null}'), 10), STRUCT(11, PARSE_JSON('{"list_data":[10,20,30]}'), 11)]) -) -SELECT - `bfcol_0` AS `rowindex`, - `bfcol_1` AS `json_col` -FROM `bfcte_0` -ORDER BY - `bfcol_2` ASC NULLS LAST \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/snapshots/test_compile_readlocal/test_compile_readlocal_w_lists_df/out.sql b/tests/unit/core/compile/sqlglot/snapshots/test_compile_readlocal/test_compile_readlocal_w_lists_df/out.sql deleted file mode 100644 index 923476aafd4..00000000000 --- a/tests/unit/core/compile/sqlglot/snapshots/test_compile_readlocal/test_compile_readlocal_w_lists_df/out.sql +++ /dev/null @@ -1,47 +0,0 @@ -WITH `bfcte_0` AS ( - SELECT - * - FROM UNNEST(ARRAY, `bfcol_2` ARRAY, `bfcol_3` ARRAY, `bfcol_4` ARRAY, `bfcol_5` ARRAY, `bfcol_6` ARRAY, `bfcol_7` ARRAY, `bfcol_8` INT64>>[STRUCT( - 0, - [1], - [TRUE], - [1.2, 2.3], - ['2021-07-21'], - ['2021-07-21 11:39:45'], - [1.2, 2.3, 3.4], - ['abc', 'de', 'f'], - 0 - ), STRUCT( - 1, - [1, 2], - [TRUE, FALSE], - [1.1], - ['2021-07-21', '1987-03-28'], - ['1999-03-14 17:22:00'], - [5.5, 2.3], - ['a', 'bc', 'de'], - 1 - ), STRUCT( - 2, - [1, 2, 3], - [TRUE], - [0.5, -1.9, 2.3], - ['2017-08-01', '2004-11-22'], - ['1979-06-03 03:20:45'], - [1.7000000000000002], - ['', 'a'], - 2 - )]) -) -SELECT - `bfcol_0` AS `rowindex`, - `bfcol_1` AS `int_list_col`, - `bfcol_2` AS `bool_list_col`, - `bfcol_3` AS `float_list_col`, - `bfcol_4` AS `date_list_col`, - `bfcol_5` AS `date_time_list_col`, - `bfcol_6` AS `numeric_list_col`, - `bfcol_7` AS `string_list_col` -FROM `bfcte_0` -ORDER BY - `bfcol_8` ASC NULLS LAST \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/snapshots/test_compile_readlocal/test_compile_readlocal_w_special_values/out.sql b/tests/unit/core/compile/sqlglot/snapshots/test_compile_readlocal/test_compile_readlocal_w_special_values/out.sql deleted file mode 100644 index ba5e0c8f1cf..00000000000 --- a/tests/unit/core/compile/sqlglot/snapshots/test_compile_readlocal/test_compile_readlocal_w_special_values/out.sql +++ /dev/null @@ -1,25 +0,0 @@ -WITH `bfcte_0` AS ( - SELECT - * - FROM UNNEST(ARRAY, `bfcol_5` STRUCT, `bfcol_6` ARRAY, `bfcol_7` INT64>>[STRUCT( - CAST(NULL AS FLOAT64), - CAST('Infinity' AS FLOAT64), - CAST('-Infinity' AS FLOAT64), - CAST(NULL AS FLOAT64), - CAST(NULL AS STRUCT), - STRUCT(CAST(NULL AS INT64) AS `foo`), - ARRAY[], - 0 - ), STRUCT(1.0, 1.0, 1.0, 1.0, STRUCT(1 AS `foo`), STRUCT(1 AS `foo`), [1, 2], 1), STRUCT(2.0, 2.0, 2.0, 2.0, STRUCT(2 AS `foo`), STRUCT(2 AS `foo`), [3, 4], 2)]) -) -SELECT - `bfcol_0` AS `col_none`, - `bfcol_1` AS `col_inf`, - `bfcol_2` AS `col_neginf`, - `bfcol_3` AS `col_nan`, - `bfcol_4` AS `col_struct_none`, - `bfcol_5` AS `col_struct_w_none`, - `bfcol_6` AS `col_list_none` -FROM `bfcte_0` -ORDER BY - `bfcol_7` ASC NULLS LAST \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/snapshots/test_compile_readlocal/test_compile_readlocal_w_structs_df/out.sql b/tests/unit/core/compile/sqlglot/snapshots/test_compile_readlocal/test_compile_readlocal_w_structs_df/out.sql deleted file mode 100644 index 58a01635b7d..00000000000 --- a/tests/unit/core/compile/sqlglot/snapshots/test_compile_readlocal/test_compile_readlocal_w_structs_df/out.sql +++ /dev/null @@ -1,145 +0,0 @@ -WITH `bfcte_0` AS ( - SELECT - * - FROM UNNEST(ARRAY>, `bfcol_2` FLOAT64, `bfcol_3` FLOAT64, `bfcol_4` FLOAT64, `bfcol_5` STRING, `bfcol_6` JSON, `bfcol_7` STRING, `bfcol_8` STRING, `bfcol_9` STRING, `bfcol_10` TIMESTAMP, `bfcol_11` STRING, `bfcol_12` FLOAT64, `bfcol_13` FLOAT64, `bfcol_14` STRING, `bfcol_15` FLOAT64, `bfcol_16` INT64>>[STRUCT( - 1, - STRUCT( - 'Alice' AS `name`, - 30 AS `age`, - STRUCT('New York' AS `city`, 'USA' AS `country`) AS `address` - ), - 1.0, - 123456789.0, - 1.25, - 'Hello World', - PARSE_JSON('{"a":1,"b":[1,2]}'), - '2026-06-24', - '12:34:56.789012', - '2026-06-24 12:34:56.789012', - CAST('2026-06-24T12:34:56.789012+00:00' AS TIMESTAMP), - 'SGVsbG8=', - 123456.789, - 123456.78901234567, - 'POINT(30 10)', - 1000.0, - 0 - ), STRUCT( - 2, - STRUCT('' AS `name`, -1 AS `age`, STRUCT('' AS `city`, '' AS `country`) AS `address`), - 0.0, - -9.223372036854776e+18, - CAST('-Infinity' AS FLOAT64), - '', - PARSE_JSON('{}'), - '0001-01-01', - '00:00:00', - '0001-01-02 00:00:00', - CAST('0001-01-02T00:00:00+00:00' AS TIMESTAMP), - '', - -1e+29, - -1e+38, - 'POINT(0 0)', - -9223372036854776.0, - 1 - ), STRUCT( - 3, - STRUCT( - 'Very Long Name...' AS `name`, - 150 AS `age`, - STRUCT('City' AS `city`, 'Country' AS `country`) AS `address` - ), - 1.0, - 9.223372036854776e+18, - CAST('Infinity' AS FLOAT64), - 'Unicode: 🚀 Spark ✨', - PARSE_JSON('{"max":true,"nested":{"val":999}}'), - '9999-12-31', - '23:59:59.999999', - '9999-12-31 23:59:59.999999', - CAST('9999-12-31T23:59:59.999999+00:00' AS TIMESTAMP), - 'dmVyeSBsb25nIGJ5dGVzIHZhbHVl', - 1e+29, - 1e+38, - 'POLYGON((0 0, 10 0, 10 10, 0 10, 0 0))', - 9223372036854776.0, - 2 - ), STRUCT( - 4, - CAST(NULL AS STRUCT>), - CAST(NULL AS FLOAT64), - CAST(NULL AS FLOAT64), - CAST(NULL AS FLOAT64), - CAST(NULL AS STRING), - CAST(NULL AS JSON), - CAST(NULL AS STRING), - CAST(NULL AS STRING), - CAST(NULL AS STRING), - CAST(NULL AS TIMESTAMP), - CAST(NULL AS STRING), - CAST(NULL AS FLOAT64), - CAST(NULL AS FLOAT64), - CAST(NULL AS STRING), - CAST(NULL AS FLOAT64), - 3 - ), STRUCT( - 5, - STRUCT( - 'Bob' AS `name`, - 0 AS `age`, - CAST(NULL AS STRUCT) AS `address` - ), - 0.0, - 0.0, - CAST(NULL AS FLOAT64), - 'Line 1\nLine 2\n"Quotes"', - PARSE_JSON('[1,"two",null]'), - '1970-01-01', - '12:00:00', - '1970-01-01 12:00:00', - CAST('1970-01-01T12:00:00+00:00' AS TIMESTAMP), - 'AA==', - 0.0, - 0.0, - 'LINESTRING(0 0, 1 1, 2 2)', - 0.0, - 4 - ), STRUCT( - 6, - CAST(NULL AS STRUCT>), - CAST(NULL AS FLOAT64), - CAST(NULL AS FLOAT64), - CAST(NULL AS FLOAT64), - CAST(NULL AS STRING), - CAST(NULL AS JSON), - CAST(NULL AS STRING), - CAST(NULL AS STRING), - CAST(NULL AS STRING), - CAST(NULL AS TIMESTAMP), - CAST(NULL AS STRING), - CAST(NULL AS FLOAT64), - CAST(NULL AS FLOAT64), - CAST(NULL AS STRING), - CAST(NULL AS FLOAT64), - 5 - )]) -) -SELECT - `bfcol_0` AS `id`, - `bfcol_1` AS `person`, - `bfcol_2` AS `bool_col`, - `bfcol_3` AS `int64_col`, - `bfcol_4` AS `float64_col`, - `bfcol_5` AS `string_col`, - `bfcol_6` AS `json_col`, - `bfcol_7` AS `date_col`, - `bfcol_8` AS `time_col`, - `bfcol_9` AS `datetime_col`, - `bfcol_10` AS `timestamp_col`, - `bfcol_11` AS `bytes_col`, - `bfcol_12` AS `numeric_col`, - `bfcol_13` AS `bignumeric_col`, - `bfcol_14` AS `geography_col`, - `bfcol_15` AS `duration_col` -FROM `bfcte_0` -ORDER BY - `bfcol_16` ASC NULLS LAST \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/snapshots/test_compile_readtable/test_compile_astype_aliases/out.sql b/tests/unit/core/compile/sqlglot/snapshots/test_compile_readtable/test_compile_astype_aliases/out.sql deleted file mode 100644 index cd056c650fd..00000000000 --- a/tests/unit/core/compile/sqlglot/snapshots/test_compile_readtable/test_compile_astype_aliases/out.sql +++ /dev/null @@ -1,5 +0,0 @@ -SELECT - `rowindex`, - CAST(`timestamp_col` AS STRING) AS `timestamp_col`, - CAST(`int64_col` AS FLOAT64) AS `int64_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` diff --git a/tests/unit/core/compile/sqlglot/snapshots/test_compile_readtable/test_compile_readtable/out.sql b/tests/unit/core/compile/sqlglot/snapshots/test_compile_readtable/test_compile_readtable/out.sql deleted file mode 100644 index 626ef80d518..00000000000 --- a/tests/unit/core/compile/sqlglot/snapshots/test_compile_readtable/test_compile_readtable/out.sql +++ /dev/null @@ -1,18 +0,0 @@ -SELECT - `rowindex`, - `bool_col`, - `bytes_col`, - `date_col`, - `datetime_col`, - `geography_col`, - `int64_col`, - `int64_too`, - `numeric_col`, - `float64_col`, - `rowindex` AS `rowindex_1`, - `rowindex_2`, - `string_col`, - `time_col`, - `timestamp_col`, - `duration_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/snapshots/test_compile_readtable/test_compile_readtable_w_columns_filters/out.sql b/tests/unit/core/compile/sqlglot/snapshots/test_compile_readtable/test_compile_readtable_w_columns_filters/out.sql deleted file mode 100644 index 4d1b822245c..00000000000 --- a/tests/unit/core/compile/sqlglot/snapshots/test_compile_readtable/test_compile_readtable_w_columns_filters/out.sql +++ /dev/null @@ -1,7 +0,0 @@ -SELECT - `rowindex`, - `int64_col`, - `string_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` -WHERE - `rowindex` > 0 AND `string_col` IN ('Hello, World!') \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/snapshots/test_compile_readtable/test_compile_readtable_w_json_types/out.sql b/tests/unit/core/compile/sqlglot/snapshots/test_compile_readtable/test_compile_readtable_w_json_types/out.sql deleted file mode 100644 index 054e850fd36..00000000000 --- a/tests/unit/core/compile/sqlglot/snapshots/test_compile_readtable/test_compile_readtable_w_json_types/out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - * -FROM `bigframes-dev`.`sqlglot_test`.`json_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/snapshots/test_compile_readtable/test_compile_readtable_w_limit/out.sql b/tests/unit/core/compile/sqlglot/snapshots/test_compile_readtable/test_compile_readtable_w_limit/out.sql deleted file mode 100644 index ff4f0656b12..00000000000 --- a/tests/unit/core/compile/sqlglot/snapshots/test_compile_readtable/test_compile_readtable_w_limit/out.sql +++ /dev/null @@ -1,7 +0,0 @@ -SELECT - `rowindex`, - `int64_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` -ORDER BY - `rowindex` ASC NULLS LAST -LIMIT 10 \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/snapshots/test_compile_readtable/test_compile_readtable_w_nested_structs_types/out.sql b/tests/unit/core/compile/sqlglot/snapshots/test_compile_readtable/test_compile_readtable_w_nested_structs_types/out.sql deleted file mode 100644 index f75fa6f722c..00000000000 --- a/tests/unit/core/compile/sqlglot/snapshots/test_compile_readtable/test_compile_readtable_w_nested_structs_types/out.sql +++ /dev/null @@ -1,5 +0,0 @@ -SELECT - `id`, - `id` AS `id_1`, - `people` -FROM `bigframes-dev`.`sqlglot_test`.`nested_structs_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/snapshots/test_compile_readtable/test_compile_readtable_w_ordering/out.sql b/tests/unit/core/compile/sqlglot/snapshots/test_compile_readtable/test_compile_readtable_w_ordering/out.sql deleted file mode 100644 index 7e6ddfd568f..00000000000 --- a/tests/unit/core/compile/sqlglot/snapshots/test_compile_readtable/test_compile_readtable_w_ordering/out.sql +++ /dev/null @@ -1,6 +0,0 @@ -SELECT - `rowindex`, - `int64_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` -ORDER BY - `int64_col` ASC NULLS LAST \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/snapshots/test_compile_readtable/test_compile_readtable_w_repeated_types/out.sql b/tests/unit/core/compile/sqlglot/snapshots/test_compile_readtable/test_compile_readtable_w_repeated_types/out.sql deleted file mode 100644 index 34b02b5209b..00000000000 --- a/tests/unit/core/compile/sqlglot/snapshots/test_compile_readtable/test_compile_readtable_w_repeated_types/out.sql +++ /dev/null @@ -1,11 +0,0 @@ -SELECT - `rowindex`, - `rowindex` AS `rowindex_1`, - `int_list_col`, - `bool_list_col`, - `float_list_col`, - `date_list_col`, - `date_time_list_col`, - `numeric_list_col`, - `string_list_col` -FROM `bigframes-dev`.`sqlglot_test`.`repeated_types` AS `bft_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/snapshots/test_compile_readtable/test_compile_readtable_w_system_time/out.sql b/tests/unit/core/compile/sqlglot/snapshots/test_compile_readtable/test_compile_readtable_w_system_time/out.sql deleted file mode 100644 index dcd40d78485..00000000000 --- a/tests/unit/core/compile/sqlglot/snapshots/test_compile_readtable/test_compile_readtable_w_system_time/out.sql +++ /dev/null @@ -1,3 +0,0 @@ -SELECT - * -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` FOR SYSTEM_TIME AS OF '2025-11-09T03:04:05.678901+00:00' \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/snapshots/test_compile_window/test_compile_window_w_groupby_rolling/out.sql b/tests/unit/core/compile/sqlglot/snapshots/test_compile_window/test_compile_window_w_groupby_rolling/out.sql deleted file mode 100644 index 1051a0fb4c1..00000000000 --- a/tests/unit/core/compile/sqlglot/snapshots/test_compile_window/test_compile_window_w_groupby_rolling/out.sql +++ /dev/null @@ -1,55 +0,0 @@ -SELECT - `bool_col`, - `rowindex`, - CASE - WHEN COALESCE( - SUM(CAST(( - `bool_col` - ) IS NOT NULL AS INT64)) OVER ( - PARTITION BY `bool_col` - ORDER BY `bool_col` ASC NULLS LAST, `rowindex` ASC NULLS LAST - ROWS BETWEEN 3 PRECEDING AND CURRENT ROW - ), - 0 - ) < 3 - THEN NULL - WHEN TRUE - THEN COALESCE( - SUM(CAST(`bool_col` AS INT64)) OVER ( - PARTITION BY `bool_col` - ORDER BY `bool_col` ASC NULLS LAST, `rowindex` ASC NULLS LAST - ROWS BETWEEN 3 PRECEDING AND CURRENT ROW - ), - 0 - ) - END AS `bool_col_1`, - CASE - WHEN COALESCE( - SUM(CAST(( - `int64_col` - ) IS NOT NULL AS INT64)) OVER ( - PARTITION BY `bool_col` - ORDER BY `bool_col` ASC NULLS LAST, `rowindex` ASC NULLS LAST - ROWS BETWEEN 3 PRECEDING AND CURRENT ROW - ), - 0 - ) < 3 - THEN NULL - WHEN TRUE - THEN COALESCE( - SUM(`int64_col`) OVER ( - PARTITION BY `bool_col` - ORDER BY `bool_col` ASC NULLS LAST, `rowindex` ASC NULLS LAST - ROWS BETWEEN 3 PRECEDING AND CURRENT ROW - ), - 0 - ) - END AS `int64_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` -WHERE - ( - `bool_col` - ) IS NOT NULL -ORDER BY - `bool_col` ASC NULLS LAST, - `rowindex` ASC NULLS LAST \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/snapshots/test_compile_window/test_compile_window_w_range_rolling/out.sql b/tests/unit/core/compile/sqlglot/snapshots/test_compile_window/test_compile_window_w_range_rolling/out.sql deleted file mode 100644 index 887e7e9212d..00000000000 --- a/tests/unit/core/compile/sqlglot/snapshots/test_compile_window/test_compile_window_w_range_rolling/out.sql +++ /dev/null @@ -1,31 +0,0 @@ -WITH `bfcte_0` AS ( - SELECT - * - FROM UNNEST(ARRAY>[STRUCT(CAST('2025-01-01T00:00:00+00:00' AS TIMESTAMP), 0, 0), STRUCT(CAST('2025-01-01T00:00:01+00:00' AS TIMESTAMP), 1, 1), STRUCT(CAST('2025-01-01T00:00:02+00:00' AS TIMESTAMP), 2, 2), STRUCT(CAST('2025-01-01T00:00:03+00:00' AS TIMESTAMP), 3, 3), STRUCT(CAST('2025-01-01T00:00:04+00:00' AS TIMESTAMP), 0, 4), STRUCT(CAST('2025-01-01T00:00:05+00:00' AS TIMESTAMP), 1, 5), STRUCT(CAST('2025-01-01T00:00:06+00:00' AS TIMESTAMP), 2, 6), STRUCT(CAST('2025-01-01T00:00:07+00:00' AS TIMESTAMP), 3, 7), STRUCT(CAST('2025-01-01T00:00:08+00:00' AS TIMESTAMP), 0, 8), STRUCT(CAST('2025-01-01T00:00:09+00:00' AS TIMESTAMP), 1, 9), STRUCT(CAST('2025-01-01T00:00:10+00:00' AS TIMESTAMP), 2, 10), STRUCT(CAST('2025-01-01T00:00:11+00:00' AS TIMESTAMP), 3, 11), STRUCT(CAST('2025-01-01T00:00:12+00:00' AS TIMESTAMP), 0, 12), STRUCT(CAST('2025-01-01T00:00:13+00:00' AS TIMESTAMP), 1, 13), STRUCT(CAST('2025-01-01T00:00:14+00:00' AS TIMESTAMP), 2, 14), STRUCT(CAST('2025-01-01T00:00:15+00:00' AS TIMESTAMP), 3, 15), STRUCT(CAST('2025-01-01T00:00:16+00:00' AS TIMESTAMP), 0, 16), STRUCT(CAST('2025-01-01T00:00:17+00:00' AS TIMESTAMP), 1, 17), STRUCT(CAST('2025-01-01T00:00:18+00:00' AS TIMESTAMP), 2, 18), STRUCT(CAST('2025-01-01T00:00:19+00:00' AS TIMESTAMP), 3, 19)]) -) -SELECT - `bfcol_0` AS `ts_col`, - CASE - WHEN COALESCE( - SUM(CAST(( - `bfcol_1` - ) IS NOT NULL AS INT64)) OVER ( - ORDER BY UNIX_MICROS(`bfcol_0`) ASC - RANGE BETWEEN 2999999 PRECEDING AND CURRENT ROW - ), - 0 - ) < 1 - THEN NULL - WHEN TRUE - THEN COALESCE( - SUM(`bfcol_1`) OVER ( - ORDER BY UNIX_MICROS(`bfcol_0`) ASC - RANGE BETWEEN 2999999 PRECEDING AND CURRENT ROW - ), - 0 - ) - END AS `int_col` -FROM `bfcte_0` -ORDER BY - `bfcol_0` ASC NULLS LAST, - `bfcol_2` ASC NULLS LAST \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/snapshots/test_compile_window/test_compile_window_w_skips_nulls_op/out.sql b/tests/unit/core/compile/sqlglot/snapshots/test_compile_window/test_compile_window_w_skips_nulls_op/out.sql deleted file mode 100644 index 21bb8d5f088..00000000000 --- a/tests/unit/core/compile/sqlglot/snapshots/test_compile_window/test_compile_window_w_skips_nulls_op/out.sql +++ /dev/null @@ -1,19 +0,0 @@ -SELECT - `rowindex`, - CASE - WHEN COALESCE( - SUM(CAST(( - `int64_col` - ) IS NOT NULL AS INT64)) OVER (ORDER BY `rowindex` ASC NULLS LAST ROWS BETWEEN 2 PRECEDING AND CURRENT ROW), - 0 - ) < 3 - THEN NULL - WHEN TRUE - THEN COALESCE( - SUM(`int64_col`) OVER (ORDER BY `rowindex` ASC NULLS LAST ROWS BETWEEN 2 PRECEDING AND CURRENT ROW), - 0 - ) - END AS `int64_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` -ORDER BY - `rowindex` ASC NULLS LAST \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/snapshots/test_compile_window/test_compile_window_wo_skips_nulls_op/out.sql b/tests/unit/core/compile/sqlglot/snapshots/test_compile_window/test_compile_window_wo_skips_nulls_op/out.sql deleted file mode 100644 index 6ae1fffab7a..00000000000 --- a/tests/unit/core/compile/sqlglot/snapshots/test_compile_window/test_compile_window_wo_skips_nulls_op/out.sql +++ /dev/null @@ -1,13 +0,0 @@ -SELECT - `rowindex`, - CASE - WHEN COUNT(( - `int64_col` - ) IS NOT NULL) OVER (ORDER BY `rowindex` ASC NULLS LAST ROWS BETWEEN 4 PRECEDING AND CURRENT ROW) < 5 - THEN NULL - WHEN TRUE - THEN COUNT(`int64_col`) OVER (ORDER BY `rowindex` ASC NULLS LAST ROWS BETWEEN 4 PRECEDING AND CURRENT ROW) - END AS `int64_col` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` -ORDER BY - `rowindex` ASC NULLS LAST \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/snapshots/test_dataframe_accessor/test_bigframes_sql_scalar/out.sql b/tests/unit/core/compile/sqlglot/snapshots/test_dataframe_accessor/test_bigframes_sql_scalar/out.sql deleted file mode 100644 index 80b3137b0b5..00000000000 --- a/tests/unit/core/compile/sqlglot/snapshots/test_dataframe_accessor/test_bigframes_sql_scalar/out.sql +++ /dev/null @@ -1,4 +0,0 @@ -SELECT - `rowindex`, - ROUND(`int64_col` + `int64_too`) AS `0` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` diff --git a/tests/unit/core/compile/sqlglot/snapshots/test_dataframe_accessor/test_sql_scalar/out.sql b/tests/unit/core/compile/sqlglot/snapshots/test_dataframe_accessor/test_sql_scalar/out.sql deleted file mode 100644 index 80b3137b0b5..00000000000 --- a/tests/unit/core/compile/sqlglot/snapshots/test_dataframe_accessor/test_sql_scalar/out.sql +++ /dev/null @@ -1,4 +0,0 @@ -SELECT - `rowindex`, - ROUND(`int64_col` + `int64_too`) AS `0` -FROM `bigframes-dev`.`sqlglot_test`.`scalar_types` AS `bft_0` diff --git a/tests/unit/core/compile/sqlglot/sql/snapshots/test_ddl/test_create_external_table/out.sql b/tests/unit/core/compile/sqlglot/sql/snapshots/test_ddl/test_create_external_table/out.sql deleted file mode 100644 index 867282de0e7..00000000000 --- a/tests/unit/core/compile/sqlglot/sql/snapshots/test_ddl/test_create_external_table/out.sql +++ /dev/null @@ -1,7 +0,0 @@ -CREATE EXTERNAL TABLE `my-project.my_dataset.my_table` ( - `col1` INT64, - `col2` STRING -) OPTIONS ( - format='CSV', - uris=['gs://bucket/path*'] -) \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/sql/snapshots/test_ddl/test_create_external_table_all_options/out.sql b/tests/unit/core/compile/sqlglot/sql/snapshots/test_ddl/test_create_external_table_all_options/out.sql deleted file mode 100644 index a08ddf5ee5d..00000000000 --- a/tests/unit/core/compile/sqlglot/sql/snapshots/test_ddl/test_create_external_table_all_options/out.sql +++ /dev/null @@ -1,10 +0,0 @@ -CREATE OR REPLACE EXTERNAL TABLE `my-project.my_dataset.my_table` ( - `col1` INT64, - `col2` STRING -) WITH CONNECTION `my-connection` WITH PARTITION COLUMNS ( - `part1` DATE, - `part2` STRING -) OPTIONS ( - format='CSV', - uris=['gs://bucket/path*'] -) \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/sql/snapshots/test_ddl/test_create_external_table_if_not_exists/out.sql b/tests/unit/core/compile/sqlglot/sql/snapshots/test_ddl/test_create_external_table_if_not_exists/out.sql deleted file mode 100644 index e05a553317b..00000000000 --- a/tests/unit/core/compile/sqlglot/sql/snapshots/test_ddl/test_create_external_table_if_not_exists/out.sql +++ /dev/null @@ -1,6 +0,0 @@ -CREATE EXTERNAL TABLE IF NOT EXISTS `my-project.my_dataset.my_table` ( - `col1` INT64 -) OPTIONS ( - format='CSV', - uris=['gs://bucket/path*'] -) \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/sql/snapshots/test_ddl/test_load_data_all_options/out.sql b/tests/unit/core/compile/sqlglot/sql/snapshots/test_ddl/test_load_data_all_options/out.sql deleted file mode 100644 index 781019a0680..00000000000 --- a/tests/unit/core/compile/sqlglot/sql/snapshots/test_ddl/test_load_data_all_options/out.sql +++ /dev/null @@ -1,10 +0,0 @@ -LOAD DATA OVERWRITE INTO `my-project.my_dataset.my_table` ( - `col1` INT64, - `col2` STRING -) PARTITION BY `date_col` CLUSTER BY - `cluster_col` OPTIONS ( - description='my table' -) FROM FILES (format='CSV', uris=['gs://bucket/path*']) WITH PARTITION COLUMNS ( - `part1` DATE, - `part2` STRING -) WITH CONNECTION `my-connection` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/sql/snapshots/test_ddl/test_load_data_minimal/out.sql b/tests/unit/core/compile/sqlglot/sql/snapshots/test_ddl/test_load_data_minimal/out.sql deleted file mode 100644 index c5f66003257..00000000000 --- a/tests/unit/core/compile/sqlglot/sql/snapshots/test_ddl/test_load_data_minimal/out.sql +++ /dev/null @@ -1 +0,0 @@ -LOAD DATA INTO `my-project.my_dataset.my_table` FROM FILES (format='CSV', uris=['gs://bucket/path*']) \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/sql/snapshots/test_dml/test_insert_from_select/out.sql b/tests/unit/core/compile/sqlglot/sql/snapshots/test_dml/test_insert_from_select/out.sql deleted file mode 100644 index e2e9225c9f7..00000000000 --- a/tests/unit/core/compile/sqlglot/sql/snapshots/test_dml/test_insert_from_select/out.sql +++ /dev/null @@ -1,6 +0,0 @@ -INSERT INTO `bigframes-dev`.`sqlglot_test`.`dest_table` -( - SELECT - * - FROM `source_table` -) \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/sql/snapshots/test_dml/test_insert_from_table/out.sql b/tests/unit/core/compile/sqlglot/sql/snapshots/test_dml/test_insert_from_table/out.sql deleted file mode 100644 index 2486d8d0a3b..00000000000 --- a/tests/unit/core/compile/sqlglot/sql/snapshots/test_dml/test_insert_from_table/out.sql +++ /dev/null @@ -1,2 +0,0 @@ -INSERT INTO `bigframes-dev`.`sqlglot_test`.`dest_table` -`source_table` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/sql/snapshots/test_dml/test_replace_from_select/out.sql b/tests/unit/core/compile/sqlglot/sql/snapshots/test_dml/test_replace_from_select/out.sql deleted file mode 100644 index c4f43f390ed..00000000000 --- a/tests/unit/core/compile/sqlglot/sql/snapshots/test_dml/test_replace_from_select/out.sql +++ /dev/null @@ -1,9 +0,0 @@ -MERGE INTO `bigframes-dev`.`sqlglot_test`.`dest_table` -USING ( - SELECT - * - FROM `source_table` -) -ON FALSE -WHEN NOT MATCHED BY SOURCE THEN DELETE -WHEN NOT MATCHED THEN INSERT ROW \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/sql/snapshots/test_dml/test_replace_from_table/out.sql b/tests/unit/core/compile/sqlglot/sql/snapshots/test_dml/test_replace_from_table/out.sql deleted file mode 100644 index bfc1532ca2d..00000000000 --- a/tests/unit/core/compile/sqlglot/sql/snapshots/test_dml/test_replace_from_table/out.sql +++ /dev/null @@ -1,5 +0,0 @@ -MERGE INTO `bigframes-dev`.`sqlglot_test`.`dest_table` -USING `source_table` -ON FALSE -WHEN NOT MATCHED BY SOURCE THEN DELETE -WHEN NOT MATCHED THEN INSERT ROW \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/sql/test_base.py b/tests/unit/core/compile/sqlglot/sql/test_base.py deleted file mode 100644 index 617f3636d40..00000000000 --- a/tests/unit/core/compile/sqlglot/sql/test_base.py +++ /dev/null @@ -1,173 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import datetime -import decimal -import re - -import numpy as np -import pandas as pd -import pyarrow as pa -import pytest -import shapely.geometry # type: ignore - -import bigframes.core.compile.sqlglot.sql.base as sql - - -@pytest.mark.parametrize( - ("value", "expected_pattern"), - ( - pytest.param(None, "NULL", id="null"), - pytest.param(True, "TRUE", id="true"), - pytest.param(False, "FALSE", id="false"), - pytest.param(123, "123", id="int"), - pytest.param(123.75, "123.75", id="float"), - pytest.param("abc", "'abc'", id="string"), - pytest.param( - b"\x01\x02\x03ABC", "CAST(b'\\x01\\x02\\x03ABC' AS BYTES)", id="bytes" - ), - pytest.param( - decimal.Decimal("123.75"), "CAST(123.75 AS NUMERIC)", id="decimal" - ), - pytest.param( - datetime.date(2025, 1, 1), "CAST('2025-01-01' AS DATE)", id="date" - ), - pytest.param( - datetime.datetime(2025, 1, 2, 3, 45, 6, 789123), - "CAST('2025-01-02T03:45:06.789123' AS DATETIME)", - id="datetime", - ), - pytest.param( - datetime.time(12, 34, 56, 789123), - "CAST('12:34:56.789123' AS TIME)", - id="time", - ), - pytest.param( - datetime.datetime( - 2025, 1, 2, 3, 45, 6, 789123, tzinfo=datetime.timezone.utc - ), - "CAST('2025-01-02T03:45:06.789123+00:00' AS TIMESTAMP)", - id="timestamp", - ), - pytest.param(np.int64(123), "123", id="np_int64"), - pytest.param(np.float64(123.75), "123.75", id="np_float64"), - pytest.param(float("inf"), "CAST('Infinity' AS FLOAT64)", id="inf"), - pytest.param(float("-inf"), "CAST('-Infinity' AS FLOAT64)", id="neg_inf"), - pytest.param(float("nan"), "NULL", id="nan"), - pytest.param(pd.NA, "NULL", id="pd_na"), - pytest.param(datetime.timedelta(seconds=1), "1000000", id="timedelta"), - pytest.param("POINT (0 1)", "'POINT (0 1)'", id="string_geo"), - ), -) -def test_literal(value, expected_pattern): - got = sql.to_sql(sql.literal(value)) - assert got == expected_pattern - - -def test_literal_for_geo(): - value = shapely.geometry.Point(0, 1) - expected_pattern = r"ST_GEOGFROMTEXT\('POINT \(0[.]?0* 1[.]?0*\)'\)" - got = sql.to_sql(sql.literal(value)) - assert re.match(expected_pattern, got) is not None - - -@pytest.mark.parametrize( - ("value", "dtype", "expected"), - ( - pytest.param( - decimal.Decimal("1.23"), - sql.dtypes.BIGNUMERIC_DTYPE, - "CAST(1.23 AS BIGNUMERIC)", - id="bignumeric", - ), - pytest.param( - [], - pd.ArrowDtype(pa.list_(pa.int64())), - "ARRAY[]", - id="empty_array", - ), - pytest.param( - {"a": 1, "b": "hello"}, - pd.ArrowDtype(pa.struct([("a", pa.int64()), ("b", pa.string())])), - "STRUCT(1 AS `a`, 'hello' AS `b`)", - id="struct", - ), - pytest.param( - float("nan"), - sql.dtypes.FLOAT_DTYPE, - "CAST('NaN' AS FLOAT64)", - id="explicit_nan", - ), - pytest.param( - pa.scalar(123, type=pa.int64()), - None, - "123", - id="pa_scalar_int", - ), - pytest.param( - pa.scalar(None, type=pa.int64()), - None, - "CAST(NULL AS INT64)", - id="pa_scalar_null", - ), - pytest.param( - {"a": 10}, - sql.dtypes.JSON_DTYPE, - "PARSE_JSON('{\\'a\\': 10}')", - id="json", - ), - ), -) -def test_literal_explicit_dtype(value, dtype, expected): - got = sql.to_sql(sql.literal(value, dtype=dtype)) - assert got == expected - - -@pytest.mark.parametrize( - ("value", "expected"), - ( - pytest.param([True, False], "[TRUE, FALSE]", id="bool"), - pytest.param([123, 456], "[123, 456]", id="int"), - pytest.param( - [123.75, 456.78, float("nan"), float("inf"), float("-inf")], - "[\n 123.75,\n 456.78,\n CAST('NaN' AS FLOAT64),\n CAST('Infinity' AS FLOAT64),\n CAST('-Infinity' AS FLOAT64)\n]", - id="float", - ), - pytest.param( - [b"\x01\x02\x03ABC", b"\x01\x02\x03ABC"], - "[CAST(b'\\x01\\x02\\x03ABC' AS BYTES), CAST(b'\\x01\\x02\\x03ABC' AS BYTES)]", - id="bytes", - ), - pytest.param( - [datetime.date(2025, 1, 1), datetime.date(2025, 1, 1)], - "[CAST('2025-01-01' AS DATE), CAST('2025-01-01' AS DATE)]", - id="date", - ), - ), -) -def test_literal_for_list(value: list, expected: str): - got = sql.to_sql(sql.literal(value)) - assert got == expected - - -def test_literal_null_type(): - import unittest.mock as mock - - mock_dtype = mock.Mock() - with mock.patch( - "bigframes.core.compile.sqlglot.sql.base.sgt.from_bigframes_dtype", - return_value="NULL", - ): - got = sql.to_sql(sql.literal(None, dtype=mock_dtype)) - assert got == "NULL" diff --git a/tests/unit/core/compile/sqlglot/sql/test_ddl.py b/tests/unit/core/compile/sqlglot/sql/test_ddl.py deleted file mode 100644 index 48080cd6b9c..00000000000 --- a/tests/unit/core/compile/sqlglot/sql/test_ddl.py +++ /dev/null @@ -1,87 +0,0 @@ -# Copyright 2026 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from unittest import mock - -import pytest - -import bigframes.bigquery -import bigframes.core.compile.sqlglot.sql as sql -import bigframes.session - -pytest.importorskip("pytest_snapshot") - - -@pytest.fixture -def mock_session(): - return mock.create_autospec(spec=bigframes.session.Session) - - -def test_load_data_minimal(snapshot): - expr = sql.load_data( - "my-project.my_dataset.my_table", - from_files_options={"format": "CSV", "uris": ["gs://bucket/path*"]}, - ) - snapshot.assert_match(sql.to_sql(expr), "out.sql") - - -def test_load_data_all_options(snapshot): - expr = sql.load_data( - "my-project.my_dataset.my_table", - write_disposition="OVERWRITE", - columns={"col1": "INT64", "col2": "STRING"}, - partition_by=["date_col"], - cluster_by=["cluster_col"], - table_options={"description": "my table"}, - from_files_options={"format": "CSV", "uris": ["gs://bucket/path*"]}, - with_partition_columns={"part1": "DATE", "part2": "STRING"}, - connection_name="my-connection", - ) - snapshot.assert_match(sql.to_sql(expr), "out.sql") - - -@mock.patch("bigframes.bigquery._operations.table._get_table_metadata") -def test_create_external_table(get_table_metadata_mock, mock_session, snapshot): - bigframes.bigquery.create_external_table( - "my-project.my_dataset.my_table", - columns={"col1": "INT64", "col2": "STRING"}, - options={"format": "CSV", "uris": ["gs://bucket/path*"]}, - session=mock_session, - ) - mock_session.read_gbq_query.assert_called_once() - generated_sql = mock_session.read_gbq_query.call_args[0][0] - snapshot.assert_match(generated_sql, "out.sql") - get_table_metadata_mock.assert_called_once() - - -def test_create_external_table_all_options(snapshot): - expr = sql.create_external_table( - "my-project.my_dataset.my_table", - replace=True, - columns={"col1": "INT64", "col2": "STRING"}, - partition_columns={"part1": "DATE", "part2": "STRING"}, - connection_name="my-connection", - options={"format": "CSV", "uris": ["gs://bucket/path*"]}, - ) - snapshot.assert_match(sql.to_sql(expr), "out.sql") - - -def test_create_external_table_if_not_exists(snapshot): - expr = sql.create_external_table( - "my-project.my_dataset.my_table", - if_not_exists=True, - columns={"col1": "INT64"}, - options={"format": "CSV", "uris": ["gs://bucket/path*"]}, - ) - snapshot.assert_match(sql.to_sql(expr), "out.sql") diff --git a/tests/unit/core/compile/sqlglot/sql/test_dml.py b/tests/unit/core/compile/sqlglot/sql/test_dml.py deleted file mode 100644 index 99f10892d90..00000000000 --- a/tests/unit/core/compile/sqlglot/sql/test_dml.py +++ /dev/null @@ -1,73 +0,0 @@ -# Copyright 2026 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import bigframes_vendored.sqlglot.expressions as sge -import pytest -from google.cloud import bigquery - -from bigframes.core.compile.sqlglot.sql import base, dml - -pytest.importorskip("pytest_snapshot") - - -def test_insert_from_select(snapshot): - query = sge.select("*").from_( - sge.Table(this=sge.Identifier(this="source_table", quoted=True)) - ) - destination = bigquery.TableReference.from_string( - "bigframes-dev.sqlglot_test.dest_table" - ) - - expr = dml.insert(query, destination) - sql = base.to_sql(expr) - - snapshot.assert_match(sql, "out.sql") - - -def test_insert_from_table(snapshot): - query = sge.Table(this=sge.Identifier(this="source_table", quoted=True)) - destination = bigquery.TableReference.from_string( - "bigframes-dev.sqlglot_test.dest_table" - ) - - expr = dml.insert(query, destination) - sql = base.to_sql(expr) - - snapshot.assert_match(sql, "out.sql") - - -def test_replace_from_select(snapshot): - query = sge.select("*").from_( - sge.Table(this=sge.Identifier(this="source_table", quoted=True)) - ) - destination = bigquery.TableReference.from_string( - "bigframes-dev.sqlglot_test.dest_table" - ) - - expr = dml.replace(query, destination) - sql = base.to_sql(expr) - - snapshot.assert_match(sql, "out.sql") - - -def test_replace_from_table(snapshot): - query = sge.Table(this=sge.Identifier(this="source_table", quoted=True)) - destination = bigquery.TableReference.from_string( - "bigframes-dev.sqlglot_test.dest_table" - ) - - expr = dml.replace(query, destination) - sql = base.to_sql(expr) - - snapshot.assert_match(sql, "out.sql") diff --git a/tests/unit/core/compile/sqlglot/test_compile_aggregate.py b/tests/unit/core/compile/sqlglot/test_compile_aggregate.py deleted file mode 100644 index d59c5e50687..00000000000 --- a/tests/unit/core/compile/sqlglot/test_compile_aggregate.py +++ /dev/null @@ -1,33 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import pytest - -import bigframes.pandas as bpd - -pytest.importorskip("pytest_snapshot") - - -def test_compile_aggregate(scalar_types_df: bpd.DataFrame, snapshot): - result = scalar_types_df["int64_too"].groupby(scalar_types_df["bool_col"]).sum() - snapshot.assert_match(result.to_frame().sql, "out.sql") - - -def test_compile_aggregate_wo_dropna(scalar_types_df: bpd.DataFrame, snapshot): - result = ( - scalar_types_df["int64_too"] - .groupby(scalar_types_df["bool_col"], dropna=False) - .sum() - ) - snapshot.assert_match(result.to_frame().sql, "out.sql") diff --git a/tests/unit/core/compile/sqlglot/test_compile_concat.py b/tests/unit/core/compile/sqlglot/test_compile_concat.py deleted file mode 100644 index d13da8ec570..00000000000 --- a/tests/unit/core/compile/sqlglot/test_compile_concat.py +++ /dev/null @@ -1,48 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import pytest - -import bigframes.pandas as bpd -from bigframes.core import ordering - -pytest.importorskip("pytest_snapshot") - - -def test_compile_concat(scalar_types_df: bpd.DataFrame, snapshot): - # TODO: concat two same dataframes, which SQL does not get reused. - df1 = scalar_types_df[["rowindex", "int64_col", "string_col"]] - concat_df = bpd.concat([df1, df1]) - snapshot.assert_match(concat_df.sql, "out.sql") - - -def test_compile_concat_filter_sorted(scalar_types_df: bpd.DataFrame, snapshot): - scalars_array_value = scalar_types_df._block.expr - input_1 = scalars_array_value.select_columns(["float64_col", "int64_col"]).order_by( - [ordering.ascending_over("int64_col")] - ) - input_2 = scalars_array_value.filter_by_id("bool_col").select_columns( - ["float64_col", "int64_too"] - ) - - result = input_1.concat([input_2, input_1, input_2]) - - new_names = ["float64_col", "int64_col"] - col_ids = { - old_name: new_name for old_name, new_name in zip(result.column_ids, new_names) - } - result = result.rename_columns(col_ids).select_columns(new_names) - - sql = result.session._executor.to_sql(result, enable_cache=False) - snapshot.assert_match(sql, "out.sql") diff --git a/tests/unit/core/compile/sqlglot/test_compile_explode.py b/tests/unit/core/compile/sqlglot/test_compile_explode.py deleted file mode 100644 index 34adbbd23ab..00000000000 --- a/tests/unit/core/compile/sqlglot/test_compile_explode.py +++ /dev/null @@ -1,31 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import pytest - -import bigframes.pandas as bpd - -pytest.importorskip("pytest_snapshot") - - -# TODO: check order by with offset -def test_compile_explode_series(repeated_types_df: bpd.DataFrame, snapshot): - s = repeated_types_df["int_list_col"].explode() - snapshot.assert_match(s.to_frame().sql, "out.sql") - - -def test_compile_explode_dataframe(repeated_types_df: bpd.DataFrame, snapshot): - exploded_columns = ["int_list_col", "string_list_col"] - df = repeated_types_df[["rowindex", *exploded_columns]].explode(exploded_columns) - snapshot.assert_match(df.sql, "out.sql") diff --git a/tests/unit/core/compile/sqlglot/test_compile_filter.py b/tests/unit/core/compile/sqlglot/test_compile_filter.py deleted file mode 100644 index 0afb5eb45b9..00000000000 --- a/tests/unit/core/compile/sqlglot/test_compile_filter.py +++ /dev/null @@ -1,25 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import pytest - -import bigframes.pandas as bpd - -pytest.importorskip("pytest_snapshot") - - -def test_compile_filter(scalar_types_df: bpd.DataFrame, snapshot): - bf_df = scalar_types_df[["rowindex", "int64_col"]] - bf_filter = bf_df[bf_df["rowindex"] >= 1] - snapshot.assert_match(bf_filter.sql, "out.sql") diff --git a/tests/unit/core/compile/sqlglot/test_compile_fromrange.py b/tests/unit/core/compile/sqlglot/test_compile_fromrange.py deleted file mode 100644 index 8c25ca0310c..00000000000 --- a/tests/unit/core/compile/sqlglot/test_compile_fromrange.py +++ /dev/null @@ -1,35 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import pandas as pd -import pytest - -import bigframes.pandas as bpd - -pytest.importorskip("pytest_snapshot") - - -def test_compile_fromrange(compiler_session, snapshot): - data = { - "timestamp_col": pd.date_range( - start="2021-01-01 13:00:00", periods=30, freq="1s" - ), - "int64_col": range(30), - "int64_too": range(10, 40), - } - df = bpd.DataFrame(data, session=compiler_session).set_index("timestamp_col") - sql, _, _ = df.resample(rule="7s")._block.to_sql_query( - include_index=True, enable_cache=False - ) - snapshot.assert_match(sql.strip() + "\n", "out.sql") diff --git a/tests/unit/core/compile/sqlglot/test_compile_geo.py b/tests/unit/core/compile/sqlglot/test_compile_geo.py deleted file mode 100644 index 4aad2dfa315..00000000000 --- a/tests/unit/core/compile/sqlglot/test_compile_geo.py +++ /dev/null @@ -1,55 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import pytest -from shapely.geometry import LineString # type: ignore - -import bigframes.bigquery as bbq -import bigframes.geopandas as gpd - -pytest.importorskip("pytest_snapshot") - - -def test_st_regionstats(compiler_session, snapshot): - geos = gpd.GeoSeries(["POINT(1 1)"], session=compiler_session) - result = bbq.st_regionstats( - geos, - "ee://some/raster/uri", - band="band1", - include="some equation", - options={"scale": 100}, - ) - assert "area" in result.struct.dtypes.index - snapshot.assert_match(result.struct.explode().sql, "out.sql") - - -def test_st_regionstats_without_optional_args(compiler_session, snapshot): - geos = gpd.GeoSeries(["POINT(1 1)"], session=compiler_session) - result = bbq.st_regionstats( - geos, - "ee://some/raster/uri", - ) - assert "area" in result.struct.dtypes.index - snapshot.assert_match(result.struct.explode().sql, "out.sql") - - -def test_st_simplify(compiler_session, snapshot): - geos = gpd.GeoSeries( - [LineString([(0, 0), (1, 1), (2, 0)])], session=compiler_session - ) - result = bbq.st_simplify( - geos, - tolerance_meters=123.125, - ) - snapshot.assert_match(result.to_frame().sql, "out.sql") diff --git a/tests/unit/core/compile/sqlglot/test_compile_isin.py b/tests/unit/core/compile/sqlglot/test_compile_isin.py deleted file mode 100644 index 8b3e7f7291f..00000000000 --- a/tests/unit/core/compile/sqlglot/test_compile_isin.py +++ /dev/null @@ -1,31 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import pytest - -import bigframes.pandas as bpd - -pytest.importorskip("pytest_snapshot") - - -def test_compile_isin(scalar_types_df: bpd.DataFrame, snapshot): - bf_isin = scalar_types_df["int64_col"].isin(scalar_types_df["int64_too"]).to_frame() - snapshot.assert_match(bf_isin.sql, "out.sql") - - -def test_compile_isin_not_nullable(scalar_types_df: bpd.DataFrame, snapshot): - bf_isin = ( - scalar_types_df["rowindex_2"].isin(scalar_types_df["rowindex_2"]).to_frame() - ) - snapshot.assert_match(bf_isin.sql, "out.sql") diff --git a/tests/unit/core/compile/sqlglot/test_compile_join.py b/tests/unit/core/compile/sqlglot/test_compile_join.py deleted file mode 100644 index ac016eec020..00000000000 --- a/tests/unit/core/compile/sqlglot/test_compile_join.py +++ /dev/null @@ -1,61 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import pytest - -import bigframes.pandas as bpd - -pytest.importorskip("pytest_snapshot") - - -def test_compile_join(scalar_types_df: bpd.DataFrame, snapshot): - left = scalar_types_df[["int64_col"]] - right = scalar_types_df.set_index("int64_col")[["int64_too"]] - join = left.join(right) - snapshot.assert_match(join.sql, "out.sql") - - -def test_compile_join_w_how(scalar_types_df: bpd.DataFrame): - left = scalar_types_df[["int64_col"]] - right = scalar_types_df.set_index("int64_col")[["int64_too"]] - - join_sql = left.join(right, how="left").sql - assert "LEFT JOIN" in join_sql - assert "ON" in join_sql - - join_sql = left.join(right, how="right").sql - assert "RIGHT JOIN" in join_sql - assert "ON" in join_sql - - join_sql = left.join(right, how="outer").sql - assert "FULL OUTER JOIN" in join_sql - assert "ON" in join_sql - - join_sql = left.join(right, how="inner").sql - assert "INNER JOIN" in join_sql - assert "ON" in join_sql - - join_sql = left.merge(right, how="cross").sql - assert "CROSS JOIN" in join_sql - assert "ON" not in join_sql - - -@pytest.mark.parametrize( - ("on"), - ["bool_col", "int64_col", "float64_col", "string_col", "time_col", "numeric_col"], -) -def test_compile_join_w_on(scalar_types_df: bpd.DataFrame, on: str, snapshot): - df = scalar_types_df[["rowindex", on]] - merge = df.merge(df, left_on=on, right_on=on) - snapshot.assert_match(merge.sql, "out.sql") diff --git a/tests/unit/core/compile/sqlglot/test_compile_random_sample.py b/tests/unit/core/compile/sqlglot/test_compile_random_sample.py deleted file mode 100644 index 6aec633238c..00000000000 --- a/tests/unit/core/compile/sqlglot/test_compile_random_sample.py +++ /dev/null @@ -1,35 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import pytest - -import bigframes.core as core -import bigframes.core.compile as compile -from bigframes.core import nodes - -pytest.importorskip("pytest_snapshot") - - -def test_compile_random_sample( - scalar_types_array_value: core.ArrayValue, - snapshot, -): - """This test verifies the SQL compilation of a RandomSampleNode. - - Because BigFrames doesn't expose a public API for creating a random sample - operation, this test constructs the node directly and then compiles it to SQL. - """ - node = nodes.RandomSampleNode(scalar_types_array_value.node, fraction=0.1) - sql = compile.sqlglot.compile_sql(compile.CompileRequest(node, sort_rows=True)).sql - snapshot.assert_match(sql, "out.sql") diff --git a/tests/unit/core/compile/sqlglot/test_compile_readlocal.py b/tests/unit/core/compile/sqlglot/test_compile_readlocal.py deleted file mode 100644 index 03a8b39d9a0..00000000000 --- a/tests/unit/core/compile/sqlglot/test_compile_readlocal.py +++ /dev/null @@ -1,78 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import numpy as np -import pandas as pd -import pytest - -import bigframes -import bigframes.pandas as bpd - -pytest.importorskip("pytest_snapshot") - - -def test_compile_readlocal( - scalar_types_pandas_df: pd.DataFrame, compiler_session: bigframes.Session, snapshot -): - bf_df = bpd.DataFrame(scalar_types_pandas_df, session=compiler_session) - snapshot.assert_match(bf_df.sql, "out.sql") - - -def test_compile_readlocal_w_structs_df( - nested_structs_pandas_df: pd.DataFrame, - compiler_session_w_nested_structs_types: bigframes.Session, - snapshot, -): - bf_df = bpd.DataFrame( - nested_structs_pandas_df, session=compiler_session_w_nested_structs_types - ) - snapshot.assert_match(bf_df.sql, "out.sql") - - -def test_compile_readlocal_w_lists_df( - repeated_types_pandas_df: pd.DataFrame, - compiler_session_w_repeated_types: bigframes.Session, - snapshot, -): - bf_df = bpd.DataFrame( - repeated_types_pandas_df, session=compiler_session_w_repeated_types - ) - snapshot.assert_match(bf_df.sql, "out.sql") - - -def test_compile_readlocal_w_json_df( - json_pandas_df: pd.DataFrame, - compiler_session_w_json_types: bigframes.Session, - snapshot, -): - bf_df = bpd.DataFrame(json_pandas_df, session=compiler_session_w_json_types) - snapshot.assert_match(bf_df.sql, "out.sql") - - -def test_compile_readlocal_w_special_values( - compiler_session: bigframes.Session, snapshot -): - df = pd.DataFrame( - { - "col_none": [None, 1, 2], - "col_inf": [np.inf, 1.0, 2.0], - "col_neginf": [-np.inf, 1.0, 2.0], - "col_nan": [np.nan, 1.0, 2.0], - "col_struct_none": [None, {"foo": 1}, {"foo": 2}], - "col_struct_w_none": [{"foo": None}, {"foo": 1}, {"foo": 2}], - "col_list_none": [None, [1, 2], [3, 4]], - } - ) - bf_df = bpd.DataFrame(df, session=compiler_session) - snapshot.assert_match(bf_df.sql, "out.sql") diff --git a/tests/unit/core/compile/sqlglot/test_compile_readtable.py b/tests/unit/core/compile/sqlglot/test_compile_readtable.py deleted file mode 100644 index 0f2058f21f6..00000000000 --- a/tests/unit/core/compile/sqlglot/test_compile_readtable.py +++ /dev/null @@ -1,94 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import datetime - -import google.cloud.bigquery as bigquery -import pytest - -import bigframes.pandas as bpd -from bigframes.core import bq_data - -pytest.importorskip("pytest_snapshot") - - -def test_compile_readtable(scalar_types_df: bpd.DataFrame, snapshot): - snapshot.assert_match(scalar_types_df.sql, "out.sql") - - -def test_compile_readtable_w_repeated_types(repeated_types_df: bpd.DataFrame, snapshot): - snapshot.assert_match(repeated_types_df.sql, "out.sql") - - -def test_compile_readtable_w_nested_structs_types( - nested_structs_types_df: bpd.DataFrame, snapshot -): - snapshot.assert_match(nested_structs_types_df.sql, "out.sql") - - -def test_compile_readtable_w_json_types(json_types_df: bpd.DataFrame, snapshot): - snapshot.assert_match(json_types_df.sql, "out.sql") - - -def test_compile_readtable_w_ordering(scalar_types_df: bpd.DataFrame, snapshot): - bf_df = scalar_types_df[["int64_col"]] - bf_df = bf_df.sort_values("int64_col") - snapshot.assert_match(bf_df.sql, "out.sql") - - -def test_compile_readtable_w_limit(scalar_types_df: bpd.DataFrame, snapshot): - bf_df = scalar_types_df[["int64_col"]] - bf_df = bf_df.sort_index().head(10) - snapshot.assert_match(bf_df.sql, "out.sql") - - -def test_compile_readtable_w_system_time( - compiler_session, scalar_types_table_schema, snapshot -): - table_ref = bigquery.TableReference( - bigquery.DatasetReference("bigframes-dev", "sqlglot_test"), - "scalar_types", - ) - table = bigquery.Table(table_ref, tuple(scalar_types_table_schema)) - table._properties["location"] = compiler_session._location - compiler_session._loader._df_snapshot[str(table_ref)] = ( - datetime.datetime(2025, 11, 9, 3, 4, 5, 678901, tzinfo=datetime.timezone.utc), - bq_data.GbqNativeTable.from_table(table), - ) - bf_df = compiler_session.read_gbq_table(str(table_ref)) - snapshot.assert_match(bf_df.sql, "out.sql") - - -def test_compile_readtable_w_columns_filters(compiler_session, snapshot): - columns = ["rowindex", "int64_col", "string_col"] - filters = [("rowindex", ">", 0), ("string_col", "in", ["Hello, World!"])] - bf_df = compiler_session._loader.read_gbq_table( - "bigframes-dev.sqlglot_test.scalar_types", - enable_snapshot=False, - columns=columns, - filters=filters, - ) - snapshot.assert_match(bf_df.sql, "out.sql") - - -def test_compile_astype_aliases(scalar_types_df: bpd.DataFrame, snapshot): - # Test case for issue #17394 (CAST columns lose their aliases) - bf_df = scalar_types_df[["timestamp_col", "int64_col"]] - result = bf_df.astype( - { - "timestamp_col": "string[pyarrow]", - "int64_col": "Float64", - } - ) - snapshot.assert_match(result.sql + "\n", "out.sql") diff --git a/tests/unit/core/compile/sqlglot/test_compile_window.py b/tests/unit/core/compile/sqlglot/test_compile_window.py deleted file mode 100644 index 1602ec2c478..00000000000 --- a/tests/unit/core/compile/sqlglot/test_compile_window.py +++ /dev/null @@ -1,61 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import numpy as np -import pandas as pd -import pytest - -import bigframes.pandas as bpd - -pytest.importorskip("pytest_snapshot") - - -def test_compile_window_w_skips_nulls_op(scalar_types_df: bpd.DataFrame, snapshot): - bf_df = scalar_types_df[["int64_col"]].sort_index() - # The SumOp's skips_nulls is True - result = bf_df.rolling(window=3).sum() - snapshot.assert_match(result.sql, "out.sql") - - -def test_compile_window_wo_skips_nulls_op(scalar_types_df: bpd.DataFrame, snapshot): - bf_df = scalar_types_df[["int64_col"]].sort_index() - # The CountOp's skips_nulls is False - result = bf_df.rolling(window=5).count() - snapshot.assert_match(result.sql, "out.sql") - - -def test_compile_window_w_groupby_rolling(scalar_types_df: bpd.DataFrame, snapshot): - bf_df = scalar_types_df[["bool_col", "int64_col"]].sort_index() - result = ( - bf_df.groupby(scalar_types_df["bool_col"]) - .rolling(window=3, closed="both") - .sum() - ) - snapshot.assert_match(result.sql, "out.sql") - - -def test_compile_window_w_range_rolling(compiler_session, snapshot): - # TODO: use `duration_col` instead. - values = np.arange(20) - pd_df = pd.DataFrame( - { - "ts_col": pd.Timestamp("20250101", tz="UTC") + pd.to_timedelta(values, "s"), - "int_col": values % 4, - "float_col": values / 2, - } - ) - bf_df = compiler_session.read_pandas(pd_df) - bf_series = bf_df.set_index("ts_col")["int_col"].sort_index() - result = bf_series.rolling(window="3s").sum() - snapshot.assert_match(result.to_frame().sql, "out.sql") diff --git a/tests/unit/core/compile/sqlglot/test_dataframe_accessor.py b/tests/unit/core/compile/sqlglot/test_dataframe_accessor.py deleted file mode 100644 index e430f566497..00000000000 --- a/tests/unit/core/compile/sqlglot/test_dataframe_accessor.py +++ /dev/null @@ -1,64 +0,0 @@ -# Copyright 2026 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import unittest.mock as mock - -import pandas as pd -import pytest - -import bigframes.pandas as bpd -import bigframes.session - -pytest.importorskip("pytest_snapshot") - -# Only test on the latest pandas since column naming behavior is slightly -# different across versions, e.g. unnamed vs 0 for unnamed Series. -pytest.importorskip("pandas", minversion="3.0.0") - - -def test_sql_scalar(scalar_types_df: bpd.DataFrame, snapshot, monkeypatch): - session = mock.create_autospec(bigframes.session.Session) - session.read_pandas.return_value = scalar_types_df - - def to_pandas(series, *, ordered): - assert ordered is True - sql, _, _ = series.to_frame()._to_sql_query(include_index=True) - return sql - - monkeypatch.setattr(bpd.Series, "to_pandas", to_pandas) - - df = pd.DataFrame({"int64_col": [1, 2], "int64_too": [3, 4]}) - result = df.bigquery.sql_scalar( - "ROUND({int64_col} + {int64_too})", - output_dtype=pd.Int64Dtype(), - session=session, - ) - - session.read_pandas.assert_called_once() - snapshot.assert_match(result.strip() + "\n", "out.sql") - - -def test_bigframes_sql_scalar(scalar_types_df: bpd.DataFrame, snapshot): - session = mock.create_autospec(bigframes.session.Session) - - result = scalar_types_df.bigquery.sql_scalar( - "ROUND({int64_col} + {int64_too})", - output_dtype=pd.Int64Dtype(), - session=session, - ) - - session.read_pandas.assert_not_called() - # Bigframes implementation returns a bigframes.series.Series - sql, _, _ = result.to_frame()._to_sql_query(include_index=True) - snapshot.assert_match(sql.strip() + "\n", "out.sql") diff --git a/tests/unit/core/compile/sqlglot/test_scalar_compiler.py b/tests/unit/core/compile/sqlglot/test_scalar_compiler.py deleted file mode 100644 index d8a59420452..00000000000 --- a/tests/unit/core/compile/sqlglot/test_scalar_compiler.py +++ /dev/null @@ -1,226 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import unittest.mock as mock - -import bigframes_vendored.sqlglot.expressions as sge -import pytest - -import bigframes.core.compile.sqlglot.expression_compiler as expression_compiler -import bigframes.operations as ops -from bigframes.core.compile.sqlglot.expressions.typed_expr import TypedExpr - - -def test_register_unary_op(): - compiler = expression_compiler.ExpressionCompiler() - - class MockUnaryOp(ops.UnaryOp): - name = "mock_unary_op" - - mock_op = MockUnaryOp() - mock_impl = mock.Mock() - - @compiler.register_unary_op(mock_op) - def _(expr: TypedExpr) -> sge.Expression: - mock_impl(expr) - return sge.Identifier(this="output") - - arg = TypedExpr(sge.Identifier(this="input"), "string") - result = compiler.compile_row_op(mock_op, [arg]) - assert result == sge.Identifier(this="output") - mock_impl.assert_called_once_with(arg) - - -def test_register_unary_op_pass_op(): - compiler = expression_compiler.ExpressionCompiler() - - class MockUnaryOp(ops.UnaryOp): - name = "mock_unary_op_pass_op" - - mock_op = MockUnaryOp() - mock_impl = mock.Mock() - - @compiler.register_unary_op(mock_op, pass_op=True) - def _(expr: TypedExpr, op: ops.UnaryOp) -> sge.Expression: - mock_impl(expr, op) - return sge.Identifier(this="output") - - arg = TypedExpr(sge.Identifier(this="input"), "string") - result = compiler.compile_row_op(mock_op, [arg]) - assert result == sge.Identifier(this="output") - mock_impl.assert_called_once_with(arg, mock_op) - - -def test_register_binary_op(): - compiler = expression_compiler.ExpressionCompiler() - - class MockBinaryOp(ops.BinaryOp): - name = "mock_binary_op" - - mock_op = MockBinaryOp() - mock_impl = mock.Mock() - - @compiler.register_binary_op(mock_op) - def _(left: TypedExpr, right: TypedExpr) -> sge.Expression: - mock_impl(left, right) - return sge.Identifier(this="output") - - arg1 = TypedExpr(sge.Identifier(this="input1"), "string") - arg2 = TypedExpr(sge.Identifier(this="input2"), "string") - result = compiler.compile_row_op(mock_op, [arg1, arg2]) - assert result == sge.Identifier(this="output") - mock_impl.assert_called_once_with(arg1, arg2) - - -def test_register_binary_op_pass_on(): - compiler = expression_compiler.ExpressionCompiler() - - class MockBinaryOp(ops.BinaryOp): - name = "mock_binary_op_pass_op" - - mock_op = MockBinaryOp() - mock_impl = mock.Mock() - - @compiler.register_binary_op(mock_op, pass_op=True) - def _(left: TypedExpr, right: TypedExpr, op: ops.BinaryOp) -> sge.Expression: - mock_impl(left, right, op) - return sge.Identifier(this="output") - - arg1 = TypedExpr(sge.Identifier(this="input1"), "string") - arg2 = TypedExpr(sge.Identifier(this="input2"), "string") - result = compiler.compile_row_op(mock_op, [arg1, arg2]) - assert result == sge.Identifier(this="output") - mock_impl.assert_called_once_with(arg1, arg2, mock_op) - - -def test_register_ternary_op(): - compiler = expression_compiler.ExpressionCompiler() - - class MockTernaryOp(ops.TernaryOp): - name = "mock_ternary_op" - - mock_op = MockTernaryOp() - mock_impl = mock.Mock() - - @compiler.register_ternary_op(mock_op) - def _(arg1: TypedExpr, arg2: TypedExpr, arg3: TypedExpr) -> sge.Expression: - mock_impl(arg1, arg2, arg3) - return sge.Identifier(this="output") - - arg1 = TypedExpr(sge.Identifier(this="input1"), "string") - arg2 = TypedExpr(sge.Identifier(this="input2"), "string") - arg3 = TypedExpr(sge.Identifier(this="input3"), "string") - result = compiler.compile_row_op(mock_op, [arg1, arg2, arg3]) - assert result == sge.Identifier(this="output") - mock_impl.assert_called_once_with(arg1, arg2, arg3) - - -def test_register_nary_op(): - compiler = expression_compiler.ExpressionCompiler() - - class MockNaryOp(ops.NaryOp): - name = "mock_nary_op" - - mock_op = MockNaryOp() - mock_impl = mock.Mock() - - @compiler.register_nary_op(mock_op) - def _(*args: TypedExpr) -> sge.Expression: - mock_impl(*args) - return sge.Identifier(this="output") - - arg1 = TypedExpr(sge.Identifier(this="input1"), "string") - arg2 = TypedExpr(sge.Identifier(this="input2"), "string") - result = compiler.compile_row_op(mock_op, [arg1, arg2]) - assert result == sge.Identifier(this="output") - mock_impl.assert_called_once_with(arg1, arg2) - - -def test_register_nary_op_pass_on(): - compiler = expression_compiler.ExpressionCompiler() - - class MockNaryOp(ops.NaryOp): - name = "mock_nary_op_pass_op" - - mock_op = MockNaryOp() - mock_impl = mock.Mock() - - @compiler.register_nary_op(mock_op, pass_op=True) - def _(*args: TypedExpr, op: ops.NaryOp) -> sge.Expression: - mock_impl(*args, op=op) - return sge.Identifier(this="output") - - arg1 = TypedExpr(sge.Identifier(this="input1"), "string") - arg2 = TypedExpr(sge.Identifier(this="input2"), "string") - arg3 = TypedExpr(sge.Identifier(this="input3"), "string") - arg4 = TypedExpr(sge.Identifier(this="input4"), "string") - result = compiler.compile_row_op(mock_op, [arg1, arg2, arg3, arg4]) - assert result == sge.Identifier(this="output") - mock_impl.assert_called_once_with(arg1, arg2, arg3, arg4, op=mock_op) - - -def test_binary_op_parentheses(): - compiler = expression_compiler.ExpressionCompiler() - - class MockAddOp(ops.BinaryOp): - name = "mock_add_op" - - class MockMulOp(ops.BinaryOp): - name = "mock_mul_op" - - add_op = MockAddOp() - mul_op = MockMulOp() - - @compiler.register_binary_op(add_op) - def _(left: TypedExpr, right: TypedExpr) -> sge.Expression: - return sge.Add(this=left.expr, expression=right.expr) - - @compiler.register_binary_op(mul_op) - def _(left: TypedExpr, right: TypedExpr) -> sge.Expression: - return sge.Mul(this=left.expr, expression=right.expr) - - a = TypedExpr(sge.Identifier(this="a"), "int") - b = TypedExpr(sge.Identifier(this="b"), "int") - c = TypedExpr(sge.Identifier(this="c"), "int") - - # (a + b) * c - add_expr = compiler.compile_row_op(add_op, [a, b]) - add_typed_expr = TypedExpr(add_expr, "int") - result1 = compiler.compile_row_op(mul_op, [add_typed_expr, c]) - assert result1.sql() == "(a + b) * c" - - # a * (b + c) - add_expr_2 = compiler.compile_row_op(add_op, [b, c]) - add_typed_expr_2 = TypedExpr(add_expr_2, "int") - result2 = compiler.compile_row_op(mul_op, [a, add_typed_expr_2]) - assert result2.sql() == "a * (b + c)" - - -def test_register_duplicate_op_raises(): - compiler = expression_compiler.ExpressionCompiler() - - class MockUnaryOp(ops.UnaryOp): - name = "mock_unary_op_duplicate" - - mock_op = MockUnaryOp() - - @compiler.register_unary_op(mock_op) - def _(expr: TypedExpr) -> sge.Expression: - return sge.Identifier(this="output") - - with pytest.raises(ValueError): - - @compiler.register_unary_op(mock_op) - def _(expr: TypedExpr) -> sge.Expression: - return sge.Identifier(this="output2") diff --git a/tests/unit/core/compile/sqlglot/test_sqlglot_types.py b/tests/unit/core/compile/sqlglot/test_sqlglot_types.py deleted file mode 100644 index 5c2d84383d7..00000000000 --- a/tests/unit/core/compile/sqlglot/test_sqlglot_types.py +++ /dev/null @@ -1,64 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import pandas as pd -import pyarrow as pa - -import bigframes.core.compile.sqlglot.sqlglot_types as sgt -import bigframes.dtypes as dtypes - - -def test_from_bigframes_simple_dtypes(): - assert sgt.from_bigframes_dtype(dtypes.INT_DTYPE) == "INT64" - assert sgt.from_bigframes_dtype(dtypes.FLOAT_DTYPE) == "FLOAT64" - assert sgt.from_bigframes_dtype(dtypes.STRING_DTYPE) == "STRING" - assert sgt.from_bigframes_dtype(dtypes.BOOL_DTYPE) == "BOOLEAN" - assert sgt.from_bigframes_dtype(dtypes.DATE_DTYPE) == "DATE" - assert sgt.from_bigframes_dtype(dtypes.TIME_DTYPE) == "TIME" - assert sgt.from_bigframes_dtype(dtypes.DATETIME_DTYPE) == "DATETIME" - assert sgt.from_bigframes_dtype(dtypes.TIMESTAMP_DTYPE) == "TIMESTAMP" - assert sgt.from_bigframes_dtype(dtypes.BYTES_DTYPE) == "BYTES" - assert sgt.from_bigframes_dtype(dtypes.NUMERIC_DTYPE) == "NUMERIC" - assert sgt.from_bigframes_dtype(dtypes.BIGNUMERIC_DTYPE) == "BIGNUMERIC" - assert sgt.from_bigframes_dtype(dtypes.JSON_DTYPE) == "JSON" - assert sgt.from_bigframes_dtype(dtypes.GEO_DTYPE) == "GEOGRAPHY" - - -def test_from_bigframes_struct_dtypes(): - fields = [pa.field("int_col", pa.int64()), pa.field("bool_col", pa.bool_())] - struct_type = pd.ArrowDtype(pa.struct(fields)) - expected = "STRUCT" - assert sgt.from_bigframes_dtype(struct_type) == expected - - -def test_from_bigframes_array_dtypes(): - int_array_type = pd.ArrowDtype(pa.list_(pa.int64())) - assert sgt.from_bigframes_dtype(int_array_type) == "ARRAY" - - string_array_type = pd.ArrowDtype(pa.list_(pa.string())) - assert sgt.from_bigframes_dtype(string_array_type) == "ARRAY" - - -def test_from_bigframes_multi_nested_dtypes(): - fields = [ - pa.field("string_col", pa.string()), - pa.field("date_col", pa.date32()), - pa.field("array_col", pa.list_(pa.timestamp("us"))), - ] - array_type = pd.ArrowDtype(pa.list_(pa.struct(fields))) - - expected = ( - "ARRAY>>" - ) - assert sgt.from_bigframes_dtype(array_type) == expected diff --git a/tests/unit/core/compile/sqlglot/tpch/conftest.py b/tests/unit/core/compile/sqlglot/tpch/conftest.py deleted file mode 100644 index b351b6988eb..00000000000 --- a/tests/unit/core/compile/sqlglot/tpch/conftest.py +++ /dev/null @@ -1,165 +0,0 @@ -# Copyright 2026 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import datetime -import functools -import unittest.mock as mock - -import pytest -from google.cloud import bigquery - -import bigframes.testing.mocks as mocks -from bigframes.testing import compiler_session - -freezegun = pytest.importorskip("freezegun") - -PROJECT_NAME = "bigframes-dev-perf" -DATASET_NAME = "tpch_0001t" -LOCATION_NAME = "test-region" - -TPCH_SCHEMAS = { - "LINEITEM": [ - bigquery.SchemaField("L_ORDERKEY", "INTEGER", mode="REQUIRED"), - bigquery.SchemaField("L_PARTKEY", "INTEGER", mode="REQUIRED"), - bigquery.SchemaField("L_SUPPKEY", "INTEGER", mode="REQUIRED"), - bigquery.SchemaField("L_LINENUMBER", "INTEGER", mode="REQUIRED"), - bigquery.SchemaField("L_QUANTITY", "FLOAT", mode="REQUIRED"), - bigquery.SchemaField("L_EXTENDEDPRICE", "FLOAT", mode="REQUIRED"), - bigquery.SchemaField("L_DISCOUNT", "FLOAT", mode="REQUIRED"), - bigquery.SchemaField("L_TAX", "FLOAT", mode="REQUIRED"), - bigquery.SchemaField("L_RETURNFLAG", "STRING", mode="REQUIRED"), - bigquery.SchemaField("L_LINESTATUS", "STRING", mode="REQUIRED"), - bigquery.SchemaField("L_SHIPDATE", "DATE", mode="REQUIRED"), - bigquery.SchemaField("L_COMMITDATE", "DATE", mode="REQUIRED"), - bigquery.SchemaField("L_RECEIPTDATE", "DATE", mode="REQUIRED"), - bigquery.SchemaField("L_SHIPINSTRUCT", "STRING", mode="REQUIRED"), - bigquery.SchemaField("L_SHIPMODE", "STRING", mode="REQUIRED"), - bigquery.SchemaField("L_COMMENT", "STRING"), - ], - "ORDERS": [ - bigquery.SchemaField("O_ORDERKEY", "INTEGER", mode="REQUIRED"), - bigquery.SchemaField("O_CUSTKEY", "INTEGER", mode="REQUIRED"), - bigquery.SchemaField("O_ORDERSTATUS", "STRING", mode="REQUIRED"), - bigquery.SchemaField("O_TOTALPRICE", "FLOAT", mode="REQUIRED"), - bigquery.SchemaField("O_ORDERDATE", "DATE", mode="REQUIRED"), - bigquery.SchemaField("O_ORDERPRIORITY", "STRING", mode="REQUIRED"), - bigquery.SchemaField("O_CLERK", "STRING", mode="REQUIRED"), - bigquery.SchemaField("O_SHIPPRIORITY", "INTEGER", mode="REQUIRED"), - bigquery.SchemaField("O_COMMENT", "STRING"), - ], - "PART": [ - bigquery.SchemaField("P_PARTKEY", "INTEGER", mode="REQUIRED"), - bigquery.SchemaField("P_NAME", "STRING", mode="REQUIRED"), - bigquery.SchemaField("P_MFGR", "STRING", mode="REQUIRED"), - bigquery.SchemaField("P_BRAND", "STRING", mode="REQUIRED"), - bigquery.SchemaField("P_TYPE", "STRING", mode="REQUIRED"), - bigquery.SchemaField("P_SIZE", "INTEGER", mode="REQUIRED"), - bigquery.SchemaField("P_CONTAINER", "STRING", mode="REQUIRED"), - bigquery.SchemaField("P_RETAILPRICE", "FLOAT", mode="REQUIRED"), - bigquery.SchemaField("P_COMMENT", "STRING"), - ], - "SUPPLIER": [ - bigquery.SchemaField("S_SUPPKEY", "INTEGER", mode="REQUIRED"), - bigquery.SchemaField("S_NAME", "STRING", mode="REQUIRED"), - bigquery.SchemaField("S_ADDRESS", "STRING", mode="REQUIRED"), - bigquery.SchemaField("S_NATIONKEY", "INTEGER", mode="REQUIRED"), - bigquery.SchemaField("S_PHONE", "STRING", mode="REQUIRED"), - bigquery.SchemaField("S_ACCTBAL", "FLOAT", mode="REQUIRED"), - bigquery.SchemaField("S_COMMENT", "STRING"), - ], - "PARTSUPP": [ - bigquery.SchemaField("PS_PARTKEY", "INTEGER", mode="REQUIRED"), - bigquery.SchemaField("PS_SUPPKEY", "INTEGER", mode="REQUIRED"), - bigquery.SchemaField("PS_AVAILQTY", "INTEGER", mode="REQUIRED"), - bigquery.SchemaField("PS_SUPPLYCOST", "FLOAT", mode="REQUIRED"), - bigquery.SchemaField("PS_COMMENT", "STRING"), - ], - "CUSTOMER": [ - bigquery.SchemaField("C_CUSTKEY", "INTEGER", mode="REQUIRED"), - bigquery.SchemaField("C_NAME", "STRING", mode="REQUIRED"), - bigquery.SchemaField("C_ADDRESS", "STRING", mode="REQUIRED"), - bigquery.SchemaField("C_NATIONKEY", "INTEGER", mode="REQUIRED"), - bigquery.SchemaField("C_PHONE", "STRING", mode="REQUIRED"), - bigquery.SchemaField("C_ACCTBAL", "FLOAT", mode="REQUIRED"), - bigquery.SchemaField("C_MKTSEGMENT", "STRING", mode="REQUIRED"), - bigquery.SchemaField("C_COMMENT", "STRING"), - ], - "NATION": [ - bigquery.SchemaField("N_NATIONKEY", "INTEGER", mode="REQUIRED"), - bigquery.SchemaField("N_NAME", "STRING", mode="REQUIRED"), - bigquery.SchemaField("N_REGIONKEY", "INTEGER", mode="REQUIRED"), - bigquery.SchemaField("N_COMMENT", "STRING"), - ], - "REGION": [ - bigquery.SchemaField("R_REGIONKEY", "INTEGER", mode="REQUIRED"), - bigquery.SchemaField("R_NAME", "STRING", mode="REQUIRED"), - bigquery.SchemaField("R_COMMENT", "STRING"), - ], -} - - -def _create_mock_bqclient(): - """Helper function to create a compiler session.""" - - bqclient = mock.create_autospec(bigquery.Client, instance=True) - bqclient.project = DATASET_NAME - bqclient.location = LOCATION_NAME - table_create_time = datetime.datetime.now() - - def get_table_mock(table_ref): - if isinstance(table_ref, str): - table_ref = bigquery.TableReference.from_string(table_ref) - - table_id = table_ref.table_id - schema = TPCH_SCHEMAS.get(table_id, []) - - table = mock.create_autospec(bigquery.Table, instance=True) - table._properties = {} - type(table).created = mock.PropertyMock(return_value=table_create_time) - type(table).location = mock.PropertyMock(return_value=LOCATION_NAME) - type(table).schema = mock.PropertyMock(return_value=schema) - type(table).project = table_ref.project - type(table).dataset_id = table_ref.dataset_id - type(table).table_id = table_id - type(table).num_rows = mock.PropertyMock(return_value=1000000000) - return table - - bqclient.get_table.side_effect = get_table_mock - return bqclient - - -@pytest.fixture(scope="session") -def tpch_session(): - anonymous_dataset = bigquery.DatasetReference.from_string( - f"{PROJECT_NAME}.{DATASET_NAME}" - ) - session = mocks.create_bigquery_session( - bqclient=_create_mock_bqclient(), - anonymous_dataset=anonymous_dataset, - ) - - # Disable snapshotting for TPC-H tests to keep snapshots clean - original_read_gbq_table = session._loader.read_gbq_table - - @functools.wraps(original_read_gbq_table) - def read_gbq_table_no_snapshot(*args, **kwargs): - kwargs["enable_snapshot"] = False - return original_read_gbq_table(*args, **kwargs) - - session._executor = compiler_session.SQLCompilerExecutor() - - with mock.patch.object( - session._loader, "read_gbq_table", new=read_gbq_table_no_snapshot - ): - yield session diff --git a/tests/unit/core/compile/sqlglot/tpch/snapshots/test_tpch/test_tpch_query/1/out.sql b/tests/unit/core/compile/sqlglot/tpch/snapshots/test_tpch/test_tpch_query/1/out.sql deleted file mode 100644 index 84ed65ec174..00000000000 --- a/tests/unit/core/compile/sqlglot/tpch/snapshots/test_tpch/test_tpch_query/1/out.sql +++ /dev/null @@ -1,75 +0,0 @@ -WITH `bfcte_0` AS ( - SELECT - `L_QUANTITY`, - `L_EXTENDEDPRICE`, - `L_DISCOUNT`, - `L_TAX`, - `L_RETURNFLAG`, - `L_LINESTATUS`, - `L_SHIPDATE`, - `L_QUANTITY` AS `bfcol_7`, - `L_EXTENDEDPRICE` AS `bfcol_8`, - `L_DISCOUNT` AS `bfcol_9`, - `L_TAX` AS `bfcol_10`, - `L_RETURNFLAG` AS `bfcol_11`, - `L_LINESTATUS` AS `bfcol_12`, - `L_SHIPDATE` <= CAST('1998-09-02' AS DATE) AS `bfcol_13`, - `L_QUANTITY` AS `bfcol_27`, - `L_EXTENDEDPRICE` AS `bfcol_28`, - `L_DISCOUNT` AS `bfcol_29`, - `L_TAX` AS `bfcol_30`, - `L_RETURNFLAG` AS `bfcol_31`, - `L_LINESTATUS` AS `bfcol_32`, - `L_EXTENDEDPRICE` * ( - 1.0 - `L_DISCOUNT` - ) AS `bfcol_33`, - `L_QUANTITY` AS `bfcol_41`, - `L_EXTENDEDPRICE` AS `bfcol_42`, - `L_DISCOUNT` AS `bfcol_43`, - `L_RETURNFLAG` AS `bfcol_44`, - `L_LINESTATUS` AS `bfcol_45`, - `L_EXTENDEDPRICE` * ( - 1.0 - `L_DISCOUNT` - ) AS `bfcol_46`, - ( - `L_EXTENDEDPRICE` * ( - 1.0 - `L_DISCOUNT` - ) - ) * ( - 1.0 + `L_TAX` - ) AS `bfcol_47` - FROM `bigframes-dev-perf`.`tpch_0001t`.`LINEITEM` AS `bft_0` - WHERE - `L_SHIPDATE` <= CAST('1998-09-02' AS DATE) -), `bfcte_1` AS ( - SELECT - `bfcol_44`, - `bfcol_45`, - COALESCE(SUM(`bfcol_41`), 0) AS `bfcol_55`, - COALESCE(SUM(`bfcol_42`), 0) AS `bfcol_56`, - COALESCE(SUM(`bfcol_46`), 0) AS `bfcol_57`, - COALESCE(SUM(`bfcol_47`), 0) AS `bfcol_58`, - AVG(`bfcol_41`) AS `bfcol_59`, - AVG(`bfcol_42`) AS `bfcol_60`, - AVG(`bfcol_43`) AS `bfcol_61`, - COUNT(`bfcol_41`) AS `bfcol_62` - FROM `bfcte_0` - GROUP BY - `bfcol_44`, - `bfcol_45` -) -SELECT - `bfcol_44` AS `L_RETURNFLAG`, - `bfcol_45` AS `L_LINESTATUS`, - `bfcol_55` AS `SUM_QTY`, - `bfcol_56` AS `SUM_BASE_PRICE`, - `bfcol_57` AS `SUM_DISC_PRICE`, - `bfcol_58` AS `SUM_CHARGE`, - `bfcol_59` AS `AVG_QTY`, - `bfcol_60` AS `AVG_PRICE`, - `bfcol_61` AS `AVG_DISC`, - `bfcol_62` AS `COUNT_ORDER` -FROM `bfcte_1` -ORDER BY - `bfcol_44` ASC NULLS LAST, - `bfcol_45` ASC NULLS LAST \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/tpch/snapshots/test_tpch/test_tpch_query/10/out.sql b/tests/unit/core/compile/sqlglot/tpch/snapshots/test_tpch/test_tpch_query/10/out.sql deleted file mode 100644 index 39dc2484342..00000000000 --- a/tests/unit/core/compile/sqlglot/tpch/snapshots/test_tpch/test_tpch_query/10/out.sql +++ /dev/null @@ -1,162 +0,0 @@ -WITH `bfcte_0` AS ( - SELECT - `N_NATIONKEY` AS `bfcol_0`, - `N_NAME` AS `bfcol_1` - FROM `bigframes-dev-perf`.`tpch_0001t`.`NATION` AS `bft_3` -), `bfcte_1` AS ( - SELECT - `L_ORDERKEY` AS `bfcol_2`, - `L_EXTENDEDPRICE` AS `bfcol_3`, - `L_DISCOUNT` AS `bfcol_4`, - `L_RETURNFLAG` AS `bfcol_5` - FROM `bigframes-dev-perf`.`tpch_0001t`.`LINEITEM` AS `bft_2` -), `bfcte_2` AS ( - SELECT - `O_ORDERKEY` AS `bfcol_6`, - `O_CUSTKEY` AS `bfcol_7`, - `O_ORDERDATE` AS `bfcol_8` - FROM `bigframes-dev-perf`.`tpch_0001t`.`ORDERS` AS `bft_1` -), `bfcte_3` AS ( - SELECT - `C_CUSTKEY` AS `bfcol_9`, - `C_NAME` AS `bfcol_10`, - `C_ADDRESS` AS `bfcol_11`, - `C_NATIONKEY` AS `bfcol_12`, - `C_PHONE` AS `bfcol_13`, - `C_ACCTBAL` AS `bfcol_14`, - `C_COMMENT` AS `bfcol_15` - FROM `bigframes-dev-perf`.`tpch_0001t`.`CUSTOMER` AS `bft_0` -), `bfcte_4` AS ( - SELECT - `bfcol_9` AS `bfcol_16`, - `bfcol_10` AS `bfcol_17`, - `bfcol_11` AS `bfcol_18`, - `bfcol_12` AS `bfcol_19`, - `bfcol_13` AS `bfcol_20`, - `bfcol_14` AS `bfcol_21`, - `bfcol_15` AS `bfcol_22`, - `bfcol_6` AS `bfcol_23`, - `bfcol_8` AS `bfcol_24` - FROM `bfcte_3` - INNER JOIN `bfcte_2` - ON `bfcol_9` = `bfcol_7` -), `bfcte_5` AS ( - SELECT - `bfcol_16` AS `bfcol_25`, - `bfcol_17` AS `bfcol_26`, - `bfcol_18` AS `bfcol_27`, - `bfcol_19` AS `bfcol_28`, - `bfcol_20` AS `bfcol_29`, - `bfcol_21` AS `bfcol_30`, - `bfcol_22` AS `bfcol_31`, - `bfcol_24` AS `bfcol_32`, - `bfcol_3` AS `bfcol_33`, - `bfcol_4` AS `bfcol_34`, - `bfcol_5` AS `bfcol_35` - FROM `bfcte_4` - INNER JOIN `bfcte_1` - ON `bfcol_23` = `bfcol_2` -), `bfcte_6` AS ( - SELECT - `bfcol_25`, - `bfcol_26`, - `bfcol_27`, - `bfcol_28`, - `bfcol_29`, - `bfcol_30`, - `bfcol_31`, - `bfcol_32`, - `bfcol_33`, - `bfcol_34`, - `bfcol_35`, - `bfcol_0`, - `bfcol_1`, - `bfcol_25` AS `bfcol_47`, - `bfcol_26` AS `bfcol_48`, - `bfcol_27` AS `bfcol_49`, - `bfcol_29` AS `bfcol_50`, - `bfcol_30` AS `bfcol_51`, - `bfcol_31` AS `bfcol_52`, - `bfcol_33` AS `bfcol_53`, - `bfcol_34` AS `bfcol_54`, - `bfcol_1` AS `bfcol_55`, - ( - ( - `bfcol_32` >= CAST('1993-10-01' AS DATE) - ) - AND ( - `bfcol_32` < CAST('1994-01-01' AS DATE) - ) - ) - AND ( - `bfcol_35` = 'R' - ) AS `bfcol_56`, - `bfcol_25` AS `bfcol_76`, - `bfcol_26` AS `bfcol_77`, - `bfcol_27` AS `bfcol_78`, - `bfcol_29` AS `bfcol_79`, - `bfcol_30` AS `bfcol_80`, - `bfcol_31` AS `bfcol_81`, - `bfcol_1` AS `bfcol_82`, - ROUND(( - `bfcol_33` * ( - 1 - `bfcol_34` - ) - ), 2) AS `bfcol_83` - FROM `bfcte_5` - INNER JOIN `bfcte_0` - ON `bfcol_28` = `bfcol_0` - WHERE - ( - ( - `bfcol_32` >= CAST('1993-10-01' AS DATE) - ) - AND ( - `bfcol_32` < CAST('1994-01-01' AS DATE) - ) - ) - AND ( - `bfcol_35` = 'R' - ) -), `bfcte_7` AS ( - SELECT - `bfcol_76`, - `bfcol_77`, - `bfcol_80`, - `bfcol_79`, - `bfcol_82`, - `bfcol_78`, - `bfcol_81`, - COALESCE(SUM(`bfcol_83`), 0) AS `bfcol_92` - FROM `bfcte_6` - WHERE - NOT `bfcol_81` IS NULL - GROUP BY - `bfcol_76`, - `bfcol_77`, - `bfcol_80`, - `bfcol_79`, - `bfcol_82`, - `bfcol_78`, - `bfcol_81` -) -SELECT - `bfcol_76` AS `C_CUSTKEY`, - `bfcol_77` AS `C_NAME`, - `bfcol_92` AS `REVENUE`, - `bfcol_80` AS `C_ACCTBAL`, - `bfcol_82` AS `N_NAME`, - `bfcol_78` AS `C_ADDRESS`, - `bfcol_79` AS `C_PHONE`, - `bfcol_81` AS `C_COMMENT` -FROM `bfcte_7` -ORDER BY - `bfcol_92` DESC, - `bfcol_76` ASC NULLS LAST, - `bfcol_77` ASC NULLS LAST, - `bfcol_80` ASC NULLS LAST, - `bfcol_79` ASC NULLS LAST, - `bfcol_82` ASC NULLS LAST, - `bfcol_78` ASC NULLS LAST, - `bfcol_81` ASC NULLS LAST -LIMIT 20 \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/tpch/snapshots/test_tpch/test_tpch_query/11/out.sql b/tests/unit/core/compile/sqlglot/tpch/snapshots/test_tpch/test_tpch_query/11/out.sql deleted file mode 100644 index 31a357be7c0..00000000000 --- a/tests/unit/core/compile/sqlglot/tpch/snapshots/test_tpch/test_tpch_query/11/out.sql +++ /dev/null @@ -1,115 +0,0 @@ -WITH `bfcte_0` AS ( - SELECT - * - FROM UNNEST(ARRAY>[STRUCT(0.0, 0, 0)]) -), `bfcte_1` AS ( - SELECT - `PS_SUPPKEY` AS `bfcol_0`, - `PS_AVAILQTY` AS `bfcol_1`, - `PS_SUPPLYCOST` AS `bfcol_2` - FROM `bigframes-dev-perf`.`tpch_0001t`.`PARTSUPP` AS `bft_2` -), `bfcte_2` AS ( - SELECT - `PS_PARTKEY` AS `bfcol_10`, - `PS_SUPPKEY` AS `bfcol_11`, - `PS_AVAILQTY` AS `bfcol_12`, - `PS_SUPPLYCOST` AS `bfcol_13` - FROM `bigframes-dev-perf`.`tpch_0001t`.`PARTSUPP` AS `bft_2` -), `bfcte_3` AS ( - SELECT - `S_SUPPKEY` AS `bfcol_3`, - `S_NATIONKEY` AS `bfcol_4` - FROM `bigframes-dev-perf`.`tpch_0001t`.`SUPPLIER` AS `bft_1` -), `bfcte_4` AS ( - SELECT - `N_NATIONKEY` AS `bfcol_18` - FROM `bigframes-dev-perf`.`tpch_0001t`.`NATION` AS `bft_0` - WHERE - `N_NAME` = 'GERMANY' -), `bfcte_5` AS ( - SELECT - `bfcol_3` AS `bfcol_19` - FROM `bfcte_4` - INNER JOIN `bfcte_3` - ON `bfcol_18` = `bfcol_4` -), `bfcte_6` AS ( - SELECT - `bfcol_19`, - `bfcol_0`, - `bfcol_1`, - `bfcol_2`, - `bfcol_1` AS `bfcol_25`, - `bfcol_2` AS `bfcol_26`, - `bfcol_2` AS `bfcol_33`, - `bfcol_1` AS `bfcol_34`, - `bfcol_2` * `bfcol_1` AS `bfcol_40` - FROM `bfcte_5` - INNER JOIN `bfcte_1` - ON `bfcol_19` = `bfcol_0` -), `bfcte_7` AS ( - SELECT - `bfcol_19`, - `bfcol_10`, - `bfcol_11`, - `bfcol_12`, - `bfcol_13`, - `bfcol_10` AS `bfcol_27`, - `bfcol_13` * `bfcol_12` AS `bfcol_28` - FROM `bfcte_5` - INNER JOIN `bfcte_2` - ON `bfcol_19` = `bfcol_11` -), `bfcte_8` AS ( - SELECT - COALESCE(SUM(`bfcol_40`), 0) AS `bfcol_44` - FROM `bfcte_6` -), `bfcte_9` AS ( - SELECT - `bfcol_27`, - COALESCE(SUM(`bfcol_28`), 0) AS `bfcol_35` - FROM `bfcte_7` - GROUP BY - `bfcol_27` -), `bfcte_10` AS ( - SELECT - `bfcol_44`, - 0 AS `bfcol_45` - FROM `bfcte_8` -), `bfcte_11` AS ( - SELECT - `bfcol_27` AS `bfcol_41`, - ROUND(`bfcol_35`, 2) AS `bfcol_42` - FROM `bfcte_9` -), `bfcte_12` AS ( - SELECT - `bfcol_7`, - `bfcol_8`, - `bfcol_9`, - `bfcol_44`, - `bfcol_45`, - CASE WHEN `bfcol_9` = 0 THEN `bfcol_44` END AS `bfcol_46`, - IF(`bfcol_45` = 0, CASE WHEN `bfcol_9` = 0 THEN `bfcol_44` END, NULL) AS `bfcol_51` - FROM `bfcte_0` - CROSS JOIN `bfcte_10` -), `bfcte_13` AS ( - SELECT - `bfcol_7`, - `bfcol_8`, - ANY_VALUE(`bfcol_51`) AS `bfcol_55` - FROM `bfcte_12` - GROUP BY - `bfcol_7`, - `bfcol_8` -), `bfcte_14` AS ( - SELECT - `bfcol_55` * 0.0001 AS `bfcol_58` - FROM `bfcte_13` -) -SELECT - `bfcol_41` AS `PS_PARTKEY`, - `bfcol_42` AS `VALUE` -FROM `bfcte_11` -CROSS JOIN `bfcte_14` -WHERE - `bfcol_42` > `bfcol_58` -ORDER BY - `bfcol_42` DESC \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/tpch/snapshots/test_tpch/test_tpch_query/12/out.sql b/tests/unit/core/compile/sqlglot/tpch/snapshots/test_tpch/test_tpch_query/12/out.sql deleted file mode 100644 index d5ab954a20b..00000000000 --- a/tests/unit/core/compile/sqlglot/tpch/snapshots/test_tpch/test_tpch_query/12/out.sql +++ /dev/null @@ -1,90 +0,0 @@ -WITH `bfcte_0` AS ( - SELECT - `L_ORDERKEY` AS `bfcol_0`, - `L_SHIPDATE` AS `bfcol_1`, - `L_COMMITDATE` AS `bfcol_2`, - `L_RECEIPTDATE` AS `bfcol_3`, - `L_SHIPMODE` AS `bfcol_4` - FROM `bigframes-dev-perf`.`tpch_0001t`.`LINEITEM` AS `bft_1` -), `bfcte_1` AS ( - SELECT - `O_ORDERKEY` AS `bfcol_5`, - `O_ORDERPRIORITY` AS `bfcol_6` - FROM `bigframes-dev-perf`.`tpch_0001t`.`ORDERS` AS `bft_0` -), `bfcte_2` AS ( - SELECT - `bfcol_5`, - `bfcol_6`, - `bfcol_0`, - `bfcol_1`, - `bfcol_2`, - `bfcol_3`, - `bfcol_4`, - `bfcol_6` AS `bfcol_12`, - `bfcol_4` AS `bfcol_13`, - ( - ( - ( - COALESCE(COALESCE(`bfcol_4` IN ('MAIL', 'SHIP'), FALSE), FALSE) - AND ( - `bfcol_2` < `bfcol_3` - ) - ) - AND ( - `bfcol_1` < `bfcol_2` - ) - ) - AND ( - `bfcol_3` >= CAST('1994-01-01' AS DATE) - ) - ) - AND ( - `bfcol_3` < CAST('1995-01-01' AS DATE) - ) AS `bfcol_14`, - `bfcol_6` AS `bfcol_20`, - `bfcol_4` AS `bfcol_21`, - CAST(COALESCE(COALESCE(`bfcol_6` IN ('1-URGENT', '2-HIGH'), FALSE), FALSE) AS INT64) AS `bfcol_22`, - `bfcol_4` AS `bfcol_26`, - CAST(COALESCE(COALESCE(`bfcol_6` IN ('1-URGENT', '2-HIGH'), FALSE), FALSE) AS INT64) AS `bfcol_27`, - CAST(NOT ( - COALESCE(COALESCE(`bfcol_6` IN ('1-URGENT', '2-HIGH'), FALSE), FALSE) - ) AS INT64) AS `bfcol_28` - FROM `bfcte_1` - INNER JOIN `bfcte_0` - ON `bfcol_5` = `bfcol_0` - WHERE - ( - ( - ( - COALESCE(COALESCE(`bfcol_4` IN ('MAIL', 'SHIP'), FALSE), FALSE) - AND ( - `bfcol_2` < `bfcol_3` - ) - ) - AND ( - `bfcol_1` < `bfcol_2` - ) - ) - AND ( - `bfcol_3` >= CAST('1994-01-01' AS DATE) - ) - ) - AND ( - `bfcol_3` < CAST('1995-01-01' AS DATE) - ) -), `bfcte_3` AS ( - SELECT - `bfcol_26`, - COALESCE(SUM(`bfcol_27`), 0) AS `bfcol_32`, - COALESCE(SUM(`bfcol_28`), 0) AS `bfcol_33` - FROM `bfcte_2` - GROUP BY - `bfcol_26` -) -SELECT - `bfcol_26` AS `L_SHIPMODE`, - `bfcol_32` AS `HIGH_LINE_COUNT`, - `bfcol_33` AS `LOW_LINE_COUNT` -FROM `bfcte_3` -ORDER BY - `bfcol_26` ASC NULLS LAST \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/tpch/snapshots/test_tpch/test_tpch_query/13/out.sql b/tests/unit/core/compile/sqlglot/tpch/snapshots/test_tpch/test_tpch_query/13/out.sql deleted file mode 100644 index 6aab2b4fec7..00000000000 --- a/tests/unit/core/compile/sqlglot/tpch/snapshots/test_tpch/test_tpch_query/13/out.sql +++ /dev/null @@ -1,39 +0,0 @@ -WITH `bfcte_0` AS ( - SELECT - `O_ORDERKEY` AS `bfcol_10`, - `O_CUSTKEY` AS `bfcol_11` - FROM `bigframes-dev-perf`.`tpch_0001t`.`ORDERS` AS `bft_1` - WHERE - NOT ( - REGEXP_CONTAINS(`O_COMMENT`, 'special.*requests') - ) -), `bfcte_1` AS ( - SELECT - `C_CUSTKEY` AS `bfcol_3` - FROM `bigframes-dev-perf`.`tpch_0001t`.`CUSTOMER` AS `bft_0` -), `bfcte_2` AS ( - SELECT - `bfcol_3`, - COUNT(`bfcol_10`) AS `bfcol_14` - FROM `bfcte_1` - LEFT JOIN `bfcte_0` - ON `bfcol_3` = `bfcol_11` - GROUP BY - `bfcol_3` -), `bfcte_3` AS ( - SELECT - `bfcol_14`, - COUNT(1) AS `bfcol_16` - FROM `bfcte_2` - WHERE - NOT `bfcol_14` IS NULL - GROUP BY - `bfcol_14` -) -SELECT - `bfcol_14` AS `C_COUNT`, - `bfcol_16` AS `CUSTDIST` -FROM `bfcte_3` -ORDER BY - `bfcol_16` DESC, - `bfcol_14` DESC \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/tpch/snapshots/test_tpch/test_tpch_query/14/out.sql b/tests/unit/core/compile/sqlglot/tpch/snapshots/test_tpch/test_tpch_query/14/out.sql deleted file mode 100644 index bde638a0f4c..00000000000 --- a/tests/unit/core/compile/sqlglot/tpch/snapshots/test_tpch/test_tpch_query/14/out.sql +++ /dev/null @@ -1,164 +0,0 @@ -WITH `bfcte_0` AS ( - SELECT - * - FROM UNNEST(ARRAY>[STRUCT('TEMP', 0, 0)]) -), `bfcte_1` AS ( - SELECT - * - FROM UNNEST(ARRAY>[STRUCT('TEMP', 0, 0)]) -), `bfcte_2` AS ( - SELECT - `L_PARTKEY` AS `bfcol_0`, - `L_EXTENDEDPRICE` AS `bfcol_1`, - `L_DISCOUNT` AS `bfcol_2`, - `L_SHIPDATE` AS `bfcol_3` - FROM `bigframes-dev-perf`.`tpch_0001t`.`LINEITEM` AS `bft_1` -), `bfcte_3` AS ( - SELECT - `P_PARTKEY` AS `bfcol_4` - FROM `bigframes-dev-perf`.`tpch_0001t`.`PART` AS `bft_0` -), `bfcte_4` AS ( - SELECT - `P_PARTKEY` AS `bfcol_8`, - `P_TYPE` AS `bfcol_9` - FROM `bigframes-dev-perf`.`tpch_0001t`.`PART` AS `bft_0` -), `bfcte_5` AS ( - SELECT - `bfcol_4`, - `bfcol_0`, - `bfcol_1`, - `bfcol_2`, - `bfcol_3`, - `bfcol_1` AS `bfcol_20`, - `bfcol_2` AS `bfcol_21`, - ( - `bfcol_3` >= CAST('1995-09-01' AS DATE) - ) - AND ( - `bfcol_3` < CAST('1995-10-01' AS DATE) - ) AS `bfcol_22`, - `bfcol_1` AS `bfcol_39`, - `bfcol_2` AS `bfcol_40`, - `bfcol_1` AS `bfcol_45`, - 1 - `bfcol_2` AS `bfcol_46`, - `bfcol_1` * ( - 1 - `bfcol_2` - ) AS `bfcol_51` - FROM `bfcte_3` - INNER JOIN `bfcte_2` - ON `bfcol_4` = `bfcol_0` - WHERE - ( - `bfcol_3` >= CAST('1995-09-01' AS DATE) - ) - AND ( - `bfcol_3` < CAST('1995-10-01' AS DATE) - ) -), `bfcte_6` AS ( - SELECT - `bfcol_8`, - `bfcol_9`, - `bfcol_0`, - `bfcol_1`, - `bfcol_2`, - `bfcol_3`, - `bfcol_9` AS `bfcol_23`, - `bfcol_1` AS `bfcol_24`, - `bfcol_2` AS `bfcol_25`, - ( - `bfcol_3` >= CAST('1995-09-01' AS DATE) - ) - AND ( - `bfcol_3` < CAST('1995-10-01' AS DATE) - ) AS `bfcol_26`, - ( - `bfcol_1` * ( - 1 - `bfcol_2` - ) - ) * CAST(REGEXP_CONTAINS(`bfcol_9`, 'PROMO') AS INT64) AS `bfcol_41` - FROM `bfcte_4` - INNER JOIN `bfcte_2` - ON `bfcol_8` = `bfcol_0` - WHERE - ( - `bfcol_3` >= CAST('1995-09-01' AS DATE) - ) - AND ( - `bfcol_3` < CAST('1995-10-01' AS DATE) - ) -), `bfcte_7` AS ( - SELECT - COALESCE(SUM(`bfcol_51`), 0) AS `bfcol_54` - FROM `bfcte_5` -), `bfcte_8` AS ( - SELECT - COALESCE(SUM(`bfcol_41`), 0) AS `bfcol_47` - FROM `bfcte_6` -), `bfcte_9` AS ( - SELECT - `bfcol_54`, - 0 AS `bfcol_59` - FROM `bfcte_7` -), `bfcte_10` AS ( - SELECT - `bfcol_47`, - 0 AS `bfcol_50` - FROM `bfcte_8` -), `bfcte_11` AS ( - SELECT - `bfcol_5`, - `bfcol_6`, - `bfcol_7`, - `bfcol_54`, - `bfcol_59`, - CASE WHEN `bfcol_7` = 0 THEN `bfcol_54` END AS `bfcol_64`, - IF(`bfcol_59` = 0, CASE WHEN `bfcol_7` = 0 THEN `bfcol_54` END, NULL) AS `bfcol_72` - FROM `bfcte_0` - CROSS JOIN `bfcte_9` -), `bfcte_12` AS ( - SELECT - `bfcol_10`, - `bfcol_11`, - `bfcol_12`, - `bfcol_47`, - `bfcol_50`, - CASE WHEN `bfcol_12` = 0 THEN `bfcol_47` END AS `bfcol_53`, - IF(`bfcol_50` = 0, CASE WHEN `bfcol_12` = 0 THEN `bfcol_47` END, NULL) AS `bfcol_60` - FROM `bfcte_1` - CROSS JOIN `bfcte_10` -), `bfcte_13` AS ( - SELECT - `bfcol_5`, - `bfcol_6`, - ANY_VALUE(`bfcol_72`) AS `bfcol_79` - FROM `bfcte_11` - GROUP BY - `bfcol_5`, - `bfcol_6` -), `bfcte_14` AS ( - SELECT - `bfcol_10`, - `bfcol_11`, - ANY_VALUE(`bfcol_60`) AS `bfcol_65` - FROM `bfcte_12` - GROUP BY - `bfcol_10`, - `bfcol_11` -), `bfcte_15` AS ( - SELECT - `bfcol_5` AS `bfcol_80`, - `bfcol_79` AS `bfcol_81` - FROM `bfcte_13` -), `bfcte_16` AS ( - SELECT - `bfcol_10` AS `bfcol_77`, - 100.0 * `bfcol_65` AS `bfcol_78` - FROM `bfcte_14` -) -SELECT - ROUND(IEEE_DIVIDE(`bfcol_78`, `bfcol_81`), 2) AS `PROMO_REVENUE` -FROM `bfcte_16` -FULL OUTER JOIN `bfcte_15` - ON `bfcol_77` = `bfcol_80` -ORDER BY - COALESCE(`bfcol_77`, `bfcol_80`) ASC NULLS LAST \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/tpch/snapshots/test_tpch/test_tpch_query/15/out.sql b/tests/unit/core/compile/sqlglot/tpch/snapshots/test_tpch/test_tpch_query/15/out.sql deleted file mode 100644 index e3cc2bd9743..00000000000 --- a/tests/unit/core/compile/sqlglot/tpch/snapshots/test_tpch/test_tpch_query/15/out.sql +++ /dev/null @@ -1,112 +0,0 @@ -WITH `bfcte_0` AS ( - SELECT - * - FROM UNNEST(ARRAY>[STRUCT('TOTAL_REVENUE', 0, 0)]) -), `bfcte_1` AS ( - SELECT - `L_SUPPKEY`, - `L_EXTENDEDPRICE`, - `L_DISCOUNT`, - `L_SHIPDATE`, - `L_SUPPKEY` AS `bfcol_12`, - `L_EXTENDEDPRICE` AS `bfcol_13`, - `L_DISCOUNT` AS `bfcol_14`, - ( - `L_SHIPDATE` >= CAST('1996-01-01' AS DATE) - ) - AND ( - `L_SHIPDATE` < CAST('1996-04-01' AS DATE) - ) AS `bfcol_15`, - `L_SUPPKEY` AS `bfcol_23`, - `L_EXTENDEDPRICE` * ( - 1 - `L_DISCOUNT` - ) AS `bfcol_24` - FROM `bigframes-dev-perf`.`tpch_0001t`.`LINEITEM` AS `bft_1` - WHERE - ( - `L_SHIPDATE` >= CAST('1996-01-01' AS DATE) - ) - AND ( - `L_SHIPDATE` < CAST('1996-04-01' AS DATE) - ) -), `bfcte_2` AS ( - SELECT - `S_SUPPKEY` AS `bfcol_4` - FROM `bigframes-dev-perf`.`tpch_0001t`.`SUPPLIER` AS `bft_0` -), `bfcte_3` AS ( - SELECT - `S_SUPPKEY` AS `bfcol_8`, - `S_NAME` AS `bfcol_9`, - `S_ADDRESS` AS `bfcol_10`, - `S_PHONE` AS `bfcol_11` - FROM `bigframes-dev-perf`.`tpch_0001t`.`SUPPLIER` AS `bft_0` -), `bfcte_4` AS ( - SELECT - `bfcol_23`, - COALESCE(SUM(`bfcol_24`), 0) AS `bfcol_27` - FROM `bfcte_1` - GROUP BY - `bfcol_23` -), `bfcte_5` AS ( - SELECT - `bfcol_23` AS `bfcol_30`, - ROUND(`bfcol_27`, 2) AS `bfcol_31` - FROM `bfcte_4` -), `bfcte_6` AS ( - SELECT - MAX(`bfcol_31`) AS `bfcol_38` - FROM `bfcte_2` - INNER JOIN `bfcte_5` - ON `bfcol_4` = `bfcol_30` -), `bfcte_7` AS ( - SELECT - `bfcol_8` AS `bfcol_33`, - `bfcol_9` AS `bfcol_34`, - `bfcol_10` AS `bfcol_35`, - `bfcol_11` AS `bfcol_36`, - `bfcol_31` AS `bfcol_37` - FROM `bfcte_3` - INNER JOIN `bfcte_5` - ON `bfcol_8` = `bfcol_30` -), `bfcte_8` AS ( - SELECT - `bfcol_38`, - 0 AS `bfcol_39` - FROM `bfcte_6` -), `bfcte_9` AS ( - SELECT - `bfcol_5`, - `bfcol_6`, - `bfcol_7`, - `bfcol_38`, - `bfcol_39`, - CASE WHEN `bfcol_7` = 0 THEN `bfcol_38` END AS `bfcol_40`, - IF(`bfcol_39` = 0, CASE WHEN `bfcol_7` = 0 THEN `bfcol_38` END, NULL) AS `bfcol_45` - FROM `bfcte_0` - CROSS JOIN `bfcte_8` -), `bfcte_10` AS ( - SELECT - `bfcol_5`, - `bfcol_6`, - ANY_VALUE(`bfcol_45`) AS `bfcol_49` - FROM `bfcte_9` - GROUP BY - `bfcol_5`, - `bfcol_6` -), `bfcte_11` AS ( - SELECT - `bfcol_49` AS `bfcol_50` - FROM `bfcte_10` -) -SELECT - `bfcol_33` AS `S_SUPPKEY`, - `bfcol_34` AS `S_NAME`, - `bfcol_35` AS `S_ADDRESS`, - `bfcol_36` AS `S_PHONE`, - `bfcol_37` AS `TOTAL_REVENUE` -FROM `bfcte_7` -CROSS JOIN `bfcte_11` -WHERE - `bfcol_37` = `bfcol_50` -ORDER BY - `bfcol_33` ASC NULLS LAST \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/tpch/snapshots/test_tpch/test_tpch_query/16/out.sql b/tests/unit/core/compile/sqlglot/tpch/snapshots/test_tpch/test_tpch_query/16/out.sql deleted file mode 100644 index 228d51a76c7..00000000000 --- a/tests/unit/core/compile/sqlglot/tpch/snapshots/test_tpch/test_tpch_query/16/out.sql +++ /dev/null @@ -1,88 +0,0 @@ -WITH `bfcte_0` AS ( - SELECT - `S_SUPPKEY`, - `S_COMMENT`, - `S_SUPPKEY` AS `bfcol_8`, - NOT ( - REGEXP_CONTAINS(`S_COMMENT`, 'Customer.*Complaints') - ) AS `bfcol_9` - FROM `bigframes-dev-perf`.`tpch_0001t`.`SUPPLIER` AS `bft_2` - WHERE - NOT ( - REGEXP_CONTAINS(`S_COMMENT`, 'Customer.*Complaints') - ) -), `bfcte_1` AS ( - SELECT - `PS_PARTKEY` AS `bfcol_2`, - `PS_SUPPKEY` AS `bfcol_3` - FROM `bigframes-dev-perf`.`tpch_0001t`.`PARTSUPP` AS `bft_1` -), `bfcte_2` AS ( - SELECT - `P_PARTKEY` AS `bfcol_4`, - `P_BRAND` AS `bfcol_5`, - `P_TYPE` AS `bfcol_6`, - `P_SIZE` AS `bfcol_7` - FROM `bigframes-dev-perf`.`tpch_0001t`.`PART` AS `bft_0` -), `bfcte_3` AS ( - SELECT - `bfcol_8` - FROM `bfcte_0` - GROUP BY - `bfcol_8` -), `bfcte_4` AS ( - SELECT - `bfcol_5` AS `bfcol_55`, - `bfcol_6` AS `bfcol_56`, - `bfcol_7` AS `bfcol_57`, - `bfcol_3` AS `bfcol_58` - FROM `bfcte_2` - INNER JOIN `bfcte_1` - ON `bfcol_4` = `bfcol_2` - WHERE - `bfcol_5` <> 'Brand#45' - AND NOT ( - REGEXP_CONTAINS(`bfcol_6`, 'MEDIUM POLISHED') - ) - AND COALESCE(COALESCE(`bfcol_7` IN (49, 14, 23, 45, 19, 3, 36, 9), FALSE), FALSE) -), `bfcte_5` AS ( - SELECT - `bfcol_8` AS `bfcol_21` - FROM `bfcte_3` -), `bfcte_6` AS ( - SELECT - *, - COALESCE(`bfcol_58` IN (( - SELECT - * - FROM `bfcte_5` - )), FALSE) AS `bfcol_59` - FROM `bfcte_4` -), `bfcte_7` AS ( - SELECT - * - FROM `bfcte_6` - WHERE - `bfcol_59` -), `bfcte_8` AS ( - SELECT - `bfcol_55`, - `bfcol_56`, - `bfcol_57`, - COUNT(DISTINCT `bfcol_58`) AS `bfcol_69` - FROM `bfcte_7` - GROUP BY - `bfcol_55`, - `bfcol_56`, - `bfcol_57` -) -SELECT - `bfcol_55` AS `P_BRAND`, - `bfcol_56` AS `P_TYPE`, - `bfcol_57` AS `P_SIZE`, - `bfcol_69` AS `SUPPLIER_CNT` -FROM `bfcte_8` -ORDER BY - `bfcol_69` DESC, - `bfcol_55` ASC NULLS LAST, - `bfcol_56` ASC NULLS LAST, - `bfcol_57` ASC NULLS LAST \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/tpch/snapshots/test_tpch/test_tpch_query/17/out.sql b/tests/unit/core/compile/sqlglot/tpch/snapshots/test_tpch/test_tpch_query/17/out.sql deleted file mode 100644 index 40aacf917f1..00000000000 --- a/tests/unit/core/compile/sqlglot/tpch/snapshots/test_tpch/test_tpch_query/17/out.sql +++ /dev/null @@ -1,97 +0,0 @@ -WITH `bfcte_0` AS ( - SELECT - * - FROM UNNEST(ARRAY>[STRUCT('L_EXTENDEDPRICE', 0, 0)]) -), `bfcte_1` AS ( - SELECT - `P_PARTKEY` AS `bfcol_15` - FROM `bigframes-dev-perf`.`tpch_0001t`.`PART` AS `bft_1` - WHERE - ( - `P_BRAND` = 'Brand#23' - ) AND ( - `P_CONTAINER` = 'MED BOX' - ) -), `bfcte_2` AS ( - SELECT - `L_PARTKEY` AS `bfcol_3`, - `L_QUANTITY` AS `bfcol_4`, - `L_EXTENDEDPRICE` AS `bfcol_5` - FROM `bigframes-dev-perf`.`tpch_0001t`.`LINEITEM` AS `bft_0` -), `bfcte_3` AS ( - SELECT - `L_PARTKEY` AS `bfcol_6`, - `L_QUANTITY` AS `bfcol_7` - FROM `bigframes-dev-perf`.`tpch_0001t`.`LINEITEM` AS `bft_0` -), `bfcte_4` AS ( - SELECT - `bfcol_4` AS `bfcol_16`, - `bfcol_5` AS `bfcol_17`, - `bfcol_15` AS `bfcol_18` - FROM `bfcte_2` - RIGHT JOIN `bfcte_1` - ON `bfcol_3` = `bfcol_15` -), `bfcte_5` AS ( - SELECT - `bfcol_15`, - AVG(`bfcol_7`) AS `bfcol_21` - FROM `bfcte_3` - RIGHT JOIN `bfcte_1` - ON `bfcol_6` = `bfcol_15` - GROUP BY - `bfcol_15` -), `bfcte_6` AS ( - SELECT - `bfcol_15` AS `bfcol_24`, - `bfcol_21` * 0.2 AS `bfcol_25` - FROM `bfcte_5` -), `bfcte_7` AS ( - SELECT - `bfcol_24`, - `bfcol_25`, - `bfcol_16`, - `bfcol_17`, - `bfcol_18`, - `bfcol_17` AS `bfcol_29`, - `bfcol_16` < `bfcol_25` AS `bfcol_30` - FROM `bfcte_6` - INNER JOIN `bfcte_4` - ON `bfcol_24` = `bfcol_18` - WHERE - `bfcol_16` < `bfcol_25` -), `bfcte_8` AS ( - SELECT - COALESCE(SUM(`bfcol_29`), 0) AS `bfcol_34` - FROM `bfcte_7` -), `bfcte_9` AS ( - SELECT - `bfcol_34`, - 0 AS `bfcol_35` - FROM `bfcte_8` -), `bfcte_10` AS ( - SELECT - `bfcol_8`, - `bfcol_9`, - `bfcol_10`, - `bfcol_34`, - `bfcol_35`, - CASE WHEN `bfcol_10` = 0 THEN `bfcol_34` END AS `bfcol_36`, - IF(`bfcol_35` = 0, CASE WHEN `bfcol_10` = 0 THEN `bfcol_34` END, NULL) AS `bfcol_41` - FROM `bfcte_0` - CROSS JOIN `bfcte_9` -), `bfcte_11` AS ( - SELECT - `bfcol_8`, - `bfcol_9`, - ANY_VALUE(`bfcol_41`) AS `bfcol_45` - FROM `bfcte_10` - GROUP BY - `bfcol_8`, - `bfcol_9` -) -SELECT - ROUND(IEEE_DIVIDE(`bfcol_45`, 7.0), 2) AS `AVG_YEARLY` -FROM `bfcte_11` -ORDER BY - `bfcol_9` ASC NULLS LAST, - `bfcol_8` ASC NULLS LAST \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/tpch/snapshots/test_tpch/test_tpch_query/18/out.sql b/tests/unit/core/compile/sqlglot/tpch/snapshots/test_tpch/test_tpch_query/18/out.sql deleted file mode 100644 index 6fcdb343940..00000000000 --- a/tests/unit/core/compile/sqlglot/tpch/snapshots/test_tpch/test_tpch_query/18/out.sql +++ /dev/null @@ -1,104 +0,0 @@ -WITH `bfcte_0` AS ( - SELECT - `C_CUSTKEY` AS `bfcol_0`, - `C_NAME` AS `bfcol_1` - FROM `bigframes-dev-perf`.`tpch_0001t`.`CUSTOMER` AS `bft_2` -), `bfcte_1` AS ( - SELECT - `L_ORDERKEY` AS `bfcol_2`, - `L_QUANTITY` AS `bfcol_3` - FROM `bigframes-dev-perf`.`tpch_0001t`.`LINEITEM` AS `bft_1` -), `bfcte_2` AS ( - SELECT - `O_ORDERKEY` AS `bfcol_4`, - `O_CUSTKEY` AS `bfcol_5`, - `O_TOTALPRICE` AS `bfcol_6`, - `O_ORDERDATE` AS `bfcol_7` - FROM `bigframes-dev-perf`.`tpch_0001t`.`ORDERS` AS `bft_0` -), `bfcte_3` AS ( - SELECT - `bfcol_2`, - COALESCE(SUM(`bfcol_3`), 0) AS `bfcol_8` - FROM `bfcte_1` - GROUP BY - `bfcol_2` -), `bfcte_4` AS ( - SELECT - `bfcol_2`, - `bfcol_8`, - `bfcol_2` AS `bfcol_9`, - `bfcol_8` > 300 AS `bfcol_10` - FROM `bfcte_3` - WHERE - `bfcol_8` > 300 -), `bfcte_5` AS ( - SELECT - `bfcol_9` - FROM `bfcte_4` - GROUP BY - `bfcol_9` -), `bfcte_6` AS ( - SELECT - `bfcol_9` AS `bfcol_13` - FROM `bfcte_5` -), `bfcte_7` AS ( - SELECT - *, - COALESCE(`bfcol_4` IN (( - SELECT - * - FROM `bfcte_6` - )), FALSE) AS `bfcol_14` - FROM `bfcte_2` -), `bfcte_8` AS ( - SELECT - `bfcol_4` AS `bfcol_20`, - `bfcol_5` AS `bfcol_21`, - `bfcol_6` AS `bfcol_22`, - `bfcol_7` AS `bfcol_23` - FROM `bfcte_7` - WHERE - `bfcol_14` -), `bfcte_9` AS ( - SELECT - `bfcol_20` AS `bfcol_24`, - `bfcol_21` AS `bfcol_25`, - `bfcol_22` AS `bfcol_26`, - `bfcol_23` AS `bfcol_27`, - `bfcol_3` AS `bfcol_28` - FROM `bfcte_8` - INNER JOIN `bfcte_1` - ON `bfcol_20` = `bfcol_2` -), `bfcte_10` AS ( - SELECT - `bfcol_1`, - `bfcol_0`, - `bfcol_24`, - `bfcol_27`, - `bfcol_26`, - COALESCE(SUM(`bfcol_28`), 0) AS `bfcol_35` - FROM `bfcte_9` - INNER JOIN `bfcte_0` - ON `bfcol_25` = `bfcol_0` - GROUP BY - `bfcol_1`, - `bfcol_0`, - `bfcol_24`, - `bfcol_27`, - `bfcol_26` -) -SELECT - `bfcol_1` AS `C_NAME`, - `bfcol_0` AS `C_CUSTKEY`, - `bfcol_24` AS `O_ORDERKEY`, - `bfcol_27` AS `O_ORDERDAT`, - `bfcol_26` AS `O_TOTALPRICE`, - `bfcol_35` AS `COL6` -FROM `bfcte_10` -ORDER BY - `bfcol_26` DESC, - `bfcol_27` ASC NULLS LAST, - `bfcol_1` ASC NULLS LAST, - `bfcol_0` ASC NULLS LAST, - `bfcol_24` ASC NULLS LAST -LIMIT 100 \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/tpch/snapshots/test_tpch/test_tpch_query/19/out.sql b/tests/unit/core/compile/sqlglot/tpch/snapshots/test_tpch/test_tpch_query/19/out.sql deleted file mode 100644 index e7b817ecfd9..00000000000 --- a/tests/unit/core/compile/sqlglot/tpch/snapshots/test_tpch/test_tpch_query/19/out.sql +++ /dev/null @@ -1,226 +0,0 @@ -WITH `bfcte_0` AS ( - SELECT - * - FROM UNNEST(ARRAY>[STRUCT(0)]) -), `bfcte_1` AS ( - SELECT - `L_PARTKEY` AS `bfcol_1`, - `L_QUANTITY` AS `bfcol_2`, - `L_EXTENDEDPRICE` AS `bfcol_3`, - `L_DISCOUNT` AS `bfcol_4`, - `L_SHIPINSTRUCT` AS `bfcol_5`, - `L_SHIPMODE` AS `bfcol_6` - FROM `bigframes-dev-perf`.`tpch_0001t`.`LINEITEM` AS `bft_1` -), `bfcte_2` AS ( - SELECT - `P_PARTKEY` AS `bfcol_7`, - `P_BRAND` AS `bfcol_8`, - `P_SIZE` AS `bfcol_9`, - `P_CONTAINER` AS `bfcol_10` - FROM `bigframes-dev-perf`.`tpch_0001t`.`PART` AS `bft_0` -), `bfcte_3` AS ( - SELECT - `bfcol_7`, - `bfcol_8`, - `bfcol_9`, - `bfcol_10`, - `bfcol_1`, - `bfcol_2`, - `bfcol_3`, - `bfcol_4`, - `bfcol_5`, - `bfcol_6`, - `bfcol_3` AS `bfcol_19`, - `bfcol_4` AS `bfcol_20`, - ( - COALESCE(COALESCE(`bfcol_6` IN ('AIR', 'AIR REG'), FALSE), FALSE) - AND ( - `bfcol_5` = 'DELIVER IN PERSON' - ) - ) - AND ( - ( - ( - ( - ( - ( - `bfcol_8` = 'Brand#12' - ) - AND COALESCE(COALESCE(`bfcol_10` IN ('SM CASE', 'SM BOX', 'SM PACK', 'SM PKG'), FALSE), FALSE) - ) - AND ( - ( - `bfcol_2` >= 1 - ) AND ( - `bfcol_2` <= 11 - ) - ) - ) - AND ( - ( - `bfcol_9` >= 1 - ) AND ( - `bfcol_9` <= 5 - ) - ) - ) - OR ( - ( - ( - ( - `bfcol_8` = 'Brand#23' - ) - AND COALESCE( - COALESCE(`bfcol_10` IN ('MED BAG', 'MED BOX', 'MED PKG', 'MED PACK'), FALSE), - FALSE - ) - ) - AND ( - ( - `bfcol_2` >= 10 - ) AND ( - `bfcol_2` <= 20 - ) - ) - ) - AND ( - ( - `bfcol_9` >= 1 - ) AND ( - `bfcol_9` <= 10 - ) - ) - ) - ) - OR ( - ( - ( - ( - `bfcol_8` = 'Brand#34' - ) - AND COALESCE(COALESCE(`bfcol_10` IN ('LG CASE', 'LG BOX', 'LG PACK', 'LG PKG'), FALSE), FALSE) - ) - AND ( - ( - `bfcol_2` >= 20 - ) AND ( - `bfcol_2` <= 30 - ) - ) - ) - AND ( - ( - `bfcol_9` >= 1 - ) AND ( - `bfcol_9` <= 15 - ) - ) - ) - ) AS `bfcol_21`, - `bfcol_3` AS `bfcol_27`, - 1 - `bfcol_4` AS `bfcol_28`, - `bfcol_3` * ( - 1 - `bfcol_4` - ) AS `bfcol_31` - FROM `bfcte_2` - INNER JOIN `bfcte_1` - ON `bfcol_7` = `bfcol_1` - WHERE - ( - COALESCE(COALESCE(`bfcol_6` IN ('AIR', 'AIR REG'), FALSE), FALSE) - AND ( - `bfcol_5` = 'DELIVER IN PERSON' - ) - ) - AND ( - ( - ( - ( - ( - ( - `bfcol_8` = 'Brand#12' - ) - AND COALESCE(COALESCE(`bfcol_10` IN ('SM CASE', 'SM BOX', 'SM PACK', 'SM PKG'), FALSE), FALSE) - ) - AND ( - ( - `bfcol_2` >= 1 - ) AND ( - `bfcol_2` <= 11 - ) - ) - ) - AND ( - ( - `bfcol_9` >= 1 - ) AND ( - `bfcol_9` <= 5 - ) - ) - ) - OR ( - ( - ( - ( - `bfcol_8` = 'Brand#23' - ) - AND COALESCE( - COALESCE(`bfcol_10` IN ('MED BAG', 'MED BOX', 'MED PKG', 'MED PACK'), FALSE), - FALSE - ) - ) - AND ( - ( - `bfcol_2` >= 10 - ) AND ( - `bfcol_2` <= 20 - ) - ) - ) - AND ( - ( - `bfcol_9` >= 1 - ) AND ( - `bfcol_9` <= 10 - ) - ) - ) - ) - OR ( - ( - ( - ( - `bfcol_8` = 'Brand#34' - ) - AND COALESCE(COALESCE(`bfcol_10` IN ('LG CASE', 'LG BOX', 'LG PACK', 'LG PKG'), FALSE), FALSE) - ) - AND ( - ( - `bfcol_2` >= 20 - ) AND ( - `bfcol_2` <= 30 - ) - ) - ) - AND ( - ( - `bfcol_9` >= 1 - ) AND ( - `bfcol_9` <= 15 - ) - ) - ) - ) -), `bfcte_4` AS ( - SELECT - COALESCE(SUM(`bfcol_31`), 0) AS `bfcol_33` - FROM `bfcte_3` -), `bfcte_5` AS ( - SELECT - * - FROM `bfcte_4` -) -SELECT - CASE WHEN `bfcol_0` = 0 THEN `bfcol_33` END AS `REVENUE` -FROM `bfcte_5` -CROSS JOIN `bfcte_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/tpch/snapshots/test_tpch/test_tpch_query/2/out.sql b/tests/unit/core/compile/sqlglot/tpch/snapshots/test_tpch/test_tpch_query/2/out.sql deleted file mode 100644 index ae7be6a71da..00000000000 --- a/tests/unit/core/compile/sqlglot/tpch/snapshots/test_tpch/test_tpch_query/2/out.sql +++ /dev/null @@ -1,197 +0,0 @@ -WITH `bfcte_0` AS ( - SELECT - `R_REGIONKEY` AS `bfcol_0`, - `R_NAME` AS `bfcol_1` - FROM `bigframes-dev-perf`.`tpch_0001t`.`REGION` AS `bft_4` -), `bfcte_1` AS ( - SELECT - `N_NATIONKEY` AS `bfcol_2`, - `N_NAME` AS `bfcol_3`, - `N_REGIONKEY` AS `bfcol_4` - FROM `bigframes-dev-perf`.`tpch_0001t`.`NATION` AS `bft_3` -), `bfcte_2` AS ( - SELECT - `N_NATIONKEY` AS `bfcol_19`, - `N_REGIONKEY` AS `bfcol_20` - FROM `bigframes-dev-perf`.`tpch_0001t`.`NATION` AS `bft_3` -), `bfcte_3` AS ( - SELECT - `S_SUPPKEY` AS `bfcol_5`, - `S_NAME` AS `bfcol_6`, - `S_ADDRESS` AS `bfcol_7`, - `S_NATIONKEY` AS `bfcol_8`, - `S_PHONE` AS `bfcol_9`, - `S_ACCTBAL` AS `bfcol_10`, - `S_COMMENT` AS `bfcol_11` - FROM `bigframes-dev-perf`.`tpch_0001t`.`SUPPLIER` AS `bft_2` -), `bfcte_4` AS ( - SELECT - `S_SUPPKEY` AS `bfcol_21`, - `S_NATIONKEY` AS `bfcol_22` - FROM `bigframes-dev-perf`.`tpch_0001t`.`SUPPLIER` AS `bft_2` -), `bfcte_5` AS ( - SELECT - `PS_PARTKEY` AS `bfcol_12`, - `PS_SUPPKEY` AS `bfcol_13`, - `PS_SUPPLYCOST` AS `bfcol_14` - FROM `bigframes-dev-perf`.`tpch_0001t`.`PARTSUPP` AS `bft_1` -), `bfcte_6` AS ( - SELECT - `P_PARTKEY` AS `bfcol_15`, - `P_MFGR` AS `bfcol_16`, - `P_TYPE` AS `bfcol_17`, - `P_SIZE` AS `bfcol_18` - FROM `bigframes-dev-perf`.`tpch_0001t`.`PART` AS `bft_0` -), `bfcte_7` AS ( - SELECT - `P_PARTKEY` AS `bfcol_23`, - `P_TYPE` AS `bfcol_24`, - `P_SIZE` AS `bfcol_25` - FROM `bigframes-dev-perf`.`tpch_0001t`.`PART` AS `bft_0` -), `bfcte_8` AS ( - SELECT - `bfcol_15` AS `bfcol_26`, - `bfcol_16` AS `bfcol_27`, - `bfcol_17` AS `bfcol_28`, - `bfcol_18` AS `bfcol_29`, - `bfcol_13` AS `bfcol_30`, - `bfcol_14` AS `bfcol_31` - FROM `bfcte_6` - INNER JOIN `bfcte_5` - ON `bfcol_15` = `bfcol_12` -), `bfcte_9` AS ( - SELECT - `bfcol_23` AS `bfcol_32`, - `bfcol_24` AS `bfcol_33`, - `bfcol_25` AS `bfcol_34`, - `bfcol_13` AS `bfcol_35`, - `bfcol_14` AS `bfcol_36` - FROM `bfcte_7` - INNER JOIN `bfcte_5` - ON `bfcol_23` = `bfcol_12` -), `bfcte_10` AS ( - SELECT - `bfcol_26` AS `bfcol_37`, - `bfcol_27` AS `bfcol_38`, - `bfcol_28` AS `bfcol_39`, - `bfcol_29` AS `bfcol_40`, - `bfcol_31` AS `bfcol_41`, - `bfcol_6` AS `bfcol_42`, - `bfcol_7` AS `bfcol_43`, - `bfcol_8` AS `bfcol_44`, - `bfcol_9` AS `bfcol_45`, - `bfcol_10` AS `bfcol_46`, - `bfcol_11` AS `bfcol_47` - FROM `bfcte_8` - INNER JOIN `bfcte_3` - ON `bfcol_30` = `bfcol_5` -), `bfcte_11` AS ( - SELECT - `bfcol_32` AS `bfcol_48`, - `bfcol_33` AS `bfcol_49`, - `bfcol_34` AS `bfcol_50`, - `bfcol_36` AS `bfcol_51`, - `bfcol_22` AS `bfcol_52` - FROM `bfcte_9` - INNER JOIN `bfcte_4` - ON `bfcol_35` = `bfcol_21` -), `bfcte_12` AS ( - SELECT - `bfcol_37` AS `bfcol_53`, - `bfcol_38` AS `bfcol_54`, - `bfcol_39` AS `bfcol_55`, - `bfcol_40` AS `bfcol_56`, - `bfcol_41` AS `bfcol_57`, - `bfcol_42` AS `bfcol_58`, - `bfcol_43` AS `bfcol_59`, - `bfcol_45` AS `bfcol_60`, - `bfcol_46` AS `bfcol_61`, - `bfcol_47` AS `bfcol_62`, - `bfcol_3` AS `bfcol_63`, - `bfcol_4` AS `bfcol_64` - FROM `bfcte_10` - INNER JOIN `bfcte_1` - ON `bfcol_44` = `bfcol_2` -), `bfcte_13` AS ( - SELECT - `bfcol_48` AS `bfcol_65`, - `bfcol_49` AS `bfcol_66`, - `bfcol_50` AS `bfcol_67`, - `bfcol_51` AS `bfcol_68`, - `bfcol_20` AS `bfcol_69` - FROM `bfcte_11` - INNER JOIN `bfcte_2` - ON `bfcol_52` = `bfcol_19` -), `bfcte_14` AS ( - SELECT - `bfcol_53` AS `bfcol_205`, - `bfcol_54` AS `bfcol_206`, - `bfcol_57` AS `bfcol_207`, - `bfcol_58` AS `bfcol_208`, - `bfcol_59` AS `bfcol_209`, - `bfcol_60` AS `bfcol_210`, - `bfcol_61` AS `bfcol_211`, - `bfcol_62` AS `bfcol_212`, - `bfcol_63` AS `bfcol_213` - FROM `bfcte_12` - INNER JOIN `bfcte_0` - ON `bfcol_64` = `bfcol_0` - WHERE - `bfcol_56` = 15 AND ENDS_WITH(`bfcol_55`, 'BRASS') AND `bfcol_1` = 'EUROPE' -), `bfcte_15` AS ( - SELECT - `bfcol_65`, - `bfcol_66`, - `bfcol_67`, - `bfcol_68`, - `bfcol_69`, - `bfcol_0`, - `bfcol_1`, - `bfcol_65` AS `bfcol_99`, - `bfcol_66` AS `bfcol_100`, - `bfcol_68` AS `bfcol_101`, - `bfcol_1` AS `bfcol_102`, - `bfcol_67` = 15 AS `bfcol_103`, - `bfcol_65` AS `bfcol_147`, - `bfcol_68` AS `bfcol_148`, - `bfcol_1` AS `bfcol_149`, - ENDS_WITH(`bfcol_66`, 'BRASS') AS `bfcol_150`, - `bfcol_65` AS `bfcol_189`, - `bfcol_68` AS `bfcol_190`, - `bfcol_1` = 'EUROPE' AS `bfcol_191` - FROM `bfcte_13` - INNER JOIN `bfcte_0` - ON `bfcol_69` = `bfcol_0` - WHERE - `bfcol_67` = 15 AND ENDS_WITH(`bfcol_66`, 'BRASS') AND `bfcol_1` = 'EUROPE' -), `bfcte_16` AS ( - SELECT - `bfcol_189`, - MIN(`bfcol_190`) AS `bfcol_216` - FROM `bfcte_15` - GROUP BY - `bfcol_189` -), `bfcte_17` AS ( - SELECT - `bfcol_189` AS `bfcol_214`, - `bfcol_216` - FROM `bfcte_16` -) -SELECT - `bfcol_211` AS `S_ACCTBAL`, - `bfcol_208` AS `S_NAME`, - `bfcol_213` AS `N_NAME`, - `bfcol_214` AS `P_PARTKEY`, - `bfcol_206` AS `P_MFGR`, - `bfcol_209` AS `S_ADDRESS`, - `bfcol_210` AS `S_PHONE`, - `bfcol_212` AS `S_COMMENT` -FROM `bfcte_17` -INNER JOIN `bfcte_14` - ON `bfcol_214` = `bfcol_205` AND `bfcol_216` = `bfcol_207` -ORDER BY - `bfcol_211` DESC, - `bfcol_213` ASC NULLS LAST, - `bfcol_208` ASC NULLS LAST, - `bfcol_214` ASC NULLS LAST -LIMIT 100 \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/tpch/snapshots/test_tpch/test_tpch_query/20/out.sql b/tests/unit/core/compile/sqlglot/tpch/snapshots/test_tpch/test_tpch_query/20/out.sql deleted file mode 100644 index 197588f5c84..00000000000 --- a/tests/unit/core/compile/sqlglot/tpch/snapshots/test_tpch/test_tpch_query/20/out.sql +++ /dev/null @@ -1,144 +0,0 @@ -WITH `bfcte_0` AS ( - SELECT - `P_PARTKEY`, - `P_NAME`, - `P_PARTKEY` AS `bfcol_15`, - STARTS_WITH(`P_NAME`, 'forest') AS `bfcol_16` - FROM `bigframes-dev-perf`.`tpch_0001t`.`PART` AS `bft_4` - WHERE - STARTS_WITH(`P_NAME`, 'forest') -), `bfcte_1` AS ( - SELECT - `PS_PARTKEY` AS `bfcol_2`, - `PS_SUPPKEY` AS `bfcol_3`, - `PS_AVAILQTY` AS `bfcol_4` - FROM `bigframes-dev-perf`.`tpch_0001t`.`PARTSUPP` AS `bft_3` -), `bfcte_2` AS ( - SELECT - `L_PARTKEY`, - `L_SUPPKEY`, - `L_QUANTITY`, - `L_SHIPDATE`, - `L_PARTKEY` AS `bfcol_17`, - `L_SUPPKEY` AS `bfcol_18`, - `L_QUANTITY` AS `bfcol_19`, - ( - `L_SHIPDATE` >= CAST('1994-01-01' AS DATE) - ) - AND ( - `L_SHIPDATE` < CAST('1995-01-01' AS DATE) - ) AS `bfcol_20` - FROM `bigframes-dev-perf`.`tpch_0001t`.`LINEITEM` AS `bft_2` - WHERE - ( - `L_SHIPDATE` >= CAST('1994-01-01' AS DATE) - ) - AND ( - `L_SHIPDATE` < CAST('1995-01-01' AS DATE) - ) -), `bfcte_3` AS ( - SELECT - `N_NATIONKEY` AS `bfcol_35` - FROM `bigframes-dev-perf`.`tpch_0001t`.`NATION` AS `bft_1` - WHERE - `N_NAME` = 'CANADA' -), `bfcte_4` AS ( - SELECT - `S_SUPPKEY` AS `bfcol_11`, - `S_NAME` AS `bfcol_12`, - `S_ADDRESS` AS `bfcol_13`, - `S_NATIONKEY` AS `bfcol_14` - FROM `bigframes-dev-perf`.`tpch_0001t`.`SUPPLIER` AS `bft_0` -), `bfcte_5` AS ( - SELECT - `bfcol_15` - FROM `bfcte_0` - GROUP BY - `bfcol_15` -), `bfcte_6` AS ( - SELECT - `bfcol_17`, - `bfcol_18`, - COALESCE(SUM(`bfcol_19`), 0) AS `bfcol_36` - FROM `bfcte_2` - GROUP BY - `bfcol_17`, - `bfcol_18` -), `bfcte_7` AS ( - SELECT - `bfcol_11` AS `bfcol_41`, - `bfcol_12` AS `bfcol_42`, - `bfcol_13` AS `bfcol_43` - FROM `bfcte_4` - INNER JOIN `bfcte_3` - ON `bfcol_14` = `bfcol_35` -), `bfcte_8` AS ( - SELECT - `bfcol_15` AS `bfcol_31` - FROM `bfcte_5` -), `bfcte_9` AS ( - SELECT - `bfcol_17` AS `bfcol_48`, - `bfcol_18` AS `bfcol_49`, - `bfcol_36` * 0.5 AS `bfcol_50` - FROM `bfcte_6` -), `bfcte_10` AS ( - SELECT - *, - COALESCE(`bfcol_2` IN (( - SELECT - * - FROM `bfcte_8` - )), FALSE) AS `bfcol_37` - FROM `bfcte_1` -), `bfcte_11` AS ( - SELECT - `bfcol_2` AS `bfcol_51`, - `bfcol_3` AS `bfcol_52`, - `bfcol_4` AS `bfcol_53` - FROM `bfcte_10` - WHERE - `bfcol_37` -), `bfcte_12` AS ( - SELECT - `bfcol_48`, - `bfcol_49`, - `bfcol_50`, - `bfcol_51`, - `bfcol_52`, - `bfcol_53`, - `bfcol_52` AS `bfcol_57`, - `bfcol_53` > `bfcol_50` AS `bfcol_58` - FROM `bfcte_9` - INNER JOIN `bfcte_11` - ON `bfcol_49` = `bfcol_52` AND `bfcol_48` = `bfcol_51` - WHERE - `bfcol_53` > `bfcol_50` -), `bfcte_13` AS ( - SELECT - `bfcol_57` - FROM `bfcte_12` - GROUP BY - `bfcol_57` -), `bfcte_14` AS ( - SELECT - `bfcol_57` AS `bfcol_61` - FROM `bfcte_13` -), `bfcte_15` AS ( - SELECT - *, - COALESCE(`bfcol_41` IN (( - SELECT - * - FROM `bfcte_14` - )), FALSE) AS `bfcol_62` - FROM `bfcte_7` -) -SELECT - `bfcol_42` AS `S_NAME`, - `bfcol_43` AS `S_ADDRESS` -FROM `bfcte_15` -WHERE - `bfcol_62` -ORDER BY - `bfcol_42` ASC NULLS LAST \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/tpch/snapshots/test_tpch/test_tpch_query/21/out.sql b/tests/unit/core/compile/sqlglot/tpch/snapshots/test_tpch/test_tpch_query/21/out.sql deleted file mode 100644 index 0caf29ca617..00000000000 --- a/tests/unit/core/compile/sqlglot/tpch/snapshots/test_tpch/test_tpch_query/21/out.sql +++ /dev/null @@ -1,142 +0,0 @@ -WITH `bfcte_0` AS ( - SELECT - `O_ORDERKEY` AS `bfcol_0`, - `O_ORDERSTATUS` AS `bfcol_1` - FROM `bigframes-dev-perf`.`tpch_0001t`.`ORDERS` AS `bft_3` -), `bfcte_1` AS ( - SELECT - `N_NATIONKEY` AS `bfcol_2`, - `N_NAME` AS `bfcol_3` - FROM `bigframes-dev-perf`.`tpch_0001t`.`NATION` AS `bft_2` -), `bfcte_2` AS ( - SELECT - `S_SUPPKEY` AS `bfcol_4`, - `S_NAME` AS `bfcol_5`, - `S_NATIONKEY` AS `bfcol_6` - FROM `bigframes-dev-perf`.`tpch_0001t`.`SUPPLIER` AS `bft_1` -), `bfcte_3` AS ( - SELECT - `L_ORDERKEY` AS `bfcol_30`, - `L_SUPPKEY` AS `bfcol_31` - FROM `bigframes-dev-perf`.`tpch_0001t`.`LINEITEM` AS `bft_0` - WHERE - `L_RECEIPTDATE` > `L_COMMITDATE` -), `bfcte_4` AS ( - SELECT - `L_ORDERKEY` - FROM `bigframes-dev-perf`.`tpch_0001t`.`LINEITEM` AS `bft_0` -), `bfcte_5` AS ( - SELECT - `L_ORDERKEY` AS `bfcol_32` - FROM `bigframes-dev-perf`.`tpch_0001t`.`LINEITEM` AS `bft_0` - WHERE - `L_RECEIPTDATE` > `L_COMMITDATE` -), `bfcte_6` AS ( - SELECT - `L_ORDERKEY`, - COUNT(1) AS `bfcol_18` - FROM `bfcte_4` - GROUP BY - `L_ORDERKEY` -), `bfcte_7` AS ( - SELECT - `L_ORDERKEY` AS `bfcol_33` - FROM `bfcte_6` - WHERE - `bfcol_18` > 1 -), `bfcte_8` AS ( - SELECT - `bfcol_33` AS `bfcol_34`, - `bfcol_31` AS `bfcol_35` - FROM `bfcte_7` - INNER JOIN `bfcte_3` - ON `bfcol_33` = `bfcol_30` -), `bfcte_9` AS ( - SELECT - `bfcol_33`, - COUNT(1) AS `bfcol_37` - FROM `bfcte_7` - INNER JOIN `bfcte_5` - ON `bfcol_33` = `bfcol_32` - GROUP BY - `bfcol_33` -), `bfcte_10` AS ( - SELECT - `bfcol_33` AS `bfcol_36`, - `bfcol_37` - FROM `bfcte_9` -), `bfcte_11` AS ( - SELECT - `bfcol_36` AS `bfcol_38`, - `bfcol_37` AS `bfcol_39`, - `bfcol_35` AS `bfcol_40` - FROM `bfcte_10` - INNER JOIN `bfcte_8` - ON `bfcol_36` = `bfcol_34` -), `bfcte_12` AS ( - SELECT - `bfcol_38` AS `bfcol_41`, - `bfcol_39` AS `bfcol_42`, - `bfcol_5` AS `bfcol_43`, - `bfcol_6` AS `bfcol_44` - FROM `bfcte_11` - INNER JOIN `bfcte_2` - ON `bfcol_40` = `bfcol_4` -), `bfcte_13` AS ( - SELECT - `bfcol_41` AS `bfcol_45`, - `bfcol_42` AS `bfcol_46`, - `bfcol_43` AS `bfcol_47`, - `bfcol_3` AS `bfcol_48` - FROM `bfcte_12` - INNER JOIN `bfcte_1` - ON `bfcol_44` = `bfcol_2` -), `bfcte_14` AS ( - SELECT - `bfcol_45`, - `bfcol_46`, - `bfcol_47`, - `bfcol_48`, - `bfcol_0`, - `bfcol_1`, - `bfcol_47` AS `bfcol_53`, - ( - ( - `bfcol_46` = 1 - ) AND ( - `bfcol_48` = 'SAUDI ARABIA' - ) - ) - AND ( - `bfcol_1` = 'F' - ) AS `bfcol_54` - FROM `bfcte_13` - INNER JOIN `bfcte_0` - ON `bfcol_45` = `bfcol_0` - WHERE - ( - ( - `bfcol_46` = 1 - ) AND ( - `bfcol_48` = 'SAUDI ARABIA' - ) - ) - AND ( - `bfcol_1` = 'F' - ) -), `bfcte_15` AS ( - SELECT - `bfcol_53`, - COUNT(1) AS `bfcol_58` - FROM `bfcte_14` - GROUP BY - `bfcol_53` -) -SELECT - `bfcol_53` AS `S_NAME`, - `bfcol_58` AS `NUMWAIT` -FROM `bfcte_15` -ORDER BY - `bfcol_58` DESC, - `bfcol_53` ASC NULLS LAST -LIMIT 100 \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/tpch/snapshots/test_tpch/test_tpch_query/22/out.sql b/tests/unit/core/compile/sqlglot/tpch/snapshots/test_tpch/test_tpch_query/22/out.sql deleted file mode 100644 index 5ab22d3cdaf..00000000000 --- a/tests/unit/core/compile/sqlglot/tpch/snapshots/test_tpch/test_tpch_query/22/out.sql +++ /dev/null @@ -1,132 +0,0 @@ -WITH `bfcte_0` AS ( - SELECT - * - FROM UNNEST(ARRAY>[STRUCT('C_ACCTBAL', 0, 0)]) -), `bfcte_1` AS ( - SELECT - `O_CUSTKEY` - FROM `bigframes-dev-perf`.`tpch_0001t`.`ORDERS` AS `bft_1` -), `bfcte_2` AS ( - SELECT - `C_PHONE`, - `C_ACCTBAL`, - `C_ACCTBAL` AS `bfcol_9`, - SUBSTRING(`C_PHONE`, 1, 2) AS `bfcol_10`, - `C_ACCTBAL` AS `bfcol_19`, - COALESCE( - COALESCE(SUBSTRING(`C_PHONE`, 1, 2) IN ('13', '31', '23', '29', '30', '18', '17'), FALSE), - FALSE - ) AS `bfcol_20`, - `C_ACCTBAL` AS `bfcol_35`, - `C_ACCTBAL` > 0.0 AS `bfcol_36` - FROM `bigframes-dev-perf`.`tpch_0001t`.`CUSTOMER` AS `bft_0` - WHERE - COALESCE( - COALESCE(SUBSTRING(`C_PHONE`, 1, 2) IN ('13', '31', '23', '29', '30', '18', '17'), FALSE), - FALSE - ) - AND `C_ACCTBAL` > 0.0 -), `bfcte_3` AS ( - SELECT - `C_CUSTKEY` AS `bfcol_32`, - `C_ACCTBAL` AS `bfcol_33`, - SUBSTRING(`C_PHONE`, 1, 2) AS `bfcol_34` - FROM `bigframes-dev-perf`.`tpch_0001t`.`CUSTOMER` AS `bft_0` - WHERE - COALESCE( - COALESCE(SUBSTRING(`C_PHONE`, 1, 2) IN ('13', '31', '23', '29', '30', '18', '17'), FALSE), - FALSE - ) -), `bfcte_4` AS ( - SELECT - `O_CUSTKEY` - FROM `bfcte_1` - GROUP BY - `O_CUSTKEY` -), `bfcte_5` AS ( - SELECT - AVG(`bfcol_35`) AS `bfcol_40` - FROM `bfcte_2` -), `bfcte_6` AS ( - SELECT - `O_CUSTKEY` AS `bfcol_0` - FROM `bfcte_4` -), `bfcte_7` AS ( - SELECT - `bfcol_40`, - 0 AS `bfcol_41` - FROM `bfcte_5` -), `bfcte_8` AS ( - SELECT - `bfcol_3`, - `bfcol_4`, - `bfcol_5`, - `bfcol_40`, - `bfcol_41`, - CASE WHEN `bfcol_5` = 0 THEN `bfcol_40` END AS `bfcol_42`, - IF(`bfcol_41` = 0, CASE WHEN `bfcol_5` = 0 THEN `bfcol_40` END, NULL) AS `bfcol_47` - FROM `bfcte_0` - CROSS JOIN `bfcte_7` -), `bfcte_9` AS ( - SELECT - `bfcol_3`, - `bfcol_4`, - ANY_VALUE(`bfcol_47`) AS `bfcol_51` - FROM `bfcte_8` - GROUP BY - `bfcol_3`, - `bfcol_4` -), `bfcte_10` AS ( - SELECT - `bfcol_51` AS `bfcol_52` - FROM `bfcte_9` -), `bfcte_11` AS ( - SELECT - `bfcol_32` AS `bfcol_61`, - `bfcol_33` AS `bfcol_62`, - `bfcol_34` AS `bfcol_63` - FROM `bfcte_3` - CROSS JOIN `bfcte_10` - WHERE - `bfcol_33` > `bfcol_52` -), `bfcte_12` AS ( - SELECT - *, - COALESCE(`bfcol_61` IN (( - SELECT - * - FROM `bfcte_6` - )), FALSE) AS `bfcol_64` - FROM `bfcte_11` -), `bfcte_13` AS ( - SELECT - `bfcol_61`, - `bfcol_62`, - `bfcol_63`, - `bfcol_64`, - NOT ( - `bfcol_64` - ) AS `bfcol_65` - FROM `bfcte_12` - WHERE - NOT ( - `bfcol_64` - ) -), `bfcte_14` AS ( - SELECT - `bfcol_63`, - COUNT(`bfcol_61`) AS `bfcol_73`, - COALESCE(SUM(`bfcol_62`), 0) AS `bfcol_74` - FROM `bfcte_13` - WHERE - NOT `bfcol_63` IS NULL - GROUP BY - `bfcol_63` -) -SELECT - `bfcol_63` AS `CNTRYCODE`, - `bfcol_73` AS `NUMCUST`, - `bfcol_74` AS `TOTACCTBAL` -FROM `bfcte_14` -ORDER BY - `bfcol_63` ASC NULLS LAST \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/tpch/snapshots/test_tpch/test_tpch_query/3/out.sql b/tests/unit/core/compile/sqlglot/tpch/snapshots/test_tpch/test_tpch_query/3/out.sql deleted file mode 100644 index 71779e1adf9..00000000000 --- a/tests/unit/core/compile/sqlglot/tpch/snapshots/test_tpch/test_tpch_query/3/out.sql +++ /dev/null @@ -1,76 +0,0 @@ -WITH `bfcte_0` AS ( - SELECT - `O_ORDERKEY` AS `bfcol_32`, - `O_CUSTKEY` AS `bfcol_33`, - `O_ORDERDATE` AS `bfcol_34`, - `O_SHIPPRIORITY` AS `bfcol_35` - FROM `bigframes-dev-perf`.`tpch_0001t`.`ORDERS` AS `bft_2` - WHERE - `O_ORDERDATE` < CAST('1995-03-15' AS DATE) -), `bfcte_1` AS ( - SELECT - `L_ORDERKEY` AS `bfcol_36`, - `L_EXTENDEDPRICE` AS `bfcol_37`, - `L_DISCOUNT` AS `bfcol_38` - FROM `bigframes-dev-perf`.`tpch_0001t`.`LINEITEM` AS `bft_1` - WHERE - `L_SHIPDATE` > CAST('1995-03-15' AS DATE) -), `bfcte_2` AS ( - SELECT - `C_CUSTKEY` AS `bfcol_39` - FROM `bigframes-dev-perf`.`tpch_0001t`.`CUSTOMER` AS `bft_0` - WHERE - `C_MKTSEGMENT` = 'BUILDING' -), `bfcte_3` AS ( - SELECT - `bfcol_37` AS `bfcol_40`, - `bfcol_38` AS `bfcol_41`, - `bfcol_32` AS `bfcol_42`, - `bfcol_33` AS `bfcol_43`, - `bfcol_34` AS `bfcol_44`, - `bfcol_35` AS `bfcol_45` - FROM `bfcte_1` - INNER JOIN `bfcte_0` - ON `bfcol_36` = `bfcol_32` -), `bfcte_4` AS ( - SELECT - `bfcol_39`, - `bfcol_40`, - `bfcol_41`, - `bfcol_42`, - `bfcol_43`, - `bfcol_44`, - `bfcol_45`, - `bfcol_42` AS `bfcol_51`, - `bfcol_44` AS `bfcol_52`, - `bfcol_45` AS `bfcol_53`, - `bfcol_40` * ( - 1 - `bfcol_41` - ) AS `bfcol_54` - FROM `bfcte_2` - INNER JOIN `bfcte_3` - ON `bfcol_39` = `bfcol_43` -), `bfcte_5` AS ( - SELECT - `bfcol_51`, - `bfcol_52`, - `bfcol_53`, - COALESCE(SUM(`bfcol_54`), 0) AS `bfcol_59` - FROM `bfcte_4` - GROUP BY - `bfcol_51`, - `bfcol_52`, - `bfcol_53` -) -SELECT - `bfcol_51` AS `L_ORDERKEY`, - `bfcol_59` AS `REVENUE`, - `bfcol_52` AS `O_ORDERDATE`, - `bfcol_53` AS `O_SHIPPRIORITY` -FROM `bfcte_5` -ORDER BY - `bfcol_59` DESC, - `bfcol_52` ASC NULLS LAST, - `bfcol_51` ASC NULLS LAST, - `bfcol_53` ASC NULLS LAST -LIMIT 10 \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/tpch/snapshots/test_tpch/test_tpch_query/4/out.sql b/tests/unit/core/compile/sqlglot/tpch/snapshots/test_tpch/test_tpch_query/4/out.sql deleted file mode 100644 index 3235239710e..00000000000 --- a/tests/unit/core/compile/sqlglot/tpch/snapshots/test_tpch/test_tpch_query/4/out.sql +++ /dev/null @@ -1,67 +0,0 @@ -WITH `bfcte_0` AS ( - SELECT - `O_ORDERKEY` AS `bfcol_0`, - `O_ORDERDATE` AS `bfcol_1`, - `O_ORDERPRIORITY` AS `bfcol_2` - FROM `bigframes-dev-perf`.`tpch_0001t`.`ORDERS` AS `bft_1` -), `bfcte_1` AS ( - SELECT - `L_ORDERKEY` AS `bfcol_3`, - `L_COMMITDATE` AS `bfcol_4`, - `L_RECEIPTDATE` AS `bfcol_5` - FROM `bigframes-dev-perf`.`tpch_0001t`.`LINEITEM` AS `bft_0` -), `bfcte_2` AS ( - SELECT - `bfcol_3`, - `bfcol_4`, - `bfcol_5`, - `bfcol_0`, - `bfcol_1`, - `bfcol_2`, - `bfcol_3` AS `bfcol_11`, - `bfcol_4` AS `bfcol_12`, - `bfcol_5` AS `bfcol_13`, - `bfcol_2` AS `bfcol_14`, - ( - `bfcol_1` >= CAST('1993-07-01' AS DATE) - ) - AND ( - `bfcol_1` < CAST('1993-10-01' AS DATE) - ) AS `bfcol_15`, - `bfcol_3` AS `bfcol_25`, - `bfcol_2` AS `bfcol_26`, - `bfcol_4` < `bfcol_5` AS `bfcol_27` - FROM `bfcte_1` - INNER JOIN `bfcte_0` - ON `bfcol_3` = `bfcol_0` - WHERE - ( - `bfcol_1` >= CAST('1993-07-01' AS DATE) - ) - AND ( - `bfcol_1` < CAST('1993-10-01' AS DATE) - ) - AND `bfcol_4` < `bfcol_5` -), `bfcte_3` AS ( - SELECT - `bfcol_26`, - `bfcol_25`, - COUNT(1) AS `bfcol_33` - FROM `bfcte_2` - GROUP BY - `bfcol_26`, - `bfcol_25` -), `bfcte_4` AS ( - SELECT - `bfcol_26`, - COUNT(`bfcol_25`) AS `bfcol_36` - FROM `bfcte_3` - GROUP BY - `bfcol_26` -) -SELECT - `bfcol_26` AS `O_ORDERPRIORITY`, - `bfcol_36` AS `ORDER_COUNT` -FROM `bfcte_4` -ORDER BY - `bfcol_26` ASC NULLS LAST \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/tpch/snapshots/test_tpch/test_tpch_query/5/out.sql b/tests/unit/core/compile/sqlglot/tpch/snapshots/test_tpch/test_tpch_query/5/out.sql deleted file mode 100644 index 5b707ce59ac..00000000000 --- a/tests/unit/core/compile/sqlglot/tpch/snapshots/test_tpch/test_tpch_query/5/out.sql +++ /dev/null @@ -1,91 +0,0 @@ -WITH `bfcte_0` AS ( - SELECT - `S_SUPPKEY` AS `bfcol_0`, - `S_NATIONKEY` AS `bfcol_1` - FROM `bigframes-dev-perf`.`tpch_0001t`.`SUPPLIER` AS `bft_5` -), `bfcte_1` AS ( - SELECT - `C_CUSTKEY` AS `bfcol_2`, - `C_NATIONKEY` AS `bfcol_3` - FROM `bigframes-dev-perf`.`tpch_0001t`.`CUSTOMER` AS `bft_4` -), `bfcte_2` AS ( - SELECT - `N_NATIONKEY` AS `bfcol_4`, - `N_NAME` AS `bfcol_5`, - `N_REGIONKEY` AS `bfcol_6` - FROM `bigframes-dev-perf`.`tpch_0001t`.`NATION` AS `bft_3` -), `bfcte_3` AS ( - SELECT - `R_REGIONKEY` AS `bfcol_32` - FROM `bigframes-dev-perf`.`tpch_0001t`.`REGION` AS `bft_2` - WHERE - `R_NAME` = 'ASIA' -), `bfcte_4` AS ( - SELECT - `O_ORDERKEY` AS `bfcol_33`, - `O_CUSTKEY` AS `bfcol_34` - FROM `bigframes-dev-perf`.`tpch_0001t`.`ORDERS` AS `bft_1` - WHERE - ( - `O_ORDERDATE` >= CAST('1994-01-01' AS DATE) - ) - AND ( - `O_ORDERDATE` < CAST('1995-01-01' AS DATE) - ) -), `bfcte_5` AS ( - SELECT - `L_ORDERKEY` AS `bfcol_29`, - `L_SUPPKEY` AS `bfcol_30`, - `L_EXTENDEDPRICE` * ( - 1.0 - `L_DISCOUNT` - ) AS `bfcol_31` - FROM `bigframes-dev-perf`.`tpch_0001t`.`LINEITEM` AS `bft_0` -), `bfcte_6` AS ( - SELECT - `bfcol_4` AS `bfcol_35`, - `bfcol_5` AS `bfcol_36` - FROM `bfcte_3` - INNER JOIN `bfcte_2` - ON `bfcol_32` = `bfcol_6` -), `bfcte_7` AS ( - SELECT - `bfcol_35` AS `bfcol_37`, - `bfcol_36` AS `bfcol_38`, - `bfcol_2` AS `bfcol_39` - FROM `bfcte_6` - INNER JOIN `bfcte_1` - ON `bfcol_35` = `bfcol_3` -), `bfcte_8` AS ( - SELECT - `bfcol_33` AS `bfcol_40`, - `bfcol_37` AS `bfcol_41`, - `bfcol_38` AS `bfcol_42` - FROM `bfcte_4` - INNER JOIN `bfcte_7` - ON `bfcol_34` = `bfcol_39` -), `bfcte_9` AS ( - SELECT - `bfcol_30` AS `bfcol_43`, - `bfcol_31` AS `bfcol_44`, - `bfcol_41` AS `bfcol_45`, - `bfcol_42` AS `bfcol_46` - FROM `bfcte_5` - INNER JOIN `bfcte_8` - ON `bfcol_29` = `bfcol_40` -), `bfcte_10` AS ( - SELECT - `bfcol_46`, - COALESCE(SUM(`bfcol_44`), 0) AS `bfcol_49` - FROM `bfcte_9` - INNER JOIN `bfcte_0` - ON `bfcol_43` = `bfcol_0` AND `bfcol_45` = `bfcol_1` - GROUP BY - `bfcol_46` -) -SELECT - `bfcol_46` AS `N_NAME`, - `bfcol_49` AS `REVENUE` -FROM `bfcte_10` -ORDER BY - `bfcol_49` DESC, - `bfcol_46` ASC NULLS LAST \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/tpch/snapshots/test_tpch/test_tpch_query/6/out.sql b/tests/unit/core/compile/sqlglot/tpch/snapshots/test_tpch/test_tpch_query/6/out.sql deleted file mode 100644 index 3544fd18e48..00000000000 --- a/tests/unit/core/compile/sqlglot/tpch/snapshots/test_tpch/test_tpch_query/6/out.sql +++ /dev/null @@ -1,61 +0,0 @@ -WITH `bfcte_0` AS ( - SELECT - * - FROM UNNEST(ARRAY>[STRUCT(0)]) -), `bfcte_1` AS ( - SELECT - `L_QUANTITY`, - `L_EXTENDEDPRICE`, - `L_DISCOUNT`, - `L_SHIPDATE`, - `L_QUANTITY` AS `bfcol_5`, - `L_EXTENDEDPRICE` AS `bfcol_6`, - `L_DISCOUNT` AS `bfcol_7`, - ( - `L_SHIPDATE` >= CAST('1994-01-01' AS DATE) - ) - AND ( - `L_SHIPDATE` < CAST('1995-01-01' AS DATE) - ) AS `bfcol_8`, - `L_QUANTITY` AS `bfcol_16`, - `L_EXTENDEDPRICE` AS `bfcol_17`, - `L_DISCOUNT` AS `bfcol_18`, - ( - `L_DISCOUNT` >= 0.05 - ) AND ( - `L_DISCOUNT` <= 0.07 - ) AS `bfcol_19`, - `L_EXTENDEDPRICE` AS `bfcol_27`, - `L_DISCOUNT` AS `bfcol_28`, - `L_QUANTITY` < 24 AS `bfcol_29`, - `L_EXTENDEDPRICE` AS `bfcol_35`, - `L_DISCOUNT` AS `bfcol_36`, - `L_EXTENDEDPRICE` * `L_DISCOUNT` AS `bfcol_39` - FROM `bigframes-dev-perf`.`tpch_0001t`.`LINEITEM` AS `bft_0` - WHERE - ( - `L_SHIPDATE` >= CAST('1994-01-01' AS DATE) - ) - AND ( - `L_SHIPDATE` < CAST('1995-01-01' AS DATE) - ) - AND ( - `L_DISCOUNT` >= 0.05 - ) - AND ( - `L_DISCOUNT` <= 0.07 - ) - AND `L_QUANTITY` < 24 -), `bfcte_2` AS ( - SELECT - COALESCE(SUM(`bfcol_39`), 0) AS `bfcol_41` - FROM `bfcte_1` -), `bfcte_3` AS ( - SELECT - * - FROM `bfcte_2` -) -SELECT - CASE WHEN `bfcol_0` = 0 THEN `bfcol_41` END AS `REVENUE` -FROM `bfcte_3` -CROSS JOIN `bfcte_0` \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/tpch/snapshots/test_tpch/test_tpch_query/7/out.sql b/tests/unit/core/compile/sqlglot/tpch/snapshots/test_tpch/test_tpch_query/7/out.sql deleted file mode 100644 index f180ca1b3ea..00000000000 --- a/tests/unit/core/compile/sqlglot/tpch/snapshots/test_tpch/test_tpch_query/7/out.sql +++ /dev/null @@ -1,138 +0,0 @@ -WITH `bfcte_0` AS ( - SELECT - `N_NATIONKEY` AS `bfcol_22`, - `N_NAME` AS `bfcol_23`, - COALESCE(COALESCE(`N_NAME` IN ('FRANCE', 'GERMANY'), FALSE), FALSE) AS `bfcol_24` - FROM `bigframes-dev-perf`.`tpch_0001t`.`NATION` AS `bft_4` - WHERE - COALESCE(COALESCE(`N_NAME` IN ('FRANCE', 'GERMANY'), FALSE), FALSE) -), `bfcte_1` AS ( - SELECT - `S_SUPPKEY` AS `bfcol_2`, - `S_NATIONKEY` AS `bfcol_3` - FROM `bigframes-dev-perf`.`tpch_0001t`.`SUPPLIER` AS `bft_3` -), `bfcte_2` AS ( - SELECT - `L_ORDERKEY` AS `bfcol_31`, - `L_SUPPKEY` AS `bfcol_32`, - `L_EXTENDEDPRICE` AS `bfcol_33`, - `L_DISCOUNT` AS `bfcol_34`, - `L_SHIPDATE` AS `bfcol_35` - FROM `bigframes-dev-perf`.`tpch_0001t`.`LINEITEM` AS `bft_2` - WHERE - ( - `L_SHIPDATE` >= CAST('1995-01-01' AS DATE) - ) - AND ( - `L_SHIPDATE` <= CAST('1996-12-31' AS DATE) - ) -), `bfcte_3` AS ( - SELECT - `O_ORDERKEY` AS `bfcol_9`, - `O_CUSTKEY` AS `bfcol_10` - FROM `bigframes-dev-perf`.`tpch_0001t`.`ORDERS` AS `bft_1` -), `bfcte_4` AS ( - SELECT - `C_CUSTKEY` AS `bfcol_11`, - `C_NATIONKEY` AS `bfcol_12` - FROM `bigframes-dev-perf`.`tpch_0001t`.`CUSTOMER` AS `bft_0` -), `bfcte_5` AS ( - SELECT - `bfcol_22` AS `bfcol_36`, - `bfcol_23` AS `bfcol_37` - FROM `bfcte_0` -), `bfcte_6` AS ( - SELECT - `bfcol_22` AS `bfcol_38`, - `bfcol_23` AS `bfcol_39` - FROM `bfcte_0` -), `bfcte_7` AS ( - SELECT - `bfcol_11` AS `bfcol_40`, - `bfcol_39` AS `bfcol_41` - FROM `bfcte_4` - INNER JOIN `bfcte_6` - ON `bfcol_12` = `bfcol_38` -), `bfcte_8` AS ( - SELECT - `bfcol_41` AS `bfcol_42`, - `bfcol_9` AS `bfcol_43` - FROM `bfcte_7` - INNER JOIN `bfcte_3` - ON `bfcol_40` = `bfcol_10` -), `bfcte_9` AS ( - SELECT - `bfcol_42` AS `bfcol_44`, - `bfcol_32` AS `bfcol_45`, - `bfcol_33` AS `bfcol_46`, - `bfcol_34` AS `bfcol_47`, - `bfcol_35` AS `bfcol_48` - FROM `bfcte_8` - INNER JOIN `bfcte_2` - ON `bfcol_43` = `bfcol_31` -), `bfcte_10` AS ( - SELECT - `bfcol_44` AS `bfcol_49`, - `bfcol_46` AS `bfcol_50`, - `bfcol_47` AS `bfcol_51`, - `bfcol_48` AS `bfcol_52`, - `bfcol_3` AS `bfcol_53` - FROM `bfcte_9` - INNER JOIN `bfcte_1` - ON `bfcol_45` = `bfcol_2` -), `bfcte_11` AS ( - SELECT - `bfcol_49`, - `bfcol_50`, - `bfcol_51`, - `bfcol_52`, - `bfcol_53`, - `bfcol_36`, - `bfcol_37`, - `bfcol_49` AS `bfcol_59`, - `bfcol_50` AS `bfcol_60`, - `bfcol_51` AS `bfcol_61`, - `bfcol_52` AS `bfcol_62`, - `bfcol_37` AS `bfcol_63`, - `bfcol_49` <> `bfcol_37` AS `bfcol_64`, - `bfcol_49` AS `bfcol_76`, - `bfcol_52` AS `bfcol_77`, - `bfcol_37` AS `bfcol_78`, - `bfcol_50` * ( - 1.0 - `bfcol_51` - ) AS `bfcol_79`, - `bfcol_49` AS `bfcol_84`, - `bfcol_37` AS `bfcol_85`, - `bfcol_50` * ( - 1.0 - `bfcol_51` - ) AS `bfcol_86`, - EXTRACT(YEAR FROM `bfcol_52`) AS `bfcol_87` - FROM `bfcte_10` - INNER JOIN `bfcte_5` - ON `bfcol_53` = `bfcol_36` - WHERE - `bfcol_49` <> `bfcol_37` -), `bfcte_12` AS ( - SELECT - `bfcol_85`, - `bfcol_84`, - `bfcol_87`, - COALESCE(SUM(`bfcol_86`), 0) AS `bfcol_92` - FROM `bfcte_11` - WHERE - NOT `bfcol_87` IS NULL - GROUP BY - `bfcol_85`, - `bfcol_84`, - `bfcol_87` -) -SELECT - `bfcol_85` AS `SUPP_NATION`, - `bfcol_84` AS `CUST_NATION`, - `bfcol_87` AS `L_YEAR`, - `bfcol_92` AS `REVENUE` -FROM `bfcte_12` -ORDER BY - `bfcol_85` ASC NULLS LAST, - `bfcol_84` ASC NULLS LAST, - `bfcol_87` ASC NULLS LAST \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/tpch/snapshots/test_tpch/test_tpch_query/8/out.sql b/tests/unit/core/compile/sqlglot/tpch/snapshots/test_tpch/test_tpch_query/8/out.sql deleted file mode 100644 index edaf51f18b8..00000000000 --- a/tests/unit/core/compile/sqlglot/tpch/snapshots/test_tpch/test_tpch_query/8/out.sql +++ /dev/null @@ -1,186 +0,0 @@ -WITH `bfcte_0` AS ( - SELECT - `N_NATIONKEY` AS `bfcol_0`, - `N_NAME` AS `bfcol_1` - FROM `bigframes-dev-perf`.`tpch_0001t`.`NATION` AS `bft_6` -), `bfcte_1` AS ( - SELECT - `N_NATIONKEY` AS `bfcol_4`, - `N_REGIONKEY` AS `bfcol_5` - FROM `bigframes-dev-perf`.`tpch_0001t`.`NATION` AS `bft_6` -), `bfcte_2` AS ( - SELECT - `R_REGIONKEY` AS `bfcol_2`, - `R_NAME` AS `bfcol_3` - FROM `bigframes-dev-perf`.`tpch_0001t`.`REGION` AS `bft_5` -), `bfcte_3` AS ( - SELECT - `C_CUSTKEY` AS `bfcol_6`, - `C_NATIONKEY` AS `bfcol_7` - FROM `bigframes-dev-perf`.`tpch_0001t`.`CUSTOMER` AS `bft_4` -), `bfcte_4` AS ( - SELECT - `O_ORDERKEY` AS `bfcol_8`, - `O_CUSTKEY` AS `bfcol_9`, - `O_ORDERDATE` AS `bfcol_10` - FROM `bigframes-dev-perf`.`tpch_0001t`.`ORDERS` AS `bft_3` -), `bfcte_5` AS ( - SELECT - `S_SUPPKEY` AS `bfcol_11`, - `S_NATIONKEY` AS `bfcol_12` - FROM `bigframes-dev-perf`.`tpch_0001t`.`SUPPLIER` AS `bft_2` -), `bfcte_6` AS ( - SELECT - `L_ORDERKEY` AS `bfcol_13`, - `L_PARTKEY` AS `bfcol_14`, - `L_SUPPKEY` AS `bfcol_15`, - `L_EXTENDEDPRICE` AS `bfcol_16`, - `L_DISCOUNT` AS `bfcol_17` - FROM `bigframes-dev-perf`.`tpch_0001t`.`LINEITEM` AS `bft_1` -), `bfcte_7` AS ( - SELECT - `P_PARTKEY` AS `bfcol_18`, - `P_TYPE` AS `bfcol_19` - FROM `bigframes-dev-perf`.`tpch_0001t`.`PART` AS `bft_0` -), `bfcte_8` AS ( - SELECT - `bfcol_19` AS `bfcol_20`, - `bfcol_13` AS `bfcol_21`, - `bfcol_15` AS `bfcol_22`, - `bfcol_16` AS `bfcol_23`, - `bfcol_17` AS `bfcol_24` - FROM `bfcte_7` - INNER JOIN `bfcte_6` - ON `bfcol_18` = `bfcol_14` -), `bfcte_9` AS ( - SELECT - `bfcol_20` AS `bfcol_25`, - `bfcol_21` AS `bfcol_26`, - `bfcol_23` AS `bfcol_27`, - `bfcol_24` AS `bfcol_28`, - `bfcol_12` AS `bfcol_29` - FROM `bfcte_8` - INNER JOIN `bfcte_5` - ON `bfcol_22` = `bfcol_11` -), `bfcte_10` AS ( - SELECT - `bfcol_25` AS `bfcol_30`, - `bfcol_27` AS `bfcol_31`, - `bfcol_28` AS `bfcol_32`, - `bfcol_29` AS `bfcol_33`, - `bfcol_9` AS `bfcol_34`, - `bfcol_10` AS `bfcol_35` - FROM `bfcte_9` - INNER JOIN `bfcte_4` - ON `bfcol_26` = `bfcol_8` -), `bfcte_11` AS ( - SELECT - `bfcol_30` AS `bfcol_36`, - `bfcol_31` AS `bfcol_37`, - `bfcol_32` AS `bfcol_38`, - `bfcol_33` AS `bfcol_39`, - `bfcol_35` AS `bfcol_40`, - `bfcol_7` AS `bfcol_41` - FROM `bfcte_10` - INNER JOIN `bfcte_3` - ON `bfcol_34` = `bfcol_6` -), `bfcte_12` AS ( - SELECT - `bfcol_36` AS `bfcol_42`, - `bfcol_37` AS `bfcol_43`, - `bfcol_38` AS `bfcol_44`, - `bfcol_39` AS `bfcol_45`, - `bfcol_40` AS `bfcol_46`, - `bfcol_5` AS `bfcol_47` - FROM `bfcte_11` - INNER JOIN `bfcte_1` - ON `bfcol_41` = `bfcol_4` -), `bfcte_13` AS ( - SELECT - `bfcol_42` AS `bfcol_66`, - `bfcol_43` AS `bfcol_67`, - `bfcol_44` AS `bfcol_68`, - `bfcol_45` AS `bfcol_69`, - `bfcol_46` AS `bfcol_70` - FROM `bfcte_12` - INNER JOIN `bfcte_2` - ON `bfcol_47` = `bfcol_2` - WHERE - `bfcol_3` = 'AMERICA' -), `bfcte_14` AS ( - SELECT - `bfcol_66`, - `bfcol_67`, - `bfcol_68`, - `bfcol_69`, - `bfcol_70`, - `bfcol_0`, - `bfcol_1`, - `bfcol_66` AS `bfcol_76`, - `bfcol_67` AS `bfcol_77`, - `bfcol_68` AS `bfcol_78`, - `bfcol_70` AS `bfcol_79`, - `bfcol_1` AS `bfcol_80`, - ( - `bfcol_70` >= CAST('1995-01-01' AS DATE) - ) - AND ( - `bfcol_70` <= CAST('1996-12-31' AS DATE) - ) AS `bfcol_81`, - `bfcol_67` AS `bfcol_93`, - `bfcol_68` AS `bfcol_94`, - `bfcol_70` AS `bfcol_95`, - `bfcol_1` AS `bfcol_96`, - `bfcol_66` = 'ECONOMY ANODIZED STEEL' AS `bfcol_97`, - `bfcol_67` AS `bfcol_107`, - `bfcol_68` AS `bfcol_108`, - `bfcol_1` AS `bfcol_109`, - EXTRACT(YEAR FROM `bfcol_70`) AS `bfcol_110`, - `bfcol_1` AS `bfcol_115`, - EXTRACT(YEAR FROM `bfcol_70`) AS `bfcol_116`, - `bfcol_67` * ( - 1.0 - `bfcol_68` - ) AS `bfcol_117`, - EXTRACT(YEAR FROM `bfcol_70`) AS `bfcol_121`, - `bfcol_67` * ( - 1.0 - `bfcol_68` - ) AS `bfcol_122`, - IF(`bfcol_1` = 'BRAZIL', `bfcol_67` * ( - 1.0 - `bfcol_68` - ), 0) AS `bfcol_123`, - EXTRACT(YEAR FROM `bfcol_70`) AS `bfcol_127`, - IF(`bfcol_1` = 'BRAZIL', `bfcol_67` * ( - 1.0 - `bfcol_68` - ), 0) AS `bfcol_128`, - `bfcol_67` * ( - 1.0 - `bfcol_68` - ) AS `bfcol_129` - FROM `bfcte_13` - INNER JOIN `bfcte_0` - ON `bfcol_69` = `bfcol_0` - WHERE - ( - `bfcol_70` >= CAST('1995-01-01' AS DATE) - ) - AND ( - `bfcol_70` <= CAST('1996-12-31' AS DATE) - ) - AND `bfcol_66` = 'ECONOMY ANODIZED STEEL' -), `bfcte_15` AS ( - SELECT - `bfcol_127`, - COALESCE(SUM(`bfcol_128`), 0) AS `bfcol_133`, - COALESCE(SUM(`bfcol_129`), 0) AS `bfcol_134` - FROM `bfcte_14` - WHERE - NOT `bfcol_127` IS NULL - GROUP BY - `bfcol_127` -) -SELECT - `bfcol_127` AS `O_YEAR`, - ROUND(IEEE_DIVIDE(`bfcol_133`, `bfcol_134`), 2) AS `MKT_SHARE` -FROM `bfcte_15` -ORDER BY - `bfcol_127` ASC NULLS LAST, - `bfcol_127` ASC NULLS LAST \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/tpch/snapshots/test_tpch/test_tpch_query/9/out.sql b/tests/unit/core/compile/sqlglot/tpch/snapshots/test_tpch/test_tpch_query/9/out.sql deleted file mode 100644 index 949d45e8dae..00000000000 --- a/tests/unit/core/compile/sqlglot/tpch/snapshots/test_tpch/test_tpch_query/9/out.sql +++ /dev/null @@ -1,143 +0,0 @@ -WITH `bfcte_0` AS ( - SELECT - `N_NATIONKEY` AS `bfcol_0`, - `N_NAME` AS `bfcol_1` - FROM `bigframes-dev-perf`.`tpch_0001t`.`NATION` AS `bft_5` -), `bfcte_1` AS ( - SELECT - `O_ORDERKEY` AS `bfcol_2`, - `O_ORDERDATE` AS `bfcol_3` - FROM `bigframes-dev-perf`.`tpch_0001t`.`ORDERS` AS `bft_4` -), `bfcte_2` AS ( - SELECT - `S_SUPPKEY` AS `bfcol_4`, - `S_NATIONKEY` AS `bfcol_5` - FROM `bigframes-dev-perf`.`tpch_0001t`.`SUPPLIER` AS `bft_3` -), `bfcte_3` AS ( - SELECT - `PS_PARTKEY` AS `bfcol_6`, - `PS_SUPPKEY` AS `bfcol_7`, - `PS_SUPPLYCOST` AS `bfcol_8` - FROM `bigframes-dev-perf`.`tpch_0001t`.`PARTSUPP` AS `bft_2` -), `bfcte_4` AS ( - SELECT - `L_ORDERKEY` AS `bfcol_9`, - `L_PARTKEY` AS `bfcol_10`, - `L_SUPPKEY` AS `bfcol_11`, - `L_QUANTITY` AS `bfcol_12`, - `L_EXTENDEDPRICE` AS `bfcol_13`, - `L_DISCOUNT` AS `bfcol_14` - FROM `bigframes-dev-perf`.`tpch_0001t`.`LINEITEM` AS `bft_1` -), `bfcte_5` AS ( - SELECT - `P_PARTKEY` AS `bfcol_15`, - `P_NAME` AS `bfcol_16` - FROM `bigframes-dev-perf`.`tpch_0001t`.`PART` AS `bft_0` -), `bfcte_6` AS ( - SELECT - `bfcol_16` AS `bfcol_17`, - `bfcol_9` AS `bfcol_18`, - `bfcol_10` AS `bfcol_19`, - `bfcol_11` AS `bfcol_20`, - `bfcol_12` AS `bfcol_21`, - `bfcol_13` AS `bfcol_22`, - `bfcol_14` AS `bfcol_23` - FROM `bfcte_5` - INNER JOIN `bfcte_4` - ON `bfcol_15` = `bfcol_10` -), `bfcte_7` AS ( - SELECT - `bfcol_17` AS `bfcol_24`, - `bfcol_18` AS `bfcol_25`, - `bfcol_20` AS `bfcol_26`, - `bfcol_21` AS `bfcol_27`, - `bfcol_22` AS `bfcol_28`, - `bfcol_23` AS `bfcol_29`, - `bfcol_8` AS `bfcol_30` - FROM `bfcte_6` - INNER JOIN `bfcte_3` - ON `bfcol_20` = `bfcol_7` AND `bfcol_19` = `bfcol_6` -), `bfcte_8` AS ( - SELECT - `bfcol_24` AS `bfcol_31`, - `bfcol_25` AS `bfcol_32`, - `bfcol_27` AS `bfcol_33`, - `bfcol_28` AS `bfcol_34`, - `bfcol_29` AS `bfcol_35`, - `bfcol_30` AS `bfcol_36`, - `bfcol_5` AS `bfcol_37` - FROM `bfcte_7` - INNER JOIN `bfcte_2` - ON `bfcol_26` = `bfcol_4` -), `bfcte_9` AS ( - SELECT - `bfcol_31` AS `bfcol_38`, - `bfcol_33` AS `bfcol_39`, - `bfcol_34` AS `bfcol_40`, - `bfcol_35` AS `bfcol_41`, - `bfcol_36` AS `bfcol_42`, - `bfcol_37` AS `bfcol_43`, - `bfcol_3` AS `bfcol_44` - FROM `bfcte_8` - INNER JOIN `bfcte_1` - ON `bfcol_32` = `bfcol_2` -), `bfcte_10` AS ( - SELECT - `bfcol_38`, - `bfcol_39`, - `bfcol_40`, - `bfcol_41`, - `bfcol_42`, - `bfcol_43`, - `bfcol_44`, - `bfcol_0`, - `bfcol_1`, - `bfcol_39` AS `bfcol_52`, - `bfcol_40` AS `bfcol_53`, - `bfcol_41` AS `bfcol_54`, - `bfcol_42` AS `bfcol_55`, - `bfcol_44` AS `bfcol_56`, - `bfcol_1` AS `bfcol_57`, - REGEXP_CONTAINS(`bfcol_38`, 'green') AS `bfcol_58`, - `bfcol_39` AS `bfcol_72`, - `bfcol_40` AS `bfcol_73`, - `bfcol_41` AS `bfcol_74`, - `bfcol_42` AS `bfcol_75`, - `bfcol_1` AS `bfcol_76`, - EXTRACT(YEAR FROM `bfcol_44`) AS `bfcol_77`, - `bfcol_1` AS `bfcol_84`, - EXTRACT(YEAR FROM `bfcol_44`) AS `bfcol_85`, - ( - `bfcol_40` * ( - 1 - `bfcol_41` - ) - ) - ( - `bfcol_42` * `bfcol_39` - ) AS `bfcol_86` - FROM `bfcte_9` - INNER JOIN `bfcte_0` - ON `bfcol_43` = `bfcol_0` - WHERE - REGEXP_CONTAINS(`bfcol_38`, 'green') -), `bfcte_11` AS ( - SELECT - `bfcol_84`, - `bfcol_85`, - COALESCE(SUM(`bfcol_86`), 0) AS `bfcol_90` - FROM `bfcte_10` - WHERE - NOT `bfcol_85` IS NULL - GROUP BY - `bfcol_84`, - `bfcol_85` -) -SELECT - `bfcol_84` AS `NATION`, - `bfcol_85` AS `O_YEAR`, - ROUND(`bfcol_90`, 2) AS `SUM_PROFIT` -FROM `bfcte_11` -ORDER BY - `bfcol_84` ASC NULLS LAST, - `bfcol_85` DESC, - `bfcol_84` ASC NULLS LAST, - `bfcol_85` ASC NULLS LAST \ No newline at end of file diff --git a/tests/unit/core/compile/sqlglot/tpch/test_tpch.py b/tests/unit/core/compile/sqlglot/tpch/test_tpch.py deleted file mode 100644 index 8988a5512f2..00000000000 --- a/tests/unit/core/compile/sqlglot/tpch/test_tpch.py +++ /dev/null @@ -1,47 +0,0 @@ -# Copyright 2026 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import re - -import pytest - -freezegun = pytest.importorskip("freezegun") -pytest.importorskip("pytest_snapshot") - - -@pytest.mark.parametrize("query_num", range(1, 23)) -def test_tpch_query(tpch_session, query_num, snapshot): - project_id = "bigframes-dev-perf" - dataset_id = "tpch_0001t" - - query_file_path = f"third_party/bigframes_vendored/tpch/queries/q{query_num}.py" - - with open(query_file_path, "r") as f: - query_code = f.read() - - # We want to capture the result dataframe instead of running next(result.to_pandas_batches(...)) - modified_code = re.sub( - r"next\((\w+)\.to_pandas_batches\((.*?)\)\)", - r"return \1", - query_code, - ) - - exec_globals = {} # type: ignore[var-annotated] - exec(modified_code, exec_globals) - q_func = exec_globals["q"] - - result = q_func(project_id, dataset_id, tpch_session) - - # result should be a DataFrame - snapshot.assert_match(result.sql, "out.sql") diff --git a/tests/unit/core/logging/__init__.py b/tests/unit/core/logging/__init__.py deleted file mode 100644 index 58d482ea386..00000000000 --- a/tests/unit/core/logging/__init__.py +++ /dev/null @@ -1,13 +0,0 @@ -# Copyright 2026 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. diff --git a/tests/unit/core/logging/test_data_types.py b/tests/unit/core/logging/test_data_types.py deleted file mode 100644 index 09b3429f00d..00000000000 --- a/tests/unit/core/logging/test_data_types.py +++ /dev/null @@ -1,54 +0,0 @@ -# Copyright 2026 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import pandas as pd -import pyarrow as pa -import pytest - -from bigframes import dtypes -from bigframes.core.logging import data_types - -UNKNOWN_TYPE = pd.ArrowDtype(pa.time64("ns")) - -PA_STRUCT_TYPE = pa.struct([("city", pa.string()), ("pop", pa.int64())]) - -PA_LIST_TYPE = pa.list_(pa.int64()) - - -@pytest.mark.parametrize( - ("dtype", "expected_mask"), - [ - (None, 0), - (UNKNOWN_TYPE, 1 << 0), - (dtypes.INT_DTYPE, 1 << 1), - (dtypes.FLOAT_DTYPE, 1 << 2), - (dtypes.BOOL_DTYPE, 1 << 3), - (dtypes.STRING_DTYPE, 1 << 4), - (dtypes.BYTES_DTYPE, 1 << 5), - (dtypes.DATE_DTYPE, 1 << 6), - (dtypes.TIME_DTYPE, 1 << 7), - (dtypes.DATETIME_DTYPE, 1 << 8), - (dtypes.TIMESTAMP_DTYPE, 1 << 9), - (dtypes.TIMEDELTA_DTYPE, 1 << 10), - (dtypes.NUMERIC_DTYPE, 1 << 11), - (dtypes.BIGNUMERIC_DTYPE, 1 << 12), - (dtypes.GEO_DTYPE, 1 << 13), - (dtypes.JSON_DTYPE, 1 << 14), - (pd.ArrowDtype(PA_STRUCT_TYPE), 1 << 15), - (pd.ArrowDtype(PA_LIST_TYPE), 1 << 16), - (dtypes.OBJ_REF_DTYPE, (1 << 15) | (1 << 17)), - ], -) -def test_get_dtype_mask(dtype, expected_mask): - assert data_types._get_dtype_mask(dtype) == expected_mask diff --git a/tests/unit/core/logging/test_log_adapter.py b/tests/unit/core/logging/test_log_adapter.py deleted file mode 100644 index 0722ef62a29..00000000000 --- a/tests/unit/core/logging/test_log_adapter.py +++ /dev/null @@ -1,265 +0,0 @@ -# Copyright 2023 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from unittest import mock - -import pytest -from google.cloud import bigquery - -from bigframes.core.logging import log_adapter - -# The limit is 64 (https://cloud.google.com/bigquery/docs/labels-intro#requirements), -# but leave a few spare for internal labels to be added. -# See internal issue 386825477. -MAX_LABELS_COUNT = 56 - - -@pytest.fixture -def mock_bqclient(): - mock_bqclient = mock.create_autospec(spec=bigquery.Client) - return mock_bqclient - - -@pytest.fixture -def test_instance(): - # Create a simple class for testing - @log_adapter.class_logger - class TestClass: - def method1(self): - pass - - def method2(self): - self.method3() - - def method3(self): - pass - - @log_adapter.log_name_override("override_name") - def method4(self): - pass - - @property - def my_field(self): - return 0 - - return TestClass() - - -@pytest.fixture -def test_method(): - @log_adapter.method_logger - def method1(): - pass - - return method1 - - -@pytest.fixture -def test_method_w_custom_base(): - def method1(): - pass - - _decorated_method = log_adapter.method_logger(method1, custom_base_name="pandas") - - return _decorated_method - - -def test_class_attribute_logging(test_instance): - test_instance.method1() - test_instance.method2() - test_instance.method4() - - # Check if the methods were added to the _api_methods list - api_methods = log_adapter.get_and_reset_api_methods() - assert "testclass-method1" in api_methods - assert "testclass-method2" in api_methods - assert "testclass-method3" not in api_methods - assert "testclass-method4" not in api_methods - assert "testclass-override_name" in api_methods - - -def test_method_logging(test_method): - test_method() - api_methods = log_adapter.get_and_reset_api_methods() - assert "locals-method1" in api_methods - - -def test_method_logging_with_custom_base_name(test_method_w_custom_base): - test_method_w_custom_base() - api_methods = log_adapter.get_and_reset_api_methods() - assert "pandas-method1" in api_methods - - -def test_method_logging_with_custom_base__logger_as_decorator(): - @log_adapter.method_logger(custom_base_name="pandas") - def my_method(): - pass - - my_method() - - api_methods = log_adapter.get_and_reset_api_methods() - assert "pandas-my_method" in api_methods - - -def test_property_logging(test_instance): - test_instance.my_field - - # Check if the properties were added to the _api_methods list - api_methods = log_adapter.get_and_reset_api_methods() - assert "testclass-my_field" in api_methods - - -def test_add_api_method_limit(test_instance): - # Ensure that add_api_method correctly adds a method to _api_methods - for i in range(70): - test_instance.method2() - assert len(log_adapter._api_methods) == MAX_LABELS_COUNT - - -def test_get_and_reset_api_methods(test_instance): - # Ensure that get_and_reset_api_methods returns a copy and resets the list - test_instance.method1() - test_instance.method2() - previous_methods = log_adapter.get_and_reset_api_methods() - assert previous_methods is not None - assert log_adapter._api_methods == [] - - -@pytest.mark.parametrize( - ("class_name", "method_name", "args", "kwargs", "task", "expected_labels"), - ( - ( - "DataFrame", - "resample", - ["a", "b", "c"], - {"aa": "bb", "rule": "1s"}, - log_adapter.PANDAS_API_TRACKING_TASK, - { - "task": log_adapter.PANDAS_API_TRACKING_TASK, - "class_name": "dataframe", - "method_name": "resample", - "args_count": 3, - "kwargs_0": "rule", - }, - ), - ( - "Series", - "resample", - [], - {"aa": "bb", "rule": "1s"}, - log_adapter.PANDAS_PARAM_TRACKING_TASK, - { - "task": log_adapter.PANDAS_PARAM_TRACKING_TASK, - "class_name": "series", - "method_name": "resample", - "args_count": 0, - "kwargs_0": "rule", - }, - ), - ( - "DataFrame", - "resample", - [], - {"aa": "bb"}, - log_adapter.PANDAS_API_TRACKING_TASK, - { - "task": log_adapter.PANDAS_API_TRACKING_TASK, - "class_name": "dataframe", - "method_name": "resample", - "args_count": 0, - }, - ), - ( - "DataFrame", - "resample", - [], - {}, - log_adapter.PANDAS_API_TRACKING_TASK, - { - "task": log_adapter.PANDAS_API_TRACKING_TASK, - "class_name": "dataframe", - "method_name": "resample", - "args_count": 0, - }, - ), - ( - "pandas", - "concat", - [[None, None]], - {"axis": 1}, - log_adapter.PANDAS_API_TRACKING_TASK, - { - "task": log_adapter.PANDAS_API_TRACKING_TASK, - "class_name": "pandas", - "method_name": "concat", - "args_count": 1, - "kwargs_0": "axis", - }, - ), - ), -) -def test_submit_pandas_labels( - mock_bqclient, class_name, method_name, args, kwargs, task, expected_labels -): - log_adapter.submit_pandas_labels( - mock_bqclient, class_name, method_name, args, kwargs, task - ) - - mock_bqclient.query.assert_called_once() - - query_call_args = mock_bqclient.query.call_args_list[0] - labels = query_call_args[1]["job_config"].labels - assert labels == expected_labels - - -def test_submit_pandas_labels_without_valid_params_for_param_logging(mock_bqclient): - log_adapter.submit_pandas_labels( - mock_bqclient, - "Series", - "resample", - task=log_adapter.PANDAS_PARAM_TRACKING_TASK, - ) - - # For param tracking task without kwargs, we won't submit labels - mock_bqclient.query.assert_not_called() - - -@pytest.mark.parametrize( - ("class_name", "method_name"), - ( - ("Series", "_repr_latex_"), - ( - "DataFrame", - # __call__ should be excluded. - # It's implemented on the pd.DataFrame class but not pd.DataFrame instances. - "__call__", - ), - ( - "Series", - # __call__ should be excluded. - # It's implemented on the pd.Series class but not pd.Series instances. - "__call__", - ), - ), -) -def test_submit_pandas_labels_with_internal_method( - mock_bqclient, class_name, method_name -): - log_adapter.submit_pandas_labels( - mock_bqclient, - class_name, - method_name, - task=log_adapter.PANDAS_API_TRACKING_TASK, - ) - mock_bqclient.query.assert_not_called() diff --git a/tests/unit/core/rewrite/conftest.py b/tests/unit/core/rewrite/conftest.py deleted file mode 100644 index ab168427f29..00000000000 --- a/tests/unit/core/rewrite/conftest.py +++ /dev/null @@ -1,84 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -import unittest.mock as mock - -import google.cloud.bigquery -import pytest - -import bigframes -import bigframes.core as core -from bigframes.core import bq_data - -TABLE_REF = google.cloud.bigquery.TableReference.from_string("project.dataset.table") -SCHEMA = ( - google.cloud.bigquery.SchemaField("col_a", "INTEGER"), - google.cloud.bigquery.SchemaField("col_b", "INTEGER"), -) -TABLE = google.cloud.bigquery.Table( - table_ref=TABLE_REF, - schema=SCHEMA, -) -FAKE_SESSION = mock.create_autospec(bigframes.Session, instance=True) -type(FAKE_SESSION)._strictly_ordered = mock.PropertyMock(return_value=True) - - -@pytest.fixture -def table(): - table_ref = google.cloud.bigquery.TableReference.from_string( - "project.dataset.table" - ) - schema = ( - google.cloud.bigquery.SchemaField("col_a", "INTEGER"), - google.cloud.bigquery.SchemaField("col_b", "INTEGER"), - ) - return google.cloud.bigquery.Table( - table_ref=table_ref, - schema=schema, - ) - - -@pytest.fixture -def table_too(): - table_ref = google.cloud.bigquery.TableReference.from_string( - "project.dataset.table_too" - ) - schema = ( - google.cloud.bigquery.SchemaField("col_a", "INTEGER"), - google.cloud.bigquery.SchemaField("col_c", "INTEGER"), - ) - return google.cloud.bigquery.Table( - table_ref=table_ref, - schema=schema, - ) - - -@pytest.fixture -def fake_session(): - return FAKE_SESSION - - -@pytest.fixture -def leaf(fake_session, table): - return core.ArrayValue.from_table( - session=fake_session, - table=bq_data.GbqNativeTable.from_table(table), - ).node - - -@pytest.fixture -def leaf_too(fake_session, table_too): - return core.ArrayValue.from_table( - session=fake_session, - table=bq_data.GbqNativeTable.from_table(table_too), - ).node diff --git a/tests/unit/core/rewrite/test_identifiers.py b/tests/unit/core/rewrite/test_identifiers.py deleted file mode 100644 index 4d4609bb0fa..00000000000 --- a/tests/unit/core/rewrite/test_identifiers.py +++ /dev/null @@ -1,203 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -import typing - -import bigframes.core as core -import bigframes.core.agg_expressions as agg_ex -import bigframes.core.expression as ex -import bigframes.core.identifiers as identifiers -import bigframes.core.nodes as nodes -import bigframes.core.rewrite.identifiers as id_rewrite -import bigframes.operations.aggregations as agg_ops -from bigframes.core import bq_data - - -def test_remap_variables_single_node(leaf): - node = leaf - id_generator = (identifiers.ColumnId(f"id_{i}") for i in range(100)) - new_node, mapping = id_rewrite.remap_variables(node, id_generator) - assert new_node is not node - assert len(mapping) == 2 - assert set(mapping.keys()) == {f.id for f in node.fields} - assert set(mapping.values()) == { - identifiers.ColumnId("id_0"), - identifiers.ColumnId("id_1"), - } - - -def test_remap_variables_projection(leaf): - node = nodes.ProjectionNode( - leaf, - ( - ( - core.expression.DerefOp(leaf.fields[0].id), - identifiers.ColumnId("new_col"), - ), - ), - ) - id_generator = (identifiers.ColumnId(f"id_{i}") for i in range(100)) - new_node, mapping = id_rewrite.remap_variables(node, id_generator) - assert new_node is not node - assert len(mapping) == 3 - assert set(mapping.keys()) == {f.id for f in node.fields} - assert set(mapping.values()) == {identifiers.ColumnId(f"id_{i}") for i in range(3)} - - -def test_remap_variables_aggregate(leaf): - # Aggregation: sum(col_a) AS sum_a - # Group by nothing - agg_op = agg_ex.UnaryAggregation( - op=agg_ops.sum_op, - arg=ex.DerefOp(leaf.fields[0].id), - ) - node = nodes.AggregateNode( - child=leaf, - aggregations=((agg_op, identifiers.ColumnId("sum_a")),), - by_column_ids=(), - ) - - id_generator = (identifiers.ColumnId(f"id_{i}") for i in range(100)) - _, mapping = id_rewrite.remap_variables(node, id_generator) - - # leaf has 2 columns: col_a, col_b - # AggregateNode defines 1 column: sum_a - # Output of AggregateNode should only be sum_a - assert len(mapping) == 1 - assert identifiers.ColumnId("sum_a") in mapping - - -def test_remap_variables_aggregate_with_grouping(leaf): - # Aggregation: sum(col_b) AS sum_b - # Group by col_a - agg_op = agg_ex.UnaryAggregation( - op=agg_ops.sum_op, - arg=ex.DerefOp(leaf.fields[1].id), - ) - node = nodes.AggregateNode( - child=leaf, - aggregations=((agg_op, identifiers.ColumnId("sum_b")),), - by_column_ids=(ex.DerefOp(leaf.fields[0].id),), - ) - - id_generator = (identifiers.ColumnId(f"id_{i}") for i in range(100)) - _, mapping = id_rewrite.remap_variables(node, id_generator) - - # Output should have 2 columns: col_a (grouping) and sum_b (agg) - assert len(mapping) == 2 - assert leaf.fields[0].id in mapping - assert identifiers.ColumnId("sum_b") in mapping - - -def test_remap_variables_nested_join_stability(leaf, fake_session, table): - # Create two more distinct leaf nodes - leaf2_uncached = core.ArrayValue.from_table( - session=fake_session, - table=bq_data.GbqNativeTable.from_table(table), - ).node - leaf2 = leaf2_uncached.remap_vars( - { - field.id: identifiers.ColumnId(f"leaf2_{field.id.name}") - for field in leaf2_uncached.fields - } - ) - leaf3_uncached = core.ArrayValue.from_table( - session=fake_session, - table=bq_data.GbqNativeTable.from_table(table), - ).node - leaf3 = leaf3_uncached.remap_vars( - { - field.id: identifiers.ColumnId(f"leaf3_{field.id.name}") - for field in leaf3_uncached.fields - } - ) - - # Create a nested join: (leaf JOIN leaf2) JOIN leaf3 - inner_join = nodes.JoinNode( - left_child=leaf, - right_child=leaf2, - conditions=( - ( - core.expression.DerefOp(leaf.fields[0].id), - core.expression.DerefOp(leaf2.fields[0].id), - ), - ), - type="inner", - propogate_order=False, - nulls_equal=True, - ) - outer_join = nodes.JoinNode( - left_child=inner_join, - right_child=leaf3, - conditions=( - ( - core.expression.DerefOp(inner_join.fields[0].id), - core.expression.DerefOp(leaf3.fields[0].id), - ), - ), - type="inner", - propogate_order=False, - nulls_equal=True, - ) - - # Run remap_variables twice and assert stability - id_generator1 = (identifiers.ColumnId(f"id_{i}") for i in range(100)) - new_node1, mapping1 = id_rewrite.remap_variables(outer_join, id_generator1) - - id_generator2 = (identifiers.ColumnId(f"id_{i}") for i in range(100)) - new_node2, mapping2 = id_rewrite.remap_variables(outer_join, id_generator2) - - assert new_node1 == new_node2 - assert mapping1 == mapping2 - - -def test_remap_variables_concat_self_stability(leaf): - # Create a concat node with the same child twice - node = nodes.ConcatNode( - children=(leaf, leaf), - output_ids=( - identifiers.ColumnId("concat_a"), - identifiers.ColumnId("concat_b"), - ), - ) - - # Run remap_variables twice and assert stability - id_generator1 = (identifiers.ColumnId(f"id_{i}") for i in range(100)) - new_node1, mapping1 = id_rewrite.remap_variables(node, id_generator1) - - id_generator2 = (identifiers.ColumnId(f"id_{i}") for i in range(100)) - new_node2, mapping2 = id_rewrite.remap_variables(node, id_generator2) - - assert new_node1 == new_node2 - assert mapping1 == mapping2 - - -def test_remap_variables_in_node_converts_dag_to_tree(leaf, leaf_too): - # Create an InNode with the same child twice, should create a tree from a DAG - right = nodes.SelectionNode( - leaf_too, (nodes.AliasedRef.identity(identifiers.ColumnId("col_a")),) - ) - node = nodes.InNode( - left_child=leaf, - right_child=right, - left_col=ex.DerefOp(identifiers.ColumnId("col_a")), - indicator_col=identifiers.ColumnId("indicator"), - ) - - id_generator = (identifiers.ColumnId(f"id_{i}") for i in range(100)) - new_node, _ = id_rewrite.remap_variables(node, id_generator) - new_node = typing.cast(nodes.InNode, new_node) - - left_col_id = new_node.left_col.id.name - new_node.validate_tree() - assert left_col_id.startswith("id_") diff --git a/tests/unit/core/rewrite/test_slices.py b/tests/unit/core/rewrite/test_slices.py deleted file mode 100644 index 6d49ffb80a7..00000000000 --- a/tests/unit/core/rewrite/test_slices.py +++ /dev/null @@ -1,34 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -import bigframes.core.nodes as nodes -import bigframes.core.rewrite.slices - - -def test_rewrite_noop_slice(leaf): - slice = nodes.SliceNode(leaf, None, None) - result = bigframes.core.rewrite.slices.rewrite_slice(slice) - assert result == leaf - - -def test_rewrite_reverse_slice(leaf): - slice = nodes.SliceNode(leaf, None, None, -1) - result = bigframes.core.rewrite.slices.rewrite_slice(slice) - assert result == nodes.ReversedNode(leaf) - - -def test_rewrite_filter_slice(leaf): - slice = nodes.SliceNode(leaf, None, 2) - result = bigframes.core.rewrite.slices.rewrite_slice(slice) - assert list(result.fields) == list(leaf.fields) - assert isinstance(result.child, nodes.FilterNode) diff --git a/tests/unit/core/sql/snapshots/test_ml/test_create_model_basic/create_model_basic.sql b/tests/unit/core/sql/snapshots/test_ml/test_create_model_basic/create_model_basic.sql deleted file mode 100644 index 9affd870e3e..00000000000 --- a/tests/unit/core/sql/snapshots/test_ml/test_create_model_basic/create_model_basic.sql +++ /dev/null @@ -1,3 +0,0 @@ -CREATE MODEL `my_project.my_dataset.my_model` -OPTIONS(model_type = 'LINEAR_REG', input_label_cols = ['label']) -AS SELECT * FROM my_table diff --git a/tests/unit/core/sql/snapshots/test_ml/test_create_model_hparam_tuning/create_model_hparam_tuning.sql b/tests/unit/core/sql/snapshots/test_ml/test_create_model_hparam_tuning/create_model_hparam_tuning.sql deleted file mode 100644 index c7ed32e54fc..00000000000 --- a/tests/unit/core/sql/snapshots/test_ml/test_create_model_hparam_tuning/create_model_hparam_tuning.sql +++ /dev/null @@ -1,3 +0,0 @@ -CREATE MODEL `my_model` -OPTIONS(model_type = 'LINEAR_REG', learn_rate = HPARAM_RANGE(0.0001, 1.0), optimizer = HPARAM_CANDIDATES(['ADAGRAD', 'SGD'])) -AS SELECT * FROM t diff --git a/tests/unit/core/sql/snapshots/test_ml/test_create_model_if_not_exists/create_model_if_not_exists.sql b/tests/unit/core/sql/snapshots/test_ml/test_create_model_if_not_exists/create_model_if_not_exists.sql deleted file mode 100644 index b67ea139673..00000000000 --- a/tests/unit/core/sql/snapshots/test_ml/test_create_model_if_not_exists/create_model_if_not_exists.sql +++ /dev/null @@ -1,3 +0,0 @@ -CREATE MODEL IF NOT EXISTS `my_model` -OPTIONS(model_type = 'KMEANS') -AS SELECT * FROM t diff --git a/tests/unit/core/sql/snapshots/test_ml/test_create_model_list_option/create_model_list_option.sql b/tests/unit/core/sql/snapshots/test_ml/test_create_model_list_option/create_model_list_option.sql deleted file mode 100644 index 723a4b037d8..00000000000 --- a/tests/unit/core/sql/snapshots/test_ml/test_create_model_list_option/create_model_list_option.sql +++ /dev/null @@ -1,3 +0,0 @@ -CREATE MODEL `my_model` -OPTIONS(hidden_units = [32, 16], dropout = 0.2) -AS SELECT * FROM t diff --git a/tests/unit/core/sql/snapshots/test_ml/test_create_model_remote/create_model_remote.sql b/tests/unit/core/sql/snapshots/test_ml/test_create_model_remote/create_model_remote.sql deleted file mode 100644 index 878afe0823b..00000000000 --- a/tests/unit/core/sql/snapshots/test_ml/test_create_model_remote/create_model_remote.sql +++ /dev/null @@ -1,5 +0,0 @@ -CREATE MODEL `my_remote_model` -INPUT (prompt STRING) -OUTPUT (content STRING) -REMOTE WITH CONNECTION `my_project.us.my_connection` -OPTIONS(endpoint = 'gemini-pro') diff --git a/tests/unit/core/sql/snapshots/test_ml/test_create_model_remote_default/create_model_remote_default.sql b/tests/unit/core/sql/snapshots/test_ml/test_create_model_remote_default/create_model_remote_default.sql deleted file mode 100644 index 9bbea44259b..00000000000 --- a/tests/unit/core/sql/snapshots/test_ml/test_create_model_remote_default/create_model_remote_default.sql +++ /dev/null @@ -1,3 +0,0 @@ -CREATE MODEL `my_remote_model` -REMOTE WITH CONNECTION DEFAULT -OPTIONS(endpoint = 'gemini-pro') diff --git a/tests/unit/core/sql/snapshots/test_ml/test_create_model_replace/create_model_replace.sql b/tests/unit/core/sql/snapshots/test_ml/test_create_model_replace/create_model_replace.sql deleted file mode 100644 index 7fe9d492da3..00000000000 --- a/tests/unit/core/sql/snapshots/test_ml/test_create_model_replace/create_model_replace.sql +++ /dev/null @@ -1,3 +0,0 @@ -CREATE OR REPLACE MODEL `my_model` -OPTIONS(model_type = 'LOGISTIC_REG') -AS SELECT * FROM t diff --git a/tests/unit/core/sql/snapshots/test_ml/test_create_model_training_data_and_holiday/create_model_training_data_and_holiday.sql b/tests/unit/core/sql/snapshots/test_ml/test_create_model_training_data_and_holiday/create_model_training_data_and_holiday.sql deleted file mode 100644 index da7b6ba6724..00000000000 --- a/tests/unit/core/sql/snapshots/test_ml/test_create_model_training_data_and_holiday/create_model_training_data_and_holiday.sql +++ /dev/null @@ -1,5 +0,0 @@ -CREATE MODEL `my_arima_model` -OPTIONS(model_type = 'ARIMA_PLUS') -AS ( - training_data AS (SELECT * FROM sales), custom_holiday AS (SELECT * FROM holidays) -) \ No newline at end of file diff --git a/tests/unit/core/sql/snapshots/test_ml/test_create_model_transform/create_model_transform.sql b/tests/unit/core/sql/snapshots/test_ml/test_create_model_transform/create_model_transform.sql deleted file mode 100644 index e460400be23..00000000000 --- a/tests/unit/core/sql/snapshots/test_ml/test_create_model_transform/create_model_transform.sql +++ /dev/null @@ -1,4 +0,0 @@ -CREATE MODEL `my_model` -TRANSFORM (ML.STANDARD_SCALER(c1) OVER() AS c1_scaled, c2) -OPTIONS(model_type = 'LINEAR_REG') -AS SELECT c1, c2, label FROM t diff --git a/tests/unit/core/sql/snapshots/test_ml/test_evaluate_model_basic/evaluate_model_basic.sql b/tests/unit/core/sql/snapshots/test_ml/test_evaluate_model_basic/evaluate_model_basic.sql deleted file mode 100644 index 5889e342e4d..00000000000 --- a/tests/unit/core/sql/snapshots/test_ml/test_evaluate_model_basic/evaluate_model_basic.sql +++ /dev/null @@ -1 +0,0 @@ -SELECT * FROM ML.EVALUATE(MODEL `my_project.my_dataset.my_model`) diff --git a/tests/unit/core/sql/snapshots/test_ml/test_evaluate_model_with_options/evaluate_model_with_options.sql b/tests/unit/core/sql/snapshots/test_ml/test_evaluate_model_with_options/evaluate_model_with_options.sql deleted file mode 100644 index cdb66bbf0e1..00000000000 --- a/tests/unit/core/sql/snapshots/test_ml/test_evaluate_model_with_options/evaluate_model_with_options.sql +++ /dev/null @@ -1 +0,0 @@ -SELECT * FROM ML.EVALUATE(MODEL `my_model`, STRUCT(FALSE AS `perform_aggregation`, 10 AS `horizon`, 0.95 AS `confidence_level`)) diff --git a/tests/unit/core/sql/snapshots/test_ml/test_evaluate_model_with_table/evaluate_model_with_table.sql b/tests/unit/core/sql/snapshots/test_ml/test_evaluate_model_with_table/evaluate_model_with_table.sql deleted file mode 100644 index e1d4fdecd62..00000000000 --- a/tests/unit/core/sql/snapshots/test_ml/test_evaluate_model_with_table/evaluate_model_with_table.sql +++ /dev/null @@ -1 +0,0 @@ -SELECT * FROM ML.EVALUATE(MODEL `my_project.my_dataset.my_model`, (SELECT * FROM evaluation_data)) diff --git a/tests/unit/core/sql/snapshots/test_ml/test_explain_predict_model_basic/explain_predict_model_basic.sql b/tests/unit/core/sql/snapshots/test_ml/test_explain_predict_model_basic/explain_predict_model_basic.sql deleted file mode 100644 index 1d755b34ddd..00000000000 --- a/tests/unit/core/sql/snapshots/test_ml/test_explain_predict_model_basic/explain_predict_model_basic.sql +++ /dev/null @@ -1 +0,0 @@ -SELECT * FROM ML.EXPLAIN_PREDICT(MODEL `my_project.my_dataset.my_model`, (SELECT * FROM new_data)) diff --git a/tests/unit/core/sql/snapshots/test_ml/test_explain_predict_model_with_options/explain_predict_model_with_options.sql b/tests/unit/core/sql/snapshots/test_ml/test_explain_predict_model_with_options/explain_predict_model_with_options.sql deleted file mode 100644 index 7569463ea2d..00000000000 --- a/tests/unit/core/sql/snapshots/test_ml/test_explain_predict_model_with_options/explain_predict_model_with_options.sql +++ /dev/null @@ -1 +0,0 @@ -SELECT * FROM ML.EXPLAIN_PREDICT(MODEL `my_model`, (SELECT * FROM new_data), STRUCT(5 AS `top_k_features`)) diff --git a/tests/unit/core/sql/snapshots/test_ml/test_generate_embedding_model_basic/generate_embedding_model_basic.sql b/tests/unit/core/sql/snapshots/test_ml/test_generate_embedding_model_basic/generate_embedding_model_basic.sql deleted file mode 100644 index 7294f1655f7..00000000000 --- a/tests/unit/core/sql/snapshots/test_ml/test_generate_embedding_model_basic/generate_embedding_model_basic.sql +++ /dev/null @@ -1 +0,0 @@ -SELECT * FROM ML.GENERATE_EMBEDDING(MODEL `my_project.my_dataset.my_model`, (SELECT * FROM new_data)) diff --git a/tests/unit/core/sql/snapshots/test_ml/test_generate_embedding_model_with_options/generate_embedding_model_with_options.sql b/tests/unit/core/sql/snapshots/test_ml/test_generate_embedding_model_with_options/generate_embedding_model_with_options.sql deleted file mode 100644 index 3be957079cf..00000000000 --- a/tests/unit/core/sql/snapshots/test_ml/test_generate_embedding_model_with_options/generate_embedding_model_with_options.sql +++ /dev/null @@ -1,5 +0,0 @@ -SELECT * FROM ML.GENERATE_EMBEDDING(MODEL `my_project.my_dataset.my_model`, (SELECT * FROM new_data), STRUCT( - TRUE AS `flatten_json_output`, - 'RETRIEVAL_DOCUMENT' AS `task_type`, - 256 AS `output_dimensionality` -)) diff --git a/tests/unit/core/sql/snapshots/test_ml/test_generate_text_model_basic/generate_text_model_basic.sql b/tests/unit/core/sql/snapshots/test_ml/test_generate_text_model_basic/generate_text_model_basic.sql deleted file mode 100644 index 9d986876448..00000000000 --- a/tests/unit/core/sql/snapshots/test_ml/test_generate_text_model_basic/generate_text_model_basic.sql +++ /dev/null @@ -1 +0,0 @@ -SELECT * FROM ML.GENERATE_TEXT(MODEL `my_project.my_dataset.my_model`, (SELECT * FROM new_data)) diff --git a/tests/unit/core/sql/snapshots/test_ml/test_generate_text_model_with_options/generate_text_model_with_options.sql b/tests/unit/core/sql/snapshots/test_ml/test_generate_text_model_with_options/generate_text_model_with_options.sql deleted file mode 100644 index 0ea26747287..00000000000 --- a/tests/unit/core/sql/snapshots/test_ml/test_generate_text_model_with_options/generate_text_model_with_options.sql +++ /dev/null @@ -1,10 +0,0 @@ -SELECT * FROM ML.GENERATE_TEXT(MODEL `my_project.my_dataset.my_model`, (SELECT * FROM new_data), STRUCT( - 0.5 AS `temperature`, - 128 AS `max_output_tokens`, - 20 AS `top_k`, - 0.9 AS `top_p`, - TRUE AS `flatten_json_output`, - ['a', 'b'] AS `stop_sequences`, - TRUE AS `ground_with_google_search`, - 'TYPE' AS `request_type` -)) diff --git a/tests/unit/core/sql/snapshots/test_ml/test_get_insights_model_basic/get_insights_model_basic.sql b/tests/unit/core/sql/snapshots/test_ml/test_get_insights_model_basic/get_insights_model_basic.sql deleted file mode 100644 index a3f2680c179..00000000000 --- a/tests/unit/core/sql/snapshots/test_ml/test_get_insights_model_basic/get_insights_model_basic.sql +++ /dev/null @@ -1 +0,0 @@ -SELECT * FROM ML.GET_INSIGHTS(MODEL `my_project.my_dataset.my_model`) diff --git a/tests/unit/core/sql/snapshots/test_ml/test_global_explain_model_basic/global_explain_model_basic.sql b/tests/unit/core/sql/snapshots/test_ml/test_global_explain_model_basic/global_explain_model_basic.sql deleted file mode 100644 index 4fc8250dab2..00000000000 --- a/tests/unit/core/sql/snapshots/test_ml/test_global_explain_model_basic/global_explain_model_basic.sql +++ /dev/null @@ -1 +0,0 @@ -SELECT * FROM ML.GLOBAL_EXPLAIN(MODEL `my_project.my_dataset.my_model`) diff --git a/tests/unit/core/sql/snapshots/test_ml/test_global_explain_model_with_options/global_explain_model_with_options.sql b/tests/unit/core/sql/snapshots/test_ml/test_global_explain_model_with_options/global_explain_model_with_options.sql deleted file mode 100644 index 396648aa1db..00000000000 --- a/tests/unit/core/sql/snapshots/test_ml/test_global_explain_model_with_options/global_explain_model_with_options.sql +++ /dev/null @@ -1 +0,0 @@ -SELECT * FROM ML.GLOBAL_EXPLAIN(MODEL `my_model`, STRUCT(TRUE AS `class_level_explain`)) diff --git a/tests/unit/core/sql/snapshots/test_ml/test_predict_model_basic/predict_model_basic.sql b/tests/unit/core/sql/snapshots/test_ml/test_predict_model_basic/predict_model_basic.sql deleted file mode 100644 index a1ac0b2b459..00000000000 --- a/tests/unit/core/sql/snapshots/test_ml/test_predict_model_basic/predict_model_basic.sql +++ /dev/null @@ -1 +0,0 @@ -SELECT * FROM ML.PREDICT(MODEL `my_project.my_dataset.my_model`, (SELECT * FROM new_data)) diff --git a/tests/unit/core/sql/snapshots/test_ml/test_predict_model_with_options/predict_model_with_options.sql b/tests/unit/core/sql/snapshots/test_ml/test_predict_model_with_options/predict_model_with_options.sql deleted file mode 100644 index e19f39eebba..00000000000 --- a/tests/unit/core/sql/snapshots/test_ml/test_predict_model_with_options/predict_model_with_options.sql +++ /dev/null @@ -1 +0,0 @@ -SELECT * FROM ML.PREDICT(MODEL `my_model`, (SELECT * FROM new_data), STRUCT(TRUE AS `keep_original_columns`)) diff --git a/tests/unit/core/sql/snapshots/test_ml/test_transform_model_basic/transform_model_basic.sql b/tests/unit/core/sql/snapshots/test_ml/test_transform_model_basic/transform_model_basic.sql deleted file mode 100644 index e6cedc16477..00000000000 --- a/tests/unit/core/sql/snapshots/test_ml/test_transform_model_basic/transform_model_basic.sql +++ /dev/null @@ -1 +0,0 @@ -SELECT * FROM ML.TRANSFORM(MODEL `my_project.my_dataset.my_model`, (SELECT * FROM new_data)) diff --git a/tests/unit/core/sql/test_ml.py b/tests/unit/core/sql/test_ml.py deleted file mode 100644 index a03d8cd805a..00000000000 --- a/tests/unit/core/sql/test_ml.py +++ /dev/null @@ -1,243 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import pytest - -import bigframes.bigquery as bbq -import bigframes.core.sql.ml - -pytest.importorskip("pytest_snapshot") - - -def test_create_model_basic(snapshot): - sql = bigframes.core.sql.ml.create_model_ddl( - model_name="my_project.my_dataset.my_model", - options={"model_type": "LINEAR_REG", "input_label_cols": ["label"]}, - training_data="SELECT * FROM my_table", - ) - snapshot.assert_match(sql, "create_model_basic.sql") - - -def test_create_model_replace(snapshot): - sql = bigframes.core.sql.ml.create_model_ddl( - model_name="my_model", - replace=True, - options={"model_type": "LOGISTIC_REG"}, - training_data="SELECT * FROM t", - ) - snapshot.assert_match(sql, "create_model_replace.sql") - - -def test_create_model_if_not_exists(snapshot): - sql = bigframes.core.sql.ml.create_model_ddl( - model_name="my_model", - if_not_exists=True, - options={"model_type": "KMEANS"}, - training_data="SELECT * FROM t", - ) - snapshot.assert_match(sql, "create_model_if_not_exists.sql") - - -def test_create_model_transform(snapshot): - sql = bigframes.core.sql.ml.create_model_ddl( - model_name="my_model", - transform=["ML.STANDARD_SCALER(c1) OVER() AS c1_scaled", "c2"], - options={"model_type": "LINEAR_REG"}, - training_data="SELECT c1, c2, label FROM t", - ) - snapshot.assert_match(sql, "create_model_transform.sql") - - -def test_create_model_remote(snapshot): - sql = bigframes.core.sql.ml.create_model_ddl( - model_name="my_remote_model", - connection_name="my_project.us.my_connection", - options={"endpoint": "gemini-pro"}, - input_schema={"prompt": "STRING"}, - output_schema={"content": "STRING"}, - ) - snapshot.assert_match(sql, "create_model_remote.sql") - - -def test_create_model_remote_default(snapshot): - sql = bigframes.core.sql.ml.create_model_ddl( - model_name="my_remote_model", - connection_name="DEFAULT", - options={"endpoint": "gemini-pro"}, - ) - snapshot.assert_match(sql, "create_model_remote_default.sql") - - -def test_create_model_training_data_and_holiday(snapshot): - sql = bigframes.core.sql.ml.create_model_ddl( - model_name="my_arima_model", - options={"model_type": "ARIMA_PLUS"}, - training_data="SELECT * FROM sales", - custom_holiday="SELECT * FROM holidays", - ) - snapshot.assert_match(sql, "create_model_training_data_and_holiday.sql") - - -def test_create_model_list_option(snapshot): - sql = bigframes.core.sql.ml.create_model_ddl( - model_name="my_model", - options={"hidden_units": [32, 16], "dropout": 0.2}, - training_data="SELECT * FROM t", - ) - snapshot.assert_match(sql, "create_model_list_option.sql") - - -def test_create_model_hparam_tuning(snapshot): - sql = bigframes.core.sql.ml.create_model_ddl( - model_name="my_model", - options={ - "model_type": "LINEAR_REG", - "learn_rate": bbq.hparam_range(0.0001, 1.0), - "optimizer": bbq.hparam_candidates(["ADAGRAD", "SGD"]), - }, - training_data="SELECT * FROM t", - ) - snapshot.assert_match(sql, "create_model_hparam_tuning.sql") - - -def test_evaluate_model_basic(snapshot): - sql = bigframes.core.sql.ml.evaluate( - model_name="my_project.my_dataset.my_model", - ) - snapshot.assert_match(sql, "evaluate_model_basic.sql") - - -def test_evaluate_model_with_table(snapshot): - sql = bigframes.core.sql.ml.evaluate( - model_name="my_project.my_dataset.my_model", - table="SELECT * FROM evaluation_data", - ) - snapshot.assert_match(sql, "evaluate_model_with_table.sql") - - -def test_evaluate_model_with_options(snapshot): - sql = bigframes.core.sql.ml.evaluate( - model_name="my_model", - perform_aggregation=False, - horizon=10, - confidence_level=0.95, - ) - snapshot.assert_match(sql, "evaluate_model_with_options.sql") - - -def test_predict_model_basic(snapshot): - sql = bigframes.core.sql.ml.predict( - model_name="my_project.my_dataset.my_model", - table="SELECT * FROM new_data", - ) - snapshot.assert_match(sql, "predict_model_basic.sql") - - -def test_predict_model_with_options(snapshot): - sql = bigframes.core.sql.ml.predict( - model_name="my_model", - table="SELECT * FROM new_data", - keep_original_columns=True, - ) - snapshot.assert_match(sql, "predict_model_with_options.sql") - - -def test_explain_predict_model_basic(snapshot): - sql = bigframes.core.sql.ml.explain_predict( - model_name="my_project.my_dataset.my_model", - table="SELECT * FROM new_data", - ) - snapshot.assert_match(sql, "explain_predict_model_basic.sql") - - -def test_explain_predict_model_with_options(snapshot): - sql = bigframes.core.sql.ml.explain_predict( - model_name="my_model", - table="SELECT * FROM new_data", - top_k_features=5, - ) - snapshot.assert_match(sql, "explain_predict_model_with_options.sql") - - -def test_global_explain_model_basic(snapshot): - sql = bigframes.core.sql.ml.global_explain( - model_name="my_project.my_dataset.my_model", - ) - snapshot.assert_match(sql, "global_explain_model_basic.sql") - - -def test_global_explain_model_with_options(snapshot): - sql = bigframes.core.sql.ml.global_explain( - model_name="my_model", - class_level_explain=True, - ) - snapshot.assert_match(sql, "global_explain_model_with_options.sql") - - -def test_transform_model_basic(snapshot): - sql = bigframes.core.sql.ml.transform( - model_name="my_project.my_dataset.my_model", - table="SELECT * FROM new_data", - ) - snapshot.assert_match(sql, "transform_model_basic.sql") - - -def test_generate_text_model_basic(snapshot): - sql = bigframes.core.sql.ml.generate_text( - model_name="my_project.my_dataset.my_model", - table="SELECT * FROM new_data", - ) - snapshot.assert_match(sql, "generate_text_model_basic.sql") - - -def test_generate_text_model_with_options(snapshot): - sql = bigframes.core.sql.ml.generate_text( - model_name="my_project.my_dataset.my_model", - table="SELECT * FROM new_data", - temperature=0.5, - max_output_tokens=128, - top_k=20, - top_p=0.9, - flatten_json_output=True, - stop_sequences=["a", "b"], - ground_with_google_search=True, - request_type="TYPE", - ) - snapshot.assert_match(sql, "generate_text_model_with_options.sql") - - -def test_get_insights_model_basic(snapshot): - sql = bigframes.core.sql.ml.get_insights( - model_name="my_project.my_dataset.my_model", - ) - snapshot.assert_match(sql, "get_insights_model_basic.sql") - - -def test_generate_embedding_model_basic(snapshot): - sql = bigframes.core.sql.ml.generate_embedding( - model_name="my_project.my_dataset.my_model", - table="SELECT * FROM new_data", - ) - snapshot.assert_match(sql, "generate_embedding_model_basic.sql") - - -def test_generate_embedding_model_with_options(snapshot): - sql = bigframes.core.sql.ml.generate_embedding( - model_name="my_project.my_dataset.my_model", - table="SELECT * FROM new_data", - flatten_json_output=True, - task_type="RETRIEVAL_DOCUMENT", - output_dimensionality=256, - ) - snapshot.assert_match(sql, "generate_embedding_model_with_options.sql") diff --git a/tests/unit/core/test_bf_utils.py b/tests/unit/core/test_bf_utils.py index 6fb796329f5..fc34f35d9c2 100644 --- a/tests/unit/core/test_bf_utils.py +++ b/tests/unit/core/test_bf_utils.py @@ -12,12 +12,6 @@ # See the License for the specific language governing permissions and # limitations under the License. -import datetime - -import numpy as np -import pandas as pd -import pytest - from bigframes.core import utils @@ -31,8 +25,8 @@ def test_get_standardized_ids_columns(): "0", utils.UNNAMED_COLUMN_ID, "duplicate", - "duplicate_1", - "with space", + "duplicate.1", + "with_space", ] assert idx_ids == [] @@ -41,15 +35,15 @@ def test_get_standardized_ids_indexes(): col_labels = ["duplicate"] idx_labels = ["string", 0, None, "duplicate", "duplicate", "with space"] - col_ids, idx_ids = utils.get_standardized_ids(col_labels, idx_labels, strict=True) + col_ids, idx_ids = utils.get_standardized_ids(col_labels, idx_labels) - assert col_ids == ["duplicate_2"] + assert col_ids == ["duplicate.2"] assert idx_ids == [ "string", - "_0", + "0", utils.UNNAMED_INDEX_ID, "duplicate", - "duplicate_1", + "duplicate.1", "with_space", ] @@ -59,16 +53,4 @@ def test_get_standardized_ids_tuple(): col_ids, _ = utils.get_standardized_ids(col_labels) - assert col_ids == ["_'foo'_ 1_", "_'foo'_ 2_", "_'bar'_ 1_"] - - -@pytest.mark.parametrize( - "input", - [ - datetime.timedelta(days=2, hours=3, seconds=4, milliseconds=5, microseconds=6), - pd.Timedelta("2d3h4s5ms6us"), - np.timedelta64(pd.Timedelta("2d3h4s5ms6us")), - ], -) -def test_timedelta_to_micros(input): - assert utils.timedelta_to_micros(input) == 183604005006 + assert col_ids == ["('foo',_1)", "('foo',_2)", "('bar',_1)"] diff --git a/tests/unit/core/test_blocks.py b/tests/unit/core/test_blocks.py index 7c06bedfd3d..86715d090cb 100644 --- a/tests/unit/core/test_blocks.py +++ b/tests/unit/core/test_blocks.py @@ -12,15 +12,11 @@ # See the License for the specific language governing permissions and # limitations under the License. -from unittest import mock - import pandas import pandas.testing import pytest -import bigframes import bigframes.core.blocks as blocks -import bigframes.session.bq_caching_executor @pytest.mark.parametrize( @@ -78,23 +74,9 @@ ) def test_block_from_local(data): expected = pandas.DataFrame(data) - mock_session = mock.create_autospec(spec=bigframes.Session) - mock_executor = mock.create_autospec( - spec=bigframes.session.bq_caching_executor.BigQueryCachingExecutor - ) - - # hard-coded the returned dimension of the session for that each of the test case contains 3 rows. - mock_session._executor = mock_executor - block = blocks.Block.from_local(pandas.DataFrame(data), mock_session) + block = blocks.block_from_local(data) pandas.testing.assert_index_equal(block.column_labels, expected.columns) - assert tuple(block.index.names) == tuple(expected.index.names) - - -def test_block_compute_dry_run__raises_error_when_sampling_is_enabled(): - mock_session = mock.create_autospec(spec=bigframes.Session) - block = blocks.Block.from_local(pandas.DataFrame(), mock_session) - - with pytest.raises(NotImplementedError): - block._compute_dry_run(sampling_method="UNIFORM") + assert tuple(block.index_labels) == tuple(expected.index.names) + assert block.shape == expected.shape diff --git a/tests/unit/core/test_bytecode.py b/tests/unit/core/test_bytecode.py deleted file mode 100644 index 036e3f00e8f..00000000000 --- a/tests/unit/core/test_bytecode.py +++ /dev/null @@ -1,82 +0,0 @@ -# Copyright 2026 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import math - -import pytest - -import bigframes.core.expression as ex -import bigframes.operations as ops -from bigframes.core.bytecode import py_to_expression - - -def test_py_to_expression_simple_arithmetic(): - func = lambda x: x + 1 - expr = py_to_expression(func) - assert expr is not None - - expected = ops.add_op.as_expr(ex.free_var("x"), ex.const(1)) - assert expr == expected - - -def test_py_to_expression_math_function(): - func = lambda x: math.sin(x) - expr = py_to_expression(func) - assert expr is not None - - expected = ops.numeric_ops.sin_op.as_expr(ex.free_var("x")) - assert expr == expected - - -def test_py_to_expression_negation(): - func = lambda x: -x - expr = py_to_expression(func) - assert expr is not None - - expected = ops.numeric_ops.neg_op.as_expr(ex.free_var("x")) - assert expr == expected - - -def test_py_to_expression_comparison(): - func = lambda x, y: x == y - expr = py_to_expression(func) - assert expr is not None - - expected = ops.comparison_ops.eq_op.as_expr(ex.free_var("x"), ex.free_var("y")) - assert expr == expected - - -def test_py_to_expression_unsupported(): - # Control flow or unsupported structures should return None - def func_with_loop(x): - res = 0 - for val in range(int(x)): - res += val - return res - - with pytest.raises(ValueError): - py_to_expression(func_with_loop) - - -global_none_val = None - - -def test_py_to_expression_global_none(): - # Test resolving a global variable explicitly set to None - func = lambda x: x == global_none_val - expr = py_to_expression(func) - assert expr is not None - - expected = ops.comparison_ops.eq_op.as_expr(ex.free_var("x"), ex.const(None)) - assert expr == expected diff --git a/tests/unit/core/test_expression.py b/tests/unit/core/test_expression.py deleted file mode 100644 index 68fc3a2b540..00000000000 --- a/tests/unit/core/test_expression.py +++ /dev/null @@ -1,118 +0,0 @@ -# Copyright 2023 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import typing - -import pytest - -import bigframes.core.expression as ex -import bigframes.core.identifiers as ids -import bigframes.dtypes as dtypes -import bigframes.operations as ops -from bigframes.core import field - - -def test_simple_expression_dtype(): - expression = ops.add_op.as_expr("a", "b") - field_bindings = _create_field_bindings( - {"a": dtypes.INT_DTYPE, "b": dtypes.INT_DTYPE} - ) - - result = ex.bind_schema_fields(expression, field_bindings) - - _assert_output_type(result, dtypes.INT_DTYPE) - - -def test_nested_expression_dtype(): - expression = ops.add_op.as_expr( - "a", ops.abs_op.as_expr(ops.sub_op.as_expr("b", ex.const(3.14))) - ) - field_bindings = _create_field_bindings( - {"a": dtypes.INT_DTYPE, "b": dtypes.INT_DTYPE} - ) - - result = ex.bind_schema_fields(expression, field_bindings) - - _assert_output_type(result, dtypes.FLOAT_DTYPE) - - -def test_where_op_dtype(): - expression = ops.where_op.as_expr(ex.const(3), ex.const(True), ex.const(None)) - - _assert_output_type(expression, dtypes.INT_DTYPE) - - -def test_astype_op_dtype(): - expression = ops.AsTypeOp(dtypes.INT_DTYPE).as_expr(ex.const(3.14159)) - - _assert_output_type(expression, dtypes.INT_DTYPE) - - -def test_deref_op_dtype_unavailable(): - expression = ex.deref("mycol") - - assert not expression.is_resolved - with pytest.raises(ValueError): - expression.output_type - - -def test_deref_op_dtype_resolution(): - expression = ex.deref("mycol") - field_bindings = _create_field_bindings({"mycol": dtypes.STRING_DTYPE}) - - result = ex.bind_schema_fields(expression, field_bindings) - - _assert_output_type(result, dtypes.STRING_DTYPE) - - -def test_field_ref_expr_dtype_resolution_short_circuit(): - expression = ex.ResolvedDerefOp( - id=ids.ColumnId("mycol"), dtype=dtypes.INT_DTYPE, is_nullable=True - ) - field_bindings = _create_field_bindings({"anotherCol": dtypes.STRING_DTYPE}) - - result = ex.bind_schema_fields(expression, field_bindings) - - _assert_output_type(result, dtypes.INT_DTYPE) - - -def test_nested_expression_dtypes_are_cached(): - expression = ops.add_op.as_expr(ex.deref("left_col"), ex.deref("right_col")) - field_bindings = _create_field_bindings( - { - "right_col": dtypes.INT_DTYPE, - "left_col": dtypes.FLOAT_DTYPE, - } - ) - - result = ex.bind_schema_fields(expression, field_bindings) - - _assert_output_type(result, dtypes.FLOAT_DTYPE) - assert isinstance(result, ex.OpExpression) - _assert_output_type(result.inputs[0], dtypes.FLOAT_DTYPE) - _assert_output_type(result.inputs[1], dtypes.INT_DTYPE) - - -def _create_field_bindings( - col_dtypes: typing.Dict[str, dtypes.Dtype], -) -> typing.Dict[ids.ColumnId, field.Field]: - return { - ids.ColumnId(col): field.Field(ids.ColumnId(col), dtype) - for col, dtype in col_dtypes.items() - } - - -def _assert_output_type(expr: ex.Expression, dtype: dtypes.Dtype): - assert expr.is_resolved - assert expr.output_type == dtype diff --git a/tests/unit/core/test_googlesql.py b/tests/unit/core/test_googlesql.py deleted file mode 100644 index e83391e4b33..00000000000 --- a/tests/unit/core/test_googlesql.py +++ /dev/null @@ -1,268 +0,0 @@ -# Copyright 2026 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import unittest.mock as mock - -import pandas as pd - -import bigframes.core.col as col -import bigframes.core.expression as ex -import bigframes.core.global_session -import bigframes.core.googlesql as core_googlesql -import bigframes.series as series -from bigframes.operations import googlesql -from bigframes.testing import mocks - -# Define a test op -_TEST_OP = googlesql.GoogleSqlScalarOp( - "TEST_OP", - args=(googlesql.ArgSpec(), googlesql.ArgSpec()), - signature=lambda *args: None, -) - - -def test_apply_googlesql_scalar_op_expressions(): - # Only expressions - result = core_googlesql.apply_googlesql_scalar_op( - _TEST_OP, - col.col("a"), - col.col("b"), - ) - assert isinstance(result, col.Expression) - - -def test_apply_googlesql_scalar_op_pandas_series_global_session(monkeypatch): - # Setup mock session - session = mocks.create_bigquery_session() - monkeypatch.setattr(bigframes.core.global_session, "_global_session", session) - bigframes.options.bigquery._session_started = True - - # Create a real-ish Series to return from read_pandas - df = mocks.create_dataframe(monkeypatch, session=session, data={"col": [1, 2, 3]}) - bf_series = df["col"] - - # Mock read_pandas on the session - mock_read_pandas = mock.MagicMock(return_value=bf_series) - session.read_pandas = mock_read_pandas # type: ignore - - # Mock _apply_nary_op on Series class to avoid real compilation/execution - mock_apply_nary_op = mock.MagicMock(return_value=bf_series) - monkeypatch.setattr(series.Series, "_apply_nary_op", mock_apply_nary_op) - - pd_series = pd.Series([1, 2, 3]) - - # Call the function with a pandas Series and a literal - result = core_googlesql.apply_googlesql_scalar_op(_TEST_OP, pd_series, 42) - - # Verify read_pandas was called on the global session - mock_read_pandas.assert_called_once_with(pd_series) - - # Verify _apply_nary_op was called on the converted series - mock_apply_nary_op.assert_called_once() - # First arg to _apply_nary_op is the op, second is the processed_args - assert mock_apply_nary_op.call_args[0][0] == _TEST_OP - # processed_args should contain the converted bf_series and the literal 42 - processed_args = mock_apply_nary_op.call_args[0][1] - assert processed_args[0] is bf_series - assert processed_args[1] == 42 - - # Verify result is a Series - assert isinstance(result, series.Series) - - -def test_apply_googlesql_scalar_op_pandas_series_with_bf_series(monkeypatch): - # Setup mock session 1 (global) and session 2 (associated with bf_series) - global_session = mocks.create_bigquery_session(session_id="global") - monkeypatch.setattr( - bigframes.core.global_session, "_global_session", global_session - ) - bigframes.options.bigquery._session_started = True - - bf_session = mocks.create_bigquery_session(session_id="bf_session") - - # Create a bf_series associated with bf_session - df = mocks.create_dataframe( - monkeypatch, session=bf_session, data={"col": [1, 2, 3]} - ) - bf_series = df["col"] - - assert bf_series._session == bf_session - - # Mock read_pandas on both sessions - mock_global_read_pandas = mock.MagicMock() - global_session.read_pandas = mock_global_read_pandas # type: ignore - - mock_bf_read_pandas = mock.MagicMock(return_value=bf_series) - bf_session.read_pandas = mock_bf_read_pandas # type: ignore - - # Mock _apply_nary_op - mock_apply_nary_op = mock.MagicMock(return_value=bf_series) - monkeypatch.setattr(series.Series, "_apply_nary_op", mock_apply_nary_op) - - pd_series = pd.Series([1, 2, 3]) - - # Call with both pandas Series and BigFrames Series - result = core_googlesql.apply_googlesql_scalar_op(_TEST_OP, pd_series, bf_series) - - # Verify read_pandas was called on bf_session, NOT global_session - mock_bf_read_pandas.assert_called_once_with(pd_series) - mock_global_read_pandas.assert_not_called() - - # Verify _apply_nary_op was called - mock_apply_nary_op.assert_called_once() - processed_args = mock_apply_nary_op.call_args[0][1] - # Both arguments to the op should now be BigFrames Series - assert processed_args[0] is bf_series - assert processed_args[1] is bf_series - - assert isinstance(result, series.Series) - - -def test_apply_googlesql_scalar_op_mixed_args(monkeypatch): - session = mocks.create_bigquery_session() - monkeypatch.setattr(bigframes.core.global_session, "_global_session", session) - bigframes.options.bigquery._session_started = True - - df = mocks.create_dataframe(monkeypatch, session=session, data={"col": [1, 2, 3]}) - bf_series = df["col"] - - mock_read_pandas = mock.MagicMock(return_value=bf_series) - session.read_pandas = mock_read_pandas # type: ignore - - mock_apply_nary_op = mock.MagicMock(return_value=bf_series) - monkeypatch.setattr(series.Series, "_apply_nary_op", mock_apply_nary_op) - - pd_series = pd.Series([1, 2, 3]) - expr = col.Expression(ex.const(10)) - - # Call with pandas Series, Expression, and Literal - result = core_googlesql.apply_googlesql_scalar_op(_TEST_OP, pd_series, expr, 42) - - # Verify pandas Series was converted - mock_read_pandas.assert_called_once_with(pd_series) - - # Verify _apply_nary_op was called - mock_apply_nary_op.assert_called_once() - processed_args = mock_apply_nary_op.call_args[0][1] - - # Processed args should be: - # 1. bf_series (converted from pd_series) - # 2. A new Series (projected from the expression onto bf_series' block) - # 3. Literal 42 - assert isinstance(processed_args[0], series.Series) - assert processed_args[0] is bf_series - - assert isinstance(processed_args[1], series.Series) - assert processed_args[1] is not bf_series - - assert processed_args[2] == 42 - - assert isinstance(result, series.Series) - - -def test_apply_googlesql_scalar_op_pandas_series_with_bf_dataframe(monkeypatch): - # Setup mock session 2 (associated with bf_dataframe) - bf_session = mocks.create_bigquery_session(session_id="bf_session") - - # Create a bf_dataframe associated with bf_session - bf_dataframe = mocks.create_dataframe( - monkeypatch, session=bf_session, data={"col": [1, 2, 3]} - ) - bf_series = bf_dataframe["col"] - - # Setup mock session 1 (global) AFTER creating the dataframe - global_session = mocks.create_bigquery_session(session_id="global") - monkeypatch.setattr( - bigframes.core.global_session, "_global_session", global_session - ) - bigframes.options.bigquery._session_started = True - - assert bf_dataframe._session == bf_session - - # Mock read_pandas on both sessions - mock_global_read_pandas = mock.MagicMock() - global_session.read_pandas = mock_global_read_pandas # type: ignore - - mock_bf_read_pandas = mock.MagicMock(return_value=bf_series) - bf_session.read_pandas = mock_bf_read_pandas # type: ignore - - # Mock _apply_nary_op - mock_apply_nary_op = mock.MagicMock(return_value=bf_series) - monkeypatch.setattr(series.Series, "_apply_nary_op", mock_apply_nary_op) - - pd_series = pd.Series([1, 2, 3]) - - # Call with pandas Series and BigFrames DataFrame - result = core_googlesql.apply_googlesql_scalar_op(_TEST_OP, pd_series, bf_dataframe) - - # Verify read_pandas was called on bf_session, NOT global_session - mock_bf_read_pandas.assert_called_once_with(pd_series) - mock_global_read_pandas.assert_not_called() - - # Verify _apply_nary_op was called - mock_apply_nary_op.assert_called_once() - processed_args = mock_apply_nary_op.call_args[0][1] - assert processed_args[0] is bf_series - assert processed_args[1] is bf_dataframe - - assert isinstance(result, series.Series) - - -def test_apply_googlesql_scalar_op_pandas_series_with_bf_index(monkeypatch): - # Setup mock session 2 (associated with bf_index) - bf_session = mocks.create_bigquery_session(session_id="bf_session") - - # Create a bf_dataframe associated with bf_session to get an index - bf_dataframe = mocks.create_dataframe( - monkeypatch, session=bf_session, data={"col": [1, 2, 3]} - ) - bf_index = bf_dataframe.index - bf_series = bf_dataframe["col"] - - # Setup mock session 1 (global) AFTER creating the dataframe - global_session = mocks.create_bigquery_session(session_id="global") - monkeypatch.setattr( - bigframes.core.global_session, "_global_session", global_session - ) - bigframes.options.bigquery._session_started = True - - assert bf_index._session == bf_session - - # Mock read_pandas on both sessions - mock_global_read_pandas = mock.MagicMock() - global_session.read_pandas = mock_global_read_pandas # type: ignore - - mock_bf_read_pandas = mock.MagicMock(return_value=bf_series) - bf_session.read_pandas = mock_bf_read_pandas # type: ignore - - # Mock _apply_nary_op - mock_apply_nary_op = mock.MagicMock(return_value=bf_series) - monkeypatch.setattr(series.Series, "_apply_nary_op", mock_apply_nary_op) - - pd_series = pd.Series([1, 2, 3]) - - # Call with pandas Series and BigFrames Index - result = core_googlesql.apply_googlesql_scalar_op(_TEST_OP, pd_series, bf_index) - - # Verify read_pandas was called on bf_session, NOT global_session - mock_bf_read_pandas.assert_called_once_with(pd_series) - mock_global_read_pandas.assert_not_called() - - # Verify _apply_nary_op was called - mock_apply_nary_op.assert_called_once() - processed_args = mock_apply_nary_op.call_args[0][1] - assert processed_args[0] is bf_series - assert processed_args[1] is bf_index - - assert isinstance(result, series.Series) diff --git a/tests/unit/core/test_groupby.py b/tests/unit/core/test_groupby.py deleted file mode 100644 index b23199da331..00000000000 --- a/tests/unit/core/test_groupby.py +++ /dev/null @@ -1,264 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import pandas as pd -import pandas.testing -import pytest - -import bigframes.core.utils as utils -import bigframes.pandas as bpd -import bigframes.testing.utils - -pytest.importorskip("polars") -pytest.importorskip("pandas", minversion="2.0.0") - - -def test_groupby_df_iter_by_key_singular(polars_session): - pd_df = pd.DataFrame({"colA": ["a", "a", "b", "c", "c"], "colB": [1, 2, 3, 4, 5]}) - bf_df = bpd.DataFrame(pd_df, session=polars_session) - - for bf_group, pd_group in zip(bf_df.groupby("colA"), pd_df.groupby("colA")): # type: ignore - bf_key, bf_group_df = bf_group - bf_result = bf_group_df.to_pandas() - pd_key, pd_result = pd_group - assert bf_key == pd_key - bigframes.testing.utils.assert_frame_equal( - bf_result, pd_result, check_dtype=False, check_index_type=False - ) - - -def test_groupby_df_iter_by_key_list(polars_session): - pd_df = pd.DataFrame({"colA": ["a", "a", "b", "c", "c"], "colB": [1, 2, 3, 4, 5]}) - bf_df = bpd.DataFrame(pd_df, session=polars_session) - - for bf_group, pd_group in zip(bf_df.groupby(["colA"]), pd_df.groupby(["colA"])): # type: ignore - bf_key, bf_group_df = bf_group - bf_result = bf_group_df.to_pandas() - pd_key, pd_result = pd_group - assert bf_key == pd_key - bigframes.testing.utils.assert_frame_equal( - bf_result, pd_result, check_dtype=False, check_index_type=False - ) - - -def test_groupby_df_iter_by_key_list_multiple(polars_session): - pd_df = pd.DataFrame( - { - "colA": ["a", "a", "b", "c", "c"], - "colB": [1, 2, 3, 4, 5], - "colC": [True, False, True, False, True], - } - ) - bf_df = bpd.DataFrame(pd_df, session=polars_session) - - for bf_group, pd_group in zip( # type: ignore - bf_df.groupby(["colA", "colB"]), pd_df.groupby(["colA", "colB"]) - ): - bf_key, bf_group_df = bf_group - bf_result = bf_group_df.to_pandas() - pd_key, pd_result = pd_group - assert bf_key == pd_key - bigframes.testing.utils.assert_frame_equal( - bf_result, pd_result, check_dtype=False, check_index_type=False - ) - - -def test_groupby_df_iter_by_level_singular(polars_session): - pd_df = pd.DataFrame( - {"colA": ["a", "a", "b", "c", "c"], "colB": [1, 2, 3, 4, 5]} - ).set_index("colA") - bf_df = bpd.DataFrame(pd_df, session=polars_session) - - for bf_group, pd_group in zip(bf_df.groupby(level=0), pd_df.groupby(level=0)): # type: ignore - bf_key, bf_group_df = bf_group - bf_result = bf_group_df.to_pandas() - pd_key, pd_result = pd_group - assert bf_key == pd_key - bigframes.testing.utils.assert_frame_equal( - bf_result, pd_result, check_dtype=False, check_index_type=False - ) - - -def test_groupby_df_iter_by_level_list_one_item(polars_session): - pd_df = pd.DataFrame( - {"colA": ["a", "a", "b", "c", "c"], "colB": [1, 2, 3, 4, 5]} - ).set_index("colA") - bf_df = bpd.DataFrame(pd_df, session=polars_session) - - for bf_group, pd_group in zip(bf_df.groupby(level=[0]), pd_df.groupby(level=[0])): # type: ignore - bf_key, bf_group_df = bf_group - bf_result = bf_group_df.to_pandas() - pd_key, pd_result = pd_group - - # In pandas 2.x, we get a warning from pandas: "Creating a Groupby - # object with a length-1 list-like level parameter will yield indexes - # as tuples in a future version. To keep indexes as scalars, create - # Groupby objects with a scalar level parameter instead. - if utils.is_list_like(pd_key): - assert bf_key == tuple(pd_key) - else: - assert bf_key == (pd_key,) - bigframes.testing.utils.assert_frame_equal( - bf_result, pd_result, check_dtype=False, check_index_type=False - ) - - -def test_groupby_df_iter_by_level_list_multiple(polars_session): - pd_df = pd.DataFrame( - { - "colA": ["a", "a", "b", "c", "c"], - "colB": [1, 2, 3, 4, 5], - "colC": [True, False, True, False, True], - } - ).set_index(["colA", "colB"]) - bf_df = bpd.DataFrame(pd_df, session=polars_session) - - for bf_group, pd_group in zip( # type: ignore - bf_df.groupby(level=[0, 1]), pd_df.groupby(level=[0, 1]) - ): - bf_key, bf_group_df = bf_group - bf_result = bf_group_df.to_pandas() - pd_key, pd_result = pd_group - assert bf_key == pd_key - bigframes.testing.utils.assert_frame_equal( - bf_result, pd_result, check_dtype=False, check_index_type=False - ) - - -def test_groupby_series_iter_by_level_singular(polars_session): - series_index = ["a", "a", "b"] - pd_series = pd.Series([1, 2, 3], index=series_index) - bf_series = bpd.Series(pd_series, session=polars_session) - bf_series.name = pd_series.name - - for bf_group, pd_group in zip( # type: ignore - bf_series.groupby(level=0), pd_series.groupby(level=0) - ): - bf_key, bf_group_series = bf_group - bf_result = bf_group_series.to_pandas() - pd_key, pd_result = pd_group - assert bf_key == pd_key - bigframes.testing.utils.assert_series_equal( - bf_result, pd_result, check_dtype=False, check_index_type=False - ) - - -def test_groupby_series_iter_by_level_list_one_item(polars_session): - series_index = ["a", "a", "b"] - pd_series = pd.Series([1, 2, 3], index=series_index) - bf_series = bpd.Series(pd_series, session=polars_session) - bf_series.name = pd_series.name - - for bf_group, pd_group in zip( # type: ignore - bf_series.groupby(level=[0]), pd_series.groupby(level=[0]) - ): - bf_key, bf_group_series = bf_group - bf_result = bf_group_series.to_pandas() - pd_key, pd_result = pd_group - - # In pandas 2.x, we get a warning from pandas: "Creating a Groupby - # object with a length-1 list-like level parameter will yield indexes - # as tuples in a future version. To keep indexes as scalars, create - # Groupby objects with a scalar level parameter instead. - if utils.is_list_like(pd_key): - assert bf_key == tuple(pd_key) - else: - assert bf_key == (pd_key,) - bigframes.testing.utils.assert_series_equal( - bf_result, pd_result, check_dtype=False, check_index_type=False - ) - - -def test_groupby_series_iter_by_level_list_multiple(polars_session): - pd_df = pd.DataFrame( - { - "colA": ["a", "a", "b", "c", "c"], - "colB": [1, 2, 3, 4, 5], - "colC": [True, False, True, False, True], - } - ).set_index(["colA", "colB"]) - pd_series = pd_df["colC"] - bf_df = bpd.DataFrame(pd_df, session=polars_session) - bf_series = bf_df["colC"] - - for bf_group, pd_group in zip( # type: ignore - bf_series.groupby(level=[0, 1]), pd_series.groupby(level=[0, 1]) - ): - bf_key, bf_group_df = bf_group - bf_result = bf_group_df.to_pandas() - pd_key, pd_result = pd_group - assert bf_key == pd_key - bigframes.testing.utils.assert_series_equal( - bf_result, pd_result, check_dtype=False, check_index_type=False - ) - - -def test_groupby_series_iter_by_series(polars_session): - pd_groups = pd.Series(["a", "a", "b"]) - bf_groups = bpd.Series(pd_groups, session=polars_session) - pd_series = pd.Series([1, 2, 3]) - bf_series = bpd.Series(pd_series, session=polars_session) - bf_series.name = pd_series.name - - for bf_group, pd_group in zip( # type: ignore - bf_series.groupby(bf_groups), pd_series.groupby(pd_groups) - ): - bf_key, bf_group_series = bf_group - bf_result = bf_group_series.to_pandas() - pd_key, pd_result = pd_group - assert bf_key == pd_key - bigframes.testing.utils.assert_series_equal( - bf_result, pd_result, check_dtype=False, check_index_type=False - ) - - -def test_groupby_series_iter_by_series_list_one_item(polars_session): - pd_groups = pd.Series(["a", "a", "b"]) - bf_groups = bpd.Series(pd_groups, session=polars_session) - pd_series = pd.Series([1, 2, 3]) - bf_series = bpd.Series(pd_series, session=polars_session) - bf_series.name = pd_series.name - - for bf_group, pd_group in zip( # type: ignore - bf_series.groupby([bf_groups]), pd_series.groupby([pd_groups]) - ): - bf_key, bf_group_series = bf_group - bf_result = bf_group_series.to_pandas() - pd_key, pd_result = pd_group - assert bf_key == pd_key - bigframes.testing.utils.assert_series_equal( - bf_result, pd_result, check_dtype=False, check_index_type=False - ) - - -def test_groupby_series_iter_by_series_list_multiple(polars_session): - pd_group_a = pd.Series(["a", "a", "b", "c", "c"]) - bf_group_a = bpd.Series(pd_group_a, session=polars_session) - pd_group_b = pd.Series([0, 0, 0, 1, 1]) - bf_group_b = bpd.Series(pd_group_b, session=polars_session) - pd_series = pd.Series([1, 2, 3, 4, 5]) - bf_series = bpd.Series(pd_series, session=polars_session) - bf_series.name = pd_series.name - - for bf_group, pd_group in zip( # type: ignore - bf_series.groupby([bf_group_a, bf_group_b]), - pd_series.groupby([pd_group_a, pd_group_b]), - ): - bf_key, bf_group_series = bf_group - bf_result = bf_group_series.to_pandas() - pd_key, pd_result = pd_group - assert bf_key == pd_key - bigframes.testing.utils.assert_series_equal( - bf_result, pd_result, check_dtype=False, check_index_type=False - ) diff --git a/tests/unit/core/test_guid.py b/tests/unit/core/test_guid.py deleted file mode 100644 index c7334848eed..00000000000 --- a/tests/unit/core/test_guid.py +++ /dev/null @@ -1,41 +0,0 @@ -import types -import unittest - -from bigframes.core.guid import SequentialUIDGenerator - - -class TestSequentialUIDGenerator(unittest.TestCase): - def test_get_uid_stream_returns_generator(self): - generator = SequentialUIDGenerator() - stream = generator.get_uid_stream("prefix") - self.assertIsInstance(stream, types.GeneratorType) - - def test_generator_yields_correct_uids(self): - generator = SequentialUIDGenerator() - stream = generator.get_uid_stream("prefix") - self.assertEqual(next(stream), "prefix0") - self.assertEqual(next(stream), "prefix1") - self.assertEqual(next(stream), "prefix2") - - def test_generator_yields_different_uids_for_different_prefixes(self): - generator = SequentialUIDGenerator() - stream_a = generator.get_uid_stream("prefixA") - stream_b = generator.get_uid_stream("prefixB") - self.assertEqual(next(stream_a), "prefixA0") - self.assertEqual(next(stream_b), "prefixB0") - self.assertEqual(next(stream_a), "prefixA1") - self.assertEqual(next(stream_b), "prefixB1") - - def test_multiple_calls_continue_generation(self): - generator = SequentialUIDGenerator() - stream1 = generator.get_uid_stream("prefix") - self.assertEqual(next(stream1), "prefix0") - self.assertEqual(next(stream1), "prefix1") - - stream2 = generator.get_uid_stream("prefix") - self.assertEqual(next(stream2), "prefix2") - self.assertEqual(next(stream2), "prefix3") - - -if __name__ == "__main__": - unittest.main() diff --git a/tests/unit/core/test_ibis_types.py b/tests/unit/core/test_ibis_types.py deleted file mode 100644 index 427e726179e..00000000000 --- a/tests/unit/core/test_ibis_types.py +++ /dev/null @@ -1,250 +0,0 @@ -# Copyright 2023 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import bigframes_vendored.ibis.backends.bigquery.datatypes as ibis_bq_types -import bigframes_vendored.ibis.expr.datatypes as ibis_dtypes -import bigframes_vendored.ibis.expr.types as ibis_types -import geopandas as gpd # type: ignore -import numpy as np -import pandas as pd -import pyarrow as pa # type: ignore -import pytest - -import bigframes.core.compile.ibis_types -import bigframes.dtypes - - -@pytest.mark.parametrize( - ["ibis_dtype", "bigframes_dtype"], - [ - # TODO(bmil): Add ARRAY, INTERVAL, STRUCT to cover all the standard - # BigQuery data types as they appear in Ibis: - # https://cloud.google.com/bigquery/docs/reference/standard-sql/data-types - pytest.param( - ibis_dtypes.Decimal(precision=76, scale=38, nullable=True), - pd.ArrowDtype(pa.decimal256(76, 38)), - id="bignumeric", - ), - pytest.param(ibis_dtypes.boolean, pd.BooleanDtype(), id="bool"), - pytest.param(ibis_dtypes.binary, pd.ArrowDtype(pa.binary()), id="bytes"), - pytest.param(ibis_dtypes.date, pd.ArrowDtype(pa.date32()), id="date"), - pytest.param( - ibis_dtypes.Timestamp(), pd.ArrowDtype(pa.timestamp("us")), id="datetime" - ), - pytest.param(ibis_dtypes.float64, pd.Float64Dtype(), id="float"), - pytest.param( - ibis_dtypes.GeoSpatial(geotype="geography", srid=4326, nullable=True), - gpd.array.GeometryDtype(), - id="geography", - ), - pytest.param(ibis_dtypes.int8, pd.Int64Dtype(), id="int8-as-int64"), - pytest.param(ibis_dtypes.int64, pd.Int64Dtype(), id="int64"), - # TODO(tswast): custom dtype (or at least string dtype) for JSON objects - pytest.param( - ibis_dtypes.Decimal(precision=38, scale=9, nullable=True), - pd.ArrowDtype(pa.decimal128(38, 9)), - id="numeric", - ), - pytest.param( - ibis_dtypes.string, pd.StringDtype(storage="pyarrow"), id="string" - ), - pytest.param(ibis_dtypes.time, pd.ArrowDtype(pa.time64("us")), id="time"), - pytest.param( - ibis_dtypes.Timestamp(timezone="UTC"), - pd.ArrowDtype(pa.timestamp("us", tz="UTC")), # type: ignore - id="timestamp", - ), - ], -) -def test_ibis_dtype_converts(ibis_dtype, bigframes_dtype): - """Test all the Ibis data types needed to read BigQuery tables""" - result = bigframes.core.compile.ibis_types.ibis_dtype_to_bigframes_dtype(ibis_dtype) - assert result == bigframes_dtype - - -def test_ibis_timestamp_pst_raises_unexpected_datatype(): - """BigQuery timestamp only supports UTC time""" - with pytest.raises(ValueError, match="'PST'"): - bigframes.core.compile.ibis_types.ibis_dtype_to_bigframes_dtype( - ibis_dtypes.Timestamp(timezone="PST") - ) - - -def test_ibis_float32_raises_unexpected_datatype(): - """Other Ibis types not read from BigQuery are not expected""" - with pytest.raises(ValueError, match="Unexpected Ibis data type"): - bigframes.core.compile.ibis_types.ibis_dtype_to_bigframes_dtype( - ibis_dtypes.float32 - ) - - -IBIS_ARROW_DTYPES = ( - (ibis_dtypes.boolean, pa.bool_()), - (ibis_dtypes.date, pa.date32()), - (ibis_dtypes.Timestamp(), pa.timestamp("us")), - (ibis_dtypes.float64, pa.float64()), - ( - ibis_dtypes.Timestamp(timezone="UTC"), - pa.timestamp("us", tz="UTC"), - ), - ( - ibis_dtypes.Struct.from_tuples( - [ - ("name", ibis_dtypes.string()), - ("version", ibis_dtypes.int64()), - ] - ), - pa.struct( - [ - ("name", pa.string()), - ("version", pa.int64()), - ] - ), - ), - ( - ibis_dtypes.Struct.from_tuples( - [ - ( - "nested", - ibis_dtypes.Struct.from_tuples( - [ - ("field", ibis_dtypes.string()), - ] - ), - ), - ] - ), - pa.struct( - [ - ( - "nested", - pa.struct( - [ - ("field", pa.string()), - ] - ), - ), - ] - ), - ), -) - - -@pytest.mark.parametrize(("ibis_dtype", "arrow_dtype"), IBIS_ARROW_DTYPES) -def test_arrow_dtype_to_ibis_dtype(ibis_dtype, arrow_dtype): - result = bigframes.core.compile.ibis_types._arrow_dtype_to_ibis_dtype(arrow_dtype) - assert result == ibis_dtype - - -@pytest.mark.parametrize(("ibis_dtype", "arrow_dtype"), IBIS_ARROW_DTYPES) -def test_ibis_dtype_to_arrow_dtype(ibis_dtype, arrow_dtype): - result = bigframes.core.compile.ibis_types._ibis_dtype_to_arrow_dtype(ibis_dtype) - assert result == arrow_dtype - - -@pytest.mark.parametrize( - ("ibis_dtype", "bigquery_type"), - [(ibis_dtypes.String(), "STRING"), (ibis_dtypes.String(nullable=False), "STRING")], -) -def test_ibis_dtype_to_bigquery_type(ibis_dtype, bigquery_type): - result = ibis_bq_types.BigQueryType.from_ibis(ibis_dtype) - assert result == bigquery_type - - -@pytest.mark.parametrize( - ["bigframes_dtype", "ibis_dtype"], - [ - # This test covers all dtypes that BigQuery DataFrames can exactly map to Ibis - (pd.BooleanDtype(), ibis_dtypes.boolean), - (pd.ArrowDtype(pa.date32()), ibis_dtypes.date), - (pd.ArrowDtype(pa.timestamp("us")), ibis_dtypes.Timestamp()), - (pd.Float64Dtype(), ibis_dtypes.float64), - (pd.Int64Dtype(), ibis_dtypes.int64), - (pd.StringDtype(storage="pyarrow"), ibis_dtypes.string), - (pd.ArrowDtype(pa.time64("us")), ibis_dtypes.time), - ( - pd.ArrowDtype(pa.timestamp("us", tz="UTC")), # type: ignore - ibis_dtypes.Timestamp(timezone="UTC"), - ), - ], - ids=[ - "boolean", - "date", - "datetime", - "float", - "int", - "string", - "time", - "timestamp", - ], -) -def test_bigframes_dtype_converts(ibis_dtype, bigframes_dtype): - """Test all the Ibis data types needed to read BigQuery tables""" - result = bigframes.core.compile.ibis_types.bigframes_dtype_to_ibis_dtype( - bigframes_dtype - ) - assert result == ibis_dtype - - -@pytest.mark.parametrize( - ["bigframes_dtype_str", "ibis_dtype"], - [ - # This test covers all dtypes that BigQuery DataFrames can exactly map to Ibis - ("boolean", ibis_dtypes.boolean), - ("date32[day][pyarrow]", ibis_dtypes.date), - ("timestamp[us][pyarrow]", ibis_dtypes.Timestamp()), - ("Float64", ibis_dtypes.float64), - ("Int64", ibis_dtypes.int64), - ("string[pyarrow]", ibis_dtypes.string), - ("time64[us][pyarrow]", ibis_dtypes.time), - ( - "timestamp[us, tz=UTC][pyarrow]", - ibis_dtypes.Timestamp(timezone="UTC"), - ), - # Special case - "string" is acceptable for "string[pyarrow]" - ("string", ibis_dtypes.string), - ], -) -def test_bigframes_string_dtype_converts(ibis_dtype, bigframes_dtype_str): - """Test all the Ibis data types needed to read BigQuery tables""" - result = bigframes.core.compile.ibis_types.bigframes_dtype_to_ibis_dtype( - bigframes.dtypes.bigframes_type(bigframes_dtype_str) - ) - assert result == ibis_dtype - - -def test_unsupported_dtype_raises_unexpected_datatype(): - """Incompatible dtypes should fail when passed into BigQuery DataFrames""" - with pytest.raises(ValueError, match="Datatype has no ibis type mapping"): - bigframes.core.compile.ibis_types.bigframes_dtype_to_ibis_dtype(np.float32) - - -def test_unsupported_dtype_str_raises_unexpected_datatype(): - """Incompatible dtypes should fail when passed into BigQuery DataFrames""" - with pytest.raises(ValueError, match="Datatype has no ibis type mapping"): - bigframes.core.compile.ibis_types.bigframes_dtype_to_ibis_dtype("int64") - - -@pytest.mark.parametrize( - ["literal", "ibis_scalar"], - [ - (True, ibis_types.literal(True, ibis_dtypes.boolean)), - (5, ibis_types.literal(5, ibis_dtypes.int64)), - (-33.2, ibis_types.literal(-33.2, ibis_dtypes.float64)), - ], -) -def test_literal_to_ibis_scalar_converts(literal, ibis_scalar): - assert bigframes.core.compile.ibis_types.literal_to_ibis_scalar(literal).equals( - ibis_scalar - ) diff --git a/tests/unit/core/test_indexes.py b/tests/unit/core/test_indexes.py deleted file mode 100644 index 6e739c9dc98..00000000000 --- a/tests/unit/core/test_indexes.py +++ /dev/null @@ -1,39 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import bigframes.core.indexes - - -def test_index_repr_with_uninitialized_object(): - """Ensures Index.__init__ can be paused in a visual debugger without crashing. - - Regression test for https://github.com/googleapis/python-bigquery-dataframes/issues/728 - """ - # Avoid calling __init__ to simulate pausing __init__ in a debugger. - # https://stackoverflow.com/a/6384982/101923 - index = object.__new__(bigframes.core.indexes.Index) - got = repr(index) - assert "Index" in got - - -def test_multiindex_repr_with_uninitialized_object(): - """Ensures MultiIndex.__init__ can be paused in a visual debugger without crashing. - - Regression test for https://github.com/googleapis/python-bigquery-dataframes/issues/728 - """ - # Avoid calling __init__ to simulate pausing __init__ in a debugger. - # https://stackoverflow.com/a/6384982/101923 - index = object.__new__(bigframes.core.indexes.MultiIndex) - got = repr(index) - assert "MultiIndex" in got diff --git a/tests/unit/core/test_pyarrow_utils.py b/tests/unit/core/test_pyarrow_utils.py deleted file mode 100644 index 155c36d268e..00000000000 --- a/tests/unit/core/test_pyarrow_utils.py +++ /dev/null @@ -1,65 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import itertools - -import numpy as np -import pyarrow as pa -import pytest - -from bigframes.core import pyarrow_utils - -PA_TABLE = pa.table({f"col_{i}": np.random.rand(1000) for i in range(10)}) - -# 17, 3, 929 coprime -N = 17 -MANY_SMALL_BATCHES = PA_TABLE.to_batches(max_chunksize=3) -FEW_BIG_BATCHES = PA_TABLE.to_batches(max_chunksize=929) - - -@pytest.mark.parametrize( - ["batches", "page_size"], - [ - (MANY_SMALL_BATCHES, N), - (FEW_BIG_BATCHES, N), - ], -) -def test_chunk_by_row_count(batches, page_size): - results = list(pyarrow_utils.chunk_by_row_count(batches, page_size=page_size)) - - for i, batches in enumerate(results): - if i != len(results) - 1: - assert sum(map(lambda x: x.num_rows, batches)) == page_size - else: - # final page can be smaller - assert sum(map(lambda x: x.num_rows, batches)) <= page_size - - reconstructed = pa.Table.from_batches(itertools.chain.from_iterable(results)) - assert reconstructed.equals(PA_TABLE) - - -@pytest.mark.parametrize( - ["batches", "max_rows"], - [ - (MANY_SMALL_BATCHES, N), - (FEW_BIG_BATCHES, N), - ], -) -def test_truncate_pyarrow_iterable(batches, max_rows): - results = list( - pyarrow_utils.truncate_pyarrow_iterable(batches, max_results=max_rows) - ) - - reconstructed = pa.Table.from_batches(results) - assert reconstructed.equals(PA_TABLE.slice(length=max_rows)) diff --git a/tests/unit/core/test_pyformat.py b/tests/unit/core/test_pyformat.py deleted file mode 100644 index 239a59237f6..00000000000 --- a/tests/unit/core/test_pyformat.py +++ /dev/null @@ -1,698 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -"""Tests for the pyformat feature.""" - -# TODO(tswast): consolidate with pandas-gbq and bigquery-magics. See: -# https://github.com/googleapis/python-bigquery-magics/blob/main/tests/unit/bigquery/test_pyformat.py - -from __future__ import annotations - -import datetime -import decimal -from typing import Any, Dict, List - -import db_dtypes # type: ignore -import geopandas # type: ignore -import google.cloud.bigquery -import google.cloud.bigquery.table -import numpy -import pandas -import pyarrow -import pytest -import shapely.geometry # type: ignore - -from bigframes.core import pyformat -from bigframes.testing import mocks - - -@pytest.fixture -def session(): - return mocks.create_bigquery_session() - - -@pytest.mark.parametrize( - ("sql_template", "expected"), - ( - ( - "{my_project}.{my_dataset}.{my_table}", - ["my_project", "my_dataset", "my_table"], - ), - ( - "{{not a format variable}}", - [], - ), - ), -) -def test_parse_fields(sql_template: str, expected: List[str]): - fields = pyformat._parse_fields(sql_template) - fields.sort() - expected.sort() - assert fields == expected - - -def test_get_error_context_at_pos_invalid_pos(): - assert pyformat.get_error_context_at_pos("SELECT 1", -1) == "" - assert pyformat.get_error_context_at_pos("SELECT 1", 100) == "" - - -def test_get_error_context_at_pos_single_line(): - sql = "SELECT {foo}" - # pos of '{' is 7 - context = pyformat.get_error_context_at_pos(sql, 7) - expected = " 1: SELECT {foo}\n ^" - assert context == expected - - -def test_get_error_context_at_pos_multi_line(): - sql = "SELECT 1\nFROM my_table\nWHERE col = {foo}\nAND active = True\nLIMIT 10" - # Lines: - # 1: SELECT 1 (len 9 including \n) - # 2: FROM my_table (len 14 including \n) -> total 23 - # 3: WHERE col = {foo} -> '{' is at 23 + 12 = 35 - - context = pyformat.get_error_context_at_pos(sql, 35) - expected = ( - " 1: SELECT 1\n" - " 2: FROM my_table\n" - " 3: WHERE col = {foo}\n" - " ^\n" - " 4: AND active = True\n" - " 5: LIMIT 10" - ) - assert context == expected - - -def test_get_error_context_at_pos_multi_line_limits(): - # Test that it only shows at most 2 lines before and 2 lines after - sql = ( - "LINE 1\n" - "LINE 2\n" - "LINE 3\n" - "LINE 4\n" - "LINE 5\n" - "TARGET {foo}\n" - "LINE 7\n" - "LINE 8\n" - "LINE 9\n" - "LINE 10" - ) - # Line lengths: - # LINE 1\n (7) - # LINE 2\n (7) -> 14 - # LINE 3\n (7) -> 21 - # LINE 4\n (7) -> 28 - # LINE 5\n (7) -> 35 - # TARGET {foo}\n -> '{' is at 35 + 7 = 42 - - context = pyformat.get_error_context_at_pos(sql, 42) - expected = ( - " 4: LINE 4\n" - " 5: LINE 5\n" - " 6: TARGET {foo}\n" - " ^\n" - " 7: LINE 7\n" - " 8: LINE 8" - ) - assert context == expected - - -def test_pyformat_with_unsupported_type_raises_typeerror(session): - pyformat_args = {"my_object": object()} - sql = "SELECT {my_object}" - - with pytest.raises(TypeError, match="my_object has unsupported type: "): - pyformat.pyformat(sql, pyformat_args=pyformat_args, session=session) - - -def test_pyformat_with_missing_variable_raises_valueerror(session): - pyformat_args: Dict[str, Any] = {} - sql = "SELECT {my_object}" - - with pytest.raises(ValueError) as exc_info: - pyformat.pyformat(sql, pyformat_args=pyformat_args, session=session) - - err_msg = str(exc_info.value) - assert "Undetected variable 'my_object' in SQL template" in err_msg - assert "Did you mean to escape '{' and '}'" in err_msg - assert " 1: SELECT {my_object}" in err_msg - assert " ^" in err_msg - - -def test_pyformat_with_unescaped_braces_raises_valueerror_with_context(session): - pyformat_args = {"active": True} - sql = """SELECT * FROM my_table -WHERE json_col = { "generation_config": { "temperature": 0.9 } } -AND active = {active} -""" - - with pytest.raises(ValueError) as exc_info: - pyformat.pyformat(sql, pyformat_args=pyformat_args, session=session) - - err_msg = str(exc_info.value) - assert "Undetected variable ' \"generation_config\"' in SQL template" in err_msg - assert "Did you mean to escape '{' and '}'" in err_msg - # The triple quote string starts with SELECT immediately, so lines are: - # 1: SELECT * FROM my_table - # 2: WHERE json_col = { "generation_config": { "temperature": 0.9 } } - # 3: AND active = {active} - assert " 1: SELECT * FROM my_table" in err_msg - assert ( - ' 2: WHERE json_col = { "generation_config": { "temperature": 0.9 } }' - in err_msg - ) - assert " ^" in err_msg - assert " 3: AND active = {active}" in err_msg - - -@pytest.mark.parametrize( - ("sql_template", "expected_error"), - ( - pytest.param( - "SELECT {foo", - "expected '}' before end of string", - id="missing_closing_brace", - ), - pytest.param( - "SELECT foo}", - "Single '}' encountered in format string", - id="missing_opening_brace", - ), - ), -) -def test_pyformat_with_malformed_template_raises_valueerror( - session, sql_template: str, expected_error: str -): - pyformat_args: Dict[str, Any] = {} - - # Case 1: Single '{' (unmatched) - with pytest.raises(ValueError) as exc_info: - pyformat.pyformat(sql_template, pyformat_args=pyformat_args, session=session) - - error_message = str(exc_info.value) - assert "Failed to parse SQL template" in error_message - assert "Did you mean to escape '{' and '}'" in error_message - assert expected_error in error_message - - -def test_pyformat_with_no_variables(session): - pyformat_args: Dict[str, Any] = {} - sql = "SELECT '{{escaped curly brackets}}'" - expected_sql = "SELECT '{escaped curly brackets}'" - got_sql = pyformat.pyformat(sql, pyformat_args=pyformat_args, session=session) - assert got_sql == expected_sql - - -@pytest.mark.parametrize( - ("df_pd", "expected_struct"), - ( - pytest.param( - pandas.DataFrame(), - "STRUCT<>", - id="empty", - ), - pytest.param( - # Empty columns default to floating point, just like pandas. - pandas.DataFrame({"empty column": []}), - "STRUCT<`empty column` FLOAT64>", - id="empty column", - ), - # Regression tests for b/428190014. - # - # Test every BigQuery type we support, especially those where the legacy - # SQL type name differs from the GoogleSQL type name. - # - # See: - # https://cloud.google.com/bigquery/docs/reference/standard-sql/data-types - # and compare to the legacy types at - # https://cloud.google.com/bigquery/docs/data-types - # - # Test these against the real BigQuery dry run API in - # tests/system/small/pandas/io/api/test_read_gbq_colab.py - pytest.param( - pandas.DataFrame( - { - "ints": pandas.Series( - [[1], [2], [3]], - dtype=pandas.ArrowDtype(pyarrow.list_(pyarrow.int64())), - ), - "floats": pandas.Series( - [[1.0], [2.0], [3.0]], - dtype=pandas.ArrowDtype(pyarrow.list_(pyarrow.float64())), - ), - } - ), - "STRUCT<`ints` ARRAY, `floats` ARRAY>", - id="arrays", - ), - pytest.param( - pandas.DataFrame( - { - "bool": pandas.Series([True, False, True], dtype="bool"), - "boolean": pandas.Series([True, None, True], dtype="boolean"), - "object": pandas.Series([True, None, True], dtype="object"), - "arrow": pandas.Series( - [True, None, True], dtype=pandas.ArrowDtype(pyarrow.bool_()) - ), - } - ), - "STRUCT<`bool` BOOL, `boolean` BOOL, `object` BOOL, `arrow` BOOL>", - id="bools", - ), - pytest.param( - pandas.DataFrame( - { - "bytes": pandas.Series([b"a", b"b", b"c"], dtype=numpy.bytes_), - "object": pandas.Series([b"a", None, b"c"], dtype="object"), - "arrow": pandas.Series( - [b"a", None, b"c"], dtype=pandas.ArrowDtype(pyarrow.binary()) - ), - } - ), - "STRUCT<`bytes` BYTES, `object` BYTES, `arrow` BYTES>", - id="bytes", - ), - pytest.param( - pandas.DataFrame( - { - "object": pandas.Series( - [ - datetime.date(2023, 11, 23), - None, - datetime.date(1970, 1, 1), - ], - dtype="object", - ), - "arrow": pandas.Series( - [ - datetime.date(2023, 11, 23), - None, - datetime.date(1970, 1, 1), - ], - dtype=pandas.ArrowDtype(pyarrow.date32()), - ), - } - ), - "STRUCT<`object` DATE, `arrow` DATE>", - id="dates", - ), - pytest.param( - pandas.DataFrame( - { - "object": pandas.Series( - [ - datetime.datetime(2023, 11, 23, 13, 14, 15), - None, - datetime.datetime(1970, 1, 1, 0, 0, 0), - ], - dtype="object", - ), - "datetime64": pandas.Series( - [ - datetime.datetime(2023, 11, 23, 13, 14, 15), - None, - datetime.datetime(1970, 1, 1, 0, 0, 0), - ], - dtype="datetime64[us]", - ), - "arrow": pandas.Series( - [ - datetime.datetime(2023, 11, 23, 13, 14, 15), - None, - datetime.datetime(1970, 1, 1, 0, 0, 0), - ], - dtype=pandas.ArrowDtype(pyarrow.timestamp("us")), - ), - } - ), - "STRUCT<`object` DATETIME, `datetime64` DATETIME, `arrow` DATETIME>", - id="datetimes", - ), - pytest.param( - pandas.DataFrame( - { - "object": pandas.Series( - [ - shapely.geometry.Point(145.0, -37.8), - None, - shapely.geometry.Point(-122.3, 47.6), - ], - dtype="object", - ), - "geopandas": geopandas.GeoSeries( - [ - shapely.geometry.Point(145.0, -37.8), - None, - shapely.geometry.Point(-122.3, 47.6), - ] - ), - } - ), - "STRUCT<`object` GEOGRAPHY, `geopandas` GEOGRAPHY>", - id="geographys", - ), - # TODO(tswast): Add INTERVAL once BigFrames supports it. - pytest.param( - pandas.DataFrame( - { - # TODO(tswast): Is there an equivalent object type we can use here? - # TODO(tswast): Add built-in Arrow extension type - "db_dtypes": pandas.Series( - ["{}", None, "123"], - dtype=pandas.ArrowDtype(db_dtypes.JSONArrowType()), - ), - } - ), - "STRUCT<`db_dtypes` JSON>", - id="jsons", - ), - pytest.param( - pandas.DataFrame( - { - "int64": pandas.Series([1, 2, 3], dtype="int64"), - "Int64": pandas.Series([1, None, 3], dtype="Int64"), - "object": pandas.Series([1, None, 3], dtype="object"), - "arrow": pandas.Series( - [1, None, 3], dtype=pandas.ArrowDtype(pyarrow.int64()) - ), - } - ), - "STRUCT<`int64` INT64, `Int64` INT64, `object` INT64, `arrow` INT64>", - id="ints", - ), - pytest.param( - pandas.DataFrame( - { - "object": pandas.Series( - [decimal.Decimal("1.23"), None, decimal.Decimal("4.56")], - dtype="object", - ), - "arrow": pandas.Series( - [decimal.Decimal("1.23"), None, decimal.Decimal("4.56")], - dtype=pandas.ArrowDtype(pyarrow.decimal128(38, 9)), - ), - } - ), - "STRUCT<`object` NUMERIC, `arrow` NUMERIC>", - id="numerics", - ), - pytest.param( - pandas.DataFrame( - { - # TODO(tswast): Add object type for BIGNUMERIC. Can bigframes disambiguate? - "arrow": pandas.Series( - [decimal.Decimal("1.23"), None, decimal.Decimal("4.56")], - dtype=pandas.ArrowDtype(pyarrow.decimal256(76, 38)), - ), - } - ), - "STRUCT<`arrow` BIGNUMERIC>", - id="bignumerics", - ), - pytest.param( - pandas.DataFrame( - { - "float64": pandas.Series([1.23, None, 4.56], dtype="float64"), - "Float64": pandas.Series([1.23, None, 4.56], dtype="Float64"), - "object": pandas.Series([1.23, None, 4.56], dtype="object"), - "arrow": pandas.Series( - [1.23, None, 4.56], dtype=pandas.ArrowDtype(pyarrow.float64()) - ), - } - ), - "STRUCT<`float64` FLOAT64, `Float64` FLOAT64, `object` FLOAT64, `arrow` FLOAT64>", - id="floats", - ), - # TODO(tswast): Add RANGE once BigFrames supports it. - pytest.param( - pandas.DataFrame( - { - "string": pandas.Series(["a", "b", "c"], dtype="string[python]"), - "object": pandas.Series(["a", None, "c"], dtype="object"), - "arrow": pandas.Series(["a", None, "c"], dtype="string[pyarrow]"), - } - ), - "STRUCT<`string` STRING, `object` STRING, `arrow` STRING>", - id="strings", - ), - pytest.param( - pandas.DataFrame( - { - # TODO(tswast): Add object type for STRUCT? How to tell apart from JSON? - "arrow": pandas.Series( - [{"a": 1, "b": 1.0, "c": "c"}], - dtype=pandas.ArrowDtype( - pyarrow.struct( - [ - ("a", pyarrow.int64()), - ("b", pyarrow.float64()), - ("c", pyarrow.string()), - ] - ) - ), - ), - } - ), - "STRUCT<`arrow` STRUCT<`a` INT64, `b` FLOAT64, `c` STRING>>", - id="structs", - ), - pytest.param( - pandas.DataFrame( - { - "object": pandas.Series( - [ - datetime.time(0, 0, 0), - None, - datetime.time(13, 7, 11), - ], - dtype="object", - ), - "arrow": pandas.Series( - [ - datetime.time(0, 0, 0), - None, - datetime.time(13, 7, 11), - ], - dtype=pandas.ArrowDtype(pyarrow.time64("us")), - ), - } - ), - "STRUCT<`object` TIME, `arrow` TIME>", - id="times", - ), - pytest.param( - pandas.DataFrame( - { - "object": pandas.Series( - [ - datetime.datetime( - 2023, 11, 23, 13, 14, 15, tzinfo=datetime.timezone.utc - ), - None, - datetime.datetime( - 1970, 1, 1, 0, 0, 0, tzinfo=datetime.timezone.utc - ), - ], - dtype="object", - ), - "datetime64": pandas.Series( - [ - datetime.datetime(2023, 11, 23, 13, 14, 15), - None, - datetime.datetime(1970, 1, 1, 0, 0, 0), - ], - dtype="datetime64[us]", - ).dt.tz_localize("UTC"), - "arrow": pandas.Series( - [ - datetime.datetime( - 2023, 11, 23, 13, 14, 15, tzinfo=datetime.timezone.utc - ), - None, - datetime.datetime( - 1970, 1, 1, 0, 0, 0, tzinfo=datetime.timezone.utc - ), - ], - dtype=pandas.ArrowDtype(pyarrow.timestamp("us", "UTC")), - ), - } - ), - "STRUCT<`object` TIMESTAMP, `datetime64` TIMESTAMP, `arrow` TIMESTAMP>", - id="timestamps", - ), - # More complicated edge cases: - pytest.param( - pandas.DataFrame( - { - "array of struct col": [ - [{"subfield": {"subsubfield": 1}, "subfield2": 2}], - ], - } - ), - "STRUCT<`array of struct col` ARRAY, `subfield2` INT64>>>", - id="array_of_structs", - ), - pytest.param( - pandas.DataFrame({"c1": [1, 2, 3], "c2": ["a", "b", "c"]}).rename( - columns={"c1": "c", "c2": "c"} - ), - "STRUCT<`c` INT64, `c_1` STRING>", - id="duplicate_column_names", - ), - ), -) -def test_pyformat_with_pandas_dataframe_dry_run_no_session(df_pd, expected_struct): - pyformat_args: Dict[str, Any] = {"my_pandas_df": df_pd} - sql = "SELECT * FROM {my_pandas_df}" - expected_sql = f"SELECT * FROM UNNEST(ARRAY<{expected_struct}>[])" - got_sql = pyformat.pyformat( - sql, pyformat_args=pyformat_args, dry_run=True, session=None - ) - assert got_sql == expected_sql - - -def test_pyformat_with_pandas_dataframe_not_dry_run_no_session_raises_valueerror(): - pyformat_args: Dict[str, Any] = {"my_pandas_df": pandas.DataFrame()} - sql = "SELECT * FROM {my_pandas_df}" - - with pytest.raises(ValueError, match="my_pandas_df"): - pyformat.pyformat(sql, pyformat_args=pyformat_args) - - -def test_pyformat_with_query_string_replaces_variables(session): - pyformat_args = { - "my_string": "`my_table`", - "max_value": 2.25, - "year": 2025, - "null_value": None, - # Unreferenced values of unsupported type shouldn't cause issues. - "my_object": object(), - } - - sql = """ - SELECT {year} - year AS age, - @myparam AS myparam, - '{{my_string}}' AS escaped_string, - * - FROM {my_string} - WHERE height < {max_value} - """.strip() - - expected_sql = """ - SELECT 2025 - year AS age, - @myparam AS myparam, - '{my_string}' AS escaped_string, - * - FROM `my_table` - WHERE height < 2.25 - """.strip() - - got_sql = pyformat.pyformat(sql, pyformat_args=pyformat_args, session=session) - assert got_sql == expected_sql - - -@pytest.mark.parametrize( - ("table", "expected_sql"), - ( - ( - google.cloud.bigquery.Table("my-project.my_dataset.my_table"), - "SELECT * FROM `my-project`.`my_dataset`.`my_table`", - ), - ( - google.cloud.bigquery.TableReference( - google.cloud.bigquery.DatasetReference("some-project", "some_dataset"), - "some_table", - ), - "SELECT * FROM `some-project`.`some_dataset`.`some_table`", - ), - ( - google.cloud.bigquery.table.TableListItem( - { - "tableReference": { - "projectId": "ListedProject", - "datasetId": "ListedDataset", - "tableId": "ListedTable", - } - } - ), - "SELECT * FROM `ListedProject`.`ListedDataset`.`ListedTable`", - ), - ( - google.cloud.bigquery.TableReference( - google.cloud.bigquery.DatasetReference( - "my-project", "my-catalog.my-namespace" - ), - "my-table", - ), - "SELECT * FROM `my-project`.`my-catalog`.`my-namespace`.`my-table`", - ), - ), -) -def test_pyformat_with_table_replaces_variables(table, expected_sql, session=session): - pyformat_args = { - "table": table, - # Unreferenced values of unsupported type shouldn't cause issues. - "my_object": object(), - } - sql = "SELECT * FROM {table}" - got_sql = pyformat.pyformat(sql, pyformat_args=pyformat_args, session=session) - assert got_sql == expected_sql - - -def test_pyformat_with_bigframes_dataframe_biglake_table(session): - # Create a real BigFrames DataFrame that points to a BigLake table. - import bigframes.core.array_value as array_value - import bigframes.core.blocks as blocks - import bigframes.core.bq_data as bq_data - import bigframes.dataframe - - # Define the BigLake table - project_id = "my-project" - catalog_id = "my-catalog" - namespace_id = "my-namespace" - table_id = "my-table" - schema = (google.cloud.bigquery.SchemaField("col", "INTEGER"),) - - biglake_table = bq_data.BiglakeIcebergTable( - project_id=project_id, - catalog_id=catalog_id, - namespace_id=namespace_id, - table_id=table_id, - physical_schema=schema, - cluster_cols=(), - metadata=bq_data.TableMetadata( - location=bq_data.BigQueryRegion("us-central1"), - type="TABLE", - ), - ) - - # ArrayValue.from_table is what read_gbq uses. - av = array_value.ArrayValue.from_table(biglake_table, session) - block = blocks.Block(av, index_columns=[], column_labels=["col"]) - df = bigframes.dataframe.DataFrame(block) - - pyformat_args = {"df": df} - sql = "SELECT * FROM {df}" - - got_sql = pyformat.pyformat(sql, pyformat_args=pyformat_args, session=session) - - # For BigLake, we now expect a SUBQUERY, not a view reference. - # The subquery should have correctly quoted 4-part ID. - assert "SELECT" in got_sql - assert project_id in got_sql - assert catalog_id in got_sql - assert namespace_id in got_sql - assert table_id in got_sql - assert got_sql.startswith("SELECT * FROM (SELECT") - assert got_sql.endswith(")") diff --git a/tests/unit/core/test_slices.py b/tests/unit/core/test_slices.py deleted file mode 100644 index 745db45eab6..00000000000 --- a/tests/unit/core/test_slices.py +++ /dev/null @@ -1,61 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import pytest - -import bigframes.core.slices as slices - - -@pytest.mark.parametrize( - ["slice", "input_rows", "expected"], - [ - ((1, 2, 3), 3, 1), - ((-3, 400, None), 401, 2), - ((5, 505, None), 300, 295), - ((1, 10, 4), 10, 3), - ((1, 9, 4), 10, 2), - ((-1, -10, -4), 10, 3), - ((-1, -10, 4), 10, 0), - ((99, 100, 1), 9, 0), - ], -) -def test_slice_row_count(slice, input_rows, expected): - assert expected == slices.slice_output_rows(slice, input_rows) - - -@pytest.mark.parametrize( - ["slice", "input_rows", "expected"], - [ - ((1, 2, 3), 3, (1, 2, 3)), - ((-3, 400, None), 401, (-3, 400, None)), - ((5, 505, None), 300, (5, None, None)), - ((99, 100, 1), 9, (99, None, None)), - ], -) -def test_remove_unused_parts(slice, input_rows, expected): - assert expected == slices.remove_unused_parts(slice, input_rows) - - -@pytest.mark.parametrize( - ["slice", "input_rows", "expected"], - [ - ((1, 2, 3), 3, (1, 2, 3)), - ((-3, 400, None), 401, (398, 400, 1)), - ((5, 505, None), 300, (5, 300, 1)), - ((None, None, None), 300, (0, None, 1)), - ((None, None, -1), 300, (299, None, -1)), - ], -) -def test_to_forward_offsets(slice, input_rows, expected): - assert expected == slices.to_forward_offsets(slice, input_rows) diff --git a/tests/unit/core/test_sql.py b/tests/unit/core/test_sql.py deleted file mode 100644 index 04ebb28764d..00000000000 --- a/tests/unit/core/test_sql.py +++ /dev/null @@ -1,66 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from bigframes.core import sql - - -def test_create_vector_search_sql_simple(): - result_query = sql.create_vector_search_sql( - sql_string="SELECT embedding FROM my_embeddings_table WHERE id = 1", - base_table="my_base_table", - column_to_search="my_embedding_column", - ) - assert ( - result_query - == """ - SELECT - query.*, - base.*, - distance, - FROM VECTOR_SEARCH(TABLE `my_base_table`, -'my_embedding_column', -(SELECT embedding FROM my_embeddings_table WHERE id = 1)) - """ - ) - - -def test_create_vector_search_sql_all_named_parameters(): - result_query = sql.create_vector_search_sql( - sql_string="SELECT embedding FROM my_embeddings_table WHERE id = 1", - base_table="my_base_table", - column_to_search="my_embedding_column", - query_column_to_search="another_embedding_column", - top_k=10, - distance_type="cosine", - options={ - "fraction_lists_to_search": 0.1, - "use_brute_force": False, - }, - ) - assert ( - result_query - == """ - SELECT - query.*, - base.*, - distance, - FROM VECTOR_SEARCH(TABLE `my_base_table`, -'my_embedding_column', -(SELECT embedding FROM my_embeddings_table WHERE id = 1), -query_column_to_search => 'another_embedding_column', -top_k=> 10, -distance_type => 'cosine', -options => '{"fraction_lists_to_search": 0.1, "use_brute_force": false}') - """ - ) diff --git a/tests/unit/core/test_windowspec.py b/tests/unit/core/test_windowspec.py deleted file mode 100644 index b9de7641363..00000000000 --- a/tests/unit/core/test_windowspec.py +++ /dev/null @@ -1,50 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import pytest - -from bigframes.core import window_spec - - -@pytest.mark.parametrize(("start", "end"), [(-1, -2), (1, -2), (2, 1)]) -def test_invalid_rows_window_boundary_raise_error(start, end): - with pytest.raises(ValueError): - window_spec.RowsWindowBounds(start, end) - - -@pytest.mark.parametrize(("start", "end"), [(-1, -2), (1, -2), (2, 1)]) -def test_invalid_range_window_boundary_raise_error(start, end): - with pytest.raises(ValueError): - window_spec.RangeWindowBounds(start, end) - - -@pytest.mark.parametrize( - ("window", "closed", "start", "end"), - [ - pytest.param(3, "left", -3, -1, id="left"), - pytest.param(3, "right", -2, 0, id="right"), - pytest.param(3, "neither", -2, -1, id="neither"), - pytest.param(3, "both", -3, 0, id="both"), - ], -) -def test_rows_window_bounds_from_window_size(window, closed, start, end): - actual_result = window_spec.RowsWindowBounds.from_window_size(window, closed) - - expected_result = window_spec.RowsWindowBounds(start, end) - assert actual_result == expected_result - - -def test_rows_window_bounds_from_window_size_invalid_closed_raise_error(): - with pytest.raises(ValueError): - window_spec.RowsWindowBounds.from_window_size(3, "whatever") # type:ignore diff --git a/tests/unit/core/tools/__init__.py b/tests/unit/core/tools/__init__.py deleted file mode 100644 index 0a2669d7a25..00000000000 --- a/tests/unit/core/tools/__init__.py +++ /dev/null @@ -1,13 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. diff --git a/tests/unit/core/tools/test_bigquery_schema.py b/tests/unit/core/tools/test_bigquery_schema.py deleted file mode 100644 index 2b6693c13e6..00000000000 --- a/tests/unit/core/tools/test_bigquery_schema.py +++ /dev/null @@ -1,191 +0,0 @@ -import pytest -from google.cloud import bigquery - -from bigframes.core.tools import bigquery_schema - - -# --- Tests for _type_to_sql --- -@pytest.mark.parametrize( - "field, expected_sql", - [ - # Simple types - # Note: the REST API will return Legacy SQL data types, but we need to - # map to GoogleSQL. See internal issue b/428190014. - (bigquery.SchemaField("test_field", "INTEGER"), "INT64"), - (bigquery.SchemaField("test_field", "STRING"), "STRING"), - (bigquery.SchemaField("test_field", "BOOLEAN"), "BOOL"), - # RECORD/STRUCT types with nested fields directly - ( - bigquery.SchemaField( - "test_field", - "RECORD", - fields=(bigquery.SchemaField("sub_field", "STRING"),), - ), - "STRUCT<`sub_field` STRING>", - ), - ( - bigquery.SchemaField( - "test_field", - "STRUCT", - fields=( - bigquery.SchemaField("sub_field", "INTEGER"), - bigquery.SchemaField("another", "BOOLEAN"), - ), - ), - "STRUCT<`sub_field` INT64, `another` BOOL>", - ), - # Array is handled by _field_to_sql, instead. - (bigquery.SchemaField("test_field", "NUMERIC", mode="REPEATED"), "NUMERIC"), - ( - bigquery.SchemaField( - "test_field", - "RECORD", - mode="REPEATED", - fields=(bigquery.SchemaField("sub_field", "STRING"),), - ), - "STRUCT<`sub_field` STRING>", - ), - ], -) -def test_type_to_sql(field, expected_sql): - assert bigquery_schema._type_to_sql(field) == expected_sql - - -# --- Tests for _field_to_sql --- -@pytest.mark.parametrize( - "field, expected_sql", - [ - # Simple field - # Note: the REST API will return Legacy SQL data types, but we need to - # map to GoogleSQL. See internal issue b/428190014. - (bigquery.SchemaField("id", "INTEGER", "NULLABLE"), "`id` INT64"), - (bigquery.SchemaField("name", "STRING", "NULLABLE"), "`name` STRING"), - # Repeated field - (bigquery.SchemaField("tags", "STRING", "REPEATED"), "`tags` ARRAY"), - # Repeated RECORD - ( - bigquery.SchemaField( - "addresses", - "RECORD", - "REPEATED", - fields=( - bigquery.SchemaField("street", "STRING"), - bigquery.SchemaField("zip", "INTEGER"), - ), - ), - "`addresses` ARRAY>", - ), - # Simple STRUCT - ( - bigquery.SchemaField( - "person", - "STRUCT", - "NULLABLE", - fields=( - bigquery.SchemaField("age", "INTEGER"), - bigquery.SchemaField("city", "STRING"), - ), - ), - "`person` STRUCT<`age` INT64, `city` STRING>", - ), - ], -) -def test_field_to_sql(field, expected_sql): - assert bigquery_schema._field_to_sql(field) == expected_sql - - -# --- Tests for _to_struct --- -@pytest.mark.parametrize( - "bqschema, expected_sql", - [ - # Empty schema - ((), "STRUCT<>"), - # Simple fields - ( - ( - bigquery.SchemaField("id", "INTEGER"), - bigquery.SchemaField("name", "STRING"), - ), - "STRUCT<`id` INT64, `name` STRING>", - ), - # Nested RECORD/STRUCT - ( - ( - bigquery.SchemaField("item_id", "INTEGER"), - bigquery.SchemaField( - "details", - "RECORD", - "NULLABLE", - fields=( - bigquery.SchemaField("price", "NUMERIC"), - bigquery.SchemaField("currency", "STRING"), - ), - ), - ), - "STRUCT<`item_id` INT64, `details` STRUCT<`price` NUMERIC, `currency` STRING>>", - ), - # Repeated field - ( - ( - bigquery.SchemaField("user_id", "STRING"), - bigquery.SchemaField("emails", "STRING", "REPEATED"), - ), - "STRUCT<`user_id` STRING, `emails` ARRAY>", - ), - # Mixed types including complex nested repeated - ( - ( - bigquery.SchemaField("event_name", "STRING"), - bigquery.SchemaField( - "participants", - "RECORD", - "REPEATED", - fields=( - bigquery.SchemaField("p_id", "INTEGER"), - bigquery.SchemaField("roles", "STRING", "REPEATED"), - ), - ), - bigquery.SchemaField("timestamp", "TIMESTAMP"), - ), - "STRUCT<`event_name` STRING, `participants` ARRAY>>, `timestamp` TIMESTAMP>", - ), - ], -) -def test_to_struct(bqschema, expected_sql): - assert bigquery_schema._to_struct(bqschema) == expected_sql - - -# --- Tests for to_sql_dry_run --- -@pytest.mark.parametrize( - "bqschema, expected_sql", - [ - # Empty schema - ((), "UNNEST(ARRAY>[])"), - # Simple schema - ( - ( - bigquery.SchemaField("id", "INTEGER"), - bigquery.SchemaField("name", "STRING"), - ), - "UNNEST(ARRAY>[])", - ), - # Complex schema with nested and repeated fields - ( - ( - bigquery.SchemaField("order_id", "STRING"), - bigquery.SchemaField( - "items", - "RECORD", - "REPEATED", - fields=( - bigquery.SchemaField("item_name", "STRING"), - bigquery.SchemaField("quantity", "INTEGER"), - ), - ), - ), - "UNNEST(ARRAY>>>[])", - ), - ], -) -def test_to_sql_dry_run(bqschema, expected_sql): - assert bigquery_schema.to_sql_dry_run(bqschema) == expected_sql diff --git a/tests/unit/core/tools/test_datetimes.py b/tests/unit/core/tools/test_datetimes.py deleted file mode 100644 index 96a6b14ef8d..00000000000 --- a/tests/unit/core/tools/test_datetimes.py +++ /dev/null @@ -1,43 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from typing import cast -from unittest import mock - -import bigframes.core.tools.datetimes -import bigframes.dtypes -import bigframes.pandas -import bigframes.testing.mocks - - -def test_to_datetime_with_series_and_format_doesnt_cache(monkeypatch): - df = bigframes.testing.mocks.create_dataframe(monkeypatch) - series = mock.Mock(spec=bigframes.pandas.Series, wraps=df["col"]) - dt_series = cast( - bigframes.pandas.Series, - bigframes.core.tools.datetimes.to_datetime(series, format="%Y%m%d"), - ) - series._cached.assert_not_called() - assert dt_series.dtype == bigframes.dtypes.DATETIME_DTYPE - - -def test_to_datetime_with_series_and_format_utc_doesnt_cache(monkeypatch): - df = bigframes.testing.mocks.create_dataframe(monkeypatch) - series = mock.Mock(spec=bigframes.pandas.Series, wraps=df["col"]) - dt_series = cast( - bigframes.pandas.Series, - bigframes.core.tools.datetimes.to_datetime(series, format="%Y%m%d", utc=True), - ) - series._cached.assert_not_called() - assert dt_series.dtype == bigframes.dtypes.TIMESTAMP_DTYPE diff --git a/tests/unit/display/test_anywidget.py b/tests/unit/display/test_anywidget.py deleted file mode 100644 index 25f19cf495c..00000000000 --- a/tests/unit/display/test_anywidget.py +++ /dev/null @@ -1,543 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import signal -import unittest.mock as mock - -import pandas as pd -import pyarrow as pa -import pytest - -import bigframes - -# Skip if anywidget/traitlets not installed, though they should be in the dev env -pytest.importorskip("anywidget") -pytest.importorskip("traitlets") - -import bigframes.dataframe -import bigframes.dtypes -import bigframes.series -from bigframes.display.anywidget import TableWidget - - -def test_navigation_to_invalid_page_resets_to_valid_page_without_deadlock(): - """ - Given a widget on a page beyond available data, when navigating, - then it should reset to the last valid page without deadlock. - """ - mock_df = mock.create_autospec(bigframes.dataframe.DataFrame, instance=True) - mock_df.columns = ["col1"] - mock_df.dtypes = {"col1": "object"} - - mock_block = mock.Mock() - mock_block.has_index = False - mock_df._block = mock_block - - # We mock _initial_load to avoid complex setup - with ( - mock.patch.object(TableWidget, "_initial_load"), - bigframes.option_context( - "display.render_mode", "anywidget", "display.max_rows", 10 - ), - ): - widget = TableWidget(mock_df) - - # Simulate "loaded data but unknown total rows" state - widget.page_size = 10 - widget.row_count = None - widget._all_data_loaded = True - - # Populate cache with 1 page of data (10 rows). Page 0 is valid, page 1+ are invalid. - widget._cached_batches = [pd.DataFrame({"col1": range(10)})] - - # Mark initial load as complete so observers fire - widget._initial_load_complete = True - - # Setup timeout to fail fast if deadlock occurs - # signal.SIGALRM is not available on Windows - has_sigalrm = hasattr(signal, "SIGALRM") - if has_sigalrm: - - def handler(signum, frame): - raise TimeoutError("Deadlock detected!") - - signal.signal(signal.SIGALRM, handler) - signal.alarm(2) # 2 seconds timeout - - try: - # Trigger navigation to page 5 (invalid), which should reset to page 0 - widget.page = 5 - - assert widget.page == 0 - - finally: - if has_sigalrm: - signal.alarm(0) - - -def test_css_contains_dark_mode_selectors(): - """Test that the CSS for dark mode is loaded with all required selectors.""" - mock_df = mock.create_autospec(bigframes.dataframe.DataFrame, instance=True) - # mock_df.columns and mock_df.dtypes are needed for __init__ - mock_df.columns = ["col1"] - mock_df.dtypes = {"col1": "object"} - - # Mock _block to avoid AttributeError during _set_table_html - mock_block = mock.Mock() - mock_block.has_index = False - mock_df._block = mock_block - - with mock.patch.object(TableWidget, "_initial_load"): - widget = TableWidget(mock_df) - css = widget._css - assert "@media (prefers-color-scheme: dark)" in css - assert 'html[theme="dark"]' in css - assert 'body[data-theme="dark"]' in css - - -@pytest.fixture -def mock_df(): - """A mock DataFrame that can be used in multiple tests.""" - df = mock.create_autospec(bigframes.dataframe.DataFrame, instance=True) - df.columns = ["col1", "col2"] - df.dtypes = {"col1": "int64", "col2": "int64"} - - mock_block = mock.Mock() - mock_block.has_index = False - df._block = mock_block - - # Mock to_pandas_batches to return empty iterator or simple data - batch_df = pd.DataFrame({"col1": [1, 2], "col2": [3, 4]}) - batches = mock.MagicMock() - batches.__iter__.return_value = iter([batch_df]) - batches.total_rows = 2 - df.to_pandas_batches.return_value = batches - - # Mock sort_values to return self (for chaining) - df.sort_values.return_value = df - - return df - - -def test_sorting_single_column(mock_df): - """Test that the widget can be sorted by a single column.""" - with bigframes.option_context("display.render_mode", "anywidget"): - widget = TableWidget(mock_df) - - # Verify initial state - assert widget.sort_context == [] - - # Apply sort - widget.sort_context = [{"column": "col1", "ascending": True}] - - # This should trigger _sort_changed -> _set_table_html - # which calls df.sort_values - - mock_df.sort_values.assert_called_with(by=["col1"], ascending=[True]) - - -def test_sorting_multi_column(mock_df): - """Test that the widget can be sorted by multiple columns.""" - with bigframes.option_context("display.render_mode", "anywidget"): - widget = TableWidget(mock_df) - - # Apply multi-column sort - widget.sort_context = [ - {"column": "col1", "ascending": True}, - {"column": "col2", "ascending": False}, - ] - - mock_df.sort_values.assert_called_with(by=["col1", "col2"], ascending=[True, False]) - - -def test_page_size_change_resets_sort(mock_df): - """Test that changing the page size resets the sorting.""" - with bigframes.option_context("display.render_mode", "anywidget"): - widget = TableWidget(mock_df) - - # Set sort state - widget.sort_context = [{"column": "col1", "ascending": True}] - - # Change page size - widget.page_size = 50 - - # Sort should be reset - assert widget.sort_context == [] - - # to_pandas_batches called again (reset) - assert mock_df.to_pandas_batches.call_count >= 2 - - -def test_cell_execution_count_propagation(mock_df): - """Test that the captured cell_execution_count is propagated to to_pandas_batches.""" - with ( - mock.patch("bigframes.core.utils.get_ipython_execution_count", return_value=42), - bigframes.option_context("display.render_mode", "anywidget"), - ): - widget = TableWidget(mock_df) - - assert widget._cell_execution_count == 42 - - mock_df.to_pandas_batches.assert_called_with( - page_size=widget.page_size, - cell_execution_count=42, - ) - - -def test_json_column_converted_to_string_for_display(polars_session): - series = bigframes.series.Series( - ['{"a": 1}', '{"b": 2}'], - dtype=bigframes.dtypes.JSON_DTYPE, - session=polars_session, - ) - df = series.to_frame("col_json") - - result = df._prepare_display_df() - - assert result["col_json"].dtype == bigframes.dtypes.STRING_DTYPE - - -def test_struct_column_with_nested_json_converted_to_string_for_display( - polars_session, -): - if not hasattr(pa, "json_"): - pytest.skip(reason=f"pyarrow=={pa.__version__} does not support json_") - - # Arrange - json_type = pa.json_(storage_type=pa.utf8()) - json_data = pa.array(['{"a": 1}'], type=json_type) - string_data = pa.array(["hello"], type=pa.string()) - struct_data = pa.StructArray.from_arrays( - [string_data, json_data], names=["field1", "field2"] - ) - nested_data = pa.table([struct_data], names=["nested"]) - df = polars_session.read_arrow(nested_data) - exploded = df["nested"].struct.explode() - # Ensure that we are actually using the JSON dtype in this test. - assert exploded["field2"].dtype == bigframes.dtypes.JSON_DTYPE - - # Act - result = df._prepare_display_df() - - # Assert - assert result["nested"].dtype == bigframes.dtypes.STRING_DTYPE - - -@pytest.fixture -def mock_df_deferred(): - with mock.patch("bigframes.display.anywidget._ANYWIDGET_INSTALLED", True): - df = mock.Mock(spec=bigframes.dataframe.DataFrame) - df.shape = (100, 4) - df.columns = ["A", "B", "C", "D"] - df.dtypes = { - "A": bigframes.dtypes.INT_DTYPE, - "B": bigframes.dtypes.STRING_DTYPE, - "C": bigframes.dtypes.FLOAT_DTYPE, - "D": bigframes.dtypes.BOOL_DTYPE, - } - - df.to_pandas_batches.return_value = iter( - [pd.DataFrame({"A": [1], "B": ["a"], "C": [1.0], "D": [True]})] - ) - - df.sort_values.return_value = df - - df._block = mock.Mock() - df._block.has_index = False - df._prepare_display_df.return_value = df - - yield df - - -@pytest.fixture -def mock_deferred_df(): - from bigframes.session.deferred import DeferredBigQueryDataFrame - - with mock.patch("bigframes.display.anywidget._ANYWIDGET_INSTALLED", True): - # We create a mock that subclasses DeferredBigQueryDataFrame so isinstance passes - class MockDeferredBigQueryDataFrame(DeferredBigQueryDataFrame): - def __init__(self): - pass - - df = mock.MagicMock(spec=MockDeferredBigQueryDataFrame) - df.__class__ = DeferredBigQueryDataFrame # type: ignore[assignment] - yield df - - -def test_init_raises_if_anywidget_not_installed(): - with ( - mock.patch("bigframes.display.anywidget._ANYWIDGET_INSTALLED", False), - pytest.raises(ImportError), - ): - from bigframes.display.anywidget import TableWidget - - TableWidget(mock.Mock()) - - -def test_init_initializes_attributes(mock_df_deferred): - from bigframes.display.anywidget import TableWidget - - with ( - bigframes.option_context("display.render_mode", "anywidget"), - mock.patch.object(TableWidget, "_initial_load"), - ): - widget = TableWidget(mock_df_deferred) - - assert widget._dataframe is mock_df_deferred - assert widget.page == 0 - assert widget.page_size > 0 - assert widget.orderable_columns == [ - "A", - "B", - "C", - "D", - ] - - -def test_init_calls_initial_load(mock_df_deferred): - from bigframes.display.anywidget import TableWidget - - with mock.patch.object(TableWidget, "_initial_load") as mock_load: - TableWidget(mock_df_deferred) - mock_load.assert_called_once() - - -def test_validate_page_clamping(mock_df_deferred): - from bigframes.display.anywidget import TableWidget - - with mock.patch.object(TableWidget, "_initial_load"): - widget = TableWidget(mock_df_deferred) - widget.row_count = 100 - widget.page_size = 10 - - widget.page = 5 - assert widget.page == 5 - - with pytest.raises(ValueError): - widget.page = -1 - - widget.page = 100 - assert widget.page == 9 - - -def test_validate_page_size(mock_df_deferred): - from bigframes.display.anywidget import TableWidget - - with ( - bigframes.option_context("display.render_mode", "anywidget"), - mock.patch.object(TableWidget, "_initial_load"), - ): - widget = TableWidget(mock_df_deferred) - - widget.page_size = 50 - assert widget.page_size == 50 - - original_size = widget.page_size - widget.page_size = -5 - assert widget.page_size == original_size - - widget.page_size = 10000 - assert widget.page_size == 1000 - - -def test_page_size_change_resets_page_and_sort(mock_df_deferred): - from bigframes.display.anywidget import TableWidget - - with mock.patch.object(TableWidget, "_initial_load"): - widget = TableWidget(mock_df_deferred) - widget._initial_load_complete = True - widget.page = 5 - widget.sort_context = [{"column": "A", "ascending": True}] - - widget.page_size = 20 - - assert widget.page == 0 - assert widget.sort_context == [] - - -def test_page_size_change_resets_batches(mock_df_deferred): - from bigframes.display.anywidget import TableWidget - - with mock.patch.object(TableWidget, "_initial_load"): - widget = TableWidget(mock_df_deferred) - widget._initial_load_complete = True - - widget.page_size = 50 - - mock_df_deferred.to_pandas_batches.assert_called() - - -def test_sort_change_resets_batches(mock_df_deferred): - from bigframes.display.anywidget import TableWidget - - with ( - bigframes.option_context("display.render_mode", "anywidget"), - mock.patch.object(TableWidget, "_initial_load"), - ): - widget = TableWidget(mock_df_deferred) - widget._initial_load_complete = True - - mock_df_deferred.to_pandas_batches.reset_mock() - - widget.sort_context = [{"column": "B", "ascending": False}] - - assert mock_df_deferred.to_pandas_batches.call_count >= 1 - - -def test_deferred_mode_initialization(mock_deferred_df): - from bigframes.display.anywidget import TableWidget - - with mock.patch.object(TableWidget, "_initial_load") as mock_load: - widget = TableWidget(mock_deferred_df) - - assert widget.is_deferred_mode is True - mock_load.assert_not_called() - - -def test_deferred_mode_execution(mock_deferred_df, mock_df_deferred): - from bigframes.display.anywidget import TableWidget - - mock_deferred_df.execute.return_value = mock_df_deferred - - widget = TableWidget(mock_deferred_df) - - assert widget.is_deferred_mode is True - - import bigframes - - with bigframes.option_context( - "display.render_mode", bigframes.options.display.render_mode - ): - widget.start_execution = True - - thread = getattr(widget, "_execution_thread", None) - if thread is not None: - thread.join(timeout=5) - - mock_deferred_df.execute.assert_called_once() - mock_df_deferred.to_pandas_batches.assert_called_once() - assert widget.is_deferred_mode is False - - -def test_deferred_mode_execution_updates_table_html(mock_deferred_df, mock_df_deferred): - from bigframes.display.anywidget import TableWidget - - mock_deferred_df.execute.return_value = mock_df_deferred - - batches = mock.MagicMock() - batch_df = pd.DataFrame({"A": [1], "B": ["a"], "C": [1.0], "D": [True]}) - batches.__iter__.return_value = iter([batch_df]) - batches.total_rows = 1 - mock_df_deferred.to_pandas_batches.return_value = batches - - with bigframes.option_context("display.render_mode", "anywidget"): - widget = TableWidget(mock_deferred_df) - widget.is_deferred_mode = True - widget._deferred_dataframe = mock_deferred_df - assert widget.table_html == "" - - widget.start_execution = True - thread = getattr(widget, "_execution_thread", None) - if thread is not None: - thread.join(timeout=5) - - assert widget.is_deferred_mode is False - assert widget.table_html != "" - assert "table" in widget.table_html - - -def test_deferred_mode_execution_error(mock_deferred_df): - from bigframes.display.anywidget import TableWidget - - mock_deferred_df.execute.side_effect = RuntimeError("Query Failed") - - with mock.patch.object(TableWidget, "_initial_load"): - widget = TableWidget(mock_deferred_df) - - import bigframes - - with bigframes.option_context( - "display.render_mode", bigframes.options.display.render_mode - ): - widget.start_execution = True - - thread = getattr(widget, "_execution_thread", None) - if thread is not None: - thread.join(timeout=5) - - assert widget.is_deferred_mode is True - assert widget._error_message == "Query Failed" - - -def test_deferred_mode_execution_does_not_reset_page_on_navigation( - mock_deferred_df, mock_df_deferred -): - from bigframes.display.anywidget import TableWidget - - mock_deferred_df.execute.return_value = mock_df_deferred - - batches = mock.MagicMock() - batch_df = pd.DataFrame({"A": [1], "B": ["a"], "C": [1.0], "D": [True]}) - batches.__iter__.return_value = iter([batch_df]) - batches.total_rows = 50 - mock_df_deferred.to_pandas_batches.return_value = batches - - with bigframes.option_context("display.render_mode", "anywidget"): - widget = TableWidget(mock_deferred_df) - widget.page_size = 10 - widget.start_execution = True - - thread = getattr(widget, "_execution_thread", None) - if thread is not None: - thread.join(timeout=5) - - assert widget.page == 0 - widget.page = 1 - assert widget.page == 1 - - -def test_deferred_mode_execution_in_colab(mock_deferred_df, mock_df_deferred): - import sys - - from bigframes.display.anywidget import TableWidget - - mock_deferred_df.execute.return_value = mock_df_deferred - - batches = mock.MagicMock() - batch_df = pd.DataFrame({"A": [1], "B": ["a"], "C": [1.0], "D": [True]}) - batches.__iter__.return_value = iter([batch_df]) - batches.total_rows = 1 - mock_df_deferred.to_pandas_batches.return_value = batches - - with ( - mock.patch.dict(sys.modules, {"google.colab": mock.MagicMock()}), - bigframes.option_context("display.render_mode", "anywidget"), - ): - widget = TableWidget(mock_deferred_df) - widget.is_deferred_mode = True - - widget.start_execution = True - - thread = getattr(widget, "_execution_thread", None) - if thread is not None: - thread.join(timeout=5) - - assert widget.is_deferred_mode is True - assert widget.table_html == "" - - # Simulate frontend ping callback - widget.ping = 1 - - assert widget.is_deferred_mode is False - assert widget.table_html != "" diff --git a/tests/unit/display/test_html.py b/tests/unit/display/test_html.py deleted file mode 100644 index 239033861a4..00000000000 --- a/tests/unit/display/test_html.py +++ /dev/null @@ -1,222 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import datetime -from unittest.mock import Mock, patch - -import pandas as pd -import pyarrow as pa -import pytest - -import bigframes as bf -import bigframes.display.html as bf_html - - -@pytest.mark.parametrize( - ("data", "expected_alignments", "expected_strings"), - [ - pytest.param( - { - "string_col": ["a", "b", "c"], - "int_col": [1, 2, 3], - "float_col": [1.1, 2.2, 3.3], - "bool_col": [True, False, True], - }, - { - "string_col": "left", - "int_col": "right", - "float_col": "right", - "bool_col": "left", - }, - ["1.100000", "2.200000", "3.300000"], - id="scalars", - ), - pytest.param( - { - "timestamp_col": pa.array( - [ - datetime.datetime.fromisoformat(value) - for value in [ - "2024-01-01 00:00:00", - "2024-01-01 00:00:01", - "2024-01-01 00:00:02", - ] - ], - pa.timestamp("us", tz="UTC"), - ), - "datetime_col": pa.array( - [ - datetime.datetime.fromisoformat(value) - for value in [ - "2027-06-05 04:03:02.001", - "2027-01-01 00:00:01", - "2027-01-01 00:00:02", - ] - ], - pa.timestamp("us"), - ), - "date_col": pa.array( - [ - datetime.date(1999, 1, 1), - datetime.date(1999, 1, 2), - datetime.date(1999, 1, 3), - ], - pa.date32(), - ), - "time_col": pa.array( - [ - datetime.time(11, 11, 0), - datetime.time(11, 11, 1), - datetime.time(11, 11, 2), - ], - pa.time64("us"), - ), - }, - { - "timestamp_col": "left", - "datetime_col": "left", - "date_col": "left", - "time_col": "left", - }, - [ - "2024-01-01 00:00:00", - "2027-06-05 04:03:02.001", - "1999-01-01", - "11:11:01", - ], - id="datetimes", - ), - pytest.param( - { - "array_col": pd.Series( - [[1, 2, 3], [4, 5, 6], [7, 8, 9]], - dtype=pd.ArrowDtype(pa.list_(pa.int64())), - ), - }, - { - "array_col": "left", - }, - ["[1, 2, 3]", "[4, 5, 6]", "[7, 8, 9]"], - id="array", - ), - pytest.param( - { - "struct_col": pd.Series( - [{"v": 1}, {"v": 2}, {"v": 3}], - dtype=pd.ArrowDtype(pa.struct([("v", pa.int64())])), - ), - }, - { - "struct_col": "left", - }, - ["{'v': 1}", "{'v': 2}", "{'v': 3}"], - id="struct", - ), - ], -) -def test_render_html_alignment_and_precision( - data, expected_alignments, expected_strings -): - df = pd.DataFrame(data) - html = bf_html.render_html(dataframe=df, table_id="test-table") - - for align in expected_alignments.values(): - assert f'class="cell-align-{align}"' in html - - for expected_string in expected_strings: - assert expected_string in html - - -def test_render_html_precision(): - data = {"float_col": [3.14159265]} - df = pd.DataFrame(data) - - with bf.option_context("display.precision", 4): - html = bf_html.render_html(dataframe=df, table_id="test-table") - assert "3.1416" in html - - # Make sure we reset to default - html = bf_html.render_html(dataframe=df, table_id="test-table") - assert "3.141593" in html - - -def test_render_html_max_columns_truncation(): - # Create a DataFrame with 10 columns - data = {f"col_{i}": [i] for i in range(10)} - df = pd.DataFrame(data) - - # Test max_columns=4 - # max_columns=4 -> 2 left, 2 right. col_0, col_1 ... col_8, col_9 - html = bf_html.render_html(dataframe=df, table_id="test", max_columns=4) - - assert "col_0" in html - assert "col_1" in html - assert "col_2" not in html - assert "col_7" not in html - assert "col_8" in html - assert "col_9" in html - assert "..." in html - - # Test max_columns=3 - # 3 // 2 = 1. Left: col_0. Right: 3 - 1 = 2. col_8, col_9. - # Total displayed: col_0, ..., col_8, col_9. (3 data cols + 1 ellipsis) - html = bf_html.render_html(dataframe=df, table_id="test", max_columns=3) - assert "col_0" in html - assert "col_1" not in html - assert "col_7" not in html - assert "col_8" in html - assert "col_9" in html - - # Test max_columns=1 - # 1 // 2 = 0. Left: []. Right: 1. col_9. - # Total: ..., col_9. - html = bf_html.render_html(dataframe=df, table_id="test", max_columns=1) - assert "col_0" not in html - assert "col_8" not in html - assert "col_9" in html - assert "..." in html - - -def test_repr_mimebundle_head(): - mock_df = Mock() - mock_df.columns = ["col1"] - - mock_df._prepare_display_df.return_value = mock_df - - # Mock the call to retrieve_repr_request_results - pandas_df = pd.DataFrame({"col1": [1, 2, 3]}) - mock_df._block.retrieve_repr_request_results.return_value = ( - pandas_df, - 3, - Mock(), # query_job - ) - - # Mock _get_obj_metadata - with ( - patch("bigframes.display.html._get_obj_metadata", return_value=(False, False)), - patch( - "bigframes.display.html.create_html_representation", return_value="" - ) as mock_create_html, - patch( - "bigframes.display.plaintext.create_text_representation", - return_value="text", - ) as mock_create_text, - ): - bundle = bf_html.repr_mimebundle_head(mock_df) - - assert bundle == {"text/html": "", "text/plain": "text"} - mock_df._prepare_display_df.assert_called_once() - mock_df._block.retrieve_repr_request_results.assert_called_once() - mock_create_html.assert_called_once() - mock_create_text.assert_called_once() diff --git a/tests/unit/display/test_render_mode.py b/tests/unit/display/test_render_mode.py deleted file mode 100644 index 478bfd30eaf..00000000000 --- a/tests/unit/display/test_render_mode.py +++ /dev/null @@ -1,120 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import unittest.mock as mock - -import pytest - -import bigframes.display.html as bf_html -import bigframes.pandas as bpd - - -def test_render_mode_options(): - assert bpd.options.display.render_mode == "html" - - with bpd.option_context("display.render_mode", "plaintext"): - assert bpd.options.display.render_mode == "plaintext" - - with bpd.option_context("display.render_mode", "html"): - assert bpd.options.display.render_mode == "html" - - with bpd.option_context("display.render_mode", "anywidget"): - assert bpd.options.display.render_mode == "anywidget" - - -def test_repr_mimebundle_selection_logic(): - mock_obj = mock.Mock() - - # Mocking dependencies - with ( - mock.patch("bigframes.display.html.repr_mimebundle_head") as mock_head, - mock.patch("bigframes.display.html.get_anywidget_bundle") as mock_anywidget, - mock.patch("bigframes.display.html.repr_mimebundle_deferred") as mock_deferred, - ): - mock_head.side_effect = lambda obj: {"text/plain": "plain", "text/html": "html"} - mock_anywidget.return_value = ( - { - "application/vnd.jupyter.widget-view+json": {}, - "text/plain": "plain", - "text/html": "html", - }, - {}, - ) - mock_deferred.return_value = {"text/plain": "deferred"} - - # Test deferred repr_mode when anywidget is available - with bpd.option_context("display.repr_mode", "deferred"): - bundle = bf_html.repr_mimebundle(mock_obj) - assert "application/vnd.jupyter.widget-view+json" in bundle[0] - mock_anywidget.assert_called_once() - mock_deferred.assert_not_called() - - mock_anywidget.reset_mock() - - # Test fallback to static deferred repr when anywidget fails - mock_anywidget.side_effect = Exception("Anywidget failed") - with ( - bpd.option_context("display.repr_mode", "deferred"), - pytest.warns(UserWarning, match="Anywidget mode is not available"), - ): - bundle = bf_html.repr_mimebundle(mock_obj) - assert bundle == {"text/plain": "deferred"} - mock_deferred.assert_called_once() - - mock_anywidget.side_effect = None - mock_deferred.reset_mock() - mock_anywidget.reset_mock() - - # Test plaintext render_mode - with bpd.option_context("display.render_mode", "plaintext"): - bundle = bf_html.repr_mimebundle(mock_obj) - assert "text/plain" in bundle - assert "text/html" not in bundle - mock_head.assert_called_once() - - mock_head.reset_mock() - - # Test html render_mode - with bpd.option_context("display.render_mode", "html"): - bundle = bf_html.repr_mimebundle(mock_obj) - assert "text/plain" in bundle - assert "text/html" in bundle - mock_head.assert_called_once() - - mock_head.reset_mock() - - # Test anywidget render_mode - with bpd.option_context("display.render_mode", "anywidget"): - bundle = bf_html.repr_mimebundle(mock_obj) - assert "application/vnd.jupyter.widget-view+json" in bundle[0] - mock_anywidget.assert_called_once() - mock_head.assert_not_called() - - mock_anywidget.reset_mock() - - # Test anywidget repr_mode (backward compatibility) - with bpd.option_context("display.repr_mode", "anywidget"): - bundle = bf_html.repr_mimebundle(mock_obj) - assert "application/vnd.jupyter.widget-view+json" in bundle[0] - mock_anywidget.assert_called_once() - mock_head.assert_not_called() - - mock_anywidget.reset_mock() - - # Test default render_mode (should be "html") - bundle = bf_html.repr_mimebundle(mock_obj) - assert "text/plain" in bundle - assert "text/html" in bundle - mock_head.assert_called_once() - mock_anywidget.assert_not_called() diff --git a/tests/unit/extensions/__init__.py b/tests/unit/extensions/__init__.py deleted file mode 100644 index 58d482ea386..00000000000 --- a/tests/unit/extensions/__init__.py +++ /dev/null @@ -1,13 +0,0 @@ -# Copyright 2026 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. diff --git a/tests/unit/extensions/bigframes/__init__.py b/tests/unit/extensions/bigframes/__init__.py deleted file mode 100644 index 58d482ea386..00000000000 --- a/tests/unit/extensions/bigframes/__init__.py +++ /dev/null @@ -1,13 +0,0 @@ -# Copyright 2026 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. diff --git a/tests/unit/extensions/bigframes/test_series_accessor.py b/tests/unit/extensions/bigframes/test_series_accessor.py deleted file mode 100644 index 4c74b60a1a0..00000000000 --- a/tests/unit/extensions/bigframes/test_series_accessor.py +++ /dev/null @@ -1,78 +0,0 @@ -# Copyright 2026 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from typing import cast -from unittest.mock import MagicMock, patch - -import pytest - -import bigframes.series as series -from bigframes.testing import mocks - - -def test_bigframes_series_has_accessor(monkeypatch: pytest.MonkeyPatch): - # Arrange - from bigframes.extensions.bigframes.series_accessor import ( - BigframesBigQuerySeriesAccessor, - ) - - bf_df = mocks.create_dataframe(monkeypatch, data={"col": [1, 2]}) - bf_series = cast(series.Series, bf_df["col"]) - - # Act - has_bq = hasattr(bf_series, "bigquery") - bq_obj = bf_series.bigquery - - # Assert - assert has_bq - assert isinstance(bq_obj, BigframesBigQuerySeriesAccessor) - - -@patch("bigframes.operations.googlesql.global_namespace.array.array_length") -def test_bigframes_series_accessor_global_routing( - mock_array_length, monkeypatch: pytest.MonkeyPatch -): - # Arrange - bf_df = mocks.create_dataframe(monkeypatch, data={"col": [[1, 2], [3, 4, 5]]}) - bf_series = cast(series.Series, bf_df["col"]) - mock_result_series = MagicMock() - mock_array_length.return_value = mock_result_series - - # Act - result = bf_series.bigquery.array_length() - - # Assert - mock_array_length.assert_called_once_with(bf_series) - assert result is mock_result_series - - -@patch("bigframes.operations.googlesql.aead.encrypt") -def test_bigframes_series_accessor_namespaced_routing( - mock_encrypt, monkeypatch: pytest.MonkeyPatch -): - # Arrange - bf_df = mocks.create_dataframe(monkeypatch, data={"keyset": [b"key1", b"key2"]}) - keyset_series = cast(series.Series, bf_df["keyset"]) - mock_result_series = MagicMock() - mock_encrypt.return_value = mock_result_series - - plaintext = "my secret" - additional_data = "context" - - # Act - result = keyset_series.bigquery.aead.encrypt(plaintext, additional_data) - - # Assert - mock_encrypt.assert_called_once_with(keyset_series, plaintext, additional_data) - assert result is mock_result_series diff --git a/tests/unit/extensions/core/__init__.py b/tests/unit/extensions/core/__init__.py deleted file mode 100644 index 58d482ea386..00000000000 --- a/tests/unit/extensions/core/__init__.py +++ /dev/null @@ -1,13 +0,0 @@ -# Copyright 2026 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. diff --git a/tests/unit/extensions/core/test_dataframe_accessor.py b/tests/unit/extensions/core/test_dataframe_accessor.py deleted file mode 100644 index c207070bb15..00000000000 --- a/tests/unit/extensions/core/test_dataframe_accessor.py +++ /dev/null @@ -1,525 +0,0 @@ -# Copyright 2026 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import unittest.mock as mock - -import pandas as pd - -import bigframes.bigquery.ai -import bigframes.pandas as bpd -import bigframes.session - - -def test_ai_forecast(monkeypatch): - session = mock.create_autospec(bigframes.session.Session) - bf_df = mock.create_autospec(bpd.DataFrame) - session.read_pandas.return_value = bf_df - - mock_forecast = mock.MagicMock() - forecast_result_df = mock.create_autospec(bpd.DataFrame) - mock_forecast.return_value = forecast_result_df - expected_result = mock.create_autospec(pd.DataFrame) - forecast_result_df.to_pandas.return_value = expected_result - - monkeypatch.setattr(bigframes.bigquery.ai, "forecast", mock_forecast) - - df = pd.DataFrame({"date": ["2020-01-01"], "value": [1.0]}) - actual_result = df.bigquery.ai.forecast( - timestamp_col="date", - data_col="value", - horizon=5, - session=session, - ) - - session.read_pandas.assert_called_once() - - mock_forecast.assert_called_once_with( - bf_df, - timestamp_col="date", - data_col="value", - model="TimesFM 2.0", - id_cols=None, - horizon=5, - confidence_level=0.95, - context_window=None, - output_historical_time_series=False, - ) - forecast_result_df.to_pandas.assert_called_once() - assert actual_result is expected_result - - -def test_bigframes_ai_forecast(scalar_types_df: bpd.DataFrame, monkeypatch): - session = mock.create_autospec(bigframes.session.Session) - forecast_result = mock.create_autospec(bpd.DataFrame) - mock_forecast = mock.MagicMock() - mock_forecast.return_value = forecast_result - - monkeypatch.setattr(bigframes.bigquery.ai, "forecast", mock_forecast) - - actual_result = scalar_types_df.bigquery.ai.forecast( - timestamp_col="date", - data_col="value", - horizon=5, - session=session, - ) - - session.read_pandas.assert_not_called() - mock_forecast.assert_called_once() - args, kwargs = mock_forecast.call_args - assert args[0] is scalar_types_df - assert kwargs == { - "timestamp_col": "date", - "data_col": "value", - "model": "TimesFM 2.0", - "id_cols": None, - "horizon": 5, - "confidence_level": 0.95, - "context_window": None, - "output_historical_time_series": False, - } - # BigFrames accessor returns the bf_df directly without calling to_pandas - forecast_result.to_pandas.assert_not_called() - assert actual_result is forecast_result - - -def test_ai_generate(monkeypatch): - mock_generate = mock.MagicMock() - result_series = mock.create_autospec(bpd.Series) - mock_generate.return_value = result_series - expected_result = mock.create_autospec(pd.Series) - result_series.to_pandas.return_value = expected_result - - monkeypatch.setattr(bigframes.bigquery.ai, "generate", mock_generate) - - prompt = mock.create_autospec(pd.Series) - df = pd.DataFrame({"text_input": ["Is this a positive review?"]}) - actual_result = df.bigquery.ai.generate( - prompt, - connection_id="conn", - endpoint="endpoint", - request_type="dedicated", - model_params={"temp": 0.5}, - output_schema={"res": "STRING"}, - ) - - mock_generate.assert_called_once_with( - prompt, - connection_id="conn", - endpoint="endpoint", - request_type="dedicated", - model_params={"temp": 0.5}, - output_schema={"res": "STRING"}, - ) - result_series.to_pandas.assert_called_once() - assert actual_result is expected_result - - -def test_bigframes_ai_generate(scalar_types_df: bpd.DataFrame, monkeypatch): - bf_series = mock.create_autospec(bpd.Series) - result_series = mock.create_autospec(bpd.Series) - - mock_generate = mock.MagicMock() - mock_generate.return_value = result_series - - monkeypatch.setattr(bigframes.bigquery.ai, "generate", mock_generate) - - actual_result = scalar_types_df.bigquery.ai.generate( - bf_series, - connection_id="conn", - endpoint="endpoint", - request_type="dedicated", - model_params={"temp": 0.5}, - output_schema={"res": "STRING"}, - ) - - mock_generate.assert_called_once() - args, kwargs = mock_generate.call_args - assert args[0] is bf_series - assert kwargs == { - "connection_id": "conn", - "endpoint": "endpoint", - "request_type": "dedicated", - "model_params": {"temp": 0.5}, - "output_schema": {"res": "STRING"}, - } - result_series.to_pandas.assert_not_called() - assert actual_result is result_series - - -def test_ai_generate_bool(monkeypatch): - mock_generate_bool = mock.MagicMock() - result_series = mock.create_autospec(bpd.Series) - mock_generate_bool.return_value = result_series - expected_result = mock.create_autospec(pd.Series) - result_series.to_pandas.return_value = expected_result - - monkeypatch.setattr(bigframes.bigquery.ai, "generate_bool", mock_generate_bool) - - prompt = mock.create_autospec(pd.Series) - df = pd.DataFrame({"text_input": ["Is this a positive review?"]}) - actual_result = df.bigquery.ai.generate_bool( - prompt, - connection_id="conn", - endpoint="endpoint", - request_type="dedicated", - model_params={"temp": 0.5}, - ) - - mock_generate_bool.assert_called_once_with( - prompt, - connection_id="conn", - endpoint="endpoint", - request_type="dedicated", - model_params={"temp": 0.5}, - ) - result_series.to_pandas.assert_called_once() - assert actual_result is expected_result - - -def test_bigframes_ai_generate_bool(scalar_types_df: bpd.DataFrame, monkeypatch): - bf_series = mock.create_autospec(bpd.Series) - result_series = mock.create_autospec(bpd.Series) - - mock_generate_bool = mock.MagicMock() - mock_generate_bool.return_value = result_series - - monkeypatch.setattr(bigframes.bigquery.ai, "generate_bool", mock_generate_bool) - - actual_result = scalar_types_df.bigquery.ai.generate_bool( - bf_series, - connection_id="conn", - endpoint="endpoint", - request_type="dedicated", - model_params={"temp": 0.5}, - ) - - mock_generate_bool.assert_called_once() - args, kwargs = mock_generate_bool.call_args - assert args[0] is bf_series - assert kwargs == { - "connection_id": "conn", - "endpoint": "endpoint", - "request_type": "dedicated", - "model_params": {"temp": 0.5}, - } - result_series.to_pandas.assert_not_called() - assert actual_result is result_series - - -def test_ai_generate_int(monkeypatch): - mock_generate_int = mock.MagicMock() - result_series = mock.create_autospec(bpd.Series) - mock_generate_int.return_value = result_series - expected_result = mock.create_autospec(pd.Series) - result_series.to_pandas.return_value = expected_result - - monkeypatch.setattr(bigframes.bigquery.ai, "generate_int", mock_generate_int) - - prompt = mock.create_autospec(pd.Series) - df = pd.DataFrame({"text_input": ["How many legs?"]}) - actual_result = df.bigquery.ai.generate_int( - prompt, - connection_id="conn", - endpoint="endpoint", - request_type="dedicated", - model_params={"temp": 0.5}, - ) - - mock_generate_int.assert_called_once_with( - prompt, - connection_id="conn", - endpoint="endpoint", - request_type="dedicated", - model_params={"temp": 0.5}, - ) - result_series.to_pandas.assert_called_once() - assert actual_result is expected_result - - -def test_bigframes_ai_generate_int(scalar_types_df: bpd.DataFrame, monkeypatch): - bf_series = mock.create_autospec(bpd.Series) - result_series = mock.create_autospec(bpd.Series) - - mock_generate_int = mock.MagicMock() - mock_generate_int.return_value = result_series - - monkeypatch.setattr(bigframes.bigquery.ai, "generate_int", mock_generate_int) - - actual_result = scalar_types_df.bigquery.ai.generate_int( - bf_series, - connection_id="conn", - endpoint="endpoint", - request_type="dedicated", - model_params={"temp": 0.5}, - ) - - mock_generate_int.assert_called_once() - args, kwargs = mock_generate_int.call_args - assert args[0] is bf_series - assert kwargs == { - "connection_id": "conn", - "endpoint": "endpoint", - "request_type": "dedicated", - "model_params": {"temp": 0.5}, - } - result_series.to_pandas.assert_not_called() - assert actual_result is result_series - - -def test_ai_generate_double(monkeypatch): - mock_generate_double = mock.MagicMock() - result_series = mock.create_autospec(bpd.Series) - mock_generate_double.return_value = result_series - expected_result = mock.create_autospec(pd.Series) - result_series.to_pandas.return_value = expected_result - - monkeypatch.setattr(bigframes.bigquery.ai, "generate_double", mock_generate_double) - - prompt = mock.create_autospec(pd.Series) - df = pd.DataFrame({"text_input": ["How tall?"]}) - actual_result = df.bigquery.ai.generate_double( - prompt, - connection_id="conn", - endpoint="endpoint", - request_type="dedicated", - model_params={"temp": 0.5}, - ) - - mock_generate_double.assert_called_once_with( - prompt, - connection_id="conn", - endpoint="endpoint", - request_type="dedicated", - model_params={"temp": 0.5}, - ) - result_series.to_pandas.assert_called_once() - assert actual_result is expected_result - - -def test_bigframes_ai_generate_double(scalar_types_df: bpd.DataFrame, monkeypatch): - bf_series = mock.create_autospec(bpd.Series) - result_series = mock.create_autospec(bpd.Series) - - mock_generate_double = mock.MagicMock() - mock_generate_double.return_value = result_series - - monkeypatch.setattr(bigframes.bigquery.ai, "generate_double", mock_generate_double) - - actual_result = scalar_types_df.bigquery.ai.generate_double( - bf_series, - connection_id="conn", - endpoint="endpoint", - request_type="dedicated", - model_params={"temp": 0.5}, - ) - - mock_generate_double.assert_called_once() - args, kwargs = mock_generate_double.call_args - assert args[0] is bf_series - assert kwargs == { - "connection_id": "conn", - "endpoint": "endpoint", - "request_type": "dedicated", - "model_params": {"temp": 0.5}, - } - result_series.to_pandas.assert_not_called() - assert actual_result is result_series - - -def test_ai_classify(monkeypatch): - mock_classify = mock.MagicMock() - result_series = mock.create_autospec(bpd.Series) - mock_classify.return_value = result_series - expected_result = mock.create_autospec(pd.Series) - result_series.to_pandas.return_value = expected_result - - monkeypatch.setattr(bigframes.bigquery.ai, "classify", mock_classify) - - input_prompt = mock.create_autospec(pd.Series) - df = pd.DataFrame({"text_input": ["Is this a positive review?"]}) - actual_result = df.bigquery.ai.classify( - input_prompt, - categories=["Mammal", "Fish"], - examples=[("Cat", "Mammal")], - connection_id="conn", - endpoint="endpoint", - output_mode="single", - optimization_mode="minimize_cost", - max_error_ratio=0.1, - ) - - mock_classify.assert_called_once_with( - input_prompt, - ["Mammal", "Fish"], - examples=[("Cat", "Mammal")], - connection_id="conn", - endpoint="endpoint", - output_mode="single", - optimization_mode="minimize_cost", - max_error_ratio=0.1, - ) - result_series.to_pandas.assert_called_once() - assert actual_result is expected_result - - -def test_bigframes_ai_classify(scalar_types_df: bpd.DataFrame, monkeypatch): - bf_series = mock.create_autospec(bpd.Series) - result_series = mock.create_autospec(bpd.Series) - - mock_classify = mock.MagicMock() - mock_classify.return_value = result_series - - monkeypatch.setattr(bigframes.bigquery.ai, "classify", mock_classify) - - actual_result = scalar_types_df.bigquery.ai.classify( - bf_series, - categories=["Mammal", "Fish"], - examples=[("Cat", "Mammal")], - connection_id="conn", - endpoint="endpoint", - output_mode="single", - optimization_mode="minimize_cost", - max_error_ratio=0.1, - ) - - mock_classify.assert_called_once() - args, kwargs = mock_classify.call_args - assert args[0] is bf_series - assert args[1] == ["Mammal", "Fish"] - assert kwargs == { - "examples": [("Cat", "Mammal")], - "connection_id": "conn", - "endpoint": "endpoint", - "output_mode": "single", - "optimization_mode": "minimize_cost", - "max_error_ratio": 0.1, - } - result_series.to_pandas.assert_not_called() - assert actual_result is result_series - - -def test_ai_if(monkeypatch): - mock_if = mock.MagicMock() - result_series = mock.create_autospec(bpd.Series) - mock_if.return_value = result_series - expected_result = mock.create_autospec(pd.Series) - result_series.to_pandas.return_value = expected_result - - monkeypatch.setattr(bigframes.bigquery.ai, "if_", mock_if) - - prompt = mock.create_autospec(pd.Series) - df = pd.DataFrame({"text_input": ["Is this a positive review?"]}) - actual_result = df.bigquery.ai.if_( - prompt, - connection_id="conn", - endpoint="endpoint", - optimization_mode="minimize_cost", - max_error_ratio=0.1, - ) - - mock_if.assert_called_once_with( - prompt, - connection_id="conn", - endpoint="endpoint", - optimization_mode="minimize_cost", - max_error_ratio=0.1, - ) - result_series.to_pandas.assert_called_once() - assert actual_result is expected_result - - -def test_bigframes_ai_if(scalar_types_df: bpd.DataFrame, monkeypatch): - bf_series = mock.create_autospec(bpd.Series) - result_series = mock.create_autospec(bpd.Series) - - mock_if = mock.MagicMock() - mock_if.return_value = result_series - - monkeypatch.setattr(bigframes.bigquery.ai, "if_", mock_if) - - actual_result = scalar_types_df.bigquery.ai.if_( - bf_series, - connection_id="conn", - endpoint="endpoint", - optimization_mode="minimize_cost", - max_error_ratio=0.1, - ) - - mock_if.assert_called_once() - args, kwargs = mock_if.call_args - assert args[0] is bf_series - assert kwargs == { - "connection_id": "conn", - "endpoint": "endpoint", - "optimization_mode": "minimize_cost", - "max_error_ratio": 0.1, - } - result_series.to_pandas.assert_not_called() - assert actual_result is result_series - - -def test_ai_score(monkeypatch): - mock_score = mock.MagicMock() - result_series = mock.create_autospec(bpd.Series) - mock_score.return_value = result_series - expected_result = mock.create_autospec(pd.Series) - result_series.to_pandas.return_value = expected_result - - monkeypatch.setattr(bigframes.bigquery.ai, "score", mock_score) - - prompt = mock.create_autospec(pd.Series) - df = pd.DataFrame({"text_input": ["Is this a positive review?"]}) - actual_result = df.bigquery.ai.score( - prompt, - connection_id="conn", - endpoint="endpoint", - max_error_ratio=0.1, - ) - - mock_score.assert_called_once_with( - prompt, - connection_id="conn", - endpoint="endpoint", - max_error_ratio=0.1, - ) - result_series.to_pandas.assert_called_once() - assert actual_result is expected_result - - -def test_bigframes_ai_score(scalar_types_df: bpd.DataFrame, monkeypatch): - bf_series = mock.create_autospec(bpd.Series) - result_series = mock.create_autospec(bpd.Series) - - mock_score = mock.MagicMock() - mock_score.return_value = result_series - - monkeypatch.setattr(bigframes.bigquery.ai, "score", mock_score) - - actual_result = scalar_types_df.bigquery.ai.score( - bf_series, - connection_id="conn", - endpoint="endpoint", - max_error_ratio=0.1, - ) - - mock_score.assert_called_once() - args, kwargs = mock_score.call_args - assert args[0] is bf_series - assert kwargs == { - "connection_id": "conn", - "endpoint": "endpoint", - "max_error_ratio": 0.1, - } - result_series.to_pandas.assert_not_called() - assert actual_result is result_series diff --git a/tests/unit/extensions/core/test_series_mixins.py b/tests/unit/extensions/core/test_series_mixins.py deleted file mode 100644 index c6e7e4078b1..00000000000 --- a/tests/unit/extensions/core/test_series_mixins.py +++ /dev/null @@ -1,384 +0,0 @@ -# Copyright 2026 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import unittest.mock as mock - -import pandas as pd - -import bigframes.bigquery.ai -import bigframes.pandas as bpd -import bigframes.session - - -def test_ai_generate_embedding(monkeypatch): - session = mock.create_autospec(bigframes.session.Session) - bf_series = mock.create_autospec(bpd.Series) - session.read_pandas.return_value = bf_series - - mock_generate_embedding = mock.MagicMock() - result_df = mock.create_autospec(bpd.DataFrame) - mock_generate_embedding.return_value = result_df - expected_result = mock.create_autospec(pd.DataFrame) - result_df.to_pandas.return_value = expected_result - - monkeypatch.setattr( - bigframes.bigquery.ai, "generate_embedding", mock_generate_embedding - ) - - series = pd.Series(["apple"], name="content") - actual_result = series.bigquery.ai.generate_embedding( # type: ignore - model="my_model", - output_dimensionality=256, - task_type="retrieval_document", - start_second=1.0, - end_second=2.0, - interval_seconds=3.0, - trial_id=4, - session=session, - ) - - session.read_pandas.assert_called_once() - mock_generate_embedding.assert_called_once_with( - "my_model", - bf_series, - output_dimensionality=256, - task_type="retrieval_document", - start_second=1.0, - end_second=2.0, - interval_seconds=3.0, - trial_id=4, - ) - result_df.to_pandas.assert_called_once() - assert actual_result is expected_result - - -def test_bigframes_ai_generate_embedding(scalar_types_df: bpd.DataFrame, monkeypatch): - session = mock.create_autospec(bigframes.session.Session) - result_df = mock.create_autospec(bpd.DataFrame) - - mock_generate_embedding = mock.MagicMock() - mock_generate_embedding.return_value = result_df - - monkeypatch.setattr( - bigframes.bigquery.ai, "generate_embedding", mock_generate_embedding - ) - - scalar_types_series = scalar_types_df["string_col"] - actual_result = scalar_types_series.bigquery.ai.generate_embedding( - model="my_model", - output_dimensionality=256, - session=session, - ) - - session.read_pandas.assert_not_called() - mock_generate_embedding.assert_called_once() - args, kwargs = mock_generate_embedding.call_args - assert args[0] == "my_model" - assert args[1] is scalar_types_series - assert kwargs == { - "output_dimensionality": 256, - "task_type": None, - "start_second": None, - "end_second": None, - "interval_seconds": None, - "trial_id": None, - } - result_df.to_pandas.assert_not_called() - assert actual_result is result_df - - -def test_ai_generate_text(monkeypatch): - session = mock.create_autospec(bigframes.session.Session) - bf_series = mock.create_autospec(bpd.Series) - session.read_pandas.return_value = bf_series - - mock_generate_text = mock.MagicMock() - result_df = mock.create_autospec(bpd.DataFrame) - mock_generate_text.return_value = result_df - expected_result = mock.create_autospec(pd.DataFrame) - result_df.to_pandas.return_value = expected_result - - monkeypatch.setattr(bigframes.bigquery.ai, "generate_text", mock_generate_text) - - series = pd.Series(["write a poem"], name="prompt") - actual_result = series.bigquery.ai.generate_text( # type: ignore - model="my_model", - temperature=0.7, - max_output_tokens=100, - top_k=50, - top_p=0.9, - stop_sequences=["\n"], - ground_with_google_search=True, - request_type="dedicated", - session=session, - ) - - session.read_pandas.assert_called_once() - mock_generate_text.assert_called_once_with( - "my_model", - bf_series, - temperature=0.7, - max_output_tokens=100, - top_k=50, - top_p=0.9, - stop_sequences=["\n"], - ground_with_google_search=True, - request_type="dedicated", - ) - result_df.to_pandas.assert_called_once() - assert actual_result is expected_result - - -def test_bigframes_ai_generate_text(scalar_types_df: bpd.DataFrame, monkeypatch): - session = mock.create_autospec(bigframes.session.Session) - result_df = mock.create_autospec(bpd.DataFrame) - - mock_generate_text = mock.MagicMock() - mock_generate_text.return_value = result_df - - monkeypatch.setattr(bigframes.bigquery.ai, "generate_text", mock_generate_text) - - scalar_types_series = scalar_types_df["string_col"] - actual_result = scalar_types_series.bigquery.ai.generate_text( - model="my_model", - temperature=0.7, - session=session, - ) - - session.read_pandas.assert_not_called() - mock_generate_text.assert_called_once() - args, kwargs = mock_generate_text.call_args - assert args[0] == "my_model" - assert args[1] is scalar_types_series - assert kwargs == { - "temperature": 0.7, - "max_output_tokens": None, - "top_k": None, - "top_p": None, - "stop_sequences": None, - "ground_with_google_search": None, - "request_type": None, - } - result_df.to_pandas.assert_not_called() - assert actual_result is result_df - - -def test_ai_generate_table(monkeypatch): - session = mock.create_autospec(bigframes.session.Session) - bf_series = mock.create_autospec(bpd.Series) - session.read_pandas.return_value = bf_series - - mock_generate_table = mock.MagicMock() - result_df = mock.create_autospec(bpd.DataFrame) - mock_generate_table.return_value = result_df - expected_result = mock.create_autospec(pd.DataFrame) - result_df.to_pandas.return_value = expected_result - - monkeypatch.setattr(bigframes.bigquery.ai, "generate_table", mock_generate_table) - - series = pd.Series(["generate something"], name="prompt") - actual_result = series.bigquery.ai.generate_table( # type: ignore - model="my_model", - output_schema="category STRING", - temperature=0.7, - top_p=0.9, - max_output_tokens=100, - stop_sequences=["\n"], - request_type="dedicated", - session=session, - ) - - session.read_pandas.assert_called_once() - mock_generate_table.assert_called_once_with( - "my_model", - bf_series, - output_schema="category STRING", - temperature=0.7, - top_p=0.9, - max_output_tokens=100, - stop_sequences=["\n"], - request_type="dedicated", - ) - result_df.to_pandas.assert_called_once() - assert actual_result is expected_result - - -def test_bigframes_ai_generate_table(scalar_types_df: bpd.DataFrame, monkeypatch): - session = mock.create_autospec(bigframes.session.Session) - result_df = mock.create_autospec(bpd.DataFrame) - - mock_generate_table = mock.MagicMock() - mock_generate_table.return_value = result_df - - monkeypatch.setattr(bigframes.bigquery.ai, "generate_table", mock_generate_table) - - scalar_types_series = scalar_types_df["string_col"] - actual_result = scalar_types_series.bigquery.ai.generate_table( - model="my_model", - output_schema="category STRING", - temperature=0.7, - session=session, - ) - - session.read_pandas.assert_not_called() - mock_generate_table.assert_called_once() - args, kwargs = mock_generate_table.call_args - assert args[0] == "my_model" - assert args[1] is scalar_types_series - assert kwargs == { - "output_schema": "category STRING", - "temperature": 0.7, - "top_p": None, - "max_output_tokens": None, - "stop_sequences": None, - "request_type": None, - } - result_df.to_pandas.assert_not_called() - assert actual_result is result_df - - -def test_ai_embed(monkeypatch): - session = mock.create_autospec(bigframes.session.Session) - bf_series = mock.create_autospec(bpd.Series) - session.read_pandas.return_value = bf_series - - mock_embed = mock.MagicMock() - result_series = mock.create_autospec(bpd.Series) - mock_embed.return_value = result_series - expected_result = mock.create_autospec(pd.Series) - result_series.to_pandas.return_value = expected_result - - monkeypatch.setattr(bigframes.bigquery.ai, "embed", mock_embed) - - series = pd.Series(["hello world"], name="content") - actual_result = series.bigquery.ai.embed( # type: ignore - endpoint="my_endpoint", - model="my_model", - task_type="retrieval_query", - title="my_title", - model_params={"key": "val"}, - connection_id="my_connection", - session=session, - ) - - session.read_pandas.assert_called_once() - mock_embed.assert_called_once_with( - bf_series, - endpoint="my_endpoint", - model="my_model", - task_type="retrieval_query", - title="my_title", - model_params={"key": "val"}, - connection_id="my_connection", - ) - result_series.to_pandas.assert_called_once() - assert actual_result is expected_result - - -def test_bigframes_ai_embed(scalar_types_df: bpd.DataFrame, monkeypatch): - session = mock.create_autospec(bigframes.session.Session) - result_series = mock.create_autospec(bpd.Series) - - mock_embed = mock.MagicMock() - mock_embed.return_value = result_series - - monkeypatch.setattr(bigframes.bigquery.ai, "embed", mock_embed) - - scalar_types_series = scalar_types_df["string_col"] - actual_result = scalar_types_series.bigquery.ai.embed( - endpoint="my_endpoint", - session=session, - ) - - session.read_pandas.assert_not_called() - mock_embed.assert_called_once() - args, kwargs = mock_embed.call_args - assert args[0] is scalar_types_series - assert kwargs == { - "endpoint": "my_endpoint", - "model": None, - "task_type": None, - "title": None, - "model_params": None, - "connection_id": None, - } - result_series.to_pandas.assert_not_called() - assert actual_result is result_series - - -def test_ai_similarity(monkeypatch): - session = mock.create_autospec(bigframes.session.Session) - bf_series = mock.create_autospec(bpd.Series) - session.read_pandas.return_value = bf_series - - mock_similarity = mock.MagicMock() - result_series = mock.create_autospec(bpd.Series) - mock_similarity.return_value = result_series - expected_result = mock.create_autospec(pd.Series) - result_series.to_pandas.return_value = expected_result - - monkeypatch.setattr(bigframes.bigquery.ai, "similarity", mock_similarity) - - series = pd.Series(["apple"], name="content") - actual_result = series.bigquery.ai.similarity( # type: ignore - "banana", - endpoint="my_endpoint", - model="my_model", - model_params={"key": "val"}, - connection_id="my_connection", - session=session, - ) - - session.read_pandas.assert_called_once() - mock_similarity.assert_called_once_with( - bf_series, - "banana", - endpoint="my_endpoint", - model="my_model", - model_params={"key": "val"}, - connection_id="my_connection", - ) - result_series.to_pandas.assert_called_once() - assert actual_result is expected_result - - -def test_bigframes_ai_similarity(scalar_types_df: bpd.DataFrame, monkeypatch): - session = mock.create_autospec(bigframes.session.Session) - result_series = mock.create_autospec(bpd.Series) - - mock_similarity = mock.MagicMock() - mock_similarity.return_value = result_series - - monkeypatch.setattr(bigframes.bigquery.ai, "similarity", mock_similarity) - - scalar_types_series = scalar_types_df["string_col"] - actual_result = scalar_types_series.bigquery.ai.similarity( - "other_text", - endpoint="my_endpoint", - session=session, - ) - - session.read_pandas.assert_not_called() - mock_similarity.assert_called_once() - args, kwargs = mock_similarity.call_args - assert args[0] is scalar_types_series - assert args[1] == "other_text" - assert kwargs == { - "endpoint": "my_endpoint", - "model": None, - "model_params": None, - "connection_id": None, - } - result_series.to_pandas.assert_not_called() - assert actual_result is result_series diff --git a/tests/unit/extensions/pandas/__init__.py b/tests/unit/extensions/pandas/__init__.py deleted file mode 100644 index 58d482ea386..00000000000 --- a/tests/unit/extensions/pandas/__init__.py +++ /dev/null @@ -1,13 +0,0 @@ -# Copyright 2026 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. diff --git a/tests/unit/extensions/pandas/test_registration.py b/tests/unit/extensions/pandas/test_registration.py deleted file mode 100644 index 7007d6f9f2f..00000000000 --- a/tests/unit/extensions/pandas/test_registration.py +++ /dev/null @@ -1,29 +0,0 @@ -# Copyright 2026 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import pandas as pd - -# Importing bigframes registers the accessor. -import bigframes # noqa: F401 - - -def test_bigframes_import_registers_accessor(): - df = pd.DataFrame({"a": [1]}) - # If bigframes was imported, df.bigquery should exist - assert hasattr(df, "bigquery") - from bigframes.extensions.pandas.dataframe_accessor import ( - PandasBigQueryDataFrameAccessor, - ) - - assert isinstance(df.bigquery, PandasBigQueryDataFrameAccessor) diff --git a/tests/unit/extensions/pandas/test_series_accessor.py b/tests/unit/extensions/pandas/test_series_accessor.py deleted file mode 100644 index bfb68323f6d..00000000000 --- a/tests/unit/extensions/pandas/test_series_accessor.py +++ /dev/null @@ -1,136 +0,0 @@ -# Copyright 2026 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from unittest.mock import MagicMock, patch - -import pandas as pd - -import bigframes # noqa: F401 registers pandas extensions -import bigframes.series as series - - -def test_pandas_series_registers_accessor(): - # Arrange - from bigframes.extensions.pandas.series_accessor import ( - PandasBigQuerySeriesAccessor, - ) - - s = pd.Series([1, 2]) - - # Act - has_bq = hasattr(s, "bigquery") - bq_obj = s.bigquery - - # Assert - assert has_bq - assert isinstance(bq_obj, PandasBigQuerySeriesAccessor) - - -@patch("bigframes.operations.googlesql.global_namespace.array.array_length") -def test_pandas_series_accessor_global_routing(mock_array_length): - # Arrange - mock_bf_series = MagicMock() - mock_bf_series.to_pandas.return_value = pd.Series([2, 3]) - mock_array_length.return_value = mock_bf_series - mock_session = MagicMock() - mock_bf_self = MagicMock() - mock_session.read_pandas.return_value = mock_bf_self - - s = pd.Series([[1, 2], [3, 4, 5]]) - - # Act - result = s.bigquery.array_length(session=mock_session) - - # Assert - mock_session.read_pandas.assert_called_once_with(s) - mock_array_length.assert_called_once_with(mock_bf_self) - mock_bf_series.to_pandas.assert_called_once_with(ordered=True) - pd.testing.assert_series_equal(result, pd.Series([2, 3])) - - -@patch("bigframes.operations.googlesql.aead.encrypt") -def test_pandas_series_accessor_namespaced_routing(mock_encrypt): - # Arrange - mock_bf_series = MagicMock() - mock_bf_series.to_pandas.return_value = pd.Series([b"encrypted1", b"encrypted2"]) - mock_encrypt.return_value = mock_bf_series - mock_session = MagicMock() - mock_bf_self = MagicMock() - mock_session.read_pandas.return_value = mock_bf_self - - keyset_series = pd.Series([b"key1", b"key2"]) - plaintext = "my secret" - additional_data = "context" - - # Act - result = keyset_series.bigquery.aead.encrypt( # type: ignore - plaintext, additional_data, session=mock_session - ) - - # Assert - mock_session.read_pandas.assert_called_once_with(keyset_series) - mock_encrypt.assert_called_once_with(mock_bf_self, plaintext, additional_data) - mock_bf_series.to_pandas.assert_called_once_with(ordered=True) - pd.testing.assert_series_equal(result, pd.Series([b"encrypted1", b"encrypted2"])) - - -@patch("bigframes.operations.googlesql.global_namespace.array.array_concat") -def test_pandas_series_accessor_global_routing_uses_series_session(mock_array_concat): - # Arrange - mock_bf_series = MagicMock() - mock_bf_series.to_pandas.return_value = pd.Series([[1, 2, 3, 4]]) - mock_array_concat.return_value = mock_bf_series - mock_session = MagicMock() - mock_bf_other = MagicMock(spec=series.Series) - mock_bf_other._session = mock_session - mock_bf_self = MagicMock() - mock_session.read_pandas.return_value = mock_bf_self - s = pd.Series([[1, 2]]) - - # Act - result = s.bigquery.array_concat(mock_bf_other) - - # Assert - assert result is not None - mock_session.read_pandas.assert_called_once_with(s) - mock_array_concat.assert_called_once_with(mock_bf_self, mock_bf_other) - - -@patch("bigframes.operations.googlesql.aead.encrypt") -def test_pandas_series_accessor_namespaced_routing_uses_series_session( - mock_encrypt, -): - # Arrange - mock_bf_series = MagicMock() - mock_bf_series.to_pandas.return_value = pd.Series([b"encrypted1", b"encrypted2"]) - mock_encrypt.return_value = mock_bf_series - mock_session = MagicMock() - mock_bf_plaintext = MagicMock(spec=series.Series) - mock_bf_plaintext._session = mock_session - mock_bf_self = MagicMock() - mock_session.read_pandas.return_value = mock_bf_self - keyset_series = pd.Series([b"key1", b"key2"]) - additional_data = "context" - - # Act - result = keyset_series.bigquery.aead.encrypt( # type: ignore - mock_bf_plaintext, additional_data - ) - - # Assert - assert result is not None - mock_session.read_pandas.assert_called_once_with(keyset_series) - mock_encrypt.assert_called_once_with( - mock_bf_self, mock_bf_plaintext, additional_data - ) diff --git a/tests/unit/functions/__init__.py b/tests/unit/functions/__init__.py deleted file mode 100644 index 0a2669d7a25..00000000000 --- a/tests/unit/functions/__init__.py +++ /dev/null @@ -1,13 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. diff --git a/tests/unit/functions/test_function_template.py b/tests/unit/functions/test_function_template.py deleted file mode 100644 index 11db01ed9ee..00000000000 --- a/tests/unit/functions/test_function_template.py +++ /dev/null @@ -1,193 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import json - -import pandas as pd -import pandas.testing -import pyarrow -import pytest - -import bigframes.dtypes -import bigframes.functions.function_template as bff_template - -HELLO_WORLD_BASE64_BYTES = b"SGVsbG8sIFdvcmxkIQ==" -HELLO_WORLD_BASE64_STR = "SGVsbG8sIFdvcmxkIQ==" - - -@pytest.mark.parametrize( - ["type_", "json_value", "expected"], - ( - pytest.param( - # Type names should match those in BigQueryType.from_ibis in - # third_party/bigframes_vendored/ibis/backends/bigquery/datatypes.py - "BOOLEAN", - True, - True, - ), - pytest.param( - "BYTES", - HELLO_WORLD_BASE64_STR, - b"Hello, World!", - ), - pytest.param( - "FLOAT64", - 1.25, - 1.25, - ), - pytest.param( - "INT64", - 123, - 123, - ), - pytest.param( - "STRING", - "Hello, World!", - "Hello, World!", - ), - ), -) -def test_convert_from_bq_json(type_, json_value, expected): - got = bff_template.convert_from_bq_json(type_, json_value) - assert got == expected - - -@pytest.mark.parametrize( - "type_", - [ - # Type names should match those in BigQueryType.from_ibis in - # third_party/bigframes_vendored/ibis/backends/bigquery/datatypes.py - "BOOLEAN", - "BYTES", - "FLOAT64", - "INT64", - "STRING", - ], -) -def test_convert_from_bq_json_none(type_): - got = bff_template.convert_from_bq_json(type_, None) - assert got is None - - -@pytest.mark.parametrize( - ["type_", "value", "expected"], - ( - pytest.param( - # Type names should match those in BigQueryType.from_ibis in - # third_party/bigframes_vendored/ibis/backends/bigquery/datatypes.py - "BOOLEAN", - True, - True, - ), - pytest.param( - "BYTES", - b"Hello, World!", - HELLO_WORLD_BASE64_STR, - ), - pytest.param( - "FLOAT64", - 1.25, - 1.25, - ), - pytest.param( - "INT64", - 123, - 123, - ), - pytest.param( - "STRING", - "Hello, World!", - "Hello, World!", - ), - ), -) -def test_convert_to_bq_json(type_, value, expected): - got = bff_template.convert_to_bq_json(type_, value) - assert got == expected - - -@pytest.mark.parametrize( - "type_", - [ - # Type names should match those in BigQueryType.from_ibis in - # third_party/bigframes_vendored/ibis/backends/bigquery/datatypes.py - "BOOLEAN", - "BYTES", - "FLOAT64", - "INT64", - "STRING", - ], -) -def test_convert_to_bq_json_none(type_): - got = bff_template.convert_to_bq_json(type_, None) - assert got is None - - -@pytest.mark.parametrize( - ["row_json", "expected"], - ( - pytest.param( - json.dumps( - { - "names": ["'my-index'", "'col1'", "'col2'", "'col3'"], - "types": ["string", "Int64", "Int64", "Int64"], - "values": ["my-index-value", "1", None, "-1"], - "indexlength": 1, - "dtype": "Int64", - } - ), - pd.Series( - [1, pd.NA, -1], - dtype="Int64", - index=["col1", "col2", "col3"], - name="my-index-value", - ), - id="int64-string-index", - ), - pytest.param( - json.dumps( - { - "names": ["'col1'", "'col2'", "'col3'"], - "types": ["binary[pyarrow]", "binary[pyarrow]", "binary[pyarrow]"], - "values": [HELLO_WORLD_BASE64_STR, "dGVzdDI=", "dGVzdDM="], - "indexlength": 0, - "dtype": "binary[pyarrow]", - } - ), - pd.Series( - [b"Hello, World!", b"test2", b"test3"], - dtype=pd.ArrowDtype(pyarrow.binary()), - index=["col1", "col2", "col3"], - name=(), - ), - id="binary-no-index", - ), - ), -) -def test_get_pd_series(row_json, expected): - got = bff_template.get_pd_series(row_json) - pandas.testing.assert_series_equal(got, expected) - - -def test_get_pd_series_converter_dtypes(): - """Ensures the string format of the dtype doesn't change from that expected by get_pd_series.""" - - # Keep in sync with value_converters in get_pd_series. - # NOTE: Any change here is a red flag that there has been a breaking change - # that will affect deployed axis=1 remote functions. - assert str(bigframes.dtypes.BOOL_DTYPE) == "boolean" - assert str(bigframes.dtypes.BYTES_DTYPE) == "binary[pyarrow]" - assert str(bigframes.dtypes.FLOAT_DTYPE) == "Float64" - assert str(bigframes.dtypes.INT_DTYPE) == "Int64" - assert str(bigframes.dtypes.STRING_DTYPE) == "string" diff --git a/tests/unit/functions/test_function_typing.py b/tests/unit/functions/test_function_typing.py deleted file mode 100644 index 46ae19555aa..00000000000 --- a/tests/unit/functions/test_function_typing.py +++ /dev/null @@ -1,50 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import datetime -import decimal - -import pytest - -from bigframes.functions import function_typing - - -def test_unsupported_type_error_init_with_dict(): - err = function_typing.UnsupportedTypeError( - decimal.Decimal, {int: "INT64", float: "FLOAT64"} - ) - - message = str(err) - - assert "Decimal" in message - assert "float, int" in message - - -def test_unsupported_type_error_init_with_set(): - err = function_typing.UnsupportedTypeError(decimal.Decimal, {int, float}) - - message = str(err) - - assert "Decimal" in message - assert "float, int" in message - - -def test_sdk_type_from_python_type_raises_unsupported_type_error(): - with pytest.raises(function_typing.UnsupportedTypeError) as excinfo: - function_typing.sdk_type_from_python_type(datetime.datetime) - - message = str(excinfo.value) - - assert "datetime" in message - assert "bool, bytes, float, int, str" in message diff --git a/tests/unit/functions/test_remote_function.py b/tests/unit/functions/test_remote_function.py deleted file mode 100644 index 19de301790d..00000000000 --- a/tests/unit/functions/test_remote_function.py +++ /dev/null @@ -1,76 +0,0 @@ -# Copyright 2023 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import pytest - -from bigframes.testing import mocks - - -def test_missing_input_types(): - session = mocks.create_bigquery_session() - remote_function_decorator = session._function_session.remote_function( - cloud_function_service_account="default" - ) - - def function_without_parameter_annotations(myparam) -> str: - return str(myparam) - - assert function_without_parameter_annotations(42) == "42" - - with pytest.raises( - ValueError, - match="'input_types' was not set .* 'myparam' is missing a type annotation", - ): - remote_function_decorator(function_without_parameter_annotations) - - -def test_missing_output_type(): - session = mocks.create_bigquery_session() - remote_function_decorator = session._function_session.remote_function( - cloud_function_service_account="default" - ) - - def function_without_return_annotation(myparam: int): - return str(myparam) - - assert function_without_return_annotation(42) == "42" - - with pytest.raises( - ValueError, - match="'output_type' was not set .* missing a return type annotation", - ): - remote_function_decorator(function_without_return_annotation) - - -def test_deploy_udf(): - session = mocks.create_bigquery_session() - - def my_remote_func(x: int) -> int: - return x * 2 - - deployed = session.deploy_udf(my_remote_func) - - assert deployed.udf_def is not None - - -def test_deploy_udf_with_name(): - session = mocks.create_bigquery_session() - - def my_remote_func(x: int) -> int: - return x * 2 - - deployed = session.deploy_udf(my_remote_func, name="my_custom_name") - - # Test that the function would have been deployed somewhere. - assert "my_custom_name" in deployed.bigframes_bigquery_function diff --git a/tests/unit/functions/test_remote_function_utils.py b/tests/unit/functions/test_remote_function_utils.py deleted file mode 100644 index cbdb289e265..00000000000 --- a/tests/unit/functions/test_remote_function_utils.py +++ /dev/null @@ -1,375 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import inspect -import sys -from unittest.mock import patch - -import bigframes_vendored.constants as constants -import pytest - -from bigframes.functions import _utils, function_typing - - -@pytest.mark.parametrize( - ("input_location", "expected_cf_region"), - [ - ("us", "us-central1"), - ("eu", "europe-west1"), - ("US-east4", "us-east4"), - ], -) -def test_gcf_location_from_bq_location(input_location, expected_cf_region): - """Tests getting cloud function locations for various BigQuery locations.""" - gcf_location = _utils.gcf_location_from_bq_location(input_location) - - assert gcf_location == expected_cf_region - - -@patch("bigframes.functions._utils.numpy.__version__", "1.24.4") -@patch("bigframes.functions._utils.pyarrow.__version__", "14.0.1") -@patch("bigframes.functions._utils.pandas.__version__", "2.0.3") -@patch("bigframes.functions._utils.cloudpickle.__version__", "2.2.1") -def test_get_updated_package_requirements_is_row_processor_with_versions(): - """Tests with is_row_processor=True and specific versions.""" - expected = [ - "cloudpickle==2.2.1", - "numpy==1.24.4", - "pandas==2.0.3", - "pyarrow==14.0.1", - ] - result = _utils.get_updated_package_requirements(is_row_processor=True) - - assert result == expected - - -@patch("bigframes.functions._utils.warnings.warn") -@patch("bigframes.functions._utils.cloudpickle.__version__", "2.2.1") -def test_get_updated_package_requirements_ignore_version(mock_warn): - """ - Tests with is_row_processor=True and ignore_package_version=True. - Should add packages without versions and raise a warning. - """ - expected = ["cloudpickle==2.2.1", "numpy", "pandas", "pyarrow"] - result = _utils.get_updated_package_requirements( - is_row_processor=True, ignore_package_version=True - ) - - assert result == expected - # Verify that a warning was issued. - mock_warn.assert_called_once() - - -@patch("bigframes.functions._utils.numpy.__version__", "1.24.4") -@patch("bigframes.functions._utils.pyarrow.__version__", "14.0.1") -@patch("bigframes.functions._utils.pandas.__version__", "2.0.3") -def test_get_updated_package_requirements_capture_references_false(): - """ - Tests with capture_references=False. - Should not add cloudpickle but should add others if requested. - """ - # Case 1: Only capture_references=False. - result_1 = _utils.get_updated_package_requirements(capture_references=False) - - assert len(result_1) == 0 - - # Case 2: capture_references=False but is_row_processor=True. - expected_2 = ["numpy==1.24.4", "pandas==2.0.3", "pyarrow==14.0.1"] - result_2 = _utils.get_updated_package_requirements( - is_row_processor=True, capture_references=False - ) - - assert result_2 == expected_2 - - -@patch("bigframes.functions._utils.numpy.__version__", "1.24.4") -@patch("bigframes.functions._utils.pyarrow.__version__", "14.0.1") -@patch("bigframes.functions._utils.pandas.__version__", "2.0.3") -@patch("bigframes.functions._utils.cloudpickle.__version__", "2.2.1") -def test_get_updated_package_requirements_non_overlapping_packages(): - """Tests providing an initial list of packages that do not overlap.""" - initial_packages = ["scikit-learn==1.3.0", "xgboost"] - expected = [ - "cloudpickle==2.2.1", - "numpy==1.24.4", - "pandas==2.0.3", - "pyarrow==14.0.1", - "scikit-learn==1.3.0", - "xgboost", - ] - result = _utils.get_updated_package_requirements( - package_requirements=initial_packages, is_row_processor=True - ) - - assert result == expected - - -@patch("bigframes.functions._utils.numpy.__version__", "1.24.4") -@patch("bigframes.functions._utils.pyarrow.__version__", "14.0.1") -@patch("bigframes.functions._utils.pandas.__version__", "2.0.3") -@patch("bigframes.functions._utils.cloudpickle.__version__", "2.2.1") -def test_get_updated_package_requirements_overlapping_packages(): - """Tests that packages are not added if they already exist.""" - # The function should respect the pre-existing pandas version. - initial_packages = ["pandas==1.5.3", "numpy"] - expected = [ - "cloudpickle==2.2.1", - "numpy", - "pandas==1.5.3", - "pyarrow==14.0.1", - ] - result = _utils.get_updated_package_requirements( - package_requirements=initial_packages, is_row_processor=True - ) - - assert result == expected - - -@patch("bigframes.functions._utils.cloudpickle.__version__", "2.2.1") -def test_get_updated_package_requirements_with_existing_cloudpickle(): - """Tests that cloudpickle is not added if it already exists.""" - initial_packages = ["cloudpickle==2.0.0"] - expected = ["cloudpickle==2.0.0"] - result = _utils.get_updated_package_requirements( - package_requirements=initial_packages - ) - - assert result == expected - - -# Dynamically generate expected python versions for the test -_major = sys.version_info.major -_minor = sys.version_info.minor -_compat_version = f"python{_major}{_minor}" -_standard_version = f"python-{_major}.{_minor}" - - -@pytest.mark.parametrize( - "is_compat, expected_version", - [ - (True, _compat_version), - (False, _standard_version), - ], -) -def test_get_python_version(is_compat, expected_version): - """Tests the python version for both standard and compat modes.""" - result = _utils.get_python_version(is_compat=is_compat) - assert result == expected_version - - -def test_package_existed_helper(): - """Tests the _package_existed helper function directly.""" - reqs = ["pandas==1.0", "numpy", "scikit-learn>=1.2.0"] - - # Exact match - assert _utils._package_existed(reqs, "pandas==1.0") - # Different version - assert _utils._package_existed(reqs, "pandas==2.0") - # No version specified - assert _utils._package_existed(reqs, "numpy") - # Not in list - assert not _utils._package_existed(reqs, "xgboost") - # Empty list - assert not _utils._package_existed([], "pandas") - - -# Helper functions for signature inspection tests -def _func_one_arg_annotated(x: int) -> int: - """A function with one annotated arg and an annotated return type.""" - return x - - -def _func_one_arg_unannotated(x): - """A function with one unannotated arg and no return type annotation.""" - return x - - -def _func_two_args_annotated(x: int, y: str): - """A function with two annotated args and no return type annotation.""" - return f"{x}{y}" - - -def _func_two_args_unannotated(x, y): - """A function with two unannotated args and no return type annotation.""" - return f"{x}{y}" - - -def test_has_conflict_input_type_too_few_inputs(): - """Tests conflict when there are fewer input types than parameters.""" - signature = inspect.signature(_func_one_arg_annotated) - assert _utils.has_conflict_input_type(signature, input_types=[]) - - -def test_has_conflict_input_type_too_many_inputs(): - """Tests conflict when there are more input types than parameters.""" - signature = inspect.signature(_func_one_arg_annotated) - assert _utils.has_conflict_input_type(signature, input_types=[int, str]) - - -def test_has_conflict_input_type_type_mismatch(): - """Tests has_conflict_input_type with a conflicting type annotation.""" - signature = inspect.signature(_func_two_args_annotated) - - # The second type (bool) conflicts with the annotation (str). - assert _utils.has_conflict_input_type(signature, input_types=[int, bool]) - - -def test_has_conflict_input_type_no_conflict_annotated(): - """Tests that a matching, annotated signature is compatible.""" - signature = inspect.signature(_func_two_args_annotated) - assert not _utils.has_conflict_input_type(signature, input_types=[int, str]) - - -def test_has_conflict_input_type_no_conflict_unannotated(): - """Tests that a signature with no annotations is always compatible.""" - signature = inspect.signature(_func_two_args_unannotated) - assert not _utils.has_conflict_input_type(signature, input_types=[int, float]) - - -def test_has_conflict_output_type_no_conflict(): - """Tests has_conflict_output_type with type annotation.""" - signature = inspect.signature(_func_one_arg_annotated) - - assert _utils.has_conflict_output_type(signature, output_type=float) - assert not _utils.has_conflict_output_type(signature, output_type=int) - - -def test_has_conflict_output_type_no_annotation(): - """Tests has_conflict_output_type without type annotation.""" - signature = inspect.signature(_func_one_arg_unannotated) - - assert not _utils.has_conflict_output_type(signature, output_type=int) - assert not _utils.has_conflict_output_type(signature, output_type=float) - - -@pytest.mark.parametrize( - ["metadata_options", "metadata_string"], - ( - pytest.param( - {}, - '{"value": {}}', - id="empty", - ), - pytest.param( - {"python_output_type": None}, - '{"value": {}}', - id="None", - ), - pytest.param( - {"python_output_type": list[bool]}, - '{"value": {"python_array_output_type": "bool"}}', - id="list-bool", - ), - pytest.param( - {"python_output_type": list[float]}, - '{"value": {"python_array_output_type": "float"}}', - id="list-float", - ), - pytest.param( - {"python_output_type": list[int]}, - '{"value": {"python_array_output_type": "int"}}', - id="list-int", - ), - pytest.param( - {"python_output_type": list[str]}, - '{"value": {"python_array_output_type": "str"}}', - id="list-str", - ), - ), -) -def test_get_bigframes_metadata(metadata_options, metadata_string): - assert _utils.get_bigframes_metadata(**metadata_options) == metadata_string - - -@pytest.mark.parametrize( - ["output_type"], - ( - pytest.param(bool), - pytest.param(bytes), - pytest.param(float), - pytest.param(int), - pytest.param(str), - pytest.param(list), - pytest.param(list[bytes], id="list-bytes"), - ), -) -def test_get_bigframes_metadata_array_type_not_serializable(output_type): - with pytest.raises(ValueError) as context: - _utils.get_bigframes_metadata(python_output_type=output_type) - - assert str(context.value) == ( - f"python_output_type {output_type} is not serializable. {constants.FEEDBACK_LINK}" - ) - - -@pytest.mark.parametrize( - ["metadata_string", "python_output_type"], - ( - pytest.param( - None, - None, - id="None", - ), - pytest.param( - "", - None, - id="empty", - ), - pytest.param( - "{}", - None, - id="empty-dict", - ), - pytest.param( - '{"value": {}}', - None, - id="empty-value", - ), - pytest.param( - '{"value": {"python_array_output_type": "bool"}}', - list[bool], - id="list-bool", - ), - pytest.param( - '{"value": {"python_array_output_type": "float"}}', - list[float], - id="list-float", - ), - pytest.param( - '{"value": {"python_array_output_type": "int"}}', - list[int], - id="list-int", - ), - pytest.param( - '{"value": {"python_array_output_type": "str"}}', - list[str], - id="list-str", - ), - ), -) -def test_get_python_output_type_from_bigframes_metadata( - metadata_string, python_output_type -): - assert ( - _utils.get_python_output_type_from_bigframes_metadata(metadata_string) - == python_output_type - ) - - -def test_metadata_roundtrip_supported_array_types(): - for array_of in function_typing.RF_SUPPORTED_ARRAY_OUTPUT_PYTHON_TYPES: - ser = _utils.get_bigframes_metadata(python_output_type=list[array_of]) # type: ignore - deser = _utils.get_python_output_type_from_bigframes_metadata(ser) - - assert deser == list[array_of] # type: ignore diff --git a/tests/unit/ml/test_api_primitives.py b/tests/unit/ml/test_api_primitives.py index dd2ceff1432..da77a180a8d 100644 --- a/tests/unit/ml/test_api_primitives.py +++ b/tests/unit/ml/test_api_primitives.py @@ -13,6 +13,8 @@ # limitations under the License. import pytest +import sklearn.decomposition as sklearn_decomposition # type: ignore +import sklearn.linear_model as sklearn_linear_model # type: ignore import bigframes.ml.decomposition import bigframes.ml.linear_model @@ -28,14 +30,12 @@ def test_base_estimator_repr(): estimator = bigframes.ml.linear_model.LinearRegression(fit_intercept=True) assert estimator.__repr__() == "LinearRegression()" - # TODO(b/340891292): fix type error - pca_estimator = bigframes.ml.decomposition.PCA(n_components=7) - assert pca_estimator.__repr__() == "PCA(n_components=7)" + estimator = bigframes.ml.decomposition.PCA(n_components=7) + assert estimator.__repr__() == "PCA(n_components=7)" +@pytest.mark.skipif(sklearn_linear_model is None, reason="requires sklearn") def test_base_estimator_repr_matches_sklearn(): - sklearn_decomposition = pytest.importorskip("sklearn.decomposition") - sklearn_linear_model = pytest.importorskip("sklearn.linear_model") estimator = bigframes.ml.linear_model.LinearRegression() sklearn_estimator = sklearn_linear_model.LinearRegression() assert estimator.__repr__() == sklearn_estimator.__repr__() @@ -48,7 +48,6 @@ def test_base_estimator_repr_matches_sklearn(): sklearn_estimator = sklearn_linear_model.LinearRegression(fit_intercept=True) assert estimator.__repr__() == sklearn_estimator.__repr__() - # TODO(b/340891292): fix type error - pca_estimator = bigframes.ml.decomposition.PCA(n_components=7) + estimator = bigframes.ml.decomposition.PCA(n_components=7) sklearn_estimator = sklearn_decomposition.PCA(n_components=7) - assert pca_estimator.__repr__() == sklearn_estimator.__repr__() + assert estimator.__repr__() == sklearn_estimator.__repr__() diff --git a/tests/unit/ml/test_compose.py b/tests/unit/ml/test_compose.py index 7779bafadfa..60dcc75b63c 100644 --- a/tests/unit/ml/test_compose.py +++ b/tests/unit/ml/test_compose.py @@ -11,15 +11,11 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. -from unittest import mock -import pytest -from google.cloud import bigquery +import sklearn.compose as sklearn_compose # type: ignore +import sklearn.preprocessing as sklearn_preprocessing # type: ignore -import bigframes.pandas as bpd from bigframes.ml import compose, preprocessing -from bigframes.ml.compose import ColumnTransformer, SQLScalarColumnTransformer -from bigframes.ml.core import BqmlModel def test_columntransformer_init_expectedtransforms(): @@ -117,8 +113,6 @@ def test_columntransformer_repr(): def test_columntransformer_repr_matches_sklearn(): - sklearn_compose = pytest.importorskip("sklearn.compose") - sklearn_preprocessing = pytest.importorskip("sklearn.preprocessing") bf_column_transformer = compose.ColumnTransformer( [ ( @@ -179,404 +173,3 @@ def test_columntransformer_repr_matches_sklearn(): ) assert bf_column_transformer.__repr__() == sk_column_transformer.__repr__() - - -@pytest.fixture(scope="session") -def mock_X(): - mock_df = mock.create_autospec(spec=bpd.DataFrame) - return mock_df - - -def test_columntransformer_init_with_sqltransformers(): - ident_transformer = SQLScalarColumnTransformer("{0}", target_column="ident_{0}") - len1_transformer = SQLScalarColumnTransformer( - "CASE WHEN {0} IS NULL THEN -2 ELSE LENGTH({0}) END", target_column="len1_{0}" - ) - len2_transformer = SQLScalarColumnTransformer( - "CASE WHEN {0} IS NULL THEN 99 ELSE LENGTH({0}) END", target_column="len2_{0}" - ) - label_transformer = preprocessing.LabelEncoder() - column_transformer = compose.ColumnTransformer( - [ - ( - "ident_trafo", - ident_transformer, - ["culmen_length_mm", "flipper_length_mm"], - ), - ("len1_trafo", len1_transformer, ["species"]), - ("len2_trafo", len2_transformer, ["species"]), - ("label", label_transformer, "species"), - ] - ) - - assert column_transformer.transformers_ == [ - ("ident_trafo", ident_transformer, "culmen_length_mm"), - ("ident_trafo", ident_transformer, "flipper_length_mm"), - ("len1_trafo", len1_transformer, "species"), - ("len2_trafo", len2_transformer, "species"), - ("label", label_transformer, "species"), - ] - - -def test_columntransformer_repr_sqltransformers(): - ident_transformer = SQLScalarColumnTransformer("{0}", target_column="ident_{0}") - len1_transformer = SQLScalarColumnTransformer( - "CASE WHEN {0} IS NULL THEN -2 ELSE LENGTH({0}) END", target_column="len1_{0}" - ) - len2_transformer = SQLScalarColumnTransformer( - "CASE WHEN {0} IS NULL THEN 99 ELSE LENGTH({0}) END", target_column="len2_{0}" - ) - label_transformer = preprocessing.LabelEncoder() - column_transformer = compose.ColumnTransformer( - [ - ( - "ident_trafo", - ident_transformer, - ["culmen_length_mm", "flipper_length_mm"], - ), - ("len1_trafo", len1_transformer, ["species"]), - ("len2_trafo", len2_transformer, ["species"]), - ("label", label_transformer, "species"), - ] - ) - - expected = """ColumnTransformer(transformers=[('ident_trafo', - SQLScalarColumnTransformer(sql='{0}', target_column='ident_{0}'), - ['culmen_length_mm', 'flipper_length_mm']), - ('len1_trafo', - SQLScalarColumnTransformer(sql='CASE WHEN {0} IS NULL THEN -2 ELSE LENGTH({0}) END', target_column='len1_{0}'), - ['species']), - ('len2_trafo', - SQLScalarColumnTransformer(sql='CASE WHEN {0} IS NULL THEN 99 ELSE LENGTH({0}) END', target_column='len2_{0}'), - ['species']), - ('label', LabelEncoder(), 'species')])""" - actual = column_transformer.__repr__() - assert expected == actual - - -def test_customtransformer_compile_sql(mock_X): - ident_trafo = SQLScalarColumnTransformer("{0}", target_column="ident_{0}") - sqls = ident_trafo._compile_to_sql(X=mock_X, columns=["col1", "col2"]) - assert sqls == [ - "`col1` AS `ident_col1`", - "`col2` AS `ident_col2`", - ] - - len1_trafo = SQLScalarColumnTransformer( - "CASE WHEN {0} IS NULL THEN -5 ELSE LENGTH({0}) END", target_column="len1_{0}" - ) - sqls = len1_trafo._compile_to_sql(X=mock_X, columns=["col1", "col2"]) - assert sqls == [ - "CASE WHEN `col1` IS NULL THEN -5 ELSE LENGTH(`col1`) END AS `len1_col1`", - "CASE WHEN `col2` IS NULL THEN -5 ELSE LENGTH(`col2`) END AS `len1_col2`", - ] - - len2_trafo = SQLScalarColumnTransformer( - "CASE WHEN {0} IS NULL THEN 99 ELSE LENGTH({0}) END", target_column="len2_{0}" - ) - sqls = len2_trafo._compile_to_sql(X=mock_X, columns=["col1", "col2"]) - assert sqls == [ - "CASE WHEN `col1` IS NULL THEN 99 ELSE LENGTH(`col1`) END AS `len2_col1`", - "CASE WHEN `col2` IS NULL THEN 99 ELSE LENGTH(`col2`) END AS `len2_col2`", - ] - - -def create_bq_model_mock(monkeypatch, transform_columns, feature_columns=None): - properties = {"transformColumns": transform_columns} - mock_bq_model = bigquery.Model("model_project.model_dataset.model_id") - type(mock_bq_model)._properties = mock.PropertyMock(return_value=properties) - if feature_columns: - result = [ - bigquery.standard_sql.StandardSqlField(col, None) for col in feature_columns - ] - monkeypatch.setattr( - type(mock_bq_model), - "feature_columns", - mock.PropertyMock(return_value=result), - ) - - return mock_bq_model - - -@pytest.fixture -def bq_model_good(monkeypatch): - return create_bq_model_mock( - monkeypatch, - [ - { - "name": "ident_culmen_length_mm", - "type": {"typeKind": "INT64"}, - "transformSql": "culmen_length_mm /*CT.IDENT()*/", - }, - { - "name": "ident_flipper_length_mm", - "type": {"typeKind": "INT64"}, - "transformSql": "flipper_length_mm /*CT.IDENT()*/", - }, - { - "name": "len1_species", - "type": {"typeKind": "INT64"}, - "transformSql": "CASE WHEN species IS NULL THEN -5 ELSE LENGTH(species) END /*CT.LEN1()*/", - }, - { - "name": "len2_species", - "type": {"typeKind": "INT64"}, - "transformSql": "CASE WHEN species IS NULL THEN 99 ELSE LENGTH(species) END /*CT.LEN2([99])*/", - }, - { - "name": "labelencoded_county", - "type": {"typeKind": "INT64"}, - "transformSql": "ML.LABEL_ENCODER(county, 1000000, 0) OVER()", - }, - { - "name": "labelencoded_species", - "type": {"typeKind": "INT64"}, - "transformSql": "ML.LABEL_ENCODER(species, 1000000, 0) OVER()", - }, - ], - ) - - -@pytest.fixture -def bq_model_merge(monkeypatch): - return create_bq_model_mock( - monkeypatch, - [ - { - "name": "labelencoded_county", - "type": {"typeKind": "INT64"}, - "transformSql": "ML.LABEL_ENCODER(county, 1000000, 0) OVER()", - }, - { - "name": "labelencoded_species", - "type": {"typeKind": "INT64"}, - "transformSql": "ML.LABEL_ENCODER(species, 1000000, 0) OVER()", - }, - ], - ["county", "species"], - ) - - -@pytest.fixture -def bq_model_no_merge(monkeypatch): - return create_bq_model_mock( - monkeypatch, - [ - { - "name": "ident_culmen_length_mm", - "type": {"typeKind": "INT64"}, - "transformSql": "culmen_length_mm /*CT.IDENT()*/", - } - ], - ["culmen_length_mm"], - ) - - -@pytest.fixture -def bq_model_unknown_ML(monkeypatch): - return create_bq_model_mock( - monkeypatch, - [ - { - "name": "unknownml_culmen_length_mm", - "type": {"typeKind": "INT64"}, - "transformSql": "ML.UNKNOWN(culmen_length_mm)", - }, - { - "name": "labelencoded_county", - "type": {"typeKind": "INT64"}, - "transformSql": "ML.LABEL_ENCODER(county, 1000000, 0) OVER()", - }, - ], - ) - - -@pytest.fixture -def bq_model_flexnames(monkeypatch): - return create_bq_model_mock( - monkeypatch, - [ - { - "name": "Flex Name culmen_length_mm", - "type": {"typeKind": "INT64"}, - "transformSql": "culmen_length_mm", - }, - { - "name": "transformed_Culmen Length MM", - "type": {"typeKind": "INT64"}, - "transformSql": "`Culmen Length MM`*/", - }, - # test workaround for bug in get_model - { - "name": "Flex Name flipper_length_mm", - "type": {"typeKind": "INT64"}, - "transformSql": "flipper_length_mm AS `Flex Name flipper_length_mm`", - }, - { - "name": "transformed_Flipper Length MM", - "type": {"typeKind": "INT64"}, - "transformSql": "`Flipper Length MM` AS `transformed_Flipper Length MM`*/", - }, - ], - ) - - -def test_columntransformer_extract_from_bq_model_good(bq_model_good): - col_trans = ColumnTransformer._extract_from_bq_model(bq_model_good) - assert len(col_trans.transformers) == 6 - # normalize the representation for string comparing - col_trans.transformers.sort(key=lambda trafo: str(trafo)) - actual = col_trans.__repr__() - expected = """ColumnTransformer(transformers=[('label_encoder', - LabelEncoder(max_categories=1000001, - min_frequency=0), - 'county'), - ('label_encoder', - LabelEncoder(max_categories=1000001, - min_frequency=0), - 'species'), - ('sql_scalar_column_transformer', - SQLScalarColumnTransformer(sql='CASE WHEN species IS NULL THEN -5 ELSE LENGTH(species) END /*CT.LEN1()*/', target_column='len1_species'), - '?len1_species'), - ('sql_scalar_column_transformer', - SQLScalarColumnTransformer(sql='CASE WHEN species IS NULL THEN 99 ELSE LENGTH(species) END /*CT.LEN2([99])*/', target_column='len2_species'), - '?len2_species'), - ('sql_scalar_column_transformer', - SQLScalarColumnTransformer(sql='culmen_length_mm /*CT.IDENT()*/', target_column='ident_culmen_length_mm'), - '?ident_culmen_length_mm'), - ('sql_scalar_column_transformer', - SQLScalarColumnTransformer(sql='flipper_length_mm /*CT.IDENT()*/', target_column='ident_flipper_length_mm'), - '?ident_flipper_length_mm')])""" - assert expected == actual - - -def test_columntransformer_extract_from_bq_model_merge(bq_model_merge): - col_trans = ColumnTransformer._extract_from_bq_model(bq_model_merge) - assert isinstance(col_trans, ColumnTransformer) - merged_col_trans = col_trans._merge(bq_model_merge) - assert isinstance(merged_col_trans, preprocessing.LabelEncoder) - assert ( - merged_col_trans.__repr__() - == """LabelEncoder(max_categories=1000001, min_frequency=0)""" - ) - assert merged_col_trans._output_names == [ - "labelencoded_county", - "labelencoded_species", - ] - - -def test_columntransformer_extract_from_bq_model_no_merge(bq_model_no_merge): - col_trans = ColumnTransformer._extract_from_bq_model(bq_model_no_merge) - merged_col_trans = col_trans._merge(bq_model_no_merge) - assert isinstance(merged_col_trans, ColumnTransformer) - expected = """ColumnTransformer(transformers=[('sql_scalar_column_transformer', - SQLScalarColumnTransformer(sql='culmen_length_mm /*CT.IDENT()*/', target_column='ident_culmen_length_mm'), - '?ident_culmen_length_mm')])""" - actual = merged_col_trans.__repr__() - assert expected == actual - - -def test_columntransformer_extract_from_bq_model_unknown_ML(bq_model_unknown_ML): - try: - _ = ColumnTransformer._extract_from_bq_model(bq_model_unknown_ML) - assert False - except NotImplementedError as e: - assert "Unsupported transformer type" in e.args[0] - - -def test_columntransformer_extract_output_names(bq_model_good): - class BQMLModel(BqmlModel): - def __init__(self, bq_model): - self._model = bq_model - - col_trans = ColumnTransformer._extract_from_bq_model(bq_model_good) - col_trans._bqml_model = BQMLModel(bq_model_good) - col_trans._extract_output_names() - assert col_trans._output_names == [ - "ident_culmen_length_mm", - "ident_flipper_length_mm", - "len1_species", - "len2_species", - "labelencoded_county", - "labelencoded_species", - ] - - -def test_columntransformer_compile_to_sql(mock_X): - ident_transformer = SQLScalarColumnTransformer("{0}", target_column="ident_{0}") - len1_transformer = SQLScalarColumnTransformer( - "CASE WHEN {0} IS NULL THEN -2 ELSE LENGTH({0}) END", target_column="len1_{0}" - ) - len2_transformer = SQLScalarColumnTransformer( - "CASE WHEN {0} IS NULL THEN 99 ELSE LENGTH({0}) END", target_column="len2_{0}" - ) - label_transformer = preprocessing.LabelEncoder() - column_transformer = compose.ColumnTransformer( - [ - ( - "ident_trafo", - ident_transformer, - ["culmen_length_mm", "flipper_length_mm"], - ), - ("len1_trafo", len1_transformer, ["species"]), - ("len2_trafo", len2_transformer, ["species"]), - ("label", label_transformer, "species"), - ] - ) - sqls = column_transformer._compile_to_sql(mock_X) - assert sqls == [ - "`culmen_length_mm` AS `ident_culmen_length_mm`", - "`flipper_length_mm` AS `ident_flipper_length_mm`", - "CASE WHEN `species` IS NULL THEN -2 ELSE LENGTH(`species`) END AS `len1_species`", - "CASE WHEN `species` IS NULL THEN 99 ELSE LENGTH(`species`) END AS `len2_species`", - "ML.LABEL_ENCODER(`species`, 1000000, 0) OVER() AS `labelencoded_species`", - ] - - -def test_columntransformer_flexible_column_names(mock_X): - ident_transformer = SQLScalarColumnTransformer("{0}", target_column="ident {0}") - len1_transformer = SQLScalarColumnTransformer( - "CASE WHEN {0} IS NULL THEN -2 ELSE LENGTH({0}) END", target_column="len1_{0}" - ) - len2_transformer = SQLScalarColumnTransformer( - "CASE WHEN {0} IS NULL THEN 99 ELSE LENGTH({0}) END", target_column="len2_{0}" - ) - column_transformer = compose.ColumnTransformer( - [ - ( - "ident_trafo", - ident_transformer, - ["culmen_length_mm", "flipper_length_mm"], - ), - ("len1_trafo", len1_transformer, ["species shortname"]), - ("len2_trafo", len2_transformer, ["species longname"]), - ] - ) - sqls = column_transformer._compile_to_sql(mock_X) - assert sqls == [ - "`culmen_length_mm` AS `ident culmen_length_mm`", - "`flipper_length_mm` AS `ident flipper_length_mm`", - "CASE WHEN `species shortname` IS NULL THEN -2 ELSE LENGTH(`species shortname`) END AS `len1_species shortname`", - "CASE WHEN `species longname` IS NULL THEN 99 ELSE LENGTH(`species longname`) END AS `len2_species longname`", - ] - - -def test_columntransformer_extract_from_bq_model_flexnames(bq_model_flexnames): - col_trans = ColumnTransformer._extract_from_bq_model(bq_model_flexnames) - assert len(col_trans.transformers) == 4 - # normalize the representation for string comparing - col_trans.transformers.sort(key=lambda trafo: str(trafo)) - actual = col_trans.__repr__() - expected = """ColumnTransformer(transformers=[('sql_scalar_column_transformer', - SQLScalarColumnTransformer(sql='`Culmen Length MM`*/', target_column='transformed_Culmen Length MM'), - '?transformed_Culmen Length MM'), - ('sql_scalar_column_transformer', - SQLScalarColumnTransformer(sql='`Flipper Length MM` AS `transformed_Flipper Length MM`*/', target_column='transformed_Flipper Length MM'), - '?transformed_Flipper Length MM'), - ('sql_scalar_column_transformer', - SQLScalarColumnTransformer(sql='culmen_length_mm', target_column='Flex Name culmen_length_mm'), - '?Flex Name culmen_length_mm'), - ('sql_scalar_column_transformer', - SQLScalarColumnTransformer(sql='flipper_length_mm', target_column='Flex Name flipper_length_mm'), - '?Flex Name flipper_length_mm')])""" - assert expected == actual diff --git a/tests/unit/ml/test_forecasting.py b/tests/unit/ml/test_forecasting.py deleted file mode 100644 index 3bbf4c777e0..00000000000 --- a/tests/unit/ml/test_forecasting.py +++ /dev/null @@ -1,58 +0,0 @@ -# Copyright 2023 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import re - -import pytest - -from bigframes.ml import forecasting - - -def test_predict_explain_low_confidence_level(): - confidence_level = -0.5 - - model = forecasting.ARIMAPlus() - - with pytest.raises( - ValueError, - match=re.escape( - f"confidence_level must be [0.0, 1.0), but is {confidence_level}." - ), - ): - model.predict_explain(horizon=4, confidence_level=confidence_level) - - -def test_predict_high_explain_confidence_level(): - confidence_level = 2.1 - - model = forecasting.ARIMAPlus() - - with pytest.raises( - ValueError, - match=re.escape( - f"confidence_level must be [0.0, 1.0), but is {confidence_level}." - ), - ): - model.predict_explain(horizon=4, confidence_level=confidence_level) - - -def test_predict_explain_low_horizon(): - horizon = -1 - - model = forecasting.ARIMAPlus() - - with pytest.raises( - ValueError, match=f"horizon must be at least 1, but is {horizon}." - ): - model.predict_explain(horizon=horizon, confidence_level=0.9) diff --git a/tests/unit/ml/test_golden_sql.py b/tests/unit/ml/test_golden_sql.py index 7babf476117..3ca7e144a53 100644 --- a/tests/unit/ml/test_golden_sql.py +++ b/tests/unit/ml/test_golden_sql.py @@ -14,60 +14,30 @@ from unittest import mock +from google.cloud import bigquery import pandas as pd import pytest -from google.cloud import bigquery +import pytest_mock import bigframes -import bigframes.ml.core +from bigframes.ml import core, linear_model import bigframes.pandas as bpd -from bigframes.ml import core, decomposition, linear_model - -TEMP_MODEL_ID = bigquery.ModelReference.from_string( - "test-project._anon123.temp_model_id" -) @pytest.fixture def mock_session(): mock_session = mock.create_autospec(spec=bigframes.Session) - mock_session._anonymous_dataset = bigquery.DatasetReference( - TEMP_MODEL_ID.project, TEMP_MODEL_ID.dataset_id - ) - mock_session._bq_kms_key_name = None - mock_session._metrics = None - - query_job = mock.create_autospec(bigquery.QueryJob) - type(query_job).destination = mock.PropertyMock( - return_value=bigquery.TableReference( - mock_session._anonymous_dataset, TEMP_MODEL_ID.model_id - ) - ) - mock_session._start_query_ml_ddl.return_value = (None, query_job) + # return values we don't care about, but need to provide to continue the program when calling session._start_query() + mock_session._start_query.return_value = (None, mock.MagicMock()) return mock_session @pytest.fixture -def bqml_model_factory(monkeypatch): - monkeypatch.setattr( - bigframes.ml.core.BqmlModelFactory, - "_create_model_ref", - mock.Mock(return_value=TEMP_MODEL_ID), - ) - bqml_model_factory = core.BqmlModelFactory() - - return bqml_model_factory - - -@pytest.fixture -def mock_y(mock_session): +def mock_y(): mock_y = mock.create_autospec(spec=bpd.DataFrame) - mock_y._session = mock_session mock_y.columns = pd.Index(["input_column_label"]) - mock_y.cache.return_value = mock_y - mock_y.copy.return_value = mock_y return mock_y @@ -81,30 +51,25 @@ def mock_X(mock_y, mock_session): ["index_column_id"], ["index_column_label"], ) - type(mock_X).sql = mock.PropertyMock(return_value="input_X_sql_property") - mock_X.reset_index(drop=True).cache().sql = "input_X_no_index_sql" mock_X.join(mock_y).sql = "input_X_y_sql" - mock_X.join(mock_y).cache.return_value = mock_X.join(mock_y) mock_X.join(mock_y)._to_sql_query.return_value = ( "input_X_y_sql", ["index_column_id"], ["index_column_label"], ) - mock_X.join(mock_y).reset_index(drop=True).sql = "input_X_y_no_index_sql" - mock_X.join(mock_y).reset_index(drop=True).cache.return_value = mock_X.join( - mock_y - ).reset_index(drop=True) - mock_X.join(mock_y).reset_index(drop=True)._to_sql_query.return_value = ( - "input_X_y_no_index_sql", - ["index_column_id"], - ["index_column_label"], - ) + return mock_X - mock_X.cache.return_value = mock_X - mock_X.copy.return_value = mock_X - return mock_X +@pytest.fixture +def bqml_model_factory(mocker: pytest_mock.MockerFixture): + mocker.patch( + "bigframes.ml.core.BqmlModelFactory._create_temp_model_id", + return_value="temp_model_id", + ) + bqml_model_factory = core.BqmlModelFactory() + + return bqml_model_factory @pytest.fixture @@ -123,8 +88,8 @@ def test_linear_regression_default_fit( model._bqml_model_factory = bqml_model_factory model.fit(mock_X, mock_y) - mock_session._start_query_ml_ddl.assert_called_once_with( - "CREATE OR REPLACE MODEL `test-project`.`_anon123`.`temp_model_id`\nOPTIONS(\n model_type='LINEAR_REG',\n data_split_method='NO_SPLIT',\n optimize_strategy='auto_strategy',\n fit_intercept=TRUE,\n l2_reg=0.0,\n max_iterations=20,\n learn_rate_strategy='line_search',\n min_rel_progress=0.01,\n calculate_p_values=FALSE,\n enable_global_explain=FALSE,\n INPUT_LABEL_COLS=['input_column_label'])\nAS input_X_y_no_index_sql" + mock_session._start_query.assert_called_once_with( + 'CREATE TEMP MODEL `temp_model_id`\nOPTIONS(\n model_type="LINEAR_REG",\n data_split_method="NO_SPLIT",\n optimize_strategy="normal_equation",\n fit_intercept=True,\n l2_reg=0.0,\n max_iterations=20,\n learn_rate_strategy="line_search",\n early_stop=True,\n min_rel_progress=0.01,\n ls_init_learn_rate=0.1,\n calculate_p_values=False,\n enable_global_explain=False,\n INPUT_LABEL_COLS=["input_column_label"])\nAS input_X_y_sql' ) @@ -133,8 +98,8 @@ def test_linear_regression_params_fit(bqml_model_factory, mock_session, mock_X, model._bqml_model_factory = bqml_model_factory model.fit(mock_X, mock_y) - mock_session._start_query_ml_ddl.assert_called_once_with( - "CREATE OR REPLACE MODEL `test-project`.`_anon123`.`temp_model_id`\nOPTIONS(\n model_type='LINEAR_REG',\n data_split_method='NO_SPLIT',\n optimize_strategy='auto_strategy',\n fit_intercept=FALSE,\n l2_reg=0.0,\n max_iterations=20,\n learn_rate_strategy='line_search',\n min_rel_progress=0.01,\n calculate_p_values=FALSE,\n enable_global_explain=FALSE,\n INPUT_LABEL_COLS=['input_column_label'])\nAS input_X_y_no_index_sql" + mock_session._start_query.assert_called_once_with( + 'CREATE TEMP MODEL `temp_model_id`\nOPTIONS(\n model_type="LINEAR_REG",\n data_split_method="NO_SPLIT",\n optimize_strategy="normal_equation",\n fit_intercept=False,\n l2_reg=0.0,\n max_iterations=20,\n learn_rate_strategy="line_search",\n early_stop=True,\n min_rel_progress=0.01,\n ls_init_learn_rate=0.1,\n calculate_p_values=False,\n enable_global_explain=False,\n INPUT_LABEL_COLS=["input_column_label"])\nAS input_X_y_sql' ) @@ -143,10 +108,9 @@ def test_linear_regression_predict(mock_session, bqml_model, mock_X): model._bqml_model = bqml_model model.predict(mock_X) - mock_session.read_gbq_query.assert_called_once_with( - "SELECT * FROM ML.PREDICT(MODEL `model_project`.`model_dataset`.`model_id`,\n (input_X_sql))", + mock_session.read_gbq.assert_called_once_with( + "SELECT * FROM ML.PREDICT(MODEL `model_project.model_dataset.model_id`,\n (input_X_sql))", index_col=["index_column_id"], - allow_large_results=True, ) @@ -155,9 +119,8 @@ def test_linear_regression_score(mock_session, bqml_model, mock_X, mock_y): model._bqml_model = bqml_model model.score(mock_X, mock_y) - mock_session.read_gbq_query.assert_called_once_with( - "SELECT * FROM ML.EVALUATE(MODEL `model_project`.`model_dataset`.`model_id`,\n (input_X_y_sql))", - allow_large_results=True, + mock_session.read_gbq.assert_called_once_with( + "SELECT * FROM ML.EVALUATE(MODEL `model_project.model_dataset.model_id`,\n (input_X_y_sql))" ) @@ -168,8 +131,8 @@ def test_logistic_regression_default_fit( model._bqml_model_factory = bqml_model_factory model.fit(mock_X, mock_y) - mock_session._start_query_ml_ddl.assert_called_once_with( - "CREATE OR REPLACE MODEL `test-project`.`_anon123`.`temp_model_id`\nOPTIONS(\n model_type='LOGISTIC_REG',\n data_split_method='NO_SPLIT',\n fit_intercept=TRUE,\n auto_class_weights=FALSE,\n optimize_strategy='auto_strategy',\n l2_reg=0.0,\n max_iterations=20,\n learn_rate_strategy='line_search',\n min_rel_progress=0.01,\n calculate_p_values=FALSE,\n enable_global_explain=FALSE,\n INPUT_LABEL_COLS=['input_column_label'])\nAS input_X_y_no_index_sql", + mock_session._start_query.assert_called_once_with( + 'CREATE TEMP MODEL `temp_model_id`\nOPTIONS(\n model_type="LOGISTIC_REG",\n data_split_method="NO_SPLIT",\n fit_intercept=True,\n auto_class_weights=False,\n INPUT_LABEL_COLS=["input_column_label"])\nAS input_X_y_sql' ) @@ -177,21 +140,13 @@ def test_logistic_regression_params_fit( bqml_model_factory, mock_session, mock_X, mock_y ): model = linear_model.LogisticRegression( - fit_intercept=False, - class_weight="balanced", - l2_reg=0.2, - tol=0.02, - l1_reg=0.2, - max_iterations=30, - optimize_strategy="batch_gradient_descent", - learning_rate_strategy="constant", - learning_rate=0.2, + fit_intercept=False, class_weights="balanced" ) model._bqml_model_factory = bqml_model_factory model.fit(mock_X, mock_y) - mock_session._start_query_ml_ddl.assert_called_once_with( - "CREATE OR REPLACE MODEL `test-project`.`_anon123`.`temp_model_id`\nOPTIONS(\n model_type='LOGISTIC_REG',\n data_split_method='NO_SPLIT',\n fit_intercept=FALSE,\n auto_class_weights=TRUE,\n optimize_strategy='batch_gradient_descent',\n l2_reg=0.2,\n max_iterations=30,\n learn_rate_strategy='constant',\n min_rel_progress=0.02,\n calculate_p_values=FALSE,\n enable_global_explain=FALSE,\n l1_reg=0.2,\n learn_rate=0.2,\n INPUT_LABEL_COLS=['input_column_label'])\nAS input_X_y_no_index_sql" + mock_session._start_query.assert_called_once_with( + 'CREATE TEMP MODEL `temp_model_id`\nOPTIONS(\n model_type="LOGISTIC_REG",\n data_split_method="NO_SPLIT",\n fit_intercept=False,\n auto_class_weights=True,\n INPUT_LABEL_COLS=["input_column_label"])\nAS input_X_y_sql' ) @@ -200,10 +155,9 @@ def test_logistic_regression_predict(mock_session, bqml_model, mock_X): model._bqml_model = bqml_model model.predict(mock_X) - mock_session.read_gbq_query.assert_called_once_with( - "SELECT * FROM ML.PREDICT(MODEL `model_project`.`model_dataset`.`model_id`,\n (input_X_sql))", + mock_session.read_gbq.assert_called_once_with( + "SELECT * FROM ML.PREDICT(MODEL `model_project.model_dataset.model_id`,\n (input_X_sql))", index_col=["index_column_id"], - allow_large_results=True, ) @@ -212,77 +166,6 @@ def test_logistic_regression_score(mock_session, bqml_model, mock_X, mock_y): model._bqml_model = bqml_model model.score(mock_X, mock_y) - mock_session.read_gbq_query.assert_called_once_with( - "SELECT * FROM ML.EVALUATE(MODEL `model_project`.`model_dataset`.`model_id`,\n (input_X_y_sql))", - allow_large_results=True, - ) - - -def test_decomposition_mf_default_fit(bqml_model_factory, mock_session, mock_X): - model = decomposition.MatrixFactorization( - num_factors=34, - feedback_type="explicit", - user_col="user_id", - item_col="item_col", - rating_col="rating_col", - l2_reg=9.83, - ) - model._bqml_model_factory = bqml_model_factory - model.fit(mock_X) - - mock_session._start_query_ml_ddl.assert_called_once_with( - "CREATE OR REPLACE MODEL `test-project`.`_anon123`.`temp_model_id`\nOPTIONS(\n model_type='matrix_factorization',\n feedback_type='explicit',\n user_col='user_id',\n item_col='item_col',\n rating_col='rating_col',\n l2_reg=9.83,\n num_factors=34)\nAS input_X_no_index_sql" - ) - - -def test_decomposition_mf_predict(mock_session, bqml_model, mock_X): - model = decomposition.MatrixFactorization( - num_factors=34, - feedback_type="explicit", - user_col="user_id", - item_col="item_col", - rating_col="rating_col", - l2_reg=9.83, - ) - model._bqml_model = bqml_model - model.predict(mock_X) - - mock_session.read_gbq_query.assert_called_once_with( - "SELECT * FROM ML.RECOMMEND(MODEL `model_project`.`model_dataset`.`model_id`,\n (input_X_sql))", - index_col=["index_column_id"], - allow_large_results=True, - ) - - -def test_decomposition_mf_score(mock_session, bqml_model): - model = decomposition.MatrixFactorization( - num_factors=34, - feedback_type="explicit", - user_col="user_id", - item_col="item_col", - rating_col="rating_col", - l2_reg=9.83, - ) - model._bqml_model = bqml_model - model.score() - mock_session.read_gbq_query.assert_called_once_with( - "SELECT * FROM ML.EVALUATE(MODEL `model_project`.`model_dataset`.`model_id`)", - allow_large_results=True, - ) - - -def test_decomposition_mf_score_with_x(mock_session, bqml_model, mock_X): - model = decomposition.MatrixFactorization( - num_factors=34, - feedback_type="explicit", - user_col="user_id", - item_col="item_col", - rating_col="rating_col", - l2_reg=9.83, - ) - model._bqml_model = bqml_model - model.score(mock_X) - mock_session.read_gbq_query.assert_called_once_with( - "SELECT * FROM ML.EVALUATE(MODEL `model_project`.`model_dataset`.`model_id`,\n (input_X_sql_property))", - allow_large_results=True, + mock_session.read_gbq.assert_called_once_with( + "SELECT * FROM ML.EVALUATE(MODEL `model_project.model_dataset.model_id`,\n (input_X_y_sql))" ) diff --git a/tests/unit/ml/test_matrix_factorization.py b/tests/unit/ml/test_matrix_factorization.py deleted file mode 100644 index 92691ba9d4a..00000000000 --- a/tests/unit/ml/test_matrix_factorization.py +++ /dev/null @@ -1,182 +0,0 @@ -# Copyright 2023 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -import pytest - -from bigframes.ml import decomposition - - -def test_decomposition_mf_model(): - model = decomposition.MatrixFactorization( - num_factors=16, - feedback_type="implicit", - user_col="user_id", - item_col="item_col", - rating_col="rating_col", - l2_reg=9, - ) - assert model.num_factors == 16 - assert model.feedback_type == "implicit" - assert model.user_col == "user_id" - assert model.item_col == "item_col" - assert model.rating_col == "rating_col" - - -def test_decomposition_mf_feedback_type_explicit(): - model = decomposition.MatrixFactorization( - num_factors=16, - feedback_type="explicit", - user_col="user_id", - item_col="item_col", - rating_col="rating_col", - l2_reg=9.83, - ) - assert model.feedback_type == "explicit" - - -def test_decomposition_mf_invalid_feedback_type_raises(): - feedback_type = "explimp" - with pytest.raises( - ValueError, - match="Expected feedback_type to be `explicit` or `implicit`.", - ): - decomposition.MatrixFactorization( - # Intentionally pass in the wrong type. This will fail if the user is using - # a type checker, but we can't assume that everyone is doing so, especially - # not in notebook environments. - num_factors=16, - feedback_type=feedback_type, # type: ignore - user_col="user_id", - item_col="item_col", - rating_col="rating_col", - l2_reg=9.83, - ) - - -def test_decomposition_mf_num_factors_low(): - model = decomposition.MatrixFactorization( - num_factors=0, - feedback_type="explicit", - user_col="user_id", - item_col="item_col", - rating_col="rating_col", - l2_reg=9.83, - ) - assert model.num_factors == 0 - - -def test_decomposition_mf_negative_num_factors_raises(): - num_factors = -2 - with pytest.raises( - ValueError, - match=f"Expected num_factors to be a positive integer, but got {num_factors}.", - ): - decomposition.MatrixFactorization( - num_factors=num_factors, # type: ignore - feedback_type="explicit", - user_col="user_id", - item_col="item_col", - rating_col="rating_col", - l2_reg=9.83, - ) - - -def test_decomposition_mf_invalid_num_factors_raises(): - num_factors = 0.5 - with pytest.raises( - TypeError, - match=f"Expected num_factors to be an int, but got {type(num_factors)}.", - ): - decomposition.MatrixFactorization( - num_factors=num_factors, # type: ignore - feedback_type="explicit", - user_col="user_id", - item_col="item_col", - rating_col="rating_col", - l2_reg=9.83, - ) - - -def test_decomposition_mf_invalid_user_col_raises(): - user_col = 123 - with pytest.raises( - TypeError, match=f"Expected user_col to be a str, but got {type(user_col)}." - ): - decomposition.MatrixFactorization( - num_factors=16, - feedback_type="explicit", - user_col=user_col, # type: ignore - item_col="item_col", - rating_col="rating_col", - l2_reg=9.83, - ) - - -def test_decomposition_mf_invalid_item_col_raises(): - item_col = 123 - with pytest.raises( - TypeError, match=f"Expected item_col to be STR, but got {type(item_col)}." - ): - decomposition.MatrixFactorization( - num_factors=16, - feedback_type="explicit", - user_col="user_id", - item_col=item_col, # type: ignore - rating_col="rating_col", - l2_reg=9.83, - ) - - -def test_decomposition_mf_invalid_rating_col_raises(): - rating_col = 4 - with pytest.raises( - TypeError, match=f"Expected rating_col to be a str, but got {type(rating_col)}." - ): - decomposition.MatrixFactorization( - num_factors=16, - feedback_type="explicit", - user_col="user_id", - item_col="item_col", - rating_col=rating_col, # type: ignore - l2_reg=9.83, - ) - - -def test_decomposition_mf_l2_reg(): - model = decomposition.MatrixFactorization( - num_factors=16, - feedback_type="explicit", - user_col="user_id", - item_col="item_col", - rating_col="rating_col", - l2_reg=6.02, # type: ignore - ) - assert model.l2_reg == 6.02 - - -def test_decomposition_mf_invalid_l2_reg_raises(): - l2_reg = "6.02" - with pytest.raises( - TypeError, - match=f"Expected l2_reg to be a float or int, but got {type(l2_reg)}.", - ): - decomposition.MatrixFactorization( - num_factors=16, - feedback_type="explicit", - user_col="user_id", - item_col="item_col", - rating_col="rating_col", - l2_reg=l2_reg, # type: ignore - ) diff --git a/tests/unit/ml/test_pipeline.py b/tests/unit/ml/test_pipeline.py index beebb9f2821..ed5c621b1df 100644 --- a/tests/unit/ml/test_pipeline.py +++ b/tests/unit/ml/test_pipeline.py @@ -13,6 +13,10 @@ # limitations under the License. import pytest +import sklearn.compose as sklearn_compose # type: ignore +import sklearn.linear_model as sklearn_linear_model # type: ignore +import sklearn.pipeline as sklearn_pipeline # type: ignore +import sklearn.preprocessing as sklearn_preprocessing # type: ignore from bigframes.ml import compose, forecasting, linear_model, pipeline, preprocessing @@ -53,11 +57,8 @@ def test_pipeline_repr(): ) +@pytest.mark.skipif(sklearn_pipeline is None, reason="requires sklearn") def test_pipeline_repr_matches_sklearn(): - sklearn_compose = pytest.importorskip("sklearn.compose") - sklearn_linear_model = pytest.importorskip("sklearn.linear_model") - sklearn_pipeline = pytest.importorskip("sklearn.pipeline") - sklearn_preprocessing = pytest.importorskip("sklearn.preprocessing") bf_pl = pipeline.Pipeline( [ ( diff --git a/tests/unit/ml/test_sql.py b/tests/unit/ml/test_sql.py index d605b571f3e..34a02edd424 100644 --- a/tests/unit/ml/test_sql.py +++ b/tests/unit/ml/test_sql.py @@ -14,7 +14,6 @@ from unittest import mock -import google.cloud.bigquery as bigquery import pytest import bigframes.ml.sql as ml_sql @@ -28,15 +27,13 @@ def base_sql_generator() -> ml_sql.BaseSqlGenerator: @pytest.fixture(scope="session") def model_creation_sql_generator() -> ml_sql.ModelCreationSqlGenerator: - return ml_sql.ModelCreationSqlGenerator() + return ml_sql.ModelCreationSqlGenerator(model_id="my_model_id") @pytest.fixture(scope="session") def model_manipulation_sql_generator() -> ml_sql.ModelManipulationSqlGenerator: return ml_sql.ModelManipulationSqlGenerator( - model_ref=bigquery.ModelReference.from_string( - "my_project_id.my_dataset_id.my_model_id" - ) + model_name="my_project_id.my_dataset_id.my_model_id" ) @@ -49,30 +46,20 @@ def mock_df(): return mock_df -def test_ml_arima_coefficients( - model_manipulation_sql_generator: ml_sql.ModelManipulationSqlGenerator, -): - sql = model_manipulation_sql_generator.ml_arima_coefficients() - assert ( - sql - == """SELECT * FROM ML.ARIMA_COEFFICIENTS(MODEL `my_project_id`.`my_dataset_id`.`my_model_id`)""" - ) - - -def test_options_correct(base_sql_generator: ml_sql.BaseSqlGenerator): +def test_options_produces_correct_sql(base_sql_generator: ml_sql.BaseSqlGenerator): sql = base_sql_generator.options( model_type="lin_reg", input_label_cols=["col_a"], l1_reg=0.6 ) assert ( sql == """OPTIONS( - model_type='lin_reg', - input_label_cols=['col_a'], + model_type="lin_reg", + input_label_cols=["col_a"], l1_reg=0.6)""" ) -def test_transform_correct(base_sql_generator: ml_sql.BaseSqlGenerator): +def test_transform_produces_correct_sql(base_sql_generator: ml_sql.BaseSqlGenerator): sql = base_sql_generator.transform( "ML.STANDARD_SCALER(col_a) OVER(col_a) AS scaled_col_a", "ML.ONE_HOT_ENCODER(col_b) OVER(col_b) AS encoded_col_b", @@ -87,131 +74,76 @@ def test_transform_correct(base_sql_generator: ml_sql.BaseSqlGenerator): ) -def test_standard_scaler_correct( +def test_standard_scaler_produces_correct_sql( base_sql_generator: ml_sql.BaseSqlGenerator, ): sql = base_sql_generator.ml_standard_scaler("col_a", "scaled_col_a") - assert sql == "ML.STANDARD_SCALER(`col_a`) OVER() AS `scaled_col_a`" + assert sql == "ML.STANDARD_SCALER(col_a) OVER() AS scaled_col_a" -def test_max_abs_scaler_correct( +def test_max_abs_scaler_produces_correct_sql( base_sql_generator: ml_sql.BaseSqlGenerator, ): sql = base_sql_generator.ml_max_abs_scaler("col_a", "scaled_col_a") - assert sql == "ML.MAX_ABS_SCALER(`col_a`) OVER() AS `scaled_col_a`" + assert sql == "ML.MAX_ABS_SCALER(col_a) OVER() AS scaled_col_a" -def test_min_max_scaler_correct( +def test_min_max_scaler_produces_correct_sql( base_sql_generator: ml_sql.BaseSqlGenerator, ): sql = base_sql_generator.ml_min_max_scaler("col_a", "scaled_col_a") - assert sql == "ML.MIN_MAX_SCALER(`col_a`) OVER() AS `scaled_col_a`" + assert sql == "ML.MIN_MAX_SCALER(col_a) OVER() AS scaled_col_a" -def test_imputer_correct( - base_sql_generator: ml_sql.BaseSqlGenerator, -): - sql = base_sql_generator.ml_imputer("col_a", "mean", "scaled_col_a") - assert sql == "ML.IMPUTER(`col_a`, 'mean') OVER() AS `scaled_col_a`" - - -def test_k_bins_discretizer_correct( +def test_k_bins_discretizer_produces_correct_sql( base_sql_generator: ml_sql.BaseSqlGenerator, ): sql = base_sql_generator.ml_bucketize("col_a", [1, 2, 3, 4], "scaled_col_a") - assert sql == "ML.BUCKETIZE(`col_a`, [1, 2, 3, 4], FALSE) AS `scaled_col_a`" - - -def test_k_bins_discretizer_quantile_correct( - base_sql_generator: ml_sql.BaseSqlGenerator, -): - sql = base_sql_generator.ml_quantile_bucketize("col_a", 5, "scaled_col_a") - assert sql == "ML.QUANTILE_BUCKETIZE(`col_a`, 5) OVER() AS `scaled_col_a`" + assert sql == "ML.BUCKETIZE(col_a, [1, 2, 3, 4], FALSE) AS scaled_col_a" -def test_one_hot_encoder_correct( +def test_one_hot_encoder_produces_correct_sql( base_sql_generator: ml_sql.BaseSqlGenerator, ): sql = base_sql_generator.ml_one_hot_encoder( "col_a", "none", 1000000, 0, "encoded_col_a" ) assert ( - sql - == "ML.ONE_HOT_ENCODER(`col_a`, 'none', 1000000, 0) OVER() AS `encoded_col_a`" + sql == "ML.ONE_HOT_ENCODER(col_a, 'none', 1000000, 0) OVER() AS encoded_col_a" ) -def test_label_encoder_correct( +def test_label_encoder_produces_correct_sql( base_sql_generator: ml_sql.BaseSqlGenerator, ): sql = base_sql_generator.ml_label_encoder("col_a", 1000000, 0, "encoded_col_a") - assert sql == "ML.LABEL_ENCODER(`col_a`, 1000000, 0) OVER() AS `encoded_col_a`" - - -def test_polynomial_expand( - base_sql_generator: ml_sql.BaseSqlGenerator, -): - sql = base_sql_generator.ml_polynomial_expand(["col_a", "col_b"], 2, "poly_exp") - assert sql == "ML.POLYNOMIAL_EXPAND(STRUCT(`col_a`, `col_b`), 2) AS `poly_exp`" - - -def test_ai_forecast_correct( - base_sql_generator: ml_sql.BaseSqlGenerator, - mock_df: bpd.DataFrame, -): - sql = base_sql_generator.ai_forecast( - source_sql=mock_df.sql, - options={ - "model": "TimesFM 2.0", - "data_col": "data1", - "timestamp_col": "time1", - "id_cols": ("id1", "id2"), - "horizon": 10, - "confidence_level": 0.95, - }, - ) - assert ( - sql - == """SELECT * FROM AI.FORECAST((input_X_y_sql), - model => 'TimesFM 2.0', - data_col => 'data1', - timestamp_col => 'time1', - id_cols => ['id1', 'id2'], - horizon => 10, - confidence_level => 0.95)""" - ) + assert sql == "ML.LABEL_ENCODER(col_a, 1000000, 0) OVER() AS encoded_col_a" -def test_create_model_correct( +def test_create_model_produces_correct_sql( model_creation_sql_generator: ml_sql.ModelCreationSqlGenerator, mock_df: bpd.DataFrame, ): sql = model_creation_sql_generator.create_model( - source_sql=mock_df.sql, - model_ref=bigquery.ModelReference.from_string( - "test-proj._anonXYZ.create_model_correct_sql" - ), + source_df=mock_df, options={"option_key1": "option_value1", "option_key2": 2}, ) assert ( sql - == """CREATE OR REPLACE MODEL `test-proj`.`_anonXYZ`.`create_model_correct_sql` + == """CREATE TEMP MODEL `my_model_id` OPTIONS( - option_key1='option_value1', + option_key1="option_value1", option_key2=2) AS input_X_y_sql""" ) -def test_create_model_transform_correct( +def test_create_model_transform_produces_correct_sql( model_creation_sql_generator: ml_sql.ModelCreationSqlGenerator, mock_df: bpd.DataFrame, ): sql = model_creation_sql_generator.create_model( - source_sql=mock_df.sql, - model_ref=bigquery.ModelReference.from_string( - "test-proj._anonXYZ.create_model_transform" - ), + source_df=mock_df, options={"option_key1": "option_value1", "option_key2": 2}, transforms=[ "ML.STANDARD_SCALER(col_a) OVER(col_a) AS scaled_col_a", @@ -220,124 +152,45 @@ def test_create_model_transform_correct( ) assert ( sql - == """CREATE OR REPLACE MODEL `test-proj`.`_anonXYZ`.`create_model_transform` + == """CREATE TEMP MODEL `my_model_id` TRANSFORM( ML.STANDARD_SCALER(col_a) OVER(col_a) AS scaled_col_a, ML.ONE_HOT_ENCODER(col_b) OVER(col_b) AS encoded_col_b) OPTIONS( - option_key1='option_value1', + option_key1="option_value1", option_key2=2) AS input_X_y_sql""" ) -def test_create_llm_remote_model_correct( - model_creation_sql_generator: ml_sql.ModelCreationSqlGenerator, - mock_df: bpd.DataFrame, -): - sql = model_creation_sql_generator.create_llm_remote_model( - source_sql=mock_df.sql, - connection_name="my_project.us.my_connection", - model_ref=bigquery.ModelReference.from_string( - "test-proj._anonXYZ.create_remote_model" - ), - options={"option_key1": "option_value1", "option_key2": 2}, - ) - assert ( - sql - == """CREATE OR REPLACE MODEL `test-proj`.`_anonXYZ`.`create_remote_model` -REMOTE WITH CONNECTION `my_project.us.my_connection` -OPTIONS( - option_key1='option_value1', - option_key2=2) -AS input_X_y_sql""" - ) - - -def test_create_remote_model_correct( - model_creation_sql_generator: ml_sql.ModelCreationSqlGenerator, -): - sql = model_creation_sql_generator.create_remote_model( - connection_name="my_project.us.my_connection", - model_ref=bigquery.ModelReference.from_string( - "test-proj._anonXYZ.create_remote_model" - ), - options={"option_key1": "option_value1", "option_key2": 2}, - ) - assert ( - sql - == """CREATE OR REPLACE MODEL `test-proj`.`_anonXYZ`.`create_remote_model` -REMOTE WITH CONNECTION `my_project.us.my_connection` -OPTIONS( - option_key1='option_value1', - option_key2=2)""" - ) - - -def test_create_remote_model_with_params_correct( +def test_create_remote_model_produces_correct_sql( model_creation_sql_generator: ml_sql.ModelCreationSqlGenerator, ): sql = model_creation_sql_generator.create_remote_model( connection_name="my_project.us.my_connection", - model_ref=bigquery.ModelReference.from_string( - "test-proj._anonXYZ.create_remote_model" - ), - input={"column1": "int64"}, - output={"result": "array"}, options={"option_key1": "option_value1", "option_key2": 2}, ) assert ( sql - == """CREATE OR REPLACE MODEL `test-proj`.`_anonXYZ`.`create_remote_model` -INPUT( - `column1` int64) -OUTPUT( - `result` array) + == """CREATE TEMP MODEL `my_model_id` REMOTE WITH CONNECTION `my_project.us.my_connection` OPTIONS( - option_key1='option_value1', + option_key1="option_value1", option_key2=2)""" ) -def test_create_imported_model_correct( +def test_create_imported_model_produces_correct_sql( model_creation_sql_generator: ml_sql.ModelCreationSqlGenerator, ): sql = model_creation_sql_generator.create_imported_model( - model_ref=bigquery.ModelReference.from_string( - "test-proj._anonXYZ.create_imported_model" - ), options={"option_key1": "option_value1", "option_key2": 2}, ) assert ( sql - == """CREATE OR REPLACE MODEL `test-proj`.`_anonXYZ`.`create_imported_model` + == """CREATE TEMP MODEL `my_model_id` OPTIONS( - option_key1='option_value1', - option_key2=2)""" - ) - - -def test_create_xgboost_imported_model_produces_correct_sql( - model_creation_sql_generator: ml_sql.ModelCreationSqlGenerator, -): - sql = model_creation_sql_generator.create_xgboost_imported_model( - model_ref=bigquery.ModelReference.from_string( - "test-proj._anonXYZ.create_xgboost_imported_model" - ), - input={"column1": "int64"}, - output={"result": "array"}, - options={"option_key1": "option_value1", "option_key2": 2}, - ) - assert ( - sql - == """CREATE OR REPLACE MODEL `test-proj`.`_anonXYZ`.`create_xgboost_imported_model` -INPUT( - `column1` int64) -OUTPUT( - `result` array) -OPTIONS( - option_key1='option_value1', + option_key1="option_value1", option_key2=2)""" ) @@ -350,182 +203,106 @@ def test_alter_model_correct_sql( ) assert ( sql - == """ALTER MODEL `my_project_id`.`my_dataset_id`.`my_model_id` + == """ALTER MODEL `my_project_id.my_dataset_id.my_model_id` SET OPTIONS( - option_key1='option_value1', + option_key1="option_value1", option_key2=2)""" ) -def test_ml_predict_correct( +def test_ml_predict_produces_correct_sql( model_manipulation_sql_generator: ml_sql.ModelManipulationSqlGenerator, mock_df: bpd.DataFrame, ): - sql = model_manipulation_sql_generator.ml_predict(source_sql=mock_df.sql) + sql = model_manipulation_sql_generator.ml_predict(source_df=mock_df) assert ( sql - == """SELECT * FROM ML.PREDICT(MODEL `my_project_id`.`my_dataset_id`.`my_model_id`, - (input_X_y_sql))""" + == """SELECT * FROM ML.PREDICT(MODEL `my_project_id.my_dataset_id.my_model_id`, + (input_X_sql))""" ) -def test_ml_llm_evaluate_correct( +def test_ml_evaluate_produces_correct_sql( model_manipulation_sql_generator: ml_sql.ModelManipulationSqlGenerator, mock_df: bpd.DataFrame, ): - sql = model_manipulation_sql_generator.ml_llm_evaluate( - source_sql=mock_df.sql, task_type="CLASSIFICATION" - ) - assert ( - sql - == """SELECT * FROM ML.EVALUATE(MODEL `my_project_id`.`my_dataset_id`.`my_model_id`, - (input_X_y_sql), STRUCT("CLASSIFICATION" AS task_type))""" - ) - - -def test_ml_evaluate_correct( - model_manipulation_sql_generator: ml_sql.ModelManipulationSqlGenerator, - mock_df: bpd.DataFrame, -): - sql = model_manipulation_sql_generator.ml_evaluate(source_sql=mock_df.sql) - assert ( - sql - == """SELECT * FROM ML.EVALUATE(MODEL `my_project_id`.`my_dataset_id`.`my_model_id`, - (input_X_y_sql))""" - ) - - -def test_ml_arima_evaluate_correct( - model_manipulation_sql_generator: ml_sql.ModelManipulationSqlGenerator, -): - sql = model_manipulation_sql_generator.ml_arima_evaluate( - show_all_candidate_models=True - ) + sql = model_manipulation_sql_generator.ml_evaluate(source_df=mock_df) assert ( sql - == """SELECT * FROM ML.ARIMA_EVALUATE(MODEL `my_project_id`.`my_dataset_id`.`my_model_id`, - STRUCT(True AS show_all_candidate_models))""" + == """SELECT * FROM ML.EVALUATE(MODEL `my_project_id.my_dataset_id.my_model_id`, + (input_X_sql))""" ) -def test_ml_evaluate_no_source_correct( +def test_ml_evaluate_no_source_produces_correct_sql( model_manipulation_sql_generator: ml_sql.ModelManipulationSqlGenerator, ): sql = model_manipulation_sql_generator.ml_evaluate() assert ( sql - == """SELECT * FROM ML.EVALUATE(MODEL `my_project_id`.`my_dataset_id`.`my_model_id`)""" + == """SELECT * FROM ML.EVALUATE(MODEL `my_project_id.my_dataset_id.my_model_id`)""" ) -def test_ml_centroids_correct( +def test_ml_centroids_produces_correct_sql( model_manipulation_sql_generator: ml_sql.ModelManipulationSqlGenerator, ): sql = model_manipulation_sql_generator.ml_centroids() assert ( sql - == """SELECT * FROM ML.CENTROIDS(MODEL `my_project_id`.`my_dataset_id`.`my_model_id`)""" + == """SELECT * FROM ML.CENTROIDS(MODEL `my_project_id.my_dataset_id.my_model_id`)""" ) -def test_ml_forecast_correct_sql( - model_manipulation_sql_generator: ml_sql.ModelManipulationSqlGenerator, -): - sql = model_manipulation_sql_generator.ml_forecast( - struct_options={"option_key1": 1, "option_key2": 2.2}, - ) - assert ( - sql - == """SELECT * FROM ML.FORECAST(MODEL `my_project_id`.`my_dataset_id`.`my_model_id`, - STRUCT( - 1 AS `option_key1`, - 2.2 AS `option_key2`))""" - ) - - -def test_ml_generate_text_correct( +def test_ml_generate_text_produces_correct_sql( model_manipulation_sql_generator: ml_sql.ModelManipulationSqlGenerator, mock_df: bpd.DataFrame, ): sql = model_manipulation_sql_generator.ml_generate_text( - source_sql=mock_df.sql, - struct_options={"option_key1": 1, "option_key2": 2.2}, - ) - assert ( - sql - == """SELECT * FROM ML.GENERATE_TEXT(MODEL `my_project_id`.`my_dataset_id`.`my_model_id`, - (input_X_y_sql), STRUCT( - 1 AS `option_key1`, - 2.2 AS `option_key2`))""" - ) - - -def test_ml_generate_embedding_correct( - model_manipulation_sql_generator: ml_sql.ModelManipulationSqlGenerator, - mock_df: bpd.DataFrame, -): - sql = model_manipulation_sql_generator.ml_generate_embedding( - source_sql=mock_df.sql, + source_df=mock_df, struct_options={"option_key1": 1, "option_key2": 2.2}, ) assert ( sql - == """SELECT * FROM ML.GENERATE_EMBEDDING(MODEL `my_project_id`.`my_dataset_id`.`my_model_id`, - (input_X_y_sql), STRUCT( - 1 AS `option_key1`, - 2.2 AS `option_key2`))""" - ) - - -def test_ml_explain_predict_correct( - model_manipulation_sql_generator: ml_sql.ModelManipulationSqlGenerator, - mock_df: bpd.DataFrame, -): - sql = model_manipulation_sql_generator.ml_explain_predict( - source_sql=mock_df.sql, - struct_options={"option_key1": 1, "option_key2": 2.25}, - ) - assert ( - sql - == """SELECT * FROM ML.EXPLAIN_PREDICT(MODEL `my_project_id`.`my_dataset_id`.`my_model_id`, - (input_X_y_sql), STRUCT( - 1 AS `option_key1`, - 2.25 AS `option_key2`))""" + == """SELECT * FROM ML.GENERATE_TEXT(MODEL `my_project_id.my_dataset_id.my_model_id`, + (input_X_sql), STRUCT( + 1 AS option_key1, + 2.2 AS option_key2))""" ) -def test_ml_detect_anomalies_correct_sql( +def test_ml_generate_text_embedding_produces_correct_sql( model_manipulation_sql_generator: ml_sql.ModelManipulationSqlGenerator, mock_df: bpd.DataFrame, ): - sql = model_manipulation_sql_generator.ml_detect_anomalies( - source_sql=mock_df.sql, + sql = model_manipulation_sql_generator.ml_generate_text_embedding( + source_df=mock_df, struct_options={"option_key1": 1, "option_key2": 2.2}, ) assert ( sql - == """SELECT * FROM ML.DETECT_ANOMALIES(MODEL `my_project_id`.`my_dataset_id`.`my_model_id`, - STRUCT( - 1 AS `option_key1`, - 2.2 AS `option_key2`), (input_X_y_sql))""" + == """SELECT * FROM ML.GENERATE_TEXT_EMBEDDING(MODEL `my_project_id.my_dataset_id.my_model_id`, + (input_X_sql), STRUCT( + 1 AS option_key1, + 2.2 AS option_key2))""" ) -def test_ml_principal_components_correct( +def test_ml_principal_components_produces_correct_sql( model_manipulation_sql_generator: ml_sql.ModelManipulationSqlGenerator, ): sql = model_manipulation_sql_generator.ml_principal_components() assert ( sql - == """SELECT * FROM ML.PRINCIPAL_COMPONENTS(MODEL `my_project_id`.`my_dataset_id`.`my_model_id`)""" + == """SELECT * FROM ML.PRINCIPAL_COMPONENTS(MODEL `my_project_id.my_dataset_id.my_model_id`)""" ) -def test_ml_principal_component_info_correct( +def test_ml_principal_component_info_produces_correct_sql( model_manipulation_sql_generator: ml_sql.ModelManipulationSqlGenerator, ): sql = model_manipulation_sql_generator.ml_principal_component_info() assert ( sql - == """SELECT * FROM ML.PRINCIPAL_COMPONENT_INFO(MODEL `my_project_id`.`my_dataset_id`.`my_model_id`)""" + == """SELECT * FROM ML.PRINCIPAL_COMPONENT_INFO(MODEL `my_project_id.my_dataset_id.my_model_id`)""" ) diff --git a/tests/unit/operations/__init__.py b/tests/unit/operations/__init__.py deleted file mode 100644 index 6d5e14bcf4a..00000000000 --- a/tests/unit/operations/__init__.py +++ /dev/null @@ -1,13 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. diff --git a/tests/unit/operations/test_output_schemas.py b/tests/unit/operations/test_output_schemas.py deleted file mode 100644 index 204078a5ceb..00000000000 --- a/tests/unit/operations/test_output_schemas.py +++ /dev/null @@ -1,99 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import pyarrow as pa -import pytest - -from bigframes.operations import output_schemas - - -@pytest.mark.parametrize( - ("sql", "expected"), - [ - ("INT64", pa.int64()), - (" INT64 ", pa.int64()), - ("int64", pa.int64()), - ("FLOAT64", pa.float64()), - ("STRING", pa.string()), - ("BOOL", pa.bool_()), - ("ARRAY", pa.list_(pa.int64())), - ( - "STRUCT", - pa.struct((pa.field("x", pa.int64()), pa.field("y", pa.float64()))), - ), - ( - "STRUCT< x INT64, y FLOAT64>", - pa.struct((pa.field("x", pa.int64()), pa.field("y", pa.float64()))), - ), - ( - "STRUCT", - pa.struct((pa.field("x", pa.float64()), pa.field("y", pa.int64()))), - ), - ( - "ARRAY>", - pa.list_(pa.struct((pa.field("x", pa.int64()), pa.field("y", pa.int64())))), - ), - ( - "STRUCT, x ARRAY>", - pa.struct( - ( - pa.field("x", pa.list_(pa.float64())), - pa.field( - "y", - pa.struct( - (pa.field("a", pa.bool_()), pa.field("b", pa.string())) - ), - ), - ) - ), - ), - ], -) -def test_parse_sql_to_pyarrow_dtype(sql, expected): - assert output_schemas.parse_sql_type(sql) == expected - - -@pytest.mark.parametrize( - "sql", - [ - "a INT64", - "ARRAY<>", - "ARRAYARRAYSTRUCT<>", - "DATE", - "STRUCT", - "ARRAY>", - ], -) -def test_parse_sql_to_pyarrow_dtype_invalid_input_raies_error(sql): - with pytest.raises(ValueError): - output_schemas.parse_sql_type(sql) - - -@pytest.mark.parametrize( - ("sql", "expected"), - [ - ("x INT64", (pa.field("x", pa.int64()),)), - ( - "x INT64, y FLOAT64", - (pa.field("x", pa.int64()), pa.field("y", pa.float64())), - ), - ( - "y FLOAT64, x INT64", - (pa.field("x", pa.int64()), pa.field("y", pa.float64())), - ), - ], -) -def test_parse_sql_fields(sql, expected): - assert output_schemas.parse_sql_fields(sql) == expected diff --git a/tests/unit/pandas/io/test_api.py b/tests/unit/pandas/io/test_api.py deleted file mode 100644 index dbdf427d91b..00000000000 --- a/tests/unit/pandas/io/test_api.py +++ /dev/null @@ -1,129 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from unittest import mock - -import google.cloud.bigquery -import pytest - -import bigframes._config.auth -import bigframes.dataframe -import bigframes.pandas -import bigframes.pandas.io.api as bf_io_api -import bigframes.session -import bigframes.session.clients - -# _read_gbq_colab requires the polars engine. -pytest.importorskip("polars") - - -@mock.patch( - "bigframes.pandas.io.api._set_default_session_location_if_possible_deferred_query" -) -@mock.patch("bigframes.core.global_session.with_default_session") -def test_read_gbq_colab_dry_run_doesnt_call_set_location( - mock_with_default_session, mock_set_location -): - """ - Ensure that we don't bind to a location too early. If it's a dry run, the - user might not be done typing. - """ - mock_df = mock.create_autospec(bigframes.dataframe.DataFrame) - mock_with_default_session.return_value = mock_df - - query_or_table = "SELECT {param1} AS param1" - sample_pyformat_args = {"param1": "value1"} - bf_io_api._read_gbq_colab( - query_or_table, pyformat_args=sample_pyformat_args, dry_run=True - ) - - mock_set_location.assert_not_called() - - -@mock.patch("bigframes._config.auth.pydata_google_auth.default") -@mock.patch("bigframes.core.global_session.with_default_session") -def test_read_gbq_colab_dry_run_doesnt_authenticate_multiple_times( - mock_with_default_session, mock_get_credentials, monkeypatch -): - """ - Ensure that we authenticate too often, which is an expensive operation, - performance-wise (2+ seconds). - """ - bigframes.pandas.close_session() - - mock_get_credentials.return_value = (mock.Mock(), "unit-test-project") - mock_create_bq_client = mock.Mock() - mock_bq_client = mock.create_autospec(google.cloud.bigquery.Client, instance=True) - mock_create_bq_client.return_value = mock_bq_client - mock_query_job = mock.create_autospec(google.cloud.bigquery.QueryJob, instance=True) - type(mock_query_job).schema = mock.PropertyMock(return_value=[]) - mock_query_job._properties = {} - mock_bq_client.query.return_value = mock_query_job - monkeypatch.setattr( - bigframes.session.clients.ClientsProvider, - "_create_bigquery_client", - mock_create_bq_client, - ) - mock_df = mock.create_autospec(bigframes.dataframe.DataFrame) - mock_with_default_session.return_value = mock_df - - bigframes._config.auth._cached_credentials = None - query_or_table = "SELECT {param1} AS param1" - sample_pyformat_args = {"param1": "value1"} - bf_io_api._read_gbq_colab( - query_or_table, pyformat_args=sample_pyformat_args, dry_run=True - ) - - mock_get_credentials.assert_called() - mock_with_default_session.assert_not_called() - mock_get_credentials.reset_mock() - - # Repeat the operation so that the credentials would have have been cached. - bf_io_api._read_gbq_colab( - query_or_table, pyformat_args=sample_pyformat_args, dry_run=True - ) - mock_get_credentials.assert_not_called() - - -@mock.patch( - "bigframes.pandas.io.api._set_default_session_location_if_possible_deferred_query" -) -@mock.patch("bigframes.core.global_session.with_default_session") -def test_read_gbq_colab_calls_set_location( - mock_with_default_session, mock_set_location -): - # Configure the mock for with_default_session to return a DataFrame mock - mock_df = mock.create_autospec(bigframes.dataframe.DataFrame) - mock_with_default_session.return_value = mock_df - - query_or_table = "SELECT {param1} AS param1" - sample_pyformat_args = {"param1": "'value1'"} - result = bf_io_api._read_gbq_colab( - query_or_table, pyformat_args=sample_pyformat_args, dry_run=False - ) - - # Make sure that we format the SQL first to prevent syntax errors. - formatted_query = "SELECT 'value1' AS param1" - mock_set_location.assert_called_once() - args, _ = mock_set_location.call_args - assert formatted_query == args[0]() - mock_with_default_session.assert_called_once() - - # Check the actual arguments passed to with_default_session - args, kwargs = mock_with_default_session.call_args - assert args[0] == bigframes.session.Session._read_gbq_colab - assert args[1] == query_or_table - assert kwargs["pyformat_args"] == sample_pyformat_args - assert not kwargs["dry_run"] - assert isinstance(result, bigframes.dataframe.DataFrame) diff --git a/tests/unit/resources.py b/tests/unit/resources.py new file mode 100644 index 00000000000..8fc8acd1759 --- /dev/null +++ b/tests/unit/resources.py @@ -0,0 +1,113 @@ +# Copyright 2023 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import Dict, List, Optional +import unittest.mock as mock + +import google.auth.credentials +import google.cloud.bigquery +import ibis +import pandas +import pytest + +import bigframes +import bigframes.core as core +import bigframes.core.ordering +import bigframes.dataframe +import bigframes.session.clients + +"""Utilities for creating test resources.""" + + +def create_bigquery_session( + bqclient: Optional[mock.Mock] = None, + session_id: str = "abcxyz", + anonymous_dataset: Optional[google.cloud.bigquery.DatasetReference] = None, +) -> bigframes.Session: + credentials = mock.create_autospec( + google.auth.credentials.Credentials, instance=True + ) + + if bqclient is None: + bqclient = mock.create_autospec(google.cloud.bigquery.Client, instance=True) + bqclient.project = "test-project" + + if anonymous_dataset is None: + anonymous_dataset = google.cloud.bigquery.DatasetReference( + "test-project", + "test_dataset", + ) + + query_job = mock.create_autospec(google.cloud.bigquery.QueryJob) + type(query_job).destination = mock.PropertyMock( + return_value=anonymous_dataset.table("test_table"), + ) + type(query_job).session_info = google.cloud.bigquery.SessionInfo( + {"sessionInfo": {"sessionId": session_id}}, + ) + bqclient.query.return_value = query_job + + clients_provider = mock.create_autospec(bigframes.session.clients.ClientsProvider) + type(clients_provider).bqclient = mock.PropertyMock(return_value=bqclient) + clients_provider._credentials = credentials + + bqoptions = bigframes.BigQueryOptions( + credentials=credentials, location="test-region" + ) + session = bigframes.Session(context=bqoptions, clients_provider=clients_provider) + session._session_id = session_id + return session + + +def create_dataframe( + monkeypatch: pytest.MonkeyPatch, session: Optional[bigframes.Session] = None +) -> bigframes.dataframe.DataFrame: + if session is None: + session = create_bigquery_session() + + # Since this may create a ReadLocalNode, the session we explicitly pass in + # might not actually be used. Mock out the global session, too. + monkeypatch.setattr(bigframes.core.global_session, "_global_session", session) + bigframes.options.bigquery._session_started = True + return bigframes.dataframe.DataFrame({}, session=session) + + +def create_pandas_session(tables: Dict[str, pandas.DataFrame]) -> bigframes.Session: + # TODO(tswast): Refactor to make helper available for all tests. Consider + # providing a proper "local Session" for use by downstream developers. + session = mock.create_autospec(bigframes.Session, instance=True) + ibis_client = ibis.pandas.connect(tables) + type(session).ibis_client = mock.PropertyMock(return_value=ibis_client) + return session + + +def create_arrayvalue( + df: pandas.DataFrame, total_ordering_columns: List[str] +) -> core.ArrayValue: + session = create_pandas_session({"test_table": df}) + ibis_table = session.ibis_client.table("test_table") + columns = tuple(ibis_table[key] for key in ibis_table.columns) + ordering = bigframes.core.ordering.ExpressionOrdering( + tuple( + [core.OrderingColumnReference(column) for column in total_ordering_columns] + ), + total_ordering_columns=frozenset(total_ordering_columns), + ) + return core.ArrayValue.from_ibis( + session=session, + table=ibis_table, + columns=columns, + hidden_ordering_columns=(), + ordering=ordering, + ) diff --git a/tests/unit/session/test_clients.py b/tests/unit/session/test_clients.py index 0de6c75e01b..f1b2a5045a3 100644 --- a/tests/unit/session/test_clients.py +++ b/tests/unit/session/test_clients.py @@ -12,25 +12,25 @@ # See the License for the specific language governing permissions and # limitations under the License. -import os -import pathlib -import tempfile +from typing import Optional import unittest.mock as mock -from typing import Optional, cast +import google.api_core.client_info +import google.api_core.client_options +import google.api_core.exceptions +import google.api_core.gapic_v1.client_info import google.auth.credentials import google.cloud.bigquery import google.cloud.bigquery_connection_v1 import google.cloud.bigquery_storage_v1 import google.cloud.functions_v2 import google.cloud.resourcemanager_v3 -import requests.adapters import bigframes.session.clients as clients import bigframes.version -def create_clients_provider(application_name: Optional[str] = None, **kwargs): +def create_clients_provider(application_name: Optional[str] = None): credentials = mock.create_autospec(google.auth.credentials.Credentials) return clients.ClientsProvider( project="test-project", @@ -38,16 +38,12 @@ def create_clients_provider(application_name: Optional[str] = None, **kwargs): use_regional_endpoints=False, credentials=credentials, application_name=application_name, - bq_kms_key_name="projects/my-project/locations/us/keyRings/myKeyRing/cryptoKeys/myKey", - **kwargs, ) def monkeypatch_client_constructors(monkeypatch): bqclient = mock.create_autospec(google.cloud.bigquery.Client) bqclient.return_value = bqclient - # Assume we have a new client library in the unit tests. - bqclient.default_job_creation_mode = None # type: ignore monkeypatch.setattr(google.cloud.bigquery, "Client", bqclient) bqconnectionclient = mock.create_autospec( @@ -85,11 +81,6 @@ def monkeypatch_client_constructors(monkeypatch): ) -def assert_bqclient_sets_default_job_creation_mode(provider: clients.ClientsProvider): - bqclient = provider.bqclient - assert bqclient.default_job_creation_mode == "JOB_CREATION_OPTIONAL" - - def assert_constructed_w_user_agent(mock_client: mock.Mock, expected_user_agent: str): assert ( expected_user_agent @@ -107,51 +98,6 @@ def assert_clients_w_user_agent( assert_constructed_w_user_agent(provider.resourcemanagerclient, expected_user_agent) -def assert_constructed_wo_user_agent( - mock_client: mock.Mock, not_expected_user_agent: str -): - assert ( - not_expected_user_agent - not in mock_client.call_args.kwargs["client_info"].to_user_agent() - ) - - -def assert_clients_wo_user_agent( - provider: clients.ClientsProvider, not_expected_user_agent: str -): - assert_constructed_wo_user_agent(provider.bqclient, not_expected_user_agent) - assert_constructed_wo_user_agent( - provider.bqconnectionclient, not_expected_user_agent - ) - assert_constructed_wo_user_agent( - provider.bqstoragereadclient, not_expected_user_agent - ) - assert_constructed_wo_user_agent( - provider.cloudfunctionsclient, not_expected_user_agent - ) - assert_constructed_wo_user_agent( - provider.resourcemanagerclient, not_expected_user_agent - ) - - -def test_requests_transport_adapters_pool_maxsize(monkeypatch): - monkeypatch_client_constructors(monkeypatch) - requests_transport_adapters = ( - ("http://", requests.adapters.HTTPAdapter(pool_maxsize=123)), - ("https://", requests.adapters.HTTPAdapter(pool_maxsize=123)), - ) # doctest: +SKIP - provider = create_clients_provider( - requests_transport_adapters=requests_transport_adapters - ) - - _, kwargs = cast(mock.Mock, provider.bqclient).call_args - requests_session = kwargs.get("_http") - adapter: requests.adapters.HTTPAdapter = requests_session.get_adapter( - "https://bigquery.googleapis.com/" - ) - assert adapter._pool_maxsize == 123 # type: ignore - - def test_user_agent_default(monkeypatch): monkeypatch_client_constructors(monkeypatch) provider = create_clients_provider(application_name=None) @@ -166,113 +112,3 @@ def test_user_agent_custom(monkeypatch): # We still need to include attribution to bigframes, even if there's also a # partner using the package. assert_clients_w_user_agent(provider, f"bigframes/{bigframes.version.__version__}") - - -@mock.patch.dict(os.environ, {}, clear=True) -def test_user_agent_not_in_vscode(monkeypatch): - monkeypatch_client_constructors(monkeypatch) - provider = create_clients_provider() - assert_clients_wo_user_agent(provider, "vscode") - assert_clients_wo_user_agent(provider, "googlecloudtools.cloudcode") - - # We still need to include attribution to bigframes - assert_clients_w_user_agent(provider, f"bigframes/{bigframes.version.__version__}") - - -@mock.patch.dict(os.environ, {"VSCODE_PID": "12345"}, clear=True) -def test_user_agent_in_vscode(monkeypatch): - monkeypatch_client_constructors(monkeypatch) - - with tempfile.TemporaryDirectory() as tmpdir: - user_home = pathlib.Path(tmpdir) - with mock.patch("pathlib.Path.home", return_value=user_home): - provider = create_clients_provider() - assert_clients_w_user_agent(provider, "vscode") - assert_clients_wo_user_agent(provider, "googlecloudtools.cloudcode") - - # We still need to include attribution to bigframes - assert_clients_w_user_agent( - provider, f"bigframes/{bigframes.version.__version__}" - ) - - -@mock.patch.dict(os.environ, {"VSCODE_PID": "12345"}, clear=True) -def test_user_agent_in_vscode_w_extension(monkeypatch): - monkeypatch_client_constructors(monkeypatch) - - with tempfile.TemporaryDirectory() as tmpdir: - user_home = pathlib.Path(tmpdir) - extension_dir = ( - user_home / ".vscode" / "extensions" / "googlecloudtools.cloudcode-0.12" - ) - extension_config = extension_dir / "package.json" - - # originally extension config does not exist - assert not extension_config.exists() - - # simulate extension installation by creating extension config on disk - extension_dir.mkdir(parents=True) - with open(extension_config, "w") as f: - f.write("{}") - - with mock.patch("pathlib.Path.home", return_value=user_home): - provider = create_clients_provider() - assert_clients_w_user_agent(provider, "vscode") - assert_clients_w_user_agent(provider, "googlecloudtools.cloudcode") - - # We still need to include attribution to bigframes - assert_clients_w_user_agent( - provider, f"bigframes/{bigframes.version.__version__}" - ) - - -@mock.patch.dict(os.environ, {}, clear=True) -def test_user_agent_not_in_jupyter(monkeypatch): - monkeypatch_client_constructors(monkeypatch) - provider = create_clients_provider() - assert_clients_wo_user_agent(provider, "jupyter") - assert_clients_wo_user_agent(provider, "bigquery_jupyter_plugin") - - # We still need to include attribution to bigframes - assert_clients_w_user_agent(provider, f"bigframes/{bigframes.version.__version__}") - - -@mock.patch.dict(os.environ, {"JPY_PARENT_PID": "12345"}, clear=True) -def test_user_agent_in_jupyter(monkeypatch): - monkeypatch_client_constructors(monkeypatch) - provider = create_clients_provider() - assert_clients_w_user_agent(provider, "jupyter") - assert_clients_wo_user_agent(provider, "bigquery_jupyter_plugin") - - # We still need to include attribution to bigframes - assert_clients_w_user_agent(provider, f"bigframes/{bigframes.version.__version__}") - - -@mock.patch.dict(os.environ, {"JPY_PARENT_PID": "12345"}, clear=True) -def test_user_agent_in_jupyter_with_plugin(monkeypatch): - monkeypatch_client_constructors(monkeypatch) - - def custom_import_module_side_effect(name, package=None): - if name == "bigquery_jupyter_plugin": - return mock.MagicMock() - else: - import importlib - - return importlib.import_module(name, package) - - assert isinstance( - custom_import_module_side_effect("bigquery_jupyter_plugin"), mock.MagicMock - ) - assert custom_import_module_side_effect("bigframes") is bigframes - - with mock.patch( - "importlib.import_module", side_effect=custom_import_module_side_effect - ): - provider = create_clients_provider() - assert_clients_w_user_agent(provider, "jupyter") - assert_clients_w_user_agent(provider, "bigquery_jupyter_plugin") - - # We still need to include attribution to bigframes - assert_clients_w_user_agent( - provider, f"bigframes/{bigframes.version.__version__}" - ) diff --git a/tests/unit/session/test_io_arrow.py b/tests/unit/session/test_io_arrow.py deleted file mode 100644 index d5266220d9a..00000000000 --- a/tests/unit/session/test_io_arrow.py +++ /dev/null @@ -1,133 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import datetime - -import pyarrow as pa -import pytest - -import bigframes.pandas as bpd -from bigframes.testing import mocks - - -@pytest.fixture(scope="module") -def session(): - # Use the mock session from bigframes.testing - return mocks.create_bigquery_session() - - -def test_read_arrow_empty_table(session): - empty_table = pa.Table.from_pydict( - { - "col_a": pa.array([], type=pa.int64()), - "col_b": pa.array([], type=pa.string()), - } - ) - df = session.read_arrow(empty_table) - assert isinstance(df, bpd.DataFrame) - assert df.shape == (0, 2) - assert list(df.columns) == ["col_a", "col_b"] - pd_df = df.to_pandas() - assert pd_df.empty - assert list(pd_df.columns) == ["col_a", "col_b"] - assert pd_df["col_a"].dtype == "Int64" - assert pd_df["col_b"].dtype == "string[pyarrow]" - - -@pytest.mark.parametrize( - "data,arrow_type,expected_bq_type_kind", - [ - ([1, 2], pa.int8(), "INTEGER"), - ([1, 2], pa.int16(), "INTEGER"), - ([1, 2], pa.int32(), "INTEGER"), - ([1, 2], pa.int64(), "INTEGER"), - ([1.0, 2.0], pa.float32(), "FLOAT"), - ([1.0, 2.0], pa.float64(), "FLOAT"), - ([True, False], pa.bool_(), "BOOLEAN"), - (["a", "b"], pa.string(), "STRING"), - (["a", "b"], pa.large_string(), "STRING"), - ([b"a", b"b"], pa.binary(), "BYTES"), - ([b"a", b"b"], pa.large_binary(), "BYTES"), - ( - [ - pa.scalar(1000, type=pa.duration("s")), - pa.scalar(2000, type=pa.duration("s")), - ], - pa.duration("s"), - "INTEGER", - ), - ([datetime.date(2023, 1, 1)], pa.date32(), "DATE"), - ( - [datetime.datetime(2023, 1, 1, 12, 0, 0, tzinfo=datetime.timezone.utc)], - pa.timestamp("s", tz="UTC"), - "TIMESTAMP", - ), - ( - [datetime.datetime(2023, 1, 1, 12, 0, 0, tzinfo=datetime.timezone.utc)], - pa.timestamp("ms", tz="UTC"), - "TIMESTAMP", - ), - ( - [datetime.datetime(2023, 1, 1, 12, 0, 0, tzinfo=datetime.timezone.utc)], - pa.timestamp("us", tz="UTC"), - "TIMESTAMP", - ), - ([datetime.time(12, 34, 56, 789000)], pa.time64("us"), "TIME"), - ], -) -def test_read_arrow_type_mappings(session, data, arrow_type, expected_bq_type_kind): - """ - Tests that various arrow types are mapped to the expected BigQuery types. - This is an indirect check via the resulting DataFrame's schema. - """ - pa_table = pa.Table.from_arrays([pa.array(data, type=arrow_type)], names=["col"]) - df = session.read_arrow(pa_table) - - bigquery_schema = df._block.expr.schema.to_bigquery() - assert len(bigquery_schema) == 2 # offsets + value - field = bigquery_schema[-1] - assert field.field_type.upper() == expected_bq_type_kind - - # Also check pandas dtype after conversion for good measure - pd_df = df.to_pandas() - assert pd_df["col"].shape == (len(data),) - - -def test_read_arrow_list_type(session): - pa_table = pa.Table.from_arrays( - [pa.array([[1, 2], [3, 4, 5]], type=pa.list_(pa.int64()))], names=["list_col"] - ) - df = session.read_arrow(pa_table) - - bigquery_schema = df._block.expr.schema.to_bigquery() - assert len(bigquery_schema) == 2 # offsets + value - field = bigquery_schema[-1] - assert field.mode.upper() == "REPEATED" - assert field.field_type.upper() == "INTEGER" - - -def test_read_arrow_struct_type(session): - struct_type = pa.struct([("a", pa.int64()), ("b", pa.string())]) - pa_table = pa.Table.from_arrays( - [pa.array([{"a": 1, "b": "x"}, {"a": 2, "b": "y"}], type=struct_type)], - names=["struct_col"], - ) - df = session.read_arrow(pa_table) - - bigquery_schema = df._block.expr.schema.to_bigquery() - assert len(bigquery_schema) == 2 # offsets + value - field = bigquery_schema[-1] - assert field.field_type.upper() == "RECORD" - assert field.fields[0].name == "a" - assert field.fields[1].name == "b" diff --git a/tests/unit/session/test_io_bigquery.py b/tests/unit/session/test_io_bigquery.py index e6fa7a901ec..03470208e42 100644 --- a/tests/unit/session/test_io_bigquery.py +++ b/tests/unit/session/test_io_bigquery.py @@ -13,238 +13,57 @@ # limitations under the License. import datetime -import re -from typing import Iterable, Optional -from unittest import mock +from typing import Iterable +import unittest.mock as mock import google.cloud.bigquery as bigquery -import google.cloud.bigquery.job -import google.cloud.bigquery.table import pytest -import bigframes -import bigframes.core.events -import bigframes.pandas as bpd import bigframes.session._io.bigquery -import bigframes.session._io.bigquery as io_bq -from bigframes.core.logging import log_adapter -from bigframes.testing import mocks -@pytest.fixture(scope="function") -def mock_bq_client(): - mock_client = mock.create_autospec(bigquery.Client) - mock_query_job = mock.create_autospec(bigquery.QueryJob) - mock_row_iterator = mock.create_autospec(google.cloud.bigquery.table.RowIterator) - - mock_query_job.result.return_value = mock_row_iterator - - mock_destination = bigquery.DatasetReference( - project="mock_project", dataset_id="mock_dataset" - ) - mock_query_job.destination = mock_destination - - mock_client.query.return_value = mock_query_job - - return mock_client - - -def test_create_job_configs_labels_is_none(): - api_methods = ["agg", "series-mode"] - labels = io_bq.create_job_configs_labels( - job_configs_labels=None, api_methods=api_methods - ) - expected_dict = {"bigframes-api": "agg", "recent-bigframes-api-0": "series-mode"} - assert labels is not None - assert labels == expected_dict - - -def test_create_job_configs_labels_always_includes_bigframes_api(): - labels = io_bq.create_job_configs_labels(None, []) - assert labels == { - "bigframes-api": "unknown", - } - - -def test_create_job_configs_labels_length_limit_not_met(): - cur_labels = { - "source": "bigquery-dataframes-temp", - } - api_methods = ["agg", "series-mode"] - labels = io_bq.create_job_configs_labels( - job_configs_labels=cur_labels, api_methods=api_methods - ) - expected_dict = { - "source": "bigquery-dataframes-temp", - "bigframes-api": "agg", - "recent-bigframes-api-0": "series-mode", - } - assert labels is not None - assert len(labels) == 3 - assert labels == expected_dict - - -def test_create_job_configs_labels_log_adaptor_call_method_under_length_limit(): - log_adapter.get_and_reset_api_methods() - cur_labels = { - "source": "bigquery-dataframes-temp", - } - api_methods = [ - "dataframe-columns", - "dataframe-max", - "dataframe-head", - "dataframe-__init__", - ] - - labels = io_bq.create_job_configs_labels( - job_configs_labels=cur_labels, api_methods=api_methods +def test_create_snapshot_sql_doesnt_timetravel_anonymous_datasets(): + table_ref = bigquery.TableReference.from_string( + "my-test-project._e8166e0cdb.anonbb92cd" ) - expected_labels = { - "source": "bigquery-dataframes-temp", - "bigframes-api": "dataframe-columns", - "recent-bigframes-api-0": "dataframe-max", - "recent-bigframes-api-1": "dataframe-head", - "recent-bigframes-api-2": "dataframe-__init__", - } - # Asserts that all items in expected_labels are present in labels - assert labels.items() >= expected_labels.items() - -def test_create_job_configs_labels_length_limit_met_and_labels_is_none(): - log_adapter.get_and_reset_api_methods() - # Test running methods more than the labels' length limit - api_methods = list(["dataframe-head"] * 100) - - with bpd.option_context("compute.extra_query_labels", {}): - labels = io_bq.create_job_configs_labels( - job_configs_labels=None, api_methods=api_methods - ) - assert labels is not None - assert len(labels) == log_adapter.MAX_LABELS_COUNT - assert "dataframe-head" in labels.values() - - -def test_create_job_configs_labels_length_limit_met(): - log_adapter.get_and_reset_api_methods() - cur_labels = { - "bigframes-api": "read_pandas", - "source": "bigquery-dataframes-temp", - } - for i in range(53): - key = f"bigframes-api-test-{i}" - value = f"test{i}" - cur_labels[key] = value - # If cur_labels length is 62, we can only add one label from api_methods - # Test running two methods - api_methods = ["dataframe-max", "dataframe-head"] - - with bpd.option_context("compute.extra_query_labels", {}): - labels = io_bq.create_job_configs_labels( - job_configs_labels=cur_labels, api_methods=api_methods - ) - - assert labels is not None - assert "dataframe-max" in labels.values() - assert "dataframe-head" not in labels.values() - assert "bigframes-api" in labels.keys() - assert "source" in labels.keys() - - -def test_add_and_trim_labels_length_limit_met(): - log_adapter.get_and_reset_api_methods() - cur_labels = { - "bigframes-api": "read_pandas", - "source": "bigquery-dataframes-temp", - } - for i in range(10): - key = f"bigframes-api-test-{i}" - value = f"test{i}" - cur_labels[key] = value - - df = bpd.DataFrame( - {"col1": [1, 2], "col2": [3, 4]}, session=mocks.create_bigquery_session() + sql = bigframes.session._io.bigquery.create_snapshot_sql( + table_ref, datetime.datetime.now(datetime.timezone.utc) ) - job_config = google.cloud.bigquery.job.QueryJobConfig() - job_config.labels = cur_labels + # Anonymous query results tables don't support time travel. + assert "SYSTEM_TIME" not in sql - df.max() - for _ in range(52): - df.head() + # Need fully-qualified table name. + assert "`my-test-project`.`_e8166e0cdb`.`anonbb92cd`" in sql - io_bq.add_and_trim_labels(job_config=job_config, session=df._session) - assert job_config.labels is not None - assert len(job_config.labels) == 56 - assert "dataframe-max" not in job_config.labels.values() - assert "dataframe-head" in job_config.labels.values() - assert "bigframes-api" in job_config.labels.keys() - assert "source" in job_config.labels.keys() +def test_create_snapshot_sql_doesnt_timetravel_session_tables(): + table_ref = bigquery.TableReference.from_string("my-test-project._session.abcdefg") -@pytest.mark.parametrize( - ("timeout", "api_name"), - [(None, None), (30.0, "test_api")], -) -def test_start_query_with_job_labels_length_limit_met( - mock_bq_client: bigquery.Client, timeout: Optional[float], api_name -): - sql = "select * from abc" - cur_labels = { - "bigframes-api": "read_pandas", - "source": "bigquery-dataframes-temp", - } - for i in range(10): - key = f"bigframes-api-test-{i}" - value = f"test{i}" - cur_labels[key] = value - - df = bpd.DataFrame( - {"col1": [1, 2], "col2": [3, 4]}, session=mocks.create_bigquery_session() + sql = bigframes.session._io.bigquery.create_snapshot_sql( + table_ref, datetime.datetime.now(datetime.timezone.utc) ) - job_config = google.cloud.bigquery.job.QueryJobConfig() - job_config.labels = cur_labels - - df.max() - for _ in range(52): - df.head() + # We aren't modifying _SESSION tables, so don't use time travel. + assert "SYSTEM_TIME" not in sql - io_bq.start_query_with_job( - mock_bq_client, - sql, - job_config=job_config, - location=None, - project=None, - timeout=timeout, - metrics=None, - publisher=bigframes.core.events.Publisher(), - session=df._session, - ) - - assert job_config.labels is not None - assert len(job_config.labels) == 56 - assert "dataframe-max" not in job_config.labels.values() - assert "dataframe-head" in job_config.labels.values() - assert "bigframes-api" in job_config.labels.keys() - assert "source" in job_config.labels.keys() + # Don't need the project ID for _SESSION tables. + assert "my-test-project" not in sql def test_create_temp_table_default_expiration(): """Make sure the created table has an expiration.""" + bqclient = mock.create_autospec(bigquery.Client) + dataset = bigquery.DatasetReference("test-project", "test_dataset") expiration = datetime.datetime( 2023, 11, 2, 13, 44, 55, 678901, datetime.timezone.utc ) - session = mocks.create_bigquery_session() - table_ref = bigquery.TableReference.from_string( - "test-project.test_dataset.bqdf_new_random_table" - ) - bigframes.session._io.bigquery.create_temp_table( - session.bqclient, table_ref, expiration - ) + bigframes.session._io.bigquery.create_temp_table(bqclient, dataset, expiration) - session.bqclient.create_table.assert_called_once() - call_args = session.bqclient.create_table.call_args + bqclient.create_table.assert_called_once() + call_args = bqclient.create_table.call_args table = call_args.args[0] assert table.project == "test-project" assert table.dataset_id == "test_dataset" @@ -306,139 +125,5 @@ def test_create_temp_table_default_expiration(): ), ) def test_bq_schema_to_sql(schema: Iterable[bigquery.SchemaField], expected: str): - sql = io_bq.bq_schema_to_sql(schema) + sql = bigframes.session._io.bigquery.bq_schema_to_sql(schema) assert sql == expected - - -@pytest.mark.parametrize( - ( - "query_or_table", - "columns", - "filters", - "max_results", - "time_travel_timestamp", - "expected_output", - ), - [ - pytest.param( - "test_table", - ["row_index", "string_col"], - [ - (("rowindex", "not in", [0, 6]),), - (("string_col", "in", ["Hello, World!", "こんにちは"]),), - ], - 123, # max_results, - datetime.datetime( - 2024, 5, 14, 12, 42, 36, 125125, tzinfo=datetime.timezone.utc - ), - ( - "SELECT `_bf_source`.`row_index`, `_bf_source`.`string_col` FROM `test_table` AS _bf_source " - "FOR SYSTEM_TIME AS OF CAST('2024-05-14T12:42:36.125125+00:00' AS TIMESTAMP) " - "WHERE `rowindex` NOT IN (0, 6) OR `string_col` IN ('Hello, World!', " - "'こんにちは') LIMIT 123" - ), - id="table-all_params-filter_or_operation", - ), - pytest.param( - ( - """SELECT - rowindex, - string_col, - FROM `test_table` AS t - """ - ), - ["rowindex", "string_col"], - [ - ("rowindex", "<", 4), - ("string_col", "==", "Hello, World!"), - ], - 123, # max_results, - datetime.datetime( - 2024, 5, 14, 12, 42, 36, 125125, tzinfo=datetime.timezone.utc - ), - ( - """SELECT `_bf_source`.`rowindex`, `_bf_source`.`string_col` FROM (SELECT - rowindex, - string_col, - FROM `test_table` AS t - ) AS _bf_source """ - "FOR SYSTEM_TIME AS OF CAST('2024-05-14T12:42:36.125125+00:00' AS TIMESTAMP) " - "WHERE `rowindex` < 4 AND `string_col` = 'Hello, World!' " - "LIMIT 123" - ), - id="subquery-all_params-filter_and_operation", - ), - pytest.param( - "test_table", - ["col_a", "col_b"], - [], - None, # max_results - None, # time_travel_timestampe - "SELECT `_bf_source`.`col_a`, `_bf_source`.`col_b` FROM `test_table` AS _bf_source", - id="table-columns", - ), - pytest.param( - "test_table", - [], - [("date_col", ">", "2022-10-20")], - None, # max_results - None, # time_travel_timestampe - "SELECT * FROM `test_table` AS _bf_source WHERE `date_col` > '2022-10-20'", - id="table-filter", - ), - pytest.param( - "test_table*", - [], - [], - None, # max_results - None, # time_travel_timestampe - "SELECT * FROM `test_table*` AS _bf_source", - id="wildcard-no_params", - ), - pytest.param( - "test_table*", - [], - [("_TABLE_SUFFIX", ">", "2022-10-20")], - None, # max_results - None, # time_travel_timestampe - "SELECT * FROM `test_table*` AS _bf_source WHERE `_TABLE_SUFFIX` > '2022-10-20'", - id="wildcard-filter", - ), - ], -) -def test_to_query( - query_or_table, - columns, - filters, - max_results, - time_travel_timestamp, - expected_output, -): - query = io_bq.to_query( - query_or_table, - columns=columns, - sql_predicate=io_bq.compile_filters(filters), - max_results=max_results, - time_travel_timestamp=time_travel_timestamp, - ) - assert query == expected_output - - -@pytest.mark.parametrize( - ("filters", "expected_message"), - ( - pytest.param( - ["date_col", ">", "2022-10-20"], - "Elements of filters must be tuples of length 3, but got 'd'", - ), - ), -) -def test_to_query_fails_with_bad_filters(filters, expected_message): - with pytest.raises(ValueError, match=re.escape(expected_message)): - io_bq.to_query( - "test_table", - columns=(), - sql_predicate=io_bq.compile_filters(filters), - max_results=None, - time_travel_timestamp=None, - ) diff --git a/tests/unit/session/test_io_pandas.py b/tests/unit/session/test_io_pandas.py index f4141ec8a23..0f6f5dae03b 100644 --- a/tests/unit/session/test_io_pandas.py +++ b/tests/unit/session/test_io_pandas.py @@ -13,8 +13,6 @@ # limitations under the License. import datetime -import re -import unittest.mock as mock from typing import Dict, Union import geopandas # type: ignore @@ -25,30 +23,7 @@ import pyarrow # type: ignore import pytest -import bigframes.core.schema -import bigframes.features -import bigframes.pandas import bigframes.session._io.pandas -from bigframes.testing import mocks - -_LIST_OF_SCALARS = [ - [1, 2, 3], - [], - [4, 5, 6], -] -_LIST_OF_STRUCTS = [ - [ - {"version": 1, "package": "numpy"}, - {"version": 2, "package": "pandas"}, - {"version": 3, "package": "pyarrow"}, - ], - [], - [ - {"version": 4, "package": "awkward-pandas"}, - {"version": 5, "package": "cyberpandas"}, - {"version": 6, "package": "geopandas"}, - ], -] @pytest.mark.parametrize( @@ -209,111 +184,6 @@ ), id="arrow-dtypes", ), - pytest.param( - pyarrow.Table.from_pydict( - { - "listofscalars": pyarrow.array( - _LIST_OF_SCALARS, - type=pyarrow.list_(pyarrow.int64()), - ), - "listofstructs": pyarrow.array( - _LIST_OF_STRUCTS, - type=pyarrow.list_( - pyarrow.struct( - [ - ("version", pyarrow.int64()), - ("package", pyarrow.string()), - ] - ) - ), - ), - }, - ), - { - "listofscalars": pandas.ArrowDtype(pyarrow.list_(pyarrow.int64())), - "listofstructs": pandas.ArrowDtype( - pyarrow.list_( - pyarrow.struct( - [ - ("version", pyarrow.int64()), - ("package", pyarrow.string()), - ] - ) - ) - ), - }, - pandas.DataFrame( - { - "listofscalars": pandas.Series(_LIST_OF_SCALARS, dtype="object"), - "listofstructs": pandas.Series(_LIST_OF_STRUCTS, dtype="object"), - }, - ), - marks=pytest.mark.skipif( - bigframes.features.PANDAS_VERSIONS.is_arrow_list_dtype_usable, - reason="no need to use object dtype for ARRAY in pandas 2.x", - ), - id="nested-dtypes-pandas-1-x", - ), - pytest.param( - pyarrow.Table.from_pydict( - { - "listofscalars": pyarrow.array( - _LIST_OF_SCALARS, - type=pyarrow.list_(pyarrow.int64()), - ), - "listofstructs": pyarrow.array( - _LIST_OF_STRUCTS, - type=pyarrow.list_( - pyarrow.struct( - [ - ("version", pyarrow.int64()), - ("package", pyarrow.string()), - ] - ) - ), - ), - }, - ), - { - "listofscalars": pandas.ArrowDtype(pyarrow.list_(pyarrow.int64())), - "listofstructs": pandas.ArrowDtype( - pyarrow.list_( - pyarrow.struct( - [ - ("version", pyarrow.int64()), - ("package", pyarrow.string()), - ] - ) - ) - ), - }, - pandas.DataFrame( - { - "listofscalars": pandas.Series( - _LIST_OF_SCALARS, - dtype=pandas.ArrowDtype(pyarrow.list_(pyarrow.int64())), - ), - "listofstructs": pandas.Series( - _LIST_OF_STRUCTS, - dtype=pandas.ArrowDtype( - pyarrow.list_( - pyarrow.struct( - [ - ("version", pyarrow.int64()), - ("package", pyarrow.string()), - ] - ) - ) - ), - ), - }, - ), - marks=pytest.mark.skipif( - not bigframes.features.PANDAS_VERSIONS.is_arrow_list_dtype_usable, - reason="Arrow list type broken in pandas 1.x", - ), - id="nested-dtypes-pandas-2-x", - ), pytest.param( pyarrow.Table.from_pydict( { @@ -445,13 +315,7 @@ def test_arrow_to_pandas( dtypes: Dict, expected: pandas.DataFrame, ): - schema = bigframes.core.schema.ArraySchema( - tuple( - bigframes.core.schema.SchemaItem(name, dtype) - for name, dtype in dtypes.items() - ) - ) - actual = bigframes.session._io.pandas.arrow_to_pandas(arrow_table, schema) + actual = bigframes.session._io.pandas.arrow_to_pandas(arrow_table, dtypes) pandas.testing.assert_series_equal(actual.dtypes, expected.dtypes) # assert_frame_equal is converting to numpy internally, which causes some @@ -484,21 +348,5 @@ def test_arrow_to_pandas( def test_arrow_to_pandas_wrong_size_dtypes( arrow_table: Union[pyarrow.Table, pyarrow.RecordBatch], dtypes: Dict ): - schema = bigframes.core.schema.ArraySchema( - tuple( - bigframes.core.schema.SchemaItem(name, dtype) - for name, dtype in dtypes.items() - ) - ) - with pytest.raises(ValueError, match=f"Number of types {len(schema)}"): - bigframes.session._io.pandas.arrow_to_pandas(arrow_table, schema) - - -def test_read_pandas_with_bigframes_dataframe(): - session = mocks.create_bigquery_session() - df = mock.create_autospec(bigframes.pandas.DataFrame, instance=True) - - with pytest.raises( - ValueError, match=re.escape("read_pandas() expects a pandas.DataFrame") - ): - session.read_pandas(df) + with pytest.raises(ValueError, match=f"Number of types {len(dtypes)}"): + bigframes.session._io.pandas.arrow_to_pandas(arrow_table, dtypes) diff --git a/tests/unit/session/test_local_scan_executor.py b/tests/unit/session/test_local_scan_executor.py deleted file mode 100644 index 66dcdf590ce..00000000000 --- a/tests/unit/session/test_local_scan_executor.py +++ /dev/null @@ -1,107 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -from __future__ import annotations - -import asyncio - -import pyarrow -import pytest - -from bigframes.core import identifiers, local_data, nodes -from bigframes.session import execution_spec, local_scan_executor -from bigframes.testing import mocks - -SPEC = execution_spec.ExecutionSpec( - ordered=True, -) - - -@pytest.fixture -def object_under_test(): - return local_scan_executor.LocalScanExecutor() - - -def create_read_local_node(arrow_table: pyarrow.Table): - session = mocks.create_bigquery_session() - local_data_source = local_data.ManagedArrowTable.from_pyarrow(arrow_table) - return nodes.ReadLocalNode( - local_data_source=local_data_source, - session=session, - scan_list=nodes.ScanList( - items=tuple( - nodes.ScanItem( - id=identifiers.ColumnId(column_name), - source_id=column_name, - ) - for column_name in arrow_table.column_names - ), - ), - ) - - -@pytest.mark.parametrize( - ("start", "stop", "expected_rows"), - ( - # No-op slices. - (None, None, 10), - (0, None, 10), - (None, 10, 10), - # Slices equivalent to limits. - (None, 7, 7), - (0, 3, 3), - ), -) -def test_local_scan_executor_with_slice(start, stop, expected_rows, object_under_test): - pyarrow_table = pyarrow.Table.from_pydict( - { - "rowindex": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], - "letters": ["a", "b", "c", "d", "e", "f", "g", "h", "i", "j"], - } - ) - assert pyarrow_table.num_rows == 10 - - local_node = create_read_local_node(pyarrow_table) - plan = nodes.SliceNode( - child=local_node, - start=start, - stop=stop, - ) - - result = asyncio.run(object_under_test.execute(plan, SPEC)) - result_table = pyarrow.Table.from_batches(result.batches().arrow_batches) - assert result_table.num_rows == expected_rows - - -@pytest.mark.parametrize( - ("start", "stop", "step"), - ( - (-1, None, 1), - (None, -1, 1), - (None, None, 2), - (None, None, -1), - (4, None, 6), - (1, 9, 8), - ), -) -def test_local_scan_executor_with_slice_unsupported_inputs( - start, stop, step, object_under_test -): - local_node = create_read_local_node(pyarrow.Table.from_pydict({"col": [1, 2, 3]})) - plan = nodes.SliceNode( - child=local_node, - start=start, - stop=stop, - step=step, - ) - assert asyncio.run(object_under_test.execute(plan, SPEC)) is None diff --git a/tests/unit/session/test_metrics.py b/tests/unit/session/test_metrics.py deleted file mode 100644 index 4e550b1c77a..00000000000 --- a/tests/unit/session/test_metrics.py +++ /dev/null @@ -1,302 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import datetime -import os -import unittest.mock - -import google.cloud.bigquery as bigquery -import pytest - -import bigframes.session.metrics as metrics - -NOW = datetime.datetime.now(datetime.timezone.utc) - - -def test_count_job_stats_with_row_iterator(): - row_iterator = unittest.mock.create_autospec( - bigquery.table.RowIterator, instance=True - ) - row_iterator.total_bytes_processed = 1024 - row_iterator.query = "SELECT * FROM table" - row_iterator.slot_millis = 1234 - execution_metrics = metrics.ExecutionMetrics() - execution_metrics.count_job_stats(row_iterator=row_iterator) - - assert execution_metrics.execution_count == 1 - assert execution_metrics.bytes_processed == 1024 - assert execution_metrics.query_char_count == 19 - assert execution_metrics.slot_millis == 1234 - - -def test_count_job_stats_with_row_iterator_missing_stats(): - row_iterator = unittest.mock.create_autospec( - bigquery.table.RowIterator, instance=True - ) - # Simulate properties not being present on the object - del row_iterator.total_bytes_processed - del row_iterator.query - del row_iterator.slot_millis - execution_metrics = metrics.ExecutionMetrics() - execution_metrics.count_job_stats(row_iterator=row_iterator) - - assert execution_metrics.execution_count == 1 - assert execution_metrics.bytes_processed == 0 - assert execution_metrics.query_char_count == 0 - assert execution_metrics.slot_millis == 0 - - -def test_count_job_stats_with_row_iterator_none_stats(): - row_iterator = unittest.mock.create_autospec( - bigquery.table.RowIterator, instance=True - ) - row_iterator.total_bytes_processed = None - row_iterator.query = None - row_iterator.slot_millis = None - execution_metrics = metrics.ExecutionMetrics() - execution_metrics.count_job_stats(row_iterator=row_iterator) - - assert execution_metrics.execution_count == 1 - assert execution_metrics.bytes_processed == 0 - assert execution_metrics.query_char_count == 0 - assert execution_metrics.slot_millis == 0 - - -def test_count_job_stats_with_dry_run(): - query_job = unittest.mock.create_autospec(bigquery.QueryJob, instance=True) - query_job.configuration.dry_run = True - query_job.query = "SELECT * FROM table" - execution_metrics = metrics.ExecutionMetrics() - execution_metrics.count_job_stats(query_job=query_job) - - # Dry run jobs shouldn't count as "executed" - assert execution_metrics.execution_count == 0 - assert execution_metrics.bytes_processed == 0 - assert execution_metrics.query_char_count == 0 - assert execution_metrics.slot_millis == 0 - - -def test_count_job_stats_with_valid_job(): - query_job = unittest.mock.create_autospec(bigquery.QueryJob, instance=True) - query_job.configuration.dry_run = False - query_job.query = "SELECT * FROM table" - query_job.total_bytes_processed = 2048 - query_job.slot_millis = 5678 - query_job.created = NOW - query_job.ended = NOW + datetime.timedelta(seconds=2) - execution_metrics = metrics.ExecutionMetrics() - execution_metrics.count_job_stats(query_job=query_job) - - assert execution_metrics.execution_count == 1 - assert execution_metrics.bytes_processed == 2048 - assert execution_metrics.query_char_count == 19 - assert execution_metrics.slot_millis == 5678 - assert execution_metrics.execution_secs == pytest.approx(2.0) - - -def test_count_job_stats_with_cached_job(): - query_job = unittest.mock.create_autospec(bigquery.QueryJob, instance=True) - query_job.configuration.dry_run = False - query_job.query = "SELECT * FROM table" - # Cache hit jobs don't have total_bytes_processed or slot_millis - query_job.total_bytes_processed = None - query_job.slot_millis = None - query_job.created = NOW - query_job.ended = NOW + datetime.timedelta(seconds=1) - execution_metrics = metrics.ExecutionMetrics() - execution_metrics.count_job_stats(query_job=query_job) - - assert execution_metrics.execution_count == 1 - assert execution_metrics.bytes_processed == 0 - assert execution_metrics.query_char_count == 19 - assert execution_metrics.slot_millis == 0 - assert execution_metrics.execution_secs == pytest.approx(1.0) - - -def test_count_job_stats_with_unsupported_job(): - query_job = unittest.mock.create_autospec(bigquery.QueryJob, instance=True) - query_job.configuration.dry_run = False - query_job.query = "SELECT * FROM table" - # Some jobs, such as scripts, don't have these properties. - query_job.total_bytes_processed = None - query_job.slot_millis = None - query_job.created = None - query_job.ended = None - execution_metrics = metrics.ExecutionMetrics() - execution_metrics.count_job_stats(query_job=query_job) - - # Don't count jobs if we can't get performance stats. - assert execution_metrics.execution_count == 0 - assert execution_metrics.bytes_processed == 0 - assert execution_metrics.query_char_count == 0 - assert execution_metrics.slot_millis == 0 - assert execution_metrics.execution_secs == pytest.approx(0.0) - - -def test_get_performance_stats_with_valid_job(): - query_job = unittest.mock.create_autospec(bigquery.QueryJob, instance=True) - query_job.configuration.dry_run = False - query_job.query = "SELECT * FROM table" - query_job.total_bytes_processed = 2048 - query_job.slot_millis = 5678 - query_job.created = NOW - query_job.ended = NOW + datetime.timedelta(seconds=2) - stats = metrics.get_performance_stats(query_job) - assert stats is not None - query_char_count, bytes_processed, slot_millis, exec_seconds = stats - assert query_char_count == 19 - assert bytes_processed == 2048 - assert slot_millis == 5678 - assert exec_seconds == pytest.approx(2.0) - - -def test_get_performance_stats_with_dry_run(): - query_job = unittest.mock.create_autospec(bigquery.QueryJob, instance=True) - query_job.configuration.dry_run = True - stats = metrics.get_performance_stats(query_job) - assert stats is None - - -def test_get_performance_stats_with_missing_timestamps(): - query_job = unittest.mock.create_autospec(bigquery.QueryJob, instance=True) - query_job.configuration.dry_run = False - query_job.created = None - query_job.ended = NOW - stats = metrics.get_performance_stats(query_job) - assert stats is None - - query_job.created = NOW - query_job.ended = None - stats = metrics.get_performance_stats(query_job) - assert stats is None - - -def test_get_performance_stats_with_mocked_types(): - query_job = unittest.mock.create_autospec(bigquery.QueryJob, instance=True) - query_job.configuration.dry_run = False - query_job.created = NOW - query_job.ended = NOW - query_job.total_bytes_processed = unittest.mock.Mock() - query_job.slot_millis = 123 - stats = metrics.get_performance_stats(query_job) - assert stats is None - - query_job.total_bytes_processed = 123 - query_job.slot_millis = unittest.mock.Mock() - stats = metrics.get_performance_stats(query_job) - assert stats is None - - -@pytest.fixture -def mock_environ(monkeypatch): - """Fixture to mock os.environ.""" - monkeypatch.setenv(metrics.LOGGING_NAME_ENV_VAR, "my_test_case") - - -def test_write_stats_to_disk_writes_files(tmp_path, mock_environ): - os.chdir(tmp_path) - test_name = os.environ[metrics.LOGGING_NAME_ENV_VAR] - metrics.write_stats_to_disk( - query_char_count=100, - bytes_processed=200, - slot_millis=300, - exec_seconds=1.23, - ) - - slot_file = tmp_path / (test_name + ".slotmillis") - assert slot_file.exists() - with open(slot_file) as f: - assert f.read() == "300\n" - - exec_time_file = tmp_path / (test_name + ".bq_exec_time_seconds") - assert exec_time_file.exists() - with open(exec_time_file) as f: - assert f.read() == "1.23\n" - - query_char_count_file = tmp_path / (test_name + ".query_char_count") - assert query_char_count_file.exists() - with open(query_char_count_file) as f: - assert f.read() == "100\n" - - bytes_file = tmp_path / (test_name + ".bytesprocessed") - assert bytes_file.exists() - with open(bytes_file) as f: - assert f.read() == "200\n" - - -def test_write_stats_to_disk_no_env_var(tmp_path, monkeypatch): - monkeypatch.delenv(metrics.LOGGING_NAME_ENV_VAR, raising=False) - os.chdir(tmp_path) - metrics.write_stats_to_disk( - query_char_count=100, - bytes_processed=200, - slot_millis=300, - exec_seconds=1.23, - ) - assert len(list(tmp_path.iterdir())) == 0 - - -def test_on_event_with_local_execute_result(): - import bigframes.core.events - from bigframes.session.executor import LocalExecuteResult - - # fmt: off - local_result = unittest.mock.create_autospec( - LocalExecuteResult, instance=True - ) - # fmt: on - local_result.total_bytes_processed = 1024 - - event = bigframes.core.events.ExecutionFinished(result=local_result) - envelope = bigframes.core.events.EventEnvelope(event) - execution_metrics = metrics.ExecutionMetrics() - execution_metrics.on_event(envelope) - - assert execution_metrics.execution_count == 1 - assert len(execution_metrics.jobs) == 1 - assert execution_metrics.jobs[0].job_type == "polars" - assert execution_metrics.jobs[0].status == "DONE" - assert execution_metrics.jobs[0].total_bytes_processed == 1024 - - -def test_count_job_stats_with_explicit_cell_execution_count(): - row_iterator = unittest.mock.create_autospec( - bigquery.table.RowIterator, instance=True - ) - row_iterator.total_bytes_processed = 1024 - row_iterator.query = "SELECT * FROM table" - row_iterator.slot_millis = 1234 - execution_metrics = metrics.ExecutionMetrics() - execution_metrics.count_job_stats( - row_iterator=row_iterator, cell_execution_count=42 - ) - - assert len(execution_metrics.jobs) == 1 - assert execution_metrics.jobs[0].cell_execution_count == 42 - - -def test_on_event_with_explicit_cell_execution_count(): - import bigframes.core.events - from bigframes.session.executor import LocalExecuteResult - - local_result = unittest.mock.create_autospec(LocalExecuteResult, instance=True) - local_result.total_bytes_processed = 1024 - - event = bigframes.core.events.ExecutionFinished(result=local_result) - envelope = bigframes.core.events.EventEnvelope(event=event, cell_execution_count=42) - execution_metrics = metrics.ExecutionMetrics() - execution_metrics.on_event(envelope) - - assert len(execution_metrics.jobs) == 1 - assert execution_metrics.jobs[0].cell_execution_count == 42 diff --git a/tests/unit/session/test_proxy_executor.py b/tests/unit/session/test_proxy_executor.py deleted file mode 100644 index c20fd57236b..00000000000 --- a/tests/unit/session/test_proxy_executor.py +++ /dev/null @@ -1,198 +0,0 @@ -# Copyright 2026 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from unittest import mock - -import google.cloud.bigquery as bigquery -import google.cloud.exceptions -import pytest - -import bigframes -from bigframes.session.proxy_executor import DualCompilerProxyExecutor - - -@pytest.fixture -def mock_executor(): - bqclient = mock.create_autospec(bigquery.Client) - bqclient.project = "test-project" - storage_manager = mock.Mock() - bqstoragereadclient = mock.Mock() - loader = mock.Mock() - publisher = mock.Mock() - function_manager = mock.Mock() - return DualCompilerProxyExecutor( - bqclient, - storage_manager, - bqstoragereadclient, - loader, - publisher=publisher, - function_manager=function_manager, - ) - - -def test_execute_legacy_routes_to_ibis(mock_executor, monkeypatch): - array_value = mock.Mock(spec=bigframes.core.ArrayValue) - execution_spec = mock.Mock(spec=bigframes.session.execution_spec.ExecutionSpec) - execution_spec.with_bq_labels.return_value = execution_spec - - mock_executor._ibis_executor = mock.Mock() - mock_executor._sqlglot_executor = mock.Mock() - - monkeypatch.setattr(bigframes.options.experiments, "sql_compiler", "legacy") - mock_executor.execute(array_value, execution_spec) - - execution_spec.with_bq_labels.assert_called_once_with( - {"bigframes-compiler": "ibis"} - ) - mock_executor._ibis_executor.execute.assert_called_once_with( - array_value, execution_spec - ) - mock_executor._sqlglot_executor.execute.assert_not_called() - - -def test_execute_experimental_routes_to_sqlglot(mock_executor, monkeypatch): - array_value = mock.Mock(spec=bigframes.core.ArrayValue) - execution_spec = mock.Mock(spec=bigframes.session.execution_spec.ExecutionSpec) - execution_spec.with_bq_labels.return_value = execution_spec - - mock_executor._ibis_executor = mock.Mock() - mock_executor._sqlglot_executor = mock.Mock() - - monkeypatch.setattr(bigframes.options.experiments, "sql_compiler", "experimental") - mock_executor.execute(array_value, execution_spec) - - execution_spec.with_bq_labels.assert_called_once_with( - {"bigframes-compiler": "sqlglot"} - ) - mock_executor._sqlglot_executor.execute.assert_called_once_with( - array_value, execution_spec - ) - mock_executor._ibis_executor.execute.assert_not_called() - - -def test_execute_stable_routes_to_sqlglot_success(mock_executor, monkeypatch): - array_value = mock.Mock(spec=bigframes.core.ArrayValue) - execution_spec = mock.Mock(spec=bigframes.session.execution_spec.ExecutionSpec) - execution_spec.with_bq_labels.return_value = execution_spec - - mock_executor._ibis_executor = mock.Mock() - mock_executor._sqlglot_executor = mock.Mock() - - monkeypatch.setattr(bigframes.options.experiments, "sql_compiler", "stable") - with mock.patch("uuid.uuid1") as mock_uuid: - mock_uuid.return_value.hex = "1234567890123456" - mock_executor.execute(array_value, execution_spec) - - execution_spec.with_bq_labels.assert_called_once_with( - {"bigframes-compiler": "sqlglot-123456789012"} - ) - mock_executor._sqlglot_executor.execute.assert_called_once_with( - array_value, execution_spec - ) - mock_executor._ibis_executor.execute.assert_not_called() - - -def test_execute_stable_routes_to_sqlglot_fallback_to_ibis(mock_executor, monkeypatch): - array_value = mock.Mock(spec=bigframes.core.ArrayValue) - execution_spec = mock.Mock(spec=bigframes.session.execution_spec.ExecutionSpec) - - spec_sqlglot = mock.Mock(spec=bigframes.session.execution_spec.ExecutionSpec) - spec_ibis = mock.Mock(spec=bigframes.session.execution_spec.ExecutionSpec) - execution_spec.with_bq_labels.side_effect = [spec_sqlglot, spec_ibis] - - mock_executor._ibis_executor = mock.Mock() - mock_executor._sqlglot_executor = mock.Mock() - - mock_executor._sqlglot_executor.execute.side_effect = ( - google.cloud.exceptions.BadRequest("test error") - ) - - monkeypatch.setattr(bigframes.options.experiments, "sql_compiler", "stable") - with mock.patch("uuid.uuid1") as mock_uuid: - mock_uuid.return_value.hex = "1234567890123456" - with pytest.warns( - UserWarning, match="Compiler ID 123456789012: Exception on sqlglot" - ): - mock_executor.execute(array_value, execution_spec) - - execution_spec.with_bq_labels.assert_has_calls( - [ - mock.call({"bigframes-compiler": "sqlglot-123456789012"}), - mock.call({"bigframes-compiler": "ibis-123456789012"}), - ] - ) - - mock_executor._sqlglot_executor.execute.assert_called_once_with( - array_value, spec_sqlglot - ) - mock_executor._ibis_executor.execute.assert_called_once_with(array_value, spec_ibis) - - -def test_cached_legacy_routes_to_ibis(mock_executor, monkeypatch): - array_value = mock.Mock(spec=bigframes.core.ArrayValue) - config = mock.Mock() - - mock_executor._ibis_executor = mock.Mock() - mock_executor._sqlglot_executor = mock.Mock() - - monkeypatch.setattr(bigframes.options.experiments, "sql_compiler", "legacy") - mock_executor.cached(array_value, config=config) - - mock_executor._ibis_executor.cached.assert_called_once_with( - array_value, config=config - ) - mock_executor._sqlglot_executor.cached.assert_not_called() - - -def test_cached_experimental_routes_to_sqlglot(mock_executor, monkeypatch): - array_value = mock.Mock(spec=bigframes.core.ArrayValue) - config = mock.Mock() - - mock_executor._ibis_executor = mock.Mock() - mock_executor._sqlglot_executor = mock.Mock() - - monkeypatch.setattr(bigframes.options.experiments, "sql_compiler", "experimental") - mock_executor.cached(array_value, config=config) - - mock_executor._sqlglot_executor.cached.assert_called_once_with( - array_value, config=config - ) - mock_executor._ibis_executor.cached.assert_not_called() - - -def test_cached_stable_routes_to_sqlglot_fallback_to_ibis(mock_executor, monkeypatch): - array_value = mock.Mock(spec=bigframes.core.ArrayValue) - config = mock.Mock() - - mock_executor._ibis_executor = mock.Mock() - mock_executor._sqlglot_executor = mock.Mock() - - mock_executor._sqlglot_executor.cached.side_effect = ( - google.cloud.exceptions.BadRequest("test error") - ) - - monkeypatch.setattr(bigframes.options.experiments, "sql_compiler", "stable") - with mock.patch("uuid.uuid1") as mock_uuid: - mock_uuid.return_value.hex = "1234567890123456" - with pytest.warns( - UserWarning, match="Compiler ID 123456789012: Exception on sqlglot" - ): - mock_executor.cached(array_value, config=config) - - mock_executor._sqlglot_executor.cached.assert_called_once_with( - array_value, config=config - ) - mock_executor._ibis_executor.cached.assert_called_once_with( - array_value, config=config - ) diff --git a/tests/unit/session/test_read_gbq_colab.py b/tests/unit/session/test_read_gbq_colab.py deleted file mode 100644 index fc4181b6a2b..00000000000 --- a/tests/unit/session/test_read_gbq_colab.py +++ /dev/null @@ -1,246 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -"""Unit tests for read_gbq_colab helper functions.""" - -import itertools -import textwrap -from unittest import mock - -import numpy -import pandas -import pytest -from google.cloud import bigquery - -from bigframes.testing import mocks - - -def test_read_gbq_colab_includes_label(): - """Make sure we can tell direct colab usage apart from regular read_gbq usage.""" - bqclient = mock.create_autospec(bigquery.Client, instance=True) - bqclient.project = "proj" - session = mocks.create_bigquery_session(bqclient=bqclient) - _ = session._read_gbq_colab("SELECT 'read-gbq-colab-test'") - - label_values = [] - for kall in itertools.chain( - bqclient.query_and_wait.call_args_list, - bqclient._query_and_wait_bigframes.call_args_list, - bqclient.query.call_args_list, - ): - job_config = kall.kwargs.get("job_config") - if job_config is None: - continue - label_values.extend(job_config.labels.values()) - - assert "session-read_gbq_colab" in label_values - - -def test_read_gbq_colab_includes_label_in_anywidget_mode(): - """Make sure read_gbq_colab label is preserved in recent-bigframes-api labels in anywidget mode.""" - pytest.importorskip("anywidget") - pytest.importorskip("traitlets") - - import bigframes - import bigframes.display.html as bf_html - - bqclient = mock.create_autospec(bigquery.Client, instance=True) - bqclient.project = "proj" - session = mocks.create_bigquery_session(bqclient=bqclient) - df = session._read_gbq_colab("SELECT 'read-gbq-colab-test'") - - with bigframes.option_context("display.render_mode", "anywidget"): - _ = bf_html.get_anywidget_bundle(df) - - label_values = [] - for kall in itertools.chain( - bqclient.query_and_wait.call_args_list, - bqclient._query_and_wait_bigframes.call_args_list, - bqclient.query.call_args_list, - ): - job_config = kall.kwargs.get("job_config") - if job_config is None: - continue - label_values.extend(job_config.labels.values()) - - assert "session-read_gbq_colab" in label_values - - -@pytest.mark.parametrize("dry_run", [True, False]) -def test_read_gbq_colab_includes_formatted_values_in_dry_run(monkeypatch, dry_run): - bqclient = mock.create_autospec(bigquery.Client, instance=True) - bqclient.project = "proj" - session = mocks.create_bigquery_session(bqclient=bqclient) - bf_df = mocks.create_dataframe(monkeypatch, session=session) - session._create_temp_table = mock.Mock( # type: ignore - return_value=bigquery.TableReference.from_string("proj.dset.temp_table") - ) - session._create_temp_view = mock.Mock( # type: ignore - return_value=bigquery.TableReference.from_string("proj.dset.temp_view") - ) - - # To avoid trouble with get_table() calls getting out of sync with mock - # "uploaded" data, make sure this is small enough to inline in the SQL as a - # view. - pd_df = pandas.DataFrame({"rowindex": numpy.arange(3), "value": numpy.arange(3)}) - - pyformat_args = { - "some_integer": 123, - "some_string": "some_column", - "bf_df": bf_df, - "pd_df": pd_df, - # This is not a supported type, but ignored if not referenced. - "some_object": object(), - } - - _ = session._read_gbq_colab( - textwrap.dedent( - """ - SELECT {some_integer} as some_integer, - {some_string} as some_string, - '{{escaped}}' as escaped - FROM {bf_df} AS bf_df - FULL OUTER JOIN {pd_df} AS pd_df - ON bf_df.rowindex = pd_df.rowindex - """ - ), - pyformat_args=pyformat_args, - dry_run=dry_run, - ) - expected = textwrap.dedent( - f""" - SELECT 123 as some_integer, - some_column as some_string, - '{{escaped}}' as escaped - FROM `proj`.`dset`.`temp_{"table" if dry_run else "view"}` AS bf_df - FULL OUTER JOIN `proj`.`dset`.`temp_{"table" if dry_run else "view"}` AS pd_df - ON bf_df.rowindex = pd_df.rowindex - """ - ) - - # This should be the most recent query. - query = session._queries[-1] # type: ignore - config = session._job_configs[-1] # type: ignore - - if dry_run: - assert config.dry_run - else: - # Allow for any "False-y" value. - assert not config.dry_run - - assert query.strip() == expected.strip() - - -def test_read_gbq_colab_doesnt_set_destination_table(): - """For best performance, we don't try to workaround the 10 GB query results limitation.""" - session = mocks.create_bigquery_session() - - _ = session._read_gbq_colab("SELECT 'my-test-query';") - queries = session._queries # type: ignore - configs = session._job_configs # type: ignore - - for query, config in zip(queries, configs): - if query == "SELECT 'my-test-query';" and not config.dry_run: - break - - assert query == "SELECT 'my-test-query';" - assert config.destination is None - - -def test_read_gbq_colab_with_callback(): - """Make sure callback receives events during execution.""" - session = mocks.create_bigquery_session() - callback = mock.Mock() - - _ = session._read_gbq_colab("SELECT 'my-test-query';", callback=callback) - - assert callback.call_count > 0 - - -def test_read_gbq_colab_filters_by_cell(): - """Verify that callbacks are scoped to individual executions.""" - session = mocks.create_bigquery_session() - callback1 = mock.Mock() - callback2 = mock.Mock() - - _ = session._read_gbq_colab("SELECT 'cell_1_query';", callback=callback1) - callback1_initial_count = callback1.call_count - - _ = session._read_gbq_colab("SELECT 'cell_2_query';", callback=callback2) - - # Verify callback1 was automatically unsubscribed upon completion - # of the first query. - assert callback1.call_count == callback1_initial_count - assert callback2.call_count > 0 - - -def test_execution_history_filtering(): - """Verify that execution_history can be filtered by job_ids or events.""" - from bigframes.session import metrics - - session = mocks.create_bigquery_session() - - job1 = metrics.JobMetadata(job_id="job_1", job_type="query", query="SELECT 1") - job2 = metrics.JobMetadata(job_id="job_2", job_type="query", query="SELECT 2") - session._metrics.jobs.extend([job1, job2]) - - history_job1 = session.execution_history(job_ids=["job_1"]).to_dataframe() - assert len(history_job1) == 1 - assert history_job1.iloc[0]["job_id"] == "job_1" - - event2 = mock.Mock() - event2.job_id = "job_2" - history_job2 = session.execution_history(events=[event2]).to_dataframe() - assert len(history_job2) == 1 - assert history_job2.iloc[0]["job_id"] == "job_2" - - -def test_execution_history_returns_all_executions_by_default(): - """Verify that execution_history returns all executions by default.""" - from bigframes.session import metrics - - session = mocks.create_bigquery_session() - job1 = metrics.JobMetadata( - job_id="job_1", job_type="query", query="SELECT 1", cell_execution_count=10 - ) - job2 = metrics.JobMetadata( - job_id="job_2", job_type="query", query="SELECT 2", cell_execution_count=20 - ) - session._metrics.jobs.extend([job1, job2]) - - history = session.execution_history().to_dataframe() - - assert len(history) == 2 - - -def test_execution_history_filters_by_notebook_cell_when_all_cells_is_false(): - """Verify that execution_history filters to the current cell when all_cells is False.""" - from bigframes.session import metrics - - session = mocks.create_bigquery_session() - job1 = metrics.JobMetadata( - job_id="job_1", job_type="query", query="SELECT 1", cell_execution_count=10 - ) - job2 = metrics.JobMetadata( - job_id="job_2", job_type="query", query="SELECT 2", cell_execution_count=20 - ) - session._metrics.jobs.extend([job1, job2]) - - with mock.patch( - "bigframes.core.utils.get_ipython_execution_count", return_value=20 - ): - history = session.execution_history(all_cells=False).to_dataframe() - - assert len(history) == 1 - assert history.iloc[0]["job_id"] == "job_2" diff --git a/tests/unit/session/test_read_gbq_query.py b/tests/unit/session/test_read_gbq_query.py deleted file mode 100644 index d078c64af72..00000000000 --- a/tests/unit/session/test_read_gbq_query.py +++ /dev/null @@ -1,38 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -"""Unit tests for read_gbq_query functions.""" - -from bigframes.testing import mocks - - -def test_read_gbq_query_sets_destination_table(): - """Workaround the 10 GB query results limitation by setting a destination table. - - See internal issue b/303057336. - """ - # Use partial ordering mode to skip column uniqueness checks. - session = mocks.create_bigquery_session(ordering_mode="partial") - - _ = session.read_gbq_query("SELECT 'my-test-query';", allow_large_results=True) - queries = session._queries # type: ignore - configs = session._job_configs # type: ignore - - for query, config in zip(queries, configs): - if query == "SELECT 'my-test-query';" and not config.dry_run: - break - - assert query == "SELECT 'my-test-query';" - assert config.destination is not None - session.close() diff --git a/tests/unit/session/test_read_gbq_table.py b/tests/unit/session/test_read_gbq_table.py deleted file mode 100644 index 97ac0efb753..00000000000 --- a/tests/unit/session/test_read_gbq_table.py +++ /dev/null @@ -1,191 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -"""Unit tests for read_gbq_table helper functions.""" - -import unittest.mock as mock -import warnings - -import google.cloud.bigquery -import pytest - -import bigframes.enums -import bigframes.exceptions -import bigframes.session._io.bigquery.read_gbq_table as bf_read_gbq_table -from bigframes.core import bq_data -from bigframes.testing import mocks - - -@pytest.mark.parametrize( - ("index_cols", "primary_keys", "expected"), - ( - (["col1", "col2"], ["col1", "col2", "col3"], ("col1", "col2", "col3")), - ( - ["col1", "col2", "col3"], - ["col1", "col2", "col3"], - ("col1", "col2", "col3"), - ), - ( - ["col2", "col3", "col1"], - [ - "col3", - "col2", - ], - ("col2", "col3"), - ), - (["col1", "col2"], [], ()), - ([], ["col1", "col2", "col3"], ("col1", "col2", "col3")), - ([], [], ()), - ), -) -def test_infer_unique_columns(index_cols, primary_keys, expected): - """If a primary key is set on the table, we use that as the index column - by default, no error should be raised in this case. - - See internal issue 335727141. - """ - table = google.cloud.bigquery.Table.from_api_repr( - { - "tableReference": { - "projectId": "my-project", - "datasetId": "my_dataset", - "tableId": "my_table", - }, - "clustering": { - "fields": ["col1", "col2"], - }, - }, - ) - table.schema = ( - google.cloud.bigquery.SchemaField("col1", "INT64"), - google.cloud.bigquery.SchemaField("col2", "INT64"), - google.cloud.bigquery.SchemaField("col3", "INT64"), - google.cloud.bigquery.SchemaField("col4", "INT64"), - ) - - # TODO(b/305264153): use setter for table_constraints in client library - # when available. - table._properties["tableConstraints"] = { - "primaryKey": { - "columns": primary_keys, - }, - } - - result = bf_read_gbq_table.infer_unique_columns( - bq_data.GbqNativeTable.from_table(table), index_cols - ) - - assert result == expected - - -@pytest.mark.parametrize( - ("index_cols", "values_distinct", "expected"), - ( - ( - ["col1", "col2", "col3"], - True, - ("col1", "col2", "col3"), - ), - ( - ["col2", "col3", "col1"], - True, - ("col2", "col3", "col1"), - ), - (["col1", "col2"], False, ()), - ([], False, ()), - ), -) -def test_check_if_index_columns_are_unique(index_cols, values_distinct, expected): - table = google.cloud.bigquery.Table.from_api_repr( - { - "tableReference": { - "projectId": "my-project", - "datasetId": "my_dataset", - "tableId": "my_table", - }, - "clustering": { - "fields": ["col1", "col2"], - }, - }, - ) - table.schema = ( - google.cloud.bigquery.SchemaField("col1", "INT64"), - google.cloud.bigquery.SchemaField("col2", "INT64"), - google.cloud.bigquery.SchemaField("col3", "INT64"), - google.cloud.bigquery.SchemaField("col4", "INT64"), - ) - - bqclient = mock.create_autospec(google.cloud.bigquery.Client, instance=True) - bqclient.project = "test-project" - session = mocks.create_bigquery_session( - bqclient=bqclient, table_schema=table.schema - ) - - # Mock bqclient _after_ creating session to override its mocks. - bqclient.get_table.return_value = table - bqclient._query_and_wait_bigframes.side_effect = None - bqclient._query_and_wait_bigframes.return_value = ( - {"total_count": 3, "distinct_count": 3 if values_distinct else 2}, - ) - - table._properties["location"] = session._location - - result = bf_read_gbq_table.check_if_index_columns_are_unique( - bqclient=bqclient, - table=bq_data.GbqNativeTable.from_table(table), - index_cols=index_cols, - publisher=session._publisher, - ) - - assert result == expected - - -def test_get_index_cols_warns_if_clustered_but_sequential_index(): - table = google.cloud.bigquery.Table.from_api_repr( - { - "tableReference": { - "projectId": "my-project", - "datasetId": "my_dataset", - "tableId": "my_table", - }, - "clustering": { - "fields": ["col1", "col2"], - }, - }, - ) - table.schema = ( - google.cloud.bigquery.SchemaField("col1", "INT64"), - google.cloud.bigquery.SchemaField("col2", "INT64"), - google.cloud.bigquery.SchemaField("col3", "INT64"), - google.cloud.bigquery.SchemaField("col4", "INT64"), - ) - - with pytest.warns(bigframes.exceptions.DefaultIndexWarning, match="is clustered"): - bf_read_gbq_table.get_index_cols( - bq_data.GbqNativeTable.from_table(table), - index_col=(), - default_index_type=bigframes.enums.DefaultIndexKind.SEQUENTIAL_INT64, - ) - - # Ensure that we don't raise if using a NULL index by default, such as in - # partial ordering mode. See: internal issue b/356872356. - with warnings.catch_warnings(): - warnings.simplefilter( - "error", category=bigframes.exceptions.DefaultIndexWarning - ) - bf_read_gbq_table.get_index_cols( - bq_data.GbqNativeTable.from_table(table), - index_col=(), - default_index_type=bigframes.enums.DefaultIndexKind.NULL, - ) diff --git a/tests/unit/session/test_session.py b/tests/unit/session/test_session.py index a6c8446967e..18fd42e0f31 100644 --- a/tests/unit/session/test_session.py +++ b/tests/unit/session/test_session.py @@ -12,510 +12,41 @@ # See the License for the specific language governing permissions and # limitations under the License. -import copy -import datetime import os -import re -import warnings from unittest import mock import google.api_core.exceptions -import google.cloud.bigquery -import pandas as pd import pytest import bigframes -import bigframes.enums -import bigframes.exceptions -from bigframes import version -from bigframes.core import bq_data -from bigframes.testing import mocks -TABLE_REFERENCE = { - "projectId": "my-project", - "datasetId": "my_dataset", - "tableId": "my_table", -} -SCHEMA = { - "fields": [ - {"name": "col1", "type": "INTEGER"}, - {"name": "col2", "type": "INTEGER"}, - {"name": "col3", "type": "INTEGER"}, - {"name": "col4", "type": "INTEGER"}, - ] -} -CLUSTERED_OR_PARTITIONED_TABLES = [ - pytest.param( - google.cloud.bigquery.Table.from_api_repr( - { - "tableReference": TABLE_REFERENCE, - "clustering": { - "fields": ["col1", "col2"], - }, - "schema": SCHEMA, - }, - ), - id="clustered", - ), - pytest.param( - google.cloud.bigquery.Table.from_api_repr( - { - "tableReference": TABLE_REFERENCE, - "rangePartitioning": { - "field": "col1", - "range": { - "start": 1, - "end": 100, - "interval": 1, - }, - }, - "schema": SCHEMA, - }, - ), - id="range-partitioned", - ), - pytest.param( - google.cloud.bigquery.Table.from_api_repr( - { - "tableReference": TABLE_REFERENCE, - "timePartitioning": { - "type": "MONTH", - "field": "col1", - }, - "schema": SCHEMA, - }, - ), - id="time-partitioned", - ), - pytest.param( - google.cloud.bigquery.Table.from_api_repr( - { - "tableReference": TABLE_REFERENCE, - "clustering": { - "fields": ["col1", "col2"], - }, - "timePartitioning": { - "type": "MONTH", - "field": "col1", - }, - "schema": SCHEMA, - }, - ), - id="time-partitioned-and-clustered", - ), -] - - -@pytest.mark.parametrize( - ("kwargs", "match"), - [ - pytest.param( - {"engine": "bigquery", "usecols": [1, 2]}, - "BigQuery engine only supports an iterable of strings for `usecols`.", - id="with_usecols_invalid", - ), - pytest.param( - {"engine": "bigquery", "encoding": "ASCII"}, - "BigQuery engine only supports the following encodings", - id="with_encoding_invalid", - ), - ], -) -def test_read_csv_w_bq_engine_raises_error(kwargs, match): - session = mocks.create_bigquery_session() - - with pytest.raises(NotImplementedError, match=match): - session.read_csv("", **kwargs) - - -@pytest.mark.parametrize( - ("engine",), - ( - ("c",), - ("python",), - ("pyarrow",), - ("python-fwf",), - ), -) -def test_read_csv_w_pandas_engines_raises_error_for_sequential_int64_index_col(engine): - session = mocks.create_bigquery_session() - - with pytest.raises(NotImplementedError, match="index_col"): - session.read_csv( - "path/to/csv.csv", - engine=engine, - index_col=bigframes.enums.DefaultIndexKind.SEQUENTIAL_INT64, - ) - - -@pytest.mark.parametrize( - ("kwargs"), - [ - pytest.param({"chunksize": 5}, id="with_chunksize"), - pytest.param({"iterator": True}, id="with_iterator"), - ], -) -def test_read_csv_w_pandas_engines_raises_error_for_unsupported_args(kwargs): - session = mocks.create_bigquery_session() - with pytest.raises( - NotImplementedError, - match="'chunksize' and 'iterator' arguments are not supported.", - ): - session.read_csv("path/to/csv.csv", **kwargs) - - -@pytest.mark.parametrize( - ("engine", "write_engine"), - ( - # Can't use bigquery parsing if parsing the data locally to upload. - ("bigquery", "bigquery_streaming"), - ("bigquery", "bigquery_inline"), - # No local parsing engines are compatible with bigquery_external_table. - (None, "bigquery_external_table"), - ("c", "bigquery_external_table"), - ("pyarrow", "bigquery_external_table"), - ("python", "bigquery_external_table"), - ("python-fwf", "bigquery_external_table"), - ), -) -def test_read_csv_with_incompatible_write_engine(engine, write_engine): - session = mocks.create_bigquery_session() - - with pytest.raises( - NotImplementedError, - match=re.escape( - f"Can't use parsing engine={repr(engine)} with write_engine={repr(write_engine)}, which" - ), - ): - session.read_csv( - "gs://cloud-samples-data/bigquery/us-states/us-states.csv", - engine=engine, - write_engine=write_engine, - ) - - -@pytest.mark.parametrize( - ("names", "error_message"), - ( - pytest.param("abc", "Names should be an ordered collection."), - pytest.param({"a", "b", "c"}, "Names should be an ordered collection."), - pytest.param(["a", "a"], "Duplicated names are not allowed."), - ), -) -def test_read_csv_w_bigquery_engine_raises_error_for_invalid_names( - names, error_message -): - session = mocks.create_bigquery_session() - - with pytest.raises(ValueError, match=error_message): - session.read_csv("path/to/csv.csv", engine="bigquery", names=names) - - -def test_read_csv_w_bigquery_engine_raises_error_for_invalid_dtypes(): - session = mocks.create_bigquery_session() - - with pytest.raises(ValueError, match="dtype should be a dict-like object."): - session.read_csv( - "path/to/csv.csv", - engine="bigquery", - dtype=["a", "b", "c"], # type: ignore[arg-type] - ) +from .. import resources @pytest.mark.parametrize("missing_parts_table_id", [(""), ("table")]) def test_read_gbq_missing_parts(missing_parts_table_id): - session = mocks.create_bigquery_session() + session = resources.create_bigquery_session() with pytest.raises(ValueError): session.read_gbq(missing_parts_table_id) -def test_read_gbq_cached_table(): - session = mocks.create_bigquery_session() - table_ref = google.cloud.bigquery.TableReference( - google.cloud.bigquery.DatasetReference("my-project", "my_dataset"), - "my_table", - ) - table = google.cloud.bigquery.Table( - table_ref, (google.cloud.bigquery.SchemaField("col", "INTEGER"),) - ) - table._properties["location"] = session._location - table._properties["numRows"] = "1000000000" - table._properties["type"] = "TABLE" - session._loader._df_snapshot[str(table_ref)] = ( - datetime.datetime(1999, 1, 2, 3, 4, 5, 678901, tzinfo=datetime.timezone.utc), - bq_data.GbqNativeTable.from_table(table), - ) - - session.bqclient._query_and_wait_bigframes = mock.MagicMock( - return_value=({"total_count": 3, "distinct_count": 2},) - ) - session.bqclient.get_table.return_value = table - - with pytest.warns( - bigframes.exceptions.TimeTravelCacheWarning, match=re.escape("use_cache=False") - ): - df = session.read_gbq("my-project.my_dataset.my_table") - - assert "1999-01-02T03:04:05.678901" in df.sql - - -def test_read_gbq_cached_table_doesnt_warn_for_anonymous_tables_and_doesnt_include_time_travel(): - session = mocks.create_bigquery_session() - table_ref = google.cloud.bigquery.TableReference( - google.cloud.bigquery.DatasetReference("my-project", "_anonymous_dataset"), - "my_table", - ) - table = google.cloud.bigquery.Table( - table_ref, (google.cloud.bigquery.SchemaField("col", "INTEGER"),) - ) - table._properties["location"] = session._location - table._properties["numRows"] = "1000000000" - table._properties["location"] = session._location - table._properties["type"] = "TABLE" - session._loader._df_snapshot[str(table_ref)] = ( - datetime.datetime(1999, 1, 2, 3, 4, 5, 678901, tzinfo=datetime.timezone.utc), - bq_data.GbqNativeTable.from_table(table), - ) - - session.bqclient._query_and_wait_bigframes = mock.MagicMock( - return_value=({"total_count": 3, "distinct_count": 2},) - ) - session.bqclient.get_table.return_value = table - - with warnings.catch_warnings(): - warnings.simplefilter( - "error", category=bigframes.exceptions.TimeTravelCacheWarning - ) - df = session.read_gbq("my-project._anonymous_dataset.my_table") - - assert "1999-01-02T03:04:05.678901" not in df.sql - - -@pytest.mark.parametrize("table", CLUSTERED_OR_PARTITIONED_TABLES) -def test_default_index_warning_raised_by_read_gbq(table): - """Because of the windowing operation to create a default index, row - filters can't push down to the clustering column. - - Raise an exception in this case so that the user is directed to supply a - unique index column or filter if possible. - - See internal issue 335727141. - """ - table = copy.deepcopy(table) - bqclient = mock.create_autospec(google.cloud.bigquery.Client, instance=True) - bqclient.project = "test-project" - bqclient.get_table.return_value = table - bqclient._query_and_wait_bigframes.return_value = ( - {"total_count": 3, "distinct_count": 2}, - ) - session = mocks.create_bigquery_session( - bqclient=bqclient, - # DefaultIndexWarning is only relevant for strict mode. - ordering_mode="strict", - ) - table._properties["location"] = session._location - - with pytest.warns(bigframes.exceptions.DefaultIndexWarning): - session.read_gbq("my-project.my_dataset.my_table") - - -@pytest.mark.parametrize("table", CLUSTERED_OR_PARTITIONED_TABLES) -def test_default_index_warning_not_raised_by_read_gbq_index_col_sequential_int64( - table, -): - """Because of the windowing operation to create a default index, row - filters can't push down to the clustering column. - - Allow people to use the default index only if they explicitly request it. - - See internal issue 335727141. - """ - table = copy.deepcopy(table) - bqclient = mock.create_autospec(google.cloud.bigquery.Client, instance=True) - bqclient.project = "test-project" - bqclient.get_table.return_value = table - bqclient._query_and_wait_bigframes.return_value = ( - {"total_count": 4, "distinct_count": 3}, - ) - session = mocks.create_bigquery_session( - bqclient=bqclient, - # DefaultIndexWarning is only relevant for strict mode. - ordering_mode="strict", - ) - table._properties["location"] = session._location - - # No warnings raised because we set the option allowing the default indexes. - with warnings.catch_warnings(): - warnings.simplefilter("error", bigframes.exceptions.DefaultIndexWarning) - df = session.read_gbq( - "my-project.my_dataset.my_table", - index_col=bigframes.enums.DefaultIndexKind.SEQUENTIAL_INT64, - ) - - # We expect a window operation because we specificaly requested a sequential index and named it. - df.index.name = "named_index" - generated_sql = df.sql.casefold() - assert "OVER".casefold() in generated_sql - assert "ROW_NUMBER()".casefold() in generated_sql - - -@pytest.mark.parametrize( - ("total_count", "distinct_count"), - ( - (0, 0), - (123, 123), - # Should still have a positive effect, even if the index is not unique. - (123, 111), - ), -) -@pytest.mark.parametrize("table", CLUSTERED_OR_PARTITIONED_TABLES) -def test_default_index_warning_not_raised_by_read_gbq_index_col_columns( - total_count, - distinct_count, - table, -): - table = copy.deepcopy(table) - table.schema = ( - google.cloud.bigquery.SchemaField("idx_1", "INT64"), - google.cloud.bigquery.SchemaField("idx_2", "INT64"), - google.cloud.bigquery.SchemaField("col_1", "INT64"), - google.cloud.bigquery.SchemaField("col_2", "INT64"), - ) - - bqclient = mock.create_autospec(google.cloud.bigquery.Client, instance=True) - bqclient.project = "test-project" - bqclient.get_table.return_value = table - bqclient._query_and_wait_bigframes.return_value = ( - {"total_count": total_count, "distinct_count": distinct_count}, - ) - session = mocks.create_bigquery_session( - bqclient=bqclient, - table_schema=table.schema, - # DefaultIndexWarning is only relevant for strict mode. - ordering_mode="strict", - ) - table._properties["location"] = session._location - - # No warning raised because there are columns to use as the index. - with warnings.catch_warnings(): - warnings.simplefilter("error", bigframes.exceptions.DefaultIndexWarning) - df = session.read_gbq( - "my-project.my_dataset.my_table", index_col=("idx_1", "idx_2") - ) - - # There should be no analytic operators to prevent row filtering pushdown. - assert "OVER" not in df.sql - assert tuple(df.index.names) == ("idx_1", "idx_2") - - -@pytest.mark.parametrize("table", CLUSTERED_OR_PARTITIONED_TABLES) -def test_default_index_warning_not_raised_by_read_gbq_primary_key(table): - """If a primary key is set on the table, we use that as the index column - by default, no error should be raised in this case. - - See internal issue 335727141. - """ - table = copy.deepcopy(table) - table.schema = ( - google.cloud.bigquery.SchemaField("pk_1", "INT64"), - google.cloud.bigquery.SchemaField("pk_2", "INT64"), - google.cloud.bigquery.SchemaField("col_1", "INT64"), - google.cloud.bigquery.SchemaField("col_2", "INT64"), - ) - - # TODO(b/305264153): use setter for table_constraints in client library - # when available. - table._properties["tableConstraints"] = { - "primaryKey": { - "columns": ["pk_1", "pk_2"], - }, - } - bqclient = mock.create_autospec(google.cloud.bigquery.Client, instance=True) - bqclient.project = "test-project" - bqclient.get_table.return_value = table - session = mocks.create_bigquery_session( - bqclient=bqclient, - table_schema=table.schema, - # DefaultIndexWarning is only relevant for strict mode. - ordering_mode="strict", - ) - table._properties["location"] = session._location - - # No warning raised because there is a primary key to use as the index. - with warnings.catch_warnings(): - warnings.simplefilter("error", bigframes.exceptions.DefaultIndexWarning) - df = session.read_gbq("my-project.my_dataset.my_table") - - # There should be no analytic operators to prevent row filtering pushdown. - assert "OVER" not in df.sql - assert tuple(df.index.names) == ("pk_1", "pk_2") - - @pytest.mark.parametrize( "not_found_table_id", [("unknown.dataset.table"), ("project.unknown.table"), ("project.dataset.unknown")], ) -def test_read_gbq_not_found_tables(not_found_table_id): +def test_read_gdb_not_found_tables(not_found_table_id): bqclient = mock.create_autospec(google.cloud.bigquery.Client, instance=True) bqclient.project = "test-project" bqclient.get_table.side_effect = google.api_core.exceptions.NotFound( "table not found" ) - session = mocks.create_bigquery_session(bqclient=bqclient) + session = resources.create_bigquery_session(bqclient=bqclient) with pytest.raises(google.api_core.exceptions.NotFound): session.read_gbq(not_found_table_id) -@pytest.mark.parametrize( - ("api_name", "query_or_table"), - [ - ("read_gbq", "project.dataset.table"), - ("read_gbq_table", "project.dataset.table"), - ("read_gbq", "SELECT * FROM project.dataset.table"), - ("read_gbq_query", "SELECT * FROM project.dataset.table"), - ], - ids=[ - "read_gbq_on_table", - "read_gbq_table", - "read_gbq_on_query", - "read_gbq_query", - ], -) -def test_read_gbq_external_table_no_drive_access(api_name, query_or_table): - session = mocks.create_bigquery_session() - session_query_mock = session.bqclient.query - - def query_mock(query, *args, **kwargs): - if query.lstrip().startswith("SELECT *"): - raise google.api_core.exceptions.Forbidden( - "Access Denied: BigQuery BigQuery: Permission denied while getting Drive credentials." - ) - - return session_query_mock(query, *args, **kwargs) - - session.bqclient.query_and_wait = query_mock - session.bqclient._query_and_wait_bigframes = query_mock - - def get_table_mock(table_ref): - table = google.cloud.bigquery.Table( - table_ref, (google.cloud.bigquery.SchemaField("col", "INTEGER"),) - ) - table._properties["numRows"] = 1000000000 - table._properties["location"] = session._location - return table - - session.bqclient.get_table = get_table_mock - - api = getattr(session, api_name) - with pytest.raises( - google.api_core.exceptions.Forbidden, - match="Check https://cloud.google.com/bigquery/docs/query-drive-data#Google_Drive_permissions.", - ): - api(query_or_table).to_pandas() - - @mock.patch.dict(os.environ, {}, clear=True) def test_session_init_fails_with_no_project(): with pytest.raises( @@ -526,36 +57,3 @@ def test_session_init_fails_with_no_project(): credentials=mock.Mock(spec=google.auth.credentials.Credentials) ) ) - - -def test_session_init_warns_if_bf_version_is_too_old(monkeypatch): - release_date = datetime.datetime.strptime(version.__release_date__, "%Y-%m-%d") - current_date = release_date + datetime.timedelta(days=366) - - class FakeDatetime(datetime.datetime): - @classmethod - def today(cls): - return current_date - - monkeypatch.setattr(datetime, "datetime", FakeDatetime) - - with pytest.warns(bigframes.exceptions.ObsoleteVersionWarning): - mocks.create_bigquery_session() - - -@mock.patch("bigframes.constants.MAX_INLINE_BYTES", 1) -def test_read_pandas_inline_exceeds_limit_raises_error(): - session = mocks.create_bigquery_session() - pd_df = pd.DataFrame([[1, 2, 3], [4, 5, 6]]) - with pytest.raises( - ValueError, - match=r"DataFrame size \(.* bytes\) exceeds the maximum allowed for inline data \(1 bytes\)\.", - ): - session.read_pandas(pd_df, write_engine="bigquery_inline") - - -def test_read_pandas_inline_w_interval_type_raises_error(): - session = mocks.create_bigquery_session() - df = pd.DataFrame(pd.arrays.IntervalArray.from_breaks([0, 10, 20, 30, 40, 50])) - with pytest.raises(TypeError): - session.read_pandas(df, write_engine="bigquery_inline") diff --git a/tests/unit/session/test_time.py b/tests/unit/session/test_time.py deleted file mode 100644 index 39a231c3cef..00000000000 --- a/tests/unit/session/test_time.py +++ /dev/null @@ -1,70 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import datetime -import unittest.mock as mock - -import google.cloud.bigquery -import pytest - -import bigframes.session.time - -INITIAL_BQ_TIME = datetime.datetime( - year=2020, - month=4, - day=24, - hour=8, - minute=55, - second=29, - tzinfo=datetime.timezone.utc, -) - - -@pytest.fixture() -def bq_client(): - bqclient = mock.create_autospec(google.cloud.bigquery.Client, instance=True) - - def query_and_wait_mock(query, *args, **kwargs): - if query.startswith("SELECT CURRENT_TIMESTAMP()"): - return iter([[INITIAL_BQ_TIME]]) - else: - return ValueError(f"mock cannot handle query : {query}") - - bqclient.query_and_wait = query_and_wait_mock - return bqclient - - -def test_bqsyncedclock_get_time(bq_client): - freezegun = pytest.importorskip("freezegun") - - # this initial local time is actually irrelevant, only the ticks matter - initial_local_datetime = datetime.datetime( - year=1, month=7, day=12, hour=15, minute=6, second=3 - ) - - with freezegun.freeze_time(initial_local_datetime) as frozen_datetime: - clock = bigframes.session.time.BigQuerySyncedClock(bq_client) - - t1 = clock.get_time() - assert t1 == INITIAL_BQ_TIME - - frozen_datetime.tick(datetime.timedelta(seconds=3)) - t2 = clock.get_time() - assert t2 == INITIAL_BQ_TIME + datetime.timedelta(seconds=3) - - frozen_datetime.tick(datetime.timedelta(seconds=23529385)) - t3 = clock.get_time() - assert t3 == INITIAL_BQ_TIME + datetime.timedelta( - seconds=3 - ) + datetime.timedelta(seconds=23529385) diff --git a/tests/unit/test_clients.py b/tests/unit/test_clients.py index 08d111b8662..f89cc21397c 100644 --- a/tests/unit/test_clients.py +++ b/tests/unit/test_clients.py @@ -12,84 +12,38 @@ # See the License for the specific language governing permissions and # limitations under the License. -from unittest import mock - import pytest -from google.cloud import bigquery_connection_v1, resourcemanager_v3 -from google.iam.v1 import policy_pb2 from bigframes import clients -def test_get_canonical_bq_connection_id_connection_id_only(): - connection_id = clients.get_canonical_bq_connection_id( +def test_get_connection_name_full_connection_id(): + connection_name = clients.BqConnectionManager.resolve_full_connection_name( "connection-id", default_project="default-project", default_location="us" ) - assert connection_id == "default-project.us.connection-id" + assert connection_name == "default-project.us.connection-id" -def test_get_canonical_bq_connection_id_location_and_connection_id(): - connection_id = clients.get_canonical_bq_connection_id( +def test_get_connection_name_full_location_connection_id(): + connection_name = clients.BqConnectionManager.resolve_full_connection_name( "eu.connection-id", default_project="default-project", default_location="us" ) - assert connection_id == "default-project.eu.connection-id" + assert connection_name == "default-project.eu.connection-id" -def test_get_canonical_bq_connection_id_already_canonical(): - connection_id = clients.get_canonical_bq_connection_id( +def test_get_connection_name_full_all(): + connection_name = clients.BqConnectionManager.resolve_full_connection_name( "my-project.eu.connection-id", default_project="default-project", default_location="us", ) - assert connection_id == "my-project.eu.connection-id" + assert connection_name == "my-project.eu.connection-id" -def test_get_canonical_bq_connection_id_invalid(): - with pytest.raises(ValueError, match="Invalid connection id format"): - clients.get_canonical_bq_connection_id( +def test_get_connection_name_full_raise_value_error(): + with pytest.raises(ValueError): + clients.BqConnectionManager.resolve_full_connection_name( "my-project.eu.connection-id.extra_field", default_project="default-project", default_location="us", ) - - -def test_get_canonical_bq_connection_id_valid_path(): - connection_id = clients.get_canonical_bq_connection_id( - "projects/project_id/locations/northamerica-northeast1/connections/connection-id", - default_project="default-project", - default_location="us", - ) - assert connection_id == "project_id.northamerica-northeast1.connection-id" - - -def test_get_canonical_bq_connection_id_invalid_path(): - with pytest.raises(ValueError, match="Invalid connection id format"): - clients.get_canonical_bq_connection_id( - "/projects/project_id/locations/northamerica-northeast1/connections/connection-id", - default_project="default-project", - default_location="us", - ) - - -def test_ensure_iam_binding(): - bq_connection_client = mock.create_autospec( - bigquery_connection_v1.ConnectionServiceClient, instance=True - ) - resource_manager_client = mock.create_autospec( - resourcemanager_v3.ProjectsClient, instance=True - ) - resource_manager_client.get_iam_policy.return_value = policy_pb2.Policy( - bindings=[ - policy_pb2.Binding( - role="roles/test.role1", members=["serviceAccount:serviceAccount1"] - ) - ] - ) - bq_connection_manager = clients.BqConnectionManager( - bq_connection_client, resource_manager_client - ) - bq_connection_manager._IAM_WAIT_SECONDS = 0 # no need to wait in test - bq_connection_manager._ensure_iam_binding( - "test-project", "serviceAccount2", "roles/test.role2" - ) - resource_manager_client.set_iam_policy.assert_called_once() diff --git a/tests/unit/test_col.py b/tests/unit/test_col.py deleted file mode 100644 index c8caf9136c0..00000000000 --- a/tests/unit/test_col.py +++ /dev/null @@ -1,269 +0,0 @@ -# Copyright 2026 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import operator -import pathlib -from typing import Generator - -import numpy as np -import pandas as pd -import pytest - -import bigframes -import bigframes.pandas as bpd -from bigframes.testing.utils import assert_frame_equal, convert_pandas_dtypes - -pytest.importorskip("polars") -pytest.importorskip("pandas", minversion="3.0.0") - - -CURRENT_DIR = pathlib.Path(__file__).parent -DATA_DIR = CURRENT_DIR.parent / "data" - - -@pytest.fixture(scope="module", autouse=True) -def session() -> Generator[bigframes.Session, None, None]: - import bigframes.core.global_session - from bigframes.testing import polars_session - - session = polars_session.TestSession() - with bigframes.core.global_session._GlobalSessionContext(session): - yield session - - -@pytest.fixture(scope="module") -def scalars_pandas_df_index() -> pd.DataFrame: - """pd.DataFrame pointing at test data.""" - - df = pd.read_json( - DATA_DIR / "scalars.jsonl", - lines=True, - ) - convert_pandas_dtypes(df, bytes_col=True) - - df = df.set_index("rowindex", drop=False) - df.index.name = None - return df.set_index("rowindex").sort_index() - - -@pytest.fixture(scope="module") -def scalars_df_index( - session: bigframes.Session, scalars_pandas_df_index -) -> bpd.DataFrame: - return session.read_pandas(scalars_pandas_df_index) - - -@pytest.fixture(scope="module") -def scalars_df_2_index( - session: bigframes.Session, scalars_pandas_df_index -) -> bpd.DataFrame: - return session.read_pandas(scalars_pandas_df_index) - - -@pytest.fixture(scope="module") -def scalars_dfs( - scalars_df_index, - scalars_pandas_df_index, -): - return scalars_df_index, scalars_pandas_df_index - - -@pytest.mark.parametrize( - ("op",), - [ - (operator.invert,), - ], -) -def test_pd_col_unary_operators(scalars_dfs, op): - scalars_df, scalars_pandas_df = scalars_dfs - bf_kwargs = { - "result": op(bpd.col("bool_col")), - } - pd_kwargs = { - "result": op(pd.col("bool_col")), # type: ignore - } - df = scalars_df.assign(**bf_kwargs) - - bf_result = df.to_pandas() - pd_result = scalars_pandas_df.assign(**pd_kwargs) - - assert_frame_equal(bf_result, pd_result) - - -@pytest.mark.parametrize( - ("op"), - [ - (lambda x: x.sum()), - (lambda x: x.mean()), - (lambda x: x.min()), - (lambda x: x.max()), - (lambda x: x.std()), - (lambda x: x.var()), - ], - ids=[ - "sum", - "mean", - "min", - "max", - "std", - "var", - ], -) -def test_pd_col_aggregate_op(scalars_dfs, op): - scalars_df, scalars_pandas_df = scalars_dfs - bf_kwargs = { - "result": op(bpd.col("float64_col")), - } - pd_kwargs = { - "result": op(pd.col("float64_col")), # type: ignore - } - df = scalars_df.assign(**bf_kwargs) - - bf_result = df.to_pandas() - pd_result = scalars_pandas_df.assign(**pd_kwargs) - - assert_frame_equal(bf_result, pd_result) - - -def test_pd_col_aggregate_of_aggregate(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - bf_kwargs = { - "result": (bpd.col("int64_col") - bpd.col("int64_col").mean()).mean(), - } - pd_kwargs = { - "result": (pd.col("int64_col") - pd.col("int64_col").mean()).mean(), # type: ignore - } - df = scalars_df.assign(**bf_kwargs) - - bf_result = df.to_pandas() - pd_result = scalars_pandas_df.assign(**pd_kwargs) - - assert_frame_equal(bf_result, pd_result) - - -@pytest.mark.parametrize( - ("op",), - [ - (operator.add,), - (operator.sub,), - (operator.mul,), - (operator.truediv,), - (operator.floordiv,), - (operator.gt,), - (operator.lt,), - (operator.ge,), - (operator.le,), - (operator.eq,), - (operator.mod,), - ], -) -def test_pd_col_binary_operators(scalars_dfs, op): - scalars_df, scalars_pandas_df = scalars_dfs - bf_kwargs = { - "result": op(bpd.col("float64_col"), 2.4), - "reverse_result": op(2.4, bpd.col("float64_col")), - } - pd_kwargs = { - "result": op(pd.col("float64_col"), 2.4), # type: ignore - "reverse_result": op(2.4, pd.col("float64_col")), # type: ignore - } - df = scalars_df.assign(**bf_kwargs) - - bf_result = df.to_pandas() - pd_result = scalars_pandas_df.assign(**pd_kwargs) - - assert_frame_equal(bf_result, pd_result) - - -@pytest.mark.parametrize( - ("op",), - [ - (operator.and_,), - (operator.or_,), - (operator.xor,), - ], -) -def test_pd_col_binary_bool_operators(scalars_dfs, op): - scalars_df, scalars_pandas_df = scalars_dfs - bf_kwargs = { - "result": op(bpd.col("bool_col"), True), - "reverse_result": op(False, bpd.col("bool_col")), - } - pd_kwargs = { - "result": op(pd.col("bool_col"), True), # type: ignore - "reverse_result": op(False, pd.col("bool_col")), # type: ignore - } - df = scalars_df.assign(**bf_kwargs) - - bf_result = df.to_pandas() - pd_result = scalars_pandas_df.assign(**pd_kwargs) - - assert_frame_equal(bf_result, pd_result) - - -def test_loc_with_pd_col(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - - bf_result = scalars_df.loc[bpd.col("float64_col") > 4].to_pandas() - pd_result = scalars_pandas_df.loc[pd.col("float64_col") > 4] # type: ignore - - assert_frame_equal(bf_result, pd_result) - - -def test_getitem_with_pd_col(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - - bf_result = scalars_df[bpd.col("float64_col") > 4].to_pandas() - pd_result = scalars_pandas_df[pd.col("float64_col") > 4] # type: ignore - - assert_frame_equal(bf_result, pd_result) - - -def test_col_str_accessor(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - - bf_result = scalars_df.assign(result=bpd.col("string_col").str.lower()).to_pandas() - pd_result = scalars_pandas_df.assign(result=pd.col("string_col").str.lower()) # type: ignore - - assert_frame_equal(bf_result, pd_result) - - -def test_col_dt_accessor(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - - bf_result = scalars_df.assign(result=bpd.col("date_col").dt.year).to_pandas() - pd_result = scalars_pandas_df.assign(result=pd.col("date_col").dt.year) # type: ignore - - # int64[pyarrow] vs Int64 - assert_frame_equal(bf_result, pd_result, check_dtype=False) - - -def test_col_numpy_ufunc(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - - bf_result = scalars_df.assign( - sqrt=np.sqrt(bpd.col("float64_col")), # type: ignore - add_const=np.add(bpd.col("float64_col"), 2.4), # type: ignore - radd_const=np.add(2.4, bpd.col("float64_col")), # type: ignore - add_cols=np.add(bpd.col("float64_col"), bpd.col("int64_col")), # type: ignore - ).to_pandas() - pd_result = scalars_pandas_df.assign( - sqrt=np.sqrt(pd.col("float64_col")), # type: ignore - add_const=np.add(pd.col("float64_col"), 2.4), # type: ignore - radd_const=np.add(2.4, pd.col("float64_col")), # type: ignore - add_cols=np.add(pd.col("float64_col"), pd.col("int64_col")), # type: ignore - ) - - # int64[pyarrow] vs Int64 - assert_frame_equal(bf_result, pd_result, check_dtype=False) diff --git a/tests/unit/test_compute_options.py b/tests/unit/test_compute_options.py new file mode 100644 index 00000000000..499a0a5fefa --- /dev/null +++ b/tests/unit/test_compute_options.py @@ -0,0 +1,30 @@ +# Copyright 2023 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import bigframes as bf + +from . import resources + + +def test_maximum_bytes_option(): + session = resources.create_bigquery_session() + num_query_calls = 0 + with bf.option_context("compute.maximum_bytes_billed", 10000): + # clear initial method calls + session.bqclient.method_calls = [] + session._start_query("query") + for call in session.bqclient.method_calls: + _, _, kwargs = call + num_query_calls += 1 + assert kwargs["job_config"].maximum_bytes_billed == 10000 + assert num_query_calls > 0 diff --git a/tests/unit/test_constants.py b/tests/unit/test_constants.py deleted file mode 100644 index 4e11419077f..00000000000 --- a/tests/unit/test_constants.py +++ /dev/null @@ -1,23 +0,0 @@ -# Copyright 2023 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import bigframes_vendored.constants - -import bigframes.version - - -def test_feedback_link_includes_version(): - version = bigframes.version.__version__ - assert len(version) > 0 - assert version in bigframes_vendored.constants.FEEDBACK_LINK diff --git a/tests/unit/test_core.py b/tests/unit/test_core.py new file mode 100644 index 00000000000..d9672b2635b --- /dev/null +++ b/tests/unit/test_core.py @@ -0,0 +1,221 @@ +# Copyright 2023 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import ibis.expr.types as ibis_types +import pandas + +import bigframes.core as core +import bigframes.core.ordering +import bigframes.operations as ops +import bigframes.operations.aggregations as agg_ops + +from . import resources + + +def test_arrayvalue_constructor_from_ibis_table_adds_all_columns(): + session = resources.create_pandas_session( + { + "test_table": pandas.DataFrame( + { + "col1": [1, 2, 3], + "not_included": [True, False, True], + "col2": ["a", "b", "c"], + "col3": [0.1, 0.2, 0.3], + } + ) + } + ) + ibis_table = session.ibis_client.table("test_table") + columns = (ibis_table["col1"], ibis_table["col2"], ibis_table["col3"]) + ordering = bigframes.core.ordering.ExpressionOrdering( + tuple([core.OrderingColumnReference("col1")]), + total_ordering_columns=frozenset(["col1"]), + ) + actual = core.ArrayValue.from_ibis( + session=session, + table=ibis_table, + columns=columns, + ordering=ordering, + hidden_ordering_columns=(), + ) + assert actual.compile()._table is ibis_table + assert len(actual.column_ids) == 3 + + +def test_arrayvalue_with_get_column_type(): + value = resources.create_arrayvalue( + pandas.DataFrame( + { + "col1": [1, 2, 3], + "col2": ["a", "b", "c"], + "col3": [0.1, 0.2, 0.3], + } + ), + total_ordering_columns=["col1"], + ) + col1_type = value.get_column_type("col1") + col2_type = value.get_column_type("col2") + col3_type = value.get_column_type("col3") + assert isinstance(col1_type, pandas.Int64Dtype) + assert isinstance(col2_type, pandas.StringDtype) + assert isinstance(col3_type, pandas.Float64Dtype) + + +def test_arrayvalue_with_get_column(): + value = resources.create_arrayvalue( + pandas.DataFrame( + { + "col1": [1, 2, 3], + "col2": ["a", "b", "c"], + "col3": [0.1, 0.2, 0.3], + } + ), + total_ordering_columns=["col1"], + ) + col1 = value.compile()._get_ibis_column("col1") + assert isinstance(col1, ibis_types.Value) + assert col1.get_name() == "col1" + assert col1.type().is_int64() + + +def test_arrayvalues_to_ibis_expr_with_get_column(): + value = resources.create_arrayvalue( + pandas.DataFrame( + { + "col1": [1, 2, 3], + "col2": ["a", "b", "c"], + "col3": [0.1, 0.2, 0.3], + } + ), + total_ordering_columns=["col1"], + ) + expr = value.compile()._get_ibis_column("col1") + assert expr.get_name() == "col1" + assert expr.type().is_int64() + + +def test_arrayvalues_to_ibis_expr_with_concat(): + value = resources.create_arrayvalue( + pandas.DataFrame( + { + "col1": [1, 2, 3], + "col2": ["a", "b", "c"], + "col3": [0.1, 0.2, 0.3], + } + ), + total_ordering_columns=["col1"], + ) + expr = value.concat([value]) + actual = expr.compile()._to_ibis_expr("unordered") + assert len(actual.columns) == 3 + # TODO(ashleyxu, b/299631930): test out the union expression + assert actual.columns[0] == "column_0" + assert actual.columns[1] == "column_1" + assert actual.columns[2] == "column_2" + + +def test_arrayvalues_to_ibis_expr_with_project_unary_op(): + value = resources.create_arrayvalue( + pandas.DataFrame( + { + "col1": [1, 2, 3], + "col2": ["a", "b", "c"], + "col3": [0.1, 0.2, 0.3], + } + ), + total_ordering_columns=["col1"], + ) + expr = value.project_unary_op("col1", ops.AsTypeOp("string")).compile() + assert value.compile().columns[0].type().is_int64() + assert expr.columns[0].type().is_string() + + +def test_arrayvalues_to_ibis_expr_with_project_binary_op(): + value = resources.create_arrayvalue( + pandas.DataFrame( + { + "col1": [1, 2, 3], + "col2": [0.2, 0.3, 0.4], + "col3": [0.1, 0.2, 0.3], + } + ), + total_ordering_columns=["col1"], + ) + expr = value.project_binary_op("col2", "col3", ops.add_op, "col4").compile() + assert expr.columns[3].type().is_float64() + actual = expr._to_ibis_expr("unordered") + assert len(expr.columns) == 4 + assert actual.columns[3] == "col4" + + +def test_arrayvalues_to_ibis_expr_with_project_ternary_op(): + value = resources.create_arrayvalue( + pandas.DataFrame( + { + "col1": [1, 2, 3], + "col2": [0.2, 0.3, 0.4], + "col3": [True, False, False], + "col4": [0.1, 0.2, 0.3], + } + ), + total_ordering_columns=["col1"], + ) + expr = value.project_ternary_op( + "col2", "col3", "col4", ops.where_op, "col5" + ).compile() + assert expr.columns[4].type().is_float64() + actual = expr._to_ibis_expr("unordered") + assert len(expr.columns) == 5 + assert actual.columns[4] == "col5" + + +def test_arrayvalue_to_ibis_expr_with_aggregate(): + value = resources.create_arrayvalue( + pandas.DataFrame( + { + "col1": [1, 2, 3], + "col2": ["a", "b", "c"], + "col3": [0.1, 0.2, 0.3], + } + ), + total_ordering_columns=["col1"], + ) + expr = value.aggregate( + aggregations=(("col1", agg_ops.sum_op, "col4"),), + by_column_ids=["col1"], + dropna=False, + ).compile() + actual = expr._to_ibis_expr("unordered") + assert len(expr.columns) == 2 + assert actual.columns[0] == "col1" + assert actual.columns[1] == "col4" + assert expr.columns[1].type().is_int64() + + +def test_arrayvalue_to_ibis_expr_with_corr_aggregate(): + value = resources.create_arrayvalue( + pandas.DataFrame( + { + "col1": [1, 2, 3], + "col2": ["a", "b", "c"], + "col3": [0.1, 0.2, 0.3], + } + ), + total_ordering_columns=["col1"], + ) + expr = value.corr_aggregate(corr_aggregations=[("col1", "col3", "col4")]).compile() + actual = expr._to_ibis_expr("unordered") + assert len(expr.columns) == 1 + assert actual.columns[0] == "col4" + assert expr.columns[0].type().is_float64() diff --git a/tests/unit/test_daemon.py b/tests/unit/test_daemon.py deleted file mode 100644 index 6b3acd7d7dc..00000000000 --- a/tests/unit/test_daemon.py +++ /dev/null @@ -1,42 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import datetime -import time -from unittest.mock import MagicMock - -from bigframes.session.bigquery_session import RecurringTaskDaemon - - -def test_recurring_task_daemon_calls(): - mock_task = MagicMock() - daemon = RecurringTaskDaemon( - task=mock_task, frequency=datetime.timedelta(seconds=0.1) - ) - daemon.start() - time.sleep(1.0) - daemon.stop() - time.sleep(0.5) - # be lenient, but number of calls should be in this ballpark regardless of scheduling hiccups - assert mock_task.call_count > 6 - assert mock_task.call_count < 12 - - -def test_recurring_task_daemon_never_started(): - mock_task = MagicMock() - _ = RecurringTaskDaemon( - task=mock_task, frequency=datetime.timedelta(seconds=0.0001) - ) - time.sleep(0.1) - assert mock_task.call_count == 0 diff --git a/tests/unit/test_dataframe.py b/tests/unit/test_dataframe.py index d045bf7c3fc..17a82908893 100644 --- a/tests/unit/test_dataframe.py +++ b/tests/unit/test_dataframe.py @@ -13,115 +13,20 @@ # limitations under the License. import google.cloud.bigquery -import pandas as pd import pytest -import bigframes.dataframe -import bigframes.session -from bigframes.testing import mocks - - -def test_dataframe_dropna_axis_1_subset_not_implememented( - monkeypatch: pytest.MonkeyPatch, -): - dataframe = mocks.create_dataframe(monkeypatch) - - with pytest.raises(NotImplementedError, match="subset"): - dataframe.dropna(axis=1, subset=["col1", "col2"]) - - -def test_dataframe_repr_with_uninitialized_object(): - """Ensures DataFrame.__init__ can be paused in a visual debugger without crashing. - - Regression test for https://github.com/googleapis/python-bigquery-dataframes/issues/728 - """ - # Avoid calling __init__ to simulate pausing __init__ in a debugger. - # https://stackoverflow.com/a/6384982/101923 - dataframe = bigframes.dataframe.DataFrame.__new__(bigframes.dataframe.DataFrame) - got = repr(dataframe) - assert "DataFrame" in got - - -@pytest.mark.parametrize( - "rule", - [ - pd.DateOffset(weeks=1), - pd.Timedelta(hours=8), - # According to - # https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.resample.html - # these all default to "right" for closed and label, which isn't yet supported. - "ME", - "YE", - "QE", - "BME", - "BA", - "BQE", - "W", - ], -) -def test_dataframe_rule_not_implememented( - monkeypatch: pytest.MonkeyPatch, - rule, -): - dataframe = mocks.create_dataframe(monkeypatch) - - with pytest.raises(NotImplementedError, match="rule"): - dataframe.resample(rule=rule) - - -def test_dataframe_closed_not_implememented( - monkeypatch: pytest.MonkeyPatch, -): - dataframe = mocks.create_dataframe(monkeypatch) - - with pytest.raises(NotImplementedError, match="Only closed='left'"): - dataframe.resample(rule="1d", closed="right") - - -def test_dataframe_label_not_implememented( - monkeypatch: pytest.MonkeyPatch, -): - dataframe = mocks.create_dataframe(monkeypatch) - - with pytest.raises(NotImplementedError, match="Only label='left'"): - dataframe.resample(rule="1d", label="right") - - -@pytest.mark.parametrize( - "origin", - [ - "end", - "end_day", - ], -) -def test_dataframe_origin_not_implememented( - monkeypatch: pytest.MonkeyPatch, - origin, -): - dataframe = mocks.create_dataframe(monkeypatch) - - with pytest.raises(NotImplementedError, match="origin"): - dataframe.resample(rule="1d", origin=origin) - - -def test_dataframe_setattr_with_uninitialized_object(): - """Ensures DataFrame can be subclassed without trying to set attributes as columns.""" - # Avoid calling __init__ since it might be called later in a subclass. - # https://stackoverflow.com/a/6384982/101923 - dataframe = bigframes.dataframe.DataFrame.__new__(bigframes.dataframe.DataFrame) - dataframe.lineage = "my-test-value" - assert dataframe.lineage == "my-test-value" # Should just be a regular attribute. +from . import resources def test_dataframe_to_gbq_invalid_destination(monkeypatch: pytest.MonkeyPatch): - dataframe = mocks.create_dataframe(monkeypatch) + dataframe = resources.create_dataframe(monkeypatch) with pytest.raises(ValueError, match="no_dataset_or_project"): dataframe.to_gbq("no_dataset_or_project") def test_dataframe_to_gbq_invalid_if_exists(monkeypatch: pytest.MonkeyPatch): - dataframe = mocks.create_dataframe(monkeypatch) + dataframe = resources.create_dataframe(monkeypatch) with pytest.raises(ValueError, match="notreallyanoption"): # Even though the type is annotated with the literals we accept, users @@ -133,7 +38,7 @@ def test_dataframe_to_gbq_invalid_if_exists(monkeypatch: pytest.MonkeyPatch): def test_dataframe_to_gbq_invalid_if_exists_no_destination( monkeypatch: pytest.MonkeyPatch, ): - dataframe = mocks.create_dataframe(monkeypatch) + dataframe = resources.create_dataframe(monkeypatch) with pytest.raises(ValueError, match="append"): dataframe.to_gbq(if_exists="append") @@ -146,80 +51,9 @@ def test_dataframe_to_gbq_writes_to_anonymous_dataset( anonymous_dataset = google.cloud.bigquery.DatasetReference.from_string( anonymous_dataset_id ) - session = mocks.create_bigquery_session(anonymous_dataset=anonymous_dataset) - dataframe = mocks.create_dataframe(monkeypatch, session=session) + session = resources.create_bigquery_session(anonymous_dataset=anonymous_dataset) + dataframe = resources.create_dataframe(monkeypatch, session=session) destination = dataframe.to_gbq() assert destination.startswith(anonymous_dataset_id) - - -def test_dataframe_rename_columns(monkeypatch: pytest.MonkeyPatch): - dataframe = mocks.create_dataframe( - monkeypatch, data={"col1": [], "col2": [], "col3": []} - ) - assert dataframe.columns.to_list() == ["col1", "col2", "col3"] - renamed = dataframe.rename(columns={"col1": "a", "col2": "b", "col3": "c"}) - assert renamed.columns.to_list() == ["a", "b", "c"] - - -def test_dataframe_rename_columns_inplace_returns_none(monkeypatch: pytest.MonkeyPatch): - dataframe = mocks.create_dataframe( - monkeypatch, data={"col1": [], "col2": [], "col3": []} - ) - assert dataframe.columns.to_list() == ["col1", "col2", "col3"] - assert ( - dataframe.rename(columns={"col1": "a", "col2": "b", "col3": "c"}, inplace=True) - is None - ) - assert dataframe.columns.to_list() == ["a", "b", "c"] - - -def test_dataframe_rename_axis(monkeypatch: pytest.MonkeyPatch): - dataframe = mocks.create_dataframe( - monkeypatch, data={"index1": [], "index2": [], "col1": [], "col2": []} - ).set_index(["index1", "index2"]) - assert list(dataframe.index.names) == ["index1", "index2"] - renamed = dataframe.rename_axis(["a", "b"]) - assert list(renamed.index.names) == ["a", "b"] - - -def test_dataframe_rename_axis_inplace_returns_none(monkeypatch: pytest.MonkeyPatch): - dataframe = mocks.create_dataframe( - monkeypatch, data={"index1": [], "index2": [], "col1": [], "col2": []} - ).set_index(["index1", "index2"]) - assert list(dataframe.index.names) == ["index1", "index2"] - assert dataframe.rename_axis(["a", "b"], inplace=True) is None - assert list(dataframe.index.names) == ["a", "b"] - - -def test_dataframe_drop_columns_inplace_returns_none(monkeypatch: pytest.MonkeyPatch): - dataframe = mocks.create_dataframe( - monkeypatch, data={"col1": [1], "col2": [2], "col3": [3]} - ) - assert dataframe.columns.to_list() == ["col1", "col2", "col3"] - assert dataframe.drop(columns=["col1", "col3"], inplace=True) is None - assert dataframe.columns.to_list() == ["col2"] - - -def test_dataframe_drop_index_inplace_returns_none( - # Drop index depends on the actual data, not just metadata, so use the - # local engine for more robust testing. - polars_session: bigframes.session.Session, -): - dataframe = polars_session.read_pandas( - pd.DataFrame({"col1": [1, 2, 3], "index_col": [0, 1, 2]}).set_index("index_col") - ) - assert dataframe.index.to_list() == [0, 1, 2] - assert dataframe.drop(index=[0, 2], inplace=True) is None - assert dataframe.index.to_list() == [1] - - -def test_dataframe_drop_columns_returns_new_dataframe(monkeypatch: pytest.MonkeyPatch): - dataframe = mocks.create_dataframe( - monkeypatch, data={"col1": [1], "col2": [2], "col3": [3]} - ) - assert dataframe.columns.to_list() == ["col1", "col2", "col3"] - new_dataframe = dataframe.drop(columns=["col1", "col3"]) - assert dataframe.columns.to_list() == ["col1", "col2", "col3"] - assert new_dataframe.columns.to_list() == ["col2"] diff --git a/tests/unit/test_dataframe_io.py b/tests/unit/test_dataframe_io.py deleted file mode 100644 index f2c02413963..00000000000 --- a/tests/unit/test_dataframe_io.py +++ /dev/null @@ -1,56 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from unittest import mock - -import pytest - -from bigframes.testing import mocks - - -@pytest.fixture -def mock_df(monkeypatch: pytest.MonkeyPatch): - dataframe = mocks.create_dataframe(monkeypatch) - monkeypatch.setattr(dataframe, "to_pandas", mock.Mock()) - return dataframe - - -@pytest.mark.parametrize( - "api_name, kwargs", - [ - ("to_csv", {"allow_large_results": True}), - ("to_json", {"allow_large_results": True}), - ("to_numpy", {"allow_large_results": True}), - ("to_parquet", {"allow_large_results": True}), - ("to_dict", {"allow_large_results": True}), - ("to_excel", {"excel_writer": "abc", "allow_large_results": True}), - ("to_latex", {"allow_large_results": True}), - ("to_records", {"allow_large_results": True}), - ("to_string", {"allow_large_results": True}), - ("to_html", {"allow_large_results": True}), - ("to_markdown", {"allow_large_results": True}), - ("to_pickle", {"path": "abc", "allow_large_results": True}), - ("to_orc", {"allow_large_results": True}), - ], -) -def test_dataframe_to_pandas(mock_df, api_name, kwargs): - getattr(mock_df, api_name)(**kwargs) - mock_df.to_pandas.assert_called_once_with( - allow_large_results=kwargs["allow_large_results"] - ) - - -def test_to_gbq_if_exists_invalid(mock_df): - with pytest.raises(ValueError, match="Got invalid value 'invalid' for if_exists."): - mock_df.to_gbq("a.b.c", if_exists="invalid") diff --git a/tests/unit/test_dataframe_polars.py b/tests/unit/test_dataframe_polars.py deleted file mode 100644 index c2dc979b71e..00000000000 --- a/tests/unit/test_dataframe_polars.py +++ /dev/null @@ -1,4535 +0,0 @@ -# Copyright 2023 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import io -import operator -import pathlib -import tempfile -import typing -from typing import Generator, List, Tuple - -import numpy as np -import pandas as pd -import pandas.testing -import pytest - -import bigframes -import bigframes._config.display_options as display_options -import bigframes.core.indexes as bf_indexes -import bigframes.dataframe as dataframe -import bigframes.pandas as bpd -import bigframes.series as series -from bigframes.testing.utils import ( - assert_dfs_equivalent, - assert_frame_equal, - assert_series_equal, - assert_series_equivalent, - convert_pandas_dtypes, -) - -pytest.importorskip("polars") -pytest.importorskip("pandas", minversion="2.0.0") - -CURRENT_DIR = pathlib.Path(__file__).parent -DATA_DIR = CURRENT_DIR.parent / "data" - - -@pytest.fixture(scope="module", autouse=True) -def session() -> Generator[bigframes.Session, None, None]: - import bigframes.core.global_session - from bigframes.testing import polars_session - - session = polars_session.TestSession() - with bigframes.core.global_session._GlobalSessionContext(session): - yield session - - -@pytest.fixture(scope="module") -def scalars_pandas_df_index() -> pd.DataFrame: - """pd.DataFrame pointing at test data.""" - - df = pd.read_json( - DATA_DIR / "scalars.jsonl", - lines=True, - ) - convert_pandas_dtypes(df, bytes_col=True) - - df = df.set_index("rowindex", drop=False) - df.index.name = None - return df.set_index("rowindex").sort_index() - - -@pytest.fixture(scope="module") -def scalars_df_index( - session: bigframes.Session, scalars_pandas_df_index -) -> bpd.DataFrame: - return session.read_pandas(scalars_pandas_df_index) - - -@pytest.fixture(scope="module") -def scalars_df_2_index( - session: bigframes.Session, scalars_pandas_df_index -) -> bpd.DataFrame: - return session.read_pandas(scalars_pandas_df_index) - - -@pytest.fixture(scope="module") -def scalars_dfs( - scalars_df_index, - scalars_pandas_df_index, -): - return scalars_df_index, scalars_pandas_df_index - - -def test_df_construct_copy(scalars_dfs): - columns = ["int64_col", "string_col", "float64_col"] - scalars_df, scalars_pandas_df = scalars_dfs - # Make the mapping from label to col_id non-trivial - bf_df = scalars_df.copy() - bf_df["int64_col"] = bf_df["int64_col"] / 2 - pd_df = scalars_pandas_df.copy() - pd_df["int64_col"] = pd_df["int64_col"] / 2 - - bf_result = dataframe.DataFrame(bf_df, columns=columns).to_pandas() - - pd_result = pd.DataFrame(pd_df, columns=columns) - pandas.testing.assert_frame_equal(bf_result, pd_result) - - -def test_df_construct_pandas_default(scalars_dfs): - # This should trigger the inlined codepath - columns = [ - "int64_too", - "int64_col", - "float64_col", - "bool_col", - "string_col", - "date_col", - "datetime_col", - "numeric_col", - "float64_col", - "time_col", - "timestamp_col", - ] - _, scalars_pandas_df = scalars_dfs - bf_result = dataframe.DataFrame(scalars_pandas_df, columns=columns).to_pandas() - pd_result = pd.DataFrame(scalars_pandas_df, columns=columns) - pandas.testing.assert_frame_equal(bf_result, pd_result) - - -def test_df_construct_structs(session): - pd_frame = pd.Series( - [ - {"version": 1, "project": "pandas"}, - {"version": 2, "project": "pandas"}, - {"version": 1, "project": "numpy"}, - ] - ).to_frame() - bf_series = session.read_pandas(pd_frame) - pd.testing.assert_frame_equal( - bf_series.to_pandas(), pd_frame, check_index_type=False, check_dtype=False - ) - - -def test_df_construct_pandas_set_dtype(scalars_dfs): - columns = [ - "int64_too", - "int64_col", - "float64_col", - "bool_col", - ] - _, scalars_pandas_df = scalars_dfs - bf_result = dataframe.DataFrame( - scalars_pandas_df, columns=columns, dtype="Float64" - ).to_pandas() - pd_result = pd.DataFrame(scalars_pandas_df, columns=columns, dtype="Float64") - pandas.testing.assert_frame_equal(bf_result, pd_result) - - -def test_df_construct_from_series(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - bf_result = dataframe.DataFrame( - {"a": scalars_df["int64_col"], "b": scalars_df["string_col"]}, - dtype="string[pyarrow]", - ) - pd_result = pd.DataFrame( - {"a": scalars_pandas_df["int64_col"], "b": scalars_pandas_df["string_col"]}, - dtype="string[pyarrow]", - ) - assert_dfs_equivalent(pd_result, bf_result) - - -def test_df_construct_from_dict(): - input_dict = { - "Animal": ["Falcon", "Falcon", "Parrot", "Parrot"], - # With a space in column name. We use standardized SQL schema ids to solve the problem that BQ schema doesn't support column names with spaces. b/296751058 - "Max Speed": [380.0, 370.0, 24.0, 26.0], - } - bf_result = dataframe.DataFrame(input_dict).to_pandas() - pd_result = pd.DataFrame(input_dict) - - pandas.testing.assert_frame_equal( - bf_result, pd_result, check_dtype=False, check_index_type=False - ) - - -def test_df_construct_dtype(): - data = { - "int_col": [1, 2, 3], - "string_col": ["1.1", "2.0", "3.5"], - "float_col": [1.0, 2.0, 3.0], - } - dtype = pd.StringDtype(storage="pyarrow") - bf_result = dataframe.DataFrame(data, dtype=dtype) - pd_result = pd.DataFrame(data, dtype=dtype) - pd_result.index = pd_result.index.astype("Int64") - pandas.testing.assert_frame_equal(bf_result.to_pandas(), pd_result) - - -def test_get_column(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - col_name = "int64_col" - series = scalars_df[col_name] - bf_result = series.to_pandas() - pd_result = scalars_pandas_df[col_name] - assert_series_equal(bf_result, pd_result) - - -def test_get_column_nonstring(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - series = scalars_df.rename(columns={"int64_col": 123.1})[123.1] - bf_result = series.to_pandas() - pd_result = scalars_pandas_df.rename(columns={"int64_col": 123.1})[123.1] - assert_series_equal(bf_result, pd_result) - - -@pytest.mark.parametrize( - "row_slice", - [ - (slice(1, 7, 2)), - (slice(1, 7, None)), - (slice(None, -3, None)), - ], -) -def test_get_rows_with_slice(scalars_dfs, row_slice): - scalars_df, scalars_pandas_df = scalars_dfs - bf_result = scalars_df[row_slice].to_pandas() - pd_result = scalars_pandas_df[row_slice] - assert_frame_equal(bf_result, pd_result) - - -def test_hasattr(scalars_dfs): - scalars_df, _ = scalars_dfs - assert hasattr(scalars_df, "int64_col") - assert hasattr(scalars_df, "head") - assert not hasattr(scalars_df, "not_exist") - - -@pytest.mark.parametrize( - ("ordered"), - [ - (True), - (False), - ], -) -def test_head_with_custom_column_labels( - scalars_df_index, scalars_pandas_df_index, ordered -): - rename_mapping = { - "int64_col": "Integer Column", - "string_col": "言語列", - } - bf_df = scalars_df_index.rename(columns=rename_mapping).head(3) - bf_result = bf_df.to_pandas(ordered=ordered) - pd_result = scalars_pandas_df_index.rename(columns=rename_mapping).head(3) - assert_frame_equal(bf_result, pd_result, ignore_order=not ordered) - - -def test_tail_with_custom_column_labels(scalars_df_index, scalars_pandas_df_index): - rename_mapping = { - "int64_col": "Integer Column", - "string_col": "言語列", - } - bf_df = scalars_df_index.rename(columns=rename_mapping).tail(3) - bf_result = bf_df.to_pandas() - pd_result = scalars_pandas_df_index.rename(columns=rename_mapping).tail(3) - pandas.testing.assert_frame_equal(bf_result, pd_result) - - -def test_get_column_by_attr(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - series = scalars_df.int64_col - bf_result = series.to_pandas() - pd_result = scalars_pandas_df.int64_col - assert_series_equal(bf_result, pd_result) - - -def test_get_columns(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - col_names = ["bool_col", "float64_col", "int64_col"] - df_subset = scalars_df.get(col_names) - df_pandas = df_subset.to_pandas() - pd.testing.assert_index_equal( - df_pandas.columns, scalars_pandas_df[col_names].columns - ) - - -def test_get_columns_default(scalars_dfs): - scalars_df, _ = scalars_dfs - col_names = ["not", "column", "names"] - result = scalars_df.get(col_names, "default_val") - assert result == "default_val" - - -@pytest.mark.parametrize( - ("loc", "column", "value", "allow_duplicates"), - [ - (0, 666, 2, False), - (5, "float64_col", 2.2, True), - (13, "rowindex_2", [8, 7, 6, 5, 4, 3, 2, 1, 0], True), - pytest.param( - 14, - "test", - 2, - False, - marks=pytest.mark.xfail( - raises=IndexError, - ), - ), - pytest.param( - 12, - "int64_col", - 2, - False, - marks=pytest.mark.xfail( - raises=ValueError, - ), - ), - ], -) -def test_insert(scalars_dfs, loc, column, value, allow_duplicates): - scalars_df, scalars_pandas_df = scalars_dfs - # insert works inplace, so will influence other tests. - # make a copy to avoid inplace changes. - bf_df = scalars_df.copy() - pd_df = scalars_pandas_df.copy() - bf_df.insert(loc, column, value, allow_duplicates) - pd_df.insert(loc, column, value, allow_duplicates) - - pd.testing.assert_frame_equal(bf_df.to_pandas(), pd_df, check_dtype=False) - - -def test_where_series_cond(scalars_df_index, scalars_pandas_df_index): - # Condition is dataframe, other is None (as default). - cond_bf = scalars_df_index["int64_col"] > 0 - cond_pd = scalars_pandas_df_index["int64_col"] > 0 - bf_result = scalars_df_index.where(cond_bf).to_pandas() - pd_result = scalars_pandas_df_index.where(cond_pd) - pandas.testing.assert_frame_equal(bf_result, pd_result) - - -def test_mask_series_cond(scalars_df_index, scalars_pandas_df_index): - cond_bf = scalars_df_index["int64_col"] > 0 - cond_pd = scalars_pandas_df_index["int64_col"] > 0 - - bf_df = scalars_df_index[["int64_too", "int64_col", "float64_col"]] - pd_df = scalars_pandas_df_index[["int64_too", "int64_col", "float64_col"]] - bf_result = bf_df.mask(cond_bf, bf_df + 1).to_pandas() - pd_result = pd_df.mask(cond_pd, pd_df + 1) - pandas.testing.assert_frame_equal(bf_result, pd_result) - - -def test_where_series_multi_index(scalars_df_index, scalars_pandas_df_index): - # Test when a dataframe has multi-index or multi-columns. - columns = ["int64_col", "float64_col"] - dataframe_bf = scalars_df_index[columns] - - dataframe_bf.columns = pd.MultiIndex.from_tuples( - [("str1", 1), ("str2", 2)], names=["STR", "INT"] - ) - cond_bf = dataframe_bf["str1"] > 0 - - with pytest.raises(NotImplementedError) as context: - dataframe_bf.where(cond_bf).to_pandas() - assert ( - str(context.value) - == "The dataframe.where() method does not support multi-column." - ) - - -def test_where_series_cond_const_other(scalars_df_index, scalars_pandas_df_index): - # Condition is a series, other is a constant. - columns = ["int64_col", "float64_col"] - dataframe_bf = scalars_df_index[columns] - dataframe_pd = scalars_pandas_df_index[columns] - dataframe_bf.columns.name = "test_name" - dataframe_pd.columns.name = "test_name" - - cond_bf = dataframe_bf["int64_col"] > 0 - cond_pd = dataframe_pd["int64_col"] > 0 - other = 0 - - bf_result = dataframe_bf.where(cond_bf, other).to_pandas() - pd_result = dataframe_pd.where(cond_pd, other) - pandas.testing.assert_frame_equal(bf_result, pd_result) - - -def test_where_series_cond_dataframe_other(scalars_df_index, scalars_pandas_df_index): - # Condition is a series, other is a dataframe. - columns = ["int64_col", "float64_col"] - dataframe_bf = scalars_df_index[columns] - dataframe_pd = scalars_pandas_df_index[columns] - - cond_bf = dataframe_bf["int64_col"] > 0 - cond_pd = dataframe_pd["int64_col"] > 0 - other_bf = -dataframe_bf - other_pd = -dataframe_pd - - bf_result = dataframe_bf.where(cond_bf, other_bf).to_pandas() - pd_result = dataframe_pd.where(cond_pd, other_pd) - pandas.testing.assert_frame_equal(bf_result, pd_result) - - -def test_where_dataframe_cond(scalars_df_index, scalars_pandas_df_index): - # Condition is a dataframe, other is None. - columns = ["int64_col", "float64_col"] - dataframe_bf = scalars_df_index[columns] - dataframe_pd = scalars_pandas_df_index[columns] - - cond_bf = dataframe_bf > 0 - cond_pd = dataframe_pd > 0 - - bf_result = dataframe_bf.where(cond_bf, None).to_pandas() - pd_result = dataframe_pd.where(cond_pd, None) - pandas.testing.assert_frame_equal(bf_result, pd_result) - - -def test_where_dataframe_cond_const_other(scalars_df_index, scalars_pandas_df_index): - # Condition is a dataframe, other is a constant. - columns = ["int64_col", "float64_col"] - dataframe_bf = scalars_df_index[columns] - dataframe_pd = scalars_pandas_df_index[columns] - - cond_bf = dataframe_bf > 0 - cond_pd = dataframe_pd > 0 - other_bf = 10 - other_pd = 10 - - bf_result = dataframe_bf.where(cond_bf, other_bf).to_pandas() - pd_result = dataframe_pd.where(cond_pd, other_pd) - pandas.testing.assert_frame_equal(bf_result, pd_result) - - -def test_where_dataframe_cond_dataframe_other( - scalars_df_index, scalars_pandas_df_index -): - # Condition is a dataframe, other is a dataframe. - columns = ["int64_col", "float64_col"] - dataframe_bf = scalars_df_index[columns] - dataframe_pd = scalars_pandas_df_index[columns] - - cond_bf = dataframe_bf > 0 - cond_pd = dataframe_pd > 0 - other_bf = dataframe_bf * 2 - other_pd = dataframe_pd * 2 - - bf_result = dataframe_bf.where(cond_bf, other_bf).to_pandas() - pd_result = dataframe_pd.where(cond_pd, other_pd) - pandas.testing.assert_frame_equal(bf_result, pd_result) - - -def test_drop_column(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - col_name = "int64_col" - df_pandas = scalars_df.drop(columns=col_name).to_pandas() - pd.testing.assert_index_equal( - df_pandas.columns, scalars_pandas_df.drop(columns=col_name).columns - ) - - -def test_drop_columns(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - col_names = ["int64_col", "geography_col", "time_col"] - df_pandas = scalars_df.drop(columns=col_names).to_pandas() - pd.testing.assert_index_equal( - df_pandas.columns, scalars_pandas_df.drop(columns=col_names).columns - ) - - -def test_drop_labels_axis_1(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - labels = ["int64_col", "geography_col", "time_col"] - - pd_result = scalars_pandas_df.drop(labels=labels, axis=1) - bf_result = scalars_df.drop(labels=labels, axis=1).to_pandas() - - pd.testing.assert_frame_equal(pd_result, bf_result) - - -def test_drop_with_custom_column_labels(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - rename_mapping = { - "int64_col": "Integer Column", - "string_col": "言語列", - } - dropped_columns = [ - "言語列", - "timestamp_col", - ] - bf_df = scalars_df.rename(columns=rename_mapping).drop(columns=dropped_columns) - bf_result = bf_df.to_pandas() - pd_result = scalars_pandas_df.rename(columns=rename_mapping).drop( - columns=dropped_columns - ) - assert_frame_equal(bf_result, pd_result) - - -def test_df_memory_usage(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - - pd_result = scalars_pandas_df.memory_usage() - bf_result = scalars_df.memory_usage() - - pd.testing.assert_series_equal(pd_result, bf_result, rtol=1.5) - - -def test_df_info(scalars_dfs): - expected = ( - "\n" - "Index: 9 entries, 0 to 8\n" - "Data columns (total 14 columns):\n" - " # Column Non-Null Count Dtype\n" - "--- ------------- ---------------- ------------------------------\n" - " 0 bool_col 8 non-null boolean\n" - " 1 bytes_col 6 non-null binary[pyarrow]\n" - " 2 date_col 7 non-null date32[day][pyarrow]\n" - " 3 datetime_col 6 non-null timestamp[us][pyarrow]\n" - " 4 geography_col 4 non-null geometry\n" - " 5 int64_col 8 non-null Int64\n" - " 6 int64_too 9 non-null Int64\n" - " 7 numeric_col 6 non-null decimal128(38, 9)[pyarrow]\n" - " 8 float64_col 7 non-null Float64\n" - " 9 rowindex_2 9 non-null Int64\n" - " 10 string_col 8 non-null string\n" - " 11 time_col 6 non-null time64[us][pyarrow]\n" - " 12 timestamp_col 6 non-null timestamp[us, tz=UTC][pyarrow]\n" - " 13 duration_col 7 non-null duration[us][pyarrow]\n" - "dtypes: Float64(1), Int64(3), binary[pyarrow](1), boolean(1), date32[day][pyarrow](1), decimal128(38, 9)[pyarrow](1), duration[us][pyarrow](1), geometry(1), string(1), time64[us][pyarrow](1), timestamp[us, tz=UTC][pyarrow](1), timestamp[us][pyarrow](1)\n" - "memory usage: 1341 bytes\n" - ) - - scalars_df, _ = scalars_dfs - bf_result = io.StringIO() - - scalars_df.info(buf=bf_result) - - assert expected == bf_result.getvalue() - - -@pytest.mark.parametrize( - ("include", "exclude"), - [ - ("Int64", None), - (["int"], None), - ("number", None), - ([pd.Int64Dtype(), pd.BooleanDtype()], None), - (None, [pd.Int64Dtype(), pd.BooleanDtype()]), - ("Int64", ["boolean"]), - ], -) -def test_select_dtypes(scalars_dfs, include, exclude): - scalars_df, scalars_pandas_df = scalars_dfs - - pd_result = scalars_pandas_df.select_dtypes(include=include, exclude=exclude) - bf_result = scalars_df.select_dtypes(include=include, exclude=exclude).to_pandas() - - pd.testing.assert_frame_equal(pd_result, bf_result) - - -def test_drop_index(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - - pd_result = scalars_pandas_df.drop(index=[4, 1, 2]) - bf_result = scalars_df.drop(index=[4, 1, 2]).to_pandas() - - pd.testing.assert_frame_equal(pd_result, bf_result) - - -def test_drop_pandas_index(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - drop_index = scalars_pandas_df.iloc[[4, 1, 2]].index - - pd_result = scalars_pandas_df.drop(index=drop_index) - bf_result = scalars_df.drop(index=drop_index).to_pandas() - - pd.testing.assert_frame_equal(pd_result, bf_result) - - -def test_drop_bigframes_index(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - drop_index = scalars_df.loc[[4, 1, 2]].index - drop_pandas_index = scalars_pandas_df.loc[[4, 1, 2]].index - - pd_result = scalars_pandas_df.drop(index=drop_pandas_index) - bf_result = scalars_df.drop(index=drop_index).to_pandas() - - pd.testing.assert_frame_equal(pd_result, bf_result) - - -def test_drop_bigframes_index_with_na(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - scalars_df = scalars_df.copy() - scalars_pandas_df = scalars_pandas_df.copy() - scalars_df = scalars_df.set_index("bytes_col") - scalars_pandas_df = scalars_pandas_df.set_index("bytes_col") - drop_index = scalars_df.iloc[[2, 5]].index - drop_pandas_index = scalars_pandas_df.iloc[[2, 5]].index - - pd_result = scalars_pandas_df.drop(index=drop_pandas_index) # drop_pandas_index) - bf_result = scalars_df.drop(index=drop_index).to_pandas() - - pd.testing.assert_frame_equal(pd_result, bf_result) - - -def test_drop_bigframes_multiindex(scalars_dfs): - # TODO: supply a reason why this isn't compatible with pandas 1.x - pytest.importorskip("pandas", minversion="2.0.0") - scalars_df, scalars_pandas_df = scalars_dfs - scalars_df = scalars_df.copy() - scalars_pandas_df = scalars_pandas_df.copy() - sub_df = scalars_df.iloc[[4, 1, 2]] - sub_pandas_df = scalars_pandas_df.iloc[[4, 1, 2]] - sub_df = sub_df.set_index(["bytes_col", "numeric_col"]) - sub_pandas_df = sub_pandas_df.set_index(["bytes_col", "numeric_col"]) - drop_index = sub_df.index - drop_pandas_index = sub_pandas_df.index - - scalars_df = scalars_df.set_index(["bytes_col", "numeric_col"]) - scalars_pandas_df = scalars_pandas_df.set_index(["bytes_col", "numeric_col"]) - bf_result = scalars_df.drop(index=drop_index).to_pandas() - pd_result = scalars_pandas_df.drop(index=drop_pandas_index) - - pd.testing.assert_frame_equal(pd_result, bf_result) - - -def test_drop_labels_axis_0(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - - pd_result = scalars_pandas_df.drop(labels=[4, 1, 2], axis=0) - bf_result = scalars_df.drop(labels=[4, 1, 2], axis=0).to_pandas() - - pd.testing.assert_frame_equal(pd_result, bf_result) - - -def test_drop_index_and_columns(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - - pd_result = scalars_pandas_df.drop(index=[4, 1, 2], columns="int64_col") - bf_result = scalars_df.drop(index=[4, 1, 2], columns="int64_col").to_pandas() - - pd.testing.assert_frame_equal(pd_result, bf_result) - - -def test_rename(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - col_name_dict = {"bool_col": 1.2345} - df_pandas = scalars_df.rename(columns=col_name_dict).to_pandas() - pd.testing.assert_index_equal( - df_pandas.columns, scalars_pandas_df.rename(columns=col_name_dict).columns - ) - - -def test_df_peek(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - - peek_result = scalars_df.peek(n=3, force=False, allow_large_results=True) - - pd.testing.assert_index_equal(scalars_pandas_df.columns, peek_result.columns) - assert len(peek_result) == 3 - - -def test_df_peek_with_large_results_not_allowed(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - - peek_result = scalars_df.peek(n=3, force=False, allow_large_results=False) - - pd.testing.assert_index_equal(scalars_pandas_df.columns, peek_result.columns) - assert len(peek_result) == 3 - - -def test_df_peek_filtered(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - peek_result = scalars_df[scalars_df.int64_col != 0].peek(n=3, force=False) - pd.testing.assert_index_equal(scalars_pandas_df.columns, peek_result.columns) - assert len(peek_result) == 3 - - -def test_df_peek_force_default(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - peek_result = scalars_df[["int64_col", "int64_too"]].cumsum().peek(n=3) - pd.testing.assert_index_equal( - scalars_pandas_df[["int64_col", "int64_too"]].columns, peek_result.columns - ) - assert len(peek_result) == 3 - - -def test_df_peek_reset_index(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - peek_result = ( - scalars_df[["int64_col", "int64_too"]].reset_index(drop=True).peek(n=3) - ) - pd.testing.assert_index_equal( - scalars_pandas_df[["int64_col", "int64_too"]].columns, peek_result.columns - ) - assert len(peek_result) == 3 - - -def test_repr_w_all_rows(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - - # Remove columns with flaky formatting, like NUMERIC columns (which use the - # object dtype). Also makes a copy so that mutating the index name doesn't - # break other tests. - scalars_df = scalars_df.drop(columns=["numeric_col"]) - scalars_pandas_df = scalars_pandas_df.drop(columns=["numeric_col"]) - - # When there are 10 or fewer rows, the outputs should be identical. - actual = repr(scalars_df.head(10)) - - with display_options.pandas_repr(bigframes.options.display): - expected = repr(scalars_pandas_df.head(10)) - - assert actual == expected - - -def test_join_repr(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - - scalars_df = ( - scalars_df[["int64_col"]] - .join(scalars_df.set_index("int64_col")[["int64_too"]]) - .sort_index() - ) - scalars_pandas_df = ( - scalars_pandas_df[["int64_col"]] - .join(scalars_pandas_df.set_index("int64_col")[["int64_too"]]) - .sort_index() - ) - # Pandas join result index name seems to depend on the index values in a way that bigframes can't match exactly - scalars_pandas_df.index.name = None - - actual = repr(scalars_df) - - with display_options.pandas_repr(bigframes.options.display): - expected = repr(scalars_pandas_df) - - assert actual == expected - - -def test_mimebundle_html_repr_w_all_rows(scalars_dfs, session): - scalars_df, _ = scalars_dfs - # get a pandas df of the expected format - df, _ = scalars_df._block.to_pandas() - pandas_df = df.set_axis(scalars_df._block.column_labels, axis=1) - pandas_df.index.name = scalars_df.index.name - - # When there are 10 or fewer rows, the outputs should be identical except for the extra note. - bundle = scalars_df.head(10)._repr_mimebundle_() - actual = bundle["text/html"] - - with display_options.pandas_repr(bigframes.options.display): - pandas_repr = pandas_df.head(10)._repr_html_() - - expected = ( - pandas_repr - + f"[{len(pandas_df.index)} rows x {len(pandas_df.columns)} columns in total]" - ) - assert actual == expected - - -def test_df_column_name_with_space(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - col_name_dict = {"bool_col": "bool col"} - df_pandas = scalars_df.rename(columns=col_name_dict).to_pandas() - pd.testing.assert_index_equal( - df_pandas.columns, scalars_pandas_df.rename(columns=col_name_dict).columns - ) - - -def test_df_column_name_duplicate(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - col_name_dict = {"int64_too": "int64_col"} - df_pandas = scalars_df.rename(columns=col_name_dict).to_pandas() - pd.testing.assert_index_equal( - df_pandas.columns, scalars_pandas_df.rename(columns=col_name_dict).columns - ) - - -def test_get_df_column_name_duplicate(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - col_name_dict = {"int64_too": "int64_col"} - - bf_result = scalars_df.rename(columns=col_name_dict)["int64_col"].to_pandas() - pd_result = scalars_pandas_df.rename(columns=col_name_dict)["int64_col"] - pd.testing.assert_index_equal(bf_result.columns, pd_result.columns) - - -@pytest.mark.parametrize( - ("indices", "axis"), - [ - ([1, 3, 5], 0), - ([2, 4, 6], 1), - ([1, -3, -5, -6], "index"), - ([-2, -4, -6], "columns"), - ], -) -def test_take_df(scalars_dfs, indices, axis): - scalars_df, scalars_pandas_df = scalars_dfs - - bf_result = scalars_df.take(indices, axis=axis).to_pandas() - pd_result = scalars_pandas_df.take(indices, axis=axis) - - assert_frame_equal(bf_result, pd_result) - - -def test_filter_df(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - - bf_bool_series = scalars_df["bool_col"] - bf_result = scalars_df[bf_bool_series].to_pandas() - - pd_bool_series = scalars_pandas_df["bool_col"] - pd_result = scalars_pandas_df[pd_bool_series] - - assert_frame_equal(bf_result, pd_result) - - -def test_assign_new_column(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - kwargs = {"new_col": 2} - df = scalars_df.assign(**kwargs) - bf_result = df.to_pandas() - pd_result = scalars_pandas_df.assign(**kwargs) - - # Convert default pandas dtypes `int64` to match BigQuery DataFrames dtypes. - pd_result["new_col"] = pd_result["new_col"].astype("Int64") - - assert_frame_equal(bf_result, pd_result) - - -def test_assign_using_pd_col(scalars_dfs): - if pd.__version__.startswith("1.") or pd.__version__.startswith("2."): - pytest.skip("col expression interface only supported for pandas 3+") - scalars_df, scalars_pandas_df = scalars_dfs - bf_kwargs = { - "new_col_1": 4 - bpd.col("int64_col"), - "new_col_2": bpd.col("int64_col") / (bpd.col("float64_col") * 0.5), - } - pd_kwargs = { - "new_col_1": 4 - pd.col("int64_col"), # type: ignore - "new_col_2": pd.col("int64_col") / (pd.col("float64_col") * 0.5), # type: ignore - } - - df = scalars_df.assign(**bf_kwargs) - bf_result = df.to_pandas() - pd_result = scalars_pandas_df.assign(**pd_kwargs) - - assert_frame_equal(bf_result, pd_result) - - -def test_assign_new_column_w_loc(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - bf_df = scalars_df.copy() - pd_df = scalars_pandas_df.copy() - bf_df.loc[:, "new_col"] = 2 - pd_df.loc[:, "new_col"] = 2 - bf_result = bf_df.to_pandas() - pd_result = pd_df - - # Convert default pandas dtypes `int64` to match BigQuery DataFrames dtypes. - pd_result["new_col"] = pd_result["new_col"].astype("Int64") - - pd.testing.assert_frame_equal(bf_result, pd_result) - - -@pytest.mark.parametrize( - ("scalar",), - [ - (2.1,), - (None,), - ], -) -def test_assign_new_column_w_setitem(scalars_dfs, scalar): - scalars_df, scalars_pandas_df = scalars_dfs - bf_df = scalars_df.copy() - pd_df = scalars_pandas_df.copy() - bf_df["new_col"] = scalar - pd_df["new_col"] = scalar - bf_result = bf_df.to_pandas() - pd_result = pd_df - - # Convert default pandas dtypes `float64` to match BigQuery DataFrames dtypes. - pd_result["new_col"] = pd_result["new_col"].astype("Float64") - - pd.testing.assert_frame_equal(bf_result, pd_result) - - -def test_assign_new_column_w_setitem_dataframe(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - bf_df = scalars_df.copy() - pd_df = scalars_pandas_df.copy() - bf_df["int64_col"] = bf_df["int64_too"].to_frame() - pd_df["int64_col"] = pd_df["int64_too"].to_frame() - - # Convert default pandas dtypes `int64` to match BigQuery DataFrames dtypes. - pd_df["int64_col"] = pd_df["int64_col"].astype("Int64") - - pd.testing.assert_frame_equal(bf_df.to_pandas(), pd_df) - - -def test_assign_new_column_w_setitem_dataframe_error(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - bf_df = scalars_df.copy() - pd_df = scalars_pandas_df.copy() - - with pytest.raises(ValueError): - bf_df["impossible_col"] = bf_df[["int64_too", "string_col"]] - with pytest.raises(ValueError): - pd_df["impossible_col"] = pd_df[["int64_too", "string_col"]] - - -def test_assign_new_column_w_setitem_list(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - bf_df = scalars_df.copy() - pd_df = scalars_pandas_df.copy() - bf_df["new_col"] = [9, 8, 7, 6, 5, 4, 3, 2, 1] - pd_df["new_col"] = [9, 8, 7, 6, 5, 4, 3, 2, 1] - bf_result = bf_df.to_pandas() - pd_result = pd_df - - # Convert default pandas dtypes `int64` to match BigQuery DataFrames dtypes. - pd_result["new_col"] = pd_result["new_col"].astype("Int64") - - pd.testing.assert_frame_equal(bf_result, pd_result) - - -def test_assign_new_column_w_setitem_list_repeated(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - bf_df = scalars_df.copy() - pd_df = scalars_pandas_df.copy() - bf_df["new_col"] = [9, 8, 7, 6, 5, 4, 3, 2, 1] - pd_df["new_col"] = [9, 8, 7, 6, 5, 4, 3, 2, 1] - bf_df["new_col_2"] = [1, 3, 2, 5, 4, 7, 6, 9, 8] - pd_df["new_col_2"] = [1, 3, 2, 5, 4, 7, 6, 9, 8] - bf_result = bf_df.to_pandas() - pd_result = pd_df - - # Convert default pandas dtypes `int64` to match BigQuery DataFrames dtypes. - pd_result["new_col"] = pd_result["new_col"].astype("Int64") - pd_result["new_col_2"] = pd_result["new_col_2"].astype("Int64") - - pd.testing.assert_frame_equal(bf_result, pd_result) - - -def test_assign_new_column_w_setitem_list_custom_index(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - bf_df = scalars_df.copy() - pd_df = scalars_pandas_df.copy() - - # set the custom index - pd_df = pd_df.set_index(["string_col", "int64_col"]) - bf_df = bf_df.set_index(["string_col", "int64_col"]) - - bf_df["new_col"] = [9, 8, 7, 6, 5, 4, 3, 2, 1] - pd_df["new_col"] = [9, 8, 7, 6, 5, 4, 3, 2, 1] - bf_result = bf_df.to_pandas() - pd_result = pd_df - - # Convert default pandas dtypes `int64` to match BigQuery DataFrames dtypes. - pd_result["new_col"] = pd_result["new_col"].astype("Int64") - - pd.testing.assert_frame_equal(bf_result, pd_result) - - -def test_assign_new_column_w_setitem_list_error(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - bf_df = scalars_df.copy() - pd_df = scalars_pandas_df.copy() - - with pytest.raises(ValueError): - pd_df["new_col"] = [1, 2, 3] # should be len 9, is 3 - with pytest.raises(ValueError): - bf_df["new_col"] = [1, 2, 3] - - -def test_assign_existing_column(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - kwargs = {"int64_col": 2} - df = scalars_df.assign(**kwargs) - bf_result = df.to_pandas() - pd_result = scalars_pandas_df.assign(**kwargs) - - # Convert default pandas dtypes `int64` to match BigQuery DataFrames dtypes. - pd_result["int64_col"] = pd_result["int64_col"].astype("Int64") - - assert_frame_equal(bf_result, pd_result) - - -def test_assign_listlike_to_empty_df(session): - empty_df = dataframe.DataFrame(session=session) - empty_pandas_df = pd.DataFrame() - - bf_result = empty_df.assign(new_col=[1, 2, 3]) - pd_result = empty_pandas_df.assign(new_col=[1, 2, 3]) - - pd_result["new_col"] = pd_result["new_col"].astype("Int64") - pd_result.index = pd_result.index.astype("Int64") - assert_frame_equal(bf_result.to_pandas(), pd_result) - - -def test_assign_to_empty_df_multiindex_error(session): - empty_df = dataframe.DataFrame(session=session) - empty_pandas_df = pd.DataFrame() - - empty_df["empty_col_1"] = typing.cast(series.Series, []) - empty_df["empty_col_2"] = typing.cast(series.Series, []) - empty_pandas_df["empty_col_1"] = [] - empty_pandas_df["empty_col_2"] = [] - empty_df = empty_df.set_index(["empty_col_1", "empty_col_2"]) - empty_pandas_df = empty_pandas_df.set_index(["empty_col_1", "empty_col_2"]) - - with pytest.raises(ValueError): - empty_df.assign(new_col=[1, 2, 3, 4, 5, 6, 7, 8, 9]) - with pytest.raises(ValueError): - empty_pandas_df.assign(new_col=[1, 2, 3, 4, 5, 6, 7, 8, 9]) - - -@pytest.mark.parametrize( - ("ordered"), - [ - (True), - (False), - ], -) -def test_assign_series(scalars_dfs, ordered): - scalars_df, scalars_pandas_df = scalars_dfs - column_name = "int64_col" - df = scalars_df.assign(new_col=scalars_df[column_name]) - bf_result = df.to_pandas(ordered=ordered) - pd_result = scalars_pandas_df.assign(new_col=scalars_pandas_df[column_name]) - - assert_frame_equal(bf_result, pd_result, ignore_order=not ordered) - - -def test_assign_series_overwrite(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - column_name = "int64_col" - df = scalars_df.assign(**{column_name: scalars_df[column_name] + 3}) - bf_result = df.to_pandas() - pd_result = scalars_pandas_df.assign( - **{column_name: scalars_pandas_df[column_name] + 3} - ) - - assert_frame_equal(bf_result, pd_result) - - -def test_assign_sequential(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - kwargs = {"int64_col": 2, "new_col": 3, "new_col2": 4} - df = scalars_df.assign(**kwargs) - bf_result = df.to_pandas() - pd_result = scalars_pandas_df.assign(**kwargs) - - # Convert default pandas dtypes `int64` to match BigQuery DataFrames dtypes. - pd_result["int64_col"] = pd_result["int64_col"].astype("Int64") - pd_result["new_col"] = pd_result["new_col"].astype("Int64") - pd_result["new_col2"] = pd_result["new_col2"].astype("Int64") - - assert_frame_equal(bf_result, pd_result) - - -# Require an index so that the self-join is consistent each time. -def test_assign_same_table_different_index_performs_self_join( - scalars_df_index, scalars_pandas_df_index -): - column_name = "int64_col" - bf_df = scalars_df_index.assign( - alternative_index=scalars_df_index["rowindex_2"] + 2 - ) - pd_df = scalars_pandas_df_index.assign( - alternative_index=scalars_pandas_df_index["rowindex_2"] + 2 - ) - bf_df_2 = bf_df.set_index("alternative_index") - pd_df_2 = pd_df.set_index("alternative_index") - bf_result = bf_df.assign(new_col=bf_df_2[column_name] * 10).to_pandas() - pd_result = pd_df.assign(new_col=pd_df_2[column_name] * 10) - - pandas.testing.assert_frame_equal(bf_result, pd_result) - - -# Different table expression must have Index -def test_assign_different_df( - scalars_df_index, scalars_df_2_index, scalars_pandas_df_index -): - column_name = "int64_col" - df = scalars_df_index.assign(new_col=scalars_df_2_index[column_name]) - bf_result = df.to_pandas() - # Doesn't matter to pandas if it comes from the same DF or a different DF. - pd_result = scalars_pandas_df_index.assign( - new_col=scalars_pandas_df_index[column_name] - ) - - assert_frame_equal(bf_result, pd_result) - - -def test_assign_different_df_w_loc( - scalars_df_index, scalars_df_2_index, scalars_pandas_df_index -): - bf_df = scalars_df_index.copy() - bf_df2 = scalars_df_2_index.copy() - pd_df = scalars_pandas_df_index.copy() - assert "int64_col" in bf_df.columns - assert "int64_col" in pd_df.columns - bf_df.loc[:, "int64_col"] = bf_df2.loc[:, "int64_col"] + 1 - pd_df.loc[:, "int64_col"] = pd_df.loc[:, "int64_col"] + 1 - bf_result = bf_df.to_pandas() - pd_result = pd_df - - # Convert default pandas dtypes `int64` to match BigQuery DataFrames dtypes. - pd_result["int64_col"] = pd_result["int64_col"].astype("Int64") - - pd.testing.assert_frame_equal(bf_result, pd_result) - - -def test_assign_different_df_w_setitem( - scalars_df_index, scalars_df_2_index, scalars_pandas_df_index -): - bf_df = scalars_df_index.copy() - bf_df2 = scalars_df_2_index.copy() - pd_df = scalars_pandas_df_index.copy() - assert "int64_col" in bf_df.columns - assert "int64_col" in pd_df.columns - bf_df["int64_col"] = bf_df2["int64_col"] + 1 - pd_df["int64_col"] = pd_df["int64_col"] + 1 - bf_result = bf_df.to_pandas() - pd_result = pd_df - - # Convert default pandas dtypes `int64` to match BigQuery DataFrames dtypes. - pd_result["int64_col"] = pd_result["int64_col"].astype("Int64") - - pd.testing.assert_frame_equal(bf_result, pd_result) - - -def test_assign_callable_lambda(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - kwargs = {"new_col": lambda x: x["int64_col"] + x["int64_too"]} - df = scalars_df.assign(**kwargs) - bf_result = df.to_pandas() - pd_result = scalars_pandas_df.assign(**kwargs) - - # Convert default pandas dtypes `int64` to match BigQuery DataFrames dtypes. - pd_result["new_col"] = pd_result["new_col"].astype("Int64") - - assert_frame_equal(bf_result, pd_result) - - -@pytest.mark.parametrize( - ("axis", "how", "ignore_index", "subset"), - [ - (0, "any", False, None), - (0, "any", True, None), - (0, "all", False, ["bool_col", "time_col"]), - (0, "any", False, ["bool_col", "time_col"]), - (0, "all", False, "time_col"), - (1, "any", False, None), - (1, "all", False, None), - ], -) -def test_df_dropna(scalars_dfs, axis, how, ignore_index, subset): - # TODO: supply a reason why this isn't compatible with pandas 1.x - pytest.importorskip("pandas", minversion="2.0.0") - scalars_df, scalars_pandas_df = scalars_dfs - df = scalars_df.dropna(axis=axis, how=how, ignore_index=ignore_index, subset=subset) - bf_result = df.to_pandas() - pd_result = scalars_pandas_df.dropna( - axis=axis, how=how, ignore_index=ignore_index, subset=subset - ) - - # Pandas uses int64 instead of Int64 (nullable) dtype. - pd_result.index = pd_result.index.astype(pd.Int64Dtype()) - pandas.testing.assert_frame_equal(bf_result, pd_result) - - -def test_df_dropna_range_columns(scalars_dfs): - # TODO: supply a reason why this isn't compatible with pandas 1.x - pytest.importorskip("pandas", minversion="2.0.0") - scalars_df, scalars_pandas_df = scalars_dfs - scalars_df = scalars_df.copy() - scalars_pandas_df = scalars_pandas_df.copy() - scalars_df.columns = pandas.RangeIndex(0, len(scalars_df.columns)) - scalars_pandas_df.columns = pandas.RangeIndex(0, len(scalars_pandas_df.columns)) - - df = scalars_df.dropna() - bf_result = df.to_pandas() - pd_result = scalars_pandas_df.dropna() - - pandas.testing.assert_frame_equal(bf_result, pd_result) - - -def test_df_interpolate(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - columns = ["int64_col", "int64_too", "float64_col"] - bf_result = scalars_df[columns].interpolate().to_pandas() - # Pandas can only interpolate on "float64" columns - # https://github.com/pandas-dev/pandas/issues/40252 - pd_result = scalars_pandas_df[columns].astype("float64").interpolate() - - pandas.testing.assert_frame_equal( - bf_result, - pd_result, - check_index_type=False, - check_dtype=False, - ) - - -@pytest.mark.parametrize( - "col, fill_value", - [ - (["int64_col", "float64_col"], 3), - (["string_col"], "A"), - (["datetime_col"], pd.Timestamp("2023-01-01")), - ], -) -def test_df_fillna(scalars_dfs, col, fill_value): - scalars_df, scalars_pandas_df = scalars_dfs - bf_result = scalars_df[col].fillna(fill_value).to_pandas() - pd_result = scalars_pandas_df[col].fillna(fill_value) - - pd.testing.assert_frame_equal(bf_result, pd_result, check_dtype=False) - - -@pytest.mark.skip("b/436316698 unit test failed for python 3.12") -def test_df_ffill(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - bf_result = scalars_df[["int64_col", "float64_col"]].ffill(limit=1).to_pandas() - pd_result = scalars_pandas_df[["int64_col", "float64_col"]].ffill(limit=1) - - pandas.testing.assert_frame_equal(bf_result, pd_result) - - -def test_df_bfill(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - bf_result = scalars_df[["int64_col", "float64_col"]].bfill().to_pandas() - pd_result = scalars_pandas_df[["int64_col", "float64_col"]].bfill() - - pandas.testing.assert_frame_equal(bf_result, pd_result) - - -def test_apply_series_series_callable( - scalars_df_index, - scalars_pandas_df_index, -): - columns = ["int64_too", "int64_col"] - - def foo(series, arg1, arg2, *, kwarg1=0, kwarg2=0): - return series**2 + (arg1 * arg2 % 4) + (kwarg1 * kwarg2 % 7) - - bf_result = ( - scalars_df_index[columns] - .apply(foo, args=(33, 61), kwarg1=52, kwarg2=21) - .to_pandas() - ) - - pd_result = scalars_pandas_df_index[columns].apply( - foo, args=(33, 61), kwarg1=52, kwarg2=21 - ) - - pandas.testing.assert_frame_equal(bf_result, pd_result) - - -def test_apply_series_listlike_callable( - scalars_df_index, - scalars_pandas_df_index, -): - columns = ["int64_too", "int64_col"] - bf_result = ( - scalars_df_index[columns].apply(lambda x: [len(x), x.min(), 24]).to_pandas() - ) - - pd_result = scalars_pandas_df_index[columns].apply(lambda x: [len(x), x.min(), 24]) - - # Convert default pandas dtypes `int64` to match BigQuery DataFrames dtypes. - pd_result.index = pd_result.index.astype("Int64") - pd_result = pd_result.astype("Int64") - pandas.testing.assert_frame_equal(bf_result, pd_result) - - -def test_apply_series_scalar_callable( - scalars_df_index, - scalars_pandas_df_index, -): - columns = ["int64_too", "int64_col"] - bf_result = scalars_df_index[columns].apply(lambda x: x.sum()) - - pd_result = scalars_pandas_df_index[columns].apply(lambda x: x.sum()) - - pandas.testing.assert_series_equal(bf_result, pd_result) - - -def test_df_map_with_udf(session): - df = bpd.DataFrame({"x": [1, 2, None, 4], "y": [5, None, 7, 8]}, dtype="Int64") - - @session.udf() - def foo(row: pd.Series) -> int: - if pd.isna(row["x"]) or pd.isna(row["y"]): - return -1 - return int(row["x"] * row["y"]) - - bf_result = df.apply(foo, axis=1).to_pandas() - pd_result = pd.Series([5, -1, -1, 32]) - assert_series_equal(bf_result, pd_result, check_dtype=False) - - -def test_df_apply_complex_udf(session): - df = bpd.DataFrame( - {"x": [1, 2, 3], "y": ["a", "b", "c"]}, - index=["row0", "row1", "row2"], - ) - - @session.udf() - def foo(row: pd.Series) -> str: - idx = str(row.name) - items_str = ";".join(f"{k}={v}" for k, v in row.items()) - return f"({idx}) -> {items_str}" - - bf_result = df.apply(foo, axis=1).to_pandas() - - pd_df = pd.DataFrame( - {"x": [1, 2, 3], "y": ["a", "b", "c"]}, - index=["row0", "row1", "row2"], - ) - - def pd_foo(row): - idx = str(row.name) - items_str = ";".join(f"{k}={v}" for k, v in row.items()) - return f"({idx}) -> {items_str}" - - pd_result = pd_df.apply(pd_foo, axis=1) - - assert_series_equal(bf_result, pd_result, check_dtype=False, check_index_type=False) - - -def test_df_pipe( - scalars_df_index, - scalars_pandas_df_index, -): - columns = ["int64_too", "int64_col"] - - def foo(x: int, y: int, df): - return (df + x) % y - - bf_result = ( - scalars_df_index[columns] - .pipe((foo, "df"), x=7, y=9) - .pipe(lambda x: x**2) - .to_pandas() - ) - - pd_result = ( - scalars_pandas_df_index[columns] - .pipe((foo, "df"), x=7, y=9) - .pipe(lambda x: x**2) - ) - - pandas.testing.assert_frame_equal(bf_result, pd_result) - - -def test_df_keys( - scalars_df_index, - scalars_pandas_df_index, -): - pandas.testing.assert_index_equal( - scalars_df_index.keys(), scalars_pandas_df_index.keys() - ) - - -def test_df_iter( - scalars_df_index, - scalars_pandas_df_index, -): - for bf_i, df_i in zip(scalars_df_index, scalars_pandas_df_index): - assert bf_i == df_i - - -def test_iterrows( - scalars_df_index, - scalars_pandas_df_index, -): - # TODO: supply a reason why this isn't compatible with pandas 1.x - pytest.importorskip("pandas", minversion="2.0.0") - scalars_df_index = scalars_df_index.add_suffix("_suffix", axis=1) - scalars_pandas_df_index = scalars_pandas_df_index.add_suffix("_suffix", axis=1) - for (bf_index, bf_series), (pd_index, pd_series) in zip( - scalars_df_index.iterrows(), scalars_pandas_df_index.iterrows() - ): - assert bf_index == pd_index - pandas.testing.assert_series_equal(bf_series, pd_series) - - -@pytest.mark.parametrize( - ( - "index", - "name", - ), - [ - ( - True, - "my_df", - ), - (False, None), - ], -) -def test_itertuples(scalars_df_index, index, name): - # Numeric has slightly different representation as a result of conversions. - bf_tuples = scalars_df_index.itertuples(index, name) - pd_tuples = scalars_df_index.to_pandas().itertuples(index, name) - for bf_tuple, pd_tuple in zip(bf_tuples, pd_tuples): - assert bf_tuple == pd_tuple - - -def test_df_cross_merge(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - left_columns = ["int64_col", "float64_col", "rowindex_2"] - right_columns = ["int64_col", "bool_col", "string_col", "rowindex_2"] - - left = scalars_df[left_columns] - # Offset the rows somewhat so that outer join can have an effect. - right = scalars_df[right_columns].assign(rowindex_2=scalars_df["rowindex_2"] + 2) - - bf_result = left.merge(right, "cross").to_pandas() - - pd_result = scalars_pandas_df[left_columns].merge( - scalars_pandas_df[right_columns].assign( - rowindex_2=scalars_pandas_df["rowindex_2"] + 2 - ), - "cross", - ) - pd.testing.assert_frame_equal(bf_result, pd_result, check_index_type=False) - - -@pytest.mark.parametrize( - ("merge_how",), - [ - ("inner",), - ("outer",), - ("left",), - ("right",), - ], -) -def test_df_merge(scalars_dfs, merge_how): - scalars_df, scalars_pandas_df = scalars_dfs - on = "rowindex_2" - left_columns = ["int64_col", "float64_col", "rowindex_2"] - right_columns = ["int64_col", "bool_col", "string_col", "rowindex_2"] - - left = scalars_df[left_columns] - # Offset the rows somewhat so that outer join can have an effect. - right = scalars_df[right_columns].assign(rowindex_2=scalars_df["rowindex_2"] + 2) - - df = left.merge(right, merge_how, on, sort=True) - bf_result = df.to_pandas() - - pd_result = scalars_pandas_df[left_columns].merge( - scalars_pandas_df[right_columns].assign( - rowindex_2=scalars_pandas_df["rowindex_2"] + 2 - ), - merge_how, - on, - sort=True, - ) - - assert_frame_equal(bf_result, pd_result, ignore_order=True, check_index_type=False) - - -@pytest.mark.parametrize( - ("left_on", "right_on"), - [ - (["int64_col", "rowindex_2"], ["int64_col", "rowindex_2"]), - (["rowindex_2", "int64_col"], ["int64_col", "rowindex_2"]), - # Polars engine is currently strict on join key types - # (["rowindex_2", "float64_col"], ["int64_col", "rowindex_2"]), - ], -) -def test_df_merge_multi_key(scalars_dfs, left_on, right_on): - scalars_df, scalars_pandas_df = scalars_dfs - left_columns = ["int64_col", "float64_col", "rowindex_2"] - right_columns = ["int64_col", "bool_col", "string_col", "rowindex_2"] - - left = scalars_df[left_columns] - # Offset the rows somewhat so that outer join can have an effect. - right = scalars_df[right_columns].assign(rowindex_2=scalars_df["rowindex_2"] + 2) - - df = left.merge(right, "outer", left_on=left_on, right_on=right_on, sort=True) - bf_result = df.to_pandas() - - pd_result = scalars_pandas_df[left_columns].merge( - scalars_pandas_df[right_columns].assign( - rowindex_2=scalars_pandas_df["rowindex_2"] + 2 - ), - "outer", - left_on=left_on, - right_on=right_on, - sort=True, - ) - - assert_frame_equal(bf_result, pd_result, ignore_order=True, check_index_type=False) - - -@pytest.mark.parametrize( - ("merge_how",), - [ - ("inner",), - ("outer",), - ("left",), - ("right",), - ], -) -def test_merge_custom_col_name(scalars_dfs, merge_how): - scalars_df, scalars_pandas_df = scalars_dfs - left_columns = ["int64_col", "float64_col"] - right_columns = ["int64_col", "bool_col", "string_col"] - on = "int64_col" - rename_columns = {"float64_col": "f64_col"} - - left = scalars_df[left_columns] - left = left.rename(columns=rename_columns) - right = scalars_df[right_columns] - df = left.merge(right, merge_how, on, sort=True) - bf_result = df.to_pandas() - - pandas_left_df = scalars_pandas_df[left_columns] - pandas_left_df = pandas_left_df.rename(columns=rename_columns) - pandas_right_df = scalars_pandas_df[right_columns] - pd_result = pandas_left_df.merge(pandas_right_df, merge_how, on, sort=True) - - assert_frame_equal(bf_result, pd_result, ignore_order=True, check_index_type=False) - - -@pytest.mark.parametrize( - ("merge_how",), - [ - ("inner",), - ("outer",), - ("left",), - ("right",), - ], -) -def test_merge_left_on_right_on(scalars_dfs, merge_how): - scalars_df, scalars_pandas_df = scalars_dfs - left_columns = ["int64_col", "float64_col", "int64_too"] - right_columns = ["int64_col", "bool_col", "string_col", "rowindex_2"] - - left = scalars_df[left_columns] - right = scalars_df[right_columns] - - df = left.merge( - right, merge_how, left_on="int64_too", right_on="rowindex_2", sort=True - ) - bf_result = df.to_pandas() - - pd_result = scalars_pandas_df[left_columns].merge( - scalars_pandas_df[right_columns], - merge_how, - left_on="int64_too", - right_on="rowindex_2", - sort=True, - ) - - assert_frame_equal(bf_result, pd_result, ignore_order=True, check_index_type=False) - - -def test_shape(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - bf_result = scalars_df.shape - pd_result = scalars_pandas_df.shape - - assert bf_result == pd_result - - -def test_len(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - bf_result = len(scalars_df) - pd_result = len(scalars_pandas_df) - - assert bf_result == pd_result - - -@pytest.mark.parametrize( - ("n_rows",), - [ - (50,), - (10000,), - ], -) -def test_df_len_local(session, n_rows): - assert ( - len( - session.read_pandas( - pd.DataFrame(np.random.randint(1, 7, n_rows), columns=["one"]), - ) - ) - == n_rows - ) - - -def test_size(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - bf_result = scalars_df.size - pd_result = scalars_pandas_df.size - - assert bf_result == pd_result - - -def test_ndim(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - bf_result = scalars_df.ndim - pd_result = scalars_pandas_df.ndim - - assert bf_result == pd_result - - -def test_empty_false(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - - bf_result = scalars_df.empty - pd_result = scalars_pandas_df.empty - - assert bf_result == pd_result - - -def test_empty_true_column_filter(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - - bf_result = scalars_df[[]].empty - pd_result = scalars_pandas_df[[]].empty - - assert bf_result == pd_result - - -def test_empty_true_row_filter(scalars_dfs: Tuple[dataframe.DataFrame, pd.DataFrame]): - scalars_df, scalars_pandas_df = scalars_dfs - bf_bool: series.Series = typing.cast(series.Series, scalars_df["bool_col"]) - pd_bool: pd.Series = scalars_pandas_df["bool_col"] - bf_false = bf_bool.notna() & (bf_bool != bf_bool) - pd_false = pd_bool.notna() & (pd_bool != pd_bool) - - bf_result = scalars_df[bf_false].empty - pd_result = scalars_pandas_df[pd_false].empty - - assert pd_result - assert bf_result == pd_result - - -def test_empty_true_memtable(session: bigframes.Session): - bf_df = dataframe.DataFrame(session=session) - pd_df = pd.DataFrame() - - bf_result = bf_df.empty - pd_result = pd_df.empty - - assert pd_result - assert bf_result == pd_result - - -@pytest.mark.parametrize( - ("drop",), - ((True,), (False,)), -) -def test_reset_index(scalars_df_index, scalars_pandas_df_index, drop): - df = scalars_df_index.reset_index(drop=drop) - assert df.index.name is None - - bf_result = df.to_pandas() - pd_result = scalars_pandas_df_index.reset_index(drop=drop) - - # Pandas uses int64 instead of Int64 (nullable) dtype. - pd_result.index = pd_result.index.astype(pd.Int64Dtype()) - - # reset_index should maintain the original ordering. - pandas.testing.assert_frame_equal(bf_result, pd_result) - - -def test_reset_index_then_filter( - scalars_df_index, - scalars_pandas_df_index, -): - bf_filter = scalars_df_index["bool_col"].fillna(True) - bf_df = scalars_df_index.reset_index()[bf_filter] - bf_result = bf_df.to_pandas() - pd_filter = scalars_pandas_df_index["bool_col"].fillna(True) - pd_result = scalars_pandas_df_index.reset_index()[pd_filter] - - # Pandas uses int64 instead of Int64 (nullable) dtype. - pd_result.index = pd_result.index.astype(pd.Int64Dtype()) - - # reset_index should maintain the original ordering and index keys - # post-filter will have gaps. - pandas.testing.assert_frame_equal(bf_result, pd_result) - - -def test_reset_index_with_unnamed_index( - scalars_df_index, - scalars_pandas_df_index, -): - scalars_df_index = scalars_df_index.copy() - scalars_pandas_df_index = scalars_pandas_df_index.copy() - - scalars_df_index.index.name = None - scalars_pandas_df_index.index.name = None - df = scalars_df_index.reset_index(drop=False) - assert df.index.name is None - - # reset_index(drop=False) creates a new column "index". - assert df.columns[0] == "index" - - bf_result = df.to_pandas() - pd_result = scalars_pandas_df_index.reset_index(drop=False) - - # Pandas uses int64 instead of Int64 (nullable) dtype. - pd_result.index = pd_result.index.astype(pd.Int64Dtype()) - - # reset_index should maintain the original ordering. - pandas.testing.assert_frame_equal(bf_result, pd_result) - - -def test_reset_index_with_unnamed_multiindex(session): - bf_df = dataframe.DataFrame( - ([1, 2, 3], [2, 5, 7]), - index=pd.MultiIndex.from_tuples([("a", "aa"), ("a", "aa")]), - session=session, - ) - pd_df = pd.DataFrame( - ([1, 2, 3], [2, 5, 7]), - index=pd.MultiIndex.from_tuples([("a", "aa"), ("a", "aa")]), - ) - - bf_df = bf_df.reset_index() - pd_df = pd_df.reset_index() - - assert pd_df.columns[0] == "level_0" - assert bf_df.columns[0] == "level_0" - assert pd_df.columns[1] == "level_1" - assert bf_df.columns[1] == "level_1" - - -def test_reset_index_with_unnamed_index_and_index_column( - scalars_df_index, - scalars_pandas_df_index, -): - scalars_df_index = scalars_df_index.copy() - scalars_pandas_df_index = scalars_pandas_df_index.copy() - - scalars_df_index.index.name = None - scalars_pandas_df_index.index.name = None - df = scalars_df_index.assign(index=scalars_df_index["int64_col"]).reset_index( - drop=False - ) - assert df.index.name is None - - # reset_index(drop=False) creates a new column "level_0" if the "index" column already exists. - assert df.columns[0] == "level_0" - - bf_result = df.to_pandas() - pd_result = scalars_pandas_df_index.assign( - index=scalars_pandas_df_index["int64_col"] - ).reset_index(drop=False) - - # Pandas uses int64 instead of Int64 (nullable) dtype. - pd_result.index = pd_result.index.astype(pd.Int64Dtype()) - - # reset_index should maintain the original ordering. - pandas.testing.assert_frame_equal(bf_result, pd_result) - - -@pytest.mark.parametrize( - ("drop",), - ( - (True,), - (False,), - ), -) -@pytest.mark.parametrize( - ("append",), - ( - (True,), - (False,), - ), -) -@pytest.mark.parametrize( - ("index_column",), - (("int64_too",), ("string_col",), ("timestamp_col",)), -) -def test_set_index(scalars_dfs, index_column, drop, append): - scalars_df, scalars_pandas_df = scalars_dfs - df = scalars_df.set_index(index_column, append=append, drop=drop) - bf_result = df.to_pandas() - pd_result = scalars_pandas_df.set_index(index_column, append=append, drop=drop) - - # Sort to disambiguate when there are duplicate index labels. - # Note: Doesn't use assert_pandas_df_equal_ignore_ordering because we get - # "ValueError: 'timestamp_col' is both an index level and a column label, - # which is ambiguous" when trying to sort by a column with the same name as - # the index. - bf_result = bf_result.sort_values("rowindex_2") - pd_result = pd_result.sort_values("rowindex_2") - - pandas.testing.assert_frame_equal(bf_result, pd_result) - - -def test_set_index_key_error(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - with pytest.raises(KeyError): - scalars_pandas_df.set_index(["not_a_col"]) - with pytest.raises(KeyError): - scalars_df.set_index(["not_a_col"]) - - -@pytest.mark.parametrize( - ("ascending",), - ((True,), (False,)), -) -@pytest.mark.parametrize( - ("na_position",), - (("first",), ("last",)), -) -@pytest.mark.parametrize( - ("axis",), - ((0,), ("columns",)), -) -def test_sort_index(scalars_dfs, ascending, na_position, axis): - index_column = "int64_col" - scalars_df, scalars_pandas_df = scalars_dfs - df = scalars_df.set_index(index_column) - bf_result = df.sort_index( - ascending=ascending, na_position=na_position, axis=axis - ).to_pandas() - pd_result = scalars_pandas_df.set_index(index_column).sort_index( - ascending=ascending, na_position=na_position, axis=axis - ) - pandas.testing.assert_frame_equal(bf_result, pd_result) - - -def test_dataframe_sort_index_inplace(scalars_dfs): - index_column = "int64_col" - scalars_df, scalars_pandas_df = scalars_dfs - df = scalars_df.copy().set_index(index_column) - df.sort_index(ascending=False, inplace=True) - bf_result = df.to_pandas() - - pd_result = scalars_pandas_df.set_index(index_column).sort_index(ascending=False) - pandas.testing.assert_frame_equal(bf_result, pd_result) - - -def test_df_abs(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - columns = ["int64_col", "int64_too", "float64_col"] - - bf_result = scalars_df[columns].abs() - pd_result = scalars_pandas_df[columns].abs() - - assert_dfs_equivalent(pd_result, bf_result) - - -def test_df_pos(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - bf_result = (+scalars_df[["int64_col", "numeric_col"]]).to_pandas() - pd_result = +scalars_pandas_df[["int64_col", "numeric_col"]] - - assert_frame_equal(pd_result, bf_result) - - -def test_df_neg(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - bf_result = (-scalars_df[["int64_col", "numeric_col"]]).to_pandas() - pd_result = -scalars_pandas_df[["int64_col", "numeric_col"]] - - assert_frame_equal(pd_result, bf_result) - - -def test_df_invert(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - columns = ["int64_col", "bool_col"] - - bf_result = (~scalars_df[columns]).to_pandas() - pd_result = ~scalars_pandas_df[columns] - - assert_frame_equal(bf_result, pd_result) - - -def test_df_isnull(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - - columns = ["int64_col", "int64_too", "string_col", "bool_col"] - bf_result = scalars_df[columns].isnull().to_pandas() - pd_result = scalars_pandas_df[columns].isnull() - - # One of dtype mismatches to be documented. Here, the `bf_result.dtype` is - # `BooleanDtype` but the `pd_result.dtype` is `bool`. - pd_result["int64_col"] = pd_result["int64_col"].astype(pd.BooleanDtype()) - pd_result["int64_too"] = pd_result["int64_too"].astype(pd.BooleanDtype()) - pd_result["string_col"] = pd_result["string_col"].astype(pd.BooleanDtype()) - pd_result["bool_col"] = pd_result["bool_col"].astype(pd.BooleanDtype()) - - assert_frame_equal(bf_result, pd_result) - - -def test_df_notnull(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - - columns = ["int64_col", "int64_too", "string_col", "bool_col"] - bf_result = scalars_df[columns].notnull().to_pandas() - pd_result = scalars_pandas_df[columns].notnull() - - # One of dtype mismatches to be documented. Here, the `bf_result.dtype` is - # `BooleanDtype` but the `pd_result.dtype` is `bool`. - pd_result["int64_col"] = pd_result["int64_col"].astype(pd.BooleanDtype()) - pd_result["int64_too"] = pd_result["int64_too"].astype(pd.BooleanDtype()) - pd_result["string_col"] = pd_result["string_col"].astype(pd.BooleanDtype()) - pd_result["bool_col"] = pd_result["bool_col"].astype(pd.BooleanDtype()) - - assert_frame_equal(bf_result, pd_result) - - -@pytest.mark.parametrize( - ("left_labels", "right_labels", "overwrite", "fill_value"), - [ - (["a", "b", "c"], ["c", "a", "b"], True, None), - (["a", "b", "c"], ["c", "a", "b"], False, None), - (["a", "b", "c"], ["a", "b", "c"], False, 2), - ], - ids=[ - "one_one_match_overwrite", - "one_one_match_no_overwrite", - "exact_match", - ], -) -def test_combine( - scalars_df_index, - scalars_df_2_index, - scalars_pandas_df_index, - left_labels, - right_labels, - overwrite, - fill_value, -): - if pd.__version__.startswith("1."): - pytest.skip("pd.NA vs NaN not handled well in pandas 1.x.") - columns = ["int64_too", "int64_col", "float64_col"] - - bf_df_a = scalars_df_index[columns] - bf_df_a.columns = left_labels - bf_df_b = scalars_df_2_index[columns] - bf_df_b.columns = right_labels - bf_result = bf_df_a.combine( - bf_df_b, - lambda x, y: x**2 + 2 * x * y + y**2, - overwrite=overwrite, - fill_value=fill_value, - ).to_pandas() - - pd_df_a = scalars_pandas_df_index[columns] - pd_df_a.columns = left_labels - pd_df_b = scalars_pandas_df_index[columns] - pd_df_b.columns = right_labels - pd_result = pd_df_a.combine( - pd_df_b, - lambda x, y: x**2 + 2 * x * y + y**2, - overwrite=overwrite, - fill_value=fill_value, - ) - - # Some dtype inconsistency for all-NULL columns - pd.testing.assert_frame_equal(bf_result, pd_result, check_dtype=False) - - -@pytest.mark.parametrize( - ("overwrite", "filter_func"), - [ - (True, None), - (False, None), - (True, lambda x: x.isna() | (x % 2 == 0)), - ], - ids=[ - "default", - "overwritefalse", - "customfilter", - ], -) -def test_df_update(overwrite, filter_func): - if pd.__version__.startswith("1."): - pytest.skip("dtype handled differently in pandas 1.x.") - - index1: pandas.Index = pandas.Index([1, 2, 3, 4], dtype="Int64") - - index2: pandas.Index = pandas.Index([1, 2, 4, 5], dtype="Int64") - pd_df1 = pandas.DataFrame( - {"a": [1, None, 3, 4], "b": [5, 6, None, 8]}, dtype="Int64", index=index1 - ) - pd_df2 = pandas.DataFrame( - {"a": [None, 20, 30, 40], "c": [90, None, 110, 120]}, - dtype="Int64", - index=index2, - ) - - bf_df1 = dataframe.DataFrame(pd_df1) - bf_df2 = dataframe.DataFrame(pd_df2) - - bf_df1.update(bf_df2, overwrite=overwrite, filter_func=filter_func) - pd_df1.update(pd_df2, overwrite=overwrite, filter_func=filter_func) - - pd.testing.assert_frame_equal(bf_df1.to_pandas(), pd_df1) - - -def test_df_idxmin(): - pd_df = pd.DataFrame( - {"a": [1, 2, 3], "b": [7, None, 3], "c": [4, 4, 4]}, index=["x", "y", "z"] - ) - bf_df = dataframe.DataFrame(pd_df) - - bf_result = bf_df.idxmin().to_pandas() - pd_result = pd_df.idxmin() - - pd.testing.assert_series_equal( - bf_result, pd_result, check_index_type=False, check_dtype=False - ) - - -def test_df_idxmax(): - pd_df = pd.DataFrame( - {"a": [1, 2, 3], "b": [7, None, 3], "c": [4, 4, 4]}, index=["x", "y", "z"] - ) - bf_df = dataframe.DataFrame(pd_df) - - bf_result = bf_df.idxmax().to_pandas() - pd_result = pd_df.idxmax() - - pd.testing.assert_series_equal( - bf_result, pd_result, check_index_type=False, check_dtype=False - ) - - -@pytest.mark.parametrize( - ("join", "axis"), - [ - ("outer", None), - ("outer", 0), - ("outer", 1), - ("left", 0), - ("right", 1), - ("inner", None), - ("inner", 1), - ], -) -def test_df_align(join, axis): - index1: pandas.Index = pandas.Index([1, 2, 3, 4], dtype="Int64") - - index2: pandas.Index = pandas.Index([1, 2, 4, 5], dtype="Int64") - pd_df1 = pandas.DataFrame( - {"a": [1, None, 3, 4], "b": [5, 6, None, 8]}, dtype="Int64", index=index1 - ) - pd_df2 = pandas.DataFrame( - {"a": [None, 20, 30, 40], "c": [90, None, 110, 120]}, - dtype="Int64", - index=index2, - ) - - bf_df1 = dataframe.DataFrame(pd_df1) - bf_df2 = dataframe.DataFrame(pd_df2) - - bf_result1, bf_result2 = bf_df1.align(bf_df2, join=join, axis=axis) - pd_result1, pd_result2 = pd_df1.align(pd_df2, join=join, axis=axis) - - # Don't check dtype as pandas does unnecessary float conversion - assert isinstance(bf_result1, dataframe.DataFrame) and isinstance( - bf_result2, dataframe.DataFrame - ) - pd.testing.assert_frame_equal(bf_result1.to_pandas(), pd_result1, check_dtype=False) - pd.testing.assert_frame_equal(bf_result2.to_pandas(), pd_result2, check_dtype=False) - - -def test_combine_first( - scalars_df_index, - scalars_df_2_index, - scalars_pandas_df_index, -): - if pd.__version__.startswith("1."): - pytest.skip("pd.NA vs NaN not handled well in pandas 1.x.") - columns = ["int64_too", "int64_col", "float64_col"] - - bf_df_a = scalars_df_index[columns].iloc[0:6] - bf_df_a.columns = ["a", "b", "c"] - bf_df_b = scalars_df_2_index[columns].iloc[2:8] - bf_df_b.columns = ["b", "a", "d"] - bf_result = bf_df_a.combine_first(bf_df_b).to_pandas() - - pd_df_a = scalars_pandas_df_index[columns].iloc[0:6] - pd_df_a.columns = ["a", "b", "c"] - pd_df_b = scalars_pandas_df_index[columns].iloc[2:8] - pd_df_b.columns = ["b", "a", "d"] - pd_result = pd_df_a.combine_first(pd_df_b) - - # Some dtype inconsistency for all-NULL columns - pd.testing.assert_frame_equal(bf_result, pd_result, check_dtype=False) - - -def test_df_corr_w_invalid_parameters(scalars_dfs): - columns = ["int64_too", "int64_col", "float64_col"] - scalars_df, _ = scalars_dfs - - with pytest.raises(NotImplementedError): - scalars_df[columns].corr(method="kendall") - - with pytest.raises(NotImplementedError): - scalars_df[columns].corr(min_periods=1) - - -@pytest.mark.parametrize( - ("columns", "numeric_only"), - [ - (["bool_col", "int64_col", "float64_col"], True), - (["bool_col", "int64_col", "float64_col"], False), - (["bool_col", "int64_col", "float64_col", "string_col"], True), - pytest.param( - ["bool_col", "int64_col", "float64_col", "string_col"], - False, - marks=pytest.mark.xfail( - raises=NotImplementedError, - ), - ), - ], -) -def test_cov_w_numeric_only(scalars_dfs, columns, numeric_only): - scalars_df, scalars_pandas_df = scalars_dfs - bf_result = scalars_df[columns].cov(numeric_only=numeric_only).to_pandas() - pd_result = scalars_pandas_df[columns].cov(numeric_only=numeric_only) - # BigFrames and Pandas differ in their data type handling: - # - Column types: BigFrames uses Float64, Pandas uses float64. - # - Index types: BigFrames uses strign, Pandas uses object. - pd.testing.assert_index_equal(bf_result.columns, pd_result.columns) - # Only check row order in ordered mode. - pd.testing.assert_frame_equal( - bf_result, - pd_result, - check_dtype=False, - check_index_type=False, - check_like=~scalars_df._block.session._strictly_ordered, - ) - - -def test_df_corrwith_df(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - - l_cols = ["int64_col", "float64_col", "int64_too"] - r_cols = ["int64_too", "float64_col"] - - bf_result = scalars_df[l_cols].corrwith(scalars_df[r_cols]).to_pandas() - pd_result = scalars_pandas_df[l_cols].corrwith(scalars_pandas_df[r_cols]) - - # BigFrames and Pandas differ in their data type handling: - # - Column types: BigFrames uses Float64, Pandas uses float64. - # - Index types: BigFrames uses strign, Pandas uses object. - pd.testing.assert_series_equal( - bf_result, pd_result, check_dtype=False, check_index_type=False - ) - - -def test_df_corrwith_df_numeric_only(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - - l_cols = ["int64_col", "float64_col", "int64_too", "string_col"] - r_cols = ["int64_too", "float64_col", "bool_col"] - - bf_result = ( - scalars_df[l_cols].corrwith(scalars_df[r_cols], numeric_only=True).to_pandas() - ) - pd_result = scalars_pandas_df[l_cols].corrwith( - scalars_pandas_df[r_cols], numeric_only=True - ) - - # BigFrames and Pandas differ in their data type handling: - # - Column types: BigFrames uses Float64, Pandas uses float64. - # - Index types: BigFrames uses strign, Pandas uses object. - pd.testing.assert_series_equal( - bf_result, pd_result, check_dtype=False, check_index_type=False - ) - - -def test_df_corrwith_df_non_numeric_error(scalars_dfs): - scalars_df, _ = scalars_dfs - - l_cols = ["int64_col", "float64_col", "int64_too", "string_col"] - r_cols = ["int64_too", "float64_col", "bool_col"] - - with pytest.raises(NotImplementedError): - scalars_df[l_cols].corrwith(scalars_df[r_cols], numeric_only=False) - - -def test_df_corrwith_series(scalars_dfs): - # TODO: supply a reason why this isn't compatible with pandas 1.x - pytest.importorskip("pandas", minversion="2.0.0") - scalars_df, scalars_pandas_df = scalars_dfs - - l_cols = ["int64_col", "float64_col", "int64_too"] - r_col = "float64_col" - - bf_result = scalars_df[l_cols].corrwith(scalars_df[r_col]).to_pandas() - pd_result = scalars_pandas_df[l_cols].corrwith(scalars_pandas_df[r_col]) - - # BigFrames and Pandas differ in their data type handling: - # - Column types: BigFrames uses Float64, Pandas uses float64. - # - Index types: BigFrames uses strign, Pandas uses object. - pd.testing.assert_series_equal( - bf_result, pd_result, check_dtype=False, check_index_type=False - ) - - -@pytest.mark.parametrize( - ("op"), - [ - operator.add, - operator.sub, - operator.mul, - operator.truediv, - operator.floordiv, - operator.eq, - operator.ne, - operator.gt, - operator.ge, - operator.lt, - operator.le, - ], - ids=[ - "add", - "subtract", - "multiply", - "true_divide", - "floor_divide", - "eq", - "ne", - "gt", - "ge", - "lt", - "le", - ], -) -# TODO(garrettwu): deal with NA values -@pytest.mark.parametrize(("other_scalar"), [1, 2.5, 0, 0.0]) -@pytest.mark.parametrize(("reverse_operands"), [True, False]) -def test_scalar_binop(scalars_dfs, op, other_scalar, reverse_operands): - scalars_df, scalars_pandas_df = scalars_dfs - columns = ["int64_col", "float64_col"] - - maybe_reversed_op = (lambda x, y: op(y, x)) if reverse_operands else op - - bf_result = maybe_reversed_op(scalars_df[columns], other_scalar).to_pandas() - pd_result = maybe_reversed_op(scalars_pandas_df[columns], other_scalar) - - assert_frame_equal(bf_result, pd_result) - - -@pytest.mark.parametrize(("other_scalar"), [1, -2]) -def test_mod(scalars_dfs, other_scalar): - # Zero case excluded as pandas produces 0 result for Int64 inputs rather than NA/NaN. - # This is likely a pandas bug as mod 0 is undefined in other dtypes, and most programming languages. - scalars_df, scalars_pandas_df = scalars_dfs - - bf_result = (scalars_df[["int64_col", "int64_too"]] % other_scalar).to_pandas() - pd_result = scalars_pandas_df[["int64_col", "int64_too"]] % other_scalar - - assert_frame_equal(bf_result, pd_result) - - -def test_scalar_binop_str_exception(scalars_dfs): - scalars_df, _ = scalars_dfs - columns = ["string_col"] - with pytest.raises(TypeError, match="Cannot add dtypes"): - (scalars_df[columns] + 1).to_pandas() - - -@pytest.mark.parametrize( - ("op"), - [ - (lambda x, y: x.add(y, axis="index")), - (lambda x, y: x.radd(y, axis="index")), - (lambda x, y: x.sub(y, axis="index")), - (lambda x, y: x.rsub(y, axis="index")), - (lambda x, y: x.mul(y, axis="index")), - (lambda x, y: x.rmul(y, axis="index")), - (lambda x, y: x.truediv(y, axis="index")), - (lambda x, y: x.rtruediv(y, axis="index")), - (lambda x, y: x.floordiv(y, axis="index")), - (lambda x, y: x.floordiv(y, axis="index")), - (lambda x, y: x.gt(y, axis="index")), - (lambda x, y: x.ge(y, axis="index")), - (lambda x, y: x.lt(y, axis="index")), - (lambda x, y: x.le(y, axis="index")), - ], - ids=[ - "add", - "radd", - "sub", - "rsub", - "mul", - "rmul", - "truediv", - "rtruediv", - "floordiv", - "rfloordiv", - "gt", - "ge", - "lt", - "le", - ], -) -def test_series_binop_axis_index( - scalars_dfs, - op, -): - scalars_df, scalars_pandas_df = scalars_dfs - df_columns = ["int64_col", "float64_col"] - series_column = "int64_too" - - bf_result = op(scalars_df[df_columns], scalars_df[series_column]).to_pandas() - pd_result = op(scalars_pandas_df[df_columns], scalars_pandas_df[series_column]) - - assert_frame_equal(bf_result, pd_result) - - -@pytest.mark.parametrize( - ("input"), - [ - ((1000, 2000, 3000)), - (pd.Index([1000, 2000, 3000])), - (pd.Series((1000, 2000), index=["int64_too", "float64_col"])), - ], - ids=[ - "tuple", - "pd_index", - "pd_series", - ], -) -def test_listlike_binop_axis_1_in_memory_data(scalars_dfs, input): - # TODO: supply a reason why this isn't compatible with pandas 1.x - pytest.importorskip("pandas", minversion="2.0.0") - scalars_df, scalars_pandas_df = scalars_dfs - - df_columns = ["int64_col", "float64_col", "int64_too"] - - bf_result = scalars_df[df_columns].add(input, axis=1).to_pandas() - if hasattr(input, "to_pandas"): - input = input.to_pandas() - pd_result = scalars_pandas_df[df_columns].add(input, axis=1) - - assert_frame_equal(bf_result, pd_result, check_dtype=False) - - -def test_df_reverse_binop_pandas(scalars_dfs): - # TODO: supply a reason why this isn't compatible with pandas 1.x - pytest.importorskip("pandas", minversion="2.0.0") - scalars_df, scalars_pandas_df = scalars_dfs - - pd_series = pd.Series([100, 200, 300]) - - df_columns = ["int64_col", "float64_col", "int64_too"] - - bf_result = pd_series + scalars_df[df_columns].to_pandas() - pd_result = pd_series + scalars_pandas_df[df_columns] - - assert_frame_equal(bf_result, pd_result, check_dtype=False) - - -def test_listlike_binop_axis_1_bf_index(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - - df_columns = ["int64_col", "float64_col", "int64_too"] - - bf_result = ( - scalars_df[df_columns] - .add(bf_indexes.Index([1000, 2000, 3000]), axis=1) - .to_pandas() - ) - pd_result = scalars_pandas_df[df_columns].add(pd.Index([1000, 2000, 3000]), axis=1) - - assert_frame_equal(bf_result, pd_result, check_dtype=False) - - -def test_binop_with_self_aggregate(session, scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - - df_columns = ["int64_col", "float64_col", "int64_too"] - - bf_df = scalars_df[df_columns] - bf_result = (bf_df - bf_df.mean()).to_pandas() - - pd_df = scalars_pandas_df[df_columns] - pd_result = pd_df - pd_df.mean() - - assert_frame_equal(bf_result, pd_result, check_dtype=False) - - -@pytest.mark.parametrize( - ("left_labels", "right_labels"), - [ - (["a", "a", "b"], ["c", "c", "d"]), - (["a", "b", "c"], ["c", "a", "b"]), - (["a", "c", "c"], ["c", "a", "c"]), - (["a", "b", "c"], ["a", "b", "c"]), - ], - ids=[ - "no_overlap", - "one_one_match", - "multi_match", - "exact_match", - ], -) -def test_binop_df_df_binary_op( - scalars_df_index, - scalars_df_2_index, - scalars_pandas_df_index, - left_labels, - right_labels, -): - if pd.__version__.startswith("1."): - pytest.skip("pd.NA vs NaN not handled well in pandas 1.x.") - columns = ["int64_too", "int64_col", "float64_col"] - - bf_df_a = scalars_df_index[columns] - bf_df_a.columns = left_labels - bf_df_b = scalars_df_2_index[columns] - bf_df_b.columns = right_labels - bf_result = (bf_df_a - bf_df_b).to_pandas() - - pd_df_a = scalars_pandas_df_index[columns] - pd_df_a.columns = left_labels - pd_df_b = scalars_pandas_df_index[columns] - pd_df_b.columns = right_labels - pd_result = pd_df_a - pd_df_b - - # Some dtype inconsistency for all-NULL columns - pd.testing.assert_frame_equal(bf_result, pd_result, check_dtype=False) - - -# Differnt table will only work for explicit index, since default index orders are arbitrary. -@pytest.mark.parametrize( - ("ordered"), - [ - (True), - (False), - ], -) -def test_series_binop_add_different_table( - scalars_df_index, scalars_pandas_df_index, scalars_df_2_index, ordered -): - df_columns = ["int64_col", "float64_col"] - series_column = "int64_too" - - bf_result = ( - scalars_df_index[df_columns] - .add(scalars_df_2_index[series_column], axis="index") - .to_pandas(ordered=ordered) - ) - pd_result = scalars_pandas_df_index[df_columns].add( - scalars_pandas_df_index[series_column], axis="index" - ) - - assert_frame_equal(bf_result, pd_result, ignore_order=not ordered) - - -# TODO(garrettwu): Test series binop with different index - -all_joins = pytest.mark.parametrize( - ("how",), - (("outer",), ("left",), ("right",), ("inner",), ("cross",)), -) - - -@all_joins -def test_join_same_table(scalars_dfs, how): - bf_df, pd_df = scalars_dfs - if not bf_df._session._strictly_ordered and how == "cross": - pytest.skip("Cross join not supported in partial ordering mode.") - - bf_df_a = bf_df.set_index("int64_too")[["string_col", "int64_col"]] - bf_df_a = bf_df_a.sort_index() - - bf_df_b = bf_df.set_index("int64_too")[["float64_col"]] - bf_df_b = bf_df_b[bf_df_b.float64_col > 0] - bf_df_b = bf_df_b.sort_values("float64_col") - - bf_result = bf_df_a.join(bf_df_b, how=how).to_pandas() - - pd_df_a = pd_df.set_index("int64_too")[["string_col", "int64_col"]].sort_index() - pd_df_a = pd_df_a.sort_index() - - pd_df_b = pd_df.set_index("int64_too")[["float64_col"]] - pd_df_b = pd_df_b[pd_df_b.float64_col > 0] - pd_df_b = pd_df_b.sort_values("float64_col") - - pd_result = pd_df_a.join(pd_df_b, how=how) - - assert_frame_equal(bf_result, pd_result, ignore_order=True) - - -@all_joins -def test_join_different_table( - scalars_df_index, scalars_df_2_index, scalars_pandas_df_index, how -): - bf_df_a = scalars_df_index[["string_col", "int64_col"]] - bf_df_b = scalars_df_2_index.dropna()[["float64_col"]] - bf_result = bf_df_a.join(bf_df_b, how=how).to_pandas() - pd_df_a = scalars_pandas_df_index[["string_col", "int64_col"]] - pd_df_b = scalars_pandas_df_index.dropna()[["float64_col"]] - pd_result = pd_df_a.join(pd_df_b, how=how) - assert_frame_equal(bf_result, pd_result, ignore_order=True) - - -@all_joins -def test_join_raise_when_param_on_duplicate_with_column(scalars_df_index, how): - if how == "cross": - return - bf_df_a = scalars_df_index[["string_col", "int64_col"]].rename( - columns={"int64_col": "string_col"} - ) - bf_df_b = scalars_df_index.dropna()["string_col"] - with pytest.raises( - ValueError, match="The column label 'string_col' is not unique." - ): - bf_df_a.join(bf_df_b, on="string_col", how=how, lsuffix="_l", rsuffix="_r") - - -def test_join_duplicate_columns_raises_value_error(scalars_dfs): - scalars_df, _ = scalars_dfs - df_a = scalars_df[["string_col", "float64_col"]] - df_b = scalars_df[["float64_col"]] - with pytest.raises(ValueError, match="columns overlap but no suffix specified"): - df_a.join(df_b, how="outer") - - -@all_joins -def test_join_param_on_duplicate_with_index_raises_value_error(scalars_df_index, how): - if how == "cross": - return - bf_df_a = scalars_df_index[["string_col"]] - bf_df_a.index.name = "string_col" - bf_df_b = scalars_df_index.dropna()["string_col"] - with pytest.raises( - ValueError, - match="'string_col' is both an index level and a column label, which is ambiguous.", - ): - bf_df_a.join(bf_df_b, on="string_col", how=how, lsuffix="_l", rsuffix="_r") - - -@all_joins -def test_join_param_on(scalars_dfs, how): - bf_df, pd_df = scalars_dfs - - bf_df_a = bf_df[["string_col", "int64_col", "rowindex_2"]] - bf_df_a = bf_df_a.assign(rowindex_2=bf_df_a["rowindex_2"] + 2) - bf_df_b = bf_df[["float64_col"]] - - if how == "cross": - with pytest.raises(ValueError, match="'on' is not supported for cross join."): - bf_df_a.join(bf_df_b, on="rowindex_2", how=how) - else: - bf_result = bf_df_a.join(bf_df_b, on="rowindex_2", how=how).to_pandas() - - pd_df_a = pd_df[["string_col", "int64_col", "rowindex_2"]] - pd_df_a = pd_df_a.assign(rowindex_2=pd_df_a["rowindex_2"] + 2) - pd_df_b = pd_df[["float64_col"]] - pd_result = pd_df_a.join(pd_df_b, on="rowindex_2", how=how) - assert_frame_equal(bf_result, pd_result, ignore_order=True) - - -@all_joins -def test_df_join_series(scalars_dfs, how): - bf_df, pd_df = scalars_dfs - - bf_df_a = bf_df[["string_col", "int64_col", "rowindex_2"]] - bf_df_a = bf_df_a.assign(rowindex_2=bf_df_a["rowindex_2"] + 2) - bf_series_b = bf_df["float64_col"] - - if how == "cross": - with pytest.raises(ValueError): - bf_df_a.join(bf_series_b, on="rowindex_2", how=how) - else: - bf_result = bf_df_a.join(bf_series_b, on="rowindex_2", how=how).to_pandas() - - pd_df_a = pd_df[["string_col", "int64_col", "rowindex_2"]] - pd_df_a = pd_df_a.assign(rowindex_2=pd_df_a["rowindex_2"] + 2) - pd_series_b = pd_df["float64_col"] - pd_result = pd_df_a.join(pd_series_b, on="rowindex_2", how=how) - assert_frame_equal(bf_result, pd_result, ignore_order=True) - - -@pytest.mark.parametrize( - ("by", "ascending", "na_position"), - [ - ("int64_col", True, "first"), - (["bool_col", "int64_col"], True, "last"), - ("int64_col", False, "first"), - (["bool_col", "int64_col"], [False, True], "last"), - (["bool_col", "int64_col"], [True, False], "first"), - ], -) -def test_dataframe_sort_values( - scalars_df_index, scalars_pandas_df_index, by, ascending, na_position -): - # Test needs values to be unique - bf_result = scalars_df_index.sort_values( - by, ascending=ascending, na_position=na_position - ).to_pandas() - pd_result = scalars_pandas_df_index.sort_values( - by, ascending=ascending, na_position=na_position - ) - - pandas.testing.assert_frame_equal( - bf_result, - pd_result, - ) - - -@pytest.mark.parametrize( - ("by", "ascending", "na_position"), - [ - ("int64_col", True, "first"), - (["bool_col", "int64_col"], True, "last"), - ], -) -def test_dataframe_sort_values_inplace( - scalars_df_index, scalars_pandas_df_index, by, ascending, na_position -): - # Test needs values to be unique - bf_sorted = scalars_df_index.copy() - bf_sorted.sort_values( - by, ascending=ascending, na_position=na_position, inplace=True - ) - bf_result = bf_sorted.to_pandas() - pd_result = scalars_pandas_df_index.sort_values( - by, ascending=ascending, na_position=na_position - ) - - pandas.testing.assert_frame_equal( - bf_result, - pd_result, - ) - - -def test_dataframe_sort_values_invalid_input(scalars_df_index): - with pytest.raises(KeyError): - scalars_df_index.sort_values(by=scalars_df_index["int64_col"]) - - -def test_dataframe_sort_values_stable(scalars_df_index, scalars_pandas_df_index): - bf_result = ( - scalars_df_index.sort_values("int64_col", kind="stable") - .sort_values("bool_col", kind="stable") - .to_pandas() - ) - pd_result = scalars_pandas_df_index.sort_values( - "int64_col", kind="stable" - ).sort_values("bool_col", kind="stable") - - pandas.testing.assert_frame_equal( - bf_result, - pd_result, - ) - - -@pytest.mark.parametrize( - ("operator", "columns"), - [ - pytest.param(lambda x: x.cumsum(), ["float64_col", "int64_too"]), - # pytest.param(lambda x: x.cumprod(), ["float64_col", "int64_too"]), - pytest.param( - lambda x: x.cumprod(), - ["string_col"], - marks=pytest.mark.xfail( - raises=ValueError, - ), - ), - ], - ids=[ - "cumsum", - # "cumprod", - "non-numeric", - ], -) -def test_dataframe_numeric_analytic_op( - scalars_df_index, scalars_pandas_df_index, operator, columns -): - # TODO: Add nullable ints (pandas 1.x has poor behavior on these) - bf_series = operator(scalars_df_index[columns]) - pd_series = operator(scalars_pandas_df_index[columns]) - bf_result = bf_series.to_pandas() - pd.testing.assert_frame_equal(pd_series, bf_result, check_dtype=False) - - -@pytest.mark.parametrize( - ("operator"), - [ - (lambda x: x.cummin()), - (lambda x: x.cummax()), - (lambda x: x.shift(2)), - (lambda x: x.shift(-2)), - ], - ids=[ - "cummin", - "cummax", - "shiftpostive", - "shiftnegative", - ], -) -def test_dataframe_general_analytic_op( - scalars_df_index, scalars_pandas_df_index, operator -): - col_names = ["int64_too", "float64_col", "int64_col", "bool_col"] - bf_series = operator(scalars_df_index[col_names]) - pd_series = operator(scalars_pandas_df_index[col_names]) - bf_result = bf_series.to_pandas() - pd.testing.assert_frame_equal( - pd_series, - bf_result, - ) - - -@pytest.mark.parametrize( - ("periods",), - [ - (1,), - (2,), - (-1,), - ], -) -def test_dataframe_diff(scalars_df_index, scalars_pandas_df_index, periods): - col_names = ["int64_too", "float64_col", "int64_col"] - bf_result = scalars_df_index[col_names].diff(periods=periods).to_pandas() - pd_result = scalars_pandas_df_index[col_names].diff(periods=periods) - pd.testing.assert_frame_equal( - pd_result, - bf_result, - ) - - -@pytest.mark.parametrize( - ("periods",), - [ - (1,), - (2,), - (-1,), - ], -) -def test_dataframe_pct_change(scalars_df_index, scalars_pandas_df_index, periods): - col_names = ["int64_too", "float64_col", "int64_col"] - bf_result = scalars_df_index[col_names].pct_change(periods=periods).to_pandas() - # pandas 3.0 does not automatically ffill anymore - pd_result = scalars_pandas_df_index[col_names].ffill().pct_change(periods=periods) - assert_frame_equal( - pd_result, - bf_result, - nulls_are_nan=True, - ) - - -def test_dataframe_agg_single_string(scalars_dfs): - numeric_cols = ["int64_col", "int64_too", "float64_col"] - scalars_df, scalars_pandas_df = scalars_dfs - - bf_result = scalars_df[numeric_cols].agg("sum").to_pandas() - pd_result = scalars_pandas_df[numeric_cols].agg("sum") - - assert bf_result.dtype == "Float64" - pd.testing.assert_series_equal( - pd_result, bf_result, check_dtype=False, check_index_type=False - ) - - -@pytest.mark.parametrize( - ("agg",), - ( - ("sum",), - ("size",), - ), -) -def test_dataframe_agg_int_single_string(scalars_dfs, agg): - numeric_cols = ["int64_col", "int64_too", "bool_col"] - scalars_df, scalars_pandas_df = scalars_dfs - - bf_result = scalars_df[numeric_cols].agg(agg).to_pandas() - pd_result = scalars_pandas_df[numeric_cols].agg(agg) - - assert bf_result.dtype == "Int64" - pd.testing.assert_series_equal( - pd_result, bf_result, check_dtype=False, check_index_type=False - ) - - -def test_dataframe_agg_multi_string(scalars_dfs): - numeric_cols = ["int64_col", "int64_too", "float64_col"] - aggregations = [ - "sum", - "mean", - "median", - "std", - "var", - "min", - "max", - "nunique", - "count", - ] - scalars_df, scalars_pandas_df = scalars_dfs - bf_result = scalars_df[numeric_cols].agg(aggregations) - pd_result = scalars_pandas_df[numeric_cols].agg(aggregations) - - # Pandas may produce narrower numeric types, but bigframes always produces Float64 - pd_result = pd_result.astype("Float64") - - # Drop median, as it's an approximation. - bf_median = bf_result.loc["median", :] - bf_result = bf_result.drop(labels=["median"]) - pd_result = pd_result.drop(labels=["median"]) - - assert_dfs_equivalent(pd_result, bf_result, check_index_type=False) - - # Double-check that median is at least plausible. - assert ( - (bf_result.loc["min", :] <= bf_median) & (bf_median <= bf_result.loc["max", :]) - ).all() - - -def test_dataframe_agg_int_multi_string(scalars_dfs): - numeric_cols = ["int64_col", "int64_too", "bool_col"] - aggregations = [ - "sum", - "nunique", - "count", - "size", - ] - scalars_df, scalars_pandas_df = scalars_dfs - bf_result = scalars_df[numeric_cols].agg(aggregations).to_pandas() - pd_result = scalars_pandas_df[numeric_cols].agg(aggregations) - - for dtype in bf_result.dtypes: - assert dtype == "Int64" - - # Pandas may produce narrower numeric types - # Pandas has object index type - pd.testing.assert_frame_equal( - pd_result, bf_result, check_dtype=False, check_index_type=False - ) - - -def test_df_transpose(): - # Include some floats to ensure type coercion - values = [[0, 3.5, True], [1, 4.5, False], [2, 6.5, None]] - # Test complex case of both axes being multi-indices with non-unique elements - - columns: pandas.Index = pd.Index( - ["A", "B", "A"], dtype=pd.StringDtype(storage="pyarrow") - ) - columns_multi = pd.MultiIndex.from_arrays([columns, columns], names=["c1", "c2"]) - - index: pandas.Index = pd.Index( - ["b", "a", "a"], dtype=pd.StringDtype(storage="pyarrow") - ) - rows_multi = pd.MultiIndex.from_arrays([index, index], names=["r1", "r2"]) - - pd_df = pandas.DataFrame(values, index=rows_multi, columns=columns_multi) - bf_df = dataframe.DataFrame(values, index=rows_multi, columns=columns_multi) - - pd_result = pd_df.T - bf_result = bf_df.T.to_pandas() - - assert_frame_equal(pd_result, bf_result, check_dtype=False, nulls_are_nan=True) - - -def test_df_transpose_error(): - with pytest.raises(TypeError, match="Cannot coerce.*to a common type."): - dataframe.DataFrame([[1, "hello"], [2, "world"]]).transpose() - - -def test_df_transpose_repeated_uses_cache(): - bf_df = dataframe.DataFrame([[1, 2.5], [2, 3.5]]) - pd_df = pandas.DataFrame([[1, 2.5], [2, 3.5]]) - # Transposing many times so that operation will fail from complexity if not using cache - for i in range(10): - # Cache still works even with simple scalar binop - bf_df = bf_df.transpose() + i - pd_df = pd_df.transpose() + i - - pd.testing.assert_frame_equal( - pd_df, bf_df.to_pandas(), check_dtype=False, check_index_type=False - ) - - -def test_df_stack(scalars_dfs): - if pandas.__version__.startswith("1.") or pandas.__version__.startswith("2.0"): - pytest.skip("pandas <2.1 uses different stack implementation") - scalars_df, scalars_pandas_df = scalars_dfs - # To match bigquery dataframes - scalars_pandas_df = scalars_pandas_df.copy() - scalars_pandas_df.columns = scalars_pandas_df.columns.astype("string[pyarrow]") - # Can only stack identically-typed columns - columns = ["int64_col", "int64_too", "rowindex_2"] - - bf_result = scalars_df[columns].stack().to_pandas() - pd_result = scalars_pandas_df[columns].stack(future_stack=True) - - # Pandas produces NaN, where bq dataframes produces pd.NA - assert_series_equal(bf_result, pd_result, check_dtype=False) - - -def test_df_melt_default(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - # To match bigquery dataframes - scalars_pandas_df = scalars_pandas_df.copy() - scalars_pandas_df.columns = scalars_pandas_df.columns.astype("string[pyarrow]") - # Can only stack identically-typed columns - columns = ["int64_col", "int64_too", "rowindex_2"] - - bf_result = scalars_df[columns].melt().to_pandas() - pd_result = scalars_pandas_df[columns].melt() - - # Pandas produces int64 index, Bigframes produces Int64 (nullable) - pd.testing.assert_frame_equal( - bf_result, - pd_result, - check_index_type=False, - check_dtype=False, - ) - - -def test_df_melt_parameterized(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - # To match bigquery dataframes - scalars_pandas_df = scalars_pandas_df.copy() - scalars_pandas_df.columns = scalars_pandas_df.columns.astype("string[pyarrow]") - # Can only stack identically-typed columns - - bf_result = scalars_df.melt( - var_name="alice", - value_name="bob", - id_vars=["string_col"], - value_vars=["int64_col", "int64_too"], - ).to_pandas() - pd_result = scalars_pandas_df.melt( - var_name="alice", - value_name="bob", - id_vars=["string_col"], - value_vars=["int64_col", "int64_too"], - ) - - # Pandas produces int64 index, Bigframes produces Int64 (nullable) - pd.testing.assert_frame_equal( - bf_result, pd_result, check_index_type=False, check_dtype=False - ) - - -@pytest.mark.parametrize( - ("ordered"), - [ - (True), - (False), - ], -) -def test_df_unstack(scalars_dfs, ordered): - scalars_df, scalars_pandas_df = scalars_dfs - # To match bigquery dataframes - scalars_pandas_df = scalars_pandas_df.copy() - scalars_pandas_df.columns = scalars_pandas_df.columns.astype("string[pyarrow]") - # Can only stack identically-typed columns - columns = [ - "rowindex_2", - "int64_col", - "int64_too", - ] - - # unstack on mono-index produces series - bf_result = scalars_df[columns].unstack().to_pandas(ordered=ordered) - pd_result = scalars_pandas_df[columns].unstack() - - # Pandas produces NaN, where bq dataframes produces pd.NA - assert_series_equal( - bf_result, pd_result, check_dtype=False, ignore_order=not ordered - ) - - -def test_ipython_key_completions_with_drop(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - col_names = "string_col" - bf_dataframe = scalars_df.drop(columns=col_names) - pd_dataframe = scalars_pandas_df.drop(columns=col_names) - expected = pd_dataframe.columns.tolist() - - results = bf_dataframe._ipython_key_completions_() - - assert col_names not in results - assert results == expected - # _ipython_key_completions_ is called with square brackets - # so only column names are relevant with tab completion - assert "to_gbq" not in results - assert "merge" not in results - assert "drop" not in results - - -def test_ipython_key_completions_with_rename(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - col_name_dict = {"string_col": "a_renamed_column"} - bf_dataframe = scalars_df.rename(columns=col_name_dict) - pd_dataframe = scalars_pandas_df.rename(columns=col_name_dict) - expected = pd_dataframe.columns.tolist() - - results = bf_dataframe._ipython_key_completions_() - - assert "string_col" not in results - assert "a_renamed_column" in results - assert results == expected - # _ipython_key_completions_ is called with square brackets - # so only column names are relevant with tab completion - assert "to_gbq" not in results - assert "merge" not in results - assert "drop" not in results - - -def test__dir__with_drop(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - col_names = "string_col" - bf_dataframe = scalars_df.drop(columns=col_names) - pd_dataframe = scalars_pandas_df.drop(columns=col_names) - expected = pd_dataframe.columns.tolist() - - results = dir(bf_dataframe) - - assert col_names not in results - assert frozenset(expected) <= frozenset(results) - # __dir__ is called with a '.' and displays all methods, columns names, etc. - assert "to_gbq" in results - assert "merge" in results - assert "drop" in results - - -def test__dir__with_rename(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - col_name_dict = {"string_col": "a_renamed_column"} - bf_dataframe = scalars_df.rename(columns=col_name_dict) - pd_dataframe = scalars_pandas_df.rename(columns=col_name_dict) - expected = pd_dataframe.columns.tolist() - - results = dir(bf_dataframe) - - assert "string_col" not in results - assert "a_renamed_column" in results - assert frozenset(expected) <= frozenset(results) - # __dir__ is called with a '.' and displays all methods, columns names, etc. - assert "to_gbq" in results - assert "merge" in results - assert "drop" in results - - -@pytest.mark.parametrize( - ("start", "stop", "step"), - [ - (0, 0, None), - (None, None, None), - (1, None, None), - (None, 4, None), - (None, None, 2), - (None, 50000000000, 1), - (5, 4, None), - (3, None, 2), - (1, 7, 2), - (1, 7, 50000000000), - ], -) -def test_iloc_slice(scalars_df_index, scalars_pandas_df_index, start, stop, step): - bf_result = scalars_df_index.iloc[start:stop:step].to_pandas() - pd_result = scalars_pandas_df_index.iloc[start:stop:step] - pd.testing.assert_frame_equal( - bf_result, - pd_result, - ) - - -def test_iloc_slice_zero_step(scalars_df_index): - with pytest.raises(ValueError): - scalars_df_index.iloc[0:0:0] - - -@pytest.mark.parametrize( - ("ordered"), - [ - (True), - (False), - ], -) -def test_iloc_slice_nested(scalars_df_index, scalars_pandas_df_index, ordered): - bf_result = scalars_df_index.iloc[1:].iloc[1:].to_pandas(ordered=ordered) - pd_result = scalars_pandas_df_index.iloc[1:].iloc[1:] - - assert_frame_equal(bf_result, pd_result, ignore_order=not ordered) - - -@pytest.mark.parametrize( - "index", - [0, 5, -2, (2,)], -) -def test_iloc_single_integer(scalars_df_index, scalars_pandas_df_index, index): - bf_result = scalars_df_index.iloc[index] - pd_result = scalars_pandas_df_index.iloc[index] - - pd.testing.assert_series_equal( - bf_result, - pd_result, - ) - - -@pytest.mark.parametrize( - "index", - [(2, 5), (5, 0), (0, 0)], -) -def test_iloc_tuple(scalars_df_index, scalars_pandas_df_index, index): - bf_result = scalars_df_index.iloc[index] - pd_result = scalars_pandas_df_index.iloc[index] - - assert bf_result == pd_result - - -@pytest.mark.parametrize( - "index", - [(slice(None), [1, 2, 3]), (slice(1, 7, 2), [2, 5, 3])], -) -def test_iloc_tuple_multi_columns(scalars_df_index, scalars_pandas_df_index, index): - bf_result = scalars_df_index.iloc[index].to_pandas() - pd_result = scalars_pandas_df_index.iloc[index] - - pd.testing.assert_frame_equal(bf_result, pd_result) - - -def test_iloc_tuple_multi_columns_single_row(scalars_df_index, scalars_pandas_df_index): - index = (2, [2, 1, 3, -4]) - bf_result = scalars_df_index.iloc[index] - pd_result = scalars_pandas_df_index.iloc[index] - pd.testing.assert_series_equal(bf_result, pd_result) - - -@pytest.mark.parametrize( - ("index", "error"), - [ - ((1, 1, 1), pd.errors.IndexingError), - (("asd", "asd", "asd"), pd.errors.IndexingError), - (("asd"), TypeError), - ], -) -def test_iloc_tuple_errors(scalars_df_index, scalars_pandas_df_index, index, error): - with pytest.raises(error): - scalars_df_index.iloc[index] - with pytest.raises(error): - scalars_pandas_df_index.iloc[index] - - -@pytest.mark.parametrize( - "index", - [(2, 5), (5, 0), (0, 0)], -) -def test_iat(scalars_df_index, scalars_pandas_df_index, index): - bf_result = scalars_df_index.iat[index] - pd_result = scalars_pandas_df_index.iat[index] - - assert bf_result == pd_result - - -@pytest.mark.parametrize( - ("index", "error"), - [ - (0, TypeError), - ("asd", ValueError), - ((1, 2, 3), TypeError), - (("asd", "asd"), ValueError), - ], -) -def test_iat_errors(scalars_df_index, scalars_pandas_df_index, index, error): - with pytest.raises(error): - scalars_pandas_df_index.iat[index] - with pytest.raises(error): - scalars_df_index.iat[index] - - -def test_iloc_single_integer_out_of_bound_error( - scalars_df_index, scalars_pandas_df_index -): - with pytest.raises(IndexError, match="single positional indexer is out-of-bounds"): - scalars_df_index.iloc[99] - - -def test_loc_bool_series(scalars_df_index, scalars_pandas_df_index): - bf_result = scalars_df_index.loc[scalars_df_index.bool_col].to_pandas() - pd_result = scalars_pandas_df_index.loc[scalars_pandas_df_index.bool_col] - - pd.testing.assert_frame_equal( - bf_result, - pd_result, - ) - - -def test_loc_select_column(scalars_df_index, scalars_pandas_df_index): - bf_result = scalars_df_index.loc[:, "int64_col"].to_pandas() - pd_result = scalars_pandas_df_index.loc[:, "int64_col"] - pd.testing.assert_series_equal( - bf_result, - pd_result, - ) - - -def test_loc_select_with_column_condition(scalars_df_index, scalars_pandas_df_index): - bf_result = scalars_df_index.loc[:, scalars_df_index.dtypes == "Int64"].to_pandas() - pd_result = scalars_pandas_df_index.loc[ - :, scalars_pandas_df_index.dtypes == "Int64" - ] - pd.testing.assert_frame_equal( - bf_result, - pd_result, - ) - - -def test_loc_select_with_column_condition_bf_series( - scalars_df_index, scalars_pandas_df_index -): - # (b/347072677) GEOGRAPH type doesn't support DISTINCT op - columns = [ - item for item in scalars_pandas_df_index.columns if item != "geography_col" - ] - scalars_df_index = scalars_df_index[columns] - scalars_pandas_df_index = scalars_pandas_df_index[columns] - - size_half = len(scalars_pandas_df_index) / 2 - bf_result = scalars_df_index.loc[ - :, scalars_df_index.nunique() > size_half - ].to_pandas() - pd_result = scalars_pandas_df_index.loc[ - :, scalars_pandas_df_index.nunique() > size_half - ] - pd.testing.assert_frame_equal( - bf_result, - pd_result, - ) - - -def test_loc_single_index_with_duplicate(scalars_df_index, scalars_pandas_df_index): - scalars_df_index = scalars_df_index.set_index("string_col", drop=False) - scalars_pandas_df_index = scalars_pandas_df_index.set_index( - "string_col", drop=False - ) - index = "Hello, World!" - bf_result = scalars_df_index.loc[index] - pd_result = scalars_pandas_df_index.loc[index] - pd.testing.assert_frame_equal( - bf_result.to_pandas(), - pd_result, - ) - - -def test_loc_single_index_no_duplicate(scalars_df_index, scalars_pandas_df_index): - scalars_df_index = scalars_df_index.set_index("int64_too", drop=False) - scalars_pandas_df_index = scalars_pandas_df_index.set_index("int64_too", drop=False) - index = -2345 - bf_result = scalars_df_index.loc[index] - pd_result = scalars_pandas_df_index.loc[index] - pd.testing.assert_series_equal( - bf_result, - pd_result, - ) - - -def test_at_with_duplicate(scalars_df_index, scalars_pandas_df_index): - scalars_df_index = scalars_df_index.set_index("string_col", drop=False) - scalars_pandas_df_index = scalars_pandas_df_index.set_index( - "string_col", drop=False - ) - index = "Hello, World!" - bf_result = scalars_df_index.at[index, "int64_too"] - pd_result = scalars_pandas_df_index.at[index, "int64_too"] - pd.testing.assert_series_equal( - bf_result.to_pandas(), - pd_result, - ) - - -def test_at_no_duplicate(scalars_df_index, scalars_pandas_df_index): - scalars_df_index = scalars_df_index.set_index("int64_too", drop=False) - scalars_pandas_df_index = scalars_pandas_df_index.set_index("int64_too", drop=False) - index = -2345 - bf_result = scalars_df_index.at[index, "string_col"] - pd_result = scalars_pandas_df_index.at[index, "string_col"] - assert bf_result == pd_result - - -def test_loc_setitem_bool_series_scalar_new_col(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - bf_df = scalars_df.copy() - pd_df = scalars_pandas_df.copy() - bf_df.loc[bf_df["int64_too"] == 0, "new_col"] = 99 - pd_df.loc[pd_df["int64_too"] == 0, "new_col"] = 99 - - # pandas uses float64 instead - pd_df["new_col"] = pd_df["new_col"].astype("Float64") - - pd.testing.assert_frame_equal( - bf_df.to_pandas(), - pd_df, - ) - - -@pytest.mark.parametrize( - ("col", "value"), - [ - ("string_col", "hello"), - ("int64_col", 3), - ("float64_col", 3.5), - ], -) -def test_loc_setitem_bool_series_scalar_existing_col(scalars_dfs, col, value): - if pd.__version__.startswith("1."): - pytest.skip("this loc overload not supported in pandas 1.x.") - - scalars_df, scalars_pandas_df = scalars_dfs - bf_df = scalars_df.copy() - pd_df = scalars_pandas_df.copy() - bf_df.loc[bf_df["int64_too"] == 1, col] = value - pd_df.loc[pd_df["int64_too"] == 1, col] = value - - pd.testing.assert_frame_equal( - bf_df.to_pandas(), - pd_df, - ) - - -def test_loc_setitem_bool_series_scalar_error(scalars_dfs): - if pd.__version__.startswith("1."): - pytest.skip("this loc overload not supported in pandas 1.x.") - - scalars_df, scalars_pandas_df = scalars_dfs - bf_df = scalars_df.copy() - pd_df = scalars_pandas_df.copy() - - with pytest.raises(Exception): - bf_df.loc[bf_df["int64_too"] == 1, "string_col"] = 99 - with pytest.raises(Exception): - pd_df.loc[pd_df["int64_too"] == 1, "string_col"] = 99 - - -@pytest.mark.parametrize( - ("col", "op"), - [ - # Int aggregates - pytest.param("int64_col", lambda x: x.sum(), id="int-sum"), - pytest.param("int64_col", lambda x: x.min(), id="int-min"), - pytest.param("int64_col", lambda x: x.max(), id="int-max"), - pytest.param("int64_col", lambda x: x.count(), id="int-count"), - pytest.param("int64_col", lambda x: x.nunique(), id="int-nunique"), - # Float aggregates - pytest.param("float64_col", lambda x: x.count(), id="float-count"), - pytest.param("float64_col", lambda x: x.nunique(), id="float-nunique"), - # Bool aggregates - pytest.param("bool_col", lambda x: x.sum(), id="bool-sum"), - pytest.param("bool_col", lambda x: x.count(), id="bool-count"), - pytest.param("bool_col", lambda x: x.nunique(), id="bool-nunique"), - # String aggregates - pytest.param("string_col", lambda x: x.count(), id="string-count"), - pytest.param("string_col", lambda x: x.nunique(), id="string-nunique"), - ], -) -def test_dataframe_aggregate_int(scalars_df_index, scalars_pandas_df_index, col, op): - bf_result = op(scalars_df_index[[col]]).to_pandas() - pd_result = op(scalars_pandas_df_index[[col]]) - - # Check dtype separately - assert bf_result.dtype == "Int64" - # Is otherwise "object" dtype - pd_result.index = pd_result.index.astype("string[pyarrow]") - # Pandas may produce narrower numeric types - assert_series_equal(pd_result, bf_result, check_dtype=False, check_index_type=False) - - -@pytest.mark.parametrize( - ("col", "op"), - [ - pytest.param("bool_col", lambda x: x.min(), id="bool-min"), - pytest.param("bool_col", lambda x: x.max(), id="bool-max"), - ], -) -def test_dataframe_aggregate_bool(scalars_df_index, scalars_pandas_df_index, col, op): - bf_result = op(scalars_df_index[[col]]).to_pandas() - pd_result = op(scalars_pandas_df_index[[col]]) - - # Check dtype separately - assert bf_result.dtype == "boolean" - - # Pandas may produce narrower numeric types - # Pandas has object index type - pd_result.index = pd_result.index.astype("string[pyarrow]") - assert_series_equal(pd_result, bf_result, check_dtype=False, check_index_type=False) - - -@pytest.mark.parametrize( - ("op", "bf_dtype"), - [ - (lambda x: x.sum(numeric_only=True), "Float64"), - (lambda x: x.mean(numeric_only=True), "Float64"), - (lambda x: x.min(numeric_only=True), "Float64"), - (lambda x: x.max(numeric_only=True), "Float64"), - (lambda x: x.std(numeric_only=True), "Float64"), - (lambda x: x.var(numeric_only=True), "Float64"), - (lambda x: x.count(numeric_only=False), "Int64"), - (lambda x: x.nunique(), "Int64"), - ], - ids=["sum", "mean", "min", "max", "std", "var", "count", "nunique"], -) -def test_dataframe_aggregates(scalars_dfs, op, bf_dtype): - scalars_df_index, scalars_pandas_df_index = scalars_dfs - col_names = ["int64_too", "float64_col", "string_col", "int64_col", "bool_col"] - bf_series = op(scalars_df_index[col_names]) - bf_result = bf_series - pd_result = op(scalars_pandas_df_index[col_names]) - - # Check dtype separately - assert bf_result.dtype == bf_dtype - - # Pandas may produce narrower numeric types, but bigframes always produces Float64 - # Pandas has object index type - pd_result.index = pd_result.index.astype("string[pyarrow]") - assert_series_equivalent( - pd_result, - bf_result, - check_dtype=False, - check_index_type=False, - ) - - -@pytest.mark.parametrize( - ("op"), - [ - (lambda x: x.sum(axis=1, numeric_only=True)), - (lambda x: x.mean(axis=1, numeric_only=True)), - (lambda x: x.min(axis=1, numeric_only=True)), - (lambda x: x.max(axis=1, numeric_only=True)), - (lambda x: x.std(axis=1, numeric_only=True)), - (lambda x: x.var(axis=1, numeric_only=True)), - ], - ids=["sum", "mean", "min", "max", "std", "var"], -) -def test_dataframe_aggregates_axis_1(scalars_df_index, scalars_pandas_df_index, op): - col_names = ["int64_too", "int64_col", "float64_col", "bool_col", "string_col"] - bf_result = op(scalars_df_index[col_names]).to_pandas() - pd_result = op(scalars_pandas_df_index[col_names]) - - # Pandas may produce narrower numeric types, but bigframes always produces Float64 - # Pandas has object index type - assert_series_equal(pd_result, bf_result, check_index_type=False, check_dtype=False) - - -@pytest.mark.parametrize( - ("op"), - [ - (lambda x: x.all(bool_only=True)), - (lambda x: x.any(bool_only=True)), - (lambda x: x.all(axis=1, bool_only=True)), - (lambda x: x.any(axis=1, bool_only=True)), - ], - ids=["all_axis0", "any_axis0", "all_axis1", "any_axis1"], -) -def test_dataframe_bool_aggregates(scalars_df_index, scalars_pandas_df_index, op): - # Pandas will drop nullable 'boolean' dtype so we convert first to bool, then cast back later - scalars_df_index = scalars_df_index.assign( - bool_col=scalars_df_index.bool_col.fillna(False) - ) - scalars_pandas_df_index = scalars_pandas_df_index.assign( - bool_col=scalars_pandas_df_index.bool_col.fillna(False).astype("bool") - ) - bf_series = op(scalars_df_index) - pd_series = op(scalars_pandas_df_index).astype("boolean") - bf_result = bf_series.to_pandas() - - pd_series.index = pd_series.index.astype(bf_result.index.dtype) - pd.testing.assert_series_equal(pd_series, bf_result, check_index_type=False) - - -def test_dataframe_prod(scalars_df_index, scalars_pandas_df_index): - col_names = ["int64_too", "float64_col"] - bf_series = scalars_df_index[col_names].prod() - pd_series = scalars_pandas_df_index[col_names].prod() - bf_result = bf_series.to_pandas() - - # Pandas may produce narrower numeric types, but bigframes always produces Float64 - pd_series = pd_series.astype("Float64") - # Pandas has object index type - pd.testing.assert_series_equal(pd_series, bf_result, check_index_type=False) - - -def test_df_skew_too_few_values(scalars_dfs): - columns = ["float64_col", "int64_col"] - scalars_df, scalars_pandas_df = scalars_dfs - bf_result = scalars_df[columns].head(2).skew().to_pandas() - pd_result = scalars_pandas_df[columns].head(2).skew() - - # Pandas may produce narrower numeric types, but bigframes always produces Float64 - pd_result = pd_result.astype("Float64") - - pd.testing.assert_series_equal(pd_result, bf_result, check_index_type=False) - - -@pytest.mark.parametrize( - ("ordered"), - [ - (True), - (False), - ], -) -def test_df_skew(scalars_dfs, ordered): - columns = ["float64_col", "int64_col"] - scalars_df, scalars_pandas_df = scalars_dfs - bf_result = scalars_df[columns].skew().to_pandas(ordered=ordered) - pd_result = scalars_pandas_df[columns].skew() - - # Pandas may produce narrower numeric types, but bigframes always produces Float64 - pd_result = pd_result.astype("Float64") - - assert_series_equal( - pd_result, bf_result, check_index_type=False, ignore_order=not ordered - ) - - -def test_df_kurt_too_few_values(scalars_dfs): - columns = ["float64_col", "int64_col"] - scalars_df, scalars_pandas_df = scalars_dfs - bf_result = scalars_df[columns].head(2).kurt().to_pandas() - pd_result = scalars_pandas_df[columns].head(2).kurt() - - # Pandas may produce narrower numeric types, but bigframes always produces Float64 - pd_result = pd_result.astype("Float64") - - pd.testing.assert_series_equal(pd_result, bf_result, check_index_type=False) - - -def test_df_kurt(scalars_dfs): - columns = ["float64_col", "int64_col"] - scalars_df, scalars_pandas_df = scalars_dfs - bf_result = scalars_df[columns].kurt().to_pandas() - pd_result = scalars_pandas_df[columns].kurt() - - # Pandas may produce narrower numeric types, but bigframes always produces Float64 - pd_result = pd_result.astype("Float64") - - pd.testing.assert_series_equal(pd_result, bf_result, check_index_type=False) - - -def test_sample_raises_value_error(scalars_dfs): - scalars_df, _ = scalars_dfs - with pytest.raises( - ValueError, match="Only one of 'n' or 'frac' parameter can be specified." - ): - scalars_df.sample(frac=0.5, n=4) - - -@pytest.mark.parametrize( - ("axis",), - [ - (None,), - (0,), - (1,), - ], -) -def test_df_add_prefix(scalars_df_index, scalars_pandas_df_index, axis): - if pd.__version__.startswith("1."): - pytest.skip("add_prefix axis parameter not supported in pandas 1.x.") - bf_result = scalars_df_index.add_prefix("prefix_", axis).to_pandas() - - pd_result = scalars_pandas_df_index.add_prefix("prefix_", axis) - - pd.testing.assert_frame_equal( - bf_result, - pd_result, - check_index_type=False, - ) - - -@pytest.mark.parametrize( - ("axis",), - [ - (0,), - (1,), - ], -) -def test_df_add_suffix(scalars_df_index, scalars_pandas_df_index, axis): - if pd.__version__.startswith("1."): - pytest.skip("add_prefix axis parameter not supported in pandas 1.x.") - bf_result = scalars_df_index.add_suffix("_suffix", axis).to_pandas() - - pd_result = scalars_pandas_df_index.add_suffix("_suffix", axis) - - pd.testing.assert_frame_equal( - bf_result, - pd_result, - check_index_type=False, - ) - - -def test_df_astype_error_error(session): - input = pd.DataFrame(["hello", "world", "3.11", "4000"]) - with pytest.raises(ValueError): - session.read_pandas(input).astype("Float64", errors="bad_value") - - -def test_df_columns_filter_items(scalars_df_index, scalars_pandas_df_index): - if pd.__version__.startswith("2.0") or pd.__version__.startswith("1."): - pytest.skip("pandas filter items behavior different pre-2.1") - bf_result = scalars_df_index.filter(items=["string_col", "int64_col"]).to_pandas() - - pd_result = scalars_pandas_df_index.filter(items=["string_col", "int64_col"]) - # Ignore column ordering as pandas order differently depending on version - pd.testing.assert_frame_equal( - bf_result.sort_index(axis=1), - pd_result.sort_index(axis=1), - ) - - -def test_df_columns_filter_like(scalars_df_index, scalars_pandas_df_index): - bf_result = scalars_df_index.filter(like="64_col").to_pandas() - - pd_result = scalars_pandas_df_index.filter(like="64_col") - - pd.testing.assert_frame_equal( - bf_result, - pd_result, - ) - - -def test_df_columns_filter_regex(scalars_df_index, scalars_pandas_df_index): - bf_result = scalars_df_index.filter(regex="^[^_]+$").to_pandas() - - pd_result = scalars_pandas_df_index.filter(regex="^[^_]+$") - - pd.testing.assert_frame_equal( - bf_result, - pd_result, - ) - - -def test_df_reindex_rows_list(scalars_dfs): - scalars_df_index, scalars_pandas_df_index = scalars_dfs - bf_result = scalars_df_index.reindex(index=[5, 1, 3, 99, 1]) - - pd_result = scalars_pandas_df_index.reindex(index=[5, 1, 3, 99, 1]) - - # Pandas uses int64 instead of Int64 (nullable) dtype. - pd_result.index = pd_result.index.astype(pd.Int64Dtype()) - assert_dfs_equivalent( - pd_result, - bf_result, - ) - - -def test_df_reindex_rows_index(scalars_df_index, scalars_pandas_df_index): - bf_result = scalars_df_index.reindex( - index=pd.Index([5, 1, 3, 99, 1], name="newname") - ).to_pandas() - - pd_result = scalars_pandas_df_index.reindex( - index=pd.Index([5, 1, 3, 99, 1], name="newname") - ) - - # Pandas uses int64 instead of Int64 (nullable) dtype. - pd_result.index = pd_result.index.astype(pd.Int64Dtype()) - pd.testing.assert_frame_equal( - bf_result, - pd_result, - ) - - -def test_df_reindex_nonunique(scalars_df_index): - with pytest.raises(ValueError): - # int64_too is non-unique - scalars_df_index.set_index("int64_too").reindex( - index=[5, 1, 3, 99, 1], validate=True - ) - - -def test_df_reindex_columns(scalars_df_index, scalars_pandas_df_index): - bf_result = scalars_df_index.reindex( - columns=["not_a_col", "int64_col", "int64_too"] - ).to_pandas() - - pd_result = scalars_pandas_df_index.reindex( - columns=["not_a_col", "int64_col", "int64_too"] - ) - - # Pandas uses float64 as default for newly created empty column, bf uses Float64 - pd_result.not_a_col = pd_result.not_a_col.astype(pandas.Float64Dtype()) - pd.testing.assert_frame_equal( - bf_result, - pd_result, - ) - - -def test_df_reindex_columns_with_same_order(scalars_df_index, scalars_pandas_df_index): - # First, make sure the two dataframes have the same columns in order. - columns = ["int64_col", "int64_too"] - bf = scalars_df_index[columns] - pd_df = scalars_pandas_df_index[columns] - - bf_result = bf.reindex(columns=columns).to_pandas() - pd_result = pd_df.reindex(columns=columns) - - pd.testing.assert_frame_equal( - bf_result, - pd_result, - ) - - -def test_df_equals_identical(scalars_df_index, scalars_pandas_df_index): - unsupported = [ - "geography_col", - ] - scalars_df_index = scalars_df_index.drop(columns=unsupported) - scalars_pandas_df_index = scalars_pandas_df_index.drop(columns=unsupported) - - bf_result = scalars_df_index.equals(scalars_df_index) - pd_result = scalars_pandas_df_index.equals(scalars_pandas_df_index) - - assert pd_result == bf_result - - -def test_df_equals_series(scalars_df_index, scalars_pandas_df_index): - bf_result = scalars_df_index[["int64_col"]].equals(scalars_df_index["int64_col"]) - pd_result = scalars_pandas_df_index[["int64_col"]].equals( - scalars_pandas_df_index["int64_col"] - ) - - assert pd_result == bf_result - - -def test_df_equals_different_dtype(scalars_df_index, scalars_pandas_df_index): - columns = ["int64_col", "int64_too"] - scalars_df_index = scalars_df_index[columns] - scalars_pandas_df_index = scalars_pandas_df_index[columns] - - bf_modified = scalars_df_index.copy() - bf_modified = bf_modified.astype("Float64") - - pd_modified = scalars_pandas_df_index.copy() - pd_modified = pd_modified.astype("Float64") - - bf_result = scalars_df_index.equals(bf_modified) - pd_result = scalars_pandas_df_index.equals(pd_modified) - - assert pd_result == bf_result - - -def test_df_equals_different_values(scalars_df_index, scalars_pandas_df_index): - columns = ["int64_col", "int64_too"] - scalars_df_index = scalars_df_index[columns] - scalars_pandas_df_index = scalars_pandas_df_index[columns] - - bf_modified = scalars_df_index.copy() - bf_modified["int64_col"] = bf_modified.int64_col + 1 - - pd_modified = scalars_pandas_df_index.copy() - pd_modified["int64_col"] = pd_modified.int64_col + 1 - - bf_result = scalars_df_index.equals(bf_modified) - pd_result = scalars_pandas_df_index.equals(pd_modified) - - assert pd_result == bf_result - - -def test_df_equals_extra_column(scalars_df_index, scalars_pandas_df_index): - columns = ["int64_col", "int64_too"] - more_columns = ["int64_col", "int64_too", "float64_col"] - - bf_result = scalars_df_index[columns].equals(scalars_df_index[more_columns]) - pd_result = scalars_pandas_df_index[columns].equals( - scalars_pandas_df_index[more_columns] - ) - - assert pd_result == bf_result - - -def test_df_reindex_like(scalars_df_index, scalars_pandas_df_index): - reindex_target_bf = scalars_df_index.reindex( - columns=["not_a_col", "int64_col", "int64_too"], index=[5, 1, 3, 99, 1] - ) - bf_result = scalars_df_index.reindex_like(reindex_target_bf).to_pandas() - - reindex_target_pd = scalars_pandas_df_index.reindex( - columns=["not_a_col", "int64_col", "int64_too"], index=[5, 1, 3, 99, 1] - ) - pd_result = scalars_pandas_df_index.reindex_like(reindex_target_pd) - - # Pandas uses float64 as default for newly created empty column, bf uses Float64 - # Pandas uses int64 instead of Int64 (nullable) dtype. - pd_result.index = pd_result.index.astype(pd.Int64Dtype()) - # Pandas uses float64 as default for newly created empty column, bf uses Float64 - pd_result.not_a_col = pd_result.not_a_col.astype(pandas.Float64Dtype()) - pd.testing.assert_frame_equal( - bf_result, - pd_result, - ) - - -def test_df_values(scalars_df_index, scalars_pandas_df_index): - bf_result = scalars_df_index.values - - pd_result = scalars_pandas_df_index.values - # Numpy isn't equipped to compare non-numeric objects, so convert back to dataframe - pd.testing.assert_frame_equal( - pd.DataFrame(bf_result), pd.DataFrame(pd_result), check_dtype=False - ) - - -def test_df_to_numpy(scalars_df_index, scalars_pandas_df_index): - bf_result = scalars_df_index.to_numpy() - - pd_result = scalars_pandas_df_index.to_numpy() - # Numpy isn't equipped to compare non-numeric objects, so convert back to dataframe - pd.testing.assert_frame_equal( - pd.DataFrame(bf_result), pd.DataFrame(pd_result), check_dtype=False - ) - - -def test_df___array__(scalars_df_index, scalars_pandas_df_index): - bf_result = scalars_df_index.__array__() - - pd_result = scalars_pandas_df_index.__array__() - # Numpy isn't equipped to compare non-numeric objects, so convert back to dataframe - pd.testing.assert_frame_equal( - pd.DataFrame(bf_result), pd.DataFrame(pd_result), check_dtype=False - ) - - -def test_df_getattr_attribute_error_when_pandas_has(scalars_df_index): - # swapaxes is implemented in pandas but not in bigframes - with pytest.raises(AttributeError): - scalars_df_index.swapaxes() - - -def test_df_getattr_attribute_error(scalars_df_index): - with pytest.raises(AttributeError): - scalars_df_index.not_a_method() - - -def test_df_getattr_axes(): - df = dataframe.DataFrame( - [[1, 1, 1], [1, 1, 1]], columns=["index", "columns", "my_column"] - ) - assert isinstance(df.index, bigframes.core.indexes.Index) - assert isinstance(df.columns, pandas.Index) - assert isinstance(df.my_column, series.Series) - - -def test_df_setattr_index(): - pd_df = pandas.DataFrame( - [[1, 1, 1], [1, 1, 1]], columns=["index", "columns", "my_column"] - ) - bf_df = dataframe.DataFrame(pd_df) - - pd_df.index = pandas.Index([4, 5]) - bf_df.index = [4, 5] - - assert_frame_equal( - pd_df, bf_df.to_pandas(), check_index_type=False, check_dtype=False - ) - - -def test_df_setattr_columns(): - pd_df = pandas.DataFrame( - [[1, 1, 1], [1, 1, 1]], columns=["index", "columns", "my_column"] - ) - bf_df = dataframe.DataFrame(pd_df) - - pd_df.columns = typing.cast(pandas.Index, pandas.Index([4, 5, 6])) - - bf_df.columns = pandas.Index([4, 5, 6]) - - assert_frame_equal( - pd_df, bf_df.to_pandas(), check_index_type=False, check_dtype=False - ) - - -def test_df_setattr_modify_column(): - pd_df = pandas.DataFrame( - [[1, 1, 1], [1, 1, 1]], columns=["index", "columns", "my_column"] - ) - bf_df = dataframe.DataFrame(pd_df) - pd_df.my_column = [4, 5] - bf_df.my_column = [4, 5] - - assert_frame_equal( - pd_df, bf_df.to_pandas(), check_index_type=False, check_dtype=False - ) - - -def test_loc_list_string_index(scalars_df_index, scalars_pandas_df_index): - index_list = scalars_pandas_df_index.string_col.iloc[[0, 1, 1, 5]].values - - scalars_df_index = scalars_df_index.set_index("string_col") - scalars_pandas_df_index = scalars_pandas_df_index.set_index("string_col") - - bf_result = scalars_df_index.loc[index_list].to_pandas() - pd_result = scalars_pandas_df_index.loc[index_list] - - pd.testing.assert_frame_equal( - bf_result, - pd_result, - ) - - -def test_loc_list_integer_index(scalars_df_index, scalars_pandas_df_index): - index_list = [3, 2, 1, 3, 2, 1] - - bf_result = scalars_df_index.loc[index_list] - pd_result = scalars_pandas_df_index.loc[index_list] - - pd.testing.assert_frame_equal( - bf_result.to_pandas(), - pd_result, - ) - - -def test_loc_list_multiindex(scalars_dfs): - scalars_df_index, scalars_pandas_df_index = scalars_dfs - scalars_df_multiindex = scalars_df_index.set_index(["string_col", "int64_col"]) - scalars_pandas_df_multiindex = scalars_pandas_df_index.set_index( - ["string_col", "int64_col"] - ) - index_list = [("Hello, World!", -234892), ("Hello, World!", 123456789)] - - bf_result = scalars_df_multiindex.loc[index_list] - pd_result = scalars_pandas_df_multiindex.loc[index_list] - - assert_dfs_equivalent( - pd_result, - bf_result, - ) - - -@pytest.mark.parametrize( - "index_list", - [ - [0, 1, 2, 3, 4, 4], - [0, 0, 0, 5, 4, 7, -2, -5, 3], - [-1, -2, -3, -4, -5, -5], - ], -) -def test_iloc_list(scalars_df_index, scalars_pandas_df_index, index_list): - bf_result = scalars_df_index.iloc[index_list] - pd_result = scalars_pandas_df_index.iloc[index_list] - - pd.testing.assert_frame_equal( - bf_result.to_pandas(), - pd_result, - ) - - -def test_iloc_list_multiindex(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - scalars_df = scalars_df.copy() - scalars_pandas_df = scalars_pandas_df.copy() - scalars_df = scalars_df.set_index(["bytes_col", "numeric_col"]) - scalars_pandas_df = scalars_pandas_df.set_index(["bytes_col", "numeric_col"]) - - index_list = [0, 0, 0, 5, 4, 7] - - bf_result = scalars_df.iloc[index_list] - pd_result = scalars_pandas_df.iloc[index_list] - - pd.testing.assert_frame_equal( - bf_result.to_pandas(), - pd_result, - ) - - -def test_iloc_empty_list(scalars_df_index, scalars_pandas_df_index): - index_list: List[int] = [] - - bf_result = scalars_df_index.iloc[index_list] - pd_result = scalars_pandas_df_index.iloc[index_list] - - bf_result = bf_result.to_pandas() - assert bf_result.shape == pd_result.shape # types are known to be different - - -def test_rename_axis(scalars_df_index, scalars_pandas_df_index): - bf_result = scalars_df_index.rename_axis("newindexname") - pd_result = scalars_pandas_df_index.rename_axis("newindexname") - - pd.testing.assert_frame_equal( - bf_result.to_pandas(), - pd_result, - ) - - -def test_rename_axis_nonstring(scalars_df_index, scalars_pandas_df_index): - bf_result = scalars_df_index.rename_axis((4,)) - pd_result = scalars_pandas_df_index.rename_axis((4,)) - - pd.testing.assert_frame_equal( - bf_result.to_pandas(), - pd_result, - ) - - -def test_loc_bf_series_string_index(scalars_df_index, scalars_pandas_df_index): - pd_string_series = scalars_pandas_df_index.string_col.iloc[[0, 5, 1, 1, 5]] - bf_string_series = scalars_df_index.string_col.iloc[[0, 5, 1, 1, 5]] - - scalars_df_index = scalars_df_index.set_index("string_col") - scalars_pandas_df_index = scalars_pandas_df_index.set_index("string_col") - - bf_result = scalars_df_index.loc[bf_string_series] - pd_result = scalars_pandas_df_index.loc[pd_string_series] - - pd.testing.assert_frame_equal( - bf_result.to_pandas(), - pd_result, - ) - - -def test_loc_bf_series_multiindex(scalars_df_index, scalars_pandas_df_index): - pd_string_series = scalars_pandas_df_index.string_col.iloc[[0, 5, 1, 1, 5]] - bf_string_series = scalars_df_index.string_col.iloc[[0, 5, 1, 1, 5]] - - scalars_df_multiindex = scalars_df_index.set_index(["string_col", "int64_col"]) - scalars_pandas_df_multiindex = scalars_pandas_df_index.set_index( - ["string_col", "int64_col"] - ) - - bf_result = scalars_df_multiindex.loc[bf_string_series] - pd_result = scalars_pandas_df_multiindex.loc[pd_string_series] - - pd.testing.assert_frame_equal( - bf_result.to_pandas(), - pd_result, - ) - - -def test_loc_bf_index_integer_index(scalars_df_index, scalars_pandas_df_index): - pd_index = scalars_pandas_df_index.iloc[[0, 5, 1, 1, 5]].index - bf_index = scalars_df_index.iloc[[0, 5, 1, 1, 5]].index - - bf_result = scalars_df_index.loc[bf_index] - pd_result = scalars_pandas_df_index.loc[pd_index] - - pd.testing.assert_frame_equal( - bf_result.to_pandas(), - pd_result, - ) - - -def test_loc_bf_index_integer_index_renamed_col( - scalars_df_index, scalars_pandas_df_index -): - scalars_df_index = scalars_df_index.rename(columns={"int64_col": "rename"}) - scalars_pandas_df_index = scalars_pandas_df_index.rename( - columns={"int64_col": "rename"} - ) - - pd_index = scalars_pandas_df_index.iloc[[0, 5, 1, 1, 5]].index - bf_index = scalars_df_index.iloc[[0, 5, 1, 1, 5]].index - - bf_result = scalars_df_index.loc[bf_index] - pd_result = scalars_pandas_df_index.loc[pd_index] - - pd.testing.assert_frame_equal( - bf_result.to_pandas(), - pd_result, - ) - - -@pytest.mark.parametrize( - ("subset"), - [ - None, - "bool_col", - ["bool_col", "int64_too"], - ], -) -@pytest.mark.parametrize( - ("keep",), - [ - (False,), - ], -) -def test_df_drop_duplicates(scalars_df_index, scalars_pandas_df_index, keep, subset): - columns = ["bool_col", "int64_too", "int64_col"] - bf_df = scalars_df_index[columns].drop_duplicates(subset, keep=keep).to_pandas() - pd_df = scalars_pandas_df_index[columns].drop_duplicates(subset, keep=keep) - pd.testing.assert_frame_equal( - pd_df, - bf_df, - ) - - -@pytest.mark.parametrize( - ("subset"), - [ - None, - ["bool_col"], - ], -) -@pytest.mark.parametrize( - ("keep",), - [ - (False,), - ], -) -def test_df_duplicated(scalars_df_index, scalars_pandas_df_index, keep, subset): - columns = ["bool_col", "int64_too", "int64_col"] - bf_series = scalars_df_index[columns].duplicated(subset, keep=keep).to_pandas() - pd_series = scalars_pandas_df_index[columns].duplicated(subset, keep=keep) - pd.testing.assert_series_equal(pd_series, bf_series, check_dtype=False) - - -def test_df_from_dict_columns_orient(): - data = {"a": [1, 2], "b": [3.3, 2.4]} - bf_result = dataframe.DataFrame.from_dict(data, orient="columns").to_pandas() - pd_result = pd.DataFrame.from_dict(data, orient="columns") - assert_frame_equal(pd_result, bf_result, check_dtype=False, check_index_type=False) - - -def test_df_from_dict_index_orient(): - data = {"a": [1, 2], "b": [3.3, 2.4]} - bf_result = dataframe.DataFrame.from_dict( - data, orient="index", columns=["col1", "col2"] - ).to_pandas() - pd_result = pd.DataFrame.from_dict(data, orient="index", columns=["col1", "col2"]) - assert_frame_equal(pd_result, bf_result, check_dtype=False, check_index_type=False) - - -def test_df_from_dict_tight_orient(): - data = { - "index": [("i1", "i2"), ("i3", "i4")], - "columns": ["col1", "col2"], - "data": [[1, 2.6], [3, 4.5]], - "index_names": ["in1", "in2"], - "column_names": ["column_axis"], - } - - bf_result = dataframe.DataFrame.from_dict(data, orient="tight").to_pandas() - pd_result = pd.DataFrame.from_dict(data, orient="tight") - assert_frame_equal(pd_result, bf_result, check_dtype=False, check_index_type=False) - - -def test_df_from_records(): - records = ((1, "a"), (2.5, "b"), (3.3, "c"), (4.9, "d")) - - bf_result = dataframe.DataFrame.from_records( - records, columns=["c1", "c2"] - ).to_pandas() - pd_result = pd.DataFrame.from_records(records, columns=["c1", "c2"]) - assert_frame_equal(pd_result, bf_result, check_dtype=False, check_index_type=False) - - -def test_df_to_dict(scalars_df_index, scalars_pandas_df_index): - unsupported = ["numeric_col"] # formatted differently - bf_result = scalars_df_index.drop(columns=unsupported).to_dict() - pd_result = scalars_pandas_df_index.drop(columns=unsupported).to_dict() - - assert bf_result == pd_result - - -def test_df_to_json_local_str(scalars_df_index, scalars_pandas_df_index): - # pandas 3.0 bugged for serializing date col - bf_result = scalars_df_index.drop(columns="date_col").to_json() - # default_handler for arrow types that have no default conversion - pd_result = scalars_pandas_df_index.drop(columns="date_col").to_json( - default_handler=str - ) - - assert bf_result == pd_result - - -def test_df_to_json_local_file(scalars_df_index, scalars_pandas_df_index): - # TODO: supply a reason why this isn't compatible with pandas 1.x - pytest.importorskip("pandas", minversion="2.0.0") - # duration not fully supported at pandas level - scalars_df_index = scalars_df_index.drop(columns="duration_col") - scalars_pandas_df_index = scalars_pandas_df_index.drop(columns="duration_col") - with ( - tempfile.TemporaryFile() as bf_result_file, - tempfile.TemporaryFile() as pd_result_file, - ): - scalars_df_index.to_json(bf_result_file, orient="table") - # default_handler for arrow types that have no default conversion - scalars_pandas_df_index.to_json( - pd_result_file, orient="table", default_handler=str - ) - - bf_result = bf_result_file.read() - pd_result = pd_result_file.read() - - assert bf_result == pd_result - - -def test_df_to_csv_local_str(scalars_df_index, scalars_pandas_df_index): - bf_result = scalars_df_index.to_csv() - # default_handler for arrow types that have no default conversion - pd_result = scalars_pandas_df_index.to_csv() - - assert bf_result == pd_result - - -def test_df_to_csv_local_file(scalars_df_index, scalars_pandas_df_index): - with ( - tempfile.TemporaryFile() as bf_result_file, - tempfile.TemporaryFile() as pd_result_file, - ): - scalars_df_index.to_csv(bf_result_file) - scalars_pandas_df_index.to_csv(pd_result_file) - - bf_result = bf_result_file.read() - pd_result = pd_result_file.read() - - assert bf_result == pd_result - - -def test_df_to_parquet_local_bytes(scalars_df_index, scalars_pandas_df_index): - # GEOGRAPHY not supported in parquet export. - unsupported = ["geography_col"] - - bf_result = scalars_df_index.drop(columns=unsupported).to_parquet() - # default_handler for arrow types that have no default conversion - pd_result = scalars_pandas_df_index.drop(columns=unsupported).to_parquet() - - assert bf_result == pd_result - - -def test_df_to_parquet_local_file(scalars_df_index, scalars_pandas_df_index): - # GEOGRAPHY not supported in parquet export. - unsupported = ["geography_col"] - with ( - tempfile.TemporaryFile() as bf_result_file, - tempfile.TemporaryFile() as pd_result_file, - ): - scalars_df_index.drop(columns=unsupported).to_parquet(bf_result_file) - scalars_pandas_df_index.drop(columns=unsupported).to_parquet(pd_result_file) - - bf_result = bf_result_file.read() - pd_result = pd_result_file.read() - - assert bf_result == pd_result - - -def test_df_to_records(scalars_df_index, scalars_pandas_df_index): - unsupported = ["numeric_col"] - bf_result = scalars_df_index.drop(columns=unsupported).to_records() - pd_result = scalars_pandas_df_index.drop(columns=unsupported).to_records() - - for bfi, pdi in zip(bf_result, pd_result): - for bfj, pdj in zip(bfi, pdi): - assert pd.isna(bfj) and pd.isna(pdj) or bfj == pdj - - -def test_df_to_string(scalars_df_index, scalars_pandas_df_index): - unsupported = ["numeric_col"] # formatted differently - - bf_result = scalars_df_index.drop(columns=unsupported).to_string() - pd_result = scalars_pandas_df_index.drop(columns=unsupported).to_string() - - assert bf_result == pd_result - - -def test_df_to_html(scalars_df_index, scalars_pandas_df_index): - unsupported = ["numeric_col"] # formatted differently - - bf_result = scalars_df_index.drop(columns=unsupported).to_html() - pd_result = scalars_pandas_df_index.drop(columns=unsupported).to_html() - - assert bf_result == pd_result - - -def test_df_to_markdown(scalars_df_index, scalars_pandas_df_index): - # Nulls have bug from tabulate https://github.com/astanin/python-tabulate/issues/231 - bf_result = scalars_df_index.dropna().to_markdown() - pd_result = scalars_pandas_df_index.dropna().to_markdown() - - assert bf_result == pd_result - - -def test_df_to_pickle(scalars_df_index, scalars_pandas_df_index): - with ( - tempfile.TemporaryFile() as bf_result_file, - tempfile.TemporaryFile() as pd_result_file, - ): - scalars_df_index.to_pickle(bf_result_file) - scalars_pandas_df_index.to_pickle(pd_result_file) - bf_result = bf_result_file.read() - pd_result = pd_result_file.read() - - assert bf_result == pd_result - - -def test_df_to_orc(scalars_df_index, scalars_pandas_df_index): - pytest.importorskip("pyarrow.orc") - unsupported = [ - "numeric_col", - "bytes_col", - "date_col", - "datetime_col", - "time_col", - "timestamp_col", - "geography_col", - "duration_col", - ] - - bf_result_file = tempfile.TemporaryFile() - pd_result_file = tempfile.TemporaryFile() - scalars_df_index.drop(columns=unsupported).to_orc(bf_result_file) - scalars_pandas_df_index.drop(columns=unsupported).reset_index().to_orc( - pd_result_file - ) - bf_result = bf_result_file.read() - pd_result = bf_result_file.read() - - assert bf_result == pd_result - - -@pytest.mark.parametrize( - ("expr",), - [ - ("new_col = int64_col + int64_too",), - ("new_col = (rowindex > 3) | bool_col",), - ("int64_too = bool_col\nnew_col2 = rowindex",), - ], -) -def test_df_eval(scalars_dfs, expr): - # TODO: supply a reason why this isn't compatible with pandas 1.x - pytest.importorskip("pandas", minversion="2.0.0") - scalars_df, scalars_pandas_df = scalars_dfs - - bf_result = scalars_df.eval(expr).to_pandas() - pd_result = scalars_pandas_df.eval(expr) - - pd.testing.assert_frame_equal(bf_result, pd_result) - - -@pytest.mark.parametrize( - ("expr",), - [ - ("int64_col > int64_too",), - ("bool_col",), - ("((int64_col - int64_too) % @local_var) == 0",), - ], -) -def test_df_query(scalars_dfs, expr): - # TODO: supply a reason why this isn't compatible with pandas 1.x - pytest.importorskip("pandas", minversion="2.0.0") - # local_var is referenced in expressions - local_var = 3 # NOQA - scalars_df, scalars_pandas_df = scalars_dfs - - bf_result = scalars_df.query(expr).to_pandas() - pd_result = scalars_pandas_df.query(expr) - - pd.testing.assert_frame_equal(bf_result, pd_result) - - -@pytest.mark.parametrize( - ("subset", "normalize", "ascending", "dropna"), - [ - (None, False, False, False), - (None, True, True, True), - ("bool_col", True, False, True), - ], -) -def test_df_value_counts(scalars_dfs, subset, normalize, ascending, dropna): - if pd.__version__.startswith("1."): - pytest.skip("pandas 1.x produces different column labels.") - scalars_df, scalars_pandas_df = scalars_dfs - - bf_result = ( - scalars_df[["string_col", "bool_col"]] - .value_counts(subset, normalize=normalize, ascending=ascending, dropna=dropna) - .to_pandas() - ) - pd_result = scalars_pandas_df[["string_col", "bool_col"]].value_counts( - subset, normalize=normalize, ascending=ascending, dropna=dropna - ) - - assert_series_equal( - bf_result, - pd_result, - check_dtype=False, - check_index_type=False, - # different pandas versions inconsistent for tie-handling - ignore_order=True, - ) - - -def test_df_bool_interpretation_error(scalars_df_index): - with pytest.raises(ValueError): - True if scalars_df_index else False - - -def test_assign_after_binop_row_joins(): - pd_df = pd.DataFrame( - { - "idx1": [1, 1, 1, 1, 2, 2, 2, 2], - "idx2": [10, 10, 20, 20, 10, 10, 20, 20], - "metric1": [10, 14, 2, 13, 6, 2, 9, 5], - "metric2": [25, -3, 8, 2, -1, 0, 0, -4], - }, - dtype=pd.Int64Dtype(), - ).set_index(["idx1", "idx2"]) - bf_df = dataframe.DataFrame(pd_df) - - # Expect implicit joiner to be used, preserving input cardinality rather than getting relational join - bf_df["metric_diff"] = bf_df.metric1 - bf_df.metric2 - pd_df["metric_diff"] = pd_df.metric1 - pd_df.metric2 - - assert_frame_equal(bf_df.to_pandas(), pd_df) - - -def test_df_dot_inline(session): - df1 = pd.DataFrame([[1, 2, 3], [2, 5, 7]]) - df2 = pd.DataFrame([[2, 4, 8], [1, 5, 10], [3, 6, 9]]) - - bf1 = session.read_pandas(df1) - bf2 = session.read_pandas(df2) - bf_result = bf1.dot(bf2).to_pandas() - pd_result = df1.dot(df2) - - # Patch pandas dtypes for testing parity - # Pandas uses int64 instead of Int64 (nullable) dtype. - for name in pd_result.columns: - pd_result[name] = pd_result[name].astype(pd.Int64Dtype()) - pd_result.index = pd_result.index.astype(pd.Int64Dtype()) - - pd.testing.assert_frame_equal( - bf_result, - pd_result, - ) - - -def test_df_dot_series_inline(): - left = [[1, 2, 3], [2, 5, 7]] - right = [2, 1, 3] - - bf1 = dataframe.DataFrame(left) - bf2 = series.Series(right) - bf_result = bf1.dot(bf2).to_pandas() - - df1 = pd.DataFrame(left) - df2 = pd.Series(right) - pd_result = df1.dot(df2) - - # Patch pandas dtypes for testing parity - # Pandas result is int64 instead of Int64 (nullable) dtype. - pd_result = pd_result.astype(pd.Int64Dtype()) - pd_result.index = pd_result.index.astype(pd.Int64Dtype()) - - pd.testing.assert_series_equal( - bf_result, - pd_result, - ) - - -@pytest.mark.parametrize( - ("col_names", "ignore_index"), - [ - pytest.param(["A"], False, id="one_array_false"), - pytest.param(["A"], True, id="one_array_true"), - pytest.param(["B"], False, id="one_float_false"), - pytest.param(["B"], True, id="one_float_true"), - pytest.param(["A", "C"], False, id="two_arrays_false"), - pytest.param(["A", "C"], True, id="two_arrays_true"), - ], -) -def test_dataframe_explode(col_names, ignore_index, session): - data = { - "A": [[0, 1, 2], [], [3, 4]], - "B": 3, - "C": [["a", "b", "c"], np.nan, ["d", "e"]], - } - - df = bpd.DataFrame(data, session=session) - pd_df = df.to_pandas() - pd_result = pd_df.explode(col_names, ignore_index=ignore_index) - bf_result = df.explode(col_names, ignore_index=ignore_index) - - # Check that to_pandas() results in at most a single query execution - bf_materialized = bf_result.to_pandas() - - pd.testing.assert_frame_equal( - bf_materialized, - pd_result, - check_index_type=False, - check_dtype=False, - ) - - -@pytest.mark.parametrize( - ("ignore_index", "ordered"), - [ - pytest.param(True, True, id="include_index_ordered"), - pytest.param(True, False, id="include_index_unordered"), - pytest.param(False, True, id="ignore_index_ordered"), - ], -) -def test_dataframe_explode_reserve_order(session, ignore_index, ordered): - data = { - "a": [np.random.randint(0, 10, 10) for _ in range(10)], - "b": [np.random.randint(0, 10, 10) for _ in range(10)], - } - df = bpd.DataFrame(data) - pd_df = pd.DataFrame(data) - - res = df.explode(["a", "b"], ignore_index=ignore_index).to_pandas(ordered=ordered) - pd_res = pd_df.explode(["a", "b"], ignore_index=ignore_index).astype( - pd.Int64Dtype() - ) - pd.testing.assert_frame_equal( - res if ordered else res.sort_index(), - pd_res, - check_index_type=False, - ) - - -@pytest.mark.parametrize( - ("col_names"), - [ - pytest.param([], id="empty", marks=pytest.mark.xfail(raises=ValueError)), - pytest.param( - ["A", "A"], id="duplicate", marks=pytest.mark.xfail(raises=ValueError) - ), - pytest.param("unknown", id="unknown", marks=pytest.mark.xfail(raises=KeyError)), - ], -) -def test_dataframe_explode_xfail(col_names): - df = bpd.DataFrame({"A": [[0, 1, 2], [], [3, 4]]}) - df.explode(col_names) - - -def test_recursion_limit_unit(scalars_df_index): - scalars_df_index = scalars_df_index[["int64_too", "int64_col", "float64_col"]] - for i in range(250): - scalars_df_index = scalars_df_index + 4 - scalars_df_index.to_pandas() diff --git a/tests/unit/test_dtypes.py b/tests/unit/test_dtypes.py index bb2b57d4090..6ceaaf911b9 100644 --- a/tests/unit/test_dtypes.py +++ b/tests/unit/test_dtypes.py @@ -1,4 +1,4 @@ -# Copyright 2025 Google LLC +# Copyright 2023 Google LLC # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -12,70 +12,240 @@ # See the License for the specific language governing permissions and # limitations under the License. -import db_dtypes # type: ignore +import geopandas as gpd # type: ignore +import ibis +import ibis.expr.datatypes as ibis_dtypes +import numpy as np +import pandas as pd import pyarrow as pa # type: ignore import pytest -import shapely.geometry # type: ignore import bigframes.dtypes @pytest.mark.parametrize( - ["python_type", "expected_dtype"], + ["ibis_dtype", "bigframes_dtype"], [ - (bool, bigframes.dtypes.BOOL_DTYPE), - (int, bigframes.dtypes.INT_DTYPE), - (str, bigframes.dtypes.STRING_DTYPE), - (shapely.geometry.Point, bigframes.dtypes.GEO_DTYPE), - (shapely.geometry.Polygon, bigframes.dtypes.GEO_DTYPE), - (shapely.geometry.base.BaseGeometry, bigframes.dtypes.GEO_DTYPE), + # TODO(bmil): Add ARRAY, INTERVAL, STRUCT to cover all the standard + # BigQuery data types as they appear in Ibis: + # https://cloud.google.com/bigquery/docs/reference/standard-sql/data-types + pytest.param( + ibis_dtypes.Decimal(precision=76, scale=38, nullable=True), + np.dtype("O"), + id="bignumeric", + ), + pytest.param(ibis_dtypes.boolean, pd.BooleanDtype(), id="bool"), + pytest.param(ibis_dtypes.binary, np.dtype("O"), id="bytes"), + pytest.param(ibis_dtypes.date, pd.ArrowDtype(pa.date32()), id="date"), + pytest.param( + ibis_dtypes.Timestamp(), pd.ArrowDtype(pa.timestamp("us")), id="datetime" + ), + pytest.param(ibis_dtypes.float64, pd.Float64Dtype(), id="float"), + pytest.param( + ibis_dtypes.GeoSpatial(geotype="geography", srid=4326, nullable=True), + gpd.array.GeometryDtype(), + id="geography", + ), + pytest.param(ibis_dtypes.int8, pd.Int64Dtype(), id="int8-as-int64"), + pytest.param(ibis_dtypes.int64, pd.Int64Dtype(), id="int64"), + # TODO(tswast): custom dtype (or at least string dtype) for JSON objects + pytest.param(ibis_dtypes.json, np.dtype("O"), id="json"), + pytest.param( + ibis_dtypes.Decimal(precision=38, scale=9, nullable=True), + np.dtype("O"), + id="numeric", + ), + pytest.param( + ibis_dtypes.string, pd.StringDtype(storage="pyarrow"), id="string" + ), + pytest.param(ibis_dtypes.time, pd.ArrowDtype(pa.time64("us")), id="time"), + pytest.param( + ibis_dtypes.Timestamp(timezone="UTC"), + pd.ArrowDtype(pa.timestamp("us", tz="UTC")), # type: ignore + id="timestamp", + ), ], ) -def test_bigframes_type_supports_python_types(python_type, expected_dtype): - got_dtype = bigframes.dtypes.bigframes_type(python_type) - assert got_dtype == expected_dtype +def test_ibis_dtype_converts(ibis_dtype, bigframes_dtype): + """Test all the Ibis data types needed to read BigQuery tables""" + result = bigframes.dtypes.ibis_dtype_to_bigframes_dtype(ibis_dtype) + assert result == bigframes_dtype + + +def test_ibis_timestamp_pst_raises_unexpected_datatype(): + """BigQuery timestamp only supports UTC time""" + with pytest.raises(ValueError, match="Unexpected Ibis data type"): + bigframes.dtypes.ibis_dtype_to_bigframes_dtype( + ibis_dtypes.Timestamp(timezone="PST") + ) + + +def test_ibis_float32_raises_unexpected_datatype(): + """Other Ibis types not read from BigQuery are not expected""" + with pytest.raises(ValueError, match="Unexpected Ibis data type"): + bigframes.dtypes.ibis_dtype_to_bigframes_dtype(ibis_dtypes.float32) + + +IBIS_ARROW_DTYPES = ( + (ibis_dtypes.boolean, pa.bool_()), + (ibis_dtypes.date, pa.date32()), + (ibis_dtypes.Timestamp(), pa.timestamp("us")), + (ibis_dtypes.float64, pa.float64()), + ( + ibis_dtypes.Timestamp(timezone="UTC"), + pa.timestamp("us", tz="UTC"), + ), + ( + ibis_dtypes.Struct.from_tuples( + [ + ("name", ibis_dtypes.string()), + ("version", ibis_dtypes.int64()), + ] + ), + pa.struct( + [ + ("name", pa.string()), + ("version", pa.int64()), + ] + ), + ), + ( + ibis_dtypes.Struct.from_tuples( + [ + ( + "nested", + ibis_dtypes.Struct.from_tuples( + [ + ("field", ibis_dtypes.string()), + ] + ), + ), + ] + ), + pa.struct( + [ + ( + "nested", + pa.struct( + [ + ("field", pa.string()), + ] + ), + ), + ] + ), + ), +) + + +@pytest.mark.parametrize(("ibis_dtype", "arrow_dtype"), IBIS_ARROW_DTYPES) +def test_arrow_dtype_to_ibis_dtype(ibis_dtype, arrow_dtype): + result = bigframes.dtypes.arrow_dtype_to_ibis_dtype(arrow_dtype) + assert result == ibis_dtype + + +@pytest.mark.parametrize(("ibis_dtype", "arrow_dtype"), IBIS_ARROW_DTYPES) +def test_ibis_dtype_to_arrow_dtype(ibis_dtype, arrow_dtype): + result = bigframes.dtypes.ibis_dtype_to_arrow_dtype(ibis_dtype) + assert result == arrow_dtype @pytest.mark.parametrize( - ["scalar", "expected_dtype"], + ["bigframes_dtype", "ibis_dtype"], [ - (pa.scalar(1_000_000_000, type=pa.int64()), bigframes.dtypes.INT_DTYPE), - (pa.scalar(True, type=pa.bool_()), bigframes.dtypes.BOOL_DTYPE), - (pa.scalar("hello", type=pa.string()), bigframes.dtypes.STRING_DTYPE), - # Support NULL scalars. - (pa.scalar(None, type=pa.int64()), bigframes.dtypes.INT_DTYPE), - (pa.scalar(None, type=pa.bool_()), bigframes.dtypes.BOOL_DTYPE), - (pa.scalar(None, type=pa.string()), bigframes.dtypes.STRING_DTYPE), + # This test covers all dtypes that BigQuery DataFrames can exactly map to Ibis + (pd.BooleanDtype(), ibis_dtypes.boolean), + (pd.ArrowDtype(pa.date32()), ibis_dtypes.date), + (pd.ArrowDtype(pa.timestamp("us")), ibis_dtypes.Timestamp()), + (pd.Float64Dtype(), ibis_dtypes.float64), + (pd.Int64Dtype(), ibis_dtypes.int64), + (pd.StringDtype(storage="pyarrow"), ibis_dtypes.string), + (pd.ArrowDtype(pa.time64("us")), ibis_dtypes.time), + ( + pd.ArrowDtype(pa.timestamp("us", tz="UTC")), # type: ignore + ibis_dtypes.Timestamp(timezone="UTC"), + ), + ], + ids=[ + "boolean", + "date", + "datetime", + "float", + "int", + "string", + "time", + "timestamp", ], ) -def test_infer_literal_type_arrow_scalar(scalar, expected_dtype): - assert bigframes.dtypes.infer_literal_type(scalar) == expected_dtype +def test_bigframes_dtype_converts(ibis_dtype, bigframes_dtype): + """Test all the Ibis data types needed to read BigQuery tables""" + result = bigframes.dtypes.bigframes_dtype_to_ibis_dtype(bigframes_dtype) + assert result == ibis_dtype @pytest.mark.parametrize( - ["type_", "expected"], + ["bigframes_dtype_str", "ibis_dtype"], [ - (pa.int64(), False), - (db_dtypes.JSONArrowType(), True), - (pa.struct([("int", pa.int64()), ("str", pa.string())]), False), - (pa.struct([("int", pa.int64()), ("json", db_dtypes.JSONArrowType())]), True), - (pa.list_(pa.int64()), False), - (pa.list_(db_dtypes.JSONArrowType()), True), + # This test covers all dtypes that BigQuery DataFrames can exactly map to Ibis + ("boolean", ibis_dtypes.boolean), + ("date32[day][pyarrow]", ibis_dtypes.date), + ("timestamp[us][pyarrow]", ibis_dtypes.Timestamp()), + ("Float64", ibis_dtypes.float64), + ("Int64", ibis_dtypes.int64), + ("string[pyarrow]", ibis_dtypes.string), + ("time64[us][pyarrow]", ibis_dtypes.time), ( - pa.list_( - pa.struct([("int", pa.int64()), ("json", db_dtypes.JSONArrowType())]) - ), - True, + "timestamp[us, tz=UTC][pyarrow]", + ibis_dtypes.Timestamp(timezone="UTC"), ), + # Special case - "string" is acceptable for "string[pyarrow]" + ("string", ibis_dtypes.string), ], ) -def test_contains_db_dtypes_json_arrow_type(type_, expected): - assert bigframes.dtypes.contains_db_dtypes_json_arrow_type(type_) == expected +def test_bigframes_string_dtype_converts(ibis_dtype, bigframes_dtype_str): + """Test all the Ibis data types needed to read BigQuery tables""" + result = bigframes.dtypes.bigframes_dtype_to_ibis_dtype(bigframes_dtype_str) + assert result == ibis_dtype + + +def test_unsupported_dtype_raises_unexpected_datatype(): + """Incompatible dtypes should fail when passed into BigQuery DataFrames""" + with pytest.raises(ValueError, match="Unexpected data type"): + bigframes.dtypes.bigframes_dtype_to_ibis_dtype(np.float32) + + +def test_unsupported_dtype_str_raises_unexpected_datatype(): + """Incompatible dtypes should fail when passed into BigQuery DataFrames""" + with pytest.raises(ValueError, match="Unexpected data type"): + bigframes.dtypes.bigframes_dtype_to_ibis_dtype("int64") + + +@pytest.mark.parametrize( + ["literal", "ibis_scalar"], + [ + (True, ibis.literal(True, ibis_dtypes.boolean)), + (5, ibis.literal(5, ibis_dtypes.int64)), + (-33.2, ibis.literal(-33.2, ibis_dtypes.float64)), + ], +) +def test_literal_to_ibis_scalar_converts(literal, ibis_scalar): + assert bigframes.dtypes.literal_to_ibis_scalar(literal).equals(ibis_scalar) + + +def test_literal_to_ibis_scalar_throws_on_incompatible_literal(): + with pytest.raises( + ValueError, + ): + bigframes.dtypes.literal_to_ibis_scalar({"mykey": "myval"}) + + +def test_remote_function_io_types_are_supported_bigframes_types(): + from ibis.expr.datatypes.core import dtype as python_type_to_bigquery_type + from bigframes.remote_function import ( + SUPPORTED_IO_PYTHON_TYPES as rf_supported_io_types, + ) -def test_convert_to_schema_field_list_description(): - bf_dtype = bigframes.dtypes.OBJ_REF_DTYPE - list_bf_dtype = bigframes.dtypes.list_type(bf_dtype) - field = bigframes.dtypes.convert_to_schema_field("my_list", list_bf_dtype) - assert field.description == "bigframes_dtype: OBJ_REF_DTYPE" - assert field.mode == "REPEATED" + for python_type in rf_supported_io_types: + ibis_type = python_type_to_bigquery_type(python_type) + assert ibis_type in bigframes.dtypes.IBIS_TO_BIGFRAMES diff --git a/tests/unit/test_features.py b/tests/unit/test_features.py deleted file mode 100644 index 20642aec343..00000000000 --- a/tests/unit/test_features.py +++ /dev/null @@ -1,49 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import pandas -import pytest - -import bigframes.features - - -def test_pandas_installed_version_returns_cached(): - versions = bigframes.features.PandasVersions() - versions._installed_version = object() - assert versions.installed_version is versions._installed_version - - -def test_pandas_installed_version_returns_parsed_version(monkeypatch): - versions = bigframes.features.PandasVersions() - monkeypatch.setattr(pandas, "__version__", "1.2.3") - major, minor, micro = versions.installed_version - assert major == "1" - assert minor == "2" - assert micro == "3" - - -@pytest.mark.parametrize( - ("version", "expected"), - ( - ("1.2.3", False), - ("1.5.3", False), - ("2.0.0", True), - ("2.2.3", True), - ("3.0.0", True), - ), -) -def test_pandas_is_arrow_list_dtype_usable(version, expected): - versions = bigframes.features.PandasVersions() - versions._installed_version = version.split(".") - assert versions.is_arrow_list_dtype_usable == expected diff --git a/tests/unit/test_formatting_helper.py b/tests/unit/test_formatting_helper.py new file mode 100644 index 00000000000..ea29869e824 --- /dev/null +++ b/tests/unit/test_formatting_helper.py @@ -0,0 +1,17 @@ +import pytest + +import bigframes.formatting_helpers as formatter + + +@pytest.mark.parametrize( + "test_input, expected", [(None, "N/A"), ("string", "N/A"), (100000, "100.0 kB")] +) +def test_get_formatted_bytes(test_input, expected): + assert formatter.get_formatted_bytes(test_input) == expected + + +@pytest.mark.parametrize( + "test_input, expected", [(None, None), ("string", "string"), (100000, "a minute")] +) +def test_get_formatted_time(test_input, expected): + assert formatter.get_formatted_time(test_input) == expected diff --git a/tests/unit/test_formatting_helpers.py b/tests/unit/test_formatting_helpers.py index 8917f540501..9db9b372e21 100644 --- a/tests/unit/test_formatting_helpers.py +++ b/tests/unit/test_formatting_helpers.py @@ -14,14 +14,12 @@ import unittest.mock as mock -import bigframes_vendored.constants as constants import google.api_core.exceptions as api_core_exceptions import google.cloud.bigquery as bigquery import pytest -import bigframes.core.events as bfevents +import bigframes.constants as constants import bigframes.formatting_helpers as formatting_helpers -import bigframes.version def test_wait_for_query_job_error_includes_feedback_link(): @@ -31,7 +29,7 @@ def test_wait_for_query_job_error_includes_feedback_link(): ) with pytest.raises(api_core_exceptions.BadRequest) as cap_exc: - formatting_helpers.wait_for_job(mock_query_job) + formatting_helpers.wait_for_query_job(mock_query_job) cap_exc.match("Test message 123.") cap_exc.match(constants.FEEDBACK_LINK) @@ -46,195 +44,3 @@ def test_wait_for_job_error_includes_feedback_link(): cap_exc.match("Test message 123.") cap_exc.match(constants.FEEDBACK_LINK) - - -def test_wait_for_job_error_includes_version(): - mock_job = mock.create_autospec(bigquery.LoadJob) - mock_job.result.side_effect = api_core_exceptions.BadRequest("Test message 123.") - - with pytest.raises(api_core_exceptions.BadRequest) as cap_exc: - formatting_helpers.wait_for_job(mock_job) - - cap_exc.match("Test message 123.") - cap_exc.match(bigframes.version.__version__) - - -@pytest.mark.parametrize( - "test_input, expected", [(None, "N/A"), ("string", "N/A"), (100000, "100.0 kB")] -) -def test_get_formatted_bytes(test_input, expected): - assert formatting_helpers.get_formatted_bytes(test_input) == expected - - -@pytest.mark.parametrize( - "test_input, expected", [(None, None), ("string", "string"), (66000, "a minute")] -) -def test_get_formatted_time(test_input, expected): - assert formatting_helpers.get_formatted_time(test_input) == expected - - -def test_render_bqquery_sent_event_html(): - event = bfevents.BigQuerySentEvent( - query="SELECT * FROM my_table", - job_id="my-job-id", - location="us-central1", - billing_project="my-project", - ) - html = formatting_helpers.render_bqquery_sent_event_html(event) - assert "SELECT * FROM my_table" in html - assert "my-job-id" in html - assert "us-central1" in html - assert "my-project" in html - assert "
" in html - - -def test_render_bqquery_sent_event_plaintext(): - event = bfevents.BigQuerySentEvent( - query="SELECT * FROM my_table", - job_id="my-job-id", - location="us-central1", - billing_project="my-project", - ) - text = formatting_helpers.render_bqquery_sent_event_plaintext(event) - assert "my-job-id" in text - assert "us-central1" in text - assert "my-project" in text - assert "SELECT * FROM my_table" not in text - - -def test_render_bqquery_retry_event_html(): - event = bfevents.BigQueryRetryEvent( - query="SELECT * FROM my_table", - job_id="my-job-id", - location="us-central1", - billing_project="my-project", - ) - html = formatting_helpers.render_bqquery_retry_event_html(event) - assert "Retrying query" in html - assert "SELECT * FROM my_table" in html - assert "my-job-id" in html - assert "us-central1" in html - assert "my-project" in html - assert "
" in html - - -def test_render_bqquery_retry_event_plaintext(): - event = bfevents.BigQueryRetryEvent( - query="SELECT * FROM my_table", - job_id="my-job-id", - location="us-central1", - billing_project="my-project", - ) - text = formatting_helpers.render_bqquery_retry_event_plaintext(event) - assert "Retrying query" in text - assert "my-job-id" in text - assert "us-central1" in text - assert "my-project" in text - assert "SELECT * FROM my_table" not in text - - -def test_render_bqquery_received_event_html(): - mock_plan_entry = mock.create_autospec( - bigquery.job.query.QueryPlanEntry, instance=True - ) - mock_plan_entry.__str__.return_value = "mocked plan" - event = bfevents.BigQueryReceivedEvent( - job_id="my-job-id", - location="us-central1", - billing_project="my-project", - state="RUNNING", - query_plan=[mock_plan_entry], - ) - html = formatting_helpers.render_bqquery_received_event_html(event) - assert "Query" in html - assert "my-job-id" in html - assert "is RUNNING" in html - assert "
" in html - assert "mocked plan" in html - - -def test_render_bqquery_received_event_plaintext(): - event = bfevents.BigQueryReceivedEvent( - job_id="my-job-id", - location="us-central1", - billing_project="my-project", - state="RUNNING", - query_plan=[], - ) - text = formatting_helpers.render_bqquery_received_event_plaintext(event) - assert "Query" in text - assert "my-job-id" in text - assert "is RUNNING" in text - assert "Query Plan" not in text - - -def test_render_bqquery_finished_event_html(): - event = bfevents.BigQueryFinishedEvent( - job_id="my-job-id", - location="us-central1", - billing_project="my-project", - total_bytes_processed=1000, - slot_millis=2000, - ) - html = formatting_helpers.render_bqquery_finished_event_html(event) - assert "Query" in html - assert "my-job-id" in html - assert "processed 1.0 kB" in html - assert "2 seconds of slot time" in html - - -def test_render_bqquery_finished_event_plaintext(): - event = bfevents.BigQueryFinishedEvent( - job_id="my-job-id", - location="us-central1", - billing_project="my-project", - total_bytes_processed=1000, - slot_millis=2000, - ) - text = formatting_helpers.render_bqquery_finished_event_plaintext(event) - assert "Query" in text - assert "my-job-id" in text - assert "finished" in text - assert "1.0 kB processed" in text - assert "Slot time: 2 seconds" in text - - -def test_get_job_url(): - job_id = "my-job-id" - location = "us-central1" - project_id = "my-project" - expected_url = ( - f"https://console.cloud.google.com/bigquery?project={project_id}" - f"&j=bq:{location}:{job_id}&page=queryresults" - ) - - actual_url = formatting_helpers.get_job_url( - job_id=job_id, location=location, project_id=project_id - ) - assert actual_url == expected_url - - -def test_progress_callback_falls_back_to_global(): - event = bfevents.BigQuerySentEvent( - query="SELECT * FROM my_table", - ) - envelope = bfevents.EventEnvelope(event=event, progress_bar=bfevents._DEFAULT) - - with mock.patch("bigframes._config.options.display.progress_bar", "terminal"): - with mock.patch("bigframes.formatting_helpers.in_ipython", return_value=False): - with mock.patch("builtins.print") as mock_print: - formatting_helpers.create_progress_callback()(envelope) - mock_print.assert_called_once() - - -def test_progress_callback_respects_envelope_progress_bar(): - event = bfevents.BigQuerySentEvent( - query="SELECT * FROM my_table", - ) - envelope = bfevents.EventEnvelope(event=event, progress_bar=None) - - with mock.patch("bigframes._config.options.display.progress_bar", "terminal"): - with mock.patch("bigframes.formatting_helpers.in_ipython", return_value=False): - with mock.patch("builtins.print") as mock_print: - formatting_helpers.create_progress_callback()(envelope) - mock_print.assert_not_called() diff --git a/tests/unit/test_groupby_transpile.py b/tests/unit/test_groupby_transpile.py deleted file mode 100644 index 4f841bc1b2d..00000000000 --- a/tests/unit/test_groupby_transpile.py +++ /dev/null @@ -1,200 +0,0 @@ -# Copyright 2026 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from pathlib import Path - -import pandas as pd -import pytest -from pandas.testing import assert_frame_equal, assert_series_equal - -import bigframes -import bigframes.core.global_session -import bigframes.pandas as bpd -from bigframes.testing.utils import convert_pandas_dtypes - -pytest.importorskip("polars") -pytest.importorskip("pandas", minversion="2.0.0") - -CURRENT_DIR = Path(__file__).parent -DATA_DIR = CURRENT_DIR.parent / "data" - - -@pytest.fixture(scope="module") -def scalars_pandas_df_index(): - df = pd.read_json( - DATA_DIR / "scalars.jsonl", - lines=True, - ) - convert_pandas_dtypes(df, bytes_col=True) - - df = df.set_index("rowindex", drop=False) - df.index.name = None - return df.set_index("rowindex").sort_index() - - -@pytest.fixture(scope="module", autouse=True) -def session(): - # import inline to allow polars importorskip to happen first - from bigframes.testing import polars_session - - with bpd.option_context("experiments.enable_python_transpiler", True): - session = polars_session.TestSession() - with bigframes.core.global_session._GlobalSessionContext(session): - yield session - - -@pytest.fixture(scope="module") -def scalars_df_index( - session: bigframes.Session, scalars_pandas_df_index -) -> bpd.DataFrame: - return session.read_pandas(scalars_pandas_df_index) - - -# Tests for groupby.agg custom lambdas - - -def test_series_groupby_agg_transpile(scalars_df_index, scalars_pandas_df_index): - def custom_agg(s): - return s.sum() - s.mean() - - bf_df = scalars_df_index.dropna(subset=["int64_col", "bool_col"]) - pd_df = scalars_pandas_df_index.dropna(subset=["int64_col", "bool_col"]) - - bf_result = bf_df.groupby("bool_col")["int64_col"].agg(custom_agg).to_pandas() - pd_result = pd_df.groupby("bool_col")["int64_col"].agg(custom_agg) - - assert_series_equal(bf_result, pd_result, check_dtype=False) - - -def test_dataframe_groupby_agg_func_transpile( - scalars_df_index, scalars_pandas_df_index -): - def custom_agg(s): - return (s.max() - s.min()) / s.count() - - bf_df = scalars_df_index.dropna(subset=["int64_col", "int64_too", "bool_col"]) - pd_df = scalars_pandas_df_index.dropna( - subset=["int64_col", "int64_too", "bool_col"] - ) - - bf_result = ( - bf_df[["int64_col", "int64_too", "bool_col"]] - .groupby("bool_col") - .agg(custom_agg) - .to_pandas() - ) - pd_result = ( - pd_df[["int64_col", "int64_too", "bool_col"]] - .groupby("bool_col") - .agg(custom_agg) - ) - - assert_frame_equal(bf_result, pd_result, check_dtype=False) - - -def test_dataframe_groupby_agg_dict_transpile( - scalars_df_index, scalars_pandas_df_index -): - def custom_agg1(s): - return s.sum() - s.mean() - - def custom_agg2(s): - return s.max() - s.min() - - bf_df = scalars_df_index.dropna(subset=["int64_col", "int64_too", "bool_col"]) - pd_df = scalars_pandas_df_index.dropna( - subset=["int64_col", "int64_too", "bool_col"] - ) - - bf_result = ( - bf_df.groupby("bool_col") - .agg({"int64_col": custom_agg1, "int64_too": custom_agg2}) - .to_pandas() - ) - pd_result = pd_df.groupby("bool_col").agg( - {"int64_col": custom_agg1, "int64_too": custom_agg2} - ) - - assert_frame_equal(bf_result, pd_result, check_dtype=False) - - -def test_dataframe_groupby_agg_list_transpile( - scalars_df_index, scalars_pandas_df_index -): - def custom_agg1(s): - return s.sum() - s.mean() - - def custom_agg2(s): - return s.max() - s.min() - - bf_df = scalars_df_index.dropna(subset=["int64_col", "bool_col"]) - pd_df = scalars_pandas_df_index.dropna(subset=["int64_col", "bool_col"]) - - bf_result = ( - bf_df[["int64_col", "bool_col"]] - .groupby("bool_col") - .agg([custom_agg1, custom_agg2]) - .to_pandas() - ) - pd_result = ( - pd_df[["int64_col", "bool_col"]] - .groupby("bool_col") - .agg([custom_agg1, custom_agg2]) - ) - - assert_frame_equal(bf_result, pd_result, check_dtype=False) - - -# Tests for groupby.transform broadcasting lambdas - - -def test_series_groupby_transform_transpile(scalars_df_index, scalars_pandas_df_index): - def custom_transform(s): - return s - s.mean() - - bf_df = scalars_df_index.dropna(subset=["int64_col", "bool_col"]) - pd_df = scalars_pandas_df_index.dropna(subset=["int64_col", "bool_col"]) - - bf_result = ( - bf_df.groupby("bool_col")["int64_col"].transform(custom_transform).to_pandas() - ) - pd_result = pd_df.groupby("bool_col")["int64_col"].transform(custom_transform) - - assert_series_equal(bf_result, pd_result, check_dtype=False) - - -def test_dataframe_groupby_transform_transpile( - scalars_df_index, scalars_pandas_df_index -): - def custom_transform(s): - return (s - s.min()) / (s.max() - s.min()) - - bf_df = scalars_df_index.dropna(subset=["int64_col", "int64_too", "bool_col"]) - pd_df = scalars_pandas_df_index.dropna( - subset=["int64_col", "int64_too", "bool_col"] - ) - - bf_result = ( - bf_df[["int64_col", "int64_too", "bool_col"]] - .groupby("bool_col") - .transform(custom_transform) - .to_pandas() - ) - pd_result = ( - pd_df[["int64_col", "int64_too", "bool_col"]] - .groupby("bool_col") - .transform(custom_transform) - ) - - assert_frame_equal(bf_result, pd_result, check_dtype=False) diff --git a/tests/unit/test_iloc_getitem.py b/tests/unit/test_iloc_getitem.py deleted file mode 100644 index 7f030a16c92..00000000000 --- a/tests/unit/test_iloc_getitem.py +++ /dev/null @@ -1,297 +0,0 @@ -# Copyright 2026 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from typing import Generator - -import numpy as np -import pandas as pd -import pyarrow as pa -import pytest - -import bigframes -import bigframes.pandas as bpd -from bigframes.testing.utils import assert_frame_equal, assert_series_equal - -pytest.importorskip("polars") - - -@pytest.fixture(scope="module", autouse=True) -def session() -> Generator[bigframes.Session, None, None]: - import bigframes.core.global_session - from bigframes.testing import polars_session - - session = polars_session.TestSession() - with bigframes.core.global_session._GlobalSessionContext(session): - yield session - - -@pytest.fixture -def sample_df() -> bpd.DataFrame: - pd_df = pd.DataFrame( - { - "A": [1, 2, 3], - "B": [4, 5, 6], - "C": [7, 8, 9], - } - ) - return bpd.read_pandas(pd_df) - - -@pytest.fixture -def unordered_sample_df( - sample_df: bpd.DataFrame, -) -> Generator[bpd.DataFrame, None, None]: - session = sample_df._session - original_strictly_ordered = session._strictly_ordered - original_allow_ambiguity = session._allow_ambiguity - - try: - session._strictly_ordered = False - session._allow_ambiguity = True - - import unittest.mock as mock - - with ( - mock.patch.object( - type(sample_df._block.expr), - "order_ambiguous", - new_callable=mock.PropertyMock, - ) as mock_ambiguous, - mock.patch.object( - type(sample_df._block), - "explicitly_ordered", - new_callable=mock.PropertyMock, - ) as mock_explicit, - ): - mock_ambiguous.return_value = True - mock_explicit.return_value = False - yield sample_df - finally: - session._strictly_ordered = original_strictly_ordered - session._allow_ambiguity = original_allow_ambiguity - - -@pytest.fixture -def duplicate_columns_df() -> bpd.DataFrame: - pd_df = pd.DataFrame( - [[1, 2, 3], [4, 5, 6], [7, 8, 9]], - columns=["A", "B", "A"], - ) - return bpd.read_pandas(pd_df) - - -def test_iloc_getitem_column_single_integer(sample_df): - bf_df = sample_df - pd_df = sample_df.to_pandas() - - bf_result = bf_df.iloc[:, 1].to_pandas() - pd_result = pd_df.iloc[:, 1] - - assert_series_equal(bf_result, pd_result) - - -def test_iloc_getitem_column_numpy_scalar(sample_df): - bf_df = sample_df - pd_df = sample_df.to_pandas() - - bf_result = bf_df.iloc[:, np.int64(1)].to_pandas() - pd_result = pd_df.iloc[:, np.int64(1)] - - assert_series_equal(bf_result, pd_result) - - -def test_iloc_getitem_columns_numpy_array(sample_df): - bf_df = sample_df - pd_df = sample_df.to_pandas() - - bf_result = bf_df.iloc[:, np.array([0, 2], dtype=np.int64)].to_pandas() - pd_result = pd_df.iloc[:, np.array([0, 2], dtype=np.int64)] - - assert_frame_equal(bf_result, pd_result) - - -def test_iloc_getitem_column_pyarrow_scalar(sample_df): - bf_df = sample_df - pd_df = sample_df.to_pandas() - - bf_result = bf_df.iloc[:, pa.scalar(1, type=pa.int64())].to_pandas() - pd_result = pd_df.iloc[:, 1] - - assert_series_equal(bf_result, pd_result) - - -def test_iloc_getitem_columns_pyarrow_array(sample_df): - bf_df = sample_df - pd_df = sample_df.to_pandas() - - bf_result = bf_df.iloc[:, pa.array([0, 2], type=pa.int64())].to_pandas() - pd_result = pd_df.iloc[:, pa.array([0, 2], type=pa.int64())] - - assert_frame_equal(bf_result, pd_result) - - -def test_iloc_getitem_row_numpy_scalar(sample_df): - bf_df = sample_df - pd_df = sample_df.to_pandas() - - bf_result = bf_df.iloc[np.int64(1)] - pd_result = pd_df.iloc[np.int64(1)] - - assert_series_equal(bf_result, pd_result) - - -def test_iloc_getitem_rows_numpy_array(sample_df): - bf_df = sample_df - pd_df = sample_df.to_pandas() - - bf_result = bf_df.iloc[np.array([0, 2], dtype=np.int64)].to_pandas() - pd_result = pd_df.iloc[np.array([0, 2], dtype=np.int64)] - - assert_frame_equal(bf_result, pd_result) - - -def test_iloc_getitem_row_pyarrow_scalar(sample_df): - bf_df = sample_df - pd_df = sample_df.to_pandas() - - bf_result = bf_df.iloc[pa.scalar(1, type=pa.int64())] - pd_result = pd_df.iloc[1] - - assert_series_equal(bf_result, pd_result) - - -def test_iloc_getitem_rows_pyarrow_array(sample_df): - bf_df = sample_df - pd_df = sample_df.to_pandas() - - bf_result = bf_df.iloc[pa.array([0, 2], type=pa.int64())].to_pandas() - pd_result = pd_df.iloc[pa.array([0, 2], type=pa.int64())] - - assert_frame_equal(bf_result, pd_result) - - -@pytest.mark.parametrize( - ["key", "value", "expected_error"], - [ - pytest.param((slice(None), 1), None, None, id="col_index"), - pytest.param((slice(0, None), 1), None, None, id="col_index_slice_0_none"), - pytest.param( - (slice(None, None, 1), 1), None, None, id="col_index_slice_none_none_1" - ), - pytest.param( - (slice(1, None), 1), - None, - bigframes.exceptions.OrderRequiredError, - id="col_index_slice_1_none", - ), - pytest.param( - (slice(None, 2), 1), - None, - bigframes.exceptions.OrderRequiredError, - id="col_index_slice_none_2", - ), - pytest.param((slice(None), 1), 99, None, id="col_setitem"), - pytest.param( - (1, slice(None)), - None, - bigframes.exceptions.OrderRequiredError, - id="row_index_slice", - ), - pytest.param( - 1, - None, - bigframes.exceptions.OrderRequiredError, - id="single_row_index", - ), - ], -) -def test_iloc_getitem_unordered(unordered_sample_df, key, value, expected_error): - if value is not None: - bf_df = unordered_sample_df.copy() - bf_df.iloc[key] = value - elif expected_error is not None: - with pytest.raises(expected_error): - unordered_sample_df.iloc[key] - else: - unordered_sample_df.iloc[key] - - -def test_iloc_getitem_duplicate_columns_single_integer(duplicate_columns_df): - bf_df = duplicate_columns_df - pd_df = duplicate_columns_df.to_pandas() - - bf_result = bf_df.iloc[:, 2].to_pandas() - pd_result = pd_df.iloc[:, 2] - - assert_series_equal(bf_result, pd_result) - - -def test_iloc_getitem_duplicate_columns_list_integer(duplicate_columns_df): - bf_df = duplicate_columns_df - pd_df = duplicate_columns_df.to_pandas() - - bf_result = bf_df.iloc[:, [0, 2]].to_pandas() - pd_result = pd_df.iloc[:, [0, 2]] - - assert_frame_equal(bf_result, pd_result) - - -def test_iloc_getitem_duplicate_columns_slice(duplicate_columns_df): - bf_df = duplicate_columns_df - pd_df = duplicate_columns_df.to_pandas() - - bf_result = bf_df.iloc[:, 1:3].to_pandas() - pd_result = pd_df.iloc[:, 1:3] - - assert_frame_equal(bf_result, pd_result) - - -def test_iloc_getitem_duplicate_columns_numpy_scalar(duplicate_columns_df): - bf_df = duplicate_columns_df - pd_df = duplicate_columns_df.to_pandas() - - bf_result = bf_df.iloc[:, np.int64(2)].to_pandas() - pd_result = pd_df.iloc[:, np.int64(2)] - - assert_series_equal(bf_result, pd_result) - - -def test_iloc_getitem_duplicate_columns_numpy_array(duplicate_columns_df): - bf_df = duplicate_columns_df - pd_df = duplicate_columns_df.to_pandas() - - bf_result = bf_df.iloc[:, np.array([0, 2], dtype=np.int64)].to_pandas() - pd_result = pd_df.iloc[:, np.array([0, 2], dtype=np.int64)] - - assert_frame_equal(bf_result, pd_result) - - -def test_iloc_getitem_duplicate_columns_pyarrow_scalar(duplicate_columns_df): - bf_df = duplicate_columns_df - pd_df = duplicate_columns_df.to_pandas() - - bf_result = bf_df.iloc[:, pa.scalar(2, type=pa.int64())].to_pandas() - pd_result = pd_df.iloc[:, 2] - - assert_series_equal(bf_result, pd_result) - - -def test_iloc_getitem_duplicate_columns_pyarrow_array(duplicate_columns_df): - bf_df = duplicate_columns_df - pd_df = duplicate_columns_df.to_pandas() - - bf_result = bf_df.iloc[:, pa.array([0, 2], type=pa.int64())].to_pandas() - pd_result = pd_df.iloc[:, pa.array([0, 2], type=pa.int64())] - - assert_frame_equal(bf_result, pd_result) diff --git a/tests/unit/test_iloc_setitem.py b/tests/unit/test_iloc_setitem.py deleted file mode 100644 index 98c515fd4ee..00000000000 --- a/tests/unit/test_iloc_setitem.py +++ /dev/null @@ -1,245 +0,0 @@ -# Copyright 2026 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from typing import Generator - -import numpy as np -import pandas as pd -import pyarrow as pa -import pytest - -import bigframes -import bigframes.pandas as bpd -from bigframes.testing.utils import assert_frame_equal - -pytest.importorskip("polars") - - -@pytest.fixture(scope="module", autouse=True) -def session() -> Generator[bigframes.Session, None, None]: - import bigframes.core.global_session - from bigframes.testing import polars_session - - session = polars_session.TestSession() - with bigframes.core.global_session._GlobalSessionContext(session): - yield session - - -@pytest.fixture -def sample_df() -> bpd.DataFrame: - pd_df = pd.DataFrame( - { - "A": [1, 2, 3], - "B": [4, 5, 6], - "C": [7, 8, 9], - } - ) - return bpd.read_pandas(pd_df) - - -def test_iloc_setitem_column_single_integer(sample_df): - bf_df = sample_df.copy() - pd_df = sample_df.to_pandas() - - bf_df.iloc[:, 1] = 99 - pd_df.iloc[:, 1] = 99 - - assert_frame_equal(bf_df.to_pandas(), pd_df) - - -def test_iloc_setitem_column_single_integer_negative(sample_df): - bf_df = sample_df.copy() - pd_df = sample_df.to_pandas() - - bf_df.iloc[:, -1] = 99 - pd_df.iloc[:, -1] = 99 - - assert_frame_equal(bf_df.to_pandas(), pd_df) - - -def test_iloc_setitem_columns_list_integer(sample_df): - bf_df = sample_df.copy() - pd_df = sample_df.to_pandas() - - bf_df.iloc[:, [0, 2]] = [99, 88] - pd_df.iloc[:, [0, 2]] = [99, 88] - - assert_frame_equal(bf_df.to_pandas(), pd_df) - - -def test_iloc_setitem_columns_slice(sample_df): - bf_df = sample_df.copy() - pd_df = sample_df.to_pandas() - - bf_df.iloc[:, 0:2] = 99 - pd_df.iloc[:, 0:2] = 99 - - assert_frame_equal(bf_df.to_pandas(), pd_df) - - -def test_iloc_setitem_columns_boolean_mask(sample_df): - bf_df = sample_df.copy() - pd_df = sample_df.to_pandas() - - mask = [True, False, True] - bf_df.iloc[:, mask] = 99 - pd_df.iloc[:, np.array(mask)] = 99 - - assert_frame_equal(bf_df.to_pandas(), pd_df) - - -def test_iloc_setitem_columns_dataframe(sample_df): - bf_df = sample_df.copy() - pd_df = sample_df.to_pandas() - - value_df = bpd.DataFrame({"B": [99, 88, 77], "C": [66, 55, 44]}) - bf_df.iloc[:, 1:3] = value_df - pd_df.iloc[:, 1:3] = value_df.to_pandas() - - assert_frame_equal(bf_df.to_pandas(), pd_df) - - -def test_iloc_setitem_column_numpy_scalar(sample_df): - bf_df = sample_df.copy() - pd_df = sample_df.to_pandas() - - bf_df.iloc[:, np.int64(1)] = 99 - pd_df.iloc[:, np.int64(1)] = 99 - - assert_frame_equal(bf_df.to_pandas(), pd_df) - - -def test_iloc_setitem_columns_numpy_array(sample_df): - bf_df = sample_df.copy() - pd_df = sample_df.to_pandas() - - bf_df.iloc[:, np.array([0, 2], dtype=np.int64)] = [99, 88] - pd_df.iloc[:, np.array([0, 2], dtype=np.int64)] = [99, 88] - - assert_frame_equal(bf_df.to_pandas(), pd_df) - - -def test_iloc_setitem_column_pyarrow_scalar(sample_df): - bf_df = sample_df.copy() - pd_df = sample_df.to_pandas() - - bf_df.iloc[:, pa.scalar(1, type=pa.int64())] = 99 - pd_df.iloc[:, 1] = 99 - - assert_frame_equal(bf_df.to_pandas(), pd_df) - - -def test_iloc_setitem_columns_pyarrow_array(sample_df): - bf_df = sample_df.copy() - pd_df = sample_df.to_pandas() - - bf_df.iloc[:, pa.array([0, 2], type=pa.int64())] = [99, 88] - pd_df.iloc[:, pa.array([0, 2], type=pa.int64())] = [99, 88] - - assert_frame_equal(bf_df.to_pandas(), pd_df) - - -@pytest.mark.parametrize( - ["key", "expected_error"], - [ - pytest.param((slice(None), 3), IndexError, id="out_of_bounds_positive"), - pytest.param((slice(None), -4), IndexError, id="out_of_bounds_negative"), - pytest.param((0, 1), NotImplementedError, id="invalid_row_indexer"), - pytest.param((slice(None), "B"), TypeError, id="invalid_col_indexer_type"), - ], -) -def test_iloc_setitem_column_errors(sample_df, key, expected_error): - bf_df = sample_df.copy() - - with pytest.raises(expected_error): - bf_df.iloc[key] = 99 - - -@pytest.fixture -def duplicate_columns_df() -> bpd.DataFrame: - pd_df = pd.DataFrame( - [[1, 2, 3], [4, 5, 6], [7, 8, 9]], - columns=["A", "B", "A"], - ) - return bpd.read_pandas(pd_df) - - -def test_iloc_setitem_duplicate_columns_single_integer(duplicate_columns_df): - bf_df = duplicate_columns_df.copy() - pd_df = duplicate_columns_df.to_pandas() - - bf_df.iloc[:, 2] = 99 - pd_df.iloc[:, 2] = 99 - - assert_frame_equal(bf_df.to_pandas(), pd_df) - - -def test_iloc_setitem_duplicate_columns_list_integer(duplicate_columns_df): - bf_df = duplicate_columns_df.copy() - pd_df = duplicate_columns_df.to_pandas() - - bf_df.iloc[:, [0, 2]] = [99, 88] - pd_df.iloc[:, [0, 2]] = [99, 88] - - assert_frame_equal(bf_df.to_pandas(), pd_df) - - -def test_iloc_setitem_duplicate_columns_slice(duplicate_columns_df): - bf_df = duplicate_columns_df.copy() - pd_df = duplicate_columns_df.to_pandas() - - bf_df.iloc[:, 1:3] = 99 - pd_df.iloc[:, 1:3] = 99 - - assert_frame_equal(bf_df.to_pandas(), pd_df) - - -def test_iloc_setitem_duplicate_columns_numpy_scalar(duplicate_columns_df): - bf_df = duplicate_columns_df.copy() - pd_df = duplicate_columns_df.to_pandas() - - bf_df.iloc[:, np.int64(2)] = 99 - pd_df.iloc[:, np.int64(2)] = 99 - - assert_frame_equal(bf_df.to_pandas(), pd_df) - - -def test_iloc_setitem_duplicate_columns_numpy_array(duplicate_columns_df): - bf_df = duplicate_columns_df.copy() - pd_df = duplicate_columns_df.to_pandas() - - bf_df.iloc[:, np.array([0, 2], dtype=np.int64)] = [99, 88] - pd_df.iloc[:, np.array([0, 2], dtype=np.int64)] = [99, 88] - - assert_frame_equal(bf_df.to_pandas(), pd_df) - - -def test_iloc_setitem_duplicate_columns_pyarrow_scalar(duplicate_columns_df): - bf_df = duplicate_columns_df.copy() - pd_df = duplicate_columns_df.to_pandas() - - bf_df.iloc[:, pa.scalar(2, type=pa.int64())] = 99 - pd_df.iloc[:, 2] = 99 - - assert_frame_equal(bf_df.to_pandas(), pd_df) - - -def test_iloc_setitem_duplicate_columns_pyarrow_array(duplicate_columns_df): - bf_df = duplicate_columns_df.copy() - pd_df = duplicate_columns_df.to_pandas() - - bf_df.iloc[:, pa.array([0, 2], type=pa.int64())] = [99, 88] - pd_df.iloc[:, pa.array([0, 2], type=pa.int64())] = [99, 88] - - assert_frame_equal(bf_df.to_pandas(), pd_df) diff --git a/tests/unit/test_index.py b/tests/unit/test_index.py deleted file mode 100644 index b875d56e7a0..00000000000 --- a/tests/unit/test_index.py +++ /dev/null @@ -1,51 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import pandas as pd -import pytest - -from bigframes.testing import mocks - - -def test_index_rename(monkeypatch: pytest.MonkeyPatch): - dataframe = mocks.create_dataframe( - monkeypatch, data={"idx": [], "col": []} - ).set_index("idx") - index = dataframe.index - assert index.name == "idx" - renamed = index.rename("my_index_name") - assert renamed.name == "my_index_name" - - -def test_index_rename_inplace_returns_none(monkeypatch: pytest.MonkeyPatch): - dataframe = mocks.create_dataframe( - monkeypatch, data={"idx": [], "col": []} - ).set_index("idx") - index = dataframe.index - assert index.name == "idx" - assert index.rename("my_index_name", inplace=True) is None - - # Make sure the linked DataFrame is updated, too. - assert dataframe.index.name == "my_index_name" - assert index.name == "my_index_name" - - -def test_index_to_list(monkeypatch: pytest.MonkeyPatch): - pd_index = pd.Index([1, 2, 3], name="my_index") - df = mocks.create_dataframe( - monkeypatch, - data={"my_index": [1, 2, 3]}, - ).set_index("my_index") - bf_index = df.index - assert bf_index.to_list() == pd_index.to_list() diff --git a/tests/unit/test_interchange.py b/tests/unit/test_interchange.py deleted file mode 100644 index 87f6c91e237..00000000000 --- a/tests/unit/test_interchange.py +++ /dev/null @@ -1,108 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import pathlib -from typing import Generator - -import pandas as pd -import pandas.api.interchange as pd_interchange -import pandas.testing -import pytest - -import bigframes -import bigframes.pandas as bpd -from bigframes.testing.utils import convert_pandas_dtypes - -pytest.importorskip("polars") -pytest.importorskip("pandas", minversion="2.0.0") - -CURRENT_DIR = pathlib.Path(__file__).parent -DATA_DIR = CURRENT_DIR.parent / "data" - - -@pytest.fixture(scope="module", autouse=True) -def session() -> Generator[bigframes.Session, None, None]: - import bigframes.core.global_session - from bigframes.testing import polars_session - - session = polars_session.TestSession() - with bigframes.core.global_session._GlobalSessionContext(session): - yield session - - -@pytest.fixture(scope="module") -def scalars_pandas_df_index() -> pd.DataFrame: - """pd.DataFrame pointing at test data.""" - - df = pd.read_json( - DATA_DIR / "scalars.jsonl", - lines=True, - ) - convert_pandas_dtypes(df, bytes_col=True) - - df = df.set_index("rowindex", drop=False) - df.index.name = None - return df.set_index("rowindex").sort_index() - - -def test_interchange_df_logical_properties(session): - df = bpd.DataFrame({"a": [1, 2, 3], 2: [4, 5, 6]}, session=session) - interchange_df = df.__dataframe__() - assert interchange_df.num_columns() == 2 - assert interchange_df.num_rows() == 3 - assert interchange_df.column_names() == ["a", "2"] - - -def test_interchange_column_logical_properties(session): - df = bpd.DataFrame( - { - "nums": [1, 2, 3, None, None], - "animals": ["cat", "dog", "mouse", "horse", "turtle"], - }, - session=session, - ) - interchange_df = df.__dataframe__() - - assert interchange_df.get_column_by_name("nums").size() == 5 - assert interchange_df.get_column(0).null_count == 2 - - assert interchange_df.get_column_by_name("animals").size() == 5 - assert interchange_df.get_column(1).null_count == 0 - - -def test_interchange_to_pandas(session, scalars_pandas_df_index): - # A few limitations: - # 1) Limited datatype support - # 2) Pandas converts null to NaN/False, rather than use nullable or pyarrow types - # 3) Indices aren't preserved by interchange format - unsupported_cols = [ - "bytes_col", - "date_col", - "numeric_col", - "time_col", - "duration_col", - "geography_col", - ] - scalars_pandas_df_index = scalars_pandas_df_index.drop(columns=unsupported_cols) - scalars_pandas_df_index = scalars_pandas_df_index.bfill().ffill() - bf_df = session.read_pandas(scalars_pandas_df_index) - - from_ix = pd_interchange.from_dataframe(bf_df) - - # interchange format does not include index, so just reset both indices before comparison - pandas.testing.assert_frame_equal( - scalars_pandas_df_index.reset_index(drop=True), - from_ix.reset_index(drop=True), - check_dtype=False, - ) diff --git a/tests/unit/test_local_data.py b/tests/unit/test_local_data.py deleted file mode 100644 index 1537c896fb2..00000000000 --- a/tests/unit/test_local_data.py +++ /dev/null @@ -1,186 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -import pandas as pd -import pandas.testing -import pyarrow as pa - -from bigframes import dtypes -from bigframes.core import local_data - -pd_data = pd.DataFrame( - { - "ints": [10, 20, 30, 40, 50], - "nested_ints": [[1, 2], [], [3, 4, 5], [], [20, 30]], - "structs": [{"a": 100}, None, {}, {"b": 200}, {"b": 300}], - } -) - -pd_data_normalized = pd.DataFrame( - { - "ints": pd.Series([10, 20, 30, 40, 50], dtype=dtypes.INT_DTYPE), - "nested_ints": pd.Series( - [[1, 2], [], [3, 4, 5], [], [20, 30]], - dtype=pd.ArrowDtype(pa.list_(pa.int64())), - ), - "structs": pd.Series( - [{"a": 100}, None, {}, {"b": 200}, {"b": 300}], - dtype=pd.ArrowDtype(pa.struct({"a": pa.int64(), "b": pa.int64()})), - ), - } -) - - -def test_local_data_well_formed_round_trip(): - local_entry = local_data.ManagedArrowTable.from_pandas(pd_data) - result = pd.DataFrame(local_entry.itertuples(), columns=pd_data.columns) - result = result.assign( - **{ - col: result[col].astype(pd_data_normalized[col].dtype) - for col in pd_data_normalized.columns - } - ) - pandas.testing.assert_frame_equal(pd_data_normalized, result, check_dtype=False) - - -def test_local_data_small_sizes_round_trip(): - pyarrow_version = int(pa.__version__.split(".")[0]) - - int8s = [126, 127, -127, -128, 0, 1, -1] - uint8s = [254, 255, 1, 0, 128, 129, 127] - int16s = [32766, 32767, -32766, -32767, 0, 1, -1] - uint16s = [65534, 65535, 1, 0, 32768, 32769, 32767] - int32s = [2**31 - 2, 2**31 - 1, -(2**31) + 1, -(2**31), 0, 1, -1] - uint32s = [2**32 - 2, 2**32 - 1, 1, 0, 2**31, 2**31 + 1, 2**31 - 1] - float16s = [ - # Test some edge cases from: - # https://en.wikipedia.org/wiki/Half-precision_floating-point_format#Precision_limitations - float.fromhex("0x1.0p-24"), # (2 ** -24).hex() - float.fromhex("-0x1.0p-24"), - float.fromhex("0x1.ffcp-13"), # ((2 ** -12) - (2 ** -23)).hex() - float.fromhex("-0x1.ffcp-13"), - 0, - float.fromhex("0x1.ffcp+14"), # (32768.0 - 16).hex() - float.fromhex("-0x1.ffcp+14"), - ] - float32s = [ - # Test some edge cases from: - # https://en.wikipedia.org/wiki/Single-precision_floating-point_format#Notable_single-precision_cases - # and - # https://en.wikipedia.org/wiki/Single-precision_floating-point_format#Precision_limitations_on_decimal_values_(between_1_and_16777216) - float.fromhex("0x1.0p-149"), # (2 ** -149).hex() - float.fromhex("-0x1.0p-149"), # (2 ** -149).hex() - float.fromhex("0x1.fffffep-1"), # (1.0 - (2 ** -24)).hex() - float.fromhex("-0x1.fffffep-1"), - 0, - float.fromhex("0x1.fffffcp-127"), # ((2 ** -126) * (1 - 2 ** -23)).hex() - float.fromhex("-0x1.fffffcp-127"), # ((2 ** -126) * (1 - 2 ** -23)).hex() - ] - small_data = { - "int8": pd.Series(int8s, dtype=pd.Int8Dtype()), - "int16": pd.Series(int16s, dtype=pd.Int16Dtype()), - "int32": pd.Series(int32s, dtype=pd.Int32Dtype()), - "uint8": pd.Series(uint8s, dtype=pd.UInt8Dtype()), - "uint16": pd.Series(uint16s, dtype=pd.UInt16Dtype()), - "uint32": pd.Series(uint32s, dtype=pd.UInt32Dtype()), - "float32": pd.Series(float32s, dtype="float32"), - } - expected_data = { - "int8": pd.Series(int8s, dtype=pd.Int64Dtype()), - "int16": pd.Series(int16s, dtype=pd.Int64Dtype()), - "int32": pd.Series(int32s, dtype=pd.Int64Dtype()), - "uint8": pd.Series(uint8s, dtype=pd.Int64Dtype()), - "uint16": pd.Series(uint16s, dtype=pd.Int64Dtype()), - "uint32": pd.Series(uint32s, dtype=pd.Int64Dtype()), - "float32": pd.Series(float32s, dtype=pd.Float64Dtype()), - } - - # Casting from float16 added in version 16. - # https://arrow.apache.org/blog/2024/04/20/16.0.0-release/#:~:text=Enhancements,New%20Features - if pyarrow_version >= 16: - small_data["float16"] = pd.Series(float16s, dtype="float16") - expected_data["float16"] = pd.Series(float16s, dtype=pd.Float64Dtype()) - - small_pd = pd.DataFrame(small_data) - local_entry = local_data.ManagedArrowTable.from_pandas(small_pd) - result = pd.DataFrame(local_entry.itertuples(), columns=small_pd.columns) - - expected = pd.DataFrame(expected_data) - pandas.testing.assert_frame_equal(expected, result, check_dtype=False) - - -def test_local_data_well_formed_round_trip_chunked(): - pa_table = pa.Table.from_pandas(pd_data, preserve_index=False) - as_rechunked_pyarrow = pa.Table.from_batches(pa_table.to_batches(max_chunksize=2)) - local_entry = local_data.ManagedArrowTable.from_pyarrow(as_rechunked_pyarrow) - result = pd.DataFrame(local_entry.itertuples(), columns=pd_data.columns) - result = result.assign( - **{ - col: result[col].astype(pd_data_normalized[col].dtype) - for col in pd_data_normalized.columns - } - ) - pandas.testing.assert_frame_equal(pd_data_normalized, result, check_dtype=False) - - -def test_local_data_well_formed_round_trip_sliced(): - pa_table = pa.Table.from_pandas(pd_data, preserve_index=False) - as_rechunked_pyarrow = pa.Table.from_batches(pa_table.slice(0, 4).to_batches()) - local_entry = local_data.ManagedArrowTable.from_pyarrow(as_rechunked_pyarrow) - result = pd.DataFrame(local_entry.itertuples(), columns=pd_data.columns) - result = result.assign( - **{ - col: result[col].astype(pd_data_normalized[col].dtype) - for col in pd_data_normalized.columns - } - ) - pandas.testing.assert_frame_equal( - pd_data_normalized[0:4].reset_index(drop=True), - result.reset_index(drop=True), - check_dtype=False, - ) - - -def test_local_data_equal_self(): - local_entry = local_data.ManagedArrowTable.from_pandas(pd_data) - assert local_entry == local_entry - assert hash(local_entry) == hash(local_entry) - - -def test_local_data_not_equal_other(): - local_entry = local_data.ManagedArrowTable.from_pandas(pd_data) - local_entry2 = local_data.ManagedArrowTable.from_pandas(pd_data[::2]) - assert local_entry != local_entry2 - assert hash(local_entry) != hash(local_entry2) - - -def test_local_data_itertuples_struct_none(): - pd_data = pd.DataFrame( - { - "structs": [{"a": 100}, None, {"b": 200}, {"b": 300}], - } - ) - local_entry = local_data.ManagedArrowTable.from_pandas(pd_data) - result = list(local_entry.itertuples()) - assert result[1][0] is None - - -def test_local_data_itertuples_list_none(): - pd_data = pd.DataFrame( - { - "lists": [[1, 2], None, [3, 4]], - } - ) - local_entry = local_data.ManagedArrowTable.from_pandas(pd_data) - result = list(local_entry.itertuples()) - assert result[1][0] == [] diff --git a/tests/unit/test_local_engine.py b/tests/unit/test_local_engine.py deleted file mode 100644 index fe5052771f2..00000000000 --- a/tests/unit/test_local_engine.py +++ /dev/null @@ -1,240 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import pandas as pd -import pandas.testing -import pyarrow as pa -import pytest - -import bigframes -import bigframes.pandas as bpd -from bigframes.testing.utils import assert_frame_equal, assert_series_equal - -pytest.importorskip("polars") -pytest.importorskip("pandas", minversion="2.0.0") - - -@pytest.fixture(scope="module") -def small_inline_frame() -> pd.DataFrame: - df = pd.DataFrame( - { - "int1": pd.Series([1, 2, 3], dtype="Int64"), - "int2": pd.Series([-10, 20, 30], dtype="Int64"), - "bools": pd.Series([True, None, False], dtype="boolean"), - "strings": pd.Series(["b", "aa", "ccc"], dtype="string[pyarrow]"), - "intLists": pd.Series( - [[1, 2, 3], [4, 5, 6, 7], []], - dtype=pd.ArrowDtype(pa.list_(pa.int64())), - ), - }, - ) - df.index = df.index.astype("Int64") - return df - - -def test_polars_local_engine_series(polars_session: bigframes.Session): - bf_series = bpd.Series([1, 2, 3], session=polars_session) - pd_series = pd.Series([1, 2, 3], dtype=bf_series.dtype) - bf_result = bf_series.to_pandas() - pd_result = pd_series - assert_series_equal(bf_result, pd_result, check_index_type=False) - - -def test_polars_local_engine_add( - small_inline_frame: pd.DataFrame, polars_session: bigframes.Session -): - pd_df = small_inline_frame - bf_df = bpd.DataFrame(pd_df, session=polars_session) - - bf_result = (bf_df["int1"] + bf_df["int2"]).to_pandas() - pd_result = pd_df.int1 + pd_df.int2 - pandas.testing.assert_series_equal(bf_result, pd_result) - - -def test_polars_local_engine_order_by(small_inline_frame: pd.DataFrame, polars_session): - pd_df = small_inline_frame - bf_df = bpd.DataFrame(pd_df, session=polars_session) - - bf_result = bf_df.sort_values("strings").to_pandas() - pd_result = pd_df.sort_values("strings") - pandas.testing.assert_frame_equal(bf_result, pd_result) - - -def test_polars_local_engine_filter(small_inline_frame: pd.DataFrame, polars_session): - pd_df = small_inline_frame - bf_df = bpd.DataFrame(pd_df, session=polars_session) - - bf_result = bf_df[bf_df["int2"] >= 1].to_pandas() - pd_result = pd_df[pd_df["int2"] >= 1] # type: ignore - assert_frame_equal(bf_result, pd_result) - - -def test_polars_local_engine_series_rename_with_mapping(polars_session): - pd_series = pd.Series( - ["a", "b", "c"], index=[1, 2, 3], dtype="string[pyarrow]", name="test_name" - ) - bf_series = bpd.Series(pd_series, session=polars_session) - - bf_result = bf_series.rename({1: 100, 2: 200, 3: 300}).to_pandas() - pd_result = pd_series.rename({1: 100, 2: 200, 3: 300}) - # pd default index is int64, bf is Int64 - assert_series_equal(bf_result, pd_result, check_index_type=False) - - -def test_polars_local_engine_series_rename_with_mapping_inplace(polars_session): - pd_series = pd.Series( - ["a", "b", "c"], index=[1, 2, 3], dtype="string[pyarrow]", name="test_name" - ) - bf_series = bpd.Series(pd_series, session=polars_session) - - pd_series.rename({1: 100, 2: 200, 3: 300}, inplace=True) - assert bf_series.rename({1: 100, 2: 200, 3: 300}, inplace=True) is None - - bf_result = bf_series.to_pandas() - pd_result = pd_series - # pd default index is int64, bf is Int64 - assert_series_equal(bf_result, pd_result, check_index_type=False) - - -def test_polars_local_engine_reset_index( - small_inline_frame: pd.DataFrame, polars_session -): - pd_df = small_inline_frame - bf_df = bpd.DataFrame(pd_df, session=polars_session) - - bf_result = bf_df.reset_index().to_pandas() - pd_result = pd_df.reset_index() - # pd default index is int64, bf is Int64 - pandas.testing.assert_frame_equal(bf_result, pd_result, check_index_type=False) - - -def test_polars_local_engine_join_binop(polars_session): - pd_df_1 = pd.DataFrame({"colA": [1, None, 3], "colB": [3, 1, 2]}, index=[1, 2, 3]) - pd_df_2 = pd.DataFrame( - {"colA": [100, 200, 300], "colB": [30, 10, 40]}, index=[2, 1, 4] - ) - bf_df_1 = bpd.DataFrame(pd_df_1, session=polars_session) - bf_df_2 = bpd.DataFrame(pd_df_2, session=polars_session) - - bf_result = (bf_df_1 + bf_df_2).to_pandas() - pd_result = pd_df_1 + pd_df_2 - # Sort since different join ordering - assert_frame_equal( - bf_result.sort_index(), - pd_result.sort_index(), - check_dtype=False, - check_index_type=False, - nulls_are_nan=True, - ) - - -@pytest.mark.parametrize( - "join_type", - ["inner", "left", "right", "outer"], -) -def test_polars_local_engine_joins(join_type, polars_session): - pd_df_1 = pd.DataFrame( - {"colA": [1, None, 3], "colB": [3, 1, 2]}, index=[1, 2, 3], dtype="Int64" - ) - pd_df_2 = pd.DataFrame( - {"colC": [100, 200, 300], "colD": [30, 10, 40]}, index=[2, 1, 4], dtype="Int64" - ) - bf_df_1 = bpd.DataFrame(pd_df_1, session=polars_session) - bf_df_2 = bpd.DataFrame(pd_df_2, session=polars_session) - - bf_result = bf_df_1.join(bf_df_2, how=join_type).to_pandas() - pd_result = pd_df_1.join(pd_df_2, how=join_type) - # Sort by index because ordering logic isn't same as pandas - pandas.testing.assert_frame_equal( - bf_result.sort_index(), pd_result.sort_index(), check_index_type=False - ) - - -def test_polars_local_engine_agg(polars_session): - pd_df = pd.DataFrame( - {"colA": [True, False, True, False, True], "colB": [1, 2, 3, 4, 5]} - ) - bf_df = bpd.DataFrame(pd_df, session=polars_session) - - bf_result = bf_df.agg(["sum", "count"]).to_pandas() - pd_result = pd_df.agg(["sum", "count"]) - # local engine appears to produce uint32 - pandas.testing.assert_frame_equal( - bf_result, # type: ignore[arg-type] - pd_result, - check_dtype=False, - check_index_type=False, - ) - - -def test_polars_local_engine_groupby_sum(polars_session): - pd_df = pd.DataFrame( - {"colA": [True, False, True, False, True], "colB": [1, 2, 3, 4, 5]} - ) - bf_df = bpd.DataFrame(pd_df, session=polars_session) - - bf_result = bf_df.groupby("colA").sum().to_pandas() - pd_result = pd_df.groupby("colA").sum() - pandas.testing.assert_frame_equal( - bf_result, pd_result, check_dtype=False, check_index_type=False - ) - - -def test_polars_local_engine_cumsum(small_inline_frame, polars_session): - pd_df = small_inline_frame[["int1", "int2"]] - bf_df = bpd.DataFrame(pd_df, session=polars_session) - - bf_result = bf_df.cumsum().to_pandas() - pd_result = pd_df.cumsum() - pandas.testing.assert_frame_equal(bf_result, pd_result) - - -def test_polars_local_engine_explode(small_inline_frame, polars_session): - pd_df = small_inline_frame - bf_df = bpd.DataFrame(pd_df, session=polars_session) - - bf_result = bf_df.explode(["intLists"]).to_pandas() - pd_result = pd_df.explode(["intLists"]) - pandas.testing.assert_frame_equal(bf_result, pd_result, check_dtype=False) - - -@pytest.mark.parametrize( - ("start", "stop", "step"), - [ - (1, None, None), - (None, 4, None), - (None, None, 2), - (None, 50_000_000_000, 1), - (5, 4, None), - (3, None, 2), - (1, 7, 2), - (1, 7, 50_000_000_000), - (-1, -7, -2), - (None, -7, -2), - (-1, None, -2), - (-7, -1, 2), - (-7, -1, None), - (-7, 7, None), - (7, -7, -2), - ], -) -def test_polars_local_engine_slice( - small_inline_frame, polars_session, start, stop, step -): - pd_df = small_inline_frame - bf_df = bpd.DataFrame(pd_df, session=polars_session) - - bf_result = bf_df.iloc[start:stop:step].to_pandas() - pd_result = pd_df.iloc[start:stop:step] - pandas.testing.assert_frame_equal(bf_result, pd_result, check_dtype=False) diff --git a/tests/unit/test_notebook.py b/tests/unit/test_notebook.py deleted file mode 100644 index 3feacd52b29..00000000000 --- a/tests/unit/test_notebook.py +++ /dev/null @@ -1,26 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import pathlib - -REPO_ROOT = pathlib.Path(__file__).parent.parent.parent - - -def test_template_notebook_exists(): - # This notebook is meant for being used as a BigFrames usage template and - # could be dynamically linked in places such as BQ Studio and IDE extensions. - # Let's make sure it exists in the well known path. - assert ( - REPO_ROOT / "notebooks" / "getting_started" / "bq_dataframes_template.ipynb" - ).exists() diff --git a/tests/unit/test_pandas.py b/tests/unit/test_pandas.py index c85d92e024d..70c5441c685 100644 --- a/tests/unit/test_pandas.py +++ b/tests/unit/test_pandas.py @@ -14,16 +14,20 @@ import inspect import re +import sys import unittest.mock as mock +import google.api_core.exceptions +import google.cloud.bigquery import pandas as pd import pytest import bigframes.core.global_session -import bigframes.dataframe import bigframes.pandas as bpd import bigframes.session +from . import resources + leading_whitespace = re.compile(r"^\s+", flags=re.MULTILINE) @@ -34,10 +38,6 @@ def all_session_methods(): if not attribute.startswith("_") ) session_attributes.remove("close") - # streaming isn't in pandas - session_attributes.remove("read_gbq_table_streaming") - # execution_history is in base namespace, not pandas - session_attributes.remove("execution_history") for attribute in sorted(session_attributes): session_method = getattr(bigframes.session.Session, attribute) @@ -53,6 +53,11 @@ def all_session_methods(): [(method_name,) for method_name in all_session_methods()], ) def test_method_matches_session(method_name: str): + if sys.version_info <= (3, 10): + pytest.skip( + "Need Python 3.10 to reconcile deferred annotations." + ) # pragma: no cover + session_method = getattr(bigframes.session.Session, method_name) session_doc = inspect.getdoc(session_method) assert session_doc is not None, "docstrings are required" @@ -60,119 +65,86 @@ def test_method_matches_session(method_name: str): pandas_method = getattr(bigframes.pandas, method_name) pandas_doc = inspect.getdoc(pandas_method) assert pandas_doc is not None, "docstrings are required" - - pandas_doc_stripped = re.sub(leading_whitespace, "", pandas_doc) - session_doc_stripped = re.sub(leading_whitespace, "", session_doc) - assert ( - pandas_doc_stripped == session_doc_stripped - or ":`bigframes.pandas" in session_doc_stripped + assert re.sub(leading_whitespace, "", pandas_doc) == re.sub( + leading_whitespace, "", session_doc ) # Add `eval_str = True` so that deferred annotations are turned into their # corresponding type objects. Need Python 3.10 for eval_str parameter. - session_signature = inspect.signature( - session_method, - eval_str=True, - globals={**vars(bigframes.session), **{"dataframe": bigframes.dataframe}}, - ) - session_args = [ - # Kind includes position, which will be an offset. - parameter.replace(kind=inspect.Parameter.POSITIONAL_ONLY) - for parameter in session_signature.parameters.values() - # Don't include the first parameter, which is `self: Session` - ][1:] + session_signature = inspect.signature(session_method, eval_str=True) pandas_signature = inspect.signature(pandas_method, eval_str=True) - pandas_args = [ + assert [ # Kind includes position, which will be an offset. parameter.replace(kind=inspect.Parameter.POSITIONAL_ONLY) for parameter in pandas_signature.parameters.values() - ] - assert session_args == pandas_args or ["args", "kwargs"] == [ - parameter.name for parameter in session_args + ] == [ + # Kind includes position, which will be an offset. + parameter.replace(kind=inspect.Parameter.POSITIONAL_ONLY) + for parameter in session_signature.parameters.values() + # Don't include the first parameter, which is `self: Session` + ][ + 1: ] assert pandas_signature.return_annotation == session_signature.return_annotation -@pytest.mark.parametrize( - ("bins", "labels", "error_message"), - [ - pytest.param( - 5, - True, - "Bin labels must either be False, None or passed in as a list-like argument", - id="true", - ), - pytest.param( - 5, - 1.5, - "Bin labels must either be False, None or passed in as a list-like argument", - id="invalid_types", - ), - pytest.param( - 2, - ["A"], - "must be same as the value of bins", - id="int_bins_mismatch", - ), - pytest.param( - [1, 2, 3], - ["A"], - "must be same as the number of bin edges", - id="iterator_bins_mismatch", - ), - ], -) -def test_cut_raises_with_invalid_labels(bins: int, labels, error_message: str): - mock_series = mock.create_autospec(bigframes.pandas.Series, instance=True) - mock_series.__len__.return_value = 5 - with pytest.raises(ValueError, match=error_message): - bigframes.pandas.cut(mock_series, bins, labels=labels) - - -def test_cut_raises_with_unsupported_labels(): - mock_series = mock.create_autospec(bigframes.pandas.Series, instance=True) - labels = [1, 2] - with pytest.raises( - NotImplementedError, match=r".*only iterables of strings are supported.*" - ): - bigframes.pandas.cut(mock_series, 2, labels=labels) # type: ignore +def test_cut_raises_with_labels(): + with pytest.raises(NotImplementedError, match="Only labels=False"): + mock_series = mock.create_autospec(bigframes.pandas.Series, instance=True) + bigframes.pandas.cut(mock_series, 4, labels=["a", "b", "c", "d"]) @pytest.mark.parametrize( - ("bins", "error_message"), - [ - pytest.param(1.5, "`bins` must be an integer or interable.", id="float"), - pytest.param(0, "`bins` should be a positive integer.", id="zero_int"), - pytest.param(-1, "`bins` should be a positive integer.", id="neg_int"), - pytest.param( - ["notabreak"], - "`bins` iterable should contain tuples or numerics", - id="iterable_w_wrong_type", - ), - pytest.param( - [10, 3], - "left side of interval must be <= right side", - id="decreased_breaks", - ), - pytest.param( - [(1, 10), (2, 25)], - "Overlapping IntervalIndex is not accepted.", - id="overlapping_intervals", - ), - ], + ("bins",), + ( + (0,), + (-1,), + ), ) -def test_cut_raises_with_invalid_bins(bins: int, error_message: str): - mock_series = mock.create_autospec(bigframes.pandas.Series, instance=True) - mock_series.__len__.return_value = 5 - - with pytest.raises(ValueError, match=error_message): +def test_cut_raises_with_invalid_bins(bins: int): + with pytest.raises(ValueError, match="`bins` should be a positive integer."): + mock_series = mock.create_autospec(bigframes.pandas.Series, instance=True) bigframes.pandas.cut(mock_series, bins, labels=False) def test_pandas_attribute(): - assert pd.NA is pd.NA + assert bpd.NA is pd.NA assert bpd.BooleanDtype is pd.BooleanDtype assert bpd.Float64Dtype is pd.Float64Dtype assert bpd.Int64Dtype is pd.Int64Dtype assert bpd.StringDtype is pd.StringDtype assert bpd.ArrowDtype is pd.ArrowDtype + + +def test_close_session_after_bq_session_ended(monkeypatch: pytest.MonkeyPatch): + bqclient = mock.create_autospec(google.cloud.bigquery.Client, instance=True) + bqclient.project = "test-project" + session = resources.create_bigquery_session( + bqclient=bqclient, session_id="JUST_A_TEST" + ) + + # Simulate that the session has already expired. + # Note: this needs to be done after the Session is constructed, as the + # initializer sends a query to start the BigQuery Session. + query_job = mock.create_autospec(google.cloud.bigquery.QueryJob, instance=True) + query_job.result.side_effect = google.api_core.exceptions.BadRequest( + "Session JUST_A_TEST has expired and is no longer available." + ) + bqclient.query.return_value = query_job + + # Simulate that the session has already started. + monkeypatch.setattr(bigframes.core.global_session, "_global_session", session) + bpd.options.bigquery._session_started = True + + # Confirm that as a result bigframes.pandas interface is unusable + with pytest.raises( + google.api_core.exceptions.BadRequest, + match="Session JUST_A_TEST has expired and is no longer available.", + ): + bpd.read_gbq("SELECT 'ABC'") + + # Even though the query to stop the session raises an exception, we should + # still be able to close it without raising an error to the user. + bpd.close_session() + assert "CALL BQ.ABORT_SESSION('JUST_A_TEST')" in bqclient.query.call_args.args[0] + assert bigframes.core.global_session._global_session is None diff --git a/tests/unit/test_planner.py b/tests/unit/test_planner.py deleted file mode 100644 index 36a568a4165..00000000000 --- a/tests/unit/test_planner.py +++ /dev/null @@ -1,117 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -from __future__ import annotations - -import unittest.mock as mock - -import google.cloud.bigquery -import pandas as pd - -import bigframes.core as core -import bigframes.core.bq_data -import bigframes.core.expression as ex -import bigframes.core.identifiers as ids -import bigframes.operations as ops -import bigframes.session.planner as planner - -TABLE_REF = google.cloud.bigquery.TableReference.from_string("project.dataset.table") -SCHEMA = ( - google.cloud.bigquery.SchemaField("col_a", "INTEGER"), - google.cloud.bigquery.SchemaField("col_b", "INTEGER"), -) -TABLE = google.cloud.bigquery.Table( - table_ref=TABLE_REF, - schema=SCHEMA, -) -FAKE_SESSION = mock.create_autospec(bigframes.Session, instance=True) -type(FAKE_SESSION)._strictly_ordered = mock.PropertyMock(return_value=True) -LEAF: core.ArrayValue = core.ArrayValue.from_table( - session=FAKE_SESSION, - table=bigframes.core.bq_data.GbqNativeTable.from_table(TABLE), -) - - -def test_session_aware_caching_project_filter(): - """ - Test that if a node is filtered by a column, the node is cached pre-filter and clustered by the filter column. - """ - session_objects = [LEAF, LEAF.create_constant(4, pd.Int64Dtype())[0]] - target, _ = LEAF.create_constant(4, pd.Int64Dtype()) - target = target.filter(ops.gt_op.as_expr("col_a", ex.const(3))) - result, cluster_cols = planner.session_aware_cache_plan( - target.node, [obj.node for obj in session_objects] - ) - assert result == LEAF.node - assert cluster_cols == [ids.ColumnId("col_a")] - - -def test_session_aware_caching_project_multi_filter(): - """ - Test that if a node is filtered by multiple columns, all of them are in the cluster cols - """ - obj1 = LEAF - obj2, _ = LEAF.create_constant(4, pd.Int64Dtype()) - session_objects = [obj1, obj2] - predicate_1a = ops.gt_op.as_expr("col_a", ex.const(3)) - predicate_1b = ops.lt_op.as_expr("col_a", ex.const(55)) - predicate_1 = ops.and_op.as_expr(predicate_1a, predicate_1b) - predicate_3 = ops.eq_op.as_expr("col_b", ex.const(1)) - target = ( - LEAF.filter(predicate_1) - .create_constant(4, pd.Int64Dtype())[0] - .filter(predicate_3) - ) - result, cluster_cols = planner.session_aware_cache_plan( - target.node, [obj.node for obj in session_objects] - ) - assert result == LEAF.node - assert cluster_cols == [ids.ColumnId("col_a"), ids.ColumnId("col_b")] - - -def test_session_aware_caching_unusable_filter(): - """ - Test that if a node is filtered by multiple columns in the same comparison, the node is cached pre-filter and not clustered by either column. - - Most filters with multiple column references cannot be used for scan pruning, as they cannot be converted to fixed value ranges. - """ - session_objects = [LEAF, LEAF.create_constant(4, pd.Int64Dtype())[0]] - target = LEAF.create_constant(4, pd.Int64Dtype())[0].filter( - ops.gt_op.as_expr("col_a", "col_b") - ) - result, cluster_cols = planner.session_aware_cache_plan( - target.node, [obj.node for obj in session_objects] - ) - assert result == LEAF.node - assert cluster_cols == [] - - -def test_session_aware_caching_fork_after_window_op(): - """ - Test that caching happens only after an windowed operation, but before filtering, projecting. - - Windowing is expensive, so caching should always compute the window function, in order to avoid later recomputation. - """ - leaf_with_offsets = LEAF.promote_offsets()[0] - other = leaf_with_offsets.create_constant(5, pd.Int64Dtype())[0] - target = leaf_with_offsets.create_constant(4, pd.Int64Dtype())[0].filter( - ops.eq_op.as_expr("col_a", ops.add_op.as_expr(ex.const(4), ex.const(3))) - ) - result, cluster_cols = planner.session_aware_cache_plan( - target.node, - [ - other.node, - ], - ) - assert result == leaf_with_offsets.node - assert cluster_cols == [ids.ColumnId("col_a")] diff --git a/tests/unit/test_py_udf.py b/tests/unit/test_py_udf.py deleted file mode 100644 index dcd6fa28658..00000000000 --- a/tests/unit/test_py_udf.py +++ /dev/null @@ -1,761 +0,0 @@ -# Copyright 2023 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import pathlib -from typing import Generator - -import numpy as np -import pandas as pd -import pandas.testing -import pyarrow as pa -import pytest - -import bigframes -import bigframes.core.global_session -import bigframes.pandas as bpd -from bigframes.core.bytecode import py_to_expression -from bigframes.testing.utils import ( - assert_frame_equal, - assert_series_equal, - convert_pandas_dtypes, -) - -pytest.importorskip("polars") -pytest.importorskip("pandas", minversion="2.0.0") - -CURRENT_DIR = pathlib.Path(__file__).parent -DATA_DIR = CURRENT_DIR.parent / "data" - - -@pytest.fixture(scope="module", autouse=True) -def session() -> Generator[bigframes.Session, None, None]: - # import inline to allow polars importorskip to happen first - from bigframes.testing import polars_session - - with bpd.option_context("experiments.enable_python_transpiler", True): - session = polars_session.TestSession() - with bigframes.core.global_session._GlobalSessionContext(session): - yield session - - -@pytest.fixture(scope="module") -def scalars_pandas_df_index() -> pd.DataFrame: - """pd.DataFrame pointing at test data.""" - - df = pd.read_json( - DATA_DIR / "scalars.jsonl", - lines=True, - ) - convert_pandas_dtypes(df, bytes_col=True) - - df = df.set_index("rowindex", drop=False) - df.index.name = None - return df.set_index("rowindex").sort_index() - - -@pytest.fixture(scope="module") -def scalars_df_index( - session: bigframes.Session, scalars_pandas_df_index -) -> bpd.DataFrame: - return session.read_pandas(scalars_pandas_df_index) - - -@pytest.fixture(scope="module") -def scalars_dfs( - scalars_df_index, - scalars_pandas_df_index, -): - return scalars_df_index, scalars_pandas_df_index - - -def test_dataframe_map_transpile( - scalars_df_index, - scalars_pandas_df_index, -): - columns = ["int64_too", "int64_col"] - - def foo(input): - return input * 3 + 12 - - bf_result = scalars_df_index[columns].map(foo, na_action="ignore").to_pandas() - - pd_result = ( - scalars_pandas_df_index[columns].map(foo, na_action="ignore").astype("Int64") - ) - - assert_frame_equal(bf_result, pd_result) - - -def test_dataframe_apply_axis_1_transpile( - scalars_df_index, - scalars_pandas_df_index, -): - columns = ["int64_too", "int64_col"] - - def foo(input): - return input.int64_too + input.int64_col - - bf_result = scalars_df_index[columns].apply(foo, axis=1).to_pandas() - - pd_result = scalars_pandas_df_index[columns].apply(foo, axis=1).astype("Int64") - - assert_series_equal(bf_result, pd_result) - - -def test_series_combine_transpile( - scalars_df_index, - scalars_pandas_df_index, -): - def which_smaller(left, right): - return (left * right) + 3 - - bf_result = ( - scalars_df_index["int64_too"] - .combine(scalars_df_index["int64_col"], which_smaller) - .to_pandas() - ) - - pd_result = scalars_pandas_df_index["int64_too"].combine( - scalars_pandas_df_index["int64_col"], which_smaller - ) - - assert_series_equal(bf_result, pd_result) - - -def test_dataframe_apply_axis_1_transpile_with_defaults( - scalars_df_index, - scalars_pandas_df_index, -): - columns = ["int64_too", "int64_col"] - - def foo(input, x=10, y=5): - return input.int64_too + input.int64_col + x + y - - bf_result = scalars_df_index[columns].apply(foo, axis=1).to_pandas() - pd_result = scalars_pandas_df_index[columns].apply(foo, axis=1).astype("Int64") - - assert_series_equal(bf_result, pd_result) - - -def test_dataframe_apply_axis_1_transpile_with_args( - scalars_df_index, - scalars_pandas_df_index, -): - columns = ["int64_too", "int64_col"] - - def foo(input, x, y=5): - return input.int64_too + input.int64_col + x + y - - bf_result = ( - scalars_df_index[columns].apply(foo, axis=1, args=(12,), y=20).to_pandas() - ) - pd_result = ( - scalars_pandas_df_index[columns] - .apply(foo, axis=1, args=(12,), y=20) - .astype("Int64") - ) - - assert_series_equal(bf_result, pd_result) - - -def test_dataframe_apply_axis_1_transpile_invalid_bindings( - scalars_df_index, -): - columns = ["int64_too", "int64_col"] - - def foo(input, x, y=5): - return input.int64_too + input.int64_col + x + y - - # 1. Unexpected keyword argument - with pytest.raises(TypeError, match="unexpected keyword argument 'z'"): - scalars_df_index[columns].apply(foo, axis=1, args=(10,), z=20) - - # 2. Multiple values for keyword argument 'x' - with pytest.raises(TypeError, match="multiple values for argument 'x'"): - scalars_df_index[columns].apply(foo, axis=1, args=(10,), x=20) - - # 3. Too many positional arguments - with pytest.raises(TypeError, match="too many positional arguments"): - scalars_df_index[columns].apply(foo, axis=1, args=(10, 20, 30)) - - # 4. Missing required argument 'x' - with pytest.raises(TypeError, match="missing a required argument: 'x'"): - scalars_df_index[columns].apply(foo, axis=1) - - -def test_series_apply_transpile( - scalars_df_index, - scalars_pandas_df_index, -): - def foo(x, y=10): - return x * 2 + y - - bf_result = scalars_df_index["int64_col"].apply(foo, args=(5,)).to_pandas() - pd_result = ( - scalars_pandas_df_index["int64_col"].apply(foo, args=(5,)).astype("Int64") - ) - - assert_series_equal(bf_result, pd_result) - - -def test_series_apply_transpile_invalid_bindings( - scalars_df_index, -): - def foo(x, y): - return x + y - - # Too many positional args: foo takes 2 args (x, y), we pass self and 2 more args (total 3 positional) - with pytest.raises( - TypeError, match="too many positional arguments: expected 2, got 3" - ): - scalars_df_index["int64_col"].apply(foo, args=(10, 20)) - - # Missing required argument: foo takes 2 args, we only pass self (so y is missing) - with pytest.raises(TypeError, match="missing required argument: 'y'"): - scalars_df_index["int64_col"].apply(foo) - - -def test_transpilation_unsupported_ops_raise( - scalars_df_index, -): - def foo_with_loop(x): - total = 0 - for i in range(x): - total += i - return total - - with pytest.raises(ValueError): - scalars_df_index["int64_col"].apply(foo_with_loop) - - -def my_foo(x: int): - return x + 1 - - -def test_local_series_apply_simple(scalars_df_index, scalars_pandas_df_index): - bf_result = scalars_df_index["int64_col"].apply(my_foo).to_pandas() - pd_result = scalars_pandas_df_index["int64_col"].apply(my_foo) - - assert_series_equal(bf_result, pd_result, check_dtype=False) - - -def my_numpy_foo(x: int): - return np.add(x, x) * (np.cos(x) - np.sin(3)) - - -def test_local_series_apply_w_numpy(scalars_df_index, scalars_pandas_df_index): - bf_result = scalars_df_index["int64_col"].apply(my_numpy_foo).to_pandas() - pd_result = scalars_pandas_df_index["int64_col"].apply(my_numpy_foo) - - assert_series_equal(bf_result, pd_result, check_dtype=False) - - -def test_local_series_apply_w_simple_lamdba(scalars_df_index, scalars_pandas_df_index): - bf_result = scalars_df_index["int64_col"].apply(lambda x: x + 3).to_pandas() - pd_result = scalars_pandas_df_index["int64_col"].apply(lambda x: x + 3) - - assert_series_equal(bf_result, pd_result, check_dtype=False) - - -def test_local_series_apply_w_ternary_lamdba(scalars_df_index, scalars_pandas_df_index): - bf_result = ( - scalars_df_index["int64_col"] - .apply(lambda x: "positive" if x > 0 else "negative") - .to_pandas() - ) - pd_result = scalars_pandas_df_index["int64_col"].apply( - lambda x: "positive" if x > 0 else "negative" - ) - - assert_series_equal(bf_result, pd_result, check_dtype=False) - - -def test_local_series_apply_w_nested_fizzbuzz(session): - # challenging: closure, multiple exits, mutating variables - foo_div = 3 - buzz_div = 5 - pd_series = pd.Series( - range(20), - dtype="Int64", - index=pd.Index(range(20), dtype="Int64"), - name="integers", - ) - bf_series = bpd.Series(pd_series, session=session) - - def fizzbuzz(x): - if (x % 3) and (x % 5): - return str(x) - val = "" - if (x % foo_div) == 0: - val += "fizz" - if (x % buzz_div) == 0: - val += "buzz" - return val - - bf_result = bf_series.apply(fizzbuzz).to_pandas() - pd_result = pd_series.apply(fizzbuzz).astype(pd.StringDtype(storage="pyarrow")) - - assert_series_equal(bf_result, pd_result, check_dtype=False) - - -def test_local_dataframe_apply_w_ternary_lamdba( - scalars_df_index, scalars_pandas_df_index -): - bf_result = scalars_df_index.apply( - lambda x: x.int64_col if x.rowindex_2 > 5 else x.float64_col, axis=1 - ).to_pandas() - pd_result = scalars_pandas_df_index.apply( - lambda x: x.int64_col if x.rowindex_2 > 5 else x.float64_col, axis=1 - ).astype("Float64") - - assert_series_equal(bf_result, pd_result, check_dtype=False) - - -def test_local_series_apply_w_nested_ifs(scalars_df_index, scalars_pandas_df_index): - def nested_ifs(x): - if x > 0: - if x > 100: - return x * 10 - else: - return x * 2 - else: - if x < -100: - return x * 20 - return x * -1 - - bf_result = scalars_df_index["int64_col"].apply(nested_ifs).to_pandas() - pd_result = scalars_pandas_df_index["int64_col"].apply(nested_ifs) - - assert_series_equal(bf_result, pd_result, check_dtype=False) - - -def test_local_series_apply_w_elif(scalars_df_index, scalars_pandas_df_index): - def elif_fn(x): - if x > 100: - return 1 - elif x > 50: - return 2 - elif x > 0: - return 3 - else: - return 4 - - bf_result = scalars_df_index["int64_col"].apply(elif_fn).to_pandas() - pd_result = scalars_pandas_df_index["int64_col"].apply(elif_fn) - - assert_series_equal(bf_result, pd_result, check_dtype=False) - - -def test_local_series_apply_w_logical_not(scalars_df_index, scalars_pandas_df_index): - def logical_not_fn(x): - if not (x > 0): - return -x - return x - - bf_result = scalars_df_index["int64_col"].apply(logical_not_fn).to_pandas() - pd_result = scalars_pandas_df_index["int64_col"].apply(logical_not_fn) - - assert_series_equal(bf_result, pd_result, check_dtype=False) - - -def test_local_series_apply_w_short_circuit(scalars_df_index, scalars_pandas_df_index): - def short_circuit(x): - if (x > 0 and x < 100) or x == 55555: - return 1 - return 0 - - bf_result = scalars_df_index["int64_col"].apply(short_circuit).to_pandas() - pd_result = scalars_pandas_df_index["int64_col"].apply(short_circuit) - - assert_series_equal(bf_result, pd_result, check_dtype=False) - - -def test_local_series_apply_w_var_assignments( - scalars_df_index, scalars_pandas_df_index -): - def var_assign(x): - val = x - if x > 0: - val = val + 10 - if val > 100: - val = val * 2 - else: - val = val - 10 - return val - - bf_result = scalars_df_index["int64_col"].apply(var_assign).to_pandas() - pd_result = scalars_pandas_df_index["int64_col"].apply(var_assign) - - assert_series_equal(bf_result, pd_result, check_dtype=False) - - -def test_local_series_apply_w_logical_and_val( - scalars_df_index, scalars_pandas_df_index -): - def logical_and_val(x): - return (x % 3) and 100 - - bf_result = ( - scalars_df_index["int64_col"].dropna().apply(logical_and_val).to_pandas() - ) - pd_result = scalars_pandas_df_index["int64_col"].dropna().apply(logical_and_val) - - assert_series_equal(bf_result, pd_result, check_dtype=False) - - -def test_local_series_apply_w_logical_or_val(scalars_df_index, scalars_pandas_df_index): - def logical_or_val(x): - return (x % 3) or 200 - - bf_result = scalars_df_index["int64_col"].dropna().apply(logical_or_val).to_pandas() - pd_result = scalars_pandas_df_index["int64_col"].dropna().apply(logical_or_val) - - assert_series_equal(bf_result, pd_result, check_dtype=False) - - -def test_local_series_apply_w_logical_and_mixed( - scalars_df_index, -): - def logical_and_mixed(x): - return (x % 3) and "hello" - - with pytest.raises(TypeError, match="Cannot coerce"): - scalars_df_index["int64_col"].apply(logical_and_mixed) - - -def test_local_series_apply_w_logical_not_val( - scalars_df_index, scalars_pandas_df_index -): - def logical_not_val(x): - return not x - - bf_result = scalars_df_index["bool_col"].dropna().apply(logical_not_val).to_pandas() - pd_result = scalars_pandas_df_index["bool_col"].dropna().apply(logical_not_val) - - assert_series_equal(bf_result, pd_result, check_dtype=False) - - -def test_local_series_apply_w_compare_chain(scalars_df_index, scalars_pandas_df_index): - def compare_chain(x): - return 0 < x < 1000 - - bf_result = scalars_df_index["int64_col"].dropna().apply(compare_chain).to_pandas() - pd_result = scalars_pandas_df_index["int64_col"].dropna().apply(compare_chain) - - assert_series_equal(bf_result, pd_result, check_dtype=False) - - -def test_dataframe_apply_axis_1_with_integer_subscript( - scalars_df_index, scalars_pandas_df_index -): - columns = ["int64_too", "int64_col"] - bf_df = scalars_df_index[columns].rename(columns={"int64_too": 0, "int64_col": 1}) - pd_df = scalars_pandas_df_index[columns].rename( - columns={"int64_too": 0, "int64_col": 1} - ) - - def foo(input): - return input[0] + input[1] - - bf_result = bf_df.apply(foo, axis=1).to_pandas() - pd_result = pd_df.apply(foo, axis=1).astype("Int64") - - assert_series_equal(bf_result, pd_result) - - -def test_dataframe_apply_axis_1_with_invalid_subscript_raises( - scalars_df_index, -): - columns = ["int64_too", "int64_col"] - - def foo_invalid_label(input): - return input["non_existent_column"] - - with pytest.raises(KeyError, match="non_existent_column"): - scalars_df_index[columns].apply(foo_invalid_label, axis=1) - - -def test_series_map_with_struct_subscript(session): - # Struct setup - struct_pa_type = pa.struct([("str_field", pa.string()), ("int_field", pa.int64())]) - pd_struct_series = pd.Series( - pa.array([{"str_field": "hello", "int_field": 1}], struct_pa_type), - dtype=pd.ArrowDtype(struct_pa_type), - ) - bf_struct_series = bpd.Series(pd_struct_series, session=session) - - # Struct subscripting in UDF - def get_struct_val(x): - return x["str_field"] - - bf_struct_res = bf_struct_series.map(get_struct_val).to_pandas() - pd_struct_res: pd.Series = pd_struct_series.map(get_struct_val) - assert_series_equal(bf_struct_res, pd_struct_res, check_dtype=False) - - -def test_series_map_with_array_subscript(session): - # Array setup - array_pa_type = pa.list_(pa.int64()) - pd_array_series = pd.Series( - pa.array([[10, 20]], array_pa_type), - dtype=pd.ArrowDtype(array_pa_type), - ) - bf_array_series = bpd.Series(pd_array_series, session=session) - - # Array subscripting in UDF - def get_array_val(x): - return x[1] - - bf_array_res = bf_array_series.map(get_array_val).to_pandas() - pd_array_res: pd.Series = pd_array_series.map(get_array_val) - assert_series_equal(bf_array_res, pd_array_res, check_dtype=False) - - -def test_series_map_with_string_subscript(session): - # String setup - pd_string_series = pd.Series(["hello", "world"]) - bf_string_series = bpd.Series(pd_string_series, session=session) - - # String subscripting in UDF - def get_string_val(x): - return x[1] - - bf_string_res = bf_string_series.map(get_string_val).to_pandas() - pd_string_res = pd_string_series.map(get_string_val) # type: ignore - assert_series_equal(bf_string_res, pd_string_res, check_dtype=False) - - -def test_dataframe_apply_axis_1_with_dynamic_subscript_raises( - scalars_df_index, -): - columns = ["int64_too", "int64_col"] - - def foo_dynamic(input): - return input[input[0]] - - with pytest.raises( - NotImplementedError, match="Dynamic column lookup is not supported" - ): - scalars_df_index[columns].apply(foo_dynamic, axis=1) - - -def test_dataframe_apply_axis_1_with_dynamic_array_subscript(session): - array_pa_type = pa.list_(pa.int64()) - pd_df = pd.DataFrame( - { - "array_col": pd.Series( - pa.array([[10, 20], [30, 40, 50], [60]], array_pa_type), - dtype=pd.ArrowDtype(array_pa_type), - ), - "index_col": pd.Series([1, 2, 0], dtype="Int64"), - } - ) - bf_df = bpd.DataFrame(pd_df, session=session) - - def foo(row): - return row["array_col"][row["index_col"]] - - bf_result = bf_df.apply(foo, axis=1).to_pandas() - pd_result = pd_df.apply(foo, axis=1).astype("Int64") - - assert_series_equal(bf_result, pd_result) - - -def test_series_apply_fstrings(session): - pd_series = pd.Series(["apple", "banana", None], dtype="string") - bf_series = bpd.Series(pd_series, session=session) - - def format_udf(x): - if x is None: - return "Null value" - return f"Fruit: {x}!" - - bf_result = bf_series.apply(format_udf).to_pandas() - pd_result = pd.Series( - ["Fruit: apple!", "Fruit: banana!", "Null value"], dtype="string" - ) - assert_series_equal(bf_result, pd_result, check_dtype=False) - - -def test_series_apply_nullity_jumps(session): - pd_series = pd.Series([10, None, 20], dtype="Int64") - bf_series = bpd.Series(pd_series, session=session) - - def nullity_udf(x): - if x is None: - return "Absent" - if x is not None: - return "Present" - return "Unknown" - - bf_result = bf_series.apply(nullity_udf).to_pandas() - pd_result = pd.Series(["Present", "Absent", "Present"], dtype="string") - assert_series_equal(bf_result, pd_result, check_dtype=False) - - -def test_series_apply_string_ops(session): - pd_series = pd.Series(["hello world", "BigFrames", "123a"], dtype="string") - bf_series = bpd.Series(pd_series, session=session) - - def str_udf(x): - if x is None: - return None - return x.upper() + " " + x.lower() + " " + str.upper(x) + " " + x.capitalize() - - bf_result = bf_series.apply(str_udf).to_pandas() - pd_result = pd.Series( - [ - "HELLO WORLD hello world HELLO WORLD Hello world", - "BIGFRAMES bigframes BIGFRAMES Bigframes", - "123A 123a 123A 123a", - ], - dtype="string", - ) - assert_series_equal(bf_result, pd_result, check_dtype=False) - - -def test_series_apply_string_predicates(session): - pd_series = pd.Series( - ["hello world", "abc123", "123", "HELLO!", None], dtype="string" - ) - bf_series = bpd.Series(pd_series, session=session) - - def predicates_udf(x): - if x is None: - return None - return f"{x.islower()}_{x.isupper()}" - - bf_result = bf_series.apply(predicates_udf).to_pandas() - pd_result = pd.Series( - ["True_False", "True_False", "False_False", "False_True", None], dtype="string" - ) - assert_series_equal(bf_result, pd_result, check_dtype=False) - - -def test_fstring_multiple_placeholders(session): - pd_series = pd.Series(["apple", "banana"], dtype="string") - bf_series = bpd.Series(pd_series, session=session) - - def multiple_placeholders(x): - return f"{x} and {x.upper()}!" - - bf_res = bf_series.apply(multiple_placeholders).to_pandas() - pd_res = pd.Series(["apple and APPLE!", "banana and BANANA!"], dtype="string") - assert_series_equal(bf_res, pd_res, check_dtype=False) - - -def test_fstring_empty(session): - pd_series = pd.Series(["apple", "banana"], dtype="string") - bf_series = bpd.Series(pd_series, session=session) - - def empty_fstring(x): - return "" - - bf_res = bf_series.apply(empty_fstring).to_pandas() - pd_res = pd.Series(["", ""], dtype="string") - assert_series_equal(bf_res, pd_res, check_dtype=False) - - -def test_fstring_consecutive_placeholders(session): - pd_series = pd.Series(["apple", "banana"], dtype="string") - bf_series = bpd.Series(pd_series, session=session) - - def consecutive_placeholders(x): - return f"{x}{x.upper()}" - - bf_res = bf_series.apply(consecutive_placeholders).to_pandas() - pd_res = pd.Series(["appleAPPLE", "bananaBANANA"], dtype="string") - assert_series_equal(bf_res, pd_res, check_dtype=False) - - -def test_fstring_with_specifier_raises(): - def format_with_spec(x): - return f"{x:2d}" - - with pytest.raises( - NotImplementedError, match="Formatting with specifier is not supported" - ): - py_to_expression(format_with_spec) - - -def test_fstring_with_repr_raises(): - def format_with_repr(x): - return f"{x!r}" - - with pytest.raises( - NotImplementedError, - match="repr\\(\\) and ascii\\(\\) conversions are not supported", - ): - py_to_expression(format_with_repr) - - -def test_fstring_with_ascii_raises(): - def format_with_ascii(x): - return f"{x!a}" - - with pytest.raises( - NotImplementedError, - match="repr\\(\\) and ascii\\(\\) conversions are not supported", - ): - py_to_expression(format_with_ascii) - - -def test_identity_unsupported_raises(): - def is_true_udf(x): - return x is True - - with pytest.raises( - NotImplementedError, - match="Identity comparison \\(is/is not\\) is only supported for None", - ): - py_to_expression(is_true_udf) - - -def test_fstring_int_input(session): - pd_int_series = pd.Series([10, 20, None], dtype="Int64") - bf_int_series = bpd.Series(pd_int_series, session=session) - bf_res_int = bf_int_series.apply(lambda x: f"val: {x}").to_pandas() - pd_res_int = pd.Series(["val: 10", "val: 20", None], dtype="string") - assert_series_equal(bf_res_int, pd_res_int, check_dtype=False) - - -def test_fstring_float_input(session): - pd_float_series = pd.Series([1.5, 2.75], dtype="Float64") - bf_float_series = bpd.Series(pd_float_series, session=session) - bf_res_float = bf_float_series.apply(lambda x: f"val: {x}").to_pandas() - pd_res_float = pd.Series(["val: 1.5", "val: 2.75"], dtype="string") - assert_series_equal(bf_res_float, pd_res_float, check_dtype=False) - - -def test_fstring_bool_input(session): - pd_bool_series = pd.Series([True, False], dtype="boolean") - bf_bool_series = bpd.Series(pd_bool_series, session=session) - bf_res_bool = bf_bool_series.apply(lambda x: f"val: {x}").to_pandas() - pd_res_bool = pd.Series(["val: True", "val: False"], dtype="string") - assert_series_equal(bf_res_bool, pd_res_bool, check_dtype=False) - - -def test_fstring_list_input_raises(session): - array_pa_type = pa.list_(pa.int64()) - pd_series = pd.Series( - pa.array([[10, 20]], array_pa_type), - dtype=pd.ArrowDtype(array_pa_type), - ) - bf_series = bpd.Series(pd_series, session=session) - - def udf_with_list(x): - return f"list: {x}" - - with pytest.raises((TypeError, ValueError)): - bf_series.apply(udf_with_list) diff --git a/tests/unit/test_remote_function.py b/tests/unit/test_remote_function.py new file mode 100644 index 00000000000..540f4020d36 --- /dev/null +++ b/tests/unit/test_remote_function.py @@ -0,0 +1,28 @@ +# Copyright 2023 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from ibis.backends.bigquery import datatypes as bq_types +from ibis.expr import datatypes as ibis_types + +from bigframes import remote_function as rf + + +def test_supported_types_correspond(): + # The same types should be representable by the supported Python and BigQuery types. + ibis_types_from_python = {ibis_types.dtype(t) for t in rf.SUPPORTED_IO_PYTHON_TYPES} + ibis_types_from_bigquery = { + bq_types.BigQueryType.to_ibis(tk) for tk in rf.SUPPORTED_IO_BIGQUERY_TYPEKINDS + } + + assert ibis_types_from_python == ibis_types_from_bigquery diff --git a/tests/unit/test_sequences.py b/tests/unit/test_sequences.py deleted file mode 100644 index d901670b9b8..00000000000 --- a/tests/unit/test_sequences.py +++ /dev/null @@ -1,55 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from __future__ import annotations - -import itertools -from typing import Sequence - -import pytest - -from bigframes.core import sequences - -LARGE_LIST = list(range(100, 500)) -SMALL_LIST = list(range(1, 5)) -CHAINED_LIST = sequences.ChainedSequence([SMALL_LIST for i in range(100)]) - - -def _build_reference(*parts): - return tuple(itertools.chain(*parts)) - - -def _check_equivalence(expected: Sequence, actual: Sequence): - assert len(expected) == len(actual) - assert tuple(expected) == tuple(actual) - assert expected[10:1:-2] == actual[10:1:-2] - if len(expected) > 0: - assert expected[len(expected) - 1] == expected[len(actual) - 1] - - -@pytest.mark.parametrize( - ("parts",), - [ - ([],), - ([[]],), - ([[0, 1, 2]],), - ([LARGE_LIST, SMALL_LIST, LARGE_LIST],), - ([SMALL_LIST * 100],), - ([CHAINED_LIST, LARGE_LIST, CHAINED_LIST, SMALL_LIST],), - ], -) -def test_init_chained_sequence_single_slist(parts): - value = sequences.ChainedSequence(*parts) - expected = _build_reference(*parts) - _check_equivalence(expected, value) diff --git a/tests/unit/test_series.py b/tests/unit/test_series.py deleted file mode 100644 index 8a083d7e4ae..00000000000 --- a/tests/unit/test_series.py +++ /dev/null @@ -1,64 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from typing import cast - -import pytest - -import bigframes.series -from bigframes.testing import mocks - - -def test_series_rename(monkeypatch: pytest.MonkeyPatch): - series = cast(bigframes.series.Series, mocks.create_dataframe(monkeypatch)["col"]) - assert series.name == "col" - renamed = series.rename("renamed_col") - assert renamed.name == "renamed_col" - - -def test_series_rename_inplace_returns_none(monkeypatch: pytest.MonkeyPatch): - series = cast(bigframes.series.Series, mocks.create_dataframe(monkeypatch)["col"]) - assert series.name == "col" - assert series.rename("renamed_col", inplace=True) is None - assert series.name == "renamed_col" - - -def test_series_rename_axis(monkeypatch: pytest.MonkeyPatch): - series = mocks.create_dataframe( - monkeypatch, data={"index1": [], "index2": [], "col1": [], "col2": []} - ).set_index(["index1", "index2"])["col1"] - assert list(series.index.names) == ["index1", "index2"] - renamed = series.rename_axis(["a", "b"]) - assert list(renamed.index.names) == ["a", "b"] - - -def test_series_rename_axis_inplace_returns_none(monkeypatch: pytest.MonkeyPatch): - series = mocks.create_dataframe( - monkeypatch, data={"index1": [], "index2": [], "col1": [], "col2": []} - ).set_index(["index1", "index2"])["col1"] - assert list(series.index.names) == ["index1", "index2"] - assert series.rename_axis(["a", "b"], inplace=True) is None - assert list(series.index.names) == ["a", "b"] - - -def test_series_repr_with_uninitialized_object(): - """Ensures Series.__init__ can be paused in a visual debugger without crashing. - - Regression test for https://github.com/googleapis/python-bigquery-dataframes/issues/728 - """ - # Avoid calling __init__ to simulate pausing __init__ in a debugger. - # https://stackoverflow.com/a/6384982/101923 - series = bigframes.series.Series.__new__(bigframes.series.Series) - got = repr(series) - assert "Series" in got diff --git a/tests/unit/test_series_io.py b/tests/unit/test_series_io.py deleted file mode 100644 index bb0ea150535..00000000000 --- a/tests/unit/test_series_io.py +++ /dev/null @@ -1,50 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from unittest import mock - -import pytest - -from bigframes.testing import mocks - - -@pytest.fixture -def mock_series(monkeypatch: pytest.MonkeyPatch): - dataframe = mocks.create_dataframe(monkeypatch) - series = dataframe["col"] - monkeypatch.setattr(series, "to_pandas", mock.Mock()) - return series - - -@pytest.mark.parametrize( - "api_name, kwargs", - [ - ("to_csv", {"allow_large_results": True}), - ("to_dict", {"allow_large_results": True}), - ("to_excel", {"excel_writer": "abc", "allow_large_results": True}), - ("to_json", {"allow_large_results": True}), - ("to_latex", {"allow_large_results": True}), - ("to_list", {"allow_large_results": True}), - ("to_markdown", {"allow_large_results": True}), - ("to_numpy", {"allow_large_results": True}), - ("to_pickle", {"path": "abc", "allow_large_results": True}), - ("to_string", {"allow_large_results": True}), - ("to_xarray", {"allow_large_results": True}), - ], -) -def test_series_allow_large_results_param_passing(mock_series, api_name, kwargs): - getattr(mock_series, api_name)(**kwargs) - mock_series.to_pandas.assert_called_once_with( - allow_large_results=kwargs["allow_large_results"] - ) diff --git a/tests/unit/test_series_polars.py b/tests/unit/test_series_polars.py deleted file mode 100644 index 8b6d97d8b4b..00000000000 --- a/tests/unit/test_series_polars.py +++ /dev/null @@ -1,5201 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import datetime as dt -import json -import math -import operator -import pathlib -import re -import tempfile -from typing import Generator - -import db_dtypes # type: ignore -import geopandas as gpd # type: ignore -import google.api_core.exceptions -import numpy -import pandas as pd -import pyarrow as pa # type: ignore -import pytest -import shapely.geometry # type: ignore -from packaging.version import Version - -import bigframes -import bigframes.dtypes as dtypes -import bigframes.features -import bigframes.pandas -import bigframes.pandas as bpd -import bigframes.series as series -from bigframes.testing.utils import ( - assert_frame_equal, - assert_series_equal, - convert_pandas_dtypes, - get_first_file_from_wildcard, - pandas_major_version, -) - -pytest.importorskip("polars") -pytest.importorskip("pandas", minversion="2.0.0") - -CURRENT_DIR = pathlib.Path(__file__).parent -DATA_DIR = CURRENT_DIR.parent / "data" - - -@pytest.fixture(scope="module", autouse=True) -def session() -> Generator[bigframes.Session, None, None]: - import bigframes.core.global_session - from bigframes.testing import polars_session - - session = polars_session.TestSession() - with bigframes.core.global_session._GlobalSessionContext(session): - yield session - - -@pytest.fixture(scope="module") -def scalars_pandas_df_index() -> pd.DataFrame: - """pd.DataFrame pointing at test data.""" - - df = pd.read_json( - DATA_DIR / "scalars.jsonl", - lines=True, - ) - convert_pandas_dtypes(df, bytes_col=True) - - df = df.set_index("rowindex", drop=False) - df.index.name = None - return df.set_index("rowindex").sort_index() - - -@pytest.fixture(scope="module") -def scalars_df_default_index( - session: bigframes.Session, scalars_pandas_df_index -) -> bpd.DataFrame: - return session.read_pandas(scalars_pandas_df_index).reset_index(drop=False) - - -@pytest.fixture(scope="module") -def scalars_df_2_default_index( - session: bigframes.Session, scalars_pandas_df_index -) -> bpd.DataFrame: - return session.read_pandas(scalars_pandas_df_index).reset_index(drop=False) - - -@pytest.fixture(scope="module") -def scalars_df_index( - session: bigframes.Session, scalars_pandas_df_index -) -> bpd.DataFrame: - return session.read_pandas(scalars_pandas_df_index) - - -@pytest.fixture(scope="module") -def scalars_df_2_index( - session: bigframes.Session, scalars_pandas_df_index -) -> bpd.DataFrame: - return session.read_pandas(scalars_pandas_df_index) - - -@pytest.fixture(scope="module") -def scalars_dfs( - scalars_df_index, - scalars_pandas_df_index, -): - return scalars_df_index, scalars_pandas_df_index - - -def test_series_construct_copy(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - bf_result = series.Series( - scalars_df["int64_col"], name="test_series", dtype="Float64" - ).to_pandas() - pd_result = pd.Series( - scalars_pandas_df["int64_col"], name="test_series", dtype="Float64" - ) - pd.testing.assert_series_equal(bf_result, pd_result) - - -def test_series_construct_nullable_ints(): - bf_result = series.Series( - [1, 3, bigframes.pandas.NA], index=[0, 4, bigframes.pandas.NA] - ).to_pandas() - - # TODO(b/340885567): fix type error - expected_index = pd.Index( # type: ignore - [0, 4, None], - dtype=pd.Int64Dtype(), - ) - expected = pd.Series([1, 3, pd.NA], dtype=pd.Int64Dtype(), index=expected_index) - - pd.testing.assert_series_equal(bf_result, expected) - - -def test_series_construct_timestamps(): - datetimes = [ - dt.datetime(2020, 1, 20, 20, 20, 20, 20), - dt.datetime(2019, 1, 20, 20, 20, 20, 20), - None, - ] - bf_result = series.Series(datetimes).to_pandas() - pd_result = pd.Series(datetimes, dtype=pd.ArrowDtype(pa.timestamp("us"))) - - assert_series_equal(bf_result, pd_result, check_index_type=False) - - -def test_series_construct_copy_with_index(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - bf_result = series.Series( - scalars_df["int64_col"], - name="test_series", - dtype="Float64", - index=scalars_df["int64_too"], - ).to_pandas() - pd_result = pd.Series( - scalars_pandas_df["int64_col"], - name="test_series", - dtype="Float64", - index=scalars_pandas_df["int64_too"], - ) - pd.testing.assert_series_equal(bf_result, pd_result) - - -def test_series_construct_copy_index(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - bf_result = series.Series( - scalars_df.index, - name="test_series", - dtype="Float64", - index=scalars_df["int64_too"], - ).to_pandas() - pd_result = pd.Series( - scalars_pandas_df.index, - name="test_series", - dtype="Float64", - index=scalars_pandas_df["int64_too"], - ) - pd.testing.assert_series_equal(bf_result, pd_result) - - -def test_series_construct_pandas(scalars_dfs): - _, scalars_pandas_df = scalars_dfs - bf_result = series.Series( - scalars_pandas_df["int64_col"], name="test_series", dtype="Float64" - ) - pd_result = pd.Series( - scalars_pandas_df["int64_col"], name="test_series", dtype="Float64" - ) - assert bf_result.shape == pd_result.shape - pd.testing.assert_series_equal(bf_result.to_pandas(), pd_result) - - -def test_series_construct_from_list(): - bf_result = series.Series([1, 1, 2, 3, 5, 8, 13], dtype="Int64").to_pandas() - pd_result = pd.Series([1, 1, 2, 3, 5, 8, 13], dtype="Int64") - - # BigQuery DataFrame default indices use nullable Int64 always - pd_result.index = pd_result.index.astype("Int64") - - pd.testing.assert_series_equal(bf_result, pd_result) - - -def test_series_construct_reindex(): - bf_result = series.Series( - series.Series({1: 10, 2: 30, 3: 30}), index=[3, 2], dtype="Int64" - ).to_pandas() - pd_result = pd.Series(pd.Series({1: 10, 2: 30, 3: 30}), index=[3, 2], dtype="Int64") - - # BigQuery DataFrame default indices use nullable Int64 always - pd_result.index = pd_result.index.astype("Int64") - pd.testing.assert_series_equal(bf_result, pd_result) - - -def test_series_construct_from_list_w_index(): - bf_result = series.Series( - [1, 1, 2, 3, 5, 8, 13], index=[10, 20, 30, 40, 50, 60, 70], dtype="Int64" - ).to_pandas() - pd_result = pd.Series( - [1, 1, 2, 3, 5, 8, 13], index=[10, 20, 30, 40, 50, 60, 70], dtype="Int64" - ) - - # BigQuery DataFrame default indices use nullable Int64 always - pd_result.index = pd_result.index.astype("Int64") - - pd.testing.assert_series_equal(bf_result, pd_result) - - -def test_series_construct_empty(session: bigframes.Session): - bf_series: series.Series = series.Series(session=session) - pd_series: pd.Series = pd.Series() - - bf_result = bf_series.empty - pd_result = pd_series.empty - - assert pd_result - assert bf_result == pd_result - - -def test_series_construct_scalar_no_index(): - bf_result = series.Series("hello world", dtype="string[pyarrow]").to_pandas() - pd_result = pd.Series("hello world", dtype="string[pyarrow]") - - # BigQuery DataFrame default indices use nullable Int64 always - pd_result.index = pd_result.index.astype("Int64") - - pd.testing.assert_series_equal(bf_result, pd_result) - - -def test_series_construct_scalar_w_index(): - bf_result = series.Series( - "hello world", dtype="string[pyarrow]", index=[0, 2, 1] - ).to_pandas() - pd_result = pd.Series("hello world", dtype="string[pyarrow]", index=[0, 2, 1]) - - # BigQuery DataFrame default indices use nullable Int64 always - pd_result.index = pd_result.index.astype("Int64") - - pd.testing.assert_series_equal(bf_result, pd_result) - - -def test_series_construct_nan(): - bf_result = series.Series(numpy.nan).to_pandas() - pd_result = pd.Series(numpy.nan) - - pd_result.index = pd_result.index.astype("Int64") - pd_result = pd_result.astype("Float64") - - pd.testing.assert_series_equal(bf_result, pd_result) - - -def test_series_construct_scalar_w_bf_index(): - bf_result = series.Series( - "hello", index=bigframes.pandas.Index([1, 2, 3]) - ).to_pandas() - pd_result = pd.Series("hello", index=pd.Index([1, 2, 3], dtype="Int64")) - - pd_result = pd_result.astype("string[pyarrow]") - - pd.testing.assert_series_equal(bf_result, pd_result) - - -def test_series_construct_from_list_escaped_strings(): - """Check that special characters are supported.""" - strings = [ - "string\nwith\nnewline", - "string\twith\ttabs", - "string\\with\\backslashes", - ] - bf_result = series.Series(strings, name="test_series", dtype="string[pyarrow]") - pd_result = pd.Series(strings, name="test_series", dtype="string[pyarrow]") - - # BigQuery DataFrame default indices use nullable Int64 always - pd_result.index = pd_result.index.astype("Int64") - - pd.testing.assert_series_equal(bf_result.to_pandas(), pd_result) - - -def test_series_construct_geodata(): - pd_series = pd.Series( - [ - shapely.geometry.Point(1, 1), - shapely.geometry.Point(2, 2), - shapely.geometry.Point(3, 3), - ], - dtype=gpd.array.GeometryDtype(), - ) - - series = bigframes.pandas.Series(pd_series) - - assert_series_equal(pd_series, series.to_pandas(), check_index_type=False) - - -@pytest.mark.parametrize( - ("dtype"), - [ - pytest.param(pd.Int64Dtype(), id="int"), - pytest.param(pd.Float64Dtype(), id="float"), - pytest.param(pd.StringDtype(storage="pyarrow"), id="string"), - ], -) -def test_series_construct_w_dtype(dtype): - data = [1, 2, 3] - expected = pd.Series(data, dtype=dtype) - expected.index = expected.index.astype("Int64") - series = bigframes.pandas.Series(data, dtype=dtype) - pd.testing.assert_series_equal(series.to_pandas(), expected) - - -def test_series_construct_w_dtype_for_struct(): - # The data shows the struct fields are disordered and correctly handled during - # construction. - data = [ - {"a": 1, "c": "pandas", "b": dt.datetime(2020, 1, 20, 20, 20, 20, 20)}, - {"a": 2, "c": "pandas", "b": dt.datetime(2019, 1, 20, 20, 20, 20, 20)}, - {"a": 1, "c": "numpy", "b": None}, - ] - dtype = pd.ArrowDtype( - pa.struct([("a", pa.int64()), ("c", pa.string()), ("b", pa.timestamp("us"))]) - ) - series = bigframes.pandas.Series(data, dtype=dtype) - expected = pd.Series(data, dtype=dtype) - expected.index = expected.index.astype("Int64") - pd.testing.assert_series_equal(series.to_pandas(), expected) - - -def test_series_construct_w_dtype_for_array_string(): - data = [["1", "2", "3"], [], ["4", "5"]] - dtype = pd.ArrowDtype(pa.list_(pa.string())) - series = bigframes.pandas.Series(data, dtype=dtype) - expected = pd.Series(data, dtype=dtype) - expected.index = expected.index.astype("Int64") - - # Skip dtype check due to internal issue b/321013333. This issue causes array types - # to be converted to the `object` dtype when calling `to_pandas()`, resulting in - # a mismatch with the expected Pandas type. - if bigframes.features.PANDAS_VERSIONS.is_arrow_list_dtype_usable: - check_dtype = True - else: - check_dtype = False - - pd.testing.assert_series_equal( - series.to_pandas(), expected, check_dtype=check_dtype - ) - - -def test_series_construct_w_dtype_for_array_struct(): - data = [[{"a": 1, "c": "aa"}, {"a": 2, "c": "bb"}], [], [{"a": 3, "c": "cc"}]] - dtype = pd.ArrowDtype(pa.list_(pa.struct([("a", pa.int64()), ("c", pa.string())]))) - series = bigframes.pandas.Series(data, dtype=dtype) - expected = pd.Series(data, dtype=dtype) - expected.index = expected.index.astype("Int64") - - # Skip dtype check due to internal issue b/321013333. This issue causes array types - # to be converted to the `object` dtype when calling `to_pandas()`, resulting in - # a mismatch with the expected Pandas type. - if bigframes.features.PANDAS_VERSIONS.is_arrow_list_dtype_usable: - check_dtype = True - else: - check_dtype = False - - pd.testing.assert_series_equal( - series.to_pandas(), expected, check_dtype=check_dtype - ) - - -def test_series_construct_local_unordered_has_sequential_index(session): - series = bigframes.pandas.Series( - ["Sun", "Mon", "Tues", "Wed", "Thurs", "Fri", "Sat"], session=session - ) - expected: pd.Index = pd.Index([0, 1, 2, 3, 4, 5, 6], dtype=pd.Int64Dtype()) - pd.testing.assert_index_equal(series.index.to_pandas(), expected) - - -@pytest.mark.parametrize( - ("json_type"), - [ - pytest.param(dtypes.JSON_DTYPE), - pytest.param("json"), - ], -) -def test_series_construct_w_json_dtype(json_type): - data = [ - "1", - '"str"', - "false", - '["a", {"b": 1}, null]', - None, - '{"a": {"b": [1, 2, 3], "c": true}}', - ] - s = bigframes.pandas.Series(data, dtype=json_type) - - assert s.dtype == dtypes.JSON_DTYPE - assert s[0] == "1" - assert s[1] == '"str"' - assert s[2] == "false" - assert s[3] == '["a",{"b":1},null]' - assert pd.isna(s[4]) - assert s[5] == '{"a":{"b":[1,2,3],"c":true}}' - - -def test_series_keys(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - bf_result = scalars_df["int64_col"].keys().to_pandas() - pd_result = scalars_pandas_df["int64_col"].keys() - pd.testing.assert_index_equal(bf_result, pd_result) - - -@pytest.mark.parametrize( - ["data", "index"], - [ - (["a", "b", "c"], None), - ([1, 2, 3], ["a", "b", "c"]), - ([1, 2, None], ["a", "b", "c"]), - ([1, 2, 3], [pd.NA, "b", "c"]), - ([numpy.nan, 2, 3], ["a", "b", "c"]), - ], -) -def test_series_items(data, index): - bf_series = series.Series(data, index=index) - pd_series = pd.Series(data, index=index) - - for (bf_index, bf_value), (pd_index, pd_value) in zip( - bf_series.items(), pd_series.items() - ): - # TODO(jialuo): Remove the if conditions after b/373699458 is addressed. - if not pd.isna(bf_index) or not pd.isna(pd_index): - assert bf_index == pd_index - if not pd.isna(bf_value) or not pd.isna(pd_value): - assert bf_value == pd_value - - -@pytest.mark.parametrize( - ["col_name", "expected_dtype"], - [ - ("bool_col", pd.BooleanDtype()), - # TODO(swast): Use a more efficient type. - ("bytes_col", pd.ArrowDtype(pa.binary())), - ("date_col", pd.ArrowDtype(pa.date32())), - ("datetime_col", pd.ArrowDtype(pa.timestamp("us"))), - ("float64_col", pd.Float64Dtype()), - ("geography_col", gpd.array.GeometryDtype()), - ("int64_col", pd.Int64Dtype()), - # TODO(swast): Use a more efficient type. - ("numeric_col", pd.ArrowDtype(pa.decimal128(38, 9))), - ("int64_too", pd.Int64Dtype()), - ("string_col", pd.StringDtype(storage="pyarrow")), - ("time_col", pd.ArrowDtype(pa.time64("us"))), - ("timestamp_col", pd.ArrowDtype(pa.timestamp("us", tz="UTC"))), - ], -) -def test_get_column(scalars_dfs, col_name, expected_dtype): - scalars_df, scalars_pandas_df = scalars_dfs - series = scalars_df[col_name] - series_pandas = series.to_pandas() - assert series_pandas.dtype == expected_dtype - assert series_pandas.shape[0] == scalars_pandas_df.shape[0] - - -def test_series_get_column_default(scalars_dfs): - scalars_df, _ = scalars_dfs - result = scalars_df.get(123123123123123, "default_val") - assert result == "default_val" - - -@pytest.mark.parametrize( - ("key",), - [ - ("hello",), - (2,), - ("int64_col",), - (None,), - ], -) -def test_series_contains(scalars_df_index, scalars_pandas_df_index, key): - bf_result = key in scalars_df_index["int64_col"] - pd_result = key in scalars_pandas_df_index["int64_col"] - - assert bf_result == pd_result - - -def test_series_equals_identical(scalars_df_index, scalars_pandas_df_index): - bf_result = scalars_df_index.int64_col.equals(scalars_df_index.int64_col) - pd_result = scalars_pandas_df_index.int64_col.equals( - scalars_pandas_df_index.int64_col - ) - - assert pd_result == bf_result - - -def test_series_equals_df(scalars_df_index, scalars_pandas_df_index): - bf_result = scalars_df_index["int64_col"].equals(scalars_df_index[["int64_col"]]) - pd_result = scalars_pandas_df_index["int64_col"].equals( - scalars_pandas_df_index[["int64_col"]] - ) - - assert pd_result == bf_result - - -def test_series_equals_different_dtype(scalars_df_index, scalars_pandas_df_index): - bf_series = scalars_df_index["int64_col"] - pd_series = scalars_pandas_df_index["int64_col"] - - bf_result = bf_series.equals(bf_series.astype("Float64")) - pd_result = pd_series.equals(pd_series.astype("Float64")) - - assert pd_result == bf_result - - -def test_series_equals_different_values(scalars_df_index, scalars_pandas_df_index): - bf_series = scalars_df_index["int64_col"] - pd_series = scalars_pandas_df_index["int64_col"] - - bf_result = bf_series.equals(bf_series + 1) - pd_result = pd_series.equals(pd_series + 1) - - assert pd_result == bf_result - - -def test_series_get_with_default_index(scalars_dfs): - col_name = "float64_col" - key = 2 - scalars_df, scalars_pandas_df = scalars_dfs - bf_result = scalars_df[col_name].get(key) - pd_result = scalars_pandas_df[col_name].get(key) - assert bf_result == pd_result - - -@pytest.mark.parametrize( - ("index_col", "key"), - ( - ("int64_too", 2), - ("string_col", "Hello, World!"), - ("int64_too", slice(2, 6)), - ), -) -def test_series___getitem__(scalars_dfs, index_col, key): - col_name = "float64_col" - scalars_df, scalars_pandas_df = scalars_dfs - scalars_df = scalars_df.set_index(index_col, drop=False) - scalars_pandas_df = scalars_pandas_df.set_index(index_col, drop=False) - bf_result = scalars_df[col_name][key] - pd_result = scalars_pandas_df[col_name][key] - pd.testing.assert_series_equal(bf_result.to_pandas(), pd_result) - - -@pytest.mark.parametrize( - ("key",), - ( - (-2,), - (-1,), - (0,), - (1,), - ), -) -def test_series___getitem___with_int_key(scalars_dfs, key): - if pd.__version__.startswith("3."): - pytest.skip("pandas 3.0 dropped getitem with int key") - col_name = "int64_too" - index_col = "string_col" - scalars_df, scalars_pandas_df = scalars_dfs - scalars_df = scalars_df.set_index(index_col, drop=False) - scalars_pandas_df = scalars_pandas_df.set_index(index_col, drop=False) - bf_result = scalars_df[col_name][key] - pd_result = scalars_pandas_df[col_name][key] - assert bf_result == pd_result - - -def test_series___getitem___with_default_index(scalars_dfs): - col_name = "float64_col" - key = 2 - scalars_df, scalars_pandas_df = scalars_dfs - bf_result = scalars_df[col_name][key] - pd_result = scalars_pandas_df[col_name][key] - assert bf_result == pd_result - - -@pytest.mark.parametrize( - ("index_col", "key", "value"), - ( - ("int64_too", 2, "new_string_value"), - ("string_col", "Hello, World!", "updated_value"), - ("int64_too", 0, None), - ), -) -def test_series___setitem__(scalars_dfs, index_col, key, value): - col_name = "string_col" - scalars_df, scalars_pandas_df = scalars_dfs - scalars_df = scalars_df.set_index(index_col, drop=False) - scalars_pandas_df = scalars_pandas_df.set_index(index_col, drop=False) - - bf_series = scalars_df[col_name] - pd_series = scalars_pandas_df[col_name].copy() - - bf_series[key] = value - pd_series[key] = value - - pd.testing.assert_series_equal(bf_series.to_pandas(), pd_series) - - -@pytest.mark.parametrize( - ("key", "value"), - ( - (0, 999), - (1, 888), - (0, None), - (-2345, 777), - ), -) -def test_series___setitem___with_int_key_numeric(scalars_dfs, key, value): - col_name = "int64_col" - index_col = "int64_too" - scalars_df, scalars_pandas_df = scalars_dfs - scalars_df = scalars_df.set_index(index_col, drop=False) - scalars_pandas_df = scalars_pandas_df.set_index(index_col, drop=False) - - bf_series = scalars_df[col_name] - pd_series = scalars_pandas_df[col_name].copy() - - bf_series[key] = value - pd_series[key] = value - - pd.testing.assert_series_equal(bf_series.to_pandas(), pd_series) - - -def test_series___setitem___with_default_index(scalars_dfs): - col_name = "float64_col" - key = 2 - value = 123.456 - scalars_df, scalars_pandas_df = scalars_dfs - - bf_series = scalars_df[col_name] - pd_series = scalars_pandas_df[col_name].copy() - - bf_series[key] = value - pd_series[key] = value - - assert bf_series.to_pandas().iloc[key] == pd_series.iloc[key] - - -@pytest.mark.parametrize( - ("col_name",), - ( - ("float64_col",), - ("int64_too",), - ), -) -def test_abs(scalars_dfs, col_name): - scalars_df, scalars_pandas_df = scalars_dfs - bf_result = scalars_df[col_name].abs().to_pandas() - pd_result = scalars_pandas_df[col_name].abs() - - assert_series_equal(pd_result, bf_result) - - -@pytest.mark.parametrize( - ("col_name",), - ( - ("float64_col",), - ("int64_too",), - ), -) -def test_series_pos(scalars_dfs, col_name): - scalars_df, scalars_pandas_df = scalars_dfs - bf_result = (+scalars_df[col_name]).to_pandas() - pd_result = +scalars_pandas_df[col_name] - - assert_series_equal(pd_result, bf_result) - - -@pytest.mark.parametrize( - ("col_name",), - ( - ("float64_col",), - ("int64_too",), - ), -) -def test_series_neg(scalars_dfs, col_name): - scalars_df, scalars_pandas_df = scalars_dfs - bf_result = (-scalars_df[col_name]).to_pandas() - pd_result = -scalars_pandas_df[col_name] - - assert_series_equal(pd_result, bf_result) - - -@pytest.mark.parametrize( - ("col_name",), - ( - ("bool_col",), - ("int64_col",), - ), -) -def test_series_invert(scalars_dfs, col_name): - scalars_df, scalars_pandas_df = scalars_dfs - bf_result = (~scalars_df[col_name]).to_pandas() - pd_result = ~scalars_pandas_df[col_name] - - assert_series_equal(pd_result, bf_result) - - -def test_fillna(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - col_name = "string_col" - bf_result = scalars_df[col_name].fillna("Missing").to_pandas() - pd_result = scalars_pandas_df[col_name].fillna("Missing") - assert_series_equal( - pd_result, - bf_result, - ) - - -def test_series_replace_scalar_scalar(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - col_name = "string_col" - bf_result = ( - scalars_df[col_name].replace("Hello, World!", "Howdy, Planet!").to_pandas() - ) - pd_result = scalars_pandas_df[col_name].replace("Hello, World!", "Howdy, Planet!") - - pd.testing.assert_series_equal( - pd_result, - bf_result, - ) - - -def test_series_replace_list_scalar(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - col_name = "string_col" - bf_result = ( - scalars_df[col_name] - .replace(["Hello, World!", "T"], "Howdy, Planet!") - .to_pandas() - ) - pd_result = scalars_pandas_df[col_name].replace( - ["Hello, World!", "T"], "Howdy, Planet!" - ) - - pd.testing.assert_series_equal( - pd_result, - bf_result, - ) - - -@pytest.mark.parametrize( - ("replacement_dict",), - (({},),), - ids=[ - "empty", - ], -) -def test_series_replace_dict(scalars_dfs, replacement_dict): - scalars_df, scalars_pandas_df = scalars_dfs - col_name = "string_col" - bf_result = scalars_df[col_name].replace(replacement_dict).to_pandas() - pd_result = scalars_pandas_df[col_name].replace(replacement_dict) - - pd.testing.assert_series_equal( - pd_result, - bf_result, - ) - - -@pytest.mark.parametrize( - ("method",), - ( - ("linear",), - ("values",), - ("slinear",), - ("nearest",), - ("zero",), - ("pad",), - ), -) -def test_series_interpolate(method): - pytest.importorskip("scipy") - if method == "pad" and pd.__version__.startswith("3."): - pytest.skip("pandas 3.0 dropped method='pad'") - - values = [None, 1, 2, None, None, 16, None] - index = [-3.2, 11.4, 3.56, 4, 4.32, 5.55, 76.8] - pd_series = pd.Series(values, index) - bf_series = series.Series(pd_series) - - # Pandas can only interpolate on "float64" columns - # https://github.com/pandas-dev/pandas/issues/40252 - pd_result = pd_series.astype("float64").interpolate(method=method) - bf_result = bf_series.interpolate(method=method).to_pandas() - - # pd uses non-null types, while bf uses nullable types - assert_series_equal( - pd_result, - bf_result, - check_index_type=False, - check_dtype=False, - nulls_are_nan=True, - ) - - -@pytest.mark.parametrize( - ("ignore_index",), - ( - (True,), - (False,), - ), -) -def test_series_dropna(scalars_dfs, ignore_index): - if pd.__version__.startswith("1."): - pytest.skip("ignore_index parameter not supported in pandas 1.x.") - scalars_df, scalars_pandas_df = scalars_dfs - col_name = "string_col" - bf_result = scalars_df[col_name].dropna(ignore_index=ignore_index).to_pandas() - pd_result = scalars_pandas_df[col_name].dropna(ignore_index=ignore_index) - assert_series_equal(pd_result, bf_result, check_index_type=False) - - -@pytest.mark.parametrize( - ("agg",), - ( - ("sum",), - ("size",), - ), -) -def test_series_agg_single_string(scalars_dfs, agg): - scalars_df, scalars_pandas_df = scalars_dfs - bf_result = scalars_df["int64_col"].agg(agg) - pd_result = scalars_pandas_df["int64_col"].agg(agg) - assert math.isclose(pd_result, bf_result) - - -def test_series_agg_multi_string(scalars_dfs): - aggregations = [ - "sum", - "mean", - "std", - "var", - "min", - "max", - "nunique", - "count", - "size", - ] - scalars_df, scalars_pandas_df = scalars_dfs - bf_result = scalars_df["int64_col"].agg(aggregations).to_pandas() - pd_result = scalars_pandas_df["int64_col"].agg(aggregations) - - # Pandas may produce narrower numeric types, but bigframes always produces Float64 - pd_result = pd_result.astype("Float64") - - pd.testing.assert_series_equal(pd_result, bf_result, check_index_type=False) - - -@pytest.mark.parametrize( - ("col_name",), - ( - ("string_col",), - ("int64_col",), - ), -) -def test_max(scalars_dfs, col_name): - scalars_df, scalars_pandas_df = scalars_dfs - bf_result = scalars_df[col_name].max() - pd_result = scalars_pandas_df[col_name].max() - assert pd_result == bf_result - - -@pytest.mark.parametrize( - ("col_name",), - ( - ("string_col",), - ("int64_col",), - ), -) -def test_min(scalars_dfs, col_name): - scalars_df, scalars_pandas_df = scalars_dfs - bf_result = scalars_df[col_name].min() - pd_result = scalars_pandas_df[col_name].min() - assert pd_result == bf_result - - -@pytest.mark.parametrize( - ("col_name",), - ( - ("float64_col",), - ("int64_col",), - ), -) -def test_std(scalars_dfs, col_name): - scalars_df, scalars_pandas_df = scalars_dfs - bf_result = scalars_df[col_name].std() - pd_result = scalars_pandas_df[col_name].std() - assert math.isclose(pd_result, bf_result) - - -@pytest.mark.parametrize( - ("col_name",), - ( - ("float64_col",), - ("int64_col",), - ), -) -def test_kurt(scalars_dfs, col_name): - scalars_df, scalars_pandas_df = scalars_dfs - bf_result = scalars_df[col_name].kurt() - pd_result = scalars_pandas_df[col_name].kurt() - assert math.isclose(pd_result, bf_result) - - -@pytest.mark.parametrize( - ("col_name",), - ( - ("float64_col",), - ("int64_col",), - ), -) -def test_skew(scalars_dfs, col_name): - scalars_df, scalars_pandas_df = scalars_dfs - bf_result = scalars_df[col_name].skew() - pd_result = scalars_pandas_df[col_name].skew() - assert math.isclose(pd_result, bf_result) - - -def test_skew_undefined(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - bf_result = scalars_df["int64_col"].iloc[:2].skew() - pd_result = scalars_pandas_df["int64_col"].iloc[:2].skew() - # both should be pd.NA - assert pd_result is bf_result - - -def test_kurt_undefined(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - bf_result = scalars_df["int64_col"].iloc[:3].kurt() - pd_result = scalars_pandas_df["int64_col"].iloc[:3].kurt() - # both should be pd.NA - assert pd_result is bf_result - - -@pytest.mark.parametrize( - ("col_name",), - ( - ("float64_col",), - ("int64_col",), - ), -) -def test_var(scalars_dfs, col_name): - scalars_df, scalars_pandas_df = scalars_dfs - bf_result = scalars_df[col_name].var() - pd_result = scalars_pandas_df[col_name].var() - assert math.isclose(pd_result, bf_result) - - -@pytest.mark.parametrize( - ("col_name",), - ( - ("bool_col",), - ("int64_col",), - ), -) -def test_mode_stat(scalars_df_index, scalars_pandas_df_index, col_name): - bf_result = scalars_df_index[col_name].mode().to_pandas() - pd_result = scalars_pandas_df_index[col_name].mode() - - ## Mode implicitly resets index, and bigframes default indices use nullable Int64 - pd_result.index = pd_result.index.astype("Int64") - - pd.testing.assert_series_equal( - bf_result, - pd_result, - ) - - -@pytest.mark.parametrize( - ("operator"), - [ - (lambda x, y: x + y), - (lambda x, y: x - y), - (lambda x, y: x * y), - (lambda x, y: x / y), - (lambda x, y: x // y), - (lambda x, y: x < y), - (lambda x, y: x > y), - (lambda x, y: x <= y), - (lambda x, y: x >= y), - ], - ids=[ - "add", - "subtract", - "multiply", - "divide", - "floordivide", - "less_than", - "greater_than", - "less_than_equal", - "greater_than_equal", - ], -) -@pytest.mark.parametrize( - ("other_scalar"), - [ - -1, - 0, - 14, - # TODO(tswast): Support pd.NA, - ], -) -@pytest.mark.parametrize(("reverse_operands"), [True, False]) -def test_series_int_int_operators_scalar( - scalars_dfs, operator, other_scalar, reverse_operands -): - scalars_df, scalars_pandas_df = scalars_dfs - - maybe_reversed_op = (lambda x, y: operator(y, x)) if reverse_operands else operator - - bf_result = maybe_reversed_op(scalars_df["int64_col"], other_scalar).to_pandas() - pd_result = maybe_reversed_op(scalars_pandas_df["int64_col"], other_scalar) - - # don't check dtype, as pandas is a bit unstable here across versions, esp floordiv - assert_series_equal(pd_result, bf_result, check_dtype=False) - - -def test_series_pow_scalar(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - - bf_result = (scalars_df["int64_col"] ** 2).to_pandas() - pd_result = scalars_pandas_df["int64_col"] ** 2 - - assert_series_equal(pd_result, bf_result) - - -def test_series_pow_scalar_reverse(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - - bf_result = (0.8 ** scalars_df["int64_col"]).to_pandas() - pd_result = 0.8 ** scalars_pandas_df["int64_col"] - - assert_series_equal(pd_result, bf_result) - - -@pytest.mark.parametrize( - ("operator"), - [ - (lambda x, y: x & y), - (lambda x, y: x | y), - (lambda x, y: x ^ y), - ], - ids=[ - "and", - "or", - "xor", - ], -) -@pytest.mark.parametrize( - ("other_scalar"), - [ - True, - False, - pytest.param( - pd.NA, - marks=[ - pytest.mark.skip( - reason="https://github.com/pola-rs/polars/issues/24809" - ) - ], - id="NULL", - ), - ], -) -@pytest.mark.parametrize(("reverse_operands"), [True, False]) -def test_series_bool_bool_operators_scalar( - scalars_dfs, operator, other_scalar, reverse_operands -): - scalars_df, scalars_pandas_df = scalars_dfs - - maybe_reversed_op = (lambda x, y: operator(y, x)) if reverse_operands else operator - - bf_result = maybe_reversed_op(scalars_df["bool_col"], other_scalar).to_pandas() - pd_result = maybe_reversed_op(scalars_pandas_df["bool_col"], other_scalar) - - assert_series_equal(pd_result.astype(pd.BooleanDtype()), bf_result) - - -@pytest.mark.parametrize( - ("operator"), - [ - (lambda x, y: x + y), - (lambda x, y: x - y), - (lambda x, y: x * y), - (lambda x, y: x / y), - (lambda x, y: x < y), - (lambda x, y: x > y), - (lambda x, y: x <= y), - (lambda x, y: x >= y), - (lambda x, y: x % y), - (lambda x, y: x // y), - (lambda x, y: x & y), - (lambda x, y: x | y), - (lambda x, y: x ^ y), - ], - ids=[ - "add", - "subtract", - "multiply", - "divide", - "less_than", - "greater_than", - "less_than_equal", - "greater_than_equal", - "modulo", - "floordivide", - "bitwise_and", - "bitwise_or", - "bitwise_xor", - ], -) -def test_series_int_int_operators_series(scalars_dfs, operator): - scalars_df, scalars_pandas_df = scalars_dfs - bf_result = operator(scalars_df["int64_col"], scalars_df["int64_too"]).to_pandas() - pd_result = operator(scalars_pandas_df["int64_col"], scalars_pandas_df["int64_too"]) - assert_series_equal(pd_result, bf_result) - - -@pytest.mark.parametrize( - ("col_x",), - [ - ("int64_col",), - ("int64_too",), - ("float64_col",), - ], -) -@pytest.mark.parametrize( - ("col_y",), - [ - ("int64_col",), - ("int64_too",), - ("float64_col",), - ], -) -@pytest.mark.parametrize( - ("method",), - [ - ("mod",), - ("rmod",), - ], -) -def test_mods(scalars_dfs, col_x, col_y, method): - scalars_df, scalars_pandas_df = scalars_dfs - x_bf = scalars_df[col_x] - y_bf = scalars_df[col_y] - bf_series = getattr(x_bf, method)(y_bf) - # BigQuery's mod functions return [BIG]NUMERIC values unless both arguments are integers. - # https://cloud.google.com/bigquery/docs/reference/standard-sql/mathematical_functions#mod - if x_bf.dtype == pd.Int64Dtype() and y_bf.dtype == pd.Int64Dtype(): - bf_result = bf_series.to_pandas() - else: - bf_result = bf_series.astype("Float64").to_pandas() - pd_result = getattr(scalars_pandas_df[col_x], method)(scalars_pandas_df[col_y]) - assert_series_equal(pd_result, bf_result, nulls_are_nan=True) - - -# We work around a pandas bug that doesn't handle correlating nullable dtypes by doing this -# manually with dumb self-correlation instead of parameterized as test_mods is above. -def test_series_corr(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - bf_result = scalars_df["int64_too"].corr(scalars_df["int64_too"]) - pd_result = ( - scalars_pandas_df["int64_too"] - .astype("int64") - .corr(scalars_pandas_df["int64_too"].astype("int64")) - ) - assert math.isclose(pd_result, bf_result) - - -def test_series_autocorr(scalars_dfs): - # TODO: supply a reason why this isn't compatible with pandas 1.x - pytest.importorskip("pandas", minversion="2.0.0") - scalars_df, scalars_pandas_df = scalars_dfs - bf_result = scalars_df["float64_col"].autocorr(2) - pd_result = scalars_pandas_df["float64_col"].autocorr(2) - assert math.isclose(pd_result, bf_result) - - -def test_series_cov(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - bf_result = scalars_df["int64_too"].cov(scalars_df["int64_too"]) - pd_result = ( - scalars_pandas_df["int64_too"] - .astype("int64") - .cov(scalars_pandas_df["int64_too"].astype("int64")) - ) - assert math.isclose(pd_result, bf_result) - - -@pytest.mark.parametrize( - ("col_x",), - [ - ("int64_col",), - ("float64_col",), - ], -) -@pytest.mark.parametrize( - ("col_y",), - [ - ("int64_col",), - ("float64_col",), - ], -) -@pytest.mark.parametrize( - ("method",), - [ - ("divmod",), - ("rdivmod",), - ], -) -def test_divmods_series(scalars_dfs, col_x, col_y, method): - scalars_df, scalars_pandas_df = scalars_dfs - bf_div_result, bf_mod_result = getattr(scalars_df[col_x], method)(scalars_df[col_y]) - pd_div_result, pd_mod_result = getattr(scalars_pandas_df[col_x], method)( - scalars_pandas_df[col_y] - ) - # BigQuery's mod functions return NUMERIC values for non-INT64 inputs. - if bf_div_result.dtype == pd.Int64Dtype(): - pd.testing.assert_series_equal(pd_div_result, bf_div_result.to_pandas()) - else: - pd.testing.assert_series_equal( - pd_div_result, bf_div_result.astype("Float64").to_pandas() - ) - - if bf_mod_result.dtype == pd.Int64Dtype(): - pd.testing.assert_series_equal(pd_mod_result, bf_mod_result.to_pandas()) - else: - pd.testing.assert_series_equal( - pd_mod_result, bf_mod_result.astype("Float64").to_pandas() - ) - - -@pytest.mark.parametrize( - ("col_x",), - [ - ("int64_col",), - ("float64_col",), - ], -) -@pytest.mark.parametrize( - ("other",), - [ - (-1000,), - (678,), - ], -) -@pytest.mark.parametrize( - ("method",), - [ - ("divmod",), - ("rdivmod",), - ], -) -def test_divmods_scalars(scalars_dfs, col_x, other, method): - scalars_df, scalars_pandas_df = scalars_dfs - bf_div_result, bf_mod_result = getattr(scalars_df[col_x], method)(other) - pd_div_result, pd_mod_result = getattr(scalars_pandas_df[col_x], method)(other) - # BigQuery's mod functions return NUMERIC values for non-INT64 inputs. - if bf_div_result.dtype == pd.Int64Dtype(): - pd.testing.assert_series_equal(pd_div_result, bf_div_result.to_pandas()) - else: - pd.testing.assert_series_equal( - pd_div_result, bf_div_result.astype("Float64").to_pandas() - ) - - if bf_mod_result.dtype == pd.Int64Dtype(): - pd.testing.assert_series_equal(pd_mod_result, bf_mod_result.to_pandas()) - else: - pd.testing.assert_series_equal( - pd_mod_result, bf_mod_result.astype("Float64").to_pandas() - ) - - -@pytest.mark.parametrize( - ("other",), - [ - (3,), - (-6.2,), - ], -) -def test_series_add_scalar(scalars_dfs, other): - scalars_df, scalars_pandas_df = scalars_dfs - bf_result = (scalars_df["float64_col"] + other).to_pandas() - pd_result = scalars_pandas_df["float64_col"] + other - - assert_series_equal(pd_result, bf_result) - - -@pytest.mark.parametrize( - ("left_col", "right_col"), - [ - ("float64_col", "float64_col"), - ("int64_col", "float64_col"), - ("int64_col", "int64_too"), - ], -) -def test_series_add_bigframes_series(scalars_dfs, left_col, right_col): - scalars_df, scalars_pandas_df = scalars_dfs - bf_result = (scalars_df[left_col] + scalars_df[right_col]).to_pandas() - pd_result = scalars_pandas_df[left_col] + scalars_pandas_df[right_col] - - assert_series_equal(pd_result, bf_result) - - -@pytest.mark.parametrize( - ("left_col", "right_col", "righter_col"), - [ - ("float64_col", "float64_col", "float64_col"), - ("int64_col", "int64_col", "int64_col"), - ], -) -def test_series_add_bigframes_series_nested( - scalars_dfs, left_col, right_col, righter_col -): - """Test that we can correctly add multiple times.""" - scalars_df, scalars_pandas_df = scalars_dfs - bf_result = ( - (scalars_df[left_col] + scalars_df[right_col]) + scalars_df[righter_col] - ).to_pandas() - pd_result = ( - scalars_pandas_df[left_col] + scalars_pandas_df[right_col] - ) + scalars_pandas_df[righter_col] - - assert_series_equal(pd_result, bf_result) - - -def test_series_add_different_table_default_index( - scalars_df_default_index, - scalars_df_2_default_index, -): - bf_result = ( - scalars_df_default_index["float64_col"] - + scalars_df_2_default_index["float64_col"] - ).to_pandas() - pd_result = ( - # Default index may not have a well defined order, but it should at - # least be consistent across to_pandas() calls. - scalars_df_default_index["float64_col"].to_pandas() - + scalars_df_2_default_index["float64_col"].to_pandas() - ) - # TODO(swast): Can remove sort_index() when there's default ordering. - pd.testing.assert_series_equal(bf_result.sort_index(), pd_result.sort_index()) - - -def test_series_add_different_table_with_index( - scalars_df_index, scalars_df_2_index, scalars_pandas_df_index -): - scalars_pandas_df = scalars_pandas_df_index - bf_result = scalars_df_index["float64_col"] + scalars_df_2_index["int64_col"] - # When index values are unique, we can emulate with values from the same - # DataFrame. - pd_result = scalars_pandas_df["float64_col"] + scalars_pandas_df["int64_col"] - pd.testing.assert_series_equal(bf_result.to_pandas(), pd_result) - - -def test_reset_index_drop(scalars_df_index, scalars_pandas_df_index): - scalars_pandas_df = scalars_pandas_df_index - bf_result = ( - scalars_df_index["float64_col"] - .sort_index(ascending=False) - .reset_index(drop=True) - ).iloc[::2] - pd_result = ( - scalars_pandas_df["float64_col"] - .sort_index(ascending=False) - .reset_index(drop=True) - ).iloc[::2] - - # BigQuery DataFrames default indices use nullable Int64 always - pd_result.index = pd_result.index.astype("Int64") - - pd.testing.assert_series_equal(bf_result.to_pandas(), pd_result) - - -def test_series_reset_index_allow_duplicates(scalars_df_index, scalars_pandas_df_index): - bf_series = scalars_df_index["int64_col"].copy() - bf_series.index.name = "int64_col" - df = bf_series.reset_index(allow_duplicates=True, drop=False) - assert df.index.name is None - - bf_result = df.to_pandas() - - pd_series = scalars_pandas_df_index["int64_col"].copy() - pd_series.index.name = "int64_col" - pd_result = pd_series.reset_index(allow_duplicates=True, drop=False) - - # Pandas uses int64 instead of Int64 (nullable) dtype. - pd_result.index = pd_result.index.astype(pd.Int64Dtype()) - - # reset_index should maintain the original ordering. - pd.testing.assert_frame_equal(bf_result, pd_result) - - -def test_series_reset_index_duplicates_error(scalars_df_index): - scalars_df_index = scalars_df_index["int64_col"].copy() - scalars_df_index.index.name = "int64_col" - with pytest.raises(ValueError): - scalars_df_index.reset_index(allow_duplicates=False, drop=False) - - -def test_series_reset_index_inplace(scalars_df_index, scalars_pandas_df_index): - bf_result = scalars_df_index.sort_index(ascending=False)["float64_col"] - bf_result.reset_index(drop=True, inplace=True) - pd_result = scalars_pandas_df_index.sort_index(ascending=False)["float64_col"] - pd_result.reset_index(drop=True, inplace=True) - - # BigQuery DataFrames default indices use nullable Int64 always - pd_result.index = pd_result.index.astype("Int64") - - pd.testing.assert_series_equal(bf_result.to_pandas(), pd_result) - - -@pytest.mark.parametrize( - ("name",), - [ - ("some_name",), - (None,), - ], -) -def test_reset_index_no_drop(scalars_df_index, scalars_pandas_df_index, name): - scalars_pandas_df = scalars_pandas_df_index - kw_args = {"name": name} if name else {} - bf_result = ( - scalars_df_index["float64_col"] - .sort_index(ascending=False) - .reset_index(drop=False, **kw_args) - ) - pd_result = ( - scalars_pandas_df["float64_col"] - .sort_index(ascending=False) - .reset_index(drop=False, **kw_args) - ) - - # BigQuery DataFrames default indices use nullable Int64 always - pd_result.index = pd_result.index.astype("Int64") - - pd.testing.assert_frame_equal(bf_result.to_pandas(), pd_result) - - -def test_copy(scalars_df_index, scalars_pandas_df_index): - col_name = "float64_col" - # Expect mutation on original not to effect_copy - bf_series = scalars_df_index[col_name].copy() - bf_copy = bf_series.copy() - bf_copy.loc[0] = 5.6 - bf_series.loc[0] = 3.4 - - pd_series = scalars_pandas_df_index[col_name].copy() - pd_copy = pd_series.copy() - pd_copy.loc[0] = 5.6 - pd_series.loc[0] = 3.4 - - assert bf_copy.to_pandas().loc[0] != bf_series.to_pandas().loc[0] - pd.testing.assert_series_equal(bf_copy.to_pandas(), pd_copy) - - -def test_isin_raise_error(scalars_df_index, scalars_pandas_df_index): - col_name = "int64_too" - with pytest.raises(TypeError): - scalars_df_index[col_name].isin("whatever").to_pandas() - - -@pytest.mark.parametrize( - ( - "col_name", - "test_set", - ), - [ - ( - "int64_col", - [314159, 2.0, 3, pd.NA], - ), - ( - "int64_col", - [2, 55555, 4], - ), - ( - "float64_col", - [-123.456, 1.25, pd.NA], - ), - ( - "int64_too", - [1, 2, pd.NA], - ), - ( - "string_col", - ["Hello, World!", "Hi", "こんにちは"], - ), - ], -) -def test_isin(scalars_dfs, col_name, test_set): - scalars_df, scalars_pandas_df = scalars_dfs - bf_result = scalars_df[col_name].isin(test_set).to_pandas() - pd_result = scalars_pandas_df[col_name].isin(test_set).astype("boolean") - pd.testing.assert_series_equal( - pd_result, - bf_result, - ) - - -@pytest.mark.parametrize( - ( - "col_name", - "test_set", - ), - [ - ( - "int64_col", - [314159, 2.0, 3, pd.NA], - ), - ( - "int64_col", - [2, 55555, 4], - ), - ( - "float64_col", - [-123.456, 1.25, pd.NA], - ), - ( - "int64_too", - [1, 2, pd.NA], - ), - ( - "string_col", - ["Hello, World!", "Hi", "こんにちは"], - ), - ], -) -def test_isin_bigframes_values(scalars_dfs, col_name, test_set, session): - scalars_df, scalars_pandas_df = scalars_dfs - bf_result = ( - scalars_df[col_name].isin(series.Series(test_set, session=session)).to_pandas() - ) - pd_result = scalars_pandas_df[col_name].isin(test_set).astype("boolean") - pd.testing.assert_series_equal( - pd_result, - bf_result, - ) - - -def test_isin_bigframes_index(scalars_dfs, session): - scalars_df, scalars_pandas_df = scalars_dfs - bf_result = ( - scalars_df["string_col"] - .isin( - bigframes.pandas.Index( - ["Hello, World!", "Hi", "こんにちは"], session=session - ) - ) - .to_pandas() - ) - pd_result = ( - scalars_pandas_df["string_col"] - .isin(pd.Index(["Hello, World!", "Hi", "こんにちは"])) - .astype("boolean") - ) - pd.testing.assert_series_equal( - pd_result, - bf_result, - ) - - -@pytest.mark.skip(reason="fixture 'scalars_dfs_maybe_ordered' not found") -@pytest.mark.parametrize( - ( - "col_name", - "test_set", - ), - [ - ( - "int64_col", - [314159, 2.0, 3, pd.NA], - ), - ( - "int64_col", - [2, 55555, 4], - ), - ( - "float64_col", - [-123.456, 1.25, pd.NA], - ), - ( - "int64_too", - [1, 2, pd.NA], - ), - ( - "string_col", - ["Hello, World!", "Hi", "こんにちは"], - ), - ], -) -def test_isin_bigframes_values_as_predicate( - scalars_dfs_maybe_ordered, col_name, test_set -): - scalars_df, scalars_pandas_df = scalars_dfs_maybe_ordered - bf_predicate = scalars_df[col_name].isin( - series.Series(test_set, session=scalars_df._session) - ) - bf_result = scalars_df[bf_predicate].to_pandas() - pd_predicate = scalars_pandas_df[col_name].isin(test_set) - pd_result = scalars_pandas_df[pd_predicate] - - pd.testing.assert_frame_equal( - pd_result.reset_index(), - bf_result.reset_index(), - ) - - -def test_isnull(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - col_name = "float64_col" - bf_series = scalars_df[col_name].isnull().to_pandas() - pd_series = scalars_pandas_df[col_name].isnull() - - # One of dtype mismatches to be documented. Here, the `bf_series.dtype` is `BooleanDtype` but - # the `pd_series.dtype` is `bool`. - assert_series_equal(pd_series.astype(pd.BooleanDtype()), bf_series) - - -def test_notnull(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - col_name = "string_col" - bf_series = scalars_df[col_name].notnull().to_pandas() - pd_series = scalars_pandas_df[col_name].notnull() - - # One of dtype mismatches to be documented. Here, the `bf_series.dtype` is `BooleanDtype` but - # the `pd_series.dtype` is `bool`. - assert_series_equal(pd_series.astype(pd.BooleanDtype()), bf_series) - - -def test_eq_scalar(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - col_name = "int64_too" - bf_result = scalars_df[col_name].eq(0).to_pandas() - pd_result = scalars_pandas_df[col_name].eq(0) - - assert_series_equal(pd_result, bf_result) - - -def test_eq_wider_type_scalar(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - col_name = "int64_too" - bf_result = scalars_df[col_name].eq(1.0).to_pandas() - pd_result = scalars_pandas_df[col_name].eq(1.0) - - assert_series_equal(pd_result, bf_result) - - -def test_ne_scalar(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - col_name = "int64_too" - bf_result = (scalars_df[col_name] != 0).to_pandas() - pd_result = scalars_pandas_df[col_name] != 0 - - assert_series_equal(pd_result, bf_result) - - -def test_eq_int_scalar(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - col_name = "int64_too" - bf_result = (scalars_df[col_name] == 0).to_pandas() - pd_result = scalars_pandas_df[col_name] == 0 - - assert_series_equal(pd_result, bf_result) - - -@pytest.mark.parametrize( - ("col_name",), - ( - ("string_col",), - ("float64_col",), - ("int64_too",), - ), -) -def test_eq_same_type_series(scalars_dfs, col_name): - scalars_df, scalars_pandas_df = scalars_dfs - col_name = "string_col" - bf_result = (scalars_df[col_name] == scalars_df[col_name]).to_pandas() - pd_result = scalars_pandas_df[col_name] == scalars_pandas_df[col_name] - - # One of dtype mismatches to be documented. Here, the `bf_series.dtype` is `BooleanDtype` but - # the `pd_series.dtype` is `bool`. - assert_series_equal(pd_result.astype(pd.BooleanDtype()), bf_result) - - -def test_loc_setitem_cell(scalars_df_index, scalars_pandas_df_index): - bf_original = scalars_df_index["string_col"] - bf_series = scalars_df_index["string_col"] - pd_original = scalars_pandas_df_index["string_col"] - pd_series = scalars_pandas_df_index["string_col"].copy() - bf_series.loc[2] = "This value isn't in the test data." - pd_series.loc[2] = "This value isn't in the test data." - bf_result = bf_series.to_pandas() - pd_result = pd_series - pd.testing.assert_series_equal(bf_result, pd_result) - # Per Copy-on-Write semantics, other references to the original DataFrame - # should remain unchanged. - pd.testing.assert_series_equal(bf_original.to_pandas(), pd_original) - - -def test_at_setitem_row_label_scalar(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - bf_series = scalars_df["int64_col"] - pd_series = scalars_pandas_df["int64_col"].copy() - bf_series.at[1] = 1000 - pd_series.at[1] = 1000 - bf_result = bf_series.to_pandas() - pd_result = pd_series.astype("Int64") - pd.testing.assert_series_equal(bf_result, pd_result) - - -def test_ne_obj_series(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - col_name = "string_col" - bf_result = (scalars_df[col_name] != scalars_df[col_name]).to_pandas() - pd_result = scalars_pandas_df[col_name] != scalars_pandas_df[col_name] - - # One of dtype mismatches to be documented. Here, the `bf_series.dtype` is `BooleanDtype` but - # the `pd_series.dtype` is `bool`. - assert_series_equal(pd_result.astype(pd.BooleanDtype()), bf_result) - - -def test_indexing_using_unselected_series(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - col_name = "string_col" - bf_result = scalars_df[col_name][scalars_df["int64_too"].eq(0)].to_pandas() - pd_result = scalars_pandas_df[col_name][scalars_pandas_df["int64_too"].eq(0)] - - assert_series_equal( - pd_result, - bf_result, - ) - - -def test_indexing_using_selected_series(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - col_name = "string_col" - bf_result = scalars_df[col_name][ - scalars_df["string_col"].eq("Hello, World!") - ].to_pandas() - pd_result = scalars_pandas_df[col_name][ - scalars_pandas_df["string_col"].eq("Hello, World!") - ] - - assert_series_equal( - pd_result, - bf_result, - ) - - -@pytest.mark.parametrize( - ("indices"), - [ - ([1, 3, 5]), - ([5, -3, -5, -6]), - ([-2, -4, -6]), - ], -) -def test_take(scalars_dfs, indices): - scalars_df, scalars_pandas_df = scalars_dfs - - bf_result = scalars_df.take(indices).to_pandas() - pd_result = scalars_pandas_df.take(indices) - - assert_frame_equal(bf_result, pd_result) - - -def test_nested_filter(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - string_col = scalars_df["string_col"] - int64_too = scalars_df["int64_too"] - bool_col = scalars_df["bool_col"] == bool( - True - ) # Convert from nullable bool to nonnullable bool usable as indexer - bf_result = string_col[int64_too == 0][~bool_col].to_pandas() - - pd_string_col = scalars_pandas_df["string_col"] - pd_int64_too = scalars_pandas_df["int64_too"] - pd_bool_col = scalars_pandas_df["bool_col"] == bool( - True - ) # Convert from nullable bool to nonnullable bool usable as indexer - pd_result = pd_string_col[pd_int64_too == 0][~pd_bool_col] - - assert_series_equal( - pd_result, - bf_result, - ) - - -def test_binop_opposite_filters(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - int64_col1 = scalars_df["int64_col"] - int64_col2 = scalars_df["int64_col"] - bool_col = scalars_df["bool_col"] - bf_result = (int64_col1[bool_col] + int64_col2[bool_col.__invert__()]).to_pandas() - - pd_int64_col1 = scalars_pandas_df["int64_col"] - pd_int64_col2 = scalars_pandas_df["int64_col"] - pd_bool_col = scalars_pandas_df["bool_col"] - pd_result = pd_int64_col1[pd_bool_col] + pd_int64_col2[pd_bool_col.__invert__()] - - # Passes with ignore_order=False only with some dependency sets - # TODO: Determine desired behavior and make test more strict - assert_series_equal(bf_result, pd_result, ignore_order=True) - - -def test_binop_left_filtered(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - int64_col = scalars_df["int64_col"] - float64_col = scalars_df["float64_col"] - bool_col = scalars_df["bool_col"] - bf_result = (int64_col[bool_col] + float64_col).to_pandas() - - pd_int64_col = scalars_pandas_df["int64_col"] - pd_float64_col = scalars_pandas_df["float64_col"] - pd_bool_col = scalars_pandas_df["bool_col"] - pd_result = pd_int64_col[pd_bool_col] + pd_float64_col - - # Passes with ignore_order=False only with some dependency sets - # TODO: Determine desired behavior and make test more strict - assert_series_equal(bf_result, pd_result, ignore_order=True) - - -def test_binop_right_filtered(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - int64_col = scalars_df["int64_col"] - float64_col = scalars_df["float64_col"] - bool_col = scalars_df["bool_col"] - bf_result = (float64_col + int64_col[bool_col]).to_pandas() - - pd_int64_col = scalars_pandas_df["int64_col"] - pd_float64_col = scalars_pandas_df["float64_col"] - pd_bool_col = scalars_pandas_df["bool_col"] - pd_result = pd_float64_col + pd_int64_col[pd_bool_col] - - assert_series_equal( - bf_result, - pd_result, - ) - - -@pytest.mark.parametrize( - ("other",), - [ - ([-1.4, 2.3, None],), - (pd.Index([-1.4, 2.3, None]),), - (pd.Series([-1.4, 2.3, None], index=[44, 2, 1]),), - ], -) -def test_series_binop_w_other_types(scalars_dfs, other): - # TODO: supply a reason why this isn't compatible with pandas 1.x - pytest.importorskip("pandas", minversion="2.0.0") - scalars_df, scalars_pandas_df = scalars_dfs - - bf_result = (scalars_df["int64_col"].head(3) + other).to_pandas() - pd_result = scalars_pandas_df["int64_col"].head(3) + other - - if isinstance(other, pd.Series): - # pandas 3.0 preserves series name, bigframe, earlier pandas do not - pd_result.index.name = bf_result.index.name - - assert_series_equal( - bf_result, - pd_result, - ) - - -@pytest.mark.parametrize( - ("other",), - [ - ([-1.4, 2.3, None],), - (pd.Index([-1.4, 2.3, None]),), - (pd.Series([-1.4, 2.3, None], index=[44, 2, 1]),), - ], -) -def test_series_reverse_binop_w_other_types(scalars_dfs, other): - # TODO: supply a reason why this isn't compatible with pandas 1.x - pytest.importorskip("pandas", minversion="2.0.0") - scalars_df, scalars_pandas_df = scalars_dfs - - bf_result = (other + scalars_df["int64_col"].head(3)).to_pandas() - pd_result = other + scalars_pandas_df["int64_col"].head(3) - - assert_series_equal( - bf_result, - pd_result, - ) - - -def test_series_combine_first(scalars_dfs): - # TODO: supply a reason why this isn't compatible with pandas 1.x - pytest.importorskip("pandas", minversion="2.0.0") - scalars_df, scalars_pandas_df = scalars_dfs - int64_col = scalars_df["int64_col"].head(7) - float64_col = scalars_df["float64_col"].tail(7) - bf_result = int64_col.combine_first(float64_col).to_pandas() - - pd_int64_col = scalars_pandas_df["int64_col"].head(7) - pd_float64_col = scalars_pandas_df["float64_col"].tail(7) - pd_result = pd_int64_col.combine_first(pd_float64_col) - - assert_series_equal( - bf_result, - pd_result, - ) - - -def test_series_update(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - int64_col = scalars_df["int64_col"].head(7) - float64_col = scalars_df["float64_col"].tail(7).copy() - float64_col.update(int64_col) - - pd_int64_col = scalars_pandas_df["int64_col"].head(7) - pd_float64_col = scalars_pandas_df["float64_col"].tail(7).copy() - pd_float64_col.update(pd_int64_col) - - assert_series_equal( - float64_col.to_pandas(), - pd_float64_col, - ) - - -def test_mean(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - col_name = "int64_col" - bf_result = scalars_df[col_name].mean() - pd_result = scalars_pandas_df[col_name].mean() - assert math.isclose(pd_result, bf_result) - - -@pytest.mark.parametrize( - ("col_name"), - [ - pytest.param( - "int64_col", - marks=[ - pytest.mark.skip( - reason="pyarrow.lib.ArrowInvalid: Float value 27778.500000 was truncated converting to int64" - ) - ], - ), - # Non-numeric column - pytest.param( - "bytes_col", - marks=[ - pytest.mark.skip( - reason="polars.exceptions.InvalidOperationError: `median` operation not supported for dtype `binary`" - ) - ], - ), - "date_col", - "datetime_col", - pytest.param( - "time_col", - marks=[ - pytest.mark.skip( - reason="pyarrow.lib.ArrowInvalid: Casting from time64[ns] to time64[us] would lose data: 42651538080500" - ) - ], - ), - "timestamp_col", - pytest.param( - "string_col", - marks=[ - pytest.mark.skip( - reason="polars.exceptions.InvalidOperationError: `median` operation not supported for dtype `str`" - ) - ], - ), - ], -) -def test_median(scalars_dfs, col_name): - scalars_df, scalars_pandas_df = scalars_dfs - bf_result = scalars_df[col_name].median(exact=False) - pd_max = scalars_pandas_df[col_name].max() - pd_min = scalars_pandas_df[col_name].min() - # Median is approximate, so just check for plausibility. - assert pd_min < bf_result < pd_max - - -def test_numeric_literal(scalars_dfs): - scalars_df, _ = scalars_dfs - col_name = "numeric_col" - assert scalars_df[col_name].dtype == pd.ArrowDtype(pa.decimal128(38, 9)) - bf_result = scalars_df[col_name] + 42 - assert bf_result.size == scalars_df[col_name].size - assert bf_result.dtype == pd.ArrowDtype(pa.decimal128(38, 9)) - - -def test_series_small_repr(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - - col_name = "int64_col" - bf_series = scalars_df[col_name] - pd_series = scalars_pandas_df[col_name] - with bigframes.pandas.option_context("display.repr_mode", "head"): - assert repr(bf_series) == pd_series.to_string( - length=False, dtype=True, name=True - ) - - -def test_sum(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - col_name = "int64_col" - bf_result = scalars_df[col_name].sum() - pd_result = scalars_pandas_df[col_name].sum() - assert pd_result == bf_result - - -def test_product(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - col_name = "float64_col" - bf_result = scalars_df[col_name].product() - pd_result = scalars_pandas_df[col_name].product() - assert math.isclose(pd_result, bf_result) - - -def test_cumprod(scalars_dfs): - if pd.__version__.startswith("1."): - pytest.skip("Series.cumprod NA mask are different in pandas 1.x.") - scalars_df, scalars_pandas_df = scalars_dfs - col_name = "float64_col" - bf_result = scalars_df[col_name].cumprod() - pd_result = scalars_pandas_df[col_name].cumprod() - pd.testing.assert_series_equal( - pd_result, - bf_result.to_pandas(), - ) - - -def test_count(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - col_name = "int64_col" - bf_result = scalars_df[col_name].count() - pd_result = scalars_pandas_df[col_name].count() - assert pd_result == bf_result - - -def test_nunique(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - col_name = "int64_col" - bf_result = (scalars_df[col_name] % 3).nunique() - pd_result = (scalars_pandas_df[col_name] % 3).nunique() - assert pd_result == bf_result - - -def test_all(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - col_name = "int64_col" - bf_result = scalars_df[col_name].all() - pd_result = scalars_pandas_df[col_name].all() - assert pd_result == bf_result - - -def test_any(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - col_name = "int64_col" - bf_result = scalars_df[col_name].any() - pd_result = scalars_pandas_df[col_name].any() - assert pd_result == bf_result - - -def test_groupby_sum(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - col_name = "int64_too" - bf_series = ( - scalars_df[col_name] - .groupby([scalars_df["bool_col"], ~scalars_df["bool_col"]]) - .sum() - ) - pd_series = ( - scalars_pandas_df[col_name] - .groupby([scalars_pandas_df["bool_col"], ~scalars_pandas_df["bool_col"]]) - .sum() - ) - # TODO(swast): Update groupby to use index based on group by key(s). - bf_result = bf_series.to_pandas() - assert_series_equal( - pd_series, - bf_result, - check_exact=False, - ) - - -def test_groupby_std(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - col_name = "int64_too" - bf_series = scalars_df[col_name].groupby(scalars_df["string_col"]).std() - pd_series = ( - scalars_pandas_df[col_name] - .groupby(scalars_pandas_df["string_col"]) - .std() - .astype(pd.Float64Dtype()) - ) - bf_result = bf_series.to_pandas() - assert_series_equal( - pd_series, - bf_result, - check_exact=False, - ) - - -def test_groupby_var(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - col_name = "int64_too" - bf_series = scalars_df[col_name].groupby(scalars_df["string_col"]).var() - pd_series = ( - scalars_pandas_df[col_name].groupby(scalars_pandas_df["string_col"]).var() - ) - bf_result = bf_series.to_pandas() - assert_series_equal( - pd_series, - bf_result, - check_exact=False, - ) - - -def test_groupby_level_sum(scalars_dfs): - # TODO(tbergeron): Use a non-unique index once that becomes possible in tests - scalars_df, scalars_pandas_df = scalars_dfs - col_name = "int64_too" - - bf_series = scalars_df[col_name].groupby(level=0).sum() - pd_series = scalars_pandas_df[col_name].groupby(level=0).sum() - # TODO(swast): Update groupby to use index based on group by key(s). - pd.testing.assert_series_equal( - pd_series.sort_index(), - bf_series.to_pandas().sort_index(), - ) - - -def test_groupby_level_list_sum(scalars_dfs): - # TODO(tbergeron): Use a non-unique index once that becomes possible in tests - scalars_df, scalars_pandas_df = scalars_dfs - col_name = "int64_too" - - bf_series = scalars_df[col_name].groupby(level=["rowindex"]).sum() - pd_series = scalars_pandas_df[col_name].groupby(level=["rowindex"]).sum() - # TODO(swast): Update groupby to use index based on group by key(s). - pd.testing.assert_series_equal( - pd_series.sort_index(), - bf_series.to_pandas().sort_index(), - ) - - -def test_groupby_mean(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - col_name = "int64_too" - bf_series = ( - scalars_df[col_name].groupby(scalars_df["string_col"], dropna=False).mean() - ) - pd_series = ( - scalars_pandas_df[col_name] - .groupby(scalars_pandas_df["string_col"], dropna=False) - .mean() - ) - # TODO(swast): Update groupby to use index based on group by key(s). - bf_result = bf_series.to_pandas() - assert_series_equal( - pd_series, - bf_result, - ) - - -@pytest.mark.skip( - reason="Aggregate op QuantileOp(q=0.5, should_floor_result=False) not yet supported in polars engine." -) -def test_groupby_median_exact(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - col_name = "int64_too" - bf_result = ( - scalars_df[col_name].groupby(scalars_df["string_col"], dropna=False).median() - ) - pd_result = ( - scalars_pandas_df[col_name] - .groupby(scalars_pandas_df["string_col"], dropna=False) - .median() - ) - - assert_series_equal( - pd_result, - bf_result.to_pandas(), - ) - - -@pytest.mark.skip( - reason="pyarrow.lib.ArrowInvalid: Float value -1172.500000 was truncated converting to int64" -) -def test_groupby_median_inexact(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - col_name = "int64_too" - bf_series = ( - scalars_df[col_name] - .groupby(scalars_df["string_col"], dropna=False) - .median(exact=False) - ) - pd_max = ( - scalars_pandas_df[col_name] - .groupby(scalars_pandas_df["string_col"], dropna=False) - .max() - ) - pd_min = ( - scalars_pandas_df[col_name] - .groupby(scalars_pandas_df["string_col"], dropna=False) - .min() - ) - # TODO(swast): Update groupby to use index based on group by key(s). - bf_result = bf_series.to_pandas() - - # Median is approximate, so just check that it's plausible. - assert ((pd_min <= bf_result) & (bf_result <= pd_max)).all() - - -def test_groupby_prod(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - col_name = "int64_too" - bf_series = scalars_df[col_name].groupby(scalars_df["int64_col"]).prod() - pd_series = ( - scalars_pandas_df[col_name].groupby(scalars_pandas_df["int64_col"]).prod() - ).astype(pd.Float64Dtype()) - # TODO(swast): Update groupby to use index based on group by key(s). - bf_result = bf_series.to_pandas() - assert_series_equal( - pd_series, - bf_result, - ) - - -@pytest.mark.skip(reason="AssertionError: Series are different") -@pytest.mark.parametrize( - ("operator"), - [ - (lambda x: x.cumsum()), - (lambda x: x.cumcount()), - (lambda x: x.cummin()), - (lambda x: x.cummax()), - # Pandas 2.2 casts to cumprod to float. - (lambda x: x.cumprod().astype("Float64")), - (lambda x: x.diff()), - (lambda x: x.shift(2)), - (lambda x: x.shift(-2)), - ], - ids=[ - "cumsum", - "cumcount", - "cummin", - "cummax", - "cumprod", - "diff", - "shiftpostive", - "shiftnegative", - ], -) -def test_groupby_window_ops(scalars_df_index, scalars_pandas_df_index, operator): - col_name = "int64_col" - group_key = "int64_too" # has some duplicates values, good for grouping - bf_series = ( - operator(scalars_df_index[col_name].groupby(scalars_df_index[group_key])) - ).to_pandas() - pd_series = operator( - scalars_pandas_df_index[col_name].groupby(scalars_pandas_df_index[group_key]) - ).astype(bf_series.dtype) - - pd.testing.assert_series_equal( - pd_series, - bf_series, - ) - - -@pytest.mark.parametrize( - ("label", "col_name"), - [ - (0, "bool_col"), - (1, "int64_col"), - ], -) -def test_drop_label(scalars_df_index, scalars_pandas_df_index, label, col_name): - bf_series = scalars_df_index[col_name].drop(label).to_pandas() - pd_series = scalars_pandas_df_index[col_name].drop(label) - pd.testing.assert_series_equal( - pd_series, - bf_series, - ) - - -def test_drop_label_list(scalars_df_index, scalars_pandas_df_index): - col_name = "int64_col" - bf_series = scalars_df_index[col_name].drop([1, 3]).to_pandas() - pd_series = scalars_pandas_df_index[col_name].drop([1, 3]) - pd.testing.assert_series_equal( - pd_series, - bf_series, - ) - - -@pytest.mark.skip(reason="AssertionError: Series.index are different") -@pytest.mark.parametrize( - ("col_name",), - [ - ("bool_col",), - ("int64_too",), - ], -) -@pytest.mark.parametrize( - ("keep",), - [ - ("first",), - ("last",), - (False,), - ], -) -def test_drop_duplicates(scalars_df_index, scalars_pandas_df_index, keep, col_name): - bf_series = scalars_df_index[col_name].drop_duplicates(keep=keep).to_pandas() - pd_series = scalars_pandas_df_index[col_name].drop_duplicates(keep=keep) - pd.testing.assert_series_equal( - pd_series, - bf_series, - ) - - -@pytest.mark.skip(reason="TypeError: boolean value of NA is ambiguous") -@pytest.mark.parametrize( - ("col_name",), - [ - ("bool_col",), - ("int64_too",), - ], -) -def test_unique(scalars_df_index, scalars_pandas_df_index, col_name): - bf_uniq = scalars_df_index[col_name].unique().to_numpy(na_value=None) - pd_uniq = scalars_pandas_df_index[col_name].unique() - numpy.array_equal(pd_uniq, bf_uniq) - - -@pytest.mark.skip(reason="AssertionError: Series are different") -@pytest.mark.parametrize( - ("col_name",), - [ - ("bool_col",), - ("int64_too",), - ], -) -@pytest.mark.parametrize( - ("keep",), - [ - ("first",), - ("last",), - (False,), - ], -) -def test_duplicated(scalars_df_index, scalars_pandas_df_index, keep, col_name): - bf_series = scalars_df_index[col_name].duplicated(keep=keep).to_pandas() - pd_series = scalars_pandas_df_index[col_name].duplicated(keep=keep) - pd.testing.assert_series_equal(pd_series, bf_series, check_dtype=False) - - -def test_shape(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - - bf_result = scalars_df["string_col"].shape - pd_result = scalars_pandas_df["string_col"].shape - - assert pd_result == bf_result - - -def test_len(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - - bf_result = len(scalars_df["string_col"]) - pd_result = len(scalars_pandas_df["string_col"]) - - assert pd_result == bf_result - - -def test_size(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - - bf_result = scalars_df["string_col"].size - pd_result = scalars_pandas_df["string_col"].size - - assert pd_result == bf_result - - -def test_series_hasnans_true(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - - bf_result = scalars_df["string_col"].hasnans - pd_result = scalars_pandas_df["string_col"].hasnans - - assert pd_result == bf_result - - -def test_series_hasnans_false(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - - bf_result = scalars_df["string_col"].dropna().hasnans - pd_result = scalars_pandas_df["string_col"].dropna().hasnans - - assert pd_result == bf_result - - -def test_empty_false(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - - bf_result = scalars_df["string_col"].empty - pd_result = scalars_pandas_df["string_col"].empty - - assert pd_result == bf_result - - -def test_empty_true_row_filter(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - - bf_result = scalars_df["string_col"][ - scalars_df["string_col"] == "won't find this" - ].empty - pd_result = scalars_pandas_df["string_col"][ - scalars_pandas_df["string_col"] == "won't find this" - ].empty - - assert pd_result - assert pd_result == bf_result - - -def test_series_names(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - - bf_result = scalars_df["string_col"].copy() - bf_result.index.name = "new index name" - bf_result.name = "new series name" - - pd_result = scalars_pandas_df["string_col"].copy() - pd_result.index.name = "new index name" - pd_result.name = "new series name" - - assert pd_result.name == bf_result.name - assert pd_result.index.name == bf_result.index.name - - -def test_dtype(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - - bf_result = scalars_df["string_col"].dtype - pd_result = scalars_pandas_df["string_col"].dtype - - assert pd_result == bf_result - - -def test_dtypes(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - - bf_result = scalars_df["int64_col"].dtypes - pd_result = scalars_pandas_df["int64_col"].dtypes - - assert pd_result == bf_result - - -def test_head(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - - bf_result = scalars_df["string_col"].head(2).to_pandas() - pd_result = scalars_pandas_df["string_col"].head(2) - - assert_series_equal( - pd_result, - bf_result, - ) - - -def test_tail(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - - bf_result = scalars_df["string_col"].tail(2).to_pandas() - pd_result = scalars_pandas_df["string_col"].tail(2) - - assert_series_equal( - pd_result, - bf_result, - ) - - -def test_head_then_scalar_operation(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - - bf_result = (scalars_df["float64_col"].head(1) + 4).to_pandas() - pd_result = scalars_pandas_df["float64_col"].head(1) + 4 - - pd.testing.assert_series_equal( - bf_result, - pd_result, - ) - - -def test_head_then_series_operation(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - - bf_result = ( - scalars_df["float64_col"].head(4) + scalars_df["float64_col"].head(2) - ).to_pandas() - pd_result = scalars_pandas_df["float64_col"].head(4) + scalars_pandas_df[ - "float64_col" - ].head(2) - - pd.testing.assert_series_equal( - bf_result, - pd_result, - ) - - -def test_series_peek(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - - peek_result = scalars_df["float64_col"].peek(n=3, force=False) - - pd.testing.assert_series_equal( - peek_result, - scalars_pandas_df["float64_col"].reindex_like(peek_result), - ) - assert len(peek_result) == 3 - - -def test_series_peek_with_large_results_not_allowed(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - - session = scalars_df._block.session - slot_millis_sum = session.slot_millis_sum - peek_result = scalars_df["float64_col"].peek( - n=3, force=False, allow_large_results=False - ) - - # The metrics won't be fully updated when we call query_and_wait. - print(session.slot_millis_sum - slot_millis_sum) - assert session.slot_millis_sum - slot_millis_sum < 500 - pd.testing.assert_series_equal( - peek_result, - scalars_pandas_df["float64_col"].reindex_like(peek_result), - ) - assert len(peek_result) == 3 - - -def test_series_peek_multi_index(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - bf_series = scalars_df.set_index(["string_col", "bool_col"])["float64_col"] - bf_series.name = ("2-part", "name") - pd_series = scalars_pandas_df.set_index(["string_col", "bool_col"])["float64_col"] - pd_series.name = ("2-part", "name") - peek_result = bf_series.peek(n=3, force=False) - pd.testing.assert_series_equal( - peek_result, - pd_series.reindex_like(peek_result), - ) - - -def test_series_peek_filtered(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - peek_result = scalars_df[scalars_df.int64_col > 0]["float64_col"].peek( - n=3, force=False - ) - pd_result = scalars_pandas_df[scalars_pandas_df.int64_col > 0]["float64_col"] - pd.testing.assert_series_equal( - peek_result, - pd_result.reindex_like(peek_result), - ) - - -def test_series_peek_force(scalars_dfs): - # TODO: supply a reason why this isn't compatible with pandas 1.x - pytest.importorskip("pandas", minversion="2.0.0") - scalars_df, scalars_pandas_df = scalars_dfs - - cumsum_df = scalars_df[["int64_col", "int64_too"]].cumsum() - df_filtered = cumsum_df[cumsum_df.int64_col > 0]["int64_too"] - peek_result = df_filtered.peek(n=3, force=True) - pd_cumsum_df = scalars_pandas_df[["int64_col", "int64_too"]].cumsum() - pd_result = pd_cumsum_df[pd_cumsum_df.int64_col > 0]["int64_too"] - pd.testing.assert_series_equal( - peek_result, - pd_result.reindex_like(peek_result), - ) - - -def test_series_peek_force_float(scalars_dfs): - # TODO: supply a reason why this isn't compatible with pandas 1.x - pytest.importorskip("pandas", minversion="2.0.0") - scalars_df, scalars_pandas_df = scalars_dfs - - cumsum_df = scalars_df[["int64_col", "float64_col"]].cumsum() - df_filtered = cumsum_df[cumsum_df.float64_col > 0]["float64_col"] - peek_result = df_filtered.peek(n=3, force=True) - pd_cumsum_df = scalars_pandas_df[["int64_col", "float64_col"]].cumsum() - pd_result = pd_cumsum_df[pd_cumsum_df.float64_col > 0]["float64_col"] - pd.testing.assert_series_equal( - peek_result, - pd_result.reindex_like(peek_result), - ) - - -def test_shift(scalars_df_index, scalars_pandas_df_index): - col_name = "int64_col" - bf_result = scalars_df_index[col_name].shift().to_pandas() - # cumsum does not behave well on nullable ints in pandas, produces object type and never ignores NA - pd_result = scalars_pandas_df_index[col_name].shift().astype(pd.Int64Dtype()) - - pd.testing.assert_series_equal( - bf_result, - pd_result, - ) - - -def test_series_ffill(scalars_df_index, scalars_pandas_df_index): - col_name = "numeric_col" - bf_result = scalars_df_index[col_name].ffill(limit=1).to_pandas() - pd_result = scalars_pandas_df_index[col_name].ffill(limit=1) - - pd.testing.assert_series_equal( - bf_result, - pd_result, - ) - - -def test_series_bfill(scalars_df_index, scalars_pandas_df_index): - col_name = "numeric_col" - bf_result = scalars_df_index[col_name].bfill(limit=2).to_pandas() - pd_result = scalars_pandas_df_index[col_name].bfill(limit=2) - - pd.testing.assert_series_equal( - bf_result, - pd_result, - ) - - -def test_cumsum_int(scalars_df_index, scalars_pandas_df_index): - if pd.__version__.startswith("1."): - pytest.skip("Series.cumsum NA mask are different in pandas 1.x.") - - col_name = "int64_col" - bf_result = scalars_df_index[col_name].cumsum().to_pandas() - # cumsum does not behave well on nullable ints in pandas, produces object type and never ignores NA - pd_result = scalars_pandas_df_index[col_name].cumsum().astype(pd.Int64Dtype()) - - pd.testing.assert_series_equal( - bf_result, - pd_result, - ) - - -def test_cumsum_int_ordered(scalars_df_index, scalars_pandas_df_index): - if pd.__version__.startswith("1."): - pytest.skip("Series.cumsum NA mask are different in pandas 1.x.") - - col_name = "int64_col" - bf_result = ( - scalars_df_index.sort_values(by="rowindex_2")[col_name].cumsum().to_pandas() - ) - # cumsum does not behave well on nullable ints in pandas, produces object type and never ignores NA - pd_result = ( - scalars_pandas_df_index.sort_values(by="rowindex_2")[col_name] - .cumsum() - .astype(pd.Int64Dtype()) - ) - - pd.testing.assert_series_equal( - bf_result, - pd_result, - ) - - -@pytest.mark.skip( - reason="NotImplementedError: Aggregate op RankOp() not yet supported in polars engine." -) -@pytest.mark.parametrize( - ("keep",), - [ - ("first",), - ("last",), - ("all",), - ], -) -def test_series_nlargest(scalars_df_index, scalars_pandas_df_index, keep): - col_name = "bool_col" - bf_result = scalars_df_index[col_name].nlargest(4, keep=keep).to_pandas() - pd_result = scalars_pandas_df_index[col_name].nlargest(4, keep=keep) - - pd.testing.assert_series_equal( - bf_result, - pd_result, - ) - - -@pytest.mark.parametrize( - ("periods",), - [ - (1,), - (2,), - (-1,), - ], -) -def test_diff(scalars_df_index, scalars_pandas_df_index, periods): - bf_result = scalars_df_index["int64_col"].diff(periods=periods).to_pandas() - # cumsum does not behave well on nullable ints in pandas, produces object type and never ignores NA - pd_result = ( - scalars_pandas_df_index["int64_col"] - .diff(periods=periods) - .astype(pd.Int64Dtype()) - ) - - pd.testing.assert_series_equal( - bf_result, - pd_result, - ) - - -@pytest.mark.parametrize( - ("periods",), - [ - (1,), - (2,), - (-1,), - ], -) -def test_series_pct_change(scalars_df_index, scalars_pandas_df_index, periods): - bf_result = scalars_df_index["int64_col"].pct_change(periods=periods).to_pandas() - # cumsum does not behave well on nullable ints in pandas, produces object type and never ignores NA - pd_result = scalars_pandas_df_index["int64_col"].ffill().pct_change(periods=periods) - - assert_series_equal(bf_result, pd_result, nulls_are_nan=True) - - -@pytest.mark.skip( - reason="NotImplementedError: Aggregate op RankOp() not yet supported in polars engine." -) -@pytest.mark.parametrize( - ("keep",), - [ - ("first",), - ("last",), - ("all",), - ], -) -def test_series_nsmallest(scalars_df_index, scalars_pandas_df_index, keep): - col_name = "bool_col" - bf_result = scalars_df_index[col_name].nsmallest(2, keep=keep).to_pandas() - pd_result = scalars_pandas_df_index[col_name].nsmallest(2, keep=keep) - - pd.testing.assert_series_equal( - bf_result, - pd_result, - ) - - -@pytest.mark.skip( - reason="NotImplementedError: Aggregate op DenseRankOp() not yet supported in polars engine." -) -@pytest.mark.parametrize( - ("na_option", "method", "ascending", "numeric_only", "pct"), - [ - ("keep", "average", True, True, False), - ("top", "min", False, False, True), - ("bottom", "max", False, False, False), - ("top", "first", False, False, True), - ("bottom", "dense", False, False, False), - ], -) -def test_series_rank( - scalars_df_index, - scalars_pandas_df_index, - na_option, - method, - ascending, - numeric_only, - pct, -): - col_name = "int64_too" - bf_result = ( - scalars_df_index[col_name] - .rank( - na_option=na_option, - method=method, - ascending=ascending, - numeric_only=numeric_only, - pct=pct, - ) - .to_pandas() - ) - pd_result = ( - scalars_pandas_df_index[col_name] - .rank( - na_option=na_option, - method=method, - ascending=ascending, - numeric_only=numeric_only, - pct=pct, - ) - .astype(pd.Float64Dtype()) - ) - - pd.testing.assert_series_equal( - bf_result, - pd_result, - ) - - -def test_cast_float_to_int(scalars_df_index, scalars_pandas_df_index): - col_name = "float64_col" - bf_result = scalars_df_index[col_name].astype(pd.Int64Dtype()).to_pandas() - # cumsum does not behave well on nullable floats in pandas, produces object type and never ignores NA - pd_result = scalars_pandas_df_index[col_name].astype(pd.Int64Dtype()) - - pd.testing.assert_series_equal( - bf_result, - pd_result, - ) - - -def test_cast_float_to_bool(scalars_df_index, scalars_pandas_df_index): - col_name = "float64_col" - bf_result = scalars_df_index[col_name].astype(pd.BooleanDtype()).to_pandas() - # cumsum does not behave well on nullable floats in pandas, produces object type and never ignores NA - pd_result = scalars_pandas_df_index[col_name].astype(pd.BooleanDtype()) - - pd.testing.assert_series_equal( - bf_result, - pd_result, - ) - - -def test_cumsum_nested(scalars_df_index, scalars_pandas_df_index): - col_name = "float64_col" - bf_result = scalars_df_index[col_name].cumsum().cumsum().cumsum().to_pandas() - # cumsum does not behave well on nullable ints in pandas, produces object type and never ignores NA - pd_result = ( - scalars_pandas_df_index[col_name] - .cumsum() - .cumsum() - .cumsum() - .astype(pd.Float64Dtype()) - ) - - pd.testing.assert_series_equal( - bf_result, - pd_result, - ) - - -@pytest.mark.skip( - reason="NotImplementedError: min_period not yet supported for polars engine" -) -def test_nested_analytic_ops_align(scalars_df_index, scalars_pandas_df_index): - # TODO: supply a reason why this isn't compatible with pandas 1.x - pytest.importorskip("pandas", minversion="2.0.0") - col_name = "float64_col" - # set non-unique index to check implicit alignment - bf_series = scalars_df_index.set_index("bool_col")[col_name].fillna(0.0) - pd_series = scalars_pandas_df_index.set_index("bool_col")[col_name].fillna(0.0) - - bf_result = ( - (bf_series + 5) - + (bf_series.cumsum().cumsum().cumsum() + bf_series.rolling(window=3).mean()) - + bf_series.expanding().max() - ).to_pandas() - # cumsum does not behave well on nullable ints in pandas, produces object type and never ignores NA - pd_result = ( - (pd_series + 5) - + ( - pd_series.cumsum().cumsum().cumsum().astype(pd.Float64Dtype()) - + pd_series.rolling(window=3).mean() - ) - + pd_series.expanding().max() - ) - - pd.testing.assert_series_equal( - bf_result, - pd_result, - ) - - -def test_cumsum_int_filtered(scalars_df_index, scalars_pandas_df_index): - col_name = "int64_col" - - bf_col = scalars_df_index[col_name] - bf_result = bf_col[bf_col > -2].cumsum().to_pandas() - - pd_col = scalars_pandas_df_index[col_name] - # cumsum does not behave well on nullable ints in pandas, produces object type and never ignores NA - pd_result = pd_col[pd_col > -2].cumsum().astype(pd.Int64Dtype()) - - pd.testing.assert_series_equal( - bf_result, - pd_result, - ) - - -def test_cumsum_float(scalars_df_index, scalars_pandas_df_index): - col_name = "float64_col" - bf_result = scalars_df_index[col_name].cumsum().to_pandas() - # cumsum does not behave well on nullable floats in pandas, produces object type and never ignores NA - pd_result = scalars_pandas_df_index[col_name].cumsum().astype(pd.Float64Dtype()) - - pd.testing.assert_series_equal( - bf_result, - pd_result, - ) - - -def test_cummin_int(scalars_df_index, scalars_pandas_df_index): - col_name = "int64_col" - bf_result = scalars_df_index[col_name].cummin().to_pandas() - pd_result = scalars_pandas_df_index[col_name].cummin() - - pd.testing.assert_series_equal( - bf_result, - pd_result, - ) - - -def test_cummax_int(scalars_df_index, scalars_pandas_df_index): - col_name = "int64_col" - bf_result = scalars_df_index[col_name].cummax().to_pandas() - pd_result = scalars_pandas_df_index[col_name].cummax() - - pd.testing.assert_series_equal( - bf_result, - pd_result, - ) - - -@pytest.mark.parametrize( - ("kwargs"), - [ - {}, - {"normalize": True}, - {"ascending": True}, - ], - ids=[ - "default", - "normalize", - "ascending", - ], -) -def test_value_counts(scalars_dfs, kwargs): - if pd.__version__.startswith("1."): - pytest.skip("pandas 1.x produces different column labels.") - scalars_df, scalars_pandas_df = scalars_dfs - col_name = "int64_too" - - # Pandas `value_counts` can produce non-deterministic results with tied counts. - # Remove duplicates to enforce a consistent output. - s = scalars_df[col_name].drop(0) - pd_s = scalars_pandas_df[col_name].drop(0) - - bf_result = s.value_counts(**kwargs).to_pandas() - pd_result = pd_s.value_counts(**kwargs) - - pd.testing.assert_series_equal( - bf_result, - pd_result, - ) - - -def test_value_counts_with_na(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - col_name = "int64_col" - - bf_result = scalars_df[col_name].value_counts(dropna=False).to_pandas() - pd_result = scalars_pandas_df[col_name].value_counts(dropna=False) - - # Older pandas version may not have these values, bigframes tries to emulate 2.0+ - pd_result.name = "count" - pd_result.index.name = col_name - - assert_series_equal( - bf_result, - pd_result, - # bigframes values_counts does not honor ordering in the original data - ignore_order=True, - ) - - -@pytest.mark.skip( - reason="NotImplementedError: Aggregate op CutOp(bins=3, right=True, labels=False) not yet supported in polars engine." -) -def test_value_counts_w_cut(scalars_dfs): - if pd.__version__.startswith("1."): - pytest.skip("value_counts results different in pandas 1.x.") - scalars_df, scalars_pandas_df = scalars_dfs - col_name = "int64_col" - - bf_cut = bigframes.pandas.cut(scalars_df[col_name], 3, labels=False) - pd_cut = pd.cut(scalars_pandas_df[col_name], 3, labels=False) - - bf_result = bf_cut.value_counts().to_pandas() - pd_result = pd_cut.value_counts() - pd_result.index = pd_result.index.astype(pd.Int64Dtype()) - - pd.testing.assert_series_equal( - bf_result, - pd_result.astype(pd.Int64Dtype()), - ) - - -def test_iloc_nested(scalars_df_index, scalars_pandas_df_index): - bf_result = scalars_df_index["string_col"].iloc[1:].iloc[1:].to_pandas() - pd_result = scalars_pandas_df_index["string_col"].iloc[1:].iloc[1:] - - pd.testing.assert_series_equal( - bf_result, - pd_result, - ) - - -@pytest.mark.parametrize( - ("start", "stop", "step"), - [ - (1, None, None), - (None, 4, None), - (None, None, 2), - (None, 50000000000, 1), - (5, 4, None), - (3, None, 2), - (1, 7, 2), - (1, 7, 50000000000), - (-1, -7, -2), - (None, -7, -2), - (-1, None, -2), - (-7, -1, 2), - (-7, -1, None), - (-7, 7, None), - (7, -7, -2), - ], -) -def test_series_iloc(scalars_df_index, scalars_pandas_df_index, start, stop, step): - bf_result = scalars_df_index["string_col"].iloc[start:stop:step].to_pandas() - pd_result = scalars_pandas_df_index["string_col"].iloc[start:stop:step] - pd.testing.assert_series_equal( - bf_result, - pd_result, - ) - - -def test_at(scalars_df_index, scalars_pandas_df_index): - scalars_df_index = scalars_df_index.set_index("int64_too", drop=False) - scalars_pandas_df_index = scalars_pandas_df_index.set_index("int64_too", drop=False) - index = -2345 - bf_result = scalars_df_index["string_col"].at[index] - pd_result = scalars_pandas_df_index["string_col"].at[index] - - assert bf_result == pd_result - - -def test_iat(scalars_df_index, scalars_pandas_df_index): - bf_result = scalars_df_index["int64_too"].iat[3] - pd_result = scalars_pandas_df_index["int64_too"].iat[3] - - assert bf_result == pd_result - - -def test_iat_error(scalars_df_index, scalars_pandas_df_index): - with pytest.raises(ValueError): - scalars_pandas_df_index["int64_too"].iat["asd"] - with pytest.raises(ValueError): - scalars_df_index["int64_too"].iat["asd"] - - -def test_series_add_prefix(scalars_df_index, scalars_pandas_df_index): - bf_result = scalars_df_index["int64_too"].add_prefix("prefix_").to_pandas() - - pd_result = scalars_pandas_df_index["int64_too"].add_prefix("prefix_") - - # Index will be object type in pandas, string type in bigframes, but same values - pd.testing.assert_series_equal( - bf_result, - pd_result, - check_index_type=False, - ) - - -def test_series_add_suffix(scalars_df_index, scalars_pandas_df_index): - bf_result = scalars_df_index["int64_too"].add_suffix("_suffix").to_pandas() - - pd_result = scalars_pandas_df_index["int64_too"].add_suffix("_suffix") - - # Index will be object type in pandas, string type in bigframes, but same values - pd.testing.assert_series_equal( - bf_result, - pd_result, - check_index_type=False, - ) - - -def test_series_filter_items(scalars_df_index, scalars_pandas_df_index): - if pd.__version__.startswith("2.0") or pd.__version__.startswith("1."): - pytest.skip("pandas filter items behavior different pre-2.1") - bf_result = scalars_df_index["float64_col"].filter(items=[5, 1, 3]).to_pandas() - - pd_result = scalars_pandas_df_index["float64_col"].filter(items=[5, 1, 3]) - - # Pandas uses int64 instead of Int64 (nullable) dtype. - pd_result.index = pd_result.index.astype(pd.Int64Dtype()) - # Ignore ordering as pandas order differently depending on version - assert_series_equal(bf_result, pd_result, check_names=False, ignore_order=True) - - -def test_series_filter_like(scalars_df_index, scalars_pandas_df_index): - scalars_df_index = scalars_df_index.copy().set_index("string_col") - scalars_pandas_df_index = scalars_pandas_df_index.copy().set_index("string_col") - - bf_result = scalars_df_index["float64_col"].filter(like="ello").to_pandas() - - pd_result = scalars_pandas_df_index["float64_col"].filter(like="ello") - - pd.testing.assert_series_equal( - bf_result, - pd_result, - ) - - -def test_series_filter_regex(scalars_df_index, scalars_pandas_df_index): - scalars_df_index = scalars_df_index.copy().set_index("string_col") - scalars_pandas_df_index = scalars_pandas_df_index.copy().set_index("string_col") - - bf_result = scalars_df_index["float64_col"].filter(regex="^[GH].*").to_pandas() - - pd_result = scalars_pandas_df_index["float64_col"].filter(regex="^[GH].*") - - pd.testing.assert_series_equal( - bf_result, - pd_result, - ) - - -def test_series_reindex(scalars_df_index, scalars_pandas_df_index): - bf_result = ( - scalars_df_index["float64_col"].reindex(index=[5, 1, 3, 99, 1]).to_pandas() - ) - - pd_result = scalars_pandas_df_index["float64_col"].reindex(index=[5, 1, 3, 99, 1]) - - # Pandas uses int64 instead of Int64 (nullable) dtype. - pd_result.index = pd_result.index.astype(pd.Int64Dtype()) - pd.testing.assert_series_equal( - bf_result, - pd_result, - ) - - -def test_series_reindex_nonunique(scalars_df_index): - with pytest.raises(ValueError): - # int64_too is non-unique - scalars_df_index.set_index("int64_too")["float64_col"].reindex( - index=[5, 1, 3, 99, 1], validate=True - ) - - -def test_series_reindex_like(scalars_df_index, scalars_pandas_df_index): - bf_reindex_target = scalars_df_index["float64_col"].reindex(index=[5, 1, 3, 99, 1]) - bf_result = ( - scalars_df_index["int64_too"].reindex_like(bf_reindex_target).to_pandas() - ) - - pd_reindex_target = scalars_pandas_df_index["float64_col"].reindex( - index=[5, 1, 3, 99, 1] - ) - pd_result = scalars_pandas_df_index["int64_too"].reindex_like(pd_reindex_target) - - # Pandas uses int64 instead of Int64 (nullable) dtype. - pd_result.index = pd_result.index.astype(pd.Int64Dtype()) - pd.testing.assert_series_equal( - bf_result, - pd_result, - ) - - -def test_where_with_series(scalars_df_index, scalars_pandas_df_index): - bf_result = ( - scalars_df_index["int64_col"] - .where(scalars_df_index["bool_col"], scalars_df_index["int64_too"]) - .to_pandas() - ) - pd_result = scalars_pandas_df_index["int64_col"].where( - scalars_pandas_df_index["bool_col"], scalars_pandas_df_index["int64_too"] - ) - - pd.testing.assert_series_equal( - bf_result, - pd_result, - ) - - -def test_where_with_different_indices(scalars_df_index, scalars_pandas_df_index): - bf_result = ( - scalars_df_index["int64_col"] - .iloc[::2] - .where( - scalars_df_index["bool_col"].iloc[2:], - scalars_df_index["int64_too"].iloc[:5], - ) - .to_pandas() - ) - pd_result = ( - scalars_pandas_df_index["int64_col"] - .iloc[::2] - .where( - scalars_pandas_df_index["bool_col"].iloc[2:], - scalars_pandas_df_index["int64_too"].iloc[:5], - ) - ) - - pd.testing.assert_series_equal( - bf_result, - pd_result, - ) - - -def test_where_with_default(scalars_df_index, scalars_pandas_df_index): - bf_result = ( - scalars_df_index["int64_col"].where(scalars_df_index["bool_col"]).to_pandas() - ) - pd_result = scalars_pandas_df_index["int64_col"].where( - scalars_pandas_df_index["bool_col"] - ) - - pd.testing.assert_series_equal( - bf_result, - pd_result, - ) - - -def test_where_with_callable(scalars_df_index, scalars_pandas_df_index): - def _is_positive(x): - return x > 0 - - # Both cond and other are callable. - bf_result = ( - scalars_df_index["int64_col"] - .where(cond=_is_positive, other=lambda x: x * 10) - .to_pandas() - ) - pd_result = scalars_pandas_df_index["int64_col"].where( - cond=_is_positive, other=lambda x: x * 10 - ) - - pd.testing.assert_series_equal( - bf_result, - pd_result, - ) - - -@pytest.mark.skip( - reason="NotImplementedError: Polars compiler hasn't implemented ClipOp()" -) -@pytest.mark.parametrize( - ("ordered"), - [ - (True), - (False), - ], -) -def test_clip(scalars_df_index, scalars_pandas_df_index, ordered): - col_bf = scalars_df_index["int64_col"] - lower_bf = scalars_df_index["int64_too"] - 1 - upper_bf = scalars_df_index["int64_too"] + 1 - bf_result = col_bf.clip(lower_bf, upper_bf).to_pandas(ordered=ordered) - - col_pd = scalars_pandas_df_index["int64_col"] - lower_pd = scalars_pandas_df_index["int64_too"] - 1 - upper_pd = scalars_pandas_df_index["int64_too"] + 1 - pd_result = col_pd.clip(lower_pd, upper_pd) - - assert_series_equal(bf_result, pd_result, ignore_order=not ordered) - - -@pytest.mark.skip( - reason="NotImplementedError: Polars compiler hasn't implemented ClipOp()" -) -def test_clip_int_with_float_bounds(scalars_df_index, scalars_pandas_df_index): - col_bf = scalars_df_index["int64_too"] - bf_result = col_bf.clip(-100, 3.14151593).to_pandas() - - col_pd = scalars_pandas_df_index["int64_too"] - # pandas doesn't work with Int64 and clip with floats - pd_result = col_pd.astype("int64").clip(-100, 3.14151593).astype("Float64") - - assert_series_equal(bf_result, pd_result) - - -@pytest.mark.skip( - reason="NotImplementedError: Polars compiler hasn't implemented ClipOp()" -) -def test_clip_filtered_two_sided(scalars_df_index, scalars_pandas_df_index): - col_bf = scalars_df_index["int64_col"].iloc[::2] - lower_bf = scalars_df_index["int64_too"].iloc[2:] - 1 - upper_bf = scalars_df_index["int64_too"].iloc[:5] + 1 - bf_result = col_bf.clip(lower_bf, upper_bf).to_pandas() - - col_pd = scalars_pandas_df_index["int64_col"].iloc[::2] - lower_pd = scalars_pandas_df_index["int64_too"].iloc[2:] - 1 - upper_pd = scalars_pandas_df_index["int64_too"].iloc[:5] + 1 - pd_result = col_pd.clip(lower_pd, upper_pd) - - pd.testing.assert_series_equal( - bf_result, - pd_result, - ) - - -@pytest.mark.skip( - reason="NotImplementedError: Polars compiler hasn't implemented maximum()" -) -def test_clip_filtered_one_sided(scalars_df_index, scalars_pandas_df_index): - col_bf = scalars_df_index["int64_col"].iloc[::2] - lower_bf = scalars_df_index["int64_too"].iloc[2:] - 1 - bf_result = col_bf.clip(lower_bf, None).to_pandas() - - col_pd = scalars_pandas_df_index["int64_col"].iloc[::2] - lower_pd = scalars_pandas_df_index["int64_too"].iloc[2:] - 1 - pd_result = col_pd.clip(lower_pd, None) - - pd.testing.assert_series_equal( - bf_result, - pd_result, - ) - - -def test_dot(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - bf_result = scalars_df["int64_too"] @ scalars_df["int64_too"] - - pd_result = scalars_pandas_df["int64_too"] @ scalars_pandas_df["int64_too"] - - assert bf_result == pd_result - - -@pytest.mark.parametrize( - ("left", "right", "inclusive"), - [ - (-234892, 55555, "left"), - (-234892, 55555, "both"), - (-234892, 55555, "neither"), - (-234892, 55555, "right"), - ], -) -def test_between(scalars_df_index, scalars_pandas_df_index, left, right, inclusive): - bf_result = ( - scalars_df_index["int64_col"].between(left, right, inclusive).to_pandas() - ) - pd_result = scalars_pandas_df_index["int64_col"].between(left, right, inclusive) - - pd.testing.assert_series_equal( - bf_result, - pd_result.astype(pd.BooleanDtype()), - ) - - -@pytest.mark.skip(reason="fixture 'scalars_dfs_maybe_ordered' not found") -def test_series_case_when(scalars_dfs_maybe_ordered): - pytest.importorskip( - "pandas", - minversion="2.2.0", - reason="case_when added in pandas 2.2.0", - ) - scalars_df, scalars_pandas_df = scalars_dfs_maybe_ordered - - bf_series = scalars_df["int64_col"] - pd_series = scalars_pandas_df["int64_col"] - - # TODO(tswast): pandas case_when appears to assume True when a value is - # null. I suspect this should be considered a bug in pandas. - - # Generate 150 conditions to test case_when with a large number of conditions - bf_conditions = ( - [((bf_series > 645).fillna(True), bf_series - 1)] - + [((bf_series > (-100 + i * 5)).fillna(True), i) for i in range(148, 0, -1)] - + [((bf_series <= -100).fillna(True), pd.NA)] - ) - - pd_conditions = ( - [((pd_series > 645), pd_series - 1)] - + [((pd_series > (-100 + i * 5)), i) for i in range(148, 0, -1)] - + [(pd_series <= -100, pd.NA)] - ) - - assert len(bf_conditions) == 150 - - bf_result = bf_series.case_when(bf_conditions).to_pandas() - pd_result = pd_series.case_when(pd_conditions) - - pd.testing.assert_series_equal( - bf_result, - pd_result.astype(pd.Int64Dtype()), - ) - - -@pytest.mark.skip(reason="fixture 'scalars_dfs_maybe_ordered' not found") -def test_series_case_when_change_type(scalars_dfs_maybe_ordered): - pytest.importorskip( - "pandas", - minversion="2.2.0", - reason="case_when added in pandas 2.2.0", - ) - scalars_df, scalars_pandas_df = scalars_dfs_maybe_ordered - - bf_series = scalars_df["int64_col"] - pd_series = scalars_pandas_df["int64_col"] - - # TODO(tswast): pandas case_when appears to assume True when a value is - # null. I suspect this should be considered a bug in pandas. - - bf_conditions = [ - ((bf_series > 645).fillna(True), scalars_df["string_col"]), - ((bf_series <= -100).fillna(True), pd.NA), - (True, "not_found"), - ] - - pd_conditions = [ - ((pd_series > 645).fillna(True), scalars_pandas_df["string_col"]), - ((pd_series <= -100).fillna(True), pd.NA), - # pandas currently fails if both the condition and the value are literals. - ([True] * len(pd_series), ["not_found"] * len(pd_series)), - ] - - bf_result = bf_series.case_when(bf_conditions).to_pandas() - pd_result = pd_series.case_when(pd_conditions) - - pd.testing.assert_series_equal( - bf_result, - pd_result.astype("string[pyarrow]"), - ) - - -def test_to_frame(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - - bf_result = scalars_df["int64_col"].to_frame().to_pandas() - pd_result = scalars_pandas_df["int64_col"].to_frame() - - assert_frame_equal(bf_result, pd_result) - - -def test_to_frame_no_name(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - - bf_result = scalars_df["int64_col"].rename(None).to_frame().to_pandas() - pd_result = scalars_pandas_df["int64_col"].rename(None).to_frame() - - assert_frame_equal(bf_result, pd_result) - - -@pytest.mark.skip(reason="fixture 'gcs_folder' not found") -def test_to_json(gcs_folder, scalars_df_index, scalars_pandas_df_index): - path = gcs_folder + "test_series_to_json*.jsonl" - scalars_df_index["int64_col"].to_json(path, lines=True, orient="records") - gcs_df = pd.read_json(get_first_file_from_wildcard(path), lines=True) - - pd.testing.assert_series_equal( - gcs_df["int64_col"].astype(pd.Int64Dtype()), - scalars_pandas_df_index["int64_col"], - check_dtype=False, - check_index=False, - ) - - -@pytest.mark.skip(reason="fixture 'gcs_folder' not found") -def test_to_csv(gcs_folder, scalars_df_index, scalars_pandas_df_index): - path = gcs_folder + "test_series_to_csv*.csv" - scalars_df_index["int64_col"].to_csv(path) - gcs_df = pd.read_csv(get_first_file_from_wildcard(path)) - - pd.testing.assert_series_equal( - gcs_df["int64_col"].astype(pd.Int64Dtype()), - scalars_pandas_df_index["int64_col"], - check_dtype=False, - check_index=False, - ) - - -def test_to_latex(scalars_df_index, scalars_pandas_df_index): - pytest.importorskip("jinja2") - bf_result = scalars_df_index["int64_col"].to_latex() - pd_result = scalars_pandas_df_index["int64_col"].to_latex() - - assert bf_result == pd_result - - -def test_series_to_json_local_str(scalars_df_index, scalars_pandas_df_index): - bf_result = scalars_df_index.int64_col.to_json() - pd_result = scalars_pandas_df_index.int64_col.to_json() - - assert bf_result == pd_result - - -def test_series_to_json_local_file(scalars_df_index, scalars_pandas_df_index): - # TODO: supply a reason why this isn't compatible with pandas 1.x - pytest.importorskip("pandas", minversion="2.0.0") - with ( - tempfile.TemporaryFile() as bf_result_file, - tempfile.TemporaryFile() as pd_result_file, - ): - scalars_df_index.int64_col.to_json(bf_result_file) - scalars_pandas_df_index.int64_col.to_json(pd_result_file) - - bf_result = bf_result_file.read() - pd_result = pd_result_file.read() - - assert bf_result == pd_result - - -def test_series_to_csv_local_str(scalars_df_index, scalars_pandas_df_index): - bf_result = scalars_df_index.int64_col.to_csv() - # default_handler for arrow types that have no default conversion - pd_result = scalars_pandas_df_index.int64_col.to_csv() - - assert bf_result == pd_result - - -def test_series_to_csv_local_file(scalars_df_index, scalars_pandas_df_index): - with ( - tempfile.TemporaryFile() as bf_result_file, - tempfile.TemporaryFile() as pd_result_file, - ): - scalars_df_index.int64_col.to_csv(bf_result_file) - scalars_pandas_df_index.int64_col.to_csv(pd_result_file) - - bf_result = bf_result_file.read() - pd_result = pd_result_file.read() - - assert bf_result == pd_result - - -def test_to_dict(scalars_df_index, scalars_pandas_df_index): - bf_result = scalars_df_index["int64_too"].to_dict() - - pd_result = scalars_pandas_df_index["int64_too"].to_dict() - - assert bf_result == pd_result - - -def test_to_excel(scalars_df_index, scalars_pandas_df_index): - pytest.importorskip("openpyxl") - bf_result_file = tempfile.TemporaryFile() - pd_result_file = tempfile.TemporaryFile() - scalars_df_index["int64_too"].to_excel(bf_result_file) - scalars_pandas_df_index["int64_too"].to_excel(pd_result_file) - bf_result = bf_result_file.read() - pd_result = bf_result_file.read() - - assert bf_result == pd_result - - -def test_to_pickle(scalars_df_index, scalars_pandas_df_index): - bf_result_file = tempfile.TemporaryFile() - pd_result_file = tempfile.TemporaryFile() - scalars_df_index["int64_too"].to_pickle(bf_result_file) - scalars_pandas_df_index["int64_too"].to_pickle(pd_result_file) - bf_result = bf_result_file.read() - pd_result = bf_result_file.read() - - assert bf_result == pd_result - - -def test_to_string(scalars_df_index, scalars_pandas_df_index): - bf_result = scalars_df_index["int64_too"].to_string() - - pd_result = scalars_pandas_df_index["int64_too"].to_string() - - assert bf_result == pd_result - - -def test_to_list(scalars_df_index, scalars_pandas_df_index): - bf_result = scalars_df_index["int64_too"].to_list() - - pd_result = scalars_pandas_df_index["int64_too"].to_list() - - assert bf_result == pd_result - - -def test_to_numpy(scalars_df_index, scalars_pandas_df_index): - bf_result = scalars_df_index["int64_too"].to_numpy() - - pd_result = scalars_pandas_df_index["int64_too"].to_numpy() - - assert (bf_result == pd_result).all() - - -def test_to_xarray(scalars_df_index, scalars_pandas_df_index): - pytest.importorskip("xarray") - bf_result = scalars_df_index["int64_too"].to_xarray() - - pd_result = scalars_pandas_df_index["int64_too"].to_xarray() - - assert bf_result.equals(pd_result) - - -def test_to_markdown(scalars_df_index, scalars_pandas_df_index): - bf_result = scalars_df_index["int64_too"].to_markdown() - - pd_result = scalars_pandas_df_index["int64_too"].to_markdown() - - assert bf_result == pd_result - - -def test_series_values(scalars_df_index, scalars_pandas_df_index): - bf_result = scalars_df_index["int64_too"].values - - pd_result = scalars_pandas_df_index["int64_too"].values - # Numpy isn't equipped to compare non-numeric objects, so convert back to dataframe - pd.testing.assert_series_equal( - pd.Series(bf_result), pd.Series(pd_result), check_dtype=False - ) - - -def test_series___array__(scalars_df_index, scalars_pandas_df_index): - bf_result = scalars_df_index["float64_col"].__array__() - - pd_result = scalars_pandas_df_index["float64_col"].__array__() - # Numpy isn't equipped to compare non-numeric objects, so convert back to dataframe - numpy.array_equal(bf_result, pd_result) - - -@pytest.mark.parametrize( - ("ascending", "na_position"), - [ - (True, "first"), - (True, "last"), - (False, "first"), - (False, "last"), - ], -) -def test_sort_values(scalars_df_index, scalars_pandas_df_index, ascending, na_position): - # Test needs values to be unique - bf_result = ( - scalars_df_index["int64_col"] - .sort_values(ascending=ascending, na_position=na_position) - .to_pandas() - ) - pd_result = scalars_pandas_df_index["int64_col"].sort_values( - ascending=ascending, na_position=na_position - ) - - pd.testing.assert_series_equal( - bf_result, - pd_result, - ) - - -def test_series_sort_values_inplace(scalars_df_index, scalars_pandas_df_index): - # Test needs values to be unique - bf_series = scalars_df_index["int64_col"].copy() - bf_series.sort_values(ascending=False, inplace=True) - bf_result = bf_series.to_pandas() - pd_result = scalars_pandas_df_index["int64_col"].sort_values(ascending=False) - - pd.testing.assert_series_equal( - bf_result, - pd_result, - ) - - -@pytest.mark.parametrize( - ("ascending"), - [ - (True,), - (False,), - ], -) -def test_sort_index(scalars_df_index, scalars_pandas_df_index, ascending): - bf_result = ( - scalars_df_index["int64_too"].sort_index(ascending=ascending).to_pandas() - ) - pd_result = scalars_pandas_df_index["int64_too"].sort_index(ascending=ascending) - - pd.testing.assert_series_equal( - bf_result, - pd_result, - ) - - -def test_series_sort_index_inplace(scalars_df_index, scalars_pandas_df_index): - bf_series = scalars_df_index["int64_too"].copy() - bf_series.sort_index(ascending=False, inplace=True) - bf_result = bf_series.to_pandas() - pd_result = scalars_pandas_df_index["int64_too"].sort_index(ascending=False) - - pd.testing.assert_series_equal( - bf_result, - pd_result, - ) - - -def test_mask_default_value(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - - bf_col = scalars_df["int64_col"] - bf_col_masked = bf_col.mask(bf_col % 2 == 1) - bf_result = bf_col.to_frame().assign(int64_col_masked=bf_col_masked).to_pandas() - - pd_col = scalars_pandas_df["int64_col"] - pd_col_masked = pd_col.mask(pd_col % 2 == 1) - pd_result = pd_col.to_frame().assign(int64_col_masked=pd_col_masked) - - assert_frame_equal(bf_result, pd_result) - - -def test_mask_custom_value(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - - bf_col = scalars_df["int64_col"] - bf_col_masked = bf_col.mask(bf_col % 2 == 1, -1) - bf_result = bf_col.to_frame().assign(int64_col_masked=bf_col_masked).to_pandas() - - pd_col = scalars_pandas_df["int64_col"] - pd_col_masked = pd_col.mask(pd_col % 2 == 1, -1) - pd_result = pd_col.to_frame().assign(int64_col_masked=pd_col_masked) - - # TODO(shobs): There is a pd.NA value in the original series, which is not - # odd so should be left as is, but it is being masked in pandas. - # Accidentally the bigframes bahavior matches, but it should be updated - # after the resolution of https://github.com/pandas-dev/pandas/issues/52955 - assert_frame_equal(bf_result, pd_result) - - -def test_mask_with_callable(scalars_df_index, scalars_pandas_df_index): - def _ten_times(x): - return x * 10 - - # Both cond and other are callable. - bf_result = ( - scalars_df_index["int64_col"] - .mask(cond=lambda x: x > 0, other=_ten_times) - .to_pandas() - ) - pd_result = scalars_pandas_df_index["int64_col"].mask( - cond=lambda x: x > 0, other=_ten_times - ) - - pd.testing.assert_series_equal( - bf_result, - pd_result, - ) - - -@pytest.mark.parametrize( - ("lambda_",), - [ - pytest.param(lambda x: x > 0), - pytest.param( - lambda x: True if x > 0 else False, - marks=pytest.mark.xfail( - raises=ValueError, - ), - ), - ], - ids=[ - "lambda_arithmatic", - "lambda_arbitrary", - ], -) -def test_mask_lambda(scalars_dfs, lambda_): - scalars_df, scalars_pandas_df = scalars_dfs - - bf_col = scalars_df["int64_col"] - bf_result = bf_col.mask(lambda_).to_pandas() - - pd_col = scalars_pandas_df["int64_col"] - pd_result = pd_col.mask(lambda_) - - # ignore dtype check, which are Int64 and object respectively - assert_series_equal(bf_result, pd_result, check_dtype=False) - - -def test_mask_simple_udf(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - - def foo(x): - return x < 1000000 - - bf_col = scalars_df["int64_col"] - bf_result = bf_col.mask(foo).to_pandas() - - pd_col = scalars_pandas_df["int64_col"] - pd_result = pd_col.mask(foo) - - # ignore dtype check, which are Int64 and object respectively - assert_series_equal(bf_result, pd_result, check_dtype=False) - - -@pytest.mark.skip( - reason="polars.exceptions.InvalidOperationError: decimal precision should be <= 38 & >= 1" -) -@pytest.mark.parametrize("errors", ["raise", "null"]) -@pytest.mark.parametrize( - ("column", "to_type"), - [ - ("int64_col", "Float64"), - ("int64_col", "Int64"), # No-op - ("int64_col", pd.Float64Dtype()), - ("int64_col", "string[pyarrow]"), - ("int64_col", "boolean"), - ("int64_col", pd.ArrowDtype(pa.decimal128(38, 9))), - ("int64_col", pd.ArrowDtype(pa.decimal256(76, 38))), - ("int64_col", pd.ArrowDtype(pa.timestamp("us"))), - ("int64_col", pd.ArrowDtype(pa.timestamp("us", tz="UTC"))), - ("int64_col", "time64[us][pyarrow]"), - ("int64_col", pd.ArrowDtype(db_dtypes.JSONArrowType())), - ("bool_col", "Int64"), - ("bool_col", "string[pyarrow]"), - ("bool_col", "Float64"), - ("bool_col", pd.ArrowDtype(db_dtypes.JSONArrowType())), - ("string_col", "binary[pyarrow]"), - ("bytes_col", "string[pyarrow]"), - # pandas actually doesn't let folks convert to/from naive timestamp and - # raises a deprecation warning to use tz_localize/tz_convert instead, - # but BigQuery always stores values as UTC and doesn't have to deal - # with timezone conversions, so we'll allow it. - ("timestamp_col", "date32[day][pyarrow]"), - ("timestamp_col", "time64[us][pyarrow]"), - ("timestamp_col", pd.ArrowDtype(pa.timestamp("us"))), - ("datetime_col", "date32[day][pyarrow]"), - pytest.param( - "datetime_col", - "string[pyarrow]", - marks=pytest.mark.skipif( - pd.__version__.startswith("2.2"), - reason="pandas 2.2 uses T as date/time separator whereas earlier versions use space", - ), - ), - ("datetime_col", "time64[us][pyarrow]"), - ("datetime_col", pd.ArrowDtype(pa.timestamp("us", tz="UTC"))), - ("date_col", "string[pyarrow]"), - ("date_col", pd.ArrowDtype(pa.timestamp("us"))), - ("date_col", pd.ArrowDtype(pa.timestamp("us", tz="UTC"))), - ("time_col", "string[pyarrow]"), - # TODO(bmil): fix Ibis bug: BigQuery backend rounds to nearest int - # ("float64_col", "Int64"), - # TODO(bmil): decide whether to fix Ibis bug: BigQuery backend - # formats floats with no decimal places if they have no fractional - # part, and does not switch to scientific notation for > 10^15 - # ("float64_col", "string[pyarrow]") - # TODO(bmil): add any other compatible conversions per - # https://cloud.google.com/bigquery/docs/reference/standard-sql/conversion_functions - ], -) -def test_astype(scalars_df_index, scalars_pandas_df_index, column, to_type, errors): - # TODO: supply a reason why this isn't compatible with pandas 1.x - pytest.importorskip("pandas", minversion="2.0.0") - bf_result = scalars_df_index[column].astype(to_type, errors=errors).to_pandas() - pd_result = scalars_pandas_df_index[column].astype(to_type) - pd.testing.assert_series_equal(bf_result, pd_result) - - -@pytest.mark.skip( - reason="AttributeError: 'DataFrame' object has no attribute 'dtype'. Did you mean: 'dtypes'?" -) -def test_series_astype_python(session): - input = pd.Series(["hello", "world", "3.11", "4000"]) - exepcted = pd.Series( - [None, None, 3.11, 4000], - dtype="Float64", - index=pd.Index([0, 1, 2, 3], dtype="Int64"), - ) - result = session.read_pandas(input).astype(float, errors="null").to_pandas() - pd.testing.assert_series_equal(result, exepcted) - - -@pytest.mark.skip( - reason="AttributeError: 'DataFrame' object has no attribute 'dtype'. Did you mean: 'dtypes'?" -) -def test_astype_safe(session): - input = pd.Series(["hello", "world", "3.11", "4000"]) - exepcted = pd.Series( - [None, None, 3.11, 4000], - dtype="Float64", - index=pd.Index([0, 1, 2, 3], dtype="Int64"), - ) - result = session.read_pandas(input).astype("Float64", errors="null").to_pandas() - pd.testing.assert_series_equal(result, exepcted) - - -def test_series_astype_w_invalid_error(session): - input = pd.Series(["hello", "world", "3.11", "4000"]) - with pytest.raises(ValueError): - session.read_pandas(input).astype("Float64", errors="bad_value") - - -@pytest.mark.parametrize( - ("column", "to_type"), - [ - ("timestamp_col", "int64[pyarrow]"), - ("datetime_col", "int64[pyarrow]"), - ("time_col", "int64[pyarrow]"), - ], -) -def test_date_time_astype_int( - scalars_df_index, scalars_pandas_df_index, column, to_type -): - # TODO: supply a reason why this isn't compatible with pandas 1.x - pytest.importorskip("pandas", minversion="2.0.0") - bf_result = scalars_df_index[column].astype(to_type).to_pandas() - pd_result = scalars_pandas_df_index[column].astype(to_type) - pd.testing.assert_series_equal(bf_result, pd_result, check_dtype=False) - assert bf_result.dtype == "Int64" - - -@pytest.mark.skip( - reason="polars.exceptions.InvalidOperationError: conversion from `str` to `i64` failed in column 'column_0' for 1 out of 4 values: [' -03']" -) -def test_string_astype_int(): - pd_series = pd.Series(["4", "-7", "0", " -03"]) - bf_series = series.Series(pd_series) - - pd_result = pd_series.astype("Int64") - bf_result = bf_series.astype("Int64").to_pandas() - - pd.testing.assert_series_equal(bf_result, pd_result, check_index_type=False) - - -@pytest.mark.skip( - reason="polars.exceptions.InvalidOperationError: conversion from `str` to `f64` failed in column 'column_0' for 1 out of 10 values: [' -03.235']" -) -def test_string_astype_float(): - pd_series = pd.Series( - ["1", "-1", "-0", "000", " -03.235", "naN", "-inf", "INf", ".33", "7.235e-8"] - ) - - bf_series = series.Series(pd_series) - - pd_result = pd_series.astype("Float64") - bf_result = bf_series.astype("Float64").to_pandas() - - pd.testing.assert_series_equal(bf_result, pd_result, check_index_type=False) - - -def test_string_astype_date(): - if int(pa.__version__.split(".")[0]) < 15: - pytest.skip( - "Avoid pyarrow.lib.ArrowNotImplementedError: " - "Unsupported cast from string to date32 using function cast_date32." - ) - - pd_series = pd.Series(["2014-08-15", "2215-08-15", "2016-02-29"]).astype( - pd.ArrowDtype(pa.string()) - ) - - bf_series = series.Series(pd_series) - - # TODO(b/340885567): fix type error - pd_result = pd_series.astype("date32[day][pyarrow]") # type: ignore - bf_result = bf_series.astype("date32[day][pyarrow]").to_pandas() - - assert_series_equal(bf_result, pd_result, check_index_type=False) - - -def test_string_astype_datetime(): - pd_series = pd.Series( - ["2014-08-15 08:15:12", "2015-08-15 08:15:12.654754", "2016-02-29 00:00:00"] - ).astype(pd.ArrowDtype(pa.string())) - - bf_series = series.Series(pd_series) - - pd_result = pd_series.astype(pd.ArrowDtype(pa.timestamp("us"))) - bf_result = bf_series.astype(pd.ArrowDtype(pa.timestamp("us"))).to_pandas() - - assert_series_equal(bf_result, pd_result, check_index_type=False) - - -def test_string_astype_timestamp(): - pd_series = pd.Series( - [ - "2014-08-15 08:15:12+00:00", - "2015-08-15 08:15:12.654754+05:00", - "2016-02-29 00:00:00+08:00", - ] - ).astype(pd.ArrowDtype(pa.string())) - - bf_series = series.Series(pd_series) - - pd_result = pd_series.astype(pd.ArrowDtype(pa.timestamp("us", tz="UTC"))) - bf_result = bf_series.astype( - pd.ArrowDtype(pa.timestamp("us", tz="UTC")) - ).to_pandas() - - assert_series_equal(bf_result, pd_result, check_index_type=False) - - -@pytest.mark.skip(reason="AssertionError: Series are different") -def test_timestamp_astype_string(): - bf_series = series.Series( - [ - "2014-08-15 08:15:12+00:00", - "2015-08-15 08:15:12.654754+05:00", - "2016-02-29 00:00:00+08:00", - ] - ).astype(pd.ArrowDtype(pa.timestamp("us", tz="UTC"))) - - expected_result = pd.Series( - [ - "2014-08-15 08:15:12+00", - "2015-08-15 03:15:12.654754+00", - "2016-02-28 16:00:00+00", - ] - ) - bf_result = bf_series.astype(pa.string()).to_pandas() - - pd.testing.assert_series_equal( - bf_result, expected_result, check_index_type=False, check_dtype=False - ) - assert bf_result.dtype == "string[pyarrow]" - - -@pytest.mark.skip(reason="AssertionError: Series are different") -@pytest.mark.parametrize("errors", ["raise", "null"]) -def test_float_astype_json(errors): - data = ["1.25", "2500000000", None, "-12323.24"] - bf_series = series.Series(data, dtype=dtypes.FLOAT_DTYPE) - - bf_result = bf_series.astype(dtypes.JSON_DTYPE, errors=errors) - assert bf_result.dtype == dtypes.JSON_DTYPE - - expected_result = pd.Series(data, dtype=dtypes.JSON_DTYPE) - expected_result.index = expected_result.index.astype("Int64") - pd.testing.assert_series_equal(bf_result.to_pandas(), expected_result) - - -@pytest.mark.skip(reason="AssertionError: Series are different") -def test_float_astype_json_str(): - data = ["1.25", "2500000000", None, "-12323.24"] - bf_series = series.Series(data, dtype=dtypes.FLOAT_DTYPE) - - bf_result = bf_series.astype("json") - assert bf_result.dtype == dtypes.JSON_DTYPE - - expected_result = pd.Series(data, dtype=dtypes.JSON_DTYPE) - expected_result.index = expected_result.index.astype("Int64") - pd.testing.assert_series_equal(bf_result.to_pandas(), expected_result) - - -@pytest.mark.parametrize("errors", ["raise", "null"]) -def test_string_astype_json(errors): - data = [ - "1", - None, - '["1","3","5"]', - '{"a":1,"b":["x","y"],"c":{"x":[],"z":false}}', - ] - bf_series = series.Series(data, dtype=dtypes.STRING_DTYPE) - - bf_result = bf_series.astype(dtypes.JSON_DTYPE, errors=errors) - assert bf_result.dtype == dtypes.JSON_DTYPE - - pd_result = bf_series.to_pandas().astype(dtypes.JSON_DTYPE) - pd.testing.assert_series_equal(bf_result.to_pandas(), pd_result) - - -@pytest.mark.skip(reason="AssertionError: Series NA mask are different") -def test_string_astype_json_in_safe_mode(): - data = ["this is not a valid json string"] - bf_series = series.Series(data, dtype=dtypes.STRING_DTYPE) - bf_result = bf_series.astype(dtypes.JSON_DTYPE, errors="null") - assert bf_result.dtype == dtypes.JSON_DTYPE - - expected = pd.Series([None], dtype=dtypes.JSON_DTYPE) - expected.index = expected.index.astype("Int64") - pd.testing.assert_series_equal(bf_result.to_pandas(), expected) - - -@pytest.mark.skip( - reason="Failed: DID NOT RAISE " -) -def test_string_astype_json_raise_error(): - data = ["this is not a valid json string"] - bf_series = series.Series(data, dtype=dtypes.STRING_DTYPE) - with pytest.raises( - google.api_core.exceptions.BadRequest, - match="syntax error while parsing value", - ): - bf_series.astype(dtypes.JSON_DTYPE, errors="raise").to_pandas() - - -@pytest.mark.parametrize("errors", ["raise", "null"]) -@pytest.mark.parametrize( - ("data", "to_type"), - [ - pytest.param(["1", "10.0", None], dtypes.INT_DTYPE, id="to_int"), - pytest.param(["0.0001", "2500000000", None], dtypes.FLOAT_DTYPE, id="to_float"), - pytest.param(["true", "false", None], dtypes.BOOL_DTYPE, id="to_bool"), - pytest.param(['"str"', None], dtypes.STRING_DTYPE, id="to_string"), - pytest.param( - ['"str"', None], - dtypes.TIME_DTYPE, - id="invalid", - marks=pytest.mark.xfail(raises=TypeError), - ), - ], -) -def test_json_astype_others(data, to_type, errors): - bf_series = series.Series(data, dtype=dtypes.JSON_DTYPE) - - bf_result = bf_series.astype(to_type, errors=errors) - assert bf_result.dtype == to_type - - load_data = [json.loads(item) if item is not None else None for item in data] - expected = pd.Series(load_data, dtype=to_type) - expected.index = expected.index.astype("Int64") - pd.testing.assert_series_equal(bf_result.to_pandas(), expected) - - -@pytest.mark.skip( - reason="Failed: DID NOT RAISE " -) -@pytest.mark.parametrize( - ("data", "to_type"), - [ - pytest.param(["10.2", None], dtypes.INT_DTYPE, id="to_int"), - pytest.param(["false", None], dtypes.FLOAT_DTYPE, id="to_float"), - pytest.param(["10.2", None], dtypes.BOOL_DTYPE, id="to_bool"), - pytest.param(["true", None], dtypes.STRING_DTYPE, id="to_string"), - ], -) -def test_json_astype_others_raise_error(data, to_type): - bf_series = series.Series(data, dtype=dtypes.JSON_DTYPE) - with pytest.raises(google.api_core.exceptions.BadRequest): - bf_series.astype(to_type, errors="raise").to_pandas() - - -@pytest.mark.skip(reason="AssertionError: Series NA mask are different") -@pytest.mark.parametrize( - ("data", "to_type"), - [ - pytest.param(["10.2", None], dtypes.INT_DTYPE, id="to_int"), - pytest.param(["false", None], dtypes.FLOAT_DTYPE, id="to_float"), - pytest.param(["10.2", None], dtypes.BOOL_DTYPE, id="to_bool"), - pytest.param(["true", None], dtypes.STRING_DTYPE, id="to_string"), - ], -) -def test_json_astype_others_in_safe_mode(data, to_type): - bf_series = series.Series(data, dtype=dtypes.JSON_DTYPE) - bf_result = bf_series.astype(to_type, errors="null") - assert bf_result.dtype == to_type - - expected = pd.Series([None, None], dtype=to_type) - expected.index = expected.index.astype("Int64") - pd.testing.assert_series_equal(bf_result.to_pandas(), expected) - - -@pytest.mark.parametrize( - "index", - [0, 5, -2], -) -def test_iloc_single_integer(scalars_df_index, scalars_pandas_df_index, index): - bf_result = scalars_df_index.string_col.iloc[index] - pd_result = scalars_pandas_df_index.string_col.iloc[index] - - assert bf_result == pd_result - - -def test_iloc_single_integer_out_of_bound_error(scalars_df_index): - with pytest.raises(IndexError, match="single positional indexer is out-of-bounds"): - scalars_df_index.string_col.iloc[99] - - -def test_loc_bool_series_explicit_index(scalars_df_index, scalars_pandas_df_index): - bf_result = scalars_df_index.string_col.loc[scalars_df_index.bool_col].to_pandas() - pd_result = scalars_pandas_df_index.string_col.loc[scalars_pandas_df_index.bool_col] - - pd.testing.assert_series_equal( - bf_result, - pd_result, - ) - - -@pytest.mark.skip(reason="fixture 'scalars_pandas_df_default_index' not found") -def test_loc_bool_series_default_index( - scalars_df_default_index, scalars_pandas_df_default_index -): - bf_result = scalars_df_default_index.string_col.loc[ - scalars_df_default_index.bool_col - ].to_pandas() - pd_result = scalars_pandas_df_default_index.string_col.loc[ - scalars_pandas_df_default_index.bool_col - ] - - assert_frame_equal( - bf_result.to_frame(), - pd_result.to_frame(), - ) - - -def test_argmin(scalars_df_index, scalars_pandas_df_index): - bf_result = scalars_df_index.string_col.argmin() - pd_result = scalars_pandas_df_index.string_col.argmin() - assert bf_result == pd_result - - -def test_argmax(scalars_df_index, scalars_pandas_df_index): - bf_result = scalars_df_index.int64_too.argmax() - pd_result = scalars_pandas_df_index.int64_too.argmax() - assert bf_result == pd_result - - -def test_series_idxmin(scalars_df_index, scalars_pandas_df_index): - bf_result = scalars_df_index.string_col.idxmin() - pd_result = scalars_pandas_df_index.string_col.idxmin() - assert bf_result == pd_result - - -def test_series_idxmax(scalars_df_index, scalars_pandas_df_index): - bf_result = scalars_df_index.int64_too.idxmax() - pd_result = scalars_pandas_df_index.int64_too.idxmax() - assert bf_result == pd_result - - -def test_getattr_attribute_error_when_pandas_has(scalars_df_index): - # asof is implemented in pandas but not in bigframes - with pytest.raises(AttributeError): - scalars_df_index.string_col.asof() - - -def test_getattr_attribute_error(scalars_df_index): - with pytest.raises(AttributeError): - scalars_df_index.string_col.not_a_method() - - -def test_rename(scalars_df_index, scalars_pandas_df_index): - bf_result = scalars_df_index.string_col.rename("newname") - pd_result = scalars_pandas_df_index.string_col.rename("newname") - - pd.testing.assert_series_equal( - bf_result.to_pandas(), - pd_result, - ) - - -def test_rename_nonstring(scalars_df_index, scalars_pandas_df_index): - bf_result = scalars_df_index.string_col.rename((4, 2)) - pd_result = scalars_pandas_df_index.string_col.rename((4, 2)) - - pd.testing.assert_series_equal( - bf_result.to_pandas(), - pd_result, - ) - - -def test_rename_dict_same_type(scalars_df_index, scalars_pandas_df_index): - bf_result = scalars_df_index.string_col.rename({1: 100, 2: 200}) - pd_result = scalars_pandas_df_index.string_col.rename({1: 100, 2: 200}) - - pd_result.index = pd_result.index.astype("Int64") - - pd.testing.assert_series_equal( - bf_result.to_pandas(), - pd_result, - ) - - -def test_rename_axis(scalars_df_index, scalars_pandas_df_index): - bf_result = scalars_df_index.string_col.rename_axis("newindexname") - pd_result = scalars_pandas_df_index.string_col.rename_axis("newindexname") - - pd.testing.assert_series_equal( - bf_result.to_pandas(), - pd_result, - ) - - -def test_loc_list_string_index(scalars_df_index, scalars_pandas_df_index): - index_list = scalars_pandas_df_index.string_col.iloc[[0, 1, 1, 5]].values - - scalars_df_index = scalars_df_index.set_index("string_col", drop=False) - scalars_pandas_df_index = scalars_pandas_df_index.set_index( - "string_col", drop=False - ) - - bf_result = scalars_df_index.string_col.loc[index_list] - pd_result = scalars_pandas_df_index.string_col.loc[index_list] - - pd.testing.assert_series_equal( - bf_result.to_pandas(), - pd_result, - ) - - -def test_loc_list_integer_index(scalars_df_index, scalars_pandas_df_index): - index_list = [3, 2, 1, 3, 2, 1] - - bf_result = scalars_df_index.bool_col.loc[index_list] - pd_result = scalars_pandas_df_index.bool_col.loc[index_list] - - pd.testing.assert_series_equal( - bf_result.to_pandas(), - pd_result, - ) - - -def test_loc_list_multiindex(scalars_df_index, scalars_pandas_df_index): - scalars_df_multiindex = scalars_df_index.set_index(["string_col", "int64_col"]) - scalars_pandas_df_multiindex = scalars_pandas_df_index.set_index( - ["string_col", "int64_col"] - ) - index_list = [("Hello, World!", -234892), ("Hello, World!", 123456789)] - - bf_result = scalars_df_multiindex.int64_too.loc[index_list] - pd_result = scalars_pandas_df_multiindex.int64_too.loc[index_list] - - pd.testing.assert_series_equal( - bf_result.to_pandas(), - pd_result, - ) - - -def test_iloc_list(scalars_df_index, scalars_pandas_df_index): - index_list = [0, 0, 0, 5, 4, 7] - - bf_result = scalars_df_index.string_col.iloc[index_list] - pd_result = scalars_pandas_df_index.string_col.iloc[index_list] - - pd.testing.assert_series_equal( - bf_result.to_pandas(), - pd_result, - ) - - -def test_iloc_list_nameless(scalars_df_index, scalars_pandas_df_index): - index_list = [0, 0, 0, 5, 4, 7] - - bf_series = scalars_df_index.string_col.rename(None) - bf_result = bf_series.iloc[index_list] - pd_series = scalars_pandas_df_index.string_col.rename(None) - pd_result = pd_series.iloc[index_list] - - pd.testing.assert_series_equal( - bf_result.to_pandas(), - pd_result, - ) - - -def test_loc_list_nameless(scalars_df_index, scalars_pandas_df_index): - index_list = [0, 0, 0, 5, 4, 7] - - bf_series = scalars_df_index.string_col.rename(None) - bf_result = bf_series.loc[index_list] - - pd_series = scalars_pandas_df_index.string_col.rename(None) - pd_result = pd_series.loc[index_list] - - pd.testing.assert_series_equal( - bf_result.to_pandas(), - pd_result, - ) - - -def test_loc_bf_series_string_index(scalars_df_index, scalars_pandas_df_index): - pd_string_series = scalars_pandas_df_index.string_col.iloc[[0, 5, 1, 1, 5]] - bf_string_series = scalars_df_index.string_col.iloc[[0, 5, 1, 1, 5]] - - scalars_df_index = scalars_df_index.set_index("string_col") - scalars_pandas_df_index = scalars_pandas_df_index.set_index("string_col") - - bf_result = scalars_df_index.date_col.loc[bf_string_series] - pd_result = scalars_pandas_df_index.date_col.loc[pd_string_series] - - pd.testing.assert_series_equal( - bf_result.to_pandas(), - pd_result, - ) - - -def test_loc_bf_series_multiindex(scalars_df_index, scalars_pandas_df_index): - pd_string_series = scalars_pandas_df_index.string_col.iloc[[0, 5, 1, 1, 5]] - bf_string_series = scalars_df_index.string_col.iloc[[0, 5, 1, 1, 5]] - - scalars_df_multiindex = scalars_df_index.set_index(["string_col", "int64_col"]) - scalars_pandas_df_multiindex = scalars_pandas_df_index.set_index( - ["string_col", "int64_col"] - ) - - bf_result = scalars_df_multiindex.int64_too.loc[bf_string_series] - pd_result = scalars_pandas_df_multiindex.int64_too.loc[pd_string_series] - - pd.testing.assert_series_equal( - bf_result.to_pandas(), - pd_result, - ) - - -def test_loc_bf_index_integer_index(scalars_df_index, scalars_pandas_df_index): - pd_index = scalars_pandas_df_index.iloc[[0, 5, 1, 1, 5]].index - bf_index = scalars_df_index.iloc[[0, 5, 1, 1, 5]].index - - bf_result = scalars_df_index.date_col.loc[bf_index] - pd_result = scalars_pandas_df_index.date_col.loc[pd_index] - - pd.testing.assert_series_equal( - bf_result.to_pandas(), - pd_result, - ) - - -def test_loc_single_index_with_duplicate(scalars_df_index, scalars_pandas_df_index): - scalars_df_index = scalars_df_index.set_index("string_col", drop=False) - scalars_pandas_df_index = scalars_pandas_df_index.set_index( - "string_col", drop=False - ) - index = "Hello, World!" - bf_result = scalars_df_index.date_col.loc[index] - pd_result = scalars_pandas_df_index.date_col.loc[index] - pd.testing.assert_series_equal( - bf_result.to_pandas(), - pd_result, - ) - - -def test_loc_single_index_no_duplicate(scalars_df_index, scalars_pandas_df_index): - scalars_df_index = scalars_df_index.set_index("int64_too", drop=False) - scalars_pandas_df_index = scalars_pandas_df_index.set_index("int64_too", drop=False) - index = -2345 - bf_result = scalars_df_index.date_col.loc[index] - pd_result = scalars_pandas_df_index.date_col.loc[index] - assert bf_result == pd_result - - -def test_series_bool_interpretation_error(scalars_df_index): - with pytest.raises(ValueError): - True if scalars_df_index["string_col"] else False - - -@pytest.mark.skip( - reason="NotImplementedError: dry_run not implemented for this executor" -) -def test_query_job_setters(scalars_dfs): - # if allow_large_results=False, might not create query job - with bigframes.option_context("compute.allow_large_results", True): - job_ids = set() - df, _ = scalars_dfs - series = df["int64_col"] - assert series.query_job is not None - repr(series) - job_ids.add(series.query_job.job_id) - series.to_pandas() - job_ids.add(series.query_job.job_id) - assert len(job_ids) == 2 - - -@pytest.mark.parametrize( - ("series_input",), - [ - ([1, 2, 3, 4, 5],), - ([1, 1, 3, 5, 5],), - ([1, pd.NA, 4, 5, 5],), - ([1, 3, 2, 5, 4],), - ([pd.NA, pd.NA],), - ([1, 1, 1, 1, 1],), - ], -) -def test_is_monotonic_increasing(series_input): - scalars_df = series.Series(series_input, dtype=pd.Int64Dtype()) - scalars_pandas_df = pd.Series(series_input, dtype=pd.Int64Dtype()) - assert ( - scalars_df.is_monotonic_increasing == scalars_pandas_df.is_monotonic_increasing - ) - - -@pytest.mark.parametrize( - ("series_input",), - [ - ([1],), - ([5, 4, 3, 2, 1],), - ([5, 5, 3, 1, 1],), - ([1, pd.NA, 4, 5, 5],), - ([5, pd.NA, 4, 2, 1],), - ([1, 1, 1, 1, 1],), - ], -) -def test_is_monotonic_decreasing(series_input): - scalars_df = series.Series(series_input) - scalars_pandas_df = pd.Series(series_input) - assert ( - scalars_df.is_monotonic_decreasing == scalars_pandas_df.is_monotonic_decreasing - ) - - -def test_map_dict_input(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - - local_map = dict() - # construct a local map, incomplete to cover behavior - for s in scalars_pandas_df.string_col[:-3]: - if isinstance(s, str): - local_map[s] = ord(s[0]) - - pd_result = scalars_pandas_df.string_col.map(local_map) - pd_result = pd_result.astype("Int64") # pandas type differences - bf_result = scalars_df.string_col.map(local_map) - - pd.testing.assert_series_equal( - bf_result.to_pandas(), - pd_result, - ) - - -def test_map_series_input(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - - new_index = scalars_pandas_df.int64_too.drop_duplicates() - pd_map_series = scalars_pandas_df.string_col.iloc[0 : len(new_index)] - pd_map_series.index = new_index - bf_map_series = series.Series( - pd_map_series, session=scalars_df._get_block().expr.session - ) - - pd_result = scalars_pandas_df.int64_too.map(pd_map_series) - bf_result = scalars_df.int64_too.map(bf_map_series) - - pd.testing.assert_series_equal( - bf_result.to_pandas(), - pd_result, - ) - - -def test_map_series_input_duplicates_error(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - - new_index = scalars_pandas_df.int64_too - pd_map_series = scalars_pandas_df.string_col.iloc[0 : len(new_index)] - pd_map_series.index = new_index - bf_map_series = series.Series( - pd_map_series, session=scalars_df._get_block().expr.session - ) - - with pytest.raises(pd.errors.InvalidIndexError): - scalars_pandas_df.int64_too.map(pd_map_series) - with pytest.raises(pd.errors.InvalidIndexError): - scalars_df.int64_too.map(bf_map_series, verify_integrity=True) - - -def test_series_map_with_udf(session): - series = bpd.Series([1, 2, None, 4], dtype="Int64") - - @session.udf(input_types=[int], output_type=int) - def foo(x): - if x is None: - return -1 - return x * 2 - - bf_result = series.map(foo).to_pandas() - pd_result = pd.Series([2, 4, -1, 8]) - assert_series_equal(bf_result, pd_result, check_dtype=False) - - -@pytest.mark.skip( - reason="NotImplementedError: Polars compiler hasn't implemented hash()" -) -@pytest.mark.parametrize( - ("frac", "n", "random_state"), - [ - (None, 4, None), - (0.5, None, None), - (None, 4, 10), - (0.5, None, 10), - (None, None, None), - ], - ids=[ - "n_wo_random_state", - "frac_wo_random_state", - "n_w_random_state", - "frac_w_random_state", - "n_default", - ], -) -def test_sample(scalars_dfs, frac, n, random_state): - scalars_df, _ = scalars_dfs - df = scalars_df.int64_col.sample(frac=frac, n=n, random_state=random_state) - bf_result = df.to_pandas() - - n = 1 if n is None else n - expected_sample_size = round(frac * scalars_df.shape[0]) if frac is not None else n - assert bf_result.shape[0] == expected_sample_size - - -def test_series_iter( - scalars_df_index, - scalars_pandas_df_index, -): - for bf_i, pd_i in zip( - scalars_df_index["int64_too"], scalars_pandas_df_index["int64_too"] - ): - assert bf_i == pd_i - - -@pytest.mark.parametrize( - ( - "col", - "lambda_", - ), - [ - pytest.param("int64_col", lambda x: x * x + x + 1), - pytest.param("int64_col", lambda x: x % 2 == 1), - pytest.param("string_col", lambda x: x + "_suffix"), - ], - ids=[ - "lambda_int_int", - "lambda_int_bool", - "lambda_str_str", - ], -) -def test_apply_lambda(scalars_dfs, col, lambda_): - scalars_df, scalars_pandas_df = scalars_dfs - - bf_col = scalars_df[col] - - # Can't be applied to BigFrames Series without by_row=False - with pytest.raises(ValueError, match="by_row=False"): - bf_col.apply(lambda_) - - bf_result = bf_col.apply(lambda_, by_row=False).to_pandas() - - pd_col = scalars_pandas_df[col] - if pd.__version__[:3] in ("2.2", "2.3") or pandas_major_version() >= 3: - pd_result = pd_col.apply(lambda_, by_row=False) - else: - pd_result = pd_col.apply(lambda_) - - # ignore dtype check, which are Int64 and object respectively - # Some columns implicitly convert to floating point. Use check_exact=False to ensure we're "close enough" - assert_series_equal( - bf_result, - pd_result, - check_dtype=False, - check_exact=False, - rtol=0.001, - nulls_are_nan=True, - ) - - -@pytest.mark.parametrize( - ("ufunc",), - [ - pytest.param(numpy.cos, id="cos"), - pytest.param(numpy.log, id="log"), - pytest.param(numpy.log10, id="log10"), - pytest.param(numpy.log1p, id="log1p"), - pytest.param(numpy.sqrt, id="sqrt"), - pytest.param(numpy.sin, id="sin"), - ], -) -def test_apply_numpy_ufunc(scalars_dfs, ufunc): - scalars_df, scalars_pandas_df = scalars_dfs - - bf_col = scalars_df["int64_col"] - - # Can't be applied to BigFrames Series without by_row=False - with pytest.raises(ValueError, match="by_row=False"): - bf_col.apply(ufunc) - - bf_result = bf_col.apply(ufunc, by_row=False).to_pandas() - - pd_col = scalars_pandas_df["int64_col"] - pd_result = pd_col.apply(ufunc) - - assert_series_equal(bf_result, pd_result) - - -@pytest.mark.parametrize( - ("ufunc",), - [ - pytest.param(math.log), - pytest.param(math.log10), - pytest.param(math.sin), - pytest.param(math.cos), - pytest.param(math.tan), - pytest.param(math.sinh), - pytest.param(math.cosh), - pytest.param(math.tanh), - pytest.param(math.asin), - pytest.param(math.acos), - pytest.param(math.atan), - pytest.param(abs), - ], -) -@pytest.mark.parametrize( - ("col",), - [pytest.param("float64_col"), pytest.param("int64_col")], -) -def test_series_apply_python_numeric_fns(scalars_dfs, ufunc, col): - scalars_df, scalars_pandas_df = scalars_dfs - - bf_col = scalars_df[col] - bf_result = bf_col.apply(ufunc).to_pandas() - - pd_col = scalars_pandas_df[col] - - def wrapped(x): - try: - return ufunc(x) - except ValueError: - return pd.NA - except OverflowError: - if ufunc == math.sinh and x < 0: - return float("-inf") - return float("inf") - - pd_result = pd_col.apply(wrapped) - - assert_series_equal(bf_result, pd_result, check_dtype=False, nulls_are_nan=True) - - -@pytest.mark.parametrize( - ("ufunc",), - [ - pytest.param(str.upper), - pytest.param(str.lower), - pytest.param(len), - ], -) -def test_series_apply_python_string_fns(scalars_dfs, ufunc): - scalars_df, scalars_pandas_df = scalars_dfs - - bf_col = scalars_df["string_col"] - bf_result = bf_col.apply(ufunc).to_pandas() - - pd_col = scalars_pandas_df["string_col"] - - def wrapped(x): - return ufunc(x) if isinstance(x, str) else None - - pd_result = pd_col.apply(wrapped) - - assert_series_equal(bf_result, pd_result, check_dtype=False) - - -@pytest.mark.parametrize( - ("ufunc",), - [ - pytest.param(numpy.add), - pytest.param(numpy.divide), - ], - ids=[ - "add", - "divide", - ], -) -def test_combine_series_ufunc(scalars_dfs, ufunc): - scalars_df, scalars_pandas_df = scalars_dfs - - bf_col = scalars_df["int64_col"].dropna() - bf_result = bf_col.combine(bf_col, ufunc).to_pandas() - - pd_col = scalars_pandas_df["int64_col"].dropna() - pd_result = pd_col.combine(pd_col, ufunc) - - assert_series_equal(bf_result, pd_result, check_dtype=False) - - -@pytest.mark.parametrize( - ("func",), - [ - pytest.param(operator.add), - pytest.param(operator.truediv), - ], - ids=[ - "add", - "divide", - ], -) -def test_combine_series_pyfunc(scalars_dfs, func): - scalars_df, scalars_pandas_df = scalars_dfs - - bf_col = scalars_df["int64_col"].dropna() - bf_result = bf_col.combine(bf_col, func).to_pandas() - - pd_col = scalars_pandas_df["int64_col"].dropna() - pd_result = pd_col.combine(pd_col, func) - - assert_series_equal(bf_result, pd_result, check_dtype=False) - - -def test_combine_scalar_ufunc(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - - bf_col = scalars_df["int64_col"].dropna() - bf_result = bf_col.combine(2.5, numpy.add).to_pandas() - - pd_col = scalars_pandas_df["int64_col"].dropna() - pd_result = pd_col.combine(2.5, numpy.add) - - assert_series_equal(bf_result, pd_result, check_dtype=False) - - -def test_apply_simple_udf(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - - def foo(x): - return x * x + 2 * x + 3 - - bf_col = scalars_df["int64_col"] - - # Can't be applied to BigFrames Series without by_row=False - with pytest.raises(ValueError, match="by_row=False"): - bf_col.apply(foo) - - bf_result = bf_col.apply(foo, by_row=False).to_pandas() - - pd_col = scalars_pandas_df["int64_col"] - - if pd.__version__[:3] in ("2.2", "2.3"): - pd_result = pd_col.apply(foo, by_row=False) - else: - pd_result = pd_col.apply(foo) - - # ignore dtype check, which are Int64 and object respectively - # Some columns implicitly convert to floating point. Use check_exact=False to ensure we're "close enough" - assert_series_equal( - bf_result, - pd_result, - check_dtype=False, - check_exact=False, - rtol=0.001, - nulls_are_nan=True, - ) - - -@pytest.mark.parametrize( - ("col", "lambda_", "exception"), - [ - pytest.param("int64_col", {1: 2, 3: 4}, ValueError), - pytest.param("int64_col", numpy.square, TypeError), - pytest.param("string_col", lambda x: x.capitalize(), AttributeError), - ], - ids=[ - "not_callable", - "numpy_ufunc", - "custom_lambda", - ], -) -def test_apply_not_supported(scalars_dfs, col, lambda_, exception): - scalars_df, _ = scalars_dfs - - bf_col = scalars_df[col] - with pytest.raises(exception): - bf_col.apply(lambda_, by_row=False) - - -def test_series_pipe( - scalars_df_index, - scalars_pandas_df_index, -): - column = "int64_too" - - def foo(x: int, y: int, df): - return (df + x) % y - - bf_result = ( - scalars_df_index[column] - .pipe((foo, "df"), x=7, y=9) - .pipe(lambda x: x**2) - .to_pandas() - ) - - pd_result = ( - scalars_pandas_df_index[column].pipe((foo, "df"), x=7, y=9).pipe(lambda x: x**2) - ) - - assert_series_equal(bf_result, pd_result) - - -@pytest.mark.parametrize( - ("data"), - [ - pytest.param([1, 2, 3], id="int"), - pytest.param([[1, 2, 3], [], numpy.nan, [3, 4]], id="int_array"), - pytest.param( - [["A", "AA", "AAA"], ["BB", "B"], numpy.nan, [], ["C"]], id="string_array" - ), - pytest.param( - [ - {"A": {"x": 1.0}, "B": "b"}, - {"A": {"y": 2.0}, "B": "bb"}, - {"A": {"z": 4.0}}, - {}, - numpy.nan, - ], - id="struct_array", - ), - ], -) -def test_series_explode(data): - s = bigframes.pandas.Series(data) - pd_s = s.to_pandas() - pd.testing.assert_series_equal( - s.explode().to_pandas(), - pd_s.explode(), - check_index_type=False, - check_dtype=False, - ) - - -@pytest.mark.parametrize( - ("index", "ignore_index"), - [ - pytest.param(None, True, id="default_index"), - pytest.param(None, False, id="ignore_default_index"), - pytest.param([5, 1, 3, 2], True, id="unordered_index"), - pytest.param([5, 1, 3, 2], False, id="ignore_unordered_index"), - pytest.param(["z", "x", "a", "b"], True, id="str_index"), - pytest.param(["z", "x", "a", "b"], False, id="ignore_str_index"), - pytest.param( - pd.Index(["z", "x", "a", "b"], name="idx"), True, id="str_named_index" - ), - pytest.param( - pd.Index(["z", "x", "a", "b"], name="idx"), - False, - id="ignore_str_named_index", - ), - pytest.param( - pd.MultiIndex.from_frame( - pd.DataFrame({"idx0": [5, 1, 3, 2], "idx1": ["z", "x", "a", "b"]}) - ), - True, - id="multi_index", - ), - pytest.param( - pd.MultiIndex.from_frame( - pd.DataFrame({"idx0": [5, 1, 3, 2], "idx1": ["z", "x", "a", "b"]}) - ), - False, - id="ignore_multi_index", - ), - ], -) -def test_series_explode_w_index(index, ignore_index): - data = [[], [200.0, 23.12], [4.5, -9.0], [1.0]] - s = bigframes.pandas.Series(data, index=index) - pd_s = pd.Series(data, index=index) - # TODO(b/340885567): fix type error - assert_series_equal( - s.explode(ignore_index=ignore_index).to_pandas(), # type: ignore - pd_s.explode(ignore_index=ignore_index).astype(pd.Float64Dtype()), # type: ignore - check_index_type=False, - ) - - -@pytest.mark.parametrize( - ("ignore_index", "ordered"), - [ - pytest.param(True, True, id="include_index_ordered"), - pytest.param(True, False, id="include_index_unordered"), - pytest.param(False, True, id="ignore_index_ordered"), - ], -) -def test_series_explode_reserve_order(ignore_index, ordered): - data = [numpy.random.randint(0, 10, 10) for _ in range(10)] - s = bigframes.pandas.Series(data) - pd_s = pd.Series(data) - - # TODO(b/340885567): fix type error - res = s.explode(ignore_index=ignore_index).to_pandas(ordered=ordered) # type: ignore - # TODO(b/340885567): fix type error - pd_res = pd_s.explode(ignore_index=ignore_index).astype(pd.Int64Dtype()) # type: ignore - pd_res.index = pd_res.index.astype(pd.Int64Dtype()) - pd.testing.assert_series_equal( - res if ordered else res.sort_index(), - pd_res, - ) - - -def test_series_explode_w_aggregate(): - data = [[1, 2, 3], [], numpy.nan, [3, 4]] - s = bigframes.pandas.Series(data) - pd_s = pd.Series(data) - assert s.explode().sum() == pd_s.explode().sum() - - -def test_series_construct_empty_array(): - # TODO: supply a reason why this isn't compatible with pandas 1.x - pytest.importorskip("pandas", minversion="2.0.0") - s = bigframes.pandas.Series([[]]) - expected = pd.Series( - [[]], - dtype=pd.ArrowDtype(pa.list_(pa.float64())), - index=pd.Index([0], dtype=pd.Int64Dtype()), - ) - pd.testing.assert_series_equal( - expected, - s.to_pandas(), - ) - - -@pytest.mark.parametrize( - ("data"), - [ - pytest.param(numpy.nan, id="null"), - pytest.param([numpy.nan], id="null_array"), - pytest.param([[]], id="empty_array"), - pytest.param([numpy.nan, []], id="null_and_empty_array"), - ], -) -def test_series_explode_null(data): - s = bigframes.pandas.Series(data) - pd.testing.assert_series_equal( - s.explode().to_pandas(), - s.to_pandas().explode(), - check_dtype=False, - ) - - -@pytest.mark.skip( - reason="NotImplementedError: Polars compiler hasn't implemented IntegerLabelToDatetimeOp(freq=<75 * Days>, label=None, origin='start_day')" -) -@pytest.mark.parametrize( - ("append", "level", "col", "rule"), - [ - pytest.param(False, None, "timestamp_col", "75D"), - pytest.param(True, 1, "timestamp_col", "25W"), - pytest.param(False, None, "datetime_col", "3ME"), - pytest.param(True, "timestamp_col", "timestamp_col", "1YE"), - ], -) -def test_resample(scalars_df_index, scalars_pandas_df_index, append, level, col, rule): - # TODO: supply a reason why this isn't compatible with pandas 1.x - pytest.importorskip("pandas", minversion="2.0.0") - scalars_df_index = scalars_df_index.set_index(col, append=append)["int64_col"] - scalars_pandas_df_index = scalars_pandas_df_index.set_index(col, append=append)[ - "int64_col" - ] - bf_result = scalars_df_index.resample(rule=rule, level=level).min().to_pandas() - pd_result = scalars_pandas_df_index.resample(rule=rule, level=level).min() - pd.testing.assert_series_equal(bf_result, pd_result) - - -@pytest.mark.skip(reason="fixture 'nested_structs_df' not found") -def test_series_struct_get_field_by_attribute( - nested_structs_df, nested_structs_pandas_df -): - if Version(pd.__version__) < Version("2.2.0"): - pytest.skip("struct accessor is not supported before pandas 2.2") - - bf_series = nested_structs_df["person"] - df_series = nested_structs_pandas_df["person"] - - pd.testing.assert_series_equal( - bf_series.address.city.to_pandas(), - df_series.struct.field("address").struct.field("city"), - check_dtype=False, - check_index=False, - ) - pd.testing.assert_series_equal( - bf_series.address.country.to_pandas(), - df_series.struct.field("address").struct.field("country"), - check_dtype=False, - check_index=False, - ) - - -@pytest.mark.skip(reason="fixture 'nested_structs_df' not found") -def test_series_struct_fields_in_dir(nested_structs_df): - series = nested_structs_df["person"] - - assert "age" in dir(series) - assert "address" in dir(series) - assert "city" in dir(series.address) - assert "country" in dir(series.address) - - -@pytest.mark.skip(reason="fixture 'nested_structs_df' not found") -def test_series_struct_class_attributes_shadow_struct_fields(nested_structs_df): - series = nested_structs_df["person"] - - assert series.name == "person" - - -@pytest.mark.skip( - reason="NotImplementedError: dry_run not implemented for this executor" -) -def test_series_to_pandas_dry_run(scalars_df_index): - bf_series = scalars_df_index["int64_col"] - - result = bf_series.to_pandas(dry_run=True) - - assert isinstance(result, pd.Series) - assert len(result) > 0 - - -def test_series_item(session): - # Test with a single item - bf_s_single = bigframes.pandas.Series([42], session=session) - pd_s_single = pd.Series([42]) - assert bf_s_single.item() == pd_s_single.item() - - -def test_series_item_with_multiple(session): - # Test with multiple items - bf_s_multiple = bigframes.pandas.Series([1, 2, 3], session=session) - pd_s_multiple = pd.Series([1, 2, 3]) - - try: - pd_s_multiple.item() - except ValueError as e: - expected_message = str(e) - else: - raise AssertionError("Expected ValueError from pandas, but didn't get one") - - with pytest.raises(ValueError, match=re.escape(expected_message)): - bf_s_multiple.item() - - -def test_series_item_with_empty(session): - # Test with an empty Series - bf_s_empty = bigframes.pandas.Series([], dtype="Int64", session=session) - pd_s_empty = pd.Series([], dtype="Int64") - - try: - pd_s_empty.item() - except ValueError as e: - expected_message = str(e) - else: - raise AssertionError("Expected ValueError from pandas, but didn't get one") - - with pytest.raises(ValueError, match=re.escape(expected_message)): - bf_s_empty.item() - - -def test_series_dt_total_seconds(scalars_df_index, scalars_pandas_df_index): - bf_result = scalars_df_index["duration_col"].dt.total_seconds().to_pandas() - - pd_result = scalars_pandas_df_index["duration_col"].dt.total_seconds() - - # Index will be object type in pandas, string type in bigframes, but same values - pd.testing.assert_series_equal( - bf_result, - pd_result, - check_index_type=False, - # bigframes uses Float64, newer pandas may use double[pyarrow] - check_dtype=False, - ) - - -def test_series_where_with_expression(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - s1 = scalars_df["float64_col"] - s2 = scalars_df["bool_col"] - - bf_result = s1.where(s2, bpd.col("bool_col")).to_pandas() - - s1_pd = scalars_pandas_df["float64_col"] - s2_pd = scalars_pandas_df["bool_col"] - - pd_result = s1_pd.where(s2_pd, s2_pd) - - pd.testing.assert_series_equal(bf_result, pd_result, check_dtype=False) - - -def test_series_expression_unbound_fails(scalars_dfs): - scalars_df, _ = scalars_dfs - s1 = scalars_df["float64_col"] - s2 = scalars_df["bool_col"] - - with pytest.raises(ValueError, match="remains unbound"): - s1.where(s2, bpd.col("non_existent_column")) - - -def test_series_where_with_expression_resolving_to_self(scalars_dfs): - scalars_df, scalars_pandas_df = scalars_dfs - s1 = scalars_df["float64_col"] - s2 = scalars_df["bool_col"] - - bf_result = s1.where(s2, bpd.col("float64_col")).to_pandas() - - s1_pd = scalars_pandas_df["float64_col"] - s2_pd = scalars_pandas_df["bool_col"] - - pd_result = s1_pd.where(s2_pd, s1_pd) - - pd.testing.assert_series_equal(bf_result, pd_result, check_dtype=False) diff --git a/tests/unit/test_series_struct.py b/tests/unit/test_series_struct.py deleted file mode 100644 index f99d5859a56..00000000000 --- a/tests/unit/test_series_struct.py +++ /dev/null @@ -1,138 +0,0 @@ -# Copyright 2025 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from __future__ import annotations - -import pathlib -from typing import TYPE_CHECKING, Generator - -import pandas as pd -import pandas.testing -import pyarrow as pa # type: ignore -import pytest - -import bigframes - -if TYPE_CHECKING: - from bigframes.testing import polars_session - -pytest.importorskip("polars") -pytest.importorskip("pandas", minversion="2.2.0") - -CURRENT_DIR = pathlib.Path(__file__).parent -DATA_DIR = CURRENT_DIR.parent / "data" - - -@pytest.fixture(scope="module", autouse=True) -def session() -> Generator[bigframes.Session, None, None]: - import bigframes.core.global_session - from bigframes.testing import polars_session - - session = polars_session.TestSession() - with bigframes.core.global_session._GlobalSessionContext(session): - yield session - - -@pytest.fixture -def struct_df(session: polars_session.TestSession): - pa_type = pa.struct( - [ - ("str_field", pa.string()), - ("int_field", pa.int64()), - ] - ) - return session.DataFrame( - { - "struct_col": pd.Series( - pa.array( - [ - { - "str_field": "my string", - "int_field": 1, - }, - { - "str_field": None, - "int_field": 2, - }, - { - "str_field": "another string", - "int_field": None, - }, - { - "str_field": "some string", - "int_field": 3, - }, - ], - pa_type, - ), - dtype=pd.ArrowDtype(pa_type), - ), - } - ) - - -@pytest.fixture -def struct_series(struct_df): - return struct_df["struct_col"] - - -def test_struct_dtypes(struct_series): - bf_series = struct_series - pd_series = struct_series.to_pandas() - assert isinstance(pd_series.dtype, pd.ArrowDtype) - - bf_result = bf_series.struct.dtypes - pd_result = pd_series.struct.dtypes - - pandas.testing.assert_series_equal(bf_result, pd_result) - - -@pytest.mark.parametrize( - ("field_name", "common_dtype"), - ( - ("str_field", "string[pyarrow]"), - ("int_field", "int64[pyarrow]"), - # TODO(tswast): Support referencing fields by number, too. - ), -) -def test_struct_field(struct_series, field_name, common_dtype): - bf_series = struct_series - pd_series = struct_series.to_pandas() - assert isinstance(pd_series.dtype, pd.ArrowDtype) - - bf_result = bf_series.struct.field(field_name).to_pandas() - pd_result = pd_series.struct.field(field_name) - - # TODO(tswast): if/when we support arrowdtype for int/string, we can remove - # this cast. - bf_result = bf_result.astype(common_dtype) - pd_result = pd_result.astype(common_dtype) - - pandas.testing.assert_series_equal(bf_result, pd_result) - - -def test_struct_explode(struct_series): - bf_series = struct_series - pd_series = struct_series.to_pandas() - assert isinstance(pd_series.dtype, pd.ArrowDtype) - - bf_result = bf_series.struct.explode().to_pandas() - pd_result = pd_series.struct.explode() - - pandas.testing.assert_frame_equal( - bf_result, - pd_result, - # TODO(tswast): remove if/when we support arrowdtype for int/string. - check_dtype=False, - ) diff --git a/third_party/bigframes_vendored/constants.py b/third_party/bigframes_vendored/constants.py deleted file mode 100644 index aa331483a9c..00000000000 --- a/third_party/bigframes_vendored/constants.py +++ /dev/null @@ -1,60 +0,0 @@ -# Copyright 2023 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -"""Constants used across BigQuery DataFrames and bigframes_vendored. - -This module should not depend on any others in the package. -""" - -import typing -from typing import Literal - -import bigframes_vendored.version - -FEEDBACK_LINK = ( - "Share your use case with the BigQuery DataFrames team at the " - "https://bit.ly/bigframes-feedback survey. " - f"You are currently running BigFrames version {bigframes_vendored.version.__version__}." -) - -ABSTRACT_METHOD_ERROR_MESSAGE = ( - "Abstract method. You have likely encountered a bug. " - "Please share this stacktrace and how you reached it with the BigQuery DataFrames team. " - f"{FEEDBACK_LINK}" -) - -WRITE_ENGINE_TEMPLATE = ( - "Can't use parsing engine={engine} with write_engine={write_engine}, which " -) -WRITE_ENGINE_REQUIRES_LOCAL_ENGINE_TEMPLATE = ( - WRITE_ENGINE_TEMPLATE + "requires a local parsing engine. " + FEEDBACK_LINK -) -WRITE_ENGINE_REQUIRES_BIGQUERY_ENGINE_TEMPLATE = ( - WRITE_ENGINE_TEMPLATE - + "requires the engine='bigquery' parsing engine. " - + FEEDBACK_LINK -) - -WriteEngineType = Literal[ - "default", - "bigquery_inline", - "bigquery_load", - "bigquery_streaming", - "bigquery_write", - "_deferred", -] -VALID_WRITE_ENGINES = typing.get_args(WriteEngineType) - -DEFAULT_SORT_KIND = "stable" -STABLE_SORT_KINDS = ("stable", "mergesort") diff --git a/third_party/bigframes_vendored/cpython/_pprint.py b/third_party/bigframes_vendored/cpython/_pprint.py index 62450985816..617c14df0d9 100644 --- a/third_party/bigframes_vendored/cpython/_pprint.py +++ b/third_party/bigframes_vendored/cpython/_pprint.py @@ -70,11 +70,11 @@ # - removed global get_config, set _changed_only=True # - replace is_scalar_nan with isinstance(x, numbers.Real) and math.isnan +from collections import OrderedDict import inspect import math import numbers import pprint -from collections import OrderedDict from bigframes.ml.base import BaseEstimator @@ -110,7 +110,6 @@ def has_changed(k, v): # try to avoid calling repr on nested estimators if isinstance(v, BaseEstimator) and v.__class__ != init_params[k].__class__: return True - # Use repr as a last resort. It may be expensive. def is_scalar_nan(x): return isinstance(x, numbers.Real) and math.isnan(x) diff --git a/third_party/bigframes_vendored/db_benchmark/LICENSE b/third_party/bigframes_vendored/db_benchmark/LICENSE deleted file mode 100644 index a612ad9813b..00000000000 --- a/third_party/bigframes_vendored/db_benchmark/LICENSE +++ /dev/null @@ -1,373 +0,0 @@ -Mozilla Public License Version 2.0 -================================== - -1. Definitions --------------- - -1.1. "Contributor" - means each individual or legal entity that creates, contributes to - the creation of, or owns Covered Software. - -1.2. "Contributor Version" - means the combination of the Contributions of others (if any) used - by a Contributor and that particular Contributor's Contribution. - -1.3. "Contribution" - means Covered Software of a particular Contributor. - -1.4. "Covered Software" - means Source Code Form to which the initial Contributor has attached - the notice in Exhibit A, the Executable Form of such Source Code - Form, and Modifications of such Source Code Form, in each case - including portions thereof. - -1.5. "Incompatible With Secondary Licenses" - means - - (a) that the initial Contributor has attached the notice described - in Exhibit B to the Covered Software; or - - (b) that the Covered Software was made available under the terms of - version 1.1 or earlier of the License, but not also under the - terms of a Secondary License. - -1.6. "Executable Form" - means any form of the work other than Source Code Form. - -1.7. "Larger Work" - means a work that combines Covered Software with other material, in - a separate file or files, that is not Covered Software. - -1.8. "License" - means this document. - -1.9. "Licensable" - means having the right to grant, to the maximum extent possible, - whether at the time of the initial grant or subsequently, any and - all of the rights conveyed by this License. - -1.10. "Modifications" - means any of the following: - - (a) any file in Source Code Form that results from an addition to, - deletion from, or modification of the contents of Covered - Software; or - - (b) any new file in Source Code Form that contains any Covered - Software. - -1.11. "Patent Claims" of a Contributor - means any patent claim(s), including without limitation, method, - process, and apparatus claims, in any patent Licensable by such - Contributor that would be infringed, but for the grant of the - License, by the making, using, selling, offering for sale, having - made, import, or transfer of either its Contributions or its - Contributor Version. - -1.12. "Secondary License" - means either the GNU General Public License, Version 2.0, the GNU - Lesser General Public License, Version 2.1, the GNU Affero General - Public License, Version 3.0, or any later versions of those - licenses. - -1.13. "Source Code Form" - means the form of the work preferred for making modifications. - -1.14. "You" (or "Your") - means an individual or a legal entity exercising rights under this - License. For legal entities, "You" includes any entity that - controls, is controlled by, or is under common control with You. For - purposes of this definition, "control" means (a) the power, direct - or indirect, to cause the direction or management of such entity, - whether by contract or otherwise, or (b) ownership of more than - fifty percent (50%) of the outstanding shares or beneficial - ownership of such entity. - -2. License Grants and Conditions --------------------------------- - -2.1. Grants - -Each Contributor hereby grants You a world-wide, royalty-free, -non-exclusive license: - -(a) under intellectual property rights (other than patent or trademark) - Licensable by such Contributor to use, reproduce, make available, - modify, display, perform, distribute, and otherwise exploit its - Contributions, either on an unmodified basis, with Modifications, or - as part of a Larger Work; and - -(b) under Patent Claims of such Contributor to make, use, sell, offer - for sale, have made, import, and otherwise transfer either its - Contributions or its Contributor Version. - -2.2. Effective Date - -The licenses granted in Section 2.1 with respect to any Contribution -become effective for each Contribution on the date the Contributor first -distributes such Contribution. - -2.3. Limitations on Grant Scope - -The licenses granted in this Section 2 are the only rights granted under -this License. No additional rights or licenses will be implied from the -distribution or licensing of Covered Software under this License. -Notwithstanding Section 2.1(b) above, no patent license is granted by a -Contributor: - -(a) for any code that a Contributor has removed from Covered Software; - or - -(b) for infringements caused by: (i) Your and any other third party's - modifications of Covered Software, or (ii) the combination of its - Contributions with other software (except as part of its Contributor - Version); or - -(c) under Patent Claims infringed by Covered Software in the absence of - its Contributions. - -This License does not grant any rights in the trademarks, service marks, -or logos of any Contributor (except as may be necessary to comply with -the notice requirements in Section 3.4). - -2.4. Subsequent Licenses - -No Contributor makes additional grants as a result of Your choice to -distribute the Covered Software under a subsequent version of this -License (see Section 10.2) or under the terms of a Secondary License (if -permitted under the terms of Section 3.3). - -2.5. Representation - -Each Contributor represents that the Contributor believes its -Contributions are its original creation(s) or it has sufficient rights -to grant the rights to its Contributions conveyed by this License. - -2.6. Fair Use - -This License is not intended to limit any rights You have under -applicable copyright doctrines of fair use, fair dealing, or other -equivalents. - -2.7. Conditions - -Sections 3.1, 3.2, 3.3, and 3.4 are conditions of the licenses granted -in Section 2.1. - -3. Responsibilities -------------------- - -3.1. Distribution of Source Form - -All distribution of Covered Software in Source Code Form, including any -Modifications that You create or to which You contribute, must be under -the terms of this License. You must inform recipients that the Source -Code Form of the Covered Software is governed by the terms of this -License, and how they can obtain a copy of this License. You may not -attempt to alter or restrict the recipients' rights in the Source Code -Form. - -3.2. Distribution of Executable Form - -If You distribute Covered Software in Executable Form then: - -(a) such Covered Software must also be made available in Source Code - Form, as described in Section 3.1, and You must inform recipients of - the Executable Form how they can obtain a copy of such Source Code - Form by reasonable means in a timely manner, at a charge no more - than the cost of distribution to the recipient; and - -(b) You may distribute such Executable Form under the terms of this - License, or sublicense it under different terms, provided that the - license for the Executable Form does not attempt to limit or alter - the recipients' rights in the Source Code Form under this License. - -3.3. Distribution of a Larger Work - -You may create and distribute a Larger Work under terms of Your choice, -provided that You also comply with the requirements of this License for -the Covered Software. If the Larger Work is a combination of Covered -Software with a work governed by one or more Secondary Licenses, and the -Covered Software is not Incompatible With Secondary Licenses, this -License permits You to additionally distribute such Covered Software -under the terms of such Secondary License(s), so that the recipient of -the Larger Work may, at their option, further distribute the Covered -Software under the terms of either this License or such Secondary -License(s). - -3.4. Notices - -You may not remove or alter the substance of any license notices -(including copyright notices, patent notices, disclaimers of warranty, -or limitations of liability) contained within the Source Code Form of -the Covered Software, except that You may alter any license notices to -the extent required to remedy known factual inaccuracies. - -3.5. Application of Additional Terms - -You may choose to offer, and to charge a fee for, warranty, support, -indemnity or liability obligations to one or more recipients of Covered -Software. However, You may do so only on Your own behalf, and not on -behalf of any Contributor. You must make it absolutely clear that any -such warranty, support, indemnity, or liability obligation is offered by -You alone, and You hereby agree to indemnify every Contributor for any -liability incurred by such Contributor as a result of warranty, support, -indemnity or liability terms You offer. You may include additional -disclaimers of warranty and limitations of liability specific to any -jurisdiction. - -4. Inability to Comply Due to Statute or Regulation ---------------------------------------------------- - -If it is impossible for You to comply with any of the terms of this -License with respect to some or all of the Covered Software due to -statute, judicial order, or regulation then You must: (a) comply with -the terms of this License to the maximum extent possible; and (b) -describe the limitations and the code they affect. Such description must -be placed in a text file included with all distributions of the Covered -Software under this License. Except to the extent prohibited by statute -or regulation, such description must be sufficiently detailed for a -recipient of ordinary skill to be able to understand it. - -5. Termination --------------- - -5.1. The rights granted under this License will terminate automatically -if You fail to comply with any of its terms. However, if You become -compliant, then the rights granted under this License from a particular -Contributor are reinstated (a) provisionally, unless and until such -Contributor explicitly and finally terminates Your grants, and (b) on an -ongoing basis, if such Contributor fails to notify You of the -non-compliance by some reasonable means prior to 60 days after You have -come back into compliance. Moreover, Your grants from a particular -Contributor are reinstated on an ongoing basis if such Contributor -notifies You of the non-compliance by some reasonable means, this is the -first time You have received notice of non-compliance with this License -from such Contributor, and You become compliant prior to 30 days after -Your receipt of the notice. - -5.2. If You initiate litigation against any entity by asserting a patent -infringement claim (excluding declaratory judgment actions, -counter-claims, and cross-claims) alleging that a Contributor Version -directly or indirectly infringes any patent, then the rights granted to -You by any and all Contributors for the Covered Software under Section -2.1 of this License shall terminate. - -5.3. In the event of termination under Sections 5.1 or 5.2 above, all -end user license agreements (excluding distributors and resellers) which -have been validly granted by You or Your distributors under this License -prior to termination shall survive termination. - -************************************************************************ -* * -* 6. Disclaimer of Warranty * -* ------------------------- * -* * -* Covered Software is provided under this License on an "as is" * -* basis, without warranty of any kind, either expressed, implied, or * -* statutory, including, without limitation, warranties that the * -* Covered Software is free of defects, merchantable, fit for a * -* particular purpose or non-infringing. The entire risk as to the * -* quality and performance of the Covered Software is with You. * -* Should any Covered Software prove defective in any respect, You * -* (not any Contributor) assume the cost of any necessary servicing, * -* repair, or correction. This disclaimer of warranty constitutes an * -* essential part of this License. No use of any Covered Software is * -* authorized under this License except under this disclaimer. * -* * -************************************************************************ - -************************************************************************ -* * -* 7. Limitation of Liability * -* -------------------------- * -* * -* Under no circumstances and under no legal theory, whether tort * -* (including negligence), contract, or otherwise, shall any * -* Contributor, or anyone who distributes Covered Software as * -* permitted above, be liable to You for any direct, indirect, * -* special, incidental, or consequential damages of any character * -* including, without limitation, damages for lost profits, loss of * -* goodwill, work stoppage, computer failure or malfunction, or any * -* and all other commercial damages or losses, even if such party * -* shall have been informed of the possibility of such damages. This * -* limitation of liability shall not apply to liability for death or * -* personal injury resulting from such party's negligence to the * -* extent applicable law prohibits such limitation. Some * -* jurisdictions do not allow the exclusion or limitation of * -* incidental or consequential damages, so this exclusion and * -* limitation may not apply to You. * -* * -************************************************************************ - -8. Litigation -------------- - -Any litigation relating to this License may be brought only in the -courts of a jurisdiction where the defendant maintains its principal -place of business and such litigation shall be governed by laws of that -jurisdiction, without reference to its conflict-of-law provisions. -Nothing in this Section shall prevent a party's ability to bring -cross-claims or counter-claims. - -9. Miscellaneous ----------------- - -This License represents the complete agreement concerning the subject -matter hereof. If any provision of this License is held to be -unenforceable, such provision shall be reformed only to the extent -necessary to make it enforceable. Any law or regulation which provides -that the language of a contract shall be construed against the drafter -shall not be used to construe this License against a Contributor. - -10. Versions of the License ---------------------------- - -10.1. New Versions - -Mozilla Foundation is the license steward. Except as provided in Section -10.3, no one other than the license steward has the right to modify or -publish new versions of this License. Each version will be given a -distinguishing version number. - -10.2. Effect of New Versions - -You may distribute the Covered Software under the terms of the version -of the License under which You originally received the Covered Software, -or under the terms of any subsequent version published by the license -steward. - -10.3. Modified Versions - -If you create software not governed by this License, and you want to -create a new license for such software, you may create and use a -modified version of this License if you rename the license and remove -any references to the name of the license steward (except to note that -such modified license differs from this License). - -10.4. Distributing Source Code Form that is Incompatible With Secondary -Licenses - -If You choose to distribute Source Code Form that is Incompatible With -Secondary Licenses under the terms of this version of the License, the -notice described in Exhibit B of this License must be attached. - -Exhibit A - Source Code Form License Notice -------------------------------------------- - - This Source Code Form is subject to the terms of the Mozilla Public - License, v. 2.0. If a copy of the MPL was not distributed with this - file, You can obtain one at http://mozilla.org/MPL/2.0/. - -If it is not possible or desirable to put the notice in a particular -file, then You may include the notice in a location (such as a LICENSE -file in a relevant directory) where a recipient would be likely to look -for such a notice. - -You may add additional accurate notices of copyright ownership. - -Exhibit B - "Incompatible With Secondary Licenses" Notice ---------------------------------------------------------- - - This Source Code Form is "Incompatible With Secondary Licenses", as - defined by the Mozilla Public License, v. 2.0. diff --git a/third_party/bigframes_vendored/db_benchmark/METADATA b/third_party/bigframes_vendored/db_benchmark/METADATA deleted file mode 100644 index 6163ac69b79..00000000000 --- a/third_party/bigframes_vendored/db_benchmark/METADATA +++ /dev/null @@ -1,18 +0,0 @@ -name: "db-benchmark" -description: - "This repository contains a reproducible benchmarking suite for evaluating " - "database-like operations in single-node environments. It assesses " - "scalability across varying data volumes and complexities." - -third_party { - identifier { - type: "Git" - value: "https://github.com/h2oai/db-benchmark" - primary_source: true - version: "Latest Commit on Main Branch as of Access" - } - version: "Latest Commit on Main Branch as of Access" - last_upgrade_date { year: 2024 month: 7 day: 12 } - license_type: RECIPROCAL - local_modifications: "Modified the queries to test and benchmark the BigFrames project" -} diff --git a/third_party/bigframes_vendored/db_benchmark/README.md b/third_party/bigframes_vendored/db_benchmark/README.md deleted file mode 100644 index aba227b0ebf..00000000000 --- a/third_party/bigframes_vendored/db_benchmark/README.md +++ /dev/null @@ -1,76 +0,0 @@ -Repository for reproducible benchmarking of database-like operations in single-node environment. -Benchmark report is available at [h2oai.github.io/db-benchmark](https://h2oai.github.io/db-benchmark). -We focused mainly on portability and reproducibility. Benchmark is routinely re-run to present up-to-date timings. Most of solutions used are automatically upgraded to their stable or development versions. -This benchmark is meant to compare scalability both in data volume and data complexity. -Contribution and feedback are very welcome! - -# Tasks - - - [x] groupby - - [x] join - - [x] groupby2014 - -# Solutions - - - [x] [dask](https://github.com/dask/dask) - - [x] [data.table](https://github.com/Rdatatable/data.table) - - [x] [dplyr](https://github.com/tidyverse/dplyr) - - [x] [DataFrames.jl](https://github.com/JuliaData/DataFrames.jl) - - [x] [pandas](https://github.com/pandas-dev/pandas) - - [x] [(py)datatable](https://github.com/h2oai/datatable) - - [x] [spark](https://github.com/apache/spark) - - [x] [cuDF](https://github.com/rapidsai/cudf) - - [x] [ClickHouse](https://github.com/yandex/ClickHouse) - - [x] [Polars](https://github.com/ritchie46/polars) - - [x] [Arrow](https://github.com/apache/arrow) - - [x] [DuckDB](https://github.com/duckdb/duckdb) - -More solutions has been proposed. Status of those can be tracked in issues tracker of our project repository by using [_new solution_](https://github.com/h2oai/db-benchmark/issues?q=is%3Aissue+is%3Aopen+label%3A%22new+solution%22) label. - -# Reproduce - -## Batch benchmark run - -- edit `path.env` and set `julia` and `java` paths -- if solution uses python create new `virtualenv` as `$solution/py-$solution`, example for `pandas` use `virtualenv pandas/py-pandas --python=/usr/bin/python3.6` -- install every solution, follow `$solution/setup-$solution.sh` scripts -- edit `run.conf` to define solutions and tasks to benchmark -- generate data, for `groupby` use `Rscript _data/groupby-datagen.R 1e7 1e2 0 0` to create `G1_1e7_1e2_0_0.csv`, re-save to binary format where needed (see below), create `data` directory and keep all data files there -- edit `_control/data.csv` to define data sizes to benchmark using `active` flag -- ensure SWAP is disabled and ClickHouse server is not yet running -- start benchmark with `./run.sh` - -## Single solution benchmark - -- install solution software - - for python we recommend to use `virtualenv` for better isolation - - for R ensure that library is installed in a solution subdirectory, so that `library("dplyr", lib.loc="./dplyr/r-dplyr")` or `library("data.table", lib.loc="./datatable/r-datatable")` works - - note that some solutions may require another to be installed to speed-up csv data load, for example, `dplyr` requires `data.table` and similarly `pandas` requires (py)`datatable` -- generate data using `_data/*-datagen.R` scripts, for example, `Rscript _data/groupby-datagen.R 1e7 1e2 0 0` creates `G1_1e7_1e2_0_0.csv`, put data files in `data` directory -- run benchmark for a single solution using `./_launcher/solution.R --solution=data.table --task=groupby --nrow=1e7` -- run other data cases by passing extra parameters `--k=1e2 --na=0 --sort=0` -- use `--quiet=true` to suppress script's output and print timings only, using `--print=question,run,time_sec` specify columns to be printed to console, to print all use `--print=*` -- use `--out=time.csv` to write timings to a file rather than console - -## Running script interactively - -- install software in expected location, details above -- ensure data name to be used in env var below is present in `./data` dir -- source python virtual environment if needed -- call `SRC_DATANAME=G1_1e7_1e2_0_0 R`, if desired replace `R` with `python` or `julia` -- proceed pasting code from benchmark script - -## Extra care needed - -- `cudf` uses `conda` instead of `virtualenv` - -# Example environment - -- setting up r3-8xlarge: 244GB RAM, 32 cores: [Amazon EC2 for beginners](https://github.com/Rdatatable/data.table/wiki/Amazon-EC2-for-beginners) -- (slightly outdated) full reproduce script on clean Ubuntu 16.04: [_utils/repro.sh](https://github.com/h2oai/db-benchmark/blob/master/_utils/repro.sh) - -# Acknowledgment - -Timings for some solutions might be missing for particular data sizes or questions. Some functions are not yet implemented in all solutions so we were unable to answer all questions in all solutions. Some solutions might also run out of memory when running benchmark script which results the process to be killed by OS. Lastly we also added timeout for single benchmark script to run, once timeout value is reached script is terminated. -Please check [_exceptions_](https://github.com/h2oai/db-benchmark/issues?q=is%3Aissue+is%3Aopen+label%3Aexceptions) label in our repository for a list of issues/defects in solutions, that makes us unable to provide all timings. -There is also [_no documentation_](https://github.com/h2oai/db-benchmark/labels/no%20documentation) label that lists issues that are blocked by missing documentation in solutions we are benchmarking. diff --git a/third_party/bigframes_vendored/db_benchmark/__init__.py b/third_party/bigframes_vendored/db_benchmark/__init__.py deleted file mode 100644 index e69de29bb2d..00000000000 diff --git a/third_party/bigframes_vendored/db_benchmark/groupby_queries.py b/third_party/bigframes_vendored/db_benchmark/groupby_queries.py deleted file mode 100644 index 7758496db59..00000000000 --- a/third_party/bigframes_vendored/db_benchmark/groupby_queries.py +++ /dev/null @@ -1,123 +0,0 @@ -# Contains code from https://github.com/duckdblabs/db-benchmark/blob/master/pandas/groupby-pandas.py - -import bigframes -import bigframes.session - - -def q1(project_id: str, dataset_id: str, table_id: str, session: bigframes.Session): - print("Groupby benchmark 1: sum v1 by id1") - - x = session.read_gbq(f"{project_id}.{dataset_id}.{table_id}") - - ans = x.groupby("id1", as_index=False, dropna=False).agg({"v1": "sum"}) - print(ans.shape) - chk = [ans["v1"].sum()] - print(chk) - - -def q2(project_id: str, dataset_id: str, table_id: str, session: bigframes.Session): - print("Groupby benchmark 2: sum v1 by id1:id2") - - x = session.read_gbq(f"{project_id}.{dataset_id}.{table_id}") - - ans = x.groupby(["id1", "id2"], as_index=False, dropna=False).agg({"v1": "sum"}) - print(ans.shape) - chk = [ans["v1"].sum()] - print(chk) - - -def q3(project_id: str, dataset_id: str, table_id: str, session: bigframes.Session): - print("Groupby benchmark 3: sum v1 mean v3 by id3") - - x = session.read_gbq(f"{project_id}.{dataset_id}.{table_id}") - - ans = x.groupby("id3", as_index=False, dropna=False).agg( - {"v1": "sum", "v3": "mean"} - ) - print(ans.shape) - chk = [ans["v1"].sum(), ans["v3"].sum()] - print(chk) - - -def q4(project_id: str, dataset_id: str, table_id: str, session: bigframes.Session): - print("Groupby benchmark 4: mean v1:v3 by id4") - - x = session.read_gbq(f"{project_id}.{dataset_id}.{table_id}") - - ans = x.groupby("id4", as_index=False, dropna=False).agg( - {"v1": "mean", "v2": "mean", "v3": "mean"} - ) - print(ans.shape) - chk = [ans["v1"].sum(), ans["v2"].sum(), ans["v3"].sum()] - print(chk) - - -def q5(project_id: str, dataset_id: str, table_id: str, session: bigframes.Session): - print("Groupby benchmark 5: sum v1:v3 by id6") - - x = session.read_gbq(f"{project_id}.{dataset_id}.{table_id}") - - ans = x.groupby("id6", as_index=False, dropna=False).agg( - {"v1": "sum", "v2": "sum", "v3": "sum"} - ) - print(ans.shape) - chk = [ans["v1"].sum(), ans["v2"].sum(), ans["v3"].sum()] - print(chk) - - -def q6(project_id: str, dataset_id: str, table_id: str, session: bigframes.Session): - print("Groupby benchmark 6: median v3 sd v3 by id4 id5") - - x = session.read_gbq(f"{project_id}.{dataset_id}.{table_id}") - - ans = x.groupby(["id4", "id5"], as_index=False, dropna=False).agg( - {"v3": ["median", "std"]} - ) - print(ans.shape) - chk = [ans["v3"]["median"].sum(), ans["v3"]["std"].sum()] - print(chk) - - -def q7(project_id: str, dataset_id: str, table_id: str, session: bigframes.Session): - print("Groupby benchmark 7: max v1 - min v2 by id3") - - x = session.read_gbq(f"{project_id}.{dataset_id}.{table_id}") - - ans = ( - x.groupby("id3", as_index=False, dropna=False) - .agg({"v1": "max", "v2": "min"}) - .assign(range_v1_v2=lambda x: x["v1"] - x["v2"])[["id3", "range_v1_v2"]] - ) - print(ans.shape) - chk = [ans["range_v1_v2"].sum()] - print(chk) - - -def q8(project_id: str, dataset_id: str, table_id: str, session: bigframes.Session): - print("Groupby benchmark 8: largest two v3 by id6") - - x = session.read_gbq(f"{project_id}.{dataset_id}.{table_id}") - - ans = ( - x[~x["v3"].isna()][["id6", "v3"]] - .sort_values("v3", ascending=False) - .groupby("id6", as_index=False, dropna=False) - .head(2) - ) - ans = ans.reset_index(drop=True) - print(ans.shape) - chk = [ans["v3"].sum()] - print(chk) - - -def q10(project_id: str, dataset_id: str, table_id: str, session: bigframes.Session): - print("Groupby benchmark 10: sum v3 count by id1:id6") - - x = session.read_gbq(f"{project_id}.{dataset_id}.{table_id}") - - ans = x.groupby( - ["id1", "id2", "id3", "id4", "id5", "id6"], as_index=False, dropna=False - ).agg({"v3": "sum", "v1": "size"}) - print(ans.shape) - chk = [ans["v3"].sum(), ans["v1"].sum()] - print(chk) diff --git a/third_party/bigframes_vendored/db_benchmark/join_queries.py b/third_party/bigframes_vendored/db_benchmark/join_queries.py deleted file mode 100644 index f0073436c02..00000000000 --- a/third_party/bigframes_vendored/db_benchmark/join_queries.py +++ /dev/null @@ -1,91 +0,0 @@ -# Contains code from https://github.com/duckdblabs/db-benchmark/blob/master/pandas/join-pandas.py -# and https://github.com/duckdblabs/db-benchmark/blob/main/_helpers/helpers.py - -import bigframes - - -def q1(project_id: str, dataset_id: str, table_id: str, session: bigframes.Session): - print("Join benchmark 1: small inner on int") - - x = session.read_gbq(f"{project_id}.{dataset_id}.{table_id}") - small = session.read_gbq( - f"{project_id}.{dataset_id}.{_get_join_table_id(table_id, 'small')}" - ) - - ans = x.merge(small, on="id1") - print(ans.shape) - - chk = [ans["v1"].sum(), ans["v2"].sum()] - print(chk) - - -def q2(project_id: str, dataset_id: str, table_id: str, session: bigframes.Session): - print("Join benchmark 2: medium inner on int") - - x = session.read_gbq(f"{project_id}.{dataset_id}.{table_id}") - medium = session.read_gbq( - f"{project_id}.{dataset_id}.{_get_join_table_id(table_id, 'medium')}" - ) - - ans = x.merge(medium, on="id2") - print(ans.shape) - - chk = [ans["v1"].sum(), ans["v2"].sum()] - print(chk) - - -def q3(project_id: str, dataset_id: str, table_id: str, session: bigframes.Session): - print("Join benchmark 3: medium outer on int") - - x = session.read_gbq(f"{project_id}.{dataset_id}.{table_id}") - medium = session.read_gbq( - f"{project_id}.{dataset_id}.{_get_join_table_id(table_id, 'medium')}" - ) - - ans = x.merge(medium, how="left", on="id2") - print(ans.shape) - - chk = [ans["v1"].sum(), ans["v2"].sum()] - print(chk) - - -def q4(project_id: str, dataset_id: str, table_id: str, session: bigframes.Session): - print("Join benchmark 4: medium inner on factor") - - x = session.read_gbq(f"{project_id}.{dataset_id}.{table_id}") - medium = session.read_gbq( - f"{project_id}.{dataset_id}.{_get_join_table_id(table_id, 'medium')}" - ) - - ans = x.merge(medium, on="id5") - print(ans.shape) - - chk = [ans["v1"].sum(), ans["v2"].sum()] - print(chk) - - -def q5(project_id: str, dataset_id: str, table_id: str, session: bigframes.Session): - print("Join benchmark 5: big inner on int") - - x = session.read_gbq(f"{project_id}.{dataset_id}.{table_id}") - big = session.read_gbq( - f"{project_id}.{dataset_id}.{_get_join_table_id(table_id, 'big')}" - ) - - ans = x.merge(big, on="id3") - print(ans.shape) - - chk = [ans["v1"].sum(), ans["v2"].sum()] - print(chk) - - -def _get_join_table_id(table_id, join_size): - x_n = int(float(table_id.split("_")[1])) - - if join_size == "small": - y_n = "{:.0e}".format(x_n / 1e6) - elif join_size == "medium": - y_n = "{:.0e}".format(x_n / 1e3) - else: - y_n = "{:.0e}".format(x_n) - return table_id.replace("NA", y_n).replace("+0", "") diff --git a/third_party/bigframes_vendored/db_benchmark/sort_queries.py b/third_party/bigframes_vendored/db_benchmark/sort_queries.py deleted file mode 100644 index bbaf46cf279..00000000000 --- a/third_party/bigframes_vendored/db_benchmark/sort_queries.py +++ /dev/null @@ -1,18 +0,0 @@ -# Contains code from https://github.com/duckdblabs/db-benchmark/blob/master/pandas/sort-pandas.py - -import bigframes -import bigframes.session - - -def q1( - project_id: str, dataset_id: str, table_id: str, session: bigframes.Session -) -> None: - print("Sort benchmark 1: sort by int id2") - - x = session.read_gbq(f"{project_id}.{dataset_id}.{table_id}") - - ans = x.sort_values("id2") - print(ans.shape) - - chk = [ans["v1"].sum()] - print(chk) diff --git a/third_party/bigframes_vendored/geopandas/LICENSE.txt b/third_party/bigframes_vendored/geopandas/LICENSE.txt deleted file mode 100644 index 028603be208..00000000000 --- a/third_party/bigframes_vendored/geopandas/LICENSE.txt +++ /dev/null @@ -1,25 +0,0 @@ -Copyright (c) 2013-2022, GeoPandas developers. -All rights reserved. - -Redistribution and use in source and binary forms, with or without -modification, are permitted provided that the following conditions are met: - - * Redistributions of source code must retain the above copyright notice, this - list of conditions and the following disclaimer. - * Redistributions in binary form must reproduce the above copyright notice, - this list of conditions and the following disclaimer in the documentation - and/or other materials provided with the distribution. - * Neither the name of GeoPandas nor the names of its contributors may - be used to endorse or promote products derived from this software without - specific prior written permission. - -THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND -ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED -WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE -DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR -ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES -(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; -LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON -ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT -(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS -SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. diff --git a/third_party/bigframes_vendored/geopandas/geoseries.py b/third_party/bigframes_vendored/geopandas/geoseries.py deleted file mode 100644 index 6b56a318011..00000000000 --- a/third_party/bigframes_vendored/geopandas/geoseries.py +++ /dev/null @@ -1,528 +0,0 @@ -# contains code from https://github.com/geopandas/geopandas/blob/main/geopandas/geoseries.py -from __future__ import annotations - -from typing import TYPE_CHECKING - -from bigframes import constants - -if TYPE_CHECKING: - import bigframes.series - - -class GeoSeries: - """ - A Series object designed to store geometry objects. - - **Examples:** - - >>> import bigframes.geopandas - >>> import bigframes.pandas as bpd - >>> from shapely.geometry import Point - - >>> s = bigframes.geopandas.GeoSeries([Point(1, 1), Point(2, 2), Point(3, 3)]) - >>> s - 0 POINT (1 1) - 1 POINT (2 2) - 2 POINT (3 3) - dtype: geometry - - Args: - data (array-like, dict, scalar value, bigframes.pandas.Series): - The geometries to store in the GeoSeries. - index (array-like, pandas.Index, bigframes.pandas.Index): - The index for the GeoSeries. - kwargs (dict): - Additional arguments passed to the Series constructor, - e.g. ``name``. - """ - - # GeoSeries.area overrides Series.area with something totally different. - # Ignore this type error, as we are trying to be as close to geopandas as - # we can. - @property - def area(self, crs=None) -> bigframes.series.Series: # type: ignore - """[Not Implemented] Use ``bigframes.bigquery.st_area(series)``, - instead to return the area in square meters. - - In GeoPandas, this returns a Series containing the area of each geometry - in the GeoSeries expressed in the units of the CRS. - - Args: - crs (optional): - Coordinate Reference System of the geometry objects. Can be - anything accepted by pyproj.CRS.from_user_input(), such as an - authority string (eg “EPSG:4326”) or a WKT string. - - Returns: - bigframes.pandas.Series: - Series of float representing the areas. - - Raises: - NotImplementedError: - GeoSeries.area is not supported. Use bigframes.bigquery.st_area(series), instead. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - @property - def x(self) -> bigframes.series.Series: - """Return the x location of point geometries in a GeoSeries - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> import geopandas.array - >>> import shapely.geometry - - >>> series = bpd.Series( - ... [shapely.geometry.Point(1, 2), shapely.geometry.Point(2, 3), shapely.geometry.Point(3, 4)], - ... dtype=geopandas.array.GeometryDtype() - ... ) - >>> series.geo.x - 0 1.0 - 1 2.0 - 2 3.0 - dtype: Float64 - - Returns: - bigframes.pandas.Series: - Return the x location (longitude) of point geometries. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - @property - def y(self) -> bigframes.series.Series: - """Return the y location of point geometries in a GeoSeries - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> import geopandas.array - >>> import shapely.geometry - - >>> series = bpd.Series( - ... [shapely.geometry.Point(1, 2), shapely.geometry.Point(2, 3), shapely.geometry.Point(3, 4)], - ... dtype=geopandas.array.GeometryDtype() - ... ) - >>> series.geo.y - 0 2.0 - 1 3.0 - 2 4.0 - dtype: Float64 - - Returns: - bigframes.pandas.Series: - Return the y location (latitude) of point geometries. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - @property - def boundary(self) -> bigframes.geopandas.GeoSeries: - """ - Returns a GeoSeries of lower dimensional objects representing each - geometry's set-theoretic boundary. - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> import geopandas.array - >>> import shapely.geometry - - >>> from shapely.geometry import Polygon, LineString, Point - >>> s = geopandas.GeoSeries( - ... [ - ... Polygon([(0, 0), (1, 1), (0, 1)]), - ... LineString([(0, 0), (1, 1), (1, 0)]), - ... Point(0, 0), - ... ] - ... ) - >>> s - 0 POLYGON ((0 0, 1 1, 0 1, 0 0)) - 1 LINESTRING (0 0, 1 1, 1 0) - 2 POINT (0 0) - dtype: geometry - - >>> s.boundary - 0 LINESTRING (0 0, 1 1, 0 1, 0 0) - 1 MULTIPOINT ((0 0), (1 0)) - 2 GEOMETRYCOLLECTION EMPTY - dtype: geometry - - Returns: - bigframes.geopandas.GeoSeries: - A GeoSeries of lower dimensional objects representing each - geometry's set-theoretic boundary - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - @classmethod - def from_xy(cls, x, y, index=None, **kwargs) -> bigframes.geopandas.GeoSeries: - """ - Alternate constructor to create a GeoSeries of Point geometries from - lists or arrays of x, y coordinates. - - In case of geographic coordinates, it is assumed that longitude is - captured by x coordinates and latitude by y. - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> import bigframes.geopandas - - >>> x = [2.5, 5, -3.0] - >>> y = [0.5, 1, 1.5] - - >>> s = bigframes.geopandas.GeoSeries.from_xy(x, y) - >>> s - 0 POINT (2.5 0.5) - 1 POINT (5 1) - 2 POINT (-3 1.5) - dtype: geometry - - Args: - x, y (array-like): - longitude is x coordinates and latitude y coordinates. - - index (array-like or Index, optional): - The index for the GeoSeries. If not given and all coordinate - inputs are Series with an equal index, that index is used. - - **kwargs: - Additional arguments passed to the Series constructor, e.g. `name`. - - Returns: - bigframes.geopandas.GeoSeries: - A GeoSeries of Point geometries. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - @classmethod - def from_wkt(cls, data, index=None) -> bigframes.geopandas.GeoSeries: - """ - Alternate constructor to create a GeoSeries from a list or array of - WKT objects. - - **Examples:** - - >>> import bigframes as bpd - >>> import bigframes.geopandas - - >>> wkts = [ - ... 'POINT (1 1)', - ... 'POINT (2 2)', - ... 'POINT (3 3)', - ... ] - >>> s = bigframes.geopandas.GeoSeries.from_wkt(wkts) - >>> s - 0 POINT (1 1) - 1 POINT (2 2) - 2 POINT (3 3) - dtype: geometry - - Args: - data (array-like): - Series, list, or array of WKT objects. - - index (array-like or Index, optional): - The index for the GeoSeries. - - Returns: - bigframes.geopandas.GeoSeries: - A GeoSeries of geometries. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def to_wkt(self) -> bigframes.series.Series: - """ - Convert GeoSeries geometries to WKT - - **Examples:** - - >>> import bigframes as bpd - >>> import bigframes.geopandas - >>> from shapely.geometry import Point - - >>> s = bigframes.geopandas.GeoSeries([Point(1, 1), Point(2, 2), Point(3, 3)]) - >>> s - 0 POINT (1 1) - 1 POINT (2 2) - 2 POINT (3 3) - dtype: geometry - - >>> s.to_wkt() - 0 POINT(1 1) - 1 POINT(2 2) - 2 POINT(3 3) - dtype: string - - Returns: - bigframes.series.Series: - WKT representations of the geometries. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def difference(self: GeoSeries, other: GeoSeries) -> GeoSeries: # type: ignore - """ - Returns a GeoSeries of the points in each aligned geometry that are not - in other. - - The operation works on a 1-to-1 row-wise manner. - - **Examples:** - - >>> import bigframes as bpd - >>> import bigframes.geopandas - >>> from shapely.geometry import Polygon, LineString, Point - - We can check two GeoSeries against each other, row by row: - - >>> s1 = bigframes.geopandas.GeoSeries( - ... [ - ... Polygon([(0, 0), (2, 2), (0, 2)]), - ... Polygon([(0, 0), (2, 2), (0, 2)]), - ... LineString([(0, 0), (2, 2)]), - ... LineString([(2, 0), (0, 2)]), - ... Point(0, 1), - ... ], - ... ) - >>> s2 = bigframes.geopandas.GeoSeries( - ... [ - ... Polygon([(0, 0), (1, 1), (0, 1)]), - ... LineString([(1, 0), (1, 3)]), - ... LineString([(2, 0), (0, 2)]), - ... Point(1, 1), - ... Point(0, 1), - ... ], - ... index=range(1, 6), - ... ) - - >>> s1 - 0 POLYGON ((0 0, 2 2, 0 2, 0 0)) - 1 POLYGON ((0 0, 2 2, 0 2, 0 0)) - 2 LINESTRING (0 0, 2 2) - 3 LINESTRING (2 0, 0 2) - 4 POINT (0 1) - dtype: geometry - - >>> s2 - 1 POLYGON ((0 0, 1 1, 0 1, 0 0)) - 2 LINESTRING (1 0, 1 3) - 3 LINESTRING (2 0, 0 2) - 4 POINT (1 1) - 5 POINT (0 1) - dtype: geometry - - >>> s1.difference(s2) - 0 None - 1 POLYGON ((0.99954 1, 2 2, 0 2, 0 1, 0.99954 1)) - 2 LINESTRING (0 0, 1 1.00046, 2 2) - 3 GEOMETRYCOLLECTION EMPTY - 4 POINT (0 1) - 5 None - dtype: geometry - - We can also check difference of single shapely geometries: - - >>> polygon_s1 = bigframes.geopandas.GeoSeries( - ... [ - ... Polygon([(0, 0), (10, 0), (10, 10), (0, 0)]) - ... ] - ... ) - >>> polygon_s2 = bigframes.geopandas.GeoSeries( - ... [ - ... Polygon([(4, 2), (6, 2), (8, 6), (4, 2)]) - ... ] - ... ) - - >>> polygon_s1 - 0 POLYGON ((0 0, 10 0, 10 10, 0 0)) - dtype: geometry - - >>> polygon_s2 - 0 POLYGON ((4 2, 6 2, 8 6, 4 2)) - dtype: geometry - - >>> polygon_s1.difference(polygon_s2) - 0 POLYGON ((0 0, 10 0, 10 10, 0 0), (8 6, 6 2, 4... - dtype: geometry - - Additionally, we can check difference of a GeoSeries against a single shapely geometry: - - >>> s1.difference(polygon_s2) - 0 POLYGON ((0 0, 2 2, 0 2, 0 0)) - 1 None - 2 None - 3 None - 4 None - dtype: geometry - - Args: - other (bigframes.geopandas.GeoSeries or geometric object): - The GeoSeries (elementwise) or geometric object to find the - difference to. - - Returns: - bigframes.geopandas.GeoSeries: - A GeoSeries of the points in each aligned geometry that are not - in other. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def distance(self: GeoSeries, other: GeoSeries) -> bigframes.series.Series: - """ - [Not Implemented] Use ``bigframes.bigquery.st_distance(series, other)`` - instead to return the shorted distance between two - ``GEOGRAPHY`` objects in meters. - - In GeoPandas, this returns a Series of the distances between each - aligned geometry in the expressed in the units of the CRS. - - Args: - other: - The Geoseries (elementwise) or geometric object to find the distance to. - - Returns: - bigframes.pandas.Series: - Series of float representing the distances. - - Raises: - NotImplementedError: - GeoSeries.distance is not supported. Use - ``bigframes.bigquery.st_distance(series, other)``, instead. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def intersection(self: GeoSeries, other: GeoSeries) -> GeoSeries: # type: ignore - """ - Returns a GeoSeries of the intersection of points in each aligned - geometry with other. - - The operation works on a 1-to-1 row-wise manner. - - **Examples:** - - >>> import bigframes as bpd - >>> import bigframes.geopandas - >>> from shapely.geometry import Polygon, LineString, Point - - We can check two GeoSeries against each other, row by row. - - >>> s1 = bigframes.geopandas.GeoSeries( - ... [ - ... Polygon([(0, 0), (2, 2), (0, 2)]), - ... Polygon([(0, 0), (2, 2), (0, 2)]), - ... LineString([(0, 0), (2, 2)]), - ... LineString([(2, 0), (0, 2)]), - ... Point(0, 1), - ... ], - ... ) - >>> s2 = bigframes.geopandas.GeoSeries( - ... [ - ... Polygon([(0, 0), (1, 1), (0, 1)]), - ... LineString([(1, 0), (1, 3)]), - ... LineString([(2, 0), (0, 2)]), - ... Point(1, 1), - ... Point(0, 1), - ... ], - ... index=range(1, 6), - ... ) - - >>> s1 - 0 POLYGON ((0 0, 2 2, 0 2, 0 0)) - 1 POLYGON ((0 0, 2 2, 0 2, 0 0)) - 2 LINESTRING (0 0, 2 2) - 3 LINESTRING (2 0, 0 2) - 4 POINT (0 1) - dtype: geometry - - >>> s2 - 1 POLYGON ((0 0, 1 1, 0 1, 0 0)) - 2 LINESTRING (1 0, 1 3) - 3 LINESTRING (2 0, 0 2) - 4 POINT (1 1) - 5 POINT (0 1) - dtype: geometry - - >>> s1.intersection(s2) - 0 None - 1 POLYGON ((0 0, 0.99954 1, 0 1, 0 0)) - 2 POINT (1 1.00046) - 3 LINESTRING (2 0, 0 2) - 4 GEOMETRYCOLLECTION EMPTY - 5 None - dtype: geometry - - - We can also do intersection of each geometry and a single shapely geometry: - - >>> s1.intersection(bigframes.geopandas.GeoSeries([Polygon([(0, 0), (1, 1), (0, 1)])])) - 0 POLYGON ((0 0, 0.99954 1, 0 1, 0 0)) - 1 None - 2 None - 3 None - 4 None - dtype: geometry - - - Args: - other (GeoSeries or geometric object): - The Geoseries (elementwise) or geometric object to find the - intersection with. - - Returns: - bigframes.geopandas.GeoSeries: - The Geoseries (elementwise) of the intersection of points in - each aligned geometry with other. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - @property - def is_closed(self: GeoSeries) -> bigframes.series.Series: - """ - [Not Implemented] Use ``bigframes.bigquery.st_isclosed(series)`` - instead to return a boolean indicating if a shape is closed. - - In GeoPandas, this returns a Series of booleans with value True if a - LineString's or LinearRing's first and last points are equal. - - Returns False for any other geometry type. - - Returns: - bigframes.pandas.Series: - Series of booleans. - - Raises: - NotImplementedError: - GeoSeries.is_closed is not supported. Use - ``bigframes.bigquery.st_isclosed(series)``, instead. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def simplify( - self, tolerance: float, preserve_topology: bool = True - ) -> bigframes.series.Series: # type: ignore - """[Not Implemented] Use ``bigframes.bigquery.st_simplify(series, tolerance_meters)``, - instead to set the tolerance in meters. - - In GeoPandas, this returns a GeoSeries containing a simplified - representation of each geometry. - - Args: - tolerance (float): - All parts of a simplified geometry will be no more than - tolerance distance from the original. It has the same units as - the coordinate reference system of the GeoSeries. For example, - using tolerance=100 in a projected CRS with meters as units - means a distance of 100 meters in reality. - preserve_topology (bool): - Default True. False uses a quicker algorithm, but may produce - self-intersecting or otherwise invalid geometries. - - Returns: - bigframes.geopandas.GeoSeries: - Series of simplified geometries. - - Raises: - NotImplementedError: - GeoSeries.simplify is not supported. Use bigframes.bigquery.st_simplify(series, tolerance_meters), instead. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) diff --git a/third_party/bigframes_vendored/google_cloud_bigquery/_pandas_helpers.py b/third_party/bigframes_vendored/google_cloud_bigquery/_pandas_helpers.py index 3e35b1382e3..5e2a7a7ef0f 100644 --- a/third_party/bigframes_vendored/google_cloud_bigquery/_pandas_helpers.py +++ b/third_party/bigframes_vendored/google_cloud_bigquery/_pandas_helpers.py @@ -17,7 +17,6 @@ import warnings -import db_dtypes import google.cloud.bigquery.schema as schema import pyarrow @@ -62,7 +61,6 @@ def pyarrow_timestamp(): "TIME": pyarrow_time, "TIMESTAMP": pyarrow_timestamp, "BIGNUMERIC": pyarrow_bignumeric, - "JSON": db_dtypes.JSONArrowType, } ARROW_SCALAR_IDS_TO_BQ = { # https://arrow.apache.org/docs/python/api/datatypes.html#type-classes diff --git a/third_party/bigframes_vendored/google_cloud_bigquery/retry.py b/third_party/bigframes_vendored/google_cloud_bigquery/retry.py deleted file mode 100644 index 9117d7aa569..00000000000 --- a/third_party/bigframes_vendored/google_cloud_bigquery/retry.py +++ /dev/null @@ -1,220 +0,0 @@ -# Original: https://github.com/googleapis/python-bigquery/blob/main/google/cloud/bigquery/retry.py -# Copyright 2018 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import google.api_core.future.polling -import requests.exceptions -from google.api_core import exceptions, retry -from google.auth import exceptions as auth_exceptions # type: ignore - -_RETRYABLE_REASONS = frozenset( - ["rateLimitExceeded", "backendError", "internalError", "badGateway"] -) - -_UNSTRUCTURED_RETRYABLE_TYPES = ( - ConnectionError, - exceptions.TooManyRequests, - exceptions.InternalServerError, - exceptions.BadGateway, - exceptions.ServiceUnavailable, - requests.exceptions.ChunkedEncodingError, - requests.exceptions.ConnectionError, - requests.exceptions.Timeout, - auth_exceptions.TransportError, -) - -_MINUTE_IN_SECONDS = 60.0 -_HOUR_IN_SECONDS = 60.0 * _MINUTE_IN_SECONDS -_DEFAULT_RETRY_DEADLINE = 10.0 * _MINUTE_IN_SECONDS - -# Ambiguous errors (e.g. internalError, backendError, rateLimitExceeded) retry -# until the full `_DEFAULT_RETRY_DEADLINE`. This is because the -# `jobs.getQueryResults` REST API translates a job failure into an HTTP error. -# -# TODO(https://github.com/googleapis/python-bigquery/issues/1903): Investigate -# if we can fail early for ambiguous errors in `QueryJob.result()`'s call to -# the `jobs.getQueryResult` API. -# -# We need `_DEFAULT_JOB_DEADLINE` to be some multiple of -# `_DEFAULT_RETRY_DEADLINE` to allow for a few retries after the retry -# timeout is reached. -# -# Note: This multiple should actually be a multiple of -# (2 * _DEFAULT_RETRY_DEADLINE). After an ambiguous exception, the first -# call from `job_retry()` refreshes the job state without actually restarting -# the query. The second `job_retry()` actually restarts the query. For a more -# detailed explanation, see the comments where we set `restart_query_job = True` -# in `QueryJob.result()`'s inner `is_job_done()` function. -_DEFAULT_JOB_DEADLINE = 2.0 * (2.0 * _DEFAULT_RETRY_DEADLINE) - - -def _should_retry(exc): - """Predicate for determining when to retry. - - We retry if and only if the 'reason' is 'backendError' - or 'rateLimitExceeded'. - """ - if not hasattr(exc, "errors") or len(exc.errors) == 0: - # Check for unstructured error returns, e.g. from GFE - return isinstance(exc, _UNSTRUCTURED_RETRYABLE_TYPES) - - reason = exc.errors[0]["reason"] - return reason in _RETRYABLE_REASONS - - -DEFAULT_RETRY = retry.Retry(predicate=_should_retry, deadline=_DEFAULT_RETRY_DEADLINE) -"""The default retry object. - -Any method with a ``retry`` parameter will be retried automatically, -with reasonable defaults. To disable retry, pass ``retry=None``. -To modify the default retry behavior, call a ``with_XXX`` method -on ``DEFAULT_RETRY``. For example, to change the deadline to 30 seconds, -pass ``retry=bigquery.DEFAULT_RETRY.with_deadline(30)``. -""" - - -def _should_retry_get_job_conflict(exc): - """Predicate for determining when to retry a jobs.get call after a conflict error. - - Sometimes we get a 404 after a Conflict. In this case, we - have pretty high confidence that by retrying the 404, we'll - (hopefully) eventually recover the job. - https://github.com/googleapis/python-bigquery/issues/2134 - - Note: we may be able to extend this to user-specified predicates - after https://github.com/googleapis/python-api-core/issues/796 - to tweak existing Retry object predicates. - """ - return isinstance(exc, exceptions.NotFound) or _should_retry(exc) - - -# Pick a deadline smaller than our other deadlines since we want to timeout -# before those expire. -_DEFAULT_GET_JOB_CONFLICT_DEADLINE = _DEFAULT_RETRY_DEADLINE / 3.0 -_DEFAULT_GET_JOB_CONFLICT_RETRY = retry.Retry( - predicate=_should_retry_get_job_conflict, - deadline=_DEFAULT_GET_JOB_CONFLICT_DEADLINE, -) -"""Private, may be removed in future.""" - - -# Note: Take care when updating DEFAULT_TIMEOUT to anything but None. We -# briefly had a default timeout, but even setting it at more than twice the -# theoretical server-side default timeout of 2 minutes was not enough for -# complex queries. See: -# https://github.com/googleapis/python-bigquery/issues/970#issuecomment-921934647 -DEFAULT_TIMEOUT = None -"""The default API timeout. - -This is the time to wait per request. To adjust the total wait time, set a -deadline on the retry object. -""" - -job_retry_reasons = ( - "rateLimitExceeded", - "backendError", - "internalError", - "jobBackendError", - "jobInternalError", - "jobRateLimitExceeded", -) - - -def _job_should_retry(exc): - # Sometimes we have ambiguous errors, such as 'backendError' which could - # be due to an API problem or a job problem. For these, make sure we retry - # our is_job_done() function. - # - # Note: This won't restart the job unless we know for sure it's because of - # the job status and set restart_query_job = True in that loop. This means - # that we might end up calling this predicate twice for the same job - # but from different paths: (1) from jobs.getQueryResults RetryError and - # (2) from translating the job error from the body of a jobs.get response. - # - # Note: If we start retrying job types other than queries where we don't - # call the problematic getQueryResults API to check the status, we need - # to provide a different predicate, as there shouldn't be ambiguous - # errors in those cases. - if isinstance(exc, exceptions.RetryError): - exc = exc.cause - - # Per https://github.com/googleapis/python-bigquery/issues/1929, sometimes - # retriable errors make their way here. Because of the separate - # `restart_query_job` logic to make sure we aren't restarting non-failed - # jobs, it should be safe to continue and not totally fail our attempt at - # waiting for the query to complete. - if _should_retry(exc): - return True - - if not hasattr(exc, "errors") or len(exc.errors) == 0: - return False - - reason = exc.errors[0]["reason"] - return reason in job_retry_reasons - - -DEFAULT_JOB_RETRY = retry.Retry( - predicate=_job_should_retry, deadline=_DEFAULT_JOB_DEADLINE -) -""" -The default job retry object. -""" - - -DEFAULT_ML_JOB_RETRY = retry.Retry( - predicate=_job_should_retry, deadline=_HOUR_IN_SECONDS -) -""" -The default job retry object for AI/ML jobs. - -Such jobs can take a long time to fail. See: b/436586523. -""" - - -def _query_job_insert_should_retry(exc): - # Per https://github.com/googleapis/python-bigquery/issues/2134, sometimes - # we get a 404 error. In this case, if we get this far, assume that the job - # doesn't actually exist and try again. We can't add 404 to the default - # job_retry because that happens for errors like "this table does not - # exist", which probably won't resolve with a retry. - if isinstance(exc, exceptions.RetryError): - exc = exc.cause - - if isinstance(exc, exceptions.NotFound): - message = exc.message - # Don't try to retry table/dataset not found, just job not found. - # The URL contains jobs, so use whitespace to disambiguate. - return message is not None and " job" in message.lower() - - return _job_should_retry(exc) - - -_DEFAULT_QUERY_JOB_INSERT_RETRY = retry.Retry( - predicate=_query_job_insert_should_retry, - # jobs.insert doesn't wait for the job to complete, so we don't need the - # long _DEFAULT_JOB_DEADLINE for this part. - deadline=_DEFAULT_RETRY_DEADLINE, -) -"""Private, may be removed in future.""" - - -DEFAULT_GET_JOB_TIMEOUT = 128 -""" -Default timeout for Client.get_job(). -""" - -POLLING_DEFAULT_VALUE = google.api_core.future.polling.PollingFuture._DEFAULT_VALUE -""" -Default value defined in google.api_core.future.polling.PollingFuture. -""" diff --git a/third_party/bigframes_vendored/google_cloud_bigquery/tests/unit/test_pandas_helpers.py b/third_party/bigframes_vendored/google_cloud_bigquery/tests/unit/test_pandas_helpers.py index c87444f41e2..dc4a09cc541 100644 --- a/third_party/bigframes_vendored/google_cloud_bigquery/tests/unit/test_pandas_helpers.py +++ b/third_party/bigframes_vendored/google_cloud_bigquery/tests/unit/test_pandas_helpers.py @@ -16,16 +16,16 @@ import functools import warnings +from google.cloud.bigquery import schema import pyarrow import pyarrow.parquet import pyarrow.types import pytest -from google.cloud.bigquery import schema @pytest.fixture def module_under_test(): - from bigframes_vendored.google_cloud_bigquery import _pandas_helpers + from third_party.bigframes_vendored.google_cloud_bigquery import _pandas_helpers return _pandas_helpers diff --git a/third_party/bigframes_vendored/ibis/README.md b/third_party/bigframes_vendored/ibis/README.md index fa8224214f4..8a00750e920 100644 --- a/third_party/bigframes_vendored/ibis/README.md +++ b/third_party/bigframes_vendored/ibis/README.md @@ -1,6 +1,7 @@ # Ibis [![Documentation Status](https://img.shields.io/badge/docs-docs.ibis--project.org-blue.svg)](http://ibis-project.org) +[![Anaconda-Server Badge](https://anaconda.org/conda-forge/ibis-framework/badges/version.svg)](https://anaconda.org/conda-forge/ibis-framework) [![PyPI](https://img.shields.io/pypi/v/ibis-framework.svg)](https://pypi.org/project/ibis-framework) [![Build status](https://github.com/ibis-project/ibis/actions/workflows/ibis-main.yml/badge.svg)](https://github.com/ibis-project/ibis/actions/workflows/ibis-main.yml?query=branch%3Amaster) [![Build status](https://github.com/ibis-project/ibis/actions/workflows/ibis-backends.yml/badge.svg)](https://github.com/ibis-project/ibis/actions/workflows/ibis-backends.yml?query=branch%3Amaster) @@ -82,14 +83,28 @@ Install Ibis from PyPI with: pip install 'ibis-framework[duckdb]' ``` +Or from conda-forge with: + +```bash +conda install ibis-framework -c conda-forge +``` + (It’s a common mistake to `pip install ibis`. If you try to use Ibis and get errors early on try uninstalling `ibis` and installing `ibis-framework`) +To discover ibis, we suggest starting with the DuckDB backend (which is included by default in the conda-forge package). The DuckDB backend is performant and fully featured. + To use ibis with other backends, include the backend name in brackets for PyPI: ```bash pip install 'ibis-framework[postgres]' ``` +Or use `ibis-$BACKEND` where `$BACKEND` is the specific backend you want to use when installing from conda-forge: + +```bash +conda install ibis-postgres -c conda-forge +``` + ## Getting Started with Ibis We provide a number of tutorial and example notebooks in the diff --git a/third_party/bigframes_vendored/ibis/__init__.py b/third_party/bigframes_vendored/ibis/__init__.py index 54a896da889..e69de29bb2d 100644 --- a/third_party/bigframes_vendored/ibis/__init__.py +++ b/third_party/bigframes_vendored/ibis/__init__.py @@ -1,109 +0,0 @@ -# Contains code from https://github.com/ibis-project/ibis/blob/9.2.0/ibis/__init__.py - -"""Initialize Ibis module.""" - -from __future__ import annotations - -__version__ = "9.2.0" - -import warnings -from typing import Any - -import bigframes_vendored.ibis.backends.bigquery as bigquery -from bigframes_vendored.ibis import util -from bigframes_vendored.ibis.backends import BaseBackend -from bigframes_vendored.ibis.common.exceptions import IbisError -from bigframes_vendored.ibis.config import options -from bigframes_vendored.ibis.expr import api -from bigframes_vendored.ibis.expr import types as ir -from bigframes_vendored.ibis.expr.api import * # noqa: F403 -from bigframes_vendored.ibis.expr.operations import udf - -__all__ = [ # noqa: PLE0604 - "api", - "ir", - "udf", - "util", - "BaseBackend", - "IbisError", - "options", - *api.__all__, -] - -_KNOWN_BACKENDS = ["heavyai"] - - -def load_backend(name: str) -> BaseBackend: - """Load backends in a lazy way with `ibis.`. - - This also registers the backend options. - - Examples - -------- - >>> import ibis - >>> con = ibis.sqlite.connect(...) - - When accessing the `sqlite` attribute of the `ibis` module, this function - is called, and a backend with the `sqlite` name is tried to load from - the `ibis.backends` entrypoints. If successful, the `ibis.sqlite` - attribute is "cached", so this function is only called the first time. - - """ - backend = bigquery.Backend() - # The first time a backend is loaded, we register its options, and we set - # it as an attribute of `ibis`, so `__getattr__` is not called again for it - backend.register_options() - - # We don't want to expose all the methods on an unconnected backend to the user. - # In lieu of a full redesign, we create a proxy module and add only the methods - # that are valid to call without a connect call. These are: - # - # - connect - # - compile - # - has_operation - # - _from_url - # - _to_sqlglot - # - # We also copy over the docstring from `do_connect` to the proxy `connect` - # method, since that's where all the backend-specific kwargs are currently - # documented. This is all admittedly gross, but it works and doesn't - # require a backend redesign yet. - - def connect(*args, **kwargs): - return backend.connect(*args, **kwargs) - - connect.__doc__ = backend.do_connect.__doc__ - connect.__wrapped__ = backend.do_connect - connect.__module__ = f"bigframes_vendored.ibis.{name}" - - import types - - import bigframes_vendored.ibis - - proxy = types.ModuleType(f"bigframes_vendored.ibis.{name}") - setattr(bigframes_vendored.ibis, name, proxy) - proxy.connect = connect - proxy.compile = backend.compile - proxy.has_operation = backend.has_operation - proxy.name = name - proxy._from_url = backend._from_url - proxy._to_sqlglot = backend._to_sqlglot - # Add any additional methods that should be exposed at the top level - for attr in getattr(backend, "_top_level_methods", ()): - setattr(proxy, attr, getattr(backend, attr)) - - return proxy - - -def __getattr__(name: str) -> Any: - if name == "NA": - warnings.warn( - "The 'ibis.NA' constant is deprecated as of v9.1 and will be removed in a future " - "version. Use 'ibis.null()' instead.", - DeprecationWarning, - stacklevel=2, - ) - - return null() # noqa: F405 - else: - return load_backend(name) diff --git a/third_party/bigframes_vendored/ibis/backends/__init__.py b/third_party/bigframes_vendored/ibis/backends/__init__.py index 0d0feca9d38..e69de29bb2d 100644 --- a/third_party/bigframes_vendored/ibis/backends/__init__.py +++ b/third_party/bigframes_vendored/ibis/backends/__init__.py @@ -1,1431 +0,0 @@ -# Contains code from https://github.com/ibis-project/ibis/blob/9.2.0/ibis/backends/__init__.py - -from __future__ import annotations - -import abc -import collections.abc -import functools -import importlib.metadata -import keyword -import re -import urllib.parse -from pathlib import Path -from typing import TYPE_CHECKING, Any, ClassVar - -import bigframes_vendored.ibis -import bigframes_vendored.ibis.common.exceptions as exc -import bigframes_vendored.ibis.config -import bigframes_vendored.ibis.expr.operations as ops -import bigframes_vendored.ibis.expr.types as ir -from bigframes_vendored.ibis import util -from bigframes_vendored.ibis.common.caching import RefCountedCache - -if TYPE_CHECKING: - from collections.abc import Iterable, Iterator, Mapping, MutableMapping - from urllib.parse import ParseResult - - import bigframes_vendored.sqlglot as sg - import pandas as pd - import polars as pl - import pyarrow as pa - import torch - -__all__ = ("BaseBackend", "connect") - - -class TablesAccessor(collections.abc.Mapping): - """A mapping-like object for accessing tables off a backend. - - Tables may be accessed by name using either index or attribute access: - - Examples - -------- - >>> con = ibis.sqlite.connect("example.db") - >>> people = con.tables["people"] # access via index - >>> people = con.tables.people # access via attribute - - """ - - def __init__(self, backend: BaseBackend): - self._backend = backend - - def __getitem__(self, name) -> ir.Table: - try: - return self._backend.table(name) - except Exception as exc: - raise KeyError(name) from exc - - def __getattr__(self, name) -> ir.Table: - if name.startswith("_"): - raise AttributeError(name) - try: - return self._backend.table(name) - except Exception as exc: - raise AttributeError(name) from exc - - def __iter__(self) -> Iterator[str]: - return iter(sorted(self._backend.list_tables())) - - def __len__(self) -> int: - return len(self._backend.list_tables()) - - def __dir__(self) -> list[str]: - o = set() - o.update(dir(type(self))) - o.update( - name - for name in self._backend.list_tables() - if name.isidentifier() and not keyword.iskeyword(name) - ) - return list(o) - - def __repr__(self) -> str: - tables = self._backend.list_tables() - rows = ["Tables", "------"] - rows.extend(f"- {name}" for name in sorted(tables)) - return "\n".join(rows) - - def _ipython_key_completions_(self) -> list[str]: - return self._backend.list_tables() - - -class _FileIOHandler: - @staticmethod - def _import_pyarrow(): - try: - import pyarrow # noqa: ICN001 - except ImportError: - raise ModuleNotFoundError( - "Exporting to arrow formats requires `pyarrow` but it is not installed" - ) - else: - import pyarrow_hotfix # noqa: F401 - - return pyarrow - - def to_pandas( - self, - expr: ir.Expr, - *, - params: Mapping[ir.Scalar, Any] | None = None, - limit: int | str | None = None, - **kwargs: Any, - ) -> pd.DataFrame | pd.Series | Any: - """Execute an Ibis expression and return a pandas `DataFrame`, `Series`, or scalar. - - ::: {.callout-note} - This method is a wrapper around `execute`. - ::: - - Parameters - ---------- - expr - Ibis expression to execute. - params - Mapping of scalar parameter expressions to value. - limit - An integer to effect a specific row limit. A value of `None` means - "no limit". The default is in `ibis/config.py`. - kwargs - Keyword arguments - - """ - return self.execute(expr, params=params, limit=limit, **kwargs) - - def to_pandas_batches( - self, - expr: ir.Expr, - *, - params: Mapping[ir.Scalar, Any] | None = None, - limit: int | str | None = None, - chunk_size: int = 1_000_000, - **kwargs: Any, - ) -> Iterator[pd.DataFrame | pd.Series | Any]: - """Execute an Ibis expression and return an iterator of pandas `DataFrame`s. - - Parameters - ---------- - expr - Ibis expression to execute. - params - Mapping of scalar parameter expressions to value. - limit - An integer to effect a specific row limit. A value of `None` means - "no limit". The default is in `ibis/config.py`. - chunk_size - Maximum number of rows in each returned `DataFrame` batch. This may have - no effect depending on the backend. - kwargs - Keyword arguments - - Returns - ------- - Iterator[pd.DataFrame] - An iterator of pandas `DataFrame`s. - - """ - from bigframes_vendored.ibis.formats.pandas import PandasData - - orig_expr = expr - expr = expr.as_table() - schema = expr.schema() - yield from ( - orig_expr.__pandas_result__( - PandasData.convert_table(batch.to_pandas(), schema) - ) - for batch in self.to_pyarrow_batches( - expr, params=params, limit=limit, chunk_size=chunk_size, **kwargs - ) - ) - - @util.experimental - def to_pyarrow( - self, - expr: ir.Expr, - *, - params: Mapping[ir.Scalar, Any] | None = None, - limit: int | str | None = None, - **kwargs: Any, - ) -> pa.Table: - """Execute expression and return results in as a pyarrow table. - - This method is eager and will execute the associated expression - immediately. - - Parameters - ---------- - expr - Ibis expression to export to pyarrow - params - Mapping of scalar parameter expressions to value. - limit - An integer to effect a specific row limit. A value of `None` means - "no limit". The default is in `ibis/config.py`. - kwargs - Keyword arguments - - Returns - ------- - Table - A pyarrow table holding the results of the executed expression. - - """ - pa = self._import_pyarrow() - self._run_pre_execute_hooks(expr) - - table_expr = expr.as_table() - schema = table_expr.schema() - arrow_schema = schema.to_pyarrow() - with self.to_pyarrow_batches( - table_expr, params=params, limit=limit, **kwargs - ) as reader: - table = pa.Table.from_batches(reader, schema=arrow_schema) - - return expr.__pyarrow_result__( - table.rename_columns(table_expr.columns).cast(arrow_schema) - ) - - @util.experimental - def to_polars( - self, - expr: ir.Expr, - *, - params: Mapping[ir.Scalar, Any] | None = None, - limit: int | str | None = None, - **kwargs: Any, - ) -> pl.DataFrame: - """Execute expression and return results in as a polars DataFrame. - - This method is eager and will execute the associated expression - immediately. - - Parameters - ---------- - expr - Ibis expression to export to polars. - params - Mapping of scalar parameter expressions to value. - limit - An integer to effect a specific row limit. A value of `None` means - "no limit". The default is in `ibis/config.py`. - kwargs - Keyword arguments - - Returns - ------- - dataframe - A polars DataFrame holding the results of the executed expression. - - """ - import polars as pl - - table = self.to_pyarrow(expr.as_table(), params=params, limit=limit, **kwargs) - return expr.__polars_result__(pl.from_arrow(table)) - - @util.experimental - def to_pyarrow_batches( - self, - expr: ir.Expr, - *, - params: Mapping[ir.Scalar, Any] | None = None, - limit: int | str | None = None, - chunk_size: int = 1_000_000, - **kwargs: Any, - ) -> pa.ipc.RecordBatchReader: - """Execute expression and return a RecordBatchReader. - - This method is eager and will execute the associated expression - immediately. - - Parameters - ---------- - expr - Ibis expression to export to pyarrow - limit - An integer to effect a specific row limit. A value of `None` means - "no limit". The default is in `ibis/config.py`. - params - Mapping of scalar parameter expressions to value. - chunk_size - Maximum number of rows in each returned record batch. - kwargs - Keyword arguments - - Returns - ------- - results - RecordBatchReader - - """ - raise NotImplementedError - - @util.experimental - def to_torch( - self, - expr: ir.Expr, - *, - params: Mapping[ir.Scalar, Any] | None = None, - limit: int | str | None = None, - **kwargs: Any, - ) -> dict[str, torch.Tensor]: - """Execute an expression and return results as a dictionary of torch tensors. - - Parameters - ---------- - expr - Ibis expression to execute. - params - Parameters to substitute into the expression. - limit - An integer to effect a specific row limit. A value of `None` means no limit. - kwargs - Keyword arguments passed into the backend's `to_torch` implementation. - - Returns - ------- - dict[str, torch.Tensor] - A dictionary of torch tensors, keyed by column name. - - """ - import torch - - t = self.to_pyarrow(expr, params=params, limit=limit, **kwargs) - # without .copy() the arrays are read-only and thus writing to them is - # undefined behavior; we can't ignore this warning from torch because - # we're going out of ibis and downstream code can do whatever it wants - # with the data - return { - name: torch.from_numpy(t[name].to_numpy().copy()) for name in t.schema.names - } - - def read_parquet( - self, path: str | Path, table_name: str | None = None, **kwargs: Any - ) -> ir.Table: - """Register a parquet file as a table in the current backend. - - Parameters - ---------- - path - The data source. - table_name - An optional name to use for the created table. This defaults to - a sequentially generated name. - **kwargs - Additional keyword arguments passed to the backend loading function. - - Returns - ------- - ir.Table - The just-registered table - - """ - raise NotImplementedError( - f"{self.name} does not support direct registration of parquet data." - ) - - def read_csv( - self, path: str | Path, table_name: str | None = None, **kwargs: Any - ) -> ir.Table: - """Register a CSV file as a table in the current backend. - - Parameters - ---------- - path - The data source. A string or Path to the CSV file. - table_name - An optional name to use for the created table. This defaults to - a sequentially generated name. - **kwargs - Additional keyword arguments passed to the backend loading function. - - Returns - ------- - ir.Table - The just-registered table - - """ - raise NotImplementedError( - f"{self.name} does not support direct registration of CSV data." - ) - - def read_json( - self, path: str | Path, table_name: str | None = None, **kwargs: Any - ) -> ir.Table: - """Register a JSON file as a table in the current backend. - - Parameters - ---------- - path - The data source. A string or Path to the JSON file. - table_name - An optional name to use for the created table. This defaults to - a sequentially generated name. - **kwargs - Additional keyword arguments passed to the backend loading function. - - Returns - ------- - ir.Table - The just-registered table - - """ - raise NotImplementedError( - f"{self.name} does not support direct registration of JSON data." - ) - - def read_delta( - self, source: str | Path, table_name: str | None = None, **kwargs: Any - ): - """Register a Delta Lake table in the current database. - - Parameters - ---------- - source - The data source. Must be a directory - containing a Delta Lake table. - table_name - An optional name to use for the created table. This defaults to - a sequentially generated name. - **kwargs - Additional keyword arguments passed to the underlying backend or library. - - Returns - ------- - ir.Table - The just-registered table. - - """ - raise NotImplementedError( - f"{self.name} does not support direct registration of DeltaLake tables." - ) - - @util.experimental - def to_parquet( - self, - expr: ir.Table, - path: str | Path, - *, - params: Mapping[ir.Scalar, Any] | None = None, - **kwargs: Any, - ) -> None: - """Write the results of executing the given expression to a parquet file. - - This method is eager and will execute the associated expression - immediately. - - Parameters - ---------- - expr - The ibis expression to execute and persist to parquet. - path - The data source. A string or Path to the parquet file. - params - Mapping of scalar parameter expressions to value. - **kwargs - Additional keyword arguments passed to pyarrow.parquet.ParquetWriter - - https://arrow.apache.org/docs/python/generated/pyarrow.parquet.ParquetWriter.html - - """ - self._import_pyarrow() - import pyarrow.parquet as pq - - with expr.to_pyarrow_batches(params=params) as batch_reader: - with pq.ParquetWriter(path, batch_reader.schema, **kwargs) as writer: - for batch in batch_reader: - writer.write_batch(batch) - - @util.experimental - def to_csv( - self, - expr: ir.Table, - path: str | Path, - *, - params: Mapping[ir.Scalar, Any] | None = None, - **kwargs: Any, - ) -> None: - """Write the results of executing the given expression to a CSV file. - - This method is eager and will execute the associated expression - immediately. - - Parameters - ---------- - expr - The ibis expression to execute and persist to CSV. - path - The data source. A string or Path to the CSV file. - params - Mapping of scalar parameter expressions to value. - kwargs - Additional keyword arguments passed to pyarrow.csv.CSVWriter - - https://arrow.apache.org/docs/python/generated/pyarrow.csv.CSVWriter.html - - """ - self._import_pyarrow() - import pyarrow.csv as pcsv - - with expr.to_pyarrow_batches(params=params) as batch_reader: - with pcsv.CSVWriter(path, batch_reader.schema, **kwargs) as writer: - for batch in batch_reader: - writer.write_batch(batch) - - @util.experimental - def to_delta( - self, - expr: ir.Table, - path: str | Path, - *, - params: Mapping[ir.Scalar, Any] | None = None, - **kwargs: Any, - ) -> None: - """Write the results of executing the given expression to a Delta Lake table. - - This method is eager and will execute the associated expression - immediately. - - Parameters - ---------- - expr - The ibis expression to execute and persist to Delta Lake table. - path - The data source. A string or Path to the Delta Lake table. - params - Mapping of scalar parameter expressions to value. - kwargs - Additional keyword arguments passed to deltalake.writer.write_deltalake method - - """ - try: - from deltalake.writer import write_deltalake - except ImportError: - raise ImportError( - "The deltalake extra is required to use the " - "to_delta method. You can install it using pip:\n\n" - "pip install 'ibis-framework[deltalake]'\n" - ) - - with expr.to_pyarrow_batches(params=params) as batch_reader: - write_deltalake(path, batch_reader, **kwargs) - - -class CanListCatalog(abc.ABC): - @abc.abstractmethod - def list_catalogs(self, like: str | None = None) -> list[str]: - """List existing catalogs in the current connection. - - ::: {.callout-note} - ## Ibis does not use the word `schema` to refer to database hierarchy. - - A collection of `table` is referred to as a `database`. - A collection of `database` is referred to as a `catalog`. - - These terms are mapped onto the corresponding features in each - backend (where available), regardless of whether the backend itself - uses the same terminology. - ::: - - Parameters - ---------- - like - A pattern in Python's regex format to filter returned database - names. - - Returns - ------- - list[str] - The catalog names that exist in the current connection, that match - the `like` pattern if provided. - - """ - - @property - @abc.abstractmethod - def current_catalog(self) -> str: - """The current catalog in use.""" - - -class CanCreateCatalog(CanListCatalog): - @abc.abstractmethod - def create_catalog(self, name: str, force: bool = False) -> None: - """Create a new catalog. - - ::: {.callout-note} - ## Ibis does not use the word `schema` to refer to database hierarchy. - - A collection of `table` is referred to as a `database`. - A collection of `database` is referred to as a `catalog`. - - These terms are mapped onto the corresponding features in each - backend (where available), regardless of whether the backend itself - uses the same terminology. - ::: - - Parameters - ---------- - name - Name of the new catalog. - force - If `False`, an exception is raised if the catalog already exists. - - """ - - @abc.abstractmethod - def drop_catalog(self, name: str, force: bool = False) -> None: - """Drop a catalog with name `name`. - - ::: {.callout-note} - ## Ibis does not use the word `schema` to refer to database hierarchy. - - A collection of `table` is referred to as a `database`. - A collection of `database` is referred to as a `catalog`. - - These terms are mapped onto the corresponding features in each - backend (where available), regardless of whether the backend itself - uses the same terminology. - ::: - - Parameters - ---------- - name - Catalog to drop. - force - If `False`, an exception is raised if the catalog does not exist. - - """ - - -class CanListDatabase(abc.ABC): - @abc.abstractmethod - def list_databases( - self, like: str | None = None, catalog: str | None = None - ) -> list[str]: - """List existing databases in the current connection. - - ::: {.callout-note} - ## Ibis does not use the word `schema` to refer to database hierarchy. - - A collection of `table` is referred to as a `database`. - A collection of `database` is referred to as a `catalog`. - - These terms are mapped onto the corresponding features in each - backend (where available), regardless of whether the backend itself - uses the same terminology. - ::: - - Parameters - ---------- - like - A pattern in Python's regex format to filter returned database - names. - catalog - The catalog to list databases from. If `None`, the current catalog - is searched. - - Returns - ------- - list[str] - The database names that exist in the current connection, that match - the `like` pattern if provided. - - """ - - @property - @abc.abstractmethod - def current_database(self) -> str: - """The current database in use.""" - - -class CanCreateDatabase(CanListDatabase): - @abc.abstractmethod - def create_database( - self, name: str, catalog: str | None = None, force: bool = False - ) -> None: - """Create a database named `name` in `catalog`. - - Parameters - ---------- - name - Name of the database to create. - catalog - Name of the catalog in which to create the database. If `None`, the - current catalog is used. - force - If `False`, an exception is raised if the database exists. - - """ - - @abc.abstractmethod - def drop_database( - self, name: str, catalog: str | None = None, force: bool = False - ) -> None: - """Drop the database with `name` in `catalog`. - - Parameters - ---------- - name - Name of the schema to drop. - catalog - Name of the catalog to drop the database from. If `None`, the - current catalog is used. - force - If `False`, an exception is raised if the database does not exist. - - """ - - -# TODO: remove this for 10.0 -class CanListSchema: - @util.deprecated( - instead="Use `list_databases` instead`", as_of="9.0", removed_in="10.0" - ) - def list_schemas( - self, like: str | None = None, database: str | None = None - ) -> list[str]: - return self.list_databases(like=like, catalog=database) - - @property - @util.deprecated( - instead="Use `Backend.current_database` instead.", - as_of="9.0", - removed_in="10.0", - ) - def current_schema(self) -> str: - return self.current_database - - -class CanCreateSchema(CanListSchema): - @util.deprecated( - instead="Use `create_database` instead", as_of="9.0", removed_in="10.0" - ) - def create_schema( - self, name: str, database: str | None = None, force: bool = False - ) -> None: - self.create_database(name=name, catalog=database, force=force) - - @util.deprecated( - instead="Use `drop_database` instead", as_of="9.0", removed_in="10.0" - ) - def drop_schema( - self, name: str, database: str | None = None, force: bool = False - ) -> None: - self.drop_database(name=name, catalog=database, force=force) - - -class BaseBackend(abc.ABC, _FileIOHandler): - """Base backend class. - - All Ibis backends must subclass this class and implement all the - required methods. - """ - - name: ClassVar[str] - - supports_temporary_tables = False - supports_python_udfs = False - supports_in_memory_tables = True - - def __init__(self, *args, **kwargs): - self._con_args: tuple[Any] = args - self._con_kwargs: dict[str, Any] = kwargs - self._can_reconnect: bool = True - # expression cache - self._query_cache = RefCountedCache( - populate=self._load_into_cache, - lookup=lambda name: self.table(name).op(), - finalize=self._clean_up_cached_table, - ) - - @property - @abc.abstractmethod - def dialect(self) -> sg.Dialect | None: - """The sqlglot dialect for this backend, where applicable. - - Returns None if the backend is not a SQL backend. - """ - - def __getstate__(self): - return dict(_con_args=self._con_args, _con_kwargs=self._con_kwargs) - - def __rich_repr__(self): - yield "name", self.name - - def __hash__(self): - return hash(self.db_identity) - - def __eq__(self, other): - return self.db_identity == other.db_identity - - @functools.cached_property - def db_identity(self) -> str: - """Return the identity of the database. - - Multiple connections to the same - database will return the same value for `db_identity`. - - The default implementation assumes connection parameters uniquely - specify the database. - - Returns - ------- - Hashable - Database identity - - """ - parts = [self.__class__] - parts.extend(self._con_args) - parts.extend(f"{k}={v}" for k, v in self._con_kwargs.items()) - return "_".join(map(str, parts)) - - # TODO(kszucs): this should be a classmethod returning with a new backend - # instance which does instantiate the connection - def connect(self, *args, **kwargs) -> BaseBackend: - """Connect to the database. - - Parameters - ---------- - *args - Mandatory connection parameters, see the docstring of `do_connect` - for details. - **kwargs - Extra connection parameters, see the docstring of `do_connect` for - details. - - Notes - ----- - This creates a new backend instance with saved `args` and `kwargs`, - then calls `reconnect` and finally returns the newly created and - connected backend instance. - - Returns - ------- - BaseBackend - An instance of the backend - - """ - new_backend = self.__class__(*args, **kwargs) - new_backend.reconnect() - return new_backend - - @abc.abstractmethod - def disconnect(self) -> None: - """Close the connection to the backend.""" - - @staticmethod - def _convert_kwargs(kwargs: MutableMapping) -> None: - """Manipulate keyword arguments to `.connect` method.""" - - # TODO(kszucs): should call self.connect(*self._con_args, **self._con_kwargs) - def reconnect(self) -> None: - """Reconnect to the database already configured with connect.""" - if self._can_reconnect: - self.do_connect(*self._con_args, **self._con_kwargs) - else: - raise exc.IbisError("Cannot reconnect to unconfigured {self.name} backend") - - def do_connect(self, *args, **kwargs) -> None: - """Connect to database specified by `args` and `kwargs`.""" - - @staticmethod - def _filter_with_like(values: Iterable[str], like: str | None = None) -> list[str]: - """Filter names with a `like` pattern (regex). - - The methods `list_databases` and `list_tables` accept a `like` - argument, which filters the returned tables with tables that match the - provided pattern. - - We provide this method in the base backend, so backends can use it - instead of reinventing the wheel. - - Parameters - ---------- - values - Iterable of strings to filter - like - Pattern to use for filtering names - - Returns - ------- - list[str] - Names filtered by the `like` pattern. - - """ - if like is None: - return sorted(values) - - pattern = re.compile(like) - return sorted(filter(pattern.findall, values)) - - @abc.abstractmethod - def list_tables( - self, like: str | None = None, database: tuple[str, str] | str | None = None - ) -> list[str]: - """Return the list of table names in the current database. - - For some backends, the tables may be files in a directory, - or other equivalent entities in a SQL database. - - ::: {.callout-note} - ## Ibis does not use the word `schema` to refer to database hierarchy. - - A collection of tables is referred to as a `database`. - A collection of `database` is referred to as a `catalog`. - - These terms are mapped onto the corresponding features in each - backend (where available), regardless of whether the backend itself - uses the same terminology. - ::: - - Parameters - ---------- - like - A pattern in Python's regex format. - database - The database from which to list tables. - If not provided, the current database is used. - For backends that support multi-level table hierarchies, you can - pass in a dotted string path like `"catalog.database"` or a tuple of - strings like `("catalog", "database")`. - - Returns - ------- - list[str] - The list of the table names that match the pattern `like`. - - """ - - @abc.abstractmethod - def table( - self, name: str, database: tuple[str, str] | str | None = None - ) -> ir.Table: - """Construct a table expression. - - ::: {.callout-note} - ## Ibis does not use the word `schema` to refer to database hierarchy. - - A collection of tables is referred to as a `database`. - A collection of `database` is referred to as a `catalog`. - - These terms are mapped onto the corresponding features in each - backend (where available), regardless of whether the backend itself - uses the same terminology. - ::: - - Parameters - ---------- - name - Table name - database - Database name - If not provided, the current database is used. - For backends that support multi-level table hierarchies, you can - pass in a dotted string path like `"catalog.database"` or a tuple of - strings like `("catalog", "database")`. - - Returns - ------- - Table - Table expression - - """ - - @functools.cached_property - def tables(self): - """An accessor for tables in the database. - - Tables may be accessed by name using either index or attribute access: - - Examples - -------- - >>> con = ibis.sqlite.connect("example.db") - >>> people = con.tables["people"] # access via index - >>> people = con.tables.people # access via attribute - - """ - return TablesAccessor(self) - - @property - @abc.abstractmethod - def version(self) -> str: - """Return the version of the backend engine. - - For database servers, return the server version. - - For others such as SQLite and pandas return the version of the - underlying library or application. - - Returns - ------- - str - The backend version - - """ - - @classmethod - def register_options(cls) -> None: - """Register custom backend options.""" - options = bigframes_vendored.ibis.config.options - backend_name = cls.name - try: - backend_options = cls.Options() - except AttributeError: - pass - else: - try: - setattr(options, backend_name, backend_options) - except ValueError as e: - raise exc.BackendConfigurationNotRegistered(backend_name) from e - - def _register_udfs(self, expr: ir.Expr) -> None: - """Register UDFs contained in `expr` with the backend.""" - if self.supports_python_udfs: - raise NotImplementedError(self.name) - - def _register_in_memory_tables(self, expr: ir.Expr) -> None: - for memtable in expr.op().find(ops.InMemoryTable): - self._register_in_memory_table(memtable) - - def _register_in_memory_table(self, op: ops.InMemoryTable): - if self.supports_in_memory_tables: - raise NotImplementedError( - f"{self.name} must implement `_register_in_memory_table` to support in-memory tables" - ) - - def _run_pre_execute_hooks(self, expr: ir.Expr) -> None: - """Backend-specific hooks to run before an expression is executed.""" - self._define_udf_translation_rules(expr) - self._register_udfs(expr) - self._register_in_memory_tables(expr) - - def _define_udf_translation_rules(self, expr: ir.Expr): - if self.supports_python_udfs: - raise NotImplementedError(self.name) - - def compile( - self, - expr: ir.Expr, - params: Mapping[ir.Expr, Any] | None = None, - ) -> Any: - """Compile an expression.""" - return self.compiler.to_sql(expr, params=params) - - def _to_sqlglot(self, expr: ir.Expr, **kwargs) -> sg.exp.Expression: - """Convert an Ibis expression to a sqlglot expression. - - Called by `ibis.to_sql`; gives the backend an opportunity to generate - nicer SQL for human consumption. - """ - raise NotImplementedError(f"Backend '{self.name}' backend doesn't support SQL") - - def execute(self, expr: ir.Expr) -> Any: - """Execute an expression.""" - - @abc.abstractmethod - def create_table( - self, - name: str, - obj: pd.DataFrame | pa.Table | ir.Table | None = None, - *, - schema: bigframes_vendored.ibis.Schema | None = None, - database: str | None = None, - temp: bool = False, - overwrite: bool = False, - ) -> ir.Table: - """Create a new table. - - Parameters - ---------- - name - Name of the new table. - obj - An Ibis table expression or pandas table that will be used to - extract the schema and the data of the new table. If not provided, - `schema` must be given. - schema - The schema for the new table. Only one of `schema` or `obj` can be - provided. - database - Name of the database where the table will be created, if not the - default. - temp - Whether a table is temporary or not - overwrite - Whether to clobber existing data - - Returns - ------- - Table - The table that was created. - - """ - - @abc.abstractmethod - def drop_table( - self, - name: str, - *, - database: str | None = None, - force: bool = False, - ) -> None: - """Drop a table. - - Parameters - ---------- - name - Name of the table to drop. - database - Name of the database where the table exists, if not the default. - force - If `False`, an exception is raised if the table does not exist. - - """ - raise NotImplementedError( - f'Backend "{self.name}" does not implement "drop_table"' - ) - - def rename_table(self, old_name: str, new_name: str) -> None: - """Rename an existing table. - - Parameters - ---------- - old_name - The old name of the table. - new_name - The new name of the table. - - """ - raise NotImplementedError( - f'Backend "{self.name}" does not implement "rename_table"' - ) - - @abc.abstractmethod - def create_view( - self, - name: str, - obj: ir.Table, - *, - database: str | None = None, - overwrite: bool = False, - ) -> ir.Table: - """Create a new view from an expression. - - Parameters - ---------- - name - Name of the new view. - obj - An Ibis table expression that will be used to create the view. - database - Name of the database where the view will be created, if not - provided the database's default is used. - overwrite - Whether to clobber an existing view with the same name - - Returns - ------- - Table - The view that was created. - - """ - - @abc.abstractmethod - def drop_view( - self, name: str, *, database: str | None = None, force: bool = False - ) -> None: - """Drop a view. - - Parameters - ---------- - name - Name of the view to drop. - database - Name of the database where the view exists, if not the default. - force - If `False`, an exception is raised if the view does not exist. - - """ - - @classmethod - def has_operation(cls, operation: type[ops.Value]) -> bool: - """Return whether the backend implements support for `operation`. - - Parameters - ---------- - operation - A class corresponding to an operation. - - Returns - ------- - bool - Whether the backend implements the operation. - - Examples - -------- - >>> import ibis - >>> import ibis.expr.operations as ops - >>> ibis.sqlite.has_operation(ops.ArrayIndex) - False - >>> ibis.postgres.has_operation(ops.ArrayIndex) - True - - """ - raise NotImplementedError( - f"{cls.name} backend has not implemented `has_operation` API" - ) - - def _cached(self, expr: ir.Table): - """Cache the provided expression. - - All subsequent operations on the returned expression will be performed on the cached data. - - Parameters - ---------- - expr - Table expression to cache - - Returns - ------- - Expr - Cached table - - """ - op = expr.op() - if (result := self._query_cache.get(op)) is None: - result = self._query_cache.store(expr) - return ir.CachedTable(result) - - def _release_cached(self, expr: ir.CachedTable) -> None: - """Releases the provided cached expression. - - Parameters - ---------- - expr - Cached expression to release - - """ - self._query_cache.release(expr.op().name) - - def _load_into_cache(self, name, expr): - raise NotImplementedError(self.name) - - def _clean_up_cached_table(self, name): - raise NotImplementedError(self.name) - - def _transpile_sql(self, query: str, *, dialect: str | None = None) -> str: - # only transpile if dialect was passed - if dialect is None: - return query - - import bigframes_vendored.sqlglot as sg - - # only transpile if the backend dialect doesn't match the input dialect - name = self.name - if (output_dialect := self.dialect) is None: - raise NotImplementedError(f"No known sqlglot dialect for backend {name}") - - if dialect != output_dialect: - (query,) = sg.transpile(query, read=dialect, write=output_dialect) - return query - - -@functools.cache -def _get_backend_names(*, exclude: tuple[str] = ()) -> frozenset[str]: - """Return the set of known backend names. - - Parameters - ---------- - exclude - Exclude these backend names from the result - - Notes - ----- - This function returns a frozenset to prevent cache pollution. - - If a `set` is used, then any in-place modifications to the set - are visible to every caller of this function. - - """ - - entrypoints = importlib.metadata.entry_points(group="ibis.backends") - return frozenset(ep.name for ep in entrypoints).difference(exclude) - - -def connect(resource: Path | str, **kwargs: Any) -> BaseBackend: - """Connect to `resource`, inferring the backend automatically. - - The general pattern for `ibis.connect` is - - ```python - con = ibis.connect("backend://connection-parameters") - ``` - - With many backends that looks like - - ```python - con = ibis.connect("backend://user:password@host:port/database") - ``` - - See the connection syntax for each backend for details about URL connection - requirements. - - Parameters - ---------- - resource - A URL or path to the resource to be connected to. - kwargs - Backend specific keyword arguments - - Examples - -------- - Connect to an in-memory DuckDB database: - - >>> import ibis - >>> con = ibis.connect("duckdb://") - - Connect to an on-disk SQLite database: - - >>> con = ibis.connect("sqlite://relative.db") - >>> con = ibis.connect( - ... "sqlite:///absolute/path/to/data.db" - ... ) # quartodoc: +SKIP # doctest: +SKIP - - Connect to a PostgreSQL server: - - >>> con = ibis.connect( - ... "postgres://user:password@hostname:5432" - ... ) # quartodoc: +SKIP # doctest: +SKIP - - Connect to BigQuery: - - >>> con = ibis.connect( - ... "bigquery://my-project/my-dataset" - ... ) # quartodoc: +SKIP # doctest: +SKIP - - """ - url = resource = str(resource) - - if re.match("[A-Za-z]:", url): - # windows path with drive, treat it as a file - url = f"file://{url}" - - parsed = urllib.parse.urlparse(url) - scheme = parsed.scheme or "file" - - kwargs = dict(urllib.parse.parse_qsl(parsed.query)) - - # convert single parameter lists value to single values - for name, value in kwargs.items(): - if len(value) == 1: - kwargs[name] = value[0] - - if scheme == "file": - raise ValueError(f"Don't know how to connect to {resource!r}") - - # Treat `postgres://` and `postgresql://` the same - scheme = scheme.replace("postgresql", "postgres") - - try: - backend = getattr(bigframes_vendored.ibis, scheme) - except AttributeError: - raise ValueError(f"Don't know how to connect to {resource!r}") from None - - return backend._from_url(parsed, **kwargs) - - -class UrlFromPath: - __slots__ = () - - def _from_url(self, url: ParseResult, **kwargs: Any) -> BaseBackend: - """Connect to a backend using a URL `url`. - - Parameters - ---------- - url - URL with which to connect to a backend. - kwargs - Additional keyword arguments - - Returns - ------- - BaseBackend - A backend instance - - """ - netloc = url.netloc - parts = list(filter(None, (netloc, url.path[bool(netloc) :]))) - database = Path(*parts) if parts and parts != [":memory:"] else ":memory:" - if (strdatabase := str(database)).startswith("md:") or strdatabase.startswith( - "motherduck:" - ): - database = strdatabase - elif isinstance(database, Path): - database = database.absolute() - - self._convert_kwargs(kwargs) - return self.connect(database=database, **kwargs) - - -class NoUrl: - __slots__ = () - - name: str - - def _from_url(self, url: ParseResult, **kwargs) -> BaseBackend: - """Connect to the backend with empty url. - - Parameters - ---------- - url : str - The URL with which to connect to the backend. This parameter is not used - in this method but is kept for consistency. - kwargs - Additional keyword arguments. - - Returns - ------- - BaseBackend - A backend instance - - """ - return self.connect(**kwargs) diff --git a/third_party/bigframes_vendored/ibis/backends/bigquery/__init__.py b/third_party/bigframes_vendored/ibis/backends/bigquery/__init__.py index 5a84a6a80fd..e69de29bb2d 100644 --- a/third_party/bigframes_vendored/ibis/backends/bigquery/__init__.py +++ b/third_party/bigframes_vendored/ibis/backends/bigquery/__init__.py @@ -1,1294 +0,0 @@ -# Contains code from https://github.com/ibis-project/ibis/blob/9.2.0/ibis/backends/bigquery/__init__.py - -"""BigQuery public API.""" - -from __future__ import annotations - -import concurrent.futures -import contextlib -import glob -import os -import re -from typing import TYPE_CHECKING, Any, Optional - -import bigframes_vendored.ibis -import bigframes_vendored.ibis.common.exceptions as com -import bigframes_vendored.ibis.expr.datatypes as ibis_dtypes -import bigframes_vendored.ibis.expr.operations as ops -import bigframes_vendored.ibis.expr.schema as sch -import bigframes_vendored.ibis.expr.types as ir -import bigframes_vendored.sqlglot as sg -import bigframes_vendored.sqlglot.expressions as sge -import google.api_core.exceptions -import google.auth.credentials -import google.cloud.bigquery as bq -import google.cloud.bigquery_storage_v1 as bqstorage -import pydata_google_auth -from bigframes_vendored.ibis import util -from bigframes_vendored.ibis.backends import CanCreateDatabase, CanCreateSchema -from bigframes_vendored.ibis.backends.bigquery.client import ( - bigquery_param, - parse_project_and_dataset, - rename_partitioned_column, - schema_from_bigquery_table, -) -from bigframes_vendored.ibis.backends.bigquery.datatypes import BigQuerySchema -from bigframes_vendored.ibis.backends.sql import SQLBackend -from bigframes_vendored.ibis.backends.sql.compilers import BigQueryCompiler -from bigframes_vendored.ibis.backends.sql.datatypes import BigQueryType -from pydata_google_auth import cache - -if TYPE_CHECKING: - from collections.abc import Iterable, Mapping - from pathlib import Path - from urllib.parse import ParseResult - - import pandas as pd - import polars as pl - import pyarrow as pa - - -SCOPES = ["https://www.googleapis.com/auth/bigquery"] -EXTERNAL_DATA_SCOPES = [ - "https://www.googleapis.com/auth/bigquery", - "https://www.googleapis.com/auth/cloud-platform", - "https://www.googleapis.com/auth/drive", -] -CLIENT_ID = "546535678771-gvffde27nd83kfl6qbrnletqvkdmsese.apps.googleusercontent.com" -CLIENT_SECRET = "iU5ohAF2qcqrujegE3hQ1cPt" # noqa: S105 - - -def _create_user_agent(application_name: str) -> str: - user_agent = [] - - if application_name: - user_agent.append(application_name) - - user_agent_default_template = f"ibis/{bigframes_vendored.ibis.__version__}" - user_agent.append(user_agent_default_template) - - return " ".join(user_agent) - - -def _create_client_info(application_name): - from google.api_core.client_info import ClientInfo - - return ClientInfo(user_agent=_create_user_agent(application_name)) - - -def _create_client_info_gapic(application_name): - from google.api_core.gapic_v1.client_info import ClientInfo - - return ClientInfo(user_agent=_create_user_agent(application_name)) - - -_MEMTABLE_PATTERN = re.compile( - r"^_?ibis_(?:[A-Za-z_][A-Za-z_0-9]*)_memtable_[a-z0-9]{26}$" -) - - -def _qualify_memtable( - node: sge.Expression, *, dataset: str | None, project: str | None -) -> sge.Expression: - """Add a BigQuery dataset and project to memtable references.""" - if isinstance(node, sge.Table) and _MEMTABLE_PATTERN.match(node.name) is not None: - node.args["db"] = dataset - node.args["catalog"] = project - # make sure to quote table location - node = _force_quote_table(node) - return node - - -def _remove_null_ordering_from_unsupported_window( - node: sge.Expression, -) -> sge.Expression: - """Remove null ordering in window frame clauses not supported by BigQuery. - - BigQuery has only partial support for NULL FIRST/LAST in RANGE windows so - we remove it from any window frame clause that doesn't support it. - - Here's the support matrix: - - ✅ sum(x) over (order by y desc nulls last) - 🚫 sum(x) over (order by y asc nulls last) - ✅ sum(x) over (order by y asc nulls first) - 🚫 sum(x) over (order by y desc nulls first) - """ - if isinstance(node, sge.Window): - order = node.args.get("order") - if order is not None: - for key in order.args["expressions"]: - kargs = key.args - if kargs.get("desc") is True and kargs.get("nulls_first", False): - kargs["nulls_first"] = False - elif kargs.get("desc") is False and not kargs.setdefault( - "nulls_first", True - ): - kargs["nulls_first"] = True - return node - - -def _force_quote_table(table: sge.Table) -> sge.Table: - """Force quote all the parts of a bigquery path. - - The BigQuery identifier quoting semantics are bonkers - https://cloud.google.com/bigquery/docs/reference/standard-sql/lexical#identifiers - - my-table is OK, but not mydataset.my-table - - mytable-287 is OK, but not mytable-287a - - Just quote everything. - """ - for key in ("this", "db", "catalog"): - if (val := table.args[key]) is not None: - if isinstance(val, sg.exp.Identifier) and not val.quoted: - val.args["quoted"] = True - else: - table.args[key] = sg.to_identifier(val, quoted=True) - return table - - -class Backend(SQLBackend, CanCreateDatabase, CanCreateSchema): - name = "bigquery" - compiler = BigQueryCompiler() - supports_in_memory_tables = True - supports_python_udfs = False - - def __init__(self, *args, **kwargs) -> None: - super().__init__(*args, **kwargs) - self.__session_dataset: bq.DatasetReference | None = None - self._query_cache.lookup = lambda name: self.table( - name, - database=(self._session_dataset.project, self._session_dataset.dataset_id), - ).op() - - @property - def _session_dataset(self): - if self.__session_dataset is None: - self.__session_dataset = self._make_session() - return self.__session_dataset - - def _register_in_memory_table(self, op: ops.InMemoryTable) -> None: - raw_name = op.name - - session_dataset = self._session_dataset - project = session_dataset.project - dataset = session_dataset.dataset_id - - table_ref = bq.TableReference(session_dataset, raw_name) - try: - self.client.get_table(table_ref) - except google.api_core.exceptions.NotFound: - table_id = sg.table( - raw_name, db=dataset, catalog=project, quoted=False - ).sql(dialect=self.name) - bq_schema = BigQuerySchema.from_ibis(op.schema) - load_job = self.client.load_table_from_dataframe( - op.data.to_frame(), - table_id, - job_config=bq.LoadJobConfig( - # fail if the table already exists and contains data - write_disposition=bq.WriteDisposition.WRITE_EMPTY, - schema=bq_schema, - ), - ) - load_job.result() - - def _read_file( - self, - path: str | Path, - *, - table_name: str | None = None, - job_config: bq.LoadJobConfig, - ) -> ir.Table: - self._make_session() - - if table_name is None: - table_name = util.gen_name(f"bq_read_{job_config.source_format}") - - table_ref = self._session_dataset.table(table_name) - - database = self._session_dataset.dataset_id - catalog = self._session_dataset.project - - # drop the table if it exists - # - # we could do this with write_disposition = WRITE_TRUNCATE but then the - # concurrent append jobs aren't possible - # - # dropping the table first means all write_dispositions can be - # WRITE_APPEND - self.drop_table(table_name, database=(catalog, database), force=True) - - if os.path.isdir(path): - raise NotImplementedError("Reading from a directory is not supported.") - elif str(path).startswith("gs://"): - load_job = self.client.load_table_from_uri( - path, table_ref, job_config=job_config - ) - load_job.result() - else: - - def load(file: str) -> None: - with open(file, mode="rb") as f: - load_job = self.client.load_table_from_file( - f, table_ref, job_config=job_config - ) - load_job.result() - - job_config.write_disposition = bq.WriteDisposition.WRITE_APPEND - - with concurrent.futures.ThreadPoolExecutor() as executor: - for fut in concurrent.futures.as_completed( - executor.submit(load, file) for file in glob.glob(str(path)) - ): - fut.result() - - return self.table(table_name, database=(catalog, database)) - - def read_parquet( - self, path: str | Path, table_name: str | None = None, **kwargs: Any - ): - """Read Parquet data into a BigQuery table. - - Parameters - ---------- - path - Path to a Parquet file on GCS or the local filesystem. Globs are supported. - table_name - Optional table name - kwargs - Additional keyword arguments passed to `google.cloud.bigquery.LoadJobConfig`. - - Returns - ------- - Table - An Ibis table expression - - """ - return self._read_file( - path, - table_name=table_name, - job_config=bq.LoadJobConfig( - source_format=bq.SourceFormat.PARQUET, **kwargs - ), - ) - - def read_csv( - self, path: str | Path, table_name: str | None = None, **kwargs: Any - ) -> ir.Table: - """Read CSV data into a BigQuery table. - - Parameters - ---------- - path - Path to a CSV file on GCS or the local filesystem. Globs are supported. - table_name - Optional table name - kwargs - Additional keyword arguments passed to - `google.cloud.bigquery.LoadJobConfig`. - - Returns - ------- - Table - An Ibis table expression - - """ - job_config = bq.LoadJobConfig( - source_format=bq.SourceFormat.CSV, - autodetect=True, - skip_leading_rows=1, - **kwargs, - ) - return self._read_file(path, table_name=table_name, job_config=job_config) - - def read_json( - self, path: str | Path, table_name: str | None = None, **kwargs: Any - ) -> ir.Table: - """Read newline-delimited JSON data into a BigQuery table. - - Parameters - ---------- - path - Path to a newline-delimited JSON file on GCS or the local - filesystem. Globs are supported. - table_name - Optional table name - kwargs - Additional keyword arguments passed to - `google.cloud.bigquery.LoadJobConfig`. - - Returns - ------- - Table - An Ibis table expression - - """ - job_config = bq.LoadJobConfig( - source_format=bq.SourceFormat.NEWLINE_DELIMITED_JSON, - autodetect=True, - **kwargs, - ) - return self._read_file(path, table_name=table_name, job_config=job_config) - - def _from_url(self, url: ParseResult, **kwargs): - return self.connect( - project_id=url.netloc or kwargs.get("project_id", [""])[0], - dataset_id=url.path[1:] or kwargs.get("dataset_id", [""])[0], - **kwargs, - ) - - def do_connect( - self, - project_id: str | None = None, - dataset_id: str = "", - credentials: google.auth.credentials.Credentials | None = None, - application_name: str | None = None, - auth_local_webserver: bool = True, - auth_external_data: bool = False, - auth_cache: str = "default", - partition_column: str | None = "PARTITIONTIME", - client: bq.Client | None = None, - storage_client: bqstorage.BigQueryReadClient | None = None, - location: str | None = None, - ) -> Backend: - """Create a `Backend` for use with Ibis. - - Parameters - ---------- - project_id - A BigQuery project id. - dataset_id - A dataset id that lives inside of the project indicated by - `project_id`. - credentials - Optional credentials. - application_name - A string identifying your application to Google API endpoints. - auth_local_webserver - Use a local webserver for the user authentication. Binds a - webserver to an open port on localhost between 8080 and 8089, - inclusive, to receive authentication token. If not set, defaults to - False, which requests a token via the console. - auth_external_data - Authenticate using additional scopes required to `query external - data sources - `_, - such as Google Sheets, files in Google Cloud Storage, or files in - Google Drive. If not set, defaults to False, which requests the - default BigQuery scopes. - auth_cache - Selects the behavior of the credentials cache. - - ``'default'`` - Reads credentials from disk if available, otherwise - authenticates and caches credentials to disk. - - ``'reauth'`` - Authenticates and caches credentials to disk. - - ``'none'`` - Authenticates and does **not** cache credentials. - - Defaults to ``'default'``. - partition_column - Identifier to use instead of default ``_PARTITIONTIME`` partition - column. Defaults to ``'PARTITIONTIME'``. - client - A ``Client`` from the ``google.cloud.bigquery`` package. If not - set, one is created using the ``project_id`` and ``credentials``. - storage_client - A ``BigQueryReadClient`` from the - ``google.cloud.bigquery_storage_v1`` package. If not set, one is - created using the ``project_id`` and ``credentials``. - location - Default location for BigQuery objects. - - Returns - ------- - Backend - An instance of the BigQuery backend. - - """ - default_project_id = client.project if client is not None else project_id - - # Only need `credentials` to create a `client` and - # `storage_client`, so only one or the other needs to be set. - if (client is None or storage_client is None) and credentials is None: - scopes = SCOPES - if auth_external_data: - scopes = EXTERNAL_DATA_SCOPES - - if auth_cache == "default": - credentials_cache = cache.ReadWriteCredentialsCache( - filename="ibis.json" - ) - elif auth_cache == "reauth": - credentials_cache = cache.WriteOnlyCredentialsCache( - filename="ibis.json" - ) - elif auth_cache == "none": - credentials_cache = cache.NOOP - else: - raise ValueError( - f"Got unexpected value for auth_cache = '{auth_cache}'. " - "Expected one of 'default', 'reauth', or 'none'." - ) - - credentials, default_project_id = pydata_google_auth.default( - scopes, - client_id=CLIENT_ID, - client_secret=CLIENT_SECRET, - credentials_cache=credentials_cache, - use_local_webserver=auth_local_webserver, - ) - - project_id = project_id or default_project_id - - ( - self.data_project, - self.billing_project, - self.dataset, - ) = parse_project_and_dataset(project_id, dataset_id) - - if client is not None: - self.client = client - else: - self.client = bq.Client( - project=self.billing_project, - credentials=credentials, - client_info=_create_client_info(application_name), - location=location, - ) - - if self.client.default_query_job_config is None: - self.client.default_query_job_config = bq.QueryJobConfig() - - self.client.default_query_job_config.use_legacy_sql = False - self.client.default_query_job_config.allow_large_results = True - - if storage_client is not None: - self.storage_client = storage_client - else: - self.storage_client = bqstorage.BigQueryReadClient( - credentials=credentials, - client_info=_create_client_info_gapic(application_name), - ) - - self.partition_column = partition_column - - @util.experimental - @classmethod - def from_connection( - cls, - client: bq.Client, - partition_column: str | None = "PARTITIONTIME", - storage_client: bqstorage.BigQueryReadClient | None = None, - dataset_id: str = "", - ) -> Backend: - """Create a BigQuery `Backend` from an existing ``Client``. - - Parameters - ---------- - client - A `Client` from the `google.cloud.bigquery` package. - partition_column - Identifier to use instead of default `_PARTITIONTIME` partition - column. Defaults to `'PARTITIONTIME'`. - storage_client - A `BigQueryReadClient` from the `google.cloud.bigquery_storage_v1` - package. - dataset_id - A dataset id that lives inside of the project attached to `client`. - """ - return bigframes_vendored.ibis.bigquery.connect( - client=client, - partition_column=partition_column, - storage_client=storage_client, - dataset_id=dataset_id, - ) - - def disconnect(self) -> None: - self.client.close() - - def _parse_project_and_dataset(self, dataset) -> tuple[str, str]: - if isinstance(dataset, sge.Table): - dataset = dataset.sql(self.dialect) - if not dataset and not self.dataset: - raise ValueError("Unable to determine BigQuery dataset.") - project, _, dataset = parse_project_and_dataset( - self.billing_project, - dataset or f"{self.data_project}.{self.dataset}", - ) - return project, dataset - - @property - def project_id(self): - return self.data_project - - @property - def dataset_id(self): - return self.dataset - - def create_database( - self, - name: str, - catalog: str | None = None, - force: bool = False, - collate: str | None = None, - **options: Any, - ) -> None: - properties = [ - sge.Property(this=sg.to_identifier(name), value=sge.convert(value)) - for name, value in (options or {}).items() - ] - - if collate is not None: - properties.append( - sge.CollateProperty(this=sge.convert(collate), default=True) - ) - - stmt = sge.Create( - kind="SCHEMA", - this=sg.table(name, db=catalog), - exists=force, - properties=sge.Properties(expressions=properties), - ) - - self.raw_sql(stmt.sql(self.name)) - - def drop_database( - self, - name: str, - catalog: str | None = None, - force: bool = False, - cascade: bool = False, - ) -> None: - """Drop a BigQuery dataset.""" - stmt = sge.Drop( - kind="SCHEMA", - this=sg.table(name, db=catalog), - exists=force, - cascade=cascade, - ) - - self.raw_sql(stmt.sql(self.name)) - - def table( - self, name: str, database: str | None = None, schema: str | None = None - ) -> ir.Table: - table_loc = self._warn_and_create_table_loc(database, schema) - table = sg.parse_one(f"`{name}`", into=sge.Table, read=self.name) - - # Bigquery, unlike other backends, had existing support for specifying - # table hierarchy in the table name, e.g. con.table("dataset.table_name") - # so here we have an extra layer of disambiguation to handle. - - # Default `catalog` to None unless we've parsed it out of the database/schema kwargs - # Raise if there are path specifications in both the name and as a kwarg - catalog = None if table_loc is None else table_loc.catalog - if table.catalog: - if table_loc is not None and table_loc.catalog: - raise com.IbisInputError( - "Cannot specify catalog both in the table name and as an argument" - ) - else: - catalog = table.catalog - - # Default `db` to None unless we've parsed it out of the database/schema kwargs - db = None if table_loc is None else table_loc.db - if table.db: - if table_loc is not None and table_loc.db: - raise com.IbisInputError( - "Cannot specify database both in the table name and as an argument" - ) - else: - db = table.db - - database = ( - sg.table(None, db=db, catalog=catalog, quoted=False).sql(dialect=self.name) - or None - ) - - project, dataset = self._parse_project_and_dataset(database) - - bq_table = self.client.get_table( - bq.TableReference( - bq.DatasetReference(project=project, dataset_id=dataset), - table.name, - ) - ) - - node = ops.DatabaseTable( - table.name, - # https://cloud.google.com/bigquery/docs/querying-wildcard-tables#filtering_selected_tables_using_table_suffix - schema=schema_from_bigquery_table(bq_table, wildcard=table.name[-1] == "*"), - source=self, - namespace=ops.Namespace(database=dataset, catalog=project), - ) - table_expr = node.to_expr() - return rename_partitioned_column(table_expr, bq_table, self.partition_column) - - def _make_session(self) -> tuple[str, str]: - if (client := getattr(self, "client", None)) is not None: - job_config = bq.QueryJobConfig(use_query_cache=False) - query = client.query( - "SELECT 1", job_config=job_config, project=self.billing_project - ) - query.result() - - return bq.DatasetReference( - project=query.destination.project, - dataset_id=query.destination.dataset_id, - ) - return None - - def _get_schema_using_query(self, query: str) -> sch.Schema: - job = self.client.query( - query, - job_config=bq.QueryJobConfig(dry_run=True, use_query_cache=False), - project=self.billing_project, - ) - return BigQuerySchema.to_ibis(job.schema) - - def _to_sqlglot( - self, - expr: ir.Expr, - limit: str | None = None, - params: Mapping[ir.Expr, Any] | None = None, - **kwargs, - ) -> Any: - """Compile an Ibis expression. - - Parameters - ---------- - expr - Ibis expression - limit - For expressions yielding result sets; retrieve at most this number - of values/rows. Overrides any limit already set on the expression. - params - Named unbound parameters - kwargs - Keyword arguments passed to the compiler - - Returns - ------- - Any - The output of compilation. The type of this value depends on the - backend. - - """ - self._define_udf_translation_rules(expr) - sql = super()._to_sqlglot(expr, limit=limit, params=params, **kwargs) - - query = sql.transform( - _qualify_memtable, - dataset=getattr(self._session_dataset, "dataset_id", None), - project=getattr(self._session_dataset, "project", None), - ).transform(_remove_null_ordering_from_unsupported_window) - return query - - def raw_sql(self, query: str, params=None, page_size: int | None = None): - query_parameters = [ - bigquery_param( - param.type(), - value, - ( - param.get_name() - if not isinstance(op := param.op(), ops.Alias) - else op.arg.name - ), - ) - for param, value in (params or {}).items() - ] - with contextlib.suppress(AttributeError): - query = query.sql(self.dialect) - - job_config = bq.job.QueryJobConfig(query_parameters=query_parameters or []) - return self.client.query_and_wait( - query, - job_config=job_config, - project=self.billing_project, - page_size=page_size, - ) - - @property - def current_catalog(self) -> str: - return self.data_project - - @property - def current_database(self) -> str | None: - return self.dataset - - def compile( - self, expr: ir.Expr, limit: str | None = None, params=None, **kwargs: Any - ): - """Compile an Ibis expression to a SQL string.""" - query = self._to_sqlglot(expr, limit=limit, params=params, **kwargs) - sql = query.sql(dialect=self.name, pretty=True) - self._log(sql) - return sql - - def execute(self, expr, params=None, limit="default", **kwargs): - """Compile and execute the given Ibis expression. - - Compile and execute Ibis expression using this backend client - interface, returning results in-memory in the appropriate object type - - Parameters - ---------- - expr - Ibis expression to execute - limit - Retrieve at most this number of values/rows. Overrides any limit - already set on the expression. - params - Query parameters - kwargs - Extra arguments specific to the backend - - Returns - ------- - pd.DataFrame | pd.Series | scalar - Output from execution - - """ - from bigframes_vendored.ibis.backends.bigquery.converter import ( - BigQueryPandasData, - ) - - self._run_pre_execute_hooks(expr) - - schema = expr.as_table().schema() - bigframes_vendored.ibis.schema( - {"_TABLE_SUFFIX": ibis_dtypes.string()} - ) - - sql = self.compile(expr, limit=limit, params=params, **kwargs) - self._log(sql) - query = self.raw_sql(sql, params=params, **kwargs) - - arrow_t = query.to_arrow( - progress_bar_type=None, bqstorage_client=self.storage_client - ) - - result = BigQueryPandasData.convert_table( - arrow_t.to_pandas(timestamp_as_object=True), schema - ) - - return expr.__pandas_result__(result, schema=schema) - - def insert( - self, - table_name: str, - obj: pd.DataFrame | ir.Table | list | dict, - schema: str | None = None, - database: str | None = None, - overwrite: bool = False, - ): - """Insert data into a table. - - Parameters - ---------- - table_name - The name of the table to which data needs will be inserted - obj - The source data or expression to insert - schema - The name of the schema that the table is located in - database - Name of the attached database that the table is located in. - overwrite - If `True` then replace existing contents of table - - """ - table_loc = self._warn_and_create_table_loc(database, schema) - catalog, db = self._to_catalog_db_tuple(table_loc) - if catalog is None: - catalog = self.current_catalog - if db is None: - db = self.current_database - - return super().insert( - table_name, - obj, - database=(catalog, db), - overwrite=overwrite, - ) - - def to_pyarrow( - self, - expr: ir.Expr, - *, - params: Mapping[ir.Scalar, Any] | None = None, - limit: int | str | None = None, - **kwargs: Any, - ) -> pa.Table: - self._import_pyarrow() - self._register_in_memory_tables(expr) - sql = self.compile(expr, limit=limit, params=params, **kwargs) - self._log(sql) - query = self.raw_sql(sql, params=params, **kwargs) - table = query.to_arrow( - progress_bar_type=None, bqstorage_client=self.storage_client - ) - table = table.rename_columns(list(expr.as_table().schema().names)) - return expr.__pyarrow_result__(table) - - def to_pyarrow_batches( - self, - expr: ir.Expr, - *, - params: Mapping[ir.Scalar, Any] | None = None, - limit: int | str | None = None, - chunk_size: int = 1_000_000, - **kwargs: Any, - ): - pa = self._import_pyarrow() - - schema = expr.as_table().schema() - - self._register_in_memory_tables(expr) - sql = self.compile(expr, limit=limit, params=params, **kwargs) - self._log(sql) - query = self.raw_sql(sql, params=params, page_size=chunk_size, **kwargs) - batch_iter = query.to_arrow_iterable(bqstorage_client=self.storage_client) - return pa.ipc.RecordBatchReader.from_batches(schema.to_pyarrow(), batch_iter) - - def _gen_udf_name(self, name: str, schema: Optional[str]) -> str: - func = ".".join(filter(None, (schema, name))) - if "." in func: - return ".".join(f"`{part}`" for part in func.split(".")) - return func - - def get_schema( - self, - name, - *, - catalog: str | None = None, - database: str | None = None, - ): - table_ref = bq.TableReference( - bq.DatasetReference( - project=catalog or self.data_project, - dataset_id=database or self.current_database, - ), - name, - ) - return schema_from_bigquery_table( - self.client.get_table(table_ref), - # https://cloud.google.com/bigquery/docs/querying-wildcard-tables#filtering_selected_tables_using_table_suffix - wildcard=name[-1] == "*", - ) - - def list_databases( - self, like: str | None = None, catalog: str | None = None - ) -> list[str]: - results = [ - dataset.dataset_id - for dataset in self.client.list_datasets( - project=catalog if catalog is not None else self.data_project - ) - ] - return self._filter_with_like(results, like) - - def list_tables( - self, - like: str | None = None, - database: tuple[str, str] | str | None = None, - schema: str | None = None, - ) -> list[str]: - """List the tables in the database. - - Parameters - ---------- - like - A pattern to use for listing tables. - database - The database location to perform the list against. - - By default uses the current `dataset` (`self.current_database`) and - `project` (`self.current_catalog`). - - To specify a table in a separate BigQuery dataset, you can pass in the - dataset and project as a string `"dataset.project"`, or as a tuple of - strings `("dataset", "project")`. - - ::: {.callout-note} - ## Ibis does not use the word `schema` to refer to database hierarchy. - - A collection of tables is referred to as a `database`. - A collection of `database` is referred to as a `catalog`. - - These terms are mapped onto the corresponding features in each - backend (where available), regardless of whether the backend itself - uses the same terminology. - ::: - schema - [deprecated] The schema (dataset) inside `database` to perform the list against. - """ - table_loc = self._warn_and_create_table_loc(database, schema) - - project, dataset = self._parse_project_and_dataset(table_loc) - dataset_ref = bq.DatasetReference(project, dataset) - result = [table.table_id for table in self.client.list_tables(dataset_ref)] - return self._filter_with_like(result, like) - - def set_database(self, name): - self.data_project, self.dataset = self._parse_project_and_dataset(name) - - @property - def version(self): - return bq.__version__ - - def create_table( - self, - name: str, - obj: ir.Table - | pd.DataFrame - | pa.Table - | pl.DataFrame - | pl.LazyFrame - | None = None, - *, - schema: bigframes_vendored.ibis.Schema | None = None, - database: str | None = None, - temp: bool = False, - overwrite: bool = False, - default_collate: str | None = None, - partition_by: str | None = None, - cluster_by: Iterable[str] | None = None, - options: Mapping[str, Any] | None = None, - ) -> ir.Table: - """Create a table in BigQuery. - - Parameters - ---------- - name - Name of the table to create - obj - The data with which to populate the table; optional, but one of `obj` - or `schema` must be specified - schema - The schema of the table to create; optional, but one of `obj` or - `schema` must be specified - database - The BigQuery *dataset* in which to create the table; optional - temp - Whether the table is temporary - overwrite - If `True`, replace the table if it already exists, otherwise fail if - the table exists - default_collate - Default collation for string columns. See BigQuery's documentation - for more details: https://cloud.google.com/bigquery/docs/reference/standard-sql/collation-concepts - partition_by - Partition the table by the given expression. See BigQuery's documentation - for more details: https://cloud.google.com/bigquery/docs/reference/standard-sql/data-definition-language#partition_expression - cluster_by - List of columns to cluster the table by. See BigQuery's documentation - for more details: https://cloud.google.com/bigquery/docs/reference/standard-sql/data-definition-language#clustering_column_list - options - BigQuery-specific table options; see the BigQuery documentation for - details: https://cloud.google.com/bigquery/docs/reference/standard-sql/data-definition-language#table_option_list - - Returns - ------- - Table - The table that was just created - - """ - if obj is None and schema is None: - raise com.IbisError("One of the `schema` or `obj` parameter is required") - - if isinstance(obj, ir.Table) and schema is not None: - if not schema.equals(obj.schema()): - raise com.IbisTypeError( - "Provided schema and Ibis table schema are incompatible. Please " - "align the two schemas, or provide only one of the two arguments." - ) - - project_id, dataset = self._parse_project_and_dataset(database) - - properties = [] - - if default_collate is not None: - properties.append( - sge.CollateProperty(this=sge.convert(default_collate), default=True) - ) - - if partition_by is not None: - properties.append( - sge.PartitionedByProperty( - this=sge.Tuple( - expressions=list(map(sg.to_identifier, partition_by)) - ) - ) - ) - - if cluster_by is not None: - properties.append( - sge.Cluster(expressions=list(map(sg.to_identifier, cluster_by))) - ) - - properties.extend( - sge.Property(this=sg.to_identifier(name), value=sge.convert(value)) - for name, value in (options or {}).items() - ) - - if obj is not None and not isinstance(obj, ir.Table): - obj = bigframes_vendored.ibis.memtable(obj, schema=schema) - - if obj is not None: - self._register_in_memory_tables(obj) - - if temp: - dataset = self._session_dataset.dataset_id - if database is not None: - raise com.IbisInputError("Cannot specify database for temporary table") - database = self._session_dataset.project - else: - dataset = database or self.current_database - - try: - table = sg.parse_one(name, into=sge.Table, read="bigquery") - except sg.ParseError: - table = sg.table( - name, - db=dataset, - catalog=project_id, - quoted=self.compiler.quoted, - ) - else: - if table.args["db"] is None: - table.args["db"] = dataset - - if table.args["catalog"] is None: - table.args["catalog"] = project_id - - table = _force_quote_table(table) - - column_defs = [ - sge.ColumnDef( - this=sg.to_identifier(name, quoted=self.compiler.quoted), - kind=BigQueryType.from_ibis(typ), - constraints=( - None - if typ.nullable or typ.is_array() - else [sge.ColumnConstraint(kind=sge.NotNullColumnConstraint())] - ), - ) - for name, typ in (schema or {}).items() - ] - - stmt = sge.Create( - kind="TABLE", - this=sge.Schema(this=table, expressions=column_defs or None), - replace=overwrite, - properties=sge.Properties(expressions=properties), - expression=None if obj is None else self.compile(obj), - ) - - sql = stmt.sql(self.name) - - self.raw_sql(sql) - return self.table(table.name, database=(table.catalog, table.db)) - - def drop_table( - self, - name: str, - *, - schema: str | None = None, - database: tuple[str | str] | str | None = None, - force: bool = False, - ) -> None: - table_loc = self._warn_and_create_table_loc(database, schema) - catalog, db = self._to_catalog_db_tuple(table_loc) - stmt = sge.Drop( - kind="TABLE", - this=sg.table( - name, - db=db or self.current_database, - catalog=catalog or self.billing_project, - ), - exists=force, - ) - self.raw_sql(stmt.sql(self.name)) - - def create_view( - self, - name: str, - obj: ir.Table, - *, - schema: str | None = None, - database: str | None = None, - overwrite: bool = False, - ) -> ir.Table: - table_loc = self._warn_and_create_table_loc(database, schema) - catalog, db = self._to_catalog_db_tuple(table_loc) - - stmt = sge.Create( - kind="VIEW", - this=sg.table( - name, - db=db or self.current_database, - catalog=catalog or self.billing_project, - ), - expression=self.compile(obj), - replace=overwrite, - ) - self._register_in_memory_tables(obj) - self.raw_sql(stmt.sql(self.name)) - return self.table(name, database=(catalog, database)) - - def drop_view( - self, - name: str, - *, - schema: str | None = None, - database: str | None = None, - force: bool = False, - ) -> None: - table_loc = self._warn_and_create_table_loc(database, schema) - catalog, db = self._to_catalog_db_tuple(table_loc) - - stmt = sge.Drop( - kind="VIEW", - this=sg.table( - name, - db=db or self.current_database, - catalog=catalog or self.billing_project, - ), - exists=force, - ) - self.raw_sql(stmt.sql(self.name)) - - def _load_into_cache(self, name, expr): - self.create_table(name, expr, schema=expr.schema(), temp=True) - - def _clean_up_cached_table(self, name): - self.drop_table( - name, - database=(self._session_dataset.project, self._session_dataset.dataset_id), - force=True, - ) - - def _register_udfs(self, expr: ir.Expr) -> None: - """No op because UDFs made with CREATE TEMPORARY FUNCTION must be followed by a query.""" - - @contextlib.contextmanager - def _safe_raw_sql(self, *args, **kwargs): - yield self.raw_sql(*args, **kwargs) - - # TODO: remove when the schema kwarg is removed - def _warn_and_create_table_loc(self, database=None, schema=None): - if schema is not None: - self._warn_schema() - if database is not None and schema is not None: - if isinstance(database, str): - table_loc = f"{database}.{schema}" - elif isinstance(database, tuple): - table_loc = database + schema - elif schema is not None: - table_loc = schema - elif database is not None: - table_loc = database - else: - table_loc = None - - table_loc = self._to_sqlglot_table(table_loc) - - if table_loc is not None: - if (sg_cat := table_loc.args["catalog"]) is not None: - sg_cat.args["quoted"] = False - if (sg_db := table_loc.args["db"]) is not None: - sg_db.args["quoted"] = False - - return table_loc - - -def compile(expr, params=None, **kwargs): - """Compile an expression for BigQuery.""" - backend = Backend() - return backend.compile(expr, params=params, **kwargs) - - -def connect( - project_id: str | None = None, - dataset_id: str = "", - credentials: google.auth.credentials.Credentials | None = None, - application_name: str | None = None, - auth_local_webserver: bool = False, - auth_external_data: bool = False, - auth_cache: str = "default", - partition_column: str | None = "PARTITIONTIME", -) -> Backend: - """Create a :class:`Backend` for use with Ibis. - - Parameters - ---------- - project_id - A BigQuery project id. - dataset_id - A dataset id that lives inside of the project indicated by - `project_id`. - credentials - Optional credentials. - application_name - A string identifying your application to Google API endpoints. - auth_local_webserver - Use a local webserver for the user authentication. Binds a - webserver to an open port on localhost between 8080 and 8089, - inclusive, to receive authentication token. If not set, defaults - to False, which requests a token via the console. - auth_external_data - Authenticate using additional scopes required to `query external - data sources - `_, - such as Google Sheets, files in Google Cloud Storage, or files in - Google Drive. If not set, defaults to False, which requests the - default BigQuery scopes. - auth_cache - Selects the behavior of the credentials cache. - - ``'default'`` - Reads credentials from disk if available, otherwise - authenticates and caches credentials to disk. - - ``'reauth'`` - Authenticates and caches credentials to disk. - - ``'none'`` - Authenticates and does **not** cache credentials. - - Defaults to ``'default'``. - partition_column - Identifier to use instead of default ``_PARTITIONTIME`` partition - column. Defaults to ``'PARTITIONTIME'``. - - Returns - ------- - Backend - An instance of the BigQuery backend - - """ - backend = Backend() - return backend.connect( - project_id=project_id, - dataset_id=dataset_id, - credentials=credentials, - application_name=application_name, - auth_local_webserver=auth_local_webserver, - auth_external_data=auth_external_data, - auth_cache=auth_cache, - partition_column=partition_column, - ) - - -__all__ = [ - "Backend", - "compile", - "connect", -] diff --git a/third_party/bigframes_vendored/ibis/backends/bigquery/backend.py b/third_party/bigframes_vendored/ibis/backends/bigquery/backend.py deleted file mode 100644 index 312b284bbb8..00000000000 --- a/third_party/bigframes_vendored/ibis/backends/bigquery/backend.py +++ /dev/null @@ -1,1187 +0,0 @@ -# Contains code from https://github.com/ibis-project/ibis/blob/9.2.0/ibis/backends/bigquery/__init__.py - - -"""BigQuery public API.""" - -from __future__ import annotations - -import concurrent.futures -import contextlib -import glob -import os -from typing import TYPE_CHECKING, Any, Optional - -import bigframes_vendored.ibis -import bigframes_vendored.ibis.backends.sql.compilers as sc -import bigframes_vendored.ibis.common.exceptions as com -import bigframes_vendored.ibis.expr.operations as ops -import bigframes_vendored.ibis.expr.schema as sch -import bigframes_vendored.ibis.expr.types as ir -import bigframes_vendored.sqlglot as sg -import bigframes_vendored.sqlglot.expressions as sge -import google.api_core.exceptions -import google.auth.credentials -import google.cloud.bigquery as bq -import google.cloud.bigquery_storage_v1 as bqstorage -import pydata_google_auth -from bigframes_vendored.ibis import util -from bigframes_vendored.ibis.backends import CanCreateDatabase, CanCreateSchema -from bigframes_vendored.ibis.backends.bigquery.client import ( - bigquery_param, - parse_project_and_dataset, - rename_partitioned_column, - schema_from_bigquery_table, -) -from bigframes_vendored.ibis.backends.bigquery.datatypes import ( - BigQuerySchema, - BigQueryType, -) -from bigframes_vendored.ibis.backends.sql import SQLBackend -from pydata_google_auth import cache - -if TYPE_CHECKING: - from collections.abc import Iterable, Mapping - from pathlib import Path - from urllib.parse import ParseResult - - import pandas as pd - import polars as pl - import pyarrow as pa - - -SCOPES = ["https://www.googleapis.com/auth/bigquery"] -EXTERNAL_DATA_SCOPES = [ - "https://www.googleapis.com/auth/bigquery", - "https://www.googleapis.com/auth/cloud-platform", - "https://www.googleapis.com/auth/drive", -] -CLIENT_ID = "546535678771-gvffde27nd83kfl6qbrnletqvkdmsese.apps.googleusercontent.com" -CLIENT_SECRET = "iU5ohAF2qcqrujegE3hQ1cPt" # noqa: S105 - - -def _create_user_agent(application_name: str) -> str: - user_agent = [] - - if application_name: - user_agent.append(application_name) - - user_agent_default_template = f"ibis/{bigframes_vendored.ibis.__version__}" - user_agent.append(user_agent_default_template) - - return " ".join(user_agent) - - -def _create_client_info(application_name): - from google.api_core.client_info import ClientInfo - - return ClientInfo(user_agent=_create_user_agent(application_name)) - - -def _create_client_info_gapic(application_name): - from google.api_core.gapic_v1.client_info import ClientInfo - - return ClientInfo(user_agent=_create_user_agent(application_name)) - - -def _force_quote_table(table: sge.Table) -> sge.Table: - """Force quote all the parts of a bigquery path. - - https://cloud.google.com/bigquery/docs/reference/standard-sql/lexical#identifiers - - my-table is OK, but not mydataset.my-table - - mytable-287 is OK, but not mytable-287a - - Just quote everything. - """ - for key in ("this", "db", "catalog"): - if (val := table.args[key]) is not None: - if isinstance(val, sg.exp.Identifier) and not val.quoted: - val.args["quoted"] = True - else: - table.args[key] = sg.to_identifier(val, quoted=True) - return table - - -class Backend(SQLBackend, CanCreateDatabase, CanCreateSchema): - name = "bigquery" - compiler = sc.bigquery.compiler - supports_in_memory_tables = True - supports_python_udfs = False - - def __init__(self, *args, **kwargs) -> None: - super().__init__(*args, **kwargs) - self.__session_dataset: bq.DatasetReference | None = None - self._query_cache.lookup = lambda name: self.table( - name, - database=(self._session_dataset.project, self._session_dataset.dataset_id), - ).op() - - @property - def _session_dataset(self): - if self.__session_dataset is None: - self.__session_dataset = self._make_session() - return self.__session_dataset - - def _read_file( - self, - path: str | Path, - *, - table_name: str | None = None, - job_config: bq.LoadJobConfig, - ) -> ir.Table: - self._make_session() - - if table_name is None: - table_name = util.gen_name(f"bq_read_{job_config.source_format}") - - table_ref = self._session_dataset.table(table_name) - - database = self._session_dataset.dataset_id - catalog = self._session_dataset.project - - # drop the table if it exists - # - # we could do this with write_disposition = WRITE_TRUNCATE but then the - # concurrent append jobs aren't possible - # - # dropping the table first means all write_dispositions can be - # WRITE_APPEND - self.drop_table(table_name, database=(catalog, database), force=True) - - if os.path.isdir(path): - raise NotImplementedError("Reading from a directory is not supported.") - elif str(path).startswith("gs://"): - load_job = self.client.load_table_from_uri( - path, table_ref, job_config=job_config - ) - load_job.result() - else: - - def load(file: str) -> None: - with open(file, mode="rb") as f: - load_job = self.client.load_table_from_file( - f, table_ref, job_config=job_config - ) - load_job.result() - - job_config.write_disposition = bq.WriteDisposition.WRITE_APPEND - - with concurrent.futures.ThreadPoolExecutor() as executor: - for fut in concurrent.futures.as_completed( - executor.submit(load, file) for file in glob.glob(str(path)) - ): - fut.result() - - return self.table(table_name, database=(catalog, database)) - - def read_parquet( - self, path: str | Path, table_name: str | None = None, **kwargs: Any - ): - """Read Parquet data into a BigQuery table. - - Parameters - ---------- - path - Path to a Parquet file on GCS or the local filesystem. Globs are supported. - table_name - Optional table name - kwargs - Additional keyword arguments passed to `google.cloud.bigquery.LoadJobConfig`. - - Returns - ------- - Table - An Ibis table expression - - """ - return self._read_file( - path, - table_name=table_name, - job_config=bq.LoadJobConfig( - source_format=bq.SourceFormat.PARQUET, **kwargs - ), - ) - - def read_csv( - self, path: str | Path, table_name: str | None = None, **kwargs: Any - ) -> ir.Table: - """Read CSV data into a BigQuery table. - - Parameters - ---------- - path - Path to a CSV file on GCS or the local filesystem. Globs are supported. - table_name - Optional table name - kwargs - Additional keyword arguments passed to - `google.cloud.bigquery.LoadJobConfig`. - - Returns - ------- - Table - An Ibis table expression - - """ - job_config = bq.LoadJobConfig( - source_format=bq.SourceFormat.CSV, - autodetect=True, - skip_leading_rows=1, - **kwargs, - ) - return self._read_file(path, table_name=table_name, job_config=job_config) - - def read_json( - self, path: str | Path, table_name: str | None = None, **kwargs: Any - ) -> ir.Table: - """Read newline-delimited JSON data into a BigQuery table. - - Parameters - ---------- - path - Path to a newline-delimited JSON file on GCS or the local - filesystem. Globs are supported. - table_name - Optional table name - kwargs - Additional keyword arguments passed to - `google.cloud.bigquery.LoadJobConfig`. - - Returns - ------- - Table - An Ibis table expression - - """ - job_config = bq.LoadJobConfig( - source_format=bq.SourceFormat.NEWLINE_DELIMITED_JSON, - autodetect=True, - **kwargs, - ) - return self._read_file(path, table_name=table_name, job_config=job_config) - - def _from_url(self, url: ParseResult, **kwargs): - return self.connect( - project_id=url.netloc or kwargs.get("project_id", [""])[0], - dataset_id=url.path[1:] or kwargs.get("dataset_id", [""])[0], - **kwargs, - ) - - def do_connect( - self, - project_id: str | None = None, - dataset_id: str = "", - credentials: google.auth.credentials.Credentials | None = None, - application_name: str | None = None, - auth_local_webserver: bool = True, - auth_external_data: bool = False, - auth_cache: str = "default", - partition_column: str | None = "PARTITIONTIME", - client: bq.Client | None = None, - storage_client: bqstorage.BigQueryReadClient | None = None, - location: str | None = None, - ) -> Backend: - """Create a `Backend` for use with Ibis. - - Parameters - ---------- - project_id - A BigQuery project id. - dataset_id - A dataset id that lives inside of the project indicated by - `project_id`. - credentials - Optional credentials. - application_name - A string identifying your application to Google API endpoints. - auth_local_webserver - Use a local webserver for the user authentication. Binds a - webserver to an open port on localhost between 8080 and 8089, - inclusive, to receive authentication token. If not set, defaults to - False, which requests a token via the console. - auth_external_data - Authenticate using additional scopes required to `query external - data sources - `_, - such as Google Sheets, files in Google Cloud Storage, or files in - Google Drive. If not set, defaults to False, which requests the - default BigQuery scopes. - auth_cache - Selects the behavior of the credentials cache. - - `'default'`` - Reads credentials from disk if available, otherwise - authenticates and caches credentials to disk. - - `'reauth'`` - Authenticates and caches credentials to disk. - - `'none'`` - Authenticates and does **not** cache credentials. - - Defaults to `'default'`. - partition_column - Identifier to use instead of default `_PARTITIONTIME` partition - column. Defaults to `'PARTITIONTIME'`. - client - A `Client` from the `google.cloud.bigquery` package. If not - set, one is created using the `project_id` and `credentials`. - storage_client - A `BigQueryReadClient` from the - `google.cloud.bigquery_storage_v1` package. If not set, one is - created using the `project_id` and `credentials`. - location - Default location for BigQuery objects. - - Returns - ------- - Backend - An instance of the BigQuery backend. - - """ - default_project_id = client.project if client is not None else project_id - - # Only need `credentials` to create a `client` and - # `storage_client`, so only one or the other needs to be set. - if (client is None or storage_client is None) and credentials is None: - scopes = SCOPES - if auth_external_data: - scopes = EXTERNAL_DATA_SCOPES - - if auth_cache == "default": - credentials_cache = cache.ReadWriteCredentialsCache( - filename="ibis.json" - ) - elif auth_cache == "reauth": - credentials_cache = cache.WriteOnlyCredentialsCache( - filename="ibis.json" - ) - elif auth_cache == "none": - credentials_cache = cache.NOOP - else: - raise ValueError( - f"Got unexpected value for auth_cache = '{auth_cache}'. " - "Expected one of 'default', 'reauth', or 'none'." - ) - - credentials, default_project_id = pydata_google_auth.default( - scopes, - client_id=CLIENT_ID, - client_secret=CLIENT_SECRET, - credentials_cache=credentials_cache, - use_local_webserver=auth_local_webserver, - ) - - project_id = project_id or default_project_id - - ( - self.data_project, - self.billing_project, - self.dataset, - ) = parse_project_and_dataset(project_id, dataset_id) - - if client is not None: - self.client = client - else: - self.client = bq.Client( - project=self.billing_project, - credentials=credentials, - client_info=_create_client_info(application_name), - location=location, - ) - - if self.client.default_query_job_config is None: - self.client.default_query_job_config = bq.QueryJobConfig() - - self.client.default_query_job_config.use_legacy_sql = False - self.client.default_query_job_config.allow_large_results = True - - if storage_client is not None: - self.storage_client = storage_client - else: - self.storage_client = bqstorage.BigQueryReadClient( - credentials=credentials, - client_info=_create_client_info_gapic(application_name), - ) - - self.partition_column = partition_column - - @util.experimental - @classmethod - def from_connection( - cls, - client: bq.Client, - partition_column: str | None = "PARTITIONTIME", - storage_client: bqstorage.BigQueryReadClient | None = None, - dataset_id: str = "", - ) -> Backend: - """Create a BigQuery `Backend` from an existing `Client`. - - Parameters - ---------- - client - A `Client` from the `google.cloud.bigquery` package. - partition_column - Identifier to use instead of default `_PARTITIONTIME` partition - column. Defaults to `'PARTITIONTIME'`. - storage_client - A `BigQueryReadClient` from the `google.cloud.bigquery_storage_v1` - package. - dataset_id - A dataset id that lives inside of the project attached to `client`. - """ - return bigframes_vendored.ibis.bigquery.connect( - client=client, - partition_column=partition_column, - storage_client=storage_client, - dataset_id=dataset_id, - ) - - def disconnect(self) -> None: - self.client.close() - - def _parse_project_and_dataset(self, dataset) -> tuple[str, str]: - if isinstance(dataset, sge.Table): - dataset = dataset.sql(self.dialect) - if not dataset and not self.dataset: - raise ValueError("Unable to determine BigQuery dataset.") - project, _, dataset = parse_project_and_dataset( - self.billing_project, - dataset or f"{self.data_project}.{self.dataset}", - ) - return project, dataset - - @property - def project_id(self): - return self.data_project - - @property - def dataset_id(self): - return self.dataset - - def create_database( - self, - name: str, - catalog: str | None = None, - force: bool = False, - collate: str | None = None, - **options: Any, - ) -> None: - properties = [ - sge.Property(this=sg.to_identifier(name), value=sge.convert(value)) - for name, value in (options or {}).items() - ] - - if collate is not None: - properties.append( - sge.CollateProperty(this=sge.convert(collate), default=True) - ) - - stmt = sge.Create( - kind="SCHEMA", - this=sg.table(name, db=catalog), - exists=force, - properties=sge.Properties(expressions=properties), - ) - - self.raw_sql(stmt.sql(self.name)) - - def drop_database( - self, - name: str, - catalog: str | None = None, - force: bool = False, - cascade: bool = False, - ) -> None: - """Drop a BigQuery dataset.""" - stmt = sge.Drop( - kind="SCHEMA", - this=sg.table(name, db=catalog), - exists=force, - cascade=cascade, - ) - - self.raw_sql(stmt.sql(self.name)) - - def table( - self, name: str, database: str | None = None, schema: str | None = None - ) -> ir.Table: - table_loc = self._warn_and_create_table_loc(database, schema) - table = sg.parse_one(f"`{name}`", into=sge.Table, read=self.name) - - # Bigquery, unlike other backends, had existing support for specifying - # table hierarchy in the table name, e.g. con.table("dataset.table_name") - # so here we have an extra layer of disambiguation to handle. - - # Default `catalog` to None unless we've parsed it out of the database/schema kwargs - # Raise if there are path specifications in both the name and as a kwarg - catalog = table_loc.args["catalog"] # args access will return None, not '' - if table.catalog: - if table_loc.catalog: - raise com.IbisInputError( - "Cannot specify catalog both in the table name and as an argument" - ) - else: - catalog = table.catalog - - # Default `db` to None unless we've parsed it out of the database/schema kwargs - db = table_loc.args["db"] # args access will return None, not '' - if table.db: - if table_loc.db: - raise com.IbisInputError( - "Cannot specify database both in the table name and as an argument" - ) - else: - db = table.db - - database = ( - sg.table(None, db=db, catalog=catalog, quoted=False).sql(dialect=self.name) - or None - ) - - project, dataset = self._parse_project_and_dataset(database) - - bq_table = self.client.get_table( - bq.TableReference( - bq.DatasetReference(project=project, dataset_id=dataset), - table.name, - ) - ) - - node = ops.DatabaseTable( - table.name, - # https://cloud.google.com/bigquery/docs/querying-wildcard-tables#filtering_selected_tables_using_table_suffix - schema=schema_from_bigquery_table(bq_table, wildcard=table.name[-1] == "*"), - source=self, - namespace=ops.Namespace(database=dataset, catalog=project), - ) - table_expr = node.to_expr() - return rename_partitioned_column(table_expr, bq_table, self.partition_column) - - def _make_session(self) -> tuple[str, str]: - if (client := getattr(self, "client", None)) is not None: - job_config = bq.QueryJobConfig(use_query_cache=False) - query = client.query( - "SELECT 1", job_config=job_config, project=self.billing_project - ) - query.result() - - return bq.DatasetReference( - project=query.destination.project, - dataset_id=query.destination.dataset_id, - ) - return None - - def _get_schema_using_query(self, query: str) -> sch.Schema: - job = self.client.query( - query, - job_config=bq.QueryJobConfig(dry_run=True, use_query_cache=False), - project=self.billing_project, - ) - return BigQuerySchema.to_ibis(job.schema) - - def raw_sql(self, query: str, params=None, page_size: int | None = None): - query_parameters = [ - bigquery_param( - param.type(), - value, - ( - param.get_name() - if not isinstance(op := param.op(), ops.Alias) - else op.arg.name - ), - ) - for param, value in (params or {}).items() - ] - with contextlib.suppress(AttributeError): - query = query.sql(self.dialect) - - job_config = bq.job.QueryJobConfig(query_parameters=query_parameters or []) - return self.client.query_and_wait( - query, - job_config=job_config, - project=self.billing_project, - page_size=page_size, - ) - - @property - def current_catalog(self) -> str: - return self.data_project - - @property - def current_database(self) -> str | None: - return self.dataset - - def compile( - self, - expr: ir.Expr, - limit: str | None = None, - params=None, - pretty: bool = True, - **kwargs: Any, - ): - """Compile an Ibis expression to a SQL string.""" - session_dataset = self._session_dataset - query = self.compiler.to_sqlglot( - expr, - limit=limit, - params=params, - session_dataset_id=getattr(session_dataset, "dataset_id", None), - session_project=getattr(session_dataset, "project", None), - **kwargs, - ) - queries = query if isinstance(query, list) else [query] - sql = ";\n".join(query.sql(self.dialect, pretty=pretty) for query in queries) - self._log(sql) - return sql - - def execute(self, expr, params=None, limit="default", **kwargs): - """Compile and execute the given Ibis expression. - Compile and execute Ibis expression using this backend client - interface, returning results in-memory in the appropriate object type - Parameters - ---------- - expr - Ibis expression to execute - limit - Retrieve at most this number of values/rows. Overrides any limit - already set on the expression. - params - Query parameters - kwargs - Extra arguments specific to the backend - Returns - ------- - pd.DataFrame | pd.Series | scalar - Output from execution - """ - from bigframes_vendored.ibis.backends.bigquery.converter import ( - BigQueryPandasData, - ) - - self._run_pre_execute_hooks(expr) - - schema = expr.as_table().schema() - bigframes_vendored.ibis.schema( - {"_TABLE_SUFFIX": "string"} - ) - - sql = self.compile(expr, limit=limit, params=params, **kwargs) - self._log(sql) - query = self.raw_sql(sql, params=params, **kwargs) - - arrow_t = query.to_arrow( - progress_bar_type=None, bqstorage_client=self.storage_client - ) - - result = BigQueryPandasData.convert_table( - arrow_t.to_pandas(timestamp_as_object=True), schema - ) - - return expr.__pandas_result__(result, schema=schema) - - def insert( - self, - table_name: str, - obj: pd.DataFrame | ir.Table | list | dict, - schema: str | None = None, - database: str | None = None, - overwrite: bool = False, - ): - """Insert data into a table. - - Parameters - ---------- - table_name - The name of the table to which data needs will be inserted - obj - The source data or expression to insert - schema - The name of the schema that the table is located in - database - Name of the attached database that the table is located in. - overwrite - If `True` then replace existing contents of table - - """ - table_loc = self._warn_and_create_table_loc(database, schema) - catalog, db = self._to_catalog_db_tuple(table_loc) - if catalog is None: - catalog = self.current_catalog - if db is None: - db = self.current_database - - return super().insert( - table_name, - obj, - database=(catalog, db), - overwrite=overwrite, - ) - - def to_pyarrow( - self, - expr: ir.Expr, - *, - params: Mapping[ir.Scalar, Any] | None = None, - limit: int | str | None = None, - **kwargs: Any, - ) -> pa.Table: - self._import_pyarrow() - sql = self.compile(expr, limit=limit, params=params, **kwargs) - self._log(sql) - query = self.raw_sql(sql, params=params, **kwargs) - table = query.to_arrow( - progress_bar_type=None, bqstorage_client=self.storage_client - ) - table = table.rename_columns(list(expr.as_table().schema().names)) - return expr.__pyarrow_result__(table) - - def to_pyarrow_batches( - self, - expr: ir.Expr, - *, - params: Mapping[ir.Scalar, Any] | None = None, - limit: int | str | None = None, - chunk_size: int = 1_000_000, - **kwargs: Any, - ): - pa = self._import_pyarrow() - - schema = expr.as_table().schema() - - sql = self.compile(expr, limit=limit, params=params, **kwargs) - self._log(sql) - query = self.raw_sql(sql, params=params, page_size=chunk_size, **kwargs) - batch_iter = query.to_arrow_iterable(bqstorage_client=self.storage_client) - return pa.ipc.RecordBatchReader.from_batches(schema.to_pyarrow(), batch_iter) - - def _gen_udf_name(self, name: str, schema: Optional[str]) -> str: - func = ".".join(filter(None, (schema, name))) - if "." in func: - return ".".join(f"`{part}`" for part in func.split(".")) - return func - - def get_schema( - self, - name, - *, - catalog: str | None = None, - database: str | None = None, - ): - table_ref = bq.TableReference( - bq.DatasetReference( - project=catalog or self.data_project, - dataset_id=database or self.current_database, - ), - name, - ) - return schema_from_bigquery_table( - self.client.get_table(table_ref), - # https://cloud.google.com/bigquery/docs/querying-wildcard-tables#filtering_selected_tables_using_table_suffix - wildcard=name[-1] == "*", - ) - - def list_databases( - self, like: str | None = None, catalog: str | None = None - ) -> list[str]: - results = [ - dataset.dataset_id - for dataset in self.client.list_datasets( - project=catalog if catalog is not None else self.data_project - ) - ] - return self._filter_with_like(results, like) - - def list_tables( - self, - like: str | None = None, - database: tuple[str, str] | str | None = None, - schema: str | None = None, - ) -> list[str]: - """List the tables in the database. - - Parameters - ---------- - like - A pattern to use for listing tables. - database - The database location to perform the list against. - - By default uses the current `dataset` (`self.current_database`) and - `project` (`self.current_catalog`). - - To specify a table in a separate BigQuery dataset, you can pass in the - dataset and project as a string `"dataset.project"`, or as a tuple of - strings `("dataset", "project")`. - - ::: {.callout-note} - ## Ibis does not use the word `schema` to refer to database hierarchy. - - A collection of tables is referred to as a `database`. - A collection of `database` is referred to as a `catalog`. - - These terms are mapped onto the corresponding features in each - backend (where available), regardless of whether the backend itself - uses the same terminology. - ::: - schema - [deprecated] The schema (dataset) inside `database` to perform the list against. - """ - table_loc = self._warn_and_create_table_loc(database, schema) - - project, dataset = self._parse_project_and_dataset(table_loc) - dataset_ref = bq.DatasetReference(project, dataset) - result = [table.table_id for table in self.client.list_tables(dataset_ref)] - return self._filter_with_like(result, like) - - def set_database(self, name): - self.data_project, self.dataset = self._parse_project_and_dataset(name) - - @property - def version(self): - return bq.__version__ - - def create_table( - self, - name: str, - obj: ir.Table - | pd.DataFrame - | pa.Table - | pl.DataFrame - | pl.LazyFrame - | None = None, - *, - schema: sch.SchemaLike | None = None, - database: str | None = None, - temp: bool = False, - overwrite: bool = False, - default_collate: str | None = None, - partition_by: str | None = None, - cluster_by: Iterable[str] | None = None, - options: Mapping[str, Any] | None = None, - ) -> ir.Table: - """Create a table in BigQuery. - - Parameters - ---------- - name - Name of the table to create - obj - The data with which to populate the table; optional, but one of `obj` - or `schema` must be specified - schema - The schema of the table to create; optional, but one of `obj` or - `schema` must be specified - database - The BigQuery *dataset* in which to create the table; optional - temp - Whether the table is temporary - overwrite - If `True`, replace the table if it already exists, otherwise fail if - the table exists - default_collate - Default collation for string columns. See BigQuery's documentation - for more details: https://cloud.google.com/bigquery/docs/reference/standard-sql/collation-concepts - partition_by - Partition the table by the given expression. See BigQuery's documentation - for more details: https://cloud.google.com/bigquery/docs/reference/standard-sql/data-definition-language#partition_expression - cluster_by - List of columns to cluster the table by. See BigQuery's documentation - for more details: https://cloud.google.com/bigquery/docs/reference/standard-sql/data-definition-language#clustering_column_list - options - BigQuery-specific table options; see the BigQuery documentation for - details: https://cloud.google.com/bigquery/docs/reference/standard-sql/data-definition-language#table_option_list - - Returns - ------- - Table - The table that was just created - - """ - if obj is None and schema is None: - raise com.IbisError("One of the `schema` or `obj` parameter is required") - if schema is not None: - schema = bigframes_vendored.ibis.schema(schema) - - if isinstance(obj, ir.Table) and schema is not None: - if not schema.equals(obj.schema()): - raise com.IbisTypeError( - "Provided schema and Ibis table schema are incompatible. Please " - "align the two schemas, or provide only one of the two arguments." - ) - - project_id, dataset = self._parse_project_and_dataset(database) - - properties = [] - - if default_collate is not None: - properties.append( - sge.CollateProperty(this=sge.convert(default_collate), default=True) - ) - - if partition_by is not None: - properties.append( - sge.PartitionedByProperty( - this=sge.Tuple( - expressions=list(map(sg.to_identifier, partition_by)) - ) - ) - ) - - if cluster_by is not None: - properties.append( - sge.Cluster(expressions=list(map(sg.to_identifier, cluster_by))) - ) - - properties.extend( - sge.Property(this=sg.to_identifier(name), value=sge.convert(value)) - for name, value in (options or {}).items() - ) - - if obj is not None and not isinstance(obj, ir.Table): - obj = bigframes_vendored.ibis.memtable(obj, schema=schema) - - if temp: - dataset = self._session_dataset.dataset_id - if database is not None: - raise com.IbisInputError("Cannot specify database for temporary table") - database = self._session_dataset.project - else: - dataset = database or self.current_database - - try: - table = sg.parse_one(name, into=sge.Table, read="bigquery") - except sg.ParseError: - table = sg.table( - name, - db=dataset, - catalog=project_id, - quoted=self.compiler.quoted, - ) - else: - if table.args["db"] is None: - table.args["db"] = dataset - - if table.args["catalog"] is None: - table.args["catalog"] = project_id - - table = _force_quote_table(table) - - column_defs = [ - sge.ColumnDef( - this=sg.to_identifier(name, quoted=self.compiler.quoted), - kind=BigQueryType.from_ibis(typ), - constraints=( - None - if typ.nullable or typ.is_array() - else [sge.ColumnConstraint(kind=sge.NotNullColumnConstraint())] - ), - ) - for name, typ in (schema or {}).items() - ] - - stmt = sge.Create( - kind="TABLE", - this=sge.Schema(this=table, expressions=column_defs or None), - replace=overwrite, - properties=sge.Properties(expressions=properties), - expression=None if obj is None else self.compile(obj), - ) - - sql = stmt.sql(self.name) - - self.raw_sql(sql) - return self.table(table.name, database=(table.catalog, table.db)) - - def drop_table( - self, - name: str, - *, - schema: str | None = None, - database: tuple[str | str] | str | None = None, - force: bool = False, - ) -> None: - table_loc = self._warn_and_create_table_loc(database, schema) - catalog, db = self._to_catalog_db_tuple(table_loc) - stmt = sge.Drop( - kind="TABLE", - this=sg.table( - name, - db=db or self.current_database, - catalog=catalog or self.billing_project, - ), - exists=force, - ) - self.raw_sql(stmt.sql(self.name)) - - def create_view( - self, - name: str, - obj: ir.Table, - *, - schema: str | None = None, - database: str | None = None, - overwrite: bool = False, - ) -> ir.Table: - table_loc = self._warn_and_create_table_loc(database, schema) - catalog, db = self._to_catalog_db_tuple(table_loc) - - stmt = sge.Create( - kind="VIEW", - this=sg.table( - name, - db=db or self.current_database, - catalog=catalog or self.billing_project, - ), - expression=self.compile(obj), - replace=overwrite, - ) - self.raw_sql(stmt.sql(self.name)) - return self.table(name, database=(catalog, database)) - - def drop_view( - self, - name: str, - *, - schema: str | None = None, - database: str | None = None, - force: bool = False, - ) -> None: - table_loc = self._warn_and_create_table_loc(database, schema) - catalog, db = self._to_catalog_db_tuple(table_loc) - - stmt = sge.Drop( - kind="VIEW", - this=sg.table( - name, - db=db or self.current_database, - catalog=catalog or self.billing_project, - ), - exists=force, - ) - self.raw_sql(stmt.sql(self.name)) - - def _drop_cached_table(self, name): - self.drop_table( - name, - database=(self._session_dataset.project, self._session_dataset.dataset_id), - force=True, - ) - - def _register_udfs(self, expr: ir.Expr) -> None: - """No op because UDFs made with CREATE TEMPORARY FUNCTION must be followed by a query.""" - - @contextlib.contextmanager - def _safe_raw_sql(self, *args, **kwargs): - yield self.raw_sql(*args, **kwargs) - - # TODO: remove when the schema kwarg is removed - def _warn_and_create_table_loc(self, database=None, schema=None): - if schema is not None: - self._warn_schema() - if database is not None and schema is not None: - if isinstance(database, str): - table_loc = f"{database}.{schema}" - elif isinstance(database, tuple): - table_loc = database + schema - elif schema is not None: - table_loc = schema - elif database is not None: - table_loc = database - else: - table_loc = None - - table_loc = self._to_sqlglot_table(table_loc) - - if table_loc is not None: - if (sg_cat := table_loc.args["catalog"]) is not None: - sg_cat.args["quoted"] = False - if (sg_db := table_loc.args["db"]) is not None: - sg_db.args["quoted"] = False - - return table_loc - - -def compile(expr, params=None, **kwargs): - """Compile an expression for BigQuery.""" - backend = Backend() - return backend.compile(expr, params=params, **kwargs) - - -def connect( - project_id: str | None = None, - dataset_id: str = "", - credentials: google.auth.credentials.Credentials | None = None, - application_name: str | None = None, - auth_local_webserver: bool = False, - auth_external_data: bool = False, - auth_cache: str = "default", - partition_column: str | None = "PARTITIONTIME", -) -> Backend: - """Create a :class:`Backend` for use with Ibis. - - Parameters - ---------- - project_id - A BigQuery project id. - dataset_id - A dataset id that lives inside of the project indicated by - `project_id`. - credentials - Optional credentials. - application_name - A string identifying your application to Google API endpoints. - auth_local_webserver - Use a local webserver for the user authentication. Binds a - webserver to an open port on localhost between 8080 and 8089, - inclusive, to receive authentication token. If not set, defaults - to False, which requests a token via the console. - auth_external_data - Authenticate using additional scopes required to `query external - data sources - `_, - such as Google Sheets, files in Google Cloud Storage, or files in - Google Drive. If not set, defaults to False, which requests the - default BigQuery scopes. - auth_cache - Selects the behavior of the credentials cache. - - `'default'`` - Reads credentials from disk if available, otherwise - authenticates and caches credentials to disk. - - `'reauth'`` - Authenticates and caches credentials to disk. - - `'none'`` - Authenticates and does **not** cache credentials. - - Defaults to `'default'`. - partition_column - Identifier to use instead of default `_PARTITIONTIME` partition - column. Defaults to `'PARTITIONTIME'`. - - Returns - ------- - Backend - An instance of the BigQuery backend - - """ - backend = Backend() - return backend.connect( - project_id=project_id, - dataset_id=dataset_id, - credentials=credentials, - application_name=application_name, - auth_local_webserver=auth_local_webserver, - auth_external_data=auth_external_data, - auth_cache=auth_cache, - partition_column=partition_column, - ) - - -__all__ = [ - "Backend", - "compile", - "connect", -] diff --git a/third_party/bigframes_vendored/ibis/backends/bigquery/client.py b/third_party/bigframes_vendored/ibis/backends/bigquery/client.py deleted file mode 100644 index 0af2f924cfb..00000000000 --- a/third_party/bigframes_vendored/ibis/backends/bigquery/client.py +++ /dev/null @@ -1,178 +0,0 @@ -# Contains code from https://github.com/ibis-project/ibis/blob/9.2.0/ibis/backends/bigquery/client.py - -"""BigQuery ibis client implementation.""" - -from __future__ import annotations - -import functools - -import bigframes_vendored.ibis.common.exceptions as com -import bigframes_vendored.ibis.expr.datatypes as dt -import google.cloud.bigquery as bq -import pandas as pd -from bigframes_vendored.ibis.backends.bigquery.datatypes import ( - BigQuerySchema, - BigQueryType, -) - -NATIVE_PARTITION_COL = "_PARTITIONTIME" - - -def schema_from_bigquery_table(table, *, wildcard: bool): - schema = BigQuerySchema.to_ibis(table.schema) - - # Check for partitioning information - partition_info = table.time_partitioning - if partition_info is not None: - # We have a partitioned table - partition_field = partition_info.field or NATIVE_PARTITION_COL - # Only add a new column if it's not already a column in the schema - if partition_field not in schema: - schema |= {partition_field: dt.Timestamp(timezone="UTC")} - - if wildcard: - schema |= {"_TABLE_SUFFIX": dt.string} - - return schema - - -@functools.singledispatch -def bigquery_param(dtype, value, name): - raise NotADirectoryError(dtype) - - -@bigquery_param.register -def bq_param_struct(dtype: dt.Struct, value, name): - fields = dtype.fields - field_params = [bigquery_param(fields[k], v, k) for k, v in value.items()] - result = bq.StructQueryParameter(name, *field_params) - return result - - -@bigquery_param.register -def bq_param_array(dtype: dt.Array, value, name): - value_type = dtype.value_type - - try: - bigquery_type = BigQueryType.to_string(value_type) - except NotImplementedError: - raise com.UnsupportedBackendType(dtype) - else: - if isinstance(value_type, dt.Array): - raise TypeError("ARRAY> is not supported in BigQuery") - elif isinstance(value_type, dt.Struct): - query_value = [ - bigquery_param(dtype.value_type, struct, f"element_{i:d}") - for i, struct in enumerate(value) - ] - bigquery_type = "STRUCT" - else: - query_value = value - return bq.ArrayQueryParameter(name, bigquery_type, query_value) - - -@bigquery_param.register -def bq_param_timestamp(_: dt.Timestamp, value, name): - # TODO(phillipc): Not sure if this is the correct way to do this. - timestamp_value = pd.Timestamp(value, tz="UTC").to_pydatetime() - return bq.ScalarQueryParameter(name, "TIMESTAMP", timestamp_value) - - -@bigquery_param.register -def bq_param_string(_: dt.String, value, name): - return bq.ScalarQueryParameter(name, "STRING", value) - - -@bigquery_param.register -def bq_param_integer(_: dt.Integer, value, name): - return bq.ScalarQueryParameter(name, "INT64", value) - - -@bigquery_param.register -def bq_param_double(_: dt.Floating, value, name): - return bq.ScalarQueryParameter(name, "FLOAT64", value) - - -@bigquery_param.register -def bq_param_boolean(_: dt.Boolean, value, name): - return bq.ScalarQueryParameter(name, "BOOL", value) - - -@bigquery_param.register -def bq_param_date(_: dt.Date, value, name): - return bq.ScalarQueryParameter( - name, "DATE", pd.Timestamp(value).to_pydatetime().date() - ) - - -def rename_partitioned_column(table_expr, bq_table, partition_col): - """Rename native partition column to user-defined name.""" - partition_info = bq_table.time_partitioning - - # If we don't have any partition information, the table isn't partitioned - if partition_info is None: - return table_expr - - # If we have a partition, but no "field" field in the table properties, - # then use NATIVE_PARTITION_COL as the default - partition_field = partition_info.field or NATIVE_PARTITION_COL - - # The partition field must be in table_expr columns - assert partition_field in table_expr.columns - - # No renaming if the config option is set to None or the partition field - # is not _PARTITIONTIME - if partition_col is None or partition_field != NATIVE_PARTITION_COL: - return table_expr - return table_expr.rename({partition_col: NATIVE_PARTITION_COL}) - - -def parse_project_and_dataset(project: str, dataset: str = "") -> tuple[str, str, str]: - """Compute the billing project, data project, and dataset if available. - - This function figure out the project id under which queries will run versus - the project of where the data live as well as what dataset to use. - - Parameters - ---------- - project : str - A project name - dataset : Optional[str] - A ``.`` string or just a dataset name - - Examples - -------- - >>> data_project, billing_project, dataset = parse_project_and_dataset( - ... "ibis-gbq", "foo-bar.my_dataset" - ... ) - >>> data_project - 'foo-bar' - >>> billing_project - 'ibis-gbq' - >>> dataset - 'my_dataset' - >>> data_project, billing_project, dataset = parse_project_and_dataset( - ... "ibis-gbq", "my_dataset" - ... ) - >>> data_project - 'ibis-gbq' - >>> billing_project - 'ibis-gbq' - >>> dataset - 'my_dataset' - >>> data_project, billing_project, _dataset = parse_project_and_dataset("ibis-gbq") - >>> data_project - 'ibis-gbq' - - """ - if dataset.count(".") > 1: - raise ValueError( - f"{dataset} is not a BigQuery dataset. More info https://cloud.google.com/bigquery/docs/datasets-intro" - ) - elif dataset.count(".") == 1: - data_project, dataset = dataset.split(".") - billing_project = project - else: - billing_project = data_project = project - - return data_project, billing_project, dataset diff --git a/third_party/bigframes_vendored/ibis/backends/bigquery/converter.py b/third_party/bigframes_vendored/ibis/backends/bigquery/converter.py deleted file mode 100644 index 2afccd454af..00000000000 --- a/third_party/bigframes_vendored/ibis/backends/bigquery/converter.py +++ /dev/null @@ -1,18 +0,0 @@ -# Contains code from https://github.com/ibis-project/ibis/blob/9.2.0/ibis/backends/bigquery/converter.py - -from __future__ import annotations - -from bigframes_vendored.ibis.formats.pandas import PandasData - - -class BigQueryPandasData(PandasData): - @classmethod - def convert_GeoSpatial(cls, s, dtype, pandas_type): - import geopandas as gpd - import shapely as shp - - return gpd.GeoSeries(shp.from_wkt(s)) - - convert_Point = convert_LineString = convert_Polygon = convert_MultiLineString = ( - convert_MultiPoint - ) = convert_MultiPolygon = convert_GeoSpatial diff --git a/third_party/bigframes_vendored/ibis/backends/bigquery/datatypes.py b/third_party/bigframes_vendored/ibis/backends/bigquery/datatypes.py deleted file mode 100644 index aa9ac062a17..00000000000 --- a/third_party/bigframes_vendored/ibis/backends/bigquery/datatypes.py +++ /dev/null @@ -1,181 +0,0 @@ -# Contains code from https://github.com/ibis-project/ibis/blob/9.2.0/ibis/backends/bigquery/datatypes.py - -from __future__ import annotations - -import bigframes_vendored.ibis -import bigframes_vendored.ibis.expr.datatypes as dt -import bigframes_vendored.ibis.expr.schema as sch -import bigframes_vendored.sqlglot as sg -import google.cloud.bigquery as bq -from bigframes_vendored.ibis.formats import SchemaMapper, TypeMapper - -_from_bigquery_types = { - "INT64": dt.Int64, - "INTEGER": dt.Int64, - "FLOAT": dt.Float64, - "FLOAT64": dt.Float64, - "BOOL": dt.Boolean, - "BOOLEAN": dt.Boolean, - "STRING": dt.String, - "DATE": dt.Date, - "TIME": dt.Time, - "BYTES": dt.Binary, - "JSON": dt.JSON, -} - - -class BigQueryType(TypeMapper): - @classmethod - def to_ibis(cls, typ: str, nullable: bool = True) -> dt.DataType: - if typ == "DATETIME": - return dt.Timestamp(timezone=None, nullable=nullable) - elif typ == "TIMESTAMP": - return dt.Timestamp(timezone="UTC", nullable=nullable) - elif typ == "NUMERIC": - return dt.Decimal(38, 9, nullable=nullable) - elif typ == "BIGNUMERIC": - return dt.Decimal(76, 38, nullable=nullable) - elif typ == "GEOGRAPHY": - return dt.GeoSpatial(geotype="geography", srid=4326, nullable=nullable) - else: - try: - return _from_bigquery_types[typ](nullable=nullable) - except KeyError: - raise TypeError(f"Unable to convert BigQuery type to ibis: {typ}") - - @classmethod - def from_ibis(cls, dtype: dt.DataType) -> str: - if dtype.is_floating(): - return "FLOAT64" - elif dtype.is_uint64(): - raise TypeError( - "Conversion from uint64 to BigQuery integer type (int64) is lossy" - ) - elif dtype.is_integer(): - return "INT64" - elif dtype.is_boolean(): - return "BOOLEAN" - elif dtype.is_binary(): - return "BYTES" - elif dtype.is_string(): - return "STRING" - elif dtype.is_date(): - return "DATE" - elif dtype.is_timestamp(): - if dtype.timezone is None: - return "DATETIME" - elif dtype.timezone == "UTC": - return "TIMESTAMP" - else: - raise TypeError( - "BigQuery does not support timestamps with timezones other than 'UTC'" - ) - elif dtype.is_decimal(): - if (dtype.precision, dtype.scale) == (76, 38): - return "BIGNUMERIC" - if (dtype.precision, dtype.scale) in [(38, 9), (None, None)]: - return "NUMERIC" - raise TypeError( - "BigQuery only supports decimal types with precision of 38 and " - f"scale of 9 (NUMERIC) or precision of 76 and scale of 38 (BIGNUMERIC). " - f"Current precision: {dtype.precision}. Current scale: {dtype.scale}" - ) - elif dtype.is_array(): - return f"ARRAY<{cls.from_ibis(dtype.value_type)}>" - elif dtype.is_struct(): - fields = ( - f"{sg.to_identifier(k).sql('bigquery')} {cls.from_ibis(v)}" - for k, v in dtype.fields.items() - ) - return "STRUCT<{}>".format(", ".join(fields)) - elif dtype.is_json(): - return "JSON" - elif dtype.is_geospatial(): - if (dtype.geotype, dtype.srid) == ("geography", 4326): - return "GEOGRAPHY" - raise TypeError( - "BigQuery geography uses points on WGS84 reference ellipsoid." - f"Current geotype: {dtype.geotype}, Current srid: {dtype.srid}" - ) - elif dtype.is_map(): - raise NotImplementedError("Maps are not supported in BigQuery") - else: - return str(dtype).upper() - - -class BigQuerySchema(SchemaMapper): - @classmethod - def from_ibis(cls, schema: sch.Schema) -> list[bq.SchemaField]: - schema_fields = [] - - for name, typ in bigframes_vendored.ibis.schema(schema).items(): - if typ.is_array(): - value_type = typ.value_type - if value_type.is_array(): - raise TypeError("Nested arrays are not supported in BigQuery") - - is_struct = value_type.is_struct() - - field_type = ( - "RECORD" if is_struct else BigQueryType.from_ibis(typ.value_type) - ) - mode = "REPEATED" - fields = cls.from_ibis( - bigframes_vendored.ibis.schema(getattr(value_type, "fields", {})) - ) - elif typ.is_struct(): - field_type = "RECORD" - mode = "NULLABLE" if typ.nullable else "REQUIRED" - fields = cls.from_ibis(bigframes_vendored.ibis.schema(typ.fields)) - else: - field_type = BigQueryType.from_ibis(typ) - mode = "NULLABLE" if typ.nullable else "REQUIRED" - fields = [] - - schema_fields.append( - bq.SchemaField(name, field_type=field_type, mode=mode, fields=fields) - ) - return schema_fields - - @classmethod - def _dtype_from_bigquery_field(cls, field: bq.SchemaField) -> dt.DataType: - typ = field.field_type - if typ == "RECORD": - assert field.fields, "RECORD fields are empty" - fields = {f.name: cls._dtype_from_bigquery_field(f) for f in field.fields} - dtype = dt.Struct(fields) - else: - dtype = BigQueryType.to_ibis(typ) - - mode = field.mode - if mode == "NULLABLE": - return dtype.copy(nullable=True) - elif mode == "REQUIRED": - return dtype.copy(nullable=False) - elif mode == "REPEATED": - # arrays with NULL elements aren't supported - return dt.Array(dtype.copy(nullable=False)) - else: - raise TypeError(f"Unknown BigQuery field.mode: {mode}") - - @classmethod - def to_ibis(cls, fields: list[bq.SchemaField]) -> sch.Schema: - return sch.Schema({f.name: cls._dtype_from_bigquery_field(f) for f in fields}) - - -# TODO(kszucs): we can eliminate this function by making dt.DataType traversible -# using ibis.common.graph.Node, similarly to how we traverse ops.Node instances: -# node.find(types) -def spread_type(dt: dt.DataType): - """Returns a generator that contains all the types in the given type. - - For complex types like set and array, it returns the types of the elements. - """ - if dt.is_array(): - yield from spread_type(dt.value_type) - elif dt.is_struct(): - for type_ in dt.types: - yield from spread_type(type_) - elif dt.is_map(): - raise NotImplementedError("Maps are not supported in BigQuery") - yield dt diff --git a/third_party/bigframes_vendored/ibis/backends/bigquery/registry.py b/third_party/bigframes_vendored/ibis/backends/bigquery/registry.py new file mode 100644 index 00000000000..a4e61ca0f91 --- /dev/null +++ b/third_party/bigframes_vendored/ibis/backends/bigquery/registry.py @@ -0,0 +1,31 @@ +# Contains code from https://github.com/ibis-project/ibis/blob/master/ibis/backends/bigquery/registry.py +"""Module to convert from Ibis expression to SQL string.""" + +from ibis.backends.bigquery.registry import OPERATION_REGISTRY + +import third_party.bigframes_vendored.ibis.expr.operations as vendored_ibis_ops + + +def _approx_quantiles(translator, op: vendored_ibis_ops.ApproximateMultiQuantile): + arg = translator.translate(op.arg) + num_bins = translator.translate(op.num_bins) + return f"APPROX_QUANTILES({arg}, {num_bins})" + + +def _first_non_null_value(translator, op: vendored_ibis_ops.FirstNonNullValue): + arg = translator.translate(op.arg) + return f"FIRST_VALUE({arg} IGNORE NULLS)" + + +def _last_non_null_value(translator, op: vendored_ibis_ops.LastNonNullValue): + arg = translator.translate(op.arg) + return f"LAST_VALUE({arg} IGNORE NULLS)" + + +patched_ops = { + vendored_ibis_ops.ApproximateMultiQuantile: _approx_quantiles, + vendored_ibis_ops.FirstNonNullValue: _first_non_null_value, + vendored_ibis_ops.LastNonNullValue: _last_non_null_value, +} + +OPERATION_REGISTRY.update(patched_ops) diff --git a/third_party/bigframes_vendored/ibis/backends/sql/__init__.py b/third_party/bigframes_vendored/ibis/backends/sql/__init__.py deleted file mode 100644 index 9035bb0755a..00000000000 --- a/third_party/bigframes_vendored/ibis/backends/sql/__init__.py +++ /dev/null @@ -1,650 +0,0 @@ -# Contains code from https://github.com/ibis-project/ibis/blob/9.2.0/ibis/backends/sql/__init__.py - -from __future__ import annotations - -import abc -from functools import partial -from typing import TYPE_CHECKING, Any, ClassVar - -import bigframes_vendored.ibis -import bigframes_vendored.ibis.common.exceptions as exc -import bigframes_vendored.ibis.expr.operations as ops -import bigframes_vendored.ibis.expr.schema as sch -import bigframes_vendored.ibis.expr.types as ir -import bigframes_vendored.sqlglot as sg -import bigframes_vendored.sqlglot.expressions as sge -from bigframes_vendored.ibis import util -from bigframes_vendored.ibis.backends import BaseBackend -from bigframes_vendored.ibis.backends.sql.compilers.base import STAR - -if TYPE_CHECKING: - from collections.abc import Iterable, Mapping - - import pandas as pd - import pyarrow as pa - from bigframes_vendored.ibis.backends.sql.compilers.base import SQLGlotCompiler - from bigframes_vendored.ibis.expr.schema import SchemaLike - - -class _DatabaseSchemaHandler: - """Temporary mixin collecting several helper functions and code snippets. - - Help to 'gracefully' deprecate the use of `schema` as a hierarchical term. - """ - - @staticmethod - def _warn_schema(): - util.warn_deprecated( - name="schema", - as_of="9.0", - removed_in="10.0", - instead="Use the `database` kwarg with one of the following patterns:" - '\ndatabase="database"' - '\ndatabase=("catalog", "database")' - '\ndatabase="catalog.database"', - # TODO: add option for namespace object - ) - - def _warn_and_create_table_loc(self, database=None, schema=None): - if schema is not None: - self._warn_schema() - - if database is not None and schema is not None: - if isinstance(database, str): - table_loc = f"{database}.{schema}" - elif isinstance(database, tuple): - table_loc = database + schema - elif schema is not None: - table_loc = schema - elif database is not None: - table_loc = database - else: - table_loc = None - - table_loc = self._to_sqlglot_table(table_loc) - - return table_loc - - -class SQLBackend(BaseBackend, _DatabaseSchemaHandler): - compiler: ClassVar[SQLGlotCompiler] - name: ClassVar[str] - - _top_level_methods = ("from_connection",) - - @property - def dialect(self) -> sg.Dialect: - return self.compiler.dialect - - @classmethod - def has_operation(cls, operation: type[ops.Value]) -> bool: - compiler = cls.compiler - if operation in compiler.extra_supported_ops: - return True - method = getattr(compiler, f"visit_{operation.__name__}", None) - return method not in ( - None, - compiler.visit_Undefined, - compiler.visit_Unsupported, - ) - - def _fetch_from_cursor(self, cursor, schema: sch.Schema) -> pd.DataFrame: - import pandas as pd - from bigframes_vendored.ibis.formats.pandas import PandasData - - try: - df = pd.DataFrame.from_records( - cursor, columns=schema.names, coerce_float=True - ) - except Exception: - # clean up the cursor if we fail to create the DataFrame - # - # in the sqlite case failing to close the cursor results in - # artificially locked tables - cursor.close() - raise - df = PandasData.convert_table(df, schema) - return df - - def table( - self, - name: str, - schema: str | None = None, - database: tuple[str, str] | str | None = None, - ) -> ir.Table: - """Construct a table expression. - - Parameters - ---------- - name - Table name - schema - [deprecated] Schema name - database - Database name - - Returns - ------- - Table - Table expression - - """ - table_loc = self._warn_and_create_table_loc(database, schema) - - catalog, database = None, None - if table_loc is not None: - catalog = table_loc.catalog or None - database = table_loc.db or None - - table_schema = self.get_schema(name, catalog=catalog, database=database) - return ops.DatabaseTable( - name, - schema=table_schema, - source=self, - namespace=ops.Namespace(catalog=catalog, database=database), - ).to_expr() - - def _to_sqlglot( - self, expr: ir.Expr, *, limit: str | None = None, params=None, **_: Any - ): - """Compile an Ibis expression to a sqlglot object.""" - table_expr = expr.as_table() - - if limit == "default": - limit = bigframes_vendored.ibis.options.sql.default_limit - if limit is not None: - table_expr = table_expr.limit(limit) - - if params is None: - params = {} - - sql = self.compiler.translate(table_expr.op(), params=params) - assert not isinstance(sql, sge.Subquery) - - if isinstance(sql, sge.Table): - sql = sg.select(STAR, copy=False).from_(sql, copy=False) - - assert not isinstance(sql, sge.Subquery) - return sql - - def compile( - self, - expr: ir.Expr, - limit: str | None = None, - params=None, - pretty: bool = False, - **kwargs: Any, - ): - """Compile an Ibis expression to a SQL string.""" - query = self._to_sqlglot(expr, limit=limit, params=params, **kwargs) - sql = query.sql(dialect=self.dialect, pretty=pretty, copy=False) - self._log(sql) - return sql - - def _log(self, sql: str) -> None: - """Log `sql`. - - This method can be implemented by subclasses. Logging occurs when - `ibis.options.verbose` is `True`. - """ - from bigframes_vendored.ibis import util - - util.log(sql) - - def sql( - self, - query: str, - schema: SchemaLike | None = None, - dialect: str | None = None, - ) -> ir.Table: - query = self._transpile_sql(query, dialect=dialect) - if schema is None: - schema = self._get_schema_using_query(query) - return ops.SQLQueryResult( - query, bigframes_vendored.ibis.schema(schema), self - ).to_expr() - - @abc.abstractmethod - def _get_schema_using_query(self, query: str) -> sch.Schema: - """Return an ibis Schema from a backend-specific SQL string. - - Parameters - ---------- - query - Backend-specific SQL string - - Returns - ------- - Schema - The schema inferred from `query` - """ - - def _get_sql_string_view_schema(self, name, table, query) -> sch.Schema: - compiler = self.compiler - dialect = compiler.dialect - - cte = self._to_sqlglot(table) - parsed = sg.parse_one(query, read=dialect) - parsed.args["with"] = cte.args.pop("with", []) - parsed = parsed.with_( - sg.to_identifier(name, quoted=compiler.quoted), as_=cte, dialect=dialect - ) - - sql = parsed.sql(dialect) - return self._get_schema_using_query(sql) - - def create_view( - self, - name: str, - obj: ir.Table, - *, - database: str | None = None, - schema: str | None = None, - overwrite: bool = False, - ) -> ir.Table: - table_loc = self._warn_and_create_table_loc(database, schema) - catalog, db = self._to_catalog_db_tuple(table_loc) - - src = sge.Create( - this=sg.table(name, db=db, catalog=catalog, quoted=self.compiler.quoted), - kind="VIEW", - replace=overwrite, - expression=self.compile(obj), - ) - self._register_in_memory_tables(obj) - with self._safe_raw_sql(src): - pass - return self.table(name, database=(catalog, db)) - - def drop_view( - self, - name: str, - *, - database: str | None = None, - schema: str | None = None, - force: bool = False, - ) -> None: - table_loc = self._warn_and_create_table_loc(database, schema) - catalog, db = self._to_catalog_db_tuple(table_loc) - - src = sge.Drop( - this=sg.table(name, db=db, catalog=catalog, quoted=self.compiler.quoted), - kind="VIEW", - exists=force, - ) - with self._safe_raw_sql(src): - pass - - def _load_into_cache(self, name, expr): - self.create_table(name, expr, schema=expr.schema(), temp=True) - - def _clean_up_cached_table(self, name): - self.drop_table(name, force=True) - - def execute( - self, - expr: ir.Expr, - params: Mapping | None = None, - limit: str | None = "default", - **kwargs: Any, - ) -> Any: - """Execute an expression.""" - - self._run_pre_execute_hooks(expr) - table = expr.as_table() - sql = self.compile(table, params=params, limit=limit, **kwargs) - - schema = table.schema() - - # TODO(kszucs): these methods should be abstractmethods or this default - # implementation should be removed - with self._safe_raw_sql(sql) as cur: - result = self._fetch_from_cursor(cur, schema) - return expr.__pandas_result__(result) - - def drop_table( - self, - name: str, - database: tuple[str, str] | str | None = None, - force: bool = False, - ) -> None: - table_loc = self._warn_and_create_table_loc(database, None) - catalog, db = self._to_catalog_db_tuple(table_loc) - - drop_stmt = sg.exp.Drop( - kind="TABLE", - this=sg.table(name, db=db, catalog=catalog, quoted=self.compiler.quoted), - exists=force, - ) - with self._safe_raw_sql(drop_stmt): - pass - - def _cursor_batches( - self, - expr: ir.Expr, - params: Mapping[ir.Scalar, Any] | None = None, - limit: int | str | None = None, - chunk_size: int = 1 << 20, - ) -> Iterable[list]: - self._run_pre_execute_hooks(expr) - - with self._safe_raw_sql( - self.compile(expr, limit=limit, params=params) - ) as cursor: - while batch := cursor.fetchmany(chunk_size): - yield batch - - @util.experimental - def to_pyarrow_batches( - self, - expr: ir.Expr, - *, - params: Mapping[ir.Scalar, Any] | None = None, - limit: int | str | None = None, - chunk_size: int = 1_000_000, - **_: Any, - ) -> pa.ipc.RecordBatchReader: - """Execute expression and return an iterator of pyarrow record batches. - - This method is eager and will execute the associated expression - immediately. - - Parameters - ---------- - expr - Ibis expression to export to pyarrow - limit - An integer to effect a specific row limit. A value of `None` means - "no limit". The default is in `ibis/config.py`. - params - Mapping of scalar parameter expressions to value. - chunk_size - Maximum number of rows in each returned record batch. - - Returns - ------- - RecordBatchReader - Collection of pyarrow `RecordBatch`s. - - """ - pa = self._import_pyarrow() - - schema = expr.as_table().schema() - array_type = schema.as_struct().to_pyarrow() - arrays = ( - pa.array(map(tuple, batch), type=array_type) - for batch in self._cursor_batches( - expr, params=params, limit=limit, chunk_size=chunk_size - ) - ) - batches = map(pa.RecordBatch.from_struct_array, arrays) - - return pa.ipc.RecordBatchReader.from_batches(schema.to_pyarrow(), batches) - - def insert( - self, - table_name: str, - obj: pd.DataFrame | ir.Table | list | dict, - schema: str | None = None, - database: str | None = None, - overwrite: bool = False, - ) -> None: - """Insert data into a table. - - ::: {.callout-note} - ## Ibis does not use the word `schema` to refer to database hierarchy. - - A collection of `table` is referred to as a `database`. - A collection of `database` is referred to as a `catalog`. - - These terms are mapped onto the corresponding features in each - backend (where available), regardless of whether the backend itself - uses the same terminology. - ::: - - Parameters - ---------- - table_name - The name of the table to which data needs will be inserted - obj - The source data or expression to insert - schema - [deprecated] The name of the schema that the table is located in - database - Name of the attached database that the table is located in. - - For backends that support multi-level table hierarchies, you can - pass in a dotted string path like `"catalog.database"` or a tuple of - strings like `("catalog", "database")`. - overwrite - If `True` then replace existing contents of table - - """ - table_loc = self._warn_and_create_table_loc(database, schema) - catalog, db = self._to_catalog_db_tuple(table_loc) - - if overwrite: - self.truncate_table(table_name, database=(catalog, db)) - - if not isinstance(obj, ir.Table): - obj = bigframes_vendored.ibis.memtable(obj) - - self._run_pre_execute_hooks(obj) - - query = self._build_insert_from_table( - target=table_name, source=obj, db=db, catalog=catalog - ) - - with self._safe_raw_sql(query): - pass - - def _build_insert_from_table( - self, *, target: str, source, db: str | None = None, catalog: str | None = None - ): - compiler = self.compiler - quoted = compiler.quoted - # Compare the columns between the target table and the object to be inserted - # If they don't match, assume auto-generated column names and use positional - # ordering. - source_cols = source.columns - columns = ( - source_cols - if not set(target_cols := self.get_schema(target).names).difference( - source_cols - ) - else target_cols - ) - - query = sge.insert( - expression=self.compile(source), - into=sg.table(target, db=db, catalog=catalog, quoted=quoted), - columns=[sg.to_identifier(col, quoted=quoted) for col in columns], - dialect=compiler.dialect, - ) - return query - - def _build_insert_template( - self, - name, - *, - schema: sch.Schema, - catalog: str | None = None, - columns: bool = False, - placeholder: str = "?", - ) -> str: - """Builds an INSERT INTO table VALUES query string with placeholders. - - Parameters - ---------- - name - Name of the table to insert into - schema - Ibis schema of the table to insert into - catalog - Catalog name of the table to insert into - columns - Whether to render the columns to insert into - placeholder - Placeholder string. Can be a format string with a single `{i}` spec. - - Returns - ------- - str - The query string - """ - quoted = self.compiler.quoted - return sge.insert( - sge.Values( - expressions=[ - sge.Tuple( - expressions=[ - sge.Var(this=placeholder.format(i=i)) - for i in range(len(schema)) - ] - ) - ] - ), - into=sg.table(name, catalog=catalog, quoted=quoted), - columns=( - map(partial(sg.to_identifier, quoted=quoted), schema.keys()) - if columns - else None - ), - ).sql(self.dialect) - - def truncate_table( - self, name: str, database: str | None = None, schema: str | None = None - ) -> None: - """Delete all rows from a table. - - ::: {.callout-note} - ## Ibis does not use the word `schema` to refer to database hierarchy. - - A collection of tables is referred to as a `database`. - A collection of `database` is referred to as a `catalog`. - These terms are mapped onto the corresponding features in each - backend (where available), regardless of whether the backend itself - uses the same terminology. - ::: - - Parameters - ---------- - name - Table name - database - Name of the attached database that the table is located in. - - For backends that support multi-level table hierarchies, you can - pass in a dotted string path like `"catalog.database"` or a tuple of - strings like `("catalog", "database")`. - schema - [deprecated] Schema name - - """ - table_loc = self._warn_and_create_table_loc(database, schema) - catalog, db = self._to_catalog_db_tuple(table_loc) - - ident = sg.table(name, db=db, catalog=catalog, quoted=self.compiler.quoted).sql( - self.dialect - ) - with self._safe_raw_sql(f"TRUNCATE TABLE {ident}"): - pass - - @util.experimental - @classmethod - def from_connection(cls, con: Any, **kwargs: Any) -> BaseBackend: - """Create an Ibis client from an existing connection. - - Parameters - ---------- - con - An existing connection. - **kwargs - Extra arguments to be applied to the newly-created backend. - """ - raise NotImplementedError( - f"{cls.name} backend cannot be constructed from an existing connection" - ) - - def disconnect(self): - # This is part of the Python DB-API specification so should work for - # _most_ sqlglot backends - self.con.close() - - def _compile_builtin_udf(self, udf_node: ops.ScalarUDF | ops.AggUDF) -> None: - """Compile a built-in UDF. No-op by default.""" - - def _compile_python_udf(self, udf_node: ops.ScalarUDF) -> None: - raise NotImplementedError( - f"Python UDFs are not supported in the {self.name} backend" - ) - - def _compile_pyarrow_udf(self, udf_node: ops.ScalarUDF) -> None: - raise NotImplementedError( - f"PyArrow UDFs are not supported in the {self.name} backend" - ) - - def _compile_pandas_udf(self, udf_node: ops.ScalarUDF) -> str: - raise NotImplementedError( - f"pandas UDFs are not supported in the {self.name} backend" - ) - - def _to_catalog_db_tuple(self, table_loc: sge.Table): - if table_loc is None or table_loc == (None, None): - return None, None - - if (sg_cat := table_loc.args["catalog"]) is not None: - sg_cat.args["quoted"] = False - sg_cat = sg_cat.sql(self.name) - if (sg_db := table_loc.args["db"]) is not None: - sg_db.args["quoted"] = False - sg_db = sg_db.sql(self.name) - - return sg_cat, sg_db - - def _to_sqlglot_table(self, database): - if database is None: - return None - elif isinstance(database, (list, tuple)): - if len(database) > 2: - raise ValueError( - "Only database hierarchies of two or fewer levels are supported." - "\nYou can specify ('catalog', 'database')." - ) - elif len(database) == 2: - catalog, database = database - elif len(database) == 1: - database = database[0] - catalog = None - else: - raise ValueError( - f"Malformed database tuple {database} provided" - "\nPlease specify one of:" - '\n("catalog", "database")' - '\n("database",)' - ) - database = sg.exp.Table( - catalog=sg.to_identifier(catalog, quoted=self.compiler.quoted), - db=sg.to_identifier(database, quoted=self.compiler.quoted), - ) - elif isinstance(database, str): - # There is no definition of a sqlglot catalog.database hierarchy outside - # of the standard table expression. - # sqlglot parsing of the string will assume that it's a Table - # so we unpack the arguments into a new sqlglot object, switching - # table (this) -> database (db) and database (db) -> catalog - table = sg.parse_one(database, into=sg.exp.Table, dialect=self.dialect) - if table.args["catalog"] is not None: - raise exc.IbisInputError( - f"Overspecified table hierarchy provided: `{table.sql(self.dialect)}`" - ) - catalog = table.args["db"] - db = table.args["this"] - database = sg.exp.Table(catalog=catalog, db=db) - else: - raise ValueError( - """Invalid database hierarchy format. Please use either dotted - strings ('catalog.database') or tuples ('catalog', 'database').""" - ) - - return database diff --git a/third_party/bigframes_vendored/ibis/backends/sql/compilers/__init__.py b/third_party/bigframes_vendored/ibis/backends/sql/compilers/__init__.py deleted file mode 100644 index 73ae551ae40..00000000000 --- a/third_party/bigframes_vendored/ibis/backends/sql/compilers/__init__.py +++ /dev/null @@ -1,9 +0,0 @@ -# Contains code from https://github.com/ibis-project/ibis/blob/9.2.0/ibis/backends/sql/compilers/__init__.py - -from __future__ import annotations - -__all__ = [ - "BigQueryCompiler", -] - -from bigframes_vendored.ibis.backends.sql.compilers.bigquery import BigQueryCompiler diff --git a/third_party/bigframes_vendored/ibis/backends/sql/compilers/base.py b/third_party/bigframes_vendored/ibis/backends/sql/compilers/base.py deleted file mode 100644 index e6ab427be5e..00000000000 --- a/third_party/bigframes_vendored/ibis/backends/sql/compilers/base.py +++ /dev/null @@ -1,1687 +0,0 @@ -# Contains code from https://github.com/ibis-project/ibis/blob/9.2.0/ibis/backends/sql/compilers/base.py - -from __future__ import annotations - -import abc -import calendar -import itertools -import math -import operator -import string -from functools import partial, reduce -from typing import TYPE_CHECKING, Any, ClassVar - -import bigframes_vendored.ibis.common.exceptions as ibis_exceptions -import bigframes_vendored.ibis.common.patterns as pats -import bigframes_vendored.ibis.expr.datatypes as dt -import bigframes_vendored.ibis.expr.operations as ops -import bigframes_vendored.sqlglot as sg -import bigframes_vendored.sqlglot.expressions as sge -from bigframes_vendored.ibis.backends.sql.rewrites import ( - FirstValue, - LastValue, - add_one_to_nth_value_input, - add_order_by_to_empty_ranking_window_functions, - empty_in_values_right_side, - lower_bucket, - lower_capitalize, - lower_sample, - one_to_zero_index, - sqlize, -) -from bigframes_vendored.ibis.config import options -from bigframes_vendored.ibis.expr.operations.udf import InputType -from bigframes_vendored.ibis.expr.rewrites import lower_stringslice -from public import public - -try: - from bigframes_vendored.sqlglot.expressions import Alter -except ImportError: - from bigframes_vendored.sqlglot.expressions import AlterTable -else: - - def AlterTable(*args, kind="TABLE", **kwargs): - return Alter(*args, kind=kind, **kwargs) - - -if TYPE_CHECKING: - from collections.abc import Callable, Iterable, Mapping - - import bigframes_vendored.ibis.expr.schema as sch - import bigframes_vendored.ibis.expr.types as ir - from bigframes_vendored.ibis.backends.bigquery.datatypes import SqlglotType - - -def get_leaf_classes(op): - for child_class in op.__subclasses__(): - if not child_class.__subclasses__(): - yield child_class - else: - yield from get_leaf_classes(child_class) - - -ALL_OPERATIONS = frozenset(get_leaf_classes(ops.Node)) - - -class AggGen: - """A descriptor for compiling aggregate functions. - - Common cases can be handled by setting configuration flags, - special cases should override the `aggregate` method directly. - - Parameters - ---------- - supports_filter - Whether the backend supports a FILTER clause in the aggregate. - Defaults to False. - supports_order_by - Whether the backend supports an ORDER BY clause in (relevant) - aggregates. Defaults to False. - """ - - class _Accessor: - """An internal type to handle getattr/getitem access.""" - - __slots__ = ("handler", "compiler") - - def __init__(self, handler: Callable, compiler: SQLGlotCompiler): - self.handler = handler - self.compiler = compiler - - def __getattr__(self, name: str) -> Callable: - return partial(self.handler, self.compiler, name) - - __getitem__ = __getattr__ - - __slots__ = ("supports_filter", "supports_order_by") - - def __init__( - self, *, supports_filter: bool = False, supports_order_by: bool = False - ): - self.supports_filter = supports_filter - self.supports_order_by = supports_order_by - - def __get__(self, instance, owner=None): - if instance is None: - return self - - return AggGen._Accessor(self.aggregate, instance) - - def aggregate( - self, - compiler: SQLGlotCompiler, - name: str, - *args: Any, - where: Any = None, - order_by: tuple = (), - ): - """Compile the specified aggregate. - - Parameters - ---------- - compiler - The backend's compiler. - name - The aggregate name (e.g. `"sum"`). - args - Any arguments to pass to the aggregate. - where - An optional column filter to apply before performing the aggregate. - order_by - Optional ordering keys to use to order the rows before performing - the aggregate. - """ - func = compiler.f[name] - - if order_by and not self.supports_order_by: - raise ibis_exceptions.UnsupportedOperationError( - "ordering of order-sensitive aggregations via `order_by` is " - f"not supported for the {compiler.dialect} backend" - ) - - if where is not None and not self.supports_filter: - args = tuple(compiler.if_(where, arg, NULL) for arg in args) - - if order_by and self.supports_order_by: - *rest, last = args - out = func(*rest, sge.Order(this=last, expressions=order_by)) - else: - out = func(*args) - - if where is not None and self.supports_filter: - out = sge.Filter(this=out, expression=sge.Where(this=where)) - - return out - - -class VarGen: - __slots__ = () - - def __getattr__(self, name: str) -> sge.Var: - return sge.Var(this=name) - - def __getitem__(self, key: str) -> sge.Var: - return sge.Var(this=key) - - -class AnonymousFuncGen: - __slots__ = () - - def __getattr__(self, name: str) -> Callable[..., sge.Anonymous]: - return lambda *args: sge.Anonymous( - this=name, expressions=list(map(sge.convert, args)) - ) - - def __getitem__(self, key: str) -> Callable[..., sge.Anonymous]: - return getattr(self, key) - - -class FuncGen: - __slots__ = ("namespace", "anon", "copy") - - def __init__(self, namespace: str | None = None, copy: bool = False) -> None: - self.namespace = namespace - self.anon = AnonymousFuncGen() - self.copy = copy - - def __getattr__(self, name: str) -> Callable[..., sge.Func]: - name = ".".join(filter(None, (self.namespace, name))) - return lambda *args, **kwargs: sg.func( - name, *map(sge.convert, args), **kwargs, copy=self.copy - ) - - def __getitem__(self, key: str) -> Callable[..., sge.Func]: - return getattr(self, key) - - def array(self, *args: Any) -> sge.Array: - if not args: - return sge.Array(expressions=[]) - - first, *rest = args - - if isinstance(first, sge.Select): - assert not rest, ( - "only one argument allowed when `first` is a select statement" - ) - - return sge.Array(expressions=list(map(sge.convert, (first, *rest)))) - - def tuple(self, *args: Any) -> sge.Anonymous: - return self.anon.tuple(*args) - - def exists(self, query: sge.Expression) -> sge.Exists: - return sge.Exists(this=query) - - def concat(self, *args: Any) -> sge.Concat: - return sge.Concat(expressions=list(map(sge.convert, args))) - - def map(self, keys: Iterable, values: Iterable) -> sge.Map: - return sge.Map(keys=keys, values=values) - - -class ColGen: - __slots__ = ("table",) - - def __init__(self, table: str | None = None) -> None: - self.table = table - - def __getattr__(self, name: str) -> sge.Column: - return sg.column(name, table=self.table, copy=False) - - def __getitem__(self, key: str) -> sge.Column: - return sg.column(key, table=self.table, copy=False) - - -C = ColGen() -F = FuncGen() -NULL = sge.Null() -FALSE = sge.false() -TRUE = sge.true() -STAR = sge.Star() - - -def parenthesize_inputs(f): - """Decorate a translation rule to parenthesize inputs.""" - - def wrapper(self, op, *, left, right): - return f( - self, - op, - left=self._add_parens(op.left, left), - right=self._add_parens(op.right, right), - ) - - return wrapper - - -@public -class SQLGlotCompiler(abc.ABC): - __slots__ = "f", "v" - - agg = AggGen() - """A generator for handling aggregate functions""" - - rewrites: tuple[type[pats.Replace], ...] = ( - empty_in_values_right_side, - add_order_by_to_empty_ranking_window_functions, - one_to_zero_index, - add_one_to_nth_value_input, - ) - """A sequence of rewrites to apply to the expression tree before SQL-specific transforms.""" - - post_rewrites: tuple[type[pats.Replace], ...] = () - """A sequence of rewrites to apply to the expression tree after SQL-specific transforms.""" - - no_limit_value: sge.Null | None = None - """The value to use to indicate no limit.""" - - quoted: bool = True - """Whether to always quote identifiers.""" - - copy_func_args: bool = False - """Whether to copy function arguments when generating SQL.""" - - supports_qualify: bool = False - """Whether the backend supports the QUALIFY clause.""" - - NAN: ClassVar[sge.Expression] = sge.Cast( - this=sge.convert("NaN"), to=sge.DataType(this=sge.DataType.Type.DOUBLE) - ) - """Backend's NaN literal.""" - - POS_INF: ClassVar[sge.Expression] = sge.Cast( - this=sge.convert("Inf"), to=sge.DataType(this=sge.DataType.Type.DOUBLE) - ) - """Backend's positive infinity literal.""" - - NEG_INF: ClassVar[sge.Expression] = sge.Cast( - this=sge.convert("-Inf"), to=sge.DataType(this=sge.DataType.Type.DOUBLE) - ) - """Backend's negative infinity literal.""" - - EXTRA_SUPPORTED_OPS: tuple[type[ops.Node], ...] = ( - ops.Project, - ops.Filter, - ops.Sort, - ops.WindowFunction, - ) - """A tuple of ops classes that are supported, but don't have explicit - `visit_*` methods (usually due to being handled by rewrite rules). Used by - `has_operation`""" - - UNSUPPORTED_OPS: tuple[type[ops.Node], ...] = () - """Tuple of operations the backend doesn't support.""" - - LOWERED_OPS: dict[type[ops.Node], pats.Replace | None] = { - ops.Bucket: lower_bucket, - ops.Capitalize: lower_capitalize, - ops.Sample: lower_sample, - ops.StringSlice: lower_stringslice, - } - """A mapping from an operation class to either a rewrite rule for rewriting that - operation to one composed of lower-level operations ("lowering"), or `None` to - remove an existing rewrite rule for that operation added in a base class""" - - SIMPLE_OPS = { - ops.Abs: "abs", - ops.Acos: "acos", - ops.All: "bool_and", - ops.Any: "bool_or", - ops.ApproxCountDistinct: "approx_distinct", - ops.ArgMax: "max_by", - ops.ArgMin: "min_by", - ops.ArrayContains: "array_contains", - ops.ArrayFlatten: "flatten", - ops.ArrayLength: "array_size", - ops.ArraySort: "array_sort", - ops.ArrayStringJoin: "array_to_string", - ops.Asin: "asin", - ops.Atan2: "atan2", - ops.Atan: "atan", - ops.Cos: "cos", - ops.Cot: "cot", - ops.Count: "count", - ops.CumeDist: "cume_dist", - ops.Date: "date", - ops.DateFromYMD: "datefromparts", - ops.Degrees: "degrees", - ops.DenseRank: "dense_rank", - ops.Exp: "exp", - FirstValue: "first_value", - ops.GroupConcat: "group_concat", - ops.IfElse: "if", - ops.IsInf: "isinf", - ops.IsNan: "isnan", - ops.JSONGetItem: "json_extract", - ops.LPad: "lpad", - LastValue: "last_value", - ops.Levenshtein: "levenshtein", - ops.Ln: "ln", - ops.Log10: "log", - ops.Log2: "log2", - ops.Lowercase: "lower", - ops.Map: "map", - ops.Median: "median", - ops.MinRank: "rank", - ops.NTile: "ntile", - ops.NthValue: "nth_value", - ops.NullIf: "nullif", - ops.PercentRank: "percent_rank", - ops.Pi: "pi", - ops.Power: "pow", - ops.RPad: "rpad", - ops.Radians: "radians", - ops.RegexSearch: "regexp_like", - ops.RegexSplit: "regexp_split", - ops.Repeat: "repeat", - ops.Reverse: "reverse", - ops.RowNumber: "row_number", - ops.Sign: "sign", - ops.Sin: "sin", - ops.Sqrt: "sqrt", - ops.StartsWith: "starts_with", - ops.StrRight: "right", - ops.StringAscii: "ascii", - ops.StringContains: "contains", - ops.StringLength: "length", - ops.StringReplace: "replace", - ops.StringSplit: "split", - ops.StringToDate: "str_to_date", - ops.StringToTimestamp: "str_to_time", - ops.Tan: "tan", - ops.Translate: "translate", - ops.Unnest: "explode", - ops.Uppercase: "upper", - } - - BINARY_INFIX_OPS = ( - # Binary operations - ops.Add, - ops.Subtract, - ops.Multiply, - ops.Divide, - ops.Modulus, - ops.Power, - # Comparisons - ops.GreaterEqual, - ops.Greater, - ops.LessEqual, - ops.Less, - ops.Equals, - ops.NotEquals, - # Boolean comparisons - ops.And, - ops.Or, - ops.Xor, - # Bitwise business - ops.BitwiseLeftShift, - ops.BitwiseRightShift, - ops.BitwiseAnd, - ops.BitwiseOr, - ops.BitwiseXor, - # Time arithmetic - ops.DateAdd, - ops.DateSub, - ops.DateDiff, - ops.TimestampAdd, - ops.TimestampSub, - ops.TimestampDiff, - # Interval Marginalia - ops.IntervalAdd, - ops.IntervalMultiply, - ops.IntervalSubtract, - ) - - NEEDS_PARENS = BINARY_INFIX_OPS + (ops.IsNull, ops.NotNull) - - # Constructed dynamically in `__init_subclass__` from their respective - # UPPERCASE values to handle inheritance, do not modify directly here. - extra_supported_ops: ClassVar[frozenset[type[ops.Node]]] = frozenset() - lowered_ops: ClassVar[dict[type[ops.Node], pats.Replace]] = {} - - def __init__(self) -> None: - self.f = FuncGen(copy=self.__class__.copy_func_args) - self.v = VarGen() - - def __init_subclass__(cls, **kwargs): - super().__init_subclass__(**kwargs) - - def methodname(op: type) -> str: - assert isinstance(type(op), type), type(op) - return f"visit_{op.__name__}" - - def make_impl(op, target_name): - assert isinstance(type(op), type), type(op) - - if issubclass(op, ops.Reduction): - - def impl( - self, _, *, _name: str = target_name, where, order_by=(), **kw - ): - return self.agg[_name](*kw.values(), where=where, order_by=order_by) - - else: - - def impl(self, _, *, _name: str = target_name, **kw): - return self.f[_name](*kw.values()) - - return impl - - for op, target_name in cls.SIMPLE_OPS.items(): - setattr(cls, methodname(op), make_impl(op, target_name)) - - # unconditionally raise an exception for unsupported operations - # - # these *must* be defined after SIMPLE_OPS to handle compilers that - # subclass other compilers - for op in cls.UNSUPPORTED_OPS: - # change to visit_Unsupported in a follow up - # TODO: handle geoespatial ops as a separate case? - setattr(cls, methodname(op), cls.visit_Undefined) - - # raise on any remaining unsupported operations - for op in ALL_OPERATIONS: - name = methodname(op) - if not hasattr(cls, name): - setattr(cls, name, cls.visit_Undefined) - - # Amend `lowered_ops` and `extra_supported_ops` using their - # respective UPPERCASE classvar values. - extra_supported_ops = set(cls.extra_supported_ops) - lowered_ops = dict(cls.lowered_ops) - extra_supported_ops.update(cls.EXTRA_SUPPORTED_OPS) - for op_cls, rewrite in cls.LOWERED_OPS.items(): - if rewrite is not None: - lowered_ops[op_cls] = rewrite - extra_supported_ops.add(op_cls) - else: - lowered_ops.pop(op_cls, None) - extra_supported_ops.discard(op_cls) - cls.lowered_ops = lowered_ops - cls.extra_supported_ops = frozenset(extra_supported_ops) - - @property - @abc.abstractmethod - def dialect(self) -> str: - """Backend dialect.""" - - @property - @abc.abstractmethod - def type_mapper(self) -> type[SqlglotType]: - """The type mapper for the backend.""" - - def _compile_builtin_udf(self, udf_node: ops.ScalarUDF) -> None: # noqa: B027 - """No-op.""" - - def _compile_python_udf(self, udf_node: ops.ScalarUDF) -> None: - raise NotImplementedError( - f"Python UDFs are not supported in the {self.dialect} backend" - ) - - def _compile_pyarrow_udf(self, udf_node: ops.ScalarUDF) -> None: - raise NotImplementedError( - f"PyArrow UDFs are not supported in the {self.dialect} backend" - ) - - def _compile_pandas_udf(self, udf_node: ops.ScalarUDF) -> str: - raise NotImplementedError( - f"pandas UDFs are not supported in the {self.dialect} backend" - ) - - # Concrete API - - def if_(self, condition, true, false: sge.Expression | None = None) -> sge.If: - return sge.If( - this=sge.convert(condition), - true=sge.convert(true), - false=None if false is None else sge.convert(false), - ) - - def cast(self, arg, to: dt.DataType, format=None) -> sge.Cast: - return sge.Cast( - this=sge.convert(arg), to=self.type_mapper.from_ibis(to), copy=False - ) - - def _prepare_params(self, params): - result = {} - for param, value in params.items(): - node = param.op() - if isinstance(node, ops.Alias): - node = node.arg - result[node] = value - return result - - def to_sqlglot( - self, - expr: ir.Expr, - *, - limit: str | None = None, - params: Mapping[ir.Expr, Any] | None = None, - ): - import bigframes_vendored.ibis - - table_expr = expr.as_table() - - if limit == "default": - limit = bigframes_vendored.ibis.options.sql.default_limit - if limit is not None: - table_expr = table_expr.limit(limit) - - if params is None: - params = {} - - sql = self.translate(table_expr.op(), params=params) - assert not isinstance(sql, sge.Subquery) - - if isinstance(sql, sge.Table): - sql = sg.select(STAR, copy=False).from_(sql, copy=False) - - assert not isinstance(sql, sge.Subquery) - return sql - - def translate(self, op, *, params: Mapping[ir.Value, Any]) -> sge.Expression: - """Translate an ibis operation to a sqlglot expression. - - Parameters - ---------- - op - An ibis operation - params - A mapping of expressions to concrete values - compiler - An instance of SQLGlotCompiler - translate_rel - Relation node translator - translate_val - Value node translator - - Returns - ------- - sqlglot.expressions.Expression - A sqlglot expression - - """ - # substitute parameters immediately to avoid having to define a - # ScalarParameter translation rule - params = self._prepare_params(params) - if self.lowered_ops: - op = op.replace(reduce(operator.or_, self.lowered_ops.values())) - op, ctes = sqlize( - op, - params=params, - rewrites=self.rewrites, - fuse_selects=options.sql.fuse_selects, - ) - - aliases = {} - counter = itertools.count() - - def fn(node, _, **kwargs): - result = self.visit_node(node, **kwargs) - - # if it's not a relation then we don't need to do anything special - if node is op or not isinstance(node, ops.Relation): - return result - - # alias ops.Views to their explicitly assigned name otherwise generate - alias = node.name if isinstance(node, ops.View) else f"t{next(counter)}" - aliases[node] = alias - - alias = sg.to_identifier(alias, quoted=self.quoted) - if isinstance(result, sge.Subquery): - return result.as_(alias, quoted=self.quoted) - else: - try: - return result.subquery(alias, copy=False) - except AttributeError: - return result.as_(alias, quoted=self.quoted) - - # apply translate rules in topological order - results = op.map(fn) - - # get the root node as a sqlglot select statement - out = results[op] - if isinstance(out, sge.Table): - out = sg.select(STAR, copy=False).from_(out, copy=False) - elif isinstance(out, sge.Subquery): - out = out.this - - # add cte definitions to the select statement - for cte in ctes: - alias = sg.to_identifier(aliases[cte], quoted=self.quoted) - out = out.with_( - alias, as_=results[cte].this, dialect=self.dialect, copy=False - ) - - return out - - def visit_node(self, op: ops.Node, **kwargs): - if isinstance(op, ops.ScalarUDF): - return self.visit_ScalarUDF(op, **kwargs) - elif isinstance(op, ops.AggUDF): - return self.visit_AggUDF(op, **kwargs) - else: - method = getattr(self, f"visit_{type(op).__name__}", None) - if method is not None: - return method(op, **kwargs) - else: - raise ibis_exceptions.OperationNotDefinedError( - f"No translation rule for {type(op).__name__}" - ) - - def visit_Field(self, op, *, rel, name): - return sg.column( - self._gen_valid_name(name), table=rel.alias_or_name, quoted=self.quoted - ) - - def visit_Cast(self, op, *, arg, to): - from_ = op.arg.dtype - - if from_.is_integer() and to.is_interval(): - return self._make_interval(arg, to.unit) - - return self.cast(arg, to) - - def visit_ScalarSubquery(self, op, *, rel): - return rel.this.subquery(copy=False) - - def visit_Alias(self, op, *, arg, name): - return arg - - def visit_Literal(self, op, *, value, dtype): - """Compile a literal value. - - This is the default implementation for compiling literal values. - - Most backends should not need to override this method unless they want - to handle NULL literals as well as every other type of non-null literal - including integers, floating point numbers, decimals, strings, etc. - - The logic here is: - - 1. If the value is None and the type is nullable, return NULL - 1. If the value is None and the type is not nullable, raise an error - 1. Call `visit_NonNullLiteral` method. - 1. If the previous returns `None`, call `visit_DefaultLiteral` method - else return the result of the previous step. - """ - if value is None: - if dtype.is_array(): - # hack: bq arrays are like semi-nullable, but want to treat as non-nullable for simplicity - # instead, use empty array as missing value sentinel - return self.cast(self.f.array(), dtype) - if dtype.nullable: - return NULL if dtype.is_null() else self.cast(NULL, dtype) - raise ibis_exceptions.UnsupportedOperationError( - f"Unsupported NULL for non-nullable type: {dtype!r}" - ) - else: - result = self.visit_NonNullLiteral(op, value=value, dtype=dtype) - if result is None: - return self.visit_DefaultLiteral(op, value=value, dtype=dtype) - return result - - def visit_NonNullLiteral(self, op, *, value, dtype): - """Compile a non-null literal differently than the default implementation. - - Most backends should implement this, but only when they need to handle - some non-null literal differently than the default implementation - (`visit_DefaultLiteral`). - - Return `None` from an override of this method to fall back to - `visit_DefaultLiteral`. - """ - return self.visit_DefaultLiteral(op, value=value, dtype=dtype) - - def visit_DefaultLiteral(self, op, *, value, dtype): - """Compile a literal with a non-null value. - - This is the default implementation for compiling non-null literals. - - Most backends should not need to override this method unless they want - to handle compiling every kind of non-null literal value. - """ - if dtype.is_integer(): - return sge.convert(value) - elif dtype.is_floating(): - if math.isnan(value): - return self.NAN - elif math.isinf(value): - return self.POS_INF if value > 0 else self.NEG_INF - return sge.convert(value) - elif dtype.is_decimal(): - return self.cast(str(value), dtype) - elif dtype.is_interval(): - return sge.Interval( - this=sge.convert(str(value)), - unit=sge.Var(this=dtype.resolution.upper()), - ) - elif dtype.is_boolean(): - return sge.Boolean(this=bool(value)) - elif dtype.is_string(): - return sge.convert(value) - elif dtype.is_inet() or dtype.is_macaddr(): - return sge.convert(str(value)) - elif dtype.is_timestamp() or dtype.is_time(): - return self.cast(value.isoformat(), dtype) - elif dtype.is_date(): - return self.f.datefromparts(value.year, value.month, value.day) - elif dtype.is_array(): - # array type is ambiguous if no elements - value_type = dtype.value_type - values = self.f.array( - *( - self.visit_Literal( - ops.Literal(v, value_type), value=v, dtype=value_type - ) - for v in value - ) - ) - return values if len(value) > 0 else self.cast(values, dtype) - elif dtype.is_map(): - key_type = dtype.key_type - keys = self.f.array( - *( - self.visit_Literal( - ops.Literal(k, key_type), value=k, dtype=key_type - ) - for k in value.keys() - ) - ) - - value_type = dtype.value_type - values = self.f.array( - *( - self.visit_Literal( - ops.Literal(v, value_type), value=v, dtype=value_type - ) - for v in value.values() - ) - ) - - return self.f.map(keys, values) - elif dtype.is_struct(): - items = [ - self.visit_Literal( - ops.Literal(v, field_dtype), value=v, dtype=field_dtype - ).as_(k, quoted=self.quoted) - for field_dtype, (k, v) in zip(dtype.types, value.items()) - ] - return sge.Struct.from_arg_list(items) - elif dtype.is_uuid(): - return self.cast(str(value), dtype) - elif dtype.is_json(): - return sge.JSON(this=sge.convert(str(value))) - elif dtype.is_geospatial(): - wkt = value if isinstance(value, str) else value.wkt - return self.f.st_geogfromtext(wkt) - - raise NotImplementedError(f"Unsupported type: {dtype!r}") - - def visit_BitwiseNot(self, op, *, arg): - return sge.BitwiseNot(this=arg) - - ### Mathematical Calisthenics - - def visit_E(self, op): - return self.f.exp(1) - - def visit_Log(self, op, *, arg, base): - if base is None: - return self.f.ln(arg) - elif str(base) in ("2", "10"): - return self.f[f"log{base}"](arg) - else: - return self.f.ln(arg) / self.f.ln(base) - - def visit_Clip(self, op, *, arg, lower, upper): - if upper is not None: - arg = self.if_(arg.is_(NULL), arg, self.f.least(upper, arg)) - - if lower is not None: - arg = self.if_(arg.is_(NULL), arg, self.f.greatest(lower, arg)) - - return arg - - def visit_FloorDivide(self, op, *, left, right): - return self.cast(self.f.floor(left / right), op.dtype) - - def visit_Ceil(self, op, *, arg): - return self.cast(self.f.ceil(arg), op.dtype) - - def visit_Floor(self, op, *, arg): - return self.cast(self.f.floor(arg), op.dtype) - - def visit_Round(self, op, *, arg, digits): - if digits is not None: - return sge.Round(this=arg, decimals=digits) - return sge.Round(this=arg) - - ### Random Noise - - def visit_RandomScalar(self, op, **kwargs): - return self.f.rand() - - def visit_RandomUUID(self, op, **kwargs): - return self.f.uuid() - - ### Dtype Dysmorphia - - def visit_TryCast(self, op, *, arg, to): - return sge.TryCast(this=arg, to=self.type_mapper.from_ibis(to)) - - ### Comparator Conundrums - - def visit_Between(self, op, *, arg, lower_bound, upper_bound): - return sge.Between(this=arg, low=lower_bound, high=upper_bound) - - def visit_Negate(self, op, *, arg): - return -sge.paren(arg, copy=False) - - def visit_Not(self, op, *, arg): - if isinstance(arg, sge.Filter): - return sge.Filter( - this=sg.not_(arg.this, copy=False), expression=arg.expression - ) - return sg.not_(sge.paren(arg, copy=False)) - - ### Timey McTimeFace - - def visit_Time(self, op, *, arg): - return self.cast(arg, to=dt.time) - - def visit_TimestampNow(self, op): - return sge.CurrentTimestamp() - - def visit_DateNow(self, op): - return sge.CurrentDate() - - def visit_Strftime(self, op, *, arg, format_str): - return sge.TimeToStr(this=arg, format=format_str) - - def visit_ExtractEpochSeconds(self, op, *, arg): - return self.f.epoch(self.cast(arg, dt.timestamp)) - - def visit_ExtractYear(self, op, *, arg): - return self.f.extract(self.v.year, arg) - - def visit_ExtractMonth(self, op, *, arg): - return self.f.extract(self.v.month, arg) - - def visit_ExtractDay(self, op, *, arg): - return self.f.extract(self.v.day, arg) - - def visit_ExtractDayOfYear(self, op, *, arg): - return self.f.extract(self.v.dayofyear, arg) - - def visit_ExtractQuarter(self, op, *, arg): - return self.f.extract(self.v.quarter, arg) - - def visit_ExtractWeekOfYear(self, op, *, arg): - return self.f.extract(self.v.week, arg) - - def visit_ExtractHour(self, op, *, arg): - return self.f.extract(self.v.hour, arg) - - def visit_ExtractMinute(self, op, *, arg): - return self.f.extract(self.v.minute, arg) - - def visit_ExtractSecond(self, op, *, arg): - return self.f.extract(self.v.second, arg) - - def visit_TimestampTruncate(self, op, *, arg, unit): - unit_mapping = { - "Y": "year", - "Q": "quarter", - "M": "month", - "W": "week", - "D": "day", - "h": "hour", - "m": "minute", - "s": "second", - "ms": "ms", - "us": "us", - } - - if (raw_unit := unit_mapping.get(unit.short)) is None: - raise ibis_exceptions.UnsupportedOperationError( - f"Unsupported truncate unit {unit.short!r}" - ) - - return self.f.date_trunc(raw_unit, arg) - - def visit_DateTruncate(self, op, *, arg, unit): - return self.visit_TimestampTruncate(op, arg=arg, unit=unit) - - def visit_TimeTruncate(self, op, *, arg, unit): - return self.visit_TimestampTruncate(op, arg=arg, unit=unit) - - def visit_DayOfWeekIndex(self, op, *, arg): - return (self.f.dayofweek(arg) + 6) % 7 - - def visit_DayOfWeekName(self, op, *, arg): - # day of week number is 0-indexed - # Sunday == 0 - # Saturday == 6 - return sge.Case( - this=(self.f.dayofweek(arg) + 6) % 7, - ifs=list(itertools.starmap(self.if_, enumerate(calendar.day_name))), - ) - - def _make_interval(self, arg, unit): - return sge.Interval(this=arg, unit=self.v[unit.singular]) - - def visit_IntervalFromInteger(self, op, *, arg, unit): - return self._make_interval(arg, unit) - - ### String Instruments - def visit_Strip(self, op, *, arg): - return self.f.trim(arg, string.whitespace) - - def visit_RStrip(self, op, *, arg): - return self.f.rtrim(arg, string.whitespace) - - def visit_LStrip(self, op, *, arg): - return self.f.ltrim(arg, string.whitespace) - - def visit_Substring(self, op, *, arg, start, length): - if isinstance(op.length, ops.Literal) and (value := op.length.value) < 0: - raise ibis_exceptions.IbisInputError( - f"Length parameter must be a non-negative value; got {value}" - ) - start += 1 - start = self.if_(start >= 1, start, start + self.f.length(arg)) - if length is None: - return self.f.substring(arg, start) - return self.f.substring(arg, start, length) - - def visit_StringFind(self, op, *, arg, substr, start, end): - if end is not None: - raise ibis_exceptions.UnsupportedOperationError( - "String find doesn't support `end` argument" - ) - - if start is not None: - arg = self.f.substr(arg, start + 1) - pos = self.f.strpos(arg, substr) - return self.if_(pos > 0, pos + start, 0) - - return self.f.strpos(arg, substr) - - def visit_RegexReplace(self, op, *, arg, pattern, replacement): - return self.f.regexp_replace(arg, pattern, replacement, "g") - - def visit_StringConcat(self, op, *, arg): - return self.f.concat(*arg) - - def visit_StringJoin(self, op, *, sep, arg): - return self.f.concat_ws(sep, *arg) - - def visit_StringSQLLike(self, op, *, arg, pattern, escape): - return arg.like(pattern) - - def visit_StringSQLILike(self, op, *, arg, pattern, escape): - return arg.ilike(pattern) - - ### NULL PLAYER CHARACTER - def visit_IsNull(self, op, *, arg): - return arg.is_(NULL) - - def visit_NotNull(self, op, *, arg): - return self._add_parens(op, arg).is_(sg.not_(NULL, copy=False)) - - def visit_InValues(self, op, *, value, options): - return value.isin(*options) - - ### Counting - - def visit_CountDistinct(self, op, *, arg, where): - return self.agg.count(sge.Distinct(expressions=[arg]), where=where) - - def visit_CountDistinctStar(self, op, *, arg, where): - return self.agg.count(sge.Distinct(expressions=[STAR]), where=where) - - def visit_CountStar(self, op, *, arg, where): - return self.agg.count(STAR, where=where) - - def visit_Sum(self, op, *, arg, where): - if op.arg.dtype.is_boolean(): - arg = self.cast(arg, dt.int32) - return self.agg.sum(arg, where=where) - - def visit_Mean(self, op, *, arg, where): - if op.arg.dtype.is_boolean(): - arg = self.cast(arg, dt.int32) - return self.agg.avg(arg, where=where) - - def visit_Min(self, op, *, arg, where): - if op.arg.dtype.is_boolean(): - return self.agg.bool_and(arg, where=where) - return self.agg.min(arg, where=where) - - def visit_Max(self, op, *, arg, where): - if op.arg.dtype.is_boolean(): - return self.agg.bool_or(arg, where=where) - return self.agg.max(arg, where=where) - - ### Stats - - def visit_VarianceStandardDevCovariance(self, op, *, how, where, **kw): - hows = {"sample": "samp", "pop": "pop"} - funcs = { - ops.Variance: "var", - ops.StandardDev: "stddev", - ops.Covariance: "covar", - } - - args = [] - - for oparg, arg in zip(op.args, kw.values()): - if (arg_dtype := oparg.dtype).is_boolean(): - arg = self.cast(arg, dt.Int32(nullable=arg_dtype.nullable)) - args.append(arg) - - funcname = f"{funcs[type(op)]}_{hows[how]}" - return self.agg[funcname](*args, where=where) - - visit_Variance = visit_StandardDev = visit_Covariance = ( - visit_VarianceStandardDevCovariance - ) - - def visit_SimpleCase(self, op, *, base=None, cases, results, default): - return sge.Case( - this=base, ifs=list(map(self.if_, cases, results)), default=default - ) - - visit_SearchedCase = visit_SimpleCase - - def visit_SqlScalar(self, op, *, sql_template, values, output_type): - # TODO: can we include a string in the sqlglot expression without parsing? - return sg.parse_one( - sql_template.format(*[value.sql(dialect="bigquery") for value in values]), - dialect="bigquery", - ) - - def visit_ExistsSubquery(self, op, *, rel): - select = rel.this.select(1, append=False) - return self.f.exists(select) - - def visit_InSubquery(self, op, *, rel, needle): - query = rel.this - if not isinstance(query, sge.Select): - query = sg.select(STAR).from_(query) - return needle.isin(query=query) - - def visit_Array(self, op, *, exprs): - return self.f.array(*exprs) - - def visit_StructColumn(self, op, *, names, values): - return sge.Struct.from_arg_list( - [value.as_(name, quoted=self.quoted) for name, value in zip(names, values)] - ) - - def visit_StructField(self, op, *, arg, field): - return sge.Dot(this=arg, expression=sg.to_identifier(field, quoted=self.quoted)) - - def visit_IdenticalTo(self, op, *, left, right): - return sge.NullSafeEQ(this=left, expression=right) - - def visit_Greatest(self, op, *, arg): - return self.f.greatest(*arg) - - def visit_Least(self, op, *, arg): - return self.f.least(*arg) - - def visit_Coalesce(self, op, *, arg): - return self.f.coalesce(*arg) - - ### Ordering and window functions - - def visit_SortKey(self, op, *, expr, ascending: bool, nulls_first: bool = False): - return sge.Ordered(this=expr, desc=not ascending, nulls_first=nulls_first) - - def visit_ApproxMedian(self, op, *, arg, where): - return self.agg.approx_quantile(arg, 0.5, where=where) - - def visit_WindowBoundary(self, op, *, value, preceding): - # TODO: bit of a hack to return a dict, but there's no sqlglot expression - # that corresponds to _only_ this information - return {"value": value, "side": "preceding" if preceding else "following"} - - def visit_WindowFunction(self, op, *, how, func, start, end, group_by, order_by): - if start is None: - start = {} - if end is None: - end = {} - - start_value = start.get("value", "UNBOUNDED") - start_side = start.get("side", "PRECEDING") - end_value = end.get("value", "UNBOUNDED") - end_side = end.get("side", "FOLLOWING") - - if getattr(start_value, "this", None) == "0": - start_value = "CURRENT ROW" - start_side = None - - if getattr(end_value, "this", None) == "0": - end_value = "CURRENT ROW" - end_side = None - - spec = sge.WindowSpec( - kind=how.upper(), - start=start_value, - start_side=start_side, - end=end_value, - end_side=end_side, - over="OVER", - ) - order = sge.Order(expressions=order_by) if order_by else None - - spec = self._minimize_spec(op.start, op.end, spec) - - return sge.Window(this=func, partition_by=group_by, order=order, spec=spec) - - @staticmethod - def _minimize_spec(start, end, spec): - return spec - - def visit_LagLead(self, op, *, arg, offset, default): - args = [arg] - - if default is not None: - if offset is None: - offset = 1 - - args.append(offset) - args.append(default) - elif offset is not None: - args.append(offset) - - return self.f[type(op).__name__.lower()](*args) - - visit_Lag = visit_Lead = visit_LagLead - - def visit_Argument(self, op, *, name: str, shape, dtype): - return sg.to_identifier(op.param) - - def visit_RowID(self, op, *, table): - return sg.column( - op.name, table=table.alias_or_name, quoted=self.quoted, copy=False - ) - - # TODO(kszucs): this should be renamed to something UDF related - def __sql_name__(self, op: ops.ScalarUDF | ops.AggUDF) -> str: - # for builtin functions use the exact function name, otherwise use the - # generated name to handle the case of redefinition - funcname = ( - op.__func_name__ - if op.__input_type__ == InputType.BUILTIN - else type(op).__name__ - ) - - # not actually a table, but easier to quote individual namespace - # components this way - namespace = op.__udf_namespace__ - - # Function names prefixed with "SAFE.", such as `SAFE.PARSE_JSON`, - # are typically not quoted. - if funcname.startswith("SAFE."): - return funcname - - return sg.table(funcname, db=namespace.database, catalog=namespace.catalog).sql( - self.dialect - ) - - def visit_ScalarUDF(self, op, **kw): - return self.f[self.__sql_name__(op)](*kw.values()) - - def visit_AggUDF(self, op, *, where, **kw): - return self.agg[self.__sql_name__(op)](*kw.values(), where=where) - - def visit_TimestampDelta(self, op, *, part, left, right): - # dialect is necessary due to sqlglot's default behavior - # of `part` coming last - return sge.DateDiff( - this=left, expression=right, unit=part, dialect=self.dialect - ) - - visit_TimeDelta = visit_DateDelta = visit_TimestampDelta - - def visit_TimestampBucket(self, op, *, arg, interval, offset): - origin = self.f.cast("epoch", self.type_mapper.from_ibis(dt.timestamp)) - if offset is not None: - origin += offset - return self.f.time_bucket(interval, arg, origin) - - def visit_ArrayConcat(self, op, *, arg): - return sge.ArrayConcat(this=arg[0], expressions=list(arg[1:])) - - ## relations - - @staticmethod - def _gen_valid_name(name: str) -> str: - """Generate a valid name for a value expression. - - Override this method if the dialect has restrictions on valid - identifiers even when quoted. - - See the BigQuery backend's implementation for an example. - """ - return name - - def _cleanup_names(self, exprs: Mapping[str, sge.Expression]): - """Compose `_gen_valid_name` and `_dedup_name` to clean up names in projections.""" - - for name, value in exprs.items(): - name = self._gen_valid_name(name) - if isinstance(value, sge.Column) and name == value.name: - # don't alias columns that are already named the same as their alias - yield value - else: - yield value.as_(name, quoted=self.quoted, copy=False) - - def visit_Select(self, op, *, parent, selections, predicates, qualified, sort_keys): - # if we've constructed a useless projection return the parent relation - if not (selections or predicates or qualified or sort_keys): - return parent - - result = parent - - if selections: - # if there are `qualify` predicates then sqlglot adds a hidden - # column to implement the functionality if the dialect doesn't - # support it - # - # using STAR in that case would lead to an extra column, so in that - # case we have to spell out the columns - if op.is_star_selection() and (not qualified or self.supports_qualify): - fields = [STAR] - else: - fields = self._cleanup_names(selections) - result = sg.select(*fields, copy=False).from_(result, copy=False) - - if predicates: - result = result.where(*predicates, copy=False) - - if qualified: - result = result.qualify(*qualified, copy=False) - - if sort_keys: - result = result.order_by(*sort_keys, copy=False) - - return result - - def visit_DummyTable(self, op, *, values): - return sg.select(*self._cleanup_names(values), copy=False) - - def visit_UnboundTable( - self, op, *, name: str, schema: sch.Schema, namespace: ops.Namespace - ) -> sg.Table: - return sg.table( - name, db=namespace.database, catalog=namespace.catalog, quoted=self.quoted - ) - - def visit_InMemoryTable( - self, op, *, name: str, schema: sch.Schema, data - ) -> sg.Table: - return sg.table(name, quoted=self.quoted) - - def visit_DatabaseTable( - self, - op, - *, - name: str, - schema: sch.Schema, - source: Any, - namespace: ops.Namespace, - ) -> sg.Table: - return sg.table( - name, db=namespace.database, catalog=namespace.catalog, quoted=self.quoted - ) - - def visit_SelfReference(self, op, *, parent, identifier): - return parent - - visit_JoinReference = visit_SelfReference - - def visit_JoinChain(self, op, *, first, rest, values): - result = sg.select(*self._cleanup_names(values), copy=False).from_( - first, copy=False - ) - - for link in rest: - if isinstance(link, sge.Alias): - link = link.this - result = result.join(link, copy=False) - return result - - def visit_JoinLink(self, op, *, how, table, predicates): - sides = { - "inner": None, - "left": "left", - "right": "right", - "semi": "left", - "anti": "left", - "cross": None, - "outer": "full", - "asof": "asof", - "any_left": "left", - "any_inner": None, - "positional": None, - } - kinds = { - "any_left": "any", - "any_inner": "any", - "asof": "left", - "inner": "inner", - "left": "outer", - "right": "outer", - "semi": "semi", - "anti": "anti", - "cross": "cross", - "outer": "outer", - "positional": "positional", - } - assert predicates or how in { - "cross", - "positional", - }, "expected non-empty predicates when not a cross join" - on = sg.and_(*predicates) if predicates else None - return sge.Join(this=table, side=sides[how], kind=kinds[how], on=on) - - @staticmethod - def _generate_groups(groups): - return map(sge.convert, range(1, len(groups) + 1)) - - def visit_Aggregate(self, op, *, parent, groups, metrics): - exprs = [] - if groups: - exprs.extend(self._cleanup_names(groups)) - if metrics: - exprs.extend(self._cleanup_names(metrics)) - - if not exprs: - # Empty aggregated projections are invalid in BigQuery - exprs = [sge.Literal.number(1)] - - sel = sg.select(*exprs, copy=False).from_(parent, copy=False) - - if groups: - sel = sel.group_by(*self._generate_groups(groups.values()), copy=False) - - return sel - - @classmethod - def _add_parens(cls, op, sg_expr): - # Patch for https://github.com/ibis-project/ibis/issues/9975 - if isinstance(op, cls.NEEDS_PARENS) or ( - isinstance(op, ops.Alias) and isinstance(op.arg, cls.NEEDS_PARENS) - ): - return sge.paren(sg_expr, copy=False) - return sg_expr - - def visit_Union(self, op, *, left, right, distinct): - if isinstance(left, (sge.Table, sge.Subquery)): - left = sg.select(STAR, copy=False).from_(left, copy=False) - - if isinstance(right, (sge.Table, sge.Subquery)): - right = sg.select(STAR, copy=False).from_(right, copy=False) - - return sg.union( - left.args.get("this", left), - right.args.get("this", right), - distinct=distinct, - copy=False, - ) - - def visit_Intersection(self, op, *, left, right, distinct): - if isinstance(left, (sge.Table, sge.Subquery)): - left = sg.select(STAR, copy=False).from_(left, copy=False) - - if isinstance(right, (sge.Table, sge.Subquery)): - right = sg.select(STAR, copy=False).from_(right, copy=False) - - return sg.intersect( - left.args.get("this", left), - right.args.get("this", right), - distinct=distinct, - copy=False, - ) - - def visit_Difference(self, op, *, left, right, distinct): - if isinstance(left, (sge.Table, sge.Subquery)): - left = sg.select(STAR, copy=False).from_(left, copy=False) - - if isinstance(right, (sge.Table, sge.Subquery)): - right = sg.select(STAR, copy=False).from_(right, copy=False) - - return sg.except_( - left.args.get("this", left), - right.args.get("this", right), - distinct=distinct, - copy=False, - ) - - def visit_Limit(self, op, *, parent, n, offset): - # push limit/offset into subqueries - if isinstance(parent, sge.Subquery) and parent.this.args.get("limit") is None: - result = parent.this.copy() - alias = parent.alias - else: - result = sg.select(STAR, copy=False).from_(parent, copy=False) - alias = None - - if isinstance(n, int): - result = result.limit(n, copy=False) - elif n is not None: - result = result.limit( - sg.select(n, copy=False).from_(parent, copy=False).subquery(copy=False), - copy=False, - ) - else: - assert n is None, n - if self.no_limit_value is not None: - result = result.limit(self.no_limit_value, copy=False) - - assert offset is not None, "offset is None" - - if not isinstance(offset, int): - skip = offset - skip = ( - sg.select(skip, copy=False) - .from_(parent, copy=False) - .subquery(copy=False) - ) - elif not offset: - if alias is not None: - return result.subquery(alias, copy=False) - return result - else: - skip = offset - - result = result.offset(skip, copy=False) - if alias is not None: - return result.subquery(alias, copy=False) - return result - - def visit_Distinct(self, op, *, parent): - return ( - sg.select(STAR, copy=False).distinct(copy=False).from_(parent, copy=False) - ) - - def visit_CTE(self, op, *, parent): - return sg.table(parent.alias_or_name, quoted=self.quoted) - - def visit_View(self, op, *, child, name: str): - if isinstance(child, sge.Table): - child = sg.select(STAR, copy=False).from_(child, copy=False) - else: - child = child.copy() - - if isinstance(child, sge.Subquery): - return child.as_(name, quoted=self.quoted) - else: - try: - return child.subquery(name, copy=False) - except AttributeError: - return child.as_(name, quoted=self.quoted) - - def visit_SQLStringView(self, op, *, query: str, child, schema): - return sg.parse_one(query, read=self.dialect) - - def visit_SQLQueryResult(self, op, *, query, schema, source): - return sg.parse_one(query, dialect=self.dialect).subquery(copy=False) - - def visit_RegexExtract(self, op, *, arg, pattern, index): - return self.f.regexp_extract(arg, pattern, index, dialect=self.dialect) - - @parenthesize_inputs - def visit_Add(self, op, *, left, right): - return sge.Add(this=left, expression=right) - - visit_DateAdd = visit_TimestampAdd = visit_IntervalAdd = visit_Add - - @parenthesize_inputs - def visit_Subtract(self, op, *, left, right): - return sge.Sub(this=left, expression=right) - - visit_DateSub = visit_DateDiff = visit_TimestampSub = visit_TimestampDiff = ( - visit_IntervalSubtract - ) = visit_Subtract - - @parenthesize_inputs - def visit_Multiply(self, op, *, left, right): - return sge.Mul(this=left, expression=right) - - visit_IntervalMultiply = visit_Multiply - - @parenthesize_inputs - def visit_Divide(self, op, *, left, right): - return sge.Div(this=left, expression=right) - - @parenthesize_inputs - def visit_Modulus(self, op, *, left, right): - return sge.Mod(this=left, expression=right) - - @parenthesize_inputs - def visit_Power(self, op, *, left, right): - return sge.Pow(this=left, expression=right) - - @parenthesize_inputs - def visit_GreaterEqual(self, op, *, left, right): - return sge.GTE(this=left, expression=right) - - @parenthesize_inputs - def visit_Greater(self, op, *, left, right): - return sge.GT(this=left, expression=right) - - @parenthesize_inputs - def visit_LessEqual(self, op, *, left, right): - return sge.LTE(this=left, expression=right) - - @parenthesize_inputs - def visit_Less(self, op, *, left, right): - return sge.LT(this=left, expression=right) - - @parenthesize_inputs - def visit_Equals(self, op, *, left, right): - return sge.EQ(this=left, expression=right) - - @parenthesize_inputs - def visit_NotEquals(self, op, *, left, right): - return sge.NEQ(this=left, expression=right) - - @parenthesize_inputs - def visit_And(self, op, *, left, right): - return sge.And(this=left, expression=right) - - @parenthesize_inputs - def visit_Or(self, op, *, left, right): - return sge.Or(this=left, expression=right) - - @parenthesize_inputs - def visit_Xor(self, op, *, left, right): - return sge.Xor(this=left, expression=right) - - @parenthesize_inputs - def visit_BitwiseLeftShift(self, op, *, left, right): - return sge.BitwiseLeftShift(this=left, expression=right) - - @parenthesize_inputs - def visit_BitwiseRightShift(self, op, *, left, right): - return sge.BitwiseRightShift(this=left, expression=right) - - @parenthesize_inputs - def visit_BitwiseAnd(self, op, *, left, right): - return sge.BitwiseAnd(this=left, expression=right) - - @parenthesize_inputs - def visit_BitwiseOr(self, op, *, left, right): - return sge.BitwiseOr(this=left, expression=right) - - @parenthesize_inputs - def visit_BitwiseXor(self, op, *, left, right): - return sge.BitwiseXor(this=left, expression=right) - - def visit_Undefined(self, op, **_): - raise ibis_exceptions.OperationNotDefinedError( - f"Compilation rule for {type(op).__name__!r} operation is not defined" - ) - - def visit_Unsupported(self, op, **_): - raise ibis_exceptions.UnsupportedOperationError( - f"{type(op).__name__!r} operation is not supported in the {self.dialect} backend" - ) - - def visit_DropColumns(self, op, *, parent, columns_to_drop): - # the generated query will be huge for wide tables - # - # TODO: figure out a way to produce an IR that only contains exactly - # what is used - parent_alias = parent.alias_or_name - quoted = self.quoted - columns_to_keep = ( - sg.column(column, table=parent_alias, quoted=quoted) - for column in op.schema.names - ) - return sg.select(*columns_to_keep).from_(parent) - - def add_query_to_expr(self, *, name: str, table: ir.Table, query: str) -> str: - dialect = self.dialect - - compiled_ibis_expr = self.to_sqlglot(table) - - # pull existing CTEs from the compiled Ibis expression and combine them - # with the new query - parsed = reduce( - lambda parsed, cte: parsed.with_(cte.args["alias"], as_=cte.args["this"]), - compiled_ibis_expr.ctes, - sg.parse_one(query, read=dialect), - ) - - # remove all ctes from the compiled expression, since they're now in - # our larger expression - compiled_ibis_expr.args.pop("with", None) - - # add the new str query as a CTE - parsed = parsed.with_( - sg.to_identifier(name, quoted=self.quoted), as_=compiled_ibis_expr - ) - - # generate the SQL string - return parsed.sql(dialect) - - def _make_sample_backwards_compatible(self, *, sample, parent): - # sample was changed to be owned by the table being sampled in 25.17.0 - # - # this is a small workaround for backwards compatibility - if "this" in sample.__class__.arg_types: - sample.args["this"] = parent - else: - parent.args["sample"] = sample - return sg.select(STAR).from_(parent) - - -# `__init_subclass__` is uncalled for subclasses - we manually call it here to -# autogenerate the base class implementations as well. -SQLGlotCompiler.__init_subclass__() diff --git a/third_party/bigframes_vendored/ibis/backends/sql/compilers/bigquery/__init__.py b/third_party/bigframes_vendored/ibis/backends/sql/compilers/bigquery/__init__.py deleted file mode 100644 index e47164f6c46..00000000000 --- a/third_party/bigframes_vendored/ibis/backends/sql/compilers/bigquery/__init__.py +++ /dev/null @@ -1,1220 +0,0 @@ -# Contains code from https://github.com/ibis-project/ibis/blob/main/ibis/backends/sql/compilers/bigquery/__init__.py -"""Module to convert from Ibis expression to SQL string.""" - -from __future__ import annotations - -import datetime -import decimal -import math -import re -from typing import TYPE_CHECKING, Any - -import bigframes_vendored.ibis.backends.bigquery.datatypes as bq_datatypes -import bigframes_vendored.ibis.common.exceptions as ibis_exceptions -import bigframes_vendored.ibis.expr.datatypes as dt -import bigframes_vendored.ibis.expr.operations as ops -import bigframes_vendored.sqlglot as sg -import bigframes_vendored.sqlglot.expressions as sge -import numpy as np -from bigframes_vendored.ibis import util -from bigframes_vendored.ibis.backends.sql.compilers.base import ( - NULL, - STAR, - AggGen, - SQLGlotCompiler, -) -from bigframes_vendored.ibis.backends.sql.datatypes import BigQueryType, BigQueryUDFType -from bigframes_vendored.ibis.backends.sql.rewrites import ( - exclude_unsupported_window_frame_from_ops, - exclude_unsupported_window_frame_from_rank, - exclude_unsupported_window_frame_from_row_number, -) -from bigframes_vendored.ibis.common.temporal import ( - DateUnit, - IntervalUnit, - TimestampUnit, - TimeUnit, -) -from bigframes_vendored.sqlglot.dialects import BigQuery - -if TYPE_CHECKING: - from collections.abc import Mapping - - import bigframes_vendored.ibis.expr.types as ir - - -_NAME_REGEX = re.compile(r'[^!"$()*,./;?@[\\\]^`{}~\n]+') - - -_MEMTABLE_PATTERN = re.compile( - r"^_?ibis_(?:[A-Za-z_][A-Za-z_0-9]*)_memtable_[a-z0-9]{26}$" -) - - -def _qualify_memtable( - node: sge.Expression, *, dataset: str | None, project: str | None -) -> sge.Expression: - """Add a BigQuery dataset and project to memtable references.""" - if isinstance(node, sge.Table) and _MEMTABLE_PATTERN.match(node.name) is not None: - node.args["db"] = dataset - node.args["catalog"] = project - return node - - -def _remove_null_ordering_from_unsupported_window( - node: sge.Expression, -) -> sge.Expression: - """Remove null ordering in window frame clauses not supported by BigQuery. - BigQuery has only partial support for NULL FIRST/LAST in RANGE windows so - we remove it from any window frame clause that doesn't support it. - Here's the support matrix: - ✅ sum(x) over (order by y desc nulls last) - 🚫 sum(x) over (order by y asc nulls last) - ✅ sum(x) over (order by y asc nulls first) - 🚫 sum(x) over (order by y desc nulls first) - """ - if isinstance(node, sge.Window): - order = node.args.get("order") - if order is not None: - for key in order.args["expressions"]: - kargs = key.args - if kargs.get("desc") is True and kargs.get("nulls_first", False): - kargs["nulls_first"] = False - elif kargs.get("desc") is False and not kargs.setdefault( - "nulls_first", True - ): - kargs["nulls_first"] = True - return node - - -class BigQueryCompiler(SQLGlotCompiler): - dialect = BigQuery - type_mapper = BigQueryType - udf_type_mapper = BigQueryUDFType - - agg = AggGen(supports_order_by=True) - - rewrites = ( - exclude_unsupported_window_frame_from_ops, - exclude_unsupported_window_frame_from_row_number, - exclude_unsupported_window_frame_from_rank, - *SQLGlotCompiler.rewrites, - ) - - supports_qualify = True - - UNSUPPORTED_OPS = ( - ops.DateDiff, - ops.ExtractAuthority, - ops.ExtractUserInfo, - ops.FindInSet, - ops.Median, - ops.RegexSplit, - ops.RowID, - ops.TimestampDiff, - ) - - NAN = sge.Cast( - this=sge.convert("NaN"), to=sge.DataType(this=sge.DataType.Type.DOUBLE) - ) - POS_INF = sge.Cast( - this=sge.convert("Infinity"), to=sge.DataType(this=sge.DataType.Type.DOUBLE) - ) - NEG_INF = sge.Cast( - this=sge.convert("-Infinity"), to=sge.DataType(this=sge.DataType.Type.DOUBLE) - ) - - SIMPLE_OPS = { - ops.Arbitrary: "any_value", - ops.StringAscii: "ascii", - ops.BitAnd: "bit_and", - ops.BitOr: "bit_or", - ops.BitXor: "bit_xor", - ops.DateFromYMD: "date", - ops.Divide: "ieee_divide", - ops.EndsWith: "ends_with", - ops.GeoArea: "st_area", - ops.GeoAsBinary: "st_asbinary", - ops.GeoAsText: "st_astext", - ops.GeoAzimuth: "st_azimuth", - ops.GeoBuffer: "st_buffer", - ops.GeoCentroid: "st_centroid", - ops.GeoContains: "st_contains", - ops.GeoCoveredBy: "st_coveredby", - ops.GeoCovers: "st_covers", - ops.GeoDWithin: "st_dwithin", - ops.GeoDifference: "st_difference", - ops.GeoDisjoint: "st_disjoint", - ops.GeoDistance: "st_distance", - ops.GeoEndPoint: "st_endpoint", - ops.GeoEquals: "st_equals", - ops.GeoGeometryType: "st_geometrytype", - ops.GeoIntersection: "st_intersection", - ops.GeoIntersects: "st_intersects", - ops.GeoLength: "st_length", - ops.GeoMaxDistance: "st_maxdistance", - ops.GeoNPoints: "st_numpoints", - ops.GeoPerimeter: "st_perimeter", - ops.GeoPoint: "st_geogpoint", - ops.GeoPointN: "st_pointn", - ops.GeoStartPoint: "st_startpoint", - ops.GeoTouches: "st_touches", - ops.GeoUnaryUnion: "st_union_agg", - ops.GeoUnion: "st_union", - ops.GeoWithin: "st_within", - ops.GeoX: "st_x", - ops.GeoY: "st_y", - ops.Hash: "farm_fingerprint", - ops.IsInf: "is_inf", - ops.IsNan: "is_nan", - ops.Log10: "log10", - ops.LPad: "lpad", - ops.RPad: "rpad", - ops.Levenshtein: "edit_distance", - ops.Modulus: "mod", - ops.RegexReplace: "regexp_replace", - ops.RegexSearch: "regexp_contains", - ops.Time: "time", - ops.TimeFromHMS: "time_from_parts", - ops.TimestampNow: "current_timestamp", - ops.ExtractHost: "net.host", - } - - def to_sqlglot( - self, - expr: ir.Expr, - *, - limit: str | None = None, - params: Mapping[ir.Expr, Any] | None = None, - session_dataset_id: str | None = None, - session_project: str | None = None, - ) -> Any: - """Compile an Ibis expression. - Parameters - ---------- - expr - Ibis expression - limit - For expressions yielding result sets; retrieve at most this number - of values/rows. Overrides any limit already set on the expression. - params - Named unbound parameters - session_dataset_id - Optional dataset ID to qualify memtable references. - session_project - Optional project ID to qualify memtable references. - Returns - ------- - Any - The output of compilation. The type of this value depends on the - backend. - """ - sql = super().to_sqlglot(expr, limit=limit, params=params) - - table_expr = expr.as_table() - geocols = getattr(table_expr.schema(), "geospatial", None) - - result = sql.transform( - _qualify_memtable, - dataset=session_dataset_id, - project=session_project, - ).transform(_remove_null_ordering_from_unsupported_window) - - if geocols: - # if there are any geospatial columns, we have to convert them to WKB, - # so interactive mode knows how to display them - # - # by default bigquery returns data to python as WKT, and there's really - # no point in supporting both if we don't need to. - quoted = self.quoted - result = sg.select( - sge.Star( - replace=[ - self.f.st_asbinary(sg.column(col, quoted=quoted)).as_( - col, quoted=quoted - ) - for col in geocols - ] - ) - ).from_(result.subquery()) - - sources = [] - - for udf_node in table_expr.op().find(ops.ScalarUDF): - compile_func = getattr( - self, f"_compile_{udf_node.__input_type__.name.lower()}_udf" - ) - if sql := compile_func(udf_node): - sources.append(sql) - - if not sources: - return result - - sources.append(result) - return sources - - def visit_BoundingBox(self, op, *, arg): - name = type(op).__name__[len("Geo") :].lower() - return sge.Dot( - this=self.f.st_boundingbox(arg), expression=sg.to_identifier(name) - ) - - visit_GeoXMax = visit_GeoXMin = visit_GeoYMax = visit_GeoYMin = visit_BoundingBox - - def visit_GeoRegionStats(self, op, *, arg, raster_id, band, include, options): - args = [arg, raster_id] - if op.band: - args.append(sge.Kwarg(this="band", expression=band)) - if op.include: - args.append(sge.Kwarg(this="include", expression=include)) - if op.options: - args.append(sge.Kwarg(this="options", expression=options)) - return sge.func("ST_REGIONSTATS", *args) - - def visit_GeoSimplify(self, op, *, arg, tolerance, preserve_collapsed): - if ( - not isinstance(op.preserve_collapsed, ops.Literal) - or op.preserve_collapsed.value - ): - raise ibis_exceptions.UnsupportedOperationError( - "BigQuery simplify does not support preserving collapsed geometries, " - "pass preserve_collapsed=False" - ) - return self.f.st_simplify(arg, tolerance) - - def visit_ApproxMedian(self, op, *, arg, where): - return self.agg.approx_quantiles(arg, 2, where=where)[self.f.offset(1)] - - def visit_Pi(self, op): - return self.f.acos(-1) - - def visit_E(self, op): - return self.f.exp(1) - - def visit_TimeDelta(self, op, *, left, right, part): - return self.f.time_diff(left, right, part, dialect=self.dialect) - - def visit_DateDelta(self, op, *, left, right, part): - return self.f.date_diff(left, right, part, dialect=self.dialect) - - def visit_TimestampDelta(self, op, *, left, right, part): - left_tz = op.left.dtype.timezone - right_tz = op.right.dtype.timezone - - if left_tz is None and right_tz is None: - return self.f.datetime_diff(left, right, part) - elif left_tz is not None and right_tz is not None: - return self.f.timestamp_diff(left, right, part) - - raise ibis_exceptions.UnsupportedOperationError( - "timestamp difference with mixed timezone/timezoneless values is not implemented" - ) - - def visit_GroupConcat(self, op, *, arg, sep, where, order_by): - if where is not None: - arg = self.if_(where, arg, NULL) - - if order_by: - sep = sge.Order(this=sep, expressions=order_by) - - return sge.GroupConcat(this=arg, separator=sep) - - def visit_ApproxQuantile(self, op, *, arg, quantile, where): - if not isinstance(op.quantile, ops.Literal): - raise ibis_exceptions.UnsupportedOperationError( - "quantile must be a literal in BigQuery" - ) - - # BigQuery syntax is `APPROX_QUANTILES(col, resolution)` to return - # `resolution + 1` quantiles array. To handle this, we compute the - # resolution ourselves then restructure the output array as needed. - # To avoid excessive resolution we arbitrarily cap it at 100,000 - - # since these are approximate quantiles anyway this seems fine. - quantiles = util.promote_list(op.quantile.value) - fracs = [decimal.Decimal(str(q)).as_integer_ratio() for q in quantiles] - resolution = min(math.lcm(*(den for _, den in fracs)), 100_000) - indices = [(num * resolution) // den for num, den in fracs] - - if where is not None: - arg = self.if_(where, arg, NULL) - - if not op.arg.dtype.is_floating(): - arg = self.cast(arg, dt.float64) - - array = self.f.approx_quantiles( - arg, sge.IgnoreNulls(this=sge.convert(resolution)) - ) - if isinstance(op, ops.ApproxQuantile): - return array[indices[0]] - - if indices == list(range(resolution + 1)): - return array - else: - return sge.Array(expressions=[array[i] for i in indices]) - - visit_ApproxMultiQuantile = visit_ApproxQuantile - - def visit_FloorDivide(self, op, *, left, right): - return self.cast(self.f.floor(self.f.ieee_divide(left, right)), op.dtype) - - def visit_Log2(self, op, *, arg): - return self.f.log(arg, 2, dialect=self.dialect) - - def visit_Log(self, op, *, arg, base): - if base is None: - return self.f.ln(arg) - return self.f.log(arg, base, dialect=self.dialect) - - def visit_ArrayRepeat(self, op, *, arg, times): - start = step = 1 - array_length = self.f.array_length(arg) - stop = self.f.greatest(times, 0) * array_length - i = sg.to_identifier("i") - idx = self.f.coalesce( - self.f.nullif(self.f.mod(i, array_length), 0), array_length - ) - series = self.f.generate_array(start, stop, step) - return self.f.array( - sg.select(arg[self.f.safe_ordinal(idx)]).from_(self._unnest(series, as_=i)) - ) - - def visit_NthValue(self, op, *, arg, nth): - if not isinstance(op.nth, ops.Literal): - raise ibis_exceptions.UnsupportedOperationError( - f"BigQuery `nth` must be a literal; got {type(op.nth)}" - ) - return self.f.nth_value(arg, nth) - - def visit_StrRight(self, op, *, arg, nchars): - return self.f.substr(arg, -self.f.least(self.f.length(arg), nchars)) - - def visit_StringJoin(self, op, *, arg, sep): - return self.f.array_to_string(self.f.array(*arg), sep) - - def visit_DayOfWeekIndex(self, op, *, arg): - return self.f.mod(self.f.extract(self.v.dayofweek, arg) + 5, 7) - - def visit_DayOfWeekName(self, op, *, arg): - return self.f.initcap(sge.Cast(this=arg, to="STRING FORMAT 'DAY'")) - - def visit_StringToTimestamp(self, op, *, arg, format_str): - if (timezone := op.dtype.timezone) is not None: - return self.f.parse_timestamp(format_str, arg, timezone) - return self.f.parse_datetime(format_str, arg) - - def visit_ArrayCollect(self, op, *, arg, where, order_by, include_null): - if where is not None and include_null: - raise ibis_exceptions.UnsupportedOperationError( - "Combining `include_null=True` and `where` is not supported by bigquery" - ) - out = self.agg.array_agg(arg, where=where, order_by=order_by) - if not include_null: - out = sge.IgnoreNulls(this=out) - return out - - def _neg_idx_to_pos(self, arg, idx): - return self.if_(idx < 0, self.f.array_length(arg) + idx, idx) - - def visit_ArraySlice(self, op, *, arg, start, stop): - index = sg.to_identifier("bq_arr_slice") - cond = [index >= self._neg_idx_to_pos(arg, start)] - - if stop is not None: - cond.append(index < self._neg_idx_to_pos(arg, stop)) - - el = sg.to_identifier("el") - return self.f.array( - sg.select(el).from_(self._unnest(arg, as_=el, offset=index)).where(*cond) - ) - - def visit_ArrayIndex(self, op, *, arg, index): - return arg[self.f.safe_offset(index)] - - def visit_ArrayContains(self, op, *, arg, other): - name = sg.to_identifier(util.gen_name("bq_arr_contains")) - return sge.Exists( - this=sg.select(sge.convert(1)) - .from_(self._unnest(arg, as_=name)) - .where(name.eq(other)) - ) - - def visit_StringContains(self, op, *, haystack, needle): - return self.f.strpos(haystack, needle) > 0 - - def visit_StringFind(self, op, *, arg, substr, start, end): - if start is not None: - raise NotImplementedError( - "`start` not implemented for BigQuery string find" - ) - if end is not None: - raise NotImplementedError("`end` not implemented for BigQuery string find") - return self.f.strpos(arg, substr) - - def visit_TimestampFromYMDHMS( - self, op, *, year, month, day, hours, minutes, seconds - ): - return self.f.anon.DATETIME(year, month, day, hours, minutes, seconds) - - def visit_NonNullLiteral(self, op, *, value, dtype): - if dtype.is_inet() or dtype.is_macaddr(): - return sge.convert(str(value)) - elif dtype.is_timestamp(): - funcname = "DATETIME" if dtype.timezone is None else "TIMESTAMP" - return self.f.anon[funcname](value.isoformat()) - elif dtype.is_date(): - return self.f.date_from_parts(value.year, value.month, value.day) - elif dtype.is_time(): - time = self.f.time_from_parts(value.hour, value.minute, value.second) - if micros := value.microsecond: - # bigquery doesn't support `time(12, 34, 56.789101)`, AKA a - # float seconds specifier, so add any non-zero micros to the - # time value - return sge.TimeAdd( - this=time, expression=sge.convert(micros), unit=self.v.MICROSECOND - ) - return time - elif dtype.is_binary(): - return sge.Cast( - this=sge.convert(value.hex()), - to=sge.DataType(this=sge.DataType.Type.BINARY), - format=sge.convert("HEX"), - ) - elif dtype.is_interval(): - if dtype.unit == IntervalUnit.NANOSECOND: - raise ibis_exceptions.UnsupportedOperationError( - "BigQuery does not support nanosecond intervals" - ) - elif dtype.is_uuid(): - return sge.convert(str(value)) - - elif dtype.is_int64(): - # allows directly using values out of a duration arrow array - if isinstance(value, datetime.timedelta): - value = ( - (value.days * 3600 * 24) + value.seconds - ) * 1_000_000 + value.microseconds - return sge.convert(np.int64(value)) - return None - - def visit_IntervalFromInteger(self, op, *, arg, unit): - if unit == IntervalUnit.NANOSECOND: - raise ibis_exceptions.UnsupportedOperationError( - "BigQuery does not support nanosecond intervals" - ) - return sge.Interval(this=arg, unit=self.v[unit.singular]) - - def visit_Strftime(self, op, *, arg, format_str): - arg_dtype = op.arg.dtype - if arg_dtype.is_timestamp(): - if (timezone := arg_dtype.timezone) is None: - return self.f.format_datetime(format_str, arg) - else: - return self.f.format_timestamp(format_str, arg, timezone) - elif arg_dtype.is_date(): - return self.f.format_date(format_str, arg) - else: - assert arg_dtype.is_time(), arg_dtype - return self.f.format_time(format_str, arg) - - def visit_IntervalMultiply(self, op, *, left, right): - unit = self.v[op.left.dtype.resolution.upper()] - return sge.Interval(this=self.f.extract(unit, left) * right, unit=unit) - - def visit_TimestampFromUNIX(self, op, *, arg, unit): - unit = op.unit - if unit == TimestampUnit.SECOND: - return self.f.timestamp_seconds(arg) - elif unit == TimestampUnit.MILLISECOND: - return self.f.timestamp_millis(arg) - elif unit == TimestampUnit.MICROSECOND: - return self.f.timestamp_micros(arg) - elif unit == TimestampUnit.NANOSECOND: - return self.f.timestamp_micros( - self.cast(self.f.round(arg / 1_000), dt.int64) - ) - else: - raise ibis_exceptions.UnsupportedOperationError( - f"Unit not supported: {unit}" - ) - - def visit_Cast(self, op, *, arg, to): - from_ = op.arg.dtype - if to.is_null(): - return sge.Null() - if arg is NULL or ( - isinstance(arg, sge.Cast) - and getattr(arg, "to", None) is not None - and str(arg.to).upper() == "NULL" - ): - if to.is_struct() or to.is_array(): - return sge.Cast(this=NULL, to=self.type_mapper.from_ibis(to)) - if from_.is_timestamp() and to.is_integer(): - return self.f.unix_micros(arg) - elif from_.is_integer() and to.is_timestamp(): - return self.f.timestamp_seconds(arg) - elif from_.is_interval() and to.is_integer(): - if from_.unit in { - IntervalUnit.WEEK, - IntervalUnit.QUARTER, - IntervalUnit.NANOSECOND, - }: - raise ibis_exceptions.UnsupportedOperationError( - f"BigQuery does not allow extracting date part `{from_.unit}` from intervals" - ) - return self.f.extract(self.v[to.resolution.upper()], arg) - elif (from_.is_floating() or from_.is_decimal()) and to.is_integer(): - return self.cast(self.f.trunc(arg), dt.int64) - return super().visit_Cast(op, arg=arg, to=to) - - def visit_JSONGetItem(self, op, *, arg, index): - return arg[index] - - def visit_UnwrapJSONString(self, op, *, arg): - return self.f.anon["safe.string"](arg) - - def visit_UnwrapJSONInt64(self, op, *, arg): - return self.f.anon["safe.int64"](arg) - - def visit_UnwrapJSONFloat64(self, op, *, arg): - return self.f.anon["safe.float64"](arg) - - def visit_UnwrapJSONBoolean(self, op, *, arg): - return self.f.anon["safe.bool"](arg) - - def visit_ExtractEpochSeconds(self, op, *, arg): - return self.f.unix_seconds(arg) - - def visit_ExtractWeekOfYear(self, op, *, arg): - return self.f.extract(self.v.isoweek, arg) - - def visit_ExtractIsoYear(self, op, *, arg): - return self.f.extract(self.v.isoyear, arg) - - def visit_ExtractMillisecond(self, op, *, arg): - return self.f.extract(self.v.millisecond, arg) - - def visit_ExtractMicrosecond(self, op, *, arg): - return self.f.extract(self.v.microsecond, arg) - - def visit_TimestampTruncate(self, op, *, arg, unit): - if unit == IntervalUnit.NANOSECOND: - raise ibis_exceptions.UnsupportedOperationError( - f"BigQuery does not support truncating {op.arg.dtype} values to unit {unit!r}" - ) - elif unit == IntervalUnit.WEEK: - unit = "WEEK(MONDAY)" - else: - unit = unit.name - return self.f.timestamp_trunc(arg, self.v[unit], dialect=self.dialect) - - def visit_DateTruncate(self, op, *, arg, unit): - if unit == DateUnit.WEEK: - unit = "WEEK(MONDAY)" - else: - unit = unit.name - return self.f.date_trunc(arg, self.v[unit], dialect=self.dialect) - - def visit_TimeTruncate(self, op, *, arg, unit): - if unit == TimeUnit.NANOSECOND: - raise ibis_exceptions.UnsupportedOperationError( - f"BigQuery does not support truncating {op.arg.dtype} values to unit {unit!r}" - ) - else: - unit = unit.name - return self.f.time_trunc(arg, self.v[unit], dialect=self.dialect) - - def _nullifzero(self, step, zero, step_dtype): - if step_dtype.is_interval(): - return self.if_(step.eq(zero), NULL, step) - return self.f.nullif(step, zero) - - def _zero(self, dtype): - if dtype.is_interval(): - return self.f.make_interval() - return sge.convert(0) - - def _sign(self, value, dtype): - if dtype.is_interval(): - zero = self._zero(dtype) - return sge.Case( - ifs=[ - self.if_(value < zero, -1), - self.if_(value.eq(zero), 0), - self.if_(value > zero, 1), - ], - default=NULL, - ) - return self.f.sign(value) - - def _make_range(self, func, start, stop, step, step_dtype): - step_sign = self._sign(step, step_dtype) - delta_sign = self._sign(stop - start, step_dtype) - zero = self._zero(step_dtype) - nullifzero = self._nullifzero(step, zero, step_dtype) - condition = sg.and_(sg.not_(nullifzero.is_(NULL)), step_sign.eq(delta_sign)) - gen_array = func(start, stop, step) - name = sg.to_identifier(util.gen_name("bq_arr_range")) - inner = ( - sg.select(name) - .from_(self._unnest(gen_array, as_=name)) - .where(name.neq(stop)) - ) - return self.if_(condition, self.f.array(inner), self.f.array()) - - def visit_IntegerRange(self, op, *, start, stop, step): - return self._make_range(self.f.generate_array, start, stop, step, op.step.dtype) - - def visit_TimestampRange(self, op, *, start, stop, step): - if op.start.dtype.timezone is None or op.stop.dtype.timezone is None: - raise ibis_exceptions.IbisTypeError( - "Timestamps without timezone values are not supported when generating timestamp ranges" - ) - return self._make_range( - self.f.generate_timestamp_array, start, stop, step, op.step.dtype - ) - - def visit_First(self, op, *, arg, where, order_by, include_null): - if where is not None: - arg = self.if_(where, arg, NULL) - if include_null: - raise ibis_exceptions.UnsupportedOperationError( - "Combining `include_null=True` and `where` is not supported " - "by bigquery" - ) - - if order_by: - arg = sge.Order(this=arg, expressions=order_by) - - if not include_null: - arg = sge.IgnoreNulls(this=arg) - - array = self.f.array_agg(sge.Limit(this=arg, expression=sge.convert(1))) - return array[self.f.safe_offset(0)] - - def visit_Last(self, op, *, arg, where, order_by, include_null): - if where is not None: - arg = self.if_(where, arg, NULL) - if include_null: - raise ibis_exceptions.UnsupportedOperationError( - "Combining `include_null=True` and `where` is not supported " - "by bigquery" - ) - - if order_by: - arg = sge.Order(this=arg, expressions=order_by) - - if not include_null: - arg = sge.IgnoreNulls(this=arg) - - array = self.f.array_reverse(self.f.array_agg(arg)) - return array[self.f.safe_offset(0)] - - def visit_ArrayFilter(self, op, *, arg, body, param): - return self.f.array( - sg.select(param).from_(self._unnest(arg, as_=param)).where(body) - ) - - def visit_ArrayMap(self, op, *, arg, body, param): - return self.f.array(sg.select(body).from_(self._unnest(arg, as_=param))) - - def visit_ArrayReduce(self, op, *, arg, body, param): - return sg.select(body).from_(self._unnest(arg, as_=param)).subquery() - - def visit_ArrayZip(self, op, *, arg): - lengths = [self.f.array_length(arr) - 1 for arr in arg] - idx = sg.to_identifier(util.gen_name("bq_arr_idx")) - indices = self._unnest( - self.f.generate_array(0, self.f.greatest(*lengths)), as_=idx - ) - struct_fields = [ - arr[self.f.safe_offset(idx)].as_(name) - for name, arr in zip(op.dtype.value_type.names, arg) - ] - return self.f.array( - sge.Select(kind="STRUCT", expressions=struct_fields).from_(indices) - ) - - def visit_ArrayPosition(self, op, *, arg, other): - name = sg.to_identifier(util.gen_name("bq_arr")) - idx = sg.to_identifier(util.gen_name("bq_arr_idx")) - unnest = self._unnest(arg, as_=name, offset=idx) - return self.f.coalesce( - sg.select(idx + 1).from_(unnest).where(name.eq(other)).limit(1).subquery(), - 0, - ) - - def _unnest(self, expression, *, as_, offset=None): - alias = sge.TableAlias(columns=[sg.to_identifier(as_)]) - return sge.Unnest(expressions=[expression], alias=alias, offset=offset) - - def visit_ArrayRemove(self, op, *, arg, other): - name = sg.to_identifier(util.gen_name("bq_arr")) - unnest = self._unnest(arg, as_=name) - both_null = sg.and_(name.is_(NULL), other.is_(NULL)) - cond = sg.or_(name.neq(other), both_null) - return self.f.array(sg.select(name).from_(unnest).where(cond)) - - def visit_ArrayDistinct(self, op, *, arg): - name = util.gen_name("bq_arr") - return self.f.array( - sg.select(name).distinct().from_(self._unnest(arg, as_=name)) - ) - - def visit_ArraySort(self, op, *, arg): - name = util.gen_name("bq_arr") - return self.f.array( - sg.select(name).from_(self._unnest(arg, as_=name)).order_by(name) - ) - - def visit_ArrayUnion(self, op, *, left, right): - lname = util.gen_name("bq_arr_left") - rname = util.gen_name("bq_arr_right") - lhs = sg.select(lname).from_(self._unnest(left, as_=lname)) - rhs = sg.select(rname).from_(self._unnest(right, as_=rname)) - return self.f.array(sg.union(lhs, rhs, distinct=True)) - - def visit_ArrayIntersect(self, op, *, left, right): - lname = util.gen_name("bq_arr_left") - rname = util.gen_name("bq_arr_right") - lhs = sg.select(lname).from_(self._unnest(left, as_=lname)) - rhs = sg.select(rname).from_(self._unnest(right, as_=rname)) - return self.f.array(sg.intersect(lhs, rhs, distinct=True)) - - def visit_RegexExtract(self, op, *, arg, pattern, index): - matches = self.f.regexp_contains(arg, pattern) - nonzero_index_replace = self.f.regexp_replace( - arg, - self.f.concat(".*?", pattern, ".*"), - self.f.concat("\\", self.cast(index, dt.string)), - ) - zero_index_replace = self.f.regexp_replace( - arg, self.f.concat(".*?", self.f.concat("(", pattern, ")"), ".*"), "\\1" - ) - extract = self.if_(index.eq(0), zero_index_replace, nonzero_index_replace) - return self.if_(matches, extract, NULL) - - def visit_TimestampAddSub(self, op, *, left, right): - if not isinstance(right, sge.Interval): - raise ibis_exceptions.OperationNotDefinedError( - "BigQuery does not support non-literals on the right side of timestamp add/subtract" - ) - if (unit := op.right.dtype.unit) == IntervalUnit.NANOSECOND: - raise ibis_exceptions.UnsupportedOperationError( - f"BigQuery does not allow binary operation {type(op).__name__} with " - f"INTERVAL offset {unit}" - ) - - opname = type(op).__name__[len("Timestamp") :] - funcname = f"TIMESTAMP_{opname.upper()}" - return self.f.anon[funcname](left, right) - - visit_TimestampAdd = visit_TimestampSub = visit_TimestampAddSub - - def visit_DateAddSub(self, op, *, left, right): - if not isinstance(right, sge.Interval): - raise ibis_exceptions.OperationNotDefinedError( - "BigQuery does not support non-literals on the right side of date add/subtract" - ) - if not (unit := op.right.dtype.unit).is_date(): - raise ibis_exceptions.UnsupportedOperationError( - f"BigQuery does not allow binary operation {type(op).__name__} with " - f"INTERVAL offset {unit}" - ) - opname = type(op).__name__[len("Date") :] - funcname = f"DATE_{opname.upper()}" - return self.f.anon[funcname](left, right) - - visit_DateAdd = visit_DateSub = visit_DateAddSub - - def visit_Covariance(self, op, *, left, right, how, where): - if where is not None: - left = self.if_(where, left, NULL) - right = self.if_(where, right, NULL) - - if op.left.dtype.is_boolean(): - left = self.cast(left, dt.int64) - - if op.right.dtype.is_boolean(): - right = self.cast(right, dt.int64) - - how = op.how[:4].upper() - assert how in ("POP", "SAMP"), 'how not in ("POP", "SAMP")' - return self.agg[f"COVAR_{how}"](left, right, where=where) - - def visit_Correlation(self, op, *, left, right, how, where): - if how == "sample": - raise ValueError(f"Correlation with how={how!r} is not supported.") - - if where is not None: - left = self.if_(where, left, NULL) - right = self.if_(where, right, NULL) - - if op.left.dtype.is_boolean(): - left = self.cast(left, dt.int64) - - if op.right.dtype.is_boolean(): - right = self.cast(right, dt.int64) - - return self.agg.corr(left, right, where=where) - - def visit_TypeOf(self, op, *, arg): - return self._pudf("typeof", arg) - - def visit_Xor(self, op, *, left, right): - return sg.or_(sg.and_(left, sg.not_(right)), sg.and_(sg.not_(left), right)) - - def visit_HashBytes(self, op, *, arg, how): - if how not in ("md5", "sha1", "sha256", "sha512"): - raise NotImplementedError(how) - return self.f[how](arg) - - @staticmethod - def _gen_valid_name(name: str) -> str: - candidate = "_".join(map(str.strip, _NAME_REGEX.findall(name))) or "tmp" - # column names cannot be longer than 300 characters - # - # https://cloud.google.com/bigquery/docs/schemas#column_names - # - # it's easy to rename columns, so raise an exception telling the user - # to do so - # - # we could potentially relax this and support arbitrary-length columns - # by compressing the information using hashing, but there's no reason - # to solve that problem until someone encounters this error and cannot - # rename their columns - limit = 300 - if len(candidate) > limit: - raise ibis_exceptions.IbisError( - f"BigQuery does not allow column names longer than {limit:d} characters. " - "Please rename your columns to have fewer characters." - ) - return candidate - - def visit_CountStar(self, op, *, arg, where): - if where is not None: - return self.f.countif(where) - return self.f.count(STAR) - - def visit_CountDistinctStar(self, op, *, where, arg): - # Bigquery does not support count(distinct a,b,c) or count(distinct (a, b, c)) - # as expressions must be "groupable": - # https://cloud.google.com/bigquery/docs/reference/standard-sql/query-syntax#group_by_grouping_item - # - # Instead, convert the entire expression to a string - # SELECT COUNT(DISTINCT concat(to_json_string(a), to_json_string(b))) - # This works with an array of datatypes which generates a unique string - # https://cloud.google.com/bigquery/docs/reference/standard-sql/json_functions#json_encodings - row = sge.Concat( - expressions=[ - self.f.to_json_string(sg.column(x, quoted=self.quoted)) - for x in op.arg.schema.keys() - ] - ) - if where is not None: - row = self.if_(where, row, NULL) - return self.f.count(sge.Distinct(expressions=[row])) - - def visit_Degrees(self, op, *, arg): - return self._pudf("degrees", arg) - - def visit_Radians(self, op, *, arg): - return self._pudf("radians", arg) - - def visit_CountDistinct(self, op, *, arg, where): - if where is not None: - arg = self.if_(where, arg, NULL) - return self.f.count(sge.Distinct(expressions=[arg])) - - def visit_RandomUUID(self, op, **kwargs): - return self.f.generate_uuid() - - def visit_ExtractFile(self, op, *, arg): - return self._pudf("cw_url_extract_file", arg) - - def visit_ExtractFragment(self, op, *, arg): - return self._pudf("cw_url_extract_fragment", arg) - - def visit_ExtractPath(self, op, *, arg): - return self._pudf("cw_url_extract_path", arg) - - def visit_ExtractProtocol(self, op, *, arg): - return self._pudf("cw_url_extract_protocol", arg) - - def visit_ExtractQuery(self, op, *, arg, key): - if key is not None: - return self._pudf("cw_url_extract_parameter", arg, key) - else: - return self._pudf("cw_url_extract_query", arg) - - def _pudf(self, name, *args): - name = sg.table(name, db="persistent_udfs", catalog="bigquery-public-data").sql( - self.dialect - ) - return self.f[name](*args) - - def visit_DropColumns(self, op, *, parent, columns_to_drop): - quoted = self.quoted - excludes = [sg.column(column, quoted=quoted) for column in columns_to_drop] - star = sge.Star(**{"except": excludes}) - table = sg.to_identifier(parent.alias_or_name, quoted=quoted) - column = sge.Column(this=star, table=table) - return sg.select(column).from_(parent) - - def visit_TableUnnest( - self, op, *, parent, column, offset: str | None, keep_empty: bool - ): - quoted = self.quoted - - column_alias = sg.to_identifier( - util.gen_name("table_unnest_column"), quoted=quoted - ) - - selcols = [] - - table = sg.to_identifier(parent.alias_or_name, quoted=quoted) - - opname = op.column.name - overlaps_with_parent = opname in op.parent.schema - computed_column = column_alias.as_(opname, quoted=quoted) - - # replace the existing column if the unnested column hasn't been - # renamed - # - # e.g., table.unnest("x") - if overlaps_with_parent: - selcols.append( - sge.Column(this=sge.Star(replace=[computed_column]), table=table) - ) - else: - selcols.append(sge.Column(this=STAR, table=table)) - selcols.append(computed_column) - - if offset is not None: - offset = sg.to_identifier(offset, quoted=quoted) - selcols.append(offset) - - unnest = sge.Unnest( - expressions=[column], - alias=sge.TableAlias(columns=[column_alias]), - offset=offset, - ) - return ( - sg.select(*selcols) - .from_(parent) - .join(unnest, join_type="CROSS" if not keep_empty else "LEFT") - ) - - def visit_TimestampBucket(self, op, *, arg, interval, offset): - arg_dtype = op.arg.dtype - if arg_dtype.timezone is not None: - funcname = "timestamp" - else: - funcname = "datetime" - - func = self.f[f"{funcname}_bucket"] - - origin = sge.convert("1970-01-01") - if offset is not None: - origin = self.f.anon[f"{funcname}_add"](origin, offset) - - return func(arg, interval, origin) - - def _array_reduction(self, *, arg, reduction): - name = sg.to_identifier(util.gen_name(f"bq_arr_{reduction}")) - return ( - sg.select(self.f[reduction](name)) - .from_(self._unnest(arg, as_=name)) - .subquery() - ) - - def visit_ArrayMin(self, op, *, arg): - return self._array_reduction(arg=arg, reduction="min") - - def visit_ArrayMax(self, op, *, arg): - return self._array_reduction(arg=arg, reduction="max") - - def visit_ArraySum(self, op, *, arg): - return self._array_reduction(arg=arg, reduction="sum") - - def visit_ArrayMean(self, op, *, arg): - return self._array_reduction(arg=arg, reduction="avg") - - def visit_ArrayAny(self, op, *, arg): - return self._array_reduction(arg=arg, reduction="logical_or") - - def visit_ArrayAll(self, op, *, arg): - return self._array_reduction(arg=arg, reduction="logical_and") - - # Customized ops for bigframes - - def visit_InMemoryTable(self, op, *, name, schema, data): - # Avoid creating temp tables for small data, which is how memtable is - # used in BigQuery DataFrames. Inspired by: - # https://github.com/ibis-project/ibis/blob/efa6fb72bf4c790450d00a926d7bd809dade5902/ibis/backends/druid/compiler.py#L95 - rows = data.to_pyarrow(schema=None).to_pylist() # type: ignore - quoted = self.quoted - columns = [sg.column(col, quoted=quoted) for col in schema.names] - array_expr = sge.DataType( - this=sge.DataType.Type.STRUCT, - expressions=[ - sge.ColumnDef( - this=sge.to_identifier(field, quoted=self.quoted), - kind=bq_datatypes.BigQueryType.from_ibis(type_), - ) - for field, type_ in zip(schema.names, schema.types) - ], - nested=True, - ) - array_values = [ - sge.Struct( - expressions=tuple( - self.visit_Literal(None, value=value, dtype=type_) - for value, type_ in zip(row.values(), schema.types) - ) - ) - for row in rows - ] - expr = sge.Unnest( - expressions=[ - sge.DataType( - this=sge.DataType.Type.ARRAY, - expressions=[array_expr], - nested=True, - values=array_values, - ), - ], - alias=sge.TableAlias( - this=sg.to_identifier(name, quoted=quoted), - columns=columns, - ), - ) - return sg.select(sge.Star()).from_(expr) - - def visit_ArrayAggregate(self, op, *, arg, order_by, where): - if len(order_by) > 0: - expr = sge.Order( - this=arg, - expressions=[ - # Avoid adding NULLS FIRST / NULLS LAST in SQL, which is - # unsupported in ARRAY_AGG by reconstructing the node as - # plain SQL text. - f"({order_column.args['this'].sql(dialect='bigquery')}) {'DESC' if order_column.args.get('desc') else 'ASC'}" - for order_column in order_by - ], - ) - else: - expr = arg - return sge.IgnoreNulls(this=self.agg.array_agg(expr, where=where)) - - def visit_StringAgg(self, op, *, arg, sep, order_by, where): - if len(order_by) > 0: - expr = sge.Order( - this=arg, - expressions=[ - # Avoid adding NULLS FIRST / NULLS LAST in SQL, which is - # unsupported in ARRAY_AGG by reconstructing the node as - # plain SQL text. - f"({order_column.args['this'].sql(dialect='bigquery')}) {'DESC' if order_column.args.get('desc') else 'ASC'}" - for order_column in order_by - ], - ) - else: - expr = arg - return self.agg.string_agg(expr, sep, where=where) - - def visit_AIGenerate(self, op, **kwargs): - return sge.func("AI.GENERATE", *self._compile_ai_args(**kwargs)) - - def visit_AIGenerateBool(self, op, **kwargs): - return sge.func("AI.GENERATE_BOOL", *self._compile_ai_args(**kwargs)) - - def visit_AIGenerateInt(self, op, **kwargs): - return sge.func("AI.GENERATE_INT", *self._compile_ai_args(**kwargs)) - - def visit_AIGenerateDouble(self, op, **kwargs): - return sge.func("AI.GENERATE_DOUBLE", *self._compile_ai_args(**kwargs)) - - def visit_AIEmbed(self, op, **kwargs): - return sge.func("AI.EMBED", *self._compile_ai_args(**kwargs)) - - def visit_AIIf(self, op, **kwargs): - return sge.func("AI.IF", *self._compile_ai_args(**kwargs)) - - def visit_AIClassify(self, op, **kwargs): - return sge.func("AI.CLASSIFY", *self._compile_ai_args(**kwargs)) - - def visit_AIScore(self, op, **kwargs): - return sge.func("AI.SCORE", *self._compile_ai_args(**kwargs)) - - def visit_AISimilarity(self, op, **kwargs): - return sge.func("AI.SIMILARITY", *self._compile_ai_args(**kwargs)) - - def _compile_ai_args(self, **kwargs): - args = [] - - for key, val in kwargs.items(): - if val is None: - continue - - if key == "model_params": - val = sge.JSON(this=val) - - args.append(sge.Kwarg(this=sge.Identifier(this=key), expression=val)) - - return args - - def visit_FirstNonNullValue(self, op, *, arg): - return sge.IgnoreNulls(this=sge.FirstValue(this=arg)) - - def visit_LastNonNullValue(self, op, *, arg): - return sge.IgnoreNulls(this=sge.LastValue(this=arg)) - - def visit_ToJsonString(self, op, *, arg): - return self.f.to_json_string(arg) - - def visit_Quantile(self, op, *, arg, quantile, where): - return sge.PercentileCont(this=arg, expression=quantile) - - def visit_WindowFunction(self, op, *, how, func, start, end, group_by, order_by): - # Patch for https://github.com/ibis-project/ibis/issues/9872 - - if start is None: - start = {} - if end is None: - end = {} - - start_value = start.get("value", "UNBOUNDED") - start_side = start.get("side", "PRECEDING") - end_value = end.get("value", "UNBOUNDED") - end_side = end.get("side", "FOLLOWING") - - if getattr(start_value, "this", None) == "0": - start_value = "CURRENT ROW" - start_side = None - - if getattr(end_value, "this", None) == "0": - end_value = "CURRENT ROW" - end_side = None - - if how != "none": - spec = sge.WindowSpec( - kind=how.upper(), - start=start_value, - start_side=start_side, - end=end_value, - end_side=end_side, - over="OVER", - ) - else: - spec = None - - # If unordered, unbound range window is implicit - if (not order_by) and (not start) and (not end): - spec = None - - order = sge.Order(expressions=order_by) if order_by else None - - return sge.Window(this=func, partition_by=group_by, order=order, spec=spec) - - -compiler = BigQueryCompiler() diff --git a/third_party/bigframes_vendored/ibis/backends/sql/datatypes.py b/third_party/bigframes_vendored/ibis/backends/sql/datatypes.py deleted file mode 100644 index 7a71ecf5efb..00000000000 --- a/third_party/bigframes_vendored/ibis/backends/sql/datatypes.py +++ /dev/null @@ -1,540 +0,0 @@ -# Contains code from https://github.com/ibis-project/ibis/blob/9.2.0/ibis/backends/sql/datatypes.py - -from __future__ import annotations - -from functools import partial -from typing import NoReturn - -import bigframes_vendored.ibis.common.exceptions as com -import bigframes_vendored.ibis.expr.datatypes as dt -import bigframes_vendored.sqlglot as sg -import bigframes_vendored.sqlglot.expressions as sge -from bigframes_vendored.ibis.formats import TypeMapper - -typecode = sge.DataType.Type - -_from_sqlglot_types = { - typecode.BIGDECIMAL: partial(dt.Decimal, 76, 38), - typecode.BIGINT: dt.Int64, - typecode.BINARY: dt.Binary, - typecode.BOOLEAN: dt.Boolean, - typecode.CHAR: dt.String, - typecode.DATE: dt.Date, - typecode.DATE32: dt.Date, - typecode.DOUBLE: dt.Float64, - typecode.ENUM: dt.String, - typecode.ENUM8: dt.String, - typecode.ENUM16: dt.String, - typecode.FLOAT: dt.Float32, - typecode.FIXEDSTRING: dt.String, - typecode.HSTORE: partial(dt.Map, dt.string, dt.string), - typecode.INET: dt.INET, - typecode.INT128: partial(dt.Decimal, 38, 0), - typecode.INT256: partial(dt.Decimal, 76, 0), - typecode.INT: dt.Int32, - typecode.IPADDRESS: dt.INET, - typecode.JSON: dt.JSON, - typecode.JSONB: partial(dt.JSON, binary=True), - typecode.LONGBLOB: dt.Binary, - typecode.LONGTEXT: dt.String, - typecode.MEDIUMBLOB: dt.Binary, - typecode.MEDIUMTEXT: dt.String, - typecode.MONEY: dt.Decimal(19, 4), - typecode.NCHAR: dt.String, - typecode.UUID: dt.UUID, - typecode.NAME: dt.String, - typecode.NULL: dt.Null, - typecode.NVARCHAR: dt.String, - typecode.OBJECT: partial(dt.Map, dt.string, dt.json), - typecode.ROWVERSION: partial(dt.Binary, nullable=False), - typecode.SMALLINT: dt.Int16, - typecode.SMALLMONEY: dt.Decimal(10, 4), - typecode.TEXT: dt.String, - typecode.TIME: dt.Time, - typecode.TIMETZ: dt.Time, - typecode.TINYBLOB: dt.Binary, - typecode.TINYINT: dt.Int8, - typecode.TINYTEXT: dt.String, - typecode.UBIGINT: dt.UInt64, - typecode.UINT: dt.UInt32, - typecode.USMALLINT: dt.UInt16, - typecode.UTINYINT: dt.UInt8, - typecode.UUID: dt.UUID, - typecode.VARBINARY: dt.Binary, - typecode.VARCHAR: dt.String, - typecode.VARIANT: dt.JSON, - typecode.SET: partial(dt.Array, dt.string), - ############################# - # Unsupported sqlglot types # - ############################# - # BIT = auto() # mysql - # BIGSERIAL = auto() - # DATETIME64 = auto() # clickhouse - # ENUM = auto() - # INT4RANGE = auto() - # INT4MULTIRANGE = auto() - # INT8RANGE = auto() - # INT8MULTIRANGE = auto() - # NUMRANGE = auto() - # NUMMULTIRANGE = auto() - # TSRANGE = auto() - # TSMULTIRANGE = auto() - # TSTZRANGE = auto() - # TSTZMULTIRANGE = auto() - # DATERANGE = auto() - # DATEMULTIRANGE = auto() - # HLLSKETCH = auto() - # IMAGE = auto() - # IPPREFIX = auto() - # SERIAL = auto() - # SET = auto() - # SMALLSERIAL = auto() - # SUPER = auto() - # TIMESTAMPLTZ = auto() - # UNKNOWN = auto() # Sentinel value, useful for type annotation - # UINT128 = auto() - # UINT256 = auto() - # USERDEFINED = "USER-DEFINED" - # XML = auto() -} - -_to_sqlglot_types = { - dt.Null: typecode.NULL, - dt.Boolean: typecode.BOOLEAN, - dt.Int8: typecode.TINYINT, - dt.Int16: typecode.SMALLINT, - dt.Int32: typecode.INT, - dt.Int64: typecode.BIGINT, - dt.UInt8: typecode.UTINYINT, - dt.UInt16: typecode.USMALLINT, - dt.UInt32: typecode.UINT, - dt.UInt64: typecode.UBIGINT, - dt.Float16: typecode.FLOAT, - dt.Float32: typecode.FLOAT, - dt.Float64: typecode.DOUBLE, - dt.String: typecode.VARCHAR, - dt.Binary: typecode.VARBINARY, - dt.INET: typecode.INET, - dt.UUID: typecode.UUID, - dt.MACADDR: typecode.VARCHAR, - dt.Date: typecode.DATE, - dt.Time: typecode.TIME, -} - -_geotypes = { - "POINT": dt.Point, - "LINESTRING": dt.LineString, - "POLYGON": dt.Polygon, - "MULTIPOINT": dt.MultiPoint, - "MULTILINESTRING": dt.MultiLineString, - "MULTIPOLYGON": dt.MultiPolygon, -} - - -class SqlglotType(TypeMapper): - dialect: str | None = None - """The dialect this parser is for.""" - - default_nullable = True - """Default nullability when not specified.""" - - default_decimal_precision: int | None = None - """Default decimal precision when not specified.""" - - default_decimal_scale: int | None = None - """Default decimal scale when not specified.""" - - default_temporal_scale: int | None = None - """Default temporal scale when not specified.""" - - default_interval_precision: str | None = None - """Default interval precision when not specified.""" - - unknown_type_strings: dict[str, dt.DataType] = {} - """String to ibis datatype mapping to use when converting unknown types.""" - - @classmethod - def to_ibis(cls, typ: sge.DataType, nullable: bool | None = None) -> dt.DataType: - """Convert a sqlglot type to an ibis type.""" - typecode = typ.this - - # broken sqlglot thing - if isinstance(typecode, sge.Interval): - typ = sge.DataType( - this=sge.DataType.Type.INTERVAL, - expressions=[typecode.unit], - ) - typecode = typ.this - - if method := getattr(cls, f"_from_sqlglot_{typecode.name}", None): - dtype = method(*typ.expressions) - else: - dtype = _from_sqlglot_types[typecode](nullable=cls.default_nullable) - - if nullable is not None: - return dtype.copy(nullable=nullable) - else: - return dtype - - @classmethod - def from_ibis(cls, dtype: dt.DataType) -> sge.DataType: - """Convert an Ibis dtype to an sqlglot dtype.""" - - if method := getattr(cls, f"_from_ibis_{dtype.name}", None): - return method(dtype) - else: - return sge.DataType(this=_to_sqlglot_types[type(dtype)]) - - @classmethod - def from_string(cls, text: str, nullable: bool | None = None) -> dt.DataType: - if dtype := cls.unknown_type_strings.get(text.lower()): - return dtype - - try: - sgtype = sg.parse_one(text, into=sge.DataType, read=cls.dialect) - return cls.to_ibis(sgtype, nullable=nullable) - except sg.errors.ParseError: - # If sqlglot can't parse the type fall back to `dt.unknown` - pass - - return dt.unknown - - @classmethod - def to_string(cls, dtype: dt.DataType) -> str: - return cls.from_ibis(dtype).sql(dialect=cls.dialect) - - @classmethod - def _from_sqlglot_ARRAY(cls, value_type: sge.DataType) -> dt.Array: - return dt.Array(cls.to_ibis(value_type), nullable=cls.default_nullable) - - @classmethod - def _from_sqlglot_MAP( - cls, key_type: sge.DataType, value_type: sge.DataType - ) -> dt.Map: - return dt.Map( - cls.to_ibis(key_type), - cls.to_ibis(value_type), - nullable=cls.default_nullable, - ) - - @classmethod - def _from_sqlglot_STRUCT(cls, *fields: sge.ColumnDef) -> dt.Struct: - types = {} - for i, field in enumerate(fields): - if isinstance(field, sge.ColumnDef): - types[field.name] = cls.to_ibis(field.args["kind"]) - else: - types[f"f{i:d}"] = cls.from_string(str(field)) - return dt.Struct(types, nullable=cls.default_nullable) - - @classmethod - def _from_sqlglot_TIMESTAMP(cls, scale=None) -> dt.Timestamp: - return dt.Timestamp( - scale=cls.default_temporal_scale if scale is None else int(scale.this.this), - nullable=cls.default_nullable, - ) - - @classmethod - def _from_sqlglot_TIMESTAMPTZ(cls, scale=None) -> dt.Timestamp: - return dt.Timestamp( - timezone="UTC", - scale=cls.default_temporal_scale if scale is None else int(scale.this.this), - nullable=cls.default_nullable, - ) - - @classmethod - def _from_sqlglot_TIMESTAMPLTZ(cls, scale=None) -> dt.Timestamp: - return dt.Timestamp( - timezone="UTC", - scale=cls.default_temporal_scale if scale is None else int(scale.this.this), - nullable=cls.default_nullable, - ) - - @classmethod - def _from_sqlglot_TIMESTAMPNTZ(cls, scale=None) -> dt.Timestamp: - return dt.Timestamp( - timezone=None, - scale=cls.default_temporal_scale if scale is None else int(scale.this.this), - nullable=cls.default_nullable, - ) - - @classmethod - def _from_sqlglot_INTERVAL( - cls, precision_or_span: sge.IntervalSpan | None = None - ) -> dt.Interval: - nullable = cls.default_nullable - if precision_or_span is None: - precision_or_span = cls.default_interval_precision - - if isinstance(precision_or_span, str): - return dt.Interval(precision_or_span, nullable=nullable) - elif isinstance(precision_or_span, sge.IntervalSpan): - if (expression := precision_or_span.expression) is not None: - unit = expression.this - else: - unit = precision_or_span.this.this - return dt.Interval(unit=unit, nullable=nullable) - elif isinstance(precision_or_span, sge.Var): - return dt.Interval(unit=precision_or_span.this, nullable=nullable) - elif precision_or_span is None: - raise com.IbisTypeError("Interval precision is None") - else: - raise com.IbisTypeError(precision_or_span) - - @classmethod - def _from_sqlglot_DECIMAL( - cls, - precision: sge.DataTypeParam | None = None, - scale: sge.DataTypeParam | None = None, - ) -> dt.Decimal: - if precision is None: - precision = cls.default_decimal_precision - else: - precision = int(precision.this.this) - - if scale is None: - scale = cls.default_decimal_scale - else: - scale = int(scale.this.this) - - return dt.Decimal(precision, scale, nullable=cls.default_nullable) - - @classmethod - def _from_sqlglot_GEOMETRY( - cls, arg: sge.DataTypeParam | None = None, srid: sge.DataTypeParam | None = None - ) -> sge.DataType: - if arg is not None: - typeclass = _geotypes[arg.this.this] - else: - typeclass = dt.GeoSpatial - if srid is not None: - srid = int(srid.this.this) - return typeclass(geotype="geometry", nullable=cls.default_nullable, srid=srid) - - @classmethod - def _from_sqlglot_GEOGRAPHY( - cls, arg: sge.DataTypeParam | None = None, srid: sge.DataTypeParam | None = None - ) -> sge.DataType: - if arg is not None: - typeclass = _geotypes[arg.this.this] - else: - typeclass = dt.GeoSpatial - if srid is not None: - srid = int(srid.this.this) - return typeclass(geotype="geography", nullable=cls.default_nullable, srid=srid) - - @classmethod - def _from_ibis_JSON(cls, dtype: dt.JSON) -> sge.DataType: - return sge.DataType(this=typecode.JSONB if dtype.binary else typecode.JSON) - - @classmethod - def _from_ibis_Interval(cls, dtype: dt.Interval) -> sge.DataType: - assert dtype.unit is not None, "interval unit cannot be None" - return sge.DataType( - this=typecode.INTERVAL, - expressions=[sge.Var(this=dtype.unit.name)], - ) - - @classmethod - def _from_ibis_Array(cls, dtype: dt.Array) -> sge.DataType: - value_type = cls.from_ibis(dtype.value_type) - return sge.DataType(this=typecode.ARRAY, expressions=[value_type], nested=True) - - @classmethod - def _from_ibis_Map(cls, dtype: dt.Map) -> sge.DataType: - key_type = cls.from_ibis(dtype.key_type) - value_type = cls.from_ibis(dtype.value_type) - return sge.DataType( - this=typecode.MAP, expressions=[key_type, value_type], nested=True - ) - - @classmethod - def _from_ibis_Struct(cls, dtype: dt.Struct) -> sge.DataType: - fields = [ - sge.ColumnDef( - # always quote struct fields to allow reserved words as field names - this=sg.to_identifier(name, quoted=True), - kind=cls.from_ibis(field), - ) - for name, field in dtype.items() - ] - return sge.DataType(this=typecode.STRUCT, expressions=fields, nested=True) - - @classmethod - def _from_ibis_Decimal(cls, dtype: dt.Decimal) -> sge.DataType: - if (precision := dtype.precision) is None: - precision = cls.default_decimal_precision - - if (scale := dtype.scale) is None: - scale = cls.default_decimal_scale - - expressions = [] - - if precision is not None: - expressions.append(sge.DataTypeParam(this=sge.Literal.number(precision))) - - if scale is not None: - if precision is None: - raise com.IbisTypeError( - "Decimal scale cannot be specified without precision" - ) - expressions.append(sge.DataTypeParam(this=sge.Literal.number(scale))) - - return sge.DataType(this=typecode.DECIMAL, expressions=expressions or None) - - @classmethod - def _from_ibis_Timestamp(cls, dtype: dt.Timestamp) -> sge.DataType: - code = typecode.TIMESTAMP if dtype.timezone is None else typecode.TIMESTAMPTZ - if dtype.scale is not None: - scale = sge.DataTypeParam(this=sge.Literal.number(dtype.scale)) - return sge.DataType(this=code, expressions=[scale]) - else: - return sge.DataType(this=code) - - @classmethod - def _from_ibis_GeoSpatial(cls, dtype: dt.GeoSpatial): - expressions = [None] - - if (srid := dtype.srid) is not None: - expressions.append(sge.DataTypeParam(this=sge.convert(srid))) - - this = getattr(typecode, dtype.geotype.upper()) - - return sge.DataType(this=this, expressions=expressions) - - @classmethod - def _from_ibis_SpecificGeometry(cls, dtype: dt.GeoSpatial): - expressions = [ - sge.DataTypeParam(this=sge.Var(this=dtype.__class__.__name__.upper())) - ] - - if (srid := dtype.srid) is not None: - expressions.append(sge.DataTypeParam(this=sge.convert(srid))) - - this = getattr(typecode, dtype.geotype.upper()) - return sge.DataType(this=this, expressions=expressions) - - _from_ibis_Point = _from_ibis_LineString = _from_ibis_Polygon = ( - _from_ibis_MultiLineString - ) = _from_ibis_MultiPoint = _from_ibis_MultiPolygon = _from_ibis_SpecificGeometry - - -class BigQueryType(SqlglotType): - dialect = "bigquery" - - default_decimal_precision = 38 - default_decimal_scale = 9 - - @classmethod - def _from_sqlglot_NUMERIC(cls) -> dt.Decimal: - return dt.Decimal( - cls.default_decimal_precision, - cls.default_decimal_scale, - nullable=cls.default_nullable, - ) - - @classmethod - def _from_sqlglot_BIGNUMERIC(cls) -> dt.Decimal: - return dt.Decimal(76, 38, nullable=cls.default_nullable) - - @classmethod - def _from_sqlglot_DATETIME(cls) -> dt.Timestamp: - return dt.Timestamp(timezone=None, nullable=cls.default_nullable) - - @classmethod - def _from_sqlglot_TIMESTAMP(cls) -> dt.Timestamp: - return dt.Timestamp(timezone=None, nullable=cls.default_nullable) - - @classmethod - def _from_sqlglot_TIMESTAMPTZ(cls) -> dt.Timestamp: - return dt.Timestamp(timezone="UTC", nullable=cls.default_nullable) - - @classmethod - def _from_sqlglot_GEOGRAPHY( - cls, arg: sge.DataTypeParam | None = None, srid: sge.DataTypeParam | None = None - ) -> dt.GeoSpatial: - return dt.GeoSpatial( - geotype="geography", srid=4326, nullable=cls.default_nullable - ) - - @classmethod - def _from_sqlglot_TINYINT(cls) -> dt.Int64: - return dt.Int64(nullable=cls.default_nullable) - - _from_sqlglot_UINT = _from_sqlglot_USMALLINT = _from_sqlglot_UTINYINT = ( - _from_sqlglot_INT - ) = _from_sqlglot_SMALLINT = _from_sqlglot_TINYINT - - @classmethod - def _from_sqlglot_UBIGINT(cls) -> NoReturn: - raise com.UnsupportedBackendType( - "Unsigned BIGINT isn't representable in BigQuery INT64" - ) - - @classmethod - def _from_sqlglot_FLOAT(cls) -> dt.Float64: - return dt.Float64(nullable=cls.default_nullable) - - @classmethod - def _from_sqlglot_MAP(cls) -> NoReturn: - raise com.UnsupportedBackendType("Maps are not supported in BigQuery") - - @classmethod - def _from_ibis_Map(cls, dtype: dt.Map) -> NoReturn: - raise com.UnsupportedBackendType("Maps are not supported in BigQuery") - - @classmethod - def _from_ibis_Timestamp(cls, dtype: dt.Timestamp) -> sge.DataType: - if dtype.timezone is None: - return sge.DataType(this=sge.DataType.Type.DATETIME) - elif dtype.timezone == "UTC": - return sge.DataType(this=sge.DataType.Type.TIMESTAMPTZ) - else: - raise com.UnsupportedBackendType( - "BigQuery does not support timestamps with timezones other than 'UTC'" - ) - - @classmethod - def _from_ibis_Decimal(cls, dtype: dt.Decimal) -> sge.DataType: - precision = dtype.precision - scale = dtype.scale - if (precision, scale) == (76, 38): - return sge.DataType(this=sge.DataType.Type.BIGDECIMAL) - elif (precision, scale) in ((38, 9), (None, None)): - return sge.DataType(this=sge.DataType.Type.DECIMAL) - else: - raise com.UnsupportedBackendType( - "BigQuery only supports decimal types with precision of 38 and " - f"scale of 9 (NUMERIC) or precision of 76 and scale of 38 (BIGNUMERIC). " - f"Current precision: {dtype.precision}. Current scale: {dtype.scale}" - ) - - @classmethod - def _from_ibis_UInt64(cls, dtype: dt.UInt64) -> NoReturn: - raise com.UnsupportedBackendType( - f"Conversion from {dtype} to BigQuery integer type (Int64) is lossy" - ) - - @classmethod - def _from_ibis_UInt32(cls, dtype: dt.UInt32) -> sge.DataType: - return sge.DataType(this=sge.DataType.Type.BIGINT) - - _from_ibis_UInt8 = _from_ibis_UInt16 = _from_ibis_UInt32 - - @classmethod - def _from_ibis_GeoSpatial(cls, dtype: dt.GeoSpatial) -> sge.DataType: - if (dtype.geotype, dtype.srid) == ("geography", 4326): - return sge.DataType(this=sge.DataType.Type.GEOGRAPHY) - else: - raise com.UnsupportedBackendType( - "BigQuery geography uses points on WGS84 reference ellipsoid." - f"Current geotype: {dtype.geotype}, Current srid: {dtype.srid}" - ) - - -class BigQueryUDFType(BigQueryType): - @classmethod - def _from_ibis_Int64(cls, dtype: dt.Int64) -> NoReturn: - raise com.UnsupportedBackendType( - "int64 is not a supported input or output type in BigQuery UDFs; use float64 instead" - ) diff --git a/third_party/bigframes_vendored/ibis/backends/sql/rewrites.py b/third_party/bigframes_vendored/ibis/backends/sql/rewrites.py deleted file mode 100644 index dbdce90517c..00000000000 --- a/third_party/bigframes_vendored/ibis/backends/sql/rewrites.py +++ /dev/null @@ -1,519 +0,0 @@ -# Contains code from https://github.com/ibis-project/ibis/blob/main/ibis/backends/sql/rewrites.py - -"""Lower the ibis expression graph to a SQL-like relational algebra.""" - -from __future__ import annotations - -import operator -from collections.abc import Mapping -from functools import reduce -from typing import TYPE_CHECKING, Any - -import bigframes_vendored.ibis.common.exceptions as ibis_exceptions -import bigframes_vendored.ibis.expr.datatypes as dt -import bigframes_vendored.ibis.expr.operations as ops -import toolz -from bigframes_vendored.ibis.common.annotations import attribute -from bigframes_vendored.ibis.common.collections import FrozenDict # noqa: TCH001 -from bigframes_vendored.ibis.common.deferred import var -from bigframes_vendored.ibis.common.graph import Graph -from bigframes_vendored.ibis.common.patterns import InstanceOf, Object, Pattern, replace -from bigframes_vendored.ibis.common.typing import VarTuple # noqa: TCH001 -from bigframes_vendored.ibis.expr.rewrites import d, p, replace_parameter -from bigframes_vendored.ibis.expr.schema import Schema -from public import public - -if TYPE_CHECKING: - from collections.abc import Sequence - -x = var("x") -y = var("y") - - -@public -class CTE(ops.Relation): - """Common table expression.""" - - parent: ops.Relation - - @attribute - def schema(self): - return self.parent.schema - - @attribute - def values(self): - return self.parent.values - - -@public -class Select(ops.Relation): - """Relation modelled after SQL's SELECT statement.""" - - parent: ops.Relation - selections: FrozenDict[str, ops.Value] = {} - predicates: VarTuple[ops.Value[dt.Boolean]] = () - qualified: VarTuple[ops.Value[dt.Boolean]] = () - sort_keys: VarTuple[ops.SortKey] = () - - def is_star_selection(self): - return tuple(self.values.items()) == tuple(self.parent.fields.items()) - - @attribute - def values(self): - return self.selections - - @attribute - def schema(self): - return Schema({k: v.dtype for k, v in self.selections.items()}) - - -@public -class FirstValue(ops.Analytic): - """Retrieve the first element.""" - - arg: ops.Column[dt.Any] - - @attribute - def dtype(self): - return self.arg.dtype - - -@public -class LastValue(ops.Analytic): - """Retrieve the last element.""" - - arg: ops.Column[dt.Any] - - @attribute - def dtype(self): - return self.arg.dtype - - -# TODO(kszucs): there is a better strategy to rewrite the relational operations -# to Select nodes by wrapping the leaf nodes in a Select node and then merging -# Project, Filter, Sort, etc. incrementally into the Select node. This way we -# can have tighter control over simplification logic. - - -@replace(p.Project) -def project_to_select(_, **kwargs): - """Convert a Project node to a Select node.""" - return Select(_.parent, selections=_.values) - - -def partition_predicates(predicates): - qualified = [] - unqualified = [] - - for predicate in predicates: - if predicate.find(ops.WindowFunction, filter=ops.Value): - qualified.append(predicate) - else: - unqualified.append(predicate) - - return unqualified, qualified - - -@replace(p.Filter) -def filter_to_select(_, **kwargs): - """Convert a Filter node to a Select node.""" - predicates, qualified = partition_predicates(_.predicates) - return Select( - _.parent, selections=_.values, predicates=predicates, qualified=qualified - ) - - -@replace(p.Sort) -def sort_to_select(_, **kwargs): - """Convert a Sort node to a Select node.""" - return Select(_.parent, selections=_.values, sort_keys=_.keys) - - -if hasattr(p, "DropColumns"): - - @replace(p.DropColumns) - def drop_columns_to_select(_, **kwargs): - """Convert a DropColumns node to a Select node.""" - # if we're dropping fewer than 50% of the parent table's columns then the - # compiled query will likely be smaller than if we list everything *NOT* - # being dropped - if len(_.columns_to_drop) < len(_.schema) // 2: - return _ - return Select(_.parent, selections=_.values) - - -if hasattr(p, "FillNull"): - - @replace(p.FillNull) - def fill_null_to_select(_, **kwargs): - """Rewrite FillNull to a Select node.""" - if isinstance(_.replacements, Mapping): - mapping = _.replacements - else: - mapping = { - name: _.replacements - for name, type in _.parent.schema.items() - if type.nullable - } - - if not mapping: - return _.parent - - selections = {} - for name in _.parent.schema.names: - col = ops.Field(_.parent, name) - if (value := mapping.get(name)) is not None: - col = ops.Alias(ops.Coalesce((col, value)), name) - selections[name] = col - - return Select(_.parent, selections=selections) - - -if hasattr(p, "DropNull"): - - @replace(p.DropNull) - def drop_null_to_select(_, **kwargs): - """Rewrite DropNull to a Select node.""" - if _.subset is None: - columns = [ops.Field(_.parent, name) for name in _.parent.schema.names] - else: - columns = _.subset - - if columns: - preds = [ - reduce( - ops.And if _.how == "any" else ops.Or, - [ops.NotNull(c) for c in columns], - ) - ] - elif _.how == "all": - preds = [ops.Literal(False, dtype=dt.bool)] - else: - return _.parent - - return Select(_.parent, selections=_.values, predicates=tuple(preds)) - - -@replace(p.WindowFunction(p.First | p.Last)) -def first_to_firstvalue(_, **kwargs): - """Convert a First or Last node to a FirstValue or LastValue node.""" - if _.func.where is not None: - raise ibis_exceptions.UnsupportedOperationError( - f"`{type(_.func).__name__.lower()}` with `where` is unsupported " - "in a window function" - ) - klass = FirstValue if isinstance(_.func, ops.First) else LastValue - return _.copy(func=klass(_.func.arg)) - - -def complexity(node): - """Assign a complexity score to a node. - - Subsequent projections can be merged into a single projection by replacing - the fields referenced in the outer projection with the computed expressions - from the inner projection. This inlining can result in very complex value - expressions depending on the projections. In order to prevent excessive - inlining, we assign a complexity score to each node. - - The complexity score assigns 1 to each value expression and adds up in the - tree hierarchy unless there is a Field node where we don't add up the - complexity of the referenced relation. This way we treat fields kind of like - reusable variables considering them less complex than they were inlined. - """ - - def accum(node, *args): - if isinstance(node, ops.Field): - return 1 - else: - return 1 + sum(args) - - return node.map_nodes(accum)[node] - - -@replace(Object(Select, Object(Select))) -def merge_select_select(_, **kwargs): - """Merge subsequent Select relations into one. - - This rewrites eliminates `_.parent` by merging the outer and the inner - `predicates`, `sort_keys` and keeping the outer `selections`. All selections - from the inner Select are inlined into the outer Select. - """ - # don't merge if either the outer or the inner select has window functions - blocking = ( - ops.WindowFunction, - ops.ExistsSubquery, - ops.InSubquery, - ops.Unnest, - ops.Impure, - # This is used for remote functions, which we don't want to copy - ops.ScalarUDF, - ) - if _.find_below(blocking, filter=ops.Value): - return _ - if _.parent.find_below(blocking, filter=ops.Value): - return _ - - subs = {ops.Field(_.parent, k): v for k, v in _.parent.values.items()} - selections = {k: v.replace(subs, filter=ops.Value) for k, v in _.selections.items()} - - predicates = tuple(p.replace(subs, filter=ops.Value) for p in _.predicates) - unique_predicates = toolz.unique(_.parent.predicates + predicates) - - qualified = tuple(p.replace(subs, filter=ops.Value) for p in _.qualified) - unique_qualified = toolz.unique(_.parent.qualified + qualified) - - sort_keys = tuple(s.replace(subs, filter=ops.Value) for s in _.sort_keys) - sort_key_exprs = {s.expr for s in sort_keys} - parent_sort_keys = tuple( - k for k in _.parent.sort_keys if k.expr not in sort_key_exprs - ) - unique_sort_keys = sort_keys + parent_sort_keys - - result = Select( - _.parent.parent, - selections=selections, - predicates=unique_predicates, - qualified=unique_qualified, - sort_keys=unique_sort_keys, - ) - return result if complexity(result) <= complexity(_) else _ - - -def extract_ctes(node: ops.Relation) -> set[ops.Relation]: - cte_types = (Select, ops.Aggregate, ops.JoinChain, ops.Set, ops.Limit, ops.Sample) - dont_count = (ops.Field, ops.CountStar, ops.CountDistinctStar) - - g = Graph.from_bfs(node, filter=~InstanceOf(dont_count)) - result = set() - for op, dependents in g.invert().items(): - if isinstance(op, ops.View) or ( - len(dependents) > 1 and isinstance(op, cte_types) - ): - result.add(op) - - return result - - -def sqlize( - node: ops.Node, - params: Mapping[ops.ScalarParameter, Any], - rewrites: Sequence[Pattern] = (), - fuse_selects: bool = True, -) -> tuple[ops.Node, list[ops.Node]]: - """Lower the ibis expression graph to a SQL-like relational algebra. - - Parameters - ---------- - node - The root node of the expression graph. - params - A mapping of scalar parameters to their values. - rewrites - Supplementary rewrites to apply to the expression graph. - fuse_selects - Whether to merge subsequent Select nodes into one where possible. - - Returns - ------- - Tuple of the rewritten expression graph and a list of CTEs. - - """ - assert isinstance(node, ops.Relation) - - # apply the backend specific rewrites - if rewrites: - node = node.replace(reduce(operator.or_, rewrites)) - - # lower the expression graph to a SQL-like relational algebra - context = {"params": params} - replacements = ( - replace_parameter | project_to_select | filter_to_select | sort_to_select - ) - - if hasattr(p, "FillNull"): - replacements = replacements | fill_null_to_select - - if hasattr(p, "DropNull"): - replacements = replacements | drop_null_to_select - - if hasattr(p, "DropColumns"): - replacements = replacements | drop_columns_to_select - - replacements = replacements | first_to_firstvalue - sqlized = node.replace( - replacements, - context=context, - ) - - # squash subsequent Select nodes into one - if fuse_selects: - simplified = sqlized.replace(merge_select_select) - else: - simplified = sqlized - - # extract common table expressions while wrapping them in a CTE node - ctes = extract_ctes(simplified) - - def wrap(node, _, **kwargs): - new = node.__recreate__(kwargs) - return CTE(new) if node in ctes else new - - result = simplified.replace(wrap) - ctes = [cte.parent for cte in result.find(CTE, ordered=True)] - - return result, ctes - - -# supplemental rewrites selectively used on a per-backend basis - - -@replace(p.WindowFunction(func=p.NTile(y), order_by=())) -def add_order_by_to_empty_ranking_window_functions(_, **kwargs): - """Add an ORDER BY clause to rank window functions that don't have one.""" - return _.copy(order_by=(y,)) - - -"""Replace checks against an empty right side with `False`.""" -empty_in_values_right_side = p.InValues(options=()) >> d.Literal(False, dtype=dt.bool) - - -@replace( - p.WindowFunction(p.RankBase | p.NTile) - | p.StringFind - | p.FindInSet - | p.ArrayPosition -) -def one_to_zero_index(_, **kwargs): - """Subtract one from one-index functions.""" - return ops.Subtract(_, 1) - - -@replace(ops.NthValue) -def add_one_to_nth_value_input(_, **kwargs): - if isinstance(_.nth, ops.Literal): - nth = ops.Literal(_.nth.value + 1, dtype=_.nth.dtype) - else: - nth = ops.Add(_.nth, 1) - return _.copy(nth=nth) - - -@replace(p.WindowFunction(order_by=())) -def rewrite_empty_order_by_window(_, **kwargs): - return _.copy(order_by=(ops.NULL,)) - - -@replace(p.WindowFunction(p.RowNumber | p.NTile | p.MinRank | p.DenseRank)) -def exclude_unsupported_window_frame_from_row_number(_, **kwargs): - # These functions do not support window bounds, only an ordering. - # Also, its kind of messy to insert subtract here, should probably be in visitor - return ops.Subtract( - _.copy(how="none", start=None, end=None, order_by=_.order_by or (ops.NULL,)), 1 - ) - - -@replace(p.WindowFunction(p.PercentRank | p.CumeDist, start=None)) -def exclude_unsupported_window_frame_from_rank(_, **kwargs): - # These functions do not support window bounds, only an ordering. - # Also, its kind of messy to insert subtract here, should probably be in visitor - return _.copy(how="none", start=None, end=None, order_by=_.order_by or (ops.NULL,)) - - -@replace(p.WindowFunction(p.Lag | p.Lead, start=None)) -def exclude_unsupported_window_frame_from_ops(_, **kwargs): - # lag/lead dont' support bounds, but do support ordering - return _.copy(how="none", start=None, end=None, order_by=_.order_by or (ops.NULL,)) - - -# Rewrite rules for lowering a high-level operation into one composed of more -# primitive operations. - - -@replace(p.Log2) -def lower_log2(_, **kwargs): - """Rewrite `log2` as `log`.""" - return ops.Log(_.arg, base=2) - - -@replace(p.Log10) -def lower_log10(_, **kwargs): - """Rewrite `log10` as `log`.""" - return ops.Log(_.arg, base=10) - - -@replace(p.Bucket) -def lower_bucket(_, **kwargs): - """Rewrite `Bucket` as `SearchedCase`.""" - cases = [] - results = [] - - if _.closed == "left": - l_cmp = ops.LessEqual - r_cmp = ops.Less - else: - l_cmp = ops.Less - r_cmp = ops.LessEqual - - user_num_buckets = len(_.buckets) - 1 - - bucket_id = 0 - if _.include_under: - if user_num_buckets > 0: - cmp = ops.Less if _.close_extreme else r_cmp - else: - cmp = ops.LessEqual if _.closed == "right" else ops.Less - cases.append(cmp(_.arg, _.buckets[0])) - results.append(bucket_id) - bucket_id += 1 - - for j, (lower, upper) in enumerate(zip(_.buckets, _.buckets[1:])): - if _.close_extreme and ( - (_.closed == "right" and j == 0) - or (_.closed == "left" and j == (user_num_buckets - 1)) - ): - cases.append( - ops.And(ops.LessEqual(lower, _.arg), ops.LessEqual(_.arg, upper)) - ) - results.append(bucket_id) - else: - cases.append(ops.And(l_cmp(lower, _.arg), r_cmp(_.arg, upper))) - results.append(bucket_id) - bucket_id += 1 - - if _.include_over: - if user_num_buckets > 0: - cmp = ops.Less if _.close_extreme else l_cmp - else: - cmp = ops.Less if _.closed == "right" else ops.LessEqual - - cases.append(cmp(_.buckets[-1], _.arg)) - results.append(bucket_id) - bucket_id += 1 - - return ops.SearchedCase( - cases=tuple(cases), results=tuple(results), default=ops.NULL - ) - - -@replace(p.Capitalize) -def lower_capitalize(_, **kwargs): - """Rewrite Capitalize in terms of substring, concat, upper, and lower.""" - first = ops.Uppercase(ops.Substring(_.arg, start=0, length=1)) - # use length instead of length - 1 to avoid backends complaining about - # asking for negative length - # - # there are at most length - 1 characters, so asking for length is fine - rest = ops.Lowercase(ops.Substring(_.arg, start=1, length=ops.StringLength(_.arg))) - return ops.StringConcat((first, rest)) - - -@replace(p.Sample) -def lower_sample(_, **kwargs): - """Rewrite Sample as `t.filter(random() <= fraction)`. - - Errors as unsupported if a `seed` is specified. - """ - if _.seed is not None: - raise ibis_exceptions.UnsupportedOperationError( - "`Table.sample` with a random seed is unsupported" - ) - return ops.Filter(_.parent, (ops.LessEqual(ops.RandomScalar(), _.fraction),)) diff --git a/third_party/bigframes_vendored/ibis/common/__init__.py b/third_party/bigframes_vendored/ibis/common/__init__.py deleted file mode 100644 index e69de29bb2d..00000000000 diff --git a/third_party/bigframes_vendored/ibis/common/annotations.py b/third_party/bigframes_vendored/ibis/common/annotations.py deleted file mode 100644 index 9eb0de4ee24..00000000000 --- a/third_party/bigframes_vendored/ibis/common/annotations.py +++ /dev/null @@ -1,651 +0,0 @@ -# Contains code from https://github.com/ibis-project/ibis/blob/9.2.0/ibis/common/annotations.py - -from __future__ import annotations - -import functools -import inspect -import types -from typing import TYPE_CHECKING -from typing import Any as AnyType - -from bigframes_vendored.ibis.common.bases import Immutable, Slotted -from bigframes_vendored.ibis.common.patterns import ( - Any, - FrozenDictOf, - NoMatch, - Option, - Pattern, - TupleOf, -) -from bigframes_vendored.ibis.common.patterns import pattern as ensure_pattern -from bigframes_vendored.ibis.common.typing import format_typehint, get_type_hints - -if TYPE_CHECKING: - from collections.abc import Callable, Sequence - -EMPTY = inspect.Parameter.empty # marker for missing argument -KEYWORD_ONLY = inspect.Parameter.KEYWORD_ONLY -POSITIONAL_ONLY = inspect.Parameter.POSITIONAL_ONLY -POSITIONAL_OR_KEYWORD = inspect.Parameter.POSITIONAL_OR_KEYWORD -VAR_KEYWORD = inspect.Parameter.VAR_KEYWORD -VAR_POSITIONAL = inspect.Parameter.VAR_POSITIONAL - - -_any = Any() - - -class ValidationError(Exception): - __slots__ = () - - -class AttributeValidationError(ValidationError): - __slots__ = ("name", "value", "pattern") - - def __init__(self, name: str, value: AnyType, pattern: Pattern): - self.name = name - self.value = value - self.pattern = pattern - - def __str__(self): - return f"Failed to validate attribute `{self.name}`: {self.value!r} is not {self.pattern.describe()}" - - -class ReturnValidationError(ValidationError): - __slots__ = ("func", "value", "pattern") - - def __init__(self, func: Callable, value: AnyType, pattern: Pattern): - self.func = func - self.value = value - self.pattern = pattern - - def __str__(self): - return f"Failed to validate return value of `{self.func.__name__}`: {self.value!r} is not {self.pattern.describe()}" - - -class SignatureValidationError(ValidationError): - __slots__ = ("msg", "sig", "func", "args", "kwargs", "errors") - - def __init__( - self, - msg: str, - sig: Signature, - func: Callable, - args: tuple[AnyType, ...], - kwargs: dict[str, AnyType], - errors: Sequence[tuple[str, AnyType, Pattern]] = (), - ): - self.msg = msg - self.sig = sig - self.func = func - self.args = args - self.kwargs = kwargs - self.errors = errors - - def __str__(self): - args = tuple(repr(arg) for arg in self.args) - args += tuple(f"{k}={v!r}" for k, v in self.kwargs.items()) - call = f"{self.func.__name__}({', '.join(args)})" - - errors = "" - for name, value, pattern in self.errors: - errors += f"\n `{name}`: {value!r} is not {pattern.describe()}" - - sig = f"{self.func.__name__}{self.sig}" - cause = str(self.__cause__) if self.__cause__ else "" - - return self.msg.format(sig=sig, call=call, cause=cause, errors=errors) - - -class Annotation(Slotted, Immutable): - """Base class for all annotations. - - Annotations are used to mark fields in a class and to validate them. - """ - - __slots__ = ("pattern", "default") - pattern: Pattern - default: AnyType - - def validate(self, name: str, value: AnyType, this: AnyType) -> AnyType: - """Validate the field. - - Parameters - ---------- - name - The name of the attribute. - value - The value of the attribute. - this - The instance of the class the attribute is defined on. - - Returns - ------- - The validated value for the field. - - """ - result = self.pattern.match(value, this) - if result is NoMatch: - raise AttributeValidationError( - name=name, - value=value, - pattern=self.pattern, - ) - return result - - -class Attribute(Annotation): - """Annotation to mark a field in a class. - - An optional pattern can be provider to validate the field every time it - is set. - - Parameters - ---------- - pattern : Pattern, default noop - Pattern to validate the field. - default : Callable, default EMPTY - Callable to compute the default value of the field. - - """ - - def __init__(self, pattern: Pattern = _any, default: AnyType = EMPTY): - super().__init__(pattern=ensure_pattern(pattern), default=default) - - def has_default(self): - """Check if the field has a default value. - - Returns - ------- - bool - - """ - return self.default is not EMPTY - - def get_default(self, name: str, this: AnyType) -> AnyType: - """Get the default value of the field. - - Parameters - ---------- - name - The name of the attribute. - this - The instance of the class the attribute is defined on. - - Returns - ------- - The default value for the field. - - """ - if callable(self.default): - value = self.default(this) - else: - value = self.default - return self.validate(name, value, this) - - def __call__(self, default): - """Needed to support the decorator syntax.""" - return self.__class__(self.pattern, default) - - -class Argument(Annotation): - """Annotation type for all fields which should be passed as arguments. - - Parameters - ---------- - pattern - Optional pattern to validate the argument. - default - Optional default value of the argument. - typehint - Optional typehint of the argument. - kind - Kind of the argument, one of `inspect.Parameter` constants. - Defaults to positional or keyword. - - """ - - __slots__ = ("typehint", "kind") - typehint: AnyType - kind: int - - def __init__( - self, - pattern: Pattern = _any, - default: AnyType = EMPTY, - typehint: type | None = None, - kind: int = POSITIONAL_OR_KEYWORD, - ): - super().__init__( - pattern=ensure_pattern(pattern), - default=default, - typehint=typehint, - kind=kind, - ) - - -def attribute(pattern=_any, default=EMPTY): - """Annotation to mark a field in a class.""" - if default is EMPTY and isinstance(pattern, (types.FunctionType, types.MethodType)): - return Attribute(default=pattern) - else: - return Attribute(pattern, default=default) - - -def argument(pattern=_any, default=EMPTY, typehint=None): - """Annotation type for all fields which should be passed as arguments.""" - return Argument(pattern, default=default, typehint=typehint) - - -def optional(pattern=_any, default=None, typehint=None): - """Annotation to allow and treat `None` values as missing arguments.""" - if pattern is None: - pattern = Option(Any(), default=default) - else: - pattern = Option(pattern, default=default) - return Argument(pattern, default=None, typehint=typehint) - - -def varargs(pattern=_any, typehint=None): - """Annotation to mark a variable length positional arguments.""" - return Argument(TupleOf(pattern), kind=VAR_POSITIONAL, typehint=typehint) - - -def varkwargs(pattern=_any, typehint=None): - """Annotation to mark a variable length keyword arguments.""" - return Argument(FrozenDictOf(_any, pattern), kind=VAR_KEYWORD, typehint=typehint) - - -class Parameter(inspect.Parameter): - """Augmented Parameter class to additionally hold a pattern object.""" - - __slots__ = () - - def __init__(self, name, annotation): - if not isinstance(annotation, Argument): - raise TypeError( - f"annotation must be an instance of Argument, got {annotation}" - ) - super().__init__( - name, - kind=annotation.kind, - default=annotation.default, - annotation=annotation, - ) - - def __str__(self): - formatted = self._name - - if self._annotation is not EMPTY: - typehint = format_typehint(self._annotation.typehint) - formatted = f"{formatted}: {typehint}" - - if self._default is not EMPTY: - if self._annotation is not EMPTY: - formatted = f"{formatted} = {self._default!r}" - else: - formatted = f"{formatted}={self._default!r}" - - if self._kind == VAR_POSITIONAL: - formatted = "*" + formatted - elif self._kind == VAR_KEYWORD: - formatted = "**" + formatted - - return formatted - - -class Signature(inspect.Signature): - """Validatable signature. - - Primarily used in the implementation of `ibis.common.grounds.Annotable`. - """ - - __slots__ = () - - @classmethod - def merge(cls, *signatures, **annotations): - """Merge multiple signatures. - - In addition to concatenating the parameters, it also reorders the - parameters so that optional arguments come after mandatory arguments. - - Parameters - ---------- - *signatures : Signature - Signature instances to merge. - **annotations : dict - Annotations to add to the merged signature. - - Returns - ------- - Signature - - """ - params = {} - for sig in signatures: - params.update(sig.parameters) - - inherited = set(params.keys()) - for name, annot in annotations.items(): - params[name] = Parameter(name, annotation=annot) - - # mandatory fields without default values must precede the optional - # ones in the function signature, the partial ordering will be kept - var_args, var_kwargs = [], [] - new_args, new_kwargs = [], [] - old_args, old_kwargs = [], [] - - for name, param in params.items(): - if param.kind == VAR_POSITIONAL: - if var_args: - raise TypeError("only one variadic *args parameter is allowed") - var_args.append(param) - elif param.kind == VAR_KEYWORD: - if var_kwargs: - raise TypeError("only one variadic **kwargs parameter is allowed") - var_kwargs.append(param) - elif name in inherited: - if param.default is EMPTY: - old_args.append(param) - else: - old_kwargs.append(param) - elif param.default is EMPTY: - new_args.append(param) - else: - new_kwargs.append(param) - - return cls( - old_args + new_args + var_args + new_kwargs + old_kwargs + var_kwargs - ) - - @classmethod - def from_callable(cls, fn, patterns=None, return_pattern=None): - """Create a validateable signature from a callable. - - Parameters - ---------- - fn : Callable - Callable to create a signature from. - patterns : list or dict, default None - Pass patterns to add missing or override existing argument type - annotations. - return_pattern : Pattern, default None - Pattern for the return value of the callable. - - Returns - ------- - Signature - - """ - sig = super().from_callable(fn) - typehints = get_type_hints(fn) - - if patterns is None: - patterns = {} - elif isinstance(patterns, (list, tuple)): - # create a mapping of parameter name to pattern - patterns = dict(zip(sig.parameters.keys(), patterns)) - elif not isinstance(patterns, dict): - raise TypeError(f"patterns must be a list or dict, got {type(patterns)}") - - parameters = [] - for param in sig.parameters.values(): - name = param.name - kind = param.kind - default = param.default - typehint = typehints.get(name) - - if name in patterns: - pattern = patterns[name] - elif typehint is not None: - pattern = Pattern.from_typehint(typehint) - else: - pattern = _any - - if kind is VAR_POSITIONAL: - annot = varargs(pattern, typehint=typehint) - elif kind is VAR_KEYWORD: - annot = varkwargs(pattern, typehint=typehint) - else: - annot = Argument(pattern, kind=kind, default=default, typehint=typehint) - - parameters.append(Parameter(param.name, annot)) - - if return_pattern is not None: - return_annotation = return_pattern - elif (typehint := typehints.get("return")) is not None: - return_annotation = Pattern.from_typehint(typehint) - else: - return_annotation = EMPTY - - return cls(parameters, return_annotation=return_annotation) - - def unbind(self, this: dict[str, Any]) -> tuple[tuple[Any, ...], dict[str, Any]]: - """Reverse bind of the parameters. - - Attempts to reconstructs the original arguments as keyword only arguments. - - Parameters - ---------- - this : Any - Object with attributes matching the signature parameters. - - Returns - ------- - args : (args, kwargs) - Tuple of positional and keyword arguments. - - """ - # does the reverse of bind, but doesn't apply defaults - args: list = [] - kwargs: dict = {} - for name, param in self.parameters.items(): - value = this[name] - if param.kind is POSITIONAL_OR_KEYWORD: - args.append(value) - elif param.kind is VAR_POSITIONAL: - args.extend(value) - elif param.kind is VAR_KEYWORD: - kwargs.update(value) - elif param.kind is KEYWORD_ONLY: - kwargs[name] = value - elif param.kind is POSITIONAL_ONLY: - args.append(value) - else: - raise TypeError(f"unsupported parameter kind {param.kind}") - return tuple(args), kwargs - - def validate(self, func, args, kwargs): - """Validate the arguments against the signature. - - Parameters - ---------- - func : Callable - Callable to validate the arguments for. - args : tuple - Positional arguments. - kwargs : dict - Keyword arguments. - - Returns - ------- - validated : dict - Dictionary of validated arguments. - - """ - try: - bound = self.bind(*args, **kwargs) - bound.apply_defaults() - except TypeError as err: - raise SignatureValidationError( - "{call} {cause}\n\nExpected signature: {sig}", - sig=self, - func=func, - args=args, - kwargs=kwargs, - ) from err - - this, errors = {}, [] - for name, value in bound.arguments.items(): - param = self.parameters[name] - pattern = param.annotation.pattern - - result = pattern.match(value, this) - if result is NoMatch: - errors.append((name, value, pattern)) - else: - this[name] = result - - if errors: - raise SignatureValidationError( - "{call} has failed due to the following errors:{errors}\n\nExpected signature: {sig}", - sig=self, - func=func, - args=args, - kwargs=kwargs, - errors=errors, - ) - - return this - - def validate_nobind(self, func, kwargs): - """Validate the arguments against the signature without binding.""" - this, errors = {}, [] - for name, param in self.parameters.items(): - value = kwargs.get(name, param.default) - if value is EMPTY: - raise TypeError(f"missing required argument `{name!r}`") - - pattern = param.annotation.pattern - result = pattern.match(value, this) - if result is NoMatch: - errors.append((name, value, pattern)) - else: - this[name] = result - - if errors: - raise SignatureValidationError( - "{call} has failed due to the following errors:{errors}\n\nExpected signature: {sig}", - sig=self, - func=func, - args=(), - kwargs=kwargs, - errors=errors, - ) - - return this - - def validate_return(self, func, value): - """Validate the return value of a function. - - Parameters - ---------- - func : Callable - Callable to validate the return value for. - value : Any - Return value of the function. - - Returns - ------- - validated : Any - Validated return value. - - """ - if self.return_annotation is EMPTY: - return value - - result = self.return_annotation.match(value, {}) - if result is NoMatch: - raise ReturnValidationError( - func=func, - value=value, - pattern=self.return_annotation, - ) - - return result - - -def annotated(_1=None, _2=None, _3=None, **kwargs): - """Create functions with arguments validated at runtime. - - There are various ways to apply this decorator: - - 1. With type annotations - - >>> @annotated - ... def foo(x: int, y: str) -> float: - ... return float(x) + float(y) - - 2. With argument patterns passed as keyword arguments - - >>> from ibis.common.patterns import InstanceOf as instance_of - >>> @annotated(x=instance_of(int), y=instance_of(str)) - ... def foo(x, y): - ... return float(x) + float(y) - - 3. With mixing type annotations and patterns where the latter takes precedence - - >>> @annotated(x=instance_of(float)) - ... def foo(x: int, y: str) -> float: - ... return float(x) + float(y) - - 4. With argument patterns passed as a list and/or an optional return pattern - - >>> @annotated([instance_of(int), instance_of(str)], instance_of(float)) - ... def foo(x, y): - ... return float(x) + float(y) - - Parameters - ---------- - *args : Union[ - tuple[Callable], - tuple[list[Pattern], Callable], - tuple[list[Pattern], Pattern, Callable] - ] - Positional arguments. - - If a single callable is passed, it's wrapped with the signature - - If two arguments are passed, the first one is a list of patterns for the - arguments and the second one is the callable to wrap - - If three arguments are passed, the first one is a list of patterns for the - arguments, the second one is a pattern for the return value and the third - one is the callable to wrap - **kwargs : dict[str, Pattern] - Patterns for the arguments. - - Returns - ------- - Callable - - """ - if _1 is None: - return functools.partial(annotated, **kwargs) - elif _2 is None: - if callable(_1): - func, patterns, return_pattern = _1, None, None - else: - return functools.partial(annotated, _1, **kwargs) - elif _3 is None: - if not isinstance(_2, Pattern): - func, patterns, return_pattern = _2, _1, None - else: - return functools.partial(annotated, _1, _2, **kwargs) - else: - func, patterns, return_pattern = _3, _1, _2 - - sig = Signature.from_callable( - func, patterns=patterns or kwargs, return_pattern=return_pattern - ) - - @functools.wraps(func) - def wrapped(*args, **kwargs): - # 1. Validate the passed arguments - values = sig.validate(func, args, kwargs) - # 2. Reconstruction of the original arguments - args, kwargs = sig.unbind(values) - # 3. Call the function with the validated arguments - result = func(*args, **kwargs) - # 4. Validate the return value - return sig.validate_return(func, result) - - wrapped.__signature__ = sig - - return wrapped diff --git a/third_party/bigframes_vendored/ibis/common/bases.py b/third_party/bigframes_vendored/ibis/common/bases.py deleted file mode 100644 index 2d5d798c318..00000000000 --- a/third_party/bigframes_vendored/ibis/common/bases.py +++ /dev/null @@ -1,246 +0,0 @@ -# Contains code from https://github.com/ibis-project/ibis/blob/9.2.0/ibis/common/bases.py - -from __future__ import annotations - -import collections.abc -from abc import abstractmethod -from typing import TYPE_CHECKING, Any -from weakref import WeakValueDictionary - -if TYPE_CHECKING: - from collections.abc import Mapping - - from typing_extensions import Self - - -class AbstractMeta(type): - """Base metaclass for many of the ibis core classes. - - Enforce the subclasses to define a `__slots__` attribute and provide a - `__create__` classmethod to change the instantiation behavior of the class. - - Support abstract methods without extending `abc.ABCMeta`. While it provides - a reduced feature set compared to `abc.ABCMeta` (no way to register virtual - subclasses) but avoids expensive instance checks by enforcing explicit - subclassing. - """ - - __slots__ = () - - def __new__(metacls, clsname, bases, dct, **kwargs): - # enforce slot definitions - dct.setdefault("__slots__", ()) - - # construct the class object - cls = super().__new__(metacls, clsname, bases, dct, **kwargs) - - # calculate abstract methods existing in the class - abstracts = { - name - for name, value in dct.items() - if getattr(value, "__isabstractmethod__", False) - } - for parent in bases: - for name in getattr(parent, "__abstractmethods__", set()): - value = getattr(cls, name, None) - if getattr(value, "__isabstractmethod__", False): - abstracts.add(name) - - # set the abstract methods for the class - cls.__abstractmethods__ = frozenset(abstracts) - - return cls - - def __call__(cls, *args, **kwargs): - """Create a new instance of the class. - - The subclass may override the `__create__` classmethod to change the - instantiation behavior. This is similar to overriding the `__new__` - method, but without conditionally calling the `__init__` based on the - return type. - - Parameters - ---------- - args : tuple - Positional arguments eventually passed to the `__init__` method. - kwargs : dict - Keyword arguments eventually passed to the `__init__` method. - - Returns - ------- - The newly created instance of the class. No extra initialization - - """ - return cls.__create__(*args, **kwargs) - - -class Abstract(metaclass=AbstractMeta): - """Base class for many of the ibis core classes, see `AbstractMeta`.""" - - __slots__ = ("__weakref__",) - __create__ = classmethod(type.__call__) # type: ignore - - -class Immutable(Abstract): - """Prohibit attribute assignment on the instance.""" - - def __copy__(self): - return self - - def __deepcopy__(self, memo): - return self - - def __setattr__(self, name: str, _: Any) -> None: - raise AttributeError( - f"Attribute {name!r} cannot be assigned to immutable instance of " - f"type {type(self)}" - ) - - -class Singleton(Abstract): - """Cache instances of the class based on instantiation arguments.""" - - __instances__: Mapping[Any, Self] = WeakValueDictionary() - - @classmethod - def __create__(cls, *args, **kwargs): - key = (cls, args, tuple(kwargs.items())) - try: - return cls.__instances__[key] - except KeyError: - instance = super().__create__(*args, **kwargs) - cls.__instances__[key] = instance - return instance - - -class Final(Abstract): - """Prohibit subclassing.""" - - def __init_subclass__(cls, **kwargs): - super().__init_subclass__(**kwargs) - cls.__init_subclass__ = cls.__prohibit_inheritance__ - - @classmethod - def __prohibit_inheritance__(cls, **kwargs): - raise TypeError(f"Cannot inherit from final class {cls}") - - -@collections.abc.Hashable.register -class Hashable(Abstract): - @abstractmethod - def __hash__(self) -> int: ... - - -class Comparable(Abstract): - """Enable quick equality comparisons. - - The subclasses must implement the `__equals__` method that returns a boolean - value indicating whether the two instances are equal. This method is called - only if the two instances are of the same type and the result is cached for - future comparisons. - - Since the class holds a global cache of comparison results, it is important - to make sure that the instances are not kept alive longer than necessary. - """ - - __cache__ = {} - - @abstractmethod - def __equals__(self, other) -> bool: ... - - def __eq__(self, other) -> bool: - if self is other: - return True - - # type comparison should be cheap - if type(self) is not type(other): - return False - - id1 = id(self) - id2 = id(other) - try: - return self.__cache__[id1][id2] - except KeyError: - result = self.__equals__(other) - self.__cache__.setdefault(id1, {})[id2] = result - self.__cache__.setdefault(id2, {})[id1] = result - return result - - def __del__(self): - id1 = id(self) - for id2 in self.__cache__.pop(id1, ()): - eqs2 = self.__cache__[id2] - del eqs2[id1] - if not eqs2: - del self.__cache__[id2] - - -class SlottedMeta(AbstractMeta): - def __new__(metacls, clsname, bases, dct, **kwargs): - fields = dct.get("__fields__", dct.get("__slots__", ())) - inherited = (getattr(base, "__fields__", ()) for base in bases) - dct["__fields__"] = sum(inherited, ()) + fields - return super().__new__(metacls, clsname, bases, dct, **kwargs) - - -class Slotted(Abstract, metaclass=SlottedMeta): - """A lightweight alternative to `ibis.common.grounds.Annotable`. - - The class is mostly used to reduce boilerplate code. - """ - - def __init__(self, **kwargs) -> None: - for field in self.__fields__: - object.__setattr__(self, field, kwargs[field]) - - def __eq__(self, other) -> bool: - if self is other: - return True - if type(self) is not type(other): - return NotImplemented - return all(getattr(self, n) == getattr(other, n) for n in self.__fields__) - - def __getstate__(self): - return {k: getattr(self, k) for k in self.__fields__} - - def __setstate__(self, state): - for name, value in state.items(): - object.__setattr__(self, name, value) - - def __repr__(self): - fields = {k: getattr(self, k) for k in self.__fields__} - fieldstring = ", ".join(f"{k}={v!r}" for k, v in fields.items()) - return f"{self.__class__.__name__}({fieldstring})" - - def __rich_repr__(self): - for name in self.__fields__: - yield name, getattr(self, name) - - -class FrozenSlotted(Slotted, Immutable, Hashable): - """A lightweight alternative to `ibis.common.grounds.Concrete`. - - This class is used to create immutable dataclasses with slots and a precomputed - hash value for quicker dictionary lookups. - """ - - __slots__ = ("__precomputed_hash__",) - __fields__ = () - __precomputed_hash__: int - - def __init__(self, **kwargs) -> None: - values = [] - for field in self.__fields__: - values.append(value := kwargs[field]) - object.__setattr__(self, field, value) - hashvalue = hash((self.__class__, tuple(values))) - object.__setattr__(self, "__precomputed_hash__", hashvalue) - - def __setstate__(self, state): - for name, value in state.items(): - object.__setattr__(self, name, value) - hashvalue = hash((self.__class__, tuple(state.values()))) - object.__setattr__(self, "__precomputed_hash__", hashvalue) - - def __hash__(self) -> int: - return self.__precomputed_hash__ diff --git a/third_party/bigframes_vendored/ibis/common/caching.py b/third_party/bigframes_vendored/ibis/common/caching.py deleted file mode 100644 index b4257410e1e..00000000000 --- a/third_party/bigframes_vendored/ibis/common/caching.py +++ /dev/null @@ -1,98 +0,0 @@ -# Contains code from https://github.com/ibis-project/ibis/blob/9.2.0/ibis/common/caching.py - -from __future__ import annotations - -import functools -import sys -from collections import namedtuple -from typing import TYPE_CHECKING, Any -from weakref import finalize, ref - -if TYPE_CHECKING: - from collections.abc import Callable - - -def memoize(func: Callable) -> Callable: - """Memoize a function.""" - cache: dict = {} - - @functools.wraps(func) - def wrapper(*args, **kwargs): - key = (args, tuple(kwargs.items())) - try: - return cache[key] - except KeyError: - result = func(*args, **kwargs) - cache[key] = result - return result - - return wrapper - - -CacheEntry = namedtuple("CacheEntry", ["name", "ref", "finalizer"]) - - -class RefCountedCache: - """A cache with implicitly reference-counted values. - - We could implement `MutableMapping`, but the `__setitem__` implementation - doesn't make sense and the `len` and `__iter__` methods aren't used. - - We can implement that interface if and when we need to. - """ - - def __init__( - self, - *, - populate: Callable[[str, Any], None], - lookup: Callable[[str], Any], - finalize: Callable[[Any], None], - ) -> None: - self.populate = populate - self.lookup = lookup - self.finalize = finalize - - self.cache: dict[Any, CacheEntry] = dict() - - def get(self, key, default=None): - if (entry := self.cache.get(key)) is not None: - op = entry.ref() - return op if op is not None else default - return default - - def __getitem__(self, key): - op = self.cache[key].ref() - if op is None: - raise KeyError(key) - return op - - def store(self, input): - """Compute and store a reference to `key`.""" - from bigframes_vendored.ibis.util import gen_name - - key = input.op() - name = gen_name("cache") - self.populate(name, input) - cached = self.lookup(name) - finalizer = finalize(cached, self._release, key) - - self.cache[key] = CacheEntry(name, ref(cached), finalizer) - - return cached - - def release(self, name: str) -> None: - # Could be sped up with an inverse dictionary - for key, entry in self.cache.items(): - if entry.name == name: - self._release(key) - return - - def _release(self, key) -> None: - entry = self.cache.pop(key) - try: - self.finalize(entry.name) - except Exception: - # suppress exceptions during interpreter shutdown - if not sys.is_finalizing(): - raise - entry.finalizer.detach() diff --git a/third_party/bigframes_vendored/ibis/common/collections.py b/third_party/bigframes_vendored/ibis/common/collections.py deleted file mode 100644 index 718b94235dd..00000000000 --- a/third_party/bigframes_vendored/ibis/common/collections.py +++ /dev/null @@ -1,372 +0,0 @@ -# Contains code from https://github.com/ibis-project/ibis/blob/9.2.0/ibis/common/collections.py - -from __future__ import annotations - -import collections.abc -from abc import abstractmethod -from itertools import tee -from typing import TYPE_CHECKING, Any, Generic, TypeVar - -from bigframes_vendored.ibis.common.bases import Abstract, Hashable -from bigframes_vendored.ibis.common.exceptions import ConflictingValuesError -from public import public - -if TYPE_CHECKING: - from typing_extensions import Self - -K = TypeVar("K", bound=collections.abc.Hashable) -V = TypeVar("V") - - -# The following classes provide an alternative to the `collections.abc` module -# which can be used with `ibis.common.bases` without metaclass conflicts but -# remains compatible with the `collections.abc` module. The main advantage is -# faster `isinstance` checks. - - -@collections.abc.Iterable.register -class Iterable(Abstract, Generic[V]): - """Iterable abstract base class for quicker isinstance checks.""" - - @abstractmethod - def __iter__(self): ... - - -@collections.abc.Reversible.register -class Reversible(Iterable[V]): - """Reverse iterable abstract base class for quicker isinstance checks.""" - - @abstractmethod - def __reversed__(self): ... - - -@collections.abc.Iterator.register -class Iterator(Iterable[V]): - """Iterator abstract base class for quicker isinstance checks.""" - - @abstractmethod - def __next__(self): ... - - def __iter__(self): - return self - - -@collections.abc.Sized.register -class Sized(Abstract): - """Sized abstract base class for quicker isinstance checks.""" - - @abstractmethod - def __len__(self): ... - - -@collections.abc.Container.register -class Container(Abstract, Generic[V]): - """Container abstract base class for quicker isinstance checks.""" - - @abstractmethod - def __contains__(self, x): ... - - -@collections.abc.Collection.register -class Collection(Sized, Iterable[V], Container[V]): - """Collection abstract base class for quicker isinstance checks.""" - - -@collections.abc.Sequence.register -class Sequence(Reversible[V], Collection[V]): - """Sequence abstract base class for quicker isinstance checks.""" - - @abstractmethod - def __getitem__(self, index): ... - - def __iter__(self): - i = 0 - try: - while True: - yield self[i] - i += 1 - except IndexError: - return - - def __contains__(self, value): - return any(v is value or v == value for v in self) - - def __reversed__(self): - for i in reversed(range(len(self))): - yield self[i] - - def index(self, value, start=0, stop=None): - if start is not None and start < 0: - start = max(len(self) + start, 0) - if stop is not None and stop < 0: - stop += len(self) - - i = start - while stop is None or i < stop: - try: - v = self[i] - except IndexError: - break - if v is value or v == value: - return i - i += 1 - raise ValueError - - def count(self, value): - return sum(1 for v in self if v is value or v == value) - - -@collections.abc.Mapping.register -class Mapping(Collection[K], Generic[K, V]): - """Mapping abstract base class for quicker isinstance checks.""" - - @abstractmethod - def __getitem__(self, key): ... - - def get(self, key, default=None): - try: - return self[key] - except KeyError: - return default - - def __contains__(self, key): - try: - self[key] - except KeyError: - return False - else: - return True - - def keys(self): - return collections.abc.KeysView(self) - - def items(self): - return collections.abc.ItemsView(self) - - def values(self): - return collections.abc.ValuesView(self) - - def __eq__(self, other): - if not isinstance(other, collections.abc.Mapping): - return NotImplemented - return dict(self.items()) == dict(other.items()) - - -@public -class MapSet(Mapping[K, V]): - """A mapping that also supports set-like operations. - - It is an altered version of `collections.abc.Mapping` that supports set-like - operations. The `__iter__`, `__len__`, and `__getitem__` methods must be - implemented. - - The set-like operations' other operand must be a `Mapping`. If the two - operands contain common keys but with different values, then the operation - becomes ambiguous and an exception will be raised. - - Examples - -------- - >>> from ibis.common.collections import MapSet - >>> class MyMap(MapSet): - ... __slots__ = ("_data",) - ... - ... def __init__(self, *args, **kwargs): - ... self._data = dict(*args, **kwargs) - ... - ... def __iter__(self): - ... return iter(self._data) - ... - ... def __len__(self): - ... return len(self._data) - ... - ... def __getitem__(self, key): - ... return self._data[key] - ... - ... def __repr__(self): - ... return f"MyMap({repr(self._data)})" - >>> m = MyMap(a=1, b=2) - >>> n = dict(a=1, b=2, c=3) - >>> m <= n - True - >>> m < n - True - >>> n - m - MyMap({'c': 3}) - >>> m & n - MyMap({'a': 1, 'b': 2}) - >>> m | n - MyMap({'a': 1, 'b': 2, 'c': 3}) - - """ - - def _check_conflict(self, other: collections.abc.Mapping) -> set[K]: - # Check if there are conflicting key-value pairs between self and other. - # A key-value pair is conflicting if the key is the same but the value is - # different. - common_keys = self.keys() & other.keys() - conflicts = { - (key, self[key], other[key]) - for key in common_keys - if self[key] != other[key] - } - if conflicts: - raise ConflictingValuesError(conflicts) - return common_keys - - def __ge__(self, other: collections.abc.Mapping) -> bool: - if not isinstance(other, collections.abc.Mapping): - return NotImplemented - common_keys = self._check_conflict(other) - return other.keys() == common_keys - - def __gt__(self, other: collections.abc.Mapping) -> bool: - if not isinstance(other, collections.abc.Mapping): - return NotImplemented - return len(self) > len(other) and self.__ge__(other) - - def __le__(self, other: collections.abc.Mapping) -> bool: - if not isinstance(other, collections.abc.Mapping): - return NotImplemented - common_keys = self._check_conflict(other) - return self.keys() == common_keys - - def __lt__(self, other: collections.abc.Mapping) -> bool: - if not isinstance(other, collections.abc.Mapping): - return NotImplemented - return len(self) < len(other) and self.__le__(other) - - def __and__(self, other: collections.abc.Mapping) -> Self: - if not isinstance(other, collections.abc.Mapping): - return NotImplemented - common_keys = self._check_conflict(other) - intersection = {k: v for k, v in self.items() if k in common_keys} - return self.__class__(intersection) - - def __sub__(self, other: collections.abc.Mapping) -> Self: - if not isinstance(other, collections.abc.Mapping): - return NotImplemented - common_keys = self._check_conflict(other) - difference = {k: v for k, v in self.items() if k not in common_keys} - return self.__class__(difference) - - def __rsub__(self, other: collections.abc.Mapping) -> Self: - if not isinstance(other, collections.abc.Mapping): - return NotImplemented - common_keys = self._check_conflict(other) - difference = {k: v for k, v in other.items() if k not in common_keys} - return self.__class__(difference) - - def __or__(self, other: collections.abc.Mapping) -> Self: - if not isinstance(other, collections.abc.Mapping): - return NotImplemented - self._check_conflict(other) - union = {**self, **other} - return self.__class__(union) - - def __xor__(self, other: collections.abc.Mapping) -> Self: - if not isinstance(other, collections.abc.Mapping): - return NotImplemented - left = self - other - right = other - self - left._check_conflict(right) - union = {**left, **right} - return self.__class__(union) - - def isdisjoint(self, other: collections.abc.Mapping) -> bool: - common_keys = self._check_conflict(other) - return not common_keys - - -@public -class FrozenDict(dict, Mapping[K, V], Hashable): - __slots__ = ("__precomputed_hash__",) - __precomputed_hash__: int - - def __init__(self, *args, **kwargs): - super().__init__(*args, **kwargs) - hashable = frozenset(self.items()) - object.__setattr__(self, "__precomputed_hash__", hash(hashable)) - - def __hash__(self) -> int: - return self.__precomputed_hash__ - - def __setitem__(self, key: K, value: V) -> None: - raise TypeError( - f"'{self.__class__.__name__}' object does not support item assignment" - ) - - def __setattr__(self, name: str, _: Any) -> None: - raise TypeError(f"Attribute {name!r} cannot be assigned to frozendict") - - def __reduce__(self) -> tuple: - return (self.__class__, (dict(self),)) - - -@public -class FrozenOrderedDict(FrozenDict[K, V]): - def __init__(self, *args, **kwargs): - super(FrozenDict, self).__init__(*args, **kwargs) - hashable = tuple(self.items()) - object.__setattr__(self, "__precomputed_hash__", hash(hashable)) - - def __hash__(self) -> int: - return self.__precomputed_hash__ - - def __eq__(self, other: Any) -> bool: - if not isinstance(other, collections.abc.Mapping): - return NotImplemented - return tuple(self.items()) == tuple(other.items()) - - def __ne__(self, other: Any) -> bool: - return not self == other - - -class RewindableIterator(Iterator[V]): - """Iterator that can be rewound to a checkpoint. - - Examples - -------- - >>> it = RewindableIterator(range(5)) - >>> next(it) - 0 - >>> next(it) - 1 - >>> it.checkpoint() - >>> next(it) - 2 - >>> next(it) - 3 - >>> it.rewind() - >>> next(it) - 2 - >>> next(it) - 3 - >>> next(it) - 4 - - """ - - __slots__ = ("_iterator", "_checkpoint") - - def __init__(self, iterable): - self._iterator = iter(iterable) - self._checkpoint = None - - def __next__(self): - return next(self._iterator) - - def rewind(self): - """Rewind the iterator to the last checkpoint.""" - if self._checkpoint is None: - raise ValueError("No checkpoint to rewind to.") - self._iterator, self._checkpoint = tee(self._checkpoint) - - def checkpoint(self): - """Create a checkpoint of the current iterator state.""" - self._iterator, self._checkpoint = tee(self._iterator) - - -# Need to provide type hint as else a static type checker does not recognize -# that frozendict exists in this module -frozendict: type[FrozenDict] -public(frozendict=FrozenDict) diff --git a/third_party/bigframes_vendored/ibis/common/deferred.py b/third_party/bigframes_vendored/ibis/common/deferred.py deleted file mode 100644 index 70e54be1503..00000000000 --- a/third_party/bigframes_vendored/ibis/common/deferred.py +++ /dev/null @@ -1,629 +0,0 @@ -# Contains code from https://github.com/ibis-project/ibis/blob/9.2.0/ibis/common/deferred.py - -from __future__ import annotations - -import collections.abc -import functools -import inspect -import operator -from abc import abstractmethod -from collections.abc import Callable -from typing import Any, TypeVar, overload - -from bigframes_vendored.ibis.common.bases import ( - Final, - FrozenSlotted, - Hashable, - Immutable, - Slotted, -) -from bigframes_vendored.ibis.common.collections import FrozenDict -from bigframes_vendored.ibis.common.typing import Coercible, CoercionError -from bigframes_vendored.ibis.util import PseudoHashable - - -class Resolver(Coercible, Hashable): - """Specification about constructing a value given a context. - - The context is a dictionary that contains all the captured values and - information relevant for the builder. - - The builder is used in the right hand side of the replace pattern: - `Replace(pattern, builder)`. Semantically when a match occurs for the - replace pattern, the builder is called with the context and the result - of the builder is used as the replacement value. - """ - - @abstractmethod - def resolve(self, context: dict): - """Construct a new object from the context. - - Parameters - ---------- - context - A dictionary containing all the captured values and information - relevant for the deferred. - - Returns - ------- - The constructed object. - - """ - - @abstractmethod - def __eq__(self, other: Resolver) -> bool: ... - - @classmethod - def __coerce__(cls, value): - if isinstance(value, cls): - return value - elif isinstance(value, Deferred): - return value._resolver - else: - raise CoercionError( - f"Cannot coerce {type(value).__name__!r} to {cls.__name__!r}" - ) - - -class Deferred(Slotted, Immutable, Final): - """The user facing wrapper object providing syntactic sugar for deferreds. - - Provides a natural-like syntax for constructing deferred expressions by - overloading all of the available dunder methods including the equality - operator. - - Its sole purpose is to provide a nicer syntax for constructing deferred - expressions, thus it gets unwrapped to the underlying deferred expression - when used by the rest of the library. - - Parameters - ---------- - deferred - The deferred object to provide syntax sugar for. - repr - An optional fixed string to use when repr-ing the deferred expression, - instead of the default. This is useful for complex deferred expressions - where the arguments don't necessarily make sense to be user facing in - the repr. - - """ - - __slots__ = ("_resolver", "_repr") - - def __init__(self, obj, repr=None): - super().__init__(_resolver=resolver(obj), _repr=repr) - - # TODO(kszucs): consider to make this method protected - def resolve(self, _=None, **kwargs): - context = {"_": _, **kwargs} - return self._resolver.resolve(context) - - def __repr__(self): - return repr(self._resolver) if self._repr is None else self._repr - - def __getattr__(self, name): - return Deferred(Attr(self, name)) - - def __iter__(self): - raise TypeError(f"{self.__class__.__name__!r} object is not iterable") - - def __bool__(self): - raise TypeError( - f"The truth value of {self.__class__.__name__} objects is not defined" - ) - - def __getitem__(self, name): - return Deferred(Item(self, name)) - - def __call__(self, *args, **kwargs): - return Deferred(Call(self, *args, **kwargs)) - - def __invert__(self) -> Deferred: - return Deferred(UnaryOperator(operator.invert, self)) - - def __neg__(self) -> Deferred: - return Deferred(UnaryOperator(operator.neg, self)) - - def __add__(self, other: Any) -> Deferred: - return Deferred(BinaryOperator(operator.add, self, other)) - - def __radd__(self, other: Any) -> Deferred: - return Deferred(BinaryOperator(operator.add, other, self)) - - def __sub__(self, other: Any) -> Deferred: - return Deferred(BinaryOperator(operator.sub, self, other)) - - def __rsub__(self, other: Any) -> Deferred: - return Deferred(BinaryOperator(operator.sub, other, self)) - - def __mul__(self, other: Any) -> Deferred: - return Deferred(BinaryOperator(operator.mul, self, other)) - - def __rmul__(self, other: Any) -> Deferred: - return Deferred(BinaryOperator(operator.mul, other, self)) - - def __truediv__(self, other: Any) -> Deferred: - return Deferred(BinaryOperator(operator.truediv, self, other)) - - def __rtruediv__(self, other: Any) -> Deferred: - return Deferred(BinaryOperator(operator.truediv, other, self)) - - def __floordiv__(self, other: Any) -> Deferred: - return Deferred(BinaryOperator(operator.floordiv, self, other)) - - def __rfloordiv__(self, other: Any) -> Deferred: - return Deferred(BinaryOperator(operator.floordiv, other, self)) - - def __pow__(self, other: Any) -> Deferred: - return Deferred(BinaryOperator(operator.pow, self, other)) - - def __rpow__(self, other: Any) -> Deferred: - return Deferred(BinaryOperator(operator.pow, other, self)) - - def __mod__(self, other: Any) -> Deferred: - return Deferred(BinaryOperator(operator.mod, self, other)) - - def __rmod__(self, other: Any) -> Deferred: - return Deferred(BinaryOperator(operator.mod, other, self)) - - def __rshift__(self, other: Any) -> Deferred: - return Deferred(BinaryOperator(operator.rshift, self, other)) - - def __rrshift__(self, other: Any) -> Deferred: - return Deferred(BinaryOperator(operator.rshift, other, self)) - - def __lshift__(self, other: Any) -> Deferred: - return Deferred(BinaryOperator(operator.lshift, self, other)) - - def __rlshift__(self, other: Any) -> Deferred: - return Deferred(BinaryOperator(operator.lshift, other, self)) - - def __eq__(self, other: Any) -> Deferred: # type: ignore - return Deferred(BinaryOperator(operator.eq, self, other)) - - def __ne__(self, other: Any) -> Deferred: # type: ignore - return Deferred(BinaryOperator(operator.ne, self, other)) - - def __lt__(self, other: Any) -> Deferred: - return Deferred(BinaryOperator(operator.lt, self, other)) - - def __le__(self, other: Any) -> Deferred: - return Deferred(BinaryOperator(operator.le, self, other)) - - def __gt__(self, other: Any) -> Deferred: - return Deferred(BinaryOperator(operator.gt, self, other)) - - def __ge__(self, other: Any) -> Deferred: - return Deferred(BinaryOperator(operator.ge, self, other)) - - def __and__(self, other: Any) -> Deferred: - return Deferred(BinaryOperator(operator.and_, self, other)) - - def __rand__(self, other: Any) -> Deferred: - return Deferred(BinaryOperator(operator.and_, other, self)) - - def __or__(self, other: Any) -> Deferred: - return Deferred(BinaryOperator(operator.or_, self, other)) - - def __ror__(self, other: Any) -> Deferred: - return Deferred(BinaryOperator(operator.or_, other, self)) - - def __xor__(self, other: Any) -> Deferred: - return Deferred(BinaryOperator(operator.xor, self, other)) - - def __rxor__(self, other: Any) -> Deferred: - return Deferred(BinaryOperator(operator.xor, other, self)) - - -class Variable(FrozenSlotted, Resolver): - """Retrieve a value from the context. - - Parameters - ---------- - name - The key to retrieve from the state. - - """ - - __slots__ = ("name",) - name: Any - - def __init__(self, name): - super().__init__(name=name) - - def __repr__(self): - return str(self.name) - - def resolve(self, context): - return context[self.name] - - -class Just(FrozenSlotted, Resolver): - """Construct exactly the given value. - - Parameters - ---------- - value - The value to return when the deferred is called. - - """ - - __slots__ = ("value",) - value: Any - - @classmethod - def __create__(cls, value): - if isinstance(value, cls): - return value - elif isinstance(value, (Deferred, Resolver)): - raise TypeError(f"{value} cannot be used as a Just value") - elif isinstance(value, collections.abc.Hashable): - return super().__create__(value) - else: - return JustUnhashable(value) - - def __init__(self, value): - super().__init__(value=value) - - def __repr__(self): - obj = self.value - if hasattr(obj, "__deferred_repr__"): - return obj.__deferred_repr__() - elif callable(obj): - return getattr(obj, "__name__", repr(obj)) - else: - return repr(obj) - - def resolve(self, context): - return self.value - - -class JustUnhashable(FrozenSlotted, Resolver): - """Construct exactly the given unhashable value. - - Parameters - ---------- - value - The value to return when the deferred is called. - - """ - - __slots__ = ("value",) - - def __init__(self, value): - hashable_value = PseudoHashable(value) - super().__init__(value=hashable_value) - - def __repr__(self): - obj = self.value.obj - if hasattr(obj, "__deferred_repr__"): - return obj.__deferred_repr__() - elif callable(obj): - return getattr(obj, "__name__", repr(obj)) - else: - return repr(obj) - - def resolve(self, context): - return self.value.obj - - -class Factory(FrozenSlotted, Resolver): - """Construct a value by calling a function. - - The function is called with two positional arguments: - 1. the value being matched - 2. the context dictionary - - The function must return the constructed value. - - Parameters - ---------- - func - The function to apply. - - """ - - __slots__ = ("func",) - func: Callable - - def __init__(self, func): - assert callable(func) - super().__init__(func=func) - - def resolve(self, context): - return self.func(**context) - - -class Attr(FrozenSlotted, Resolver): - __slots__ = ("obj", "name") - obj: Resolver - name: str - - def __init__(self, obj, name): - super().__init__(obj=resolver(obj), name=resolver(name)) - - def __repr__(self): - if isinstance(self.name, Just): - return f"{self.obj!r}.{self.name.value}" - else: - return f"Attr({self.obj!r}, {self.name!r})" - - def resolve(self, context): - obj = self.obj.resolve(context) - name = self.name.resolve(context) - return getattr(obj, name) - - -class Item(FrozenSlotted, Resolver): - __slots__ = ("obj", "name") - obj: Resolver - name: str - - def __init__(self, obj, name): - super().__init__(obj=resolver(obj), name=resolver(name)) - - def __repr__(self): - if isinstance(self.name, Just): - return f"{self.obj!r}[{self.name.value!r}]" - else: - return f"Item({self.obj!r}, {self.name!r})" - - def resolve(self, context): - obj = self.obj.resolve(context) - name = self.name.resolve(context) - return obj[name] - - -class Call(FrozenSlotted, Resolver): - """Pattern that calls a function with the given arguments. - - Both positional and keyword arguments are coerced into patterns. - - Parameters - ---------- - func - The function to call. - args - The positional argument patterns. - kwargs - The keyword argument patterns. - - """ - - __slots__ = ("func", "args", "kwargs") - func: Resolver - args: tuple[Resolver, ...] - kwargs: dict[str, Resolver] - - def __init__(self, func, *args, **kwargs): - if isinstance(func, Deferred): - func = func._resolver - elif isinstance(func, Resolver): - pass - elif callable(func): - func = Just(func) - else: - raise TypeError(f"Invalid callable {func!r}") - args = tuple(map(resolver, args)) - kwargs = FrozenDict({k: resolver(v) for k, v in kwargs.items()}) - super().__init__(func=func, args=args, kwargs=kwargs) - - def resolve(self, context): - func = self.func.resolve(context) - args = tuple(arg.resolve(context) for arg in self.args) - kwargs = {k: v.resolve(context) for k, v in self.kwargs.items()} - return func(*args, **kwargs) - - def __repr__(self): - func = repr(self.func) - args = ", ".join(map(repr, self.args)) - kwargs = ", ".join(f"{k}={v!r}" for k, v in self.kwargs.items()) - if args and kwargs: - return f"{func}({args}, {kwargs})" - elif args: - return f"{func}({args})" - elif kwargs: - return f"{func}({kwargs})" - else: - return f"{func}()" - - -_operator_symbols = { - operator.add: "+", - operator.sub: "-", - operator.mul: "*", - operator.truediv: "/", - operator.floordiv: "//", - operator.pow: "**", - operator.mod: "%", - operator.eq: "==", - operator.ne: "!=", - operator.lt: "<", - operator.le: "<=", - operator.gt: ">", - operator.ge: ">=", - operator.and_: "&", - operator.or_: "|", - operator.xor: "^", - operator.rshift: ">>", - operator.lshift: "<<", - operator.inv: "~", - operator.neg: "-", - operator.invert: "~", -} - - -class UnaryOperator(FrozenSlotted, Resolver): - __slots__ = ("func", "arg") - func: Callable - arg: Resolver - - def __init__(self, func, arg): - assert func in _operator_symbols - super().__init__(func=func, arg=resolver(arg)) - - def __repr__(self): - symbol = _operator_symbols[self.func] - return f"{symbol}{self.arg!r}" - - def resolve(self, context): - arg = self.arg.resolve(context) - return self.func(arg) - - -class BinaryOperator(FrozenSlotted, Resolver): - __slots__ = ("func", "left", "right") - func: Callable - left: Resolver - right: Resolver - - def __init__(self, func, left, right): - assert func in _operator_symbols - super().__init__(func=func, left=resolver(left), right=resolver(right)) - - def __repr__(self): - symbol = _operator_symbols[self.func] - return f"({self.left!r} {symbol} {self.right!r})" - - def resolve(self, context): - left = self.left.resolve(context) - right = self.right.resolve(context) - return self.func(left, right) - - -class Mapping(FrozenSlotted, Resolver): - __slots__ = ("typ", "values") - - def __init__(self, values): - typ = type(values) - values = FrozenDict({k: resolver(v) for k, v in values.items()}) - super().__init__(typ=typ, values=values) - - def __repr__(self): - items = ", ".join(f"{k!r}: {v!r}" for k, v in self.values.items()) - if self.typ is dict: - return f"{{{items}}}" - else: - return f"{self.typ.__name__}({{{items}}})" - - def resolve(self, context): - items = {k: v.resolve(context) for k, v in self.values.items()} - return self.typ(items) - - -class Sequence(FrozenSlotted, Resolver): - __slots__ = ("typ", "values") - typ: type - - def __init__(self, values): - typ = type(values) - values = tuple(map(resolver, values)) - super().__init__(typ=typ, values=values) - - def __repr__(self): - elems = ", ".join(map(repr, self.values)) - if self.typ is tuple: - return f"({elems})" - elif self.typ is list: - return f"[{elems}]" - else: - return f"{self.typ.__name__}({elems})" - - def resolve(self, context): - return self.typ(v.resolve(context) for v in self.values) - - -def resolver(obj): - if isinstance(obj, Deferred): - return obj._resolver - elif isinstance(obj, Resolver): - return obj - elif isinstance(obj, collections.abc.Mapping): - # allow nesting deferred patterns in dicts - return Mapping(obj) - elif isinstance(obj, collections.abc.Sequence): - # allow nesting deferred patterns in tuples/lists - if isinstance(obj, (str, bytes)): - return Just(obj) - else: - return Sequence(obj) - else: - # the object is used as a constant value - return Just(obj) - - -def deferred(obj): - return Deferred(resolver(obj)) - - -def var(name): - return Deferred(Variable(name)) - - -def const(value): - return Deferred(Just(value)) - - -def _contains_deferred(obj: Any) -> bool: - if isinstance(obj, (Resolver, Deferred)): - return True - elif (typ := type(obj)) in (tuple, list, set): - return any(_contains_deferred(o) for o in obj) - elif typ is dict: - return any(_contains_deferred(o) for o in obj.values()) - return False - - -F = TypeVar("F", bound=Callable) - - -@overload -def deferrable(*, repr: str | None = None) -> Callable[[F], F]: ... - - -@overload -def deferrable(func: F) -> F: ... - - -def deferrable(func=None, *, repr=None): - """Wrap a top-level expr function to support deferred arguments. - - When a deferrable function is called, the args & kwargs are traversed to - look for `Deferred` values (through builtin collections like - `list`/`tuple`/`set`/`dict`). If any `Deferred` arguments are found, then - the result is also `Deferred`. Otherwise the function is called directly. - - Parameters - ---------- - func - A callable to make deferrable - repr - An optional fixed string to use when repr-ing the deferred expression, - instead of the usual. This is useful for complex deferred expressions - where the arguments don't necessarily make sense to be user facing - in the repr. - - """ - - def wrapper(func): - # Parse the signature of func so we can validate deferred calls eagerly, - # erroring for invalid/missing arguments at call time not resolve time. - sig = inspect.signature(func) - - @functools.wraps(func) - def inner(*args, **kwargs): - if _contains_deferred((args, kwargs)): - # Try to bind the arguments now, raising a nice error - # immediately if the function was called incorrectly - sig.bind(*args, **kwargs) - builder = Call(func, *args, **kwargs) - return Deferred(builder, repr=repr) - return func(*args, **kwargs) - - return inner # type: ignore - - return wrapper if func is None else wrapper(func) - - -# reserved variable name for the value being matched -_ = var("_") diff --git a/third_party/bigframes_vendored/ibis/common/dispatch.py b/third_party/bigframes_vendored/ibis/common/dispatch.py deleted file mode 100644 index 9808d1fdb2b..00000000000 --- a/third_party/bigframes_vendored/ibis/common/dispatch.py +++ /dev/null @@ -1,218 +0,0 @@ -# Contains code from https://github.com/ibis-project/ibis/blob/9.2.0/ibis/common/dispatch.py - -from __future__ import annotations - -import abc -import functools -import inspect -import re -import sys -from collections import defaultdict -from typing import Union - -from bigframes_vendored.ibis.common.typing import ( - evaluate_annotations, - get_args, - get_origin, -) -from bigframes_vendored.ibis.util import import_object, unalias_package - -if sys.version_info >= (3, 10): - from types import UnionType -else: - from bigframes_vendored.ibis.common.typing import UnionType - - -def normalize(r: str | re.Pattern): - """Normalize a expression by wrapping it with `'^'` and `'$'`. - - Parameters - ---------- - r - The pattern to normalize. - - Returns - ------- - Pattern - The compiled regex. - - """ - r = getattr(r, "pattern", r) - return re.compile("^" + r.lstrip("^").rstrip("$") + "$") - - -class SingleDispatch: - def __init__(self, func, typ=None): - self.lookup = {} - self.abc_lookup = {} - self.lazy_lookup = defaultdict(dict) - self.func = func - self.add(func, typ) - - def add(self, func, typ=None): - if typ is None: - annots = getattr(func, "__annotations__", {}) - typehints = evaluate_annotations(annots, func.__module__, best_effort=True) - if typehints: - typ, *_ = typehints.values() - if get_origin(typ) in (Union, UnionType): - for t in get_args(typ): - self.add(func, t) - else: - self.add(func, typ) - else: - self.add(func, object) - elif isinstance(typ, tuple): - for t in typ: - self.add(func, t) - elif isinstance(typ, abc.ABCMeta): - if typ in self.abc_lookup: - raise TypeError(f"{typ} is already registered") - self.abc_lookup[typ] = func - elif isinstance(typ, str): - package, rest = typ.split(".", 1) - package = unalias_package(package) - typ = f"{package}.{rest}" - if typ in self.lazy_lookup[package]: - raise TypeError(f"{typ} is already registered") - self.lazy_lookup[package][typ] = func - else: - if typ in self.lookup: - raise TypeError(f"{typ} is already registered") - self.lookup[typ] = func - return func - - def register(self, typ, func=None): - """Register a new implementation for arguments of type `cls`.""" - - def inner(func): - self.add(func, typ) - return func - - return inner if func is None else inner(func) - - def dispatch(self, typ): - """Return the implementation for the given `cls`.""" - for klass in typ.__mro__: - # 1. Check for a concrete implementation - try: - impl = self.lookup[klass] - except KeyError: - pass - else: - if typ is not klass: - # Cache implementation - self.lookup[typ] = impl - return impl - # 2. Check lazy implementations - package = klass.__module__.split(".", 1)[0] - if lazy := self.lazy_lookup.get(package): - # Import all lazy implementations first before registering - # (which should never fail), to ensure an error anywhere - # doesn't result in a half-registered state. - new = {import_object(name): func for name, func in lazy.items()} - self.lookup.update(new) - # drop lazy implementations, idempotent for thread safety - self.lazy_lookup.pop(package, None) - return self.dispatch(typ) - # 3. Check for abcs - for abc_class, impl in self.abc_lookup.items(): - if issubclass(typ, abc_class): - self.lookup[typ] = impl - return impl - raise TypeError(f"Could not find implementation for {typ}") - - def __call__(self, arg, *args, **kwargs): - impl = self.dispatch(type(arg)) - return impl(arg, *args, **kwargs) - - def __get__(self, obj, cls=None): - def _method(*args, **kwargs): - method = self.dispatch(type(args[0])) - method = method.__get__(obj, cls) - return method(*args, **kwargs) - - functools.update_wrapper(_method, self.func) - return _method - - -def lazy_singledispatch(func): - """A `singledispatch` implementation that supports lazily registering implementations.""" - - dispatcher = SingleDispatch(func, object) - - @functools.wraps(func) - def call(arg, *args, **kwargs): - impl = dispatcher.dispatch(type(arg)) - return impl(arg, *args, **kwargs) - - call.dispatch = dispatcher.dispatch - call.register = dispatcher.register - return call - - -class _MultiDict(dict): - """A dictionary that allows multiple values for a single key.""" - - def __setitem__(self, key, value): - if key in self: - self[key].append(value) - else: - super().__setitem__(key, [value]) - - -class DispatchedMeta(type): - """Metaclass that allows multiple implementations of a method to be defined.""" - - def __new__(cls, name, bases, dct): - namespace = {} - for key, value in dct.items(): - if len(value) == 1: - # there is just a single attribute so pick that - namespace[key] = value[0] - elif all(inspect.isfunction(v) for v in value): - # multiple functions are defined with the same name, so create - # a dispatcher function - first, *rest = value - func = SingleDispatch(first) - for impl in rest: - func.add(impl) - namespace[key] = func - elif all(isinstance(v, classmethod) for v in value): - first, *rest = value - func = SingleDispatch(first.__func__) - for impl in rest: - func.add(impl.__func__) - namespace[key] = classmethod(func) - elif all(isinstance(v, staticmethod) for v in value): - first, *rest = value - func = SingleDispatch(first.__func__) - for impl in rest: - func.add(impl.__func__) - namespace[key] = staticmethod(func) - else: - raise TypeError(f"Multiple attributes are defined with name {key}") - - return type.__new__(cls, name, bases, namespace) - - @classmethod - def __prepare__(cls, name, bases): - return _MultiDict() - - -class Dispatched(metaclass=DispatchedMeta): - """Base class supporting multiple implementations of a method. - - Methods with the same name can be defined multiple times. The first method - defined is the default implementation, and subsequent methods are registered - as implementations for specific types of the first argument. - - The constructed methods are equivalent as if they were defined with - `functools.singledispatchmethod` but without the need to use the decorator - syntax. The recommended application of this class is to implement visitor - patterns. - - Besides ordinary methods, classmethods and staticmethods are also supported. - The implementation can be extended to overload multiple arguments by using - `multimethod` instead of `singledispatchmethod` as the dispatcher. - """ diff --git a/third_party/bigframes_vendored/ibis/common/egraph.py b/third_party/bigframes_vendored/ibis/common/egraph.py deleted file mode 100644 index 437e85eda63..00000000000 --- a/third_party/bigframes_vendored/ibis/common/egraph.py +++ /dev/null @@ -1,830 +0,0 @@ -# Contains code from https://github.com/ibis-project/ibis/blob/9.2.0/ibis/common/egraph.py - -from __future__ import annotations - -import collections -import itertools -import math -from collections.abc import Callable, Hashable, Iterable, Iterator, Mapping -from typing import Any, TypeVar - -from bigframes_vendored.ibis.common.bases import FrozenSlotted as Slotted -from bigframes_vendored.ibis.common.graph import Node -from bigframes_vendored.ibis.util import promote_list - -K = TypeVar("K", bound=Hashable) - - -class DisjointSet(Mapping[K, set[K]]): - """Disjoint set data structure. - - Also known as union-find data structure. It is a data structure that keeps - track of a set of elements partitioned into a number of disjoint (non-overlapping) - subsets. It provides near-constant-time operations to add new sets, to merge - existing sets, and to determine whether elements are in the same set. - - Parameters - ---------- - data : - Initial data to add to the disjoint set. - - Examples - -------- - >>> ds = DisjointSet() - >>> ds.add(1) - 1 - >>> ds.add(2) - 2 - >>> ds.add(3) - 3 - >>> ds.union(1, 2) - True - >>> ds.union(2, 3) - True - >>> ds.find(1) - 1 - >>> ds.find(2) - 1 - >>> ds.find(3) - 1 - >>> ds.union(1, 3) - False - - """ - - __slots__ = ("_parents", "_classes") - _parents: dict - _classes: dict - - def __init__(self, data: Iterable[K] | None = None): - self._parents = {} - self._classes = {} - if data is not None: - for id in data: - self.add(id) - - def __contains__(self, id) -> bool: - """Check if the given id is in the disjoint set. - - Parameters - ---------- - id : - The id to check. - - Returns - ------- - ined: - True if the id is in the disjoint set, False otherwise. - - """ - return id in self._parents - - def __getitem__(self, id) -> set[K]: - """Get the set of ids that are in the same class as the given id. - - Parameters - ---------- - id : - The id to get the class for. - - Returns - ------- - class: - The set of ids that are in the same class as the given id, including - the given id. - - """ - id = self._parents[id] - return self._classes[id] - - def __iter__(self) -> Iterator[K]: - """Iterate over the ids in the disjoint set.""" - return iter(self._parents) - - def __len__(self) -> int: - """Get the number of ids in the disjoint set.""" - return len(self._parents) - - def __eq__(self, other: object) -> bool: - """Check if the disjoint set is equal to another disjoint set. - - Parameters - ---------- - other : - The other disjoint set to compare to. - - Returns - ------- - equal: - True if the disjoint sets are equal, False otherwise. - - """ - if not isinstance(other, DisjointSet): - return NotImplemented - return self._parents == other._parents - - def copy(self) -> DisjointSet: - """Make a copy of the disjoint set. - - Returns - ------- - copy: - A copy of the disjoint set. - - """ - ds = DisjointSet() - ds._parents = self._parents.copy() - ds._classes = self._classes.copy() - return ds - - def add(self, id: K) -> K: - """Add a new id to the disjoint set. - - If the id is not in the disjoint set, it will be added to the disjoint set - along with a new class containing only the given id. - - Parameters - ---------- - id : - The id to add to the disjoint set. - - Returns - ------- - id: - The id that was added to the disjoint set. - - """ - if id in self._parents: - return self._parents[id] - self._parents[id] = id - self._classes[id] = {id} - return id - - def find(self, id: K) -> K: - """Find the root of the class that the given id is in. - - Also called as the canonicalized id or the representative id. - - Parameters - ---------- - id : - The id to find the canonicalized id for. - - Returns - ------- - id: - The canonicalized id for the given id. - - """ - return self._parents[id] - - def union(self, id1, id2) -> bool: - """Merge the classes that the given ids are in. - - If the ids are already in the same class, this will return False. Otherwise - it will merge the classes and return True. - - Parameters - ---------- - id1 : - The first id to merge the classes for. - id2 : - The second id to merge the classes for. - - Returns - ------- - merged: - True if the classes were merged, False otherwise. - - """ - # Find the root of each class - id1 = self._parents[id1] - id2 = self._parents[id2] - if id1 == id2: - return False - - # Merge the smaller eclass into the larger one, aka. union-find by size - class1 = self._classes[id1] - class2 = self._classes[id2] - if len(class1) >= len(class2): - id1, id2 = id2, id1 - class1, class2 = class2, class1 - - # Update the parent pointers, this is called path compression but done - # during the union operation to keep the find operation minimal - for id in class1: - self._parents[id] = id2 - - # Do the actual merging and clear the other eclass - class2 |= class1 - class1.clear() - - return True - - def connected(self, id1, id2): - """Check if the given ids are in the same class. - - True if both ids have the same canonicalized id, False otherwise. - - Parameters - ---------- - id1 : - The first id to check. - id2 : - The second id to check. - - Returns - ------- - connected: - True if the ids are connected, False otherwise. - - """ - return self._parents[id1] == self._parents[id2] - - def verify(self): - """Verify that the disjoint set is not corrupted. - - Check that each id's canonicalized id's class. In general corruption - should not happen if the public API is used, but this is a sanity check - to make sure that the internal data structures are not corrupted. - - Returns - ------- - verified: - True if the disjoint set is not corrupted, False otherwise. - - """ - for id in self._parents: - if id not in self._classes[self._parents[id]]: - raise RuntimeError( - f"DisjointSet is corrupted: {id} is not in its class" - ) - - -class Variable(Slotted): - """A named capture in a pattern. - - Parameters - ---------- - name : str - The name of the variable. - - """ - - __slots__ = ("name",) - name: str - - def __init__(self, name: str): - if name is None: - raise ValueError("Variable name cannot be None") - super().__init__(name=name) - - def __repr__(self): - return f"${self.name}" - - def substitute(self, egraph, enode, subst): - """Substitute the variable with the corresponding value in the substitution. - - Parameters - ---------- - egraph : EGraph - The egraph instance. - enode : ENode - The matched enode. - subst : dict - The substitution dictionary. - - Returns - ------- - value : Any - The substituted value. - - """ - return subst[self.name] - - -# Pattern corresponds to a selection which is flattened to a join of selections -class Pattern(Slotted): - """A non-ground term, tree of enodes possibly containing variables. - - This class is used to represent a pattern in a query. The pattern is almost - identical to an ENode, except that it can contain variables. - - Parameters - ---------- - head : type - The head or python type of the ENode to match against. - args : tuple - The arguments of the pattern. The arguments can be enodes, patterns, - variables or leaf values. - name : str, optional - The name of the pattern which is used to refer to it in a rewrite rule. - - """ - - __slots__ = ("head", "args", "name") - head: type - args: tuple - name: str | None - - # TODO(kszucs): consider to raise if the pattern matches none - def __init__(self, head, args, name=None, conditions=None): - # TODO(kszucs): ensure that args are either patterns, variables or leaf values - assert all(not isinstance(arg, (ENode, Node)) for arg in args) - super().__init__(head=head, args=tuple(args), name=name) - - def matches_none(self): - """Evaluate whether the pattern is guaranteed to match nothing. - - This can be evaluated before the matching loop starts, so eventually can - be eliminated from the flattened query. - """ - return len(self.head.__argnames__) != len(self.args) - - def matches_all(self): - """Evaluate whether the pattern is guaranteed to match everything. - - This can be evaluated before the matching loop starts, so eventually can - be eliminated from the flattened query. - """ - return not self.matches_none() and all( - isinstance(arg, Variable) for arg in self.args - ) - - def __repr__(self): - argstring = ", ".join(map(repr, self.args)) - return f"P{self.head.__name__}({argstring})" - - def __rshift__(self, rhs): - """Syntax sugar to create a rewrite rule.""" - return Rewrite(self, rhs) - - def __rmatmul__(self, name): - """Syntax sugar to create a named pattern.""" - return self.__class__(self.head, self.args, name) - - def flatten(self, var=None, counter=None): - """Recursively flatten the pattern to a join of selections. - - `Pattern(Add, (Pattern(Mul, ($x, 1)), $y))` is turned into a join of - selections by introducing auxiliary variables where each selection gets - executed as a dictionary lookup. - - In SQL terms this is equivalent to the following query: - SELECT m.0 AS $x, a.1 AS $y FROM Add a JOIN Mul m ON a.0 = m.id WHERE m.1 = 1 - - Parameters - ---------- - var : Variable - The variable to assign to the flattened pattern. - counter : Iterator[int] - The counter to generate unique variable names for auxiliary variables - connecting the selections. - - Yields - ------ - (var, pattern) : tuple[Variable, Pattern] - The variable and the flattened pattern where the flattened pattern - cannot contain any patterns just variables. - - """ - # TODO(kszucs): convert a pattern to a query object instead by flattening it - counter = counter or itertools.count() - - if var is None: - if self.name is None: - var = Variable(next(counter)) - else: - var = Variable(self.name) - - args = [] - for arg in self.args: - if isinstance(arg, Pattern): - if arg.name is None: - aux = Variable(next(counter)) - else: - aux = Variable(arg.name) - yield from arg.flatten(aux, counter) - args.append(aux) - else: - args.append(arg) - - yield (var, Pattern(self.head, args)) - - def substitute(self, egraph, enode, subst): - """Substitute the variables in the pattern with the corresponding values. - - Parameters - ---------- - egraph : EGraph - The egraph instance. - enode : ENode - The matched enode. - subst : dict - The substitution dictionary. - - Returns - ------- - enode : ENode - The substituted pattern which is a ground term aka. an ENode. - - """ - args = [] - for arg in self.args: - if isinstance(arg, (Variable, Pattern)): - arg = arg.substitute(egraph, enode, subst) - args.append(arg) - return ENode(self.head, tuple(args)) - - -class DynamicApplier(Slotted): - """A dynamic applier which calls a function to compute the result.""" - - __slots__ = ("func",) - func: Callable - - def __init__(self, func): - super().__init__(func=func) - - def substitute(self, egraph, enode, subst): - kwargs = {k: v for k, v in subst.items() if isinstance(k, str)} - result = self.func(egraph, enode, **kwargs) - if not isinstance(result, ENode): - raise TypeError(f"applier must return an ENode, got {type(result)}") - return result - - -class Rewrite(Slotted): - """A rewrite rule which matches a pattern and applies a pattern or a function.""" - - __slots__ = ("matcher", "applier") - matcher: Pattern - applier: Callable | Pattern | Variable - - def __init__(self, matcher, applier): - if callable(applier): - applier = DynamicApplier(applier) - elif not isinstance(applier, (Pattern, Variable)): - raise TypeError( - "applier must be a Pattern or a Variable returning an ENode" - ) - super().__init__(matcher=matcher, applier=applier) - - def __repr__(self): - return f"{self.lhs} >> {self.rhs}" - - -class ENode(Slotted, Node): - """A ground term which is a node in the EGraph, called ENode. - - Parameters - ---------- - head : type - The type of the Node the ENode represents. - args : tuple - The arguments of the ENode which are either ENodes or leaf values. - - """ - - __slots__ = ("head", "args") - head: type - args: tuple - - def __init__(self, head, args): - # TODO(kszucs): ensure that it is a ground term, this check should be removed - assert all(not isinstance(arg, (Pattern, Variable)) for arg in args) - super().__init__(head=head, args=tuple(args)) - - @property - def __argnames__(self): - """Implementation for the `ibis.common.graph.Node` protocol.""" - return self.head.__argnames__ - - @property - def __args__(self): - """Implementation for the `ibis.common.graph.Node` protocol.""" - return self.args - - def __repr__(self): - argstring = ", ".join(map(repr, self.args)) - return f"E{self.head.__name__}({argstring})" - - def __lt__(self, other): - return False - - @classmethod - def from_node(cls, node: Any): - """Convert an `ibis.common.graph.Node` to an `ENode`.""" - - def mapper(node, _, **kwargs): - return cls(node.__class__, kwargs.values()) - - return node.map(mapper)[node] - - def to_node(self): - """Convert the ENode back to an `ibis.common.graph.Node`.""" - - def mapper(node, _, **kwargs): - return node.head(**kwargs) - - return self.map(mapper)[self] - - -# TODO: move every E* into the Egraph so its API only uses Nodes -# TODO: track whether the egraph is saturated or not -# TODO: support parent classes in etables (Join <= InnerJoin) - - -class EGraph: - __slots__ = ("_nodes", "_etables", "_eclasses") - _nodes: dict - _etables: collections.defaultdict - _eclasses: DisjointSet - - def __init__(self): - # store the nodes before converting them to enodes, so we can spare the initial - # node traversal and omit the creation of enodes - self._nodes = {} - # map enode heads to their eclass ids and their arguments, this is required for - # the relational e-matching (Node => dict[type, tuple[Union[ENode, Any], ...]]) - self._etables = collections.defaultdict(dict) - # map enodes to their eclass, this is the heart of the egraph - self._eclasses = DisjointSet() - - def __repr__(self): - return f"EGraph({self._eclasses})" - - def _as_enode(self, node: Node) -> ENode: - """Convert a node to an enode.""" - # order is important here since ENode is a subclass of Node - if isinstance(node, ENode): - return node - elif isinstance(node, Node): - return self._nodes.get(node) or ENode.from_node(node) - else: - raise TypeError(node) - - def add(self, node: Node) -> ENode: - """Add a node to the egraph. - - The node is converted to an enode and added to the egraph. If the enode is - already present in the egraph, then the canonical enode is returned. - - Parameters - ---------- - node : - The node to add to the egraph. - - Returns - ------- - enode : - The canonical enode. - - """ - enode = self._as_enode(node) - if enode in self._eclasses: - return self._eclasses.find(enode) - - args = [] - for arg in enode.args: - if isinstance(arg, ENode): - args.append(self.add(arg)) - else: - args.append(arg) - - enode = ENode(enode.head, args) - self._eclasses.add(enode) - self._etables[enode.head][enode] = tuple(args) - - return enode - - def union(self, node1: Node, node2: Node) -> ENode: - """Union two nodes in the egraph. - - The nodes are converted to enodes which must be present in the egraph. - The eclasses of the nodes are merged and the canonical enode is returned. - - Parameters - ---------- - node1 : - The first node to union. - node2 : - The second node to union. - - Returns - ------- - enode : - The canonical enode. - - """ - enode1 = self._as_enode(node1) - enode2 = self._as_enode(node2) - return self._eclasses.union(enode1, enode2) - - def _match_args(self, args, patargs): - """Match the arguments of an enode against a pattern's arguments. - - An enode matches a pattern if each of the arguments are: - - both leaf values and equal - - both enodes and in the same eclass - - an enode and a variable, in which case the variable gets bound to the enode - - Parameters - ---------- - args : tuple - The arguments of the enode. Since an enode is a ground term, the arguments - are either enodes or leaf values. - patargs : tuple - The arguments of the pattern. Since a pattern is a flat term (flattened - using auxiliary variables), the arguments are either variables or leaf - values. - - Returns - ------- - dict[str, Any] : - The mapping of variable names to enodes or leaf values. - - """ - subst = {} - for arg, patarg in zip(args, patargs): - if isinstance(patarg, Variable): - if isinstance(arg, ENode): - subst[patarg.name] = self._eclasses.find(arg) - else: - subst[patarg.name] = arg - # TODO(kszucs): this is not needed since patarg is either a variable or a - # leaf value due to the pattern flattening, though we may choose to - # support this in the future - # elif isinstance(arg, ENode): - # if self._eclasses.find(arg) != self._eclasses.find(arg): - # return None - elif patarg != arg: - return None - return subst - - def match(self, pattern: Pattern) -> dict[ENode, dict[str, Any]]: - """Match a pattern in the egraph. - - The pattern is converted to a conjunctive query (list of flat patterns) and - matched against the relations represented by the egraph. This is called the - relational e-matching. - - Parameters - ---------- - pattern : - The pattern to match in the egraph. - - Returns - ------- - matches : - A dictionary mapping the matched enodes to their substitutions. - - """ - # patterns could be reordered to match on the most selective one first - patterns = dict(reversed(list(pattern.flatten()))) - if any(pat.matches_none() for pat in patterns.values()): - return {} - - # extract the first pattern - (auxvar, pattern), *rest = patterns.items() - matches = {} - - # match the first pattern and create the initial substitutions - rel = self._etables[pattern.head] - for enode, args in rel.items(): - if (subst := self._match_args(args, pattern.args)) is not None: - subst[auxvar.name] = enode - matches[enode] = subst - - # match the rest of the patterns and extend the substitutions - for auxvar, pattern in rest: - rel = self._etables[pattern.head] - tmp = {} - for enode, subst in matches.items(): - if args := rel.get(subst[auxvar.name]): - if (newsubst := self._match_args(args, pattern.args)) is not None: - tmp[enode] = {**subst, **newsubst} - matches = tmp - - return matches - - def apply(self, rewrites: list[Rewrite]) -> int: - """Apply the given rewrites to the egraph. - - Iteratively match the patterns and apply the rewrites to the graph. The returned - number of changes is the number of eclasses that were merged. This is the - number of changes made to the egraph. The egraph is saturated if the number of - changes is zero. - - Parameters - ---------- - rewrites : - A list of rewrites to apply. - - Returns - ------- - n_changes - The number of changes made to the egraph. - - """ - n_changes = 0 - for rewrite in promote_list(rewrites): - for match, subst in self.match(rewrite.matcher).items(): - enode = rewrite.applier.substitute(self, match, subst) - enode = self.add(enode) - n_changes += self._eclasses.union(match, enode) - return n_changes - - def run(self, rewrites: list[Rewrite], n: int = 10) -> bool: - """Run the match-apply cycles for the given number of iterations. - - Parameters - ---------- - rewrites : - A list of rewrites to apply. - n : - The number of iterations to run. - - Returns - ------- - saturated : - True if the egraph is saturated, False otherwise. - - """ - return any(not self.apply(rewrites) for _i in range(n)) - - # TODO(kszucs): investigate whether the costs and best enodes could be maintained - # during the union operations after each match-apply cycle - def extract(self, node: Node) -> Node: - """Extract a node from the egraph. - - The node is converted to an enode which recursively gets converted to an - enode having the lowest cost according to equivalence classes. Currently - the cost function is hardcoded as the depth of the enode. - - Parameters - ---------- - node : - The node to extract from the egraph. - - Returns - ------- - node : - The extracted node. - - """ - enode = self._as_enode(node) - enode = self._eclasses.find(enode) - costs = {en: (math.inf, None) for en in self._eclasses.keys()} - - def enode_cost(enode): - cost = 1 - for arg in enode.args: - if isinstance(arg, ENode): - cost += costs[arg][0] - else: - cost += 1 - return cost - - changed = True - while changed: - changed = False - for en, enodes in self._eclasses.items(): - new_cost = min((enode_cost(en), en) for en in enodes) - if costs[en][0] != new_cost[0]: - changed = True - costs[en] = new_cost - - def extract(en): - if not isinstance(en, ENode): - return en - best = costs[en][1] - args = tuple(extract(a) for a in best.args) - return best.head(*args) - - return extract(enode) - - def equivalent(self, node1: Node, node2: Node) -> bool: - """Check if two nodes are equivalent. - - The nodes are converted to enodes and checked for equivalence: they are - equivalent if they are in the same equivalence class. - - Parameters - ---------- - node1 : - The first node. - node2 : - The second node. - - Returns - ------- - equivalent : - True if the nodes are equivalent, False otherwise. - - """ - enode1 = self._as_enode(node1) - enode2 = self._as_enode(node2) - enode1 = self._eclasses.find(enode1) - enode2 = self._eclasses.find(enode2) - return enode1 == enode2 diff --git a/third_party/bigframes_vendored/ibis/common/exceptions.py b/third_party/bigframes_vendored/ibis/common/exceptions.py deleted file mode 100644 index 4c6392cfc6a..00000000000 --- a/third_party/bigframes_vendored/ibis/common/exceptions.py +++ /dev/null @@ -1,173 +0,0 @@ -# Contains code from https://github.com/ibis-project/ibis/blob/9.2.0/ibis/common/exceptions.py - -# Copyright 2014 Cloudera Inc. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -"""Module for exceptions.""" - -from __future__ import annotations - -from typing import TYPE_CHECKING, Any - -if TYPE_CHECKING: - from collections.abc import Callable - - -class IbisError(Exception): - """IbisError.""" - - -class InternalError(IbisError): - """InternalError.""" - - -class IntegrityError(IbisError): - """IntegrityError.""" - - -class ExpressionError(IbisError): - """ExpressionError.""" - - -class RelationError(ExpressionError): - """RelationError.""" - - -class TranslationError(IbisError): - """TranslationError.""" - - -class OperationNotDefinedError(TranslationError): - """OperationNotDefinedError.""" - - -class UnsupportedOperationError(TranslationError): - """UnsupportedOperationError.""" - - -class UnsupportedBackendType(TranslationError): - """UnsupportedBackendType.""" - - -class UnboundExpressionError(ValueError, IbisError): - """UnboundExpressionError.""" - - -class IbisInputError(ValueError, IbisError): - """IbisInputError.""" - - -class IbisTypeError(TypeError, IbisError): - """IbisTypeError.""" - - -class InputTypeError(IbisTypeError): - """InputTypeError.""" - - -class UnsupportedArgumentError(IbisError): - """UnsupportedArgumentError.""" - - -class BackendConversionError(IbisError): - """A backend cannot convert an input to its native type.""" - - -class BackendConfigurationNotRegistered(IbisError): - """A backend has options but isn't registered in ibis/config.py.""" - - def __init__(self, backend_name: str) -> None: - super().__init__(backend_name) - - def __str__(self) -> str: - (backend_name,) = self.args - return f"Please register options for the `{backend_name}` backend in ibis/config.py" - - -class DuplicateUDFError(IbisError): - def __init__(self, name: str) -> None: - super().__init__(name) - - def __str__(self) -> str: - (name,) = self.args - return f"More than one function with `{name}` found." - - -class MissingUDFError(IbisError): - def __init__(self, name: str) -> None: - super().__init__(name) - - def __str__(self) -> str: - (name,) = self.args - return f"No user-defined function found with name `{name}`" - - -class AmbiguousUDFError(IbisError): - def __init__(self, name: str) -> None: - super().__init__(name) - - def __str__(self) -> str: - (name,) = self.args - return f"Multiple implementations of function `{name}`. Only one implementation is supported." - - -class MissingReturnAnnotationError(IbisError): - def __init__(self, func_name: str): - super().__init__(func_name) - - def __str__(self): - (func_name,) = self.args - return f"function `{func_name}` has no return type annotation" - - -class MissingParameterAnnotationError(IbisError): - def __init__(self, func_name: str, param_name: str): - super().__init__(func_name, param_name) - - def __str__(self): - func_name, param_name = self.args - return f"parameter `{param_name}` in function `{func_name}` is missing a type annotation" - - -class InvalidDecoratorError(IbisError): - def __init__(self, name: str, lines: str): - super().__init__(name, lines) - - def __str__(self) -> str: - name, lines = self.args - return f"Only the `@udf` decorator is allowed in user-defined function: `{name}`; found lines {lines}" - - -class ConflictingValuesError(ValueError): - """A single key has conflicting values in two different mappings.""" - - def __init__(self, conflicts: set[tuple[Any, Any, Any]]): - self.conflicts = conflicts - msgs = [f" `{key}`: {v1} != {v2}" for key, v1, v2 in conflicts] - msg = "Conflicting values for keys:\n" + "\n".join(msgs) - super().__init__(msg) - - -def mark_as_unsupported(f: Callable) -> Callable: - """Decorate an unsupported method.""" - - # function that raises UnsupportedOperationError - def _mark_as_unsupported(self): - raise UnsupportedOperationError( - f"Method `{f.__name__}` is unsupported by class `{self.__class__.__name__}`." - ) - - _mark_as_unsupported.__doc__ = f.__doc__ - _mark_as_unsupported.__name__ = f.__name__ - - return _mark_as_unsupported diff --git a/third_party/bigframes_vendored/ibis/common/graph.py b/third_party/bigframes_vendored/ibis/common/graph.py deleted file mode 100644 index 9bf13e93ecf..00000000000 --- a/third_party/bigframes_vendored/ibis/common/graph.py +++ /dev/null @@ -1,780 +0,0 @@ -# Contains code from https://github.com/ibis-project/ibis/blob/9.2.0/ibis/common/graph.py - -"""Various traversal utilities for the expression graph.""" - -from __future__ import annotations - -import itertools -from abc import abstractmethod -from collections import deque -from collections.abc import Callable, Iterable, Iterator, KeysView, Mapping, Sequence -from typing import TYPE_CHECKING, Any, Optional, TypeVar, Union - -from bigframes_vendored.ibis.common.bases import Hashable -from bigframes_vendored.ibis.common.patterns import NoMatch, Pattern -from bigframes_vendored.ibis.common.typing import _ClassInfo -from bigframes_vendored.ibis.util import experimental, promote_list - -if TYPE_CHECKING: - from typing_extensions import Self - - N = TypeVar("N") - - -Finder = Callable[["Node"], bool] -FinderLike = Union[Finder, Pattern, _ClassInfo] - -Replacer = Callable[["Node", dict["Node", Any]], "Node"] -ReplacerLike = Union[Replacer, Pattern, Mapping] - - -def _flatten_collections(node: Any) -> Iterator[N]: - """Flatten collections of nodes into a single iterator. - - We treat common collection types inherently traversable (e.g. list, tuple, dict) - but as undesired in a graph representation, so we traverse them implicitly. - - Parameters - ---------- - node - Flattaneble object. - - Returns - ------- - A flat generator of the filtered nodes. - - Examples - -------- - >>> from ibis.common.grounds import Concrete - >>> from ibis.common.graph import Node - >>> - >>> class MyNode(Concrete, Node): - ... number: int - ... string: str - ... children: tuple[Node, ...] - >>> a = MyNode(4, "a", ()) - >>> - >>> b = MyNode(3, "b", ()) - >>> c = MyNode(2, "c", (a, b)) - >>> d = MyNode(1, "d", (c,)) - >>> - >>> assert list(_flatten_collections((c,))) == [c] - >>> assert list(_flatten_collections([a, b, (c, a)])) == [a, b, c, a] - >>> assert list(_flatten_collections([{"b": b, "a": a}])) == [b, a] - - """ - for item in node: - if isinstance(item, Node): - yield item - elif isinstance(item, (tuple, list)): - yield from _flatten_collections(item) - elif isinstance(item, dict): - items = itertools.chain.from_iterable(item.items()) - yield from _flatten_collections(items) - - -def _recursive_lookup(obj: Any, dct: dict) -> Any: - """Recursively replace objects in a nested structure with values from a dict. - - Since we treat common collection types inherently traversable, so we need to - traverse them implicitly and replace the values given a result mapping. - - Parameters - ---------- - obj - Object to replace. - dct - Mapping of objects to replace with their values. - - Returns - ------- - Object with replaced values. - - Examples - -------- - >>> from ibis.common.collections import frozendict - >>> from ibis.common.grounds import Concrete - >>> from ibis.common.graph import Node - >>> - >>> class MyNode(Concrete, Node): - ... number: int - ... string: str - ... children: tuple[Node, ...] - >>> a = MyNode(4, "a", ()) - >>> - >>> b = MyNode(3, "b", ()) - >>> c = MyNode(2, "c", (a, b)) - >>> d = MyNode(1, "d", (c,)) - >>> - >>> dct = {a: "A", b: "B"} - >>> _recursive_lookup(a, dct) - 'A' - >>> _recursive_lookup((a, b), dct) - ('A', 'B') - >>> _recursive_lookup({1: a, 2: b}, dct) - {1: 'A', 2: 'B'} - >>> _recursive_lookup((a, frozendict({1: c})), dct) - ('A', {1: MyNode(number=2, ...)}) - - """ - if isinstance(obj, Node): - return dct.get(obj, obj) - elif isinstance(obj, (tuple, list)): - return tuple(_recursive_lookup(o, dct) for o in obj) - elif isinstance(obj, dict): - return { - _recursive_lookup(k, dct): _recursive_lookup(v, dct) for k, v in obj.items() - } - else: - return obj - - -def _coerce_finder(obj: FinderLike, context: Optional[dict] = None) -> Finder: - """Coerce an object into a callable finder function. - - Parameters - ---------- - obj - A callable accepting the node, a pattern or a type to match on. - context - Optional context to use if the finder is a pattern. - - Returns - ------- - A callable finder function which can be used to match nodes. - - """ - if isinstance(obj, Pattern): - ctx = context or {} - - def fn(node): - return obj.match(node, ctx) is not NoMatch - - elif isinstance(obj, (tuple, type)): - - def fn(node): - return isinstance(node, obj) - - elif callable(obj): - fn = obj - else: - raise TypeError("finder must be callable, type, tuple of types or a pattern") - - return fn - - -def _coerce_replacer(obj: ReplacerLike, context: Optional[dict] = None) -> Replacer: - """Coerce an object into a callable replacer function. - - Parameters - ---------- - obj - A Pattern, a Mapping or a callable which can be fed to `node.map()` - to replace nodes. - context - Optional context to use if the replacer is a pattern. - - Returns - ------- - A callable replacer function which can be used to replace nodes. - - """ - if isinstance(obj, Pattern): - ctx = context or {} - - def fn(node, _, **kwargs): - # need to first reconstruct the node from the possible rewritten - # children, so we can match on the new node containing the rewritten - # child arguments, this way we can propagate the rewritten nodes - # upward in the hierarchy, using a specialized __recreate__ method - # improves the performance by 17% compared node.__class__(**kwargs) - recreated = node.__recreate__(kwargs) - if (result := obj.match(recreated, ctx)) is NoMatch: - return recreated - else: - return result - - elif isinstance(obj, Mapping): - - def fn(node, _, **kwargs): - try: - return obj[node] - except KeyError: - return node.__recreate__(kwargs) - - elif callable(obj): - fn = obj - else: - raise TypeError("replacer must be callable, mapping or a pattern") - - return fn - - -class Node(Hashable): - __slots__ = () - - @classmethod - def __recreate__(cls, kwargs: Any) -> Self: - """Reconstruct the node from the given arguments.""" - return cls(**kwargs) - - @property - @abstractmethod - def __args__(self) -> tuple[Any, ...]: - """Sequence of arguments to traverse.""" - - @property - @abstractmethod - def __argnames__(self) -> tuple[str, ...]: - """Sequence of argument names.""" - - @property - def __children__(self) -> tuple[Node, ...]: - """Sequence of children nodes.""" - return tuple(_flatten_collections(self.__args__)) - - def __rich_repr__(self): - """Support for rich reprerentation of the node.""" - return zip(self.__argnames__, self.__args__) - - def map(self, fn: Callable, filter: Optional[Finder] = None) -> dict[Node, Any]: - """Apply a function to all nodes in the graph. - - The traversal is done in a topological order, so the function receives the - results of its immediate children as keyword arguments. - - Parameters - ---------- - fn - Function to apply to each node. It receives the node as the first argument, - the results as the second and the results of the children as keyword - arguments. - filter - Pattern-like object to filter out nodes from the traversal. The traversal - will only visit nodes that match the given pattern and stop otherwise. - - Returns - ------- - A mapping of nodes to their results. - - """ - results: dict[Node, Any] = {} - - graph, _ = Graph.from_bfs(self, filter=filter).toposort() - for node in graph: - # minor optimization to directly recurse into the children - kwargs = { - k: _recursive_lookup(v, results) - for k, v in zip(node.__argnames__, node.__args__) - } - results[node] = fn(node, results, **kwargs) - - return results - - @experimental - def map_clear(self, fn: Callable, filter: Optional[Finder] = None) -> Any: - """Apply a function to all nodes in the graph more memory efficiently. - - Alternative implementation of `map` to reduce memory usage. While `map` keeps - all the results in memory until the end of the traversal, this method removes - intermediate results as soon as they are not needed anymore. - - Prefer this method over `map` if the results consume significant amount of - memory and if the intermediate results are not needed. - - Parameters - ---------- - fn - Function to apply to each node. It receives the node as the first argument, - the results as the second and the results of the children as keyword - arguments. - filter - Pattern-like object to filter out nodes from the traversal. The traversal - will only visit nodes that match the given pattern and stop otherwise. - - Returns - ------- - In contrast to `map`, this method returns the result of the root node only since - the rest of the results are already discarded. - - """ - results: dict[Node, Any] = {} - - graph, dependents = Graph.from_bfs(self, filter=filter).toposort() - dependents = {k: set(v) for k, v in dependents.items()} - - for node, dependencies in graph.items(): - # minor optimization to directly recurse into the children - kwargs = { - k: _recursive_lookup(v, results) - for k, v in zip(node.__argnames__, node.__args__) - } - results[node] = fn(node, results, **kwargs) - - # remove the results belonging to the dependencies if they are not - # needed by other nodes during the rest of the traversal - for dependency in set(dependencies): - dependents[dependency].remove(node) - if not dependents[dependency]: - del results[dependency] - - return results[self] - - @experimental - def map_nodes(self, fn: Callable, filter: Optional[Finder] = None) -> Any: - """Apply a function to all nodes in the graph more memory efficiently. - - Alternative implementation of `map` passing only node results to the function - as positional arguments. This method is useful for calculations where the - nodes don't need to be reconstructed. - """ - results: dict[Node, Any] = {} - - graph, _ = Graph.from_bfs(self, filter=filter).toposort() - for node, children in graph.items(): - args = _recursive_lookup(children, results) - results[node] = fn(node, *args) - - return results - - # TODO(kszucs): perhaps rename it to find_all() for better clarity - def find( - self, - finder: FinderLike, - filter: Optional[FinderLike] = None, - context: Optional[dict] = None, - ordered: bool = False, - ) -> list[Node]: - """Find all nodes matching a given pattern or type in the graph. - - Allow to match nodes based on the flexible pattern matching system implemented - in the pattern module, but also provide a fast path for matching based on the - type of the node. - - Parameters - ---------- - finder - A type, tuple of types, a pattern or a callable to match upon. - filter - A type, tuple of types, a pattern or a callable to filter out nodes - from the traversal. The traversal will only visit nodes that match - the given filter and stop otherwise. - context - Optional context to use if `finder` or `filter` is a pattern. - ordered - Emit nodes in topological order if `True`. - - Returns - ------- - The list of nodes matching the given pattern. The order of the nodes is - determined by a breadth-first search. - - """ - graph = Graph.from_bfs(self, filter=filter, context=context) - finder = _coerce_finder(finder, context) - if ordered: - graph, _ = graph.toposort() - return [node for node in graph.nodes() if finder(node)] - - @experimental - def find_below( - self, - finder: FinderLike, - filter: Optional[FinderLike] = None, - context: Optional[dict] = None, - ) -> list[Node]: - """Find all nodes below the current node matching a given pattern in the graph. - - A variant of find() that only returns nodes below the current node in the graph. - - Parameters - ---------- - finder - A type, tuple of types, a pattern or a callable to match upon. - filter - A type, tuple of types, a pattern or a callable to filter out nodes - from the traversal. The traversal will only visit nodes that match - the given filter and stop otherwise. - context - Optional context to use if `finder` or `filter` is a pattern. - - Returns - ------- - The list of nodes matching the given pattern. - """ - graph = Graph.from_bfs(self.__children__, filter=filter, context=context) - finder = _coerce_finder(finder, context) - return [node for node in graph.nodes() if finder(node)] - - @experimental - def find_topmost( - self, finder: FinderLike, context: Optional[dict] = None - ) -> list[Node]: - """Find all topmost nodes matching a given pattern in the graph. - - A more advanced version of find, this method stops the traversal at the first - node that matches the given pattern and does not descend into its children. - - Parameters - ---------- - finder - A type, tuple of types, a pattern or a callable to match upon. - context - Optional context to use if `finder` is a pattern. - - Returns - ------- - The list of topmost nodes matching the given pattern. - - """ - seen = set() - queue = deque([self]) - result = [] - finder = _coerce_finder(finder, context) - - while queue: - if (node := queue.popleft()) not in seen: - if finder(node): - result.append(node) - else: - queue.extend(node.__children__) - seen.add(node) - return result - - @experimental - def replace( - self, - replacer: ReplacerLike, - filter: Optional[FinderLike] = None, - context: Optional[dict] = None, - ) -> Any: - """Match and replace nodes in the graph according to a given pattern. - - The pattern matching system is used to match nodes in the graph and replace them - with the results of the pattern. - - Parameters - ---------- - replacer - A `Pattern`, a `Mapping` or a callable which can be fed to - `node.map()` directly to replace nodes. - filter - A type, tuple of types, a pattern or a callable to filter out nodes - from the traversal. The traversal will only visit nodes that match - the given filter and stop otherwise. - context - Optional context to use for the pattern matching. - - Returns - ------- - The root node of the graph with the replaced nodes. - - """ - replacer = _coerce_replacer(replacer, context) - results = self.map(replacer, filter=filter) - return results.get(self, self) - - -class Graph(dict[Node, Sequence[Node]]): - """A mapping-like graph data structure for easier graph traversal and manipulation. - - The data structure is a mapping of nodes to their children. The children are - represented as a sequence of nodes. The graph can be constructed from a root node - using the `from_bfs` or `from_dfs` class methods. - - Parameters - ---------- - mapping : Node or Mapping[Node, Sequence[Node]], default () - Either a root node or a mapping of nodes to their children. - - """ - - def __init__(self, mapping=(), /, **kwargs): - if isinstance(mapping, Node): - mapping = self.from_bfs(mapping) - super().__init__(mapping, **kwargs) - - @classmethod - def from_bfs( - cls, - root: Node, - filter: Optional[FinderLike] = None, - context: Optional[dict] = None, - ) -> Self: - """Construct a graph from a root node using a breadth-first search. - - The traversal is implemented in an iterative fashion using a queue. - - Parameters - ---------- - root - Root node of the graph. - filter - A type, tuple of types, a pattern or a callable to filter out nodes - from the traversal. The traversal will only visit nodes that match - the given filter and stop otherwise. - context - Optional context to use for the pattern matching. - - Returns - ------- - A graph constructed from the root node. - - """ - if filter is None: - return bfs(root) - else: - filter = _coerce_finder(filter, context) - return bfs_while(root, filter=filter) - - @classmethod - def from_dfs( - cls, - root: Node, - filter: Optional[FinderLike] = None, - context: Optional[dict] = None, - ) -> Self: - """Construct a graph from a root node using a depth-first search. - - The traversal is implemented in an iterative fashion using a stack. - - Parameters - ---------- - root - Root node of the graph. - filter - A type, tuple of types, a pattern or a callable to filter out nodes - from the traversal. The traversal will only visit nodes that match - the given filter and stop otherwise. - context - Optional context to use for the pattern matching. - - Returns - ------- - A graph constructed from the root node. - - """ - if filter is None: - return dfs(root) - else: - filter = _coerce_finder(filter, None) - return dfs_while(root, filter=filter) - - def __repr__(self): - return f"{self.__class__.__name__}({super().__repr__()})" - - def nodes(self) -> KeysView[Node]: - """Return all unique nodes in the graph.""" - return self.keys() - - def invert(self) -> Self: - """Invert the data structure. - - The graph originally maps nodes to their children, this method inverts the - mapping to map nodes to their parents. - - Returns - ------- - The inverted graph. - - """ - result: dict[Node, list[Node]] = {node: [] for node in self} - for node, dependencies in self.items(): - for dependency in dependencies: - result[dependency].append(node) - return self.__class__({k: tuple(v) for k, v in result.items()}) - - def toposort(self) -> Self: - """Topologically sort the graph using Kahn's algorithm. - - The graph is sorted in a way that all the dependencies of a node are placed - before the node itself. The graph must not contain any cycles. Especially useful - for mutating the graph in a way that the dependencies of a node are mutated - before the node itself. - - Returns - ------- - The topologically sorted graph. - - """ - dependents = self.invert() - in_degree = {k: len(v) for k, v in self.items()} - - queue = deque(node for node, count in in_degree.items() if not count) - result = self.__class__() - - while queue: - node = queue.popleft() - result[node] = self[node] - - for dependent in dependents[node]: - in_degree[dependent] -= 1 - if not in_degree[dependent]: - queue.append(dependent) - - if any(in_degree.values()): - raise ValueError("cycle detected in the graph") - - return result, dependents - - -# these could be callables instead -proceed = True -halt = False - - -def traverse( - fn: Callable[[Node], tuple[bool | Iterable, Any]], node: Iterable[Node] | Node -) -> Iterator[Any]: - """Utility for generic expression tree traversal. - - Parameters - ---------- - fn - A function applied on each expression. The first element of the tuple controls - the traversal, and the second is the result if its not `None`. - node - The Node expression or a list of expressions. - - """ - nodes = list(_flatten_collections(promote_list(node))) - queue: deque[Node] = deque(reversed(nodes)) - seen: set[Node] = set() - - while queue: - node = queue.pop() - - if node in seen: - continue - seen.add(node) - - control, result = fn(node) - if result is not None: - yield result - - if control is not halt: - if control is proceed: - children = node.__children__ - elif isinstance(control, Iterable): - children = control - else: - raise TypeError( - "First item of the returned tuple must be " - "an instance of boolean or iterable" - ) - - queue.extend(reversed(children)) - - -def bfs(root: Node) -> Graph: - """Construct a graph from a root node using a breadth-first search. - - Parameters - ---------- - root - Root node of the graph. - - Returns - ------- - A graph constructed from the root node. - - """ - # fast path for the default no filter case, according to benchmarks - # this is gives a 10% speedup compared to the filtered version - nodes = _flatten_collections(promote_list(root)) - queue = deque(nodes) - graph = Graph() - - while queue: - if (node := queue.popleft()) not in graph: - children = node.__children__ - graph[node] = children - queue.extend(children) - - return graph - - -def bfs_while(root: Node, filter: Finder) -> Graph: - """Construct a graph from a root node using a breadth-first search. - - Parameters - ---------- - root - Root node of the graph. - filter - A callable which returns a boolean given a node. The traversal will only - visit nodes that match the given filter and stop otherwise. - - Returns - ------- - A graph constructed from the root node. - - """ - nodes = _flatten_collections(promote_list(root)) - queue = deque(node for node in nodes if filter(node)) - graph = Graph() - - while queue: - if (node := queue.popleft()) not in graph: - children = tuple(child for child in node.__children__ if filter(child)) - graph[node] = children - queue.extend(children) - - return graph - - -def dfs(root: Node) -> Graph: - """Construct a graph from a root node using a depth-first search. - - Parameters - ---------- - root - Root node of the graph. - - Returns - ------- - A graph constructed from the root node. - - """ - # fast path for the default no filter case, according to benchmarks - # this is gives a 10% speedup compared to the filtered version - nodes = _flatten_collections(promote_list(root)) - stack = deque(nodes) - graph = {} - - while stack: - if (node := stack.pop()) not in graph: - children = node.__children__ - graph[node] = children - stack.extend(children) - - return Graph(reversed(graph.items())) - - -def dfs_while(root: Node, filter: Finder) -> Graph: - """Construct a graph from a root node using a depth-first search. - - Parameters - ---------- - root - Root node of the graph. - filter - A callable which returns a boolean given a node. The traversal will only - visit nodes that match the given filter and stop otherwise. - - Returns - ------- - A graph constructed from the root node. - - """ - nodes = _flatten_collections(promote_list(root)) - stack = deque(node for node in nodes if filter(node)) - graph = {} - - while stack: - if (node := stack.pop()) not in graph: - children = tuple(child for child in node.__children__ if filter(child)) - graph[node] = children - stack.extend(children) - - return Graph(reversed(graph.items())) diff --git a/third_party/bigframes_vendored/ibis/common/grounds.py b/third_party/bigframes_vendored/ibis/common/grounds.py deleted file mode 100644 index 874e18c4057..00000000000 --- a/third_party/bigframes_vendored/ibis/common/grounds.py +++ /dev/null @@ -1,232 +0,0 @@ -# Contains code from https://github.com/ibis-project/ibis/blob/9.2.0/ibis/common/grounds.py -from __future__ import annotations - -import contextlib -from copy import copy -from typing import Any, ClassVar, Union, get_origin - -from bigframes_vendored.ibis.common.annotations import ( - Annotation, - Argument, - Attribute, - Signature, -) -from bigframes_vendored.ibis.common.bases import ( # noqa: F401 - Abstract, - AbstractMeta, - Comparable, - Final, - Hashable, - Immutable, - Singleton, -) -from bigframes_vendored.ibis.common.collections import FrozenDict # noqa: TCH001 -from bigframes_vendored.ibis.common.patterns import Pattern -from bigframes_vendored.ibis.common.typing import evaluate_annotations -from typing_extensions import Self, dataclass_transform - - -class AnnotableMeta(AbstractMeta): - """Metaclass to turn class annotations into a validatable function signature.""" - - __slots__ = () - - def __new__(metacls, clsname, bases, dct, **kwargs): - # inherit signature from parent classes - signatures, attributes = [], {} - for parent in bases: - with contextlib.suppress(AttributeError): - attributes.update(parent.__attributes__) - with contextlib.suppress(AttributeError): - signatures.append(parent.__signature__) - - # collection type annotations and convert them to patterns - module = dct.get("__module__") - qualname = dct.get("__qualname__") or clsname - annotations = dct.get("__annotations__", {}) - - # TODO(kszucs): pass dct as localns to evaluate_annotations - typehints = evaluate_annotations(annotations, module, clsname) - for name, typehint in typehints.items(): - if get_origin(typehint) is ClassVar: - continue - pattern = Pattern.from_typehint(typehint) - if name in dct: - dct[name] = Argument(pattern, default=dct[name], typehint=typehint) - else: - dct[name] = Argument(pattern, typehint=typehint) - - # collect the newly defined annotations - slots = list(dct.pop("__slots__", [])) - namespace, arguments = {}, {} - for name, attrib in dct.items(): - if isinstance(attrib, Pattern): - arguments[name] = Argument(attrib) - slots.append(name) - elif isinstance(attrib, Argument): - arguments[name] = attrib - slots.append(name) - elif isinstance(attrib, Attribute): - attributes[name] = attrib - slots.append(name) - else: - namespace[name] = attrib - - # merge the annotations with the parent annotations - signature = Signature.merge(*signatures, **arguments) - argnames = tuple(signature.parameters.keys()) - - namespace.update( - __module__=module, - __qualname__=qualname, - __argnames__=argnames, - __attributes__=attributes, - __match_args__=argnames, - __signature__=signature, - __slots__=tuple(slots), - ) - return super().__new__(metacls, clsname, bases, namespace, **kwargs) - - def __or__(self, other): - # required to support `dt.Numeric | dt.Floating` annotation for python<3.10 - return Union[self, other] - - -@dataclass_transform() -class Annotable(Abstract, metaclass=AnnotableMeta): - """Base class for objects with custom validation rules.""" - - __signature__: ClassVar[Signature] - """Signature of the class, containing the Argument annotations.""" - - __attributes__: ClassVar[FrozenDict[str, Annotation]] - """Mapping of the Attribute annotations.""" - - __argnames__: ClassVar[tuple[str, ...]] - """Names of the arguments.""" - - __match_args__: ClassVar[tuple[str, ...]] - """Names of the arguments to be used for pattern matching.""" - - @classmethod - def __create__(cls, *args: Any, **kwargs: Any) -> Self: - # construct the instance by passing only validated keyword arguments - kwargs = cls.__signature__.validate(cls, args, kwargs) - return super().__create__(**kwargs) - - @classmethod - def __recreate__(cls, kwargs: Any) -> Self: - # bypass signature binding by requiring keyword arguments only - kwargs = cls.__signature__.validate_nobind(cls, kwargs) - return super().__create__(**kwargs) - - def __init__(self, **kwargs: Any) -> None: - # set the already validated arguments - for name, value in kwargs.items(): - object.__setattr__(self, name, value) - # initialize the remaining attributes - for name, field in self.__attributes__.items(): - if field.has_default(): - object.__setattr__(self, name, field.get_default(name, self)) - - def __setattr__(self, name, value) -> None: - # first try to look up the argument then the attribute - if param := self.__signature__.parameters.get(name): - value = param.annotation.validate(name, value, self) - # then try to look up the attribute - elif annot := self.__attributes__.get(name): - value = annot.validate(name, value, self) - return super().__setattr__(name, value) - - def __repr__(self) -> str: - args = (f"{n}={getattr(self, n)!r}" for n in self.__argnames__) - argstring = ", ".join(args) - return f"{self.__class__.__name__}({argstring})" - - def __eq__(self, other) -> bool: - # compare types - if type(self) is not type(other): - return NotImplemented - # compare arguments - if self.__args__ != other.__args__: - return False - # compare attributes - for name in self.__attributes__: - if getattr(self, name, None) != getattr(other, name, None): - return False - return True - - @property - def __args__(self) -> tuple[Any, ...]: - return tuple(getattr(self, name) for name in self.__argnames__) - - def copy(self, **overrides: Any) -> Annotable: - """Return a copy of this object with the given overrides. - - Parameters - ---------- - overrides - Argument override values - - Returns - ------- - Annotable - New instance of the copied object - - """ - this = copy(self) - for name, value in overrides.items(): - setattr(this, name, value) - return this - - -class Concrete(Immutable, Comparable, Annotable): - """Opinionated base class for immutable data classes.""" - - __slots__ = ("__args__", "__precomputed_hash__") - - def __init__(self, **kwargs: Any) -> None: - # collect and set the arguments in a single pass - args = [] - for name in self.__argnames__: - value = kwargs[name] - args.append(value) - object.__setattr__(self, name, value) - - # precompute the hash value since the instance is immutable - args = tuple(args) - hashvalue = hash((self.__class__, args)) - object.__setattr__(self, "__args__", args) - object.__setattr__(self, "__precomputed_hash__", hashvalue) - - # initialize the remaining attributes - for name, field in self.__attributes__.items(): - if field.has_default(): - object.__setattr__(self, name, field.get_default(name, self)) - - def __reduce__(self): - # assuming immutability and idempotency of the __init__ method, we can - # reconstruct the instance from the arguments without additional attributes - state = dict(zip(self.__argnames__, self.__args__)) - return (self.__recreate__, (state,)) - - def __hash__(self) -> int: - return self.__precomputed_hash__ - - def __equals__(self, other) -> bool: - return hash(self) == hash(other) and self.__args__ == other.__args__ - - @property - def args(self): - return self.__args__ - - @property - def argnames(self) -> tuple[str, ...]: - return self.__argnames__ - - def copy(self, **overrides) -> Self: - kwargs = dict(zip(self.__argnames__, self.__args__)) - if unknown_args := overrides.keys() - kwargs.keys(): - raise AttributeError(f"Unexpected arguments: {unknown_args}") - kwargs.update(overrides) - return self.__recreate__(kwargs) diff --git a/third_party/bigframes_vendored/ibis/common/numeric.py b/third_party/bigframes_vendored/ibis/common/numeric.py deleted file mode 100644 index ad406ae340a..00000000000 --- a/third_party/bigframes_vendored/ibis/common/numeric.py +++ /dev/null @@ -1,50 +0,0 @@ -# Contains code from https://github.com/ibis-project/ibis/blob/9.2.0/ibis/common/numeric.py - -from __future__ import annotations - -from decimal import Context, Decimal, InvalidOperation - - -def normalize_decimal( - value, - precision: int | None = None, - scale: int | None = None, - strict: bool = True, -): - context = Context(prec=38 if precision is None else precision) - - try: - if isinstance(value, float): - out = Decimal(str(value)) - else: - out = Decimal(value) - except InvalidOperation: - raise TypeError(f"Unable to construct decimal from {value!r}") - - out = out.normalize(context=context) - components = out.as_tuple() - n_digits = len(components.digits) - exponent = components.exponent - - if precision is not None and precision < n_digits: - raise TypeError( - f"Decimal value {value} has too many digits for precision: {precision}" - ) - - if scale is not None: - if strict and exponent < -scale: - raise TypeError( - f"Normalizing {value} with scale {exponent} to scale -{scale} " - "would loose precision" - ) - - other = Decimal(10) ** -scale - try: - out = out.quantize(other, context=context) - except InvalidOperation: - raise TypeError( - f"Unable to normalize {value!r} as decimal with precision {precision} " - f"and scale {scale}" - ) - - return out diff --git a/third_party/bigframes_vendored/ibis/common/patterns.py b/third_party/bigframes_vendored/ibis/common/patterns.py deleted file mode 100644 index 68861aa1908..00000000000 --- a/third_party/bigframes_vendored/ibis/common/patterns.py +++ /dev/null @@ -1,1711 +0,0 @@ -# Contains code from https://github.com/ibis-project/ibis/blob/9.2.0/ibis/common/patterns.py - -from __future__ import annotations - -import math -import numbers -from abc import abstractmethod -from collections.abc import Callable, Mapping, Sequence -from enum import Enum -from inspect import Parameter -from typing import ( - Annotated, - ForwardRef, - Generic, - Literal, - Optional, - TypeVar, - Union, - get_args, - get_origin, -) -from typing import Any as AnyType - -import toolz -from bigframes_vendored.ibis.common.bases import FrozenSlotted as Slotted -from bigframes_vendored.ibis.common.bases import Hashable, Singleton -from bigframes_vendored.ibis.common.collections import ( - FrozenDict, - RewindableIterator, - frozendict, -) -from bigframes_vendored.ibis.common.deferred import ( - Deferred, - Factory, - Resolver, - Variable, - _, # noqa: F401 - resolver, -) -from bigframes_vendored.ibis.common.typing import ( - Coercible, - CoercionError, - Sentinel, - format_typehint, - get_bound_typevars, - get_type_params, -) -from bigframes_vendored.ibis.util import import_object, is_iterable, unalias_package -from typing_extensions import GenericMeta - -T_co = TypeVar("T_co", covariant=True) - - -def as_resolver(obj): - if callable(obj) and not isinstance(obj, Deferred): - return Factory(obj) - else: - return resolver(obj) - - -class NoMatch(metaclass=Sentinel): - """Marker to indicate that a pattern didn't match.""" - - -# TODO(kszucs): have an As[int] or Coerced[int] type in ibis.common.typing which -# would be used to annotate an argument as coercible to int or to a certain type -# without needing for the type to inherit from Coercible -class Pattern(Hashable): - """Base class for all patterns. - - Patterns are used to match values against a given condition. They are extensively - used by other core components of Ibis to validate and/or coerce user inputs. - """ - - @classmethod - def from_typehint(cls, annot: type, allow_coercion: bool = True) -> Pattern: - """Construct a validator from a python type annotation. - - Parameters - ---------- - annot - The typehint annotation to construct the pattern from. This must be - an already evaluated type annotation. - allow_coercion - Whether to use coercion if the typehint is a Coercible type. - - Returns - ------- - A pattern that matches the given type annotation. - - """ - # TODO(kszucs): cache the result of this function - # TODO(kszucs): explore issubclass(typ, SupportsInt) etc. - origin, args = get_origin(annot), get_args(annot) - - if origin is None: - # the typehint is not generic - if annot is Ellipsis or annot is AnyType: - # treat both `Any` and `...` as wildcard - return _any - elif isinstance(annot, type): - # the typehint is a concrete type (e.g. int, str, etc.) - if allow_coercion and issubclass(annot, Coercible): - # the type implements the Coercible protocol so we try to - # coerce the value to the given type rather than checking - return CoercedTo(annot) - else: - return InstanceOf(annot) - elif isinstance(annot, TypeVar): - # if the typehint is a type variable we try to construct a - # validator from it only if it is covariant and has a bound - if not annot.__covariant__: - raise NotImplementedError( - "Only covariant typevars are supported for now" - ) - if annot.__bound__: - return cls.from_typehint(annot.__bound__) - else: - return _any - elif isinstance(annot, Enum): - # for enums we check the value against the enum values - return EqualTo(annot) - elif isinstance(annot, str): - # for strings and forward references we check in a lazy way - return LazyInstanceOf(annot) - elif isinstance(annot, ForwardRef): - return LazyInstanceOf(annot.__forward_arg__) - else: - raise TypeError(f"Cannot create validator from annotation {annot!r}") - elif origin is CoercedTo: - return CoercedTo(args[0]) - elif origin is Literal: - # for literal types we check the value against the literal values - return IsIn(args) - elif origin is Union: - # this is slightly more complicated because we need to handle - # Optional[T] which is Union[T, None] and Union[T1, T2, ...] - *rest, last = args - if last is type(None): - # the typehint is Optional[*rest] which is equivalent to - # Union[*rest, None], so we construct an Option pattern - if len(rest) == 1: - inner = cls.from_typehint(rest[0]) - else: - inner = AnyOf(*map(cls.from_typehint, rest)) - return Option(inner) - else: - # the typehint is Union[*args] so we construct an AnyOf pattern - return AnyOf(*map(cls.from_typehint, args)) - elif origin is Annotated: - # the Annotated typehint can be used to add extra validation logic - # to the typehint, e.g. Annotated[int, Positive], the first argument - # is used for isinstance checks, the rest are applied in conjunction - annot, *extras = args - return AllOf(cls.from_typehint(annot), *extras) - elif origin is Callable: - # the Callable typehint is used to annotate functions, e.g. the - # following typehint annotates a function that takes two integers - # and returns a string: Callable[[int, int], str] - if args: - # callable with args and return typehints construct a special - # CallableWith validator - arg_hints, return_hint = args - arg_patterns = tuple(map(cls.from_typehint, arg_hints)) - return_pattern = cls.from_typehint(return_hint) - return CallableWith(arg_patterns, return_pattern) - else: - # in case of Callable without args we check for the Callable - # protocol only - return InstanceOf(Callable) - elif issubclass(origin, tuple): - # construct validators for the tuple elements, but need to treat - # variadic tuples differently, e.g. tuple[int, ...] is a variadic - # tuple of integers, while tuple[int] is a tuple with a single int - first, *rest = args - if rest == [Ellipsis]: - return TupleOf(cls.from_typehint(first)) - else: - return PatternList(map(cls.from_typehint, args), type=origin) - elif issubclass(origin, Sequence): - # construct a validator for the sequence elements where all elements - # must be of the same type, e.g. Sequence[int] is a sequence of ints - (value_inner,) = map(cls.from_typehint, args) - if allow_coercion and issubclass(origin, Coercible): - return GenericSequenceOf(value_inner, type=origin) - else: - return SequenceOf(value_inner, type=origin) - elif issubclass(origin, Mapping): - # construct a validator for the mapping keys and values, e.g. - # Mapping[str, int] is a mapping with string keys and int values - key_inner, value_inner = map(cls.from_typehint, args) - return MappingOf(key_inner, value_inner, type=origin) - elif isinstance(origin, GenericMeta): - # construct a validator for the generic type, see the specific - # Generic* validators for more details - if allow_coercion and issubclass(origin, Coercible) and args: - return GenericCoercedTo(annot) - else: - return GenericInstanceOf(annot) - else: - raise TypeError( - f"Cannot create validator from annotation {annot!r} {origin!r}" - ) - - @abstractmethod - def match(self, value: AnyType, context: dict[str, AnyType]) -> AnyType: - """Match a value against the pattern. - - Parameters - ---------- - value - The value to match the pattern against. - context - A dictionary providing arbitrary context for the pattern matching. - - Returns - ------- - The result of the pattern matching. If the pattern doesn't match - the value, then it must return the `NoMatch` sentinel value. - - """ - ... - - def describe(self, plural=False): - return f"matching {self!r}" - - @abstractmethod - def __eq__(self, other: Pattern) -> bool: ... - - def __invert__(self) -> Not: - """Syntax sugar for matching the inverse of the pattern.""" - return Not(self) - - def __or__(self, other: Pattern) -> AnyOf: - """Syntax sugar for matching either of the patterns. - - Parameters - ---------- - other - The other pattern to match against. - - Returns - ------- - New pattern that matches if either of the patterns match. - - """ - return AnyOf(self, other) - - def __and__(self, other: Pattern) -> AllOf: - """Syntax sugar for matching both of the patterns. - - Parameters - ---------- - other - The other pattern to match against. - - Returns - ------- - New pattern that matches if both of the patterns match. - - """ - return AllOf(self, other) - - def __rshift__(self, other: Deferred) -> Replace: - """Syntax sugar for replacing a value. - - Parameters - ---------- - other - The deferred to use for constructing the replacement value. - - Returns - ------- - New replace pattern. - - """ - return Replace(self, other) - - def __rmatmul__(self, name: str) -> Capture: - """Syntax sugar for capturing a value. - - Parameters - ---------- - name - The name of the capture. - - Returns - ------- - New capture pattern. - - """ - return Capture(name, self) - - def __iter__(self) -> SomeOf: - yield SomeOf(self) - - -class Is(Slotted, Pattern): - """Pattern that matches a value against a reference value. - - Parameters - ---------- - value - The reference value to match against. - - """ - - __slots__ = ("value",) - value: AnyType - - def match(self, value, context): - if value is self.value: - return value - else: - return NoMatch - - -class Any(Slotted, Singleton, Pattern): - """Pattern that accepts any value, basically a no-op.""" - - def match(self, value, context): - return value - - -_any = Any() - - -class Nothing(Slotted, Singleton, Pattern): - """Pattern that no values.""" - - def match(self, value, context): - return NoMatch - - -class Capture(Slotted, Pattern): - """Pattern that captures a value in the context. - - Parameters - ---------- - pattern - The pattern to match against. - key - The key to use in the context if the pattern matches. - - """ - - __slots__ = ("key", "pattern") - key: AnyType - pattern: Pattern - - def __init__(self, key, pat=_any): - if isinstance(key, (Deferred, Resolver)): - key = as_resolver(key) - if isinstance(key, Variable): - key = key.name - else: - raise TypeError("Only variables can be used as capture keys") - super().__init__(key=key, pattern=pattern(pat)) - - def match(self, value, context): - value = self.pattern.match(value, context) - if value is NoMatch: - return NoMatch - context[self.key] = value - return value - - -class Replace(Slotted, Pattern): - """Pattern that replaces a value with the output of another pattern. - - Parameters - ---------- - matcher - The pattern to match against. - replacer - The deferred to use as a replacement. - - """ - - __slots__ = ("matcher", "replacer") - matcher: Pattern - replacer: Resolver - - def __init__(self, matcher, replacer): - super().__init__(matcher=pattern(matcher), replacer=as_resolver(replacer)) - - def match(self, value, context): - value = self.matcher.match(value, context) - if value is NoMatch: - return NoMatch - # use the `_` reserved variable to record the value being replaced - # in the context, so that it can be used in the replacer pattern - context["_"] = value - return self.replacer.resolve(context) - - -def replace(matcher): - """More convenient syntax for replacing a value with the output of a function.""" - - def decorator(replacer): - return Replace(matcher, replacer) - - return decorator - - -class Check(Slotted, Pattern): - """Pattern that checks a value against a predicate. - - Parameters - ---------- - predicate - The predicate to use. - - """ - - __slots__ = ("predicate",) - predicate: Callable - - @classmethod - def __create__(cls, predicate): - if isinstance(predicate, (Deferred, Resolver)): - return DeferredCheck(predicate) - else: - return super().__create__(predicate) - - def __init__(self, predicate): - assert callable(predicate) - super().__init__(predicate=predicate) - - def describe(self, plural=False): - if plural: - return f"values that satisfy {self.predicate.__name__}()" - else: - return f"a value that satisfies {self.predicate.__name__}()" - - def match(self, value, context): - if self.predicate(value): - return value - else: - return NoMatch - - -class DeferredCheck(Slotted, Pattern): - __slots__ = ("resolver",) - resolver: Resolver - - def __init__(self, obj): - super().__init__(resolver=as_resolver(obj)) - - def describe(self, plural=False): - if plural: - return f"values that satisfy {self.resolver!r}" - else: - return f"a value that satisfies {self.resolver!r}" - - def match(self, value, context): - context["_"] = value - if self.resolver.resolve(context): - return value - else: - return NoMatch - - -class Custom(Slotted, Pattern): - """User defined custom matcher function. - - Parameters - ---------- - func - The function to apply. - - """ - - __slots__ = ("func",) - func: Callable - - def __init__(self, func): - assert callable(func) - super().__init__(func=func) - - def match(self, value, context): - return self.func(value, context) - - -class EqualTo(Slotted, Pattern): - """Pattern that checks a value equals to the given value. - - Parameters - ---------- - value - The value to check against. - - """ - - __slots__ = ("value",) - value: AnyType - - @classmethod - def __create__(cls, value): - if isinstance(value, (Deferred, Resolver)): - return DeferredEqualTo(value) - else: - return super().__create__(value) - - def __init__(self, value): - super().__init__(value=value) - - def match(self, value, context): - if value == self.value: - return value - else: - return NoMatch - - def describe(self, plural=False): - return repr(self.value) - - -class DeferredEqualTo(Slotted, Pattern): - """Pattern that checks a value equals to the given value. - - Parameters - ---------- - value - The value to check against. - - """ - - __slots__ = ("resolver",) - resolver: Resolver - - def __init__(self, obj): - super().__init__(resolver=as_resolver(obj)) - - def match(self, value, context): - context["_"] = value - if value == self.resolver.resolve(context): - return value - else: - return NoMatch - - def describe(self, plural=False): - return repr(self.resolver) - - -class Option(Slotted, Pattern): - """Pattern that matches `None` or a value that passes the inner validator. - - Parameters - ---------- - pattern - The inner pattern to use. - - """ - - __slots__ = ("pattern", "default") - pattern: Pattern - default: AnyType - - def __init__(self, pat, default=None): - super().__init__(pattern=pattern(pat), default=default) - - def describe(self, plural=False): - if plural: - return f"optional {self.pattern.describe(plural=True)}" - else: - return f"either None or {self.pattern.describe(plural=False)}" - - def match(self, value, context): - if value is None: - if self.default is None: - return None - else: - return self.default - else: - return self.pattern.match(value, context) - - -def _describe_type(typ, plural=False): - if isinstance(typ, tuple): - *rest, last = typ - rest = ", ".join(_describe_type(t, plural=plural) for t in rest) - last = _describe_type(last, plural=plural) - return f"{rest} or {last}" if rest else last - - name = format_typehint(typ) - if plural: - return f"{name}s" - elif name[0].lower() in "aeiou": - return f"an {name}" - else: - return f"a {name}" - - -class TypeOf(Slotted, Pattern): - """Pattern that matches a value that is of a given type.""" - - __slots__ = ("type",) - type: type - - def __init__(self, typ): - super().__init__(type=typ) - - def describe(self, plural=False): - return f"exactly {_describe_type(self.type, plural=plural)}" - - def match(self, value, context): - if type(value) is self.type: - return value - else: - return NoMatch - - -class SubclassOf(Slotted, Pattern): - """Pattern that matches a value that is a subclass of a given type. - - Parameters - ---------- - type - The type to check against. - - """ - - __slots__ = ("type",) - - def __init__(self, typ): - super().__init__(type=typ) - - def describe(self, plural=False): - if plural: - return f"subclasses of {self.type.__name__}" - else: - return f"a subclass of {self.type.__name__}" - - def match(self, value, context): - if issubclass(value, self.type): - return value - else: - return NoMatch - - -class InstanceOf(Slotted, Singleton, Pattern): - """Pattern that matches a value that is an instance of a given type. - - Parameters - ---------- - types - The type to check against. - - """ - - __slots__ = ("type",) - type: Any - - def __init__(self, typ): - super().__init__(type=typ) - - def describe(self, plural=False): - return _describe_type(self.type, plural=plural) - - def match(self, value, context): - if isinstance(value, self.type): - return value - else: - return NoMatch - - def __call__(self, *args, **kwargs): - return Object(self.type, *args, **kwargs) - - -class GenericInstanceOf(Slotted, Pattern): - """Pattern that matches a value that is an instance of a given generic type. - - Parameters - ---------- - typ - The type to check against (must be a generic type). - - Examples - -------- - >>> class MyNumber(Generic[T_co]): - ... value: T_co - ... - ... def __init__(self, value: T_co): - ... self.value = value - ... - ... def __eq__(self, other): - ... return type(self) is type(other) and self.value == other.value - >>> p = GenericInstanceOf(MyNumber[int]) - >>> assert p.match(MyNumber(1), {}) == MyNumber(1) - >>> assert p.match(MyNumber(1.0), {}) is NoMatch - >>> - >>> p = GenericInstanceOf(MyNumber[float]) - >>> assert p.match(MyNumber(1.0), {}) == MyNumber(1.0) - >>> assert p.match(MyNumber(1), {}) is NoMatch - - """ - - __slots__ = ("type", "origin", "fields") - origin: type - fields: FrozenDict[str, Pattern] - - def __init__(self, typ): - origin = get_origin(typ) - typevars = get_bound_typevars(typ) - - fields = {} - for var, (attr, type_) in typevars.items(): - if not var.__covariant__: - raise TypeError( - f"Typevar {var} is not covariant, cannot use it in a GenericInstanceOf" - ) - fields[attr] = Pattern.from_typehint(type_, allow_coercion=False) - - super().__init__(type=typ, origin=origin, fields=frozendict(fields)) - - def describe(self, plural=False): - return _describe_type(self.type, plural=plural) - - def match(self, value, context): - if not isinstance(value, self.origin): - return NoMatch - - for name, pattern in self.fields.items(): - attr = getattr(value, name) - if pattern.match(attr, context) is NoMatch: - return NoMatch - - return value - - -class LazyInstanceOf(Slotted, Pattern): - """A version of `InstanceOf` that accepts qualnames instead of imported classes. - - Useful for delaying imports. - - Parameters - ---------- - types - The types to check against. - - """ - - __fields__ = ("qualname", "package") - __slots__ = ("qualname", "package", "loaded") - qualname: str - package: str - loaded: type - - def __init__(self, qualname): - package = unalias_package(qualname.split(".", 1)[0]) - super().__init__(qualname=qualname, package=package) - - def match(self, value, context): - if hasattr(self, "loaded"): - return value if isinstance(value, self.loaded) else NoMatch - - for klass in type(value).__mro__: - package = klass.__module__.split(".", 1)[0] - if package == self.package: - typ = import_object(self.qualname) - object.__setattr__(self, "loaded", typ) - return value if isinstance(value, typ) else NoMatch - - return NoMatch - - -class CoercedTo(Slotted, Pattern, Generic[T_co]): - """Force a value to have a particular Python type. - - If a Coercible subclass is passed, the `__coerce__` method will be used to - coerce the value. Otherwise, the type will be called with the value as the - only argument. - - Parameters - ---------- - type - The type to coerce to. - - """ - - __slots__ = ("type", "func") - type: T_co - - def __init__(self, type): - func = type.__coerce__ if issubclass(type, Coercible) else type - super().__init__(type=type, func=func) - - def describe(self, plural=False): - type = _describe_type(self.type, plural=False) - if plural: - return f"coercibles to {type}" - else: - return f"coercible to {type}" - - def match(self, value, context): - try: - value = self.func(value) - except (TypeError, CoercionError): - return NoMatch - - if isinstance(value, self.type): - return value - else: - return NoMatch - - def __call__(self, *args, **kwargs): - return Object(self.type, *args, **kwargs) - - -class GenericCoercedTo(Slotted, Pattern): - """Force a value to have a particular generic Python type. - - Parameters - ---------- - typ - The type to coerce to. Must be a generic type with bound typevars. - - Examples - -------- - >>> from typing import Generic, TypeVar - >>> - >>> T = TypeVar("T", covariant=True) - >>> - >>> class MyNumber(Coercible, Generic[T]): - ... __slots__ = ("value",) - ... - ... def __init__(self, value): - ... self.value = value - ... - ... def __eq__(self, other): - ... return type(self) is type(other) and self.value == other.value - ... - ... @classmethod - ... def __coerce__(cls, value, T=None): - ... if issubclass(T, int): - ... return cls(int(value)) - ... elif issubclass(T, float): - ... return cls(float(value)) - ... else: - ... raise CoercionError(f"Cannot coerce to {T}") - >>> p = GenericCoercedTo(MyNumber[int]) - >>> assert p.match(3.14, {}) == MyNumber(3) - >>> assert p.match("15", {}) == MyNumber(15) - >>> - >>> p = GenericCoercedTo(MyNumber[float]) - >>> assert p.match(3.14, {}) == MyNumber(3.14) - >>> assert p.match("15", {}) == MyNumber(15.0) - - """ - - __slots__ = ("origin", "params", "checker") - origin: type - params: FrozenDict[str, type] - checker: GenericInstanceOf - - def __init__(self, target): - origin = get_origin(target) - checker = GenericInstanceOf(target) - params = frozendict(get_type_params(target)) - super().__init__(origin=origin, params=params, checker=checker) - - def describe(self, plural=False): - if plural: - return f"coercibles to {self.checker.describe(plural=False)}" - else: - return f"coercible to {self.checker.describe(plural=False)}" - - def match(self, value, context): - try: - value = self.origin.__coerce__(value, **self.params) - except CoercionError: - return NoMatch - - if self.checker.match(value, context) is NoMatch: - return NoMatch - - return value - - -class Not(Slotted, Pattern): - """Pattern that matches a value that does not match a given pattern. - - Parameters - ---------- - pattern - The pattern which the value should not match. - - """ - - __slots__ = ("pattern",) - pattern: Pattern - - def __init__(self, inner): - super().__init__(pattern=pattern(inner)) - - def describe(self, plural=False): - if plural: - return f"anything except {self.pattern.describe(plural=True)}" - else: - return f"anything except {self.pattern.describe(plural=False)}" - - def match(self, value, context): - if self.pattern.match(value, context) is NoMatch: - return value - else: - return NoMatch - - -class AnyOf(Slotted, Pattern): - """Pattern that if any of the given patterns match. - - Parameters - ---------- - patterns - The patterns to match against. The first pattern that matches will be - returned. - - """ - - __slots__ = ("patterns",) - patterns: tuple[Pattern, ...] - - def __init__(self, *pats): - patterns = tuple(map(pattern, pats)) - super().__init__(patterns=patterns) - - def describe(self, plural=False): - *rest, last = self.patterns - rest = ", ".join(p.describe(plural=plural) for p in rest) - last = last.describe(plural=plural) - return f"{rest} or {last}" if rest else last - - def match(self, value, context): - for pattern in self.patterns: - result = pattern.match(value, context) - if result is not NoMatch: - return result - return NoMatch - - -class AllOf(Slotted, Pattern): - """Pattern that matches if all of the given patterns match. - - Parameters - ---------- - patterns - The patterns to match against. The value will be passed through each - pattern in order. The changes applied to the value propagate through the - patterns. - - """ - - __slots__ = ("patterns",) - patterns: tuple[Pattern, ...] - - def __init__(self, *pats): - patterns = tuple(map(pattern, pats)) - super().__init__(patterns=patterns) - - def describe(self, plural=False): - *rest, last = self.patterns - rest = ", ".join(p.describe(plural=plural) for p in rest) - last = last.describe(plural=plural) - return f"{rest} then {last}" if rest else last - - def match(self, value, context): - for pattern in self.patterns: - value = pattern.match(value, context) - if value is NoMatch: - return NoMatch - return value - - -class Length(Slotted, Pattern): - """Pattern that matches if the length of a value is within a given range. - - Parameters - ---------- - exactly - The exact length of the value. If specified, `at_least` and `at_most` - must be None. - at_least - The minimum length of the value. - at_most - The maximum length of the value. - - """ - - __slots__ = ("at_least", "at_most") - at_least: int - at_most: int - - def __init__( - self, - exactly: Optional[int] = None, - at_least: Optional[int] = None, - at_most: Optional[int] = None, - ): - if exactly is not None: - if at_least is not None or at_most is not None: - raise ValueError("Can't specify both exactly and at_least/at_most") - at_least = exactly - at_most = exactly - super().__init__(at_least=at_least, at_most=at_most) - - def describe(self, plural=False): - if self.at_least is not None and self.at_most is not None: - if self.at_least == self.at_most: - return f"with length exactly {self.at_least}" - else: - return f"with length between {self.at_least} and {self.at_most}" - elif self.at_least is not None: - return f"with length at least {self.at_least}" - elif self.at_most is not None: - return f"with length at most {self.at_most}" - else: - return "with any length" - - def match(self, value, context): - length = len(value) - if self.at_least is not None and length < self.at_least: - return NoMatch - if self.at_most is not None and length > self.at_most: - return NoMatch - return value - - -class Between(Slotted, Pattern): - """Match a value between two bounds. - - Parameters - ---------- - lower - The lower bound. - upper - The upper bound. - - """ - - __slots__ = ("lower", "upper") - lower: float - upper: float - - def __init__(self, lower: float = -math.inf, upper: float = math.inf): - super().__init__(lower=lower, upper=upper) - - def match(self, value, context): - if self.lower <= value <= self.upper: - return value - else: - return NoMatch - - -class Contains(Slotted, Pattern): - """Pattern that matches if a value contains a given value. - - Parameters - ---------- - needle - The item that the passed value should contain. - - """ - - __slots__ = ("needle",) - needle: AnyType - - def __init__(self, needle): - super().__init__(needle=needle) - - def describe(self, plural=False): - return f"containing {self.needle!r}" - - def match(self, value, context): - if self.needle in value: - return value - else: - return NoMatch - - -class IsIn(Slotted, Pattern): - """Pattern that matches if a value is in a given set. - - Parameters - ---------- - haystack - The set of values that the passed value should be in. - - """ - - __slots__ = ("haystack",) - haystack: frozenset - - def __init__(self, haystack): - super().__init__(haystack=frozenset(haystack)) - - def describe(self, plural=False): - return f"in {set(self.haystack)!r}" - - def match(self, value, context): - if value in self.haystack: - return value - else: - return NoMatch - - -class SequenceOf(Slotted, Pattern): - """Pattern that matches if all of the items in a sequence match a given pattern. - - Specialization of the more flexible GenericSequenceOf pattern which uses two - additional patterns to possibly coerce the sequence type and to match on - the length of the sequence. - - Parameters - ---------- - item - The pattern to match against each item in the sequence. - type - The type to coerce the sequence to. Defaults to tuple. - - """ - - __slots__ = ("item", "type") - item: Pattern - type: type - - def __init__(self, item, type=list): - super().__init__(item=pattern(item), type=type) - - def describe(self, plural=False): - typ = _describe_type(self.type, plural=plural) - item = self.item.describe(plural=True) - return f"{typ} of {item}" - - def match(self, values, context): - if not is_iterable(values): - return NoMatch - - if self.item == _any: - # optimization to avoid unnecessary iteration - result = values - else: - result = [] - for item in values: - item = self.item.match(item, context) - if item is NoMatch: - return NoMatch - result.append(item) - - return self.type(result) - - -class GenericSequenceOf(Slotted, Pattern): - """Pattern that matches if all of the items in a sequence match a given pattern. - - Parameters - ---------- - item - The pattern to match against each item in the sequence. - type - The type to coerce the sequence to. Defaults to list. - exactly - The exact length of the sequence. - at_least - The minimum length of the sequence. - at_most - The maximum length of the sequence. - - """ - - __slots__ = ("item", "type", "length") - item: Pattern - type: Pattern - length: Length - - def __init__( - self, - item: Pattern, - type: type = list, - exactly: Optional[int] = None, - at_least: Optional[int] = None, - at_most: Optional[int] = None, - ): - item = pattern(item) - type = CoercedTo(type) - length = Length(exactly=exactly, at_least=at_least, at_most=at_most) - super().__init__(item=item, type=type, length=length) - - def match(self, values, context): - if not is_iterable(values): - return NoMatch - - if self.item == _any: - # optimization to avoid unnecessary iteration - result = values - else: - result = [] - for value in values: - value = self.item.match(value, context) - if value is NoMatch: - return NoMatch - result.append(value) - - result = self.type.match(result, context) - if result is NoMatch: - return NoMatch - - return self.length.match(result, context) - - -class GenericMappingOf(Slotted, Pattern): - """Pattern that matches if all of the keys and values match the given patterns. - - Parameters - ---------- - key - The pattern to match the keys against. - value - The pattern to match the values against. - type - The type to coerce the mapping to. Defaults to dict. - - """ - - __slots__ = ("key", "value", "type") - key: Pattern - value: Pattern - type: Pattern - - def __init__(self, key: Pattern, value: Pattern, type: type = dict): - super().__init__(key=pattern(key), value=pattern(value), type=CoercedTo(type)) - - def match(self, value, context): - if not isinstance(value, Mapping): - return NoMatch - - result = {} - for k, v in value.items(): - if (k := self.key.match(k, context)) is NoMatch: - return NoMatch - if (v := self.value.match(v, context)) is NoMatch: - return NoMatch - result[k] = v - - result = self.type.match(result, context) - if result is NoMatch: - return NoMatch - - return result - - -MappingOf = GenericMappingOf - - -class Attrs(Slotted, Pattern): - __slots__ = ("fields",) - fields: FrozenDict[str, Pattern] - - def __init__(self, **fields): - fields = frozendict(toolz.valmap(pattern, fields)) - super().__init__(fields=fields) - - def match(self, value, context): - for attr, pattern in self.fields.items(): - if not hasattr(value, attr): - return NoMatch - - v = getattr(value, attr) - if match(pattern, v, context) is NoMatch: - return NoMatch - - return value - - -class Object(Slotted, Pattern): - """Pattern that matches if the object has the given attributes and they match the given patterns. - - The type must conform the structural pattern matching protocol, e.g. it must have a - __match_args__ attribute that is a tuple of the names of the attributes to match. - - Parameters - ---------- - type - The type of the object. - *args - The positional arguments to match against the attributes of the object. - **kwargs - The keyword arguments to match against the attributes of the object. - - """ - - __slots__ = ("type", "args", "kwargs") - type: Pattern - args: tuple[Pattern, ...] - kwargs: FrozenDict[str, Pattern] - - @classmethod - def __create__(cls, type, *args, **kwargs): - if not args and not kwargs: - return InstanceOf(type) - return super().__create__(type, *args, **kwargs) - - def __init__(self, typ, *args, **kwargs): - if isinstance(typ, type) and len(typ.__match_args__) < len(args): - raise ValueError( - "The type to match has fewer `__match_args__` than the number " - "of positional arguments in the pattern" - ) - typ = pattern(typ) - args = tuple(map(pattern, args)) - kwargs = frozendict(toolz.valmap(pattern, kwargs)) - super().__init__(type=typ, args=args, kwargs=kwargs) - - def match(self, value, context): - if self.type.match(value, context) is NoMatch: - return NoMatch - - # the pattern requirest more positional arguments than the object has - if len(value.__match_args__) < len(self.args): - return NoMatch - patterns = dict(zip(value.__match_args__, self.args)) - patterns.update(self.kwargs) - - fields = {} - changed = False - for name, pattern in patterns.items(): - try: - attr = getattr(value, name) - except AttributeError: - return NoMatch - - result = pattern.match(attr, context) - if result is NoMatch: - return NoMatch - elif result != attr: - changed = True - fields[name] = result - else: - fields[name] = attr - - if changed: - return type(value)(**fields) - else: - return value - - -class Node(Slotted, Pattern): - __slots__ = ("type", "each_arg") - type: Pattern - - def __init__(self, type, each_arg): - super().__init__(type=pattern(type), each_arg=pattern(each_arg)) - - def match(self, value, context): - if self.type.match(value, context) is NoMatch: - return NoMatch - - newargs = {} - changed = False - for name, arg in zip(value.__argnames__, value.__args__): - result = self.each_arg.match(arg, context) - if result is NoMatch: - newargs[name] = arg - else: - newargs[name] = result - changed = True - - if changed: - return value.__class__(**newargs) - else: - return value - - -class CallableWith(Slotted, Pattern): - __slots__ = ("args", "return_") - args: tuple - return_: AnyType - - def __init__(self, args, return_=_any): - super().__init__(args=tuple(args), return_=return_) - - def match(self, value, context): - from bigframes_vendored.ibis.common.annotations import EMPTY, annotated - - if not callable(value): - return NoMatch - - fn = annotated(self.args, self.return_, value) - - has_varargs = False - positional, required_positional = [], [] - for p in fn.__signature__.parameters.values(): - if p.kind in (Parameter.POSITIONAL_ONLY, Parameter.POSITIONAL_OR_KEYWORD): - positional.append(p) - if p.default is EMPTY: - required_positional.append(p) - elif p.kind is Parameter.KEYWORD_ONLY and p.default is EMPTY: - raise TypeError( - "Callable has mandatory keyword-only arguments which cannot be specified" - ) - elif p.kind is Parameter.VAR_POSITIONAL: - has_varargs = True - - if len(required_positional) > len(self.args): - # Callable has more positional arguments than expected") - return NoMatch - elif len(positional) < len(self.args) and not has_varargs: - # Callable has less positional arguments than expected") - return NoMatch - else: - return fn - - -class SomeOf(Slotted, Pattern): - __slots__ = ("pattern", "delimiter") - - @classmethod - def __create__(cls, *args, **kwargs): - if len(args) == 1: - return super().__create__(*args, **kwargs) - else: - return SomeChunksOf(*args, **kwargs) - - def __init__(self, item, **kwargs): - pattern = GenericSequenceOf(item, **kwargs) - delimiter = pattern.item - super().__init__(pattern=pattern, delimiter=delimiter) - - def match(self, values, context): - return self.pattern.match(values, context) - - -class SomeChunksOf(Slotted, Pattern): - """Pattern that unpacks a value into its elements. - - Designed to be used inside a `PatternList` pattern with the `*` syntax. - """ - - __slots__ = ("pattern", "delimiter") - - def __init__(self, *args, **kwargs): - pattern = GenericSequenceOf(PatternList(args), **kwargs) - delimiter = pattern.item.patterns[0] - super().__init__(pattern=pattern, delimiter=delimiter) - - def chunk(self, values, context): - chunk = [] - for item in values: - if self.delimiter.match(item, context) is NoMatch: - chunk.append(item) - else: - if chunk: # only yield if there are items in the chunk - yield chunk - chunk = [item] # start a new chunk with the delimiter - if chunk: - yield chunk - - def match(self, values, context): - chunks = self.chunk(values, context) - result = self.pattern.match(chunks, context) - if result is NoMatch: - return NoMatch - else: - return [el for lst in result for el in lst] - - -def _maybe_unwrap_capture(obj): - return obj.pattern if isinstance(obj, Capture) else obj - - -class PatternList(Slotted, Pattern): - """Pattern that matches if the respective items in a tuple match the given patterns. - - Parameters - ---------- - fields - The patterns to match the respective items in the tuple. - - """ - - __slots__ = ("patterns", "type") - patterns: tuple[Pattern, ...] - type: type - - @classmethod - def __create__(cls, patterns, type=list): - if patterns == (): - return EqualTo(patterns) - - patterns = tuple(map(pattern, patterns)) - for pat in patterns: - pat = _maybe_unwrap_capture(pat) - if isinstance(pat, (SomeOf, SomeChunksOf)): - return VariadicPatternList(patterns, type) - - return super().__create__(patterns, type) - - def __init__(self, patterns, type): - super().__init__(patterns=patterns, type=type) - - def describe(self, plural=False): - patterns = ", ".join(f.describe(plural=False) for f in self.patterns) - if plural: - return f"tuples of ({patterns})" - else: - return f"a tuple of ({patterns})" - - def match(self, values, context): - if not is_iterable(values): - return NoMatch - - if len(values) != len(self.patterns): - return NoMatch - - result = [] - for pattern, value in zip(self.patterns, values): - value = pattern.match(value, context) - if value is NoMatch: - return NoMatch - result.append(value) - - return self.type(result) - - -class VariadicPatternList(Slotted, Pattern): - __slots__ = ("patterns", "type") - patterns: tuple[Pattern, ...] - type: type - - def __init__(self, patterns, type=list): - patterns = tuple(map(pattern, patterns)) - super().__init__(patterns=patterns, type=type) - - def match(self, value, context): - if not self.patterns: - return NoMatch if value else [] - - it = RewindableIterator(value) - result = [] - - following_patterns = self.patterns[1:] + (Nothing(),) - for current, following in zip(self.patterns, following_patterns): - original = current - current = _maybe_unwrap_capture(current) - following = _maybe_unwrap_capture(following) - - if isinstance(current, (SomeOf, SomeChunksOf)): - if isinstance(following, (SomeOf, SomeChunksOf)): - following = following.delimiter - - matches = [] - while True: - it.checkpoint() - try: - item = next(it) - except StopIteration: - break - - res = following.match(item, context) - if res is NoMatch: - matches.append(item) - else: - it.rewind() - break - - res = original.match(matches, context) - if res is NoMatch: - return NoMatch - else: - result.extend(res) - else: - try: - item = next(it) - except StopIteration: - return NoMatch - - res = original.match(item, context) - if res is NoMatch: - return NoMatch - else: - result.append(res) - - return self.type(result) - - -def NoneOf(*args) -> Pattern: - """Match none of the passed patterns.""" - return Not(AnyOf(*args)) - - -def ListOf(pattern): - """Match a list of items matching the given pattern.""" - return SequenceOf(pattern, type=list) - - -def TupleOf(pattern): - """Match a variable-length tuple of items matching the given pattern.""" - return SequenceOf(pattern, type=tuple) - - -def DictOf(key_pattern, value_pattern): - """Match a dictionary with keys and values matching the given patterns.""" - return MappingOf(key_pattern, value_pattern, type=dict) - - -def FrozenDictOf(key_pattern, value_pattern): - """Match a frozendict with keys and values matching the given patterns.""" - return MappingOf(key_pattern, value_pattern, type=frozendict) - - -def pattern(obj: AnyType) -> Pattern: - """Create a pattern from various types. - - Not that if a Coercible type is passed as argument, the constructed pattern - won't attempt to coerce the value during matching. In order to allow type - coercions use `Pattern.from_typehint()` factory method. - - Parameters - ---------- - obj - The object to create a pattern from. Can be a pattern, a type, a callable, - a mapping, an iterable or a value. - - Examples - -------- - >>> assert pattern(Any()) == Any() - >>> assert pattern(int) == InstanceOf(int) - >>> - >>> @pattern - ... def as_int(x, context): - ... return int(x) - >>> - >>> assert as_int.match(1, {}) == 1 - - Returns - ------- - The constructed pattern. - - """ - if obj is Ellipsis: - return _any - elif isinstance(obj, Pattern): - return obj - elif isinstance(obj, (Deferred, Resolver)): - return Capture(obj) - elif isinstance(obj, Mapping): - return EqualTo(FrozenDict(obj)) - elif isinstance(obj, Sequence): - if isinstance(obj, (str, bytes)): - return EqualTo(obj) - else: - return PatternList(obj, type=type(obj)) - elif isinstance(obj, type): - return InstanceOf(obj) - elif get_origin(obj): - return Pattern.from_typehint(obj, allow_coercion=False) - elif callable(obj): - return Custom(obj) - else: - return EqualTo(obj) - - -def match( - pat: Pattern, value: AnyType, context: Optional[dict[str, AnyType]] = None -) -> Any: - """Match a value against a pattern. - - Parameters - ---------- - pat - The pattern to match against. - value - The value to match. - context - Arbitrary mapping of values to be used while matching. - - Returns - ------- - The matched value if the pattern matches, otherwise :obj:`NoMatch`. - - Examples - -------- - >>> assert match(Any(), 1) == 1 - >>> assert match(1, 1) == 1 - >>> assert match(1, 2) is NoMatch - >>> assert match(1, 1, context={"x": 1}) == 1 - >>> assert match(1, 2, context={"x": 1}) is NoMatch - >>> assert match([1, int], [1, 2]) == [1, 2] - >>> assert match([1, int, "a" @ InstanceOf(str)], [1, 2, "three"]) == [ - ... 1, - ... 2, - ... "three", - ... ] - - """ - if context is None: - context = {} - - pat = pattern(pat) - result = pat.match(value, context) - return NoMatch if result is NoMatch else result - - -IsTruish = Check(lambda x: bool(x)) -IsNumber = InstanceOf(numbers.Number) & ~InstanceOf(bool) -IsString = InstanceOf(str) - -As = CoercedTo -Eq = EqualTo -In = IsIn -If = Check -Some = SomeOf diff --git a/third_party/bigframes_vendored/ibis/common/selectors.py b/third_party/bigframes_vendored/ibis/common/selectors.py deleted file mode 100644 index 4bc47622549..00000000000 --- a/third_party/bigframes_vendored/ibis/common/selectors.py +++ /dev/null @@ -1,33 +0,0 @@ -# Contains code from https://github.com/ibis-project/ibis/blob/9.2.0/ibis/common/selectors.py - -from __future__ import annotations - -import abc -from typing import TYPE_CHECKING - -from bigframes_vendored.ibis.common.grounds import Concrete - -if TYPE_CHECKING: - from collections.abc import Sequence - - import bigframes_vendored.ibis.expr.types as ir - - -class Selector(Concrete): - """A column selector.""" - - @abc.abstractmethod - def expand(self, table: ir.Table) -> Sequence[ir.Value]: - """Expand `table` into value expressions that match the selector. - - Parameters - ---------- - table - An ibis table expression - - Returns - ------- - Sequence[Value] - A sequence of value expressions that match the selector - - """ diff --git a/third_party/bigframes_vendored/ibis/common/temporal.py b/third_party/bigframes_vendored/ibis/common/temporal.py deleted file mode 100644 index 68042ad51a9..00000000000 --- a/third_party/bigframes_vendored/ibis/common/temporal.py +++ /dev/null @@ -1,267 +0,0 @@ -# Contains code from https://github.com/ibis-project/ibis/blob/9.2.0/ibis/common/temporal.py - -from __future__ import annotations - -import datetime -import numbers -from decimal import Decimal -from enum import Enum, EnumMeta - -import dateutil.parser -import dateutil.tz -import pytz -from bigframes_vendored.ibis import util -from bigframes_vendored.ibis.common.bases import AbstractMeta -from bigframes_vendored.ibis.common.dispatch import lazy_singledispatch -from bigframes_vendored.ibis.common.patterns import Coercible, CoercionError -from public import public - - -class AbstractEnumMeta(EnumMeta, AbstractMeta): - pass - - -class Unit(Coercible, Enum, metaclass=AbstractEnumMeta): - @classmethod - def __coerce__(cls, value): - if isinstance(value, cls): - return value - else: - return cls.from_string(value) - - @classmethod - def from_string(cls, value): - # TODO(kszucs): perhaps this is not needed anymore - if isinstance(value, Unit): - value = value.value - elif not isinstance(value, str): - raise CoercionError(f"Unable to coerce {value} to {cls.__name__}") - - # first look for aliases - value = cls.aliases().get(value, value) - - # then look for the enum value (unit value) - try: - return cls(value) - except ValueError: - pass - - # then look for the enum name (unit name) - if value.endswith("s"): - value = value[:-1] - try: - return cls[value.upper()] - except KeyError: - raise CoercionError(f"Unable to coerce {value} to {cls.__name__}") - - @classmethod - def aliases(cls): - return {} - - @property - def singular(self) -> str: - return self.name.lower() - - @property - def plural(self) -> str: - return self.singular + "s" - - @property - def short(self) -> str: - return self.value - - -class TemporalUnit(Unit): - @classmethod - def aliases(cls): - return { - "d": "D", - "H": "h", - "HH24": "h", - "J": "D", - "MI": "m", - "q": "Q", - "SYYYY": "Y", - "w": "W", - "y": "Y", - "YY": "Y", - "YYY": "Y", - "YYYY": "Y", - } - - -@public -class DateUnit(TemporalUnit): - YEAR = "Y" - QUARTER = "Q" - MONTH = "M" - WEEK = "W" - DAY = "D" - - -@public -class TimeUnit(TemporalUnit): - HOUR = "h" - MINUTE = "m" - SECOND = "s" - MILLISECOND = "ms" - MICROSECOND = "us" - NANOSECOND = "ns" - - -@public -class TimestampUnit(TemporalUnit): - SECOND = "s" - MILLISECOND = "ms" - MICROSECOND = "us" - NANOSECOND = "ns" - - -@public -class IntervalUnit(TemporalUnit): - YEAR = "Y" - QUARTER = "Q" - MONTH = "M" - WEEK = "W" - DAY = "D" - HOUR = "h" - MINUTE = "m" - SECOND = "s" - MILLISECOND = "ms" - MICROSECOND = "us" - NANOSECOND = "ns" - - def is_date(self) -> bool: - return self.name in DateUnit.__members__ - - def is_time(self) -> bool: - return self.name in TimeUnit.__members__ - - -def normalize_timedelta( - value: datetime.timedelta | numbers.Real, unit: IntervalUnit -) -> datetime.timedelta: - """Normalize a timedelta value to the given unit. - - Parameters - ---------- - value - The value to normalize, either a timedelta or a number. - unit - The unit to normalize to. - - Returns - ------- - The normalized timedelta value. - - Examples - -------- - >>> from datetime import timedelta - >>> normalize_timedelta(1, IntervalUnit.SECOND) - 1 - >>> normalize_timedelta(1, IntervalUnit.DAY) - 1 - >>> normalize_timedelta(timedelta(days=14), IntervalUnit.WEEK) - 2 - >>> normalize_timedelta(timedelta(seconds=3), IntervalUnit.MILLISECOND) - 3000 - >>> normalize_timedelta(timedelta(seconds=3), IntervalUnit.MICROSECOND) - 3000000 - - """ - if isinstance(value, datetime.timedelta): - # datetime.timedelta only stores days, seconds, and microseconds internally - if value.days and not (value.seconds or value.microseconds): - value = util.convert_unit(value.days, "D", unit.short, floor=False) - else: - total_seconds = Decimal(str(value.total_seconds())) - value = util.convert_unit(total_seconds, "s", unit.short, floor=False) - else: - value = Decimal(value) - - # check that value is integral - if value % 1 != 0: - raise ValueError(f"Normalizing {value} to {unit} would lose precision") - - return int(value) - - -def normalize_timezone(tz): - if tz is None: - return None - elif isinstance(tz, str): - if tz == "UTC": - return dateutil.tz.tzutc() - else: - return dateutil.tz.gettz(tz) - elif isinstance(tz, (int, float)): - return datetime.timezone(datetime.timedelta(hours=tz)) - elif isinstance(tz, (dateutil.tz.tzoffset, pytz._FixedOffset)): - # this way we have a proper tzname() output, e.g. "UTC+01:00" - return datetime.timezone(tz.utcoffset(None)) - elif isinstance(tz, datetime.tzinfo): - return tz - else: - raise TypeError(f"Unable to normalize {type(tz)} to timezone") - - -@lazy_singledispatch -def normalize_datetime(value): - raise TypeError(f"Unable to normalize {type(value)} to timestamp") - - -@normalize_datetime.register(str) -def _from_str(value): - lower = value.lower() - if lower == "now": - return datetime.datetime.now() - elif lower == "today": - return datetime.datetime.today() - - try: - value = dateutil.parser.parse(value) - except dateutil.parser.ParserError: - raise TypeError(f"Unable to normalize {value} to timestamp") - return value.replace(tzinfo=normalize_timezone(value.tzinfo)) - - -@normalize_datetime.register(numbers.Number) -def _from_number(value): - return datetime.datetime.fromtimestamp(value, dateutil.tz.UTC) - - -@normalize_datetime.register(datetime.time) -def _from_time(value): - return datetime.datetime.combine(datetime.date.today(), value) - - -@normalize_datetime.register(datetime.date) -def _from_date(value): - return datetime.datetime(year=value.year, month=value.month, day=value.day) - - -@normalize_datetime.register(datetime.datetime) -def _from_datetime(value): - return value.replace(tzinfo=normalize_timezone(value.tzinfo)) - - -@normalize_datetime.register("pandas.Timestamp") -def _from_pandas_timestamp(value): - # TODO(kszucs): it would make sense to preserve nanoseconds precision by - # keeping the pandas.Timestamp object - return value.to_pydatetime() - - -@normalize_datetime.register("numpy.datetime64") -def _from_numpy_datetime64(value): - try: - import pandas as pd - except ImportError: - raise TypeError("Unable to convert np.datetime64 without pandas") - else: - return pd.Timestamp(value).to_pydatetime() - - -@normalize_datetime.register("pyarrow.Scalar") -def _from_pyarrow_scalar(value): - return value.as_py() diff --git a/third_party/bigframes_vendored/ibis/common/typing.py b/third_party/bigframes_vendored/ibis/common/typing.py deleted file mode 100644 index c0c8ff3928c..00000000000 --- a/third_party/bigframes_vendored/ibis/common/typing.py +++ /dev/null @@ -1,281 +0,0 @@ -# Contains code from https://github.com/ibis-project/ibis/blob/9.2.0/ibis/common/typing.py - -from __future__ import annotations - -import inspect -import re -import sys -from abc import abstractmethod -from itertools import zip_longest -from typing import TYPE_CHECKING, Any, Optional, TypeVar, Union, get_args, get_origin -from typing import get_type_hints as _get_type_hints - -from bigframes_vendored.ibis.common.bases import Abstract -from bigframes_vendored.ibis.common.caching import memoize - -if TYPE_CHECKING: - from typing_extensions import Self - -if sys.version_info >= (3, 10): - from types import UnionType - from typing import TypeAlias - - # Keep this alias in sync with unittest.case._ClassInfo - _ClassInfo: TypeAlias = type | UnionType | tuple["_ClassInfo", ...] -else: - from typing_extensions import TypeAlias - - UnionType = object() - _ClassInfo: TypeAlias = Union[type, tuple["_ClassInfo", ...]] - - -T = TypeVar("T") -U = TypeVar("U") - -Namespace = dict[str, Any] -VarTuple = tuple[T, ...] - - -@memoize -def get_type_hints( - obj: Any, - include_extras: bool = True, - include_properties: bool = False, -) -> dict[str, Any]: - """Get type hints for a callable or class. - - Extension of typing.get_type_hints that supports getting type hints for - class properties. - - Parameters - ---------- - obj - Callable or class to get type hints for. - include_extras - Whether to include extra type hints such as Annotated. - include_properties - Whether to include type hints for class properties. - - Returns - ------- - Mapping of parameter or attribute name to type hint. - - """ - try: - hints = _get_type_hints(obj, include_extras=include_extras) - except TypeError: - return {} - - if include_properties: - for name in dir(obj): - attr = getattr(obj, name) - if isinstance(attr, property): - annots = _get_type_hints(attr.fget, include_extras=include_extras) - if return_annot := annots.get("return"): - hints[name] = return_annot - - return hints - - -@memoize -def get_type_params(obj: Any) -> dict[str, type]: - """Get type parameters for a generic class. - - Parameters - ---------- - obj - Generic class to get type parameters for. - - Returns - ------- - Mapping of type parameter name to type. - - Examples - -------- - >>> from typing import Dict, List - >>> class MyList(List[T]): ... - >>> get_type_params(MyList[int]) - {'T': } - >>> class MyDict(Dict[T, U]): ... - >>> get_type_params(MyDict[int, str]) - {'T': , 'U': } - - """ - args = get_args(obj) - origin = get_origin(obj) or obj - bases = getattr(origin, "__orig_bases__", ()) - params = getattr(origin, "__parameters__", ()) - - result = {} - for base in bases: - result.update(get_type_params(base)) - - param_names = (p.__name__ for p in params) - result.update(zip(param_names, args)) - - return result - - -@memoize -def get_bound_typevars(obj: Any) -> dict[TypeVar, tuple[str, type]]: - """Get type variables bound to concrete types for a generic class. - - Parameters - ---------- - obj - Generic class to get type variables for. - - Returns - ------- - Mapping of type variable to attribute name and type. - - Examples - -------- - >>> from typing import Generic - >>> class MyStruct(Generic[T, U]): - ... a: T - ... b: U - >>> get_bound_typevars(MyStruct[int, str]) - {~T: ('a', ), ~U: ('b', )} - >>> - >>> class MyStruct(Generic[T, U]): - ... a: T - ... - ... @property - ... def myprop(self) -> U: ... - >>> get_bound_typevars(MyStruct[float, bytes]) - {~T: ('a', ), ~U: ('myprop', )} - - """ - origin = get_origin(obj) or obj - hints = get_type_hints(origin, include_properties=True) - params = get_type_params(obj) - - result = {} - for attr, typ in hints.items(): - if isinstance(typ, TypeVar): - result[typ] = (attr, params[typ.__name__]) - return result - - -def evaluate_annotations( - annots: dict[str, str], - module_name: str, - class_name: Optional[str] = None, - best_effort: bool = False, -) -> dict[str, Any]: - """Evaluate type annotations that are strings. - - Parameters - ---------- - annots - Type annotations to evaluate. - module_name - The name of the module that the annotations are defined in, hence - providing global scope. - class_name - The name of the class that the annotations are defined in, hence - providing Self type. - best_effort - Whether to ignore errors when evaluating type annotations. - - Returns - ------- - Actual type hints. - - Examples - -------- - >>> annots = {"a": "dict[str, float]", "b": "int"} - >>> evaluate_annotations(annots, __name__) - {'a': dict[str, float], 'b': } - - """ - module = sys.modules.get(module_name, None) - globalns = getattr(module, "__dict__", None) - if class_name is None: - localns = None - else: - localns = dict(Self=f"{module_name}.{class_name}") - - result = {} - for k, v in annots.items(): - if isinstance(v, str): - try: - v = eval(v, globalns, localns) # noqa: S307 - except NameError: - if not best_effort: - raise - result[k] = v - - return result - - -def format_typehint(typ: Any) -> str: - if isinstance(typ, type): - return typ.__name__ - elif isinstance(typ, TypeVar): - if typ.__bound__ is None: - return str(typ) - else: - return format_typehint(typ.__bound__) - else: - # remove the module name from the typehint, including generics - return re.sub(r"(\w+\.)+", "", str(typ)) - - -class DefaultTypeVars: - """Enable using default type variables in generic classes (PEP-0696).""" - - __slots__ = () - - def __class_getitem__(cls, params): - params = params if isinstance(params, tuple) else (params,) - pairs = zip_longest(params, cls.__parameters__) - params = tuple(p.__default__ if t is None else t for t, p in pairs) - return super().__class_getitem__(params) - - -class Sentinel(type): - """Create type-annotable unique objects.""" - - def __new__(cls, name, bases, namespace, **kwargs): - if bases: - raise TypeError("Sentinels cannot be subclassed") - return super().__new__(cls, name, bases, namespace, **kwargs) - - def __call__(self, *args: Any, **kwargs: Any) -> Any: - raise TypeError("Sentinels are not constructible") - - -class CoercionError(Exception): ... - - -class Coercible(Abstract): - """Protocol for defining coercible types. - - Coercible types define a special ``__coerce__`` method that accepts an object - with an instance of the type. Used in conjunction with the ``coerced_to`` - pattern to coerce arguments to a specific type. - """ - - @classmethod - @abstractmethod - def __coerce__(cls, value: Any, **kwargs: Any) -> Self: ... - - -def get_defining_frame(obj): - """Locate the outermost frame where `obj` is defined.""" - for frame_info in inspect.stack()[::-1]: - for var in frame_info.frame.f_locals.values(): - if obj is var: - return frame_info.frame - raise ValueError(f"No defining frame found for {obj}") - - -def get_defining_scope(obj, types=None): - """Get variables in the scope where `expr` is first defined.""" - frame = get_defining_frame(obj) - scope = {**frame.f_globals, **frame.f_locals} - if types is not None: - scope = {k: v for k, v in scope.items() if isinstance(v, types)} - return scope diff --git a/third_party/bigframes_vendored/ibis/config.py b/third_party/bigframes_vendored/ibis/config.py deleted file mode 100644 index 8c2b0b1c718..00000000000 --- a/third_party/bigframes_vendored/ibis/config.py +++ /dev/null @@ -1,192 +0,0 @@ -# Contains code from https://github.com/ibis-project/ibis/blob/9.2.0/ibis/config.py - -from __future__ import annotations - -import contextlib -from collections.abc import Callable # noqa: TCH003 -from typing import Annotated, Any, Optional - -import bigframes_vendored.ibis.common.exceptions as com -from bigframes_vendored.ibis.common.grounds import Annotable -from bigframes_vendored.ibis.common.patterns import Between -from public import public - -PosInt = Annotated[int, Between(lower=0)] - - -class Config(Annotable): - def get(self, key: str) -> Any: - value = self - for field in key.split("."): - value = getattr(value, field) - return value - - def set(self, key: str, value: Any) -> None: - *prefix, key = key.split(".") - conf = self - for field in prefix: - conf = getattr(conf, field) - setattr(conf, key, value) - - @contextlib.contextmanager - def _with_temporary(self, options): - try: - old = {} - for key, value in options.items(): - old[key] = self.get(key) - self.set(key, value) - yield - finally: - for key, value in old.items(): - self.set(key, value) - - def __call__(self, options): - return self._with_temporary(options) - - -class SQL(Config): - """SQL-related options. - - Attributes - ---------- - fuse_selects : bool - Whether to fuse consecutive select queries into a single query where - possible. - default_limit : int | None - Number of rows to be retrieved for a table expression without an - explicit limit. [](`None`) means no limit. - default_dialect : str - Dialect to use for printing SQL when the backend cannot be determined. - - """ - - fuse_selects: bool = True - default_limit: Optional[PosInt] = None - default_dialect: str = "duckdb" - - -class Interactive(Config): - """Options controlling the interactive repr. - - Attributes - ---------- - max_rows : int - Maximum rows to pretty print. - max_columns : int | None - The maximum number of columns to pretty print. If 0 (the default), the - number of columns will be inferred from output console size. Set to - `None` for no limit. - max_length : int - Maximum length for pretty-printed arrays and maps. - max_string : int - Maximum length for pretty-printed strings. - max_depth : int - Maximum depth for nested data types. - show_types : bool - Show the inferred type of value expressions in the interactive repr. - - """ - - max_rows: int = 10 - max_columns: Optional[int] = 0 - max_length: int = 2 - max_string: int = 80 - max_depth: int = 1 - show_types: bool = True - - -class Repr(Config): - """Expression printing options. - - Attributes - ---------- - depth : int - The maximum number of expression nodes to print when repring. - table_columns : int - The number of columns to show in leaf table expressions. - table_rows : int - The number of rows to show for in memory tables. - query_text_length : int - The maximum number of characters to show in the `query` field repr of - SQLQueryResult operations. - show_types : bool - Show the inferred type of value expressions in the repr. - show_variables : bool - Show the variables in the repr instead of generated names. This is - an advanced option and may not work in all scenarios. - interactive : bool - Options controlling the interactive repr. - """ - - depth: Optional[PosInt] = None - table_columns: Optional[PosInt] = None - table_rows: PosInt = 10 - query_text_length: PosInt = 80 - show_types: bool = False - show_variables: bool = False - interactive: Interactive = Interactive() - - -class Options(Config): - """Ibis configuration options. - - Attributes - ---------- - interactive : bool - Show the first few rows of computing an expression when in a repl. - repr : Repr - Options controlling expression printing. - verbose : bool - Run in verbose mode if [](`True`) - verbose_log: Callable[[str], None] | None - A callable to use when logging. - graphviz_repr : bool - Render expressions as GraphViz PNGs when running in a Jupyter notebook. - default_backend : Optional[ibis.backends.BaseBackend] - The default backend to use for execution, defaults to DuckDB if not - set. - sql: SQL - SQL-related options. - clickhouse : Config | None - Clickhouse specific options. - dask : Config | None - Dask specific options. - impala : Config | None - Impala specific options. - pandas : Config | None - Pandas specific options. - pyspark : Config | None - PySpark specific options. - - """ - - interactive: bool = False - repr: Repr = Repr() - verbose: bool = False - verbose_log: Optional[Callable] = None - graphviz_repr: bool = False - default_backend: Optional[Any] = None - sql: SQL = SQL() - clickhouse: Optional[Config] = None - dask: Optional[Config] = None - impala: Optional[Config] = None - pandas: Optional[Config] = None - pyspark: Optional[Config] = None - - -def _default_backend() -> Any: - if (backend := options.default_backend) is not None: - return backend - - raise com.IbisError("You must speficy an backend to use") - - -options = Options() - - -@public -def option_context(key, new_value): - return options({key: new_value}) - - -public(options=options) diff --git a/third_party/bigframes_vendored/ibis/expr/api.py b/third_party/bigframes_vendored/ibis/expr/api.py deleted file mode 100644 index 953ecb2979f..00000000000 --- a/third_party/bigframes_vendored/ibis/expr/api.py +++ /dev/null @@ -1,2473 +0,0 @@ -# Contains code from https://github.com/ibis-project/ibis/blob/9.2.0/ibis/expr/api.py - -"""Ibis expression API definitions.""" - -from __future__ import annotations - -import builtins -import datetime -import functools -import itertools -import numbers -import operator -from collections import Counter -from typing import TYPE_CHECKING, Any, overload - -import bigframes_vendored.ibis.expr.builders as bl -import bigframes_vendored.ibis.expr.datatypes as dt -import bigframes_vendored.ibis.expr.operations as ops -import bigframes_vendored.ibis.expr.schema as sch -import bigframes_vendored.ibis.expr.types as ir -from bigframes_vendored.ibis import selectors, util -from bigframes_vendored.ibis.backends import BaseBackend, connect -from bigframes_vendored.ibis.common.deferred import Deferred, _, deferrable -from bigframes_vendored.ibis.common.dispatch import lazy_singledispatch -from bigframes_vendored.ibis.common.exceptions import IbisInputError -from bigframes_vendored.ibis.common.grounds import Concrete -from bigframes_vendored.ibis.common.temporal import ( - normalize_datetime, - normalize_timezone, -) -from bigframes_vendored.ibis.expr.decompile import decompile -from bigframes_vendored.ibis.expr.schema import Schema -from bigframes_vendored.ibis.expr.sql import parse_sql, to_sql -from bigframes_vendored.ibis.expr.types import ( - Column, - DateValue, - Expr, - Scalar, - Table, - TimestampValue, - TimeValue, - Value, - array, - literal, - map, - null, - struct, -) -from bigframes_vendored.ibis.util import experimental - -if TYPE_CHECKING: - from collections.abc import Iterable, Sequence - from pathlib import Path - - import pandas as pd - import polars as pl - import pyarrow as pa - from bigframes_vendored.ibis.expr.schema import SchemaLike - -__all__ = ( - "Column", - "Deferred", - "Expr", - "Scalar", - "Schema", - "Table", - "Value", - "_", - "aggregate", - "and_", - "array", - "asc", - "case", - "coalesce", - "connect", - "cross_join", - "cume_dist", - "cumulative_window", - "date", - "decompile", - "deferred", - "dense_rank", - "desc", - "difference", - "dtype", - "e", - "following", - "geo_area", - "geo_as_binary", - "geo_as_ewkb", - "geo_as_ewkt", - "geo_as_text", - "geo_azimuth", - "geo_buffer", - "geo_centroid", - "geo_contains", - "geo_contains_properly", - "geo_covered_by", - "geo_covers", - "geo_crosses", - "geo_d_fully_within", - "geo_d_within", - "geo_difference", - "geo_disjoint", - "geo_distance", - "geo_end_point", - "geo_envelope", - "geo_equals", - "geo_geometry_n", - "geo_geometry_type", - "geo_intersection", - "geo_intersects", - "geo_is_valid", - "geo_length", - "geo_line_locate_point", - "geo_line_merge", - "geo_line_substring", - "geo_max_distance", - "geo_n_points", - "geo_n_rings", - "geo_ordering_equals", - "geo_overlaps", - "geo_perimeter", - "geo_point", - "geo_point_n", - "geo_simplify", - "geo_srid", - "geo_start_point", - "geo_touches", - "geo_transform", - "geo_unary_union", - "geo_union", - "geo_within", - "geo_x", - "geo_x_max", - "geo_x_min", - "geo_y", - "geo_y_max", - "geo_y_min", - "get_backend", - "greatest", - "ifelse", - "infer_dtype", - "infer_schema", - "intersect", - "interval", - "join", - "least", - "literal", - "map", - "memtable", - "negate", - "now", - "ntile", - "null", - "or_", - "param", - "parse_sql", - "percent_rank", - "pi", - "preceding", - "random", - "range", - "range_window", - "rank", - "read_csv", - "read_delta", - "read_json", - "read_parquet", - "row_number", - "rows_window", - "schema", - "selectors", - "set_backend", - "struct", - "table", - "time", - "timestamp", - "to_sql", - "today", - "trailing_range_window", - "trailing_window", - "union", - "uuid", - "watermark", - "where", - "window", -) - - -dtype = dt.dtype -infer_dtype = dt.infer -infer_schema = sch.infer -aggregate = ir.Table.aggregate -cross_join = ir.Table.cross_join -join = ir.Table.join -asof_join = ir.Table.asof_join - -e = ops.E().to_expr() -pi = ops.Pi().to_expr() - - -deferred = _ -"""Deferred expression object. - -Use this object to refer to a previous table expression in a chain of -expressions. - -::: {.callout-note} -## `_` may conflict with other idioms in Python - -See https://github.com/ibis-project/ibis/issues/4704 for details. - -Use `from ibis import deferred as ` to assign a different name to -the deferred object builder. - -Another option is to use `ibis._` directly. -::: - -Examples --------- ->>> from ibis import _ ->>> t = ibis.table(dict(key="int", value="float"), name="t") ->>> expr = t.group_by(key=_.key - 1).agg(total=_.value.sum()) ->>> expr.schema() -ibis.Schema { - key int64 - total float64 -} -""" - - -def param(type: dt.DataType) -> ir.Scalar: - """Create a deferred parameter of a given type. - - Parameters - ---------- - type - The type of the unbound parameter, e.g., double, int64, date, etc. - - Returns - ------- - Scalar - A scalar expression backend by a parameter - - Examples - -------- - >>> from datetime import date - >>> import ibis - >>> start = ibis.param("date") - >>> t = ibis.memtable( - ... { - ... "date_col": [date(2013, 1, 1), date(2013, 1, 2), date(2013, 1, 3)], - ... "value": [1.0, 2.0, 3.0], - ... }, - ... ) - >>> expr = t.filter(t.date_col >= start).value.sum() - >>> expr.execute(params={start: date(2013, 1, 1)}) - np.float64(6.0) - >>> expr.execute(params={start: date(2013, 1, 2)}) - np.float64(5.0) - >>> expr.execute(params={start: date(2013, 1, 3)}) - np.float64(3.0) - """ - return ops.ScalarParameter(type).to_expr() - - -def schema( - pairs: SchemaLike | None = None, - names: Iterable[str] | None = None, - types: Iterable[str | dt.DataType] | None = None, -) -> sch.Schema: - """Validate and return a [`Schema`](./schemas.qmd#ibis.expr.schema.Schema) object. - - Parameters - ---------- - pairs - List or dictionary of name, type pairs. Mutually exclusive with `names` - and `types` arguments. - names - Field names. Mutually exclusive with `pairs`. - types - Field types. Mutually exclusive with `pairs`. - - Returns - ------- - Schema - An ibis schema - - Examples - -------- - >>> from ibis import schema, Schema - >>> sc = schema([("foo", "string"), ("bar", "int64"), ("baz", "boolean")]) - >>> sc = schema(names=["foo", "bar", "baz"], types=["string", "int64", "boolean"]) - >>> sc = schema(dict(foo="string")) - >>> sc = schema(Schema(dict(foo="string"))) # no-op - - """ - if pairs is not None: - return sch.schema(pairs) - - # validate lengths of names and types are the same - if len(names) != len(types): - raise ValueError("Schema names and types must have the same length") - - return sch.Schema.from_tuples(zip(names, types)) - - -_table_names = (f"unbound_table_{i:d}" for i in itertools.count()) - - -def table( - schema: SchemaLike | None = None, - name: str | None = None, - catalog: str | None = None, - database: str | None = None, -) -> ir.Table: - """Create a table literal or an abstract table without data. - - Ibis uses the word database to refer to a collection of tables, and the word - catalog to refer to a collection of databases. You can use a combination of - `catalog` and `database` to specify a hierarchical location for table. - - Parameters - ---------- - schema - A schema for the table - name - Name for the table. One is generated if this value is `None`. - catalog - A collection of database. - database - A collection of tables. Required if catalog is not `None`. - - Returns - ------- - Table - A table expression - - Examples - -------- - Create a table with no data backing it - - >>> import ibis - >>> ibis.options.interactive = False - >>> t = ibis.table(schema=dict(a="int", b="string"), name="t") - >>> t - UnboundTable: t - a int64 - b string - - - Create a table with no data backing it in a specific location - - >>> import ibis - >>> ibis.options.interactive = False - >>> t = ibis.table(schema=dict(a="int"), name="t", catalog="cat", database="db") - >>> t - UnboundTable: cat.db.t - a int64 - """ - if name is None: - if isinstance(schema, type): - name = schema.__name__ - else: - name = next(_table_names) - if catalog is not None and database is None: - raise ValueError( - "A catalog-only namespace is invalid in Ibis, " - "please specify a database as well." - ) - - return ops.UnboundTable( - name=name, - schema=schema, - namespace=ops.Namespace(catalog=catalog, database=database), - ).to_expr() - - -def memtable( - data, - *, - columns: Iterable[str] | None = None, - schema: SchemaLike | None = None, - name: str | None = None, -) -> Table: - """Construct an ibis table expression from in-memory data. - - Parameters - ---------- - data - A table-like object (`pandas.DataFrame`, `pyarrow.Table`, or - `polars.DataFrame`), or any data accepted by the `pandas.DataFrame` - constructor (e.g. a list of dicts). - - Note that ibis objects (e.g. `MapValue`) may not be passed in as part - of `data` and will result in an error. - - Do not depend on the underlying storage type (e.g., pyarrow.Table), - it's subject to change across non-major releases. - columns - Optional [](`typing.Iterable`) of [](`str`) column names. If provided, - must match the number of columns in `data`. - schema - Optional [`Schema`](./schemas.qmd#ibis.expr.schema.Schema). - The functions use `data` to infer a schema if not passed. - name - Optional name of the table. - - Returns - ------- - Table - A table expression backed by in-memory data. - - Examples - -------- - >>> import ibis - >>> t = ibis.memtable([{"a": 1}, {"a": 2}]) - >>> t - InMemoryTable - data: - PandasDataFrameProxy: - a - 0 1 - 1 2 - - >>> t = ibis.memtable([{"a": 1, "b": "foo"}, {"a": 2, "b": "baz"}]) - >>> t - InMemoryTable - data: - PandasDataFrameProxy: - a b - 0 1 foo - 1 2 baz - - Create a table literal without column names embedded in the data and pass - `columns` - - >>> t = ibis.memtable([(1, "foo"), (2, "baz")], columns=["a", "b"]) - >>> t - InMemoryTable - data: - PandasDataFrameProxy: - a b - 0 1 foo - 1 2 baz - - Create a table literal without column names embedded in the data. Ibis - generates column names if none are provided. - - >>> t = ibis.memtable([(1, "foo"), (2, "baz")]) - >>> t - InMemoryTable - data: - PandasDataFrameProxy: - col0 col1 - 0 1 foo - 1 2 baz - - """ - if columns is not None and schema is not None: - raise NotImplementedError( - "passing `columns` and schema` is ambiguous; " - "pass one or the other but not both" - ) - return _memtable(data, name=name, schema=schema, columns=columns) - - -@lazy_singledispatch -def _memtable( - data: pd.DataFrame | Any, - *, - columns: Iterable[str] | None = None, - schema: SchemaLike | None = None, - name: str | None = None, -) -> Table: - import pandas as pd - from bigframes_vendored.ibis.formats.pandas import PandasDataFrameProxy - - if not isinstance(data, pd.DataFrame): - df = pd.DataFrame(data, columns=columns) - else: - df = data - - if df.columns.inferred_type != "string": - cols = df.columns - newcols = getattr( - schema, - "names", - (f"col{i:d}" for i in builtins.range(len(cols))), - ) - df = df.rename(columns=dict(zip(cols, newcols))) - - if columns is not None: - if (provided_col := len(columns)) != (exist_col := len(df.columns)): - raise ValueError( - "Provided `columns` must have an entry for each column in `data`.\n" - f"`columns` has {provided_col} elements but `data` has {exist_col} columns." - ) - - df = df.rename(columns=dict(zip(df.columns, columns))) - - # verify that the DataFrame has no duplicate column names because ibis - # doesn't allow that - cols = df.columns - dupes = [name for name, count in Counter(cols).items() if count > 1] - if dupes: - raise IbisInputError( - f"Duplicate column names found in DataFrame when constructing memtable: {dupes}" - ) - - op = ops.InMemoryTable( - name=name if name is not None else util.gen_name("pandas_memtable"), - schema=sch.infer(df) if schema is None else schema, - data=PandasDataFrameProxy(df), - ) - return op.to_expr() - - -@_memtable.register("pyarrow.Table") -def _memtable_from_pyarrow_table( - data: pa.Table, - *, - name: str | None = None, - schema: SchemaLike | None = None, - columns: Iterable[str] | None = None, -): - from bigframes_vendored.ibis.formats.pyarrow import PyArrowTableProxy - - if columns is not None: - assert schema is None, "if `columns` is not `None` then `schema` must be `None`" - schema = sch.Schema(dict(zip(columns, sch.infer(data).values()))) - return ops.InMemoryTable( - name=name if name is not None else util.gen_name("pyarrow_memtable"), - schema=sch.infer(data) if schema is None else schema, - data=PyArrowTableProxy(data), - ).to_expr() - - -@_memtable.register("polars.LazyFrame") -def _memtable_from_polars_lazyframe(data: pl.LazyFrame, **kwargs): - return _memtable_from_polars_dataframe(data.collect(), **kwargs) - - -@_memtable.register("polars.DataFrame") -def _memtable_from_polars_dataframe( - data: pl.DataFrame, - *, - name: str | None = None, - schema: SchemaLike | None = None, - columns: Iterable[str] | None = None, -): - from bigframes_vendored.ibis.formats.polars import PolarsDataFrameProxy - - if columns is not None: - assert schema is None, "if `columns` is not `None` then `schema` must be `None`" - schema = sch.Schema(dict(zip(columns, sch.infer(data).values()))) - return ops.InMemoryTable( - name=name if name is not None else util.gen_name("polars_memtable"), - schema=sch.infer(data) if schema is None else schema, - data=PolarsDataFrameProxy(data), - ).to_expr() - - -def _deferred_method_call(expr, method_name, **kwargs): - method = operator.methodcaller(method_name, **kwargs) - if isinstance(expr, str): - value = _[expr] - elif isinstance(expr, Deferred): - value = expr - elif callable(expr): - value = expr(_) - else: - value = expr - return method(value) - - -def desc(expr: ir.Column | str, nulls_first: bool = False) -> ir.Value: - """Create a descending sort key from `expr` or column name. - - Parameters - ---------- - expr - The expression or column name to use for sorting - nulls_first - Bool to indicate weather to put NULL values first or not. - - See Also - -------- - [`Value.desc()`](./expression-generic.qmd#ibis.expr.types.generic.Value.desc) - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.examples.penguins.fetch() - >>> t[["species", "year"]].order_by(ibis.desc("year")).head() - ┏━━━━━━━━━┳━━━━━━━┓ - ┃ species ┃ year ┃ - ┡━━━━━━━━━╇━━━━━━━┩ - │ string │ int64 │ - ├─────────┼───────┤ - │ Adelie │ 2009 │ - │ Adelie │ 2009 │ - │ Adelie │ 2009 │ - │ Adelie │ 2009 │ - │ Adelie │ 2009 │ - └─────────┴───────┘ - - Returns - ------- - ir.ValueExpr - An expression - - """ - return _deferred_method_call(expr, "desc", nulls_first=nulls_first) - - -def asc(expr: ir.Column | str, nulls_first: bool = False) -> ir.Value: - """Create a ascending sort key from `asc` or column name. - - Parameters - ---------- - expr - The expression or column name to use for sorting - nulls_first - Bool to indicate weather to put NULL values first or not. - - See Also - -------- - [`Value.asc()`](./expression-generic.qmd#ibis.expr.types.generic.Value.asc) - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.examples.penguins.fetch() - >>> t[["species", "year"]].order_by(ibis.asc("year")).head() - ┏━━━━━━━━━┳━━━━━━━┓ - ┃ species ┃ year ┃ - ┡━━━━━━━━━╇━━━━━━━┩ - │ string │ int64 │ - ├─────────┼───────┤ - │ Adelie │ 2007 │ - │ Adelie │ 2007 │ - │ Adelie │ 2007 │ - │ Adelie │ 2007 │ - │ Adelie │ 2007 │ - └─────────┴───────┘ - - Returns - ------- - ir.ValueExpr - An expression - - """ - return _deferred_method_call(expr, "asc", nulls_first=nulls_first) - - -def preceding(value) -> ir.Value: - return ops.WindowBoundary(value, preceding=True).to_expr() - - -def following(value) -> ir.Value: - return ops.WindowBoundary(value, preceding=False).to_expr() - - -def and_(*predicates: ir.BooleanValue) -> ir.BooleanValue: - """Combine multiple predicates using `&`. - - Parameters - ---------- - predicates - Boolean value expressions - - Returns - ------- - BooleanValue - A new predicate that evaluates to True if all composing predicates are - True. If no predicates were provided, returns True. - - """ - if not predicates: - return literal(True) - return functools.reduce(operator.and_, predicates) - - -def or_(*predicates: ir.BooleanValue) -> ir.BooleanValue: - """Combine multiple predicates using `|`. - - Parameters - ---------- - predicates - Boolean value expressions - - Returns - ------- - BooleanValue - A new predicate that evaluates to True if any composing predicates are - True. If no predicates were provided, returns False. - - """ - if not predicates: - return literal(False) - return functools.reduce(operator.or_, predicates) - - -def random() -> ir.FloatingScalar: - """Return a random floating point number in the range [0.0, 1.0). - - Similar to [](`random.random`) in the Python standard library. - - ::: {.callout-note} - ## Repeated use of `random` - - `ibis.random()` will generate a column of distinct random numbers even if - the same instance of `ibis.random()` is re-used. - - When Ibis compiles an expression to SQL, each place where `random` is used - will render as a separate call to the given backend's random number - generator. - - ```python - >>> from ibis.interactive import * - >>> t = ibis.memtable({"a": range(5)}) - >>> r_a = ibis.random() - >>> t.mutate(random_1=r_a, random_2=r_a) # doctest: +SKIP - ┏━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┓ - ┃ a ┃ random_1 ┃ random_2 ┃ - ┡━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━┩ - │ int64 │ float64 │ float64 │ - ├───────┼──────────┼──────────┤ - │ 0 │ 0.191130 │ 0.098715 │ - │ 1 │ 0.255262 │ 0.828454 │ - │ 2 │ 0.011804 │ 0.392275 │ - │ 3 │ 0.309941 │ 0.347300 │ - │ 4 │ 0.482783 │ 0.095562 │ - └───────┴──────────┴──────────┘ - ``` - ::: - - Returns - ------- - FloatingScalar - Random float value expression - - """ - return ops.RandomScalar().to_expr() - - -def uuid() -> ir.UUIDScalar: - """Return a random UUID version 4 value. - - Similar to [('uuid.uuid4`) in the Python standard library. - - Examples - -------- - >>> from ibis.interactive import * - >>> ibis.uuid() # doctest: +SKIP - UUID('e57e927b-aed2-483b-9140-dc32a26cad95') - - Returns - ------- - UUIDScalar - Random UUID value expression - """ - return ops.RandomUUID().to_expr() - - -@overload -def timestamp( - value_or_year: int | ir.IntegerValue | Deferred, - month: int | ir.IntegerValue | Deferred, - day: int | ir.IntegerValue | Deferred, - hour: int | ir.IntegerValue | Deferred, - minute: int | ir.IntegerValue | Deferred, - second: int | ir.IntegerValue | Deferred, - /, - timezone: str | None = None, -) -> TimestampValue: ... - - -@overload -def timestamp(value_or_year: Any, /, timezone: str | None = None) -> TimestampValue: ... - - -@deferrable -def timestamp( - value_or_year, - month=None, - day=None, - hour=None, - minute=None, - second=None, - /, - timezone=None, -): - """Construct a timestamp scalar or column. - - Parameters - ---------- - value_or_year - Either a string value or `datetime.datetime` to coerce to a timestamp, - or an integral value representing the timestamp year component. - month - The timestamp month component; required if `value_or_year` is a year. - day - The timestamp day component; required if `value_or_year` is a year. - hour - The timestamp hour component; required if `value_or_year` is a year. - minute - The timestamp minute component; required if `value_or_year` is a year. - second - The timestamp second component; required if `value_or_year` is a year. - timezone - The timezone name, or none for a timezone-naive timestamp. - - Returns - ------- - TimestampValue - A timestamp expression - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - - Create a timestamp scalar from a string - - >>> ibis.timestamp("2023-01-02T03:04:05") - ┌──────────────────────────────────┐ - │ Timestamp('2023-01-02 03:04:05') │ - └──────────────────────────────────┘ - - Create a timestamp scalar from components - - >>> ibis.timestamp(2023, 1, 2, 3, 4, 5) - ┌──────────────────────────────────┐ - │ Timestamp('2023-01-02 03:04:05') │ - └──────────────────────────────────┘ - - Create a timestamp column from components - - >>> t = ibis.memtable({"y": [2001, 2002], "m": [1, 4], "d": [2, 5], "h": [3, 6]}) - >>> ibis.timestamp(t.y, t.m, t.d, t.h, 0, 0).name("timestamp") - ┏━━━━━━━━━━━━━━━━━━━━━┓ - ┃ timestamp ┃ - ┡━━━━━━━━━━━━━━━━━━━━━┩ - │ timestamp │ - ├─────────────────────┤ - │ 2001-01-02 03:00:00 │ - │ 2002-04-05 06:00:00 │ - └─────────────────────┘ - - """ - args = (value_or_year, month, day, hour, minute, second) - is_ymdhms = any(a is not None for a in args[1:]) - - if is_ymdhms: - if timezone is not None: - raise NotImplementedError( - "Timezone currently not supported when creating a timestamp from components" - ) - return ops.TimestampFromYMDHMS(*args).to_expr() - elif isinstance(value_or_year, (numbers.Real, ir.IntegerValue)): - raise TypeError("Use ibis.literal(...).to_timestamp() instead") - elif isinstance(value_or_year, ir.Expr): - return value_or_year.cast(dt.Timestamp(timezone=timezone)) - else: - value = normalize_datetime(value_or_year) - tzinfo = normalize_timezone(timezone or value.tzinfo) - timezone = tzinfo.tzname(value) if tzinfo is not None else None - return literal(value, type=dt.Timestamp(timezone=timezone)) - - -@overload -def date( - value_or_year: int | ir.IntegerValue | Deferred, - month: int | ir.IntegerValue | Deferred, - day: int | ir.IntegerValue | Deferred, - /, -) -> DateValue: ... - - -@overload -def date(value_or_year: Any, /) -> DateValue: ... - - -@deferrable -def date(value_or_year, month=None, day=None, /): - """Construct a date scalar or column. - - Parameters - ---------- - value_or_year - Either a string value or `datetime.date` to coerce to a date, or - an integral value representing the date year component. - month - The date month component; required if `value_or_year` is a year. - day - The date day component; required if `value_or_year` is a year. - - Returns - ------- - DateValue - A date expression - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - - Create a date scalar from a string - - >>> ibis.date("2023-01-02") - ┌───────────────────────────┐ - │ datetime.date(2023, 1, 2) │ - └───────────────────────────┘ - - Create a date scalar from year, month, and day - - >>> ibis.date(2023, 1, 2) - ┌───────────────────────────┐ - │ datetime.date(2023, 1, 2) │ - └───────────────────────────┘ - - Create a date column from year, month, and day - - >>> t = ibis.memtable(dict(year=[2001, 2002], month=[1, 3], day=[2, 4])) - >>> ibis.date(t.year, t.month, t.day).name("my_date") - ┏━━━━━━━━━━━━┓ - ┃ my_date ┃ - ┡━━━━━━━━━━━━┩ - │ date │ - ├────────────┤ - │ 2001-01-02 │ - │ 2002-03-04 │ - └────────────┘ - - """ - if month is not None or day is not None: - return ops.DateFromYMD(value_or_year, month, day).to_expr() - elif isinstance(value_or_year, ir.Expr): - return value_or_year.cast(dt.date) - else: - return literal(value_or_year, type=dt.date) - - -@overload -def time( - value_or_hour: int | ir.IntegerValue | Deferred, - minute: int | ir.IntegerValue | Deferred, - second: int | ir.IntegerValue | Deferred, - /, -) -> TimeValue: ... - - -@overload -def time(value_or_hour: Any, /) -> TimeValue: ... - - -@deferrable -def time(value_or_hour, minute=None, second=None, /): - """Return a time literal if `value` is coercible to a time. - - Parameters - ---------- - value_or_hour - Either a string value or `datetime.time` to coerce to a time, or - an integral value representing the time hour component. - minute - The time minute component; required if `value_or_hour` is an hour. - second - The time second component; required if `value_or_hour` is an hour. - - Returns - ------- - TimeValue - A time expression - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - - Create a time scalar from a string - - >>> ibis.time("01:02:03") - ┌────────────────────────┐ - │ datetime.time(1, 2, 3) │ - └────────────────────────┘ - - Create a time scalar from hour, minute, and second - - >>> ibis.time(1, 2, 3) - ┌────────────────────────┐ - │ datetime.time(1, 2, 3) │ - └────────────────────────┘ - - Create a time column from hour, minute, and second - - >>> t = ibis.memtable({"h": [1, 4], "m": [2, 5], "s": [3, 6]}) - >>> ibis.time(t.h, t.m, t.s).name("time") - ┏━━━━━━━━━━┓ - ┃ time ┃ - ┡━━━━━━━━━━┩ - │ time │ - ├──────────┤ - │ 01:02:03 │ - │ 04:05:06 │ - └──────────┘ - - """ - if minute is not None or second is not None: - return ops.TimeFromHMS(value_or_hour, minute, second).to_expr() - elif isinstance(value_or_hour, ir.Expr): - return value_or_hour.cast(dt.time) - else: - return literal(value_or_hour, type=dt.time) - - -def interval( - value: int | datetime.timedelta | None = None, - unit: str = "s", - *, - years: int | None = None, - quarters: int | None = None, - months: int | None = None, - weeks: int | None = None, - days: int | None = None, - hours: int | None = None, - minutes: int | None = None, - seconds: int | None = None, - milliseconds: int | None = None, - microseconds: int | None = None, - nanoseconds: int | None = None, -) -> ir.IntervalScalar: - """Return an interval literal expression. - - Parameters - ---------- - value - Interval value. - unit - Unit of `value` - years - Number of years - quarters - Number of quarters - months - Number of months - weeks - Number of weeks - days - Number of days - hours - Number of hours - minutes - Number of minutes - seconds - Number of seconds - milliseconds - Number of milliseconds - microseconds - Number of microseconds - nanoseconds - Number of nanoseconds - - Returns - ------- - IntervalScalar - An interval expression - - """ - keyword_value_unit = [ - ("nanoseconds", nanoseconds, "ns"), - ("microseconds", microseconds, "us"), - ("milliseconds", milliseconds, "ms"), - ("seconds", seconds, "s"), - ("minutes", minutes, "m"), - ("hours", hours, "h"), - ("days", days, "D"), - ("weeks", weeks, "W"), - ("months", months, "M"), - ("quarters", quarters, "Q"), - ("years", years, "Y"), - ] - if value is not None: - for kw, v, _abbrev in keyword_value_unit: - if v is not None: - raise TypeError(f"Cannot provide both 'value' and '{kw}'") - if isinstance(value, datetime.timedelta): - components = [ - (value.microseconds, "us"), - (value.seconds, "s"), - (value.days, "D"), - ] - components = [(v, u) for v, u in components if v] - elif isinstance(value, int): - components = [(value, unit)] - else: - raise TypeError("value must be an integer or timedelta") - else: - components = [(v, u) for _, v, u in keyword_value_unit if v is not None] - - # If no components, default to 0 s - if not components: - components.append((0, "s")) - - intervals = [literal(v, type=dt.Interval(u)) for v, u in components] - return functools.reduce(operator.add, intervals) - - -def case() -> bl.SearchedCaseBuilder: - """Begin constructing a case expression. - - Use the `.when` method on the resulting object followed by `.end` to create a - complete case expression. - - Returns - ------- - SearchedCaseBuilder - A builder object to use for constructing a case expression. - - See Also - -------- - [`Value.case()`](./expression-generic.qmd#ibis.expr.types.generic.Value.case) - - Examples - -------- - >>> import ibis - >>> from ibis import _ - >>> ibis.options.interactive = True - >>> t = ibis.memtable( - ... { - ... "left": [1, 2, 3, 4], - ... "symbol": ["+", "-", "*", "/"], - ... "right": [5, 6, 7, 8], - ... } - ... ) - >>> t.mutate( - ... result=( - ... ibis.case() - ... .when(_.symbol == "+", _.left + _.right) - ... .when(_.symbol == "-", _.left - _.right) - ... .when(_.symbol == "*", _.left * _.right) - ... .when(_.symbol == "/", _.left / _.right) - ... .end() - ... ) - ... ) - ┏━━━━━━━┳━━━━━━━━┳━━━━━━━┳━━━━━━━━━┓ - ┃ left ┃ symbol ┃ right ┃ result ┃ - ┡━━━━━━━╇━━━━━━━━╇━━━━━━━╇━━━━━━━━━┩ - │ int64 │ string │ int64 │ float64 │ - ├───────┼────────┼───────┼─────────┤ - │ 1 │ + │ 5 │ 6.0 │ - │ 2 │ - │ 6 │ -4.0 │ - │ 3 │ * │ 7 │ 21.0 │ - │ 4 │ / │ 8 │ 0.5 │ - └───────┴────────┴───────┴─────────┘ - - """ - return bl.SearchedCaseBuilder() - - -def now() -> ir.TimestampScalar: - """Return an expression that will compute the current timestamp. - - Returns - ------- - TimestampScalar - An expression representing the current timestamp. - - """ - return ops.TimestampNow().to_expr() - - -def today() -> ir.DateScalar: - """Return an expression that will compute the current date. - - Returns - ------- - DateScalar - An expression representing the current date. - - """ - return ops.DateNow().to_expr() - - -def rank() -> ir.IntegerColumn: - """Compute position of first element within each equal-value group in sorted order. - - Equivalent to SQL's `RANK()` window function. - - Returns - ------- - Int64Column - The min rank - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable({"values": [1, 2, 1, 2, 3, 2]}) - >>> t.mutate(rank=ibis.rank().over(order_by=t.values)) - ┏━━━━━━━━┳━━━━━━━┓ - ┃ values ┃ rank ┃ - ┡━━━━━━━━╇━━━━━━━┩ - │ int64 │ int64 │ - ├────────┼───────┤ - │ 1 │ 0 │ - │ 1 │ 0 │ - │ 2 │ 2 │ - │ 2 │ 2 │ - │ 2 │ 2 │ - │ 3 │ 5 │ - └────────┴───────┘ - - """ - return ops.MinRank().to_expr() - - -def dense_rank() -> ir.IntegerColumn: - """Position of first element within each group of equal values. - - Values are returned in sorted order and duplicate values are ignored. - - Equivalent to SQL's `DENSE_RANK()`. - - Returns - ------- - IntegerColumn - The rank - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable({"values": [1, 2, 1, 2, 3, 2]}) - >>> t.mutate(rank=ibis.dense_rank().over(order_by=t.values)) - ┏━━━━━━━━┳━━━━━━━┓ - ┃ values ┃ rank ┃ - ┡━━━━━━━━╇━━━━━━━┩ - │ int64 │ int64 │ - ├────────┼───────┤ - │ 1 │ 0 │ - │ 1 │ 0 │ - │ 2 │ 1 │ - │ 2 │ 1 │ - │ 2 │ 1 │ - │ 3 │ 2 │ - └────────┴───────┘ - - """ - return ops.DenseRank().to_expr() - - -def percent_rank() -> ir.FloatingColumn: - """Return the relative rank of the values in the column. - - Returns - ------- - FloatingColumn - The percent rank - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable({"values": [1, 2, 1, 2, 3, 2]}) - >>> t.mutate(pct_rank=ibis.percent_rank().over(order_by=t.values)) - ┏━━━━━━━━┳━━━━━━━━━━┓ - ┃ values ┃ pct_rank ┃ - ┡━━━━━━━━╇━━━━━━━━━━┩ - │ int64 │ float64 │ - ├────────┼──────────┤ - │ 1 │ 0.0 │ - │ 1 │ 0.0 │ - │ 2 │ 0.4 │ - │ 2 │ 0.4 │ - │ 2 │ 0.4 │ - │ 3 │ 1.0 │ - └────────┴──────────┘ - - """ - return ops.PercentRank().to_expr() - - -def cume_dist() -> ir.FloatingColumn: - """Return the cumulative distribution over a window. - - Returns - ------- - FloatingColumn - The cumulative distribution - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable({"values": [1, 2, 1, 2, 3, 2]}) - >>> t.mutate(dist=ibis.cume_dist().over(order_by=t.values)) - ┏━━━━━━━━┳━━━━━━━━━━┓ - ┃ values ┃ dist ┃ - ┡━━━━━━━━╇━━━━━━━━━━┩ - │ int64 │ float64 │ - ├────────┼──────────┤ - │ 1 │ 0.333333 │ - │ 1 │ 0.333333 │ - │ 2 │ 0.833333 │ - │ 2 │ 0.833333 │ - │ 2 │ 0.833333 │ - │ 3 │ 1.000000 │ - └────────┴──────────┘ - - """ - return ops.CumeDist().to_expr() - - -def ntile(buckets: int | ir.IntegerValue) -> ir.IntegerColumn: - """Return the integer number of a partitioning of the column values. - - Parameters - ---------- - buckets - Number of buckets to partition into - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable({"values": [1, 2, 1, 2, 3, 2]}) - >>> t.mutate(ntile=ibis.ntile(2).over(order_by=t.values)) - ┏━━━━━━━━┳━━━━━━━┓ - ┃ values ┃ ntile ┃ - ┡━━━━━━━━╇━━━━━━━┩ - │ int64 │ int64 │ - ├────────┼───────┤ - │ 1 │ 0 │ - │ 1 │ 0 │ - │ 2 │ 0 │ - │ 2 │ 1 │ - │ 2 │ 1 │ - │ 3 │ 1 │ - └────────┴───────┘ - - """ - return ops.NTile(buckets).to_expr() - - -def row_number() -> ir.IntegerColumn: - """Return an analytic function expression for the current row number. - - ::: {.callout-note} - `row_number` is normalized across backends to start at 0 - ::: - - Returns - ------- - IntegerColumn - A column expression enumerating rows - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable({"values": [1, 2, 1, 2, 3, 2]}) - >>> t.mutate(rownum=ibis.row_number()) - ┏━━━━━━━━┳━━━━━━━━┓ - ┃ values ┃ rownum ┃ - ┡━━━━━━━━╇━━━━━━━━┩ - │ int64 │ int64 │ - ├────────┼────────┤ - │ 1 │ 0 │ - │ 2 │ 1 │ - │ 1 │ 2 │ - │ 2 │ 3 │ - │ 3 │ 4 │ - │ 2 │ 5 │ - └────────┴────────┘ - - """ - return ops.RowNumber().to_expr() - - -def read_csv( - sources: str | Path | Sequence[str | Path], - table_name: str | None = None, - **kwargs: Any, -) -> ir.Table: - """Lazily load a CSV or set of CSVs. - - This function delegates to the `read_csv` method on the current default - backend (DuckDB or `ibis.config.default_backend`). - - Parameters - ---------- - sources - A filesystem path or URL or list of same. Supports CSV and TSV files. - table_name - A name to refer to the table. If not provided, a name will be generated. - kwargs - Backend-specific keyword arguments for the file type. For the DuckDB - backend used by default, please refer to: - - * CSV/TSV: https://duckdb.org/docs/data/csv/overview.html#parameters. - - Returns - ------- - ir.Table - Table expression representing a file - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> lines = '''a,b - ... 1,d - ... 2, - ... ,f - ... ''' - >>> with open("/tmp/lines.csv", mode="w") as f: - ... nbytes = f.write(lines) # nbytes is unused - >>> t = ibis.read_csv("/tmp/lines.csv") - >>> t - ┏━━━━━━━┳━━━━━━━━┓ - ┃ a ┃ b ┃ - ┡━━━━━━━╇━━━━━━━━┩ - │ int64 │ string │ - ├───────┼────────┤ - │ 1 │ d │ - │ 2 │ NULL │ - │ NULL │ f │ - └───────┴────────┘ - - """ - from bigframes_vendored.ibis.config import _default_backend - - con = _default_backend() - return con.read_csv(sources, table_name=table_name, **kwargs) - - -@experimental -def read_json( - sources: str | Path | Sequence[str | Path], - table_name: str | None = None, - **kwargs: Any, -) -> ir.Table: - """Lazily load newline-delimited JSON data. - - This function delegates to the `read_json` method on the current default - backend (DuckDB or `ibis.config.default_backend`). - - Parameters - ---------- - sources - A filesystem path or URL or list of same. - table_name - A name to refer to the table. If not provided, a name will be generated. - kwargs - Backend-specific keyword arguments for the file type. See - https://duckdb.org/docs/extensions/json.html for details. - - Returns - ------- - ir.Table - Table expression representing a file - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> lines = ''' - ... {"a": 1, "b": "d"} - ... {"a": 2, "b": null} - ... {"a": null, "b": "f"} - ... ''' - >>> with open("/tmp/lines.json", mode="w") as f: - ... nbytes = f.write(lines) # nbytes is unused - >>> t = ibis.read_json("/tmp/lines.json") - >>> t - ┏━━━━━━━┳━━━━━━━━┓ - ┃ a ┃ b ┃ - ┡━━━━━━━╇━━━━━━━━┩ - │ int64 │ string │ - ├───────┼────────┤ - │ 1 │ d │ - │ 2 │ NULL │ - │ NULL │ f │ - └───────┴────────┘ - - """ - from bigframes_vendored.ibis.config import _default_backend - - con = _default_backend() - return con.read_json(sources, table_name=table_name, **kwargs) - - -def read_parquet( - sources: str | Path | Sequence[str | Path], - table_name: str | None = None, - **kwargs: Any, -) -> ir.Table: - """Lazily load a parquet file or set of parquet files. - - This function delegates to the `read_parquet` method on the current default - backend (DuckDB or `ibis.config.default_backend`). - - Parameters - ---------- - sources - A filesystem path or URL or list of same. - table_name - A name to refer to the table. If not provided, a name will be generated. - kwargs - Backend-specific keyword arguments for the file type. For the DuckDB - backend used by default, please refer to: - - * Parquet: https://duckdb.org/docs/data/parquet - - Returns - ------- - ir.Table - Table expression representing a file - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> df = pd.DataFrame({"a": [1, 2, 3], "b": list("ghi")}) - >>> df - a b - 0 1 g - 1 2 h - 2 3 i - >>> df.to_parquet("/tmp/data.parquet") - >>> t = ibis.read_parquet("/tmp/data.parquet") - >>> t - ┏━━━━━━━┳━━━━━━━━┓ - ┃ a ┃ b ┃ - ┡━━━━━━━╇━━━━━━━━┩ - │ int64 │ string │ - ├───────┼────────┤ - │ 1 │ g │ - │ 2 │ h │ - │ 3 │ i │ - └───────┴────────┘ - - """ - from bigframes_vendored.ibis.config import _default_backend - - con = _default_backend() - return con.read_parquet(sources, table_name=table_name, **kwargs) - - -def read_delta( - source: str | Path, table_name: str | None = None, **kwargs: Any -) -> ir.Table: - """Lazily load a Delta Lake table. - - Parameters - ---------- - source - A filesystem path or URL. - table_name - A name to refer to the table. If not provided, a name will be generated. - kwargs - Backend-specific keyword arguments for the file type. - - Returns - ------- - ir.Table - Table expression representing a file - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> df = pd.DataFrame({"a": [1, 2, 3], "b": list("ghi")}) - >>> df - a b - 0 1 g - 1 2 h - 2 3 i - >>> import deltalake as dl - >>> dl.write_deltalake("/tmp/data.delta", df, mode="overwrite") - >>> t = ibis.read_delta("/tmp/data.delta") - >>> t - ┏━━━━━━━┳━━━━━━━━┓ - ┃ a ┃ b ┃ - ┡━━━━━━━╇━━━━━━━━┩ - │ int64 │ string │ - ├───────┼────────┤ - │ 1 │ g │ - │ 2 │ h │ - │ 3 │ i │ - └───────┴────────┘ - - """ - from bigframes_vendored.ibis.config import _default_backend - - con = _default_backend() - return con.read_delta(source, table_name=table_name, **kwargs) - - -def set_backend(backend: str | BaseBackend) -> None: - """Set the default Ibis backend. - - Parameters - ---------- - backend - May be a backend name or URL, or an existing backend instance. - - Examples - -------- - You can pass the backend as a name: - - >>> import ibis - >>> ibis.set_backend("polars") - - Or as a URI - - >>> ibis.set_backend( - ... "postgres://user:password@hostname:5432" - ... ) # quartodoc: +SKIP # doctest: +SKIP - - Or as an existing backend instance - - >>> ibis.set_backend(ibis.duckdb.connect()) - - """ - import bigframes_vendored.ibis - - if isinstance(backend, str) and backend.isidentifier(): - try: - backend_type = getattr(bigframes_vendored.ibis, backend) - except AttributeError: - pass - else: - backend = backend_type.connect() - if isinstance(backend, str): - backend = bigframes_vendored.ibis.connect(backend) - - bigframes_vendored.ibis.options.default_backend = backend - - -def get_backend(expr: Expr | None = None) -> BaseBackend: - """Get the current Ibis backend to use for a given expression. - - expr - An expression to get the backend from. If not passed, the default - backend is returned. - - Returns - ------- - BaseBackend - The Ibis backend. - - """ - if expr is None: - from bigframes_vendored.ibis.config import _default_backend - - return _default_backend() - return expr._find_backend(use_default=True) - - -def window( - preceding=None, - following=None, - order_by=None, - group_by=None, - *, - rows=None, - range=None, - between=None, -): - """Create a window clause for use with window functions. - - The `ROWS` window clause includes peer rows based on differences in row - **number** whereas `RANGE` includes rows based on the differences in row - **value** of a single `order_by` expression. - - All window frame bounds are inclusive. - - Parameters - ---------- - preceding - Number of preceding rows in the window - following - Number of following rows in the window - group_by - Grouping key - order_by - Ordering key - rows - Whether to use the `ROWS` window clause - range - Whether to use the `RANGE` window clause - between - Automatically infer the window kind based on the boundaries - - Returns - ------- - Window - A window frame - - """ - has_rows = rows is not None - has_range = range is not None - has_between = between is not None - has_preceding_following = preceding is not None or following is not None - if has_rows + has_range + has_between + has_preceding_following > 1: - raise IbisInputError( - "Must only specify either `rows`, `range`, `between` or `preceding`/`following`" - ) - - builder = bl.LegacyWindowBuilder().group_by(group_by).order_by(order_by) - if has_rows: - return builder.rows(*rows) - elif has_range: - return builder.range(*range) - elif has_between: - return builder.between(*between) - elif has_preceding_following: - return builder.preceding_following(preceding, following) - else: - return builder - - -def rows_window(preceding=None, following=None, group_by=None, order_by=None): - """Create a rows-based window clause for use with window functions. - - This ROWS window clause aggregates rows based upon differences in row - number. - - All window frames / ranges are inclusive. - - Parameters - ---------- - preceding - Number of preceding rows in the window - following - Number of following rows in the window - group_by - Grouping key - order_by - Ordering key - - Returns - ------- - Window - A window frame - - """ - return ( - bl.LegacyWindowBuilder() - .group_by(group_by) - .order_by(order_by) - .preceding_following(preceding, following, how="rows") - ) - - -def range_window(preceding=None, following=None, group_by=None, order_by=None): - """Create a range-based window clause for use with window functions. - - This RANGE window clause aggregates rows based upon differences in the - value of the order-by expression. - - All window frames / ranges are inclusive. - - Parameters - ---------- - preceding - Number of preceding rows in the window - following - Number of following rows in the window - group_by - Grouping key - order_by - Ordering key - - Returns - ------- - Window - A window frame - - """ - return ( - bl.LegacyWindowBuilder() - .group_by(group_by) - .order_by(order_by) - .preceding_following(preceding, following, how="range") - ) - - -def cumulative_window(group_by=None, order_by=None): - """Create a cumulative window for use with window functions. - - All window frames / ranges are inclusive. - - Parameters - ---------- - group_by - Grouping key - order_by - Ordering key - - Returns - ------- - Window - A window frame - - """ - return window(rows=(None, 0), group_by=group_by, order_by=order_by) - - -def trailing_window(preceding, group_by=None, order_by=None): - """Create a trailing window for use with window functions. - - Parameters - ---------- - preceding - The number of preceding rows - group_by - Grouping key - order_by - Ordering key - - Returns - ------- - Window - A window frame - - """ - return window( - preceding=preceding, following=0, group_by=group_by, order_by=order_by - ) - - -def trailing_rows_window(preceding, group_by=None, order_by=None): - """Create a trailing window for use with aggregate window functions. - - Parameters - ---------- - preceding - The number of preceding rows - group_by - Grouping key - order_by - Ordering key - - Returns - ------- - Window - A window frame - - """ - return rows_window( - preceding=preceding, following=0, group_by=group_by, order_by=order_by - ) - - -def trailing_range_window(preceding, order_by, group_by=None): - """Create a trailing range window for use with window functions. - - Parameters - ---------- - preceding - A value expression - order_by - Ordering key - group_by - Grouping key - - Returns - ------- - Window - A window frame - - """ - return range_window( - preceding=preceding, following=0, group_by=group_by, order_by=order_by - ) - - -def union(table: ir.Table, *rest: ir.Table, distinct: bool = False) -> ir.Table: - """Compute the set union of multiple table expressions. - - The input tables must have identical schemas. - - Parameters - ---------- - table - A table expression - *rest - Additional table expressions - distinct - Only return distinct rows - - Returns - ------- - Table - A new table containing the union of all input tables. - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t1 = ibis.memtable({"a": [1, 2]}) - >>> t1 - ┏━━━━━━━┓ - ┃ a ┃ - ┡━━━━━━━┩ - │ int64 │ - ├───────┤ - │ 1 │ - │ 2 │ - └───────┘ - >>> t2 = ibis.memtable({"a": [2, 3]}) - >>> t2 - ┏━━━━━━━┓ - ┃ a ┃ - ┡━━━━━━━┩ - │ int64 │ - ├───────┤ - │ 2 │ - │ 3 │ - └───────┘ - >>> ibis.union(t1, t2) # union all by default - ┏━━━━━━━┓ - ┃ a ┃ - ┡━━━━━━━┩ - │ int64 │ - ├───────┤ - │ 1 │ - │ 2 │ - │ 2 │ - │ 3 │ - └───────┘ - >>> ibis.union(t1, t2, distinct=True).order_by("a") - ┏━━━━━━━┓ - ┃ a ┃ - ┡━━━━━━━┩ - │ int64 │ - ├───────┤ - │ 1 │ - │ 2 │ - │ 3 │ - └───────┘ - - """ - return table.union(*rest, distinct=distinct) if rest else table - - -def intersect(table: ir.Table, *rest: ir.Table, distinct: bool = True) -> ir.Table: - """Compute the set intersection of multiple table expressions. - - The input tables must have identical schemas. - - Parameters - ---------- - table - A table expression - *rest - Additional table expressions - distinct - Only return distinct rows - - Returns - ------- - Table - A new table containing the intersection of all input tables. - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t1 = ibis.memtable({"a": [1, 2]}) - >>> t1 - ┏━━━━━━━┓ - ┃ a ┃ - ┡━━━━━━━┩ - │ int64 │ - ├───────┤ - │ 1 │ - │ 2 │ - └───────┘ - >>> t2 = ibis.memtable({"a": [2, 3]}) - >>> t2 - ┏━━━━━━━┓ - ┃ a ┃ - ┡━━━━━━━┩ - │ int64 │ - ├───────┤ - │ 2 │ - │ 3 │ - └───────┘ - >>> ibis.intersect(t1, t2) - ┏━━━━━━━┓ - ┃ a ┃ - ┡━━━━━━━┩ - │ int64 │ - ├───────┤ - │ 2 │ - └───────┘ - - """ - return table.intersect(*rest, distinct=distinct) if rest else table - - -def difference(table: ir.Table, *rest: ir.Table, distinct: bool = True) -> ir.Table: - """Compute the set difference of multiple table expressions. - - The input tables must have identical schemas. - - Parameters - ---------- - table - A table expression - *rest - Additional table expressions - distinct - Only diff distinct rows not occurring in the calling table - - Returns - ------- - Table - The rows present in `self` that are not present in `tables`. - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t1 = ibis.memtable({"a": [1, 2]}) - >>> t1 - ┏━━━━━━━┓ - ┃ a ┃ - ┡━━━━━━━┩ - │ int64 │ - ├───────┤ - │ 1 │ - │ 2 │ - └───────┘ - >>> t2 = ibis.memtable({"a": [2, 3]}) - >>> t2 - ┏━━━━━━━┓ - ┃ a ┃ - ┡━━━━━━━┩ - │ int64 │ - ├───────┤ - │ 2 │ - │ 3 │ - └───────┘ - >>> ibis.difference(t1, t2) - ┏━━━━━━━┓ - ┃ a ┃ - ┡━━━━━━━┩ - │ int64 │ - ├───────┤ - │ 1 │ - └───────┘ - - """ - return table.difference(*rest, distinct=distinct) if rest else table - - -class Watermark(Concrete): - time_col: str - allowed_delay: ir.IntervalScalar - - -def watermark(time_col: str, allowed_delay: ir.IntervalScalar) -> Watermark: - """Return a watermark object. - - Parameters - ---------- - time_col - The timestamp column that will be used to generate watermarks in event time processing. - allowed_delay - Length of time that events are allowed to be late. - - Returns - ------- - Watermark - A watermark object. - - """ - return Watermark(time_col=time_col, allowed_delay=allowed_delay) - - -@functools.singledispatch -def range(start, stop, step) -> ir.ArrayValue: - """Generate a range of values. - - Integer ranges are supported, as well as timestamp ranges. - - ::: {.callout-note} - `start` is inclusive and `stop` is exclusive, just like Python's builtin - [](`range`). - - When `step` equals 0, however, this function will return an empty array. - - Python's `range` will raise an exception when `step` is zero. - ::: - - Parameters - ---------- - start - Lower bound of the range, inclusive. - stop - Upper bound of the range, exclusive. - step - Step value. Optional, defaults to 1. - - Returns - ------- - ArrayValue - An array of values - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - - Range using only a stop argument - - >>> ibis.range(5) - ┌────────────────┐ - │ [0, 1, ... +3] │ - └────────────────┘ - - Simple range using start and stop - - >>> ibis.range(1, 5) - ┌────────────────┐ - │ [1, 2, ... +2] │ - └────────────────┘ - - Generate an empty range - - >>> ibis.range(0) - ┌────┐ - │ [] │ - └────┘ - - Negative step values are supported - - >>> ibis.range(10, 4, -2) - ┌─────────────────┐ - │ [10, 8, ... +1] │ - └─────────────────┘ - - `ibis.range` behaves the same as Python's range ... - - >>> ibis.range(0, 7, -1) - ┌────┐ - │ [] │ - └────┘ - - ... except when the step is zero, in which case `ibis.range` returns an - empty array - - >>> ibis.range(0, 5, 0) - ┌────┐ - │ [] │ - └────┘ - - Because the resulting expression is array, you can unnest the values - - >>> ibis.range(5).unnest().name("numbers") - ┏━━━━━━━━━┓ - ┃ numbers ┃ - ┡━━━━━━━━━┩ - │ int8 │ - ├─────────┤ - │ 0 │ - │ 1 │ - │ 2 │ - │ 3 │ - │ 4 │ - └─────────┘ - - Timestamp ranges are also supported - - >>> expr = ibis.range("2002-01-01", "2002-02-01", ibis.interval(days=2)).name("ts") - >>> expr - ┌──────────────────────────────────────────┐ - │ [ │ - │ datetime.datetime(2002, 1, 1, 0, 0), │ - │ datetime.datetime(2002, 1, 3, 0, 0), │ - │ ... +14 │ - │ ] │ - └──────────────────────────────────────────┘ - >>> expr.unnest() - ┏━━━━━━━━━━━━━━━━━━━━━┓ - ┃ ts ┃ - ┡━━━━━━━━━━━━━━━━━━━━━┩ - │ timestamp │ - ├─────────────────────┤ - │ 2002-01-01 00:00:00 │ - │ 2002-01-03 00:00:00 │ - │ 2002-01-05 00:00:00 │ - │ 2002-01-07 00:00:00 │ - │ 2002-01-09 00:00:00 │ - │ 2002-01-11 00:00:00 │ - │ 2002-01-13 00:00:00 │ - │ 2002-01-15 00:00:00 │ - │ 2002-01-17 00:00:00 │ - │ 2002-01-19 00:00:00 │ - │ … │ - └─────────────────────┘ - - """ - raise NotImplementedError() - - -@range.register(int) -@range.register(ir.IntegerValue) -def _int_range( - start: int, - stop: int | ir.IntegerValue | None = None, - step: int | ir.IntegerValue | None = None, -) -> ir.ArrayValue: - if stop is None: - stop = start - start = 0 - if step is None: - step = 1 - return ops.IntegerRange(start=start, stop=stop, step=step).to_expr() - - -@range.register(str) -@range.register(datetime.datetime) -@range.register(ir.TimestampValue) -def _timestamp_range( - start: datetime.datetime | ir.TimestampValue | str, - stop: datetime.datetime | ir.TimestampValue | str, - step: datetime.timedelta | ir.IntervalValue, -) -> ir.ArrayValue: - return ops.TimestampRange( - start=normalize_datetime(start) if isinstance(start, str) else start, - stop=normalize_datetime(stop) if isinstance(stop, str) else stop, - step=step, - ).to_expr() - - -def _wrap_deprecated(fn, prefix=""): - """Deprecate the top-level geo function.""" - - @functools.wraps(fn) - def wrapper(self, *args, **kwargs): - if isinstance(self, Deferred): - method = getattr(self, fn.__name__) - return method(*args, **kwargs) - return fn(self, *args, **kwargs) - - wrapper.__module__ = "ibis.expr.api" - wrapper.__qualname__ = wrapper.__name__ = prefix + fn.__name__ - dec = util.deprecated( - instead=f"use the `{fn.__qualname__}` method instead", as_of="7.0" - ) - return dec(wrapper) - - -geo_area = _wrap_deprecated(ir.GeoSpatialValue.area, "geo_") -geo_as_binary = _wrap_deprecated(ir.GeoSpatialValue.as_binary, "geo_") -geo_as_ewkb = _wrap_deprecated(ir.GeoSpatialValue.as_ewkb, "geo_") -geo_as_ewkt = _wrap_deprecated(ir.GeoSpatialValue.as_ewkt, "geo_") -geo_as_text = _wrap_deprecated(ir.GeoSpatialValue.as_text, "geo_") -geo_azimuth = _wrap_deprecated(ir.GeoSpatialValue.azimuth, "geo_") -geo_buffer = _wrap_deprecated(ir.GeoSpatialValue.buffer, "geo_") -geo_centroid = _wrap_deprecated(ir.GeoSpatialValue.centroid, "geo_") -geo_contains = _wrap_deprecated(ir.GeoSpatialValue.contains, "geo_") -geo_contains_properly = _wrap_deprecated(ir.GeoSpatialValue.contains_properly, "geo_") -geo_covers = _wrap_deprecated(ir.GeoSpatialValue.covers, "geo_") -geo_covered_by = _wrap_deprecated(ir.GeoSpatialValue.covered_by, "geo_") -geo_crosses = _wrap_deprecated(ir.GeoSpatialValue.crosses, "geo_") -geo_d_fully_within = _wrap_deprecated(ir.GeoSpatialValue.d_fully_within, "geo_") -geo_difference = _wrap_deprecated(ir.GeoSpatialValue.difference, "geo_") -geo_disjoint = _wrap_deprecated(ir.GeoSpatialValue.disjoint, "geo_") -geo_distance = _wrap_deprecated(ir.GeoSpatialValue.distance, "geo_") -geo_d_within = _wrap_deprecated(ir.GeoSpatialValue.d_within, "geo_") -geo_end_point = _wrap_deprecated(ir.GeoSpatialValue.end_point, "geo_") -geo_envelope = _wrap_deprecated(ir.GeoSpatialValue.envelope, "geo_") -geo_equals = _wrap_deprecated(ir.GeoSpatialValue.geo_equals, "geo_") -geo_geometry_n = _wrap_deprecated(ir.GeoSpatialValue.geometry_n, "geo_") -geo_geometry_type = _wrap_deprecated(ir.GeoSpatialValue.geometry_type, "geo_") -geo_intersection = _wrap_deprecated(ir.GeoSpatialValue.intersection, "geo_") -geo_intersects = _wrap_deprecated(ir.GeoSpatialValue.intersects, "geo_") -geo_is_valid = _wrap_deprecated(ir.GeoSpatialValue.is_valid, "geo_") -geo_line_locate_point = _wrap_deprecated(ir.GeoSpatialValue.line_locate_point, "geo_") -geo_line_merge = _wrap_deprecated(ir.GeoSpatialValue.line_merge, "geo_") -geo_line_substring = _wrap_deprecated(ir.GeoSpatialValue.line_substring, "geo_") -geo_length = _wrap_deprecated(ir.GeoSpatialValue.length, "geo_") -geo_max_distance = _wrap_deprecated(ir.GeoSpatialValue.max_distance, "geo_") -geo_n_points = _wrap_deprecated(ir.GeoSpatialValue.n_points, "geo_") -geo_n_rings = _wrap_deprecated(ir.GeoSpatialValue.n_rings, "geo_") -geo_ordering_equals = _wrap_deprecated(ir.GeoSpatialValue.ordering_equals, "geo_") -geo_overlaps = _wrap_deprecated(ir.GeoSpatialValue.overlaps, "geo_") -geo_perimeter = _wrap_deprecated(ir.GeoSpatialValue.perimeter, "geo_") -geo_point = _wrap_deprecated(ir.NumericValue.point, "geo_") -geo_point_n = _wrap_deprecated(ir.GeoSpatialValue.point_n, "geo_") -geo_set_srid = _wrap_deprecated(ir.GeoSpatialValue.set_srid, "geo_") -geo_simplify = _wrap_deprecated(ir.GeoSpatialValue.simplify, "geo_") -geo_srid = _wrap_deprecated(ir.GeoSpatialValue.srid, "geo_") -geo_start_point = _wrap_deprecated(ir.GeoSpatialValue.start_point, "geo_") -geo_touches = _wrap_deprecated(ir.GeoSpatialValue.touches, "geo_") -geo_transform = _wrap_deprecated(ir.GeoSpatialValue.transform, "geo_") -geo_union = _wrap_deprecated(ir.GeoSpatialValue.union, "geo_") -geo_within = _wrap_deprecated(ir.GeoSpatialValue.within, "geo_") -geo_x = _wrap_deprecated(ir.GeoSpatialValue.x, "geo_") -geo_x_max = _wrap_deprecated(ir.GeoSpatialValue.x_max, "geo_") -geo_x_min = _wrap_deprecated(ir.GeoSpatialValue.x_min, "geo_") -geo_y = _wrap_deprecated(ir.GeoSpatialValue.y, "geo_") -geo_y_max = _wrap_deprecated(ir.GeoSpatialValue.y_max, "geo_") -geo_y_min = _wrap_deprecated(ir.GeoSpatialValue.y_min, "geo_") -geo_unary_union = _wrap_deprecated(ir.GeoSpatialColumn.unary_union, "geo_") -negate = _wrap_deprecated(ir.NumericValue.negate) - - -@deferrable -def ifelse(condition: Any, true_expr: Any, false_expr: Any) -> ir.Value: - """Construct a ternary conditional expression. - - Parameters - ---------- - condition - A boolean expression - true_expr - Expression to return if `condition` evaluates to `True` - false_expr - Expression to return if `condition` evaluates to `False` or `NULL` - - Returns - ------- - Value : ir.Value - The value of `true_expr` if `condition` is `True` else `false_expr` - - See Also - -------- - [`BooleanValue.ifelse()`](./expression-numeric.qmd#ibis.expr.types.logical.BooleanValue.ifelse) - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable({"condition": [True, False, True, None]}) - >>> ibis.ifelse(t.condition, "yes", "no") - ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ IfElse(condition, 'yes', 'no') ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩ - │ string │ - ├────────────────────────────────┤ - │ yes │ - │ no │ - │ yes │ - │ no │ - └────────────────────────────────┘ - - """ - if not isinstance(condition, ir.Value): - condition = literal(condition, type="bool") - elif not condition.type().is_boolean(): - condition = condition.cast(bool) - return condition.ifelse(true_expr, false_expr) - - -@util.deprecated(instead="use `ibis.ifelse` instead", as_of="7.0") -def where(cond, true_expr, false_expr) -> ir.Value: - """Construct a ternary conditional expression. - - Parameters - ---------- - cond - Boolean conditional expression - true_expr - Expression to return if `cond` evaluates to `True` - false_expr - Expression to return if `cond` evaluates to `False` or `NULL` - - Returns - ------- - Value : ir.Value - The value of `true_expr` if `arg` is `True` else `false_expr` - """ - return ifelse(cond, true_expr, false_expr) - - -@deferrable -def coalesce(*args: Any) -> ir.Value: - """Return the first non-null value from `args`. - - Parameters - ---------- - args - Arguments from which to choose the first non-null value - - Returns - ------- - Value - Coalesced expression - - See Also - -------- - [`Value.coalesce()`](#ibis.expr.types.generic.Value.coalesce) - [`Value.fill_null()`](#ibis.expr.types.generic.Value.fill_null) - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> ibis.coalesce(None, 4, 5) - ┌────────────┐ - │ np.int8(4) │ - └────────────┘ - """ - return ops.Coalesce(args).to_expr() - - -@deferrable -def greatest(*args: Any) -> ir.Value: - """Compute the largest value among the supplied arguments. - - Parameters - ---------- - args - Arguments to choose from - - Returns - ------- - Value - Maximum of the passed arguments - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> ibis.greatest(None, 4, 5) - ┌────────────┐ - │ np.int8(5) │ - └────────────┘ - """ - return ops.Greatest(args).to_expr() - - -@deferrable -def least(*args: Any) -> ir.Value: - """Compute the smallest value among the supplied arguments. - - Parameters - ---------- - args - Arguments to choose from - - Returns - ------- - Value - Minimum of the passed arguments - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> ibis.least(None, 4, 5) - ┌────────────┐ - │ np.int8(4) │ - └────────────┘ - """ - return ops.Least(args).to_expr() - - -def omitted() -> ir.Value: - return ops.Omitted().to_expr() diff --git a/third_party/bigframes_vendored/ibis/expr/builders.py b/third_party/bigframes_vendored/ibis/expr/builders.py deleted file mode 100644 index c7b6e538ff5..00000000000 --- a/third_party/bigframes_vendored/ibis/expr/builders.py +++ /dev/null @@ -1,312 +0,0 @@ -# Contains code from https://github.com/ibis-project/ibis/blob/9.2.0/ibis/expr/builder.py - -from __future__ import annotations - -import math -from typing import TYPE_CHECKING, Any, Literal, Optional, Union - -import bigframes_vendored.ibis -import bigframes_vendored.ibis.expr.datatypes as dt -import bigframes_vendored.ibis.expr.operations as ops -import bigframes_vendored.ibis.expr.rules as rlz -import bigframes_vendored.ibis.expr.types as ir -from bigframes_vendored.ibis import util -from bigframes_vendored.ibis.common.annotations import annotated, attribute -from bigframes_vendored.ibis.common.deferred import Deferred, Resolver, deferrable -from bigframes_vendored.ibis.common.exceptions import IbisInputError -from bigframes_vendored.ibis.common.grounds import Concrete -from bigframes_vendored.ibis.common.selectors import Selector # noqa: TCH001 -from bigframes_vendored.ibis.common.typing import VarTuple # noqa: TCH001 - -if TYPE_CHECKING: - from typing_extensions import Self - - -class Builder(Concrete): - pass - - -@deferrable(repr="") -def _finish_searched_case(cases, results, default) -> ir.Value: - """Finish constructing a SearchedCase expression. - - This is split out into a separate function to allow for deferred arguments - to resolve. - """ - return ops.SearchedCase(cases=cases, results=results, default=default).to_expr() - - -class SearchedCaseBuilder(Builder): - """A case builder, used for constructing `ibis.case()` expressions.""" - - cases: VarTuple[Union[Resolver, ops.Value[dt.Boolean]]] = () - results: VarTuple[Union[Resolver, ops.Value]] = () - default: Optional[Union[Resolver, ops.Value]] = None - - def when(self, case_expr: Any, result_expr: Any) -> Self: - """Add a new condition and result to the `CASE` expression. - - Parameters - ---------- - case_expr - Predicate expression to use for this case. - result_expr - Value when the case predicate evaluates to true. - - """ - return self.copy( - cases=self.cases + (case_expr,), results=self.results + (result_expr,) - ) - - def else_(self, result_expr: Any) -> Self: - """Add a default value for the `CASE` expression. - - Parameters - ---------- - result_expr - Value to use when all case predicates evaluate to false. - - """ - return self.copy(default=result_expr) - - def end(self) -> ir.Value | Deferred: - """Finish the `CASE` expression.""" - return _finish_searched_case(self.cases, self.results, self.default) - - -class SimpleCaseBuilder(Builder): - """A case builder, used for constructing `Column.case()` expressions.""" - - base: ops.Value - cases: VarTuple[ops.Value] = () - results: VarTuple[ops.Value] = () - default: Optional[ops.Value] = None - - def when(self, case_expr: Any, result_expr: Any) -> Self: - """Add a new condition and result to the `CASE` expression. - - Parameters - ---------- - case_expr - Expression to equality-compare with base expression. Must be - comparable with the base. - result_expr - Value when the case predicate evaluates to true. - - """ - if not isinstance(case_expr, ir.Value): - case_expr = bigframes_vendored.ibis.literal(case_expr) - if not isinstance(result_expr, ir.Value): - result_expr = bigframes_vendored.ibis.literal(result_expr) - - if not rlz.comparable(self.base, case_expr.op()): - raise TypeError( - f"Base expression {rlz._arg_type_error_format(self.base)} and " - f"case {rlz._arg_type_error_format(case_expr)} are not comparable" - ) - return self.copy( - cases=self.cases + (case_expr,), results=self.results + (result_expr,) - ) - - def else_(self, result_expr: Any) -> Self: - """Add a default value for the `CASE` expression. - - Parameters - ---------- - result_expr - Value to use when all case predicates evaluate to false. - - """ - return self.copy(default=result_expr) - - def end(self) -> ir.Value: - """Finish the `CASE` expression.""" - if (default := self.default) is None: - default = bigframes_vendored.ibis.null().cast( - rlz.highest_precedence_dtype(self.results) - ) - return ops.SimpleCase( - cases=self.cases, results=self.results, default=default, base=self.base - ).to_expr() - - -RowsWindowBoundary = ops.WindowBoundary[dt.Integer] -RangeWindowBoundary = ops.WindowBoundary[dt.Numeric | dt.Interval] - - -class WindowBuilder(Builder): - """An unbound window frame specification. - - Notes - ----- - This class is patterned after SQL window frame clauses. - - Using `None` for `preceding` or `following` indicates an unbounded frame. - - Use 0 for `CURRENT ROW`. - - """ - - how: Literal["rows", "range"] = "rows" - start: Optional[RangeWindowBoundary] = None - end: Optional[RangeWindowBoundary] = None - groupings: VarTuple[Union[str, Resolver, Selector, ops.Value]] = () - orderings: VarTuple[Union[str, Resolver, Selector, ops.SortKey]] = () - - @attribute - def _table(self): - inputs = ( - self.start, - self.end, - *self.groupings, - *self.orderings, - ) - valuerels = (v.relations for v in inputs if isinstance(v, ops.Value)) - relations = frozenset().union(*valuerels) - if len(relations) == 0: - return None - elif len(relations) == 1: - (table,) = relations - return table - else: - raise IbisInputError("Window frame can only depend on a single relation") - - def _maybe_cast_boundaries(self, start, end): - if start and end: - if start.dtype.is_interval() and end.dtype.is_numeric(): - return start, ops.Cast(end.value, start.dtype) - elif start.dtype.is_numeric() and end.dtype.is_interval(): - return ops.Cast(start.value, end.dtype), end - return start, end - - def _determine_how(self, start, end): - if start and not start.dtype.is_integer(): - return self.range - elif end and not end.dtype.is_integer(): - return self.range - else: - return self.rows - - def _validate_boundaries(self, start, end): - start_, end_ = -math.inf, math.inf - if start and isinstance(lit := start.value, ops.Literal): - start_ = -lit.value if start.preceding else lit.value - if end and isinstance(lit := end.value, ops.Literal): - end_ = -lit.value if end.preceding else lit.value - - if start_ > end_: - raise IbisInputError( - "Window frame's start point must be greater than its end point" - ) - - @annotated - def rows( - self, start: Optional[RowsWindowBoundary], end: Optional[RowsWindowBoundary] - ): - self._validate_boundaries(start, end) - start, end = self._maybe_cast_boundaries(start, end) - return self.copy(how="rows", start=start, end=end) - - @annotated - def range( - self, start: Optional[RangeWindowBoundary], end: Optional[RangeWindowBoundary] - ): - self._validate_boundaries(start, end) - start, end = self._maybe_cast_boundaries(start, end) - return self.copy(how="range", start=start, end=end) - - @annotated - def between( - self, start: Optional[RangeWindowBoundary], end: Optional[RangeWindowBoundary] - ): - self._validate_boundaries(start, end) - start, end = self._maybe_cast_boundaries(start, end) - method = self._determine_how(start, end) - return method(start, end) - - def group_by(self, expr) -> Self: - return self.copy(groupings=self.groupings + util.promote_tuple(expr)) - - def order_by(self, expr) -> Self: - return self.copy(orderings=self.orderings + util.promote_tuple(expr)) - - def bind(self, table): - if table is None: - if self._table is None: - raise IbisInputError("Cannot bind window frame without a table") - else: - table = self._table.to_expr() - - return self.copy( - groupings=table.bind(self.groupings), orderings=table.bind(self.orderings) - ) - - -class LegacyWindowBuilder(WindowBuilder): - def _is_negative(self, value): - if value is None: - return False - if isinstance(value, ir.Scalar): - value = value.op().value - return value < 0 - - def preceding_following(self, preceding, following, how=None) -> Self: - preceding_tuple = has_preceding = False - following_tuple = has_following = False - if preceding is not None: - preceding_tuple = isinstance(preceding, tuple) - has_preceding = True - if following is not None: - following_tuple = isinstance(following, tuple) - has_following = True - - if (preceding_tuple and has_following) or (following_tuple and has_preceding): - raise IbisInputError( - "Can only specify one window side when you want an off-center window" - ) - elif preceding_tuple: - start, end = preceding - if end is None: - raise IbisInputError("preceding end point cannot be None") - elif self._is_negative(end): - raise IbisInputError("preceding end point must be non-negative") - elif self._is_negative(start): - raise IbisInputError("preceding start point must be non-negative") - between = ( - None if start is None else ops.WindowBoundary(start, preceding=True), - ops.WindowBoundary(end, preceding=True), - ) - elif following_tuple: - start, end = following - if start is None: - raise IbisInputError("following start point cannot be None") - elif self._is_negative(start): - raise IbisInputError("following start point must be non-negative") - elif self._is_negative(end): - raise IbisInputError("following end point must be non-negative") - between = ( - ops.WindowBoundary(start, preceding=False), - None if end is None else ops.WindowBoundary(end, preceding=False), - ) - elif has_preceding and has_following: - between = ( - ops.WindowBoundary(preceding, preceding=True), - ops.WindowBoundary(following, preceding=False), - ) - elif has_preceding: - if self._is_negative(preceding): - raise IbisInputError("preceding end point must be non-negative") - between = (ops.WindowBoundary(preceding, preceding=True), None) - elif has_following: - if self._is_negative(following): - raise IbisInputError("following end point must be non-negative") - between = (None, ops.WindowBoundary(following, preceding=False)) - - if how is None: - return self.between(*between) - elif how == "rows": - return self.rows(*between) - elif how == "range": - return self.range(*between) - else: - raise ValueError(f"Invalid window frame type: {how}") diff --git a/third_party/bigframes_vendored/ibis/expr/datashape.py b/third_party/bigframes_vendored/ibis/expr/datashape.py deleted file mode 100644 index a13c5461925..00000000000 --- a/third_party/bigframes_vendored/ibis/expr/datashape.py +++ /dev/null @@ -1,70 +0,0 @@ -# Contains code from https://github.com/ibis-project/ibis/blob/9.2.0/ibis/expr/datashape.py - -from __future__ import annotations - -from typing import Any - -from bigframes_vendored.ibis.common.grounds import Singleton -from public import public - - -@public -class DataShape(Singleton): - ndim: int - SCALAR: Scalar - COLUMNAR: Columnar - - def is_scalar(self) -> bool: - return self.ndim == 0 - - def is_columnar(self) -> bool: - return self.ndim == 1 - - def is_tabular(self) -> bool: - return self.ndim == 2 - - def __lt__(self, other: Any) -> bool: - if not isinstance(other, DataShape): - return NotImplemented - return self.ndim < other.ndim - - def __le__(self, other: Any) -> bool: - if not isinstance(other, DataShape): - return NotImplemented - return self.ndim <= other.ndim - - def __eq__(self, other: Any) -> bool: - if not isinstance(other, DataShape): - return NotImplemented - return self.ndim == other.ndim - - def __hash__(self) -> int: - return hash((self.__class__, self.ndim)) - - -@public -class Scalar(DataShape): - ndim = 0 - - -@public -class Columnar(DataShape): - ndim = 1 - - -@public -class Tabular(DataShape): - ndim = 2 - - -# for backward compat -DataShape.SCALAR = Scalar() -DataShape.COLUMNAR = Columnar() -DataShape.TABULAR = Tabular() - -scalar = Scalar() -columnar = Columnar() -tabular = Tabular() - - -public(Any=DataShape) diff --git a/third_party/bigframes_vendored/ibis/expr/datatypes/__init__.py b/third_party/bigframes_vendored/ibis/expr/datatypes/__init__.py deleted file mode 100644 index 2ff4d41ab5d..00000000000 --- a/third_party/bigframes_vendored/ibis/expr/datatypes/__init__.py +++ /dev/null @@ -1,18 +0,0 @@ -# Contains code from https://github.com/ibis-project/ibis/blob/9.2.0/ibis/expr/datatypes/__init__.py - -from __future__ import annotations - -from bigframes_vendored.ibis.expr.datatypes.cast import * # noqa: F403 -from bigframes_vendored.ibis.expr.datatypes.core import * # noqa: F403 -from bigframes_vendored.ibis.expr.datatypes.value import * # noqa: F403 - -halffloat = float16 # noqa: F405 -float = float64 # noqa: F405 -double = float64 # noqa: F405 -int = int64 # noqa: F405 -uint_ = uint64 # noqa: F405 -bool = boolean # noqa: F405 -str = string # noqa: F405 -bytes = binary # noqa: F405 - -validate_type = dtype # noqa: F405 diff --git a/third_party/bigframes_vendored/ibis/expr/datatypes/cast.py b/third_party/bigframes_vendored/ibis/expr/datatypes/cast.py deleted file mode 100644 index e7e18419441..00000000000 --- a/third_party/bigframes_vendored/ibis/expr/datatypes/cast.py +++ /dev/null @@ -1,148 +0,0 @@ -# Contains code from https://github.com/ibis-project/ibis/blob/9.2.0/ibis/expr/datatypes/cast.py - -from __future__ import annotations - -import functools -from typing import TYPE_CHECKING, Any - -import bigframes_vendored.ibis.expr.datatypes.core as dt -from bigframes_vendored.ibis.common.exceptions import IbisTypeError -from public import public - -if TYPE_CHECKING: - from collections.abc import Iterator - - -@public -def cast(source: str | dt.DataType, target: str | dt.DataType, **kwargs) -> dt.DataType: - """Attempts to implicitly cast from source dtype to target dtype.""" - source, target = dt.dtype(source), dt.dtype(target) - - if not source.castable(target, **kwargs): - raise IbisTypeError( - f"Datatype {source} cannot be implicitly casted to {target}" - ) - return target - - -@public -def castable(source: dt.DataType, target: dt.DataType, value: Any = None) -> bool: - """Return whether source ir type is implicitly castable to target.""" - from bigframes_vendored.ibis.expr.datatypes.value import normalizable - - if source == target: - return True - elif source.is_null(): - # The null type is castable to any type, even if the target type is *not* - # nullable. - # - # We handle the promotion of `null + !T -> T` at the `castable` call site. - # - # It might be possible to build a system with a single function that tries - # to promote types and use the exception to indicate castability, but that - # is a deeper refactor to be tackled later. - # - # See https://github.com/ibis-project/ibis/issues/2891 for the bug report - return True - elif target.is_boolean(): - if source.is_boolean(): - return True - elif source.is_integer(): - return value in (0, 1) - else: - return False - elif target.is_integer(): - # TODO(kszucs): ideally unsigned to signed shouldn't be allowed but that - # breaks the integral promotion rule logic in rules.py - if source.is_integer(): - if value is not None: - return normalizable(target, value) - else: - return source.nbytes <= target.nbytes - else: - return False - elif target.is_floating(): - if source.is_floating(): - return source.nbytes <= target.nbytes - else: - return source.is_integer() - elif target.is_decimal(): - if source.is_decimal(): - downcast_precision = ( - source.precision is not None - and target.precision is not None - and source.precision < target.precision - ) - downcast_scale = ( - source.scale is not None - and target.scale is not None - and source.scale < target.scale - ) - return not (downcast_precision or downcast_scale) - else: - return source.is_numeric() - elif target.is_string(): - return source.is_string() or source.is_uuid() - elif target.is_uuid(): - return source.is_uuid() or source.is_string() - elif target.is_date() or target.is_timestamp(): - if source.is_string(): - return value is not None and normalizable(target, value) - else: - return source.is_timestamp() or source.is_date() - elif target.is_interval(): - if source.is_interval(): - return source.unit == target.unit - else: - return source.is_integer() - elif target.is_time(): - if source.is_string(): - return value is not None and normalizable(target, value) - else: - return source.is_time() - elif target.is_json(): - return ( - source.is_json() - or source.is_string() - or source.is_floating() - or source.is_integer() - ) - elif target.is_array(): - return source.is_array() and castable(source.value_type, target.value_type) - elif target.is_map(): - return ( - source.is_map() - and castable(source.key_type, target.key_type) - and castable(source.value_type, target.value_type) - ) - elif target.is_struct(): - return source.is_struct() and all( - castable(source[field], target[field]) for field in target.names - ) - elif target.is_geospatial(): - return source.is_geospatial() or source.is_array() - else: - return isinstance(target, source.__class__) - - -@public -def higher_precedence(left: dt.DataType, right: dt.DataType) -> dt.DataType: - nullable = left.nullable or right.nullable - - if left.castable(right): - return right.copy(nullable=nullable) - elif right.castable(left): - return left.copy(nullable=nullable) - else: - raise IbisTypeError( - f"Cannot compute precedence for `{left}` and `{right}` types" - ) - - -@public -def highest_precedence(dtypes: Iterator[dt.DataType]) -> dt.DataType: - """Compute the highest precedence of `dtypes`.""" - if collected := list(dtypes): - return functools.reduce(higher_precedence, collected) - else: - return dt.null diff --git a/third_party/bigframes_vendored/ibis/expr/datatypes/core.py b/third_party/bigframes_vendored/ibis/expr/datatypes/core.py deleted file mode 100644 index 75bff716626..00000000000 --- a/third_party/bigframes_vendored/ibis/expr/datatypes/core.py +++ /dev/null @@ -1,1118 +0,0 @@ -# Contains code from https://github.com/ibis-project/ibis/blob/9.2.0/ibis/expr/datatypes/core.py - -from __future__ import annotations - -import datetime as pydatetime -import decimal as pydecimal -import numbers -import uuid as pyuuid -from abc import abstractmethod -from collections.abc import Iterable, Iterator, Mapping, Sequence -from numbers import Integral, Real -from typing import ( - Any, - Generic, - Literal, - NamedTuple, - Optional, - TypeVar, - get_args, - get_origin, - get_type_hints, -) - -import toolz -from bigframes_vendored.ibis.common.annotations import attribute -from bigframes_vendored.ibis.common.collections import FrozenOrderedDict, MapSet -from bigframes_vendored.ibis.common.dispatch import lazy_singledispatch -from bigframes_vendored.ibis.common.grounds import Concrete, Singleton -from bigframes_vendored.ibis.common.patterns import Coercible, CoercionError -from bigframes_vendored.ibis.common.temporal import IntervalUnit, TimestampUnit -from public import public -from typing_extensions import Self - - -@lazy_singledispatch -def dtype(value: Any, nullable: bool = True) -> DataType: - """Create a DataType object. - - Parameters - ---------- - value - The object to coerce to an Ibis DataType. Supported inputs include - strings, python type annotations, numpy dtypes, pandas dtypes, and - pyarrow types. - nullable - Whether the type should be nullable. Defaults to True. - - Examples - -------- - >>> import ibis - >>> ibis.dtype("int32") - Int32(nullable=True) - >>> ibis.dtype("array") - Array(value_type=Float64(nullable=True), nullable=True) - - DataType objects may also be created from Python types: - - >>> ibis.dtype(int) - Int64(nullable=True) - >>> ibis.dtype(list[float]) - Array(value_type=Float64(nullable=True), nullable=True) - - Or other type systems, like numpy/pandas/pyarrow types: - - >>> ibis.dtype(pa.int32()) - Int32(nullable=True) - - """ - if isinstance(value, DataType): - return value - else: - return DataType.from_typehint(value) - - -@dtype.register(str) -def from_string(value): - return DataType.from_string(value) - - -@dtype.register("numpy.dtype") -def from_numpy_dtype(value, nullable=True): - return DataType.from_numpy(value, nullable) - - -@dtype.register("pandas.core.dtypes.base.ExtensionDtype") -def from_pandas_extension_dtype(value, nullable=True): - return DataType.from_pandas(value, nullable) - - -@dtype.register("pyarrow.lib.DataType") -def from_pyarrow(value, nullable=True): - return DataType.from_pyarrow(value, nullable) - - -@dtype.register("polars.datatypes.classes.DataTypeClass") -def from_polars(value, nullable=True): - return DataType.from_polars(value, nullable) - - -# lock the dispatcher to prevent new types from being registered -del dtype.register - - -@public -class DataType(Concrete, Coercible): - """Base class for all data types. - - Instances are immutable. - """ - - nullable: bool = True - - @property - @abstractmethod - def scalar(self): ... - - @property - @abstractmethod - def column(self): ... - - # TODO(kszucs): remove it, prefer to use Annotable.__repr__ instead - @property - def _pretty_piece(self) -> str: - return "" - - # TODO(kszucs): should remove it, only used internally - @property - def name(self) -> str: - """Return the name of the data type.""" - return self.__class__.__name__ - - @classmethod - def __coerce__(cls, value, **kwargs): - if isinstance(value, cls): - return value - try: - return dtype(value) - except (TypeError, RuntimeError) as e: - raise CoercionError("Unable to coerce to a DataType") from e - - def __call__(self, **kwargs): - return self.copy(**kwargs) - - def __str__(self) -> str: - prefix = "!" * (not self.nullable) - return f"{prefix}{self.name.lower()}{self._pretty_piece}" - - def equals(self, other): - if not isinstance(other, DataType): - raise TypeError( - f"invalid equality comparison between DataType and {type(other)}" - ) - return self == other - - def cast(self, other, **kwargs): - # TODO(kszucs): remove it or deprecate it? - from bigframes_vendored.ibis.expr.datatypes.cast import cast - - return cast(self, other, **kwargs) - - def castable(self, to, **kwargs) -> bool: - """Check whether this type is castable to another.""" - from bigframes_vendored.ibis.expr.datatypes.cast import castable - - return castable(self, to, **kwargs) - - @classmethod - def from_typehint(cls, typ, nullable=True) -> Self: - origin_type = get_origin(typ) - - if origin_type is None: - if isinstance(typ, type): - if issubclass(typ, Parametric): - raise TypeError( - f"Cannot construct a parametric {typ.__name__} datatype based " - "on the type itself" - ) - elif issubclass(typ, DataType): - return typ(nullable=nullable) - elif typ is type(None): - return null - elif issubclass(typ, bool): - return Boolean(nullable=nullable) - elif issubclass(typ, bytes): - return Binary(nullable=nullable) - elif issubclass(typ, str): - return String(nullable=nullable) - elif issubclass(typ, Integral): - return Int64(nullable=nullable) - elif issubclass(typ, Real): - return Float64(nullable=nullable) - elif issubclass(typ, pydecimal.Decimal): - return Decimal(nullable=nullable) - elif issubclass(typ, pydatetime.datetime): - return Timestamp(nullable=nullable) - elif issubclass(typ, pydatetime.date): - return Date(nullable=nullable) - elif issubclass(typ, pydatetime.time): - return Time(nullable=nullable) - elif issubclass(typ, pydatetime.timedelta): - return Interval(unit="us", nullable=nullable) - elif issubclass(typ, pyuuid.UUID): - return UUID(nullable=nullable) - elif annots := get_type_hints(typ): - return Struct(toolz.valmap(dtype, annots), nullable=nullable) - else: - raise TypeError( - f"Cannot construct an ibis datatype from python type `{typ!r}`" - ) - else: - raise TypeError( - f"Cannot construct an ibis datatype from python value `{typ!r}`" - ) - elif issubclass(origin_type, (Sequence, Array)): - (value_type,) = map(dtype, get_args(typ)) - return Array(value_type) - elif issubclass(origin_type, (Mapping, Map)): - key_type, value_type = map(dtype, get_args(typ)) - return Map(key_type, value_type) - else: - raise TypeError(f"Value {typ!r} is not a valid datatype") - - @classmethod - def from_numpy(cls, numpy_type, nullable=True) -> Self: - """Return the equivalent ibis datatype.""" - from bigframes_vendored.ibis.formats.numpy import NumpyType - - return NumpyType.to_ibis(numpy_type, nullable=nullable) - - @classmethod - def from_pandas(cls, pandas_type, nullable=True) -> Self: - """Return the equivalent ibis datatype.""" - from bigframes_vendored.ibis.formats.pandas import PandasType - - return PandasType.to_ibis(pandas_type, nullable=nullable) - - @classmethod - def from_pyarrow(cls, arrow_type, nullable=True) -> Self: - """Return the equivalent ibis datatype.""" - from bigframes_vendored.ibis.formats.pyarrow import PyArrowType - - return PyArrowType.to_ibis(arrow_type, nullable=nullable) - - @classmethod - def from_polars(cls, polars_type, nullable=True) -> Self: - """Return the equivalent ibis datatype.""" - from bigframes_vendored.ibis.formats.polars import PolarsType - - return PolarsType.to_ibis(polars_type, nullable=nullable) - - def to_numpy(self): - """Return the equivalent numpy datatype.""" - from bigframes_vendored.ibis.formats.numpy import NumpyType - - return NumpyType.from_ibis(self) - - def to_pandas(self): - """Return the equivalent pandas datatype.""" - from bigframes_vendored.ibis.formats.pandas import PandasType - - return PandasType.from_ibis(self) - - def to_pyarrow(self): - """Return the equivalent pyarrow datatype.""" - from bigframes_vendored.ibis.formats.pyarrow import PyArrowType - - return PyArrowType.from_ibis(self) - - def to_polars(self): - """Return the equivalent polars datatype.""" - from bigframes_vendored.ibis.formats.polars import PolarsType - - return PolarsType.from_ibis(self) - - def is_array(self) -> bool: - """Return True if an instance of an Array type.""" - return isinstance(self, Array) - - def is_binary(self) -> bool: - """Return True if an instance of a Binary type.""" - return isinstance(self, Binary) - - def is_boolean(self) -> bool: - """Return True if an instance of a Boolean type.""" - return isinstance(self, Boolean) - - def is_date(self) -> bool: - """Return True if an instance of a Date type.""" - return isinstance(self, Date) - - def is_decimal(self) -> bool: - """Return True if an instance of a Decimal type.""" - return isinstance(self, Decimal) - - def is_enum(self) -> bool: - """Return True if an instance of an Enum type.""" - return isinstance(self, Enum) - - def is_float16(self) -> bool: - """Return True if an instance of a Float16 type.""" - return isinstance(self, Float16) - - def is_float32(self) -> bool: - """Return True if an instance of a Float32 type.""" - return isinstance(self, Float32) - - def is_float64(self) -> bool: - """Return True if an instance of a Float64 type.""" - return isinstance(self, Float64) - - def is_floating(self) -> bool: - """Return True if an instance of any Floating type.""" - return isinstance(self, Floating) - - def is_geospatial(self) -> bool: - """Return True if an instance of a Geospatial type.""" - return isinstance(self, GeoSpatial) - - def is_inet(self) -> bool: - """Return True if an instance of an Inet type.""" - return isinstance(self, INET) - - def is_int16(self) -> bool: - """Return True if an instance of an Int16 type.""" - return isinstance(self, Int16) - - def is_int32(self) -> bool: - """Return True if an instance of an Int32 type.""" - return isinstance(self, Int32) - - def is_int64(self) -> bool: - """Return True if an instance of an Int64 type.""" - return isinstance(self, Int64) - - def is_int8(self) -> bool: - """Return True if an instance of an Int8 type.""" - return isinstance(self, Int8) - - def is_integer(self) -> bool: - """Return True if an instance of any Integer type.""" - return isinstance(self, Integer) - - def is_interval(self) -> bool: - """Return True if an instance of an Interval type.""" - return isinstance(self, Interval) - - def is_json(self) -> bool: - """Return True if an instance of a JSON type.""" - return isinstance(self, JSON) - - def is_linestring(self) -> bool: - """Return True if an instance of a LineString type.""" - return isinstance(self, LineString) - - def is_macaddr(self) -> bool: - """Return True if an instance of a MACADDR type.""" - return isinstance(self, MACADDR) - - def is_map(self) -> bool: - """Return True if an instance of a Map type.""" - return isinstance(self, Map) - - def is_multilinestring(self) -> bool: - """Return True if an instance of a MultiLineString type.""" - return isinstance(self, MultiLineString) - - def is_multipoint(self) -> bool: - """Return True if an instance of a MultiPoint type.""" - return isinstance(self, MultiPoint) - - def is_multipolygon(self) -> bool: - """Return True if an instance of a MultiPolygon type.""" - return isinstance(self, MultiPolygon) - - def is_nested(self) -> bool: - """Return true if an instance of any nested (Array/Map/Struct) type.""" - return isinstance(self, (Array, Map, Struct)) - - def is_null(self) -> bool: - """Return true if an instance of a Null type.""" - return isinstance(self, Null) - - def is_numeric(self) -> bool: - """Return true if an instance of a Numeric type.""" - return isinstance(self, Numeric) - - def is_point(self) -> bool: - """Return true if an instance of a Point type.""" - return isinstance(self, Point) - - def is_polygon(self) -> bool: - """Return true if an instance of a Polygon type.""" - return isinstance(self, Polygon) - - def is_primitive(self) -> bool: - """Return true if an instance of a Primitive type.""" - return isinstance(self, Primitive) - - def is_signed_integer(self) -> bool: - """Return true if an instance of a SignedInteger type.""" - return isinstance(self, SignedInteger) - - def is_string(self) -> bool: - """Return true if an instance of a String type.""" - return isinstance(self, String) - - def is_struct(self) -> bool: - """Return true if an instance of a Struct type.""" - return isinstance(self, Struct) - - def is_temporal(self) -> bool: - """Return true if an instance of a Temporal type.""" - return isinstance(self, Temporal) - - def is_time(self) -> bool: - """Return true if an instance of a Time type.""" - return isinstance(self, Time) - - def is_timestamp(self) -> bool: - """Return true if an instance of a Timestamp type.""" - return isinstance(self, Timestamp) - - def is_uint16(self) -> bool: - """Return true if an instance of a UInt16 type.""" - return isinstance(self, UInt16) - - def is_uint32(self) -> bool: - """Return true if an instance of a UInt32 type.""" - return isinstance(self, UInt32) - - def is_uint64(self) -> bool: - """Return true if an instance of a UInt64 type.""" - return isinstance(self, UInt64) - - def is_uint8(self) -> bool: - """Return true if an instance of a UInt8 type.""" - return isinstance(self, UInt8) - - def is_unknown(self) -> bool: - """Return true if an instance of an Unknown type.""" - return isinstance(self, Unknown) - - def is_unsigned_integer(self) -> bool: - """Return true if an instance of an UnsignedInteger type.""" - return isinstance(self, UnsignedInteger) - - def is_uuid(self) -> bool: - """Return true if an instance of a UUID type.""" - return isinstance(self, UUID) - - def is_variadic(self) -> bool: - """Return true if an instance of a Variadic type.""" - return isinstance(self, Variadic) - - -@public -class Unknown(DataType, Singleton): - """An unknown type.""" - - scalar = "UnknownScalar" - column = "UnknownColumn" - - -@public -class Primitive(DataType, Singleton): - """Values with known size.""" - - -# TODO(kszucs): consider to remove since we don't actually use this information -@public -class Variadic(DataType): - """Values with unknown size.""" - - -@public -class Parametric(DataType): - """Types that can be parameterized.""" - - -@public -class Null(Primitive): - """Null values.""" - - scalar = "NullScalar" - column = "NullColumn" - - -@public -class Boolean(Primitive): - """[](`True`) or [](`False`) values.""" - - scalar = "BooleanScalar" - column = "BooleanColumn" - - -@public -class Bounds(NamedTuple): - """The lower and upper bound of a fixed-size value.""" - - lower: int - upper: int - - def __contains__(self, value: int) -> bool: - return self.lower <= value <= self.upper - - -@public -class Numeric(DataType): - """Numeric types.""" - - -@public -class Integer(Primitive, Numeric): - """Integer values.""" - - scalar = "IntegerScalar" - column = "IntegerColumn" - - @property - @abstractmethod - def nbytes(self) -> int: - """Return the number of bytes used to store values of this type.""" - - -@public -class String(Variadic, Singleton): - """A type representing a string. - - Notes - ----- - Because of differences in the way different backends handle strings, we - cannot assume that strings are UTF-8 encoded. - - """ - - scalar = "StringScalar" - column = "StringColumn" - - -@public -class Binary(Variadic, Singleton): - """A type representing a sequence of bytes. - - Notes - ----- - Some databases treat strings and blobs of equally, and some do not. - - For example, Impala doesn't make a distinction between string and binary - types but PostgreSQL has a `TEXT` type and a `BYTEA` type which are - distinct types that have different behavior. - - """ - - scalar = "BinaryScalar" - column = "BinaryColumn" - - -@public -class Temporal(DataType): - """Data types related to time.""" - - -@public -class Date(Temporal, Primitive): - """Date values.""" - - scalar = "DateScalar" - column = "DateColumn" - - -@public -class Time(Temporal, Primitive): - """Time values.""" - - scalar = "TimeScalar" - column = "TimeColumn" - - -@public -class Timestamp(Temporal, Parametric): - """Timestamp values.""" - - timezone: Optional[str] = None - """The timezone of values of this type.""" - - # Literal[*range(10)] is only supported from 3.11 - scale: Optional[Literal[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]] = None - """The scale of the timestamp if known.""" - - scalar = "TimestampScalar" - column = "TimestampColumn" - - @classmethod - def from_unit(cls, unit, timezone=None, nullable=True): - """Return a timestamp type with the given unit and timezone.""" - unit = TimestampUnit(unit) - if unit == TimestampUnit.SECOND: - scale = 0 - elif unit == TimestampUnit.MILLISECOND: - scale = 3 - elif unit == TimestampUnit.MICROSECOND: - scale = 6 - elif unit == TimestampUnit.NANOSECOND: - scale = 9 - else: - # TODO: remove raise path as it's never triggered - # Timestamp op has a restriction that only the literal - # ints from 0 through 9 can be passed as scale - raise ValueError(f"Invalid unit {unit}") - return cls(scale=scale, timezone=timezone, nullable=nullable) - - @property - def unit(self) -> str: - """Return the unit of the timestamp.""" - if self.scale is None or self.scale == 0: - return TimestampUnit.SECOND - elif 1 <= self.scale <= 3: - return TimestampUnit.MILLISECOND - elif 4 <= self.scale <= 6: - return TimestampUnit.MICROSECOND - elif 7 <= self.scale <= 9: - return TimestampUnit.NANOSECOND - else: - # TODO: remove raise path as it's never triggered - # TimestampUnit, which is a (child of) Enum - # so it'll raise in the Enum class constructor instead - raise ValueError(f"Invalid scale {self.scale}") - - @property - def _pretty_piece(self) -> str: - if self.scale is not None and self.timezone is not None: - return f"('{self.timezone}', {self.scale:d})" - elif self.timezone is not None: - return f"('{self.timezone}')" - elif self.scale is not None: - return f"({self.scale:d})" - else: - return "" - - -@public -class SignedInteger(Integer): - """Signed integer values.""" - - @property - def bounds(self): - exp = self.nbytes * 8 - 1 - upper = (1 << exp) - 1 - return Bounds(lower=~upper, upper=upper) - - -@public -class UnsignedInteger(Integer): - """Unsigned integer values.""" - - @property - def bounds(self): - exp = self.nbytes * 8 - upper = (1 << exp) - 1 - return Bounds(lower=0, upper=upper) - - -@public -class Floating(Primitive, Numeric): - """Floating point values.""" - - scalar = "FloatingScalar" - column = "FloatingColumn" - - @property - @abstractmethod - def nbytes(self) -> int: # pragma: no cover - """Return the number of bytes used to store values of this type.""" - - -@public -class Int8(SignedInteger): - """Signed 8-bit integers.""" - - nbytes = 1 - - -@public -class Int16(SignedInteger): - """Signed 16-bit integers.""" - - nbytes = 2 - - -@public -class Int32(SignedInteger): - """Signed 32-bit integers.""" - - nbytes = 4 - - -@public -class Int64(SignedInteger): - """Signed 64-bit integers.""" - - nbytes = 8 - - -@public -class UInt8(UnsignedInteger): - """Unsigned 8-bit integers.""" - - nbytes = 1 - - -@public -class UInt16(UnsignedInteger): - """Unsigned 16-bit integers.""" - - nbytes = 2 - - -@public -class UInt32(UnsignedInteger): - """Unsigned 32-bit integers.""" - - nbytes = 4 - - -@public -class UInt64(UnsignedInteger): - """Unsigned 64-bit integers.""" - - nbytes = 8 - - -@public -class Float16(Floating): - """16-bit floating point numbers.""" - - nbytes = 2 - - -@public -class Float32(Floating): - """32-bit floating point numbers.""" - - nbytes = 4 - - -@public -class Float64(Floating): - """64-bit floating point numbers.""" - - nbytes = 8 - - -@public -class Decimal(Numeric, Parametric): - """Fixed-precision decimal values.""" - - precision: Optional[int] = None - """The number of decimal places values of this type can hold.""" - - scale: Optional[int] = None - """The number of values after the decimal point.""" - - scalar = "DecimalScalar" - column = "DecimalColumn" - - def __init__( - self, - precision: int | None = None, - scale: int | None = None, - **kwargs: Any, - ) -> None: - if precision is not None: - if not isinstance(precision, numbers.Integral): - raise TypeError( - f"Decimal type precision must be an integer; got {type(precision)}" - ) - if precision < 0: - raise ValueError("Decimal type precision cannot be negative") - if not precision: - raise ValueError("Decimal type precision cannot be zero") - if scale is not None: - if not isinstance(scale, numbers.Integral): - raise TypeError("Decimal type scale must be an integer") - if scale < 0: - raise ValueError("Decimal type scale cannot be negative") - if precision is not None and precision < scale: - raise ValueError( - "Decimal type precision must be greater than or equal to " - f"scale. Got precision={precision:d} and scale={scale:d}" - ) - super().__init__(precision=precision, scale=scale, **kwargs) - - @property - def _pretty_piece(self) -> str: - precision = self.precision - scale = self.scale - if precision is None and scale is None: - return "" - - args = [str(precision) if precision is not None else "_"] - - if scale is not None: - args.append(str(scale)) - - return f"({', '.join(args)})" - - -@public -class Interval(Parametric): - """Interval values.""" - - unit: IntervalUnit - """The time unit of the interval.""" - - scalar = "IntervalScalar" - column = "IntervalColumn" - - @property - def resolution(self): - """The interval unit's name.""" - return self.unit.singular - - @property - def _pretty_piece(self) -> str: - return f"('{self.unit.value}')" - - -@public -class Struct(Parametric, MapSet): - """Structured values.""" - - fields: FrozenOrderedDict[str, DataType] - - scalar = "StructScalar" - column = "StructColumn" - - @classmethod - def from_tuples( - cls, pairs: Iterable[tuple[str, str | DataType]], nullable: bool = True - ) -> Struct: - """Construct a `Struct` type from pairs. - - Parameters - ---------- - pairs - An iterable of pairs of field name and type - nullable - Whether the type is nullable - - Returns - ------- - Struct - Struct data type instance - - """ - return cls(dict(pairs), nullable=nullable) - - @attribute - def names(self) -> tuple[str, ...]: - """Return the names of the struct's fields.""" - return tuple(self.keys()) - - @attribute - def types(self) -> tuple[DataType, ...]: - """Return the types of the struct's fields.""" - return tuple(self.values()) - - def __len__(self) -> int: - return len(self.fields) - - def __iter__(self) -> Iterator[str]: - return iter(self.fields) - - def __getitem__(self, key: str) -> DataType: - return self.fields[key] - - def __repr__(self) -> str: - return f"'{self.name}({list(self.items())}, nullable={self.nullable})" - - @property - def _pretty_piece(self) -> str: - pairs = ", ".join(map("{}: {}".format, self.names, self.types)) - return f"<{pairs}>" - - -T = TypeVar("T", bound=DataType, covariant=True) - - -@public -class Array(Variadic, Parametric, Generic[T]): - """Array values.""" - - value_type: T - - scalar = "ArrayScalar" - column = "ArrayColumn" - - @property - def _pretty_piece(self) -> str: - return f"<{self.value_type}>" - - -K = TypeVar("K", bound=DataType, covariant=True) -V = TypeVar("V", bound=DataType, covariant=True) - - -@public -class Map(Variadic, Parametric, Generic[K, V]): - """Associative array values.""" - - key_type: K - value_type: V - - scalar = "MapScalar" - column = "MapColumn" - - @property - def _pretty_piece(self) -> str: - return f"<{self.key_type}, {self.value_type}>" - - -@public -class JSON(Variadic): - """JSON values.""" - - scalar = "JSONScalar" - column = "JSONColumn" - - binary: bool = False - """True if JSON is stored as binary, e.g., JSONB in PostgreSQL.""" - - @property - def _pretty_piece(self) -> str: - return "b" * self.binary - - -@public -class GeoSpatial(DataType): - """Geospatial values.""" - - geotype: Literal["geography", "geometry"] = "geometry" - """The specific geospatial type.""" - - srid: Optional[int] = None - """The spatial reference identifier.""" - - column = "GeoSpatialColumn" - scalar = "GeoSpatialScalar" - - @property - def _pretty_piece(self) -> str: - piece = "" - if self.geotype is not None: - piece += f":{self.geotype}" - if self.srid is not None: - piece += f";{self.srid}" - return piece - - -@public -class Point(GeoSpatial): - """A point described by two coordinates.""" - - scalar = "PointScalar" - column = "PointColumn" - - -@public -class LineString(GeoSpatial): - """A sequence of 2 or more points.""" - - scalar = "LineStringScalar" - column = "LineStringColumn" - - -@public -class Polygon(GeoSpatial): - """A set of one or more closed line strings. - - The first line string represents the shape (external ring) and the - rest represent holes in that shape (internal rings). - """ - - scalar = "PolygonScalar" - column = "PolygonColumn" - - -@public -class MultiLineString(GeoSpatial): - """A set of one or more line strings.""" - - scalar = "MultiLineStringScalar" - column = "MultiLineStringColumn" - - -@public -class MultiPoint(GeoSpatial): - """A set of one or more points.""" - - scalar = "MultiPointScalar" - column = "MultiPointColumn" - - -@public -class MultiPolygon(GeoSpatial): - """A set of one or more polygons.""" - - scalar = "MultiPolygonScalar" - column = "MultiPolygonColumn" - - -@public -class UUID(DataType): - """A 128-bit number used to identify information in computer systems.""" - - scalar = "UUIDScalar" - column = "UUIDColumn" - - -@public -class MACADDR(DataType): - """Media Access Control (MAC) address of a network interface.""" - - scalar = "MACADDRScalar" - column = "MACADDRColumn" - - -@public -class INET(DataType): - """IP addresses.""" - - scalar = "INETScalar" - column = "INETColumn" - - -# --------------------------------------------------------------------- - -null = Null() -boolean = Boolean() -int8 = Int8() -int16 = Int16() -int32 = Int32() -int64 = Int64() -uint8 = UInt8() -uint16 = UInt16() -uint32 = UInt32() -uint64 = UInt64() -float16 = Float16() -float32 = Float32() -float64 = Float64() -string = String() -binary = Binary() -date = Date() -time = Time() -timestamp = Timestamp() -# geo spatial data type -geometry = GeoSpatial(geotype="geometry") -geography = GeoSpatial(geotype="geography") -point = Point() -linestring = LineString() -polygon = Polygon() -multilinestring = MultiLineString() -multipoint = MultiPoint() -multipolygon = MultiPolygon() -# json -json = JSON(binary=False) -jsonb = JSON(binary=True) -# special string based data type -uuid = UUID() -macaddr = MACADDR() -inet = INET() -decimal = Decimal() -unknown = Unknown() - -Enum = String - - -public( - Any=DataType, - null=null, - boolean=boolean, - int8=int8, - int16=int16, - int32=int32, - int64=int64, - uint8=uint8, - uint16=uint16, - uint32=uint32, - uint64=uint64, - float16=float16, - float32=float32, - float64=float64, - string=string, - binary=binary, - date=date, - time=time, - timestamp=timestamp, - dtype=dtype, - geometry=geometry, - geography=geography, - point=point, - linestring=linestring, - polygon=polygon, - multilinestring=multilinestring, - multipoint=multipoint, - multipolygon=multipolygon, - json=json, - jsonb=jsonb, - uuid=uuid, - macaddr=macaddr, - inet=inet, - decimal=decimal, - unknown=unknown, - Enum=Enum, - Geography=GeoSpatial, - Geometry=GeoSpatial, - Set=Array, -) diff --git a/third_party/bigframes_vendored/ibis/expr/datatypes/value.py b/third_party/bigframes_vendored/ibis/expr/datatypes/value.py deleted file mode 100644 index 5856cb8cf94..00000000000 --- a/third_party/bigframes_vendored/ibis/expr/datatypes/value.py +++ /dev/null @@ -1,391 +0,0 @@ -# Contains code from https://github.com/ibis-project/ibis/blob/9.2.0/ibis/expr/datatypes/value.py - -from __future__ import annotations - -import collections -import datetime -import decimal -import enum -import ipaddress -import json -import uuid -from collections.abc import Mapping, Sequence -from functools import partial -from operator import attrgetter -from typing import Any - -import bigframes_vendored.ibis.expr.datatypes as dt -import pyarrow as pa -import toolz -from bigframes_vendored.ibis.common.collections import frozendict -from bigframes_vendored.ibis.common.dispatch import lazy_singledispatch -from bigframes_vendored.ibis.common.exceptions import IbisTypeError, InputTypeError -from bigframes_vendored.ibis.common.numeric import normalize_decimal -from bigframes_vendored.ibis.common.temporal import ( - IntervalUnit, - normalize_datetime, - normalize_timedelta, - normalize_timezone, -) -from bigframes_vendored.ibis.expr.datatypes.cast import highest_precedence -from public import public - - -@lazy_singledispatch -def infer(value: Any) -> dt.DataType: - """Infer the corresponding ibis dtype for a python object.""" - raise InputTypeError( - f"Unable to infer datatype of value {value!r} with type {type(value)}" - ) - - -# TODO(kszucs): support NamedTuples and dataclasses instead of OrderedDict -# which should trigger infer_map instead -@infer.register(collections.OrderedDict) -def infer_struct(value: Mapping[str, Any]) -> dt.Struct: - """Infer the [`Struct`](./datatypes.qmd#ibis.expr.datatypes.Struct) type of `value`.""" - if not value: - raise TypeError("Empty struct type not supported") - fields = {name: infer(val) for name, val in value.items()} - return dt.Struct(fields) - - -@infer.register(collections.abc.Mapping) -def infer_map(value: Mapping[Any, Any]) -> dt.Map: - """Infer the [`Map`](./datatypes.qmd#ibis.expr.datatypes.Map) type of `value`.""" - if not value: - return dt.Map(dt.null, dt.null) - try: - return dt.Map( - highest_precedence(map(infer, value.keys())), - highest_precedence(map(infer, value.values())), - ) - except IbisTypeError: - return dt.Struct(toolz.valmap(infer, value, factory=type(value))) - - -@infer.register((list, tuple, set, frozenset)) -def infer_list(values: Sequence[Any]) -> dt.Array: - """Infer the [`Array`](./datatypes.qmd#ibis.expr.datatypes.Array) type of `value`.""" - if not values: - return dt.Array(dt.null) - return dt.Array(highest_precedence(map(infer, values))) - - -@infer.register("pyarrow.Scalar") -def infer_pyarrow_scalar(value: "pa.Scalar"): - """Infert the type of a PyArrow Scalar value.""" - import bigframes_vendored.ibis.formats.pyarrow - - return bigframes_vendored.ibis.formats.pyarrow.PyArrowType.to_ibis(value.type) - - -@infer.register(datetime.time) -def infer_time(value: datetime.time) -> dt.Time: - return dt.time - - -@infer.register(datetime.date) -def infer_date(value: datetime.date) -> dt.Date: - return dt.date - - -@infer.register(datetime.datetime) -def infer_timestamp(value: datetime.datetime) -> dt.Timestamp: - if value.tzinfo: - return dt.Timestamp(timezone=str(value.tzinfo)) - else: - return dt.timestamp - - -@infer.register(datetime.timedelta) -def infer_interval(value: datetime.timedelta) -> dt.Interval: - # datetime.timedelta only stores days, seconds, and microseconds internally - if value.days: - if value.seconds or value.microseconds: - raise ValueError( - "Unable to infer interval type from mixed units, " - "use ibis.interval(timedelta) instead" - ) - else: - return dt.Interval(IntervalUnit.DAY) - elif value.seconds: - if value.microseconds: - return dt.Interval(IntervalUnit.MICROSECOND) - else: - return dt.Interval(IntervalUnit.SECOND) - elif value.microseconds: - return dt.Interval(IntervalUnit.MICROSECOND) - else: - raise ValueError("Unable to infer interval type from zero value") - - -@infer.register(str) -def infer_string(value: str) -> dt.String: - return dt.string - - -@infer.register(bytes) -def infer_bytes(value: bytes) -> dt.Binary: - return dt.binary - - -@infer.register(float) -def infer_floating(value: float) -> dt.Float64: - return dt.float64 - - -@infer.register(int) -def infer_integer(value: int, prefer_unsigned: bool = False) -> dt.Integer: - types = (dt.uint8, dt.uint16, dt.uint32, dt.uint64) if prefer_unsigned else () - types += (dt.int8, dt.int16, dt.int32, dt.int64) - for dtype in types: - if dtype.bounds.lower <= value <= dtype.bounds.upper: - return dtype - return dt.uint64 if prefer_unsigned else dt.int64 - - -@infer.register(enum.Enum) -def infer_enum(_: enum.Enum) -> dt.String: - return dt.string - - -@infer.register(decimal.Decimal) -def infer_decimal(value: decimal.Decimal) -> dt.Decimal: - """Infer the [`Decimal`](./datatypes.qmd#ibis.expr.datatypes.Decimal) type of `value`.""" - return dt.decimal - - -@infer.register(bool) -def infer_boolean(value: bool) -> dt.Boolean: - return dt.boolean - - -@infer.register((type(None), dt.Null)) -def infer_null(value: dt.Null | None) -> dt.Null: - return dt.null - - -@infer.register((ipaddress.IPv4Address, ipaddress.IPv6Address)) -def infer_ipaddr( - _: ipaddress.IPv4Address | ipaddress.IPv6Address | None, -) -> dt.INET: - return dt.inet - - -@infer.register("numpy.generic") -def infer_numpy_scalar(value): - from bigframes_vendored.ibis.formats.numpy import NumpyType - - return NumpyType.to_ibis(value.dtype) - - -@infer.register("pandas.Timestamp") -def infer_pandas_timestamp(value): - if value.tz is not None: - return dt.Timestamp(timezone=str(value.tz)) - else: - return dt.timestamp - - -@infer.register("pandas.Timedelta") -def infer_interval_pandas(value) -> dt.Interval: - # pandas Timedelta has more granularity - units = { - "D": "d", - "H": "h", - "h": "h", - "T": "m", - "min": "m", - "S": "s", - "s": "s", - "L": "ms", - "ms": "ms", - "U": "us", - "us": "us", - "N": "ns", - "ns": "ns", - } - unit = units[value.resolution_string] - return dt.Interval(unit) - - -@infer.register("numpy.ndarray") -@infer.register("pandas.Series") -def infer_numpy_array(value): - from bigframes_vendored.ibis.formats.numpy import NumpyType - from bigframes_vendored.ibis.formats.pyarrow import PyArrowData - - if value.dtype.kind == "O": - value_dtype = PyArrowData.infer_column(value) - else: - value_dtype = NumpyType.to_ibis(value.dtype) - - return dt.Array(value_dtype) - - -@infer.register("shapely.geometry.Point") -def infer_shapely_point(value) -> dt.Point: - return dt.point - - -@infer.register("shapely.geometry.LineString") -def infer_shapely_linestring(value) -> dt.LineString: - return dt.linestring - - -@infer.register("shapely.geometry.Polygon") -def infer_shapely_polygon(value) -> dt.Polygon: - return dt.polygon - - -@infer.register("shapely.geometry.MultiLineString") -def infer_shapely_multilinestring(value) -> dt.MultiLineString: - return dt.multilinestring - - -@infer.register("shapely.geometry.MultiPoint") -def infer_shapely_multipoint(value) -> dt.MultiPoint: - return dt.multipoint - - -@infer.register("shapely.geometry.MultiPolygon") -def infer_shapely_multipolygon(value) -> dt.MultiPolygon: - return dt.multipolygon - - -# lock the dispatcher to prevent adding new implementations -del infer.register - - -# TODO(kszucs): should raise ValueError instead of TypeError -def normalize(typ, value): - """Ensure that the Python type underlying a literal resolves to a single type.""" - - if pa is not None and isinstance(value, pa.Scalar): - value = value.as_py() - - dtype = dt.dtype(typ) - if value is None: - if not dtype.nullable: - raise TypeError(f"Cannot convert `None` to non-nullable type {typ!r}") - return None - - if dtype.is_boolean(): - try: - return bool(value) - except ValueError: - raise TypeError(f"Unable to normalize {value!r} to {dtype!r}") - elif dtype.is_integer(): - try: - value = int(value) - except ValueError: - raise TypeError(f"Unable to normalize {value!r} to {dtype!r}") - if value not in dtype.bounds: - raise TypeError( - f"Value {value} is out of bounds for type {dtype!r} " - f"(bounds: {dtype.bounds})" - ) - else: - return value - elif dtype.is_floating(): - try: - return float(value) - except ValueError: - raise TypeError(f"Unable to normalize {value!r} to {dtype!r}") - elif dtype.is_json(): - if isinstance(value, str): - try: - json.loads(value) - except json.JSONDecodeError: - raise TypeError(f"Invalid JSON string: {value!r}") - else: - return value - else: - return json.dumps(value) - elif dtype.is_binary(): - return bytes(value) - elif dtype.is_string() or dtype.is_macaddr() or dtype.is_inet(): - return str(value) - elif dtype.is_decimal(): - return normalize_decimal(value, precision=dtype.precision, scale=dtype.scale) - elif dtype.is_uuid(): - return value if isinstance(value, uuid.UUID) else uuid.UUID(value) - elif dtype.is_array(): - return tuple(normalize(dtype.value_type, item) for item in value) - elif dtype.is_map(): - return frozendict({k: normalize(dtype.value_type, v) for k, v in value.items()}) - elif dtype.is_struct(): - if not isinstance(value, Mapping): - raise TypeError(f"Unable to normalize {dtype} from non-mapping {value!r}") - if missing_keys := (dtype.keys() - value.keys()): - raise TypeError( - f"Unable to normalize {value!r} to {dtype} because of missing keys {missing_keys!r}" - ) - return frozendict({k: normalize(t, value[k]) for k, t in dtype.items()}) - elif dtype.is_geospatial(): - import shapely - import shapely.geometry - - if isinstance(value, (tuple, list)): - if dtype.is_point(): - return shapely.geometry.Point(value) - elif dtype.is_linestring(): - return shapely.geometry.LineString(value) - elif dtype.is_polygon(): - return shapely.geometry.Polygon( - toolz.concat( - map( - attrgetter("coords"), - map(partial(normalize, dt.linestring), value), - ) - ) - ) - elif dtype.is_multipoint(): - return shapely.geometry.MultiPoint( - tuple(map(partial(normalize, dt.point), value)) - ) - elif dtype.is_multilinestring(): - return shapely.geometry.MultiLineString( - tuple(map(partial(normalize, dt.linestring), value)) - ) - elif dtype.is_multipolygon(): - return shapely.geometry.MultiPolygon( - map(partial(normalize, dt.polygon), value) - ) - else: - raise IbisTypeError(f"Unsupported geospatial type: {dtype}") - elif isinstance(value, shapely.geometry.base.BaseGeometry): - return value - else: - return shapely.from_wkt(value) - elif dtype.is_date(): - return normalize_datetime(value).date() - elif dtype.is_time(): - return normalize_datetime(value).time() - elif dtype.is_timestamp(): - value = normalize_datetime(value) - tzinfo = normalize_timezone(dtype.timezone) - if tzinfo is None: - return value - elif value.tzinfo is None or value.tzinfo.utcoffset(value) is None: - return value.replace(tzinfo=tzinfo) - else: - return value.astimezone(tzinfo) - elif dtype.is_interval(): - return normalize_timedelta(value, dtype.unit) - else: - raise TypeError(f"Unable to normalize {value!r} to {dtype!r}") - - -def normalizable(typ, value): - """Check if a value can be normalized to a given type.""" - try: - normalize(typ, value) - except TypeError: - return False - else: - return True - - -public(infer=infer, normalize=normalize) diff --git a/third_party/bigframes_vendored/ibis/expr/decompile.py b/third_party/bigframes_vendored/ibis/expr/decompile.py deleted file mode 100644 index 62913c9fa90..00000000000 --- a/third_party/bigframes_vendored/ibis/expr/decompile.py +++ /dev/null @@ -1,482 +0,0 @@ -# Contains code from https://github.com/ibis-project/ibis/blob/9.2.0/ibis/expr/decompile.py - -from __future__ import annotations - -import collections -import functools -import io -import itertools - -import bigframes_vendored.ibis.expr.datatypes as dt -import bigframes_vendored.ibis.expr.operations as ops -import bigframes_vendored.ibis.expr.types as ibis_types -import bigframes_vendored.ibis.expr.types as ir -from bigframes_vendored.ibis.common.graph import Graph -from bigframes_vendored.ibis.expr.rewrites import simplify -from bigframes_vendored.ibis.util import experimental - -_method_overrides = { - ops.CountDistinct: "nunique", - ops.CountStar: "count", - ops.EndsWith: "endswith", - ops.ExtractDay: "day", - ops.ExtractDayOfYear: "day_of_year", - ops.ExtractEpochSeconds: "epoch_seconds", - ops.ExtractHour: "hour", - ops.ExtractMicrosecond: "microsecond", - ops.ExtractMillisecond: "millisecond", - ops.ExtractMinute: "minute", - ops.ExtractMinute: "minute", - ops.ExtractMonth: "month", - ops.ExtractQuarter: "quarter", - ops.ExtractSecond: "second", - ops.ExtractWeekOfYear: "week_of_year", - ops.ExtractYear: "year", - ops.Intersection: "intersect", - ops.IsNull: "isnull", - ops.Lowercase: "lower", - ops.RegexSearch: "re_search", - ops.StartsWith: "startswith", - ops.StringContains: "contains", - ops.StringSQLILike: "ilike", - ops.StringSQLLike: "like", - ops.TimestampNow: "now", -} - - -def _to_snake_case(camel_case): - """Convert a camelCase string to snake_case.""" - result = list(camel_case[:1].lower()) - for char in camel_case[1:]: - if char.isupper(): - result.append("_") - result.append(char.lower()) - return "".join(result) - - -def _get_method_name(op): - typ = op.__class__ - try: - return _method_overrides[typ] - except KeyError: - return _to_snake_case(typ.__name__) - - -def _maybe_add_parens(op, string): - if isinstance(op, ops.Binary): - return f"({string})" - elif isinstance(string, CallStatement): - return string.args - else: - return string - - -class CallStatement: - def __init__(self, func, args): - self.func = func - self.args = args - - def __str__(self): - return f"{self.func}({self.args})" - - -@functools.singledispatch -def translate(op, *args, **kwargs): - """Translate an ibis operation into a Python expression.""" - raise NotImplementedError(op) - - -@translate.register(ops.Value) -def value(op, *args, **kwargs): - method = _get_method_name(op) - kwargs = [(k, v) for k, v in kwargs.items() if v is not None] - - if args: - this, *args = args - else: - (_, this), *kwargs = kwargs - - # if there is a single keyword argument prefer to pass that as positional - if not args and len(kwargs) == 1: - args = [kwargs[0][1]] - kwargs = [] - - args = ", ".join(map(str, args)) - kwargs = ", ".join(f"{k}={v}" for k, v in kwargs) - parameters = ", ".join(filter(None, [args, kwargs])) - - return f"{this}.{method}({parameters})" - - -@translate.register(ops.ScalarParameter) -def scalar_parameter(op, dtype, counter): - return f"ibis.param({str(dtype)!r})" - - -@translate.register(ops.UnboundTable) -@translate.register(ops.DatabaseTable) -def table(op, schema, name, **kwargs): - fields = dict(zip(schema.names, map(str, schema.types))) - return f"ibis.table(name={name!r}, schema={fields})" - - -def _try_unwrap(stmt): - if len(stmt) == 1: - return stmt[0] - else: - stmt = map(str, stmt) - values = ", ".join(stmt) - return f"[{values}]" - - -def _wrap_alias(values, rendered): - result = [] - for k, v in values.items(): - text = rendered[k] - if v.name != k: - if isinstance(v, ops.Binary): - text = f"({text}).name({k!r})" - else: - text = f"{text}.name({k!r})" - result.append(text) - return result - - -def _inline(args): - return ", ".join(map(str, args)) - - -@translate.register(ops.Project) -def project(op, parent, values): - out = f"{parent}" - if not values: - return out - - values = _wrap_alias(op.values, values) - return f"{out}.select({_inline(values)})" - - -@translate.register(ops.Filter) -def filter_(op, parent, predicates): - out = f"{parent}" - if predicates: - out = f"{out}.filter({_inline(predicates)})" - return out - - -@translate.register(ops.Sort) -def sort(op, parent, keys): - out = f"{parent}" - if keys: - out = f"{out}.order_by({_inline(keys)})" - return out - - -@translate.register(ops.Aggregate) -def aggregation(op, parent, groups, metrics): - groups = _wrap_alias(op.groups, groups) - metrics = _wrap_alias(op.metrics, metrics) - if groups and metrics: - return f"{parent}.aggregate([{_inline(metrics)}], by=[{_inline(groups)}])" - elif metrics: - return f"{parent}.aggregate([{_inline(metrics)}])" - else: - raise ValueError("No metrics to aggregate") - - -@translate.register(ops.Distinct) -def distinct(op, parent): - return f"{parent}.distinct()" - - -@translate.register(ops.DropColumns) -def drop(op, parent, columns_to_drop): - return f"{parent}.drop({_inline(map(repr, columns_to_drop))})" - - -@translate.register(ops.SelfReference) -def self_reference(op, parent, identifier): - return f"{parent}.view()" - - -@translate.register(ops.JoinReference) -def join_reference(op, parent, identifier): - return parent - - -@translate.register(ops.JoinLink) -def join_link(op, table, predicates, how): - return f".{how}_join({table}, {_try_unwrap(predicates)})" - - -@translate.register(ops.JoinChain) -def join(op, first, rest, values): - calls = "".join(rest) - pieces = [f"{first}{calls}"] - if values: - values = _wrap_alias(op.values, values) - pieces.append(f"select({_inline(values)})") - result = ".".join(pieces) - return result - - -@translate.register(ops.Set) -def union(op, left, right, distinct): - method = _get_method_name(op) - if distinct: - return f"{left}.{method}({right}, distinct=True)" - else: - return f"{left}.{method}({right})" - - -@translate.register(ops.Limit) -def limit(op, parent, n, offset): - if offset: - return f"{parent}.limit({n}, {offset})" - else: - return f"{parent}.limit({n})" - - -@translate.register(ops.Field) -def table_column(op, rel, name): - if name.isidentifier(): - return f"{rel}.{name}" - return f"{rel}[{name!r}]" - - -@translate.register(ops.SortKey) -def sort_key(op, expr, ascending, nulls_first): - method = "asc" if ascending else "desc" - call = f"{expr}.{method}" - if nulls_first: - return f"{call}(nulls_first={nulls_first})" - return f"{call}()" - - -@translate.register(ops.Reduction) -def reduction(op, arg, where, **kwargs): - method = _get_method_name(op) - return f"{arg}.{method}()" - - -@translate.register(ops.Alias) -def alias(op, arg, name): - arg = _maybe_add_parens(op.arg, arg) - return f"{arg}.name({name!r})" - - -@translate.register(ops.Constant) -def constant(op, **kwargs): - method = _get_method_name(op) - return f"ibis.{method}()" - - -@translate.register(ops.Literal) -def literal(op, value, dtype): - inferred = ibis_types.literal(value) - - if isinstance(op.dtype, dt.Timestamp): - return f'ibis.timestamp("{value}")' - elif isinstance(op.dtype, dt.Date): - return f"ibis.date({value!r})" - elif isinstance(op.dtype, dt.Interval): - return f"ibis.interval({value!r})" - elif inferred.type() != op.dtype: - return CallStatement("ibis.literal", f"{value!r}, {dtype}") - else: - # prefer plain python literal values if the inferred datatype is the same, - # though this makes rendering method calls on literals more complicated - return CallStatement("ibis.literal", repr(value)) - - -@translate.register(ops.Cast) -def cast(op, arg, to): - return f"{arg}.cast({str(to)!r})" - - -@translate.register(ops.Between) -def between(op, arg, lower_bound, upper_bound): - return f"{arg}.between({lower_bound}, {upper_bound})" - - -@translate.register(ops.IfElse) -def ifelse(op, bool_expr, true_expr, false_null_expr): - return f"{bool_expr}.ifelse({true_expr}, {false_null_expr})" - - -@translate.register(ops.SimpleCase) -@translate.register(ops.SearchedCase) -def switch_case(op, cases, results, default, base=None): - out = f"{base}.case()" if base else "ibis.case()" - - for case, result in zip(cases, results): - out = f"{out}.when({case}, {result})" - - if default is not None: - out = f"{out}.else_({default})" - - return f"{out}.end()" - - -_infix_ops = { - ops.Equals: "==", - ops.NotEquals: "!=", - ops.GreaterEqual: ">=", - ops.Greater: ">", - ops.LessEqual: "<=", - ops.Less: "<", - ops.And: "and", - ops.Or: "or", - ops.Add: "+", - ops.Subtract: "-", - ops.Multiply: "*", - ops.Divide: "/", - ops.Power: "**", - ops.Modulus: "%", - ops.TimestampAdd: "+", - ops.TimestampSub: "-", - ops.TimestampDiff: "-", -} - - -@translate.register(ops.Binary) -def binary(op, left, right): - operator = _infix_ops[type(op)] - left = _maybe_add_parens(op.left, left) - right = _maybe_add_parens(op.right, right) - return f"{left} {operator} {right}" - - -@translate.register(ops.InValues) -def isin(op, value, options): - return f"{value}.isin(({', '.join([str(option) for option in options])}))" - - -class CodeContext: - always_assign = ( - ops.ScalarParameter, - ops.Aggregate, - ops.PhysicalTable, - ops.SelfReference, - ) - - always_ignore = ( - ops.JoinReference, - ops.Field, - dt.Primitive, - dt.Variadic, - dt.Temporal, - ) - shorthands = { - ops.Aggregate: "agg", - ops.Literal: "lit", - ops.ScalarParameter: "param", - ops.Project: "p", - ops.Relation: "r", - ops.Filter: "f", - ops.Sort: "s", - } - - def __init__(self, assign_result_to="result"): - self.assign_result_to = assign_result_to - self._shorthand_counters = collections.defaultdict(itertools.count) - - def variable_for(self, node): - klass = type(node) - if isinstance(node, ops.Relation) and hasattr(node, "name"): - name = node.name - elif klass in self.shorthands: - name = self.shorthands[klass] - else: - name = klass.__name__.lower() - - # increment repeated type names: table, table1, table2, ... - nth = next(self._shorthand_counters[name]) or "" - return f"{name}{nth}" - - def render(self, node, code, n_dependents): - isroot = n_dependents == 0 - ignore = isinstance(node, self.always_ignore) - assign = n_dependents > 1 or isinstance(node, self.always_assign) - - # depending on the conditions return with (output code, node result) pairs - if not code: - return (None, None) - elif isroot: - if self.assign_result_to: - out = f"\n{self.assign_result_to} = {code}\n" - else: - out = str(code) - return (out, code) - elif ignore: - return (None, code) - elif assign: - var = self.variable_for(node) - out = f"{var} = {code}\n" - return (out, var) - else: - return (None, code) - - -@experimental -def decompile( - expr: ir.Expr, - render_import: bool = True, - assign_result_to: str = "result", - format: bool = False, -) -> str: - """Decompile an ibis expression into Python source code. - - Parameters - ---------- - expr - node or expression to decompile - render_import - Whether to add `import ibis` to the result. - assign_result_to - Variable name to store the result at, pass None to avoid assignment. - format - Whether to format the generated code using black code formatter. - - Returns - ------- - str - Equivalent Python source code for `node`. - - """ - if not isinstance(expr, ir.Expr): - raise TypeError(f"Expected ibis expression, got {type(expr).__name__}") - - node = expr.op() - node = simplify(node) - out = io.StringIO() - ctx = CodeContext(assign_result_to=assign_result_to) - dependents = Graph(node).invert() - - def fn(node, _, *args, **kwargs): - code = translate(node, *args, **kwargs) - n_dependents = len(dependents[node]) - - code, result = ctx.render(node, code, n_dependents) - if code: - out.write(code) - - return result - - node.map(fn) - - result = out.getvalue() - if render_import: - result = f"import bigframes_vendored.ibis\n\n\n{result}" - - if format: - try: - import black - except ImportError: - raise ImportError( - "The 'format' option requires the 'black' package to be installed" - ) - - result = black.format_str(result, mode=black.FileMode()) - - return result diff --git a/third_party/bigframes_vendored/ibis/expr/format.py b/third_party/bigframes_vendored/ibis/expr/format.py deleted file mode 100644 index 27eac21ddeb..00000000000 --- a/third_party/bigframes_vendored/ibis/expr/format.py +++ /dev/null @@ -1,373 +0,0 @@ -# Contains code from https://github.com/ibis-project/ibis/blob/9.2.0/ibis/expr/format.py - -from __future__ import annotations - -import functools -import itertools -import textwrap -import types -from collections.abc import Mapping, Sequence -from typing import Optional - -import bigframes_vendored.ibis -import bigframes_vendored.ibis.expr.datatypes as dt -import bigframes_vendored.ibis.expr.operations as ops -from bigframes_vendored.ibis import util -from bigframes_vendored.ibis.common.graph import Node -from public import public - -_infix_ops = { - # comparison operations - ops.Equals: "==", - ops.IdenticalTo: "===", - ops.NotEquals: "!=", - ops.Less: "<", - ops.LessEqual: "<=", - ops.Greater: ">", - ops.GreaterEqual: ">=", - # arithmetic operations - ops.Add: "+", - ops.Subtract: "-", - ops.Multiply: "*", - ops.Divide: "/", - ops.FloorDivide: "//", - ops.Modulus: "%", - ops.Power: "**", - # temporal operations - ops.DateAdd: "+", - ops.DateSub: "-", - ops.DateDiff: "-", - ops.TimeAdd: "+", - ops.TimeSub: "-", - ops.TimeDiff: "-", - ops.TimestampAdd: "+", - ops.TimestampSub: "-", - ops.TimestampDiff: "-", - ops.IntervalAdd: "+", - ops.IntervalSubtract: "-", - ops.IntervalMultiply: "*", - ops.IntervalFloorDivide: "//", - # boolean operators - ops.And: "&", - ops.Or: "|", - ops.Xor: "^", -} - - -def type_info(datatype) -> str: - """Format `datatype` for display next to a column.""" - return f" # {datatype}" * bigframes_vendored.ibis.options.repr.show_types - - -def truncate(pieces: Sequence[str], limit: int) -> list[str]: - if limit < 1: - raise ValueError("limit must be >= 1") - elif limit == 1: - return pieces[-1:] - elif limit >= len(pieces): - return pieces - - first_n = limit // 2 - last_m = limit - first_n - first, last = pieces[:first_n], pieces[-last_m:] - - maxlen = max(*map(len, first), *map(len, last)) - ellipsis = util.VERTICAL_ELLIPSIS.center(maxlen) - - return [*first, ellipsis, *last] - - -def render(obj, indent_level=0, limit_items=None, key_separator=":"): - if isinstance(obj, str): - result = obj - elif isinstance(obj, Mapping): - rendered = {f"{k}{key_separator}": render(v) for k, v in obj.items() if v} - if not rendered: - return "" - maxlen = max(map(len, rendered.keys())) - lines = [f"{k:<{maxlen}} {v}" for k, v in rendered.items()] - if limit_items is not None: - lines = truncate(lines, limit_items) - result = "\n".join(lines) - elif isinstance(obj, Sequence): - lines = tuple(render(item) for item in obj) - if limit_items is not None: - lines = truncate(lines, limit_items) - result = "\n".join(lines) - else: - result = str(obj) - - return util.indent(result, spaces=indent_level * 2) - - -def render_fields(fields, indent_level=0, limit_items=None): - rendered = {k: render(v, 1) for k, v in fields.items() if v} - lines = [f"{k}:\n{v}" for k, v in rendered.items()] - if limit_items is not None: - lines = truncate(lines, limit_items) - result = "\n".join(lines) - return util.indent(result, spaces=indent_level * 2) - - -def render_schema(schema, indent_level=0, limit_items=None): - if not len(schema): - return util.indent("", spaces=indent_level * 2) - if limit_items is None: - limit_items = bigframes_vendored.ibis.options.repr.table_columns - return render(schema, indent_level, limit_items, key_separator="") - - -def inline(obj): - if isinstance(obj, Mapping): - fields = ", ".join(f"{k!r}: {inline(v)}" for k, v in obj.items()) - return f"{{{fields}}}" - elif util.is_iterable(obj): - elems = ", ".join(inline(item) for item in obj) - return f"[{elems}]" - elif isinstance(obj, types.FunctionType): - return obj.__name__ - elif isinstance(obj, dt.DataType): - return str(obj) - else: - return repr(obj) - - -def inline_args(fields, prefer_positional=False): - fields = {k: inline(v) for k, v in fields.items() if v} - - if fields and prefer_positional: - first, *rest = fields.keys() - if not rest: - return fields[first] - elif first in {"arg", "expr"}: - first = fields[first] - rest = (f"{k}={fields[k]}" for k in rest) - return ", ".join((first, *rest)) - - return ", ".join(f"{k}={v}" for k, v in fields.items()) - - -class Rendered(str): - def __repr__(self): - return self - - -@public -def pretty(node: Node, scope: Optional[dict[str, Node]] = None) -> str: - """Pretty print an expression. - - Parameters - ---------- - node - The graph node to pretty print. - scope - A dictionary of expression to name mappings used to intermediate - assignments. If not provided aliases will be generated for each - relation. - - Returns - ------- - str - A pretty printed representation of the expression. - """ - if not isinstance(node, Node): - raise TypeError(f"Expected a graph node, got {type(node)}") - - refs = {} - refcnt = itertools.count() - variables = {v.op(): k for k, v in (scope or {}).items()} - - def mapper(op, _, **kwargs): - result = fmt(op, **kwargs) - if var := variables.get(op): - refs[op] = result - result = var - elif isinstance(op, ops.Relation) and not isinstance(op, ops.JoinReference): - refs[op] = result - result = f"r{next(refcnt)}" - return Rendered(result) - - results = node.map(mapper) - - out = [] - for ref, rendered in refs.items(): - if ref is not node: - out.append(f"{results[ref]} := {rendered}") - - res = refs.get(node, results[node]) - if isinstance(node, ops.Literal): - out.append(res) - elif isinstance(node, ops.Value): - out.append(f"{node.name}: {res}{type_info(node.dtype)}") - else: - out.append(res) - - return "\n\n".join(out) - - -@functools.singledispatch -def fmt(op, **kwargs): - top = f"{op.__class__.__name__}\n" - return top + render_fields(kwargs, 1) - - -@fmt.register(ops.Relation) -def _relation(op, parent=None, **kwargs): - if parent is None: - top = f"{op.__class__.__name__}\n" - else: - top = f"{op.__class__.__name__}[{parent}]\n" - kwargs["schema"] = render_schema(op.schema) - return top + render_fields(kwargs, 1) - - -@fmt.register(ops.PhysicalTable) -def _physical_table(op, name, **kwargs): - schema = render_schema(op.schema, indent_level=1) - return f"{op.__class__.__name__}: {name}\n{schema}" - - -@fmt.register(ops.UnboundTable) -@fmt.register(ops.DatabaseTable) -def _unbound_table(op, name, **kwargs): - schema = render_schema(op.schema, indent_level=1) - name = ".".join(filter(None, op.namespace.args + (name,))) - return f"{op.__class__.__name__}: {name}\n{schema}" - - -@fmt.register(ops.InMemoryTable) -def _in_memory_table(op, data, **kwargs): - import rich.pretty - - name = f"{op.__class__.__name__}\n" - data = rich.pretty.pretty_repr( - op.data, max_length=bigframes_vendored.ibis.options.repr.table_columns - ) - return name + render_fields({"data": data}, 1) - - -@fmt.register(ops.SQLQueryResult) -@fmt.register(ops.SQLStringView) -def _sql_query_result(op, query, **kwargs): - clsname = op.__class__.__name__ - - if isinstance(op, ops.SQLStringView): - child = kwargs["child"] - top = f"{clsname}[{child}]\n" - else: - top = f"{clsname}\n" - - query = textwrap.shorten( - query, - width=bigframes_vendored.ibis.options.repr.query_text_length, - placeholder=f" {util.HORIZONTAL_ELLIPSIS}", - ) - schema = render_schema(op.schema) - return top + render_fields({"query": query, "schema": schema}, 1) - - -@fmt.register(ops.FillNull) -@fmt.register(ops.DropNull) -def _fill_null(op, parent, **kwargs): - name = f"{op.__class__.__name__}[{parent}]\n" - return name + render_fields(kwargs, 1) - - -@fmt.register(ops.Aggregate) -def _aggregate(op, parent, **kwargs): - name = f"{op.__class__.__name__}[{parent}]\n" - return name + render_fields(kwargs, 1) - - -@fmt.register(ops.Sort) -def _sort(op, parent, keys): - name = f"{op.__class__.__name__}[{parent}]\n" - return name + render(keys, 1) - - -@fmt.register(ops.Set) -def _set_op(op, left, right, distinct): - args = [str(left), str(right)] - if op.distinct is not None: - args.append(f"distinct={distinct}") - return f"{op.__class__.__name__}[{', '.join(args)}]" - - -@fmt.register(ops.JoinChain) -def _join_project(op, first, rest, **kwargs): - name = f"{op.__class__.__name__}[{first}]\n" - return name + render(rest, 1) + "\n" + render_fields(kwargs, 1) - - -@fmt.register(ops.Limit) -@fmt.register(ops.Sample) -def _limit(op, parent, **kwargs): - params = inline_args(kwargs) - return f"{op.__class__.__name__}[{parent}, {params}]" - - -@fmt.register(ops.SelfReference) -@fmt.register(ops.Distinct) -def _self_reference(op, parent, **kwargs): - return f"{op.__class__.__name__}[{parent}]" - - -@fmt.register(ops.JoinReference) -def _join_reference(op, parent, **kwargs): - return parent - - -@fmt.register(ops.Literal) -def _literal(op, value, **kwargs): - if op.dtype.is_interval(): - return f"{value!r} {op.dtype.unit.short}" - elif op.dtype.is_array(): - return f"{list(value)!r}" - else: - return f"{value!r}" - - -@fmt.register(ops.Field) -def _relation_field(op, rel, name): - if name.isidentifier(): - return f"{rel}.{name}" - else: - return f"{rel}[{name!r}]" - - -@fmt.register(ops.Value) -def _value(op, **kwargs): - fields = inline_args(kwargs, prefer_positional=True) - return f"{op.__class__.__name__}({fields})" - - -@fmt.register(ops.Alias) -def _alias(op, arg, name): - return arg - - -@fmt.register(ops.Binary) -def _binary(op, left, right): - try: - symbol = _infix_ops[op.__class__] - except KeyError: - return f"{op.__class__.__name__}({left}, {right})" - else: - return f"{left} {symbol} {right}" - - -@fmt.register(ops.ScalarParameter) -def _scalar_parameter(op, dtype, **kwargs): - return f"$({dtype})" - - -@fmt.register(ops.SortKey) -def _sort_key(op, expr, **kwargs): - return f"{'asc' if op.ascending else 'desc'} {expr}" - - -@fmt.register(ops.GeoSpatialBinOp) -def _geo_bin_op(op, left, right, **kwargs): - fields = [left, right, inline_args(kwargs)] - args = ", ".join(f"{field}" for field in fields if field) - return f"{op.__class__.__name__}({args})" diff --git a/third_party/bigframes_vendored/ibis/expr/operations/__init__.py b/third_party/bigframes_vendored/ibis/expr/operations/__init__.py index 90f41a304a4..1612d9c12e5 100644 --- a/third_party/bigframes_vendored/ibis/expr/operations/__init__.py +++ b/third_party/bigframes_vendored/ibis/expr/operations/__init__.py @@ -1,22 +1,5 @@ # Contains code from https://github.com/ibis-project/ibis/blob/master/ibis/expr/operations/__init__.py from __future__ import annotations -from bigframes_vendored.ibis.expr.operations.analytic import * # noqa: F401 F403 -from bigframes_vendored.ibis.expr.operations.arrays import * # noqa: F401 F403 -from bigframes_vendored.ibis.expr.operations.core import * # noqa: F401 F403 -from bigframes_vendored.ibis.expr.operations.generic import * # noqa: F401 F403 -from bigframes_vendored.ibis.expr.operations.geospatial import * # noqa: F401 F403 -from bigframes_vendored.ibis.expr.operations.histograms import * # noqa: F401 F403 -from bigframes_vendored.ibis.expr.operations.json import * # noqa: F401 F403 -from bigframes_vendored.ibis.expr.operations.logical import * # noqa: F401 F403 -from bigframes_vendored.ibis.expr.operations.maps import * # noqa: F401 F403 -from bigframes_vendored.ibis.expr.operations.numeric import * # noqa: F401 F403 -from bigframes_vendored.ibis.expr.operations.reductions import * # noqa: F401 F403 -from bigframes_vendored.ibis.expr.operations.relations import * # noqa: F401 F403 -from bigframes_vendored.ibis.expr.operations.sortkeys import * # noqa: F401 F403 -from bigframes_vendored.ibis.expr.operations.strings import * # noqa: F401 F403 -from bigframes_vendored.ibis.expr.operations.structs import * # noqa: F401 F403 -from bigframes_vendored.ibis.expr.operations.subqueries import * # noqa: F401 F403 -from bigframes_vendored.ibis.expr.operations.temporal import * # noqa: F401 F403 -from bigframes_vendored.ibis.expr.operations.udf import * # noqa: F401 F403 -from bigframes_vendored.ibis.expr.operations.window import * # noqa: F401 F403 +from third_party.bigframes_vendored.ibis.expr.operations.analytic import * # noqa: F403 +from third_party.bigframes_vendored.ibis.expr.operations.reductions import * # noqa: F403 diff --git a/third_party/bigframes_vendored/ibis/expr/operations/ai_ops.py b/third_party/bigframes_vendored/ibis/expr/operations/ai_ops.py deleted file mode 100644 index 9fa043d0bab..00000000000 --- a/third_party/bigframes_vendored/ibis/expr/operations/ai_ops.py +++ /dev/null @@ -1,205 +0,0 @@ -# Contains code from https://github.com/ibis-project/ibis/blob/9.2.0/ibis/expr/operations/maps.py - -"""Operations for working with AI operators.""" - -from __future__ import annotations - -from typing import Optional - -import bigframes_vendored.ibis.expr.datatypes as dt -import bigframes_vendored.ibis.expr.rules as rlz -import pyarrow as pa -from bigframes_vendored.ibis.common.annotations import attribute -from bigframes_vendored.ibis.expr.operations.core import Value -from public import public - -from bigframes.operations import output_schemas - - -@public -class AIGenerate(Value): - """Generate content based on the prompt""" - - prompt: Value - connection_id: Optional[Value[dt.String]] - endpoint: Optional[Value[dt.String]] - request_type: Value[dt.String] - model_params: Optional[Value[dt.String]] - output_schema: Optional[Value[dt.String]] - - shape = rlz.shape_like("prompt") - - @attribute - def dtype(self) -> dt.Struct: - if self.output_schema is None: - output_pa_fields = (pa.field("result", pa.string()),) - else: - output_pa_fields = output_schemas.parse_sql_fields(self.output_schema.value) - - pyarrow_output_type = pa.struct( - ( - *output_pa_fields, - pa.field("full_response", pa.string()), - pa.field("status", pa.string()), - ) - ) - - return dt.Struct.from_pyarrow(pyarrow_output_type) - - -@public -class AIGenerateBool(Value): - """Generate Bool based on the prompt""" - - prompt: Value - connection_id: Optional[Value[dt.String]] - endpoint: Optional[Value[dt.String]] - request_type: Value[dt.String] - model_params: Optional[Value[dt.String]] - - shape = rlz.shape_like("prompt") - - @attribute - def dtype(self) -> dt.Struct: - return dt.Struct.from_tuples( - (("result", dt.bool), ("full_response", dt.string), ("status", dt.string)) - ) - - -@public -class AIGenerateInt(Value): - """Generate integers based on the prompt""" - - prompt: Value - connection_id: Optional[Value[dt.String]] - endpoint: Optional[Value[dt.String]] - request_type: Value[dt.String] - model_params: Optional[Value[dt.String]] - - shape = rlz.shape_like("prompt") - - @attribute - def dtype(self) -> dt.Struct: - return dt.Struct.from_tuples( - (("result", dt.int64), ("full_response", dt.string), ("status", dt.string)) - ) - - -@public -class AIGenerateDouble(Value): - """Generate doubles based on the prompt""" - - prompt: Value - connection_id: Optional[Value[dt.String]] - endpoint: Optional[Value[dt.String]] - request_type: Value[dt.String] - model_params: Optional[Value[dt.String]] - - shape = rlz.shape_like("prompt") - - @attribute - def dtype(self) -> dt.Struct: - return dt.Struct.from_tuples( - ( - ("result", dt.float64), - ("full_response", dt.string), - ("status", dt.string), - ) - ) - - -@public -class AIEmbed(Value): - """Create embeddings from text or image data.""" - - content: Value - connection_id: Optional[Value[dt.String]] - endpoint: Optional[Value[dt.String]] - model: Optional[Value[dt.String]] - task_type: Optional[Value[dt.String]] - title: Optional[Value[dt.String]] - model_params: Optional[Value[dt.String]] - - shape = rlz.shape_like("content") - - @attribute - def dtype(self) -> dt.Struct: - return dt.Struct.from_tuples( - ( - ("result", dt.Array(dt.float64)), - ("status", dt.string), - ) - ) - - -@public -class AIIf(Value): - """Generate True/False based on the prompt""" - - prompt: Value - connection_id: Optional[Value[dt.String]] - endpoint: Optional[Value[dt.String]] - optimization_mode: Optional[Value[dt.String]] - max_error_ratio: Optional[Value[dt.Float64]] - - shape = rlz.shape_like("prompt") - - @attribute - def dtype(self) -> dt.Struct: - return dt.bool - - -@public -class AIClassify(Value): - """Generate categories based on the prompt""" - - input: Value - categories: Value[dt.Array[dt.String]] - examples: Optional[Value] - connection_id: Optional[Value[dt.String]] - endpoint: Optional[Value[dt.String]] - output_mode: Optional[Value[dt.String]] - optimization_mode: Optional[Value[dt.String]] - max_error_ratio: Optional[Value[dt.Float64]] - - shape = rlz.shape_like("input") - - @attribute - def dtype(self) -> dt.DataType: - if self.output_mode is not None: - return dt.Array(dt.string) - return dt.string - - -@public -class AIScore(Value): - """Generate scores based on the prompt""" - - prompt: Value - connection_id: Optional[Value[dt.String]] - endpoint: Optional[Value[dt.String]] - max_error_ratio: Optional[Value[dt.Float64]] - - shape = rlz.shape_like("prompt") - - @attribute - def dtype(self) -> dt.DataType: - return dt.float64 - - -@public -class AISimilarity(Value): - """Calculate the similarity between two contents""" - - content1: Value - content2: Value - endpoint: Optional[Value[dt.String]] - model: Optional[Value[dt.String]] - model_params: Optional[Value[dt.String]] - connection_id: Optional[Value[dt.String]] - - shape = rlz.shape_like("content1") - - @attribute - def dtype(self) -> dt.Struct: - return dt.float64 diff --git a/third_party/bigframes_vendored/ibis/expr/operations/analytic.py b/third_party/bigframes_vendored/ibis/expr/operations/analytic.py index 584fba23f66..038987cac93 100644 --- a/third_party/bigframes_vendored/ibis/expr/operations/analytic.py +++ b/third_party/bigframes_vendored/ibis/expr/operations/analytic.py @@ -1,124 +1,26 @@ -# Contains code from https://github.com/ibis-project/ibis/blob/9.2.0/ibis/expr/operations/analytic.py - -"""Operations for analytic window functions.""" +# Contains code from https://github.com/ibis-project/ibis/blob/master/ibis/expr/operations/analytic.py from __future__ import annotations -from typing import Optional - -import bigframes_vendored.ibis.expr.datashape as ds -import bigframes_vendored.ibis.expr.datatypes as dt -import bigframes_vendored.ibis.expr.operations.udf as ibis_udf -import bigframes_vendored.ibis.expr.rules as rlz -from bigframes_vendored.ibis.expr.operations.core import Column, Scalar, Value -from public import public - - -@public -class Analytic(Value): - """Base class for analytic window function operations.""" - - shape = ds.columnar - - -class ShiftBase(Analytic): - """Base class for shift operations.""" - - arg: Column[dt.Any] - offset: Optional[Value[dt.Integer | dt.Interval]] = None - default: Optional[Value] = None - - dtype = rlz.dtype_like("arg") - - -@public -class Lag(ShiftBase): - """Shift a column forward.""" - - -@public -class Lead(ShiftBase): - """Shift a column backward.""" - - -@public -class RankBase(Analytic): - """Base class for ranking operations.""" - - dtype = dt.int64 - - -@public -class MinRank(RankBase): - pass - - -@public -class DenseRank(RankBase): - pass - +from ibis.expr.operations.analytic import Analytic +import ibis.expr.rules as rlz -@public -class RowNumber(RankBase): - """Compute the row number over a window, starting from 0.""" - -@public -class PercentRank(Analytic): - """Compute the percentile rank over a window.""" - - dtype = dt.double - - -@public -class CumeDist(Analytic): - """Compute the cumulative distribution function of a column over a window.""" - - dtype = dt.double - - -@public -class NTile(Analytic): - """Compute the percentile of a column over a window.""" - - buckets: Scalar[dt.Integer] - - dtype = dt.int64 - - -@public -class NthValue(Analytic): - """Retrieve the Nth element of a column over a window.""" - - arg: Column[dt.Any] - nth: Value[dt.Integer] - - dtype = rlz.dtype_like("arg") - - -public(AnalyticOp=Analytic) - - -# TODO(swast): We can remove this if ibis adds aggregates over scalar values. -# See: https://github.com/ibis-project/ibis/issues/8698 -@public -@ibis_udf.agg.builtin -def count(value: int) -> int: - """Count of a scalar.""" - return 0 # pragma: NO COVER - - -@public class FirstNonNullValue(Analytic): """Retrieve the first element.""" - arg: Column - dtype = rlz.dtype_like("arg") + arg = rlz.column(rlz.any) + output_dtype = rlz.dtype_like("arg") -@public class LastNonNullValue(Analytic): """Retrieve the last element.""" - arg: Column - dtype = rlz.dtype_like("arg") + arg = rlz.column(rlz.any) + output_dtype = rlz.dtype_like("arg") + + +__all__ = [ + "FirstNonNullValue", + "LastNonNullValue", +] diff --git a/third_party/bigframes_vendored/ibis/expr/operations/arrays.py b/third_party/bigframes_vendored/ibis/expr/operations/arrays.py deleted file mode 100644 index 7e10a3e26f9..00000000000 --- a/third_party/bigframes_vendored/ibis/expr/operations/arrays.py +++ /dev/null @@ -1,284 +0,0 @@ -# Contains code from https://github.com/ibis-project/ibis/blob/9.2.0/ibis/expr/operations/arrays.py - -"""Operations for array expressions.""" - -from __future__ import annotations - -from typing import Optional - -import bigframes_vendored.ibis.expr.datashape as ds -import bigframes_vendored.ibis.expr.datatypes as dt -import bigframes_vendored.ibis.expr.rules as rlz -from bigframes_vendored.ibis.common.annotations import attribute -from bigframes_vendored.ibis.common.typing import VarTuple # noqa: TCH001 -from bigframes_vendored.ibis.expr.operations.core import Unary, Value -from public import public - - -@public -class Array(Value): - """Construct an array.""" - - exprs: VarTuple[Value] - - @attribute - def shape(self): - return rlz.highest_precedence_shape(self.exprs) - - @attribute - def dtype(self): - return dt.Array(rlz.highest_precedence_dtype(self.exprs)) - - -@public -class ArrayLength(Unary): - """Compute the length of an array.""" - - arg: Value[dt.Array] - - dtype = dt.int64 - shape = rlz.shape_like("args") - - -@public -class ArraySlice(Value): - """Slice an array element.""" - - arg: Value[dt.Array] - start: Value[dt.Integer] - stop: Optional[Value[dt.Integer]] = None - - dtype = rlz.dtype_like("arg") - shape = rlz.shape_like("args") - - -@public -class ArrayIndex(Value): - """Return the element of an array at some index.""" - - arg: Value[dt.Array] - index: Value[dt.Integer] - - shape = rlz.shape_like("args") - - @attribute - def dtype(self): - return self.arg.dtype.value_type - - -@public -class ArrayConcat(Value): - """Concatenate two or more arrays into a single array.""" - - arg: VarTuple[Value[dt.Array]] - - shape = rlz.shape_like("arg") - - @attribute - def dtype(self): - return dt.Array(dt.highest_precedence(arg.dtype.value_type for arg in self.arg)) - - -@public -class ArrayRepeat(Value): - """Repeat the elements of an array.""" - - arg: Value[dt.Array] - times: Value[dt.Integer] - - dtype = rlz.dtype_like("arg") - shape = rlz.shape_like("args") - - -@public -class ArrayMap(Value): - """Apply a function to every element of an array.""" - - arg: Value[dt.Array] - body: Value - param: str - - shape = rlz.shape_like("arg") - - @attribute - def dtype(self) -> dt.DataType: - return dt.Array(self.body.dtype) - - -@public -class ArrayReduce(Value): - """Apply a function to every element of an array.""" - - arg: Value[dt.Array] - body: Value - param: str - - shape = rlz.shape_like("arg") - - @attribute - def dtype(self) -> dt.DataType: - return self.body.dtype - - -@public -class ArrayFilter(Value): - """Filter array elements with a function.""" - - arg: Value[dt.Array] - body: Value[dt.Boolean] - param: str - - shape = rlz.shape_like("arg") - dtype = rlz.dtype_like("arg") - - -@public -class Unnest(Value): - """Unnest an array value into a column.""" - - arg: Value[dt.Array] - - shape = ds.columnar - - @attribute - def dtype(self): - return self.arg.dtype.value_type - - -@public -class ArrayContains(Value): - """Return whether an array contains a specific value.""" - - arg: Value[dt.Array] - other: Value - - dtype = dt.boolean - shape = rlz.shape_like("args") - - -@public -class ArrayPosition(Value): - """Return the position of a specific value in an array.""" - - arg: Value[dt.Array] - other: Value - - dtype = dt.int64 - shape = rlz.shape_like("args") - - -@public -class ArrayRemove(Value): - """Remove an element from an array.""" - - arg: Value[dt.Array] - other: Value - - dtype = rlz.dtype_like("arg") - shape = rlz.shape_like("args") - - -@public -class ArrayDistinct(Value): - """Return the unique elements of an array.""" - - arg: Value[dt.Array] - - dtype = rlz.dtype_like("arg") - shape = rlz.shape_like("arg") - - -@public -class ArraySort(Value): - """Sort the values of an array.""" - - arg: Value[dt.Array] - - dtype = rlz.dtype_like("arg") - shape = rlz.shape_like("arg") - - -@public -class ArrayUnion(Value): - """Return the union of two arrays.""" - - left: Value[dt.Array] - right: Value[dt.Array] - - dtype = rlz.dtype_like("args") - shape = rlz.shape_like("args") - - -@public -class ArrayIntersect(Value): - """Return the intersection of two arrays.""" - - left: Value[dt.Array] - right: Value[dt.Array] - - dtype = rlz.dtype_like("args") - shape = rlz.shape_like("args") - - -@public -class ArrayZip(Value): - """Zip two or more arrays into an array of structs.""" - - arg: VarTuple[Value[dt.Array]] - - shape = rlz.shape_like("arg") - - @attribute - def dtype(self): - return dt.Array( - dt.Struct( - { - f"f{i:d}": array.dtype.value_type - for i, array in enumerate(self.arg, start=1) - } - ) - ) - - -@public -class ArrayFlatten(Value): - """Flatten a nested array one level. - - The input expression must have at least one level of nesting for flattening - to make sense. - """ - - arg: Value[dt.Array[dt.Array]] - shape = rlz.shape_like("arg") - - @property - def dtype(self): - return self.arg.dtype.value_type - - -class Range(Value): - """Base class for range-generating operations.""" - - shape = rlz.shape_like("args") - - @attribute - def dtype(self) -> dt.DataType: - return dt.Array(dt.highest_precedence((self.start.dtype, self.stop.dtype))) - - -@public -class IntegerRange(Range): - """Produce an array of integers from `start` to `stop`, moving by `step`.""" - - start: Value[dt.Integer] - stop: Value[dt.Integer] - step: Value[dt.Integer] - - -@public -class TimestampRange(Range): - """Produce an array of timestamps from `start` to `stop`, moving by `step`.""" - - start: Value[dt.Timestamp] - stop: Value[dt.Timestamp] - step: Value[dt.Interval] diff --git a/third_party/bigframes_vendored/ibis/expr/operations/core.py b/third_party/bigframes_vendored/ibis/expr/operations/core.py deleted file mode 100644 index ad0bd095b6c..00000000000 --- a/third_party/bigframes_vendored/ibis/expr/operations/core.py +++ /dev/null @@ -1,200 +0,0 @@ -# Contains code from https://github.com/ibis-project/ibis/blob/9.2.0/ibis/expr/operations/core.py - -from __future__ import annotations - -from abc import abstractmethod -from typing import Generic, Optional - -import bigframes_vendored.ibis.expr.datashape as ds -import bigframes_vendored.ibis.expr.datatypes as dt -import bigframes_vendored.ibis.expr.rules as rlz -from bigframes_vendored.ibis.common.annotations import attribute -from bigframes_vendored.ibis.common.graph import Node as Traversable -from bigframes_vendored.ibis.common.grounds import Concrete -from bigframes_vendored.ibis.common.patterns import Coercible, CoercionError -from bigframes_vendored.ibis.common.typing import DefaultTypeVars -from bigframes_vendored.ibis.util import is_iterable -from public import public -from typing_extensions import Any, Self, TypeVar - - -@public -class Node(Concrete, Traversable): - def equals(self, other) -> bool: - if not isinstance(other, Node): - raise TypeError( - f"invalid equality comparison between Node and {type(other)}" - ) - return self == other - - # Avoid custom repr for performance reasons - __repr__ = object.__repr__ - - # TODO(kszucs): hidrate the __children__ traversable attribute - # @attribute - # def __children__(self): - # return super().__children__ - - -T = TypeVar("T", bound=dt.DataType, covariant=True) -S = TypeVar("S", bound=ds.DataShape, default=ds.Any, covariant=True) - - -@public -class Value(Node, Coercible, DefaultTypeVars, Generic[T, S]): - @classmethod - def __coerce__( - cls, value: Any, T: Optional[type] = None, S: Optional[type] = None - ) -> Self: - # note that S=Shape is unused here since the pattern will check the - # shape of the value expression after executing Value.__coerce__() - from bigframes_vendored.ibis.expr.operations.generic import NULL, Literal - from bigframes_vendored.ibis.expr.types import Expr - - if isinstance(value, Expr): - value = value.op() - - if isinstance(value, Value): - if value == NULL: - # treat the NULL literal the same as None to implicitly cast to - # the requested datatype if any - value = None - else: - return value - - if T is dt.Integer: - dtype = dt.infer(int(value)) - elif T is dt.Floating: - dtype = dt.infer(float(value)) - else: - try: - dtype = dt.DataType.from_typehint(T) - except TypeError: - dtype = dt.infer(value) - - try: - return Literal(value, dtype=dtype) - except TypeError: - raise CoercionError(f"Unable to coerce {value!r} to Value[{T!r}]") - - # TODO(kszucs): cover it with tests - # TODO(kszucs): figure out how to represent not named arguments - @property - def name(self) -> str: - names = [] - for arg in self.__args__: - if is_iterable(arg): - elements = [ - element_name - for element in arg - if (element_name := getattr(element, "name", None)) is not None - ] - joined = ", ".join(elements) - fmt = "({})" if len(elements) != 1 else "({},)" - names.append(fmt.format(joined)) - elif (name := getattr(arg, "name", None)) is not None: - names.append(name) - return f"{self.__class__.__name__}({', '.join(names)})" - - @property - @abstractmethod - def dtype(self) -> T: - """Ibis datatype of the produced value expression. - - Returns - ------- - dt.DataType - - """ - - @property - @abstractmethod - def shape(self) -> S: - """Shape of the produced value expression. - - Possible values are: "scalar" and "columnar" - - Returns - ------- - ds.Shape - - """ - - @attribute - def relations(self): - """Set of relations the value node depends on.""" - children = (n.relations for n in self.__children__ if isinstance(n, Value)) - return frozenset().union(*children) - - def to_expr(self): - import bigframes_vendored.ibis.expr.types as ir - - if self.shape.is_columnar(): - typename = self.dtype.column - else: - typename = self.dtype.scalar - - return getattr(ir, typename)(self) - - @property - def omitted(self) -> bool: - return False - - -# convenience aliases -Scalar = Value[T, ds.Scalar] -Column = Value[T, ds.Columnar] - - -@public -class Alias(Value): - arg: Value - name: str - - shape = rlz.shape_like("arg") - dtype = rlz.dtype_like("arg") - - -@public -class Unary(Value): - """A unary operation.""" - - arg: Value - - @attribute - def shape(self) -> ds.DataShape: - return self.arg.shape - - @attribute - def relations(self): - return self.arg.relations - - -@public -class Binary(Value): - """A binary operation.""" - - left: Value - right: Value - - @attribute - def shape(self) -> ds.DataShape: - return max(self.left.shape, self.right.shape) - - @attribute - def relations(self): - return self.left.relations | self.right.relations - - -@public -class Argument(Value): - name: str - shape: ds.DataShape - dtype: dt.DataType - - @attribute - def param(self) -> str: - return f"__ibis_param_{self.name}__" - - -public(ValueOp=Value, UnaryOp=Unary, BinaryOp=Binary, Scalar=Scalar, Column=Column) diff --git a/third_party/bigframes_vendored/ibis/expr/operations/generic.py b/third_party/bigframes_vendored/ibis/expr/operations/generic.py deleted file mode 100644 index cc0caf21b2e..00000000000 --- a/third_party/bigframes_vendored/ibis/expr/operations/generic.py +++ /dev/null @@ -1,352 +0,0 @@ -# Contains code from https://github.com/ibis-project/ibis/blob/9.2.0/ibis/expr/operations/generic.py -"""Generic value operations.""" - -from __future__ import annotations - -import itertools -from typing import Annotated, Any, Optional -from typing import Literal as LiteralType - -import bigframes_vendored.ibis.expr.datashape as ds -import bigframes_vendored.ibis.expr.datatypes as dt -import bigframes_vendored.ibis.expr.rules as rlz -from bigframes_vendored.ibis.common.annotations import attribute -from bigframes_vendored.ibis.common.deferred import Deferred # noqa: TCH001 -from bigframes_vendored.ibis.common.grounds import Singleton -from bigframes_vendored.ibis.common.patterns import InstanceOf, Length # noqa: TCH001 -from bigframes_vendored.ibis.common.typing import VarTuple # noqa: TCH001 -from bigframes_vendored.ibis.expr.operations.core import Scalar, Unary, Value -from bigframes_vendored.ibis.expr.operations.relations import Relation # noqa: TCH001 -from public import public -from typing_extensions import TypeVar - - -@public -class RowID(Value): - """The row number of the returned result.""" - - name = "rowid" - table: Relation - - shape = ds.columnar - dtype = dt.int64 - - @attribute - def relations(self): - return frozenset({self.table}) - - -@public -class Cast(Value): - """Explicitly cast a value to a specific data type.""" - - arg: Value - to: dt.DataType - - shape = rlz.shape_like("arg") - - @property - def name(self): - return f"{self.__class__.__name__}({self.arg.name}, {self.to})" - - @property - def dtype(self): - return self.to - - -@public -class TryCast(Value): - """Try to cast a value to a specific data type.""" - - arg: Value - to: dt.DataType - - shape = rlz.shape_like("arg") - - @property - def dtype(self): - return self.to - - -@public -class TypeOf(Unary): - """Return the _database_ data type of the input expression.""" - - dtype = dt.string - - -@public -class IsNull(Unary): - """Return true if values are null.""" - - dtype = dt.boolean - - -@public -class NotNull(Unary): - """Returns true if values are not null.""" - - dtype = dt.boolean - - -@public -class NullIf(Value): - """Return NULL if an expression equals some specific value.""" - - arg: Value - null_if_expr: Value - - dtype = rlz.dtype_like("args") - shape = rlz.shape_like("args") - - -@public -class Coalesce(Value): - """Return the first non-null expression from a tuple of expressions.""" - - arg: Annotated[VarTuple[Value], Length(at_least=1)] - - shape = rlz.shape_like("arg") - dtype = rlz.dtype_like("arg") - - -@public -class Greatest(Value): - """Return the largest value from a tuple of expressions.""" - - arg: Annotated[VarTuple[Value], Length(at_least=1)] - - shape = rlz.shape_like("arg") - dtype = rlz.dtype_like("arg") - - -@public -class Least(Value): - """Return the smallest value from a tuple of expressions.""" - - arg: Annotated[VarTuple[Value], Length(at_least=1)] - - shape = rlz.shape_like("arg") - dtype = rlz.dtype_like("arg") - - -T = TypeVar("T", bound=dt.DataType, covariant=True) - - -@public -class Literal(Scalar[T]): - """A constant value.""" - - value: Annotated[Any, ~InstanceOf(Deferred)] - dtype: T - - shape = ds.scalar - - def __init__(self, value, dtype): - # normalize ensures that the value is a valid value for the given dtype - value = dt.normalize(dtype, value) - super().__init__(value=value, dtype=dtype) - - @property - def name(self): - if self.dtype.is_interval(): - return f"{self.value!r}{self.dtype.unit.short}" - return repr(self.value) - - -NULL = Literal(None, dt.null) - - -@public -class ScalarParameter(Scalar): - _counter = itertools.count() - - dtype: dt.DataType - counter: Optional[int] = None - - shape = ds.scalar - - def __init__(self, dtype, counter): - if counter is None: - counter = next(self._counter) - super().__init__(dtype=dtype, counter=counter) - - @property - def name(self): - return f"param_{self.counter:d}" - - -@public -class Constant(Scalar, Singleton): - """A function that produces a constant.""" - - shape = ds.scalar - - -@public -class Impure(Value): - pass - - -@public -class OmittedArg(Value): - pass - - -@public -class TimestampNow(Constant): - """Return the current timestamp.""" - - dtype = dt.timestamp - - -@public -class DateNow(Constant): - """Return the current date.""" - - dtype = dt.date - - -@public -class RandomScalar(Impure): - """Return a random scalar between 0 and 1.""" - - dtype = dt.float64 - shape = ds.scalar - - -@public -class RandomUUID(Impure): - """Return a random UUID.""" - - dtype = dt.uuid - shape = ds.scalar - - -@public -class E(Constant): - """The mathematical constant e.""" - - dtype = dt.float64 - - -@public -class Pi(Constant): - """The mathematical constant pi.""" - - dtype = dt.float64 - - -@public -class Hash(Value): - """Return the hash of a value.""" - - arg: Value - - dtype = dt.int64 - shape = rlz.shape_like("arg") - - -@public -class HashBytes(Value): - arg: Value[dt.String | dt.Binary] - how: LiteralType[ - "md5", # noqa: F821 - "MD5", # noqa: F821 - "sha1", # noqa: F821 - "SHA1", # noqa: F821 - "SHA224", # noqa: F821 - "sha256", # noqa: F821 - "SHA256", # noqa: F821 - "sha512", # noqa: F821 - "intHash32", # noqa: F821 - "intHash64", # noqa: F821 - "cityHash64", # noqa: F821 - "sipHash64", # noqa: F821 - "sipHash128", # noqa: F821 - ] - - dtype = dt.binary - shape = rlz.shape_like("arg") - - -@public -class HexDigest(Value): - """Return the hexadecimal digest of a value.""" - - arg: Value[dt.String | dt.Binary] - how: LiteralType[ - "md5", # noqa: F821 - "sha1", # noqa: F821 - "sha256", # noqa: F821 - "sha512", # noqa: F821 - ] - - dtype = dt.str - shape = rlz.shape_like("arg") - - -# TODO(kszucs): we should merge the case operations by making the -# cases, results and default optional arguments like they are in -# api.py -@public -class SimpleCase(Value): - """Simple case statement.""" - - base: Value - cases: VarTuple[Value] - results: VarTuple[Value] - default: Value - - shape = rlz.shape_like("base") - - def __init__(self, cases, results, **kwargs): - assert len(cases) == len(results) - super().__init__(cases=cases, results=results, **kwargs) - - @attribute - def dtype(self): - values = [*self.results, self.default] - return rlz.highest_precedence_dtype(values) - - -@public -class SearchedCase(Value): - """Searched case statement.""" - - cases: VarTuple[Value[dt.Boolean]] - results: VarTuple[Value] - default: Value - - def __init__(self, cases, results, default): - assert len(cases) == len(results) - if default.dtype.is_null(): - default = Cast(default, rlz.highest_precedence_dtype(results)) - super().__init__(cases=cases, results=results, default=default) - - @attribute - def shape(self): - return rlz.highest_precedence_shape((*self.cases, *self.results, self.default)) - - @attribute - def dtype(self): - exprs = [*self.results, self.default] - return rlz.highest_precedence_dtype(exprs) - - -@public -class SqlScalar(Value): - """Inject a SQL string as a scalar value.""" - - sql_template: str - values: VarTuple[Value] - output_type: dt.DataType - - shape = ds.scalar - - @property - def dtype(self): - return self.output_type - - -public(NULL=NULL) diff --git a/third_party/bigframes_vendored/ibis/expr/operations/geospatial.py b/third_party/bigframes_vendored/ibis/expr/operations/geospatial.py deleted file mode 100644 index efe038599a0..00000000000 --- a/third_party/bigframes_vendored/ibis/expr/operations/geospatial.py +++ /dev/null @@ -1,523 +0,0 @@ -# Contains code from https://github.com/ibis-project/ibis/blob/9.2.0/ibis/expr/operations/geospatial.py - -"""Geospatial operations.""" - -from __future__ import annotations - -import bigframes_vendored.ibis.expr.datatypes as dt -from bigframes_vendored.ibis.expr.operations.core import Binary, Unary, Value -from bigframes_vendored.ibis.expr.operations.reductions import Filterable, Reduction -from public import public - - -@public -class GeoSpatialBinOp(Binary): - """Geo Spatial base binary.""" - - left: Value[dt.GeoSpatial] - right: Value[dt.GeoSpatial] - - -@public -class GeoSpatialUnOp(Unary): - """Geo Spatial base unary.""" - - arg: Value[dt.GeoSpatial] - - -@public -class GeoDistance(GeoSpatialBinOp): - """Returns minimum distance between two geospatial operands.""" - - dtype = dt.float64 - - -@public -class GeoContains(GeoSpatialBinOp): - """Check if the first geo spatial data contains the second one.""" - - dtype = dt.boolean - - -@public -class GeoContainsProperly(GeoSpatialBinOp): - """Check if the left value contains the right one, with no shared no boundary points.""" - - dtype = dt.boolean - - -@public -class GeoCovers(GeoSpatialBinOp): - """Check if no point in the right operand is outside that of the left.""" - - dtype = dt.boolean - - -@public -class GeoCoveredBy(GeoSpatialBinOp): - """Check if no point in the left operand is outside that of the right.""" - - dtype = dt.boolean - - -@public -class GeoCrosses(GeoSpatialBinOp): - """Check if the inputs have some but not all interior points in common.""" - - dtype = dt.boolean - - -@public -class GeoDisjoint(GeoSpatialBinOp): - """Check if the Geometries do not spatially intersect.""" - - dtype = dt.boolean - - -@public -class GeoEquals(GeoSpatialBinOp): - """Returns True if the given geometries represent the same geometry.""" - - dtype = dt.boolean - - -@public -class GeoGeometryN(GeoSpatialUnOp): - """Returns the Nth Geometry of a Multi geometry.""" - - n: Value[dt.Integer] - dtype = dt.geometry - - -@public -class GeoGeometryType(GeoSpatialUnOp): - """Returns the type of the geometry.""" - - dtype = dt.string - - -@public -class GeoIntersects(GeoSpatialBinOp): - """Returns True if the Geometries/Geography “spatially intersect in 2D”. - - - (share any portion of space) and False if they don`t (they are Disjoint). - """ - - dtype = dt.boolean - - -@public -class GeoIsValid(GeoSpatialUnOp): - """Returns true if the geometry is well-formed.""" - - dtype = dt.boolean - - -@public -class GeoLineLocatePoint(GeoSpatialBinOp): - """Locate the distance a point falls along the length of a line. - - Returns a float between zero and one representing the location of - the closest point on the linestring to the given point, as a - fraction of the total 2d line length. - """ - - left: Value[dt.LineString] - right: Value[dt.Point] - - dtype = dt.halffloat - - -@public -class GeoLineMerge(GeoSpatialUnOp): - """Merge a MultiLineString into a LineString. - - Returns a (set of) LineString(s) formed by sewing together the - constituent line work of a multilinestring. If a geometry other than - a linestring or multilinestring is given, this will return an empty - geometry collection. - """ - - dtype = dt.geometry - - -@public -class GeoLineSubstring(GeoSpatialUnOp): - """Clip a substring from a LineString. - - Returns a linestring that is a substring of the input one, starting - and ending at the given fractions of the total 2d length. The second - and third arguments are floating point values between zero and one. - This only works with linestrings. - """ - - arg: Value[dt.LineString] - start: Value[dt.Floating] - end: Value[dt.Floating] - - dtype = dt.linestring - - -@public -class GeoOrderingEquals(GeoSpatialBinOp): - """Check if two geometries are equal and have the same point ordering. - - Returns true if the two geometries are equal and the coordinates are - in the same order. - """ - - dtype = dt.boolean - - -@public -class GeoOverlaps(GeoSpatialBinOp): - """Check if the inputs are of the same dimension but are not completely contained by each other.""" - - dtype = dt.boolean - - -@public -class GeoTouches(GeoSpatialBinOp): - """Check if the inputs have at least one point in common but their interiors do not intersect.""" - - dtype = dt.boolean - - -@public -class GeoUnaryUnion(Filterable, Reduction): - """Returns the pointwise union of the geometries in the column.""" - - arg: Value[dt.GeoSpatial] - - dtype = dt.geometry - - -@public -class GeoUnion(GeoSpatialBinOp): - """Returns the pointwise union of the two geometries.""" - - dtype = dt.geometry - - -@public -class GeoArea(GeoSpatialUnOp): - """Area of the geo spatial data.""" - - dtype = dt.float64 - - -@public -class GeoPerimeter(GeoSpatialUnOp): - """Perimeter of the geo spatial data.""" - - dtype = dt.float64 - - -@public -class GeoLength(GeoSpatialUnOp): - """Length of geo spatial data.""" - - dtype = dt.float64 - - -@public -class GeoMaxDistance(GeoSpatialBinOp): - """Returns the 2-dimensional max distance between two geometries in projected units. - - If g1 and g2 is the same geometry the function will return the - distance between the two vertices most far from each other in that - geometry - """ - - dtype = dt.float64 - - -@public -class GeoX(GeoSpatialUnOp): - """Return the X coordinate of the point, or NULL if not available. - - Input must be a point - """ - - dtype = dt.float64 - - -@public -class GeoY(GeoSpatialUnOp): - """Return the Y coordinate of the point, or NULL if not available. - - Input must be a point - """ - - dtype = dt.float64 - - -@public -class GeoXMin(GeoSpatialUnOp): - """Returns Y minima of a bounding box 2d or 3d or a geometry.""" - - dtype = dt.float64 - - -@public -class GeoXMax(GeoSpatialUnOp): - """Returns X maxima of a bounding box 2d or 3d or a geometry.""" - - dtype = dt.float64 - - -@public -class GeoYMin(GeoSpatialUnOp): - """Returns Y minima of a bounding box 2d or 3d or a geometry.""" - - dtype = dt.float64 - - -@public -class GeoYMax(GeoSpatialUnOp): - """Returns Y maxima of a bounding box 2d or 3d or a geometry.""" - - dtype = dt.float64 - - -@public -class GeoStartPoint(GeoSpatialUnOp): - """Return the first point of a `LINESTRING` geometry as a POINT. - - Returns `NULL` if the input is not a LINESTRING. - """ - - dtype = dt.point - - -@public -class GeoEndPoint(GeoSpatialUnOp): - """Return the last point of a `LINESTRING` geometry as a POINT. - - Returns `NULL` if the input is not a LINESTRING. - """ - - dtype = dt.point - - -@public -class GeoPoint(GeoSpatialBinOp): - """Return a point constructed from the input coordinate values. - - Constant coordinates result in construction of a POINT literal. - """ - - left: Value[dt.Numeric] - right: Value[dt.Numeric] - - dtype = dt.point - - -@public -class GeoPointN(GeoSpatialUnOp): - """Return the Nth point in a single linestring in the geometry. - - Negative values are counted backwards from the end of the - LineString, so that -1 is the last point. Returns NULL if there is - no linestring in the geometry - """ - - n: Value[dt.Integer] - dtype = dt.point - - -@public -class GeoNPoints(GeoSpatialUnOp): - """Return the number of points in a geometry.""" - - dtype = dt.int64 - - -@public -class GeoNRings(GeoSpatialUnOp): - """Return the number of rings for polygons or multipolygons. - - Outer rings are counted. - """ - - dtype = dt.int64 - - -@public -class GeoRegionStats(GeoSpatialUnOp): - """Returns results of ST_REGIONSTATS.""" - - raster_id: Value[dt.String] - band: Value[dt.String] - include: Value[dt.String] - options: Value[dt.JSON] - - dtype = dt.Struct( - fields={ - "count": dt.int64, - "min": dt.float64, - "max": dt.float64, - "stdDev": dt.float64, - "sum": dt.float64, - "mean": dt.float64, - "area": dt.float64, - } - ) - - -@public -class GeoSRID(GeoSpatialUnOp): - """Returns the spatial reference identifier for the ST_Geometry.""" - - dtype = dt.int64 - - -@public -class GeoSetSRID(GeoSpatialUnOp): - """Set the spatial reference identifier for the ST_Geometry.""" - - srid: Value[dt.Integer] - - dtype = dt.geometry - - -@public -class GeoBuffer(GeoSpatialUnOp): - """Return all points whose distance from this geometry is less than or equal to `radius`. - - Calculations are in the Spatial Reference System of this geometry. - """ - - radius: Value[dt.Floating] - dtype = dt.geometry - - -@public -class GeoCentroid(GeoSpatialUnOp): - """Returns the geometric center of a geometry.""" - - dtype = dt.point - - -@public -class GeoDFullyWithin(GeoSpatialBinOp): - """Check if the geometries are fully within `distance` of one another.""" - - distance: Value[dt.Floating] - - dtype = dt.boolean - - -@public -class GeoDWithin(GeoSpatialBinOp): - """Check if the geometries are within `distance` of one another.""" - - distance: Value[dt.Floating] - - dtype = dt.boolean - - -@public -class GeoEnvelope(GeoSpatialUnOp): - """The bounding box of the supplied geometry.""" - - dtype = dt.polygon - - -@public -class GeoAzimuth(GeoSpatialBinOp): - """Return the angle in radians from the horizontal of the vector defined by the two inputs. - - Angle is computed clockwise from down-to-up: on the clock: 12=0; - 3=PI/2; 6=PI; 9=3PI/2. - """ - - left: Value[dt.Point] - right: Value[dt.Point] - - dtype = dt.float64 - - -@public -class GeoWithin(GeoSpatialBinOp): - """Returns True if the geometry A is completely inside geometry B.""" - - dtype = dt.boolean - - -@public -class GeoIntersection(GeoSpatialBinOp): - """Return a geometry that represents the point-set intersection of the inputs.""" - - dtype = dt.geometry - - -@public -class GeoDifference(GeoSpatialBinOp): - """Return a geometry that is the delta between the left and right inputs.""" - - dtype = dt.geometry - - -@public -class GeoSimplify(GeoSpatialUnOp): - """Returns a simplified version of the given geometry.""" - - tolerance: Value[dt.Floating] - preserve_collapsed: Value[dt.Boolean] - - dtype = dt.geometry - - -@public -class GeoTransform(GeoSpatialUnOp): - """Returns a transformed version of the given geometry into a new SRID.""" - - srid: Value[dt.Integer] - - dtype = dt.geometry - - -@public -class GeoConvert(GeoSpatialUnOp): - """Returns a transformed version of the given geometry from source crs/srid to a target crs/srid.""" - - source: str - target: str - - dtype = dt.geometry - - -@public -class GeoAsBinary(GeoSpatialUnOp): - """Return the Well-Known Binary (WKB) representation of the input, without SRID meta data.""" - - dtype = dt.binary - - -@public -class GeoAsEWKB(GeoSpatialUnOp): - """Return the Well-Known Binary representation of the input, with SRID meta data.""" - - dtype = dt.binary - - -@public -class GeoAsEWKT(GeoSpatialUnOp): - """Return the Well-Known Text representation of the input, with SRID meta data.""" - - dtype = dt.string - - -@public -class GeoAsText(GeoSpatialUnOp): - """Return the Well-Known Text (WKT) representation of the input, without SRID metadata.""" - - dtype = dt.string - - -@public -class GeoFlipCoordinates(GeoSpatialUnOp): - """Returns a new geometry with the coordinates of the input geometry "flipped" so that x = y and y = x.""" - - dtype = dt.geometry diff --git a/third_party/bigframes_vendored/ibis/expr/operations/histograms.py b/third_party/bigframes_vendored/ibis/expr/operations/histograms.py deleted file mode 100644 index e7487887761..00000000000 --- a/third_party/bigframes_vendored/ibis/expr/operations/histograms.py +++ /dev/null @@ -1,53 +0,0 @@ -# Contains code from https://github.com/ibis-project/ibis/blob/9.2.0/ibis/expr/operations/histograms.py - -"""Operations for computing histograms.""" - -from __future__ import annotations - -import numbers # noqa: TCH003 -from typing import Literal - -import bigframes_vendored.ibis.expr.datashape as ds -import bigframes_vendored.ibis.expr.datatypes as dt -from bigframes_vendored.ibis.common.annotations import ValidationError, attribute -from bigframes_vendored.ibis.common.typing import VarTuple # noqa: TCH001 -from bigframes_vendored.ibis.expr.operations.core import Column, Value -from public import public - - -@public -class Bucket(Value): - """Compute the bucket number of a numeric column.""" - - arg: Column[dt.Numeric | dt.Boolean] - buckets: VarTuple[numbers.Real] - closed: Literal["left", "right"] = "left" - close_extreme: bool = True - include_under: bool = False - include_over: bool = False - - shape = ds.columnar - - @attribute - def dtype(self): - return dt.infer(self.nbuckets) - - def __init__(self, buckets, include_under, include_over, **kwargs): - if not buckets: - raise ValidationError("Must be at least one bucket edge") - elif len(buckets) == 1: - if not include_under or not include_over: - raise ValidationError( - "If one bucket edge provided, must have " - "include_under=True and include_over=True" - ) - super().__init__( - buckets=buckets, - include_under=include_under, - include_over=include_over, - **kwargs, - ) - - @property - def nbuckets(self): - return len(self.buckets) - 1 + self.include_over + self.include_under diff --git a/third_party/bigframes_vendored/ibis/expr/operations/json.py b/third_party/bigframes_vendored/ibis/expr/operations/json.py deleted file mode 100644 index 6b03cb36672..00000000000 --- a/third_party/bigframes_vendored/ibis/expr/operations/json.py +++ /dev/null @@ -1,94 +0,0 @@ -# Contains code from https://github.com/ibis-project/ibis/blob/master/ibis/expr/operations/json.py -"""Operations for working with JSON data.""" - -from __future__ import annotations - -import bigframes_vendored.ibis.expr.datatypes as dt -import bigframes_vendored.ibis.expr.rules as rlz -from bigframes_vendored.ibis.common.annotations import attribute -from bigframes_vendored.ibis.expr.operations import Unary, Value -from public import public - - -@public -class JSONGetItem(Value): - """Get a value from a JSON object or array.""" - - arg: Value[dt.JSON] - index: Value[dt.String | dt.Integer] - - dtype = rlz.dtype_like("arg") - shape = rlz.shape_like("args") - - -@public -class ToJSONArray(Value): - """Convert a value to an array of JSON objects.""" - - arg: Value[dt.JSON] - - shape = rlz.shape_like("arg") - - @attribute - def dtype(self) -> dt.DataType: - return dt.Array(self.arg.dtype) - - -@public -class ToJSONMap(Value): - """Convert a value to a map of string to JSON.""" - - arg: Value[dt.JSON] - - shape = rlz.shape_like("arg") - - @attribute - def dtype(self) -> dt.DataType: - return dt.Map(dt.string, self.arg.dtype) - - -@public -class UnwrapJSONString(Value): - """Unwrap a JSON string into an engine-native string.""" - - arg: Value[dt.JSON] - - dtype = dt.string - shape = rlz.shape_like("arg") - - -@public -class UnwrapJSONInt64(Value): - """Unwrap a JSON number into an engine-native int64.""" - - arg: Value[dt.JSON] - - dtype = dt.int64 - shape = rlz.shape_like("arg") - - -@public -class UnwrapJSONFloat64(Value): - """Unwrap a JSON number into an engine-native float64.""" - - arg: Value[dt.JSON] - - dtype = dt.float64 - shape = rlz.shape_like("arg") - - -@public -class UnwrapJSONBoolean(Value): - """Unwrap a JSON bool into an engine-native bool.""" - - arg: Value[dt.JSON] - - dtype = dt.boolean - shape = rlz.shape_like("arg") - - -# TODO(swast): Remove once supported upstream. -# See: https://github.com/ibis-project/ibis/issues/9542 -@public -class ToJsonString(Unary): - dtype = dt.string diff --git a/third_party/bigframes_vendored/ibis/expr/operations/logical.py b/third_party/bigframes_vendored/ibis/expr/operations/logical.py deleted file mode 100644 index 74ac495642a..00000000000 --- a/third_party/bigframes_vendored/ibis/expr/operations/logical.py +++ /dev/null @@ -1,171 +0,0 @@ -# Contains code from https://github.com/ibis-project/ibis/blob/9.2.0/ibis/expr/operations/logical.py -"""Logical operations.""" - -from __future__ import annotations - -import bigframes_vendored.ibis.expr.datatypes as dt -import bigframes_vendored.ibis.expr.rules as rlz -from bigframes_vendored.ibis.common.annotations import ValidationError, attribute -from bigframes_vendored.ibis.common.exceptions import IbisTypeError -from bigframes_vendored.ibis.common.typing import VarTuple # noqa: TCH001 -from bigframes_vendored.ibis.expr.operations.core import Binary, Unary, Value -from public import public - - -@public -class LogicalBinary(Binary): - """Base class for logical binary operations.""" - - left: Value[dt.Boolean] - right: Value[dt.Boolean] - - dtype = dt.boolean - - -@public -class Not(Unary): - """Logical negation.""" - - arg: Value[dt.Boolean] - - dtype = dt.boolean - - -@public -class And(LogicalBinary): - """Logical AND.""" - - -@public -class Or(LogicalBinary): - """Logical OR.""" - - -@public -class Xor(LogicalBinary): - """Logical XOR.""" - - -@public -class Comparison(Binary): - """Base class for comparison operations.""" - - left: Value - right: Value - - dtype = dt.boolean - - def __init__(self, left, right): - """Construct a comparison operation between `left` and `right`. - - Casting rules for type promotions (for resolving the output type) may - depend on the target backend. - - TODO: how are overflows handled? Can we provide anything useful in - Ibis to help the user avoid them? - """ - if not rlz.comparable(left, right): - raise IbisTypeError( - f"Arguments {rlz._arg_type_error_format(left)} and " - f"{rlz._arg_type_error_format(right)} are not comparable" - ) - super().__init__(left=left, right=right) - - -@public -class Equals(Comparison): - """Equality comparison.""" - - -@public -class NotEquals(Comparison): - """Inequality comparison.""" - - -@public -class GreaterEqual(Comparison): - """Greater than or equal to comparison.""" - - -@public -class Greater(Comparison): - """Greater than comparison.""" - - -@public -class LessEqual(Comparison): - """Less than or equal to comparison.""" - - -@public -class Less(Comparison): - """Less than comparison.""" - - -@public -class IdenticalTo(Comparison): - """Identity comparison. Considers two NULL values **equal**.""" - - -@public -class Between(Value): - """Check if a value is within a range.""" - - arg: Value - lower_bound: Value - upper_bound: Value - - dtype = dt.boolean - shape = rlz.shape_like("args") - - def __init__(self, arg, lower_bound, upper_bound): - if not rlz.comparable(arg, lower_bound): - raise ValidationError( - f"Arguments {rlz._arg_type_error_format(arg)} and " - f"{rlz._arg_type_error_format(lower_bound)} are not comparable" - ) - if not rlz.comparable(arg, upper_bound): - raise ValidationError( - f"Arguments {rlz._arg_type_error_format(arg)} and " - f"{rlz._arg_type_error_format(upper_bound)} are not comparable" - ) - super().__init__(arg=arg, lower_bound=lower_bound, upper_bound=upper_bound) - - -@public -class InValues(Value): - """Check if a value is in a set of values.""" - - value: Value - options: VarTuple[Value] - - dtype = dt.boolean - - @attribute - def shape(self): - args = [self.value, *self.options] - return rlz.highest_precedence_shape(args) - - -@public -class IfElse(Value): - """Ternary case expression. - - Equivalent to - - ```python - bool_expr.case().when(True, true_expr).else_(false_or_null_expr) - ``` - - Many backends implement this as a built-in function. - """ - - bool_expr: Value[dt.Boolean] - true_expr: Value - false_null_expr: Value - - shape = rlz.shape_like("args") - - @attribute - def dtype(self): - return rlz.highest_precedence_dtype([self.true_expr, self.false_null_expr]) diff --git a/third_party/bigframes_vendored/ibis/expr/operations/maps.py b/third_party/bigframes_vendored/ibis/expr/operations/maps.py deleted file mode 100644 index 1111e1e6898..00000000000 --- a/third_party/bigframes_vendored/ibis/expr/operations/maps.py +++ /dev/null @@ -1,101 +0,0 @@ -# Contains code from https://github.com/ibis-project/ibis/blob/9.2.0/ibis/expr/operations/maps.py - -"""Operations for working with maps.""" - -from __future__ import annotations - -import bigframes_vendored.ibis.expr.datatypes as dt -import bigframes_vendored.ibis.expr.rules as rlz -from bigframes_vendored.ibis.common.annotations import attribute -from bigframes_vendored.ibis.expr.operations.core import Unary, Value -from public import public - - -@public -class Map(Value): - """Construct a map.""" - - keys: Value[dt.Array] - values: Value[dt.Array] - - shape = rlz.shape_like("args") - - @attribute - def dtype(self): - return dt.Map( - self.keys.dtype.value_type, - self.values.dtype.value_type, - ) - - -@public -class MapLength(Unary): - """Compute the number of unique keys in a map.""" - - arg: Value[dt.Map] - dtype = dt.int64 - - -@public -class MapGet(Value): - """Get a value from a map by key.""" - - arg: Value[dt.Map] - key: Value - default: Value = None - - shape = rlz.shape_like("args") - - @attribute - def dtype(self): - return dt.higher_precedence(self.default.dtype, self.arg.dtype.value_type) - - -@public -class MapContains(Value): - """Check if a map contains a key.""" - - arg: Value[dt.Map] - key: Value - - shape = rlz.shape_like("args") - dtype = dt.bool - - -@public -class MapKeys(Unary): - """Get the keys of a map as an array.""" - - arg: Value[dt.Map] - - @attribute - def dtype(self): - return dt.Array(self.arg.dtype.key_type) - - -@public -class MapValues(Unary): - """Get the values of a map as an array.""" - - arg: Value[dt.Map] - - @attribute - def dtype(self): - return dt.Array(self.arg.dtype.value_type) - - -@public -class MapMerge(Value): - """Combine two maps into one. - - If a key is present in both maps, the value from the first is kept. - """ - - left: Value[dt.Map] - right: Value[dt.Map] - - shape = rlz.shape_like("args") - dtype = rlz.dtype_like("args") - - -public(MapValueForKey=MapGet, MapValueOrDefaultForKey=MapGet, MapConcat=MapMerge) diff --git a/third_party/bigframes_vendored/ibis/expr/operations/numeric.py b/third_party/bigframes_vendored/ibis/expr/operations/numeric.py deleted file mode 100644 index f4ba57e9d70..00000000000 --- a/third_party/bigframes_vendored/ibis/expr/operations/numeric.py +++ /dev/null @@ -1,374 +0,0 @@ -# Contains code from https://github.com/ibis-project/ibis/blob/9.2.0/ibis/expr/operations/numeric.py - -"""Operations for numeric expressions.""" - -from __future__ import annotations - -import operator -from typing import Optional - -import bigframes_vendored.ibis.expr.datatypes as dt -import bigframes_vendored.ibis.expr.rules as rlz -from bigframes_vendored.ibis import util -from bigframes_vendored.ibis.common.annotations import attribute -from bigframes_vendored.ibis.expr.operations.core import Binary, Unary, Value -from public import public - -Integer = Value[dt.Integer] -SoftNumeric = Value[dt.Numeric | dt.Boolean] -StrictNumeric = Value[dt.Numeric] - - -@public -class NumericBinary(Binary): - left: SoftNumeric - right: SoftNumeric - - -@public -class Add(NumericBinary): - """Add two values.""" - - dtype = rlz.numeric_like("args", operator.add) - - -@public -class Multiply(NumericBinary): - """Multiply two values.""" - - dtype = rlz.numeric_like("args", operator.mul) - - -@public -class Power(NumericBinary): - """Raise the left value to the power of the right value.""" - - @property - def dtype(self): - dtypes = (arg.dtype for arg in self.args) - if util.all_of(dtypes, dt.Integer): - return dt.float64 - else: - return rlz.highest_precedence_dtype(self.args) - - -@public -class Subtract(NumericBinary): - """Subtract the right value from the left value.""" - - dtype = rlz.numeric_like("args", operator.sub) - - -@public -class Divide(NumericBinary): - """Divide the left value by the right value.""" - - dtype = dt.float64 - - -@public -class FloorDivide(Divide): - """Divide the left value by the right value and round down to the nearest integer.""" - - dtype = dt.int64 - - -@public -class Modulus(NumericBinary): - """Return the remainder after the division of the left value by the right value.""" - - dtype = rlz.numeric_like("args", operator.mod) - - -@public -class Negate(Unary): - """Negate the value.""" - - arg: Value[dt.Numeric | dt.Interval] - - dtype = rlz.dtype_like("arg") - - -@public -class IsNan(Unary): - """Check if the value is NaN.""" - - arg: Value[dt.Floating] - - dtype = dt.boolean - - -@public -class IsInf(Unary): - """Check if the value is infinite.""" - - arg: Value[dt.Floating] - - dtype = dt.boolean - - -@public -class Abs(Unary): - """Absolute value.""" - - arg: SoftNumeric - - dtype = rlz.dtype_like("arg") - - -@public -class Ceil(Unary): - """Round up to the nearest integer value greater than or equal to this value.""" - - arg: SoftNumeric - - @property - def dtype(self): - if self.arg.dtype.is_decimal(): - return self.arg.dtype - else: - return dt.int64 - - -@public -class Floor(Unary): - """Round down to the nearest integer value less than or equal to this value.""" - - arg: SoftNumeric - - @property - def dtype(self): - if self.arg.dtype.is_decimal(): - return self.arg.dtype - else: - return dt.int64 - - -@public -class Round(Value): - """Round a value.""" - - arg: StrictNumeric - # TODO(kszucs): the default should be 0 instead of being None - digits: Optional[Integer] = None - - shape = rlz.shape_like("arg") - - @property - def dtype(self): - if self.arg.dtype.is_decimal(): - return self.arg.dtype - else: - return dt.double - - -@public -class Clip(Value): - """Clip a value to a specified range.""" - - arg: StrictNumeric - lower: Optional[StrictNumeric] = None - upper: Optional[StrictNumeric] = None - - dtype = rlz.dtype_like("arg") - shape = rlz.shape_like("arg") - - -@public -class BaseConvert(Value): - """Convert a number from one base to another.""" - - # TODO(kszucs): this should be Integer simply - arg: Value[dt.Integer | dt.String] - from_base: Integer - to_base: Integer - - dtype = dt.string - shape = rlz.shape_like("args") - - -@public -class MathUnary(Unary): - """Base class for unary math operations.""" - - arg: SoftNumeric - - @attribute - def dtype(self): - return dt.higher_precedence(self.arg.dtype, dt.float64) - - -class ExpandingMathUnary(MathUnary): - @attribute - def dtype(self): - if self.arg.dtype.is_decimal(): - return self.arg.dtype - else: - return dt.float64 - - -@public -class Exp(ExpandingMathUnary): - """Exponential function.""" - - -@public -class Sign(Unary): - """Sign of the value.""" - - arg: SoftNumeric - - dtype = rlz.dtype_like("arg") - - -@public -class Sqrt(MathUnary): - """Square root of the value.""" - - -@public -class Logarithm(MathUnary): - """Base class for logarithmic operations.""" - - arg: StrictNumeric - - -@public -class Log(Logarithm): - """Logarithm with a specific base.""" - - base: Optional[StrictNumeric] = None - - -@public -class Ln(Logarithm): - """Natural logarithm.""" - - -@public -class Log2(Logarithm): - """Logarithm base 2.""" - - -@public -class Log10(Logarithm): - """Logarithm base 10.""" - - -@public -class Degrees(ExpandingMathUnary): - """Converts radians to degrees.""" - - -@public -class Radians(MathUnary): - """Converts degrees to radians.""" - - -@public -class TrigonometricUnary(MathUnary): - """Trigonometric base unary.""" - - -@public -class TrigonometricBinary(Binary): - """Trigonometric base binary.""" - - left: SoftNumeric - right: SoftNumeric - - dtype = dt.float64 - - -@public -class Acos(TrigonometricUnary): - """Returns the arc cosine of x.""" - - -@public -class Asin(TrigonometricUnary): - """Returns the arc sine of x.""" - - -@public -class Atan(TrigonometricUnary): - """Returns the arc tangent of x.""" - - -@public -class Atan2(TrigonometricBinary): - """Returns the arc tangent of x and y.""" - - -@public -class Cos(TrigonometricUnary): - """Returns the cosine of x.""" - - -@public -class Cot(TrigonometricUnary): - """Returns the cotangent of x.""" - - -@public -class Sin(TrigonometricUnary): - """Returns the sine of x.""" - - -@public -class Tan(TrigonometricUnary): - """Returns the tangent of x.""" - - -@public -class BitwiseNot(Unary): - """Bitwise NOT operation.""" - - arg: Value[dt.Integer | dt.Binary] - - dtype = rlz.numeric_like("args", operator.invert) - - -@public -class BitwiseBinary(Binary): - """Base class for bitwise binary operations.""" - - left: Integer - right: Integer - - -@public -class BitwiseAnd(BitwiseBinary): - """Bitwise AND operation.""" - - dtype = rlz.numeric_like("args", operator.and_) - - -@public -class BitwiseOr(BitwiseBinary): - """Bitwise OR operation.""" - - dtype = rlz.numeric_like("args", operator.or_) - - -@public -class BitwiseXor(BitwiseBinary): - """Bitwise XOR operation.""" - - dtype = rlz.numeric_like("args", operator.xor) - - -@public -class BitwiseLeftShift(BitwiseBinary): - """Bitwise left shift operation.""" - - shape = rlz.shape_like("args") - dtype = dt.int64 - - -@public -class BitwiseRightShift(BitwiseBinary): - """Bitwise right shift operation.""" - - shape = rlz.shape_like("args") - dtype = dt.int64 diff --git a/third_party/bigframes_vendored/ibis/expr/operations/reductions.py b/third_party/bigframes_vendored/ibis/expr/operations/reductions.py index b739c7048fd..5e6ad9ecf2b 100644 --- a/third_party/bigframes_vendored/ibis/expr/operations/reductions.py +++ b/third_party/bigframes_vendored/ibis/expr/operations/reductions.py @@ -1,420 +1,23 @@ -# Contains code from https://github.com/ibis-project/ibis/blob/9.2.0/ibis/expr/operations/reductions.py - -"""Reduction operations.""" +# Contains code from https://github.com/ibis-project/ibis/blob/master/ibis/expr/operations/reductions.py from __future__ import annotations -from typing import Literal, Optional - -import bigframes_vendored.ibis.expr.datashape as ds -import bigframes_vendored.ibis.expr.datatypes as dt -import bigframes_vendored.ibis.expr.rules as rlz -from bigframes_vendored.ibis.common.annotations import attribute -from bigframes_vendored.ibis.common.typing import VarTuple -from bigframes_vendored.ibis.expr.operations.core import Column, Value -from bigframes_vendored.ibis.expr.operations.relations import Relation # noqa: TCH001 -from public import public - - -@public -class Reduction(Value): - """Base class for reduction operations.""" - - shape = ds.scalar - - -# TODO(kszucs): all reductions all filterable so we could remove Filterable -class Filterable(Value): - where: Optional[Value[dt.Boolean]] = None - - -@public -class Count(Filterable, Reduction): - """Count the number of non-null elements of a column.""" - - arg: Column[dt.Any] - - dtype = dt.int64 - - -@public -class CountStar(Filterable, Reduction): - """Count the number of rows of a relation.""" - - arg: Relation - - dtype = dt.int64 - - @attribute - def relations(self): - return frozenset({self.arg}) - - -@public -class CountDistinctStar(Filterable, Reduction): - """Count the number of distinct rows of a relation.""" - - arg: Relation - - dtype = dt.int64 - - @attribute - def relations(self): - return frozenset({self.arg}) - - -@public -class Arbitrary(Filterable, Reduction): - """Retrieve an arbitrary element. - - Returns a non-null value unless the column is empty or all values are NULL. - """ - - arg: Column[dt.Any] - - dtype = rlz.dtype_like("arg") - - -@public -class First(Filterable, Reduction): - """Retrieve the first element.""" - - arg: Column[dt.Any] - - dtype = rlz.dtype_like("arg") - - -@public -class Last(Filterable, Reduction): - """Retrieve the last element.""" - - arg: Column[dt.Any] - - dtype = rlz.dtype_like("arg") - - -@public -class BitAnd(Filterable, Reduction): - """Aggregate bitwise AND operation. - - All elements in an integer column are ANDed together. - - This can be used to determine which bit flags are set on all elements. - - See Also - -------- - * BigQuery [`BIT_AND`](https://cloud.google.com/bigquery/docs/reference/standard-sql/aggregate_functions#bit_and) - * MySQL [`BIT_AND`](https://dev.mysql.com/doc/refman/5.7/en/aggregate-functions.html#function_bit-and) - """ - - arg: Column[dt.Integer] - - dtype = rlz.dtype_like("arg") - - -@public -class BitOr(Filterable, Reduction): - """Aggregate bitwise OR operation. - - All elements in an integer column are ORed together. This can be used - to determine which bit flags are set on any element. - - See Also - -------- - * BigQuery [`BIT_OR`](https://cloud.google.com/bigquery/docs/reference/standard-sql/aggregate_functions#bit_or) - * MySQL [`BIT_OR`](https://dev.mysql.com/doc/refman/5.7/en/aggregate-functions.html#function_bit-or) - """ - - arg: Column[dt.Integer] - - dtype = rlz.dtype_like("arg") - - -@public -class BitXor(Filterable, Reduction): - """Aggregate bitwise XOR operation. - - All elements in an integer column are XORed together. This can be used - as a parity checksum of element values. - - See Also - -------- - * BigQuery [`BIT_XOR`](https://cloud.google.com/bigquery/docs/reference/standard-sql/aggregate_functions#bit_xor) - * MySQL [`BIT_XOR`](https://dev.mysql.com/doc/refman/5.7/en/aggregate-functions.html#function_bit-xor) - """ - - arg: Column[dt.Integer] - - dtype = rlz.dtype_like("arg") - - -@public -class Sum(Filterable, Reduction): - """Compute the sum of a column.""" - - arg: Column[dt.Numeric | dt.Boolean] - - @attribute - def dtype(self): - dtype = self.arg.dtype - if dtype.is_boolean(): - return dt.int64 - elif dtype.is_integer(): - return dt.int64 - elif dtype.is_unsigned_integer(): - return dt.uint64 - elif dtype.is_floating(): - return dt.float64 - elif dtype.is_decimal(): - return dt.Decimal( - precision=max(dtype.precision, 38) - if dtype.precision is not None - else None, - scale=max(dtype.scale, 2) if dtype.scale is not None else None, - ) - else: - raise TypeError(f"Cannot compute sum of {dtype} values") - - -@public -class Mean(Filterable, Reduction): - """Compute the mean of a column.""" - - arg: Column[dt.Numeric | dt.Boolean] - - @attribute - def dtype(self): - if (dtype := self.arg.dtype).is_boolean(): - return dt.float64 - else: - return dt.higher_precedence(dtype, dt.float64) - - -class QuantileBase(Filterable, Reduction): - arg: Column - - @attribute - def dtype(self): - dtype = self.arg.dtype - if dtype.is_numeric(): - dtype = dt.higher_precedence(dtype, dt.float64) - return dtype - - -@public -class Median(QuantileBase): - """Compute the median of a column.""" - - -@public -class Quantile(QuantileBase): - """Compute the quantile of a column.""" - - quantile: Value[dt.Numeric] - +import ibis.expr.datatypes as dt +from ibis.expr.operations.reductions import Filterable, Reduction +import ibis.expr.rules as rlz -@public -class MultiQuantile(Filterable, Reduction): - """Compute multiple quantiles of a column.""" - arg: Column - quantile: Value[dt.Array[dt.Numeric]] +class ApproximateMultiQuantile(Filterable, Reduction): + """Calculate (approximately) evenly-spaced quantiles. - @attribute - def dtype(self): - dtype = self.arg.dtype - if dtype.is_numeric(): - dtype = dt.higher_precedence(dtype, dt.float64) - return dt.Array(dtype) - - -class VarianceBase(Filterable, Reduction): - """Base class for variance and standard deviation.""" - - arg: Column[dt.Numeric | dt.Boolean] - how: Literal["sample", "pop"] - - @attribute - def dtype(self): - if self.arg.dtype.is_decimal(): - return self.arg.dtype - else: - return dt.float64 - - -@public -class StandardDev(VarianceBase): - """Compute the standard deviation of a column.""" - - -@public -class Variance(VarianceBase): - """Compute the variance of a column.""" - - -@public -class Correlation(Filterable, Reduction): - """Correlation coefficient of two columns.""" - - left: Column[dt.Numeric | dt.Boolean] - right: Column[dt.Numeric | dt.Boolean] - how: Literal["sample", "pop"] = "sample" - - dtype = dt.float64 - - -@public -class Covariance(Filterable, Reduction): - """Covariance of two columns.""" - - left: Column[dt.Numeric | dt.Boolean] - right: Column[dt.Numeric | dt.Boolean] - how: Literal["sample", "pop"] - - dtype = dt.float64 - - -@public -class Mode(Filterable, Reduction): - """Compute the mode of a column.""" - - arg: Column - - dtype = rlz.dtype_like("arg") - - -@public -class Max(Filterable, Reduction): - """Compute the maximum of a column.""" - - arg: Column - - dtype = rlz.dtype_like("arg") - - -@public -class Min(Filterable, Reduction): - """Compute the minimum of a column.""" - - arg: Column - - dtype = rlz.dtype_like("arg") - - -@public -class ArgMax(Filterable, Reduction): - """Compute the index of the maximum value in a column.""" - - arg: Column - key: Column - - dtype = rlz.dtype_like("arg") - - -@public -class ArgMin(Filterable, Reduction): - """Compute the index of the minimum value in a column.""" - - arg: Column - key: Column - - dtype = rlz.dtype_like("arg") - - -@public -class ApproxCountDistinct(Filterable, Reduction): - """Approximate number of unique values.""" - - arg: Column - - # Impala 2.0 and higher returns a DOUBLE - dtype = dt.int64 - - -@public -class ApproxMedian(Filterable, Reduction): - """Compute the approximate median of a set of comparable values.""" - - arg: Column - - dtype = rlz.dtype_like("arg") - - -@public -class GroupConcat(Filterable, Reduction): - """Concatenate strings in a group with a given separator character.""" - - arg: Column - sep: Value[dt.String] - - dtype = dt.string - - -@public -class CountDistinct(Filterable, Reduction): - """Count the number of distinct values in a column.""" - - arg: Column - - dtype = dt.int64 - - -@public -class ArrayCollect(Filterable, Reduction): - """Collect values into an array.""" - - arg: Column - - @attribute - def dtype(self): - return dt.Array(self.arg.dtype) - - -@public -class All(Filterable, Reduction): - """Check if all values in a column are true.""" - - arg: Column[dt.Boolean] - - dtype = dt.boolean - - -@public -class Any(Filterable, Reduction): - """Check if any value in a column is true.""" - - arg: Column[dt.Boolean] - - dtype = dt.boolean - - -@public -class ArrayAggregate(Filterable, Reduction): - """ - Collects the elements of this expression into an ordered array. Similar to - the ibis `ArrayCollect`, but adds `order_by_*` and `distinct_only` parameters. - """ - - arg: Column - order_by: VarTuple[Value] = () - - @attribute - def dtype(self): - return dt.Array(self.arg.dtype) - - -@public -class StringAgg(Filterable, Reduction): - """ - Collects the elements of this expression into a string. Similar to - the ibis `GroupConcat`, but adds `order_by_*` parameter. + See: https://cloud.google.com/bigquery/docs/reference/standard-sql/approximate_aggregate_functions#approx_quantiles """ - arg: Column - sep: Value[dt.String] + arg = rlz.any + num_bins = rlz.value(dt.int64) + output_dtype = dt.Array(dt.float64) - order_by: VarTuple[Value] = () - @attribute - def dtype(self): - return dt.string +__all__ = [ + "ApproximateMultiQuantile", +] diff --git a/third_party/bigframes_vendored/ibis/expr/operations/relations.py b/third_party/bigframes_vendored/ibis/expr/operations/relations.py deleted file mode 100644 index c230cbe20b5..00000000000 --- a/third_party/bigframes_vendored/ibis/expr/operations/relations.py +++ /dev/null @@ -1,521 +0,0 @@ -# Contains code from https://github.com/ibis-project/ibis/blob/9.2.0/ibis/expr/operations/relations.py - -"""Relational operations.""" - -from __future__ import annotations - -import itertools -import typing -from abc import abstractmethod -from typing import Annotated, Any, Literal, Optional, TypeVar - -import bigframes_vendored.ibis.expr.datashape as ds -import bigframes_vendored.ibis.expr.datatypes as dt -from bigframes_vendored.ibis.common.annotations import attribute -from bigframes_vendored.ibis.common.collections import FrozenDict, FrozenOrderedDict -from bigframes_vendored.ibis.common.exceptions import ( - IbisTypeError, - IntegrityError, - RelationError, -) -from bigframes_vendored.ibis.common.grounds import Concrete -from bigframes_vendored.ibis.common.patterns import Between, InstanceOf -from bigframes_vendored.ibis.common.typing import Coercible, VarTuple -from bigframes_vendored.ibis.expr.operations.core import ( - Alias, - Column, - Node, - Scalar, - Value, -) -from bigframes_vendored.ibis.expr.operations.sortkeys import SortKey -from bigframes_vendored.ibis.expr.schema import Schema -from bigframes_vendored.ibis.formats import TableProxy # noqa: TCH001 -from public import public - -T = TypeVar("T") - -Unaliased = Annotated[T, ~InstanceOf(Alias)] -NonSortKey = Annotated[T, ~InstanceOf(SortKey)] - - -@public -class Relation(Node, Coercible): - """Base class for relational operations.""" - - @classmethod - def __coerce__(cls, value): - from bigframes_vendored.ibis.expr.types import Table - - if isinstance(value, Relation): - return value - elif isinstance(value, Table): - return value.op() - else: - raise TypeError(f"Cannot coerce {value!r} to a Relation") - - @property - @abstractmethod - def values(self) -> FrozenOrderedDict[str, Value]: - """A mapping of column names to expressions which build up the relation. - - This attribute is heavily used in rewrites as well as during field - dereferencing in the API layer. The returned expressions must only - originate from parent relations, depending on the relation type. - """ - - @property - @abstractmethod - def schema(self) -> Schema: - """The schema of the relation. - - All relations must have a well-defined schema. - """ - ... - - @property - def fields(self) -> FrozenOrderedDict[str, Column]: - """A mapping of column names to fields of the relation. - - This calculated property shouldn't be overridden in subclasses since it - is mostly used for convenience. - """ - return FrozenOrderedDict({k: Field(self, k) for k in self.schema}) - - def to_expr(self): - from bigframes_vendored.ibis.expr.types import Table - - return Table(self) - - -@public -class Field(Value): - """A field of a relation.""" - - rel: Relation - name: str - - shape = ds.columnar - - def __init__(self, rel, name): - if name not in rel.schema: - columns_formatted = ", ".join(map(repr, rel.schema.names)) - raise IbisTypeError( - f"Column {name!r} is not found in table. " - f"Existing columns: {columns_formatted}." - ) - super().__init__(rel=rel, name=name) - - @attribute - def dtype(self): - return self.rel.schema[self.name] - - @attribute - def relations(self): - return frozenset({self.rel}) - - -def _check_integrity(values, allowed_parents): - for value in values: - for rel in value.relations: - if rel not in allowed_parents: - raise IntegrityError( - f"Cannot add {value!r} to projection, they belong to another relation" - ) - - -@public -class Project(Relation): - """Project a subset of columns from a relation.""" - - parent: Relation - values: FrozenOrderedDict[str, NonSortKey[Unaliased[Value]]] - - def __init__(self, parent, values): - _check_integrity(values.values(), {parent}) - super().__init__(parent=parent, values=values) - - @attribute - def schema(self): - return Schema({k: v.dtype for k, v in self.values.items()}) - - -class Simple(Relation): - parent: Relation - - @attribute - def values(self): - return self.parent.fields - - @attribute - def schema(self): - return self.parent.schema - - -@public -class DropColumns(Relation): - parent: Relation - columns_to_drop: VarTuple[str] - - @attribute - def schema(self): - schema = self.parent.schema.fields.copy() - for column in self.columns_to_drop: - del schema[column] - return Schema(schema) - - @attribute - def values(self): - fields = self.parent.fields.copy() - for column in self.columns_to_drop: - del fields[column] - return fields - - -@public -class Reference(Relation): - _uid_counter = itertools.count() - parent: Relation - identifier: Optional[int] = None - - def __init__(self, parent, identifier): - if identifier is None: - identifier = next(self._uid_counter) - super().__init__(parent=parent, identifier=identifier) - - @attribute - def schema(self): - return self.parent.schema - - -# TODO(kszucs): remove in favor of View -@public -class SelfReference(Reference): - values = FrozenOrderedDict() - - -@public -class JoinReference(Reference): - @attribute - def values(self): - return self.parent.fields - - -JoinKind = Literal[ - "inner", - "left", - "right", - "outer", - "asof", - "semi", - "anti", - "any_inner", - "any_left", - "cross", - "positional", -] - - -@public -class JoinLink(Node): - how: JoinKind - table: Reference - predicates: VarTuple[Value[dt.Boolean]] - - -@public -class JoinChain(Relation): - first: Reference - rest: VarTuple[JoinLink] - values: FrozenOrderedDict[str, Unaliased[Value]] - - def __init__(self, first, rest, values): - allowed_parents = {first} - for join in rest: - if join.table in allowed_parents: - raise IntegrityError( - f"Cannot add {join.table!r} to the join chain, it is already in the chain" - ) - allowed_parents.add(join.table) - _check_integrity(join.predicates, allowed_parents) - _check_integrity(values.values(), allowed_parents) - super().__init__(first=first, rest=rest, values=values) - - @property - def tables(self): - return [self.first] + [link.table for link in self.rest] - - @property - def length(self): - return len(self.rest) + 1 - - @attribute - def schema(self): - return Schema({k: v.dtype.copy(nullable=True) for k, v in self.values.items()}) - - def to_expr(self): - import bigframes_vendored.ibis.expr.types as ir - - return ir.Join(self) - - -@public -class Sort(Simple): - """Sort a table by a set of keys.""" - - keys: VarTuple[SortKey] - - def __init__(self, parent, keys): - _check_integrity(keys, {parent}) - super().__init__(parent=parent, keys=keys) - - -@public -class Filter(Simple): - """Filter a table by a set of predicates.""" - - predicates: VarTuple[Value[dt.Boolean]] - - def __init__(self, parent, predicates): - from bigframes_vendored.ibis.expr.rewrites import ReductionLike - - for pred in predicates: - if pred.find(ReductionLike, filter=Value): - raise IntegrityError( - f"Cannot add {pred!r} to filter, it is a reduction which " - "must be converted to a scalar subquery first" - ) - if pred.relations and parent not in pred.relations: - raise IntegrityError( - f"Cannot add {pred!r} to filter, they belong to another relation" - ) - super().__init__(parent=parent, predicates=predicates) - - -@public -class Limit(Simple): - """Limit and/or offset the number of records in a table.""" - - # TODO(kszucs): dynamic limit should contain ScalarSubqueries rather than - # plain scalar values - n: typing.Union[int, Scalar[dt.Integer], None] = None - offset: typing.Union[int, Scalar[dt.Integer]] = 0 - - -@public -class Aggregate(Relation): - """Aggregate a table by a set of group by columns and metrics.""" - - parent: Relation - groups: FrozenOrderedDict[str, Unaliased[Value]] - metrics: FrozenOrderedDict[str, Unaliased[Scalar]] - - def __init__(self, parent, groups, metrics): - _check_integrity(groups.values(), {parent}) - _check_integrity(metrics.values(), {parent}) - if duplicates := groups.keys() & metrics.keys(): - raise RelationError( - f"Cannot add {duplicates} to aggregate, they are already in the groupby" - ) - super().__init__(parent=parent, groups=groups, metrics=metrics) - - @attribute - def values(self): - return FrozenOrderedDict({**self.groups, **self.metrics}) - - @attribute - def schema(self): - return Schema({k: v.dtype for k, v in self.values.items()}) - - -@public -class Set(Relation): - """Base class for set operations.""" - - left: Relation - right: Relation - distinct: bool = False - values = FrozenOrderedDict() - - def __init__(self, left, right, **kwargs): - if left.schema.names != right.schema.names: - # rewrite so that both sides have the columns in the same order making it - # easier for the backends to implement set operations - cols = {name: Field(right, name) for name in left.schema.names} - right = Project(right, cols) - super().__init__(left=left, right=right, **kwargs) - - @attribute - def schema(self): - dtypes = ( - dt.higher_precedence(ltype, rtype) - for ltype, rtype in zip( - self.left.schema.values(), self.right.schema.values() - ) - ) - return Schema.from_tuples( - (name, coltype) for name, coltype in zip(self.left.schema.names, dtypes) - ) - - -@public -class Union(Set): - """Union two tables.""" - - -@public -class Intersection(Set): - """Intersect two tables.""" - - -@public -class Difference(Set): - """Subtract one table from another.""" - - -@public -class PhysicalTable(Relation): - """Base class for tables with a name.""" - - name: str - values = FrozenOrderedDict() - - -@public -class Namespace(Concrete): - """Object to model namespaces for tables. - - Maps to the concept of database and/or catalog in SQL databases that support - them. - """ - - catalog: Optional[str] = None - database: Optional[str] = None - - -@public -class UnboundTable(PhysicalTable): - """A table that is not bound to a specific backend.""" - - schema: Schema - namespace: Namespace = Namespace() - - -@public -class DatabaseTable(PhysicalTable): - """A table that is bound to a specific backend.""" - - schema: Schema - source: Any - namespace: Namespace = Namespace() - - -@public -class InMemoryTable(PhysicalTable): - """A table whose data is stored in memory.""" - - schema: Schema - data: TableProxy - - -@public -class SQLQueryResult(Relation): - """A table sourced from the result set of a SQL SELECT statement.""" - - query: str - schema: Schema - source: Any - values = FrozenOrderedDict() - - -@public -class View(PhysicalTable): - """A view created from an expression.""" - - # TODO(kszucs): rename it to parent - child: Relation - - @attribute - def schema(self): - return self.child.schema - - -@public -class SQLStringView(Relation): - """A view created from a SQL string.""" - - child: Relation - query: str - schema: Schema - values = FrozenOrderedDict() - - -@public -class DummyTable(Relation): - """A table constructed from literal values.""" - - values: FrozenOrderedDict[str, Value] - - @attribute - def schema(self): - return Schema({k: v.dtype for k, v in self.values.items()}) - - -@public -class FillNull(Simple): - """Fill null values in the table.""" - - replacements: typing.Union[Value[dt.Numeric | dt.String], FrozenDict[str, Any]] - - -@public -class DropNull(Simple): - """Drop null values in the table.""" - - how: typing.Literal["any", "all"] - subset: Optional[VarTuple[Column]] = None - - -@public -class Sample(Simple): - """Sample performs random sampling of records in a table.""" - - fraction: Annotated[float, Between(0, 1)] - method: typing.Literal["row", "block"] - seed: typing.Union[int, None] = None - - -@public -class Distinct(Simple): - """Compute the distinct rows of a table.""" - - -@public -class TableUnnest(Relation): - """Cross join unnest operation.""" - - parent: Relation - column: Value[dt.Array] - offset: typing.Union[str, None] - keep_empty: bool - - @attribute - def values(self): - return self.parent.fields - - @attribute - def schema(self): - column = self.column - offset = self.offset - - base = self.parent.schema.fields.copy() - - base[column.name] = column.dtype.value_type - - if offset is not None: - base[offset] = dt.int64 - - return Schema(base) - - -# TODO(kszucs): support t.select(*t) syntax by implementing Table.__iter__() diff --git a/third_party/bigframes_vendored/ibis/expr/operations/sortkeys.py b/third_party/bigframes_vendored/ibis/expr/operations/sortkeys.py deleted file mode 100644 index f1c5b9820ac..00000000000 --- a/third_party/bigframes_vendored/ibis/expr/operations/sortkeys.py +++ /dev/null @@ -1,43 +0,0 @@ -# Contains code from https://github.com/ibis-project/ibis/blob/9.2.0/ibis/expr/operations/sortkeys.py - -"""Sort key operations.""" - -from __future__ import annotations - -import bigframes_vendored.ibis.expr.rules as rlz -from bigframes_vendored.ibis.expr.operations.core import Value -from public import public - -# TODO(kszucs): move the content of this file to generic.py - - -# TODO(kszucs): consider to limit its shape to Columnar, we could treat random() -# as a columnar operation too -@public -class SortKey(Value): - """A sort key.""" - - # TODO(kszucs): rename expr to arg or something else except expr - expr: Value - ascending: bool = True - nulls_first: bool = False - - dtype = rlz.dtype_like("expr") - shape = rlz.shape_like("expr") - - @classmethod - def __coerce__(cls, key, T=None, S=None): - key = super().__coerce__(key, T=T, S=S) - - if isinstance(key, cls): - return key - else: - return cls(key) - - @property - def name(self) -> str: - return self.expr.name - - @property - def descending(self) -> bool: - return not self.ascending diff --git a/third_party/bigframes_vendored/ibis/expr/operations/strings.py b/third_party/bigframes_vendored/ibis/expr/operations/strings.py deleted file mode 100644 index c2dc151ae07..00000000000 --- a/third_party/bigframes_vendored/ibis/expr/operations/strings.py +++ /dev/null @@ -1,397 +0,0 @@ -# Contains code from https://github.com/ibis-project/ibis/blob/9.2.0/ibis/expr/operations/strings.py - -"""String operations.""" - -from __future__ import annotations - -from typing import Optional - -import bigframes_vendored.ibis.expr.datatypes as dt -import bigframes_vendored.ibis.expr.rules as rlz -from bigframes_vendored.ibis.common.annotations import attribute -from bigframes_vendored.ibis.common.typing import VarTuple # noqa: TCH001 -from bigframes_vendored.ibis.expr.operations.core import Unary, Value -from public import public - - -@public -class StringUnary(Unary): - """Base class for string operations accepting one argument.""" - - arg: Value[dt.String] - - dtype = dt.string - - -@public -class Uppercase(StringUnary): - """Convert a string to uppercase.""" - - -@public -class Lowercase(StringUnary): - """Convert a string to lowercase.""" - - -@public -class Reverse(StringUnary): - """Reverse a string.""" - - -@public -class Strip(StringUnary): - """Strip leading and trailing whitespace.""" - - -@public -class LStrip(StringUnary): - """Strip leading whitespace.""" - - -@public -class RStrip(StringUnary): - """Strip trailing whitespace.""" - - -@public -class Capitalize(StringUnary): - """Capitalize the first letter of a string.""" - - -@public -class Substring(Value): - """Extract a substring from a string.""" - - arg: Value[dt.String] - start: Value[dt.Integer] - length: Optional[Value[dt.Integer]] = None - - dtype = dt.string - shape = rlz.shape_like("args") - - -@public -class StringSlice(Value): - """Extract a substring from a string.""" - - arg: Value[dt.String] - start: Optional[Value[dt.Integer]] = None - end: Optional[Value[dt.Integer]] = None - - dtype = dt.string - shape = rlz.shape_like("args") - - -@public -class StrRight(Value): - """Extract a substring starting from the right of a string.""" - - arg: Value[dt.String] - nchars: Value[dt.Integer] - - shape = rlz.shape_like("args") - dtype = dt.string - - -@public -class Repeat(Value): - """Repeat a string.""" - - arg: Value[dt.String] - times: Value[dt.Integer] - - shape = rlz.shape_like("args") - dtype = dt.string - - -@public -class StringFind(Value): - """Find the position of a substring in a string.""" - - arg: Value[dt.String] - substr: Value[dt.String] - start: Optional[Value[dt.Integer]] = None - end: Optional[Value[dt.Integer]] = None - - shape = rlz.shape_like("args") - dtype = dt.int64 - - -@public -class Translate(Value): - """Translate characters in a string.""" - - arg: Value[dt.String] - from_str: Value[dt.String] - to_str: Value[dt.String] - - shape = rlz.shape_like("args") - dtype = dt.string - - -@public -class LPad(Value): - """Pad a string on the left.""" - - arg: Value[dt.String] - length: Value[dt.Integer] - pad: Optional[Value[dt.String]] = None - - shape = rlz.shape_like("args") - dtype = dt.string - - -@public -class RPad(Value): - """Pad a string on the right.""" - - arg: Value[dt.String] - length: Value[dt.Integer] - pad: Optional[Value[dt.String]] = None - - shape = rlz.shape_like("args") - dtype = dt.string - - -@public -class FindInSet(Value): - """Find the position of a string in a list of comma-separated strings.""" - - needle: Value[dt.String] - values: VarTuple[Value[dt.String]] - - shape = rlz.shape_like("needle") - dtype = dt.int64 - - -@public -class StringJoin(Value): - """Join strings with a separator.""" - - arg: VarTuple[Value[dt.String]] - sep: Value[dt.String] - - dtype = dt.string - - @attribute - def shape(self): - return rlz.highest_precedence_shape((self.sep, *self.arg)) - - -@public -class ArrayStringJoin(Value): - """Join strings in an array with a separator.""" - - arg: Value[dt.Array[dt.String]] - sep: Value[dt.String] - - dtype = dt.string - shape = rlz.shape_like("args") - - -@public -class StartsWith(Value): - """Check if a string starts with another string.""" - - arg: Value[dt.String] - start: Value[dt.String] - - dtype = dt.boolean - shape = rlz.shape_like("args") - - -@public -class EndsWith(Value): - """Check if a string ends with another string.""" - - arg: Value[dt.String] - end: Value[dt.String] - - dtype = dt.boolean - shape = rlz.shape_like("args") - - -@public -class FuzzySearch(Value): - arg: Value[dt.String] - pattern: Value[dt.String] - - dtype = dt.boolean - shape = rlz.shape_like("args") - - -@public -class StringSQLLike(FuzzySearch): - """SQL LIKE string match operation. - - Similar to globbing. - """ - - arg: Value[dt.String] - pattern: Value[dt.String] - escape: Optional[str] = None - - -@public -class StringSQLILike(StringSQLLike): - """Case-insensitive SQL LIKE string match operation. - - Similar to case-insensitive globbing. - """ - - -@public -class RegexSearch(FuzzySearch): - """Search a string with a regular expression.""" - - -@public -class RegexExtract(Value): - """Extract a substring from a string using a regular expression.""" - - arg: Value[dt.String] - pattern: Value[dt.String] - index: Value[dt.Integer] - - shape = rlz.shape_like("args") - dtype = dt.string - - -@public -class RegexSplit(Value): - """Split a string using a regular expression.""" - - arg: Value[dt.String] - pattern: Value[dt.String] - - shape = rlz.shape_like("args") - dtype = dt.Array(dt.string) - - -@public -class RegexReplace(Value): - """Replace a substring in a string using a regular expression.""" - - arg: Value[dt.String] - pattern: Value[dt.String] - replacement: Value[dt.String] - - shape = rlz.shape_like("args") - dtype = dt.string - - -@public -class StringReplace(Value): - """Replace a substring in a string with another string.""" - - arg: Value[dt.String] - pattern: Value[dt.String] - replacement: Value[dt.String] - - shape = rlz.shape_like("args") - dtype = dt.string - - -@public -class StringSplit(Value): - """Split a string using a delimiter.""" - - arg: Value[dt.String] - delimiter: Value[dt.String] - - shape = rlz.shape_like("args") - dtype = dt.Array(dt.string) - - -@public -class StringConcat(Value): - """Concatenate strings.""" - - arg: VarTuple[Value[dt.String]] - - shape = rlz.shape_like("arg") - dtype = rlz.dtype_like("arg") - - -@public -class ExtractURLField(StringUnary): - pass - - -@public -class ExtractProtocol(ExtractURLField): - """Extract the protocol from a URL.""" - - -@public -class ExtractAuthority(ExtractURLField): - """Extract the authority from a URL.""" - - -@public -class ExtractUserInfo(ExtractURLField): - """Extract the user info from a URL.""" - - -@public -class ExtractHost(ExtractURLField): - """Extract the host from a URL.""" - - -@public -class ExtractFile(ExtractURLField): - """Extract the file from a URL.""" - - -@public -class ExtractPath(ExtractURLField): - """Extract the path from a URL.""" - - -@public -class ExtractQuery(ExtractURLField): - """Extract the query from a URL.""" - - key: Optional[Value[dt.String]] = None - - -@public -class ExtractFragment(ExtractURLField): - """Extract the fragment from a URL.""" - - -@public -class StringLength(Unary): - """Compute the length of a string or binary value.""" - - arg: Value[dt.String | dt.Binary] - dtype = dt.int64 - - -@public -class StringAscii(StringUnary): - """Compute the ASCII code of the first character of a string.""" - - dtype = dt.int64 - - -@public -class StringContains(Value): - """Check if a string contains a substring.""" - - haystack: Value[dt.String] - needle: Value[dt.String] - - shape = rlz.shape_like("args") - dtype = dt.bool - - -@public -class Levenshtein(Value): - """Compute the Levenshtein distance between two strings.""" - - left: Value[dt.String] - right: Value[dt.String] - - dtype = dt.int64 - shape = rlz.shape_like("args") diff --git a/third_party/bigframes_vendored/ibis/expr/operations/structs.py b/third_party/bigframes_vendored/ibis/expr/operations/structs.py deleted file mode 100644 index aa26841d9ab..00000000000 --- a/third_party/bigframes_vendored/ibis/expr/operations/structs.py +++ /dev/null @@ -1,62 +0,0 @@ -# Contains code from https://github.com/ibis-project/ibis/blob/9.2.0/ibis/expr/operations/structs.py - -"""Operations for working with structs.""" - -from __future__ import annotations - -import bigframes_vendored.ibis.expr.datatypes as dt -import bigframes_vendored.ibis.expr.rules as rlz -from bigframes_vendored.ibis.common.annotations import ValidationError, attribute -from bigframes_vendored.ibis.common.typing import VarTuple # noqa: TCH001 -from bigframes_vendored.ibis.expr.operations.core import Value -from public import public - - -@public -class StructField(Value): - """Extract a field from a struct value.""" - - arg: Value[dt.Struct] - field: str - - shape = rlz.shape_like("arg") - - @attribute - def dtype(self) -> dt.DataType: - struct_dtype = self.arg.dtype - value_dtype = struct_dtype[self.field] - return value_dtype - - @property - def name(self) -> str: - return self.field - - -@public -class StructColumn(Value): - """Construct a struct column from literals or expressions.""" - - names: VarTuple[str] - values: VarTuple[Value] - - shape = rlz.shape_like("values") - - def __init__(self, names, values): - if len(names) != len(values): - raise ValidationError( - f"Length of names ({len(names)}) does not match length of " - f"values ({len(values)})" - ) - super().__init__(names=names, values=values) - - @property - def name(self) -> str: - pairs = ", ".join( - f"{name!r}: {op.name}" for name, op in zip(self.names, self.values) - ) - return f"{self.__class__.__name__}({{{pairs}}})" - - @attribute - def dtype(self) -> dt.DataType: - dtypes = (value.dtype for value in self.values) - return dt.Struct.from_tuples(zip(self.names, dtypes)) diff --git a/third_party/bigframes_vendored/ibis/expr/operations/subqueries.py b/third_party/bigframes_vendored/ibis/expr/operations/subqueries.py deleted file mode 100644 index c0b95a5d36c..00000000000 --- a/third_party/bigframes_vendored/ibis/expr/operations/subqueries.py +++ /dev/null @@ -1,86 +0,0 @@ -# Contains code from https://github.com/ibis-project/ibis/blob/9.2.0/ibis/expr/operations/subqueries.py -"""Subquery operations.""" - -from __future__ import annotations - -import bigframes_vendored.ibis.expr.datashape as ds -import bigframes_vendored.ibis.expr.datatypes as dt -import bigframes_vendored.ibis.expr.rules as rlz -from bigframes_vendored.ibis.common.annotations import attribute -from bigframes_vendored.ibis.common.exceptions import IntegrityError -from bigframes_vendored.ibis.expr.operations.core import Value -from bigframes_vendored.ibis.expr.operations.relations import Relation # noqa: TCH001 -from public import public - - -@public -class Subquery(Value): - """Base class for subquery operations.""" - - rel: Relation - - @attribute - def relations(self): - return frozenset() - - -@public -class ExistsSubquery(Subquery): - """Check if a subquery returns any rows.""" - - dtype = dt.boolean - shape = ds.columnar - - -@public -class ScalarSubquery(Subquery): - """A subquery that returns a single scalar value.""" - - shape = ds.scalar - - def __init__(self, rel): - if len(rel.schema) != 1: - raise IntegrityError( - "Relation passed to ScalarSubquery() must have exactly one " - f"column, got {len(rel.schema)}" - ) - super().__init__(rel=rel) - - @attribute - def value(self): - (value,) = self.rel.values.values() - return value - - @attribute - def dtype(self): - return self.value.dtype - - -@public -class InSubquery(Subquery): - """Check if a value is in the result of a subquery.""" - - needle: Value - - dtype = dt.boolean - shape = rlz.shape_like("needle") - - def __init__(self, rel, needle): - if len(rel.schema) != 1: - raise IntegrityError( - "Relation passed to InSubquery() must have exactly one " - f"column, got {len(rel.schema)}" - ) - (value,) = rel.values.values() - if not rlz.comparable(value, needle): - raise IntegrityError(f"{needle!r} is not comparable to {value!r}") - super().__init__(rel=rel, needle=needle) - - @attribute - def value(self): - (value,) = self.rel.values.values() - return value - - @attribute - def relations(self): - return self.needle.relations diff --git a/third_party/bigframes_vendored/ibis/expr/operations/temporal.py b/third_party/bigframes_vendored/ibis/expr/operations/temporal.py deleted file mode 100644 index 729b7f14b91..00000000000 --- a/third_party/bigframes_vendored/ibis/expr/operations/temporal.py +++ /dev/null @@ -1,475 +0,0 @@ -# Contains code from https://github.com/ibis-project/ibis/blob/9.2.0/ibis/expr/operations/temporal.py - -"""Temporal operations.""" - -from __future__ import annotations - -import operator -from typing import Annotated, Optional - -import bigframes_vendored.ibis.expr.datatypes as dt -import bigframes_vendored.ibis.expr.rules as rlz -from bigframes_vendored.ibis.common.annotations import attribute -from bigframes_vendored.ibis.common.patterns import As, Attrs -from bigframes_vendored.ibis.common.temporal import ( - DateUnit, - IntervalUnit, - TimestampUnit, - TimeUnit, -) -from bigframes_vendored.ibis.expr.operations.core import Binary, Scalar, Unary, Value -from bigframes_vendored.ibis.expr.operations.logical import Between -from public import public - - -@public -class TimestampTruncate(Value): - """Truncate a timestamp to a specified unit.""" - - arg: Value[dt.Timestamp] - unit: IntervalUnit - - shape = rlz.shape_like("arg") - dtype = dt.timestamp - - -@public -class DateTruncate(Value): - """Truncate a date to a specified unit.""" - - arg: Value[dt.Date] - unit: DateUnit - - shape = rlz.shape_like("arg") - dtype = dt.date - - -@public -class TimeTruncate(Value): - """Truncate a time to a specified unit.""" - - arg: Value[dt.Time] - unit: TimeUnit - - shape = rlz.shape_like("arg") - dtype = dt.time - - -@public -class TimestampBucket(Value): - """Bucketize a timestamp to a specified interval.""" - - arg: Value[dt.Timestamp] - interval: Scalar[dt.Interval] - offset: Optional[Scalar[dt.Interval]] = None - - shape = rlz.shape_like("arg") - dtype = dt.timestamp - - -@public -class Strftime(Value): - """Format a temporal value as a string.""" - - arg: Value[dt.Temporal] - format_str: Value[dt.String] - - shape = rlz.shape_like("args") - dtype = dt.string - - -@public -class StringToTimestamp(Value): - """Convert a string to a timestamp.""" - - arg: Value[dt.String] - format_str: Value[dt.String] - - shape = rlz.shape_like("args") - dtype = dt.Timestamp(timezone="UTC") - - -@public -class StringToDate(Value): - """Convert a string to a date.""" - - arg: Value[dt.String] - format_str: Value[dt.String] - - shape = rlz.shape_like("args") - dtype = dt.date - - -@public -class ExtractTemporalField(Unary): - """Extract a field from a temporal value.""" - - arg: Value[dt.Temporal] - dtype = dt.int64 - - -@public -class ExtractDateField(ExtractTemporalField): - """Extract a field from a date.""" - - arg: Value[dt.Date | dt.Timestamp] - - -@public -class ExtractTimeField(ExtractTemporalField): - """Extract a field from a time.""" - - arg: Value[dt.Time | dt.Timestamp] - - -@public -class ExtractYear(ExtractDateField): - """Extract the year from a date or timestamp.""" - - -@public -class ExtractIsoYear(ExtractDateField): - """Extract the ISO year from a date or timestamp.""" - - -@public -class ExtractMonth(ExtractDateField): - """Extract the month from a date or timestamp.""" - - -@public -class ExtractDay(ExtractDateField): - """Extract the day from a date or timestamp.""" - - -@public -class ExtractDayOfYear(ExtractDateField): - """Extract the day of the year from a date or timestamp.""" - - -@public -class ExtractQuarter(ExtractDateField): - """Extract the quarter from a date or timestamp.""" - - -@public -class ExtractEpochSeconds(ExtractDateField): - """Extract seconds since the UNIX epoch from a date or timestamp.""" - - -@public -class ExtractWeekOfYear(ExtractDateField): - """Extract the week of the year from a date or timestamp.""" - - -@public -class ExtractHour(ExtractTimeField): - """Extract the hour from a time or timestamp.""" - - -@public -class ExtractMinute(ExtractTimeField): - """Extract the minute from a time or timestamp.""" - - -@public -class ExtractSecond(ExtractTimeField): - """Extract the second from a time or timestamp.""" - - -@public -class ExtractMillisecond(ExtractTimeField): - """Extract milliseconds from a time or timestamp.""" - - -@public -class ExtractMicrosecond(ExtractTimeField): - """Extract microseconds from a time or timestamp.""" - - -@public -class DayOfWeekIndex(Unary): - """Extract the index of the day of the week from a date or timestamp.""" - - arg: Value[dt.Date | dt.Timestamp] - - dtype = dt.int16 - - -@public -class DayOfWeekName(Unary): - """Extract the name of the day of the week from a date or timestamp.""" - - arg: Value[dt.Date | dt.Timestamp] - - dtype = dt.string - - -@public -class Time(Unary): - """Extract the time from a timestamp.""" - - dtype = dt.time - - -@public -class Date(Unary): - """Extract the date from a timestamp.""" - - dtype = dt.date - - -@public -class DateFromYMD(Value): - """Construct a date from year, month, and day.""" - - year: Value[dt.Integer] - month: Value[dt.Integer] - day: Value[dt.Integer] - - dtype = dt.date - shape = rlz.shape_like("args") - - -@public -class TimeFromHMS(Value): - """Construct a time from hours, minutes, and seconds.""" - - hours: Value[dt.Integer] - minutes: Value[dt.Integer] - seconds: Value[dt.Integer] - - dtype = dt.time - shape = rlz.shape_like("args") - - -@public -class TimestampFromYMDHMS(Value): - """Construct a timestamp from components.""" - - year: Value[dt.Integer] - month: Value[dt.Integer] - day: Value[dt.Integer] - hours: Value[dt.Integer] - minutes: Value[dt.Integer] - seconds: Value[dt.Integer] - - dtype = dt.timestamp - shape = rlz.shape_like("args") - - -@public -class TimestampFromUNIX(Value): - """Construct a timestamp from a UNIX timestamp.""" - - arg: Value - unit: TimestampUnit - - dtype = dt.timestamp - shape = rlz.shape_like("arg") - - -TimeInterval = Annotated[dt.Interval, Attrs(unit=As(TimeUnit))] -DateInterval = Annotated[dt.Interval, Attrs(unit=As(DateUnit))] - - -@public -class DateAdd(Binary): - """Add an interval to a date.""" - - left: Value[dt.Date] - right: Value[DateInterval] - - dtype = rlz.dtype_like("left") - - -@public -class DateSub(Binary): - """Subtract an interval from a date.""" - - left: Value[dt.Date] - right: Value[DateInterval] - - dtype = rlz.dtype_like("left") - - -@public -class DateDiff(Binary): - """Compute the difference between two dates.""" - - left: Value[dt.Date] - right: Value[dt.Date] - - dtype = dt.Interval("D") - - -@public -class TimeAdd(Binary): - """Add an interval to a time.""" - - left: Value[dt.Time] - right: Value[TimeInterval] - - dtype = rlz.dtype_like("left") - - -@public -class TimeSub(Binary): - """Subtract an interval from a time.""" - - left: Value[dt.Time] - right: Value[TimeInterval] - - dtype = rlz.dtype_like("left") - - -@public -class TimeDiff(Binary): - """Compute the difference between two times.""" - - left: Value[dt.Time] - right: Value[dt.Time] - - dtype = dt.Interval("s") - - -@public -class TimestampAdd(Binary): - """Add an interval to a timestamp.""" - - left: Value[dt.Timestamp] - right: Value[dt.Interval] - - dtype = rlz.dtype_like("left") - - -@public -class TimestampSub(Binary): - """Subtract an interval from a timestamp.""" - - left: Value[dt.Timestamp] - right: Value[dt.Interval] - - dtype = rlz.dtype_like("left") - - -@public -class TimestampDiff(Binary): - """Compute the difference between two timestamps.""" - - left: Value[dt.Timestamp] - right: Value[dt.Timestamp] - - dtype = dt.Interval("s") - - -@public -class IntervalBinary(Binary): - """Base class for interval binary operations.""" - - @attribute - def dtype(self): - interval_unit_args = [ - arg.dtype.unit for arg in (self.left, self.right) if arg.dtype.is_interval() - ] - unit = rlz._promote_interval_resolution(interval_unit_args) - - return self.left.dtype.copy(unit=unit) - - -@public -class IntervalAdd(IntervalBinary): - """Add two intervals.""" - - left: Value[dt.Interval] - right: Value[dt.Interval] - op = operator.add - - -@public -class IntervalSubtract(IntervalBinary): - """Subtract one interval from another.""" - - left: Value[dt.Interval] - right: Value[dt.Interval] - op = operator.sub - - -@public -class IntervalMultiply(IntervalBinary): - """Multiply an interval by a scalar.""" - - left: Value[dt.Interval] - right: Value[dt.Numeric | dt.Boolean] - op = operator.mul - - -@public -class IntervalFloorDivide(IntervalBinary): - """Divide an interval by a scalar, rounding down.""" - - left: Value[dt.Interval] - right: Value[dt.Numeric | dt.Boolean] - op = operator.floordiv - - -@public -class IntervalFromInteger(Value): - """Construct an interval from an integer.""" - - arg: Value[dt.Integer] - unit: IntervalUnit - - shape = rlz.shape_like("arg") - - @attribute - def dtype(self): - return dt.Interval(self.unit) - - @property - def resolution(self): - return self.dtype.resolution - - -@public -class BetweenTime(Between): - """Check if a time is between two bounds.""" - - arg: Value[dt.Time | dt.Timestamp] - lower_bound: Value[dt.Time | dt.String] - upper_bound: Value[dt.Time | dt.String] - - -class TemporalDelta(Value): - """Base class for temporal delta operations.""" - - part: Value[dt.String] - shape = rlz.shape_like("args") - dtype = dt.int64 - - -@public -class TimeDelta(TemporalDelta): - """Compute the difference between two times as integer number of requested units.""" - - left: Value[dt.Time] - right: Value[dt.Time] - - -@public -class DateDelta(TemporalDelta): - """Compute the difference between two dates as integer number of requested units.""" - - left: Value[dt.Date] - right: Value[dt.Date] - - -@public -class TimestampDelta(TemporalDelta): - """Compute the difference between two timestamps as integer number of requested units.""" - - left: Value[dt.Timestamp] - right: Value[dt.Timestamp] - - -public(ExtractTimestampField=ExtractTemporalField) diff --git a/third_party/bigframes_vendored/ibis/expr/operations/udf.py b/third_party/bigframes_vendored/ibis/expr/operations/udf.py deleted file mode 100644 index e3e528ee90d..00000000000 --- a/third_party/bigframes_vendored/ibis/expr/operations/udf.py +++ /dev/null @@ -1,651 +0,0 @@ -# Contains code from https://github.com/ibis-project/ibis/blob/9.2.0/ibis/expr/operations/udf.py - -"""User-defined functions (UDFs) implementation.""" - -from __future__ import annotations - -import abc -import collections -import enum -import functools -import inspect -import itertools -import typing -from typing import TYPE_CHECKING, Any, Optional, TypeVar, overload - -import bigframes_vendored.ibis.common.exceptions as exc -import bigframes_vendored.ibis.expr.datashape as ds -import bigframes_vendored.ibis.expr.datatypes as dt -import bigframes_vendored.ibis.expr.operations.core as core -import bigframes_vendored.ibis.expr.operations.reductions as reductions -import bigframes_vendored.ibis.expr.operations.relations as relations -import bigframes_vendored.ibis.expr.rules as rlz -from bigframes_vendored.ibis import util -from bigframes_vendored.ibis.common.annotations import Argument, attribute -from bigframes_vendored.ibis.common.collections import FrozenDict -from bigframes_vendored.ibis.common.deferred import deferrable -from public import public - -if TYPE_CHECKING: - from collections.abc import Callable, Iterable, MutableMapping - - import bigframes_vendored.ibis.expr.types as ir - - -EMPTY = inspect.Parameter.empty - - -_udf_name_cache: MutableMapping[type[core.Node], Iterable[int]] = ( - collections.defaultdict(itertools.count) -) - - -def _make_udf_name(name: str) -> str: - definition = next(_udf_name_cache[name]) - return f"{name}_{definition:d}" - - -@enum.unique -class InputType(enum.Enum): - BUILTIN = enum.auto() - PANDAS = enum.auto() - PYARROW = enum.auto() - PYTHON = enum.auto() - - -@public -class ScalarUDF(core.Value): - @attribute - def shape(self): - if not (args := getattr(self, "args")): # noqa: B009 - # if a udf builtin takes no args then the shape check will fail - # because there are no arguments to grab the shape of. In that case - # default to a scalar shape - return ds.scalar - else: - args = args if util.is_iterable(args) else [args] - return rlz.highest_precedence_shape(args) - - -@public -class AggUDF(reductions.Reduction): - where: Optional[core.Value[dt.Boolean]] = None - - -def _wrap( - wrapper, - input_type: InputType, - fn: Callable | None = None, - **kwargs: Any, -) -> Callable: - """Wrap a function `fn` with `wrapper`, allowing zero arguments when used as part of a decorator.""" - - def wrap(fn): - return functools.update_wrapper( - deferrable(wrapper(input_type, fn, **kwargs)), fn - ) - - return wrap(fn) if fn is not None else wrap - - -S = TypeVar("S", bound=core.Value) -B = TypeVar("B", bound=core.Value) - - -class _UDF(abc.ABC): - __slots__ = () - - @property - @abc.abstractmethod - def _base(self) -> type[B]: - """Base class of the UDF.""" - - @classmethod - def _make_node( - cls, - fn: Callable, - input_type: InputType, - name: str | None = None, - database: str | None = None, - catalog: str | None = None, - signature: tuple[tuple, Any] | None = None, - param_name_overrides: tuple[str, ...] | None = None, - **kwargs, - ) -> type[S]: - """Construct a scalar user-defined function that is built-in to the backend.""" - if "schema" in kwargs: - raise exc.UnsupportedArgumentError( - """schema` is not a valid argument. - You can use the `catalog` and `database` keywords to specify a UDF location.""" - ) - - if signature is None: - annotations = typing.get_type_hints(fn) - if (return_annotation := annotations.pop("return", None)) is None: - raise exc.MissingReturnAnnotationError(fn) - fields = { - arg_name: Argument( - pattern=rlz.ValueOf(annotations.get(arg_name)), - default=param.default, - typehint=annotations.get(arg_name, Any), - ) - for arg_name, param in inspect.signature(fn).parameters.items() - } - - else: - arg_types, return_annotation = signature - arg_names = param_name_overrides or list(inspect.signature(fn).parameters) - fields = { - arg_name: Argument(pattern=rlz.ValueOf(typ), typehint=typ) - for arg_name, typ in zip(arg_names, arg_types) - } - - func_name = name if name is not None else fn.__name__ - - fields.update( - { - "dtype": dt.dtype(return_annotation), - "__input_type__": input_type, - # must wrap `fn` in a `property` otherwise `fn` is assumed to be a - # method - "__func__": property(fget=lambda _, fn=fn: fn), - "__config__": FrozenDict(kwargs), - "__udf_namespace__": relations.Namespace( - database=database, catalog=catalog - ), - "__module__": fn.__module__, - "__func_name__": func_name, - } - ) - - return type(_make_udf_name(fn.__name__), (cls._base,), fields) - - @classmethod - def _make_wrapper( - cls, input_type: InputType, fn: Callable, **kwargs: Any - ) -> Callable: - node = cls._make_node(fn, input_type, **kwargs) - - @functools.wraps(fn) - def construct(*args: Any, **kwargs: Any) -> ir.Value: - return node(*args, **kwargs).to_expr() - - return construct - - -@public -class scalar(_UDF): - """Scalar user-defined functions. - - ::: {.callout-note} - ## The `scalar` class itself is **not** a public API, its methods are. - ::: - """ - - _base = ScalarUDF - - @overload - @classmethod - def builtin(cls, fn: Callable) -> Callable[..., ir.Value]: ... - - @overload - @classmethod - def builtin( - cls, - *, - name: str | None = None, - database: str | None = None, - catalog: str | None = None, - signature: tuple[tuple[Any, ...], Any] | None = None, - **kwargs: Any, - ) -> Callable[[Callable], Callable[..., ir.Value]]: ... - - @util.experimental - @classmethod - def builtin( - cls, - fn=None, - *, - name=None, - database=None, - catalog=None, - signature=None, - **kwargs, - ): - """Construct a scalar user-defined function that is built-in to the backend. - - Parameters - ---------- - fn - The function to wrap. - name - The name of the UDF in the backend if different from the function name. - database - The database in which the builtin function resides. - catalog - The catalog in which the builtin function resides. - signature - If present, a tuple of the form `((arg0type, arg1type, ...), returntype)`. - For example, a function taking an int and a float and returning a - string would be `((int, float), str)`. If not present, the signature - will be derived from the type annotations of the wrapped function. - - For **builtin** UDFs, only the **return type** annotation is required. - See [the user guide](/how-to/extending/builtin.qmd#input-types) for - more information. - kwargs - Additional backend-specific configuration arguments for the UDF. - - Examples - -------- - >>> import ibis - >>> @ibis.udf.scalar.builtin - ... def hamming(a: str, b: str) -> int: - ... '''Compute the Hamming distance between two strings.''' - >>> expr = hamming("duck", "luck") - >>> con = ibis.connect("duckdb://") - >>> con.execute(expr) - np.int64(1) - - """ - return _wrap( - cls._make_wrapper, - InputType.BUILTIN, - fn, - name=name, - database=database, - catalog=catalog, - signature=signature, - **kwargs, - ) - - @overload - @classmethod - def python(cls, fn: Callable) -> Callable[..., ir.Value]: ... - - @overload - @classmethod - def python( - cls, - *, - name: str | None = None, - database: str | None = None, - catalog: str | None = None, - signature: tuple[tuple[Any, ...], Any] | None = None, - **kwargs: Any, - ) -> Callable[[Callable], Callable[..., ir.Value]]: ... - - @util.experimental - @classmethod - def python( - cls, - fn=None, - *, - name=None, - database=None, - catalog=None, - signature=None, - **kwargs, - ): - """Construct a **non-vectorized** scalar user-defined function that accepts Python scalar values as inputs. - - ::: {.callout-warning collapse="true"} - ## `python` UDFs are likely to be slow - - `python` UDFs are not vectorized: they are executed row by row with one - Python function call per row - - This calling pattern tends to be **much** slower than - [`pandas`](/reference/scalar-udfs.qmd#ibis.expr.operations.udf.scalar.pandas) - or - [`pyarrow`](/reference/scalar-udfs.qmd#ibis.expr.operations.udf.scalar.pyarrow)-based - vectorized UDFs. - ::: - - Parameters - ---------- - fn - The function to wrap. - name - The name of the UDF in the backend if different from the function name. - database - The database in which to create the UDF. - catalog - The catalog in which to create the UDF. - signature - If present, a tuple of the form `((arg0type, arg1type, ...), returntype)`. - For example, a function taking an int and a float and returning a - string would be `((int, float), str)`. If not present, the signature - will be derived from the type annotations of the wrapped function. - kwargs - Additional backend-specific configuration arguments for the UDF. - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable(dict(int_col=[1, 2, 3], str_col=["a", "b", "c"])) - >>> t - ┏━━━━━━━━━┳━━━━━━━━━┓ - ┃ int_col ┃ str_col ┃ - ┡━━━━━━━━━╇━━━━━━━━━┩ - │ int64 │ string │ - ├─────────┼─────────┤ - │ 1 │ a │ - │ 2 │ b │ - │ 3 │ c │ - └─────────┴─────────┘ - >>> @ibis.udf.scalar.python - ... def str_magic(x: str) -> str: - ... return f"{x}_magic" - >>> @ibis.udf.scalar.python - ... def add_one_py(x: int) -> int: - ... return x + 1 - >>> str_magic(t.str_col) - ┏━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ str_magic_0(str_col) ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━┩ - │ string │ - ├──────────────────────┤ - │ a_magic │ - │ b_magic │ - │ c_magic │ - └──────────────────────┘ - >>> add_one_py(t.int_col) - ┏━━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ add_one_py_0(int_col) ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━━┩ - │ int64 │ - ├───────────────────────┤ - │ 2 │ - │ 3 │ - │ 4 │ - └───────────────────────┘ - - See Also - -------- - - [`pandas`](/reference/scalar-udfs.qmd#ibis.expr.operations.udf.scalar.pandas) - - [`pyarrow`](/reference/scalar-udfs.qmd#ibis.expr.operations.udf.scalar.pyarrow) - - """ - return _wrap( - cls._make_wrapper, - InputType.PYTHON, - fn, - name=name, - database=database, - catalog=catalog, - signature=signature, - **kwargs, - ) - - @overload - @classmethod - def pandas(cls, fn: Callable) -> Callable[..., ir.Value]: ... - - @overload - @classmethod - def pandas( - cls, - *, - name: str | None = None, - database: str | None = None, - catalog: str | None = None, - signature: tuple[tuple[Any, ...], Any] | None = None, - **kwargs: Any, - ) -> Callable[[Callable], Callable[..., ir.Value]]: ... - - @util.experimental - @classmethod - def pandas( - cls, - fn=None, - *, - name=None, - database=None, - catalog=None, - signature=None, - **kwargs, - ): - """Construct a **vectorized** scalar user-defined function that accepts pandas Series' as inputs. - - Parameters - ---------- - fn - The function to wrap. - name - The name of the UDF in the backend if different from the function name. - database - The database in which to create the UDF. - catalog - The catalog in which to create the UDF. - signature - If present, a tuple of the form `((arg0type, arg1type, ...), returntype)`. - For example, a function taking an int and a float and returning a - string would be `((int, float), str)`. If not present, the signature - will be derived from the type annotations of the wrapped function. - kwargs - Additional backend-specific configuration arguments for the UDF. - - Examples - -------- - ```python - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable(dict(int_col=[1, 2, 3], str_col=["a", "b", "c"])) - >>> t - ┏━━━━━━━━━┳━━━━━━━━━┓ - ┃ int_col ┃ str_col ┃ - ┡━━━━━━━━━╇━━━━━━━━━┩ - │ int64 │ string │ - ├─────────┼─────────┤ - │ 1 │ a │ - │ 2 │ b │ - │ 3 │ c │ - └─────────┴─────────┘ - >>> @ibis.udf.scalar.pandas - ... def str_cap(x: str) -> str: - ... # note usage of pandas `str` method - ... return x.str.capitalize() - >>> str_cap(t.str_col) # doctest: +SKIP - ┏━━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ string_cap_0(str_col) ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━━┩ - │ string │ - ├───────────────────────┤ - │ A │ - │ B │ - │ C │ - └───────────────────────┘ - ``` - - See Also - -------- - - [`python`](/reference/scalar-udfs.qmd#ibis.expr.operations.udf.scalar.python) - - [`pyarrow`](/reference/scalar-udfs.qmd#ibis.expr.operations.udf.scalar.pyarrow) - - """ - return _wrap( - cls._make_wrapper, - InputType.PANDAS, - fn, - name=name, - database=database, - catalog=catalog, - signature=signature, - **kwargs, - ) - - @overload - @classmethod - def pyarrow(cls, fn: Callable) -> Callable[..., ir.Value]: ... - - @overload - @classmethod - def pyarrow( - cls, - *, - name: str | None = None, - database: str | None = None, - catalog: str | None = None, - signature: tuple[tuple[Any, ...], Any] | None = None, - **kwargs: Any, - ) -> Callable[[Callable], Callable[..., ir.Value]]: ... - - @util.experimental - @classmethod - def pyarrow( - cls, - fn=None, - *, - name=None, - database=None, - catalog=None, - signature=None, - **kwargs, - ): - """Construct a **vectorized** scalar user-defined function that accepts PyArrow Arrays as input. - - Parameters - ---------- - fn - The function to wrap. - name - The name of the UDF in the backend if different from the function name. - database - The database in which to create the UDF. - catalog - The catalog in which to create the UDF. - signature - If present, a tuple of the form `((arg0type, arg1type, ...), returntype)`. - For example, a function taking an int and a float and returning a - string would be `((int, float), str)`. If not present, the signature - will be derived from the type annotations of the wrapped function. - kwargs - Additional backend-specific configuration arguments for the UDF. - - Examples - -------- - >>> import ibis - >>> import pyarrow.compute as pc - >>> from datetime import date - >>> ibis.options.interactive = True - >>> t = ibis.memtable( - ... dict(start_col=[date(2024, 4, 29)], end_col=[date(2025, 4, 29)]), - ... ) - >>> @ibis.udf.scalar.pyarrow - ... def weeks_between(start: date, end: date) -> int: - ... return pc.weeks_between(start, end) - >>> weeks_between(t.start_col, t.end_col) - ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ weeks_between_0(start_col, end_col) ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩ - │ int64 │ - ├─────────────────────────────────────┤ - │ 52 │ - └─────────────────────────────────────┘ - - See Also - -------- - - [`python`](/reference/scalar-udfs.qmd#ibis.expr.operations.udf.scalar.python) - - [`pandas`](/reference/scalar-udfs.qmd#ibis.expr.operations.udf.scalar.pandas) - - """ - return _wrap( - cls._make_wrapper, - InputType.PYARROW, - fn, - name=name, - database=database, - catalog=catalog, - signature=signature, - **kwargs, - ) - - -@public -class agg(_UDF): - """Aggregate user-defined functions. - - ::: {.callout-note} - ## The `agg` class itself is **not** a public API, its methods are. - ::: - """ - - __slots__ = () - - _base = AggUDF - - @overload - @classmethod - def builtin(cls, fn: Callable) -> Callable[..., ir.Value]: ... - - @overload - @classmethod - def builtin( - cls, - *, - name: str | None = None, - database: str | None = None, - catalog: str | None = None, - signature: tuple[tuple[Any, ...], Any] | None = None, - **kwargs: Any, - ) -> Callable[[Callable], Callable[..., ir.Value]]: ... - - @util.experimental - @classmethod - def builtin( - cls, - fn=None, - *, - name=None, - database=None, - catalog=None, - signature=None, - **kwargs, - ): - """Construct an aggregate user-defined function that is built-in to the backend. - - Parameters - ---------- - fn - The function to wrap. - name - The name of the UDF in the backend if different from the function name. - database - The database in which the builtin function resides. - catalog - The catalog in which the builtin function resides. - signature - If present, a tuple of the form `((arg0type, arg1type, ...), returntype)`. - For example, a function taking an int and a float and returning a - string would be `((int, float), str)`. If not present, the signature - will be derived from the type annotations of the wrapped function. - kwargs - Additional backend-specific configuration arguments for the UDF. - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> @ibis.udf.agg.builtin - ... def favg(a: float) -> float: - ... '''Compute the average of a column using Kahan summation.''' - >>> t = ibis.examples.penguins.fetch() - >>> expr = favg(t.bill_length_mm) - >>> expr - ┌──────────────────────────────┐ - │ np.float64(43.9219298245614) │ - └──────────────────────────────┘ - - """ - return _wrap( - cls._make_wrapper, - InputType.BUILTIN, - fn, - name=name, - database=database, - catalog=catalog, - signature=signature, - **kwargs, - ) diff --git a/third_party/bigframes_vendored/ibis/expr/operations/window.py b/third_party/bigframes_vendored/ibis/expr/operations/window.py deleted file mode 100644 index c40c9db2f0b..00000000000 --- a/third_party/bigframes_vendored/ibis/expr/operations/window.py +++ /dev/null @@ -1,115 +0,0 @@ -# Contains code from https://github.com/ibis-project/ibis/blob/9.2.0/ibis/expr/operations/window.py - -"""Window operations.""" - -from __future__ import annotations - -from typing import Literal as LiteralType -from typing import Optional - -import bigframes_vendored.ibis.common.exceptions as com -import bigframes_vendored.ibis.expr.datashape as ds -import bigframes_vendored.ibis.expr.datatypes as dt -import bigframes_vendored.ibis.expr.rules as rlz -from bigframes_vendored.ibis.common.patterns import CoercionError -from bigframes_vendored.ibis.common.typing import VarTuple # noqa: TCH001 -from bigframes_vendored.ibis.expr.operations.analytic import Analytic # noqa: TCH001 -from bigframes_vendored.ibis.expr.operations.core import Column, Value -from bigframes_vendored.ibis.expr.operations.generic import Literal -from bigframes_vendored.ibis.expr.operations.numeric import Negate -from bigframes_vendored.ibis.expr.operations.reductions import Reduction # noqa: TCH001 -from bigframes_vendored.ibis.expr.operations.sortkeys import SortKey # noqa: TCH001 -from public import public -from typing_extensions import TypeVar - -T = TypeVar("T", bound=dt.Numeric | dt.Interval, covariant=True) -S = TypeVar("S", bound=ds.DataShape, default=ds.Any, covariant=True) - - -@public -class WindowBoundary(Value[T, S]): - """Window boundary object.""" - - # TODO(kszucs): consider to prefer Concrete base class here - # pretty similar to SortKey and Alias operations which wrap a single value - value: Value[T, S] - preceding: bool - - @property - def following(self) -> bool: - return not self.preceding - - @property - def shape(self) -> S: - return self.value.shape - - @property - def dtype(self) -> T: - return self.value.dtype - - @classmethod - def __coerce__(cls, value, **kwargs): - arg = super().__coerce__(value, **kwargs) - - if isinstance(arg, cls): - return arg - elif isinstance(arg, Negate): - return cls(arg.arg, preceding=True) - elif isinstance(arg, Literal): - new = arg.copy(value=abs(arg.value)) - return cls(new, preceding=arg.value < 0) - elif isinstance(arg, Value): - return cls(arg, preceding=False) - else: - raise CoercionError(f"Invalid window boundary type: {type(arg)}") - - -@public -class WindowFunction(Value): - """Window function operation.""" - - func: Analytic | Reduction - # none is a hacky way to express that window bounds are not supported (eg row_number()) - how: LiteralType["rows", "range", "none"] = "rows" # noqa: F821 - start: Optional[WindowBoundary[dt.Numeric | dt.Interval]] = None - end: Optional[WindowBoundary[dt.Numeric | dt.Interval]] = None - group_by: VarTuple[Column] = () - order_by: VarTuple[SortKey] = () - - dtype = rlz.dtype_like("func") - shape = ds.columnar - - def __init__(self, how, start, end, **kwargs): - if how == "rows": - if start and not start.dtype.is_integer(): - raise com.IbisTypeError( - "Row-based window frame start boundary must be an integer" - ) - if end and not end.dtype.is_integer(): - raise com.IbisTypeError( - "Row-based window frame end boundary must be an integer" - ) - elif how == "range": - if ( - start - and end - and not ( - (start.dtype.is_interval() and end.dtype.is_interval()) - or (start.dtype.is_numeric() and end.dtype.is_numeric()) - ) - ): - raise com.IbisTypeError( - "Window frame start and end boundaries must have the same datatype" - ) - elif how != "none": - raise com.IbisTypeError( - f"Window frame type must be either 'rows' or 'range', got {how}" - ) - super().__init__(how=how, start=start, end=end, **kwargs) - - @property - def name(self): - return self.func.name - - -public(WindowOp=WindowFunction, Window=WindowFunction) diff --git a/third_party/bigframes_vendored/ibis/expr/rewrites.py b/third_party/bigframes_vendored/ibis/expr/rewrites.py deleted file mode 100644 index 3ec5ea12714..00000000000 --- a/third_party/bigframes_vendored/ibis/expr/rewrites.py +++ /dev/null @@ -1,385 +0,0 @@ -# Contains code from https://github.com/ibis-project/ibis/blob/9.2.0/ibis/expr/rewrites.py - -"""Some common rewrite functions to be shared between backends.""" - -from __future__ import annotations - -from collections import defaultdict - -import bigframes_vendored.ibis.expr.operations as ops -import toolz -from bigframes_vendored.ibis.common.collections import FrozenDict # noqa: TCH001 -from bigframes_vendored.ibis.common.deferred import Item, _, deferred, var -from bigframes_vendored.ibis.common.exceptions import ExpressionError, IbisInputError -from bigframes_vendored.ibis.common.graph import Node as Traversable -from bigframes_vendored.ibis.common.graph import traverse -from bigframes_vendored.ibis.common.grounds import Concrete -from bigframes_vendored.ibis.common.patterns import Check, pattern, replace -from bigframes_vendored.ibis.common.typing import VarTuple # noqa: TCH001 -from bigframes_vendored.ibis.util import Namespace, promote_list - -p = Namespace(pattern, module=ops) -d = Namespace(deferred, module=ops) - - -x = var("x") -y = var("y") -name = var("name") - - -class DerefMap(Concrete, Traversable): - """Trace and replace fields from earlier relations in the hierarchy. - - In order to provide a nice user experience, we need to allow expressions - from earlier relations in the hierarchy. Consider the following example: - - t = ibis.table([('a', 'int64'), ('b', 'string')], name='t') - t1 = t.select([t.a, t.b]) - t2 = t1.filter(t.a > 0) # note that not t1.a is referenced here - t3 = t2.select(t.a) # note that not t2.a is referenced here - - However the relational operations in the IR are strictly enforcing that - the expressions are referencing the immediate parent only. So we need to - track fields upwards the hierarchy to replace `t.a` with `t1.a` and `t2.a` - in the example above. This is called dereferencing. - - Whether we can treat or not a field of a relation semantically equivalent - with a field of an earlier relation in the hierarchy depends on the - `.values` mapping of the relation. Leaf relations, like `t` in the example - above, have an empty `.values` mapping, so we cannot dereference fields - from them. On the other hand a projection, like `t1` in the example above, - has a `.values` mapping like `{'a': t.a, 'b': t.b}`, so we can deduce that - `t1.a` is semantically equivalent with `t.a` and so on. - """ - - """The relations we want the values to point to.""" - rels: VarTuple[ops.Relation] - - """Substitution mapping from values of earlier relations to the fields of `rels`.""" - subs: FrozenDict[ops.Value, ops.Field] - - """Ambiguous field references.""" - ambigs: FrozenDict[ops.Value, VarTuple[ops.Value]] - - @classmethod - def from_targets(cls, rels, extra=None): - """Create a dereference map from a list of target relations. - - Usually a single relation is passed except for joins where multiple - relations are involved. - - Parameters - ---------- - rels : list of ops.Relation - The target relations to dereference to. - extra : dict, optional - Extra substitutions to be added to the dereference map. - - Returns - ------- - DerefMap - """ - rels = promote_list(rels) - mapping = defaultdict(dict) - for rel in rels: - for field in rel.fields.values(): - for value, distance in cls.backtrack(field): - mapping[value][field] = distance - - subs, ambigs = {}, {} - for from_, to in mapping.items(): - mindist = min(to.values()) - minkeys = [k for k, v in to.items() if v == mindist] - # if all the closest fields are from the same relation, then we - # can safely substitute them and we pick the first one arbitrarily - if all(minkeys[0].relations == k.relations for k in minkeys): - subs[from_] = minkeys[0] - else: - ambigs[from_] = minkeys - - if extra is not None: - subs.update(extra) - - return cls(rels, subs, ambigs) - - @classmethod - def backtrack(cls, value): - """Backtrack the field in the relation hierarchy. - - The field is traced back until no modification is made, so only follow - ops.Field nodes not arbitrary values. - - Parameters - ---------- - value : ops.Value - The value to backtrack. - - Yields - ------ - tuple[ops.Field, int] - The value node and the distance from the original value. - """ - distance = 0 - # track down the field in the hierarchy until no modification - # is made so only follow ops.Field nodes not arbitrary values; - while isinstance(value, ops.Field): - yield value, distance - value = value.rel.values.get(value.name) - distance += 1 - if ( - value is not None - and value.relations - and not value.find(ops.Impure, filter=ops.Value) - ): - yield value, distance - - def dereference(self, value): - """Dereference a value to the target relations. - - Also check for ambiguous field references. If a field reference is found - which is marked as ambiguous, then raise an error. - - Parameters - ---------- - value : ops.Value - The value to dereference. - - Returns - ------- - ops.Value - The dereferenced value. - """ - ambigs = value.find(lambda x: x in self.ambigs, filter=ops.Value) - if ambigs: - raise IbisInputError( - f"Ambiguous field reference {ambigs!r} in expression {value!r}" - ) - return value.replace(self.subs, filter=ops.Value) - - -def flatten_predicates(node): - """Yield the expressions corresponding to the `And` nodes of a predicate. - - Examples - -------- - >>> import ibis - >>> t = ibis.table([("a", "int64"), ("b", "string")], name="t") - >>> filt = (t.a == 1) & (t.b == "foo") - >>> predicates = flatten_predicates(filt.op()) - >>> len(predicates) - 2 - >>> predicates[0].to_expr().name("left") - r0 := UnboundTable: t - a int64 - b string - left: r0.a == 1 - >>> predicates[1].to_expr().name("right") - r0 := UnboundTable: t - a int64 - b string - right: r0.b == 'foo' - - """ - - def predicate(node): - if isinstance(node, ops.And): - # proceed and don't yield the node - return True, None - else: - # halt and yield the node - return False, node - - return list(traverse(predicate, node)) - - -@replace(p.Field(p.JoinChain)) -def peel_join_field(_): - return _.rel.values[_.name] - - -@replace(p.ScalarParameter) -def replace_parameter(_, params, **kwargs): - """Replace scalar parameters with their values.""" - return ops.Literal(value=params[_], dtype=_.dtype) - - -@replace(p.StringSlice) -def lower_stringslice(_, **kwargs): - """Rewrite StringSlice in terms of Substring.""" - if _.start is None: - real_start = 0 - else: - real_start = ops.IfElse( - ops.GreaterEqual(_.start, 0), - _.start, - ops.Greatest((0, ops.Add(ops.StringLength(_.arg), _.start))), - ) - - if _.end is None: - real_end = ops.StringLength(_.arg) - else: - real_end = ops.IfElse( - ops.GreaterEqual(_.end, 0), - _.end, - ops.Greatest((0, ops.Add(ops.StringLength(_.arg), _.end))), - ) - - length = ops.Greatest((0, ops.Subtract(real_end, real_start))) - return ops.Substring(_.arg, start=real_start, length=length) - - -@replace(p.Analytic) -def project_wrap_analytic(_, rel): - # Wrap analytic functions in a window function - return ops.WindowFunction(_) - - -@replace(p.Reduction) -def project_wrap_reduction(_, rel): - # Query all the tables that the reduction depends on - if _.relations == {rel}: - # The reduction is fully originating from the `rel`, so turn - # it into a window function of `rel` - return ops.WindowFunction(_) - else: - # 1. The reduction doesn't depend on any table, constructed from - # scalar values, so turn it into a scalar subquery. - # 2. The reduction is originating from `rel` and other tables, - # so this is a correlated scalar subquery. - # 3. The reduction is originating entirely from other tables, - # so this is an uncorrelated scalar subquery. - return ops.ScalarSubquery(_.to_expr().as_table()) - - -def rewrite_project_input(value, relation): - # we need to detect reductions which are either turned into window functions - # or scalar subqueries depending on whether they are originating from the - # relation - return value.replace( - project_wrap_analytic | project_wrap_reduction, - filter=p.Value & ~p.WindowFunction & ~p.ArrayReduce, - context={"rel": relation}, - ) - - -ReductionLike = p.Reduction | p.Field(p.Aggregate(groups={})) - - -@replace(ReductionLike) -def filter_wrap_reduction(_): - # Wrap reductions or fields referencing an aggregation without a group by - - # which are scalar fields - in a scalar subquery. In the latter case we - # use the reduction value from the aggregation. - if isinstance(_, ops.Field): - value = _.rel.values[_.name] - else: - value = _ - return ops.ScalarSubquery(value.to_expr().as_table()) - - -def rewrite_filter_input(value): - return value.replace(filter_wrap_reduction, filter=p.Value & ~p.WindowFunction) - - -@replace(p.Analytic | p.Reduction) -def window_wrap_reduction(_, window): - # Wrap analytic and reduction functions in a window function. Used in the - # value.over() API. - return ops.WindowFunction( - _, - how=window.how, - start=window.start, - end=window.end, - group_by=window.groupings, - order_by=window.orderings, - ) - - -@replace(p.WindowFunction) -def window_merge_frames(_, window): - # Merge window frames, used in the value.over() and groupby.select() APIs. - if _.how != window.how: - raise ExpressionError( - f"Unable to merge {_.how} window with {window.how} window" - ) - elif _.start and window.start and _.start != window.start: - raise ExpressionError( - "Unable to merge windows with conflicting `start` boundary" - ) - elif _.end and window.end and _.end != window.end: - raise ExpressionError("Unable to merge windows with conflicting `end` boundary") - - start = _.start or window.start - end = _.end or window.end - group_by = tuple(toolz.unique(_.group_by + window.groupings)) - - order_keys = {} - for sort_key in window.orderings + _.order_by: - order_keys[sort_key.expr] = sort_key.ascending, sort_key.nulls_first - - order_by = ( - ops.SortKey(expr, ascending=ascending, nulls_first=nulls_first) - for expr, (ascending, nulls_first) in order_keys.items() - ) - return _.copy(start=start, end=end, group_by=group_by, order_by=order_by) - - -def rewrite_window_input(value, window): - context = {"window": window} - # if self is a reduction or analytic function, wrap it in a window function - node = value.replace( - window_wrap_reduction, - filter=p.Value & ~p.WindowFunction, - context=context, - ) - # if self is already a window function, merge the existing window frame - # with the requested window frame - return node.replace(window_merge_frames, filter=p.Value, context=context) - - -# TODO(kszucs): schema comparison should be updated to not distinguish between -# different column order -@replace(p.Project(y @ p.Relation) & Check(_.schema == y.schema)) -def complete_reprojection(_, y): - # TODO(kszucs): this could be moved to the pattern itself but not sure how - # to express it, especially in a shorter way then the following check - for name in _.schema: - if _.values[name] != ops.Field(y, name): - return _ - return y - - -@replace(p.Project(y @ p.Project)) -def subsequent_projects(_, y): - rule = p.Field(y, name) >> Item(y.values, name) - values = {k: v.replace(rule, filter=ops.Value) for k, v in _.values.items()} - return ops.Project(y.parent, values) - - -@replace(p.Filter(y @ p.Filter)) -def subsequent_filters(_, y): - rule = p.Field(y, name) >> d.Field(y.parent, name) - preds = tuple(v.replace(rule, filter=ops.Value) for v in _.predicates) - return ops.Filter(y.parent, y.predicates + preds) - - -@replace(p.Filter(y @ p.Project)) -def reorder_filter_project(_, y): - rule = p.Field(y, name) >> Item(y.values, name) - preds = tuple(v.replace(rule, filter=ops.Value) for v in _.predicates) - - inner = ops.Filter(y.parent, preds) - rule = p.Field(y.parent, name) >> d.Field(inner, name) - projs = {k: v.replace(rule, filter=ops.Value) for k, v in y.values.items()} - - return ops.Project(inner, projs) - - -def simplify(node): - # TODO(kszucs): add a utility to the graph module to do rewrites in multiple - # passes after each other - node = node.replace(reorder_filter_project) - node = node.replace(reorder_filter_project) - node = node.replace(subsequent_projects | subsequent_filters) - node = node.replace(complete_reprojection) - return node diff --git a/third_party/bigframes_vendored/ibis/expr/rules.py b/third_party/bigframes_vendored/ibis/expr/rules.py deleted file mode 100644 index 95050a6a5bc..00000000000 --- a/third_party/bigframes_vendored/ibis/expr/rules.py +++ /dev/null @@ -1,167 +0,0 @@ -# Contains code from https://github.com/ibis-project/ibis/blob/9.2.0/ibis/expr/rules.py - -from __future__ import annotations - -from itertools import product, starmap -from typing import Optional - -import bigframes_vendored.ibis.expr.datatypes as dt -import bigframes_vendored.ibis.expr.operations as ops -from bigframes_vendored.ibis import util -from bigframes_vendored.ibis.common.annotations import attribute -from bigframes_vendored.ibis.common.grounds import Concrete -from bigframes_vendored.ibis.common.patterns import CoercionError, NoMatch, Pattern -from bigframes_vendored.ibis.common.temporal import IntervalUnit -from public import public - - -@public -def highest_precedence_shape(nodes): - return max(node.shape for node in nodes) - - -@public -def highest_precedence_dtype(nodes): - """Return the highest precedence type from the passed expressions. - - Also verifies that there are valid implicit casts between any of the types - and the selected highest precedence type. - This is a thin wrapper around datatypes highest precedence check. - - Parameters - ---------- - nodes : Iterable[ops.Value] - A sequence of Expressions - - Returns - ------- - dtype: DataType - The highest precedence datatype - - """ - return dt.highest_precedence(node.dtype for node in nodes) - - -@public -def castable(source, target): - """Return whether source ir type is implicitly castable to target. - - Based on the underlying datatypes and the value in case of Literals - """ - value = getattr(source, "value", None) - return source.dtype.castable(target.dtype, value=value) - - -@public -def comparable(left, right): - return castable(left, right) or castable(right, left) - - -# --------------------------------------------------------------------- -# Output type functions - - -@public -def dtype_like(name): - @attribute - def dtype(self): - args = getattr(self, name) - args = args if util.is_iterable(args) else [args] - return highest_precedence_dtype(args) - - return dtype - - -@public -def shape_like(name): - @attribute - def shape(self): - args = getattr(self, name) - args = args if util.is_iterable(args) else [args] - args = [a for a in args if a is not None] - return highest_precedence_shape(args) - - return shape - - -# TODO(kszucs): might just use bounds instead of actual literal values -# that could simplify interval binop output_type methods -# TODO(kszucs): pre-generate mapping? - - -def _promote_integral_binop(exprs, op): - bounds, dtypes = [], [] - for arg in exprs: - dtypes.append(arg.dtype) - if isinstance(arg, ops.Literal): - bounds.append([arg.value]) - else: - bounds.append(arg.dtype.bounds) - - all_unsigned = dtypes and util.all_of(dtypes, dt.UnsignedInteger) - # In some cases, the bounding type might be int8, even though neither - # of the types are that small. We want to ensure the containing type is - # _at least_ as large as the smallest type in the expression. - values = starmap(op, product(*bounds)) - dtypes += [dt.infer(v, prefer_unsigned=all_unsigned) for v in values] - - return dt.highest_precedence(dtypes) - - -@public -def numeric_like(name, op): - @attribute - def dtype(self): - args = getattr(self, name) - dtypes = [arg.dtype for arg in args] - if util.all_of(dtypes, dt.Integer): - result = _promote_integral_binop(args, op) - else: - result = highest_precedence_dtype(args) - - return result - - return dtype - - -def _promote_interval_resolution(units: list[IntervalUnit]) -> IntervalUnit: - # Find the smallest unit present in units - for unit in reversed(IntervalUnit): - if unit in units: - return unit - raise AssertionError("unreachable") - - -def _arg_type_error_format(op): - if isinstance(op, ops.Literal): - return f"Literal({op.value}):{op.dtype}" - else: - return f"{op.name}:{op.dtype}" - - -class ValueOf(Concrete, Pattern): - """Match a value of a specific type **instance**. - - This is different from the Value[T] annotations which construct - GenericCoercedTo(Value[T]) validators working with datatype types - rather than instances. - - Parameters - ---------- - dtype : DataType | None - The datatype the constructed Value instance should conform to. - - """ - - dtype: Optional[dt.DataType] = None - - def match(self, value, context): - try: - value = ops.Value.__coerce__(value, self.dtype) - except CoercionError: - return NoMatch - - if self.dtype and not value.dtype.castable(self.dtype): - return NoMatch - - return value diff --git a/third_party/bigframes_vendored/ibis/expr/schema.py b/third_party/bigframes_vendored/ibis/expr/schema.py deleted file mode 100644 index edc704c3664..00000000000 --- a/third_party/bigframes_vendored/ibis/expr/schema.py +++ /dev/null @@ -1,303 +0,0 @@ -# Contains code from https://github.com/ibis-project/ibis/blob/9.2.0/ibis/expr/schema.py - -from __future__ import annotations - -from collections.abc import Iterable, Iterator, Mapping -from typing import TYPE_CHECKING, Any, Union - -import bigframes_vendored.ibis.expr.datatypes as dt -from bigframes_vendored.ibis.common.annotations import attribute -from bigframes_vendored.ibis.common.collections import FrozenOrderedDict, MapSet -from bigframes_vendored.ibis.common.dispatch import lazy_singledispatch -from bigframes_vendored.ibis.common.exceptions import InputTypeError, IntegrityError -from bigframes_vendored.ibis.common.grounds import Concrete -from bigframes_vendored.ibis.common.patterns import Coercible -from bigframes_vendored.ibis.util import indent - -if TYPE_CHECKING: - from typing import TypeAlias - - -class Schema(Concrete, Coercible, MapSet): - """An ordered mapping of str -> [datatype](./datatypes.qmd), used to hold a [Table](./expression-tables.qmd#ibis.expr.tables.Table)'s schema.""" - - fields: FrozenOrderedDict[str, dt.DataType] - """A mapping of [](`str`) to - [`DataType`](./datatypes.qmd#ibis.expr.datatypes.DataType) - objects representing the type of each column.""" - - def __repr__(self) -> str: - space = 2 + max(map(len, self.names), default=0) - return "ibis.Schema {{{}\n}}".format( - indent( - "".join( - f"\n{name.ljust(space)}{type!s}" for name, type in self.items() - ), - 2, - ) - ) - - def __rich_repr__(self): - for name, dtype in self.items(): - yield name, str(dtype) - - def __len__(self) -> int: - return len(self.fields) - - def __iter__(self) -> Iterator[str]: - return iter(self.fields) - - def __getitem__(self, name: str) -> dt.DataType: - return self.fields[name] - - @classmethod - def __coerce__(cls, value) -> Schema: - if isinstance(value, cls): - return value - return schema(value) - - @attribute - def names(self): - return tuple(self.keys()) - - @attribute - def types(self): - return tuple(self.values()) - - @attribute - def _name_locs(self) -> dict[str, int]: - return {v: i for i, v in enumerate(self.names)} - - def equals(self, other: Schema) -> bool: - """Return whether `other` is equal to `self`. - - The order of fields in the schema is taken into account when computing equality. - - Parameters - ---------- - other - Schema to compare `self` to. - - Examples - -------- - >>> import ibis - >>> xy = ibis.schema({"x": int, "y": str}) - >>> xy2 = ibis.schema({"x": int, "y": str}) - >>> yx = ibis.schema({"y": str, "x": int}) - >>> xy_float = ibis.schema({"x": float, "y": str}) - >>> assert xy.equals(xy2) - >>> assert not xy.equals(yx) - >>> assert not xy.equals(xy_float) - """ - if not isinstance(other, Schema): - raise TypeError( - f"invalid equality comparison between Schema and {type(other)}" - ) - return self == other - - @classmethod - def from_tuples( - cls, - values: Iterable[tuple[str, str | dt.DataType]], - ) -> Schema: - """Construct a `Schema` from an iterable of pairs. - - Parameters - ---------- - values - An iterable of pairs of name and type. - - Returns - ------- - Schema - A new schema - - Examples - -------- - >>> import ibis - >>> ibis.Schema.from_tuples([("a", "int"), ("b", "string")]) - ibis.Schema { - a int64 - b string - } - - """ - pairs = list(values) - if len(pairs) == 0: - return cls({}) - - names, types = zip(*pairs) - - # validate unique field names - name_locs = {v: i for i, v in enumerate(names)} - if len(name_locs) < len(names): - duplicate_names = list(names) - for v in name_locs: - duplicate_names.remove(v) - raise IntegrityError(f"Duplicate column name(s): {duplicate_names}") - - # construct the schema - return cls(dict(zip(names, types))) - - @classmethod - def from_numpy(cls, numpy_schema): - """Return the equivalent ibis schema.""" - from bigframes_vendored.ibis.formats.numpy import NumpySchema - - return NumpySchema.to_ibis(numpy_schema) - - @classmethod - def from_pandas(cls, pandas_schema): - """Return the equivalent ibis schema.""" - from bigframes_vendored.ibis.formats.pandas import PandasSchema - - return PandasSchema.to_ibis(pandas_schema) - - @classmethod - def from_pyarrow(cls, pyarrow_schema): - """Return the equivalent ibis schema.""" - from bigframes_vendored.ibis.formats.pyarrow import PyArrowSchema - - return PyArrowSchema.to_ibis(pyarrow_schema) - - @classmethod - def from_polars(cls, polars_schema): - """Return the equivalent ibis schema.""" - from bigframes_vendored.ibis.formats.polars import PolarsSchema - - return PolarsSchema.to_ibis(polars_schema) - - def to_numpy(self): - """Return the equivalent numpy dtypes.""" - from bigframes_vendored.ibis.formats.numpy import NumpySchema - - return NumpySchema.from_ibis(self) - - def to_pandas(self): - """Return the equivalent pandas datatypes.""" - from bigframes_vendored.ibis.formats.pandas import PandasSchema - - return PandasSchema.from_ibis(self) - - def to_pyarrow(self): - """Return the equivalent pyarrow schema.""" - from bigframes_vendored.ibis.formats.pyarrow import PyArrowSchema - - return PyArrowSchema.from_ibis(self) - - def __arrow_c_schema__(self): - return self.to_pyarrow().__arrow_c_schema__() - - def to_polars(self): - """Return the equivalent polars schema.""" - from bigframes_vendored.ibis.formats.polars import PolarsSchema - - return PolarsSchema.from_ibis(self) - - def as_struct(self) -> dt.Struct: - return dt.Struct(self) - - def name_at_position(self, i: int) -> str: - """Return the name of a schema column at position `i`. - - Parameters - ---------- - i - The position of the column - - Returns - ------- - str - The name of the column in the schema at position `i`. - - Examples - -------- - >>> import ibis - >>> sch = ibis.Schema({"a": "int", "b": "string"}) - >>> sch.name_at_position(0) - 'a' - >>> sch.name_at_position(1) - 'b' - - """ - return self.names[i] - - -SchemaLike: TypeAlias = Union[ - Schema, - Mapping[str, Union[str, dt.DataType]], - Iterable[tuple[str, Union[str, dt.DataType]]], -] - - -@lazy_singledispatch -def schema(value: Any) -> Schema: - """Construct ibis schema from schema-like python objects.""" - raise InputTypeError(value) - - -@lazy_singledispatch -def infer(value: Any) -> Schema: - """Infer the corresponding ibis schema for a python object.""" - raise InputTypeError(value) - - -@schema.register(Schema) -def from_schema(s): - return s - - -@schema.register(Mapping) -def from_mapping(d): - return Schema(d) - - -@schema.register(Iterable) -def from_pairs(lst): - return Schema.from_tuples(lst) - - -@schema.register(type) -def from_class(cls): - return Schema(dt.dtype(cls)) - - -@schema.register("pandas.Series") -def from_pandas_series(s): - from bigframes_vendored.ibis.formats.pandas import PandasSchema - - return PandasSchema.to_ibis(s) - - -@schema.register("pyarrow.Schema") -def from_pyarrow_schema(schema): - from bigframes_vendored.ibis.formats.pyarrow import PyArrowSchema - - return PyArrowSchema.to_ibis(schema) - - -@infer.register("pandas.DataFrame") -def infer_pandas_dataframe(df): - from bigframes_vendored.ibis.formats.pandas import PandasData - - return PandasData.infer_table(df) - - -@infer.register("pyarrow.Table") -def infer_pyarrow_table(table): - from bigframes_vendored.ibis.formats.pyarrow import PyArrowSchema - - return PyArrowSchema.to_ibis(table.schema) - - -@infer.register("polars.DataFrame") -@infer.register("polars.LazyFrame") -def infer_polars_dataframe(df): - from bigframes_vendored.ibis.formats.polars import PolarsSchema - - return PolarsSchema.to_ibis(df.collect_schema()) - - -# lock the dispatchers to avoid adding new implementations -del infer.register -del schema.register diff --git a/third_party/bigframes_vendored/ibis/expr/sql.py b/third_party/bigframes_vendored/ibis/expr/sql.py deleted file mode 100644 index f375a7351f1..00000000000 --- a/third_party/bigframes_vendored/ibis/expr/sql.py +++ /dev/null @@ -1,383 +0,0 @@ -# Contains code from https://github.com/ibis-project/ibis/blob/9.2.0/ibis/expr/sql.py - -from __future__ import annotations - -import contextlib -import operator -from functools import singledispatch - -import bigframes_vendored.ibis -import bigframes_vendored.ibis.expr.api as api -import bigframes_vendored.ibis.expr.datatypes as dt -import bigframes_vendored.ibis.expr.schema as sch -import bigframes_vendored.ibis.expr.types as ibis_types -import bigframes_vendored.ibis.expr.types as ir -import bigframes_vendored.sqlglot as sg -import bigframes_vendored.sqlglot.expressions as sge -import bigframes_vendored.sqlglot.optimizer as sgo -import bigframes_vendored.sqlglot.planner as sgp -from bigframes_vendored.ibis.util import experimental -from public import public - - -class Catalog(dict[str, sch.Schema]): - """A catalog of tables and their schemas.""" - - typemap = { - dt.Int8: "tinyint", - dt.Int16: "smallint", - dt.Int32: "int", - dt.Int64: "bigint", - dt.Float16: "halffloat", - dt.Float32: "float", - dt.Float64: "double", - dt.Decimal: "decimal", - dt.Boolean: "boolean", - dt.JSON: "json", - dt.Interval: "interval", - dt.Timestamp: "datetime", - dt.Date: "date", - dt.Binary: "varbinary", - dt.String: "varchar", - dt.Array: "array", - dt.Map: "map", - dt.UUID: "uuid", - dt.Struct: "struct", - } - - def to_sqlglot_dtype(self, dtype: dt.DataType) -> str: - if dtype.is_geospatial(): - return dtype.geotype - else: - default = dtype.__class__.__name__.lower() - return self.typemap.get(type(dtype), default) - - def to_sqlglot_schema(self, schema: sch.Schema) -> dict[str, str]: - return {name: self.to_sqlglot_dtype(dtype) for name, dtype in schema.items()} - - def to_sqlglot(self): - return { - name: self.to_sqlglot_schema(table.schema()) for name, table in self.items() - } - - def overlay(self, step): - updates = {dep.name: convert(dep, catalog=self) for dep in step.dependencies} - - # handle scan aliases: FROM foo AS bar - source = getattr(step, "source", None) - alias = getattr(source, "args", {}).get("alias") - if alias is not None and (source_name := self.get(source.name)) is not None: - self[alias.name] = source_name - - return Catalog({**self, **updates}) - - -@singledispatch -def convert(step, catalog): - raise TypeError(type(step)) - - -@convert.register(sgp.Scan) -def convert_scan(scan, catalog): - catalog = catalog.overlay(scan) - - table = catalog[scan.source.alias_or_name] - - if scan.condition: - pred = convert(scan.condition, catalog=catalog) - table = table.filter(pred) - - if scan.projections: - projs = [convert(proj, catalog=catalog) for proj in scan.projections] - table = table.select(projs) - - if isinstance(scan.limit, int): - table = table.limit(scan.limit) - - return table - - -@convert.register(sgp.Sort) -def convert_sort(sort, catalog): - catalog = catalog.overlay(sort) - - table = catalog[sort.name] - - if sort.key: - keys = [convert(key, catalog=catalog) for key in sort.key] - table = table.order_by(keys) - - if sort.projections: - projs = [convert(proj, catalog=catalog) for proj in sort.projections] - table = table.select(projs) - - return table - - -_join_types = { - "": "inner", - "LEFT": "left", - "RIGHT": "right", -} - - -@convert.register(sgp.Join) -def convert_join(join, catalog): - catalog = catalog.overlay(join) - - left_name = join.name - left_table = catalog[left_name] - - for right_name, desc in join.joins.items(): - right_table = catalog[right_name] - join_kind = _join_types[desc["side"]] - - if desc["join_key"]: - predicate = None - for left_key, right_key in zip(desc["source_key"], desc["join_key"]): - left_key = convert(left_key, catalog=catalog) - right_key = convert(right_key, catalog=catalog) - if predicate is None: - predicate = left_key == right_key - else: - predicate &= left_key == right_key - else: - condition = desc["condition"] - predicate = convert(condition, catalog=catalog) - - left_table = left_table.join(right_table, predicates=predicate, how=join_kind) - - if join.condition: - predicate = convert(join.condition, catalog=catalog) - left_table = left_table.filter(predicate) - - catalog[left_name] = left_table - - return left_table - - -@convert.register(sgp.Aggregate) -def convert_aggregate(agg, catalog): - catalog = catalog.overlay(agg) - - table = catalog[agg.source] - if agg.aggregations: - metrics = [convert(a, catalog=catalog) for a in agg.aggregations] - groups = [convert(g, catalog=catalog) for k, g in agg.group.items()] - table = table.aggregate(metrics, by=groups) - - return table - - -@convert.register(sge.Subquery) -def convert_subquery(subquery, catalog): - tree = sgo.optimize(subquery.this, catalog.to_sqlglot(), rules=sgo.RULES) - plan = sgp.Plan(tree) - return convert(plan.root, catalog=catalog) - - -@convert.register(sge.Literal) -def convert_literal(literal, catalog): - value = literal.this - if literal.is_int: - value = int(value) - elif literal.is_number: - value = float(value) - return ibis_types.literal(value) - - -@convert.register(sge.Boolean) -def convert_boolean(boolean, catalog): - return ibis_types.literal(boolean.this) - - -@convert.register(sge.Alias) -def convert_alias(alias, catalog): - this = convert(alias.this, catalog=catalog) - return this.name(alias.alias) - - -@convert.register(sge.Column) -def convert_column(column, catalog): - table = catalog[column.table] - return table[column.name] - - -@convert.register(sge.Ordered) -def convert_ordered(ordered, catalog): - this = convert(ordered.this, catalog=catalog) - desc = ordered.args["desc"] # not exposed as an attribute - - return api.desc(this) if desc else api.asc(this) - - -_unary_operations = { - sge.Paren: lambda x: x, -} - - -@convert.register(sge.Unary) -def convert_unary(unary, catalog): - op = _unary_operations[type(unary)] - this = convert(unary.this, catalog=catalog) - return op(this) - - -_binary_operations = { - sge.LT: operator.lt, - sge.LTE: operator.le, - sge.GT: operator.gt, - sge.GTE: operator.ge, - sge.EQ: operator.eq, - sge.NEQ: operator.ne, - sge.Add: operator.add, - sge.Sub: operator.sub, - sge.Mul: operator.mul, - sge.Div: operator.truediv, - sge.Pow: operator.pow, - sge.And: operator.and_, - sge.Or: operator.or_, -} - - -@convert.register(sge.Binary) -def convert_binary(binary, catalog): - op = _binary_operations[type(binary)] - this = convert(binary.this, catalog=catalog) - expr = convert(binary.expression, catalog=catalog) - - if isinstance(binary.expression, sge.Subquery): - # expr is a table expression - assert len(expr.columns) == 1 - name = expr.columns[0] - expr = expr[name] - - return op(this, expr) - - -_reduction_methods = { - sge.Max: "max", - sge.Min: "min", - sge.Quantile: "quantile", - sge.Sum: "sum", - sge.Avg: "mean", - sge.Count: "count", -} - - -@convert.register(sge.AggFunc) -def convert_sum(reduction, catalog): - method = _reduction_methods[type(reduction)] - this = convert(reduction.this, catalog=catalog) - return getattr(this, method)() - - -@convert.register(sge.In) -def convert_in(in_, catalog): - this = convert(in_.this, catalog=catalog) - candidates = [convert(expression, catalog) for expression in in_.expressions] - return this.isin(candidates) - - -@public -@experimental -def parse_sql(sqlstring, catalog, dialect=None): - """Parse a SQL string into an Ibis expression. - - Parameters - ---------- - sqlstring : str - SQL string to parse - catalog : dict - A dictionary mapping table names to either schemas or ibis table expressions. - If a schema is passed, a table expression will be created using the schema. - dialect : str, optional - The SQL dialect to use with sqlglot to parse the query string. - - Returns - ------- - expr : ir.Expr - - """ - catalog = Catalog( - {name: api.table(schema, name=name) for name, schema in catalog.items()} - ) - - expr = sg.parse_one(sqlstring, dialect) - tree = sgo.optimize(expr, catalog.to_sqlglot(), rules=sgo.RULES) - plan = sgp.Plan(tree) - - return convert(plan.root, catalog=catalog) - - -class SQLString(str): - """Object to hold a formatted SQL string. - - Syntax highlights in Jupyter notebooks. - """ - - def __repr__(self) -> str: - return f"{self.__class__.__name__}({str(self)!r})" - - def _repr_markdown_(self) -> str: - return f"```sql\n{self!s}\n```" - - def _repr_pretty_(self, p, cycle) -> str: - output = str(self) - try: - from pygments import highlight - from pygments.formatters import TerminalFormatter - from pygments.lexers import SqlLexer - except ImportError: - pass - else: - with contextlib.suppress(Exception): - output = highlight( - code=output, - lexer=SqlLexer(), - formatter=TerminalFormatter(), - ) - - # strip trailing newline - p.text(output.strip()) - - -@public -def to_sql( - expr: ir.Expr, dialect: str | None = None, pretty: bool = True, **kwargs -) -> SQLString: - """Return the formatted SQL string for an expression. - - Parameters - ---------- - expr - Ibis expression. - dialect - SQL dialect to use for compilation. - pretty - Whether to use pretty formatting. - kwargs - Scalar parameters - - Returns - ------- - str - Formatted SQL string - - """ - # try to infer from a non-str expression or if not possible fallback to - # the default pretty dialect for expressions - if dialect is None: - backend = expr._find_backend(use_default=True) - dialect = backend.dialect - else: - try: - backend = getattr(bigframes_vendored.ibis, dialect) - except AttributeError: - raise ValueError(f"Unknown dialect {dialect}") - else: - dialect = getattr(backend, "dialect", dialect) - - sg_expr = backend._to_sqlglot(expr.unbind(), **kwargs) - sql = sg_expr.sql(dialect=dialect, pretty=pretty) - return SQLString(sql) diff --git a/third_party/bigframes_vendored/ibis/expr/types/__init__.py b/third_party/bigframes_vendored/ibis/expr/types/__init__.py deleted file mode 100644 index a3a48ee9359..00000000000 --- a/third_party/bigframes_vendored/ibis/expr/types/__init__.py +++ /dev/null @@ -1,21 +0,0 @@ -# Contains code from https://github.com/ibis-project/ibis/blob/9.2.0/ibis/expr/types/__init__.py - -from __future__ import annotations - -from bigframes_vendored.ibis.expr.types.arrays import * # noqa: F403 -from bigframes_vendored.ibis.expr.types.binary import * # noqa: F403 -from bigframes_vendored.ibis.expr.types.core import * # noqa: F403 -from bigframes_vendored.ibis.expr.types.generic import * # noqa: F403 -from bigframes_vendored.ibis.expr.types.geospatial import * # noqa: F403 -from bigframes_vendored.ibis.expr.types.joins import * # noqa: F403 -from bigframes_vendored.ibis.expr.types.json import * # noqa: F403 -from bigframes_vendored.ibis.expr.types.logical import * # noqa: F403 -from bigframes_vendored.ibis.expr.types.maps import * # noqa: F403 -from bigframes_vendored.ibis.expr.types.numeric import * # noqa: F403 -from bigframes_vendored.ibis.expr.types.relations import * # noqa: F403 -from bigframes_vendored.ibis.expr.types.strings import * # noqa: F403 -from bigframes_vendored.ibis.expr.types.structs import * # noqa: F403 -from bigframes_vendored.ibis.expr.types.temporal import * # noqa: F403 -from bigframes_vendored.ibis.expr.types.uuid import * # noqa: F403 - -# ruff: noqa: I001 diff --git a/third_party/bigframes_vendored/ibis/expr/types/arrays.py b/third_party/bigframes_vendored/ibis/expr/types/arrays.py deleted file mode 100644 index ee11acd56b5..00000000000 --- a/third_party/bigframes_vendored/ibis/expr/types/arrays.py +++ /dev/null @@ -1,1146 +0,0 @@ -# Contains code from https://github.com/ibis-project/ibis/blob/9.2.0/ibis/expr/types/arrays.py - -from __future__ import annotations - -import inspect -from typing import TYPE_CHECKING - -import bigframes_vendored.ibis.expr.operations as ops -from bigframes_vendored.ibis.common.deferred import Deferred, deferrable -from bigframes_vendored.ibis.expr.types.generic import Column, Scalar, Value -from public import public - -if TYPE_CHECKING: - from collections.abc import Callable, Iterable - - import bigframes_vendored.ibis.expr.types as ir - from bigframes_vendored.ibis.expr.types.typing import V - -import bigframes_vendored.ibis.common.exceptions as com - - -@public -class ArrayValue(Value): - """An Array is a variable-length sequence of values of a single type. - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> ibis.memtable({"a": [[1, None, 3], [4], [], None]}) - ┏━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ a ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━┩ - │ array │ - ├──────────────────────┤ - │ [1, None, ... +1] │ - │ [4] │ - │ [] │ - │ NULL │ - └──────────────────────┘ - """ - - def length(self) -> ir.IntegerValue: - """Compute the length of an array. - - Returns - ------- - IntegerValue - The integer length of each element of `self` - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable({"a": [[7, 42], [3], None]}) - >>> t - ┏━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ a ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━┩ - │ array │ - ├──────────────────────┤ - │ [7, 42] │ - │ [3] │ - │ NULL │ - └──────────────────────┘ - >>> t.a.length() - ┏━━━━━━━━━━━━━━━━┓ - ┃ ArrayLength(a) ┃ - ┡━━━━━━━━━━━━━━━━┩ - │ int64 │ - ├────────────────┤ - │ 2 │ - │ 1 │ - │ NULL │ - └────────────────┘ - """ - return ops.ArrayLength(self).to_expr() - - def __getitem__(self, index: int | ir.IntegerValue | slice) -> ir.Value: - """Extract one or more elements of `self`. - - Parameters - ---------- - index - Index into `array` - - Returns - ------- - Value - - If `index` is an [](`int`) or - [`IntegerValue`](./expression-numeric.qmd#ibis.expr.types.IntegerValue) - then the return type is the element type of `self`. - - If `index` is a [](`slice`) then the return type is the same - type as the input. - - Examples - -------- - Extract a single element - - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable({"a": [[7, 42], [3], None]}) - >>> t.a[0] - ┏━━━━━━━━━━━━━━━━━━┓ - ┃ ArrayIndex(a, 0) ┃ - ┡━━━━━━━━━━━━━━━━━━┩ - │ int64 │ - ├──────────────────┤ - │ 7 │ - │ 3 │ - │ NULL │ - └──────────────────┘ - - Extract a range of elements - - >>> t = ibis.memtable({"a": [[7, 42, 72], [3] * 5, None]}) - >>> t - ┏━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ a ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━┩ - │ array │ - ├──────────────────────┤ - │ [7, 42, ... +1] │ - │ [3, 3, ... +3] │ - │ NULL │ - └──────────────────────┘ - >>> t.a[1:2] - ┏━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ ArraySlice(a, 1, 2) ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━┩ - │ array │ - ├──────────────────────┤ - │ [42] │ - │ [3] │ - │ NULL │ - └──────────────────────┘ - """ - if isinstance(index, slice): - start = index.start - stop = index.stop - step = index.step - - if step is not None and step != 1: - raise NotImplementedError("step can only be 1") - - op = ops.ArraySlice(self, start if start is not None else 0, stop) - else: - op = ops.ArrayIndex(self, index) - return op.to_expr() - - def concat(self, other: ArrayValue, *args: ArrayValue) -> ArrayValue: - """Concatenate this array with one or more arrays. - - Parameters - ---------- - other - Other array to concat with `self` - args - Other arrays to concat with `self` - - Returns - ------- - ArrayValue - `self` concatenated with `other` and `args` - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable({"a": [[7], [3], None]}) - >>> t - ┏━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ a ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━┩ - │ array │ - ├──────────────────────┤ - │ [7] │ - │ [3] │ - │ NULL │ - └──────────────────────┘ - >>> t.a.concat(t.a) - ┏━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ ArrayConcat((a, a)) ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━┩ - │ array │ - ├──────────────────────┤ - │ [7, 7] │ - │ [3, 3] │ - │ NULL │ - └──────────────────────┘ - >>> t.a.concat(ibis.literal([4], type="array")) - ┏━━━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ ArrayConcat((a, (4,))) ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━━━┩ - │ array │ - ├────────────────────────┤ - │ [7, 4] │ - │ [3, 4] │ - │ [4] │ - └────────────────────────┘ - - `concat` is also available using the `+` operator - - >>> [1] + t.a - ┏━━━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ ArrayConcat(((1,), a)) ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━━━┩ - │ array │ - ├────────────────────────┤ - │ [1, 7] │ - │ [1, 3] │ - │ [1] │ - └────────────────────────┘ - >>> t.a + [1] - ┏━━━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ ArrayConcat((a, (1,))) ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━━━┩ - │ array │ - ├────────────────────────┤ - │ [7, 1] │ - │ [3, 1] │ - │ [1] │ - └────────────────────────┘ - """ - return ops.ArrayConcat((self, other, *args)).to_expr() - - def __add__(self, other: ArrayValue) -> ArrayValue: - return self.concat(other) - - def __radd__(self, other: ArrayValue) -> ArrayValue: - return ops.ArrayConcat((other, self)).to_expr() - - def repeat(self, n: int | ir.IntegerValue) -> ArrayValue: - """Repeat this array `n` times. - - Parameters - ---------- - n - Number of times to repeat `self`. - - Returns - ------- - ArrayValue - `self` repeated `n` times - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable({"a": [[7], [3], None]}) - >>> t - ┏━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ a ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━┩ - │ array │ - ├──────────────────────┤ - │ [7] │ - │ [3] │ - │ NULL │ - └──────────────────────┘ - >>> t.a.repeat(2) - ┏━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ ArrayRepeat(a, 2) ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━┩ - │ array │ - ├──────────────────────┤ - │ [7, 7] │ - │ [3, 3] │ - │ [] │ - └──────────────────────┘ - - `repeat` is also available using the `*` operator - - >>> 2 * t.a - ┏━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ ArrayRepeat(a, 2) ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━┩ - │ array │ - ├──────────────────────┤ - │ [7, 7] │ - │ [3, 3] │ - │ [] │ - └──────────────────────┘ - """ - return ops.ArrayRepeat(self, n).to_expr() - - __mul__ = __rmul__ = repeat - - def unnest(self) -> ir.Value: - """Unnest an array into a column. - - ::: {.callout-note} - ## Empty arrays and `NULL`s are dropped in the output. - To preserve empty arrays as `NULL`s as well as existing `NULL` values, - use [`Table.unnest`](./expression-tables.qmd#ibis.expr.types.relations.Table.unnest). - ::: - - Returns - ------- - ir.Value - Unnested array - - See Also - -------- - [`Table.unnest`](./expression-tables.qmd#ibis.expr.types.relations.Table.unnest) - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable({"a": [[7, 42], [3, 3], None]}) - >>> t - ┏━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ a ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━┩ - │ array │ - ├──────────────────────┤ - │ [7, 42] │ - │ [3, 3] │ - │ NULL │ - └──────────────────────┘ - >>> t.a.unnest() - ┏━━━━━━━┓ - ┃ a ┃ - ┡━━━━━━━┩ - │ int64 │ - ├───────┤ - │ 7 │ - │ 42 │ - │ 3 │ - │ 3 │ - └───────┘ - """ - expr = ops.Unnest(self).to_expr() - try: - return expr.name(self.get_name()) - except com.ExpressionError: - return expr - - def join(self, sep: str | ir.StringValue) -> ir.StringValue: - """Join the elements of this array expression with `sep`. - - Parameters - ---------- - sep - Separator to use for joining array elements - - Returns - ------- - StringValue - Elements of `self` joined with `sep` - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable({"arr": [["a", "b", "c"], None, [], ["b", None]]}) - >>> t - ┏━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ arr ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━┩ - │ array │ - ├──────────────────────┤ - │ ['a', 'b', ... +1] │ - │ NULL │ - │ [] │ - │ ['b', None] │ - └──────────────────────┘ - >>> t.arr.join("|") - ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ ArrayStringJoin(arr, '|') ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━┩ - │ string │ - ├───────────────────────────┤ - │ a|b|c │ - │ NULL │ - │ NULL │ - │ b │ - └───────────────────────────┘ - - See Also - -------- - [`StringValue.join`](./expression-strings.qmd#ibis.expr.types.strings.StringValue.join) - """ - return ops.ArrayStringJoin(self, sep=sep).to_expr() - - def map(self, func: Deferred | Callable[[ir.Value], ir.Value]) -> ir.ArrayValue: - """Apply a `func` or `Deferred` to each element of this array expression. - - Parameters - ---------- - func - Function or `Deferred` to apply to each element of this array. - - Returns - ------- - ArrayValue - `func` applied to every element of this array expression. - - Examples - -------- - >>> import ibis - >>> from ibis import _ - >>> ibis.options.interactive = True - >>> t = ibis.memtable({"a": [[1, None, 2], [4], []]}) - >>> t - ┏━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ a ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━┩ - │ array │ - ├──────────────────────┤ - │ [1, None, ... +1] │ - │ [4] │ - │ [] │ - └──────────────────────┘ - - The most succinct way to use `map` is with `Deferred` expressions: - - >>> t.a.map((_ + 100).cast(float)) - ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ ArrayMap(a, Cast(Add(_, 100), float64)) ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩ - │ array │ - ├─────────────────────────────────────────┤ - │ [101.0, None, ... +1] │ - │ [104.0] │ - │ [] │ - └─────────────────────────────────────────┘ - - You can also use `map` with a lambda function: - - >>> t.a.map(lambda x: (x + 100).cast(float)) - ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ ArrayMap(a, Cast(Add(x, 100), float64)) ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩ - │ array │ - ├─────────────────────────────────────────┤ - │ [101.0, None, ... +1] │ - │ [104.0] │ - │ [] │ - └─────────────────────────────────────────┘ - - `.map()` also supports more complex callables like `functools.partial` - and lambdas with closures - - >>> from functools import partial - >>> def add(x, y): - ... return x + y - >>> add2 = partial(add, y=2) - >>> t.a.map(add2) - ┏━━━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ ArrayMap(a, Add(x, 2)) ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━━━┩ - │ array │ - ├────────────────────────┤ - │ [3, None, ... +1] │ - │ [6] │ - │ [] │ - └────────────────────────┘ - >>> y = 2 - >>> t.a.map(lambda x: x + y) - ┏━━━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ ArrayMap(a, Add(x, 2)) ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━━━┩ - │ array │ - ├────────────────────────┤ - │ [3, None, ... +1] │ - │ [6] │ - │ [] │ - └────────────────────────┘ - """ - if isinstance(func, Deferred): - name = "_" - resolve = func.resolve - elif callable(func): - name = next(iter(inspect.signature(func).parameters.keys())) - resolve = func - else: - raise TypeError( - f"`func` must be a Deferred or Callable, got `{type(func).__name__}`" - ) - - parameter = ops.Argument( - name=name, shape=self.op().shape, dtype=self.type().value_type - ) - body = resolve(parameter.to_expr()) - return ops.ArrayMap(self, param=parameter.param, body=body).to_expr() - - def reduce(self, func: Deferred | Callable[[ir.Value], ir.Value]) -> ir.ArrayValue: - if isinstance(func, Deferred): - name = "_" - resolve = func.resolve - elif callable(func): - name = next(iter(inspect.signature(func).parameters.keys())) - resolve = func - else: - raise TypeError( - f"`func` must be a Deferred or Callable, got `{type(func).__name__}`" - ) - - parameter = ops.Argument( - name=name, shape=self.op().shape, dtype=self.type().value_type - ) - body = resolve(parameter.to_expr()) - return ops.ArrayReduce(self, param=parameter.param, body=body).to_expr() - - def filter( - self, predicate: Deferred | Callable[[ir.Value], bool | ir.BooleanValue] - ) -> ir.ArrayValue: - """Filter array elements using `predicate` function or `Deferred`. - - Parameters - ---------- - predicate - Function or `Deferred` to use to filter array elements - - Returns - ------- - ArrayValue - Array elements filtered using `predicate` - - Examples - -------- - >>> import ibis - >>> from ibis import _ - >>> ibis.options.interactive = True - >>> t = ibis.memtable({"a": [[1, None, 2], [4], []]}) - >>> t - ┏━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ a ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━┩ - │ array │ - ├──────────────────────┤ - │ [1, None, ... +1] │ - │ [4] │ - │ [] │ - └──────────────────────┘ - - The most succinct way to use `filter` is with `Deferred` expressions: - - >>> t.a.filter(_ > 1) - ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ ArrayFilter(a, Greater(_, 1)) ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩ - │ array │ - ├───────────────────────────────┤ - │ [2] │ - │ [4] │ - │ [] │ - └───────────────────────────────┘ - - You can also use `map` with a lambda function: - - >>> t.a.filter(lambda x: x > 1) - ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ ArrayFilter(a, Greater(x, 1)) ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩ - │ array │ - ├───────────────────────────────┤ - │ [2] │ - │ [4] │ - │ [] │ - └───────────────────────────────┘ - - `.filter()` also supports more complex callables like `functools.partial` - and lambdas with closures - - >>> from functools import partial - >>> def gt(x, y): - ... return x > y - >>> gt1 = partial(gt, y=1) - >>> t.a.filter(gt1) - ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ ArrayFilter(a, Greater(x, 1)) ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩ - │ array │ - ├───────────────────────────────┤ - │ [2] │ - │ [4] │ - │ [] │ - └───────────────────────────────┘ - >>> y = 1 - >>> t.a.filter(lambda x: x > y) - ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ ArrayFilter(a, Greater(x, 1)) ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩ - │ array │ - ├───────────────────────────────┤ - │ [2] │ - │ [4] │ - │ [] │ - └───────────────────────────────┘ - """ - if isinstance(predicate, Deferred): - name = "_" - resolve = predicate.resolve - elif callable(predicate): - name = next(iter(inspect.signature(predicate).parameters.keys())) - resolve = predicate - else: - raise TypeError( - f"`predicate` must be a Deferred or Callable, got `{type(predicate).__name__}`" - ) - parameter = ops.Argument( - name=name, - shape=self.op().shape, - dtype=self.type().value_type, - ) - body = resolve(parameter.to_expr()) - return ops.ArrayFilter(self, param=parameter.param, body=body).to_expr() - - def contains(self, other: ir.Value) -> ir.BooleanValue: - """Return whether the array contains `other`. - - Parameters - ---------- - other - Ibis expression to check for existence of in `self` - - Returns - ------- - BooleanValue - Whether `other` is contained in `self` - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable({"arr": [[1], [], [42, 42], None]}) - >>> t - ┏━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ arr ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━┩ - │ array │ - ├──────────────────────┤ - │ [1] │ - │ [] │ - │ [42, 42] │ - │ NULL │ - └──────────────────────┘ - >>> t.arr.contains(42) - ┏━━━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ ArrayContains(arr, 42) ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━━━┩ - │ boolean │ - ├────────────────────────┤ - │ False │ - │ False │ - │ True │ - │ NULL │ - └────────────────────────┘ - >>> t.arr.contains(None) - ┏━━━━━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ ArrayContains(arr, None) ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━━━━━┩ - │ boolean │ - ├──────────────────────────┤ - │ NULL │ - │ NULL │ - │ NULL │ - │ NULL │ - └──────────────────────────┘ - """ - return ops.ArrayContains(self, other).to_expr() - - def index(self, other: ir.Value) -> ir.IntegerValue: - """Return the position of `other` in an array. - - Parameters - ---------- - other - Ibis expression to existence of in `self` - - Returns - ------- - BooleanValue - The position of `other` in `self` - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable({"arr": [[1], [], [42, 42], None]}) - >>> t - ┏━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ arr ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━┩ - │ array │ - ├──────────────────────┤ - │ [1] │ - │ [] │ - │ [42, 42] │ - │ NULL │ - └──────────────────────┘ - >>> t.arr.index(42) - ┏━━━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ ArrayPosition(arr, 42) ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━━━┩ - │ int64 │ - ├────────────────────────┤ - │ -1 │ - │ -1 │ - │ 0 │ - │ NULL │ - └────────────────────────┘ - >>> t.arr.index(800) - ┏━━━━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ ArrayPosition(arr, 800) ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━━━━┩ - │ int64 │ - ├─────────────────────────┤ - │ -1 │ - │ -1 │ - │ -1 │ - │ NULL │ - └─────────────────────────┘ - >>> t.arr.index(None) - ┏━━━━━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ ArrayPosition(arr, None) ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━━━━━┩ - │ int64 │ - ├──────────────────────────┤ - │ NULL │ - │ NULL │ - │ NULL │ - │ NULL │ - └──────────────────────────┘ - """ - return ops.ArrayPosition(self, other).to_expr() - - def remove(self, other: ir.Value) -> ir.ArrayValue: - """Remove `other` from `self`. - - Parameters - ---------- - other - Element to remove from `self`. - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable({"arr": [[3, 2], [], [42, 2], [2, 2], None]}) - >>> t - ┏━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ arr ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━┩ - │ array │ - ├──────────────────────┤ - │ [3, 2] │ - │ [] │ - │ [42, 2] │ - │ [2, 2] │ - │ NULL │ - └──────────────────────┘ - >>> t.arr.remove(2) - ┏━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ ArrayRemove(arr, 2) ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━┩ - │ array │ - ├──────────────────────┤ - │ [3] │ - │ [] │ - │ [42] │ - │ [] │ - │ NULL │ - └──────────────────────┘ - """ - return ops.ArrayRemove(self, other).to_expr() - - def unique(self) -> ir.ArrayValue: - """Return the unique values in an array. - - ::: {.callout-note} - ## Element ordering in array may not be retained. - ::: - - Returns - ------- - ArrayValue - Unique values in an array - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable({"arr": [[1, 3, 3], [], [42, 42, None], None]}) - >>> t.arr.unique() - ┏━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ ArrayDistinct(arr) ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━┩ - │ array │ - ├──────────────────────┤ - │ [3, 1] │ - │ [] │ - │ [42, None] │ - │ NULL │ - └──────────────────────┘ - """ - return ops.ArrayDistinct(self).to_expr() - - def sort(self) -> ir.ArrayValue: - """Sort the elements in an array. - - Returns - ------- - ArrayValue - Sorted values in an array - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable({"arr": [[3, 2], [], [42, 42], None]}) - >>> t - ┏━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ arr ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━┩ - │ array │ - ├──────────────────────┤ - │ [3, 2] │ - │ [] │ - │ [42, 42] │ - │ NULL │ - └──────────────────────┘ - >>> t.arr.sort() - ┏━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ ArraySort(arr) ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━┩ - │ array │ - ├──────────────────────┤ - │ [2, 3] │ - │ [] │ - │ [42, 42] │ - │ NULL │ - └──────────────────────┘ - """ - return ops.ArraySort(self).to_expr() - - def union(self, other: ir.ArrayValue) -> ir.ArrayValue: - """Union two arrays. - - Parameters - ---------- - other - Another array to union with `self` - - Returns - ------- - ArrayValue - Unioned arrays - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable({"arr1": [[3, 2], [], None], "arr2": [[1, 3], [None], [5]]}) - >>> t - ┏━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ arr1 ┃ arr2 ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━┩ - │ array │ array │ - ├──────────────────────┼──────────────────────┤ - │ [3, 2] │ [1, 3] │ - │ [] │ [None] │ - │ NULL │ [5] │ - └──────────────────────┴──────────────────────┘ - >>> t.arr1.union(t.arr2) - ┏━━━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ ArrayUnion(arr1, arr2) ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━━━┩ - │ array │ - ├────────────────────────┤ - │ [1, 2, ... +1] │ - │ [None] │ - │ [5] │ - └────────────────────────┘ - >>> t.arr1.union(t.arr2).contains(3) - ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ ArrayContains(ArrayUnion(arr1, arr2), 3) ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩ - │ boolean │ - ├──────────────────────────────────────────┤ - │ True │ - │ False │ - │ False │ - └──────────────────────────────────────────┘ - """ - return ops.ArrayUnion(self, other).to_expr() - - def intersect(self, other: ArrayValue) -> ArrayValue: - """Intersect two arrays. - - Parameters - ---------- - other - Another array to intersect with `self` - - Returns - ------- - ArrayValue - Intersected arrays - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable({"arr1": [[3, 2], [], None], "arr2": [[1, 3], [None], [5]]}) - >>> t - ┏━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ arr1 ┃ arr2 ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━┩ - │ array │ array │ - ├──────────────────────┼──────────────────────┤ - │ [3, 2] │ [1, 3] │ - │ [] │ [None] │ - │ NULL │ [5] │ - └──────────────────────┴──────────────────────┘ - >>> t.arr1.intersect(t.arr2) - ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ ArrayIntersect(arr1, arr2) ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩ - │ array │ - ├────────────────────────────┤ - │ [3] │ - │ [] │ - │ NULL │ - └────────────────────────────┘ - """ - return ops.ArrayIntersect(self, other).to_expr() - - def zip(self, other: ArrayValue, *others: ArrayValue) -> ArrayValue: - """Zip two or more arrays together. - - Parameters - ---------- - other - Another array to zip with `self` - others - Additional arrays to zip with `self` - - Returns - ------- - Array - Array of structs where each struct field is an element of each input - array. The fields are named `f1`, `f2`, `f3`, etc. - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> ibis.options.repr.interactive.max_depth = 2 - >>> t = ibis.memtable( - ... { - ... "numbers": [[3, 2], [6, 7], [], None], - ... "strings": [["a", "c"], ["d"], [], ["x", "y"]], - ... } - ... ) - >>> t - ┏━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ numbers ┃ strings ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━┩ - │ array │ array │ - ├──────────────────────┼──────────────────────┤ - │ [3, 2] │ ['a', 'c'] │ - │ [6, 7] │ ['d'] │ - │ [] │ [] │ - │ NULL │ ['x', 'y'] │ - └──────────────────────┴──────────────────────┘ - >>> t.numbers.zip(t.strings) - ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ ArrayZip((numbers, strings)) ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩ - │ array> │ - ├───────────────────────────────────────────────┤ - │ [{'f1': 3, 'f2': 'a'}, {'f1': 2, 'f2': 'c'}] │ - │ [{'f1': 6, 'f2': 'd'}, {'f1': 7, 'f2': None}] │ - │ [] │ - │ NULL │ - └───────────────────────────────────────────────┘ - """ - - return ops.ArrayZip((self, other, *others)).to_expr() - - def flatten(self) -> ir.ArrayValue: - """Remove one level of nesting from an array expression. - - Returns - ------- - ArrayValue - Flattened array expression - - Examples - -------- - >>> import ibis - >>> import ibis.selectors as s - >>> from ibis import _ - >>> ibis.options.interactive = True - >>> schema = { - ... "empty": "array>", - ... "happy": "array>", - ... "nulls_only": "array>>>", - ... "mixed_nulls": "array>", - ... } - >>> data = { - ... "empty": [[], [], []], - ... "happy": [[["abc"]], [["bcd"]], [["def"]]], - ... "nulls_only": [None, None, None], - ... "mixed_nulls": [[], None, [None]], - ... } - >>> t = ibis.memtable( - ... pa.Table.from_pydict( - ... data, - ... schema=ibis.schema(schema).to_pyarrow(), - ... ) - ... ) - >>> t - ┏━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━┓ - ┃ empty ┃ happy ┃ nulls_only ┃ … ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━┩ - │ array> │ array> │ array>> t.empty.flatten() - ┏━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ ArrayFlatten(empty) ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━┩ - │ array │ - ├──────────────────────┤ - │ [] │ - │ [] │ - │ [] │ - └──────────────────────┘ - >>> t.happy.flatten() - ┏━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ ArrayFlatten(happy) ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━┩ - │ array │ - ├──────────────────────┤ - │ ['abc'] │ - │ ['bcd'] │ - │ ['def'] │ - └──────────────────────┘ - >>> t.nulls_only.flatten() - ┏━━━━━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ ArrayFlatten(nulls_only) ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━━━━━┩ - │ array>> t.mixed_nulls.flatten() - ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ ArrayFlatten(mixed_nulls) ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━┩ - │ array │ - ├───────────────────────────┤ - │ [] │ - │ NULL │ - │ [] │ - └───────────────────────────┘ - >>> t.select(s.across(s.all(), _.flatten())) - ┏━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━┓ - ┃ empty ┃ happy ┃ nulls_only ┃ … ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━┩ - │ array │ array │ array ir.Column: - return ArrayValue.__getitem__(self, index) - - -@public -@deferrable -def array(values: Iterable[V]) -> ArrayValue: - """Create an array expression. - - If any values are [column expressions](../concepts/datatypes.qmd) the - result will be a column. Otherwise the result will be a - [scalar](../concepts/datatypes.qmd). - - Parameters - ---------- - values - An iterable of Ibis expressions or Python literals - - Returns - ------- - ArrayValue - - Examples - -------- - Create an array scalar from scalar values - - >>> import ibis - >>> ibis.options.interactive = True - >>> ibis.array([1.0, None]) - ┌─────────────┐ - │ [1.0, None] │ - └─────────────┘ - - Create an array from column and scalar expressions - - >>> t = ibis.memtable({"a": [1, 2, 3], "b": [4, 5, 6]}) - >>> ibis.array([t.a, 42, ibis.literal(None)]) - ┏━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ Array((a, 42, None)) ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━┩ - │ array │ - ├──────────────────────┤ - │ [1, 42, ... +1] │ - │ [2, 42, ... +1] │ - │ [3, 42, ... +1] │ - └──────────────────────┘ - - >>> ibis.array([t.a, 42 + ibis.literal(5)]) - ┏━━━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ Array((a, Add(5, 42))) ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━━━┩ - │ array │ - ├────────────────────────┤ - │ [1, 47] │ - │ [2, 47] │ - │ [3, 47] │ - └────────────────────────┘ - """ - return ops.Array(tuple(values)).to_expr() diff --git a/third_party/bigframes_vendored/ibis/expr/types/binary.py b/third_party/bigframes_vendored/ibis/expr/types/binary.py deleted file mode 100644 index b89eb6c1f1a..00000000000 --- a/third_party/bigframes_vendored/ibis/expr/types/binary.py +++ /dev/null @@ -1,56 +0,0 @@ -# Contains code from https://github.com/ibis-project/ibis/blob/9.2.0/ibis/expr/types/binary.py - -from __future__ import annotations - -from typing import TYPE_CHECKING, Literal - -if TYPE_CHECKING: - from bigframes_vendored.ibis.expr import types as ir - -import bigframes_vendored.ibis.expr.operations as ops -from bigframes_vendored.ibis.expr.types.generic import Column, Scalar, Value -from public import public - - -@public -class BinaryValue(Value): - def hashbytes( - self, - how: Literal["md5", "sha1", "sha256", "sha512"] = "sha256", - ) -> ir.BinaryValue: - """Compute the binary hash value of `arg`. - - Parameters - ---------- - how - Hash algorithm to use - - Returns - ------- - BinaryValue - Binary expression - """ - return ops.HashBytes(self, how).to_expr() - - def __invert__(self) -> BinaryValue: - return ops.BitwiseNot(self).to_expr() - - def length(self) -> ir.IntegerValue: - """Compute the length of a binary value. - - Returns - ------- - IntegerValue - The length of each binary value in the expression - """ - return ops.StringLength(self).to_expr() - - -@public -class BinaryScalar(Scalar, BinaryValue): - pass - - -@public -class BinaryColumn(Column, BinaryValue): - pass diff --git a/third_party/bigframes_vendored/ibis/expr/types/core.py b/third_party/bigframes_vendored/ibis/expr/types/core.py deleted file mode 100644 index 7a527bbda28..00000000000 --- a/third_party/bigframes_vendored/ibis/expr/types/core.py +++ /dev/null @@ -1,752 +0,0 @@ -# Contains code from https://github.com/ibis-project/ibis/blob/9.2.0/ibis/expr/types/core.py - -from __future__ import annotations - -import contextlib -import os -import webbrowser -from typing import TYPE_CHECKING, Any, NoReturn - -import bigframes_vendored.ibis -import bigframes_vendored.ibis.expr.operations as ops -import pandas as pd -from bigframes_vendored.ibis.common.annotations import ValidationError -from bigframes_vendored.ibis.common.exceptions import IbisError, TranslationError -from bigframes_vendored.ibis.common.grounds import Immutable -from bigframes_vendored.ibis.common.patterns import Coercible, CoercionError -from bigframes_vendored.ibis.common.typing import get_defining_scope -from bigframes_vendored.ibis.config import _default_backend -from bigframes_vendored.ibis.config import options as opts -from bigframes_vendored.ibis.expr.format import pretty -from bigframes_vendored.ibis.expr.types.pretty import to_rich -from bigframes_vendored.ibis.util import experimental -from public import public -from rich.console import Console -from rich.jupyter import JupyterMixin -from rich.text import Text - -if TYPE_CHECKING: - from collections.abc import Iterator, Mapping - from pathlib import Path - - import bigframes_vendored.ibis.expr.types as ir - import polars as pl - import pyarrow as pa - import torch - from bigframes_vendored.ibis.backends import BaseBackend - from bigframes_vendored.ibis.expr.visualize import ( - EdgeAttributeGetter, - NodeAttributeGetter, - ) - - -class _FixedTextJupyterMixin(JupyterMixin): - """JupyterMixin adds a spurious newline to text, this fixes the issue.""" - - def _repr_mimebundle_(self, *args, **kwargs): - bundle = super()._repr_mimebundle_(*args, **kwargs) - bundle["text/plain"] = bundle["text/plain"].rstrip() - return bundle - - -@public -class Expr(Immutable, Coercible): - """Base expression class.""" - - __slots__ = ("_arg",) - _arg: ops.Node - - def _noninteractive_repr(self) -> str: - if bigframes_vendored.ibis.options.repr.show_variables: - scope = get_defining_scope(self, types=Expr) - else: - scope = None - return pretty(self.op(), scope=scope) - - def _interactive_repr(self) -> str: - console = Console(force_terminal=False) - with console.capture() as capture: - try: - console.print(self) - except TranslationError as e: - lines = [ - "Translation to backend failed", - f"Error message: {e!r}", - "Expression repr follows:", - self._noninteractive_repr(), - ] - return "\n".join(lines) - return capture.get().rstrip() - - def __repr__(self) -> str: - if bigframes_vendored.ibis.options.interactive: - return self._interactive_repr() - else: - return self._noninteractive_repr() - - def __rich_console__(self, console: Console, options): - if console.is_jupyter: - # Rich infers a console width in jupyter notebooks, but since - # notebooks can use horizontal scroll bars we don't want to apply a - # limit here. Since rich requires an integer for max_width, we - # choose an arbitrarily large integer bound. Note that we need to - # handle this here rather than in `to_rich`, as this setting - # also needs to be forwarded to `console.render`. - options = options.update(max_width=1_000_000) - console_width = None - else: - console_width = options.max_width - - try: - if opts.interactive: - rich_object = to_rich(self, console_width=console_width) - else: - rich_object = Text(self._noninteractive_repr()) - except Exception as e: - # In IPython exceptions inside of _repr_mimebundle_ are swallowed to - # allow calling several display functions and choosing to display - # the "best" result based on some priority. - # This behavior, though, means that exceptions that bubble up inside of the interactive repr - # are silently caught. - # - # We can't stop the exception from being swallowed, but we can force - # the display of that exception as we do here. - # - # A _very_ annoying caveat is that this exception is _not_ being - # ` raise`d, it is only being printed to the console. This means - # that you cannot "catch" it. - # - # This restriction is only present in IPython, not in other REPLs. - console.print_exception() - raise e - return console.render(rich_object, options=options) - - def __init__(self, arg: ops.Node) -> None: - object.__setattr__(self, "_arg", arg) - - def __iter__(self) -> NoReturn: - raise TypeError(f"{self.__class__.__name__!r} object is not iterable") - - @classmethod - def __coerce__(cls, value): - if isinstance(value, cls): - return value - elif isinstance(value, ops.Node): - return value.to_expr() - else: - raise CoercionError("Unable to coerce value to an expression") - - def __reduce__(self): - return (self.__class__, (self._arg,)) - - def __hash__(self): - return hash((self.__class__, self._arg)) - - def equals(self, other): - """Return whether this expression is _structurally_ equivalent to `other`. - - If you want to produce an equality expression, use `==` syntax. - - Parameters - ---------- - other - Another expression - - Examples - -------- - >>> import ibis - >>> t1 = ibis.table(dict(a="int"), name="t") - >>> t2 = ibis.table(dict(a="int"), name="t") - >>> t1.equals(t2) - True - >>> v = ibis.table(dict(a="string"), name="v") - >>> t1.equals(v) - False - """ - if not isinstance(other, Expr): - raise TypeError( - f"invalid equality comparison between Expr and {type(other)}" - ) - return self._arg.equals(other._arg) - - def __bool__(self) -> bool: - raise ValueError("The truth value of an Ibis expression is not defined") - - __nonzero__ = __bool__ - - def has_name(self): - """Check whether this expression has an explicit name.""" - return hasattr(self._arg, "name") - - def get_name(self): - """Return the name of this expression.""" - return self._arg.name - - def _repr_png_(self) -> bytes | None: - if opts.interactive or not opts.graphviz_repr: - return None - try: - import bigframes_vendored.ibis.expr.visualize as viz - except ImportError: - return None - else: - # Something may go wrong, and we can't error in the notebook - # so fallback to the default text representation. - with contextlib.suppress(Exception): - return viz.to_graph(self).pipe(format="png") - - def visualize( - self, - format: str = "svg", - *, - label_edges: bool = False, - verbose: bool = False, - node_attr: Mapping[str, str] | None = None, - node_attr_getter: NodeAttributeGetter | None = None, - edge_attr: Mapping[str, str] | None = None, - edge_attr_getter: EdgeAttributeGetter | None = None, - ) -> None: - """Visualize an expression as a GraphViz graph in the browser. - - Parameters - ---------- - format - Image output format. These are specified by the ``graphviz`` Python - library. - label_edges - Show operation input names as edge labels - verbose - Print the graphviz DOT code to stderr if [](`True`) - node_attr - Mapping of ``(attribute, value)`` pairs set for all nodes. - Options are specified by the ``graphviz`` Python library. - node_attr_getter - Callback taking a node and returning a mapping of ``(attribute, value)`` pairs - for that node. Options are specified by the ``graphviz`` Python library. - edge_attr - Mapping of ``(attribute, value)`` pairs set for all edges. - Options are specified by the ``graphviz`` Python library. - edge_attr_getter - Callback taking two adjacent nodes and returning a mapping of ``(attribute, value)`` pairs - for the edge between those nodes. Options are specified by the ``graphviz`` Python library. - - Examples - -------- - Open the visualization of an expression in default browser: - - >>> import ibis - >>> import ibis.expr.operations as ops - >>> left = ibis.table(dict(a="int64", b="string"), name="left") - >>> right = ibis.table(dict(b="string", c="int64", d="string"), name="right") - >>> expr = left.inner_join(right, "b").select(left.a, b=right.c, c=right.d) - >>> expr.visualize( - ... format="svg", - ... label_edges=True, - ... node_attr={"fontname": "Roboto Mono", "fontsize": "10"}, - ... node_attr_getter=lambda node: isinstance(node, ops.Field) and {"shape": "oval"}, - ... edge_attr={"fontsize": "8"}, - ... edge_attr_getter=lambda u, v: isinstance(u, ops.Field) and {"color": "red"}, - ... ) # quartodoc: +SKIP # doctest: +SKIP - - Raises - ------ - ImportError - If ``graphviz`` is not installed. - """ - import bigframes_vendored.ibis.expr.visualize as viz - - path = viz.draw( - viz.to_graph( - self, - node_attr=node_attr, - node_attr_getter=node_attr_getter, - edge_attr=edge_attr, - edge_attr_getter=edge_attr_getter, - label_edges=label_edges, - ), - format=format, - verbose=verbose, - ) - webbrowser.open(f"file://{os.path.abspath(path)}") - - def pipe(self, f, *args: Any, **kwargs: Any) -> Expr: - """Compose `f` with `self`. - - Parameters - ---------- - f - If the expression needs to be passed as anything other than the - first argument to the function, pass a tuple with the argument - name. For example, (f, 'data') if the function f expects a 'data' - keyword - args - Positional arguments to `f` - kwargs - Keyword arguments to `f` - - Examples - -------- - >>> import ibis - >>> t = ibis.table([("a", "int64"), ("b", "string")], name="t") - >>> f = lambda a: (a + 1).name("a") - >>> g = lambda a: (a * 2).name("a") - >>> result1 = t.a.pipe(f).pipe(g) - >>> result1 - r0 := UnboundTable: t - a int64 - b string - a: r0.a + 1 * 2 - - >>> result2 = g(f(t.a)) # equivalent to the above - >>> result1.equals(result2) - True - - Returns - ------- - Expr - Result type of passed function - """ - if isinstance(f, tuple): - f, data_keyword = f - kwargs = kwargs.copy() - kwargs[data_keyword] = self - return f(*args, **kwargs) - else: - return f(self, *args, **kwargs) - - def op(self) -> ops.Node: - return self._arg - - def _find_backends(self) -> tuple[list[BaseBackend], bool]: - """Return the possible backends for an expression. - - Returns - ------- - list[BaseBackend] - A list of the backends found. - """ - - backends = set() - has_unbound = False - node_types = (ops.UnboundTable, ops.DatabaseTable, ops.SQLQueryResult) - for table in self.op().find(node_types): - if isinstance(table, ops.UnboundTable): - has_unbound = True - else: - backends.add(table.source) - - return list(backends), has_unbound - - def _find_backend(self, *, use_default: bool = False) -> BaseBackend: - """Find the backend attached to an expression. - - Parameters - ---------- - use_default - If [](`True`) and the default backend isn't set, initialize the - default backend and use that. This should only be set to `True` for - `.execute()`. For other contexts such as compilation, this option - doesn't make sense so the default value is [](`False`). - - Returns - ------- - BaseBackend - A backend that is attached to the expression - """ - backends, has_unbound = self._find_backends() - - if not backends: - if has_unbound: - raise IbisError( - "Expression contains unbound tables and therefore cannot " - "be executed. Use `.execute(expr)` to execute " - "against an explicit backend, or rebuild the expression " - "using bound tables instead." - ) - default = _default_backend() if use_default else None - if default is None: - raise IbisError( - "Expression depends on no backends, and found no default" - ) - return default - - if len(backends) > 1: - raise IbisError("Multiple backends found for this expression") - - return backends[0] - - def execute( - self, - limit: int | str | None = "default", - params: Mapping[ir.Value, Any] | None = None, - **kwargs: Any, - ): - """Execute an expression against its backend if one exists. - - Parameters - ---------- - limit - An integer to effect a specific row limit. A value of `None` means - "no limit". The default is in `ibis/config.py`. - params - Mapping of scalar parameter expressions to value - kwargs - Keyword arguments - """ - return self._find_backend(use_default=True).execute( - self, limit=limit, params=params, **kwargs - ) - - def compile( - self, - limit: int | None = None, - params: Mapping[ir.Value, Any] | None = None, - pretty: bool = False, - ): - """Compile to an execution target. - - Parameters - ---------- - limit - An integer to effect a specific row limit. A value of `None` means - "no limit". The default is in `ibis/config.py`. - params - Mapping of scalar parameter expressions to value - pretty - In case of SQL backends, return a pretty formatted SQL query. - """ - return self._find_backend().compile( - self, limit=limit, params=params, pretty=pretty - ) - - @experimental - def to_pyarrow_batches( - self, - *, - limit: int | str | None = None, - params: Mapping[ir.Value, Any] | None = None, - chunk_size: int = 1_000_000, - **kwargs: Any, - ) -> pa.ipc.RecordBatchReader: - """Execute expression and return a RecordBatchReader. - - This method is eager and will execute the associated expression - immediately. - - Parameters - ---------- - limit - An integer to effect a specific row limit. A value of `None` means - "no limit". The default is in `ibis/config.py`. - params - Mapping of scalar parameter expressions to value. - chunk_size - Maximum number of rows in each returned record batch. - kwargs - Keyword arguments - - Returns - ------- - results - RecordBatchReader - """ - return self._find_backend(use_default=True).to_pyarrow_batches( - self, - params=params, - limit=limit, - chunk_size=chunk_size, - **kwargs, - ) - - @experimental - def to_pyarrow( - self, - *, - params: Mapping[ir.Scalar, Any] | None = None, - limit: int | str | None = None, - **kwargs: Any, - ) -> pa.Table: - """Execute expression and return results in as a pyarrow table. - - This method is eager and will execute the associated expression - immediately. - - Parameters - ---------- - params - Mapping of scalar parameter expressions to value. - limit - An integer to effect a specific row limit. A value of `None` means - "no limit". The default is in `ibis/config.py`. - kwargs - Keyword arguments - - Returns - ------- - Table - A pyarrow table holding the results of the executed expression. - """ - return self._find_backend(use_default=True).to_pyarrow( - self, params=params, limit=limit, **kwargs - ) - - @experimental - def to_polars( - self, - *, - params: Mapping[ir.Scalar, Any] | None = None, - limit: int | str | None = None, - **kwargs: Any, - ) -> pl.DataFrame: - """Execute expression and return results as a polars dataframe. - - This method is eager and will execute the associated expression - immediately. - - Parameters - ---------- - params - Mapping of scalar parameter expressions to value. - limit - An integer to effect a specific row limit. A value of `None` means - "no limit". The default is in `ibis/config.py`. - kwargs - Keyword arguments - - Returns - ------- - DataFrame - A polars dataframe holding the results of the executed expression. - """ - return self._find_backend(use_default=True).to_polars( - self, params=params, limit=limit, **kwargs - ) - - @experimental - def to_pandas_batches( - self, - *, - limit: int | str | None = None, - params: Mapping[ir.Value, Any] | None = None, - chunk_size: int = 1_000_000, - **kwargs: Any, - ) -> Iterator[pd.DataFrame | pd.Series | Any]: - """Execute expression and return an iterator of pandas DataFrames. - - This method is eager and will execute the associated expression - immediately. - - Parameters - ---------- - limit - An integer to effect a specific row limit. A value of `None` means - "no limit". The default is in `ibis/config.py`. - params - Mapping of scalar parameter expressions to value. - chunk_size - Maximum number of rows in each returned `DataFrame``. - kwargs - Keyword arguments - - Returns - ------- - Iterator[pd.DataFrame] - """ - return self._find_backend(use_default=True).to_pandas_batches( - self, - params=params, - limit=limit, - chunk_size=chunk_size, - **kwargs, - ) - - @experimental - def to_parquet( - self, - path: str | Path, - *, - params: Mapping[ir.Scalar, Any] | None = None, - **kwargs: Any, - ) -> None: - """Write the results of executing the given expression to a parquet file. - - This method is eager and will execute the associated expression - immediately. - - Parameters - ---------- - path - The data source. A string or Path to the parquet file. - params - Mapping of scalar parameter expressions to value. - **kwargs - Additional keyword arguments passed to pyarrow.parquet.ParquetWriter - - https://arrow.apache.org/docs/python/generated/pyarrow.parquet.ParquetWriter.html - - Examples - -------- - Write out an expression to a single parquet file. - - >>> import ibis - >>> import tempfile - >>> penguins = ibis.examples.penguins.fetch() - >>> penguins.to_parquet(tempfile.mktemp()) - - Partition on a single column. - - >>> penguins.to_parquet(tempfile.mkdtemp(), partition_by="year") - - Partition on multiple columns. - - >>> penguins.to_parquet(tempfile.mkdtemp(), partition_by=("year", "island")) - - ::: {.callout-note} - ## Hive-partitioned output is currently only supported when using DuckDB - ::: - """ - self._find_backend(use_default=True).to_parquet(self, path, **kwargs) - - @experimental - def to_csv( - self, - path: str | Path, - *, - params: Mapping[ir.Scalar, Any] | None = None, - **kwargs: Any, - ) -> None: - """Write the results of executing the given expression to a CSV file. - - This method is eager and will execute the associated expression - immediately. - - Parameters - ---------- - path - The data source. A string or Path to the CSV file. - params - Mapping of scalar parameter expressions to value. - **kwargs - Additional keyword arguments passed to pyarrow.csv.CSVWriter - - https://arrow.apache.org/docs/python/generated/pyarrow.csv.CSVWriter.html - """ - self._find_backend(use_default=True).to_csv(self, path, **kwargs) - - @experimental - def to_delta( - self, - path: str | Path, - *, - params: Mapping[ir.Scalar, Any] | None = None, - **kwargs: Any, - ) -> None: - """Write the results of executing the given expression to a Delta Lake table. - - This method is eager and will execute the associated expression - immediately. - - Parameters - ---------- - path - The data source. A string or Path to the Delta Lake table directory. - params - Mapping of scalar parameter expressions to value. - **kwargs - Additional keyword arguments passed to deltalake.writer.write_deltalake method - """ - self._find_backend(use_default=True).to_delta(self, path, **kwargs) - - @experimental - def to_torch( - self, - *, - params: Mapping[ir.Scalar, Any] | None = None, - limit: int | str | None = None, - **kwargs: Any, - ) -> dict[str, torch.Tensor]: - """Execute an expression and return results as a dictionary of torch tensors. - - Parameters - ---------- - params - Parameters to substitute into the expression. - limit - An integer to effect a specific row limit. A value of `None` means no limit. - kwargs - Keyword arguments passed into the backend's `to_torch` implementation. - - Returns - ------- - dict[str, torch.Tensor] - A dictionary of torch tensors, keyed by column name. - """ - return self._find_backend(use_default=True).to_torch( - self, params=params, limit=limit, **kwargs - ) - - def unbind(self) -> ir.Table: - """Return an expression built on `UnboundTable` instead of backend-specific objects.""" - from bigframes_vendored.ibis.expr.rewrites import _, d, p - - rule = p.DatabaseTable >> d.UnboundTable( - name=_.name, schema=_.schema, namespace=_.namespace - ) - return self.op().replace(rule).to_expr() - - def as_table(self) -> ir.Table: - """Convert an expression to a table.""" - raise NotImplementedError( - f"{type(self)} expressions cannot be converted into tables" - ) - - def as_scalar(self) -> ir.Scalar: - """Convert an expression to a scalar.""" - raise NotImplementedError( - f"{type(self)} expressions cannot be converted into scalars" - ) - - -def _binop(op_class: type[ops.Binary], left: ir.Value, right: ir.Value) -> ir.Value: - """Try to construct a binary operation. - - Parameters - ---------- - op_class - The `ops.Binary` subclass for the operation - left - Left operand - right - Right operand - - Returns - ------- - ir.Value - A value expression - - Examples - -------- - >>> import ibis - >>> import ibis.expr.operations as ops - >>> expr = _binop(ops.TimeAdd, ibis.time("01:00"), ibis.interval(hours=1)) - >>> expr - TimeAdd(datetime.time(1, 0), 1h): datetime.time(1, 0) + 1 h - >>> _binop(ops.TimeAdd, 1, ibis.interval(hours=1)) - TimeAdd(datetime.time(0, 0, 1), 1h): datetime.time(0, 0, 1) + 1 h - """ - try: - node = op_class(left, right) - except (ValidationError, NotImplementedError): - return NotImplemented - else: - return node.to_expr() - - -def _is_null_literal(value: Any) -> bool: - """Detect whether `value` will be treated by ibis as a null literal.""" - if isinstance(value, Expr): - op = value.op() - return isinstance(op, ops.Literal) and op.value is None - if pd.isna(value): - return True - return False diff --git a/third_party/bigframes_vendored/ibis/expr/types/dataframe_interchange.py b/third_party/bigframes_vendored/ibis/expr/types/dataframe_interchange.py deleted file mode 100644 index a500fc06d79..00000000000 --- a/third_party/bigframes_vendored/ibis/expr/types/dataframe_interchange.py +++ /dev/null @@ -1,177 +0,0 @@ -# Contains code from https://github.com/ibis-project/ibis/blob/9.2.0/ibis/expr/types/dataframe_interchange.py - -from __future__ import annotations - -from functools import cached_property -from typing import TYPE_CHECKING - -if TYPE_CHECKING: - from collections.abc import Sequence - - import bigframes_vendored.ibis.expr.types as ir - import pyarrow as pa - - -class IbisDataFrame: - """An implementation of the dataframe interchange protocol. - - This is a thin shim around the pyarrow implementation to allow for: - - - Accessing a few of the metadata queries without executing the expression. - - Caching the execution on the dataframe object to avoid re-execution if - multiple methods are accessed. - - The dataframe interchange protocol may be found here: - https://data-apis.org/dataframe-protocol/latest/API.html - """ - - def __init__( - self, - table: ir.Table, - nan_as_null: bool = False, - allow_copy: bool = True, - pyarrow_table: pa.Table | None = None, - ): - self._table = table - self._nan_as_null = nan_as_null - self._allow_copy = allow_copy - self._pyarrow_table = pyarrow_table - - @cached_property - def _pyarrow_df(self): - """Returns the pyarrow implementation of the __dataframe__ protocol. - - If the backing ibis Table hasn't been executed yet, this will result - in executing and caching the result. - """ - if self._pyarrow_table is None: - self._pyarrow_table = self._table.to_pyarrow() - return self._pyarrow_table.__dataframe__( - nan_as_null=self._nan_as_null, - allow_copy=self._allow_copy, - ) - - @cached_property - def _empty_pyarrow_df(self): - """A pyarrow implementation of the __dataframe__ protocol for an empty table. - - Used for returning dtype information without executing the backing ibis - expression. - """ - return self._table.schema().to_pyarrow().empty_table().__dataframe__() - - def _get_dtype(self, name): - """Get the dtype info for a column named `name`.""" - return self._empty_pyarrow_df.get_column_by_name(name).dtype - - # These methods may all be handled without executing the query - def num_columns(self): - return len(self._table.columns) - - def column_names(self): - return self._table.columns - - def get_column(self, i: int) -> IbisColumn: - name = self._table.columns[i] - return self.get_column_by_name(name) - - def get_column_by_name(self, name: str) -> IbisColumn: - return IbisColumn(self, name) - - def get_columns(self): - return [IbisColumn(self, name) for name in self._table.columns] - - def select_columns(self, indices: Sequence[int]) -> IbisDataFrame: - names = [self._table.columns[i] for i in indices] - return self.select_columns_by_name(names) - - def select_columns_by_name(self, names: Sequence[str]) -> IbisDataFrame: - names = list(names) - table = self._table.select(names) - if (pyarrow_table := self._pyarrow_table) is not None: - pyarrow_table = pyarrow_table.select(names) - return IbisDataFrame( - table, - nan_as_null=self._nan_as_null, - allow_copy=self._allow_copy, - pyarrow_table=pyarrow_table, - ) - - def __dataframe__( - self, nan_as_null: bool = False, allow_copy: bool = True - ) -> IbisDataFrame: - return IbisDataFrame( - self._table, - nan_as_null=nan_as_null, - allow_copy=allow_copy, - pyarrow_table=self._pyarrow_table, - ) - - # These methods require executing the query - @property - def metadata(self): - return self._pyarrow_df.metadata - - def num_rows(self) -> int | None: - return self._pyarrow_df.num_rows() - - def num_chunks(self) -> int: - return self._pyarrow_df.num_chunks() - - def get_chunks(self, n_chunks: int | None = None): - return self._pyarrow_df.get_chunks(n_chunks=n_chunks) - - -class IbisColumn: - def __init__(self, df: IbisDataFrame, name: str): - self._df = df - self._name = name - - @cached_property - def _pyarrow_col(self): - """Returns the pyarrow implementation of the __dataframe__ protocol's Column type. - - If the backing ibis Table hasn't been executed yet, this will result - in executing and caching the result. - """ - return self._df._pyarrow_df.get_column_by_name(self._name) - - # These methods may all be handled without executing the query - @property - def dtype(self): - return self._df._get_dtype(self._name) - - @property - def describe_categorical(self): - raise TypeError( - "describe_categorical only works on a column with categorical dtype" - ) - - # These methods require executing the query - def size(self): - return self._pyarrow_col.size() - - @property - def offset(self): - return self._pyarrow_col.offset - - @property - def describe_null(self): - return self._pyarrow_col.describe_null - - @property - def null_count(self): - return self._pyarrow_col.null_count - - @property - def metadata(self): - return self._pyarrow_col.metadata - - def num_chunks(self) -> int: - return self._pyarrow_col.num_chunks() - - def get_chunks(self, n_chunks: int | None = None): - return self._pyarrow_col.get_chunks(n_chunks=n_chunks) - - def get_buffers(self): - return self._pyarrow_col.get_buffers() diff --git a/third_party/bigframes_vendored/ibis/expr/types/generic.py b/third_party/bigframes_vendored/ibis/expr/types/generic.py deleted file mode 100644 index 52d07183f66..00000000000 --- a/third_party/bigframes_vendored/ibis/expr/types/generic.py +++ /dev/null @@ -1,2420 +0,0 @@ -# Contains code from https://github.com/ibis-project/ibis/blob/9.2.0/ibis/expr/types/generic.py - -from __future__ import annotations - -from collections.abc import Iterable, Sequence -from typing import TYPE_CHECKING, Any - -import bigframes_vendored.ibis -import bigframes_vendored.ibis.common.exceptions as com -import bigframes_vendored.ibis.expr.builders as bl -import bigframes_vendored.ibis.expr.datatypes as dt -import bigframes_vendored.ibis.expr.operations as ops -from bigframes_vendored.ibis.common.deferred import Deferred, _, deferrable -from bigframes_vendored.ibis.common.grounds import Singleton -from bigframes_vendored.ibis.expr.rewrites import rewrite_window_input -from bigframes_vendored.ibis.expr.types.core import ( - Expr, - _binop, - _FixedTextJupyterMixin, - _is_null_literal, -) -from bigframes_vendored.ibis.expr.types.pretty import to_rich -from bigframes_vendored.ibis.util import deprecated, warn_deprecated -from public import public - -if TYPE_CHECKING: - import bigframes_vendored.ibis.expr.schema as sch - import bigframes_vendored.ibis.expr.types as ir - import pandas as pd - import polars as pl - import pyarrow as pa - import rich.table - from bigframes_vendored.ibis.formats.pyarrow import PyArrowData - - -@public -class Value(Expr): - """Base class for a data generating expression having a known type.""" - - def name(self, name): - """Rename an expression to `name`. - - Parameters - ---------- - name - The new name of the expression - - Returns - ------- - Value - `self` with name `name` - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable({"a": [1, 2]}, name="t") - >>> t.a - ┏━━━━━━━┓ - ┃ a ┃ - ┡━━━━━━━┩ - │ int64 │ - ├───────┤ - │ 1 │ - │ 2 │ - └───────┘ - >>> t.a.name("b") - ┏━━━━━━━┓ - ┃ b ┃ - ┡━━━━━━━┩ - │ int64 │ - ├───────┤ - │ 1 │ - │ 2 │ - └───────┘ - """ - # TODO(kszucs): shouldn't do simplification here, but rather later - # when simplifying the whole operation tree - # the expression's name is idendical to the new one - if self.has_name() and self.get_name() == name: - return self - - if isinstance(self.op(), ops.Alias): - # only keep a single alias operation - op = ops.Alias(arg=self.op().arg, name=name) - else: - op = ops.Alias(arg=self, name=name) - - return op.to_expr() - - # TODO(kszucs): should rename to dtype - def type(self) -> dt.DataType: - """Return the [DataType](./datatypes.qmd) of `self`.""" - return self.op().dtype - - def hash(self) -> ir.IntegerValue: - """Compute an integer hash value. - - ::: {.callout-note} - ## The hashing function used is backend-dependent. - ::: - - Returns - ------- - IntegerValue - The hash value of `self` - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> ibis.literal("hello").hash() # doctest: +SKIP - -4155090522938856779 - """ - return ops.Hash(self).to_expr() - - def cast(self, target_type: Any) -> Value: - """Cast expression to indicated data type. - - Similar to `pandas.Series.astype`. - - Parameters - ---------- - target_type - Type to cast to. Anything accepted by [`ibis.dtype()`](./datatypes.qmd#ibis.dtype) - - Returns - ------- - Value - Casted expression - - See Also - -------- - [`Value.try_cast()`](./expression-generic.qmd#ibis.expr.types.generic.Value.try_cast) - [`ibis.dtype()`](./datatypes.qmd#ibis.dtype) - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> x = ibis.examples.penguins.fetch()["bill_depth_mm"] - >>> x - ┏━━━━━━━━━━━━━━━┓ - ┃ bill_depth_mm ┃ - ┡━━━━━━━━━━━━━━━┩ - │ float64 │ - ├───────────────┤ - │ 18.7 │ - │ 17.4 │ - │ 18.0 │ - │ NULL │ - │ 19.3 │ - │ 20.6 │ - │ 17.8 │ - │ 19.6 │ - │ 18.1 │ - │ 20.2 │ - │ … │ - └───────────────┘ - - python's built-in types can be used - - >>> x.cast(int) - ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ Cast(bill_depth_mm, int64) ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩ - │ int64 │ - ├────────────────────────────┤ - │ 19 │ - │ 17 │ - │ 18 │ - │ NULL │ - │ 19 │ - │ 21 │ - │ 18 │ - │ 20 │ - │ 18 │ - │ 20 │ - │ … │ - └────────────────────────────┘ - - If you make an illegal cast, you won't know until the backend actually - executes it. Consider [`.try_cast()`](#ibis.expr.types.generic.Value.try_cast). - - >>> ibis.literal("a string").cast(int) # doctest: +SKIP - - """ - op = ops.Cast(self, to=target_type) - - if op.to == self.type(): - # noop case if passed type is the same - return self - - if op.to.is_geospatial() and not self.type().is_binary(): - from_geotype = self.type().geotype or "geometry" - to_geotype = op.to.geotype - if from_geotype == to_geotype: - return self - - return op.to_expr() - - def try_cast(self, target_type: Any) -> Value: - """Try cast expression to indicated data type. - - If the cast fails for a row, the value is returned - as null or NaN depending on target_type and backend behavior. - - Parameters - ---------- - target_type - Type to try cast to. Anything accepted by [`ibis.dtype()`](./datatypes.qmd#ibis.dtype) - - Returns - ------- - Value - Casted expression - - See Also - -------- - [`Value.cast()`](./expression-generic.qmd#ibis.expr.types.generic.Value.cast) - [`ibis.dtype()`](./datatypes.qmd#ibis.dtype) - - Examples - -------- - >>> import ibis - >>> from ibis import _ - >>> ibis.options.interactive = True - >>> t = ibis.memtable({"numbers": [1, 2, 3, 4], "strings": ["1.0", "2", "hello", "world"]}) - >>> t - ┏━━━━━━━━━┳━━━━━━━━━┓ - ┃ numbers ┃ strings ┃ - ┡━━━━━━━━━╇━━━━━━━━━┩ - │ int64 │ string │ - ├─────────┼─────────┤ - │ 1 │ 1.0 │ - │ 2 │ 2 │ - │ 3 │ hello │ - │ 4 │ world │ - └─────────┴─────────┘ - >>> t = t.mutate(numbers_to_strings=_.numbers.try_cast("string")) - >>> t = t.mutate(strings_to_numbers=_.strings.try_cast("int")) - >>> t - ┏━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━┓ - ┃ numbers ┃ strings ┃ numbers_to_strings ┃ strings_to_numbers ┃ - ┡━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━┩ - │ int64 │ string │ string │ int64 │ - ├─────────┼─────────┼────────────────────┼────────────────────┤ - │ 1 │ 1.0 │ 1 │ 1 │ - │ 2 │ 2 │ 2 │ 2 │ - │ 3 │ hello │ 3 │ NULL │ - │ 4 │ world │ 4 │ NULL │ - └─────────┴─────────┴────────────────────┴────────────────────┘ - """ - op = ops.TryCast(self, to=target_type) - - if op.to == self.type(): - # noop case if passed type is the same - return self - - return op.to_expr() - - def coalesce(self, *args: Value) -> Value: - """Return the first non-null value from `args`. - - Parameters - ---------- - args - Arguments from which to choose the first non-null value - - Returns - ------- - Value - Coalesced expression - - See Also - -------- - [`ibis.coalesce()`](./expression-generic.qmd#ibis.coalesce) - [`Value.fill_null()`](./expression-generic.qmd#ibis.expr.types.generic.Value.fill_null) - - Examples - -------- - >>> import ibis - >>> ibis.coalesce(None, 4, 5).name("x") - x: Coalesce(...) - """ - return ops.Coalesce((self, *args)).to_expr() - - @deprecated(as_of="8.0.0", instead="use ibis.greatest(self, rest...) instead") - def greatest(self, *args: ir.Value) -> ir.Value: - return ops.Greatest((self, *args)).to_expr() - - @deprecated(as_of="8.0.0", instead="use ibis.least(self, rest...) instead") - def least(self, *args: ir.Value) -> ir.Value: - return ops.Least((self, *args)).to_expr() - - def typeof(self) -> ir.StringValue: - """Return the string name of the datatype of self. - - The values of the returned strings are necessarily backend dependent. - e.g. duckdb may say "DOUBLE", while sqlite may say "real". - - Returns - ------- - StringValue - A string indicating the type of the value - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> vals = ibis.examples.penguins.fetch().head(5).bill_length_mm - >>> vals - ┏━━━━━━━━━━━━━━━━┓ - ┃ bill_length_mm ┃ - ┡━━━━━━━━━━━━━━━━┩ - │ float64 │ - ├────────────────┤ - │ 39.1 │ - │ 39.5 │ - │ 40.3 │ - │ NULL │ - │ 36.7 │ - └────────────────┘ - >>> vals.typeof() - ┏━━━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ TypeOf(bill_length_mm) ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━━━┩ - │ string │ - ├────────────────────────┤ - │ DOUBLE │ - │ DOUBLE │ - │ DOUBLE │ - │ DOUBLE │ - │ DOUBLE │ - └────────────────────────┘ - - Different backends have different names for their native types - - >>> ibis.duckdb.connect().execute(ibis.literal(5.4).typeof()) - 'DOUBLE' - >>> ibis.sqlite.connect().execute(ibis.literal(5.4).typeof()) - 'real' - """ - return ops.TypeOf(self).to_expr() - - def fill_null(self, fill_value: Scalar) -> Value: - """Replace any null values with the indicated fill value. - - Parameters - ---------- - fill_value - Value with which to replace `NULL` values in `self` - - See Also - -------- - [`Value.coalesce()`](./expression-generic.qmd#ibis.expr.types.generic.Value.coalesce) - [`ibis.coalesce()`](./expression-generic.qmd#ibis.coalesce) - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.examples.penguins.fetch().limit(5) - >>> t.sex - ┏━━━━━━━━┓ - ┃ sex ┃ - ┡━━━━━━━━┩ - │ string │ - ├────────┤ - │ male │ - │ female │ - │ female │ - │ NULL │ - │ female │ - └────────┘ - >>> t.sex.fill_null("unrecorded").name("sex") - ┏━━━━━━━━━━━━┓ - ┃ sex ┃ - ┡━━━━━━━━━━━━┩ - │ string │ - ├────────────┤ - │ male │ - │ female │ - │ female │ - │ unrecorded │ - │ female │ - └────────────┘ - - Returns - ------- - Value - `self` filled with `fill_value` where it is `NULL` - """ - return ops.Coalesce((self, fill_value)).to_expr() - - @deprecated(as_of="9.1", instead="use fill_null instead") - def fillna(self, fill_value: Scalar) -> Value: - """Deprecated - use `fill_null` instead.""" - return self.fill_null(fill_value) - - def nullif(self, null_if_expr: Value) -> Value: - """Set values to null if they equal the values `null_if_expr`. - - Commonly used to avoid divide-by-zero problems by replacing zero with - `NULL` in the divisor. - - Equivalent to `(self == null_if_expr).ifelse(ibis.null(), self)`. - - Parameters - ---------- - null_if_expr - Expression indicating what values should be NULL - - Returns - ------- - Value - Value expression - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> vals = ibis.examples.penguins.fetch().head(5).sex - >>> vals - ┏━━━━━━━━┓ - ┃ sex ┃ - ┡━━━━━━━━┩ - │ string │ - ├────────┤ - │ male │ - │ female │ - │ female │ - │ NULL │ - │ female │ - └────────┘ - >>> vals.nullif("male") - ┏━━━━━━━━━━━━━━━━━━━━━┓ - ┃ NullIf(sex, 'male') ┃ - ┡━━━━━━━━━━━━━━━━━━━━━┩ - │ string │ - ├─────────────────────┤ - │ NULL │ - │ female │ - │ female │ - │ NULL │ - │ female │ - └─────────────────────┘ - """ - return ops.NullIf(self, null_if_expr).to_expr() - - def between( - self, - lower: Value, - upper: Value, - ) -> ir.BooleanValue: - """Check if this expression is between `lower` and `upper`, inclusive. - - Parameters - ---------- - lower - Lower bound, inclusive - upper - Upper bound, inclusive - - Returns - ------- - BooleanValue - Expression indicating membership in the provided range - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.examples.penguins.fetch().limit(5) - >>> t.bill_length_mm.between(35, 38) - ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ Between(bill_length_mm, 35, 38) ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩ - │ boolean │ - ├─────────────────────────────────┤ - │ False │ - │ False │ - │ False │ - │ NULL │ - │ True │ - └─────────────────────────────────┘ - """ - return ops.Between(self, lower, upper).to_expr() - - def isin(self, values: Value | Sequence[Value]) -> ir.BooleanValue: - """Check whether this expression's values are in `values`. - - `NULL` values are propagated in the output. See examples for details. - - Parameters - ---------- - values - Values or expression to check for membership - - Returns - ------- - BooleanValue - Expression indicating membership - - See Also - -------- - [`Value.notin()`](./expression-generic.qmd#ibis.expr.types.generic.Value.notin) - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable({"a": [1, 2, 3], "b": [2, 3, 4]}) - >>> t - ┏━━━━━━━┳━━━━━━━┓ - ┃ a ┃ b ┃ - ┡━━━━━━━╇━━━━━━━┩ - │ int64 │ int64 │ - ├───────┼───────┤ - │ 1 │ 2 │ - │ 2 │ 3 │ - │ 3 │ 4 │ - └───────┴───────┘ - - Check against a literal sequence of values - - >>> t.a.isin([1, 2]) - ┏━━━━━━━━━━━━━━━━━━━━━┓ - ┃ InValues(a, (1, 2)) ┃ - ┡━━━━━━━━━━━━━━━━━━━━━┩ - │ boolean │ - ├─────────────────────┤ - │ True │ - │ True │ - │ False │ - └─────────────────────┘ - - Check against a derived expression - - >>> t.a.isin(t.b + 1) - ┏━━━━━━━━━━━━━━━┓ - ┃ InSubquery(a) ┃ - ┡━━━━━━━━━━━━━━━┩ - │ boolean │ - ├───────────────┤ - │ False │ - │ False │ - │ True │ - └───────────────┘ - - Check against a column from a different table - - >>> t2 = ibis.memtable({"x": [99, 2, 99]}) - >>> t.a.isin(t2.x) - ┏━━━━━━━━━━━━━━━┓ - ┃ InSubquery(a) ┃ - ┡━━━━━━━━━━━━━━━┩ - │ boolean │ - ├───────────────┤ - │ False │ - │ True │ - │ False │ - └───────────────┘ - - `NULL` behavior - - >>> t = ibis.memtable({"x": [1, 2]}) - >>> t.x.isin([1, None]) - ┏━━━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ InValues(x, (1, None)) ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━━━┩ - │ boolean │ - ├────────────────────────┤ - │ True │ - │ NULL │ - └────────────────────────┘ - >>> t = ibis.memtable({"x": [1, None, 2]}) - >>> t.x.isin([1]) - ┏━━━━━━━━━━━━━━━━━━━┓ - ┃ InValues(x, (1,)) ┃ - ┡━━━━━━━━━━━━━━━━━━━┩ - │ boolean │ - ├───────────────────┤ - │ True │ - │ NULL │ - │ False │ - └───────────────────┘ - >>> t.x.isin([3]) - ┏━━━━━━━━━━━━━━━━━━━┓ - ┃ InValues(x, (3,)) ┃ - ┡━━━━━━━━━━━━━━━━━━━┩ - │ boolean │ - ├───────────────────┤ - │ False │ - │ NULL │ - │ False │ - └───────────────────┘ - """ - from bigframes_vendored.ibis.expr.types import ArrayValue - - if isinstance(values, ArrayValue): - return ops.ArrayContains(values, self).to_expr() - elif isinstance(values, Column): - return ops.InSubquery(values.as_table(), needle=self).to_expr() - else: - return ops.InValues(self, values).to_expr() - - def notin(self, values: Value | Sequence[Value]) -> ir.BooleanValue: - """Check whether this expression's values are not in `values`. - - Opposite of [`Value.isin()`](./expression-generic.qmd#ibis.expr.types.generic.Value.isin). - - Parameters - ---------- - values - Values or expression to check for lack of membership - - Returns - ------- - BooleanValue - Whether `self`'s values are not contained in `values` - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.examples.penguins.fetch().limit(5) - >>> t.bill_depth_mm - ┏━━━━━━━━━━━━━━━┓ - ┃ bill_depth_mm ┃ - ┡━━━━━━━━━━━━━━━┩ - │ float64 │ - ├───────────────┤ - │ 18.7 │ - │ 17.4 │ - │ 18.0 │ - │ NULL │ - │ 19.3 │ - └───────────────┘ - >>> t.bill_depth_mm.notin([18.7, 18.1]) - ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ Not(InValues(bill_depth_mm, (18.7, 18.1))) ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩ - │ boolean │ - ├────────────────────────────────────────────┤ - │ False │ - │ True │ - │ True │ - │ NULL │ - │ True │ - └────────────────────────────────────────────┘ - """ - return ~self.isin(values) - - def substitute( - self, - value: Value | dict, - replacement: Value | None = None, - else_: Value | None = None, - ): - """Replace values given in `values` with `replacement`. - - This is similar to the pandas `replace` method. - - Parameters - ---------- - value - Expression or dict. - replacement - If an expression is passed to value, this must be - passed. - else_ - If an original value does not match `value`, then `else_` is used. - The default of `None` means leave the original value unchanged. - - Returns - ------- - Value - Replaced values - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.examples.penguins.fetch() - >>> t.island.value_counts().order_by("island") - ┏━━━━━━━━━━━┳━━━━━━━━━━━━━━┓ - ┃ island ┃ island_count ┃ - ┡━━━━━━━━━━━╇━━━━━━━━━━━━━━┩ - │ string │ int64 │ - ├───────────┼──────────────┤ - │ Biscoe │ 168 │ - │ Dream │ 124 │ - │ Torgersen │ 52 │ - └───────────┴──────────────┘ - >>> t.island.substitute({"Torgersen": "torg", "Biscoe": "bisc"}).name( - ... "island" - ... ).value_counts().order_by("island") - ┏━━━━━━━━┳━━━━━━━━━━━━━━┓ - ┃ island ┃ island_count ┃ - ┡━━━━━━━━╇━━━━━━━━━━━━━━┩ - │ string │ int64 │ - ├────────┼──────────────┤ - │ Dream │ 124 │ - │ bisc │ 168 │ - │ torg │ 52 │ - └────────┴──────────────┘ - """ - if isinstance(value, dict): - expr = bigframes_vendored.ibis.case() - try: - null_replacement = value.pop(None) - except KeyError: - pass - else: - expr = expr.when(self.isnull(), null_replacement) - for k, v in value.items(): - expr = expr.when(self == k, v) - else: - expr = self.case().when(value, replacement) - - return expr.else_(else_ if else_ is not None else self).end() - - def over( - self, - window=None, - *, - rows=None, - range=None, - group_by=None, - order_by=None, - ) -> Value: - """Construct a window expression. - - Parameters - ---------- - window - Window specification - rows - Whether to use the `ROWS` window clause - range - Whether to use the `RANGE` window clause - group_by - Grouping key - order_by - Ordering key - - Returns - ------- - Value - A window function expression - - """ - - if window is None: - window = bigframes_vendored.ibis.window( - rows=rows, - range=range, - group_by=group_by, - order_by=order_by, - ) - elif not isinstance(window, bl.WindowBuilder): - raise com.IbisTypeError("Unexpected window type: {window!r}") - - node = self.op() - if len(node.relations) == 0: - table = None - elif len(node.relations) == 1: - (table,) = node.relations - table = table.to_expr() - else: - raise com.RelationError("Cannot use window with multiple tables") - - @deferrable - def bind(table): - winfunc = rewrite_window_input( - node, window.bind(table) if (table is not None) else window - ) - if winfunc == node: - raise com.IbisTypeError( - "No reduction or analytic function found to construct a window expression" - ) - return winfunc.to_expr() - - try: - return bind(table) - except com.IbisInputError: - return bind(_) - - def isnull(self) -> ir.BooleanValue: - """Return whether this expression is NULL. - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.examples.penguins.fetch().limit(5) - >>> t.bill_depth_mm - ┏━━━━━━━━━━━━━━━┓ - ┃ bill_depth_mm ┃ - ┡━━━━━━━━━━━━━━━┩ - │ float64 │ - ├───────────────┤ - │ 18.7 │ - │ 17.4 │ - │ 18.0 │ - │ NULL │ - │ 19.3 │ - └───────────────┘ - >>> t.bill_depth_mm.isnull() - ┏━━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ IsNull(bill_depth_mm) ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━━┩ - │ boolean │ - ├───────────────────────┤ - │ False │ - │ False │ - │ False │ - │ True │ - │ False │ - └───────────────────────┘ - """ - return ops.IsNull(self).to_expr() - - def notnull(self) -> ir.BooleanValue: - """Return whether this expression is not NULL. - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.examples.penguins.fetch().limit(5) - >>> t.bill_depth_mm - ┏━━━━━━━━━━━━━━━┓ - ┃ bill_depth_mm ┃ - ┡━━━━━━━━━━━━━━━┩ - │ float64 │ - ├───────────────┤ - │ 18.7 │ - │ 17.4 │ - │ 18.0 │ - │ NULL │ - │ 19.3 │ - └───────────────┘ - >>> t.bill_depth_mm.notnull() - ┏━━━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ NotNull(bill_depth_mm) ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━━━┩ - │ boolean │ - ├────────────────────────┤ - │ True │ - │ True │ - │ True │ - │ False │ - │ True │ - └────────────────────────┘ - """ - return ops.NotNull(self).to_expr() - - def case(self) -> bl.SimpleCaseBuilder: - """Create a SimpleCaseBuilder to chain multiple if-else statements. - - Add new search expressions with the `.when()` method. These must be - comparable with this column expression. Conclude by calling `.end()`. - - Returns - ------- - SimpleCaseBuilder - A case builder - - See Also - -------- - [`Value.substitute()`](./expression-generic.qmd#ibis.expr.types.generic.Value.substitute) - [`ibis.cases()`](./expression-generic.qmd#ibis.expr.types.generic.Value.cases) - [`ibis.case()`](./expression-generic.qmd#ibis.case) - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> x = ibis.examples.penguins.fetch().head(5)["sex"] - >>> x - ┏━━━━━━━━┓ - ┃ sex ┃ - ┡━━━━━━━━┩ - │ string │ - ├────────┤ - │ male │ - │ female │ - │ female │ - │ NULL │ - │ female │ - └────────┘ - >>> x.case().when("male", "M").when("female", "F").else_("U").end() - ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ SimpleCase(sex, ('male', 'female'), ('M', 'F'), 'U') ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩ - │ string │ - ├──────────────────────────────────────────────────────┤ - │ M │ - │ F │ - │ F │ - │ U │ - │ F │ - └──────────────────────────────────────────────────────┘ - - Cases not given result in the ELSE case - - >>> x.case().when("male", "M").else_("OTHER").end() - ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ SimpleCase(sex, ('male',), ('M',), 'OTHER') ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩ - │ string │ - ├─────────────────────────────────────────────┤ - │ M │ - │ OTHER │ - │ OTHER │ - │ OTHER │ - │ OTHER │ - └─────────────────────────────────────────────┘ - - If you don't supply an ELSE, then NULL is used - - >>> x.case().when("male", "M").end() - ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ SimpleCase(sex, ('male',), ('M',), Cast(None, string)) ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩ - │ string │ - ├────────────────────────────────────────────────────────┤ - │ M │ - │ NULL │ - │ NULL │ - │ NULL │ - │ NULL │ - └────────────────────────────────────────────────────────┘ - """ - import bigframes_vendored.ibis.expr.builders as bl - - return bl.SimpleCaseBuilder(self.op()) - - def cases( - self, - case_result_pairs: Iterable[tuple[ir.BooleanValue, Value]], - default: Value | None = None, - ) -> Value: - """Create a case expression in one shot. - - Parameters - ---------- - case_result_pairs - Conditional-result pairs - default - Value to return if none of the case conditions are true - - Returns - ------- - Value - Value expression - - See Also - -------- - [`Value.substitute()`](./expression-generic.qmd#ibis.expr.types.generic.Value.substitute) - [`ibis.cases()`](./expression-generic.qmd#ibis.expr.types.generic.Value.cases) - [`ibis.case()`](./expression-generic.qmd#ibis.case) - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable({"values": [1, 2, 1, 2, 3, 2, 4]}) - >>> t - ┏━━━━━━━━┓ - ┃ values ┃ - ┡━━━━━━━━┩ - │ int64 │ - ├────────┤ - │ 1 │ - │ 2 │ - │ 1 │ - │ 2 │ - │ 3 │ - │ 2 │ - │ 4 │ - └────────┘ - >>> number_letter_map = ((1, "a"), (2, "b"), (3, "c")) - >>> t.values.cases(number_letter_map, default="unk").name("replace") - ┏━━━━━━━━━┓ - ┃ replace ┃ - ┡━━━━━━━━━┩ - │ string │ - ├─────────┤ - │ a │ - │ b │ - │ a │ - │ b │ - │ c │ - │ b │ - │ unk │ - └─────────┘ - """ - builder = self.case() - for case, result in case_result_pairs: - builder = builder.when(case, result) - return builder.else_(default).end() - - def collect(self, where: ir.BooleanValue | None = None) -> ir.ArrayScalar: - """Aggregate this expression's elements into an array. - - This function is called `array_agg`, `list_agg`, or `list` in other systems. - - Parameters - ---------- - where - Filter to apply before aggregation - - Returns - ------- - ArrayScalar - Collected array - - Examples - -------- - Basic collect usage - - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable({"key": list("aaabb"), "value": [1, 2, 3, 4, 5]}) - >>> t - ┏━━━━━━━━┳━━━━━━━┓ - ┃ key ┃ value ┃ - ┡━━━━━━━━╇━━━━━━━┩ - │ string │ int64 │ - ├────────┼───────┤ - │ a │ 1 │ - │ a │ 2 │ - │ a │ 3 │ - │ b │ 4 │ - │ b │ 5 │ - └────────┴───────┘ - >>> t.value.collect() - ┌────────────────┐ - │ [1, 2, ... +3] │ - └────────────────┘ - >>> type(t.value.collect()) - - - Collect elements per group - - >>> t.group_by("key").agg(v=lambda t: t.value.collect()).order_by("key") - ┏━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ key ┃ v ┃ - ┡━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━┩ - │ string │ array │ - ├────────┼──────────────────────┤ - │ a │ [1, 2, ... +1] │ - │ b │ [4, 5] │ - └────────┴──────────────────────┘ - - Collect elements per group using a filter - - >>> t.group_by("key").agg(v=lambda t: t.value.collect(where=t.value > 1)).order_by("key") - ┏━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ key ┃ v ┃ - ┡━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━┩ - │ string │ array │ - ├────────┼──────────────────────┤ - │ a │ [2, 3] │ - │ b │ [4, 5] │ - └────────┴──────────────────────┘ - """ - return ops.ArrayCollect(self, where=self._bind_to_parent_table(where)).to_expr() - - def identical_to(self, other: Value) -> ir.BooleanValue: - """Return whether this expression is identical to other. - - Corresponds to `IS NOT DISTINCT FROM` in SQL. - - Parameters - ---------- - other - Expression to compare to - - Returns - ------- - BooleanValue - Whether this expression is not distinct from `other` - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> one = ibis.literal(1) - >>> two = ibis.literal(2) - >>> two.identical_to(one + one) - ┌──────────┐ - │ np.True_ │ - └──────────┘ - """ - try: - return ops.IdenticalTo(self, other).to_expr() - except (com.IbisTypeError, NotImplementedError): - return NotImplemented - - def group_concat( - self, - sep: str = ",", - where: ir.BooleanValue | None = None, - ) -> ir.StringScalar: - """Concatenate values using the indicated separator to produce a string. - - Parameters - ---------- - sep - Separator will be used to join strings - where - Filter expression - - Returns - ------- - StringScalar - Concatenated string expression - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.examples.penguins.fetch().limit(5) - >>> t[["bill_length_mm", "bill_depth_mm"]] - ┏━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ - ┃ bill_length_mm ┃ bill_depth_mm ┃ - ┡━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ - │ float64 │ float64 │ - ├────────────────┼───────────────┤ - │ 39.1 │ 18.7 │ - │ 39.5 │ 17.4 │ - │ 40.3 │ 18.0 │ - │ NULL │ NULL │ - │ 36.7 │ 19.3 │ - └────────────────┴───────────────┘ - >>> t.bill_length_mm.group_concat() - ┌───────────────────────┐ - │ '39.1,39.5,40.3,36.7' │ - └───────────────────────┘ - - >>> t.bill_length_mm.group_concat(sep=": ") - ┌──────────────────────────┐ - │ '39.1: 39.5: 40.3: 36.7' │ - └──────────────────────────┘ - - >>> t.bill_length_mm.group_concat(sep=": ", where=t.bill_depth_mm > 18) - ┌──────────────┐ - │ '39.1: 36.7' │ - └──────────────┘ - """ - return ops.GroupConcat( - self, sep=sep, where=self._bind_to_parent_table(where) - ).to_expr() - - def __hash__(self) -> int: - return super().__hash__() - - def __eq__(self, other: Value) -> ir.BooleanValue: - if _is_null_literal(other): - return self.isnull() - elif _is_null_literal(self): - return other.isnull() - return _binop(ops.Equals, self, other) - - def __ne__(self, other: Value) -> ir.BooleanValue: - if _is_null_literal(other): - return self.notnull() - elif _is_null_literal(self): - return other.notnull() - return _binop(ops.NotEquals, self, other) - - def __ge__(self, other: Value) -> ir.BooleanValue: - return _binop(ops.GreaterEqual, self, other) - - def __gt__(self, other: Value) -> ir.BooleanValue: - return _binop(ops.Greater, self, other) - - def __le__(self, other: Value) -> ir.BooleanValue: - return _binop(ops.LessEqual, self, other) - - def __lt__(self, other: Value) -> ir.BooleanValue: - return _binop(ops.Less, self, other) - - def asc(self, nulls_first: bool = False) -> ir.Value: - """Sort an expression ascending.""" - return ops.SortKey(self, ascending=True, nulls_first=nulls_first).to_expr() - - def desc(self, nulls_first: bool = False) -> ir.Value: - """Sort an expression descending.""" - return ops.SortKey(self, ascending=False, nulls_first=nulls_first).to_expr() - - def to_pandas(self, **kwargs) -> pd.Series: - """Convert a column expression to a pandas Series or scalar object. - - Parameters - ---------- - kwargs - Same as keyword arguments to [`execute`](#ibis.expr.types.core.Expr.execute) - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.examples.penguins.fetch().limit(5) - >>> t.to_pandas() - species island bill_length_mm ... body_mass_g sex year - 0 Adelie Torgersen 39.1 ... 3750.0 male 2007 - 1 Adelie Torgersen 39.5 ... 3800.0 female 2007 - 2 Adelie Torgersen 40.3 ... 3250.0 female 2007 - 3 Adelie Torgersen NaN ... NaN None 2007 - 4 Adelie Torgersen 36.7 ... 3450.0 female 2007 - [5 rows x 8 columns] - """ - return self.execute(**kwargs) - - -@public -class Scalar(Value): - def __pyarrow_result__( - self, table: pa.Table, data_mapper: type[PyArrowData] | None = None - ) -> pa.Scalar: - if data_mapper is None: - from bigframes_vendored.ibis.formats.pyarrow import ( - PyArrowData as data_mapper, - ) - - return data_mapper.convert_scalar(table[0][0], self.type()) - - def __pandas_result__( - self, df: pd.DataFrame, *, schema: sch.Schema | None = None - ) -> Any: - from bigframes_vendored.ibis.formats.pandas import PandasData - - return PandasData.convert_scalar( - df, self.type() if schema is None else schema[df.columns[0]] - ) - - def __polars_result__(self, df: pl.DataFrame) -> Any: - from bigframes_vendored.ibis.formats.polars import PolarsData - - return PolarsData.convert_scalar(df, self.type()) - - def as_scalar(self): - """Inform ibis that the expression should be treated as a scalar. - - If the expression is a literal, it will be returned as is. If it depends - on a table, it will be turned to a scalar subquery. - - Returns - ------- - Scalar - A scalar subquery or a literal - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.examples.penguins.fetch() - >>> max_gentoo_weight = t.filter(t.species == "Gentoo").body_mass_g.max() - >>> light_penguins = t.filter(t.body_mass_g < max_gentoo_weight / 2) - >>> light_penguins.species.value_counts().order_by("species") - ┏━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ - ┃ species ┃ species_count ┃ - ┡━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ - │ string │ int64 │ - ├───────────┼───────────────┤ - │ Adelie │ 15 │ - │ Chinstrap │ 2 │ - └───────────┴───────────────┘ - """ - parents = self.op().relations - if parents: - return ops.ScalarSubquery(self.as_table()).to_expr() - else: - return self - - def as_table(self) -> ir.Table: - """Promote the scalar expression to a table. - - Returns - ------- - Table - A table expression - - Examples - -------- - Promote an aggregation to a table - - >>> import ibis - >>> import ibis.expr.types as ir - >>> t = ibis.table(dict(a="str"), name="t") - >>> expr = t.a.length().sum().name("len").as_table() - >>> isinstance(expr, ir.Table) - True - - Promote a literal value to a table - - >>> import ibis.expr.types as ir - >>> lit = ibis.literal(1).name("a").as_table() - >>> isinstance(lit, ir.Table) - True - """ - parents = self.op().relations - - if len(parents) == 0: - return ops.DummyTable({self.get_name(): self}).to_expr() - elif len(parents) == 1: - (parent,) = parents - return parent.to_expr().aggregate(self) - else: - raise com.RelationError( - f"The scalar expression {self} cannot be converted to a " - "table expression because it involves multiple base table " - "references" - ) - - def __deferred_repr__(self): - return f"" - - def _repr_html_(self) -> str | None: - return None - - -@public -class Column(Value, _FixedTextJupyterMixin): - # Higher than numpy & dask objects - __array_priority__ = 20 - - __array_ufunc__ = None - - def __getitem__(self, _): - raise TypeError( - f"{self.__class__.__name__!r} is not subscriptable: " - "see https://ibis-project.org/tutorial/ibis-for-pandas-users/#ibis-for-pandas-users for details." - ) - - def __array__(self, dtype=None): - return self.execute().__array__(dtype) - - def preview( - self, - *, - max_rows: int | None = None, - max_length: int | None = None, - max_string: int | None = None, - max_depth: int | None = None, - console_width: int | float | None = None, - ) -> rich.table.Table: - """Print a subset as a single-column Rich Table. - - This is an explicit version of what you get when you inspect - this object in interactive mode, except with this version you - can pass formatting options. The options are the same as those exposed - in `ibis.options.interactive`. - - Parameters - ---------- - max_rows - Maximum number of rows to display - max_length - Maximum length for pretty-printed arrays and maps. - max_string - Maximum length for pretty-printed strings. - max_depth - Maximum depth for nested data types. - console_width - Width of the console in characters. If not specified, the width - will be inferred from the console. - - Examples - -------- - >>> import ibis - >>> t = ibis.examples.penguins.fetch() - >>> t.island.preview(max_rows=3, max_string=5) # doctest: +SKIP - ┏━━━━━━━━┓ - ┃ island ┃ - ┡━━━━━━━━┩ - │ stri… │ - ├────────┤ - │ Torg… │ - │ Torg… │ - │ Torg… │ - │ … │ - └────────┘ - """ - return to_rich( - self, - max_rows=max_rows, - max_length=max_length, - max_string=max_string, - max_depth=max_depth, - console_width=console_width, - ) - - def __pyarrow_result__( - self, table: pa.Table, data_mapper: type[PyArrowData] | None = None - ) -> pa.Array | pa.ChunkedArray: - if data_mapper is None: - from bigframes_vendored.ibis.formats.pyarrow import ( - PyArrowData as data_mapper, - ) - - return data_mapper.convert_column(table[0], self.type()) - - def __pandas_result__( - self, df: pd.DataFrame, *, schema: sch.Schema | None = None - ) -> pd.Series: - from bigframes_vendored.ibis.formats.pandas import PandasData - - assert len(df.columns) == 1, ( - "more than one column when converting columnar result DataFrame to Series" - ) - # in theory we could use df.iloc[:, 0], but there seems to be a bug in - # older geopandas where df.iloc[:, 0] doesn't return the same kind of - # object as df.loc[:, column_name] when df is a GeoDataFrame - # - # the bug is that iloc[:, 0] returns a bare series whereas - # df.loc[:, column_name] returns the special GeoSeries object. - # - # this bug is fixed in later versions of geopandas - (column,) = df.columns - return PandasData.convert_column( - df.loc[:, column], self.type() if schema is None else schema[column] - ) - - def __polars_result__(self, df: pl.DataFrame) -> pl.Series: - from bigframes_vendored.ibis.formats.polars import PolarsData - - return PolarsData.convert_column(df, self.type()) - - def as_scalar(self) -> Scalar: - """Inform ibis that the expression should be treated as a scalar. - - Creates a scalar subquery from the column expression. Since ibis cannot - be sure that the column expression contains only one value, the column - expression is wrapped in a scalar subquery and treated as a scalar. - - Note that the execution of the scalar subquery will fail if the column - expression contains more than one value. - - Returns - ------- - Scalar - A scalar subquery - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.examples.penguins.fetch() - >>> heavy_gentoo = t.filter(t.species == "Gentoo", t.body_mass_g > 6200) - >>> from_that_island = t.filter(t.island == heavy_gentoo.island.as_scalar()) - >>> from_that_island.species.value_counts().order_by("species") - ┏━━━━━━━━━┳━━━━━━━━━━━━━━━┓ - ┃ species ┃ species_count ┃ - ┡━━━━━━━━━╇━━━━━━━━━━━━━━━┩ - │ string │ int64 │ - ├─────────┼───────────────┤ - │ Adelie │ 44 │ - │ Gentoo │ 124 │ - └─────────┴───────────────┘ - """ - return self.as_table().as_scalar() - - def as_table(self) -> ir.Table: - """Promote the expression to a [Table](./expression-tables.qmd#ibis.expr.types.Table). - - Returns - ------- - Table - A table expression - - Examples - -------- - >>> import ibis - >>> t = ibis.table(dict(a="str"), name="t") - >>> expr = t.a.length().name("len").as_table() - >>> expected = t.select(len=t.a.length()) - >>> expr.equals(expected) - True - """ - parents = self.op().relations - values = {self.get_name(): self} - - if len(parents) == 0: - return ops.DummyTable(values).to_expr() - elif len(parents) == 1: - (parent,) = parents - return parent.to_expr().select(self) - else: - raise com.RelationError( - f"Cannot convert {type(self)} expression involving multiple " - "base table references to a projection" - ) - - def _bind_to_parent_table(self, value) -> Value | None: - """Bind an expr to the parent table of `self`.""" - if value is None: - return None - if isinstance(value, (Deferred, str)) or callable(value): - op = self.op() - if len(op.relations) != 1: - # TODO: I don't think this line can ever be hit by a valid - # expression, since it would require a column expression to - # directly depend on multiple tables. Currently some invalid - # expressions (like t1.a.argmin(t2.b)) aren't caught at - # construction time though, so we keep the check in for now. - raise com.RelationError( - f"Unable to bind `{value!r}` - the current expression" - f"depends on multiple tables." - ) - table = next(iter(op.relations)).to_expr() - - if isinstance(value, str): - return table[value] - elif isinstance(value, Deferred): - return value.resolve(table) - else: - value = value(table) - - if not isinstance(value, Value): - return literal(value) - return value - - def __deferred_repr__(self): - return f"" - - def approx_nunique(self, where: ir.BooleanValue | None = None) -> ir.IntegerScalar: - """Return the approximate number of distinct elements in `self`. - - ::: {.callout-note} - ## The result may or may not be exact - - Whether the result is an approximation depends on the backend. - - ::: {.callout-warning} - ## Do not depend on the results being exact - ::: - - ::: - - Parameters - ---------- - where - Filter in values when `where` is `True` - - Returns - ------- - Scalar - An approximate count of the distinct elements of `self` - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.examples.penguins.fetch() - >>> t.body_mass_g.approx_nunique() - ┌──────────────┐ - │ np.int64(94) │ - └──────────────┘ - >>> t.body_mass_g.approx_nunique(where=t.species == "Adelie") - ┌──────────────┐ - │ np.int64(55) │ - └──────────────┘ - """ - return ops.ApproxCountDistinct( - self, where=self._bind_to_parent_table(where) - ).to_expr() - - def approx_median(self, where: ir.BooleanValue | None = None) -> Scalar: - """Return an approximate of the median of `self`. - - ::: {.callout-note} - ## The result may or may not be exact - - Whether the result is an approximation depends on the backend. - - ::: {.callout-warning} - ## Do not depend on the results being exact - ::: - - ::: - - Parameters - ---------- - where - Filter in values when `where` is `True` - - Returns - ------- - Scalar - An approximation of the median of `self` - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.examples.penguins.fetch() - >>> t.body_mass_g.approx_median() - ┌────────────────┐ - │ np.int64(4030) │ - └────────────────┘ - >>> t.body_mass_g.approx_median(where=t.species == "Chinstrap") - ┌────────────────┐ - │ np.int64(3700) │ - └────────────────┘ - """ - return ops.ApproxMedian(self, where=self._bind_to_parent_table(where)).to_expr() - - def mode(self, where: ir.BooleanValue | None = None) -> Scalar: - """Return the mode of a column. - - Parameters - ---------- - where - Filter in values when `where` is `True` - - Returns - ------- - Scalar - The mode of `self` - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.examples.penguins.fetch() - >>> t.body_mass_g.mode() - ┌────────────────┐ - │ np.int64(3800) │ - └────────────────┘ - >>> t.body_mass_g.mode(where=(t.species == "Gentoo") & (t.sex == "male")) - ┌────────────────┐ - │ np.int64(5550) │ - └────────────────┘ - """ - return ops.Mode(self, where=self._bind_to_parent_table(where)).to_expr() - - def max(self, where: ir.BooleanValue | None = None) -> Scalar: - """Return the maximum of a column. - - Parameters - ---------- - where - Filter in values when `where` is `True` - - Returns - ------- - Scalar - The maximum value in `self` - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.examples.penguins.fetch() - >>> t.body_mass_g.max() - ┌────────────────┐ - │ np.int64(6300) │ - └────────────────┘ - >>> t.body_mass_g.max(where=t.species == "Chinstrap") - ┌────────────────┐ - │ np.int64(4800) │ - └────────────────┘ - """ - return ops.Max(self, where=self._bind_to_parent_table(where)).to_expr() - - def min(self, where: ir.BooleanValue | None = None) -> Scalar: - """Return the minimum of a column. - - Parameters - ---------- - where - Filter in values when `where` is `True` - - Returns - ------- - Scalar - The minimum value in `self` - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.examples.penguins.fetch() - >>> t.body_mass_g.min() - ┌────────────────┐ - │ np.int64(2700) │ - └────────────────┘ - >>> t.body_mass_g.min(where=t.species == "Adelie") - ┌────────────────┐ - │ np.int64(2850) │ - └────────────────┘ - """ - return ops.Min(self, where=self._bind_to_parent_table(where)).to_expr() - - def argmax(self, key: ir.Value, where: ir.BooleanValue | None = None) -> Scalar: - """Return the value of `self` that maximizes `key`. - - Parameters - ---------- - key - Key to use for `max` computation. - where - Keep values when `where` is `True` - - Returns - ------- - Scalar - The value of `self` that maximizes `key` - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.examples.penguins.fetch() - >>> t.species.argmax(t.body_mass_g) - ┌──────────┐ - │ 'Gentoo' │ - └──────────┘ - >>> t.species.argmax(t.body_mass_g, where=t.island == "Dream") - ┌─────────────┐ - │ 'Chinstrap' │ - └─────────────┘ - """ - return ops.ArgMax( - self, - key=self._bind_to_parent_table(key), - where=self._bind_to_parent_table(where), - ).to_expr() - - def argmin(self, key: ir.Value, where: ir.BooleanValue | None = None) -> Scalar: - """Return the value of `self` that minimizes `key`. - - Parameters - ---------- - key - Key to use for `min` computation. - where - Keep values when `where` is `True` - - Returns - ------- - Scalar - The value of `self` that minimizes `key` - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.examples.penguins.fetch() - >>> t.species.argmin(t.body_mass_g) - ┌─────────────┐ - │ 'Chinstrap' │ - └─────────────┘ - - >>> t.species.argmin(t.body_mass_g, where=t.island == "Biscoe") - ┌──────────┐ - │ 'Adelie' │ - └──────────┘ - """ - return ops.ArgMin( - self, - key=self._bind_to_parent_table(key), - where=self._bind_to_parent_table(where), - ).to_expr() - - def median(self, where: ir.BooleanValue | None = None) -> Scalar: - """Return the median of the column. - - Parameters - ---------- - where - Optional boolean expression. If given, only the values where - `where` evaluates to true will be considered for the median. - - Returns - ------- - Scalar - Median of the column - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.examples.penguins.fetch() - - Compute the median of `bill_depth_mm` - - >>> t.bill_depth_mm.median() - ┌──────────────────┐ - │ np.float64(17.3) │ - └──────────────────┘ - >>> t.group_by(t.species).agg(median_bill_depth=t.bill_depth_mm.median()).order_by( - ... ibis.desc("median_bill_depth") - ... ) - ┏━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓ - ┃ species ┃ median_bill_depth ┃ - ┡━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩ - │ string │ float64 │ - ├───────────┼───────────────────┤ - │ Chinstrap │ 18.45 │ - │ Adelie │ 18.40 │ - │ Gentoo │ 15.00 │ - └───────────┴───────────────────┘ - - In addition to numeric types, any orderable non-numeric types such as - strings and dates work with `median`. - - >>> t.group_by(t.island).agg(median_species=t.species.median()).order_by( - ... ibis.desc("median_species") - ... ) - ┏━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┓ - ┃ island ┃ median_species ┃ - ┡━━━━━━━━━━━╇━━━━━━━━━━━━━━━━┩ - │ string │ string │ - ├───────────┼────────────────┤ - │ Biscoe │ Gentoo │ - │ Dream │ Chinstrap │ - │ Torgersen │ Adelie │ - └───────────┴────────────────┘ - """ - return ops.Median(self, where=self._bind_to_parent_table(where)).to_expr() - - def quantile( - self, - quantile: float | ir.NumericValue | Sequence[ir.NumericValue | float], - where: ir.BooleanValue | None = None, - ) -> Scalar: - """Return value at the given quantile. - - The output of this method is a continuous quantile if the input is - numeric, otherwise the output is a discrete quantile. - - Parameters - ---------- - quantile - `0 <= quantile <= 1`, or an array of such values - indicating the quantile or quantiles to compute - where - Boolean filter for input values - - Returns - ------- - Scalar - Quantile of the input - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.examples.penguins.fetch() - - Compute the 99th percentile of `bill_depth` - - >>> t.bill_depth_mm.quantile(0.99) - ┌──────────────────┐ - │ np.float64(21.1) │ - └──────────────────┘ - >>> t.group_by(t.species).agg(p99_bill_depth=t.bill_depth_mm.quantile(0.99)).order_by( - ... ibis.desc("p99_bill_depth") - ... ) - ┏━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┓ - ┃ species ┃ p99_bill_depth ┃ - ┡━━━━━━━━━━━╇━━━━━━━━━━━━━━━━┩ - │ string │ float64 │ - ├───────────┼────────────────┤ - │ Adelie │ 21.200 │ - │ Chinstrap │ 20.733 │ - │ Gentoo │ 17.256 │ - └───────────┴────────────────┘ - - In addition to numeric types, any orderable non-numeric types such as - strings and dates work with `quantile`. - - Let's compute the 99th percentile of the `species` column - - >>> t.group_by(t.island).agg(p99_species=t.species.quantile(0.99)).order_by( - ... ibis.desc("p99_species") - ... ) - ┏━━━━━━━━━━━┳━━━━━━━━━━━━━┓ - ┃ island ┃ p99_species ┃ - ┡━━━━━━━━━━━╇━━━━━━━━━━━━━┩ - │ string │ string │ - ├───────────┼─────────────┤ - │ Biscoe │ Gentoo │ - │ Dream │ Chinstrap │ - │ Torgersen │ Adelie │ - └───────────┴─────────────┘ - """ - if isinstance(quantile, Sequence): - op = ops.MultiQuantile - else: - op = ops.Quantile - return op(self, quantile, where=self._bind_to_parent_table(where)).to_expr() - - def nunique(self, where: ir.BooleanValue | None = None) -> ir.IntegerScalar: - """Compute the number of distinct rows in an expression. - - Parameters - ---------- - where - Filter expression - - Returns - ------- - IntegerScalar - Number of distinct elements in an expression - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.examples.penguins.fetch() - >>> t.body_mass_g.nunique() - ┌──────────────┐ - │ np.int64(94) │ - └──────────────┘ - >>> t.body_mass_g.nunique(where=t.species == "Adelie") - ┌──────────────┐ - │ np.int64(55) │ - └──────────────┘ - """ - return ops.CountDistinct( - self, where=self._bind_to_parent_table(where) - ).to_expr() - - def topk(self, k: int, by: ir.Value | None = None) -> ir.Table: - """Return a "top k" expression. - - Parameters - ---------- - k - Return this number of rows - by - An expression. Defaults to `count`. - - Returns - ------- - Table - A top-k expression - """ - from bigframes_vendored.ibis.expr.types.relations import bind - - try: - (table,) = self.op().relations - except ValueError: - raise com.IbisTypeError("TopK must depend on exactly one table.") - - table = table.to_expr() - - if by is None: - by = lambda t: t.count() - - (metric,) = bind(table, by) - - return table.aggregate(metric, by=[self]).order_by(metric.desc()).limit(k) - - def arbitrary( - self, where: ir.BooleanValue | None = None, how: Any = None - ) -> Scalar: - """Select an arbitrary value in a column. - - Returns an arbitrary (nondeterministic, backend-specific) value from - the column. The value will be non-NULL, except if the column is empty - or all values are NULL. - - Parameters - ---------- - where - A filter expression - how - DEPRECATED - - Returns - ------- - Scalar - An expression - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable({"a": [1, 2, 2], "b": list("aaa"), "c": [4.0, 4.1, 4.2]}) - >>> t - ┏━━━━━━━┳━━━━━━━━┳━━━━━━━━━┓ - ┃ a ┃ b ┃ c ┃ - ┡━━━━━━━╇━━━━━━━━╇━━━━━━━━━┩ - │ int64 │ string │ float64 │ - ├───────┼────────┼─────────┤ - │ 1 │ a │ 4.0 │ - │ 2 │ a │ 4.1 │ - │ 2 │ a │ 4.2 │ - └───────┴────────┴─────────┘ - >>> t.group_by("a").agg(arb=t.b.arbitrary(), c=t.c.sum()).order_by("a") - ┏━━━━━━━┳━━━━━━━━┳━━━━━━━━━┓ - ┃ a ┃ arb ┃ c ┃ - ┡━━━━━━━╇━━━━━━━━╇━━━━━━━━━┩ - │ int64 │ string │ float64 │ - ├───────┼────────┼─────────┤ - │ 1 │ a │ 4.0 │ - │ 2 │ a │ 8.3 │ - └───────┴────────┴─────────┘ - """ - if how is not None: - warn_deprecated( - name="how", - as_of="9.0", - removed_in="10.0", - instead="call `first` or `last` explicitly", - ) - return ops.Arbitrary(self, where=self._bind_to_parent_table(where)).to_expr() - - def count(self, where: ir.BooleanValue | None = None) -> ir.IntegerScalar: - """Compute the number of rows in an expression. - - Parameters - ---------- - where - Filter expression - - Returns - ------- - IntegerScalar - Number of elements in an expression - """ - return ops.Count(self, where=self._bind_to_parent_table(where)).to_expr() - - def value_counts(self) -> ir.Table: - """Compute a frequency table. - - Returns - ------- - Table - Frequency table expression - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable({"chars": char} for char in "aabcddd") - >>> t - ┏━━━━━━━━┓ - ┃ chars ┃ - ┡━━━━━━━━┩ - │ string │ - ├────────┤ - │ a │ - │ a │ - │ b │ - │ c │ - │ d │ - │ d │ - │ d │ - └────────┘ - >>> t.chars.value_counts().order_by("chars") - ┏━━━━━━━━┳━━━━━━━━━━━━━┓ - ┃ chars ┃ chars_count ┃ - ┡━━━━━━━━╇━━━━━━━━━━━━━┩ - │ string │ int64 │ - ├────────┼─────────────┤ - │ a │ 2 │ - │ b │ 1 │ - │ c │ 1 │ - │ d │ 3 │ - └────────┴─────────────┘ - """ - name = self.get_name() - metric = _.count().name(f"{name}_count") - return self.as_table().group_by(name).aggregate(metric) - - def first(self, where: ir.BooleanValue | None = None) -> Value: - """Return the first value of a column. - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable({"chars": ["a", "b", "c", "d"]}) - >>> t - ┏━━━━━━━━┓ - ┃ chars ┃ - ┡━━━━━━━━┩ - │ string │ - ├────────┤ - │ a │ - │ b │ - │ c │ - │ d │ - └────────┘ - >>> t.chars.first() - ┌─────┐ - │ 'a' │ - └─────┘ - >>> t.chars.first(where=t.chars != "a") - ┌─────┐ - │ 'b' │ - └─────┘ - """ - return ops.First(self, where=self._bind_to_parent_table(where)).to_expr() - - def last(self, where: ir.BooleanValue | None = None) -> Value: - """Return the last value of a column. - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable({"chars": ["a", "b", "c", "d"]}) - >>> t - ┏━━━━━━━━┓ - ┃ chars ┃ - ┡━━━━━━━━┩ - │ string │ - ├────────┤ - │ a │ - │ b │ - │ c │ - │ d │ - └────────┘ - >>> t.chars.last() - ┌─────┐ - │ 'd' │ - └─────┘ - >>> t.chars.last(where=t.chars != "d") - ┌─────┐ - │ 'c' │ - └─────┘ - """ - return ops.Last(self, where=self._bind_to_parent_table(where)).to_expr() - - def rank(self) -> ir.IntegerColumn: - """Compute position of first element within each equal-value group in sorted order. - - Equivalent to SQL's `RANK()` window function. - - Returns - ------- - Int64Column - The min rank - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable({"values": [1, 2, 1, 2, 3, 2]}) - >>> t.mutate(rank=t.values.rank()) - ┏━━━━━━━━┳━━━━━━━┓ - ┃ values ┃ rank ┃ - ┡━━━━━━━━╇━━━━━━━┩ - │ int64 │ int64 │ - ├────────┼───────┤ - │ 1 │ 0 │ - │ 1 │ 0 │ - │ 2 │ 2 │ - │ 2 │ 2 │ - │ 2 │ 2 │ - │ 3 │ 5 │ - └────────┴───────┘ - """ - return bigframes_vendored.ibis.rank().over(order_by=self) - - def dense_rank(self) -> ir.IntegerColumn: - """Position of first element within each group of equal values. - - Values are returned in sorted order and duplicate values are ignored. - - Equivalent to SQL's `DENSE_RANK()`. - - Returns - ------- - IntegerColumn - The rank - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable({"values": [1, 2, 1, 2, 3, 2]}) - >>> t.mutate(rank=t.values.dense_rank()) - ┏━━━━━━━━┳━━━━━━━┓ - ┃ values ┃ rank ┃ - ┡━━━━━━━━╇━━━━━━━┩ - │ int64 │ int64 │ - ├────────┼───────┤ - │ 1 │ 0 │ - │ 1 │ 0 │ - │ 2 │ 1 │ - │ 2 │ 1 │ - │ 2 │ 1 │ - │ 3 │ 2 │ - └────────┴───────┘ - """ - return bigframes_vendored.ibis.dense_rank().over(order_by=self) - - def percent_rank(self) -> Column: - """Return the relative rank of the values in the column.""" - return bigframes_vendored.ibis.percent_rank().over(order_by=self) - - def cume_dist(self) -> Column: - """Return the cumulative distribution over a window.""" - return bigframes_vendored.ibis.cume_dist().over(order_by=self) - - def ntile(self, buckets: int | ir.IntegerValue) -> ir.IntegerColumn: - """Return the integer number of a partitioning of the column values. - - Parameters - ---------- - buckets - Number of buckets to partition into - """ - return bigframes_vendored.ibis.ntile(buckets).over(order_by=self) - - def cummin(self, *, where=None, group_by=None, order_by=None) -> Column: - """Return the cumulative min over a window.""" - return self.min(where=where).over( - bigframes_vendored.ibis.cumulative_window( - group_by=group_by, order_by=order_by - ) - ) - - def cummax(self, *, where=None, group_by=None, order_by=None) -> Column: - """Return the cumulative max over a window.""" - return self.max(where=where).over( - bigframes_vendored.ibis.cumulative_window( - group_by=group_by, order_by=order_by - ) - ) - - def lag( - self, - offset: int | ir.IntegerValue | None = None, - default: Value | None = None, - ) -> Column: - """Return the row located at `offset` rows **before** the current row. - - Parameters - ---------- - offset - Index of row to select - default - Value used if no row exists at `offset` - """ - return ops.Lag(self, offset, default).to_expr() - - def lead( - self, - offset: int | ir.IntegerValue | None = None, - default: Value | None = None, - ) -> Column: - """Return the row located at `offset` rows **after** the current row. - - Parameters - ---------- - offset - Index of row to select - default - Value used if no row exists at `offset` - """ - return ops.Lead(self, offset, default).to_expr() - - def nth(self, n: int | ir.IntegerValue) -> Column: - """Return the `n`th value (0-indexed) over a window. - - `.nth(0)` is equivalent to `.first()`. Negative will result in `NULL`. - If the value of `n` is greater than the number of rows in the window, - `NULL` will be returned. - - Parameters - ---------- - n - Desired rank value - - Returns - ------- - Column - The nth value over a window - """ - return ops.NthValue(self, n).to_expr() - - -@public -class UnknownValue(Value): - pass - - -@public -class UnknownScalar(Scalar): - pass - - -@public -class UnknownColumn(Column): - pass - - -@public -class NullValue(Value): - pass - - -@public -class NullScalar(Scalar, NullValue, Singleton): - pass - - -@public -class NullColumn(Column, NullValue): - pass - - -@public -def null(type: dt.DataType | str | None = None) -> Value: - """Create a NULL scalar. - - `NULL`s with an unspecified type are castable and comparable to values, - but lack datatype-specific methods: - - >>> import ibis - >>> ibis.options.interactive = True - >>> ibis.null().upper() - Traceback (most recent call last): - ... - AttributeError: 'NullScalar' object has no attribute 'upper' - >>> ibis.null(str).upper() - ┌──────┐ - │ None │ - └──────┘ - >>> ibis.null(str).upper().isnull() - ┌──────────┐ - │ np.True_ │ - └──────────┘ - """ - if type is None: - type = dt.null - return ops.Literal(None, type).to_expr() - - -@public -def literal(value: Any, type: dt.DataType | str | None = None) -> Scalar: - """Create a scalar expression from a Python value. - - ::: {.callout-tip} - ## Use specific functions for arrays, structs and maps - - Ibis supports literal construction of arrays using the following - functions: - - 1. [`ibis.array`](./expression-collections.qmd#ibis.array) - 1. [`ibis.struct`](./expression-collections.qmd#ibis.struct) - 1. [`ibis.map`](./expression-collections.qmd#ibis.map) - - Constructing these types using `literal` will be deprecated in a future - release. - ::: - - Parameters - ---------- - value - A Python value - type - An instance of [`DataType`](./datatypes.qmd#ibis.expr.datatypes.DataType) or a string - indicating the ibis type of `value`. This parameter can be used - in cases where ibis's type inference isn't sufficient for discovering - the type of `value`. - - Returns - ------- - Scalar - An expression representing a literal value - - Examples - -------- - Construct an integer literal - - >>> import ibis - >>> x = ibis.literal(42) - >>> x.type() - Int8(nullable=True) - - Construct a `float64` literal from an `int` - - >>> y = ibis.literal(42, type="double") - >>> y.type() - Float64(nullable=True) - - Ibis checks for invalid types - - >>> ibis.literal("foobar", type="int64") # quartodoc: +EXPECTED_FAILURE - Traceback (most recent call last): - ... - TypeError: Value 'foobar' cannot be safely coerced to int64 - """ - if isinstance(value, Expr): - node = value.op() - if not isinstance(node, ops.Literal): - raise TypeError(f"Ibis expression {value!r} is not a Literal") - if type is None or node.dtype.castable(dt.dtype(type)): - return value - else: - raise TypeError( - f"Ibis literal {value!r} cannot be safely coerced to datatype {type}" - ) - - dtype = dt.infer(value) if type is None else dt.dtype(type) - return ops.Literal(value, dtype=dtype).to_expr() - - -public( - ValueExpr=Value, - ScalarExpr=Scalar, - ColumnExpr=Column, - AnyValue=Value, - AnyScalar=Scalar, - AnyColumn=Column, -) diff --git a/third_party/bigframes_vendored/ibis/expr/types/geospatial.py b/third_party/bigframes_vendored/ibis/expr/types/geospatial.py deleted file mode 100644 index 298e74d6de5..00000000000 --- a/third_party/bigframes_vendored/ibis/expr/types/geospatial.py +++ /dev/null @@ -1,1757 +0,0 @@ -# Contains code from https://github.com/ibis-project/ibis/blob/9.2.0/ibis/expr/types/geospatial.py - -from __future__ import annotations - -from typing import TYPE_CHECKING - -import bigframes_vendored.ibis.expr.operations as ops -from bigframes_vendored.ibis.expr.types.numeric import ( - NumericColumn, - NumericScalar, - NumericValue, -) -from public import public - -if TYPE_CHECKING: - import bigframes_vendored.ibis.expr.types as ir - - -@public -class GeoSpatialValue(NumericValue): - def area(self) -> ir.FloatingValue: - """Compute the area of a geospatial value. - - Returns - ------- - FloatingValue - The area of `self` - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.examples.zones.fetch() - >>> t.geom.area() - ┏━━━━━━━━━━━━━━━┓ - ┃ GeoArea(geom) ┃ - ┡━━━━━━━━━━━━━━━┩ - │ float64 │ - ├───────────────┤ - │ 7.903953e+07 │ - │ 1.439095e+08 │ - │ 3.168508e+07 │ - │ 8.023733e+06 │ - │ 5.041488e+07 │ - │ 4.093479e+07 │ - │ 3.934104e+07 │ - │ 2.682802e+06 │ - │ 3.416422e+07 │ - │ 4.404143e+07 │ - │ … │ - └───────────────┘ - """ - return ops.GeoArea(self).to_expr() - - def as_binary(self) -> ir.BinaryValue: - """Get the geometry as well-known bytes (WKB) without the SRID data. - - Returns - ------- - BinaryValue - Binary value - """ - return ops.GeoAsBinary(self).to_expr() - - def as_ewkt(self) -> ir.StringValue: - """Get the geometry as well-known text (WKT) with the SRID data. - - Returns - ------- - StringValue - String value - """ - return ops.GeoAsEWKT(self).to_expr() - - def as_text(self) -> ir.StringValue: - """Get the geometry as well-known text (WKT) without the SRID data. - - Returns - ------- - StringValue - String value - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.examples.zones.fetch() - >>> t.geom.as_text() - ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ GeoAsText(geom) ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩ - │ string │ - ├──────────────────────────────────────────────────────────────────────────────┤ - │ POLYGON ((933100.9183527103 192536.08569720192, 933091.0114800561 192572.17… │ - │ MULTIPOLYGON (((1033269.2435912937 172126.0078125, 1033439.6426391453 17088… │ - │ POLYGON ((1026308.7695066631 256767.6975403726, 1026495.5934945047 256638.6… │ - │ POLYGON ((992073.4667968601 203714.07598876953, 992068.6669922024 203711.50… │ - │ POLYGON ((935843.3104932606 144283.33585065603, 936046.5648079664 144173.41… │ - │ POLYGON ((966568.7466657609 158679.85468779504, 966615.255504474 158662.292… │ - │ POLYGON ((1010804.2179628164 218919.64069513977, 1011049.1648243815 218914.… │ - │ POLYGON ((1005482.2763733566 221686.46616631746, 1005304.8982993066 221499.… │ - │ POLYGON ((1043803.993348822 216615.9250395149, 1043849.7083857208 216473.16… │ - │ POLYGON ((1044355.0717166215 190734.32089698315, 1044612.1216432452 190156.… │ - │ … │ - └──────────────────────────────────────────────────────────────────────────────┘ - """ - return ops.GeoAsText(self).to_expr() - - def as_ewkb(self) -> ir.BinaryValue: - """Get the geometry as well-known bytes (WKB) with the SRID data. - - Returns - ------- - BinaryValue - WKB value - """ - return ops.GeoAsEWKB(self).to_expr() - - def contains(self, right: GeoSpatialValue) -> ir.BooleanValue: - """Check if the geometry contains the `right`. - - Parameters - ---------- - right - Right geometry - - Returns - ------- - BooleanValue - Whether `self` contains `right` - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> import shapely - >>> t = ibis.examples.zones.fetch() - >>> p = shapely.geometry.Point(935996.821, 191376.75) # centroid for zone 1 - >>> plit = ibis.literal(p, "geometry") - >>> t.geom.contains(plit).name("contains") - ┏━━━━━━━━━━┓ - ┃ contains ┃ - ┡━━━━━━━━━━┩ - │ boolean │ - ├──────────┤ - │ True │ - │ False │ - │ False │ - │ False │ - │ False │ - │ False │ - │ False │ - │ False │ - │ False │ - │ False │ - │ … │ - └──────────┘ - """ - return ops.GeoContains(self, right).to_expr() - - def contains_properly(self, right: GeoSpatialValue) -> ir.BooleanValue: - """Check if the first geometry contains the second one. - - Excludes common border points. - - Parameters - ---------- - right - Right geometry - - Returns - ------- - BooleanValue - Whether self contains right excluding border points. - """ - return ops.GeoContainsProperly(self, right).to_expr() - - def covers(self, right: GeoSpatialValue) -> ir.BooleanValue: - """Check if the first geometry covers the second one. - - Parameters - ---------- - right - Right geometry - - Returns - ------- - BooleanValue - Whether `self` covers `right` - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> import shapely - >>> t = ibis.examples.zones.fetch() - - Polygon area center in zone 1 - - >>> z1_ctr_buff = shapely.geometry.Point(935996.821, 191376.75).buffer(10) - >>> z1_ctr_buff_lit = ibis.literal(z1_ctr_buff, "geometry") - >>> t.geom.covers(z1_ctr_buff_lit).name("covers") - ┏━━━━━━━━━┓ - ┃ covers ┃ - ┡━━━━━━━━━┩ - │ boolean │ - ├─────────┤ - │ True │ - │ False │ - │ False │ - │ False │ - │ False │ - │ False │ - │ False │ - │ False │ - │ False │ - │ False │ - │ … │ - └─────────┘ - """ - return ops.GeoCovers(self, right).to_expr() - - def covered_by(self, right: GeoSpatialValue) -> ir.BooleanValue: - """Check if the first geometry is covered by the second one. - - Parameters - ---------- - right - Right geometry - - Returns - ------- - BooleanValue - Whether `self` is covered by `right` - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> import shapely - >>> t = ibis.examples.zones.fetch() - - Polygon area center in zone 1 - - >>> pol_big = shapely.geometry.Point(935996.821, 191376.75).buffer(10000) - >>> pol_big_lit = ibis.literal(pol_big, "geometry") - >>> t.geom.covered_by(pol_big_lit).name("covered_by") - ┏━━━━━━━━━━━━┓ - ┃ covered_by ┃ - ┡━━━━━━━━━━━━┩ - │ boolean │ - ├────────────┤ - │ True │ - │ False │ - │ False │ - │ False │ - │ False │ - │ False │ - │ False │ - │ False │ - │ False │ - │ False │ - │ … │ - └────────────┘ - >>> pol_small = shapely.geometry.Point(935996.821, 191376.75).buffer(100) - >>> pol_small_lit = ibis.literal(pol_small, "geometry") - >>> t.geom.covered_by(pol_small_lit).name("covered_by") - ┏━━━━━━━━━━━━┓ - ┃ covered_by ┃ - ┡━━━━━━━━━━━━┩ - │ boolean │ - ├────────────┤ - │ False │ - │ False │ - │ False │ - │ False │ - │ False │ - │ False │ - │ False │ - │ False │ - │ False │ - │ False │ - │ … │ - └────────────┘ - """ - return ops.GeoCoveredBy(self, right).to_expr() - - def crosses(self, right: GeoSpatialValue) -> ir.BooleanValue: - """Check if the geometries have at least one, but not all, interior points in common. - - Parameters - ---------- - right - Right geometry - - Returns - ------- - BooleanValue - Whether `self` and `right` have at least one common interior point. - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> import shapely - >>> t = ibis.examples.zones.fetch() - - Line from center of zone 1 to center of zone 2 - - >>> line = shapely.LineString([[935996.821, 191376.75], [1031085.719, 164018.754]]) - >>> line_lit = ibis.literal(line, "geometry") - >>> t.geom.crosses(line_lit).name("crosses") - ┏━━━━━━━━━┓ - ┃ crosses ┃ - ┡━━━━━━━━━┩ - │ boolean │ - ├─────────┤ - │ True │ - │ True │ - │ False │ - │ False │ - │ False │ - │ False │ - │ False │ - │ False │ - │ False │ - │ False │ - │ … │ - └─────────┘ - >>> t.filter(t.geom.crosses(line_lit))[["zone", "LocationID"]] - ┏━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┓ - ┃ zone ┃ LocationID ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━┩ - │ string │ int32 │ - ├────────────────────────┼────────────┤ - │ Newark Airport │ 1 │ - │ Jamaica Bay │ 2 │ - │ Canarsie │ 39 │ - │ East Flatbush/Farragut │ 71 │ - │ Erasmus │ 85 │ - │ Flatbush/Ditmas Park │ 89 │ - │ Flatlands │ 91 │ - │ Green-Wood Cemetery │ 111 │ - │ Sunset Park West │ 228 │ - │ Windsor Terrace │ 257 │ - └────────────────────────┴────────────┘ - """ - return ops.GeoCrosses(self, right).to_expr() - - def d_fully_within( - self, - right: GeoSpatialValue, - distance: ir.FloatingValue, - ) -> ir.BooleanValue: - """Check if `self` is entirely within `distance` from `right`. - - Parameters - ---------- - right - Right geometry - distance - Distance to check - - Returns - ------- - BooleanValue - Whether `self` is within a specified distance from `right`. - """ - return ops.GeoDFullyWithin(self, right, distance).to_expr() - - def disjoint(self, right: GeoSpatialValue) -> ir.BooleanValue: - """Check if the geometries have no points in common. - - Parameters - ---------- - right - Right geometry - - Returns - ------- - BooleanValue - Whether `self` and `right` are disjoint - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> import shapely - >>> t = ibis.examples.zones.fetch() - >>> p = shapely.geometry.Point(935996.821, 191376.75) # zone 1 centroid - >>> plit = ibis.literal(p, "geometry") - >>> t.geom.disjoint(plit).name("disjoint") - ┏━━━━━━━━━━┓ - ┃ disjoint ┃ - ┡━━━━━━━━━━┩ - │ boolean │ - ├──────────┤ - │ False │ - │ True │ - │ True │ - │ True │ - │ True │ - │ True │ - │ True │ - │ True │ - │ True │ - │ True │ - │ … │ - └──────────┘ - """ - return ops.GeoDisjoint(self, right).to_expr() - - def d_within( - self, - right: GeoSpatialValue, - distance: ir.FloatingValue, - ) -> ir.BooleanValue: - """Check if `self` is partially within `distance` from `right`. - - Parameters - ---------- - right - Right geometry - distance - Distance to check - - Returns - ------- - BooleanValue - Whether `self` is partially within `distance` from `right`. - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> import shapely - >>> t = ibis.examples.zones.fetch() - >>> penn_station = shapely.geometry.Point(986345.399, 211974.446) - >>> penn_lit = ibis.literal(penn_station, "geometry") - - Check zones within 1000ft of Penn Station centroid - - >>> t.geom.d_within(penn_lit, 1000).name("d_within_1000") - ┏━━━━━━━━━━━━━━━┓ - ┃ d_within_1000 ┃ - ┡━━━━━━━━━━━━━━━┩ - │ boolean │ - ├───────────────┤ - │ False │ - │ False │ - │ False │ - │ False │ - │ False │ - │ False │ - │ False │ - │ False │ - │ False │ - │ False │ - │ … │ - └───────────────┘ - >>> t.filter(t.geom.d_within(penn_lit, 1000))[["zone"]] - ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ zone ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩ - │ string │ - ├──────────────────────────────┤ - │ East Chelsea │ - │ Midtown South │ - │ Penn Station/Madison Sq West │ - └──────────────────────────────┘ - """ - return ops.GeoDWithin(self, right, distance).to_expr() - - def geo_equals(self, right: GeoSpatialValue) -> ir.BooleanValue: - """Check if the geometries are equal. - - Parameters - ---------- - right - Right geometry - - Returns - ------- - BooleanValue - Whether `self` equals `right` - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.examples.zones.fetch() - >>> t.geom.geo_equals(t.geom) - ┏━━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ GeoEquals(geom, geom) ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━━┩ - │ boolean │ - ├───────────────────────┤ - │ True │ - │ True │ - │ True │ - │ True │ - │ True │ - │ True │ - │ True │ - │ True │ - │ True │ - │ True │ - │ … │ - └───────────────────────┘ - """ - return ops.GeoEquals(self, right).to_expr() - - def geometry_n(self, n: int | ir.IntegerValue) -> GeoSpatialValue: - """Get the 1-based Nth geometry of a multi geometry. - - Parameters - ---------- - n - Nth geometry index - - Returns - ------- - GeoSpatialValue - Geometry value - """ - return ops.GeoGeometryN(self, n).to_expr() - - def geometry_type(self) -> ir.StringValue: - """Get the type of a geometry. - - Returns - ------- - StringValue - String representing the type of `self`. - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.examples.zones.fetch() - >>> t.geom.geometry_type() - ┏━━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ GeoGeometryType(geom) ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━━┩ - │ string │ - ├───────────────────────┤ - │ POLYGON │ - │ MULTIPOLYGON │ - │ POLYGON │ - │ POLYGON │ - │ POLYGON │ - │ POLYGON │ - │ POLYGON │ - │ POLYGON │ - │ POLYGON │ - │ POLYGON │ - │ … │ - └───────────────────────┘ - """ - return ops.GeoGeometryType(self).to_expr() - - def intersects(self, right: GeoSpatialValue) -> ir.BooleanValue: - """Check if the geometries share any points. - - Parameters - ---------- - right - Right geometry - - Returns - ------- - BooleanValue - Whether `self` intersects `right` - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> import shapely - >>> t = ibis.examples.zones.fetch() - >>> p = shapely.geometry.Point(935996.821, 191376.75) # zone 1 centroid - >>> plit = ibis.literal(p, "geometry") - >>> t.geom.intersects(plit).name("intersects") - ┏━━━━━━━━━━━━┓ - ┃ intersects ┃ - ┡━━━━━━━━━━━━┩ - │ boolean │ - ├────────────┤ - │ True │ - │ False │ - │ False │ - │ False │ - │ False │ - │ False │ - │ False │ - │ False │ - │ False │ - │ False │ - │ … │ - └────────────┘ - """ - return ops.GeoIntersects(self, right).to_expr() - - def is_valid(self) -> ir.BooleanValue: - """Check if the geometry is valid. - - Returns - ------- - BooleanValue - Whether `self` is valid - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.examples.zones.fetch() - >>> t.geom.is_valid() - ┏━━━━━━━━━━━━━━━━━━┓ - ┃ GeoIsValid(geom) ┃ - ┡━━━━━━━━━━━━━━━━━━┩ - │ boolean │ - ├──────────────────┤ - │ True │ - │ True │ - │ True │ - │ True │ - │ True │ - │ True │ - │ True │ - │ True │ - │ True │ - │ True │ - │ … │ - └──────────────────┘ - """ - return ops.GeoIsValid(self).to_expr() - - def ordering_equals(self, right: GeoSpatialValue) -> ir.BooleanValue: - """Check if two geometries are equal and have the same point ordering. - - Returns true if the two geometries are equal and the coordinates - are in the same order. - - Parameters - ---------- - right - Right geometry - - Returns - ------- - BooleanValue - Whether points and orderings are equal. - """ - return ops.GeoOrderingEquals(self, right).to_expr() - - def overlaps(self, right: GeoSpatialValue) -> ir.BooleanValue: - """Check if the geometries share space, have the same dimension, and are not completely contained by each other. - - Parameters - ---------- - right - Right geometry - - Returns - ------- - BooleanValue - Overlaps indicator - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> import shapely - >>> t = ibis.examples.zones.fetch() - - Polygon center in an edge point of zone 1 - - >>> p_edge_buffer = shapely.geometry.Point(933100.918, 192536.086).buffer(100) - >>> buff_lit = ibis.literal(p_edge_buffer, "geometry") - >>> t.geom.overlaps(buff_lit).name("overlaps") - ┏━━━━━━━━━━┓ - ┃ overlaps ┃ - ┡━━━━━━━━━━┩ - │ boolean │ - ├──────────┤ - │ True │ - │ False │ - │ False │ - │ False │ - │ False │ - │ False │ - │ False │ - │ False │ - │ False │ - │ False │ - │ … │ - └──────────┘ - """ - return ops.GeoOverlaps(self, right).to_expr() - - def touches(self, right: GeoSpatialValue) -> ir.BooleanValue: - """Check if the geometries have at least one point in common, but do not intersect. - - Parameters - ---------- - right - Right geometry - - Returns - ------- - BooleanValue - Whether self and right are touching - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> import shapely - >>> t = ibis.examples.zones.fetch() - - Edge point of zone 1 - - >>> p_edge = shapely.geometry.Point(933100.9183527103, 192536.08569720192) - >>> p_edge_lit = ibis.literal(p_edge, "geometry") - >>> t.geom.touches(p_edge_lit).name("touches") - ┏━━━━━━━━━┓ - ┃ touches ┃ - ┡━━━━━━━━━┩ - │ boolean │ - ├─────────┤ - │ True │ - │ False │ - │ False │ - │ False │ - │ False │ - │ False │ - │ False │ - │ False │ - │ False │ - │ False │ - │ … │ - └─────────┘ - """ - return ops.GeoTouches(self, right).to_expr() - - def distance(self, right: GeoSpatialValue) -> ir.FloatingValue: - """Compute the distance between two geospatial expressions. - - Parameters - ---------- - right - Right geometry or geography - - Returns - ------- - FloatingValue - Distance between `self` and `right` - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> import shapely - >>> t = ibis.examples.zones.fetch() - - Penn station zone centroid - - >>> penn_station = shapely.geometry.Point(986345.399, 211974.446) - >>> penn_lit = ibis.literal(penn_station, "geometry") - >>> t.geom.distance(penn_lit).name("distance_penn") - ┏━━━━━━━━━━━━━━━┓ - ┃ distance_penn ┃ - ┡━━━━━━━━━━━━━━━┩ - │ float64 │ - ├───────────────┤ - │ 47224.139856 │ - │ 55992.665470 │ - │ 54850.880098 │ - │ 8011.846870 │ - │ 84371.995209 │ - │ 54196.904809 │ - │ 15965.509896 │ - │ 20566.442476 │ - │ 54070.543584 │ - │ 56994.826531 │ - │ … │ - └───────────────┘ - """ - return ops.GeoDistance(self, right).to_expr() - - def length(self) -> ir.FloatingValue: - """Compute the length of a geospatial expression. - - Returns zero for polygons. - - Returns - ------- - FloatingValue - Length of `self` - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> con = ibis.get_backend() - >>> con.load_extension("spatial") - >>> import shapely - >>> line = shapely.LineString([[0, 0], [1, 0], [1, 1]]) - >>> line_lit = ibis.literal(line, type="geometry") - >>> line_lit.length() - ┌─────────────────┐ - │ np.float64(2.0) │ - └─────────────────┘ - >>> t = ibis.examples.zones.fetch() - >>> t.geom.length() - ┏━━━━━━━━━━━━━━━━━┓ - ┃ GeoLength(geom) ┃ - ┡━━━━━━━━━━━━━━━━━┩ - │ float64 │ - ├─────────────────┤ - │ 0.0 │ - │ 0.0 │ - │ 0.0 │ - │ 0.0 │ - │ 0.0 │ - │ 0.0 │ - │ 0.0 │ - │ 0.0 │ - │ 0.0 │ - │ 0.0 │ - │ … │ - └─────────────────┘ - """ - return ops.GeoLength(self).to_expr() - - def perimeter(self) -> ir.FloatingValue: - """Compute the perimeter of a geospatial expression. - - Returns - ------- - FloatingValue - Perimeter of `self` - """ - return ops.GeoPerimeter(self).to_expr() - - def max_distance(self, right: GeoSpatialValue) -> ir.FloatingValue: - """Returns the 2-dimensional max distance between two geometries in projected units. - - If `self` and `right` are the same geometry the function will return - the distance between the two vertices most far from each other in that - geometry. - - Parameters - ---------- - right - Right geometry - - Returns - ------- - FloatingValue - Maximum distance - """ - return ops.GeoMaxDistance(self, right).to_expr() - - def union(self, right: GeoSpatialValue) -> GeoSpatialValue: - """Merge two geometries into a union geometry. - - Returns the pointwise union of the two geometries. - - Parameters - ---------- - right - Right geometry - - Returns - ------- - GeoSpatialValue - Union of geometries - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> import shapely - >>> t = ibis.examples.zones.fetch() - - Penn station zone centroid - - >>> penn_station = shapely.geometry.Point(986345.399, 211974.446) - >>> penn_lit = ibis.literal(penn_station, "geometry") - >>> t.geom.centroid().union(penn_lit).name("union_centroid_penn") - ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ union_centroid_penn ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩ - │ geospatial:geometry │ - ├──────────────────────────────────────────────────────────────┤ - │ │ - │ │ - │ │ - │ │ - │ │ - │ │ - │ │ - │ │ - │ │ - │ │ - │ … │ - └──────────────────────────────────────────────────────────────┘ - """ - return ops.GeoUnion(self, right).to_expr() - - def x(self) -> ir.FloatingValue: - """Return the X coordinate of `self`, or NULL if not available. - - Input must be a point. - - Returns - ------- - FloatingValue - X coordinate of `self` - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.examples.zones.fetch() - >>> t.geom.centroid().x() - ┏━━━━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ GeoX(GeoCentroid(geom)) ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━━━━┩ - │ float64 │ - ├─────────────────────────┤ - │ 9.359968e+05 │ - │ 1.031086e+06 │ - │ 1.026453e+06 │ - │ 9.906340e+05 │ - │ 9.318714e+05 │ - │ 9.643197e+05 │ - │ 1.006497e+06 │ - │ 1.005552e+06 │ - │ 1.043003e+06 │ - │ 1.042224e+06 │ - │ … │ - └─────────────────────────┘ - """ - return ops.GeoX(self).to_expr() - - def y(self) -> ir.FloatingValue: - """Return the Y coordinate of `self`, or NULL if not available. - - Input must be a point. - - Returns - ------- - FloatingValue - Y coordinate of `self` - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.examples.zones.fetch() - >>> t.geom.centroid().y() - ┏━━━━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ GeoY(GeoCentroid(geom)) ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━━━━┩ - │ float64 │ - ├─────────────────────────┤ - │ 191376.749531 │ - │ 164018.754403 │ - │ 254265.478659 │ - │ 202959.782391 │ - │ 140681.351376 │ - │ 157998.935612 │ - │ 216719.218169 │ - │ 222936.087552 │ - │ 212969.849014 │ - │ 186706.496469 │ - │ … │ - └─────────────────────────┘ - """ - return ops.GeoY(self).to_expr() - - def x_min(self) -> ir.FloatingValue: - """Return the X minima of a geometry. - - Returns - ------- - FloatingValue - X minima - """ - return ops.GeoXMin(self).to_expr() - - def x_max(self) -> ir.FloatingValue: - """Return the X maxima of a geometry. - - Returns - ------- - FloatingValue - X maxima - """ - return ops.GeoXMax(self).to_expr() - - def y_min(self) -> ir.FloatingValue: - """Return the Y minima of a geometry. - - Returns - ------- - FloatingValue - Y minima - """ - return ops.GeoYMin(self).to_expr() - - def y_max(self) -> ir.FloatingValue: - """Return the Y maxima of a geometry. - - Returns - ------- - FloatingValue - Y maxima - """ - return ops.GeoYMax(self).to_expr() - - def start_point(self) -> PointValue: - """Return the first point of a `LINESTRING` geometry as a `POINT`. - - Return `NULL` if the input parameter is not a `LINESTRING` - - Returns - ------- - PointValue - Start point - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> con = ibis.get_backend() - >>> con.load_extension("spatial") - >>> import shapely - >>> line = shapely.LineString([[0, 0], [1, 0], [1, 1]]) - >>> line_lit = ibis.literal(line, type="geometry") - >>> line_lit.start_point() - ┌───────────────┐ - │ │ - └───────────────┘ - """ - return ops.GeoStartPoint(self).to_expr() - - def end_point(self) -> PointValue: - """Return the last point of a `LINESTRING` geometry as a `POINT`. - - Return `NULL` if the input parameter is not a `LINESTRING` - - Returns - ------- - PointValue - End point - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> con = ibis.get_backend() - >>> con.load_extension("spatial") - >>> import shapely - >>> line = shapely.LineString([[0, 0], [1, 0], [1, 1]]) - >>> line_lit = ibis.literal(line, type="geometry") - >>> line_lit.end_point() - ┌───────────────┐ - │ │ - └───────────────┘ - """ - return ops.GeoEndPoint(self).to_expr() - - def point_n(self, n: ir.IntegerValue) -> PointValue: - """Return the Nth point in a single linestring in the geometry. - - Negative values are counted backwards from the end of the LineString, - so that -1 is the last point. Returns NULL if there is no linestring in - the geometry. - - Parameters - ---------- - n - Nth point index - - Returns - ------- - PointValue - Nth point in `self` - """ - return ops.GeoPointN(self, n).to_expr() - - def n_points(self) -> ir.IntegerValue: - """Return the number of points in a geometry. Works for all geometries. - - Returns - ------- - IntegerValue - Number of points - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.examples.zones.fetch() - >>> t.geom.n_points() - ┏━━━━━━━━━━━━━━━━━━┓ - ┃ GeoNPoints(geom) ┃ - ┡━━━━━━━━━━━━━━━━━━┩ - │ int64 │ - ├──────────────────┤ - │ 232 │ - │ 2954 │ - │ 121 │ - │ 88 │ - │ 170 │ - │ 277 │ - │ 182 │ - │ 40 │ - │ 189 │ - │ 157 │ - │ … │ - └──────────────────┘ - """ - return ops.GeoNPoints(self).to_expr() - - def n_rings(self) -> ir.IntegerValue: - """Return the number of rings for polygons and multipolygons. - - Outer rings are counted as well. - - Returns - ------- - IntegerValue - Number of rings - """ - return ops.GeoNRings(self).to_expr() - - def srid(self) -> ir.IntegerValue: - """Return the spatial reference identifier for the ST_Geometry. - - Returns - ------- - IntegerValue - SRID - """ - return ops.GeoSRID(self).to_expr() - - def set_srid(self, srid: ir.IntegerValue) -> GeoSpatialValue: - """Set the spatial reference identifier for the `ST_Geometry`. - - Parameters - ---------- - srid - SRID integer value - - Returns - ------- - GeoSpatialValue - `self` with SRID set to `srid` - """ - return ops.GeoSetSRID(self, srid=srid).to_expr() - - def buffer(self, radius: float | ir.FloatingValue) -> GeoSpatialValue: - """Return all points whose distance from this geometry is less than or equal to `radius`. - - Calculations are in the Spatial Reference System of this Geometry. - - Parameters - ---------- - radius - Floating expression - - Returns - ------- - GeoSpatialValue - Geometry expression - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.examples.zones.fetch() - >>> p = t.x_cent.point(t.y_cent) - >>> p.buffer(10) # note buff.area.mean() ~ pi * r^2 - ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ GeoBuffer(GeoPoint(x_cent, y_cent), 10.0) ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩ - │ geospatial:geometry │ - ├──────────────────────────────────────────────────────────────────────────────┤ - │ │ - │ │ - │ │ - │ │ - │ │ - │ │ - │ │ - │ │ - │ │ - │ │ - │ … │ - └──────────────────────────────────────────────────────────────────────────────┘ - """ - return ops.GeoBuffer(self, radius=radius).to_expr() - - def centroid(self) -> PointValue: - """Returns the centroid of the geometry. - - Returns - ------- - PointValue - The centroid - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.examples.zones.fetch() - >>> t.geom.centroid() - ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ GeoCentroid(geom) ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩ - │ point:geometry │ - ├──────────────────────────────────┤ - │ │ - │ │ - │ │ - │ │ - │ │ - │ │ - │ │ - │ │ - │ │ - │ │ - │ … │ - └──────────────────────────────────┘ - """ - return ops.GeoCentroid(self).to_expr() - - def envelope(self) -> ir.PolygonValue: - """Returns a geometry representing the bounding box of `self`. - - Returns - ------- - PolygonValue - A polygon - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.examples.zones.fetch() - >>> t.geom.envelope() - ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ GeoEnvelope(geom) ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩ - │ polygon:geometry │ - ├──────────────────────────────────────────────────────────────────────────────┤ - │ │ - │ │ - │ │ - │ │ - │ │ - │ │ - │ │ - │ │ - │ │ - │ │ - │ … │ - └──────────────────────────────────────────────────────────────────────────────┘ - """ - return ops.GeoEnvelope(self).to_expr() - - def within(self, right: GeoSpatialValue) -> ir.BooleanValue: - """Check if the first geometry is completely inside of the second. - - Parameters - ---------- - right - Right geometry - - Returns - ------- - BooleanValue - Whether `self` is in `right`. - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> import shapely - >>> t = ibis.examples.zones.fetch() - >>> penn_station_buff = shapely.geometry.Point(986345.399, 211974.446).buffer(5000) - >>> penn_lit = ibis.literal(penn_station_buff, "geometry") - >>> t.filter(t.geom.within(penn_lit))["zone"] - ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ zone ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩ - │ string │ - ├──────────────────────────────┤ - │ East Chelsea │ - │ Flatiron │ - │ Garment District │ - │ Midtown South │ - │ Penn Station/Madison Sq West │ - └──────────────────────────────┘ - """ - return ops.GeoWithin(self, right).to_expr() - - def azimuth(self, right: GeoSpatialValue) -> ir.FloatingValue: - """Return the angle in radians from the horizontal of the vector defined by the inputs. - - Angle is computed clockwise from down-to-up on the clock: 12=0; 3=PI/2; 6=PI; 9=3PI/2. - - Parameters - ---------- - right - Right geometry - - Returns - ------- - FloatingValue - azimuth - """ - return ops.GeoAzimuth(self, right).to_expr() - - def intersection(self, right: GeoSpatialValue) -> GeoSpatialValue: - """Return the intersection of two geometries. - - Parameters - ---------- - right - Right geometry - - Returns - ------- - GeoSpatialValue - Intersection of `self` and `right` - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.examples.zones.fetch() - >>> t.geom.intersection(t.geom.centroid()) - ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ GeoIntersection(geom, GeoCentroid(geom)) ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩ - │ geospatial:geometry │ - ├──────────────────────────────────────────┤ - │ │ - │ │ - │ │ - │ │ - │ │ - │ │ - │ │ - │ │ - │ │ - │ │ - │ … │ - └──────────────────────────────────────────┘ - """ - return ops.GeoIntersection(self, right).to_expr() - - def difference(self, right: GeoSpatialValue) -> GeoSpatialValue: - """Return the difference of two geometries. - - Parameters - ---------- - right - Right geometry - - Returns - ------- - GeoSpatialValue - Difference of `self` and `right` - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.examples.zones.fetch() - >>> t.geom.difference(t.geom.centroid()) - ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ GeoDifference(geom, GeoCentroid(geom)) ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩ - │ geospatial:geometry │ - ├──────────────────────────────────────────────────────────────────────────────┤ - │ │ - │ │ - │ │ - │ │ - │ │ - │ │ - │ │ - │ │ - │ │ - │ │ - │ … │ - └──────────────────────────────────────────────────────────────────────────────┘ - """ - return ops.GeoDifference(self, right).to_expr() - - def simplify( - self, - tolerance: ir.FloatingValue, - preserve_collapsed: ir.BooleanValue, - ) -> GeoSpatialValue: - """Simplify a given geometry. - - Parameters - ---------- - tolerance - Tolerance - preserve_collapsed - Whether to preserve collapsed geometries - - Returns - ------- - GeoSpatialValue - Simplified geometry - """ - return ops.GeoSimplify(self, tolerance, preserve_collapsed).to_expr() - - def transform(self, srid: ir.IntegerValue) -> GeoSpatialValue: - """Transform a geometry into a new SRID. - - Parameters - ---------- - srid - Integer expression - - Returns - ------- - GeoSpatialValue - Transformed geometry - """ - return ops.GeoTransform(self, srid).to_expr() - - def convert( - self, source: ir.StringValue, target: ir.StringValue | ir.IntegerValue - ) -> GeoSpatialValue: - """Transform a geometry into a new SRID (CRS). - - Coordinates are assumed to always be XY (Longitude-Latitude). - - Parameters - ---------- - source - CRS/SRID of input geometry - target - Target CRS/SRID - - Returns - ------- - GeoSpatialValue - Transformed geometry - - See Also - -------- - [`flip_coordinates`](#ibis.expr.types.geospatial.GeoSpatialValue.flip_coordinates) - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.examples.zones.fetch() - - Data is originally in epsg:2263 - - >>> t.geom.convert("EPSG:2263", "EPSG:4326") - ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ GeoConvert(geom) ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩ - │ geospatial:geometry │ - ├──────────────────────────────────────────────────────────────────────────────┤ - │ │ - │ │ - │ │ - │ │ - │ │ - │ │ - │ │ - │ │ - │ │ - │ │ - │ … │ - └──────────────────────────────────────────────────────────────────────────────┘ - """ - return ops.GeoConvert(self, source, target).to_expr() - - def line_locate_point(self, right: PointValue) -> ir.FloatingValue: - """Locate the distance a point falls along the length of a line. - - Returns a float between zero and one representing the location of the - closest point on the linestring to the given point, as a fraction of - the total 2d line length. - - Parameters - ---------- - right - Point geometry - - Returns - ------- - FloatingValue - Fraction of the total line length - """ - return ops.GeoLineLocatePoint(self, right).to_expr() - - def line_substring( - self, start: ir.FloatingValue, end: ir.FloatingValue - ) -> ir.LineStringValue: - """Clip a substring from a LineString. - - Returns a linestring that is a substring of the input one, starting - and ending at the given fractions of the total 2d length. The second - and third arguments are floating point values between zero and one. - This only works with linestrings. - - Parameters - ---------- - start - Start value - end - End value - - Returns - ------- - LineStringValue - Clipped linestring - """ - return ops.GeoLineSubstring(self, start, end).to_expr() - - def line_merge(self) -> ir.LineStringValue: - """Merge a `MultiLineString` into a `LineString`. - - Returns a (set of) LineString(s) formed by sewing together the - constituent line work of a MultiLineString. If a geometry other than - a LineString or MultiLineString is given, this will return an empty - geometry collection. - - Returns - ------- - GeoSpatialValue - Merged linestrings - """ - return ops.GeoLineMerge(self).to_expr() - - def flip_coordinates(self) -> GeoSpatialValue: - """Flip coordinates of a geometry so that x = y and y = x. - - Returns - ------- - GeoSpatialValue - New geometry with flipped coordinates - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.examples.zones.fetch() - >>> t.geom.centroid().flip_coordinates() - ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ GeoFlipCoordinates(GeoCentroid(geom)) ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩ - │ geospatial:geometry │ - ├───────────────────────────────────────┤ - │ │ - │ │ - │ │ - │ │ - │ │ - │ │ - │ │ - │ │ - │ │ - │ │ - │ … │ - └───────────────────────────────────────┘ - - """ - return ops.GeoFlipCoordinates(self).to_expr() - - -@public -class GeoSpatialScalar(NumericScalar, GeoSpatialValue): - pass - - -@public -class GeoSpatialColumn(NumericColumn, GeoSpatialValue): - def unary_union( - self, where: bool | ir.BooleanValue | None = None - ) -> ir.GeoSpatialScalar: - """Aggregate a set of geometries into a union. - - This corresponds to the aggregate version of the union. - We give it a different name (following the corresponding method - in GeoPandas) to avoid name conflicts with the non-aggregate version. - - Parameters - ---------- - where - Filter expression - - Returns - ------- - GeoSpatialScalar - Union of geometries - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.examples.zones.fetch() - >>> t.geom.unary_union() - ┌──────────────────────────────────────────────────────────────────────────────┐ - │ │ - └──────────────────────────────────────────────────────────────────────────────┘ - - """ - return ops.GeoUnaryUnion(self, where=where).to_expr() - - -@public -class PointValue(GeoSpatialValue): - pass - - -@public -class PointScalar(GeoSpatialScalar, PointValue): - pass - - -@public -class PointColumn(GeoSpatialColumn, PointValue): - pass - - -@public -class LineStringValue(GeoSpatialValue): - pass - - -@public -class LineStringScalar(GeoSpatialScalar, LineStringValue): - pass - - -@public -class LineStringColumn(GeoSpatialColumn, LineStringValue): - pass - - -@public -class PolygonValue(GeoSpatialValue): - pass - - -@public -class PolygonScalar(GeoSpatialScalar, PolygonValue): - pass - - -@public -class PolygonColumn(GeoSpatialColumn, PolygonValue): - pass - - -@public -class MultiLineStringValue(GeoSpatialValue): - pass - - -@public -class MultiLineStringScalar(GeoSpatialScalar, MultiLineStringValue): - pass - - -@public -class MultiLineStringColumn(GeoSpatialColumn, MultiLineStringValue): - pass - - -@public -class MultiPointValue(GeoSpatialValue): - pass - - -@public -class MultiPointScalar(GeoSpatialScalar, MultiPointValue): - pass - - -@public -class MultiPointColumn(GeoSpatialColumn, MultiPointValue): - pass - - -@public -class MultiPolygonValue(GeoSpatialValue): - pass - - -@public -class MultiPolygonScalar(GeoSpatialScalar, MultiPolygonValue): - pass - - -@public -class MultiPolygonColumn(GeoSpatialColumn, MultiPolygonValue): - pass diff --git a/third_party/bigframes_vendored/ibis/expr/types/groupby.py b/third_party/bigframes_vendored/ibis/expr/types/groupby.py deleted file mode 100644 index 369eb8a0a82..00000000000 --- a/third_party/bigframes_vendored/ibis/expr/types/groupby.py +++ /dev/null @@ -1,296 +0,0 @@ -# Contains code from https://github.com/ibis-project/ibis/blob/9.2.0/ibis/expr/types/groupby.py - -# Copyright 2014 Cloudera Inc. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -"""User API for grouping operations.""" - -from __future__ import annotations - -from typing import TYPE_CHECKING, Annotated - -import bigframes_vendored.ibis -import bigframes_vendored.ibis.expr.datatypes as dt -import bigframes_vendored.ibis.expr.operations as ops -import bigframes_vendored.ibis.expr.types as ir -from bigframes_vendored.ibis.common.grounds import Concrete -from bigframes_vendored.ibis.common.patterns import Length # noqa: TCH001 -from bigframes_vendored.ibis.common.typing import VarTuple # noqa: TCH001 -from bigframes_vendored.ibis.expr.rewrites import rewrite_window_input -from public import public - -if TYPE_CHECKING: - from collections.abc import Sequence - - -@public -class GroupedTable(Concrete): - """An intermediate table expression to hold grouping information.""" - - table: ops.Relation - groupings: Annotated[VarTuple[ops.Value], Length(at_least=1)] - orderings: VarTuple[ops.SortKey] = () - havings: VarTuple[ops.Value[dt.Boolean]] = () - - def __getitem__(self, args): - # Shortcut for projection with window functions - return self.select(*args) - - def __getattr__(self, attr): - try: - field = getattr(self.table.to_expr(), attr) - except AttributeError as e: - raise AttributeError(f"GroupedTable has no attribute {attr}") from e - - if isinstance(field, ir.NumericValue): - return GroupedNumbers(field, self) - else: - return GroupedArray(field, self) - - def aggregate(self, *metrics, **kwds) -> ir.Table: - """Compute aggregates over a group by.""" - metrics = self.table.to_expr().bind(*metrics, **kwds) - return self.table.to_expr().aggregate( - metrics, by=self.groupings, having=self.havings - ) - - agg = aggregate - - def having(self, *predicates: ir.BooleanScalar) -> GroupedTable: - """Add a post-aggregation result filter `expr`. - - ::: {.callout-warning} - ## Expressions like `x is None` return `bool` and **will not** generate a SQL comparison to `NULL` - ::: - - Parameters - ---------- - predicates - Expressions that filters based on an aggregate value. - - Returns - ------- - GroupedTable - A grouped table expression - """ - table = self.table.to_expr() - havings = table.bind(*predicates) - return self.copy(havings=self.havings + havings) - - def order_by(self, *by: ir.Value) -> GroupedTable: - """Sort a grouped table expression by `expr`. - - Notes - ----- - This API call is ignored in aggregations. - - Parameters - ---------- - by - Expressions to order the results by - - Returns - ------- - GroupedTable - A sorted grouped GroupedTable - """ - table = self.table.to_expr() - orderings = table.bind(*by) - return self.copy(orderings=self.orderings + orderings) - - def mutate( - self, *exprs: ir.Value | Sequence[ir.Value], **kwexprs: ir.Value - ) -> ir.Table: - """Return a table projection with window functions applied. - - Any arguments can be functions. - - Parameters - ---------- - exprs - List of expressions - kwexprs - Expressions - - Examples - -------- - >>> import ibis - >>> import ibis.selectors as s - >>> ibis.options.interactive = True - >>> t = ibis.examples.penguins.fetch() - >>> t - ┏━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━┓ - ┃ species ┃ island ┃ bill_length_mm ┃ bill_depth_mm ┃ flipper_length_mm ┃ … ┃ - ┡━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━┩ - │ string │ string │ float64 │ float64 │ int64 │ … │ - ├─────────┼───────────┼────────────────┼───────────────┼───────────────────┼───┤ - │ Adelie │ Torgersen │ 39.1 │ 18.7 │ 181 │ … │ - │ Adelie │ Torgersen │ 39.5 │ 17.4 │ 186 │ … │ - │ Adelie │ Torgersen │ 40.3 │ 18.0 │ 195 │ … │ - │ Adelie │ Torgersen │ NULL │ NULL │ NULL │ … │ - │ Adelie │ Torgersen │ 36.7 │ 19.3 │ 193 │ … │ - │ Adelie │ Torgersen │ 39.3 │ 20.6 │ 190 │ … │ - │ Adelie │ Torgersen │ 38.9 │ 17.8 │ 181 │ … │ - │ Adelie │ Torgersen │ 39.2 │ 19.6 │ 195 │ … │ - │ Adelie │ Torgersen │ 34.1 │ 18.1 │ 193 │ … │ - │ Adelie │ Torgersen │ 42.0 │ 20.2 │ 190 │ … │ - │ … │ … │ … │ … │ … │ … │ - └─────────┴───────────┴────────────────┴───────────────┴───────────────────┴───┘ - >>> ( - ... t.select("species", "bill_length_mm") - ... .group_by("species") - ... .mutate(centered_bill_len=ibis._.bill_length_mm - ibis._.bill_length_mm.mean()) - ... .order_by(s.all()) - ... ) - ┏━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓ - ┃ species ┃ bill_length_mm ┃ centered_bill_len ┃ - ┡━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩ - │ string │ float64 │ float64 │ - ├─────────┼────────────────┼───────────────────┤ - │ Adelie │ 32.1 │ -6.691391 │ - │ Adelie │ 33.1 │ -5.691391 │ - │ Adelie │ 33.5 │ -5.291391 │ - │ Adelie │ 34.0 │ -4.791391 │ - │ Adelie │ 34.1 │ -4.691391 │ - │ Adelie │ 34.4 │ -4.391391 │ - │ Adelie │ 34.5 │ -4.291391 │ - │ Adelie │ 34.6 │ -4.191391 │ - │ Adelie │ 34.6 │ -4.191391 │ - │ Adelie │ 35.0 │ -3.791391 │ - │ … │ … │ … │ - └─────────┴────────────────┴───────────────────┘ - - Returns - ------- - Table - A table expression with window functions applied - """ - exprs = self._selectables(*exprs, **kwexprs) - return self.table.to_expr().mutate(exprs) - - def select(self, *exprs, **kwexprs) -> ir.Table: - """Project new columns out of the grouped table. - - See Also - -------- - [`GroupedTable.mutate`](#ibis.expr.types.groupby.GroupedTable.mutate) - """ - exprs = self._selectables(*exprs, **kwexprs) - return self.table.to_expr().select(exprs) - - def _selectables(self, *exprs, **kwexprs): - """Project new columns out of the grouped table. - - See Also - -------- - [`GroupedTable.mutate`](#ibis.expr.types.groupby.GroupedTable.mutate) - """ - table = self.table.to_expr() - values = table.bind(*exprs, **kwexprs) - window = bigframes_vendored.ibis.window( - group_by=self.groupings, order_by=self.orderings - ) - return [rewrite_window_input(expr.op(), window).to_expr() for expr in values] - - projection = select - - def over( - self, - window=None, - *, - rows=None, - range=None, - group_by=None, - order_by=None, - ) -> GroupedTable: - """Apply a window over the input expressions. - - Parameters - ---------- - window - Window to add to the input - rows - Whether to use the `ROWS` window clause - range - Whether to use the `RANGE` window clause - group_by - Grouping key - order_by - Ordering key - - Returns - ------- - GroupedTable - A new grouped table expression - """ - if window is None: - window = bigframes_vendored.ibis.window( - rows=rows, - range=range, - group_by=group_by, - order_by=order_by, - ) - - return self.__class__( - self.table, - self.by, - having=self._having, - order_by=self._order_by, - window=window, - ) - - def count(self) -> ir.Table: - """Computing the number of rows per group. - - Returns - ------- - Table - The aggregated table - """ - table = self.table.to_expr() - return table.aggregate(table.count(), by=self.groupings, having=self.havings) - - size = count - - -def _group_agg_dispatch(name): - def wrapper(self, *args, **kwargs): - f = getattr(self.arr, name) - metric = f(*args, **kwargs) - alias = f"{name}({self.arr.get_name()})" - return self.parent.aggregate(metric.name(alias)) - - wrapper.__name__ = name - return wrapper - - -@public -class GroupedArray: - def __init__(self, arr, parent): - self.arr = arr - self.parent = parent - - count = _group_agg_dispatch("count") - size = count - min = _group_agg_dispatch("min") - max = _group_agg_dispatch("max") - approx_nunique = _group_agg_dispatch("approx_nunique") - approx_median = _group_agg_dispatch("approx_median") - group_concat = _group_agg_dispatch("group_concat") - - -@public -class GroupedNumbers(GroupedArray): - mean = _group_agg_dispatch("mean") - sum = _group_agg_dispatch("sum") diff --git a/third_party/bigframes_vendored/ibis/expr/types/joins.py b/third_party/bigframes_vendored/ibis/expr/types/joins.py deleted file mode 100644 index 62c4a334fb5..00000000000 --- a/third_party/bigframes_vendored/ibis/expr/types/joins.py +++ /dev/null @@ -1,440 +0,0 @@ -# Contains code from https://github.com/ibis-project/ibis/blob/9.2.0/ibis/expr/types/joins.py - -from __future__ import annotations - -import functools -from typing import TYPE_CHECKING, Any - -import bigframes_vendored.ibis -import bigframes_vendored.ibis.expr.operations as ops -from bigframes_vendored.ibis import util -from bigframes_vendored.ibis.common.deferred import Deferred -from bigframes_vendored.ibis.common.egraph import DisjointSet -from bigframes_vendored.ibis.common.exceptions import ( - ExpressionError, - IbisInputError, - InputTypeError, - IntegrityError, -) -from bigframes_vendored.ibis.expr.rewrites import flatten_predicates, peel_join_field -from bigframes_vendored.ibis.expr.types.generic import Value -from bigframes_vendored.ibis.expr.types.relations import ( - DerefMap, - Table, - bind, - unwrap_aliases, -) -from public import public - -if TYPE_CHECKING: - from collections.abc import Sequence - - from bigframes_vendored.ibis.expr.operations.relations import JoinKind - - -def coerce_to_table(data): - try: - import pandas as pd - except ImportError: - pass - else: - if isinstance(data, pd.DataFrame): - return bigframes_vendored.ibis.memtable(data) - - try: - import pyarrow as pa - except ImportError: - pass - else: - if isinstance(data, pa.Table): - return bigframes_vendored.ibis.memtable(data) - - if not isinstance(data, Table): - raise TypeError(f"right operand must be a Table, got {type(data).__name__}") - return data - - -def disambiguate_fields( - how, - predicates, - equalities, - left_fields, - right_fields, - left_template, - right_template, -): - """Resolve name collisions between the left and right tables.""" - collisions = set() - left_template = left_template or "{name}" - right_template = right_template or "{name}" - - if how == "inner" and util.all_of(predicates, ops.Equals): - # for inner joins composed exclusively of equality predicates, we can - # avoid renaming columns with colliding names if their values are - # guaranteed to be equal due to the predicate - equalities = equalities.copy() - for pred in predicates: - if isinstance(pred.left, ops.Field) and isinstance(pred.right, ops.Field): - # disjoint sets are used to track the equality groups - equalities.add(pred.left) - equalities.add(pred.right) - equalities.union(pred.left, pred.right) - - if how in ("semi", "anti"): - # discard the right fields per left semi and left anty join semantics - return left_fields, collisions, equalities - - fields = {} - for name, field in left_fields.items(): - if name in right_fields: - # there is an overlap between this field and a field from the right - try: - # check if the fields are equal due to equality predicates - are_equal = equalities.connected(field, right_fields[name]) - except KeyError: - are_equal = False - if not are_equal: - # there is a name collision and the fields are not equal, so - # rename the field from the left according to the provided - # template (which is the name itself by default) - name = left_template.format(name=name) - - fields[name] = field - - for name, field in right_fields.items(): - if name in left_fields: - # there is an overlap between this field and a field from the left - try: - # check if the fields are equal due to equality predicates - are_equal = equalities.connected(field, left_fields[name]) - except KeyError: - are_equal = False - - if are_equal: - # even though there is a name collision, the fields are equal - # due to equality predicates, so we can safely discard the - # field from the right - continue - else: - # there is a name collision and the fields are not equal, so - # rename the field from the right according to the provided - # template - name = right_template.format(name=name) - - if name in fields: - # we can still have collisions after multiple joins, or a wrongly - # chosen template, so we need to track the collisions - collisions.add(name) - else: - # the field name does not collide with any field from the left - # and not occupied by any field from the right, so add it to the - # fields mapping - fields[name] = field - - return fields, collisions, equalities - - -def prepare_predicates( - chain: ops.JoinChain, - right: ops.Relation, - predicates: Sequence[Any], - comparison: type[ops.Comparison] = ops.Equals, -): - """Bind and dereference predicates to the left and right tables. - - The responsibility of this function is twofold: - 1. Convert the various input values to valid predicates, including binding. - 2. Dereference the predicates one of the ops.JoinTable(s) in the join chain - or the new JoinTable wrapping the right table. JoinTable(s) are used to - ensure that all join participants are unique, even if the same table is - joined multiple times. - - Since join predicates can be ambiguous sometimes, we do the two steps above - in the same time so that we have more contextual information to resolve - ambiguities. - - Possible inputs for the predicates: - 1. A python boolean literal, which is converted to a literal expression - 2. A boolean `Value` expression, which gets flattened and dereferenced. - If there are comparison expressions where both sides depend on the same - relation, then the left side is dereferenced to one of the join tables - already part of the join chain, while the right side is dereferenced to - the new join table wrapping the right table. - 3. A `Deferred` expression, which gets resolved on the left table and then - the same path is followed as for `Value` expressions. - 4. A pair of expression-like objects, which are getting bound to the left - and right tables respectively using the robust `bind` function handling - several cases, including `Deferred` expressions, `Selector`s, literals, - etc. Then the left are dereferenced to the join chain whereas the right - to the new join table wrapping the right table. - - Parameters - ---------- - chain - The join chain - right - The right table - predicates - Predicates to bind and dereference, see the possible values above - comparison - The comparison operation to construct if the input is a pair of - expression-like objects - """ - reverse = { - ops.Field(chain, k): v - for k, v in chain.values.items() - if isinstance(v, ops.Field) - } - deref_right = DerefMap.from_targets(right) - deref_left = DerefMap.from_targets(chain.tables, extra=reverse) - deref_both = DerefMap.from_targets([*chain.tables, right], extra=reverse) - - left, right = chain.to_expr(), right.to_expr() - for pred in util.promote_list(predicates): - if isinstance(pred, (Value, Deferred, bool)): - for bound in bind(left, pred): - yield deref_both.dereference(bound.op()) - else: - if isinstance(pred, tuple): - if len(pred) != 2: - raise ExpressionError("Join key tuple must be length 2") - lk, rk = pred - else: - lk = rk = pred - - for lhs, rhs in zip(bind(left, lk), bind(right, rk)): - lhs = deref_left.dereference(lhs.op()) - rhs = deref_right.dereference(rhs.op()) - yield comparison(lhs, rhs) - - -def finished(method): - """Decorator to ensure the join chain is finished before calling a method.""" - - @functools.wraps(method) - def wrapper(self, *args, **kwargs): - return method(self._finish(), *args, **kwargs) - - return wrapper - - -@public -class Join(Table): - __slots__ = ("_collisions", "_equalities") - - def __init__(self, arg, collisions=(), equalities=()): - assert isinstance(arg, ops.Node) - if not isinstance(arg, ops.JoinChain): - # coerce the input node to a join chain operation - arg = ops.JoinReference(arg, identifier=0) - arg = ops.JoinChain(arg, rest=(), values=arg.fields) - super().__init__(arg) - # the collisions and equalities are used to track the name collisions - # and the equality groups join fields based on equality predicates; - # these must be tracked in the join expression because the join chain - # operation doesn't hold any information about `lname` and `rname` - # parameters passed to the join methods and used to disambiguate field - # names; the collisions are used to raise an error if there are any - # name collisions after the join chain is finished - object.__setattr__(self, "_collisions", collisions or set()) - object.__setattr__(self, "_equalities", equalities or DisjointSet()) - - def _finish(self) -> Table: - """Construct a valid table expression from this join expression.""" - if self._collisions: - raise IntegrityError(f"Name collisions: {self._collisions}") - return Table(self.op()) - - @functools.wraps(Table.join) - def join( - self, - right, - predicates: Any, - how: JoinKind = "inner", - *, - lname: str = "", - rname: str = "{name}_right", - ): - right = coerce_to_table(right) - - if how == "left_semi": - how = "semi" - elif how == "asof": - raise IbisInputError("use table.asof_join(...) instead") - - chain = self.op() - right = right.op() - if not isinstance(right, ops.Reference): - right = ops.JoinReference(right, identifier=chain.length) - - # bind and dereference the predicates - preds = prepare_predicates(chain, right, predicates) - preds = flatten_predicates(preds) - if not preds and how not in {"cross", "positional"}: - # if there are no predicates, default to every row matching unless - # the join is a cross join, because a cross join already has this - # behavior - preds.append(ops.Literal(True, dtype="bool")) - - # calculate the fields based in lname and rname, this should be a best - # effort to avoid collisions, but does not raise if there are any - # if no disambiaution happens using a final .select() call, then - # the finish() method will raise due to the name collisions - values, collisions, equalities = disambiguate_fields( - how=how, - predicates=preds, - equalities=self._equalities, - left_fields=chain.values, - right_fields=right.fields, - left_template=lname, - right_template=rname, - ) - - # construct a new join link and add it to the join chain - link = ops.JoinLink(how, table=right, predicates=preds) - chain = chain.copy(rest=chain.rest + (link,), values=values) - - # return with a new JoinExpr wrapping the new join chain - return self.__class__(chain, collisions=collisions, equalities=equalities) - - @functools.wraps(Table.asof_join) - def asof_join( - self: Table, - right: Table, - on, - predicates=(), - tolerance=None, - *, - lname: str = "", - rname: str = "{name}_right", - ): - predicates = util.promote_list(predicates) - if tolerance is not None: - # `tolerance` parameter is mimicking the pandas API, but we express - # it at the expression level by a sequence of operations: - # 1. perform the `asof` join with the `on` an `predicates` parameters - # where the `on` parameter is an inequality predicate - # 2. filter the asof join result using the `tolerance` parameter and - # the `on` parameter - # 3. perform a left join between the original left table and the - # filtered asof join result using the `on` parameter but this - # time as an equality predicate - if isinstance(on, str): - # self is always a JoinChain so reference one of the join tables - left_on = self.op().values[on].to_expr() - right_on = right[on] - on = left_on >= right_on - elif isinstance(on, Value): - node = on.op() - if not isinstance(node, ops.Binary): - raise InputTypeError("`on` must be a comparison expression") - left_on = node.left.to_expr() - right_on = node.right.to_expr() - else: - raise TypeError("`on` must be a string or a ValueExpr") - - joined = self.asof_join( - right, on=on, predicates=predicates, lname=lname, rname=rname - ) - filtered = joined.filter( - left_on <= right_on + tolerance, left_on >= right_on - tolerance - ) - right_on = right_on.op().replace({right.op(): filtered.op()}).to_expr() - - # without joining twice the table would not contain the rows from - # the left table that do not match any row from the right table - # given the tolerance filter - result = self.left_join( - filtered, predicates=[left_on == right_on] + predicates - ) - values = {**filtered.op().values, **self.op().values} - - return result.select(**values) - - chain = self.op() - right = right.op() - if not isinstance(right, ops.Reference): - right = ops.JoinReference(right, identifier=chain.length) - - # TODO(kszucs): add extra validation for `on` with clear error messages - (on,) = prepare_predicates(chain, right, [on], comparison=ops.GreaterEqual) - preds = prepare_predicates(chain, right, predicates, comparison=ops.Equals) - preds = [on, *preds] - - values, collisions, equalities = disambiguate_fields( - how="asof", - predicates=preds, - equalities=self._equalities, - left_fields=chain.values, - right_fields=right.fields, - left_template=lname, - right_template=rname, - ) - - # construct a new join link and add it to the join chain - link = ops.JoinLink("asof", table=right, predicates=preds) - chain = chain.copy(rest=chain.rest + (link,), values=values) - - # return with a new JoinExpr wrapping the new join chain - return self.__class__(chain, collisions=collisions, equalities=equalities) - - @functools.wraps(Table.cross_join) - def cross_join( - self: Table, - right: Table, - *rest: Table, - lname: str = "", - rname: str = "{name}_right", - ): - left = self.join(right, how="cross", predicates=(), lname=lname, rname=rname) - for table in rest: - left = left.join( - table, how="cross", predicates=(), lname=lname, rname=rname - ) - return left - - @functools.wraps(Table.select) - def select(self, *args, **kwargs): - chain = self.op() - values = self.bind(*args, **kwargs) - values = unwrap_aliases(values) - - links = [link.table for link in chain.rest if link.how not in ("semi", "anti")] - derefmap = DerefMap.from_targets([chain.first, *links]) - - # if there are values referencing fields from the join chain constructed - # so far, we need to replace them the fields from one of the join links - values = { - k: v.replace(peel_join_field, filter=ops.Value) for k, v in values.items() - } - values = {k: derefmap.dereference(v) for k, v in values.items()} - - node = chain.copy(values=values) - return Table(node) - - aggregate = finished(Table.aggregate) - alias = finished(Table.alias) - cast = finished(Table.cast) - compile = finished(Table.compile) - count = finished(Table.count) - difference = finished(Table.difference) - distinct = finished(Table.distinct) - drop = finished(Table.drop) - dropna = finished(Table.dropna) - execute = finished(Table.execute) - fill_null = finished(Table.fill_null) - filter = finished(Table.filter) - group_by = finished(Table.group_by) - intersect = finished(Table.intersect) - limit = finished(Table.limit) - mutate = finished(Table.mutate) - nunique = finished(Table.nunique) - order_by = finished(Table.order_by) - sample = finished(Table.sample) - sql = finished(Table.sql) - unbind = finished(Table.unbind) - union = finished(Table.union) - view = finished(Table.view) - - -public(JoinExpr=Join) diff --git a/third_party/bigframes_vendored/ibis/expr/types/json.py b/third_party/bigframes_vendored/ibis/expr/types/json.py deleted file mode 100644 index 51d1642de00..00000000000 --- a/third_party/bigframes_vendored/ibis/expr/types/json.py +++ /dev/null @@ -1,492 +0,0 @@ -# Contains code from https://github.com/ibis-project/ibis/blob/9.2.0/ibis/expr/types/json.py - -"""JSON value operations.""" - -from __future__ import annotations - -from typing import TYPE_CHECKING - -import bigframes_vendored.ibis.common.exceptions as exc -import bigframes_vendored.ibis.expr.datatypes as dt -import bigframes_vendored.ibis.expr.operations as ops -from bigframes_vendored.ibis.expr.types import Column, Scalar, Value -from public import public - -if TYPE_CHECKING: - import bigframes_vendored.ibis.expr.types as ir - - -@public -class JSONValue(Value): - def __getitem__( - self, key: str | int | ir.StringValue | ir.IntegerValue - ) -> JSONValue: - """Access an JSON object's value or JSON array's element at `key`. - - Parameters - ---------- - key - Object field name or integer array index - - Returns - ------- - JSONValue - Element located at `key` - - Examples - -------- - Construct a table with a JSON column - - >>> import json, ibis - >>> ibis.options.interactive = True - >>> rows = [{"js": json.dumps({"a": [i, 1]})} for i in range(2)] - >>> t = ibis.memtable(rows, schema=ibis.schema(dict(js="json"))) - >>> t - ┏━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ js ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━┩ - │ json │ - ├──────────────────────┤ - │ {'a': [...]} │ - │ {'a': [...]} │ - └──────────────────────┘ - - Extract the `"a"` field - - >>> t.js["a"] - ┏━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ JSONGetItem(js, 'a') ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━┩ - │ json │ - ├──────────────────────┤ - │ [0, 1] │ - │ [1, 1] │ - └──────────────────────┘ - - Extract the first element of the JSON array at `"a"` - - >>> t.js["a"][0] - ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ JSONGetItem(JSONGetItem(js, 'a'), 0) ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩ - │ json │ - ├──────────────────────────────────────┤ - │ 0 │ - │ 1 │ - └──────────────────────────────────────┘ - - Extract a non-existent field - - >>> t.js["a"]["foo"] - ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ JSONGetItem(JSONGetItem(js, 'a'), 'foo') ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩ - │ json │ - ├──────────────────────────────────────────┤ - │ NULL │ - │ NULL │ - └──────────────────────────────────────────┘ - - Try to extract an array element, returns `NULL` - - >>> t.js[20] - ┏━━━━━━━━━━━━━━━━━━━━━┓ - ┃ JSONGetItem(js, 20) ┃ - ┡━━━━━━━━━━━━━━━━━━━━━┩ - │ json │ - ├─────────────────────┤ - │ NULL │ - │ NULL │ - └─────────────────────┘ - """ - return ops.JSONGetItem(self, key).to_expr() - - def unwrap_as(self, dtype: dt.DataType | str) -> ir.Value: - """Unwrap JSON into a specific data type. - - Returns - ------- - Value - An Ibis expression of a more specific type than JSON - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> data = { - ... "jstring": ['"a"', '""', None, "null"], - ... "jbool": ["true", "false", "null", None], - ... "jint": ["1", "null", None, "2"], - ... "jfloat": ["42.42", None, "null", "37.37"], - ... "jmap": ['{"a": 1}', "null", None, "{}"], - ... "jarray": ["[]", "null", None, '[{},"1",2]'], - ... } - >>> t = ibis.memtable(data, schema=dict.fromkeys(data.keys(), "json")) - >>> t - ┏━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━┳━━━┓ - ┃ jstring ┃ jbool ┃ jint ┃ … ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━╇━━━┩ - │ json │ json │ json │ … │ - ├──────────────────────┼──────────────────────┼──────────────────────┼───┤ - │ 'a' │ True │ 1 │ … │ - │ '' │ False │ None │ … │ - │ NULL │ None │ NULL │ … │ - │ None │ NULL │ 2 │ … │ - └──────────────────────┴──────────────────────┴──────────────────────┴───┘ - >>> t.select(unwrapped=t.jstring.unwrap_as(str), original=t.jstring) - ┏━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ unwrapped ┃ original ┃ - ┡━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━┩ - │ string │ json │ - ├───────────┼──────────────────────┤ - │ a │ 'a' │ - │ ~ │ '' │ - │ NULL │ NULL │ - │ NULL │ None │ - └───────────┴──────────────────────┘ - >>> t.select(unwrapped=t.jbool.unwrap_as("bool"), original=t.jbool) - ┏━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ unwrapped ┃ original ┃ - ┡━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━┩ - │ boolean │ json │ - ├───────────┼──────────────────────┤ - │ True │ True │ - │ False │ False │ - │ NULL │ None │ - │ NULL │ NULL │ - └───────────┴──────────────────────┘ - >>> t.select( - ... unwrapped_int64=t.jint.unwrap_as("int64"), - ... unwrapped_int32=t.jint.unwrap_as("int32"), - ... original=t.jint, - ... ) - ┏━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ unwrapped_int64 ┃ unwrapped_int32 ┃ original ┃ - ┡━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━┩ - │ int64 │ int32 │ json │ - ├─────────────────┼─────────────────┼──────────────────────┤ - │ 1 │ 1 │ 1 │ - │ NULL │ NULL │ None │ - │ NULL │ NULL │ NULL │ - │ 2 │ 2 │ 2 │ - └─────────────────┴─────────────────┴──────────────────────┘ - - You can cast to a more specific type than the types available in standards-compliant JSON. - - Here's an example of casting JSON numbers to `float32`: - - >>> t.select(unwrapped=t.jfloat.unwrap_as("float32"), original=t.jfloat) - ┏━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ unwrapped ┃ original ┃ - ┡━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━┩ - │ float32 │ json │ - ├───────────┼──────────────────────┤ - │ 42.419998 │ 42.42 │ - │ NULL │ NULL │ - │ NULL │ None │ - │ 37.369999 │ 37.37 │ - └───────────┴──────────────────────┘ - - You can cast JSON objects to a more specific `map` type: - - >>> t.select(unwrapped=t.jmap.unwrap_as("map"), original=t.jmap) - ┏━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ unwrapped ┃ original ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━┩ - │ map │ json │ - ├──────────────────────┼──────────────────────┤ - │ {'a': 1} │ {'a': 1} │ - │ NULL │ None │ - │ NULL │ NULL │ - │ {} │ {} │ - └──────────────────────┴──────────────────────┘ - - You can cast JSON arrays to an array type as well. In this case the - array values don't have a single element type so we cast to - `array`. - - >>> t.select(unwrapped=t.jarray.unwrap_as("array"), original=t.jarray) - ┏━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ unwrapped ┃ original ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━┩ - │ array │ json │ - ├───────────────────────┼──────────────────────┤ - │ [] │ [] │ - │ NULL │ None │ - │ NULL │ NULL │ - │ ['{}', '"1"', ... +1] │ [{...}, '1', ... +1] │ - └───────────────────────┴──────────────────────┘ - - See Also - -------- - [`JSONValue.str`](#ibis.expr.types.json.JSONValue.str) - [`JSONValue.int`](#ibis.expr.types.json.JSONValue.int) - [`JSONValue.float`](#ibis.expr.types.json.JSONValue.float) - [`JSONValue.bool`](#ibis.expr.types.json.JSONValue.bool) - [`JSONValue.map`](#ibis.expr.types.json.JSONValue.map) - [`JSONValue.array`](#ibis.expr.types.json.JSONValue.array) - [`Value.cast`](#ibis.expr.types.generic.Value.cast) - """ - dtype = dt.dtype(dtype) - if dtype.is_string(): - return self.str - elif dtype.is_boolean(): - return self.bool - elif dtype.is_integer(): - i = self.int - return i.cast(dtype) if i.type() != dtype else i - elif dtype.is_floating(): - f = self.float - return f.cast(dtype) if f.type() != dtype else f - elif dtype.is_map(): - m = self.map - return m.cast(dtype) if m.type() != dtype else m - elif dtype.is_array(): - a = self.array - return a.cast(dtype) if a.type() != dtype else a - else: - raise exc.IbisTypeError( - f"Data type {dtype} is unsupported for unwrapping JSON values. Supported " - "data types are strings, integers, floats, booleans, maps, and arrays." - ) - - @property - def map(self) -> ir.MapValue: - """Cast JSON to a map of string to JSON. - - Use this property to unlock map functionality on JSON objects. - - Returns - ------- - MapValue - Map of string to JSON - """ - return ops.ToJSONMap(self).to_expr() - - @property - def array(self) -> ir.ArrayValue: - """Cast JSON to an array of JSON. - - Use this property to unlock array functionality on JSON objects. - - Returns - ------- - ArrayValue - Array of JSON objects - """ - return ops.ToJSONArray(self).to_expr() - - @property - def int(self) -> ir.IntegerValue: - """Unwrap a JSON value into a backend-native int. - - Any non-float JSON values are returned as `NULL`. - - Examples - -------- - >>> import json, ibis - >>> ibis.options.interactive = True - >>> data = [ - ... {"name": "Alice", "json_data": '{"last_name":"Smith","age":40}'}, - ... {"name": "Bob", "json_data": '{"last_name":"Jones", "age":39}'}, - ... {"name": "Charlie", "json_data": '{"last_name":"Davies","age":54}'}, - ... ] - >>> t = ibis.memtable(data, schema={"name": "string", "json_data": "json"}) - >>> t - ┏━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ name ┃ json_data ┃ - ┡━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩ - │ string │ json │ - ├─────────┼────────────────────────────────────┤ - │ Alice │ {'last_name': 'Smith', 'age': 40} │ - │ Bob │ {'last_name': 'Jones', 'age': 39} │ - │ Charlie │ {'last_name': 'Davies', 'age': 54} │ - └─────────┴────────────────────────────────────┘ - >>> t.mutate(age=t.json_data["age"].int) - ┏━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━┓ - ┃ name ┃ json_data ┃ age ┃ - ┡━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━┩ - │ string │ json │ int64 │ - ├─────────┼────────────────────────────────────┼───────┤ - │ Alice │ {'last_name': 'Smith', 'age': 40} │ 40 │ - │ Bob │ {'last_name': 'Jones', 'age': 39} │ 39 │ - │ Charlie │ {'last_name': 'Davies', 'age': 54} │ 54 │ - └─────────┴────────────────────────────────────┴───────┘ - """ - return ops.UnwrapJSONInt64(self).to_expr() - - @property - def float(self) -> ir.FloatingValue: - """Unwrap a JSON value into a backend-native float. - - Any non-float JSON values are returned as `NULL`. - - ::: {.callout-warning} - ## The `float` property is lax with respect to integers - - The `float` property will attempt to coerce integers to floating point numbers. - ::: - - Examples - -------- - >>> import json, ibis - >>> ibis.options.interactive = True - >>> data = [ - ... {"name": "Alice", "json_data": '{"last_name":"Smith","salary":42.42}'}, - ... {"name": "Bob", "json_data": '{"last_name":"Jones", "salary":37.37}'}, - ... {"name": "Charlie", "json_data": '{"last_name":"Davies","salary":"NA"}'}, - ... {"name": "Joan", "json_data": '{"last_name":"Davies","salary":78}'}, - ... ] - >>> t = ibis.memtable(data, schema={"name": "string", "json_data": "json"}) - >>> t - ┏━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ name ┃ json_data ┃ - ┡━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩ - │ string │ json │ - ├─────────┼─────────────────────────────────────────┤ - │ Alice │ {'last_name': 'Smith', 'salary': 42.42} │ - │ Bob │ {'last_name': 'Jones', 'salary': 37.37} │ - │ Charlie │ {'last_name': 'Davies', 'salary': 'NA'} │ - │ Joan │ {'last_name': 'Davies', 'salary': 78} │ - └─────────┴─────────────────────────────────────────┘ - >>> t.mutate(salary=t.json_data["salary"].float) - ┏━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━┓ - ┃ name ┃ json_data ┃ salary ┃ - ┡━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━┩ - │ string │ json │ float64 │ - ├─────────┼─────────────────────────────────────────┼─────────┤ - │ Alice │ {'last_name': 'Smith', 'salary': 42.42} │ 42.42 │ - │ Bob │ {'last_name': 'Jones', 'salary': 37.37} │ 37.37 │ - │ Charlie │ {'last_name': 'Davies', 'salary': 'NA'} │ NULL │ - │ Joan │ {'last_name': 'Davies', 'salary': 78} │ 78.00 │ - └─────────┴─────────────────────────────────────────┴─────────┘ - """ - return ops.UnwrapJSONFloat64(self).to_expr() - - @property - def bool(self) -> ir.BooleanValue: - """Unwrap a JSON value into a backend-native boolean. - - Any non-boolean JSON values are returned as `NULL`. - - Examples - -------- - >>> import json, ibis - >>> ibis.options.interactive = True - >>> data = [ - ... {"name": "Alice", "json_data": '{"last_name":"Smith","is_bot":false}'}, - ... {"name": "Bob", "json_data": '{"last_name":"Jones","is_bot":true}'}, - ... {"name": "Charlie", "json_data": '{"last_name":"Davies","is_bot":false}'}, - ... ] - >>> t = ibis.memtable(data, schema={"name": "string", "json_data": "json"}) - >>> t - ┏━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ name ┃ json_data ┃ - ┡━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩ - │ string │ json │ - ├─────────┼──────────────────────────────────────────┤ - │ Alice │ {'last_name': 'Smith', 'is_bot': False} │ - │ Bob │ {'last_name': 'Jones', 'is_bot': True} │ - │ Charlie │ {'last_name': 'Davies', 'is_bot': False} │ - └─────────┴──────────────────────────────────────────┘ - >>> t.mutate(is_bot=t.json_data["is_bot"].bool) - ┏━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━┓ - ┃ name ┃ json_data ┃ is_bot ┃ - ┡━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━┩ - │ string │ json │ boolean │ - ├─────────┼──────────────────────────────────────────┼─────────┤ - │ Alice │ {'last_name': 'Smith', 'is_bot': False} │ False │ - │ Bob │ {'last_name': 'Jones', 'is_bot': True} │ True │ - │ Charlie │ {'last_name': 'Davies', 'is_bot': False} │ False │ - └─────────┴──────────────────────────────────────────┴─────────┘ - """ - return ops.UnwrapJSONBoolean(self).to_expr() - - @property - def str(self) -> ir.StringValue: - """Unwrap a JSON string into a backend-native string. - - Any non-string JSON values are returned as `NULL`. - - Returns - ------- - StringValue - A string expression - - Examples - -------- - >>> import json, ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable( - ... {"js": ['"a"', '"b"', "1", "{}", '[{"a": 1}]']}, - ... schema=ibis.schema(dict(js="json")), - ... ) - >>> t - ┏━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ js ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━┩ - │ json │ - ├──────────────────────┤ - │ 'a' │ - │ 'b' │ - │ 1 │ - │ {} │ - │ [{...}] │ - └──────────────────────┘ - >>> t.js.str - ┏━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ UnwrapJSONString(js) ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━┩ - │ string │ - ├──────────────────────┤ - │ a │ - │ b │ - │ NULL │ - │ NULL │ - │ NULL │ - └──────────────────────┘ - - Here's a more complex example with a table containing a JSON column - with nested fields. - - >>> data = [ - ... {"name": "Alice", "json_data": '{"last_name":"Smith"}'}, - ... {"name": "Bob", "json_data": '{"last_name":"Jones"}'}, - ... {"name": "Charlie", "json_data": '{"last_name":"Davies"}'}, - ... ] - >>> t = ibis.memtable(data, schema={"name": "string", "json_data": "json"}) - >>> t - ┏━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ name ┃ json_data ┃ - ┡━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━┩ - │ string │ json │ - ├─────────┼─────────────────────────┤ - │ Alice │ {'last_name': 'Smith'} │ - │ Bob │ {'last_name': 'Jones'} │ - │ Charlie │ {'last_name': 'Davies'} │ - └─────────┴─────────────────────────┘ - >>> t.mutate(last_name=t.json_data["last_name"].str) - ┏━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━┓ - ┃ name ┃ json_data ┃ last_name ┃ - ┡━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━┩ - │ string │ json │ string │ - ├─────────┼─────────────────────────┼───────────┤ - │ Alice │ {'last_name': 'Smith'} │ Smith │ - │ Bob │ {'last_name': 'Jones'} │ Jones │ - │ Charlie │ {'last_name': 'Davies'} │ Davies │ - └─────────┴─────────────────────────┴───────────┘ - """ - return ops.UnwrapJSONString(self).to_expr() - - -@public -class JSONScalar(Scalar, JSONValue): - pass - - -@public -class JSONColumn(Column, JSONValue): - def __getitem__( - self, key: str | int | ir.StringValue | ir.IntegerValue - ) -> JSONColumn: - return JSONValue.__getitem__(self, key) diff --git a/third_party/bigframes_vendored/ibis/expr/types/logical.py b/third_party/bigframes_vendored/ibis/expr/types/logical.py deleted file mode 100644 index 68ad6feae10..00000000000 --- a/third_party/bigframes_vendored/ibis/expr/types/logical.py +++ /dev/null @@ -1,557 +0,0 @@ -# Contains code from https://github.com/ibis-project/ibis/blob/9.2.0/ibis/expr/types/logical.py - -from __future__ import annotations - -from typing import TYPE_CHECKING - -import bigframes_vendored.ibis -import bigframes_vendored.ibis.expr.operations as ops -from bigframes_vendored.ibis.expr.types.core import _binop -from bigframes_vendored.ibis.expr.types.numeric import ( - NumericColumn, - NumericScalar, - NumericValue, -) -from public import public - -if TYPE_CHECKING: - import bigframes_vendored.ibis.expr.types as ir - - -@public -class BooleanValue(NumericValue): - def ifelse(self, true_expr: ir.Value, false_expr: ir.Value) -> ir.Value: - """Construct a ternary conditional expression. - - Parameters - ---------- - true_expr - Expression to return if `self` evaluates to `True` - false_expr - Expression to return if `self` evaluates to `False` or `NULL` - - Returns - ------- - Value - The value of `true_expr` if `arg` is `True` else `false_expr` - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable({"is_person": [True, False, True, None]}) - >>> t.is_person.ifelse("yes", "no") - ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ IfElse(is_person, 'yes', 'no') ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩ - │ string │ - ├────────────────────────────────┤ - │ yes │ - │ no │ - │ yes │ - │ no │ - └────────────────────────────────┘ - """ - # Result will be the result of promotion of true/false exprs. These - # might be conflicting types; same type resolution as case expressions - # must be used. - return ops.IfElse(self, true_expr, false_expr).to_expr() - - def __and__(self, other: BooleanValue) -> BooleanValue: - """Construct a binary AND conditional expression with `self` and `other`. - - Parameters - ---------- - self - Left operand - other - Right operand - - Returns - ------- - BooleanValue - A Boolean expression - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable({"arr": [[1], [], [42, 42], None]}) - >>> t.arr.contains(42) & (t.arr.contains(1)) - ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ And(ArrayContains(arr, 42), ArrayContains(arr, 1)) ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩ - │ boolean │ - ├────────────────────────────────────────────────────┤ - │ False │ - │ False │ - │ False │ - │ NULL │ - └────────────────────────────────────────────────────┘ - - >>> t.arr.contains(42) & (t.arr.contains(42)) - ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ And(ArrayContains(arr, 42), ArrayContains(arr, 42)) ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩ - │ boolean │ - ├─────────────────────────────────────────────────────┤ - │ False │ - │ False │ - │ True │ - │ NULL │ - └─────────────────────────────────────────────────────┘ - """ - return _binop(ops.And, self, other) - - __rand__ = __and__ - - def __or__(self, other: BooleanValue) -> BooleanValue: - """Construct a binary OR conditional expression with `self` and `other`. - - Parameters - ---------- - self - Left operand - other - Right operand - - Returns - ------- - BooleanValue - A Boolean expression - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable({"arr": [1, 2, 3, None]}) - >>> (t.arr > 1) | (t.arr > 2) - ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ Or(Greater(arr, 1), Greater(arr, 2)) ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩ - │ boolean │ - ├──────────────────────────────────────┤ - │ False │ - │ True │ - │ True │ - │ NULL │ - └──────────────────────────────────────┘ - """ - return _binop(ops.Or, self, other) - - __ror__ = __or__ - - def __xor__(self, other: BooleanValue) -> BooleanValue: - """Construct a binary XOR conditional expression with `self` and `other`. - - Parameters - ---------- - self - Left operand - other - Right operand - - Returns - ------- - BooleanValue - A Boolean expression - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable({"arr": [1, 2, 3, None]}) - >>> t.arr == 2 - ┏━━━━━━━━━━━━━━━━┓ - ┃ Equals(arr, 2) ┃ - ┡━━━━━━━━━━━━━━━━┩ - │ boolean │ - ├────────────────┤ - │ False │ - │ True │ - │ False │ - │ NULL │ - └────────────────┘ - - >>> (t.arr > 2) - ┏━━━━━━━━━━━━━━━━━┓ - ┃ Greater(arr, 2) ┃ - ┡━━━━━━━━━━━━━━━━━┩ - │ boolean │ - ├─────────────────┤ - │ False │ - │ False │ - │ True │ - │ NULL │ - └─────────────────┘ - - >>> (t.arr == 2) ^ (t.arr > 2) - ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ Xor(Equals(arr, 2), Greater(arr, 2)) ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩ - │ boolean │ - ├──────────────────────────────────────┤ - │ False │ - │ True │ - │ True │ - │ NULL │ - └──────────────────────────────────────┘ - """ - - return _binop(ops.Xor, self, other) - - __rxor__ = __xor__ - - def __invert__(self) -> BooleanValue: - """Construct a unary NOT conditional expression with `self`. - - Parameters - ---------- - self - Operand - - Returns - ------- - BooleanValue - A Boolean expression - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable({"arr": [True, False, False, None]}) - >>> ~t.arr - ┏━━━━━━━━━━┓ - ┃ Not(arr) ┃ - ┡━━━━━━━━━━┩ - │ boolean │ - ├──────────┤ - │ False │ - │ True │ - │ True │ - │ NULL │ - └──────────┘ - """ - return self.negate() - - def negate(self) -> BooleanValue: - """Negate a boolean expression. - - Returns - ------- - BooleanValue - A boolean value expression - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable({"values": [True, False, False, None]}) - >>> t.values.negate() - ┏━━━━━━━━━━━━━┓ - ┃ Not(values) ┃ - ┡━━━━━━━━━━━━━┩ - │ boolean │ - ├─────────────┤ - │ False │ - │ True │ - │ True │ - │ NULL │ - └─────────────┘ - """ - return ops.Not(self).to_expr() - - -@public -class BooleanScalar(NumericScalar, BooleanValue): - pass - - -@public -class BooleanColumn(NumericColumn, BooleanValue): - def any(self, where: BooleanValue | None = None) -> BooleanValue: - """Return whether at least one element is `True`. - - If the expression does not reference any foreign tables, the result - will be a scalar reduction, otherwise it will be a deferred expression - constructing an exists subquery when passed to a table method. - - Parameters - ---------- - where - Optional filter for the aggregation - - Returns - ------- - BooleanValue - Whether at least one element is `True`. - - Notes - ----- - Consider the following ibis expressions - - ```python - import ibis - - t = ibis.table(dict(a="string")) - s = ibis.table(dict(a="string")) - - cond = (t.a == s.a).any() - ``` - - Without knowing the table to use as the outer query there are two ways to - turn this expression into a SQL `EXISTS` predicate, depending on which of - `t` or `s` is filtered on. - - Filtering from `t`: - - ```sql - SELECT * - FROM t - WHERE EXISTS (SELECT 1 FROM s WHERE t.a = s.a) - ``` - - Filtering from `s`: - - ```sql - SELECT * - FROM s - WHERE EXISTS (SELECT 1 FROM t WHERE t.a = s.a) - ``` - - Notably the correlated subquery cannot stand on its own. - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable({"arr": [1, 2, 3, None]}) - >>> (t.arr > 2).any() - ┌──────────┐ - │ np.True_ │ - └──────────┘ - >>> (t.arr > 4).any() - ┌───────────┐ - │ np.False_ │ - └───────────┘ - >>> (t.arr == None).any(where=t.arr != None) - ┌───────────┐ - │ np.False_ │ - └───────────┘ - """ - from bigframes_vendored.ibis.common.deferred import Call, Deferred, _ - - parents = self.op().relations - - def resolve_exists_subquery(outer): - """An exists subquery whose outer leaf table is unknown.""" - (inner,) = (t for t in parents if t != outer.op()) - relation = ops.Filter(inner, [self]) - return ops.ExistsSubquery(relation).to_expr() - - if len(parents) == 2: - return Deferred(Call(resolve_exists_subquery, _)) - elif len(parents) == 1: - op = ops.Any(self, where=self._bind_to_parent_table(where)) - elif len(parents) == 0: - # array reduction case - op = ops.Any(self, where=self._bind_to_parent_table(where)) - else: - raise NotImplementedError( - f'Cannot compute "any" for expression of type {type(self)} ' - f"with multiple foreign tables" - ) - - return op.to_expr() - - def notany(self, where: BooleanValue | None = None) -> BooleanValue: - """Return whether no elements are `True`. - - Parameters - ---------- - where - Optional filter for the aggregation - - Returns - ------- - BooleanValue - Whether no elements are `True`. - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable({"arr": [1, 2, 3, 4]}) - >>> (t.arr > 1).notany() - ┌───────────┐ - │ np.False_ │ - └───────────┘ - >>> (t.arr > 4).notany() - ┌──────────┐ - │ np.True_ │ - └──────────┘ - >>> m = ibis.memtable({"arr": [True, True, True, False]}) - >>> (t.arr == None).notany(where=t.arr != None) - ┌──────────┐ - │ np.True_ │ - └──────────┘ - """ - return ~self.any(where=where) - - def all(self, where: BooleanValue | None = None) -> BooleanScalar: - """Return whether all elements are `True`. - - Parameters - ---------- - where - Optional filter for the aggregation - - Returns - ------- - BooleanValue - Whether all elements are `True` - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable({"arr": [1, 2, 3, 4]}) - >>> (t.arr >= 1).all() - ┌──────────┐ - │ np.True_ │ - └──────────┘ - >>> (t.arr > 2).all() - ┌───────────┐ - │ np.False_ │ - └───────────┘ - >>> (t.arr == 2).all(where=t.arr == 2) - ┌──────────┐ - │ np.True_ │ - └──────────┘ - >>> (t.arr == 2).all(where=t.arr >= 2) - ┌───────────┐ - │ np.False_ │ - └───────────┘ - """ - return ops.All(self, where=self._bind_to_parent_table(where)).to_expr() - - def notall(self, where: BooleanValue | None = None) -> BooleanScalar: - """Return whether not all elements are `True`. - - Parameters - ---------- - where - Optional filter for the aggregation - - Returns - ------- - BooleanValue - Whether not all elements are `True` - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable({"arr": [1, 2, 3, 4]}) - >>> (t.arr >= 1).notall() - ┌───────────┐ - │ np.False_ │ - └───────────┘ - >>> (t.arr > 2).notall() - ┌──────────┐ - │ np.True_ │ - └──────────┘ - >>> (t.arr == 2).notall(where=t.arr == 2) - ┌───────────┐ - │ np.False_ │ - └───────────┘ - >>> (t.arr == 2).notall(where=t.arr >= 2) - ┌──────────┐ - │ np.True_ │ - └──────────┘ - """ - return ~self.all(where=where) - - def cumany(self, *, where=None, group_by=None, order_by=None) -> BooleanColumn: - """Accumulate the `any` aggregate. - - Returns - ------- - BooleanColumn - A boolean column with the cumulative `any` aggregate. - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable({"arr": [1, 2, 3, 4]}) - >>> ((t.arr > 1) | (t.arr >= 1)).cumany() - ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ Any(Or(Greater(arr, 1), GreaterEqual(arr, 1))) ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩ - │ boolean │ - ├────────────────────────────────────────────────┤ - │ True │ - │ True │ - │ True │ - │ True │ - └────────────────────────────────────────────────┘ - >>> ((t.arr > 1) & (t.arr >= 1)).cumany() - ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ Any(And(Greater(arr, 1), GreaterEqual(arr, 1))) ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩ - │ boolean │ - ├─────────────────────────────────────────────────┤ - │ False │ - │ True │ - │ True │ - │ True │ - └─────────────────────────────────────────────────┘ - """ - return self.any(where=where).over( - bigframes_vendored.ibis.cumulative_window( - group_by=group_by, order_by=order_by - ) - ) - - def cumall(self, *, where=None, group_by=None, order_by=None) -> BooleanColumn: - """Accumulate the `all` aggregate. - - Returns - ------- - BooleanColumn - A boolean column with the cumulative `all` aggregate. - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable({"arr": [1, 2, 3, 4]}) - >>> ((t.arr > 1) & (t.arr >= 1)).cumall() - ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ All(And(Greater(arr, 1), GreaterEqual(arr, 1))) ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩ - │ boolean │ - ├─────────────────────────────────────────────────┤ - │ False │ - │ False │ - │ False │ - │ False │ - └─────────────────────────────────────────────────┘ - >>> ((t.arr > 0) & (t.arr >= 1)).cumall() - ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ All(And(Greater(arr, 0), GreaterEqual(arr, 1))) ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩ - │ boolean │ - ├─────────────────────────────────────────────────┤ - │ True │ - │ True │ - │ True │ - │ True │ - └─────────────────────────────────────────────────┘ - """ - return self.all(where=where).over( - bigframes_vendored.ibis.cumulative_window( - group_by=group_by, order_by=order_by - ) - ) diff --git a/third_party/bigframes_vendored/ibis/expr/types/maps.py b/third_party/bigframes_vendored/ibis/expr/types/maps.py deleted file mode 100644 index 0be7241b241..00000000000 --- a/third_party/bigframes_vendored/ibis/expr/types/maps.py +++ /dev/null @@ -1,494 +0,0 @@ -# Contains code from https://github.com/ibis-project/ibis/blob/9.2.0/ibis/expr/types/maps.py - -from __future__ import annotations - -from typing import TYPE_CHECKING, Any - -import bigframes_vendored.ibis.expr.operations as ops -from bigframes_vendored.ibis.common.deferred import deferrable -from bigframes_vendored.ibis.expr.types.generic import Column, Scalar, Value -from public import public - -if TYPE_CHECKING: - from collections.abc import Iterable, Mapping - - import bigframes_vendored.ibis.expr.types as ir - from bigframes_vendored.ibis.expr.types.arrays import ArrayValue - - -@public -class MapValue(Value): - """A dict-like collection with fixed-type keys and values. - - Maps are similar to a Python dictionary, with the restriction that all keys - must have the same type, and all values must have the same type. - - The key type and the value type can be different. - - For example, keys are `string`s, and values are `int64`s. - - Keys are unique within a given map value. - - Maps can be constructed with [`ibis.map()`](#ibis.expr.types.map). - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> tab = pa.table( - ... { - ... "m": pa.array( - ... [[("a", 1), ("b", 2)], [("a", 1)], None], - ... type=pa.map_(pa.utf8(), pa.int64()), - ... ) - ... } - ... ) - >>> t = ibis.memtable(tab) - >>> t - ┏━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ m ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━┩ - │ map │ - ├──────────────────────┤ - │ {'a': 1, 'b': 2} │ - │ {'a': 1} │ - │ NULL │ - └──────────────────────┘ - - Can use `[]` to access values: - >>> t.m["a"] - ┏━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ MapGet(m, 'a', None) ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━┩ - │ int64 │ - ├──────────────────────┤ - │ 1 │ - │ 1 │ - │ NULL │ - └──────────────────────┘ - - To provide default values, use `get`: - >>> t.m.get("b", 0) - ┏━━━━━━━━━━━━━━━━━━━┓ - ┃ MapGet(m, 'b', 0) ┃ - ┡━━━━━━━━━━━━━━━━━━━┩ - │ int64 │ - ├───────────────────┤ - │ 2 │ - │ 0 │ - │ NULL │ - └───────────────────┘ - """ - - def get(self, key: ir.Value, default: ir.Value | None = None) -> ir.Value: - """Return the value for `key` from `expr`. - - Return `default` if `key` is not in the map. - - Parameters - ---------- - key - Expression to use for key - default - Expression to return if `key` is not a key in `expr` - - Returns - ------- - Value - The element type of `self` - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> tab = pa.table( - ... { - ... "m": pa.array( - ... [[("a", 1), ("b", 2)], [("a", 1)], None], - ... type=pa.map_(pa.utf8(), pa.int64()), - ... ) - ... } - ... ) - >>> t = ibis.memtable(tab) - >>> t - ┏━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ m ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━┩ - │ map │ - ├──────────────────────┤ - │ {'a': 1, 'b': 2} │ - │ {'a': 1} │ - │ NULL │ - └──────────────────────┘ - >>> t.m.get("a") - ┏━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ MapGet(m, 'a', None) ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━┩ - │ int64 │ - ├──────────────────────┤ - │ 1 │ - │ 1 │ - │ NULL │ - └──────────────────────┘ - >>> t.m.get("b") - ┏━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ MapGet(m, 'b', None) ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━┩ - │ int64 │ - ├──────────────────────┤ - │ 2 │ - │ NULL │ - │ NULL │ - └──────────────────────┘ - >>> t.m.get("b", 0) - ┏━━━━━━━━━━━━━━━━━━━┓ - ┃ MapGet(m, 'b', 0) ┃ - ┡━━━━━━━━━━━━━━━━━━━┩ - │ int64 │ - ├───────────────────┤ - │ 2 │ - │ 0 │ - │ NULL │ - └───────────────────┘ - """ - - return ops.MapGet(self, key, default).to_expr() - - def length(self) -> ir.IntegerValue: - """Return the number of key-value pairs in the map. - - Returns - ------- - IntegerValue - The number of elements in `self` - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> tab = pa.table( - ... { - ... "m": pa.array( - ... [[("a", 1), ("b", 2)], [("a", 1)], None], - ... type=pa.map_(pa.utf8(), pa.int64()), - ... ) - ... } - ... ) - >>> t = ibis.memtable(tab) - >>> t - ┏━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ m ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━┩ - │ map │ - ├──────────────────────┤ - │ {'a': 1, 'b': 2} │ - │ {'a': 1} │ - │ NULL │ - └──────────────────────┘ - >>> t.m.length() - ┏━━━━━━━━━━━━━━┓ - ┃ MapLength(m) ┃ - ┡━━━━━━━━━━━━━━┩ - │ int64 │ - ├──────────────┤ - │ 2 │ - │ 1 │ - │ NULL │ - └──────────────┘ - """ - return ops.MapLength(self).to_expr() - - def __getitem__(self, key: ir.Value) -> ir.Value: - """Get the value for a given map `key`. - - ::: {.callout-note} - ## This operation may have different semantics depending on the backend. - - Some backends return `NULL` when a key is missing, others may fail - the query. - ::: - - Parameters - ---------- - key - A map key - - Returns - ------- - Value - An element with the value type of the map - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> tab = pa.table( - ... { - ... "m": pa.array( - ... [[("a", 1), ("b", 2)], [("a", 1)], None], - ... type=pa.map_(pa.utf8(), pa.int64()), - ... ) - ... } - ... ) - >>> t = ibis.memtable(tab) - >>> t - ┏━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ m ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━┩ - │ map │ - ├──────────────────────┤ - │ {'a': 1, 'b': 2} │ - │ {'a': 1} │ - │ NULL │ - └──────────────────────┘ - >>> t.m["a"] - ┏━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ MapGet(m, 'a', None) ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━┩ - │ int64 │ - ├──────────────────────┤ - │ 1 │ - │ 1 │ - │ NULL │ - └──────────────────────┘ - """ - return ops.MapGet(self, key).to_expr() - - def contains( - self, key: int | str | ir.IntegerValue | ir.StringValue - ) -> ir.BooleanValue: - """Return whether the map contains `key`. - - Parameters - ---------- - key - Mapping key for which to check - - Returns - ------- - BooleanValue - Boolean indicating the presence of `key` in the map expression - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> tab = pa.table( - ... { - ... "m": pa.array( - ... [[("a", 1), ("b", 2)], [("a", 1)], None], - ... type=pa.map_(pa.utf8(), pa.int64()), - ... ) - ... } - ... ) - >>> t = ibis.memtable(tab) - >>> t - ┏━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ m ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━┩ - │ map │ - ├──────────────────────┤ - │ {'a': 1, 'b': 2} │ - │ {'a': 1} │ - │ NULL │ - └──────────────────────┘ - >>> t.m.contains("b") - ┏━━━━━━━━━━━━━━━━━━━━━┓ - ┃ MapContains(m, 'b') ┃ - ┡━━━━━━━━━━━━━━━━━━━━━┩ - │ boolean │ - ├─────────────────────┤ - │ True │ - │ False │ - │ NULL │ - └─────────────────────┘ - """ - return ops.MapContains(self, key).to_expr() - - def keys(self) -> ir.ArrayValue: - """Extract the keys of a map. - - Returns - ------- - ArrayValue - The keys of `self` - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> tab = pa.table( - ... { - ... "m": pa.array( - ... [[("a", 1), ("b", 2)], [("a", 1)], None], - ... type=pa.map_(pa.utf8(), pa.int64()), - ... ) - ... } - ... ) - >>> t = ibis.memtable(tab) - >>> t - ┏━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ m ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━┩ - │ map │ - ├──────────────────────┤ - │ {'a': 1, 'b': 2} │ - │ {'a': 1} │ - │ NULL │ - └──────────────────────┘ - >>> t.m.keys() - ┏━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ MapKeys(m) ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━┩ - │ array │ - ├──────────────────────┤ - │ ['a', 'b'] │ - │ ['a'] │ - │ NULL │ - └──────────────────────┘ - """ - return ops.MapKeys(self).to_expr() - - def values(self) -> ir.ArrayValue: - """Extract the values of a map. - - Returns - ------- - ArrayValue - The values of `self` - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> m = ibis.map({"a": 1, "b": 2}) - >>> m.values() - ┌────────┐ - │ [1, 2] │ - └────────┘ - """ - return ops.MapValues(self).to_expr() - - def __add__(self, other: MapValue) -> MapValue: - """Concatenate this map with another. - - Parameters - ---------- - other - Map to concatenate with `self` - - Returns - ------- - MapValue - `self` concatenated with `other` - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> m1 = ibis.map({"a": 1, "b": 2}) - >>> m2 = ibis.map({"c": 3, "d": 4}) - >>> m1 + m2 - ┌──────────────────────────┐ - │ {'a': 1, 'b': 2, ... +2} │ - └──────────────────────────┘ - """ - return ops.MapMerge(self, other).to_expr() - - def __radd__(self, other: MapValue) -> MapValue: - """Concatenate this map with another. - - Parameters - ---------- - other - Map to concatenate with `self` - - Returns - ------- - MapValue - `self` concatenated with `other` - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> m1 = ibis.map({"a": 1, "b": 2}) - >>> m2 = ibis.map({"c": 3, "d": 4}) - >>> m1 + m2 - ┌──────────────────────────┐ - │ {'a': 1, 'b': 2, ... +2} │ - └──────────────────────────┘ - """ - return ops.MapMerge(self, other).to_expr() - - -@public -class MapScalar(Scalar, MapValue): - pass - - -@public -class MapColumn(Column, MapValue): - def __getitem__(self, key: ir.Value) -> ir.Column: - return MapValue.__getitem__(self, key) - - -@public -@deferrable -def map( - keys: Iterable[Any] | Mapping[Any, Any] | ArrayValue, - values: Iterable[Any] | ArrayValue | None = None, -) -> MapValue: - """Create a MapValue. - - If any of the `keys` or `values` are Columns, then the output will be a MapColumn. - Otherwise, the output will be a MapScalar. - - Parameters - ---------- - keys - Keys of the map or `Mapping`. If `keys` is a `Mapping`, `values` must be `None`. - values - Values of the map or `None`. If `None`, the `keys` argument must be a `Mapping`. - - Returns - ------- - MapValue - Either a MapScalar or MapColumn, depending on the input shapes. - - Examples - -------- - Create a Map scalar from a dict with the type inferred - - >>> import ibis - >>> ibis.options.interactive = True - >>> ibis.map(dict(a=1, b=2)) - ┌──────────────────┐ - │ {'a': 1, 'b': 2} │ - └──────────────────┘ - - Create a Map Column from columns with keys and values - - >>> t = ibis.memtable({"keys": [["a", "b"], ["b"]], "values": [[1, 2], [3]]}) - >>> t - ┏━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ keys ┃ values ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━┩ - │ array │ array │ - ├──────────────────────┼──────────────────────┤ - │ ['a', 'b'] │ [1, 2] │ - │ ['b'] │ [3] │ - └──────────────────────┴──────────────────────┘ - >>> ibis.map(t.keys, t.values) - ┏━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ Map(keys, values) ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━┩ - │ map │ - ├──────────────────────┤ - │ {'a': 1, 'b': 2} │ - │ {'b': 3} │ - └──────────────────────┘ - """ - if values is None: - keys, values = tuple(keys.keys()), tuple(keys.values()) - return ops.Map(keys, values).to_expr() diff --git a/third_party/bigframes_vendored/ibis/expr/types/numeric.py b/third_party/bigframes_vendored/ibis/expr/types/numeric.py deleted file mode 100644 index 84ef30b9f80..00000000000 --- a/third_party/bigframes_vendored/ibis/expr/types/numeric.py +++ /dev/null @@ -1,1219 +0,0 @@ -# Contains code from https://github.com/ibis-project/ibis/blob/9.2.0/ibis/expr/types/numeric.py - -from __future__ import annotations - -import functools -from typing import TYPE_CHECKING, Literal - -import bigframes_vendored.ibis -import bigframes_vendored.ibis.expr.operations as ops -from bigframes_vendored.ibis.common.exceptions import IbisTypeError -from bigframes_vendored.ibis.expr.types.core import _binop -from bigframes_vendored.ibis.expr.types.generic import Column, Scalar, Value -from public import public - -if TYPE_CHECKING: - from collections.abc import Iterable, Sequence - - import bigframes_vendored.ibis.expr.types as ir - - -@public -class NumericValue(Value): - def negate(self) -> NumericValue: - """Negate a numeric expression. - - Returns - ------- - NumericValue - A numeric value expression - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable({"values": [-1, 0, 1]}) - >>> t.values.negate() - ┏━━━━━━━━━━━━━━━━┓ - ┃ Negate(values) ┃ - ┡━━━━━━━━━━━━━━━━┩ - │ int64 │ - ├────────────────┤ - │ 1 │ - │ 0 │ - │ -1 │ - └────────────────┘ - """ - return ops.Negate(self).to_expr() - - def __neg__(self) -> NumericValue: - """Negate `self`. - - Returns - ------- - NumericValue - `self` negated - """ - return self.negate() - - def round(self, digits: int | IntegerValue | None = None) -> NumericValue: - """Round values to an indicated number of decimal places. - - Parameters - ---------- - digits - The number of digits to round to. - - Here's how the `digits` parameter affects the expression output - type: - - - `digits` is `False`-y; `self.type()` is `decimal` → `decimal` - - `digits` is nonzero; `self.type()` is `decimal` → `decimal` - - `digits` is `False`-y; `self.type()` is Floating → `int64` - - `digits` is nonzero; `self.type()` is Floating → `float64` - - Returns - ------- - NumericValue - The rounded expression - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable({"values": [1.22, 1.64, 2.15, 2.54]}) - >>> t - ┏━━━━━━━━━┓ - ┃ values ┃ - ┡━━━━━━━━━┩ - │ float64 │ - ├─────────┤ - │ 1.22 │ - │ 1.64 │ - │ 2.15 │ - │ 2.54 │ - └─────────┘ - >>> t.values.round() - ┏━━━━━━━━━━━━━━━┓ - ┃ Round(values) ┃ - ┡━━━━━━━━━━━━━━━┩ - │ int64 │ - ├───────────────┤ - │ 1 │ - │ 2 │ - │ 2 │ - │ 3 │ - └───────────────┘ - >>> t.values.round(digits=1) - ┏━━━━━━━━━━━━━━━━━━┓ - ┃ Round(values, 1) ┃ - ┡━━━━━━━━━━━━━━━━━━┩ - │ float64 │ - ├──────────────────┤ - │ 1.2 │ - │ 1.6 │ - │ 2.2 │ - │ 2.5 │ - └──────────────────┘ - """ - return ops.Round(self, digits).to_expr() - - def log(self, base: NumericValue | None = None) -> NumericValue: - r"""Compute $\log_{\texttt{base}}\left(\texttt{self}\right)$. - - Parameters - ---------- - base - The base of the logarithm. If `None`, base `e` is used. - - Returns - ------- - NumericValue - Logarithm of `arg` with base `base` - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> from math import e - >>> t = ibis.memtable({"values": [e, e**2, e**3]}) - >>> t.values.log() - ┏━━━━━━━━━━━━━┓ - ┃ Log(values) ┃ - ┡━━━━━━━━━━━━━┩ - │ float64 │ - ├─────────────┤ - │ 1.0 │ - │ 2.0 │ - │ 3.0 │ - └─────────────┘ - - - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable({"values": [10, 100, 1000]}) - >>> t.values.log(base=10) - ┏━━━━━━━━━━━━━━━━━┓ - ┃ Log(values, 10) ┃ - ┡━━━━━━━━━━━━━━━━━┩ - │ float64 │ - ├─────────────────┤ - │ 1.0 │ - │ 2.0 │ - │ 3.0 │ - └─────────────────┘ - """ - return ops.Log(self, base).to_expr() - - def clip( - self, - lower: NumericValue | None = None, - upper: NumericValue | None = None, - ) -> NumericValue: - """Trim values outside of `lower` and `upper` bounds. - - `NULL` values are preserved and are not replaced with bounds. - - Parameters - ---------- - lower - Lower bound - upper - Upper bound - - Returns - ------- - NumericValue - Clipped input - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable( - ... {"values": [None, 2, 3, None, 5, None, None, 8]}, - ... schema=dict(values="int"), - ... ) - >>> t.values.clip(lower=3, upper=6) - ┏━━━━━━━━━━━━━━━━━━━━┓ - ┃ Clip(values, 3, 6) ┃ - ┡━━━━━━━━━━━━━━━━━━━━┩ - │ int64 │ - ├────────────────────┤ - │ NULL │ - │ 3 │ - │ 3 │ - │ NULL │ - │ 5 │ - │ NULL │ - │ NULL │ - │ 6 │ - └────────────────────┘ - """ - if lower is None and upper is None: - raise ValueError("at least one of lower and upper must be provided") - - return ops.Clip(self, lower, upper).to_expr() - - def abs(self) -> NumericValue: - """Return the absolute value of `self`. - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable({"values": [-1, 2, -3, 4]}) - >>> t.values.abs() - ┏━━━━━━━━━━━━━┓ - ┃ Abs(values) ┃ - ┡━━━━━━━━━━━━━┩ - │ int64 │ - ├─────────────┤ - │ 1 │ - │ 2 │ - │ 3 │ - │ 4 │ - └─────────────┘ - """ - return ops.Abs(self).to_expr() - - def ceil(self) -> DecimalValue | IntegerValue: - """Return the ceiling of `self`. - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable({"values": [1, 1.1, 2, 2.1, 3.3]}) - >>> t.values.ceil() - ┏━━━━━━━━━━━━━━┓ - ┃ Ceil(values) ┃ - ┡━━━━━━━━━━━━━━┩ - │ int64 │ - ├──────────────┤ - │ 1 │ - │ 2 │ - │ 2 │ - │ 3 │ - │ 4 │ - └──────────────┘ - """ - return ops.Ceil(self).to_expr() - - def degrees(self) -> NumericValue: - """Compute the degrees of `self` radians. - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> from math import pi - >>> t = ibis.memtable({"values": [0, pi / 2, pi, 3 * pi / 2, 2 * pi]}) - >>> t.values.degrees() - ┏━━━━━━━━━━━━━━━━━┓ - ┃ Degrees(values) ┃ - ┡━━━━━━━━━━━━━━━━━┩ - │ float64 │ - ├─────────────────┤ - │ 0.0 │ - │ 90.0 │ - │ 180.0 │ - │ 270.0 │ - │ 360.0 │ - └─────────────────┘ - """ - return ops.Degrees(self).to_expr() - - rad2deg = degrees - - def exp(self) -> NumericValue: - r"""Compute $e^\texttt{self}$. - - Returns - ------- - NumericValue - $e^\texttt{self}$ - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable({"values": range(4)}) - >>> t.values.exp() - ┏━━━━━━━━━━━━━┓ - ┃ Exp(values) ┃ - ┡━━━━━━━━━━━━━┩ - │ float64 │ - ├─────────────┤ - │ 1.000000 │ - │ 2.718282 │ - │ 7.389056 │ - │ 20.085537 │ - └─────────────┘ - """ - return ops.Exp(self).to_expr() - - def floor(self) -> DecimalValue | IntegerValue: - """Return the floor of an expression. - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable({"values": [1, 1.1, 2, 2.1, 3.3]}) - >>> t.values.floor() - ┏━━━━━━━━━━━━━━━┓ - ┃ Floor(values) ┃ - ┡━━━━━━━━━━━━━━━┩ - │ int64 │ - ├───────────────┤ - │ 1 │ - │ 1 │ - │ 2 │ - │ 2 │ - │ 3 │ - └───────────────┘ - - """ - return ops.Floor(self).to_expr() - - def log2(self) -> NumericValue: - r"""Compute $\log_{2}\left(\texttt{self}\right)$. - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable({"values": [1, 2, 4, 8]}) - >>> t.values.log2() - ┏━━━━━━━━━━━━━━┓ - ┃ Log2(values) ┃ - ┡━━━━━━━━━━━━━━┩ - │ float64 │ - ├──────────────┤ - │ 0.0 │ - │ 1.0 │ - │ 2.0 │ - │ 3.0 │ - └──────────────┘ - """ - return ops.Log2(self).to_expr() - - def log10(self) -> NumericValue: - r"""Compute $\log_{10}\left(\texttt{self}\right)$. - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable({"values": [1, 10, 100]}) - >>> t.values.log10() - ┏━━━━━━━━━━━━━━━┓ - ┃ Log10(values) ┃ - ┡━━━━━━━━━━━━━━━┩ - │ float64 │ - ├───────────────┤ - │ 0.0 │ - │ 1.0 │ - │ 2.0 │ - └───────────────┘ - """ - return ops.Log10(self).to_expr() - - def ln(self) -> NumericValue: - r"""Compute $\ln\left(\texttt{self}\right)$. - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable({"values": [1, 2.718281828, 3]}) - >>> t.values.ln() - ┏━━━━━━━━━━━━┓ - ┃ Ln(values) ┃ - ┡━━━━━━━━━━━━┩ - │ float64 │ - ├────────────┤ - │ 0.000000 │ - │ 1.000000 │ - │ 1.098612 │ - └────────────┘ - """ - return ops.Ln(self).to_expr() - - def radians(self) -> NumericValue: - """Compute radians from `self` degrees. - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable({"values": [0, 90, 180, 270, 360]}) - >>> t.values.radians() - ┏━━━━━━━━━━━━━━━━━┓ - ┃ Radians(values) ┃ - ┡━━━━━━━━━━━━━━━━━┩ - │ float64 │ - ├─────────────────┤ - │ 0.000000 │ - │ 1.570796 │ - │ 3.141593 │ - │ 4.712389 │ - │ 6.283185 │ - └─────────────────┘ - """ - return ops.Radians(self).to_expr() - - deg2rad = radians - - def sign(self) -> NumericValue: - """Return the sign of the input. - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable({"values": [-1, 2, -3, 4]}) - >>> t.values.sign() - ┏━━━━━━━━━━━━━━┓ - ┃ Sign(values) ┃ - ┡━━━━━━━━━━━━━━┩ - │ int64 │ - ├──────────────┤ - │ -1 │ - │ 1 │ - │ -1 │ - │ 1 │ - └──────────────┘ - """ - return ops.Sign(self).to_expr() - - def sqrt(self) -> NumericValue: - """Compute the square root of `self`. - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable({"values": [1, 4, 9, 16]}) - >>> t.values.sqrt() - ┏━━━━━━━━━━━━━━┓ - ┃ Sqrt(values) ┃ - ┡━━━━━━━━━━━━━━┩ - │ float64 │ - ├──────────────┤ - │ 1.0 │ - │ 2.0 │ - │ 3.0 │ - │ 4.0 │ - └──────────────┘ - """ - return ops.Sqrt(self).to_expr() - - def acos(self) -> NumericValue: - """Compute the arc cosine of `self`. - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable({"values": [-1, 0, 1]}) - >>> t.values.acos() - ┏━━━━━━━━━━━━━━┓ - ┃ Acos(values) ┃ - ┡━━━━━━━━━━━━━━┩ - │ float64 │ - ├──────────────┤ - │ 3.141593 │ - │ 1.570796 │ - │ 0.000000 │ - └──────────────┘ - - """ - return ops.Acos(self).to_expr() - - def asin(self) -> NumericValue: - """Compute the arc sine of `self`. - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable({"values": [-1, 0, 1]}) - >>> t.values.asin() - ┏━━━━━━━━━━━━━━┓ - ┃ Asin(values) ┃ - ┡━━━━━━━━━━━━━━┩ - │ float64 │ - ├──────────────┤ - │ -1.570796 │ - │ 0.000000 │ - │ 1.570796 │ - └──────────────┘ - """ - return ops.Asin(self).to_expr() - - def atan(self) -> NumericValue: - """Compute the arc tangent of `self`. - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable({"values": [-1, 0, 1]}) - >>> t.values.atan() - ┏━━━━━━━━━━━━━━┓ - ┃ Atan(values) ┃ - ┡━━━━━━━━━━━━━━┩ - │ float64 │ - ├──────────────┤ - │ -0.785398 │ - │ 0.000000 │ - │ 0.785398 │ - └──────────────┘ - """ - return ops.Atan(self).to_expr() - - def atan2(self, other: NumericValue) -> NumericValue: - """Compute the two-argument version of arc tangent. - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable({"values": [-1, 0, 1]}) - >>> t.values.atan2(0) - ┏━━━━━━━━━━━━━━━━━━┓ - ┃ Atan2(values, 0) ┃ - ┡━━━━━━━━━━━━━━━━━━┩ - │ float64 │ - ├──────────────────┤ - │ -1.570796 │ - │ 0.000000 │ - │ 1.570796 │ - └──────────────────┘ - """ - return ops.Atan2(self, other).to_expr() - - def cos(self) -> NumericValue: - """Compute the cosine of `self`. - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable({"values": [-1, 0, 1]}) - >>> t.values.cos() - ┏━━━━━━━━━━━━━┓ - ┃ Cos(values) ┃ - ┡━━━━━━━━━━━━━┩ - │ float64 │ - ├─────────────┤ - │ 0.540302 │ - │ 1.000000 │ - │ 0.540302 │ - └─────────────┘ - """ - return ops.Cos(self).to_expr() - - def cot(self) -> NumericValue: - """Compute the cotangent of `self`. - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable({"values": [-1, -2, 3]}) - >>> t.values.cot() - ┏━━━━━━━━━━━━━┓ - ┃ Cot(values) ┃ - ┡━━━━━━━━━━━━━┩ - │ float64 │ - ├─────────────┤ - │ -0.642093 │ - │ 0.457658 │ - │ -7.015253 │ - └─────────────┘ - """ - return ops.Cot(self).to_expr() - - def sin(self) -> NumericValue: - """Compute the sine of `self`. - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable({"values": [-1, 0, 1]}) - >>> t.values.sin() - ┏━━━━━━━━━━━━━┓ - ┃ Sin(values) ┃ - ┡━━━━━━━━━━━━━┩ - │ float64 │ - ├─────────────┤ - │ -0.841471 │ - │ 0.000000 │ - │ 0.841471 │ - └─────────────┘ - """ - return ops.Sin(self).to_expr() - - def tan(self) -> NumericValue: - """Compute the tangent of `self`. - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable({"values": [-1, 0, 1]}) - >>> t.values.tan() - ┏━━━━━━━━━━━━━┓ - ┃ Tan(values) ┃ - ┡━━━━━━━━━━━━━┩ - │ float64 │ - ├─────────────┤ - │ -1.557408 │ - │ 0.000000 │ - │ 1.557408 │ - └─────────────┘ - """ - return ops.Tan(self).to_expr() - - def __add__(self, other: NumericValue) -> NumericValue: - """Add `self` with `other`.""" - return _binop(ops.Add, self, other) - - add = radd = __radd__ = __add__ - - def __sub__(self, other: NumericValue) -> NumericValue: - """Subtract `other` from `self`.""" - return _binop(ops.Subtract, self, other) - - sub = __sub__ - - def __rsub__(self, other: NumericValue) -> NumericValue: - """Subtract `self` from `other`.""" - return _binop(ops.Subtract, other, self) - - rsub = __rsub__ - - def __mul__(self, other: NumericValue) -> NumericValue: - """Multiply `self` and `other`.""" - return _binop(ops.Multiply, self, other) - - mul = rmul = __rmul__ = __mul__ - - def __truediv__(self, other): - """Divide `self` by `other`.""" - return _binop(ops.Divide, self, other) - - div = __div__ = __truediv__ - - def __rtruediv__(self, other: NumericValue) -> NumericValue: - """Divide `other` by `self`.""" - return _binop(ops.Divide, other, self) - - rdiv = __rdiv__ = __rtruediv__ - - def __floordiv__( - self, - other: NumericValue, - ) -> NumericValue: - """Floor divide `self` by `other`.""" - return _binop(ops.FloorDivide, self, other) - - floordiv = __floordiv__ - - def __rfloordiv__( - self, - other: NumericValue, - ) -> NumericValue: - """Floor divide `other` by `self`.""" - return _binop(ops.FloorDivide, other, self) - - rfloordiv = __rfloordiv__ - - def __pow__(self, other: NumericValue) -> NumericValue: - """Raise `self` to the `other`th power.""" - return _binop(ops.Power, self, other) - - pow = __pow__ - - def __rpow__(self, other: NumericValue) -> NumericValue: - """Raise `other` to the `self`th power.""" - return _binop(ops.Power, other, self) - - rpow = __rpow__ - - def __mod__(self, other: NumericValue) -> NumericValue: - """Compute `self` modulo `other`.""" - return _binop(ops.Modulus, self, other) - - mod = __mod__ - - def __rmod__(self, other: NumericValue) -> NumericValue: - """Compute `other` modulo `self`.""" - - return _binop(ops.Modulus, other, self) - - rmod = __rmod__ - - def point(self, right: int | float | NumericValue) -> ir.PointValue: - """Return a point constructed from the coordinate values. - - Constant coordinates result in construction of a `POINT` literal or - column. - - Parameters - ---------- - right - Y coordinate - - Returns - ------- - PointValue - Points - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.examples.zones.fetch() - >>> t.x_cent.point(t.y_cent) - ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ GeoPoint(x_cent, y_cent) ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩ - │ point:geometry │ - ├──────────────────────────────────┤ - │ │ - │ │ - │ │ - │ │ - │ │ - │ │ - │ │ - │ │ - │ │ - │ │ - │ … │ - └──────────────────────────────────┘ - """ - return ops.GeoPoint(self, right).to_expr() - - -@public -class NumericScalar(Scalar, NumericValue): - pass - - -@public -class NumericColumn(Column, NumericValue): - def std( - self, - where: ir.BooleanValue | None = None, - how: Literal["sample", "pop"] = "sample", - ) -> NumericScalar: - """Return the standard deviation of a numeric column. - - Parameters - ---------- - where - Filter - how - Sample or population standard deviation - - Returns - ------- - NumericScalar - Standard deviation of `arg` - """ - return ops.StandardDev( - self, how=how, where=self._bind_to_parent_table(where) - ).to_expr() - - def var( - self, - where: ir.BooleanValue | None = None, - how: Literal["sample", "pop"] = "sample", - ) -> NumericScalar: - """Return the variance of a numeric column. - - Parameters - ---------- - where - Filter - how - Sample or population variance - - Returns - ------- - NumericScalar - Standard deviation of `arg` - """ - return ops.Variance( - self, how=how, where=self._bind_to_parent_table(where) - ).to_expr() - - def corr( - self, - right: NumericColumn, - where: ir.BooleanValue | None = None, - how: Literal["sample", "pop"] = "sample", - ) -> NumericScalar: - """Return the correlation of two numeric columns. - - Parameters - ---------- - right - Numeric column - where - Filter - how - Population or sample correlation - - Returns - ------- - NumericScalar - The correlation of `left` and `right` - """ - return ops.Correlation( - self, - self._bind_to_parent_table(right), - how=how, - where=self._bind_to_parent_table(where), - ).to_expr() - - def cov( - self, - right: NumericColumn, - where: ir.BooleanValue | None = None, - how: Literal["sample", "pop"] = "sample", - ) -> NumericScalar: - """Return the covariance of two numeric columns. - - Parameters - ---------- - right - Numeric column - where - Filter - how - Population or sample covariance - - Returns - ------- - NumericScalar - The covariance of `self` and `right` - """ - return ops.Covariance( - self, - self._bind_to_parent_table(right), - how=how, - where=self._bind_to_parent_table(where), - ).to_expr() - - def mean( - self, - where: ir.BooleanValue | None = None, - ) -> NumericScalar: - """Return the mean of a numeric column. - - Parameters - ---------- - where - Filter - - Returns - ------- - NumericScalar - The mean of the input expression - """ - # TODO(kszucs): remove the alias from the reduction method in favor - # of default name generated by ops.Value operations - return ops.Mean(self, where=self._bind_to_parent_table(where)).to_expr() - - def cummean(self, *, where=None, group_by=None, order_by=None) -> NumericColumn: - """Return the cumulative mean of the input.""" - return self.mean(where=where).over( - bigframes_vendored.ibis.cumulative_window( - group_by=group_by, order_by=order_by - ) - ) - - def sum( - self, - where: ir.BooleanValue | None = None, - ) -> NumericScalar: - """Return the sum of a numeric column. - - Parameters - ---------- - where - Filter - - Returns - ------- - NumericScalar - The sum of the input expression - """ - return ops.Sum(self, where=self._bind_to_parent_table(where)).to_expr() - - def cumsum(self, *, where=None, group_by=None, order_by=None) -> NumericColumn: - """Return the cumulative sum of the input.""" - return self.sum(where=where).over( - bigframes_vendored.ibis.cumulative_window( - group_by=group_by, order_by=order_by - ) - ) - - def bucket( - self, - buckets: Sequence[int], - closed: Literal["left", "right"] = "left", - close_extreme: bool = True, - include_under: bool = False, - include_over: bool = False, - ) -> ir.IntegerColumn: - """Compute a discrete binning of a numeric array. - - Parameters - ---------- - buckets - List of buckets - closed - Which side of each interval is closed. For example: - - ```python - buckets = [0, 100, 200] - closed = "left" # 100 falls in 2nd bucket - closed = "right" # 100 falls in 1st bucket - ``` - close_extreme - Whether the extreme values fall in the last bucket - include_over - Include values greater than the last bucket in the last bucket - include_under - Include values less than the first bucket in the first bucket - - Returns - ------- - IntegerColumn - A categorical column expression - """ - return ops.Bucket( - self, - buckets, - closed=closed, - close_extreme=close_extreme, - include_under=include_under, - include_over=include_over, - ).to_expr() - - def histogram( - self, - nbins: int | None = None, - binwidth: float | None = None, - base: float | None = None, - eps: float = 1e-13, - ): - """Compute a histogram with fixed width bins. - - Parameters - ---------- - nbins - If supplied, will be used to compute the binwidth - binwidth - If not supplied, computed from the data (actual max and min values) - base - The value of the first histogram bin. Defaults to the minimum value - of `column`. - eps - Allowed floating point epsilon for histogram base - - Returns - ------- - Column - Bucketed column - """ - - if nbins is not None and binwidth is not None: - raise ValueError( - f"Cannot pass both `nbins` (got {nbins}) and `binwidth` (got {binwidth})" - ) - - if binwidth is None or base is None: - if nbins is None: - raise ValueError("`nbins` is required if `binwidth` is not provided") - - if base is None: - base = self.min() - eps - - binwidth = (self.max() - base) / nbins - - return ((self - base) / binwidth).floor() - - -@public -class IntegerValue(NumericValue): - def to_timestamp( - self, - unit: Literal["s", "ms", "us"] = "s", - ) -> ir.TimestampValue: - """Convert an integral UNIX timestamp to a timestamp expression. - - Parameters - ---------- - unit - The resolution of `arg` - - Returns - ------- - TimestampValue - `self` converted to a timestamp - """ - return ops.TimestampFromUNIX(self, unit).to_expr() - - def to_interval( - self, - unit: Literal["Y", "M", "W", "D", "h", "m", "s", "ms", "us", "ns"] = "s", - ) -> ir.IntervalValue: - """Convert an integer to an interval. - - Parameters - ---------- - unit - Unit for the resulting interval - - Returns - ------- - IntervalValue - An interval in units of `unit` - """ - return ops.IntervalFromInteger(self, unit).to_expr() - - def convert_base( - self, - from_base: IntegerValue, - to_base: IntegerValue, - ) -> IntegerValue: - """Convert an integer from one base to another. - - Parameters - ---------- - from_base - Numeric base of expression - to_base - New base - - Returns - ------- - IntegerValue - Converted expression - """ - return ops.BaseConvert(self, from_base, to_base).to_expr() - - def __and__(self, other: IntegerValue) -> IntegerValue: - """Bitwise and `self` with `other`.""" - return _binop(ops.BitwiseAnd, self, other) - - __rand__ = __and__ - - def __or__(self, other: IntegerValue) -> IntegerValue: - """Bitwise or `self` with `other`.""" - return _binop(ops.BitwiseOr, self, other) - - __ror__ = __or__ - - def __xor__(self, other: IntegerValue) -> IntegerValue: - """Bitwise xor `self` with `other`.""" - return _binop(ops.BitwiseXor, self, other) - - __rxor__ = __xor__ - - def __lshift__(self, other: IntegerValue) -> IntegerValue: - """Bitwise left shift `self` with `other`.""" - return _binop(ops.BitwiseLeftShift, self, other) - - def __rlshift__(self, other: IntegerValue) -> IntegerValue: - """Bitwise left shift `self` with `other`.""" - return _binop(ops.BitwiseLeftShift, other, self) - - def __rshift__(self, other: IntegerValue) -> IntegerValue: - """Bitwise right shift `self` with `other`.""" - return _binop(ops.BitwiseRightShift, self, other) - - def __rrshift__(self, other: IntegerValue) -> IntegerValue: - """Bitwise right shift `self` with `other`.""" - return _binop(ops.BitwiseRightShift, other, self) - - def __invert__(self) -> IntegerValue: - """Bitwise not of `self`. - - Returns - ------- - IntegerValue - Inverted bits of `self`. - """ - try: - node = ops.BitwiseNot(self) - except (IbisTypeError, NotImplementedError): - return NotImplemented - else: - return node.to_expr() - - def label(self, labels: Iterable[str], nulls: str | None = None) -> ir.StringValue: - """Label a set of integer values with strings. - - Parameters - ---------- - labels - An iterable of string labels. Each integer value in `self` will be mapped to - a value in `labels`. - nulls - String label to use for `NULL` values - - Returns - ------- - StringValue - `self` labeled with `labels` - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable({"a": [0, 1, 0, 2]}) - >>> t.select(t.a, labeled=t.a.label(["a", "b", "c"])) - ┏━━━━━━━┳━━━━━━━━━┓ - ┃ a ┃ labeled ┃ - ┡━━━━━━━╇━━━━━━━━━┩ - │ int64 │ string │ - ├───────┼─────────┤ - │ 0 │ a │ - │ 1 │ b │ - │ 0 │ a │ - │ 2 │ c │ - └───────┴─────────┘ - """ - return ( - functools.reduce( - lambda stmt, inputs: stmt.when(*inputs), enumerate(labels), self.case() - ) - .else_(nulls) - .end() - ) - - -@public -class IntegerScalar(NumericScalar, IntegerValue): - pass - - -@public -class IntegerColumn(NumericColumn, IntegerValue): - def bit_and(self, where: ir.BooleanValue | None = None) -> IntegerScalar: - """Aggregate the column using the bitwise and operator.""" - return ops.BitAnd(self, where=self._bind_to_parent_table(where)).to_expr() - - def bit_or(self, where: ir.BooleanValue | None = None) -> IntegerScalar: - """Aggregate the column using the bitwise or operator.""" - return ops.BitOr(self, where=self._bind_to_parent_table(where)).to_expr() - - def bit_xor(self, where: ir.BooleanValue | None = None) -> IntegerScalar: - """Aggregate the column using the bitwise exclusive or operator.""" - return ops.BitXor(self, where=self._bind_to_parent_table(where)).to_expr() - - -@public -class FloatingValue(NumericValue): - def isnan(self) -> ir.BooleanValue: - """Return whether the value is NaN.""" - return ops.IsNan(self).to_expr() - - def isinf(self) -> ir.BooleanValue: - """Return whether the value is infinity.""" - return ops.IsInf(self).to_expr() - - -@public -class FloatingScalar(NumericScalar, FloatingValue): - pass - - -@public -class FloatingColumn(NumericColumn, FloatingValue): - pass - - -@public -class DecimalValue(NumericValue): - pass - - -@public -class DecimalScalar(NumericScalar, DecimalValue): - pass - - -@public -class DecimalColumn(NumericColumn, DecimalValue): - pass diff --git a/third_party/bigframes_vendored/ibis/expr/types/pretty.py b/third_party/bigframes_vendored/ibis/expr/types/pretty.py deleted file mode 100644 index 22617d84615..00000000000 --- a/third_party/bigframes_vendored/ibis/expr/types/pretty.py +++ /dev/null @@ -1,487 +0,0 @@ -# Contains code from https://github.com/ibis-project/ibis/blob/9.2.0/ibis/expr/types/pretty.py - -from __future__ import annotations - -import datetime -import json -from functools import singledispatch -from math import isfinite -from typing import TYPE_CHECKING -from urllib.parse import urlparse - -import bigframes_vendored.ibis -import bigframes_vendored.ibis.expr.datatypes as dt -import rich -import rich.table -from rich import box -from rich.align import Align -from rich.panel import Panel -from rich.pretty import Pretty -from rich.text import Text - -if TYPE_CHECKING: - from bigframes_vendored.ibis.expr.types import Column, Expr, Scalar, Table - - -def _format_nested( - values, - *, - max_length: int | None = None, - max_string: int | None = None, - max_depth: int | None = None, -): - return [ - Pretty( - v, - max_length=max_length - or bigframes_vendored.ibis.options.repr.interactive.max_length, - max_string=max_string - or bigframes_vendored.ibis.options.repr.interactive.max_string, - max_depth=max_depth - or bigframes_vendored.ibis.options.repr.interactive.max_depth, - ) - for v in values - ] - - -@singledispatch -def format_values(dtype, values, **fmt_kwargs): - return _format_nested(values, **fmt_kwargs) - - -@format_values.register(dt.Map) -def _(dtype, values, **fmt_kwargs): - return _format_nested( - [None if v is None else dict(v) for v in values], **fmt_kwargs - ) - - -@format_values.register(dt.GeoSpatial) -def _(dtype, values, **fmt_kwargs): - import shapely - - return _format_nested( - [None if v is None else shapely.from_wkb(v) for v in values], **fmt_kwargs - ) - - -@format_values.register(dt.JSON) -def _(dtype, values, **fmt_kwargs): - def try_json(v): - if v is None: - return None - try: - return json.loads(v) - except Exception: # noqa: BLE001 - return v - - return _format_nested([try_json(v) for v in values], **fmt_kwargs) - - -@format_values.register(dt.Boolean) -@format_values.register(dt.UUID) -def _(dtype, values, **fmt_kwargs): - return [Text(str(v)) for v in values] - - -@format_values.register(dt.Decimal) -def _(dtype, values, **fmt_kwargs): - if dtype.scale is not None: - fmt = f"{{:.{dtype.scale}f}}" - return [Text.styled(fmt.format(v), "bold cyan") for v in values] - else: - # No scale specified, convert to float and repr that way - return format_values(dt.float64, [float(v) for v in values]) - - -@format_values.register(dt.Integer) -def _(dtype, values, **fmt_kwargs): - return [Text.styled(str(int(v)), "bold cyan") for v in values] - - -@format_values.register(dt.Floating) -def _(dtype, values, **fmt_kwargs): - floats = [float(v) for v in values] - # Extract and format all finite floats - finites = [f for f in floats if isfinite(f)] - if finites and all(f == 0 or 1e-6 < abs(f) < 1e6 for f in finites): - strs = [f"{f:f}" for f in finites] - # Trim matching trailing zeros - while all(s.endswith("0") for s in strs): - strs = [s[:-1] for s in strs] - strs = [s + "0" if s.endswith(".") else s for s in strs] - else: - strs = [f"{f:e}" for f in finites] - # Merge together the formatted finite floats with non-finite values - iterstrs = iter(strs) - strs2 = (next(iterstrs) if isfinite(f) else str(f) for f in floats) - return [Text.styled(s, "bold cyan") for s in strs2] - - -@format_values.register(dt.Timestamp) -def _(dtype, values, **fmt_kwargs): - if all(v.microsecond == 0 for v in values): - timespec = "seconds" - elif all(v.microsecond % 1000 == 0 for v in values): - timespec = "milliseconds" - else: - timespec = "microseconds" - return [ - Text.styled(v.isoformat(sep=" ", timespec=timespec), "magenta") for v in values - ] - - -@format_values.register(dt.Date) -def _(dtype, values, **fmt_kwargs): - dates = [v.date() if isinstance(v, datetime.datetime) else v for v in values] - return [Text.styled(d.isoformat(), "magenta") for d in dates] - - -@format_values.register(dt.Time) -def _(dtype, values, **fmt_kwargs): - times = [v.time() if isinstance(v, datetime.datetime) else v for v in values] - if all(t.microsecond == 0 for t in times): - timespec = "seconds" - elif all(t.microsecond % 1000 == 0 for t in times): - timespec = "milliseconds" - else: - timespec = "microseconds" - return [Text.styled(t.isoformat(timespec=timespec), "magenta") for t in times] - - -@format_values.register(dt.Interval) -def _(dtype, values, **fmt_kwargs): - return [Text.styled(str(v), "magenta") for v in values] - - -_str_escapes = str.maketrans( - { - "\t": r"[orange3]\t[/]", - "\r": r"[orange3]\r[/]", - "\n": r"[orange3]\n[/]", - "\v": r"[orange3]\v[/]", - "\f": r"[orange3]\f[/]", - } -) - - -@format_values.register(dt.String) -def _(dtype, values, *, max_string: int | None = None, **fmt_kwargs): - max_string = ( - max_string or bigframes_vendored.ibis.options.repr.interactive.max_string - ) - out = [] - for v in values: - v = str(v) - if v: - raw_v = v - if len(v) > max_string: - v = v[: max_string - 1] + "…" - v = v[:max_string] - # Escape all literal `[` so rich doesn't treat them as markup - v = v.replace("[", r"\[") - # Replace ascii escape characters dimmed versions of their repr - v = v.translate(_str_escapes) - if not v.isprintable(): - # display all unprintable characters as a dimmed version of - # their repr - v = "".join( - f"[dim]{repr(c)[1:-1]}[/]" if not c.isprintable() else c for c in v - ) - url = urlparse(raw_v) - # check both scheme and netloc to avoid rendering e.g., - # `https://` as link - if url.scheme and url.netloc: - v = f"[link={raw_v}]{v}[/link]" - text = Text.from_markup(v, style="green") - else: - text = Text.styled("~", "dim") - out.append(text) - return out - - -def format_column( - dtype, - values, - *, - max_length: int | None = None, - max_string: int | None = None, - max_depth: int | None = None, -): - import pandas as pd - - null_str = Text.styled("NULL", style="dim") - if dtype.is_floating(): - # We don't want to treat `nan` as `NULL` for floating point types - def isnull(x): - return x is None or x is pd.NA - - else: - - def isnull(x): - o = pd.isna(x) - # pd.isna broadcasts if `x` is an array - return o if isinstance(o, bool) else False - - nonnull = [v for v in values if not isnull(v)] - if nonnull: - formatted = format_values( - dtype, - nonnull, - max_length=max_length, - max_string=max_string, - max_depth=max_depth, - ) - next_f = iter(formatted).__next__ - out = [null_str if isnull(v) else next_f() for v in values] - else: - out = [null_str] * len(values) - - try: - max_width = max(map(len, out)) - except Exception: # noqa: BLE001 - max_width = None - min_width = 20 - else: - if dtype.is_string(): - min_width = min(20, max_width) - else: - min_width = max_width - - return out, min_width, max_width - - -def format_dtype(dtype, max_string: int) -> Text: - strtyp = str(dtype) - if len(strtyp) > max_string: - strtyp = strtyp[: max_string - 1] + "…" - return Text.styled(strtyp, "dim") - - -def to_rich( - expr: Expr, - *, - max_rows: int | None = None, - max_columns: int | None = None, - max_length: int | None = None, - max_string: int | None = None, - max_depth: int | None = None, - console_width: int | float | None = None, -) -> Pretty: - """Truncate, evaluate, and render an Ibis expression as a rich object.""" - from bigframes_vendored.ibis.expr.types import Scalar - - if isinstance(expr, Scalar): - return _to_rich_scalar( - expr, max_length=max_length, max_string=max_string, max_depth=max_depth - ) - else: - return _to_rich_table( - expr, - max_rows=max_rows, - max_columns=max_columns, - max_length=max_length, - max_string=max_string, - max_depth=max_depth, - console_width=console_width, - ) - - -def _to_rich_scalar( - expr: Scalar, - *, - max_length: int | None = None, - max_string: int | None = None, - max_depth: int | None = None, -) -> Pretty: - scalar = Pretty( - expr.execute(), - max_length=max_length - or bigframes_vendored.ibis.options.repr.interactive.max_length, - max_string=max_string - or bigframes_vendored.ibis.options.repr.interactive.max_string, - max_depth=max_depth - or bigframes_vendored.ibis.options.repr.interactive.max_depth, - ) - return Panel(scalar, expand=False, box=box.SQUARE) - - -def _to_rich_table( - tablish: Table | Column, - *, - max_rows: int | None = None, - max_columns: int | None = None, - max_length: int | None = None, - max_string: int | None = None, - max_depth: int | None = None, - console_width: int | float | None = None, -) -> rich.table.Table: - max_rows = max_rows or bigframes_vendored.ibis.options.repr.interactive.max_rows - max_columns = ( - max_columns or bigframes_vendored.ibis.options.repr.interactive.max_columns - ) - console_width = console_width or float("inf") - max_string = ( - max_string or bigframes_vendored.ibis.options.repr.interactive.max_string - ) - show_types = bigframes_vendored.ibis.options.repr.interactive.show_types - - table = tablish.as_table() - orig_ncols = len(table.columns) - - if console_width == float("inf"): - # When there's infinite display space, only subset columns - # if an explicit limit has been set. - if max_columns and max_columns < orig_ncols: - table = table.select(*table.columns[:max_columns]) - else: - # Determine the maximum subset of columns that *might* fit in the - # current console. Note that not every column here may actually fit - # later on once we know the repr'd width of the data. - computed_cols = [] - remaining = console_width - 1 # 1 char for left boundary - for c in table.columns: - needed = len(c) + 3 # padding + 1 char for right boundary - if ( - needed < remaining or not computed_cols - ): # always select at least one col - computed_cols.append(c) - remaining -= needed - else: - break - if max_columns not in (0, None): - # If an explicit limit on max columns is set, apply it - computed_cols = computed_cols[:max_columns] - if orig_ncols > len(computed_cols): - table = table.select(*computed_cols) - - result = table.limit(max_rows + 1).to_pyarrow() - # Now format the columns in order, stopping if the console width would - # be exceeded. - col_info = [] - col_data = [] - formatted_dtypes = [] - remaining = console_width - 1 # 1 char for left boundary - for name, dtype in table.schema().items(): - formatted, min_width, max_width = format_column( - dtype, - result[name].to_pylist()[:max_rows], - max_length=max_length, - max_string=max_string, - max_depth=max_depth, - ) - dtype_str = format_dtype(dtype, max_string) - if show_types and not isinstance(dtype, (dt.Struct, dt.Map, dt.Array)): - # Don't truncate non-nested dtypes - min_width = max(min_width, len(dtype_str)) - - min_width = max(min_width, len(name)) - if max_width is not None: - max_width = max(min_width, max_width) - needed = min_width + 3 # padding + 1 char for right boundary - if needed < remaining: - col_info.append((name, dtype, min_width, max_width)) - col_data.append(formatted) - formatted_dtypes.append(dtype_str) - remaining -= needed - elif not col_info: - # Always pretty print at least one column. If only one column, we - # truncate to fill the available space, leaving room for the - # ellipsis & framing. - min_width = remaining - 3 # 3 for framing - if orig_ncols > 1: - min_width -= 4 # 4 for ellipsis - col_info.append((name, dtype, min_width, min_width)) - col_data.append(formatted) - formatted_dtypes.append(dtype_str) - break - else: - if remaining < 4: - # Not enough space for ellipsis column, drop previous column - col_info.pop() - col_data.pop() - formatted_dtypes.pop() - break - - # rich's column width computations are super buggy and can result in tables - # that are much wider than the available console space. To work around this - # for now we manually compute all column widths rather than letting rich - # figure it out for us. - columns_truncated = orig_ncols > len(col_info) - col_widths = {} - if console_width == float("inf"): - # Always use the max_width if there's infinite console space - for name, _, _, max_width in col_info: - col_widths[name] = max_width - else: - # Allocate the remaining space evenly between the flexible columns - flex_cols = [] - remaining = console_width - 1 - if columns_truncated: - remaining -= 4 - for name, _, min_width, max_width in col_info: - remaining -= min_width + 3 - col_widths[name] = min_width - if min_width != max_width: - flex_cols.append((name, max_width)) - - while True: - next_flex_cols = [] - for name, max_width in flex_cols: - if remaining: - remaining -= 1 - if max_width is not None: - col_widths[name] += 1 - if max_width is None or col_widths[name] < max_width: - next_flex_cols.append((name, max_width)) - else: - break - if not next_flex_cols: - break - - rich_table = rich.table.Table(padding=(0, 1, 0, 1)) - - # Configure the columns on the rich table. - for name, dtype, _, max_width in col_info: - rich_table.add_column( - Align(name, align="left"), - justify="right" if dtype.is_numeric() else "left", - vertical="middle", - width=None if max_width is None else col_widths[name], - min_width=None if max_width is not None else col_widths[name], - no_wrap=max_width is not None, - ) - - # If the columns are truncated, add a trailing ellipsis column - if columns_truncated: - rich_table.add_column( - Align("…", align="left"), - justify="left", - vertical="middle", - width=1, - min_width=1, - no_wrap=True, - ) - - def add_row(*args, **kwargs): - rich_table.add_row(*args, Align("[dim]…[/]", align="left"), **kwargs) - - else: - add_row = rich_table.add_row - - if show_types: - add_row( - *(Align(s, align="left") for s in formatted_dtypes), - end_section=True, - ) - - for row in zip(*col_data): - add_row(*row) - - # If the rows are truncated, add a trailing ellipsis row - if len(result) > max_rows: - rich_table.add_row( - *(Align("[dim]…[/]", align=c.justify) for c in rich_table.columns) - ) - - return rich_table diff --git a/third_party/bigframes_vendored/ibis/expr/types/relations.py b/third_party/bigframes_vendored/ibis/expr/types/relations.py deleted file mode 100644 index 956bb95dfae..00000000000 --- a/third_party/bigframes_vendored/ibis/expr/types/relations.py +++ /dev/null @@ -1,4892 +0,0 @@ -# Contains code from https://github.com/ibis-project/ibis/blob/9.2.0/ibis/expr/types/relations.py - -from __future__ import annotations - -import itertools -import operator -import re -from collections import deque -from collections.abc import Callable, Iterable, Iterator, Mapping, Sequence -from keyword import iskeyword -from typing import TYPE_CHECKING, Any, Literal - -import bigframes_vendored.ibis -import bigframes_vendored.ibis.common.exceptions as com -import bigframes_vendored.ibis.expr.datatypes as dt -import bigframes_vendored.ibis.expr.operations as ops -import bigframes_vendored.ibis.expr.schema as sch -import toolz -from bigframes_vendored.ibis import util -from bigframes_vendored.ibis.common.deferred import Deferred, Resolver -from bigframes_vendored.ibis.common.selectors import Selector -from bigframes_vendored.ibis.expr.rewrites import DerefMap -from bigframes_vendored.ibis.expr.types.core import Expr, _FixedTextJupyterMixin -from bigframes_vendored.ibis.expr.types.generic import Value, literal -from bigframes_vendored.ibis.expr.types.pretty import to_rich -from bigframes_vendored.ibis.expr.types.temporal import TimestampColumn -from bigframes_vendored.ibis.util import deprecated -from public import public - -if TYPE_CHECKING: - import bigframes_vendored.ibis.expr.types as ir - import bigframes_vendored.ibis.selectors as s - import pandas as pd - import polars as pl - import pyarrow as pa - from bigframes_vendored.ibis.expr.operations.relations import JoinKind, Set - from bigframes_vendored.ibis.expr.schema import SchemaLike - from bigframes_vendored.ibis.expr.types import Table - from bigframes_vendored.ibis.expr.types.groupby import GroupedTable - from bigframes_vendored.ibis.expr.types.temporal_windows import WindowedTable - from bigframes_vendored.ibis.formats.pyarrow import PyArrowData - from bigframes_vendored.ibis.selectors import IfAnyAll - from rich.table import Table as RichTable - - -def _regular_join_method( - name: str, - how: Literal[ - "inner", - "left", - "outer", - "right", - "semi", - "anti", - "any_inner", - "any_left", - ], -): - def f( # noqa: D417 - self: ir.Table, - right: ir.Table, - predicates: ( - str - | Sequence[str | tuple[str | ir.Column, str | ir.Column] | ir.BooleanValue] - ) = (), - *, - lname: str = "", - rname: str = "{name}_right", - ) -> ir.Table: - """Perform a join between two tables. - - Parameters - ---------- - right - Right table to join - predicates - Boolean or column names to join on - lname - A format string to use to rename overlapping columns in the left - table (e.g. ``"left_{name}"``). - rname - A format string to use to rename overlapping columns in the right - table (e.g. ``"right_{name}"``). - - Returns - ------- - Table - Joined table - """ - return self.join(right, predicates, how=how, lname=lname, rname=rname) - - f.__name__ = name - return f - - -def bind(table: Table, value) -> Iterator[ir.Value]: - """Bind a value to a table expression.""" - if isinstance(value, str): - # TODO(kszucs): perhaps use getattr(table, value) instead for nicer error msg - yield ops.Field(table, value).to_expr() - elif isinstance(value, ops.Value): - yield value.to_expr() - elif isinstance(value, Value): - yield value - elif isinstance(value, Table): - for name in value.columns: - yield ops.Field(value, name).to_expr() - elif isinstance(value, Deferred): - yield value.resolve(table) - elif isinstance(value, Resolver): - yield value.resolve({"_": table}) - elif isinstance(value, Selector): - yield from value.expand(table) - elif callable(value): - # rebind, otherwise the callable is required to return an expression - # which would preclude support for expressions like lambda _: 2 - yield from bind(table, value(table)) - else: - yield literal(value) - - -def unwrap_aliases(values: Iterator[ir.Value]) -> Mapping[str, ir.Value]: - """Unwrap aliases into a mapping of {name: expression}.""" - result = {} - for value in values: - node = value.op() - if node.name in result: - raise com.IbisInputError( - f"Duplicate column name {node.name!r} in result set" - ) - if isinstance(node, ops.Alias): - result[node.name] = node.arg - else: - result[node.name] = node - return result - - -@public -class Table(Expr, _FixedTextJupyterMixin): - """An immutable and lazy dataframe. - - Analogous to a SQL table or a pandas DataFrame. A table expression contains - an [ordered set of named columns](./schemas.qmd#ibis.expr.schema.Schema), - each with a single known type. Unless explicitly ordered with an - [`.order_by()`](./expression-tables.qmd#ibis.expr.types.relations.Table.order_by), - the order of rows is undefined. - - Table immutability means that the data underlying an Ibis `Table` cannot be modified: every - method on a Table returns a new Table with those changes. Laziness - means that an Ibis `Table` expression does not run your computation every time you call one of its methods. - Instead, it is a symbolic expression that represents a set of operations - to be performed, which typically is translated into a SQL query. That - SQL query is then executed on a backend, where the data actually lives. - The result (now small enough to be manageable) can then be materialized back - into python as a pandas/pyarrow/python DataFrame/Column/scalar. - - You will not create Table objects directly. Instead, you will create one - - - from a pandas DataFrame, pyarrow table, Polars table, or raw python dicts/lists - with [`ibis.memtable(df)`](./expression-tables.qmd#ibis.memtable) - - from an existing table in a data platform with - [`connection.table("name")`](./expression-tables.qmd#ibis.backends.duckdb.Backend.table) - - from a file or URL, into a specific backend with - [`connection.read_csv/parquet/json("path/to/file")`](../backends/duckdb.qmd#ibis.backends.duckdb.Backend.read_csv) - (only some backends, typically local ones, support this) - - from a file or URL, into the default backend with - [`ibis.read_csv/read_json/read_parquet("path/to/file")`](./expression-tables.qmd#ibis.read_csv) - - See the [user guide](https://ibis-project.org/how-to/input-output/basics) for more - info. - """ - - # Higher than numpy & dask objects - __array_priority__ = 20 - - __array_ufunc__ = None - - def get_name(self) -> str: - """Return the fully qualified name of the table.""" - arg = self._arg - namespace = getattr(arg, "namespace", ops.Namespace()) - pieces = namespace.catalog, namespace.database, arg.name - return ".".join(filter(None, pieces)) - - def __array__(self, dtype=None): - return self.execute().__array__(dtype) - - def __dataframe__(self, nan_as_null: bool = False, allow_copy: bool = True): - from bigframes_vendored.ibis.expr.types.dataframe_interchange import ( - IbisDataFrame, - ) - - return IbisDataFrame(self, nan_as_null=nan_as_null, allow_copy=allow_copy) - - def __arrow_c_stream__(self, requested_schema: object | None = None) -> object: - return self.to_pyarrow().__arrow_c_stream__(requested_schema) - - def __pyarrow_result__( - self, table: pa.Table, data_mapper: type[PyArrowData] | None = None - ) -> pa.Table: - if data_mapper is None: - from bigframes_vendored.ibis.formats.pyarrow import ( - PyArrowData as data_mapper, - ) - - return data_mapper.convert_table(table, self.schema()) - - def __pandas_result__( - self, df: pd.DataFrame, schema: sch.Schema | None = None - ) -> pd.DataFrame: - from bigframes_vendored.ibis.formats.pandas import PandasData - - return PandasData.convert_table(df, self.schema() if schema is None else schema) - - def __polars_result__(self, df: pl.DataFrame) -> Any: - from bigframes_vendored.ibis.formats.polars import PolarsData - - return PolarsData.convert_table(df, self.schema()) - - def _fast_bind(self, *args, **kwargs): - # allow the first argument to be either a dictionary or a list of values - if len(args) == 1: - if isinstance(args[0], dict): - kwargs = {**args[0], **kwargs} - args = () - else: - args = util.promote_list(args[0]) - # bind positional arguments - values = [] - for arg in args: - values.extend(bind(self, arg)) - - # bind keyword arguments where each entry can produce only one value - # which is then named with the given key - for key, arg in kwargs.items(): - bindings = tuple(bind(self, arg)) - if len(bindings) != 1: - raise com.IbisInputError( - "Keyword arguments cannot produce more than one value" - ) - (value,) = bindings - values.append(value.name(key)) - return values - - def bind(self, *args: Any, **kwargs: Any) -> tuple[Value, ...]: - """Bind column values to a table expression. - - This method handles the binding of every kind of column-like value that - Ibis handles, including strings, integers, deferred expressions and - selectors, to a table expression. - - Parameters - ---------- - args - Column-like values to bind. - kwargs - Column-like values to bind, with names. - - Returns - ------- - tuple[Value, ...] - A tuple of bound values - """ - values = self._fast_bind(*args, **kwargs) - # dereference the values to `self` - dm = DerefMap.from_targets(self.op()) - result = [] - for original in values: - value = dm.dereference(original.op()).to_expr() - value = value.name(original.get_name()) - result.append(value) - return tuple(result) - - def as_scalar(self) -> ir.ScalarExpr: - """Inform ibis that the table expression should be treated as a scalar. - - Note that the table must have exactly one column and one row for this to - work. If the table has more than one column an error will be raised in - expression construction time. If the table has more than one row an - error will be raised by the backend when the expression is executed. - - Returns - ------- - Scalar - A scalar subquery - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.examples.penguins.fetch() - >>> heavy_gentoo = t.filter(t.species == "Gentoo", t.body_mass_g > 6200) - >>> from_that_island = t.filter(t.island == heavy_gentoo.select("island").as_scalar()) - >>> from_that_island.species.value_counts().order_by("species") - ┏━━━━━━━━━┳━━━━━━━━━━━━━━━┓ - ┃ species ┃ species_count ┃ - ┡━━━━━━━━━╇━━━━━━━━━━━━━━━┩ - │ string │ int64 │ - ├─────────┼───────────────┤ - │ Adelie │ 44 │ - │ Gentoo │ 124 │ - └─────────┴───────────────┘ - """ - return ops.ScalarSubquery(self).to_expr() - - def as_table(self) -> Table: - """Promote the expression to a table. - - This method is a no-op for table expressions. - - Returns - ------- - Table - A table expression - - Examples - -------- - >>> t = ibis.table(dict(a="int"), name="t") - >>> s = t.as_table() - >>> t is s - True - """ - return self - - def __contains__(self, name: str) -> bool: - """Return whether `name` is a column in the table. - - Parameters - ---------- - name - Possible column name - - Returns - ------- - bool - Whether `name` is a column in `self` - - Examples - -------- - >>> t = ibis.table(dict(a="string", b="float"), name="t") - >>> "a" in t - True - >>> "c" in t - False - """ - return name in self.schema() - - def cast(self, schema: SchemaLike) -> Table: - """Cast the columns of a table. - - Similar to `pandas.DataFrame.astype`. - - ::: {.callout-note} - ## If you need to cast columns to a single type, use [selectors](./selectors.qmd). - ::: - - Parameters - ---------- - schema - Mapping, schema or iterable of pairs to use for casting - - Returns - ------- - Table - Casted table - - Examples - -------- - >>> import ibis - >>> import ibis.selectors as s - >>> ibis.options.interactive = True - >>> t = ibis.examples.penguins.fetch() - >>> t.schema() - ibis.Schema { - species string - island string - bill_length_mm float64 - bill_depth_mm float64 - flipper_length_mm int64 - body_mass_g int64 - sex string - year int64 - } - >>> cols = ["body_mass_g", "bill_length_mm"] - >>> t[cols].head() - ┏━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┓ - ┃ body_mass_g ┃ bill_length_mm ┃ - ┡━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━┩ - │ int64 │ float64 │ - ├─────────────┼────────────────┤ - │ 3750 │ 39.1 │ - │ 3800 │ 39.5 │ - │ 3250 │ 40.3 │ - │ NULL │ NULL │ - │ 3450 │ 36.7 │ - └─────────────┴────────────────┘ - - Columns not present in the input schema will be passed through unchanged - - >>> t.columns - ['species', 'island', 'bill_length_mm', 'bill_depth_mm', 'flipper_length_mm', 'body_mass_g', 'sex', 'year'] - >>> expr = t.cast({"body_mass_g": "float64", "bill_length_mm": "int"}) - >>> expr.select(*cols).head() - ┏━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┓ - ┃ body_mass_g ┃ bill_length_mm ┃ - ┡━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━┩ - │ float64 │ int64 │ - ├─────────────┼────────────────┤ - │ 3750.0 │ 39 │ - │ 3800.0 │ 40 │ - │ 3250.0 │ 40 │ - │ NULL │ NULL │ - │ 3450.0 │ 37 │ - └─────────────┴────────────────┘ - - Columns that are in the input `schema` but not in the table raise an error - - >>> t.cast({"foo": "string"}) # quartodoc: +EXPECTED_FAILURE - Traceback (most recent call last): - ... - ibis.common.exceptions.IbisError: Cast schema has fields that are not in the table: ['foo'] - """ - return self._cast(schema, cast_method="cast") - - def try_cast(self, schema: SchemaLike) -> Table: - """Cast the columns of a table. - - If the cast fails for a row, the value is returned - as `NULL` or `NaN` depending on backend behavior. - - Parameters - ---------- - schema - Mapping, schema or iterable of pairs to use for casting - - Returns - ------- - Table - Casted table - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable({"a": ["1", "2", "3"], "b": ["2.2", "3.3", "book"]}) - >>> t.try_cast({"a": "int", "b": "float"}) - ┏━━━━━━━┳━━━━━━━━━┓ - ┃ a ┃ b ┃ - ┡━━━━━━━╇━━━━━━━━━┩ - │ int64 │ float64 │ - ├───────┼─────────┤ - │ 1 │ 2.2 │ - │ 2 │ 3.3 │ - │ 3 │ NULL │ - └───────┴─────────┘ - """ - return self._cast(schema, cast_method="try_cast") - - def _cast(self, schema: SchemaLike, cast_method: str = "cast") -> Table: - schema = sch.schema(schema) - - cols = [] - - columns = self.columns - if missing_fields := frozenset(schema.names).difference(columns): - raise com.IbisError( - f"Cast schema has fields that are not in the table: {sorted(missing_fields)}" - ) - - for col in columns: - if (new_type := schema.get(col)) is not None: - new_col = getattr(self[col], cast_method)(new_type).name(col) - else: - new_col = col - cols.append(new_col) - return self.select(*cols) - - def preview( - self, - *, - max_rows: int | None = None, - max_columns: int | None = None, - max_length: int | None = None, - max_string: int | None = None, - max_depth: int | None = None, - console_width: int | float | None = None, - ) -> RichTable: - """Return a subset as a Rich Table. - - This is an explicit version of what you get when you inspect - this object in interactive mode, except with this version you - can pass formatting options. The options are the same as those exposed - in `ibis.options.interactive`. - - Parameters - ---------- - max_rows - Maximum number of rows to display - max_columns - Maximum number of columns to display - max_length - Maximum length for pretty-printed arrays and maps - max_string - Maximum length for pretty-printed strings - max_depth - Maximum depth for nested data types - console_width - Width of the console in characters. If not specified, the width - will be inferred from the console. - - Examples - -------- - >>> import ibis - >>> t = ibis.examples.penguins.fetch() - - Because the console_width is too small, only 2 columns are shown even though - we specified up to 3. - - >>> t.preview( - ... max_rows=3, - ... max_columns=3, - ... max_string=8, - ... console_width=30, - ... ) # doctest: +SKIP - ┏━━━━━━━━━┳━━━━━━━━━━┳━━━┓ - ┃ species ┃ island ┃ … ┃ - ┡━━━━━━━━━╇━━━━━━━━━━╇━━━┩ - │ string │ string │ … │ - ├─────────┼──────────┼───┤ - │ Adelie │ Torgers… │ … │ - │ Adelie │ Torgers… │ … │ - │ Adelie │ Torgers… │ … │ - │ … │ … │ … │ - └─────────┴──────────┴───┘ - """ - return to_rich( - self, - max_columns=max_columns, - max_rows=max_rows, - max_length=max_length, - max_string=max_string, - max_depth=max_depth, - console_width=console_width, - ) - - def __getitem__(self, what): - """Select items from a table expression. - - This method implements square bracket syntax for table expressions, - including various forms of projection and filtering. - - Parameters - ---------- - what - Selection object. This can be a variety of types including strings, ints, lists. - - Returns - ------- - Table | Column - The return type depends on the input. For a single string or int - input a column is returned, otherwise a table is returned. - - Examples - -------- - >>> import ibis - >>> import ibis.selectors as s - >>> from ibis import _ - >>> ibis.options.interactive = True - >>> t = ibis.examples.penguins.fetch() - >>> t - ┏━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━┓ - ┃ species ┃ island ┃ bill_length_mm ┃ bill_depth_mm ┃ flipper_length_mm ┃ … ┃ - ┡━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━┩ - │ string │ string │ float64 │ float64 │ int64 │ … │ - ├─────────┼───────────┼────────────────┼───────────────┼───────────────────┼───┤ - │ Adelie │ Torgersen │ 39.1 │ 18.7 │ 181 │ … │ - │ Adelie │ Torgersen │ 39.5 │ 17.4 │ 186 │ … │ - │ Adelie │ Torgersen │ 40.3 │ 18.0 │ 195 │ … │ - │ Adelie │ Torgersen │ NULL │ NULL │ NULL │ … │ - │ Adelie │ Torgersen │ 36.7 │ 19.3 │ 193 │ … │ - │ Adelie │ Torgersen │ 39.3 │ 20.6 │ 190 │ … │ - │ Adelie │ Torgersen │ 38.9 │ 17.8 │ 181 │ … │ - │ Adelie │ Torgersen │ 39.2 │ 19.6 │ 195 │ … │ - │ Adelie │ Torgersen │ 34.1 │ 18.1 │ 193 │ … │ - │ Adelie │ Torgersen │ 42.0 │ 20.2 │ 190 │ … │ - │ … │ … │ … │ … │ … │ … │ - └─────────┴───────────┴────────────────┴───────────────┴───────────────────┴───┘ - - Return a column by name - - >>> t["island"] - ┏━━━━━━━━━━━┓ - ┃ island ┃ - ┡━━━━━━━━━━━┩ - │ string │ - ├───────────┤ - │ Torgersen │ - │ Torgersen │ - │ Torgersen │ - │ Torgersen │ - │ Torgersen │ - │ Torgersen │ - │ Torgersen │ - │ Torgersen │ - │ Torgersen │ - │ Torgersen │ - │ … │ - └───────────┘ - - Return the second column, starting from index 0 - - >>> t.columns[1] - 'island' - >>> t[1] - ┏━━━━━━━━━━━┓ - ┃ island ┃ - ┡━━━━━━━━━━━┩ - │ string │ - ├───────────┤ - │ Torgersen │ - │ Torgersen │ - │ Torgersen │ - │ Torgersen │ - │ Torgersen │ - │ Torgersen │ - │ Torgersen │ - │ Torgersen │ - │ Torgersen │ - │ Torgersen │ - │ … │ - └───────────┘ - - Extract a range of rows - - >>> t[:2] - ┏━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━┓ - ┃ species ┃ island ┃ bill_length_mm ┃ bill_depth_mm ┃ flipper_length_mm ┃ … ┃ - ┡━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━┩ - │ string │ string │ float64 │ float64 │ int64 │ … │ - ├─────────┼───────────┼────────────────┼───────────────┼───────────────────┼───┤ - │ Adelie │ Torgersen │ 39.1 │ 18.7 │ 181 │ … │ - │ Adelie │ Torgersen │ 39.5 │ 17.4 │ 186 │ … │ - └─────────┴───────────┴────────────────┴───────────────┴───────────────────┴───┘ - >>> t[:5] - ┏━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━┓ - ┃ species ┃ island ┃ bill_length_mm ┃ bill_depth_mm ┃ flipper_length_mm ┃ … ┃ - ┡━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━┩ - │ string │ string │ float64 │ float64 │ int64 │ … │ - ├─────────┼───────────┼────────────────┼───────────────┼───────────────────┼───┤ - │ Adelie │ Torgersen │ 39.1 │ 18.7 │ 181 │ … │ - │ Adelie │ Torgersen │ 39.5 │ 17.4 │ 186 │ … │ - │ Adelie │ Torgersen │ 40.3 │ 18.0 │ 195 │ … │ - │ Adelie │ Torgersen │ NULL │ NULL │ NULL │ … │ - │ Adelie │ Torgersen │ 36.7 │ 19.3 │ 193 │ … │ - └─────────┴───────────┴────────────────┴───────────────┴───────────────────┴───┘ - >>> t[2:5] - ┏━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━┓ - ┃ species ┃ island ┃ bill_length_mm ┃ bill_depth_mm ┃ flipper_length_mm ┃ … ┃ - ┡━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━┩ - │ string │ string │ float64 │ float64 │ int64 │ … │ - ├─────────┼───────────┼────────────────┼───────────────┼───────────────────┼───┤ - │ Adelie │ Torgersen │ 40.3 │ 18.0 │ 195 │ … │ - │ Adelie │ Torgersen │ NULL │ NULL │ NULL │ … │ - │ Adelie │ Torgersen │ 36.7 │ 19.3 │ 193 │ … │ - └─────────┴───────────┴────────────────┴───────────────┴───────────────────┴───┘ - - Some backends support negative slice indexing - - >>> t[-5:] # last 5 rows - ┏━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━┓ - ┃ species ┃ island ┃ bill_length_mm ┃ bill_depth_mm ┃ flipper_length_mm ┃ … ┃ - ┡━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━┩ - │ string │ string │ float64 │ float64 │ int64 │ … │ - ├───────────┼────────┼────────────────┼───────────────┼───────────────────┼───┤ - │ Chinstrap │ Dream │ 55.8 │ 19.8 │ 207 │ … │ - │ Chinstrap │ Dream │ 43.5 │ 18.1 │ 202 │ … │ - │ Chinstrap │ Dream │ 49.6 │ 18.2 │ 193 │ … │ - │ Chinstrap │ Dream │ 50.8 │ 19.0 │ 210 │ … │ - │ Chinstrap │ Dream │ 50.2 │ 18.7 │ 198 │ … │ - └───────────┴────────┴────────────────┴───────────────┴───────────────────┴───┘ - >>> t[-5:-3] # last 5th to 3rd rows - ┏━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━┓ - ┃ species ┃ island ┃ bill_length_mm ┃ bill_depth_mm ┃ flipper_length_mm ┃ … ┃ - ┡━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━┩ - │ string │ string │ float64 │ float64 │ int64 │ … │ - ├───────────┼────────┼────────────────┼───────────────┼───────────────────┼───┤ - │ Chinstrap │ Dream │ 55.8 │ 19.8 │ 207 │ … │ - │ Chinstrap │ Dream │ 43.5 │ 18.1 │ 202 │ … │ - └───────────┴────────┴────────────────┴───────────────┴───────────────────┴───┘ - >>> t[2:-2] # chop off the first two and last two rows - ┏━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━┓ - ┃ species ┃ island ┃ bill_length_mm ┃ bill_depth_mm ┃ flipper_length_mm ┃ … ┃ - ┡━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━┩ - │ string │ string │ float64 │ float64 │ int64 │ … │ - ├─────────┼───────────┼────────────────┼───────────────┼───────────────────┼───┤ - │ Adelie │ Torgersen │ 40.3 │ 18.0 │ 195 │ … │ - │ Adelie │ Torgersen │ NULL │ NULL │ NULL │ … │ - │ Adelie │ Torgersen │ 36.7 │ 19.3 │ 193 │ … │ - │ Adelie │ Torgersen │ 39.3 │ 20.6 │ 190 │ … │ - │ Adelie │ Torgersen │ 38.9 │ 17.8 │ 181 │ … │ - │ Adelie │ Torgersen │ 39.2 │ 19.6 │ 195 │ … │ - │ Adelie │ Torgersen │ 34.1 │ 18.1 │ 193 │ … │ - │ Adelie │ Torgersen │ 42.0 │ 20.2 │ 190 │ … │ - │ Adelie │ Torgersen │ 37.8 │ 17.1 │ 186 │ … │ - │ Adelie │ Torgersen │ 37.8 │ 17.3 │ 180 │ … │ - │ … │ … │ … │ … │ … │ … │ - └─────────┴───────────┴────────────────┴───────────────┴───────────────────┴───┘ - - Select columns - - >>> t[["island", "bill_length_mm"]].head() - ┏━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┓ - ┃ island ┃ bill_length_mm ┃ - ┡━━━━━━━━━━━╇━━━━━━━━━━━━━━━━┩ - │ string │ float64 │ - ├───────────┼────────────────┤ - │ Torgersen │ 39.1 │ - │ Torgersen │ 39.5 │ - │ Torgersen │ 40.3 │ - │ Torgersen │ NULL │ - │ Torgersen │ 36.7 │ - └───────────┴────────────────┘ - >>> t["island", "bill_length_mm"].head() - ┏━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┓ - ┃ island ┃ bill_length_mm ┃ - ┡━━━━━━━━━━━╇━━━━━━━━━━━━━━━━┩ - │ string │ float64 │ - ├───────────┼────────────────┤ - │ Torgersen │ 39.1 │ - │ Torgersen │ 39.5 │ - │ Torgersen │ 40.3 │ - │ Torgersen │ NULL │ - │ Torgersen │ 36.7 │ - └───────────┴────────────────┘ - >>> t[_.island, _.bill_length_mm].head() - ┏━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┓ - ┃ island ┃ bill_length_mm ┃ - ┡━━━━━━━━━━━╇━━━━━━━━━━━━━━━━┩ - │ string │ float64 │ - ├───────────┼────────────────┤ - │ Torgersen │ 39.1 │ - │ Torgersen │ 39.5 │ - │ Torgersen │ 40.3 │ - │ Torgersen │ NULL │ - │ Torgersen │ 36.7 │ - └───────────┴────────────────┘ - - Filtering - - >>> t[t.island.lower() != "torgersen"].head() - ┏━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━┓ - ┃ species ┃ island ┃ bill_length_mm ┃ bill_depth_mm ┃ flipper_length_mm ┃ … ┃ - ┡━━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━┩ - │ string │ string │ float64 │ float64 │ int64 │ … │ - ├─────────┼────────┼────────────────┼───────────────┼───────────────────┼───┤ - │ Adelie │ Biscoe │ 37.8 │ 18.3 │ 174 │ … │ - │ Adelie │ Biscoe │ 37.7 │ 18.7 │ 180 │ … │ - │ Adelie │ Biscoe │ 35.9 │ 19.2 │ 189 │ … │ - │ Adelie │ Biscoe │ 38.2 │ 18.1 │ 185 │ … │ - │ Adelie │ Biscoe │ 38.8 │ 17.2 │ 180 │ … │ - └─────────┴────────┴────────────────┴───────────────┴───────────────────┴───┘ - - Selectors - - >>> t[~s.numeric() | (s.numeric() & ~s.c("year"))].head() - ┏━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━┓ - ┃ species ┃ island ┃ bill_length_mm ┃ bill_depth_mm ┃ flipper_length_mm ┃ … ┃ - ┡━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━┩ - │ string │ string │ float64 │ float64 │ int64 │ … │ - ├─────────┼───────────┼────────────────┼───────────────┼───────────────────┼───┤ - │ Adelie │ Torgersen │ 39.1 │ 18.7 │ 181 │ … │ - │ Adelie │ Torgersen │ 39.5 │ 17.4 │ 186 │ … │ - │ Adelie │ Torgersen │ 40.3 │ 18.0 │ 195 │ … │ - │ Adelie │ Torgersen │ NULL │ NULL │ NULL │ … │ - │ Adelie │ Torgersen │ 36.7 │ 19.3 │ 193 │ … │ - └─────────┴───────────┴────────────────┴───────────────┴───────────────────┴───┘ - >>> t[s.r["bill_length_mm":"body_mass_g"]].head() - ┏━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┓ - ┃ bill_length_mm ┃ bill_depth_mm ┃ flipper_length_mm ┃ body_mass_g ┃ - ┡━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━┩ - │ float64 │ float64 │ int64 │ int64 │ - ├────────────────┼───────────────┼───────────────────┼─────────────┤ - │ 39.1 │ 18.7 │ 181 │ 3750 │ - │ 39.5 │ 17.4 │ 186 │ 3800 │ - │ 40.3 │ 18.0 │ 195 │ 3250 │ - │ NULL │ NULL │ NULL │ NULL │ - │ 36.7 │ 19.3 │ 193 │ 3450 │ - └────────────────┴───────────────┴───────────────────┴─────────────┘ - """ - from bigframes_vendored.ibis.expr.types.logical import BooleanValue - - if isinstance(what, slice): - limit, offset = util.slice_to_limit_offset(what, self.count()) - return self.limit(limit, offset=offset) - # skip the self.bind call for single column access with strings or ints - # because dereferencing has significant overhead - elif isinstance(what, str): - return ops.Field(self.op(), what).to_expr() - elif isinstance(what, int): - return ops.Field(self.op(), self.columns[what]).to_expr() - - args = [ - self.columns[arg] if isinstance(arg, int) else arg - for arg in util.promote_list(what) - ] - values = self.bind(args) - - if isinstance(what, (str, int)): - assert len(values) == 1 - return values[0] - elif util.all_of(values, BooleanValue): - return self.filter(values) - else: - return self.select(values) - - def __len__(self): - raise com.ExpressionError("Use .count() instead") - - def __getattr__(self, key: str) -> ir.Column: - """Return the column name of a table. - - Parameters - ---------- - key - Column name - - Returns - ------- - Column - Column expression with name `key` - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.examples.penguins.fetch() - >>> t.island - ┏━━━━━━━━━━━┓ - ┃ island ┃ - ┡━━━━━━━━━━━┩ - │ string │ - ├───────────┤ - │ Torgersen │ - │ Torgersen │ - │ Torgersen │ - │ Torgersen │ - │ Torgersen │ - │ Torgersen │ - │ Torgersen │ - │ Torgersen │ - │ Torgersen │ - │ Torgersen │ - │ … │ - └───────────┘ - """ - try: - return ops.Field(self, key).to_expr() - except com.IbisTypeError: - pass - - # A mapping of common attribute typos, mapping them to the proper name - common_typos = { - "sort": "order_by", - "sort_by": "order_by", - "sortby": "order_by", - "orderby": "order_by", - "groupby": "group_by", - } - if key in common_typos: - hint = common_typos[key] - raise AttributeError( - f"{type(self).__name__} object has no attribute {key!r}, did you mean {hint!r}" - ) - - raise AttributeError(f"'Table' object has no attribute {key!r}") - - def __dir__(self) -> list[str]: - out = set(dir(type(self))) - out.update(c for c in self.columns if c.isidentifier() and not iskeyword(c)) - return sorted(out) - - def _ipython_key_completions_(self) -> list[str]: - return self.columns - - @property - def columns(self) -> list[str]: - """The list of column names in this table. - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.examples.penguins.fetch() - >>> t.columns - ['species', - 'island', - 'bill_length_mm', - 'bill_depth_mm', - 'flipper_length_mm', - 'body_mass_g', - 'sex', - 'year'] - """ - return list(self.schema().names) - - def schema(self) -> sch.Schema: - """Return the [Schema](./schemas.qmd#ibis.expr.schema.Schema) for this table. - - Returns - ------- - Schema - The table's schema. - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.examples.penguins.fetch() - >>> t.schema() - ibis.Schema { - species string - island string - bill_length_mm float64 - bill_depth_mm float64 - flipper_length_mm int64 - body_mass_g int64 - sex string - year int64 - } - """ - return self.op().schema - - def group_by( - self, - *by: str | ir.Value | Iterable[str] | Iterable[ir.Value] | None, - **key_exprs: str | ir.Value | Iterable[str] | Iterable[ir.Value], - ) -> GroupedTable: - """Create a grouped table expression. - - Similar to SQL's GROUP BY statement, or pandas .groupby() method. - - Parameters - ---------- - by - Grouping expressions - key_exprs - Named grouping expressions - - Returns - ------- - GroupedTable - A grouped table expression - - Examples - -------- - >>> import ibis - >>> from ibis import _ - >>> ibis.options.interactive = True - >>> t = ibis.memtable( - ... { - ... "fruit": ["apple", "apple", "banana", "orange"], - ... "price": [0.5, 0.5, 0.25, 0.33], - ... } - ... ) - >>> t - ┏━━━━━━━━┳━━━━━━━━━┓ - ┃ fruit ┃ price ┃ - ┡━━━━━━━━╇━━━━━━━━━┩ - │ string │ float64 │ - ├────────┼─────────┤ - │ apple │ 0.50 │ - │ apple │ 0.50 │ - │ banana │ 0.25 │ - │ orange │ 0.33 │ - └────────┴─────────┘ - >>> t.group_by("fruit").agg(total_cost=_.price.sum(), avg_cost=_.price.mean()).order_by( - ... "fruit" - ... ) - ┏━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━┓ - ┃ fruit ┃ total_cost ┃ avg_cost ┃ - ┡━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━┩ - │ string │ float64 │ float64 │ - ├────────┼────────────┼──────────┤ - │ apple │ 1.00 │ 0.50 │ - │ banana │ 0.25 │ 0.25 │ - │ orange │ 0.33 │ 0.33 │ - └────────┴────────────┴──────────┘ - """ - from bigframes_vendored.ibis.expr.types.groupby import GroupedTable - - by = tuple(v for v in by if v is not None) - groups = self.bind(*by, **key_exprs) - return GroupedTable(self, groups) - - # TODO(kszucs): shouldn't this be ibis.rowid() instead not bound to a specific table? - def rowid(self) -> ir.IntegerValue: - """A unique integer per row. - - ::: {.callout-note} - ## This operation is only valid on physical tables - - Any further meaning behind this expression is backend dependent. - Generally this corresponds to some index into the database storage - (for example, SQLite and DuckDB's `rowid`). - - For a monotonically increasing row number, see `ibis.row_number`. - ::: - - Returns - ------- - IntegerColumn - An integer column - """ - if not isinstance(self.op(), ops.PhysicalTable): - raise com.IbisTypeError( - "rowid() is only valid for physical tables, not for generic " - "table expressions" - ) - return ops.RowID(self).to_expr() - - def view(self) -> Table: - """Create a new table expression distinct from the current one. - - Use this API for any self-referencing operations like a self-join. - - Returns - ------- - Table - Table expression - """ - if isinstance(self.op(), ops.SelfReference): - return self - else: - return ops.SelfReference(self).to_expr() - - def aggregate( - self, - metrics: Sequence[ir.Scalar] | None = (), - by: Sequence[ir.Value] | None = (), - having: Sequence[ir.BooleanValue] | None = (), - **kwargs: ir.Value, - ) -> Table: - """Aggregate a table with a given set of reductions grouping by `by`. - - Parameters - ---------- - metrics - Aggregate expressions. These can be any scalar-producing - expression, including aggregation functions like `sum` or literal - values like `ibis.literal(1)`. - by - Grouping expressions. - having - Post-aggregation filters. The shape requirements are the same - `metrics`, but the output type for `having` is `boolean`. - - ::: {.callout-warning} - ## Expressions like `x is None` return `bool` and **will not** generate a SQL comparison to `NULL` - ::: - kwargs - Named aggregate expressions - - Returns - ------- - Table - An aggregate table expression - - Examples - -------- - >>> import ibis - >>> from ibis import _ - >>> ibis.options.interactive = True - >>> t = ibis.memtable( - ... { - ... "fruit": ["apple", "apple", "banana", "orange"], - ... "price": [0.5, 0.5, 0.25, 0.33], - ... } - ... ) - >>> t - ┏━━━━━━━━┳━━━━━━━━━┓ - ┃ fruit ┃ price ┃ - ┡━━━━━━━━╇━━━━━━━━━┩ - │ string │ float64 │ - ├────────┼─────────┤ - │ apple │ 0.50 │ - │ apple │ 0.50 │ - │ banana │ 0.25 │ - │ orange │ 0.33 │ - └────────┴─────────┘ - >>> t.aggregate( - ... by=["fruit"], - ... total_cost=_.price.sum(), - ... avg_cost=_.price.mean(), - ... having=_.price.sum() < 0.5, - ... ) - ┏━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━┓ - ┃ fruit ┃ total_cost ┃ avg_cost ┃ - ┡━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━┩ - │ string │ float64 │ float64 │ - ├────────┼────────────┼──────────┤ - │ banana │ 0.25 │ 0.25 │ - │ orange │ 0.33 │ 0.33 │ - └────────┴────────────┴──────────┘ - """ - from bigframes_vendored.ibis.common.patterns import Contains, In - from bigframes_vendored.ibis.expr.rewrites import p - - node = self.op() - - groups = self.bind(by) - metrics = self.bind(metrics, **kwargs) - having = self.bind(having) - - groups = unwrap_aliases(groups) - metrics = unwrap_aliases(metrics) - - # the user doesn't need to specify the metrics used in the having clause - # explicitly, we implicitly add them to the metrics list by looking for - # any metrics depending on self which are not specified explicitly - pattern = p.Reduction(relations=Contains(node)) & ~In(set(metrics.values())) - original_metrics = metrics.copy() - for pred in having: - for metric in pred.op().find_topmost(pattern): - if metric.name in metrics: - metrics[util.get_name("metric")] = metric - else: - metrics[metric.name] = metric - - # construct the aggregate node - agg = ops.Aggregate(node, groups, metrics).to_expr() - - if having: - # apply the having clause - agg = agg.filter(having) - # remove any metrics that were only used in the having clause - if metrics != original_metrics: - agg = agg.select(*groups.keys(), *original_metrics.keys()) - - return agg - - agg = aggregate - - def distinct( - self, - *, - on: str | Iterable[str] | s.Selector | None = None, - keep: Literal["first", "last"] | None = "first", - ) -> Table: - """Return a Table with duplicate rows removed. - - Similar to `pandas.DataFrame.drop_duplicates()`. - - ::: {.callout-note} - ## Some backends do not support `keep='last'` - ::: - - Parameters - ---------- - on - Only consider certain columns for identifying duplicates. - By default deduplicate all of the columns. - keep - Determines which duplicates to keep. - - - `"first"`: Drop duplicates except for the first occurrence. - - `"last"`: Drop duplicates except for the last occurrence. - - `None`: Drop all duplicates - - Examples - -------- - >>> import ibis - >>> import ibis.examples as ex - >>> import ibis.selectors as s - >>> ibis.options.interactive = True - >>> t = ex.penguins.fetch() - >>> t - ┏━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━┓ - ┃ species ┃ island ┃ bill_length_mm ┃ bill_depth_mm ┃ flipper_length_mm ┃ … ┃ - ┡━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━┩ - │ string │ string │ float64 │ float64 │ int64 │ … │ - ├─────────┼───────────┼────────────────┼───────────────┼───────────────────┼───┤ - │ Adelie │ Torgersen │ 39.1 │ 18.7 │ 181 │ … │ - │ Adelie │ Torgersen │ 39.5 │ 17.4 │ 186 │ … │ - │ Adelie │ Torgersen │ 40.3 │ 18.0 │ 195 │ … │ - │ Adelie │ Torgersen │ NULL │ NULL │ NULL │ … │ - │ Adelie │ Torgersen │ 36.7 │ 19.3 │ 193 │ … │ - │ Adelie │ Torgersen │ 39.3 │ 20.6 │ 190 │ … │ - │ Adelie │ Torgersen │ 38.9 │ 17.8 │ 181 │ … │ - │ Adelie │ Torgersen │ 39.2 │ 19.6 │ 195 │ … │ - │ Adelie │ Torgersen │ 34.1 │ 18.1 │ 193 │ … │ - │ Adelie │ Torgersen │ 42.0 │ 20.2 │ 190 │ … │ - │ … │ … │ … │ … │ … │ … │ - └─────────┴───────────┴────────────────┴───────────────┴───────────────────┴───┘ - - Compute the distinct rows of a subset of columns - - >>> t[["species", "island"]].distinct().order_by(s.all()) - ┏━━━━━━━━━━━┳━━━━━━━━━━━┓ - ┃ species ┃ island ┃ - ┡━━━━━━━━━━━╇━━━━━━━━━━━┩ - │ string │ string │ - ├───────────┼───────────┤ - │ Adelie │ Biscoe │ - │ Adelie │ Dream │ - │ Adelie │ Torgersen │ - │ Chinstrap │ Dream │ - │ Gentoo │ Biscoe │ - └───────────┴───────────┘ - - Drop all duplicate rows except the first - - >>> t.distinct(on=["species", "island"], keep="first").order_by(s.all()) - ┏━━━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━┓ - ┃ species ┃ island ┃ bill_length_mm ┃ bill_depth_… ┃ flipper_length_mm ┃ ┃ - ┡━━━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━┩ - │ string │ string │ float64 │ float64 │ int64 │ │ - ├───────────┼───────────┼────────────────┼──────────────┼───────────────────┼──┤ - │ Adelie │ Biscoe │ 37.8 │ 18.3 │ 174 │ │ - │ Adelie │ Dream │ 39.5 │ 16.7 │ 178 │ │ - │ Adelie │ Torgersen │ 39.1 │ 18.7 │ 181 │ │ - │ Chinstrap │ Dream │ 46.5 │ 17.9 │ 192 │ │ - │ Gentoo │ Biscoe │ 46.1 │ 13.2 │ 211 │ │ - └───────────┴───────────┴────────────────┴──────────────┴───────────────────┴──┘ - - Drop all duplicate rows except the last - - >>> t.distinct(on=["species", "island"], keep="last").order_by(s.all()) - ┏━━━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━┓ - ┃ species ┃ island ┃ bill_length_mm ┃ bill_depth_… ┃ flipper_length_mm ┃ ┃ - ┡━━━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━┩ - │ string │ string │ float64 │ float64 │ int64 │ │ - ├───────────┼───────────┼────────────────┼──────────────┼───────────────────┼──┤ - │ Adelie │ Biscoe │ 42.7 │ 18.3 │ 196 │ │ - │ Adelie │ Dream │ 41.5 │ 18.5 │ 201 │ │ - │ Adelie │ Torgersen │ 43.1 │ 19.2 │ 197 │ │ - │ Chinstrap │ Dream │ 50.2 │ 18.7 │ 198 │ │ - │ Gentoo │ Biscoe │ 49.9 │ 16.1 │ 213 │ │ - └───────────┴───────────┴────────────────┴──────────────┴───────────────────┴──┘ - - Drop all duplicated rows - - >>> expr = t.distinct(on=["species", "island", "year", "bill_length_mm"], keep=None) - >>> expr.count() - ┌───────────────┐ - │ np.int64(273) │ - └───────────────┘ - >>> t.count() - ┌───────────────┐ - │ np.int64(344) │ - └───────────────┘ - - You can pass [`selectors`](./selectors.qmd) to `on` - - >>> t.distinct(on=~s.numeric()) # doctest: +SKIP - ┏━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━┓ - ┃ species ┃ island ┃ bill_length_mm ┃ bill_depth_mm ┃ flipper_length_mm ┃ … ┃ - ┡━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━┩ - │ string │ string │ float64 │ float64 │ int64 │ … │ - ├─────────┼───────────┼────────────────┼───────────────┼───────────────────┼───┤ - │ Adelie │ Torgersen │ 39.1 │ 18.7 │ 181 │ … │ - │ Adelie │ Torgersen │ 39.5 │ 17.4 │ 186 │ … │ - │ Adelie │ Torgersen │ NULL │ NULL │ NULL │ … │ - │ Adelie │ Biscoe │ 37.8 │ 18.3 │ 174 │ … │ - │ Adelie │ Biscoe │ 37.7 │ 18.7 │ 180 │ … │ - │ Adelie │ Dream │ 39.5 │ 16.7 │ 178 │ … │ - │ Adelie │ Dream │ 37.2 │ 18.1 │ 178 │ … │ - │ Adelie │ Dream │ 37.5 │ 18.9 │ 179 │ … │ - │ Gentoo │ Biscoe │ 46.1 │ 13.2 │ 211 │ … │ - │ Gentoo │ Biscoe │ 50.0 │ 16.3 │ 230 │ … │ - │ … │ … │ … │ … │ … │ … │ - └─────────┴───────────┴────────────────┴───────────────┴───────────────────┴───┘ - - The only valid values of `keep` are `"first"`, `"last"` and [`None][None] - - >>> t.distinct(on="species", keep="second") # quartodoc: +EXPECTED_FAILURE - Traceback (most recent call last): - ... - ibis.common.exceptions.IbisError: Invalid value for keep: 'second' ... - """ - - import bigframes_vendored.ibis.selectors as s - - if on is None: - # dedup everything - if keep != "first": - raise com.IbisError( - f"Only keep='first' (the default) makes sense when deduplicating all columns; got keep={keep!r}" - ) - return ops.Distinct(self).to_expr() - - on = s._to_selector(on) - - if keep is None: - having = lambda t: t.count() == 1 - method = "first" - elif keep in ("first", "last"): - having = () - method = keep - else: - raise com.IbisError( - f"Invalid value for `keep`: {keep!r}, must be 'first', 'last' or None" - ) - - aggs = {col.get_name(): getattr(col, method)() for col in (~on).expand(self)} - res = self.aggregate(aggs, by=on, having=having) - - assert len(res.columns) == len(self.columns) - if res.columns != self.columns: - return res.select(self.columns) - return res - - def sample( - self, - fraction: float, - *, - method: Literal["row", "block"] = "row", - seed: int | None = None, - ) -> Table: - """Sample a fraction of rows from a table. - - ::: {.callout-note} - ## Results may be non-repeatable - - Sampling is by definition a random operation. Some backends support - specifying a `seed` for repeatable results, but not all backends - support that option. And some backends (duckdb, for example) do support - specifying a seed but may still not have repeatable results in all - cases. - - In all cases, results are backend-specific. An execution against one - backend is unlikely to sample the same rows when executed against a - different backend, even with the same `seed` set. - ::: - - Parameters - ---------- - fraction - The percentage of rows to include in the sample, expressed as a - float between 0 and 1. - method - The sampling method to use. The default is "row", which includes - each row with a probability of ``fraction``. If method is "block", - some backends may instead perform sampling a fraction of blocks of - rows (where "block" is a backend dependent definition). This is - identical to "row" for backends lacking a blockwise sampling - implementation. For those coming from SQL, "row" and "block" - correspond to "bernoulli" and "system" respectively in a - TABLESAMPLE clause. - seed - An optional random seed to use, for repeatable sampling. The range - of possible seed values is backend specific (most support at least - `[0, 2**31 - 1]`). Backends that never support specifying a seed - for repeatable sampling will error appropriately. Note that some - backends (like DuckDB) do support specifying a seed, but may still - not have repeatable results in all cases. - - Returns - ------- - Table - The input table, with `fraction` of rows selected. - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable({"x": [1, 2, 3, 4], "y": ["a", "b", "c", "d"]}) - >>> t - ┏━━━━━━━┳━━━━━━━━┓ - ┃ x ┃ y ┃ - ┡━━━━━━━╇━━━━━━━━┩ - │ int64 │ string │ - ├───────┼────────┤ - │ 1 │ a │ - │ 2 │ b │ - │ 3 │ c │ - │ 4 │ d │ - └───────┴────────┘ - - Sample approximately half the rows, with a seed specified for - reproducibility. - - >>> t.sample(0.5, seed=1234) - ┏━━━━━━━┳━━━━━━━━┓ - ┃ x ┃ y ┃ - ┡━━━━━━━╇━━━━━━━━┩ - │ int64 │ string │ - ├───────┼────────┤ - │ 2 │ b │ - │ 3 │ c │ - └───────┴────────┘ - """ - if fraction == 1: - return self - elif fraction == 0: - return self.limit(0) - else: - return ops.Sample( - self, fraction=fraction, method=method, seed=seed - ).to_expr() - - def limit(self, n: int | None, offset: int = 0) -> Table: - """Select `n` rows from `self` starting at `offset`. - - ::: {.callout-note} - ## The result set is not deterministic without a call to [`order_by`](#ibis.expr.types.relations.Table.order_by). - ::: - - Parameters - ---------- - n - Number of rows to include. If `None`, the entire table is selected - starting from `offset`. - offset - Number of rows to skip first - - Returns - ------- - Table - The first `n` rows of `self` starting at `offset` - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable({"a": [1, 1, 2], "b": ["c", "a", "a"]}) - >>> t - ┏━━━━━━━┳━━━━━━━━┓ - ┃ a ┃ b ┃ - ┡━━━━━━━╇━━━━━━━━┩ - │ int64 │ string │ - ├───────┼────────┤ - │ 1 │ c │ - │ 1 │ a │ - │ 2 │ a │ - └───────┴────────┘ - >>> t.limit(2) - ┏━━━━━━━┳━━━━━━━━┓ - ┃ a ┃ b ┃ - ┡━━━━━━━╇━━━━━━━━┩ - │ int64 │ string │ - ├───────┼────────┤ - │ 1 │ c │ - │ 1 │ a │ - └───────┴────────┘ - - You can use `None` with `offset` to slice starting from a particular row - - >>> t.limit(None, offset=1) - ┏━━━━━━━┳━━━━━━━━┓ - ┃ a ┃ b ┃ - ┡━━━━━━━╇━━━━━━━━┩ - │ int64 │ string │ - ├───────┼────────┤ - │ 1 │ a │ - │ 2 │ a │ - └───────┴────────┘ - - See Also - -------- - [`Table.order_by`](#ibis.expr.types.relations.Table.order_by) - """ - return ops.Limit(self, n, offset).to_expr() - - def head(self, n: int = 5) -> Table: - """Select the first `n` rows of a table. - - ::: {.callout-note} - ## The result set is not deterministic without a call to [`order_by`](#ibis.expr.types.relations.Table.order_by). - ::: - - Parameters - ---------- - n - Number of rows to include - - Returns - ------- - Table - `self` limited to `n` rows - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable({"a": [1, 1, 2], "b": ["c", "a", "a"]}) - >>> t - ┏━━━━━━━┳━━━━━━━━┓ - ┃ a ┃ b ┃ - ┡━━━━━━━╇━━━━━━━━┩ - │ int64 │ string │ - ├───────┼────────┤ - │ 1 │ c │ - │ 1 │ a │ - │ 2 │ a │ - └───────┴────────┘ - >>> t.head(2) - ┏━━━━━━━┳━━━━━━━━┓ - ┃ a ┃ b ┃ - ┡━━━━━━━╇━━━━━━━━┩ - │ int64 │ string │ - ├───────┼────────┤ - │ 1 │ c │ - │ 1 │ a │ - └───────┴────────┘ - - See Also - -------- - [`Table.limit`](#ibis.expr.types.relations.Table.limit) - [`Table.order_by`](#ibis.expr.types.relations.Table.order_by) - """ - return self.limit(n=n) - - def order_by( - self, - *by: str - | ir.Column - | s.Selector - | Sequence[str] - | Sequence[ir.Column] - | Sequence[s.Selector] - | None, - ) -> Table: - """Sort a table by one or more expressions. - - Similar to `pandas.DataFrame.sort_values()`. - - Parameters - ---------- - by - Expressions to sort the table by. - - Returns - ------- - Table - Sorted table - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable( - ... { - ... "a": [3, 2, 1, 3], - ... "b": ["a", "B", "c", "D"], - ... "c": [4, 6, 5, 7], - ... } - ... ) - >>> t - ┏━━━━━━━┳━━━━━━━━┳━━━━━━━┓ - ┃ a ┃ b ┃ c ┃ - ┡━━━━━━━╇━━━━━━━━╇━━━━━━━┩ - │ int64 │ string │ int64 │ - ├───────┼────────┼───────┤ - │ 3 │ a │ 4 │ - │ 2 │ B │ 6 │ - │ 1 │ c │ 5 │ - │ 3 │ D │ 7 │ - └───────┴────────┴───────┘ - - Sort by b. Default is ascending. Note how capital letters come before lowercase - - >>> t.order_by("b") - ┏━━━━━━━┳━━━━━━━━┳━━━━━━━┓ - ┃ a ┃ b ┃ c ┃ - ┡━━━━━━━╇━━━━━━━━╇━━━━━━━┩ - │ int64 │ string │ int64 │ - ├───────┼────────┼───────┤ - │ 2 │ B │ 6 │ - │ 3 │ D │ 7 │ - │ 3 │ a │ 4 │ - │ 1 │ c │ 5 │ - └───────┴────────┴───────┘ - - Sort in descending order - - >>> t.order_by(ibis.desc("b")) - ┏━━━━━━━┳━━━━━━━━┳━━━━━━━┓ - ┃ a ┃ b ┃ c ┃ - ┡━━━━━━━╇━━━━━━━━╇━━━━━━━┩ - │ int64 │ string │ int64 │ - ├───────┼────────┼───────┤ - │ 1 │ c │ 5 │ - │ 3 │ a │ 4 │ - │ 3 │ D │ 7 │ - │ 2 │ B │ 6 │ - └───────┴────────┴───────┘ - - You can also use the deferred API to get the same result - - >>> from ibis import _ - >>> t.order_by(_.b.desc()) - ┏━━━━━━━┳━━━━━━━━┳━━━━━━━┓ - ┃ a ┃ b ┃ c ┃ - ┡━━━━━━━╇━━━━━━━━╇━━━━━━━┩ - │ int64 │ string │ int64 │ - ├───────┼────────┼───────┤ - │ 1 │ c │ 5 │ - │ 3 │ a │ 4 │ - │ 3 │ D │ 7 │ - │ 2 │ B │ 6 │ - └───────┴────────┴───────┘ - - Sort by multiple columns/expressions - - >>> t.order_by(["a", _.c.desc()]) - ┏━━━━━━━┳━━━━━━━━┳━━━━━━━┓ - ┃ a ┃ b ┃ c ┃ - ┡━━━━━━━╇━━━━━━━━╇━━━━━━━┩ - │ int64 │ string │ int64 │ - ├───────┼────────┼───────┤ - │ 1 │ c │ 5 │ - │ 2 │ B │ 6 │ - │ 3 │ D │ 7 │ - │ 3 │ a │ 4 │ - └───────┴────────┴───────┘ - - You can actually pass arbitrary expressions to use as sort keys. - For example, to ignore the case of the strings in column `b` - - >>> t.order_by(_.b.lower()) - ┏━━━━━━━┳━━━━━━━━┳━━━━━━━┓ - ┃ a ┃ b ┃ c ┃ - ┡━━━━━━━╇━━━━━━━━╇━━━━━━━┩ - │ int64 │ string │ int64 │ - ├───────┼────────┼───────┤ - │ 3 │ a │ 4 │ - │ 2 │ B │ 6 │ - │ 1 │ c │ 5 │ - │ 3 │ D │ 7 │ - └───────┴────────┴───────┘ - - This means that shuffling a Table is super simple - - >>> t.order_by(ibis.random()) # doctest: +SKIP - ┏━━━━━━━┳━━━━━━━━┳━━━━━━━┓ - ┃ a ┃ b ┃ c ┃ - ┡━━━━━━━╇━━━━━━━━╇━━━━━━━┩ - │ int64 │ string │ int64 │ - ├───────┼────────┼───────┤ - │ 1 │ c │ 5 │ - │ 3 │ D │ 7 │ - │ 3 │ a │ 4 │ - │ 2 │ B │ 6 │ - └───────┴────────┴───────┘ - - [Selectors](./selectors.qmd) are allowed as sort keys and are a concise way to sort by - multiple columns matching some criteria - - >>> import ibis.selectors as s - >>> penguins = ibis.examples.penguins.fetch() - >>> penguins[["year", "island"]].value_counts().order_by(s.startswith("year")) - ┏━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓ - ┃ year ┃ island ┃ year_island_count ┃ - ┡━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩ - │ int64 │ string │ int64 │ - ├───────┼───────────┼───────────────────┤ - │ 2007 │ Torgersen │ 20 │ - │ 2007 │ Biscoe │ 44 │ - │ 2007 │ Dream │ 46 │ - │ 2008 │ Torgersen │ 16 │ - │ 2008 │ Dream │ 34 │ - │ 2008 │ Biscoe │ 64 │ - │ 2009 │ Torgersen │ 16 │ - │ 2009 │ Dream │ 44 │ - │ 2009 │ Biscoe │ 60 │ - └───────┴───────────┴───────────────────┘ - - Use the [`across`](./selectors.qmd#ibis.selectors.across) selector to - apply a specific order to multiple columns - - >>> penguins[["year", "island"]].value_counts().order_by( - ... s.across(s.startswith("year"), _.desc()) - ... ) - ┏━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓ - ┃ year ┃ island ┃ year_island_count ┃ - ┡━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩ - │ int64 │ string │ int64 │ - ├───────┼───────────┼───────────────────┤ - │ 2009 │ Biscoe │ 60 │ - │ 2009 │ Dream │ 44 │ - │ 2009 │ Torgersen │ 16 │ - │ 2008 │ Biscoe │ 64 │ - │ 2008 │ Dream │ 34 │ - │ 2008 │ Torgersen │ 16 │ - │ 2007 │ Dream │ 46 │ - │ 2007 │ Biscoe │ 44 │ - │ 2007 │ Torgersen │ 20 │ - └───────┴───────────┴───────────────────┘ - """ - keys = self.bind(*by) - keys = unwrap_aliases(keys) - if not keys: - raise com.IbisError("At least one sort key must be provided") - - node = ops.Sort(self, keys.values()) - return node.to_expr() - - def _assemble_set_op( - self, opcls: type[Set], table: Table, *rest: Table, distinct: bool - ) -> Table: - """Assemble a set operation expression. - - This exists to workaround an issue in sqlglot where codegen blows the - Python stack because of set operation nesting. - - The implementation here uses a queue to balance the operation tree. - """ - queue = deque() - - queue.append(self) - queue.append(table) - queue.extend(rest) - - while len(queue) > 1: - left = queue.popleft() - right = queue.popleft() - node = opcls(left, right, distinct=distinct) - queue.append(node) - result = queue.popleft() - assert not queue, "items left in queue" - return result.to_expr() - - def union(self, table: Table, *rest: Table, distinct: bool = False) -> Table: - """Compute the set union of multiple table expressions. - - The input tables must have identical schemas. - - Parameters - ---------- - table - A table expression - *rest - Additional table expressions - distinct - Only return distinct rows - - Returns - ------- - Table - A new table containing the union of all input tables. - - See Also - -------- - [`ibis.union`](./expression-tables.qmd#ibis.union) - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t1 = ibis.memtable({"a": [1, 2]}) - >>> t1 - ┏━━━━━━━┓ - ┃ a ┃ - ┡━━━━━━━┩ - │ int64 │ - ├───────┤ - │ 1 │ - │ 2 │ - └───────┘ - >>> t2 = ibis.memtable({"a": [2, 3]}) - >>> t2 - ┏━━━━━━━┓ - ┃ a ┃ - ┡━━━━━━━┩ - │ int64 │ - ├───────┤ - │ 2 │ - │ 3 │ - └───────┘ - >>> t1.union(t2) # union all by default - ┏━━━━━━━┓ - ┃ a ┃ - ┡━━━━━━━┩ - │ int64 │ - ├───────┤ - │ 1 │ - │ 2 │ - │ 2 │ - │ 3 │ - └───────┘ - >>> t1.union(t2, distinct=True).order_by("a") - ┏━━━━━━━┓ - ┃ a ┃ - ┡━━━━━━━┩ - │ int64 │ - ├───────┤ - │ 1 │ - │ 2 │ - │ 3 │ - └───────┘ - """ - return self._assemble_set_op(ops.Union, table, *rest, distinct=distinct) - - def intersect(self, table: Table, *rest: Table, distinct: bool = True) -> Table: - """Compute the set intersection of multiple table expressions. - - The input tables must have identical schemas. - - Parameters - ---------- - table - A table expression - *rest - Additional table expressions - distinct - Only return distinct rows - - Returns - ------- - Table - A new table containing the intersection of all input tables. - - See Also - -------- - [`ibis.intersect`](./expression-tables.qmd#ibis.intersect) - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t1 = ibis.memtable({"a": [1, 2]}) - >>> t1 - ┏━━━━━━━┓ - ┃ a ┃ - ┡━━━━━━━┩ - │ int64 │ - ├───────┤ - │ 1 │ - │ 2 │ - └───────┘ - >>> t2 = ibis.memtable({"a": [2, 3]}) - >>> t2 - ┏━━━━━━━┓ - ┃ a ┃ - ┡━━━━━━━┩ - │ int64 │ - ├───────┤ - │ 2 │ - │ 3 │ - └───────┘ - >>> t1.intersect(t2) - ┏━━━━━━━┓ - ┃ a ┃ - ┡━━━━━━━┩ - │ int64 │ - ├───────┤ - │ 2 │ - └───────┘ - """ - return self._assemble_set_op(ops.Intersection, table, *rest, distinct=distinct) - - def difference(self, table: Table, *rest: Table, distinct: bool = True) -> Table: - """Compute the set difference of multiple table expressions. - - The input tables must have identical schemas. - - Parameters - ---------- - table: - A table expression - *rest: - Additional table expressions - distinct - Only diff distinct rows not occurring in the calling table - - See Also - -------- - [`ibis.difference`](./expression-tables.qmd#ibis.difference) - - Returns - ------- - Table - The rows present in `self` that are not present in `tables`. - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t1 = ibis.memtable({"a": [1, 2]}) - >>> t1 - ┏━━━━━━━┓ - ┃ a ┃ - ┡━━━━━━━┩ - │ int64 │ - ├───────┤ - │ 1 │ - │ 2 │ - └───────┘ - >>> t2 = ibis.memtable({"a": [2, 3]}) - >>> t2 - ┏━━━━━━━┓ - ┃ a ┃ - ┡━━━━━━━┩ - │ int64 │ - ├───────┤ - │ 2 │ - │ 3 │ - └───────┘ - >>> t1.difference(t2) - ┏━━━━━━━┓ - ┃ a ┃ - ┡━━━━━━━┩ - │ int64 │ - ├───────┤ - │ 1 │ - └───────┘ - """ - node = ops.Difference(self, table, distinct=distinct) - for expr in rest: - node = ops.Difference(node, expr, distinct=distinct) - return node.to_expr() - - @deprecated(as_of="9.0", instead="use table.as_scalar() instead") - def to_array(self) -> ir.Column: - """View a single column table as an array. - - Returns - ------- - Value - A single column view of a table - """ - schema = self.schema() - if len(schema) != 1: - raise com.ExpressionError( - "Table must have exactly one column when viewed as array" - ) - return self.as_scalar() - - def mutate(self, *exprs: Sequence[ir.Expr] | None, **mutations: ir.Value) -> Table: - """Add columns to a table expression. - - Parameters - ---------- - exprs - List of named expressions to add as columns - mutations - Named expressions using keyword arguments - - Returns - ------- - Table - Table expression with additional columns - - Examples - -------- - >>> import ibis - >>> import ibis.selectors as s - >>> from ibis import _ - >>> ibis.options.interactive = True - >>> t = ibis.examples.penguins.fetch().select("species", "year", "bill_length_mm") - >>> t - ┏━━━━━━━━━┳━━━━━━━┳━━━━━━━━━━━━━━━━┓ - ┃ species ┃ year ┃ bill_length_mm ┃ - ┡━━━━━━━━━╇━━━━━━━╇━━━━━━━━━━━━━━━━┩ - │ string │ int64 │ float64 │ - ├─────────┼───────┼────────────────┤ - │ Adelie │ 2007 │ 39.1 │ - │ Adelie │ 2007 │ 39.5 │ - │ Adelie │ 2007 │ 40.3 │ - │ Adelie │ 2007 │ NULL │ - │ Adelie │ 2007 │ 36.7 │ - │ Adelie │ 2007 │ 39.3 │ - │ Adelie │ 2007 │ 38.9 │ - │ Adelie │ 2007 │ 39.2 │ - │ Adelie │ 2007 │ 34.1 │ - │ Adelie │ 2007 │ 42.0 │ - │ … │ … │ … │ - └─────────┴───────┴────────────────┘ - - Add a new column from a per-element expression - - >>> t.mutate(next_year=_.year + 1).head() - ┏━━━━━━━━━┳━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━┓ - ┃ species ┃ year ┃ bill_length_mm ┃ next_year ┃ - ┡━━━━━━━━━╇━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━┩ - │ string │ int64 │ float64 │ int64 │ - ├─────────┼───────┼────────────────┼───────────┤ - │ Adelie │ 2007 │ 39.1 │ 2008 │ - │ Adelie │ 2007 │ 39.5 │ 2008 │ - │ Adelie │ 2007 │ 40.3 │ 2008 │ - │ Adelie │ 2007 │ NULL │ 2008 │ - │ Adelie │ 2007 │ 36.7 │ 2008 │ - └─────────┴───────┴────────────────┴───────────┘ - - Add a new column based on an aggregation. Note the automatic broadcasting. - - >>> t.select("species", bill_demean=_.bill_length_mm - _.bill_length_mm.mean()).head() - ┏━━━━━━━━━┳━━━━━━━━━━━━━┓ - ┃ species ┃ bill_demean ┃ - ┡━━━━━━━━━╇━━━━━━━━━━━━━┩ - │ string │ float64 │ - ├─────────┼─────────────┤ - │ Adelie │ -4.82193 │ - │ Adelie │ -4.42193 │ - │ Adelie │ -3.62193 │ - │ Adelie │ NULL │ - │ Adelie │ -7.22193 │ - └─────────┴─────────────┘ - - Mutate across multiple columns - - >>> t.mutate(s.across(s.numeric() & ~s.c("year"), _ - _.mean())).head() - ┏━━━━━━━━━┳━━━━━━━┳━━━━━━━━━━━━━━━━┓ - ┃ species ┃ year ┃ bill_length_mm ┃ - ┡━━━━━━━━━╇━━━━━━━╇━━━━━━━━━━━━━━━━┩ - │ string │ int64 │ float64 │ - ├─────────┼───────┼────────────────┤ - │ Adelie │ 2007 │ -4.82193 │ - │ Adelie │ 2007 │ -4.42193 │ - │ Adelie │ 2007 │ -3.62193 │ - │ Adelie │ 2007 │ NULL │ - │ Adelie │ 2007 │ -7.22193 │ - └─────────┴───────┴────────────────┘ - """ - # string and integer inputs are going to be coerced to literals instead - # of interpreted as column references like in select - node = self.op() - values = self.bind(*exprs, **mutations) - values = unwrap_aliases(values) - # allow overriding of fields, hence the mutation behavior - values = {**node.fields, **values} - return self.select(**values) - - def select( - self, - *exprs: ir.Value | str | Iterable[ir.Value | str], - **named_exprs: ir.Value | str, - ) -> Table: - """Compute a new table expression using `exprs` and `named_exprs`. - - Passing an aggregate function to this method will broadcast the - aggregate's value over the number of rows in the table and - automatically constructs a window function expression. See the examples - section for more details. - - For backwards compatibility the keyword argument `exprs` is reserved - and cannot be used to name an expression. This behavior will be removed - in v4. - - Parameters - ---------- - exprs - Column expression, string, or list of column expressions and - strings. - named_exprs - Column expressions - - Returns - ------- - Table - Table expression - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.examples.penguins.fetch() - >>> t - ┏━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━┓ - ┃ species ┃ island ┃ bill_length_mm ┃ bill_depth_mm ┃ flipper_length_mm ┃ … ┃ - ┡━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━┩ - │ string │ string │ float64 │ float64 │ int64 │ … │ - ├─────────┼───────────┼────────────────┼───────────────┼───────────────────┼───┤ - │ Adelie │ Torgersen │ 39.1 │ 18.7 │ 181 │ … │ - │ Adelie │ Torgersen │ 39.5 │ 17.4 │ 186 │ … │ - │ Adelie │ Torgersen │ 40.3 │ 18.0 │ 195 │ … │ - │ Adelie │ Torgersen │ NULL │ NULL │ NULL │ … │ - │ Adelie │ Torgersen │ 36.7 │ 19.3 │ 193 │ … │ - │ Adelie │ Torgersen │ 39.3 │ 20.6 │ 190 │ … │ - │ Adelie │ Torgersen │ 38.9 │ 17.8 │ 181 │ … │ - │ Adelie │ Torgersen │ 39.2 │ 19.6 │ 195 │ … │ - │ Adelie │ Torgersen │ 34.1 │ 18.1 │ 193 │ … │ - │ Adelie │ Torgersen │ 42.0 │ 20.2 │ 190 │ … │ - │ … │ … │ … │ … │ … │ … │ - └─────────┴───────────┴────────────────┴───────────────┴───────────────────┴───┘ - - Simple projection - - >>> t.select("island", "bill_length_mm").head() - ┏━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┓ - ┃ island ┃ bill_length_mm ┃ - ┡━━━━━━━━━━━╇━━━━━━━━━━━━━━━━┩ - │ string │ float64 │ - ├───────────┼────────────────┤ - │ Torgersen │ 39.1 │ - │ Torgersen │ 39.5 │ - │ Torgersen │ 40.3 │ - │ Torgersen │ NULL │ - │ Torgersen │ 36.7 │ - └───────────┴────────────────┘ - - In that simple case, you could also just use python's indexing syntax - - >>> t[["island", "bill_length_mm"]].head() - ┏━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┓ - ┃ island ┃ bill_length_mm ┃ - ┡━━━━━━━━━━━╇━━━━━━━━━━━━━━━━┩ - │ string │ float64 │ - ├───────────┼────────────────┤ - │ Torgersen │ 39.1 │ - │ Torgersen │ 39.5 │ - │ Torgersen │ 40.3 │ - │ Torgersen │ NULL │ - │ Torgersen │ 36.7 │ - └───────────┴────────────────┘ - - Projection by zero-indexed column position - - >>> t.select(t[0], t[4]).head() - ┏━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓ - ┃ species ┃ flipper_length_mm ┃ - ┡━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩ - │ string │ int64 │ - ├─────────┼───────────────────┤ - │ Adelie │ 181 │ - │ Adelie │ 186 │ - │ Adelie │ 195 │ - │ Adelie │ NULL │ - │ Adelie │ 193 │ - └─────────┴───────────────────┘ - - Projection with renaming and compute in one call - - >>> t.select(next_year=t.year + 1).head() - ┏━━━━━━━━━━━┓ - ┃ next_year ┃ - ┡━━━━━━━━━━━┩ - │ int64 │ - ├───────────┤ - │ 2008 │ - │ 2008 │ - │ 2008 │ - │ 2008 │ - │ 2008 │ - └───────────┘ - - You can do the same thing with a named expression, and using the - deferred API - - >>> from ibis import _ - >>> t.select((_.year + 1).name("next_year")).head() - ┏━━━━━━━━━━━┓ - ┃ next_year ┃ - ┡━━━━━━━━━━━┩ - │ int64 │ - ├───────────┤ - │ 2008 │ - │ 2008 │ - │ 2008 │ - │ 2008 │ - │ 2008 │ - └───────────┘ - - Projection with aggregation expressions - - >>> t.select("island", bill_mean=t.bill_length_mm.mean()).head() - ┏━━━━━━━━━━━┳━━━━━━━━━━━┓ - ┃ island ┃ bill_mean ┃ - ┡━━━━━━━━━━━╇━━━━━━━━━━━┩ - │ string │ float64 │ - ├───────────┼───────────┤ - │ Torgersen │ 43.92193 │ - │ Torgersen │ 43.92193 │ - │ Torgersen │ 43.92193 │ - │ Torgersen │ 43.92193 │ - │ Torgersen │ 43.92193 │ - └───────────┴───────────┘ - - Projection with a selector - - >>> import ibis.selectors as s - >>> t.select(s.numeric() & ~s.c("year")).head() - ┏━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┓ - ┃ bill_length_mm ┃ bill_depth_mm ┃ flipper_length_mm ┃ body_mass_g ┃ - ┡━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━┩ - │ float64 │ float64 │ int64 │ int64 │ - ├────────────────┼───────────────┼───────────────────┼─────────────┤ - │ 39.1 │ 18.7 │ 181 │ 3750 │ - │ 39.5 │ 17.4 │ 186 │ 3800 │ - │ 40.3 │ 18.0 │ 195 │ 3250 │ - │ NULL │ NULL │ NULL │ NULL │ - │ 36.7 │ 19.3 │ 193 │ 3450 │ - └────────────────┴───────────────┴───────────────────┴─────────────┘ - - Projection + aggregation across multiple columns - - >>> from ibis import _ - >>> t.select(s.across(s.numeric() & ~s.c("year"), _.mean())).head() - ┏━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┓ - ┃ bill_length_mm ┃ bill_depth_mm ┃ flipper_length_mm ┃ body_mass_g ┃ - ┡━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━┩ - │ float64 │ float64 │ float64 │ float64 │ - ├────────────────┼───────────────┼───────────────────┼─────────────┤ - │ 43.92193 │ 17.15117 │ 200.915205 │ 4201.754386 │ - │ 43.92193 │ 17.15117 │ 200.915205 │ 4201.754386 │ - │ 43.92193 │ 17.15117 │ 200.915205 │ 4201.754386 │ - │ 43.92193 │ 17.15117 │ 200.915205 │ 4201.754386 │ - │ 43.92193 │ 17.15117 │ 200.915205 │ 4201.754386 │ - └────────────────┴───────────────┴───────────────────┴─────────────┘ - """ - from bigframes_vendored.ibis.expr.rewrites import rewrite_project_input - - values = self.bind(*exprs, **named_exprs) - values = unwrap_aliases(values) - if not values: - raise com.IbisTypeError( - "You must select at least one column for a valid projection" - ) - - # we need to detect reductions which are either turned into window functions - # or scalar subqueries depending on whether they are originating from self - values = { - k: rewrite_project_input(v, relation=self.op()) for k, v in values.items() - } - return ops.Project(self, values).to_expr() - - projection = select - - @util.deprecated( - as_of="7.0", - instead=( - "use `Table.rename` instead (if passing a mapping, note the meaning " - "of keys and values are swapped in Table.rename)." - ), - ) - def relabel( - self, - substitutions: ( - Mapping[str, str] - | Callable[[str], str | None] - | str - | Literal["snake_case", "ALL_CAPS"] - ), - ) -> Table: - """Deprecated in favor of `Table.rename`.""" - if isinstance(substitutions, Mapping): - substitutions = {new: old for old, new in substitutions.items()} - return self.rename(substitutions) - - def rename( - self, - method: ( - str - | Callable[[str], str | None] - | Literal["snake_case", "ALL_CAPS"] - | Mapping[str, str] - | None - ) = None, - /, - **substitutions: str, - ) -> Table: - """Rename columns in the table. - - Parameters - ---------- - method - An optional method for renaming columns. May be one of: - - - A format string to use to rename all columns, like - ``"prefix_{name}"``. - - A function from old name to new name. If the function returns - ``None`` the old name is used. - - The literal strings ``"snake_case"`` or ``"ALL_CAPS"`` to - rename all columns using a ``snake_case`` or ``"ALL_CAPS"`` - naming convention respectively. - - A mapping from new name to old name. Existing columns not present - in the mapping will passthrough with their original name. - substitutions - Columns to be explicitly renamed, expressed as ``new_name=old_name`` - keyword arguments. - - Returns - ------- - Table - A renamed table expression - - Examples - -------- - >>> import ibis - >>> import ibis.selectors as s - >>> ibis.options.interactive = True - >>> first3 = s.r[:3] # first 3 columns - >>> t = ibis.examples.penguins_raw_raw.fetch().select(first3) - >>> t - ┏━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ studyName ┃ Sample Number ┃ Species ┃ - ┡━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩ - │ string │ int64 │ string │ - ├───────────┼───────────────┼─────────────────────────────────────┤ - │ PAL0708 │ 1 │ Adelie Penguin (Pygoscelis adeliae) │ - │ PAL0708 │ 2 │ Adelie Penguin (Pygoscelis adeliae) │ - │ PAL0708 │ 3 │ Adelie Penguin (Pygoscelis adeliae) │ - │ PAL0708 │ 4 │ Adelie Penguin (Pygoscelis adeliae) │ - │ PAL0708 │ 5 │ Adelie Penguin (Pygoscelis adeliae) │ - │ PAL0708 │ 6 │ Adelie Penguin (Pygoscelis adeliae) │ - │ PAL0708 │ 7 │ Adelie Penguin (Pygoscelis adeliae) │ - │ PAL0708 │ 8 │ Adelie Penguin (Pygoscelis adeliae) │ - │ PAL0708 │ 9 │ Adelie Penguin (Pygoscelis adeliae) │ - │ PAL0708 │ 10 │ Adelie Penguin (Pygoscelis adeliae) │ - │ … │ … │ … │ - └───────────┴───────────────┴─────────────────────────────────────┘ - - Rename specific columns by passing keyword arguments like - ``new_name="old_name"`` - - >>> t.rename(study_name="studyName").head(1) - ┏━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ study_name ┃ Sample Number ┃ Species ┃ - ┡━━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩ - │ string │ int64 │ string │ - ├────────────┼───────────────┼─────────────────────────────────────┤ - │ PAL0708 │ 1 │ Adelie Penguin (Pygoscelis adeliae) │ - └────────────┴───────────────┴─────────────────────────────────────┘ - - Rename all columns using a format string - - >>> t.rename("p_{name}").head(1) - ┏━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ p_studyName ┃ p_Sample Number ┃ p_Species ┃ - ┡━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩ - │ string │ int64 │ string │ - ├─────────────┼─────────────────┼─────────────────────────────────────┤ - │ PAL0708 │ 1 │ Adelie Penguin (Pygoscelis adeliae) │ - └─────────────┴─────────────────┴─────────────────────────────────────┘ - - Rename all columns using a snake_case convention - - >>> t.rename("snake_case").head(1) - ┏━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ study_name ┃ sample_number ┃ species ┃ - ┡━━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩ - │ string │ int64 │ string │ - ├────────────┼───────────────┼─────────────────────────────────────┤ - │ PAL0708 │ 1 │ Adelie Penguin (Pygoscelis adeliae) │ - └────────────┴───────────────┴─────────────────────────────────────┘ - - Rename all columns using an ALL_CAPS convention - - >>> t.rename("ALL_CAPS").head(1) - ┏━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ STUDY_NAME ┃ SAMPLE_NUMBER ┃ SPECIES ┃ - ┡━━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩ - │ string │ int64 │ string │ - ├────────────┼───────────────┼─────────────────────────────────────┤ - │ PAL0708 │ 1 │ Adelie Penguin (Pygoscelis adeliae) │ - └────────────┴───────────────┴─────────────────────────────────────┘ - - Rename all columns using a callable - - >>> t.rename(str.upper).head(1) - ┏━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ STUDYNAME ┃ SAMPLE NUMBER ┃ SPECIES ┃ - ┡━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩ - │ string │ int64 │ string │ - ├───────────┼───────────────┼─────────────────────────────────────┤ - │ PAL0708 │ 1 │ Adelie Penguin (Pygoscelis adeliae) │ - └───────────┴───────────────┴─────────────────────────────────────┘ - """ - if isinstance(method, Mapping): - substitutions.update(method) - method = None - - # A mapping from old_name -> renamed expr - renamed = {} - - for new_name, old_name in substitutions.items(): - if old_name not in renamed: - renamed[old_name] = (new_name, self[old_name].op()) - else: - raise ValueError("duplicate new names passed for renaming {old_name!r}") - - if isinstance(method, str) and method in {"snake_case", "ALL_CAPS"}: - - def rename(c): - c = c.strip() - if " " in c: - # Handle "space case possibly with-hyphens" - if method == "snake_case": - return "_".join(c.lower().split()).replace("-", "_") - elif method == "ALL_CAPS": - return "_".join(c.upper().split()).replace("-", "_") - # Handle PascalCase, camelCase, and kebab-case - c = re.sub(r"([A-Z]+)([A-Z][a-z])", r"\1_\2", c) - c = re.sub(r"([a-z\d])([A-Z])", r"\1_\2", c) - c = c.replace("-", "_") - if method == "snake_case": - return c.lower() - elif method == "ALL_CAPS": - return c.upper() - else: - return None - - elif isinstance(method, str): - - def rename(name): - return method.format(name=name) - - # Detect the case of missing or extra format string parameters - try: - dummy_name1 = "_unlikely_column_name_1_" - dummy_name2 = "_unlikely_column_name_2_" - invalid = rename(dummy_name1) == rename(dummy_name2) - except KeyError: - invalid = True - if invalid: - raise ValueError("Format strings must take a single parameter `name`") - else: - rename = method - - exprs = {} - fields = self.op().fields - for c in self.columns: - if (new_name_op := renamed.get(c)) is not None: - new_name, op = new_name_op - else: - op = fields[c] - if rename is None or (new_name := rename(c)) is None: - new_name = c - - exprs[new_name] = op - - return ops.Project(self, exprs).to_expr() - - def drop(self, *fields: str | Selector) -> Table: - """Remove fields from a table. - - Parameters - ---------- - fields - Fields to drop. Strings and selectors are accepted. - - Returns - ------- - Table - A table with all columns matching `fields` removed. - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.examples.penguins.fetch() - >>> t - ┏━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━┓ - ┃ species ┃ island ┃ bill_length_mm ┃ bill_depth_mm ┃ flipper_length_mm ┃ … ┃ - ┡━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━┩ - │ string │ string │ float64 │ float64 │ int64 │ … │ - ├─────────┼───────────┼────────────────┼───────────────┼───────────────────┼───┤ - │ Adelie │ Torgersen │ 39.1 │ 18.7 │ 181 │ … │ - │ Adelie │ Torgersen │ 39.5 │ 17.4 │ 186 │ … │ - │ Adelie │ Torgersen │ 40.3 │ 18.0 │ 195 │ … │ - │ Adelie │ Torgersen │ NULL │ NULL │ NULL │ … │ - │ Adelie │ Torgersen │ 36.7 │ 19.3 │ 193 │ … │ - │ Adelie │ Torgersen │ 39.3 │ 20.6 │ 190 │ … │ - │ Adelie │ Torgersen │ 38.9 │ 17.8 │ 181 │ … │ - │ Adelie │ Torgersen │ 39.2 │ 19.6 │ 195 │ … │ - │ Adelie │ Torgersen │ 34.1 │ 18.1 │ 193 │ … │ - │ Adelie │ Torgersen │ 42.0 │ 20.2 │ 190 │ … │ - │ … │ … │ … │ … │ … │ … │ - └─────────┴───────────┴────────────────┴───────────────┴───────────────────┴───┘ - - Drop one or more columns - - >>> t.drop("species").head() - ┏━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━┓ - ┃ island ┃ bill_length_mm ┃ bill_depth_mm ┃ flipper_length_mm ┃ … ┃ - ┡━━━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━┩ - │ string │ float64 │ float64 │ int64 │ … │ - ├───────────┼────────────────┼───────────────┼───────────────────┼───┤ - │ Torgersen │ 39.1 │ 18.7 │ 181 │ … │ - │ Torgersen │ 39.5 │ 17.4 │ 186 │ … │ - │ Torgersen │ 40.3 │ 18.0 │ 195 │ … │ - │ Torgersen │ NULL │ NULL │ NULL │ … │ - │ Torgersen │ 36.7 │ 19.3 │ 193 │ … │ - └───────────┴────────────────┴───────────────┴───────────────────┴───┘ - >>> t.drop("species", "bill_length_mm").head() - ┏━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━┳━━━┓ - ┃ island ┃ bill_depth_mm ┃ flipper_length_mm ┃ body_mass_g ┃ sex ┃ … ┃ - ┡━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━╇━━━┩ - │ string │ float64 │ int64 │ int64 │ string │ … │ - ├───────────┼───────────────┼───────────────────┼─────────────┼────────┼───┤ - │ Torgersen │ 18.7 │ 181 │ 3750 │ male │ … │ - │ Torgersen │ 17.4 │ 186 │ 3800 │ female │ … │ - │ Torgersen │ 18.0 │ 195 │ 3250 │ female │ … │ - │ Torgersen │ NULL │ NULL │ NULL │ NULL │ … │ - │ Torgersen │ 19.3 │ 193 │ 3450 │ female │ … │ - └───────────┴───────────────┴───────────────────┴─────────────┴────────┴───┘ - - Drop with selectors, mix and match - - >>> import ibis.selectors as s - >>> t.drop("species", s.startswith("bill_")).head() - ┏━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━┓ - ┃ island ┃ flipper_length_mm ┃ body_mass_g ┃ sex ┃ year ┃ - ┡━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━┩ - │ string │ int64 │ int64 │ string │ int64 │ - ├───────────┼───────────────────┼─────────────┼────────┼───────┤ - │ Torgersen │ 181 │ 3750 │ male │ 2007 │ - │ Torgersen │ 186 │ 3800 │ female │ 2007 │ - │ Torgersen │ 195 │ 3250 │ female │ 2007 │ - │ Torgersen │ NULL │ NULL │ NULL │ 2007 │ - │ Torgersen │ 193 │ 3450 │ female │ 2007 │ - └───────────┴───────────────────┴─────────────┴────────┴───────┘ - """ - if not fields: - # no-op if nothing to be dropped - return self - - columns_to_drop = tuple( - map(operator.methodcaller("get_name"), self._fast_bind(*fields)) - ) - return ops.DropColumns(parent=self, columns_to_drop=columns_to_drop).to_expr() - - def filter( - self, - *predicates: ir.BooleanValue | Sequence[ir.BooleanValue] | IfAnyAll, - ) -> Table: - """Select rows from `table` based on `predicates`. - - Parameters - ---------- - predicates - Boolean value expressions used to select rows in `table`. - - Returns - ------- - Table - Filtered table expression - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.examples.penguins.fetch() - >>> t - ┏━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━┓ - ┃ species ┃ island ┃ bill_length_mm ┃ bill_depth_mm ┃ flipper_length_mm ┃ … ┃ - ┡━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━┩ - │ string │ string │ float64 │ float64 │ int64 │ … │ - ├─────────┼───────────┼────────────────┼───────────────┼───────────────────┼───┤ - │ Adelie │ Torgersen │ 39.1 │ 18.7 │ 181 │ … │ - │ Adelie │ Torgersen │ 39.5 │ 17.4 │ 186 │ … │ - │ Adelie │ Torgersen │ 40.3 │ 18.0 │ 195 │ … │ - │ Adelie │ Torgersen │ NULL │ NULL │ NULL │ … │ - │ Adelie │ Torgersen │ 36.7 │ 19.3 │ 193 │ … │ - │ Adelie │ Torgersen │ 39.3 │ 20.6 │ 190 │ … │ - │ Adelie │ Torgersen │ 38.9 │ 17.8 │ 181 │ … │ - │ Adelie │ Torgersen │ 39.2 │ 19.6 │ 195 │ … │ - │ Adelie │ Torgersen │ 34.1 │ 18.1 │ 193 │ … │ - │ Adelie │ Torgersen │ 42.0 │ 20.2 │ 190 │ … │ - │ … │ … │ … │ … │ … │ … │ - └─────────┴───────────┴────────────────┴───────────────┴───────────────────┴───┘ - >>> t.filter([t.species == "Adelie", t.body_mass_g > 3500]).sex.value_counts().drop_null( - ... "sex" - ... ).order_by("sex") - ┏━━━━━━━━┳━━━━━━━━━━━┓ - ┃ sex ┃ sex_count ┃ - ┡━━━━━━━━╇━━━━━━━━━━━┩ - │ string │ int64 │ - ├────────┼───────────┤ - │ female │ 22 │ - │ male │ 68 │ - └────────┴───────────┘ - """ - from bigframes_vendored.ibis.expr.rewrites import ( - flatten_predicates, - rewrite_filter_input, - ) - - preds = self.bind(*predicates) - - # we can't use `unwrap_aliases` here because that function - # deduplicates based on name alone - # - # it's perfectly valid to repeat a filter, even if it might be - # useless, so enforcing uniquely named expressions here doesn't make - # sense - # - # instead, compute all distinct unaliased predicates - result = toolz.unique( - node.arg if isinstance(node := value.op(), ops.Alias) else node - for value in preds - ) - - preds = flatten_predicates(list(result)) - preds = list(map(rewrite_filter_input, preds)) - if not preds: - raise com.IbisInputError("You must pass at least one predicate to filter") - return ops.Filter(self, preds).to_expr() - - def nunique(self, where: ir.BooleanValue | None = None) -> ir.IntegerScalar: - """Compute the number of unique rows in the table. - - Parameters - ---------- - where - Optional boolean expression to filter rows when counting. - - Returns - ------- - IntegerScalar - Number of unique rows in the table - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable({"a": ["foo", "bar", "bar"]}) - >>> t - ┏━━━━━━━━┓ - ┃ a ┃ - ┡━━━━━━━━┩ - │ string │ - ├────────┤ - │ foo │ - │ bar │ - │ bar │ - └────────┘ - >>> t.nunique() - ┌─────────────┐ - │ np.int64(2) │ - └─────────────┘ - >>> t.nunique(t.a != "foo") - ┌─────────────┐ - │ np.int64(1) │ - └─────────────┘ - """ - if where is not None: - (where,) = bind(self, where) - return ops.CountDistinctStar(self, where=where).to_expr() - - def count(self, where: ir.BooleanValue | None = None) -> ir.IntegerScalar: - """Compute the number of rows in the table. - - Parameters - ---------- - where - Optional boolean expression to filter rows when counting. - - Returns - ------- - IntegerScalar - Number of rows in the table - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable({"a": ["foo", "bar", "baz"]}) - >>> t - ┏━━━━━━━━┓ - ┃ a ┃ - ┡━━━━━━━━┩ - │ string │ - ├────────┤ - │ foo │ - │ bar │ - │ baz │ - └────────┘ - >>> t.count() - ┌─────────────┐ - │ np.int64(3) │ - └─────────────┘ - >>> t.count(t.a != "foo") - ┌─────────────┐ - │ np.int64(2) │ - └─────────────┘ - >>> type(t.count()) - - """ - if where is not None: - (where,) = bind(self, where) - return ops.CountStar(self, where=where).to_expr() - - def drop_null( - self, - subset: Sequence[str] | str | None = None, - how: Literal["any", "all"] = "any", - ) -> Table: - """Remove rows with null values from the table. - - Parameters - ---------- - subset - Columns names to consider when dropping nulls. By default all columns - are considered. - how - Determine whether a row is removed if there is **at least one null - value in the row** (`'any'`), or if **all** row values are null - (`'all'`). - - Returns - ------- - Table - Table expression - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.examples.penguins.fetch() - >>> t - ┏━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━┓ - ┃ species ┃ island ┃ bill_length_mm ┃ bill_depth_mm ┃ flipper_length_mm ┃ … ┃ - ┡━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━┩ - │ string │ string │ float64 │ float64 │ int64 │ … │ - ├─────────┼───────────┼────────────────┼───────────────┼───────────────────┼───┤ - │ Adelie │ Torgersen │ 39.1 │ 18.7 │ 181 │ … │ - │ Adelie │ Torgersen │ 39.5 │ 17.4 │ 186 │ … │ - │ Adelie │ Torgersen │ 40.3 │ 18.0 │ 195 │ … │ - │ Adelie │ Torgersen │ NULL │ NULL │ NULL │ … │ - │ Adelie │ Torgersen │ 36.7 │ 19.3 │ 193 │ … │ - │ Adelie │ Torgersen │ 39.3 │ 20.6 │ 190 │ … │ - │ Adelie │ Torgersen │ 38.9 │ 17.8 │ 181 │ … │ - │ Adelie │ Torgersen │ 39.2 │ 19.6 │ 195 │ … │ - │ Adelie │ Torgersen │ 34.1 │ 18.1 │ 193 │ … │ - │ Adelie │ Torgersen │ 42.0 │ 20.2 │ 190 │ … │ - │ … │ … │ … │ … │ … │ … │ - └─────────┴───────────┴────────────────┴───────────────┴───────────────────┴───┘ - >>> t.count() - ┌───────────────┐ - │ np.int64(344) │ - └───────────────┘ - >>> t.drop_null(["bill_length_mm", "body_mass_g"]).count() - ┌───────────────┐ - │ np.int64(342) │ - └───────────────┘ - >>> t.drop_null(how="all").count() # no rows where all columns are null - ┌───────────────┐ - │ np.int64(344) │ - └───────────────┘ - """ - if subset is not None: - subset = self.bind(subset) - return ops.DropNull(self, how, subset).to_expr() - - def fill_null( - self, - replacements: ir.Scalar | Mapping[str, ir.Scalar], - ) -> Table: - """Fill null values in a table expression. - - ::: {.callout-note} - ## There is potential lack of type stability with the `fill_null` API - - For example, different library versions may impact whether a given - backend promotes integer replacement values to floats. - ::: - - Parameters - ---------- - replacements - Value with which to fill nulls. If `replacements` is a mapping, the - keys are column names that map to their replacement value. If - passed as a scalar all columns are filled with that value. - - Returns - ------- - Table - Table expression - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.examples.penguins.fetch() - >>> t.sex - ┏━━━━━━━━┓ - ┃ sex ┃ - ┡━━━━━━━━┩ - │ string │ - ├────────┤ - │ male │ - │ female │ - │ female │ - │ NULL │ - │ female │ - │ male │ - │ female │ - │ male │ - │ NULL │ - │ NULL │ - │ … │ - └────────┘ - >>> t.fill_null({"sex": "unrecorded"}).sex - ┏━━━━━━━━━━━━┓ - ┃ sex ┃ - ┡━━━━━━━━━━━━┩ - │ string │ - ├────────────┤ - │ male │ - │ female │ - │ female │ - │ unrecorded │ - │ female │ - │ male │ - │ female │ - │ male │ - │ unrecorded │ - │ unrecorded │ - │ … │ - └────────────┘ - """ - schema = self.schema() - - if isinstance(replacements, Mapping): - for col, val in replacements.items(): - if col not in schema: - columns_formatted = ", ".join(map(repr, schema.names)) - raise com.IbisTypeError( - f"Column {col!r} is not found in table. " - f"Existing columns: {columns_formatted}." - ) from None - - col_type = schema[col] - val_type = val.type() if isinstance(val, Expr) else dt.infer(val) - if not val_type.castable(col_type): - raise com.IbisTypeError( - f"Cannot fill_null on column {col!r} of type {col_type} with a " - f"value of type {val_type}" - ) - else: - val_type = ( - replacements.type() - if isinstance(replacements, Expr) - else dt.infer(replacements) - ) - for col, col_type in schema.items(): - if col_type.nullable and not val_type.castable(col_type): - raise com.IbisTypeError( - f"Cannot fill_null on column {col!r} of type {col_type} with a " - f"value of type {val_type} - pass in an explicit mapping " - f"of fill values to `fill_null` instead." - ) - return ops.FillNull(self, replacements).to_expr() - - @deprecated(as_of="9.1", instead="use drop_null instead") - def dropna( - self, - subset: Sequence[str] | str | None = None, - how: Literal["any", "all"] = "any", - ) -> Table: - """Deprecated - use `drop_null` instead.""" - - return self.drop_null(subset, how) - - @deprecated(as_of="9.1", instead="use fill_null instead") - def fillna( - self, - replacements: ir.Scalar | Mapping[str, ir.Scalar], - ) -> Table: - """Deprecated - use `fill_null` instead.""" - - return self.fill_null(replacements) - - def unpack(self, *columns: str) -> Table: - """Project the struct fields of each of `columns` into `self`. - - Existing fields are retained in the projection. - - Parameters - ---------- - columns - String column names to project into `self`. - - Returns - ------- - Table - The child table with struct fields of each of `columns` projected. - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> lines = ''' - ... {"name": "a", "pos": {"lat": 10.1, "lon": 30.3}} - ... {"name": "b", "pos": {"lat": 10.2, "lon": 30.2}} - ... {"name": "c", "pos": {"lat": 10.3, "lon": 30.1}} - ... ''' - >>> with open("/tmp/lines.json", "w") as f: - ... nbytes = f.write(lines) # nbytes is unused - >>> t = ibis.read_json("/tmp/lines.json") - >>> t - ┏━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ name ┃ pos ┃ - ┡━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩ - │ string │ struct │ - ├────────┼────────────────────────────────────┤ - │ a │ {'lat': 10.1, 'lon': 30.3} │ - │ b │ {'lat': 10.2, 'lon': 30.2} │ - │ c │ {'lat': 10.3, 'lon': 30.1} │ - └────────┴────────────────────────────────────┘ - >>> t.unpack("pos") - ┏━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━┓ - ┃ name ┃ lat ┃ lon ┃ - ┡━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━┩ - │ string │ float64 │ float64 │ - ├────────┼─────────┼─────────┤ - │ a │ 10.1 │ 30.3 │ - │ b │ 10.2 │ 30.2 │ - │ c │ 10.3 │ 30.1 │ - └────────┴─────────┴─────────┘ - - See Also - -------- - [`StructValue.lift`](./expression-collections.qmd#ibis.expr.types.structs.StructValue.lift) - """ - columns_to_unpack = frozenset(columns) - result_columns = [] - for column in self.columns: - if column in columns_to_unpack: - expr = self[column] - result_columns.extend(expr[field] for field in expr.names) - else: - result_columns.append(column) - return self[result_columns] - - def info(self) -> Table: - """Return summary information about a table. - - Returns - ------- - Table - Summary of `self` - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.examples.penguins.fetch() - >>> t.info() - ┏━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━┳━━━┓ - ┃ name ┃ type ┃ nullable ┃ nulls ┃ non_nulls ┃ null_frac ┃ … ┃ - ┡━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━╇━━━┩ - │ string │ string │ boolean │ int64 │ int64 │ float64 │ … │ - ├───────────────────┼─────────┼──────────┼───────┼───────────┼───────────┼───┤ - │ species │ string │ True │ 0 │ 344 │ 0.000000 │ … │ - │ island │ string │ True │ 0 │ 344 │ 0.000000 │ … │ - │ bill_length_mm │ float64 │ True │ 2 │ 342 │ 0.005814 │ … │ - │ bill_depth_mm │ float64 │ True │ 2 │ 342 │ 0.005814 │ … │ - │ flipper_length_mm │ int64 │ True │ 2 │ 342 │ 0.005814 │ … │ - │ body_mass_g │ int64 │ True │ 2 │ 342 │ 0.005814 │ … │ - │ sex │ string │ True │ 11 │ 333 │ 0.031977 │ … │ - │ year │ int64 │ True │ 0 │ 344 │ 0.000000 │ … │ - └───────────────────┴─────────┴──────────┴───────┴───────────┴───────────┴───┘ - """ - from bigframes_vendored.ibis import literal as lit - - aggs = [] - - for pos, colname in enumerate(self.columns): - col = self[colname] - typ = col.type() - agg = self.select( - isna=bigframes_vendored.ibis.case().when(col.isnull(), 1).else_(0).end() - ).agg( - name=lit(colname), - type=lit(str(typ)), - nullable=lit(typ.nullable), - nulls=lambda t: t.isna.sum(), - non_nulls=lambda t: (1 - t.isna).sum(), - null_frac=lambda t: t.isna.mean(), - pos=lit(pos, type=dt.int16), - ) - aggs.append(agg) - return bigframes_vendored.ibis.union(*aggs).order_by( - bigframes_vendored.ibis.asc("pos") - ) - - def describe( - self, quantile: Sequence[ir.NumericValue | float] = (0.25, 0.5, 0.75) - ) -> Table: - """Return summary information about a table. - - Parameters - ---------- - quantile - The quantiles to compute for numerical columns. Defaults to (0.25, 0.5, 0.75). - - Returns - ------- - Table - A table containing summary information about the columns of self. - - Notes - ----- - This function computes summary statistics for each column in the table. For - numerical columns, it computes statistics such as minimum, maximum, mean, - standard deviation, and quantiles. For string columns, it computes the mode - and the number of unique values. - - Examples - -------- - >>> import ibis - >>> import ibis.selectors as s - >>> ibis.options.interactive = True - >>> p = ibis.examples.penguins.fetch() - >>> p.describe() - ┏━━━━━━━━━━━━━━━━━━━┳━━━━━━━┳━━━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━━┳━━━━━━━━┳━━━┓ - ┃ name ┃ pos ┃ type ┃ count ┃ nulls ┃ unique ┃ mode ┃ … ┃ - ┡━━━━━━━━━━━━━━━━━━━╇━━━━━━━╇━━━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━━╇━━━━━━━━╇━━━┩ - │ string │ int16 │ string │ int64 │ int64 │ int64 │ string │ … │ - ├───────────────────┼───────┼─────────┼───────┼───────┼────────┼────────┼───┤ - │ species │ 0 │ string │ 344 │ 0 │ 3 │ Adelie │ … │ - │ island │ 1 │ string │ 344 │ 0 │ 3 │ Biscoe │ … │ - │ bill_length_mm │ 2 │ float64 │ 344 │ 2 │ 164 │ NULL │ … │ - │ bill_depth_mm │ 3 │ float64 │ 344 │ 2 │ 80 │ NULL │ … │ - │ flipper_length_mm │ 4 │ int64 │ 344 │ 2 │ 55 │ NULL │ … │ - │ body_mass_g │ 5 │ int64 │ 344 │ 2 │ 94 │ NULL │ … │ - │ sex │ 6 │ string │ 344 │ 11 │ 2 │ male │ … │ - │ year │ 7 │ int64 │ 344 │ 0 │ 3 │ NULL │ … │ - └───────────────────┴───────┴─────────┴───────┴───────┴────────┴────────┴───┘ - >>> p.select(s.of_type("numeric")).describe() - ┏━━━━━━━━━━━━━━━━━━━┳━━━━━━━┳━━━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━━┳━━━┓ - ┃ name ┃ pos ┃ type ┃ count ┃ nulls ┃ unique ┃ … ┃ - ┡━━━━━━━━━━━━━━━━━━━╇━━━━━━━╇━━━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━━╇━━━┩ - │ string │ int16 │ string │ int64 │ int64 │ int64 │ … │ - ├───────────────────┼───────┼─────────┼───────┼───────┼────────┼───┤ - │ flipper_length_mm │ 2 │ int64 │ 344 │ 2 │ 55 │ … │ - │ body_mass_g │ 3 │ int64 │ 344 │ 2 │ 94 │ … │ - │ year │ 4 │ int64 │ 344 │ 0 │ 3 │ … │ - │ bill_length_mm │ 0 │ float64 │ 344 │ 2 │ 164 │ … │ - │ bill_depth_mm │ 1 │ float64 │ 344 │ 2 │ 80 │ … │ - └───────────────────┴───────┴─────────┴───────┴───────┴────────┴───┘ - >>> p.select(s.of_type("string")).describe() - ┏━━━━━━━━━┳━━━━━━━┳━━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━━┳━━━━━━━━┓ - ┃ name ┃ pos ┃ type ┃ count ┃ nulls ┃ unique ┃ mode ┃ - ┡━━━━━━━━━╇━━━━━━━╇━━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━━╇━━━━━━━━┩ - │ string │ int16 │ string │ int64 │ int64 │ int64 │ string │ - ├─────────┼───────┼────────┼───────┼───────┼────────┼────────┤ - │ sex │ 2 │ string │ 344 │ 11 │ 2 │ male │ - │ species │ 0 │ string │ 344 │ 0 │ 3 │ Adelie │ - │ island │ 1 │ string │ 344 │ 0 │ 3 │ Biscoe │ - └─────────┴───────┴────────┴───────┴───────┴────────┴────────┘ - """ - import bigframes_vendored.ibis.selectors as s - from bigframes_vendored.ibis.expr.types.generic import literal as lit - - quantile = sorted(quantile) - aggs = [] - string_col = False - numeric_col = False - for pos, colname in enumerate(self.columns): - col = self[colname] - typ = col.type() - - # default statistics to None - col_mean = lit(None).cast(float) - col_std = lit(None).cast(float) - col_min = lit(None).cast(float) - col_max = lit(None).cast(float) - col_mode = lit(None).cast(str) - quantile_values = { - f"p{100 * q:.6f}".rstrip("0").rstrip("."): lit(None).cast(float) - for q in quantile - } - - if typ.is_numeric(): - numeric_col = True - col_mean = col.mean() - col_std = col.std() - col_min = col.min().cast(float) - col_max = col.max().cast(float) - quantile_values = { - f"p{100 * q:.6f}".rstrip("0").rstrip("."): col.quantile(q).cast( - float - ) - for q in quantile - } - elif typ.is_string(): - string_col = True - col_mode = col.mode() - elif typ.is_boolean(): - numeric_col = True - col_mean = col.mean() - else: - # Will not calculate statistics for other types - continue - - agg = self.agg( - name=lit(colname), - pos=lit(pos, type=dt.int16), - type=lit(str(typ)), - count=col.isnull().count(), - nulls=col.isnull().sum(), - unique=col.nunique(), - mode=col_mode, - mean=col_mean, - std=col_std, - min=col_min, - **quantile_values, - max=col_max, - ) - aggs.append(agg) - - t = bigframes_vendored.ibis.union(*aggs) - - # TODO(jiting): Need a better way to remove columns with all NULL - if string_col and not numeric_col: - t = t.select(~s.of_type("float")) - elif numeric_col and not string_col: - t = t.drop("mode") - - return t - - def join( - left: Table, - right: Table, - predicates: ( - str - | Sequence[ - str - | ir.BooleanColumn - | Literal[True] - | Literal[False] - | tuple[ - str | ir.Column | ir.Deferred, - str | ir.Column | ir.Deferred, - ] - ] - ) = (), - how: JoinKind = "inner", - *, - lname: str = "", - rname: str = "{name}_right", - ) -> Table: - """Perform a join between two tables. - - Parameters - ---------- - left - Left table to join - right - Right table to join - predicates - Condition(s) to join on. See examples for details. - how - Join method, e.g. ``"inner"`` or ``"left"``. - lname - A format string to use to rename overlapping columns in the left - table (e.g. ``"left_{name}"``). - rname - A format string to use to rename overlapping columns in the right - table (e.g. ``"right_{name}"``). - - Examples - -------- - >>> import ibis - >>> from ibis import _ - >>> ibis.options.interactive = True - >>> movies = ibis.examples.ml_latest_small_movies.fetch() - >>> movies.head() - ┏━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ movieId ┃ title ┃ genres ┃ - ┡━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩ - │ int64 │ string │ string │ - ├─────────┼──────────────────────────────────┼─────────────────────────────────┤ - │ 1 │ Toy Story (1995) │ Adventure|Animation|Children|C… │ - │ 2 │ Jumanji (1995) │ Adventure|Children|Fantasy │ - │ 3 │ Grumpier Old Men (1995) │ Comedy|Romance │ - │ 4 │ Waiting to Exhale (1995) │ Comedy|Drama|Romance │ - │ 5 │ Father of the Bride Part II (19… │ Comedy │ - └─────────┴──────────────────────────────────┴─────────────────────────────────┘ - >>> ratings = ibis.examples.ml_latest_small_ratings.fetch().drop("timestamp") - >>> ratings.head() - ┏━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━┓ - ┃ userId ┃ movieId ┃ rating ┃ - ┡━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━┩ - │ int64 │ int64 │ float64 │ - ├────────┼─────────┼─────────┤ - │ 1 │ 1 │ 4.0 │ - │ 1 │ 3 │ 4.0 │ - │ 1 │ 6 │ 4.0 │ - │ 1 │ 47 │ 5.0 │ - │ 1 │ 50 │ 5.0 │ - └────────┴─────────┴─────────┘ - - Equality left join on the shared `movieId` column. - Note the `_right` suffix added to all overlapping - columns from the right table - (in this case only the "movieId" column). - - >>> ratings.join(movies, "movieId", how="left").head(5) - ┏━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━┓ - ┃ userId ┃ movieId ┃ rating ┃ movieId_right ┃ title ┃ … ┃ - ┡━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━┩ - │ int64 │ int64 │ float64 │ int64 │ string │ … │ - ├────────┼─────────┼─────────┼───────────────┼─────────────────────────────┼───┤ - │ 1 │ 1 │ 4.0 │ 1 │ Toy Story (1995) │ … │ - │ 1 │ 3 │ 4.0 │ 3 │ Grumpier Old Men (1995) │ … │ - │ 1 │ 6 │ 4.0 │ 6 │ Heat (1995) │ … │ - │ 1 │ 47 │ 5.0 │ 47 │ Seven (a.k.a. Se7en) (1995) │ … │ - │ 1 │ 50 │ 5.0 │ 50 │ Usual Suspects, The (1995) │ … │ - └────────┴─────────┴─────────┴───────────────┴─────────────────────────────┴───┘ - - Explicit equality join using the default `how` value of `"inner"`. - Note how there is no `_right` suffix added to the `movieId` column - since this is an inner join and the `movieId` column is part of the - join condition. - - >>> ratings.join(movies, ratings.movieId == movies.movieId).head(5) - ┏━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ userId ┃ movieId ┃ rating ┃ title ┃ genres ┃ - ┡━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩ - │ int64 │ int64 │ float64 │ string │ string │ - ├────────┼─────────┼─────────┼────────────────────────┼────────────────────────┤ - │ 1 │ 1 │ 4.0 │ Toy Story (1995) │ Adventure|Animation|C… │ - │ 1 │ 3 │ 4.0 │ Grumpier Old Men (199… │ Comedy|Romance │ - │ 1 │ 6 │ 4.0 │ Heat (1995) │ Action|Crime|Thriller │ - │ 1 │ 47 │ 5.0 │ Seven (a.k.a. Se7en) … │ Mystery|Thriller │ - │ 1 │ 50 │ 5.0 │ Usual Suspects, The (… │ Crime|Mystery|Thriller │ - └────────┴─────────┴─────────┴────────────────────────┴────────────────────────┘ - - >>> tags = ibis.examples.ml_latest_small_tags.fetch() - >>> tags.head() - ┏━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┓ - ┃ userId ┃ movieId ┃ tag ┃ timestamp ┃ - ┡━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━┩ - │ int64 │ int64 │ string │ int64 │ - ├────────┼─────────┼─────────────────┼────────────┤ - │ 2 │ 60756 │ funny │ 1445714994 │ - │ 2 │ 60756 │ Highly quotable │ 1445714996 │ - │ 2 │ 60756 │ will ferrell │ 1445714992 │ - │ 2 │ 89774 │ Boxing story │ 1445715207 │ - │ 2 │ 89774 │ MMA │ 1445715200 │ - └────────┴─────────┴─────────────────┴────────────┘ - - You can join on multiple columns/conditions by passing in a - sequence. Find all instances where a user both tagged and - rated a movie: - - >>> tags.join(ratings, ["userId", "movieId"]).head(5).order_by("userId") - ┏━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━┓ - ┃ userId ┃ movieId ┃ tag ┃ timestamp ┃ rating ┃ - ┡━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━┩ - │ int64 │ int64 │ string │ int64 │ float64 │ - ├────────┼─────────┼────────────────┼────────────┼─────────┤ - │ 62 │ 2 │ Robin Williams │ 1528843907 │ 4.0 │ - │ 62 │ 110 │ sword fight │ 1528152535 │ 4.5 │ - │ 62 │ 410 │ gothic │ 1525636609 │ 4.5 │ - │ 62 │ 2023 │ mafia │ 1525636733 │ 5.0 │ - │ 62 │ 2124 │ quirky │ 1525636846 │ 5.0 │ - └────────┴─────────┴────────────────┴────────────┴─────────┘ - - To self-join a table with itself, you need to call - `.view()` on one of the arguments so the two tables - are distinct from each other. - - For crafting more complex join conditions, - a valid form of a join condition is a 2-tuple like - `({left_key}, {right_key})`, where each key can be - - - a Column - - Deferred expression - - lambda of the form (Table) -> Column - - For example, to find all movies pairings that received the same - (ignoring case) tags: - - >>> movie_tags = tags["movieId", "tag"] - >>> view = movie_tags.view() - >>> movie_tags.join( - ... view, - ... [ - ... movie_tags.movieId != view.movieId, - ... (_.tag.lower(), lambda t: t.tag.lower()), - ... ], - ... ).head().order_by(("movieId", "movieId_right")) - ┏━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓ - ┃ movieId ┃ tag ┃ movieId_right ┃ tag_right ┃ - ┡━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩ - │ int64 │ string │ int64 │ string │ - ├─────────┼───────────────────┼───────────────┼───────────────────┤ - │ 1732 │ funny │ 60756 │ funny │ - │ 1732 │ Highly quotable │ 60756 │ Highly quotable │ - │ 1732 │ drugs │ 106782 │ drugs │ - │ 5989 │ Leonardo DiCaprio │ 106782 │ Leonardo DiCaprio │ - │ 139385 │ tom hardy │ 89774 │ Tom Hardy │ - └─────────┴───────────────────┴───────────────┴───────────────────┘ - """ - from bigframes_vendored.ibis.expr.types.joins import Join - - return Join(left.op()).join( - right, predicates, how=how, lname=lname, rname=rname - ) - - def asof_join( - left: Table, - right: Table, - on: str | ir.BooleanColumn, - predicates: str | ir.Column | Sequence[str | ir.Column] = (), - tolerance: str | ir.IntervalScalar | None = None, - *, - lname: str = "", - rname: str = "{name}_right", - ) -> Table: - """Perform an "as-of" join between `left` and `right`. - - Similar to a left join except that the match is done on nearest key - rather than equal keys. - - Parameters - ---------- - left - Table expression - right - Table expression - on - Closest match inequality condition - predicates - Additional join predicates - tolerance - Amount of time to look behind when joining - lname - A format string to use to rename overlapping columns in the left - table (e.g. ``"left_{name}"``). - rname - A format string to use to rename overlapping columns in the right - table (e.g. ``"right_{name}"``). - - Returns - ------- - Table - Table expression - """ - from bigframes_vendored.ibis.expr.types.joins import Join - - return Join(left.op()).asof_join( - right, on, predicates, tolerance=tolerance, lname=lname, rname=rname - ) - - def cross_join( - left: Table, - right: Table, - *rest: Table, - lname: str = "", - rname: str = "{name}_right", - ) -> Table: - """Compute the cross join of a sequence of tables. - - Parameters - ---------- - left - Left table - right - Right table - rest - Additional tables to cross join - lname - A format string to use to rename overlapping columns in the left - table (e.g. ``"left_{name}"``). - rname - A format string to use to rename overlapping columns in the right - table (e.g. ``"right_{name}"``). - - Returns - ------- - Table - Cross join of `left`, `right` and `rest` - - Examples - -------- - >>> import ibis - >>> import ibis.selectors as s - >>> from ibis import _ - >>> ibis.options.interactive = True - >>> t = ibis.examples.penguins.fetch() - >>> t.count() - ┌───────────────┐ - │ np.int64(344) │ - └───────────────┘ - >>> agg = t.drop("year").agg(s.across(s.numeric(), _.mean())) - >>> expr = t.cross_join(agg) - >>> expr - ┏━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━┓ - ┃ species ┃ island ┃ bill_length_mm ┃ bill_depth_mm ┃ flipper_length_mm ┃ … ┃ - ┡━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━┩ - │ string │ string │ float64 │ float64 │ int64 │ … │ - ├─────────┼───────────┼────────────────┼───────────────┼───────────────────┼───┤ - │ Adelie │ Torgersen │ 39.1 │ 18.7 │ 181 │ … │ - │ Adelie │ Torgersen │ 39.5 │ 17.4 │ 186 │ … │ - │ Adelie │ Torgersen │ 40.3 │ 18.0 │ 195 │ … │ - │ Adelie │ Torgersen │ NULL │ NULL │ NULL │ … │ - │ Adelie │ Torgersen │ 36.7 │ 19.3 │ 193 │ … │ - │ Adelie │ Torgersen │ 39.3 │ 20.6 │ 190 │ … │ - │ Adelie │ Torgersen │ 38.9 │ 17.8 │ 181 │ … │ - │ Adelie │ Torgersen │ 39.2 │ 19.6 │ 195 │ … │ - │ Adelie │ Torgersen │ 34.1 │ 18.1 │ 193 │ … │ - │ Adelie │ Torgersen │ 42.0 │ 20.2 │ 190 │ … │ - │ … │ … │ … │ … │ … │ … │ - └─────────┴───────────┴────────────────┴───────────────┴───────────────────┴───┘ - >>> expr.columns - ['species', - 'island', - 'bill_length_mm', - 'bill_depth_mm', - 'flipper_length_mm', - 'body_mass_g', - 'sex', - 'year', - 'bill_length_mm_right', - 'bill_depth_mm_right', - 'flipper_length_mm_right', - 'body_mass_g_right'] - >>> expr.count() - ┌───────────────┐ - │ np.int64(344) │ - └───────────────┘ - """ - from bigframes_vendored.ibis.expr.types.joins import Join - - return Join(left.op()).cross_join(right, *rest, lname=lname, rname=rname) - - inner_join = _regular_join_method("inner_join", "inner") - left_join = _regular_join_method("left_join", "left") - outer_join = _regular_join_method("outer_join", "outer") - right_join = _regular_join_method("right_join", "right") - semi_join = _regular_join_method("semi_join", "semi") - anti_join = _regular_join_method("anti_join", "anti") - any_inner_join = _regular_join_method("any_inner_join", "any_inner") - any_left_join = _regular_join_method("any_left_join", "any_left") - - def alias(self, alias: str) -> ir.Table: - """Create a table expression with a specific name `alias`. - - This method is useful for exposing an ibis expression to the underlying - backend for use in the - [`Table.sql`](#ibis.expr.types.relations.Table.sql) method. - - ::: {.callout-note} - ## `.alias` will create a temporary view - - `.alias` creates a temporary view in the database. - - This side effect will be removed in a future version of ibis and **is - not part of the public API**. - ::: - - Parameters - ---------- - alias - Name of the child expression - - Returns - ------- - Table - An table expression - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.examples.penguins.fetch() - >>> expr = t.alias("pingüinos").sql('SELECT * FROM "pingüinos" LIMIT 5') - >>> expr - ┏━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━┓ - ┃ species ┃ island ┃ bill_length_mm ┃ bill_depth_mm ┃ flipper_length_mm ┃ … ┃ - ┡━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━┩ - │ string │ string │ float64 │ float64 │ int64 │ … │ - ├─────────┼───────────┼────────────────┼───────────────┼───────────────────┼───┤ - │ Adelie │ Torgersen │ 39.1 │ 18.7 │ 181 │ … │ - │ Adelie │ Torgersen │ 39.5 │ 17.4 │ 186 │ … │ - │ Adelie │ Torgersen │ 40.3 │ 18.0 │ 195 │ … │ - │ Adelie │ Torgersen │ NULL │ NULL │ NULL │ … │ - │ Adelie │ Torgersen │ 36.7 │ 19.3 │ 193 │ … │ - └─────────┴───────────┴────────────────┴───────────────┴───────────────────┴───┘ - """ - return ops.View(child=self, name=alias).to_expr() - - def sql(self, query: str, dialect: str | None = None) -> ir.Table: - '''Run a SQL query against a table expression. - - Parameters - ---------- - query - Query string - dialect - Optional string indicating the dialect of `query`. Defaults to the - backend's native dialect. - - Returns - ------- - Table - An opaque table expression - - Examples - -------- - >>> import ibis - >>> from ibis import _ - >>> ibis.options.interactive = True - >>> t = ibis.examples.penguins.fetch(table_name="penguins") - >>> expr = t.sql( - ... """ - ... SELECT island, mean(bill_length_mm) AS avg_bill_length - ... FROM penguins - ... GROUP BY 1 - ... ORDER BY 2 DESC - ... """ - ... ) - >>> expr - ┏━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ - ┃ island ┃ avg_bill_length ┃ - ┡━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ - │ string │ float64 │ - ├───────────┼─────────────────┤ - │ Biscoe │ 45.257485 │ - │ Dream │ 44.167742 │ - │ Torgersen │ 38.950980 │ - └───────────┴─────────────────┘ - - Mix and match ibis expressions with SQL queries - - >>> t = ibis.examples.penguins.fetch(table_name="penguins") - >>> expr = t.sql( - ... """ - ... SELECT island, mean(bill_length_mm) AS avg_bill_length - ... FROM penguins - ... GROUP BY 1 - ... ORDER BY 2 DESC - ... """ - ... ) - >>> expr = expr.mutate( - ... island=_.island.lower(), - ... avg_bill_length=_.avg_bill_length.round(1), - ... ) - >>> expr - ┏━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ - ┃ island ┃ avg_bill_length ┃ - ┡━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ - │ string │ float64 │ - ├───────────┼─────────────────┤ - │ biscoe │ 45.3 │ - │ dream │ 44.2 │ - │ torgersen │ 39.0 │ - └───────────┴─────────────────┘ - - Because ibis expressions aren't named, they aren't visible to - subsequent `.sql` calls. Use the [`alias`](#ibis.expr.types.relations.Table.alias) method - to assign a name to an expression. - - >>> expr.alias("b").sql("SELECT * FROM b WHERE avg_bill_length > 40") - ┏━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ - ┃ island ┃ avg_bill_length ┃ - ┡━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ - │ string │ float64 │ - ├────────┼─────────────────┤ - │ biscoe │ 45.3 │ - │ dream │ 44.2 │ - └────────┴─────────────────┘ - - See Also - -------- - [`Table.alias`](#ibis.expr.types.relations.Table.alias) - ''' - op = self.op() - backend = self._find_backend() - - if dialect is not None: - # only transpile if dialect was passed - query = backend._transpile_sql(query, dialect=dialect) - - if isinstance(op, ops.View): - name = op.name - expr = op.child.to_expr() - else: - name = util.gen_name("sql_query") - expr = self - - schema = backend._get_sql_string_view_schema(name, expr, query) - node = ops.SQLStringView(child=self.op(), query=query, schema=schema) - return node.to_expr() - - def to_pandas(self, **kwargs) -> pd.DataFrame: - """Convert a table expression to a pandas DataFrame. - - Parameters - ---------- - kwargs - Same as keyword arguments to [`execute`](./expression-generic.qmd#ibis.expr.types.core.Expr.execute) - """ - return self.execute(**kwargs) - - def cache(self) -> Table: - """Cache the provided expression. - - All subsequent operations on the returned expression will be performed - on the cached data. The lifetime of the cached table is tied to its - python references (ie. it is released once the last reference to it is - garbage collected). Alternatively, use the - [`with`](https://docs.python.org/3/reference/compound_stmts.html#with) - statement or call the `.release()` method for more control. - - This method is idempotent: calling it multiple times in succession will - return the same value as the first call. - - ::: {.callout-note} - ## This method eagerly evaluates the expression prior to caching - - Subsequent evaluations will not recompute the expression so method - chaining will not incur the overhead of caching more than once. - ::: - - Returns - ------- - Table - Cached table - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.examples.penguins.fetch() - >>> heavy_computation = ibis.literal("Heavy Computation") - >>> cached_penguins = t.mutate(computation=heavy_computation).cache() - >>> cached_penguins - ┏━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━┓ - ┃ species ┃ island ┃ bill_length_mm ┃ bill_depth_mm ┃ flipper_length_mm ┃ … ┃ - ┡━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━┩ - │ string │ string │ float64 │ float64 │ int64 │ … │ - ├─────────┼───────────┼────────────────┼───────────────┼───────────────────┼───┤ - │ Adelie │ Torgersen │ 39.1 │ 18.7 │ 181 │ … │ - │ Adelie │ Torgersen │ 39.5 │ 17.4 │ 186 │ … │ - │ Adelie │ Torgersen │ 40.3 │ 18.0 │ 195 │ … │ - │ Adelie │ Torgersen │ NULL │ NULL │ NULL │ … │ - │ Adelie │ Torgersen │ 36.7 │ 19.3 │ 193 │ … │ - │ Adelie │ Torgersen │ 39.3 │ 20.6 │ 190 │ … │ - │ Adelie │ Torgersen │ 38.9 │ 17.8 │ 181 │ … │ - │ Adelie │ Torgersen │ 39.2 │ 19.6 │ 195 │ … │ - │ Adelie │ Torgersen │ 34.1 │ 18.1 │ 193 │ … │ - │ Adelie │ Torgersen │ 42.0 │ 20.2 │ 190 │ … │ - │ … │ … │ … │ … │ … │ … │ - └─────────┴───────────┴────────────────┴───────────────┴───────────────────┴───┘ - - Explicit cache cleanup - - >>> with t.mutate(computation=heavy_computation).cache() as cached_penguins: - ... cached_penguins - ┏━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━┓ - ┃ species ┃ island ┃ bill_length_mm ┃ bill_depth_mm ┃ flipper_length_mm ┃ … ┃ - ┡━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━┩ - │ string │ string │ float64 │ float64 │ int64 │ … │ - ├─────────┼───────────┼────────────────┼───────────────┼───────────────────┼───┤ - │ Adelie │ Torgersen │ 39.1 │ 18.7 │ 181 │ … │ - │ Adelie │ Torgersen │ 39.5 │ 17.4 │ 186 │ … │ - │ Adelie │ Torgersen │ 40.3 │ 18.0 │ 195 │ … │ - │ Adelie │ Torgersen │ NULL │ NULL │ NULL │ … │ - │ Adelie │ Torgersen │ 36.7 │ 19.3 │ 193 │ … │ - │ Adelie │ Torgersen │ 39.3 │ 20.6 │ 190 │ … │ - │ Adelie │ Torgersen │ 38.9 │ 17.8 │ 181 │ … │ - │ Adelie │ Torgersen │ 39.2 │ 19.6 │ 195 │ … │ - │ Adelie │ Torgersen │ 34.1 │ 18.1 │ 193 │ … │ - │ Adelie │ Torgersen │ 42.0 │ 20.2 │ 190 │ … │ - │ … │ … │ … │ … │ … │ … │ - └─────────┴───────────┴────────────────┴───────────────┴───────────────────┴───┘ - """ - current_backend = self._find_backend(use_default=True) - return current_backend._cached(self) - - def pivot_longer( - self, - col: str | s.Selector, - *, - names_to: str | Iterable[str] = "name", - names_pattern: str | re.Pattern = r"(.+)", - names_transform: ( - Callable[[str], ir.Value] | Mapping[str, Callable[[str], ir.Value]] | None - ) = None, - values_to: str = "value", - values_transform: Callable[[ir.Value], ir.Value] | Deferred | None = None, - ) -> Table: - r"""Transform a table from wider to longer. - - Parameters - ---------- - col - String column name or selector. - names_to - A string or iterable of strings indicating how to name the new - pivoted columns. - names_pattern - Pattern to use to extract column names from the input. By default - the entire column name is extracted. - names_transform - Function or mapping of a name in `names_to` to a function to - transform a column name to a value. - values_to - Name of the pivoted value column. - values_transform - Apply a function to the value column. This can be a lambda or - deferred expression. - - Returns - ------- - Table - Pivoted table - - Examples - -------- - Basic usage - - >>> import ibis - >>> import ibis.selectors as s - >>> from ibis import _ - >>> ibis.options.interactive = True - >>> relig_income = ibis.examples.relig_income_raw.fetch() - >>> relig_income - ┏━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━┳━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━┳━━━┓ - ┃ religion ┃ <$10k ┃ $10-20k ┃ $20-30k ┃ $30-40k ┃ $40-50k ┃ … ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━╇━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━╇━━━┩ - │ string │ int64 │ int64 │ int64 │ int64 │ int64 │ … │ - ├─────────────────────────┼───────┼─────────┼─────────┼─────────┼─────────┼───┤ - │ Agnostic │ 27 │ 34 │ 60 │ 81 │ 76 │ … │ - │ Atheist │ 12 │ 27 │ 37 │ 52 │ 35 │ … │ - │ Buddhist │ 27 │ 21 │ 30 │ 34 │ 33 │ … │ - │ Catholic │ 418 │ 617 │ 732 │ 670 │ 638 │ … │ - │ Don’t know/refused │ 15 │ 14 │ 15 │ 11 │ 10 │ … │ - │ Evangelical Prot │ 575 │ 869 │ 1064 │ 982 │ 881 │ … │ - │ Hindu │ 1 │ 9 │ 7 │ 9 │ 11 │ … │ - │ Historically Black Prot │ 228 │ 244 │ 236 │ 238 │ 197 │ … │ - │ Jehovah's Witness │ 20 │ 27 │ 24 │ 24 │ 21 │ … │ - │ Jewish │ 19 │ 19 │ 25 │ 25 │ 30 │ … │ - │ … │ … │ … │ … │ … │ … │ … │ - └─────────────────────────┴───────┴─────────┴─────────┴─────────┴─────────┴───┘ - - Here we convert column names not matching the selector for the `religion` column - and convert those names into values - - >>> relig_income.pivot_longer(~s.c("religion"), names_to="income", values_to="count") - ┏━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━┳━━━━━━━┓ - ┃ religion ┃ income ┃ count ┃ - ┡━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━╇━━━━━━━┩ - │ string │ string │ int64 │ - ├──────────┼────────────────────┼───────┤ - │ Agnostic │ <$10k │ 27 │ - │ Agnostic │ $10-20k │ 34 │ - │ Agnostic │ $20-30k │ 60 │ - │ Agnostic │ $30-40k │ 81 │ - │ Agnostic │ $40-50k │ 76 │ - │ Agnostic │ $50-75k │ 137 │ - │ Agnostic │ $75-100k │ 122 │ - │ Agnostic │ $100-150k │ 109 │ - │ Agnostic │ >150k │ 84 │ - │ Agnostic │ Don't know/refused │ 96 │ - │ … │ … │ … │ - └──────────┴────────────────────┴───────┘ - - Similarly for a different example dataset, we convert names to values - but using a different selector and the default `values_to` value. - - >>> world_bank_pop = ibis.examples.world_bank_pop_raw.fetch() - >>> world_bank_pop.head() - ┏━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━┓ - ┃ country ┃ indicator ┃ 2000 ┃ 2001 ┃ 2002 ┃ … ┃ - ┡━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━┩ - │ string │ string │ float64 │ float64 │ float64 │ … │ - ├─────────┼─────────────┼──────────────┼──────────────┼──────────────┼───┤ - │ ABW │ SP.URB.TOTL │ 4.162500e+04 │ 4.202500e+04 │ 4.219400e+04 │ … │ - │ ABW │ SP.URB.GROW │ 1.664222e+00 │ 9.563731e-01 │ 4.013352e-01 │ … │ - │ ABW │ SP.POP.TOTL │ 8.910100e+04 │ 9.069100e+04 │ 9.178100e+04 │ … │ - │ ABW │ SP.POP.GROW │ 2.539234e+00 │ 1.768757e+00 │ 1.194718e+00 │ … │ - │ AFE │ SP.URB.TOTL │ 1.155517e+08 │ 1.197755e+08 │ 1.242275e+08 │ … │ - └─────────┴─────────────┴──────────────┴──────────────┴──────────────┴───┘ - >>> world_bank_pop.pivot_longer(s.matches(r"\d{4}"), names_to="year").head() - ┏━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━┓ - ┃ country ┃ indicator ┃ year ┃ value ┃ - ┡━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━━┩ - │ string │ string │ string │ float64 │ - ├─────────┼─────────────┼────────┼─────────┤ - │ ABW │ SP.URB.TOTL │ 2000 │ 41625.0 │ - │ ABW │ SP.URB.TOTL │ 2001 │ 42025.0 │ - │ ABW │ SP.URB.TOTL │ 2002 │ 42194.0 │ - │ ABW │ SP.URB.TOTL │ 2003 │ 42277.0 │ - │ ABW │ SP.URB.TOTL │ 2004 │ 42317.0 │ - └─────────┴─────────────┴────────┴─────────┘ - - `pivot_longer` has some preprocessing capabiltiies like stripping a prefix and applying - a function to column names - - >>> billboard = ibis.examples.billboard.fetch() - >>> billboard - ┏━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━┓ - ┃ artist ┃ track ┃ date_entered ┃ wk1 ┃ wk2 ┃ … ┃ - ┡━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━┩ - │ string │ string │ date │ int64 │ int64 │ … │ - ├────────────────┼─────────────────────────┼──────────────┼───────┼───────┼───┤ - │ 2 Pac │ Baby Don't Cry (Keep... │ 2000-02-26 │ 87 │ 82 │ … │ - │ 2Ge+her │ The Hardest Part Of ... │ 2000-09-02 │ 91 │ 87 │ … │ - │ 3 Doors Down │ Kryptonite │ 2000-04-08 │ 81 │ 70 │ … │ - │ 3 Doors Down │ Loser │ 2000-10-21 │ 76 │ 76 │ … │ - │ 504 Boyz │ Wobble Wobble │ 2000-04-15 │ 57 │ 34 │ … │ - │ 98^0 │ Give Me Just One Nig... │ 2000-08-19 │ 51 │ 39 │ … │ - │ A*Teens │ Dancing Queen │ 2000-07-08 │ 97 │ 97 │ … │ - │ Aaliyah │ I Don't Wanna │ 2000-01-29 │ 84 │ 62 │ … │ - │ Aaliyah │ Try Again │ 2000-03-18 │ 59 │ 53 │ … │ - │ Adams, Yolanda │ Open My Heart │ 2000-08-26 │ 76 │ 76 │ … │ - │ … │ … │ … │ … │ … │ … │ - └────────────────┴─────────────────────────┴──────────────┴───────┴───────┴───┘ - >>> billboard.pivot_longer( - ... s.startswith("wk"), - ... names_to="week", - ... names_pattern=r"wk(.+)", - ... names_transform=int, - ... values_to="rank", - ... values_transform=_.cast(int), - ... ).drop_null("rank") - ┏━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━┳━━━━━━━┓ - ┃ artist ┃ track ┃ date_entered ┃ week ┃ rank ┃ - ┡━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━╇━━━━━━━┩ - │ string │ string │ date │ int8 │ int64 │ - ├─────────┼─────────────────────────┼──────────────┼──────┼───────┤ - │ 2 Pac │ Baby Don't Cry (Keep... │ 2000-02-26 │ 1 │ 87 │ - │ 2 Pac │ Baby Don't Cry (Keep... │ 2000-02-26 │ 2 │ 82 │ - │ 2 Pac │ Baby Don't Cry (Keep... │ 2000-02-26 │ 3 │ 72 │ - │ 2 Pac │ Baby Don't Cry (Keep... │ 2000-02-26 │ 4 │ 77 │ - │ 2 Pac │ Baby Don't Cry (Keep... │ 2000-02-26 │ 5 │ 87 │ - │ 2 Pac │ Baby Don't Cry (Keep... │ 2000-02-26 │ 6 │ 94 │ - │ 2 Pac │ Baby Don't Cry (Keep... │ 2000-02-26 │ 7 │ 99 │ - │ 2Ge+her │ The Hardest Part Of ... │ 2000-09-02 │ 1 │ 91 │ - │ 2Ge+her │ The Hardest Part Of ... │ 2000-09-02 │ 2 │ 87 │ - │ 2Ge+her │ The Hardest Part Of ... │ 2000-09-02 │ 3 │ 92 │ - │ … │ … │ … │ … │ … │ - └─────────┴─────────────────────────┴──────────────┴──────┴───────┘ - - You can use regular expression capture groups to extract multiple - variables stored in column names - - >>> who = ibis.examples.who.fetch() - >>> who - ┏━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━┳━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━┓ - ┃ country ┃ iso2 ┃ iso3 ┃ year ┃ new_sp_m014 ┃ new_sp_m1524 ┃ … ┃ - ┡━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━╇━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━┩ - │ string │ string │ string │ int64 │ int64 │ int64 │ … │ - ├─────────────┼────────┼────────┼───────┼─────────────┼──────────────┼───┤ - │ Afghanistan │ AF │ AFG │ 1980 │ NULL │ NULL │ … │ - │ Afghanistan │ AF │ AFG │ 1981 │ NULL │ NULL │ … │ - │ Afghanistan │ AF │ AFG │ 1982 │ NULL │ NULL │ … │ - │ Afghanistan │ AF │ AFG │ 1983 │ NULL │ NULL │ … │ - │ Afghanistan │ AF │ AFG │ 1984 │ NULL │ NULL │ … │ - │ Afghanistan │ AF │ AFG │ 1985 │ NULL │ NULL │ … │ - │ Afghanistan │ AF │ AFG │ 1986 │ NULL │ NULL │ … │ - │ Afghanistan │ AF │ AFG │ 1987 │ NULL │ NULL │ … │ - │ Afghanistan │ AF │ AFG │ 1988 │ NULL │ NULL │ … │ - │ Afghanistan │ AF │ AFG │ 1989 │ NULL │ NULL │ … │ - │ … │ … │ … │ … │ … │ … │ … │ - └─────────────┴────────┴────────┴───────┴─────────────┴──────────────┴───┘ - >>> len(who.columns) - 60 - >>> who.pivot_longer( - ... s.r["new_sp_m014":"newrel_f65"], - ... names_to=["diagnosis", "gender", "age"], - ... names_pattern="new_?(.*)_(.)(.*)", - ... values_to="count", - ... ) - ┏━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━┳━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━┳━━━━━━━┓ - ┃ country ┃ iso2 ┃ iso3 ┃ year ┃ diagnosis ┃ gender ┃ age ┃ count ┃ - ┡━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━╇━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━╇━━━━━━━┩ - │ string │ string │ string │ int64 │ string │ string │ string │ int64 │ - ├─────────────┼────────┼────────┼───────┼───────────┼────────┼────────┼───────┤ - │ Afghanistan │ AF │ AFG │ 1980 │ sp │ m │ 014 │ NULL │ - │ Afghanistan │ AF │ AFG │ 1980 │ sp │ m │ 1524 │ NULL │ - │ Afghanistan │ AF │ AFG │ 1980 │ sp │ m │ 2534 │ NULL │ - │ Afghanistan │ AF │ AFG │ 1980 │ sp │ m │ 3544 │ NULL │ - │ Afghanistan │ AF │ AFG │ 1980 │ sp │ m │ 4554 │ NULL │ - │ Afghanistan │ AF │ AFG │ 1980 │ sp │ m │ 5564 │ NULL │ - │ Afghanistan │ AF │ AFG │ 1980 │ sp │ m │ 65 │ NULL │ - │ Afghanistan │ AF │ AFG │ 1980 │ sp │ f │ 014 │ NULL │ - │ Afghanistan │ AF │ AFG │ 1980 │ sp │ f │ 1524 │ NULL │ - │ Afghanistan │ AF │ AFG │ 1980 │ sp │ f │ 2534 │ NULL │ - │ … │ … │ … │ … │ … │ … │ … │ … │ - └─────────────┴────────┴────────┴───────┴───────────┴────────┴────────┴───────┘ - - `names_transform` is flexible, and can be: - - 1. A mapping of one or more names in `names_to` to callable - 2. A callable that will be applied to every name - - Let's recode gender and age to numeric values using a mapping - - >>> who.pivot_longer( - ... s.r["new_sp_m014":"newrel_f65"], - ... names_to=["diagnosis", "gender", "age"], - ... names_pattern="new_?(.*)_(.)(.*)", - ... names_transform=dict( - ... gender={"m": 1, "f": 2}.get, - ... age=dict( - ... zip( - ... ["014", "1524", "2534", "3544", "4554", "5564", "65"], - ... range(7), - ... ) - ... ).get, - ... ), - ... values_to="count", - ... ) - ┏━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━┳━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━┳━━━━━━┳━━━━━━━┓ - ┃ country ┃ iso2 ┃ iso3 ┃ year ┃ diagnosis ┃ gender ┃ age ┃ count ┃ - ┡━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━╇━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━╇━━━━━━╇━━━━━━━┩ - │ string │ string │ string │ int64 │ string │ int8 │ int8 │ int64 │ - ├─────────────┼────────┼────────┼───────┼───────────┼────────┼──────┼───────┤ - │ Afghanistan │ AF │ AFG │ 1980 │ sp │ 1 │ 0 │ NULL │ - │ Afghanistan │ AF │ AFG │ 1980 │ sp │ 1 │ 1 │ NULL │ - │ Afghanistan │ AF │ AFG │ 1980 │ sp │ 1 │ 2 │ NULL │ - │ Afghanistan │ AF │ AFG │ 1980 │ sp │ 1 │ 3 │ NULL │ - │ Afghanistan │ AF │ AFG │ 1980 │ sp │ 1 │ 4 │ NULL │ - │ Afghanistan │ AF │ AFG │ 1980 │ sp │ 1 │ 5 │ NULL │ - │ Afghanistan │ AF │ AFG │ 1980 │ sp │ 1 │ 6 │ NULL │ - │ Afghanistan │ AF │ AFG │ 1980 │ sp │ 2 │ 0 │ NULL │ - │ Afghanistan │ AF │ AFG │ 1980 │ sp │ 2 │ 1 │ NULL │ - │ Afghanistan │ AF │ AFG │ 1980 │ sp │ 2 │ 2 │ NULL │ - │ … │ … │ … │ … │ … │ … │ … │ … │ - └─────────────┴────────┴────────┴───────┴───────────┴────────┴──────┴───────┘ - - The number of match groups in `names_pattern` must match the length of `names_to` - - >>> who.pivot_longer( # quartodoc: +EXPECTED_FAILURE - ... s.r["new_sp_m014":"newrel_f65"], - ... names_to=["diagnosis", "gender", "age"], - ... names_pattern="new_?(.*)_.(.*)", - ... ) - Traceback (most recent call last): - ... - ibis.common.exceptions.IbisInputError: Number of match groups in `names_pattern` ... - - `names_transform` must be a mapping or callable - - >>> who.pivot_longer( - ... s.r["new_sp_m014":"newrel_f65"], names_transform="upper" - ... ) # quartodoc: +EXPECTED_FAILURE - Traceback (most recent call last): - ... - ibis.common.exceptions.IbisTypeError: ... Got - """ # noqa: RUF002 - import bigframes_vendored.ibis.selectors as s - - pivot_sel = s._to_selector(col) - - pivot_cols = pivot_sel.expand(self) - if not pivot_cols: - # TODO: improve the repr of selectors - raise com.IbisInputError("Selector returned no columns to pivot on") - - names_to = util.promote_list(names_to) - - names_pattern = re.compile(names_pattern) - if (ngroups := names_pattern.groups) != (nnames := len(names_to)): - raise com.IbisInputError( - f"Number of match groups in `names_pattern`" - f"{names_pattern.pattern!r} ({ngroups:d} groups) doesn't " - f"match the length of `names_to` {names_to} (length {nnames:d})" - ) - - if names_transform is None: - names_transform = dict.fromkeys(names_to, toolz.identity) - elif not isinstance(names_transform, Mapping): - if callable(names_transform): - names_transform = dict.fromkeys(names_to, names_transform) - else: - raise com.IbisTypeError( - f"`names_transform` must be a mapping or callable. Got {type(names_transform)}" - ) - - for name in names_to: - names_transform.setdefault(name, toolz.identity) - - if values_transform is None: - values_transform = toolz.identity - elif isinstance(values_transform, Deferred): - values_transform = values_transform.resolve - - pieces = [] - - for pivot_col in pivot_cols: - col_name = pivot_col.get_name() - match_result = names_pattern.match(col_name) - row = { - name: names_transform[name](value) - for name, value in zip(names_to, match_result.groups()) - } - row[values_to] = values_transform(pivot_col) - pieces.append(bigframes_vendored.ibis.struct(row)) - - # nest into an array of structs to zip unnests together - pieces = bigframes_vendored.ibis.array(pieces) - - return self.select(~pivot_sel, __pivoted__=pieces.unnest()).unpack( - "__pivoted__" - ) - - @util.experimental - def pivot_wider( - self, - *, - id_cols: s.Selector | None = None, - names_from: str | Iterable[str] | s.Selector = "name", - names_prefix: str = "", - names_sep: str = "_", - names_sort: bool = False, - names: Iterable[str] | None = None, - values_from: str | Iterable[str] | s.Selector = "value", - values_fill: int | float | str | ir.Scalar | None = None, - values_agg: str | Callable[[ir.Value], ir.Scalar] | Deferred = "arbitrary", - ) -> Table: - """Pivot a table to a wider format. - - Parameters - ---------- - id_cols - A set of columns that uniquely identify each observation. - names_from - An argument describing which column or columns to use to get the - name of the output columns. - names_prefix - String added to the start of every column name. - names_sep - If `names_from` or `values_from` contains multiple columns, this - argument will be used to join their values together into a single - string to use as a column name. - names_sort - If [](`True`) columns are sorted. If [](`False`) column names are - ordered by appearance. - names - An explicit sequence of values to look for in columns matching - `names_from`. - - * When this value is `None`, the values will be computed from - `names_from`. - * When this value is not `None`, each element's length must match - the length of `names_from`. - - See examples below for more detail. - values_from - An argument describing which column or columns to get the cell - values from. - values_fill - A scalar value that specifies what each value should be filled with - when missing. - values_agg - A function applied to the value in each cell in the output. - - Returns - ------- - Table - Wider pivoted table - - Examples - -------- - >>> import ibis - >>> import ibis.selectors as s - >>> from ibis import _ - >>> ibis.options.interactive = True - - Basic usage - - >>> fish_encounters = ibis.examples.fish_encounters.fetch() - >>> fish_encounters - ┏━━━━━━━┳━━━━━━━━━┳━━━━━━━┓ - ┃ fish ┃ station ┃ seen ┃ - ┡━━━━━━━╇━━━━━━━━━╇━━━━━━━┩ - │ int64 │ string │ int64 │ - ├───────┼─────────┼───────┤ - │ 4842 │ Release │ 1 │ - │ 4842 │ I80_1 │ 1 │ - │ 4842 │ Lisbon │ 1 │ - │ 4842 │ Rstr │ 1 │ - │ 4842 │ Base_TD │ 1 │ - │ 4842 │ BCE │ 1 │ - │ 4842 │ BCW │ 1 │ - │ 4842 │ BCE2 │ 1 │ - │ 4842 │ BCW2 │ 1 │ - │ 4842 │ MAE │ 1 │ - │ … │ … │ … │ - └───────┴─────────┴───────┘ - >>> fish_encounters.pivot_wider(names_from="station", values_from="seen") # doctest: +SKIP - ┏━━━━━━━┳━━━━━━━━━┳━━━━━━━┳━━━━━━━━┳━━━━━━━┳━━━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━┓ - ┃ fish ┃ Release ┃ I80_1 ┃ Lisbon ┃ Rstr ┃ Base_TD ┃ BCE ┃ BCW ┃ … ┃ - ┡━━━━━━━╇━━━━━━━━━╇━━━━━━━╇━━━━━━━━╇━━━━━━━╇━━━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━┩ - │ int64 │ int64 │ int64 │ int64 │ int64 │ int64 │ int64 │ int64 │ … │ - ├───────┼─────────┼───────┼────────┼───────┼─────────┼───────┼───────┼───┤ - │ 4842 │ 1 │ 1 │ 1 │ 1 │ 1 │ 1 │ 1 │ … │ - │ 4843 │ 1 │ 1 │ 1 │ 1 │ 1 │ 1 │ 1 │ … │ - │ 4844 │ 1 │ 1 │ 1 │ 1 │ 1 │ 1 │ 1 │ … │ - │ 4845 │ 1 │ 1 │ 1 │ 1 │ 1 │ NULL │ NULL │ … │ - │ 4847 │ 1 │ 1 │ 1 │ NULL │ NULL │ NULL │ NULL │ … │ - │ 4848 │ 1 │ 1 │ 1 │ 1 │ NULL │ NULL │ NULL │ … │ - │ 4849 │ 1 │ 1 │ NULL │ NULL │ NULL │ NULL │ NULL │ … │ - │ 4850 │ 1 │ 1 │ NULL │ 1 │ 1 │ 1 │ 1 │ … │ - │ 4851 │ 1 │ 1 │ NULL │ NULL │ NULL │ NULL │ NULL │ … │ - │ 4854 │ 1 │ 1 │ NULL │ NULL │ NULL │ NULL │ NULL │ … │ - │ … │ … │ … │ … │ … │ … │ … │ … │ … │ - └───────┴─────────┴───────┴────────┴───────┴─────────┴───────┴───────┴───┘ - - Fill missing pivoted values using `values_fill` - - >>> fish_encounters.pivot_wider( - ... names_from="station", values_from="seen", values_fill=0 - ... ) # doctest: +SKIP - ┏━━━━━━━┳━━━━━━━━━┳━━━━━━━┳━━━━━━━━┳━━━━━━━┳━━━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━┓ - ┃ fish ┃ Release ┃ I80_1 ┃ Lisbon ┃ Rstr ┃ Base_TD ┃ BCE ┃ BCW ┃ … ┃ - ┡━━━━━━━╇━━━━━━━━━╇━━━━━━━╇━━━━━━━━╇━━━━━━━╇━━━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━┩ - │ int64 │ int64 │ int64 │ int64 │ int64 │ int64 │ int64 │ int64 │ … │ - ├───────┼─────────┼───────┼────────┼───────┼─────────┼───────┼───────┼───┤ - │ 4842 │ 1 │ 1 │ 1 │ 1 │ 1 │ 1 │ 1 │ … │ - │ 4843 │ 1 │ 1 │ 1 │ 1 │ 1 │ 1 │ 1 │ … │ - │ 4844 │ 1 │ 1 │ 1 │ 1 │ 1 │ 1 │ 1 │ … │ - │ 4845 │ 1 │ 1 │ 1 │ 1 │ 1 │ 0 │ 0 │ … │ - │ 4847 │ 1 │ 1 │ 1 │ 0 │ 0 │ 0 │ 0 │ … │ - │ 4848 │ 1 │ 1 │ 1 │ 1 │ 0 │ 0 │ 0 │ … │ - │ 4849 │ 1 │ 1 │ 0 │ 0 │ 0 │ 0 │ 0 │ … │ - │ 4850 │ 1 │ 1 │ 0 │ 1 │ 1 │ 1 │ 1 │ … │ - │ 4851 │ 1 │ 1 │ 0 │ 0 │ 0 │ 0 │ 0 │ … │ - │ 4854 │ 1 │ 1 │ 0 │ 0 │ 0 │ 0 │ 0 │ … │ - │ … │ … │ … │ … │ … │ … │ … │ … │ … │ - └───────┴─────────┴───────┴────────┴───────┴─────────┴───────┴───────┴───┘ - - Compute multiple values columns - - >>> us_rent_income = ibis.examples.us_rent_income.fetch() - >>> us_rent_income - ┏━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━┓ - ┃ geoid ┃ name ┃ variable ┃ estimate ┃ moe ┃ - ┡━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━┩ - │ string │ string │ string │ int64 │ int64 │ - ├────────┼────────────┼──────────┼──────────┼───────┤ - │ 01 │ Alabama │ income │ 24476 │ 136 │ - │ 01 │ Alabama │ rent │ 747 │ 3 │ - │ 02 │ Alaska │ income │ 32940 │ 508 │ - │ 02 │ Alaska │ rent │ 1200 │ 13 │ - │ 04 │ Arizona │ income │ 27517 │ 148 │ - │ 04 │ Arizona │ rent │ 972 │ 4 │ - │ 05 │ Arkansas │ income │ 23789 │ 165 │ - │ 05 │ Arkansas │ rent │ 709 │ 5 │ - │ 06 │ California │ income │ 29454 │ 109 │ - │ 06 │ California │ rent │ 1358 │ 3 │ - │ … │ … │ … │ … │ … │ - └────────┴────────────┴──────────┴──────────┴───────┘ - >>> us_rent_income.pivot_wider( - ... names_from="variable", values_from=["estimate", "moe"] - ... ) # doctest: +SKIP - ┏━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━┓ - ┃ geoid ┃ name ┃ estimate_income ┃ moe_income ┃ … ┃ - ┡━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━┩ - │ string │ string │ int64 │ int64 │ … │ - ├────────┼──────────────────────┼─────────────────┼────────────┼───┤ - │ 01 │ Alabama │ 24476 │ 136 │ … │ - │ 02 │ Alaska │ 32940 │ 508 │ … │ - │ 04 │ Arizona │ 27517 │ 148 │ … │ - │ 05 │ Arkansas │ 23789 │ 165 │ … │ - │ 06 │ California │ 29454 │ 109 │ … │ - │ 08 │ Colorado │ 32401 │ 109 │ … │ - │ 09 │ Connecticut │ 35326 │ 195 │ … │ - │ 10 │ Delaware │ 31560 │ 247 │ … │ - │ 11 │ District of Columbia │ 43198 │ 681 │ … │ - │ 12 │ Florida │ 25952 │ 70 │ … │ - │ … │ … │ … │ … │ … │ - └────────┴──────────────────────┴─────────────────┴────────────┴───┘ - - The column name separator can be changed using the `names_sep` parameter - - >>> us_rent_income.pivot_wider( - ... names_from="variable", - ... names_sep=".", - ... values_from=("estimate", "moe"), - ... ) # doctest: +SKIP - ┏━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━┓ - ┃ geoid ┃ name ┃ estimate.income ┃ moe.income ┃ … ┃ - ┡━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━┩ - │ string │ string │ int64 │ int64 │ … │ - ├────────┼──────────────────────┼─────────────────┼────────────┼───┤ - │ 01 │ Alabama │ 24476 │ 136 │ … │ - │ 02 │ Alaska │ 32940 │ 508 │ … │ - │ 04 │ Arizona │ 27517 │ 148 │ … │ - │ 05 │ Arkansas │ 23789 │ 165 │ … │ - │ 06 │ California │ 29454 │ 109 │ … │ - │ 08 │ Colorado │ 32401 │ 109 │ … │ - │ 09 │ Connecticut │ 35326 │ 195 │ … │ - │ 10 │ Delaware │ 31560 │ 247 │ … │ - │ 11 │ District of Columbia │ 43198 │ 681 │ … │ - │ 12 │ Florida │ 25952 │ 70 │ … │ - │ … │ … │ … │ … │ … │ - └────────┴──────────────────────┴─────────────────┴────────────┴───┘ - - Supply an alternative function to summarize values - - >>> warpbreaks = ibis.examples.warpbreaks.fetch().select("wool", "tension", "breaks") - >>> warpbreaks - ┏━━━━━━━━┳━━━━━━━━━┳━━━━━━━━┓ - ┃ wool ┃ tension ┃ breaks ┃ - ┡━━━━━━━━╇━━━━━━━━━╇━━━━━━━━┩ - │ string │ string │ int64 │ - ├────────┼─────────┼────────┤ - │ A │ L │ 26 │ - │ A │ L │ 30 │ - │ A │ L │ 54 │ - │ A │ L │ 25 │ - │ A │ L │ 70 │ - │ A │ L │ 52 │ - │ A │ L │ 51 │ - │ A │ L │ 26 │ - │ A │ L │ 67 │ - │ A │ M │ 18 │ - │ … │ … │ … │ - └────────┴─────────┴────────┘ - >>> warpbreaks.pivot_wider( - ... names_from="wool", values_from="breaks", values_agg="mean" - ... ).select("tension", "A", "B").order_by("tension") - ┏━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━┓ - ┃ tension ┃ A ┃ B ┃ - ┡━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━┩ - │ string │ float64 │ float64 │ - ├─────────┼───────────┼───────────┤ - │ H │ 24.555556 │ 18.777778 │ - │ L │ 44.555556 │ 28.222222 │ - │ M │ 24.000000 │ 28.777778 │ - └─────────┴───────────┴───────────┘ - - Passing `Deferred` objects to `values_agg` is supported - - >>> warpbreaks.pivot_wider( - ... names_from="tension", - ... values_from="breaks", - ... values_agg=_.sum(), - ... ).select("wool", "H", "L", "M").order_by(s.all()) - ┏━━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━┓ - ┃ wool ┃ H ┃ L ┃ M ┃ - ┡━━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━┩ - │ string │ int64 │ int64 │ int64 │ - ├────────┼───────┼───────┼───────┤ - │ A │ 221 │ 401 │ 216 │ - │ B │ 169 │ 254 │ 259 │ - └────────┴───────┴───────┴───────┘ - - Use a custom aggregate function - - >>> warpbreaks.pivot_wider( - ... names_from="wool", - ... values_from="breaks", - ... values_agg=lambda col: col.std() / col.mean(), - ... ).select("tension", "A", "B").order_by("tension") - ┏━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┓ - ┃ tension ┃ A ┃ B ┃ - ┡━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━┩ - │ string │ float64 │ float64 │ - ├─────────┼──────────┼──────────┤ - │ H │ 0.418344 │ 0.260590 │ - │ L │ 0.406183 │ 0.349325 │ - │ M │ 0.360844 │ 0.327719 │ - └─────────┴──────────┴──────────┘ - - Generate some random data, setting the random seed for reproducibility - - >>> import random - >>> random.seed(0) - >>> raw = ibis.memtable( - ... [ - ... dict( - ... product=product, - ... country=country, - ... year=year, - ... production=random.random(), - ... ) - ... for product in "AB" - ... for country in ["AI", "EI"] - ... for year in range(2000, 2015) - ... ] - ... ) - >>> production = raw.filter(((_.product == "A") & (_.country == "AI")) | (_.product == "B")) - >>> production.order_by(s.all()) - ┏━━━━━━━━━┳━━━━━━━━━┳━━━━━━━┳━━━━━━━━━━━━┓ - ┃ product ┃ country ┃ year ┃ production ┃ - ┡━━━━━━━━━╇━━━━━━━━━╇━━━━━━━╇━━━━━━━━━━━━┩ - │ string │ string │ int64 │ float64 │ - ├─────────┼─────────┼───────┼────────────┤ - │ A │ AI │ 2000 │ 0.844422 │ - │ A │ AI │ 2001 │ 0.757954 │ - │ A │ AI │ 2002 │ 0.420572 │ - │ A │ AI │ 2003 │ 0.258917 │ - │ A │ AI │ 2004 │ 0.511275 │ - │ A │ AI │ 2005 │ 0.404934 │ - │ A │ AI │ 2006 │ 0.783799 │ - │ A │ AI │ 2007 │ 0.303313 │ - │ A │ AI │ 2008 │ 0.476597 │ - │ A │ AI │ 2009 │ 0.583382 │ - │ … │ … │ … │ … │ - └─────────┴─────────┴───────┴────────────┘ - - Pivoting with multiple name columns - - >>> production.pivot_wider( - ... names_from=["product", "country"], - ... values_from="production", - ... ) # doctest: +SKIP - ┏━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┓ - ┃ year ┃ B_AI ┃ B_EI ┃ A_AI ┃ - ┡━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━┩ - │ int64 │ float64 │ float64 │ float64 │ - ├───────┼──────────┼──────────┼──────────┤ - │ 2000 │ 0.477010 │ 0.870471 │ 0.844422 │ - │ 2001 │ 0.865310 │ 0.191067 │ 0.757954 │ - │ 2002 │ 0.260492 │ 0.567511 │ 0.420572 │ - │ 2003 │ 0.805028 │ 0.238616 │ 0.258917 │ - │ 2004 │ 0.548699 │ 0.967540 │ 0.511275 │ - │ 2005 │ 0.014042 │ 0.803179 │ 0.404934 │ - │ 2006 │ 0.719705 │ 0.447970 │ 0.783799 │ - │ 2007 │ 0.398824 │ 0.080446 │ 0.303313 │ - │ 2008 │ 0.824845 │ 0.320055 │ 0.476597 │ - │ 2009 │ 0.668153 │ 0.507941 │ 0.583382 │ - │ … │ … │ … │ … │ - └───────┴──────────┴──────────┴──────────┘ - - Select a subset of names. This call incurs no computation when - constructing the expression. - - >>> production.pivot_wider( - ... names_from=["product", "country"], - ... names=[("A", "AI"), ("B", "AI")], - ... values_from="production", - ... ) # doctest: +SKIP - ┏━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┓ - ┃ year ┃ A_AI ┃ B_AI ┃ - ┡━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━┩ - │ int64 │ float64 │ float64 │ - ├───────┼──────────┼──────────┤ - │ 2000 │ 0.844422 │ 0.477010 │ - │ 2001 │ 0.757954 │ 0.865310 │ - │ 2002 │ 0.420572 │ 0.260492 │ - │ 2003 │ 0.258917 │ 0.805028 │ - │ 2004 │ 0.511275 │ 0.548699 │ - │ 2005 │ 0.404934 │ 0.014042 │ - │ 2006 │ 0.783799 │ 0.719705 │ - │ 2007 │ 0.303313 │ 0.398824 │ - │ 2008 │ 0.476597 │ 0.824845 │ - │ 2009 │ 0.583382 │ 0.668153 │ - │ … │ … │ … │ - └───────┴──────────┴──────────┘ - - Sort the new columns' names - - >>> production.pivot_wider( - ... names_from=["product", "country"], - ... values_from="production", - ... names_sort=True, - ... ) # doctest: +SKIP - ┏━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┓ - ┃ year ┃ A_AI ┃ B_AI ┃ B_EI ┃ - ┡━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━┩ - │ int64 │ float64 │ float64 │ float64 │ - ├───────┼──────────┼──────────┼──────────┤ - │ 2000 │ 0.844422 │ 0.477010 │ 0.870471 │ - │ 2001 │ 0.757954 │ 0.865310 │ 0.191067 │ - │ 2002 │ 0.420572 │ 0.260492 │ 0.567511 │ - │ 2003 │ 0.258917 │ 0.805028 │ 0.238616 │ - │ 2004 │ 0.511275 │ 0.548699 │ 0.967540 │ - │ 2005 │ 0.404934 │ 0.014042 │ 0.803179 │ - │ 2006 │ 0.783799 │ 0.719705 │ 0.447970 │ - │ 2007 │ 0.303313 │ 0.398824 │ 0.080446 │ - │ 2008 │ 0.476597 │ 0.824845 │ 0.320055 │ - │ 2009 │ 0.583382 │ 0.668153 │ 0.507941 │ - │ … │ … │ … │ … │ - └───────┴──────────┴──────────┴──────────┘ - """ - import bigframes_vendored.ibis.selectors as s - import pandas as pd - from bigframes_vendored.ibis.expr.rewrites import _, p, x - - orig_names_from = util.promote_list(names_from) - - names_from = s._to_selector(orig_names_from) - values_from = s._to_selector(values_from) - - if id_cols is None: - id_cols = ~(names_from | values_from) - else: - id_cols = s._to_selector(id_cols) - - if isinstance(values_agg, str): - values_agg = operator.methodcaller(values_agg) - elif isinstance(values_agg, Deferred): - values_agg = values_agg.resolve - - if names is None: - # no names provided, compute them from the data - names = self.select(names_from).distinct().execute() - else: - if not (columns := [col.get_name() for col in names_from.expand(self)]): - raise com.IbisInputError( - f"No matching names columns in `names_from`: {orig_names_from}" - ) - names = pd.DataFrame(list(map(util.promote_list, names)), columns=columns) - - if names_sort: - names = names.sort_values(by=names.columns.tolist()) - - values_cols = values_from.expand(self) - more_than_one_value = len(values_cols) > 1 - aggs = {} - - names_cols_exprs = [self[col] for col in names.columns] - - for keys in names.itertuples(index=False): - where = bigframes_vendored.ibis.and_( - *map(operator.eq, names_cols_exprs, keys) - ) - - for values_col in values_cols: - arg = values_agg(values_col) - - # this allows users to write the aggregate without having to deal with - # the filter themselves - rules = ( - # add in the where clause to filter the appropriate values - p.Reduction(where=None) >> _.copy(where=where) - | p.Reduction(where=x) >> _.copy(where=where & x) - ) - arg = arg.op().replace(rules, filter=p.Value).to_expr() - - # build the components of the group by key - key_components = ( - # user provided prefix - names_prefix, - # include the `values` column name if there's more than one - # `values` column - values_col.get_name() * more_than_one_value, - # values computed from `names`/`names_from` - *keys, - ) - key = names_sep.join(filter(None, key_components)) - aggs[key] = arg if values_fill is None else arg.coalesce(values_fill) - - return self.group_by(id_cols).aggregate(**aggs) - - def relocate( - self, - *columns: str | s.Selector, - before: str | s.Selector | None = None, - after: str | s.Selector | None = None, - **kwargs: str, - ) -> Table: - """Relocate `columns` before or after other specified columns. - - Parameters - ---------- - columns - Columns to relocate. Selectors are accepted. - before - A column name or selector to insert the new columns before. - after - A column name or selector. Columns in `columns` are relocated after the last - column selected in `after`. - kwargs - Additional column names to relocate, renaming argument values to - keyword argument names. - - Returns - ------- - Table - A table with the columns relocated. - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> import ibis.selectors as s - >>> t = ibis.memtable(dict(a=[1], b=[1], c=[1], d=["a"], e=["a"], f=["a"])) - >>> t - ┏━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━━┳━━━━━━━━┳━━━━━━━━┓ - ┃ a ┃ b ┃ c ┃ d ┃ e ┃ f ┃ - ┡━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━━╇━━━━━━━━╇━━━━━━━━┩ - │ int64 │ int64 │ int64 │ string │ string │ string │ - ├───────┼───────┼───────┼────────┼────────┼────────┤ - │ 1 │ 1 │ 1 │ a │ a │ a │ - └───────┴───────┴───────┴────────┴────────┴────────┘ - >>> t.relocate("f") - ┏━━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━━┳━━━━━━━━┓ - ┃ f ┃ a ┃ b ┃ c ┃ d ┃ e ┃ - ┡━━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━━╇━━━━━━━━┩ - │ string │ int64 │ int64 │ int64 │ string │ string │ - ├────────┼───────┼───────┼───────┼────────┼────────┤ - │ a │ 1 │ 1 │ 1 │ a │ a │ - └────────┴───────┴───────┴───────┴────────┴────────┘ - >>> t.relocate("a", after="c") - ┏━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━━┳━━━━━━━━┳━━━━━━━━┓ - ┃ b ┃ c ┃ a ┃ d ┃ e ┃ f ┃ - ┡━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━━╇━━━━━━━━╇━━━━━━━━┩ - │ int64 │ int64 │ int64 │ string │ string │ string │ - ├───────┼───────┼───────┼────────┼────────┼────────┤ - │ 1 │ 1 │ 1 │ a │ a │ a │ - └───────┴───────┴───────┴────────┴────────┴────────┘ - >>> t.relocate("f", before="b") - ┏━━━━━━━┳━━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━━┳━━━━━━━━┓ - ┃ a ┃ f ┃ b ┃ c ┃ d ┃ e ┃ - ┡━━━━━━━╇━━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━━╇━━━━━━━━┩ - │ int64 │ string │ int64 │ int64 │ string │ string │ - ├───────┼────────┼───────┼───────┼────────┼────────┤ - │ 1 │ a │ 1 │ 1 │ a │ a │ - └───────┴────────┴───────┴───────┴────────┴────────┘ - >>> t.relocate("a", after=s.last()) - ┏━━━━━━━┳━━━━━━━┳━━━━━━━━┳━━━━━━━━┳━━━━━━━━┳━━━━━━━┓ - ┃ b ┃ c ┃ d ┃ e ┃ f ┃ a ┃ - ┡━━━━━━━╇━━━━━━━╇━━━━━━━━╇━━━━━━━━╇━━━━━━━━╇━━━━━━━┩ - │ int64 │ int64 │ string │ string │ string │ int64 │ - ├───────┼───────┼────────┼────────┼────────┼───────┤ - │ 1 │ 1 │ a │ a │ a │ 1 │ - └───────┴───────┴────────┴────────┴────────┴───────┘ - - Relocate allows renaming - - >>> t.relocate(ff="f") - ┏━━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━━┳━━━━━━━━┓ - ┃ ff ┃ a ┃ b ┃ c ┃ d ┃ e ┃ - ┡━━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━━╇━━━━━━━━┩ - │ string │ int64 │ int64 │ int64 │ string │ string │ - ├────────┼───────┼───────┼───────┼────────┼────────┤ - │ a │ 1 │ 1 │ 1 │ a │ a │ - └────────┴───────┴───────┴───────┴────────┴────────┘ - - You can relocate based on any predicate selector, such as - [`of_type`](./selectors.qmd#ibis.selectors.of_type) - - >>> t.relocate(s.of_type("string")) - ┏━━━━━━━━┳━━━━━━━━┳━━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━┓ - ┃ d ┃ e ┃ f ┃ a ┃ b ┃ c ┃ - ┡━━━━━━━━╇━━━━━━━━╇━━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━┩ - │ string │ string │ string │ int64 │ int64 │ int64 │ - ├────────┼────────┼────────┼───────┼───────┼───────┤ - │ a │ a │ a │ 1 │ 1 │ 1 │ - └────────┴────────┴────────┴───────┴───────┴───────┘ - >>> t.relocate(s.numeric(), after=s.last()) - ┏━━━━━━━━┳━━━━━━━━┳━━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━┓ - ┃ d ┃ e ┃ f ┃ a ┃ b ┃ c ┃ - ┡━━━━━━━━╇━━━━━━━━╇━━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━┩ - │ string │ string │ string │ int64 │ int64 │ int64 │ - ├────────┼────────┼────────┼───────┼───────┼───────┤ - │ a │ a │ a │ 1 │ 1 │ 1 │ - └────────┴────────┴────────┴───────┴───────┴───────┘ - >>> t.relocate(s.any_of(s.c(*"ae"))) - ┏━━━━━━━┳━━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━━┳━━━━━━━━┓ - ┃ a ┃ e ┃ b ┃ c ┃ d ┃ f ┃ - ┡━━━━━━━╇━━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━━╇━━━━━━━━┩ - │ int64 │ string │ int64 │ int64 │ string │ string │ - ├───────┼────────┼───────┼───────┼────────┼────────┤ - │ 1 │ a │ 1 │ 1 │ a │ a │ - └───────┴────────┴───────┴───────┴────────┴────────┘ - - When multiple columns are selected with `before` or `after`, those - selected columns are moved before and after the `selectors` input - - >>> t = ibis.memtable(dict(a=[1], b=["a"], c=[1], d=["a"])) - >>> t.relocate(s.numeric(), after=s.of_type("string")) - ┏━━━━━━━━┳━━━━━━━━┳━━━━━━━┳━━━━━━━┓ - ┃ b ┃ d ┃ a ┃ c ┃ - ┡━━━━━━━━╇━━━━━━━━╇━━━━━━━╇━━━━━━━┩ - │ string │ string │ int64 │ int64 │ - ├────────┼────────┼───────┼───────┤ - │ a │ a │ 1 │ 1 │ - └────────┴────────┴───────┴───────┘ - >>> t.relocate(s.numeric(), before=s.of_type("string")) - ┏━━━━━━━┳━━━━━━━┳━━━━━━━━┳━━━━━━━━┓ - ┃ a ┃ c ┃ b ┃ d ┃ - ┡━━━━━━━╇━━━━━━━╇━━━━━━━━╇━━━━━━━━┩ - │ int64 │ int64 │ string │ string │ - ├───────┼───────┼────────┼────────┤ - │ 1 │ 1 │ a │ a │ - └───────┴───────┴────────┴────────┘ - - When there are duplicate **renames** in a call to relocate, the - last one is preserved - - >>> t.relocate(e="d", f="d") - ┏━━━━━━━━┳━━━━━━━┳━━━━━━━━┳━━━━━━━┓ - ┃ f ┃ a ┃ b ┃ c ┃ - ┡━━━━━━━━╇━━━━━━━╇━━━━━━━━╇━━━━━━━┩ - │ string │ int64 │ string │ int64 │ - ├────────┼───────┼────────┼───────┤ - │ a │ 1 │ a │ 1 │ - └────────┴───────┴────────┴───────┘ - - However, if there are duplicates that are **not** part of a rename, the - order specified in the relocate call is preserved - - >>> t.relocate( - ... "b", - ... s.of_type("string"), # "b" is a string column, so the selector matches - ... ) - ┏━━━━━━━━┳━━━━━━━━┳━━━━━━━┳━━━━━━━┓ - ┃ b ┃ d ┃ a ┃ c ┃ - ┡━━━━━━━━╇━━━━━━━━╇━━━━━━━╇━━━━━━━┩ - │ string │ string │ int64 │ int64 │ - ├────────┼────────┼───────┼───────┤ - │ a │ a │ 1 │ 1 │ - └────────┴────────┴───────┴───────┘ - """ - if not columns and before is None and after is None and not kwargs: - raise com.IbisInputError( - "At least one selector or `before` or `after` must be provided" - ) - - if before is not None and after is not None: - raise com.IbisInputError("Cannot specify both `before` and `after`") - - sels = {} - - schema = self.schema() - positions = schema._name_locs - - for new_name, expr in itertools.zip_longest( - kwargs.keys(), self._fast_bind(*kwargs.values(), *columns) - ): - expr_name = expr.get_name() - pos = positions[expr_name] - renamed = new_name is not None - if renamed and pos in sels: - # **only when renaming**: make sure the last duplicate - # column wins by reinserting the position if it already - # exists - # - # to do that, we first delete the existing one, which causes - # the subsequent insertion to be at the end - del sels[pos] - sels[pos] = new_name if renamed else expr_name - - ncols = len(schema) - - if before is not None: - where = min( - (positions[expr.get_name()] for expr in self._fast_bind(before)), - default=0, - ) - elif after is not None: - where = ( - max( - (positions[expr.get_name()] for expr in self._fast_bind(after)), - default=ncols - 1, - ) - + 1 - ) - else: - assert before is None and after is None - where = 0 - - columns = schema.names - - fields = self.op().fields - - # all columns that should come BEFORE the matched selectors - exprs = { - name: fields[name] - for name in (columns[left] for left in range(where) if left not in sels) - } - - # selected columns - exprs.update((name, fields[columns[i]]) for i, name in sels.items()) - - # all columns that should come AFTER the matched selectors - exprs.update( - (name, fields[name]) - for name in ( - columns[right] for right in range(where, ncols) if right not in sels - ) - ) - - return ops.Project(self, exprs).to_expr() - - def window_by( - self, - time_col: str | ir.Value, - ) -> WindowedTable: - from bigframes_vendored.ibis.expr.types.temporal_windows import WindowedTable - - time_col = next(iter(self.bind(time_col))) - - # validate time_col is a timestamp column - if not isinstance(time_col, TimestampColumn): - raise com.IbisInputError( - f"`time_col` must be a timestamp column, got {time_col.type()}" - ) - - return WindowedTable(self, time_col) - - def value_counts(self) -> ir.Table: - """Compute a frequency table of this table's values. - - Returns - ------- - Table - Frequency table of this table's values. - - Examples - -------- - >>> from ibis import examples - >>> ibis.options.interactive = True - >>> t = examples.penguins.fetch() - >>> t.head() - ┏━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━┓ - ┃ species ┃ island ┃ bill_length_mm ┃ bill_depth_mm ┃ flipper_length_mm ┃ … ┃ - ┡━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━┩ - │ string │ string │ float64 │ float64 │ int64 │ … │ - ├─────────┼───────────┼────────────────┼───────────────┼───────────────────┼───┤ - │ Adelie │ Torgersen │ 39.1 │ 18.7 │ 181 │ … │ - │ Adelie │ Torgersen │ 39.5 │ 17.4 │ 186 │ … │ - │ Adelie │ Torgersen │ 40.3 │ 18.0 │ 195 │ … │ - │ Adelie │ Torgersen │ NULL │ NULL │ NULL │ … │ - │ Adelie │ Torgersen │ 36.7 │ 19.3 │ 193 │ … │ - └─────────┴───────────┴────────────────┴───────────────┴───────────────────┴───┘ - >>> t.year.value_counts().order_by("year") - ┏━━━━━━━┳━━━━━━━━━━━━┓ - ┃ year ┃ year_count ┃ - ┡━━━━━━━╇━━━━━━━━━━━━┩ - │ int64 │ int64 │ - ├───────┼────────────┤ - │ 2007 │ 110 │ - │ 2008 │ 114 │ - │ 2009 │ 120 │ - └───────┴────────────┘ - >>> t[["year", "island"]].value_counts().order_by("year", "island") - ┏━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓ - ┃ year ┃ island ┃ year_island_count ┃ - ┡━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩ - │ int64 │ string │ int64 │ - ├───────┼───────────┼───────────────────┤ - │ 2007 │ Biscoe │ 44 │ - │ 2007 │ Dream │ 46 │ - │ 2007 │ Torgersen │ 20 │ - │ 2008 │ Biscoe │ 64 │ - │ 2008 │ Dream │ 34 │ - │ 2008 │ Torgersen │ 16 │ - │ 2009 │ Biscoe │ 60 │ - │ 2009 │ Dream │ 44 │ - │ 2009 │ Torgersen │ 16 │ - └───────┴───────────┴───────────────────┘ - """ - columns = self.columns - return self.group_by(columns).agg( - lambda t: t.count().name("_".join(columns) + "_count") - ) - - def unnest( - self, column, offset: str | None = None, keep_empty: bool = False - ) -> Table: - """Unnest an array `column` from a table. - - When unnesting an existing column the newly unnested column replaces - the existing column. - - Parameters - ---------- - column - Array column to unnest. - offset - Name of the resulting index column. - keep_empty - Keep empty array values as `NULL` in the output table, as well as - existing `NULL` values. - - Returns - ------- - Table - Table with the array column `column` unnested. - - See Also - -------- - [`ArrayValue.unnest`](./expression-collections.qmd#ibis.expr.types.arrays.ArrayValue.unnest) - - Examples - -------- - >>> import ibis - >>> from ibis import _ - >>> ibis.options.interactive = True - - Construct a table expression with an array column. - - >>> t = ibis.memtable({"x": [[1, 2], [], None, [3, 4, 5]], "y": [1, 2, 3, 4]}) - >>> t - ┏━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━┓ - ┃ x ┃ y ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━┩ - │ array │ int64 │ - ├──────────────────────┼───────┤ - │ [1, 2] │ 1 │ - │ [] │ 2 │ - │ NULL │ 3 │ - │ [3, 4, ... +1] │ 4 │ - └──────────────────────┴───────┘ - - Unnest the array column `x`, replacing the **existing** `x` column. - - >>> t.unnest("x") - ┏━━━━━━━┳━━━━━━━┓ - ┃ x ┃ y ┃ - ┡━━━━━━━╇━━━━━━━┩ - │ int64 │ int64 │ - ├───────┼───────┤ - │ 1 │ 1 │ - │ 2 │ 1 │ - │ 3 │ 4 │ - │ 4 │ 4 │ - │ 5 │ 4 │ - └───────┴───────┘ - - Unnest the array column `x` with an offset. The `offset` parameter is - the name of the resulting index column. - - >>> t.unnest(t.x, offset="idx") - ┏━━━━━━━┳━━━━━━━┳━━━━━━━┓ - ┃ x ┃ y ┃ idx ┃ - ┡━━━━━━━╇━━━━━━━╇━━━━━━━┩ - │ int64 │ int64 │ int64 │ - ├───────┼───────┼───────┤ - │ 1 │ 1 │ 0 │ - │ 2 │ 1 │ 1 │ - │ 3 │ 4 │ 0 │ - │ 4 │ 4 │ 1 │ - │ 5 │ 4 │ 2 │ - └───────┴───────┴───────┘ - - Unnest the array column `x` keep empty array values as `NULL` in the - output table. - - >>> t.unnest(_.x, offset="idx", keep_empty=True) - ┏━━━━━━━┳━━━━━━━┳━━━━━━━┓ - ┃ x ┃ y ┃ idx ┃ - ┡━━━━━━━╇━━━━━━━╇━━━━━━━┩ - │ int64 │ int64 │ int64 │ - ├───────┼───────┼───────┤ - │ 1 │ 1 │ 0 │ - │ 2 │ 1 │ 1 │ - │ 3 │ 4 │ 0 │ - │ 4 │ 4 │ 1 │ - │ 5 │ 4 │ 2 │ - │ NULL │ 2 │ NULL │ - │ NULL │ 3 │ NULL │ - └───────┴───────┴───────┘ - - If you need to preserve the row order of the preserved empty arrays or - null values use - [`row_number`](./expression-tables.qmd#ibis.row_number) to - create an index column before calling `unnest`. - - >>> ( - ... t.mutate(original_row=ibis.row_number()) - ... .unnest("x", offset="idx", keep_empty=True) - ... .relocate("original_row") - ... .order_by("original_row") - ... ) - ┏━━━━━━━━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━┓ - ┃ original_row ┃ x ┃ y ┃ idx ┃ - ┡━━━━━━━━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━┩ - │ int64 │ int64 │ int64 │ int64 │ - ├──────────────┼───────┼───────┼───────┤ - │ 0 │ 1 │ 1 │ 0 │ - │ 0 │ 2 │ 1 │ 1 │ - │ 1 │ NULL │ 2 │ NULL │ - │ 2 │ NULL │ 3 │ NULL │ - │ 3 │ 3 │ 4 │ 0 │ - │ 3 │ 4 │ 4 │ 1 │ - │ 3 │ 5 │ 4 │ 2 │ - └──────────────┴───────┴───────┴───────┘ - - You can also unnest more complex expressions, and the resulting column - will be projected as the last expression in the result. - - >>> t.unnest(_.x.map(lambda v: v + 1).name("plus_one")) - ┏━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━┳━━━━━━━━━━┓ - ┃ x ┃ y ┃ plus_one ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━╇━━━━━━━━━━┩ - │ array │ int64 │ int64 │ - ├──────────────────────┼───────┼──────────┤ - │ [1, 2] │ 1 │ 2 │ - │ [1, 2] │ 1 │ 3 │ - │ [3, 4, ... +1] │ 4 │ 4 │ - │ [3, 4, ... +1] │ 4 │ 5 │ - │ [3, 4, ... +1] │ 4 │ 6 │ - └──────────────────────┴───────┴──────────┘ - """ - (column,) = self.bind(column) - return ops.TableUnnest( - parent=self, column=column, offset=offset, keep_empty=keep_empty - ).to_expr() - - -@public -class CachedTable(Table): - def __exit__(self, *_): - self.release() - - def __enter__(self): - return self - - def release(self): - """Release the underlying expression from the cache.""" - current_backend = self._find_backend(use_default=True) - return current_backend._release_cached(self) - - -public(Table=Table, CachedTable=CachedTable) diff --git a/third_party/bigframes_vendored/ibis/expr/types/strings.py b/third_party/bigframes_vendored/ibis/expr/types/strings.py deleted file mode 100644 index 29502740082..00000000000 --- a/third_party/bigframes_vendored/ibis/expr/types/strings.py +++ /dev/null @@ -1,1705 +0,0 @@ -# Contains code from https://github.com/ibis-project/ibis/blob/9.2.0/ibis/expr/types/strings.py - -from __future__ import annotations - -import functools -import operator -from typing import TYPE_CHECKING, Any, Literal - -import bigframes_vendored.ibis.expr.operations as ops -from bigframes_vendored.ibis import util -from bigframes_vendored.ibis.expr.types.core import _binop -from bigframes_vendored.ibis.expr.types.generic import Column, Scalar, Value -from public import public - -if TYPE_CHECKING: - from collections.abc import Iterable, Sequence - - import bigframes_vendored.ibis.expr.types as ir - - -@public -class StringValue(Value): - def __getitem__(self, key: slice | int | ir.IntegerScalar) -> StringValue: - """Index or slice a string expression. - - Parameters - ---------- - key - [](`int`), [](`slice`) or integer scalar expression - - Returns - ------- - StringValue - Indexed or sliced string value - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable({"food": ["bread", "cheese", "rice"], "idx": [1, 2, 4]}) - >>> t - ┏━━━━━━━━┳━━━━━━━┓ - ┃ food ┃ idx ┃ - ┡━━━━━━━━╇━━━━━━━┩ - │ string │ int64 │ - ├────────┼───────┤ - │ bread │ 1 │ - │ cheese │ 2 │ - │ rice │ 4 │ - └────────┴───────┘ - >>> t.food[0] - ┏━━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ Substring(food, 0, 1) ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━━┩ - │ string │ - ├───────────────────────┤ - │ b │ - │ c │ - │ r │ - └───────────────────────┘ - >>> t.food[:3] - ┏━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ StringSlice(food, 3) ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━┩ - │ string │ - ├──────────────────────┤ - │ bre │ - │ che │ - │ ric │ - └──────────────────────┘ - >>> t.food[3:5] - ┏━━━━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ StringSlice(food, 3, 5) ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━━━━┩ - │ string │ - ├─────────────────────────┤ - │ ad │ - │ es │ - │ e │ - └─────────────────────────┘ - >>> t.food[7] - ┏━━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ Substring(food, 7, 1) ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━━┩ - │ string │ - ├───────────────────────┤ - │ ~ │ - │ ~ │ - │ ~ │ - └───────────────────────┘ - """ - from bigframes_vendored.ibis.expr import types as ir - - if isinstance(key, slice): - start, stop, step = key.start, key.stop, key.step - - if isinstance(step, ir.Expr) or (step is not None and step != 1): - raise ValueError("Step can only be 1") - if start is None and stop is None: - return self - return ops.StringSlice(self, start, stop).to_expr() - elif isinstance(key, int): - return self.substr(key, 1) - raise NotImplementedError(f"string __getitem__[{key.__class__.__name__}]") - - def length(self) -> ir.IntegerValue: - """Compute the length of a string. - - Returns - ------- - IntegerValue - The length of each string in the expression - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable({"s": ["aaa", "a", "aa"]}) - >>> t.s.length() - ┏━━━━━━━━━━━━━━━━━┓ - ┃ StringLength(s) ┃ - ┡━━━━━━━━━━━━━━━━━┩ - │ int32 │ - ├─────────────────┤ - │ 3 │ - │ 1 │ - │ 2 │ - └─────────────────┘ - """ - return ops.StringLength(self).to_expr() - - def lower(self) -> StringValue: - """Convert string to all lowercase. - - Returns - ------- - StringValue - Lowercase string - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable({"s": ["AAA", "a", "AA"]}) - >>> t - ┏━━━━━━━━┓ - ┃ s ┃ - ┡━━━━━━━━┩ - │ string │ - ├────────┤ - │ AAA │ - │ a │ - │ AA │ - └────────┘ - >>> t.s.lower() - ┏━━━━━━━━━━━━━━┓ - ┃ Lowercase(s) ┃ - ┡━━━━━━━━━━━━━━┩ - │ string │ - ├──────────────┤ - │ aaa │ - │ a │ - │ aa │ - └──────────────┘ - """ - return ops.Lowercase(self).to_expr() - - def upper(self) -> StringValue: - """Convert string to all uppercase. - - Returns - ------- - StringValue - Uppercase string - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable({"s": ["aaa", "A", "aa"]}) - >>> t - ┏━━━━━━━━┓ - ┃ s ┃ - ┡━━━━━━━━┩ - │ string │ - ├────────┤ - │ aaa │ - │ A │ - │ aa │ - └────────┘ - >>> t.s.upper() - ┏━━━━━━━━━━━━━━┓ - ┃ Uppercase(s) ┃ - ┡━━━━━━━━━━━━━━┩ - │ string │ - ├──────────────┤ - │ AAA │ - │ A │ - │ AA │ - └──────────────┘ - """ - return ops.Uppercase(self).to_expr() - - def reverse(self) -> StringValue: - """Reverse the characters of a string. - - Returns - ------- - StringValue - Reversed string - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable({"s": ["abc", "def", "ghi"]}) - >>> t - ┏━━━━━━━━┓ - ┃ s ┃ - ┡━━━━━━━━┩ - │ string │ - ├────────┤ - │ abc │ - │ def │ - │ ghi │ - └────────┘ - >>> t.s.reverse() - ┏━━━━━━━━━━━━┓ - ┃ Reverse(s) ┃ - ┡━━━━━━━━━━━━┩ - │ string │ - ├────────────┤ - │ cba │ - │ fed │ - │ ihg │ - └────────────┘ - """ - return ops.Reverse(self).to_expr() - - def ascii_str(self) -> ir.IntegerValue: - """Return the numeric ASCII code of the first character of a string. - - Returns - ------- - IntegerValue - ASCII code of the first character of the input - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable({"s": ["abc", "def", "ghi"]}) - >>> t.s.ascii_str() - ┏━━━━━━━━━━━━━━━━┓ - ┃ StringAscii(s) ┃ - ┡━━━━━━━━━━━━━━━━┩ - │ int32 │ - ├────────────────┤ - │ 97 │ - │ 100 │ - │ 103 │ - └────────────────┘ - """ - return ops.StringAscii(self).to_expr() - - def strip(self) -> StringValue: - r"""Remove whitespace from left and right sides of a string. - - Returns - ------- - StringValue - Stripped string - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable({"s": ["\ta\t", "\nb\n", "\vc\t"]}) - >>> t - ┏━━━━━━━━┓ - ┃ s ┃ - ┡━━━━━━━━┩ - │ string │ - ├────────┤ - │ \ta\t │ - │ \nb\n │ - │ \vc\t │ - └────────┘ - >>> t.s.strip() - ┏━━━━━━━━━━┓ - ┃ Strip(s) ┃ - ┡━━━━━━━━━━┩ - │ string │ - ├──────────┤ - │ a │ - │ b │ - │ c │ - └──────────┘ - """ - return ops.Strip(self).to_expr() - - def lstrip(self) -> StringValue: - r"""Remove whitespace from the left side of string. - - Returns - ------- - StringValue - Left-stripped string - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable({"s": ["\ta\t", "\nb\n", "\vc\t"]}) - >>> t - ┏━━━━━━━━┓ - ┃ s ┃ - ┡━━━━━━━━┩ - │ string │ - ├────────┤ - │ \ta\t │ - │ \nb\n │ - │ \vc\t │ - └────────┘ - >>> t.s.lstrip() - ┏━━━━━━━━━━━┓ - ┃ LStrip(s) ┃ - ┡━━━━━━━━━━━┩ - │ string │ - ├───────────┤ - │ a\t │ - │ b\n │ - │ c\t │ - └───────────┘ - """ - return ops.LStrip(self).to_expr() - - def rstrip(self) -> StringValue: - r"""Remove whitespace from the right side of string. - - Returns - ------- - StringValue - Right-stripped string - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable({"s": ["\ta\t", "\nb\n", "\vc\t"]}) - >>> t - ┏━━━━━━━━┓ - ┃ s ┃ - ┡━━━━━━━━┩ - │ string │ - ├────────┤ - │ \ta\t │ - │ \nb\n │ - │ \vc\t │ - └────────┘ - >>> t.s.rstrip() - ┏━━━━━━━━━━━┓ - ┃ RStrip(s) ┃ - ┡━━━━━━━━━━━┩ - │ string │ - ├───────────┤ - │ \ta │ - │ \nb │ - │ \vc │ - └───────────┘ - """ - return ops.RStrip(self).to_expr() - - def capitalize(self) -> StringValue: - """Uppercase the first letter, lowercase the rest. - - This API matches the semantics of the Python [](`str.capitalize`) - method. - - Returns - ------- - StringValue - Capitalized string - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable({"s": ["aBC", " abc", "ab cd", None]}) - >>> t.s.capitalize() - ┏━━━━━━━━━━━━━━━┓ - ┃ Capitalize(s) ┃ - ┡━━━━━━━━━━━━━━━┩ - │ string │ - ├───────────────┤ - │ Abc │ - │ abc │ - │ Ab cd │ - │ NULL │ - └───────────────┘ - """ - return ops.Capitalize(self).to_expr() - - @util.deprecated( - instead="use the `capitalize` method", as_of="9.0", removed_in="10.0" - ) - def initcap(self) -> StringValue: - """Deprecated. Use `capitalize` instead.""" - return self.capitalize() - - def __contains__(self, *_: Any) -> bool: - raise TypeError("Use string_expr.contains(arg)") - - def contains(self, substr: str | StringValue) -> ir.BooleanValue: - """Return whether the expression contains `substr`. - - Parameters - ---------- - substr - Substring for which to check - - Returns - ------- - BooleanValue - Boolean indicating the presence of `substr` in the expression - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable({"s": ["bab", "ddd", "eaf"]}) - >>> t.s.contains("a") - ┏━━━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ StringContains(s, 'a') ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━━━┩ - │ boolean │ - ├────────────────────────┤ - │ True │ - │ False │ - │ True │ - └────────────────────────┘ - """ - return ops.StringContains(self, substr).to_expr() - - def hashbytes( - self, - how: Literal["md5", "sha1", "sha256", "sha512"] = "sha256", - ) -> ir.BinaryValue: - """Compute the binary hash value of the input. - - Parameters - ---------- - how - Hash algorithm to use - - Returns - ------- - BinaryValue - Binary expression - """ - return ops.HashBytes(self, how).to_expr() - - def hexdigest( - self, - how: Literal["md5", "sha1", "sha256", "sha512"] = "sha256", - ) -> ir.StringValue: - """Return the hash digest of the input as a hex encoded string. - - Parameters - ---------- - how - Hash algorithm to use - - Returns - ------- - StringValue - Hexadecimal representation of the hash as a string - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable({"species": ["Adelie", "Chinstrap", "Gentoo"]}) - >>> t.species.hexdigest() - ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ HexDigest(species) ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩ - │ string │ - ├──────────────────────────────────────────────────────────────────┤ - │ a4d7d46b27480037bc1e513e0e157cbf258baae6ee69e3110d0f9ff418b57a3c │ - │ cb97d113ca69899ae4f1fb581f4a90d86989db77b4a33873d604b0ee412b4cc9 │ - │ b5e90cdff65949fe6bc226823245f7698110e563a12363fc57b3eed3e4a0a612 │ - └──────────────────────────────────────────────────────────────────┘ - """ - return ops.HexDigest(self, how.lower()).to_expr() - - def substr( - self, - start: int | ir.IntegerValue, - length: int | ir.IntegerValue | None = None, - ) -> StringValue: - """Extract a substring. - - Parameters - ---------- - start - First character to start splitting, indices start at 0 - length - Maximum length of each substring. If not supplied, searches the - entire string - - Returns - ------- - StringValue - Found substring - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable({"s": ["abc", "defg", "hijlk"]}) - >>> t.s.substr(2) - ┏━━━━━━━━━━━━━━━━━┓ - ┃ Substring(s, 2) ┃ - ┡━━━━━━━━━━━━━━━━━┩ - │ string │ - ├─────────────────┤ - │ c │ - │ fg │ - │ jlk │ - └─────────────────┘ - """ - return ops.Substring(self, start, length).to_expr() - - def left(self, nchars: int | ir.IntegerValue) -> StringValue: - """Return the `nchars` left-most characters. - - Parameters - ---------- - nchars - Maximum number of characters to return - - Returns - ------- - StringValue - Characters from the start - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable({"s": ["abc", "defg", "hijlk"]}) - >>> t.s.left(2) - ┏━━━━━━━━━━━━━━━━━━━━┓ - ┃ Substring(s, 0, 2) ┃ - ┡━━━━━━━━━━━━━━━━━━━━┩ - │ string │ - ├────────────────────┤ - │ ab │ - │ de │ - │ hi │ - └────────────────────┘ - """ - return self.substr(0, length=nchars) - - def right(self, nchars: int | ir.IntegerValue) -> StringValue: - """Return up to `nchars` from the end of each string. - - Parameters - ---------- - nchars - Maximum number of characters to return - - Returns - ------- - StringValue - Characters from the end - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable({"s": ["abc", "defg", "hijlk"]}) - >>> t.s.right(2) - ┏━━━━━━━━━━━━━━━━┓ - ┃ StrRight(s, 2) ┃ - ┡━━━━━━━━━━━━━━━━┩ - │ string │ - ├────────────────┤ - │ bc │ - │ fg │ - │ lk │ - └────────────────┘ - """ - return ops.StrRight(self, nchars).to_expr() - - def repeat(self, n: int | ir.IntegerValue) -> StringValue: - """Repeat a string `n` times. - - Parameters - ---------- - n - Number of repetitions - - Returns - ------- - StringValue - Repeated string - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable({"s": ["a", "bb", "c"]}) - >>> t.s.repeat(5) - ┏━━━━━━━━━━━━━━┓ - ┃ Repeat(s, 5) ┃ - ┡━━━━━━━━━━━━━━┩ - │ string │ - ├──────────────┤ - │ aaaaa │ - │ bbbbbbbbbb │ - │ ccccc │ - └──────────────┘ - """ - return ops.Repeat(self, n).to_expr() - - def translate(self, from_str: StringValue, to_str: StringValue) -> StringValue: - """Replace `from_str` characters in `self` characters in `to_str`. - - To avoid unexpected behavior, `from_str` should be shorter than - `to_str`. - - Parameters - ---------- - from_str - Characters in `arg` to replace - to_str - Characters to use for replacement - - Returns - ------- - StringValue - Translated string - - Examples - -------- - >>> import ibis - >>> table = ibis.table(dict(string_col="string")) - >>> result = table.string_col.translate("a", "b") - """ - return ops.Translate(self, from_str, to_str).to_expr() - - def find( - self, - substr: str | StringValue, - start: int | ir.IntegerValue | None = None, - end: int | ir.IntegerValue | None = None, - ) -> ir.IntegerValue: - """Return the position of the first occurrence of substring. - - Parameters - ---------- - substr - Substring to search for - start - Zero based index of where to start the search - end - Zero based index of where to stop the search. Currently not - implemented. - - Returns - ------- - IntegerValue - Position of `substr` in `arg` starting from `start` - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable({"s": ["abc", "bac", "bca"]}) - >>> t.s.find("a") - ┏━━━━━━━━━━━━━━━━━━━━┓ - ┃ StringFind(s, 'a') ┃ - ┡━━━━━━━━━━━━━━━━━━━━┩ - │ int64 │ - ├────────────────────┤ - │ 0 │ - │ 1 │ - │ 2 │ - └────────────────────┘ - >>> t.s.find("z") - ┏━━━━━━━━━━━━━━━━━━━━┓ - ┃ StringFind(s, 'z') ┃ - ┡━━━━━━━━━━━━━━━━━━━━┩ - │ int64 │ - ├────────────────────┤ - │ -1 │ - │ -1 │ - │ -1 │ - └────────────────────┘ - """ - if end is not None: - raise NotImplementedError("`end` parameter is not yet implemented") - return ops.StringFind(self, substr, start, end).to_expr() - - def lpad( - self, - length: int | ir.IntegerValue, - pad: str | StringValue = " ", - ) -> StringValue: - """Pad `arg` by truncating on the right or padding on the left. - - Parameters - ---------- - length - Length of output string - pad - Pad character - - Returns - ------- - StringValue - Left-padded string - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable({"s": ["abc", "def", "ghij"]}) - >>> t.s.lpad(5, "-") - ┏━━━━━━━━━━━━━━━━━┓ - ┃ LPad(s, 5, '-') ┃ - ┡━━━━━━━━━━━━━━━━━┩ - │ string │ - ├─────────────────┤ - │ --abc │ - │ --def │ - │ -ghij │ - └─────────────────┘ - """ - return ops.LPad(self, length, pad).to_expr() - - def rpad( - self, - length: int | ir.IntegerValue, - pad: str | StringValue = " ", - ) -> StringValue: - """Pad `self` by truncating or padding on the right. - - Parameters - ---------- - self - String to pad - length - Length of output string - pad - Pad character - - Returns - ------- - StringValue - Right-padded string - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable({"s": ["abc", "def", "ghij"]}) - >>> t.s.rpad(5, "-") - ┏━━━━━━━━━━━━━━━━━┓ - ┃ RPad(s, 5, '-') ┃ - ┡━━━━━━━━━━━━━━━━━┩ - │ string │ - ├─────────────────┤ - │ abc-- │ - │ def-- │ - │ ghij- │ - └─────────────────┘ - """ - return ops.RPad(self, length, pad).to_expr() - - def find_in_set(self, str_list: Sequence[str]) -> ir.IntegerValue: - """Find the first occurrence of `str_list` within a list of strings. - - No string in `str_list` can have a comma. - - Parameters - ---------- - str_list - Sequence of strings - - Returns - ------- - IntegerValue - Position of `str_list` in `self`. Returns -1 if `self` isn't found - or if `self` contains `','`. - - Examples - -------- - >>> import ibis - >>> table = ibis.table(dict(string_col="string")) - >>> result = table.string_col.find_in_set(["a", "b"]) - """ - return ops.FindInSet(self, str_list).to_expr() - - def join(self, strings: Sequence[str | StringValue] | ir.ArrayValue) -> StringValue: - """Join a list of strings using `self` as the separator. - - Parameters - ---------- - strings - Strings to join with `arg` - - Returns - ------- - StringValue - Joined string - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable({"arr": [["a", "b", "c"], None, [], ["b", None]]}) - >>> t - ┏━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ arr ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━┩ - │ array │ - ├──────────────────────┤ - │ ['a', 'b', ... +1] │ - │ NULL │ - │ [] │ - │ ['b', None] │ - └──────────────────────┘ - >>> ibis.literal("|").join(t.arr) - ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ ArrayStringJoin(arr, '|') ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━┩ - │ string │ - ├───────────────────────────┤ - │ a|b|c │ - │ NULL │ - │ NULL │ - │ b │ - └───────────────────────────┘ - - See Also - -------- - [`ArrayValue.join`](./expression-collections.qmd#ibis.expr.types.arrays.ArrayValue.join) - """ - import bigframes_vendored.ibis.expr.types as ir - - if isinstance(strings, ir.ArrayValue): - cls = ops.ArrayStringJoin - else: - cls = ops.StringJoin - return cls(strings, sep=self).to_expr() - - def startswith(self, start: str | StringValue) -> ir.BooleanValue: - """Determine whether `self` starts with `end`. - - Parameters - ---------- - start - prefix to check for - - Returns - ------- - BooleanValue - Boolean indicating whether `self` starts with `start` - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable({"s": ["Ibis project", "GitHub"]}) - >>> t.s.startswith("Ibis") - ┏━━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ StartsWith(s, 'Ibis') ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━━┩ - │ boolean │ - ├───────────────────────┤ - │ True │ - │ False │ - └───────────────────────┘ - """ - return ops.StartsWith(self, start).to_expr() - - def endswith(self, end: str | StringValue) -> ir.BooleanValue: - """Determine if `self` ends with `end`. - - Parameters - ---------- - end - Suffix to check for - - Returns - ------- - BooleanValue - Boolean indicating whether `self` ends with `end` - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable({"s": ["Ibis project", "GitHub"]}) - >>> t.s.endswith("project") - ┏━━━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ EndsWith(s, 'project') ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━━━┩ - │ boolean │ - ├────────────────────────┤ - │ True │ - │ False │ - └────────────────────────┘ - """ - return ops.EndsWith(self, end).to_expr() - - def like( - self, - patterns: str | StringValue | Iterable[str | StringValue], - ) -> ir.BooleanValue: - """Match `patterns` against `self`, case-sensitive. - - This function is modeled after the SQL `LIKE` directive. Use `%` as a - multiple-character wildcard or `_` as a single-character wildcard. - - Use `re_search` or `rlike` for regular expression-based matching. - - Parameters - ---------- - patterns - If `pattern` is a list, then if any pattern matches the input then - the corresponding row in the output is `True`. - - Returns - ------- - BooleanValue - Column indicating matches - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable({"s": ["Ibis project", "GitHub"]}) - >>> t.s.like("%project") - ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ StringSQLLike(s, '%project') ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩ - │ boolean │ - ├──────────────────────────────┤ - │ True │ - │ False │ - └──────────────────────────────┘ - """ - return functools.reduce( - operator.or_, - ( - ops.StringSQLLike(self, pattern).to_expr() - for pattern in util.promote_list(patterns) - ), - ) - - def ilike( - self, - patterns: str | StringValue | Iterable[str | StringValue], - ) -> ir.BooleanValue: - """Match `patterns` against `self`, case-insensitive. - - This function is modeled after SQL's `ILIKE` directive. Use `%` as a - multiple-character wildcard or `_` as a single-character wildcard. - - Use `re_search` or `rlike` for regular expression-based matching. - - Parameters - ---------- - patterns - If `pattern` is a list, then if any pattern matches the input then - the corresponding row in the output is `True`. - - Returns - ------- - BooleanValue - Column indicating matches - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable({"s": ["Ibis project", "GitHub"]}) - >>> t.s.ilike("%PROJect") - ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ StringSQLILike(s, '%PROJect') ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩ - │ boolean │ - ├───────────────────────────────┤ - │ True │ - │ False │ - └───────────────────────────────┘ - """ - return functools.reduce( - operator.or_, - ( - ops.StringSQLILike(self, pattern).to_expr() - for pattern in util.promote_list(patterns) - ), - ) - - @util.backend_sensitive( - why="Different backends support different regular expression syntax." - ) - def re_search(self, pattern: str | StringValue) -> ir.BooleanValue: - """Return whether the values match `pattern`. - - Returns `True` if the regex matches a string and `False` otherwise. - - Parameters - ---------- - pattern - Regular expression use for searching - - Returns - ------- - BooleanValue - Indicator of matches - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable({"s": ["Ibis project", "GitHub"]}) - >>> t.s.re_search(".+Hub") - ┏━━━━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ RegexSearch(s, '.+Hub') ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━━━━┩ - │ boolean │ - ├─────────────────────────┤ - │ False │ - │ True │ - └─────────────────────────┘ - """ - return ops.RegexSearch(self, pattern).to_expr() - - rlike = re_search - - @util.backend_sensitive( - why="Different backends support different regular expression syntax." - ) - def re_extract( - self, - pattern: str | StringValue, - index: int | ir.IntegerValue, - ) -> StringValue: - """Return the specified match at `index` from a regex `pattern`. - - Parameters - ---------- - pattern - Regular expression pattern string - index - The index of the match group to return. - - The behavior of this function follows the behavior of Python's - [`match objects`](https://docs.python.org/3/library/re.html#match-objects): - when `index` is zero and there's a match, return the entire match, - otherwise return the content of the `index`-th match group. - - Returns - ------- - StringValue - Extracted match or whole string if `index` is zero - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable({"s": ["abc", "bac", "bca"]}) - - Extract a specific group - - >>> t.s.re_extract(r"^(a)bc", 1) - ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ RegexExtract(s, '^(a)bc', 1) ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩ - │ string │ - ├──────────────────────────────┤ - │ a │ - │ ~ │ - │ ~ │ - └──────────────────────────────┘ - - Extract the entire match - - >>> t.s.re_extract(r"^(a)bc", 0) - ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ RegexExtract(s, '^(a)bc', 0) ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩ - │ string │ - ├──────────────────────────────┤ - │ abc │ - │ ~ │ - │ ~ │ - └──────────────────────────────┘ - """ - return ops.RegexExtract(self, pattern, index).to_expr() - - @util.backend_sensitive( - why="Different backends support different regular expression syntax." - ) - def re_split(self, pattern: str | StringValue) -> ir.ArrayValue: - r"""Split a string by a regular expression `pattern`. - - Parameters - ---------- - pattern - Regular expression string to split by - - Returns - ------- - ArrayValue - Array of strings from splitting by `pattern` - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable(dict(s=["a.b", "b.....c", "c.........a", "def"])) - >>> t.s - ┏━━━━━━━━━━━━━┓ - ┃ s ┃ - ┡━━━━━━━━━━━━━┩ - │ string │ - ├─────────────┤ - │ a.b │ - │ b.....c │ - │ c.........a │ - │ def │ - └─────────────┘ - >>> t.s.re_split(r"\.+").name("splits") - ┏━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ splits ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━┩ - │ array │ - ├──────────────────────┤ - │ ['a', 'b'] │ - │ ['b', 'c'] │ - │ ['c', 'a'] │ - │ ['def'] │ - └──────────────────────┘ - """ - return ops.RegexSplit(self, pattern).to_expr() - - @util.backend_sensitive( - why="Different backends support different regular expression syntax." - ) - def re_replace( - self, - pattern: str | StringValue, - replacement: str | StringValue, - ) -> StringValue: - r"""Replace all matches found by regex `pattern` with `replacement`. - - Parameters - ---------- - pattern - Regular expression string - replacement - Replacement string or regular expression - - Returns - ------- - StringValue - Modified string - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable({"s": ["abc", "bac", "bca", "this has multi \t whitespace"]}) - >>> s = t.s - - Replace all "a"s that are at the beginning of the string with "b": - - >>> s.re_replace("^a", "b") - ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ RegexReplace(s, '^a', 'b') ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩ - │ string │ - ├───────────────────────────────┤ - │ bbc │ - │ bac │ - │ bca │ - │ this has multi \t whitespace │ - └───────────────────────────────┘ - - Double up any "a"s or "b"s, using capture groups and backreferences: - - >>> s.re_replace("([ab])", r"\0\0") - ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ RegexReplace(s, '()', '\\0\\0') ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩ - │ string │ - ├─────────────────────────────────────┤ - │ aabbc │ - │ bbaac │ - │ bbcaa │ - │ this haas multi \t whitespaace │ - └─────────────────────────────────────┘ - - Normalize all whitespace to a single space: - - >>> s.re_replace(r"\s+", " ") - ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ RegexReplace(s, '\\s+', ' ') ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩ - │ string │ - ├──────────────────────────────┤ - │ abc │ - │ bac │ - │ bca │ - │ this has multi whitespace │ - └──────────────────────────────┘ - """ - return ops.RegexReplace(self, pattern, replacement).to_expr() - - def replace( - self, - pattern: StringValue, - replacement: StringValue, - ) -> StringValue: - """Replace each exact match of `pattern` with `replacement`. - - Parameters - ---------- - pattern - String pattern - replacement - String replacement - - Returns - ------- - StringValue - Replaced string - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable({"s": ["abc", "bac", "bca"]}) - >>> t.s.replace("b", "z") - ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ StringReplace(s, 'b', 'z') ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩ - │ string │ - ├────────────────────────────┤ - │ azc │ - │ zac │ - │ zca │ - └────────────────────────────┘ - """ - return ops.StringReplace(self, pattern, replacement).to_expr() - - def to_timestamp(self, format_str: str) -> ir.TimestampValue: - """Parse a string and return a timestamp. - - Parameters - ---------- - format_str - Format string in `strptime` format - - Returns - ------- - TimestampValue - Parsed timestamp value - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable({"ts": ["20170206"]}) - >>> t.ts.to_timestamp("%Y%m%d") - ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ StringToTimestamp(ts, '%Y%m%d') ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩ - │ timestamp('UTC') │ - ├─────────────────────────────────┤ - │ 2017-02-06 00:00:00+00:00 │ - └─────────────────────────────────┘ - """ - return ops.StringToTimestamp(self, format_str).to_expr() - - def to_date(self, format_str: str) -> ir.DateValue: - """Parse a string and return a date. - - Parameters - ---------- - format_str - Format string in `strptime` format - - Returns - ------- - DateValue - Parsed date value - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable({"ts": ["20170206"]}) - >>> t.ts.to_date("%Y%m%d") - ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ StringToDate(ts, '%Y%m%d') ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩ - │ date │ - ├────────────────────────────┤ - │ 2017-02-06 │ - └────────────────────────────┘ - """ - return ops.StringToDate(self, format_str).to_expr() - - def protocol(self): - """Parse a URL and extract protocol. - - Examples - -------- - >>> import ibis - >>> url = ibis.literal("https://user:pass@example.com:80/docs/books") - >>> result = url.protocol() # https - - Returns - ------- - StringValue - Extracted string value - """ - return ops.ExtractProtocol(self).to_expr() - - def authority(self): - """Parse a URL and extract authority. - - Examples - -------- - >>> import ibis - >>> url = ibis.literal("https://user:pass@example.com:80/docs/books") - >>> result = url.authority() # user:pass@example.com:80 - - Returns - ------- - StringValue - Extracted string value - """ - return ops.ExtractAuthority(self).to_expr() - - def userinfo(self): - """Parse a URL and extract user info. - - Examples - -------- - >>> import ibis - >>> url = ibis.literal("https://user:pass@example.com:80/docs/books") - >>> result = url.userinfo() # user:pass - - Returns - ------- - StringValue - Extracted string value - """ - return ops.ExtractUserInfo(self).to_expr() - - def host(self): - """Parse a URL and extract host. - - Examples - -------- - >>> import ibis - >>> url = ibis.literal("https://user:pass@example.com:80/docs/books") - >>> result = url.host() # example.com - - Returns - ------- - StringValue - Extracted string value - """ - return ops.ExtractHost(self).to_expr() - - def file(self): - """Parse a URL and extract file. - - Examples - -------- - >>> import ibis - >>> url = ibis.literal( - ... "https://example.com:80/docs/books/tutorial/index.html?name=networking" - ... ) - >>> result = url.file() # docs/books/tutorial/index.html?name=networking - - Returns - ------- - StringValue - Extracted string value - """ - return ops.ExtractFile(self).to_expr() - - def path(self): - """Parse a URL and extract path. - - Examples - -------- - >>> import ibis - >>> url = ibis.literal( - ... "https://example.com:80/docs/books/tutorial/index.html?name=networking" - ... ) - >>> result = url.path() # docs/books/tutorial/index.html - - Returns - ------- - StringValue - Extracted string value - """ - return ops.ExtractPath(self).to_expr() - - def query(self, key: str | StringValue | None = None): - """Parse a URL and returns query strring or query string parameter. - - If key is passed, return the value of the query string parameter named. - If key is absent, return the query string. - - Parameters - ---------- - key - Query component to extract - - Examples - -------- - >>> import ibis - >>> url = ibis.literal( - ... "https://example.com:80/docs/books/tutorial/index.html?name=networking" - ... ) - >>> result = url.query() # name=networking - >>> query_name = url.query("name") # networking - - Returns - ------- - StringValue - Extracted string value - """ - return ops.ExtractQuery(self, key).to_expr() - - def fragment(self): - """Parse a URL and extract fragment identifier. - - Examples - -------- - >>> import ibis - >>> url = ibis.literal("https://example.com:80/docs/#DOWNLOADING") - >>> result = url.fragment() # DOWNLOADING - - Returns - ------- - StringValue - Extracted string value - """ - return ops.ExtractFragment(self).to_expr() - - def split(self, delimiter: str | StringValue) -> ir.ArrayValue: - """Split as string on `delimiter`. - - ::: {.callout-note} - ## This API only works on backends with array support. - ::: - - Parameters - ---------- - delimiter - Value to split by - - Returns - ------- - ArrayValue - The string split by `delimiter` - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable({"col": ["a,b,c", "d,e", "f"]}) - >>> t - ┏━━━━━━━━┓ - ┃ col ┃ - ┡━━━━━━━━┩ - │ string │ - ├────────┤ - │ a,b,c │ - │ d,e │ - │ f │ - └────────┘ - >>> t.col.split(",") - ┏━━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ StringSplit(col, ',') ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━━┩ - │ array │ - ├───────────────────────┤ - │ ['a', 'b', ... +1] │ - │ ['d', 'e'] │ - │ ['f'] │ - └───────────────────────┘ - """ - return ops.StringSplit(self, delimiter).to_expr() - - def concat(self, other: str | StringValue, *args: str | StringValue) -> StringValue: - """Concatenate strings. - - NULLs are propagated. This methods is equivalent to using the `+` operator. - - Parameters - ---------- - other - String to concatenate - args - Additional strings to concatenate - - Returns - ------- - StringValue - All strings concatenated - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable({"s": ["abc", None]}) - >>> t.s.concat("xyz", "123") - ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ StringConcat((s, 'xyz', '123')) ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩ - │ string │ - ├─────────────────────────────────┤ - │ abcxyz123 │ - │ NULL │ - └─────────────────────────────────┘ - >>> t.s + "xyz" - ┏━━━━━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ StringConcat((s, 'xyz')) ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━━━━━┩ - │ string │ - ├──────────────────────────┤ - │ abcxyz │ - │ NULL │ - └──────────────────────────┘ - """ - return ops.StringConcat((self, other, *args)).to_expr() - - def __add__(self, other: str | StringValue) -> StringValue: - """Concatenate strings. - - Parameters - ---------- - other - String to concatenate - - Returns - ------- - StringValue - All strings concatenated - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable({"s": ["abc", "bac", "bca"]}) - >>> t - ┏━━━━━━━━┓ - ┃ s ┃ - ┡━━━━━━━━┩ - │ string │ - ├────────┤ - │ abc │ - │ bac │ - │ bca │ - └────────┘ - >>> t.s + "z" - ┏━━━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ StringConcat((s, 'z')) ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━━━┩ - │ string │ - ├────────────────────────┤ - │ abcz │ - │ bacz │ - │ bcaz │ - └────────────────────────┘ - >>> t.s + t.s - ┏━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ StringConcat((s, s)) ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━┩ - │ string │ - ├──────────────────────┤ - │ abcabc │ - │ bacbac │ - │ bcabca │ - └──────────────────────┘ - """ - return self.concat(other) - - def __radd__(self, other: str | StringValue) -> StringValue: - """Concatenate strings. - - Parameters - ---------- - other - String to concatenate - - Returns - ------- - StringValue - All strings concatenated - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable({"s": ["abc", "bac", "bca"]}) - >>> t - ┏━━━━━━━━┓ - ┃ s ┃ - ┡━━━━━━━━┩ - │ string │ - ├────────┤ - │ abc │ - │ bac │ - │ bca │ - └────────┘ - >>> "z" + t.s - ┏━━━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ StringConcat(('z', s)) ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━━━┩ - │ string │ - ├────────────────────────┤ - │ zabc │ - │ zbac │ - │ zbca │ - └────────────────────────┘ - """ - return ops.StringConcat((other, self)).to_expr() - - def convert_base( - self, - from_base: int | ir.IntegerValue, - to_base: int | ir.IntegerValue, - ) -> ir.IntegerValue: - """Convert a string representing an integer from one base to another. - - Parameters - ---------- - from_base - Numeric base of the expression - to_base - New base - - Returns - ------- - IntegerValue - Converted expression - """ - return ops.BaseConvert(self, from_base, to_base).to_expr() - - def __mul__(self, n: int | ir.IntegerValue) -> StringValue: - return _binop(ops.Repeat, self, n) - - __rmul__ = __mul__ - - def levenshtein(self, other: StringValue) -> ir.IntegerValue: - """Return the Levenshtein distance between two strings. - - Parameters - ---------- - other - String to compare to - - Returns - ------- - IntegerValue - The edit distance between the two strings - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> s = ibis.literal("kitten") - >>> s.levenshtein("sitting") - ┌─────────────┐ - │ np.int64(3) │ - └─────────────┘ - """ - return ops.Levenshtein(self, other).to_expr() - - -@public -class StringScalar(Scalar, StringValue): - pass - - -@public -class StringColumn(Column, StringValue): - def __getitem__(self, key: slice | int | ir.IntegerScalar) -> StringColumn: - return StringValue.__getitem__(self, key) diff --git a/third_party/bigframes_vendored/ibis/expr/types/structs.py b/third_party/bigframes_vendored/ibis/expr/types/structs.py deleted file mode 100644 index eb5b5595a23..00000000000 --- a/third_party/bigframes_vendored/ibis/expr/types/structs.py +++ /dev/null @@ -1,403 +0,0 @@ -# Contains code from https://github.com/ibis-project/ibis/blob/9.2.0/ibis/expr/types/structs.py - -from __future__ import annotations - -import collections -from keyword import iskeyword -from typing import TYPE_CHECKING - -import bigframes_vendored.ibis.expr.operations as ops -from bigframes_vendored.ibis.common.deferred import deferrable -from bigframes_vendored.ibis.common.exceptions import IbisError -from bigframes_vendored.ibis.expr.types.generic import Column, Scalar, Value, literal -from public import public - -if TYPE_CHECKING: - from collections.abc import Iterable, Mapping, Sequence - - import bigframes_vendored.ibis.expr.datatypes as dt - import bigframes_vendored.ibis.expr.types as ir - from bigframes_vendored.ibis.expr.types.typing import V - - -@public -@deferrable -def struct( - value: Iterable[tuple[str, V]] | Mapping[str, V], - type: str | dt.DataType | None = None, -) -> StructValue: - """Create a struct expression. - - If any of the inputs are Columns, then the output will be a `StructColumn`. - Otherwise, the output will be a `StructScalar`. - - Parameters - ---------- - value - Either a `{str: Value}` mapping, or an iterable of tuples of the form - `(str, Value)`. - type - An instance of `ibis.expr.datatypes.DataType` or a string indicating - the Ibis type of `value`. This is only used if all of the input values - are Python literals. eg `struct`. - - Returns - ------- - StructValue - An StructScalar or StructColumn expression. - - Examples - -------- - Create a struct scalar literal from a `dict` with the type inferred - - >>> import ibis - >>> ibis.options.interactive = True - >>> ibis.struct(dict(a=1, b="foo")) - ┌──────────────────────┐ - │ {'a': 1, 'b': 'foo'} │ - └──────────────────────┘ - - Specify a type (note the 1 is now a `float`): - - >>> ibis.struct(dict(a=1, b="foo"), type="struct") - ┌────────────────────────┐ - │ {'a': 1.0, 'b': 'foo'} │ - └────────────────────────┘ - - Create a struct column from a column and a scalar literal - - >>> t = ibis.memtable({"a": [1, 2, 3]}) - >>> ibis.struct([("a", t.a), ("b", "foo")]) - ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ StructColumn({'a': a, 'b': 'foo'}) ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩ - │ struct │ - ├────────────────────────────────────┤ - │ {'a': 1, 'b': 'foo'} │ - │ {'a': 2, 'b': 'foo'} │ - │ {'a': 3, 'b': 'foo'} │ - └────────────────────────────────────┘ - """ - import bigframes_vendored.ibis.expr.operations as ops - - fields = dict(value) - if any(isinstance(value, Value) for value in fields.values()): - names = tuple(fields.keys()) - values = tuple(fields.values()) - return ops.StructColumn(names=names, values=values).to_expr() - else: - return literal(collections.OrderedDict(fields), type=type) - - -@public -class StructValue(Value): - """A Struct is a nested type with ordered fields of any type. - - For example, a Struct might have a field `a` of type `int64` and a field `b` - of type `string`. - - Structs can be constructed with [`ibis.struct()`](#ibis.expr.types.struct). - - Examples - -------- - Construct a `Struct` column with fields `a: int64` and `b: string` - - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable({"s": [{"a": 1, "b": "foo"}, {"a": 3, "b": None}, None]}) - >>> t - ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ s ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩ - │ struct │ - ├─────────────────────────────┤ - │ {'a': 1, 'b': 'foo'} │ - │ {'a': 3, 'b': None} │ - │ NULL │ - └─────────────────────────────┘ - - You can use dot notation (`.`) or square-bracket syntax (`[]`) to access - struct column fields - - >>> t.s.a - ┏━━━━━━━┓ - ┃ a ┃ - ┡━━━━━━━┩ - │ int64 │ - ├───────┤ - │ 1 │ - │ 3 │ - │ NULL │ - └───────┘ - >>> t.s["a"] - ┏━━━━━━━┓ - ┃ a ┃ - ┡━━━━━━━┩ - │ int64 │ - ├───────┤ - │ 1 │ - │ 3 │ - │ NULL │ - └───────┘ - """ - - def __dir__(self): - out = set(dir(type(self))) - out.update( - c for c in self.type().names if c.isidentifier() and not iskeyword(c) - ) - return sorted(out) - - def _ipython_key_completions_(self) -> list[str]: - return sorted(self.type().names) - - def __getitem__(self, name: str) -> ir.Value: - """Extract the `name` field from this struct. - - Parameters - ---------- - name - The name of the field to access. - - Returns - ------- - Value - An expression with the type of the field being accessed. - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable({"s": [{"a": 1, "b": "foo"}, {"a": 3, "b": None}, None]}) - >>> t - ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ s ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩ - │ struct │ - ├─────────────────────────────┤ - │ {'a': 1, 'b': 'foo'} │ - │ {'a': 3, 'b': None} │ - │ NULL │ - └─────────────────────────────┘ - >>> t.s["a"] - ┏━━━━━━━┓ - ┃ a ┃ - ┡━━━━━━━┩ - │ int64 │ - ├───────┤ - │ 1 │ - │ 3 │ - │ NULL │ - └───────┘ - >>> t.s["b"] - ┏━━━━━━━━┓ - ┃ b ┃ - ┡━━━━━━━━┩ - │ string │ - ├────────┤ - │ foo │ - │ NULL │ - │ NULL │ - └────────┘ - >>> t.s["foo_bar"] - Traceback (most recent call last): - ... - KeyError: 'foo_bar' - """ - if name not in self.names: - raise KeyError(name) - return ops.StructField(self, name).to_expr() - - def __setstate__(self, instance_dictionary): - self.__dict__ = instance_dictionary - - def __getattr__(self, name: str) -> ir.Value: - """Extract the `name` field from this struct. - - Parameters - ---------- - name - The name of the field to access. - - Returns - ------- - Value - An expression with the type of the field being accessed. - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable({"s": [{"a": 1, "b": "foo"}, {"a": 3, "b": None}, None]}) - >>> t - ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ s ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩ - │ struct │ - ├─────────────────────────────┤ - │ {'a': 1, 'b': 'foo'} │ - │ {'a': 3, 'b': None} │ - │ NULL │ - └─────────────────────────────┘ - >>> t.s.a - ┏━━━━━━━┓ - ┃ a ┃ - ┡━━━━━━━┩ - │ int64 │ - ├───────┤ - │ 1 │ - │ 3 │ - │ NULL │ - └───────┘ - >>> t.s.b - ┏━━━━━━━━┓ - ┃ b ┃ - ┡━━━━━━━━┩ - │ string │ - ├────────┤ - │ foo │ - │ NULL │ - │ NULL │ - └────────┘ - >>> t.s.foo_bar - Traceback (most recent call last): - ... - AttributeError: foo_bar - """ - try: - return self[name] - except KeyError: - raise AttributeError(name) from None - - @property - def names(self) -> Sequence[str]: - """Return the field names of the struct.""" - return self.type().names - - @property - def types(self) -> Sequence[dt.DataType]: - """Return the field types of the struct.""" - return self.type().types - - @property - def fields(self) -> Mapping[str, dt.DataType]: - """Return a mapping from field name to field type of the struct.""" - return self.type().fields - - def lift(self) -> ir.Table: - """Project the fields of `self` into a table. - - This method is useful when analyzing data that has deeply nested - structs or arrays of structs. `lift` can be chained to avoid repeating - column names and table references. - - Returns - ------- - Table - A projection with this struct expression's fields. - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable( - ... { - ... "pos": [ - ... {"lat": 10.1, "lon": 30.3}, - ... {"lat": 10.2, "lon": 30.2}, - ... {"lat": 10.3, "lon": 30.1}, - ... ] - ... } - ... ) - >>> t - ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ pos ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩ - │ struct │ - ├────────────────────────────────────┤ - │ {'lat': 10.1, 'lon': 30.3} │ - │ {'lat': 10.2, 'lon': 30.2} │ - │ {'lat': 10.3, 'lon': 30.1} │ - └────────────────────────────────────┘ - >>> t.pos.lift() - ┏━━━━━━━━━┳━━━━━━━━━┓ - ┃ lat ┃ lon ┃ - ┡━━━━━━━━━╇━━━━━━━━━┩ - │ float64 │ float64 │ - ├─────────┼─────────┤ - │ 10.1 │ 30.3 │ - │ 10.2 │ 30.2 │ - │ 10.3 │ 30.1 │ - └─────────┴─────────┘ - - See Also - -------- - [`Table.unpack`](./expression-tables.qmd#ibis.expr.types.relations.Table.unpack) - """ - try: - (table,) = self.op().relations - except ValueError: - raise IbisError("StructValue must depend on exactly one table") - - return table.to_expr().select([self[name] for name in self.names]) - - def destructure(self) -> list[ir.Value]: - """Destructure a ``StructValue`` into the corresponding struct fields. - - When assigned, a destruct value will be destructured and assigned to - multiple columns. - - Returns - ------- - list[AnyValue] - Value expressions corresponding to the struct fields. - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> t = ibis.memtable({"s": [{"a": 1, "b": "foo"}, {"a": 3, "b": None}, None]}) - >>> t - ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ s ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩ - │ struct │ - ├─────────────────────────────┤ - │ {'a': 1, 'b': 'foo'} │ - │ {'a': 3, 'b': None} │ - │ NULL │ - └─────────────────────────────┘ - >>> a, b = t.s.destructure() - >>> a - ┏━━━━━━━┓ - ┃ a ┃ - ┡━━━━━━━┩ - │ int64 │ - ├───────┤ - │ 1 │ - │ 3 │ - │ NULL │ - └───────┘ - >>> b - ┏━━━━━━━━┓ - ┃ b ┃ - ┡━━━━━━━━┩ - │ string │ - ├────────┤ - │ foo │ - │ NULL │ - │ NULL │ - └────────┘ - """ - return [self[field_name] for field_name in self.type().names] - - -@public -class StructScalar(Scalar, StructValue): - pass - - -@public -class StructColumn(Column, StructValue): - def __getitem__(self, name: str) -> ir.Column: - return StructValue.__getitem__(self, name) diff --git a/third_party/bigframes_vendored/ibis/expr/types/temporal.py b/third_party/bigframes_vendored/ibis/expr/types/temporal.py deleted file mode 100644 index 91e978d5402..00000000000 --- a/third_party/bigframes_vendored/ibis/expr/types/temporal.py +++ /dev/null @@ -1,996 +0,0 @@ -# Contains code from https://github.com/ibis-project/ibis/blob/9.2.0/ibis/expr/types/temporal.py - -from __future__ import annotations - -from typing import TYPE_CHECKING, Any, Literal - -import bigframes_vendored.ibis.expr.datashape as ds -import bigframes_vendored.ibis.expr.datatypes as dt -import bigframes_vendored.ibis.expr.operations as ops -from bigframes_vendored import ibis -from bigframes_vendored.ibis import util -from bigframes_vendored.ibis.common.annotations import annotated -from bigframes_vendored.ibis.common.temporal import IntervalUnit -from bigframes_vendored.ibis.expr.types.core import _binop -from bigframes_vendored.ibis.expr.types.generic import Column, Scalar, Value -from public import public - -if TYPE_CHECKING: - import datetime - - import bigframes_vendored.ibis.expr.types as ir - import pandas as pd - - -class _DateComponentMixin: - """Temporal expressions that have a date component.""" - - def epoch_seconds(self) -> ir.IntegerValue: - """Extract UNIX epoch in seconds.""" - return ops.ExtractEpochSeconds(self).to_expr() - - def year(self) -> ir.IntegerValue: - """Extract the year component.""" - return ops.ExtractYear(self).to_expr() - - def iso_year(self) -> ir.IntegerValue: - """Extract the ISO year component.""" - return ops.ExtractIsoYear(self).to_expr() - - def month(self) -> ir.IntegerValue: - """Extract the month component.""" - return ops.ExtractMonth(self).to_expr() - - def day(self) -> ir.IntegerValue: - """Extract the day component.""" - return ops.ExtractDay(self).to_expr() - - @property - def day_of_week(self) -> DayOfWeek: - """A namespace of methods for extracting day of week information. - - Returns - ------- - DayOfWeek - An namespace expression containing methods to use to extract - information. - """ - return DayOfWeek(self) - - def day_of_year(self) -> ir.IntegerValue: - """Extract the day of the year component.""" - return ops.ExtractDayOfYear(self).to_expr() - - def quarter(self) -> ir.IntegerValue: - """Extract the quarter component.""" - return ops.ExtractQuarter(self).to_expr() - - def week_of_year(self) -> ir.IntegerValue: - """Extract the week of the year component.""" - return ops.ExtractWeekOfYear(self).to_expr() - - -class _TimeComponentMixin: - """Temporal expressions that have a time component.""" - - def time(self) -> TimeValue: - """Return the time component of the expression. - - Returns - ------- - TimeValue - The time component of `self` - """ - return ops.Time(self).to_expr() - - def hour(self) -> ir.IntegerValue: - """Extract the hour component.""" - return ops.ExtractHour(self).to_expr() - - def minute(self) -> ir.IntegerValue: - """Extract the minute component.""" - return ops.ExtractMinute(self).to_expr() - - def second(self) -> ir.IntegerValue: - """Extract the second component.""" - return ops.ExtractSecond(self).to_expr() - - def microsecond(self) -> ir.IntegerValue: - """Extract the microsecond component.""" - return ops.ExtractMicrosecond(self).to_expr() - - def millisecond(self) -> ir.IntegerValue: - """Extract the millisecond component.""" - return ops.ExtractMillisecond(self).to_expr() - - def between( - self, - lower: str | datetime.time | TimeValue, - upper: str | datetime.time | TimeValue, - timezone: str | None = None, - ) -> ir.BooleanValue: - """Check if the expr falls between `lower` and `upper`, inclusive. - - Adjusts according to `timezone` if provided. - - Parameters - ---------- - lower - Lower bound - upper - Upper bound - timezone - Time zone - - Returns - ------- - BooleanValue - Whether `self` is between `lower` and `upper`, adjusting `timezone` - as needed. - """ - op = self.op() - if isinstance(op, ops.Time): - # Here we pull out the first argument to the underlying Time - # operation which is by definition (in _timestamp_value_methods) a - # TimestampValue. We do this so that we can potentially specialize - # the "between time" operation for - # timestamp_value_expr.time().between(). A similar mechanism is - # triggered when creating expressions like - # t.column.distinct().count(), which is turned into - # t.column.nunique(). - arg = op.arg.to_expr() - if timezone is not None: - arg = arg.cast(dt.Timestamp(timezone=timezone)) - op_cls = ops.BetweenTime - else: - arg = self - op_cls = ops.Between - - return op_cls(arg, lower, upper).to_expr() - - -@public -class TimeValue(_TimeComponentMixin, Value): - def strftime(self, format_str: str) -> ir.StringValue: - """Format a time according to `format_str`. - - Format string may depend on the backend, but we try to conform to ANSI - `strftime`. - - Parameters - ---------- - format_str - `strftime` format string - - Returns - ------- - StringValue - Formatted version of `arg` - """ - return ops.Strftime(self, format_str).to_expr() - - def truncate(self, unit: Literal["h", "m", "s", "ms", "us", "ns"]) -> TimeValue: - """Truncate the expression to a time expression in units of `unit`. - - Commonly used for time series resampling. - - Parameters - ---------- - unit - The unit to truncate to - - Returns - ------- - TimeValue - `self` truncated to `unit` - """ - return ops.TimeTruncate(self, unit).to_expr() - - def __add__( - self, - other: datetime.timedelta | pd.Timedelta | IntervalValue, - ) -> TimeValue: - """Add an interval to a time expression.""" - return _binop(ops.TimeAdd, self, other) - - add = radd = __radd__ = __add__ - """Add an interval to a time expression. - - Parameters - ---------- - other : datetime.timedelta | pd.Timedelta | IntervalValue - Interval to add to time expression - - Returns - ------- - Value : TimeValue - """ - - @annotated - def __sub__(self, other: ops.Value[dt.Interval | dt.Time, ds.Any]): - """Subtract a time or an interval from a time expression.""" - - if other.dtype.is_time(): - op = ops.TimeDiff - else: - op = ops.TimeSub # let the operation validate - - return _binop(op, self, other) - - sub = __sub__ - """Subtract a time or an interval from a time expression. - - Parameters - ---------- - other : TimeValue | IntervalValue - Interval to subtract from time expression - - Returns - ------- - Value : IntervalValue | TimeValue - """ - - @annotated - def __rsub__(self, other: ops.Value[dt.Interval | dt.Time, ds.Any]): - """Subtract a time or an interval from a time expression.""" - - if other.dtype.is_time(): - op = ops.TimeDiff - else: - op = ops.TimeSub # let the operation validate - - return _binop(op, other, self) - - rsub = __rsub__ - - def delta( - self, - other: datetime.time | Value[dt.Time], - part: Literal[ - "hour", "minute", "second", "millisecond", "microsecond", "nanosecond" - ] - | Value[dt.String], - ) -> ir.IntegerValue: - """Compute the number of `part`s between two times. - - ::: {.callout-note} - ## The order of operands matches standard subtraction - - The second argument is subtracted from the first. - ::: - - Parameters - ---------- - other - A time expression - part - The unit of time to compute the difference in - - Returns - ------- - IntegerValue - The number of `part`s between `self` and `other` - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> start = ibis.time("01:58:00") - >>> end = ibis.time("23:59:59") - >>> end.delta(start, "hour") - ┌──────────────┐ - │ np.int64(22) │ - └──────────────┘ - >>> data = '''tpep_pickup_datetime,tpep_dropoff_datetime - ... 2016-02-01T00:23:56,2016-02-01T00:42:28 - ... 2016-02-01T00:12:14,2016-02-01T00:21:41 - ... 2016-02-01T00:43:24,2016-02-01T00:46:14 - ... 2016-02-01T00:55:11,2016-02-01T01:24:34 - ... 2016-02-01T00:11:13,2016-02-01T00:16:59''' - >>> with open("/tmp/triptimes.csv", "w") as f: - ... nbytes = f.write(data) # nbytes is unused - >>> taxi = ibis.read_csv("/tmp/triptimes.csv") - >>> ride_duration = ( - ... taxi.tpep_dropoff_datetime.time() - ... .delta(taxi.tpep_pickup_datetime.time(), "minute") - ... .name("ride_minutes") - ... ) - >>> ride_duration - ┏━━━━━━━━━━━━━━┓ - ┃ ride_minutes ┃ - ┡━━━━━━━━━━━━━━┩ - │ int64 │ - ├──────────────┤ - │ 19 │ - │ 9 │ - │ 3 │ - │ 29 │ - │ 5 │ - └──────────────┘ - """ - return ops.TimeDelta(left=self, right=other, part=part).to_expr() - - -@public -class TimeScalar(Scalar, TimeValue): - pass - - -@public -class TimeColumn(Column, TimeValue): - pass - - -@public -class DateValue(Value, _DateComponentMixin): - def strftime(self, format_str: str) -> ir.StringValue: - """Format a date according to `format_str`. - - Format string may depend on the backend, but we try to conform to ANSI - `strftime`. - - Parameters - ---------- - format_str - `strftime` format string - - Returns - ------- - StringValue - Formatted version of `arg` - """ - return ops.Strftime(self, format_str).to_expr() - - def truncate(self, unit: Literal["Y", "Q", "M", "W", "D"]) -> DateValue: - """Truncate date expression to units of `unit`. - - Parameters - ---------- - unit - Unit to truncate `arg` to - - Returns - ------- - DateValue - Truncated date value expression - """ - return ops.DateTruncate(self, unit).to_expr() - - def __add__( - self, - other: datetime.timedelta | pd.Timedelta | IntervalValue, - ) -> DateValue: - """Add an interval to a date.""" - return _binop(ops.DateAdd, self, other) - - add = radd = __radd__ = __add__ - """Add an interval to a date. - - Parameters - ---------- - other : datetime.timedelta | pd.Timedelta | IntervalValue - Interval to add to DateValue - - Returns - ------- - Value : DateValue - """ - - @annotated - def __sub__(self, other: ops.Value[dt.Date | dt.Interval, ds.Any]): - """Subtract a date or an interval from a date.""" - - if other.dtype.is_date(): - op = ops.DateDiff - else: - op = ops.DateSub # let the operation validate - - return _binop(op, self, other) - - sub = __sub__ - """Subtract a date or an interval from a date. - - Parameters - ---------- - other : datetime.date | DateValue | datetime.timedelta | pd.Timedelta | IntervalValue - Interval to subtract from DateValue - - Returns - ------- - Value : DateValue - """ - - @annotated - def __rsub__(self, other: ops.Value[dt.Date | dt.Interval, ds.Any]): - """Subtract a date or an interval from a date.""" - - if other.dtype.is_date(): - op = ops.DateDiff - else: - op = ops.DateSub # let the operation validate - - return _binop(op, other, self) - - rsub = __rsub__ - - def delta( - self, - other: datetime.date | Value[dt.Date], - part: Literal["year", "quarter", "month", "week", "day"] | Value[dt.String], - ) -> ir.IntegerValue: - """Compute the number of `part`s between two dates. - - ::: {.callout-note} - ## The order of operands matches standard subtraction - - The second argument is subtracted from the first. - ::: - - Parameters - ---------- - other - A date expression - part - The unit of time to compute the difference in - - Returns - ------- - IntegerValue - The number of `part`s between `self` and `other` - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> start = ibis.date("1992-09-30") - >>> end = ibis.date("1992-10-01") - >>> end.delta(start, "day") - ┌─────────────┐ - │ np.int64(1) │ - └─────────────┘ - >>> prez = ibis.examples.presidential.fetch() - >>> prez.mutate( - ... years_in_office=prez.end.delta(prez.start, "year"), - ... hours_in_office=prez.end.delta(prez.start, "hour"), - ... ).drop("party") - ┏━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ - ┃ name ┃ start ┃ end ┃ years_in_office ┃ hours_in_office ┃ - ┡━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ - │ string │ date │ date │ int64 │ int64 │ - ├────────────┼────────────┼────────────┼─────────────────┼─────────────────┤ - │ Eisenhower │ 1953-01-20 │ 1961-01-20 │ 8 │ 70128 │ - │ Kennedy │ 1961-01-20 │ 1963-11-22 │ 2 │ 24864 │ - │ Johnson │ 1963-11-22 │ 1969-01-20 │ 6 │ 45264 │ - │ Nixon │ 1969-01-20 │ 1974-08-09 │ 5 │ 48648 │ - │ Ford │ 1974-08-09 │ 1977-01-20 │ 3 │ 21480 │ - │ Carter │ 1977-01-20 │ 1981-01-20 │ 4 │ 35064 │ - │ Reagan │ 1981-01-20 │ 1989-01-20 │ 8 │ 70128 │ - │ Bush │ 1989-01-20 │ 1993-01-20 │ 4 │ 35064 │ - │ Clinton │ 1993-01-20 │ 2001-01-20 │ 8 │ 70128 │ - │ Bush │ 2001-01-20 │ 2009-01-20 │ 8 │ 70128 │ - │ … │ … │ … │ … │ … │ - └────────────┴────────────┴────────────┴─────────────────┴─────────────────┘ - """ - return ops.DateDelta(left=self, right=other, part=part).to_expr() - - -@public -class DateScalar(Scalar, DateValue): - pass - - -@public -class DateColumn(Column, DateValue): - pass - - -@public -class TimestampValue(_DateComponentMixin, _TimeComponentMixin, Value): - def strftime(self, format_str: str) -> ir.StringValue: - """Format a timestamp according to `format_str`. - - Format string may depend on the backend, but we try to conform to ANSI - `strftime`. - - Parameters - ---------- - format_str - `strftime` format string - - Returns - ------- - StringValue - Formatted version of `arg` - """ - return ops.Strftime(self, format_str).to_expr() - - def truncate( - self, - unit: Literal["Y", "Q", "M", "W", "D", "h", "m", "s", "ms", "us", "ns"], - ) -> TimestampValue: - """Truncate timestamp expression to units of `unit`. - - Parameters - ---------- - unit - Unit to truncate to - - Returns - ------- - TimestampValue - Truncated timestamp expression - """ - return ops.TimestampTruncate(self, unit).to_expr() - - @util.experimental - def bucket( - self, - interval: Any = None, - *, - years: int | None = None, - quarters: int | None = None, - months: int | None = None, - weeks: int | None = None, - days: int | None = None, - hours: int | None = None, - minutes: int | None = None, - seconds: int | None = None, - milliseconds: int | None = None, - microseconds: int | None = None, - nanoseconds: int | None = None, - offset: Any = None, - ) -> TimestampValue: - """Truncate the timestamp to buckets of a specified interval. - - This is similar to `truncate`, but supports truncating to arbitrary - intervals rather than a single unit. Buckets are computed as fixed - intervals starting from the UNIX epoch. This origin may be offset by - specifying `offset`. - - Parameters - ---------- - interval - The bucket width as an interval. Alternatively may be specified - via component keyword arguments. - years - Number of years - quarters - Number of quarters - months - Number of months - weeks - Number of weeks - days - Number of days - hours - Number of hours - minutes - Number of minutes - seconds - Number of seconds - milliseconds - Number of milliseconds - microseconds - Number of microseconds - nanoseconds - Number of nanoseconds - offset - An interval to use to offset the start of the bucket. - - Returns - ------- - TimestampValue - The start of the bucket as a timestamp. - - Examples - -------- - >>> import ibis - >>> from ibis import _ - >>> ibis.options.interactive = True - >>> t = ibis.memtable( - ... [ - ... ("2020-04-15 08:04:00", 1), - ... ("2020-04-15 08:06:00", 2), - ... ("2020-04-15 08:09:00", 3), - ... ("2020-04-15 08:11:00", 4), - ... ], - ... columns=["ts", "val"], - ... ).cast({"ts": "timestamp"}) - - Bucket the data into 5 minute wide buckets: - - >>> t.ts.bucket(minutes=5) - ┏━━━━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ TimestampBucket(ts, 5m) ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━━━━┩ - │ timestamp │ - ├─────────────────────────┤ - │ 2020-04-15 08:00:00 │ - │ 2020-04-15 08:05:00 │ - │ 2020-04-15 08:05:00 │ - │ 2020-04-15 08:10:00 │ - └─────────────────────────┘ - - Bucket the data into 5 minute wide buckets, offset by 2 minutes: - - >>> t.ts.bucket(minutes=5, offset=ibis.interval(minutes=2)) - ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ - ┃ TimestampBucket(ts, 5m, 2m) ┃ - ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩ - │ timestamp │ - ├─────────────────────────────┤ - │ 2020-04-15 08:02:00 │ - │ 2020-04-15 08:02:00 │ - │ 2020-04-15 08:07:00 │ - │ 2020-04-15 08:07:00 │ - └─────────────────────────────┘ - - One common use of timestamp bucketing is computing statistics per - bucket. Here we compute the mean of `val` across 5 minute intervals: - - >>> mean_by_bucket = ( - ... t.group_by(t.ts.bucket(minutes=5).name("bucket")) - ... .agg(mean=_.val.mean()) - ... .order_by("bucket") - ... ) - >>> mean_by_bucket - ┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━┓ - ┃ bucket ┃ mean ┃ - ┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━┩ - │ timestamp │ float64 │ - ├─────────────────────┼─────────┤ - │ 2020-04-15 08:00:00 │ 1.0 │ - │ 2020-04-15 08:05:00 │ 2.5 │ - │ 2020-04-15 08:10:00 │ 4.0 │ - └─────────────────────┴─────────┘ - """ - - components = { - "years": years, - "quarters": quarters, - "months": months, - "weeks": weeks, - "days": days, - "hours": hours, - "minutes": minutes, - "seconds": seconds, - "milliseconds": milliseconds, - "microseconds": microseconds, - "nanoseconds": nanoseconds, - } - has_components = any(v is not None for v in components.values()) - if (interval is not None) == has_components: - raise ValueError( - "Must specify either interval value or components, but not both" - ) - if has_components: - interval = ibis.interval(**components) - return ops.TimestampBucket(self, interval, offset).to_expr() - - def date(self) -> DateValue: - """Return the date component of the expression. - - Returns - ------- - DateValue - The date component of `self` - """ - return ops.Date(self).to_expr() - - def __add__( - self, - other: datetime.timedelta | pd.Timedelta | IntervalValue, - ) -> TimestampValue: - """Add an interval to a timestamp.""" - return _binop(ops.TimestampAdd, self, other) - - add = radd = __radd__ = __add__ - """Add an interval to a timestamp. - - Parameters - ---------- - other : datetime.timedelta | pd.Timedelta | IntervalValue - Interval to subtract from timestamp - - Returns - ------- - Value : TimestampValue - """ - - @annotated - def __sub__(self, other: ops.Value[dt.Timestamp | dt.Interval, ds.Any]): - """Subtract a timestamp or an interval from a timestamp.""" - - if other.dtype.is_timestamp(): - op = ops.TimestampDiff - else: - op = ops.TimestampSub # let the operation validate - - return _binop(op, self, other) - - sub = __sub__ - """Subtract a timestamp or an interval from a timestamp. - - Parameters - ---------- - other : datetime.datetime | pd.Timestamp | TimestampValue | datetime.timedelta | pd.Timedelta | IntervalValue - Timestamp or interval to subtract from timestamp - - Returns - ------- - Value : IntervalValue | TimestampValue - """ - - @annotated - def __rsub__(self, other: ops.Value[dt.Timestamp | dt.Interval, ds.Any]): - """Subtract a timestamp or an interval from a timestamp.""" - - if other.dtype.is_timestamp(): - op = ops.TimestampDiff - else: - op = ops.TimestampSub # let the operation validate - - return _binop(op, other, self) - - rsub = __rsub__ - - def delta( - self, - other: datetime.datetime | Value[dt.Timestamp], - part: Literal[ - "year", - "quarter", - "month", - "week", - "day", - "hour", - "minute", - "second", - "millisecond", - "microsecond", - "nanosecond", - ] - | Value[dt.String], - ) -> ir.IntegerValue: - """Compute the number of `part`s between two timestamps. - - ::: {.callout-note} - ## The order of operands matches standard subtraction - - The second argument is subtracted from the first. - ::: - - Parameters - ---------- - other - A timestamp expression - part - The unit of time to compute the difference in - - Returns - ------- - IntegerValue - The number of `part`s between `self` and `other` - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> start = ibis.time("01:58:00") - >>> end = ibis.time("23:59:59") - >>> end.delta(start, "hour") - ┌──────────────┐ - │ np.int64(22) │ - └──────────────┘ - >>> data = '''tpep_pickup_datetime,tpep_dropoff_datetime - ... 2016-02-01T00:23:56,2016-02-01T00:42:28 - ... 2016-02-01T00:12:14,2016-02-01T00:21:41 - ... 2016-02-01T00:43:24,2016-02-01T00:46:14 - ... 2016-02-01T00:55:11,2016-02-01T01:24:34 - ... 2016-02-01T00:11:13,2016-02-01T00:16:59''' - >>> with open("/tmp/triptimes.csv", "w") as f: - ... nbytes = f.write(data) # nbytes is unused - >>> taxi = ibis.read_csv("/tmp/triptimes.csv") - >>> ride_duration = taxi.tpep_dropoff_datetime.delta( - ... taxi.tpep_pickup_datetime, "minute" - ... ).name("ride_minutes") - >>> ride_duration - ┏━━━━━━━━━━━━━━┓ - ┃ ride_minutes ┃ - ┡━━━━━━━━━━━━━━┩ - │ int64 │ - ├──────────────┤ - │ 19 │ - │ 9 │ - │ 3 │ - │ 29 │ - │ 5 │ - └──────────────┘ - """ - return ops.TimestampDelta(left=self, right=other, part=part).to_expr() - - -@public -class TimestampScalar(Scalar, TimestampValue): - pass - - -@public -class TimestampColumn(Column, TimestampValue): - pass - - -@public -class IntervalValue(Value): - def to_unit(self, target_unit: str) -> IntervalValue: - """Convert this interval to units of `target_unit`.""" - # TODO(kszucs): should use a separate operation for unit conversion - # which we can rewrite/simplify to integer multiplication/division - op = self.op() - current_unit = op.dtype.unit - target_unit = IntervalUnit.from_string(target_unit) - - if current_unit == target_unit: - return self - elif isinstance(op, ops.Literal): - value = util.convert_unit(op.value, current_unit.short, target_unit.short) - return ops.Literal(value, dtype=dt.Interval(target_unit)).to_expr() - else: - value = util.convert_unit( - self.cast(dt.int64), current_unit.short, target_unit.short - ) - return value.to_interval(target_unit) - - @property - def years(self) -> ir.IntegerValue: - """The number of years (IntegerValue).""" - return self.to_unit("Y") - - @property - def quarters(self) -> ir.IntegerValue: - """The number of quarters (IntegerValue).""" - return self.to_unit("Q") - - @property - def months(self) -> ir.IntegerValue: - """The number of months (IntegerValue).""" - return self.to_unit("M") - - @property - def weeks(self) -> ir.IntegerValue: - """The number of weeks (IntegerValue).""" - return self.to_unit("W") - - @property - def days(self) -> ir.IntegerValue: - """The number of days (IntegerValue).""" - return self.to_unit("D") - - @property - def hours(self) -> ir.IntegerValue: - """The number of hours (IntegerValue).""" - return self.to_unit("h") - - @property - def minutes(self) -> ir.IntegerValue: - """The number of minutes (IntegerValue).""" - return self.to_unit("m") - - @property - def seconds(self) -> ir.IntegerValue: - """The number of seconds (IntegerValue).""" - return self.to_unit("s") - - @property - def milliseconds(self) -> ir.IntegerValue: - """The number of milliseconds (IntegerValue).""" - return self.to_unit("ms") - - @property - def microseconds(self) -> ir.IntegerValue: - """The number of microseconds (IntegerValue).""" - return self.to_unit("us") - - @property - def nanoseconds(self) -> ir.IntegerValue: - """The number of nanoseconds (IntegerValue).""" - return self.to_unit("ns") - - def __add__( - self, - other: datetime.timedelta | pd.Timedelta | IntervalValue, - ) -> IntervalValue: - """Add this interval to `other`.""" - return _binop(ops.IntervalAdd, self, other) - - add = radd = __radd__ = __add__ - - def __sub__( - self, - other: datetime.timedelta | pd.Timedelta | IntervalValue, - ) -> IntervalValue: - """Subtract `other` from this interval.""" - return _binop(ops.IntervalSubtract, self, other) - - sub = __sub__ - - def __rsub__( - self, - other: datetime.timedelta | pd.Timedelta | IntervalValue, - ) -> IntervalValue: - """Subtract `other` from this interval.""" - return _binop(ops.IntervalSubtract, other, self) - - rsub = __rsub__ - - def __mul__( - self, - other: int | ir.IntegerValue, - ) -> IntervalValue: - """Multiply this interval by `other`.""" - return _binop(ops.IntervalMultiply, self, other) - - mul = rmul = __rmul__ = __mul__ - - def __floordiv__( - self, - other: ir.IntegerValue, - ) -> IntervalValue: - """Floor-divide this interval by `other`.""" - return _binop(ops.IntervalFloorDivide, self, other) - - floordiv = __floordiv__ - - def negate(self) -> ir.IntervalValue: - """Negate an interval expression. - - Returns - ------- - IntervalValue - A negated interval value expression - """ - return ops.Negate(self).to_expr() - - __neg__ = negate - - -@public -class IntervalScalar(Scalar, IntervalValue): - pass - - -@public -class IntervalColumn(Column, IntervalValue): - pass - - -@public -class DayOfWeek: - """A namespace of methods for extracting day of week information.""" - - def __init__(self, expr): - self._expr = expr - - def index(self): - """Get the index of the day of the week. - - ::: {.callout-note} - ## Ibis follows the `pandas` convention for day numbering: Monday = 0 and Sunday = 6. - ::: - - Returns - ------- - IntegerValue - The index of the day of the week. - """ - return ops.DayOfWeekIndex(self._expr).to_expr() - - def full_name(self): - """Get the name of the day of the week. - - Returns - ------- - StringValue - The name of the day of the week - """ - return ops.DayOfWeekName(self._expr).to_expr() diff --git a/third_party/bigframes_vendored/ibis/expr/types/temporal_windows.py b/third_party/bigframes_vendored/ibis/expr/types/temporal_windows.py deleted file mode 100644 index 13e917c744c..00000000000 --- a/third_party/bigframes_vendored/ibis/expr/types/temporal_windows.py +++ /dev/null @@ -1,94 +0,0 @@ -# Contains code from https://github.com/ibis-project/ibis/blob/9.2.0/ibis/expr/types/temporal_windows.py - -from __future__ import annotations - -from typing import TYPE_CHECKING, Literal - -import bigframes_vendored.ibis.common.exceptions as com -import bigframes_vendored.ibis.expr.operations as ops -import bigframes_vendored.ibis.expr.types as ir -from bigframes_vendored.ibis.common.collections import FrozenOrderedDict # noqa: TCH001 -from bigframes_vendored.ibis.common.grounds import Concrete -from bigframes_vendored.ibis.expr.operations.relations import Unaliased # noqa: TCH001 -from bigframes_vendored.ibis.expr.types.relations import unwrap_aliases -from public import public - -if TYPE_CHECKING: - from collections.abc import Sequence - - -@public -class WindowedTable(Concrete): - """An intermediate table expression to hold windowing information.""" - - parent: ir.Table - time_col: ops.Column - window_type: Literal["tumble", "hop"] | None = None - window_size: ir.IntervalScalar | None = None - window_slide: ir.IntervalScalar | None = None - window_offset: ir.IntervalScalar | None = None - groups: FrozenOrderedDict[str, Unaliased[ops.Column]] | None = None - metrics: FrozenOrderedDict[str, Unaliased[ops.Column]] | None = None - - def __init__(self, time_col: ops.Column, **kwargs): - if time_col is None: - raise com.IbisInputError( - "Window aggregations require `time_col` as an argument" - ) - super().__init__(time_col=time_col, **kwargs) - - def tumble( - self, - size: ir.IntervalScalar, - offset: ir.IntervalScalar | None = None, - ) -> WindowedTable: - return self.copy(window_type="tumble", window_size=size, window_offset=offset) - - def hop( - self, - size: ir.IntervalScalar, - slide: ir.IntervalScalar, - offset: ir.IntervalScalar | None = None, - ) -> WindowedTable: - return self.copy( - window_type="hop", - window_size=size, - window_slide=slide, - window_offset=offset, - ) - - def aggregate( - self, - metrics: Sequence[ir.Scalar] | None = (), - by: str | ir.Value | Sequence[str] | Sequence[ir.Value] | None = (), - **kwargs: ir.Value, - ) -> ir.Table: - by = self.parent.bind(by) - metrics = self.parent.bind(metrics, **kwargs) - - by = unwrap_aliases(by) - metrics = unwrap_aliases(metrics) - - groups = dict(self.groups) if self.groups is not None else {} - groups.update(by) - - return ops.WindowAggregate( - self.parent, - self.window_type, - self.time_col, - groups=groups, - metrics=metrics, - window_size=self.window_size, - window_slide=self.window_slide, - window_offset=self.window_offset, - ).to_expr() - - agg = aggregate - - def group_by( - self, *by: str | ir.Value | Sequence[str] | Sequence[ir.Value] - ) -> WindowedTable: - by = tuple(v for v in by if v is not None) - groups = self.parent.bind(*by) - groups = unwrap_aliases(groups) - return self.copy(groups=groups) diff --git a/third_party/bigframes_vendored/ibis/expr/types/typing.py b/third_party/bigframes_vendored/ibis/expr/types/typing.py deleted file mode 100644 index 68c59894d05..00000000000 --- a/third_party/bigframes_vendored/ibis/expr/types/typing.py +++ /dev/null @@ -1,11 +0,0 @@ -# Contains code from https://github.com/ibis-project/ibis/blob/9.2.0/ibis/expr/types/typing.py - -from __future__ import annotations - -from collections.abc import Hashable -from typing import TypeVar - -__all__ = ["K", "V"] - -K = TypeVar("K", bound=Hashable) -V = TypeVar("V") diff --git a/third_party/bigframes_vendored/ibis/expr/types/uuid.py b/third_party/bigframes_vendored/ibis/expr/types/uuid.py deleted file mode 100644 index 9e0f07c37c4..00000000000 --- a/third_party/bigframes_vendored/ibis/expr/types/uuid.py +++ /dev/null @@ -1,21 +0,0 @@ -# Contains code from https://github.com/ibis-project/ibis/blob/9.2.0/ibis/expr/types/uuid.py - -from __future__ import annotations - -from bigframes_vendored.ibis.expr.types.generic import Column, Scalar, Value -from public import public - - -@public -class UUIDValue(Value): - pass - - -@public -class UUIDScalar(Scalar, UUIDValue): - pass - - -@public -class UUIDColumn(Column, UUIDValue): - pass diff --git a/third_party/bigframes_vendored/ibis/expr/visualize.py b/third_party/bigframes_vendored/ibis/expr/visualize.py deleted file mode 100644 index 390c9c98282..00000000000 --- a/third_party/bigframes_vendored/ibis/expr/visualize.py +++ /dev/null @@ -1,255 +0,0 @@ -# Contains code from https://github.com/ibis-project/ibis/blob/9.2.0/ibis/expr/visualize.py - -from __future__ import annotations - -import contextlib -import sys -import tempfile -from collections.abc import Callable -from html import escape -from typing import Optional - -import bigframes_vendored.ibis -import bigframes_vendored.ibis.common.exceptions as com -import bigframes_vendored.ibis.expr.operations as ops -import graphviz as gv -from bigframes_vendored.ibis.common.graph import Graph - - -def get_type(node): - with contextlib.suppress(AttributeError, NotImplementedError): - return escape(str(node.dtype)) - - try: - schema = node.schema - except (AttributeError, NotImplementedError): - # TODO(kszucs): this branch should be removed - try: - # As a last resort try get the name of the output_type class - return node.output_type.__name__ - except (AttributeError, NotImplementedError): - return "\u2205" # empty set character - except com.IbisError: - assert isinstance(node, ops.Join) - left_table_name = getattr(node.left, "name", None) or ops.genname() - left_schema = node.left.schema - right_table_name = getattr(node.right, "name", None) or ops.genname() - right_schema = node.right.schema - pairs = [ - (f"{left_table_name}.{left_column}", type) - for left_column, type in left_schema.items() - ] + [ - (f"{right_table_name}.{right_column}", type) - for right_column, type in right_schema.items() - ] - schema = bigframes_vendored.ibis.schema(pairs) - else: - # Simple relations have the same schema as their parent so avoid - # re-rendering the same schema fields for these relations - if isinstance(node, ops.relations.Simple): - return '
:: …' - - return '
' + '
'.join( - f"{escape(name)}: {escape(str(type))}" - for name, type in zip(schema.names, schema.types) - ) - - -def get_label(node): - typename = get_type(node) # Already an escaped string - name = type(node).__name__ - nodename = ( - node.name - if isinstance( - node, - ( - ops.Literal, - ops.Field, - ops.Alias, - ops.PhysicalTable, - ), - ) - else None - ) - if nodename is not None: - if isinstance(node, ops.Relation): - label_fmt = "<{}: {}{}>" - else: - label_fmt = '<{}: {}
:: {}>' - # typename is already escaped - label = label_fmt.format(escape(nodename), escape(name), typename) - else: - if isinstance(node, ops.Relation): - label_fmt = "<{}{}>" - else: - label_fmt = '<{}
:: {}>' - label = label_fmt.format(escape(name), typename) - return label - - -DEFAULT_NODE_ATTRS = {"shape": "box", "fontname": "Deja Vu Sans Mono"} -DEFAULT_EDGE_ATTRS = {"fontname": "Deja Vu Sans Mono"} - -NodeAttributeGetter = Callable[[ops.Node], Optional[dict[str, str]]] -EdgeAttributeGetter = Callable[[ops.Node, ops.Node], Optional[dict[str, str]]] - - -def to_graph( - expr, - node_attr=None, - node_attr_getter: NodeAttributeGetter | None = None, - edge_attr=None, - edge_attr_getter: EdgeAttributeGetter | None = None, - label_edges: bool = False, -): - graph = Graph.from_bfs(expr.op(), filter=ops.Node) - - g = gv.Digraph( - node_attr=DEFAULT_NODE_ATTRS | (node_attr or {}), - edge_attr=DEFAULT_EDGE_ATTRS | (edge_attr or {}), - ) - - g.attr(rankdir="BT") - - seen = set() - edges = set() - - for v, us in graph.items(): - vhash = str(hash(v)) - if v not in seen: - g.node( - vhash, - label=get_label(v), - _attributes=node_attr_getter(v) if node_attr_getter else {}, - ) - seen.add(v) - - for u in us: - uhash = str(hash(u)) - if u not in seen: - g.node( - uhash, - label=get_label(u), - _attributes=node_attr_getter(u) if node_attr_getter else {}, - ) - seen.add(u) - if (edge := (u, v)) not in edges: - if not label_edges: - label = None - else: - if isinstance(v, ops.Relation): - if (name := getattr(u, "name", None)) in v.fields: - name = f"fields[{name!r}]" - else: - name = None - else: - for name, arg in zip(v.argnames, v.args): - if isinstance(arg, tuple) and u in arg: - index = arg.index(u) - name = f"{name}[{index}]" - break - elif arg == u: - break - else: - name = None - - if name is not None: - label = f"<.{name}>" - else: - label = None - - g.edge( - uhash, - vhash, - label=label, - _attributes=edge_attr_getter(u, v) if edge_attr_getter else {}, - ) - edges.add(edge) - return g - - -def draw(graph, path=None, format="png", verbose: bool = False): - if verbose: - print(graph.source, file=sys.stderr) # noqa: T201 - - piped_source = graph.pipe(format=format) - - if path is None: - with tempfile.NamedTemporaryFile( - delete=False, suffix=f".{format}", mode="wb" - ) as f: - f.write(piped_source) - return f.name - else: - with open(path, mode="wb") as f: - f.write(piped_source) - return path - - -if __name__ == "__main__": - import json - from argparse import ArgumentParser - - from bigframes_vendored.ibis import _ - - p = ArgumentParser( - description="Render a GraphViz SVG of an example ibis expression." - ) - - p.add_argument( - "-v", - "--verbose", - action="count", - default=0, - help="Print GraphViz DOT code to stderr.", - ) - p.add_argument( - "-l", - "--label-edges", - action="store_true", - help="Show operation inputs as edge labels.", - ) - p.add_argument( - "-n", - "--node-attr", - type=lambda x: json.loads(x) if x else {}, - default="{}", - help='JSON string of node attributes. E.g., \'{"fontname": "Roboto Mono", "fontsize": "10"}\'', - ) - p.add_argument( - "-e", - "--edge-attr", - type=lambda x: json.loads(x) if x else {}, - default="{}", - help='JSON string of edge attributes. E.g., \'{"fontsize": "8"}\'', - ) - - args = p.parse_args() - - left = bigframes_vendored.ibis.table(dict(a="int64", b="string"), name="left") - right = bigframes_vendored.ibis.table( - dict(b="string", c="int64", d="string"), name="right" - ) - expr = ( - left.inner_join(right, "b") - .select(left.a, b=right.c, c=right.d) - .filter((_.a + _.b * 2 * _.b / _.b**3 > 4) & (_.b > 5)) - .group_by(_.c) - .having(_.a.mean() > 0.0) - .aggregate(a_mean=_.a.mean(), b_sum=_.b.sum()) - .order_by(_.a_mean) - .mutate( - arrays=bigframes_vendored.ibis.array([1, 2, 3]), - maps=bigframes_vendored.ibis.map({"a": 1, "b": 2}), - structs=bigframes_vendored.ibis.struct( - {"a": [1, 2, 3], "b": {"c": 1, "d": 2}} - ), - ) - ) - - expr.visualize( - verbose=args.verbose > 0, - label_edges=args.label_edges, - node_attr=args.node_attr, - edge_attr=args.edge_attr, - ) diff --git a/third_party/bigframes_vendored/ibis/formats/__init__.py b/third_party/bigframes_vendored/ibis/formats/__init__.py deleted file mode 100644 index 627299a0da1..00000000000 --- a/third_party/bigframes_vendored/ibis/formats/__init__.py +++ /dev/null @@ -1,266 +0,0 @@ -# Contains code from https://github.com/ibis-project/ibis/blob/9.2.0/ibis/formats/__init__.py - -from __future__ import annotations - -from abc import abstractmethod -from typing import TYPE_CHECKING, Generic, TypeVar - -from bigframes_vendored.ibis.util import PseudoHashable, indent - -if TYPE_CHECKING: - import pandas as pd - import polars as pl - import pyarrow as pa - from bigframes_vendored.ibis.expr.datatypes import DataType - from bigframes_vendored.ibis.expr.schema import Schema - -C = TypeVar("C") -T = TypeVar("T") -S = TypeVar("S") - - -class TypeMapper(Generic[T]): - # `T` is the format-specific type object, e.g. pyarrow.DataType - - @classmethod - def from_ibis(cls, dtype: DataType) -> T: - """Convert an Ibis DataType to a format-specific type object. - - Parameters - ---------- - dtype - The Ibis DataType to convert. - - Returns - ------- - Format-specific type object. - - """ - raise NotImplementedError - - @classmethod - def to_ibis(cls, typ: T, nullable: bool = True) -> DataType: - """Convert a format-specific type object to an Ibis DataType. - - Parameters - ---------- - typ - The format-specific type object to convert. - nullable - Whether the Ibis DataType should be nullable. - - Returns - ------- - Ibis DataType. - - """ - raise NotImplementedError - - @classmethod - def from_string(cls, text: str, nullable: bool = True) -> DataType: - """Convert a backend-specific string representation into an Ibis DataType. - - Parameters - ---------- - text - The backend-specific string representation to convert. - nullable - Whether the Ibis DataType should be nullable. - - Returns - ------- - Ibis DataType. - - """ - raise NotImplementedError - - @classmethod - def to_string(cls, dtype: DataType) -> str: - """Convert `dtype` into a backend-specific string representation. - - Parameters - ---------- - dtype - The Ibis DataType to convert. - - Returns - ------- - Backend-specific string representation. - - """ - raise NotImplementedError - - -class SchemaMapper(Generic[S]): - # `S` is the format-specific schema object, e.g. pyarrow.Schema - - @classmethod - def from_ibis(cls, schema: Schema) -> S: - """Convert an Ibis Schema to a format-specific schema object. - - Parameters - ---------- - schema - The Ibis Schema to convert. - - Returns - ------- - Format-specific schema object. - - """ - raise NotImplementedError - - @classmethod - def to_ibis(cls, obj: S) -> Schema: - """Convert a format-specific schema object to an Ibis Schema. - - Parameters - ---------- - obj - The format-specific schema object to convert. - - Returns - ------- - Ibis Schema. - - """ - raise NotImplementedError - - -class DataMapper(Generic[S, C, T]): - # `S` is the format-specific scalar object, e.g. pyarrow.Scalar - # `C` is the format-specific column object, e.g. pyarrow.Array - # `T` is the format-specific table object, e.g. pyarrow.Table - - @classmethod - def convert_scalar(cls, obj: S, dtype: DataType) -> S: - """Convert a format-specific scalar to the given ibis datatype. - - Parameters - ---------- - obj - The format-specific scalar value to convert. - dtype - The Ibis datatype to convert to. - - Returns - ------- - Format specific scalar corresponding to the given Ibis datatype. - - """ - raise NotImplementedError - - @classmethod - def convert_column(cls, obj: C, dtype: DataType) -> C: - """Convert a format-specific column to the given ibis datatype. - - Parameters - ---------- - obj - The format-specific column value to convert. - dtype - The Ibis datatype to convert to. - - Returns - ------- - Format specific column corresponding to the given Ibis datatype. - - """ - raise NotImplementedError - - @classmethod - def convert_table(cls, obj: T, schema: Schema) -> T: - """Convert a format-specific table to the given ibis schema. - - Parameters - ---------- - obj - The format-specific table-like object to convert. - schema - The Ibis schema to convert to. - - Returns - ------- - Format specific table-like object corresponding to the given Ibis schema. - - """ - raise NotImplementedError - - @classmethod - def infer_scalar(cls, obj: S) -> DataType: - """Infer the Ibis datatype of a format-specific scalar. - - Parameters - ---------- - obj - The format-specific scalar to infer the Ibis datatype of. - - Returns - ------- - Ibis datatype corresponding to the given format-specific scalar. - - """ - raise NotImplementedError - - @classmethod - def infer_column(cls, obj: C) -> DataType: - """Infer the Ibis datatype of a format-specific column. - - Parameters - ---------- - obj - The format-specific column to infer the Ibis datatype of. - - Returns - ------- - Ibis datatype corresponding to the given format-specific column. - - """ - raise NotImplementedError - - @classmethod - def infer_table(cls, obj: T) -> Schema: - """Infer the Ibis schema of a format-specific table. - - Parameters - ---------- - obj - The format-specific table to infer the Ibis schema of. - - Returns - ------- - Ibis schema corresponding to the given format-specific table. - - """ - raise NotImplementedError - - -class TableProxy(PseudoHashable[T]): - def __repr__(self) -> str: - data_repr = indent(repr(self.obj), spaces=2) - return f"{self.__class__.__name__}:\n{data_repr}" - - def __len__(self) -> int: - return len(self.obj) - - @abstractmethod - def to_frame(self) -> pd.DataFrame: # pragma: no cover - """Convert this input to a pandas DataFrame.""" - - @abstractmethod - def to_pyarrow(self, schema: Schema) -> pa.Table: # pragma: no cover - """Convert this input to a PyArrow Table.""" - - @abstractmethod - def to_polars(self, schema: Schema) -> pl.DataFrame: # pragma: no cover - """Convert this input to a Polars DataFrame.""" - - def to_pyarrow_bytes(self, schema: Schema) -> bytes: - import pyarrow as pa - import pyarrow_hotfix # noqa: F401 - - data = self.to_pyarrow(schema=schema) - out = pa.BufferOutputStream() - with pa.RecordBatchFileWriter(out, data.schema) as writer: - writer.write(data) - return out.getvalue() diff --git a/third_party/bigframes_vendored/ibis/formats/numpy.py b/third_party/bigframes_vendored/ibis/formats/numpy.py deleted file mode 100644 index 76cab2888b3..00000000000 --- a/third_party/bigframes_vendored/ibis/formats/numpy.py +++ /dev/null @@ -1,103 +0,0 @@ -# Contains code from https://github.com/ibis-project/ibis/blob/9.2.0/ibis/formats/numpy.py - -from __future__ import annotations - -import bigframes_vendored.ibis.expr.datatypes as dt -import bigframes_vendored.ibis.expr.schema as sch -import numpy as np -import toolz -from bigframes_vendored.ibis.formats import SchemaMapper, TypeMapper - -_from_numpy_types = toolz.keymap( - np.dtype, - { - np.bool_: dt.Boolean, - np.int8: dt.Int8, - np.int16: dt.Int16, - np.int32: dt.Int32, - np.int64: dt.Int64, - np.uint8: dt.UInt8, - np.uint16: dt.UInt16, - np.uint32: dt.UInt32, - np.uint64: dt.UInt64, - np.float16: dt.Float16, - np.float32: dt.Float32, - np.float64: dt.Float64, - }, -) - - -_to_numpy_types = {v: k for k, v in _from_numpy_types.items()} - - -class NumpyType(TypeMapper[np.dtype]): - @classmethod - def to_ibis(cls, typ: np.dtype, nullable: bool = True) -> dt.DataType: - if np.issubdtype(typ, np.datetime64): - # TODO(kszucs): the following code provedes proper timestamp roundtrips - # between ibis and numpy/pandas but breaks the test suite at several - # places, we should revisit this later - # unit, _ = np.datetime_data(typ) - # if unit in {'generic', 'Y', 'M', 'D', 'h', 'm'}: - # return dt.Timestamp(nullable=nullable) - # else: - # return dt.Timestamp.from_unit(unit, nullable=nullable) - return dt.Timestamp(nullable=nullable) - elif np.issubdtype(typ, np.timedelta64): - unit, _ = np.datetime_data(typ) - if unit == "generic": - unit = "s" - return dt.Interval(unit, nullable=nullable) - elif np.issubdtype(typ, np.str_): - return dt.String(nullable=nullable) - elif np.issubdtype(typ, np.bytes_): - return dt.Binary(nullable=nullable) - else: - try: - return _from_numpy_types[typ](nullable=nullable) - except KeyError: - raise TypeError(f"numpy dtype {typ!r} is not supported") - - @classmethod - def from_ibis(cls, dtype: dt.DataType) -> np.dtype: - if dtype.is_interval(): - return np.dtype(f"timedelta64[{dtype.unit.short}]") - elif dtype.is_timestamp(): - # TODO(kszucs): the following code provedes proper timestamp roundtrips - # between ibis and numpy/pandas but breaks the test suite at several - # places, we should revisit this later - # return np.dtype(f"datetime64[{dtype.unit.short}]") - return np.dtype("datetime64[ns]") - elif dtype.is_date(): - return np.dtype("datetime64[D]") - elif dtype.is_time(): - return np.dtype("timedelta64[ns]") - elif ( - dtype.is_null() - or dtype.is_decimal() - or dtype.is_struct() - or dtype.is_variadic() - or dtype.is_unknown() - or dtype.is_uuid() - or dtype.is_geospatial() - or dtype.is_inet() - or dtype.is_macaddr() - ): - return np.dtype("object") - else: - try: - return _to_numpy_types[type(dtype)] - except KeyError: - raise TypeError(f"ibis dtype {dtype!r} is not supported") - - -class NumpySchema(SchemaMapper): - @classmethod - def from_ibis(cls, schema): - numpy_types = map(NumpyType.from_ibis, schema.types) - return list(zip(schema.names, numpy_types)) - - @classmethod - def to_ibis(cls, schema): - ibis_types = {name: NumpyType.to_ibis(typ) for name, typ in schema} - return sch.Schema(ibis_types) diff --git a/third_party/bigframes_vendored/ibis/formats/pandas.py b/third_party/bigframes_vendored/ibis/formats/pandas.py deleted file mode 100644 index a24c170ac50..00000000000 --- a/third_party/bigframes_vendored/ibis/formats/pandas.py +++ /dev/null @@ -1,427 +0,0 @@ -# Contains code from https://github.com/ibis-project/ibis/blob/9.2.0/ibis/formats/pandas.py - -from __future__ import annotations - -import contextlib -import datetime -import warnings -from functools import partial -from importlib.util import find_spec as _find_spec -from typing import TYPE_CHECKING - -import bigframes_vendored.ibis.expr.datatypes as dt -import bigframes_vendored.ibis.expr.schema as sch -import numpy as np -import pandas as pd -import pandas.api.types as pdt -from bigframes_vendored.ibis import util -from bigframes_vendored.ibis.common.numeric import normalize_decimal -from bigframes_vendored.ibis.common.temporal import normalize_timezone -from bigframes_vendored.ibis.formats import DataMapper, SchemaMapper, TableProxy -from bigframes_vendored.ibis.formats.numpy import NumpyType -from bigframes_vendored.ibis.formats.pyarrow import ( - PyArrowData, - PyArrowSchema, - PyArrowType, -) - -if TYPE_CHECKING: - import polars as pl - import pyarrow as pa - -_has_arrow_dtype = hasattr(pd, "ArrowDtype") - -if not _has_arrow_dtype: - warnings.warn( - f"The `ArrowDtype` class is not available in pandas {pd.__version__}. " - "Install pandas >= 1.5.0 for interop with pandas and arrow dtype support" - ) - -geospatial_supported = _find_spec("geopandas") is not None - - -class PandasType(NumpyType): - @classmethod - def to_ibis(cls, typ, nullable=True): - if isinstance(typ, pdt.DatetimeTZDtype): - return dt.Timestamp(timezone=str(typ.tz), nullable=nullable) - elif pdt.is_datetime64_dtype(typ): - return dt.Timestamp(nullable=nullable) - elif isinstance(typ, pdt.CategoricalDtype): - if typ.categories is None or pdt.is_string_dtype(typ.categories): - return dt.String(nullable=nullable) - return cls.to_ibis(typ.categories.dtype, nullable=nullable) - elif pdt.is_extension_array_dtype(typ): - if _has_arrow_dtype and isinstance(typ, pd.ArrowDtype): - return PyArrowType.to_ibis(typ.pyarrow_dtype, nullable=nullable) - else: - name = typ.__class__.__name__.replace("Dtype", "") - klass = getattr(dt, name) - return klass(nullable=nullable) - else: - return super().to_ibis(typ, nullable=nullable) - - @classmethod - def from_ibis(cls, dtype): - if dtype.is_timestamp() and dtype.timezone: - return pdt.DatetimeTZDtype("ns", dtype.timezone) - elif dtype.is_date(): - return np.dtype("M8[s]") - elif dtype.is_interval(): - return np.dtype(f"timedelta64[{dtype.unit.short}]") - else: - return super().from_ibis(dtype) - - -class PandasSchema(SchemaMapper): - @classmethod - def to_ibis(cls, pandas_schema): - if isinstance(pandas_schema, pd.Series): - pandas_schema = pandas_schema.to_list() - - fields = {name: PandasType.to_ibis(t) for name, t in pandas_schema} - - return sch.Schema(fields) - - @classmethod - def from_ibis(cls, schema): - names = schema.names - types = [PandasType.from_ibis(t) for t in schema.types] - return list(zip(names, types)) - - -class PandasData(DataMapper): - @classmethod - def infer_scalar(cls, s): - return PyArrowData.infer_scalar(s) - - @classmethod - def infer_column(cls, s): - return PyArrowData.infer_column(s) - - @classmethod - def infer_table(cls, df): - pairs = [] - for column_name in df.dtypes.keys(): - if not isinstance(column_name, str): - raise TypeError( - "Column names must be strings to use the pandas backend" - ) - - pandas_column = df[column_name] - pandas_dtype = pandas_column.dtype - if pandas_dtype == np.object_: - ibis_dtype = cls.infer_column(pandas_column) - else: - ibis_dtype = PandasType.to_ibis(pandas_dtype) - - pairs.append((column_name, ibis_dtype)) - - return sch.Schema.from_tuples(pairs) - - concat = staticmethod(pd.concat) - - @classmethod - def convert_table(cls, df, schema): - if len(schema) != len(df.columns): - raise ValueError( - "schema column count does not match input data column count" - ) - - columns = [] - for (_, series), dtype in zip(df.items(), schema.types): - columns.append(cls.convert_column(series, dtype)) - df = cls.concat(columns, axis=1) - - # return data with the schema's columns which may be different than the - # input columns - df.columns = schema.names - - if geospatial_supported: - from geopandas import GeoDataFrame - from geopandas.array import GeometryDtype - - if ( - # pluck out the first geometry column if it exists - geom := next( - ( - name - for name, c in df.items() - if isinstance(c.dtype, GeometryDtype) - ), - None, - ) - ) is not None: - return GeoDataFrame(df, geometry=geom) - return df - - @classmethod - def convert_column(cls, obj, dtype): - pandas_type = PandasType.from_ibis(dtype) - - method_name = f"convert_{dtype.__class__.__name__}" - convert_method = getattr(cls, method_name, cls.convert_default) - - result = convert_method(obj, dtype, pandas_type) - assert not isinstance(result, np.ndarray), f"{convert_method} -> {type(result)}" - return result - - @classmethod - def convert_scalar(cls, obj, dtype): - df = PandasData.convert_table(obj, sch.Schema({obj.columns[0]: dtype})) - return df.iat[0, 0] - - @classmethod - def convert_GeoSpatial(cls, s, dtype, pandas_type): - import geopandas as gpd - - if isinstance(s.dtype, gpd.array.GeometryDtype): - return gpd.GeoSeries(s) - return gpd.GeoSeries.from_wkb(s) - - convert_Point = convert_LineString = convert_Polygon = convert_MultiLineString = ( - convert_MultiPoint - ) = convert_MultiPolygon = convert_GeoSpatial - - @classmethod - def convert_default(cls, s, dtype, pandas_type): - if s.dtype == pandas_type and dtype.is_primitive(): - return s - try: - return s.astype(pandas_type) - except Exception: # noqa: BLE001 - return s - - @classmethod - def convert_Boolean(cls, s, dtype, pandas_type): - if s.empty: - return s.astype(pandas_type) - elif pdt.is_object_dtype(s.dtype): - return s - elif s.dtype != pandas_type: - return s.map(bool, na_action="ignore") - else: - return s - - @classmethod - def convert_Timestamp(cls, s, dtype, pandas_type): - if isinstance(pandas_type, pd.DatetimeTZDtype) and isinstance( - s.dtype, pd.DatetimeTZDtype - ): - return s if s.dtype == pandas_type else s.dt.tz_convert(dtype.timezone) - elif pdt.is_datetime64_dtype(s.dtype): - return s.dt.tz_localize(dtype.timezone) - else: - try: - return s.astype(pandas_type) - except pd.errors.OutOfBoundsDatetime: # uncovered - try: - from dateutil.parser import parse as date_parse - - return s.map(date_parse, na_action="ignore") - except TypeError: - return s - except (ValueError, TypeError): - try: - return pd.to_datetime(s).dt.tz_convert(dtype.timezone) - except TypeError: - return pd.to_datetime(s).dt.tz_localize(dtype.timezone) - - @classmethod - def convert_Date(cls, s, dtype, pandas_type): - if isinstance(s.dtype, pd.DatetimeTZDtype): - s = s.dt.tz_convert("UTC").dt.tz_localize(None) - try: - return s.astype(pandas_type).dt.date - except (ValueError, TypeError, pd._libs.tslibs.OutOfBoundsDatetime): - - def try_date(v): - if isinstance(v, datetime.datetime): - return v.date() - elif isinstance(v, str): - if v.endswith("Z"): - return datetime.datetime.fromisoformat(v[:-1]).date() - return datetime.date.fromisoformat(v) - else: - return v - - return s.map(try_date, na_action="ignore") - - @classmethod - def convert_Interval(cls, s, dtype, pandas_type): - values = s.values - try: - result = values.astype(pandas_type) - except ValueError: # can happen when `column` is DateOffsets # uncovered - result = s - else: - result = s.__class__(result, index=s.index, name=s.name) - return result - - @classmethod - def convert_String(cls, s, dtype, pandas_type): - return s.astype(pandas_type, errors="ignore") - - @classmethod - def convert_Decimal(cls, s, dtype, pandas_type): - func = partial( - normalize_decimal, - precision=dtype.precision, - scale=dtype.scale, - strict=False, - ) - return s.map(func, na_action="ignore") - - @classmethod - def convert_UUID(cls, s, dtype, pandas_type): - return s.map(cls.get_element_converter(dtype), na_action="ignore") - - @classmethod - def convert_Struct(cls, s, dtype, pandas_type): - return s.map(cls.get_element_converter(dtype), na_action="ignore") - - @classmethod - def convert_Array(cls, s, dtype, pandas_type): - return s.map(cls.get_element_converter(dtype), na_action="ignore") - - @classmethod - def convert_Map(cls, s, dtype, pandas_type): - return s.map(cls.get_element_converter(dtype), na_action="ignore") - - @classmethod - def convert_JSON(cls, s, dtype, pandas_type): - return s.map(cls.get_element_converter(dtype), na_action="ignore").astype( - "object" - ) - - @classmethod - def get_element_converter(cls, dtype): - name = f"convert_{type(dtype).__name__}_element" - funcgen = getattr(cls, name, lambda _: lambda x: x) - return funcgen(dtype) - - @classmethod - def convert_Struct_element(cls, dtype): - converters = tuple(map(cls.get_element_converter, dtype.types)) - - def convert(values, names=dtype.names, converters=converters): - if values is None: - return values - - items = ( - values.items() - if isinstance(values, dict) - else zip(names, util.promote_list(values)) - ) - return { - k: converter(v) if v is not None else v - for converter, (k, v) in zip(converters, items) - } - - return convert - - @classmethod - def convert_JSON_element(cls, _): - import json - - def convert(value): - if value is None: - return value - try: - return json.loads(value) - except (TypeError, json.JSONDecodeError): - return value - - return convert - - @classmethod - def convert_Timestamp_element(cls, dtype): - def converter(value, dtype=dtype): - if value is None: - return value - - with contextlib.suppress(AttributeError): - value = value.item() - - if isinstance(value, int): - # this can only mean a numpy or pandas timestamp because they - # both support nanosecond precision - # - # when the precision is less than or equal to the value - # supported by Python datetime.dateimte a call to .item() will - # return a datetime.datetime but when the precision is higher - # than the value supported by Python the value is an integer - # - # TODO: can we do better than implicit truncation to microseconds? - import dateutil - - value = datetime.datetime.fromtimestamp(value / 1e9, dateutil.tz.UTC) - - if (tz := dtype.timezone) is not None: - return value.astimezone(normalize_timezone(tz)) - - return value.replace(tzinfo=None) - - return converter - - @classmethod - def convert_Array_element(cls, dtype): - convert_value = cls.get_element_converter(dtype.value_type) - - def convert(values): - if values is None: - return values - - return [ - convert_value(value) if value is not None else value for value in values - ] - - return convert - - @classmethod - def convert_Map_element(cls, dtype): - convert_key = cls.get_element_converter(dtype.key_type) - convert_value = cls.get_element_converter(dtype.value_type) - - def convert(raw_row): - if raw_row is None: - return raw_row - - row = dict(raw_row) - return dict( - zip(map(convert_key, row.keys()), map(convert_value, row.values())) - ) - - return convert - - @classmethod - def convert_UUID_element(cls, _): - from uuid import UUID - - def convert(value): - if value is None: - return value - elif isinstance(value, UUID): - return value - return UUID(value) - - return convert - - -class PandasDataFrameProxy(TableProxy[pd.DataFrame]): - def to_frame(self) -> pd.DataFrame: - return self.obj - - def to_pyarrow(self, schema: sch.Schema) -> pa.Table: - import pyarrow as pa - import pyarrow_hotfix # noqa: F401 - - pyarrow_schema = PyArrowSchema.from_ibis(schema) - return pa.Table.from_pandas(self.obj, schema=pyarrow_schema) - - def to_polars(self, schema: sch.Schema) -> pl.DataFrame: - import polars as pl - from bigframes_vendored.ibis.formats.polars import PolarsSchema - - pl_schema = PolarsSchema.from_ibis(schema) - return pl.from_pandas(self.obj, schema_overrides=pl_schema) diff --git a/third_party/bigframes_vendored/ibis/formats/polars.py b/third_party/bigframes_vendored/ibis/formats/polars.py deleted file mode 100644 index 1c0b38ee804..00000000000 --- a/third_party/bigframes_vendored/ibis/formats/polars.py +++ /dev/null @@ -1,194 +0,0 @@ -# Contains code from https://github.com/ibis-project/ibis/blob/9.2.0/ibis/formats/polars.py - -from __future__ import annotations - -from typing import TYPE_CHECKING, Any - -import bigframes_vendored.ibis.expr.datatypes as dt -import polars as pl -from bigframes_vendored.ibis.expr.schema import Schema -from bigframes_vendored.ibis.formats import ( - DataMapper, - SchemaMapper, - TableProxy, - TypeMapper, -) - -if TYPE_CHECKING: - from collections.abc import Sequence - - import pandas as pd - import pyarrow as pa - - -_to_polars_types = { - dt.Boolean: pl.Boolean, - dt.Null: pl.Null, - dt.String: pl.Utf8, - dt.Binary: pl.Binary, - dt.Date: pl.Date, - dt.Time: pl.Time, - dt.Int8: pl.Int8, - dt.Int16: pl.Int16, - dt.Int32: pl.Int32, - dt.Int64: pl.Int64, - dt.UInt8: pl.UInt8, - dt.UInt16: pl.UInt16, - dt.UInt32: pl.UInt32, - dt.UInt64: pl.UInt64, - dt.Float32: pl.Float32, - dt.Float64: pl.Float64, -} - -_from_polars_types = {v: k for k, v in _to_polars_types.items()} - - -class PolarsType(TypeMapper): - @classmethod - def to_ibis(cls, typ: pl.DataType, nullable=True) -> dt.DataType: - """Convert a polars type to an ibis type.""" - - base_type = typ.base_type() - if base_type is pl.Categorical: - return dt.String(nullable=nullable) - elif base_type is pl.Decimal: - return dt.Decimal( - precision=typ.precision, scale=typ.scale, nullable=nullable - ) - elif base_type is pl.Datetime: - try: - timezone = typ.time_zone - except AttributeError: # pragma: no cover - timezone = typ.tz # pragma: no cover - - # this will raise on polars for seconds "s" unit as it's not supported - return dt.Timestamp.from_unit( - unit=typ.time_unit, timezone=timezone, nullable=nullable - ) - - elif base_type is pl.Duration: - try: - time_unit = typ.time_unit - except AttributeError: # pragma: no cover - time_unit = typ.tu # pragma: no cover - return dt.Interval(unit=time_unit, nullable=nullable) - elif base_type is pl.List: - return dt.Array(cls.to_ibis(typ.inner), nullable=nullable) - elif base_type is pl.Struct: - return dt.Struct.from_tuples( - [(field.name, cls.to_ibis(field.dtype)) for field in typ.fields], - nullable=nullable, - ) - else: - return _from_polars_types[base_type](nullable=nullable) - - @classmethod - def from_ibis(cls, dtype: dt.DataType) -> pl.DataType: - """Convert an ibis type to a polars type.""" - if dtype.is_decimal(): - return pl.Decimal( - precision=dtype.precision, - scale=9 if dtype.scale is None else dtype.scale, - ) - elif dtype.is_timestamp(): - unit = dtype.unit.short - if unit in {"us", "ns", "ms"}: - return pl.Datetime(unit, dtype.timezone) - else: - # this for "s", if something else is passed, it'll raise at - # the from_unit level. - return pl.Datetime("ns", dtype.timezone) # this was the default before - - elif dtype.is_interval(): - if dtype.unit.short in {"us", "ns", "ms"}: - return pl.Duration(dtype.unit.short) - else: - raise ValueError(f"Unsupported polars duration unit: {dtype.unit}") - elif dtype.is_struct(): - fields = [ - pl.Field(name=name, dtype=cls.from_ibis(dtype)) - for name, dtype in dtype.fields.items() - ] - return pl.Struct(fields) - elif dtype.is_array(): - return pl.List(cls.from_ibis(dtype.value_type)) - else: - try: - return _to_polars_types[type(dtype)] - except KeyError: - raise NotImplementedError( - f"Converting {dtype} to polars is not supported yet" - ) - - -class PolarsSchema(SchemaMapper): - @classmethod - def from_ibis(cls, schema: Schema) -> dict[str, pl.DataType]: - """Convert a schema to a polars schema.""" - return {name: PolarsType.from_ibis(typ) for name, typ in schema.items()} - - @classmethod - def to_ibis(cls, schema: dict[str, pl.DataType]) -> Schema: - """Convert a polars schema to a schema.""" - return Schema.from_tuples( - [(name, PolarsType.to_ibis(typ)) for name, typ in schema.items()] - ) - - -class PolarsData(DataMapper): - @classmethod - def infer_scalar(cls, scalar: Any) -> dt.DataType: - """Infer the ibis type of a scalar.""" - return PolarsType.to_ibis(pl.Series(values=[scalar]).dtype) - - @classmethod - def infer_column(cls, column: Sequence) -> dt.DataType: - """Infer the ibis type of a sequence.""" - if not isinstance(column, pl.Series): - column = pl.Series(values=column) - return PolarsType.to_ibis(column.dtype) - - @classmethod - def infer_table(cls, table) -> Schema: - """Infer the schema of a table.""" - if not isinstance(table, pl.DataFrame): - table = pl.DataFrame(table) - - return PolarsSchema.to_ibis(table.schema) - - @classmethod - def convert_scalar(cls, df: pl.DataFrame, dtype: dt.DataType) -> Any: - assert df.shape == (1, 1) - df = cls.convert_table(df, Schema({df.columns[0]: dtype})) - return df[0, 0] - - @classmethod - def convert_column(cls, df: pl.DataFrame, dtype: dt.DataType) -> pl.Series: - assert df.shape[1] == 1 - df = cls.convert_table(df, Schema({df.columns[0]: dtype})) - return df[:, 0] - - @classmethod - def convert_table(cls, df: pl.DataFrame, schema: Schema) -> pl.DataFrame: - pl_schema = PolarsSchema.from_ibis(schema) - - if tuple(df.columns) != tuple(schema.names): - df = df.rename(dict(zip(df.columns, schema.names))) - - if df.schema == pl_schema: - return df - return df.cast(pl_schema) - - -class PolarsDataFrameProxy(TableProxy[pl.DataFrame]): - def to_frame(self) -> pd.DataFrame: - return self.obj.to_pandas() - - def to_pyarrow(self, schema: Schema) -> pa.Table: - from bigframes_vendored.ibis.formats.pyarrow import PyArrowData - - table = self.obj.to_arrow() - return PyArrowData.convert_table(table, schema) - - def to_polars(self, schema: Schema) -> pl.DataFrame: - return self.obj diff --git a/third_party/bigframes_vendored/ibis/formats/pyarrow.py b/third_party/bigframes_vendored/ibis/formats/pyarrow.py deleted file mode 100644 index 5428264ee7c..00000000000 --- a/third_party/bigframes_vendored/ibis/formats/pyarrow.py +++ /dev/null @@ -1,379 +0,0 @@ -# Contains code from https://github.com/ibis-project/ibis/blob/9.2.0/ibis/formats/pyarrow.py - -from __future__ import annotations - -import functools -from typing import TYPE_CHECKING, Any - -import bigframes_vendored.ibis.expr.datatypes as dt -from bigframes_vendored.ibis.expr.schema import Schema -from bigframes_vendored.ibis.formats import ( - DataMapper, - SchemaMapper, - TableProxy, - TypeMapper, -) - -if TYPE_CHECKING: - from collections.abc import Sequence - - import polars as pl - import pyarrow as pa - - -@functools.cache -def _from_pyarrow_types(): - import pyarrow as pa - - return { - pa.int8(): dt.Int8, - pa.int16(): dt.Int16, - pa.int32(): dt.Int32, - pa.int64(): dt.Int64, - pa.uint8(): dt.UInt8, - pa.uint16(): dt.UInt16, - pa.uint32(): dt.UInt32, - pa.uint64(): dt.UInt64, - pa.float16(): dt.Float16, - pa.float32(): dt.Float32, - pa.float64(): dt.Float64, - pa.string(): dt.String, - pa.binary(): dt.Binary, - pa.bool_(): dt.Boolean, - pa.date32(): dt.Date, - pa.date64(): dt.Date, - pa.null(): dt.Null, - pa.string(): dt.String, - pa.large_binary(): dt.Binary, - pa.large_string(): dt.String, - pa.binary(): dt.Binary, - } - - -@functools.cache -def _to_pyarrow_types(): - import pyarrow as pa - import pyarrow_hotfix # noqa: F401 - - return { - dt.Null: pa.null(), - dt.Boolean: pa.bool_(), - dt.Binary: pa.binary(), - dt.Int8: pa.int8(), - dt.Int16: pa.int16(), - dt.Int32: pa.int32(), - dt.Int64: pa.int64(), - dt.UInt8: pa.uint8(), - dt.UInt16: pa.uint16(), - dt.UInt32: pa.uint32(), - dt.UInt64: pa.uint64(), - dt.Float16: pa.float16(), - dt.Float32: pa.float32(), - dt.Float64: pa.float64(), - dt.String: pa.string(), - dt.Binary: pa.binary(), - # assume unknown types can be converted into strings - dt.Unknown: pa.string(), - dt.MACADDR: pa.string(), - dt.INET: pa.string(), - dt.UUID: pa.string(), - dt.JSON: pa.string(), - } - - -class PyArrowType(TypeMapper): - @classmethod - def to_ibis(cls, typ: pa.DataType, nullable=True) -> dt.DataType: - """Convert a pyarrow type to an ibis type.""" - import pyarrow as pa - - if pa.types.is_null(typ): - return dt.null - elif pa.types.is_decimal(typ): - return dt.Decimal(typ.precision, typ.scale, nullable=nullable) - elif pa.types.is_timestamp(typ): - return dt.Timestamp.from_unit(typ.unit, timezone=typ.tz, nullable=nullable) - elif pa.types.is_time(typ): - return dt.Time(nullable=nullable) - elif pa.types.is_duration(typ): - return dt.Interval(typ.unit, nullable=nullable) - elif pa.types.is_interval(typ): - raise ValueError("Arrow interval type is not supported") - elif ( - pa.types.is_list(typ) - or pa.types.is_large_list(typ) - or pa.types.is_fixed_size_list(typ) - ): - value_dtype = cls.to_ibis(typ.value_type, typ.value_field.nullable) - return dt.Array(value_dtype, nullable=nullable) - elif pa.types.is_struct(typ): - field_dtypes = { - field.name: cls.to_ibis(field.type, field.nullable) for field in typ - } - return dt.Struct(field_dtypes, nullable=nullable) - elif pa.types.is_map(typ): - # TODO(kszucs): keys_sorted has just been exposed in pyarrow - key_dtype = cls.to_ibis(typ.key_type, typ.key_field.nullable) - value_dtype = cls.to_ibis(typ.item_type, typ.item_field.nullable) - return dt.Map(key_dtype, value_dtype, nullable=nullable) - elif pa.types.is_dictionary(typ): - return cls.to_ibis(typ.value_type) - elif ( - isinstance(typ, pa.ExtensionType) - and type(typ).__module__ == "geoarrow.types.type_pyarrow" - ): - from geoarrow import types as gat - - gat.type_pyarrow.register_extension_types() - - auth_code = None - if typ.crs is not None: - crs_dict = typ.crs.to_json_dict() - if "id" in crs_dict: - crs_id = crs_dict["id"] - if "authority" in crs_id and "code" in crs_id: - auth_code = (crs_id["authority"], crs_id["code"]) - - if typ.crs is not None and auth_code is None: - # It is possible to have PROJJSON that does not have an authority/code - # attached, either because the producer didn't have that information - # (e.g., because they were reading a older shapefile). In this case, - # pyproj can often guess the authority/code. - import pyproj - - auth_code = pyproj.CRS(typ.crs.to_json()).to_authority() - if auth_code is None: - raise ValueError(f"Can't resolve SRID of crs {typ.crs}") - - if auth_code is None: - srid = None - elif auth_code == ("OGC", "CRS84"): - # OGC:CRS84 and EPSG:4326 are identical except for the order of - # coordinates (i.e., lon lat vs. lat lon) in their official definition. - # This axis ordering is ignored in all but the most obscure scenarios - # such that these are identical. OGC:CRS84 is more correct, but EPSG:4326 - # is more common. - srid = 4326 - else: - # This works because the two most common srid authorities are EPSG and ESRI - # and the "codes" are all integers and don't intersect with each other on - # purpose. This won't scale to something like OGC:CRS27 (not common). - srid = int(auth_code[1]) - - if typ.edge_type == gat.EdgeType.SPHERICAL: - geotype = "geography" - else: - geotype = "geometry" - - return dt.GeoSpatial(geotype, srid, nullable) - else: - return _from_pyarrow_types()[typ](nullable=nullable) - - @classmethod - def from_ibis(cls, dtype: dt.DataType) -> pa.DataType: - """Convert an ibis type to a pyarrow type.""" - import pyarrow as pa - import pyarrow_hotfix # noqa: F401 - - if dtype.is_decimal(): - # set default precision and scale to something; unclear how to choose this - precision = 38 if dtype.precision is None else dtype.precision - scale = 9 if dtype.scale is None else dtype.scale - - if precision > 76: - raise ValueError( - f"Unsupported precision {dtype.precision} for decimal type" - ) - elif precision > 38: - return pa.decimal256(precision, scale) - else: - return pa.decimal128(precision, scale) - elif dtype.is_timestamp(): - return pa.timestamp( - dtype.unit.short if dtype.scale is not None else "us", tz=dtype.timezone - ) - elif dtype.is_interval(): - short = dtype.unit.short - if short in {"ns", "us", "ms", "s"}: - return pa.duration(short) - else: - return pa.month_day_nano_interval() - elif dtype.is_time(): - return pa.time64("ns") - elif dtype.is_date(): - return pa.date32() - elif dtype.is_array(): - value_field = pa.field( - "item", - cls.from_ibis(dtype.value_type), - nullable=dtype.value_type.nullable, - ) - return pa.list_(value_field) - elif dtype.is_struct(): - fields = [ - pa.field(name, cls.from_ibis(dtype), nullable=dtype.nullable) - for name, dtype in dtype.items() - ] - return pa.struct(fields) - elif dtype.is_map(): - key_field = pa.field( - "key", - cls.from_ibis(dtype.key_type), - nullable=False, # pyarrow doesn't allow nullable keys - ) - value_field = pa.field( - "value", - cls.from_ibis(dtype.value_type), - nullable=dtype.value_type.nullable, - ) - return pa.map_(key_field, value_field, keys_sorted=False) - elif dtype.is_geospatial(): - from geoarrow import types as gat - - # Resolve CRS - if dtype.srid is None: - crs = None - elif dtype.srid == 4326: - crs = gat.OGC_CRS84 - else: - import pyproj - - # Assume that these are EPSG codes. An srid is more accurately a key - # into a backend/connection-specific lookup table; however, most usage - # should work with this assumption. - crs = pyproj.CRS(f"EPSG:{dtype.srid}") - - # Resolve edge type - if dtype.geotype == "geography": - edge_type = gat.EdgeType.SPHERICAL - else: - edge_type = gat.EdgeType.PLANAR - - return gat.wkb(crs=crs, edge_type=edge_type).to_pyarrow() - else: - try: - return _to_pyarrow_types()[type(dtype)] - except KeyError: - raise NotImplementedError( - f"Converting {dtype} to pyarrow is not supported yet" - ) - - -class PyArrowSchema(SchemaMapper): - @classmethod - def from_ibis(cls, schema: Schema) -> pa.Schema: - """Convert a schema to a pyarrow schema.""" - import pyarrow as pa - import pyarrow_hotfix # noqa: F401 - - fields = [ - pa.field(name, PyArrowType.from_ibis(dtype), nullable=dtype.nullable) - for name, dtype in schema.items() - ] - return pa.schema(fields) - - @classmethod - def to_ibis(cls, schema: pa.Schema) -> Schema: - """Convert a pyarrow schema to a schema.""" - fields = [(f.name, PyArrowType.to_ibis(f.type, f.nullable)) for f in schema] - return Schema.from_tuples(fields) - - -class PyArrowData(DataMapper): - @classmethod - def infer_scalar(cls, scalar: Any) -> dt.DataType: - """Infer the ibis type of a scalar.""" - import pyarrow as pa - import pyarrow_hotfix # noqa: F401 - - return PyArrowType.to_ibis(pa.scalar(scalar).type) - - @classmethod - def infer_column(cls, column: Sequence) -> dt.DataType: - """Infer the ibis type of a sequence.""" - import pyarrow as pa - import pyarrow_hotfix # noqa: F401 - - if isinstance(column, pa.Array): - return PyArrowType.to_ibis(column.type) - - try: - pyarrow_type = pa.array(column, from_pandas=True).type - # pyarrow_type = pa.infer_type(column, from_pandas=True) - except pa.ArrowInvalid: - try: - # handle embedded series objects - return dt.highest_precedence(map(dt.infer, column)) - except TypeError: - # we can still have a type error, e.g., float64 and string in the - # same array - return dt.unknown - except pa.ArrowTypeError: - # arrow can't infer the type - return dt.unknown - else: - # arrow inferred the type, now convert that type to an ibis type - return PyArrowType.to_ibis(pyarrow_type) - - @classmethod - def infer_table(cls, table) -> Schema: - """Infer the schema of a table.""" - import pyarrow as pa - import pyarrow_hotfix # noqa: F401 - - if not isinstance(table, pa.Table): - table = pa.table(table) - - return PyArrowSchema.to_ibis(table.schema) - - @classmethod - def convert_scalar(cls, scalar: pa.Scalar, dtype: dt.DataType) -> pa.Scalar: - import pyarrow as pa - import pyarrow_hotfix # noqa: F401 - - desired_type = PyArrowType.from_ibis(dtype) - scalar_type = scalar.type - if scalar_type != desired_type: - try: - return scalar.cast(desired_type) - except pa.ArrowNotImplementedError: - # pyarrow doesn't support some scalar casts that are supported - # when using arrays or tables - return pa.array([scalar.as_py()], type=scalar_type).cast(desired_type)[ - 0 - ] - else: - return scalar - - @classmethod - def convert_column(cls, column: pa.Array, dtype: dt.DataType) -> pa.Array: - desired_type = PyArrowType.from_ibis(dtype) - if column.type != desired_type: - return column.cast(desired_type) - else: - return column - - @classmethod - def convert_table(cls, table: pa.Table, schema: Schema) -> pa.Table: - desired_schema = PyArrowSchema.from_ibis(schema) - pa_schema = table.schema - - if pa_schema != desired_schema: - return table.cast(desired_schema, safe=False) - else: - return table - - -class PyArrowTableProxy(TableProxy): - def to_frame(self): - return self.obj.to_pandas() - - def to_pyarrow(self, schema: Schema) -> pa.Table: - return self.obj - - def to_polars(self, schema: Schema) -> pl.DataFrame: - import polars as pl - from bigframes_vendored.ibis.formats.polars import PolarsData - - df = pl.from_arrow(self.obj) - return PolarsData.convert_table(df, schema) diff --git a/third_party/bigframes_vendored/ibis/selectors.py b/third_party/bigframes_vendored/ibis/selectors.py deleted file mode 100644 index 3b9f0107728..00000000000 --- a/third_party/bigframes_vendored/ibis/selectors.py +++ /dev/null @@ -1,655 +0,0 @@ -# Contains code from https://github.com/ibis-project/ibis/blob/9.2.0/ibis/selectors.py - -"""Convenient column selectors. - -::: {.callout-tip} -## Check out the [blog post on selectors](../posts/selectors) for examples! -::: - -## Rationale - -Column selectors are convenience functions for selecting columns that share some property. - -## Discussion - -For example, a common task is to be able to select all numeric columns for a -subsequent computation. - -Without selectors this becomes quite verbose and tedious to write: - ->>> import ibis ->>> t = ibis.table(dict(a="int", b="string", c="array", abcd="float")) ->>> expr = t.select([t[c] for c in t.columns if t[c].type().is_numeric()]) ->>> expr.columns -['a', 'abcd'] - -Compare that to the [`numeric`](#ibis.selectors.numeric) selector: - ->>> import ibis.selectors as s ->>> expr = t.select(s.numeric()) ->>> expr.columns -['a', 'abcd'] - -When there are multiple properties to check it gets worse: - ->>> expr = t.select( -... [ -... t[c] -... for c in t.columns -... if t[c].type().is_numeric() or t[c].type().is_string() -... if ("a" in c or "b" in c or "cd" in c) -... ] -... ) ->>> expr.columns -['a', 'b', 'abcd'] - -Using a composition of selectors this is much less tiresome: - ->>> expr = t.select((s.numeric() | s.of_type("string")) & s.contains(("a", "b", "cd"))) ->>> expr.columns -['a', 'b', 'abcd'] -""" - -from __future__ import annotations - -import functools -import inspect -import operator -import re -from collections.abc import Callable, Iterable, Mapping, Sequence -from typing import Optional, Union - -import bigframes_vendored.ibis.common.exceptions as exc -import bigframes_vendored.ibis.expr.datatypes as dt -import bigframes_vendored.ibis.expr.types as ir -from bigframes_vendored.ibis import util -from bigframes_vendored.ibis.common.collections import frozendict # noqa: TCH001 -from bigframes_vendored.ibis.common.deferred import Deferred, Resolver -from bigframes_vendored.ibis.common.exceptions import IbisError -from bigframes_vendored.ibis.common.grounds import Singleton -from bigframes_vendored.ibis.common.selectors import Selector -from public import public - - -class Predicate(Selector): - predicate: Callable[[ir.Value], bool] - - def expand(self, table: ir.Table) -> Sequence[ir.Value]: - """Evaluate `self.predicate` on every column of `table`. - - Parameters - ---------- - table - An ibis table expression - - """ - return [col for column in table.columns if self.predicate(col := table[column])] - - def __and__(self, other: Selector) -> Predicate: - """Compute the conjunction of two `Selector`s. - - Parameters - ---------- - other - Another selector - - """ - return self.__class__(lambda col: self.predicate(col) and other.predicate(col)) - - def __or__(self, other: Selector) -> Predicate: - """Compute the disjunction of two `Selector`s. - - Parameters - ---------- - other - Another selector - - """ - return self.__class__(lambda col: self.predicate(col) or other.predicate(col)) - - def __invert__(self) -> Predicate: - """Compute the logical negation of two `Selector`s.""" - return self.__class__(lambda col: not self.predicate(col)) - - -@public -def where(predicate: Callable[[ir.Value], bool]) -> Predicate: - """Select columns that satisfy `predicate`. - - Use this selector when one of the other selectors does not meet your needs. - - Parameters - ---------- - predicate - A callable that accepts an ibis value expression and returns a `bool` - - Examples - -------- - >>> import ibis - >>> import ibis.selectors as s - >>> t = ibis.table(dict(a="float32"), name="t") - >>> expr = t.select(s.where(lambda col: col.get_name() == "a")) - >>> expr.columns - ['a'] - - """ - return Predicate(predicate=predicate) - - -@public -def numeric() -> Predicate: - """Return numeric columns. - - Examples - -------- - >>> import ibis - >>> import ibis.selectors as s - >>> t = ibis.table(dict(a="int", b="string", c="array"), name="t") - >>> t.columns - ['a', 'b', 'c'] - >>> expr = t.select(s.numeric()) # `a` has integer type, so it's numeric - >>> expr.columns - ['a'] - - See Also - -------- - [`of_type`](#ibis.selectors.of_type) - - """ - return of_type(dt.Numeric) - - -@public -def of_type(dtype: dt.DataType | str | type[dt.DataType]) -> Predicate: - """Select columns of type `dtype`. - - Parameters - ---------- - dtype - `DataType` instance, `str` or `DataType` class - - Examples - -------- - Select according to a specific `DataType` instance - - >>> import ibis - >>> import ibis.expr.datatypes as dt - >>> import ibis.selectors as s - >>> t = ibis.table(dict(name="string", siblings="array", parents="array")) - >>> expr = t.select(s.of_type(dt.Array(dt.string))) - >>> expr.columns - ['siblings'] - - Strings are also accepted - - >>> expr = t.select(s.of_type("array")) - >>> expr.columns - ['siblings'] - - Abstract/unparametrized types may also be specified by their string name - (e.g. "integer" for any integer type), or by passing in a `DataType` class - instead. The following options are equivalent. - - >>> expr1 = t.select(s.of_type("array")) - >>> expr2 = t.select(s.of_type(dt.Array)) - >>> expr1.equals(expr2) - True - >>> expr2.columns - ['siblings', 'parents'] - - See Also - -------- - [`numeric`](#ibis.selectors.numeric) - - """ - if isinstance(dtype, str): - # A mapping of abstract or parametric types, to allow selecting all - # subclasses/parametrizations of these types, rather than only a - # specific instance. - abstract = { - "array": dt.Array, - "decimal": dt.Decimal, - "floating": dt.Floating, - "geospatial": dt.GeoSpatial, - "integer": dt.Integer, - "map": dt.Map, - "numeric": dt.Numeric, - "struct": dt.Struct, - "temporal": dt.Temporal, - } - if cls := abstract.get(dtype.lower()): - predicate = lambda col: isinstance(col.type(), cls) # noqa: E731 - else: - dtype = dt.dtype(dtype) - predicate = lambda col: col.type() == dtype # noqa: E731 - elif inspect.isclass(dtype) and issubclass(dtype, dt.DataType): - predicate = lambda col: isinstance(col.type(), dtype) # noqa: E731 - else: - dtype = dt.dtype(dtype) - predicate = lambda col: col.type() == dtype # noqa: E731 - return where(predicate) - - -@public -def startswith(prefixes: str | tuple[str, ...]) -> Predicate: - """Select columns whose name starts with one of `prefixes`. - - Parameters - ---------- - prefixes - Prefixes to compare column names against - - Examples - -------- - >>> import ibis - >>> import ibis.selectors as s - >>> t = ibis.table(dict(apples="int", oranges="float", bananas="bool"), name="t") - >>> expr = t.select(s.startswith(("a", "b"))) - >>> expr.columns - ['apples', 'bananas'] - - See Also - -------- - [`endswith`](#ibis.selectors.endswith) - - """ - return where(lambda col: col.get_name().startswith(prefixes)) - - -@public -def endswith(suffixes: str | tuple[str, ...]) -> Predicate: - """Select columns whose name ends with one of `suffixes`. - - Parameters - ---------- - suffixes - Suffixes to compare column names against - - See Also - -------- - [`startswith`](#ibis.selectors.startswith) - - """ - return where(lambda col: col.get_name().endswith(suffixes)) - - -@public -def contains( - needles: str | tuple[str, ...], how: Callable[[Iterable[bool]], bool] = any -) -> Predicate: - """Return columns whose name contains `needles`. - - Parameters - ---------- - needles - One or more strings to search for in column names - how - A boolean reduction to allow the configuration of how `needles` are summarized. - - Examples - -------- - Select columns that contain either `"a"` or `"b"` - - >>> import ibis - >>> import ibis.selectors as s - >>> t = ibis.table( - ... dict(a="int64", b="string", c="float", d="array", ab="struct") - ... ) - >>> expr = t.select(s.contains(("a", "b"))) - >>> expr.columns - ['a', 'b', 'ab'] - - Select columns that contain all of `"a"` and `"b"`, that is, both `"a"` and - `"b"` must be in each column's name to match. - - >>> expr = t.select(s.contains(("a", "b"), how=all)) - >>> expr.columns - ['ab'] - - See Also - -------- - [`matches`](#ibis.selectors.matches) - - """ - - def predicate(col: ir.Value) -> bool: - name = col.get_name() - return how(needle in name for needle in util.promote_list(needles)) - - return where(predicate) - - -@public -def matches(regex: str | re.Pattern) -> Selector: - """Return columns whose name matches the regular expression `regex`. - - Parameters - ---------- - regex - A string or `re.Pattern` object - - Examples - -------- - >>> import ibis - >>> import ibis.selectors as s - >>> t = ibis.table(dict(ab="string", abd="int", be="array")) - >>> expr = t.select(s.matches(r"ab+")) - >>> expr.columns - ['ab', 'abd'] - - See Also - -------- - [`contains`](#ibis.selectors.contains) - - """ - pattern = re.compile(regex) - return where(lambda col: pattern.search(col.get_name()) is not None) - - -@public -def any_of(*predicates: str | Predicate) -> Predicate: - """Include columns satisfying any of `predicates`.""" - return functools.reduce(operator.or_, map(_to_selector, predicates)) - - -@public -def all_of(*predicates: str | Predicate) -> Predicate: - """Include columns satisfying all of `predicates`.""" - return functools.reduce(operator.and_, map(_to_selector, predicates)) - - -@public -def c(*names: str | ir.Column) -> Predicate: - """Select specific column names.""" - names = frozenset(col if isinstance(col, str) else col.get_name() for col in names) - - def func(col: ir.Value) -> bool: - schema = col.op().rel.schema - if extra_cols := (names - schema.keys()): - raise exc.IbisInputError( - f"Columns {extra_cols} are not present in {schema.names}" - ) - return col.get_name() in names - - return where(func) - - -class Across(Selector): - selector: Selector - funcs: Union[ - Resolver, - Callable[[ir.Value], ir.Value], - frozendict[Optional[str], Union[Resolver, Callable[[ir.Value], ir.Value]]], - ] - names: Union[str, Callable[[str, Optional[str]], str]] - - def expand(self, table: ir.Table) -> Sequence[ir.Value]: - expanded = [] - - names = self.names - cols = self.selector.expand(table) - for func_name, func in self.funcs.items(): - for orig_col in cols: - if isinstance(func, Resolver): - col = func.resolve({"_": orig_col}) - else: - col = func(orig_col) - - if callable(names): - name = names(orig_col.get_name(), func_name) - else: - name = names.format(col=orig_col.get_name(), fn=func_name) - - expanded.append(col.name(name)) - - return expanded - - -@public -def across( - selector: Selector | Iterable[str] | str, - func: Deferred - | Callable[[ir.Value], ir.Value] - | Mapping[str | None, Deferred | Callable[[ir.Value], ir.Value]], - names: str | Callable[[str, str | None], str] | None = None, -) -> Across: - """Apply data transformations across multiple columns. - - Parameters - ---------- - selector - An expression that selects columns on which the transformation function - will be applied, an iterable of `str` column names or a single `str` - column name. - func - A function (or dictionary of functions) to use to transform the data. - names - A lambda function or a format string to name the columns created by the - transformation function. - - Returns - ------- - Across - An `Across` selector object - - Examples - -------- - >>> import ibis - >>> ibis.options.interactive = True - >>> from ibis import _, selectors as s - >>> t = ibis.examples.penguins.fetch() - >>> t.select(s.startswith("bill")).mutate( - ... s.across(s.numeric(), dict(centered=_ - _.mean()), names="{fn}_{col}") - ... ) - ┏━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━┓ - ┃ bill_length_mm ┃ bill_depth_mm ┃ centered_bill_length_mm ┃ … ┃ - ┡━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━┩ - │ float64 │ float64 │ float64 │ … │ - ├────────────────┼───────────────┼─────────────────────────┼───┤ - │ 39.1 │ 18.7 │ -4.82193 │ … │ - │ 39.5 │ 17.4 │ -4.42193 │ … │ - │ 40.3 │ 18.0 │ -3.62193 │ … │ - │ NULL │ NULL │ NULL │ … │ - │ 36.7 │ 19.3 │ -7.22193 │ … │ - │ 39.3 │ 20.6 │ -4.62193 │ … │ - │ 38.9 │ 17.8 │ -5.02193 │ … │ - │ 39.2 │ 19.6 │ -4.72193 │ … │ - │ 34.1 │ 18.1 │ -9.82193 │ … │ - │ 42.0 │ 20.2 │ -1.92193 │ … │ - │ … │ … │ … │ … │ - └────────────────┴───────────────┴─────────────────────────┴───┘ - - """ - if names is None: - names = lambda col, fn: "_".join(filter(None, (col, fn))) # noqa: E731 - funcs = dict(func if isinstance(func, Mapping) else {None: func}) - if not isinstance(selector, Selector): - selector = c(*util.promote_list(selector)) - return Across(selector=selector, funcs=funcs, names=names) - - -class IfAnyAll(Selector): - selector: Selector - predicate: Union[Resolver, Callable[[ir.Value], ir.BooleanValue]] - summarizer: Callable[[ir.BooleanValue, ir.BooleanValue], ir.BooleanValue] - - def expand(self, table: ir.Table) -> Sequence[ir.Value]: - func = self.predicate - if isinstance(func, Resolver): - elems = (func.resolve({"_": col}) for col in self.selector.expand(table)) - else: - elems = (func(col) for col in self.selector.expand(table)) - - return [functools.reduce(self.summarizer, elems)] - - -@public -def if_any(selector: Selector, predicate: Deferred | Callable) -> IfAnyAll: - """Return the **disjunction** of `predicate` applied on all `selector` columns. - - Parameters - ---------- - selector - A column selector - predicate - A callable or deferred object defining a predicate to apply to each - column from `selector`. - - Examples - -------- - >>> import ibis - >>> from ibis import selectors as s, _ - >>> ibis.options.interactive = True - >>> penguins = ibis.examples.penguins.fetch() - >>> cols = s.across(s.endswith("_mm"), (_ - _.mean()) / _.std()) - >>> expr = penguins.mutate(cols).filter(s.if_any(s.endswith("_mm"), _.abs() > 2)) - >>> expr_by_hand = penguins.mutate(cols).filter( - ... (_.bill_length_mm.abs() > 2) - ... | (_.bill_depth_mm.abs() > 2) - ... | (_.flipper_length_mm.abs() > 2) - ... ) - >>> expr.equals(expr_by_hand) - True - >>> expr - ┏━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━┓ - ┃ species ┃ island ┃ bill_length_mm ┃ bill_depth_mm ┃ flipper_length_mm ┃ … ┃ - ┡━━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━┩ - │ string │ string │ float64 │ float64 │ float64 │ … │ - ├─────────┼────────┼────────────────┼───────────────┼───────────────────┼───┤ - │ Adelie │ Biscoe │ -1.103002 │ 0.733662 │ -2.056307 │ … │ - │ Gentoo │ Biscoe │ 1.113285 │ -0.431017 │ 2.068368 │ … │ - │ Gentoo │ Biscoe │ 2.871660 │ -0.076550 │ 2.068368 │ … │ - │ Gentoo │ Biscoe │ 1.900890 │ -0.734846 │ 2.139483 │ … │ - │ Gentoo │ Biscoe │ 1.076652 │ -0.177826 │ 2.068368 │ … │ - │ Gentoo │ Biscoe │ 0.856855 │ -0.582932 │ 2.068368 │ … │ - │ Gentoo │ Biscoe │ 1.497929 │ -0.076550 │ 2.068368 │ … │ - │ Gentoo │ Biscoe │ 1.388031 │ -0.431017 │ 2.068368 │ … │ - │ Gentoo │ Biscoe │ 2.047422 │ -0.582932 │ 2.068368 │ … │ - │ Adelie │ Dream │ -2.165354 │ -0.836123 │ -0.918466 │ … │ - │ … │ … │ … │ … │ … │ … │ - └─────────┴────────┴────────────────┴───────────────┴───────────────────┴───┘ - - """ - return IfAnyAll(selector=selector, predicate=predicate, summarizer=operator.or_) - - -@public -def if_all(selector: Selector, predicate: Deferred | Callable) -> IfAnyAll: - """Return the **conjunction** of `predicate` applied on all `selector` columns. - - Parameters - ---------- - selector - A column selector - predicate - A callable or deferred object defining a predicate to apply to each - column from `selector`. - - Examples - -------- - >>> import ibis - >>> from ibis import selectors as s, _ - >>> ibis.options.interactive = True - >>> penguins = ibis.examples.penguins.fetch() - >>> cols = s.across(s.endswith("_mm"), (_ - _.mean()) / _.std()) - >>> expr = penguins.mutate(cols).filter(s.if_all(s.endswith("_mm"), _.abs() > 1)) - >>> expr_by_hand = penguins.mutate(cols).filter( - ... (_.bill_length_mm.abs() > 1) - ... & (_.bill_depth_mm.abs() > 1) - ... & (_.flipper_length_mm.abs() > 1) - ... ) - >>> expr.equals(expr_by_hand) - True - >>> expr - ┏━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━┓ - ┃ species ┃ island ┃ bill_length_mm ┃ bill_depth_mm ┃ flipper_length_mm ┃ … ┃ - ┡━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━┩ - │ string │ string │ float64 │ float64 │ float64 │ … │ - ├─────────┼───────────┼────────────────┼───────────────┼───────────────────┼───┤ - │ Adelie │ Dream │ -1.157951 │ 1.088129 │ -1.416272 │ … │ - │ Adelie │ Torgersen │ -1.231217 │ 1.138768 │ -1.202926 │ … │ - │ Gentoo │ Biscoe │ 1.149917 │ -1.443781 │ 1.214987 │ … │ - │ Gentoo │ Biscoe │ 1.040019 │ -1.089314 │ 1.072757 │ … │ - │ Gentoo │ Biscoe │ 1.131601 │ -1.089314 │ 1.712792 │ … │ - │ Gentoo │ Biscoe │ 1.241499 │ -1.089314 │ 1.570562 │ … │ - │ Gentoo │ Biscoe │ 1.351398 │ -1.494420 │ 1.214987 │ … │ - └─────────┴───────────┴────────────────┴───────────────┴───────────────────┴───┘ - - """ - return IfAnyAll(selector=selector, predicate=predicate, summarizer=operator.and_) - - -class Sliceable(Singleton): - def __getitem__(self, key: str | int | slice | Iterable[int | str]) -> Predicate: - def pred(col: ir.Value) -> bool: - try: - (table,) = col.op().relations - except ValueError: - raise IbisError("Column should depend on exactly one table") - - schema = table.schema - idxs = schema._name_locs - num_names = len(schema) - colname = col.get_name() - colidx = idxs[colname] - - if isinstance(key, str): - return key == colname - elif isinstance(key, int): - return key % num_names == colidx - elif util.is_iterable(key): - return any( - (isinstance(el, int) and el % num_names == colidx) - or (isinstance(el, str) and el == colname) - for el in key - ) - else: - start = key.start or 0 - stop = key.stop or num_names - step = key.step or 1 - - if isinstance(start, str): - start = idxs[start] - - if isinstance(stop, str): - stop = idxs[stop] + 1 - - return colidx in range(start, stop, step) - - return where(pred) - - -r = Sliceable() -"""Ranges of columns.""" - - -@public -def first() -> Predicate: - """Return the first column of a table.""" - return r[0] - - -@public -def last() -> Predicate: - """Return the last column of a table.""" - return r[-1] - - -@public -def all() -> Predicate: - """Return every column from a table.""" - return r[:] - - -def _to_selector( - obj: str | Selector | ir.Column | Sequence[str | Selector | ir.Column], -) -> Selector: - """Convert an object to a `Selector`.""" - if isinstance(obj, Selector): - return obj - elif isinstance(obj, ir.Column): - return c(obj.get_name()) - elif isinstance(obj, str): - return c(obj) - else: - return any_of(*obj) diff --git a/third_party/bigframes_vendored/ibis/util.py b/third_party/bigframes_vendored/ibis/util.py deleted file mode 100644 index 7da2a7afff8..00000000000 --- a/third_party/bigframes_vendored/ibis/util.py +++ /dev/null @@ -1,699 +0,0 @@ -# Contains code from https://github.com/ibis-project/ibis/blob/9.2.0/ibis/util.py - -"""Ibis utility functions.""" - -from __future__ import annotations - -import base64 -import collections -import collections.abc -import functools -import importlib.metadata -import itertools -import operator -import os -import sys -import textwrap -import types -import uuid -import warnings -from types import ModuleType -from typing import TYPE_CHECKING, Any, Generic, TypeVar -from uuid import uuid4 - -import toolz -from bigframes_vendored.ibis.common.typing import Coercible - -if TYPE_CHECKING: - from collections.abc import Callable, Iterator, Sequence - from numbers import Real - from pathlib import Path - - import bigframes_vendored.ibis.expr.types as ir - -T = TypeVar("T", covariant=True) -U = TypeVar("U", covariant=True) -K = TypeVar("K") -V = TypeVar("V") - - -# https://www.compart.com/en/unicode/U+22EE -VERTICAL_ELLIPSIS = "\u22ee" -# https://www.compart.com/en/unicode/U+2026 -HORIZONTAL_ELLIPSIS = "\u2026" - - -def guid() -> str: - """Return a uuid4 hexadecimal value.""" - return uuid4().hex - - -def indent(text: str, spaces: int) -> str: - """Apply an indentation using the given spaces into the given text. - - Parameters - ---------- - text - Text to indent - spaces - Number of leading spaces per line - - Returns - ------- - str - Indented text - - """ - prefix = " " * spaces - return textwrap.indent(text, prefix=prefix) - - -def is_one_of(values: Sequence[T], t: type[U]) -> Iterator[bool]: - """Check if the type of each value is the same of the given type. - - Parameters - ---------- - values - Input values - t - Type to check against - - Returns - ------- - tuple - - """ - return (isinstance(x, t) for x in values) - - -any_of = toolz.compose(any, is_one_of) -all_of = toolz.compose(all, is_one_of) - - -def promote_list(val: V | Sequence[V]) -> list[V]: - """Ensure that the value is a list. - - Parameters - ---------- - val - Value to promote - - Returns - ------- - list - - """ - if isinstance(val, list): - return val - elif isinstance(val, dict): - return [val] - elif is_iterable(val): - return list(val) - elif val is None: - return [] - else: - return [val] - - -def promote_tuple(val: V | Sequence[V]) -> tuple[V]: - """Ensure that the value is a tuple. - - Parameters - ---------- - val - Value to promote - - Returns - ------- - tuple - - """ - if isinstance(val, tuple): - return val - elif is_iterable(val): - return tuple(val) - elif val is None: - return () - else: - return (val,) - - -def is_function(v: Any) -> bool: - """Check if the given object is a function. - - Returns - ------- - bool - Whether `v` is a function - - """ - return isinstance(v, (types.FunctionType, types.LambdaType)) - - -def log(msg: str) -> None: - """Log `msg` using ``options.verbose_log`` if set, otherwise ``print``.""" - from bigframes_vendored.ibis.config import options - - if options.verbose: - (options.verbose_log or print)(msg) - - -def approx_equal(a: Real, b: Real, eps: Real): - """Return whether the difference between `a` and `b` is less than `eps`. - - Raises - ------ - AssertionError - - """ - assert abs(a - b) < eps - - -def safe_index(elements: Sequence[int], value: int) -> int: - """Find the location of `value` in `elements`. - - Return -1 if `value` is not found instead of raising ``ValueError``. - - Parameters - ---------- - elements - Elements to index into - value : int - Index of the given sequence/elements - - Returns - ------- - int - - Examples - -------- - >>> sequence = [1, 2, 3] - >>> safe_index(sequence, 2) - 1 - >>> safe_index(sequence, 4) - -1 - - """ - try: - return elements.index(value) - except ValueError: - return -1 - - -def is_iterable(o: Any) -> bool: - """Return whether `o` is iterable and not a :class:`str` or :class:`bytes`. - - Parameters - ---------- - o : object - Any python object - - Returns - ------- - bool - - Examples - -------- - >>> is_iterable("1") - False - >>> is_iterable(b"1") - False - >>> is_iterable(iter("1")) - True - >>> is_iterable(i for i in range(1)) - True - >>> is_iterable(1) - False - >>> is_iterable([]) - True - - """ - if isinstance(o, (str, bytes)): - return False - - try: - iter(o) - except TypeError: - return False - else: - return True - - -def convert_unit(value, unit, to, floor: bool = True): - """Convert a value between different units. - - Convert `value`, is assumed to be in units of `unit`, to units of `to`. - If `floor` is true, then use floor division on `value` if necessary. - - Parameters - ---------- - value - Number or numeric ibis expression - unit - Unit of `value` - to - Unit to convert to - floor - Whether or not to use floor division on `value` if necessary. - - Returns - ------- - Union[numbers.Integral, ibis.expr.types.NumericValue] - Integer converted unit - - Examples - -------- - >>> one_second = 1000 - >>> x = convert_unit(one_second, "ms", "s") - >>> x - 1 - >>> one_second = 1 - >>> x = convert_unit(one_second, "s", "ms") - >>> x - 1000 - >>> x = convert_unit(one_second, "s", "s") - >>> x - 1 - >>> x = convert_unit(one_second, "s", "M") - Traceback (most recent call last): - ... - ValueError: Cannot convert to or from unit ... to unit ... - - """ - # Don't do anything if from and to units are equivalent - if unit == to: - return value - - units = ("W", "D", "h", "m", "s", "ms", "us", "ns") - factors = (7, 24, 60, 60, 1000, 1000, 1000) - - monthly_units = ("Y", "Q", "M") - monthly_factors = (4, 3) - - try: - i, j = units.index(unit), units.index(to) - except ValueError: - try: - i, j = monthly_units.index(unit), monthly_units.index(to) - factors = monthly_factors - except ValueError: - raise ValueError( - f"Cannot convert interval value from unit {unit} to unit {to}" - ) - - factor = functools.reduce(operator.mul, factors[min(i, j) : max(i, j)], 1) - assert factor > 1 - - if i < j: - op = operator.mul - else: - assert i > j - op = operator.floordiv if floor else operator.truediv - try: - return op(value.to_expr(), factor).op() - except AttributeError: - return op(value, factor) - - -# taken from the itertools documentation -def consume(iterator: Iterator[T], n: int | None = None) -> None: - """Advance `iterator` n-steps ahead. If `n` is `None`, consume entirely.""" - # Use functions that consume iterators at C speed. - if n is None: - # feed the entire iterator into a zero-length deque - collections.deque(iterator, maxlen=0) - else: - # advance to the empty slice starting at position n - next(itertools.islice(iterator, n, n), None) - - -def flatten_iterable(iterable): - """Recursively flatten the iterable `iterable`.""" - if not is_iterable(iterable): - raise TypeError("flatten is only defined for non-str iterables") - - for item in iterable: - if is_iterable(item): - yield from flatten_iterable(item) - else: - yield item - - -def deprecated_msg(name, *, instead, as_of="", removed_in=""): - msg = f"`{name}` is deprecated" - - msgs = [] - - if as_of: - msgs.append(f"as of v{as_of}") - - if removed_in: - msgs.append(f"removed in v{removed_in}") - - if msgs: - msg += f" {', '.join(msgs)}" - msg += f"; {instead}" - return msg - - -def warn_deprecated(name, *, instead, as_of="", removed_in="", stacklevel=1): - """Warn about deprecated usage. - - The message includes a stacktrace and what to do instead. - """ - - msg = deprecated_msg(name, instead=instead, as_of=as_of, removed_in=removed_in) - warnings.warn(msg, FutureWarning, stacklevel=stacklevel + 1) - - -def append_admonition( - func: Callable, *, msg: str, body: str = "", kind: str = "warning" -) -> str: - """Append a `kind` admonition with `msg` to `func`'s docstring.""" - if docstr := func.__doc__: - preamble, *rest = docstr.split("\n\n", maxsplit=1) - - # count leading spaces and add them to the deprecation warning so the - # docstring parses correctly - leading_spaces = " " * sum( - 1 for _ in itertools.takewhile(str.isspace, rest[0] if rest else []) - ) - - lines = [f"::: {{.callout-{kind}}}", f"## {msg}", ":::"] - admonition_doc = textwrap.indent("\n".join(lines), leading_spaces) - - if body: - rest = [indent(body, spaces=len(leading_spaces) + 4), *rest] - - docstr = "\n\n".join([preamble, admonition_doc, *rest]) - else: - lines = [f"::: {{.callout-{kind}}}", f"## {msg}", ":::"] - admonition_doc = "\n".join(lines) - if body: - admonition_doc += f"\n\n{indent(body, spaces=4)}" - docstr = admonition_doc - return docstr - - -def deprecated(*, instead: str, as_of: str = "", removed_in: str = ""): - """Decorate to warn of deprecated usage and what to do instead.""" - - def decorator(func): - msg = deprecated_msg( - func.__qualname__, instead=instead, as_of=as_of, removed_in=removed_in - ) - - func.__doc__ = append_admonition(func, msg=f"DEPRECATED: {msg}") - - @functools.wraps(func) - def wrapper(*args, **kwargs): - warn_deprecated( - func.__qualname__, - instead=instead, - as_of=as_of, - removed_in=removed_in, - stacklevel=2, - ) - return func(*args, **kwargs) - - return wrapper - - return decorator - - -def backend_sensitive( - *, - msg: str = "This operation differs between backends.", - why: str = "", -): - """Indicate that an API may be sensitive to a backend.""" - - def wrapper(func): - func.__doc__ = append_admonition(func, msg=msg, body=why, kind="info") - return func - - return wrapper - - -def experimental(func): - """Decorate a callable to add warning about API instability in docstring.""" - - func.__doc__ = append_admonition( - func, msg="This API is experimental and subject to change." - ) - return func - - -_common_package_aliases = { - "pa": "pyarrow", - "pd": "pandas", - "np": "numpy", - "sk": "sklearn", - "sp": "scipy", - "tf": "tensorflow", -} - - -def unalias_package(name: str) -> str: - return _common_package_aliases.get(name, name) - - -def import_object(qualname: str) -> Any: - """Attempt to import an object given its full qualname. - - Examples - -------- - >>> ex = import_object("ibis.examples") - - Is the same as - - >>> from ibis import examples as ex - - """ - mod_name, name = qualname.rsplit(".", 1) - mod = importlib.import_module(mod_name) - try: - return getattr(mod, name) - except AttributeError: - raise ImportError(f"cannot import name {name!r} from {mod_name!r}") from None - - -def normalize_filename(source: str | Path) -> str: - source = str(source) - for prefix in ( - "parquet", - "csv", - "csv.gz", - "txt", - "txt.gz", - "tsv", - "tsv.gz", - "file", - ): - source = source.removeprefix(f"{prefix}://") - - def _absolufy_paths(name): - if not name.startswith( - ("http", "s3", "az", "abfs", "abfss", "adl", "gs", "gcs", "azure") - ): - return os.path.abspath(name) - return name - - source = _absolufy_paths(source) - return source - - -def normalize_filenames(source_list): - # Promote to list - source_list = promote_list(source_list) - - return list(map(normalize_filename, source_list)) - - -def gen_name(namespace: str) -> str: - """Create a unique identifier.""" - uid = base64.b32encode(uuid.uuid4().bytes).decode().rstrip("=").lower() - return f"ibis_{namespace}_{uid}" - - -def slice_to_limit_offset( - what: slice, count: ir.IntegerScalar -) -> tuple[int | ir.IntegerScalar, int | ir.IntegerScalar]: - """Convert a Python [`slice`](slice) to a `limit`, `offset` pair. - - Parameters - ---------- - what - The slice to convert - count - The total number of rows in the table as an expression - - Returns - ------- - tuple[int | ir.IntegerScalar, int | ir.IntegerScalar] - The offset and limit to use in a `Table.limit` call - - Examples - -------- - >>> import ibis - >>> t = ibis.table(dict(a="int", b="string"), name="t") - - First 10 rows - >>> count = t.count() - >>> what = slice(0, 10) - >>> limit, offset = slice_to_limit_offset(what, count) - >>> limit - 10 - >>> offset - 0 - - Last 10 rows - >>> what = slice(-10, None) - >>> limit, offset = slice_to_limit_offset(what, count) - >>> limit - 10 - >>> offset - r0 := UnboundTable: t - a int64 - b string - - Add(CountStar(t), -10): CountStar(r0) + -10 - - From 5th row to 10th row - >>> what = slice(5, 10) - >>> limit, offset = slice_to_limit_offset(what, count) - >>> limit, offset - (5, 5) - - """ - if (step := what.step) is not None and step != 1: - raise ValueError("Slice step can only be 1") - - import bigframes_vendored.ibis.expr.api as ibis_api - - start = what.start - stop = what.stop - - if start is None or start >= 0: - offset = start or 0 - - if stop is None: - limit = None - elif stop == 0: - limit = 0 - elif stop < 0: - limit = count + (stop - offset) - else: # stop > 0 - limit = max(stop - offset, 0) - else: # start < 0 - offset = count + start - - if stop is None: - limit = -start - elif stop == 0: - limit = offset = 0 - elif stop < 0: - limit = max(stop - start, 0) - if limit == 0: - offset = 0 - else: # stop > 0 - limit = ibis_api.greatest((stop - start) - count, 0) - return limit, offset - - -class Namespace: - """Convenience class for creating patterns for various types from a module. - - Useful to reduce boilerplate when creating patterns for various types from - a module. - - Parameters - ---------- - factory - The pattern to construct with the looked up types. - module - The module object or name to look up the types. - - """ - - __slots__ = ("_factory", "_module") - _factory: Callable - _module: ModuleType - - def __init__(self, factory, module): - if isinstance(module, str): - module = sys.modules[module] - self._module = module - self._factory = factory - - def __getattr__(self, name: str): - obj = getattr(self._module, name) - return self._factory(obj) - - -class PseudoHashable(Coercible, Generic[V]): - """A wrapper that provides a best effort precomputed hash.""" - - __slots__ = ("obj", "hash") - obj: V - - def __init__(self, obj: V): - if isinstance(obj, collections.abc.Hashable): - raise TypeError(f"Cannot wrap a hashable object: {obj!r}") - elif isinstance(obj, collections.abc.Sequence): - hashable_obj = tuple(obj) - elif isinstance(obj, collections.abc.Mapping): - hashable_obj = tuple(obj.items()) - elif isinstance(obj, collections.abc.Set): - hashable_obj = frozenset(obj) - else: - hashable_obj = id(obj) - - self.obj = obj - self.hash = hash((type(obj), hashable_obj)) - - @classmethod - def __coerce__(cls, value: V) -> PseudoHashable[V]: - if isinstance(value, cls): - return value - return cls(value) - - def __hash__(self): - return self.hash - - def __eq__(self, other): - if isinstance(other, PseudoHashable): - return self.obj == other.obj - else: - return NotImplemented - - def __ne__(self, other): - if isinstance(other, PseudoHashable): - return self.obj != other.obj - else: - return NotImplemented - - -def chunks(n: int, *, chunk_size: int) -> Iterator[tuple[int, int]]: - """Return an iterator of chunk start and end indices. - - Parameters - ---------- - n - The total number of elements. - chunk_size - The size of each chunk. - - Returns - ------- - int - THE start and end indices of each chunk. - - Examples - -------- - >>> list(chunks(10, chunk_size=3)) - [(0, 3), (3, 6), (6, 9), (9, 10)] - >>> list(chunks(10, chunk_size=4)) - [(0, 4), (4, 8), (8, 10)] - """ - return ((start, min(start + chunk_size, n)) for start in range(0, n, chunk_size)) diff --git a/third_party/bigframes_vendored/pandas/README.md b/third_party/bigframes_vendored/pandas/README.md index 1aa5068d5e5..9f2bc800e84 100644 --- a/third_party/bigframes_vendored/pandas/README.md +++ b/third_party/bigframes_vendored/pandas/README.md @@ -6,6 +6,7 @@ # pandas: powerful Python data analysis toolkit [![PyPI Latest Release](https://img.shields.io/pypi/v/pandas.svg)](https://pypi.org/project/pandas/) +[![Conda Latest Release](https://anaconda.org/conda-forge/pandas/badges/version.svg)](https://anaconda.org/anaconda/pandas/) [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.3509134.svg)](https://doi.org/10.5281/zenodo.3509134) [![Package Status](https://img.shields.io/pypi/status/pandas.svg)](https://pypi.org/project/pandas/) [![License](https://img.shields.io/pypi/l/pandas.svg)](https://github.com/pandas-dev/pandas/blob/main/LICENSE) @@ -85,10 +86,15 @@ The source code is currently hosted on GitHub at: https://github.com/pandas-dev/pandas Binary installers for the latest released version are available at the [Python -Package Index (PyPI)](https://pypi.org/project/pandas). +Package Index (PyPI)](https://pypi.org/project/pandas) and on [Conda](https://docs.conda.io/en/latest/). ```sh -# PyPI +# conda +conda install -c conda-forge pandas +``` + +```sh +# or PyPI pip install pandas ``` diff --git a/third_party/bigframes_vendored/pandas/_config/config.py b/third_party/bigframes_vendored/pandas/_config/config.py index 418f5868e57..8abaca76c78 100644 --- a/third_party/bigframes_vendored/pandas/_config/config.py +++ b/third_party/bigframes_vendored/pandas/_config/config.py @@ -2,26 +2,20 @@ import contextlib import operator +import bigframes + class option_context(contextlib.ContextDecorator): """ - Context manager to temporarily set thread-local options in the `with` - statement context. + Context manager to temporarily set options in the `with` statement context. You need to invoke as ``option_context(pat, val, [(pat, val), ...])``. - .. note:: - - `"bigquery"` options can't be changed on a running session. Setting any - of these options creates a new thread-local session that only lives for - the lifetime of the context manager. - - **Examples:** - - >>> import bigframes - - >>> with bigframes.option_context('display.max_rows', 10, 'display.max_columns', 5): - ... pass + Examples + -------- + >>> import bigframes + >>> with bigframes.option_context('display.max_rows', 10, 'display.max_columns', 5): + ... pass """ def __init__(self, *args) -> None: @@ -33,47 +27,19 @@ def __init__(self, *args) -> None: self.ops = list(zip(args[::2], args[1::2])) def __enter__(self) -> None: - # Avoid problems with circular imports. - import bigframes._config - self.undo = [ - (pat, operator.attrgetter(pat)(bigframes._config.options)) - for pat, _ in self.ops - # Don't try to undo changes to bigquery options. We're starting and - # closing a new thread-local session if those are set. - if not pat.startswith("bigquery.") + (pat, operator.attrgetter(pat)(bigframes.options)) for pat, val in self.ops ] for pat, val in self.ops: self._set_option(pat, val) def __exit__(self, *args) -> None: - # Avoid problems with circular imports. - import bigframes._config - import bigframes.core.global_session - if self.undo: for pat, val in self.undo: self._set_option(pat, val) - # TODO(tswast): What to do if someone nests several context managers - # with separate "bigquery" options? We might need a "stack" of - # sessions if we allow that. - if bigframes._config.options.is_bigquery_thread_local: - bigframes.core.global_session.close_session() - - # Reset bigquery_options so that we're no longer thread-local. - bigframes._config.options._local.bigquery_options = None - def _set_option(self, pat, val): - # Avoid problems with circular imports. - import bigframes._config - root, attr = pat.rsplit(".", 1) - - # We are now using a thread-specific session. - if root == "bigquery": - bigframes._config.options._init_bigquery_thread_local() - - parent = operator.attrgetter(root)(bigframes._config.options) + parent = operator.attrgetter(root)(bigframes.options) setattr(parent, attr, val) diff --git a/third_party/bigframes_vendored/pandas/core/arrays/arrow/accessors.py b/third_party/bigframes_vendored/pandas/core/arrays/arrow/accessors.py index 94319dbc102..8e3ea06a3d4 100644 --- a/third_party/bigframes_vendored/pandas/core/arrays/arrow/accessors.py +++ b/third_party/bigframes_vendored/pandas/core/arrays/arrow/accessors.py @@ -6,67 +6,6 @@ from bigframes import constants -class ListAccessor: - """Accessor object for list data properties of the Series values.""" - - def len(self): - """Compute the length of each list in the Series. - - **See Also:** - - - :func:`StringMethods.len` : Compute the length of each element in the Series/Index. - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> s = bpd.Series( - ... [ - ... [1, 2, 3], - ... [3], - ... ], - ... dtype=bpd.ArrowDtype(pa.list_(pa.int64())), - ... ) - >>> s.list.len() - 0 3 - 1 1 - dtype: Int64 - - Returns: - bigframes.series.Series: A Series or Index of integer values indicating - the length of each element in the Series or Index. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def __getitem__(self, key: int | slice): - """Index or slice lists in the Series. - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> s = bpd.Series( - ... [ - ... [1, 2, 3], - ... [3], - ... ], - ... dtype=bpd.ArrowDtype(pa.list_(pa.int64())), - ... ) - >>> s.list[0] - 0 1 - 1 3 - dtype: Int64 - - Args: - key (int | slice): Index or slice of indices to access from each list. - For integer indices, only non-negative values are accepted. For - slices, you must use a non-negative start, a non-negative end, and - a step of 1. - - Returns: - bigframes.series.Series: The list at requested index. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - class StructAccessor: """ Accessor object for structured data properties of the Series values. @@ -79,6 +18,8 @@ def field(self, name_or_index: str | int): **Examples:** >>> import bigframes.pandas as bpd + >>> import pyarrow as pa + >>> bpd.options.display.progress_bar = None >>> s = bpd.Series( ... [ ... {"version": 1, "project": "pandas"}, @@ -123,6 +64,8 @@ def explode(self): **Examples:** >>> import bigframes.pandas as bpd + >>> import pyarrow as pa + >>> bpd.options.display.progress_bar = None >>> s = bpd.Series( ... [ ... {"version": 1, "project": "pandas"}, @@ -149,82 +92,3 @@ def explode(self): The data corresponding to all child fields. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - @property - def dtypes(self): - """ - Return the dtype object of each child field of the struct. - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> s = bpd.Series( - ... [ - ... {"version": 1, "project": "pandas"}, - ... {"version": 2, "project": "pandas"}, - ... {"version": 1, "project": "numpy"}, - ... ], - ... dtype=bpd.ArrowDtype(pa.struct( - ... [("version", pa.int64()), ("project", pa.string())] - ... )) - ... ) - >>> s.struct.dtypes - version int64[pyarrow] - project string[pyarrow] - dtype: object - - Returns: - A *pandas* Series with the data type of all child fields. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - -class StructFrameAccessor: - """ - Accessor object for structured data properties of the DataFrame values. - """ - - def explode(self, column, *, separator: str = "."): - """ - Extract all child fields of struct column(s) and add to the DataFrame. - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> countries = bpd.Series(["cn", "es", "us"]) - >>> files = bpd.Series( - ... [ - ... {"version": 1, "project": "pandas"}, - ... {"version": 2, "project": "pandas"}, - ... {"version": 1, "project": "numpy"}, - ... ], - ... dtype=bpd.ArrowDtype(pa.struct( - ... [("version", pa.int64()), ("project", pa.string())] - ... )) - ... ) - >>> downloads = bpd.Series([100, 200, 300]) - >>> df = bpd.DataFrame({"country": countries, "file": files, "download_count": downloads}) - >>> df.struct.explode("file") - country file.version file.project download_count - 0 cn 1 pandas 100 - 1 es 2 pandas 200 - 2 us 1 numpy 300 - - [3 rows x 4 columns] - - Args: - column: - Column(s) to explode. For multiple columns, specify a non-empty - list with each element be str or tuple, and all specified - columns their list-like data on same row of the frame must - have matching length. - separator: - Separator/delimiter to use to separate the original column name - from the sub-field column name. - - - Returns: - DataFrame: - Original DataFrame with exploded struct column(s). - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) diff --git a/third_party/bigframes_vendored/pandas/core/arrays/datetimelike.py b/third_party/bigframes_vendored/pandas/core/arrays/datetimelike.py deleted file mode 100644 index ace91dad1e8..00000000000 --- a/third_party/bigframes_vendored/pandas/core/arrays/datetimelike.py +++ /dev/null @@ -1,101 +0,0 @@ -# Contains code from https://github.com/pandas-dev/pandas/blob/main/pandas/core/arrays/datetimelike.py - -from bigframes import constants - - -class DatelikeOps: - def strftime(self, date_format: str): - """ - Convert to string Series using specified date_format. - - Return a Series of formatted strings specified by date_format. Details - of the string format can be found in BigQuery format elements doc: - https://cloud.google.com/bigquery/docs/reference/standard-sql/format-elements#format_elements_date_time. - - **Examples:** - - >>> import bigframes.pandas as bpd - - >>> s = bpd.to_datetime( - ... ['2014-08-15 08:15:12', '2012-02-29 08:15:12+06:00', '2015-08-15 08:15:12+05:00'], - ... utc=True - ... ).astype("timestamp[us, tz=UTC][pyarrow]") - - >>> s.dt.strftime("%B %d, %Y, %r") - 0 August 15, 2014, 08:15:12 AM - 1 February 29, 2012, 02:15:12 AM - 2 August 15, 2015, 03:15:12 AM - dtype: string - - Args: - date_format (str): - Date format string (e.g. "%Y-%m-%d"). - - Returns: - bigframes.pandas.Series: - Series of formatted strings. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def normalize(self): - """ - Convert times to midnight. - - The time component of the date-time is converted to midnight i.e. - 00:00:00. This is useful in cases when the time does not matter. - The return dtype will match the source series. - - This method is available on Series with datetime values under the - .dt accessor. - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> s = bpd.Series(pd.date_range( - ... start='2014-08-01 10:00', - ... freq='h', - ... periods=3, - ... tz='Asia/Calcutta')) # note timezones will be converted to UTC here - >>> s.dt.normalize() - 0 2014-08-01 00:00:00+00:00 - 1 2014-08-01 00:00:00+00:00 - 2 2014-08-01 00:00:00+00:00 - dtype: timestamp[us, tz=UTC][pyarrow] - - Returns: - bigframes.pandas.Series: - Series of the same dtype as the data. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def floor(self, freq: str): - """ - Perform floor operation on the data to the specified freq. - - Supported freq arguments are: 'Y' (year), 'Q' (quarter), 'M' - (month), 'W' (week), 'D' (day), 'h' (hour), 'min' (minute), 's' - (second), 'ms' (microsecond), 'us' (nanosecond), 'ns' (nanosecond) - - Behavior around clock changes (i.e. daylight savings) is determined - by the SQL engine, so "ambiguous" and "nonexistent" parameters are not - supported. Y, Q, M, and W freqs are not supported by pandas as of - version 2.2, but have been added here due to backend support. - - **Examples:** - - >>> rng = pd.date_range('1/1/2018 11:59:00', periods=3, freq='min') - >>> bpd.Series(rng).dt.floor("h") - 0 2018-01-01 11:00:00 - 1 2018-01-01 12:00:00 - 2 2018-01-01 12:00:00 - dtype: timestamp[us][pyarrow] - - Args: - freq (str): - Frequency string (e.g. "D", "min", "s"). - - Returns: - bigframes.pandas.Series: - Series of the same dtype as the data. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) diff --git a/third_party/bigframes_vendored/pandas/core/col.py b/third_party/bigframes_vendored/pandas/core/col.py deleted file mode 100644 index 9b71293a7e3..00000000000 --- a/third_party/bigframes_vendored/pandas/core/col.py +++ /dev/null @@ -1,36 +0,0 @@ -# Contains code from https://github.com/pandas-dev/pandas/blob/main/pandas/core/col.py -from __future__ import annotations - -from collections.abc import Hashable - -from bigframes import constants - - -class Expression: - """ - Class representing a deferred column. - - This is not meant to be instantiated directly. Instead, use :meth:`pandas.col`. - """ - - -def col(col_name: Hashable) -> Expression: - """ - Generate deferred object representing a column of a DataFrame. - - Any place which accepts ``lambda df: df[col_name]``, such as - :meth:`DataFrame.assign` or :meth:`DataFrame.loc`, can also accept - ``pd.col(col_name)``. - - Args: - col_name (Hashable): - Column name. - - Returns: - Expression: - A deferred object representing a column of a DataFrame. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - -__all__ = ["Expression", "col"] diff --git a/third_party/bigframes_vendored/pandas/core/common.py b/third_party/bigframes_vendored/pandas/core/common.py deleted file mode 100644 index 970ba92a91c..00000000000 --- a/third_party/bigframes_vendored/pandas/core/common.py +++ /dev/null @@ -1,68 +0,0 @@ -# Contains code from https://github.com/pandas-dev/pandas/blob/main/pandas/core/common.py -from __future__ import annotations - -from typing import TYPE_CHECKING, Callable - -from bigframes_vendored.pandas.core.dtypes.inference import iterable_not_string - -if TYPE_CHECKING: - from bigframes_vendored.pandas.pandas._typing import T - - -def pipe( - obj, func: Callable[..., T] | tuple[Callable[..., T], str], *args, **kwargs -) -> T: - """ - Apply a function ``func`` to object ``obj`` either by passing obj as the - first argument to the function or, in the case that the func is a tuple, - interpret the first element of the tuple as a function and pass the obj to - that function as a keyword argument whose key is the value of the second - element of the tuple. - - Args: - func (callable or tuple of (callable, str)): - Function to apply to this object or, alternatively, a - ``(callable, data_keyword)`` tuple where ``data_keyword`` is a - string indicating the keyword of ``callable`` that expects the - object. - args (iterable, optional): - Positional arguments passed into ``func``. - kwargs (dict, optional): - A dictionary of keyword arguments passed into ``func``. - - Returns: - object: the return type of ``func``. - """ - if isinstance(func, tuple): - func, target = func - if target in kwargs: - msg = f"{target} is both the pipe target and a keyword argument" - raise ValueError(msg) - kwargs[target] = obj - return func(*args, **kwargs) - else: - return func(obj, *args, **kwargs) - - -def flatten(line): - """ - Flatten an arbitrarily nested sequence. - - Parameters - ---------- - line : sequence - The non string sequence to flatten - - Notes - ----- - This doesn't consider strings sequences. - - Returns - ------- - flattened : generator - """ - for element in line: - if iterable_not_string(element): - yield from flatten(element) - else: - yield element diff --git a/third_party/bigframes_vendored/pandas/core/computation/align.py b/third_party/bigframes_vendored/pandas/core/computation/align.py deleted file mode 100644 index fbc53a094df..00000000000 --- a/third_party/bigframes_vendored/pandas/core/computation/align.py +++ /dev/null @@ -1,226 +0,0 @@ -# Contains code from https://github.com/pandas-dev/pandas/blob/main/pandas/core/computation/align.py -""" -Core eval alignment algorithms. -""" - -from __future__ import annotations - -import warnings -from functools import partial, wraps -from typing import TYPE_CHECKING, Callable, Union - -import bigframes_vendored.pandas.core.common as com -import numpy as np -from bigframes_vendored.pandas.core.computation.common import result_type_many -from bigframes_vendored.pandas.util._exceptions import find_stack_level -from pandas.errors import PerformanceWarning - -if TYPE_CHECKING: - from collections.abc import Sequence - - from bigframes_vendored.pandas.core.indexes.base import Index - from pandas._typing import F - - from bigframes.pandas import DataFrame, Series - - FrameT = Union[Series, DataFrame] - - -def _align_core_single_unary_op( - term, -) -> tuple[partial | FrameT, dict[str, Index] | None]: - typ: partial | FrameT - axes: dict[str, Index] | None = None - - if isinstance(term.value, np.ndarray): - typ = partial(np.asanyarray, dtype=term.value.dtype) - else: - typ = type(term.value) - if hasattr(term.value, "axes"): - axes = _zip_axes_from_type(typ, term.value.axes) - - return typ, axes - - -def _zip_axes_from_type(typ: FrameT, new_axes: Sequence[Index]) -> dict[str, Index]: - return {name: new_axes[i] for i, name in enumerate(typ._AXIS_ORDERS)} - - -def _any_pandas_objects(terms) -> bool: - """ - Check a sequence of terms for instances of PandasObject. - """ - return any(is_pandas_object(term.value) for term in terms) - - -def _filter_special_cases(f) -> Callable[[F], F]: - @wraps(f) - def wrapper(terms): - # single unary operand - if len(terms) == 1: - return _align_core_single_unary_op(terms[0]) - - term_values = (term.value for term in terms) - - # we don't have any pandas objects - if not _any_pandas_objects(terms): - return result_type_many(*term_values), None - - return f(terms) - - return wrapper - - -@_filter_special_cases -def _align_core(terms): - term_index = [i for i, term in enumerate(terms) if hasattr(term.value, "axes")] - term_dims = [terms[i].value.ndim for i in term_index] - - from pandas import Series - - ndims = Series(dict(zip(term_index, term_dims))) - - # initial axes are the axes of the largest-axis'd term - biggest = terms[ndims.idxmax()].value - typ = biggest._constructor - axes = biggest.axes - naxes = len(axes) - gt_than_one_axis = naxes > 1 - - for value in (terms[i].value for i in term_index): - value_is_series = is_series(value) - is_series_and_gt_one_axis = value_is_series and gt_than_one_axis - - for axis, items in enumerate(value.axes): - if is_series_and_gt_one_axis: - ax, itm = naxes - 1, value.index - else: - ax, itm = axis, items - - if not axes[ax].is_(itm): - axes[ax] = axes[ax].join(itm, how="outer") - - for i, ndim in ndims.items(): - for axis, items in zip(range(ndim), axes): - ti = terms[i].value - - if hasattr(ti, "reindex"): - transpose = value_is_series(ti) and naxes > 1 - reindexer = axes[naxes - 1] if transpose else items - - term_axis_size = len(ti.axes[axis]) - reindexer_size = len(reindexer) - - ordm = np.log10(max(1, abs(reindexer_size - term_axis_size))) - if ordm >= 1 and reindexer_size >= 10000: - w = ( - f"Alignment difference on axis {axis} is larger " - f"than an order of magnitude on term {repr(terms[i].name)}, " - f"by more than {ordm:.4g}; performance may suffer." - ) - warnings.warn( - w, category=PerformanceWarning, stacklevel=find_stack_level() - ) - - obj = ti.reindex(reindexer, axis=axis, copy=False) - terms[i].update(obj) - - terms[i].update(terms[i].value.values) - - return typ, _zip_axes_from_type(typ, axes) - - -def align_terms(terms): - """ - Align a set of terms. - """ - try: - # flatten the parse tree (a nested list, really) - terms = list(com.flatten(terms)) - except TypeError: - # can't iterate so it must just be a constant or single variable - if is_series_or_dataframe(terms.value): - typ = type(terms.value) - return typ, _zip_axes_from_type(typ, terms.value.axes) - return np.result_type(terms.type), None - - # if all resolved variables are numeric scalars - if all(term.is_scalar for term in terms): - return result_type_many(*(term.value for term in terms)).type, None - - # perform the main alignment - typ, axes = _align_core(terms) - return typ, axes - - -def reconstruct_object(typ, obj, axes, dtype): - """ - Reconstruct an object given its type, raw value, and possibly empty - (None) axes. - - Parameters - ---------- - typ : object - A type - obj : object - The value to use in the type constructor - axes : dict - The axes to use to construct the resulting pandas object - - Returns - ------- - ret : typ - An object of type ``typ`` with the value `obj` and possible axes - `axes`. - """ - try: - typ = typ.type - except AttributeError: - pass - - res_t = np.result_type(obj.dtype, dtype) - - if not isinstance(typ, partial) and is_pandas_type(typ): - return typ(obj, dtype=res_t, **axes) - - # special case for pathological things like ~True/~False - if hasattr(res_t, "type") and typ == np.bool_ and res_t != np.bool_: - ret_value = res_t.type(obj) - else: - ret_value = typ(obj).astype(res_t) - # The condition is to distinguish 0-dim array (returned in case of - # scalar) and 1 element array - # e.g. np.array(0) and np.array([0]) - if ( - len(obj.shape) == 1 - and len(obj) == 1 - and not isinstance(ret_value, np.ndarray) - ): - ret_value = np.array([ret_value]).astype(res_t) - - return ret_value - - -# Custom to recognize BigFrames types -def is_series(obj) -> bool: - from bigframes_vendored.pandas.core.series import Series - - return isinstance(obj, Series) - - -def is_series_or_dataframe(obj) -> bool: - from bigframes.pandas import DataFrame, Series - - return isinstance(obj, Series | DataFrame) - - -def is_pandas_object(obj) -> bool: - from bigframes.pandas import DataFrame, Index, Series - - return isinstance(obj, Series | DataFrame | Index) - - -def is_pandas_type(type) -> bool: - from bigframes.pandas import DataFrame, Index, Series - - return issubclass(type, Series | DataFrame | Index) diff --git a/third_party/bigframes_vendored/pandas/core/computation/common.py b/third_party/bigframes_vendored/pandas/core/computation/common.py deleted file mode 100644 index 7775489d0df..00000000000 --- a/third_party/bigframes_vendored/pandas/core/computation/common.py +++ /dev/null @@ -1,48 +0,0 @@ -# Contains code from https://github.com/pandas-dev/pandas/blob/main/pandas/core/computation/common.py -from __future__ import annotations - -from functools import reduce - -import numpy as np -from pandas._config import get_option - - -def ensure_decoded(s) -> str: - """ - If we have bytes, decode them to unicode. - """ - if isinstance(s, (np.bytes_, bytes)): - s = s.decode(get_option("display.encoding")) - return s - - -def result_type_many(*arrays_and_dtypes): - """ - Wrapper around numpy.result_type which overcomes the NPY_MAXARGS (32) - argument limit. - """ - try: - return np.result_type(*arrays_and_dtypes) - except ValueError: - # we have > NPY_MAXARGS terms in our expression - return reduce(np.result_type, arrays_and_dtypes) - except TypeError: - from pandas.core.dtypes.cast import find_common_type - from pandas.core.dtypes.common import is_extension_array_dtype - - arr_and_dtypes = list(arrays_and_dtypes) - ea_dtypes, non_ea_dtypes = [], [] - for arr_or_dtype in arr_and_dtypes: - if is_extension_array_dtype(arr_or_dtype): - ea_dtypes.append(arr_or_dtype) - else: - non_ea_dtypes.append(arr_or_dtype) - - if non_ea_dtypes: - try: - np_dtype = np.result_type(*non_ea_dtypes) - except ValueError: - np_dtype = reduce(np.result_type, arrays_and_dtypes) - return find_common_type(ea_dtypes + [np_dtype]) - - return find_common_type(ea_dtypes) diff --git a/third_party/bigframes_vendored/pandas/core/computation/engines.py b/third_party/bigframes_vendored/pandas/core/computation/engines.py deleted file mode 100644 index 8902bb08adb..00000000000 --- a/third_party/bigframes_vendored/pandas/core/computation/engines.py +++ /dev/null @@ -1,95 +0,0 @@ -# Contains code from https://github.com/pandas-dev/pandas/blob/main/pandas/core/computation/engines.py -""" -Engine classes for :func:`~pandas.eval` -""" - -from __future__ import annotations - -import abc - -from bigframes_vendored.pandas.core.computation.align import ( - align_terms, - reconstruct_object, -) -from pandas.io.formats import printing - - -class AbstractEngine(metaclass=abc.ABCMeta): - """Object serving as a base class for all engines.""" - - has_neg_frac = False - - def __init__(self, expr) -> None: - self.expr = expr - self.aligned_axes = None - self.result_type = None - - def convert(self) -> str: - """ - Convert an expression for evaluation. - - Defaults to return the expression as a string. - """ - return printing.pprint_thing(self.expr) - - def evaluate(self) -> object: - """ - Run the engine on the expression. - - This method performs alignment which is necessary no matter what engine - is being used, thus its implementation is in the base class. - - Returns - ------- - object - The result of the passed expression. - """ - if not self._is_aligned: - self.result_type, self.aligned_axes = align_terms(self.expr.terms) - - # make sure no names in resolvers and locals/globals clash - res = self._evaluate() - return reconstruct_object( - self.result_type, res, self.aligned_axes, self.expr.terms.return_type - ) - - @property - def _is_aligned(self) -> bool: - return self.aligned_axes is not None and self.result_type is not None - - @abc.abstractmethod - def _evaluate(self): - """ - Return an evaluated expression. - - Parameters - ---------- - env : Scope - The local and global environment in which to evaluate an - expression. - - Notes - ----- - Must be implemented by subclasses. - """ - - -class PythonEngine(AbstractEngine): - """ - Evaluate an expression in Python space. - - Mostly for testing purposes. - """ - - has_neg_frac = False - - def evaluate(self): - return self.expr() - - def _evaluate(self) -> None: - pass - - -ENGINES: dict[str, type[AbstractEngine]] = { - "python": PythonEngine, -} diff --git a/third_party/bigframes_vendored/pandas/core/computation/eval.py b/third_party/bigframes_vendored/pandas/core/computation/eval.py deleted file mode 100644 index bf7e1de3bf1..00000000000 --- a/third_party/bigframes_vendored/pandas/core/computation/eval.py +++ /dev/null @@ -1,369 +0,0 @@ -# Contains code from https://github.com/pandas-dev/pandas/blob/main/pandas/core/computation/eval.py -""" -Top level ``eval`` module. -""" - -from __future__ import annotations - -import tokenize -import warnings -from typing import TYPE_CHECKING - -from bigframes_vendored.pandas.core.computation.engines import ENGINES -from bigframes_vendored.pandas.core.computation.expr import PARSERS, Expr -from bigframes_vendored.pandas.core.computation.parsing import tokenize_string -from bigframes_vendored.pandas.core.computation.scope import ensure_scope -from bigframes_vendored.pandas.util._validators import validate_bool_kwarg -from pandas.io.formats.printing import pprint_thing - -if TYPE_CHECKING: - from pandas.core.computation.ops import BinOp - - -def _check_engine(engine: str | None) -> str: - """ - Make sure a valid engine is passed. - - Parameters - ---------- - engine : str - String to validate. - - Raises - ------ - KeyError - * If an invalid engine is passed. - - Returns - ------- - str - Engine name. - """ - - if engine is None: - engine = "python" - - if engine not in ENGINES: - valid_engines = list(ENGINES.keys()) - raise KeyError( - f"Invalid engine '{engine}' passed, valid engines are {valid_engines}" - ) - - return engine - - -def _check_parser(parser: str): - """ - Make sure a valid parser is passed. - - Parameters - ---------- - parser : str - - Raises - ------ - KeyError - * If an invalid parser is passed - """ - if parser not in PARSERS: - raise KeyError( - f"Invalid parser '{parser}' passed, valid parsers are {PARSERS.keys()}" - ) - - -def _check_resolvers(resolvers): - if resolvers is not None: - for resolver in resolvers: - if not hasattr(resolver, "__getitem__"): - name = type(resolver).__name__ - raise TypeError( - f"Resolver of type '{name}' does not " - "implement the __getitem__ method" - ) - - -def _check_expression(expr): - """ - Make sure an expression is not an empty string - - Parameters - ---------- - expr : object - An object that can be converted to a string - - Raises - ------ - ValueError - * If expr is an empty string - """ - if not expr: - raise ValueError("expr cannot be an empty string") - - -def _convert_expression(expr) -> str: - """ - Convert an object to an expression. - - This function converts an object to an expression (a unicode string) and - checks to make sure it isn't empty after conversion. This is used to - convert operators to their string representation for recursive calls to - :func:`~pandas.eval`. - - Parameters - ---------- - expr : object - The object to be converted to a string. - - Returns - ------- - str - The string representation of an object. - - Raises - ------ - ValueError - * If the expression is empty. - """ - s = pprint_thing(expr) - _check_expression(s) - return s - - -def _check_for_locals(expr: str, stack_level: int, parser: str): - at_top_of_stack = stack_level == 0 - not_pandas_parser = parser != "pandas" - - if not_pandas_parser: - msg = "The '@' prefix is only supported by the pandas parser" - elif at_top_of_stack: - msg = ( - "The '@' prefix is not allowed in top-level eval calls.\n" - "please refer to your variables by name without the '@' prefix." - ) - - if at_top_of_stack or not_pandas_parser: - for toknum, tokval in tokenize_string(expr): - if toknum == tokenize.OP and tokval == "@": - raise SyntaxError(msg) - - -def eval( - expr: str | BinOp, # we leave BinOp out of the docstr bc it isn't for users - parser: str = "pandas", - engine: str | None = None, - local_dict=None, - global_dict=None, - resolvers=(), - level: int = 0, - target=None, - inplace: bool = False, -): - """ - Evaluate a Python expression as a string using various backends. - - The following arithmetic operations are supported: ``+``, ``-``, ``*``, - ``/``, ``**``, ``%``, ``//`` (python engine only) along with the following - boolean operations: ``|`` (or), ``&`` (and), and ``~`` (not). - Additionally, the ``'pandas'`` parser allows the use of :keyword:`and`, - :keyword:`or`, and :keyword:`not` with the same semantics as the - corresponding bitwise operators. :class:`~pandas.Series` and - :class:`~pandas.DataFrame` objects are supported and behave as they would - with plain ol' Python evaluation. - - **Examples:** - - - >>> df = bpd.DataFrame({"animal": ["dog", "pig"], "age": [10, 20]}) - >>> df - animal age - 0 dog 10 - 1 pig 20 - - [2 rows x 2 columns] - - We can add a new column using ``pd.eval``: - - >>> df.eval("double_age = age * 2") - animal age double_age - 0 dog 10 20 - 1 pig 20 40 - - [2 rows x 3 columns] - - Args: - expr (str): - The expression to evaluate. This string cannot contain any Python - `statements - `__, - only Python `expressions - `__. - parser ({'pandas', 'python'}, default 'pandas'): - The parser to use to construct the syntax tree from the expression. The - default of ``'pandas'`` parses code slightly different than standard - Python. Alternatively, you can parse an expression using the - ``'python'`` parser to retain strict Python semantics. See the - :ref:`enhancing performance ` documentation for - more details. - engine ({'python'}, default None): - - The engine used to evaluate the expression. Supported engines are - - - None : defaults to ``python`` - - ``'python'`` : Performs operations as if you had ``eval``'d in top - level python. This engine is generally not that useful. - - More backends may be available in the future. - local_dict (dict or None, optional): - A dictionary of local variables, taken from locals() by default. - global_dict (dict or None, optional): - A dictionary of global variables, taken from globals() by default. - resolvers (list of dict-like or None, optional): - A list of objects implementing the ``__getitem__`` special method that - you can use to inject an additional collection of namespaces to use for - variable lookup. For example, this is used in the - :meth:`~DataFrame.query` method to inject the - ``DataFrame.index`` and ``DataFrame.columns`` - variables that refer to their respective :class:`~pandas.DataFrame` - instance attributes. - level (int, optional): - The number of prior stack frames to traverse and add to the current - scope. Most users will **not** need to change this parameter. - target (object, optional, default None): - This is the target object for assignment. It is used when there is - variable assignment in the expression. If so, then `target` must - support item assignment with string keys, and if a copy is being - returned, it must also support `.copy()`. - inplace (bool, default False): - If `target` is provided, and the expression mutates `target`, whether - to modify `target` inplace. Otherwise, return a copy of `target` with - the mutation. - - Returns: - ndarray, numeric scalar, DataFrame, Series, or None: - The completion value of evaluating the given code or None if ``inplace=True``. - - Raises: - ValueError: - There are many instances where such an error can be raised: - - - `target=None`, but the expression is multiline. - - The expression is multiline, but not all them have item assignment. - An example of such an arrangement is this: - - a = b + 1 - a + 2 - - Here, there are expressions on different lines, making it multiline, - but the last line has no variable assigned to the output of `a + 2`. - - `inplace=True`, but the expression is missing item assignment. - - Item assignment is provided, but the `target` does not support - string item assignment. - - Item assignment is provided and `inplace=False`, but the `target` - does not support the `.copy()` method - - """ - inplace = validate_bool_kwarg(inplace, "inplace") - - exprs: list[str | BinOp] - if isinstance(expr, str): - _check_expression(expr) - exprs = [e.strip() for e in expr.splitlines() if e.strip() != ""] - else: - # ops.BinOp; for internal compat, not intended to be passed by users - exprs = [expr] - multi_line = len(exprs) > 1 - - if multi_line and target is None: - raise ValueError( - "multi-line expressions are only valid in the " - "context of data, use DataFrame.eval" - ) - engine = _check_engine(engine) - _check_parser(parser) - _check_resolvers(resolvers) - - ret = None - first_expr = True - target_modified = False - - for expr in exprs: - expr = _convert_expression(expr) - _check_for_locals(expr, level, parser) - - # get our (possibly passed-in) scope - env = ensure_scope( - level + 1, - global_dict=global_dict, - local_dict=local_dict, - resolvers=resolvers, - target=target, - ) - - parsed_expr = Expr(expr, engine=engine, parser=parser, env=env) - - # construct the engine and evaluate the parsed expression - eng = ENGINES[engine] - eng_inst = eng(parsed_expr) - ret = eng_inst.evaluate() - - if parsed_expr.assigner is None: - if multi_line: - raise ValueError( - "Multi-line expressions are only valid " - "if all expressions contain an assignment" - ) - if inplace: - raise ValueError("Cannot operate inplace if there is no assignment") - - # assign if needed - assigner = parsed_expr.assigner - from bigframes.pandas import DataFrame, Series - - if env.target is not None and assigner is not None: - target_modified = True - - # if returning a copy, copy only on the first assignment - if not inplace and first_expr: - try: - target = env.target - if isinstance(target, Series | DataFrame): - target = target.copy() - except AttributeError as err: - raise ValueError("Cannot return a copy of the target") from err - else: - target = env.target - - # TypeError is most commonly raised (e.g. int, list), but you - # get IndexError if you try to do this assignment on np.ndarray. - # we will ignore numpy warnings here; e.g. if trying - # to use a non-numeric indexer - try: - with warnings.catch_warnings(record=True): - # TODO: Filter the warnings we actually care about here. - if inplace and isinstance(target, Series | DataFrame): - target.loc[:, assigner] = ret - else: - target[ # pyright: ignore[reportGeneralTypeIssues] - assigner - ] = ret - except (TypeError, IndexError) as err: - raise ValueError("Cannot assign expression output to target") from err - - if not resolvers: - resolvers = ({assigner: ret},) - else: - # existing resolver needs updated to handle - # case of mutating existing column in copy - for resolver in resolvers: - if assigner in resolver: - resolver[assigner] = ret - break - else: - resolvers += ({assigner: ret},) - - ret = None - first_expr = False - - # We want to exclude `inplace=None` as being False. - if inplace is False: - return target if target_modified else ret diff --git a/third_party/bigframes_vendored/pandas/core/computation/expr.py b/third_party/bigframes_vendored/pandas/core/computation/expr.py deleted file mode 100644 index e8def559a88..00000000000 --- a/third_party/bigframes_vendored/pandas/core/computation/expr.py +++ /dev/null @@ -1,829 +0,0 @@ -# Contains code from https://github.com/pandas-dev/pandas/blob/main/pandas/core/computation/expr.py -""" -:func:`~pandas.eval` parsers. -""" - -from __future__ import annotations - -import ast -import tokenize -from functools import partial, reduce -from keyword import iskeyword -from typing import Callable, TypeVar - -import bigframes_vendored.pandas.core.common as com -import numpy as np -from bigframes_vendored.pandas.core.computation.ops import ( - ARITH_OPS_SYMS, - BOOL_OPS_SYMS, - CMP_OPS_SYMS, - LOCAL_TAG, - UNARY_OPS_SYMS, - BinOp, - Constant, - Div, - FuncNode, - Op, - Term, - UnaryOp, - is_term, -) -from bigframes_vendored.pandas.core.computation.parsing import ( - clean_backtick_quoted_toks, - tokenize_string, -) -from bigframes_vendored.pandas.core.computation.scope import Scope -from pandas.errors import UndefinedVariableError -from pandas.io.formats import printing - - -def _rewrite_assign(tok: tuple[int, str]) -> tuple[int, str]: - """ - Rewrite the assignment operator for PyTables expressions that use ``=`` - as a substitute for ``==``. - - Parameters - ---------- - tok : tuple of int, str - ints correspond to the all caps constants in the tokenize module - - Returns - ------- - tuple of int, str - Either the input or token or the replacement values - """ - toknum, tokval = tok - return toknum, "==" if tokval == "=" else tokval - - -def _replace_booleans(tok: tuple[int, str]) -> tuple[int, str]: - """ - Replace ``&`` with ``and`` and ``|`` with ``or`` so that bitwise - precedence is changed to boolean precedence. - - Parameters - ---------- - tok : tuple of int, str - ints correspond to the all caps constants in the tokenize module - - Returns - ------- - tuple of int, str - Either the input or token or the replacement values - """ - toknum, tokval = tok - if toknum == tokenize.OP: - if tokval == "&": - return tokenize.NAME, "and" - elif tokval == "|": - return tokenize.NAME, "or" - return toknum, tokval - return toknum, tokval - - -def _replace_locals(tok: tuple[int, str]) -> tuple[int, str]: - """ - Replace local variables with a syntactically valid name. - - Parameters - ---------- - tok : tuple of int, str - ints correspond to the all caps constants in the tokenize module - - Returns - ------- - tuple of int, str - Either the input or token or the replacement values - - Notes - ----- - This is somewhat of a hack in that we rewrite a string such as ``'@a'`` as - ``'__pd_eval_local_a'`` by telling the tokenizer that ``__pd_eval_local_`` - is a ``tokenize.OP`` and to replace the ``'@'`` symbol with it. - """ - toknum, tokval = tok - if toknum == tokenize.OP and tokval == "@": - return tokenize.OP, LOCAL_TAG - return toknum, tokval - - -def _compose2(f, g): - """ - Compose 2 callables. - """ - return lambda *args, **kwargs: f(g(*args, **kwargs)) - - -def _compose(*funcs): - """ - Compose 2 or more callables. - """ - assert len(funcs) > 1, "At least 2 callables must be passed to compose" - return reduce(_compose2, funcs) - - -def _preparse( - source: str, - f=_compose( - _replace_locals, _replace_booleans, _rewrite_assign, clean_backtick_quoted_toks - ), -) -> str: - """ - Compose a collection of tokenization functions. - - Parameters - ---------- - source : str - A Python source code string - f : callable - This takes a tuple of (toknum, tokval) as its argument and returns a - tuple with the same structure but possibly different elements. Defaults - to the composition of ``_rewrite_assign``, ``_replace_booleans``, and - ``_replace_locals``. - - Returns - ------- - str - Valid Python source code - - Notes - ----- - The `f` parameter can be any callable that takes *and* returns input of the - form ``(toknum, tokval)``, where ``toknum`` is one of the constants from - the ``tokenize`` module and ``tokval`` is a string. - """ - assert callable(f), "f must be callable" - return tokenize.untokenize(f(x) for x in tokenize_string(source)) - - -def _is_type(t): - """ - Factory for a type checking function of type ``t`` or tuple of types. - """ - return lambda x: isinstance(x.value, t) - - -_is_list = _is_type(list) -_is_str = _is_type(str) - - -# partition all AST nodes -_all_nodes = frozenset( - node - for node in (getattr(ast, name) for name in dir(ast)) - if isinstance(node, type) and issubclass(node, ast.AST) -) - - -def _filter_nodes(superclass, all_nodes=_all_nodes): - """ - Filter out AST nodes that are subclasses of ``superclass``. - """ - node_names = (node.__name__ for node in all_nodes if issubclass(node, superclass)) - return frozenset(node_names) - - -_all_node_names = frozenset(x.__name__ for x in _all_nodes) -_mod_nodes = _filter_nodes(ast.mod) -_stmt_nodes = _filter_nodes(ast.stmt) -_expr_nodes = _filter_nodes(ast.expr) -_expr_context_nodes = _filter_nodes(ast.expr_context) -_boolop_nodes = _filter_nodes(ast.boolop) -_operator_nodes = _filter_nodes(ast.operator) -_unary_op_nodes = _filter_nodes(ast.unaryop) -_cmp_op_nodes = _filter_nodes(ast.cmpop) -_comprehension_nodes = _filter_nodes(ast.comprehension) -_handler_nodes = _filter_nodes(ast.excepthandler) -_arguments_nodes = _filter_nodes(ast.arguments) -_keyword_nodes = _filter_nodes(ast.keyword) -_alias_nodes = _filter_nodes(ast.alias) - - -# nodes that we don't support directly but are needed for parsing -_hacked_nodes = frozenset(["Assign", "Module", "Expr"]) - - -_unsupported_expr_nodes = frozenset( - [ - "Yield", - "GeneratorExp", - "IfExp", - "DictComp", - "SetComp", - "Repr", - "Lambda", - "Set", - "AST", - "Is", - "IsNot", - ] -) - -# these nodes are low priority or won't ever be supported (e.g., AST) -_unsupported_nodes = ( - _stmt_nodes - | _mod_nodes - | _handler_nodes - | _arguments_nodes - | _keyword_nodes - | _alias_nodes - | _expr_context_nodes - | _unsupported_expr_nodes -) - _hacked_nodes - -# we're adding a different assignment in some cases to be equality comparison -# and we don't want `stmt` and friends in their so get only the class whose -# names are capitalized -_base_supported_nodes = (_all_node_names - _unsupported_nodes) | _hacked_nodes -intersection = _unsupported_nodes & _base_supported_nodes -_msg = f"cannot both support and not support {intersection}" -assert not intersection, _msg - - -def _node_not_implemented(node_name: str) -> Callable[..., None]: - """ - Return a function that raises a NotImplementedError with a passed node name. - """ - - def f(self, *args, **kwargs): - raise NotImplementedError(f"'{node_name}' nodes are not implemented") - - return f - - -# should be bound by BaseExprVisitor but that creates a circular dependency: -# _T is used in disallow, but disallow is used to define BaseExprVisitor -# https://github.com/microsoft/pyright/issues/2315 -_T = TypeVar("_T") - - -def disallow(nodes: set[str]) -> Callable[[type[_T]], type[_T]]: - """ - Decorator to disallow certain nodes from parsing. Raises a - NotImplementedError instead. - - Returns - ------- - callable - """ - - def disallowed(cls: type[_T]) -> type[_T]: - # error: "Type[_T]" has no attribute "unsupported_nodes" - cls.unsupported_nodes = () # type: ignore[attr-defined] - for node in nodes: - new_method = _node_not_implemented(node) - name = f"visit_{node}" - # error: "Type[_T]" has no attribute "unsupported_nodes" - cls.unsupported_nodes += (name,) # type: ignore[attr-defined] - setattr(cls, name, new_method) - return cls - - return disallowed - - -def _op_maker(op_class, op_symbol): - """ - Return a function to create an op class with its symbol already passed. - - Returns - ------- - callable - """ - - def f(self, node, *args, **kwargs): - """ - Return a partial function with an Op subclass with an operator already passed. - - Returns - ------- - callable - """ - return partial(op_class, op_symbol, *args, **kwargs) - - return f - - -_op_classes = {"binary": BinOp, "unary": UnaryOp} - - -def add_ops(op_classes): - """ - Decorator to add default implementation of ops. - """ - - def f(cls): - for op_attr_name, op_class in op_classes.items(): - ops = getattr(cls, f"{op_attr_name}_ops") - ops_map = getattr(cls, f"{op_attr_name}_op_nodes_map") - for op in ops: - op_node = ops_map[op] - if op_node is not None: - made_op = _op_maker(op_class, op) - setattr(cls, f"visit_{op_node}", made_op) - return cls - - return f - - -@disallow(_unsupported_nodes) -@add_ops(_op_classes) -class BaseExprVisitor(ast.NodeVisitor): - """ - Custom ast walker. Parsers of other engines should subclass this class - if necessary. - - Parameters - ---------- - env : Scope - engine : str - parser : str - preparser : callable - """ - - const_type: type[Term] = Constant - term_type = Term - - binary_ops = CMP_OPS_SYMS + BOOL_OPS_SYMS + ARITH_OPS_SYMS - binary_op_nodes = ( - "Gt", - "Lt", - "GtE", - "LtE", - "Eq", - "NotEq", - "In", - "NotIn", - "BitAnd", - "BitOr", - "And", - "Or", - "Add", - "Sub", - "Mult", - None, - "Pow", - "FloorDiv", - "Mod", - ) - binary_op_nodes_map = dict(zip(binary_ops, binary_op_nodes)) - - unary_ops = UNARY_OPS_SYMS - unary_op_nodes = "UAdd", "USub", "Invert", "Not" - unary_op_nodes_map = dict(zip(unary_ops, unary_op_nodes)) - - rewrite_map = { - ast.Eq: ast.In, - ast.NotEq: ast.NotIn, - ast.In: ast.In, - ast.NotIn: ast.NotIn, - } - - unsupported_nodes: tuple[str, ...] - - def __init__(self, env, engine, parser, preparser=_preparse) -> None: - self.env = env - self.engine = engine - self.parser = parser - self.preparser = preparser - self.assigner = None - - def visit(self, node, **kwargs): - if isinstance(node, str): - clean = self.preparser(node) - try: - node = ast.fix_missing_locations(ast.parse(clean)) - except SyntaxError as e: - if any(iskeyword(x) for x in clean.split()): - e.msg = "Python keyword not valid identifier in numexpr query" - raise e - - method = f"visit_{type(node).__name__}" - visitor = getattr(self, method) - return visitor(node, **kwargs) - - def visit_Module(self, node, **kwargs): - if len(node.body) != 1: - raise SyntaxError("only a single expression is allowed") - expr = node.body[0] - return self.visit(expr, **kwargs) - - def visit_Expr(self, node, **kwargs): - return self.visit(node.value, **kwargs) - - def _rewrite_membership_op(self, node, left, right): - # the kind of the operator (is actually an instance) - op_instance = node.op - op_type = type(op_instance) - - # must be two terms and the comparison operator must be ==/!=/in/not in - if is_term(left) and is_term(right) and op_type in self.rewrite_map: - left_list, right_list = map(_is_list, (left, right)) - left_str, right_str = map(_is_str, (left, right)) - - # if there are any strings or lists in the expression - if left_list or right_list or left_str or right_str: - op_instance = self.rewrite_map[op_type]() - - # pop the string variable out of locals and replace it with a list - # of one string, kind of a hack - if right_str: - name = self.env.add_tmp([right.value]) - right = self.term_type(name, self.env) - - if left_str: - name = self.env.add_tmp([left.value]) - left = self.term_type(name, self.env) - - op = self.visit(op_instance) - return op, op_instance, left, right - - def _maybe_transform_eq_ne(self, node, left=None, right=None): - if left is None: - left = self.visit(node.left, side="left") - if right is None: - right = self.visit(node.right, side="right") - op, op_class, left, right = self._rewrite_membership_op(node, left, right) - return op, op_class, left, right - - def _maybe_downcast_constants(self, left, right): - f32 = np.dtype(np.float32) - if ( - left.is_scalar - and hasattr(left, "value") - and not right.is_scalar - and right.return_type == f32 - ): - # right is a float32 array, left is a scalar - name = self.env.add_tmp(np.float32(left.value)) - left = self.term_type(name, self.env) - if ( - right.is_scalar - and hasattr(right, "value") - and not left.is_scalar - and left.return_type == f32 - ): - # left is a float32 array, right is a scalar - name = self.env.add_tmp(np.float32(right.value)) - right = self.term_type(name, self.env) - - return left, right - - def _maybe_eval(self, binop, eval_in_python): - # eval `in` and `not in` (for now) in "partial" python space - # things that can be evaluated in "eval" space will be turned into - # temporary variables. for example, - # [1,2] in a + 2 * b - # in that case a + 2 * b will be evaluated using numexpr, and the "in" - # call will be evaluated using isin (in python space) - return binop.evaluate( - self.env, self.engine, self.parser, self.term_type, eval_in_python - ) - - def _maybe_evaluate_binop( - self, - op, - op_class, - lhs, - rhs, - eval_in_python=("in", "not in"), - maybe_eval_in_python=("==", "!=", "<", ">", "<=", ">="), - ): - res = op(lhs, rhs) - - if res.has_invalid_return_type: - raise TypeError( - f"unsupported operand type(s) for {res.op}: " - f"'{lhs.type}' and '{rhs.type}'" - ) - - if self.engine != "pytables" and ( - res.op in CMP_OPS_SYMS - and getattr(lhs, "is_datetime", False) - or getattr(rhs, "is_datetime", False) - ): - # all date ops must be done in python bc numexpr doesn't work - # well with NaT - return self._maybe_eval(res, self.binary_ops) - - if res.op in eval_in_python: - # "in"/"not in" ops are always evaluated in python - return self._maybe_eval(res, eval_in_python) - elif self.engine != "pytables": - if ( - getattr(lhs, "return_type", None) == object - or getattr(rhs, "return_type", None) == object - ): - # evaluate "==" and "!=" in python if either of our operands - # has an object return type - return self._maybe_eval(res, eval_in_python + maybe_eval_in_python) - return res - - def visit_BinOp(self, node, **kwargs): - op, op_class, left, right = self._maybe_transform_eq_ne(node) - left, right = self._maybe_downcast_constants(left, right) - return self._maybe_evaluate_binop(op, op_class, left, right) - - def visit_Div(self, node, **kwargs): - return lambda lhs, rhs: Div(lhs, rhs) - - def visit_UnaryOp(self, node, **kwargs): - op = self.visit(node.op) - operand = self.visit(node.operand) - return op(operand) - - def visit_Name(self, node, **kwargs): - return self.term_type(node.id, self.env, **kwargs) - - # TODO(py314): deprecated since Python 3.8. Remove after Python 3.14 is min - def visit_NameConstant(self, node, **kwargs) -> Term: - return self.const_type(node.value, self.env) - - # TODO(py314): deprecated since Python 3.8. Remove after Python 3.14 is min - def visit_Num(self, node, **kwargs) -> Term: - return self.const_type(node.value, self.env) - - def visit_Constant(self, node, **kwargs) -> Term: - return self.const_type(node.value, self.env) - - # TODO(py314): deprecated since Python 3.8. Remove after Python 3.14 is min - def visit_Str(self, node, **kwargs): - name = self.env.add_tmp(node.s) - return self.term_type(name, self.env) - - def visit_List(self, node, **kwargs): - name = self.env.add_tmp([self.visit(e)(self.env) for e in node.elts]) - return self.term_type(name, self.env) - - visit_Tuple = visit_List - - def visit_Index(self, node, **kwargs): - """df.index[4]""" - return self.visit(node.value) - - def visit_Subscript(self, node, **kwargs): - from pandas import eval as pd_eval - - value = self.visit(node.value) - slobj = self.visit(node.slice) - result = pd_eval( - slobj, local_dict=self.env, engine=self.engine, parser=self.parser - ) - try: - # a Term instance - v = value.value[result] - except AttributeError: - # an Op instance - lhs = pd_eval( - value, local_dict=self.env, engine=self.engine, parser=self.parser - ) - v = lhs[result] - name = self.env.add_tmp(v) - return self.term_type(name, env=self.env) - - def visit_Slice(self, node, **kwargs): - """df.index[slice(4,6)]""" - lower = node.lower - if lower is not None: - lower = self.visit(lower).value - upper = node.upper - if upper is not None: - upper = self.visit(upper).value - step = node.step - if step is not None: - step = self.visit(step).value - - return slice(lower, upper, step) - - def visit_Assign(self, node, **kwargs): - """ - support a single assignment node, like - - c = a + b - - set the assigner at the top level, must be a Name node which - might or might not exist in the resolvers - - """ - if len(node.targets) != 1: - raise SyntaxError("can only assign a single expression") - if not isinstance(node.targets[0], ast.Name): - raise SyntaxError("left hand side of an assignment must be a single name") - if self.env.target is None: - raise ValueError("cannot assign without a target object") - - try: - assigner = self.visit(node.targets[0], **kwargs) - except UndefinedVariableError: - assigner = node.targets[0].id - - self.assigner = getattr(assigner, "name", assigner) - if self.assigner is None: - raise SyntaxError( - "left hand side of an assignment must be a single resolvable name" - ) - - return self.visit(node.value, **kwargs) - - def visit_Attribute(self, node, **kwargs): - attr = node.attr - value = node.value - - ctx = node.ctx - if isinstance(ctx, ast.Load): - # resolve the value - resolved = self.visit(value).value - try: - v = getattr(resolved, attr) - name = self.env.add_tmp(v) - return self.term_type(name, self.env) - except AttributeError: - # something like datetime.datetime where scope is overridden - if isinstance(value, ast.Name) and value.id == attr: - return resolved - raise - - raise ValueError(f"Invalid Attribute context {type(ctx).__name__}") - - def visit_Call(self, node, side=None, **kwargs): - if isinstance(node.func, ast.Attribute) and node.func.attr != "__call__": - res = self.visit_Attribute(node.func) - elif not isinstance(node.func, ast.Name): - raise TypeError("Only named functions are supported") - else: - try: - res = self.visit(node.func) - except UndefinedVariableError: - # Check if this is a supported function name - try: - res = FuncNode(node.func.id) - except ValueError: - # Raise original error - raise - - if res is None: - # error: "expr" has no attribute "id" - raise ValueError( - f"Invalid function call {node.func.id}" # type: ignore[attr-defined] - ) - if hasattr(res, "value"): - res = res.value - - if isinstance(res, FuncNode): - new_args = [self.visit(arg) for arg in node.args] - - if node.keywords: - raise TypeError( - f'Function "{res.name}" does not support keyword arguments' - ) - - return res(*new_args) - - else: - new_args = [self.visit(arg)(self.env) for arg in node.args] - - for key in node.keywords: - if not isinstance(key, ast.keyword): - # error: "expr" has no attribute "id" - raise ValueError( - "keyword error in function call " # type: ignore[attr-defined] - f"'{node.func.id}'" - ) - - if key.arg: - kwargs[key.arg] = self.visit(key.value)(self.env) - - name = self.env.add_tmp(res(*new_args, **kwargs)) - return self.term_type(name=name, env=self.env) - - def translate_In(self, op): - return op - - def visit_Compare(self, node, **kwargs): - ops = node.ops - comps = node.comparators - - # base case: we have something like a CMP b - if len(comps) == 1: - op = self.translate_In(ops[0]) - binop = ast.BinOp(op=op, left=node.left, right=comps[0]) - return self.visit(binop) - - # recursive case: we have a chained comparison, a CMP b CMP c, etc. - left = node.left - values = [] - for op, comp in zip(ops, comps): - new_node = self.visit( - ast.Compare(comparators=[comp], left=left, ops=[self.translate_In(op)]) - ) - left = comp - values.append(new_node) - return self.visit(ast.BoolOp(op=ast.And(), values=values)) - - def _try_visit_binop(self, bop): - if isinstance(bop, (Op, Term)): - return bop - return self.visit(bop) - - def visit_BoolOp(self, node, **kwargs): - def visitor(x, y): - lhs = self._try_visit_binop(x) - rhs = self._try_visit_binop(y) - - op, op_class, lhs, rhs = self._maybe_transform_eq_ne(node, lhs, rhs) - return self._maybe_evaluate_binop(op, node.op, lhs, rhs) - - operands = node.values - return reduce(visitor, operands) - - -_python_not_supported = frozenset(["Dict", "BoolOp", "In", "NotIn"]) - - -@disallow( - (_unsupported_nodes | _python_not_supported) - - (_boolop_nodes | frozenset(["BoolOp", "Attribute", "In", "NotIn", "Tuple"])) -) -class PandasExprVisitor(BaseExprVisitor): - def __init__( - self, - env, - engine, - parser, - preparser=partial( - _preparse, - f=_compose(_replace_locals, _replace_booleans, clean_backtick_quoted_toks), - ), - ) -> None: - super().__init__(env, engine, parser, preparser) - - -@disallow(_unsupported_nodes | _python_not_supported | frozenset(["Not"])) -class PythonExprVisitor(BaseExprVisitor): - def __init__( - self, env, engine, parser, preparser=lambda source, f=None: source - ) -> None: - super().__init__(env, engine, parser, preparser=preparser) - - -class Expr: - """ - Object encapsulating an expression. - - Parameters - ---------- - expr : str - engine : str, optional, default 'numexpr' - parser : str, optional, default 'pandas' - env : Scope, optional, default None - level : int, optional, default 2 - """ - - env: Scope - engine: str - parser: str - - def __init__( - self, - expr, - engine: str = "numexpr", - parser: str = "pandas", - env: Scope | None = None, - level: int = 0, - ) -> None: - self.expr = expr - self.env = env or Scope(level=level + 1) - self.engine = engine - self.parser = parser - self._visitor = PARSERS[parser](self.env, self.engine, self.parser) - self.terms = self.parse() - - @property - def assigner(self): - return getattr(self._visitor, "assigner", None) - - def __call__(self): - return self.terms(self.env) - - def __repr__(self) -> str: - return printing.pprint_thing(self.terms) - - def __len__(self) -> int: - return len(self.expr) - - def parse(self): - """ - Parse an expression. - """ - return self._visitor.visit(self.expr) - - @property - def names(self): - """ - Get the names in an expression. - """ - if is_term(self.terms): - return frozenset([self.terms.name]) - return frozenset(term.name for term in com.flatten(self.terms)) - - -PARSERS = {"python": PythonExprVisitor, "pandas": PandasExprVisitor} diff --git a/third_party/bigframes_vendored/pandas/core/computation/ops.py b/third_party/bigframes_vendored/pandas/core/computation/ops.py deleted file mode 100644 index 0dfd77daf36..00000000000 --- a/third_party/bigframes_vendored/pandas/core/computation/ops.py +++ /dev/null @@ -1,605 +0,0 @@ -# Contains code from https://github.com/pandas-dev/pandas/blob/main/pandas/core/computation/ops.py -""" -Operator classes for eval. -""" - -from __future__ import annotations - -import operator -from datetime import datetime -from functools import partial -from typing import TYPE_CHECKING, Callable, Literal - -import bigframes_vendored.pandas.core.common as com -import numpy as np -from bigframes_vendored.pandas.core.computation.common import ( - ensure_decoded, - result_type_many, -) -from bigframes_vendored.pandas.core.computation.scope import DEFAULT_GLOBALS -from pandas._libs.tslibs import Timestamp -from pandas.core.dtypes.common import is_list_like, is_scalar -from pandas.io.formats.printing import pprint_thing, pprint_thing_encoded - -if TYPE_CHECKING: - from collections.abc import Iterable, Iterator - -REDUCTIONS = ("sum", "prod", "min", "max") - -_unary_math_ops = ( - "sin", - "cos", - "exp", - "log", - "expm1", - "log1p", - "sqrt", - "sinh", - "cosh", - "tanh", - "arcsin", - "arccos", - "arctan", - "arccosh", - "arcsinh", - "arctanh", - "abs", - "log10", - "floor", - "ceil", -) -_binary_math_ops = ("arctan2",) - -MATHOPS = _unary_math_ops + _binary_math_ops - - -LOCAL_TAG = "__pd_eval_local_" - - -class Term: - def __new__(cls, name, env, side=None, encoding=None): - klass = Constant if not isinstance(name, str) else cls - # error: Argument 2 for "super" not an instance of argument 1 - supr_new = super(Term, klass).__new__ # type: ignore[misc] - return supr_new(klass) - - is_local: bool - - def __init__(self, name, env, side=None, encoding=None) -> None: - # name is a str for Term, but may be something else for subclasses - self._name = name - self.env = env - self.side = side - tname = str(name) - self.is_local = tname.startswith(LOCAL_TAG) or tname in DEFAULT_GLOBALS - self._value = self._resolve_name() - self.encoding = encoding - - @property - def local_name(self) -> str: - return self.name.replace(LOCAL_TAG, "") - - def __repr__(self) -> str: - return pprint_thing(self.name) - - def __call__(self, *args, **kwargs): - return self.value - - def evaluate(self, *args, **kwargs) -> Term: - return self - - def _resolve_name(self): - local_name = str(self.local_name) - is_local = self.is_local - if local_name in self.env.scope and isinstance( - self.env.scope[local_name], type - ): - is_local = False - - res = self.env.resolve(local_name, is_local=is_local) - self.update(res) - - if hasattr(res, "ndim") and res.ndim > 2: - raise NotImplementedError( - "N-dimensional objects, where N > 2, are not supported with eval" - ) - return res - - def update(self, value) -> None: - """ - search order for local (i.e., @variable) variables: - - scope, key_variable - [('locals', 'local_name'), - ('globals', 'local_name'), - ('locals', 'key'), - ('globals', 'key')] - """ - key = self.name - - # if it's a variable name (otherwise a constant) - if isinstance(key, str): - self.env.swapkey(self.local_name, key, new_value=value) - - self.value = value - - @property - def is_scalar(self) -> bool: - return is_scalar(self._value) - - @property - def type(self): - try: - # potentially very slow for large, mixed dtype frames - return self._value.values.dtype - except AttributeError: - try: - # ndarray - return self._value.dtype - except AttributeError: - # scalar - return type(self._value) - - return_type = type - - @property - def raw(self) -> str: - return f"{type(self).__name__}(name={repr(self.name)}, type={self.type})" - - @property - def is_datetime(self) -> bool: - try: - t = self.type.type - except AttributeError: - t = self.type - - return issubclass(t, (datetime, np.datetime64)) - - @property - def value(self): - return self._value - - @value.setter - def value(self, new_value) -> None: - self._value = new_value - - @property - def name(self): - return self._name - - @property - def ndim(self) -> int: - return self._value.ndim - - -class Constant(Term): - def _resolve_name(self): - return self._name - - @property - def name(self): - return self.value - - def __repr__(self) -> str: - # in python 2 str() of float - # can truncate shorter than repr() - return repr(self.name) - - -_bool_op_map = {"not": "~", "and": "&", "or": "|"} - - -class Op: - """ - Hold an operator of arbitrary arity. - """ - - op: str - - def __init__(self, op: str, operands: Iterable[Term | Op], encoding=None) -> None: - self.op = _bool_op_map.get(op, op) - self.operands = operands - self.encoding = encoding - - def __iter__(self) -> Iterator: - return iter(self.operands) - - def __repr__(self) -> str: - """ - Print a generic n-ary operator and its operands using infix notation. - """ - # recurse over the operands - parened = (f"({pprint_thing(opr)})" for opr in self.operands) - return pprint_thing(f" {self.op} ".join(parened)) - - @property - def return_type(self): - # clobber types to bool if the op is a boolean operator - if self.op in (CMP_OPS_SYMS + BOOL_OPS_SYMS): - return np.bool_ - return result_type_many(*(term.type for term in com.flatten(self))) - - @property - def has_invalid_return_type(self) -> bool: - types = self.operand_types - obj_dtype_set = frozenset([np.dtype("object")]) - return self.return_type == object and types - obj_dtype_set - - @property - def operand_types(self): - return frozenset(term.type for term in com.flatten(self)) - - @property - def is_scalar(self) -> bool: - return all(operand.is_scalar for operand in self.operands) - - @property - def is_datetime(self) -> bool: - try: - t = self.return_type.type - except AttributeError: - t = self.return_type - - return issubclass(t, (datetime, np.datetime64)) - - -def _in(x, y): - """ - Compute the vectorized membership of ``x in y`` if possible, otherwise - use Python. - """ - try: - return x.isin(y) - except AttributeError: - if is_list_like(x): - try: - return y.isin(x) - except AttributeError: - pass - return x in y - - -def _not_in(x, y): - """ - Compute the vectorized membership of ``x not in y`` if possible, - otherwise use Python. - """ - try: - return ~x.isin(y) - except AttributeError: - if is_list_like(x): - try: - return ~y.isin(x) - except AttributeError: - pass - return x not in y - - -CMP_OPS_SYMS = (">", "<", ">=", "<=", "==", "!=", "in", "not in") -_cmp_ops_funcs = ( - operator.gt, - operator.lt, - operator.ge, - operator.le, - operator.eq, - operator.ne, - _in, - _not_in, -) -_cmp_ops_dict = dict(zip(CMP_OPS_SYMS, _cmp_ops_funcs)) - -BOOL_OPS_SYMS = ("&", "|", "and", "or") -_bool_ops_funcs = (operator.and_, operator.or_, operator.and_, operator.or_) -_bool_ops_dict = dict(zip(BOOL_OPS_SYMS, _bool_ops_funcs)) - -ARITH_OPS_SYMS = ("+", "-", "*", "/", "**", "//", "%") -_arith_ops_funcs = ( - operator.add, - operator.sub, - operator.mul, - operator.truediv, - operator.pow, - operator.floordiv, - operator.mod, -) -_arith_ops_dict = dict(zip(ARITH_OPS_SYMS, _arith_ops_funcs)) - -SPECIAL_CASE_ARITH_OPS_SYMS = ("**", "//", "%") -_special_case_arith_ops_funcs = (operator.pow, operator.floordiv, operator.mod) -_special_case_arith_ops_dict = dict( - zip(SPECIAL_CASE_ARITH_OPS_SYMS, _special_case_arith_ops_funcs) -) - -_binary_ops_dict = {} - -for d in (_cmp_ops_dict, _bool_ops_dict, _arith_ops_dict): - _binary_ops_dict.update(d) - - -def _cast_inplace(terms, acceptable_dtypes, dtype) -> None: - """ - Cast an expression inplace. - - Parameters - ---------- - terms : Op - The expression that should cast. - acceptable_dtypes : list of acceptable numpy.dtype - Will not cast if term's dtype in this list. - dtype : str or numpy.dtype - The dtype to cast to. - """ - dt = np.dtype(dtype) - for term in terms: - if term.type in acceptable_dtypes: - continue - - try: - new_value = term.value.astype(dt) - except AttributeError: - new_value = dt.type(term.value) - term.update(new_value) - - -def is_term(obj) -> bool: - return isinstance(obj, Term) - - -class BinOp(Op): - """ - Hold a binary operator and its operands. - - Parameters - ---------- - op : str - lhs : Term or Op - rhs : Term or Op - """ - - def __init__(self, op: str, lhs, rhs) -> None: - super().__init__(op, (lhs, rhs)) - self.lhs = lhs - self.rhs = rhs - - self._disallow_scalar_only_bool_ops() - - self.convert_values() - - try: - self.func = _binary_ops_dict[op] - except KeyError as err: - # has to be made a list for python3 - keys = list(_binary_ops_dict.keys()) - raise ValueError( - f"Invalid binary operator {repr(op)}, valid operators are {keys}" - ) from err - - def __call__(self, env): - """ - Recursively evaluate an expression in Python space. - - Parameters - ---------- - env : Scope - - Returns - ------- - object - The result of an evaluated expression. - """ - # recurse over the left/right nodes - left = self.lhs(env) - right = self.rhs(env) - - return self.func(left, right) - - def evaluate(self, env, engine: str, parser, term_type, eval_in_python): - """ - Evaluate a binary operation *before* being passed to the engine. - - Parameters - ---------- - env : Scope - engine : str - parser : str - term_type : type - eval_in_python : list - - Returns - ------- - term_type - The "pre-evaluated" expression as an instance of ``term_type`` - """ - if engine == "python": - res = self(env) - else: - # recurse over the left/right nodes - - left = self.lhs.evaluate( - env, - engine=engine, - parser=parser, - term_type=term_type, - eval_in_python=eval_in_python, - ) - - right = self.rhs.evaluate( - env, - engine=engine, - parser=parser, - term_type=term_type, - eval_in_python=eval_in_python, - ) - - # base cases - if self.op in eval_in_python: - res = self.func(left.value, right.value) - else: - from pandas.core.computation.eval import eval - - res = eval(self, local_dict=env, engine=engine, parser=parser) - - name = env.add_tmp(res) - return term_type(name, env=env) - - def convert_values(self) -> None: - """ - Convert datetimes to a comparable value in an expression. - """ - - def stringify(value): - encoder: Callable - if self.encoding is not None: - encoder = partial(pprint_thing_encoded, encoding=self.encoding) - else: - encoder = pprint_thing - return encoder(value) - - lhs, rhs = self.lhs, self.rhs - - if is_term(lhs) and lhs.is_datetime and is_term(rhs) and rhs.is_scalar: - v = rhs.value - if isinstance(v, (int, float)): - v = stringify(v) - v = Timestamp(ensure_decoded(v)) - if v.tz is not None: - v = v.tz_convert("UTC") - self.rhs.update(v) - - if is_term(rhs) and rhs.is_datetime and is_term(lhs) and lhs.is_scalar: - v = lhs.value - if isinstance(v, (int, float)): - v = stringify(v) - v = Timestamp(ensure_decoded(v)) - if v.tz is not None: - v = v.tz_convert("UTC") - self.lhs.update(v) - - def _disallow_scalar_only_bool_ops(self): - rhs = self.rhs - lhs = self.lhs - - # GH#24883 unwrap dtype if necessary to ensure we have a type object - rhs_rt = rhs.return_type - rhs_rt = getattr(rhs_rt, "type", rhs_rt) - lhs_rt = lhs.return_type - lhs_rt = getattr(lhs_rt, "type", lhs_rt) - if ( - (lhs.is_scalar or rhs.is_scalar) - and self.op in _bool_ops_dict - and ( - not ( - issubclass(rhs_rt, (bool, np.bool_)) - and issubclass(lhs_rt, (bool, np.bool_)) - ) - ) - ): - raise NotImplementedError("cannot evaluate scalar only bool ops") - - -def isnumeric(dtype) -> bool: - return issubclass(np.dtype(dtype).type, np.number) - - -class Div(BinOp): - """ - Div operator to special case casting. - - Parameters - ---------- - lhs, rhs : Term or Op - The Terms or Ops in the ``/`` expression. - """ - - def __init__(self, lhs, rhs) -> None: - super().__init__("/", lhs, rhs) - - if not isnumeric(lhs.return_type) or not isnumeric(rhs.return_type): - raise TypeError( - f"unsupported operand type(s) for {self.op}: " - f"'{lhs.return_type}' and '{rhs.return_type}'" - ) - - # do not upcast float32s to float64 un-necessarily - acceptable_dtypes = [np.float32, np.float64] - _cast_inplace(com.flatten(self), acceptable_dtypes, np.float64) - - -UNARY_OPS_SYMS = ("+", "-", "~", "not") -_unary_ops_funcs = (operator.pos, operator.neg, operator.invert, operator.invert) -_unary_ops_dict = dict(zip(UNARY_OPS_SYMS, _unary_ops_funcs)) - - -class UnaryOp(Op): - """ - Hold a unary operator and its operands. - - Parameters - ---------- - op : str - The token used to represent the operator. - operand : Term or Op - The Term or Op operand to the operator. - - Raises - ------ - ValueError - * If no function associated with the passed operator token is found. - """ - - def __init__(self, op: Literal["+", "-", "~", "not"], operand) -> None: - super().__init__(op, (operand,)) - self.operand = operand - - try: - self.func = _unary_ops_dict[op] - except KeyError as err: - raise ValueError( - f"Invalid unary operator {repr(op)}, " - f"valid operators are {UNARY_OPS_SYMS}" - ) from err - - def __call__(self, env) -> MathCall: - operand = self.operand(env) - # error: Cannot call function of unknown type - return self.func(operand) # type: ignore[operator] - - def __repr__(self) -> str: - return pprint_thing(f"{self.op}({self.operand})") - - @property - def return_type(self) -> np.dtype: - operand = self.operand - if operand.return_type == np.dtype("bool"): - return np.dtype("bool") - if isinstance(operand, Op) and ( - operand.op in _cmp_ops_dict or operand.op in _bool_ops_dict - ): - return np.dtype("bool") - return np.dtype("int") - - -class MathCall(Op): - def __init__(self, func, args) -> None: - super().__init__(func.name, args) - self.func = func - - def __call__(self, env): - # error: "Op" not callable - operands = [op(env) for op in self.operands] # type: ignore[operator] - return self.func.func(*operands) - - def __repr__(self) -> str: - operands = map(str, self.operands) - return pprint_thing(f"{self.op}({','.join(operands)})") - - -class FuncNode: - def __init__(self, name: str) -> None: - if name not in MATHOPS: - raise ValueError(f'"{name}" is not a supported function') - self.name = name - self.func = getattr(np, name) - - def __call__(self, *args): - return MathCall(self, args) diff --git a/third_party/bigframes_vendored/pandas/core/computation/parsing.py b/third_party/bigframes_vendored/pandas/core/computation/parsing.py deleted file mode 100644 index 569c3c50330..00000000000 --- a/third_party/bigframes_vendored/pandas/core/computation/parsing.py +++ /dev/null @@ -1,197 +0,0 @@ -# Contains code from https://github.com/pandas-dev/pandas/blob/main/pandas/core/computation/parsing.py -""" -:func:`~pandas.eval` source string parsing functions -""" - -from __future__ import annotations - -import token -import tokenize -from io import StringIO -from keyword import iskeyword -from typing import TYPE_CHECKING - -if TYPE_CHECKING: - from collections.abc import Hashable, Iterator - -# A token value Python's tokenizer probably will never use. -BACKTICK_QUOTED_STRING = 100 - - -def create_valid_python_identifier(name: str) -> str: - """ - Create valid Python identifiers from any string. - - Check if name contains any special characters. If it contains any - special characters, the special characters will be replaced by - a special string and a prefix is added. - - Raises - ------ - SyntaxError - If the returned name is not a Python valid identifier, raise an exception. - This can happen if there is a hashtag in the name, as the tokenizer will - than terminate and not find the backtick. - But also for characters that fall out of the range of (U+0001..U+007F). - """ - if name.isidentifier() and not iskeyword(name): - return name - - # Create a dict with the special characters and their replacement string. - # EXACT_TOKEN_TYPES contains these special characters - # token.tok_name contains a readable description of the replacement string. - special_characters_replacements = { - char: f"_{token.tok_name[tokval]}_" - for char, tokval in (tokenize.EXACT_TOKEN_TYPES.items()) - } - special_characters_replacements.update( - { - " ": "_", - "?": "_QUESTIONMARK_", - "!": "_EXCLAMATIONMARK_", - "$": "_DOLLARSIGN_", - "€": "_EUROSIGN_", - "°": "_DEGREESIGN_", - # Including quotes works, but there are exceptions. - "'": "_SINGLEQUOTE_", - '"': "_DOUBLEQUOTE_", - # Currently not possible. Terminates parser and won't find backtick. - # "#": "_HASH_", - } - ) - - name = "".join([special_characters_replacements.get(char, char) for char in name]) - name = f"BACKTICK_QUOTED_STRING_{name}" - - if not name.isidentifier(): - raise SyntaxError(f"Could not convert '{name}' to a valid Python identifier.") - - return name - - -def clean_backtick_quoted_toks(tok: tuple[int, str]) -> tuple[int, str]: - """ - Clean up a column name if surrounded by backticks. - - Backtick quoted string are indicated by a certain tokval value. If a string - is a backtick quoted token it will processed by - :func:`_create_valid_python_identifier` so that the parser can find this - string when the query is executed. - In this case the tok will get the NAME tokval. - - Parameters - ---------- - tok : tuple of int, str - ints correspond to the all caps constants in the tokenize module - - Returns - ------- - tok : Tuple[int, str] - Either the input or token or the replacement values - """ - toknum, tokval = tok - if toknum == BACKTICK_QUOTED_STRING: - return tokenize.NAME, create_valid_python_identifier(tokval) - return toknum, tokval - - -def clean_column_name(name: Hashable) -> Hashable: - """ - Function to emulate the cleaning of a backtick quoted name. - - The purpose for this function is to see what happens to the name of - identifier if it goes to the process of being parsed a Python code - inside a backtick quoted string and than being cleaned - (removed of any special characters). - - Parameters - ---------- - name : hashable - Name to be cleaned. - - Returns - ------- - name : hashable - Returns the name after tokenizing and cleaning. - - Notes - ----- - For some cases, a name cannot be converted to a valid Python identifier. - In that case :func:`tokenize_string` raises a SyntaxError. - In that case, we just return the name unmodified. - - If this name was used in the query string (this makes the query call impossible) - an error will be raised by :func:`tokenize_backtick_quoted_string` instead, - which is not caught and propagates to the user level. - """ - try: - tokenized = tokenize_string(f"`{name}`") - tokval = next(tokenized)[1] - return create_valid_python_identifier(tokval) - except SyntaxError: - return name - - -def tokenize_backtick_quoted_string( - token_generator: Iterator[tokenize.TokenInfo], source: str, string_start: int -) -> tuple[int, str]: - """ - Creates a token from a backtick quoted string. - - Moves the token_generator forwards till right after the next backtick. - - Parameters - ---------- - token_generator : Iterator[tokenize.TokenInfo] - The generator that yields the tokens of the source string (Tuple[int, str]). - The generator is at the first token after the backtick (`) - - source : str - The Python source code string. - - string_start : int - This is the start of backtick quoted string inside the source string. - - Returns - ------- - tok: Tuple[int, str] - The token that represents the backtick quoted string. - The integer is equal to BACKTICK_QUOTED_STRING (100). - """ - for _, tokval, start, _, _ in token_generator: - if tokval == "`": - string_end = start[1] - break - - return BACKTICK_QUOTED_STRING, source[string_start:string_end] - - -def tokenize_string(source: str) -> Iterator[tuple[int, str]]: - """ - Tokenize a Python source code string. - - Parameters - ---------- - source : str - The Python source code string. - - Returns - ------- - tok_generator : Iterator[Tuple[int, str]] - An iterator yielding all tokens with only toknum and tokval (Tuple[ing, str]). - """ - line_reader = StringIO(source).readline - token_generator = tokenize.generate_tokens(line_reader) - - # Loop over all tokens till a backtick (`) is found. - # Then, take all tokens till the next backtick to form a backtick quoted string - for toknum, tokval, start, _, _ in token_generator: - if tokval == "`": - try: - yield tokenize_backtick_quoted_string( - token_generator, source, string_start=start[1] + 1 - ) - except Exception as err: - raise SyntaxError(f"Failed to parse backticks in '{source}'.") from err - else: - yield toknum, tokval diff --git a/third_party/bigframes_vendored/pandas/core/computation/scope.py b/third_party/bigframes_vendored/pandas/core/computation/scope.py deleted file mode 100644 index 51b15e74a27..00000000000 --- a/third_party/bigframes_vendored/pandas/core/computation/scope.py +++ /dev/null @@ -1,356 +0,0 @@ -# Contains code from https://github.com/pandas-dev/pandas/blob/main/pandas/core/computation/scope.py -""" -Module for scope operations -""" - -from __future__ import annotations - -import datetime -import inspect -import itertools -import pprint -import struct -import sys -from collections import ChainMap -from io import StringIO -from typing import TypeVar - -import numpy as np -from pandas._libs.tslibs import Timestamp -from pandas.errors import UndefinedVariableError - -_KT = TypeVar("_KT") -_VT = TypeVar("_VT") - - -# https://docs.python.org/3/library/collections.html#chainmap-examples-and-recipes -class DeepChainMap(ChainMap[_KT, _VT]): - """ - Variant of ChainMap that allows direct updates to inner scopes. - - Only works when all passed mapping are mutable. - """ - - def __setitem__(self, key: _KT, value: _VT) -> None: - for mapping in self.maps: - if key in mapping: - mapping[key] = value - return - self.maps[0][key] = value - - def __delitem__(self, key: _KT) -> None: - """ - Raises - ------ - KeyError - If `key` doesn't exist. - """ - for mapping in self.maps: - if key in mapping: - del mapping[key] - return - raise KeyError(key) - - -def ensure_scope( - level: int, global_dict=None, local_dict=None, resolvers=(), target=None -) -> Scope: - """Ensure that we are grabbing the correct scope.""" - return Scope( - level + 1, - global_dict=global_dict, - local_dict=local_dict, - resolvers=resolvers, - target=target, - ) - - -def _replacer(x) -> str: - """ - Replace a number with its hexadecimal representation. Used to tag - temporary variables with their calling scope's id. - """ - # get the hex repr of the binary char and remove 0x and pad by pad_size - # zeros - try: - hexin = ord(x) - except TypeError: - # bytes literals masquerade as ints when iterating in py3 - hexin = x - - return hex(hexin) - - -def _raw_hex_id(obj) -> str: - """Return the padded hexadecimal id of ``obj``.""" - # interpret as a pointer since that's what really what id returns - packed = struct.pack("@P", id(obj)) - return "".join([_replacer(x) for x in packed]) - - -DEFAULT_GLOBALS = { - "Timestamp": Timestamp, - "datetime": datetime.datetime, - "True": True, - "False": False, - "list": list, - "tuple": tuple, - "inf": np.inf, - "Inf": np.inf, -} - - -def _get_pretty_string(obj) -> str: - """ - Return a prettier version of obj. - - Parameters - ---------- - obj : object - Object to pretty print - - Returns - ------- - str - Pretty print object repr - """ - sio = StringIO() - pprint.pprint(obj, stream=sio) - return sio.getvalue() - - -class Scope: - """ - Object to hold scope, with a few bells to deal with some custom syntax - and contexts added by pandas. - - Parameters - ---------- - level : int - global_dict : dict or None, optional, default None - local_dict : dict or Scope or None, optional, default None - resolvers : list-like or None, optional, default None - target : object - - Attributes - ---------- - level : int - scope : DeepChainMap - target : object - temps : dict - """ - - __slots__ = ["level", "scope", "target", "resolvers", "temps"] - level: int - scope: DeepChainMap - resolvers: DeepChainMap - temps: dict - - def __init__( - self, level: int, global_dict=None, local_dict=None, resolvers=(), target=None - ) -> None: - self.level = level + 1 - - # shallow copy because we don't want to keep filling this up with what - # was there before if there are multiple calls to Scope/_ensure_scope - self.scope = DeepChainMap(DEFAULT_GLOBALS.copy()) - self.target = target - - if isinstance(local_dict, Scope): - self.scope.update(local_dict.scope) - if local_dict.target is not None: - self.target = local_dict.target - self._update(local_dict.level) - - frame = sys._getframe(self.level) - - try: - # shallow copy here because we don't want to replace what's in - # scope when we align terms (alignment accesses the underlying - # numpy array of pandas objects) - scope_global = self.scope.new_child( - (global_dict if global_dict is not None else frame.f_globals).copy() - ) - self.scope = DeepChainMap(scope_global) - if not isinstance(local_dict, Scope): - scope_local = self.scope.new_child( - (local_dict if local_dict is not None else frame.f_locals).copy() - ) - self.scope = DeepChainMap(scope_local) - finally: - del frame - - # assumes that resolvers are going from outermost scope to inner - if isinstance(local_dict, Scope): - resolvers += tuple(local_dict.resolvers.maps) - self.resolvers = DeepChainMap(*resolvers) - self.temps = {} - - def __repr__(self) -> str: - scope_keys = _get_pretty_string(list(self.scope.keys())) - res_keys = _get_pretty_string(list(self.resolvers.keys())) - return f"{type(self).__name__}(scope={scope_keys}, resolvers={res_keys})" - - @property - def has_resolvers(self) -> bool: - """ - Return whether we have any extra scope. - - For example, DataFrames pass Their columns as resolvers during calls to - ``DataFrame.eval()`` and ``DataFrame.query()``. - - Returns - ------- - hr : bool - """ - return bool(len(self.resolvers)) - - def resolve(self, key: str, is_local: bool): - """ - Resolve a variable name in a possibly local context. - - Parameters - ---------- - key : str - A variable name - is_local : bool - Flag indicating whether the variable is local or not (prefixed with - the '@' symbol) - - Returns - ------- - value : object - The value of a particular variable - """ - try: - # only look for locals in outer scope - if is_local: - return self.scope[key] - - # not a local variable so check in resolvers if we have them - if self.has_resolvers: - return self.resolvers[key] - - # if we're here that means that we have no locals and we also have - # no resolvers - assert not is_local and not self.has_resolvers - return self.scope[key] - except KeyError: - try: - # last ditch effort we look in temporaries - # these are created when parsing indexing expressions - # e.g., df[df > 0] - return self.temps[key] - except KeyError as err: - raise UndefinedVariableError(key, is_local) from err - - def swapkey(self, old_key: str, new_key: str, new_value=None) -> None: - """ - Replace a variable name, with a potentially new value. - - Parameters - ---------- - old_key : str - Current variable name to replace - new_key : str - New variable name to replace `old_key` with - new_value : object - Value to be replaced along with the possible renaming - """ - if self.has_resolvers: - maps = self.resolvers.maps + self.scope.maps - else: - maps = self.scope.maps - - maps.append(self.temps) - - for mapping in maps: - if old_key in mapping: - mapping[new_key] = new_value - return - - def _get_vars(self, stack, scopes: list[str]) -> None: - """ - Get specifically scoped variables from a list of stack frames. - - Parameters - ---------- - stack : list - A list of stack frames as returned by ``inspect.stack()`` - scopes : sequence of strings - A sequence containing valid stack frame attribute names that - evaluate to a dictionary. For example, ('locals', 'globals') - """ - variables = itertools.product(scopes, stack) - for scope, (frame, _, _, _, _, _) in variables: - try: - d = getattr(frame, f"f_{scope}") - self.scope = DeepChainMap(self.scope.new_child(d)) - finally: - # won't remove it, but DECREF it - # in Py3 this probably isn't necessary since frame won't be - # scope after the loop - del frame - - def _update(self, level: int) -> None: - """ - Update the current scope by going back `level` levels. - - Parameters - ---------- - level : int - """ - sl = level + 1 - - # add sl frames to the scope starting with the - # most distant and overwriting with more current - # makes sure that we can capture variable scope - stack = inspect.stack() - - try: - self._get_vars(stack[:sl], scopes=["locals"]) - finally: - del stack[:], stack - - def add_tmp(self, value) -> str: - """ - Add a temporary variable to the scope. - - Parameters - ---------- - value : object - An arbitrary object to be assigned to a temporary variable. - - Returns - ------- - str - The name of the temporary variable created. - """ - name = f"{type(value).__name__}_{self.ntemps}_{_raw_hex_id(self)}" - - # add to inner most scope - assert name not in self.temps - self.temps[name] = value - assert name in self.temps - - # only increment if the variable gets put in the scope - return name - - @property - def ntemps(self) -> int: - """The number of temporary variables in this scope""" - return len(self.temps) - - @property - def full_scope(self) -> DeepChainMap: - """ - Return the full scope for use with passing to engines transparently - as a mapping. - - Returns - ------- - vars : DeepChainMap - All variables in this scope. - """ - maps = [self.temps] + self.resolvers.maps + self.scope.maps - return DeepChainMap(*maps) diff --git a/third_party/bigframes_vendored/pandas/core/config_init.py b/third_party/bigframes_vendored/pandas/core/config_init.py index bd40d05154b..198654015e0 100644 --- a/third_party/bigframes_vendored/pandas/core/config_init.py +++ b/third_party/bigframes_vendored/pandas/core/config_init.py @@ -1,5 +1,4 @@ -# Contains code from -# https://github.com/pandas-dev/pandas/blob/main/pandas/core/config_init.py +# Contains code from https://github.com/pandas-dev/pandas/blob/main/pandas/core/config_init.py """ This module is imported from the pandas package __init__.py file in order to ensure that the core.config options registered here will @@ -11,229 +10,50 @@ module is imported, register them here rather than in the module. """ - from __future__ import annotations -import dataclasses -from typing import Literal, Optional - - -@dataclasses.dataclass -class DisplayOptions: - """ - Encapsulates the configuration for displaying objects. - - **Examples:** - - Define Repr mode to "deferred" will prevent job execution in repr. - - >>> import bigframes.pandas as bpd - >>> df = bpd.read_gbq("bigquery-public-data.ml_datasets.penguins") - - >>> bpd.options.display.repr_mode = "deferred" # doctest: +SKIP - >>> df.head(20) # will no longer run the job # doctest: +SKIP - Computation deferred. Computation will process 28.9 kB - - Users can also get a dry run of the job by accessing the query_job - property before they've run the job. This will return a dry run - instance of the job they can inspect. - - >>> df.query_job.total_bytes_processed # doctest: +SKIP - 28947 - - User can execute the job by calling .to_pandas() - - >>> # df.to_pandas() - - Reset repr_mode option - - >>> bpd.options.display.repr_mode = "head" # doctest: +SKIP - - Can also set the progress_bar option to see the progress bar in terminal, - - >>> bpd.options.display.progress_bar = "terminal" # doctest: +SKIP - - notebook, - - >>> bpd.options.display.progress_bar = "notebook" # doctest: +SKIP - - or just remove it. - - Setting to default value "auto" will detect and show progress bar - automatically. - - >>> bpd.options.display.progress_bar = "auto" # doctest: +SKIP - """ - - # Options borrowed from pandas. - max_columns: int = 20 - """ - Maximum number of columns to display. Default 20. - - If `max_columns` is exceeded, switch to truncate view. - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> bpd.options.display.max_columns = 50 # doctest: +SKIP - """ - - max_rows: int = 10 - """ - Maximum number of rows to display. Default 10. - - If `max_rows` is exceeded, switch to truncate view. - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> bpd.options.display.max_rows = 50 # doctest: +SKIP - """ - - precision: int = 6 - """ - Controls the floating point output precision. Defaults to 6. - - See :attr:`pandas.options.display.precision`. - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> bpd.options.display.precision = 2 # doctest: +SKIP - """ - - # Options unique to BigQuery DataFrames. - progress_bar: Optional[Literal["auto", "notebook", "terminal"]] = "auto" - """ - Determines if progress bars are shown during job runs. Default "auto". - - Valid values are `auto`, `notebook`, and `terminal`. Set - to `None` to remove progress bars. - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> bpd.options.display.progress_bar = "terminal" # doctest: +SKIP - """ - - repr_mode: Literal["head", "deferred", "anywidget"] = "head" - """ - Determines how to display a DataFrame or Series. Default "head". - - `head` - Execute, download, and display results (limited to head) from - Dataframe and Series objects during repr. - - `deferred` - Prevent executions from repr statements in DataFrame and - Series objects. - Instead, estimated bytes processed will be shown. DataFrame and Series - objects can still be computed with methods that explicitly execute and - download results. - - `anywidget` - Display as interactive widget using `anywidget` library. - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> bpd.options.display.repr_mode = "deferred" # doctest: +SKIP - """ - - render_mode: Literal["plaintext", "html", "anywidget"] = "html" - """ - Determines how to visualize a DataFrame or Series. Default "html". - - `plaintext` - Display as plain text. - - `html` - Display as HTML table. - - `anywidget` - Display as interactive widget using `anywidget` library. - """ - - max_colwidth: Optional[int] = 50 - """ - The maximum width in characters of a column in the repr. Default 50. - - When the column overflows, a "..." placeholder is embedded in the output. A - 'None' value means unlimited. - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> bpd.options.display.max_colwidth = 20 # doctest: +SKIP - """ - - max_info_columns: int = 100 - """ - Used in DataFrame.info method to decide if information in each column will - be printed. Default 100. - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> bpd.options.display.max_info_columns = 50 # doctest: +SKIP - """ - - max_info_rows: Optional[int] = 200_000 - """ - Limit null check in ``df.info()`` only to frames with smaller - dimensions than - max_info_rows. Default 200,000. - - df.info() will usually show null-counts for each column. - For large frames, this can be quite slow. max_info_rows and max_info_cols - limit this null check only to frames with smaller dimensions than - specified. - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> bpd.options.display.max_info_rows = 100 # doctest: +SKIP - """ - - memory_usage: bool = True - """ - If True, memory usage of a DataFrame should be displayed when - df.info() is called. Default True. - - Valid values True, False. - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> bpd.options.display.memory_usage = False # doctest: +SKIP - """ - - blob_display: bool = True - """ - If True, display the blob content in notebook DataFrame preview. Default - True. - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> bpd.options.display.blob_display = True # doctest: +SKIP - """ - - blob_display_width: Optional[int] = None - """ - Width in pixels that the blob constrained to. Default None.. - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> bpd.options.display.blob_display_width = 100 # doctest: +SKIP - """ - blob_display_height: Optional[int] = None - """ - Height in pixels that the blob constrained to. Default None.. - - **Examples:** +display_options_doc = """ +Encapsulates configuration for displaying objects. + +Attributes: + max_columns (int, default 20): + If `max_columns` is exceeded, switch to truncate view. + max_rows (int, default 25): + If `max_rows` is exceeded, switch to truncate view. + progress_bar (Optional(str), default "auto"): + Determines if progress bars are shown during job runs. + Valid values are `auto`, `notebook`, and `terminal`. Set + to `None` to remove progress bars. + repr_mode (Literal[`head`, `deferred`]): + `head`: + Execute, download, and display results (limited to head) from + dataframe and series objects during repr. + `deferred`: + Prevent executions from repr statements in dataframe and series objects. + Instead estimated bytes processed will be shown. Dataframe and Series + objects can still be computed with methods that explicitly execute and + download results. +""" - >>> import bigframes.pandas as bpd - >>> bpd.options.display.blob_display_height = 100 # doctest: +SKIP - """ +sampling_options_doc = """ +Encapsulates configuration for data sampling. + +Attributes: + max_download_size (int, default 500): + Download size threshold in MB. If value set to None, the download size + won't be checked. + enable_downsampling (bool, default False): + Whether to enable downsampling, If max_download_size is exceeded when + downloading data (e.g., to_pandas()), the data will be downsampled + if enable_downsampling is True, otherwise, an error will be raised. + sampling_method (str, default "uniform"): + Downsampling algorithms to be chosen from, the choices are: + "head": This algorithm returns a portion of the data from + the beginning. It is fast and requires minimal computations + to perform the downsampling.; "uniform": This algorithm returns + uniform random samples of the data. + random_state (int, default None): + The seed for the uniform downsampling algorithm. If provided, + the uniform method may take longer to execute and require more + computation. +""" diff --git a/third_party/bigframes_vendored/pandas/core/dtypes/inference.py b/third_party/bigframes_vendored/pandas/core/dtypes/inference.py deleted file mode 100644 index 7875c297bc2..00000000000 --- a/third_party/bigframes_vendored/pandas/core/dtypes/inference.py +++ /dev/null @@ -1,31 +0,0 @@ -# Contains code from https://github.com/pandas-dev/pandas/blob/main/pandas/core/dtypes/inference.py -"""basic inference routines""" - -from __future__ import annotations - -from collections import abc - - -def iterable_not_string(obj) -> bool: - """ - Check if the object is an iterable but not a string. - - Parameters - ---------- - obj : The object to check. - - Returns - ------- - is_iter_not_string : bool - Whether `obj` is a non-string iterable. - - Examples - -------- - >>> iterable_not_string([1, 2, 3]) - True - >>> iterable_not_string("foo") - False - >>> iterable_not_string(1) - False - """ - return isinstance(obj, abc.Iterable) and not isinstance(obj, str) diff --git a/third_party/bigframes_vendored/pandas/core/frame.py b/third_party/bigframes_vendored/pandas/core/frame.py index e84f46861d9..6f4f6be35d6 100644 --- a/third_party/bigframes_vendored/pandas/core/frame.py +++ b/third_party/bigframes_vendored/pandas/core/frame.py @@ -9,23 +9,20 @@ alignment and a host of useful data manipulation methods having to do with the labeling information """ - from __future__ import annotations -import datetime -from typing import Hashable, Iterable, Literal, Optional, Sequence, Union +from typing import Literal, Mapping, Optional, Sequence, Union -import bigframes_vendored.pandas.core.generic as generic import numpy as np -import pandas as pd -from bigframes_vendored import constants -from pandas.api import extensions as pd_ext + +from bigframes import constants +from third_party.bigframes_vendored.pandas.core.generic import NDFrame # ----------------------------------------------------------------------- # DataFrame class -class DataFrame(generic.NDFrame): +class DataFrame(NDFrame): """Two-dimensional, size-mutable, potentially heterogeneous tabular data. Data structure also contains labeled axes (rows and columns). @@ -41,15 +38,13 @@ def shape(self) -> tuple[int, int]: **Examples:** + >>> import bigframes.pandas as bpd + >>> bpd.options.display.progress_bar = None >>> df = bpd.DataFrame({'col1': [1, 2, 3], ... 'col2': [4, 5, 6]}) >>> df.shape (3, 2) - - Returns: - Tuple[int, int]: - Tuple of array dimensions. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -63,12 +58,14 @@ def axes(self) -> list: **Examples:** + >>> import bigframes.pandas as bpd + >>> bpd.options.display.progress_bar = None >>> df = bpd.DataFrame({'col1': [1, 2], 'col2': [3, 4]}) >>> df.axes[1:] - [Index(['col1', 'col2'], dtype='str')] + [Index(['col1', 'col2'], dtype='object')] """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) + return [self.index, self.columns] @property def values(self) -> np.ndarray: @@ -76,6 +73,8 @@ def values(self) -> np.ndarray: **Examples:** + >>> import bigframes.pandas as bpd + >>> bpd.options.display.progress_bar = None >>> df = bpd.DataFrame({'col1': [1, 2], 'col2': [3, 4]}) >>> df.values @@ -90,286 +89,19 @@ def values(self) -> np.ndarray: on another array. na_value (default None): The value to use for missing values. - - Returns: - numpy.ndarray: - The values of the DataFrame. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - @property - def T(self) -> DataFrame: - """ - The transpose of the DataFrame. - - All columns must be the same dtype (numerics can be coerced to a common supertype). - - **Examples:** - - >>> df = bpd.DataFrame({'col1': [1, 2], 'col2': [3, 4]}) - >>> df - col1 col2 - 0 1 3 - 1 2 4 - - [2 rows x 2 columns] - - >>> df.T - 0 1 - col1 1 2 - col2 3 4 - - [2 rows x 2 columns] - - Returns: - bigframes.pandas.DataFrame: The transposed DataFrame. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def transpose(self) -> DataFrame: - """ - Transpose index and columns. - - Reflect the DataFrame over its main diagonal by writing rows as columns - and vice-versa. The property :attr:`.T` is an accessor to the method - :meth:`transpose`. - - All columns must be the same dtype (numerics can be coerced to a common supertype). - - **Examples:** - - **Square DataFrame with homogeneous dtype** - - - >>> d1 = {'col1': [1, 2], 'col2': [3, 4]} - >>> df1 = bpd.DataFrame(data=d1) - >>> df1 - col1 col2 - 0 1 3 - 1 2 4 - - [2 rows x 2 columns] - - >>> df1_transposed = df1.T # or df1.transpose() - >>> df1_transposed - 0 1 - col1 1 2 - col2 3 4 - - [2 rows x 2 columns] - - When the dtype is homogeneous in the original DataFrame, we get a - transposed DataFrame with the same dtype: - - >>> df1.dtypes - col1 Int64 - col2 Int64 - dtype: object - >>> df1_transposed.dtypes - 0 Int64 - 1 Int64 - dtype: object - - Returns: - bigframes.pandas.DataFrame: The transposed DataFrame. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def info( - self, - verbose: bool | None = None, - buf=None, - max_cols: int | None = None, - memory_usage: bool | None = None, - show_counts: bool | None = None, - ) -> None: - """ - Print a concise summary of a DataFrame. - - This method prints information about a DataFrame including - the index dtypeand columns, non-null values and memory usage. - - Args: - verbose (bool, optional): - Whether to print the full summary. By default, the setting in - ``pandas.options.display.max_info_columns`` is followed. - buf (writable buffer, defaults to sys.stdout): - Where to send the output. By default, the output is printed to - sys.stdout. Pass a writable buffer if you need to further process - the output. - max_cols (int, optional): - When to switch from the verbose to the truncated output. If the - DataFrame has more than `max_cols` columns, the truncated output - is used. By default, the setting in - ``pandas.options.display.max_info_columns`` is used. - memory_usage (bool, optional): - Specifies whether total memory usage of the DataFrame - elements (including the index) should be displayed. By default, - this follows the ``pandas.options.display.memory_usage`` setting. - True always show memory usage. False never shows memory usage. - Memory estimation is made based in column dtype and number of rows - assuming values consume the same memory amount for corresponding dtypes. - show_counts (bool, optional): - Whether to show the non-null counts. By default, this is shown - only if the DataFrame is smaller than - ``pandas.options.display.max_info_rows`` and - ``pandas.options.display.max_info_columns``. A value of True always - shows the counts, and False never shows the counts. - - Returns: - None: This method prints a summary of a DataFrame and returns None. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def memory_usage(self, index: bool = True): - """ - Return the memory usage of each column in bytes. - - The memory usage can optionally include the contribution of - the index and elements of `object` dtype. - - This value is displayed in `DataFrame.info` by default. This can be - suppressed by setting ``pandas.options.display.memory_usage`` to False. - - Args: - index (bool, default True): - Specifies whether to include the memory usage of the DataFrame's - index in returned Series. If ``index=True``, the memory usage of - the index is the first item in the output. - - Returns: - bigframes.pandas.Series: A Series whose index is the original column names and whose values is the memory usage of each column in bytes. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def select_dtypes(self, include=None, exclude=None) -> DataFrame: - """ - Return a subset of the DataFrame's columns based on the column dtypes. - - **Examples:** - - - >>> df = bpd.DataFrame({'col1': [1, 2], 'col2': ["hello", "world"], 'col3': [True, False]}) - >>> df.select_dtypes(include=['Int64']) - col1 - 0 1 - 1 2 - - [2 rows x 1 columns] - - >>> df.select_dtypes(exclude=['Int64']) - col2 col3 - 0 hello True - 1 world False - - [2 rows x 2 columns] - - - Args: - include (scalar or list-like): - A selection of dtypes or strings to be included. - exclude (scalar or list-like): - A selection of dtypes or strings to be excluded. - - Returns: - bigframes.pandas.DataFrame: The subset of the frame including the dtypes in ``include`` and excluding the dtypes in ``exclude``. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) # ---------------------------------------------------------------------- # IO methods (to / from other formats) - @classmethod - def from_dict( - cls, - data: dict, - orient="columns", - dtype=None, - columns=None, - ) -> DataFrame: - """ - Construct DataFrame from dict of array-like or dicts. - - Creates DataFrame object from dictionary by columns or by index - allowing dtype specification. - - Args: - data (dict): - Of the form {field : array-like} or {field : dict}. - orient ({'columns', 'index', 'tight'}, default 'columns'): - The "orientation" of the data. If the keys of the passed dict - should be the columns of the resulting DataFrame, pass 'columns' - (default). Otherwise if the keys should be rows, pass 'index'. - If 'tight', assume a dict with keys ['index', 'columns', 'data', - 'index_names', 'column_names']. - dtype (dtype, default None): - Data type to force after DataFrame construction, otherwise infer. - columns (list, default None): - Column labels to use when ``orient='index'``. - - Raises: - ValueError: - If used with ``orient='columns'`` or ``orient='tight'``. - - Returns: - bigframes.pandas.DataFrame: DataFrame. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - @classmethod - def from_records( - cls, - data, - index=None, - exclude=None, - columns=None, - coerce_float: bool = False, - nrows: int | None = None, - ) -> DataFrame: - """ - Convert structured or record ndarray to DataFrame. - - Creates a DataFrame object from a structured ndarray, sequence of - tuples or dicts, or DataFrame. - - Args: - data (structured ndarray, sequence of tuples or dicts): - Structured input data. - index (str, list of fields, array-like): - Field of array to use as the index, alternately a specific set of - input labels to use. - exclude (sequence, default None): - Columns or fields to exclude. - columns (sequence, default None): - Column names to use. If the passed data do not have names - associated with them, this argument provides names for the - columns. Otherwise this argument indicates the order of the columns - in the result (any names not found in the data will become all-NA - columns). - coerce_float (bool, default False): - Attempt to convert values of non-string, non-numeric objects (like - decimal.Decimal) to floating point, useful for SQL result sets. - nrows (int, default None): - Number of rows to read if data is an iterator. - - Returns: - bigframes.pandas.DataFrame: DataFrame. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def to_numpy( - self, - dtype=None, - copy=False, - na_value=pd_ext.no_default, - *, - allow_large_results=None, - **kwargs, - ) -> np.ndarray: + def to_numpy(self, dtype=None, copy=False, na_value=None, **kwargs) -> np.ndarray: """ Convert the DataFrame to a NumPy array. **Examples:** + >>> import bigframes.pandas as bpd + >>> bpd.options.display.progress_bar = None >>> df = bpd.DataFrame({'col1': [1, 2], 'col2': [3, 4]}) >>> df.to_numpy() @@ -385,9 +117,7 @@ def to_numpy( na_value (Any, default None): The value to use for missing values. The default value depends on dtype and the dtypes of the DataFrame columns. - allow_large_results (bool, default None): - If not None, overrides the global setting to allow or disallow - large query results over the default size limit of 10 GB. + Returns: numpy.ndarray: The converted NumPy array. """ @@ -400,14 +130,13 @@ def to_gbq( if_exists: Optional[Literal["fail", "replace", "append"]] = None, index: bool = True, ordering_id: Optional[str] = None, - clustering_columns: Union[pd.Index, Iterable[Hashable]] = (), - labels: dict[str, str] = {}, ) -> str: """Write a DataFrame to a BigQuery table. **Examples:** >>> import bigframes.pandas as bpd + >>> bpd.options.display.progress_bar = None Write a DataFrame to a BigQuery table. @@ -421,7 +150,7 @@ def to_gbq( >>> df = bpd.DataFrame({'col1': [1, 2], 'col2': [3, 4]}) >>> destination = df.to_gbq(ordering_id="ordering_id") >>> # The table created can be read outside of the current session. - >>> bpd.close_session() # Optional, to demonstrate a new session. # doctest: +SKIP + >>> bpd.close_session() # For demonstration, only. >>> bpd.read_gbq(destination, index_col="ordering_id") col1 col2 ordering_id @@ -430,17 +159,6 @@ def to_gbq( [2 rows x 2 columns] - Write a DataFrame to a BigQuery table with clustering columns: - - >>> df = bpd.DataFrame({'col1': [1, 2], 'col2': [3, 4], 'col3': [5, 6]}) - >>> clustering_cols = ['col1', 'col3'] - >>> df.to_gbq( - ... "bigframes-dev.birds.test-clusters", - ... if_exists="replace", - ... clustering_columns=clustering_cols, - ... ) - 'bigframes-dev.birds.test-clusters' - Args: destination_table (Optional[str]): Name of table to be written, in the form ``dataset.tablename`` @@ -469,46 +187,20 @@ def to_gbq( If set, write the ordering of the DataFrame as a column in the result table with this name. - clustering_columns (Union[pd.Index, Iterable[Hashable]], default ()): - Specifies the columns for clustering in the BigQuery table. The order - of columns in this list is significant for clustering hierarchy. Index - columns may be included in clustering if the `index` parameter is set - to True, and their names are specified in this. These index columns, - if included, precede DataFrame columns in the clustering order. The - clustering order within the Index/DataFrame columns follows the order - specified in `clustering_columns`. - - labels (dict[str, str], default None): - Specifies table labels within BigQuery - Returns: str: The fully-qualified ID for the written table, in the form ``project.dataset.tablename``. - - Raises: - ValueError: - If an invalid value is provided for ``if_exists`` when ``destination_table`` - is ``None``. ``None`` or ``replace`` are the only valid values for ``if_exists``. - ValueError: - If an invalid value is provided for ``destination_table`` that is - not one of ``datasetID.tableId`` or ``projectId.datasetId.tableId``. - ValueError: - If an invalid value is provided for ``if_exists`` that is not one of - ``fail``, ``replace``, or ``append``. - - """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) def to_parquet( self, - path: Optional[str], + path: str, *, compression: Optional[Literal["snappy", "gzip"]] = "snappy", index: bool = True, - allow_large_results: Optional[bool] = None, - ) -> Optional[bytes]: + ) -> None: """Write a DataFrame to the binary Parquet format. This function writes the dataframe as a `parquet file @@ -517,82 +209,29 @@ def to_parquet( **Examples:** >>> import bigframes.pandas as bpd + >>> bpd.options.display.progress_bar = None + >>> df = bpd.DataFrame({'col1': [1, 2], 'col2': [3, 4]}) >>> gcs_bucket = "gs://bigframes-dev-testing/sample_parquet*.parquet" >>> df.to_parquet(path=gcs_bucket) Args: - path (str, path object, file-like object, or None, default None): - String, path object (implementing ``os.PathLike[str]``), or file-like - object implementing a binary ``write()`` function. If None, the result is - returned as bytes. If a string or path, it will be used as Root Directory - path when writing a partitioned dataset. + path (str): Destination URI(s) of Cloud Storage files(s) to store the extracted dataframe - should be formatted ``gs:///``. + in format of ``gs:///``. If the data size is more than 1GB, you must use a wildcard to export the data into multiple files and the size of the files varies. + compression (str, default 'snappy'): Name of the compression to use. Use ``None`` for no compression. Supported options: ``'gzip'``, ``'snappy'``. + index (bool, default True): If ``True``, include the dataframe's index(es) in the file output. If ``False``, they will not be written to the file. - allow_large_results (bool, default None): - If not None, overrides the global setting to allow or disallow large - query results over the default size limit of 10 GB. This parameter has - no effect when results are saved to Google Cloud Storage (GCS). - - Returns: - None or bytes: - bytes if no path argument is provided else None - - Raises: - ValueError: - If an invalid value provided for `compression` that is not one of - ``None``, ``snappy``, or ``gzip``. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def to_csv( - self, - path_or_buf=None, - sep=",", - *, - header: bool = True, - index: bool = True, - allow_large_results: Optional[bool] = None, - ) -> Optional[str]: - """ - Write object to a comma-separated values (csv) file. - - **Examples:** - - >>> import bigframes.pandas as bpd - - >>> df = bpd.DataFrame({'col1': [1, 2], 'col2': [3, 4]}) - >>> df.to_csv() - \',col1,col2\\n0,1,3\\n1,2,4\\n\' - - Args: - path_or_buf (str, path object, file-like object, or None, default None): - String, path object (implementing os.PathLike[str]), or file-like object - implementing a write() function. If None, the result is returned as a string. - If a non-binary file object is passed, it should be opened with newline='', - disabling universal newlines. If a binary file object is passed, - mode might need to contain a 'b'. - Must contain a wildcard character '*' if this is a GCS path. - sep (str, default ','): - String of length 1. Field delimiter for the output file. - header (bool, default True): - Write out the column names. - index (bool, default True): - Write row names (index). - allow_large_results (bool, default None): - If not None, overrides the global setting to allow or disallow large - query results over the default size limit of 10 GB. Returns: - If path_or_buf is None, returns the resulting csv format as a string. Otherwise returns None. + None. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -602,8 +241,6 @@ def to_dict( "dict", "list", "series", "split", "tight", "records", "index" ] = "dict", into: type[dict] = dict, - *, - allow_large_results: Optional[bool] = None, **kwargs, ) -> dict | list[dict]: """ @@ -614,10 +251,12 @@ def to_dict( **Examples:** + >>> import bigframes.pandas as bpd + >>> bpd.options.display.progress_bar = None >>> df = bpd.DataFrame({'col1': [1, 2], 'col2': [3, 4]}) >>> df.to_dict() - {'col1': {np.int64(0): 1, np.int64(1): 2}, 'col2': {np.int64(0): 3, np.int64(1): 4}} + {'col1': {0: 1, 1: 2}, 'col2': {0: 3, 1: 4}} You can specify the return orientation. @@ -655,13 +294,11 @@ def to_dict( in the return value. Can be the actual class or an empty instance of the mapping type you want. If you want a collections.defaultdict, you must pass it initialized. + index (bool, default True): Whether to include the index item (and index_names item if `orient` is 'tight') in the returned dictionary. Can only be ``False`` when `orient` is 'split' or 'tight'. - allow_large_results (bool, default None): - If not None, overrides the global setting to allow or disallow large - query results over the default size limit of 10 GB. Returns: dict or list of dict: Return a collections.abc.Mapping object representing the DataFrame. @@ -669,14 +306,7 @@ def to_dict( """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - def to_excel( - self, - excel_writer, - sheet_name: str = "Sheet1", - *, - allow_large_results: Optional[bool] = None, - **kwargs, - ) -> None: + def to_excel(self, excel_writer, sheet_name: str = "Sheet1", **kwargs) -> None: """ Write DataFrame to an Excel sheet. @@ -692,7 +322,9 @@ def to_excel( **Examples:** + >>> import bigframes.pandas as bpd >>> import tempfile + >>> bpd.options.display.progress_bar = None >>> df = bpd.DataFrame({'col1': [1, 2], 'col2': [3, 4]}) >>> df.to_excel(tempfile.TemporaryFile()) @@ -702,21 +334,11 @@ def to_excel( File path or existing ExcelWriter. sheet_name (str, default 'Sheet1'): Name of sheet which will contain DataFrame. - allow_large_results (bool, default None): - If not None, overrides the global setting to allow or disallow large - query results over the default size limit of 10 GB. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) def to_latex( - self, - buf=None, - columns=None, - header=True, - index=True, - *, - allow_large_results=None, - **kwargs, + self, buf=None, columns=None, header=True, index=True, **kwargs ) -> str | None: r""" Render object to a LaTeX tabular, longtable, or nested table. @@ -727,6 +349,8 @@ def to_latex( **Examples:** + >>> import bigframes.pandas as bpd + >>> bpd.options.display.progress_bar = None >>> df = bpd.DataFrame({'col1': [1, 2], 'col2': [3, 4]}) >>> print(df.to_latex()) @@ -750,23 +374,11 @@ def to_latex( it is assumed to be aliases for the column names. index (bool, default True): Write row names (index). - allow_large_results (bool, default None): - If not None, overrides the global setting to allow or disallow large - query results over the default size limit of 10 GB. - - Returns: - str or None: If buf is None, returns the result as a string. Otherwise returns - None. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) def to_records( - self, - index: bool = True, - column_dtypes=None, - index_dtypes=None, - *, - allow_large_results=None, + self, index: bool = True, column_dtypes=None, index_dtypes=None ) -> np.recarray: """ Convert DataFrame to a NumPy record array. @@ -776,11 +388,13 @@ def to_records( **Examples:** + >>> import bigframes.pandas as bpd + >>> bpd.options.display.progress_bar = None >>> df = bpd.DataFrame({'col1': [1, 2], 'col2': [3, 4]}) >>> df.to_records() rec.array([(0, 1, 3), (1, 2, 4)], - dtype=[('index', '>> import bigframes.pandas as bpd + >>> bpd.options.display.progress_bar = None >>> df = bpd.DataFrame({'col1': [1, 2], 'col2': [3, 4]}) >>> print(df.to_string()) @@ -890,9 +501,6 @@ def to_string( Max width to truncate each column in characters. By default, no limit. encoding (str, default "utf-8"): Set character encoding. - allow_large_results (bool, default None): - If not None, overrides the global setting to allow or disallow large - query results over the default size limit of 10 GB. Returns: str or None: If buf is None, returns the result as a string. Otherwise returns @@ -900,146 +508,19 @@ def to_string( """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - def to_html( - self, - buf=None, - columns: Sequence[str] | None = None, - col_space=None, - header: bool = True, - index: bool = True, - na_rep: str = "NaN", - formatters=None, - float_format=None, - sparsify: bool | None = None, - index_names: bool = True, - justify: str | None = None, - max_rows: int | None = None, - max_cols: int | None = None, - show_dimensions: bool = False, - decimal: str = ".", - bold_rows: bool = True, - classes: str | list | tuple | None = None, - escape: bool = True, - notebook: bool = False, - border: int | None = None, - table_id: str | None = None, - render_links: bool = False, - encoding: str | None = None, - *, - allow_large_results: bool | None = None, - ): - """Render a DataFrame as an HTML table. - - **Examples:** - - - >>> df = bpd.DataFrame({'col1': [1, 2], 'col2': [3, 4]}) - >>> print(df.to_html()) - - - - - - - - - - - - - - - - - - - - -
col1col2
013
124
- - Args: - buf (str, Path or StringIO-like, optional, default None): - Buffer to write to. If None, the output is returned as a string. - columns (sequence, optional, default None): - The subset of columns to write. Writes all columns by default. - col_space (str or int, list or dict of int or str, optional): - The minimum width of each column in CSS length units. An int is - assumed to be px units. - header (bool, optional): - Whether to print column labels, default True. - index (bool, optional, default True): - Whether to print index (row) labels. - na_rep (str, optional, default 'NaN'): - String representation of NAN to use. - formatters (list, tuple or dict of one-param. functions, optional): - Formatter functions to apply to columns' elements by position or - name. - The result of each function must be a unicode string. - List/tuple must be of length equal to the number of columns. - float_format (one-parameter function, optional, default None): - Formatter function to apply to columns' elements if they are - floats. This function must return a unicode string and will - be applied only to the non-NaN elements, with NaN being - handled by na_rep. - sparsify (bool, optional, default True): - Set to False for a DataFrame with a hierarchical index to print - every multiindex key at each row. - index_names (bool, optional, default True): - Prints the names of the indexes. - justify (str, default None): - How to justify the column labels. If None uses the option from - the print configuration (controlled by set_option), 'right' out - of the box. Valid values are, 'left', 'right', 'center', 'justify', - 'justify-all', 'start', 'end', 'inherit', 'match-parent', 'initial', - 'unset'. - max_rows (int, optional): - Maximum number of rows to display in the console. - max_cols (int, optional): - Maximum number of columns to display in the console. - show_dimensions (bool, default False): - Display DataFrame dimensions (number of rows by number of columns). - decimal (str, default '.'): - Character recognized as decimal separator, e.g. ',' in Europe. - bold_rows (bool, default True): - Make the row labels bold in the output. - classes (str or list or tuple, default None): - CSS class(es) to apply to the resulting html table. - escape (bool, default True): - Convert the characters <, >, and & to HTML-safe sequences. - notebook (bool, default False): - Whether the generated HTML is for IPython Notebook. - border (int): - A border=border attribute is included in the opening - tag. Default pd.options.display.html.border. - table_id (str, optional): - A css id is included in the opening
tag if specified. - render_links (bool, default False): - Convert URLs to HTML links. - encoding (str, default "utf-8"): - Set character encoding. - allow_large_results (bool, default None): - If not None, overrides the global setting to allow or disallow - large query results over the default size limit of 10 GB. - - Returns: - str or None: If buf is None, returns the result as a string. Otherwise - returns None. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - def to_markdown( self, buf=None, mode: str = "wt", index: bool = True, - *, - allow_large_results: Optional[bool] = None, **kwargs, ): """Print DataFrame in Markdown-friendly format. **Examples:** + >>> import bigframes.pandas as bpd + >>> bpd.options.display.progress_bar = None >>> df = bpd.DataFrame({'col1': [1, 2], 'col2': [3, 4]}) >>> print(df.to_markdown()) @@ -1055,23 +536,21 @@ def to_markdown( Mode in which file is opened. index (bool, optional, default True): Add index (row) labels. - allow_large_results (bool, default None): - If not None, overrides the global setting to allow or disallow - large query results over the default size limit of 10 GB. **kwargs These parameters will be passed to `tabulate `_. Returns: - str: - DataFrame in Markdown-friendly format. + DataFrame in Markdown-friendly format. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - def to_pickle(self, path, *, allow_large_results, **kwargs) -> None: + def to_pickle(self, path, **kwargs) -> None: """Pickle (serialize) object to file. **Examples:** + >>> import bigframes.pandas as bpd + >>> bpd.options.display.progress_bar = None >>> df = bpd.DataFrame({'col1': [1, 2], 'col2': [3, 4]}) >>> gcs_bucket = "gs://bigframes-dev-testing/sample_pickle_gcs.pkl" @@ -1080,18 +559,17 @@ def to_pickle(self, path, *, allow_large_results, **kwargs) -> None: Args: path (str): File path where the pickled object will be stored. - allow_large_results (bool, default None): - If not None, overrides the global setting to allow or disallow - large query results over the default size limit of 10 GB. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - def to_orc(self, path=None, *, allow_large_results=None, **kwargs) -> bytes | None: + def to_orc(self, path=None, **kwargs) -> bytes | None: """ Write a DataFrame to the ORC format. **Examples:** + >>> import bigframes.pandas as bpd + >>> bpd.options.display.progress_bar = None >>> df = bpd.DataFrame({'col1': [1, 2], 'col2': [3, 4]}) >>> import tempfile @@ -1104,20 +582,34 @@ def to_orc(self, path=None, *, allow_large_results=None, **kwargs) -> bytes | No we refer to objects with a write() method, such as a file handle (e.g. via builtin open function). If path is None, a bytes object is returned. - allow_large_results (bool, default None): - If not None, overrides the global setting to allow or disallow - large query results over the default size limit of 10 GB. - - Returns: - bytes or None: - If buf is None, returns the result as bytes. Otherwise returns - None. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) # ---------------------------------------------------------------------- # Unsorted + def equals(self, other) -> bool: + """ + Test whether two objects contain the same elements. + + This function allows two Series or DataFrames to be compared against + each other to see if they have the same shape and elements. NaNs in + the same location are considered equal. + + The row/column index do not need to have the same type, as long + as the values are considered equal. Corresponding columns must be of + the same dtype. + + Args: + other (Series or DataFrame): + The other Series or DataFrame to be compared with the first. + + Returns: + bool: True if all elements are the same in both objects, False + otherwise. + """ + raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) + def assign(self, **kwargs) -> DataFrame: r""" Assign new columns to a DataFrame. @@ -1138,8 +630,7 @@ def assign(self, **kwargs) -> DataFrame: are simply assigned to the column. Returns: - bigframes.pandas.DataFrame: - A new DataFrame with the new columns + bigframes.dataframe.DataFrame: A new DataFrame with the new columns in addition to all the existing columns. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -1173,7 +664,7 @@ def reindex( Axis to target. Can be either the axis name ('index', 'columns') or number (0, 1). Returns: - bigframes.pandas.DataFrame: DataFrame with changed index. + DataFrame: DataFrame with changed index. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -1190,55 +681,7 @@ def reindex_like(self, other): of this object. Returns: - bigframes.pandas.DataFrame or bigframes.pandas.Series: - Same type as caller, but with changed indices on each axis. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def insert(self, loc, column, value, allow_duplicates=False): - """Insert column into DataFrame at specified location. - - **Examples:** - - - >>> df = bpd.DataFrame({'col1': [1, 2], 'col2': [3, 4]}) - - Insert a new column named 'col3' between 'col1' and 'col2' with all entries set to 5. - - >>> df.insert(1, 'col3', 5) - >>> df - col1 col3 col2 - 0 1 5 3 - 1 2 5 4 - - [2 rows x 3 columns] - - Insert another column named 'col2' at the beginning of the DataFrame with values [5, 6] - - >>> df.insert(0, 'col2', [5, 6], allow_duplicates=True) - >>> df - col2 col1 col3 col2 - 0 5 1 5 3 - 1 6 2 5 4 - - [2 rows x 4 columns] - - Args: - loc (int): - Insertion index. Must verify 0 <= loc <= len(columns). - column (str, number, or hashable object): - Label of the inserted column. - value (Scalar, Series, or array-like): - Content of the inserted column. - allow_duplicates (bool, default False): - Allow duplicate column labels to be created. - - Raises: - IndexError: - If ``column`` index is out of bounds with the total count of columns. - ValueError: - If ``column`` is already contained in the DataFrame, - unless ``allow_duplicates`` is set to True. + Series or DataFrame: Same type as caller, but with changed indices on each axis. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -1249,97 +692,9 @@ def drop( Remove columns by directly specifying column names. - **Examples:** - - - >>> df = bpd.DataFrame(np.arange(12).reshape(3, 4), - ... columns=['A', 'B', 'C', 'D']) - >>> df - A B C D - 0 0 1 2 3 - 1 4 5 6 7 - 2 8 9 10 11 - - [3 rows x 4 columns] - - Drop columns: - - >>> df.drop(['B', 'C'], axis=1) - A D - 0 0 3 - 1 4 7 - 2 8 11 - - [3 rows x 2 columns] - - >>> df.drop(columns=['B', 'C']) - A D - 0 0 3 - 1 4 7 - 2 8 11 - - [3 rows x 2 columns] - - Drop a row by index: - - >>> df.drop([0, 1]) - A B C D - 2 8 9 10 11 - - [1 rows x 4 columns] - - Drop columns and/or rows of MultiIndex DataFrame: - - >>> midx = pd.MultiIndex(levels=[['llama', 'cow', 'falcon'], - ... ['speed', 'weight', 'length']], - ... codes=[[0, 0, 0, 1, 1, 1, 2, 2, 2], - ... [0, 1, 2, 0, 1, 2, 0, 1, 2]]) - >>> df = bpd.DataFrame(index=midx, columns=['big', 'small'], - ... data=[[45, 30], [200, 100], [1.5, 1], [30, 20], - ... [250, 150], [1.5, 0.8], [320, 250], - ... [1, 0.8], [0.3, 0.2]]) - >>> df - big small - llama speed 45.0 30.0 - weight 200.0 100.0 - length 1.5 1.0 - cow speed 30.0 20.0 - weight 250.0 150.0 - length 1.5 0.8 - falcon speed 320.0 250.0 - weight 1.0 0.8 - length 0.3 0.2 - - [9 rows x 2 columns] - - Drop a specific index and column combination from the MultiIndex - DataFrame, i.e., drop the index ``'cow'`` and column ``'small'``: - - >>> df.drop(index='cow', columns='small') - big - llama speed 45.0 - weight 200.0 - length 1.5 - falcon speed 320.0 - weight 1.0 - length 0.3 - - [6 rows x 1 columns] - - >>> df.drop(index='length', level=1) - big small - llama speed 45.0 30.0 - weight 200.0 100.0 - cow speed 30.0 20.0 - weight 250.0 150.0 - falcon speed 320.0 250.0 - weight 1.0 0.8 - - [6 rows x 2 columns] - Args: labels: - Index or column labels to drop. A tuple will be used as a single label and not treated as a list-like. + Index or column labels to drop. axis: Whether to drop labels from the index (0 or 'index') or columns (1 or 'columns'). @@ -1352,14 +707,10 @@ def drop( level: For MultiIndex, level from which the labels will be removed. Returns: - bigframes.pandas.DataFrame: - DataFrame without the removed column labels. + bigframes.dataframe.DataFrame: DataFrame without the removed column labels. Raises: KeyError: If any of the labels is not found in the selected axis. - ValueError: If values for both ``labels`` and ``index``/``columns`` are provided. - ValueError: If a multi-index tuple is provided as ``level``. - ValueError: If either ``labels`` or ``index``/``columns`` is not provided. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -1377,7 +728,7 @@ def align( Args: other (DataFrame or Series): - join ({'outer', 'inner', 'left', 'right'}, default 'outer'): + join ({{'outer', 'inner', 'left', 'right'}}, default 'outer'): Type of alignment to be performed. left: use only keys from left frame, preserve key order. right: use only keys from right frame, preserve key order. @@ -1389,77 +740,46 @@ def align( Align on index (0), columns (1), or both (None). Returns: - Tuple[bigframes.pandas.DataFrame or bigframes.pandas.Series, type of other]: - Aligned objects. + tuple of (DataFrame, type of other): Aligned objects. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) def rename( self, *, - columns, - inplace, - ): + columns: Mapping, + ) -> DataFrame: """Rename columns. Dict values must be unique (1-to-1). Labels not contained in a dict will be left as-is. Extra labels listed don't throw an error. - **Examples:** - - - >>> df = bpd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]}) - >>> df - A B - 0 1 4 - 1 2 5 - 2 3 6 - - [3 rows x 2 columns] - - Rename columns using a mapping: - - >>> df.rename(columns={"A": "col1", "B": "col2"}) - col1 col2 - 0 1 4 - 1 2 5 - 2 3 6 - - [3 rows x 2 columns] - Args: columns (Mapping): Dict-like from old column labels to new column labels. - inplace (bool): - Default False. Whether to modify the DataFrame rather than - creating a new one. Returns: - bigframes.pandas.DataFrame | None: - DataFrame with the renamed axis labels or None if ``inplace=True``. + bigframes.dataframe.DataFrame: DataFrame with the renamed axis labels. Raises: KeyError: If any of the labels is not found. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - def rename_axis(self, mapper, *, inplace, **kwargs): + def rename_axis(self, mapper: Optional[str], **kwargs) -> DataFrame: """ Set the name of the axis for the index. .. note:: + Currently only accepts a single string parameter (the new name of the index). Args: - mapper (str): + mapper str: Value to set the axis name attribute. - inplace (bool): - Default False. Modifies the object directly, instead of - creating a new Series or DataFrame. Returns: - bigframes.pandas.DataFrame | None: - DataFrame with the new index name or None if ``inplace=True``. + bigframes.dataframe.DataFrame: DataFrame with the new index name """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -1475,45 +795,6 @@ def set_index( Set the DataFrame index (row labels) using one existing column. The index can replace the existing index. - **Examples:** - - - >>> df = bpd.DataFrame({'month': [1, 4, 7, 10], - ... 'year': [2012, 2014, 2013, 2014], - ... 'sale': [55, 40, 84, 31]}) - >>> df - month year sale - 0 1 2012 55 - 1 4 2014 40 - 2 7 2013 84 - 3 10 2014 31 - - [4 rows x 3 columns] - - Set the 'month' column to become the index: - - >>> df.set_index('month') - year sale - month - 1 2012 55 - 4 2014 40 - 7 2013 84 - 10 2014 31 - - [4 rows x 2 columns] - - Create a MultiIndex using columns 'year' and 'month': - - >>> df.set_index(['year', 'month']) - sale - year month - 2012 1 55 - 2014 4 40 - 2013 7 84 - 2014 10 31 - - [4 rows x 1 columns] - Args: keys: A label. This parameter can be a single column key. @@ -1521,12 +802,7 @@ def set_index( Delete columns to be used as the new index. Returns: - bigframes.pandas.DataFrame: - Changed row labels. - - Raises: - KeyError: - If key(s) are not in the columns. + DataFrame: Changed row labels. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -1544,12 +820,7 @@ def reorder_levels( Where to reorder levels. Returns: - bigframes.pandas.DataFrame: - DataFrame of rearranged index. - - Raises: - ValueError: - If columns are not multi-index. + DataFrame: DataFrame of rearranged index. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -1567,12 +838,7 @@ def swaplevel(self, i, j, axis: str | int = 0) -> DataFrame: 'columns' for column-wise. Returns: - bigframes.pandas.DataFrame: - DataFrame with levels swapped in MultiIndex. - - Raises: - ValueError: - If columns are not multi-index. + DataFrame: DataFrame with levels swapped in MultiIndex. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -1591,138 +857,26 @@ def droplevel(self, level, axis: str | int = 0): * 0 or 'index': remove level(s) in column. * 1 or 'columns': remove level(s) in row. Returns: - bigframes.pandas.DataFrame: - DataFrame with requested index / column level(s) removed. - - Raises: - ValueError: - If columns are not multi-index + DataFrame: DataFrame with requested index / column level(s) removed. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) def reset_index( self, - level=None, *, drop: bool = False, - inplace: bool = False, - col_level: Hashable = 0, - col_fill: Hashable = "", - allow_duplicates: Optional[bool] = None, - names: Hashable | Sequence[Hashable] | None = None, ) -> DataFrame | None: """Reset the index. Reset the index of the DataFrame, and use the default one instead. - **Examples:** - - - >>> df = bpd.DataFrame([('bird', 389.0), - ... ('bird', 24.0), - ... ('mammal', 80.5), - ... ('mammal', np.nan)], - ... index=['falcon', 'parrot', 'lion', 'monkey'], - ... columns=('class', 'max_speed')) - >>> df - class max_speed - falcon bird 389.0 - parrot bird 24.0 - lion mammal 80.5 - monkey mammal - - [4 rows x 2 columns] - - When we reset the index, the old index is added as a column, and a new sequential index is used: - - >>> df.reset_index() - index class max_speed - 0 falcon bird 389.0 - 1 parrot bird 24.0 - 2 lion mammal 80.5 - 3 monkey mammal - - [4 rows x 3 columns] - - We can use the ``drop`` parameter to avoid the old index being added as a column: - - >>> df.reset_index(drop=True) - class max_speed - 0 bird 389.0 - 1 bird 24.0 - 2 mammal 80.5 - 3 mammal - - [4 rows x 2 columns] - - You can also use ``reset_index`` with ``MultiIndex``. - - >>> index = pd.MultiIndex.from_tuples([('bird', 'falcon'), - ... ('bird', 'parrot'), - ... ('mammal', 'lion'), - ... ('mammal', 'monkey')], - ... names=['class', 'name']) - >>> columns = ['speed', 'max'] - >>> df = bpd.DataFrame([(389.0, 'fly'), - ... (24.0, 'fly'), - ... (80.5, 'run'), - ... (np.nan, 'jump')], - ... index=index, - ... columns=columns) - >>> df - speed max - class name - bird falcon 389.0 fly - parrot 24.0 fly - mammal lion 80.5 run - monkey jump - - [4 rows x 2 columns] - - >>> df.reset_index() - class name speed max - 0 bird falcon 389.0 fly - 1 bird parrot 24.0 fly - 2 mammal lion 80.5 run - 3 mammal monkey jump - - [4 rows x 4 columns] - - >>> df.reset_index(drop=True) - speed max - 0 389.0 fly - 1 24.0 fly - 2 80.5 run - 3 jump - - [4 rows x 2 columns] - - Args: - level (int, str, tuple, or list, default None): - Only remove the given levels from the index. Removes all levels by - default. drop (bool, default False): Do not try to insert index into dataframe columns. This resets the index to the default integer index. - inplace (bool, default False): - Whether to modify the DataFrame rather than creating a new one. - col_level (int or str, default 0): - If the columns have multiple levels, determines which level the - labels are inserted into. By default it is inserted into the first - level. - col_fill (object, default ''): - If the columns have multiple levels, determines how the other - levels are named. If None then the index name is repeated. - allow_duplicates (bool, optional, default None): - Allow duplicate column labels to be created. - names (str or 1-dimensional list, default None): - Using the given string, rename the DataFrame column which contains the - index data. If the DataFrame has a MultiIndex, this has to be a list or - tuple with length equal to the number of levels Returns: - bigframes.pandas.DataFrame: DataFrame with the new index. + bigframes.dataframe.DataFrame: DataFrame with the new index. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -1749,7 +903,7 @@ def drop_duplicates( - ``False`` : Drop all duplicates. Returns: - bigframes.pandas.DataFrame: DataFrame with duplicates removed + bigframes.dataframe.DataFrame: DataFrame with duplicates removed """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -1771,7 +925,7 @@ def duplicated(self, subset=None, keep="first"): - False : Mark all duplicates as ``True``. Returns: - bigframes.pandas.Series: Boolean series for each duplicated rows. + bigframes.series.Series: Boolean series for each duplicated rows. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -1780,88 +934,11 @@ def duplicated(self, subset=None, keep="first"): def dropna( self, - *, - axis: int | str = 0, - how: str = "any", - thresh: Optional[int] = None, - subset=None, - inplace: bool = False, - ignore_index=False, ) -> DataFrame: """Remove missing values. - **Examples:** - - - >>> df = bpd.DataFrame({"name": ['Alfred', 'Batman', 'Catwoman'], - ... "toy": [np.nan, 'Batmobile', 'Bullwhip'], - ... "born": [pd.NA, "1940-04-25", pd.NA]}) - >>> df - name toy born - 0 Alfred - 1 Batman Batmobile 1940-04-25 - 2 Catwoman Bullwhip - - [3 rows x 3 columns] - - Drop the rows where at least one element is missing: - - >>> df.dropna() - name toy born - 1 Batman Batmobile 1940-04-25 - - [1 rows x 3 columns] - - Drop the columns where at least one element is missing. - - >>> df.dropna(axis='columns') - name - 0 Alfred - 1 Batman - 2 Catwoman - - [3 rows x 1 columns] - - Drop the rows where all elements are missing: - - >>> df.dropna(how='all') - name toy born - 0 Alfred - 1 Batman Batmobile 1940-04-25 - 2 Catwoman Bullwhip - - [3 rows x 3 columns] - - Keep rows with at least 2 non-null values. - - >>> df.dropna(thresh=2) - name toy born - 1 Batman Batmobile 1940-04-25 - 2 Catwoman Bullwhip - - [2 rows x 3 columns] - - Keep columns with at least 2 non-null values: - - >>> df.dropna(axis='columns', thresh=2) - name toy - 0 Alfred - 1 Batman Batmobile - 2 Catwoman Bullwhip - - [3 rows x 2 columns] - - Define in which columns to look for missing values. - - >>> df.dropna(subset=['name', 'toy']) - name toy born - 1 Batman Batmobile 1940-04-25 - 2 Catwoman Bullwhip - - [2 rows x 3 columns] - Args: - axis ({0 or 'index', 1 or 'columns'}, default 0): + axis ({0 or 'index', 1 or 'columns'}, default 'columns'): Determine if rows or columns which contain missing values are removed. @@ -1873,27 +950,12 @@ def dropna( * 'any' : If any NA values are present, drop that row or column. * 'all' : If all values are NA, drop that row or column. - thresh (int, optional): - Require that many non-NA values. Cannot be combined with how. - subset (column label or sequence of labels, optional): - Labels along other axis to consider, e.g. if you are dropping - rows these would be a list of columns to include. - Only supports axis=0. - inplace (bool, default ``False``): - Not supported. ignore_index (bool, default ``False``): If ``True``, the resulting axis will be labeled 0, 1, …, n - 1. Returns: - bigframes.pandas.DataFrame: - DataFrame with NA entries dropped from it. - - Raises: - ValueError: - If ``how`` is not one of ``any`` or ``all``. - TyperError: - If both ``how`` and ``thresh`` are specified. + bigframes.dataframe.DataFrame: DataFrame with NA entries dropped from it. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -1901,37 +963,6 @@ def isin(self, values): """ Whether each element in the DataFrame is contained in values. - **Examples:** - - - >>> df = bpd.DataFrame({'num_legs': [2, 4], 'num_wings': [2, 0]}, - ... index=['falcon', 'dog']) - >>> df - num_legs num_wings - falcon 2 2 - dog 4 0 - - [2 rows x 2 columns] - - When ``values`` is a list check whether every value in the DataFrame is - present in the list (which animals have 0 or 2 legs or wings). - - >>> df.isin([0, 2]) - num_legs num_wings - falcon True True - dog False True - - [2 rows x 2 columns] - - When ``values`` is a dict, we can pass it to check for each column separately: - - >>> df.isin({'num_wings': [0, 3]}) - num_legs num_wings - falcon False False - dog False True - - [2 rows x 2 columns] - Args: values (iterable, or dict): The result will only be true at a location if all the @@ -1939,13 +970,8 @@ def isin(self, values): the column names, which must match. Returns: - bigframes.pandas.DataFrame: - DataFrame of booleans showing whether each element - in the DataFrame is contained in values. - - Raises: - TypeError: - If values provided are not list-like objects. + DataFrame: DataFrame of booleans showing whether each element + in the DataFrame is contained in values. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -1955,18 +981,20 @@ def keys(self): This is index for Series, columns for DataFrame. + Returns: + Index: Info axis. + **Examples:** + >>> import bigframes.pandas as bpd + >>> bpd.options.display.progress_bar = None >>> df = bpd.DataFrame({ ... 'A': [1, 2, 3], ... 'B': [4, 5, 6], ... }) >>> df.keys() - Index(['A', 'B'], dtype='str') - - Returns: - pandas.Index: Info axis. + Index(['A', 'B'], dtype='object') """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -1974,23 +1002,24 @@ def iterrows(self): """ Iterate over DataFrame rows as (index, Series) pairs. + Yields: + a tuple (index, data) where data contains row values as a Series + **Examples:** + >>> import bigframes.pandas as bpd + >>> bpd.options.display.progress_bar = None >>> df = bpd.DataFrame({ ... 'A': [1, 2, 3], ... 'B': [4, 5, 6], ... }) >>> index, row = next(df.iterrows()) >>> index - np.int64(0) + 0 >>> row A 1 B 4 Name: 0, dtype: object - - Returns: - Iterable[Tuple]: - A tuple where data contains row values as a Series """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -1998,15 +1027,6 @@ def itertuples(self, index: bool = True, name: str | None = "Pandas"): """ Iterate over DataFrame rows as namedtuples. - **Examples:** - - >>> df = bpd.DataFrame({ - ... 'A': [1, 2, 3], - ... 'B': [4, 5, 6], - ... }) - >>> next(df.itertuples(name="Pair")) - Pair(Index=np.int64(0), A=np.int64(1), B=np.int64(4)) - Args: index (bool, default True): If True, return the index as the first element of the tuple. @@ -2015,10 +1035,22 @@ def itertuples(self, index: bool = True, name: str | None = "Pandas"): tuples. Returns: - Iterable[Tuple]: + iterator: An object to iterate over namedtuples for each row in the DataFrame with the first field possibly being the index and following fields being the column values. + + + **Examples:** + + >>> import bigframes.pandas as bpd + >>> bpd.options.display.progress_bar = None + >>> df = bpd.DataFrame({ + ... 'A': [1, 2, 3], + ... 'B': [4, 5, 6], + ... }) + >>> next(df.itertuples(name="Pair")) + Pair(Index=0, A=1, B=4) """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -2029,390 +1061,73 @@ def items(self): Iterates over the DataFrame columns, returning a tuple with the column name and the content as a Series. - **Examples:** - - - >>> df = bpd.DataFrame({'species': ['bear', 'bear', 'marsupial'], - ... 'population': [1864, 22000, 80000]}, - ... index=['panda', 'polar', 'koala']) - >>> df - species population - panda bear 1864 - polar bear 22000 - koala marsupial 80000 - - [3 rows x 2 columns] - - >>> for label, content in df.items(): - ... print(f'--> label: {label}') - ... print(f'--> content:\\n{content}') - ... - --> label: species - --> content: - panda bear - polar bear - koala marsupial - Name: species, dtype: string - --> label: population - --> content: - panda 1864 - polar 22000 - koala 80000 - Name: population, dtype: Int64 - Returns: Iterator: Iterator of label, Series for each column. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - def where(self, cond, other): - """Replace values where the condition is False. - - **Examples:** + # ---------------------------------------------------------------------- + # Sorting - >>> df = bpd.DataFrame({'a': [20, 10, 0], 'b': [0, 10, 20]}) - >>> df - a b - 0 20 0 - 1 10 10 - 2 0 20 - - [3 rows x 2 columns] + def sort_values( + self, + by: str | Sequence[str], + *, + ascending: bool | Sequence[bool] = True, + kind: str = "quicksort", + na_position="last", + ) -> DataFrame: + """Sort by the values along row axis. - You can filter the values in the dataframe based on a condition. The - values matching the condition would be kept, and not matching would be - replaced. The default replacement value is ``NA``. For example, when the - condition is a dataframe: + Args: + by (str or Sequence[str]): + Name or list of names to sort by. + ascending (bool or Sequence[bool], default True): + Sort ascending vs. descending. Specify list for multiple sort + orders. If this is a list of bools, must match the length of + the by. + kind (str, default 'quicksort'): + Choice of sorting algorithm. Accepts 'quicksort', 'mergesort', + 'heapsort', 'stable'. Ignored except when determining whether to + sort stably. 'mergesort' or 'stable' will result in stable reorder. + na_position ({'first', 'last'}, default `last`): + ``{'first', 'last'}``, default 'last' Puts NaNs at the beginning + if `first`; `last` puts NaNs at the end. - >>> df.where(df > 0) - a b - 0 20 - 1 10 10 - 2 20 - - [3 rows x 2 columns] + Returns: + DataFrame with sorted values. + """ + raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - You can specify a custom replacement value for non-matching values. + def sort_index( + self, + ) -> DataFrame: + """Sort object by labels (along an axis). - >>> df.where(df > 0, -1) - a b - 0 20 -1 - 1 10 10 - 2 -1 20 - - [3 rows x 2 columns] + Returns: + The original DataFrame sorted by the labels. + """ + raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - Besides dataframe, the condition can be a series too. For example: + # ---------------------------------------------------------------------- + # Arithmetic Methods - >>> df.where(df['a'] > 10, -1) - a b - 0 20 0 - 1 -1 -1 - 2 -1 -1 - - [3 rows x 2 columns] + def eq(self, other, axis: str | int = "columns") -> DataFrame: + """ + Get equal to of DataFrame and other, element-wise (binary operator `eq`). - As for the replacement, it can be a dataframe too. For example: + Among flexible wrappers (`eq`, `ne`, `le`, `lt`, `ge`, `gt`) to comparison + operators. - >>> df.where(df > 10, -df) - a b - 0 20 0 - 1 -10 -10 - 2 0 20 - - [3 rows x 2 columns] + Equivalent to `==`, `!=`, `<=`, `<`, `>=`, `>` with support to choose axis + (rows or columns) and level for comparison. - >>> df.where(df['a'] > 10, -df) - a b - 0 20 0 - 1 -10 -10 - 2 0 -20 - - [3 rows x 2 columns] + **Examples:** - Please note, replacement doesn't support Series for now. In pandas, when - specifying a Series as replacement, the axis value should be specified - at the same time, which is not supported in bigframes DataFrame. + >>> import bigframes.pandas as bpd + >>> bpd.options.display.progress_bar = None - Args: - cond (bool Series/DataFrame, array-like, or callable): - Where cond is True, keep the original value. Where False, replace - with corresponding value from other. If cond is callable, it is - computed on the Series/DataFrame and returns boolean - Series/DataFrame or array. The callable must not change input - Series/DataFrame. - other (scalar, DataFrame, or callable): - Entries where cond is False are replaced with corresponding value - from other. If other is callable, it is computed on the - DataFrame and returns scalar or DataFrame. The callable must not - change input DataFrame. If not specified, entries will be filled - with the corresponding NULL value (np.nan for numpy dtypes, - pd.NA for extension dtypes). - - Returns: - DataFrame: DataFrame after the replacement. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def mask(self, cond, other): - """Replace values where the condition is False. - - **Examples:** - - - >>> df = bpd.DataFrame({'a': [20, 10, 0], 'b': [0, 10, 20]}) - >>> df - a b - 0 20 0 - 1 10 10 - 2 0 20 - - [3 rows x 2 columns] - - You can filter the values in the dataframe based on a condition. The - values matching the condition would be kept, and not matching would be - replaced. The default replacement value is ``NA``. For example, when the - condition is a dataframe: - - >>> df.mask(df > 0) - a b - 0 0 - 1 - 2 0 - - [3 rows x 2 columns] - - You can specify a custom replacement value for non-matching values. - - >>> df.mask(df > 0, -1) - a b - 0 -1 0 - 1 -1 -1 - 2 0 -1 - - [3 rows x 2 columns] - - Besides dataframe, the condition can be a series too. For example: - - >>> df.mask(df['a'] > 10, -1) - a b - 0 -1 -1 - 1 10 10 - 2 0 20 - - [3 rows x 2 columns] - - As for the replacement, it can be a dataframe too. For example: - - >>> df.mask(df > 10, -df) - a b - 0 -20 0 - 1 10 10 - 2 0 -20 - - [3 rows x 2 columns] - - >>> df.mask(df['a'] > 10, -df) - a b - 0 -20 0 - 1 10 10 - 2 0 20 - - [3 rows x 2 columns] - - Please note, replacement doesn't support Series for now. In pandas, when - specifying a Series as replacement, the axis value should be specified - at the same time, which is not supported in bigframes DataFrame. - - Args: - cond (bool Series/DataFrame, array-like, or callable): - Where cond is False, keep the original value. Where True, replace - with corresponding value from other. If cond is callable, it is - computed on the Series/DataFrame and returns boolean - Series/DataFrame or array. The callable must not change input - Series/DataFrame (though pandas doesn’t check it). - other (scalar, DataFrame, or callable): - Entries where cond is True are replaced with corresponding value - from other. If other is callable, it is computed on the - DataFrame and returns scalar or DataFrame. The callable must not - change input DataFrame (though pandas doesn’t check it). If not - specified, entries will be filled with the corresponding NULL - value (np.nan for numpy dtypes, pd.NA for extension dtypes). - - Returns: - DataFrame: DataFrame after the replacement. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - # ---------------------------------------------------------------------- - # Sorting - - def sort_values( - self, - by: str | Sequence[str], - *, - inplace: bool = False, - ascending: bool | Sequence[bool] = True, - kind: str | None = None, - na_position: Literal["first", "last"] = "last", - ): - """Sort by the values along row axis. - - **Examples:** - - - >>> df = bpd.DataFrame({ - ... 'col1': ['A', 'A', 'B', pd.NA, 'D', 'C'], - ... 'col2': [2, 1, 9, 8, 7, 4], - ... 'col3': [0, 1, 9, 4, 2, 3], - ... 'col4': ['a', 'B', 'c', 'D', 'e', 'F'] - ... }) - >>> df - col1 col2 col3 col4 - 0 A 2 0 a - 1 A 1 1 B - 2 B 9 9 c - 3 8 4 D - 4 D 7 2 e - 5 C 4 3 F - - [6 rows x 4 columns] - - Sort by col1: - - >>> df.sort_values(by=['col1']) - col1 col2 col3 col4 - 0 A 2 0 a - 1 A 1 1 B - 2 B 9 9 c - 5 C 4 3 F - 4 D 7 2 e - 3 8 4 D - - [6 rows x 4 columns] - - Sort by multiple columns: - - >>> df.sort_values(by=['col1', 'col2']) - col1 col2 col3 col4 - 1 A 1 1 B - 0 A 2 0 a - 2 B 9 9 c - 5 C 4 3 F - 4 D 7 2 e - 3 8 4 D - - [6 rows x 4 columns] - - Sort Descending: - - >>> df.sort_values(by='col1', ascending=False) - col1 col2 col3 col4 - 4 D 7 2 e - 5 C 4 3 F - 2 B 9 9 c - 0 A 2 0 a - 1 A 1 1 B - 3 8 4 D - - [6 rows x 4 columns] - - Putting NAs first: - - >>> df.sort_values(by='col1', ascending=False, na_position='first') - col1 col2 col3 col4 - 3 8 4 D - 4 D 7 2 e - 5 C 4 3 F - 2 B 9 9 c - 0 A 2 0 a - 1 A 1 1 B - - [6 rows x 4 columns] - - Args: - by (str or Sequence[str]): - Name or list of names to sort by. - ascending (bool or Sequence[bool], default True): - Sort ascending vs. descending. Specify list for multiple sort - orders. If this is a list of bools, must match the length of - the by. - inplace (bool, default False): - If True, perform operation in-place. - kind (str, default None): - Choice of sorting algorithm. Accepts 'quicksort', 'mergesort', - 'heapsort', 'stable'. Ignored except when determining whether to - sort stably. 'mergesort' or 'stable' will result in stable reorder. - na_position ({'first', 'last'}, default `last`): - ``{'first', 'last'}``, default 'last' Puts NaNs at the beginning - if `first`; `last` puts NaNs at the end. - - Returns: - bigframes.pandas.DataFram or None: - DataFrame with sorted values or None if inplace=True. - - Raises: - ValueError: - If value of ``na_position`` is not one of ``first`` or ``last``. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def sort_index( - self, - *, - axis: str | int = 0, - ascending: bool = True, - inplace: bool = False, - kind: str | None = None, - na_position: Literal["first", "last"] = "last", - ): - """Sort object by labels (along an axis). - - Args: - axis ({0 or 'index', 1 or 'columns'}, default 0): - The axis along which to sort. The value 0 identifies the rows, - and 1 identifies the columns. - ascending (bool, default True) - Sort ascending vs. descending. - inplace (bool, default False): - Whether to modify the DataFrame rather than creating a new one. - kind (str, default None): - Choice of sorting algorithm. Accepts 'quicksort', 'mergesort', - 'heapsort', 'stable'. Ignored except when determining whether to - sort stably. 'mergesort' or 'stable' will result in stable reorder. - na_position ({'first', 'last'}, default 'last'): - Puts NaNs at the beginning if `first`; `last` puts NaNs at the end. - Not implemented for MultiIndex. - - Returns: - bigframes.pandas.DataFrame: - DataFrame with sorted values or None if inplace=True. - - Raises: - ValueError: - If value of ``na_position`` is not one of ``first`` or ``last``. - ValueError: - If length of ``ascending`` dose not equal length of ``by``. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - # ---------------------------------------------------------------------- - # Arithmetic and Logical Methods - - def eq(self, other, axis: str | int = "columns") -> DataFrame: - """ - Get equal to of DataFrame and other, element-wise (binary operator `eq`). - - Among flexible wrappers (`eq`, `ne`, `le`, `lt`, `ge`, `gt`) to comparison - operators. - - Equivalent to `==`, `!=`, `<=`, `<`, `>=`, `>` with support to choose axis - (rows or columns) and level for comparison. - - **Examples:** - - - You can use method name: + You can use method name: >>> df = bpd.DataFrame({'angles': [0, 3, 4], ... 'degrees': [360, 180, 360]}, @@ -2423,8 +1138,7 @@ def eq(self, other, axis: str | int = "columns") -> DataFrame: rectangle True Name: degrees, dtype: boolean - You can also use logical operator `==`: - + You can also use arithmetic operator ``==``: >>> df["degrees"] == 360 circle True triangle False @@ -2439,60 +1153,7 @@ def eq(self, other, axis: str | int = "columns") -> DataFrame: (1 or 'columns'). Returns: - bigframes.pandas.DataFrame: Result of the comparison. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def __eq__(self, other): - """ - Check equality of DataFrame and other, element-wise, using logical - operator `==`. - - Equivalent to `DataFrame.eq(other)`. - - **Examples:** - - - >>> df = bpd.DataFrame({ - ... 'a': [0, 3, 4], - ... 'b': [360, 0, 180] - ... }) - >>> df == 0 - a b - 0 True False - 1 False True - 2 False False - - [3 rows x 2 columns] - - Args: - other (scalar or DataFrame): - Object to be compared to the DataFrame for equality. - - Returns: - bigframes.pandas.DataFrame: The result of comparing `other` to DataFrame. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def __invert__(self) -> DataFrame: - """ - Returns the bitwise inversion of the DataFrame, element-wise - using operator `~`. - - **Examples:** - - - >>> df = bpd.DataFrame({'a':[True, False, True], 'b':[-1, 0, 1]}) - >>> ~df - a b - 0 False 0 - 1 True -1 - 2 False -2 - - [3 rows x 2 columns] - - Returns: - bigframes.pandas.DataFrame: The result of inverting elements in the input. + Result of the comparison. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -2508,6 +1169,8 @@ def ne(self, other, axis: str | int = "columns") -> DataFrame: **Examples:** + >>> import bigframes.pandas as bpd + >>> bpd.options.display.progress_bar = None You can use method name: @@ -2535,38 +1198,7 @@ def ne(self, other, axis: str | int = "columns") -> DataFrame: Whether to compare by the index (0 or 'index') or columns (1 or 'columns'). Returns: - bigframes.pandas.DataFrame: Result of the comparison. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def __ne__(self, other): - """ - Check inequality of DataFrame and other, element-wise, using logical - operator `!=`. - - Equivalent to `DataFrame.ne(other)`. - - **Examples:** - - - >>> df = bpd.DataFrame({ - ... 'a': [0, 3, 4], - ... 'b': [360, 0, 180] - ... }) - >>> df != 0 - a b - 0 False True - 1 True False - 2 True True - - [3 rows x 2 columns] - - Args: - other (scalar or DataFrame): - Object to be compared to the DataFrame for inequality. - - Returns: - bigframes.pandas.DataFrame: The result of comparing `other` to DataFrame. + DataFrame: Result of the comparison. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -2586,6 +1218,8 @@ def le(self, other, axis: str | int = "columns") -> DataFrame: **Examples:** + >>> import bigframes.pandas as bpd + >>> bpd.options.display.progress_bar = None You can use method name: @@ -2614,38 +1248,7 @@ def le(self, other, axis: str | int = "columns") -> DataFrame: (1 or 'columns'). Returns: - bigframes.pandas.DataFrame: DataFrame of bool. The result of the comparison. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def __le__(self, other): - """ - Check whether DataFrame is less than or equal to other, element-wise, - using logical operator `<=`. - - Equivalent to `DataFrame.le(other)`. - - **Examples:** - - - >>> df = bpd.DataFrame({ - ... 'a': [0, -1, 1], - ... 'b': [1, 0, -1] - ... }) - >>> df <= 0 - a b - 0 True False - 1 True True - 2 False True - - [3 rows x 2 columns] - - Args: - other (scalar or DataFrame): - Object to be compared to the DataFrame. - - Returns: - bigframes.pandas.DataFrame: The result of comparing `other` to DataFrame. + DataFrame: DataFrame of bool. The result of the comparison. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -2665,6 +1268,8 @@ def lt(self, other, axis: str | int = "columns") -> DataFrame: **Examples:** + >>> import bigframes.pandas as bpd + >>> bpd.options.display.progress_bar = None You can use method name: @@ -2693,38 +1298,7 @@ def lt(self, other, axis: str | int = "columns") -> DataFrame: (1 or 'columns'). Returns: - bigframes.pandas.DataFrame: DataFrame of bool. The result of the comparison. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def __lt__(self, other): - """ - Check whether DataFrame is less than other, element-wise, using logical - operator `<`. - - Equivalent to `DataFrame.lt(other)`. - - **Examples:** - - - >>> df = bpd.DataFrame({ - ... 'a': [0, -1, 1], - ... 'b': [1, 0, -1] - ... }) - >>> df < 0 - a b - 0 False False - 1 True False - 2 False True - - [3 rows x 2 columns] - - Args: - other (scalar or DataFrame): - Object to be compared to the DataFrame. - - Returns: - bigframes.pandas.DataFrame: The result of comparing `other` to DataFrame. + DataFrame: DataFrame of bool. The result of the comparison. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -2744,6 +1318,8 @@ def ge(self, other, axis: str | int = "columns") -> DataFrame: **Examples:** + >>> import bigframes.pandas as bpd + >>> bpd.options.display.progress_bar = None You can use method name: @@ -2772,38 +1348,7 @@ def ge(self, other, axis: str | int = "columns") -> DataFrame: (1 or 'columns'). Returns: - bigframes.pandas.DataFrame: DataFrame of bool. The result of the comparison. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def __ge__(self, other): - """ - Check whether DataFrame is greater than or equal to other, element-wise, - using logical operator `>=`. - - Equivalent to `DataFrame.ge(other)`. - - **Examples:** - - - >>> df = bpd.DataFrame({ - ... 'a': [0, -1, 1], - ... 'b': [1, 0, -1] - ... }) - >>> df >= 0 - a b - 0 True True - 1 False True - 2 True False - - [3 rows x 2 columns] - - Args: - other (scalar or DataFrame): - Object to be compared to the DataFrame. - - Returns: - bigframes.pandas.DataFrame: The result of comparing `other` to DataFrame. + DataFrame: DataFrame of bool. The result of the comparison. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -2823,6 +1368,8 @@ def gt(self, other, axis: str | int = "columns") -> DataFrame: **Examples:** + >>> import bigframes.pandas as bpd + >>> bpd.options.display.progress_bar = None >>> df = bpd.DataFrame({'angles': [0, 3, 4], ... 'degrees': [360, 180, 360]}, @@ -2849,38 +1396,7 @@ def gt(self, other, axis: str | int = "columns") -> DataFrame: (1 or 'columns'). Returns: - bigframes.pandas.DataFrame: DataFrame of bool: The result of the comparison. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def __gt__(self, other): - """ - Check whether DataFrame is greater than other, element-wise, using logical - operator `>`. - - Equivalent to `DataFrame.gt(other)`. - - **Examples:** - - - >>> df = bpd.DataFrame({ - ... 'a': [0, -1, 1], - ... 'b': [1, 0, -1] - ... }) - >>> df > 0 - a b - 0 False True - 1 False False - 2 True False - - [3 rows x 2 columns] - - Args: - other (scalar or DataFrame): - Object to be compared to the DataFrame. - - Returns: - bigframes.pandas.DataFrame: The result of comparing `other` to DataFrame. + DataFrame: DataFrame of bool: The result of the comparison. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -2897,6 +1413,8 @@ def add(self, other, axis: str | int = "columns") -> DataFrame: **Examples:** + >>> import bigframes.pandas as bpd + >>> bpd.options.display.progress_bar = None >>> df = bpd.DataFrame({ ... 'A': [1, 2, 3], @@ -2913,7 +1431,7 @@ def add(self, other, axis: str | int = "columns") -> DataFrame: You can also use arithmetic operator ``+``: - >>> df['A'] + df['B'] + >>> df['A'] + (df['B']) 0 5 1 7 2 9 @@ -2927,167 +1445,40 @@ def add(self, other, axis: str | int = "columns") -> DataFrame: (1 or 'columns'). For Series input, axis to match Series index on. Returns: - bigframes.pandas.DataFrame: DataFrame result of the arithmetic operation. + DataFrame: DataFrame result of the arithmetic operation. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - def __add__(self, other) -> DataFrame: - """Get addition of DataFrame and other, column-wise, using arithmetic - operator `+`. + def sub(self, other, axis: str | int = "columns") -> DataFrame: + """Get subtraction of DataFrame and other, element-wise (binary operator `-`). + + Equivalent to ``dataframe - other``. With reverse version, `rsub`. + + Among flexible wrappers (`add`, `sub`, `mul`, `div`, `mod`, `pow`) to + arithmetic operators: `+`, `-`, `*`, `/`, `//`, `%`, `**`. - Equivalent to ``DataFrame.add(other)``. + .. note:: + Mismatched indices will be unioned together. **Examples:** + >>> import bigframes.pandas as bpd + >>> bpd.options.display.progress_bar = None >>> df = bpd.DataFrame({ - ... 'height': [1.5, 2.6], - ... 'weight': [500, 800] - ... }, - ... index=['elk', 'moose']) - >>> df - height weight - elk 1.5 500 - moose 2.6 800 - - [2 rows x 2 columns] + ... 'A': [1, 2, 3], + ... 'B': [4, 5, 6], + ... }) - Adding a scalar affects all rows and columns. + You can use method name: - >>> df + 1.5 - height weight - elk 3.0 501.5 - moose 4.1 801.5 - - [2 rows x 2 columns] + >>> df['A'].sub(df['B']) + 0 -3 + 1 -3 + 2 -3 + dtype: Int64 - You can add another DataFrame with index and columns aligned. - - >>> delta = bpd.DataFrame({ - ... 'height': [0.5, 0.9], - ... 'weight': [50, 80] - ... }, - ... index=['elk', 'moose']) - >>> df + delta - height weight - elk 2.0 550 - moose 3.5 880 - - [2 rows x 2 columns] - - Adding any mis-aligned index and columns will result in invalid values. - - >>> delta = bpd.DataFrame({ - ... 'depth': [0.5, 0.9, 1.0], - ... 'weight': [50, 80, 100] - ... }, - ... index=['elk', 'moose', 'bison']) - >>> df + delta - depth height weight - elk 550 - moose 880 - bison - - [3 rows x 3 columns] - - Args: - other (scalar or DataFrame): - Object to be added to the DataFrame. - - Returns: - bigframes.pandas.DataFrame: The result of adding `other` to DataFrame. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def radd(self, other, axis: str | int = "columns") -> DataFrame: - """Get addition of DataFrame and other, element-wise (binary operator `+`). - - Equivalent to ``other + dataframe``. With reverse version, `add`. - - Among flexible wrappers (`add`, `sub`, `mul`, `div`, `mod`, `pow`) to - arithmetic operators: `+`, `-`, `*`, `/`, `//`, `%`, `**`. - - .. note:: - Mismatched indices will be unioned together. - - **Examples:** - - - >>> df = bpd.DataFrame({ - ... 'A': [1, 2, 3], - ... 'B': [4, 5, 6], - ... }) - - You can use method name: - - >>> df['A'].radd(df['B']) - 0 5 - 1 7 - 2 9 - dtype: Int64 - - You can also use arithmetic operator ``+``: - - >>> df['A'] + df['B'] - 0 5 - 1 7 - 2 9 - dtype: Int64 - - Args: - other (float, int, or Series): - Any single or multiple element data structure, or list-like object. - axis ({0 or 'index', 1 or 'columns'}): - Whether to compare by the index (0 or 'index') or columns. - (1 or 'columns'). For Series input, axis to match Series index on. - - Returns: - bigframes.pandas.DataFrame: DataFrame result of the arithmetic operation. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def __radd__(self, other) -> DataFrame: - """Get addition of other and DataFrame, element-wise (binary operator `+`). - - Equivalent to ``DataFrame.radd(other)``. - - Args: - other (float, int, or Series): - Any single or multiple element data structure, or list-like object. - - Returns: - bigframes.pandas.DataFrame: DataFrame result of the arithmetic operation. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def sub(self, other, axis: str | int = "columns") -> DataFrame: - """Get subtraction of DataFrame and other, element-wise (binary operator `-`). - - Equivalent to ``dataframe - other``. With reverse version, `rsub`. - - Among flexible wrappers (`add`, `sub`, `mul`, `div`, `mod`, `pow`) to - arithmetic operators: `+`, `-`, `*`, `/`, `//`, `%`, `**`. - - .. note:: - Mismatched indices will be unioned together. - - **Examples:** - - - >>> df = bpd.DataFrame({ - ... 'A': [1, 2, 3], - ... 'B': [4, 5, 6], - ... }) - - You can use method name: - - >>> df['A'].sub(df['B']) - 0 -3 - 1 -3 - 2 -3 - dtype: Int64 - - You can also use arithmetic operator ``-``: + You can also use arithmetic operator ``-``: >>> df['A'] - (df['B']) 0 -3 @@ -3103,48 +1494,7 @@ def sub(self, other, axis: str | int = "columns") -> DataFrame: (1 or 'columns'). For Series input, axis to match Series index on. Returns: - bigframes.pandas.DataFrame: DataFrame result of the arithmetic operation. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def __sub__(self, other): - """ - Get subtraction of other from DataFrame, element-wise, using operator `-`. - - Equivalent to `DataFrame.sub(other)`. - - **Examples:** - - - You can subtract a scalar: - - >>> df = bpd.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]}) - >>> df - 2 - a b - 0 -1 2 - 1 0 3 - 2 1 4 - - [3 rows x 2 columns] - - You can also subtract another DataFrame with index and column labels - aligned: - - >>> df1 = bpd.DataFrame({"a": [2, 2, 2], "b": [3, 3, 3]}) - >>> df - df1 - a b - 0 -1 1 - 1 0 2 - 2 1 3 - - [3 rows x 2 columns] - - Args: - other (scalar or DataFrame): - Object to subtract from the DataFrame. - - Returns: - bigframes.pandas.DataFrame: The result of the subtraction. + DataFrame: DataFrame result of the arithmetic operation. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -3161,6 +1511,8 @@ def rsub(self, other, axis: str | int = "columns") -> DataFrame: **Examples:** + >>> import bigframes.pandas as bpd + >>> bpd.options.display.progress_bar = None >>> df = bpd.DataFrame({ ... 'A': [1, 2, 3], @@ -3188,22 +1540,7 @@ def rsub(self, other, axis: str | int = "columns") -> DataFrame: (1 or 'columns'). For Series input, axis to match Series index on. Returns: - bigframes.pandas.DataFrame: DataFrame result of the arithmetic operation. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def __rsub__(self, other): - """ - Get subtraction of DataFrame from other, element-wise, using operator `-`. - - Equivalent to `DataFrame.rsub(other)`. - - Args: - other (scalar or DataFrame): - Object to subtract the DataFrame from. - - Returns: - bigframes.pandas.DataFrame: The result of the subtraction. + DataFrame: DataFrame result of the arithmetic operation. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -3220,6 +1557,8 @@ def mul(self, other, axis: str | int = "columns") -> DataFrame: **Examples:** + >>> import bigframes.pandas as bpd + >>> bpd.options.display.progress_bar = None >>> df = bpd.DataFrame({ ... 'A': [1, 2, 3], @@ -3250,136 +1589,7 @@ def mul(self, other, axis: str | int = "columns") -> DataFrame: (1 or 'columns'). For Series input, axis to match Series index on. Returns: - bigframes.pandas.DataFrame: DataFrame result of the arithmetic operation. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def __mul__(self, other): - """ - Get multiplication of DataFrame with other, element-wise, using operator `*`. - - Equivalent to `DataFrame.mul(other)`. - - **Examples:** - - - You can multiply with a scalar: - - >>> df = bpd.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]}) - >>> df * 3 - a b - 0 3 12 - 1 6 15 - 2 9 18 - - [3 rows x 2 columns] - - You can also multiply with another DataFrame with index and column labels - aligned: - - >>> df1 = bpd.DataFrame({"a": [2, 2, 2], "b": [3, 3, 3]}) - >>> df * df1 - a b - 0 2 12 - 1 4 15 - 2 6 18 - - [3 rows x 2 columns] - - Args: - other (scalar or DataFrame): - Object to multiply with the DataFrame. - - Returns: - bigframes.pandas.DataFrame: The result of the multiplication. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def rmul(self, other, axis: str | int = "columns") -> DataFrame: - """Get multiplication of DataFrame and other, element-wise (binary operator `*`). - - Equivalent to ``other * dataframe``. With reverse version, `mul`. - - Among flexible wrappers (`add`, `sub`, `mul`, `div`, `mod`, `pow`) to - arithmetic operators: `+`, `-`, `*`, `/`, `//`, `%`, `**`. - - .. note:: - Mismatched indices will be unioned together. - - **Examples:** - - - >>> df = bpd.DataFrame({ - ... 'A': [1, 2, 3], - ... 'B': [4, 5, 6], - ... }) - - You can use method name: - - >>> df['A'].rmul(df['B']) - 0 4 - 1 10 - 2 18 - dtype: Int64 - - You can also use arithmetic operator ``*``: - - >>> df['A'] * (df['B']) - 0 4 - 1 10 - 2 18 - dtype: Int64 - - Args: - other (float, int, or Series): - Any single or multiple element data structure, or list-like object. - axis ({0 or 'index', 1 or 'columns'}): - Whether to compare by the index (0 or 'index') or columns. - (1 or 'columns'). For Series input, axis to match Series index on. - - Returns: - bigframes.pandas.DataFrame: DataFrame result of the arithmetic operation. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def __rmul__(self, other): - """ - Get multiplication of DataFrame with other, element-wise, using operator `*`. - - Equivalent to `DataFrame.rmul(other)`. - - **Examples:** - - - You can multiply with a scalar: - - >>> df = bpd.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]}) - >>> df * 3 - a b - 0 3 12 - 1 6 15 - 2 9 18 - - [3 rows x 2 columns] - - You can also multiply with another DataFrame with index and column labels - aligned: - - >>> df1 = bpd.DataFrame({"a": [2, 2, 2], "b": [3, 3, 3]}) - >>> df * df1 - a b - 0 2 12 - 1 4 15 - 2 6 18 - - [3 rows x 2 columns] - - Args: - other (scalar or DataFrame): - Object to multiply the DataFrame with. - - Returns: - bigframes.pandas.DataFrame: The result of the multiplication. + DataFrame: DataFrame result of the arithmetic operation. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -3396,6 +1606,8 @@ def truediv(self, other, axis: str | int = "columns") -> DataFrame: **Examples:** + >>> import bigframes.pandas as bpd + >>> bpd.options.display.progress_bar = None >>> df = bpd.DataFrame({ ... 'A': [1, 2, 3], @@ -3426,48 +1638,7 @@ def truediv(self, other, axis: str | int = "columns") -> DataFrame: (1 or 'columns'). For Series input, axis to match Series index on. Returns: - bigframes.pandas.DataFrame: DataFrame result of the arithmetic operation. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def __truediv__(self, other): - """ - Get division of DataFrame by other, element-wise, using operator `/`. - - Equivalent to `DataFrame.truediv(other)`. - - **Examples:** - - - You can multiply with a scalar: - - >>> df = bpd.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]}) - >>> df / 2 - a b - 0 0.5 2.0 - 1 1.0 2.5 - 2 1.5 3.0 - - [3 rows x 2 columns] - - You can also multiply with another DataFrame with index and column labels - aligned: - - >>> denominator = bpd.DataFrame({"a": [2, 2, 2], "b": [3, 3, 3]}) - >>> df / denominator - a b - 0 0.5 1.333333 - 1 1.0 1.666667 - 2 1.5 2.0 - - [3 rows x 2 columns] - - Args: - other (scalar or DataFrame): - Object to divide the DataFrame by. - - Returns: - bigframes.pandas.DataFrame: The result of the division. + DataFrame: DataFrame result of the arithmetic operation. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -3484,6 +1655,8 @@ def rtruediv(self, other, axis: str | int = "columns") -> DataFrame: **Examples:** + >>> import bigframes.pandas as bpd + >>> bpd.options.display.progress_bar = None >>> df = bpd.DataFrame({ ... 'A': [1, 2, 3], @@ -3511,22 +1684,7 @@ def rtruediv(self, other, axis: str | int = "columns") -> DataFrame: (1 or 'columns'). For Series input, axis to match Series index on. Returns: - bigframes.pandas.DataFrame: DataFrame result of the arithmetic operation. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def __rtruediv__(self, other): - """ - Get division of other by DataFrame, element-wise, using operator `/`. - - Equivalent to `DataFrame.rtruediv(other)`. - - Args: - other (scalar or DataFrame): - Object to divide by the DataFrame. - - Returns: - bigframes.pandas.DataFrame: The result of the division. + DataFrame result of the arithmetic operation. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -3543,6 +1701,8 @@ def floordiv(self, other, axis: str | int = "columns") -> DataFrame: **Examples:** + >>> import bigframes.pandas as bpd + >>> bpd.options.display.progress_bar = None >>> df = bpd.DataFrame({ ... 'A': [1, 2, 3], @@ -3573,48 +1733,7 @@ def floordiv(self, other, axis: str | int = "columns") -> DataFrame: (1 or 'columns'). For Series input, axis to match Series index on. Returns: - bigframes.pandas.DataFrame: DataFrame result of the arithmetic operation. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def __floordiv__(self, other): - """ - Get integer division of DataFrame by other, using arithmetic operator `//`. - - Equivalent to `DataFrame.floordiv(other)`. - - **Examples:** - - - You can divide by a scalar: - - >>> df = bpd.DataFrame({"a": [15, 15, 15], "b": [30, 30, 30]}) - >>> df // 2 - a b - 0 7 15 - 1 7 15 - 2 7 15 - - [3 rows x 2 columns] - - You can also divide by another DataFrame with index and column labels - aligned: - - >>> divisor = bpd.DataFrame({"a": [2, 3, 4], "b": [5, 6, 7]}) - >>> df // divisor - a b - 0 7 6 - 1 5 5 - 2 3 4 - - [3 rows x 2 columns] - - Args: - other (scalar or DataFrame): - Object to divide the DataFrame by. - - Returns: - bigframes.pandas.DataFrame: The result of the integer divison. + DataFrame: DataFrame result of the arithmetic operation. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -3631,6 +1750,8 @@ def rfloordiv(self, other, axis: str | int = "columns") -> DataFrame: **Examples:** + >>> import bigframes.pandas as bpd + >>> bpd.options.display.progress_bar = None >>> df = bpd.DataFrame({ ... 'A': [1, 2, 3], @@ -3658,22 +1779,7 @@ def rfloordiv(self, other, axis: str | int = "columns") -> DataFrame: (1 or 'columns'). For Series input, axis to match Series index on. Returns: - bigframes.pandas.DataFrame: DataFrame result of the arithmetic operation. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def __rfloordiv__(self, other): - """ - Get integer divison of other by DataFrame. - - Equivalent to `DataFrame.rfloordiv(other)`. - - Args: - other (scalar or DataFrame): - Object to divide by the DataFrame. - - Returns: - bigframes.pandas.DataFrame: The result of the integer divison. + DataFrame: DataFrame result of the arithmetic operation. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -3690,6 +1796,8 @@ def mod(self, other, axis: str | int = "columns") -> DataFrame: **Examples:** + >>> import bigframes.pandas as bpd + >>> bpd.options.display.progress_bar = None >>> df = bpd.DataFrame({ ... 'A': [1, 2, 3], @@ -3720,48 +1828,7 @@ def mod(self, other, axis: str | int = "columns") -> DataFrame: (1 or 'columns'). For Series input, axis to match Series index on. Returns: - bigframes.pandas.DataFrame: DataFrame result of the arithmetic operation. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def __mod__(self, other): - """ - Get modulo of DataFrame with other, element-wise, using operator `%`. - - Equivalent to `DataFrame.mod(other)`. - - **Examples:** - - - You can modulo with a scalar: - - >>> df = bpd.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]}) - >>> df % 3 - a b - 0 1 1 - 1 2 2 - 2 0 0 - - [3 rows x 2 columns] - - You can also modulo with another DataFrame with index and column labels - aligned: - - >>> modulo = bpd.DataFrame({"a": [2, 2, 2], "b": [3, 3, 3]}) - >>> df % modulo - a b - 0 1 1 - 1 0 2 - 2 1 0 - - [3 rows x 2 columns] - - Args: - other (scalar or DataFrame): - Object to modulo the DataFrame by. - - Returns: - bigframes.pandas.DataFrame: The result of the modulo. + DataFrame: DataFrame result of the arithmetic operation. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -3778,6 +1845,8 @@ def rmod(self, other, axis: str | int = "columns") -> DataFrame: **Examples:** + >>> import bigframes.pandas as bpd + >>> bpd.options.display.progress_bar = None >>> df = bpd.DataFrame({ ... 'A': [1, 2, 3], @@ -3805,22 +1874,7 @@ def rmod(self, other, axis: str | int = "columns") -> DataFrame: (1 or 'columns'). For Series input, axis to match Series index on. Returns: - bigframes.pandas.DataFrame: DataFrame result of the arithmetic operation. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def __rmod__(self, other): - """ - Get integer divison of other by DataFrame. - - Equivalent to `DataFrame.rmod(other)`. - - Args: - other (scalar or DataFrame): - Object to modulo by the DataFrame. - - Returns: - bigframes.pandas.DataFrame: The result of the modulo. + DataFrame: DataFrame result of the arithmetic operation. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -3838,6 +1892,8 @@ def pow(self, other, axis: str | int = "columns") -> DataFrame: **Examples:** + >>> import bigframes.pandas as bpd + >>> bpd.options.display.progress_bar = None >>> df = bpd.DataFrame({ ... 'A': [1, 2, 3], @@ -3868,49 +1924,7 @@ def pow(self, other, axis: str | int = "columns") -> DataFrame: (1 or 'columns'). For Series input, axis to match Series index on. Returns: - bigframes.pandas.DataFrame: DataFrame result of the arithmetic operation. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def __pow__(self, other): - """ - Get exponentiation of DataFrame with other, element-wise, using operator - `**`. - - Equivalent to `DataFrame.pow(other)`. - - **Examples:** - - - You can exponentiate with a scalar: - - >>> df = bpd.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]}) - >>> df ** 2 - a b - 0 1 16 - 1 4 25 - 2 9 36 - - [3 rows x 2 columns] - - You can also exponentiate with another DataFrame with index and column - labels aligned: - - >>> exponent = bpd.DataFrame({"a": [2, 2, 2], "b": [3, 3, 3]}) - >>> df ** exponent - a b - 0 1 64 - 1 4 125 - 2 9 216 - - [3 rows x 2 columns] - - Args: - other (scalar or DataFrame): - Object to exponentiate the DataFrame with. - - Returns: - bigframes.pandas.DataFrame: The result of the exponentiation. + DataFrame: DataFrame result of the arithmetic operation. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -3928,6 +1942,8 @@ def rpow(self, other, axis: str | int = "columns") -> DataFrame: **Examples:** + >>> import bigframes.pandas as bpd + >>> bpd.options.display.progress_bar = None >>> df = bpd.DataFrame({ ... 'A': [1, 2, 3], @@ -3955,59 +1971,7 @@ def rpow(self, other, axis: str | int = "columns") -> DataFrame: (1 or 'columns'). For Series input, axis to match Series index on. Returns: - bigframes.pandas.DataFrame: DataFrame result of the arithmetic operation. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def __rpow__(self, other): - """ - Get exponentiation of other with DataFrame, element-wise, using operator - `**`. - - Equivalent to `DataFrame.rpow(other)`. - - Args: - other (scalar or DataFrame): - Object to exponentiate with the DataFrame. - - Returns: - DataFrame: The result of the exponentiation. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def __and__(self, other): - """Get bitwise AND of DataFrame and other, element-wise, using operator `&`. - - Args: - other (scalar, Series or DataFrame): - Object to bitwise AND with the DataFrame. - - Returns: - bigframes.dataframe.DataFrame: The result of the operation. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def __or__(self, other): - """Get bitwise OR of DataFrame and other, element-wise, using operator `|`. - - Args: - other (scalar, Series or DataFrame): - Object to bitwise OR with the DataFrame. - - Returns: - bigframes.dataframe.DataFrame: The result of the operation. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def __xor__(self, other): - """Get bitwise XOR of DataFrame and other, element-wise, using operator `^`. - - Args: - other (scalar, Series or DataFrame): - Object to bitwise XOR with the DataFrame. - - Returns: - bigframes.dataframe.DataFrame: The result of the operation. + DataFrame: DataFrame result of the arithmetic operation. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -4022,6 +1986,8 @@ def combine( **Examples:** + >>> import bigframes.pandas as bpd + >>> bpd.options.display.progress_bar = None >>> df1 = bpd.DataFrame({'A': [0, 0], 'B': [4, 4]}) >>> df2 = bpd.DataFrame({'A': [1, 1], 'B': [3, 3]}) @@ -4047,12 +2013,7 @@ def combine( overwritten with NaNs. Returns: - bigframes.pandas.DataFrame: - Combination of the provided DataFrames. - - Raises: - ValueError: - If ``func`` return value is not Series. + DataFrame: Combination of the provided DataFrames. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -4070,6 +2031,8 @@ def combine_first(self, other) -> DataFrame: **Examples:** + >>> import bigframes.pandas as bpd + >>> bpd.options.display.progress_bar = None >>> df1 = bpd.DataFrame({'A': [None, 0], 'B': [None, 4]}) >>> df2 = bpd.DataFrame({'A': [1, 1], 'B': [3, 3]}) @@ -4085,165 +2048,7 @@ def combine_first(self, other) -> DataFrame: Provided DataFrame to use to fill null values. Returns: - bigframes.pandas.DataFrame: - The result of combining the provided DataFrame with the other object. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def explode( - self, column: Union[str, Sequence[str]], *, ignore_index: Optional[bool] = False - ) -> DataFrame: - """ - Transform each element of an array to a row, replicating index values. - - **Examples:** - - - >>> df = bpd.DataFrame({'A': [[0, 1, 2], [], [], [3, 4]], - ... 'B': 1, - ... 'C': [['a', 'b', 'c'], np.nan, [], ['d', 'e']]}) - >>> df.explode('A') - A B C - 0 0 1 ['a' 'b' 'c'] - 0 1 1 ['a' 'b' 'c'] - 0 2 1 ['a' 'b' 'c'] - 1 1 [] - 2 1 [] - 3 3 1 ['d' 'e'] - 3 4 1 ['d' 'e'] - - [7 rows x 3 columns] - >>> df.explode(list('AC')) - A B C - 0 0 1 a - 0 1 1 b - 0 2 1 c - 1 1 - 2 1 - 3 3 1 d - 3 4 1 e - - [7 rows x 3 columns] - - Args: - column (str, Sequence[str]): - Column(s) to explode. For multiple columns, specify a non-empty list - with each element be str or tuple, and all specified columns their - list-like data on same row of the frame must have matching length. - ignore_index (bool, default False): - If True, the resulting index will be labeled 0, 1, …, n - 1. - - Returns: - bigframes.pandas.DataFrame: - Exploded lists to rows of the subset columns; - index will be duplicated for these rows. - - Raises: - ValueError: - * If columns of the frame are not unique. - * If specified columns to explode is empty list. - * If specified columns to explode have not matching count of elements rowwise in the frame. - KeyError: - If incorrect column names are provided - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def corr(self, method, min_periods, numeric_only) -> DataFrame: - """ - Compute pairwise correlation of columns, excluding NA/null values. - - **Examples:** - - - >>> df = bpd.DataFrame({'A': [1, 2, 3], - ... 'B': [400, 500, 600], - ... 'C': [0.8, 0.4, 0.9]}) - >>> df.corr(numeric_only=True) - A B C - A 1.0 1.0 0.188982 - B 1.0 1.0 0.188982 - C 0.188982 0.188982 1.0 - - [3 rows x 3 columns] - - Args: - method (string, default "pearson"): - Correlation method to use - currently only "pearson" is supported. - min_periods (int, default None): - The minimum number of observations needed to return a result. Non-default values - are not yet supported, so a result will be returned for at least two observations. - numeric_only(bool, default False): - Include only float, int, boolean, decimal data. - - Returns: - bigframes.pandas.DataFrame: Correlation matrix. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def cov(self, *, numeric_only) -> DataFrame: - """ - Compute pairwise covariance of columns, excluding NA/null values. - - **Examples:** - - - >>> df = bpd.DataFrame({'A': [1, 2, 3], - ... 'B': [400, 500, 600], - ... 'C': [0.8, 0.4, 0.9]}) - >>> df.cov(numeric_only=True) - A B C - A 1.0 100.0 0.05 - B 100.0 10000.0 5.0 - C 0.05 5.0 0.07 - - [3 rows x 3 columns] - - Args: - numeric_only(bool, default False): - Include only float, int, boolean, decimal data. - - Returns: - bigframes.pandas.DataFrame: The covariance matrix of the series of the DataFrame. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def corrwith( - self, - other, - *, - numeric_only: bool = False, - ): - """ - Compute pairwise correlation. - - Pairwise correlation is computed between rows or columns of - DataFrame with rows or columns of Series or DataFrame. DataFrames - are first aligned along both axes before computing the - correlations. - - **Examples:** - - - >>> index = ["a", "b", "c", "d", "e"] - >>> columns = ["one", "two", "three", "four"] - >>> df1 = bpd.DataFrame(np.arange(20).reshape(5, 4), index=index, columns=columns) - >>> df2 = bpd.DataFrame(np.arange(16).reshape(4, 4), index=index[:4], columns=columns) - >>> df1.corrwith(df2) - one 1.0 - two 1.0 - three 1.0 - four 1.0 - dtype: Float64 - - Args: - other (DataFrame, Series): - Object with which to compute correlations. - - numeric_only (bool, default False): - Include only `float`, `int` or `boolean` data. - - Returns: - bigframes.pandas.Series: Pairwise correlations. + DataFrame: The result of combining the provided DataFrame with the other object. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -4257,6 +2062,8 @@ def update( **Examples:** + >>> import bigframes.pandas as bpd + >>> bpd.options.display.progress_bar = None >>> df = bpd.DataFrame({'A': [1, 2, 3], ... 'B': [400, 500, 600]}) @@ -4293,10 +2100,6 @@ def update( Returns: None: This method directly changes calling object. - - Raises: - ValueError: - If a type of join other than ``left`` is provided as an argument. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -4318,57 +2121,6 @@ def groupby( used to group large amounts of data and compute operations on these groups. - **Examples:** - - - >>> df = bpd.DataFrame({'Animal': ['Falcon', 'Falcon', - ... 'Parrot', 'Parrot'], - ... 'Max Speed': [380., 370., 24., 26.]}) - >>> df - Animal Max Speed - 0 Falcon 380.0 - 1 Falcon 370.0 - 2 Parrot 24.0 - 3 Parrot 26.0 - - [4 rows x 2 columns] - - >>> df.groupby(['Animal'])['Max Speed'].mean() - Animal - Falcon 375.0 - Parrot 25.0 - Name: Max Speed, dtype: Float64 - - We can also choose to include NA in group keys or not by setting `dropna`: - - >>> df = bpd.DataFrame([[1, 2, 3],[1, None, 4], [2, 1, 3], [1, 2, 2]], - ... columns=["a", "b", "c"]) - >>> df.groupby(by=["b"]).sum() - a c - b - 1.0 2 3 - 2.0 2 5 - - [2 rows x 2 columns] - - >>> df.groupby(by=["b"], dropna=False).sum() - a c - b - 1.0 2 3 - 2.0 2 5 - 1 4 - - [3 rows x 2 columns] - - We can also choose to return object with group labels or not by setting `as_index`: - - >>> df.groupby(by=["b"], as_index=False).sum() - b a c - 0 1.0 2 3 - 1 2.0 2 5 - - [2 rows x 3 columns] - Args: by (str, Sequence[str]): A label or list of labels may be passed to group by the columns @@ -4389,14 +2141,7 @@ def groupby( values will also be treated as the key in groups. Returns: - bigframes.core.groupby.SeriesGroupBy: - A groupby object that contains information about the groups. - - Raises: - ValueError: - If both ``by`` and ``level`` are specified. - TypeError: - If one of ``by`` or `level`` is not specified. + bigframes.core.groupby.SeriesGroupBy: A groupby object that contains information about the groups. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -4410,205 +2155,38 @@ def map(self, func, na_action: Optional[str] = None) -> DataFrame: to every element of a DataFrame. .. note:: + In pandas 2.1.0, DataFrame.applymap is deprecated and renamed to DataFrame.map. - **Examples:** - - Let's use ``reuse=False`` flag to make sure a new ``remote_function`` - is created every time we run the following code, but you can skip it - to potentially reuse a previously deployed ``remote_function`` from - the same user defined function. - - >>> @bpd.remote_function(reuse=False, cloud_function_service_account="default") # doctest: +SKIP - ... def minutes_to_hours(x: int) -> float: - ... return x/60 - - >>> df_minutes = bpd.DataFrame( - ... {"system_minutes" : [0, 30, 60, 90, 120], - ... "user_minutes" : [0, 15, 75, 90, 6]}) - >>> df_minutes - system_minutes user_minutes - 0 0 0 - 1 30 15 - 2 60 75 - 3 90 90 - 4 120 6 - - [5 rows x 2 columns] - - >>> df_hours = df_minutes.map(minutes_to_hours) # doctest: +SKIP - >>> df_hours # doctest: +SKIP - system_minutes user_minutes - 0 0.0 0.0 - 1 0.5 0.25 - 2 1.0 1.25 - 3 1.5 1.5 - 4 2.0 0.1 - - [5 rows x 2 columns] - - If there are ``NA``/``None`` values in the data, you can ignore - applying the remote function on such values by specifying - ``na_action='ignore'``. - - >>> df_minutes = bpd.DataFrame( - ... { - ... "system_minutes" : [0, 30, 60, None, 90, 120, pd.NA], - ... "user_minutes" : [0, 15, 75, 90, 6, None, pd.NA] - ... }, dtype="Int64") - >>> df_hours = df_minutes.map(minutes_to_hours, na_action='ignore') # doctest: +SKIP - >>> df_hours # doctest: +SKIP - system_minutes user_minutes - 0 0.0 0.0 - 1 0.5 0.25 - 2 1.0 1.25 - 3 1.5 - 4 1.5 0.1 - 5 2.0 - 6 - - [7 rows x 2 columns] - - With experimental Python Transpiler enabled, you can use some lambda functions without - deploying them as remote functions. - - >>> bpd.options.experiments.enable_python_transpiler = True - >>> df_minutes.map(lambda hours: hours / 60) - system_minutes user_minutes - 0 0.0 0.0 - 1 0.5 0.25 - 2 1.0 1.25 - 3 1.5 - 4 1.5 0.1 - 5 2.0 - 6 - - [7 rows x 2 columns] - Args: - func (function): + func: Python function wrapped by ``remote_function`` decorator, returns a single value from a single value. na_action (Optional[str], default None): - ``{None, 'ignore'}``, default None. If `ignore`, propagate NaN + ``{None, 'ignore'}``, default None. If ‘ignore’, propagate NaN values, without passing them to func. Returns: - bigframes.pandas.DataFrame: - Transformed DataFrame. - - Raises: - TypeError: - If value provided for ``func`` is not callable. - ValueError: - If value provided for ``na_action`` is not ``None`` or ``ignore``. + bigframes.dataframe.DataFrame: Transformed DataFrame. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - applymap = map - # ---------------------------------------------------------------------- # Merging / joining methods - def join( - self, - other, - on: Optional[str] = None, - how: str = "left", - lsuffix: str = "", - rsuffix: str = "", - ) -> DataFrame: + def join(self, other, *, on: Optional[str] = None, how: str) -> DataFrame: """Join columns of another DataFrame. Join columns with `other` DataFrame on index - **Examples:** - - - Join two DataFrames by specifying how to handle the operation: - - >>> df1 = bpd.DataFrame({'col1': ['foo', 'bar'], 'col2': [1, 2]}, index=[10, 11]) - >>> df1 - col1 col2 - 10 foo 1 - 11 bar 2 - - [2 rows x 2 columns] - - >>> df2 = bpd.DataFrame({'col3': ['foo', 'baz'], 'col4': [3, 4]}, index=[11, 22]) - >>> df2 - col3 col4 - 11 foo 3 - 22 baz 4 - - [2 rows x 2 columns] - - >>> df1.join(df2) - col1 col2 col3 col4 - 10 foo 1 - 11 bar 2 foo 3 - - [2 rows x 4 columns] - - >>> df1.join(df2, how="left") - col1 col2 col3 col4 - 10 foo 1 - 11 bar 2 foo 3 - - [2 rows x 4 columns] - - >>> df1.join(df2, how="right") - col1 col2 col3 col4 - 11 bar 2 foo 3 - 22 baz 4 - - [2 rows x 4 columns] - - >>> df1.join(df2, how="outer") - col1 col2 col3 col4 - 10 foo 1 - 11 bar 2 foo 3 - 22 baz 4 - - [3 rows x 4 columns] - - >>> df1.join(df2, how="inner") - col1 col2 col3 col4 - 11 bar 2 foo 3 - - [1 rows x 4 columns] - - - Another option to join using the key columns is to use the on parameter: - - >>> df1.join(df2, on="col2", how="right") - col1 col2 col3 col4 - 11 foo 3 - 22 baz 4 - - [2 rows x 4 columns] - - If there are overlapping columns, `lsuffix` and `rsuffix` can be used: - - >>> df1 = bpd.DataFrame({'key': ['K0', 'K1', 'K2'], 'A': ['A0', 'A1', 'A2']}) - >>> df2 = bpd.DataFrame({'key': ['K0', 'K1', 'K2'], 'A': ['B0', 'B1', 'B2']}) - >>> df1.set_index('key').join(df2.set_index('key'), lsuffix='_left', rsuffix='_right') - A_left A_right - key - K0 A0 B0 - K1 A1 B1 - K2 A2 B2 - - [3 rows x 2 columns] - Args: other: - DataFrame or Series with an Index similar to the Index of this one. + DataFrame with an Index similar to the Index of this one. on: Column in the caller to join on the index in other, otherwise joins index-on-index. Like an Excel VLOOKUP operation. - how ({'left', 'right', 'outer', 'inner'}, default 'left'): + how ({'left', 'right', 'outer', 'inner'}, default 'left'`): How to handle the operation of the two objects. ``left``: use calling frame's index (or column if on is specified) ``right``: use `other`'s index. ``outer``: form union of calling @@ -4616,30 +2194,9 @@ def join( and sort it lexicographically. ``inner``: form intersection of calling frame's index (or column if on is specified) with `other`'s index, preserving the order of the calling's one. - ``cross``: creates the cartesian product from both frames, preserves - the order of the left keys. - lsuffix(str, default ''): - Suffix to use from left frame's overlapping columns. - rsuffix(str, default ''): - Suffix to use from right frame's overlapping columns. Returns: - bigframes.pandas.DataFrame: - A dataframe containing columns from both the caller and `other`. - - Raises: - ValueError: - If value for ``on`` is specified for cross join. - ValueError: - If join on columns does not match the index level of the other - DataFrame. Join on columns with multi-index is not supported. - ValueError: - If left index to join on does not have the same number of levels - as the right index. - ValueError: - If columns overlap but no suffix is specified. - ValueError: - If `on` column is not unique. + bigframes.dataframe.DataFrame: A dataframe containing columns from both the caller and `other`. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -4651,14 +2208,11 @@ def merge( "left", "outer", "right", - "cross", ] = "inner", on: Optional[str] = None, *, left_on: Optional[str] = None, right_on: Optional[str] = None, - left_index: bool = False, - right_index: bool = False, sort: bool = False, suffixes: tuple[str, str] = ("_x", "_y"), ) -> DataFrame: @@ -4675,81 +2229,11 @@ def merge( rows will be matched against each other. This is different from usual SQL join behaviour and can lead to unexpected results. - **Examples:** - - - Merge DataFrames df1 and df2 by specifying type of merge: - - >>> df1 = bpd.DataFrame({'a': ['foo', 'bar'], 'b': [1, 2]}) - >>> df1 - a b - 0 foo 1 - 1 bar 2 - - [2 rows x 2 columns] - - >>> df2 = bpd.DataFrame({'a': ['foo', 'baz'], 'c': [3, 4]}) - >>> df2 - a c - 0 foo 3 - 1 baz 4 - - [2 rows x 2 columns] - - >>> df1.merge(df2, how="inner", on="a") - a b c - 0 foo 1 3 - - [1 rows x 3 columns] - - >>> df1.merge(df2, how='left', on='a') - a b c - 0 foo 1 3 - 1 bar 2 - - [2 rows x 3 columns] - - Merge df1 and df2 on the lkey and rkey columns. The value columns have - the default suffixes, _x and _y, appended. - - >>> df1 = bpd.DataFrame({'lkey': ['foo', 'bar', 'baz', 'foo'], - ... 'value': [1, 2, 3, 5]}) - >>> df1 - lkey value - 0 foo 1 - 1 bar 2 - 2 baz 3 - 3 foo 5 - - [4 rows x 2 columns] - - >>> df2 = bpd.DataFrame({'rkey': ['foo', 'bar', 'baz', 'foo'], - ... 'value': [5, 6, 7, 8]}) - >>> df2 - rkey value - 0 foo 5 - 1 bar 6 - 2 baz 7 - 3 foo 8 - - [4 rows x 2 columns] - - >>> df1.merge(df2, left_on='lkey', right_on='rkey') - lkey value_x rkey value_y - 0 foo 1 foo 5 - 1 foo 1 foo 8 - 2 bar 2 bar 6 - 3 baz 3 baz 7 - 4 foo 5 foo 5 - 5 foo 5 foo 8 - - [6 rows x 4 columns] - Args: right: Object to merge with. how: - ``{'left', 'right', 'outer', 'inner', 'cross'}, default 'inner'`` + ``{'left', 'right', 'outer', 'inner'}, default 'inner'`` Type of merge to be performed. ``left``: use only keys from left frame, similar to a SQL left outer join; preserve key order. @@ -4759,8 +2243,6 @@ def merge( join; sort keys lexicographically. ``inner``: use intersection of keys from both frames, similar to a SQL inner join; preserve the order of the left keys. - ``cross``: creates the cartesian product from both frames, preserves the order - of the left keys. on (label or list of labels): Columns to join on. It must be found in both DataFrames. Either on or left_on + right_on @@ -4771,10 +2253,6 @@ def merge( right_on (label or list of labels): Columns to join on in the right DataFrame. Either on or left_on + right_on must be passed in. - left_index (bool, default False): - Use the index from the left DataFrame as the join key. - right_index (bool, default False): - Use the index from the right DataFrame as the join key. sort: Default False. Sort the join keys lexicographically in the result DataFrame. If False, the order of the join keys depends @@ -4788,315 +2266,20 @@ def merge( no suffix. At least one of the values must not be None. Returns: - bigframes.pandas.DataFrame: - A DataFrame of the two merged objects. - - Raises: - ValueError: - If value for ``on`` is specified for cross join. - ValueError: - If ``on`` or ``left_on`` + ``right_on`` are not specified when ``on`` is ``None``. - ValueError: - If ``on`` and ``left_on`` + ``right_on`` are specified when ``on`` is not ``None``. - ValueError: - If no column with the provided label is found in ``self`` for left join. - ValueError: - If no column with the provided label is found in ``self`` for right join. + bigframes.dataframe.DataFrame: A DataFrame of the two merged objects. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - def resample( - self, - rule: str, - *, - closed: Optional[Literal["right", "left"]] = None, - label: Optional[Literal["right", "left"]] = None, - on=None, - level=None, - origin: Union[ - Union[pd.Timestamp, datetime.datetime, np.datetime64, int, float, str], - Literal["epoch", "start", "start_day", "end", "end_day"], - ] = "start_day", - ): - """Resample time-series data. - - **Examples:** + def apply(self, func, *, args=(), **kwargs): + """Apply a function along an axis of the DataFrame. - >>> import bigframes.pandas as bpd - >>> data = { - ... "timestamp_col": pd.date_range( - ... start="2021-01-01 13:00:00", periods=30, freq="1s" - ... ), - ... "int64_col": range(30), - ... "int64_too": range(10, 40), - ... } - - Resample on a DataFrame with index: - - >>> df = bpd.DataFrame(data).set_index("timestamp_col") - >>> df.resample(rule="7s").min() - int64_col int64_too - timestamp_col - 2021-01-01 12:59:55 0 10 - 2021-01-01 13:00:02 2 12 - 2021-01-01 13:00:09 9 19 - 2021-01-01 13:00:16 16 26 - 2021-01-01 13:00:23 23 33 - - [5 rows x 2 columns] - - Resample with column and origin set to 'start': - - >>> df = bpd.DataFrame(data) - >>> df.resample(rule="7s", on = "timestamp_col", origin="start").min() - int64_col int64_too - timestamp_col - 2021-01-01 13:00:00 0 10 - 2021-01-01 13:00:07 7 17 - 2021-01-01 13:00:14 14 24 - 2021-01-01 13:00:21 21 31 - 2021-01-01 13:00:28 28 38 - - [5 rows x 2 columns] - - Args: - rule (str): - The offset string representing target conversion. - Offsets 'ME', 'YE', 'QE', 'BME', 'BA', 'BQE', and 'W' are *not* - supported. - closed (Literal['left'] | None): - Which side of bin interval is closed. The default is 'left' for - all supported frequency offsets. - label (Literal['right'] | Literal['left'] | None): - Which bin edge label to label bucket with. The default is 'left' - for all supported frequency offsets. - on (str, default None): - For a DataFrame, column to use instead of index for resampling. Column - must be datetime-like. - level (str or int, default None): - For a MultiIndex, level (name or number) to use for resampling. - level must be datetime-like. - origin(str, default 'start_day'): - The timestamp on which to adjust the grouping. Must be one of the following: - 'epoch': origin is 1970-01-01 - 'start': origin is the first value of the timeseries - 'start_day': origin is the first day at midnight of the timeseries - Origin values 'end' and 'end_day' are *not* supported. - Returns: - DataFrameGroupBy: DataFrameGroupBy object. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def round(self, decimals): - """ - Round a DataFrame to a variable number of decimal places. - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> df = bpd.DataFrame([(.21, .32), (.01, .67), (.66, .03), (.21, .18)], - ... columns=['dogs', 'cats']) - >>> df - dogs cats - 0 0.21 0.32 - 1 0.01 0.67 - 2 0.66 0.03 - 3 0.21 0.18 - - [4 rows x 2 columns] - - By providing an integer each column is rounded to the same number - of decimal places - - >>> df.round(1) - dogs cats - 0 0.2 0.3 - 1 0.0 0.7 - 2 0.7 0.0 - 3 0.2 0.2 - - [4 rows x 2 columns] - - With a dict, the number of places for specific columns can be - specified with the column names as key and the number of decimal - places as value - - >>> df.round({'dogs': 1, 'cats': 0}) - dogs cats - 0 0.2 0.0 - 1 0.0 1.0 - 2 0.7 0.0 - 3 0.2 0.0 - - [4 rows x 2 columns] - - Using a Series, the number of places for specific columns can be - specified with the column names as index and the number of - decimal places as value - - >>> decimals = pd.Series([0, 1], index=['cats', 'dogs']) - >>> df.round(decimals) - dogs cats - 0 0.2 0.0 - 1 0.0 1.0 - 2 0.7 0.0 - 3 0.2 0.0 - - [4 rows x 2 columns] - - Args: - decimals (int, dict, Series): - Number of decimal places to round each column to. If an int is - given, round each column to the same number of places. - Otherwise dict and Series round to variable numbers of places. - Column names should be in the keys if `decimals` is a - dict-like, or in the index if `decimals` is a Series. Any - columns not included in `decimals` will be left as is. Elements - of `decimals` which are not columns of the input will be - ignored. - - Returns: - bigframes.pandas.DataFrame: - A DataFrame with the affected columns rounded to the specified - number of decimal places. - - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def apply(self, func, *, axis=0, args=(), **kwargs): - """Apply a function along an axis of the DataFrame. - - Objects passed to the function are Series objects whose index is - the DataFrame's index (``axis=0``) or the DataFrame's columns (``axis=1``). - The final return type is inferred from the return type of the applied - function. - - .. note:: - ``axis=1`` scenario is in preview. - - **Examples:** - - - >>> df = bpd.DataFrame({'col1': [1, 2], 'col2': [3, 4]}) - >>> df - col1 col2 - 0 1 3 - 1 2 4 - - [2 rows x 2 columns] - - >>> def square(x): - ... return x * x - - >>> df.apply(square) - col1 col2 - 0 1 9 - 1 4 16 - - [2 rows x 2 columns] - - You could apply a user defined function to every row of the DataFrame by - creating a remote function out of it, and using it with `axis=1`. Within - the function, each row is passed as a ``pandas.Series``. It is recommended - to select only the necessary columns before calling `apply()`. Note: This - feature is currently in **preview**. - - >>> @bpd.remote_function(reuse=False, cloud_function_service_account="default") # doctest: +SKIP - ... def foo(row: pd.Series) -> int: - ... result = 1 - ... result += row["col1"] - ... result += row["col2"]*row["col2"] - ... return result - - >>> df[["col1", "col2"]].apply(foo, axis=1) # doctest: +SKIP - 0 11 - 1 19 - dtype: Int64 - - You could return an array output for every input row from the remote - function. - - >>> @bpd.remote_function(reuse=False, cloud_function_service_account="default") # doctest: +SKIP - ... def marks_analyzer(marks: pd.Series) -> list[float]: - ... import statistics - ... average = marks.mean() - ... median = marks.median() - ... gemetric_mean = statistics.geometric_mean(marks.values) - ... harmonic_mean = statistics.harmonic_mean(marks.values) - ... return [ - ... round(stat, 2) for stat in - ... (average, median, gemetric_mean, harmonic_mean) - ... ] - - >>> df = bpd.DataFrame({ - ... "physics": [67, 80, 75], - ... "chemistry": [88, 56, 72], - ... "algebra": [78, 91, 79] - ... }, index=["Alice", "Bob", "Charlie"]) - >>> stats = df.apply(marks_analyzer, axis=1) # doctest: +SKIP - >>> stats # doctest: +SKIP - Alice [77.67 78. 77.19 76.71] - Bob [75.67 80. 74.15 72.56] - Charlie [75.33 75. 75.28 75.22] - dtype: list[pyarrow] - - You could also apply a remote function which accepts multiple parameters - to every row of a DataFrame by using it with `axis=1` if the DataFrame - has matching number of columns and data types. Note: This feature is - currently in **preview**. - - >>> df = bpd.DataFrame({ - ... 'col1': [1, 2], - ... 'col2': [3, 4], - ... 'col3': [5, 5] - ... }) - >>> df - col1 col2 col3 - 0 1 3 5 - 1 2 4 5 - - [2 rows x 3 columns] - - >>> @bpd.remote_function(reuse=False, cloud_function_service_account="default") # doctest: +SKIP - ... def foo(x: int, y: int, z: int) -> float: - ... result = 1 - ... result += x - ... result += y/z - ... return result - - >>> df.apply(foo, axis=1) # doctest: +SKIP - 0 2.6 - 1 3.8 - dtype: Float64 - - With experimental Python Transpiler enabled, you can use some lambda functions without - deploying them as remote functions: - - >>> bpd.options.experiments.enable_python_transpiler = True - >>> df.apply(lambda row: 1 + row.col1 + row.col2/row.col3, axis=1) - 0 2.6 - 1 3.8 - dtype: Float64 + Objects passed to the function are Series objects whose index is + the DataFrame's index (``axis=0``) the final return type + is inferred from the return type of the applied function. Args: func (function): - Function to apply to each column or row. To apply to each row - (i.e. when `axis=1` is specified) the function can be of one of - the two types: - - (1). It accepts a single input parameter of type `Series`, in - which case each row is delivered to the function as a pandas - Series. - - (2). It accept one or more parameters, in which case column values - are delivered to the function as separate arguments (mapping - to those parameters) for each row. For this to work the - `DataFrame` must have same number of columns and matching - data types. - axis ({index (0), columns (1)}): - Axis along which the function is applied. Specify 0 or 'index' - to apply function to each column. Specify 1 or 'columns' to - apply function to each row. + Function to apply to each column or row. args (tuple): Positional arguments to pass to `func` in addition to the array/series. @@ -5105,18 +2288,7 @@ def apply(self, func, *, axis=0, args=(), **kwargs): `func`. Returns: - bigframes.pandas.DataFrame or bigframes.pandas.Series: - Result of applying ``func`` along the given axis of the DataFrame. - - Raises: - ValueError: - If a remote function is not provided when ``axis=1`` is specified. - ValueError: - If number or input params in the remote function are not the same as - the number of columns in the dataframe. - ValueError: - If the dtypes of the columns in the dataframe are not compatible with - the data types of the remote function input params. + pandas.Series or bigframes.DataFrame: Result of applying ``func`` along the given axis of the DataFrame. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -5131,31 +2303,6 @@ def any(self, *, axis=0, bool_only: bool = False): along a Dataframe axis that is True or equivalent (e.g. non-zero or non-empty). - **Examples:** - - - >>> df = bpd.DataFrame({"A": [True, True], "B": [False, False]}) - >>> df - A B - 0 True False - 1 True False - - [2 rows x 2 columns] - - Checking if each column contains at least one True element(the default behavior without an explicit axis parameter): - - >>> df.any() - A True - B False - dtype: boolean - - Checking if each row contains at least one True element: - - >>> df.any(axis=1) - 0 True - 1 True - dtype: boolean - Args: axis ({index (0), columns (1)}): Axis for the function to be applied on. @@ -5164,7 +2311,7 @@ def any(self, *, axis=0, bool_only: bool = False): Include only boolean columns. Returns: - bigframes.pandas.Series: Series indicating if any element is True per column. + Series """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -5176,31 +2323,6 @@ def all(self, axis=0, *, bool_only: bool = False): along a DataFrame axis that is False or equivalent (e.g. zero or empty). - **Examples:** - - - >>> df = bpd.DataFrame({"A": [True, True], "B": [False, False]}) - >>> df - A B - 0 True False - 1 True False - - [2 rows x 2 columns] - - Checking if all values in each column are True(the default behavior without an explicit axis parameter): - - >>> df.all() - A True - B False - dtype: boolean - - Checking across rows to see if all values are True: - - >>> df.all(axis=1) - 0 False - 1 False - dtype: boolean - Args: axis ({index (0), columns (1)}): Axis for the function to be applied on. @@ -5209,7 +2331,7 @@ def all(self, axis=0, *, bool_only: bool = False): Include only boolean columns. Returns: - bigframes.pandas.Series: Series indicating if all elements are True per column. + bigframes.series.Series: Series if all elements are True. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -5217,42 +2339,15 @@ def prod(self, axis=0, *, numeric_only: bool = False): """ Return the product of the values over the requested axis. - **Examples:** - - >>> import bigframes.pandas as bpd - >>> df = bpd.DataFrame({"A": [1, 2, 3], "B": [4.5, 5.5, 6.5]}) - >>> df - A B - 0 1 4.5 - 1 2 5.5 - 2 3 6.5 - - [3 rows x 2 columns] - - Calculating the product of each column(the default behavior without an explicit axis parameter): - - >>> df.prod() - A 6.0 - B 160.875 - dtype: Float64 - - Calculating the product of each row: - - >>> df.prod(axis=1) - 0 4.5 - 1 11.0 - 2 19.5 - dtype: Float64 - Args: - axis ({index (0), columns (1)}): + aßxis ({index (0), columns (1)}): Axis for the function to be applied on. For Series this parameter is unused and defaults to 0. numeric_only (bool. default False): Include only float, int, boolean columns. Returns: - bigframes.pandas.Series: Series with the product of the values. + bigframes.series.Series: Series with the product of the values. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -5262,31 +2357,6 @@ def min(self, axis=0, *, numeric_only: bool = False): If you want the *index* of the minimum, use ``idxmin``. This is the equivalent of the ``numpy.ndarray`` method ``argmin``. - **Examples:** - - - >>> df = bpd.DataFrame({"A": [1, 3], "B": [2, 4]}) - >>> df - A B - 0 1 2 - 1 3 4 - - [2 rows x 2 columns] - - Finding the minimum value in each column (the default behavior without an explicit axis parameter). - - >>> df.min() - A 1 - B 2 - dtype: Int64 - - Finding the minimum value in each row. - - >>> df.min(axis=1) - 0 1 - 1 3 - dtype: Int64 - Args: axis ({index (0), columns (1)}): Axis for the function to be applied on. @@ -5295,7 +2365,7 @@ def min(self, axis=0, *, numeric_only: bool = False): Default False. Include only float, int, boolean columns. Returns: - bigframes.pandas.Series: Series with the minimum of the values. + bigframes.series.Series: Series with the minimum of the values. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -5305,31 +2375,6 @@ def max(self, axis=0, *, numeric_only: bool = False): If you want the *index* of the maximum, use ``idxmax``. This is the equivalent of the ``numpy.ndarray`` method ``argmax``. - **Examples:** - - - >>> df = bpd.DataFrame({"A": [1, 3], "B": [2, 4]}) - >>> df - A B - 0 1 2 - 1 3 4 - - [2 rows x 2 columns] - - Finding the maximum value in each column (the default behavior without an explicit axis parameter). - - >>> df.max() - A 3 - B 4 - dtype: Int64 - - Finding the maximum value in each row. - - >>> df.max(axis=1) - 0 2 - 1 4 - dtype: Int64 - Args: axis ({index (0), columns (1)}): Axis for the function to be applied on. @@ -5338,7 +2383,7 @@ def max(self, axis=0, *, numeric_only: bool = False): Default False. Include only float, int, boolean columns. Returns: - bigframes.pandas.Series: Series after the maximum of values. + bigframes.series.Series: Series after the maximum of values. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -5347,31 +2392,6 @@ def sum(self, axis=0, *, numeric_only: bool = False): This is equivalent to the method ``numpy.sum``. - **Examples:** - - - >>> df = bpd.DataFrame({"A": [1, 3], "B": [2, 4]}) - >>> df - A B - 0 1 2 - 1 3 4 - - [2 rows x 2 columns] - - Calculating the sum of each column (the default behavior without an explicit axis parameter). - - >>> df.sum() - A 4 - B 6 - dtype: Int64 - - Calculating the sum of each row. - - >>> df.sum(axis=1) - 0 3 - 1 7 - dtype: Int64 - Args: axis ({index (0), columns (1)}): Axis for the function to be applied on. @@ -5380,38 +2400,13 @@ def sum(self, axis=0, *, numeric_only: bool = False): Default False. Include only float, int, boolean columns. Returns: - bigframes.pandas.Series: Series with the sum of values. + bigframes.series.Series: Series with the sum of values. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) def mean(self, axis=0, *, numeric_only: bool = False): """Return the mean of the values over the requested axis. - **Examples:** - - - >>> df = bpd.DataFrame({"A": [1, 3], "B": [2, 4]}) - >>> df - A B - 0 1 2 - 1 3 4 - - [2 rows x 2 columns] - - Calculating the mean of each column (the default behavior without an explicit axis parameter). - - >>> df.mean() - A 2.0 - B 3.0 - dtype: Float64 - - Calculating the mean of each row. - - >>> df.mean(axis=1) - 0 1.5 - 1 3.5 - dtype: Float64 - Args: axis ({index (0), columns (1)}): Axis for the function to be applied on. @@ -5420,78 +2415,22 @@ def mean(self, axis=0, *, numeric_only: bool = False): Default False. Include only float, int, boolean columns. Returns: - bigframes.pandas.Series: Series with the mean of values. + bigframes.series.Series: Series with the mean of values. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - def median(self, *, numeric_only: bool = False, exact: bool = True): - """Return the median of the values over colunms. - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> df = bpd.DataFrame({"A": [1, 3], "B": [2, 4]}) - >>> df - A B - 0 1 2 - 1 3 4 - - [2 rows x 2 columns] - - Finding the median value of each column. - - >>> df.median() - A 2.0 - B 3.0 - dtype: Float64 + def median(self, *, numeric_only: bool = False, exact: bool = False): + """Return the median of the values over the requested axis. Args: numeric_only (bool. default False): Default False. Include only float, int, boolean columns. - exact (bool. default True): - Default True. Get the exact median instead of an approximate - one. - - Returns: - bigframes.pandas.Series: Series with the median of values. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def quantile( - self, q: Union[float, Sequence[float]] = 0.5, *, numeric_only: bool = False - ): - """ - Return values at the given quantile over requested axis. - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> df = bpd.DataFrame(np.array([[1, 1], [2, 10], [3, 100], [4, 100]]), - ... columns=['a', 'b']) - >>> df.quantile(.1) - a 1.3 - b 3.7 - Name: 0.1, dtype: Float64 - >>> df.quantile([.1, .5]) - a b - 0.1 1.3 3.7 - 0.5 2.5 55.0 - - [2 rows x 2 columns] - - Args: - q (float or array-like, default 0.5 (50% quantile)): - Value between 0 <= q <= 1, the quantile(s) to compute. - numeric_only (bool, default False): - Include only `float`, `int` or `boolean` data. + exact (bool. default False): + Default False. Get the exact median instead of an approximate + one. Note: ``exact=True`` not yet supported. Returns: - bigframes.pandas.DataFrame or bigframes.pandas.Series: - If ``q`` is an array, a DataFrame will be returned where the - index is ``q``, the columns are the columns of self, and the - values are the quantiles. - If ``q`` is a float, a Series will be returned where the - index is the columns of self and the values are the quantiles. + bigframes.series.Series: Series with the median of values. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -5500,32 +2439,6 @@ def var(self, axis=0, *, numeric_only: bool = False): Normalized by N-1 by default. - **Examples:** - - - >>> df = bpd.DataFrame({"A": [1, 3], "B": [2, 4]}) - >>> df - A B - 0 1 2 - 1 3 4 - - [2 rows x 2 columns] - - Calculating the variance of each column (the default behavior without an explicit axis parameter). - - >>> df.var() - A 2.0 - B 2.0 - dtype: Float64 - - Calculating the variance of each row. - - >>> df.var(axis=1) - 0 0.5 - 1 0.5 - dtype: Float64 - - Args: axis ({index (0), columns (1)}): Axis for the function to be applied on. @@ -5534,162 +2447,66 @@ def var(self, axis=0, *, numeric_only: bool = False): Default False. Include only float, int, boolean columns. Returns: - bigframes.pandas.Series: Series with unbiased variance over requested axis. + bigframes.series.Series: Series with unbiased variance over requested axis. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) def skew(self, *, numeric_only: bool = False): - """Return unbiased skew over columns. + """Return unbiased skew over requested axis. Normalized by N-1. - **Examples:** - - - >>> df = bpd.DataFrame({'A': [1, 2, 3, 4, 5], - ... 'B': [5, 4, 3, 2, 1], - ... 'C': [2, 2, 3, 2, 2]}) - >>> df - A B C - 0 1 5 2 - 1 2 4 2 - 2 3 3 3 - 3 4 2 2 - 4 5 1 2 - - [5 rows x 3 columns] - - Calculating the skewness of each column. - - >>> df.skew() - A 0.0 - B 0.0 - C 2.236068 - dtype: Float64 - Args: numeric_only (bool, default False): Include only float, int, boolean columns. Returns: - bigframes.pandas.Series: Series. + Series """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) def kurt(self, *, numeric_only: bool = False): - """Return unbiased kurtosis over columns. + """Return unbiased kurtosis over requested axis. Kurtosis obtained using Fisher's definition of kurtosis (kurtosis of normal == 0.0). Normalized by N-1. - **Examples:** - - - >>> df = bpd.DataFrame({"A": [1, 2, 3, 4, 5], - ... "B": [3, 4, 3, 2, 1], - ... "C": [2, 2, 3, 2, 2]}) - >>> df - A B C - 0 1 3 2 - 1 2 4 2 - 2 3 3 3 - 3 4 2 2 - 4 5 1 2 - - [5 rows x 3 columns] - - Calculating the kurtosis value of each column: - - >>> df.kurt() - A -1.2 - B -0.177515 - C 5.0 - dtype: Float64 - Args: numeric_only (bool, default False): Include only float, int, boolean columns. Returns: - bigframes.pandas.Series: Series. + Series """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) def std(self, *, numeric_only: bool = False): - """Return sample standard deviation over columns. + """Return sample standard deviation over requested axis. Normalized by N-1 by default. - **Examples:** - - - >>> df = bpd.DataFrame({"A": [1, 2, 3, 4, 5], - ... "B": [3, 4, 3, 2, 1], - ... "C": [2, 2, 3, 2, 2]}) - >>> df - A B C - 0 1 3 2 - 1 2 4 2 - 2 3 3 3 - 3 4 2 2 - 4 5 1 2 - - [5 rows x 3 columns] - - Calculating the standard deviation of each column: - - >>> df.std() - A 1.581139 - B 1.140175 - C 0.447214 - dtype: Float64 - Args: numeric_only (bool. default False): Default False. Include only float, int, boolean columns. Returns: - bigframes.pandas.Series: Series with sample standard deviation. + bigframes.series.Series: Series with sample standard deviation. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) def count(self, *, numeric_only: bool = False): """ - Count non-NA cells for each column. + Count non-NA cells for each column or row. The values `None`, `NaN`, `NaT`, and optionally `numpy.inf` (depending on `pandas.options.mode.use_inf_as_na`) are considered NA. - **Examples:** - - - >>> df = bpd.DataFrame({"A": [1, None, 3, 4, 5], - ... "B": [1, 2, 3, 4, 5], - ... "C": [None, 3.5, None, 4.5, 5.0]}) - >>> df - A B C - 0 1.0 1 - 1 2 3.5 - 2 3.0 3 - 3 4.0 4 4.5 - 4 5.0 5 5.0 - - [5 rows x 3 columns] - - Counting non-NA values for each column: - - >>> df.count() - A 4 - B 5 - C 3 - dtype: Int64 - Args: numeric_only (bool, default False): Include only `float`, `int` or `boolean` data. Returns: - bigframes.pandas.Series: For each column/row the number of + bigframes.series.Series: For each column/row the number of non-NA/null entries. If `level` is specified returns a `DataFrame`. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -5706,87 +2523,32 @@ def nlargest(self, n: int, columns, keep: str = "first"): ``df.sort_values(columns, ascending=False).head(n)``, but more performant. + Args: + n (int): + Number of rows to return. + columns (label or list of labels): + Column label(s) to order by. + keep ({'first', 'last', 'all'}, default 'first'): + Where there are duplicate values: + + - ``first`` : prioritize the first occurrence(s) + - ``last`` : prioritize the last occurrence(s) + - ``all`` : do not drop any duplicates, even it means + selecting more than `n` items. + + Returns: + DataFrame: The first `n` rows ordered by the given columns in descending order. + .. note:: This function cannot be used with all column types. For example, when specifying columns with `object` or `category` dtypes, ``TypeError`` is raised. + """ + raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - **Examples:** - - >>> import bigframes.pandas as bpd - >>> df = bpd.DataFrame({"A": [1, 1, 3, 3, 5, 5], - ... "B": [5, 6, 3, 4, 1, 2], - ... "C": ['a', 'b', 'a', 'b', 'a', 'b']}) - >>> df - A B C - 0 1 5 a - 1 1 6 b - 2 3 3 a - 3 3 4 b - 4 5 1 a - 5 5 2 b - - [6 rows x 3 columns] - - Returns rows with the largest value in 'A', including all ties: - - >>> df.nlargest(1, 'A', keep = "all") - A B C - 4 5 1 a - 5 5 2 b - - [2 rows x 3 columns] - - Returns the first row with the largest value in 'A', default behavior in case of ties: - - >>> df.nlargest(1, 'A') - A B C - 4 5 1 a - - [1 rows x 3 columns] - - Returns the last row with the largest value in 'A' in case of ties: - - >>> df.nlargest(1, 'A', keep = "last") - A B C - 5 5 2 b - - [1 rows x 3 columns] - - Returns the row with the largest combined values in both 'A' and 'C': - - >>> df.nlargest(1, ['A', 'C']) - A B C - 5 5 2 b - - [1 rows x 3 columns] - - Args: - n (int): - Number of rows to return. - columns (label or list of labels): - Column label(s) to order by. - keep ({'first', 'last', 'all'}, default 'first'): - Where there are duplicate values: - - - ``first`` : prioritize the first occurrence(s) - - ``last`` : prioritize the last occurrence(s) - - ``all`` : do not drop any duplicates, even it means - selecting more than `n` items. - - Returns: - bigframes.pandas.DataFrame: - The first `n` rows ordered by the given columns in descending order. - - Raises: - ValueError: - If value of ``keep`` is not ``first``, ``last``, or ``all``. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def nsmallest(self, n: int, columns, keep: str = "first"): - """ - Return the first `n` rows ordered by `columns` in ascending order. + def nsmallest(self, n: int, columns, keep: str = "first"): + """ + Return the first `n` rows ordered by `columns` in ascending order. Return the first `n` rows with the smallest values in `columns`, in ascending order. The columns that are not specified are returned as @@ -5796,62 +2558,6 @@ def nsmallest(self, n: int, columns, keep: str = "first"): ``df.sort_values(columns, ascending=True).head(n)``, but more performant. - .. note:: - This function cannot be used with all column types. For example, when - specifying columns with `object` or `category` dtypes, ``TypeError`` is - raised. - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> df = bpd.DataFrame({"A": [1, 1, 3, 3, 5, 5], - ... "B": [5, 6, 3, 4, 1, 2], - ... "C": ['a', 'b', 'a', 'b', 'a', 'b']}) - >>> df - A B C - 0 1 5 a - 1 1 6 b - 2 3 3 a - 3 3 4 b - 4 5 1 a - 5 5 2 b - - [6 rows x 3 columns] - - Returns rows with the smallest value in 'A', including all ties: - - >>> df.nsmallest(1, 'A', keep = "all") - A B C - 0 1 5 a - 1 1 6 b - - [2 rows x 3 columns] - - Returns the first row with the smallest value in 'A', default behavior in case of ties: - - >>> df.nsmallest(1, 'A') - A B C - 0 1 5 a - - [1 rows x 3 columns] - - Returns the last row with the smallest value in 'A' in case of ties: - - >>> df.nsmallest(1, 'A', keep = "last") - A B C - 1 1 6 b - - [1 rows x 3 columns] - - Returns rows with the smallest values in 'A' and 'C' - - >>> df.nsmallest(1, ['A', 'C']) - A B C - 0 1 5 a - - [1 rows x 3 columns] - - Args: n (int): Number of rows to return. @@ -5866,68 +2572,34 @@ def nsmallest(self, n: int, columns, keep: str = "first"): selecting more than `n` items. Returns: - bigframes.pandas.DataFrame: - The first `n` rows ordered by the given columns in ascending order. + DataFrame: The first `n` rows ordered by the given columns in ascending order. - Raises: - ValueError: - If value of ``keep`` is not ``first``, ``last``, or ``all``. + .. note:: + This function cannot be used with all column types. For example, when + specifying columns with `object` or `category` dtypes, ``TypeError`` is + raised. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) def idxmin(self): """ - Return index of first occurrence of minimum over columns. + Return index of first occurrence of minimum over requested axis. NA/null values are excluded. - **Examples:** - - - >>> df = bpd.DataFrame({"A": [3, 1, 2], "B": [1, 2, 3]}) - >>> df - A B - 0 3 1 - 1 1 2 - 2 2 3 - - [3 rows x 2 columns] - - >>> df.idxmin() - A 1 - B 0 - dtype: Int64 - Returns: - bigframes.pandas.Series: Indexes of minima along the columns. + Series: Indexes of minima along the specified axis. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) def idxmax(self): """ - Return index of first occurrence of maximum over columns. + Return index of first occurrence of maximum over requested axis. NA/null values are excluded. - **Examples:** - - - >>> df = bpd.DataFrame({"A": [3, 1, 2], "B": [1, 2, 3]}) - >>> df - A B - 0 3 1 - 1 1 2 - 2 2 3 - - [3 rows x 2 columns] - - >>> df.idxmax() - A 0 - B 2 - dtype: Int64 - Returns: - bigframes.pandas.Series: Indexes of maxima along the columns. + Series: Indexes of maxima along the specified axis. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -5941,313 +2613,95 @@ def melt(self, id_vars, value_vars, var_name, value_name): the row axis, leaving just two non-identifier columns, 'variable' and 'value'. - **Examples:** - - - >>> df = bpd.DataFrame({"A": [1, None, 3, 4, 5], - ... "B": [1, 2, 3, 4, 5], - ... "C": [None, 3.5, None, 4.5, 5.0]}) - >>> df - A B C - 0 1.0 1 - 1 2 3.5 - 2 3.0 3 - 3 4.0 4 4.5 - 4 5.0 5 5.0 - - [5 rows x 3 columns] - - Using `melt` without optional arguments: - - >>> df.melt() - variable value - 0 A 1.0 - 1 A - 2 A 3.0 - 3 A 4.0 - 4 A 5.0 - 5 B 1.0 - 6 B 2.0 - 7 B 3.0 - 8 B 4.0 - 9 B 5.0 - ... - - [15 rows x 2 columns] - - Using `melt` with `id_vars` and `value_vars`: - - >>> df.melt(id_vars='A', value_vars=['B', 'C']) - A variable value - 0 1.0 B 1.0 - 1 B 2.0 - 2 3.0 B 3.0 - 3 4.0 B 4.0 - 4 5.0 B 5.0 - 5 1.0 C - 6 C 3.5 - 7 3.0 C - 8 4.0 C 4.5 - 9 5.0 C 5.0 - - [10 rows x 3 columns] - - - Args: - id_vars (tuple, list, or ndarray, optional): - Column(s) to use as identifier variables. - value_vars (tuple, list, or ndarray, optional): - Column(s) to unpivot. If not specified, uses all columns that - are not set as `id_vars`. - var_name (scalar): - Name to use for the 'variable' column. If None it uses - ``frame.columns.name`` or 'variable'. - value_name (scalar, default 'value'): - Name to use for the 'value' column. + Parameters + ---------- + id_vars (tuple, list, or ndarray, optional): + Column(s) to use as identifier variables. + value_vars (tuple, list, or ndarray, optional): + Column(s) to unpivot. If not specified, uses all columns that + are not set as `id_vars`. + var_name (scalar): + Name to use for the 'variable' column. If None it uses + ``frame.columns.name`` or 'variable'. + value_name (scalar, default 'value'): + Name to use for the 'value' column. Returns: - bigframes.pandas.DataFrame: Unpivoted DataFrame. + DataFrame: Unpivoted DataFrame. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) def nunique(self): """ - Count number of distinct elements in each column. - - **Examples:** - - - >>> df = bpd.DataFrame({"A": [3, 1, 2], "B": [1, 2, 2]}) - >>> df - A B - 0 3 1 - 1 1 2 - 2 2 2 - - [3 rows x 2 columns] - - >>> df.nunique() - A 3 - B 2 - dtype: Int64 + Count number of distinct elements in specified axis. Returns: - bigframes.pandas.Series: Series with number of distinct elements. + bigframes.series.Series: Series with number of distinct elements. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) def cummin(self) -> DataFrame: - """Return cumulative minimum over columns. + """Return cumulative minimum over a DataFrame axis. Returns a DataFrame of the same size containing the cumulative minimum. - **Examples:** - - - >>> df = bpd.DataFrame({"A": [3, 1, 2], "B": [1, 2, 3]}) - >>> df - A B - 0 3 1 - 1 1 2 - 2 2 3 - - [3 rows x 2 columns] - - >>> df.cummin() - A B - 0 3 1 - 1 1 1 - 2 1 1 - - [3 rows x 2 columns] - Returns: - bigframes.pandas.DataFrame: Return cumulative minimum of DataFrame. + bigframes.dataframe.DataFrame: Return cumulative minimum of DataFrame. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) def cummax(self) -> DataFrame: - """Return cumulative maximum over columns. + """Return cumulative maximum over a DataFrame axis. Returns a DataFrame of the same size containing the cumulative maximum. - **Examples:** - - - >>> df = bpd.DataFrame({"A": [3, 1, 2], "B": [1, 2, 3]}) - >>> df - A B - 0 3 1 - 1 1 2 - 2 2 3 - - [3 rows x 2 columns] - - >>> df.cummax() - A B - 0 3 1 - 1 3 2 - 2 3 3 - - [3 rows x 2 columns] - Returns: - bigframes.pandas.DataFrame: Return cumulative maximum of DataFrame. + bigframes.dataframe.DataFrame: Return cumulative maximum of DataFrame. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) def cumsum(self) -> DataFrame: - """Return cumulative sum over columns. + """Return cumulative sum over a DataFrame axis. Returns a DataFrame of the same size containing the cumulative sum. - **Examples:** - - - >>> df = bpd.DataFrame({"A": [3, 1, 2], "B": [1, 2, 3]}) - >>> df - A B - 0 3 1 - 1 1 2 - 2 2 3 - - [3 rows x 2 columns] - - >>> df.cumsum() - A B - 0 3 1 - 1 4 3 - 2 6 6 - - [3 rows x 2 columns] - Returns: - bigframes.pandas.DataFrame: - Return cumulative sum of DataFrame. - - Raises: - ValueError: - If values are not of numeric type. + bigframes.dataframe.DataFrame: Return cumulative sum of DataFrame. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) def cumprod(self) -> DataFrame: - """Return cumulative product over columns. + """Return cumulative product over a DataFrame axis. Returns a DataFrame of the same size containing the cumulative product. - **Examples:** - - - >>> df = bpd.DataFrame({"A": [3, 1, 2], "B": [1, 2, 3]}) - >>> df - A B - 0 3 1 - 1 1 2 - 2 2 3 - - [3 rows x 2 columns] - - >>> df.cumprod() - A B - 0 3.0 1.0 - 1 3.0 2.0 - 2 6.0 6.0 - - [3 rows x 2 columns] - Returns: - bigframes.pandas.DataFrame: - Return cumulative product of DataFrame. - - Raises: - ValueError: - If values are not of numeric type. + bigframes.dataframe.DataFrame: Return cumulative product of DataFrame. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) def diff( self, periods: int = 1, - ) -> generic.NDFrame: + ) -> NDFrame: """First discrete difference of element. Calculates the difference of a DataFrame element compared with another element in the DataFrame (default is element in previous row). - **Examples:** - - - >>> df = bpd.DataFrame({"A": [3, 1, 2], "B": [1, 2, 3]}) - >>> df - A B - 0 3 1 - 1 1 2 - 2 2 3 - - [3 rows x 2 columns] - - Calculating difference with default periods=1: - - >>> df.diff() - A B - 0 - 1 -2 1 - 2 1 1 - - [3 rows x 2 columns] - - Calculating difference with periods=-1: - - >>> df.diff(periods=-1) - A B - 0 2 -1 - 1 -1 -1 - 2 - - [3 rows x 2 columns] - Args: periods (int, default 1): Periods to shift for calculating difference, accepts negative values. Returns: - bigframes.pandas.DataFrame: First differences of the Series. + bigframes.dataframe.DataFrame: First differences of the Series. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) def agg(self, func): """ - Aggregate using one or more operations over columns. - - **Examples:** - - - >>> df = bpd.DataFrame({"A": [3, 1, 2], "B": [1, 2, 3]}) - >>> df - A B - 0 3 1 - 1 1 2 - 2 2 3 - - [3 rows x 2 columns] - - Using a single function: - - >>> df.agg('sum') - A 6 - B 6 - dtype: Int64 - - Using a list of functions: - - >>> df.agg(['sum', 'mean']) - A B - sum 6.0 6.0 - mean 2.0 2.0 - - [2 rows x 2 columns] + Aggregate using one or more operations over the specified axis. Args: func (function): @@ -6256,11 +2710,11 @@ def agg(self, func): function names, e.g. ``['sum', 'mean']``. Returns: - bigframes.pandas.DataFrame or bigframes.pandas.Series: Aggregated results. + DataFrame or bigframes.series.Series: Aggregated results. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - def describe(self, include: None | Literal["all"] = None): + def describe(self): """ Generate descriptive statistics. @@ -6268,10 +2722,7 @@ def describe(self, include: None | Literal["all"] = None): tendency, dispersion and shape of a dataset's distribution, excluding ``NaN`` values. - Args: - include ("all" or None, optional): - If "all": All columns of the input will be included in the output. - If None: The result will include all numeric columns. + Only supports numeric columns. .. note:: Percentile values are approximates only. @@ -6283,54 +2734,8 @@ def describe(self, include: None | Literal["all"] = None): upper percentile is ``75``. The ``50`` percentile is the same as the median. - **Examples:** - - >>> import bigframes.pandas as bpd - >>> df = bpd.DataFrame({"A": [3, 1, 2], "B": [0, 2, 8], "C": ["cat", "cat", "dog"]}) - >>> df - A B C - 0 3 0 cat - 1 1 2 cat - 2 2 8 dog - - [3 rows x 3 columns] - - >>> df.describe() - A B - count 3.0 3.0 - mean 2.0 3.333333 - std 1.0 4.163332 - min 1.0 0.0 - 25% 1.0 0.0 - 50% 2.0 2.0 - 75% 3.0 8.0 - max 3.0 8.0 - - [8 rows x 2 columns] - - - Using describe with include = "all": - >>> df.describe(include="all") - A B C - count 3.0 3.0 3 - nunique 2 - mean 2.0 3.333333 - std 1.0 4.163332 - min 1.0 0.0 - 25% 1.0 0.0 - 50% 2.0 2.0 - 75% 3.0 8.0 - max 3.0 8.0 - - [9 rows x 3 columns] - Returns: - bigframes.pandas.DataFrame: - Summary statistics of the Series or Dataframe provided. - - Raises: - ValueError: - If unsupported ``include`` type is provided. + bigframes.dataframe.DataFrame: Summary statistics of the Series or Dataframe provided. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -6353,50 +2758,6 @@ def pivot(self, *, columns, index=None, values=None): do not together uniquely identify input rows, the output will be silently non-deterministic. - **Examples:** - - - >>> df = bpd.DataFrame({ - ... "foo": ["one", "one", "one", "two", "two"], - ... "bar": ["A", "B", "C", "A", "B"], - ... "baz": [1, 2, 3, 4, 5], - ... "zoo": ['x', 'y', 'z', 'q', 'w'] - ... }) - - >>> df - foo bar baz zoo - 0 one A 1 x - 1 one B 2 y - 2 one C 3 z - 3 two A 4 q - 4 two B 5 w - - [5 rows x 4 columns] - - Using `pivot` without optional arguments: - - >>> df.pivot(columns='foo') - bar baz zoo - foo one two one two one two - 0 A 1 x - 1 B 2 y - 2 C 3 z - 3 A 4 q - 4 B 5 w - - [5 rows x 6 columns] - - Using `pivot` with `index` and `values`: - - >>> df.pivot(columns='foo', index='bar', values='baz') - foo one two - bar - A 1 4 - B 2 5 - C 3 - - [3 rows x 2 columns] - Args: columns (str or object or a list of str): Column to use to make new frame's columns. @@ -6410,95 +2771,11 @@ def pivot(self, *, columns, index=None, values=None): have hierarchically indexed columns. Returns: - bigframes.pandas.DataFrame: Returns reshaped DataFrame. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def pivot_table(self, values=None, index=None, columns=None, aggfunc="mean"): - """ - Create a spreadsheet-style pivot table as a DataFrame. - - The levels in the pivot table will be stored in MultiIndex objects (hierarchical indexes) - on the index and columns of the result DataFrame. - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> df = bpd.DataFrame({ - ... 'Product': ['Product A', 'Product B', 'Product A', 'Product B', 'Product A', 'Product B'], - ... 'Region': ['East', 'West', 'East', 'West', 'West', 'East'], - ... 'Sales': [100, 200, 150, 100, 200, 150], - ... 'Rating': [3, 5, 4, 3, 3, 5] - ... }) - >>> df - Product Region Sales Rating - 0 Product A East 100 3 - 1 Product B West 200 5 - 2 Product A East 150 4 - 3 Product B West 100 3 - 4 Product A West 200 3 - 5 Product B East 150 5 - - [6 rows x 4 columns] - - Using `pivot_table` with default aggfunc "mean": - - >>> pivot_table = df.pivot_table( - ... values=['Sales', 'Rating'], - ... index='Product', - ... columns='Region' - ... ) - >>> pivot_table - Rating Sales - Region East West East West - Product - Product A 3.5 3.0 125.0 200.0 - Product B 5.0 4.0 150.0 150.0 - - [2 rows x 4 columns] - - Using `pivot_table` with specified aggfunc "max": - - >>> pivot_table = df.pivot_table( - ... values=['Sales', 'Rating'], - ... index='Product', - ... columns='Region', - ... aggfunc="max" - ... ) - >>> pivot_table - Rating Sales - Region East West East West - Product - Product A 4 3 150 200 - Product B 5 5 150 200 - - [2 rows x 4 columns] - - Args: - values (str, object or a list of the previous, optional): - Column(s) to use for populating new frame's values. If not - specified, all remaining columns will be used and the result will - have hierarchically indexed columns. - - index (str or object or a list of str, optional): - Column to use to make new frame's index. If not given, uses existing index. - - columns (str or object or a list of str): - Column to use to make new frame's columns. - - aggfunc (str, default "mean"): - Aggregation function name to compute summary statistics (e.g., 'sum', 'mean'). - - fill_value (scalar, default None): - Value to replace missing values with (in the resulting pivot table, after - aggregation). - - Returns: - bigframes.pandas.DataFrame: An Excel style pivot table. + Returns reshaped DataFrame. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - def stack(self, level=-1): + def stack(self): """ Stack the prescribed level(s) from columns to index. @@ -6516,34 +2793,12 @@ def stack(self, level=-1): BigQuery DataFrames does not support stack operations that would combine columns of different dtypes. - **Examples:** - - - >>> df = bpd.DataFrame({'A': [1, 3], 'B': [2, 4]}, index=['foo', 'bar']) - >>> df - A B - foo 1 2 - bar 3 4 - - [2 rows x 2 columns] - - >>> df.stack() - foo A 1 - B 2 - bar A 3 - B 4 - dtype: Int64 - - Args: - level (int, str, or list of these, default -1 (last level)): - Level(s) to stack from the column axis onto the index axis. - Returns: - bigframes.pandas.DataFrame or bigframes.pandas.Series: Stacked dataframe or series. + DataFrame or Series: Stacked dataframe or series. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - def unstack(self, level=-1): + def unstack(self): """ Pivot a level of the (necessarily hierarchical) index labels. @@ -6553,30 +2808,8 @@ def unstack(self, level=-1): If the index is not a MultiIndex, the output will be a Series (the analogue of stack when the columns are not a MultiIndex). - **Examples:** - - - >>> df = bpd.DataFrame({'A': [1, 3], 'B': [2, 4]}, index=['foo', 'bar']) - >>> df - A B - foo 1 2 - bar 3 4 - - [2 rows x 2 columns] - - >>> df.unstack() - A foo 1 - bar 3 - B foo 2 - bar 4 - dtype: Int64 - - Args: - level (int, str, or list of these, default -1 (last level)): - Level(s) of index to unstack, can pass level name. - Returns: - bigframes.pandas.DataFrame or bigframes.pandas.Series: DataFrame or Series. + DataFrame or Series """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -6592,90 +2825,14 @@ def index(self): index is used for label-based access and alignment, and can be accessed or modified using this attribute. - **Examples:** - - - You can access the index of a DataFrame via ``index`` property. - - >>> df = bpd.DataFrame({'Name': ['Alice', 'Bob', 'Aritra'], - ... 'Age': [25, 30, 35], - ... 'Location': ['Seattle', 'New York', 'Kona']}, - ... index=([10, 20, 30])) - >>> df - Name Age Location - 10 Alice 25 Seattle - 20 Bob 30 New York - 30 Aritra 35 Kona - - [3 rows x 3 columns] - >>> df.index # doctest: +ELLIPSIS - Index([10, 20, 30], dtype='Int64') - >>> df.index.values - array([10, 20, 30]) - - Let's try setting a new index for the dataframe and see that reflect via - ``index`` property. - - >>> df1 = df.set_index(["Name", "Location"]) - >>> df1 - Age - Name Location - Alice Seattle 25 - Bob New York 30 - Aritra Kona 35 - - [3 rows x 1 columns] - >>> df1.index # doctest: +ELLIPSIS - MultiIndex([( 'Alice', 'Seattle'), - ( 'Bob', 'New York'), - ('Aritra', 'Kona')], - names=['Name', 'Location']) - >>> df1.index.values - array([('Alice', 'Seattle'), ('Bob', 'New York'), ('Aritra', 'Kona')], - dtype=object) - Returns: - Index: The index object of the DataFrame. + The index labels of the DataFrame. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @property def columns(self): - """The column labels of the DataFrame. - - **Examples:** - - - You can access the column labels of a DataFrame via ``columns`` property. - - >>> df = bpd.DataFrame({'Name': ['Alice', 'Bob', 'Aritra'], - ... 'Age': [25, 30, 35], - ... 'Location': ['Seattle', 'New York', 'Kona']}, - ... index=([10, 20, 30])) - >>> df - Name Age Location - 10 Alice 25 Seattle - 20 Bob 30 New York - 30 Aritra 35 Kona - - [3 rows x 3 columns] - >>> df.columns - Index(['Name', 'Age', 'Location'], dtype='str') - - You can also set new labels for columns. - - >>> df.columns = ["NewName", "NewAge", "NewLocation"] - >>> df - NewName NewAge NewLocation - 10 Alice 25 Seattle - 20 Bob 30 New York - 30 Aritra 35 Kona - - [3 rows x 3 columns] - >>> df.columns - Index(['NewName', 'NewAge', 'NewLocation'], dtype='str') - - """ + "The column labels of the DataFrame." raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) def value_counts( @@ -6689,60 +2846,6 @@ def value_counts( """ Return a Series containing counts of unique rows in the DataFrame. - **Examples:** - - - >>> df = bpd.DataFrame({'num_legs': [2, 4, 4, 6, 7], - ... 'num_wings': [2, 0, 0, 0, pd.NA]}, - ... index=['falcon', 'dog', 'cat', 'ant', 'octopus'], - ... dtype='Int64') - >>> df - num_legs num_wings - falcon 2 2 - dog 4 0 - cat 4 0 - ant 6 0 - octopus 7 - - [5 rows x 2 columns] - - ``value_counts`` sorts the result by counts in a descending order by default: - - >>> df.value_counts() - num_legs num_wings - 4 0 2 - 2 2 1 - 6 0 1 - Name: count, dtype: Int64 - - You can normalize the counts to return relative frequencies by setting ``normalize=True``: - - >>> df.value_counts(normalize=True) - num_legs num_wings - 4 0 0.5 - 2 2 0.25 - 6 0 0.25 - Name: proportion, dtype: Float64 - - You can get the rows in the ascending order of the counts by setting ``ascending=True``: - - >>> df.value_counts(ascending=True) - num_legs num_wings - 2 2 1 - 6 0 1 - 4 0 2 - Name: count, dtype: Int64 - - You can include the counts of the rows with ``NA`` values by setting ``dropna=False``: - - >>> df.value_counts(dropna=False) - num_legs num_wings - 4 0 2 - 2 2 1 - 6 0 1 - 7 1 - Name: count, dtype: Int64 - Args: subset (label or list of labels, optional): Columns to use when counting unique combinations. @@ -6756,266 +2859,50 @@ def value_counts( Don’t include counts of rows that contain NA values. Returns: - bigframes.pandas.Series: Series containing counts of unique rows in the DataFrame - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def eval(self, expr: str) -> DataFrame: - """ - Evaluate a string describing operations on DataFrame columns. - - Operates on columns only, not specific rows or elements. This allows - `eval` to run arbitrary code, which can make you vulnerable to code - injection if you pass user input to this function. - - **Examples:** - - - >>> df = bpd.DataFrame({'A': range(1, 6), 'B': range(10, 0, -2)}) - >>> df - A B - 0 1 10 - 1 2 8 - 2 3 6 - 3 4 4 - 4 5 2 - - [5 rows x 2 columns] - >>> df.eval('A + B') - 0 11 - 1 10 - 2 9 - 3 8 - 4 7 - dtype: Int64 - - Assignment is allowed though by default the original DataFrame is not - modified. - - >>> df.eval('C = A + B') - A B C - 0 1 10 11 - 1 2 8 10 - 2 3 6 9 - 3 4 4 8 - 4 5 2 7 - - [5 rows x 3 columns] - >>> df - A B - 0 1 10 - 1 2 8 - 2 3 6 - 3 4 4 - 4 5 2 - - [5 rows x 2 columns] - - Multiple columns can be assigned to using multi-line expressions: - - >>> df.eval( - ... ''' - ... C = A + B - ... D = A - B - ... ''' - ... ) - A B C D - 0 1 10 11 -9 - 1 2 8 10 -6 - 2 3 6 9 -3 - 3 4 4 8 0 - 4 5 2 7 3 - - [5 rows x 4 columns] - - - Args: - expr (str): - The expression string to evaluate. - - Returns: - bigframes.pandas.DataFrame: DataFrame result after the operation. + Series: Series containing counts of unique rows in the DataFrame """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - def query(self, expr: str) -> DataFrame | None: + def interpolate(self, method: str = "linear"): """ - Query the columns of a DataFrame with a boolean expression. - - **Examples:** - - - >>> df = bpd.DataFrame({'A': range(1, 6), - ... 'B': range(10, 0, -2), - ... 'C C': range(10, 5, -1)}) - >>> df - A B C C - 0 1 10 10 - 1 2 8 9 - 2 3 6 8 - 3 4 4 7 - 4 5 2 6 - - [5 rows x 3 columns] - >>> df.query('A > B') - A B C C - 4 5 2 6 - - [1 rows x 3 columns] - - The previous expression is equivalent to - - >>> df[df.A > df.B] - A B C C - 4 5 2 6 - - [1 rows x 3 columns] - - For columns with spaces in their name, you can use backtick quoting. - - >>> df.query('B == `C C`') - A B C C - 0 1 10 10 - - [1 rows x 3 columns] - - The previous expression is equivalent to - - >>> df[df.B == df['C C']] - A B C C - 0 1 10 10 - - [1 rows x 3 columns] + Fill NaN values using an interpolation method. Args: - expr (str): - The query string to evaluate. - - You can refer to variables - in the environment by prefixing them with an '@' character like - ``@a + b``. - - You can refer to column names that are not valid Python variable names - by surrounding them in backticks. Thus, column names containing spaces - or punctuations (besides underscores) or starting with digits must be - surrounded by backticks. (For example, a column named "Area (cm^2)" would - be referenced as ```Area (cm^2)```). Column names which are Python keywords - (like "list", "for", "import", etc) cannot be used. - - For example, if one of your columns is called ``a a`` and you want - to sum it with ``b``, your query should be ```a a` + b``. + method (str, default 'linear'): + Interpolation technique to use. Only 'linear' supported. + 'linear': Ignore the index and treat the values as equally spaced. + This is the only method supported on MultiIndexes. Returns: - None or bigframes.pandas.DataFrame: - DataFrame result after the query operation, otherwise None. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def interpolate(self, method: str = "linear"): - """ - Fill NA (NULL in BigQuery) values using an interpolation method. + DataFrame: + Returns the same object type as the caller, interpolated at + some or all ``NaN`` values **Examples:** + >>> import bigframes.pandas as bpd + >>> bpd.options.display.progress_bar = None >>> df = bpd.DataFrame({ ... 'A': [1, 2, 3, None, None, 6], ... 'B': [None, 6, None, 2, None, 3], - ... }, index=[0, 0.1, 0.3, 0.7, 0.9, 1.0]) + ... }) >>> df.interpolate() - A B - 0.0 1.0 - 0.1 2.0 6.0 - 0.3 3.0 4.0 - 0.7 4.0 2.0 - 0.9 5.0 2.5 - 1.0 6.0 3.0 - - [6 rows x 2 columns] - >>> df.interpolate(method="values") - A B - 0.0 1.0 - 0.1 2.0 6.0 - 0.3 3.0 4.666667 - 0.7 4.714286 2.0 - 0.9 5.571429 2.666667 - 1.0 6.0 3.0 + A B + 0 1.0 + 1 2.0 6.0 + 2 3.0 4.0 + 3 4.0 2.0 + 4 5.0 2.5 + 5 6.0 3.0 [6 rows x 2 columns] - - Args: - method (str, default 'linear'): - Interpolation technique to use. Only 'linear' supported. - 'linear': Ignore the index and treat the values as equally spaced. - This is the only method supported on MultiIndexes. - 'index', 'values': use the actual numerical values of the index. - 'pad': Fill in NaNs using existing values. - 'nearest', 'zero', 'slinear': Emulates `scipy.interpolate.interp1d` - - Returns: - bigframes.pandas.DataFrame: - Returns the same object type as the caller, interpolated at - some or all ``NaN`` values """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) def fillna(self, value): """ - Fill NA (NULL in BigQuery) values using the specified method. - - Note that empty strings ``''``, :attr:`numpy.inf`, and - :attr:`numpy.nan` are ***not*** considered NA values. This NA/NULL - logic differs from numpy, but it is the same as BigQuery and the - :class:`pandas.ArrowDtype`. - - **Examples:** - - >>> df = bpd.DataFrame( - ... [ - ... pa.array([np.nan, 2, None, 0], type=pa.float64()), - ... pa.array([3, np.nan, None, 1], type=pa.float64()), - ... pa.array([None, None, np.nan, None], type=pa.float64()), - ... pa.array([4, 5, None, np.nan], type=pa.float64()), - ... ], columns=list("ABCD"), dtype=pd.ArrowDtype(pa.float64())) - >>> df - A B C D - 0 NaN 2.0 0.0 - 1 3.0 NaN 1.0 - 2 NaN - 3 4.0 5.0 NaN - - [4 rows x 4 columns] - - Replace all NA (NULL) elements with 0s. - - >>> df.fillna(0) - A B C D - 0 NaN 2.0 0.0 0.0 - 1 3.0 NaN 0.0 1.0 - 2 0.0 0.0 NaN 0.0 - 3 4.0 5.0 0.0 NaN - - [4 rows x 4 columns] - - You can use fill values from another DataFrame: - - >>> df_fill = bpd.DataFrame(np.arange(12).reshape(3, 4), - ... columns=['A', 'B', 'C', 'D']) - >>> df_fill - A B C D - 0 0 1 2 3 - 1 4 5 6 7 - 2 8 9 10 11 - - [3 rows x 4 columns] - >>> df.fillna(df_fill) - A B C D - 0 NaN 2.0 2.0 0.0 - 1 3.0 NaN 6.0 1.0 - 2 8.0 9.0 NaN 11.0 - 3 4.0 5.0 NaN - - [4 rows x 4 columns] + Fill NA/NaN values using the specified method. Args: value (scalar, Series): @@ -7026,177 +2913,23 @@ def fillna(self, value): be a list. Returns: - bigframes.pandas.DataFrame: Object with missing values filled - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def replace( - self, - to_replace, - value=None, - *, - regex=False, - ): - """ - Replace values given in `to_replace` with `value`. - - Values of the Series/DataFrame are replaced with other values dynamically. - This differs from updating with ``.loc`` or ``.iloc``, which require - you to specify a location to update with some value. - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> df = bpd.DataFrame({ - ... 'int_col': [1, 1, 2, 3], - ... 'string_col': ["a", "b", "c", "b"], - ... }) - - Using scalar `to_replace` and `value`: - - >>> df.replace("b", "e") - int_col string_col - 0 1 a - 1 1 e - 2 2 c - 3 3 e - - [4 rows x 2 columns] - - Using dictionary: - - >>> df.replace({"a": "e", 2: 5}) - int_col string_col - 0 1 e - 1 1 b - 2 5 c - 3 3 b - - [4 rows x 2 columns] - - Using regex: - - >>> df.replace("[ab]", "e", regex=True) - int_col string_col - 0 1 e - 1 1 e - 2 2 c - 3 3 e - - [4 rows x 2 columns] - - - Args: - to_replace (str, regex, list, int, float or None): - How to find the values that will be replaced. - numeric: numeric values equal to `to_replace` will be replaced with `value` - str: string exactly matching `to_replace` will be replaced with `value` - regex: regexs matching `to_replace` will be replaced with`value` - list of str, regex, or numeric: - First, if `to_replace` and `value` are both lists, they **must** be the same length. - Second, if ``regex=True`` then all of the strings in **both** - lists will be interpreted as regexs otherwise they will match - directly. This doesn't matter much for `value` since there - are only a few possible substitution regexes you can use. - str, regex and numeric rules apply as above. - - value (scalar, default None): - Value to replace any values matching `to_replace` with. - For a DataFrame a dict of values can be used to specify which - value to use for each column (columns not in the dict will not be - filled). Regular expressions, strings and lists or dicts of such - objects are also allowed. - regex (bool, default False): - Whether to interpret `to_replace` and/or `value` as regular - expressions. If this is ``True`` then `to_replace` *must* be a - string. - - Returns: - bigframes.pandas.DataFrame or bigframes.pandas.Series: - Object after replacement. + DataFrame: Object with missing values filled """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @property def iloc(self): - """Purely integer-location based indexing for selection by position. - - Returns: - bigframes.core.indexers.ILocDataFrameIndexer: Purely integer-location Indexers. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - @property - def loc(self): - """Access a group of rows and columns by label(s) or a boolean array. - - Returns: - bigframes.core.indexers.ILocDataFrameIndexer: Indexers object. - """ + """Purely integer-location based indexing for selection by position.""" raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @property def iat(self): - """Access a single value for a row/column pair by integer position. - - **Examples:** - - - >>> df = bpd.DataFrame([[0, 2, 3], [0, 4, 1], [10, 20, 30]], - ... columns=['A', 'B', 'C']) - >>> df - A B C - 0 0 2 3 - 1 0 4 1 - 2 10 20 30 - - [3 rows x 3 columns] - - Get value at specified row/column pair - - >>> df.iat[1, 2] - np.int64(1) - - Get value within a series - - >>> df.loc[0].iat[1] - np.int64(2) - - Returns: - bigframes.core.indexers.IatDataFrameIndexer: Indexers object. - """ + """Access a single value for a row/column pair by integer position.""" raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @property def at(self): - """Access a single value for a row/column label pair. - - **Examples:** - - - >>> df = bpd.DataFrame([[0, 2, 3], [0, 4, 1], [10, 20, 30]], - ... index=[4, 5, 6], columns=['A', 'B', 'C']) - >>> df - A B C - 4 0 2 3 - 5 0 4 1 - 6 10 20 30 - - [3 rows x 3 columns] - - Get value at specified row/column pair - - >>> df.at[4, 'B'] - np.int64(2) - - Get value within a series - - >>> df.loc[5].at['B'] - np.int64(4) - - Returns: - bigframes.core.indexers.AtDataFrameIndexer: Indexers object. - """ + """Access a single value for a row/column label pair.""" raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) def dot(self, other): @@ -7214,371 +2947,17 @@ def dot(self, other): DataFrame and the index of other must contain the same values, as they will be aligned prior to the multiplication. - .. note:: The dot method for Series computes the inner product, instead of the matrix product here. - **Examples:** - - - >>> left = bpd.DataFrame([[0, 1, -2, -1], [1, 1, 1, 1]]) - >>> left - 0 1 2 3 - 0 0 1 -2 -1 - 1 1 1 1 1 - - [2 rows x 4 columns] - >>> right = bpd.DataFrame([[0, 1], [1, 2], [-1, -1], [2, 0]]) - >>> right - 0 1 - 0 0 1 - 1 1 2 - 2 -1 -1 - 3 2 0 - - [4 rows x 2 columns] - >>> left.dot(right) - 0 1 - 0 1 4 - 1 2 2 - - [2 rows x 2 columns] - - You can also use the operator ``@`` for the dot product: - - >>> left @ right - 0 1 - 0 1 4 - 1 2 2 - - [2 rows x 2 columns] - - The right input can be a Series, in which case the result will also be a - Series: - - >>> right = bpd.Series([1, 2, -1,0]) - >>> left @ right - 0 4 - 1 2 - dtype: Int64 - - Any user defined index of the left matrix and columns of the right - matrix will reflect in the result. - - >>> left = bpd.DataFrame([[1, 2, 3], [2, 5, 7]], index=["alpha", "beta"]) - >>> left - 0 1 2 - alpha 1 2 3 - beta 2 5 7 - - [2 rows x 3 columns] - >>> right = bpd.DataFrame([[2, 4, 8], [1, 5, 10], [3, 6, 9]], columns=["red", "green", "blue"]) - >>> right - red green blue - 0 2 4 8 - 1 1 5 10 - 2 3 6 9 - - [3 rows x 3 columns] - >>> left.dot(right) - red green blue - alpha 13 32 55 - beta 30 75 129 - - [2 rows x 3 columns] - Args: other (Series or DataFrame): The other object to compute the matrix product with. Returns: - bigframes.pandas.DataFrame or bigframes.pandas.Series: + Series or DataFrame If `other` is a Series, return the matrix product between self and other as a Series. If other is a DataFrame, return the matrix product of self and other in a DataFrame. - - Raises: - RuntimeError: - If unable to construct all columns. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def __matmul__(self, other): - """ - Compute the matrix multiplication between the DataFrame and other, using - operator `@`. - - Equivalent to `DataFrame.dot(other)`. - - **Examples:** - - - >>> left = bpd.DataFrame([[0, 1, -2, -1], [1, 1, 1, 1]]) - >>> left - 0 1 2 3 - 0 0 1 -2 -1 - 1 1 1 1 1 - - [2 rows x 4 columns] - >>> right = bpd.DataFrame([[0, 1], [1, 2], [-1, -1], [2, 0]]) - >>> right - 0 1 - 0 0 1 - 1 1 2 - 2 -1 -1 - 3 2 0 - - [4 rows x 2 columns] - >>> left @ right - 0 1 - 0 1 4 - 1 2 2 - - [2 rows x 2 columns] - - The operand can be a Series, in which case the result will also be a - Series: - - >>> right = bpd.Series([1, 2, -1,0]) - >>> left @ right - 0 4 - 1 2 - dtype: Int64 - - Args: - other (DataFrame or Series): - Object to be matrix multiplied with the DataFrame. - - Returns: - DataFrame or Series: The result of the matrix multiplication. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - @property - def plot(self): - """ - Make plots of Dataframes. - - Returns: - bigframes.pandas.api.typing.PlotAccessor: - An accessor making plots. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def __len__(self): - """Returns number of rows in the DataFrame, serves `len` operator. - - **Examples:** - - - >>> df = bpd.DataFrame({ - ... 'a': [0, 1, 2], - ... 'b': [3, 4, 5] - ... }) - >>> len(df) - 3 - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def __array__(self, dtype=None, copy: Optional[bool] = None): - """ - Returns the rows as NumPy array. - - Equivalent to `DataFrame.to_numpy(dtype)`. - - Users should not call this directly. Rather, it is invoked by - `numpy.array` and `numpy.asarray`. - - **Examples:** - - - >>> df = bpd.DataFrame({"a": [1, 2, 3], "b": [11, 22, 33]}) - - >>> np.array(df) - array([[1, 11], - [2, 22], - [3, 33]], dtype=object) - - >>> np.asarray(df) - array([[1, 11], - [2, 22], - [3, 33]], dtype=object) - - Args: - dtype (str or numpy.dtype, optional): - The dtype to use for the resulting NumPy array. By default, - the dtype is inferred from the data. - copy (bool or None, optional): - Whether to copy the data, False is not supported. - - Returns: - numpy.ndarray: - The rows in the DataFrame converted to a `numpy.ndarray` with - the specified dtype. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def __getitem__(self, key): - """Gets the specified column(s) from the DataFrame. - - **Examples:** - - - >>> df = bpd.DataFrame({ - ... "name" : ["alpha", "beta", "gamma"], - ... "age": [20, 30, 40], - ... "location": ["WA", "NY", "CA"] - ... }) - >>> df - name age location - 0 alpha 20 WA - 1 beta 30 NY - 2 gamma 40 CA - - [3 rows x 3 columns] - - You can specify a column label to retrieve the corresponding Series. - - >>> df["name"] - 0 alpha - 1 beta - 2 gamma - Name: name, dtype: string - - You can specify a list of column labels to retrieve a Dataframe. - - >>> df[["name", "age"]] - name age - 0 alpha 20 - 1 beta 30 - 2 gamma 40 - - [3 rows x 2 columns] - - You can specify a condition as a series of booleans to retrieve matching - rows. - - >>> df[df["age"] > 25] - name age location - 1 beta 30 NY - 2 gamma 40 CA - - [2 rows x 3 columns] - - You can specify a pandas Index with desired column labels. - - >>> df[pd.Index(["age", "location"])] - age location - 0 20 WA - 1 30 NY - 2 40 CA - - [3 rows x 2 columns] - - Args: - key (index): - Index or list of indices. It can be a column label, a list of - column labels, a Series of booleans or a pandas Index of desired - column labels - - Returns: - bigframes.pandas.Series or Any: Value(s) at the requested index(es). - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def __setitem__(self, key, value): - """Modify or insert a column into the DataFrame. - - .. note:: - This does **not** modify the original table the DataFrame was - derived from. - - **Examples:** - - - >>> df = bpd.DataFrame({ - ... "name" : ["alpha", "beta", "gamma"], - ... "age": [20, 30, 40], - ... "location": ["WA", "NY", "CA"] - ... }) - >>> df - name age location - 0 alpha 20 WA - 1 beta 30 NY - 2 gamma 40 CA - - [3 rows x 3 columns] - - You can add assign a constant to a new column. - - >>> df["country"] = "USA" - >>> df - name age location country - 0 alpha 20 WA USA - 1 beta 30 NY USA - 2 gamma 40 CA USA - - [3 rows x 4 columns] - - You can assign a Series to a new column. - - >>> df["new_age"] = df["age"] + 5 - >>> df - name age location country new_age - 0 alpha 20 WA USA 25 - 1 beta 30 NY USA 35 - 2 gamma 40 CA USA 45 - - [3 rows x 5 columns] - - You can assign a Series to an existing column. - - >>> df["new_age"] = bpd.Series([29, 39, 19], index=[1, 2, 0]) - >>> df - name age location country new_age - 0 alpha 20 WA USA 19 - 1 beta 30 NY USA 29 - 2 gamma 40 CA USA 39 - - [3 rows x 5 columns] - - You can assign a scalar to multiple columns. - - >>> df[["age", "new_age"]] = 25 - >>> df - name age location country new_age - 0 alpha 25 WA USA 25 - 1 beta 25 NY USA 25 - 2 gamma 25 CA USA 25 - - [3 rows x 5 columns] - - You can use a sequence of scalars for assignment of multiple columns: - - >>> df[["age", "is_happy"]] = [20, True] - >>> df - name age location country new_age is_happy - 0 alpha 20 WA USA 25 True - 1 beta 20 NY USA 25 True - 2 gamma 20 CA USA 25 True - - [3 rows x 6 columns] - - You can use a dataframe for assignment of multiple columns: - >>> df[["age", "new_age"]] = df[["new_age", "age"]] - >>> df - name age location country new_age is_happy - 0 alpha 25 WA USA 20 True - 1 beta 25 NY USA 20 True - 2 gamma 25 CA USA 20 True - - [3 rows x 6 columns] - - Args: - key (column index): - It can be a new column to be inserted, or an existing column to - be modified. - value (scalar, Sequence, DataFrame, or Series): - Value to be assigned to the column """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) diff --git a/third_party/bigframes_vendored/pandas/core/generic.py b/third_party/bigframes_vendored/pandas/core/generic.py index 0e4ac335c8a..127efe6a3d6 100644 --- a/third_party/bigframes_vendored/pandas/core/generic.py +++ b/third_party/bigframes_vendored/pandas/core/generic.py @@ -1,13 +1,10 @@ # Contains code from https://github.com/pandas-dev/pandas/blob/main/pandas/core/generic.py from __future__ import annotations -from typing import TYPE_CHECKING, Callable, Iterator, Literal, Optional +from typing import Iterator, Literal, Optional -import bigframes_vendored.constants as constants -from bigframes_vendored.pandas.core import indexing - -if TYPE_CHECKING: - from bigframes_vendored.pandas.pandas._typing import T +from bigframes import constants +from third_party.bigframes_vendored.pandas.core import indexing class NDFrame(indexing.IndexingMixin): @@ -16,9 +13,6 @@ class NDFrame(indexing.IndexingMixin): size-mutable, labeled data structure """ - # Explicitly mark the class as unhashable - __hash__ = None # type: ignore - # ---------------------------------------------------------------------- # Axis @@ -35,17 +29,6 @@ def ndim(self) -> int: def size(self) -> int: """Return an int representing the number of elements in this object. - **Examples:** - - - >>> s = bpd.Series({'a': 1, 'b': 2, 'c': 3}) - >>> s.size - 3 - - >>> df = bpd.DataFrame({'col1': [1, 2], 'col2': [3, 4]}) - >>> df.size - 4 - Returns: int: Return the number of rows if Series. Otherwise return the number of rows times number of columns if DataFrame. @@ -54,14 +37,14 @@ def size(self) -> int: def __iter__(self) -> Iterator: """ - Iterate over column axis for DataFrame, or values for Series. + Iterate over info axis. - Returns: - Iterator: - Iterator of DataFrame or Series values. + Returns + iterator: Info axis as iterator. **Examples:** - + >>> import bigframes.pandas as bpd + >>> bpd.options.display.progress_bar = None >>> df = bpd.DataFrame({ ... 'A': [1, 2, 3], @@ -75,9 +58,9 @@ def __iter__(self) -> Iterator: >>> series = bpd.Series(["a", "b", "c"], index=[10, 20, 30]) >>> for x in series: ... print(x) - a - b - c + 10 + 20 + 30 """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -90,8 +73,8 @@ def abs(self): This function only applies to elements that are all numeric. Returns: - bigframes.pandas.DataFrame or bigframes.pandas.Series: - A Series or DataFrame containing the absolute value of each element. + Series/DataFrame containing the absolute value of each element. + Returns a Series/DataFrame containing the absolute value of each element. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -99,90 +82,18 @@ def astype(self, dtype): """ Cast a pandas object to a specified dtype ``dtype``. - **Examples:** - - Create a DataFrame: - - >>> d = {'col1': [1, 2], 'col2': [3, 4]} - >>> df = bpd.DataFrame(data=d) - >>> df.dtypes - col1 Int64 - col2 Int64 - dtype: object - - Cast all columns to ``Float64``: - - >>> df.astype('Float64').dtypes - col1 Float64 - col2 Float64 - dtype: object - - Create a series of type ``Int64``: - - >>> ser = bpd.Series([2023010000246789, 1624123244123101, 1054834234120101], dtype='Int64') - >>> ser - 0 2023010000246789 - 1 1624123244123101 - 2 1054834234120101 - dtype: Int64 - - Convert to ``Float64`` type: - - >>> ser.astype('Float64') - 0 2023010000246789.0 - 1 1624123244123101.0 - 2 1054834234120101.0 - dtype: Float64 - - Convert to ``pd.ArrowDtype(pa.timestamp("us", tz="UTC"))`` type: - - >>> ser.astype("timestamp[us, tz=UTC][pyarrow]") - 0 2034-02-08 11:13:20.246789+00:00 - 1 2021-06-19 17:20:44.123101+00:00 - 2 2003-06-05 17:30:34.120101+00:00 - dtype: timestamp[us, tz=UTC][pyarrow] - - Note that this is equivalent of using ``to_datetime`` with ``unit='us'``: - - >>> bpd.to_datetime(ser, unit='us', utc=True) # doctest: +SKIP - 0 2034-02-08 11:13:20.246789+00:00 - 1 2021-06-19 17:20:44.123101+00:00 - 2 2003-06-05 17:30:34.120101+00:00 - dtype: timestamp[us, tz=UTC][pyarrow] - - Convert ``pd.ArrowDtype(pa.timestamp("us", tz="UTC"))`` type to ``Int64`` type: - - >>> timestamp_ser = ser.astype("timestamp[us, tz=UTC][pyarrow]") - >>> timestamp_ser.astype('Int64') - 0 2023010000246789 - 1 1624123244123101 - 2 1054834234120101 - dtype: Int64 - Args: - dtype (str, data type or pandas.ExtensionDtype): - A dtype supported by BigQuery DataFrame include ``'boolean'``, - ``'Float64'``, ``'Int64'``, ``'int64\\[pyarrow\\]'``, - ``'string'``, ``'string\\[pyarrow\\]'``, - ``'timestamp\\[us, tz=UTC\\]\\[pyarrow\\]'``, - ``'timestamp\\[us\\]\\[pyarrow\\]'``, - ``'date32\\[day\\]\\[pyarrow\\]'``, - ``'time64\\[us\\]\\[pyarrow\\]'``. - A pandas.ExtensionDtype include ``pandas.BooleanDtype()``, - ``pandas.Float64Dtype()``, ``pandas.Int64Dtype()``, - ``pandas.StringDtype(storage="pyarrow")``, - ``pd.ArrowDtype(pa.date32())``, - ``pd.ArrowDtype(pa.time64("us"))``, - ``pd.ArrowDtype(pa.timestamp("us"))``, - ``pd.ArrowDtype(pa.timestamp("us", tz="UTC"))``. - errors ({'raise', 'null'}, default 'raise'): - Control raising of exceptions on invalid data for provided dtype. - If 'raise', allow exceptions to be raised if any value fails cast - If 'null', will assign null value if value fails cast + dtype (str or pandas.ExtensionDtype): + A dtype supported by BigQuery DataFrame include 'boolean','Float64','Int64', + 'string', 'tring[pyarrow]','timestamp[us, tz=UTC][pyarrow]', + 'timestamp[us][pyarrow]','date32[day][pyarrow]','time64[us][pyarrow]' + A pandas.ExtensionDtype include pandas.BooleanDtype(), pandas.Float64Dtype(), + pandas.Int64Dtype(), pandas.StringDtype(storage="pyarrow"), + pd.ArrowDtype(pa.date32()), pd.ArrowDtype(pa.time64("us")), + pd.ArrowDtype(pa.timestamp("us")), pd.ArrowDtype(pa.timestamp("us", tz="UTC")). Returns: - bigframes.pandas.DataFrame: - A BigQuery DataFrame. + same type as caller """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -211,15 +122,14 @@ def empty(self) -> bool: def to_json( self, - path_or_buf, - orient: Optional[ - Literal["split", "records", "index", "columns", "values", "table"] - ] = None, + path_or_buf: str, + orient: Literal[ + "split", "records", "index", "columns", "values", "table" + ] = "columns", *, index: bool = True, lines: bool = False, - allow_large_results: Optional[bool] = None, - ) -> Optional[str]: + ) -> str | None: """Convert the object to a JSON string, written to Cloud Storage. Note NaN's and None will be converted to null and datetime objects @@ -229,18 +139,16 @@ def to_json( Only ``orient='records'`` and ``lines=True`` is supported so far. Args: - path_or_buf (str, path object, file-like object, or None, default None): - String, path object (implementing os.PathLike[str]), or file-like - object implementing a write() function. If None, the result is - returned as a string. - - Can be a destination URI of Cloud Storage files(s) to store the extracted + path_or_buf (str): + A destination URI of Cloud Storage files(s) to store the extracted dataframe in format of ``gs:///``. Must contain a wildcard `*` character. If the data size is more than 1GB, you must use a wildcard to export the data into multiple files and the size of the files varies. + + None, file-like objects or local file paths not yet supported. orient ({`split`, `records`, `index`, `columns`, `values`, `table`}, default 'columns): Indication of expected JSON string format. @@ -274,42 +182,18 @@ def to_json( throw ValueError if incorrect 'orient' since others are not list-like. - allow_large_results (bool, default None): - If not None, overrides the global setting to allow or disallow large - query results over the default size limit of 10 GB. This parameter has - no effect when results are saved to Google Cloud Storage (GCS). - Returns: - None or str: - If path_or_buf is None, returns the resulting json format as a - string. Otherwise returns None. - - Raises: - ValueError: - If ``lines`` is True but ``records`` is not provided as value for ``orient``. + None: String output not yet supported. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - def to_csv( - self, - path_or_buf, - *, - index: bool = True, - allow_large_results: Optional[bool] = None, - ) -> Optional[str]: + def to_csv(self, path_or_buf: str, *, index: bool = True) -> str | None: """Write object to a comma-separated values (csv) file on Cloud Storage. Args: - path_or_buf (str, path object, file-like object, or None, default None): - String, path object (implementing os.PathLike[str]), or file-like - object implementing a write() function. If None, the result is - returned as a string. If a non-binary file object is passed, it should - be opened with `newline=''`, disabling universal newlines. If a binary - file object is passed, `mode` might need to contain a `'b'`. - - Alternatively, a destination URI of Cloud Storage files(s) to store the - extracted dataframe in format of - ``gs:///``. + path_or_buf (str): + A destination URI of Cloud Storage files(s) to store the extracted dataframe + in format of ``gs:///``. If the data size is more than 1GB, you must use a wildcard to export the data into multiple files and the size of the files @@ -320,14 +204,8 @@ def to_csv( index (bool, default True): If True, write row names (index). - allow_large_results (bool, default None): - If not None, overrides the global setting to allow or disallow large - query results over the default size limit of 10 GB. This parameter has - no effect when results are saved to Google Cloud Storage (GCS). - Returns: - None or str: If path_or_buf is None, returns the resulting json format as a - string. Otherwise returns None. + None: String output not yet supported. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -340,61 +218,16 @@ def get(self, key, default=None): Returns default value if not found. - **Examples:** - - - >>> df = bpd.DataFrame( - ... [ - ... [24.3, 75.7, "high"], - ... [31, 87.8, "high"], - ... [22, 71.6, "medium"], - ... [35, 95, "medium"], - ... ], - ... columns=["temp_celsius", "temp_fahrenheit", "windspeed"], - ... index=["2014-02-12", "2014-02-13", "2014-02-14", "2014-02-15"], - ... ) - >>> df - temp_celsius temp_fahrenheit windspeed - 2014-02-12 24.3 75.7 high - 2014-02-13 31.0 87.8 high - 2014-02-14 22.0 71.6 medium - 2014-02-15 35.0 95.0 medium - - [4 rows x 3 columns] - - >>> df.get(["temp_celsius", "windspeed"]) - temp_celsius windspeed - 2014-02-12 24.3 high - 2014-02-13 31.0 high - 2014-02-14 22.0 medium - 2014-02-15 35.0 medium - - [4 rows x 2 columns] - - >>> ser = df['windspeed'] - >>> ser - 2014-02-12 high - 2014-02-13 high - 2014-02-14 medium - 2014-02-15 medium - Name: windspeed, dtype: string - >>> ser.get('2014-02-13') - 'high' - - If the key is not found, the default value will be used. - - >>> df.get(["temp_celsius", "temp_kelvin"]) - >>> df.get(["temp_celsius", "temp_kelvin"], default="default_value") - 'default_value' - Args: key: object Returns: - Any: - same type as items contained in object + same type as items contained in object """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) + try: + return self[key] + except (KeyError, ValueError, IndexError): + return default def add_prefix(self, prefix: str, axis: int | str | None = None): """Prefix labels with string `prefix`. @@ -410,8 +243,7 @@ def add_prefix(self, prefix: str, axis: int | str | None = None): to add prefix on. Returns: - bigframes.pandas.DataFrame or bigframes.pandas.Series: - New Series or DataFrame with updated labels. + New Series or DataFrame with updated labels. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -429,8 +261,7 @@ def add_suffix(self, suffix: str, axis: int | str | None = None): to add suffix on Returns: - bigframes.pandas.DataFrame or bigframes.pandas.Series: - New Series or DataFrame with updated labels. + New Series or DataFrame with updated labels. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -441,72 +272,17 @@ def head(self, n: int = 5): on position. It is useful for quickly testing if your object has the right type of data in it. - For negative values of `n`, this function returns + **Not yet supported** For negative values of `n`, this function returns all rows except the last `|n|` rows, equivalent to ``df[:n]``. If n is larger than the number of rows, this function returns all rows. - **Examples:** - - - >>> df = bpd.DataFrame({'animal': ['alligator', 'bee', 'falcon', 'lion', - ... 'monkey', 'parrot', 'shark', 'whale', 'zebra']}) - >>> df - animal - 0 alligator - 1 bee - 2 falcon - 3 lion - 4 monkey - 5 parrot - 6 shark - 7 whale - 8 zebra - - [9 rows x 1 columns] - - Viewing the first 5 lines: - - >>> df.head() - animal - 0 alligator - 1 bee - 2 falcon - 3 lion - 4 monkey - - [5 rows x 1 columns] - - Viewing the first `n` lines (three in this case): - - >>> df.head(3) - animal - 0 alligator - 1 bee - 2 falcon - - [3 rows x 1 columns] - - For negative values of `n`: - - >>> df.head(-3) - animal - 0 alligator - 1 bee - 2 falcon - 3 lion - 4 monkey - 5 parrot - - [6 rows x 1 columns] - Args: n (int, default 5): Default 5. Number of rows to select. Returns: - bigframes.pandas.DataFrame or bigframes.pandas.Series: - The first ``n`` rows of the caller object. + The first `n` rows of the caller object. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -527,8 +303,7 @@ def tail(self, n: int = 5): Number of rows to select. Returns: - bigframes.pandas.DataFrame: - The last `n` rows of the caller object. + The last `n` rows of the caller object. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -538,55 +313,11 @@ def sample( frac: Optional[float] = None, *, random_state: Optional[int] = None, - sort: Optional[bool | Literal["random"]] = "random", ): """Return a random sample of items from an axis of object. You can use `random_state` for reproducibility. - **Examples:** - - >>> import bigframes.pandas as bpd - >>> df = bpd.DataFrame({'num_legs': [2, 4, 8, 0], - ... 'num_wings': [2, 0, 0, 0], - ... 'num_specimen_seen': [10, 2, 1, 8]}, - ... index=['falcon', 'dog', 'spider', 'fish']) - >>> df - num_legs num_wings num_specimen_seen - falcon 2 2 10 - dog 4 0 2 - spider 8 0 1 - fish 0 0 8 - - [4 rows x 3 columns] - - Fetch one random row from the DataFrame (Note that we use `random_state` - to ensure reproducibility of the examples): - - >>> df.sample(random_state=1) - num_legs num_wings num_specimen_seen - dog 4 0 2 - - [1 rows x 3 columns] - - A random 50% sample of the DataFrame: - - >>> df.sample(frac=0.5, random_state=1) - num_legs num_wings num_specimen_seen - dog 4 0 2 - fish 0 0 8 - - [2 rows x 3 columns] - - Extract 3 random elements from the Series `df['num_legs']`: - - >>> s = df['num_legs'] - >>> s.sample(n=3, random_state=1) - dog 4 - fish 0 - spider 8 - Name: num_legs, dtype: Int64 - Args: n (Optional[int], default None): Number of items from axis to return. Cannot be used with `frac`. @@ -595,21 +326,10 @@ def sample( Fraction of axis items to return. Cannot be used with `n`. random_state (Optional[int], default None): Seed for random number generator. - sort (Optional[bool|Literal["random"]], default "random"): - - - 'random' (default): No specific ordering will be applied after - sampling. - - 'True' : Index columns will determine the sample's order. - - 'False': The sample will retain the original object's order. Returns: - bigframes.pandas.DataFrame or bigframes.pandas.Series: - A new object of same type as caller containing `n` items randomly - sampled from the caller object. - - Raises: - ValueError: - If both ``n`` and ``frac`` are specified. + A new object of same type as caller containing `n` items randomly + sampled from the caller object. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -624,19 +344,8 @@ def dtypes(self): The result's index is the original DataFrame's columns. Columns with mixed types aren't supported yet in BigQuery DataFrames. - **Examples:** - - - >>> df = bpd.DataFrame({'float': [1.0], 'int': [1], 'string': ['foo']}) - >>> df.dtypes - float Float64 - int Int64 - string string - dtype: object - Returns: - pandas.Series: - A *pandas* Series with the data type of each column. + A *pandas* Series with the data type of each column. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -647,68 +356,8 @@ def copy(self): and indices. Modifications to the data or indices of the copy will not be reflected in the original object. - **Examples:** - - - Modification in the original Series will not affect the copy Series: - - >>> s = bpd.Series([1, 2], index=["a", "b"]) - >>> s - a 1 - b 2 - dtype: Int64 - - >>> s_copy = s.copy() - >>> s_copy - a 1 - b 2 - dtype: Int64 - - >>> s.loc['b'] = 22 - >>> s - a 1 - b 22 - dtype: Int64 - >>> s_copy - a 1 - b 2 - dtype: Int64 - - Modification in the original DataFrame will not affect the copy DataFrame: - - >>> df = bpd.DataFrame({'a': [1, 3], 'b': [2, 4]}) - >>> df - a b - 0 1 2 - 1 3 4 - - [2 rows x 2 columns] - - >>> df_copy = df.copy() - >>> df_copy - a b - 0 1 2 - 1 3 4 - - [2 rows x 2 columns] - - >>> df.loc[df["b"] == 2, "b"] = 22 - >>> df - a b - 0 1 22 - 1 3 4 - - [2 rows x 2 columns] - >>> df_copy - a b - 0 1 2 - 1 3 4 - - [2 rows x 2 columns] - Returns: - bigframes.pandas.DataFrame or bigframes.pandas.Series: - Object type matches caller. + Object type matches caller. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -718,45 +367,6 @@ def copy(self): def ffill(self, *, limit: Optional[int] = None): """Fill NA/NaN values by propagating the last valid observation to next valid. - **Examples:** - - - >>> df = bpd.DataFrame([[np.nan, 2, np.nan, 0], - ... [3, 4, np.nan, 1], - ... [np.nan, np.nan, np.nan, np.nan], - ... [np.nan, 3, np.nan, 4]], - ... columns=list("ABCD")).astype("Float64") - >>> df - A B C D - 0 2.0 0.0 - 1 3.0 4.0 1.0 - 2 - 3 3.0 4.0 - - [4 rows x 4 columns] - - Fill NA/NaN values in DataFrames: - - >>> df.ffill() - A B C D - 0 2.0 0.0 - 1 3.0 4.0 1.0 - 2 3.0 4.0 1.0 - 3 3.0 3.0 4.0 - - [4 rows x 4 columns] - - - Fill NA/NaN values in Series: - - >>> series = bpd.Series([1, np.nan, 2, 3]) - >>> series.ffill() - 0 1.0 - 1 1.0 - 2 2.0 - 3 3.0 - dtype: Float64 - Args: limit : int, default None If method is specified, this is the maximum number of consecutive @@ -768,8 +378,7 @@ def ffill(self, *, limit: Optional[int] = None): Returns: - bigframes.pandas.DataFrame or bigframes.pandas.Series or None: - Object with missing values filled. + Series/DataFrame or None: Object with missing values filled. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -786,101 +395,21 @@ def bfill(self, *, limit: Optional[int] = None): filled. Must be greater than 0 if not None. Returns: - bigframes.pandas.DataFrame or bigframes.pandas.Series or None: - Object with missing values filled. + Series/DataFrame or None: Object with missing values filled. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) def isna(self) -> NDFrame: - """Detect missing (NULL) values. - - Return a boolean same-sized object indicating if the values are NA - (NULL in BigQuery). NA/NULL values get mapped to True values. - Everything else gets mapped to False values. + """Detect missing values. - Note that empty strings ``''``, :attr:`numpy.inf`, and - :attr:`numpy.nan` are ***not*** considered NA values. This NA/NULL - logic differs from numpy, but it is the same as BigQuery and the - :class:`pandas.ArrowDtype`. - - **Examples:** - - >>> df = bpd.DataFrame(dict( - ... age=pd.Series(pa.array( - ... [5, 6, None, 4], - ... type=pa.int64(), - ... ), dtype=pd.ArrowDtype(pa.int64())), - ... born=pd.to_datetime([pd.NA, "1940-04-25", "1940-04-25", "1941-08-25"]), - ... name=['Alfred', 'Batman', '', 'Plastic Man'], - ... toy=[None, 'Batmobile', 'Joker', 'Play dough'], - ... height=pd.Series(pa.array( - ... [6.1, 5.9, None, np.nan], - ... type=pa.float64(), - ... ), dtype=pd.ArrowDtype(pa.float64())), - ... )) - >>> df - age born name toy height - 0 5 Alfred 6.1 - 1 6 1940-04-25 00:00:00 Batman Batmobile 5.9 - 2 1940-04-25 00:00:00 Joker - 3 4 1941-08-25 00:00:00 Plastic Man Play dough NaN - - [4 rows x 5 columns] - - Show which entries in a DataFrame are NA (NULL in BigQuery): - - >>> df.isna() - age born name toy height - 0 False True False True False - 1 False False False False False - 2 True False False False True - 3 False False False False False - - [4 rows x 5 columns] - - >>> df.isnull() - age born name toy height - 0 False True False True False - 1 False False False False False - 2 True False False False True - 3 False False False False False - - [4 rows x 5 columns] - - Show which entries in a Series are NA (NULL in BigQuery): - - >>> ser = bpd.Series(pa.array( - ... [5, None, 6, np.nan, None], - ... type=pa.float64(), - ... ), dtype=pd.ArrowDtype(pa.float64())) - >>> ser - 0 5.0 - 1 - 2 6.0 - 3 NaN - 4 - dtype: Float64 - - >>> ser.isna() - 0 False - 1 True - 2 False - 3 False - 4 True - dtype: boolean - - >>> ser.isnull() - 0 False - 1 True - 2 False - 3 False - 4 True - dtype: boolean + Return a boolean same-sized object indicating if the values are NA. + NA values get mapped to True values. Everything else gets mapped to + False values. Characters such as empty strings ``''`` or + :attr:`numpy.inf` are not considered NA values. Returns: - bigframes.pandas.DataFrame or bigframes.pandas.Series: - Mask of bool values for each element that indicates whether an - element is an NA value. + Mask of bool values for each element that indicates whether an + element is an NA value. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -902,29 +431,6 @@ def notna(self) -> NDFrame: notnull = notna - def take(self, indices, axis=0, **kwargs) -> NDFrame: - """Return the elements in the given positional indices along an axis. - - This means that we are not indexing according to actual values in the index - attribute of the object. We are indexing according to the actual position of - the element in the object. - - Args: - indices (list-like): - An array of ints indicating which positions to take. - axis ({0 or 'index', 1 or 'columns', None}, default 0): - The axis on which to select elements. 0 means that we are selecting rows, - 1 means that we are selecting columns. For Series this parameter is - unused and defaults to 0. - **kwargs: - For compatibility with numpy.take(). Has no effect on the output. - - Returns: - bigframes.pandas.DataFrame or bigframes.pandas.Series: - Same type as input object. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - def filter( self, items=None, @@ -951,12 +457,7 @@ def filter( DataFrame. For `Series` this parameter is unused and defaults to `None`. Returns: - bigframes.pandas.DataFrame or bigframes.pandas.Series: - Same type as input object. - - Raises: - ValueError: - If value provided is not exactly one of ``items``, ``like``, or ``regex``. + same type as input object """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -997,7 +498,7 @@ def pct_change(self, periods: int = 1): Periods to shift for forming percent change. Returns: - bigframes.pandas.DataFrame or bigframes.pandas.Series: The same type as the calling object. + Series or DataFrame: The same type as the calling object. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -1034,13 +535,8 @@ def rank( ascending (bool, default True): Whether or not the elements should be ranked in ascending order. - pct (bool, default False): - Whether or not to display the returned rankings in percentile - form. - Returns: - bigframes.pandas.DataFrame or bigframes.pandas.Series: - Return a Series or DataFrame with data ranks as values. + same type as caller: Return a Series or DataFrame with data ranks as values. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -1048,66 +544,37 @@ def rolling( self, window, min_periods: int | None = None, - on: str | None = None, - closed: Literal["right", "left", "both", "neither"] = "right", ): """ Provide rolling window calculations. - **Examples:** - - >>> import bigframes.pandas as bpd - >>> s = bpd.Series([0,1,2,3,4]) - >>> s.rolling(window=3).min() - 0 - 1 - 2 0 - 3 1 - 4 2 - dtype: Int64 - - >>> df = bpd.DataFrame({'A': [0,1,2,3], 'B': [0,2,4,6]}) - >>> df.rolling(window=2, on='A', closed='both').sum() - A B - 0 0 - 1 1 2 - 2 2 6 - 3 3 12 - - [4 rows x 2 columns] - Args: - window (int, pandas.Timedelta, numpy.timedelta64, datetime.timedelta, str): + window (int, timedelta, str, offset, or BaseIndexer subclass): Size of the moving window. If an integer, the fixed number of observations used for each window. - If a string, the timedelta representation in string. This string - must be parsable by pandas.Timedelta(). + If a timedelta, str, or offset, the time period of each window. Each + window will be a variable sized based on the observations included in + the time-period. This is only valid for datetime-like indexes. + To learn more about the offsets & frequency strings, please see `this link + `__. - Otherwise, the time range for each window. + If a BaseIndexer subclass, the window boundaries + based on the defined ``get_window_bounds`` method. Additional rolling + keyword arguments, namely ``min_periods``, ``center``, ``closed`` and + ``step`` will be passed to ``get_window_bounds``. min_periods (int, default None): Minimum number of observations in window required to have a value; otherwise, result is ``np.nan``. + For a window that is specified by an offset, ``min_periods`` will default to 1. + For a window that is specified by an integer, ``min_periods`` will default to the size of the window. - For a window that is not spicified by an interger, ``min_periods`` will default - to 1. - - on (str, optional): - For a DataFrame, a column label on which to calculate the rolling window, - rather than the DataFrame’s index. - - closed (str, default 'right'): - If 'right', the first point in the window is excluded from calculations. - If 'left', the last point in the window is excluded from calculations. - If 'both', the no points in the window are excluded from calculations. - If 'neither', the first and last points in the window are excluded from calculations. - Returns: bigframes.core.window.Window: ``Window`` subclass if a ``win_type`` is passed. ``Rolling`` subclass if ``win_type`` is not passed. @@ -1128,128 +595,10 @@ def expanding(self, min_periods=1): """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - def pipe( - self, - func: Callable[..., T] | tuple[Callable[..., T], str], - *args, - **kwargs, - ) -> T: - """ - Apply chainable functions that expect Series or DataFrames. - - **Examples:** - - Constructing a income DataFrame from a dictionary. - - - >>> data = [[8000, 1000], [9500, np.nan], [5000, 2000]] - >>> df = bpd.DataFrame(data, columns=['Salary', 'Others']) - >>> df - Salary Others - 0 8000 1000.0 - 1 9500 - 2 5000 2000.0 - - [3 rows x 2 columns] - - Functions that perform tax reductions on an income DataFrame. - - >>> def subtract_federal_tax(df): - ... return df * 0.9 - >>> def subtract_state_tax(df, rate): - ... return df * (1 - rate) - >>> def subtract_national_insurance(df, rate, rate_increase): - ... new_rate = rate + rate_increase - ... return df * (1 - new_rate) - - Instead of writing - - >>> subtract_national_insurance( - ... subtract_state_tax(subtract_federal_tax(df), rate=0.12), - ... rate=0.05, - ... rate_increase=0.02) # doctest: +SKIP - - You can write - - >>> ( - ... df.pipe(subtract_federal_tax) - ... .pipe(subtract_state_tax, rate=0.12) - ... .pipe(subtract_national_insurance, rate=0.05, rate_increase=0.02) - ... ) - Salary Others - 0 5892.48 736.56 - 1 6997.32 - 2 3682.8 1473.12 - - [3 rows x 2 columns] - - If you have a function that takes the data as (say) the second - argument, pass a tuple indicating which keyword expects the - data. For example, suppose ``national_insurance`` takes its data as ``df`` - in the second argument: - - >>> def subtract_national_insurance(rate, df, rate_increase): - ... new_rate = rate + rate_increase - ... return df * (1 - new_rate) - >>> ( - ... df.pipe(subtract_federal_tax) - ... .pipe(subtract_state_tax, rate=0.12) - ... .pipe( - ... (subtract_national_insurance, 'df'), - ... rate=0.05, - ... rate_increase=0.02 - ... ) - ... ) - Salary Others - 0 5892.48 736.56 - 1 6997.32 - 2 3682.8 1473.12 - - [3 rows x 2 columns] - - Args: - func (function): - Function to apply to this object. - ``args``, and ``kwargs`` are passed into ``func``. - Alternatively a ``(callable, data_keyword)`` tuple where - ``data_keyword`` is a string indicating the keyword of - ``callable`` that expects this object. - args (iterable, optional): - Positional arguments passed into ``func``. - kwargs (mapping, optional): - A dictionary of keyword arguments passed into ``func``. - - Returns: - bigframes.pandas.DataFrame or bigframes.pandas.Series: - Object of same type as caller - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) + def __nonzero__(self): + raise ValueError( + f"The truth value of a {type(self).__name__} is ambiguous. " + "Use a.empty, a.bool(), a.item(), a.any() or a.all()." + ) - def __getattr__(self, name: str): - """ - After regular attribute access, try looking up the name - This allows simpler access to columns for interactive use. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def equals(self, other) -> bool: - """ - Test whether two objects contain the same elements. - - This function allows two Series or DataFrames to be compared against - each other to see if they have the same shape and elements. NaNs in - the same location are considered equal. - - The row/column index do not need to have the same type, as long - as the values are considered equal. Corresponding columns must be of - the same dtype. - - Args: - other (Series or DataFrame): - The other Series or DataFrame to be compared with the first. - - Returns: - bool: True if all elements are the same in both objects, False - otherwise. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) + __bool__ = __nonzero__ diff --git a/third_party/bigframes_vendored/pandas/core/groupby/__init__.py b/third_party/bigframes_vendored/pandas/core/groupby/__init__.py index 579765fad5f..b05319b4f7d 100644 --- a/third_party/bigframes_vendored/pandas/core/groupby/__init__.py +++ b/third_party/bigframes_vendored/pandas/core/groupby/__init__.py @@ -7,11 +7,8 @@ class providing the base-class of operations. (defined in pandas.core.groupby.generic) expose these user-facing objects to provide specific functionality. """ - from __future__ import annotations -from typing import Literal - from bigframes import constants @@ -20,96 +17,14 @@ class GroupBy: Class for grouping and aggregating relational data. """ - def describe(self, include: None | Literal["all"] = None): - """ - Generate descriptive statistics. - - Descriptive statistics include those that summarize the central - tendency, dispersion and shape of a - dataset's distribution, excluding ``NaN`` values. - - Args: - include ("all" or None, optional): - If "all": All columns of the input will be included in the output. - If None: The result will include all numeric columns. - - .. note:: - Percentile values are approximates only. - - .. note:: - For numeric data, the result's index will include ``count``, - ``mean``, ``std``, ``min``, ``max`` as well as lower, ``50`` and - upper percentiles. By default the lower percentile is ``25`` and the - upper percentile is ``75``. The ``50`` percentile is the - same as the median. - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> df = bpd.DataFrame({"A": [1, 1, 1, 2, 2], "B": [0, 2, 8, 2, 7], "C": ["cat", "cat", "dog", "mouse", "cat"]}) - >>> df - A B C - 0 1 0 cat - 1 1 2 cat - 2 1 8 dog - 3 2 2 mouse - 4 2 7 cat - - [5 rows x 3 columns] - - >>> df.groupby("A").describe(include="all") - B C - count mean std min 25% 50% 75% max count nunique - A - 1 3 3.333333 4.163332 0 0 2 8 8 3 2 - 2 2 4.5 3.535534 2 2 2 7 7 2 2 - - [2 rows x 10 columns] - - Returns: - bigframes.pandas.DataFrame: - Summary statistics of the Series or Dataframe provided. - - Raises: - ValueError: - If unsupported ``include`` type is provided. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - def any(self): """ Return True if any value in the group is true, else False. - **Examples:** - - For SeriesGroupBy: - - - >>> lst = ['a', 'a', 'b'] - >>> ser = bpd.Series([1, 2, 0], index=lst) - >>> ser.groupby(level=0).any() - a True - b False - dtype: boolean - - For DataFrameGroupBy: - - >>> data = [[1, 0, 3], [1, 0, 6], [7, 1, 9]] - >>> df = bpd.DataFrame(data, columns=["a", "b", "c"], - ... index=["ostrich", "penguin", "parrot"]) - >>> df.groupby(by=["a"]).any() - b c - a - 1 False True - 7 True True - - [2 rows x 2 columns] - Returns: - bigframes.pandas.DataFrame or bigframes.pandas.Series: - DataFrame or Series of boolean values, + Series or DataFrame: DataFrame or Series of boolean values, where a value is True if any element is True within its - respective group; otherwise False. + respective group, False otherwise. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -117,36 +32,10 @@ def all(self): """ Return True if all values in the group are true, else False. - **Examples:** - - For SeriesGroupBy: - - - >>> lst = ['a', 'a', 'b'] - >>> ser = bpd.Series([1, 2, 0], index=lst) - >>> ser.groupby(level=0).all() - a True - b False - dtype: boolean - - For DataFrameGroupBy: - - >>> data = [[1, 0, 3], [1, 5, 6], [7, 8, 9]] - >>> df = bpd.DataFrame(data, columns=["a", "b", "c"], - ... index=["ostrich", "penguin", "parrot"]) - >>> df.groupby(by=["a"]).all() - b c - a - 1 False True - 7 True True - - [2 rows x 2 columns] - Returns: - bigframes.pandas.DataFrame or bigframes.pandas.Series: - DataFrame or Series of boolean values, + Series or DataFrame: DataFrame or Series of boolean values, where a value is True if all elements are True within its - respective group; otherwise False. + respective group, False otherwise. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -154,34 +43,8 @@ def count(self): """ Compute count of group, excluding missing values. - **Examples:** - - For SeriesGroupBy: - - - >>> lst = ['a', 'a', 'b'] - >>> ser = bpd.Series([1, 2, np.nan], index=lst) - >>> ser.groupby(level=0).count() - a 2 - b 0 - dtype: Int64 - - For DataFrameGroupBy: - - >>> data = [[1, np.nan, 3], [1, np.nan, 6], [7, 8, 9]] - >>> df = bpd.DataFrame(data, columns=["a", "b", "c"], - ... index=["cow", "horse", "bull"]) - >>> df.groupby(by=["a"]).count() - b c - a - 1 0 2 - 7 1 1 - - [2 rows x 2 columns] - Returns: - bigframes.pandas.DataFrame or bigframes.pandas.Series: - Count of values within each group. + Series or DataFrame: Count of values within each group. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -192,49 +55,12 @@ def mean( """ Compute mean of groups, excluding missing values. - **Examples:** - - >>> df = bpd.DataFrame({'A': [1, 1, 2, 1, 2], - ... 'B': [np.nan, 2, 3, 4, 5], - ... 'C': [1, 2, 1, 1, 2]}, columns=['A', 'B', 'C']) - - Groupby one column and return the mean of the remaining columns in each group. - - >>> df.groupby('A').mean() - B C - A - 1 3.0 1.333333 - 2 4.0 1.5 - - [2 rows x 2 columns] - - Groupby two columns and return the mean of the remaining column. - - >>> df.groupby(['A', 'B']).mean() - C - A B - 1 2.0 2.0 - 4.0 1.0 - 2 3.0 1.0 - 5.0 2.0 - - [4 rows x 1 columns] - - Groupby one column and return the mean of only particular column in the group. - - >>> df.groupby('A')['B'].mean() - A - 1 3.0 - 2 4.0 - Name: B, dtype: Float64 - Args: numeric_only (bool, default False): Include only float, int, boolean columns. Returns: - bigframes.pandas.DataFrame or bigframes.pandas.Series: - Mean of groups. + pandas.Series or pandas.DataFrame: Mean of groups. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -242,75 +68,20 @@ def median( self, numeric_only: bool = False, *, - exact: bool = True, + exact: bool = False, ): """ Compute median of groups, excluding missing values. - **Examples:** - - For SeriesGroupBy: - - >>> import bigframes.pandas as bpd - >>> lst = ['a', 'a', 'a', 'b', 'b', 'b'] - >>> ser = bpd.Series([7, 2, 8, 4, 3, 3], index=lst) - >>> ser.groupby(level=0).median() - a 7.0 - b 3.0 - dtype: Float64 - - For DataFrameGroupBy: - - >>> data = {'a': [1, 3, 5, 7, 7, 8, 3], 'b': [1, 4, 8, 4, 4, 2, 1]} - >>> df = bpd.DataFrame(data, index=['dog', 'dog', 'dog', - ... 'mouse', 'mouse', 'mouse', 'mouse']) - >>> df.groupby(level=0).median() - a b - dog 3.0 4.0 - mouse 7.0 3.0 - - [2 rows x 2 columns] - Args: numeric_only (bool, default False): Include only float, int, boolean columns. - exact (bool, default True): - Calculate the exact median instead of an approximation. - - Returns: - bigframes.pandas.DataFrame or bigframes.pandas.Series: - Median of groups. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def quantile(self, q=0.5, *, numeric_only: bool = False): - """ - Return group values at the given quantile, a la numpy.percentile. - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> df = bpd.DataFrame([ - ... ['a', 1], ['a', 2], ['a', 3], - ... ['b', 1], ['b', 3], ['b', 5] - ... ], columns=['key', 'val']) - >>> df.groupby('key').quantile() - val - key - a 2.0 - b 3.0 - - [2 rows x 1 columns] - - Args: - q (float or array-like, default 0.5 (50% quantile)): - Value(s) between 0 and 1 providing the quantile(s) to compute. - numeric_only (bool, default False): - Include only `float`, `int` or `boolean` data. + exact (bool, default False): + Calculate the exact median instead of an approximation. Note: + ``exact=True`` not yet supported. Returns: - bigframes.pandas.DataFrame or bigframes.pandas.Series: - Return type determined by caller of GroupBy object. + pandas.Series or pandas.DataFrame: Median of groups. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -324,37 +95,12 @@ def std( For multiple groupings, the result index will be a MultiIndex. - **Examples:** - - For SeriesGroupBy: - - - >>> lst = ['a', 'a', 'a', 'b', 'b', 'b'] - >>> ser = bpd.Series([7, 2, 8, 4, 3, 3], index=lst) - >>> ser.groupby(level=0).std() - a 3.21455 - b 0.57735 - dtype: Float64 - - For DataFrameGroupBy: - - >>> data = {'a': [1, 3, 5, 7, 7, 8, 3], 'b': [1, 4, 8, 4, 4, 2, 1]} - >>> df = bpd.DataFrame(data, index=['dog', 'dog', 'dog', - ... 'mouse', 'mouse', 'mouse', 'mouse']) - >>> df.groupby(level=0).std() - a b - dog 2.0 3.511885 - mouse 2.217356 1.5 - - [2 rows x 2 columns] - Args: numeric_only (bool, default False): Include only `float`, `int` or `boolean` data. Returns: - bigframes.pandas.DataFrame or bigframes.pandas.Series: - Standard deviation of values within each group. + Series or DataFrame: Standard deviation of values within each group. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -368,110 +114,16 @@ def var( For multiple groupings, the result index will be a MultiIndex. - **Examples:** - - For SeriesGroupBy: - - - >>> lst = ['a', 'a', 'a', 'b', 'b', 'b'] - >>> ser = bpd.Series([7, 2, 8, 4, 3, 3], index=lst) - >>> ser.groupby(level=0).var() - a 10.333333 - b 0.333333 - dtype: Float64 - - For DataFrameGroupBy: - - >>> data = {'a': [1, 3, 5, 7, 7, 8, 3], 'b': [1, 4, 8, 4, 4, 2, 1]} - >>> df = bpd.DataFrame(data, index=['dog', 'dog', 'dog', - ... 'mouse', 'mouse', 'mouse', 'mouse']) - >>> df.groupby(level=0).var() - a b - dog 4.0 12.333333 - mouse 4.916667 2.25 - - [2 rows x 2 columns] - Args: numeric_only (bool, default False): Include only `float`, `int` or `boolean` data. Returns: - bigframes.pandas.DataFrame or bigframes.pandas.Series: + Series or DataFrame Variance of values within each group. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - def rank( - self, - method: str = "average", - ascending: bool = True, - na_option: str = "keep", - ): - """ - Provide the rank of values within each group. - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> df = bpd.DataFrame( - ... { - ... "group": ["a", "a", "a", "a", "a", "b", "b", "b", "b", "b"], - ... "value": [2, 4, 2, 3, 5, 1, 2, 4, 1, 5], - ... } - ... ) - >>> df - group value - 0 a 2 - 1 a 4 - 2 a 2 - 3 a 3 - 4 a 5 - 5 b 1 - 6 b 2 - 7 b 4 - 8 b 1 - 9 b 5 - - [10 rows x 2 columns] - >>> for method in ['average', 'min', 'max', 'dense', 'first']: - ... df[f'{method}_rank'] = df.groupby('group')['value'].rank(method) - >>> df - group value average_rank min_rank max_rank dense_rank first_rank - 0 a 2 1.5 1.0 2.0 1.0 1.0 - 1 a 4 4.0 4.0 4.0 3.0 4.0 - 2 a 2 1.5 1.0 2.0 1.0 2.0 - 3 a 3 3.0 3.0 3.0 2.0 3.0 - 4 a 5 5.0 5.0 5.0 4.0 5.0 - 5 b 1 1.5 1.0 2.0 1.0 1.0 - 6 b 2 3.0 3.0 3.0 2.0 3.0 - 7 b 4 4.0 4.0 4.0 3.0 4.0 - 8 b 1 1.5 1.0 2.0 1.0 2.0 - 9 b 5 5.0 5.0 5.0 4.0 5.0 - - [10 rows x 7 columns] - - Args: - method ({'average', 'min', 'max', 'first', 'dense'}, default 'average'): - * average: average rank of group. - * min: lowest rank in group. - * max: highest rank in group. - * first: ranks assigned in order they appear in the array. - * dense: like 'min', but rank always increases by 1 between groups. - ascending (bool, default True): - False for ranks by high (1) to low (N). - na_option ({'keep', 'top', 'bottom'}, default 'keep'): - * keep: leave NA values where they are. - * top: smallest rank if ascending. - * bottom: smallest rank if descending. - pct (bool, default False): - Compute percentage rank of data within each group - - Returns: - DataFrame with ranking of values within each group - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - def skew( self, *, @@ -482,26 +134,12 @@ def skew( Normalized by N-1. - **Examples:** - - For SeriesGroupBy: - - - >>> ser = bpd.Series([390., 350., 357., np.nan, 22., 20., 30.], - ... index=['Falcon', 'Falcon', 'Falcon', 'Falcon', - ... 'Parrot', 'Parrot', 'Parrot'], - ... name="Max Speed") - >>> ser.groupby(level=0).skew() - Falcon 1.525174 - Parrot 1.457863 - Name: Max Speed, dtype: Float64 - Args: numeric_only (bool, default False): Include only `float`, `int` or `boolean` data. Returns: - bigframes.pandas.DataFrame or bigframes.pandas.Series: + Series or DataFrame Variance of values within each group. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -517,128 +155,16 @@ def kurt( Kurtosis obtained using Fisher's definition of kurtosis (kurtosis of normal == 0.0). Normalized by N-1. - **Examples:** - - - >>> lst = ['a', 'a', 'a', 'a', 'b', 'b', 'b', 'b', 'b'] - >>> ser = bpd.Series([0, 1, 1, 0, 0, 1, 2, 4, 5], index=lst) - >>> ser.groupby(level=0).kurt() - a -6.0 - b -1.963223 - dtype: Float64 - Args: numeric_only (bool, default False): Include only `float`, `int` or `boolean` data. Returns: - bigframes.pandas.DataFrame or bigframes.pandas.Series: + Series or DataFrame Variance of values within each group. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - def kurtosis( - self, - *, - numeric_only: bool = False, - ): - """ - Return unbiased kurtosis over requested axis. - - Kurtosis obtained using Fisher's definition of - kurtosis (kurtosis of normal == 0.0). Normalized by N-1. - - **Examples:** - - - >>> lst = ['a', 'a', 'a', 'a', 'b', 'b', 'b', 'b', 'b'] - >>> ser = bpd.Series([0, 1, 1, 0, 0, 1, 2, 4, 5], index=lst) - >>> ser.groupby(level=0).kurtosis() - a -6.0 - b -1.963223 - dtype: Float64 - - Args: - numeric_only (bool, default False): - Include only `float`, `int` or `boolean` data. - - Returns: - bigframes.pandas.DataFrame or bigframes.pandas.Series: - Variance of values within each group. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def first(self, numeric_only: bool = False, min_count: int = -1): - """ - Compute the first entry of each column within each group. - - Defaults to skipping NA elements. - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> df = bpd.DataFrame(dict(A=[1, 1, 3], B=[None, 5, 6], C=[1, 2, 3])) - >>> df.groupby("A").first() - B C - A - 1 5.0 1 - 3 6.0 3 - - [2 rows x 2 columns] - - >>> df.groupby("A").first(min_count=2) - B C - A - 1 1 - 3 - - [2 rows x 2 columns] - - Args: - numeric_only (bool, default False): - Include only float, int, boolean columns. If None, will attempt to use - everything, then use only numeric data. - min_count (int, default -1): - The required number of valid values to perform the operation. If fewer - than ``min_count`` valid values are present the result will be NA. - - Returns: - bigframes.pandas.DataFrame or bigframes.pandas.Series: - First of values within each group. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def last(self, numeric_only: bool = False, min_count: int = -1): - """ - Compute the last entry of each column within each group. - - Defaults to skipping NA elements. - - **Examples:** - - >>> df = bpd.DataFrame(dict(A=[1, 1, 3], B=[5, None, 6], C=[1, 2, 3])) - >>> df.groupby("A").last() - B C - A - 1 5.0 2 - 3 6.0 3 - - [2 rows x 2 columns] - - Args: - numeric_only (bool, default False): - Include only float, int, boolean columns. If None, will attempt to use - everything, then use only numeric data. - min_count (int, default -1): - The required number of valid values to perform the operation. If fewer - than ``min_count`` valid values are present the result will be NA. - - Returns: - bigframes.pandas.DataFrame or bigframes.pandas.Series: - Last of values within each group. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - def sum( self, numeric_only: bool = False, @@ -647,71 +173,31 @@ def sum( """ Compute sum of group values. - **Examples:** - - For SeriesGroupBy: - - - >>> lst = ['a', 'a', 'b', 'b'] - >>> ser = bpd.Series([1, 2, 3, 4], index=lst) - >>> ser.groupby(level=0).sum() - a 3 - b 7 - dtype: Int64 - - For DataFrameGroupBy: - - >>> data = [[1, 8, 2], [1, 2, 5], [2, 5, 8], [2, 6, 9]] - >>> df = bpd.DataFrame(data, columns=["a", "b", "c"], - ... index=["tiger", "leopard", "cheetah", "lion"]) - >>> df.groupby("a").sum() - b c - a - 1 10 7 - 2 11 17 - - [2 rows x 2 columns] - Args: numeric_only (bool, default False): Include only float, int, boolean columns. min_count (int, default 0): The required number of valid values to perform the operation. If fewer - than ``min_count`` and non-NA values are present, the result will be NA. + than ``min_count`` non-NA values are present the result will be NA. Returns: - bigframes.pandas.DataFrame or bigframes.pandas.Series: - Computed sum of values within each group. + Series or DataFrame: Computed sum of values within each group. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) def prod(self, numeric_only: bool = False, min_count: int = 0): """ Compute prod of group values. - (DataFrameGroupBy functionality is not yet available.) - - **Examples:** - - For SeriesGroupBy: - - - >>> lst = ['a', 'a', 'b', 'b'] - >>> ser = bpd.Series([1, 2, 3, 4], index=lst) - >>> ser.groupby(level=0).prod() - a 2.0 - b 12.0 - dtype: Float64 Args: numeric_only (bool, default False): Include only float, int, boolean columns. min_count (int, default 0): The required number of valid values to perform the operation. If fewer - than ``min_count`` and non-NA values are present, the result will be NA. + than ``min_count`` non-NA values are present the result will be NA. Returns: - bigframes.pandas.DataFrame or bigframes.pandas.Series: - Computed prod of values within each group. + Series or DataFrame: Computed prod of values within each group. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -723,41 +209,15 @@ def min( """ Compute min of group values. - **Examples:** - - For SeriesGroupBy: - - - >>> lst = ['a', 'a', 'b', 'b'] - >>> ser = bpd.Series([1, 2, 3, 4], index=lst) - >>> ser.groupby(level=0).min() - a 1 - b 3 - dtype: Int64 - - For DataFrameGroupBy: - - >>> data = [[1, 8, 2], [1, 2, 5], [2, 5, 8], [2, 6, 9]] - >>> df = bpd.DataFrame(data, columns=["a", "b", "c"], - ... index=["tiger", "leopard", "cheetah", "lion"]) - >>> df.groupby(by=["a"]).min() - b c - a - 1 2 2 - 2 5 8 - - [2 rows x 2 columns] - Args: numeric_only (bool, default False): Include only float, int, boolean columns. min_count (int, default 0): The required number of valid values to perform the operation. If fewer - than ``min_count`` and non-NA values are present, the result will be NA. + than ``min_count`` non-NA values are present the result will be NA. Returns: - bigframes.pandas.DataFrame or bigframes.pandas.Series: - Computed min of values within each group. + Series or DataFrame: Computed min of values within each group. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -769,41 +229,15 @@ def max( """ Compute max of group values. - **Examples:** - - For SeriesGroupBy: - - - >>> lst = ['a', 'a', 'b', 'b'] - >>> ser = bpd.Series([1, 2, 3, 4], index=lst) - >>> ser.groupby(level=0).max() - a 2 - b 4 - dtype: Int64 - - For DataFrameGroupBy: - - >>> data = [[1, 8, 2], [1, 2, 5], [2, 5, 8], [2, 6, 9]] - >>> df = bpd.DataFrame(data, columns=["a", "b", "c"], - ... index=["tiger", "leopard", "cheetah", "lion"]) - >>> df.groupby(by=["a"]).max() - b c - a - 1 8 5 - 2 6 9 - - [2 rows x 2 columns] - Args: numeric_only (bool, default False): Include only float, int, boolean columns. min_count (int, default 0): The required number of valid values to perform the operation. If fewer - than ``min_count`` and non-NA values are present, the result will be NA. + than ``min_count`` non-NA values are present the result will be NA. Returns: - bigframes.pandas.DataFrame or bigframes.pandas.Series: - Computed max of values within each group. + Series or DataFrame: Computed max of values within each group. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -811,35 +245,12 @@ def cumcount(self, ascending: bool = True): """ Number each item in each group from 0 to the length of that group - 1. - **Examples:** - - For SeriesGroupBy: - - - >>> lst = ['a', 'a', 'b', 'b', 'c'] - >>> ser = bpd.Series([5, 1, 2, 3, 4], index=lst) - >>> ser.groupby(level=0).cumcount() - a 0 - a 1 - b 0 - b 1 - c 0 - dtype: Int64 - >>> ser.groupby(level=0).cumcount(ascending=False) - a 0 - a 1 - b 0 - b 1 - c 0 - dtype: Int64 - Args: ascending (bool, default True): If False, number in reverse, from length of group - 1 to 0. Returns: - bigframes.pandas.Series: - Sequence number of each element within each group. + Series: Sequence number of each element within each group. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -847,35 +258,8 @@ def cumprod(self, *args, **kwargs): """ Cumulative product for each group. - **Examples:** - - For SeriesGroupBy: - - - >>> lst = ['a', 'a', 'b'] - >>> ser = bpd.Series([6, 2, 0], index=lst) - >>> ser.groupby(level=0).cumprod() - a 6.0 - a 12.0 - b 0.0 - dtype: Float64 - - For DataFrameGroupBy: - - >>> data = [[1, 8, 2], [1, 2, 5], [2, 6, 9]] - >>> df = bpd.DataFrame(data, columns=["a", "b", "c"], - ... index=["cow", "horse", "bull"]) - >>> df.groupby("a").cumprod() - b c - cow 8.0 2.0 - horse 16.0 10.0 - bull 6.0 9.0 - - [3 rows x 2 columns] - Returns: - bigframes.pandas.DataFrame or bigframes.pandas.Series: - Cumulative product for each group. + Series or DataFrame: Cumulative product for each group. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -883,35 +267,8 @@ def cumsum(self, *args, **kwargs): """ Cumulative sum for each group. - **Examples:** - - For SeriesGroupBy: - - - >>> lst = ['a', 'a', 'b'] - >>> ser = bpd.Series([6, 2, 0], index=lst) - >>> ser.groupby(level=0).cumsum() - a 6 - a 8 - b 0 - dtype: Int64 - - For DataFrameGroupBy: - - >>> data = [[1, 8, 2], [1, 2, 5], [2, 6, 9]] - >>> df = bpd.DataFrame(data, columns=["a", "b", "c"], - ... index=["fox", "gorilla", "lion"]) - >>> df.groupby("a").cumsum() - b c - fox 8 2 - gorilla 10 7 - lion 6 9 - - [3 rows x 2 columns] - Returns: - bigframes.pandas.DataFrame or bigframes.pandas.Series: - Cumulative sum for each group. + Series or DataFrame: Cumulative sum for each group. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -919,35 +276,8 @@ def cummin(self, *args, numeric_only: bool = False, **kwargs): """ Cumulative min for each group. - **Examples:** - - For SeriesGroupBy: - - - >>> lst = ['a', 'a', 'b'] - >>> ser = bpd.Series([6, 2, 0], index=lst) - >>> ser.groupby(level=0).cummin() - a 6 - a 2 - b 0 - dtype: Int64 - - For DataFrameGroupBy: - - >>> data = [[1, 8, 2], [1, 2, 5], [2, 6, 9]] - >>> df = bpd.DataFrame(data, columns=["a", "b", "c"], - ... index=["fox", "gorilla", "lion"]) - >>> df.groupby("a").cummin() - b c - fox 8 2 - gorilla 2 2 - lion 6 9 - - [3 rows x 2 columns] - Returns: - bigframes.pandas.DataFrame or bigframes.pandas.Series: - Cumulative min for each group. + Series or DataFrame: Cumulative min for each group. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -955,35 +285,8 @@ def cummax(self, *args, numeric_only: bool = False, **kwargs): """ Cumulative max for each group. - **Examples:** - - For SeriesGroupBy: - - - >>> lst = ['a', 'a', 'b'] - >>> ser = bpd.Series([6, 2, 0], index=lst) - >>> ser.groupby(level=0).cummax() - a 6 - a 6 - b 0 - dtype: Int64 - - For DataFrameGroupBy: - - >>> data = [[1, 8, 2], [1, 2, 5], [2, 6, 9]] - >>> df = bpd.DataFrame(data, columns=["a", "b", "c"], - ... index=["fox", "gorilla", "lion"]) - >>> df.groupby("a").cummax() - b c - fox 8 2 - gorilla 8 5 - lion 6 9 - - [3 rows x 2 columns] - Returns: - bigframes.pandas.DataFrame or bigframes.pandas.Series: - Cumulative max for each group. + Series or DataFrame: Cumulative max for each group. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -993,42 +296,8 @@ def diff(self): Calculates the difference of each element compared with another element in the group (default is element in previous row). - **Examples:** - - For SeriesGroupBy: - - - >>> lst = ['a', 'a', 'a', 'b', 'b', 'b'] - >>> ser = bpd.Series([7, 2, 8, 4, 3, 3], index=lst) - >>> ser.groupby(level=0).diff() - a - a -5 - a 6 - b - b -1 - b 0 - dtype: Int64 - - For DataFrameGroupBy: - - >>> data = {'a': [1, 3, 5, 7, 7, 8, 3], 'b': [1, 4, 8, 4, 4, 2, 1]} - >>> df = bpd.DataFrame(data, index=['dog', 'dog', 'dog', - ... 'mouse', 'mouse', 'mouse', 'mouse']) - >>> df.groupby(level=0).diff() - a b - dog - dog 2 3 - dog 2 4 - mouse - mouse 0 0 - mouse 1 -2 - mouse -5 -1 - - [7 rows x 2 columns] - Returns: - bigframes.pandas.DataFrame or bigframes.pandas.Series: - First differences. + Series or DataFrame: First differences. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -1036,41 +305,12 @@ def shift(self, periods: int = 1): """ Shift each group by periods observations. - **Examples:** - - For SeriesGroupBy: - - - >>> lst = ['a', 'a', 'b', 'b'] - >>> ser = bpd.Series([1, 2, 3, 4], index=lst) - >>> ser.groupby(level=0).shift(1) - a - a 1 - b - b 3 - dtype: Int64 - - For DataFrameGroupBy: - - >>> data = [[1, 2, 3], [1, 5, 6], [2, 5, 8], [2, 6, 9]] - >>> df = bpd.DataFrame(data, columns=["a", "b", "c"], - ... index=["tuna", "salmon", "catfish", "goldfish"]) - >>> df.groupby("a").shift(1) - b c - tuna - salmon 2 3 - catfish - goldfish 5 8 - - [4 rows x 2 columns] - Args: periods (int, default 1): Number of periods to shift. Returns: - bigframes.pandas.DataFrame or bigframes.pandas.Series: - Object shifted within each group. + Series or DataFrame: Object shifted within each group. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -1078,55 +318,19 @@ def rolling(self, *args, **kwargs): """ Returns a rolling grouper, providing rolling functionality per group. - **Examples:** - - >>> import bigframes.pandas as bpd - >>> lst = ['a', 'a', 'a', 'a', 'e'] - >>> ser = bpd.Series([1, 0, -2, -1, 2], index=lst) - >>> ser.groupby(level=0).rolling(2).min() - index index - a a - a 0 - a -2 - a -2 - e e - dtype: Int64 - Args: - window (int, pandas.Timedelta, numpy.timedelta64, datetime.timedelta, str): - Size of the moving window. - - If an integer, the fixed number of observations used for - each window. - - If a string, the timedelta representation in string. This string - must be parsable by pandas.Timedelta(). - - Otherwise, the time range for each window. - min_periods (int, default None): Minimum number of observations in window required to have a value; otherwise, result is ``np.nan``. + For a window that is specified by an offset, + ``min_periods`` will default to 1. + For a window that is specified by an integer, ``min_periods`` will default to the size of the window. - For a window that is not spicified by an interger, ``min_periods`` will default - to 1. - - on (str, optional): - For a DataFrame, a column label on which to calculate the rolling window, - rather than the DataFrame’s index. - - closed (str, default 'right'): - If 'right', the first point in the window is excluded from calculations. - If 'left', the last point in the window is excluded from calculations. - If 'both', the no points in the window are excluded from calculations. - If 'neither', the first and last points in the window are excluded from calculations. - Returns: - bigframes.pandas.DataFrame or bigframes.pandas.Series: - Return a new grouper with our rolling appended. + Series or DataFrame: Return a new grouper with our rolling appended. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -1134,162 +338,8 @@ def expanding(self, *args, **kwargs): """ Provides expanding functionality. - **Examples:** - - >>> import bigframes.pandas as bpd - >>> lst = ['a', 'a', 'c', 'c', 'e'] - >>> ser = bpd.Series([1, 0, -2, -1, 2], index=lst) - >>> ser.groupby(level=0).expanding().min() - index index - a a 1 - a 0 - c c -2 - c -2 - e e 2 - dtype: Int64 - - Returns: - bigframes.pandas.DataFrame or bigframes.pandas.Series: - An expanding grouper, providing expanding functionality per group. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def head(self, n: int = 5): - """ - Return last first n rows of each group - - **Examples:** - - - >>> df = bpd.DataFrame([[1, 2], [1, 4], [5, 6]], - ... columns=['A', 'B']) - >>> df.groupby('A').head(1) - A B - 0 1 2 - 2 5 6 - [2 rows x 2 columns] - - Args: - n (int): - If positive: number of entries to include from start of each group. - If negative: number of entries to exclude from end of each group. - - Returns: - bigframes.pandas.DataFrame or bigframes.pandas.Series: - First n rows of the original DataFrame or Series - - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def size(self): - """ - Compute group sizes. - - **Examples:** - - - For SeriesGroupBy: - - >>> lst = ['a', 'a', 'b'] - >>> ser = bpd.Series([1, 2, 3], index=lst) - >>> ser - a 1 - a 2 - b 3 - dtype: Int64 - >>> ser.groupby(level=0).size() - a 2 - b 1 - dtype: Int64 - - For DataFrameGroupBy: - - >>> data = [[1, 2, 3], [1, 5, 6], [7, 8, 9]] - >>> df = bpd.DataFrame(data, columns=["a", "b", "c"], - ... index=["owl", "toucan", "eagle"]) - >>> df - a b c - owl 1 2 3 - toucan 1 5 6 - eagle 7 8 9 - [3 rows x 3 columns] - >>> df.groupby("a").size() - a - 1 2 - 7 1 - dtype: Int64 - - Returns: - bigframes.pandas.DataFrame or bigframes.pandas.Series: - Number of rows in each group as a Series if as_index is True - or a DataFrame if as_index is False. - - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def __iter__(self): - r""" - Groupby iterator. - - This method provides an iterator over the groups created by the ``resample`` - or ``groupby`` operation on the object. The method yields tuples where - the first element is the label (group key) corresponding to each group or - resampled bin, and the second element is the subset of the data that falls - within that group or bin. - - **Examples:** - - - For SeriesGroupBy: - - >>> lst = ["a", "a", "b"] - >>> ser = bpd.Series([1, 2, 3], index=lst) - >>> ser - a 1 - a 2 - b 3 - dtype: Int64 - >>> for x, y in ser.groupby(level=0): - ... print(f"{x}\n{y}\n") - a - a 1 - a 2 - dtype: Int64 - b - b 3 - dtype: Int64 - - For DataFrameGroupBy: - - >>> data = [[1, 2, 3], [1, 5, 6], [7, 8, 9]] - >>> df = bpd.DataFrame(data, columns=["a", "b", "c"]) - >>> df - a b c - 0 1 2 3 - 1 1 5 6 - 2 7 8 9 - - [3 rows x 3 columns] - >>> for x, y in df.groupby(by=["a"]): - ... print(f'{x}\n{y}\n') - (1,) - a b c - 0 1 2 3 - 1 1 5 6 - - [2 rows x 3 columns] - (7,) - - a b c - 2 7 8 9 - - [1 rows x 3 columns] - - Returns: - Iterable[Label | Tuple, bigframes.pandas.Series | bigframes.pandas.DataFrame]: - Generator yielding sequence of (name, subsetted object) - for each group. + Series or DataFrame: A expanding grouper, providing expanding functionality per group. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -1299,47 +349,6 @@ def agg(self, func): """ Aggregate using one or more operations. - **Examples:** - - - >>> s = bpd.Series([1, 2, 3, 4], index=[1, 1, 2, 2]) - >>> s.groupby(level=0).agg(['min', 'max']) - min max - 1 1 2 - 2 3 4 - - [2 rows x 2 columns] - - Args: - func : function, str, list, dict or None - Function to use for aggregating the data. - - Accepted combinations are: - - - string function name - - list of function names, e.g. ``['sum', 'mean']`` - - Returns: - bigframes.pandas.Series: - A BigQuery Series. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def aggregate(self, func): - """ - Aggregate using one or more operations. - - **Examples:** - - - >>> s = bpd.Series([1, 2, 3, 4], index=[1, 1, 2, 2]) - >>> s.groupby(level=0).aggregate(['min', 'max']) - min max - 1 1 2 - 2 3 4 - - [2 rows x 2 columns] - Args: func : function, str, list, dict or None Function to use for aggregating the data. @@ -1350,54 +359,7 @@ def aggregate(self, func): - list of function names, e.g. ``['sum', 'mean']`` Returns: - bigframes.pandas.Series: - A BigQuery Series. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def nunique(self): - """ - Return number of unique elements in the group. - - **Examples:** - - - >>> lst = ['a', 'a', 'b', 'b'] - >>> ser = bpd.Series([1, 2, 3, 3], index=lst) - >>> ser.groupby(level=0).nunique() - a 2 - b 1 - dtype: Int64 - - Returns: - bigframes.pandas.Series: - Number of unique values within each group. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def value_counts( - self, - normalize: bool = False, - sort: bool = True, - ascending: bool = False, - dropna: bool = True, - ): - """ - Return a Series or DataFrame containing counts of unique rows. - - Args: - normalize (bool, default False): - Return proportions rather than frequencies. - sort (bool, default True): - Sort by frequencies. - ascending (bool, default False): - Sort in ascending order. - dropna (bool, default True): - Don't include counts of rows that contain NA values. - - Returns: - Series or DataFrame: - Series if the groupby as_index is True, otherwise DataFrame. + Series or DataFrame """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -1407,92 +369,6 @@ def agg(self, func, **kwargs): """ Aggregate using one or more operations. - **Examples:** - - - >>> data = {"A": [1, 1, 2, 2], - ... "B": [1, 2, 3, 4], - ... "C": [0.362838, 0.227877, 1.267767, -0.562860]} - >>> df = bpd.DataFrame(data) - - The aggregation is for each column. - - >>> df.groupby('A').agg('min') - B C - A - 1 1 0.227877 - 2 3 -0.56286 - - [2 rows x 2 columns] - - Multiple aggregations - - >>> df.groupby('A').agg(['min', 'max']) - B C - min max min max - A - 1 1 2 0.227877 0.362838 - 2 3 4 -0.56286 1.267767 - - [2 rows x 4 columns] - - Args: - func (function, str, list, dict or None): - Function to use for aggregating the data. - - Accepted combinations are: - - - string function name - - list of function names, e.g. ``['sum', 'mean']`` - - dict of axis labels -> function names or list of such. - - None, in which case ``**kwargs`` are used with Named Aggregation. Here the - output has one column for each element in ``**kwargs``. The name of the - column is keyword, whereas the value determines the aggregation used to compute - the values in the column. - - kwargs - If ``func`` is None, ``**kwargs`` are used to define the output names and - aggregations via Named Aggregation. See ``func`` entry. - - Returns: - bigframes.pandas.DataFrame: - A BigQuery DataFrame. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def aggregate(self, func, **kwargs): - """ - Aggregate using one or more operations. - - **Examples:** - - - >>> data = {"A": [1, 1, 2, 2], - ... "B": [1, 2, 3, 4], - ... "C": [0.362838, 0.227877, 1.267767, -0.562860]} - >>> df = bpd.DataFrame(data) - - The aggregation is for each column. - - >>> df.groupby('A').aggregate('min') - B C - A - 1 1 0.227877 - 2 3 -0.56286 - - [2 rows x 2 columns] - - Multiple aggregations - - >>> df.groupby('A').agg(['min', 'max']) - B C - min max min max - A - 1 1 2 0.227877 0.362838 - 2 3 4 -0.56286 1.267767 - - [2 rows x 4 columns] - Args: func (function, str, list, dict or None): Function to use for aggregating the data. @@ -1512,191 +388,6 @@ def aggregate(self, func, **kwargs): aggregations via Named Aggregation. See ``func`` entry. Returns: - bigframes.pandas.DataFrame: - A BigQuery DataFrame. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def corr( - self, - *, - numeric_only: bool = False, - ): - """ - Compute pairwise correlation of columns, excluding NA/null values. - - **Examples:** - - - >>> df = bpd.DataFrame({'A': [1, 2, 3], - ... 'B': [400, 500, 600], - ... 'C': [0.8, 0.4, 0.9]}) - >>> df.corr(numeric_only=True) - A B C - A 1.0 1.0 0.188982 - B 1.0 1.0 0.188982 - C 0.188982 0.188982 1.0 - - [3 rows x 3 columns] - - Args: - numeric_only(bool, default False): - Include only float, int, boolean, decimal data. - - Returns: - bigframes.pandas.DataFrame: Correlation matrix. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def cov( - self, - *, - numeric_only: bool = False, - ): - """ - Compute pairwise covariance of columns, excluding NA/null values. - - **Examples:** - - - >>> df = bpd.DataFrame({'A': [1, 2, 3], - ... 'B': [400, 500, 600], - ... 'C': [0.8, 0.4, 0.9]}) - >>> df.cov(numeric_only=True) - A B C - A 1.0 100.0 0.05 - B 100.0 10000.0 5.0 - C 0.05 5.0 0.07 - - [3 rows x 3 columns] - - Args: - numeric_only(bool, default False): - Include only float, int, boolean, decimal data. - - Returns: - bigframes.pandas.DataFrame: The covariance matrix of the series of the DataFrame. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def nunique(self): - """ - Return DataFrame with counts of unique elements in each position. - - **Examples:** - - - >>> df = bpd.DataFrame({'id': ['spam', 'egg', 'egg', 'spam', - ... 'ham', 'ham'], - ... 'value1': [1, 5, 5, 2, 5, 5], - ... 'value2': list('abbaxy')}) - >>> df.groupby('id').nunique() - value1 value2 - id - egg 1 1 - ham 1 2 - spam 2 1 - - [3 rows x 2 columns] - - Returns: - bigframes.pandas.DataFrame: - Number of unique values within a BigQuery DataFrame. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def value_counts( - self, - subset=None, - normalize: bool = False, - sort: bool = True, - ascending: bool = False, - dropna: bool = True, - ): - """ - Return a Series or DataFrame containing counts of unique rows. - - **Examples:** - - - >>> df = bpd.DataFrame({ - ... 'gender': ['male', 'male', 'female', 'male', 'female', 'male'], - ... 'education': ['low', 'medium', 'high', 'low', 'high', 'low'], - ... 'country': ['US', 'FR', 'US', 'FR', 'FR', 'FR'] - ... }) - - >>> df - gender education country - 0 male low US - 1 male medium FR - 2 female high US - 3 male low FR - 4 female high FR - 5 male low FR - - [6 rows x 3 columns] - - >>> df.groupby('gender').value_counts() - gender education country - female high FR 1 - US 1 - male low FR 2 - US 1 - medium FR 1 - Name: count, dtype: Int64 - - >>> df.groupby('gender').value_counts(ascending=True) - gender education country - female high FR 1 - US 1 - male low US 1 - medium FR 1 - low FR 2 - Name: count, dtype: Int64 - - >>> df.groupby('gender').value_counts(normalize=True) - gender education country - female high FR 0.5 - US 0.5 - male low FR 0.5 - US 0.25 - medium FR 0.25 - Name: proportion, dtype: Float64 - - >>> df.groupby('gender', as_index=False).value_counts() - gender education country count - 0 female high FR 1 - 1 female high US 1 - 2 male low FR 2 - 3 male low US 1 - 4 male medium FR 1 - - [5 rows x 4 columns] - - >>> df.groupby('gender', as_index=False).value_counts(normalize=True) - gender education country proportion - 0 female high FR 0.5 - 1 female high US 0.5 - 2 male low FR 0.5 - 3 male low US 0.25 - 4 male medium FR 0.25 - - [5 rows x 4 columns] - - Args: - subset (list-like, optional): - Columns to use when counting unique combinations. - normalize (bool, default False): - Return proportions rather than frequencies. - sort (bool, default True): - Sort by frequencies. - ascending (bool, default False): - Sort in ascending order. - dropna (bool, default True): - Don't include counts of rows that contain NA values. - - Returns: - Series or DataFrame: - Series if the groupby as_index is True, otherwise DataFrame. + DataFrame """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) diff --git a/third_party/bigframes_vendored/pandas/core/indexes/accessor.py b/third_party/bigframes_vendored/pandas/core/indexes/accessor.py index da5f9e3b88a..2b4a3263175 100644 --- a/third_party/bigframes_vendored/pandas/core/indexes/accessor.py +++ b/third_party/bigframes_vendored/pandas/core/indexes/accessor.py @@ -1,5 +1,3 @@ -from typing import Literal - from bigframes import constants @@ -10,24 +8,7 @@ class DatetimeProperties: @property def day(self): - """The day of the datetime. - - **Examples:** - - >>> s = bpd.Series( - ... pd.date_range("2000-01-01", periods=3, freq="D") - ... ) - >>> s - 0 2000-01-01 00:00:00 - 1 2000-01-02 00:00:00 - 2 2000-01-03 00:00:00 - dtype: timestamp[us][pyarrow] - >>> s.dt.day - 0 1 - 1 2 - 2 3 - dtype: Int64 - """ + """The day of the datetime.""" raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -36,342 +17,63 @@ def dayofweek(self): """The day of the week with Monday=0, Sunday=6. Return the day of the week. It is assumed the week starts on - Monday, which is denoted by 0 and ends on Sunday, which is denoted - by 6. - - **Examples:** - - >>> s = bpd.Series( - ... pd.date_range('2016-12-31', '2017-01-08', freq='D').to_series() - ... ) - >>> s.dt.dayofweek - 2016-12-31 00:00:00 5 - 2017-01-01 00:00:00 6 - 2017-01-02 00:00:00 0 - 2017-01-03 00:00:00 1 - 2017-01-04 00:00:00 2 - 2017-01-05 00:00:00 3 - 2017-01-06 00:00:00 4 - 2017-01-07 00:00:00 5 - 2017-01-08 00:00:00 6 - dtype: Int64 - - Returns: - Series: Containing integers indicating the day number. - """ - - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - @property - def day_of_week(self): - """The day of the week with Monday=0, Sunday=6. - - Return the day of the week. It is assumed the week starts on - Monday, which is denoted by 0 and ends on Sunday, which is denoted - by 6. - - **Examples:** - - >>> s = bpd.Series( - ... pd.date_range('2016-12-31', '2017-01-08', freq='D').to_series() - ... ) - >>> s.dt.day_of_week - 2016-12-31 00:00:00 5 - 2017-01-01 00:00:00 6 - 2017-01-02 00:00:00 0 - 2017-01-03 00:00:00 1 - 2017-01-04 00:00:00 2 - 2017-01-05 00:00:00 3 - 2017-01-06 00:00:00 4 - 2017-01-07 00:00:00 5 - 2017-01-08 00:00:00 6 - dtype: Int64 - - Returns: - Series: Containing integers indicating the day number. - """ - - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - @property - def weekday(self): - """The day of the week with Monday=0, Sunday=6. - - Return the day of the week. It is assumed the week starts on - Monday, which is denoted by 0 and ends on Sunday, which is denoted - by 6. - - **Examples:** - - >>> s = bpd.Series( - ... pd.date_range('2016-12-31', '2017-01-08', freq='D').to_series() - ... ) - >>> s.dt.weekday - 2016-12-31 00:00:00 5 - 2017-01-01 00:00:00 6 - 2017-01-02 00:00:00 0 - 2017-01-03 00:00:00 1 - 2017-01-04 00:00:00 2 - 2017-01-05 00:00:00 3 - 2017-01-06 00:00:00 4 - 2017-01-07 00:00:00 5 - 2017-01-08 00:00:00 6 - dtype: Int64 - - Returns: - Series: Containing integers indicating the day number. - """ - - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - @property - def day_name(self): - """ - Return the day names in english. - - **Examples:** - >>> s = bpd.Series(pd.date_range(start="2018-01-01", freq="D", periods=3)) - >>> s - 0 2018-01-01 00:00:00 - 1 2018-01-02 00:00:00 - 2 2018-01-03 00:00:00 - dtype: timestamp[us][pyarrow] - >>> s.dt.day_name() - 0 Monday - 1 Tuesday - 2 Wednesday - dtype: string - - Returns: - Series: Series of day names. - - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - @property - def dayofyear(self): - """The ordinal day of the year. - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> s = bpd.Series( - ... pd.date_range('2016-12-28', '2017-01-03', freq='D').to_series() - ... ) - >>> s.dt.dayofyear - 2016-12-28 00:00:00 363 - 2016-12-29 00:00:00 364 - 2016-12-30 00:00:00 365 - 2016-12-31 00:00:00 366 - 2017-01-01 00:00:00 1 - 2017-01-02 00:00:00 2 - 2017-01-03 00:00:00 3 - dtype: Int64 - - Returns: - Series: Containing integers indicating the day number. - """ - - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - @property - def day_of_year(self): - """The ordinal day of the year. - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> s = bpd.Series( - ... pd.date_range('2016-12-28', '2017-01-03', freq='D').to_series() - ... ) - >>> s.dt.day_of_year - 2016-12-28 00:00:00 363 - 2016-12-29 00:00:00 364 - 2016-12-30 00:00:00 365 - 2016-12-31 00:00:00 366 - 2017-01-01 00:00:00 1 - 2017-01-02 00:00:00 2 - 2017-01-03 00:00:00 3 - dtype: Int64 + Monday, which is denoted by 0 and ends on Sunday which is denoted + by 6. This method is available on both Series with datetime + values (using the `dt` accessor) or DatetimeIndex. Returns: - Series: Containing integers indicating the day number. + Series or Index: Containing integers indicating the day number. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @property def date(self): - """Returns a Series with the date part of Timestamps without time and + """Returns numpy array of Python :class:`datetime.date` objects. + + Namely, the date part of Timestamps without time and timezone information. .. warning:: This method returns a Series whereas pandas returns a numpy array. - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> s = bpd.Series(["1/1/2020 10:00:00+00:00", "2/1/2020 11:00:00+00:00"]) - >>> s = bpd.to_datetime(s, utc=True, format="%d/%m/%Y %H:%M:%S%Ez") - >>> s - 0 2020-01-01 10:00:00+00:00 - 1 2020-01-02 11:00:00+00:00 - dtype: timestamp[us, tz=UTC][pyarrow] - >>> s.dt.date - 0 2020-01-01 - 1 2020-01-02 - dtype: date32[day][pyarrow] """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @property def hour(self): - """The hours of the datetime. - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> s = bpd.Series( - ... pd.date_range("2000-01-01", periods=3, freq="h") - ... ) - >>> s - 0 2000-01-01 00:00:00 - 1 2000-01-01 01:00:00 - 2 2000-01-01 02:00:00 - dtype: timestamp[us][pyarrow] - >>> s.dt.hour - 0 0 - 1 1 - 2 2 - dtype: Int64 - """ + """The hours of the datetime.""" raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @property def minute(self): - """The minutes of the datetime. - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> s = bpd.Series( - ... pd.date_range("2000-01-01", periods=3, freq="min") - ... ) - >>> s - 0 2000-01-01 00:00:00 - 1 2000-01-01 00:01:00 - 2 2000-01-01 00:02:00 - dtype: timestamp[us][pyarrow] - >>> s.dt.minute - 0 0 - 1 1 - 2 2 - dtype: Int64 - """ + """The minutes of the datetime.""" raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @property def month(self): - """The month as January=1, December=12. - - **Examples:** - - >>> s = bpd.Series( - ... pd.date_range("2000-01-01", periods=3, freq="ME") - ... ) - >>> s - 0 2000-01-31 00:00:00 - 1 2000-02-29 00:00:00 - 2 2000-03-31 00:00:00 - dtype: timestamp[us][pyarrow] - >>> s.dt.month - 0 1 - 1 2 - 2 3 - dtype: Int64 - """ + """The month as January=1, December=12.""" raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - def isocalendar(self): - """ - Calculate year, week, and day according to the ISO 8601 standard. - - **Examples:** - - >>> s = bpd.Series( - ... pd.date_range('2009-12-27', '2010-01-04', freq='d').to_series() - ... ) - >>> s.dt.isocalendar() - year week day - 2009-12-27 00:00:00 2009 52 7 - 2009-12-28 00:00:00 2009 53 1 - 2009-12-29 00:00:00 2009 53 2 - 2009-12-30 00:00:00 2009 53 3 - 2009-12-31 00:00:00 2009 53 4 - 2010-01-01 00:00:00 2009 53 5 - 2010-01-02 00:00:00 2009 53 6 - 2010-01-03 00:00:00 2009 53 7 - 2010-01-04 00:00:00 2010 1 1 - - [9 rows x 3 columns] - - - Returns: DataFrame - With columns year, week and day. - - - """ - @property def second(self): - """The seconds of the datetime. - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> s = bpd.Series( - ... pd.date_range("2000-01-01", periods=3, freq="s") - ... ) - >>> s - 0 2000-01-01 00:00:00 - 1 2000-01-01 00:00:01 - 2 2000-01-01 00:00:02 - dtype: timestamp[us][pyarrow] - >>> s.dt.second - 0 0 - 1 1 - 2 2 - dtype: Int64 - """ + """The seconds of the datetime.""" raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @property def time(self): - """Returns a Series with the time part of the Timestamps. + """Returns numpy array of :class:`datetime.time` objects. + + The time part of the Timestamps. .. warning:: This method returns a Series whereas pandas returns a numpy array. - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> s = bpd.Series(["1/1/2020 10:00:00+00:00", "2/1/2020 11:00:00+00:00"]) - >>> s = bpd.to_datetime(s, utc=True, format="%m/%d/%Y %H:%M:%S%Ez") - >>> s - 0 2020-01-01 10:00:00+00:00 - 1 2020-02-01 11:00:00+00:00 - dtype: timestamp[us, tz=UTC][pyarrow] - >>> s.dt.time - 0 10:00:00 - 1 11:00:00 - dtype: time64[us][pyarrow] """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -380,172 +82,15 @@ def time(self): def quarter(self): """The quarter of the date. - **Examples:** - - >>> import bigframes.pandas as bpd - >>> s = bpd.Series(["1/1/2020 10:00:00+00:00", "4/1/2020 11:00:00+00:00"]) - >>> s = bpd.to_datetime(s, utc=True, format="%m/%d/%Y %H:%M:%S%Ez") - >>> s - 0 2020-01-01 10:00:00+00:00 - 1 2020-04-01 11:00:00+00:00 - dtype: timestamp[us, tz=UTC][pyarrow] - >>> s.dt.quarter - 0 1 - 1 2 - dtype: Int64 + .. warning:: + This method returns a Series whereas pandas returns + a numpy array. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @property def year(self): - """The year of the datetime. - - **Examples:** - - >>> s = bpd.Series( - ... pd.date_range("2000-01-01", periods=3, freq="YE") - ... ) - >>> s - 0 2000-12-31 00:00:00 - 1 2001-12-31 00:00:00 - 2 2002-12-31 00:00:00 - dtype: timestamp[us][pyarrow] - >>> s.dt.year - 0 2000 - 1 2001 - 2 2002 - dtype: Int64 - """ - - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - @property - def days(self): - """The numebr of days for each element - - **Examples:** - - >>> s = bpd.Series([pd.Timedelta("4d3m2s1us")]) - >>> s - 0 4 days 00:03:02.000001 - dtype: duration[us][pyarrow] - >>> s.dt.days - 0 4 - dtype: Int64 - """ - - @property - def seconds(self): - """Number of seconds (>= 0 and less than 1 day) for each element. - - **Examples:** - - >>> s = bpd.Series([pd.Timedelta("4d3m2s1us")]) - >>> s - 0 4 days 00:03:02.000001 - dtype: duration[us][pyarrow] - >>> s.dt.seconds - 0 182 - dtype: Int64 - """ - - @property - def microseconds(self): - """Number of microseconds (>= 0 and less than 1 second) for each element. - - **Examples:** - - >>> s = bpd.Series([pd.Timedelta("4d3m2s1us")]) - >>> s - 0 4 days 00:03:02.000001 - dtype: duration[us][pyarrow] - >>> s.dt.microseconds - 0 1 - dtype: Int64 - """ - - def total_seconds(self): - """Return total duration of each element expressed in seconds. - - **Examples:** - - >>> s = bpd.Series([pd.Timedelta("1d1m1s1us")]) - >>> s - 0 1 days 00:01:01.000001 - dtype: duration[us][pyarrow] - >>> s.dt.total_seconds() - 0 86461.000001 - dtype: Float64 - """ - - @property - def tz(self): - """Return the timezone. - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> s = bpd.Series(["1/1/2020 10:00:00+00:00", "2/1/2020 11:00:00+00:00"]) - >>> s = bpd.to_datetime(s, utc=True, format="%m/%d/%Y %H:%M:%S%Ez") - >>> s - 0 2020-01-01 10:00:00+00:00 - 1 2020-02-01 11:00:00+00:00 - dtype: timestamp[us, tz=UTC][pyarrow] - >>> s.dt.tz - datetime.timezone.utc - - Returns: - datetime.tzinfo, pytz.tzinfo.BaseTZInfo, dateutil.tz.tz.tzfile, or None - """ - - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) + """The year of the datetime.""" - @property - def tz_localize(self, tz: Literal["UTC"] | None): - """Localize tz-naive Datetime Array/Index to tz-aware Datetime Array/Index. - - This method takes a time zone (tz) naive Datetime Array/Index object and makes - this time zone aware. It does not move the time to another time zone. Only "UTC" - timezone is supported. - - This method can also be used to do the inverse - to create a time zone unaware - object from an aware object. To that end, pass tz=None. - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> s = bpd.Series([pd.Timestamp(year = 2026, month=1, day=1)]) - >>> s - 0 2026-01-01 00:00:00 - dtype: timestamp[us][pyarrow] - >>> s.dt.tz_localize('UTC') - 0 2026-01-01 00:00:00+00:00 - dtype: timestamp[us, tz=UTC][pyarrow] - - Returns: - A BigFrames series with the updated timezone. - """ - - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - @property - def unit(self) -> str: - """Returns the unit of time precision. - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> s = bpd.Series(["1/1/2020 10:00:00+00:00", "2/1/2020 11:00:00+00:00"]) - >>> s = bpd.to_datetime(s, utc=True, format="%m/%d/%Y %H:%M:%S%Ez") - >>> s - 0 2020-01-01 10:00:00+00:00 - 1 2020-02-01 11:00:00+00:00 - dtype: timestamp[us, tz=UTC][pyarrow] - >>> s.dt.unit - 'us' - - Returns: - Unit as string (eg. "us"). - """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) diff --git a/third_party/bigframes_vendored/pandas/core/indexes/base.py b/third_party/bigframes_vendored/pandas/core/indexes/base.py index 632026a3311..e8737341a34 100644 --- a/third_party/bigframes_vendored/pandas/core/indexes/base.py +++ b/third_party/bigframes_vendored/pandas/core/indexes/base.py @@ -1,10 +1,6 @@ # Contains code from https://github.com/pandas-dev/pandas/blob/main/pandas/core/indexes/base.py from __future__ import annotations -import typing -from collections.abc import Hashable - -import bigframes from bigframes import constants @@ -12,415 +8,54 @@ class Index: """Immutable sequence used for indexing and alignment. The basic object storing axis labels for all objects. - - Args: - data (pandas.Series | pandas.Index | bigframes.series.Series | bigframes.core.indexes.base.Index): - Labels (1-dimensional). - dtype: - Data type for the output Index. If not specified, this will be - inferred from `data`. - name: - Name to be stored in the index. - session (Optional[bigframes.session.Session]): - BigQuery DataFrames session where queries are run. If not set, - a default session is used. """ @property def name(self): - """Returns Index name. - - **Examples:** - - - >>> idx = bpd.Index([1, 2, 3], name='x') - >>> idx - Index([1, 2, 3], dtype='Int64', name='x') - >>> idx.name - 'x' - - Returns: - blocks.Label: - Index or MultiIndex name - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - @property - def names(self): - """Returns the names of the Index. - - Returns: - Sequence[blocks.Label]: - A Sequence of Index or MultiIndex name - """ + """Returns Index name.""" raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @property def values(self): - """Return an array representing the data in the Index. - - **Examples:** - - - >>> idx = bpd.Index([1, 2, 3]) - >>> idx - Index([1, 2, 3], dtype='Int64') - - >>> idx.values - array([1, 2, 3]) - - Returns: - array: - Numpy.ndarray or ExtensionArray - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - @property - def ndim(self): - """ - Number of dimensions of the underlying data, by definition 1. - - **Examples:** - - - >>> s = bpd.Series(['Ant', 'Bear', 'Cow']) - >>> s - 0 Ant - 1 Bear - 2 Cow - dtype: string - - >>> s.ndim - 1 - - For Index: - - >>> idx = bpd.Index([1, 2, 3]) - >>> idx - Index([1, 2, 3], dtype='Int64') - - >>> idx.ndim - 1 - - Returns: - int: - Number or dimensions. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - @property - def size(self) -> int: - """ - Return the number of elements in the underlying data. - - **Examples:** - - - For Series: - - >>> s = bpd.Series(['Ant', 'Bear', 'Cow']) - >>> s - 0 Ant - 1 Bear - 2 Cow - dtype: string - - For Index: - - >>> idx = bpd.Index([1, 2, 3]) - >>> idx - Index([1, 2, 3], dtype='Int64') - - Returns: - int: - Number of elements - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - @property - def empty(self) -> bool: - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - @property - def is_monotonic_increasing(self) -> bool: - """ - Return a boolean if the values are equal or increasing. - - **Examples:** - - - >>> bool(bpd.Index([1, 2, 3]).is_monotonic_increasing) - True - - >>> bool(bpd.Index([1, 2, 2]).is_monotonic_increasing) - True - - >>> bool(bpd.Index([1, 3, 2]).is_monotonic_increasing) - False - - Returns: - bool: - True, if the values monotonically increasing, otherwise False. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - @property - def is_monotonic_decreasing(self) -> bool: - """ - Return a boolean if the values are equal or decreasing. - - **Examples:** - - - >>> bool(bpd.Index([3, 2, 1]).is_monotonic_decreasing) - True - - >>> bool(bpd.Index([3, 2, 2]).is_monotonic_decreasing) - True - - >>> bool(bpd.Index([3, 1, 2]).is_monotonic_decreasing) - False - - Returns: - bool: - True, if the values monotonically decreasing, otherwise False. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - @classmethod - def from_frame(cls, frame) -> Index: - """ - Make a MultiIndex from a DataFrame. - - **Examples:** - - - >>> df = bpd.DataFrame([['HI', 'Temp'], ['HI', 'Precip'], - ... ['NJ', 'Temp'], ['NJ', 'Precip']], - ... columns=['a', 'b']) - >>> df - a b - 0 HI Temp - 1 HI Precip - 2 NJ Temp - 3 NJ Precip - - [4 rows x 2 columns] - - >>> bpd.MultiIndex.from_frame(df) - Index([0, 1, 2, 3], dtype='Int64') - - Args: - frame (Union[bigframes.pandas.Series, bigframes.pandas.DataFrame]): - bigframes.pandas.Series or bigframes.pandas.DataFrame to convert - to bigframes.pandas.Index. - - Returns: - bigframes.pandas.Index: - The Index representation of the given Series or DataFrame. - - Raises: - bigframes.exceptions.NullIndexError: - If Index is Null. - """ + """Return an array representing the data in the Index.""" raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @property def shape(self): """ Return a tuple of the shape of the underlying data. - - **Examples:** - - - >>> idx = bpd.Index([1, 2, 3]) - >>> idx - Index([1, 2, 3], dtype='Int64') - - >>> idx.shape - (3,) - - Returns: - Tuple[int]: - A Tuple of integers representing the shape. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @property def nlevels(self) -> int: - """Integer number of levels in this MultiIndex - - **Examples:** - - - >>> mi = bpd.MultiIndex.from_arrays([['a'], ['b'], ['c']]) - >>> mi - MultiIndex([('a', 'b', 'c')], - ) - >>> mi.nlevels - 3 - - Returns: - int: - Number of levels. - """ + """Number of levels.""" raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @property def is_unique(self) -> bool: - """Return if the index has unique values. - - **Examples:** - - - >>> idx = bpd.Index([1, 5, 7, 7]) - >>> idx.is_unique - False - - >>> idx = bpd.Index([1, 5, 7]) - >>> idx.is_unique - True - - Returns: - bool: - True if the index has unique values, otherwise False. - """ + """Return if the index has unique values.""" raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @property def has_duplicates(self) -> bool: - """Check if the Index has duplicate values. - - **Examples:** - - - >>> idx = bpd.Index([1, 5, 7, 7]) - >>> bool(idx.has_duplicates) - True - - >>> idx = bpd.Index([1, 5, 7]) - >>> bool(idx.has_duplicates) - False - - Returns: - bool: - Whether or not the Index has duplicate values. - """ + """Check if the Index has duplicate values.""" raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @property def dtype(self): - """Return the dtype object of the underlying data. - - **Examples:** - - - >>> idx = bpd.Index([1, 2, 3]) - >>> idx - Index([1, 2, 3], dtype='Int64') + """Return the dtype object of the underlying data.""" - >>> idx.dtype - Int64Dtype() - """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @property def dtypes(self): - """Return the dtypes as a Series for the underlying MultiIndex. - - Returns: - Pandas.Series: - Pandas.Series of the MultiIndex dtypes. - """ + """Return the dtypes as a Series for the underlying MultiIndex.""" raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @property def T(self) -> Index: - """Return the transpose, which is by definition self. - - **Examples:** - - - >>> s = bpd.Series(['Ant', 'Bear', 'Cow']) - >>> s - 0 Ant - 1 Bear - 2 Cow - dtype: string - - >>> s.T - 0 Ant - 1 Bear - 2 Cow - dtype: string - - For Index: - - >>> idx = bpd.Index([1, 2, 3]) - >>> idx.T - Index([1, 2, 3], dtype='Int64') - - Returns: - bigframes.pandas.Index: - Index - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - @property - def str(self): - """ - Vectorized string functions for Series and Index. - - NAs stay NA unless handled otherwise by a particular method. Patterned - after Python’s string methods, with some inspiration from R’s stringr package. - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> s = bpd.Series(["A_Str_Series"]) - >>> s - 0 A_Str_Series - dtype: string - - >>> s.str.lower() - 0 a_str_series - dtype: string - - >>> s.str.replace("_", "") - 0 AStrSeries - dtype: string - - Returns: - bigframes.operations.strings.StringMethods: - An accessor containing string methods. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def copy( - self, - name=None, - ) -> Index: - """ - Make a copy of this object. - - Name is set on the new object. - - **Examples:** - - - >>> idx = bpd.Index(['a', 'b', 'c']) - >>> new_idx = idx.copy() - >>> idx is new_idx - False - - Args: - name (Label, optional): - Set name for new object. - - Returns: - bigframes.pandas.Index: - Index reference to new object, which is a copy of this object. - """ + """Return the transpose, which is by definition self.""" raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) def transpose(self) -> Index: @@ -428,7 +63,7 @@ def transpose(self) -> Index: Return the transpose, which is by definition self. Returns: - bigframes.pandas.Index + Index """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -438,122 +73,11 @@ def astype(self, dtype): The class of a new Index is determined by dtype. When conversion is impossible, a TypeError exception is raised. - **Examples:** - - - >>> idx = bpd.Index([1, 2, 3]) - >>> idx - Index([1, 2, 3], dtype='Int64') - - - Args: - dtype (str, data type, or pandas.ExtensionDtype): - A dtype supported by BigQuery DataFrame include ``'boolean'``, - ``'Float64'``, ``'Int64'``, ``'int64\\[pyarrow\\]'``, - ``'string'``, ``'string\\[pyarrow\\]'``, - ``'timestamp\\[us, tz=UTC\\]\\[pyarrow\\]'``, - ``'timestamp\\[us\\]\\[pyarrow\\]'``, - ``'date32\\[day\\]\\[pyarrow\\]'``, - ``'time64\\[us\\]\\[pyarrow\\]'``. - A pandas.ExtensionDtype include ``pandas.BooleanDtype()``, - ``pandas.Float64Dtype()``, ``pandas.Int64Dtype()``, - ``pandas.StringDtype(storage="pyarrow")``, - ``pd.ArrowDtype(pa.date32())``, - ``pd.ArrowDtype(pa.time64("us"))``, - ``pd.ArrowDtype(pa.timestamp("us"))``, - ``pd.ArrowDtype(pa.timestamp("us", tz="UTC"))``. - errors ({'raise', 'null'}, default 'raise'): - Control raising of exceptions on invalid data for provided dtype. - If 'raise', allow exceptions to be raised if any value fails cast - If 'null', will assign null value if value fails cast - - Returns: - bigframes.pandas.Index: Index with values cast to specified dtype. - - Raises: - ValueError: - If ``errors`` is not one of ``raise``. - TypeError: - MultiIndex with more than 1 level does not support ``astype``. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def get_level_values(self, level) -> Index: - """ - Return an Index of values for requested level. - - This is primarily useful to get an individual level of values from a - MultiIndex, but is provided on Index as well for compatibility. - - **Examples:** - - - >>> idx = bpd.Index(list('abc')) - >>> idx - Index(['a', 'b', 'c'], dtype='string') - - Get level values by supplying level as integer: - - >>> idx.get_level_values(0) - Index(['a', 'b', 'c'], dtype='string') - Args: - level (int or str): - It is either the integer position or the name of the level. + dtype (numpy dtype or pandas type): Returns: - bigframes.pandas.Index: - Calling object, as there is only one level in the Index. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def to_series(self): - """ - Create a Series with both index and values equal to the index keys. - - Useful with map for returning an indexer based on an index. - - **Examples:** - - - >>> idx = bpd.Index(['Ant', 'Bear', 'Cow'], name='animal') - - By default, the original index and original name is reused. - - >>> idx.to_series() - animal - Ant Ant - Bear Bear - Cow Cow - Name: animal, dtype: string - - To enforce a new index, specify new labels to index: - - >>> idx.to_series(index=[0, 1, 2]) - 0 Ant - 1 Bear - 2 Cow - Name: animal, dtype: string - - To override the name of the resulting column, specify name: - - >>> idx.to_series(name='zoo') - animal - Ant Ant - Bear Bear - Cow Cow - Name: zoo, dtype: string - - Args: - index (Index, optional): - Index of resulting Series. If None, defaults to original index. - name (str, optional): - Name of resulting Series. If None, defaults to name of original - index. - - Returns: - bigframes.pandas.Series: - The dtype will be based on the type of the Index values. + Index: Index with values cast to specified dtype. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -561,207 +85,60 @@ def isin(self, values): """ Return a boolean array where the index values are in `values`. - Compute boolean array to check whether each index value is found in the + Compute boolean array of whether each index value is found in the passed set of values. The length of the returned boolean array matches the length of the index. - **Examples:** - - - >>> idx = bpd.Index([1,2,3]) - >>> idx - Index([1, 2, 3], dtype='Int64') - - Check whether each index value in a list of values. - - >>> idx.isin([1, 4]) - Index([True, False, False], dtype='boolean') - - >>> midx = bpd.MultiIndex.from_arrays([[1,2,3], - ... ['red', 'blue', 'green']], - ... names=('number', 'color')) - >>> midx - MultiIndex([(1, 'red'), - (2, 'blue'), - (3, 'green')], - names=['number', 'color']) - Args: values (set or list-like): Sought values. Returns: - bigframes.pandas.Series: - Series of boolean values. - - Raises: - TypeError: - If object passed to ``isin()`` is not a list-like + Series: Series of boolean values. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) def all(self) -> bool: """Return whether all elements are Truthy. - **Examples:** - - - True, because nonzero integers are considered True. - - >>> bool(bpd.Index([1, 2, 3]).all()) - True - - False, because 0 is considered False. - - >>> bool(bpd.Index([0, 1, 2]).all()) - False - Returns: - bool: - A single element array-like may be converted to bool. - - Raises: - TypeError: - MultiIndex with more than 1 level does not support ``all``. + bool: A single element array-like may be converted to bool. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) def any(self) -> bool: """Return whether any element is Truthy. - **Examples:** - - - >>> index = bpd.Index([0, 1, 2]) - >>> bool(index.any()) - True - - >>> index = bpd.Index([0, 0, 0]) - >>> bool(index.any()) - False - Returns: - bool: - A single element array-like may be converted to bool. - - Raises: - TypeError: - MultiIndex with more than 1 level does not support ``any``. + bool: A single element array-like may be converted to bool. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) def min(self): """Return the minimum value of the Index. - **Examples:** - - - >>> idx = bpd.Index([3, 2, 1]) - >>> int(idx.min()) - 1 - - >>> idx = bpd.Index(['c', 'b', 'a']) - >>> idx.min() - 'a' - Returns: - scalar: - Minimum value. + scalar: Minimum value. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) def max(self): """Return the maximum value of the Index. - **Examples:** - - - >>> idx = bpd.Index([3, 2, 1]) - >>> int(idx.max()) - 3 - - >>> idx = bpd.Index(['c', 'b', 'a']) - >>> idx.max() - 'c' - Returns: - scalar: - Maximum value. + scalar: Maximum value. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) def argmin(self) -> int: """ - Return int position of the smallest value in the series. + Return int position of the smallest value in the Series. If the minimum is achieved in multiple locations, the first row position is returned. - **Examples:** - - - Consider dataset containing cereal calories - - >>> s = bpd.Series({'Corn Flakes': 100.0, 'Almond Delight': 110.0, - ... 'Cinnamon Toast Crunch': 120.0, 'Cocoa Puff': 110.0}) - >>> s - Corn Flakes 100.0 - Almond Delight 110.0 - Cinnamon Toast Crunch 120.0 - Cocoa Puff 110.0 - dtype: Float64 - - >>> int(s.argmax()) - 2 - - >>> int(s.argmin()) - 0 - - The maximum cereal calories is the third element and the minimum - cereal calories is the first element, since series is zero-indexed. - Returns: - int: - Row position of the minimum value. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def get_loc( - self, key: typing.Any - ) -> typing.Union[int, slice, bigframes.series.Series]: - """ - Get integer location, slice or boolean mask for requested label. - - **Examples:** - - - >>> unique_index = bpd.Index(list('abc')) - >>> unique_index.get_loc('b') - 1 - - >>> monotonic_index = bpd.Index(list('abbc')) - >>> monotonic_index.get_loc('b') - slice(1, 3, None) - - >>> non_monotonic_index = bpd.Index(list('abcb')) - >>> non_monotonic_index.get_loc('b') - 0 False - 1 True - 2 False - 3 True - dtype: boolean - - Args: - key: Label to get the location for. - - Returns: - Union[int, slice, bigframes.pandas.Series]: - Integer position of the label for unique indexes. - Slice object for monotonic indexes with duplicates. - Boolean Series mask for non-monotonic indexes with duplicates. - - Raises: - KeyError: If the key is not found in the index. + int: Row position of the minimum value. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -772,32 +149,8 @@ def argmax(self) -> int: If the maximum is achieved in multiple locations, the first row position is returned. - **Examples:** - - Consider dataset containing cereal calories - - - >>> s = bpd.Series({'Corn Flakes': 100.0, 'Almond Delight': 110.0, - ... 'Cinnamon Toast Crunch': 120.0, 'Cocoa Puff': 110.0}) - >>> s - Corn Flakes 100.0 - Almond Delight 110.0 - Cinnamon Toast Crunch 120.0 - Cocoa Puff 110.0 - dtype: Float64 - - >>> int(s.argmax()) - 2 - - >>> int(s.argmin()) - 0 - - The maximum cereal calories is the third element and the minimum - cereal calories is the first element, since series is zero-indexed. - Returns: - int: - Row position of the maximum value. + int: Row position of the maximum value. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -806,33 +159,13 @@ def nunique(self) -> int: Excludes NA values by default. - **Examples:** - - - >>> s = bpd.Series([1, 3, 5, 7, 7]) - >>> s - 0 1 - 1 3 - 2 5 - 3 7 - 4 7 - dtype: Int64 - - >>> int(s.nunique()) - 4 - Returns: - int: - Number of unique elements + int """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) def sort_values( - self, - *, - ascending: bool = True, - kind: str | None = None, - na_position: str = "last", + self, *, ascending: bool = True, na_position: str = "last" ) -> Index: """ Return a sorted copy of the index. @@ -840,35 +173,15 @@ def sort_values( Return a sorted copy of the index, and optionally return the indices that sorted the index itself. - **Examples:** - - - >>> idx = bpd.Index([10, 100, 1, 1000]) - >>> idx - Index([10, 100, 1, 1000], dtype='Int64') - - Sort values in ascending order (default behavior). - - >>> idx.sort_values() - Index([1, 10, 100, 1000], dtype='Int64') - Args: ascending (bool, default True): Should the index values be sorted in an ascending order. - kind (str, default None): - Choice of sorting algorithm. Accepts 'quicksort', 'mergesort', - 'heapsort', 'stable'. Ignored except when determining whether to - sort stably. 'mergesort' or 'stable' will result in stable reorder. na_position ({'first' or 'last'}, default 'last'): Argument 'first' puts NaNs at the beginning, 'last' puts NaNs at the end. Returns: pandas.Index: Sorted copy of the index. - - Raises: - ValueError: - If ``no_position`` is not one of ``first`` or ``last``. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -886,43 +199,9 @@ def value_counts( first element is the most frequently-occurring element. Excludes NA values by default. - **Examples:** - - - >>> index = bpd.Index([3, 1, 2, 3, 4, np.nan]) - >>> index.value_counts() - 3.0 2 - 1.0 1 - 2.0 1 - 4.0 1 - Name: count, dtype: Int64 - - With normalize set to True, returns the relative frequency by - dividing all values by the sum of values. - - >>> s = bpd.Series([3, 1, 2, 3, 4, np.nan]) - >>> s.value_counts(normalize=True) - 3.0 0.4 - 1.0 0.2 - 2.0 0.2 - 4.0 0.2 - Name: proportion, dtype: Float64 - - ``dropna`` - - With dropna set to False we can also see NaN index values. - - >>> s.value_counts(dropna=False) - 3.0 2 - 1.0 1 - 2.0 1 - 4.0 1 - 1 - Name: count, dtype: Int64 - Args: normalize (bool, default False): - If True, then the object returned will contain the relative + If True then the object returned will contain the relative frequencies of the unique values. sort (bool, default True): Sort by frequencies. @@ -932,29 +211,13 @@ def value_counts( Don't include counts of NaN. Returns: - bigframes.pandas.Series + Series """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) def fillna(self, value) -> Index: """ - Fill NA (NULL in BigQuery) values using the specified method. - - Note that empty strings ``''``, :attr:`numpy.inf`, and - :attr:`numpy.nan` are ***not*** considered NA values. This NA/NULL - logic differs from numpy, but it is the same as BigQuery and the - :class:`pandas.ArrowDtype`. - - **Examples:** - - >>> idx = bpd.Index( - ... pa.array([None, np.nan, 3, None], type=pa.float64()), - ... dtype=pd.ArrowDtype(pa.float64()), - ... ) - >>> idx - Index([, nan, 3.0, ], dtype='Float64') - >>> idx.fillna(0) - Index([0.0, nan, 3.0, 0.0], dtype='Float64') + Fill NA/NaN values with the specified value. Args: value (scalar): @@ -962,42 +225,23 @@ def fillna(self, value) -> Index: This value cannot be a list-likes. Returns: - bigframes.pandas.Index - - Raises: - TypeError: - MultiIndex with more than 1 level does not support ``fillna``. + Index """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - def rename(self, name, *, inplace): + def rename(self, name) -> Index: """ Alter Index or MultiIndex name. Able to set new names without level. Defaults to returning new index. Length of names must match number of levels in MultiIndex. - **Examples:** - - - >>> idx = bpd.Index(['A', 'C', 'A', 'B'], name='score') - >>> idx.rename('grade') - Index(['A', 'C', 'A', 'B'], dtype='string', name='grade') - Args: name (label or list of labels): Name(s) to set. - inplace (bool): - Default False. Modifies the object directly, instead of - creating a new Index or MultiIndex. Returns: - bigframes.pandas.Index | None: - The same type as the caller or None if ``inplace=True``. - - Raises: - ValueError: - If ``name`` is not the same length as levels. + Index: The same type as the caller. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -1005,42 +249,24 @@ def drop(self, labels) -> Index: """ Make new Index with passed list of labels deleted. - **Examples:** - - - >>> idx = bpd.Index(['a', 'b', 'c']) - >>> idx.drop(['a']) - Index(['b', 'c'], dtype='string') - Args: labels (array-like or scalar): Returns: - bigframes.pandas.Index: Will be same type as self. + Index: Will be same type as self """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - def dropna(self, how: typing.Literal["all", "any"] = "any"): + def dropna(self, how: str = "any"): """Return Index without NA/NaN values. - **Examples:** - - - >>> idx = bpd.Index([1, np.nan, 3]) - >>> idx.dropna() - Index([1.0, 3.0], dtype='Float64') - Args: how ({'any', 'all'}, default 'any'): If the Index is a MultiIndex, drop the value when any or all levels are NaN. Returns: - bigframes.pandas.Index - - Raises: - ValueError: - If ``how`` is not ``any`` or ``all`` + Index """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -1048,32 +274,6 @@ def drop_duplicates(self, *, keep: str = "first"): """ Return Index with duplicate values removed. - **Examples:** - - >>> import bigframes.pandas as bpd - - Generate an pandas.Index with duplicate values. - - >>> idx = bpd.Index(['lama', 'cow', 'lama', 'beetle', 'lama', 'hippo']) - - The keep parameter controls which duplicate values are removed. - The value ``first`` keeps the first occurrence for each set of - duplicated entries. The default value of keep is ``first``. - - >>> idx.drop_duplicates(keep='first') - Index(['lama', 'cow', 'beetle', 'hippo'], dtype='string') - - The value ``last`` keeps the last occurrence for each set of - duplicated entries. - - >>> idx.drop_duplicates(keep='last') - Index(['cow', 'beetle', 'lama', 'hippo'], dtype='string') - - The value ``False`` discards all sets of duplicated entries. - - >>> idx.drop_duplicates(keep=False) - Index(['cow', 'beetle', 'hippo'], dtype='string') - Args: keep ({'first', 'last', ``False``}, default 'first'): One of: @@ -1082,57 +282,17 @@ def drop_duplicates(self, *, keep: str = "first"): ``False`` : Drop all duplicates. Returns: - bigframes.pandas.Index - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def unique(self, level: Hashable | int | None = None): - """ - Returns unique values in the index. - - **Examples:** - - >>> idx = bpd.Index([1, 1, 2, 3, 3]) - >>> idx.unique() - Index([1, 2, 3], dtype='Int64') - - Args: - level (int or hashable, optional): - Only return values from specified level (for MultiIndex). - If int, gets the level by integer position, else by level name. - - Returns: - bigframes.pandas.Index - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def item(self, *args, **kwargs): - """Return the first element of the underlying data as a Python scalar. - - **Examples:** - - >>> s = bpd.Series([1], index=['a']) - >>> s.index.item() - 'a' - - Returns: - scalar: The first element of Index. - - Raises: - ValueError: If the data is not length = 1. + Index """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - def to_numpy(self, dtype, *, allow_large_results=None): + def to_numpy(self, dtype): """ A NumPy ndarray representing the values in this Series or Index. Args: dtype: The dtype to pass to :meth:`numpy.asarray`. - allow_large_results (bool, default None): - If not None, overrides the global setting to allow or disallow - large query results over the default size limit of 10 GB. **kwargs: Additional keywords passed through to the ``to_numpy`` method of the underlying array (for extension arrays). diff --git a/third_party/bigframes_vendored/pandas/core/indexes/datetimes.py b/third_party/bigframes_vendored/pandas/core/indexes/datetimes.py deleted file mode 100644 index f22554e174d..00000000000 --- a/third_party/bigframes_vendored/pandas/core/indexes/datetimes.py +++ /dev/null @@ -1,88 +0,0 @@ -# Contains code from https://github.com/pandas-dev/pandas/blob/main/pandas/core/indexes/datetimes.py - -from __future__ import annotations - -from bigframes_vendored import constants -from bigframes_vendored.pandas.core.indexes import base - - -class DatetimeIndex(base.Index): - """Immutable sequence used for indexing and alignment with datetime-like values""" - - @property - def year(self) -> base.Index: - """The year of the datetime - - **Examples:** - - - >>> idx = bpd.Index([pd.Timestamp("20250215")]) - >>> idx.year - Index([2025], dtype='Int64') - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - @property - def month(self) -> base.Index: - """The month as January=1, December=12. - - **Examples:** - - - >>> idx = bpd.Index([pd.Timestamp("20250215")]) - >>> idx.month - Index([2], dtype='Int64') - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - @property - def day(self) -> base.Index: - """The day of the datetime. - - **Examples:** - - - >>> idx = bpd.Index([pd.Timestamp("20250215")]) - >>> idx.day - Index([15], dtype='Int64') - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - @property - def day_of_week(self) -> base.Index: - """The day of the week with Monday=0, Sunday=6. - - **Examples:** - - - >>> idx = bpd.Index([pd.Timestamp("20250215")]) - >>> idx.day_of_week - Index([5], dtype='Int64') - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - @property - def dayofweek(self) -> base.Index: - """The day of the week with Monday=0, Sunday=6. - - **Examples:** - - - >>> idx = bpd.Index([pd.Timestamp("20250215")]) - >>> idx.dayofweek - Index([5], dtype='Int64') - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - @property - def weekday(self) -> base.Index: - """The day of the week with Monday=0, Sunday=6. - - **Examples:** - - - >>> idx = bpd.Index([pd.Timestamp("20250215")]) - >>> idx.weekday - Index([5], dtype='Int64') - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) diff --git a/third_party/bigframes_vendored/pandas/core/indexes/multi.py b/third_party/bigframes_vendored/pandas/core/indexes/multi.py deleted file mode 100644 index 018e638de35..00000000000 --- a/third_party/bigframes_vendored/pandas/core/indexes/multi.py +++ /dev/null @@ -1,84 +0,0 @@ -# Contains code from https://github.com/pandas-dev/pandas/blob/main/pandas/core/indexes/multi.py -from __future__ import annotations - -from typing import Hashable, Iterable, Sequence - -import bigframes_vendored.pandas.core.indexes.base - -from bigframes import constants - - -class MultiIndex(bigframes_vendored.pandas.core.indexes.base.Index): - """ - A multi-level, or hierarchical, index object for pandas objects. - """ - - @classmethod - def from_tuples( - cls, - tuples: Iterable[tuple[Hashable, ...]], - sortorder: int | None = None, - names: Sequence[Hashable] | Hashable | None = None, - ) -> MultiIndex: - """ - Convert list of tuples to MultiIndex. - - **Examples:** - - >>> tuples = [(1, 'red'), (1, 'blue'), - ... (2, 'red'), (2, 'blue')] - >>> bpd.MultiIndex.from_tuples(tuples, names=('number', 'color')) - MultiIndex([(1, 'red'), - (1, 'blue'), - (2, 'red'), - (2, 'blue')], - names=['number', 'color']) - - Args: - tuples (list / sequence of tuple-likes): - Each tuple is the index of one row/column. - sortorder (int or None): - Level of sortedness (must be lexicographically sorted by that - level). - names (list / sequence of str, optional): - Names for the levels in the index. - - Returns: - MultiIndex - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - @classmethod - def from_arrays( - cls, - arrays, - sortorder: int | None = None, - names=None, - ) -> MultiIndex: - """ - Convert arrays to MultiIndex. - - **Examples:** - - >>> arrays = [[1, 1, 2, 2], ['red', 'blue', 'red', 'blue']] - >>> bpd.MultiIndex.from_arrays(arrays, names=('number', 'color')) - MultiIndex([(1, 'red'), - (1, 'blue'), - (2, 'red'), - (2, 'blue')], - names=['number', 'color']) - - Args: - arrays (list / sequence of array-likes): - Each array-like gives one level's value for each data point. - len(arrays) is the number of levels. - sortorder (int or None): - Level of sortedness (must be lexicographically sorted by that - level). - names (list / sequence of str, optional): - Names for the levels in the index. - - Returns: - MultiIndex - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) diff --git a/third_party/bigframes_vendored/pandas/core/indexing.py b/third_party/bigframes_vendored/pandas/core/indexing.py index a188c7197e2..fae5d6261f2 100644 --- a/third_party/bigframes_vendored/pandas/core/indexing.py +++ b/third_party/bigframes_vendored/pandas/core/indexing.py @@ -1,6 +1,6 @@ # Contains code from https://github.com/pandas-dev/pandas/blob/main/pandas/core/indexing.py -import bigframes_vendored.constants as constants +from bigframes import constants class IndexingMixin: diff --git a/third_party/bigframes_vendored/pandas/core/reshape/concat.py b/third_party/bigframes_vendored/pandas/core/reshape/concat.py index 0a6c4153bd7..b0472c524ab 100644 --- a/third_party/bigframes_vendored/pandas/core/reshape/concat.py +++ b/third_party/bigframes_vendored/pandas/core/reshape/concat.py @@ -2,7 +2,6 @@ """ Concat routines """ - from __future__ import annotations from bigframes import constants @@ -15,7 +14,8 @@ def concat( join: str = "outer", ignore_index: bool = False, ): - """Concatenate BigQuery DataFrames objects along a particular axis. + """ + Concatenate BigQuery DataFrames objects along a particular axis. Allows optional set logic along the other axes. @@ -23,116 +23,118 @@ def concat( which may be useful if the labels are the same (or overlapping) on the passed axis number. - .. note:: - It is not recommended to build DataFrames by adding single rows in a - for loop. Build a list of rows and make a DataFrame in a single concat. - - **Examples:** - - >>> import bigframes.pandas as pd - >>> pd.options.display.progress_bar = None - + Parameters + ---------- + objs: + Objects to concatenate. Any None objects will be dropped silently unless + they are all None in which case a ValueError will be raised. + axis : {0/'index', 1/'columns'}, default 0 + The axis to concatenate along. + join: {'inner', 'outer'}, default 'outer' + How to handle indexes on other axis (or axes). + ignore_index : bool, default False + If True, do not use the index values along the concatenation axis. The + resulting axis will be labeled 0, ..., n - 1. This is useful if you are + concatenating objects where the concatenation axis does not have + meaningful indexing information. Note the index values on the other + axes are still respected in the join. + + Returns + ------- + object, type of objs + When concatenating all ``Series`` along the index (axis=0), a + ``Series`` is returned. When ``objs`` contains at least one + ``DataFrame``, a ``DataFrame`` is returned. + + Notes + ----- + It is not recommended to build DataFrames by adding single rows in a + for loop. Build a list of rows and make a DataFrame in a single concat. + + Examples + -------- Combine two ``Series``. - >>> s1 = pd.Series(['a', 'b']) - >>> s2 = pd.Series(['c', 'd']) - >>> pd.concat([s1, s2]) - 0 a - 1 b - 0 c - 1 d - dtype: string + >>> import bigframes.pandas as pd + >>> pd.options.display.progress_bar = None + >>> s1 = pd.Series(['a', 'b']) + >>> s2 = pd.Series(['c', 'd']) + >>> pd.concat([s1, s2]) + 0 a + 1 b + 0 c + 1 d + dtype: string Clear the existing index and reset it in the result by setting the ``ignore_index`` option to ``True``. - >>> pd.concat([s1, s2], ignore_index=True) - 0 a - 1 b - 2 c - 3 d - dtype: string + >>> pd.concat([s1, s2], ignore_index=True) + 0 a + 1 b + 2 c + 3 d + dtype: string Combine two ``DataFrame`` objects with identical columns. - >>> df1 = pd.DataFrame([['a', 1], ['b', 2]], - ... columns=['letter', 'number']) - >>> df1 - letter number - 0 a 1 - 1 b 2 - - [2 rows x 2 columns] - >>> df2 = pd.DataFrame([['c', 3], ['d', 4]], - ... columns=['letter', 'number']) - >>> df2 - letter number - 0 c 3 - 1 d 4 - - [2 rows x 2 columns] - >>> pd.concat([df1, df2]) - letter number - 0 a 1 - 1 b 2 - 0 c 3 - 1 d 4 - - [4 rows x 2 columns] + >>> df1 = pd.DataFrame([['a', 1], ['b', 2]], + ... columns=['letter', 'number']) + >>> df1 + letter number + 0 a 1 + 1 b 2 + + [2 rows x 2 columns] + >>> df2 = pd.DataFrame([['c', 3], ['d', 4]], + ... columns=['letter', 'number']) + >>> df2 + letter number + 0 c 3 + 1 d 4 + + [2 rows x 2 columns] + >>> pd.concat([df1, df2]) + letter number + 0 a 1 + 1 b 2 + 0 c 3 + 1 d 4 + + [4 rows x 2 columns] Combine ``DataFrame`` objects with overlapping columns and return everything. Columns outside the intersection will be filled with ``NaN`` values. - >>> df3 = pd.DataFrame([['c', 3, 'cat'], ['d', 4, 'dog']], - ... columns=['letter', 'number', 'animal']) - >>> df3 - letter number animal - 0 c 3 cat - 1 d 4 dog - - [2 rows x 3 columns] - >>> pd.concat([df1, df3]) - letter number animal - 0 a 1 - 1 b 2 - 0 c 3 cat - 1 d 4 dog - - [4 rows x 3 columns] + >>> df3 = pd.DataFrame([['c', 3, 'cat'], ['d', 4, 'dog']], + ... columns=['letter', 'number', 'animal']) + >>> df3 + letter number animal + 0 c 3 cat + 1 d 4 dog + + [2 rows x 3 columns] + >>> pd.concat([df1, df3]) + letter number animal + 0 a 1 + 1 b 2 + 0 c 3 cat + 1 d 4 dog + + [4 rows x 3 columns] Combine ``DataFrame`` objects with overlapping columns and return only those that are shared by passing ``inner`` to the ``join`` keyword argument. - >>> pd.concat([df1, df3], join="inner") - letter number - 0 a 1 - 1 b 2 - 0 c 3 - 1 d 4 - - [4 rows x 2 columns] - - Args: - objs (list of objects): - Objects to concatenate. Any None objects will be dropped silently unless - they are all None in which case a ValueError will be raised. - axis ({0 or 'index', 1 or 'columns'}, default 0): - The axis to concatenate along. - join ({'inner', 'outer'}, default 'outer'): - How to handle indexes on other axis (or axes). - ignore_index (bool, default False): - If True, do not use the index values along the concatenation axis. The - resulting axis will be labeled 0, ..., n - 1. This is useful if you are - concatenating objects where the concatenation axis does not have - meaningful indexing information. Note the index values on the other - axes are still respected in the join. - - Returns: - object, type of objs: - When concatenating all ``Series`` along the index (axis=0), a - ``Series`` is returned. When ``objs`` contains at least one - ``DataFrame``, a ``DataFrame`` is returned. + >>> pd.concat([df1, df3], join="inner") + letter number + 0 a 1 + 1 b 2 + 0 c 3 + 1 d 4 + + [4 rows x 2 columns] """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) diff --git a/third_party/bigframes_vendored/pandas/core/reshape/encoding.py b/third_party/bigframes_vendored/pandas/core/reshape/encoding.py index 8d3b26a2a2b..da92b58f505 100644 --- a/third_party/bigframes_vendored/pandas/core/reshape/encoding.py +++ b/third_party/bigframes_vendored/pandas/core/reshape/encoding.py @@ -2,7 +2,6 @@ """ Encoding routines """ - from __future__ import annotations from bigframes import constants @@ -26,7 +25,6 @@ def get_dummies( prepended to the value. **Examples:** - >>> import bigframes.pandas as pd >>> pd.options.display.progress_bar = None >>> s = pd.Series(list('abca')) @@ -114,9 +112,8 @@ def get_dummies( Data type for new columns. Only a single dtype is allowed. Returns: - bigframes.pandas.DataFrame: - Dummy-coded data. If data contains other columns than the - dummy-coded one(s), these will be prepended, unaltered, to the - result. + DataFrame: Dummy-coded data. If data contains other columns than the + dummy-coded one(s), these will be prepended, unaltered, to the + result. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) diff --git a/third_party/bigframes_vendored/pandas/core/reshape/merge.py b/third_party/bigframes_vendored/pandas/core/reshape/merge.py index 448b21819c0..b03f366fca7 100644 --- a/third_party/bigframes_vendored/pandas/core/reshape/merge.py +++ b/third_party/bigframes_vendored/pandas/core/reshape/merge.py @@ -2,7 +2,6 @@ """ SQL-style merge routines """ - from __future__ import annotations @@ -14,8 +13,6 @@ def merge( *, left_on=None, right_on=None, - left_index: bool = False, - right_index: bool = False, sort=False, suffixes=("_x", "_y"), ): @@ -52,8 +49,6 @@ def merge( join; sort keys lexicographically. ``inner``: use intersection of keys from both frames, similar to a SQL inner join; preserve the order of the left keys. - ``cross``: creates the cartesian product from both frames, preserves the order - of the left keys. on (label or list of labels): Columns to join on. It must be found in both DataFrames. Either on or left_on + right_on @@ -64,10 +59,6 @@ def merge( right_on (label or list of labels): Columns to join on in the right DataFrame. Either on or left_on + right_on must be passed in. - left_index (bool, default False): - Use the index from the left DataFrame as the join key. - right_index (bool, default False): - Use the index from the right DataFrame as the join key. sort: Default False. Sort the join keys lexicographically in the result DataFrame. If False, the order of the join keys depends @@ -81,7 +72,6 @@ def merge( no suffix. At least one of the values must not be None. Returns: - bigframes.pandas.DataFrame: - A DataFrame of the two merged objects. + bigframes.dataframe.DataFrame: A DataFrame of the two merged objects. """ raise NotImplementedError("abstract method") diff --git a/third_party/bigframes_vendored/pandas/core/reshape/pivot.py b/third_party/bigframes_vendored/pandas/core/reshape/pivot.py deleted file mode 100644 index 8cc33525a4b..00000000000 --- a/third_party/bigframes_vendored/pandas/core/reshape/pivot.py +++ /dev/null @@ -1,57 +0,0 @@ -# Contains code from https://github.com/pandas-dev/pandas/blob/main/pandas/core/reshape/pivot.py -from __future__ import annotations - -from bigframes import constants - - -def crosstab( - index, - columns, - values=None, - rownames=None, - colnames=None, - aggfunc=None, -): - """ - Compute a simple cross tabulation of two (or more) factors. - - By default, computes a frequency table of the factors unless an - array of values and an aggregation function are passed. - - **Examples:** - >>> a = np.array(["foo", "foo", "foo", "foo", "bar", "bar", - ... "bar", "bar", "foo", "foo", "foo"], dtype=object) - >>> b = np.array(["one", "one", "one", "two", "one", "one", - ... "one", "two", "two", "two", "one"], dtype=object) - >>> c = np.array(["dull", "dull", "shiny", "dull", "dull", "shiny", - ... "shiny", "dull", "shiny", "shiny", "shiny"], - ... dtype=object) - >>> bpd.crosstab(a, [b, c], rownames=['a'], colnames=['b', 'c']) - b one two - c dull shiny dull shiny - a - bar 1 2 1 0 - foo 2 2 1 2 - - [2 rows x 4 columns] - - Args: - index (array-like, Series, or list of arrays/Series): - Values to group by in the rows. - columns (array-like, Series, or list of arrays/Series): - Values to group by in the columns. - values (array-like, optional): - Array of values to aggregate according to the factors. - Requires `aggfunc` be specified. - rownames (sequence, default None): - If passed, must match number of row arrays passed. - colnames (sequence, default None): - If passed, must match number of column arrays passed. - aggfunc (function, optional): - If specified, requires `values` be specified as well. - - Returns: - DataFrame: - Cross tabulation of the data. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) diff --git a/third_party/bigframes_vendored/pandas/core/reshape/tile.py b/third_party/bigframes_vendored/pandas/core/reshape/tile.py index 546a65b73c2..d4471ed68e4 100644 --- a/third_party/bigframes_vendored/pandas/core/reshape/tile.py +++ b/third_party/bigframes_vendored/pandas/core/reshape/tile.py @@ -2,26 +2,16 @@ """ Quantilization functions and related routines """ - from __future__ import annotations -import typing - -import pandas as pd - from bigframes import constants def cut( x, - bins: typing.Union[ - int, - pd.IntervalIndex, - typing.Iterable, - ], + bins, *, - right: bool = True, - labels: typing.Union[typing.Iterable[str], bool, None] = None, + labels=None, ): """ Bin values into discrete intervals. @@ -32,114 +22,42 @@ def cut( age ranges. Supports binning into an equal number of bins, or a pre-specified array of bins. - **Examples:** - - >>> import bigframes.pandas as bpd - >>> s = bpd.Series([0, 1, 5, 10]) - >>> s - 0 0 - 1 1 - 2 5 - 3 10 - dtype: Int64 - - Cut with an integer (equal-width bins): + ``labels=False`` implies you just want the bins back. - >>> bpd.cut(s, bins=4) - 0 {'left_exclusive': -0.01, 'right_inclusive': 2.5} - 1 {'left_exclusive': -0.01, 'right_inclusive': 2.5} - 2 {'left_exclusive': 2.5, 'right_inclusive': 5.0} - 3 {'left_exclusive': 7.5, 'right_inclusive': 10.0} - dtype: struct[pyarrow] + Examples: - Cut with the same bins, but assign them specific labels: + .. code-block:: - >>> bpd.cut(s, bins=3, labels=["bad", "medium", "good"]) - 0 bad - 1 bad - 2 medium - 3 good - dtype: string + import bigframes.pandas as pd - `labels=False` implies you want the bins back. + pd.options.display.progress_bar = None + s = pd.Series([0, 1, 1, 2]) + pd.cut(s, bins=4, labels=False) - >>> bpd.cut(s, bins=4, labels=False) 0 0 - 1 0 + 1 1 2 1 3 3 dtype: Int64 - Cut with pd.IntervalIndex, requires importing pandas for IntervalIndex: - - >>> interval_index = pd.IntervalIndex.from_tuples([(0, 1), (1, 5), (5, 20)]) - >>> bpd.cut(s, bins=interval_index) - 0 - 1 {'left_exclusive': 0, 'right_inclusive': 1} - 2 {'left_exclusive': 1, 'right_inclusive': 5} - 3 {'left_exclusive': 5, 'right_inclusive': 20} - dtype: struct[pyarrow] - - Cut with an iterable of tuples: - - >>> bins_tuples = [(0, 1), (1, 4), (5, 20)] - >>> bpd.cut(s, bins=bins_tuples) - 0 - 1 {'left_exclusive': 0, 'right_inclusive': 1} - 2 - 3 {'left_exclusive': 5, 'right_inclusive': 20} - dtype: struct[pyarrow] - - Cut with an iterable of ints: - - >>> bins_ints = [0, 1, 5, 20] - >>> bpd.cut(s, bins=bins_ints) - 0 - 1 {'left_exclusive': 0, 'right_inclusive': 1} - 2 {'left_exclusive': 1, 'right_inclusive': 5} - 3 {'left_exclusive': 5, 'right_inclusive': 20} - dtype: struct[pyarrow] - - Cut with an interable of ints, where intervals are left-inclusive and right-exclusive. - - >>> bins_ints = [0, 1, 5, 20] - >>> bpd.cut(s, bins=bins_ints, right=False) - 0 {'left_inclusive': 0, 'right_exclusive': 1} - 1 {'left_inclusive': 1, 'right_exclusive': 5} - 2 {'left_inclusive': 5, 'right_exclusive': 20} - 3 {'left_inclusive': 5, 'right_exclusive': 20} - dtype: struct[pyarrow] - Args: - x (array-like): + x (Series): The input Series to be binned. Must be 1-dimensional. - bins (int, pd.IntervalIndex, Iterable): + bins (int): The criteria to bin by. - int: Defines the number of equal-width bins in the range of `x`. The + int : Defines the number of equal-width bins in the range of `x`. The range of `x` is extended by .1% on each side to include the minimum and maximum values of `x`. - - pd.IntervalIndex or Iterable of tuples: Defines the exact bins to be used. - It's important to ensure that these bins are non-overlapping. - - Iterable of numerics: Defines the exact bins by using the interval - between each item and its following item. The items must be monotonically - increasing. - right (bool, default True): - Indicates whether `bins` includes the rightmost edge or not. If - ``right == True`` (the default), then the `bins` ``[1, 2, 3, 4]`` - indicate (1,2], (2,3], (3,4]. This argument is ignored when - `bins` is an IntervalIndex. - labels (bool, Iterable, default None): + labels (None): Specifies the labels for the returned bins. Must be the same length as the resulting bins. If False, returns only integer indicators of the - bins. This affects the type of the output container. This argument is - ignored when `bins` is an IntervalIndex. If True, raises an error. + bins. This affects the type of the output container (see below). + If True, raises an error. When `ordered=False`, labels must be + provided. Returns: - bigframes.pandas.Series: - A Series representing the respective bin for each value + Series: A Series representing the respective bin for each value of `x`. The type depends on the value of `labels`. sequence of scalars : returns a Series for Series `x` or a Categorical for all other inputs. The values stored within @@ -171,8 +89,7 @@ def qcut(x, q, *, labels=None, duplicates="error"): If bin edges are not unique, raise ValueError or drop non-uniques. Returns: - bigframes.pandas.Series: - Categorical or Series of integers if labels is False + Series: Categorical or Series of integers if labels is False The return type (Categorical or Series) depends on the input: a Series of type category if input is a Series else Categorical. Bins are represented as categories when categorical data is returned. diff --git a/third_party/bigframes_vendored/pandas/core/series.py b/third_party/bigframes_vendored/pandas/core/series.py index 183f36ef5a4..b569e5699c7 100644 --- a/third_party/bigframes_vendored/pandas/core/series.py +++ b/third_party/bigframes_vendored/pandas/core/series.py @@ -1,89 +1,28 @@ """ Data structure for 1-dimensional cross-sectional and time series data """ - from __future__ import annotations -import datetime -from typing import ( - IO, - TYPE_CHECKING, - Hashable, - List, - Literal, - Mapping, - Optional, - Sequence, - Tuple, - Union, -) - -import numpy +from typing import Hashable, IO, Literal, Mapping, Sequence, TYPE_CHECKING + import numpy as np -import pandas as pd -from bigframes_vendored.pandas.core.generic import NDFrame +from pandas._libs import lib from pandas._typing import Axis, FilePath, NaPosition, WriteBuffer -from pandas.api import extensions as pd_ext from bigframes import constants +from third_party.bigframes_vendored.pandas.core.generic import NDFrame if TYPE_CHECKING: - from bigframes_vendored.pandas.core.frame import DataFrame - from bigframes_vendored.pandas.core.groupby import SeriesGroupBy + from third_party.bigframes_vendored.pandas.core.frame import DataFrame + from third_party.bigframes_vendored.pandas.core.groupby import SeriesGroupBy class Series(NDFrame): # type: ignore[misc] - """ - One-dimensional ndarray with axis labels (including time series). - """ - @property def dt(self): """ Accessor object for datetime-like properties of the Series values. - **Examples:** - - >>> import bigframes.pandas as bpd - >>> seconds_series = bpd.Series(pd.date_range("2000-01-01", periods=3, freq="s")) - >>> seconds_series - 0 2000-01-01 00:00:00 - 1 2000-01-01 00:00:01 - 2 2000-01-01 00:00:02 - dtype: timestamp[us][pyarrow] - - >>> seconds_series.dt.second - 0 0 - 1 1 - 2 2 - dtype: Int64 - - >>> hours_series = bpd.Series(pd.date_range("2000-01-01", periods=3, freq="h")) - >>> hours_series - 0 2000-01-01 00:00:00 - 1 2000-01-01 01:00:00 - 2 2000-01-01 02:00:00 - dtype: timestamp[us][pyarrow] - - >>> hours_series.dt.hour - 0 0 - 1 1 - 2 2 - dtype: Int64 - - >>> quarters_series = bpd.Series(pd.date_range("2000-01-01", periods=3, freq="QE")) - >>> quarters_series - 0 2000-03-31 00:00:00 - 1 2000-06-30 00:00:00 - 2 2000-09-30 00:00:00 - dtype: timestamp[us][pyarrow] - - >>> quarters_series.dt.quarter - 0 1 - 1 2 - 2 3 - dtype: Int64 - Returns: bigframes.operations.datetimes.DatetimeMethods: An accessor containing datetime methods. @@ -105,85 +44,25 @@ def struct(self): @property def index(self): - """The index (axis labels) of the Series. - - The index of a Series is used to label and identify each element of the - underlying data. The index can be thought of as an immutable ordered set - (technically a multi-set, as it may contain duplicate labels), and is - used to index and align data. - - **Examples:** - - - You can access the index of a Series via ``index`` property. - - >>> df = bpd.DataFrame({'Name': ['Alice', 'Bob', 'Aritra'], - ... 'Age': [25, 30, 35], - ... 'Location': ['Seattle', 'New York', 'Kona']}, - ... index=([10, 20, 30])) - >>> s = df["Age"] - >>> s - 10 25 - 20 30 - 30 35 - Name: Age, dtype: Int64 - >>> s.index # doctest: +ELLIPSIS - Index([10, 20, 30], dtype='Int64') - >>> s.index.values - array([10, 20, 30]) - - Let's try setting a multi-index case reflect via ``index`` property. - - >>> df1 = df.set_index(["Name", "Location"]) - >>> s1 = df1["Age"] - >>> s1 - Name Location - Alice Seattle 25 - Bob New York 30 - Aritra Kona 35 - Name: Age, dtype: Int64 - >>> s1.index # doctest: +ELLIPSIS - MultiIndex([( 'Alice', 'Seattle'), - ( 'Bob', 'New York'), - ('Aritra', 'Kona')], - names=['Name', 'Location']) - >>> s1.index.values - array([('Alice', 'Seattle'), ('Bob', 'New York'), ('Aritra', 'Kona')], - dtype=object) - - Returns: - Index: - The index object of the Series. - """ + """The index (axis labels) of the Series.""" raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @property def shape(self): - """Return a tuple of the shape of the underlying data. - - **Examples:** - - - >>> s = bpd.Series([1, 4, 9, 16]) - >>> s.shape - (4,) - >>> s = bpd.Series(['Alice', 'Bob', pd.NA]) - >>> s.shape - (3,) - """ + """Return a tuple of the shape of the underlying data.""" raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @property def dtype(self): """ Return the dtype object of the underlying data. + """ + raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - **Examples:** - - - >>> s = bpd.Series([1, 2, 3]) - >>> s.dtype - Int64Dtype() + @property + def dtypes(self): + """ + Return the dtype object of the underlying data. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -196,127 +75,31 @@ def name(self) -> Hashable: to form a DataFrame. It is also used whenever displaying the Series using the interpreter. - **Examples:** - - - For a Series: - - >>> s = bpd.Series([1, 2, 3], dtype="Int64", name='Numbers') - >>> s - 0 1 - 1 2 - 2 3 - Name: Numbers, dtype: Int64 - >>> s.name - 'Numbers' - - >>> s.name = "Integers" - >>> s - 0 1 - 1 2 - 2 3 - Name: Integers, dtype: Int64 - - If the Series is part of a DataFrame: - - >>> df = bpd.DataFrame({'col1': [1, 2], 'col2': [3, 4]}) - >>> df - col1 col2 - 0 1 3 - 1 2 4 - - [2 rows x 2 columns] - >>> s = df["col1"] - >>> s.name - 'col1' - Returns: - hashable object: - The name of the Series, also the column name + hashable object: The name of the Series, also the column name if part of a DataFrame. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - @property - def hasnans(self) -> bool: - """ - Return True if there are any NaNs. - - **Examples:** - - - >>> s = bpd.Series([1, 2, 3, None]) - >>> s - 0 1.0 - 1 2.0 - 2 3.0 - 3 - dtype: Float64 - >>> s.hasnans - np.True_ - - Returns: - bool - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - @property def T(self) -> Series: - """Return the transpose, which is by definition self. - - **Examples:** - - - >>> s = bpd.Series(['Ant', 'Bear', 'Cow']) - >>> s - 0 Ant - 1 Bear - 2 Cow - dtype: string - - >>> s.T - 0 Ant - 1 Bear - 2 Cow - dtype: string - - """ + """Return the transpose, which is by definition self.""" raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) def transpose(self) -> Series: """ Return the transpose, which is by definition self. - **Examples:** - - - >>> s = bpd.Series(['Ant', 'Bear', 'Cow']) - >>> s - 0 Ant - 1 Bear - 2 Cow - dtype: string - - >>> s.transpose() - 0 Ant - 1 Bear - 2 Cow - dtype: string - Returns: - bigframes.pandas.Series: - Series. + Series """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) def reset_index( self, - level=None, *, drop: bool = False, - name=pd_ext.no_default, - inplace: bool = False, - allow_duplicates: Optional[bool] = None, + name=lib.no_default, ) -> DataFrame | Series | None: """ Generate a new DataFrame or Series with the index reset. @@ -325,73 +108,7 @@ def reset_index( when the index is meaningless and needs to be reset to the default before another operation. - **Examples:** - - - >>> s = bpd.Series([1, 2, 3, 4], name='foo', - ... index=['a', 'b', 'c', 'd']) - >>> s.index.name = "idx" - >>> s - idx - a 1 - b 2 - c 3 - d 4 - Name: foo, dtype: Int64 - - Generate a DataFrame with default index. - - >>> s.reset_index() - idx foo - 0 a 1 - 1 b 2 - 2 c 3 - 3 d 4 - - [4 rows x 2 columns] - - To specify the name of the new column use ``name`` param. - - >>> s.reset_index(name="bar") - idx bar - 0 a 1 - 1 b 2 - 2 c 3 - 3 d 4 - - [4 rows x 2 columns] - - To generate a new Series with the default index set param ``drop=True``. - - >>> s.reset_index(drop=True) - 0 1 - 1 2 - 2 3 - 3 4 - Name: foo, dtype: Int64 - - >>> arrays = [np.array(['bar', 'bar', 'baz', 'baz']), - ... np.array(['one', 'two', 'one', 'two'])] - >>> s2 = bpd.Series( - ... range(4), name='foo', - ... index=pd.MultiIndex.from_arrays(arrays, - ... names=['a', 'b'])) - - If level is not set, all levels are removed from the Index. - - >>> s2.reset_index() - a b foo - 0 bar one 0 - 1 bar two 1 - 2 baz one 2 - 3 baz two 3 - - [4 rows x 3 columns] - Args: - level (int, str, tuple, or list, default optional): - For a Series with a MultiIndex, only remove the specified levels - from the index. Removes all levels by default. drop (bool, default False): Just reset the index, without inserting it as a column in the new DataFrame. @@ -399,14 +116,9 @@ def reset_index( The name to use for the column containing the original Series values. Uses ``self.name`` by default. This argument is ignored when `drop` is True. - inplace (bool, default False): - Modify the Series in place (do not create a new object). - allow_duplicates (bool, optional, default None): - Allow duplicate column labels to be created. Returns: - bigframes.pandas.Series or bigframes.pandas.DataFrame or None: - When `drop` is False (the default), + Series or DataFrame or None; When `drop` is False (the default), a DataFrame is returned. The newly created columns will come first in the DataFrame, followed by the original Series values. When `drop` is True, a `Series` is returned. @@ -421,23 +133,6 @@ def __repr__(self) -> str: """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - def keys(self): - """ - Return alias for index. - - **Examples:** - - - >>> s = bpd.Series([1, 2, 3], index=[0, 1, 2]) - >>> s.keys() - Index([0, 1, 2], dtype='Int64') - - Returns: - Index: - Index of the Series. - """ - return self.index - # ---------------------------------------------------------------------- # IO methods (to / from other formats) @@ -453,8 +148,6 @@ def to_string( name: bool = False, max_rows: int | None = None, min_rows: int | None = None, - *, - allow_large_results: Optional[bool] = None, ) -> str | None: """ Render a string representation of the Series. @@ -483,13 +176,10 @@ def to_string( min_rows (int, optional): The number of rows to display in a truncated repr (when number of rows is above `max_rows`). - allow_large_results (bool, default None): - If not None, overrides the global setting to allow or disallow large - query results over the default size limit of 10 GB. Returns: - str or None: - String representation of Series if ``buf=None``, otherwise None. + str or None: String representation of Series if ``buf=None``, + otherwise None. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -498,137 +188,37 @@ def to_markdown( buf: IO[str] | None = None, mode: str = "wt", index: bool = True, - *, - allow_large_results: Optional[bool] = None, **kwargs, ) -> str | None: """ - Print Series in Markdown-friendly format. - - **Examples:** - - - >>> s = bpd.Series(["elk", "pig", "dog", "quetzal"], name="animal") - >>> print(s.to_markdown()) - | | animal | - |---:|:---------| - | 0 | elk | - | 1 | pig | - | 2 | dog | - | 3 | quetzal | - - Output markdown with a tabulate option. - - >>> print(s.to_markdown(tablefmt="grid")) - +----+----------+ - | | animal | - +====+==========+ - | 0 | elk | - +----+----------+ - | 1 | pig | - +----+----------+ - | 2 | dog | - +----+----------+ - | 3 | quetzal | - +----+----------+ + Print {klass} in Markdown-friendly format. Args: buf (str, Path or StringIO-like, optional, default None): Buffer to write to. If None, the output is returned as a string. mode (str, optional): Mode in which file is opened, "wt" by default. - allow_large_results (bool, default None): - If not None, overrides the global setting to allow or disallow - large query results over the default size limit of 10 GB. index (bool, optional, default True): Add index (row) labels. Returns: - str: - Series in Markdown-friendly format. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def to_csv( - self, - path_or_buf=None, - sep=",", - *, - header: bool = True, - index: bool = True, - allow_large_results: Optional[bool] = None, - ) -> Optional[str]: - """ - Write object to a comma-separated values (csv) file. - - **Examples:** - - >>> import bigframes.pandas as bpd - - >>> s = bpd.Series([1,2,3], name='my_series') - >>> s.to_csv() - \',my_series\\n0,1\\n1,2\\n2,3\\n\' - - Args: - path_or_buf (str, path object, file-like object, or None, default None): - String, path object (implementing os.PathLike[str]), or file-like object - implementing a write() function. If None, the result is returned as a string. - If a non-binary file object is passed, it should be opened with newline='', - disabling universal newlines. If a binary file object is passed, - mode might need to contain a 'b'. - Must contain a wildcard character '*' if this is a GCS path. - sep (str, default ','): - String of length 1. Field delimiter for the output file. - header (bool, default True): - Write out the column names. - index (bool, default True): - Write row names (index). - allow_large_results (bool, default None): - If not None, overrides the global setting to allow or disallow large - query results over the default size limit of 10 GB. - - Returns: - If path_or_buf is None, returns the resulting csv format as a string. Otherwise returns None. + str: {klass} in Markdown-friendly format. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - def to_dict( - self, - into: type[dict] = dict, - *, - allow_large_results: Optional[bool] = None, - ) -> Mapping: + def to_dict(self, into: type[dict] = dict) -> Mapping: """ Convert Series to {label -> value} dict or dict-like object. - **Examples:** - - >>> from collections import OrderedDict, defaultdict - - >>> s = bpd.Series([1, 2, 3, 4]) - >>> s.to_dict() - {np.int64(0): 1, np.int64(1): 2, np.int64(2): 3, np.int64(3): 4} - - >>> s.to_dict(into=OrderedDict) - OrderedDict({np.int64(0): 1, np.int64(1): 2, np.int64(2): 3, np.int64(3): 4}) - - >>> dd = defaultdict(list) - >>> s.to_dict(into=dd) - defaultdict(, {np.int64(0): 1, np.int64(1): 2, np.int64(2): 3, np.int64(3): 4}) - Args: into (class, default dict): The collections.abc.Mapping subclass to use as the return object. Can be the actual class or an empty instance of the mapping type you want. If you want a collections.defaultdict, you must pass it initialized. - allow_large_results (bool, default None): - If not None, overrides the global setting to allow or disallow large - query results over the default size limit of 10 GB. Returns: - collections.abc.Mapping: - Key-value representation of Series. + collections.abc.Mapping: Key-value representation of Series. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -639,35 +229,15 @@ def to_frame(self, name=None) -> DataFrame: The column in the new dataframe will be named name (the keyword parameter) if the name parameter is provided and not None. - **Examples:** - - - >>> s = bpd.Series(["a", "b", "c"], - ... name="vals") - >>> s.to_frame() - vals - 0 a - 1 b - 2 c - - [3 rows x 1 columns] - Args: name (Hashable, default None) Returns: - bigframes.pandas.DataFrame: - DataFrame representation of Series. + bigframes.dataframe.DataFrame: DataFrame representation of Series. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - def to_excel( - self, - excel_writer, - sheet_name, - *, - allow_large_results=None, - ): + def to_excel(self, excel_writer, sheet_name): """ Write Series to an Excel sheet. @@ -686,22 +256,10 @@ def to_excel( File path or existing ExcelWriter. sheet_name (str, default 'Sheet1'): Name of sheet to contain Series. - allow_large_results (bool, default None): - If not None, overrides the global setting to allow or disallow large - query results over the default size limit of 10 GB. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - def to_latex( - self, - buf=None, - columns=None, - header=True, - index=True, - *, - allow_large_results=None, - **kwargs, - ): + def to_latex(self, buf=None, columns=None, header=True, index=True, **kwargs): """ Render object to a LaTeX tabular, longtable, or nested table. @@ -715,18 +273,14 @@ def to_latex( it is assumed to be aliases for the column names. index (bool, default True): Write row names (index). - allow_large_results (bool, default None): - If not None, overrides the global setting to allow or disallow large - query results over the default size limit of 10 GB. Returns: - str or None: - If buf is None, returns the result as a string. + str or None: If buf is None, returns the result as a string. Otherwise returns None. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - def tolist(self, *, allow_large_results: Optional[bool] = None) -> list: + def tolist(self) -> list: """ Return a list of the values. @@ -734,61 +288,17 @@ def tolist(self, *, allow_large_results: Optional[bool] = None) -> list: (for str, int, float) or a pandas scalar (for Timestamp/Timedelta/Interval/Period). - **Examples:** - - - >>> s = bpd.Series([1, 2, 3]) - >>> s - 0 1 - 1 2 - 2 3 - dtype: Int64 - - >>> s.to_list() - [1, 2, 3] - - Args: - allow_large_results (bool, default None): - If not None, overrides the global setting to allow or disallow - large query results over the default size limit of 10 GB. - Returns: - list: - list of the values. + list: list of the values """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) to_list = tolist - def to_numpy( - self, dtype, copy=False, na_value=pd_ext.no_default, *, allow_large_results=None - ): + def to_numpy(self, dtype, copy=False, na_value=None): """ A NumPy ndarray representing the values in this Series or Index. - **Examples:** - - - >>> ser = bpd.Series(pd.Categorical(['a', 'b', 'a'])) - >>> ser.to_numpy() - array(['a', 'b', 'a'], dtype=object) - - Specify the dtype to control how datetime-aware data is represented. Use - dtype=object to return an ndarray of pandas Timestamp objects, each with - the correct tz. - - >>> ser = bpd.Series(pd.date_range('2000', periods=2, tz="CET")) - >>> ser.to_numpy(dtype=object) - array([Timestamp('1999-12-31 23:00:00+0000', tz='UTC'), - Timestamp('2000-01-01 23:00:00+0000', tz='UTC')], dtype=object) - - Or ``dtype=datetime64[ns]`` to return an ndarray of native datetime64 values. - The values are converted to UTC and the timezone info is dropped. - - >>> ser.to_numpy(dtype="datetime64[ns]") - array(['1999-12-31T23:00:00.000000000', '2000-01-01T23:00:00.000000000'], - dtype='datetime64[ns]') - Args: dtype (str or numpy.dtype, optional): The dtype to pass to :meth:`numpy.asarray`. @@ -800,102 +310,95 @@ def to_numpy( na_value (Any, optional): The value to use for missing values. The default value depends on `dtype` and the type of the array. - allow_large_results (bool, default None): - If not None, overrides the global setting to allow or disallow - large query results over the default size limit of 10 GB. ``**kwargs``: Additional keywords passed through to the ``to_numpy`` method of the underlying array (for extension arrays). Returns: - numpy.ndarray: - A NumPy ndarray representing the values in this + numpy.ndarray: A NumPy ndarray representing the values in this Series or Index. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - def to_pickle(self, path, *, allow_large_results=None, **kwargs): + def to_pickle(self, path, **kwargs): """ Pickle (serialize) object to file. - **Examples:** - - - >>> original_df = bpd.DataFrame({"foo": range(5), "bar": range(5, 10)}) - >>> original_df - foo bar - 0 0 5 - 1 1 6 - 2 2 7 - 3 3 8 - 4 4 9 - - [5 rows x 2 columns] - - >>> original_df.to_pickle("./dummy.pkl") - - >>> unpickled_df = bpd.read_pickle("./dummy.pkl") - >>> unpickled_df - foo bar - 0 0 5 - 1 1 6 - 2 2 7 - 3 3 8 - 4 4 9 - - [5 rows x 2 columns] - Args: path (str, path object, or file-like object): String, path object (implementing ``os.PathLike[str]``), or file-like object implementing a binary ``write()`` function. File path where the pickled object will be stored. - allow_large_results (bool, default None): - If not None, overrides the global setting to allow or disallow - large query results over the default size limit of 10 GB. - - Returns: - None """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - def to_xarray(self, *, allow_large_results=None): + def to_xarray(self): """ Return an xarray object from the pandas object. Returns: - xarray.DataArray or xarray.Dataset: - Data in the pandas structure + xarray.DataArray or xarray.Dataset: Data in the pandas structure converted to Dataset if the object is a DataFrame, or a DataArray if the object is a Series. - allow_large_results (bool, default None): - If not None, overrides the global setting to allow or disallow large - query results over the default size limit of 10 GB. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - def agg(self, func): + def to_json( + self, + path_or_buf=None, + orient: Literal[ + "split", "records", "index", "columns", "values", "table" + ] = "columns", + **kwarg, + ) -> str | None: """ - Aggregate using one or more operations over the specified axis. + Convert the object to a JSON string. - **Examples:** + Note NaN's and None will be converted to null and datetime objects + will be converted to UNIX timestamps. + + Args: + path_or_buf (str, path object, file-like object, or None, default None): + String, path object (implementing os.PathLike[str]), or file-like + object implementing a write() function. If None, the result is + returned as a string. + orient ({"split", "records", "index", "columns", "values", "table"}, default "columns"): + Indication of expected JSON string format. + 'split' : dict like {{'index' -> [index], 'columns' -> [columns],'data' -> [values]}} + 'records' : list like [{{column -> value}}, ... , {{column -> value}}] + 'index' : dict like {{index -> {{column -> value}}}} + 'columns' : dict like {{column -> {{index -> value}}}} + 'values' : just the values array + 'table' : dict like {{'schema': {{schema}}, 'data': {{data}}}} + Describing the data, where data component is like ``orient='records'``. + Returns: + None or str: If path_or_buf is None, returns the resulting json format as a + string. Otherwise returns None. + """ + raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) + + def to_csv(self, path_or_buf: str, *, index: bool = True) -> str | None: + """ + Write object to a comma-separated values (csv) file. - >>> s = bpd.Series([1, 2, 3, 4]) - >>> s - 0 1 - 1 2 - 2 3 - 3 4 - dtype: Int64 + Args: + path_or_buf (str, path object, file-like object, or None, default None): + String, path object (implementing os.PathLike[str]), or file-like + object implementing a write() function. If None, the result is + returned as a string. If a non-binary file object is passed, it should + be opened with `newline=''`, disabling universal newlines. If a binary + file object is passed, `mode` might need to contain a `'b'`. - >>> s.agg('min') - np.int64(1) + Returns: + None or str: If path_or_buf is None, returns the resulting csv format + as a string. Otherwise returns None. + """ + raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - >>> s.agg(['min', 'max']) - min 1 - max 4 - dtype: Int64 + def agg(self, func): + """ + Aggregate using one or more operations over the specified axis. Args: func (function): @@ -904,8 +407,7 @@ def agg(self, func): function names, e.g. ``['sum', 'mean']``. Returns: - scalar or bigframes.pandas.Series: - Aggregated results. + scalar or Series: Aggregated results """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -913,21 +415,9 @@ def count(self): """ Return number of non-NA/null observations in the Series. - **Examples:** - - - >>> s = bpd.Series([0.0, 1.0, pd.NA]) - >>> s - 0 0.0 - 1 1.0 - 2 - dtype: Float64 - >>> s.count() - np.int64(2) - Returns: - int or bigframes.pandas.Series (if level specified): - Number of non-null values in the Series. + int or Series (if level specified): Number of non-null values in the + Series. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -937,69 +427,8 @@ def nunique(self) -> int: Excludes NA values by default. - **Examples:** - - - >>> s = bpd.Series([1, 3, 5, 7, 7]) - >>> s - 0 1 - 1 3 - 2 5 - 3 7 - 4 7 - dtype: Int64 - - >>> s.nunique() - np.int64(4) - - Returns: - int: - number of unique elements in the object. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def unique(self, keep_order=True) -> Series: - """ - Return unique values of Series object. - - By default, uniques are returned in order of appearance. Hash table-based unique, - therefore does NOT sort. - - Args: - keep_order (bool, default True): - If True, preserves the order of the first appearance of each unique value. - If False, returns the elements in ascending order, which can be faster. - - **Examples:** - - - >>> s = bpd.Series([2, 1, 3, 3], name='A') - >>> s - 0 2 - 1 1 - 2 3 - 3 3 - Name: A, dtype: Int64 - - Example with order preservation: Slower, but keeps order - - >>> s.unique() - 0 2 - 1 1 - 2 3 - Name: A, dtype: Int64 - - Example without order preservation: Faster, but loses original order - - >>> s.unique(keep_order=False) - 0 1 - 1 2 - 2 3 - Name: A, dtype: Int64 - Returns: - bigframes.pandas.Series: - The unique values returned as a Series. + int: number of unique elements in the object. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -1011,18 +440,8 @@ def mode(self) -> Series: Always returns Series even if only one value is returned. - **Examples:** - - - >>> s = bpd.Series([2, 4, 8, 2, 4, None]) - >>> s.mode() - 0 2.0 - 1 4.0 - dtype: Float64 - Returns: - bigframes.pandas.Series: - Modes of the Series in sorted order. + bigframes.series.Series: Modes of the Series in sorted order. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -1034,51 +453,6 @@ def drop_duplicates( """ Return Series with duplicate values removed. - **Examples:** - - Generate a Series with duplicated entries. - - >>> import bigframes.pandas as bpd - >>> s = bpd.Series(['llama', 'cow', 'llama', 'beetle', 'llama', 'hippo'], - ... name='animal') - >>> s - 0 llama - 1 cow - 2 llama - 3 beetle - 4 llama - 5 hippo - Name: animal, dtype: string - - With the 'keep' parameter, the selection behaviour of duplicated values - can be changed. The value 'first' keeps the first occurrence for each set - of duplicated entries. The default value of keep is 'first'. - - >>> s.drop_duplicates() - 0 llama - 1 cow - 3 beetle - 5 hippo - Name: animal, dtype: string - - The value 'last' for parameter 'keep' keeps the last occurrence for - each set of duplicated entries. - - >>> s.drop_duplicates(keep='last') - 1 cow - 3 beetle - 4 llama - 5 hippo - Name: animal, dtype: string - - The value False for parameter 'keep' discards all sets of duplicated entries. - - >>> s.drop_duplicates(keep=False) - 1 cow - 3 beetle - 5 hippo - Name: animal, dtype: string - Args: keep ({'first', 'last', ``False``}, default 'first'): Method to handle dropping duplicates: @@ -1088,8 +462,7 @@ def drop_duplicates( ``False`` : Drop all duplicates. Returns: - bigframes.pandas.Series: - Series with duplicates dropped or None if ``inplace=True``. + bigframes.series.Series: Series with duplicates dropped or None if ``inplace=True``. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -1101,53 +474,6 @@ def duplicated(self, keep="first") -> Series: Series. Either all duplicates, all except the first or all except the last occurrence of duplicates can be indicated. - **Examples:** - - >>> import bigframes.pandas as bpd - - By default, for each set of duplicated values, the first occurrence is - set on False and all others on True: - - >>> animals = bpd.Series(['llama', 'cow', 'llama', 'beetle', 'llama']) - >>> animals.duplicated() - 0 False - 1 False - 2 True - 3 False - 4 True - dtype: boolean - - which is equivalent to - - >>> animals.duplicated(keep='first') - 0 False - 1 False - 2 True - 3 False - 4 True - dtype: boolean - - By using 'last', the last occurrence of each set of duplicated values - is set on False and all others on True: - - >>> animals.duplicated(keep='last') - 0 True - 1 False - 2 True - 3 False - 4 False - dtype: boolean - - By setting keep on False, all duplicates are True: - - >>> animals.duplicated(keep=False) - 0 True - 1 False - 2 True - 3 False - 4 True - dtype: boolean - Args: keep ({'first', 'last', False}, default 'first'): Method to handle dropping duplicates: @@ -1159,8 +485,7 @@ def duplicated(self, keep="first") -> Series: ``False`` : Mark all duplicates as ``True``. Returns: - bigframes.pandas.Series: - Series indicating whether each value has occurred in the + bigframes.series.Series: Series indicating whether each value has occurred in the preceding values. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -1172,21 +497,6 @@ def idxmin(self) -> Hashable: If multiple values equal the minimum, the first row label with that value is returned. - **Examples:** - - - >>> s = bpd.Series(data=[1, None, 4, 1], - ... index=['A', 'B', 'C', 'D']) - >>> s - A 1.0 - B - C 4.0 - D 1.0 - dtype: Float64 - - >>> s.idxmin() - 'A' - Returns: Index: Label of the minimum value. """ @@ -1199,22 +509,6 @@ def idxmax(self) -> Hashable: If multiple values equal the maximum, the first row label with that value is returned. - **Examples:** - - - >>> s = bpd.Series(data=[1, None, 4, 3, 4], - ... index=['A', 'B', 'C', 'D', 'E']) - >>> s - A 1.0 - B - C 4.0 - D 3.0 - E 4.0 - dtype: Float64 - - >>> s.idxmax() - 'C' - Returns: Index: Label of the maximum value. """ @@ -1224,64 +518,13 @@ def round(self, decimals: int = 0) -> Series: """ Round each value in a Series to the given number of decimals. - **Examples:** - - >>> import bigframes.pandas as bpd - >>> s = bpd.Series([0.1, 1.3, 2.7]) - >>> s.round() - 0 0.0 - 1 1.0 - 2 3.0 - dtype: Float64 - - >>> s = bpd.Series([0.123, 1.345, 2.789]) - >>> s.round(decimals=2) - 0 0.12 - 1 1.34 - 2 2.79 - dtype: Float64 - Args: decimals (int, default 0): Number of decimal places to round to. If decimals is negative, it specifies the number of positions to the left of the decimal point. Returns: - bigframes.pandas.Series: - Rounded values of the Series. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def explode(self, *, ignore_index: Optional[bool] = False) -> Series: - """ - Transform each element of a list-like to a row. - - **Examples:** - - - >>> s = bpd.Series([[1, 2, 3], [], [3, 4]]) - >>> s - 0 [1 2 3] - 1 [] - 2 [3 4] - dtype: list[pyarrow] - - >>> s.explode() - 0 1 - 0 2 - 0 3 - 1 - 2 3 - 2 4 - dtype: Int64 - - Args: - ignore_index (bool, default False): - If True, the resulting index will be labeled 0, 1, …, n - 1. - - Returns: - bigframes.pandas.Series: - Exploded lists to rows; index will be duplicated for these rows. + bigframes.series.Series: Rounded values of the Series. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -1293,19 +536,6 @@ def corr(self, other, method="pearson", min_periods=None) -> float: Uses the "Pearson" method of correlation. Numbers are converted to float before calculation, so the result may be unstable. - **Examples:** - - - >>> s1 = bpd.Series([.2, .0, .6, .2]) - >>> s2 = bpd.Series([.3, .6, .0, .1]) - >>> s1.corr(s2) - np.float64(-0.8510644963469901) - - >>> s1 = bpd.Series([1, 2, 3], index=[0, 1, 2]) - >>> s2 = bpd.Series([1, 2, 3], index=[2, 1, 0]) - >>> s1.corr(s2) - np.float64(-1.0) - Args: other (Series): The series with which this is to be correlated. @@ -1316,127 +546,25 @@ def corr(self, other, method="pearson", min_periods=None) -> float: are not yet supported, so a result will be returned for at least two observations. Returns: - float: - Will return NaN if there are fewer than two numeric pairs, either series has a + float; Will return NaN if there are fewer than two numeric pairs, either series has a variance or covariance of zero, or any input value is infinite. """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) + raise NotImplementedError("abstract method") - def autocorr(self, lag: int = 1) -> float: + def diff(self) -> Series: """ - Compute the lag-N autocorrelation. - - This method computes the Pearson correlation between - the Series and its shifted self. + First discrete difference of element. - **Examples:** + Calculates the difference of a {klass} element compared with another + element in the {klass} (default is element in previous row). - - >>> s = bpd.Series([0.25, 0.5, 0.2, -0.05]) - >>> float(s.autocorr()) # doctest: +ELLIPSIS - 0.1035526330902... - - >>> float(s.autocorr(lag=2)) - -1.0 - - If the Pearson correlation is not well defined, then 'NaN' is returned. - - >>> s = bpd.Series([1, 0, 0, 0]) - >>> float(s.autocorr()) - nan - - Args: - lag (int, default 1): - Number of lags to apply before performing autocorrelation. - - Returns: - float: - The Pearson correlation between self and self.shift(lag). - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def cov( - self, - other, - ) -> float: - """ - Compute covariance with Series, excluding missing values. - - The two `Series` objects are not required to be the same length and - will be aligned internally before the covariance is calculated. - - **Examples:** - - - >>> s1 = bpd.Series([0.90010907, 0.13484424, 0.62036035]) - >>> s2 = bpd.Series([0.12528585, 0.26962463, 0.51111198]) - >>> s1.cov(s2) - np.float64(-0.01685762652715874) - - Args: - other (Series): - Series with which to compute the covariance. - - Returns: - float: - Covariance between Series and other normalized by N-1 - (unbiased estimator). - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def diff(self) -> Series: - """ - First discrete difference of element. - - Calculates the difference of a Series element compared with another - element in the Series (default is element in previous row). - - - **Examples:** - - - Difference with previous row - - >>> s = bpd.Series([1, 1, 2, 3, 5, 8]) - >>> s.diff() - 0 - 1 0 - 2 1 - 3 1 - 4 2 - 5 3 - dtype: Int64 - - Difference with 3rd previous row - - >>> s.diff(periods=3) - 0 - 1 - 2 - 3 2 - 4 4 - 5 6 - dtype: Int64 - - Difference with following row - - >>> s.diff(periods=-1) - 0 0 - 1 -1 - 2 -1 - 3 -2 - 4 -3 - 5 - dtype: Int64 - - Args: - periods (int, default 1): - Periods to shift for calculating difference, accepts negative - values. + Args: + periods (int, default 1): + Periods to shift for calculating difference, accepts negative + values. Returns: - bigframes.pandas.Series: - First differences of the Series. + {klass}: First differences of the Series. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -1456,26 +584,12 @@ def dot(self, other) -> Series | np.ndarray: BigQuery Dataframes does not validate this property and will produce incorrect results if indices are not equal. - **Examples:** - - - >>> s = bpd.Series([0, 1, 2, 3]) - >>> other = bpd.Series([-1, 2, -3, 4]) - >>> s.dot(other) - np.int64(8) - - You can also use the operator ``@`` for the dot product: - - >>> s @ other - np.int64(8) - Args: other (Series): The other object to compute the dot product with its columns. Returns: - scalar, bigframes.pandas.Series or numpy.ndarray: - Return the dot product of the Series + scalar, Series or numpy.ndarray: Return the dot product of the Series and other if other is a Series, the Series of the dot product of Series and each rows of other if other is a DataFrame or a numpy.ndarray between the Series and each columns of the numpy array. @@ -1486,110 +600,45 @@ def dot(self, other) -> Series | np.ndarray: def __matmul__(self, other): """ - Matrix multiplication using binary `@` operator. + Matrix multiplication using binary `@` operator in Python>=3.5. """ - return NotImplemented + raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) def __rmatmul__(self, other): """ - Matrix multiplication using binary `@` operator. + Matrix multiplication using binary `@` operator in Python>=3.5. """ - return NotImplemented + raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) def sort_values( self, *, axis: Axis = 0, - inplace: bool = False, ascending: bool | int | Sequence[bool] | Sequence[int] = True, - kind: str | None = None, + kind: str = "quicksort", na_position: str = "last", - ): + ) -> Series | None: """ Sort by the values. Sort a Series in ascending or descending order by some criterion. - **Examples:** - - - >>> s = bpd.Series([np.nan, 1, 3, 10, 5]) - >>> s - 0 - 1 1.0 - 2 3.0 - 3 10.0 - 4 5.0 - dtype: Float64 - - Sort values ascending order (default behaviour): - - >>> s.sort_values(ascending=True) - 1 1.0 - 2 3.0 - 4 5.0 - 3 10.0 - 0 - dtype: Float64 - - Sort values descending order: - - >>> s.sort_values(ascending=False) - 3 10.0 - 4 5.0 - 2 3.0 - 1 1.0 - 0 - dtype: Float64 - - Sort values putting NAs first: - - >>> s.sort_values(na_position='first') - 0 - 1 1.0 - 2 3.0 - 4 5.0 - 3 10.0 - dtype: Float64 - - Sort a series of strings: - - >>> s = bpd.Series(['z', 'b', 'd', 'a', 'c']) - >>> s - 0 z - 1 b - 2 d - 3 a - 4 c - dtype: string - - >>> s.sort_values() - 3 a - 1 b - 4 c - 2 d - 0 z - dtype: string - Args: axis (0 or 'index'): Unused. Parameter needed for compatibility with DataFrame. - inplace (bool, default False): - Whether to modify the Series rather than creating a new one. ascending (bool or list of bools, default True): If True, sort values in ascending order, otherwise descending. - kind (str, default to None): - Choice of sorting algorithm. Accepts quicksort', 'mergesort', - 'heapsort', 'stable'. Ignored except when determining whether to + kind (str, default to 'quicksort'): + Choice of sorting algorithm. Accepts 'quicksort’, ‘mergesort’, + ‘heapsort’, ‘stable’. Ignored except when determining whether to sort stably. 'mergesort' or 'stable' will result in stable reorder na_position ({'first' or 'last'}, default 'last'): Argument 'first' puts NaNs at the beginning, 'last' puts NaNs at the end. Returns: - bigframes.pandas.Series or None: - Series ordered by values or None if ``inplace=True``. + bigframes.series.Series: Series ordered by values or None if ``inplace=True``. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -1597,67 +646,27 @@ def sort_index( self, *, axis: Axis = 0, - inplace: bool = False, ascending: bool | Sequence[bool] = True, - kind: str | None = None, na_position: NaPosition = "last", - ): + ) -> Series | None: """ Sort Series by index labels. Returns a new Series sorted by label if `inplace` argument is ``False``, otherwise updates the original series and returns None. - **Examples:** - - - >>> s = bpd.Series(['a', 'b', 'c', 'd'], index=[3, 2, 1, 4]) - >>> s.sort_index() - 1 c - 2 b - 3 a - 4 d - dtype: string - - Sort Descending - - >>> s.sort_index(ascending=False) - 4 d - 3 a - 2 b - 1 c - dtype: string - - By default NaNs are put at the end, but use na_position to place them at - the beginning - - >>> s = bpd.Series(['a', 'b', 'c', 'd'], index=[3, 2, 1, np.nan]) - >>> s.sort_index(na_position='first') - d - 1.0 c - 2.0 b - 3.0 a - dtype: string - Args: axis ({0 or 'index'}): Unused. Parameter needed for compatibility with DataFrame. - inplace (bool, default False): - Whether to modify the Series rather than creating a new one. ascending (bool or list-like of bools, default True): Sort ascending vs. descending. When the index is a MultiIndex the sort direction can be controlled for each level individually. - kind (str, default None): - Choice of sorting algorithm. Accepts 'quicksort', 'mergesort', - 'heapsort', 'stable'. Ignored except when determining whether to - sort stably. 'mergesort' or 'stable' will result in stable reorder. na_position ({'first', 'last'}, default 'last'): If 'first' puts NaNs at the beginning, 'last' puts NaNs at the end. Not implemented for MultiIndex. Returns: - bigframes.pandas.Series or None: - The original Series sorted by the labels or None if + bigframes.series.Series: The original Series sorted by the labels or None if ``inplace=True``. """ @@ -1670,67 +679,6 @@ def nlargest( """ Return the largest `n` elements. - **Examples:** - - >>> import bigframes.pandas as bpd - >>> countries_population = {"Italy": 59000000, "France": 65000000, - ... "Malta": 434000, "Maldives": 434000, - ... "Brunei": 434000, "Iceland": 337000, - ... "Nauru": 11300, "Tuvalu": 11300, - ... "Anguilla": 11300, "Montserrat": 5200} - >>> s = bpd.Series(countries_population) - >>> s - Italy 59000000 - France 65000000 - Malta 434000 - Maldives 434000 - Brunei 434000 - Iceland 337000 - Nauru 11300 - Tuvalu 11300 - Anguilla 11300 - Montserrat 5200 - dtype: Int64 - - The n largest elements where `n=5` by default. - - >>> s.nlargest() - France 65000000 - Italy 59000000 - Malta 434000 - Maldives 434000 - Brunei 434000 - dtype: Int64 - - The n largest elements where `n=3`. Default keep value is `first` so Malta - will be kept. - - >>> s.nlargest(3) - France 65000000 - Italy 59000000 - Malta 434000 - dtype: Int64 - - The n largest elements where `n=3` and keeping the last duplicates. Brunei - will be kept since it is the last with value 434000 based on the index order. - - >>> s.nlargest(3, keep='last') - France 65000000 - Italy 59000000 - Brunei 434000 - dtype: Int64 - - The n largest elements where n`=3` with all duplicates kept. Note that the - returned Series has five elements due to the three duplicates. - - >>> s.nlargest(3, keep='all') - France 65000000 - Italy 59000000 - Malta 434000 - Maldives 434000 - Brunei 434000 - dtype: Int64 - Args: n (int, default 5): Return this many descending sorted values. @@ -1745,8 +693,7 @@ def nlargest( size larger than `n`. Returns: - bigframes.pandas.Series: - The `n` largest values in the Series, sorted in decreasing order. + bigframes.series.Series: The `n` largest values in the Series, sorted in decreasing order. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -1754,57 +701,6 @@ def nsmallest(self, n: int = 5, keep: str = "first") -> Series: """ Return the smallest `n` elements. - **Examples:** - - >>> import bigframes.pandas as bpd - >>> countries_population = {"Italy": 59000000, "France": 65000000, - ... "Malta": 434000, "Maldives": 434000, - ... "Brunei": 434000, "Iceland": 337000, - ... "Nauru": 11300, "Tuvalu": 11300, - ... "Anguilla": 11300, "Montserrat": 5200} - >>> s = bpd.Series(countries_population) - >>> s - Italy 59000000 - France 65000000 - Malta 434000 - Maldives 434000 - Brunei 434000 - Iceland 337000 - Nauru 11300 - Tuvalu 11300 - Anguilla 11300 - Montserrat 5200 - dtype: Int64 - - The n smallest elements where `n=5` by default. - - >>> s.nsmallest() - Montserrat 5200 - Nauru 11300 - Tuvalu 11300 - Anguilla 11300 - Iceland 337000 - dtype: Int64 - - The n smallest elements where `n=3`. Default keep value is `first` so - Nauru and Tuvalu will be kept. - - >>> s.nsmallest(3) - Montserrat 5200 - Nauru 11300 - Tuvalu 11300 - dtype: Int64 - - The n smallest elements where `n=3` with all duplicates kept. Note that - the returned Series has four elements due to the three duplicates. - - >>> s.nsmallest(3, keep='all') - Montserrat 5200 - Nauru 11300 - Tuvalu 11300 - Anguilla 11300 - dtype: Int64 - Args: n (int, default 5): Return this many ascending sorted values. @@ -1820,8 +716,7 @@ def nsmallest(self, n: int = 5, keep: str = "first") -> Series: size larger than `n`. Returns: - bigframes.pandas.Series: - The `n` smallest values in the Series, sorted in increasing order. + bigframes.series.Series: The `n` smallest values in the Series, sorted in increasing order. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -1831,193 +726,20 @@ def nsmallest(self, n: int = 5, keep: str = "first") -> Series: def apply( self, func, - by_row="compat", ) -> DataFrame | Series: """ - Invoke function on values of a Series. - - Can be ufunc (a NumPy function that applies to the entire Series) or a - Python function that only works on single values. If it is an arbitrary - python function then converting it into a `remote_function` is recommended. - - **Examples:** - - Simple vectorized functions, lambdas or ufuncs can be applied directly - with `by_row=False`. - - >>> nums = bpd.Series([1, 2, 3, 4]) - >>> nums - 0 1 - 1 2 - 2 3 - 3 4 - dtype: Int64 - >>> nums.apply(lambda x: x*x + 2*x + 1, by_row=False) - 0 4 - 1 9 - 2 16 - 3 25 - dtype: Int64 - - >>> def is_odd(num): - ... return num % 2 == 1 - >>> nums.apply(is_odd, by_row=False) - 0 True - 1 False - 2 True - 3 False - dtype: boolean - - >>> nums.apply(np.log, by_row=False) - 0 0.0 - 1 0.693147 - 2 1.098612 - 3 1.386294 - dtype: Float64 - - Use `remote_function` to apply an arbitrary Python function. - Set ``reuse=False`` flag to make sure a new `remote_function` - is created every time you run the following code. Omit it - to reuse a previously deployed `remote_function` from - the same user defined function if the hash of the function definition - hasn't changed. - - >>> @bpd.remote_function(reuse=False, cloud_function_service_account="default") # doctest: +SKIP - ... def minutes_to_hours(x: int) -> float: - ... return x/60 - - >>> minutes = bpd.Series([0, 30, 60, 90, 120]) - >>> minutes - 0 0 - 1 30 - 2 60 - 3 90 - 4 120 - dtype: Int64 - - >>> hours = minutes.apply(minutes_to_hours) # doctest: +SKIP - >>> hours # doctest: +SKIP - 0 0.0 - 1 0.5 - 2 1.0 - 3 1.5 - 4 2.0 - dtype: Float64 - - To turn a user defined function with external package dependencies into - a `remote_function`, you would provide the names of the packages via - `packages` param. - - >>> @bpd.remote_function( # doctest: +SKIP - ... reuse=False, - ... packages=["cryptography"], - ... cloud_function_service_account="default" - ... ) - ... def get_hash(input: str) -> str: - ... from cryptography.fernet import Fernet - ... - ... # handle missing value - ... if input is None: - ... input = "" - ... - ... key = Fernet.generate_key() - ... f = Fernet(key) - ... return f.encrypt(input.encode()).decode() - - >>> names = bpd.Series(["Alice", "Bob"]) - >>> hashes = names.apply(get_hash) # doctest: +SKIP - - You could return an array output from the remote function. - - >>> @bpd.remote_function(reuse=False, cloud_function_service_account="default") # doctest: +SKIP - ... def text_analyzer(text: str) -> list[int]: - ... words = text.count(" ") + 1 - ... periods = text.count(".") - ... exclamations = text.count("!") - ... questions = text.count("?") - ... return [words, periods, exclamations, questions] - - >>> texts = bpd.Series([ - ... "The quick brown fox jumps over the lazy dog.", - ... "I love this product! It's amazing.", - ... "Hungry? Wanna eat? Lets go!" - ... ]) - >>> features = texts.apply(text_analyzer) # doctest: +SKIP - >>> features # doctest: +SKIP - 0 [9 1 0 0] - 1 [6 1 1 0] - 2 [5 0 1 2] - dtype: list[pyarrow] - - Args: - func (function): - BigFrames DataFrames ``remote_function`` to apply. The function - should take a scalar and return a scalar. It will be applied to - every element in the ``Series``. - by_row (False or "compat", default "compat"): - If `"compat"` , func must be a remote function which will be - passed each element of the Series, like `Series.map`. If False, - the func will be passed the whole Series at once. - - Returns: - bigframes.pandas.Series: - A new Series with values representing the - return value of the ``func`` applied to each element of the - original Series. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def combine( - self, - other: Series | Hashable, - func, - ) -> Series: - """ - Combine the Series with a Series or scalar according to `func`. - - Combine the Series and `other` using `func` to perform elementwise - selection for combined Series. - `fill_value` is assumed when value is missing at some index - from one of the two objects being combined. - - **Examples:** - - Consider 2 Datasets ``s1`` and ``s2`` containing - highest clocked speeds of different birds. - - >>> import bigframes.pandas as bpd - >>> s1 = bpd.Series({'falcon': 330.0, 'eagle': 160.0}) - >>> s1 - falcon 330.0 - eagle 160.0 - dtype: Float64 - >>> s2 = bpd.Series({'falcon': 345.0, 'eagle': 200.0, 'duck': 30.0}) - >>> s2 - falcon 345.0 - eagle 200.0 - duck 30.0 - dtype: Float64 + Invoke function on values of Series. - Now, to combine the two datasets and view the highest speeds - of the birds across the two datasets - - >>> s1.combine(s2, np.maximum) - falcon 345.0 - eagle 200.0 - duck - dtype: Float64 + Can be ufunc (a NumPy function that applies to the entire Series) + or a Python function that only works on single values. Args: - other (Series or scalar): - The value(s) to be combined with the `Series`. func (function): - BigFrames DataFrames ``remote_function`` to apply. - Takes two scalars as inputs and returns an element. - Also accepts some numpy binary functions. + Python function or NumPy ufunc to apply. Returns: - bigframes.pandas.Series: - The result of combining the Series with the other object. + bigframes.series.Series: If func returns a Series object the result + will be a DataFrame. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -2037,92 +759,6 @@ def groupby( used to group large amounts of data and compute operations on these groups. - **Examples:** - - - You can group by a named index level. - - >>> s = bpd.Series([380, 370., 24., 26.], - ... index=["Falcon", "Falcon", "Parrot", "Parrot"], - ... name="Max Speed") - >>> s.index.name="Animal" - >>> s - Animal - Falcon 380.0 - Falcon 370.0 - Parrot 24.0 - Parrot 26.0 - Name: Max Speed, dtype: Float64 - >>> s.groupby("Animal").mean() - Animal - Falcon 375.0 - Parrot 25.0 - Name: Max Speed, dtype: Float64 - - You can also group by more than one index levels. - - >>> s = bpd.Series([380, 370., 24., 26.], - ... index=pd.MultiIndex.from_tuples( - ... [("Falcon", "Clear"), - ... ("Falcon", "Cloudy"), - ... ("Parrot", "Clear"), - ... ("Parrot", "Clear")], - ... names=["Animal", "Sky"]), - ... name="Max Speed") - >>> s - Animal Sky - Falcon Clear 380.0 - Cloudy 370.0 - Parrot Clear 24.0 - Clear 26.0 - Name: Max Speed, dtype: Float64 - - >>> s.groupby("Animal").mean() - Animal - Falcon 375.0 - Parrot 25.0 - Name: Max Speed, dtype: Float64 - - >>> s.groupby("Sky").mean() - Sky - Clear 143.333333 - Cloudy 370.0 - Name: Max Speed, dtype: Float64 - - >>> s.groupby(["Animal", "Sky"]).mean() - Animal Sky - Falcon Clear 380.0 - Cloudy 370.0 - Parrot Clear 25.0 - Name: Max Speed, dtype: Float64 - - You can also group by values in a Series provided the index matches with - the original series. - - >>> df = bpd.DataFrame({'Animal': ['Falcon', 'Falcon', 'Parrot', 'Parrot'], - ... 'Max Speed': [380., 370., 24., 26.], - ... 'Age': [10., 20., 4., 6.]}) - >>> df - Animal Max Speed Age - 0 Falcon 380.0 10.0 - 1 Falcon 370.0 20.0 - 2 Parrot 24.0 4.0 - 3 Parrot 26.0 6.0 - - [4 rows x 3 columns] - - >>> df['Max Speed'].groupby(df['Animal']).mean() - Animal - Falcon 375.0 - Parrot 25.0 - Name: Max Speed, dtype: Float64 - - >>> df['Age'].groupby(df['Animal']).max() - Animal - Falcon 20.0 - Parrot 6.0 - Name: Age, dtype: Float64 - Args: by (mapping, function, label, pd.Grouper or list of such, default None): Used to determine the groups for the groupby. @@ -2156,8 +792,7 @@ def groupby( If False, NA values will also be treated as the key in groups. Returns: - bigframes.core.groupby.SeriesGroupBy: - Returns a groupby object that contains + bigframes.core.groupby.SeriesGroupBy: Returns a groupby object that contains information about the groups. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -2207,52 +842,6 @@ def drop( When using a multi-index, labels on different levels can be removed by specifying the level. - **Examples:** - - - >>> s = bpd.Series(data=np.arange(3), index=['A', 'B', 'C']) - >>> s - A 0 - B 1 - C 2 - dtype: Int64 - - Drop labels B and C: - - >>> s.drop(labels=['B', 'C']) - A 0 - dtype: Int64 - - Drop 2nd level label in MultiIndex Series: - - >>> midx = pd.MultiIndex(levels=[['llama', 'cow', 'falcon'], - ... ['speed', 'weight', 'length']], - ... codes=[[0, 0, 0, 1, 1, 1, 2, 2, 2], - ... [0, 1, 2, 0, 1, 2, 0, 1, 2]]) - - >>> s = bpd.Series([45, 200, 1.2, 30, 250, 1.5, 320, 1, 0.3], - ... index=midx) - >>> s - llama speed 45.0 - weight 200.0 - length 1.2 - cow speed 30.0 - weight 250.0 - length 1.5 - falcon speed 320.0 - weight 1.0 - length 0.3 - dtype: Float64 - - >>> s.drop(labels='weight', level=1) - llama speed 45.0 - length 1.2 - cow speed 30.0 - length 1.5 - falcon speed 320.0 - length 0.3 - dtype: Float64 - Args: labels (single label or list-like): Index labels to drop. @@ -2267,13 +856,11 @@ def drop( For MultiIndex, level for which the labels will be removed. Returns: - bigframes.pandas.Series or None: - Series with specified index labels removed + bigframes.series.Series: Series with specified index labels removed or None if ``inplace=True``. Raises: - KeyError: - If none of the labels are found in the index. + KeyError: If none of the labels are found in the index. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -2307,8 +894,7 @@ def swaplevel(self, i, j): Levels of the indices to be swapped. Can pass level name as string. Returns: - bigframes.pandas.Series: - Series with levels swapped in MultiIndex + Series: Series with levels swapped in MultiIndex """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -2326,8 +912,7 @@ def droplevel(self, level, axis): For `Series` this parameter is unused and defaults to 0. Returns: - bigframes.pandas.Series: - Series with requested index / column level(s) removed. + Series with requested index / column level(s) removed. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -2335,38 +920,31 @@ def interpolate(self, method: str = "linear"): """ Fill NaN values using an interpolation method. - **Examples:** - - - Filling in NaN in a Series via linear interpolation. - - >>> s = bpd.Series([0, 1, np.nan, 3]) - >>> s - 0 0.0 - 1 1.0 - 2 - 3 3.0 - dtype: Float64 - - >>> s.interpolate() - 0 0.0 - 1 1.0 - 2 2.0 - 3 3.0 - dtype: Float64 - Args: method (str, default 'linear'): Interpolation technique to use. Only 'linear' supported. 'linear': Ignore the index and treat the values as equally spaced. This is the only method supported on MultiIndexes. - 'index', 'values': use the actual numerical values of the index. - 'pad': Fill in NaNs using existing values. - 'nearest', 'zero', 'slinear': Emulates `scipy.interpolate.interp1d` + Returns: - bigframes.pandas.Series: + Series: Returns the same object type as the caller, interpolated at some or all ``NaN`` values + + **Examples:** + + >>> import bigframes.pandas as bpd + >>> bpd.options.display.progress_bar = None + + >>> series = bpd.Series([1, 2, 3, None, None, 6]) + >>> series.interpolate() + 0 1.0 + 1 2.0 + 2 3.0 + 3 4.0 + 4 5.0 + 5 6.0 + dtype: Float64 """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -2375,52 +953,14 @@ def fillna( value=None, ) -> Series | None: """ - Fill NA (NULL in BigQuery) values using the specified method. - - Note that empty strings ``''``, :attr:`numpy.inf`, and - :attr:`numpy.nan` are ***not*** considered NA values. This NA/NULL - logic differs from numpy, but it is the same as BigQuery and the - :class:`pandas.ArrowDtype`. - - **Examples:** - - >>> s = bpd.Series( - ... pa.array([np.nan, 2, None, -1], type=pa.float64()), - ... dtype=pd.ArrowDtype(pa.float64()), - ... ) - >>> s - 0 NaN - 1 2.0 - 2 - 3 -1.0 - dtype: Float64 - - Replace all NA (NULL) elements with 0s. - - >>> s.fillna(0) - 0 NaN - 1 2.0 - 2 0.0 - 3 -1.0 - dtype: Float64 - - You can use fill values from another Series: - - >>> s_fill = bpd.Series([11, 22, 33]) - >>> s.fillna(s_fill) - 0 NaN - 1 2.0 - 2 33.0 - 3 -1.0 - dtype: Float64 + Fill NA/NaN values using the specified method. Args: value (scalar, dict, Series, or DataFrame, default None): Value to use to fill holes (e.g. 0). Returns: - bigframes.pandas.Series or None: - Object with missing values filled or None. + Series or None: Object with missing values filled or None. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -2436,75 +976,6 @@ def replace( This differs from updating with ``.loc`` or ``.iloc``, which require you to specify a location to update with some value. - **Examples:** - - >>> import bigframes.pandas as bpd - >>> s = bpd.Series([1, 2, 3, 4, 5]) - >>> s - 0 1 - 1 2 - 2 3 - 3 4 - 4 5 - dtype: Int64 - - >>> s.replace(1, 5) - 0 5 - 1 2 - 2 3 - 3 4 - 4 5 - dtype: Int64 - - You can replace a list of values: - - >>> s.replace([1, 3, 5], -1) - 0 -1 - 1 2 - 2 -1 - 3 4 - 4 -1 - dtype: Int64 - - You can use a replacement mapping: - - >>> s.replace({1: 5, 3: 10}) - 0 5 - 1 2 - 2 10 - 3 4 - 4 5 - dtype: Int64 - - With a string Series you can use a simple string replacement or a regex - replacement: - - >>> s = bpd.Series(["Hello", "Another Hello"]) - >>> s.replace("Hello", "Hi") - 0 Hi - 1 Another Hello - dtype: string - - >>> s.replace("Hello", "Hi", regex=True) - 0 Hi - 1 Another Hi - dtype: string - - >>> s.replace("^Hello", "Hi", regex=True) - 0 Hi - 1 Another Hello - dtype: string - - >>> s.replace("Hello$", "Hi", regex=True) - 0 Hi - 1 Another Hi - dtype: string - - >>> s.replace("[Hh]e", "__", regex=True) - 0 __llo - 1 Anot__r __llo - dtype: string - Args: to_replace (str, regex, list, int, float or None): How to find the values that will be replaced. @@ -2540,8 +1011,7 @@ def replace( string. Returns: - bigframes.pandas.Series or bigframes.pandas.DataFrame: - Object after replacement. + Series/DataFrame: Object after replacement. Raises: TypeError: @@ -2557,107 +1027,10 @@ def replace( """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - def resample( - self, - rule: str, - *, - closed: Optional[Literal["right", "left"]] = None, - label: Optional[Literal["right", "left"]] = None, - level=None, - origin: Union[ - Union[pd.Timestamp, datetime.datetime, numpy.datetime64, int, float, str], - Literal["epoch", "start", "start_day", "end", "end_day"], - ] = "start_day", - ): - """Resample time-series data. - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> data = { - ... "timestamp_col": pd.date_range( - ... start="2021-01-01 13:00:00", periods=30, freq="1s" - ... ), - ... "int64_col": range(30), - ... } - >>> s = bpd.DataFrame(data).set_index("timestamp_col") - >>> s.resample(rule="7s", origin="epoch").min() - int64_col - timestamp_col - 2021-01-01 12:59:56 0 - 2021-01-01 13:00:03 3 - 2021-01-01 13:00:10 10 - 2021-01-01 13:00:17 17 - 2021-01-01 13:00:24 24 - - [5 rows x 1 columns] - - Args: - rule (str): - The offset string representing target conversion. - Offsets 'ME', 'YE', 'QE', 'BME', 'BA', 'BQE', and 'W' are *not* - supported. - closed (Literal['left'] | None): - Which side of bin interval is closed. The default is 'left' for - all supported frequency offsets. - label (Literal['right'] | Literal['left'] | None): - Which bin edge label to label bucket with. The default is 'left' - for all supported frequency offsets. - on (str, default None): - For a DataFrame, column to use instead of index for resampling. Column - must be datetime-like. - level (str or int, default None): - For a MultiIndex, level (name or number) to use for resampling. - level must be datetime-like. - origin(str, default 'start_day'): - The timestamp on which to adjust the grouping. Must be one of the following: - 'epoch': origin is 1970-01-01 - 'start': origin is the first value of the timeseries - 'start_day': origin is the first day at midnight of the timeseries - Origin values 'end' and 'end_day' are *not* supported. - Returns: - SeriesGroupBy: SeriesGroupBy object. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - def dropna(self, *, axis=0, inplace: bool = False, how=None) -> Series: """ Return a new Series with missing values removed. - **Examples:** - - - Drop NA values from a Series: - - >>> ser = bpd.Series([1., 2., np.nan]) - >>> ser - 0 1.0 - 1 2.0 - 2 - dtype: Float64 - - >>> ser.dropna() - 0 1.0 - 1 2.0 - dtype: Float64 - - Empty strings are not considered NA values. ``None`` is considered an NA value. - - >>> ser = bpd.Series(['2', pd.NA, '', None, 'I stay'], dtype='object') - >>> ser - 0 2 - 1 - 2 - 3 - 4 I stay - dtype: string - - >>> ser.dropna() - 0 2 - 2 - 4 I stay - dtype: string - Args: axis (0 or 'index'): Unused. Parameter needed for compatibility with DataFrame. @@ -2667,8 +1040,7 @@ def dropna(self, *, axis=0, inplace: bool = False, how=None) -> Series: Not in use. Kept for compatibility. Returns: - bigframes.pandas.Series: - Series with NA entries dropped from it. + Series: Series with NA entries dropped from it. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -2685,40 +1057,6 @@ def between( corresponding Series element is between the boundary values `left` and `right`. NA values are treated as `False`. - **Examples:** - - - Boundary values are included by default: - - >>> s = bpd.Series([2, 0, 4, 8, np.nan]) - >>> s.between(1, 4) - 0 True - 1 False - 2 True - 3 False - 4 - dtype: boolean - - With inclusive set to "neither" boundary values are excluded: - - >>> s.between(1, 4, inclusive="neither") - 0 True - 1 False - 2 False - 3 False - 4 - dtype: boolean - - left and right can be any scalar value: - - >>> s = bpd.Series(['Alice', 'Bob', 'Carol', 'Eve']) - >>> s.between('Anna', 'Daniel') - 0 False - 1 True - 2 True - 3 False - dtype: boolean - Args: left (scalar or list-like): Left boundary. @@ -2728,75 +1066,9 @@ def between( Include boundaries. Whether to set each bound as closed or open. Returns: - bigframes.pandas.Series: - Series representing whether each element is between left and - right (inclusive). - - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def case_when( - self, - caselist: List[Tuple[Series, Series]], - ) -> Series: - """Replace values where the conditions are True. - - **Examples:** - - - >>> c = bpd.Series([6, 7, 8, 9], name="c") - >>> a = bpd.Series([0, 0, 1, 2]) - >>> b = bpd.Series([0, 3, 4, 5]) - - >>> c.case_when( - ... caselist=[ - ... (a.gt(0), a), # condition, replacement - ... (b.gt(0), b), - ... ] - ... ) - 0 6 - 1 3 - 2 1 - 3 2 - Name: c, dtype: Int64 - - If you'd like to change the type, add a case with the condition True at the end of the case list - - >>> c.case_when( - ... caselist=[ - ... (a.gt(0), 'a'), # condition, replacement - ... (b.gt(0), 'b'), - ... (True, 'c'), - ... ] - ... ) - 0 c - 1 b - 2 a - 3 a - Name: c, dtype: string - - **See also:** - - - :func:`bigframes.pandas.Series.mask` : Replace values where the condition is True. - - Args: - caselist (A list of tuples of conditions and expected replacements): - Takes the form: ``(condition0, replacement0)``, - ``(condition1, replacement1)``, ... . - ``condition`` should be a 1-D boolean array-like object - or a callable. If ``condition`` is a callable, - it is computed on the Series - and should return a boolean Series or array. - The callable must not change the input Series - (though pandas doesn`t check it). ``replacement`` should be a - 1-D array-like object, a scalar or a callable. - If ``replacement`` is a callable, it is computed on the Series - and should return a scalar or Series. The callable - must not change the input Series - (though pandas doesn`t check it). + Series: Series representing whether each element is between left and + right (inclusive). - Returns: - bigframes.pandas.Series """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -2807,31 +1079,8 @@ def cumprod(self): Returns a DataFrame or Series of the same size containing the cumulative product. - **Examples:** - - >>> import bigframes.pandas as bpd - >>> s = bpd.Series([2, np.nan, 5, -1, 0]) - >>> s - 0 2.0 - 1 - 2 5.0 - 3 -1.0 - 4 0.0 - dtype: Float64 - - By default, NA values are ignored. - - >>> s.cumprod() - 0 2.0 - 1 - 2 10.0 - 3 -10.0 - 4 0.0 - dtype: Float64 - Returns: - bigframes.pandas.Series: - Return cumulative sum of scalar or Series. + bigframes.series.Series: Return cumulative sum of scalar or Series. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -2842,36 +1091,13 @@ def cumsum(self): Returns a DataFrame or Series of the same size containing the cumulative sum. - **Examples:** - - - >>> s = bpd.Series([2, np.nan, 5, -1, 0]) - >>> s - 0 2.0 - 1 - 2 5.0 - 3 -1.0 - 4 0.0 - dtype: Float64 - - By default, NA values are ignored. - - >>> s.cumsum() - 0 2.0 - 1 - 2 7.0 - 3 6.0 - 4 6.0 - dtype: Float64 - Args: axis ({0 or 'index', 1 or 'columns'}, default 0): The index or the name of the axis. 0 is equivalent to None or 'index'. For `Series` this parameter is unused and defaults to 0. Returns: - bigframes.pandas.Series: - Return cumulative sum of scalar or Series. + scalar or Series: Return cumulative sum of scalar or Series. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -2882,32 +1108,13 @@ def cummax(self): Returns a DataFrame or Series of the same size containing the cumulative maximum. - **Examples:** - - - >>> s = bpd.Series([2, np.nan, 5, -1, 0]) - >>> s - 0 2.0 - 1 - 2 5.0 - 3 -1.0 - 4 0.0 - dtype: Float64 - - By default, NA values are ignored. - - >>> s.cummax() - 0 2.0 - 1 - 2 5.0 - 3 5.0 - 4 5.0 - dtype: Float64 - + Args: + axis ({{0 or 'index', 1 or 'columns'}}, default 0): + The index or the name of the axis. 0 is equivalent to None or 'index'. + For `Series` this parameter is unused and defaults to 0. Returns: - bigframes.pandas.Series: - Return cumulative maximum of scalar or Series. + bigframes.series.Series: Return cumulative maximum of scalar or Series. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -2918,69 +1125,30 @@ def cummin(self): Returns a DataFrame or Series of the same size containing the cumulative minimum. - **Examples:** - - - >>> s = bpd.Series([2, np.nan, 5, -1, 0]) - >>> s - 0 2.0 - 1 - 2 5.0 - 3 -1.0 - 4 0.0 - dtype: Float64 - - By default, NA values are ignored. - - >>> s.cummin() - 0 2.0 - 1 - 2 2.0 - 3 -1.0 - 4 -1.0 - dtype: Float64 + Args: + axis ({0 or 'index', 1 or 'columns'}, default 0): + The index or the name of the axis. 0 is equivalent to None or 'index'. + For `Series` this parameter is unused and defaults to 0. + skipna (bool, default True): + Exclude NA/null values. If an entire row/column is NA, the result + will be NA. + `*args`, `**kwargs`: + Additional keywords have no effect but might be accepted for + compatibility with NumPy. Returns: - bigframes.pandas.Series: - Return cumulative minimum of scalar or Series. + bigframes.series.Series: Return cumulative minimum of scalar or Series. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) def eq(self, other) -> Series: """Return equal of Series and other, element-wise (binary operator eq). - Equivalent to ``other == series``, but with support to substitute a - fill_value for missing data in either one of the inputs. - - **Examples:** - - - >>> a = bpd.Series([1, 1, 1, np.nan], index=['a', 'b', 'c', 'd']) - >>> a - a 1.0 - b 1.0 - c 1.0 - d - dtype: Float64 - - >>> b = bpd.Series([1, np.nan, 1, np.nan], index=['a', 'b', 'd', 'e']) - >>> b - a 1.0 - b - d 1.0 - e - dtype: Float64 - - >>> a.eq(b) - a True - b - c - d - e - dtype: boolean + Equivalent to ``other == series``, but with support to substitute a fill_value for + missing data in either one of the inputs. Args: - other (Series or scalar value) + other (Series, or scalar value): Returns: Series: The result of the operation. @@ -2991,1985 +1159,469 @@ def eq(self, other) -> Series: def ne(self, other) -> Series: """Return not equal of Series and other, element-wise (binary operator ne). - Equivalent to ``other != series``, but with support to substitute a - fill_value for missing data in either one of the inputs. + Equivalent to ``other != series``, but with support to substitute a fill_value for + missing data in either one of the inputs. - **Examples:** - - - >>> a = bpd.Series([1, 1, 1, np.nan], index=['a', 'b', 'c', 'd']) - >>> a - a 1.0 - b 1.0 - c 1.0 - d - dtype: Float64 - - >>> b = bpd.Series([1, np.nan, 1, np.nan], index=['a', 'b', 'd', 'e']) - >>> b - a 1.0 - b - d 1.0 - e - dtype: Float64 - - >>> a.ne(b) - a False - b - c - d - e - dtype: boolean - - Args: - other (Series or scalar value) + Args: + other (Series, or scalar value): Returns: - bigframes.pandas.Series: - The result of the operation. + bigframes.series.Series: The result of the operation. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) def le(self, other) -> Series: - """Get 'less than or equal to' of Series and other, element-wise (binary - operator le). - - Equivalent to ``series <= other``, but with support to substitute a - fill_value for missing data in either one of the inputs. - - **Examples:** - - - >>> a = bpd.Series([1, 1, 1, np.nan], index=['a', 'b', 'c', 'd']) - >>> a - a 1.0 - b 1.0 - c 1.0 - d - dtype: Float64 - - >>> b = bpd.Series([1, np.nan, 1, np.nan], index=['a', 'b', 'd', 'e']) - >>> b - a 1.0 - b - d 1.0 - e - dtype: Float64 + """Get 'less than or equal to' of Series and other, element-wise (binary operator `<=`). - >>> a.le(b) - a True - b - c - d - e - dtype: boolean + Equivalent to ``series <= other``, but with support to substitute a fill_value for + missing data in either one of the inputs. Args: other: Series, or scalar value Returns: - bigframes.pandas.Series: - The result of the comparison. + bigframes.series.Series. The result of the comparison. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) def lt(self, other) -> Series: - """Get 'less than' of Series and other, element-wise (binary operator lt). + """Get 'less than' of Series and other, element-wise (binary operator `<`). - Equivalent to ``series < other``, but with support to substitute a - fill_value for missing data in either one of the inputs. - - **Examples:** - - - >>> a = bpd.Series([1, 1, 1, np.nan], index=['a', 'b', 'c', 'd']) - >>> a - a 1.0 - b 1.0 - c 1.0 - d - dtype: Float64 - - >>> b = bpd.Series([1, np.nan, 1, np.nan], index=['a', 'b', 'd', 'e']) - >>> b - a 1.0 - b - d 1.0 - e - dtype: Float64 - - >>> a.lt(b) - a False - b - c - d - e - dtype: boolean + Equivalent to ``series < other``, but with support to substitute a fill_value for + missing data in either one of the inputs. Args: - other (Series or scalar value) + other (Series, or scalar value): - Returns: - bigframes.pandas.Series: - The result of the operation. + Returns: + bigframes.series.Series: The result of the operation. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) def ge(self, other) -> Series: - """Get 'greater than or equal to' of Series and other, element-wise - (binary operator ge). - - Equivalent to ``series >= other``, but with support to substitute a - fill_value for missing data in either one of the inputs. - - **Examples:** - - - >>> a = bpd.Series([1, 1, 1, np.nan], index=['a', 'b', 'c', 'd']) - >>> a - a 1.0 - b 1.0 - c 1.0 - d - dtype: Float64 - - >>> b = bpd.Series([1, np.nan, 1, np.nan], index=['a', 'b', 'd', 'e']) - >>> b - a 1.0 - b - d 1.0 - e - dtype: Float64 + """Get 'greater than or equal to' of Series and other, element-wise (binary operator `>=`). - >>> a.ge(b) - a True - b - c - d - e - dtype: boolean + Equivalent to ``series >= other``, but with support to substitute a fill_value for + missing data in either one of the inputs. Args: - other (Series or scalar value) + other (Series, or scalar value): Returns: - bigframes.pandas.Series: - The result of the operation. + bigframes.series.Series: The result of the operation. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) def gt(self, other) -> Series: - """Return Greater than of series and other, element-wise - (binary operator gt). - - Equivalent to ``series <= other``, but with support to substitute a - fill_value for missing data in either one of the inputs. - - **Examples:** - - - >>> a = bpd.Series([1, 1, 1, np.nan], index=['a', 'b', 'c', 'd']) - >>> a - a 1.0 - b 1.0 - c 1.0 - d - dtype: Float64 - - >>> b = bpd.Series([1, np.nan, 1, np.nan], index=['a', 'b', 'd', 'e']) - >>> b - a 1.0 - b - d 1.0 - e - dtype: Float64 + """Get 'less than or equal to' of Series and other, element-wise (binary operator `<=`). - >>> a.gt(b) - a False - b - c - d - e - dtype: boolean + Equivalent to ``series <= other``, but with support to substitute a fill_value for + missing data in either one of the inputs. Args: - other (Series or scalar value) + other (Series, or scalar value): Returns: - bigframes.pandas.Series: The result of the operation. + bigframes.series.Series: The result of the operation. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) def add(self, other) -> Series: - """Return addition of Series and other, element-wise (binary operator - add). - - Equivalent to ``series + other``, but with support to substitute a - fill_value for missing data in either one of the inputs. - - **Examples:** + """Return addition of Series and other, element-wise (binary operator add). - - >>> a = bpd.Series([1, 2, 3, pd.NA]) - >>> a - 0 1 - 1 2 - 2 3 - 3 - dtype: Int64 - - >>> b = bpd.Series([10, 20, 30, 40]) - >>> b - 0 10 - 1 20 - 2 30 - 3 40 - dtype: Int64 - - >>> a.add(b) - 0 11 - 1 22 - 2 33 - 3 - dtype: Int64 - - You can also use the mathematical operator ``+``: - - >>> a + b - 0 11 - 1 22 - 2 33 - 3 - dtype: Int64 - - Adding two Series with explicit indexes: - - >>> a = bpd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd']) - >>> b = bpd.Series([10, 20, 30, 40], index=['a', 'b', 'd', 'e']) - >>> a.add(b) - a 11 - b 22 - c - d 34 - e - dtype: Int64 + Equivalent to ``series + other``, but with support to substitute a fill_value for + missing data in either one of the inputs. Args: - other (Series or scalar value) + other (Series, or scalar value): Returns: - bigframes.pandas.Series: - The result of the operation. + bigframes.series.Series: The result of the operation. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - def __add__(self, other): - """Get addition of Series and other, element-wise, using operator `+`. - - Equivalent to `Series.add(other)`. - - **Examples:** - - - >>> s = bpd.Series([1.5, 2.6], index=['elk', 'moose']) - >>> s - elk 1.5 - moose 2.6 - dtype: Float64 + def radd(self, other) -> Series: + """Return addition of Series and other, element-wise (binary operator radd). - You can add a scalar. + Equivalent to ``other + series``, but with support to substitute a fill_value for + missing data in either one of the inputs. - >>> s + 1.5 - elk 3.0 - moose 4.1 - dtype: Float64 + Args: + other (Series, or scalar value): - You can add another Series with index aligned. + Returns: + bigframes.series.Series: The result of the operation. - >>> delta = bpd.Series([1.5, 2.6], index=['elk', 'moose']) - >>> s + delta - elk 3.0 - moose 5.2 - dtype: Float64 + """ + raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - Adding any mis-aligned index will result in invalid values. + def sub( + self, + other, + ) -> Series: + """Return subtraction of Series and other, element-wise (binary operator sub). - >>> delta = bpd.Series([1.5, 2.6], index=['moose', 'bison']) - >>> s + delta - elk - moose 4.1 - bison - dtype: Float64 + Equivalent to ``series - other``, but with support to substitute a fill_value for + missing data in either one of the inputs. Args: - other (scalar or Series): - Object to be added to the Series. + other (Series, or scalar value): Returns: - bigframes.pandas.Series: - The result of adding `other` to Series. + bigframes.series.Series: The result of the operation. + """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - def radd(self, other) -> Series: - """Return addition of Series and other, element-wise (binary operator - radd). + def rsub(self, other) -> Series: + """Return subtraction of Series and other, element-wise (binary operator rsub). - Equivalent to ``other + series``, but with support to substitute a - fill_value for missing data in either one of the inputs. + Equivalent to ``other - series``, but with support to substitute a fill_value for + missing data in either one of the inputs. - **Examples:** + Args: + other (Series, or scalar value): + Returns: + bigframes.series.Series: The result of the operation. - >>> a = bpd.Series([1, 1, 1, np.nan], index=['a', 'b', 'c', 'd']) - >>> a - a 1.0 - b 1.0 - c 1.0 - d - dtype: Float64 + """ + raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - >>> b = bpd.Series([1, np.nan, 1, np.nan], index=['a', 'b', 'd', 'e']) - >>> b - a 1.0 - b - d 1.0 - e - dtype: Float64 + def mul(self, other) -> Series: + """Return multiplication of Series and other, element-wise (binary operator mul). - >>> a.add(b) - a 2.0 - b - c - d - e - dtype: Float64 + Equivalent to ``other * series``, but with support to substitute a fill_value for + missing data in either one of the inputs. Args: - other (Series, or scalar value) + other (Series, or scalar value): Returns: - bigframes.pandas.Series: - The result of the operation. + bigframes.series.Series: The result of the operation. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - def __radd__(self, other): - """Get addition of Series and other, element-wise, using operator `+`. + def rmul(self, other) -> Series: + """Return multiplication of Series and other, element-wise (binary operator mul). - Equivalent to `Series.radd(other)`. + Equivalent to ``series * others``, but with support to substitute a fill_value for + missing data in either one of the inputs. Args: - other (scalar or Series): - Object to which Series should be added. + other (Series, or scalar value): Returns: - bigframes.pandas.Series: - The result of adding Series to `other`. + Series: The result of the operation. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - def sub( - self, - other, - ) -> Series: - """Return subtraction of Series and other, element-wise (binary operator - sub). - - Equivalent to ``series - other``, but with support to substitute a - fill_value for missing data in either one of the inputs. - - **Examples:** - - - >>> a = bpd.Series([1, 1, 1, np.nan], index=['a', 'b', 'c', 'd']) - >>> a - a 1.0 - b 1.0 - c 1.0 - d - dtype: Float64 - - >>> b = bpd.Series([1, np.nan, 1, np.nan], index=['a', 'b', 'd', 'e']) - >>> b - a 1.0 - b - d 1.0 - e - dtype: Float64 + def truediv(self, other) -> Series: + """Return floating division of Series and other, element-wise (binary operator truediv). - >>> a.subtract(b) - a 0.0 - b - c - d - e - dtype: Float64 + Equivalent to ``series / other``, but with support to substitute a fill_value for + missing data in either one of the inputs. Args: - other (Series, or scalar value) + other (Series, or scalar value): Returns: - bigframes.pandas.Series: - The result of the operation. + bigframes.series.Series: The result of the operation. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - def __sub__(self, other): - """Get subtraction of other from Series, element-wise, using operator `-`. - - Equivalent to `Series.sub(other)`. - - **Examples:** - - - >>> s = bpd.Series([1.5, 2.6], index=['elk', 'moose']) - >>> s - elk 1.5 - moose 2.6 - dtype: Float64 - - You can subtract a scalar. - - >>> s - 1.5 - elk 0.0 - moose 1.1 - dtype: Float64 - - You can subtract another Series with index aligned. - - >>> delta = bpd.Series([0.5, 1.0], index=['elk', 'moose']) - >>> s - delta - elk 1.0 - moose 1.6 - dtype: Float64 - - Adding any mis-aligned index will result in invalid values. + def rtruediv(self, other) -> Series: + """Return floating division of Series and other, element-wise (binary operator rtruediv). - >>> delta = bpd.Series([0.5, 1.0], index=['moose', 'bison']) - >>> s - delta - elk - moose 2.1 - bison - dtype: Float64 + Equivalent to ``other / series``, but with support to substitute a fill_value for + missing data in either one of the inputs. Args: - other (scalar or Series): - Object to subtract from the Series. + other (Series, or scalar value): Returns: - bigframes.pandas.Series: - The result of subtraction. + bigframes.series.Series: The result of the operation. + """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - def rsub(self, other) -> Series: - """Return subtraction of Series and other, element-wise (binary operator - rsub). - - Equivalent to ``other - series``, but with support to substitute a - fill_value for missing data in either one of the inputs. - - **Examples:** - - - >>> a = bpd.Series([1, 1, 1, np.nan], index=['a', 'b', 'c', 'd']) - >>> a - a 1.0 - b 1.0 - c 1.0 - d - dtype: Float64 - - >>> b = bpd.Series([1, np.nan, 1, np.nan], index=['a', 'b', 'd', 'e']) - >>> b - a 1.0 - b - d 1.0 - e - dtype: Float64 + def floordiv(self, other) -> Series: + """Return integer division of Series and other, element-wise (binary operator floordiv). - >>> a.subtract(b) - a 0.0 - b - c - d - e - dtype: Float64 + Equivalent to ``series // other``, but with support to substitute a fill_value for + missing data in either one of the inputs. Args: - other (Series, or scalar value) + other (Series, or scalar value): Returns: - bigframes.pandas.Series: - The result of the operation. + bigframes.series.Series: The result of the operation. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - def __rsub__(self, other): - """Get subtraction of Series from other, element-wise, using operator `-`. + def rfloordiv(self, other) -> Series: + """Return integer division of Series and other, element-wise (binary operator rfloordiv). - Equivalent to `Series.rsub(other)`. + Equivalent to ``other // series``, but with support to substitute a fill_value for + missing data in either one of the inputs. Args: - other (scalar or Series): - Object to subtract the Series from. + other (Series, or scalar value): Returns: - bigframes.pandas.Series: - The result of subtraction. + bigframes.series.Series: The result of the operation. + """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - def mul(self, other) -> Series: - """Return multiplication of Series and other, element-wise (binary - operator mul). + def mod(self, other) -> Series: + """Return modulo of Series and other, element-wise (binary operator mod). - Equivalent to ``other * series``, but with support to substitute a - fill_value for missing data in either one of the inputs. + Equivalent to ``series % other``, but with support to substitute a fill_value for + missing data in either one of the inputs. - **Examples:** + Args: + other (Series, or scalar value): + Returns: + bigframes.series.Series: The result of the operation. - >>> a = bpd.Series([1, 1, 1, np.nan], index=['a', 'b', 'c', 'd']) - >>> a - a 1.0 - b 1.0 - c 1.0 - d - dtype: Float64 + """ + raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - >>> b = bpd.Series([1, np.nan, 1, np.nan], index=['a', 'b', 'd', 'e']) - >>> b - a 1.0 - b - d 1.0 - e - dtype: Float64 + def rmod(self, other) -> Series: + """Return modulo of Series and other, element-wise (binary operator mod). - >>> a.multiply(b) - a 1.0 - b - c - d - e - dtype: Float64 + Equivalent to ``series % other``, but with support to substitute a fill_value for + missing data in either one of the inputs. Args: - other (Series or scalar value) + other (Series, or scalar value): Returns: - bigframes.pandas.Series: - The result of the operation. + bigframes.series.Series: The result of the operation. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - def __mul__(self, other): - """ - Get multiplication of Series with other, element-wise, using operator `*`. - - Equivalent to `Series.mul(other)`. + def pow(self, other) -> Series: + """Return Exponential power of series and other, element-wise (binary operator `pow`). - **Examples:** + Equivalent to ``series ** other``, but with support to substitute a fill_value for + missing data in either one of the inputs. + Args: + other (Series, or scalar value): - You can multiply with a scalar: + Returns: + bigframes.series.Series: The result of the operation. - >>> s = bpd.Series([1, 2, 3]) - >>> s * 3 - 0 3 - 1 6 - 2 9 - dtype: Int64 + """ + raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - You can also multiply with another Series: + def rpow(self, other) -> Series: + """Return Exponential power of series and other, element-wise (binary operator `rpow`). - >>> s1 = bpd.Series([2, 3, 4]) - >>> s * s1 - 0 2 - 1 6 - 2 12 - dtype: Int64 + Equivalent to ``other ** series``, but with support to substitute a fill_value for + missing data in either one of the inputs. Args: - other (scalar or Series): - Object to multiply with the Series. + other (Series, or scalar value): Returns: - bigframes.pandas.Series: - The result of the multiplication. + bigframes.series.Series: The result of the operation. + """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - def rmul(self, other) -> Series: - """Return multiplication of Series and other, element-wise (binary - operator mul). + def divmod(self, other) -> Series: + """Return integer division and modulo of Series and other, element-wise (binary operator divmod). - Equivalent to ``series * others``, but with support to substitute a - fill_value for missing data in either one of the inputs. + Equivalent to divmod(series, other). - **Examples:** + Args: + other: Series, or scalar value + Returns: + 2-Tuple of Series. The result of the operation. The result is always + consistent with (floordiv, mod) (though pandas may not). - >>> a = bpd.Series([1, 1, 1, np.nan], index=['a', 'b', 'c', 'd']) - >>> a - a 1.0 - b 1.0 - c 1.0 - d - dtype: Float64 + """ + raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - >>> b = bpd.Series([1, np.nan, 1, np.nan], index=['a', 'b', 'd', 'e']) - >>> b - a 1.0 - b - d 1.0 - e - dtype: Float64 + def rdivmod(self, other) -> Series: + """Return integer division and modulo of Series and other, element-wise (binary operator rdivmod). - >>> a.multiply(b) - a 1.0 - b - c - d - e - dtype: Float64 + Equivalent to other divmod series. Args: - other (Series or scalar value) + other: Series, or scalar value Returns: - bigframes.pandas.Series: - The result of the operation. + 2-Tuple of Series. The result of the operation. The result is always + consistent with (rfloordiv, rmod) (though pandas may not). + """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - def __rmul__(self, other): + def all( + self, + ): """ - Get multiplication of other with Series, element-wise, using operator `*`. - - Equivalent to `Series.rmul(other)`. + Return whether all elements are True, potentially over an axis. - Args: - other (scalar or Series): - Object to multiply the Series with. + Returns True unless there at least one element within a Series or along a + DataFrame axis that is False or equivalent (e.g. zero or empty). Returns: - bigframes.pandas.Series: The result of the multiplication. + scalar or Series: If level is specified, then, Series is returned; + otherwise, scalar is returned. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - def truediv(self, other) -> Series: - """Return floating division of Series and other, element-wise (binary - operator truediv). - - Equivalent to ``series / other``, but with support to substitute a - fill_value for missing data in either one of the inputs. - - **Examples:** + def any( + self, + ): + """ + Return whether any element is True, potentially over an axis. + Returns False unless there is at least one element within a series or along + a Dataframe axis that is True or equivalent (e.g. non-zero or non-empty). - >>> a = bpd.Series([1, 1, 1, np.nan], index=['a', 'b', 'c', 'd']) - >>> a - a 1.0 - b 1.0 - c 1.0 - d - dtype: Float64 + Returns: + scalar or Series: If level is specified, then, Series is returned; + otherwise, scalar is returned. + """ + raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - >>> b = bpd.Series([1, np.nan, 1, np.nan], index=['a', 'b', 'd', 'e']) - >>> b - a 1.0 - b - d 1.0 - e - dtype: Float64 + def max( + self, + ): + """ + Return the maximum of the values over the requested axis. - >>> a.divide(b) - a 1.0 - b - c - d - e - dtype: Float64 + If you want the index of the maximum, use ``idxmax``. This is the equivalent + of the ``numpy.ndarray`` method ``argmax``. - Args: - other (Series or scalar value) Returns: - bigframes.pandas.Series: - The result of the operation. - + scalar or scalar """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - def __truediv__(self, other): + def min( + self, + ): """ - Get division of Series by other, element-wise, using operator `/`. + Return the maximum of the values over the requested axis. - Equivalent to `Series.truediv(other)`. + If you want the index of the minimum, use ``idxmin``. This is the equivalent + of the ``numpy.ndarray`` method ``argmin``. - **Examples:** + Returns: + scalar or scalar + """ + raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) + def std( + self, + ): + """ + Return sample standard deviation over requested axis. - You can multiply with a scalar: + Normalized by N-1 by default. - >>> s = bpd.Series([1, 2, 3]) - >>> s / 2 - 0 0.5 - 1 1.0 - 2 1.5 - dtype: Float64 - You can also multiply with another Series: + Returns + ------- + scalar or Series (if level specified) + """ + raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - >>> denominator = bpd.Series([2, 3, 4]) - >>> s / denominator - 0 0.5 - 1 0.666667 - 2 0.75 - dtype: Float64 + def var( + self, + ): + """ + Return unbiased variance over requested axis. - Args: - other (scalar or Series): - Object to divide the Series by. + Normalized by N-1 by default. Returns: - bigframes.pandas.Series: - The result of the division. + scalar or Series (if level specified) """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - def rtruediv(self, other) -> Series: - """Return floating division of Series and other, element-wise (binary - operator rtruediv). - - Equivalent to ``other / series``, but with support to substitute a - fill_value for missing data in either one of the inputs. + def sum(self): + """Return the sum of the values over the requested axis. - **Examples:** + This is equivalent to the method ``numpy.sum``. + Returns: + scalar + """ + raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - >>> a = bpd.Series([1, 1, 1, np.nan], index=['a', 'b', 'c', 'd']) - >>> a - a 1.0 - b 1.0 - c 1.0 - d - dtype: Float64 + def mean(self): + """Return the mean of the values over the requested axis. - >>> b = bpd.Series([1, np.nan, 1, np.nan], index=['a', 'b', 'd', 'e']) - >>> b - a 1.0 - b - d 1.0 - e - dtype: Float64 + Returns: + scalar + """ + raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - >>> a.divide(b) - a 1.0 - b - c - d - e - dtype: Float64 + def median(self, *, exact: bool = False): + """Return the median of the values over the requested axis. Args: - other (Series or scalar value) + exact (bool. default False): + Default False. Get the exact median instead of an approximate + one. Note: ``exact=True`` not yet supported. Returns: - bigframes.pandas.Series: - The result of the operation. - + scalar """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - def __rtruediv__(self, other): + def prod(self): + """Return the product of the values over the requested axis. + + Returns: + scalar """ - Get division of other by Series, element-wise, using operator `/`. + raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - Equivalent to `Series.rtruediv(other)`. + def skew(self): + """Return unbiased skew over requested axis. - Args: - other (scalar or Series): - Object to divide by the Series. + Normalized by N-1. Returns: - bigframes.pandas.Series: The result of the division. + scalar """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - def floordiv(self, other) -> Series: - """Return integer division of Series and other, element-wise - (binary operator floordiv). - - Equivalent to ``series // other``, but with support to substitute a - fill_value for missing data in either one of the inputs. + def kurt(self): + """Return unbiased kurtosis over requested axis. - **Examples:** - - - >>> a = bpd.Series([1, 1, 1, np.nan], index=['a', 'b', 'c', 'd']) - >>> a - a 1.0 - b 1.0 - c 1.0 - d - dtype: Float64 - - >>> b = bpd.Series([1, np.nan, 1, np.nan], index=['a', 'b', 'd', 'e']) - >>> b - a 1.0 - b - d 1.0 - e - dtype: Float64 - - >>> a.floordiv(b) - a 1.0 - b - c - d - e - dtype: Float64 - - Args: - other (Series or scalar value) - - Returns: - bigframes.pandas.Series: - The result of the operation. - - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def __floordiv__(self, other): - """ - Get integer division of Series by other, using arithmetic operator `//`. - - Equivalent to `Series.floordiv(other)`. - - **Examples:** - - - You can divide by a scalar: - - >>> s = bpd.Series([15, 30, 45]) - >>> s // 2 - 0 7 - 1 15 - 2 22 - dtype: Int64 - - You can also divide by another DataFrame: - - >>> divisor = bpd.Series([3, 4, 4]) - >>> s // divisor - 0 5 - 1 7 - 2 11 - dtype: Int64 - - Args: - other (scalar or Series): - Object to divide the Series by. - - Returns: - bigframes.pandas.Series: - The result of the integer division. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def rfloordiv(self, other) -> Series: - """Return integer division of Series and other, element-wise (binary - operator rfloordiv). - - Equivalent to ``other // series``, but with support to substitute a - fill_value for missing data in either one of the inputs. - - **Examples:** - - - >>> a = bpd.Series([1, 1, 1, np.nan], index=['a', 'b', 'c', 'd']) - >>> a - a 1.0 - b 1.0 - c 1.0 - d - dtype: Float64 - - >>> b = bpd.Series([1, np.nan, 1, np.nan], index=['a', 'b', 'd', 'e']) - >>> b - a 1.0 - b - d 1.0 - e - dtype: Float64 - - >>> a.floordiv(b) - a 1.0 - b - c - d - e - dtype: Float64 - - Args: - other (Series or scalar value) - - Returns: - bigframes.pandas.Series: - The result of the operation. - - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def __rfloordiv__(self, other): - """ - Get integer division of other by Series, using arithmetic operator `//`. - - Equivalent to `Series.rfloordiv(other)`. - - Args: - other (scalar or Series): - Object to divide by the Series. - - Returns: - bigframes.pandas.Series: - The result of the integer division. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def mod(self, other) -> Series: - """Return modulo of Series and other, element-wise (binary operator mod). - - Equivalent to ``series % other``, but with support to substitute a - fill_value for missing data in either one of the inputs. - - **Examples:** - - - >>> a = bpd.Series([1, 1, 1, np.nan], index=['a', 'b', 'c', 'd']) - >>> a - a 1.0 - b 1.0 - c 1.0 - d - dtype: Float64 - - >>> b = bpd.Series([1, np.nan, 1, np.nan], index=['a', 'b', 'd', 'e']) - >>> b - a 1.0 - b - d 1.0 - e - dtype: Float64 - - >>> a.mod(b) - a 0.0 - b - c - d - e - dtype: Float64 - - Args: - other (Series or scalar value) - - Returns: - bigframes.pandas.Series: - The result of the operation. - - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def __mod__(self, other): - """ - Get modulo of Series with other, element-wise, using operator `%`. - - Equivalent to `Series.mod(other)`. - - **Examples:** - - - You can modulo with a scalar: - - >>> s = bpd.Series([1, 2, 3]) - >>> s % 3 - 0 1 - 1 2 - 2 0 - dtype: Int64 - - You can also modulo with another Series: - - >>> modulo = bpd.Series([3, 3, 3]) - >>> s % modulo - 0 1 - 1 2 - 2 0 - dtype: Int64 - - Args: - other (scalar or Series): - Object to modulo the Series by. - - Returns: - bigframes.pandas.Series: - The result of the modulo. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def rmod(self, other) -> Series: - """Return modulo of Series and other, element-wise (binary operator mod). - - Equivalent to ``series % other``, but with support to substitute a - fill_value for missing data in either one of the inputs. - - **Examples:** - - - >>> a = bpd.Series([1, 1, 1, np.nan], index=['a', 'b', 'c', 'd']) - >>> a - a 1.0 - b 1.0 - c 1.0 - d - dtype: Float64 - - >>> b = bpd.Series([1, np.nan, 1, np.nan], index=['a', 'b', 'd', 'e']) - >>> b - a 1.0 - b - d 1.0 - e - dtype: Float64 - - >>> a.mod(b) - a 0.0 - b - c - d - e - dtype: Float64 - - Args: - other (Series or scalar value) - - Returns: - bigframes.pandas.Series: - The result of the operation. - - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def __rmod__(self, other): - """ - Get modulo of other with Series, element-wise, using operator `%`. - - Equivalent to `Series.rmod(other)`. - - Args: - other (scalar or Series): - Object to modulo by the Series. - - Returns: - bigframes.pandas.Series: - The result of the modulo. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def pow(self, other) -> Series: - """Return Exponential power of series and other, element-wise (binary - operator `pow`). - - Equivalent to ``series ** other``, but with support to substitute a - fill_value for missing data in either one of the inputs. - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> a = bpd.Series([1, 1, 1, np.nan], index=['a', 'b', 'c', 'd']) - >>> a - a 1.0 - b 1.0 - c 1.0 - d - dtype: Float64 - - >>> b = bpd.Series([1, np.nan, 1, np.nan], index=['a', 'b', 'd', 'e']) - >>> b - a 1.0 - b - d 1.0 - e - dtype: Float64 - - >>> a.pow(b) - a 1.0 - b 1.0 - c 1.0 - d - e - dtype: Float64 - - Args: - other (Series or scalar value) - - Returns: - bigframes.pandas.Series: - The result of the operation. - - """ - # TODO(b/452366836): adjust sample if needed to match pyarrow semantics. - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def __pow__(self, other): - """ - Get exponentiation of Series with other, element-wise, using operator - `**`. - - Equivalent to `Series.pow(other)`. - - **Examples:** - - - You can exponentiate with a scalar: - - >>> s = bpd.Series([1, 2, 3]) - >>> s ** 2 - 0 1 - 1 4 - 2 9 - dtype: Int64 - - You can also exponentiate with another Series: - - >>> exponent = bpd.Series([3, 2, 1]) - >>> s ** exponent - 0 1 - 1 4 - 2 3 - dtype: Int64 - - Args: - other (scalar or Series): - Object to exponentiate the Series with. - - Returns: - bigframes.pandas.Series: - The result of the exponentiation. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def rpow(self, other) -> Series: - """Return Exponential power of series and other, element-wise (binary - operator `rpow`). - - Equivalent to ``other ** series``, but with support to substitute a - fill_value for missing data in either one of the inputs. - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> a = bpd.Series([1, 1, 1, np.nan], index=['a', 'b', 'c', 'd']) - >>> a - a 1.0 - b 1.0 - c 1.0 - d - dtype: Float64 - - >>> b = bpd.Series([1, np.nan, 1, np.nan], index=['a', 'b', 'd', 'e']) - >>> b - a 1.0 - b - d 1.0 - e - dtype: Float64 - - >>> a.pow(b) - a 1.0 - b 1.0 - c 1.0 - d - e - dtype: Float64 - - Args: - other (Series or scalar value) - - Returns: - bigframes.pandas.Series: - The result of the operation. - - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def __rpow__(self, other): - """ - Get exponentiation of other with Series, element-wise, using operator - `**`. - - Equivalent to `Series.rpow(other)`. - - Args: - other (scalar or Series): - Object to exponentiate with the Series. - - Returns: - bigframes.pandas.Series: - The result of the exponentiation. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def divmod(self, other) -> Series: - """Return integer division and modulo of Series and other, element-wise - (binary operator divmod). - - Equivalent to divmod(series, other). - - **Examples:** - - - >>> a = bpd.Series([1, 1, 1, np.nan], index=['a', 'b', 'c', 'd']) - >>> a - a 1.0 - b 1.0 - c 1.0 - d - dtype: Float64 - - >>> b = bpd.Series([1, np.nan, 1, np.nan], index=['a', 'b', 'd', 'e']) - >>> b - a 1.0 - b - d 1.0 - e - dtype: Float64 - - >>> a.divmod(b) - (a 1.0 - b - c - d - e - dtype: Float64, - a 0.0 - b - c - d - e - dtype: Float64) - - Args: - other: Series, or scalar value - - Returns: - Tuple[bigframes.pandas.Series, bigframes.pandas.Series]: - The result of the operation. The result is always - consistent with (floordiv, mod) (though pandas may not). - - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def rdivmod(self, other) -> Series: - """Return integer division and modulo of Series and other, element-wise (binary operator rdivmod). - - Equivalent to other divmod series. - - **Examples:** - - - >>> a = bpd.Series([1, 1, 1, np.nan], index=['a', 'b', 'c', 'd']) - >>> a - a 1.0 - b 1.0 - c 1.0 - d - dtype: Float64 - - >>> b = bpd.Series([1, np.nan, 1, np.nan], index=['a', 'b', 'd', 'e']) - >>> b - a 1.0 - b - d 1.0 - e - dtype: Float64 - - >>> a.divmod(b) - (a 1.0 - b - c - d - e - dtype: Float64, - a 0.0 - b - c - d - e - dtype: Float64) - - Args: - other: Series, or scalar value - - Returns: - Tuple[bigframes.pandas.Series, bigframes.pandas.Series]: - The result of the operation. The result is always - consistent with (rfloordiv, rmod) (though pandas may not). - - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def combine_first(self, other) -> Series: - """ - Update null elements with value in the same location in 'other'. - - Combine two Series objects by filling null values in one Series with - non-null values from the other Series. Result index will be the union - of the two indexes. - - **Examples:** - - - >>> s1 = bpd.Series([1, np.nan]) - >>> s2 = bpd.Series([3, 4, 5]) - >>> s1.combine_first(s2) - 0 1.0 - 1 4.0 - 2 5.0 - dtype: Float64 - - Null values still persist if the location of that null value - does not exist in `other` - - >>> s1 = bpd.Series({'falcon': np.nan, 'eagle': 160.0}) - >>> s2 = bpd.Series({'eagle': 200.0, 'duck': 30.0}) - >>> s1.combine_first(s2) - falcon - eagle 160.0 - duck 30.0 - dtype: Float64 - - Args: - other (Series): - The value(s) to be used for filling null values. - - Returns: - bigframes.pandas.Series: - The result of combining the provided Series with the other object. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def update(self, other) -> None: - """ - Modify Series in place using values from passed Series. - - Uses non-NA values from passed Series to make updates. Aligns - on index. - - **Examples:** - - - >>> s = bpd.Series([1, 2, 3]) - >>> s.update(bpd.Series([4, 5, 6])) - >>> s - 0 4 - 1 5 - 2 6 - dtype: Int64 - - >>> s = bpd.Series(['a', 'b', 'c']) - >>> s.update(bpd.Series(['d', 'e'], index=[0, 2])) - >>> s - 0 d - 1 b - 2 e - dtype: string - - >>> s = bpd.Series([1, 2, 3]) - >>> s.update(bpd.Series([4, 5, 6, 7, 8])) - >>> s - 0 4 - 1 5 - 2 6 - dtype: Int64 - - If ``other`` contains NA (NULL values) the corresponding values are not updated - in the original Series. - - >>> s = bpd.Series([1, 2, 3]) - >>> s.update(bpd.Series([4, np.nan, 6], dtype=pd.Int64Dtype())) - >>> s - 0 4 - 1 2 - 2 6 - dtype: Int64 - - ``other`` can also be a non-Series object type - that is coercible into a Series - - >>> s = bpd.Series([1, 2, 3]) - >>> s.update([4, np.nan, 6]) - >>> s - 0 4.0 - 1 2.0 - 2 6.0 - dtype: Float64 - - >>> s = bpd.Series([1, 2, 3]) - >>> s.update({1: 9}) - >>> s - 0 1 - 1 9 - 2 3 - dtype: Int64 - - Args: - other (Series, or object coercible into Series) - - Returns: - None - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def all( - self, - ): - """ - Return whether all elements are True, potentially over an axis. - - Returns True unless there at least one element within a Series or along a - DataFrame axis that is False or equivalent (e.g. zero or empty). - - Returns: - scalar or bigframes.pandas.Series: - If level is specified, then, Series is returned; - otherwise, scalar is returned. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def any( - self, - ): - """ - Return whether any element is True, potentially over an axis. - - Returns False unless there is at least one element within a series or along - a Dataframe axis that is True or equivalent (e.g. non-zero or non-empty). - - **Examples:** - - - For Series input, the output is a scalar indicating whether any element is True. - - >>> bpd.Series([False, False]).any() - np.False_ - - >>> bpd.Series([True, False]).any() - np.True_ - - >>> bpd.Series([], dtype="float64").any() - np.False_ - - >>> bpd.Series([np.nan]).any() - np.False_ - - Returns: - scalar or bigframes.pandas.Series: - If level is specified, then, Series is returned; - otherwise, scalar is returned. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def max( - self, - ): - """ - Return the maximum of the values over the requested axis. - - If you want the index of the maximum, use ``idxmax``. This is the equivalent - of the ``numpy.ndarray`` method ``argmax``. - - **Examples:** - - - Calculating the max of a Series: - - >>> s = bpd.Series([1, 3]) - >>> s - 0 1 - 1 3 - dtype: Int64 - - >>> s.max() - np.int64(3) - - Calculating the max of a Series containing ``NA`` values: - - >>> s = bpd.Series([1, 3, pd.NA]) - >>> s - 0 1 - 1 3 - 2 - dtype: Int64 - - >>> s.max() - np.int64(3) - - Returns: - scalar: Scalar. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def min( - self, - ): - """ - Return the maximum of the values over the requested axis. - - If you want the index of the minimum, use ``idxmin``. This is the equivalent - of the ``numpy.ndarray`` method ``argmin``. - - **Examples:** - - - Calculating the min of a Series: - - >>> s = bpd.Series([1, 3]) - >>> s - 0 1 - 1 3 - dtype: Int64 - - >>> s.min() - np.int64(1) - - Calculating the min of a Series containing ``NA`` values: - - >>> s = bpd.Series([1, 3, pd.NA]) - >>> s - 0 1 - 1 3 - 2 - dtype: Int64 - - >>> s.min() - np.int64(1) - - Returns: - scalar: Scalar. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def std( - self, - ): - """ - Return sample standard deviation over requested axis. - - Normalized by N-1 by default. - - **Examples:** - - - >>> df = bpd.DataFrame({'person_id': [0, 1, 2, 3], - ... 'age': [21, 25, 62, 43], - ... 'height': [1.61, 1.87, 1.49, 2.01]} - ... ).set_index('person_id') - >>> df - age height - person_id - 0 21 1.61 - 1 25 1.87 - 2 62 1.49 - 3 43 2.01 - - [4 rows x 2 columns] - - >>> df.std() - age 18.786076 - height 0.237417 - dtype: Float64 - - Returns: - scalar or bigframes.pandas.Series (if level specified) - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def var( - self, - ): - """ - Return unbiased variance over requested axis. - - Normalized by N-1 by default. - - Returns: - scalar or bigframes.pandas.Series (if level specified): - Variance. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def sum(self): - """Return the sum of the values over the requested axis. - - This is equivalent to the method ``numpy.sum``. - - **Examples:** - - - Calculating the sum of a Series: - - >>> s = bpd.Series([1, 3]) - >>> s - 0 1 - 1 3 - dtype: Int64 - - >>> s.sum() - np.int64(4) - - Calculating the sum of a Series containing ``NA`` values: - - >>> s = bpd.Series([1, 3, pd.NA]) - >>> s - 0 1 - 1 3 - 2 - dtype: Int64 - - >>> s.sum() - np.int64(4) - - Returns: - scalar: Scalar. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def mean(self): - """Return the mean of the values over the requested axis. - - **Examples:** - - - Calculating the mean of a Series: - - >>> s = bpd.Series([1, 3]) - >>> s - 0 1 - 1 3 - dtype: Int64 - - >>> s.mean() - np.float64(2.0) - - Calculating the mean of a Series containing ``NA`` values: - - >>> s = bpd.Series([1, 3, pd.NA]) - >>> s - 0 1 - 1 3 - 2 - dtype: Int64 - - >>> s.mean() - np.float64(2.0) - - Returns: - scalar: Scalar. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def median(self, *, exact: bool = True): - """Return the median of the values over the requested axis. - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> s = bpd.Series([1, 2, 3]) - >>> s.median() - np.float64(2.0) - - With a DataFrame - - >>> df = bpd.DataFrame({'a': [1, 2], 'b': [2, 3]}, index=['tiger', 'zebra']) - >>> df - a b - tiger 1 2 - zebra 2 3 - - [2 rows x 2 columns] - - >>> df.median() - a 1.5 - b 2.5 - dtype: Float64 - - Args: - exact (bool. default True): - Default True. Get the exact median instead of an approximate - one. - - Returns: - scalar: Scalar. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def quantile( - self, - q: Union[float, Sequence[float]] = 0.5, - ) -> Union[Series, float]: - """ - Return value at the given quantile. - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> s = bpd.Series([1, 2, 3, 4]) - >>> s.quantile(.5) - np.float64(2.5) - - >>> s.quantile([.25, .5, .75]) - 0.25 1.75 - 0.5 2.5 - 0.75 3.25 - dtype: Float64 - - Args: - q (Union[float, Sequence[float], default 0.5 (50% quantile)): - The quantile(s) to compute, which can lie in range: 0 <= q <= 1. - - Returns: - Union[float, bigframes.pandas.Series]: - If ``q`` is an array, a Series will be returned where the - index is ``q`` and the values are the quantiles, otherwise - a float will be returned. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def prod(self): - """Return the product of the values over the requested axis. - - Returns: - scalar: Scalar. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def describe(self): - """ - Generate descriptive statistics. - - Descriptive statistics include those that summarize the central - tendency, dispersion and shape of a - dataset's distribution, excluding ``NaN`` values. - - .. note:: - Percentile values are approximates only. - - .. note:: - For numeric data, the result's index will include ``count``, - ``mean``, ``std``, ``min``, ``max`` as well as lower, ``50`` and - upper percentiles. By default the lower percentile is ``25`` and the - upper percentile is ``75``. The ``50`` percentile is the - same as the median. - - **Examples:** - - - >>> s = bpd.Series(['A', 'A', 'B']) - >>> s - 0 A - 1 A - 2 B - dtype: string - - >>> s.describe() - count 3 - nunique 2 - Name: 0, dtype: Int64 - - Returns: - bigframes.pandas.Series: - Summary statistics of the Series. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def skew(self): - """Return unbiased skew over requested axis. - - Normalized by N-1. - - **Examples:** - - - >>> s = bpd.Series([1, 2, 3]) - >>> s.skew() - np.float64(0.0) - - With a DataFrame - - >>> df = bpd.DataFrame({'a': [1, 2, 3], 'b': [2, 3, 4], 'c': [1, 3, 5]}, - ... index=['tiger', 'zebra', 'cow']) - >>> df - a b c - tiger 1 2 1 - zebra 2 3 3 - cow 3 4 5 - - [3 rows x 3 columns] - - >>> df.skew() - a 0.0 - b 0.0 - c 0.0 - dtype: Float64 - - Returns: - scalar: Scalar. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def kurt(self): - """Return unbiased kurtosis over requested axis. - - Kurtosis obtained using Fisher’s definition of kurtosis (kurtosis of - normal == 0.0). Normalized by N-1. - - **Examples:** - - - >>> s = bpd.Series([1, 2, 2, 3], index=['cat', 'dog', 'dog', 'mouse']) - >>> s - cat 1 - dog 2 - dog 2 - mouse 3 - dtype: Int64 - - >>> s.kurt() - np.float64(1.5) - - With a DataFrame - - >>> df = bpd.DataFrame({'a': [1, 2, 2, 3], 'b': [3, 4, 4, 4]}, - ... index=['cat', 'dog', 'dog', 'mouse']) - >>> df - a b - cat 1 3 - dog 2 4 - dog 2 4 - mouse 3 4 - - [4 rows x 2 columns] - - >>> df.kurt() - a 1.5 - b 4.0 - dtype: Float64 - - Returns: - scalar or scalar: - Unbiased kurtosis over requested axis. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def item(self: Series, *args, **kwargs): - """Return the first element of the underlying data as a Python scalar. - - **Examples:** - - >>> s = bpd.Series([1]) - >>> s.item() - np.int64(1) - - Returns: - scalar: The first element of Series. - - Raises: - ValueError: If the data is not length = 1. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def items(self): - """ - Lazily iterate over (index, value) tuples. - - This method returns an iterable tuple (index, value). - This is convenient if you want to create a lazy iterator. - - **Examples:** - - - >>> s = bpd.Series(['A', 'B', 'C']) - >>> for index, value in s.items(): - ... print(f"Index : {index}, Value : {value}") - Index : 0, Value : A - Index : 1, Value : B - Index : 2, Value : C + Kurtosis obtained using Fisher’s definition of kurtosis (kurtosis of normal == 0.0). Normalized by N-1. Returns: - iterable: - Iterable of tuples containing the (index, value) pairs from a - Series. + scalar or scalar: Unbiased kurtosis over requested axis. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) def where(self, cond, other): """Replace values where the condition is False. - **Examples:** - - - >>> s = bpd.Series([10, 11, 12, 13, 14]) - >>> s - 0 10 - 1 11 - 2 12 - 3 13 - 4 14 - dtype: Int64 - - You can filter the values in the Series based on a condition. The values - matching the condition would be kept, and not matching would be replaced. - The default replacement value is ``NA``. - - >>> s.where(s % 2 == 0) - 0 10 - 1 - 2 12 - 3 - 4 14 - dtype: Int64 - - You can specify a custom replacement value for non-matching values. - - >>> s.where(s % 2 == 0, -1) - 0 10 - 1 -1 - 2 12 - 3 -1 - 4 14 - dtype: Int64 - >>> s.where(s % 2 == 0, 100*s) - 0 10 - 1 1100 - 2 12 - 3 1300 - 4 14 - dtype: Int64 - Args: cond (bool Series/DataFrame, array-like, or callable): Where cond is True, keep the original value. Where False, replace @@ -4987,132 +1639,35 @@ def where(self, cond, other): extension dtypes). Returns: - bigframes.pandas.Series: - Series after the replacement. + bigframes.series.Series: Series after the replacement. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) def mask(self, cond, other): """Replace values where the condition is True. - **Examples:** - - >>> s = bpd.Series([10, 11, 12, 13, 14]) - >>> s - 0 10 - 1 11 - 2 12 - 3 13 - 4 14 - dtype: Int64 - - You can mask the values in the Series based on a condition. The values - matching the condition would be masked. The condition can be provided in - formm of a Series. - - >>> s.mask(s % 2 == 0) - 0 - 1 11 - 2 - 3 13 - 4 - dtype: Int64 - - You can specify a custom mask value. - - >>> s.mask(s % 2 == 0, -1) - 0 -1 - 1 11 - 2 -1 - 3 13 - 4 -1 - dtype: Int64 - >>> s.mask(s % 2 == 0, 100*s) - 0 1000 - 1 11 - 2 1200 - 3 13 - 4 1400 - dtype: Int64 - - You can also use a remote function to evaluate the mask condition. This - is useful in situation such as the following, where the mask - condition is evaluated based on a complicated business logic which cannot - be expressed in form of a Series. - - >>> @bpd.remote_function(reuse=False, cloud_function_service_account="default") # doctest: +SKIP - ... def should_mask(name: str) -> bool: - ... hash = 0 - ... for char_ in name: - ... hash += ord(char_) - ... return hash % 2 == 0 - - >>> s = bpd.Series(["Alice", "Bob", "Caroline"]) - >>> s - 0 Alice - 1 Bob - 2 Caroline - dtype: string - >>> s.mask(should_mask) # doctest: +SKIP - 0 - 1 Bob - 2 Caroline - dtype: string - >>> s.mask(should_mask, "REDACTED") # doctest: +SKIP - 0 REDACTED - 1 Bob - 2 Caroline - dtype: string - - Simple vectorized (i.e. they only perform operations supported on a - Series) lambdas or python functions can be used directly. - - >>> nums = bpd.Series([1, 2, 3, 4], name="nums") - >>> nums - 0 1 - 1 2 - 2 3 - 3 4 - Name: nums, dtype: Int64 - >>> nums.mask(lambda x: (x+1) % 2 == 1) - 0 1 - 1 - 2 3 - 3 - Name: nums, dtype: Int64 - - >>> def is_odd(num): - ... return num % 2 == 1 - >>> nums.mask(is_odd) - 0 - 1 2 - 2 - 3 4 - Name: nums, dtype: Int64 - Args: cond (bool Series/DataFrame, array-like, or callable): Where cond is False, keep the original value. Where True, replace with corresponding value from other. If cond is callable, it is computed on the Series/DataFrame and should return boolean Series/DataFrame or array. The callable must not change input - Series/DataFrame (though pandas doesn't check it). + Series/DataFrame (though pandas doesn’t check it). other (scalar, Series/DataFrame, or callable): Entries where cond is True are replaced with corresponding value from other. If other is callable, it is computed on the Series/DataFrame and should return scalar or Series/DataFrame. The callable must not change input Series/DataFrame (though pandas - doesn't check it). If not specified, entries will be filled with + doesn’t check it). If not specified, entries will be filled with the corresponding NULL value (np.nan for numpy dtypes, pd.NA for extension dtypes). Returns: - bigframes.pandas.Series: - Series after the replacement. + bigframes.series.Series: Series after the replacement. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - def clip(self, lower, upper): + def clip(self): """Trim values at input threshold(s). Assigns values outside boundary to boundary values. Thresholds can be @@ -5121,16 +1676,13 @@ def clip(self, lower, upper): Args: lower (float or array-like, default None): - Minimum threshold value. All values below this threshold will - be set to it. A missing threshold (e.g NA) will not clip the value. + Minimum threshold value. All values below this threshold will be set to it. A missing threshold (e.g NA) will not clip the value. upper (float or array-like, default None): - Maximum threshold value. All values above this threshold will - be set to it. A missing threshold (e.g NA) will not clip the value. + Maximum threshold value. All values above this threshold will be set to it. A missing threshold (e.g NA) will not clip the value. Returns: - bigframes.pandas.Series: - Series. + Series. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -5138,84 +1690,38 @@ def unstack(self, level): """ Unstack, also known as pivot, Series with MultiIndex to produce DataFrame. + Args: + level (int, str, or list of these, default last level): + Level(s) to unstack, can pass level name. + Returns: - bigframes.pandas.DataFrame: Unstacked Series. + DataFrame: Unstacked Series. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) def argmax(self): """ - Return int position of the largest value in the series. - - If the maximum is achieved in multiple locations, the first row position - is returned. - - **Examples:** - - - Consider dataset containing cereal calories. - - >>> s = bpd.Series({'Corn Flakes': 100.0, 'Almond Delight': 110.0, - ... 'Cinnamon Toast Crunch': 120.0, 'Cocoa Puff': 110.0}) - >>> s - Corn Flakes 100.0 - Almond Delight 110.0 - Cinnamon Toast Crunch 120.0 - Cocoa Puff 110.0 - dtype: Float64 - - >>> s.argmax() - np.int64(2) - - >>> s.argmin() - np.int64(0) + Return int position of the smallest value in the Series. - The maximum cereal calories is the third element and the minimum cereal - calories is the first element, since series is zero-indexed. + If the minimum is achieved in multiple locations, the first row position is returned. Returns: - int: - Row position of the maximum value. + Series: Row position of the maximum value. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) def argmin(self): """ - Return int position of the smallest value in the Series. - - If the minimum is achieved in multiple locations, the first row position - is returned. - - **Examples:** - - - Consider dataset containing cereal calories. - - >>> s = bpd.Series({'Corn Flakes': 100.0, 'Almond Delight': 110.0, - ... 'Cinnamon Toast Crunch': 120.0, 'Cocoa Puff': 110.0}) - >>> s - Corn Flakes 100.0 - Almond Delight 110.0 - Cinnamon Toast Crunch 120.0 - Cocoa Puff 110.0 - dtype: Float64 - - >>> s.argmax() - np.int64(2) + Return int position of the largest value in the Series. - >>> s.argmin() - np.int64(0) - - The maximum cereal calories is the third element and the minimum cereal - calories is the first element, since series is zero-indexed. + If the maximum is achieved in multiple locations, the first row position is returned. Returns: - int: - Row position of the minimum value. + Series: Row position of the minimum value. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - def rename(self, index, *, inplace, **kwargs): + def rename(self, index, **kwargs) -> Series | None: """ Alter Series index labels or name. @@ -5225,49 +1731,20 @@ def rename(self, index, *, inplace, **kwargs): Alternatively, change ``Series.name`` with a scalar value. - **Examples:** - - - >>> s = bpd.Series([1, 2, 3]) - >>> s - 0 1 - 1 2 - 2 3 - dtype: Int64 - - You can changes the Series name by specifying a string scalar: - - >>> s.rename("my_name") - 0 1 - 1 2 - 2 3 - Name: my_name, dtype: Int64 - - You can change the labels by specifying a mapping: - - >>> s.rename({1: 3, 2: 5}) - 0 1 - 3 2 - 5 3 - dtype: Int64 - Args: index (scalar, hashable sequence, dict-like or function optional): Functions or dict-like are transformations to apply to the index. Scalar or hashable sequence-like will alter the ``Series.name`` attribute. - inplace (bool): - Default False. Whether to return a new Series. Returns: - bigframes.pandas.Series | None: - Series with index labels or None if ``inplace=True``. + bigframes.series.Series: Series with index labels. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - def rename_axis(self, mapper, *, inplace, **kwargs): + def rename_axis(self, mapper, **kwargs): """ Set the name of the axis for the index or columns. @@ -5275,51 +1752,8 @@ def rename_axis(self, mapper, *, inplace, **kwargs): mapper (scalar, list-like, optional): Value to set the axis name attribute. - **Examples:** - - - Series - - >>> s = bpd.Series(["dog", "cat", "monkey"]) - >>> s - 0 dog - 1 cat - 2 monkey - dtype: string - - >>> s.rename_axis("animal") - animal - 0 dog - 1 cat - 2 monkey - dtype: string - - DataFrame - - >>> df = bpd.DataFrame({"num_legs": [4, 4, 2], - ... "num_arms": [0, 0, 2]}, - ... ["dog", "cat", "monkey"]) - >>> df - num_legs num_arms - dog 4 0 - cat 4 0 - monkey 2 2 - - [3 rows x 2 columns] - - >>> df = df.rename_axis("animal") - >>> df - num_legs num_arms - animal - dog 4 0 - cat 4 0 - monkey 2 2 - - [3 rows x 2 columns] - Returns: - bigframes.pandas.Series or bigframes.pandas.DataFrame: - The same type as the caller. + bigframes.series.Series: Series with the name of the axis set. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -5338,57 +1772,6 @@ def value_counts( first element is the most frequently-occurring element. Excludes NA values by default. - **Examples:** - - - >>> s = bpd.Series([3, 1, 2, 3, 4, pd.NA], dtype="Int64") - - >>> s - 0 3 - 1 1 - 2 2 - 3 3 - 4 4 - 5 - dtype: Int64 - - ``value_counts`` sorts the result by counts in a descending order by default: - - >>> s.value_counts() - 3 2 - 1 1 - 2 1 - 4 1 - Name: count, dtype: Int64 - - You can normalize the counts to return relative frequencies by setting ``normalize=True``: - - >>> s.value_counts(normalize=True) - 3 0.4 - 1 0.2 - 2 0.2 - 4 0.2 - Name: proportion, dtype: Float64 - - You can get the values in the ascending order of the counts by setting ``ascending=True``: - - >>> s.value_counts(ascending=True) - 1 1 - 2 1 - 4 1 - 3 2 - Name: count, dtype: Int64 - - You can include the counts of the ``NA`` values by setting ``dropna=False``: - - >>> s.value_counts(dropna=False) - 3 2 - 1 1 - 2 1 - 4 1 - 1 - Name: count, dtype: Int64 - Args: normalize (bool, default False): If True then the object returned will contain the relative @@ -5401,8 +1784,7 @@ def value_counts( Don't include counts of NaN. Returns: - bigframes.pandas.Series: - Series containing counts of unique values. + Series: Series containing counts of unique values. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -5414,47 +1796,12 @@ def str(self): NAs stay NA unless handled otherwise by a particular method. Patterned after Python’s string methods, with some inspiration from R’s stringr package. - **Examples:** - - >>> import bigframes.pandas as bpd - >>> s = bpd.Series(["A_Str_Series"]) - >>> s - 0 A_Str_Series - dtype: string - - >>> s.str.lower() - 0 a_str_series - dtype: string - - >>> s.str.replace("_", "") - 0 AStrSeries - dtype: string - Returns: bigframes.operations.strings.StringMethods: An accessor containing string methods. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - @property - def plot(self): - """ - Make plots of Series. - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> ser = bpd.Series([1, 2, 3, 3]) - >>> plot = ser.plot(kind='hist', title="My plot") - >>> plot - - - Returns: - bigframes.pandas.api.typing.PlotAccessor: - An accessor making plots. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - def isin(self, values): """ Whether elements in Series are contained in values. @@ -5467,59 +1814,13 @@ def isin(self, values): the same. That is, if any form of NaN is present in values, all forms of NaN in the series will be considered a match. (though pandas may not) - **Examples:** - - - >>> s = bpd.Series(['llama', 'cow', 'llama', 'beetle', 'llama', - ... 'hippo'], name='animal') - >>> s - 0 llama - 1 cow - 2 llama - 3 beetle - 4 llama - 5 hippo - Name: animal, dtype: string - - To invert the boolean values, use the ~ operator: - - >>> ~s.isin(['cow', 'llama']) - 0 False - 1 False - 2 False - 3 True - 4 False - 5 True - Name: animal, dtype: boolean - - Passing a single string as s.isin('llama') will raise an error. Use a - list of one element instead: - - >>> s.isin(['llama']) - 0 True - 1 False - 2 True - 3 False - 4 True - 5 False - Name: animal, dtype: boolean - - Strings and integers are distinct and are therefore not comparable: - - >>> bpd.Series([1]).isin(['1']) - 0 False - dtype: boolean - >>> bpd.Series([1.1]).isin(['1.1']) - 0 False - dtype: boolean - Args: values (list-like): The sequence of values to test. Passing in a single string will raise a TypeError. Instead, turn a single string into a list of one element. Returns: - bigframes.pandas.Series: Series of booleans indicating if each element is in values. + bigframes.series.Series: Series of booleans indicating if each element is in values. Raises: TypeError: If input is not list-like. @@ -5531,20 +1832,8 @@ def is_monotonic_increasing(self) -> bool: """ Return boolean if values in the object are monotonically increasing. - **Examples:** - - - >>> s = bpd.Series([1, 2, 2]) - >>> s.is_monotonic_increasing - np.True_ - - >>> s = bpd.Series([3, 2, 1]) - >>> s.is_monotonic_increasing - np.False_ - Returns: - bool: - Boolean. + bool """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -5553,20 +1842,8 @@ def is_monotonic_decreasing(self) -> bool: """ Return boolean if values in the object are monotonically decreasing. - **Examples:** - - - >>> s = bpd.Series([3, 2, 2, 1]) - >>> s.is_monotonic_decreasing - np.True_ - - >>> s = bpd.Series([1, 2, 3]) - >>> s.is_monotonic_decreasing - np.False_ - Returns: - bool: - Boolean. + bool """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -5593,55 +1870,6 @@ def map( ``__missing__`` (i.e. provide a method for default values). These are treated the same as ``dict``. - **Examples:** - - >>> s = bpd.Series(['cat', 'dog', pd.NA, 'rabbit']) - >>> s - 0 cat - 1 dog - 2 - 3 rabbit - dtype: string - - `map` can accepts a `dict`. Values that are not found in the `dict` are - converted to `NA`: - - >>> s.map({'cat': 'kitten', 'dog': 'puppy'}) - 0 kitten - 1 puppy - 2 - 3 - dtype: string - - It also accepts a remote function: - - >>> @bpd.remote_function(cloud_function_service_account="default") # doctest: +SKIP - ... def my_mapper(val: str) -> str: - ... vowels = ["a", "e", "i", "o", "u"] - ... if val: - ... return "".join([ - ... ch.upper() if ch in vowels else ch for ch in val - ... ]) - ... return "N/A" - - >>> s.map(my_mapper) # doctest: +SKIP - 0 cAt - 1 dOg - 2 N/A - 3 rAbbIt - dtype: string - - With experimental Python Transpiler enabled, you can use some lambda functions without - deploying them as remote functions: - - >>> bpd.options.experiments.enable_python_transpiler = True - >>> s.map(lambda val: val + "fish") - 0 catfish - 1 dogfish - 2 - 3 rabbitfish - dtype: string - Args: arg (function, Mapping, Series): remote function, collections.abc.Mapping subclass or Series @@ -5658,487 +1886,21 @@ def map( index entry. Returns: - bigframes.pandas.Series: - Same index as caller. + Series: Same index as caller. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @property def iloc(self): - """Purely integer-location based indexing for selection by position. - - **Examples:** - - - >>> mydict = [{'a': 1, 'b': 2, 'c': 3, 'd': 4}, - ... {'a': 100, 'b': 200, 'c': 300, 'd': 400}, - ... {'a': 1000, 'b': 2000, 'c': 3000, 'd': 4000}] - >>> df = bpd.DataFrame(mydict) - >>> df - a b c d - 0 1 2 3 4 - 1 100 200 300 400 - 2 1000 2000 3000 4000 - - [3 rows x 4 columns] - - Indexing just the rows - - With a scalar integer. - - >>> type(df.iloc[0]) # doctest: +ELLIPSIS - - - >>> df.iloc[0] - a 1 - b 2 - c 3 - d 4 - Name: 0, dtype: Int64 - - With a list of integers. - - >>> df.iloc[0] - a 1 - b 2 - c 3 - d 4 - Name: 0, dtype: Int64 - - >>> type(df.iloc[[0]]) - - - >>> df.iloc[[0, 1]] - a b c d - 0 1 2 3 4 - 1 100 200 300 400 - - [2 rows x 4 columns] - - With a slice object. - - >>> df.iloc[:3] - a b c d - 0 1 2 3 4 - 1 100 200 300 400 - 2 1000 2000 3000 4000 - - [3 rows x 4 columns] - - Indexing both axes - - You can mix the indexer types for the index and columns. Use : to select - the entire axis. - - With scalar integers. - - >>> df.iloc[0, 1] - np.int64(2) - - Returns: - bigframes.core.indexers.IlocSeriesIndexer: - Purely integer-location Indexers. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - @property - def loc(self): - """Access a group of rows and columns by label(s) or a boolean array. - - **Examples:** - - - >>> df = bpd.DataFrame([[1, 2], [4, 5], [7, 8]], - ... index=['cobra', 'viper', 'sidewinder'], - ... columns=['max_speed', 'shield']) - >>> df - max_speed shield - cobra 1 2 - viper 4 5 - sidewinder 7 8 - - [3 rows x 2 columns] - - Single label. Note this returns the row as a Series. - - >>> df.loc['viper'] - max_speed 4 - shield 5 - Name: viper, dtype: Int64 - - List of labels. Note using [[]] returns a DataFrame. - - >>> df.loc[['viper', 'sidewinder']] - max_speed shield - viper 4 5 - sidewinder 7 8 - - [2 rows x 2 columns] - - Slice with labels for row and single label for column. As mentioned - above, note that both the start and stop of the slice are included. - - >>> df.loc['cobra', 'shield'] - np.int64(2) - - Index (same behavior as df.reindex) - - >>> df.loc[bpd.Index(["cobra", "viper"], name="foo")] - max_speed shield - cobra 1 2 - viper 4 5 - - [2 rows x 2 columns] - - Conditional that returns a boolean Series with column labels specified - - >>> df.loc[df['shield'] > 6, ['max_speed']] - max_speed - sidewinder 7 - - [1 rows x 1 columns] - - Multiple conditional using | that returns a boolean Series - - >>> df.loc[(df['max_speed'] > 4) | (df['shield'] < 5)] - max_speed shield - cobra 1 2 - sidewinder 7 8 - - [2 rows x 2 columns] - - Please ensure that each condition is wrapped in parentheses (). - - Set value for an entire column - - >>> df.loc[:, 'max_speed'] = 30 - >>> df - max_speed shield - cobra 30 2 - viper 30 5 - sidewinder 30 8 - - [3 rows x 2 columns] - - Returns: - bigframes.core.indexers.LocSeriesIndexer: - Indexers object. - """ + """Purely integer-location based indexing for selection by position.""" raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @property def iat(self): - """Access a single value for a row/column pair by integer position. - - **Examples:** - - - >>> df = bpd.DataFrame([[0, 2, 3], [0, 4, 1], [10, 20, 30]], - ... columns=['A', 'B', 'C']) - >>> df - A B C - 0 0 2 3 - 1 0 4 1 - 2 10 20 30 - - [3 rows x 3 columns] - - Get value at specified row/column pair - - >>> df.iat[1, 2] - np.int64(1) - - Get value within a series - - >>> df.loc[0].iat[1] - np.int64(2) - - Returns: - bigframes.core.indexers.IatSeriesIndexer: - Indexers object. - """ + """Access a single value for a row/column pair by integer position.""" raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @property def at(self): - """Access a single value for a row/column label pair. - - **Examples:** - - - >>> df = bpd.DataFrame([[0, 2, 3], [0, 4, 1], [10, 20, 30]], - ... index=[4, 5, 6], columns=['A', 'B', 'C']) - >>> df - A B C - 4 0 2 3 - 5 0 4 1 - 6 10 20 30 - - [3 rows x 3 columns] - - Get value at specified row/column pair - - >>> df.at[4, 'B'] - np.int64(2) - - Get value at specified row label - - >>> df.loc[5].at['B'] - np.int64(4) - - Returns: - bigframes.core.indexers.AtSeriesIndexer: - Indexers object. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - @property - def values(self): - """ - Return Series as ndarray or ndarray-like depending on the dtype. - - **Examples:** - - - >>> bpd.Series([1, 2, 3]).values - array([1, 2, 3]) - - >>> bpd.Series(list('aabc')).values - array(['a', 'a', 'b', 'c'], dtype=object) - - Returns: - numpy.ndarray or ndarray-like: - Values in the Series. - - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - @property - def size(self) -> int: - """Return the number of elements in the underlying data. - - **Examples:** - - - For Series: - - >>> s = bpd.Series(['Ant', 'Bear', 'Cow']) - >>> s - 0 Ant - 1 Bear - 2 Cow - dtype: string - >>> s.size - 3 - - For Index: - - >>> idx = bpd.Index(bpd.Series([1, 2, 3])) - >>> idx.size - 3 - - Returns: - int: - Return the number of elements in the underlying data. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def __array__(self, dtype=None, copy: Optional[bool] = None) -> numpy.ndarray: - """ - Returns the values as NumPy array. - - Equivalent to `Series.to_numpy(dtype)`. - - Users should not call this directly. Rather, it is invoked by - `numpy.array` and `numpy.asarray`. - - **Examples:** - - - >>> ser = bpd.Series([1, 2, 3]) - - >>> np.asarray(ser) - array([1, 2, 3]) - - Args: - dtype (str or numpy.dtype, optional): - The dtype to use for the resulting NumPy array. By default, - the dtype is inferred from the data. - copy (bool or None, optional): - Whether to copy the data, False is not supported. - - Returns: - numpy.ndarray: - The values in the series converted to a `numpy.ndarray` with the - specified dtype. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def __len__(self): - """Returns number of values in the Series, serves `len` operator. - - **Examples:** - - - >>> s = bpd.Series([1, 2, 3]) - >>> len(s) - 3 - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def __invert__(self): - """ - Returns the logical inversion (binary NOT) of the Series, element-wise - using operator `~`. - - **Examples:** - - - >>> ser = bpd.Series([True, False, True]) - >>> ~ser - 0 False - 1 True - 2 False - dtype: boolean - - Returns: - bigframes.pandas.Series: - The inverted values in the series. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def __and__(self, other): - """Get bitwise AND of Series and other, element-wise, using operator `&`. - - **Examples:** - - - >>> s = bpd.Series([0, 1, 2, 3]) - - You can operate with a scalar. - - >>> s & 6 - 0 0 - 1 0 - 2 2 - 3 2 - dtype: Int64 - - You can operate with another Series. - - >>> s1 = bpd.Series([5, 6, 7, 8]) - >>> s & s1 - 0 0 - 1 0 - 2 2 - 3 0 - dtype: Int64 - - Args: - other (scalar or Series): - Object to bitwise AND with the Series. - - Returns: - bigframes.pandas.Series: - The result of the operation. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def __or__(self, other): - """Get bitwise OR of Series and other, element-wise, using operator `|`. - - **Examples:** - - - >>> s = bpd.Series([0, 1, 2, 3]) - - You can operate with a scalar. - - >>> s | 6 - 0 6 - 1 7 - 2 6 - 3 7 - dtype: Int64 - - You can operate with another Series. - - >>> s1 = bpd.Series([5, 6, 7, 8]) - >>> s | s1 - 0 5 - 1 7 - 2 7 - 3 11 - dtype: Int64 - - Args: - other (scalar or Series): - Object to bitwise OR with the Series. - - Returns: - bigframes.pandas.Series: - The result of the operation. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def __xor__(self, other): - """Get bitwise XOR of Series and other, element-wise, using operator `^`. - - **Examples:** - - - >>> s = bpd.Series([0, 1, 2, 3]) - - You can operate with a scalar. - - >>> s ^ 6 - 0 6 - 1 7 - 2 4 - 3 5 - dtype: Int64 - - You can operate with another Series. - - >>> s1 = bpd.Series([5, 6, 7, 8]) - >>> s ^ s1 - 0 5 - 1 7 - 2 5 - 3 11 - dtype: Int64 - - Args: - other (scalar or Series): - Object to bitwise XOR with the Series. - - Returns: - bigframes.pandas.Series: - The result of the operation. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def __getitem__(self, indexer): - """Gets the specified index from the Series. - - **Examples:** - - - >>> s = bpd.Series([15, 30, 45]) - >>> s[1] - np.int64(30) - - >>> s[0:2] - 0 15 - 1 30 - dtype: Int64 - - Args: - indexer (int or slice): - Index or slice of indices. - - Returns: - bigframes.pandas.Series or Value: - Value(s) at the requested index(es). - """ + """Access a single value for a row/column label pair.""" raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) diff --git a/third_party/bigframes_vendored/pandas/core/strings/accessor.py b/third_party/bigframes_vendored/pandas/core/strings/accessor.py index 9a72b98aee8..ecdd9547d54 100644 --- a/third_party/bigframes_vendored/pandas/core/strings/accessor.py +++ b/third_party/bigframes_vendored/pandas/core/strings/accessor.py @@ -13,36 +13,6 @@ class StringMethods: R's stringr package. """ - def __getitem__(self, key: typing.Union[int, slice]): - """ - Index or slice string or list in the Series. - - **Examples:** - - >>> import bigframes.pandas as bpd - - >>> s = bpd.Series(['Alice', 'Bob', 'Charlie']) - >>> s.str[0] - 0 A - 1 B - 2 C - dtype: string - - >>> s.str[0:3] - 0 Ali - 1 Bob - 2 Cha - dtype: string - - Args: - key (int | slice): - Index or slice of indices to access from each string or list. - - Returns: - bigframes.series.Series: The list at requested index. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - def extract(self, pat: str, flags: int = 0): """ Extract capture groups in the regex `pat` as columns in a DataFrame. @@ -50,56 +20,19 @@ def extract(self, pat: str, flags: int = 0): For each subject string in the Series, extract groups from the first match of regular expression `pat`. - **Examples:** - - >>> import bigframes.pandas as bpd - - A pattern with two groups will return a DataFrame with two columns. - Non-matches will be `NaN`. - - >>> s = bpd.Series(['a1', 'b2', 'c3']) - >>> s.str.extract(r'([ab])(\\d)') - 0 1 - 0 a 1 - 1 b 2 - 2 - - [3 rows x 2 columns] - - Named groups will become column names in the result. - - >>> s.str.extract(r'(?P[ab])(?P\\d)') - letter digit - 0 a 1 - 1 b 2 - 2 - - [3 rows x 2 columns] - - A pattern with one group will return a DataFrame with one column. - - >>> s.str.extract(r'[ab](\\d)') - 0 - 0 1 - 1 2 - 2 - - [3 rows x 1 columns] - Args: - pat (str): + pat: Regular expression pattern with capturing groups. - flags (int, default 0 (no flags)): + flags: Flags from the ``re`` module, e.g. ``re.IGNORECASE``, that modify regular expression matching for things like case, spaces, etc. For more details, see :mod:`re`. Returns: - bigframes.dataframe.DataFrame: - A DataFrame with one row for each subject string, and one - column for each group. Any capture group names in regular - expression pat will be used for column names; otherwise - capture group numbers will be used. + A DataFrame with one row for each subject string, and one + column for each group. Any capture group names in regular + expression pat will be used for column names; otherwise + capture group numbers will be used. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -110,23 +43,12 @@ def find(self, sub, start: int = 0, end=None): substring is fully contained between [start:end]. Return -1 on failure. Equivalent to standard :meth:`str.find`. - **Examples:** - - >>> import bigframes.pandas as bpd - - >>> ser = bpd.Series(["cow_", "duck_", "do_ve"]) - >>> ser.str.find("_") - 0 3 - 1 4 - 2 2 - dtype: Int64 - Args: - sub (str): + sub: Substring being searched. start (int, default 0): Left edge index. - end (int, default None): + end (None): Right edge index. Returns: @@ -140,19 +62,6 @@ def len(self): The element may be a sequence (such as a string, tuple or list) or a collection (such as a dictionary). - **Examples:** - - >>> import bigframes.pandas as bpd - - Returns the length (number of characters) in a string. - - >>> s = bpd.Series(['dog', '', pd.NA]) - >>> s.str.len() - 0 3 - 1 0 - 2 - dtype: Int64 - Returns: bigframes.series.Series: A Series or Index of integer values indicating the length of each element in the Series or Index. @@ -165,21 +74,6 @@ def lower(self): Equivalent to :meth:`str.lower`. - **Examples:** - - >>> import bigframes.pandas as bpd - - >>> s = bpd.Series(['lower', - ... 'CAPITALS', - ... 'this is a sentence', - ... 'SwApCaSe']) - >>> s.str.lower() - 0 lower - 1 capitals - 2 this is a sentence - 3 swapcase - dtype: string - Returns: bigframes.series.Series: Series with lowercase. """ @@ -189,35 +83,6 @@ def lower(self): def slice(self, start=None, stop=None): """Slice substrings from each element in the Series or Index. - **Examples:** - - >>> import bigframes.pandas as bpd - - >>> s = bpd.Series(["koala", "dog", "chameleon"]) - >>> s - 0 koala - 1 dog - 2 chameleon - dtype: string - - >>> s.str.slice(start=1) - 0 oala - 1 og - 2 hameleon - dtype: string - - >>> s.str.slice(stop=2) - 0 ko - 1 do - 2 ch - dtype: string - - >>> s.str.slice(start=2, stop=5) - 0 ala - 1 g - 2 ame - dtype: string - Args: start (int, optional): Start position for slice operation. @@ -233,7 +98,7 @@ def slice(self, start=None, stop=None): raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - def strip(self, to_strip: typing.Optional[str] = None): + def strip(self): """Remove leading and trailing characters. Strip whitespaces (including newlines) or a set of specified characters @@ -241,36 +106,6 @@ def strip(self, to_strip: typing.Optional[str] = None): Replaces any non-strings in Series with NaNs. Equivalent to :meth:`str.strip`. - **Examples:** - - >>> import bigframes.pandas as bpd - - >>> s = bpd.Series([ - ... '1. Ant.', - ... ' 2. Bee? ', - ... '\\t3. Cat!\\n', - ... pd.NA, - ... ]) - >>> s.str.strip() - 0 1. Ant. - 1 2. Bee? - 2 3. Cat! - 3 - dtype: string - - >>> s.str.strip('123.!? \\n\\t') - 0 Ant - 1 Bee - 2 Cat - 3 - dtype: string - - Args: - to_strip (str, default None): - Specifying the set of characters to be removed. All combinations - of this set of characters will be stripped. If None then - whitespaces are removed. - Returns: bigframes.series.Series: Series or Index without leading and trailing characters. @@ -283,21 +118,6 @@ def upper(self): Equivalent to :meth:`str.upper`. - **Examples:** - - >>> import bigframes.pandas as bpd - - >>> s = bpd.Series(['lower', - ... 'CAPITALS', - ... 'this is a sentence', - ... 'SwApCaSe']) - >>> s.str.upper() - 0 LOWER - 1 CAPITALS - 2 THIS IS A SENTENCE - 3 SWAPCASE - dtype: string - Returns: bigframes.series.Series: Series with uppercase strings. """ @@ -311,18 +131,6 @@ def isnumeric(self): :meth:`str.isnumeric` for each element of the Series/Index. If a string has zero characters, ``False`` is returned for that check. - **Examples:** - - >>> import bigframes.pandas as bpd - - >>> s1 = bpd.Series(['one', 'one1', '1', '']) - >>> s1.str.isnumeric() - 0 False - 1 False - 2 True - 3 False - dtype: boolean - Returns: bigframes.series.Series: Series or Index of boolean values with the same length as the original Series/Index. @@ -337,18 +145,6 @@ def isalpha(self): :meth:`str.isalpha` for each element of the Series/Index. If a string has zero characters, ``False`` is returned for that check. - **Examples:** - - >>> import bigframes.pandas as bpd - - >>> s1 = bpd.Series(['one', 'one1', '1', '']) - >>> s1.str.isalpha() - 0 True - 1 False - 2 False - 3 False - dtype: boolean - Returns: bigframes.series.Series: Series with the same length as the originalSeries/Index. """ @@ -362,18 +158,6 @@ def isdigit(self): :meth:`str.isdigit` for each element of the Series/Index. If a string has zero characters, ``False`` is returned for that check. - **Examples:** - - >>> import bigframes.pandas as bpd - - >>> s = bpd.Series(['23', '1a', '1/5', '']) - >>> s.str.isdigit() - 0 True - 1 False - 2 False - 3 False - dtype: boolean - Returns: bigframes.series.Series: Series with the same length as the originalSeries/Index. """ @@ -387,29 +171,6 @@ def isalnum(self): :meth:`str.isalnum` for each element of the Series/Index. If a string has zero characters, ``False`` is returned for that check. - **Examples:** - - >>> import bigframes.pandas as bpd - - >>> s1 = bpd.Series(['one', 'one1', '1', '']) - >>> s1.str.isalnum() - 0 True - 1 True - 2 True - 3 False - dtype: boolean - - Note that checks against characters mixed with any additional - punctuation or whitespace will evaluate to false for an alphanumeric - check. - - >>> s2 = bpd.Series(['A B', '1.5', '3,000']) - >>> s2.str.isalnum() - 0 False - 1 False - 2 False - dtype: boolean - Returns: bigframes.series.Series: Series or Index of boolean values with the same length as the original Series/Index. @@ -424,17 +185,6 @@ def isspace(self): :meth:`str.isspace` for each element of the Series/Index. If a string has zero characters, ``False`` is returned for that check. - **Examples:** - - >>> import bigframes.pandas as bpd - - >>> s = bpd.Series([' ', '\\t\\r\\n ', '']) - >>> s.str.isspace() - 0 True - 1 True - 2 False - dtype: boolean - Returns: bigframes.series.Series: Series or Index of boolean values with the same length as the original Series/Index. @@ -449,18 +199,6 @@ def islower(self): :meth:`str.islower` for each element of the Series/Index. If a string has zero characters, ``False`` is returned for that check. - **Examples:** - - >>> import bigframes.pandas as bpd - - >>> s = bpd.Series(['leopard', 'Golden Eagle', 'SNAKE', '']) - >>> s.str.islower() - 0 True - 1 False - 2 False - 3 False - dtype: boolean - Returns: bigframes.series.Series: Series or Index of boolean values with the same length as the original Series/Index. @@ -475,18 +213,6 @@ def isupper(self): :meth:`str.isupper` for each element of the Series/Index. If a string has zero characters, ``False`` is returned for that check. - **Examples:** - - >>> import bigframes.pandas as bpd - - >>> s = bpd.Series(['leopard', 'Golden Eagle', 'SNAKE', '']) - >>> s.str.isupper() - 0 False - 1 False - 2 True - 3 False - dtype: boolean - Returns: bigframes.series.Series: Series or Index of boolean values with the same length as the original Series/Index. @@ -501,21 +227,6 @@ def isdecimal(self): :meth:`str.isdecimal` for each element of the Series/Index. If a string has zero characters, ``False`` is returned for that check. - **Examples:** - - >>> import bigframes.pandas as bpd - - The `isdecimal` method checks for characters used to form numbers in - base 10. - - >>> s = bpd.Series(['23', '³', '⅕', '']) - >>> s.str.isdecimal() - 0 True - 1 False - 2 False - 3 False - dtype: boolean - Returns: bigframes.series.Series: Series or Index of boolean values with the same length as the original Series/Index. @@ -523,64 +234,28 @@ def isdecimal(self): raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - def rstrip(self, to_strip: typing.Optional[str] = None): - r"""Remove trailing characters. + def rstrip(self): + """Remove trailing characters. Strip whitespaces (including newlines) or a set of specified characters from each string in the Series/Index from right side. Replaces any non-strings in Series with NaNs. Equivalent to :meth:`str.rstrip`. - **Examples:** - - >>> import bigframes.pandas as bpd - - >>> s = bpd.Series(['Ant', ' Bee ', '\tCat\n', pd.NA]) - >>> s.str.rstrip() - 0 Ant - 1 Bee - 2 \tCat - 3 - dtype: string - - Args: - to_strip (str, default None): - Specifying the set of characters to be removed. All combinations - of this set of characters will be stripped. If None then - whitespaces are removed. - Returns: bigframes.series.Series: Series without trailing characters. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - def lstrip(self, to_strip: typing.Optional[str] = None): - r"""Remove leading characters. + def lstrip(self): + """Remove leading characters. Strip whitespaces (including newlines) or a set of specified characters from each string in the Series/Index from left side. Replaces any non-strings in Series with NaNs. Equivalent to :meth:`str.lstrip`. - **Examples:** - - >>> import bigframes.pandas as bpd - - >>> s = bpd.Series(['Ant', ' Bee ', '\tCat\n', pd.NA]) - >>> s.str.lstrip() - 0 Ant - 1 Bee - 2 Cat\n - 3 - dtype: string - - Args: - to_strip (str, default None): - Specifying the set of characters to be removed. All combinations - of this set of characters will be stripped. If None then - whitespaces are removed. - Returns: bigframes.series.Series: Series without leading characters. """ @@ -590,23 +265,6 @@ def lstrip(self, to_strip: typing.Optional[str] = None): def repeat(self, repeats: int): """Duplicate each string in the Series or Index. - **Examples:** - - >>> import bigframes.pandas as bpd - - >>> s = bpd.Series(['a', 'b', 'c']) - >>> s - 0 a - 1 b - 2 c - dtype: string - - >>> s.str.repeat(repeats=2) - 0 aa - 1 bb - 2 cc - dtype: string - Args: repeats : int or sequence of int Same value for all (int) or different value per (sequence). @@ -623,21 +281,6 @@ def capitalize(self): Equivalent to :meth:`str.capitalize`. - **Examples:** - - >>> import bigframes.pandas as bpd - - >>> s = bpd.Series(['lower', - ... 'CAPITALS', - ... 'this is a sentence', - ... 'SwApCaSe']) - >>> s.str.capitalize() - 0 Lower - 1 Capitals - 2 This is a sentence - 3 Swapcase - dtype: string - Returns: bigframes.series.Series: Series with captitalized strings. """ @@ -650,42 +293,8 @@ def cat(self, others, *, join): If `others` is specified, this function concatenates the Series/Index and elements of `others` element-wise. - **Examples:** - - >>> import bigframes.pandas as bpd - - You can concatenate each string in a Series to another string. - - >>> s = bpd.Series(['Jane', 'John']) - >>> s.str.cat(" Doe") - 0 Jane Doe - 1 John Doe - dtype: string - - You can concatenate another Series. By default left join is performed to - align the corresponding elements. - - >>> s.str.cat(bpd.Series([" Doe", " Foe", " Roe"])) - 0 Jane Doe - 1 John Foe - dtype: string - - >>> s.str.cat(bpd.Series([" Doe", " Foe", " Roe"], index=[2, 0, 1])) - 0 Jane Foe - 1 John Roe - dtype: string - - You can enforce an outer join. - - >>> s.str.cat(bpd.Series([" Doe", " Foe", " Roe"]), join="outer") - 0 Jane Doe - 1 John Foe - 2 - dtype: string - Args: - others (str or Series): - A string or a Series of strings. + others (Series): join ({'left', 'outer'}, default 'left'): Determines the join-style between the calling Series and any @@ -706,76 +315,6 @@ def contains(self, pat, case: bool = True, flags: int = 0, *, regex: bool = True Return boolean Series or Index based on whether a given pattern or regex is contained within a string of a Series or Index. - **Examples:** - - >>> import bigframes.pandas as bpd - - Returning a Series of booleans using only a literal pattern. - - >>> s1 = bpd.Series(['Mouse', 'dog', 'house and parrot', '23', None]) - >>> s1.str.contains('og') - 0 False - 1 True - 2 False - 3 False - 4 - dtype: boolean - - Specifying case sensitivity using `case`. - - >>> s1.str.contains('oG', case=True) - 0 False - 1 False - 2 False - 3 False - 4 - dtype: boolean - - Returning 'house' or 'dog' when either expression occurs in a string. - - >>> s1.str.contains('house|dog', regex=True) - 0 False - 1 True - 2 True - 3 False - 4 - dtype: boolean - - Ignoring case sensitivity using `flags` with regex. - - >>> import re - >>> s1.str.contains('PARROT', flags=re.IGNORECASE, regex=True) - 0 False - 1 False - 2 True - 3 False - 4 - dtype: boolean - - Returning any digit using regular expression. - - >>> s1.str.contains('\\d', regex=True) - 0 False - 1 False - 2 False - 3 True - 4 - dtype: boolean - - Ensure `pat` is a not a literal pattern when `regex` is set to True. - Note in the following example one might expect only *s2[1]* and *s2[3]* - to return `True`. However, '.0' as a regex matches any character - followed by a 0. - - >>> s2 = bpd.Series(['40', '40.0', '41', '41.0', '35']) - >>> s2.str.contains('.0', regex=True) - 0 True - 1 True - 2 False - 3 True - 4 False - dtype: boolean - Args: pat (str, re.Pattern): Character sequence or regular expression. @@ -809,31 +348,6 @@ def replace( Equivalent to :meth:`str.replace` or :func:`re.sub`, depending on the regex value. - **Examples:** - - >>> import bigframes.pandas as bpd - - When *pat* is a string and *regex* is True, the given *pat* is compiled - as a regex. When *repl* is a string, it replaces matching regex patterns - as with `re.sub()`. NaN value(s) in the Series are left as is: - - >>> s = bpd.Series(['foo', 'fuz', pd.NA]) - >>> s.str.replace('f.', 'ba', regex=True) - 0 bao - 1 baz - 2 - dtype: string - - When *pat* is a string and *regex* is False, every *pat* is replaced - with *repl* as with `str.replace()`: - - >>> s = bpd.Series(['f.o', 'fuz', pd.NA]) - >>> s.str.replace('f.', 'ba', regex=False) - 0 bao - 1 fuz - 2 - dtype: string - Args: pat (str, re.Pattern): String can be a character sequence or regular expression. @@ -870,32 +384,6 @@ def startswith( """ Test if the start of each string element matches a pattern. - **Examples:** - - >>> import bigframes.pandas as bpd - - >>> s = bpd.Series(['bat', 'Bear', 'caT', pd.NA]) - >>> s - 0 bat - 1 Bear - 2 caT - 3 - dtype: string - - >>> s.str.startswith('b') - 0 True - 1 False - 2 False - 3 - dtype: boolean - - >>> s.str.startswith(('b', 'B')) - 0 True - 1 True - 2 False - 3 - dtype: boolean - Args: pat (str, tuple[str, ...]): Character sequence or tuple of strings. Regular expressions are not @@ -914,32 +402,6 @@ def endswith( """ Test if the end of each string element matches a pattern. - **Examples:** - - >>> import bigframes.pandas as bpd - - >>> s = bpd.Series(['bat', 'bear', 'caT', pd.NA]) - >>> s - 0 bat - 1 bear - 2 caT - 3 - dtype: string - - >>> s.str.endswith('t') - 0 True - 1 False - 2 False - 3 - dtype: boolean - - >>> s.str.endswith(('t', 'T')) - 0 True - 1 False - 2 True - 3 - dtype: boolean - Args: pat (str, tuple[str, ...]): Character sequence or tuple of strings. Regular expressions are not @@ -951,67 +413,10 @@ def endswith( """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - def split( - self, - pat: str = " ", - regex: typing.Union[bool, None] = None, - ): - """ - Split strings around given separator/delimiter. - - **Examples:** - - >>> import bigframes.pandas as bpd - - >>> s = bpd.Series( - ... [ - ... "a regular sentence", - ... "https://docs.python.org/index.html", - ... np.nan - ... ] - ... ) - >>> s.str.split() - 0 ['a' 'regular' 'sentence'] - 1 ['https://docs.python.org/index.html'] - 2 [] - dtype: list[pyarrow] - - The pat parameter can be used to split by other characters. - - >>> s.str.split("//", regex=False) - 0 ['a regular sentence'] - 1 ['https:' 'docs.python.org/index.html'] - 2 [] - dtype: list[pyarrow] - - Args: - pat (str, default " "): - String to split on. If not specified, split on whitespace. - regex (bool, default None): - Determines if the passed-in pattern is a regular expression. Regular - expressions aren't currently supported. Please set `regex=False` when - `pat` length is not 1. - - Returns: - bigframes.series.Series: Type matches caller. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - def match(self, pat: str, case: bool = True, flags: int = 0): """ Determine if each string starts with a match of a regular expression. - **Examples:** - - >>> import bigframes.pandas as bpd - - >>> ser = bpd.Series(["horse", "eagle", "donkey"]) - >>> ser.str.match("e") - 0 False - 1 True - 2 False - dtype: boolean - Args: pat (str): Character sequence or regular expression. @@ -1029,17 +434,6 @@ def fullmatch(self, pat: str, case: bool = True, flags: int = 0): """ Determine if each string entirely matches a regular expression. - **Examples:** - - >>> import bigframes.pandas as bpd - - >>> ser = bpd.Series(["cat", "duck", "dove"]) - >>> ser.str.fullmatch(r'd.+') - 0 False - 1 True - 2 True - dtype: boolean - Args: pat (str): Character sequence or regular expression. @@ -1060,17 +454,6 @@ def get(self, i: int): Extract element from lists, tuples, dict, or strings in each element in the Series/Index. - **Examples:** - - >>> import bigframes.pandas as bpd - - >>> s = bpd.Series(["apple", "banana", "fig"]) - >>> s.str.get(3) - 0 l - 1 a - 2 - dtype: string - Args: i (int): Position or key of element to extract. @@ -1089,31 +472,6 @@ def pad( """ Pad strings in the Series/Index up to width. - **Examples:** - - >>> import bigframes.pandas as bpd - - >>> s = bpd.Series(["caribou", "tiger"]) - >>> s - 0 caribou - 1 tiger - dtype: string - - >>> s.str.pad(width=10) - 0 caribou - 1 tiger - dtype: string - - >>> s.str.pad(width=10, side='right', fillchar='-') - 0 caribou--- - 1 tiger----- - dtype: string - - >>> s.str.pad(width=10, side='both', fillchar='-') - 0 -caribou-- - 1 --tiger--- - dtype: string - Args: width (int): Minimum width of resulting string; additional characters will be filled @@ -1136,17 +494,6 @@ def ljust( """ Pad right side of strings in the Series/Index up to width. - **Examples:** - - >>> import bigframes.pandas as bpd - - >>> ser = bpd.Series(['dog', 'bird', 'mouse']) - >>> ser.str.ljust(8, fillchar='.') - 0 dog..... - 1 bird.... - 2 mouse... - dtype: string - Args: width (int): Minimum width of resulting string; additional characters will be filled @@ -1167,17 +514,6 @@ def rjust( """ Pad left side of strings in the Series/Index up to width. - **Examples:** - - >>> import bigframes.pandas as bpd - - >>> ser = bpd.Series(['dog', 'bird', 'mouse']) - >>> ser.str.rjust(8, fillchar='.') - 0 .....dog - 1 ....bird - 2 ...mouse - dtype: string - Args: width (int): Minimum width of resulting string; additional characters will be filled @@ -1202,25 +538,6 @@ def zfill( in the Series/Index with length greater or equal to `width` are unchanged. - **Examples:** - - >>> import bigframes.pandas as bpd - - >>> s = bpd.Series(['-1', '1', '1000', pd.NA]) - >>> s - 0 -1 - 1 1 - 2 1000 - 3 - dtype: string - - >>> s.str.zfill(3) - 0 -01 - 1 001 - 2 1000 - 3 - dtype: string - Args: width (int): Minimum length of resulting string; strings with length less @@ -1241,17 +558,6 @@ def center( Equivalent to :meth:`str.center`. - **Examples:** - - >>> import bigframes.pandas as bpd - - >>> ser = bpd.Series(['dog', 'bird', 'mouse']) - >>> ser.str.center(8, fillchar='.') - 0 ..dog... - 1 ..bird.. - 2 .mouse.. - dtype: string - Args: width (int): Minimum width of resulting string; additional characters will be filled @@ -1263,41 +569,3 @@ def center( bigframes.series.Series: Returns Series or Index with minimum number of char in object. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def join(self, sep: str): - """ - Join lists contained as elements in the Series/Index with passed delimiter. - - If the elements of a Series are lists themselves, join the content of these - lists using the delimiter passed to the function. - This function is an equivalent to :meth:`str.join`. - - **Examples:** - - >>> import bigframes.pandas as bpd - - Example with a list that contains non-string elements. - - >>> s = bpd.Series([['lion', 'elephant', 'zebra'], - ... ['dragon'], - ... ['duck', 'swan', 'fish', 'guppy']]) - >>> s - 0 ['lion' 'elephant' 'zebra'] - 1 ['dragon'] - 2 ['duck' 'swan' 'fish' 'guppy'] - dtype: list[pyarrow] - - >>> s.str.join('-') - 0 lion-elephant-zebra - 1 dragon - 2 duck-swan-fish-guppy - dtype: string - - Args: - sep (str): - Delimiter to use between list entries. - - Returns: - bigframes.series.Series: The list entries concatenated by intervening occurrences of the delimiter. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) diff --git a/third_party/bigframes_vendored/pandas/core/tools/__init__.py b/third_party/bigframes_vendored/pandas/core/tools/__init__.py deleted file mode 100644 index e69de29bb2d..00000000000 diff --git a/third_party/bigframes_vendored/pandas/core/tools/datetimes.py b/third_party/bigframes_vendored/pandas/core/tools/datetimes.py deleted file mode 100644 index c5f9f8330f6..00000000000 --- a/third_party/bigframes_vendored/pandas/core/tools/datetimes.py +++ /dev/null @@ -1,83 +0,0 @@ -# Contains code from https://github.com/pandas-dev/pandas/blob/main/pandas/core/tools/datetimes.py - -from __future__ import annotations - -from datetime import date, datetime -from typing import List, Mapping, Tuple, Union - -import pandas as pd - -from bigframes import constants, dataframe, series - -local_iterables = Union[List, Tuple, pd.Series, pd.DataFrame, Mapping] - - -def to_datetime( - arg: Union[ - Union[int, float, str, datetime, date], - local_iterables, - series.Series, - dataframe.DataFrame, - ], - *, - utc=False, - format=None, - unit=None, -) -> Union[pd.Timestamp, datetime, series.Series]: - """ - This function converts a scalar, array-like or Series to a datetime object. - - .. note:: - BigQuery only supports precision up to microseconds (us). Therefore, when working - with timestamps that have a finer granularity than microseconds, be aware that - the additional precision will not be represented in BigQuery. - - .. note:: - The format strings for specifying datetime representations in BigQuery and pandas - are not completely identical. Ensure that the format string provided is compatible - with BigQuery (https://cloud.google.com/bigquery/docs/reference/standard-sql/format-elements#format_elements_date_time). - - **Examples:** - - >>> import bigframes.pandas as bpd - - Converting a Scalar to datetime: - - >>> scalar = 123456.789 - >>> bpd.to_datetime(scalar, unit = 's') - Timestamp('1970-01-02 10:17:36.789000') - - Converting a List of Strings without Timezone Information: - - >>> list_str = ["01-31-2021 14:30", "02-28-2021 15:45"] - >>> bpd.to_datetime(list_str, format="%m-%d-%Y %H:%M", utc=True) - 0 2021-01-31 14:30:00+00:00 - 1 2021-02-28 15:45:00+00:00 - dtype: timestamp[us, tz=UTC][pyarrow] - - Converting a Series of Strings with Timezone Information: - - >>> series_str = bpd.Series(["01-31-2021 14:30+08:00", "02-28-2021 15:45+00:00"]) - >>> bpd.to_datetime(series_str, format="%m-%d-%Y %H:%M%Z", utc=True) - 0 2021-01-31 06:30:00+00:00 - 1 2021-02-28 15:45:00+00:00 - dtype: timestamp[us, tz=UTC][pyarrow] - - Args: - arg (int, float, str, datetime, date, list, tuple, 1-d array, Series): - The object to convert to a datetime. - utc (bool, default False): - Control timezone-related parsing, localization and conversion. If True, the - function always returns a timezone-aware UTC-localized timestamp or series. - If False (default), inputs will not be coerced to UTC. - format (str, default None): - The strftime to parse time, e.g. "%d/%m/%Y". - unit (str, default 'ns'): - The unit of the arg (D,s,ms,us,ns) denote the unit, which is an integer or - float number. - - Returns: - Union[pandas.Timestamp, datetime.datetime or bigframes.pandas.Series]: - Return type depends on input. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) diff --git a/third_party/bigframes_vendored/pandas/core/tools/timedeltas.py b/third_party/bigframes_vendored/pandas/core/tools/timedeltas.py deleted file mode 100644 index 92cac856a59..00000000000 --- a/third_party/bigframes_vendored/pandas/core/tools/timedeltas.py +++ /dev/null @@ -1,97 +0,0 @@ -# Contains code from https://github.com/pandas-dev/pandas/blob/v2.2.3/pandas/core/tools/timedeltas.py - -import typing - -import pandas as pd -from bigframes_vendored import constants - -from bigframes import series - -UnitChoices = typing.Literal[ - "W", - "w", - "D", - "d", - "days", - "day", - "hours", - "hour", - "hr", - "h", - "m", - "minute", - "min", - "minutes", - "s", - "seconds", - "sec", - "second", - "ms", - "milliseconds", - "millisecond", - "milli", - "millis", - "us", - "microseconds", - "microsecond", - "µs", - "micro", - "micros", -] - - -def to_timedelta( - arg: typing.Union[series.Series, str, int, float], - unit: typing.Optional[UnitChoices] = None, -) -> typing.Union[series.Series, pd.Timedelta]: - """ - Converts a scalar or Series to a timedelta object. - - .. note:: - BigQuery only supports precision up to microseconds (us). Therefore, when working - with timedeltas that have a finer granularity than microseconds, be aware that - the additional precision will not be represented in BigQuery. - - **Examples:** - - Converting a Scalar to timedelta - - >>> import bigframes.pandas as bpd - >>> scalar = 2 - >>> bpd.to_timedelta(scalar, unit='s') - Timedelta('0 days 00:00:02') - - Converting a Series of integers to a Series of timedeltas - - >>> int_series = bpd.Series([1,2,3]) - >>> bpd.to_timedelta(int_series, unit='s') - 0 0 days 00:00:01 - 1 0 days 00:00:02 - 2 0 days 00:00:03 - dtype: duration[us][pyarrow] - - Args: - arg (int, float, str, Series): - The object to convert to a dataframe - unit (str, default 'us'): - Denotes the unit of the arg for numeric `arg`. Defaults to ``"us"``. - - Possible values: - - * 'W' - * 'D' / 'days' / 'day' - * 'hours' / 'hour' / 'hr' / 'h' / 'H' - * 'm' / 'minute' / 'min' / 'minutes' - * 's' / 'seconds' / 'sec' / 'second' - * 'ms' / 'milliseconds' / 'millisecond' / 'milli' / 'millis' - * 'us' / 'microseconds' / 'microsecond' / 'micro' / 'micros' - - Returns: - Union[pandas.Timedelta, bigframes.pandas.Series]: - Return type depends on input - - Series: Series of duration[us][pyarrow] dtype - - scalar: timedelta - - """ - - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) diff --git a/third_party/bigframes_vendored/pandas/core/window/rolling.py b/third_party/bigframes_vendored/pandas/core/window/rolling.py index 7ca676fbe6d..a869c86e72a 100644 --- a/third_party/bigframes_vendored/pandas/core/window/rolling.py +++ b/third_party/bigframes_vendored/pandas/core/window/rolling.py @@ -37,52 +37,3 @@ def max(self): def min(self): """Calculate the weighted window minimum.""" raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def agg(self, func): - """ - Aggregate using one or more operations over the specified axis. - - **Examples:** - - >>> import bigframes.pandas as bpd - - >>> df = bpd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6], "C": [7, 8, 9]}) - >>> df - A B C - 0 1 4 7 - 1 2 5 8 - 2 3 6 9 - - [3 rows x 3 columns] - - >>> df.rolling(2).sum() - A B C - 0 - 1 3 9 15 - 2 5 11 17 - - [3 rows x 3 columns] - - >>> df.rolling(2).agg({"A": "sum", "B": "min"}) - A B - 0 - 1 3 4 - 2 5 5 - - [3 rows x 2 columns] - - Args: - func (function, str, list or dict): - Function to use for aggregating the data. - - Accepted combinations are: - - - string function name - - list of function names, e.g. ``['sum', 'mean']`` - - dict of axis labels -> function names or list of such. - - Returns: - Series or DataFrame - - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) diff --git a/third_party/bigframes_vendored/pandas/io/common.py b/third_party/bigframes_vendored/pandas/io/common.py index cab3c36cc9f..506984e64da 100644 --- a/third_party/bigframes_vendored/pandas/io/common.py +++ b/third_party/bigframes_vendored/pandas/io/common.py @@ -1,6 +1,5 @@ # Contains code from https://github.com/pandas-dev/pandas/blob/main/pandas/io/common.py """Common IO api utilities""" - from __future__ import annotations from collections import defaultdict @@ -14,13 +13,13 @@ def dedup_names( """ Rename column names if duplicates exist. - Currently the renaming is done by appending a underscore and an - autonumeric, but a custom pattern may be supported in the future. + Currently the renaming is done by appending a period and an autonumeric, + but a custom pattern may be supported in the future. Examples ``` dedup_names(["x", "y", "x", "x"], is_potential_multiindex=False) - ['x', 'y', 'x_1', 'x_2'] + ['x', 'y', 'x.1', 'x.2'] ``` """ names = list(names) # so we can index @@ -35,9 +34,9 @@ def dedup_names( if is_potential_multiindex: # for mypy assert isinstance(col, tuple) - col = col[:-1] + (f"{col[-1]}_{cur_count}",) + col = col[:-1] + (f"{col[-1]}.{cur_count}",) else: - col = f"{col}_{cur_count}" + col = f"{col}.{cur_count}" cur_count = counts[col] names[i] = col diff --git a/third_party/bigframes_vendored/pandas/io/gbq.py b/third_party/bigframes_vendored/pandas/io/gbq.py index 242d2c50c8d..575c5016187 100644 --- a/third_party/bigframes_vendored/pandas/io/gbq.py +++ b/third_party/bigframes_vendored/pandas/io/gbq.py @@ -1,52 +1,31 @@ # Contains code from https://github.com/pandas-dev/pandas/blob/main/pandas/io/gbq.py -"""Google BigQuery support""" +""" Google BigQuery support """ from __future__ import annotations -from typing import Any, Dict, Iterable, Literal, Optional, Tuple, Union +from typing import Iterable, Optional -import bigframes.enums from bigframes import constants -FilterOps = Literal["in", "not in", "<", "<=", "==", "!=", ">=", ">", "LIKE"] -FilterType = Tuple[str, FilterOps, Any] -FiltersType = Union[Iterable[FilterType], Iterable[Iterable[FilterType]]] - class GBQIOMixin: def read_gbq( self, query_or_table: str, *, - index_col: Union[Iterable[str], str, bigframes.enums.DefaultIndexKind] = (), - columns: Iterable[str] = (), - configuration: Optional[Dict] = None, - max_results: Optional[int] = None, - filters: FiltersType = (), - use_cache: Optional[bool] = None, + index_col: Iterable[str] | str = (), col_order: Iterable[str] = (), - allow_large_results: Optional[bool] = None, + max_results: Optional[int] = None, ): """Loads a DataFrame from BigQuery. - BigQuery tables are an unordered, unindexed data source. To add support - pandas-compatibility, the following indexing options are supported via - the ``index_col`` parameter: - - * (Empty iterable, default) A default index. **Behavior may change.** - Explicitly set ``index_col`` if your application makes use of - specific index values. - - If a table has primary key(s), those are used as the index, - otherwise a sequential index is generated. - * (:attr:`bigframes.enums.DefaultIndexKind.SEQUENTIAL_INT64`) Add an - arbitrary sequential index and ordering. **Warning** This uses an - analytic windowed operation that prevents filtering push down. Avoid - using on large clustered or partitioned tables. - * (Recommended) Set the ``index_col`` argument to one or more columns. - Unique values for the row labels are recommended. Duplicate labels - are possible, but note that joins on a non-unique index can duplicate - rows via pandas-compatible outer join behavior. + BigQuery tables are an unordered, unindexed data source. By default, + the DataFrame will have an arbitrary index and ordering. + + Set the `index_col` argument to one or more columns to choose an + index. The resulting DataFrame is sorted by the index columns. For the + best performance, ensure the index columns don't contain duplicate + values. .. note:: By default, even SQL query inputs with an ORDER BY clause create a @@ -61,14 +40,21 @@ def read_gbq( **Examples:** >>> import bigframes.pandas as bpd + >>> bpd.options.display.progress_bar = None If the input is a table ID: >>> df = bpd.read_gbq("bigquery-public-data.ml_datasets.penguins") - - Read table path with wildcard suffix and filters: - - >>> df = bpd.read_gbq_table("bigquery-public-data.noaa_gsod.gsod19*", filters=[("_table_suffix", ">=", "30"), ("_table_suffix", "<=", "39")]) + >>> df.head(2) + species island culmen_length_mm \\ + 0 Adelie Penguin (Pygoscelis adeliae) Dream 36.6 + 1 Adelie Penguin (Pygoscelis adeliae) Dream 39.8 + + culmen_depth_mm flipper_length_mm body_mass_g sex + 0 18.4 184.0 3475.0 FEMALE + 1 19.1 184.0 4650.0 MALE + + [2 rows x 7 columns] Preserve ordering in a query input. @@ -86,96 +72,29 @@ def read_gbq( ... WHERE year = 2016 ... GROUP BY pitcherFirstName, pitcherLastName ... ''', index_col="rowindex") - >>> print("START_OF_OUTPUT"); df.head(2) # doctest: +ELLIPSIS,+NORMALIZE_WHITESPACE - START_OF_OUTPUT - ... + >>> df.head(2) pitcherFirstName pitcherLastName averagePitchSpeed - ... + rowindex 1 Albertin Chapman 96.514113 2 Zachary Britton 94.591039 [2 rows x 3 columns] - Reading data with `columns` and `filters` parameters: - - >>> columns = ['pitcherFirstName', 'pitcherLastName', 'year', 'pitchSpeed'] - >>> filters = [('year', '==', 2016), ('pitcherFirstName', 'in', ['John', 'Doe']), ('pitcherLastName', 'in', ['Gant']), ('pitchSpeed', '>', 94)] - >>> df = bpd.read_gbq( - ... "bigquery-public-data.baseball.games_wide", - ... columns=columns, - ... filters=filters, - ... ) - >>> df.head(1) - pitcherFirstName pitcherLastName year pitchSpeed - 0 John Gant 2016 95 - - [1 rows x 4 columns] - Args: query_or_table (str): A SQL string to be executed or a BigQuery table to be read. The table must be specified in the format of `project.dataset.tablename` or `dataset.tablename`. - Can also take wildcard table name, such as `project.dataset.table_prefix*`. - In tha case, will read all the matched table as one DataFrame. - index_col (Iterable[str], str, bigframes.enums.DefaultIndexKind): + index_col (Iterable[str] or str): Name of result column(s) to use for index in results DataFrame. - - If an empty iterable, such as ``()``, a default index is - generated. Do not depend on specific index values in this case. - - **New in bigframes version 1.3.0**: If ``index_cols`` is not - set, the primary key(s) of the table are used as the index. - - **New in bigframes version 1.4.0**: Support - :class:`bigframes.enums.DefaultIndexKind` to override default index - behavior. - columns (Iterable[str]): + col_order (Iterable[str]): List of BigQuery column names in the desired order for results DataFrame. - configuration (dict, optional): - Query config parameters for job processing. - For example: configuration = {'query': {'useQueryCache': False}}. - For more information see `BigQuery REST API Reference - `__. max_results (Optional[int], default None): If set, limit the maximum number of rows to fetch from the query results. - filters (Union[Iterable[FilterType], Iterable[Iterable[FilterType]]], default ()): To - filter out data. Filter syntax: [[(column, op, val), …],…] where - op is [==, >, >=, <, <=, !=, in, not in, LIKE]. The innermost tuples - are transposed into a set of filters applied through an AND - operation. The outer Iterable combines these sets of filters - through an OR operation. A single Iterable of tuples can also - be used, meaning that no OR operation between set of filters - is to be conducted. - If using wildcard table suffix in query_or_table, can specify - '_table_suffix' pseudo column to filter the tables to be read - into the DataFrame. - use_cache (Optional[bool], default None): - Caches query results if set to `True`. When `None`, it behaves - as `True`, but should not be combined with `useQueryCache` in - `configuration` to avoid conflicts. - col_order (Iterable[str]): - Alias for columns, retained for backwards compatibility. - allow_large_results (bool, optional): - Whether to allow large query results. If ``True``, the query - results can be larger than the maximum response size. This - option is only applicable when ``query_or_table`` is a query. - Defaults to ``bpd.options.compute.allow_large_results``. - - Raises: - bigframes.exceptions.DefaultIndexWarning: - Using the default index is discouraged, such as with clustered - or partitioned tables without primary keys. - ValueError: - When both ``columns`` and ``col_order`` are specified. - ValueError: - If ``configuration`` is specified when directly reading - from a table. Returns: - bigframes.pandas.DataFrame: - A DataFrame representing results of the query or table. + bigframes.dataframe.DataFrame: A DataFrame representing results of the query or table. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) diff --git a/third_party/bigframes_vendored/pandas/io/parquet.py b/third_party/bigframes_vendored/pandas/io/parquet.py index cfb653481bf..f97bd386a40 100644 --- a/third_party/bigframes_vendored/pandas/io/parquet.py +++ b/third_party/bigframes_vendored/pandas/io/parquet.py @@ -1,6 +1,5 @@ # Contains code from https://github.com/pandas-dev/pandas/blob/main/pandas/io/parquet.py -"""parquet compat""" - +""" parquet compat """ from __future__ import annotations from bigframes import constants @@ -10,8 +9,6 @@ class ParquetIOMixin: def read_parquet( self, path: str, - *, - engine: str = "auto", ): r"""Load a Parquet object from the file path (local or Cloud Storage), returning a DataFrame. @@ -20,27 +17,25 @@ def read_parquet( Instead, set a serialized index column as the index and sort by that in the resulting DataFrame. - .. note:: - For non-"bigquery" engine, data is inlined in the query SQL if it is - small enough (roughly 5MB or less in memory). Larger size data is - loaded to a BigQuery table instead. - **Examples:** >>> import bigframes.pandas as bpd + >>> bpd.options.display.progress_bar = None >>> gcs_path = "gs://cloud-samples-data/bigquery/us-states/us-states.parquet" - >>> df = bpd.read_parquet(path=gcs_path, engine="bigquery") + >>> df = bpd.read_parquet(path=gcs_path) + >>> df.head(2) + name post_abbr + 0 Alabama AL + 1 Alaska AK + + [2 rows x 2 columns] Args: path (str): Local or Cloud Storage path to Parquet file. - engine (str): - One of ``'auto', 'pyarrow', 'fastparquet'``, or ``'bigquery'``. - Parquet library to parse the file. If set to ``'bigquery'``, - order is not preserved. Default, ``'auto'``. Returns: - bigframes.pandas.DataFrame: A BigQuery DataFrames. + bigframes.dataframe.DataFrame: A BigQuery DataFrames. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) diff --git a/third_party/bigframes_vendored/pandas/io/parsers/readers.py b/third_party/bigframes_vendored/pandas/io/parsers/readers.py index 537974b5f32..e8ed6182a6f 100644 --- a/third_party/bigframes_vendored/pandas/io/parsers/readers.py +++ b/third_party/bigframes_vendored/pandas/io/parsers/readers.py @@ -4,13 +4,12 @@ GH#48849 provides a convenient way of deprecating keyword arguments """ - from __future__ import annotations from typing import ( - IO, Any, Dict, + IO, Literal, MutableSequence, Optional, @@ -21,7 +20,6 @@ import numpy as np -import bigframes.enums from bigframes import constants @@ -36,13 +34,7 @@ def read_csv( Union[MutableSequence[Any], np.ndarray[Any, Any], Tuple[Any, ...], range] ] = None, index_col: Optional[ - Union[ - int, - str, - Sequence[Union[str, int]], - bigframes.enums.DefaultIndexKind, - Literal[False], - ] + Union[int, str, Sequence[Union[str, int]], Literal[False]] ] = None, usecols=None, dtype: Optional[Dict] = None, @@ -50,10 +42,10 @@ def read_csv( Literal["c", "python", "pyarrow", "python-fwf", "bigquery"] ] = None, encoding: Optional[str] = None, - write_engine="default", **kwargs, ): - """Loads data from a comma-separated values (csv) file into a DataFrame. + """Loads DataFrame from comma-separated values (csv) file locally or from + Cloud Storage. The CSV file data will be persisted as a temporary BigQuery table, which can be automatically recycled after the Session is closed. @@ -61,17 +53,12 @@ def read_csv( .. note:: using `engine="bigquery"` will not guarantee the same ordering as the file. Instead, set a serialized index column as the index and sort by - that in the resulting DataFrame. Only files stored on your local machine - or in Google Cloud Storage are supported. - - .. note:: - For non-bigquery engine, data is inlined in the query SQL if it is - small enough (roughly 5MB or less in memory). Larger size data is - loaded to a BigQuery table instead. + that in the resulting DataFrame. **Examples:** >>> import bigframes.pandas as bpd + >>> bpd.options.display.progress_bar = None >>> gcs_path = "gs://cloud-samples-data/bigquery/us-states/us-states.csv" >>> df = bpd.read_csv(filepath_or_buffer=gcs_path) @@ -114,7 +101,7 @@ def read_csv( names (default None): a list of column names to use. If the file contains a header row and you want to pass this parameter, then `header=0` should be passed as well so the - first (header) row is ignored. + first (header) row is ignored. Only to be used with default engine. index_col (default None): column(s) to use as the row labels of the DataFrame, either given as string name or column index. `index_col=False` can be used with the default @@ -143,22 +130,12 @@ def read_csv( documentation for a comprehensive list, https://docs.python.org/3/library/codecs.html#standard-encodings The BigQuery engine only supports `UTF-8` and `ISO-8859-1`. - write_engine (str): - How data should be written to BigQuery (if at all). See - :func:`bigframes.pandas.read_pandas` for a full description of - supported values. - **kwargs: keyword arguments for `pandas.read_csv` when not using the BigQuery engine. - Returns: - bigframes.pandas.DataFrame: - A BigQuery DataFrames. - Raises: - bigframes.exceptions.DefaultIndexWarning: - Using the default index is discouraged, such as with clustered - or partitioned tables without primary keys. + Returns: + bigframes.dataframe.DataFrame: A BigQuery DataFrames. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -173,7 +150,6 @@ def read_json( encoding: Optional[str] = None, lines: bool = False, engine: Literal["ujson", "pyarrow", "bigquery"] = "ujson", - write_engine="default", **kwargs, ): """ @@ -184,14 +160,10 @@ def read_json( file. Instead, set a serialized index column as the index and sort by that in the resulting DataFrame. - .. note:: - For non-bigquery engine, data is inlined in the query SQL if it is - small enough (roughly 5MB or less in memory). Larger size data is - loaded to a BigQuery table instead. - **Examples:** >>> import bigframes.pandas as bpd + >>> bpd.options.display.progress_bar = None >>> gcs_path = "gs://bigframes-dev-testing/sample1.json" >>> df = bpd.read_json(path_or_buf=gcs_path, lines=True, orient="records") @@ -233,23 +205,11 @@ def read_json( engine ({{"ujson", "pyarrow", "bigquery"}}, default "ujson"): Type of engine to use. If `engine="bigquery"` is specified, then BigQuery's load API will be used. Otherwise, the engine will be passed to `pandas.read_json`. - write_engine (str): - How data should be written to BigQuery (if at all). See - :func:`bigframes.pandas.read_pandas` for a full description of - supported values. - **kwargs: keyword arguments for `pandas.read_json` when not using the BigQuery engine. Returns: - bigframes.pandas.DataFrame: + bigframes.dataframe.DataFrame: The DataFrame representing JSON contents. - - Raises: - bigframes.exceptions.DefaultIndexWarning: - Using the default index is discouraged, such as with clustered - or partitioned tables without primary keys. - ValueError: - ``lines`` is only valid when ``orient`` is ``records``. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) diff --git a/third_party/bigframes_vendored/pandas/io/pickle.py b/third_party/bigframes_vendored/pandas/io/pickle.py index 10ceab3c2fa..053ba4871c1 100644 --- a/third_party/bigframes_vendored/pandas/io/pickle.py +++ b/third_party/bigframes_vendored/pandas/io/pickle.py @@ -1,6 +1,5 @@ # Contains code from https://github.com/pandas-dev/pandas/blob/main/pandas/io/pickle.py -"""pickle compat""" - +""" pickle compat """ from __future__ import annotations from pandas._typing import ( @@ -19,8 +18,6 @@ def read_pickle( filepath_or_buffer: FilePath | ReadPickleBuffer, compression: CompressionOptions = "infer", storage_options: StorageOptions = None, - *, - write_engine="default", ): """Load pickled BigFrames object (or any object) from file. @@ -28,17 +25,23 @@ def read_pickle( If the content of the pickle file is a Series and its name attribute is None, the name will be set to '0' by default. - .. note:: - Data is inlined in the query SQL if it is small enough (roughly 5MB - or less in memory). Larger size data is loaded to a BigQuery table - instead. - **Examples:** >>> import bigframes.pandas as bpd + >>> bpd.options.display.progress_bar = None >>> gcs_path = "gs://bigframes-dev-testing/test_pickle.pkl" >>> df = bpd.read_pickle(filepath_or_buffer=gcs_path) + >>> df.head(2) + species island culmen_length_mm \\ + 0 Adelie Penguin (Pygoscelis adeliae) Dream 36.6 + 1 Adelie Penguin (Pygoscelis adeliae) Dream 39.8 + + culmen_depth_mm flipper_length_mm body_mass_g sex + 0 18.4 184.0 3475.0 FEMALE + 1 19.1 184.0 4650.0 MALE + + [2 rows x 7 columns] Args: filepath_or_buffer (str, path object, or file-like object): @@ -64,13 +67,9 @@ def read_pickle( starting with “s3://”, and “gcs://”) the key-value pairs are forwarded to fsspec.open. Please see fsspec and urllib for more details, and for more examples on storage options refer here. - write_engine (str): - How data should be written to BigQuery (if at all). See - :func:`bigframes.pandas.read_pandas` for a full description of - supported values. Returns: - bigframes.pandas.DataFrame or bigframes.pandas.Series: same type as object + bigframes.dataframe.DataFrame or bigframes.series.Series: same type as object stored in file. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) diff --git a/third_party/bigframes_vendored/pandas/pandas/_typing.py b/third_party/bigframes_vendored/pandas/pandas/_typing.py index 3640ba25163..e665339fc83 100644 --- a/third_party/bigframes_vendored/pandas/pandas/_typing.py +++ b/third_party/bigframes_vendored/pandas/pandas/_typing.py @@ -1,11 +1,10 @@ # Copied from https://github.com/pandas-dev/pandas/blob/main/pandas/_typing.py from __future__ import annotations -import sys from datetime import datetime, timedelta, tzinfo from os import PathLike +import sys from typing import ( - TYPE_CHECKING, Any, Callable, Dict, @@ -18,10 +17,9 @@ Protocol, Sequence, Tuple, - TypeVar, - Union, ) from typing import Type as type_t +from typing import TYPE_CHECKING, TypeVar, Union import numpy as np @@ -236,11 +234,13 @@ def flush(self) -> Any: class ReadPickleBuffer(ReadBuffer[bytes], Protocol): - def readline(self) -> bytes: ... + def readline(self) -> bytes: + ... class WriteExcelBuffer(WriteBuffer[bytes], Protocol): - def truncate(self, size: int | None = ...) -> int: ... + def truncate(self, size: int | None = ...) -> int: + ... class ReadCsvBuffer(ReadBuffer[AnyStr_co], Protocol): diff --git a/third_party/bigframes_vendored/pandas/plotting/_core.py b/third_party/bigframes_vendored/pandas/plotting/_core.py deleted file mode 100644 index 6c2aed970de..00000000000 --- a/third_party/bigframes_vendored/pandas/plotting/_core.py +++ /dev/null @@ -1,441 +0,0 @@ -import typing - -from bigframes import constants - - -class PlotAccessor: - """ - Make plots of Series or DataFrame with the `matplotlib` backend. - - **Examples:** - For Series: - - >>> import bigframes.pandas as bpd - >>> ser = bpd.Series([1, 2, 3, 3]) - >>> plot = ser.plot(kind='hist', title="My plot") - - For DataFrame: - - >>> df = bpd.DataFrame({'length': [1.5, 0.5, 1.2, 0.9, 3], - ... 'width': [0.7, 0.2, 0.15, 0.2, 1.1]}, - ... index=['pig', 'rabbit', 'duck', 'chicken', 'horse']) - >>> plot = df.plot(title="DataFrame Plot") - - Args: - data (Series or DataFrame): - The object for which the method is called. - kind (str): - The kind of plot to produce: - - - 'line' : line plot (default) - - 'hist' : histogram - - 'area' : area plot - - 'scatter' : scatter plot (DataFrame only) - - **kwargs: - Options to pass to `pandas.DataFrame.plot` method. See pandas - documentation online for more on these arguments. - - Returns: - matplotlib.axes.Axes or np.ndarray of them: - An ndarray is returned with one :class:`matplotlib.axes.Axes` - per column when ``subplots=True``. - """ - - def hist( - self, by: typing.Optional[typing.Sequence[str]] = None, bins: int = 10, **kwargs - ): - """ - Draw one histogram of the DataFrame’s columns. - - A histogram is a representation of the distribution of data. - This function groups the values of all given Series in the DataFrame - into bins and draws all bins in one :class:`matplotlib.axes.Axes`. - This is useful when the DataFrame's Series are in a similar scale. - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> df = bpd.DataFrame(np.random.randint(1, 7, 6000), columns=['one']) - >>> df['two'] = np.random.randint(1, 7, 6000) + np.random.randint(1, 7, 6000) - >>> ax = df.plot.hist(bins=12, alpha=0.5) - - Args: - by (str or sequence, optional): - Column in the DataFrame to group by. It is not supported yet. - bins (int, default 10): - Number of histogram bins to be used. - **kwargs: - Additional keyword arguments are documented in - :meth:`DataFrame.plot`. - - Returns: - class:`matplotlib.AxesSubplot`: A histogram plot. - - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def line( - self, - x: typing.Optional[typing.Hashable] = None, - y: typing.Optional[typing.Hashable] = None, - **kwargs, - ): - """ - Plot Series or DataFrame as lines. This function is useful to plot lines - using DataFrame's values as coordinates. - - This function calls `pandas.plot` to generate a plot with a random sample - of items. For consistent results, the random sampling is reproducible. - Use the `sampling_random_state` parameter to modify the sampling seed. - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> df = bpd.DataFrame( - ... { - ... 'one': [1, 2, 3, 4], - ... 'three': [3, 6, 9, 12], - ... 'reverse_ten': [40, 30, 20, 10], - ... } - ... ) - >>> ax = df.plot.line(x='one') - - Args: - x (label or position, optional): - Allows plotting of one column versus another. If not specified, - the index of the DataFrame is used. - y (label or position, optional): - Allows plotting of one column versus another. If not specified, - all numerical columns are used. - color (str, array-like, or dict, optional): - The color for each of the DataFrame's columns. Possible values are: - - - A single color string referred to by name, RGB or RGBA code, - for instance 'red' or '#a98d19'. - - - A sequence of color strings referred to by name, RGB or RGBA - code, which will be used for each column recursively. For - instance ['green','yellow'] each column's %(kind)s will be filled in - green or yellow, alternatively. If there is only a single column to - be plotted, then only the first color from the color list will be - used. - - - A dict of the form {column name : color}, so that each column will be - colored accordingly. For example, if your columns are called `a` and - `b`, then passing {'a': 'green', 'b': 'red'} will color %(kind)ss for - column `a` in green and %(kind)ss for column `b` in red. - sampling_n (int, default 100): - Number of random items for plotting. - sampling_random_state (int, default 0): - Seed for random number generator. - **kwargs: - Additional keyword arguments are documented in - :meth:`DataFrame.plot`. - - Returns: - matplotlib.axes.Axes or np.ndarray of them: - An ndarray is returned with one :class:`matplotlib.axes.Axes` - per column when ``subplots=True``. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def area( - self, - x: typing.Optional[typing.Hashable] = None, - y: typing.Optional[typing.Hashable] = None, - stacked: bool = True, - **kwargs, - ): - """ - Draw a stacked area plot. An area plot displays quantitative data visually. - - This function calls `pandas.plot` to generate a plot with a random sample - of items. For consistent results, the random sampling is reproducible. - Use the `sampling_random_state` parameter to modify the sampling seed. - - **Examples:** - - Draw an area plot based on basic business metrics: - - >>> import bigframes.pandas as bpd - >>> df = bpd.DataFrame( - ... { - ... 'sales': [3, 2, 3, 9, 10, 6], - ... 'signups': [5, 5, 6, 12, 14, 13], - ... 'visits': [20, 42, 28, 62, 81, 50], - ... }, - ... index=["01-31", "02-28", "03-31", "04-30", "05-31", "06-30"] - ... ) - >>> ax = df.plot.area() - - Area plots are stacked by default. To produce an unstacked plot, - pass ``stacked=False``: - - >>> ax = df.plot.area(stacked=False) - - Draw an area plot for a single column: - - >>> ax = df.plot.area(y='sales') - - Draw with a different `x`: - - >>> df = bpd.DataFrame({ - ... 'sales': [3, 2, 3], - ... 'visits': [20, 42, 28], - ... 'day': [1, 2, 3], - ... }) - >>> ax = df.plot.area(x='day') - - Args: - x (label or position, optional): - Coordinates for the X axis. By default uses the index. - y (label or position, optional): - Column to plot. By default uses all columns. - stacked (bool, default True): - Area plots are stacked by default. Set to False to create a - unstacked plot. - sampling_n (int, default 100): - Number of random items for plotting. - sampling_random_state (int, default 0): - Seed for random number generator. - **kwargs: - Additional keyword arguments are documented in - :meth:`DataFrame.plot`. - - Returns: - matplotlib.axes.Axes or numpy.ndarray: - Area plot, or array of area plots if subplots is True. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def bar( - self, - x: typing.Optional[typing.Hashable] = None, - y: typing.Optional[typing.Hashable] = None, - **kwargs, - ): - """ - Draw a vertical bar plot. - - This function calls `pandas.plot` to generate a plot with a random sample - of items. For consistent results, the random sampling is reproducible. - Use the `sampling_random_state` parameter to modify the sampling seed. - - **Examples:** - - Basic plot. - - >>> import bigframes.pandas as bpd - >>> df = bpd.DataFrame({'lab':['A', 'B', 'C'], 'val':[10, 30, 20]}) - >>> ax = df.plot.bar(x='lab', y='val', rot=0) - - Plot a whole dataframe to a bar plot. Each column is assigned a distinct color, - and each row is nested in a group along the horizontal axis. - - >>> speed = [0.1, 17.5, 40, 48, 52, 69, 88] - >>> lifespan = [2, 8, 70, 1.5, 25, 12, 28] - >>> index = ['snail', 'pig', 'elephant', - ... 'rabbit', 'giraffe', 'coyote', 'horse'] - >>> df = bpd.DataFrame({'speed': speed, 'lifespan': lifespan}, index=index) - >>> ax = df.plot.bar(rot=0) - - Plot stacked bar charts for the DataFrame. - - >>> ax = df.plot.bar(stacked=True) - - If you don’t like the default colours, you can specify how you’d like each column - to be colored. - - >>> axes = df.plot.bar( - ... rot=0, subplots=True, color={"speed": "red", "lifespan": "green"} - ... ) - - Args: - x (label or position, optional): - Allows plotting of one column versus another. If not specified, the index - of the DataFrame is used. - y (label or position, optional): - Allows plotting of one column versus another. If not specified, all numerical - columns are used. - **kwargs: - Additional keyword arguments are documented in - :meth:`DataFrame.plot`. - - Returns: - matplotlib.axes.Axes or numpy.ndarray: - Area plot, or array of area plots if subplots is True. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def barh( - self, - x: typing.Optional[typing.Hashable] = None, - y: typing.Optional[typing.Hashable] = None, - **kwargs, - ): - """ - Draw a horizontal bar plot. - - This function calls `pandas.plot` to generate a plot with a random sample - of items. For consistent results, the random sampling is reproducible. - Use the `sampling_random_state` parameter to modify the sampling seed. - - **Examples:** - - Basic plot. - - >>> import bigframes.pandas as bpd - >>> df = bpd.DataFrame({'lab':['A', 'B', 'C'], 'val':[10, 30, 20]}) - >>> ax = df.plot.barh(x='lab', y='val', rot=0) - - Plot a whole dataframe to a barh plot. Each column is assigned a distinct color, - and each row is nested in a group along the horizontal axis. - - >>> speed = [0.1, 17.5, 40, 48, 52, 69, 88] - >>> lifespan = [2, 8, 70, 1.5, 25, 12, 28] - >>> index = ['snail', 'pig', 'elephant', - ... 'rabbit', 'giraffe', 'coyote', 'horse'] - >>> df = bpd.DataFrame({'speed': speed, 'lifespan': lifespan}, index=index) - >>> ax = df.plot.barh(rot=0) - - Plot stacked barh charts for the DataFrame. - - >>> ax = df.plot.barh(stacked=True) - - If you don’t like the default colours, you can specify how you’d like each column - to be colored. - - >>> axes = df.plot.barh( - ... rot=0, subplots=True, color={"speed": "red", "lifespan": "green"} - ... ) - - Args: - x (label or position, optional): - Allows plotting of one column versus another. If not specified, the index - of the DataFrame is used. - y (label or position, optional): - Allows plotting of one column versus another. If not specified, all numerical - columns are used. - **kwargs: - Additional keyword arguments are documented in - :meth:`DataFrame.plot`. - - Returns: - matplotlib.axes.Axes or numpy.ndarray: - Area plot, or array of area plots if subplots is True. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def pie( - self, - y: typing.Optional[typing.Hashable] = None, - **kwargs, - ): - """ - Generate a pie plot. - - A pie plot is a proportional representation of the numerical data in a - column. This function wraps :meth:`matplotlib.pyplot.pie` for the - specified column. If no column reference is passed and - ``subplots=True`` a pie plot is drawn for each numerical column - independently. - - **Examples:** - - In the example below we have a DataFrame with the information about - planet's mass and radius. We pass the 'mass' column to the - pie function to get a pie plot. - - >>> import bigframes.pandas as bpd - - >>> df = bpd.DataFrame({'mass': [0.330, 4.87 , 5.97], - ... 'radius': [2439.7, 6051.8, 6378.1]}, - ... index=['Mercury', 'Venus', 'Earth']) - >>> plot = df.plot.pie(y='mass', figsize=(5, 5)) - - >>> plot = df.plot.pie(subplots=True, figsize=(11, 6)) - - Args: - y (int or label, optional): - Label or position of the column to plot. - If not provided, ``subplots=True`` argument must be passed. - **kwargs: - Keyword arguments to pass on to :meth:`DataFrame.plot`. - - Returns: - matplotlib.axes.Axes or np.ndarray: - A NumPy array is returned when `subplots` is True. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def scatter( - self, - x: typing.Optional[typing.Hashable] = None, - y: typing.Optional[typing.Hashable] = None, - s: typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]] = None, - c: typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]] = None, - **kwargs, - ): - """ - Create a scatter plot with varying marker point size and color. - - This function calls `pandas.plot` to generate a plot with a random sample - of items. For consistent results, the random sampling is reproducible. - Use the `sampling_random_state` parameter to modify the sampling seed. - - **Examples:** - - Let's see how to draw a scatter plot using coordinates from the values - in a DataFrame's columns. - - >>> import bigframes.pandas as bpd - >>> df = bpd.DataFrame([[5.1, 3.5, 0], [4.9, 3.0, 0], [7.0, 3.2, 1], - ... [6.4, 3.2, 1], [5.9, 3.0, 2]], - ... columns=['length', 'width', 'species']) - >>> ax1 = df.plot.scatter(x='length', - ... y='width', - ... c='DarkBlue') - - And now with the color determined by a column as well. - - >>> ax2 = df.plot.scatter(x='length', - ... y='width', - ... c='species', - ... colormap='viridis') - - Args: - x (int or str): - The column name or column position to be used as horizontal - coordinates for each point. - y (int or str): - The column name or column position to be used as vertical - coordinates for each point. - s (str, scalar or array-like, optional): - The size of each point. Possible values are: - - - A string with the name of the column to be used for marker's size. - - A single scalar so all points have the same size. - - c (str, int or array-like, optional): - The color of each point. Possible values are: - - - A single color string referred to by name, RGB or RGBA code, - for instance 'red' or '#a98d19'. - - A column name or position whose values will be used to color the - marker points according to a colormap. - - sampling_n (int, default 100): - Number of random items for plotting. - sampling_random_state (int, default 0): - Seed for random number generator. - **kwargs: - Additional keyword arguments are documented in - :meth:`DataFrame.plot`. - - Returns: - matplotlib.axes.Axes or np.ndarray of them: - An ndarray is returned with one :class:`matplotlib.axes.Axes` - per column when ``subplots=True``. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) diff --git a/third_party/bigframes_vendored/pandas/util/_exceptions.py b/third_party/bigframes_vendored/pandas/util/_exceptions.py deleted file mode 100644 index 4ca649153a0..00000000000 --- a/third_party/bigframes_vendored/pandas/util/_exceptions.py +++ /dev/null @@ -1,29 +0,0 @@ -# Contains code from https://github.com/pandas-dev/pandas/blob/main/pandas/util/_exceptions.py -from __future__ import annotations - -import inspect -import os - - -def find_stack_level() -> int: - """ - Find the first place in the stack that is not inside pandas - (tests notwithstanding). - """ - - import pandas as pd - - pkg_dir = os.path.dirname(pd.__file__) - test_dir = os.path.join(pkg_dir, "tests") - - # https://stackoverflow.com/questions/17407119/python-inspect-stack-is-slow - frame = inspect.currentframe() - n = 0 - while frame: - fname = inspect.getfile(frame) - if fname.startswith(pkg_dir) and not fname.startswith(test_dir): - frame = frame.f_back - n += 1 - else: - break - return n diff --git a/third_party/bigframes_vendored/pandas/util/_validators.py b/third_party/bigframes_vendored/pandas/util/_validators.py deleted file mode 100644 index fe8c9b5d9c6..00000000000 --- a/third_party/bigframes_vendored/pandas/util/_validators.py +++ /dev/null @@ -1,59 +0,0 @@ -# Contains code from https://github.com/pandas-dev/pandas/blob/main/pandas/util/_validators.py -""" -Module that contains many useful utilities -for validating data or function arguments -""" - -from __future__ import annotations - -from typing import TypeVar - -from pandas.core.dtypes.common import is_bool - -BoolishT = TypeVar("BoolishT", bool, int) -BoolishNoneT = TypeVar("BoolishNoneT", bool, int, None) - - -def validate_bool_kwarg( - value: BoolishNoneT, - arg_name: str, - none_allowed: bool = True, - int_allowed: bool = False, -) -> BoolishNoneT: - """ - Ensure that argument passed in arg_name can be interpreted as boolean. - - Parameters - ---------- - value : bool - Value to be validated. - arg_name : str - Name of the argument. To be reflected in the error message. - none_allowed : bool, default True - Whether to consider None to be a valid boolean. - int_allowed : bool, default False - Whether to consider integer value to be a valid boolean. - - Returns - ------- - value - The same value as input. - - Raises - ------ - ValueError - If the value is not a valid boolean. - """ - good_value = is_bool(value) - if none_allowed: - good_value = good_value or (value is None) - - if int_allowed: - good_value = good_value or isinstance(value, int) - - if not good_value: - raise ValueError( - f'For argument "{arg_name}" expected type bool, received ' - f"type {type(value).__name__}." - ) - return value # pyright: ignore[reportGeneralTypeIssues] diff --git a/third_party/bigframes_vendored/py.typed b/third_party/bigframes_vendored/py.typed deleted file mode 100644 index e69de29bb2d..00000000000 diff --git a/third_party/bigframes_vendored/sklearn/base.py b/third_party/bigframes_vendored/sklearn/base.py index 57c9e79f8de..768328e5529 100644 --- a/third_party/bigframes_vendored/sklearn/base.py +++ b/third_party/bigframes_vendored/sklearn/base.py @@ -81,13 +81,13 @@ class ClassifierMixin: def score(self, X, y): """Return the mean accuracy on the given test data and labels. - In multi-label classification, this is the subset accuracy, - which is a harsh metric since you require that - each label set be correctly predicted for each sample. + In multi-label classification, this is the subset accuracy + which is a harsh metric since you require for each sample that + each label set be correctly predicted. .. note:: - Output matches that of the BigQuery ML.EVALUATE function. + Output matches that of the BigQuery ML.EVALUTE function. See: https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-evaluate#classification_models for the outputs relevant to this model type. @@ -115,7 +115,7 @@ def score(self, X, y): .. note:: - Output matches that of the BigQuery ML.EVALUATE function. + Output matches that of the BigQuery ML.EVALUTE function. See: https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-evaluate#regression_models for the outputs relevant to this model type. @@ -153,7 +153,7 @@ def fit_transform(self, X, y=None): Target values (None for unsupervised transformations). Returns: - bigframes.dataframe.DataFrame: DataFrame of shape (n_samples, n_features_new). + bigframes.dataframe.DataFrame: DataFrame of shape (n_samples, n_features_new) Transformed DataFrame. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) diff --git a/third_party/bigframes_vendored/sklearn/cluster/_kmeans.py b/third_party/bigframes_vendored/sklearn/cluster/_kmeans.py index 2b1778eec8a..5369d3662d7 100644 --- a/third_party/bigframes_vendored/sklearn/cluster/_kmeans.py +++ b/third_party/bigframes_vendored/sklearn/cluster/_kmeans.py @@ -13,73 +13,35 @@ from abc import ABC -from bigframes_vendored.sklearn.base import BaseEstimator - from bigframes import constants +from third_party.bigframes_vendored.sklearn.base import BaseEstimator class _BaseKMeans(BaseEstimator, ABC): """Base class for KMeans and MiniBatchKMeans""" - pass - - -class KMeans(_BaseKMeans): - """K-Means clustering. - - **Examples:** + def predict(self, X): + """Predict the closest cluster each sample in X belongs to. - >>> import bigframes.pandas as bpd - >>> from bigframes.ml.cluster import KMeans + Args: + X (bigframes.dataframe.DataFrame or bigframes.series.Series): + Series or DataFrame of shape (n_samples, n_features). The data matrix for + which we want to get the predictions. - >>> X = bpd.DataFrame({"feat0": [1, 1, 1, 10, 10, 10], "feat1": [2, 4, 0, 2, 4, 0]}) - >>> kmeans = KMeans(n_clusters=2).fit(X) - >>> kmeans.predict(bpd.DataFrame({"feat0": [0, 12], "feat1": [0, 3]}))["CENTROID_ID"] # doctest:+SKIP - 0 1 - 1 2 - Name: CENTROID_ID, dtype: Int64 + Returns: + bigframes.dataframe.DataFrame: DataFrame of shape (n_samples,), containing the + class labels for each sample. + """ + raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - >>> kmeans.cluster_centers_ # doctest:+SKIP - centroid_id feature numerical_value categorical_value - 0 1 feat0 5.5 [] - 1 1 feat1 1.0 [] - 2 2 feat0 5.5 [] - 3 2 feat1 4.0 [] - [4 rows x 4 columns] +class KMeans(_BaseKMeans): + """K-Means clustering. Args: n_clusters (int, default 8): The number of clusters to form as well as the number of centroids to generate. Default to 8. - - init ("kmeans++", "random" or "custom", default "kmeans++"): - The method of initializing the clusters. Default to "kmeans++" - - kmeas++: Initializes a number of centroids equal to the n_clusters value by using the k-means++ algorithm. Using this approach usually trains a better model than using random cluster initialization. - random: Initializes the centroids by randomly selecting a number of data points equal to the n_clusters value from the input data. - custom: Initializes the centroids using a provided column of type bool. Uses the rows with a value of True as the initial centroids. You specify the column to use by using the init_col option. - - init_col (str or None, default None): - The name of the column to use to initialize the centroids. This column must have a type of bool. If this column contains a value of True for a given row, then uses that row as an initial centroid. The number of True rows in this column must be equal to the value you have specified for the n_clusters option. - Only works with init method "custom". Default to None. - - distance_type ("euclidean" or "cosine", default "euclidean"): - The type of metric to use to compute the distance between two points. - Default to "euclidean". - - max_iter (int, default 20): - The maximum number of training iterations, where one iteration represents a single pass of the entire training data. Default to 20. - - tol (float, default 0.01): - The minimum relative loss improvement that is necessary to continue training. For example, a value of 0.01 specifies that each iteration must reduce the loss by 1% for training to continue. - Default to 0.01. - - warm_start (bool, default False): - Determines whether to train a model with new training data, new model options, or both. Unless you explicitly override them, the initial options used to train the model are used for the warm start run. - Default to False. - - """ def fit( @@ -90,13 +52,13 @@ def fit( """Compute k-means clustering. Args: - X (bigframes.dataframe.DataFrame or bigframes.series.Series or pandas.core.frame.DataFrame or pandas.core.series.Series): + X (bigframes.dataframe.DataFrame or bigframes.series.Series): DataFrame of shape (n_samples, n_features). Training data. y (default None): Not used, present here for API consistency by convention. Returns: - KMeans: Fitted estimator. + KMeans: Fitted Estimator. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -107,31 +69,11 @@ def predict( """Predict the closest cluster each sample in X belongs to. Args: - X (bigframes.dataframe.DataFrame or bigframes.series.Series or pandas.core.frame.DataFrame or pandas.core.series.Series): + X (bigframes.dataframe.DataFrame or bigframes.series.Series): DataFrame of shape (n_samples, n_features). New data to predict. Returns: - bigframes.dataframe.DataFrame: DataFrame of shape (n_samples, n_input_columns + n_prediction_columns). Returns predicted labels. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def fit_predict( - self, - X, - y=None, - ): - """Compute cluster centers and predict cluster index for each sample. - - Convenience method; equivalent to calling fit(X) followed by predict(X). - - Args: - X (bigframes.dataframe.DataFrame or bigframes.series.Series or pandas.core.frame.DataFrame or pandas.core.series.Series): - DataFrame of shape (n_samples, n_features). Training data. - y (default None): - Not used, present here for API consistency by convention. - - Returns: - bigframes.dataframe.DataFrame: DataFrame of shape (n_samples, n_input_columns + n_prediction_columns). Returns predicted labels. + bigframes.dataframe.DataFrame: DataFrame of the cluster each sample belongs to. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -144,12 +86,12 @@ def score( .. note:: - Output matches that of the BigQuery ML.EVALUATE function. + Output matches that of the BigQuery ML.EVALUTE function. See: https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-evaluate#k-means_models for the outputs relevant to this model type. Args: - X (bigframes.dataframe.DataFrame or bigframes.series.Series or pandas.core.frame.DataFrame or pandas.core.series.Series): + X (bigframes.dataframe.DataFrame or bigframes.series.Series): DataFrame of shape (n_samples, n_features). New Data. y (default None) Not used, present here for API consistency by convention. diff --git a/third_party/bigframes_vendored/sklearn/compose/_column_transformer.py b/third_party/bigframes_vendored/sklearn/compose/_column_transformer.py index e0c03536e09..dead173b2df 100644 --- a/third_party/bigframes_vendored/sklearn/compose/_column_transformer.py +++ b/third_party/bigframes_vendored/sklearn/compose/_column_transformer.py @@ -3,11 +3,11 @@ # Andreas Mueller # License: BSD -from abc import ABCMeta -from bigframes_vendored.sklearn.base import BaseEstimator +from abc import ABCMeta from bigframes import constants +from third_party.bigframes_vendored.sklearn.base import BaseEstimator class _BaseComposition(BaseEstimator, metaclass=ABCMeta): @@ -18,9 +18,9 @@ class ColumnTransformer(_BaseComposition): """Applies transformers to columns of BigQuery DataFrames. This estimator allows different columns or column subsets of the input - to be transformed separately, and the features generated by each transformer + to be transformed separately and the features generated by each transformer will be concatenated to form a single feature space. - This is useful for heterogeneous or columnar data to combine several + This is useful for heterogeneous or columnar data, to combine several feature extraction mechanisms or transformations into a single transformer. Args: @@ -36,7 +36,7 @@ def fit( """Fit all transformers using X. Args: - X (bigframes.dataframe.DataFrame or bigframes.series.Series or pandas.core.frame.DataFrame or pandas.core.series.Series): + X (bigframes.dataframe.DataFrame or bigframes.series.Series): The Series or DataFrame of shape (n_samples, n_features). Training vector, where `n_samples` is the number of samples and `n_features` is the number of features. @@ -53,7 +53,7 @@ def transform( """Transform X separately by each transformer, concatenate results. Args: - X (bigframes.dataframe.DataFrame or bigframes.series.Series or pandas.core.frame.DataFrame or pandas.core.series.Series): + X (bigframes.dataframe.DataFrame or bigframes.series.Series): The Series or DataFrame to be transformed by subset. Returns: diff --git a/third_party/bigframes_vendored/sklearn/decomposition/_mf.py b/third_party/bigframes_vendored/sklearn/decomposition/_mf.py deleted file mode 100644 index 0ce79995d0c..00000000000 --- a/third_party/bigframes_vendored/sklearn/decomposition/_mf.py +++ /dev/null @@ -1,115 +0,0 @@ -"""Matrix Factorization.""" - -# Author: Alexandre Gramfort -# Olivier Grisel -# Mathieu Blondel -# Denis A. Engemann -# Michael Eickenberg -# Giorgio Patrini -# -# License: BSD 3 clause - -from abc import ABCMeta - -from bigframes_vendored.sklearn.base import BaseEstimator - -from bigframes import constants - - -class MatrixFactorization(BaseEstimator, metaclass=ABCMeta): - """Matrix Factorization (MF). - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> from bigframes.ml.decomposition import MatrixFactorization - >>> X = bpd.DataFrame({ - ... "row": [0, 0, 1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6], - ... "column": [0,1] * 7, - ... "value": [1, 1, 2, 1, 3, 1.2, 4, 1, 5, 0.8, 6, 1, 2, 3], - ... }) - >>> model = MatrixFactorization(feedback_type='explicit', num_factors=6, user_col='row', item_col='column', rating_col='value', l2_reg=2.06) - >>> W = model.fit(X) # doctest: +SKIP - - Args: - feedback_type ('explicit' | 'implicit'): - Specifies the feedback type for the model. The feedback type determines the algorithm that is used during training. - num_factors (int or auto, default auto): - Specifies the number of latent factors to use. - user_col (str): - The user column name. - item_col (str): - The item column name. - l2_reg (float, default 1.0): - A floating point value for L2 regularization. The default value is 1.0. - """ - - def fit(self, X, y=None): - """Fit the model according to the given training data. - - Args: - X (bigframes.dataframe.DataFrame or bigframes.series.Series or pandas.core.frame.DataFrame or pandas.core.series.Series): - Series or DataFrame of shape (n_samples, n_features). Training vector, - where `n_samples` is the number of samples and `n_features` is - the number of features. - - y (default None): - Ignored. - - Returns: - bigframes.ml.decomposition.MatrixFactorization: Fitted estimator. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def score(self, X=None, y=None): - """Calculate evaluation metrics of the model. - - .. note:: - - Output matches that of the BigQuery ML.EVALUATE function. - See: https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-evaluate#matrix_factorization_models - for the outputs relevant to this model type. - - Args: - X (bigframes.dataframe.DataFrame | bigframes.series.Series | None): - DataFrame of shape (n_samples, n_features). Test samples. - - y (bigframes.dataframe.DataFrame | bigframes.series.Series | None): - DataFrame of shape (n_samples,) or (n_samples, n_outputs). True - labels for `X`. - - Returns: - bigframes.dataframe.DataFrame: DataFrame that represents model metrics. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def predict(self, X): - """Generate a predicted rating for every user-item row combination for a matrix factorization model. - - Args: - X (bigframes.dataframe.DataFrame or bigframes.series.Series or pandas.core.frame.DataFrame or pandas.core.series.Series): - Series or a DataFrame to predict. - - Returns: - bigframes.dataframe.DataFrame: Predicted DataFrames.""" - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def fit_predict( - self, - X, - y=None, - ): - """Fit the model with X and generate a predicted rating for every user-item row combination for a matrix factorization model. on X. - - Convenience method; equivalent to calling fit(X) followed by predict(X). - - Args: - X (bigframes.dataframe.DataFrame or bigframes.series.Series or pandas.core.frame.DataFrame or pandas.core.series.Series): - DataFrame of shape (n_samples, n_features). Training data. - y (default None): - Not used, present here for API consistency by convention. - - Returns: - bigframes.dataframe.DataFrame: DataFrame of shape (n_samples, n_input_columns + n_prediction_columns). Returns predicted labels. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) diff --git a/third_party/bigframes_vendored/sklearn/decomposition/_pca.py b/third_party/bigframes_vendored/sklearn/decomposition/_pca.py index 138b7772b24..011ecc06dd3 100644 --- a/third_party/bigframes_vendored/sklearn/decomposition/_pca.py +++ b/third_party/bigframes_vendored/sklearn/decomposition/_pca.py @@ -1,4 +1,5 @@ -"""Principal Component Analysis.""" +""" Principal Component Analysis. +""" # Author: Alexandre Gramfort # Olivier Grisel @@ -11,44 +12,28 @@ from abc import ABCMeta -from bigframes_vendored.sklearn.base import BaseEstimator - from bigframes import constants +from third_party.bigframes_vendored.sklearn.base import BaseEstimator class PCA(BaseEstimator, metaclass=ABCMeta): """Principal component analysis (PCA). - **Examples:** - - >>> import bigframes.pandas as bpd - >>> from bigframes.ml.decomposition import PCA - >>> X = bpd.DataFrame({"feat0": [-1, -2, -3, 1, 2, 3], "feat1": [-1, -1, -2, 1, 1, 2]}) - >>> pca = PCA(n_components=2).fit(X) - >>> pca.predict(X) # doctest:+SKIP - principal_component_1 principal_component_2 - 0 -0.755243 0.157628 - 1 -1.05405 -0.141179 - 2 -1.809292 0.016449 - 3 0.755243 -0.157628 - 4 1.05405 0.141179 - 5 1.809292 -0.016449 - - [6 rows x 2 columns] - >>> pca.explained_variance_ratio_ # doctest:+SKIP - principal_component_id explained_variance_ratio - 0 1 0.00901 - 1 0 0.99099 - - [2 rows x 2 columns] + Linear dimensionality reduction using Singular Value Decomposition of the + data to project it to a lower dimensional space. The input data is centered + but not scaled for each feature before applying the SVD. + + It uses the LAPACK implementation of the full SVD or a randomized truncated + SVD by the method of Halko et al. 2009, depending on the shape of the input + data and the number of components to extract. + + It can also use the scipy.sparse.linalg ARPACK implementation of the + truncated SVD. Args: - n_components (int, float or None, default None): - Number of components to keep. If n_components is not set, all - components are kept, n_components = min(n_samples, n_features). - If 0 < n_components < 1, select the number of components such that the amount of variance that needs to be explained is greater than the percentage specified by n_components. - svd_solver ("full", "randomized" or "auto", default "auto"): - The solver to use to calculate the principal components. Details: https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-create-pca#pca_solver. + n_components (Optional[int], default 3): + Number of components to keep. if n_components is not set all components + are kept. """ @@ -56,7 +41,7 @@ def fit(self, X, y=None): """Fit the model according to the given training data. Args: - X (bigframes.dataframe.DataFrame or bigframes.series.Series or pandas.core.frame.DataFrame or pandas.core.series.Series): + X (bigframes.dataframe.DataFrame or bigframes.series.Series): Series or DataFrame of shape (n_samples, n_features). Training vector, where `n_samples` is the number of samples and `n_features` is the number of features. @@ -74,7 +59,7 @@ def score(self, X=None, y=None): .. note:: - Output matches that of the BigQuery ML.EVALUATE function. + Output matches that of the BigQuery ML.EVALUTE function. See: https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-evaluate#pca_models for the outputs relevant to this model type. @@ -93,31 +78,11 @@ def predict(self, X): """Predict the closest cluster for each sample in X. Args: - X (bigframes.dataframe.DataFrame or bigframes.series.Series or pandas.core.frame.DataFrame or pandas.core.series.Series): + X (bigframes.dataframe.DataFrame or bigframes.series.Series): Series or a DataFrame to predict. Returns: - bigframes.dataframe.DataFrame: Predicted DataFrames.""" - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def fit_predict( - self, - X, - y=None, - ): - """Fit the model with X and apply the dimensionality reduction on X. - - Convenience method; equivalent to calling fit(X) followed by predict(X). - - Args: - X (bigframes.dataframe.DataFrame or bigframes.series.Series or pandas.core.frame.DataFrame or pandas.core.series.Series): - DataFrame of shape (n_samples, n_features). Training data. - y (default None): - Not used, present here for API consistency by convention. - - Returns: - bigframes.dataframe.DataFrame: DataFrame of shape (n_samples, n_input_columns + n_prediction_columns). Returns predicted labels. - """ + bigframes.dataframe.DataFrame: predicted DataFrames.""" raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @property @@ -132,7 +97,7 @@ def components_(self): numerical_value: If feature is numeric, the value of feature for the principal component that principal_component_id identifies. If feature isn't numeric, the value is NULL. - categorical_value: A list of mappings containing information about categorical features. Each mapping contains the following fields: + categorical_value: An list of mappings containing information about categorical features. Each mapping contains the following fields: categorical_value.category: The name of each category. categorical_value.value: The value of categorical_value.category for the centroid that centroid_id identifies. diff --git a/third_party/bigframes_vendored/sklearn/ensemble/_forest.py b/third_party/bigframes_vendored/sklearn/ensemble/_forest.py index fb81bd66847..6be41bf9aa5 100644 --- a/third_party/bigframes_vendored/sklearn/ensemble/_forest.py +++ b/third_party/bigframes_vendored/sklearn/ensemble/_forest.py @@ -47,23 +47,16 @@ def fit(self, X, y): """Build a forest of trees from the training set (X, y). Args: - X (bigframes.dataframe.DataFrame or bigframes.series.Series or pandas.core.frame.DataFrame or pandas.core.series.Series): + X: Series or DataFrame of shape (n_samples, n_features). Training data. - y (bigframes.dataframe.DataFrame or bigframes.series.Series or pandas.core.frame.DataFrame or pandas.core.series.Series): + y: Series or DataFrame of shape (n_samples,) or (n_samples, n_targets). Target values. Will be cast to X's dtype if necessary. - X_eval (bigframes.dataframe.DataFrame or bigframes.series.Series or pandas.core.frame.DataFrame or pandas.core.series.Series): - Series or DataFrame of shape (n_samples, n_features). Evaluation data. - - y_eval (bigframes.dataframe.DataFrame or bigframes.series.Series or pandas.core.frame.DataFrame or pandas.core.series.Series): - Series or DataFrame of shape (n_samples,) or (n_samples, n_targets). - Evaluation target values. Will be cast to X_eval's dtype if necessary. - Returns: - ForestModel: Fitted estimator. + Fitted Estimator. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -80,12 +73,12 @@ def predict(self, X): mean predicted regression targets of the trees in the forest. Args: - X (bigframes.dataframe.DataFrame or bigframes.series.Series or pandas.core.frame.DataFrame or pandas.core.series.Series): + X: Series or DataFrame of shape (n_samples, n_features). The data matrix for which we want to get the predictions. Returns: - bigframes.dataframe.DataFrame: The predicted values. + The predicted values. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -98,36 +91,38 @@ class RandomForestRegressor(ForestRegressor): to improve the predictive accuracy and control over-fitting. Args: - n_estimators (Optional[int]): + num_parallel_tree: Optional[int] Number of parallel trees constructed during each iteration. Default to 100. Minimum value is 2. - tree_method (Optional[str]): + tree_method: Optional[str] Specify which tree method to use. Default to "auto". If this parameter is set to - default, XGBoost will choose the most conservative option available. Possible values: "exact", "approx", + default, XGBoost will choose the most conservative option available. Possible values: ""exact", "approx", "hist". - min_child_weight (Optional[float]): + min_child_weight : Optional[float] Minimum sum of instance weight(hessian) needed in a child. Default to 1. - colsample_bytree (Optional[float]): + colsample_bytree : Optional[float] Subsample ratio of columns when constructing each tree. Default to 1.0. The value should be between 0 and 1. - colsample_bylevel (Optional[float]): + colsample_bylevel : Optional[float] Subsample ratio of columns for each level. Default to 1.0. The value should be between 0 and 1. - colsample_bynode (Optional[float]): + colsample_bynode : Optional[float] Subsample ratio of columns for each split. Default to 0.8. The value should be between 0 and 1. - gamma (Optional[float]): + gamma : Optional[float] (min_split_loss) Minimum loss reduction required to make a further partition on a leaf node of the tree. Default to 0.0. - max_depth (Optional[int]): + max_depth : Optional[int] Maximum tree depth for base learners. Default to 15. The value should be greater than 0 and less than 1. - subsample (Optional[float]: + subsample : Optional[float] Subsample ratio of the training instance. Default to 0.8. The value should be greater than 0 and less than 1. - reg_alpha (Optional[float]): + reg_alpha : Optional[float] L1 regularization term on weights (xgb's alpha). Default to 0.0. - reg_lambda (Optional[float]): + reg_lambda : Optional[float] L2 regularization term on weights (xgb's lambda). Default to 1.0. - tol (Optional[float]): - Minimum relative loss improvement necessary to continue training. Default to 0.01. - enable_global_explain (Optional[bool]): + early_stop: Optional[bool] + Whether training should stop after the first iteration. Default to True. + min_rel_progress: Optional[float] + Minimum relative loss improvement necessary to continue training when early_stop is set to True. Default to 0.01. + enable_global_explain: Optional[bool] Whether to compute global explanations using explainable AI to evaluate global feature importance to the model. Default to False. - xgboost_version (Optional[str]): + xgboost_version: Optional[str] Specifies the Xgboost version for model training. Default to "0.9". Possible values: "0.9", "1.1". """ @@ -149,7 +144,7 @@ def predict(self, X): which we want to get the predictions. Returns: - bigframes.dataframe.DataFrame: The predicted values. + The predicted values. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -163,35 +158,37 @@ class RandomForestClassifier(ForestClassifier): improve the predictive accuracy and control over-fitting. Args: - n_estimators (Optional[int]): + num_parallel_tree: Optional[int] Number of parallel trees constructed during each iteration. Default to 100. Minimum value is 2. - tree_method (Optional[str]): + tree_method: Optional[str] Specify which tree method to use. Default to "auto". If this parameter is set to - default, XGBoost will choose the most conservative option available. Possible values: "exact", "approx", + default, XGBoost will choose the most conservative option available. Possible values: ""exact", "approx", "hist". - min_child_weight (Optional[float]): + min_child_weight : Optional[float] Minimum sum of instance weight(hessian) needed in a child. Default to 1. - colsample_bytree (Optional[float]): + colsample_bytree : Optional[float] Subsample ratio of columns when constructing each tree. Default to 1.0. The value should be between 0 and 1. - colsample_bylevel (Optional[float]): + colsample_bylevel : Optional[float] Subsample ratio of columns for each level. Default to 1.0. The value should be between 0 and 1. - colsample_bynode (Optional[float]): + colsample_bynode : Optional[float] Subsample ratio of columns for each split. Default to 0.8. The value should be between 0 and 1. - gamma (Optional[float]): + gamma : Optional[float] (min_split_loss) Minimum loss reduction required to make a further partition on a leaf node of the tree. Default to 0.0. - max_depth (Optional[int]): + max_depth : Optional[int] Maximum tree depth for base learners. Default to 15. The value should be greater than 0 and less than 1. - subsample (Optional[float]): + subsample : Optional[float] Subsample ratio of the training instance. Default to 0.8. The value should be greater than 0 and less than 1. - reg_alpha (Optional[float]): + reg_alpha : Optional[float] L1 regularization term on weights (xgb's alpha). Default to 0.0. - reg_lambda (Optional[float]): + reg_lambda : Optional[float] L2 regularization term on weights (xgb's lambda). Default to 1.0. - tol (Optional[float]): - Minimum relative loss improvement necessary to continue training. Default to 0.01. - enable_global_explain (Optional[bool]): + early_stop: Optional[bool] + Whether training should stop after the first iteration. Default to True. + min_rel_progress: Optional[float] + Minimum relative loss improvement necessary to continue training when early_stop is set to True. Default to 0.01. + enable_global_explain: Optional[bool] Whether to compute global explanations using explainable AI to evaluate global feature importance to the model. Default to False. - xgboost_version (Optional[str]): + xgboost_version: Optional[str] Specifies the Xgboost version for model training. Default to "0.9". Possible values: "0.9", "1.1".ß """ diff --git a/third_party/bigframes_vendored/sklearn/impute/_base.py b/third_party/bigframes_vendored/sklearn/impute/_base.py deleted file mode 100644 index 175ad86b21b..00000000000 --- a/third_party/bigframes_vendored/sklearn/impute/_base.py +++ /dev/null @@ -1,68 +0,0 @@ -# Authors: Nicolas Tresegnie -# Sergey Feldman -# License: BSD 3 clause - -from bigframes_vendored.sklearn.base import BaseEstimator, TransformerMixin - -from bigframes import constants - - -class _BaseImputer(TransformerMixin, BaseEstimator): - """Base class for all imputers.""" - - -class SimpleImputer(_BaseImputer): - """ - Univariate imputer for completing missing values with simple strategies. - - Replace missing values using a descriptive statistic (e.g. mean, median, or - most frequent) along each column. - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> from bigframes.ml.impute import SimpleImputer - >>> X_train = bpd.DataFrame({"feat0": [7.0, 4.0, 10.0], "feat1": [2.0, None, 5.0], "feat2": [3.0, 6.0, 9.0]}) - >>> imp_mean = SimpleImputer().fit(X_train) - >>> X_test = bpd.DataFrame({"feat0": [None, 4.0, 10.0], "feat1": [2.0, None, None], "feat2": [3.0, 6.0, 9.0]}) - >>> imp_mean.transform(X_test) - imputer_feat0 imputer_feat1 imputer_feat2 - 0 7.0 2.0 3.0 - 1 4.0 3.5 6.0 - 2 10.0 3.5 9.0 - - [3 rows x 3 columns] - - Args: - strategy ({'mean', 'median', 'most_frequent'}, default='mean'): - The imputation strategy. 'mean': replace missing values using the mean along - the axis. 'median':replace missing values using the median along - the axis. 'most_frequent', replace missing using the most frequent - value along the axis. - """ - - def fit(self, X, y=None): - """Fit the imputer on X. - - Args: - X (bigframes.dataframe.DataFrame or bigframes.series.Series or pandas.core.frame.DataFrame or pandas.core.series.Series): - The Dataframe or Series with training data. - - y (default None): - Ignored. - - Returns: - SimpleImputer: Fitted scaler. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def transform(self, X): - """Impute all missing values in X. - - Args: - X (bigframes.dataframe.DataFrame or bigframes.series.Series or pandas.core.frame.DataFrame or pandas.core.series.Series): - The DataFrame or Series to be transformed. - - Returns: - bigframes.dataframe.DataFrame: Transformed result.""" - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) diff --git a/third_party/bigframes_vendored/sklearn/linear_model/_base.py b/third_party/bigframes_vendored/sklearn/linear_model/_base.py index 7543edd10b7..8dc3b6280a4 100644 --- a/third_party/bigframes_vendored/sklearn/linear_model/_base.py +++ b/third_party/bigframes_vendored/sklearn/linear_model/_base.py @@ -16,26 +16,26 @@ # Original location: https://github.com/scikit-learn/scikit-learn/blob/main/sklearn/linear_model/_base.py from abc import ABCMeta +from typing import List, Optional -from bigframes_vendored.sklearn.base import ( +from bigframes import constants +from third_party.bigframes_vendored.sklearn.base import ( BaseEstimator, ClassifierMixin, RegressorMixin, ) -from bigframes import constants - class LinearModel(BaseEstimator, metaclass=ABCMeta): def predict(self, X): """Predict using the linear model. Args: - X (bigframes.dataframe.DataFrame or bigframes.series.Series or pandas.core.frame.DataFrame or pandas.core.series.Series): + X (bigframes.dataframe.DataFrame or bigframes.series.Series): Series or DataFrame of shape (n_samples, n_features). Samples. Returns: - bigframes.dataframe.DataFrame: DataFrame of shape (n_samples, n_input_columns + n_prediction_columns). Returns predicted values. + bigframes.dataframe.DataFrame: DataFrame of shape (n_samples,). Returns predicted values. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -45,12 +45,13 @@ def predict(self, X): """Predict class labels for samples in X. Args: - X (bigframes.dataframe.DataFrame or bigframes.series.Series or pandas.core.frame.DataFrame or pandas.core.series.Series): + X (bigframes.dataframe.DataFrame or bigframes.series.Series): Series or DataFrame of shape (n_samples, n_features). The data matrix for which we want to get the predictions. Returns: - bigframes.dataframe.DataFrame: DataFrame of shape (n_samples, n_input_columns + n_prediction_columns). Returns predicted values. + bigframes.dataframe.DataFrame: DataFrame of shape (n_samples,), containing + the class labels for each sample. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -62,54 +63,27 @@ class LinearRegression(RegressorMixin, LinearModel): to minimize the residual sum of squares between the observed targets in the dataset, and the targets predicted by the linear approximation. - **Examples:** - - >>> from bigframes.ml.linear_model import LinearRegression - >>> import bigframes.pandas as bpd - >>> X = bpd.DataFrame({ \ - "feature0": [20, 21, 19, 18], \ - "feature1": [0, 1, 1, 0], \ - "feature2": [0.2, 0.3, 0.4, 0.5]}) - >>> y = bpd.DataFrame({"outcome": [0, 0, 1, 1]}) - >>> # Create the linear model - >>> model = LinearRegression() - >>> model.fit(X, y) - LinearRegression() - - >>> # Score the model - >>> score = model.score(X, y) - >>> print(score) # doctest:+SKIP - mean_absolute_error mean_squared_error mean_squared_log_error \ - 0 0.022812 0.000602 0.00035 - median_absolute_error r2_score explained_variance - 0 0.015077 0.997591 0.997591 - - Args: - optimize_strategy (str, default "auto_strategy"): + optimize_strategy (str, default "normal_equation"): The strategy to train linear regression models. Possible values are "auto_strategy", "batch_gradient_descent", "normal_equation". Default - to "auto_strategy". + to "normal_equation". fit_intercept (bool, default True): Default ``True``. Whether to calculate the intercept for this model. If set to False, no intercept will be used in calculations (i.e. data is expected to be centered). - l1_reg (float or None, default None): - The amount of L1 regularization applied. Default to None. Can't be set in "normal_equation" mode. If unset, value 0 is used. l2_reg (float, default 0.0): The amount of L2 regularization applied. Default to 0. max_iterations (int, default 20): The maximum number of training iterations or steps. Default to 20. - warm_start (bool, default False): - Determines whether to train a model with new training data, new model options, or both. Unless you explicitly override them, the initial options used to train the model are used for the warm start run. Default to False. - learning_rate (float or None, default None): - The learn rate for gradient descent when learning_rate_strategy='constant'. If unset, value 0.1 is used. If learning_rate_strategy='line_search', an error is returned. - learning_rate_strategy (str, default "line_search"): + learn_rate_strategy (str, default "line_search"): The strategy for specifying the learning rate during training. Default to "line_search". - tol (float, default 0.01): + early_stop (bool, default True): + Whether training should stop after the first iteration in which the relative loss improvement is less than the value specified for min_rel_progress. Default to True. + min_rel_progress (float, default 0.01): The minimum relative loss improvement that is necessary to continue training when EARLY_STOP is set to true. For example, a value of 0.01 specifies that each iteration must reduce the loss by 1% for training to continue. Default to 0.01. - ls_init_learning_rate (float or None, default None): - Sets the initial learning rate that learning_rate_strategy='line_search' uses. This option can only be used if line_search is specified. If unset, value 0.1 is used. + ls_init_learn_rate (float, default 0.1): + Sets the initial learning rate that learn_rate_strategy='line_search' uses. This option can only be used if line_search is specified. Default to 0.1. calculate_p_values (bool, default False): Specifies whether to compute p-values and standard errors during training. Default to False. enable_global_explain (bool, default False): @@ -124,21 +98,14 @@ def fit( """Fit linear model. Args: - X (bigframes.dataframe.DataFrame or bigframes.series.Series or pandas.core.frame.DataFrame or pandas.core.series.Series): + X (bigframes.dataframe.DataFrame or bigframes.series.Series): Series or DataFrame of shape (n_samples, n_features). Training data. - y (bigframes.dataframe.DataFrame or bigframes.series.Series or pandas.core.frame.DataFrame or pandas.core.series.Series): + y (bigframes.dataframe.DataFrame or bigframes.series.Series): Series or DataFrame of shape (n_samples,) or (n_samples, n_targets). Target values. Will be cast to X's dtype if necessary. - X_eval (bigframes.dataframe.DataFrame or bigframes.series.Series or pandas.core.frame.DataFrame or pandas.core.series.Series): - Series or DataFrame of shape (n_samples, n_features). Evaluation data. - - y_eval (bigframes.dataframe.DataFrame or bigframes.series.Series or pandas.core.frame.DataFrame or pandas.core.series.Series): - Series or DataFrame of shape (n_samples,) or (n_samples, n_targets). - Evaluation target values. Will be cast to X_eval's dtype if necessary. - Returns: - LinearRegression: Fitted estimator. + LinearRegression: Fitted Estimator. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) diff --git a/third_party/bigframes_vendored/sklearn/linear_model/_logistic.py b/third_party/bigframes_vendored/sklearn/linear_model/_logistic.py index a309166c90c..989ca03c827 100644 --- a/third_party/bigframes_vendored/sklearn/linear_model/_logistic.py +++ b/third_party/bigframes_vendored/sklearn/linear_model/_logistic.py @@ -11,87 +11,31 @@ # Arthur Mensch >> from bigframes.ml.linear_model import LogisticRegression - >>> import bigframes.pandas as bpd - >>> X = bpd.DataFrame({ \ - "feature0": [20, 21, 19, 18], \ - "feature1": [0, 1, 1, 0], \ - "feature2": [0.2, 0.3, 0.4, 0.5]}) - >>> y = bpd.DataFrame({"outcome": [0, 0, 1, 1]}) - >>> # Create the LogisticRegression - >>> model = LogisticRegression() - >>> model.fit(X, y) - LogisticRegression() - >>> model.predict(X) # doctest:+SKIP - predicted_outcome predicted_outcome_probs feature0 feature1 feature2 - 0 0 [{'label': 1, 'prob': 3.1895929877221615e-07} ... 20 0 0.2 - 1 0 [{'label': 1, 'prob': 5.662891265051953e-06} ... 21 1 0.3 - 2 1 [{'label': 1, 'prob': 0.9999917826885262} {'l... 19 1 0.4 - 3 1 [{'label': 1, 'prob': 0.9999999993659574} {'l... 18 0 0.5 - 4 rows × 5 columns - - [4 rows x 5 columns in total] - - >>> # Score the model - >>> score = model.score(X, y) - >>> score # doctest:+SKIP - precision recall accuracy f1_score log_loss roc_auc - 0 1.0 1.0 1.0 1.0 0.000004 1.0 - 1 rows × 6 columns - - [1 rows x 6 columns in total] - Args: - optimize_strategy (str, default "auto_strategy"): - The strategy to train logistic regression models. Possible values are - "auto_strategy" and "batch_gradient_descent". The two are equilevant since - "auto_strategy" will fall back to "batch_gradient_descent". The API is kept - for consistency. - Default to "auto_strategy". fit_intercept (default True): Default True. Specifies if a constant (a.k.a. bias or intercept) should be added to the decision function. - class_weight (dict or 'balanced', default None): + class_weights (dict or 'balanced', default None): Default None. Weights associated with classes in the form ``{class_label: weight}``.If not given, all classes are supposed to have weight one. The "balanced" mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as ``n_samples / (n_classes * np.bincount(y))``. Dict isn't - supported. - l1_reg (float or None, default None): - The amount of L1 regularization applied. Default to None. Can't be set in "normal_equation" mode. If unset, value 0 is used. - l2_reg (float, default 0.0): - The amount of L2 regularization applied. Default to 0. - max_iterations (int, default 20): - The maximum number of training iterations or steps. Default to 20. - warm_start (bool, default False): - Determines whether to train a model with new training data, new model options, or both. Unless you explicitly override them, the initial options used to train the model are used for the warm start run. Default to False. - learning_rate (float or None, default None): - The learn rate for gradient descent when learning_rate_strategy='constant'. If unset, value 0.1 is used. If learning_rate_strategy='line_search', an error is returned. - learning_rate_strategy (str, default "line_search"): - The strategy for specifying the learning rate during training. Default to "line_search". - tol (float, default 0.01): - The minimum relative loss improvement that is necessary to continue training when EARLY_STOP is set to true. For example, a value of 0.01 specifies that each iteration must reduce the loss by 1% for training to continue. Default to 0.01. - ls_init_learning_rate (float or None, default None): - Sets the initial learning rate that learning_rate_strategy='line_search' uses. This option can only be used if line_search is specified. If unset, value 0.1 is used. - calculate_p_values (bool, default False): - Specifies whether to compute p-values and standard errors during training. Default to False. - enable_global_explain (bool, default False): - Whether to compute global explanations using explainable AI to evaluate global feature importance to the model. Default to False. + supported now. """ def fit( @@ -102,24 +46,16 @@ def fit( """Fit the model according to the given training data. Args: - X (bigframes.dataframe.DataFrame or bigframes.series.Series or pandas.core.frame.DataFrame or pandas.core.series.Series): + X (bigframes.dataframe.DataFrame or bigframes.series.Series): Series or DataFrame of shape (n_samples, n_features). Training vector, where `n_samples` is the number of samples and `n_features` is the number of features. - y (bigframes.dataframe.DataFrame or bigframes.series.Series or pandas.core.frame.DataFrame or pandas.core.series.Series): + y (bigframes.dataframe.DataFrame or bigframes.series.Series): DataFrame of shape (n_samples,). Target vector relative to X. - X_eval (bigframes.dataframe.DataFrame or bigframes.series.Series or pandas.core.frame.DataFrame or pandas.core.series.Series): - Series or DataFrame of shape (n_samples, n_features). Evaluation vector, - where `n_samples` is the number of samples and `n_features` is - the number of features. - - y_eval (bigframes.dataframe.DataFrame or bigframes.series.Series or pandas.core.frame.DataFrame or pandas.core.series.Series): - DataFrame of shape (n_samples,). Target vector relative to X_eval. - Returns: - LogisticRegression: Fitted estimator. + LogisticRegression: Fitted Estimator. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) diff --git a/third_party/bigframes_vendored/sklearn/metrics/_classification.py b/third_party/bigframes_vendored/sklearn/metrics/_classification.py index 085388b0456..a9d8038e59b 100644 --- a/third_party/bigframes_vendored/sklearn/metrics/_classification.py +++ b/third_party/bigframes_vendored/sklearn/metrics/_classification.py @@ -26,23 +26,6 @@ def accuracy_score(y_true, y_pred, normalize=True) -> float: """Accuracy classification score. - **Examples:** - - >>> import bigframes.pandas as bpd - >>> import bigframes.ml.metrics - - >>> y_true = bpd.DataFrame([0, 2, 1, 3]) - >>> y_pred = bpd.DataFrame([0, 1, 2, 3]) - >>> accuracy_score = bigframes.ml.metrics.accuracy_score(y_true, y_pred) - >>> accuracy_score - np.float64(0.5) - - If False, return the number of correctly classified samples: - - >>> accuracy_score = bigframes.ml.metrics.accuracy_score(y_true, y_pred, normalize=False) - >>> accuracy_score - np.int64(2) - Args: y_true (Series or DataFrame of shape (n_samples,)): Ground truth (correct) labels. @@ -75,29 +58,6 @@ def confusion_matrix( :math:`C_{0,0}`, false negatives is :math:`C_{1,0}`, true positives is :math:`C_{1,1}` and false positives is :math:`C_{0,1}`. - **Examples:** - - >>> import bigframes.pandas as bpd - >>> import bigframes.ml.metrics - - >>> y_true = bpd.DataFrame([2, 0, 2, 2, 0, 1]) - >>> y_pred = bpd.DataFrame([0, 0, 2, 2, 0, 2]) - >>> confusion_matrix = bigframes.ml.metrics.confusion_matrix(y_true, y_pred) - >>> confusion_matrix - 0 1 2 - 0 2 0 0 - 1 0 0 1 - 2 1 0 2 - - >>> y_true = bpd.DataFrame(["cat", "ant", "cat", "cat", "ant", "bird"]) - >>> y_pred = bpd.DataFrame(["ant", "ant", "cat", "cat", "ant", "cat"]) - >>> confusion_matrix = bigframes.ml.metrics.confusion_matrix(y_true, y_pred) - >>> confusion_matrix - ant bird cat - ant 2 0 0 - bird 0 0 1 - cat 1 0 2 - Args: y_true (Series or DataFrame of shape (n_samples,)): Ground truth (correct) target values. @@ -120,27 +80,12 @@ def recall_score( ): """Compute the recall. - The recall is the ratio ``tp / (tp + fn)``, where ``tp`` is the number of + The recall is the ratio ``tp / (tp + fn)`` where ``tp`` is the number of true positives and ``fn`` the number of false negatives. The recall is intuitively the ability of the classifier to find all the positive samples. The best value is 1 and the worst value is 0. - **Examples:** - - >>> import bigframes.pandas as bpd - >>> import bigframes.ml.metrics - - >>> y_true = bpd.DataFrame([0, 1, 2, 0, 1, 2]) - >>> y_pred = bpd.DataFrame([0, 2, 1, 0, 0, 1]) - >>> recall_score = bigframes.ml.metrics.recall_score(y_true, y_pred, average=None) - >>> recall_score - 0 1.0 - 1 0.0 - 2 0.0 - dtype: float64 - - Args: y_true (Series or DataFrame of shape (n_samples,)): Ground truth (correct) target values. @@ -150,7 +95,6 @@ def recall_score( default='binary'): This parameter is required for multiclass/multilabel targets. Possible values are 'None', 'micro', 'macro', 'samples', 'weighted', 'binary'. - Only average=None is supported. Returns: float (if average is not None) or Series of float of shape n_unique_labels,): Recall @@ -167,27 +111,13 @@ def precision_score( ): """Compute the precision. - The precision is the ratio ``tp / (tp + fp)``, where ``tp`` is the number of + The precision is the ratio ``tp / (tp + fp)`` where ``tp`` is the number of true positives and ``fp`` the number of false positives. The precision is intuitively the ability of the classifier not to label as positive a sample that is negative. The best value is 1 and the worst value is 0. - **Examples:** - - >>> import bigframes.pandas as bpd - >>> import bigframes.ml.metrics - - >>> y_true = bpd.DataFrame([0, 1, 2, 0, 1, 2]) - >>> y_pred = bpd.DataFrame([0, 2, 1, 0, 0, 1]) - >>> precision_score = bigframes.ml.metrics.precision_score(y_true, y_pred, average=None) - >>> precision_score - 0 0.666667 - 1 0.000000 - 2 0.000000 - dtype: float64 - Args: y_true: Series or DataFrame of shape (n_samples,) Ground truth (correct) target values. @@ -197,7 +127,6 @@ def precision_score( default='binary' This parameter is required for multiclass/multilabel targets. Possible values are 'None', 'micro', 'macro', 'samples', 'weighted', 'binary'. - Only None and 'binary' is supported. Returns: precision: float (if average is not None) or Series of float of shape \ @@ -224,24 +153,10 @@ def f1_score( the F1 score of each class with weighting depending on the ``average`` parameter. - **Examples:** - - >>> import bigframes.pandas as bpd - >>> import bigframes.ml.metrics - - >>> y_true = bpd.DataFrame([0, 1, 2, 0, 1, 2]) - >>> y_pred = bpd.DataFrame([0, 2, 1, 0, 0, 1]) - >>> f1_score = bigframes.ml.metrics.f1_score(y_true, y_pred, average=None) - >>> f1_score - 0 0.8 - 1 0.0 - 2 0.0 - dtype: float64 - Args: - y_true: Series or DataFrame of shape (n_samples,). + y_true: Series or DataFrame of shape (n_samples,) Ground truth (correct) target values. - y_pred: Series or DataFrame of shape (n_samples,). + y_pred: Series or DataFrame of shape (n_samples,) Estimated targets as returned by a classifier. average: {'micro', 'macro', 'samples', 'weighted', 'binary'} or None, \ default='binary' diff --git a/third_party/bigframes_vendored/sklearn/metrics/_ranking.py b/third_party/bigframes_vendored/sklearn/metrics/_ranking.py index cd5bd2cbcd5..ac919edbe33 100644 --- a/third_party/bigframes_vendored/sklearn/metrics/_ranking.py +++ b/third_party/bigframes_vendored/sklearn/metrics/_ranking.py @@ -16,8 +16,6 @@ # Michal Karbownik # License: BSD 3 clause -import numpy as np - from bigframes import constants @@ -29,28 +27,6 @@ def auc(x, y) -> float: way to summarize a precision-recall curve, see :func:`average_precision_score`. - **Examples:** - - >>> import bigframes.pandas as bpd - >>> import bigframes.ml.metrics - - >>> x = bpd.DataFrame([1, 1, 2, 2]) - >>> y = bpd.DataFrame([2, 3, 4, 5]) - >>> auc = bigframes.ml.metrics.auc(x, y) - >>> auc - np.float64(3.5) - - The input can be Series: - - >>> df = bpd.DataFrame( - ... {"x": [1, 1, 2, 2], - ... "y": [2, 3, 4, 5],} - ... ) - >>> auc = bigframes.ml.metrics.auc(df["x"], df["y"]) - >>> auc - np.float64(3.5) - - Args: x (Series or DataFrame of shape (n_samples,)): X coordinates. These must be either monotonic increasing or monotonic @@ -61,50 +37,13 @@ def auc(x, y) -> float: Returns: float: Area Under the Curve. """ - if len(x) < 2: - raise ValueError( - f"At least 2 points are needed to compute area under curve, but x.shape = {len(x)}" - ) - - if x.is_monotonic_decreasing: - d = -1 - elif x.is_monotonic_increasing: - d = 1 - else: - raise ValueError(f"x is neither increasing nor decreasing : {x}.") - - if hasattr(np, "trapezoid"): - # new in numpy 2.0 - return d * np.trapezoid(y, x) - # np.trapz has been deprecated in 2.0 - return d * np.trapz(y, x) # type: ignore + raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) def roc_auc_score(y_true, y_score) -> float: """Compute Area Under the Receiver Operating Characteristic Curve (ROC AUC) \ from prediction scores. - **Examples:** - - >>> import bigframes.pandas as bpd - >>> import bigframes.ml.metrics - - >>> y_true = bpd.DataFrame([0, 0, 1, 1, 0, 1, 0, 1, 1, 1]) - >>> y_score = bpd.DataFrame([0.1, 0.4, 0.35, 0.8, 0.65, 0.9, 0.5, 0.3, 0.6, 0.45]) - >>> roc_auc_score = bigframes.ml.metrics.roc_auc_score(y_true, y_score) - >>> roc_auc_score - np.float64(0.625) - - The input can be Series: - - >>> df = bpd.DataFrame( - ... {"y_true": [0, 0, 1, 1, 0, 1, 0, 1, 1, 1], - ... "y_score": [0.1, 0.4, 0.35, 0.8, 0.65, 0.9, 0.5, 0.3, 0.6, 0.45],} - ... ) - >>> roc_auc_score = bigframes.ml.metrics.roc_auc_score(df["y_true"], df["y_score"]) - >>> roc_auc_score - np.float64(0.625) - Args: y_true (Series or DataFrame of shape (n_samples,)): True labels or binary label indicators. The binary and multiclass cases @@ -133,38 +72,6 @@ def roc_curve( ): """Compute Receiver operating characteristic (ROC). - **Examples:** - - >>> import bigframes.pandas as bpd - >>> import bigframes.ml.metrics - - >>> y_true = bpd.DataFrame([1, 1, 2, 2]) - >>> y_score = bpd.DataFrame([0.1, 0.4, 0.35, 0.8]) - >>> fpr, tpr, thresholds = bigframes.ml.metrics.roc_curve(y_true, y_score, drop_intermediate=False) - >>> fpr - 0 0.0 - 1 0.0 - 2 0.0 - 3 0.0 - 4 0.0 - Name: fpr, dtype: Float64 - - >>> tpr - 0 0.0 - 1 0.333333 - 2 0.5 - 3 0.833333 - 4 1.0 - Name: tpr, dtype: Float64 - - >>> thresholds - 0 inf - 1 0.8 - 2 0.4 - 3 0.35 - 4 0.1 - Name: thresholds, dtype: Float64 - Args: y_true: Series or DataFrame of shape (n_samples,) True binary labels. If labels are not either {-1, 1} or {0, 1}, then diff --git a/third_party/bigframes_vendored/sklearn/metrics/_regression.py b/third_party/bigframes_vendored/sklearn/metrics/_regression.py index 85f0c1ecf94..9740c540e90 100644 --- a/third_party/bigframes_vendored/sklearn/metrics/_regression.py +++ b/third_party/bigframes_vendored/sklearn/metrics/_regression.py @@ -42,17 +42,6 @@ def r2_score(y_true, y_pred, force_finite=True) -> float: these cases are replaced with 1.0 (perfect predictions) or 0.0 (imperfect predictions) respectively. - **Examples:** - - >>> import bigframes.pandas as bpd - >>> import bigframes.ml.metrics - - >>> y_true = bpd.DataFrame([3, -0.5, 2, 7]) - >>> y_pred = bpd.DataFrame([2.5, 0.0, 2, 8]) - >>> r2_score = bigframes.ml.metrics.r2_score(y_true, y_pred) - >>> r2_score - np.float64(0.9486081370449679) - Args: y_true (Series or DataFrame of shape (n_samples,)): Ground truth (correct) target values. @@ -63,55 +52,3 @@ def r2_score(y_true, y_pred, force_finite=True) -> float: float: The :math:`R^2` score. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - -def mean_squared_error(y_true, y_pred) -> float: - """Mean squared error regression loss. - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> import bigframes.ml.metrics - - >>> y_true = bpd.DataFrame([3, -0.5, 2, 7]) - >>> y_pred = bpd.DataFrame([2.5, 0.0, 2, 8]) - >>> mse = bigframes.ml.metrics.mean_squared_error(y_true, y_pred) - >>> mse - np.float64(0.375) - - Args: - y_true (Series or DataFrame of shape (n_samples,)): - Ground truth (correct) target values. - y_pred (Series or DataFrame of shape (n_samples,)): - Estimated target values. - - Returns: - float: Mean squared error. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - -def mean_absolute_error(y_true, y_pred) -> float: - """Mean absolute error regression loss. - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> import bigframes.ml.metrics - - >>> y_true = bpd.DataFrame([3, -0.5, 2, 7]) - >>> y_pred = bpd.DataFrame([2.5, 0.0, 2, 8]) - >>> mae = bigframes.ml.metrics.mean_absolute_error(y_true, y_pred) - >>> mae - np.float64(0.5) - - Args: - y_true (Series or DataFrame of shape (n_samples,)): - Ground truth (correct) target values. - y_pred (Series or DataFrame of shape (n_samples,)): - Estimated target values. - - Returns: - float: Mean absolute error. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) diff --git a/third_party/bigframes_vendored/sklearn/metrics/pairwise.py b/third_party/bigframes_vendored/sklearn/metrics/pairwise.py deleted file mode 100644 index 37cbf23b29c..00000000000 --- a/third_party/bigframes_vendored/sklearn/metrics/pairwise.py +++ /dev/null @@ -1,56 +0,0 @@ -# Authors: Alexandre Gramfort -# Mathieu Blondel -# Robert Layton -# Andreas Mueller -# Philippe Gervais -# Lars Buitinck -# Joel Nothman -# License: BSD 3 clause - -import bigframes.pandas as bpd -from bigframes import constants - - -def paired_cosine_distances(X, Y) -> bpd.DataFrame: - """Compute the paired cosine distances between X and Y. - - Args: - X (Series or single column DataFrame of array of numeric type): - Input data. - Y (Series or single column DataFrame of array of numeric type): - Input data. X and Y are mapped by indexes, must have the same index. - - Returns: - bigframes.dataframe.DataFrame: DataFrame with columns of X, Y and cosine_distance. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - -def paired_manhattan_distance(X, Y) -> bpd.DataFrame: - """Compute the L1 distances between the vectors in X and Y. - - Args: - X (Series or single column DataFrame of array of numeric type): - Input data. - Y (Series or single column DataFrame of array of numeric type): - Input data. X and Y are mapped by indexes, must have the same index. - - Returns: - bigframes.dataframe.DataFrame: DataFrame with columns of X, Y and manhattan_distance. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - -def paired_euclidean_distances(X, Y) -> bpd.DataFrame: - """Compute the paired euclidean distances between X and Y. - - Args: - X (Series or single column DataFrame of array of numeric type): - Input data. - Y (Series or single column DataFrame of array of numeric type): - Input data. X and Y are mapped by indexes, must have the same index. - - Returns: - bigframes.dataframe.DataFrame: DataFrame with columns of X, Y and euclidean_distance. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) diff --git a/third_party/bigframes_vendored/sklearn/model_selection/_split.py b/third_party/bigframes_vendored/sklearn/model_selection/_split.py deleted file mode 100644 index 2398cbe77ca..00000000000 --- a/third_party/bigframes_vendored/sklearn/model_selection/_split.py +++ /dev/null @@ -1,215 +0,0 @@ -""" -The :mod:`sklearn.model_selection._split` module includes classes and -functions to split the data based on a preset strategy. -""" - -# Author: Alexandre Gramfort -# Gael Varoquaux -# Olivier Grisel -# Raghav RV -# Leandro Hermida -# Rodion Martynov -# License: BSD 3 clause - -from abc import ABCMeta - -from bigframes import constants - - -class _BaseKFold(metaclass=ABCMeta): - """Base class for K-Fold cross-validators.""" - - def split(self, X, y=None): - """Generate indices to split data into training and test set. - - Args: - X (bigframes.dataframe.DataFrame or bigframes.series.Series): - BigFrames DataFrame or Series of shape (n_samples, n_features) - Training data, where `n_samples` is the number of samples - and `n_features` is the number of features. - - y (bigframes.dataframe.DataFrame, bigframes.series.Series or None): - BigFrames DataFrame, Series of shape (n_samples,) or None. - The target variable for supervised learning problems. Default to None. - - Yields: - X_train (bigframes.dataframe.DataFrame or bigframes.series.Series): - The training data for that split. - - X_test (bigframes.dataframe.DataFrame or bigframes.series.Series): - The testing data for that split. - - y_train (bigframes.dataframe.DataFrame, bigframes.series.Series or None): - The training label for that split. - - y_test (bigframes.dataframe.DataFrame, bigframes.series.Series or None): - The testing label for that split. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def get_n_splits(self): - """Returns the number of splitting iterations in the cross-validator. - - Returns: - int: the number of splitting iterations in the cross-validator. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - -class KFold(_BaseKFold): - """K-Fold cross-validator. - - Split data in train/test sets. Split dataset into k consecutive folds. - - Each fold is then used once as a validation while the k - 1 remaining - folds form the training set. - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> from bigframes.ml.model_selection import KFold - >>> X = bpd.DataFrame({"feat0": [1, 3, 5], "feat1": [2, 4, 6]}) - >>> y = bpd.DataFrame({"label": [1, 2, 3]}) - >>> kf = KFold(n_splits=3, random_state=42) - >>> for i, (X_train, X_test, y_train, y_test) in enumerate(kf.split(X, y)): - ... print(f"Fold {i}:") - ... print(f" X_train: {X_train}") - ... print(f" X_test: {X_test}") - ... print(f" y_train: {y_train}") - ... print(f" y_test: {y_test}") - ... - Fold 0: - X_train: feat0 feat1 - 1 3 4 - 2 5 6 - - [2 rows x 2 columns] - X_test: feat0 feat1 - 0 1 2 - - [1 rows x 2 columns] - y_train: label - 1 2 - 2 3 - - [2 rows x 1 columns] - y_test: label - 0 1 - - [1 rows x 1 columns] - Fold 1: - X_train: feat0 feat1 - 0 1 2 - 2 5 6 - - [2 rows x 2 columns] - X_test: feat0 feat1 - 1 3 4 - - [1 rows x 2 columns] - y_train: label - 0 1 - 2 3 - - [2 rows x 1 columns] - y_test: label - 1 2 - - [1 rows x 1 columns] - Fold 2: - X_train: feat0 feat1 - 0 1 2 - 1 3 4 - - [2 rows x 2 columns] - X_test: feat0 feat1 - 2 5 6 - - [1 rows x 2 columns] - y_train: label - 0 1 - 1 2 - - [2 rows x 1 columns] - y_test: label - 2 3 - - [1 rows x 1 columns] - - - Args: - n_splits (int): - Number of folds. Must be at least 2. Default to 5. - - random_state (Optional[int]): - A seed to use for randomly choosing the rows of the split. If not - set, a random split will be generated each time. Default to None. - """ - - -def train_test_split( - *arrays, - test_size=None, - train_size=None, - random_state=None, - stratify=None, -): - """Splits dataframes or series into random train and test subsets. - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> from bigframes.ml.model_selection import train_test_split - >>> X = bpd.DataFrame({"feat0": [0, 2, 4, 6, 8], "feat1": [1, 3, 5, 7, 9]}) - >>> y = bpd.DataFrame({"label": [0, 1, 2, 3, 4]}) - >>> X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42) - >>> X_train - feat0 feat1 - 0 0 1 - 1 2 3 - 4 8 9 - - [3 rows x 2 columns] - >>> y_train - label - 0 0 - 1 1 - 4 4 - - [3 rows x 1 columns] - >>> X_test - feat0 feat1 - 2 4 5 - 3 6 7 - - [2 rows x 2 columns] - >>> y_test - label - 2 2 - 3 3 - - [2 rows x 1 columns] - - Args: - *arrays (bigframes.dataframe.DataFrame or bigframes.series.Series): - A sequence of BigQuery DataFrames or Series that can be joined on - their indexes. - test_size (default None): - The proportion of the dataset to include in the test split. If - None, this will default to the complement of train_size. If both - are none, it will be set to 0.25. - train_size (default None): - The proportion of the dataset to include in the train split. If - None, this will default to the complement of test_size. - random_state (default None): - A seed to use for randomly choosing the rows of the split. If not - set, a random split will be generated each time. - stratify: (bigframes.series.Series or None, default None): - If not None, data is split in a stratified fashion, using this as the class labels. Each split has the same distribution of the class labels with the original dataset. - Default to None. - Note: By setting the stratify parameter, the memory consumption and generated SQL will be linear to the unique values in the Series. May return errors if the unique values size is too large. - - Returns: - List[Union[bigframes.dataframe.DataFrame, bigframes.series.Series]]: A list of BigQuery DataFrames or Series. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) diff --git a/third_party/bigframes_vendored/sklearn/model_selection/_validation.py b/third_party/bigframes_vendored/sklearn/model_selection/_validation.py deleted file mode 100644 index 6f840188534..00000000000 --- a/third_party/bigframes_vendored/sklearn/model_selection/_validation.py +++ /dev/null @@ -1,62 +0,0 @@ -""" -The :mod:`sklearn.model_selection._validation` module includes classes and -functions to validate the model. -""" - -# Author: Alexandre Gramfort -# Gael Varoquaux -# Olivier Grisel -# Raghav RV -# Michal Karbownik -# License: BSD 3 clause - - -def cross_validate(estimator, X, y=None, *, cv=None): - """Evaluate metric(s) by cross-validation and also record fit/score times. - - **Examples:** - - >>> import bigframes.pandas as bpd - >>> from bigframes.ml.model_selection import cross_validate, KFold - >>> from bigframes.ml.linear_model import LinearRegression - >>> X = bpd.DataFrame({"feat0": [1, 3, 5], "feat1": [2, 4, 6]}) - >>> y = bpd.DataFrame({"label": [1, 2, 3]}) - >>> model = LinearRegression() - >>> scores = cross_validate(model, X, y, cv=3) # doctest: +SKIP - >>> for score in scores["test_score"]: # doctest: +SKIP - ... print(score["mean_squared_error"][0]) - ... - 5.218167286047954e-19 - 2.726229944928669e-18 - 1.6197635612324266e-17 - - Args: - estimator: - bigframes.ml model that implements fit(). - The object to use to fit the data. - - X (bigframes.dataframe.DataFrame or bigframes.series.Series): - The data to fit. - - y (bigframes.dataframe.DataFrame, bigframes.series.Series or None): - The target variable to try to predict in the case of supe()rvised learning. Default to None. - - cv (int, bigframes.ml.model_selection.KFold or None): - Determines the cross-validation splitting strategy. - Possible inputs for cv are: - - - None, to use the default 5-fold cross validation, - - int, to specify the number of folds in a `KFold`, - - bigframes.ml.model_selection.KFold instance. - - Returns: - Dict[str, List]: A dict of arrays containing the score/time arrays for each scorer is returned. The keys for this ``dict`` are: - - ``test_score`` - The score array for test scores on each cv split. - ``fit_time`` - The time for fitting the estimator on the train - set for each cv split. - ``score_time`` - The time for scoring the estimator on the test set for each - cv split.""" diff --git a/third_party/bigframes_vendored/sklearn/pipeline.py b/third_party/bigframes_vendored/sklearn/pipeline.py index 96eaa903be7..4b8eb25a973 100644 --- a/third_party/bigframes_vendored/sklearn/pipeline.py +++ b/third_party/bigframes_vendored/sklearn/pipeline.py @@ -11,22 +11,21 @@ from abc import ABCMeta -from bigframes_vendored.sklearn.base import BaseEstimator - from bigframes import constants +from third_party.bigframes_vendored.sklearn.base import BaseEstimator class Pipeline(BaseEstimator, metaclass=ABCMeta): """Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. - Intermediate steps of the pipeline must be `transforms`. That is, they + Intermediate steps of the pipeline must be `transforms`, that is, they must implement `fit` and `transform` methods. The final estimator only needs to implement `fit`. The purpose of the pipeline is to assemble several steps that can be - cross-validated together while setting different parameters. This simplifies code and allows for - deploying an estimator and preprocessing together, e.g. with `Pipeline.to_gbq(...).` + cross-validated together while setting different parameters. This simplifies code, and allows deploying an estimator + and peprocessing together, e.g. with `Pipeline.to_gbq(...).` """ def fit( diff --git a/third_party/bigframes_vendored/sklearn/preprocessing/_data.py b/third_party/bigframes_vendored/sklearn/preprocessing/_data.py index b051cb24b43..5ce102d573f 100644 --- a/third_party/bigframes_vendored/sklearn/preprocessing/_data.py +++ b/third_party/bigframes_vendored/sklearn/preprocessing/_data.py @@ -7,9 +7,8 @@ # Eric Chang # License: BSD 3 clause -from bigframes_vendored.sklearn.base import BaseEstimator, TransformerMixin - from bigframes import constants +from third_party.bigframes_vendored.sklearn.base import BaseEstimator, TransformerMixin class StandardScaler(BaseEstimator, TransformerMixin): @@ -48,7 +47,7 @@ def fit(self, X, y=None): """Compute the mean and std to be used for later scaling. Args: - X (bigframes.dataframe.DataFrame or bigframes.series.Series or pandas.core.frame.DataFrame or pandas.core.series.Series): + X (bigframes.dataframe.DataFrame or bigframes.series.Series): The Dataframe or Series with training data. y (default None): @@ -63,7 +62,7 @@ def transform(self, X): """Perform standardization by centering and scaling. Args: - X (bigframes.dataframe.DataFrame or bigframes.series.Series or pandas.core.frame.DataFrame or pandas.core.series.Series): + X (bigframes.dataframe.DataFrame or bigframes.series.Series): The DataFrame or Series to be transformed. Returns: @@ -85,7 +84,7 @@ def fit(self, X, y=None): """Compute the maximum absolute value to be used for later scaling. Args: - X (bigframes.dataframe.DataFrame or bigframes.series.Series or pandas.core.frame.DataFrame or pandas.core.series.Series): + X (bigframes.dataframe.DataFrame or bigframes.series.Series): The Dataframe or Series with training data. y (default None): @@ -100,7 +99,7 @@ def transform(self, X): """Scale the data. Args: - X (bigframes.dataframe.DataFrame or bigframes.series.Series or pandas.core.frame.DataFrame or pandas.core.series.Series): + X (bigframes.dataframe.DataFrame or bigframes.series.Series): The DataFrame or Series to be transformed. Returns: @@ -121,7 +120,7 @@ def fit(self, X, y=None): """Compute the minimum and maximum to be used for later scaling. Args: - X (bigframes.dataframe.DataFrame or bigframes.series.Series or pandas.core.frame.DataFrame or pandas.core.series.Series): + X (bigframes.dataframe.DataFrame or bigframes.series.Series): The Dataframe or Series with training data. y (default None): @@ -136,7 +135,7 @@ def transform(self, X): """Scale the data. Args: - X (bigframes.dataframe.DataFrame or bigframes.series.Series or pandas.core.frame.DataFrame or pandas.core.series.Series): + X (bigframes.dataframe.DataFrame or bigframes.series.Series): The DataFrame or Series to be transformed. Returns: diff --git a/third_party/bigframes_vendored/sklearn/preprocessing/_discretization.py b/third_party/bigframes_vendored/sklearn/preprocessing/_discretization.py index 5fa84d2d159..0236558dd4b 100644 --- a/third_party/bigframes_vendored/sklearn/preprocessing/_discretization.py +++ b/third_party/bigframes_vendored/sklearn/preprocessing/_discretization.py @@ -3,9 +3,8 @@ # License: BSD -from bigframes_vendored.sklearn.base import BaseEstimator, TransformerMixin - from bigframes import constants +from third_party.bigframes_vendored.sklearn.base import BaseEstimator, TransformerMixin class KBinsDiscretizer(TransformerMixin, BaseEstimator): @@ -18,14 +17,14 @@ class KBinsDiscretizer(TransformerMixin, BaseEstimator): strategy ({'uniform', 'quantile'}, default='quantile'): Strategy used to define the widths of the bins. 'uniform': All bins in each feature have identical widths. 'quantile': All bins in each - feature have the same number of points. + feature have the same number of points. Only `uniform` is supported now. """ def fit(self, X, y=None): """Fit the estimator. Args: - X (bigframes.dataframe.DataFrame or bigframes.series.Series or pandas.core.frame.DataFrame or pandas.core.series.Series): + X (bigframes.dataframe.DataFrame or bigframes.series.Series): The Dataframe or Series with training data. y (default None): @@ -40,7 +39,7 @@ def transform(self, X): """Discretize the data. Args: - X (bigframes.dataframe.DataFrame or bigframes.series.Series or pandas.core.frame.DataFrame or pandas.core.series.Series): + X (bigframes.dataframe.DataFrame or bigframes.series.Series): The DataFrame or Series to be transformed. Returns: diff --git a/third_party/bigframes_vendored/sklearn/preprocessing/_encoder.py b/third_party/bigframes_vendored/sklearn/preprocessing/_encoder.py index 1301ef329ab..8da9a98c53f 100644 --- a/third_party/bigframes_vendored/sklearn/preprocessing/_encoder.py +++ b/third_party/bigframes_vendored/sklearn/preprocessing/_encoder.py @@ -2,9 +2,8 @@ # Joris Van den Bossche # License: BSD 3 clause -from bigframes_vendored.sklearn.base import BaseEstimator - from bigframes import constants +from third_party.bigframes_vendored.sklearn.base import BaseEstimator class OneHotEncoder(BaseEstimator): @@ -23,20 +22,15 @@ class OneHotEncoder(BaseEstimator): Given a dataset with two features, we let the encoder find the unique values per feature and transform the data to a binary one-hot encoding. - >>> from bigframes.ml.preprocessing import OneHotEncoder - >>> import bigframes.pandas as bpd + .. code-block:: - >>> enc = OneHotEncoder() - >>> X = bpd.DataFrame({"a": ["Male", "Female", "Female"], "b": ["1", "3", "2"]}) - >>> enc.fit(X) - OneHotEncoder() + from bigframes.ml.preprocessing import OneHotEncoder + import bigframes.pandas as bpd - >>> print(enc.transform(bpd.DataFrame({"a": ["Female", "Male"], "b": ["1", "4"]}))) - onehotencoded_a onehotencoded_b - 0 [{'index': 1, 'value': 1.0}] [{'index': 1, 'value': 1.0}] - 1 [{'index': 2, 'value': 1.0}] [{'index': 0, 'value': 1.0}] - - [2 rows x 2 columns] + enc = OneHotEncoder() + X = bpd.DataFrame({"a": ["Male", "Female", "Female"], "b": ["1", "3", "2"]}) + enc.fit(X) + print(enc.transform(bpd.DataFrame({"a": ["Female", "Male"], "b": ["1", "4"]}))) Args: drop (Optional[Literal["most_frequent"]], default None): @@ -57,14 +51,14 @@ class OneHotEncoder(BaseEstimator): Specifies an upper limit to the number of output features for each input feature when considering infrequent categories. If there are infrequent categories, max_categories includes the category representing the infrequent categories along with the frequent categories. - Default None. Set limit to 1,000,000. + Default None, set limit to 1,000,000. """ def fit(self, X, y=None): """Fit OneHotEncoder to X. Args: - X (bigframes.dataframe.DataFrame or bigframes.series.Series or pandas.core.frame.DataFrame or pandas.core.series.Series): + X (bigframes.dataframe.DataFrame or bigframes.series.Series): The DataFrame or Series with training data. y (default None): @@ -79,11 +73,10 @@ def transform(self, X): """Transform X using one-hot encoding. Args: - X (bigframes.dataframe.DataFrame or bigframes.series.Series or pandas.core.frame.DataFrame or pandas.core.series.Series): + X (bigframes.dataframe.DataFrame or bigframes.series.Series): The DataFrame or Series to be transformed. Returns: - bigframes.dataframe.DataFrame: The result is categorized as index: number, value: number, - where index is the position of the dict seeing the category, and value is 0 or 1. - """ + bigframes.dataframe.DataFrame: The result is categorized as index: number, value: number. + Where index is the position of the dict that seeing the category, and value is 0 or 1.""" raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) diff --git a/third_party/bigframes_vendored/sklearn/preprocessing/_label.py b/third_party/bigframes_vendored/sklearn/preprocessing/_label.py index 74b3ca347a2..83f8eb0f9c2 100644 --- a/third_party/bigframes_vendored/sklearn/preprocessing/_label.py +++ b/third_party/bigframes_vendored/sklearn/preprocessing/_label.py @@ -6,9 +6,8 @@ # Hamzeh Alsalhi # License: BSD 3 clause -from bigframes_vendored.sklearn.base import BaseEstimator - from bigframes import constants +from third_party.bigframes_vendored.sklearn.base import BaseEstimator class LabelEncoder(BaseEstimator): @@ -26,14 +25,14 @@ class LabelEncoder(BaseEstimator): Specifies an upper limit to the number of output features for each input feature when considering infrequent categories. If there are infrequent categories, max_categories includes the category representing the infrequent categories along with the frequent categories. - Default None. Set limit to 1,000,000. + Default None, set limit to 1,000,000. """ def fit(self, y): """Fit label encoder. Args: - y (bigframes.dataframe.DataFrame or bigframes.series.Series or pandas.core.frame.DataFrame or pandas.core.series.Series): + y (bigframes.dataframe.DataFrame or bigframes.series.Series): The DataFrame or Series with training data. Returns: @@ -45,7 +44,7 @@ def transform(self, y): """Transform y using label encoding. Args: - y (bigframes.dataframe.DataFrame or bigframes.series.Series or pandas.core.frame.DataFrame or pandas.core.series.Series): + y (bigframes.dataframe.DataFrame or bigframes.series.Series): The DataFrame or Series to be transformed. Returns: diff --git a/third_party/bigframes_vendored/sklearn/preprocessing/_polynomial.py b/third_party/bigframes_vendored/sklearn/preprocessing/_polynomial.py deleted file mode 100644 index aeed4dce922..00000000000 --- a/third_party/bigframes_vendored/sklearn/preprocessing/_polynomial.py +++ /dev/null @@ -1,43 +0,0 @@ -""" -This file contains preprocessing tools based on polynomials. -""" - -from bigframes_vendored.sklearn.base import BaseEstimator, TransformerMixin - -from bigframes import constants - - -class PolynomialFeatures(TransformerMixin, BaseEstimator): - """Generate polynomial and interaction features. - - Args: - degree (int): - Specifies the maximal degree of the polynomial features. Valid values [1, 4]. Default to 2. - """ - - def fit(self, X, y=None): - """Compute number of output features. - - Args: - X (bigframes.dataframe.DataFrame or bigframes.series.Series or pandas.core.frame.DataFrame or pandas.core.series.Series): - The Dataframe or Series with training data. - - y (default None): - Ignored. - - Returns: - PolynomialFeatures: Fitted transformer. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) - - def transform(self, X): - """Transform data to polynomial features. - - Args: - X (bigframes.dataframe.DataFrame or bigframes.series.Series or pandas.core.frame.DataFrame or pandas.core.series.Series): - The DataFrame or Series to be transformed. - - Returns: - bigframes.dataframe.DataFrame: Transformed result. - """ - raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) diff --git a/third_party/bigframes_vendored/sqlglot/LICENSE b/third_party/bigframes_vendored/sqlglot/LICENSE deleted file mode 100644 index 72c4dbcc54f..00000000000 --- a/third_party/bigframes_vendored/sqlglot/LICENSE +++ /dev/null @@ -1,21 +0,0 @@ -MIT License - -Copyright (c) 2025 Toby Mao - -Permission is hereby granted, free of charge, to any person obtaining a copy -of this software and associated documentation files (the "Software"), to deal -in the Software without restriction, including without limitation the rights -to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -copies of the Software, and to permit persons to whom the Software is -furnished to do so, subject to the following conditions: - -The above copyright notice and this permission notice shall be included in all -copies or substantial portions of the Software. - -THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -SOFTWARE. diff --git a/third_party/bigframes_vendored/sqlglot/__init__.py b/third_party/bigframes_vendored/sqlglot/__init__.py deleted file mode 100644 index 7369b9b444b..00000000000 --- a/third_party/bigframes_vendored/sqlglot/__init__.py +++ /dev/null @@ -1,189 +0,0 @@ -# Contains code from https://github.com/tobymao/sqlglot/blob/v28.5.0/sqlglot/__init__.py - -# ruff: noqa: F401 -""" -.. include:: ../README.md - ----- -""" - -from __future__ import annotations - -import logging -import typing as t - -from bigframes_vendored.sqlglot import expressions as exp -from bigframes_vendored.sqlglot.dialects.dialect import Dialect as Dialect # noqa: F401 -from bigframes_vendored.sqlglot.dialects.dialect import ( # noqa: F401 - Dialects as Dialects, -) -from bigframes_vendored.sqlglot.diff import diff as diff # noqa: F401 -from bigframes_vendored.sqlglot.errors import ErrorLevel as ErrorLevel -from bigframes_vendored.sqlglot.errors import ParseError as ParseError -from bigframes_vendored.sqlglot.errors import TokenError as TokenError # noqa: F401 -from bigframes_vendored.sqlglot.errors import ( # noqa: F401 - UnsupportedError as UnsupportedError, -) -from bigframes_vendored.sqlglot.expressions import ( # noqa: F401 - Expression as Expression, -) -from bigframes_vendored.sqlglot.expressions import alias_ as alias # noqa: F401 -from bigframes_vendored.sqlglot.expressions import and_ as and_ # noqa: F401 -from bigframes_vendored.sqlglot.expressions import case as case # noqa: F401 -from bigframes_vendored.sqlglot.expressions import cast as cast # noqa: F401 -from bigframes_vendored.sqlglot.expressions import column as column # noqa: F401 -from bigframes_vendored.sqlglot.expressions import condition as condition # noqa: F401 -from bigframes_vendored.sqlglot.expressions import delete as delete # noqa: F401 -from bigframes_vendored.sqlglot.expressions import except_ as except_ # noqa: F401 -from bigframes_vendored.sqlglot.expressions import ( # noqa: F401 - find_tables as find_tables, -) -from bigframes_vendored.sqlglot.expressions import from_ as from_ # noqa: F401 -from bigframes_vendored.sqlglot.expressions import func as func # noqa: F401 -from bigframes_vendored.sqlglot.expressions import insert as insert # noqa: F401 -from bigframes_vendored.sqlglot.expressions import intersect as intersect # noqa: F401 -from bigframes_vendored.sqlglot.expressions import ( # noqa: F401 - maybe_parse as maybe_parse, -) -from bigframes_vendored.sqlglot.expressions import merge as merge # noqa: F401 -from bigframes_vendored.sqlglot.expressions import not_ as not_ # noqa: F401 -from bigframes_vendored.sqlglot.expressions import or_ as or_ # noqa: F401 -from bigframes_vendored.sqlglot.expressions import select as select # noqa: F401 -from bigframes_vendored.sqlglot.expressions import subquery as subquery # noqa: F401 -from bigframes_vendored.sqlglot.expressions import table_ as table # noqa: F401 -from bigframes_vendored.sqlglot.expressions import to_column as to_column # noqa: F401 -from bigframes_vendored.sqlglot.expressions import ( # noqa: F401 - to_identifier as to_identifier, -) -from bigframes_vendored.sqlglot.expressions import to_table as to_table # noqa: F401 -from bigframes_vendored.sqlglot.expressions import union as union # noqa: F401 -from bigframes_vendored.sqlglot.generator import Generator as Generator # noqa: F401 -from bigframes_vendored.sqlglot.parser import Parser as Parser # noqa: F401 -from bigframes_vendored.sqlglot.schema import ( # noqa: F401 - MappingSchema as MappingSchema, -) -from bigframes_vendored.sqlglot.schema import Schema as Schema # noqa: F401 -from bigframes_vendored.sqlglot.tokens import Token as Token # noqa: F401 -from bigframes_vendored.sqlglot.tokens import Tokenizer as Tokenizer # noqa: F401 -from bigframes_vendored.sqlglot.tokens import TokenType as TokenType # noqa: F401 - -if t.TYPE_CHECKING: - from bigframes_vendored.sqlglot._typing import E - from bigframes_vendored.sqlglot.dialects.dialect import DialectType as DialectType - -logger = logging.getLogger("sqlglot") - - -pretty = False -"""Whether to format generated SQL by default.""" - - -def tokenize( - sql: str, read: DialectType = None, dialect: DialectType = None -) -> t.List[Token]: - """ - Tokenizes the given SQL string. - - Args: - sql: the SQL code string to tokenize. - read: the SQL dialect to apply during tokenizing (eg. "spark", "hive", "presto", "mysql"). - dialect: the SQL dialect (alias for read). - - Returns: - The resulting list of tokens. - """ - return Dialect.get_or_raise(read or dialect).tokenize(sql) - - -def parse( - sql: str, read: DialectType = None, dialect: DialectType = None, **opts -) -> t.List[t.Optional[Expression]]: - """ - Parses the given SQL string into a collection of syntax trees, one per parsed SQL statement. - - Args: - sql: the SQL code string to parse. - read: the SQL dialect to apply during parsing (eg. "spark", "hive", "presto", "mysql"). - dialect: the SQL dialect (alias for read). - **opts: other `sqlglot.parser.Parser` options. - - Returns: - The resulting syntax tree collection. - """ - return Dialect.get_or_raise(read or dialect).parse(sql, **opts) - - -@t.overload -def parse_one(sql: str, *, into: t.Type[E], **opts) -> E: ... - - -@t.overload -def parse_one(sql: str, **opts) -> Expression: ... - - -def parse_one( - sql: str, - read: DialectType = None, - dialect: DialectType = None, - into: t.Optional[exp.IntoType] = None, - **opts, -) -> Expression: - """ - Parses the given SQL string and returns a syntax tree for the first parsed SQL statement. - - Args: - sql: the SQL code string to parse. - read: the SQL dialect to apply during parsing (eg. "spark", "hive", "presto", "mysql"). - dialect: the SQL dialect (alias for read) - into: the SQLGlot Expression to parse into. - **opts: other `sqlglot.parser.Parser` options. - - Returns: - The syntax tree for the first parsed statement. - """ - - dialect = Dialect.get_or_raise(read or dialect) - - if into: - result = dialect.parse_into(into, sql, **opts) - else: - result = dialect.parse(sql, **opts) - - for expression in result: - if not expression: - raise ParseError(f"No expression was parsed from '{sql}'") - return expression - else: - raise ParseError(f"No expression was parsed from '{sql}'") - - -def transpile( - sql: str, - read: DialectType = None, - write: DialectType = None, - identity: bool = True, - error_level: t.Optional[ErrorLevel] = None, - **opts, -) -> t.List[str]: - """ - Parses the given SQL string in accordance with the source dialect and returns a list of SQL strings transformed - to conform to the target dialect. Each string in the returned list represents a single transformed SQL statement. - - Args: - sql: the SQL code string to transpile. - read: the source dialect used to parse the input string (eg. "spark", "hive", "presto", "mysql"). - write: the target dialect into which the input should be transformed (eg. "spark", "hive", "presto", "mysql"). - identity: if set to `True` and if the target dialect is not specified the source dialect will be used as both: - the source and the target dialect. - error_level: the desired error level of the parser. - **opts: other `sqlglot.generator.Generator` options. - - Returns: - The list of transpiled SQL statements. - """ - write = (read if write is None else write) if identity else write - write = Dialect.get_or_raise(write) - return [ - write.generate(expression, copy=False, **opts) if expression else "" - for expression in parse(sql, read, error_level=error_level) - ] diff --git a/third_party/bigframes_vendored/sqlglot/dialects/__init__.py b/third_party/bigframes_vendored/sqlglot/dialects/__init__.py deleted file mode 100644 index 78285be445a..00000000000 --- a/third_party/bigframes_vendored/sqlglot/dialects/__init__.py +++ /dev/null @@ -1,99 +0,0 @@ -# Contains code from https://github.com/tobymao/sqlglot/blob/v28.5.0/sqlglot/dialects/__init__.py - -# ruff: noqa: F401 -""" -## Dialects - -While there is a SQL standard, most SQL engines support a variation of that standard. This makes it difficult -to write portable SQL code. SQLGlot bridges all the different variations, called "dialects", with an extensible -SQL transpilation framework. - -The base `sqlglot.dialects.dialect.Dialect` class implements a generic dialect that aims to be as universal as possible. - -Each SQL variation has its own `Dialect` subclass, extending the corresponding `Tokenizer`, `Parser` and `Generator` -classes as needed. - -### Implementing a custom Dialect - -Creating a new SQL dialect may seem complicated at first, but it is actually quite simple in SQLGlot: - -```python -from sqlglot import exp -from sqlglot.dialects.dialect import Dialect -from sqlglot.generator import Generator -from sqlglot.tokens import Tokenizer, TokenType - - -class Custom(Dialect): - class Tokenizer(Tokenizer): - QUOTES = ["'", '"'] # Strings can be delimited by either single or double quotes - IDENTIFIERS = ["`"] # Identifiers can be delimited by backticks - - # Associates certain meaningful words with tokens that capture their intent - KEYWORDS = { - **Tokenizer.KEYWORDS, - "INT64": TokenType.BIGINT, - "FLOAT64": TokenType.DOUBLE, - } - - class Generator(Generator): - # Specifies how AST nodes, i.e. subclasses of exp.Expression, should be converted into SQL - TRANSFORMS = { - exp.Array: lambda self, e: f"[{self.expressions(e)}]", - } - - # Specifies how AST nodes representing data types should be converted into SQL - TYPE_MAPPING = { - exp.DataType.Type.TINYINT: "INT64", - exp.DataType.Type.SMALLINT: "INT64", - exp.DataType.Type.INT: "INT64", - exp.DataType.Type.BIGINT: "INT64", - exp.DataType.Type.DECIMAL: "NUMERIC", - exp.DataType.Type.FLOAT: "FLOAT64", - exp.DataType.Type.DOUBLE: "FLOAT64", - exp.DataType.Type.BOOLEAN: "BOOL", - exp.DataType.Type.TEXT: "STRING", - } -``` - -The above example demonstrates how certain parts of the base `Dialect` class can be overridden to match a different -specification. Even though it is a fairly realistic starting point, we strongly encourage the reader to study existing -dialect implementations in order to understand how their various components can be modified, depending on the use-case. - ----- -""" - -import importlib -import threading - -DIALECTS = [ - "BigQuery", -] - -MODULE_BY_DIALECT = {name: name.lower() for name in DIALECTS} -DIALECT_MODULE_NAMES = MODULE_BY_DIALECT.values() - -MODULE_BY_ATTRIBUTE = { - **MODULE_BY_DIALECT, - "Dialect": "dialect", - "Dialects": "dialect", -} - -__all__ = list(MODULE_BY_ATTRIBUTE) - -# We use a reentrant lock because a dialect may depend on (i.e., import) other dialects. -# Without it, the first dialect import would never be completed, because subsequent -# imports would be blocked on the lock held by the first import. -_import_lock = threading.RLock() - - -def __getattr__(name): - module_name = MODULE_BY_ATTRIBUTE.get(name) - if module_name: - with _import_lock: - module = importlib.import_module( - f"bigframes_vendored.sqlglot.dialects.{module_name}" - ) - return getattr(module, name) - - raise AttributeError(f"module {__name__} has no attribute {name}") diff --git a/third_party/bigframes_vendored/sqlglot/dialects/bigquery.py b/third_party/bigframes_vendored/sqlglot/dialects/bigquery.py deleted file mode 100644 index 7da30231ff4..00000000000 --- a/third_party/bigframes_vendored/sqlglot/dialects/bigquery.py +++ /dev/null @@ -1,1681 +0,0 @@ -# Contains code from https://github.com/tobymao/sqlglot/blob/v28.5.0/sqlglot/dialects/bigquery.py - -from __future__ import annotations - -import logging -import re -import typing as t - -from bigframes_vendored.sqlglot import ( - exp, - generator, - jsonpath, - parser, - tokens, - transforms, -) -from bigframes_vendored.sqlglot.dialects.dialect import ( - Dialect, - NormalizationStrategy, - arg_max_or_min_no_count, - binary_from_function, - build_date_delta_with_interval, - build_formatted_time, - date_add_interval_sql, - datestrtodate_sql, - filter_array_using_unnest, - groupconcat_sql, - if_sql, - inline_array_unless_query, - max_or_greatest, - min_or_least, - no_ilike_sql, - regexp_replace_sql, - rename_func, - sha2_digest_sql, - sha256_sql, - strposition_sql, - timestrtotime_sql, - ts_or_ds_add_cast, - unit_to_var, -) -from bigframes_vendored.sqlglot.expressions import Expression as E -from bigframes_vendored.sqlglot.generator import unsupported_args -from bigframes_vendored.sqlglot.helper import seq_get, split_num_words -from bigframes_vendored.sqlglot.optimizer.annotate_types import TypeAnnotator -from bigframes_vendored.sqlglot.tokens import TokenType -from bigframes_vendored.sqlglot.typing.bigquery import EXPRESSION_METADATA - -if t.TYPE_CHECKING: - from bigframes_vendored.sqlglot._typing import Lit - -logger = logging.getLogger("sqlglot") - - -JSON_EXTRACT_TYPE = t.Union[ - exp.JSONExtract, exp.JSONExtractScalar, exp.JSONExtractArray -] - -DQUOTES_ESCAPING_JSON_FUNCTIONS = ("JSON_QUERY", "JSON_VALUE", "JSON_QUERY_ARRAY") - -MAKE_INTERVAL_KWARGS = ["year", "month", "day", "hour", "minute", "second"] - - -def _derived_table_values_to_unnest( - self: BigQuery.Generator, expression: exp.Values -) -> str: - if not expression.find_ancestor(exp.From, exp.Join): - return self.values_sql(expression) - - structs = [] - alias = expression.args.get("alias") - for tup in expression.find_all(exp.Tuple): - field_aliases = ( - alias.columns - if alias and alias.columns - else (f"_c{i}" for i in range(len(tup.expressions))) - ) - expressions = [ - exp.PropertyEQ(this=exp.to_identifier(name), expression=fld) - for name, fld in zip(field_aliases, tup.expressions) - ] - structs.append(exp.Struct(expressions=expressions)) - - # Due to `UNNEST_COLUMN_ONLY`, it is expected that the table alias be contained in the columns expression - alias_name_only = exp.TableAlias(columns=[alias.this]) if alias else None - return self.unnest_sql( - exp.Unnest(expressions=[exp.array(*structs, copy=False)], alias=alias_name_only) - ) - - -def _returnsproperty_sql( - self: BigQuery.Generator, expression: exp.ReturnsProperty -) -> str: - this = expression.this - if isinstance(this, exp.Schema): - this = f"{self.sql(this, 'this')} <{self.expressions(this)}>" - else: - this = self.sql(this) - return f"RETURNS {this}" - - -def _create_sql(self: BigQuery.Generator, expression: exp.Create) -> str: - returns = expression.find(exp.ReturnsProperty) - if expression.kind == "FUNCTION" and returns and returns.args.get("is_table"): - expression.set("kind", "TABLE FUNCTION") - - if isinstance(expression.expression, (exp.Subquery, exp.Literal)): - expression.set("expression", expression.expression.this) - - return self.create_sql(expression) - - -# https://issuetracker.google.com/issues/162294746 -# workaround for bigquery bug when grouping by an expression and then ordering -# WITH x AS (SELECT 1 y) -# SELECT y + 1 z -# FROM x -# GROUP BY x + 1 -# ORDER by z -def _alias_ordered_group(expression: exp.Expression) -> exp.Expression: - if isinstance(expression, exp.Select): - group = expression.args.get("group") - order = expression.args.get("order") - - if group and order: - aliases = { - select.this: select.args["alias"] - for select in expression.selects - if isinstance(select, exp.Alias) - } - - for grouped in group.expressions: - if grouped.is_int: - continue - alias = aliases.get(grouped) - if alias: - grouped.replace(exp.column(alias)) - - return expression - - -def _pushdown_cte_column_names(expression: exp.Expression) -> exp.Expression: - """BigQuery doesn't allow column names when defining a CTE, so we try to push them down.""" - if isinstance(expression, exp.CTE) and expression.alias_column_names: - cte_query = expression.this - - if cte_query.is_star: - logger.warning( - "Can't push down CTE column names for star queries. Run the query through" - " the optimizer or use 'qualify' to expand the star projections first." - ) - return expression - - column_names = expression.alias_column_names - expression.args["alias"].set("columns", None) - - for name, select in zip(column_names, cte_query.selects): - to_replace = select - - if isinstance(select, exp.Alias): - select = select.this - - # Inner aliases are shadowed by the CTE column names - to_replace.replace(exp.alias_(select, name)) - - return expression - - -def _build_parse_timestamp(args: t.List) -> exp.StrToTime: - this = build_formatted_time(exp.StrToTime, "bigquery")( - [seq_get(args, 1), seq_get(args, 0)] - ) - this.set("zone", seq_get(args, 2)) - return this - - -def _build_timestamp(args: t.List) -> exp.Timestamp: - timestamp = exp.Timestamp.from_arg_list(args) - timestamp.set("with_tz", True) - return timestamp - - -def _build_date(args: t.List) -> exp.Date | exp.DateFromParts: - expr_type = exp.DateFromParts if len(args) == 3 else exp.Date - return expr_type.from_arg_list(args) - - -def _build_to_hex(args: t.List) -> exp.Hex | exp.MD5: - # TO_HEX(MD5(..)) is common in BigQuery, so it's parsed into MD5 to simplify its transpilation - arg = seq_get(args, 0) - return ( - exp.MD5(this=arg.this) - if isinstance(arg, exp.MD5Digest) - else exp.LowerHex(this=arg) - ) - - -def _build_json_strip_nulls(args: t.List) -> exp.JSONStripNulls: - expression = exp.JSONStripNulls(this=seq_get(args, 0)) - - for arg in args[1:]: - if isinstance(arg, exp.Kwarg): - expression.set(arg.this.name.lower(), arg) - else: - expression.set("expression", arg) - - return expression - - -def _array_contains_sql(self: BigQuery.Generator, expression: exp.ArrayContains) -> str: - return self.sql( - exp.Exists( - this=exp.select("1") - .from_( - exp.Unnest(expressions=[expression.left]).as_("_unnest", table=["_col"]) - ) - .where(exp.column("_col").eq(expression.right)) - ) - ) - - -def _ts_or_ds_add_sql(self: BigQuery.Generator, expression: exp.TsOrDsAdd) -> str: - return date_add_interval_sql("DATE", "ADD")(self, ts_or_ds_add_cast(expression)) - - -def _ts_or_ds_diff_sql(self: BigQuery.Generator, expression: exp.TsOrDsDiff) -> str: - expression.this.replace(exp.cast(expression.this, exp.DataType.Type.TIMESTAMP)) - expression.expression.replace( - exp.cast(expression.expression, exp.DataType.Type.TIMESTAMP) - ) - unit = unit_to_var(expression) - return self.func("DATE_DIFF", expression.this, expression.expression, unit) - - -def _unix_to_time_sql(self: BigQuery.Generator, expression: exp.UnixToTime) -> str: - scale = expression.args.get("scale") - timestamp = expression.this - - if scale in (None, exp.UnixToTime.SECONDS): - return self.func("TIMESTAMP_SECONDS", timestamp) - if scale == exp.UnixToTime.MILLIS: - return self.func("TIMESTAMP_MILLIS", timestamp) - if scale == exp.UnixToTime.MICROS: - return self.func("TIMESTAMP_MICROS", timestamp) - - unix_seconds = exp.cast( - exp.Div(this=timestamp, expression=exp.func("POW", 10, scale)), - exp.DataType.Type.BIGINT, - ) - return self.func("TIMESTAMP_SECONDS", unix_seconds) - - -def _build_time(args: t.List) -> exp.Func: - if len(args) == 1: - return exp.TsOrDsToTime(this=args[0]) - if len(args) == 2: - return exp.Time.from_arg_list(args) - return exp.TimeFromParts.from_arg_list(args) - - -def _build_datetime(args: t.List) -> exp.Func: - if len(args) == 1: - return exp.TsOrDsToDatetime.from_arg_list(args) - if len(args) == 2: - return exp.Datetime.from_arg_list(args) - return exp.TimestampFromParts.from_arg_list(args) - - -def build_date_diff(args: t.List) -> exp.Expression: - expr = exp.DateDiff( - this=seq_get(args, 0), - expression=seq_get(args, 1), - unit=seq_get(args, 2), - date_part_boundary=True, - ) - - # Normalize plain WEEK to WEEK(SUNDAY) to preserve the semantic in the AST to facilitate transpilation - # This is done post exp.DateDiff construction since the TimeUnit mixin performs canonicalizations in its constructor too - unit = expr.args.get("unit") - - if isinstance(unit, exp.Var) and unit.name.upper() == "WEEK": - expr.set("unit", exp.WeekStart(this=exp.var("SUNDAY"))) - - return expr - - -def _build_regexp_extract( - expr_type: t.Type[E], default_group: t.Optional[exp.Expression] = None -) -> t.Callable[[t.List, BigQuery], E]: - def _builder(args: t.List, dialect: BigQuery) -> E: - try: - group = re.compile(args[1].name).groups == 1 - except re.error: - group = False - - # Default group is used for the transpilation of REGEXP_EXTRACT_ALL - return expr_type( - this=seq_get(args, 0), - expression=seq_get(args, 1), - position=seq_get(args, 2), - occurrence=seq_get(args, 3), - group=exp.Literal.number(1) if group else default_group, - **( - { - "null_if_pos_overflow": dialect.REGEXP_EXTRACT_POSITION_OVERFLOW_RETURNS_NULL - } - if expr_type is exp.RegexpExtract - else {} - ), - ) - - return _builder - - -def _build_extract_json_with_default_path( - expr_type: t.Type[E], -) -> t.Callable[[t.List, Dialect], E]: - def _builder(args: t.List, dialect: Dialect) -> E: - if len(args) == 1: - # The default value for the JSONPath is '$' i.e all of the data - args.append(exp.Literal.string("$")) - return parser.build_extract_json_with_path(expr_type)(args, dialect) - - return _builder - - -def _str_to_datetime_sql( - self: BigQuery.Generator, expression: exp.StrToDate | exp.StrToTime -) -> str: - this = self.sql(expression, "this") - dtype = "DATE" if isinstance(expression, exp.StrToDate) else "TIMESTAMP" - - if expression.args.get("safe"): - fmt = self.format_time( - expression, - self.dialect.INVERSE_FORMAT_MAPPING, - self.dialect.INVERSE_FORMAT_TRIE, - ) - return f"SAFE_CAST({this} AS {dtype} FORMAT {fmt})" - - fmt = self.format_time(expression) - return self.func(f"PARSE_{dtype}", fmt, this, expression.args.get("zone")) - - -@unsupported_args("ins_cost", "del_cost", "sub_cost") -def _levenshtein_sql(self: BigQuery.Generator, expression: exp.Levenshtein) -> str: - max_dist = expression.args.get("max_dist") - if max_dist: - max_dist = exp.Kwarg(this=exp.var("max_distance"), expression=max_dist) - - return self.func("EDIT_DISTANCE", expression.this, expression.expression, max_dist) - - -def _build_levenshtein(args: t.List) -> exp.Levenshtein: - max_dist = seq_get(args, 2) - return exp.Levenshtein( - this=seq_get(args, 0), - expression=seq_get(args, 1), - max_dist=max_dist.expression if max_dist else None, - ) - - -def _build_format_time( - expr_type: t.Type[exp.Expression], -) -> t.Callable[[t.List], exp.TimeToStr]: - def _builder(args: t.List) -> exp.TimeToStr: - formatted_time = build_formatted_time(exp.TimeToStr, "bigquery")( - [expr_type(this=seq_get(args, 1)), seq_get(args, 0)] - ) - formatted_time.set("zone", seq_get(args, 2)) - return formatted_time - - return _builder - - -def _build_contains_substring(args: t.List) -> exp.Contains: - # Lowercase the operands in case of transpilation, as exp.Contains - # is case-sensitive on other dialects - this = exp.Lower(this=seq_get(args, 0)) - expr = exp.Lower(this=seq_get(args, 1)) - - return exp.Contains(this=this, expression=expr, json_scope=seq_get(args, 2)) - - -def _json_extract_sql(self: BigQuery.Generator, expression: JSON_EXTRACT_TYPE) -> str: - name = (expression._meta and expression.meta.get("name")) or expression.sql_name() - upper = name.upper() - - dquote_escaping = upper in DQUOTES_ESCAPING_JSON_FUNCTIONS - - if dquote_escaping: - self._quote_json_path_key_using_brackets = False - - sql = rename_func(upper)(self, expression) - - if dquote_escaping: - self._quote_json_path_key_using_brackets = True - - return sql - - -class BigQuery(Dialect): - WEEK_OFFSET = -1 - UNNEST_COLUMN_ONLY = True - SUPPORTS_USER_DEFINED_TYPES = False - SUPPORTS_SEMI_ANTI_JOIN = False - LOG_BASE_FIRST = False - HEX_LOWERCASE = True - FORCE_EARLY_ALIAS_REF_EXPANSION = True - EXPAND_ONLY_GROUP_ALIAS_REF = True - PRESERVE_ORIGINAL_NAMES = True - HEX_STRING_IS_INTEGER_TYPE = True - BYTE_STRING_IS_BYTES_TYPE = True - UUID_IS_STRING_TYPE = True - ANNOTATE_ALL_SCOPES = True - PROJECTION_ALIASES_SHADOW_SOURCE_NAMES = True - TABLES_REFERENCEABLE_AS_COLUMNS = True - SUPPORTS_STRUCT_STAR_EXPANSION = True - EXCLUDES_PSEUDOCOLUMNS_FROM_STAR = True - QUERY_RESULTS_ARE_STRUCTS = True - JSON_EXTRACT_SCALAR_SCALAR_ONLY = True - LEAST_GREATEST_IGNORES_NULLS = False - DEFAULT_NULL_TYPE = exp.DataType.Type.BIGINT - PRIORITIZE_NON_LITERAL_TYPES = True - - # https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/string_functions#initcap - INITCAP_DEFAULT_DELIMITER_CHARS = ' \t\n\r\f\v\\[\\](){}/|<>!?@"^#$&~_,.:;*%+\\-' - - # https://cloud.google.com/bigquery/docs/reference/standard-sql/lexical#case_sensitivity - NORMALIZATION_STRATEGY = NormalizationStrategy.CASE_INSENSITIVE - - # bigquery udfs are case sensitive - NORMALIZE_FUNCTIONS = False - - # https://cloud.google.com/bigquery/docs/reference/standard-sql/format-elements#format_elements_date_time - TIME_MAPPING = { - "%x": "%m/%d/%y", - "%D": "%m/%d/%y", - "%E6S": "%S.%f", - "%e": "%-d", - "%F": "%Y-%m-%d", - "%T": "%H:%M:%S", - "%c": "%a %b %e %H:%M:%S %Y", - } - - INVERSE_TIME_MAPPING = { - # Preserve %E6S instead of expanding to %T.%f - since both %E6S & %T.%f are semantically different in BigQuery - # %E6S is semantically different from %T.%f: %E6S works as a single atomic specifier for seconds with microseconds, while %T.%f expands incorrectly and fails to parse. - "%H:%M:%S.%f": "%H:%M:%E6S", - } - - FORMAT_MAPPING = { - "DD": "%d", - "MM": "%m", - "MON": "%b", - "MONTH": "%B", - "YYYY": "%Y", - "YY": "%y", - "HH": "%I", - "HH12": "%I", - "HH24": "%H", - "MI": "%M", - "SS": "%S", - "SSSSS": "%f", - "TZH": "%z", - } - - # The _PARTITIONTIME and _PARTITIONDATE pseudo-columns are not returned by a SELECT * statement - # https://cloud.google.com/bigquery/docs/querying-partitioned-tables#query_an_ingestion-time_partitioned_table - # https://cloud.google.com/bigquery/docs/querying-wildcard-tables#scanning_a_range_of_tables_using_table_suffix - # https://cloud.google.com/bigquery/docs/query-cloud-storage-data#query_the_file_name_pseudo-column - PSEUDOCOLUMNS = { - "_PARTITIONTIME", - "_PARTITIONDATE", - "_TABLE_SUFFIX", - "_FILE_NAME", - "_DBT_MAX_PARTITION", - } - - # All set operations require either a DISTINCT or ALL specifier - SET_OP_DISTINCT_BY_DEFAULT = dict.fromkeys( - (exp.Except, exp.Intersect, exp.Union), None - ) - - # https://cloud.google.com/bigquery/docs/reference/standard-sql/navigation_functions#percentile_cont - COERCES_TO = { - **TypeAnnotator.COERCES_TO, - exp.DataType.Type.BIGDECIMAL: {exp.DataType.Type.DOUBLE}, - } - COERCES_TO[exp.DataType.Type.DECIMAL] |= {exp.DataType.Type.BIGDECIMAL} - COERCES_TO[exp.DataType.Type.BIGINT] |= {exp.DataType.Type.BIGDECIMAL} - COERCES_TO[exp.DataType.Type.VARCHAR] |= { - exp.DataType.Type.DATE, - exp.DataType.Type.DATETIME, - exp.DataType.Type.TIME, - exp.DataType.Type.TIMESTAMP, - exp.DataType.Type.TIMESTAMPTZ, - } - - EXPRESSION_METADATA = EXPRESSION_METADATA.copy() - - def normalize_identifier(self, expression: E) -> E: - if ( - isinstance(expression, exp.Identifier) - and self.normalization_strategy is NormalizationStrategy.CASE_INSENSITIVE - ): - parent = expression.parent - while isinstance(parent, exp.Dot): - parent = parent.parent - - # In BigQuery, CTEs are case-insensitive, but UDF and table names are case-sensitive - # by default. The following check uses a heuristic to detect tables based on whether - # they are qualified. This should generally be correct, because tables in BigQuery - # must be qualified with at least a dataset, unless @@dataset_id is set. - case_sensitive = ( - isinstance(parent, exp.UserDefinedFunction) - or ( - isinstance(parent, exp.Table) - and parent.db - and ( - parent.meta.get("quoted_table") - or not parent.meta.get("maybe_column") - ) - ) - or expression.meta.get("is_table") - ) - if not case_sensitive: - expression.set("this", expression.this.lower()) - - return t.cast(E, expression) - - return super().normalize_identifier(expression) - - class JSONPathTokenizer(jsonpath.JSONPathTokenizer): - VAR_TOKENS = { - TokenType.DASH, - TokenType.VAR, - } - - class Tokenizer(tokens.Tokenizer): - QUOTES = ["'", '"', '"""', "'''"] - COMMENTS = ["--", "#", ("/*", "*/")] - IDENTIFIERS = ["`"] - STRING_ESCAPES = ["\\"] - - HEX_STRINGS = [("0x", ""), ("0X", "")] - - BYTE_STRINGS = [ - (prefix + q, q) - for q in t.cast(t.List[str], QUOTES) - for prefix in ("b", "B") - ] - - RAW_STRINGS = [ - (prefix + q, q) - for q in t.cast(t.List[str], QUOTES) - for prefix in ("r", "R") - ] - - NESTED_COMMENTS = False - - KEYWORDS = { - **tokens.Tokenizer.KEYWORDS, - "ANY TYPE": TokenType.VARIANT, - "BEGIN": TokenType.COMMAND, - "BEGIN TRANSACTION": TokenType.BEGIN, - "BYTEINT": TokenType.INT, - "BYTES": TokenType.BINARY, - "CURRENT_DATETIME": TokenType.CURRENT_DATETIME, - "DATETIME": TokenType.TIMESTAMP, - "DECLARE": TokenType.DECLARE, - "ELSEIF": TokenType.COMMAND, - "EXCEPTION": TokenType.COMMAND, - "EXPORT": TokenType.EXPORT, - "FLOAT64": TokenType.DOUBLE, - "FOR SYSTEM_TIME": TokenType.TIMESTAMP_SNAPSHOT, - "LOOP": TokenType.COMMAND, - "MODEL": TokenType.MODEL, - "NOT DETERMINISTIC": TokenType.VOLATILE, - "RECORD": TokenType.STRUCT, - "REPEAT": TokenType.COMMAND, - "TIMESTAMP": TokenType.TIMESTAMPTZ, - "WHILE": TokenType.COMMAND, - } - KEYWORDS.pop("DIV") - KEYWORDS.pop("VALUES") - KEYWORDS.pop("/*+") - - class Parser(parser.Parser): - PREFIXED_PIVOT_COLUMNS = True - LOG_DEFAULTS_TO_LN = True - SUPPORTS_IMPLICIT_UNNEST = True - JOINS_HAVE_EQUAL_PRECEDENCE = True - - # BigQuery does not allow ASC/DESC to be used as an identifier, allows GRANT as an identifier - ID_VAR_TOKENS = { - *parser.Parser.ID_VAR_TOKENS, - TokenType.GRANT, - } - {TokenType.ASC, TokenType.DESC} - - ALIAS_TOKENS = { - *parser.Parser.ALIAS_TOKENS, - TokenType.GRANT, - } - {TokenType.ASC, TokenType.DESC} - - TABLE_ALIAS_TOKENS = { - *parser.Parser.TABLE_ALIAS_TOKENS, - TokenType.GRANT, - } - {TokenType.ASC, TokenType.DESC} - - COMMENT_TABLE_ALIAS_TOKENS = { - *parser.Parser.COMMENT_TABLE_ALIAS_TOKENS, - TokenType.GRANT, - } - {TokenType.ASC, TokenType.DESC} - - UPDATE_ALIAS_TOKENS = { - *parser.Parser.UPDATE_ALIAS_TOKENS, - TokenType.GRANT, - } - {TokenType.ASC, TokenType.DESC} - - FUNCTIONS = { - **parser.Parser.FUNCTIONS, - "APPROX_TOP_COUNT": exp.ApproxTopK.from_arg_list, - "BIT_AND": exp.BitwiseAndAgg.from_arg_list, - "BIT_OR": exp.BitwiseOrAgg.from_arg_list, - "BIT_XOR": exp.BitwiseXorAgg.from_arg_list, - "BIT_COUNT": exp.BitwiseCount.from_arg_list, - "BOOL": exp.JSONBool.from_arg_list, - "CONTAINS_SUBSTR": _build_contains_substring, - "DATE": _build_date, - "DATE_ADD": build_date_delta_with_interval(exp.DateAdd), - "DATE_DIFF": build_date_diff, - "DATE_SUB": build_date_delta_with_interval(exp.DateSub), - "DATE_TRUNC": lambda args: exp.DateTrunc( - unit=seq_get(args, 1), - this=seq_get(args, 0), - zone=seq_get(args, 2), - ), - "DATETIME": _build_datetime, - "DATETIME_ADD": build_date_delta_with_interval(exp.DatetimeAdd), - "DATETIME_SUB": build_date_delta_with_interval(exp.DatetimeSub), - "DIV": binary_from_function(exp.IntDiv), - "EDIT_DISTANCE": _build_levenshtein, - "FORMAT_DATE": _build_format_time(exp.TsOrDsToDate), - "GENERATE_ARRAY": exp.GenerateSeries.from_arg_list, - "JSON_EXTRACT_SCALAR": _build_extract_json_with_default_path( - exp.JSONExtractScalar - ), - "JSON_EXTRACT_ARRAY": _build_extract_json_with_default_path( - exp.JSONExtractArray - ), - "JSON_EXTRACT_STRING_ARRAY": _build_extract_json_with_default_path( - exp.JSONValueArray - ), - "JSON_KEYS": exp.JSONKeysAtDepth.from_arg_list, - "JSON_QUERY": parser.build_extract_json_with_path(exp.JSONExtract), - "JSON_QUERY_ARRAY": _build_extract_json_with_default_path( - exp.JSONExtractArray - ), - "JSON_STRIP_NULLS": _build_json_strip_nulls, - "JSON_VALUE": _build_extract_json_with_default_path(exp.JSONExtractScalar), - "JSON_VALUE_ARRAY": _build_extract_json_with_default_path( - exp.JSONValueArray - ), - "LENGTH": lambda args: exp.Length(this=seq_get(args, 0), binary=True), - "MD5": exp.MD5Digest.from_arg_list, - "SHA1": exp.SHA1Digest.from_arg_list, - "NORMALIZE_AND_CASEFOLD": lambda args: exp.Normalize( - this=seq_get(args, 0), form=seq_get(args, 1), is_casefold=True - ), - "OCTET_LENGTH": exp.ByteLength.from_arg_list, - "TO_HEX": _build_to_hex, - "PARSE_DATE": lambda args: build_formatted_time(exp.StrToDate, "bigquery")( - [seq_get(args, 1), seq_get(args, 0)] - ), - "PARSE_TIME": lambda args: build_formatted_time(exp.ParseTime, "bigquery")( - [seq_get(args, 1), seq_get(args, 0)] - ), - "PARSE_TIMESTAMP": _build_parse_timestamp, - "PARSE_DATETIME": lambda args: build_formatted_time( - exp.ParseDatetime, "bigquery" - )([seq_get(args, 1), seq_get(args, 0)]), - "REGEXP_CONTAINS": exp.RegexpLike.from_arg_list, - "REGEXP_EXTRACT": _build_regexp_extract(exp.RegexpExtract), - "REGEXP_SUBSTR": _build_regexp_extract(exp.RegexpExtract), - "REGEXP_EXTRACT_ALL": _build_regexp_extract( - exp.RegexpExtractAll, default_group=exp.Literal.number(0) - ), - "SHA256": lambda args: exp.SHA2Digest( - this=seq_get(args, 0), length=exp.Literal.number(256) - ), - "SHA512": lambda args: exp.SHA2( - this=seq_get(args, 0), length=exp.Literal.number(512) - ), - "SPLIT": lambda args: exp.Split( - # https://cloud.google.com/bigquery/docs/reference/standard-sql/string_functions#split - this=seq_get(args, 0), - expression=seq_get(args, 1) or exp.Literal.string(","), - ), - "STRPOS": exp.StrPosition.from_arg_list, - "TIME": _build_time, - "TIME_ADD": build_date_delta_with_interval(exp.TimeAdd), - "TIME_SUB": build_date_delta_with_interval(exp.TimeSub), - "TIMESTAMP": _build_timestamp, - "TIMESTAMP_ADD": build_date_delta_with_interval(exp.TimestampAdd), - "TIMESTAMP_SUB": build_date_delta_with_interval(exp.TimestampSub), - "TIMESTAMP_MICROS": lambda args: exp.UnixToTime( - this=seq_get(args, 0), scale=exp.UnixToTime.MICROS - ), - "TIMESTAMP_MILLIS": lambda args: exp.UnixToTime( - this=seq_get(args, 0), scale=exp.UnixToTime.MILLIS - ), - "TIMESTAMP_SECONDS": lambda args: exp.UnixToTime(this=seq_get(args, 0)), - "TO_JSON": lambda args: exp.JSONFormat( - this=seq_get(args, 0), options=seq_get(args, 1), to_json=True - ), - "TO_JSON_STRING": exp.JSONFormat.from_arg_list, - "FORMAT_DATETIME": _build_format_time(exp.TsOrDsToDatetime), - "FORMAT_TIMESTAMP": _build_format_time(exp.TsOrDsToTimestamp), - "FORMAT_TIME": _build_format_time(exp.TsOrDsToTime), - "FROM_HEX": exp.Unhex.from_arg_list, - "WEEK": lambda args: exp.WeekStart(this=exp.var(seq_get(args, 0))), - } - # Remove SEARCH to avoid parameter routing issues - let it fall back to Anonymous function - FUNCTIONS.pop("SEARCH") - - FUNCTION_PARSERS = { - **parser.Parser.FUNCTION_PARSERS, - "ARRAY": lambda self: self.expression( - exp.Array, - expressions=[self._parse_statement()], - struct_name_inheritance=True, - ), - "JSON_ARRAY": lambda self: self.expression( - exp.JSONArray, expressions=self._parse_csv(self._parse_bitwise) - ), - "MAKE_INTERVAL": lambda self: self._parse_make_interval(), - "PREDICT": lambda self: self._parse_ml(exp.Predict), - "TRANSLATE": lambda self: self._parse_translate(), - "FEATURES_AT_TIME": lambda self: self._parse_features_at_time(), - "GENERATE_EMBEDDING": lambda self: self._parse_ml(exp.GenerateEmbedding), - "GENERATE_TEXT_EMBEDDING": lambda self: self._parse_ml( - exp.GenerateEmbedding, is_text=True - ), - "VECTOR_SEARCH": lambda self: self._parse_vector_search(), - "FORECAST": lambda self: self._parse_ml(exp.MLForecast), - } - FUNCTION_PARSERS.pop("TRIM") - - NO_PAREN_FUNCTIONS = { - **parser.Parser.NO_PAREN_FUNCTIONS, - TokenType.CURRENT_DATETIME: exp.CurrentDatetime, - } - - NESTED_TYPE_TOKENS = { - *parser.Parser.NESTED_TYPE_TOKENS, - TokenType.TABLE, - } - - PROPERTY_PARSERS = { - **parser.Parser.PROPERTY_PARSERS, - "NOT DETERMINISTIC": lambda self: self.expression( - exp.StabilityProperty, this=exp.Literal.string("VOLATILE") - ), - "OPTIONS": lambda self: self._parse_with_property(), - } - - CONSTRAINT_PARSERS = { - **parser.Parser.CONSTRAINT_PARSERS, - "OPTIONS": lambda self: exp.Properties( - expressions=self._parse_with_property() - ), - } - - RANGE_PARSERS = parser.Parser.RANGE_PARSERS.copy() - RANGE_PARSERS.pop(TokenType.OVERLAPS) - - DASHED_TABLE_PART_FOLLOW_TOKENS = { - TokenType.DOT, - TokenType.L_PAREN, - TokenType.R_PAREN, - } - - STATEMENT_PARSERS = { - **parser.Parser.STATEMENT_PARSERS, - TokenType.ELSE: lambda self: self._parse_as_command(self._prev), - TokenType.END: lambda self: self._parse_as_command(self._prev), - TokenType.FOR: lambda self: self._parse_for_in(), - TokenType.EXPORT: lambda self: self._parse_export_data(), - TokenType.DECLARE: lambda self: self._parse_declare(), - } - - BRACKET_OFFSETS = { - "OFFSET": (0, False), - "ORDINAL": (1, False), - "SAFE_OFFSET": (0, True), - "SAFE_ORDINAL": (1, True), - } - - def _parse_for_in(self) -> t.Union[exp.ForIn, exp.Command]: - index = self._index - this = self._parse_range() - self._match_text_seq("DO") - if self._match(TokenType.COMMAND): - self._retreat(index) - return self._parse_as_command(self._prev) - return self.expression( - exp.ForIn, this=this, expression=self._parse_statement() - ) - - def _parse_table_part(self, schema: bool = False) -> t.Optional[exp.Expression]: - this = super()._parse_table_part(schema=schema) or self._parse_number() - - # https://cloud.google.com/bigquery/docs/reference/standard-sql/lexical#table_names - if isinstance(this, exp.Identifier): - table_name = this.name - while self._match(TokenType.DASH, advance=False) and self._next: - start = self._curr - while self._is_connected() and not self._match_set( - self.DASHED_TABLE_PART_FOLLOW_TOKENS, advance=False - ): - self._advance() - - if start == self._curr: - break - - table_name += self._find_sql(start, self._prev) - - this = exp.Identifier( - this=table_name, quoted=this.args.get("quoted") - ).update_positions(this) - elif isinstance(this, exp.Literal): - table_name = this.name - - if self._is_connected() and self._parse_var(any_token=True): - table_name += self._prev.text - - this = exp.Identifier(this=table_name, quoted=True).update_positions( - this - ) - - return this - - def _parse_table_parts( - self, - schema: bool = False, - is_db_reference: bool = False, - wildcard: bool = False, - ) -> exp.Table: - table = super()._parse_table_parts( - schema=schema, is_db_reference=is_db_reference, wildcard=True - ) - - # proj-1.db.tbl -- `1.` is tokenized as a float so we need to unravel it here - if not table.catalog: - if table.db: - previous_db = table.args["db"] - parts = table.db.split(".") - if len(parts) == 2 and not table.args["db"].quoted: - table.set( - "catalog", - exp.Identifier(this=parts[0]).update_positions(previous_db), - ) - table.set( - "db", - exp.Identifier(this=parts[1]).update_positions(previous_db), - ) - else: - previous_this = table.this - parts = table.name.split(".") - if len(parts) == 2 and not table.this.quoted: - table.set( - "db", - exp.Identifier(this=parts[0]).update_positions( - previous_this - ), - ) - table.set( - "this", - exp.Identifier(this=parts[1]).update_positions( - previous_this - ), - ) - - if isinstance(table.this, exp.Identifier) and any( - "." in p.name for p in table.parts - ): - alias = table.this - catalog, db, this, *rest = ( - exp.to_identifier(p, quoted=True) - for p in split_num_words( - ".".join(p.name for p in table.parts), ".", 3 - ) - ) - - for part in (catalog, db, this): - if part: - part.update_positions(table.this) - - if rest and this: - this = exp.Dot.build([this, *rest]) # type: ignore - - table = exp.Table( - this=this, db=db, catalog=catalog, pivots=table.args.get("pivots") - ) - table.meta["quoted_table"] = True - else: - alias = None - - # The `INFORMATION_SCHEMA` views in BigQuery need to be qualified by a region or - # dataset, so if the project identifier is omitted we need to fix the ast so that - # the `INFORMATION_SCHEMA.X` bit is represented as a single (quoted) Identifier. - # Otherwise, we wouldn't correctly qualify a `Table` node that references these - # views, because it would seem like the "catalog" part is set, when it'd actually - # be the region/dataset. Merging the two identifiers into a single one is done to - # avoid producing a 4-part Table reference, which would cause issues in the schema - # module, when there are 3-part table names mixed with information schema views. - # - # See: https://cloud.google.com/bigquery/docs/information-schema-intro#syntax - table_parts = table.parts - if ( - len(table_parts) > 1 - and table_parts[-2].name.upper() == "INFORMATION_SCHEMA" - ): - # We need to alias the table here to avoid breaking existing qualified columns. - # This is expected to be safe, because if there's an actual alias coming up in - # the token stream, it will overwrite this one. If there isn't one, we are only - # exposing the name that can be used to reference the view explicitly (a no-op). - exp.alias_( - table, - t.cast(exp.Identifier, alias or table_parts[-1]), - table=True, - copy=False, - ) - - info_schema_view = f"{table_parts[-2].name}.{table_parts[-1].name}" - new_this = exp.Identifier( - this=info_schema_view, quoted=True - ).update_positions( - line=table_parts[-2].meta.get("line"), - col=table_parts[-1].meta.get("col"), - start=table_parts[-2].meta.get("start"), - end=table_parts[-1].meta.get("end"), - ) - table.set("this", new_this) - table.set("db", seq_get(table_parts, -3)) - table.set("catalog", seq_get(table_parts, -4)) - - return table - - def _parse_column(self) -> t.Optional[exp.Expression]: - column = super()._parse_column() - if isinstance(column, exp.Column): - parts = column.parts - if any("." in p.name for p in parts): - catalog, db, table, this, *rest = ( - exp.to_identifier(p, quoted=True) - for p in split_num_words( - ".".join(p.name for p in parts), ".", 4 - ) - ) - - if rest and this: - this = exp.Dot.build([this, *rest]) # type: ignore - - column = exp.Column(this=this, table=table, db=db, catalog=catalog) - column.meta["quoted_column"] = True - - return column - - @t.overload - def _parse_json_object(self, agg: Lit[False]) -> exp.JSONObject: ... - - @t.overload - def _parse_json_object(self, agg: Lit[True]) -> exp.JSONObjectAgg: ... - - def _parse_json_object(self, agg=False): - json_object = super()._parse_json_object() - array_kv_pair = seq_get(json_object.expressions, 0) - - # Converts BQ's "signature 2" of JSON_OBJECT into SQLGlot's canonical representation - # https://cloud.google.com/bigquery/docs/reference/standard-sql/json_functions#json_object_signature2 - if ( - array_kv_pair - and isinstance(array_kv_pair.this, exp.Array) - and isinstance(array_kv_pair.expression, exp.Array) - ): - keys = array_kv_pair.this.expressions - values = array_kv_pair.expression.expressions - - json_object.set( - "expressions", - [ - exp.JSONKeyValue(this=k, expression=v) - for k, v in zip(keys, values) - ], - ) - - return json_object - - def _parse_bracket( - self, this: t.Optional[exp.Expression] = None - ) -> t.Optional[exp.Expression]: - bracket = super()._parse_bracket(this) - - if isinstance(bracket, exp.Array): - bracket.set("struct_name_inheritance", True) - - if this is bracket: - return bracket - - if isinstance(bracket, exp.Bracket): - for expression in bracket.expressions: - name = expression.name.upper() - - if name not in self.BRACKET_OFFSETS: - break - - offset, safe = self.BRACKET_OFFSETS[name] - bracket.set("offset", offset) - bracket.set("safe", safe) - expression.replace(expression.expressions[0]) - - return bracket - - def _parse_unnest(self, with_alias: bool = True) -> t.Optional[exp.Unnest]: - unnest = super()._parse_unnest(with_alias=with_alias) - - if not unnest: - return None - - unnest_expr = seq_get(unnest.expressions, 0) - if unnest_expr: - from bigframes_vendored.sqlglot.optimizer.annotate_types import ( - annotate_types, - ) - - unnest_expr = annotate_types(unnest_expr, dialect=self.dialect) - - # Unnesting a nested array (i.e array of structs) explodes the top-level struct fields, - # in contrast to other dialects such as DuckDB which flattens only the array by default - if unnest_expr.is_type(exp.DataType.Type.ARRAY) and any( - array_elem.is_type(exp.DataType.Type.STRUCT) - for array_elem in unnest_expr._type.expressions - ): - unnest.set("explode_array", True) - - return unnest - - def _parse_make_interval(self) -> exp.MakeInterval: - expr = exp.MakeInterval() - - for arg_key in MAKE_INTERVAL_KWARGS: - value = self._parse_lambda() - - if not value: - break - - # Non-named arguments are filled sequentially, (optionally) followed by named arguments - # that can appear in any order e.g MAKE_INTERVAL(1, minute => 5, day => 2) - if isinstance(value, exp.Kwarg): - arg_key = value.this.name - - expr.set(arg_key, value) - - self._match(TokenType.COMMA) - - return expr - - def _parse_ml(self, expr_type: t.Type[E], **kwargs) -> E: - self._match_text_seq("MODEL") - this = self._parse_table() - - self._match(TokenType.COMMA) - self._match_text_seq("TABLE") - - # Certain functions like ML.FORECAST require a STRUCT argument but not a TABLE/SELECT one - expression = ( - self._parse_table() - if not self._match(TokenType.STRUCT, advance=False) - else None - ) - - self._match(TokenType.COMMA) - - return self.expression( - expr_type, - this=this, - expression=expression, - params_struct=self._parse_bitwise(), - **kwargs, - ) - - def _parse_translate(self) -> exp.Translate | exp.MLTranslate: - # Check if this is ML.TRANSLATE by looking at previous tokens - token = seq_get(self._tokens, self._index - 4) - if token and token.text.upper() == "ML": - return self._parse_ml(exp.MLTranslate) - - return exp.Translate.from_arg_list(self._parse_function_args()) - - def _parse_features_at_time(self) -> exp.FeaturesAtTime: - self._match(TokenType.TABLE) - this = self._parse_table() - - expr = self.expression(exp.FeaturesAtTime, this=this) - - while self._match(TokenType.COMMA): - arg = self._parse_lambda() - - # Get the LHS of the Kwarg and set the arg to that value, e.g - # "num_rows => 1" sets the expr's `num_rows` arg - if arg: - expr.set(arg.this.name, arg) - - return expr - - def _parse_vector_search(self) -> exp.VectorSearch: - self._match(TokenType.TABLE) - base_table = self._parse_table() - - self._match(TokenType.COMMA) - - column_to_search = self._parse_bitwise() - self._match(TokenType.COMMA) - - self._match(TokenType.TABLE) - query_table = self._parse_table() - - expr = self.expression( - exp.VectorSearch, - this=base_table, - column_to_search=column_to_search, - query_table=query_table, - ) - - while self._match(TokenType.COMMA): - # query_column_to_search can be named argument or positional - if self._match(TokenType.STRING, advance=False): - query_column = self._parse_string() - expr.set("query_column_to_search", query_column) - else: - arg = self._parse_lambda() - if arg: - expr.set(arg.this.name, arg) - - return expr - - def _parse_export_data(self) -> exp.Export: - self._match_text_seq("DATA") - - return self.expression( - exp.Export, - connection=self._match_text_seq("WITH", "CONNECTION") - and self._parse_table_parts(), - options=self._parse_properties(), - this=self._match_text_seq("AS") and self._parse_select(), - ) - - def _parse_column_ops( - self, this: t.Optional[exp.Expression] - ) -> t.Optional[exp.Expression]: - this = super()._parse_column_ops(this) - - if isinstance(this, exp.Dot): - prefix_name = this.this.name.upper() - func_name = this.name.upper() - if prefix_name == "NET": - if func_name == "HOST": - this = self.expression( - exp.NetHost, this=seq_get(this.expression.expressions, 0) - ) - elif prefix_name == "SAFE": - if func_name == "TIMESTAMP": - this = _build_timestamp(this.expression.expressions) - this.set("safe", True) - - return this - - class Generator(generator.Generator): - INTERVAL_ALLOWS_PLURAL_FORM = False - JOIN_HINTS = False - QUERY_HINTS = False - TABLE_HINTS = False - LIMIT_FETCH = "LIMIT" - RENAME_TABLE_WITH_DB = False - NVL2_SUPPORTED = False - UNNEST_WITH_ORDINALITY = False - COLLATE_IS_FUNC = True - LIMIT_ONLY_LITERALS = True - SUPPORTS_TABLE_ALIAS_COLUMNS = False - UNPIVOT_ALIASES_ARE_IDENTIFIERS = False - JSON_KEY_VALUE_PAIR_SEP = "," - NULL_ORDERING_SUPPORTED = False - IGNORE_NULLS_IN_FUNC = True - JSON_PATH_SINGLE_QUOTE_ESCAPE = True - CAN_IMPLEMENT_ARRAY_ANY = True - SUPPORTS_TO_NUMBER = False - NAMED_PLACEHOLDER_TOKEN = "@" - HEX_FUNC = "TO_HEX" - WITH_PROPERTIES_PREFIX = "OPTIONS" - SUPPORTS_EXPLODING_PROJECTIONS = False - EXCEPT_INTERSECT_SUPPORT_ALL_CLAUSE = False - SUPPORTS_UNIX_SECONDS = True - - SAFE_JSON_PATH_KEY_RE = re.compile(r"^[_\-a-zA-Z][\-\w]*$") - - TS_OR_DS_TYPES = ( - exp.TsOrDsToDatetime, - exp.TsOrDsToTimestamp, - exp.TsOrDsToTime, - exp.TsOrDsToDate, - ) - - TRANSFORMS = { - **generator.Generator.TRANSFORMS, - exp.ApproxTopK: rename_func("APPROX_TOP_COUNT"), - exp.ApproxDistinct: rename_func("APPROX_COUNT_DISTINCT"), - exp.ArgMax: arg_max_or_min_no_count("MAX_BY"), - exp.ArgMin: arg_max_or_min_no_count("MIN_BY"), - exp.Array: inline_array_unless_query, - exp.ArrayContains: _array_contains_sql, - exp.ArrayFilter: filter_array_using_unnest, - exp.ArrayRemove: filter_array_using_unnest, - exp.BitwiseAndAgg: rename_func("BIT_AND"), - exp.BitwiseOrAgg: rename_func("BIT_OR"), - exp.BitwiseXorAgg: rename_func("BIT_XOR"), - exp.BitwiseCount: rename_func("BIT_COUNT"), - exp.ByteLength: rename_func("BYTE_LENGTH"), - exp.Cast: transforms.preprocess( - [transforms.remove_precision_parameterized_types] - ), - exp.CollateProperty: lambda self, e: ( - f"DEFAULT COLLATE {self.sql(e, 'this')}" - if e.args.get("default") - else f"COLLATE {self.sql(e, 'this')}" - ), - exp.Commit: lambda *_: "COMMIT TRANSACTION", - exp.CountIf: rename_func("COUNTIF"), - exp.Create: _create_sql, - exp.CTE: transforms.preprocess([_pushdown_cte_column_names]), - exp.DateAdd: date_add_interval_sql("DATE", "ADD"), - exp.DateDiff: lambda self, e: self.func( - "DATE_DIFF", e.this, e.expression, unit_to_var(e) - ), - exp.DateFromParts: rename_func("DATE"), - exp.DateStrToDate: datestrtodate_sql, - exp.DateSub: date_add_interval_sql("DATE", "SUB"), - exp.DatetimeAdd: date_add_interval_sql("DATETIME", "ADD"), - exp.DatetimeSub: date_add_interval_sql("DATETIME", "SUB"), - exp.DateFromUnixDate: rename_func("DATE_FROM_UNIX_DATE"), - exp.FromTimeZone: lambda self, e: self.func( - "DATETIME", self.func("TIMESTAMP", e.this, e.args.get("zone")), "'UTC'" - ), - exp.GenerateSeries: rename_func("GENERATE_ARRAY"), - exp.GroupConcat: lambda self, e: groupconcat_sql( - self, e, func_name="STRING_AGG", within_group=False, sep=None - ), - exp.Hex: lambda self, e: self.func( - "UPPER", self.func("TO_HEX", self.sql(e, "this")) - ), - exp.HexString: lambda self, e: self.hexstring_sql( - e, binary_function_repr="FROM_HEX" - ), - exp.If: if_sql(false_value="NULL"), - exp.ILike: no_ilike_sql, - exp.IntDiv: rename_func("DIV"), - exp.Int64: rename_func("INT64"), - exp.JSONBool: rename_func("BOOL"), - exp.JSONExtract: _json_extract_sql, - exp.JSONExtractArray: _json_extract_sql, - exp.JSONExtractScalar: _json_extract_sql, - exp.JSONFormat: lambda self, e: self.func( - "TO_JSON" if e.args.get("to_json") else "TO_JSON_STRING", - e.this, - e.args.get("options"), - ), - exp.JSONKeysAtDepth: rename_func("JSON_KEYS"), - exp.JSONValueArray: rename_func("JSON_VALUE_ARRAY"), - exp.Levenshtein: _levenshtein_sql, - exp.Max: max_or_greatest, - exp.MD5: lambda self, e: self.func("TO_HEX", self.func("MD5", e.this)), - exp.MD5Digest: rename_func("MD5"), - exp.Min: min_or_least, - exp.Normalize: lambda self, e: self.func( - "NORMALIZE_AND_CASEFOLD" if e.args.get("is_casefold") else "NORMALIZE", - e.this, - e.args.get("form"), - ), - exp.PartitionedByProperty: lambda self, - e: f"PARTITION BY {self.sql(e, 'this')}", - exp.RegexpExtract: lambda self, e: self.func( - "REGEXP_EXTRACT", - e.this, - e.expression, - e.args.get("position"), - e.args.get("occurrence"), - ), - exp.RegexpExtractAll: lambda self, e: self.func( - "REGEXP_EXTRACT_ALL", e.this, e.expression - ), - exp.RegexpReplace: regexp_replace_sql, - exp.RegexpLike: rename_func("REGEXP_CONTAINS"), - exp.ReturnsProperty: _returnsproperty_sql, - exp.Rollback: lambda *_: "ROLLBACK TRANSACTION", - exp.ParseTime: lambda self, e: self.func( - "PARSE_TIME", self.format_time(e), e.this - ), - exp.ParseDatetime: lambda self, e: self.func( - "PARSE_DATETIME", self.format_time(e), e.this - ), - exp.Select: transforms.preprocess( - [ - transforms.explode_projection_to_unnest(), - transforms.unqualify_unnest, - transforms.eliminate_distinct_on, - _alias_ordered_group, - transforms.eliminate_semi_and_anti_joins, - ] - ), - exp.SHA: rename_func("SHA1"), - exp.SHA2: sha256_sql, - exp.SHA1Digest: rename_func("SHA1"), - exp.SHA2Digest: sha2_digest_sql, - exp.StabilityProperty: lambda self, e: ( - "DETERMINISTIC" if e.name == "IMMUTABLE" else "NOT DETERMINISTIC" - ), - exp.String: rename_func("STRING"), - exp.StrPosition: lambda self, e: ( - strposition_sql( - self, - e, - func_name="INSTR", - supports_position=True, - supports_occurrence=True, - ) - ), - exp.StrToDate: _str_to_datetime_sql, - exp.StrToTime: _str_to_datetime_sql, - exp.SessionUser: lambda *_: "SESSION_USER()", - exp.TimeAdd: date_add_interval_sql("TIME", "ADD"), - exp.TimeFromParts: rename_func("TIME"), - exp.TimestampFromParts: rename_func("DATETIME"), - exp.TimeSub: date_add_interval_sql("TIME", "SUB"), - exp.TimestampAdd: date_add_interval_sql("TIMESTAMP", "ADD"), - exp.TimestampDiff: rename_func("TIMESTAMP_DIFF"), - exp.TimestampSub: date_add_interval_sql("TIMESTAMP", "SUB"), - exp.TimeStrToTime: timestrtotime_sql, - exp.Transaction: lambda *_: "BEGIN TRANSACTION", - exp.TsOrDsAdd: _ts_or_ds_add_sql, - exp.TsOrDsDiff: _ts_or_ds_diff_sql, - exp.TsOrDsToTime: rename_func("TIME"), - exp.TsOrDsToDatetime: rename_func("DATETIME"), - exp.TsOrDsToTimestamp: rename_func("TIMESTAMP"), - exp.Unhex: rename_func("FROM_HEX"), - exp.UnixDate: rename_func("UNIX_DATE"), - exp.UnixToTime: _unix_to_time_sql, - exp.Uuid: lambda *_: "GENERATE_UUID()", - exp.Values: _derived_table_values_to_unnest, - exp.VariancePop: rename_func("VAR_POP"), - exp.SafeDivide: rename_func("SAFE_DIVIDE"), - } - - SUPPORTED_JSON_PATH_PARTS = { - exp.JSONPathKey, - exp.JSONPathRoot, - exp.JSONPathSubscript, - } - - TYPE_MAPPING = { - **generator.Generator.TYPE_MAPPING, - exp.DataType.Type.BIGDECIMAL: "BIGNUMERIC", - exp.DataType.Type.BIGINT: "INT64", - exp.DataType.Type.BINARY: "BYTES", - exp.DataType.Type.BLOB: "BYTES", - exp.DataType.Type.BOOLEAN: "BOOL", - exp.DataType.Type.CHAR: "STRING", - exp.DataType.Type.DECIMAL: "NUMERIC", - exp.DataType.Type.DOUBLE: "FLOAT64", - exp.DataType.Type.FLOAT: "FLOAT64", - exp.DataType.Type.INT: "INT64", - exp.DataType.Type.NCHAR: "STRING", - exp.DataType.Type.NVARCHAR: "STRING", - exp.DataType.Type.SMALLINT: "INT64", - exp.DataType.Type.TEXT: "STRING", - exp.DataType.Type.TIMESTAMP: "DATETIME", - exp.DataType.Type.TIMESTAMPNTZ: "DATETIME", - exp.DataType.Type.TIMESTAMPTZ: "TIMESTAMP", - exp.DataType.Type.TIMESTAMPLTZ: "TIMESTAMP", - exp.DataType.Type.TINYINT: "INT64", - exp.DataType.Type.ROWVERSION: "BYTES", - exp.DataType.Type.UUID: "STRING", - exp.DataType.Type.VARBINARY: "BYTES", - exp.DataType.Type.VARCHAR: "STRING", - exp.DataType.Type.VARIANT: "ANY TYPE", - } - - PROPERTIES_LOCATION = { - **generator.Generator.PROPERTIES_LOCATION, - exp.PartitionedByProperty: exp.Properties.Location.POST_SCHEMA, - exp.VolatileProperty: exp.Properties.Location.UNSUPPORTED, - } - - # WINDOW comes after QUALIFY - # https://cloud.google.com/bigquery/docs/reference/standard-sql/query-syntax#window_clause - AFTER_HAVING_MODIFIER_TRANSFORMS = { - "qualify": generator.Generator.AFTER_HAVING_MODIFIER_TRANSFORMS["qualify"], - "windows": generator.Generator.AFTER_HAVING_MODIFIER_TRANSFORMS["windows"], - } - - # from: https://cloud.google.com/bigquery/docs/reference/standard-sql/lexical#reserved_keywords - RESERVED_KEYWORDS = { - "all", - "and", - "any", - "array", - "as", - "asc", - "assert_rows_modified", - "at", - "between", - "by", - "case", - "cast", - "collate", - "contains", - "create", - "cross", - "cube", - "current", - "default", - "define", - "desc", - "distinct", - "else", - "end", - "enum", - "escape", - "except", - "exclude", - "exists", - "extract", - "false", - "fetch", - "following", - "for", - "from", - "full", - "group", - "grouping", - "groups", - "hash", - "having", - "if", - "ignore", - "in", - "inner", - "intersect", - "interval", - "into", - "is", - "join", - "lateral", - "left", - "like", - "limit", - "lookup", - "merge", - "natural", - "new", - "no", - "not", - "null", - "nulls", - "of", - "on", - "or", - "order", - "outer", - "over", - "partition", - "preceding", - "proto", - "qualify", - "range", - "recursive", - "respect", - "right", - "rollup", - "rows", - "select", - "set", - "some", - "struct", - "tablesample", - "then", - "to", - "treat", - "true", - "unbounded", - "union", - "unnest", - "using", - "when", - "where", - "window", - "with", - "within", - } - - def datetrunc_sql(self, expression: exp.DateTrunc) -> str: - unit = expression.unit - unit_sql = unit.name if unit.is_string else self.sql(unit) - return self.func( - "DATE_TRUNC", expression.this, unit_sql, expression.args.get("zone") - ) - - def mod_sql(self, expression: exp.Mod) -> str: - this = expression.this - expr = expression.expression - return self.func( - "MOD", - this.unnest() if isinstance(this, exp.Paren) else this, - expr.unnest() if isinstance(expr, exp.Paren) else expr, - ) - - def column_parts(self, expression: exp.Column) -> str: - if expression.meta.get("quoted_column"): - # If a column reference is of the form `dataset.table`.name, we need - # to preserve the quoted table path, otherwise the reference breaks - table_parts = ".".join(p.name for p in expression.parts[:-1]) - table_path = self.sql(exp.Identifier(this=table_parts, quoted=True)) - return f"{table_path}.{self.sql(expression, 'this')}" - - return super().column_parts(expression) - - def table_parts(self, expression: exp.Table) -> str: - # Depending on the context, `x.y` may not resolve to the same data source as `x`.`y`, so - # we need to make sure the correct quoting is used in each case. - # - # For example, if there is a CTE x that clashes with a schema name, then the former will - # return the table y in that schema, whereas the latter will return the CTE's y column: - # - # - WITH x AS (SELECT [1, 2] AS y) SELECT * FROM x, `x.y` -> cross join - # - WITH x AS (SELECT [1, 2] AS y) SELECT * FROM x, `x`.`y` -> implicit unnest - if expression.meta.get("quoted_table"): - table_parts = ".".join(p.name for p in expression.parts) - return self.sql(exp.Identifier(this=table_parts, quoted=True)) - - return super().table_parts(expression) - - def timetostr_sql(self, expression: exp.TimeToStr) -> str: - this = expression.this - if isinstance(this, exp.TsOrDsToDatetime): - func_name = "FORMAT_DATETIME" - elif isinstance(this, exp.TsOrDsToTimestamp): - func_name = "FORMAT_TIMESTAMP" - elif isinstance(this, exp.TsOrDsToTime): - func_name = "FORMAT_TIME" - else: - func_name = "FORMAT_DATE" - - time_expr = this if isinstance(this, self.TS_OR_DS_TYPES) else expression - return self.func( - func_name, - self.format_time(expression), - time_expr.this, - expression.args.get("zone"), - ) - - def eq_sql(self, expression: exp.EQ) -> str: - # Operands of = cannot be NULL in BigQuery - if isinstance(expression.left, exp.Null) or isinstance( - expression.right, exp.Null - ): - if not isinstance(expression.parent, exp.Update): - return "NULL" - - return self.binary(expression, "=") - - def attimezone_sql(self, expression: exp.AtTimeZone) -> str: - parent = expression.parent - - # BigQuery allows CAST(.. AS {STRING|TIMESTAMP} [FORMAT [AT TIME ZONE ]]). - # Only the TIMESTAMP one should use the below conversion, when AT TIME ZONE is included. - if not isinstance(parent, exp.Cast) or not parent.to.is_type("text"): - return self.func( - "TIMESTAMP", - self.func("DATETIME", expression.this, expression.args.get("zone")), - ) - - return super().attimezone_sql(expression) - - def trycast_sql(self, expression: exp.TryCast) -> str: - return self.cast_sql(expression, safe_prefix="SAFE_") - - def bracket_sql(self, expression: exp.Bracket) -> str: - this = expression.this - expressions = expression.expressions - - if ( - len(expressions) == 1 - and this - and this.is_type(exp.DataType.Type.STRUCT) - ): - arg = expressions[0] - if arg.type is None: - from bigframes_vendored.sqlglot.optimizer.annotate_types import ( - annotate_types, - ) - - arg = annotate_types(arg, dialect=self.dialect) - - if arg.type and arg.type.this in exp.DataType.TEXT_TYPES: - # BQ doesn't support bracket syntax with string values for structs - return f"{self.sql(this)}.{arg.name}" - - expressions_sql = self.expressions(expression, flat=True) - offset = expression.args.get("offset") - - if offset == 0: - expressions_sql = f"OFFSET({expressions_sql})" - elif offset == 1: - expressions_sql = f"ORDINAL({expressions_sql})" - elif offset is not None: - self.unsupported(f"Unsupported array offset: {offset}") - - if expression.args.get("safe"): - expressions_sql = f"SAFE_{expressions_sql}" - - return f"{self.sql(this)}[{expressions_sql}]" - - def in_unnest_op(self, expression: exp.Unnest) -> str: - return self.sql(expression) - - def version_sql(self, expression: exp.Version) -> str: - if expression.name == "TIMESTAMP": - expression.set("this", "SYSTEM_TIME") - return super().version_sql(expression) - - def contains_sql(self, expression: exp.Contains) -> str: - this = expression.this - expr = expression.expression - - if isinstance(this, exp.Lower) and isinstance(expr, exp.Lower): - this = this.this - expr = expr.this - - return self.func( - "CONTAINS_SUBSTR", this, expr, expression.args.get("json_scope") - ) - - def cast_sql( - self, expression: exp.Cast, safe_prefix: t.Optional[str] = None - ) -> str: - this = expression.this - - # This ensures that inline type-annotated ARRAY literals like ARRAY[1, 2, 3] - # are roundtripped unaffected. The inner check excludes ARRAY(SELECT ...) expressions, - # because they aren't literals and so the above syntax is invalid BigQuery. - if isinstance(this, exp.Array): - elem = seq_get(this.expressions, 0) - if not (elem and elem.find(exp.Query)): - return f"{self.sql(expression, 'to')}{self.sql(this)}" - - return super().cast_sql(expression, safe_prefix=safe_prefix) - - def declareitem_sql(self, expression: exp.DeclareItem) -> str: - variables = self.expressions(expression, "this") - default = self.sql(expression, "default") - default = f" DEFAULT {default}" if default else "" - kind = self.sql(expression, "kind") - kind = f" {kind}" if kind else "" - - return f"{variables}{kind}{default}" - - def timestamp_sql(self, expression: exp.Timestamp) -> str: - prefix = "SAFE." if expression.args.get("safe") else "" - return self.func( - f"{prefix}TIMESTAMP", expression.this, expression.args.get("zone") - ) diff --git a/third_party/bigframes_vendored/sqlglot/dialects/dialect.py b/third_party/bigframes_vendored/sqlglot/dialects/dialect.py deleted file mode 100644 index 8e26b777abd..00000000000 --- a/third_party/bigframes_vendored/sqlglot/dialects/dialect.py +++ /dev/null @@ -1,2368 +0,0 @@ -# Contains code from https://github.com/tobymao/sqlglot/blob/v28.5.0/sqlglot/dialects/dialect.py - -from __future__ import annotations - -import importlib -import logging -import sys -import typing as t -from enum import Enum, auto -from functools import reduce - -from bigframes_vendored.sqlglot import exp -from bigframes_vendored.sqlglot.dialects import DIALECT_MODULE_NAMES -from bigframes_vendored.sqlglot.errors import ParseError -from bigframes_vendored.sqlglot.generator import Generator, unsupported_args -from bigframes_vendored.sqlglot.helper import ( - AutoName, - flatten, - is_int, - seq_get, - suggest_closest_match_and_fail, - to_bool, -) -from bigframes_vendored.sqlglot.jsonpath import JSONPathTokenizer -from bigframes_vendored.sqlglot.jsonpath import parse as parse_json_path -from bigframes_vendored.sqlglot.parser import Parser -from bigframes_vendored.sqlglot.time import TIMEZONES, format_time, subsecond_precision -from bigframes_vendored.sqlglot.tokens import Token, Tokenizer, TokenType -from bigframes_vendored.sqlglot.trie import new_trie -from bigframes_vendored.sqlglot.typing import EXPRESSION_METADATA - -DATE_ADD_OR_DIFF = t.Union[ - exp.DateAdd, - exp.DateDiff, - exp.DateSub, - exp.TsOrDsAdd, - exp.TsOrDsDiff, -] -DATE_ADD_OR_SUB = t.Union[exp.DateAdd, exp.TsOrDsAdd, exp.DateSub] -JSON_EXTRACT_TYPE = t.Union[ - exp.JSONExtract, exp.JSONExtractScalar, exp.JSONBExtract, exp.JSONBExtractScalar -] -DATETIME_DELTA = t.Union[ - exp.DateAdd, - exp.DatetimeAdd, - exp.DatetimeSub, - exp.TimeAdd, - exp.TimeSub, - exp.TimestampAdd, - exp.TimestampSub, - exp.TsOrDsAdd, -] -DATETIME_ADD = ( - exp.DateAdd, - exp.TimeAdd, - exp.DatetimeAdd, - exp.TsOrDsAdd, - exp.TimestampAdd, -) - -if t.TYPE_CHECKING: - from sqlglot._typing import B, E, F - -logger = logging.getLogger("sqlglot") - -UNESCAPED_SEQUENCES = { - "\\a": "\a", - "\\b": "\b", - "\\f": "\f", - "\\n": "\n", - "\\r": "\r", - "\\t": "\t", - "\\v": "\v", - "\\\\": "\\", -} - - -class Dialects(str, Enum): - """Dialects supported by SQLGLot.""" - - DIALECT = "" - - ATHENA = "athena" - BIGQUERY = "bigquery" - CLICKHOUSE = "clickhouse" - DATABRICKS = "databricks" - DORIS = "doris" - DREMIO = "dremio" - DRILL = "drill" - DRUID = "druid" - DUCKDB = "duckdb" - DUNE = "dune" - FABRIC = "fabric" - HIVE = "hive" - MATERIALIZE = "materialize" - MYSQL = "mysql" - ORACLE = "oracle" - POSTGRES = "postgres" - PRESTO = "presto" - PRQL = "prql" - REDSHIFT = "redshift" - RISINGWAVE = "risingwave" - SNOWFLAKE = "snowflake" - SOLR = "solr" - SPARK = "spark" - SPARK2 = "spark2" - SQLITE = "sqlite" - STARROCKS = "starrocks" - TABLEAU = "tableau" - TERADATA = "teradata" - TRINO = "trino" - TSQL = "tsql" - EXASOL = "exasol" - - -class NormalizationStrategy(str, AutoName): - """Specifies the strategy according to which identifiers should be normalized.""" - - LOWERCASE = auto() - """Unquoted identifiers are lowercased.""" - - UPPERCASE = auto() - """Unquoted identifiers are uppercased.""" - - CASE_SENSITIVE = auto() - """Always case-sensitive, regardless of quotes.""" - - CASE_INSENSITIVE = auto() - """Always case-insensitive (lowercase), regardless of quotes.""" - - CASE_INSENSITIVE_UPPERCASE = auto() - """Always case-insensitive (uppercase), regardless of quotes.""" - - -class _Dialect(type): - _classes: t.Dict[str, t.Type[Dialect]] = {} - - def __eq__(cls, other: t.Any) -> bool: - if cls is other: - return True - if isinstance(other, str): - return cls is cls.get(other) - if isinstance(other, Dialect): - return cls is type(other) - - return False - - def __hash__(cls) -> int: - return hash(cls.__name__.lower()) - - @property - def classes(cls): - if len(DIALECT_MODULE_NAMES) != len(cls._classes): - for key in DIALECT_MODULE_NAMES: - cls._try_load(key) - - return cls._classes - - @classmethod - def _try_load(cls, key: str | Dialects) -> None: - if isinstance(key, Dialects): - key = key.value - - # This import will lead to a new dialect being loaded, and hence, registered. - # We check that the key is an actual sqlglot module to avoid blindly importing - # files. Custom user dialects need to be imported at the top-level package, in - # order for them to be registered as soon as possible. - if key in DIALECT_MODULE_NAMES: - importlib.import_module(f"bigframes_vendored.sqlglot.dialects.{key}") - - @classmethod - def __getitem__(cls, key: str) -> t.Type[Dialect]: - if key not in cls._classes: - cls._try_load(key) - - return cls._classes[key] - - @classmethod - def get( - cls, key: str, default: t.Optional[t.Type[Dialect]] = None - ) -> t.Optional[t.Type[Dialect]]: - if key not in cls._classes: - cls._try_load(key) - - return cls._classes.get(key, default) - - def __new__(cls, clsname, bases, attrs): - klass = super().__new__(cls, clsname, bases, attrs) - enum = Dialects.__members__.get(clsname.upper()) - cls._classes[enum.value if enum is not None else clsname.lower()] = klass - - klass.TIME_TRIE = new_trie(klass.TIME_MAPPING) - klass.FORMAT_TRIE = ( - new_trie(klass.FORMAT_MAPPING) if klass.FORMAT_MAPPING else klass.TIME_TRIE - ) - # Merge class-defined INVERSE_TIME_MAPPING with auto-generated mappings - # This allows dialects to define custom inverse mappings for roundtrip correctness - klass.INVERSE_TIME_MAPPING = {v: k for k, v in klass.TIME_MAPPING.items()} | ( - klass.__dict__.get("INVERSE_TIME_MAPPING") or {} - ) - klass.INVERSE_TIME_TRIE = new_trie(klass.INVERSE_TIME_MAPPING) - klass.INVERSE_FORMAT_MAPPING = {v: k for k, v in klass.FORMAT_MAPPING.items()} - klass.INVERSE_FORMAT_TRIE = new_trie(klass.INVERSE_FORMAT_MAPPING) - - klass.INVERSE_CREATABLE_KIND_MAPPING = { - v: k for k, v in klass.CREATABLE_KIND_MAPPING.items() - } - - base = seq_get(bases, 0) - base_tokenizer = (getattr(base, "tokenizer_class", Tokenizer),) - base_jsonpath_tokenizer = ( - getattr(base, "jsonpath_tokenizer_class", JSONPathTokenizer), - ) - base_parser = (getattr(base, "parser_class", Parser),) - base_generator = (getattr(base, "generator_class", Generator),) - - klass.tokenizer_class = klass.__dict__.get( - "Tokenizer", type("Tokenizer", base_tokenizer, {}) - ) - klass.jsonpath_tokenizer_class = klass.__dict__.get( - "JSONPathTokenizer", type("JSONPathTokenizer", base_jsonpath_tokenizer, {}) - ) - klass.parser_class = klass.__dict__.get( - "Parser", type("Parser", base_parser, {}) - ) - klass.generator_class = klass.__dict__.get( - "Generator", type("Generator", base_generator, {}) - ) - - klass.QUOTE_START, klass.QUOTE_END = list( - klass.tokenizer_class._QUOTES.items() - )[0] - klass.IDENTIFIER_START, klass.IDENTIFIER_END = list( - klass.tokenizer_class._IDENTIFIERS.items() - )[0] - - def get_start_end( - token_type: TokenType, - ) -> t.Tuple[t.Optional[str], t.Optional[str]]: - return next( - ( - (s, e) - for s, (e, t) in klass.tokenizer_class._FORMAT_STRINGS.items() - if t == token_type - ), - (None, None), - ) - - klass.BIT_START, klass.BIT_END = get_start_end(TokenType.BIT_STRING) - klass.HEX_START, klass.HEX_END = get_start_end(TokenType.HEX_STRING) - klass.BYTE_START, klass.BYTE_END = get_start_end(TokenType.BYTE_STRING) - klass.UNICODE_START, klass.UNICODE_END = get_start_end(TokenType.UNICODE_STRING) - - if "\\" in klass.tokenizer_class.STRING_ESCAPES: - klass.UNESCAPED_SEQUENCES = { - **UNESCAPED_SEQUENCES, - **klass.UNESCAPED_SEQUENCES, - } - - klass.ESCAPED_SEQUENCES = {v: k for k, v in klass.UNESCAPED_SEQUENCES.items()} - - klass.SUPPORTS_COLUMN_JOIN_MARKS = "(+)" in klass.tokenizer_class.KEYWORDS - - if enum not in ("", "bigquery", "snowflake"): - klass.INITCAP_SUPPORTS_CUSTOM_DELIMITERS = False - - if enum not in ("", "bigquery"): - klass.generator_class.SELECT_KINDS = () - - if enum not in ("", "athena", "presto", "trino", "duckdb"): - klass.generator_class.TRY_SUPPORTED = False - klass.generator_class.SUPPORTS_UESCAPE = False - - if enum not in ("", "databricks", "hive", "spark", "spark2"): - modifier_transforms = ( - klass.generator_class.AFTER_HAVING_MODIFIER_TRANSFORMS.copy() - ) - for modifier in ("cluster", "distribute", "sort"): - modifier_transforms.pop(modifier, None) - - klass.generator_class.AFTER_HAVING_MODIFIER_TRANSFORMS = modifier_transforms - - if enum not in ("", "doris", "mysql"): - klass.parser_class.ID_VAR_TOKENS = klass.parser_class.ID_VAR_TOKENS | { - TokenType.STRAIGHT_JOIN, - } - klass.parser_class.TABLE_ALIAS_TOKENS = ( - klass.parser_class.TABLE_ALIAS_TOKENS - | { - TokenType.STRAIGHT_JOIN, - } - ) - - if enum not in ("", "databricks", "oracle", "redshift", "snowflake", "spark"): - klass.generator_class.SUPPORTS_DECODE_CASE = False - - if not klass.SUPPORTS_SEMI_ANTI_JOIN: - klass.parser_class.TABLE_ALIAS_TOKENS = ( - klass.parser_class.TABLE_ALIAS_TOKENS - | { - TokenType.ANTI, - TokenType.SEMI, - } - ) - - if enum not in ( - "", - "postgres", - "duckdb", - "redshift", - "snowflake", - "presto", - "trino", - "mysql", - "singlestore", - ): - no_paren_functions = klass.parser_class.NO_PAREN_FUNCTIONS.copy() - no_paren_functions.pop(TokenType.LOCALTIME, None) - if enum != "oracle": - no_paren_functions.pop(TokenType.LOCALTIMESTAMP, None) - klass.parser_class.NO_PAREN_FUNCTIONS = no_paren_functions - - if enum in ( - "", - "postgres", - "duckdb", - "trino", - ): - no_paren_functions = klass.parser_class.NO_PAREN_FUNCTIONS.copy() - no_paren_functions[TokenType.CURRENT_CATALOG] = exp.CurrentCatalog - klass.parser_class.NO_PAREN_FUNCTIONS = no_paren_functions - else: - # For dialects that don't support this keyword, treat it as a regular identifier - # This fixes the "Unexpected token" error in BQ, Spark, etc. - klass.parser_class.ID_VAR_TOKENS = klass.parser_class.ID_VAR_TOKENS | { - TokenType.CURRENT_CATALOG, - } - - if enum in ( - "", - "duckdb", - "spark", - "postgres", - "tsql", - ): - no_paren_functions = klass.parser_class.NO_PAREN_FUNCTIONS.copy() - no_paren_functions[TokenType.SESSION_USER] = exp.SessionUser - klass.parser_class.NO_PAREN_FUNCTIONS = no_paren_functions - else: - klass.parser_class.ID_VAR_TOKENS = klass.parser_class.ID_VAR_TOKENS | { - TokenType.SESSION_USER, - } - - klass.VALID_INTERVAL_UNITS = { - *klass.VALID_INTERVAL_UNITS, - *klass.DATE_PART_MAPPING.keys(), - *klass.DATE_PART_MAPPING.values(), - } - - return klass - - -class Dialect(metaclass=_Dialect): - INDEX_OFFSET = 0 - """The base index offset for arrays.""" - - WEEK_OFFSET = 0 - """First day of the week in DATE_TRUNC(week). Defaults to 0 (Monday). -1 would be Sunday.""" - - UNNEST_COLUMN_ONLY = False - """Whether `UNNEST` table aliases are treated as column aliases.""" - - ALIAS_POST_TABLESAMPLE = False - """Whether the table alias comes after tablesample.""" - - TABLESAMPLE_SIZE_IS_PERCENT = False - """Whether a size in the table sample clause represents percentage.""" - - NORMALIZATION_STRATEGY = NormalizationStrategy.LOWERCASE - """Specifies the strategy according to which identifiers should be normalized.""" - - IDENTIFIERS_CAN_START_WITH_DIGIT = False - """Whether an unquoted identifier can start with a digit.""" - - DPIPE_IS_STRING_CONCAT = True - """Whether the DPIPE token (`||`) is a string concatenation operator.""" - - STRICT_STRING_CONCAT = False - """Whether `CONCAT`'s arguments must be strings.""" - - SUPPORTS_USER_DEFINED_TYPES = True - """Whether user-defined data types are supported.""" - - SUPPORTS_SEMI_ANTI_JOIN = True - """Whether `SEMI` or `ANTI` joins are supported.""" - - SUPPORTS_COLUMN_JOIN_MARKS = False - """Whether the old-style outer join (+) syntax is supported.""" - - COPY_PARAMS_ARE_CSV = True - """Separator of COPY statement parameters.""" - - NORMALIZE_FUNCTIONS: bool | str = "upper" - """ - Determines how function names are going to be normalized. - Possible values: - "upper" or True: Convert names to uppercase. - "lower": Convert names to lowercase. - False: Disables function name normalization. - """ - - PRESERVE_ORIGINAL_NAMES: bool = False - """ - Whether the name of the function should be preserved inside the node's metadata, - can be useful for roundtripping deprecated vs new functions that share an AST node - e.g JSON_VALUE vs JSON_EXTRACT_SCALAR in BigQuery - """ - - LOG_BASE_FIRST: t.Optional[bool] = True - """ - Whether the base comes first in the `LOG` function. - Possible values: `True`, `False`, `None` (two arguments are not supported by `LOG`) - """ - - NULL_ORDERING = "nulls_are_small" - """ - Default `NULL` ordering method to use if not explicitly set. - Possible values: `"nulls_are_small"`, `"nulls_are_large"`, `"nulls_are_last"` - """ - - TYPED_DIVISION = False - """ - Whether the behavior of `a / b` depends on the types of `a` and `b`. - False means `a / b` is always float division. - True means `a / b` is integer division if both `a` and `b` are integers. - """ - - SAFE_DIVISION = False - """Whether division by zero throws an error (`False`) or returns NULL (`True`).""" - - CONCAT_COALESCE = False - """A `NULL` arg in `CONCAT` yields `NULL` by default, but in some dialects it yields an empty string.""" - - HEX_LOWERCASE = False - """Whether the `HEX` function returns a lowercase hexadecimal string.""" - - DATE_FORMAT = "'%Y-%m-%d'" - DATEINT_FORMAT = "'%Y%m%d'" - TIME_FORMAT = "'%Y-%m-%d %H:%M:%S'" - - TIME_MAPPING: t.Dict[str, str] = {} - """Associates this dialect's time formats with their equivalent Python `strftime` formats.""" - - # https://cloud.google.com/bigquery/docs/reference/standard-sql/format-elements#format_model_rules_date_time - # https://docs.teradata.com/r/Teradata-Database-SQL-Functions-Operators-Expressions-and-Predicates/March-2017/Data-Type-Conversions/Character-to-DATE-Conversion/Forcing-a-FORMAT-on-CAST-for-Converting-Character-to-DATE - FORMAT_MAPPING: t.Dict[str, str] = {} - """ - Helper which is used for parsing the special syntax `CAST(x AS DATE FORMAT 'yyyy')`. - If empty, the corresponding trie will be constructed off of `TIME_MAPPING`. - """ - - UNESCAPED_SEQUENCES: t.Dict[str, str] = {} - """Mapping of an escaped sequence (`\\n`) to its unescaped version (`\n`).""" - - PSEUDOCOLUMNS: t.Set[str] = set() - """ - Columns that are auto-generated by the engine corresponding to this dialect. - For example, such columns may be excluded from `SELECT *` queries. - """ - - PREFER_CTE_ALIAS_COLUMN = False - """ - Some dialects, such as Snowflake, allow you to reference a CTE column alias in the - HAVING clause of the CTE. This flag will cause the CTE alias columns to override - any projection aliases in the subquery. - - For example, - WITH y(c) AS ( - SELECT SUM(a) FROM (SELECT 1 a) AS x HAVING c > 0 - ) SELECT c FROM y; - - will be rewritten as - - WITH y(c) AS ( - SELECT SUM(a) AS c FROM (SELECT 1 AS a) AS x HAVING c > 0 - ) SELECT c FROM y; - """ - - COPY_PARAMS_ARE_CSV = True - """ - Whether COPY statement parameters are separated by comma or whitespace - """ - - FORCE_EARLY_ALIAS_REF_EXPANSION = False - """ - Whether alias reference expansion (_expand_alias_refs()) should run before column qualification (_qualify_columns()). - - For example: - WITH data AS ( - SELECT - 1 AS id, - 2 AS my_id - ) - SELECT - id AS my_id - FROM - data - WHERE - my_id = 1 - GROUP BY - my_id, - HAVING - my_id = 1 - - In most dialects, "my_id" would refer to "data.my_id" across the query, except: - - BigQuery, which will forward the alias to GROUP BY + HAVING clauses i.e - it resolves to "WHERE my_id = 1 GROUP BY id HAVING id = 1" - - Clickhouse, which will forward the alias across the query i.e it resolves - to "WHERE id = 1 GROUP BY id HAVING id = 1" - """ - - EXPAND_ONLY_GROUP_ALIAS_REF = False - """Whether alias reference expansion before qualification should only happen for the GROUP BY clause.""" - - ANNOTATE_ALL_SCOPES = False - """Whether to annotate all scopes during optimization. Used by BigQuery for UNNEST support.""" - - DISABLES_ALIAS_REF_EXPANSION = False - """ - Whether alias reference expansion is disabled for this dialect. - - Some dialects like Oracle do NOT support referencing aliases in projections or WHERE clauses. - The original expression must be repeated instead. - - For example, in Oracle: - SELECT y.foo AS bar, bar * 2 AS baz FROM y -- INVALID - SELECT y.foo AS bar, y.foo * 2 AS baz FROM y -- VALID - """ - - SUPPORTS_ALIAS_REFS_IN_JOIN_CONDITIONS = False - """ - Whether alias references are allowed in JOIN ... ON clauses. - - Most dialects do not support this, but Snowflake allows alias expansion in the JOIN ... ON - clause (and almost everywhere else) - - For example, in Snowflake: - SELECT a.id AS user_id FROM a JOIN b ON user_id = b.id -- VALID - - Reference: https://docs.snowflake.com/en/sql-reference/sql/select#usage-notes - """ - - SUPPORTS_ORDER_BY_ALL = False - """ - Whether ORDER BY ALL is supported (expands to all the selected columns) as in DuckDB, Spark3/Databricks - """ - - PROJECTION_ALIASES_SHADOW_SOURCE_NAMES = False - """ - Whether projection alias names can shadow table/source names in GROUP BY and HAVING clauses. - - In BigQuery, when a projection alias has the same name as a source table, the alias takes - precedence in GROUP BY and HAVING clauses, and the table becomes inaccessible by that name. - - For example, in BigQuery: - SELECT id, ARRAY_AGG(col) AS custom_fields - FROM custom_fields - GROUP BY id - HAVING id >= 1 - - The "custom_fields" source is shadowed by the projection alias, so we cannot qualify "id" - with "custom_fields" in GROUP BY/HAVING. - """ - - TABLES_REFERENCEABLE_AS_COLUMNS = False - """ - Whether table names can be referenced as columns (treated as structs). - - BigQuery allows tables to be referenced as columns in queries, automatically treating - them as struct values containing all the table's columns. - - For example, in BigQuery: - SELECT t FROM my_table AS t -- Returns entire row as a struct - """ - - SUPPORTS_STRUCT_STAR_EXPANSION = False - """ - Whether the dialect supports expanding struct fields using star notation (e.g., struct_col.*). - - BigQuery allows struct fields to be expanded with the star operator: - SELECT t.struct_col.* FROM table t - RisingWave also allows struct field expansion with the star operator using parentheses: - SELECT (t.struct_col).* FROM table t - - This expands to all fields within the struct. - """ - - EXCLUDES_PSEUDOCOLUMNS_FROM_STAR = False - """ - Whether pseudocolumns should be excluded from star expansion (SELECT *). - - Pseudocolumns are special dialect-specific columns (e.g., Oracle's ROWNUM, ROWID, LEVEL, - or BigQuery's _PARTITIONTIME, _PARTITIONDATE) that are implicitly available but not part - of the table schema. When this is True, SELECT * will not include these pseudocolumns; - they must be explicitly selected. - """ - - QUERY_RESULTS_ARE_STRUCTS = False - """ - Whether query results are typed as structs in metadata for type inference. - - In BigQuery, subqueries store their column types as a STRUCT in metadata, - enabling special type inference for ARRAY(SELECT ...) expressions: - ARRAY(SELECT x, y FROM t) → ARRAY> - - For single column subqueries, BigQuery unwraps the struct: - ARRAY(SELECT x FROM t) → ARRAY - - This is metadata-only for type inference. - """ - - REQUIRES_PARENTHESIZED_STRUCT_ACCESS = False - """ - Whether struct field access requires parentheses around the expression. - - RisingWave requires parentheses for struct field access in certain contexts: - SELECT (col.field).subfield FROM table -- Parentheses required - - Without parentheses, the parser may not correctly interpret nested struct access. - - Reference: https://docs.risingwave.com/sql/data-types/struct#retrieve-data-in-a-struct - """ - - SUPPORTS_NULL_TYPE = False - """ - Whether NULL/VOID is supported as a valid data type (not just a value). - - Databricks and Spark v3+ support NULL as an actual type, allowing expressions like: - SELECT NULL AS col -- Has type NULL, not just value NULL - CAST(x AS VOID) -- Valid type cast - """ - - COALESCE_COMPARISON_NON_STANDARD = False - """ - Whether COALESCE in comparisons has non-standard NULL semantics. - - We can't convert `COALESCE(x, 1) = 2` into `NOT x IS NULL AND x = 2` for redshift, - because they are not always equivalent. For example, if `x` is `NULL` and it comes - from a table, then the result is `NULL`, despite `FALSE AND NULL` evaluating to `FALSE`. - - In standard SQL and most dialects, these expressions are equivalent, but Redshift treats - table NULLs differently in this context. - """ - - HAS_DISTINCT_ARRAY_CONSTRUCTORS = False - """ - Whether the ARRAY constructor is context-sensitive, i.e in Redshift ARRAY[1, 2, 3] != ARRAY(1, 2, 3) - as the former is of type INT[] vs the latter which is SUPER - """ - - SUPPORTS_FIXED_SIZE_ARRAYS = False - """ - Whether expressions such as x::INT[5] should be parsed as fixed-size array defs/casts e.g. - in DuckDB. In dialects which don't support fixed size arrays such as Snowflake, this should - be interpreted as a subscript/index operator. - """ - - STRICT_JSON_PATH_SYNTAX = True - """Whether failing to parse a JSON path expression using the JSONPath dialect will log a warning.""" - - ON_CONDITION_EMPTY_BEFORE_ERROR = True - """Whether "X ON EMPTY" should come before "X ON ERROR" (for dialects like T-SQL, MySQL, Oracle).""" - - ARRAY_AGG_INCLUDES_NULLS: t.Optional[bool] = True - """Whether ArrayAgg needs to filter NULL values.""" - - PROMOTE_TO_INFERRED_DATETIME_TYPE = False - """ - This flag is used in the optimizer's canonicalize rule and determines whether x will be promoted - to the literal's type in x::DATE < '2020-01-01 12:05:03' (i.e., DATETIME). When false, the literal - is cast to x's type to match it instead. - """ - - SUPPORTS_VALUES_DEFAULT = True - """Whether the DEFAULT keyword is supported in the VALUES clause.""" - - NUMBERS_CAN_BE_UNDERSCORE_SEPARATED = False - """Whether number literals can include underscores for better readability""" - - HEX_STRING_IS_INTEGER_TYPE: bool = False - """Whether hex strings such as x'CC' evaluate to integer or binary/blob type""" - - REGEXP_EXTRACT_DEFAULT_GROUP = 0 - """The default value for the capturing group.""" - - REGEXP_EXTRACT_POSITION_OVERFLOW_RETURNS_NULL = True - """Whether REGEXP_EXTRACT returns NULL when the position arg exceeds the string length.""" - - SET_OP_DISTINCT_BY_DEFAULT: t.Dict[t.Type[exp.Expression], t.Optional[bool]] = { - exp.Except: True, - exp.Intersect: True, - exp.Union: True, - } - """ - Whether a set operation uses DISTINCT by default. This is `None` when either `DISTINCT` or `ALL` - must be explicitly specified. - """ - - CREATABLE_KIND_MAPPING: dict[str, str] = {} - """ - Helper for dialects that use a different name for the same creatable kind. For example, the Clickhouse - equivalent of CREATE SCHEMA is CREATE DATABASE. - """ - - ALTER_TABLE_SUPPORTS_CASCADE = False - """ - Hive by default does not update the schema of existing partitions when a column is changed. - the CASCADE clause is used to indicate that the change should be propagated to all existing partitions. - the Spark dialect, while derived from Hive, does not support the CASCADE clause. - """ - - # Whether ADD is present for each column added by ALTER TABLE - ALTER_TABLE_ADD_REQUIRED_FOR_EACH_COLUMN = True - - # Whether the value/LHS of the TRY_CAST( AS ) should strictly be a - # STRING type (Snowflake's case) or can be of any type - TRY_CAST_REQUIRES_STRING: t.Optional[bool] = None - - # Whether the double negation can be applied - # Not safe with MySQL and SQLite due to type coercion (may not return boolean) - SAFE_TO_ELIMINATE_DOUBLE_NEGATION = True - - # Whether the INITCAP function supports custom delimiter characters as the second argument - # Default delimiter characters for INITCAP function: whitespace and non-alphanumeric characters - INITCAP_SUPPORTS_CUSTOM_DELIMITERS = True - INITCAP_DEFAULT_DELIMITER_CHARS = ( - " \t\n\r\f\v!\"#$%&'()*+,\\-./:;<=>?@\\[\\]^_`{|}~" - ) - - BYTE_STRING_IS_BYTES_TYPE: bool = False - """ - Whether byte string literals (ex: BigQuery's b'...') are typed as BYTES/BINARY - """ - - UUID_IS_STRING_TYPE: bool = False - """ - Whether a UUID is considered a string or a UUID type. - """ - - JSON_EXTRACT_SCALAR_SCALAR_ONLY = False - """ - Whether JSON_EXTRACT_SCALAR returns null if a non-scalar value is selected. - """ - - DEFAULT_FUNCTIONS_COLUMN_NAMES: t.Dict[ - t.Type[exp.Func], t.Union[str, t.Tuple[str, ...]] - ] = {} - """ - Maps function expressions to their default output column name(s). - - For example, in Postgres, generate_series function outputs a column named "generate_series" by default, - so we map the ExplodingGenerateSeries expression to "generate_series" string. - """ - - DEFAULT_NULL_TYPE = exp.DataType.Type.UNKNOWN - """ - The default type of NULL for producing the correct projection type. - - For example, in BigQuery the default type of the NULL value is INT64. - """ - - LEAST_GREATEST_IGNORES_NULLS = True - """ - Whether LEAST/GREATEST functions ignore NULL values, e.g: - - BigQuery, Snowflake, MySQL, Presto/Trino: LEAST(1, NULL, 2) -> NULL - - Spark, Postgres, DuckDB, TSQL: LEAST(1, NULL, 2) -> 1 - """ - - PRIORITIZE_NON_LITERAL_TYPES = False - """ - Whether to prioritize non-literal types over literals during type annotation. - """ - - # --- Autofilled --- - - tokenizer_class = Tokenizer - jsonpath_tokenizer_class = JSONPathTokenizer - parser_class = Parser - generator_class = Generator - - # A trie of the time_mapping keys - TIME_TRIE: t.Dict = {} - FORMAT_TRIE: t.Dict = {} - - INVERSE_TIME_MAPPING: t.Dict[str, str] = {} - INVERSE_TIME_TRIE: t.Dict = {} - INVERSE_FORMAT_MAPPING: t.Dict[str, str] = {} - INVERSE_FORMAT_TRIE: t.Dict = {} - - INVERSE_CREATABLE_KIND_MAPPING: dict[str, str] = {} - - ESCAPED_SEQUENCES: t.Dict[str, str] = {} - - # Delimiters for string literals and identifiers - QUOTE_START = "'" - QUOTE_END = "'" - IDENTIFIER_START = '"' - IDENTIFIER_END = '"' - - VALID_INTERVAL_UNITS: t.Set[str] = set() - - # Delimiters for bit, hex, byte and unicode literals - BIT_START: t.Optional[str] = None - BIT_END: t.Optional[str] = None - HEX_START: t.Optional[str] = None - HEX_END: t.Optional[str] = None - BYTE_START: t.Optional[str] = None - BYTE_END: t.Optional[str] = None - UNICODE_START: t.Optional[str] = None - UNICODE_END: t.Optional[str] = None - - DATE_PART_MAPPING = { - "Y": "YEAR", - "YY": "YEAR", - "YYY": "YEAR", - "YYYY": "YEAR", - "YR": "YEAR", - "YEARS": "YEAR", - "YRS": "YEAR", - "MM": "MONTH", - "MON": "MONTH", - "MONS": "MONTH", - "MONTHS": "MONTH", - "D": "DAY", - "DD": "DAY", - "DAYS": "DAY", - "DAYOFMONTH": "DAY", - "DAY OF WEEK": "DAYOFWEEK", - "WEEKDAY": "DAYOFWEEK", - "DOW": "DAYOFWEEK", - "DW": "DAYOFWEEK", - "WEEKDAY_ISO": "DAYOFWEEKISO", - "DOW_ISO": "DAYOFWEEKISO", - "DW_ISO": "DAYOFWEEKISO", - "DAYOFWEEK_ISO": "DAYOFWEEKISO", - "DAY OF YEAR": "DAYOFYEAR", - "DOY": "DAYOFYEAR", - "DY": "DAYOFYEAR", - "W": "WEEK", - "WK": "WEEK", - "WEEKOFYEAR": "WEEK", - "WOY": "WEEK", - "WY": "WEEK", - "WEEK_ISO": "WEEKISO", - "WEEKOFYEARISO": "WEEKISO", - "WEEKOFYEAR_ISO": "WEEKISO", - "Q": "QUARTER", - "QTR": "QUARTER", - "QTRS": "QUARTER", - "QUARTERS": "QUARTER", - "H": "HOUR", - "HH": "HOUR", - "HR": "HOUR", - "HOURS": "HOUR", - "HRS": "HOUR", - "M": "MINUTE", - "MI": "MINUTE", - "MIN": "MINUTE", - "MINUTES": "MINUTE", - "MINS": "MINUTE", - "S": "SECOND", - "SEC": "SECOND", - "SECONDS": "SECOND", - "SECS": "SECOND", - "MS": "MILLISECOND", - "MSEC": "MILLISECOND", - "MSECS": "MILLISECOND", - "MSECOND": "MILLISECOND", - "MSECONDS": "MILLISECOND", - "MILLISEC": "MILLISECOND", - "MILLISECS": "MILLISECOND", - "MILLISECON": "MILLISECOND", - "MILLISECONDS": "MILLISECOND", - "US": "MICROSECOND", - "USEC": "MICROSECOND", - "USECS": "MICROSECOND", - "MICROSEC": "MICROSECOND", - "MICROSECS": "MICROSECOND", - "USECOND": "MICROSECOND", - "USECONDS": "MICROSECOND", - "MICROSECONDS": "MICROSECOND", - "NS": "NANOSECOND", - "NSEC": "NANOSECOND", - "NANOSEC": "NANOSECOND", - "NSECOND": "NANOSECOND", - "NSECONDS": "NANOSECOND", - "NANOSECS": "NANOSECOND", - "EPOCH_SECOND": "EPOCH", - "EPOCH_SECONDS": "EPOCH", - "EPOCH_MILLISECONDS": "EPOCH_MILLISECOND", - "EPOCH_MICROSECONDS": "EPOCH_MICROSECOND", - "EPOCH_NANOSECONDS": "EPOCH_NANOSECOND", - "TZH": "TIMEZONE_HOUR", - "TZM": "TIMEZONE_MINUTE", - "DEC": "DECADE", - "DECS": "DECADE", - "DECADES": "DECADE", - "MIL": "MILLENNIUM", - "MILS": "MILLENNIUM", - "MILLENIA": "MILLENNIUM", - "C": "CENTURY", - "CENT": "CENTURY", - "CENTS": "CENTURY", - "CENTURIES": "CENTURY", - } - - # Specifies what types a given type can be coerced into - COERCES_TO: t.Dict[exp.DataType.Type, t.Set[exp.DataType.Type]] = {} - - # Specifies type inference & validation rules for expressions - EXPRESSION_METADATA = EXPRESSION_METADATA.copy() - - # Determines the supported Dialect instance settings - SUPPORTED_SETTINGS = { - "normalization_strategy", - "version", - } - - @classmethod - def get_or_raise(cls, dialect: DialectType) -> Dialect: - """ - Look up a dialect in the global dialect registry and return it if it exists. - - Args: - dialect: The target dialect. If this is a string, it can be optionally followed by - additional key-value pairs that are separated by commas and are used to specify - dialect settings, such as whether the dialect's identifiers are case-sensitive. - - Example: - >>> dialect = dialect_class = get_or_raise("duckdb") - >>> dialect = get_or_raise("mysql, normalization_strategy = case_sensitive") - - Returns: - The corresponding Dialect instance. - """ - - if not dialect: - return cls() - if isinstance(dialect, _Dialect): - return dialect() - if isinstance(dialect, Dialect): - return dialect - if isinstance(dialect, str): - try: - dialect_name, *kv_strings = dialect.split(",") - kv_pairs = (kv.split("=") for kv in kv_strings) - kwargs = {} - for pair in kv_pairs: - key = pair[0].strip() - value: t.Union[bool | str | None] = None - - if len(pair) == 1: - # Default initialize standalone settings to True - value = True - elif len(pair) == 2: - value = pair[1].strip() - - kwargs[key] = to_bool(value) - - except ValueError: - raise ValueError( - f"Invalid dialect format: '{dialect}'. " - "Please use the correct format: 'dialect [, k1 = v2 [, ...]]'." - ) - - result = cls.get(dialect_name.strip()) - if not result: - suggest_closest_match_and_fail( - "dialect", dialect_name, list(DIALECT_MODULE_NAMES) - ) - - assert result is not None - return result(**kwargs) - - raise ValueError(f"Invalid dialect type for '{dialect}': '{type(dialect)}'.") - - @classmethod - def format_time( - cls, expression: t.Optional[str | exp.Expression] - ) -> t.Optional[exp.Expression]: - """Converts a time format in this dialect to its equivalent Python `strftime` format.""" - if isinstance(expression, str): - return exp.Literal.string( - # the time formats are quoted - format_time(expression[1:-1], cls.TIME_MAPPING, cls.TIME_TRIE) - ) - - if expression and expression.is_string: - return exp.Literal.string( - format_time(expression.this, cls.TIME_MAPPING, cls.TIME_TRIE) - ) - - return expression - - def __init__(self, **kwargs) -> None: - parts = str(kwargs.pop("version", sys.maxsize)).split(".") - parts.extend(["0"] * (3 - len(parts))) - self.version = tuple(int(p) for p in parts[:3]) - - normalization_strategy = kwargs.pop("normalization_strategy", None) - if normalization_strategy is None: - self.normalization_strategy = self.NORMALIZATION_STRATEGY - else: - self.normalization_strategy = NormalizationStrategy( - normalization_strategy.upper() - ) - - self.settings = kwargs - - for unsupported_setting in kwargs.keys() - self.SUPPORTED_SETTINGS: - suggest_closest_match_and_fail( - "setting", unsupported_setting, self.SUPPORTED_SETTINGS - ) - - def __eq__(self, other: t.Any) -> bool: - # Does not currently take dialect state into account - return isinstance(self, other.__class__) - - def __hash__(self) -> int: - # Does not currently take dialect state into account - return hash(type(self)) - - def normalize_identifier(self, expression: E) -> E: - """ - Transforms an identifier in a way that resembles how it'd be resolved by this dialect. - - For example, an identifier like `FoO` would be resolved as `foo` in Postgres, because it - lowercases all unquoted identifiers. On the other hand, Snowflake uppercases them, so - it would resolve it as `FOO`. If it was quoted, it'd need to be treated as case-sensitive, - and so any normalization would be prohibited in order to avoid "breaking" the identifier. - - There are also dialects like Spark, which are case-insensitive even when quotes are - present, and dialects like MySQL, whose resolution rules match those employed by the - underlying operating system, for example they may always be case-sensitive in Linux. - - Finally, the normalization behavior of some engines can even be controlled through flags, - like in Redshift's case, where users can explicitly set enable_case_sensitive_identifier. - - SQLGlot aims to understand and handle all of these different behaviors gracefully, so - that it can analyze queries in the optimizer and successfully capture their semantics. - """ - if ( - isinstance(expression, exp.Identifier) - and self.normalization_strategy is not NormalizationStrategy.CASE_SENSITIVE - and ( - not expression.quoted - or self.normalization_strategy - in ( - NormalizationStrategy.CASE_INSENSITIVE, - NormalizationStrategy.CASE_INSENSITIVE_UPPERCASE, - ) - ) - ): - normalized = ( - expression.this.upper() - if self.normalization_strategy - in ( - NormalizationStrategy.UPPERCASE, - NormalizationStrategy.CASE_INSENSITIVE_UPPERCASE, - ) - else expression.this.lower() - ) - expression.set("this", normalized) - - return expression - - def case_sensitive(self, text: str) -> bool: - """Checks if text contains any case sensitive characters, based on the dialect's rules.""" - if self.normalization_strategy is NormalizationStrategy.CASE_INSENSITIVE: - return False - - unsafe = ( - str.islower - if self.normalization_strategy is NormalizationStrategy.UPPERCASE - else str.isupper - ) - return any(unsafe(char) for char in text) - - def can_quote( - self, identifier: exp.Identifier, identify: str | bool = "safe" - ) -> bool: - """Checks if an identifier can be quoted - - Args: - identifier: The identifier to check. - identify: - `True`: Always returns `True` except for certain cases. - `"safe"`: Only returns `True` if the identifier is case-insensitive. - `"unsafe"`: Only returns `True` if the identifier is case-sensitive. - - Returns: - Whether the given text can be identified. - """ - if identifier.quoted: - return True - if not identify: - return False - if isinstance(identifier.parent, exp.Func): - return False - if identify is True: - return True - - is_safe = not self.case_sensitive(identifier.this) and bool( - exp.SAFE_IDENTIFIER_RE.match(identifier.this) - ) - - if identify == "safe": - return is_safe - if identify == "unsafe": - return not is_safe - - raise ValueError(f"Unexpected argument for identify: '{identify}'") - - def quote_identifier(self, expression: E, identify: bool = True) -> E: - """ - Adds quotes to a given expression if it is an identifier. - - Args: - expression: The expression of interest. If it's not an `Identifier`, this method is a no-op. - identify: If set to `False`, the quotes will only be added if the identifier is deemed - "unsafe", with respect to its characters and this dialect's normalization strategy. - """ - if isinstance(expression, exp.Identifier): - expression.set("quoted", self.can_quote(expression, identify or "unsafe")) - return expression - - def to_json_path( - self, path: t.Optional[exp.Expression] - ) -> t.Optional[exp.Expression]: - if isinstance(path, exp.Literal): - path_text = path.name - if path.is_number: - path_text = f"[{path_text}]" - try: - return parse_json_path(path_text, self) - except ParseError as e: - if self.STRICT_JSON_PATH_SYNTAX and not path_text.lstrip().startswith( - ("lax", "strict") - ): - logger.warning(f"Invalid JSON path syntax. {str(e)}") - - return path - - def parse(self, sql: str, **opts) -> t.List[t.Optional[exp.Expression]]: - return self.parser(**opts).parse(self.tokenize(sql), sql) - - def parse_into( - self, expression_type: exp.IntoType, sql: str, **opts - ) -> t.List[t.Optional[exp.Expression]]: - return self.parser(**opts).parse_into(expression_type, self.tokenize(sql), sql) - - def generate(self, expression: exp.Expression, copy: bool = True, **opts) -> str: - return self.generator(**opts).generate(expression, copy=copy) - - def transpile(self, sql: str, **opts) -> t.List[str]: - return [ - self.generate(expression, copy=False, **opts) if expression else "" - for expression in self.parse(sql) - ] - - def tokenize(self, sql: str, **opts) -> t.List[Token]: - return self.tokenizer(**opts).tokenize(sql) - - def tokenizer(self, **opts) -> Tokenizer: - return self.tokenizer_class(**{"dialect": self, **opts}) - - def jsonpath_tokenizer(self, **opts) -> JSONPathTokenizer: - return self.jsonpath_tokenizer_class(**{"dialect": self, **opts}) - - def parser(self, **opts) -> Parser: - return self.parser_class(**{"dialect": self, **opts}) - - def generator(self, **opts) -> Generator: - return self.generator_class(**{"dialect": self, **opts}) - - def generate_values_aliases(self, expression: exp.Values) -> t.List[exp.Identifier]: - return [ - exp.to_identifier(f"_col_{i}") - for i, _ in enumerate(expression.expressions[0].expressions) - ] - - -DialectType = t.Union[str, Dialect, t.Type[Dialect], None] - - -def rename_func(name: str) -> t.Callable[[Generator, exp.Expression], str]: - return lambda self, expression: self.func(name, *flatten(expression.args.values())) - - -@unsupported_args("accuracy") -def approx_count_distinct_sql(self: Generator, expression: exp.ApproxDistinct) -> str: - return self.func("APPROX_COUNT_DISTINCT", expression.this) - - -def if_sql( - name: str = "IF", false_value: t.Optional[exp.Expression | str] = None -) -> t.Callable[[Generator, exp.If], str]: - def _if_sql(self: Generator, expression: exp.If) -> str: - return self.func( - name, - expression.this, - expression.args.get("true"), - expression.args.get("false") or false_value, - ) - - return _if_sql - - -def arrow_json_extract_sql(self: Generator, expression: JSON_EXTRACT_TYPE) -> str: - this = expression.this - if ( - self.JSON_TYPE_REQUIRED_FOR_EXTRACTION - and isinstance(this, exp.Literal) - and this.is_string - ): - this.replace(exp.cast(this, exp.DataType.Type.JSON)) - - return self.binary( - expression, "->" if isinstance(expression, exp.JSONExtract) else "->>" - ) - - -def inline_array_sql(self: Generator, expression: exp.Expression) -> str: - return f"[{self.expressions(expression, dynamic=True, new_line=True, skip_first=True, skip_last=True)}]" - - -def inline_array_unless_query(self: Generator, expression: exp.Expression) -> str: - elem = seq_get(expression.expressions, 0) - if ( - len(expression.expressions) == 1 - and isinstance(elem, exp.Expression) - and ( - isinstance(elem, exp.Query) - or (isinstance(elem, exp.Subquery) and isinstance(elem.this, exp.Query)) - ) - ): - return self.func("ARRAY", elem) - return inline_array_sql(self, expression) - - -def no_ilike_sql(self: Generator, expression: exp.ILike) -> str: - return self.like_sql( - exp.Like( - this=exp.Lower(this=expression.this), - expression=exp.Lower(this=expression.expression), - ) - ) - - -def no_paren_current_date_sql(self: Generator, expression: exp.CurrentDate) -> str: - zone = self.sql(expression, "this") - return f"CURRENT_DATE AT TIME ZONE {zone}" if zone else "CURRENT_DATE" - - -def no_recursive_cte_sql(self: Generator, expression: exp.With) -> str: - if expression.args.get("recursive"): - self.unsupported("Recursive CTEs are unsupported") - expression.set("recursive", False) - return self.with_sql(expression) - - -def no_tablesample_sql(self: Generator, expression: exp.TableSample) -> str: - self.unsupported("TABLESAMPLE unsupported") - return self.sql(expression.this) - - -def no_pivot_sql(self: Generator, expression: exp.Pivot) -> str: - self.unsupported("PIVOT unsupported") - return "" - - -def no_trycast_sql(self: Generator, expression: exp.TryCast) -> str: - return self.cast_sql(expression) - - -def no_comment_column_constraint_sql( - self: Generator, expression: exp.CommentColumnConstraint -) -> str: - self.unsupported("CommentColumnConstraint unsupported") - return "" - - -def no_map_from_entries_sql(self: Generator, expression: exp.MapFromEntries) -> str: - self.unsupported("MAP_FROM_ENTRIES unsupported") - return "" - - -def property_sql(self: Generator, expression: exp.Property) -> str: - return f"{self.property_name(expression, string_key=True)}={self.sql(expression, 'value')}" - - -def strposition_sql( - self: Generator, - expression: exp.StrPosition, - func_name: str = "STRPOS", - supports_position: bool = False, - supports_occurrence: bool = False, - use_ansi_position: bool = True, -) -> str: - string = expression.this - substr = expression.args.get("substr") - position = expression.args.get("position") - occurrence = expression.args.get("occurrence") - zero = exp.Literal.number(0) - one = exp.Literal.number(1) - - if supports_occurrence and occurrence and supports_position and not position: - position = one - - transpile_position = position and not supports_position - if transpile_position: - string = exp.Substring(this=string, start=position) - - if func_name == "POSITION" and use_ansi_position: - func = exp.Anonymous( - this=func_name, expressions=[exp.In(this=substr, field=string)] - ) - else: - args = ( - [substr, string] - if func_name in ("LOCATE", "CHARINDEX") - else [string, substr] - ) - if supports_position: - args.append(position) - if occurrence: - if supports_occurrence: - args.append(occurrence) - else: - self.unsupported( - f"{func_name} does not support the occurrence parameter." - ) - func = exp.Anonymous(this=func_name, expressions=args) - - if transpile_position: - func_with_offset = exp.Sub(this=func + position, expression=one) - func_wrapped = exp.If(this=func.eq(zero), true=zero, false=func_with_offset) - return self.sql(func_wrapped) - - return self.sql(func) - - -def struct_extract_sql(self: Generator, expression: exp.StructExtract) -> str: - return f"{self.sql(expression, 'this')}.{self.sql(exp.to_identifier(expression.expression.name))}" - - -def var_map_sql( - self: Generator, expression: exp.Map | exp.VarMap, map_func_name: str = "MAP" -) -> str: - keys = expression.args.get("keys") - values = expression.args.get("values") - - if not isinstance(keys, exp.Array) or not isinstance(values, exp.Array): - self.unsupported("Cannot convert array columns into map.") - return self.func(map_func_name, keys, values) - - args = [] - for key, value in zip(keys.expressions, values.expressions): - args.append(self.sql(key)) - args.append(self.sql(value)) - - return self.func(map_func_name, *args) - - -def months_between_sql(self: Generator, expression: exp.MonthsBetween) -> str: - """ - Transpile MONTHS_BETWEEN to dialects that don't have native support. - - Snowflake's MONTHS_BETWEEN returns whole months + fractional part where: - - Fractional part = (DAY(date1) - DAY(date2)) / 31 - - Special case: If both dates are last day of month, fractional part = 0 - - Formula: DATEDIFF('month', date2, date1) + (DAY(date1) - DAY(date2)) / 31.0 - """ - date1 = expression.this - date2 = expression.expression - - # Cast to DATE to ensure consistent behavior - date1_cast = exp.cast(date1, exp.DataType.Type.DATE, copy=False) - date2_cast = exp.cast(date2, exp.DataType.Type.DATE, copy=False) - - # Whole months: DATEDIFF('month', date2, date1) - whole_months = exp.DateDiff( - this=date1_cast, expression=date2_cast, unit=exp.var("month") - ) - - # Day components - day1 = exp.Day(this=date1_cast.copy()) - day2 = exp.Day(this=date2_cast.copy()) - - # Last day of month components - last_day_of_month1 = exp.LastDay(this=date1_cast.copy()) - last_day_of_month2 = exp.LastDay(this=date2_cast.copy()) - - day_of_last_day1 = exp.Day(this=last_day_of_month1) - day_of_last_day2 = exp.Day(this=last_day_of_month2) - - # Check if both are last day of month - last_day1 = exp.EQ(this=day1.copy(), expression=day_of_last_day1) - last_day2 = exp.EQ(this=day2.copy(), expression=day_of_last_day2) - both_last_day = exp.And(this=last_day1, expression=last_day2) - - # Fractional part: (DAY(date1) - DAY(date2)) / 31.0 - fractional = exp.Div( - this=exp.Paren(this=exp.Sub(this=day1.copy(), expression=day2.copy())), - expression=exp.Literal.number("31.0"), - ) - - # If both are last day of month, fractional = 0, else calculate fractional - fractional_with_check = exp.If( - this=both_last_day, true=exp.Literal.number("0"), false=fractional - ) - - # Final result: whole_months + fractional - result = exp.Add(this=whole_months, expression=fractional_with_check) - - return self.sql(result) - - -def build_formatted_time( - exp_class: t.Type[E], dialect: str, default: t.Optional[bool | str] = None -) -> t.Callable[[t.List], E]: - """Helper used for time expressions. - - Args: - exp_class: the expression class to instantiate. - dialect: target sql dialect. - default: the default format, True being time. - - Returns: - A callable that can be used to return the appropriately formatted time expression. - """ - - def _builder(args: t.List): - return exp_class( - this=seq_get(args, 0), - format=Dialect[dialect].format_time( - seq_get(args, 1) - or ( - Dialect[dialect].TIME_FORMAT if default is True else default or None - ) - ), - ) - - return _builder - - -def time_format( - dialect: DialectType = None, -) -> t.Callable[[Generator, exp.UnixToStr | exp.StrToUnix], t.Optional[str]]: - def _time_format( - self: Generator, expression: exp.UnixToStr | exp.StrToUnix - ) -> t.Optional[str]: - """ - Returns the time format for a given expression, unless it's equivalent - to the default time format of the dialect of interest. - """ - time_format = self.format_time(expression) - return ( - time_format - if time_format != Dialect.get_or_raise(dialect).TIME_FORMAT - else None - ) - - return _time_format - - -def build_date_delta( - exp_class: t.Type[E], - unit_mapping: t.Optional[t.Dict[str, str]] = None, - default_unit: t.Optional[str] = "DAY", - supports_timezone: bool = False, -) -> t.Callable[[t.List], E]: - def _builder(args: t.List) -> E: - unit_based = len(args) >= 3 - has_timezone = len(args) == 4 - this = args[2] if unit_based else seq_get(args, 0) - unit = None - if unit_based or default_unit: - unit = args[0] if unit_based else exp.Literal.string(default_unit) - unit = ( - exp.var(unit_mapping.get(unit.name.lower(), unit.name)) - if unit_mapping - else unit - ) - expression = exp_class(this=this, expression=seq_get(args, 1), unit=unit) - if supports_timezone and has_timezone: - expression.set("zone", args[-1]) - return expression - - return _builder - - -def build_date_delta_with_interval( - expression_class: t.Type[E], -) -> t.Callable[[t.List], t.Optional[E]]: - def _builder(args: t.List) -> t.Optional[E]: - if len(args) < 2: - return None - - interval = args[1] - - if not isinstance(interval, exp.Interval): - raise ParseError(f"INTERVAL expression expected but got '{interval}'") - - return expression_class( - this=args[0], expression=interval.this, unit=unit_to_str(interval) - ) - - return _builder - - -def date_trunc_to_time(args: t.List) -> exp.DateTrunc | exp.TimestampTrunc: - unit = seq_get(args, 0) - this = seq_get(args, 1) - - if isinstance(this, exp.Cast) and this.is_type("date"): - return exp.DateTrunc(unit=unit, this=this) - return exp.TimestampTrunc(this=this, unit=unit) - - -def date_add_interval_sql( - data_type: str, kind: str -) -> t.Callable[[Generator, exp.Expression], str]: - def func(self: Generator, expression: exp.Expression) -> str: - this = self.sql(expression, "this") - interval = exp.Interval( - this=expression.expression, unit=unit_to_var(expression) - ) - return f"{data_type}_{kind}({this}, {self.sql(interval)})" - - return func - - -def timestamptrunc_sql( - func: str = "DATE_TRUNC", zone: bool = False -) -> t.Callable[[Generator, exp.TimestampTrunc], str]: - def _timestamptrunc_sql(self: Generator, expression: exp.TimestampTrunc) -> str: - args = [unit_to_str(expression), expression.this] - if zone: - args.append(expression.args.get("zone")) - return self.func(func, *args) - - return _timestamptrunc_sql - - -def no_timestamp_sql(self: Generator, expression: exp.Timestamp) -> str: - zone = expression.args.get("zone") - if not zone: - from sqlglot.optimizer.annotate_types import annotate_types - - target_type = ( - annotate_types(expression, dialect=self.dialect).type - or exp.DataType.Type.TIMESTAMP - ) - return self.sql(exp.cast(expression.this, target_type)) - if zone.name.lower() in TIMEZONES: - return self.sql( - exp.AtTimeZone( - this=exp.cast(expression.this, exp.DataType.Type.TIMESTAMP), - zone=zone, - ) - ) - return self.func("TIMESTAMP", expression.this, zone) - - -def no_time_sql(self: Generator, expression: exp.Time) -> str: - # Transpile BQ's TIME(timestamp, zone) to CAST(TIMESTAMPTZ AT TIME ZONE AS TIME) - this = exp.cast(expression.this, exp.DataType.Type.TIMESTAMPTZ) - expr = exp.cast( - exp.AtTimeZone(this=this, zone=expression.args.get("zone")), - exp.DataType.Type.TIME, - ) - return self.sql(expr) - - -def no_datetime_sql(self: Generator, expression: exp.Datetime) -> str: - this = expression.this - expr = expression.expression - - if expr.name.lower() in TIMEZONES: - # Transpile BQ's DATETIME(timestamp, zone) to CAST(TIMESTAMPTZ AT TIME ZONE AS TIMESTAMP) - this = exp.cast(this, exp.DataType.Type.TIMESTAMPTZ) - this = exp.cast( - exp.AtTimeZone(this=this, zone=expr), exp.DataType.Type.TIMESTAMP - ) - return self.sql(this) - - this = exp.cast(this, exp.DataType.Type.DATE) - expr = exp.cast(expr, exp.DataType.Type.TIME) - - return self.sql( - exp.cast(exp.Add(this=this, expression=expr), exp.DataType.Type.TIMESTAMP) - ) - - -def left_to_substring_sql(self: Generator, expression: exp.Left) -> str: - return self.sql( - exp.Substring( - this=expression.this, - start=exp.Literal.number(1), - length=expression.expression, - ) - ) - - -def right_to_substring_sql(self: Generator, expression: exp.Left) -> str: - return self.sql( - exp.Substring( - this=expression.this, - start=exp.Length(this=expression.this) - - exp.paren(expression.expression - 1), - ) - ) - - -def timestrtotime_sql( - self: Generator, - expression: exp.TimeStrToTime, - include_precision: bool = False, -) -> str: - datatype = exp.DataType.build( - exp.DataType.Type.TIMESTAMPTZ - if expression.args.get("zone") - else exp.DataType.Type.TIMESTAMP - ) - - if isinstance(expression.this, exp.Literal) and include_precision: - precision = subsecond_precision(expression.this.name) - if precision > 0: - datatype = exp.DataType.build( - datatype.this, - expressions=[exp.DataTypeParam(this=exp.Literal.number(precision))], - ) - - return self.sql(exp.cast(expression.this, datatype, dialect=self.dialect)) - - -def datestrtodate_sql(self: Generator, expression: exp.DateStrToDate) -> str: - return self.sql(exp.cast(expression.this, exp.DataType.Type.DATE)) - - -# Used for Presto and Duckdb which use functions that don't support charset, and assume utf-8 -def encode_decode_sql( - self: Generator, expression: exp.Expression, name: str, replace: bool = True -) -> str: - charset = expression.args.get("charset") - if charset and charset.name.lower() != "utf-8": - self.unsupported(f"Expected utf-8 character set, got {charset}.") - - return self.func( - name, expression.this, expression.args.get("replace") if replace else None - ) - - -def min_or_least(self: Generator, expression: exp.Min) -> str: - name = "LEAST" if expression.expressions else "MIN" - return rename_func(name)(self, expression) - - -def max_or_greatest(self: Generator, expression: exp.Max) -> str: - name = "GREATEST" if expression.expressions else "MAX" - return rename_func(name)(self, expression) - - -def count_if_to_sum(self: Generator, expression: exp.CountIf) -> str: - cond = expression.this - - if isinstance(expression.this, exp.Distinct): - cond = expression.this.expressions[0] - self.unsupported("DISTINCT is not supported when converting COUNT_IF to SUM") - - return self.func("sum", exp.func("if", cond, 1, 0)) - - -def trim_sql(self: Generator, expression: exp.Trim, default_trim_type: str = "") -> str: - target = self.sql(expression, "this") - trim_type = self.sql(expression, "position") or default_trim_type - remove_chars = self.sql(expression, "expression") - collation = self.sql(expression, "collation") - - # Use TRIM/LTRIM/RTRIM syntax if the expression isn't database-specific - if not remove_chars: - return self.trim_sql(expression) - - trim_type = f"{trim_type} " if trim_type else "" - remove_chars = f"{remove_chars} " if remove_chars else "" - from_part = "FROM " if trim_type or remove_chars else "" - collation = f" COLLATE {collation}" if collation else "" - return f"TRIM({trim_type}{remove_chars}{from_part}{target}{collation})" - - -def str_to_time_sql(self: Generator, expression: exp.Expression) -> str: - return self.func("STRPTIME", expression.this, self.format_time(expression)) - - -def concat_to_dpipe_sql(self: Generator, expression: exp.Concat) -> str: - return self.sql( - reduce(lambda x, y: exp.DPipe(this=x, expression=y), expression.expressions) - ) - - -def concat_ws_to_dpipe_sql(self: Generator, expression: exp.ConcatWs) -> str: - delim, *rest_args = expression.expressions - return self.sql( - reduce( - lambda x, y: exp.DPipe( - this=x, expression=exp.DPipe(this=delim, expression=y) - ), - rest_args, - ) - ) - - -@unsupported_args("position", "occurrence", "parameters") -def regexp_extract_sql( - self: Generator, expression: exp.RegexpExtract | exp.RegexpExtractAll -) -> str: - group = expression.args.get("group") - - # Do not render group if it's the default value for this dialect - if group and group.name == str(self.dialect.REGEXP_EXTRACT_DEFAULT_GROUP): - group = None - - return self.func( - expression.sql_name(), expression.this, expression.expression, group - ) - - -@unsupported_args("position", "occurrence", "modifiers") -def regexp_replace_sql(self: Generator, expression: exp.RegexpReplace) -> str: - return self.func( - "REGEXP_REPLACE", - expression.this, - expression.expression, - expression.args["replacement"], - ) - - -def pivot_column_names( - aggregations: t.List[exp.Expression], dialect: DialectType -) -> t.List[str]: - names = [] - for agg in aggregations: - if isinstance(agg, exp.Alias): - names.append(agg.alias) - else: - """ - This case corresponds to aggregations without aliases being used as suffixes - (e.g. col_avg(foo)). We need to unquote identifiers because they're going to - be quoted in the base parser's `_parse_pivot` method, due to `to_identifier`. - Otherwise, we'd end up with `col_avg(`foo`)` (notice the double quotes). - """ - agg_all_unquoted = agg.transform( - lambda node: ( - exp.Identifier(this=node.name, quoted=False) - if isinstance(node, exp.Identifier) - else node - ) - ) - names.append( - agg_all_unquoted.sql(dialect=dialect, normalize_functions="lower") - ) - - return names - - -def binary_from_function(expr_type: t.Type[B]) -> t.Callable[[t.List], B]: - return lambda args: expr_type(this=seq_get(args, 0), expression=seq_get(args, 1)) - - -# Used to represent DATE_TRUNC in Doris, Postgres and Starrocks dialects -def build_timestamp_trunc(args: t.List) -> exp.TimestampTrunc: - return exp.TimestampTrunc(this=seq_get(args, 1), unit=seq_get(args, 0)) - - -def any_value_to_max_sql(self: Generator, expression: exp.AnyValue) -> str: - return self.func("MAX", expression.this) - - -def bool_xor_sql(self: Generator, expression: exp.Xor) -> str: - a = self.sql(expression.left) - b = self.sql(expression.right) - return f"({a} AND (NOT {b})) OR ((NOT {a}) AND {b})" - - -def is_parse_json(expression: exp.Expression) -> bool: - return isinstance(expression, exp.ParseJSON) or ( - isinstance(expression, exp.Cast) and expression.is_type("json") - ) - - -def isnull_to_is_null(args: t.List) -> exp.Expression: - return exp.Paren(this=exp.Is(this=seq_get(args, 0), expression=exp.null())) - - -def generatedasidentitycolumnconstraint_sql( - self: Generator, expression: exp.GeneratedAsIdentityColumnConstraint -) -> str: - start = self.sql(expression, "start") or "1" - increment = self.sql(expression, "increment") or "1" - return f"IDENTITY({start}, {increment})" - - -def arg_max_or_min_no_count( - name: str, -) -> t.Callable[[Generator, exp.ArgMax | exp.ArgMin], str]: - @unsupported_args("count") - def _arg_max_or_min_sql( - self: Generator, expression: exp.ArgMax | exp.ArgMin - ) -> str: - return self.func(name, expression.this, expression.expression) - - return _arg_max_or_min_sql - - -def ts_or_ds_add_cast(expression: exp.TsOrDsAdd) -> exp.TsOrDsAdd: - this = expression.this.copy() - - return_type = expression.return_type - if return_type.is_type(exp.DataType.Type.DATE): - # If we need to cast to a DATE, we cast to TIMESTAMP first to make sure we - # can truncate timestamp strings, because some dialects can't cast them to DATE - this = exp.cast(this, exp.DataType.Type.TIMESTAMP) - - expression.this.replace(exp.cast(this, return_type)) - return expression - - -def date_delta_sql( - name: str, cast: bool = False -) -> t.Callable[[Generator, DATE_ADD_OR_DIFF], str]: - def _delta_sql(self: Generator, expression: DATE_ADD_OR_DIFF) -> str: - if cast and isinstance(expression, exp.TsOrDsAdd): - expression = ts_or_ds_add_cast(expression) - - return self.func( - name, - unit_to_var(expression), - expression.expression, - expression.this, - ) - - return _delta_sql - - -def date_delta_to_binary_interval_op( - cast: bool = True, -) -> t.Callable[[Generator, DATETIME_DELTA], str]: - def date_delta_to_binary_interval_op_sql( - self: Generator, expression: DATETIME_DELTA - ) -> str: - this = expression.this - unit = unit_to_var(expression) - op = "+" if isinstance(expression, DATETIME_ADD) else "-" - - to_type: t.Optional[exp.DATA_TYPE] = None - if cast: - if isinstance(expression, exp.TsOrDsAdd): - to_type = expression.return_type - elif this.is_string: - # Cast string literals (i.e function parameters) to the appropriate type for +/- interval to work - to_type = ( - exp.DataType.Type.DATETIME - if isinstance(expression, (exp.DatetimeAdd, exp.DatetimeSub)) - else exp.DataType.Type.DATE - ) - - this = exp.cast(this, to_type) if to_type else this - - expr = expression.expression - interval = ( - expr - if isinstance(expr, exp.Interval) - else exp.Interval(this=expr, unit=unit) - ) - - return f"{self.sql(this)} {op} {self.sql(interval)}" - - return date_delta_to_binary_interval_op_sql - - -def unit_to_str( - expression: exp.Expression, default: str = "DAY" -) -> t.Optional[exp.Expression]: - unit = expression.args.get("unit") - if not unit: - return exp.Literal.string(default) if default else None - - if isinstance(unit, exp.Placeholder) or type(unit) not in (exp.Var, exp.Literal): - return unit - - return exp.Literal.string(unit.name) - - -def unit_to_var( - expression: exp.Expression, default: str = "DAY" -) -> t.Optional[exp.Expression]: - unit = expression.args.get("unit") - - if isinstance(unit, (exp.Var, exp.Placeholder, exp.WeekStart, exp.Column)): - return unit - - value = unit.name if unit else default - return exp.Var(this=value) if value else None - - -@t.overload -def map_date_part(part: exp.Expression, dialect: DialectType = Dialect) -> exp.Var: - pass - - -@t.overload -def map_date_part( - part: t.Optional[exp.Expression], dialect: DialectType = Dialect -) -> t.Optional[exp.Expression]: - pass - - -def map_date_part(part, dialect: DialectType = Dialect): - mapped = ( - Dialect.get_or_raise(dialect).DATE_PART_MAPPING.get(part.name.upper()) - if part and not (isinstance(part, exp.Column) and len(part.parts) != 1) - else None - ) - if mapped: - return exp.Literal.string(mapped) if part.is_string else exp.var(mapped) - - return part - - -def no_last_day_sql(self: Generator, expression: exp.LastDay) -> str: - trunc_curr_date = exp.func("date_trunc", "month", expression.this) - plus_one_month = exp.func("date_add", trunc_curr_date, 1, "month") - minus_one_day = exp.func("date_sub", plus_one_month, 1, "day") - - return self.sql(exp.cast(minus_one_day, exp.DataType.Type.DATE)) - - -def merge_without_target_sql(self: Generator, expression: exp.Merge) -> str: - """Remove table refs from columns in when statements.""" - alias = expression.this.args.get("alias") - - def normalize(identifier: t.Optional[exp.Identifier]) -> t.Optional[str]: - return ( - self.dialect.normalize_identifier(identifier).name if identifier else None - ) - - targets = {normalize(expression.this.this)} - - if alias: - targets.add(normalize(alias.this)) - - for when in expression.args["whens"].expressions: - # only remove the target table names from certain parts of WHEN MATCHED / WHEN NOT MATCHED - # they are still valid in the , the right hand side of each UPDATE and the VALUES part - # (not the column list) of the INSERT - then: exp.Insert | exp.Update | None = when.args.get("then") - if then: - if isinstance(then, exp.Update): - for equals in then.find_all(exp.EQ): - equal_lhs = equals.this - if ( - isinstance(equal_lhs, exp.Column) - and normalize(equal_lhs.args.get("table")) in targets - ): - equal_lhs.replace(exp.column(equal_lhs.this)) - if isinstance(then, exp.Insert): - column_list = then.this - if isinstance(column_list, exp.Tuple): - for column in column_list.expressions: - if normalize(column.args.get("table")) in targets: - column.replace(exp.column(column.this)) - - return self.merge_sql(expression) - - -def build_json_extract_path( - expr_type: t.Type[F], - zero_based_indexing: bool = True, - arrow_req_json_type: bool = False, - json_type: t.Optional[str] = None, -) -> t.Callable[[t.List], F]: - def _builder(args: t.List) -> F: - segments: t.List[exp.JSONPathPart] = [exp.JSONPathRoot()] - for arg in args[1:]: - if not isinstance(arg, exp.Literal): - # We use the fallback parser because we can't really transpile non-literals safely - return expr_type.from_arg_list(args) - - text = arg.name - if is_int(text) and (not arrow_req_json_type or not arg.is_string): - index = int(text) - segments.append( - exp.JSONPathSubscript( - this=index if zero_based_indexing else index - 1 - ) - ) - else: - segments.append(exp.JSONPathKey(this=text)) - - # This is done to avoid failing in the expression validator due to the arg count - del args[2:] - kwargs = { - "this": seq_get(args, 0), - "expression": exp.JSONPath(expressions=segments), - } - - is_jsonb = issubclass(expr_type, (exp.JSONBExtract, exp.JSONBExtractScalar)) - if not is_jsonb: - kwargs["only_json_types"] = arrow_req_json_type - - if json_type is not None: - kwargs["json_type"] = json_type - - return expr_type(**kwargs) - - return _builder - - -def json_extract_segments( - name: str, quoted_index: bool = True, op: t.Optional[str] = None -) -> t.Callable[[Generator, JSON_EXTRACT_TYPE], str]: - def _json_extract_segments(self: Generator, expression: JSON_EXTRACT_TYPE) -> str: - path = expression.expression - if not isinstance(path, exp.JSONPath): - return rename_func(name)(self, expression) - - escape = path.args.get("escape") - - segments = [] - for segment in path.expressions: - path = self.sql(segment) - if path: - if isinstance(segment, exp.JSONPathPart) and ( - quoted_index or not isinstance(segment, exp.JSONPathSubscript) - ): - if escape: - path = self.escape_str(path) - - path = f"{self.dialect.QUOTE_START}{path}{self.dialect.QUOTE_END}" - - segments.append(path) - - if op: - return f" {op} ".join([self.sql(expression.this), *segments]) - return self.func(name, expression.this, *segments) - - return _json_extract_segments - - -def json_path_key_only_name(self: Generator, expression: exp.JSONPathKey) -> str: - if isinstance(expression.this, exp.JSONPathWildcard): - self.unsupported("Unsupported wildcard in JSONPathKey expression") - - return expression.name - - -def filter_array_using_unnest( - self: Generator, expression: exp.ArrayFilter | exp.ArrayRemove -) -> str: - cond = expression.expression - if isinstance(cond, exp.Lambda) and len(cond.expressions) == 1: - alias = cond.expressions[0] - cond = cond.this - elif isinstance(cond, exp.Predicate): - alias = "_u" - elif isinstance(expression, exp.ArrayRemove): - alias = "_u" - cond = exp.NEQ(this=alias, expression=expression.expression) - else: - self.unsupported("Unsupported filter condition") - return "" - - unnest = exp.Unnest(expressions=[expression.this]) - filtered = ( - exp.select(alias).from_(exp.alias_(unnest, None, table=[alias])).where(cond) - ) - return self.sql(exp.Array(expressions=[filtered])) - - -def remove_from_array_using_filter(self: Generator, expression: exp.ArrayRemove) -> str: - lambda_id = exp.to_identifier("_u") - cond = exp.NEQ(this=lambda_id, expression=expression.expression) - return self.sql( - exp.ArrayFilter( - this=expression.this, - expression=exp.Lambda(this=cond, expressions=[lambda_id]), - ) - ) - - -def to_number_with_nls_param(self: Generator, expression: exp.ToNumber) -> str: - return self.func( - "TO_NUMBER", - expression.this, - expression.args.get("format"), - expression.args.get("nlsparam"), - ) - - -def build_default_decimal_type( - precision: t.Optional[int] = None, scale: t.Optional[int] = None -) -> t.Callable[[exp.DataType], exp.DataType]: - def _builder(dtype: exp.DataType) -> exp.DataType: - if dtype.expressions or precision is None: - return dtype - - params = f"{precision}{f', {scale}' if scale is not None else ''}" - return exp.DataType.build(f"DECIMAL({params})") - - return _builder - - -def build_timestamp_from_parts(args: t.List) -> exp.Func: - if len(args) == 2: - # Other dialects don't have the TIMESTAMP_FROM_PARTS(date, time) concept, - # so we parse this into Anonymous for now instead of introducing complexity - return exp.Anonymous(this="TIMESTAMP_FROM_PARTS", expressions=args) - - return exp.TimestampFromParts.from_arg_list(args) - - -def sha256_sql(self: Generator, expression: exp.SHA2) -> str: - return self.func(f"SHA{expression.text('length') or '256'}", expression.this) - - -def sha2_digest_sql(self: Generator, expression: exp.SHA2Digest) -> str: - return self.func(f"SHA{expression.text('length') or '256'}", expression.this) - - -def sequence_sql( - self: Generator, expression: exp.GenerateSeries | exp.GenerateDateArray -) -> str: - start = expression.args.get("start") - end = expression.args.get("end") - step = expression.args.get("step") - - if isinstance(start, exp.Cast): - target_type = start.to - elif isinstance(end, exp.Cast): - target_type = end.to - else: - target_type = None - - if start and end: - if target_type and target_type.is_type("date", "timestamp"): - if isinstance(start, exp.Cast) and target_type is start.to: - end = exp.cast(end, target_type) - else: - start = exp.cast(start, target_type) - - if expression.args.get("is_end_exclusive"): - step_value = step or exp.Literal.number(1) - end = exp.paren(exp.Sub(this=end, expression=step_value), copy=False) - - sequence_call = exp.Anonymous( - this="SEQUENCE", expressions=[e for e in (start, end, step) if e] - ) - zero = exp.Literal.number(0) - should_return_empty = exp.or_( - exp.EQ(this=step_value.copy(), expression=zero.copy()), - exp.and_( - exp.GT(this=step_value.copy(), expression=zero.copy()), - exp.GTE(this=start.copy(), expression=end.copy()), - ), - exp.and_( - exp.LT(this=step_value.copy(), expression=zero.copy()), - exp.LTE(this=start.copy(), expression=end.copy()), - ), - ) - empty_array_or_sequence = exp.If( - this=should_return_empty, - true=exp.Array(expressions=[]), - false=sequence_call, - ) - return self.sql(self._simplify_unless_literal(empty_array_or_sequence)) - - return self.func("SEQUENCE", start, end, step) - - -def build_like( - expr_type: t.Type[E], not_like: bool = False -) -> t.Callable[[t.List], exp.Expression]: - def _builder(args: t.List) -> exp.Expression: - like_expr: exp.Expression = expr_type( - this=seq_get(args, 0), expression=seq_get(args, 1) - ) - - if escape := seq_get(args, 2): - like_expr = exp.Escape(this=like_expr, expression=escape) - - if not_like: - like_expr = exp.Not(this=like_expr) - - return like_expr - - return _builder - - -def build_regexp_extract(expr_type: t.Type[E]) -> t.Callable[[t.List, Dialect], E]: - def _builder(args: t.List, dialect: Dialect) -> E: - # The "position" argument specifies the index of the string character to start matching from. - # `null_if_pos_overflow` reflects the dialect's behavior when position is greater than the string - # length. If true, returns NULL. If false, returns an empty string. `null_if_pos_overflow` is - # only needed for exp.RegexpExtract - exp.RegexpExtractAll always returns an empty array if - # position overflows. - return expr_type( - this=seq_get(args, 0), - expression=seq_get(args, 1), - group=seq_get(args, 2) - or exp.Literal.number(dialect.REGEXP_EXTRACT_DEFAULT_GROUP), - parameters=seq_get(args, 3), - **( - { - "null_if_pos_overflow": dialect.REGEXP_EXTRACT_POSITION_OVERFLOW_RETURNS_NULL - } - if expr_type is exp.RegexpExtract - else {} - ), - ) - - return _builder - - -def explode_to_unnest_sql(self: Generator, expression: exp.Lateral) -> str: - if isinstance(expression.this, exp.Explode): - return self.sql( - exp.Join( - this=exp.Unnest( - expressions=[expression.this.this], - alias=expression.args.get("alias"), - offset=isinstance(expression.this, exp.Posexplode), - ), - kind="cross", - ) - ) - return self.lateral_sql(expression) - - -def timestampdiff_sql( - self: Generator, expression: exp.DatetimeDiff | exp.TimestampDiff -) -> str: - return self.func( - "TIMESTAMPDIFF", expression.unit, expression.expression, expression.this - ) - - -def no_make_interval_sql( - self: Generator, expression: exp.MakeInterval, sep: str = ", " -) -> str: - args = [] - for unit, value in expression.args.items(): - if isinstance(value, exp.Kwarg): - value = value.expression - - args.append(f"{value} {unit}") - - return f"INTERVAL '{self.format_args(*args, sep=sep)}'" - - -def length_or_char_length_sql(self: Generator, expression: exp.Length) -> str: - length_func = "LENGTH" if expression.args.get("binary") else "CHAR_LENGTH" - return self.func(length_func, expression.this) - - -def groupconcat_sql( - self: Generator, - expression: exp.GroupConcat, - func_name="LISTAGG", - sep: t.Optional[str] = ",", - within_group: bool = True, - on_overflow: bool = False, -) -> str: - this = expression.this - separator = self.sql( - expression.args.get("separator") or (exp.Literal.string(sep) if sep else None) - ) - - on_overflow_sql = self.sql(expression, "on_overflow") - on_overflow_sql = ( - f" ON OVERFLOW {on_overflow_sql}" if (on_overflow and on_overflow_sql) else "" - ) - - if isinstance(this, exp.Limit) and this.this: - limit = this - this = limit.this.pop() - else: - limit = None - - order = this.find(exp.Order) - - if order and order.this: - this = order.this.pop() - - args = self.format_args( - this, f"{separator}{on_overflow_sql}" if separator or on_overflow_sql else None - ) - - listagg: exp.Expression = exp.Anonymous(this=func_name, expressions=[args]) - - modifiers = self.sql(limit) - - if order: - if within_group: - listagg = exp.WithinGroup(this=listagg, expression=order) - else: - modifiers = f"{self.sql(order)}{modifiers}" - - if modifiers: - listagg.set("expressions", [f"{args}{modifiers}"]) - - return self.sql(listagg) - - -def build_timetostr_or_tochar( - args: t.List, dialect: DialectType -) -> exp.TimeToStr | exp.ToChar: - if len(args) == 2: - this = args[0] - if not this.type: - from sqlglot.optimizer.annotate_types import annotate_types - - annotate_types(this, dialect=dialect) - - if this.is_type(*exp.DataType.TEMPORAL_TYPES): - dialect_name = dialect.__class__.__name__.lower() - return build_formatted_time(exp.TimeToStr, dialect_name, default=True)(args) - - return exp.ToChar.from_arg_list(args) - - -def build_replace_with_optional_replacement(args: t.List) -> exp.Replace: - return exp.Replace( - this=seq_get(args, 0), - expression=seq_get(args, 1), - replacement=seq_get(args, 2) or exp.Literal.string(""), - ) - - -def regexp_replace_global_modifier( - expression: exp.RegexpReplace, -) -> exp.Expression | None: - modifiers = expression.args.get("modifiers") - single_replace = expression.args.get("single_replace") - occurrence = expression.args.get("occurrence") - - if not single_replace and ( - not occurrence or (occurrence.is_int and occurrence.to_py() == 0) - ): - if not modifiers or modifiers.is_string: - # Append 'g' to the modifiers if they are not provided since - # the semantics of REGEXP_REPLACE from the input dialect - # is to replace all occurrences of the pattern. - value = "" if not modifiers else modifiers.name - modifiers = exp.Literal.string(value + "g") - - return modifiers diff --git a/third_party/bigframes_vendored/sqlglot/diff.py b/third_party/bigframes_vendored/sqlglot/diff.py deleted file mode 100644 index e0e4eb4b0be..00000000000 --- a/third_party/bigframes_vendored/sqlglot/diff.py +++ /dev/null @@ -1,513 +0,0 @@ -# Contains code from https://github.com/tobymao/sqlglot/blob/v28.5.0/sqlglot/diff.py - -""" -.. include:: ../posts/sql_diff.md - ----- -""" - -from __future__ import annotations - -import typing as t -from collections import defaultdict -from dataclasses import dataclass -from heapq import heappop, heappush -from itertools import chain - -from bigframes_vendored.sqlglot import Dialect -from bigframes_vendored.sqlglot import expressions as exp -from bigframes_vendored.sqlglot.helper import seq_get - -if t.TYPE_CHECKING: - from bigframes_vendored.sqlglot.dialects.dialect import DialectType - - -@dataclass(frozen=True) -class Insert: - """Indicates that a new node has been inserted""" - - expression: exp.Expression - - -@dataclass(frozen=True) -class Remove: - """Indicates that an existing node has been removed""" - - expression: exp.Expression - - -@dataclass(frozen=True) -class Move: - """Indicates that an existing node's position within the tree has changed""" - - source: exp.Expression - target: exp.Expression - - -@dataclass(frozen=True) -class Update: - """Indicates that an existing node has been updated""" - - source: exp.Expression - target: exp.Expression - - -@dataclass(frozen=True) -class Keep: - """Indicates that an existing node hasn't been changed""" - - source: exp.Expression - target: exp.Expression - - -if t.TYPE_CHECKING: - from bigframes_vendored.sqlglot._typing import T - - Edit = t.Union[Insert, Remove, Move, Update, Keep] - - -def diff( - source: exp.Expression, - target: exp.Expression, - matchings: t.List[t.Tuple[exp.Expression, exp.Expression]] | None = None, - delta_only: bool = False, - **kwargs: t.Any, -) -> t.List[Edit]: - """ - Returns the list of changes between the source and the target expressions. - - Examples: - >>> diff(parse_one("a + b"), parse_one("a + c")) - [ - Remove(expression=(COLUMN this: (IDENTIFIER this: b, quoted: False))), - Insert(expression=(COLUMN this: (IDENTIFIER this: c, quoted: False))), - Keep( - source=(ADD this: ...), - target=(ADD this: ...) - ), - Keep( - source=(COLUMN this: (IDENTIFIER this: a, quoted: False)), - target=(COLUMN this: (IDENTIFIER this: a, quoted: False)) - ), - ] - - Args: - source: the source expression. - target: the target expression against which the diff should be calculated. - matchings: the list of pre-matched node pairs which is used to help the algorithm's - heuristics produce better results for subtrees that are known by a caller to be matching. - Note: expression references in this list must refer to the same node objects that are - referenced in the source / target trees. - delta_only: excludes all `Keep` nodes from the diff. - kwargs: additional arguments to pass to the ChangeDistiller instance. - - Returns: - the list of Insert, Remove, Move, Update and Keep objects for each node in the source and the - target expression trees. This list represents a sequence of steps needed to transform the source - expression tree into the target one. - """ - matchings = matchings or [] - - def compute_node_mappings( - old_nodes: tuple[exp.Expression, ...], new_nodes: tuple[exp.Expression, ...] - ) -> t.Dict[int, exp.Expression]: - node_mapping = {} - for old_node, new_node in zip(reversed(old_nodes), reversed(new_nodes)): - new_node._hash = hash(new_node) - node_mapping[id(old_node)] = new_node - - return node_mapping - - # if the source and target have any shared objects, that means there's an issue with the ast - # the algorithm won't work because the parent / hierarchies will be inaccurate - source_nodes = tuple(source.walk()) - target_nodes = tuple(target.walk()) - source_ids = {id(n) for n in source_nodes} - target_ids = {id(n) for n in target_nodes} - - copy = ( - len(source_nodes) != len(source_ids) - or len(target_nodes) != len(target_ids) - or source_ids & target_ids - ) - - source_copy = source.copy() if copy else source - target_copy = target.copy() if copy else target - - try: - # We cache the hash of each new node here to speed up equality comparisons. If the input - # trees aren't copied, these hashes will be evicted before returning the edit script. - if copy and matchings: - source_mapping = compute_node_mappings( - source_nodes, tuple(source_copy.walk()) - ) - target_mapping = compute_node_mappings( - target_nodes, tuple(target_copy.walk()) - ) - matchings = [ - (source_mapping[id(s)], target_mapping[id(t)]) for s, t in matchings - ] - else: - for node in chain(reversed(source_nodes), reversed(target_nodes)): - node._hash = hash(node) - - edit_script = ChangeDistiller(**kwargs).diff( - source_copy, - target_copy, - matchings=matchings, - delta_only=delta_only, - ) - finally: - if not copy: - for node in chain(source_nodes, target_nodes): - node._hash = None - - return edit_script - - -# The expression types for which Update edits are allowed. -UPDATABLE_EXPRESSION_TYPES = ( - exp.Alias, - exp.Boolean, - exp.Column, - exp.DataType, - exp.Lambda, - exp.Literal, - exp.Table, - exp.Window, -) - -IGNORED_LEAF_EXPRESSION_TYPES = (exp.Identifier,) - - -class ChangeDistiller: - """ - The implementation of the Change Distiller algorithm described by Beat Fluri and Martin Pinzger in - their paper https://ieeexplore.ieee.org/document/4339230, which in turn is based on the algorithm by - Chawathe et al. described in http://ilpubs.stanford.edu:8090/115/1/1995-46.pdf. - """ - - def __init__( - self, f: float = 0.6, t: float = 0.6, dialect: DialectType = None - ) -> None: - self.f = f - self.t = t - self._sql_generator = Dialect.get_or_raise(dialect).generator() - - def diff( - self, - source: exp.Expression, - target: exp.Expression, - matchings: t.List[t.Tuple[exp.Expression, exp.Expression]] | None = None, - delta_only: bool = False, - ) -> t.List[Edit]: - matchings = matchings or [] - pre_matched_nodes = {id(s): id(t) for s, t in matchings} - - self._source = source - self._target = target - self._source_index = { - id(n): n - for n in self._source.bfs() - if not isinstance(n, IGNORED_LEAF_EXPRESSION_TYPES) - } - self._target_index = { - id(n): n - for n in self._target.bfs() - if not isinstance(n, IGNORED_LEAF_EXPRESSION_TYPES) - } - self._unmatched_source_nodes = set(self._source_index) - set(pre_matched_nodes) - self._unmatched_target_nodes = set(self._target_index) - set( - pre_matched_nodes.values() - ) - self._bigram_histo_cache: t.Dict[int, t.DefaultDict[str, int]] = {} - - matching_set = self._compute_matching_set() | set(pre_matched_nodes.items()) - return self._generate_edit_script(dict(matching_set), delta_only) - - def _generate_edit_script( - self, matchings: t.Dict[int, int], delta_only: bool - ) -> t.List[Edit]: - edit_script: t.List[Edit] = [] - for removed_node_id in self._unmatched_source_nodes: - edit_script.append(Remove(self._source_index[removed_node_id])) - for inserted_node_id in self._unmatched_target_nodes: - edit_script.append(Insert(self._target_index[inserted_node_id])) - for kept_source_node_id, kept_target_node_id in matchings.items(): - source_node = self._source_index[kept_source_node_id] - target_node = self._target_index[kept_target_node_id] - - identical_nodes = source_node == target_node - - if ( - not isinstance(source_node, UPDATABLE_EXPRESSION_TYPES) - or identical_nodes - ): - if identical_nodes: - source_parent = source_node.parent - target_parent = target_node.parent - - if ( - (source_parent and not target_parent) - or (not source_parent and target_parent) - or ( - source_parent - and target_parent - and matchings.get(id(source_parent)) != id(target_parent) - ) - ): - edit_script.append(Move(source=source_node, target=target_node)) - else: - edit_script.extend( - self._generate_move_edits(source_node, target_node, matchings) - ) - - source_non_expression_leaves = dict( - _get_non_expression_leaves(source_node) - ) - target_non_expression_leaves = dict( - _get_non_expression_leaves(target_node) - ) - - if source_non_expression_leaves != target_non_expression_leaves: - edit_script.append(Update(source_node, target_node)) - elif not delta_only: - edit_script.append(Keep(source_node, target_node)) - else: - edit_script.append(Update(source_node, target_node)) - - return edit_script - - def _generate_move_edits( - self, - source: exp.Expression, - target: exp.Expression, - matchings: t.Dict[int, int], - ) -> t.List[Move]: - source_args = [id(e) for e in _expression_only_args(source)] - target_args = [id(e) for e in _expression_only_args(target)] - - args_lcs = set( - _lcs( - source_args, - target_args, - lambda ll, r: matchings.get(t.cast(int, ll)) == r, - ) - ) - - move_edits = [] - for a in source_args: - if a not in args_lcs and a not in self._unmatched_source_nodes: - move_edits.append( - Move( - source=self._source_index[a], - target=self._target_index[matchings[a]], - ) - ) - - return move_edits - - def _compute_matching_set(self) -> t.Set[t.Tuple[int, int]]: - leaves_matching_set = self._compute_leaf_matching_set() - matching_set = leaves_matching_set.copy() - - ordered_unmatched_source_nodes = { - id(n): None - for n in self._source.bfs() - if id(n) in self._unmatched_source_nodes - } - ordered_unmatched_target_nodes = { - id(n): None - for n in self._target.bfs() - if id(n) in self._unmatched_target_nodes - } - - for source_node_id in ordered_unmatched_source_nodes: - for target_node_id in ordered_unmatched_target_nodes: - source_node = self._source_index[source_node_id] - target_node = self._target_index[target_node_id] - if _is_same_type(source_node, target_node): - source_leaf_ids = { - id(ll) for ll in _get_expression_leaves(source_node) - } - target_leaf_ids = { - id(ll) for ll in _get_expression_leaves(target_node) - } - - max_leaves_num = max(len(source_leaf_ids), len(target_leaf_ids)) - if max_leaves_num: - common_leaves_num = sum( - 1 if s in source_leaf_ids and t in target_leaf_ids else 0 - for s, t in leaves_matching_set - ) - leaf_similarity_score = common_leaves_num / max_leaves_num - else: - leaf_similarity_score = 0.0 - - adjusted_t = ( - self.t - if min(len(source_leaf_ids), len(target_leaf_ids)) > 4 - else 0.4 - ) - - if leaf_similarity_score >= 0.8 or ( - leaf_similarity_score >= adjusted_t - and self._dice_coefficient(source_node, target_node) >= self.f - ): - matching_set.add((source_node_id, target_node_id)) - self._unmatched_source_nodes.remove(source_node_id) - self._unmatched_target_nodes.remove(target_node_id) - ordered_unmatched_target_nodes.pop(target_node_id, None) - break - - return matching_set - - def _compute_leaf_matching_set(self) -> t.Set[t.Tuple[int, int]]: - candidate_matchings: t.List[ - t.Tuple[float, int, int, exp.Expression, exp.Expression] - ] = [] - source_expression_leaves = list(_get_expression_leaves(self._source)) - target_expression_leaves = list(_get_expression_leaves(self._target)) - for source_leaf in source_expression_leaves: - for target_leaf in target_expression_leaves: - if _is_same_type(source_leaf, target_leaf): - similarity_score = self._dice_coefficient(source_leaf, target_leaf) - if similarity_score >= self.f: - heappush( - candidate_matchings, - ( - -similarity_score, - -_parent_similarity_score(source_leaf, target_leaf), - len(candidate_matchings), - source_leaf, - target_leaf, - ), - ) - - # Pick best matchings based on the highest score - matching_set = set() - while candidate_matchings: - _, _, _, source_leaf, target_leaf = heappop(candidate_matchings) - if ( - id(source_leaf) in self._unmatched_source_nodes - and id(target_leaf) in self._unmatched_target_nodes - ): - matching_set.add((id(source_leaf), id(target_leaf))) - self._unmatched_source_nodes.remove(id(source_leaf)) - self._unmatched_target_nodes.remove(id(target_leaf)) - - return matching_set - - def _dice_coefficient( - self, source: exp.Expression, target: exp.Expression - ) -> float: - source_histo = self._bigram_histo(source) - target_histo = self._bigram_histo(target) - - total_grams = sum(source_histo.values()) + sum(target_histo.values()) - if not total_grams: - return 1.0 if source == target else 0.0 - - overlap_len = 0 - overlapping_grams = set(source_histo) & set(target_histo) - for g in overlapping_grams: - overlap_len += min(source_histo[g], target_histo[g]) - - return 2 * overlap_len / total_grams - - def _bigram_histo(self, expression: exp.Expression) -> t.DefaultDict[str, int]: - if id(expression) in self._bigram_histo_cache: - return self._bigram_histo_cache[id(expression)] - - expression_str = self._sql_generator.generate(expression) - count = max(0, len(expression_str) - 1) - bigram_histo: t.DefaultDict[str, int] = defaultdict(int) - for i in range(count): - bigram_histo[expression_str[i : i + 2]] += 1 - - self._bigram_histo_cache[id(expression)] = bigram_histo - return bigram_histo - - -def _get_expression_leaves(expression: exp.Expression) -> t.Iterator[exp.Expression]: - has_child_exprs = False - - for node in expression.iter_expressions(): - if not isinstance(node, IGNORED_LEAF_EXPRESSION_TYPES): - has_child_exprs = True - yield from _get_expression_leaves(node) - - if not has_child_exprs: - yield expression - - -def _get_non_expression_leaves( - expression: exp.Expression, -) -> t.Iterator[t.Tuple[str, t.Any]]: - for arg, value in expression.args.items(): - if ( - value is None - or isinstance(value, exp.Expression) - or ( - isinstance(value, list) - and isinstance(seq_get(value, 0), exp.Expression) - ) - ): - continue - - yield (arg, value) - - -def _is_same_type(source: exp.Expression, target: exp.Expression) -> bool: - if type(source) is type(target): - if isinstance(source, exp.Join): - return source.args.get("side") == target.args.get("side") - - if isinstance(source, exp.Anonymous): - return source.this == target.this - - return True - - return False - - -def _parent_similarity_score( - source: t.Optional[exp.Expression], target: t.Optional[exp.Expression] -) -> int: - if source is None or target is None or type(source) is not type(target): - return 0 - - return 1 + _parent_similarity_score(source.parent, target.parent) - - -def _expression_only_args(expression: exp.Expression) -> t.Iterator[exp.Expression]: - yield from ( - arg - for arg in expression.iter_expressions() - if not isinstance(arg, IGNORED_LEAF_EXPRESSION_TYPES) - ) - - -def _lcs( - seq_a: t.Sequence[T], seq_b: t.Sequence[T], equal: t.Callable[[T, T], bool] -) -> t.Sequence[t.Optional[T]]: - """Calculates the longest common subsequence""" - - len_a = len(seq_a) - len_b = len(seq_b) - lcs_result = [[None] * (len_b + 1) for i in range(len_a + 1)] - - for i in range(len_a + 1): - for j in range(len_b + 1): - if i == 0 or j == 0: - lcs_result[i][j] = [] # type: ignore - elif equal(seq_a[i - 1], seq_b[j - 1]): - lcs_result[i][j] = lcs_result[i - 1][j - 1] + [seq_a[i - 1]] # type: ignore - else: - lcs_result[i][j] = ( - lcs_result[i - 1][j] - if len(lcs_result[i - 1][j]) > len(lcs_result[i][j - 1]) # type: ignore - else lcs_result[i][j - 1] - ) - - return lcs_result[len_a][len_b] # type: ignore diff --git a/third_party/bigframes_vendored/sqlglot/errors.py b/third_party/bigframes_vendored/sqlglot/errors.py deleted file mode 100644 index fe8e31d1960..00000000000 --- a/third_party/bigframes_vendored/sqlglot/errors.py +++ /dev/null @@ -1,167 +0,0 @@ -# Contains code from https://github.com/tobymao/sqlglot/blob/v28.5.0/sqlglot/errors.py - -from __future__ import annotations - -import typing as t -from enum import auto - -from bigframes_vendored.sqlglot.helper import AutoName - -# ANSI escape codes for error formatting -ANSI_UNDERLINE = "\033[4m" -ANSI_RESET = "\033[0m" -ERROR_MESSAGE_CONTEXT_DEFAULT = 100 - - -class ErrorLevel(AutoName): - IGNORE = auto() - """Ignore all errors.""" - - WARN = auto() - """Log all errors.""" - - RAISE = auto() - """Collect all errors and raise a single exception.""" - - IMMEDIATE = auto() - """Immediately raise an exception on the first error found.""" - - -class SqlglotError(Exception): - pass - - -class UnsupportedError(SqlglotError): - pass - - -class ParseError(SqlglotError): - def __init__( - self, - message: str, - errors: t.Optional[t.List[t.Dict[str, t.Any]]] = None, - ): - super().__init__(message) - self.errors = errors or [] - - @classmethod - def new( - cls, - message: str, - description: t.Optional[str] = None, - line: t.Optional[int] = None, - col: t.Optional[int] = None, - start_context: t.Optional[str] = None, - highlight: t.Optional[str] = None, - end_context: t.Optional[str] = None, - into_expression: t.Optional[str] = None, - ) -> ParseError: - return cls( - message, - [ - { - "description": description, - "line": line, - "col": col, - "start_context": start_context, - "highlight": highlight, - "end_context": end_context, - "into_expression": into_expression, - } - ], - ) - - -class TokenError(SqlglotError): - pass - - -class OptimizeError(SqlglotError): - pass - - -class SchemaError(SqlglotError): - pass - - -class ExecuteError(SqlglotError): - pass - - -def highlight_sql( - sql: str, - positions: t.List[t.Tuple[int, int]], - context_length: int = ERROR_MESSAGE_CONTEXT_DEFAULT, -) -> t.Tuple[str, str, str, str]: - """ - Highlight a SQL string using ANSI codes at the given positions. - - Args: - sql: The complete SQL string. - positions: List of (start, end) tuples where both start and end are inclusive 0-based - indexes. For example, to highlight "foo" in "SELECT foo", use (7, 9). - The positions will be sorted and de-duplicated if they overlap. - context_length: Number of characters to show before the first highlight and after - the last highlight. - - Returns: - A tuple of (formatted_sql, start_context, highlight, end_context) where: - - formatted_sql: The SQL with ANSI underline codes applied to highlighted sections - - start_context: Plain text before the first highlight - - highlight: Plain text from the first highlight start to the last highlight end, - including any non-highlighted text in between (no ANSI) - - end_context: Plain text after the last highlight - - Note: - If positions is empty, raises a ValueError. - """ - if not positions: - raise ValueError("positions must contain at least one (start, end) tuple") - - start_context = "" - end_context = "" - first_highlight_start = 0 - formatted_parts = [] - previous_part_end = 0 - sorted_positions = sorted(positions, key=lambda pos: pos[0]) - - if sorted_positions[0][0] > 0: - first_highlight_start = sorted_positions[0][0] - start_context = sql[ - max(0, first_highlight_start - context_length) : first_highlight_start - ] - formatted_parts.append(start_context) - previous_part_end = first_highlight_start - - for start, end in sorted_positions: - highlight_start = max(start, previous_part_end) - highlight_end = end + 1 - if highlight_start >= highlight_end: - continue # Skip invalid or overlapping highlights - if highlight_start > previous_part_end: - formatted_parts.append(sql[previous_part_end:highlight_start]) - formatted_parts.append( - f"{ANSI_UNDERLINE}{sql[highlight_start:highlight_end]}{ANSI_RESET}" - ) - previous_part_end = highlight_end - - if previous_part_end < len(sql): - end_context = sql[previous_part_end : previous_part_end + context_length] - formatted_parts.append(end_context) - - formatted_sql = "".join(formatted_parts) - highlight = sql[first_highlight_start:previous_part_end] - - return formatted_sql, start_context, highlight, end_context - - -def concat_messages(errors: t.Sequence[t.Any], maximum: int) -> str: - msg = [str(e) for e in errors[:maximum]] - remaining = len(errors) - maximum - if remaining > 0: - msg.append(f"... and {remaining} more") - return "\n\n".join(msg) - - -def merge_errors(errors: t.Sequence[ParseError]) -> t.List[t.Dict[str, t.Any]]: - return [e_dict for error in errors for e_dict in error.errors] diff --git a/third_party/bigframes_vendored/sqlglot/expressions.py b/third_party/bigframes_vendored/sqlglot/expressions.py deleted file mode 100644 index e8e4cc8e10d..00000000000 --- a/third_party/bigframes_vendored/sqlglot/expressions.py +++ /dev/null @@ -1,10471 +0,0 @@ -# Contains code from https://github.com/tobymao/sqlglot/blob/v28.5.0/sqlglot/expressions.py - -""" -## Expressions - -Every AST node in SQLGlot is represented by a subclass of `Expression`. - -This module contains the implementation of all supported `Expression` types. Additionally, -it exposes a number of helper functions, which are mainly used to programmatically build -SQL expressions, such as `sqlglot.expressions.select`. - ----- -""" - -from __future__ import annotations - -import datetime -import math -import numbers -import re -import sys -import textwrap -import typing as t -from collections import deque -from copy import deepcopy -from decimal import Decimal -from enum import auto -from functools import reduce - -from bigframes_vendored.sqlglot.errors import ErrorLevel, ParseError -from bigframes_vendored.sqlglot.helper import ( - AutoName, - camel_to_snake_case, - ensure_collection, - ensure_list, - seq_get, - split_num_words, - subclasses, - to_bool, -) -from bigframes_vendored.sqlglot.tokens import Token, TokenError - -if t.TYPE_CHECKING: - from bigframes_vendored.sqlglot._typing import E, Lit - from bigframes_vendored.sqlglot.dialects.dialect import DialectType - from typing_extensions import Self - - Q = t.TypeVar("Q", bound="Query") - S = t.TypeVar("S", bound="SetOperation") - - -class _Expression(type): - def __new__(cls, clsname, bases, attrs): - klass = super().__new__(cls, clsname, bases, attrs) - - # When an Expression class is created, its key is automatically set - # to be the lowercase version of the class' name. - klass.key = clsname.lower() - klass.required_args = {k for k, v in klass.arg_types.items() if v} - - # This is so that docstrings are not inherited in pdoc - klass.__doc__ = klass.__doc__ or "" - - return klass - - -SQLGLOT_META = "sqlglot.meta" -SQLGLOT_ANONYMOUS = "sqlglot.anonymous" -TABLE_PARTS = ("this", "db", "catalog") -COLUMN_PARTS = ("this", "table", "db", "catalog") -POSITION_META_KEYS = ("line", "col", "start", "end") -UNITTEST = "unittest" in sys.modules or "pytest" in sys.modules - - -class Expression(metaclass=_Expression): - """ - The base class for all expressions in a syntax tree. Each Expression encapsulates any necessary - context, such as its child expressions, their names (arg keys), and whether a given child expression - is optional or not. - - Attributes: - key: a unique key for each class in the Expression hierarchy. This is useful for hashing - and representing expressions as strings. - arg_types: determines the arguments (child nodes) supported by an expression. It maps - arg keys to booleans that indicate whether the corresponding args are optional. - parent: a reference to the parent expression (or None, in case of root expressions). - arg_key: the arg key an expression is associated with, i.e. the name its parent expression - uses to refer to it. - index: the index of an expression if it is inside of a list argument in its parent. - comments: a list of comments that are associated with a given expression. This is used in - order to preserve comments when transpiling SQL code. - type: the `sqlglot.expressions.DataType` type of an expression. This is inferred by the - optimizer, in order to enable some transformations that require type information. - meta: a dictionary that can be used to store useful metadata for a given expression. - - Example: - >>> class Foo(Expression): - ... arg_types = {"this": True, "expression": False} - - The above definition informs us that Foo is an Expression that requires an argument called - "this" and may also optionally receive an argument called "expression". - - Args: - args: a mapping used for retrieving the arguments of an expression, given their arg keys. - """ - - key = "expression" - arg_types = {"this": True} - required_args = {"this"} - __slots__ = ( - "args", - "parent", - "arg_key", - "index", - "comments", - "_type", - "_meta", - "_hash", - ) - - def __init__(self, **args: t.Any): - self.args: t.Dict[str, t.Any] = args - self.parent: t.Optional[Expression] = None - self.arg_key: t.Optional[str] = None - self.index: t.Optional[int] = None - self.comments: t.Optional[t.List[str]] = None - self._type: t.Optional[DataType] = None - self._meta: t.Optional[t.Dict[str, t.Any]] = None - self._hash: t.Optional[int] = None - - for arg_key, value in self.args.items(): - self._set_parent(arg_key, value) - - def __eq__(self, other) -> bool: - return self is other or ( - type(self) is type(other) and hash(self) == hash(other) - ) - - def __hash__(self) -> int: - if self._hash is None: - nodes = [] - queue = deque([self]) - - while queue: - node = queue.popleft() - nodes.append(node) - - for v in node.iter_expressions(): - if v._hash is None: - queue.append(v) - - for node in reversed(nodes): - hash_ = hash(node.key) - t = type(node) - - if t is Literal or t is Identifier: - for k, v in sorted(node.args.items()): - if v: - hash_ = hash((hash_, k, v)) - else: - for k, v in sorted(node.args.items()): - t = type(v) - - if t is list: - for x in v: - if x is not None and x is not False: - hash_ = hash( - (hash_, k, x.lower() if type(x) is str else x) - ) - else: - hash_ = hash((hash_, k)) - elif v is not None and v is not False: - hash_ = hash((hash_, k, v.lower() if t is str else v)) - - node._hash = hash_ - assert self._hash - return self._hash - - def __reduce__(self) -> t.Tuple[t.Callable, t.Tuple[t.List[t.Dict[str, t.Any]]]]: - from bigframes_vendored.sqlglot.serde import dump, load - - return (load, (dump(self),)) - - @property - def this(self) -> t.Any: - """ - Retrieves the argument with key "this". - """ - return self.args.get("this") - - @property - def expression(self) -> t.Any: - """ - Retrieves the argument with key "expression". - """ - return self.args.get("expression") - - @property - def expressions(self) -> t.List[t.Any]: - """ - Retrieves the argument with key "expressions". - """ - return self.args.get("expressions") or [] - - def text(self, key) -> str: - """ - Returns a textual representation of the argument corresponding to "key". This can only be used - for args that are strings or leaf Expression instances, such as identifiers and literals. - """ - field = self.args.get(key) - if isinstance(field, str): - return field - if isinstance(field, (Identifier, Literal, Var)): - return field.this - if isinstance(field, (Star, Null)): - return field.name - return "" - - @property - def is_string(self) -> bool: - """ - Checks whether a Literal expression is a string. - """ - return isinstance(self, Literal) and self.args["is_string"] - - @property - def is_number(self) -> bool: - """ - Checks whether a Literal expression is a number. - """ - return (isinstance(self, Literal) and not self.args["is_string"]) or ( - isinstance(self, Neg) and self.this.is_number - ) - - def to_py(self) -> t.Any: - """ - Returns a Python object equivalent of the SQL node. - """ - raise ValueError(f"{self} cannot be converted to a Python object.") - - @property - def is_int(self) -> bool: - """ - Checks whether an expression is an integer. - """ - return self.is_number and isinstance(self.to_py(), int) - - @property - def is_star(self) -> bool: - """Checks whether an expression is a star.""" - return isinstance(self, Star) or ( - isinstance(self, Column) and isinstance(self.this, Star) - ) - - @property - def alias(self) -> str: - """ - Returns the alias of the expression, or an empty string if it's not aliased. - """ - if isinstance(self.args.get("alias"), TableAlias): - return self.args["alias"].name - return self.text("alias") - - @property - def alias_column_names(self) -> t.List[str]: - table_alias = self.args.get("alias") - if not table_alias: - return [] - return [c.name for c in table_alias.args.get("columns") or []] - - @property - def name(self) -> str: - return self.text("this") - - @property - def alias_or_name(self) -> str: - return self.alias or self.name - - @property - def output_name(self) -> str: - """ - Name of the output column if this expression is a selection. - - If the Expression has no output name, an empty string is returned. - - Example: - >>> from sqlglot import parse_one - >>> parse_one("SELECT a").expressions[0].output_name - 'a' - >>> parse_one("SELECT b AS c").expressions[0].output_name - 'c' - >>> parse_one("SELECT 1 + 2").expressions[0].output_name - '' - """ - return "" - - @property - def type(self) -> t.Optional[DataType]: - return self._type - - @type.setter - def type(self, dtype: t.Optional[DataType | DataType.Type | str]) -> None: - if dtype and not isinstance(dtype, DataType): - dtype = DataType.build(dtype) - self._type = dtype # type: ignore - - def is_type(self, *dtypes) -> bool: - return self.type is not None and self.type.is_type(*dtypes) - - def is_leaf(self) -> bool: - return not any( - isinstance(v, (Expression, list)) and v for v in self.args.values() - ) - - @property - def meta(self) -> t.Dict[str, t.Any]: - if self._meta is None: - self._meta = {} - return self._meta - - def __deepcopy__(self, memo): - root = self.__class__() - stack = [(self, root)] - - while stack: - node, copy = stack.pop() - - if node.comments is not None: - copy.comments = deepcopy(node.comments) - if node._type is not None: - copy._type = deepcopy(node._type) - if node._meta is not None: - copy._meta = deepcopy(node._meta) - if node._hash is not None: - copy._hash = node._hash - - for k, vs in node.args.items(): - if hasattr(vs, "parent"): - stack.append((vs, vs.__class__())) - copy.set(k, stack[-1][-1]) - elif type(vs) is list: - copy.args[k] = [] - - for v in vs: - if hasattr(v, "parent"): - stack.append((v, v.__class__())) - copy.append(k, stack[-1][-1]) - else: - copy.append(k, v) - else: - copy.args[k] = vs - - return root - - def copy(self) -> Self: - """ - Returns a deep copy of the expression. - """ - return deepcopy(self) - - def add_comments( - self, comments: t.Optional[t.List[str]] = None, prepend: bool = False - ) -> None: - if self.comments is None: - self.comments = [] - - if comments: - for comment in comments: - _, *meta = comment.split(SQLGLOT_META) - if meta: - for kv in "".join(meta).split(","): - k, *v = kv.split("=") - value = v[0].strip() if v else True - self.meta[k.strip()] = to_bool(value) - - if not prepend: - self.comments.append(comment) - - if prepend: - self.comments = comments + self.comments - - def pop_comments(self) -> t.List[str]: - comments = self.comments or [] - self.comments = None - return comments - - def append(self, arg_key: str, value: t.Any) -> None: - """ - Appends value to arg_key if it's a list or sets it as a new list. - - Args: - arg_key (str): name of the list expression arg - value (Any): value to append to the list - """ - if type(self.args.get(arg_key)) is not list: - self.args[arg_key] = [] - self._set_parent(arg_key, value) - values = self.args[arg_key] - if hasattr(value, "parent"): - value.index = len(values) - values.append(value) - - def set( - self, - arg_key: str, - value: t.Any, - index: t.Optional[int] = None, - overwrite: bool = True, - ) -> None: - """ - Sets arg_key to value. - - Args: - arg_key: name of the expression arg. - value: value to set the arg to. - index: if the arg is a list, this specifies what position to add the value in it. - overwrite: assuming an index is given, this determines whether to overwrite the - list entry instead of only inserting a new value (i.e., like list.insert). - """ - expression: t.Optional[Expression] = self - - while expression and expression._hash is not None: - expression._hash = None - expression = expression.parent - - if index is not None: - expressions = self.args.get(arg_key) or [] - - if seq_get(expressions, index) is None: - return - if value is None: - expressions.pop(index) - for v in expressions[index:]: - v.index = v.index - 1 - return - - if isinstance(value, list): - expressions.pop(index) - expressions[index:index] = value - elif overwrite: - expressions[index] = value - else: - expressions.insert(index, value) - - value = expressions - elif value is None: - self.args.pop(arg_key, None) - return - - self.args[arg_key] = value - self._set_parent(arg_key, value, index) - - def _set_parent( - self, arg_key: str, value: t.Any, index: t.Optional[int] = None - ) -> None: - if hasattr(value, "parent"): - value.parent = self - value.arg_key = arg_key - value.index = index - elif type(value) is list: - for index, v in enumerate(value): - if hasattr(v, "parent"): - v.parent = self - v.arg_key = arg_key - v.index = index - - @property - def depth(self) -> int: - """ - Returns the depth of this tree. - """ - if self.parent: - return self.parent.depth + 1 - return 0 - - def iter_expressions(self, reverse: bool = False) -> t.Iterator[Expression]: - """Yields the key and expression for all arguments, exploding list args.""" - for vs in reversed(self.args.values()) if reverse else self.args.values(): # type: ignore - if type(vs) is list: - for v in reversed(vs) if reverse else vs: # type: ignore - if hasattr(v, "parent"): - yield v - elif hasattr(vs, "parent"): - yield vs - - def find(self, *expression_types: t.Type[E], bfs: bool = True) -> t.Optional[E]: - """ - Returns the first node in this tree which matches at least one of - the specified types. - - Args: - expression_types: the expression type(s) to match. - bfs: whether to search the AST using the BFS algorithm (DFS is used if false). - - Returns: - The node which matches the criteria or None if no such node was found. - """ - return next(self.find_all(*expression_types, bfs=bfs), None) - - def find_all(self, *expression_types: t.Type[E], bfs: bool = True) -> t.Iterator[E]: - """ - Returns a generator object which visits all nodes in this tree and only - yields those that match at least one of the specified expression types. - - Args: - expression_types: the expression type(s) to match. - bfs: whether to search the AST using the BFS algorithm (DFS is used if false). - - Returns: - The generator object. - """ - for expression in self.walk(bfs=bfs): - if isinstance(expression, expression_types): - yield expression - - def find_ancestor(self, *expression_types: t.Type[E]) -> t.Optional[E]: - """ - Returns a nearest parent matching expression_types. - - Args: - expression_types: the expression type(s) to match. - - Returns: - The parent node. - """ - ancestor = self.parent - while ancestor and not isinstance(ancestor, expression_types): - ancestor = ancestor.parent - return ancestor # type: ignore - - @property - def parent_select(self) -> t.Optional[Select]: - """ - Returns the parent select statement. - """ - return self.find_ancestor(Select) - - @property - def same_parent(self) -> bool: - """Returns if the parent is the same class as itself.""" - return type(self.parent) is self.__class__ - - def root(self) -> Expression: - """ - Returns the root expression of this tree. - """ - expression = self - while expression.parent: - expression = expression.parent - return expression - - def walk( - self, bfs: bool = True, prune: t.Optional[t.Callable[[Expression], bool]] = None - ) -> t.Iterator[Expression]: - """ - Returns a generator object which visits all nodes in this tree. - - Args: - bfs: if set to True the BFS traversal order will be applied, - otherwise the DFS traversal will be used instead. - prune: callable that returns True if the generator should stop traversing - this branch of the tree. - - Returns: - the generator object. - """ - if bfs: - yield from self.bfs(prune=prune) - else: - yield from self.dfs(prune=prune) - - def dfs( - self, prune: t.Optional[t.Callable[[Expression], bool]] = None - ) -> t.Iterator[Expression]: - """ - Returns a generator object which visits all nodes in this tree in - the DFS (Depth-first) order. - - Returns: - The generator object. - """ - stack = [self] - - while stack: - node = stack.pop() - - yield node - - if prune and prune(node): - continue - - for v in node.iter_expressions(reverse=True): - stack.append(v) - - def bfs( - self, prune: t.Optional[t.Callable[[Expression], bool]] = None - ) -> t.Iterator[Expression]: - """ - Returns a generator object which visits all nodes in this tree in - the BFS (Breadth-first) order. - - Returns: - The generator object. - """ - queue = deque([self]) - - while queue: - node = queue.popleft() - - yield node - - if prune and prune(node): - continue - - for v in node.iter_expressions(): - queue.append(v) - - def unnest(self): - """ - Returns the first non parenthesis child or self. - """ - expression = self - while type(expression) is Paren: - expression = expression.this - return expression - - def unalias(self): - """ - Returns the inner expression if this is an Alias. - """ - if isinstance(self, Alias): - return self.this - return self - - def unnest_operands(self): - """ - Returns unnested operands as a tuple. - """ - return tuple(arg.unnest() for arg in self.iter_expressions()) - - def flatten(self, unnest=True): - """ - Returns a generator which yields child nodes whose parents are the same class. - - A AND B AND C -> [A, B, C] - """ - for node in self.dfs( - prune=lambda n: n.parent and type(n) is not self.__class__ - ): - if type(node) is not self.__class__: - yield ( - node.unnest() if unnest and not isinstance(node, Subquery) else node - ) - - def __str__(self) -> str: - return self.sql() - - def __repr__(self) -> str: - return _to_s(self) - - def to_s(self) -> str: - """ - Same as __repr__, but includes additional information which can be useful - for debugging, like empty or missing args and the AST nodes' object IDs. - """ - return _to_s(self, verbose=True) - - def sql(self, dialect: DialectType = None, **opts) -> str: - """ - Returns SQL string representation of this tree. - - Args: - dialect: the dialect of the output SQL string (eg. "spark", "hive", "presto", "mysql"). - opts: other `sqlglot.generator.Generator` options. - - Returns: - The SQL string. - """ - from bigframes_vendored.sqlglot.dialects import Dialect - - return Dialect.get_or_raise(dialect).generate(self, **opts) - - def transform( - self, fun: t.Callable, *args: t.Any, copy: bool = True, **kwargs - ) -> Expression: - """ - Visits all tree nodes (excluding already transformed ones) - and applies the given transformation function to each node. - - Args: - fun: a function which takes a node as an argument and returns a - new transformed node or the same node without modifications. If the function - returns None, then the corresponding node will be removed from the syntax tree. - copy: if set to True a new tree instance is constructed, otherwise the tree is - modified in place. - - Returns: - The transformed tree. - """ - root = None - new_node = None - - for node in (self.copy() if copy else self).dfs( - prune=lambda n: n is not new_node - ): - parent, arg_key, index = node.parent, node.arg_key, node.index - new_node = fun(node, *args, **kwargs) - - if not root: - root = new_node - elif parent and arg_key and new_node is not node: - parent.set(arg_key, new_node, index) - - assert root - return root.assert_is(Expression) - - @t.overload - def replace(self, expression: E) -> E: ... - - @t.overload - def replace(self, expression: None) -> None: ... - - def replace(self, expression): - """ - Swap out this expression with a new expression. - - For example:: - - >>> tree = Select().select("x").from_("tbl") - >>> tree.find(Column).replace(column("y")) - Column( - this=Identifier(this=y, quoted=False)) - >>> tree.sql() - 'SELECT y FROM tbl' - - Args: - expression: new node - - Returns: - The new expression or expressions. - """ - parent = self.parent - - if not parent or parent is expression: - return expression - - key = self.arg_key - value = parent.args.get(key) - - if type(expression) is list and isinstance(value, Expression): - # We are trying to replace an Expression with a list, so it's assumed that - # the intention was to really replace the parent of this expression. - value.parent.replace(expression) - else: - parent.set(key, expression, self.index) - - if expression is not self: - self.parent = None - self.arg_key = None - self.index = None - - return expression - - def pop(self: E) -> E: - """ - Remove this expression from its AST. - - Returns: - The popped expression. - """ - self.replace(None) - return self - - def assert_is(self, type_: t.Type[E]) -> E: - """ - Assert that this `Expression` is an instance of `type_`. - - If it is NOT an instance of `type_`, this raises an assertion error. - Otherwise, this returns this expression. - - Examples: - This is useful for type security in chained expressions: - - >>> import sqlglot - >>> sqlglot.parse_one("SELECT x from y").assert_is(Select).select("z").sql() - 'SELECT x, z FROM y' - """ - if not isinstance(self, type_): - raise AssertionError(f"{self} is not {type_}.") - return self - - def error_messages(self, args: t.Optional[t.Sequence] = None) -> t.List[str]: - """ - Checks if this expression is valid (e.g. all mandatory args are set). - - Args: - args: a sequence of values that were used to instantiate a Func expression. This is used - to check that the provided arguments don't exceed the function argument limit. - - Returns: - A list of error messages for all possible errors that were found. - """ - errors: t.List[str] = [] - - if UNITTEST: - for k in self.args: - if k not in self.arg_types: - raise TypeError(f"Unexpected keyword: '{k}' for {self.__class__}") - - for k in self.required_args: - v = self.args.get(k) - if v is None or (type(v) is list and not v): - errors.append(f"Required keyword: '{k}' missing for {self.__class__}") - - if ( - args - and isinstance(self, Func) - and len(args) > len(self.arg_types) - and not self.is_var_len_args - ): - errors.append( - f"The number of provided arguments ({len(args)}) is greater than " - f"the maximum number of supported arguments ({len(self.arg_types)})" - ) - - return errors - - def dump(self): - """ - Dump this Expression to a JSON-serializable dict. - """ - from bigframes_vendored.sqlglot.serde import dump - - return dump(self) - - @classmethod - def load(cls, obj): - """ - Load a dict (as returned by `Expression.dump`) into an Expression instance. - """ - from bigframes_vendored.sqlglot.serde import load - - return load(obj) - - def and_( - self, - *expressions: t.Optional[ExpOrStr], - dialect: DialectType = None, - copy: bool = True, - wrap: bool = True, - **opts, - ) -> Condition: - """ - AND this condition with one or multiple expressions. - - Example: - >>> condition("x=1").and_("y=1").sql() - 'x = 1 AND y = 1' - - Args: - *expressions: the SQL code strings to parse. - If an `Expression` instance is passed, it will be used as-is. - dialect: the dialect used to parse the input expression. - copy: whether to copy the involved expressions (only applies to Expressions). - wrap: whether to wrap the operands in `Paren`s. This is true by default to avoid - precedence issues, but can be turned off when the produced AST is too deep and - causes recursion-related issues. - opts: other options to use to parse the input expressions. - - Returns: - The new And condition. - """ - return and_(self, *expressions, dialect=dialect, copy=copy, wrap=wrap, **opts) - - def or_( - self, - *expressions: t.Optional[ExpOrStr], - dialect: DialectType = None, - copy: bool = True, - wrap: bool = True, - **opts, - ) -> Condition: - """ - OR this condition with one or multiple expressions. - - Example: - >>> condition("x=1").or_("y=1").sql() - 'x = 1 OR y = 1' - - Args: - *expressions: the SQL code strings to parse. - If an `Expression` instance is passed, it will be used as-is. - dialect: the dialect used to parse the input expression. - copy: whether to copy the involved expressions (only applies to Expressions). - wrap: whether to wrap the operands in `Paren`s. This is true by default to avoid - precedence issues, but can be turned off when the produced AST is too deep and - causes recursion-related issues. - opts: other options to use to parse the input expressions. - - Returns: - The new Or condition. - """ - return or_(self, *expressions, dialect=dialect, copy=copy, wrap=wrap, **opts) - - def not_(self, copy: bool = True): - """ - Wrap this condition with NOT. - - Example: - >>> condition("x=1").not_().sql() - 'NOT x = 1' - - Args: - copy: whether to copy this object. - - Returns: - The new Not instance. - """ - return not_(self, copy=copy) - - def update_positions( - self: E, - other: t.Optional[Token | Expression] = None, - line: t.Optional[int] = None, - col: t.Optional[int] = None, - start: t.Optional[int] = None, - end: t.Optional[int] = None, - ) -> E: - """ - Update this expression with positions from a token or other expression. - - Args: - other: a token or expression to update this expression with. - line: the line number to use if other is None - col: column number - start: start char index - end: end char index - - Returns: - The updated expression. - """ - if other is None: - self.meta["line"] = line - self.meta["col"] = col - self.meta["start"] = start - self.meta["end"] = end - elif hasattr(other, "meta"): - for k in POSITION_META_KEYS: - self.meta[k] = other.meta[k] - else: - self.meta["line"] = other.line - self.meta["col"] = other.col - self.meta["start"] = other.start - self.meta["end"] = other.end - return self - - def as_( - self, - alias: str | Identifier, - quoted: t.Optional[bool] = None, - dialect: DialectType = None, - copy: bool = True, - **opts, - ) -> Alias: - return alias_(self, alias, quoted=quoted, dialect=dialect, copy=copy, **opts) - - def _binop(self, klass: t.Type[E], other: t.Any, reverse: bool = False) -> E: - this = self.copy() - other = convert(other, copy=True) - if not isinstance(this, klass) and not isinstance(other, klass): - this = _wrap(this, Binary) - other = _wrap(other, Binary) - if reverse: - return klass(this=other, expression=this) - return klass(this=this, expression=other) - - def __getitem__(self, other: ExpOrStr | t.Tuple[ExpOrStr]) -> Bracket: - return Bracket( - this=self.copy(), - expressions=[convert(e, copy=True) for e in ensure_list(other)], - ) - - def __iter__(self) -> t.Iterator: - if "expressions" in self.arg_types: - return iter(self.args.get("expressions") or []) - # We define this because __getitem__ converts Expression into an iterable, which is - # problematic because one can hit infinite loops if they do "for x in some_expr: ..." - # See: https://peps.python.org/pep-0234/ - raise TypeError(f"'{self.__class__.__name__}' object is not iterable") - - def isin( - self, - *expressions: t.Any, - query: t.Optional[ExpOrStr] = None, - unnest: t.Optional[ExpOrStr] | t.Collection[ExpOrStr] = None, - copy: bool = True, - **opts, - ) -> In: - subquery = maybe_parse(query, copy=copy, **opts) if query else None - if subquery and not isinstance(subquery, Subquery): - subquery = subquery.subquery(copy=False) - - return In( - this=maybe_copy(self, copy), - expressions=[convert(e, copy=copy) for e in expressions], - query=subquery, - unnest=( - Unnest( - expressions=[ - maybe_parse(t.cast(ExpOrStr, e), copy=copy, **opts) - for e in ensure_list(unnest) - ] - ) - if unnest - else None - ), - ) - - def between( - self, - low: t.Any, - high: t.Any, - copy: bool = True, - symmetric: t.Optional[bool] = None, - **opts, - ) -> Between: - between = Between( - this=maybe_copy(self, copy), - low=convert(low, copy=copy, **opts), - high=convert(high, copy=copy, **opts), - ) - if symmetric is not None: - between.set("symmetric", symmetric) - - return between - - def is_(self, other: ExpOrStr) -> Is: - return self._binop(Is, other) - - def like(self, other: ExpOrStr) -> Like: - return self._binop(Like, other) - - def ilike(self, other: ExpOrStr) -> ILike: - return self._binop(ILike, other) - - def eq(self, other: t.Any) -> EQ: - return self._binop(EQ, other) - - def neq(self, other: t.Any) -> NEQ: - return self._binop(NEQ, other) - - def rlike(self, other: ExpOrStr) -> RegexpLike: - return self._binop(RegexpLike, other) - - def div(self, other: ExpOrStr, typed: bool = False, safe: bool = False) -> Div: - div = self._binop(Div, other) - div.set("typed", typed) - div.set("safe", safe) - return div - - def asc(self, nulls_first: bool = True) -> Ordered: - return Ordered(this=self.copy(), nulls_first=nulls_first) - - def desc(self, nulls_first: bool = False) -> Ordered: - return Ordered(this=self.copy(), desc=True, nulls_first=nulls_first) - - def __lt__(self, other: t.Any) -> LT: - return self._binop(LT, other) - - def __le__(self, other: t.Any) -> LTE: - return self._binop(LTE, other) - - def __gt__(self, other: t.Any) -> GT: - return self._binop(GT, other) - - def __ge__(self, other: t.Any) -> GTE: - return self._binop(GTE, other) - - def __add__(self, other: t.Any) -> Add: - return self._binop(Add, other) - - def __radd__(self, other: t.Any) -> Add: - return self._binop(Add, other, reverse=True) - - def __sub__(self, other: t.Any) -> Sub: - return self._binop(Sub, other) - - def __rsub__(self, other: t.Any) -> Sub: - return self._binop(Sub, other, reverse=True) - - def __mul__(self, other: t.Any) -> Mul: - return self._binop(Mul, other) - - def __rmul__(self, other: t.Any) -> Mul: - return self._binop(Mul, other, reverse=True) - - def __truediv__(self, other: t.Any) -> Div: - return self._binop(Div, other) - - def __rtruediv__(self, other: t.Any) -> Div: - return self._binop(Div, other, reverse=True) - - def __floordiv__(self, other: t.Any) -> IntDiv: - return self._binop(IntDiv, other) - - def __rfloordiv__(self, other: t.Any) -> IntDiv: - return self._binop(IntDiv, other, reverse=True) - - def __mod__(self, other: t.Any) -> Mod: - return self._binop(Mod, other) - - def __rmod__(self, other: t.Any) -> Mod: - return self._binop(Mod, other, reverse=True) - - def __pow__(self, other: t.Any) -> Pow: - return self._binop(Pow, other) - - def __rpow__(self, other: t.Any) -> Pow: - return self._binop(Pow, other, reverse=True) - - def __and__(self, other: t.Any) -> And: - return self._binop(And, other) - - def __rand__(self, other: t.Any) -> And: - return self._binop(And, other, reverse=True) - - def __or__(self, other: t.Any) -> Or: - return self._binop(Or, other) - - def __ror__(self, other: t.Any) -> Or: - return self._binop(Or, other, reverse=True) - - def __neg__(self) -> Neg: - return Neg(this=_wrap(self.copy(), Binary)) - - def __invert__(self) -> Not: - return not_(self.copy()) - - -IntoType = t.Union[ - str, - t.Type[Expression], - t.Collection[t.Union[str, t.Type[Expression]]], -] -ExpOrStr = t.Union[str, Expression] - - -class Condition(Expression): - """Logical conditions like x AND y, or simply x""" - - -class Predicate(Condition): - """Relationships like x = y, x > 1, x >= y.""" - - -class DerivedTable(Expression): - @property - def selects(self) -> t.List[Expression]: - return self.this.selects if isinstance(self.this, Query) else [] - - @property - def named_selects(self) -> t.List[str]: - return [select.output_name for select in self.selects] - - -class Query(Expression): - def subquery( - self, alias: t.Optional[ExpOrStr] = None, copy: bool = True - ) -> Subquery: - """ - Returns a `Subquery` that wraps around this query. - - Example: - >>> subquery = Select().select("x").from_("tbl").subquery() - >>> Select().select("x").from_(subquery).sql() - 'SELECT x FROM (SELECT x FROM tbl)' - - Args: - alias: an optional alias for the subquery. - copy: if `False`, modify this expression instance in-place. - """ - instance = maybe_copy(self, copy) - if not isinstance(alias, Expression): - alias = TableAlias(this=to_identifier(alias)) if alias else None - - return Subquery(this=instance, alias=alias) - - def limit( - self: Q, - expression: ExpOrStr | int, - dialect: DialectType = None, - copy: bool = True, - **opts, - ) -> Q: - """ - Adds a LIMIT clause to this query. - - Example: - >>> select("1").union(select("1")).limit(1).sql() - 'SELECT 1 UNION SELECT 1 LIMIT 1' - - Args: - expression: the SQL code string to parse. - This can also be an integer. - If a `Limit` instance is passed, it will be used as-is. - If another `Expression` instance is passed, it will be wrapped in a `Limit`. - dialect: the dialect used to parse the input expression. - copy: if `False`, modify this expression instance in-place. - opts: other options to use to parse the input expressions. - - Returns: - A limited Select expression. - """ - return _apply_builder( - expression=expression, - instance=self, - arg="limit", - into=Limit, - prefix="LIMIT", - dialect=dialect, - copy=copy, - into_arg="expression", - **opts, - ) - - def offset( - self: Q, - expression: ExpOrStr | int, - dialect: DialectType = None, - copy: bool = True, - **opts, - ) -> Q: - """ - Set the OFFSET expression. - - Example: - >>> Select().from_("tbl").select("x").offset(10).sql() - 'SELECT x FROM tbl OFFSET 10' - - Args: - expression: the SQL code string to parse. - This can also be an integer. - If a `Offset` instance is passed, this is used as-is. - If another `Expression` instance is passed, it will be wrapped in a `Offset`. - dialect: the dialect used to parse the input expression. - copy: if `False`, modify this expression instance in-place. - opts: other options to use to parse the input expressions. - - Returns: - The modified Select expression. - """ - return _apply_builder( - expression=expression, - instance=self, - arg="offset", - into=Offset, - prefix="OFFSET", - dialect=dialect, - copy=copy, - into_arg="expression", - **opts, - ) - - def order_by( - self: Q, - *expressions: t.Optional[ExpOrStr], - append: bool = True, - dialect: DialectType = None, - copy: bool = True, - **opts, - ) -> Q: - """ - Set the ORDER BY expression. - - Example: - >>> Select().from_("tbl").select("x").order_by("x DESC").sql() - 'SELECT x FROM tbl ORDER BY x DESC' - - Args: - *expressions: the SQL code strings to parse. - If a `Group` instance is passed, this is used as-is. - If another `Expression` instance is passed, it will be wrapped in a `Order`. - append: if `True`, add to any existing expressions. - Otherwise, this flattens all the `Order` expression into a single expression. - dialect: the dialect used to parse the input expression. - copy: if `False`, modify this expression instance in-place. - opts: other options to use to parse the input expressions. - - Returns: - The modified Select expression. - """ - return _apply_child_list_builder( - *expressions, - instance=self, - arg="order", - append=append, - copy=copy, - prefix="ORDER BY", - into=Order, - dialect=dialect, - **opts, - ) - - @property - def ctes(self) -> t.List[CTE]: - """Returns a list of all the CTEs attached to this query.""" - with_ = self.args.get("with_") - return with_.expressions if with_ else [] - - @property - def selects(self) -> t.List[Expression]: - """Returns the query's projections.""" - raise NotImplementedError("Query objects must implement `selects`") - - @property - def named_selects(self) -> t.List[str]: - """Returns the output names of the query's projections.""" - raise NotImplementedError("Query objects must implement `named_selects`") - - def select( - self: Q, - *expressions: t.Optional[ExpOrStr], - append: bool = True, - dialect: DialectType = None, - copy: bool = True, - **opts, - ) -> Q: - """ - Append to or set the SELECT expressions. - - Example: - >>> Select().select("x", "y").sql() - 'SELECT x, y' - - Args: - *expressions: the SQL code strings to parse. - If an `Expression` instance is passed, it will be used as-is. - append: if `True`, add to any existing expressions. - Otherwise, this resets the expressions. - dialect: the dialect used to parse the input expressions. - copy: if `False`, modify this expression instance in-place. - opts: other options to use to parse the input expressions. - - Returns: - The modified Query expression. - """ - raise NotImplementedError("Query objects must implement `select`") - - def where( - self: Q, - *expressions: t.Optional[ExpOrStr], - append: bool = True, - dialect: DialectType = None, - copy: bool = True, - **opts, - ) -> Q: - """ - Append to or set the WHERE expressions. - - Examples: - >>> Select().select("x").from_("tbl").where("x = 'a' OR x < 'b'").sql() - "SELECT x FROM tbl WHERE x = 'a' OR x < 'b'" - - Args: - *expressions: the SQL code strings to parse. - If an `Expression` instance is passed, it will be used as-is. - Multiple expressions are combined with an AND operator. - append: if `True`, AND the new expressions to any existing expression. - Otherwise, this resets the expression. - dialect: the dialect used to parse the input expressions. - copy: if `False`, modify this expression instance in-place. - opts: other options to use to parse the input expressions. - - Returns: - The modified expression. - """ - return _apply_conjunction_builder( - *[expr.this if isinstance(expr, Where) else expr for expr in expressions], - instance=self, - arg="where", - append=append, - into=Where, - dialect=dialect, - copy=copy, - **opts, - ) - - def with_( - self: Q, - alias: ExpOrStr, - as_: ExpOrStr, - recursive: t.Optional[bool] = None, - materialized: t.Optional[bool] = None, - append: bool = True, - dialect: DialectType = None, - copy: bool = True, - scalar: t.Optional[bool] = None, - **opts, - ) -> Q: - """ - Append to or set the common table expressions. - - Example: - >>> Select().with_("tbl2", as_="SELECT * FROM tbl").select("x").from_("tbl2").sql() - 'WITH tbl2 AS (SELECT * FROM tbl) SELECT x FROM tbl2' - - Args: - alias: the SQL code string to parse as the table name. - If an `Expression` instance is passed, this is used as-is. - as_: the SQL code string to parse as the table expression. - If an `Expression` instance is passed, it will be used as-is. - recursive: set the RECURSIVE part of the expression. Defaults to `False`. - materialized: set the MATERIALIZED part of the expression. - append: if `True`, add to any existing expressions. - Otherwise, this resets the expressions. - dialect: the dialect used to parse the input expression. - copy: if `False`, modify this expression instance in-place. - scalar: if `True`, this is a scalar common table expression. - opts: other options to use to parse the input expressions. - - Returns: - The modified expression. - """ - return _apply_cte_builder( - self, - alias, - as_, - recursive=recursive, - materialized=materialized, - append=append, - dialect=dialect, - copy=copy, - scalar=scalar, - **opts, - ) - - def union( - self, - *expressions: ExpOrStr, - distinct: bool = True, - dialect: DialectType = None, - **opts, - ) -> Union: - """ - Builds a UNION expression. - - Example: - >>> import sqlglot - >>> sqlglot.parse_one("SELECT * FROM foo").union("SELECT * FROM bla").sql() - 'SELECT * FROM foo UNION SELECT * FROM bla' - - Args: - expressions: the SQL code strings. - If `Expression` instances are passed, they will be used as-is. - distinct: set the DISTINCT flag if and only if this is true. - dialect: the dialect used to parse the input expression. - opts: other options to use to parse the input expressions. - - Returns: - The new Union expression. - """ - return union(self, *expressions, distinct=distinct, dialect=dialect, **opts) - - def intersect( - self, - *expressions: ExpOrStr, - distinct: bool = True, - dialect: DialectType = None, - **opts, - ) -> Intersect: - """ - Builds an INTERSECT expression. - - Example: - >>> import sqlglot - >>> sqlglot.parse_one("SELECT * FROM foo").intersect("SELECT * FROM bla").sql() - 'SELECT * FROM foo INTERSECT SELECT * FROM bla' - - Args: - expressions: the SQL code strings. - If `Expression` instances are passed, they will be used as-is. - distinct: set the DISTINCT flag if and only if this is true. - dialect: the dialect used to parse the input expression. - opts: other options to use to parse the input expressions. - - Returns: - The new Intersect expression. - """ - return intersect(self, *expressions, distinct=distinct, dialect=dialect, **opts) - - def except_( - self, - *expressions: ExpOrStr, - distinct: bool = True, - dialect: DialectType = None, - **opts, - ) -> Except: - """ - Builds an EXCEPT expression. - - Example: - >>> import sqlglot - >>> sqlglot.parse_one("SELECT * FROM foo").except_("SELECT * FROM bla").sql() - 'SELECT * FROM foo EXCEPT SELECT * FROM bla' - - Args: - expressions: the SQL code strings. - If `Expression` instance are passed, they will be used as-is. - distinct: set the DISTINCT flag if and only if this is true. - dialect: the dialect used to parse the input expression. - opts: other options to use to parse the input expressions. - - Returns: - The new Except expression. - """ - return except_(self, *expressions, distinct=distinct, dialect=dialect, **opts) - - -class UDTF(DerivedTable): - @property - def selects(self) -> t.List[Expression]: - alias = self.args.get("alias") - return alias.columns if alias else [] - - -class Cache(Expression): - arg_types = { - "this": True, - "lazy": False, - "options": False, - "expression": False, - } - - -class Uncache(Expression): - arg_types = {"this": True, "exists": False} - - -class Refresh(Expression): - arg_types = {"this": True, "kind": True} - - -class DDL(Expression): - @property - def ctes(self) -> t.List[CTE]: - """Returns a list of all the CTEs attached to this statement.""" - with_ = self.args.get("with_") - return with_.expressions if with_ else [] - - @property - def selects(self) -> t.List[Expression]: - """If this statement contains a query (e.g. a CTAS), this returns the query's projections.""" - return self.expression.selects if isinstance(self.expression, Query) else [] - - @property - def named_selects(self) -> t.List[str]: - """ - If this statement contains a query (e.g. a CTAS), this returns the output - names of the query's projections. - """ - return ( - self.expression.named_selects if isinstance(self.expression, Query) else [] - ) - - -# https://docs.teradata.com/r/Enterprise_IntelliFlex_VMware/SQL-Data-Manipulation-Language/Statement-Syntax/LOCKING-Request-Modifier/LOCKING-Request-Modifier-Syntax -class LockingStatement(Expression): - arg_types = {"this": True, "expression": True} - - -class DML(Expression): - def returning( - self, - expression: ExpOrStr, - dialect: DialectType = None, - copy: bool = True, - **opts, - ) -> "Self": - """ - Set the RETURNING expression. Not supported by all dialects. - - Example: - >>> delete("tbl").returning("*", dialect="postgres").sql() - 'DELETE FROM tbl RETURNING *' - - Args: - expression: the SQL code strings to parse. - If an `Expression` instance is passed, it will be used as-is. - dialect: the dialect used to parse the input expressions. - copy: if `False`, modify this expression instance in-place. - opts: other options to use to parse the input expressions. - - Returns: - Delete: the modified expression. - """ - return _apply_builder( - expression=expression, - instance=self, - arg="returning", - prefix="RETURNING", - dialect=dialect, - copy=copy, - into=Returning, - **opts, - ) - - -class Create(DDL): - arg_types = { - "with_": False, - "this": True, - "kind": True, - "expression": False, - "exists": False, - "properties": False, - "replace": False, - "refresh": False, - "unique": False, - "indexes": False, - "no_schema_binding": False, - "begin": False, - "end": False, - "clone": False, - "concurrently": False, - "clustered": False, - } - - @property - def kind(self) -> t.Optional[str]: - kind = self.args.get("kind") - return kind and kind.upper() - - -class SequenceProperties(Expression): - arg_types = { - "increment": False, - "minvalue": False, - "maxvalue": False, - "cache": False, - "start": False, - "owned": False, - "options": False, - } - - -class TruncateTable(Expression): - arg_types = { - "expressions": True, - "is_database": False, - "exists": False, - "only": False, - "cluster": False, - "identity": False, - "option": False, - "partition": False, - } - - -# https://docs.snowflake.com/en/sql-reference/sql/create-clone -# https://cloud.google.com/bigquery/docs/reference/standard-sql/data-definition-language#create_table_clone_statement -# https://cloud.google.com/bigquery/docs/reference/standard-sql/data-definition-language#create_table_copy -class Clone(Expression): - arg_types = {"this": True, "shallow": False, "copy": False} - - -class Describe(Expression): - arg_types = { - "this": True, - "style": False, - "kind": False, - "expressions": False, - "partition": False, - "format": False, - } - - -# https://duckdb.org/docs/sql/statements/attach.html#attach -class Attach(Expression): - arg_types = {"this": True, "exists": False, "expressions": False} - - -# https://duckdb.org/docs/sql/statements/attach.html#detach -class Detach(Expression): - arg_types = {"this": True, "exists": False} - - -# https://duckdb.org/docs/sql/statements/load_and_install.html -class Install(Expression): - arg_types = {"this": True, "from_": False, "force": False} - - -# https://duckdb.org/docs/guides/meta/summarize.html -class Summarize(Expression): - arg_types = {"this": True, "table": False} - - -class Kill(Expression): - arg_types = {"this": True, "kind": False} - - -class Pragma(Expression): - pass - - -class Declare(Expression): - arg_types = {"expressions": True} - - -class DeclareItem(Expression): - arg_types = {"this": True, "kind": False, "default": False} - - -class Set(Expression): - arg_types = {"expressions": False, "unset": False, "tag": False} - - -class Heredoc(Expression): - arg_types = {"this": True, "tag": False} - - -class SetItem(Expression): - arg_types = { - "this": False, - "expressions": False, - "kind": False, - "collate": False, # MySQL SET NAMES statement - "global_": False, - } - - -class QueryBand(Expression): - arg_types = {"this": True, "scope": False, "update": False} - - -class Show(Expression): - arg_types = { - "this": True, - "history": False, - "terse": False, - "target": False, - "offset": False, - "starts_with": False, - "limit": False, - "from_": False, - "like": False, - "where": False, - "db": False, - "scope": False, - "scope_kind": False, - "full": False, - "mutex": False, - "query": False, - "channel": False, - "global_": False, - "log": False, - "position": False, - "types": False, - "privileges": False, - "for_table": False, - "for_group": False, - "for_user": False, - "for_role": False, - "into_outfile": False, - "json": False, - } - - -class UserDefinedFunction(Expression): - arg_types = {"this": True, "expressions": False, "wrapped": False} - - -class CharacterSet(Expression): - arg_types = {"this": True, "default": False} - - -class RecursiveWithSearch(Expression): - arg_types = {"kind": True, "this": True, "expression": True, "using": False} - - -class With(Expression): - arg_types = {"expressions": True, "recursive": False, "search": False} - - @property - def recursive(self) -> bool: - return bool(self.args.get("recursive")) - - -class WithinGroup(Expression): - arg_types = {"this": True, "expression": False} - - -# clickhouse supports scalar ctes -# https://clickhouse.com/docs/en/sql-reference/statements/select/with -class CTE(DerivedTable): - arg_types = { - "this": True, - "alias": True, - "scalar": False, - "materialized": False, - "key_expressions": False, - } - - -class ProjectionDef(Expression): - arg_types = {"this": True, "expression": True} - - -class TableAlias(Expression): - arg_types = {"this": False, "columns": False} - - @property - def columns(self): - return self.args.get("columns") or [] - - -class BitString(Condition): - pass - - -class HexString(Condition): - arg_types = {"this": True, "is_integer": False} - - -class ByteString(Condition): - arg_types = {"this": True, "is_bytes": False} - - -class RawString(Condition): - pass - - -class UnicodeString(Condition): - arg_types = {"this": True, "escape": False} - - -class Column(Condition): - arg_types = { - "this": True, - "table": False, - "db": False, - "catalog": False, - "join_mark": False, - } - - @property - def table(self) -> str: - return self.text("table") - - @property - def db(self) -> str: - return self.text("db") - - @property - def catalog(self) -> str: - return self.text("catalog") - - @property - def output_name(self) -> str: - return self.name - - @property - def parts(self) -> t.List[Identifier]: - """Return the parts of a column in order catalog, db, table, name.""" - return [ - t.cast(Identifier, self.args[part]) - for part in ("catalog", "db", "table", "this") - if self.args.get(part) - ] - - def to_dot(self, include_dots: bool = True) -> Dot | Identifier: - """Converts the column into a dot expression.""" - parts = self.parts - parent = self.parent - - if include_dots: - while isinstance(parent, Dot): - parts.append(parent.expression) - parent = parent.parent - - return Dot.build(deepcopy(parts)) if len(parts) > 1 else parts[0] - - -class Pseudocolumn(Column): - pass - - -class ColumnPosition(Expression): - arg_types = {"this": False, "position": True} - - -class ColumnDef(Expression): - arg_types = { - "this": True, - "kind": False, - "constraints": False, - "exists": False, - "position": False, - "default": False, - "output": False, - } - - @property - def constraints(self) -> t.List[ColumnConstraint]: - return self.args.get("constraints") or [] - - @property - def kind(self) -> t.Optional[DataType]: - return self.args.get("kind") - - -class AlterColumn(Expression): - arg_types = { - "this": True, - "dtype": False, - "collate": False, - "using": False, - "default": False, - "drop": False, - "comment": False, - "allow_null": False, - "visible": False, - "rename_to": False, - } - - -# https://dev.mysql.com/doc/refman/8.0/en/invisible-indexes.html -class AlterIndex(Expression): - arg_types = {"this": True, "visible": True} - - -# https://docs.aws.amazon.com/redshift/latest/dg/r_ALTER_TABLE.html -class AlterDistStyle(Expression): - pass - - -class AlterSortKey(Expression): - arg_types = {"this": False, "expressions": False, "compound": False} - - -class AlterSet(Expression): - arg_types = { - "expressions": False, - "option": False, - "tablespace": False, - "access_method": False, - "file_format": False, - "copy_options": False, - "tag": False, - "location": False, - "serde": False, - } - - -class RenameColumn(Expression): - arg_types = {"this": True, "to": True, "exists": False} - - -class AlterRename(Expression): - pass - - -class SwapTable(Expression): - pass - - -class Comment(Expression): - arg_types = { - "this": True, - "kind": True, - "expression": True, - "exists": False, - "materialized": False, - } - - -class Comprehension(Expression): - arg_types = { - "this": True, - "expression": True, - "position": False, - "iterator": True, - "condition": False, - } - - -# https://clickhouse.com/docs/en/engines/table-engines/mergetree-family/mergetree#mergetree-table-ttl -class MergeTreeTTLAction(Expression): - arg_types = { - "this": True, - "delete": False, - "recompress": False, - "to_disk": False, - "to_volume": False, - } - - -# https://clickhouse.com/docs/en/engines/table-engines/mergetree-family/mergetree#mergetree-table-ttl -class MergeTreeTTL(Expression): - arg_types = { - "expressions": True, - "where": False, - "group": False, - "aggregates": False, - } - - -# https://dev.mysql.com/doc/refman/8.0/en/create-table.html -class IndexConstraintOption(Expression): - arg_types = { - "key_block_size": False, - "using": False, - "parser": False, - "comment": False, - "visible": False, - "engine_attr": False, - "secondary_engine_attr": False, - } - - -class ColumnConstraint(Expression): - arg_types = {"this": False, "kind": True} - - @property - def kind(self) -> ColumnConstraintKind: - return self.args["kind"] - - -class ColumnConstraintKind(Expression): - pass - - -class AutoIncrementColumnConstraint(ColumnConstraintKind): - pass - - -class ZeroFillColumnConstraint(ColumnConstraint): - arg_types = {} - - -class PeriodForSystemTimeConstraint(ColumnConstraintKind): - arg_types = {"this": True, "expression": True} - - -class CaseSpecificColumnConstraint(ColumnConstraintKind): - arg_types = {"not_": True} - - -class CharacterSetColumnConstraint(ColumnConstraintKind): - arg_types = {"this": True} - - -class CheckColumnConstraint(ColumnConstraintKind): - arg_types = {"this": True, "enforced": False} - - -class ClusteredColumnConstraint(ColumnConstraintKind): - pass - - -class CollateColumnConstraint(ColumnConstraintKind): - pass - - -class CommentColumnConstraint(ColumnConstraintKind): - pass - - -class CompressColumnConstraint(ColumnConstraintKind): - arg_types = {"this": False} - - -class DateFormatColumnConstraint(ColumnConstraintKind): - arg_types = {"this": True} - - -class DefaultColumnConstraint(ColumnConstraintKind): - pass - - -class EncodeColumnConstraint(ColumnConstraintKind): - pass - - -# https://www.postgresql.org/docs/current/sql-createtable.html#SQL-CREATETABLE-EXCLUDE -class ExcludeColumnConstraint(ColumnConstraintKind): - pass - - -class EphemeralColumnConstraint(ColumnConstraintKind): - arg_types = {"this": False} - - -class WithOperator(Expression): - arg_types = {"this": True, "op": True} - - -class GeneratedAsIdentityColumnConstraint(ColumnConstraintKind): - # this: True -> ALWAYS, this: False -> BY DEFAULT - arg_types = { - "this": False, - "expression": False, - "on_null": False, - "start": False, - "increment": False, - "minvalue": False, - "maxvalue": False, - "cycle": False, - "order": False, - } - - -class GeneratedAsRowColumnConstraint(ColumnConstraintKind): - arg_types = {"start": False, "hidden": False} - - -# https://dev.mysql.com/doc/refman/8.0/en/create-table.html -# https://github.com/ClickHouse/ClickHouse/blob/master/src/Parsers/ParserCreateQuery.h#L646 -class IndexColumnConstraint(ColumnConstraintKind): - arg_types = { - "this": False, - "expressions": False, - "kind": False, - "index_type": False, - "options": False, - "expression": False, # Clickhouse - "granularity": False, - } - - -class InlineLengthColumnConstraint(ColumnConstraintKind): - pass - - -class NonClusteredColumnConstraint(ColumnConstraintKind): - pass - - -class NotForReplicationColumnConstraint(ColumnConstraintKind): - arg_types = {} - - -# https://docs.snowflake.com/en/sql-reference/sql/create-table -class MaskingPolicyColumnConstraint(ColumnConstraintKind): - arg_types = {"this": True, "expressions": False} - - -class NotNullColumnConstraint(ColumnConstraintKind): - arg_types = {"allow_null": False} - - -# https://dev.mysql.com/doc/refman/5.7/en/timestamp-initialization.html -class OnUpdateColumnConstraint(ColumnConstraintKind): - pass - - -class PrimaryKeyColumnConstraint(ColumnConstraintKind): - arg_types = {"desc": False, "options": False} - - -class TitleColumnConstraint(ColumnConstraintKind): - pass - - -class UniqueColumnConstraint(ColumnConstraintKind): - arg_types = { - "this": False, - "index_type": False, - "on_conflict": False, - "nulls": False, - "options": False, - } - - -class UppercaseColumnConstraint(ColumnConstraintKind): - arg_types: t.Dict[str, t.Any] = {} - - -# https://docs.risingwave.com/processing/watermarks#syntax -class WatermarkColumnConstraint(Expression): - arg_types = {"this": True, "expression": True} - - -class PathColumnConstraint(ColumnConstraintKind): - pass - - -# https://docs.snowflake.com/en/sql-reference/sql/create-table -class ProjectionPolicyColumnConstraint(ColumnConstraintKind): - pass - - -# computed column expression -# https://learn.microsoft.com/en-us/sql/t-sql/statements/create-table-transact-sql?view=sql-server-ver16 -class ComputedColumnConstraint(ColumnConstraintKind): - arg_types = { - "this": True, - "persisted": False, - "not_null": False, - "data_type": False, - } - - -class Constraint(Expression): - arg_types = {"this": True, "expressions": True} - - -class Delete(DML): - arg_types = { - "with_": False, - "this": False, - "using": False, - "where": False, - "returning": False, - "order": False, - "limit": False, - "tables": False, # Multiple-Table Syntax (MySQL) - "cluster": False, # Clickhouse - } - - def delete( - self, - table: ExpOrStr, - dialect: DialectType = None, - copy: bool = True, - **opts, - ) -> Delete: - """ - Create a DELETE expression or replace the table on an existing DELETE expression. - - Example: - >>> delete("tbl").sql() - 'DELETE FROM tbl' - - Args: - table: the table from which to delete. - dialect: the dialect used to parse the input expression. - copy: if `False`, modify this expression instance in-place. - opts: other options to use to parse the input expressions. - - Returns: - Delete: the modified expression. - """ - return _apply_builder( - expression=table, - instance=self, - arg="this", - dialect=dialect, - into=Table, - copy=copy, - **opts, - ) - - def where( - self, - *expressions: t.Optional[ExpOrStr], - append: bool = True, - dialect: DialectType = None, - copy: bool = True, - **opts, - ) -> Delete: - """ - Append to or set the WHERE expressions. - - Example: - >>> delete("tbl").where("x = 'a' OR x < 'b'").sql() - "DELETE FROM tbl WHERE x = 'a' OR x < 'b'" - - Args: - *expressions: the SQL code strings to parse. - If an `Expression` instance is passed, it will be used as-is. - Multiple expressions are combined with an AND operator. - append: if `True`, AND the new expressions to any existing expression. - Otherwise, this resets the expression. - dialect: the dialect used to parse the input expressions. - copy: if `False`, modify this expression instance in-place. - opts: other options to use to parse the input expressions. - - Returns: - Delete: the modified expression. - """ - return _apply_conjunction_builder( - *expressions, - instance=self, - arg="where", - append=append, - into=Where, - dialect=dialect, - copy=copy, - **opts, - ) - - -class Drop(Expression): - arg_types = { - "this": False, - "kind": False, - "expressions": False, - "exists": False, - "temporary": False, - "materialized": False, - "cascade": False, - "constraints": False, - "purge": False, - "cluster": False, - "concurrently": False, - } - - @property - def kind(self) -> t.Optional[str]: - kind = self.args.get("kind") - return kind and kind.upper() - - -# https://cloud.google.com/bigquery/docs/reference/standard-sql/export-statements -class Export(Expression): - arg_types = {"this": True, "connection": False, "options": True} - - -class Filter(Expression): - arg_types = {"this": True, "expression": True} - - -class Check(Expression): - pass - - -class Changes(Expression): - arg_types = {"information": True, "at_before": False, "end": False} - - -# https://docs.snowflake.com/en/sql-reference/constructs/connect-by -class Connect(Expression): - arg_types = {"start": False, "connect": True, "nocycle": False} - - -class CopyParameter(Expression): - arg_types = {"this": True, "expression": False, "expressions": False} - - -class Copy(DML): - arg_types = { - "this": True, - "kind": True, - "files": False, - "credentials": False, - "format": False, - "params": False, - } - - -class Credentials(Expression): - arg_types = { - "credentials": False, - "encryption": False, - "storage": False, - "iam_role": False, - "region": False, - } - - -class Prior(Expression): - pass - - -class Directory(Expression): - arg_types = {"this": True, "local": False, "row_format": False} - - -# https://docs.snowflake.com/en/user-guide/data-load-dirtables-query -class DirectoryStage(Expression): - pass - - -class ForeignKey(Expression): - arg_types = { - "expressions": False, - "reference": False, - "delete": False, - "update": False, - "options": False, - } - - -class ColumnPrefix(Expression): - arg_types = {"this": True, "expression": True} - - -class PrimaryKey(Expression): - arg_types = {"this": False, "expressions": True, "options": False, "include": False} - - -# https://www.postgresql.org/docs/9.1/sql-selectinto.html -# https://docs.aws.amazon.com/redshift/latest/dg/r_SELECT_INTO.html#r_SELECT_INTO-examples -class Into(Expression): - arg_types = { - "this": False, - "temporary": False, - "unlogged": False, - "bulk_collect": False, - "expressions": False, - } - - -class From(Expression): - @property - def name(self) -> str: - return self.this.name - - @property - def alias_or_name(self) -> str: - return self.this.alias_or_name - - -class Having(Expression): - pass - - -class Hint(Expression): - arg_types = {"expressions": True} - - -class JoinHint(Expression): - arg_types = {"this": True, "expressions": True} - - -class Identifier(Expression): - arg_types = {"this": True, "quoted": False, "global_": False, "temporary": False} - - @property - def quoted(self) -> bool: - return bool(self.args.get("quoted")) - - @property - def output_name(self) -> str: - return self.name - - -# https://www.postgresql.org/docs/current/indexes-opclass.html -class Opclass(Expression): - arg_types = {"this": True, "expression": True} - - -class Index(Expression): - arg_types = { - "this": False, - "table": False, - "unique": False, - "primary": False, - "amp": False, # teradata - "params": False, - } - - -class IndexParameters(Expression): - arg_types = { - "using": False, - "include": False, - "columns": False, - "with_storage": False, - "partition_by": False, - "tablespace": False, - "where": False, - "on": False, - } - - -class Insert(DDL, DML): - arg_types = { - "hint": False, - "with_": False, - "is_function": False, - "this": False, - "expression": False, - "conflict": False, - "returning": False, - "overwrite": False, - "exists": False, - "alternative": False, - "where": False, - "ignore": False, - "by_name": False, - "stored": False, - "partition": False, - "settings": False, - "source": False, - "default": False, - } - - def with_( - self, - alias: ExpOrStr, - as_: ExpOrStr, - recursive: t.Optional[bool] = None, - materialized: t.Optional[bool] = None, - append: bool = True, - dialect: DialectType = None, - copy: bool = True, - **opts, - ) -> Insert: - """ - Append to or set the common table expressions. - - Example: - >>> insert("SELECT x FROM cte", "t").with_("cte", as_="SELECT * FROM tbl").sql() - 'WITH cte AS (SELECT * FROM tbl) INSERT INTO t SELECT x FROM cte' - - Args: - alias: the SQL code string to parse as the table name. - If an `Expression` instance is passed, this is used as-is. - as_: the SQL code string to parse as the table expression. - If an `Expression` instance is passed, it will be used as-is. - recursive: set the RECURSIVE part of the expression. Defaults to `False`. - materialized: set the MATERIALIZED part of the expression. - append: if `True`, add to any existing expressions. - Otherwise, this resets the expressions. - dialect: the dialect used to parse the input expression. - copy: if `False`, modify this expression instance in-place. - opts: other options to use to parse the input expressions. - - Returns: - The modified expression. - """ - return _apply_cte_builder( - self, - alias, - as_, - recursive=recursive, - materialized=materialized, - append=append, - dialect=dialect, - copy=copy, - **opts, - ) - - -class ConditionalInsert(Expression): - arg_types = {"this": True, "expression": False, "else_": False} - - -class MultitableInserts(Expression): - arg_types = {"expressions": True, "kind": True, "source": True} - - -class OnConflict(Expression): - arg_types = { - "duplicate": False, - "expressions": False, - "action": False, - "conflict_keys": False, - "constraint": False, - "where": False, - } - - -class OnCondition(Expression): - arg_types = {"error": False, "empty": False, "null": False} - - -class Returning(Expression): - arg_types = {"expressions": True, "into": False} - - -# https://dev.mysql.com/doc/refman/8.0/en/charset-introducer.html -class Introducer(Expression): - arg_types = {"this": True, "expression": True} - - -# national char, like n'utf8' -class National(Expression): - pass - - -class LoadData(Expression): - arg_types = { - "this": True, - "local": False, - "overwrite": False, - "inpath": True, - "partition": False, - "input_format": False, - "serde": False, - } - - -class Partition(Expression): - arg_types = {"expressions": True, "subpartition": False} - - -class PartitionRange(Expression): - arg_types = {"this": True, "expression": False, "expressions": False} - - -# https://clickhouse.com/docs/en/sql-reference/statements/alter/partition#how-to-set-partition-expression -class PartitionId(Expression): - pass - - -class Fetch(Expression): - arg_types = { - "direction": False, - "count": False, - "limit_options": False, - } - - -class Grant(Expression): - arg_types = { - "privileges": True, - "kind": False, - "securable": True, - "principals": True, - "grant_option": False, - } - - -class Revoke(Expression): - arg_types = {**Grant.arg_types, "cascade": False} - - -class Group(Expression): - arg_types = { - "expressions": False, - "grouping_sets": False, - "cube": False, - "rollup": False, - "totals": False, - "all": False, - } - - -class Cube(Expression): - arg_types = {"expressions": False} - - -class Rollup(Expression): - arg_types = {"expressions": False} - - -class GroupingSets(Expression): - arg_types = {"expressions": True} - - -class Lambda(Expression): - arg_types = {"this": True, "expressions": True, "colon": False} - - -class Limit(Expression): - arg_types = { - "this": False, - "expression": True, - "offset": False, - "limit_options": False, - "expressions": False, - } - - -class LimitOptions(Expression): - arg_types = { - "percent": False, - "rows": False, - "with_ties": False, - } - - -class Literal(Condition): - arg_types = {"this": True, "is_string": True} - - @classmethod - def number(cls, number) -> Literal: - return cls(this=str(number), is_string=False) - - @classmethod - def string(cls, string) -> Literal: - return cls(this=str(string), is_string=True) - - @property - def output_name(self) -> str: - return self.name - - def to_py(self) -> int | str | Decimal: - if self.is_number: - try: - return int(self.this) - except ValueError: - return Decimal(self.this) - return self.this - - -class Join(Expression): - arg_types = { - "this": True, - "on": False, - "side": False, - "kind": False, - "using": False, - "method": False, - "global_": False, - "hint": False, - "match_condition": False, # Snowflake - "expressions": False, - "pivots": False, - } - - @property - def method(self) -> str: - return self.text("method").upper() - - @property - def kind(self) -> str: - return self.text("kind").upper() - - @property - def side(self) -> str: - return self.text("side").upper() - - @property - def hint(self) -> str: - return self.text("hint").upper() - - @property - def alias_or_name(self) -> str: - return self.this.alias_or_name - - @property - def is_semi_or_anti_join(self) -> bool: - return self.kind in ("SEMI", "ANTI") - - def on( - self, - *expressions: t.Optional[ExpOrStr], - append: bool = True, - dialect: DialectType = None, - copy: bool = True, - **opts, - ) -> Join: - """ - Append to or set the ON expressions. - - Example: - >>> import sqlglot - >>> sqlglot.parse_one("JOIN x", into=Join).on("y = 1").sql() - 'JOIN x ON y = 1' - - Args: - *expressions: the SQL code strings to parse. - If an `Expression` instance is passed, it will be used as-is. - Multiple expressions are combined with an AND operator. - append: if `True`, AND the new expressions to any existing expression. - Otherwise, this resets the expression. - dialect: the dialect used to parse the input expressions. - copy: if `False`, modify this expression instance in-place. - opts: other options to use to parse the input expressions. - - Returns: - The modified Join expression. - """ - join = _apply_conjunction_builder( - *expressions, - instance=self, - arg="on", - append=append, - dialect=dialect, - copy=copy, - **opts, - ) - - if join.kind == "CROSS": - join.set("kind", None) - - return join - - def using( - self, - *expressions: t.Optional[ExpOrStr], - append: bool = True, - dialect: DialectType = None, - copy: bool = True, - **opts, - ) -> Join: - """ - Append to or set the USING expressions. - - Example: - >>> import sqlglot - >>> sqlglot.parse_one("JOIN x", into=Join).using("foo", "bla").sql() - 'JOIN x USING (foo, bla)' - - Args: - *expressions: the SQL code strings to parse. - If an `Expression` instance is passed, it will be used as-is. - append: if `True`, concatenate the new expressions to the existing "using" list. - Otherwise, this resets the expression. - dialect: the dialect used to parse the input expressions. - copy: if `False`, modify this expression instance in-place. - opts: other options to use to parse the input expressions. - - Returns: - The modified Join expression. - """ - join = _apply_list_builder( - *expressions, - instance=self, - arg="using", - append=append, - dialect=dialect, - copy=copy, - **opts, - ) - - if join.kind == "CROSS": - join.set("kind", None) - - return join - - -class Lateral(UDTF): - arg_types = { - "this": True, - "view": False, - "outer": False, - "alias": False, - "cross_apply": False, # True -> CROSS APPLY, False -> OUTER APPLY - "ordinality": False, - } - - -# https://docs.snowflake.com/sql-reference/literals-table -# https://docs.snowflake.com/en/sql-reference/functions-table#using-a-table-function -class TableFromRows(UDTF): - arg_types = { - "this": True, - "alias": False, - "joins": False, - "pivots": False, - "sample": False, - } - - -class MatchRecognizeMeasure(Expression): - arg_types = { - "this": True, - "window_frame": False, - } - - -class MatchRecognize(Expression): - arg_types = { - "partition_by": False, - "order": False, - "measures": False, - "rows": False, - "after": False, - "pattern": False, - "define": False, - "alias": False, - } - - -# Clickhouse FROM FINAL modifier -# https://clickhouse.com/docs/en/sql-reference/statements/select/from/#final-modifier -class Final(Expression): - pass - - -class Offset(Expression): - arg_types = {"this": False, "expression": True, "expressions": False} - - -class Order(Expression): - arg_types = {"this": False, "expressions": True, "siblings": False} - - -# https://clickhouse.com/docs/en/sql-reference/statements/select/order-by#order-by-expr-with-fill-modifier -class WithFill(Expression): - arg_types = { - "from_": False, - "to": False, - "step": False, - "interpolate": False, - } - - -# hive specific sorts -# https://cwiki.apache.org/confluence/display/Hive/LanguageManual+SortBy -class Cluster(Order): - pass - - -class Distribute(Order): - pass - - -class Sort(Order): - pass - - -class Ordered(Expression): - arg_types = {"this": True, "desc": False, "nulls_first": True, "with_fill": False} - - @property - def name(self) -> str: - return self.this.name - - -class Property(Expression): - arg_types = {"this": True, "value": True} - - -class GrantPrivilege(Expression): - arg_types = {"this": True, "expressions": False} - - -class GrantPrincipal(Expression): - arg_types = {"this": True, "kind": False} - - -class AllowedValuesProperty(Expression): - arg_types = {"expressions": True} - - -class AlgorithmProperty(Property): - arg_types = {"this": True} - - -class AutoIncrementProperty(Property): - arg_types = {"this": True} - - -# https://docs.aws.amazon.com/prescriptive-guidance/latest/materialized-views-redshift/refreshing-materialized-views.html -class AutoRefreshProperty(Property): - arg_types = {"this": True} - - -class BackupProperty(Property): - arg_types = {"this": True} - - -# https://doris.apache.org/docs/sql-manual/sql-statements/table-and-view/async-materialized-view/CREATE-ASYNC-MATERIALIZED-VIEW/ -class BuildProperty(Property): - arg_types = {"this": True} - - -class BlockCompressionProperty(Property): - arg_types = { - "autotemp": False, - "always": False, - "default": False, - "manual": False, - "never": False, - } - - -class CharacterSetProperty(Property): - arg_types = {"this": True, "default": True} - - -class ChecksumProperty(Property): - arg_types = {"on": False, "default": False} - - -class CollateProperty(Property): - arg_types = {"this": True, "default": False} - - -class CopyGrantsProperty(Property): - arg_types = {} - - -class DataBlocksizeProperty(Property): - arg_types = { - "size": False, - "units": False, - "minimum": False, - "maximum": False, - "default": False, - } - - -class DataDeletionProperty(Property): - arg_types = {"on": True, "filter_column": False, "retention_period": False} - - -class DefinerProperty(Property): - arg_types = {"this": True} - - -class DistKeyProperty(Property): - arg_types = {"this": True} - - -# https://docs.starrocks.io/docs/sql-reference/sql-statements/data-definition/CREATE_TABLE/#distribution_desc -# https://doris.apache.org/docs/sql-manual/sql-statements/Data-Definition-Statements/Create/CREATE-TABLE?_highlight=create&_highlight=table#distribution_desc -class DistributedByProperty(Property): - arg_types = {"expressions": False, "kind": True, "buckets": False, "order": False} - - -class DistStyleProperty(Property): - arg_types = {"this": True} - - -class DuplicateKeyProperty(Property): - arg_types = {"expressions": True} - - -class EngineProperty(Property): - arg_types = {"this": True} - - -class HeapProperty(Property): - arg_types = {} - - -class ToTableProperty(Property): - arg_types = {"this": True} - - -class ExecuteAsProperty(Property): - arg_types = {"this": True} - - -class ExternalProperty(Property): - arg_types = {"this": False} - - -class FallbackProperty(Property): - arg_types = {"no": True, "protection": False} - - -# https://docs.databricks.com/aws/en/sql/language-manual/sql-ref-syntax-ddl-create-table-hiveformat -class FileFormatProperty(Property): - arg_types = {"this": False, "expressions": False, "hive_format": False} - - -class CredentialsProperty(Property): - arg_types = {"expressions": True} - - -class FreespaceProperty(Property): - arg_types = {"this": True, "percent": False} - - -class GlobalProperty(Property): - arg_types = {} - - -class IcebergProperty(Property): - arg_types = {} - - -class InheritsProperty(Property): - arg_types = {"expressions": True} - - -class InputModelProperty(Property): - arg_types = {"this": True} - - -class OutputModelProperty(Property): - arg_types = {"this": True} - - -class IsolatedLoadingProperty(Property): - arg_types = {"no": False, "concurrent": False, "target": False} - - -class JournalProperty(Property): - arg_types = { - "no": False, - "dual": False, - "before": False, - "local": False, - "after": False, - } - - -class LanguageProperty(Property): - arg_types = {"this": True} - - -class EnviromentProperty(Property): - arg_types = {"expressions": True} - - -# spark ddl -class ClusteredByProperty(Property): - arg_types = {"expressions": True, "sorted_by": False, "buckets": True} - - -class DictProperty(Property): - arg_types = {"this": True, "kind": True, "settings": False} - - -class DictSubProperty(Property): - pass - - -class DictRange(Property): - arg_types = {"this": True, "min": True, "max": True} - - -class DynamicProperty(Property): - arg_types = {} - - -# Clickhouse CREATE ... ON CLUSTER modifier -# https://clickhouse.com/docs/en/sql-reference/distributed-ddl -class OnCluster(Property): - arg_types = {"this": True} - - -# Clickhouse EMPTY table "property" -class EmptyProperty(Property): - arg_types = {} - - -class LikeProperty(Property): - arg_types = {"this": True, "expressions": False} - - -class LocationProperty(Property): - arg_types = {"this": True} - - -class LockProperty(Property): - arg_types = {"this": True} - - -class LockingProperty(Property): - arg_types = { - "this": False, - "kind": True, - "for_or_in": False, - "lock_type": True, - "override": False, - } - - -class LogProperty(Property): - arg_types = {"no": True} - - -class MaterializedProperty(Property): - arg_types = {"this": False} - - -class MergeBlockRatioProperty(Property): - arg_types = {"this": False, "no": False, "default": False, "percent": False} - - -class NoPrimaryIndexProperty(Property): - arg_types = {} - - -class OnProperty(Property): - arg_types = {"this": True} - - -class OnCommitProperty(Property): - arg_types = {"delete": False} - - -class PartitionedByProperty(Property): - arg_types = {"this": True} - - -class PartitionedByBucket(Property): - arg_types = {"this": True, "expression": True} - - -class PartitionByTruncate(Property): - arg_types = {"this": True, "expression": True} - - -# https://docs.starrocks.io/docs/sql-reference/sql-statements/table_bucket_part_index/CREATE_TABLE/ -class PartitionByRangeProperty(Property): - arg_types = {"partition_expressions": True, "create_expressions": True} - - -# https://docs.starrocks.io/docs/table_design/data_distribution/#range-partitioning -class PartitionByRangePropertyDynamic(Expression): - arg_types = {"this": False, "start": True, "end": True, "every": True} - - -# https://doris.apache.org/docs/table-design/data-partitioning/manual-partitioning -class PartitionByListProperty(Property): - arg_types = {"partition_expressions": True, "create_expressions": True} - - -# https://doris.apache.org/docs/table-design/data-partitioning/manual-partitioning -class PartitionList(Expression): - arg_types = {"this": True, "expressions": True} - - -# https://doris.apache.org/docs/sql-manual/sql-statements/table-and-view/async-materialized-view/CREATE-ASYNC-MATERIALIZED-VIEW -class RefreshTriggerProperty(Property): - arg_types = { - "method": True, - "kind": False, - "every": False, - "unit": False, - "starts": False, - } - - -# https://docs.starrocks.io/docs/sql-reference/sql-statements/table_bucket_part_index/CREATE_TABLE/ -class UniqueKeyProperty(Property): - arg_types = {"expressions": True} - - -# https://www.postgresql.org/docs/current/sql-createtable.html -class PartitionBoundSpec(Expression): - # this -> IN / MODULUS, expression -> REMAINDER, from_expressions -> FROM (...), to_expressions -> TO (...) - arg_types = { - "this": False, - "expression": False, - "from_expressions": False, - "to_expressions": False, - } - - -class PartitionedOfProperty(Property): - # this -> parent_table (schema), expression -> FOR VALUES ... / DEFAULT - arg_types = {"this": True, "expression": True} - - -class StreamingTableProperty(Property): - arg_types = {} - - -class RemoteWithConnectionModelProperty(Property): - arg_types = {"this": True} - - -class ReturnsProperty(Property): - arg_types = {"this": False, "is_table": False, "table": False, "null": False} - - -class StrictProperty(Property): - arg_types = {} - - -class RowFormatProperty(Property): - arg_types = {"this": True} - - -class RowFormatDelimitedProperty(Property): - # https://cwiki.apache.org/confluence/display/hive/languagemanual+dml - arg_types = { - "fields": False, - "escaped": False, - "collection_items": False, - "map_keys": False, - "lines": False, - "null": False, - "serde": False, - } - - -class RowFormatSerdeProperty(Property): - arg_types = {"this": True, "serde_properties": False} - - -# https://spark.apache.org/docs/3.1.2/sql-ref-syntax-qry-select-transform.html -class QueryTransform(Expression): - arg_types = { - "expressions": True, - "command_script": True, - "schema": False, - "row_format_before": False, - "record_writer": False, - "row_format_after": False, - "record_reader": False, - } - - -class SampleProperty(Property): - arg_types = {"this": True} - - -# https://prestodb.io/docs/current/sql/create-view.html#synopsis -class SecurityProperty(Property): - arg_types = {"this": True} - - -class SchemaCommentProperty(Property): - arg_types = {"this": True} - - -class SemanticView(Expression): - arg_types = { - "this": True, - "metrics": False, - "dimensions": False, - "facts": False, - "where": False, - } - - -class SerdeProperties(Property): - arg_types = {"expressions": True, "with_": False} - - -class SetProperty(Property): - arg_types = {"multi": True} - - -class SharingProperty(Property): - arg_types = {"this": False} - - -class SetConfigProperty(Property): - arg_types = {"this": True} - - -class SettingsProperty(Property): - arg_types = {"expressions": True} - - -class SortKeyProperty(Property): - arg_types = {"this": True, "compound": False} - - -class SqlReadWriteProperty(Property): - arg_types = {"this": True} - - -class SqlSecurityProperty(Property): - arg_types = {"this": True} - - -class StabilityProperty(Property): - arg_types = {"this": True} - - -class StorageHandlerProperty(Property): - arg_types = {"this": True} - - -class TemporaryProperty(Property): - arg_types = {"this": False} - - -class SecureProperty(Property): - arg_types = {} - - -# https://docs.snowflake.com/en/sql-reference/sql/create-table -class Tags(ColumnConstraintKind, Property): - arg_types = {"expressions": True} - - -class TransformModelProperty(Property): - arg_types = {"expressions": True} - - -class TransientProperty(Property): - arg_types = {"this": False} - - -class UnloggedProperty(Property): - arg_types = {} - - -# https://docs.snowflake.com/en/sql-reference/sql/create-table#create-table-using-template -class UsingTemplateProperty(Property): - arg_types = {"this": True} - - -# https://learn.microsoft.com/en-us/sql/t-sql/statements/create-view-transact-sql?view=sql-server-ver16 -class ViewAttributeProperty(Property): - arg_types = {"this": True} - - -class VolatileProperty(Property): - arg_types = {"this": False} - - -class WithDataProperty(Property): - arg_types = {"no": True, "statistics": False} - - -class WithJournalTableProperty(Property): - arg_types = {"this": True} - - -class WithSchemaBindingProperty(Property): - arg_types = {"this": True} - - -class WithSystemVersioningProperty(Property): - arg_types = { - "on": False, - "this": False, - "data_consistency": False, - "retention_period": False, - "with_": True, - } - - -class WithProcedureOptions(Property): - arg_types = {"expressions": True} - - -class EncodeProperty(Property): - arg_types = {"this": True, "properties": False, "key": False} - - -class IncludeProperty(Property): - arg_types = {"this": True, "alias": False, "column_def": False} - - -class ForceProperty(Property): - arg_types = {} - - -class Properties(Expression): - arg_types = {"expressions": True} - - NAME_TO_PROPERTY = { - "ALGORITHM": AlgorithmProperty, - "AUTO_INCREMENT": AutoIncrementProperty, - "CHARACTER SET": CharacterSetProperty, - "CLUSTERED_BY": ClusteredByProperty, - "COLLATE": CollateProperty, - "COMMENT": SchemaCommentProperty, - "CREDENTIALS": CredentialsProperty, - "DEFINER": DefinerProperty, - "DISTKEY": DistKeyProperty, - "DISTRIBUTED_BY": DistributedByProperty, - "DISTSTYLE": DistStyleProperty, - "ENGINE": EngineProperty, - "EXECUTE AS": ExecuteAsProperty, - "FORMAT": FileFormatProperty, - "LANGUAGE": LanguageProperty, - "LOCATION": LocationProperty, - "LOCK": LockProperty, - "PARTITIONED_BY": PartitionedByProperty, - "RETURNS": ReturnsProperty, - "ROW_FORMAT": RowFormatProperty, - "SORTKEY": SortKeyProperty, - "ENCODE": EncodeProperty, - "INCLUDE": IncludeProperty, - } - - PROPERTY_TO_NAME = {v: k for k, v in NAME_TO_PROPERTY.items()} - - # CREATE property locations - # Form: schema specified - # create [POST_CREATE] - # table a [POST_NAME] - # (b int) [POST_SCHEMA] - # with ([POST_WITH]) - # index (b) [POST_INDEX] - # - # Form: alias selection - # create [POST_CREATE] - # table a [POST_NAME] - # as [POST_ALIAS] (select * from b) [POST_EXPRESSION] - # index (c) [POST_INDEX] - class Location(AutoName): - POST_CREATE = auto() - POST_NAME = auto() - POST_SCHEMA = auto() - POST_WITH = auto() - POST_ALIAS = auto() - POST_EXPRESSION = auto() - POST_INDEX = auto() - UNSUPPORTED = auto() - - @classmethod - def from_dict(cls, properties_dict: t.Dict) -> Properties: - expressions = [] - for key, value in properties_dict.items(): - property_cls = cls.NAME_TO_PROPERTY.get(key.upper()) - if property_cls: - expressions.append(property_cls(this=convert(value))) - else: - expressions.append( - Property(this=Literal.string(key), value=convert(value)) - ) - - return cls(expressions=expressions) - - -class Qualify(Expression): - pass - - -class InputOutputFormat(Expression): - arg_types = {"input_format": False, "output_format": False} - - -# https://www.ibm.com/docs/en/ias?topic=procedures-return-statement-in-sql -class Return(Expression): - pass - - -class Reference(Expression): - arg_types = {"this": True, "expressions": False, "options": False} - - -class Tuple(Expression): - arg_types = {"expressions": False} - - def isin( - self, - *expressions: t.Any, - query: t.Optional[ExpOrStr] = None, - unnest: t.Optional[ExpOrStr] | t.Collection[ExpOrStr] = None, - copy: bool = True, - **opts, - ) -> In: - return In( - this=maybe_copy(self, copy), - expressions=[convert(e, copy=copy) for e in expressions], - query=maybe_parse(query, copy=copy, **opts) if query else None, - unnest=( - Unnest( - expressions=[ - maybe_parse(t.cast(ExpOrStr, e), copy=copy, **opts) - for e in ensure_list(unnest) - ] - ) - if unnest - else None - ), - ) - - -QUERY_MODIFIERS = { - "match": False, - "laterals": False, - "joins": False, - "connect": False, - "pivots": False, - "prewhere": False, - "where": False, - "group": False, - "having": False, - "qualify": False, - "windows": False, - "distribute": False, - "sort": False, - "cluster": False, - "order": False, - "limit": False, - "offset": False, - "locks": False, - "sample": False, - "settings": False, - "format": False, - "options": False, -} - - -# https://learn.microsoft.com/en-us/sql/t-sql/queries/option-clause-transact-sql?view=sql-server-ver16 -# https://learn.microsoft.com/en-us/sql/t-sql/queries/hints-transact-sql-query?view=sql-server-ver16 -class QueryOption(Expression): - arg_types = {"this": True, "expression": False} - - -# https://learn.microsoft.com/en-us/sql/t-sql/queries/hints-transact-sql-table?view=sql-server-ver16 -class WithTableHint(Expression): - arg_types = {"expressions": True} - - -# https://dev.mysql.com/doc/refman/8.0/en/index-hints.html -class IndexTableHint(Expression): - arg_types = {"this": True, "expressions": False, "target": False} - - -# https://docs.snowflake.com/en/sql-reference/constructs/at-before -class HistoricalData(Expression): - arg_types = {"this": True, "kind": True, "expression": True} - - -# https://docs.snowflake.com/en/sql-reference/sql/put -class Put(Expression): - arg_types = {"this": True, "target": True, "properties": False} - - -# https://docs.snowflake.com/en/sql-reference/sql/get -class Get(Expression): - arg_types = {"this": True, "target": True, "properties": False} - - -class Table(Expression): - arg_types = { - "this": False, - "alias": False, - "db": False, - "catalog": False, - "laterals": False, - "joins": False, - "pivots": False, - "hints": False, - "system_time": False, - "version": False, - "format": False, - "pattern": False, - "ordinality": False, - "when": False, - "only": False, - "partition": False, - "changes": False, - "rows_from": False, - "sample": False, - "indexed": False, - } - - @property - def name(self) -> str: - if not self.this or isinstance(self.this, Func): - return "" - return self.this.name - - @property - def db(self) -> str: - return self.text("db") - - @property - def catalog(self) -> str: - return self.text("catalog") - - @property - def selects(self) -> t.List[Expression]: - return [] - - @property - def named_selects(self) -> t.List[str]: - return [] - - @property - def parts(self) -> t.List[Expression]: - """Return the parts of a table in order catalog, db, table.""" - parts: t.List[Expression] = [] - - for arg in ("catalog", "db", "this"): - part = self.args.get(arg) - - if isinstance(part, Dot): - parts.extend(part.flatten()) - elif isinstance(part, Expression): - parts.append(part) - - return parts - - def to_column(self, copy: bool = True) -> Expression: - parts = self.parts - last_part = parts[-1] - - if isinstance(last_part, Identifier): - col: Expression = column(*reversed(parts[0:4]), fields=parts[4:], copy=copy) # type: ignore - else: - # This branch will be reached if a function or array is wrapped in a `Table` - col = last_part - - alias = self.args.get("alias") - if alias: - col = alias_(col, alias.this, copy=copy) - - return col - - -class SetOperation(Query): - arg_types = { - "with_": False, - "this": True, - "expression": True, - "distinct": False, - "by_name": False, - "side": False, - "kind": False, - "on": False, - **QUERY_MODIFIERS, - } - - def select( - self: S, - *expressions: t.Optional[ExpOrStr], - append: bool = True, - dialect: DialectType = None, - copy: bool = True, - **opts, - ) -> S: - this = maybe_copy(self, copy) - this.this.unnest().select( - *expressions, append=append, dialect=dialect, copy=False, **opts - ) - this.expression.unnest().select( - *expressions, append=append, dialect=dialect, copy=False, **opts - ) - return this - - @property - def named_selects(self) -> t.List[str]: - expression = self - while isinstance(expression, SetOperation): - expression = expression.this.unnest() - return expression.named_selects - - @property - def is_star(self) -> bool: - return self.this.is_star or self.expression.is_star - - @property - def selects(self) -> t.List[Expression]: - expression = self - while isinstance(expression, SetOperation): - expression = expression.this.unnest() - return expression.selects - - @property - def left(self) -> Query: - return self.this - - @property - def right(self) -> Query: - return self.expression - - @property - def kind(self) -> str: - return self.text("kind").upper() - - @property - def side(self) -> str: - return self.text("side").upper() - - -class Union(SetOperation): - pass - - -class Except(SetOperation): - pass - - -class Intersect(SetOperation): - pass - - -class Update(DML): - arg_types = { - "with_": False, - "this": False, - "expressions": False, - "from_": False, - "where": False, - "returning": False, - "order": False, - "limit": False, - "options": False, - } - - def table( - self, - expression: ExpOrStr, - dialect: DialectType = None, - copy: bool = True, - **opts, - ) -> Update: - """ - Set the table to update. - - Example: - >>> Update().table("my_table").set_("x = 1").sql() - 'UPDATE my_table SET x = 1' - - Args: - expression : the SQL code strings to parse. - If a `Table` instance is passed, this is used as-is. - If another `Expression` instance is passed, it will be wrapped in a `Table`. - dialect: the dialect used to parse the input expression. - copy: if `False`, modify this expression instance in-place. - opts: other options to use to parse the input expressions. - - Returns: - The modified Update expression. - """ - return _apply_builder( - expression=expression, - instance=self, - arg="this", - into=Table, - prefix=None, - dialect=dialect, - copy=copy, - **opts, - ) - - def set_( - self, - *expressions: ExpOrStr, - append: bool = True, - dialect: DialectType = None, - copy: bool = True, - **opts, - ) -> Update: - """ - Append to or set the SET expressions. - - Example: - >>> Update().table("my_table").set_("x = 1").sql() - 'UPDATE my_table SET x = 1' - - Args: - *expressions: the SQL code strings to parse. - If `Expression` instance(s) are passed, they will be used as-is. - Multiple expressions are combined with a comma. - append: if `True`, add the new expressions to any existing SET expressions. - Otherwise, this resets the expressions. - dialect: the dialect used to parse the input expressions. - copy: if `False`, modify this expression instance in-place. - opts: other options to use to parse the input expressions. - """ - return _apply_list_builder( - *expressions, - instance=self, - arg="expressions", - append=append, - into=Expression, - prefix=None, - dialect=dialect, - copy=copy, - **opts, - ) - - def where( - self, - *expressions: t.Optional[ExpOrStr], - append: bool = True, - dialect: DialectType = None, - copy: bool = True, - **opts, - ) -> Select: - """ - Append to or set the WHERE expressions. - - Example: - >>> Update().table("tbl").set_("x = 1").where("x = 'a' OR x < 'b'").sql() - "UPDATE tbl SET x = 1 WHERE x = 'a' OR x < 'b'" - - Args: - *expressions: the SQL code strings to parse. - If an `Expression` instance is passed, it will be used as-is. - Multiple expressions are combined with an AND operator. - append: if `True`, AND the new expressions to any existing expression. - Otherwise, this resets the expression. - dialect: the dialect used to parse the input expressions. - copy: if `False`, modify this expression instance in-place. - opts: other options to use to parse the input expressions. - - Returns: - Select: the modified expression. - """ - return _apply_conjunction_builder( - *expressions, - instance=self, - arg="where", - append=append, - into=Where, - dialect=dialect, - copy=copy, - **opts, - ) - - def from_( - self, - expression: t.Optional[ExpOrStr] = None, - dialect: DialectType = None, - copy: bool = True, - **opts, - ) -> Update: - """ - Set the FROM expression. - - Example: - >>> Update().table("my_table").set_("x = 1").from_("baz").sql() - 'UPDATE my_table SET x = 1 FROM baz' - - Args: - expression : the SQL code strings to parse. - If a `From` instance is passed, this is used as-is. - If another `Expression` instance is passed, it will be wrapped in a `From`. - If nothing is passed in then a from is not applied to the expression - dialect: the dialect used to parse the input expression. - copy: if `False`, modify this expression instance in-place. - opts: other options to use to parse the input expressions. - - Returns: - The modified Update expression. - """ - if not expression: - return maybe_copy(self, copy) - - return _apply_builder( - expression=expression, - instance=self, - arg="from_", - into=From, - prefix="FROM", - dialect=dialect, - copy=copy, - **opts, - ) - - def with_( - self, - alias: ExpOrStr, - as_: ExpOrStr, - recursive: t.Optional[bool] = None, - materialized: t.Optional[bool] = None, - append: bool = True, - dialect: DialectType = None, - copy: bool = True, - **opts, - ) -> Update: - """ - Append to or set the common table expressions. - - Example: - >>> Update().table("my_table").set_("x = 1").from_("baz").with_("baz", "SELECT id FROM foo").sql() - 'WITH baz AS (SELECT id FROM foo) UPDATE my_table SET x = 1 FROM baz' - - Args: - alias: the SQL code string to parse as the table name. - If an `Expression` instance is passed, this is used as-is. - as_: the SQL code string to parse as the table expression. - If an `Expression` instance is passed, it will be used as-is. - recursive: set the RECURSIVE part of the expression. Defaults to `False`. - materialized: set the MATERIALIZED part of the expression. - append: if `True`, add to any existing expressions. - Otherwise, this resets the expressions. - dialect: the dialect used to parse the input expression. - copy: if `False`, modify this expression instance in-place. - opts: other options to use to parse the input expressions. - - Returns: - The modified expression. - """ - return _apply_cte_builder( - self, - alias, - as_, - recursive=recursive, - materialized=materialized, - append=append, - dialect=dialect, - copy=copy, - **opts, - ) - - -# DuckDB supports VALUES followed by https://duckdb.org/docs/stable/sql/query_syntax/limit -class Values(UDTF): - arg_types = { - "expressions": True, - "alias": False, - "order": False, - "limit": False, - "offset": False, - } - - -class Var(Expression): - pass - - -class Version(Expression): - """ - Time travel, iceberg, bigquery etc - https://trino.io/docs/current/connector/iceberg.html?highlight=snapshot#using-snapshots - https://www.databricks.com/blog/2019/02/04/introducing-delta-time-travel-for-large-scale-data-lakes.html - https://cloud.google.com/bigquery/docs/reference/standard-sql/query-syntax#for_system_time_as_of - https://learn.microsoft.com/en-us/sql/relational-databases/tables/querying-data-in-a-system-versioned-temporal-table?view=sql-server-ver16 - this is either TIMESTAMP or VERSION - kind is ("AS OF", "BETWEEN") - """ - - arg_types = {"this": True, "kind": True, "expression": False} - - -class Schema(Expression): - arg_types = {"this": False, "expressions": False} - - -# https://dev.mysql.com/doc/refman/8.0/en/select.html -# https://docs.oracle.com/en/database/oracle/oracle-database/19/sqlrf/SELECT.html -class Lock(Expression): - arg_types = {"update": True, "expressions": False, "wait": False, "key": False} - - -class Select(Query): - arg_types = { - "with_": False, - "kind": False, - "expressions": False, - "hint": False, - "distinct": False, - "into": False, - "from_": False, - "operation_modifiers": False, - **QUERY_MODIFIERS, - } - - def from_( - self, - expression: ExpOrStr, - dialect: DialectType = None, - copy: bool = True, - **opts, - ) -> Select: - """ - Set the FROM expression. - - Example: - >>> Select().from_("tbl").select("x").sql() - 'SELECT x FROM tbl' - - Args: - expression : the SQL code strings to parse. - If a `From` instance is passed, this is used as-is. - If another `Expression` instance is passed, it will be wrapped in a `From`. - dialect: the dialect used to parse the input expression. - copy: if `False`, modify this expression instance in-place. - opts: other options to use to parse the input expressions. - - Returns: - The modified Select expression. - """ - return _apply_builder( - expression=expression, - instance=self, - arg="from_", - into=From, - prefix="FROM", - dialect=dialect, - copy=copy, - **opts, - ) - - def group_by( - self, - *expressions: t.Optional[ExpOrStr], - append: bool = True, - dialect: DialectType = None, - copy: bool = True, - **opts, - ) -> Select: - """ - Set the GROUP BY expression. - - Example: - >>> Select().from_("tbl").select("x", "COUNT(1)").group_by("x").sql() - 'SELECT x, COUNT(1) FROM tbl GROUP BY x' - - Args: - *expressions: the SQL code strings to parse. - If a `Group` instance is passed, this is used as-is. - If another `Expression` instance is passed, it will be wrapped in a `Group`. - If nothing is passed in then a group by is not applied to the expression - append: if `True`, add to any existing expressions. - Otherwise, this flattens all the `Group` expression into a single expression. - dialect: the dialect used to parse the input expression. - copy: if `False`, modify this expression instance in-place. - opts: other options to use to parse the input expressions. - - Returns: - The modified Select expression. - """ - if not expressions: - return self if not copy else self.copy() - - return _apply_child_list_builder( - *expressions, - instance=self, - arg="group", - append=append, - copy=copy, - prefix="GROUP BY", - into=Group, - dialect=dialect, - **opts, - ) - - def sort_by( - self, - *expressions: t.Optional[ExpOrStr], - append: bool = True, - dialect: DialectType = None, - copy: bool = True, - **opts, - ) -> Select: - """ - Set the SORT BY expression. - - Example: - >>> Select().from_("tbl").select("x").sort_by("x DESC").sql(dialect="hive") - 'SELECT x FROM tbl SORT BY x DESC' - - Args: - *expressions: the SQL code strings to parse. - If a `Group` instance is passed, this is used as-is. - If another `Expression` instance is passed, it will be wrapped in a `SORT`. - append: if `True`, add to any existing expressions. - Otherwise, this flattens all the `Order` expression into a single expression. - dialect: the dialect used to parse the input expression. - copy: if `False`, modify this expression instance in-place. - opts: other options to use to parse the input expressions. - - Returns: - The modified Select expression. - """ - return _apply_child_list_builder( - *expressions, - instance=self, - arg="sort", - append=append, - copy=copy, - prefix="SORT BY", - into=Sort, - dialect=dialect, - **opts, - ) - - def cluster_by( - self, - *expressions: t.Optional[ExpOrStr], - append: bool = True, - dialect: DialectType = None, - copy: bool = True, - **opts, - ) -> Select: - """ - Set the CLUSTER BY expression. - - Example: - >>> Select().from_("tbl").select("x").cluster_by("x DESC").sql(dialect="hive") - 'SELECT x FROM tbl CLUSTER BY x DESC' - - Args: - *expressions: the SQL code strings to parse. - If a `Group` instance is passed, this is used as-is. - If another `Expression` instance is passed, it will be wrapped in a `Cluster`. - append: if `True`, add to any existing expressions. - Otherwise, this flattens all the `Order` expression into a single expression. - dialect: the dialect used to parse the input expression. - copy: if `False`, modify this expression instance in-place. - opts: other options to use to parse the input expressions. - - Returns: - The modified Select expression. - """ - return _apply_child_list_builder( - *expressions, - instance=self, - arg="cluster", - append=append, - copy=copy, - prefix="CLUSTER BY", - into=Cluster, - dialect=dialect, - **opts, - ) - - def select( - self, - *expressions: t.Optional[ExpOrStr], - append: bool = True, - dialect: DialectType = None, - copy: bool = True, - **opts, - ) -> Select: - return _apply_list_builder( - *expressions, - instance=self, - arg="expressions", - append=append, - dialect=dialect, - into=Expression, - copy=copy, - **opts, - ) - - def lateral( - self, - *expressions: t.Optional[ExpOrStr], - append: bool = True, - dialect: DialectType = None, - copy: bool = True, - **opts, - ) -> Select: - """ - Append to or set the LATERAL expressions. - - Example: - >>> Select().select("x").lateral("OUTER explode(y) tbl2 AS z").from_("tbl").sql() - 'SELECT x FROM tbl LATERAL VIEW OUTER EXPLODE(y) tbl2 AS z' - - Args: - *expressions: the SQL code strings to parse. - If an `Expression` instance is passed, it will be used as-is. - append: if `True`, add to any existing expressions. - Otherwise, this resets the expressions. - dialect: the dialect used to parse the input expressions. - copy: if `False`, modify this expression instance in-place. - opts: other options to use to parse the input expressions. - - Returns: - The modified Select expression. - """ - return _apply_list_builder( - *expressions, - instance=self, - arg="laterals", - append=append, - into=Lateral, - prefix="LATERAL VIEW", - dialect=dialect, - copy=copy, - **opts, - ) - - def join( - self, - expression: ExpOrStr, - on: t.Optional[ExpOrStr] = None, - using: t.Optional[ExpOrStr | t.Collection[ExpOrStr]] = None, - append: bool = True, - join_type: t.Optional[str] = None, - join_alias: t.Optional[Identifier | str] = None, - dialect: DialectType = None, - copy: bool = True, - **opts, - ) -> Select: - """ - Append to or set the JOIN expressions. - - Example: - >>> Select().select("*").from_("tbl").join("tbl2", on="tbl1.y = tbl2.y").sql() - 'SELECT * FROM tbl JOIN tbl2 ON tbl1.y = tbl2.y' - - >>> Select().select("1").from_("a").join("b", using=["x", "y", "z"]).sql() - 'SELECT 1 FROM a JOIN b USING (x, y, z)' - - Use `join_type` to change the type of join: - - >>> Select().select("*").from_("tbl").join("tbl2", on="tbl1.y = tbl2.y", join_type="left outer").sql() - 'SELECT * FROM tbl LEFT OUTER JOIN tbl2 ON tbl1.y = tbl2.y' - - Args: - expression: the SQL code string to parse. - If an `Expression` instance is passed, it will be used as-is. - on: optionally specify the join "on" criteria as a SQL string. - If an `Expression` instance is passed, it will be used as-is. - using: optionally specify the join "using" criteria as a SQL string. - If an `Expression` instance is passed, it will be used as-is. - append: if `True`, add to any existing expressions. - Otherwise, this resets the expressions. - join_type: if set, alter the parsed join type. - join_alias: an optional alias for the joined source. - dialect: the dialect used to parse the input expressions. - copy: if `False`, modify this expression instance in-place. - opts: other options to use to parse the input expressions. - - Returns: - Select: the modified expression. - """ - parse_args: t.Dict[str, t.Any] = {"dialect": dialect, **opts} - - try: - expression = maybe_parse(expression, into=Join, prefix="JOIN", **parse_args) - except ParseError: - expression = maybe_parse(expression, into=(Join, Expression), **parse_args) - - join = expression if isinstance(expression, Join) else Join(this=expression) - - if isinstance(join.this, Select): - join.this.replace(join.this.subquery()) - - if join_type: - method: t.Optional[Token] - side: t.Optional[Token] - kind: t.Optional[Token] - - method, side, kind = maybe_parse(join_type, into="JOIN_TYPE", **parse_args) # type: ignore - - if method: - join.set("method", method.text) - if side: - join.set("side", side.text) - if kind: - join.set("kind", kind.text) - - if on: - on = and_(*ensure_list(on), dialect=dialect, copy=copy, **opts) - join.set("on", on) - - if using: - join = _apply_list_builder( - *ensure_list(using), - instance=join, - arg="using", - append=append, - copy=copy, - into=Identifier, - **opts, - ) - - if join_alias: - join.set("this", alias_(join.this, join_alias, table=True)) - - return _apply_list_builder( - join, - instance=self, - arg="joins", - append=append, - copy=copy, - **opts, - ) - - def having( - self, - *expressions: t.Optional[ExpOrStr], - append: bool = True, - dialect: DialectType = None, - copy: bool = True, - **opts, - ) -> Select: - """ - Append to or set the HAVING expressions. - - Example: - >>> Select().select("x", "COUNT(y)").from_("tbl").group_by("x").having("COUNT(y) > 3").sql() - 'SELECT x, COUNT(y) FROM tbl GROUP BY x HAVING COUNT(y) > 3' - - Args: - *expressions: the SQL code strings to parse. - If an `Expression` instance is passed, it will be used as-is. - Multiple expressions are combined with an AND operator. - append: if `True`, AND the new expressions to any existing expression. - Otherwise, this resets the expression. - dialect: the dialect used to parse the input expressions. - copy: if `False`, modify this expression instance in-place. - opts: other options to use to parse the input expressions. - - Returns: - The modified Select expression. - """ - return _apply_conjunction_builder( - *expressions, - instance=self, - arg="having", - append=append, - into=Having, - dialect=dialect, - copy=copy, - **opts, - ) - - def window( - self, - *expressions: t.Optional[ExpOrStr], - append: bool = True, - dialect: DialectType = None, - copy: bool = True, - **opts, - ) -> Select: - return _apply_list_builder( - *expressions, - instance=self, - arg="windows", - append=append, - into=Window, - dialect=dialect, - copy=copy, - **opts, - ) - - def qualify( - self, - *expressions: t.Optional[ExpOrStr], - append: bool = True, - dialect: DialectType = None, - copy: bool = True, - **opts, - ) -> Select: - return _apply_conjunction_builder( - *expressions, - instance=self, - arg="qualify", - append=append, - into=Qualify, - dialect=dialect, - copy=copy, - **opts, - ) - - def distinct( - self, *ons: t.Optional[ExpOrStr], distinct: bool = True, copy: bool = True - ) -> Select: - """ - Set the OFFSET expression. - - Example: - >>> Select().from_("tbl").select("x").distinct().sql() - 'SELECT DISTINCT x FROM tbl' - - Args: - ons: the expressions to distinct on - distinct: whether the Select should be distinct - copy: if `False`, modify this expression instance in-place. - - Returns: - Select: the modified expression. - """ - instance = maybe_copy(self, copy) - on = ( - Tuple(expressions=[maybe_parse(on, copy=copy) for on in ons if on]) - if ons - else None - ) - instance.set("distinct", Distinct(on=on) if distinct else None) - return instance - - def ctas( - self, - table: ExpOrStr, - properties: t.Optional[t.Dict] = None, - dialect: DialectType = None, - copy: bool = True, - **opts, - ) -> Create: - """ - Convert this expression to a CREATE TABLE AS statement. - - Example: - >>> Select().select("*").from_("tbl").ctas("x").sql() - 'CREATE TABLE x AS SELECT * FROM tbl' - - Args: - table: the SQL code string to parse as the table name. - If another `Expression` instance is passed, it will be used as-is. - properties: an optional mapping of table properties - dialect: the dialect used to parse the input table. - copy: if `False`, modify this expression instance in-place. - opts: other options to use to parse the input table. - - Returns: - The new Create expression. - """ - instance = maybe_copy(self, copy) - table_expression = maybe_parse(table, into=Table, dialect=dialect, **opts) - - properties_expression = None - if properties: - properties_expression = Properties.from_dict(properties) - - return Create( - this=table_expression, - kind="TABLE", - expression=instance, - properties=properties_expression, - ) - - def lock(self, update: bool = True, copy: bool = True) -> Select: - """ - Set the locking read mode for this expression. - - Examples: - >>> Select().select("x").from_("tbl").where("x = 'a'").lock().sql("mysql") - "SELECT x FROM tbl WHERE x = 'a' FOR UPDATE" - - >>> Select().select("x").from_("tbl").where("x = 'a'").lock(update=False).sql("mysql") - "SELECT x FROM tbl WHERE x = 'a' FOR SHARE" - - Args: - update: if `True`, the locking type will be `FOR UPDATE`, else it will be `FOR SHARE`. - copy: if `False`, modify this expression instance in-place. - - Returns: - The modified expression. - """ - inst = maybe_copy(self, copy) - inst.set("locks", [Lock(update=update)]) - - return inst - - def hint( - self, *hints: ExpOrStr, dialect: DialectType = None, copy: bool = True - ) -> Select: - """ - Set hints for this expression. - - Examples: - >>> Select().select("x").from_("tbl").hint("BROADCAST(y)").sql(dialect="spark") - 'SELECT /*+ BROADCAST(y) */ x FROM tbl' - - Args: - hints: The SQL code strings to parse as the hints. - If an `Expression` instance is passed, it will be used as-is. - dialect: The dialect used to parse the hints. - copy: If `False`, modify this expression instance in-place. - - Returns: - The modified expression. - """ - inst = maybe_copy(self, copy) - inst.set( - "hint", - Hint( - expressions=[maybe_parse(h, copy=copy, dialect=dialect) for h in hints] - ), - ) - - return inst - - @property - def named_selects(self) -> t.List[str]: - selects = [] - - for e in self.expressions: - if e.alias_or_name: - selects.append(e.output_name) - elif isinstance(e, Aliases): - selects.extend([a.name for a in e.aliases]) - return selects - - @property - def is_star(self) -> bool: - return any(expression.is_star for expression in self.expressions) - - @property - def selects(self) -> t.List[Expression]: - return self.expressions - - -UNWRAPPED_QUERIES = (Select, SetOperation) - - -class Subquery(DerivedTable, Query): - arg_types = { - "this": True, - "alias": False, - "with_": False, - **QUERY_MODIFIERS, - } - - def unnest(self): - """Returns the first non subquery.""" - expression = self - while isinstance(expression, Subquery): - expression = expression.this - return expression - - def unwrap(self) -> Subquery: - expression = self - while expression.same_parent and expression.is_wrapper: - expression = t.cast(Subquery, expression.parent) - return expression - - def select( - self, - *expressions: t.Optional[ExpOrStr], - append: bool = True, - dialect: DialectType = None, - copy: bool = True, - **opts, - ) -> Subquery: - this = maybe_copy(self, copy) - this.unnest().select( - *expressions, append=append, dialect=dialect, copy=False, **opts - ) - return this - - @property - def is_wrapper(self) -> bool: - """ - Whether this Subquery acts as a simple wrapper around another expression. - - SELECT * FROM (((SELECT * FROM t))) - ^ - This corresponds to a "wrapper" Subquery node - """ - return all(v is None for k, v in self.args.items() if k != "this") - - @property - def is_star(self) -> bool: - return self.this.is_star - - @property - def output_name(self) -> str: - return self.alias - - -class TableSample(Expression): - arg_types = { - "expressions": False, - "method": False, - "bucket_numerator": False, - "bucket_denominator": False, - "bucket_field": False, - "percent": False, - "rows": False, - "size": False, - "seed": False, - } - - -class Tag(Expression): - """Tags are used for generating arbitrary sql like SELECT x.""" - - arg_types = { - "this": False, - "prefix": False, - "postfix": False, - } - - -# Represents both the standard SQL PIVOT operator and DuckDB's "simplified" PIVOT syntax -# https://duckdb.org/docs/sql/statements/pivot -class Pivot(Expression): - arg_types = { - "this": False, - "alias": False, - "expressions": False, - "fields": False, - "unpivot": False, - "using": False, - "group": False, - "columns": False, - "include_nulls": False, - "default_on_null": False, - "into": False, - "with_": False, - } - - @property - def unpivot(self) -> bool: - return bool(self.args.get("unpivot")) - - @property - def fields(self) -> t.List[Expression]: - return self.args.get("fields", []) - - -# https://duckdb.org/docs/sql/statements/unpivot#simplified-unpivot-syntax -# UNPIVOT ... INTO [NAME VALUE ][...,] -class UnpivotColumns(Expression): - arg_types = {"this": True, "expressions": True} - - -class Window(Condition): - arg_types = { - "this": True, - "partition_by": False, - "order": False, - "spec": False, - "alias": False, - "over": False, - "first": False, - } - - -class WindowSpec(Expression): - arg_types = { - "kind": False, - "start": False, - "start_side": False, - "end": False, - "end_side": False, - "exclude": False, - } - - -class PreWhere(Expression): - pass - - -class Where(Expression): - pass - - -class Star(Expression): - arg_types = {"except_": False, "replace": False, "rename": False} - - @property - def name(self) -> str: - return "*" - - @property - def output_name(self) -> str: - return self.name - - -class Parameter(Condition): - arg_types = {"this": True, "expression": False} - - -class SessionParameter(Condition): - arg_types = {"this": True, "kind": False} - - -# https://www.databricks.com/blog/parameterized-queries-pyspark -# https://jdbc.postgresql.org/documentation/query/#using-the-statement-or-preparedstatement-interface -class Placeholder(Condition): - arg_types = {"this": False, "kind": False, "widget": False, "jdbc": False} - - @property - def name(self) -> str: - return self.this or "?" - - -class Null(Condition): - arg_types: t.Dict[str, t.Any] = {} - - @property - def name(self) -> str: - return "NULL" - - def to_py(self) -> Lit[None]: - return None - - -class Boolean(Condition): - def to_py(self) -> bool: - return self.this - - -class DataTypeParam(Expression): - arg_types = {"this": True, "expression": False} - - @property - def name(self) -> str: - return self.this.name - - -# The `nullable` arg is helpful when transpiling types from other dialects to ClickHouse, which -# assumes non-nullable types by default. Values `None` and `True` mean the type is nullable. -class DataType(Expression): - arg_types = { - "this": True, - "expressions": False, - "nested": False, - "values": False, - "prefix": False, - "kind": False, - "nullable": False, - } - - class Type(AutoName): - ARRAY = auto() - AGGREGATEFUNCTION = auto() - SIMPLEAGGREGATEFUNCTION = auto() - BIGDECIMAL = auto() - BIGINT = auto() - BIGNUM = auto() - BIGSERIAL = auto() - BINARY = auto() - BIT = auto() - BLOB = auto() - BOOLEAN = auto() - BPCHAR = auto() - CHAR = auto() - DATE = auto() - DATE32 = auto() - DATEMULTIRANGE = auto() - DATERANGE = auto() - DATETIME = auto() - DATETIME2 = auto() - DATETIME64 = auto() - DECIMAL = auto() - DECIMAL32 = auto() - DECIMAL64 = auto() - DECIMAL128 = auto() - DECIMAL256 = auto() - DECFLOAT = auto() - DOUBLE = auto() - DYNAMIC = auto() - ENUM = auto() - ENUM8 = auto() - ENUM16 = auto() - FILE = auto() - FIXEDSTRING = auto() - FLOAT = auto() - GEOGRAPHY = auto() - GEOGRAPHYPOINT = auto() - GEOMETRY = auto() - POINT = auto() - RING = auto() - LINESTRING = auto() - MULTILINESTRING = auto() - POLYGON = auto() - MULTIPOLYGON = auto() - HLLSKETCH = auto() - HSTORE = auto() - IMAGE = auto() - INET = auto() - INT = auto() - INT128 = auto() - INT256 = auto() - INT4MULTIRANGE = auto() - INT4RANGE = auto() - INT8MULTIRANGE = auto() - INT8RANGE = auto() - INTERVAL = auto() - IPADDRESS = auto() - IPPREFIX = auto() - IPV4 = auto() - IPV6 = auto() - JSON = auto() - JSONB = auto() - LIST = auto() - LONGBLOB = auto() - LONGTEXT = auto() - LOWCARDINALITY = auto() - MAP = auto() - MEDIUMBLOB = auto() - MEDIUMINT = auto() - MEDIUMTEXT = auto() - MONEY = auto() - NAME = auto() - NCHAR = auto() - NESTED = auto() - NOTHING = auto() - NULL = auto() - NUMMULTIRANGE = auto() - NUMRANGE = auto() - NVARCHAR = auto() - OBJECT = auto() - RANGE = auto() - ROWVERSION = auto() - SERIAL = auto() - SET = auto() - SMALLDATETIME = auto() - SMALLINT = auto() - SMALLMONEY = auto() - SMALLSERIAL = auto() - STRUCT = auto() - SUPER = auto() - TEXT = auto() - TINYBLOB = auto() - TINYTEXT = auto() - TIME = auto() - TIMETZ = auto() - TIME_NS = auto() - TIMESTAMP = auto() - TIMESTAMPNTZ = auto() - TIMESTAMPLTZ = auto() - TIMESTAMPTZ = auto() - TIMESTAMP_S = auto() - TIMESTAMP_MS = auto() - TIMESTAMP_NS = auto() - TINYINT = auto() - TSMULTIRANGE = auto() - TSRANGE = auto() - TSTZMULTIRANGE = auto() - TSTZRANGE = auto() - UBIGINT = auto() - UINT = auto() - UINT128 = auto() - UINT256 = auto() - UMEDIUMINT = auto() - UDECIMAL = auto() - UDOUBLE = auto() - UNION = auto() - UNKNOWN = auto() # Sentinel value, useful for type annotation - USERDEFINED = "USER-DEFINED" - USMALLINT = auto() - UTINYINT = auto() - UUID = auto() - VARBINARY = auto() - VARCHAR = auto() - VARIANT = auto() - VECTOR = auto() - XML = auto() - YEAR = auto() - TDIGEST = auto() - - STRUCT_TYPES = { - Type.FILE, - Type.NESTED, - Type.OBJECT, - Type.STRUCT, - Type.UNION, - } - - ARRAY_TYPES = { - Type.ARRAY, - Type.LIST, - } - - NESTED_TYPES = { - *STRUCT_TYPES, - *ARRAY_TYPES, - Type.MAP, - } - - TEXT_TYPES = { - Type.CHAR, - Type.NCHAR, - Type.NVARCHAR, - Type.TEXT, - Type.VARCHAR, - Type.NAME, - } - - SIGNED_INTEGER_TYPES = { - Type.BIGINT, - Type.INT, - Type.INT128, - Type.INT256, - Type.MEDIUMINT, - Type.SMALLINT, - Type.TINYINT, - } - - UNSIGNED_INTEGER_TYPES = { - Type.UBIGINT, - Type.UINT, - Type.UINT128, - Type.UINT256, - Type.UMEDIUMINT, - Type.USMALLINT, - Type.UTINYINT, - } - - INTEGER_TYPES = { - *SIGNED_INTEGER_TYPES, - *UNSIGNED_INTEGER_TYPES, - Type.BIT, - } - - FLOAT_TYPES = { - Type.DOUBLE, - Type.FLOAT, - } - - REAL_TYPES = { - *FLOAT_TYPES, - Type.BIGDECIMAL, - Type.DECIMAL, - Type.DECIMAL32, - Type.DECIMAL64, - Type.DECIMAL128, - Type.DECIMAL256, - Type.DECFLOAT, - Type.MONEY, - Type.SMALLMONEY, - Type.UDECIMAL, - Type.UDOUBLE, - } - - NUMERIC_TYPES = { - *INTEGER_TYPES, - *REAL_TYPES, - } - - TEMPORAL_TYPES = { - Type.DATE, - Type.DATE32, - Type.DATETIME, - Type.DATETIME2, - Type.DATETIME64, - Type.SMALLDATETIME, - Type.TIME, - Type.TIMESTAMP, - Type.TIMESTAMPNTZ, - Type.TIMESTAMPLTZ, - Type.TIMESTAMPTZ, - Type.TIMESTAMP_MS, - Type.TIMESTAMP_NS, - Type.TIMESTAMP_S, - Type.TIMETZ, - } - - @classmethod - def build( - cls, - dtype: DATA_TYPE, - dialect: DialectType = None, - udt: bool = False, - copy: bool = True, - **kwargs, - ) -> DataType: - """ - Constructs a DataType object. - - Args: - dtype: the data type of interest. - dialect: the dialect to use for parsing `dtype`, in case it's a string. - udt: when set to True, `dtype` will be used as-is if it can't be parsed into a - DataType, thus creating a user-defined type. - copy: whether to copy the data type. - kwargs: additional arguments to pass in the constructor of DataType. - - Returns: - The constructed DataType object. - """ - from bigframes_vendored.sqlglot import parse_one - - if isinstance(dtype, str): - if dtype.upper() == "UNKNOWN": - return DataType(this=DataType.Type.UNKNOWN, **kwargs) - - try: - data_type_exp = parse_one( - dtype, read=dialect, into=DataType, error_level=ErrorLevel.IGNORE - ) - except ParseError: - if udt: - return DataType( - this=DataType.Type.USERDEFINED, kind=dtype, **kwargs - ) - raise - elif isinstance(dtype, (Identifier, Dot)) and udt: - return DataType(this=DataType.Type.USERDEFINED, kind=dtype, **kwargs) - elif isinstance(dtype, DataType.Type): - data_type_exp = DataType(this=dtype) - elif isinstance(dtype, DataType): - return maybe_copy(dtype, copy) - else: - raise ValueError( - f"Invalid data type: {type(dtype)}. Expected str or DataType.Type" - ) - - return DataType(**{**data_type_exp.args, **kwargs}) - - def is_type(self, *dtypes: DATA_TYPE, check_nullable: bool = False) -> bool: - """ - Checks whether this DataType matches one of the provided data types. Nested types or precision - will be compared using "structural equivalence" semantics, so e.g. array != array. - - Args: - dtypes: the data types to compare this DataType to. - check_nullable: whether to take the NULLABLE type constructor into account for the comparison. - If false, it means that NULLABLE is equivalent to INT. - - Returns: - True, if and only if there is a type in `dtypes` which is equal to this DataType. - """ - self_is_nullable = self.args.get("nullable") - for dtype in dtypes: - other_type = DataType.build(dtype, copy=False, udt=True) - other_is_nullable = other_type.args.get("nullable") - if ( - other_type.expressions - or (check_nullable and (self_is_nullable or other_is_nullable)) - or self.this == DataType.Type.USERDEFINED - or other_type.this == DataType.Type.USERDEFINED - ): - matches = self == other_type - else: - matches = self.this == other_type.this - - if matches: - return True - return False - - -# https://www.postgresql.org/docs/15/datatype-pseudo.html -class PseudoType(DataType): - arg_types = {"this": True} - - -# https://www.postgresql.org/docs/15/datatype-oid.html -class ObjectIdentifier(DataType): - arg_types = {"this": True} - - -# WHERE x EXISTS|ALL|ANY|SOME(SELECT ...) -class SubqueryPredicate(Predicate): - pass - - -class All(SubqueryPredicate): - pass - - -class Any(SubqueryPredicate): - pass - - -# Commands to interact with the databases or engines. For most of the command -# expressions we parse whatever comes after the command's name as a string. -class Command(Expression): - arg_types = {"this": True, "expression": False} - - -class Transaction(Expression): - arg_types = {"this": False, "modes": False, "mark": False} - - -class Commit(Expression): - arg_types = {"chain": False, "this": False, "durability": False} - - -class Rollback(Expression): - arg_types = {"savepoint": False, "this": False} - - -class Alter(Expression): - arg_types = { - "this": False, - "kind": True, - "actions": True, - "exists": False, - "only": False, - "options": False, - "cluster": False, - "not_valid": False, - "check": False, - "cascade": False, - } - - @property - def kind(self) -> t.Optional[str]: - kind = self.args.get("kind") - return kind and kind.upper() - - @property - def actions(self) -> t.List[Expression]: - return self.args.get("actions") or [] - - -class AlterSession(Expression): - arg_types = {"expressions": True, "unset": False} - - -class Analyze(Expression): - arg_types = { - "kind": False, - "this": False, - "options": False, - "mode": False, - "partition": False, - "expression": False, - "properties": False, - } - - -class AnalyzeStatistics(Expression): - arg_types = { - "kind": True, - "option": False, - "this": False, - "expressions": False, - } - - -class AnalyzeHistogram(Expression): - arg_types = { - "this": True, - "expressions": True, - "expression": False, - "update_options": False, - } - - -class AnalyzeSample(Expression): - arg_types = {"kind": True, "sample": True} - - -class AnalyzeListChainedRows(Expression): - arg_types = {"expression": False} - - -class AnalyzeDelete(Expression): - arg_types = {"kind": False} - - -class AnalyzeWith(Expression): - arg_types = {"expressions": True} - - -class AnalyzeValidate(Expression): - arg_types = { - "kind": True, - "this": False, - "expression": False, - } - - -class AnalyzeColumns(Expression): - pass - - -class UsingData(Expression): - pass - - -class AddConstraint(Expression): - arg_types = {"expressions": True} - - -class AddPartition(Expression): - arg_types = {"this": True, "exists": False, "location": False} - - -class AttachOption(Expression): - arg_types = {"this": True, "expression": False} - - -class DropPartition(Expression): - arg_types = {"expressions": True, "exists": False} - - -# https://clickhouse.com/docs/en/sql-reference/statements/alter/partition#replace-partition -class ReplacePartition(Expression): - arg_types = {"expression": True, "source": True} - - -# Binary expressions like (ADD a b) -class Binary(Condition): - arg_types = {"this": True, "expression": True} - - @property - def left(self) -> Expression: - return self.this - - @property - def right(self) -> Expression: - return self.expression - - -class Add(Binary): - pass - - -class Connector(Binary): - pass - - -class BitwiseAnd(Binary): - arg_types = {"this": True, "expression": True, "padside": False} - - -class BitwiseLeftShift(Binary): - pass - - -class BitwiseOr(Binary): - arg_types = {"this": True, "expression": True, "padside": False} - - -class BitwiseRightShift(Binary): - pass - - -class BitwiseXor(Binary): - arg_types = {"this": True, "expression": True, "padside": False} - - -class Div(Binary): - arg_types = {"this": True, "expression": True, "typed": False, "safe": False} - - -class Overlaps(Binary): - pass - - -class ExtendsLeft(Binary): - pass - - -class ExtendsRight(Binary): - pass - - -class Dot(Binary): - @property - def is_star(self) -> bool: - return self.expression.is_star - - @property - def name(self) -> str: - return self.expression.name - - @property - def output_name(self) -> str: - return self.name - - @classmethod - def build(self, expressions: t.Sequence[Expression]) -> Dot: - """Build a Dot object with a sequence of expressions.""" - if len(expressions) < 2: - raise ValueError("Dot requires >= 2 expressions.") - - return t.cast(Dot, reduce(lambda x, y: Dot(this=x, expression=y), expressions)) - - @property - def parts(self) -> t.List[Expression]: - """Return the parts of a table / column in order catalog, db, table.""" - this, *parts = self.flatten() - - parts.reverse() - - for arg in COLUMN_PARTS: - part = this.args.get(arg) - - if isinstance(part, Expression): - parts.append(part) - - parts.reverse() - return parts - - -DATA_TYPE = t.Union[str, Identifier, Dot, DataType, DataType.Type] - - -class DPipe(Binary): - arg_types = {"this": True, "expression": True, "safe": False} - - -class EQ(Binary, Predicate): - pass - - -class NullSafeEQ(Binary, Predicate): - pass - - -class NullSafeNEQ(Binary, Predicate): - pass - - -# Represents e.g. := in DuckDB which is mostly used for setting parameters -class PropertyEQ(Binary): - pass - - -class Distance(Binary): - pass - - -class Escape(Binary): - pass - - -class Glob(Binary, Predicate): - pass - - -class GT(Binary, Predicate): - pass - - -class GTE(Binary, Predicate): - pass - - -class ILike(Binary, Predicate): - pass - - -class IntDiv(Binary): - pass - - -class Is(Binary, Predicate): - pass - - -class Kwarg(Binary): - """Kwarg in special functions like func(kwarg => y).""" - - -class Like(Binary, Predicate): - pass - - -class Match(Binary, Predicate): - pass - - -class LT(Binary, Predicate): - pass - - -class LTE(Binary, Predicate): - pass - - -class Mod(Binary): - pass - - -class Mul(Binary): - pass - - -class NEQ(Binary, Predicate): - pass - - -# https://www.postgresql.org/docs/current/ddl-schemas.html#DDL-SCHEMAS-PATH -class Operator(Binary): - arg_types = {"this": True, "operator": True, "expression": True} - - -class SimilarTo(Binary, Predicate): - pass - - -class Sub(Binary): - pass - - -# https://www.postgresql.org/docs/current/functions-range.html -# Represents range adjacency operator: -|- -class Adjacent(Binary): - pass - - -# Unary Expressions -# (NOT a) -class Unary(Condition): - pass - - -class BitwiseNot(Unary): - pass - - -class Not(Unary): - pass - - -class Paren(Unary): - @property - def output_name(self) -> str: - return self.this.name - - -class Neg(Unary): - def to_py(self) -> int | Decimal: - if self.is_number: - return self.this.to_py() * -1 - return super().to_py() - - -class Alias(Expression): - arg_types = {"this": True, "alias": False} - - @property - def output_name(self) -> str: - return self.alias - - -# BigQuery requires the UNPIVOT column list aliases to be either strings or ints, but -# other dialects require identifiers. This enables us to transpile between them easily. -class PivotAlias(Alias): - pass - - -# Represents Snowflake's ANY [ ORDER BY ... ] syntax -# https://docs.snowflake.com/en/sql-reference/constructs/pivot -class PivotAny(Expression): - arg_types = {"this": False} - - -class Aliases(Expression): - arg_types = {"this": True, "expressions": True} - - @property - def aliases(self): - return self.expressions - - -# https://docs.aws.amazon.com/redshift/latest/dg/query-super.html -class AtIndex(Expression): - arg_types = {"this": True, "expression": True} - - -class AtTimeZone(Expression): - arg_types = {"this": True, "zone": True} - - -class FromTimeZone(Expression): - arg_types = {"this": True, "zone": True} - - -class FormatPhrase(Expression): - """Format override for a column in Teradata. - Can be expanded to additional dialects as needed - - https://docs.teradata.com/r/Enterprise_IntelliFlex_VMware/SQL-Data-Types-and-Literals/Data-Type-Formats-and-Format-Phrases/FORMAT - """ - - arg_types = {"this": True, "format": True} - - -class Between(Predicate): - arg_types = {"this": True, "low": True, "high": True, "symmetric": False} - - -class Bracket(Condition): - # https://cloud.google.com/bigquery/docs/reference/standard-sql/operators#array_subscript_operator - arg_types = { - "this": True, - "expressions": True, - "offset": False, - "safe": False, - "returns_list_for_maps": False, - } - - @property - def output_name(self) -> str: - if len(self.expressions) == 1: - return self.expressions[0].output_name - - return super().output_name - - -class Distinct(Expression): - arg_types = {"expressions": False, "on": False} - - -class In(Predicate): - arg_types = { - "this": True, - "expressions": False, - "query": False, - "unnest": False, - "field": False, - "is_global": False, - } - - -# https://cloud.google.com/bigquery/docs/reference/standard-sql/procedural-language#for-in -class ForIn(Expression): - arg_types = {"this": True, "expression": True} - - -class TimeUnit(Expression): - """Automatically converts unit arg into a var.""" - - arg_types = {"unit": False} - - UNABBREVIATED_UNIT_NAME = { - "D": "DAY", - "H": "HOUR", - "M": "MINUTE", - "MS": "MILLISECOND", - "NS": "NANOSECOND", - "Q": "QUARTER", - "S": "SECOND", - "US": "MICROSECOND", - "W": "WEEK", - "Y": "YEAR", - } - - VAR_LIKE = (Column, Literal, Var) - - def __init__(self, **args): - unit = args.get("unit") - if type(unit) in self.VAR_LIKE and not ( - isinstance(unit, Column) and len(unit.parts) != 1 - ): - args["unit"] = Var( - this=(self.UNABBREVIATED_UNIT_NAME.get(unit.name) or unit.name).upper() - ) - elif isinstance(unit, Week): - unit.set("this", Var(this=unit.this.name.upper())) - - super().__init__(**args) - - @property - def unit(self) -> t.Optional[Var | IntervalSpan]: - return self.args.get("unit") - - -class IntervalOp(TimeUnit): - arg_types = {"unit": False, "expression": True} - - def interval(self): - return Interval( - this=self.expression.copy(), - unit=self.unit.copy() if self.unit else None, - ) - - -# https://www.oracletutorial.com/oracle-basics/oracle-interval/ -# https://trino.io/docs/current/language/types.html#interval-day-to-second -# https://docs.databricks.com/en/sql/language-manual/data-types/interval-type.html -class IntervalSpan(DataType): - arg_types = {"this": True, "expression": True} - - -class Interval(TimeUnit): - arg_types = {"this": False, "unit": False} - - -class IgnoreNulls(Expression): - pass - - -class RespectNulls(Expression): - pass - - -# https://cloud.google.com/bigquery/docs/reference/standard-sql/aggregate-function-calls#max_min_clause -class HavingMax(Expression): - arg_types = {"this": True, "expression": True, "max": True} - - -# Functions -class Func(Condition): - """ - The base class for all function expressions. - - Attributes: - is_var_len_args (bool): if set to True the last argument defined in arg_types will be - treated as a variable length argument and the argument's value will be stored as a list. - _sql_names (list): the SQL name (1st item in the list) and aliases (subsequent items) for this - function expression. These values are used to map this node to a name during parsing as - well as to provide the function's name during SQL string generation. By default the SQL - name is set to the expression's class name transformed to snake case. - """ - - is_var_len_args = False - - @classmethod - def from_arg_list(cls, args): - if cls.is_var_len_args: - all_arg_keys = list(cls.arg_types) - # If this function supports variable length argument treat the last argument as such. - non_var_len_arg_keys = ( - all_arg_keys[:-1] if cls.is_var_len_args else all_arg_keys - ) - num_non_var = len(non_var_len_arg_keys) - - args_dict = { - arg_key: arg for arg, arg_key in zip(args, non_var_len_arg_keys) - } - args_dict[all_arg_keys[-1]] = args[num_non_var:] - else: - args_dict = {arg_key: arg for arg, arg_key in zip(args, cls.arg_types)} - - return cls(**args_dict) - - @classmethod - def sql_names(cls): - if cls is Func: - raise NotImplementedError( - "SQL name is only supported by concrete function implementations" - ) - if "_sql_names" not in cls.__dict__: - cls._sql_names = [camel_to_snake_case(cls.__name__)] - return cls._sql_names - - @classmethod - def sql_name(cls): - sql_names = cls.sql_names() - assert sql_names, f"Expected non-empty 'sql_names' for Func: {cls.__name__}." - return sql_names[0] - - @classmethod - def default_parser_mappings(cls): - return {name: cls.from_arg_list for name in cls.sql_names()} - - -class Typeof(Func): - pass - - -class Acos(Func): - pass - - -class Acosh(Func): - pass - - -class Asin(Func): - pass - - -class Asinh(Func): - pass - - -class Atan(Func): - arg_types = {"this": True, "expression": False} - - -class Atanh(Func): - pass - - -class Atan2(Func): - arg_types = {"this": True, "expression": True} - - -class Cot(Func): - pass - - -class Coth(Func): - pass - - -class Cos(Func): - pass - - -class Csc(Func): - pass - - -class Csch(Func): - pass - - -class Sec(Func): - pass - - -class Sech(Func): - pass - - -class Sin(Func): - pass - - -class Sinh(Func): - pass - - -class Tan(Func): - pass - - -class Tanh(Func): - pass - - -class Degrees(Func): - pass - - -class Cosh(Func): - pass - - -class CosineDistance(Func): - arg_types = {"this": True, "expression": True} - - -class DotProduct(Func): - arg_types = {"this": True, "expression": True} - - -class EuclideanDistance(Func): - arg_types = {"this": True, "expression": True} - - -class ManhattanDistance(Func): - arg_types = {"this": True, "expression": True} - - -class JarowinklerSimilarity(Func): - arg_types = {"this": True, "expression": True} - - -class AggFunc(Func): - pass - - -class BitwiseAndAgg(AggFunc): - pass - - -class BitwiseOrAgg(AggFunc): - pass - - -class BitwiseXorAgg(AggFunc): - pass - - -class BoolxorAgg(AggFunc): - pass - - -class BitwiseCount(Func): - pass - - -class BitmapBucketNumber(Func): - pass - - -class BitmapCount(Func): - pass - - -class BitmapBitPosition(Func): - pass - - -class BitmapConstructAgg(AggFunc): - pass - - -class BitmapOrAgg(AggFunc): - pass - - -class ByteLength(Func): - pass - - -class Boolnot(Func): - pass - - -class Booland(Func): - arg_types = {"this": True, "expression": True} - - -class Boolor(Func): - arg_types = {"this": True, "expression": True} - - -# https://cloud.google.com/bigquery/docs/reference/standard-sql/json_functions#bool_for_json -class JSONBool(Func): - pass - - -class ArrayRemove(Func): - arg_types = {"this": True, "expression": True} - - -class ParameterizedAgg(AggFunc): - arg_types = {"this": True, "expressions": True, "params": True} - - -class Abs(Func): - pass - - -class ArgMax(AggFunc): - arg_types = {"this": True, "expression": True, "count": False} - _sql_names = ["ARG_MAX", "ARGMAX", "MAX_BY"] - - -class ArgMin(AggFunc): - arg_types = {"this": True, "expression": True, "count": False} - _sql_names = ["ARG_MIN", "ARGMIN", "MIN_BY"] - - -class ApproxTopK(AggFunc): - arg_types = {"this": True, "expression": False, "counters": False} - - -# https://docs.snowflake.com/en/sql-reference/functions/approx_top_k_accumulate -# https://spark.apache.org/docs/preview/api/sql/index.html#approx_top_k_accumulate -class ApproxTopKAccumulate(AggFunc): - arg_types = {"this": True, "expression": False} - - -# https://docs.snowflake.com/en/sql-reference/functions/approx_top_k_combine -class ApproxTopKCombine(AggFunc): - arg_types = {"this": True, "expression": False} - - -class ApproxTopKEstimate(Func): - arg_types = {"this": True, "expression": False} - - -class ApproxTopSum(AggFunc): - arg_types = {"this": True, "expression": True, "count": True} - - -class ApproxQuantiles(AggFunc): - arg_types = {"this": True, "expression": False} - - -# https://docs.snowflake.com/en/sql-reference/functions/approx_percentile_combine -class ApproxPercentileCombine(AggFunc): - pass - - -# https://docs.snowflake.com/en/sql-reference/functions/minhash -class Minhash(AggFunc): - arg_types = {"this": True, "expressions": True} - is_var_len_args = True - - -# https://docs.snowflake.com/en/sql-reference/functions/minhash_combine -class MinhashCombine(AggFunc): - pass - - -# https://docs.snowflake.com/en/sql-reference/functions/approximate_similarity -class ApproximateSimilarity(AggFunc): - _sql_names = ["APPROXIMATE_SIMILARITY", "APPROXIMATE_JACCARD_INDEX"] - - -class FarmFingerprint(Func): - arg_types = {"expressions": True} - is_var_len_args = True - _sql_names = ["FARM_FINGERPRINT", "FARMFINGERPRINT64"] - - -class Flatten(Func): - arg_types = {"this": True, "depth": False} - - -class Float64(Func): - arg_types = {"this": True, "expression": False} - - -# https://spark.apache.org/docs/latest/api/sql/index.html#transform -class Transform(Func): - arg_types = {"this": True, "expression": True} - - -class Translate(Func): - arg_types = {"this": True, "from_": True, "to": True} - - -class Grouping(AggFunc): - arg_types = {"expressions": True} - is_var_len_args = True - - -class GroupingId(AggFunc): - arg_types = {"expressions": True} - is_var_len_args = True - - -class Anonymous(Func): - arg_types = {"this": True, "expressions": False} - is_var_len_args = True - - @property - def name(self) -> str: - return self.this if isinstance(self.this, str) else self.this.name - - -class AnonymousAggFunc(AggFunc): - arg_types = {"this": True, "expressions": False} - is_var_len_args = True - - -# https://clickhouse.com/docs/en/sql-reference/aggregate-functions/combinators -class CombinedAggFunc(AnonymousAggFunc): - arg_types = {"this": True, "expressions": False} - - -class CombinedParameterizedAgg(ParameterizedAgg): - arg_types = {"this": True, "expressions": True, "params": True} - - -# https://docs.snowflake.com/en/sql-reference/functions/hash_agg -class HashAgg(AggFunc): - arg_types = {"this": True, "expressions": False} - is_var_len_args = True - - -# https://docs.snowflake.com/en/sql-reference/functions/hll -# https://docs.aws.amazon.com/redshift/latest/dg/r_HLL_function.html -class Hll(AggFunc): - arg_types = {"this": True, "expressions": False} - is_var_len_args = True - - -class ApproxDistinct(AggFunc): - arg_types = {"this": True, "accuracy": False} - _sql_names = ["APPROX_DISTINCT", "APPROX_COUNT_DISTINCT"] - - -class Apply(Func): - arg_types = {"this": True, "expression": True} - - -class Array(Func): - arg_types = { - "expressions": False, - "bracket_notation": False, - "struct_name_inheritance": False, - } - is_var_len_args = True - - -class Ascii(Func): - pass - - -# https://docs.snowflake.com/en/sql-reference/functions/to_array -class ToArray(Func): - pass - - -class ToBoolean(Func): - arg_types = {"this": True, "safe": False} - - -# https://materialize.com/docs/sql/types/list/ -class List(Func): - arg_types = {"expressions": False} - is_var_len_args = True - - -# String pad, kind True -> LPAD, False -> RPAD -class Pad(Func): - arg_types = { - "this": True, - "expression": True, - "fill_pattern": False, - "is_left": True, - } - - -# https://docs.snowflake.com/en/sql-reference/functions/to_char -# https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/TO_CHAR-number.html -class ToChar(Func): - arg_types = { - "this": True, - "format": False, - "nlsparam": False, - "is_numeric": False, - } - - -class ToCodePoints(Func): - pass - - -# https://docs.snowflake.com/en/sql-reference/functions/to_decimal -# https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/TO_NUMBER.html -class ToNumber(Func): - arg_types = { - "this": True, - "format": False, - "nlsparam": False, - "precision": False, - "scale": False, - "safe": False, - "safe_name": False, - } - - -# https://docs.snowflake.com/en/sql-reference/functions/to_double -class ToDouble(Func): - arg_types = { - "this": True, - "format": False, - "safe": False, - } - - -# https://docs.snowflake.com/en/sql-reference/functions/to_decfloat -class ToDecfloat(Func): - arg_types = { - "this": True, - "format": False, - } - - -# https://docs.snowflake.com/en/sql-reference/functions/try_to_decfloat -class TryToDecfloat(Func): - arg_types = { - "this": True, - "format": False, - } - - -# https://docs.snowflake.com/en/sql-reference/functions/to_file -class ToFile(Func): - arg_types = { - "this": True, - "path": False, - "safe": False, - } - - -class CodePointsToBytes(Func): - pass - - -class Columns(Func): - arg_types = {"this": True, "unpack": False} - - -# https://learn.microsoft.com/en-us/sql/t-sql/functions/cast-and-convert-transact-sql?view=sql-server-ver16#syntax -class Convert(Func): - arg_types = {"this": True, "expression": True, "style": False, "safe": False} - - -# https://docs.oracle.com/en/database/oracle/oracle-database/19/sqlrf/CONVERT.html -class ConvertToCharset(Func): - arg_types = {"this": True, "dest": True, "source": False} - - -class ConvertTimezone(Func): - arg_types = { - "source_tz": False, - "target_tz": True, - "timestamp": True, - "options": False, - } - - -class CodePointsToString(Func): - pass - - -class GenerateSeries(Func): - arg_types = {"start": True, "end": True, "step": False, "is_end_exclusive": False} - - -# Postgres' GENERATE_SERIES function returns a row set, i.e. it implicitly explodes when it's -# used in a projection, so this expression is a helper that facilitates transpilation to other -# dialects. For example, we'd generate UNNEST(GENERATE_SERIES(...)) in DuckDB -class ExplodingGenerateSeries(GenerateSeries): - pass - - -class ArrayAgg(AggFunc): - arg_types = {"this": True, "nulls_excluded": False} - - -class ArrayUniqueAgg(AggFunc): - pass - - -class AIAgg(AggFunc): - arg_types = {"this": True, "expression": True} - _sql_names = ["AI_AGG"] - - -class AISummarizeAgg(AggFunc): - _sql_names = ["AI_SUMMARIZE_AGG"] - - -class AIClassify(Func): - arg_types = {"this": True, "categories": True, "config": False} - _sql_names = ["AI_CLASSIFY"] - - -class ArrayAll(Func): - arg_types = {"this": True, "expression": True} - - -# Represents Python's `any(f(x) for x in array)`, where `array` is `this` and `f` is `expression` -class ArrayAny(Func): - arg_types = {"this": True, "expression": True} - - -class ArrayConcat(Func): - _sql_names = ["ARRAY_CONCAT", "ARRAY_CAT"] - arg_types = {"this": True, "expressions": False} - is_var_len_args = True - - -class ArrayConcatAgg(AggFunc): - pass - - -class ArrayConstructCompact(Func): - arg_types = {"expressions": False} - is_var_len_args = True - - -class ArrayContains(Binary, Func): - arg_types = {"this": True, "expression": True, "ensure_variant": False} - _sql_names = ["ARRAY_CONTAINS", "ARRAY_HAS"] - - -class ArrayContainsAll(Binary, Func): - _sql_names = ["ARRAY_CONTAINS_ALL", "ARRAY_HAS_ALL"] - - -class ArrayFilter(Func): - arg_types = {"this": True, "expression": True} - _sql_names = ["FILTER", "ARRAY_FILTER"] - - -class ArrayFirst(Func): - pass - - -class ArrayLast(Func): - pass - - -class ArrayReverse(Func): - pass - - -class ArraySlice(Func): - arg_types = {"this": True, "start": True, "end": False, "step": False} - - -class ArrayToString(Func): - arg_types = {"this": True, "expression": True, "null": False} - _sql_names = ["ARRAY_TO_STRING", "ARRAY_JOIN"] - - -class ArrayIntersect(Func): - arg_types = {"expressions": True} - is_var_len_args = True - _sql_names = ["ARRAY_INTERSECT", "ARRAY_INTERSECTION"] - - -class StPoint(Func): - arg_types = {"this": True, "expression": True, "null": False} - _sql_names = ["ST_POINT", "ST_MAKEPOINT"] - - -class StDistance(Func): - arg_types = {"this": True, "expression": True, "use_spheroid": False} - - -# https://cloud.google.com/bigquery/docs/reference/standard-sql/timestamp_functions#string -class String(Func): - arg_types = {"this": True, "zone": False} - - -class StringToArray(Func): - arg_types = {"this": True, "expression": False, "null": False} - _sql_names = ["STRING_TO_ARRAY", "SPLIT_BY_STRING", "STRTOK_TO_ARRAY"] - - -class ArrayOverlaps(Binary, Func): - pass - - -class ArraySize(Func): - arg_types = {"this": True, "expression": False} - _sql_names = ["ARRAY_SIZE", "ARRAY_LENGTH"] - - -class ArraySort(Func): - arg_types = {"this": True, "expression": False} - - -class ArraySum(Func): - arg_types = {"this": True, "expression": False} - - -class ArrayUnionAgg(AggFunc): - pass - - -class Avg(AggFunc): - pass - - -class AnyValue(AggFunc): - pass - - -class Lag(AggFunc): - arg_types = {"this": True, "offset": False, "default": False} - - -class Lead(AggFunc): - arg_types = {"this": True, "offset": False, "default": False} - - -# some dialects have a distinction between first and first_value, usually first is an aggregate func -# and first_value is a window func -class First(AggFunc): - arg_types = {"this": True, "expression": False} - - -class Last(AggFunc): - arg_types = {"this": True, "expression": False} - - -class FirstValue(AggFunc): - pass - - -class LastValue(AggFunc): - pass - - -class NthValue(AggFunc): - arg_types = {"this": True, "offset": True} - - -class ObjectAgg(AggFunc): - arg_types = {"this": True, "expression": True} - - -class Case(Func): - arg_types = {"this": False, "ifs": True, "default": False} - - def when( - self, condition: ExpOrStr, then: ExpOrStr, copy: bool = True, **opts - ) -> Case: - instance = maybe_copy(self, copy) - instance.append( - "ifs", - If( - this=maybe_parse(condition, copy=copy, **opts), - true=maybe_parse(then, copy=copy, **opts), - ), - ) - return instance - - def else_(self, condition: ExpOrStr, copy: bool = True, **opts) -> Case: - instance = maybe_copy(self, copy) - instance.set("default", maybe_parse(condition, copy=copy, **opts)) - return instance - - -class Cast(Func): - arg_types = { - "this": True, - "to": True, - "format": False, - "safe": False, - "action": False, - "default": False, - } - - @property - def name(self) -> str: - return self.this.name - - @property - def to(self) -> DataType: - return self.args["to"] - - @property - def output_name(self) -> str: - return self.name - - def is_type(self, *dtypes: DATA_TYPE) -> bool: - """ - Checks whether this Cast's DataType matches one of the provided data types. Nested types - like arrays or structs will be compared using "structural equivalence" semantics, so e.g. - array != array. - - Args: - dtypes: the data types to compare this Cast's DataType to. - - Returns: - True, if and only if there is a type in `dtypes` which is equal to this Cast's DataType. - """ - return self.to.is_type(*dtypes) - - -class TryCast(Cast): - arg_types = {**Cast.arg_types, "requires_string": False} - - -# https://clickhouse.com/docs/sql-reference/data-types/newjson#reading-json-paths-as-sub-columns -class JSONCast(Cast): - pass - - -class JustifyDays(Func): - pass - - -class JustifyHours(Func): - pass - - -class JustifyInterval(Func): - pass - - -class Try(Func): - pass - - -class CastToStrType(Func): - arg_types = {"this": True, "to": True} - - -class CheckJson(Func): - arg_types = {"this": True} - - -class CheckXml(Func): - arg_types = {"this": True, "disable_auto_convert": False} - - -# https://docs.teradata.com/r/Enterprise_IntelliFlex_VMware/SQL-Functions-Expressions-and-Predicates/String-Operators-and-Functions/TRANSLATE/TRANSLATE-Function-Syntax -class TranslateCharacters(Expression): - arg_types = {"this": True, "expression": True, "with_error": False} - - -class Collate(Binary, Func): - pass - - -class Collation(Func): - pass - - -class Ceil(Func): - arg_types = {"this": True, "decimals": False, "to": False} - _sql_names = ["CEIL", "CEILING"] - - -class Coalesce(Func): - arg_types = {"this": True, "expressions": False, "is_nvl": False, "is_null": False} - is_var_len_args = True - _sql_names = ["COALESCE", "IFNULL", "NVL"] - - -class Chr(Func): - arg_types = {"expressions": True, "charset": False} - is_var_len_args = True - _sql_names = ["CHR", "CHAR"] - - -class Concat(Func): - arg_types = {"expressions": True, "safe": False, "coalesce": False} - is_var_len_args = True - - -class ConcatWs(Concat): - _sql_names = ["CONCAT_WS"] - - -# https://cloud.google.com/bigquery/docs/reference/standard-sql/string_functions#contains_substr -class Contains(Func): - arg_types = {"this": True, "expression": True, "json_scope": False} - - -# https://docs.oracle.com/cd/B13789_01/server.101/b10759/operators004.htm#i1035022 -class ConnectByRoot(Func): - pass - - -class Count(AggFunc): - arg_types = {"this": False, "expressions": False, "big_int": False} - is_var_len_args = True - - -class CountIf(AggFunc): - _sql_names = ["COUNT_IF", "COUNTIF"] - - -# cube root -class Cbrt(Func): - pass - - -class CurrentAccount(Func): - arg_types = {} - - -class CurrentAccountName(Func): - arg_types = {} - - -class CurrentAvailableRoles(Func): - arg_types = {} - - -class CurrentClient(Func): - arg_types = {} - - -class CurrentIpAddress(Func): - arg_types = {} - - -class CurrentDatabase(Func): - arg_types = {} - - -class CurrentSchemas(Func): - arg_types = {"this": False} - - -class CurrentSecondaryRoles(Func): - arg_types = {} - - -class CurrentSession(Func): - arg_types = {} - - -class CurrentStatement(Func): - arg_types = {} - - -class CurrentVersion(Func): - arg_types = {} - - -class CurrentTransaction(Func): - arg_types = {} - - -class CurrentWarehouse(Func): - arg_types = {} - - -class CurrentDate(Func): - arg_types = {"this": False} - - -class CurrentDatetime(Func): - arg_types = {"this": False} - - -class CurrentTime(Func): - arg_types = {"this": False} - - -# https://www.postgresql.org/docs/current/functions-datetime.html#FUNCTIONS-DATETIME-CURRENT -# In Postgres, the difference between CURRENT_TIME vs LOCALTIME etc is that the latter does not have tz -class Localtime(Func): - arg_types = {"this": False} - - -class Localtimestamp(Func): - arg_types = {"this": False} - - -class CurrentTimestamp(Func): - arg_types = {"this": False, "sysdate": False} - - -class CurrentTimestampLTZ(Func): - arg_types = {} - - -class CurrentTimezone(Func): - arg_types = {} - - -class CurrentOrganizationName(Func): - arg_types = {} - - -class CurrentSchema(Func): - arg_types = {"this": False} - - -class CurrentUser(Func): - arg_types = {"this": False} - - -class CurrentCatalog(Func): - arg_types = {} - - -class CurrentRegion(Func): - arg_types = {} - - -class CurrentRole(Func): - arg_types = {} - - -class CurrentRoleType(Func): - arg_types = {} - - -class CurrentOrganizationUser(Func): - arg_types = {} - - -class SessionUser(Func): - arg_types = {} - - -class UtcDate(Func): - arg_types = {} - - -class UtcTime(Func): - arg_types = {"this": False} - - -class UtcTimestamp(Func): - arg_types = {"this": False} - - -class DateAdd(Func, IntervalOp): - arg_types = {"this": True, "expression": True, "unit": False} - - -class DateBin(Func, IntervalOp): - arg_types = { - "this": True, - "expression": True, - "unit": False, - "zone": False, - "origin": False, - } - - -class DateSub(Func, IntervalOp): - arg_types = {"this": True, "expression": True, "unit": False} - - -class DateDiff(Func, TimeUnit): - _sql_names = ["DATEDIFF", "DATE_DIFF"] - arg_types = { - "this": True, - "expression": True, - "unit": False, - "zone": False, - "big_int": False, - "date_part_boundary": False, - } - - -class DateTrunc(Func): - arg_types = {"unit": True, "this": True, "zone": False} - - def __init__(self, **args): - # Across most dialects it's safe to unabbreviate the unit (e.g. 'Q' -> 'QUARTER') except Oracle - # https://docs.oracle.com/en/database/oracle/oracle-database/21/sqlrf/ROUND-and-TRUNC-Date-Functions.html - unabbreviate = args.pop("unabbreviate", True) - - unit = args.get("unit") - if isinstance(unit, TimeUnit.VAR_LIKE) and not ( - isinstance(unit, Column) and len(unit.parts) != 1 - ): - unit_name = unit.name.upper() - if unabbreviate and unit_name in TimeUnit.UNABBREVIATED_UNIT_NAME: - unit_name = TimeUnit.UNABBREVIATED_UNIT_NAME[unit_name] - - args["unit"] = Literal.string(unit_name) - - super().__init__(**args) - - @property - def unit(self) -> Expression: - return self.args["unit"] - - -# https://cloud.google.com/bigquery/docs/reference/standard-sql/datetime_functions#datetime -# expression can either be time_expr or time_zone -class Datetime(Func): - arg_types = {"this": True, "expression": False} - - -class DatetimeAdd(Func, IntervalOp): - arg_types = {"this": True, "expression": True, "unit": False} - - -class DatetimeSub(Func, IntervalOp): - arg_types = {"this": True, "expression": True, "unit": False} - - -class DatetimeDiff(Func, TimeUnit): - arg_types = {"this": True, "expression": True, "unit": False} - - -class DatetimeTrunc(Func, TimeUnit): - arg_types = {"this": True, "unit": True, "zone": False} - - -class DateFromUnixDate(Func): - pass - - -class DayOfWeek(Func): - _sql_names = ["DAY_OF_WEEK", "DAYOFWEEK"] - - -# https://duckdb.org/docs/sql/functions/datepart.html#part-specifiers-only-usable-as-date-part-specifiers -# ISO day of week function in duckdb is ISODOW -class DayOfWeekIso(Func): - _sql_names = ["DAYOFWEEK_ISO", "ISODOW"] - - -class DayOfMonth(Func): - _sql_names = ["DAY_OF_MONTH", "DAYOFMONTH"] - - -class DayOfYear(Func): - _sql_names = ["DAY_OF_YEAR", "DAYOFYEAR"] - - -class Dayname(Func): - arg_types = {"this": True, "abbreviated": False} - - -class ToDays(Func): - pass - - -class WeekOfYear(Func): - _sql_names = ["WEEK_OF_YEAR", "WEEKOFYEAR"] - - -class YearOfWeek(Func): - _sql_names = ["YEAR_OF_WEEK", "YEAROFWEEK"] - - -class YearOfWeekIso(Func): - _sql_names = ["YEAR_OF_WEEK_ISO", "YEAROFWEEKISO"] - - -class MonthsBetween(Func): - arg_types = {"this": True, "expression": True, "roundoff": False} - - -class MakeInterval(Func): - arg_types = { - "year": False, - "month": False, - "week": False, - "day": False, - "hour": False, - "minute": False, - "second": False, - } - - -class LastDay(Func, TimeUnit): - _sql_names = ["LAST_DAY", "LAST_DAY_OF_MONTH"] - arg_types = {"this": True, "unit": False} - - -class PreviousDay(Func): - arg_types = {"this": True, "expression": True} - - -class LaxBool(Func): - pass - - -class LaxFloat64(Func): - pass - - -class LaxInt64(Func): - pass - - -class LaxString(Func): - pass - - -class Extract(Func): - arg_types = {"this": True, "expression": True} - - -class Exists(Func, SubqueryPredicate): - arg_types = {"this": True, "expression": False} - - -class Elt(Func): - arg_types = {"this": True, "expressions": True} - is_var_len_args = True - - -class Timestamp(Func): - arg_types = {"this": False, "zone": False, "with_tz": False, "safe": False} - - -class TimestampAdd(Func, TimeUnit): - arg_types = {"this": True, "expression": True, "unit": False} - - -class TimestampSub(Func, TimeUnit): - arg_types = {"this": True, "expression": True, "unit": False} - - -class TimestampDiff(Func, TimeUnit): - _sql_names = ["TIMESTAMPDIFF", "TIMESTAMP_DIFF"] - arg_types = {"this": True, "expression": True, "unit": False} - - -class TimestampTrunc(Func, TimeUnit): - arg_types = {"this": True, "unit": True, "zone": False} - - -class TimeSlice(Func, TimeUnit): - arg_types = {"this": True, "expression": True, "unit": True, "kind": False} - - -class TimeAdd(Func, TimeUnit): - arg_types = {"this": True, "expression": True, "unit": False} - - -class TimeSub(Func, TimeUnit): - arg_types = {"this": True, "expression": True, "unit": False} - - -class TimeDiff(Func, TimeUnit): - arg_types = {"this": True, "expression": True, "unit": False} - - -class TimeTrunc(Func, TimeUnit): - arg_types = {"this": True, "unit": True, "zone": False} - - -class DateFromParts(Func): - _sql_names = ["DATE_FROM_PARTS", "DATEFROMPARTS"] - arg_types = {"year": True, "month": False, "day": False} - - -class TimeFromParts(Func): - _sql_names = ["TIME_FROM_PARTS", "TIMEFROMPARTS"] - arg_types = { - "hour": True, - "min": True, - "sec": True, - "nano": False, - "fractions": False, - "precision": False, - } - - -class DateStrToDate(Func): - pass - - -class DateToDateStr(Func): - pass - - -class DateToDi(Func): - pass - - -# https://cloud.google.com/bigquery/docs/reference/standard-sql/date_functions#date -class Date(Func): - arg_types = {"this": False, "zone": False, "expressions": False} - is_var_len_args = True - - -class Day(Func): - pass - - -class Decode(Func): - arg_types = {"this": True, "charset": True, "replace": False} - - -class DecodeCase(Func): - arg_types = {"expressions": True} - is_var_len_args = True - - -class DenseRank(AggFunc): - arg_types = {"expressions": False} - is_var_len_args = True - - -class DiToDate(Func): - pass - - -class Encode(Func): - arg_types = {"this": True, "charset": True} - - -class EqualNull(Func): - arg_types = {"this": True, "expression": True} - - -class Exp(Func): - pass - - -class Factorial(Func): - pass - - -# https://docs.snowflake.com/en/sql-reference/functions/flatten -class Explode(Func, UDTF): - arg_types = {"this": True, "expressions": False} - is_var_len_args = True - - -# https://spark.apache.org/docs/latest/api/sql/#inline -class Inline(Func): - pass - - -class ExplodeOuter(Explode): - pass - - -class Posexplode(Explode): - pass - - -class PosexplodeOuter(Posexplode, ExplodeOuter): - pass - - -class PositionalColumn(Expression): - pass - - -class Unnest(Func, UDTF): - arg_types = { - "expressions": True, - "alias": False, - "offset": False, - "explode_array": False, - } - - @property - def selects(self) -> t.List[Expression]: - columns = super().selects - offset = self.args.get("offset") - if offset: - columns = columns + [to_identifier("offset") if offset is True else offset] - return columns - - -class Floor(Func): - arg_types = {"this": True, "decimals": False, "to": False} - - -class FromBase32(Func): - pass - - -class FromBase64(Func): - pass - - -class ToBase32(Func): - pass - - -class ToBase64(Func): - pass - - -class ToBinary(Func): - arg_types = {"this": True, "format": False, "safe": False} - - -# https://docs.snowflake.com/en/sql-reference/functions/base64_decode_binary -class Base64DecodeBinary(Func): - arg_types = {"this": True, "alphabet": False} - - -# https://docs.snowflake.com/en/sql-reference/functions/base64_decode_string -class Base64DecodeString(Func): - arg_types = {"this": True, "alphabet": False} - - -# https://docs.snowflake.com/en/sql-reference/functions/base64_encode -class Base64Encode(Func): - arg_types = {"this": True, "max_line_length": False, "alphabet": False} - - -# https://docs.snowflake.com/en/sql-reference/functions/try_base64_decode_binary -class TryBase64DecodeBinary(Func): - arg_types = {"this": True, "alphabet": False} - - -# https://docs.snowflake.com/en/sql-reference/functions/try_base64_decode_string -class TryBase64DecodeString(Func): - arg_types = {"this": True, "alphabet": False} - - -# https://docs.snowflake.com/en/sql-reference/functions/try_hex_decode_binary -class TryHexDecodeBinary(Func): - pass - - -# https://docs.snowflake.com/en/sql-reference/functions/try_hex_decode_string -class TryHexDecodeString(Func): - pass - - -# https://trino.io/docs/current/functions/datetime.html#from_iso8601_timestamp -class FromISO8601Timestamp(Func): - _sql_names = ["FROM_ISO8601_TIMESTAMP"] - - -class GapFill(Func): - arg_types = { - "this": True, - "ts_column": True, - "bucket_width": True, - "partitioning_columns": False, - "value_columns": False, - "origin": False, - "ignore_nulls": False, - } - - -# https://cloud.google.com/bigquery/docs/reference/standard-sql/array_functions#generate_date_array -class GenerateDateArray(Func): - arg_types = {"start": True, "end": True, "step": False} - - -# https://cloud.google.com/bigquery/docs/reference/standard-sql/array_functions#generate_timestamp_array -class GenerateTimestampArray(Func): - arg_types = {"start": True, "end": True, "step": True} - - -# https://docs.snowflake.com/en/sql-reference/functions/get -class GetExtract(Func): - arg_types = {"this": True, "expression": True} - - -class Getbit(Func): - arg_types = {"this": True, "expression": True} - - -class Greatest(Func): - arg_types = {"this": True, "expressions": False, "ignore_nulls": True} - is_var_len_args = True - - -# Trino's `ON OVERFLOW TRUNCATE [filler_string] {WITH | WITHOUT} COUNT` -# https://trino.io/docs/current/functions/aggregate.html#listagg -class OverflowTruncateBehavior(Expression): - arg_types = {"this": False, "with_count": True} - - -class GroupConcat(AggFunc): - arg_types = {"this": True, "separator": False, "on_overflow": False} - - -class Hex(Func): - pass - - -# https://docs.snowflake.com/en/sql-reference/functions/hex_decode_string -class HexDecodeString(Func): - pass - - -# https://docs.snowflake.com/en/sql-reference/functions/hex_encode -class HexEncode(Func): - arg_types = {"this": True, "case": False} - - -class Hour(Func): - pass - - -class Minute(Func): - pass - - -class Second(Func): - pass - - -# T-SQL: https://learn.microsoft.com/en-us/sql/t-sql/functions/compress-transact-sql?view=sql-server-ver17 -# Snowflake: https://docs.snowflake.com/en/sql-reference/functions/compress -class Compress(Func): - arg_types = {"this": True, "method": False} - - -# Snowflake: https://docs.snowflake.com/en/sql-reference/functions/decompress_binary -class DecompressBinary(Func): - arg_types = {"this": True, "method": True} - - -# Snowflake: https://docs.snowflake.com/en/sql-reference/functions/decompress_string -class DecompressString(Func): - arg_types = {"this": True, "method": True} - - -class LowerHex(Hex): - pass - - -class And(Connector, Func): - pass - - -class Or(Connector, Func): - pass - - -class Xor(Connector, Func): - arg_types = {"this": False, "expression": False, "expressions": False} - - -class If(Func): - arg_types = {"this": True, "true": True, "false": False} - _sql_names = ["IF", "IIF"] - - -class Nullif(Func): - arg_types = {"this": True, "expression": True} - - -class Initcap(Func): - arg_types = {"this": True, "expression": False} - - -class IsAscii(Func): - pass - - -class IsNan(Func): - _sql_names = ["IS_NAN", "ISNAN"] - - -# https://cloud.google.com/bigquery/docs/reference/standard-sql/json_functions#int64_for_json -class Int64(Func): - pass - - -class IsInf(Func): - _sql_names = ["IS_INF", "ISINF"] - - -class IsNullValue(Func): - pass - - -# https://www.postgresql.org/docs/current/functions-json.html -class JSON(Expression): - arg_types = {"this": False, "with_": False, "unique": False} - - -class JSONPath(Expression): - arg_types = {"expressions": True, "escape": False} - - @property - def output_name(self) -> str: - last_segment = self.expressions[-1].this - return last_segment if isinstance(last_segment, str) else "" - - -class JSONPathPart(Expression): - arg_types = {} - - -class JSONPathFilter(JSONPathPart): - arg_types = {"this": True} - - -class JSONPathKey(JSONPathPart): - arg_types = {"this": True} - - -class JSONPathRecursive(JSONPathPart): - arg_types = {"this": False} - - -class JSONPathRoot(JSONPathPart): - pass - - -class JSONPathScript(JSONPathPart): - arg_types = {"this": True} - - -class JSONPathSlice(JSONPathPart): - arg_types = {"start": False, "end": False, "step": False} - - -class JSONPathSelector(JSONPathPart): - arg_types = {"this": True} - - -class JSONPathSubscript(JSONPathPart): - arg_types = {"this": True} - - -class JSONPathUnion(JSONPathPart): - arg_types = {"expressions": True} - - -class JSONPathWildcard(JSONPathPart): - pass - - -class FormatJson(Expression): - pass - - -class Format(Func): - arg_types = {"this": True, "expressions": False} - is_var_len_args = True - - -class JSONKeyValue(Expression): - arg_types = {"this": True, "expression": True} - - -# https://cloud.google.com/bigquery/docs/reference/standard-sql/json_functions#json_keys -class JSONKeysAtDepth(Func): - arg_types = {"this": True, "expression": False, "mode": False} - - -class JSONObject(Func): - arg_types = { - "expressions": False, - "null_handling": False, - "unique_keys": False, - "return_type": False, - "encoding": False, - } - - -class JSONObjectAgg(AggFunc): - arg_types = { - "expressions": False, - "null_handling": False, - "unique_keys": False, - "return_type": False, - "encoding": False, - } - - -# https://www.postgresql.org/docs/9.5/functions-aggregate.html -class JSONBObjectAgg(AggFunc): - arg_types = {"this": True, "expression": True} - - -# https://docs.oracle.com/en/database/oracle/oracle-database/19/sqlrf/JSON_ARRAY.html -class JSONArray(Func): - arg_types = { - "expressions": False, - "null_handling": False, - "return_type": False, - "strict": False, - } - - -# https://docs.oracle.com/en/database/oracle/oracle-database/19/sqlrf/JSON_ARRAYAGG.html -class JSONArrayAgg(AggFunc): - arg_types = { - "this": True, - "order": False, - "null_handling": False, - "return_type": False, - "strict": False, - } - - -class JSONExists(Func): - arg_types = { - "this": True, - "path": True, - "passing": False, - "on_condition": False, - "from_dcolonqmark": False, - } - - -# https://docs.oracle.com/en/database/oracle/oracle-database/19/sqlrf/JSON_TABLE.html -# Note: parsing of JSON column definitions is currently incomplete. -class JSONColumnDef(Expression): - arg_types = { - "this": False, - "kind": False, - "path": False, - "nested_schema": False, - "ordinality": False, - } - - -class JSONSchema(Expression): - arg_types = {"expressions": True} - - -class JSONSet(Func): - arg_types = {"this": True, "expressions": True} - is_var_len_args = True - _sql_names = ["JSON_SET"] - - -# https://cloud.google.com/bigquery/docs/reference/standard-sql/json_functions#json_strip_nulls -class JSONStripNulls(Func): - arg_types = { - "this": True, - "expression": False, - "include_arrays": False, - "remove_empty": False, - } - _sql_names = ["JSON_STRIP_NULLS"] - - -# https://dev.mysql.com/doc/refman/8.4/en/json-search-functions.html#function_json-value -class JSONValue(Expression): - arg_types = { - "this": True, - "path": True, - "returning": False, - "on_condition": False, - } - - -class JSONValueArray(Func): - arg_types = {"this": True, "expression": False} - - -class JSONRemove(Func): - arg_types = {"this": True, "expressions": True} - is_var_len_args = True - _sql_names = ["JSON_REMOVE"] - - -# https://docs.oracle.com/en/database/oracle/oracle-database/19/sqlrf/JSON_TABLE.html -class JSONTable(Func): - arg_types = { - "this": True, - "schema": True, - "path": False, - "error_handling": False, - "empty_handling": False, - } - - -# https://cloud.google.com/bigquery/docs/reference/standard-sql/json_functions#json_type -# https://doris.apache.org/docs/sql-manual/sql-functions/scalar-functions/json-functions/json-type#description -class JSONType(Func): - arg_types = {"this": True, "expression": False} - _sql_names = ["JSON_TYPE"] - - -# https://docs.snowflake.com/en/sql-reference/functions/object_insert -class ObjectInsert(Func): - arg_types = { - "this": True, - "key": True, - "value": True, - "update_flag": False, - } - - -class OpenJSONColumnDef(Expression): - arg_types = {"this": True, "kind": True, "path": False, "as_json": False} - - -class OpenJSON(Func): - arg_types = {"this": True, "path": False, "expressions": False} - - -class JSONBContains(Binary, Func): - _sql_names = ["JSONB_CONTAINS"] - - -# https://www.postgresql.org/docs/9.5/functions-json.html -class JSONBContainsAnyTopKeys(Binary, Func): - pass - - -# https://www.postgresql.org/docs/9.5/functions-json.html -class JSONBContainsAllTopKeys(Binary, Func): - pass - - -class JSONBExists(Func): - arg_types = {"this": True, "path": True} - _sql_names = ["JSONB_EXISTS"] - - -# https://www.postgresql.org/docs/9.5/functions-json.html -class JSONBDeleteAtPath(Binary, Func): - pass - - -class JSONExtract(Binary, Func): - arg_types = { - "this": True, - "expression": True, - "only_json_types": False, - "expressions": False, - "variant_extract": False, - "json_query": False, - "option": False, - "quote": False, - "on_condition": False, - "requires_json": False, - } - _sql_names = ["JSON_EXTRACT"] - is_var_len_args = True - - @property - def output_name(self) -> str: - return self.expression.output_name if not self.expressions else "" - - -# https://trino.io/docs/current/functions/json.html#json-query -class JSONExtractQuote(Expression): - arg_types = { - "option": True, - "scalar": False, - } - - -class JSONExtractArray(Func): - arg_types = {"this": True, "expression": False} - _sql_names = ["JSON_EXTRACT_ARRAY"] - - -class JSONExtractScalar(Binary, Func): - arg_types = { - "this": True, - "expression": True, - "only_json_types": False, - "expressions": False, - "json_type": False, - "scalar_only": False, - } - _sql_names = ["JSON_EXTRACT_SCALAR"] - is_var_len_args = True - - @property - def output_name(self) -> str: - return self.expression.output_name - - -class JSONBExtract(Binary, Func): - _sql_names = ["JSONB_EXTRACT"] - - -class JSONBExtractScalar(Binary, Func): - arg_types = {"this": True, "expression": True, "json_type": False} - _sql_names = ["JSONB_EXTRACT_SCALAR"] - - -class JSONFormat(Func): - arg_types = {"this": False, "options": False, "is_json": False, "to_json": False} - _sql_names = ["JSON_FORMAT"] - - -class JSONArrayAppend(Func): - arg_types = {"this": True, "expressions": True} - is_var_len_args = True - _sql_names = ["JSON_ARRAY_APPEND"] - - -# https://dev.mysql.com/doc/refman/8.0/en/json-search-functions.html#operator_member-of -class JSONArrayContains(Binary, Predicate, Func): - arg_types = {"this": True, "expression": True, "json_type": False} - _sql_names = ["JSON_ARRAY_CONTAINS"] - - -class JSONArrayInsert(Func): - arg_types = {"this": True, "expressions": True} - is_var_len_args = True - _sql_names = ["JSON_ARRAY_INSERT"] - - -class ParseBignumeric(Func): - pass - - -class ParseNumeric(Func): - pass - - -class ParseJSON(Func): - # BigQuery, Snowflake have PARSE_JSON, Presto has JSON_PARSE - # Snowflake also has TRY_PARSE_JSON, which is represented using `safe` - _sql_names = ["PARSE_JSON", "JSON_PARSE"] - arg_types = {"this": True, "expression": False, "safe": False} - - -# Snowflake: https://docs.snowflake.com/en/sql-reference/functions/parse_url -# Databricks: https://docs.databricks.com/aws/en/sql/language-manual/functions/parse_url -class ParseUrl(Func): - arg_types = { - "this": True, - "part_to_extract": False, - "key": False, - "permissive": False, - } - - -class ParseIp(Func): - arg_types = {"this": True, "type": True, "permissive": False} - - -class ParseTime(Func): - arg_types = {"this": True, "format": True} - - -class ParseDatetime(Func): - arg_types = {"this": True, "format": False, "zone": False} - - -class Least(Func): - arg_types = {"this": True, "expressions": False, "ignore_nulls": True} - is_var_len_args = True - - -class Left(Func): - arg_types = {"this": True, "expression": True} - - -class Right(Func): - arg_types = {"this": True, "expression": True} - - -class Reverse(Func): - pass - - -class Length(Func): - arg_types = {"this": True, "binary": False, "encoding": False} - _sql_names = ["LENGTH", "LEN", "CHAR_LENGTH", "CHARACTER_LENGTH"] - - -class RtrimmedLength(Func): - pass - - -class BitLength(Func): - pass - - -class Levenshtein(Func): - arg_types = { - "this": True, - "expression": False, - "ins_cost": False, - "del_cost": False, - "sub_cost": False, - "max_dist": False, - } - - -class Ln(Func): - pass - - -class Log(Func): - arg_types = {"this": True, "expression": False} - - -class LogicalOr(AggFunc): - _sql_names = ["LOGICAL_OR", "BOOL_OR", "BOOLOR_AGG"] - - -class LogicalAnd(AggFunc): - _sql_names = ["LOGICAL_AND", "BOOL_AND", "BOOLAND_AGG"] - - -class Lower(Func): - _sql_names = ["LOWER", "LCASE"] - - -class Map(Func): - arg_types = {"keys": False, "values": False} - - @property - def keys(self) -> t.List[Expression]: - keys = self.args.get("keys") - return keys.expressions if keys else [] - - @property - def values(self) -> t.List[Expression]: - values = self.args.get("values") - return values.expressions if values else [] - - -# Represents the MAP {...} syntax in DuckDB - basically convert a struct to a MAP -class ToMap(Func): - pass - - -class MapFromEntries(Func): - pass - - -class MapCat(Func): - arg_types = {"this": True, "expression": True} - - -class MapContainsKey(Func): - arg_types = {"this": True, "key": True} - - -class MapDelete(Func): - arg_types = {"this": True, "expressions": True} - is_var_len_args = True - - -class MapInsert(Func): - arg_types = {"this": True, "key": False, "value": True, "update_flag": False} - - -class MapKeys(Func): - pass - - -class MapPick(Func): - arg_types = {"this": True, "expressions": True} - is_var_len_args = True - - -class MapSize(Func): - pass - - -# https://learn.microsoft.com/en-us/sql/t-sql/language-elements/scope-resolution-operator-transact-sql?view=sql-server-ver16 -class ScopeResolution(Expression): - arg_types = {"this": False, "expression": True} - - -class Slice(Expression): - arg_types = {"this": False, "expression": False, "step": False} - - -class Stream(Expression): - pass - - -class StarMap(Func): - pass - - -class VarMap(Func): - arg_types = {"keys": True, "values": True} - is_var_len_args = True - - @property - def keys(self) -> t.List[Expression]: - return self.args["keys"].expressions - - @property - def values(self) -> t.List[Expression]: - return self.args["values"].expressions - - -# https://dev.mysql.com/doc/refman/8.0/en/fulltext-search.html -class MatchAgainst(Func): - arg_types = {"this": True, "expressions": True, "modifier": False} - - -class Max(AggFunc): - arg_types = {"this": True, "expressions": False} - is_var_len_args = True - - -class MD5(Func): - _sql_names = ["MD5"] - - -# Represents the variant of the MD5 function that returns a binary value -class MD5Digest(Func): - _sql_names = ["MD5_DIGEST"] - - -# https://docs.snowflake.com/en/sql-reference/functions/md5_number_lower64 -class MD5NumberLower64(Func): - pass - - -# https://docs.snowflake.com/en/sql-reference/functions/md5_number_upper64 -class MD5NumberUpper64(Func): - pass - - -class Median(AggFunc): - pass - - -class Mode(AggFunc): - arg_types = {"this": False, "deterministic": False} - - -class Min(AggFunc): - arg_types = {"this": True, "expressions": False} - is_var_len_args = True - - -class Month(Func): - pass - - -class Monthname(Func): - arg_types = {"this": True, "abbreviated": False} - - -class AddMonths(Func): - arg_types = {"this": True, "expression": True, "preserve_end_of_month": False} - - -class Nvl2(Func): - arg_types = {"this": True, "true": True, "false": False} - - -class Ntile(AggFunc): - arg_types = {"this": False} - - -class Normalize(Func): - arg_types = {"this": True, "form": False, "is_casefold": False} - - -class Normal(Func): - arg_types = {"this": True, "stddev": True, "gen": True} - - -# https://cloud.google.com/bigquery/docs/reference/standard-sql/net_functions#nethost -class NetHost(Func): - _sql_names = ["NET.HOST"] - - -class Overlay(Func): - arg_types = {"this": True, "expression": True, "from_": True, "for_": False} - - -# https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-predict#mlpredict_function -class Predict(Func): - arg_types = {"this": True, "expression": True, "params_struct": False} - - -# https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-translate#mltranslate_function -class MLTranslate(Func): - arg_types = {"this": True, "expression": True, "params_struct": True} - - -# https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-feature-time -class FeaturesAtTime(Func): - arg_types = { - "this": True, - "time": False, - "num_rows": False, - "ignore_feature_nulls": False, - } - - -# https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-generate-embedding -class GenerateEmbedding(Func): - arg_types = { - "this": True, - "expression": True, - "params_struct": False, - "is_text": False, - } - - -class MLForecast(Func): - arg_types = {"this": True, "expression": False, "params_struct": False} - - -# Represents Snowflake's ! syntax. For example: SELECT model!PREDICT(INPUT_DATA => {*}) -# See: https://docs.snowflake.com/en/guides-overview-ml-functions -class ModelAttribute(Expression): - arg_types = {"this": True, "expression": True} - - -# https://cloud.google.com/bigquery/docs/reference/standard-sql/search_functions#vector_search -class VectorSearch(Func): - arg_types = { - "this": True, - "column_to_search": True, - "query_table": True, - "query_column_to_search": False, - "top_k": False, - "distance_type": False, - "options": False, - } - - -class Pi(Func): - arg_types = {} - - -class Pow(Binary, Func): - _sql_names = ["POWER", "POW"] - - -class PercentileCont(AggFunc): - arg_types = {"this": True, "expression": False} - - -class PercentileDisc(AggFunc): - arg_types = {"this": True, "expression": False} - - -class PercentRank(AggFunc): - arg_types = {"expressions": False} - is_var_len_args = True - - -class Quantile(AggFunc): - arg_types = {"this": True, "quantile": True} - - -class ApproxQuantile(Quantile): - arg_types = { - "this": True, - "quantile": True, - "accuracy": False, - "weight": False, - "error_tolerance": False, - } - - -# https://docs.snowflake.com/en/sql-reference/functions/approx_percentile_accumulate -class ApproxPercentileAccumulate(AggFunc): - pass - - -# https://docs.snowflake.com/en/sql-reference/functions/approx_percentile_estimate -class ApproxPercentileEstimate(Func): - arg_types = {"this": True, "percentile": True} - - -class Quarter(Func): - pass - - -# https://docs.teradata.com/r/Enterprise_IntelliFlex_VMware/SQL-Functions-Expressions-and-Predicates/Arithmetic-Trigonometric-Hyperbolic-Operators/Functions/RANDOM/RANDOM-Function-Syntax -# teradata lower and upper bounds -class Rand(Func): - _sql_names = ["RAND", "RANDOM"] - arg_types = {"this": False, "lower": False, "upper": False} - - -class Randn(Func): - arg_types = {"this": False} - - -class Randstr(Func): - arg_types = {"this": True, "generator": False} - - -class RangeN(Func): - arg_types = {"this": True, "expressions": True, "each": False} - - -class RangeBucket(Func): - arg_types = {"this": True, "expression": True} - - -class Rank(AggFunc): - arg_types = {"expressions": False} - is_var_len_args = True - - -class ReadCSV(Func): - _sql_names = ["READ_CSV"] - is_var_len_args = True - arg_types = {"this": True, "expressions": False} - - -class ReadParquet(Func): - is_var_len_args = True - arg_types = {"expressions": True} - - -class Reduce(Func): - arg_types = {"this": True, "initial": True, "merge": True, "finish": False} - - -class RegexpExtract(Func): - arg_types = { - "this": True, - "expression": True, - "position": False, - "occurrence": False, - "parameters": False, - "group": False, - "null_if_pos_overflow": False, # for transpilation target behavior - } - - -class RegexpExtractAll(Func): - arg_types = { - "this": True, - "expression": True, - "group": False, - "parameters": False, - "position": False, - "occurrence": False, - } - - -class RegexpReplace(Func): - arg_types = { - "this": True, - "expression": True, - "replacement": False, - "position": False, - "occurrence": False, - "modifiers": False, - "single_replace": False, - } - - -class RegexpLike(Binary, Func): - arg_types = {"this": True, "expression": True, "flag": False} - - -class RegexpILike(Binary, Func): - arg_types = {"this": True, "expression": True, "flag": False} - - -class RegexpFullMatch(Binary, Func): - arg_types = {"this": True, "expression": True, "options": False} - - -class RegexpInstr(Func): - arg_types = { - "this": True, - "expression": True, - "position": False, - "occurrence": False, - "option": False, - "parameters": False, - "group": False, - } - - -# https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/api/pyspark.sql.functions.split.html -# limit is the number of times a pattern is applied -class RegexpSplit(Func): - arg_types = {"this": True, "expression": True, "limit": False} - - -class RegexpCount(Func): - arg_types = { - "this": True, - "expression": True, - "position": False, - "parameters": False, - } - - -class RegrValx(AggFunc): - arg_types = {"this": True, "expression": True} - - -class RegrValy(AggFunc): - arg_types = {"this": True, "expression": True} - - -class RegrAvgy(AggFunc): - arg_types = {"this": True, "expression": True} - - -class RegrAvgx(AggFunc): - arg_types = {"this": True, "expression": True} - - -class RegrCount(AggFunc): - arg_types = {"this": True, "expression": True} - - -class RegrIntercept(AggFunc): - arg_types = {"this": True, "expression": True} - - -class RegrR2(AggFunc): - arg_types = {"this": True, "expression": True} - - -class RegrSxx(AggFunc): - arg_types = {"this": True, "expression": True} - - -class RegrSxy(AggFunc): - arg_types = {"this": True, "expression": True} - - -class RegrSyy(AggFunc): - arg_types = {"this": True, "expression": True} - - -class RegrSlope(AggFunc): - arg_types = {"this": True, "expression": True} - - -class Repeat(Func): - arg_types = {"this": True, "times": True} - - -# Some dialects like Snowflake support two argument replace -class Replace(Func): - arg_types = {"this": True, "expression": True, "replacement": False} - - -class Radians(Func): - pass - - -# https://learn.microsoft.com/en-us/sql/t-sql/functions/round-transact-sql?view=sql-server-ver16 -# tsql third argument function == trunctaion if not 0 -class Round(Func): - arg_types = { - "this": True, - "decimals": False, - "truncate": False, - "casts_non_integer_decimals": False, - } - - -class RowNumber(Func): - arg_types = {"this": False} - - -class SafeAdd(Func): - arg_types = {"this": True, "expression": True} - - -class SafeDivide(Func): - arg_types = {"this": True, "expression": True} - - -class SafeMultiply(Func): - arg_types = {"this": True, "expression": True} - - -class SafeNegate(Func): - pass - - -class SafeSubtract(Func): - arg_types = {"this": True, "expression": True} - - -class SafeConvertBytesToString(Func): - pass - - -class SHA(Func): - _sql_names = ["SHA", "SHA1"] - - -class SHA2(Func): - _sql_names = ["SHA2"] - arg_types = {"this": True, "length": False} - - -# Represents the variant of the SHA1 function that returns a binary value -class SHA1Digest(Func): - pass - - -# Represents the variant of the SHA2 function that returns a binary value -class SHA2Digest(Func): - arg_types = {"this": True, "length": False} - - -class Sign(Func): - _sql_names = ["SIGN", "SIGNUM"] - - -class SortArray(Func): - arg_types = {"this": True, "asc": False, "nulls_first": False} - - -class Soundex(Func): - pass - - -# https://docs.snowflake.com/en/sql-reference/functions/soundex_p123 -class SoundexP123(Func): - pass - - -class Split(Func): - arg_types = {"this": True, "expression": True, "limit": False} - - -# https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/api/pyspark.sql.functions.split_part.html -# https://docs.snowflake.com/en/sql-reference/functions/split_part -# https://docs.snowflake.com/en/sql-reference/functions/strtok -class SplitPart(Func): - arg_types = {"this": True, "delimiter": False, "part_index": False} - - -# Start may be omitted in the case of postgres -# https://www.postgresql.org/docs/9.1/functions-string.html @ Table 9-6 -class Substring(Func): - _sql_names = ["SUBSTRING", "SUBSTR"] - arg_types = {"this": True, "start": False, "length": False} - - -class SubstringIndex(Func): - """ - SUBSTRING_INDEX(str, delim, count) - - *count* > 0 → left slice before the *count*-th delimiter - *count* < 0 → right slice after the |count|-th delimiter - """ - - arg_types = {"this": True, "delimiter": True, "count": True} - - -class StandardHash(Func): - arg_types = {"this": True, "expression": False} - - -class StartsWith(Func): - _sql_names = ["STARTS_WITH", "STARTSWITH"] - arg_types = {"this": True, "expression": True} - - -class EndsWith(Func): - _sql_names = ["ENDS_WITH", "ENDSWITH"] - arg_types = {"this": True, "expression": True} - - -class StrPosition(Func): - arg_types = { - "this": True, - "substr": True, - "position": False, - "occurrence": False, - } - - -# Snowflake: https://docs.snowflake.com/en/sql-reference/functions/search -# BigQuery: https://cloud.google.com/bigquery/docs/reference/standard-sql/search_functions#search -class Search(Func): - arg_types = { - "this": True, # data_to_search / search_data - "expression": True, # search_query / search_string - "json_scope": False, # BigQuery: JSON_VALUES | JSON_KEYS | JSON_KEYS_AND_VALUES - "analyzer": False, # Both: analyzer / ANALYZER - "analyzer_options": False, # BigQuery: analyzer_options_values - "search_mode": False, # Snowflake: OR | AND - } - - -# Snowflake: https://docs.snowflake.com/en/sql-reference/functions/search_ip -class SearchIp(Func): - arg_types = {"this": True, "expression": True} - - -class StrToDate(Func): - arg_types = {"this": True, "format": False, "safe": False} - - -class StrToTime(Func): - arg_types = { - "this": True, - "format": True, - "zone": False, - "safe": False, - "target_type": False, - } - - -# Spark allows unix_timestamp() -# https://spark.apache.org/docs/3.1.3/api/python/reference/api/pyspark.sql.functions.unix_timestamp.html -class StrToUnix(Func): - arg_types = {"this": False, "format": False} - - -# https://prestodb.io/docs/current/functions/string.html -# https://spark.apache.org/docs/latest/api/sql/index.html#str_to_map -class StrToMap(Func): - arg_types = { - "this": True, - "pair_delim": False, - "key_value_delim": False, - "duplicate_resolution_callback": False, - } - - -class NumberToStr(Func): - arg_types = {"this": True, "format": True, "culture": False} - - -class FromBase(Func): - arg_types = {"this": True, "expression": True} - - -class Space(Func): - """ - SPACE(n) → string consisting of n blank characters - """ - - pass - - -class Struct(Func): - arg_types = {"expressions": False} - is_var_len_args = True - - -class StructExtract(Func): - arg_types = {"this": True, "expression": True} - - -# https://learn.microsoft.com/en-us/sql/t-sql/functions/stuff-transact-sql?view=sql-server-ver16 -# https://docs.snowflake.com/en/sql-reference/functions/insert -class Stuff(Func): - _sql_names = ["STUFF", "INSERT"] - arg_types = {"this": True, "start": True, "length": True, "expression": True} - - -class Sum(AggFunc): - pass - - -class Sqrt(Func): - pass - - -class Stddev(AggFunc): - _sql_names = ["STDDEV", "STDEV"] - - -class StddevPop(AggFunc): - pass - - -class StddevSamp(AggFunc): - pass - - -# https://cloud.google.com/bigquery/docs/reference/standard-sql/time_functions#time -class Time(Func): - arg_types = {"this": False, "zone": False} - - -class TimeToStr(Func): - arg_types = {"this": True, "format": True, "culture": False, "zone": False} - - -class TimeToTimeStr(Func): - pass - - -class TimeToUnix(Func): - pass - - -class TimeStrToDate(Func): - pass - - -class TimeStrToTime(Func): - arg_types = {"this": True, "zone": False} - - -class TimeStrToUnix(Func): - pass - - -class Trim(Func): - arg_types = { - "this": True, - "expression": False, - "position": False, - "collation": False, - } - - -class TsOrDsAdd(Func, TimeUnit): - # return_type is used to correctly cast the arguments of this expression when transpiling it - arg_types = {"this": True, "expression": True, "unit": False, "return_type": False} - - @property - def return_type(self) -> DataType: - return DataType.build(self.args.get("return_type") or DataType.Type.DATE) - - -class TsOrDsDiff(Func, TimeUnit): - arg_types = {"this": True, "expression": True, "unit": False} - - -class TsOrDsToDateStr(Func): - pass - - -class TsOrDsToDate(Func): - arg_types = {"this": True, "format": False, "safe": False} - - -class TsOrDsToDatetime(Func): - pass - - -class TsOrDsToTime(Func): - arg_types = {"this": True, "format": False, "safe": False} - - -class TsOrDsToTimestamp(Func): - pass - - -class TsOrDiToDi(Func): - pass - - -class Unhex(Func): - arg_types = {"this": True, "expression": False} - - -class Unicode(Func): - pass - - -class Uniform(Func): - arg_types = {"this": True, "expression": True, "gen": False, "seed": False} - - -# https://cloud.google.com/bigquery/docs/reference/standard-sql/date_functions#unix_date -class UnixDate(Func): - pass - - -class UnixToStr(Func): - arg_types = {"this": True, "format": False} - - -# https://prestodb.io/docs/current/functions/datetime.html -# presto has weird zone/hours/minutes -class UnixToTime(Func): - arg_types = { - "this": True, - "scale": False, - "zone": False, - "hours": False, - "minutes": False, - "format": False, - } - - SECONDS = Literal.number(0) - DECIS = Literal.number(1) - CENTIS = Literal.number(2) - MILLIS = Literal.number(3) - DECIMILLIS = Literal.number(4) - CENTIMILLIS = Literal.number(5) - MICROS = Literal.number(6) - DECIMICROS = Literal.number(7) - CENTIMICROS = Literal.number(8) - NANOS = Literal.number(9) - - -class UnixToTimeStr(Func): - pass - - -class UnixSeconds(Func): - pass - - -class UnixMicros(Func): - pass - - -class UnixMillis(Func): - pass - - -class Uuid(Func): - _sql_names = ["UUID", "GEN_RANDOM_UUID", "GENERATE_UUID", "UUID_STRING"] - - arg_types = {"this": False, "name": False, "is_string": False} - - -TIMESTAMP_PARTS = { - "year": False, - "month": False, - "day": False, - "hour": False, - "min": False, - "sec": False, - "nano": False, -} - - -class TimestampFromParts(Func): - _sql_names = ["TIMESTAMP_FROM_PARTS", "TIMESTAMPFROMPARTS"] - arg_types = { - **TIMESTAMP_PARTS, - "zone": False, - "milli": False, - "this": False, - "expression": False, - } - - -class TimestampLtzFromParts(Func): - _sql_names = ["TIMESTAMP_LTZ_FROM_PARTS", "TIMESTAMPLTZFROMPARTS"] - arg_types = TIMESTAMP_PARTS.copy() - - -class TimestampTzFromParts(Func): - _sql_names = ["TIMESTAMP_TZ_FROM_PARTS", "TIMESTAMPTZFROMPARTS"] - arg_types = { - **TIMESTAMP_PARTS, - "zone": False, - } - - -class Upper(Func): - _sql_names = ["UPPER", "UCASE"] - - -class Corr(Binary, AggFunc): - pass - - -# https://docs.oracle.com/en/database/oracle/oracle-database/19/sqlrf/CUME_DIST.html -class CumeDist(AggFunc): - arg_types = {"expressions": False} - is_var_len_args = True - - -class Variance(AggFunc): - _sql_names = ["VARIANCE", "VARIANCE_SAMP", "VAR_SAMP"] - - -class VariancePop(AggFunc): - _sql_names = ["VARIANCE_POP", "VAR_POP"] - - -class Skewness(AggFunc): - pass - - -class WidthBucket(Func): - arg_types = { - "this": True, - "min_value": True, - "max_value": True, - "num_buckets": True, - } - - -class CovarSamp(Binary, AggFunc): - pass - - -class CovarPop(Binary, AggFunc): - pass - - -class Week(Func): - arg_types = {"this": True, "mode": False} - - -class WeekStart(Expression): - pass - - -class NextDay(Func): - arg_types = {"this": True, "expression": True} - - -class XMLElement(Func): - _sql_names = ["XMLELEMENT"] - arg_types = {"this": True, "expressions": False} - - -class XMLGet(Func): - _sql_names = ["XMLGET"] - arg_types = {"this": True, "expression": True, "instance": False} - - -class XMLTable(Func): - arg_types = { - "this": True, - "namespaces": False, - "passing": False, - "columns": False, - "by_ref": False, - } - - -class XMLNamespace(Expression): - pass - - -# https://learn.microsoft.com/en-us/sql/t-sql/queries/select-for-clause-transact-sql?view=sql-server-ver17#syntax -class XMLKeyValueOption(Expression): - arg_types = {"this": True, "expression": False} - - -class Year(Func): - pass - - -class Zipf(Func): - arg_types = {"this": True, "elementcount": True, "gen": True} - - -class Use(Expression): - arg_types = {"this": False, "expressions": False, "kind": False} - - -class Merge(DML): - arg_types = { - "this": True, - "using": True, - "on": False, - "using_cond": False, - "whens": True, - "with_": False, - "returning": False, - } - - -class When(Expression): - arg_types = {"matched": True, "source": False, "condition": False, "then": True} - - -class Whens(Expression): - """Wraps around one or more WHEN [NOT] MATCHED [...] clauses.""" - - arg_types = {"expressions": True} - - -# https://docs.oracle.com/javadb/10.8.3.0/ref/rrefsqljnextvaluefor.html -# https://learn.microsoft.com/en-us/sql/t-sql/functions/next-value-for-transact-sql?view=sql-server-ver16 -class NextValueFor(Func): - arg_types = {"this": True, "order": False} - - -# Refers to a trailing semi-colon. This is only used to preserve trailing comments -# select 1; -- my comment -class Semicolon(Expression): - arg_types = {} - - -# BigQuery allows SELECT t FROM t and treats the projection as a struct value. This expression -# type is intended to be constructed by qualify so that we can properly annotate its type later -class TableColumn(Expression): - pass - - -ALL_FUNCTIONS = subclasses(__name__, Func, {AggFunc, Anonymous, Func}) -FUNCTION_BY_NAME = {name: func for func in ALL_FUNCTIONS for name in func.sql_names()} - -JSON_PATH_PARTS = subclasses(__name__, JSONPathPart, {JSONPathPart}) - -PERCENTILES = (PercentileCont, PercentileDisc) - - -# Helpers -@t.overload -def maybe_parse( - sql_or_expression: ExpOrStr, - *, - into: t.Type[E], - dialect: DialectType = None, - prefix: t.Optional[str] = None, - copy: bool = False, - **opts, -) -> E: ... - - -@t.overload -def maybe_parse( - sql_or_expression: str | E, - *, - into: t.Optional[IntoType] = None, - dialect: DialectType = None, - prefix: t.Optional[str] = None, - copy: bool = False, - **opts, -) -> E: ... - - -def maybe_parse( - sql_or_expression: ExpOrStr, - *, - into: t.Optional[IntoType] = None, - dialect: DialectType = None, - prefix: t.Optional[str] = None, - copy: bool = False, - **opts, -) -> Expression: - """Gracefully handle a possible string or expression. - - Example: - >>> maybe_parse("1") - Literal(this=1, is_string=False) - >>> maybe_parse(to_identifier("x")) - Identifier(this=x, quoted=False) - - Args: - sql_or_expression: the SQL code string or an expression - into: the SQLGlot Expression to parse into - dialect: the dialect used to parse the input expressions (in the case that an - input expression is a SQL string). - prefix: a string to prefix the sql with before it gets parsed - (automatically includes a space) - copy: whether to copy the expression. - **opts: other options to use to parse the input expressions (again, in the case - that an input expression is a SQL string). - - Returns: - Expression: the parsed or given expression. - """ - if isinstance(sql_or_expression, Expression): - if copy: - return sql_or_expression.copy() - return sql_or_expression - - if sql_or_expression is None: - raise ParseError("SQL cannot be None") - - import bigframes_vendored.sqlglot - - sql = str(sql_or_expression) - if prefix: - sql = f"{prefix} {sql}" - - return bigframes_vendored.sqlglot.parse_one(sql, read=dialect, into=into, **opts) - - -@t.overload -def maybe_copy(instance: None, copy: bool = True) -> None: ... - - -@t.overload -def maybe_copy(instance: E, copy: bool = True) -> E: ... - - -def maybe_copy(instance, copy=True): - return instance.copy() if copy and instance else instance - - -def _to_s( - node: t.Any, verbose: bool = False, level: int = 0, repr_str: bool = False -) -> str: - """Generate a textual representation of an Expression tree""" - indent = "\n" + (" " * (level + 1)) - delim = f",{indent}" - - if isinstance(node, Expression): - args = { - k: v for k, v in node.args.items() if (v is not None and v != []) or verbose - } - - if (node.type or verbose) and not isinstance(node, DataType): - args["_type"] = node.type - if node.comments or verbose: - args["_comments"] = node.comments - - if verbose: - args["_id"] = id(node) - - # Inline leaves for a more compact representation - if node.is_leaf(): - indent = "" - delim = ", " - - repr_str = node.is_string or (isinstance(node, Identifier) and node.quoted) - items = delim.join( - [ - f"{k}={_to_s(v, verbose, level + 1, repr_str=repr_str)}" - for k, v in args.items() - ] - ) - return f"{node.__class__.__name__}({indent}{items})" - - if isinstance(node, list): - items = delim.join(_to_s(i, verbose, level + 1) for i in node) - items = f"{indent}{items}" if items else "" - return f"[{items}]" - - # We use the representation of the string to avoid stripping out important whitespace - if repr_str and isinstance(node, str): - node = repr(node) - - # Indent multiline strings to match the current level - return indent.join(textwrap.dedent(str(node).strip("\n")).splitlines()) - - -def _is_wrong_expression(expression, into): - return isinstance(expression, Expression) and not isinstance(expression, into) - - -def _apply_builder( - expression, - instance, - arg, - copy=True, - prefix=None, - into=None, - dialect=None, - into_arg="this", - **opts, -): - if _is_wrong_expression(expression, into): - expression = into(**{into_arg: expression}) - instance = maybe_copy(instance, copy) - expression = maybe_parse( - sql_or_expression=expression, - prefix=prefix, - into=into, - dialect=dialect, - **opts, - ) - instance.set(arg, expression) - return instance - - -def _apply_child_list_builder( - *expressions, - instance, - arg, - append=True, - copy=True, - prefix=None, - into=None, - dialect=None, - properties=None, - **opts, -): - instance = maybe_copy(instance, copy) - parsed = [] - properties = {} if properties is None else properties - - for expression in expressions: - if expression is not None: - if _is_wrong_expression(expression, into): - expression = into(expressions=[expression]) - - expression = maybe_parse( - expression, - into=into, - dialect=dialect, - prefix=prefix, - **opts, - ) - for k, v in expression.args.items(): - if k == "expressions": - parsed.extend(v) - else: - properties[k] = v - - existing = instance.args.get(arg) - if append and existing: - parsed = existing.expressions + parsed - - child = into(expressions=parsed) - for k, v in properties.items(): - child.set(k, v) - instance.set(arg, child) - - return instance - - -def _apply_list_builder( - *expressions, - instance, - arg, - append=True, - copy=True, - prefix=None, - into=None, - dialect=None, - **opts, -): - inst = maybe_copy(instance, copy) - - expressions = [ - maybe_parse( - sql_or_expression=expression, - into=into, - prefix=prefix, - dialect=dialect, - **opts, - ) - for expression in expressions - if expression is not None - ] - - existing_expressions = inst.args.get(arg) - if append and existing_expressions: - expressions = existing_expressions + expressions - - inst.set(arg, expressions) - return inst - - -def _apply_conjunction_builder( - *expressions, - instance, - arg, - into=None, - append=True, - copy=True, - dialect=None, - **opts, -): - expressions = [exp for exp in expressions if exp is not None and exp != ""] - if not expressions: - return instance - - inst = maybe_copy(instance, copy) - - existing = inst.args.get(arg) - if append and existing is not None: - expressions = [existing.this if into else existing] + list(expressions) - - node = and_(*expressions, dialect=dialect, copy=copy, **opts) - - inst.set(arg, into(this=node) if into else node) - return inst - - -def _apply_cte_builder( - instance: E, - alias: ExpOrStr, - as_: ExpOrStr, - recursive: t.Optional[bool] = None, - materialized: t.Optional[bool] = None, - append: bool = True, - dialect: DialectType = None, - copy: bool = True, - scalar: t.Optional[bool] = None, - **opts, -) -> E: - alias_expression = maybe_parse(alias, dialect=dialect, into=TableAlias, **opts) - as_expression = maybe_parse(as_, dialect=dialect, copy=copy, **opts) - if scalar and not isinstance(as_expression, Subquery): - # scalar CTE must be wrapped in a subquery - as_expression = Subquery(this=as_expression) - cte = CTE( - this=as_expression, - alias=alias_expression, - materialized=materialized, - scalar=scalar, - ) - return _apply_child_list_builder( - cte, - instance=instance, - arg="with_", - append=append, - copy=copy, - into=With, - properties={"recursive": recursive} if recursive else {}, - ) - - -def _combine( - expressions: t.Sequence[t.Optional[ExpOrStr]], - operator: t.Type[Connector], - dialect: DialectType = None, - copy: bool = True, - wrap: bool = True, - **opts, -) -> Expression: - conditions = [ - condition(expression, dialect=dialect, copy=copy, **opts) - for expression in expressions - if expression is not None - ] - - this, *rest = conditions - if rest and wrap: - this = _wrap(this, Connector) - for expression in rest: - this = operator( - this=this, expression=_wrap(expression, Connector) if wrap else expression - ) - - return this - - -@t.overload -def _wrap(expression: None, kind: t.Type[Expression]) -> None: ... - - -@t.overload -def _wrap(expression: E, kind: t.Type[Expression]) -> E | Paren: ... - - -def _wrap(expression: t.Optional[E], kind: t.Type[Expression]) -> t.Optional[E] | Paren: - return Paren(this=expression) if isinstance(expression, kind) else expression - - -def _apply_set_operation( - *expressions: ExpOrStr, - set_operation: t.Type[S], - distinct: bool = True, - dialect: DialectType = None, - copy: bool = True, - **opts, -) -> S: - return reduce( - lambda x, y: set_operation(this=x, expression=y, distinct=distinct, **opts), - (maybe_parse(e, dialect=dialect, copy=copy, **opts) for e in expressions), - ) - - -def union( - *expressions: ExpOrStr, - distinct: bool = True, - dialect: DialectType = None, - copy: bool = True, - **opts, -) -> Union: - """ - Initializes a syntax tree for the `UNION` operation. - - Example: - >>> union("SELECT * FROM foo", "SELECT * FROM bla").sql() - 'SELECT * FROM foo UNION SELECT * FROM bla' - - Args: - expressions: the SQL code strings, corresponding to the `UNION`'s operands. - If `Expression` instances are passed, they will be used as-is. - distinct: set the DISTINCT flag if and only if this is true. - dialect: the dialect used to parse the input expression. - copy: whether to copy the expression. - opts: other options to use to parse the input expressions. - - Returns: - The new Union instance. - """ - assert len(expressions) >= 2, "At least two expressions are required by `union`." - return _apply_set_operation( - *expressions, - set_operation=Union, - distinct=distinct, - dialect=dialect, - copy=copy, - **opts, - ) - - -def intersect( - *expressions: ExpOrStr, - distinct: bool = True, - dialect: DialectType = None, - copy: bool = True, - **opts, -) -> Intersect: - """ - Initializes a syntax tree for the `INTERSECT` operation. - - Example: - >>> intersect("SELECT * FROM foo", "SELECT * FROM bla").sql() - 'SELECT * FROM foo INTERSECT SELECT * FROM bla' - - Args: - expressions: the SQL code strings, corresponding to the `INTERSECT`'s operands. - If `Expression` instances are passed, they will be used as-is. - distinct: set the DISTINCT flag if and only if this is true. - dialect: the dialect used to parse the input expression. - copy: whether to copy the expression. - opts: other options to use to parse the input expressions. - - Returns: - The new Intersect instance. - """ - assert len(expressions) >= 2, ( - "At least two expressions are required by `intersect`." - ) - return _apply_set_operation( - *expressions, - set_operation=Intersect, - distinct=distinct, - dialect=dialect, - copy=copy, - **opts, - ) - - -def except_( - *expressions: ExpOrStr, - distinct: bool = True, - dialect: DialectType = None, - copy: bool = True, - **opts, -) -> Except: - """ - Initializes a syntax tree for the `EXCEPT` operation. - - Example: - >>> except_("SELECT * FROM foo", "SELECT * FROM bla").sql() - 'SELECT * FROM foo EXCEPT SELECT * FROM bla' - - Args: - expressions: the SQL code strings, corresponding to the `EXCEPT`'s operands. - If `Expression` instances are passed, they will be used as-is. - distinct: set the DISTINCT flag if and only if this is true. - dialect: the dialect used to parse the input expression. - copy: whether to copy the expression. - opts: other options to use to parse the input expressions. - - Returns: - The new Except instance. - """ - assert len(expressions) >= 2, "At least two expressions are required by `except_`." - return _apply_set_operation( - *expressions, - set_operation=Except, - distinct=distinct, - dialect=dialect, - copy=copy, - **opts, - ) - - -def select(*expressions: ExpOrStr, dialect: DialectType = None, **opts) -> Select: - """ - Initializes a syntax tree from one or multiple SELECT expressions. - - Example: - >>> select("col1", "col2").from_("tbl").sql() - 'SELECT col1, col2 FROM tbl' - - Args: - *expressions: the SQL code string to parse as the expressions of a - SELECT statement. If an Expression instance is passed, this is used as-is. - dialect: the dialect used to parse the input expressions (in the case that an - input expression is a SQL string). - **opts: other options to use to parse the input expressions (again, in the case - that an input expression is a SQL string). - - Returns: - Select: the syntax tree for the SELECT statement. - """ - return Select().select(*expressions, dialect=dialect, **opts) - - -def from_(expression: ExpOrStr, dialect: DialectType = None, **opts) -> Select: - """ - Initializes a syntax tree from a FROM expression. - - Example: - >>> from_("tbl").select("col1", "col2").sql() - 'SELECT col1, col2 FROM tbl' - - Args: - *expression: the SQL code string to parse as the FROM expressions of a - SELECT statement. If an Expression instance is passed, this is used as-is. - dialect: the dialect used to parse the input expression (in the case that the - input expression is a SQL string). - **opts: other options to use to parse the input expressions (again, in the case - that the input expression is a SQL string). - - Returns: - Select: the syntax tree for the SELECT statement. - """ - return Select().from_(expression, dialect=dialect, **opts) - - -def update( - table: str | Table, - properties: t.Optional[dict] = None, - where: t.Optional[ExpOrStr] = None, - from_: t.Optional[ExpOrStr] = None, - with_: t.Optional[t.Dict[str, ExpOrStr]] = None, - dialect: DialectType = None, - **opts, -) -> Update: - """ - Creates an update statement. - - Example: - >>> update("my_table", {"x": 1, "y": "2", "z": None}, from_="baz_cte", where="baz_cte.id > 1 and my_table.id = baz_cte.id", with_={"baz_cte": "SELECT id FROM foo"}).sql() - "WITH baz_cte AS (SELECT id FROM foo) UPDATE my_table SET x = 1, y = '2', z = NULL FROM baz_cte WHERE baz_cte.id > 1 AND my_table.id = baz_cte.id" - - Args: - properties: dictionary of properties to SET which are - auto converted to sql objects eg None -> NULL - where: sql conditional parsed into a WHERE statement - from_: sql statement parsed into a FROM statement - with_: dictionary of CTE aliases / select statements to include in a WITH clause. - dialect: the dialect used to parse the input expressions. - **opts: other options to use to parse the input expressions. - - Returns: - Update: the syntax tree for the UPDATE statement. - """ - update_expr = Update(this=maybe_parse(table, into=Table, dialect=dialect)) - if properties: - update_expr.set( - "expressions", - [ - EQ(this=maybe_parse(k, dialect=dialect, **opts), expression=convert(v)) - for k, v in properties.items() - ], - ) - if from_: - update_expr.set( - "from_", - maybe_parse(from_, into=From, dialect=dialect, prefix="FROM", **opts), - ) - if isinstance(where, Condition): - where = Where(this=where) - if where: - update_expr.set( - "where", - maybe_parse(where, into=Where, dialect=dialect, prefix="WHERE", **opts), - ) - if with_: - cte_list = [ - alias_( - CTE(this=maybe_parse(qry, dialect=dialect, **opts)), alias, table=True - ) - for alias, qry in with_.items() - ] - update_expr.set( - "with_", - With(expressions=cte_list), - ) - return update_expr - - -def delete( - table: ExpOrStr, - where: t.Optional[ExpOrStr] = None, - returning: t.Optional[ExpOrStr] = None, - dialect: DialectType = None, - **opts, -) -> Delete: - """ - Builds a delete statement. - - Example: - >>> delete("my_table", where="id > 1").sql() - 'DELETE FROM my_table WHERE id > 1' - - Args: - where: sql conditional parsed into a WHERE statement - returning: sql conditional parsed into a RETURNING statement - dialect: the dialect used to parse the input expressions. - **opts: other options to use to parse the input expressions. - - Returns: - Delete: the syntax tree for the DELETE statement. - """ - delete_expr = Delete().delete(table, dialect=dialect, copy=False, **opts) - if where: - delete_expr = delete_expr.where(where, dialect=dialect, copy=False, **opts) - if returning: - delete_expr = delete_expr.returning( - returning, dialect=dialect, copy=False, **opts - ) - return delete_expr - - -def insert( - expression: ExpOrStr, - into: ExpOrStr, - columns: t.Optional[t.Sequence[str | Identifier]] = None, - overwrite: t.Optional[bool] = None, - returning: t.Optional[ExpOrStr] = None, - dialect: DialectType = None, - copy: bool = True, - **opts, -) -> Insert: - """ - Builds an INSERT statement. - - Example: - >>> insert("VALUES (1, 2, 3)", "tbl").sql() - 'INSERT INTO tbl VALUES (1, 2, 3)' - - Args: - expression: the sql string or expression of the INSERT statement - into: the tbl to insert data to. - columns: optionally the table's column names. - overwrite: whether to INSERT OVERWRITE or not. - returning: sql conditional parsed into a RETURNING statement - dialect: the dialect used to parse the input expressions. - copy: whether to copy the expression. - **opts: other options to use to parse the input expressions. - - Returns: - Insert: the syntax tree for the INSERT statement. - """ - expr = maybe_parse(expression, dialect=dialect, copy=copy, **opts) - this: Table | Schema = maybe_parse( - into, into=Table, dialect=dialect, copy=copy, **opts - ) - - if columns: - this = Schema( - this=this, expressions=[to_identifier(c, copy=copy) for c in columns] - ) - - insert = Insert(this=this, expression=expr, overwrite=overwrite) - - if returning: - insert = insert.returning(returning, dialect=dialect, copy=False, **opts) - - return insert - - -def merge( - *when_exprs: ExpOrStr, - into: ExpOrStr, - using: ExpOrStr, - on: ExpOrStr, - returning: t.Optional[ExpOrStr] = None, - dialect: DialectType = None, - copy: bool = True, - **opts, -) -> Merge: - """ - Builds a MERGE statement. - - Example: - >>> merge("WHEN MATCHED THEN UPDATE SET col1 = source_table.col1", - ... "WHEN NOT MATCHED THEN INSERT (col1) VALUES (source_table.col1)", - ... into="my_table", - ... using="source_table", - ... on="my_table.id = source_table.id").sql() - 'MERGE INTO my_table USING source_table ON my_table.id = source_table.id WHEN MATCHED THEN UPDATE SET col1 = source_table.col1 WHEN NOT MATCHED THEN INSERT (col1) VALUES (source_table.col1)' - - Args: - *when_exprs: The WHEN clauses specifying actions for matched and unmatched rows. - into: The target table to merge data into. - using: The source table to merge data from. - on: The join condition for the merge. - returning: The columns to return from the merge. - dialect: The dialect used to parse the input expressions. - copy: Whether to copy the expression. - **opts: Other options to use to parse the input expressions. - - Returns: - Merge: The syntax tree for the MERGE statement. - """ - expressions: t.List[Expression] = [] - for when_expr in when_exprs: - expression = maybe_parse( - when_expr, dialect=dialect, copy=copy, into=Whens, **opts - ) - expressions.extend( - [expression] if isinstance(expression, When) else expression.expressions - ) - - merge = Merge( - this=maybe_parse(into, dialect=dialect, copy=copy, **opts), - using=maybe_parse(using, dialect=dialect, copy=copy, **opts), - on=maybe_parse(on, dialect=dialect, copy=copy, **opts), - whens=Whens(expressions=expressions), - ) - if returning: - merge = merge.returning(returning, dialect=dialect, copy=False, **opts) - - if isinstance(using_clause := merge.args.get("using"), Alias): - using_clause.replace( - alias_(using_clause.this, using_clause.args["alias"], table=True) - ) - - return merge - - -def condition( - expression: ExpOrStr, dialect: DialectType = None, copy: bool = True, **opts -) -> Condition: - """ - Initialize a logical condition expression. - - Example: - >>> condition("x=1").sql() - 'x = 1' - - This is helpful for composing larger logical syntax trees: - >>> where = condition("x=1") - >>> where = where.and_("y=1") - >>> Select().from_("tbl").select("*").where(where).sql() - 'SELECT * FROM tbl WHERE x = 1 AND y = 1' - - Args: - *expression: the SQL code string to parse. - If an Expression instance is passed, this is used as-is. - dialect: the dialect used to parse the input expression (in the case that the - input expression is a SQL string). - copy: Whether to copy `expression` (only applies to expressions). - **opts: other options to use to parse the input expressions (again, in the case - that the input expression is a SQL string). - - Returns: - The new Condition instance - """ - return maybe_parse( - expression, - into=Condition, - dialect=dialect, - copy=copy, - **opts, - ) - - -def and_( - *expressions: t.Optional[ExpOrStr], - dialect: DialectType = None, - copy: bool = True, - wrap: bool = True, - **opts, -) -> Condition: - """ - Combine multiple conditions with an AND logical operator. - - Example: - >>> and_("x=1", and_("y=1", "z=1")).sql() - 'x = 1 AND (y = 1 AND z = 1)' - - Args: - *expressions: the SQL code strings to parse. - If an Expression instance is passed, this is used as-is. - dialect: the dialect used to parse the input expression. - copy: whether to copy `expressions` (only applies to Expressions). - wrap: whether to wrap the operands in `Paren`s. This is true by default to avoid - precedence issues, but can be turned off when the produced AST is too deep and - causes recursion-related issues. - **opts: other options to use to parse the input expressions. - - Returns: - The new condition - """ - return t.cast( - Condition, _combine(expressions, And, dialect, copy=copy, wrap=wrap, **opts) - ) - - -def or_( - *expressions: t.Optional[ExpOrStr], - dialect: DialectType = None, - copy: bool = True, - wrap: bool = True, - **opts, -) -> Condition: - """ - Combine multiple conditions with an OR logical operator. - - Example: - >>> or_("x=1", or_("y=1", "z=1")).sql() - 'x = 1 OR (y = 1 OR z = 1)' - - Args: - *expressions: the SQL code strings to parse. - If an Expression instance is passed, this is used as-is. - dialect: the dialect used to parse the input expression. - copy: whether to copy `expressions` (only applies to Expressions). - wrap: whether to wrap the operands in `Paren`s. This is true by default to avoid - precedence issues, but can be turned off when the produced AST is too deep and - causes recursion-related issues. - **opts: other options to use to parse the input expressions. - - Returns: - The new condition - """ - return t.cast( - Condition, _combine(expressions, Or, dialect, copy=copy, wrap=wrap, **opts) - ) - - -def xor( - *expressions: t.Optional[ExpOrStr], - dialect: DialectType = None, - copy: bool = True, - wrap: bool = True, - **opts, -) -> Condition: - """ - Combine multiple conditions with an XOR logical operator. - - Example: - >>> xor("x=1", xor("y=1", "z=1")).sql() - 'x = 1 XOR (y = 1 XOR z = 1)' - - Args: - *expressions: the SQL code strings to parse. - If an Expression instance is passed, this is used as-is. - dialect: the dialect used to parse the input expression. - copy: whether to copy `expressions` (only applies to Expressions). - wrap: whether to wrap the operands in `Paren`s. This is true by default to avoid - precedence issues, but can be turned off when the produced AST is too deep and - causes recursion-related issues. - **opts: other options to use to parse the input expressions. - - Returns: - The new condition - """ - return t.cast( - Condition, _combine(expressions, Xor, dialect, copy=copy, wrap=wrap, **opts) - ) - - -def not_( - expression: ExpOrStr, dialect: DialectType = None, copy: bool = True, **opts -) -> Not: - """ - Wrap a condition with a NOT operator. - - Example: - >>> not_("this_suit='black'").sql() - "NOT this_suit = 'black'" - - Args: - expression: the SQL code string to parse. - If an Expression instance is passed, this is used as-is. - dialect: the dialect used to parse the input expression. - copy: whether to copy the expression or not. - **opts: other options to use to parse the input expressions. - - Returns: - The new condition. - """ - this = condition( - expression, - dialect=dialect, - copy=copy, - **opts, - ) - return Not(this=_wrap(this, Connector)) - - -def paren(expression: ExpOrStr, copy: bool = True) -> Paren: - """ - Wrap an expression in parentheses. - - Example: - >>> paren("5 + 3").sql() - '(5 + 3)' - - Args: - expression: the SQL code string to parse. - If an Expression instance is passed, this is used as-is. - copy: whether to copy the expression or not. - - Returns: - The wrapped expression. - """ - return Paren(this=maybe_parse(expression, copy=copy)) - - -SAFE_IDENTIFIER_RE: t.Pattern[str] = re.compile(r"^[_a-zA-Z][\w]*$") - - -@t.overload -def to_identifier( - name: None, quoted: t.Optional[bool] = None, copy: bool = True -) -> None: ... - - -@t.overload -def to_identifier( - name: str | Identifier, quoted: t.Optional[bool] = None, copy: bool = True -) -> Identifier: ... - - -def to_identifier(name, quoted=None, copy=True): - """Builds an identifier. - - Args: - name: The name to turn into an identifier. - quoted: Whether to force quote the identifier. - copy: Whether to copy name if it's an Identifier. - - Returns: - The identifier ast node. - """ - - if name is None: - return None - - if isinstance(name, Identifier): - identifier = maybe_copy(name, copy) - elif isinstance(name, str): - identifier = Identifier( - this=name, - quoted=not SAFE_IDENTIFIER_RE.match(name) if quoted is None else quoted, - ) - else: - raise ValueError( - f"Name needs to be a string or an Identifier, got: {name.__class__}" - ) - return identifier - - -def parse_identifier(name: str | Identifier, dialect: DialectType = None) -> Identifier: - """ - Parses a given string into an identifier. - - Args: - name: The name to parse into an identifier. - dialect: The dialect to parse against. - - Returns: - The identifier ast node. - """ - try: - expression = maybe_parse(name, dialect=dialect, into=Identifier) - except (ParseError, TokenError): - expression = to_identifier(name) - - return expression - - -INTERVAL_STRING_RE = re.compile(r"\s*(-?[0-9]+(?:\.[0-9]+)?)\s*([a-zA-Z]+)\s*") - -# Matches day-time interval strings that contain -# - A number of days (possibly negative or with decimals) -# - At least one space -# - Portions of a time-like signature, potentially negative -# - Standard format [-]h+:m+:s+[.f+] -# - Just minutes/seconds/frac seconds [-]m+:s+.f+ -# - Just hours, minutes, maybe colon [-]h+:m+[:] -# - Just hours, maybe colon [-]h+[:] -# - Just colon : -INTERVAL_DAY_TIME_RE = re.compile( - r"\s*-?\s*\d+(?:\.\d+)?\s+(?:-?(?:\d+:)?\d+:\d+(?:\.\d+)?|-?(?:\d+:){1,2}|:)\s*" -) - - -def to_interval(interval: str | Literal) -> Interval: - """Builds an interval expression from a string like '1 day' or '5 months'.""" - if isinstance(interval, Literal): - if not interval.is_string: - raise ValueError("Invalid interval string.") - - interval = interval.this - - interval = maybe_parse(f"INTERVAL {interval}") - assert isinstance(interval, Interval) - return interval - - -def to_table( - sql_path: str | Table, dialect: DialectType = None, copy: bool = True, **kwargs -) -> Table: - """ - Create a table expression from a `[catalog].[schema].[table]` sql path. Catalog and schema are optional. - If a table is passed in then that table is returned. - - Args: - sql_path: a `[catalog].[schema].[table]` string. - dialect: the source dialect according to which the table name will be parsed. - copy: Whether to copy a table if it is passed in. - kwargs: the kwargs to instantiate the resulting `Table` expression with. - - Returns: - A table expression. - """ - if isinstance(sql_path, Table): - return maybe_copy(sql_path, copy=copy) - - try: - table = maybe_parse(sql_path, into=Table, dialect=dialect) - except ParseError: - catalog, db, this = split_num_words(sql_path, ".", 3) - - if not this: - raise - - table = table_(this, db=db, catalog=catalog) - - for k, v in kwargs.items(): - table.set(k, v) - - return table - - -def to_column( - sql_path: str | Column, - quoted: t.Optional[bool] = None, - dialect: DialectType = None, - copy: bool = True, - **kwargs, -) -> Column: - """ - Create a column from a `[table].[column]` sql path. Table is optional. - If a column is passed in then that column is returned. - - Args: - sql_path: a `[table].[column]` string. - quoted: Whether or not to force quote identifiers. - dialect: the source dialect according to which the column name will be parsed. - copy: Whether to copy a column if it is passed in. - kwargs: the kwargs to instantiate the resulting `Column` expression with. - - Returns: - A column expression. - """ - if isinstance(sql_path, Column): - return maybe_copy(sql_path, copy=copy) - - try: - col = maybe_parse(sql_path, into=Column, dialect=dialect) - except ParseError: - return column(*reversed(sql_path.split(".")), quoted=quoted, **kwargs) - - for k, v in kwargs.items(): - col.set(k, v) - - if quoted: - for i in col.find_all(Identifier): - i.set("quoted", True) - - return col - - -def alias_( - expression: ExpOrStr, - alias: t.Optional[str | Identifier], - table: bool | t.Sequence[str | Identifier] = False, - quoted: t.Optional[bool] = None, - dialect: DialectType = None, - copy: bool = True, - **opts, -): - """Create an Alias expression. - - Example: - >>> alias_('foo', 'bar').sql() - 'foo AS bar' - - >>> alias_('(select 1, 2)', 'bar', table=['a', 'b']).sql() - '(SELECT 1, 2) AS bar(a, b)' - - Args: - expression: the SQL code strings to parse. - If an Expression instance is passed, this is used as-is. - alias: the alias name to use. If the name has - special characters it is quoted. - table: Whether to create a table alias, can also be a list of columns. - quoted: whether to quote the alias - dialect: the dialect used to parse the input expression. - copy: Whether to copy the expression. - **opts: other options to use to parse the input expressions. - - Returns: - Alias: the aliased expression - """ - exp = maybe_parse(expression, dialect=dialect, copy=copy, **opts) - alias = to_identifier(alias, quoted=quoted) - - if table: - table_alias = TableAlias(this=alias) - exp.set("alias", table_alias) - - if not isinstance(table, bool): - for column in table: - table_alias.append("columns", to_identifier(column, quoted=quoted)) - - return exp - - # We don't set the "alias" arg for Window expressions, because that would add an IDENTIFIER node in - # the AST, representing a "named_window" [1] construct (eg. bigquery). What we want is an ALIAS node - # for the complete Window expression. - # - # [1]: https://cloud.google.com/bigquery/docs/reference/standard-sql/window-function-calls - - if "alias" in exp.arg_types and not isinstance(exp, Window): - exp.set("alias", alias) - return exp - return Alias(this=exp, alias=alias) - - -def subquery( - expression: ExpOrStr, - alias: t.Optional[Identifier | str] = None, - dialect: DialectType = None, - **opts, -) -> Select: - """ - Build a subquery expression that's selected from. - - Example: - >>> subquery('select x from tbl', 'bar').select('x').sql() - 'SELECT x FROM (SELECT x FROM tbl) AS bar' - - Args: - expression: the SQL code strings to parse. - If an Expression instance is passed, this is used as-is. - alias: the alias name to use. - dialect: the dialect used to parse the input expression. - **opts: other options to use to parse the input expressions. - - Returns: - A new Select instance with the subquery expression included. - """ - - expression = maybe_parse(expression, dialect=dialect, **opts).subquery( - alias, **opts - ) - return Select().from_(expression, dialect=dialect, **opts) - - -@t.overload -def column( - col: str | Identifier, - table: t.Optional[str | Identifier] = None, - db: t.Optional[str | Identifier] = None, - catalog: t.Optional[str | Identifier] = None, - *, - fields: t.Collection[t.Union[str, Identifier]], - quoted: t.Optional[bool] = None, - copy: bool = True, -) -> Dot: - pass - - -@t.overload -def column( - col: str | Identifier | Star, - table: t.Optional[str | Identifier] = None, - db: t.Optional[str | Identifier] = None, - catalog: t.Optional[str | Identifier] = None, - *, - fields: Lit[None] = None, - quoted: t.Optional[bool] = None, - copy: bool = True, -) -> Column: - pass - - -def column( - col, - table=None, - db=None, - catalog=None, - *, - fields=None, - quoted=None, - copy=True, -): - """ - Build a Column. - - Args: - col: Column name. - table: Table name. - db: Database name. - catalog: Catalog name. - fields: Additional fields using dots. - quoted: Whether to force quotes on the column's identifiers. - copy: Whether to copy identifiers if passed in. - - Returns: - The new Column instance. - """ - if not isinstance(col, Star): - col = to_identifier(col, quoted=quoted, copy=copy) - - this = Column( - this=col, - table=to_identifier(table, quoted=quoted, copy=copy), - db=to_identifier(db, quoted=quoted, copy=copy), - catalog=to_identifier(catalog, quoted=quoted, copy=copy), - ) - - if fields: - this = Dot.build( - ( - this, - *(to_identifier(field, quoted=quoted, copy=copy) for field in fields), - ) - ) - return this - - -def cast( - expression: ExpOrStr, - to: DATA_TYPE, - copy: bool = True, - dialect: DialectType = None, - **opts, -) -> Cast: - """Cast an expression to a data type. - - Example: - >>> cast('x + 1', 'int').sql() - 'CAST(x + 1 AS INT)' - - Args: - expression: The expression to cast. - to: The datatype to cast to. - copy: Whether to copy the supplied expressions. - dialect: The target dialect. This is used to prevent a re-cast in the following scenario: - - The expression to be cast is already a exp.Cast expression - - The existing cast is to a type that is logically equivalent to new type - - For example, if :expression='CAST(x as DATETIME)' and :to=Type.TIMESTAMP, - but in the target dialect DATETIME is mapped to TIMESTAMP, then we will NOT return `CAST(x (as DATETIME) as TIMESTAMP)` - and instead just return the original expression `CAST(x as DATETIME)`. - - This is to prevent it being output as a double cast `CAST(x (as TIMESTAMP) as TIMESTAMP)` once the DATETIME -> TIMESTAMP - mapping is applied in the target dialect generator. - - Returns: - The new Cast instance. - """ - expr = maybe_parse(expression, copy=copy, dialect=dialect, **opts) - data_type = DataType.build(to, copy=copy, dialect=dialect, **opts) - - # dont re-cast if the expression is already a cast to the correct type - if isinstance(expr, Cast): - from bigframes_vendored.sqlglot.dialects.dialect import Dialect - - target_dialect = Dialect.get_or_raise(dialect) - type_mapping = target_dialect.generator_class.TYPE_MAPPING - - existing_cast_type: DataType.Type = expr.to.this - new_cast_type: DataType.Type = data_type.this - types_are_equivalent = type_mapping.get( - existing_cast_type, existing_cast_type.value - ) == type_mapping.get(new_cast_type, new_cast_type.value) - - if expr.is_type(data_type) or types_are_equivalent: - return expr - - expr = Cast(this=expr, to=data_type) - expr.type = data_type - - return expr - - -def table_( - table: Identifier | str, - db: t.Optional[Identifier | str] = None, - catalog: t.Optional[Identifier | str] = None, - quoted: t.Optional[bool] = None, - alias: t.Optional[Identifier | str] = None, -) -> Table: - """Build a Table. - - Args: - table: Table name. - db: Database name. - catalog: Catalog name. - quote: Whether to force quotes on the table's identifiers. - alias: Table's alias. - - Returns: - The new Table instance. - """ - return Table( - this=to_identifier(table, quoted=quoted) if table else None, - db=to_identifier(db, quoted=quoted) if db else None, - catalog=to_identifier(catalog, quoted=quoted) if catalog else None, - alias=TableAlias(this=to_identifier(alias)) if alias else None, - ) - - -def values( - values: t.Iterable[t.Tuple[t.Any, ...]], - alias: t.Optional[str] = None, - columns: t.Optional[t.Iterable[str] | t.Dict[str, DataType]] = None, -) -> Values: - """Build VALUES statement. - - Example: - >>> values([(1, '2')]).sql() - "VALUES (1, '2')" - - Args: - values: values statements that will be converted to SQL - alias: optional alias - columns: Optional list of ordered column names or ordered dictionary of column names to types. - If either are provided then an alias is also required. - - Returns: - Values: the Values expression object - """ - if columns and not alias: - raise ValueError("Alias is required when providing columns") - - return Values( - expressions=[convert(tup) for tup in values], - alias=( - TableAlias( - this=to_identifier(alias), columns=[to_identifier(x) for x in columns] - ) - if columns - else (TableAlias(this=to_identifier(alias)) if alias else None) - ), - ) - - -def var(name: t.Optional[ExpOrStr]) -> Var: - """Build a SQL variable. - - Example: - >>> repr(var('x')) - 'Var(this=x)' - - >>> repr(var(column('x', table='y'))) - 'Var(this=x)' - - Args: - name: The name of the var or an expression who's name will become the var. - - Returns: - The new variable node. - """ - if not name: - raise ValueError("Cannot convert empty name into var.") - - if isinstance(name, Expression): - name = name.name - return Var(this=name) - - -def rename_table( - old_name: str | Table, - new_name: str | Table, - dialect: DialectType = None, -) -> Alter: - """Build ALTER TABLE... RENAME... expression - - Args: - old_name: The old name of the table - new_name: The new name of the table - dialect: The dialect to parse the table. - - Returns: - Alter table expression - """ - old_table = to_table(old_name, dialect=dialect) - new_table = to_table(new_name, dialect=dialect) - return Alter( - this=old_table, - kind="TABLE", - actions=[ - AlterRename(this=new_table), - ], - ) - - -def rename_column( - table_name: str | Table, - old_column_name: str | Column, - new_column_name: str | Column, - exists: t.Optional[bool] = None, - dialect: DialectType = None, -) -> Alter: - """Build ALTER TABLE... RENAME COLUMN... expression - - Args: - table_name: Name of the table - old_column: The old name of the column - new_column: The new name of the column - exists: Whether to add the `IF EXISTS` clause - dialect: The dialect to parse the table/column. - - Returns: - Alter table expression - """ - table = to_table(table_name, dialect=dialect) - old_column = to_column(old_column_name, dialect=dialect) - new_column = to_column(new_column_name, dialect=dialect) - return Alter( - this=table, - kind="TABLE", - actions=[ - RenameColumn(this=old_column, to=new_column, exists=exists), - ], - ) - - -def convert(value: t.Any, copy: bool = False) -> Expression: - """Convert a python value into an expression object. - - Raises an error if a conversion is not possible. - - Args: - value: A python object. - copy: Whether to copy `value` (only applies to Expressions and collections). - - Returns: - The equivalent expression object. - """ - if isinstance(value, Expression): - return maybe_copy(value, copy) - if isinstance(value, str): - return Literal.string(value) - if isinstance(value, bool): - return Boolean(this=value) - if value is None or (isinstance(value, float) and math.isnan(value)): - return null() - if isinstance(value, numbers.Number): - return Literal.number(value) - if isinstance(value, bytes): - return HexString(this=value.hex()) - if isinstance(value, datetime.datetime): - datetime_literal = Literal.string(value.isoformat(sep=" ")) - - tz = None - if value.tzinfo: - # this works for zoneinfo.ZoneInfo, pytz.timezone and datetime.datetime.utc to return IANA timezone names like "America/Los_Angeles" - # instead of abbreviations like "PDT". This is for consistency with other timezone handling functions in SQLGlot - tz = Literal.string(str(value.tzinfo)) - - return TimeStrToTime(this=datetime_literal, zone=tz) - if isinstance(value, datetime.date): - date_literal = Literal.string(value.strftime("%Y-%m-%d")) - return DateStrToDate(this=date_literal) - if isinstance(value, datetime.time): - time_literal = Literal.string(value.isoformat()) - return TsOrDsToTime(this=time_literal) - if isinstance(value, tuple): - if hasattr(value, "_fields"): - return Struct( - expressions=[ - PropertyEQ( - this=to_identifier(k), - expression=convert(getattr(value, k), copy=copy), - ) - for k in value._fields - ] - ) - return Tuple(expressions=[convert(v, copy=copy) for v in value]) - if isinstance(value, list): - return Array(expressions=[convert(v, copy=copy) for v in value]) - if isinstance(value, dict): - return Map( - keys=Array(expressions=[convert(k, copy=copy) for k in value]), - values=Array(expressions=[convert(v, copy=copy) for v in value.values()]), - ) - if hasattr(value, "__dict__"): - return Struct( - expressions=[ - PropertyEQ(this=to_identifier(k), expression=convert(v, copy=copy)) - for k, v in value.__dict__.items() - ] - ) - raise ValueError(f"Cannot convert {value}") - - -def replace_children(expression: Expression, fun: t.Callable, *args, **kwargs) -> None: - """ - Replace children of an expression with the result of a lambda fun(child) -> exp. - """ - for k, v in tuple(expression.args.items()): - is_list_arg = type(v) is list - - child_nodes = v if is_list_arg else [v] - new_child_nodes = [] - - for cn in child_nodes: - if isinstance(cn, Expression): - for child_node in ensure_collection(fun(cn, *args, **kwargs)): - new_child_nodes.append(child_node) - else: - new_child_nodes.append(cn) - - expression.set( - k, new_child_nodes if is_list_arg else seq_get(new_child_nodes, 0) - ) - - -def replace_tree( - expression: Expression, - fun: t.Callable, - prune: t.Optional[t.Callable[[Expression], bool]] = None, -) -> Expression: - """ - Replace an entire tree with the result of function calls on each node. - - This will be traversed in reverse dfs, so leaves first. - If new nodes are created as a result of function calls, they will also be traversed. - """ - stack = list(expression.dfs(prune=prune)) - - while stack: - node = stack.pop() - new_node = fun(node) - - if new_node is not node: - node.replace(new_node) - - if isinstance(new_node, Expression): - stack.append(new_node) - - return new_node - - -def find_tables(expression: Expression) -> t.Set[Table]: - """ - Find all tables referenced in a query. - - Args: - expressions: The query to find the tables in. - - Returns: - A set of all the tables. - """ - from bigframes_vendored.sqlglot.optimizer.scope import traverse_scope - - return { - table - for scope in traverse_scope(expression) - for table in scope.tables - if table.name and table.name not in scope.cte_sources - } - - -def column_table_names(expression: Expression, exclude: str = "") -> t.Set[str]: - """ - Return all table names referenced through columns in an expression. - - Example: - >>> import sqlglot - >>> sorted(column_table_names(sqlglot.parse_one("a.b AND c.d AND c.e"))) - ['a', 'c'] - - Args: - expression: expression to find table names. - exclude: a table name to exclude - - Returns: - A list of unique names. - """ - return { - table - for table in (column.table for column in expression.find_all(Column)) - if table and table != exclude - } - - -def table_name( - table: Table | str, dialect: DialectType = None, identify: bool = False -) -> str: - """Get the full name of a table as a string. - - Args: - table: Table expression node or string. - dialect: The dialect to generate the table name for. - identify: Determines when an identifier should be quoted. Possible values are: - False (default): Never quote, except in cases where it's mandatory by the dialect. - True: Always quote. - - Examples: - >>> from sqlglot import exp, parse_one - >>> table_name(parse_one("select * from a.b.c").find(exp.Table)) - 'a.b.c' - - Returns: - The table name. - """ - - table = maybe_parse(table, into=Table, dialect=dialect) - - if not table: - raise ValueError(f"Cannot parse {table}") - - return ".".join( - ( - part.sql(dialect=dialect, identify=True, copy=False, comments=False) - if identify or not SAFE_IDENTIFIER_RE.match(part.name) - else part.name - ) - for part in table.parts - ) - - -def normalize_table_name( - table: str | Table, dialect: DialectType = None, copy: bool = True -) -> str: - """Returns a case normalized table name without quotes. - - Args: - table: the table to normalize - dialect: the dialect to use for normalization rules - copy: whether to copy the expression. - - Examples: - >>> normalize_table_name("`A-B`.c", dialect="bigquery") - 'A-B.c' - """ - from bigframes_vendored.sqlglot.optimizer.normalize_identifiers import ( - normalize_identifiers, - ) - - return ".".join( - p.name - for p in normalize_identifiers( - to_table(table, dialect=dialect, copy=copy), dialect=dialect - ).parts - ) - - -def replace_tables( - expression: E, - mapping: t.Dict[str, str], - dialect: DialectType = None, - copy: bool = True, -) -> E: - """Replace all tables in expression according to the mapping. - - Args: - expression: expression node to be transformed and replaced. - mapping: mapping of table names. - dialect: the dialect of the mapping table - copy: whether to copy the expression. - - Examples: - >>> from sqlglot import exp, parse_one - >>> replace_tables(parse_one("select * from a.b"), {"a.b": "c"}).sql() - 'SELECT * FROM c /* a.b */' - - Returns: - The mapped expression. - """ - - mapping = {normalize_table_name(k, dialect=dialect): v for k, v in mapping.items()} - - def _replace_tables(node: Expression) -> Expression: - if isinstance(node, Table) and node.meta.get("replace") is not False: - original = normalize_table_name(node, dialect=dialect) - new_name = mapping.get(original) - - if new_name: - table = to_table( - new_name, - **{k: v for k, v in node.args.items() if k not in TABLE_PARTS}, - dialect=dialect, - ) - table.add_comments([original]) - return table - return node - - return expression.transform(_replace_tables, copy=copy) # type: ignore - - -def replace_placeholders(expression: Expression, *args, **kwargs) -> Expression: - """Replace placeholders in an expression. - - Args: - expression: expression node to be transformed and replaced. - args: positional names that will substitute unnamed placeholders in the given order. - kwargs: keyword arguments that will substitute named placeholders. - - Examples: - >>> from sqlglot import exp, parse_one - >>> replace_placeholders( - ... parse_one("select * from :tbl where ? = ?"), - ... exp.to_identifier("str_col"), "b", tbl=exp.to_identifier("foo") - ... ).sql() - "SELECT * FROM foo WHERE str_col = 'b'" - - Returns: - The mapped expression. - """ - - def _replace_placeholders(node: Expression, args, **kwargs) -> Expression: - if isinstance(node, Placeholder): - if node.this: - new_name = kwargs.get(node.this) - if new_name is not None: - return convert(new_name) - else: - try: - return convert(next(args)) - except StopIteration: - pass - return node - - return expression.transform(_replace_placeholders, iter(args), **kwargs) - - -def expand( - expression: Expression, - sources: t.Dict[str, Query | t.Callable[[], Query]], - dialect: DialectType = None, - copy: bool = True, -) -> Expression: - """Transforms an expression by expanding all referenced sources into subqueries. - - Examples: - >>> from sqlglot import parse_one - >>> expand(parse_one("select * from x AS z"), {"x": parse_one("select * from y")}).sql() - 'SELECT * FROM (SELECT * FROM y) AS z /* source: x */' - - >>> expand(parse_one("select * from x AS z"), {"x": parse_one("select * from y"), "y": parse_one("select * from z")}).sql() - 'SELECT * FROM (SELECT * FROM (SELECT * FROM z) AS y /* source: y */) AS z /* source: x */' - - Args: - expression: The expression to expand. - sources: A dict of name to query or a callable that provides a query on demand. - dialect: The dialect of the sources dict or the callable. - copy: Whether to copy the expression during transformation. Defaults to True. - - Returns: - The transformed expression. - """ - normalized_sources = { - normalize_table_name(k, dialect=dialect): v for k, v in sources.items() - } - - def _expand(node: Expression): - if isinstance(node, Table): - name = normalize_table_name(node, dialect=dialect) - source = normalized_sources.get(name) - - if source: - # Create a subquery with the same alias (or table name if no alias) - parsed_source = source() if callable(source) else source - subquery = parsed_source.subquery(node.alias or name) - subquery.comments = [f"source: {name}"] - - # Continue expanding within the subquery - return subquery.transform(_expand, copy=False) - - return node - - return expression.transform(_expand, copy=copy) - - -def func( - name: str, *args, copy: bool = True, dialect: DialectType = None, **kwargs -) -> Func: - """ - Returns a Func expression. - - Examples: - >>> func("abs", 5).sql() - 'ABS(5)' - - >>> func("cast", this=5, to=DataType.build("DOUBLE")).sql() - 'CAST(5 AS DOUBLE)' - - Args: - name: the name of the function to build. - args: the args used to instantiate the function of interest. - copy: whether to copy the argument expressions. - dialect: the source dialect. - kwargs: the kwargs used to instantiate the function of interest. - - Note: - The arguments `args` and `kwargs` are mutually exclusive. - - Returns: - An instance of the function of interest, or an anonymous function, if `name` doesn't - correspond to an existing `sqlglot.expressions.Func` class. - """ - if args and kwargs: - raise ValueError("Can't use both args and kwargs to instantiate a function.") - - from bigframes_vendored.sqlglot.dialects.dialect import Dialect - - dialect = Dialect.get_or_raise(dialect) - - converted: t.List[Expression] = [ - maybe_parse(arg, dialect=dialect, copy=copy) for arg in args - ] - kwargs = { - key: maybe_parse(value, dialect=dialect, copy=copy) - for key, value in kwargs.items() - } - - constructor = dialect.parser_class.FUNCTIONS.get(name.upper()) - if constructor: - if converted: - if "dialect" in constructor.__code__.co_varnames: - function = constructor(converted, dialect=dialect) - else: - function = constructor(converted) - elif constructor.__name__ == "from_arg_list": - function = constructor.__self__(**kwargs) # type: ignore - else: - constructor = FUNCTION_BY_NAME.get(name.upper()) - if constructor: - function = constructor(**kwargs) - else: - raise ValueError( - f"Unable to convert '{name}' into a Func. Either manually construct " - "the Func expression of interest or parse the function call." - ) - else: - kwargs = kwargs or {"expressions": converted} - function = Anonymous(this=name, **kwargs) - - for error_message in function.error_messages(converted): - raise ValueError(error_message) - - return function - - -def case( - expression: t.Optional[ExpOrStr] = None, - **opts, -) -> Case: - """ - Initialize a CASE statement. - - Example: - case().when("a = 1", "foo").else_("bar") - - Args: - expression: Optionally, the input expression (not all dialects support this) - **opts: Extra keyword arguments for parsing `expression` - """ - if expression is not None: - this = maybe_parse(expression, **opts) - else: - this = None - return Case(this=this, ifs=[]) - - -def array( - *expressions: ExpOrStr, copy: bool = True, dialect: DialectType = None, **kwargs -) -> Array: - """ - Returns an array. - - Examples: - >>> array(1, 'x').sql() - 'ARRAY(1, x)' - - Args: - expressions: the expressions to add to the array. - copy: whether to copy the argument expressions. - dialect: the source dialect. - kwargs: the kwargs used to instantiate the function of interest. - - Returns: - An array expression. - """ - return Array( - expressions=[ - maybe_parse(expression, copy=copy, dialect=dialect, **kwargs) - for expression in expressions - ] - ) - - -def tuple_( - *expressions: ExpOrStr, copy: bool = True, dialect: DialectType = None, **kwargs -) -> Tuple: - """ - Returns an tuple. - - Examples: - >>> tuple_(1, 'x').sql() - '(1, x)' - - Args: - expressions: the expressions to add to the tuple. - copy: whether to copy the argument expressions. - dialect: the source dialect. - kwargs: the kwargs used to instantiate the function of interest. - - Returns: - A tuple expression. - """ - return Tuple( - expressions=[ - maybe_parse(expression, copy=copy, dialect=dialect, **kwargs) - for expression in expressions - ] - ) - - -def true() -> Boolean: - """ - Returns a true Boolean expression. - """ - return Boolean(this=True) - - -def false() -> Boolean: - """ - Returns a false Boolean expression. - """ - return Boolean(this=False) - - -def null() -> Null: - """ - Returns a Null expression. - """ - return Null() - - -NONNULL_CONSTANTS = ( - Literal, - Boolean, -) - -CONSTANTS = ( - Literal, - Boolean, - Null, -) diff --git a/third_party/bigframes_vendored/sqlglot/generator.py b/third_party/bigframes_vendored/sqlglot/generator.py deleted file mode 100644 index 80546fadc44..00000000000 --- a/third_party/bigframes_vendored/sqlglot/generator.py +++ /dev/null @@ -1,5850 +0,0 @@ -# Contains code from https://github.com/tobymao/sqlglot/blob/v28.5.0/sqlglot/generator.py - -from __future__ import annotations - -import logging -import re -import typing as t -from collections import defaultdict -from functools import reduce, wraps - -from bigframes_vendored.sqlglot import exp -from bigframes_vendored.sqlglot.errors import ( - ErrorLevel, - UnsupportedError, - concat_messages, -) -from bigframes_vendored.sqlglot.helper import ( - apply_index_offset, - csv, - name_sequence, - seq_get, -) -from bigframes_vendored.sqlglot.jsonpath import ( - ALL_JSON_PATH_PARTS, - JSON_PATH_PART_TRANSFORMS, -) -from bigframes_vendored.sqlglot.time import format_time -from bigframes_vendored.sqlglot.tokens import TokenType - -if t.TYPE_CHECKING: - from bigframes_vendored.sqlglot._typing import E - from bigframes_vendored.sqlglot.dialects.dialect import DialectType - - G = t.TypeVar("G", bound="Generator") - GeneratorMethod = t.Callable[[G, E], str] - -logger = logging.getLogger("sqlglot") - -ESCAPED_UNICODE_RE = re.compile(r"\\(\d+)") -UNSUPPORTED_TEMPLATE = ( - "Argument '{}' is not supported for expression '{}' when targeting {}." -) - - -def unsupported_args( - *args: t.Union[str, t.Tuple[str, str]], -) -> t.Callable[[GeneratorMethod], GeneratorMethod]: - """ - Decorator that can be used to mark certain args of an `Expression` subclass as unsupported. - It expects a sequence of argument names or pairs of the form (argument_name, diagnostic_msg). - """ - diagnostic_by_arg: t.Dict[str, t.Optional[str]] = {} - for arg in args: - if isinstance(arg, str): - diagnostic_by_arg[arg] = None - else: - diagnostic_by_arg[arg[0]] = arg[1] - - def decorator(func: GeneratorMethod) -> GeneratorMethod: - @wraps(func) - def _func(generator: G, expression: E) -> str: - expression_name = expression.__class__.__name__ - dialect_name = generator.dialect.__class__.__name__ - - for arg_name, diagnostic in diagnostic_by_arg.items(): - if expression.args.get(arg_name): - diagnostic = diagnostic or UNSUPPORTED_TEMPLATE.format( - arg_name, expression_name, dialect_name - ) - generator.unsupported(diagnostic) - - return func(generator, expression) - - return _func - - return decorator - - -class _Generator(type): - def __new__(cls, clsname, bases, attrs): - klass = super().__new__(cls, clsname, bases, attrs) - - # Remove transforms that correspond to unsupported JSONPathPart expressions - for part in ALL_JSON_PATH_PARTS - klass.SUPPORTED_JSON_PATH_PARTS: - klass.TRANSFORMS.pop(part, None) - - return klass - - -class Generator(metaclass=_Generator): - """ - Generator converts a given syntax tree to the corresponding SQL string. - - Args: - pretty: Whether to format the produced SQL string. - Default: False. - identify: Determines when an identifier should be quoted. Possible values are: - False (default): Never quote, except in cases where it's mandatory by the dialect. - True: Always quote except for specials cases. - 'safe': Only quote identifiers that are case insensitive. - normalize: Whether to normalize identifiers to lowercase. - Default: False. - pad: The pad size in a formatted string. For example, this affects the indentation of - a projection in a query, relative to its nesting level. - Default: 2. - indent: The indentation size in a formatted string. For example, this affects the - indentation of subqueries and filters under a `WHERE` clause. - Default: 2. - normalize_functions: How to normalize function names. Possible values are: - "upper" or True (default): Convert names to uppercase. - "lower": Convert names to lowercase. - False: Disables function name normalization. - unsupported_level: Determines the generator's behavior when it encounters unsupported expressions. - Default ErrorLevel.WARN. - max_unsupported: Maximum number of unsupported messages to include in a raised UnsupportedError. - This is only relevant if unsupported_level is ErrorLevel.RAISE. - Default: 3 - leading_comma: Whether the comma is leading or trailing in select expressions. - This is only relevant when generating in pretty mode. - Default: False - max_text_width: The max number of characters in a segment before creating new lines in pretty mode. - The default is on the smaller end because the length only represents a segment and not the true - line length. - Default: 80 - comments: Whether to preserve comments in the output SQL code. - Default: True - """ - - TRANSFORMS: t.Dict[t.Type[exp.Expression], t.Callable[..., str]] = { - **JSON_PATH_PART_TRANSFORMS, - exp.Adjacent: lambda self, e: self.binary(e, "-|-"), - exp.AllowedValuesProperty: lambda self, - e: f"ALLOWED_VALUES {self.expressions(e, flat=True)}", - exp.AnalyzeColumns: lambda self, e: self.sql(e, "this"), - exp.AnalyzeWith: lambda self, e: self.expressions(e, prefix="WITH ", sep=" "), - exp.ArrayContainsAll: lambda self, e: self.binary(e, "@>"), - exp.ArrayOverlaps: lambda self, e: self.binary(e, "&&"), - exp.AutoRefreshProperty: lambda self, e: f"AUTO REFRESH {self.sql(e, 'this')}", - exp.BackupProperty: lambda self, e: f"BACKUP {self.sql(e, 'this')}", - exp.CaseSpecificColumnConstraint: lambda _, - e: f"{'NOT ' if e.args.get('not_') else ''}CASESPECIFIC", - exp.Ceil: lambda self, e: self.ceil_floor(e), - exp.CharacterSetColumnConstraint: lambda self, - e: f"CHARACTER SET {self.sql(e, 'this')}", - exp.CharacterSetProperty: lambda self, - e: f"{'DEFAULT ' if e.args.get('default') else ''}CHARACTER SET={self.sql(e, 'this')}", - exp.ClusteredColumnConstraint: lambda self, - e: f"CLUSTERED ({self.expressions(e, 'this', indent=False)})", - exp.CollateColumnConstraint: lambda self, e: f"COLLATE {self.sql(e, 'this')}", - exp.CommentColumnConstraint: lambda self, e: f"COMMENT {self.sql(e, 'this')}", - exp.ConnectByRoot: lambda self, e: f"CONNECT_BY_ROOT {self.sql(e, 'this')}", - exp.ConvertToCharset: lambda self, e: self.func( - "CONVERT", e.this, e.args["dest"], e.args.get("source") - ), - exp.CopyGrantsProperty: lambda *_: "COPY GRANTS", - exp.CredentialsProperty: lambda self, - e: f"CREDENTIALS=({self.expressions(e, 'expressions', sep=' ')})", - exp.CurrentCatalog: lambda *_: "CURRENT_CATALOG", - exp.SessionUser: lambda *_: "SESSION_USER", - exp.DateFormatColumnConstraint: lambda self, e: f"FORMAT {self.sql(e, 'this')}", - exp.DefaultColumnConstraint: lambda self, e: f"DEFAULT {self.sql(e, 'this')}", - exp.DynamicProperty: lambda *_: "DYNAMIC", - exp.EmptyProperty: lambda *_: "EMPTY", - exp.EncodeColumnConstraint: lambda self, e: f"ENCODE {self.sql(e, 'this')}", - exp.EnviromentProperty: lambda self, - e: f"ENVIRONMENT ({self.expressions(e, flat=True)})", - exp.EphemeralColumnConstraint: lambda self, - e: f"EPHEMERAL{(' ' + self.sql(e, 'this')) if e.this else ''}", - exp.ExcludeColumnConstraint: lambda self, - e: f"EXCLUDE {self.sql(e, 'this').lstrip()}", - exp.ExecuteAsProperty: lambda self, e: self.naked_property(e), - exp.Except: lambda self, e: self.set_operations(e), - exp.ExternalProperty: lambda *_: "EXTERNAL", - exp.Floor: lambda self, e: self.ceil_floor(e), - exp.Get: lambda self, e: self.get_put_sql(e), - exp.GlobalProperty: lambda *_: "GLOBAL", - exp.HeapProperty: lambda *_: "HEAP", - exp.IcebergProperty: lambda *_: "ICEBERG", - exp.InheritsProperty: lambda self, - e: f"INHERITS ({self.expressions(e, flat=True)})", - exp.InlineLengthColumnConstraint: lambda self, - e: f"INLINE LENGTH {self.sql(e, 'this')}", - exp.InputModelProperty: lambda self, e: f"INPUT{self.sql(e, 'this')}", - exp.Intersect: lambda self, e: self.set_operations(e), - exp.IntervalSpan: lambda self, - e: f"{self.sql(e, 'this')} TO {self.sql(e, 'expression')}", - exp.Int64: lambda self, e: self.sql(exp.cast(e.this, exp.DataType.Type.BIGINT)), - exp.JSONBContainsAnyTopKeys: lambda self, e: self.binary(e, "?|"), - exp.JSONBContainsAllTopKeys: lambda self, e: self.binary(e, "?&"), - exp.JSONBDeleteAtPath: lambda self, e: self.binary(e, "#-"), - exp.LanguageProperty: lambda self, e: self.naked_property(e), - exp.LocationProperty: lambda self, e: self.naked_property(e), - exp.LogProperty: lambda _, e: f"{'NO ' if e.args.get('no') else ''}LOG", - exp.MaterializedProperty: lambda *_: "MATERIALIZED", - exp.NonClusteredColumnConstraint: lambda self, - e: f"NONCLUSTERED ({self.expressions(e, 'this', indent=False)})", - exp.NoPrimaryIndexProperty: lambda *_: "NO PRIMARY INDEX", - exp.NotForReplicationColumnConstraint: lambda *_: "NOT FOR REPLICATION", - exp.OnCommitProperty: lambda _, - e: f"ON COMMIT {'DELETE' if e.args.get('delete') else 'PRESERVE'} ROWS", - exp.OnProperty: lambda self, e: f"ON {self.sql(e, 'this')}", - exp.OnUpdateColumnConstraint: lambda self, - e: f"ON UPDATE {self.sql(e, 'this')}", - exp.Operator: lambda self, e: self.binary( - e, "" - ), # The operator is produced in `binary` - exp.OutputModelProperty: lambda self, e: f"OUTPUT{self.sql(e, 'this')}", - exp.ExtendsLeft: lambda self, e: self.binary(e, "&<"), - exp.ExtendsRight: lambda self, e: self.binary(e, "&>"), - exp.PathColumnConstraint: lambda self, e: f"PATH {self.sql(e, 'this')}", - exp.PartitionedByBucket: lambda self, e: self.func( - "BUCKET", e.this, e.expression - ), - exp.PartitionByTruncate: lambda self, e: self.func( - "TRUNCATE", e.this, e.expression - ), - exp.PivotAny: lambda self, e: f"ANY{self.sql(e, 'this')}", - exp.PositionalColumn: lambda self, e: f"#{self.sql(e, 'this')}", - exp.ProjectionPolicyColumnConstraint: lambda self, - e: f"PROJECTION POLICY {self.sql(e, 'this')}", - exp.ZeroFillColumnConstraint: lambda self, e: "ZEROFILL", - exp.Put: lambda self, e: self.get_put_sql(e), - exp.RemoteWithConnectionModelProperty: lambda self, - e: f"REMOTE WITH CONNECTION {self.sql(e, 'this')}", - exp.ReturnsProperty: lambda self, e: ( - "RETURNS NULL ON NULL INPUT" - if e.args.get("null") - else self.naked_property(e) - ), - exp.SampleProperty: lambda self, e: f"SAMPLE BY {self.sql(e, 'this')}", - exp.SecureProperty: lambda *_: "SECURE", - exp.SecurityProperty: lambda self, e: f"SECURITY {self.sql(e, 'this')}", - exp.SetConfigProperty: lambda self, e: self.sql(e, "this"), - exp.SetProperty: lambda _, e: f"{'MULTI' if e.args.get('multi') else ''}SET", - exp.SettingsProperty: lambda self, - e: f"SETTINGS{self.seg('')}{(self.expressions(e))}", - exp.SharingProperty: lambda self, e: f"SHARING={self.sql(e, 'this')}", - exp.SqlReadWriteProperty: lambda _, e: e.name, - exp.SqlSecurityProperty: lambda self, e: f"SQL SECURITY {self.sql(e, 'this')}", - exp.StabilityProperty: lambda _, e: e.name, - exp.Stream: lambda self, e: f"STREAM {self.sql(e, 'this')}", - exp.StreamingTableProperty: lambda *_: "STREAMING", - exp.StrictProperty: lambda *_: "STRICT", - exp.SwapTable: lambda self, e: f"SWAP WITH {self.sql(e, 'this')}", - exp.TableColumn: lambda self, e: self.sql(e.this), - exp.Tags: lambda self, e: f"TAG ({self.expressions(e, flat=True)})", - exp.TemporaryProperty: lambda *_: "TEMPORARY", - exp.TitleColumnConstraint: lambda self, e: f"TITLE {self.sql(e, 'this')}", - exp.ToMap: lambda self, e: f"MAP {self.sql(e, 'this')}", - exp.ToTableProperty: lambda self, e: f"TO {self.sql(e.this)}", - exp.TransformModelProperty: lambda self, e: self.func( - "TRANSFORM", *e.expressions - ), - exp.TransientProperty: lambda *_: "TRANSIENT", - exp.Union: lambda self, e: self.set_operations(e), - exp.UnloggedProperty: lambda *_: "UNLOGGED", - exp.UsingTemplateProperty: lambda self, - e: f"USING TEMPLATE {self.sql(e, 'this')}", - exp.UsingData: lambda self, e: f"USING DATA {self.sql(e, 'this')}", - exp.UppercaseColumnConstraint: lambda *_: "UPPERCASE", - exp.UtcDate: lambda self, e: self.sql( - exp.CurrentDate(this=exp.Literal.string("UTC")) - ), - exp.UtcTime: lambda self, e: self.sql( - exp.CurrentTime(this=exp.Literal.string("UTC")) - ), - exp.UtcTimestamp: lambda self, e: self.sql( - exp.CurrentTimestamp(this=exp.Literal.string("UTC")) - ), - exp.VarMap: lambda self, e: self.func("MAP", e.args["keys"], e.args["values"]), - exp.ViewAttributeProperty: lambda self, e: f"WITH {self.sql(e, 'this')}", - exp.VolatileProperty: lambda *_: "VOLATILE", - exp.WithJournalTableProperty: lambda self, - e: f"WITH JOURNAL TABLE={self.sql(e, 'this')}", - exp.WithProcedureOptions: lambda self, - e: f"WITH {self.expressions(e, flat=True)}", - exp.WithSchemaBindingProperty: lambda self, - e: f"WITH SCHEMA {self.sql(e, 'this')}", - exp.WithOperator: lambda self, - e: f"{self.sql(e, 'this')} WITH {self.sql(e, 'op')}", - exp.ForceProperty: lambda *_: "FORCE", - } - - # Whether null ordering is supported in order by - # True: Full Support, None: No support, False: No support for certain cases - # such as window specifications, aggregate functions etc - NULL_ORDERING_SUPPORTED: t.Optional[bool] = True - - # Whether ignore nulls is inside the agg or outside. - # FIRST(x IGNORE NULLS) OVER vs FIRST (x) IGNORE NULLS OVER - IGNORE_NULLS_IN_FUNC = False - - # Whether locking reads (i.e. SELECT ... FOR UPDATE/SHARE) are supported - LOCKING_READS_SUPPORTED = False - - # Whether the EXCEPT and INTERSECT operations can return duplicates - EXCEPT_INTERSECT_SUPPORT_ALL_CLAUSE = True - - # Wrap derived values in parens, usually standard but spark doesn't support it - WRAP_DERIVED_VALUES = True - - # Whether create function uses an AS before the RETURN - CREATE_FUNCTION_RETURN_AS = True - - # Whether MERGE ... WHEN MATCHED BY SOURCE is allowed - MATCHED_BY_SOURCE = True - - # Whether the INTERVAL expression works only with values like '1 day' - SINGLE_STRING_INTERVAL = False - - # Whether the plural form of date parts like day (i.e. "days") is supported in INTERVALs - INTERVAL_ALLOWS_PLURAL_FORM = True - - # Whether limit and fetch are supported (possible values: "ALL", "LIMIT", "FETCH") - LIMIT_FETCH = "ALL" - - # Whether limit and fetch allows expresions or just limits - LIMIT_ONLY_LITERALS = False - - # Whether a table is allowed to be renamed with a db - RENAME_TABLE_WITH_DB = True - - # The separator for grouping sets and rollups - GROUPINGS_SEP = "," - - # The string used for creating an index on a table - INDEX_ON = "ON" - - # Whether join hints should be generated - JOIN_HINTS = True - - # Whether table hints should be generated - TABLE_HINTS = True - - # Whether query hints should be generated - QUERY_HINTS = True - - # What kind of separator to use for query hints - QUERY_HINT_SEP = ", " - - # Whether comparing against booleans (e.g. x IS TRUE) is supported - IS_BOOL_ALLOWED = True - - # Whether to include the "SET" keyword in the "INSERT ... ON DUPLICATE KEY UPDATE" statement - DUPLICATE_KEY_UPDATE_WITH_SET = True - - # Whether to generate the limit as TOP instead of LIMIT - LIMIT_IS_TOP = False - - # Whether to generate INSERT INTO ... RETURNING or INSERT INTO RETURNING ... - RETURNING_END = True - - # Whether to generate an unquoted value for EXTRACT's date part argument - EXTRACT_ALLOWS_QUOTES = True - - # Whether TIMETZ / TIMESTAMPTZ will be generated using the "WITH TIME ZONE" syntax - TZ_TO_WITH_TIME_ZONE = False - - # Whether the NVL2 function is supported - NVL2_SUPPORTED = True - - # https://cloud.google.com/bigquery/docs/reference/standard-sql/query-syntax - SELECT_KINDS: t.Tuple[str, ...] = ("STRUCT", "VALUE") - - # Whether VALUES statements can be used as derived tables. - # MySQL 5 and Redshift do not allow this, so when False, it will convert - # SELECT * VALUES into SELECT UNION - VALUES_AS_TABLE = True - - # Whether the word COLUMN is included when adding a column with ALTER TABLE - ALTER_TABLE_INCLUDE_COLUMN_KEYWORD = True - - # UNNEST WITH ORDINALITY (presto) instead of UNNEST WITH OFFSET (bigquery) - UNNEST_WITH_ORDINALITY = True - - # Whether FILTER (WHERE cond) can be used for conditional aggregation - AGGREGATE_FILTER_SUPPORTED = True - - # Whether JOIN sides (LEFT, RIGHT) are supported in conjunction with SEMI/ANTI join kinds - SEMI_ANTI_JOIN_WITH_SIDE = True - - # Whether to include the type of a computed column in the CREATE DDL - COMPUTED_COLUMN_WITH_TYPE = True - - # Whether CREATE TABLE .. COPY .. is supported. False means we'll generate CLONE instead of COPY - SUPPORTS_TABLE_COPY = True - - # Whether parentheses are required around the table sample's expression - TABLESAMPLE_REQUIRES_PARENS = True - - # Whether a table sample clause's size needs to be followed by the ROWS keyword - TABLESAMPLE_SIZE_IS_ROWS = True - - # The keyword(s) to use when generating a sample clause - TABLESAMPLE_KEYWORDS = "TABLESAMPLE" - - # Whether the TABLESAMPLE clause supports a method name, like BERNOULLI - TABLESAMPLE_WITH_METHOD = True - - # The keyword to use when specifying the seed of a sample clause - TABLESAMPLE_SEED_KEYWORD = "SEED" - - # Whether COLLATE is a function instead of a binary operator - COLLATE_IS_FUNC = False - - # Whether data types support additional specifiers like e.g. CHAR or BYTE (oracle) - DATA_TYPE_SPECIFIERS_ALLOWED = False - - # Whether conditions require booleans WHERE x = 0 vs WHERE x - ENSURE_BOOLS = False - - # Whether the "RECURSIVE" keyword is required when defining recursive CTEs - CTE_RECURSIVE_KEYWORD_REQUIRED = True - - # Whether CONCAT requires >1 arguments - SUPPORTS_SINGLE_ARG_CONCAT = True - - # Whether LAST_DAY function supports a date part argument - LAST_DAY_SUPPORTS_DATE_PART = True - - # Whether named columns are allowed in table aliases - SUPPORTS_TABLE_ALIAS_COLUMNS = True - - # Whether UNPIVOT aliases are Identifiers (False means they're Literals) - UNPIVOT_ALIASES_ARE_IDENTIFIERS = True - - # What delimiter to use for separating JSON key/value pairs - JSON_KEY_VALUE_PAIR_SEP = ":" - - # INSERT OVERWRITE TABLE x override - INSERT_OVERWRITE = " OVERWRITE TABLE" - - # Whether the SELECT .. INTO syntax is used instead of CTAS - SUPPORTS_SELECT_INTO = False - - # Whether UNLOGGED tables can be created - SUPPORTS_UNLOGGED_TABLES = False - - # Whether the CREATE TABLE LIKE statement is supported - SUPPORTS_CREATE_TABLE_LIKE = True - - # Whether the LikeProperty needs to be specified inside of the schema clause - LIKE_PROPERTY_INSIDE_SCHEMA = False - - # Whether DISTINCT can be followed by multiple args in an AggFunc. If not, it will be - # transpiled into a series of CASE-WHEN-ELSE, ultimately using a tuple conseisting of the args - MULTI_ARG_DISTINCT = True - - # Whether the JSON extraction operators expect a value of type JSON - JSON_TYPE_REQUIRED_FOR_EXTRACTION = False - - # Whether bracketed keys like ["foo"] are supported in JSON paths - JSON_PATH_BRACKETED_KEY_SUPPORTED = True - - # Whether to escape keys using single quotes in JSON paths - JSON_PATH_SINGLE_QUOTE_ESCAPE = False - - # The JSONPathPart expressions supported by this dialect - SUPPORTED_JSON_PATH_PARTS = ALL_JSON_PATH_PARTS.copy() - - # Whether any(f(x) for x in array) can be implemented by this dialect - CAN_IMPLEMENT_ARRAY_ANY = False - - # Whether the function TO_NUMBER is supported - SUPPORTS_TO_NUMBER = True - - # Whether EXCLUDE in window specification is supported - SUPPORTS_WINDOW_EXCLUDE = False - - # Whether or not set op modifiers apply to the outer set op or select. - # SELECT * FROM x UNION SELECT * FROM y LIMIT 1 - # True means limit 1 happens after the set op, False means it it happens on y. - SET_OP_MODIFIERS = True - - # Whether parameters from COPY statement are wrapped in parentheses - COPY_PARAMS_ARE_WRAPPED = True - - # Whether values of params are set with "=" token or empty space - COPY_PARAMS_EQ_REQUIRED = False - - # Whether COPY statement has INTO keyword - COPY_HAS_INTO_KEYWORD = True - - # Whether the conditional TRY(expression) function is supported - TRY_SUPPORTED = True - - # Whether the UESCAPE syntax in unicode strings is supported - SUPPORTS_UESCAPE = True - - # Function used to replace escaped unicode codes in unicode strings - UNICODE_SUBSTITUTE: t.Optional[t.Callable[[re.Match[str]], str]] = None - - # The keyword to use when generating a star projection with excluded columns - STAR_EXCEPT = "EXCEPT" - - # The HEX function name - HEX_FUNC = "HEX" - - # The keywords to use when prefixing & separating WITH based properties - WITH_PROPERTIES_PREFIX = "WITH" - - # Whether to quote the generated expression of exp.JsonPath - QUOTE_JSON_PATH = True - - # Whether the text pattern/fill (3rd) parameter of RPAD()/LPAD() is optional (defaults to space) - PAD_FILL_PATTERN_IS_REQUIRED = False - - # Whether a projection can explode into multiple rows, e.g. by unnesting an array. - SUPPORTS_EXPLODING_PROJECTIONS = True - - # Whether ARRAY_CONCAT can be generated with varlen args or if it should be reduced to 2-arg version - ARRAY_CONCAT_IS_VAR_LEN = True - - # Whether CONVERT_TIMEZONE() is supported; if not, it will be generated as exp.AtTimeZone - SUPPORTS_CONVERT_TIMEZONE = False - - # Whether MEDIAN(expr) is supported; if not, it will be generated as PERCENTILE_CONT(expr, 0.5) - SUPPORTS_MEDIAN = True - - # Whether UNIX_SECONDS(timestamp) is supported - SUPPORTS_UNIX_SECONDS = False - - # Whether to wrap in `AlterSet`, e.g., ALTER ... SET () - ALTER_SET_WRAPPED = False - - # Whether to normalize the date parts in EXTRACT( FROM ) into a common representation - # For instance, to extract the day of week in ISO semantics, one can use ISODOW, DAYOFWEEKISO etc depending on the dialect. - # TODO: The normalization should be done by default once we've tested it across all dialects. - NORMALIZE_EXTRACT_DATE_PARTS = False - - # The name to generate for the JSONPath expression. If `None`, only `this` will be generated - PARSE_JSON_NAME: t.Optional[str] = "PARSE_JSON" - - # The function name of the exp.ArraySize expression - ARRAY_SIZE_NAME: str = "ARRAY_LENGTH" - - # The syntax to use when altering the type of a column - ALTER_SET_TYPE = "SET DATA TYPE" - - # Whether exp.ArraySize should generate the dimension arg too (valid for Postgres & DuckDB) - # None -> Doesn't support it at all - # False (DuckDB) -> Has backwards-compatible support, but preferably generated without - # True (Postgres) -> Explicitly requires it - ARRAY_SIZE_DIM_REQUIRED: t.Optional[bool] = None - - # Whether a multi-argument DECODE(...) function is supported. If not, a CASE expression is generated - SUPPORTS_DECODE_CASE = True - - # Whether SYMMETRIC and ASYMMETRIC flags are supported with BETWEEN expression - SUPPORTS_BETWEEN_FLAGS = False - - # Whether LIKE and ILIKE support quantifiers such as LIKE ANY/ALL/SOME - SUPPORTS_LIKE_QUANTIFIERS = True - - # Prefix which is appended to exp.Table expressions in MATCH AGAINST - MATCH_AGAINST_TABLE_PREFIX: t.Optional[str] = None - - # Whether to include the VARIABLE keyword for SET assignments - SET_ASSIGNMENT_REQUIRES_VARIABLE_KEYWORD = False - - TYPE_MAPPING = { - exp.DataType.Type.DATETIME2: "TIMESTAMP", - exp.DataType.Type.NCHAR: "CHAR", - exp.DataType.Type.NVARCHAR: "VARCHAR", - exp.DataType.Type.MEDIUMTEXT: "TEXT", - exp.DataType.Type.LONGTEXT: "TEXT", - exp.DataType.Type.TINYTEXT: "TEXT", - exp.DataType.Type.BLOB: "VARBINARY", - exp.DataType.Type.MEDIUMBLOB: "BLOB", - exp.DataType.Type.LONGBLOB: "BLOB", - exp.DataType.Type.TINYBLOB: "BLOB", - exp.DataType.Type.INET: "INET", - exp.DataType.Type.ROWVERSION: "VARBINARY", - exp.DataType.Type.SMALLDATETIME: "TIMESTAMP", - } - - UNSUPPORTED_TYPES: set[exp.DataType.Type] = set() - - TIME_PART_SINGULARS = { - "MICROSECONDS": "MICROSECOND", - "SECONDS": "SECOND", - "MINUTES": "MINUTE", - "HOURS": "HOUR", - "DAYS": "DAY", - "WEEKS": "WEEK", - "MONTHS": "MONTH", - "QUARTERS": "QUARTER", - "YEARS": "YEAR", - } - - AFTER_HAVING_MODIFIER_TRANSFORMS = { - "cluster": lambda self, e: self.sql(e, "cluster"), - "distribute": lambda self, e: self.sql(e, "distribute"), - "sort": lambda self, e: self.sql(e, "sort"), - "windows": lambda self, e: ( - self.seg("WINDOW ") + self.expressions(e, key="windows", flat=True) - if e.args.get("windows") - else "" - ), - "qualify": lambda self, e: self.sql(e, "qualify"), - } - - TOKEN_MAPPING: t.Dict[TokenType, str] = {} - - STRUCT_DELIMITER = ("<", ">") - - PARAMETER_TOKEN = "@" - NAMED_PLACEHOLDER_TOKEN = ":" - - EXPRESSION_PRECEDES_PROPERTIES_CREATABLES: t.Set[str] = set() - - PROPERTIES_LOCATION = { - exp.AllowedValuesProperty: exp.Properties.Location.POST_SCHEMA, - exp.AlgorithmProperty: exp.Properties.Location.POST_CREATE, - exp.AutoIncrementProperty: exp.Properties.Location.POST_SCHEMA, - exp.AutoRefreshProperty: exp.Properties.Location.POST_SCHEMA, - exp.BackupProperty: exp.Properties.Location.POST_SCHEMA, - exp.BlockCompressionProperty: exp.Properties.Location.POST_NAME, - exp.CharacterSetProperty: exp.Properties.Location.POST_SCHEMA, - exp.ChecksumProperty: exp.Properties.Location.POST_NAME, - exp.CollateProperty: exp.Properties.Location.POST_SCHEMA, - exp.CopyGrantsProperty: exp.Properties.Location.POST_SCHEMA, - exp.Cluster: exp.Properties.Location.POST_SCHEMA, - exp.ClusteredByProperty: exp.Properties.Location.POST_SCHEMA, - exp.DistributedByProperty: exp.Properties.Location.POST_SCHEMA, - exp.DuplicateKeyProperty: exp.Properties.Location.POST_SCHEMA, - exp.DataBlocksizeProperty: exp.Properties.Location.POST_NAME, - exp.DataDeletionProperty: exp.Properties.Location.POST_SCHEMA, - exp.DefinerProperty: exp.Properties.Location.POST_CREATE, - exp.DictRange: exp.Properties.Location.POST_SCHEMA, - exp.DictProperty: exp.Properties.Location.POST_SCHEMA, - exp.DynamicProperty: exp.Properties.Location.POST_CREATE, - exp.DistKeyProperty: exp.Properties.Location.POST_SCHEMA, - exp.DistStyleProperty: exp.Properties.Location.POST_SCHEMA, - exp.EmptyProperty: exp.Properties.Location.POST_SCHEMA, - exp.EncodeProperty: exp.Properties.Location.POST_EXPRESSION, - exp.EngineProperty: exp.Properties.Location.POST_SCHEMA, - exp.EnviromentProperty: exp.Properties.Location.POST_SCHEMA, - exp.ExecuteAsProperty: exp.Properties.Location.POST_SCHEMA, - exp.ExternalProperty: exp.Properties.Location.POST_CREATE, - exp.FallbackProperty: exp.Properties.Location.POST_NAME, - exp.FileFormatProperty: exp.Properties.Location.POST_WITH, - exp.FreespaceProperty: exp.Properties.Location.POST_NAME, - exp.GlobalProperty: exp.Properties.Location.POST_CREATE, - exp.HeapProperty: exp.Properties.Location.POST_WITH, - exp.InheritsProperty: exp.Properties.Location.POST_SCHEMA, - exp.IcebergProperty: exp.Properties.Location.POST_CREATE, - exp.IncludeProperty: exp.Properties.Location.POST_SCHEMA, - exp.InputModelProperty: exp.Properties.Location.POST_SCHEMA, - exp.IsolatedLoadingProperty: exp.Properties.Location.POST_NAME, - exp.JournalProperty: exp.Properties.Location.POST_NAME, - exp.LanguageProperty: exp.Properties.Location.POST_SCHEMA, - exp.LikeProperty: exp.Properties.Location.POST_SCHEMA, - exp.LocationProperty: exp.Properties.Location.POST_SCHEMA, - exp.LockProperty: exp.Properties.Location.POST_SCHEMA, - exp.LockingProperty: exp.Properties.Location.POST_ALIAS, - exp.LogProperty: exp.Properties.Location.POST_NAME, - exp.MaterializedProperty: exp.Properties.Location.POST_CREATE, - exp.MergeBlockRatioProperty: exp.Properties.Location.POST_NAME, - exp.NoPrimaryIndexProperty: exp.Properties.Location.POST_EXPRESSION, - exp.OnProperty: exp.Properties.Location.POST_SCHEMA, - exp.OnCommitProperty: exp.Properties.Location.POST_EXPRESSION, - exp.Order: exp.Properties.Location.POST_SCHEMA, - exp.OutputModelProperty: exp.Properties.Location.POST_SCHEMA, - exp.PartitionedByProperty: exp.Properties.Location.POST_WITH, - exp.PartitionedOfProperty: exp.Properties.Location.POST_SCHEMA, - exp.PrimaryKey: exp.Properties.Location.POST_SCHEMA, - exp.Property: exp.Properties.Location.POST_WITH, - exp.RemoteWithConnectionModelProperty: exp.Properties.Location.POST_SCHEMA, - exp.ReturnsProperty: exp.Properties.Location.POST_SCHEMA, - exp.RowFormatProperty: exp.Properties.Location.POST_SCHEMA, - exp.RowFormatDelimitedProperty: exp.Properties.Location.POST_SCHEMA, - exp.RowFormatSerdeProperty: exp.Properties.Location.POST_SCHEMA, - exp.SampleProperty: exp.Properties.Location.POST_SCHEMA, - exp.SchemaCommentProperty: exp.Properties.Location.POST_SCHEMA, - exp.SecureProperty: exp.Properties.Location.POST_CREATE, - exp.SecurityProperty: exp.Properties.Location.POST_SCHEMA, - exp.SerdeProperties: exp.Properties.Location.POST_SCHEMA, - exp.Set: exp.Properties.Location.POST_SCHEMA, - exp.SettingsProperty: exp.Properties.Location.POST_SCHEMA, - exp.SetProperty: exp.Properties.Location.POST_CREATE, - exp.SetConfigProperty: exp.Properties.Location.POST_SCHEMA, - exp.SharingProperty: exp.Properties.Location.POST_EXPRESSION, - exp.SequenceProperties: exp.Properties.Location.POST_EXPRESSION, - exp.SortKeyProperty: exp.Properties.Location.POST_SCHEMA, - exp.SqlReadWriteProperty: exp.Properties.Location.POST_SCHEMA, - exp.SqlSecurityProperty: exp.Properties.Location.POST_CREATE, - exp.StabilityProperty: exp.Properties.Location.POST_SCHEMA, - exp.StorageHandlerProperty: exp.Properties.Location.POST_SCHEMA, - exp.StreamingTableProperty: exp.Properties.Location.POST_CREATE, - exp.StrictProperty: exp.Properties.Location.POST_SCHEMA, - exp.Tags: exp.Properties.Location.POST_WITH, - exp.TemporaryProperty: exp.Properties.Location.POST_CREATE, - exp.ToTableProperty: exp.Properties.Location.POST_SCHEMA, - exp.TransientProperty: exp.Properties.Location.POST_CREATE, - exp.TransformModelProperty: exp.Properties.Location.POST_SCHEMA, - exp.MergeTreeTTL: exp.Properties.Location.POST_SCHEMA, - exp.UnloggedProperty: exp.Properties.Location.POST_CREATE, - exp.UsingTemplateProperty: exp.Properties.Location.POST_SCHEMA, - exp.ViewAttributeProperty: exp.Properties.Location.POST_SCHEMA, - exp.VolatileProperty: exp.Properties.Location.POST_CREATE, - exp.WithDataProperty: exp.Properties.Location.POST_EXPRESSION, - exp.WithJournalTableProperty: exp.Properties.Location.POST_NAME, - exp.WithProcedureOptions: exp.Properties.Location.POST_SCHEMA, - exp.WithSchemaBindingProperty: exp.Properties.Location.POST_SCHEMA, - exp.WithSystemVersioningProperty: exp.Properties.Location.POST_SCHEMA, - exp.ForceProperty: exp.Properties.Location.POST_CREATE, - } - - # Keywords that can't be used as unquoted identifier names - RESERVED_KEYWORDS: t.Set[str] = set() - - # Expressions whose comments are separated from them for better formatting - WITH_SEPARATED_COMMENTS: t.Tuple[t.Type[exp.Expression], ...] = ( - exp.Command, - exp.Create, - exp.Describe, - exp.Delete, - exp.Drop, - exp.From, - exp.Insert, - exp.Join, - exp.MultitableInserts, - exp.Order, - exp.Group, - exp.Having, - exp.Select, - exp.SetOperation, - exp.Update, - exp.Where, - exp.With, - ) - - # Expressions that should not have their comments generated in maybe_comment - EXCLUDE_COMMENTS: t.Tuple[t.Type[exp.Expression], ...] = ( - exp.Binary, - exp.SetOperation, - ) - - # Expressions that can remain unwrapped when appearing in the context of an INTERVAL - UNWRAPPED_INTERVAL_VALUES: t.Tuple[t.Type[exp.Expression], ...] = ( - exp.Column, - exp.Literal, - exp.Neg, - exp.Paren, - ) - - PARAMETERIZABLE_TEXT_TYPES = { - exp.DataType.Type.NVARCHAR, - exp.DataType.Type.VARCHAR, - exp.DataType.Type.CHAR, - exp.DataType.Type.NCHAR, - } - - # Expressions that need to have all CTEs under them bubbled up to them - EXPRESSIONS_WITHOUT_NESTED_CTES: t.Set[t.Type[exp.Expression]] = set() - - RESPECT_IGNORE_NULLS_UNSUPPORTED_EXPRESSIONS: t.Tuple[ - t.Type[exp.Expression], ... - ] = () - - SAFE_JSON_PATH_KEY_RE = exp.SAFE_IDENTIFIER_RE - - SENTINEL_LINE_BREAK = "__SQLGLOT__LB__" - - __slots__ = ( - "pretty", - "identify", - "normalize", - "pad", - "_indent", - "normalize_functions", - "unsupported_level", - "max_unsupported", - "leading_comma", - "max_text_width", - "comments", - "dialect", - "unsupported_messages", - "_escaped_quote_end", - "_escaped_byte_quote_end", - "_escaped_identifier_end", - "_next_name", - "_identifier_start", - "_identifier_end", - "_quote_json_path_key_using_brackets", - ) - - def __init__( - self, - pretty: t.Optional[bool] = None, - identify: str | bool = False, - normalize: bool = False, - pad: int = 2, - indent: int = 2, - normalize_functions: t.Optional[str | bool] = None, - unsupported_level: ErrorLevel = ErrorLevel.WARN, - max_unsupported: int = 3, - leading_comma: bool = False, - max_text_width: int = 80, - comments: bool = True, - dialect: DialectType = None, - ): - import bigframes_vendored.sqlglot - from bigframes_vendored.sqlglot.dialects import Dialect - - self.pretty = ( - pretty if pretty is not None else bigframes_vendored.sqlglot.pretty - ) - self.identify = identify - self.normalize = normalize - self.pad = pad - self._indent = indent - self.unsupported_level = unsupported_level - self.max_unsupported = max_unsupported - self.leading_comma = leading_comma - self.max_text_width = max_text_width - self.comments = comments - self.dialect = Dialect.get_or_raise(dialect) - - # This is both a Dialect property and a Generator argument, so we prioritize the latter - self.normalize_functions = ( - self.dialect.NORMALIZE_FUNCTIONS - if normalize_functions is None - else normalize_functions - ) - - self.unsupported_messages: t.List[str] = [] - self._escaped_quote_end: str = ( - self.dialect.tokenizer_class.STRING_ESCAPES[0] + self.dialect.QUOTE_END - ) - self._escaped_byte_quote_end: str = ( - self.dialect.tokenizer_class.STRING_ESCAPES[0] + self.dialect.BYTE_END - if self.dialect.BYTE_END - else "" - ) - self._escaped_identifier_end = self.dialect.IDENTIFIER_END * 2 - - self._next_name = name_sequence("_t") - - self._identifier_start = self.dialect.IDENTIFIER_START - self._identifier_end = self.dialect.IDENTIFIER_END - - self._quote_json_path_key_using_brackets = True - - def generate(self, expression: exp.Expression, copy: bool = True) -> str: - """ - Generates the SQL string corresponding to the given syntax tree. - - Args: - expression: The syntax tree. - copy: Whether to copy the expression. The generator performs mutations so - it is safer to copy. - - Returns: - The SQL string corresponding to `expression`. - """ - if copy: - expression = expression.copy() - - expression = self.preprocess(expression) - - self.unsupported_messages = [] - sql = self.sql(expression).strip() - - if self.pretty: - sql = sql.replace(self.SENTINEL_LINE_BREAK, "\n") - - if self.unsupported_level == ErrorLevel.IGNORE: - return sql - - if self.unsupported_level == ErrorLevel.WARN: - for msg in self.unsupported_messages: - logger.warning(msg) - elif self.unsupported_level == ErrorLevel.RAISE and self.unsupported_messages: - raise UnsupportedError( - concat_messages(self.unsupported_messages, self.max_unsupported) - ) - - return sql - - def preprocess(self, expression: exp.Expression) -> exp.Expression: - """Apply generic preprocessing transformations to a given expression.""" - expression = self._move_ctes_to_top_level(expression) - - if self.ENSURE_BOOLS: - from bigframes_vendored.sqlglot.transforms import ensure_bools - - expression = ensure_bools(expression) - - return expression - - def _move_ctes_to_top_level(self, expression: E) -> E: - if ( - not expression.parent - and type(expression) in self.EXPRESSIONS_WITHOUT_NESTED_CTES - and any( - node.parent is not expression for node in expression.find_all(exp.With) - ) - ): - from bigframes_vendored.sqlglot.transforms import move_ctes_to_top_level - - expression = move_ctes_to_top_level(expression) - return expression - - def unsupported(self, message: str) -> None: - if self.unsupported_level == ErrorLevel.IMMEDIATE: - raise UnsupportedError(message) - self.unsupported_messages.append(message) - - def sep(self, sep: str = " ") -> str: - return f"{sep.strip()}\n" if self.pretty else sep - - def seg(self, sql: str, sep: str = " ") -> str: - return f"{self.sep(sep)}{sql}" - - def sanitize_comment(self, comment: str) -> str: - comment = " " + comment if comment[0].strip() else comment - comment = comment + " " if comment[-1].strip() else comment - - if not self.dialect.tokenizer_class.NESTED_COMMENTS: - # Necessary workaround to avoid syntax errors due to nesting: /* ... */ ... */ - comment = comment.replace("*/", "* /") - - return comment - - def maybe_comment( - self, - sql: str, - expression: t.Optional[exp.Expression] = None, - comments: t.Optional[t.List[str]] = None, - separated: bool = False, - ) -> str: - comments = ( - ((expression and expression.comments) if comments is None else comments) # type: ignore - if self.comments - else None - ) - - if not comments or isinstance(expression, self.EXCLUDE_COMMENTS): - return sql - - comments_sql = " ".join( - f"/*{self.sanitize_comment(comment)}*/" for comment in comments if comment - ) - - if not comments_sql: - return sql - - comments_sql = self._replace_line_breaks(comments_sql) - - if separated or isinstance(expression, self.WITH_SEPARATED_COMMENTS): - return ( - f"{self.sep()}{comments_sql}{sql}" - if not sql or sql[0].isspace() - else f"{comments_sql}{self.sep()}{sql}" - ) - - return f"{sql} {comments_sql}" - - def wrap(self, expression: exp.Expression | str) -> str: - this_sql = ( - self.sql(expression) - if isinstance(expression, exp.UNWRAPPED_QUERIES) - else self.sql(expression, "this") - ) - if not this_sql: - return "()" - - this_sql = self.indent(this_sql, level=1, pad=0) - return f"({self.sep('')}{this_sql}{self.seg(')', sep='')}" - - def no_identify(self, func: t.Callable[..., str], *args, **kwargs) -> str: - original = self.identify - self.identify = False - result = func(*args, **kwargs) - self.identify = original - return result - - def normalize_func(self, name: str) -> str: - if self.normalize_functions == "upper" or self.normalize_functions is True: - return name.upper() - if self.normalize_functions == "lower": - return name.lower() - return name - - def indent( - self, - sql: str, - level: int = 0, - pad: t.Optional[int] = None, - skip_first: bool = False, - skip_last: bool = False, - ) -> str: - if not self.pretty or not sql: - return sql - - pad = self.pad if pad is None else pad - lines = sql.split("\n") - - return "\n".join( - ( - line - if (skip_first and i == 0) or (skip_last and i == len(lines) - 1) - else f"{' ' * (level * self._indent + pad)}{line}" - ) - for i, line in enumerate(lines) - ) - - def sql( - self, - expression: t.Optional[str | exp.Expression], - key: t.Optional[str] = None, - comment: bool = True, - ) -> str: - if not expression: - return "" - - if isinstance(expression, str): - return expression - - if key: - value = expression.args.get(key) - if value: - return self.sql(value) - return "" - - transform = self.TRANSFORMS.get(expression.__class__) - - if callable(transform): - sql = transform(self, expression) - elif isinstance(expression, exp.Expression): - exp_handler_name = f"{expression.key}_sql" - - if hasattr(self, exp_handler_name): - sql = getattr(self, exp_handler_name)(expression) - elif isinstance(expression, exp.Func): - sql = self.function_fallback_sql(expression) - elif isinstance(expression, exp.Property): - sql = self.property_sql(expression) - else: - raise ValueError( - f"Unsupported expression type {expression.__class__.__name__}" - ) - else: - raise ValueError( - f"Expected an Expression. Received {type(expression)}: {expression}" - ) - - return self.maybe_comment(sql, expression) if self.comments and comment else sql - - def uncache_sql(self, expression: exp.Uncache) -> str: - table = self.sql(expression, "this") - exists_sql = " IF EXISTS" if expression.args.get("exists") else "" - return f"UNCACHE TABLE{exists_sql} {table}" - - def cache_sql(self, expression: exp.Cache) -> str: - lazy = " LAZY" if expression.args.get("lazy") else "" - table = self.sql(expression, "this") - options = expression.args.get("options") - options = ( - f" OPTIONS({self.sql(options[0])} = {self.sql(options[1])})" - if options - else "" - ) - sql = self.sql(expression, "expression") - sql = f" AS{self.sep()}{sql}" if sql else "" - sql = f"CACHE{lazy} TABLE {table}{options}{sql}" - return self.prepend_ctes(expression, sql) - - def characterset_sql(self, expression: exp.CharacterSet) -> str: - if isinstance(expression.parent, exp.Cast): - return f"CHAR CHARACTER SET {self.sql(expression, 'this')}" - default = "DEFAULT " if expression.args.get("default") else "" - return f"{default}CHARACTER SET={self.sql(expression, 'this')}" - - def column_parts(self, expression: exp.Column) -> str: - return ".".join( - self.sql(part) - for part in ( - expression.args.get("catalog"), - expression.args.get("db"), - expression.args.get("table"), - expression.args.get("this"), - ) - if part - ) - - def column_sql(self, expression: exp.Column) -> str: - join_mark = " (+)" if expression.args.get("join_mark") else "" - - if join_mark and not self.dialect.SUPPORTS_COLUMN_JOIN_MARKS: - join_mark = "" - self.unsupported( - "Outer join syntax using the (+) operator is not supported." - ) - - return f"{self.column_parts(expression)}{join_mark}" - - def pseudocolumn_sql(self, expression: exp.Pseudocolumn) -> str: - return self.column_sql(expression) - - def columnposition_sql(self, expression: exp.ColumnPosition) -> str: - this = self.sql(expression, "this") - this = f" {this}" if this else "" - position = self.sql(expression, "position") - return f"{position}{this}" - - def columndef_sql(self, expression: exp.ColumnDef, sep: str = " ") -> str: - column = self.sql(expression, "this") - kind = self.sql(expression, "kind") - constraints = self.expressions( - expression, key="constraints", sep=" ", flat=True - ) - exists = "IF NOT EXISTS " if expression.args.get("exists") else "" - kind = f"{sep}{kind}" if kind else "" - constraints = f" {constraints}" if constraints else "" - position = self.sql(expression, "position") - position = f" {position}" if position else "" - - if ( - expression.find(exp.ComputedColumnConstraint) - and not self.COMPUTED_COLUMN_WITH_TYPE - ): - kind = "" - - return f"{exists}{column}{kind}{constraints}{position}" - - def columnconstraint_sql(self, expression: exp.ColumnConstraint) -> str: - this = self.sql(expression, "this") - kind_sql = self.sql(expression, "kind").strip() - return f"CONSTRAINT {this} {kind_sql}" if this else kind_sql - - def computedcolumnconstraint_sql( - self, expression: exp.ComputedColumnConstraint - ) -> str: - this = self.sql(expression, "this") - if expression.args.get("not_null"): - persisted = " PERSISTED NOT NULL" - elif expression.args.get("persisted"): - persisted = " PERSISTED" - else: - persisted = "" - - return f"AS {this}{persisted}" - - def autoincrementcolumnconstraint_sql(self, _) -> str: - return self.token_sql(TokenType.AUTO_INCREMENT) - - def compresscolumnconstraint_sql( - self, expression: exp.CompressColumnConstraint - ) -> str: - if isinstance(expression.this, list): - this = self.wrap(self.expressions(expression, key="this", flat=True)) - else: - this = self.sql(expression, "this") - - return f"COMPRESS {this}" - - def generatedasidentitycolumnconstraint_sql( - self, expression: exp.GeneratedAsIdentityColumnConstraint - ) -> str: - this = "" - if expression.this is not None: - on_null = " ON NULL" if expression.args.get("on_null") else "" - this = " ALWAYS" if expression.this else f" BY DEFAULT{on_null}" - - start = expression.args.get("start") - start = f"START WITH {start}" if start else "" - increment = expression.args.get("increment") - increment = f" INCREMENT BY {increment}" if increment else "" - minvalue = expression.args.get("minvalue") - minvalue = f" MINVALUE {minvalue}" if minvalue else "" - maxvalue = expression.args.get("maxvalue") - maxvalue = f" MAXVALUE {maxvalue}" if maxvalue else "" - cycle = expression.args.get("cycle") - cycle_sql = "" - - if cycle is not None: - cycle_sql = f"{' NO' if not cycle else ''} CYCLE" - cycle_sql = cycle_sql.strip() if not start and not increment else cycle_sql - - sequence_opts = "" - if start or increment or cycle_sql: - sequence_opts = f"{start}{increment}{minvalue}{maxvalue}{cycle_sql}" - sequence_opts = f" ({sequence_opts.strip()})" - - expr = self.sql(expression, "expression") - expr = f"({expr})" if expr else "IDENTITY" - - return f"GENERATED{this} AS {expr}{sequence_opts}" - - def generatedasrowcolumnconstraint_sql( - self, expression: exp.GeneratedAsRowColumnConstraint - ) -> str: - start = "START" if expression.args.get("start") else "END" - hidden = " HIDDEN" if expression.args.get("hidden") else "" - return f"GENERATED ALWAYS AS ROW {start}{hidden}" - - def periodforsystemtimeconstraint_sql( - self, expression: exp.PeriodForSystemTimeConstraint - ) -> str: - return f"PERIOD FOR SYSTEM_TIME ({self.sql(expression, 'this')}, {self.sql(expression, 'expression')})" - - def notnullcolumnconstraint_sql( - self, expression: exp.NotNullColumnConstraint - ) -> str: - return f"{'' if expression.args.get('allow_null') else 'NOT '}NULL" - - def primarykeycolumnconstraint_sql( - self, expression: exp.PrimaryKeyColumnConstraint - ) -> str: - desc = expression.args.get("desc") - if desc is not None: - return f"PRIMARY KEY{' DESC' if desc else ' ASC'}" - options = self.expressions(expression, key="options", flat=True, sep=" ") - options = f" {options}" if options else "" - return f"PRIMARY KEY{options}" - - def uniquecolumnconstraint_sql(self, expression: exp.UniqueColumnConstraint) -> str: - this = self.sql(expression, "this") - this = f" {this}" if this else "" - index_type = expression.args.get("index_type") - index_type = f" USING {index_type}" if index_type else "" - on_conflict = self.sql(expression, "on_conflict") - on_conflict = f" {on_conflict}" if on_conflict else "" - nulls_sql = " NULLS NOT DISTINCT" if expression.args.get("nulls") else "" - options = self.expressions(expression, key="options", flat=True, sep=" ") - options = f" {options}" if options else "" - return f"UNIQUE{nulls_sql}{this}{index_type}{on_conflict}{options}" - - def createable_sql(self, expression: exp.Create, locations: t.DefaultDict) -> str: - return self.sql(expression, "this") - - def create_sql(self, expression: exp.Create) -> str: - kind = self.sql(expression, "kind") - kind = self.dialect.INVERSE_CREATABLE_KIND_MAPPING.get(kind) or kind - properties = expression.args.get("properties") - properties_locs = ( - self.locate_properties(properties) if properties else defaultdict() - ) - - this = self.createable_sql(expression, properties_locs) - - properties_sql = "" - if properties_locs.get( - exp.Properties.Location.POST_SCHEMA - ) or properties_locs.get(exp.Properties.Location.POST_WITH): - props_ast = exp.Properties( - expressions=[ - *properties_locs[exp.Properties.Location.POST_SCHEMA], - *properties_locs[exp.Properties.Location.POST_WITH], - ] - ) - props_ast.parent = expression - properties_sql = self.sql(props_ast) - - if properties_locs.get(exp.Properties.Location.POST_SCHEMA): - properties_sql = self.sep() + properties_sql - elif not self.pretty: - # Standalone POST_WITH properties need a leading whitespace in non-pretty mode - properties_sql = f" {properties_sql}" - - begin = " BEGIN" if expression.args.get("begin") else "" - end = " END" if expression.args.get("end") else "" - - expression_sql = self.sql(expression, "expression") - if expression_sql: - expression_sql = f"{begin}{self.sep()}{expression_sql}{end}" - - if self.CREATE_FUNCTION_RETURN_AS or not isinstance( - expression.expression, exp.Return - ): - postalias_props_sql = "" - if properties_locs.get(exp.Properties.Location.POST_ALIAS): - postalias_props_sql = self.properties( - exp.Properties( - expressions=properties_locs[ - exp.Properties.Location.POST_ALIAS - ] - ), - wrapped=False, - ) - postalias_props_sql = ( - f" {postalias_props_sql}" if postalias_props_sql else "" - ) - expression_sql = f" AS{postalias_props_sql}{expression_sql}" - - postindex_props_sql = "" - if properties_locs.get(exp.Properties.Location.POST_INDEX): - postindex_props_sql = self.properties( - exp.Properties( - expressions=properties_locs[exp.Properties.Location.POST_INDEX] - ), - wrapped=False, - prefix=" ", - ) - - indexes = self.expressions(expression, key="indexes", indent=False, sep=" ") - indexes = f" {indexes}" if indexes else "" - index_sql = indexes + postindex_props_sql - - replace = " OR REPLACE" if expression.args.get("replace") else "" - refresh = " OR REFRESH" if expression.args.get("refresh") else "" - unique = " UNIQUE" if expression.args.get("unique") else "" - - clustered = expression.args.get("clustered") - if clustered is None: - clustered_sql = "" - elif clustered: - clustered_sql = " CLUSTERED COLUMNSTORE" - else: - clustered_sql = " NONCLUSTERED COLUMNSTORE" - - postcreate_props_sql = "" - if properties_locs.get(exp.Properties.Location.POST_CREATE): - postcreate_props_sql = self.properties( - exp.Properties( - expressions=properties_locs[exp.Properties.Location.POST_CREATE] - ), - sep=" ", - prefix=" ", - wrapped=False, - ) - - modifiers = "".join( - (clustered_sql, replace, refresh, unique, postcreate_props_sql) - ) - - postexpression_props_sql = "" - if properties_locs.get(exp.Properties.Location.POST_EXPRESSION): - postexpression_props_sql = self.properties( - exp.Properties( - expressions=properties_locs[exp.Properties.Location.POST_EXPRESSION] - ), - sep=" ", - prefix=" ", - wrapped=False, - ) - - concurrently = " CONCURRENTLY" if expression.args.get("concurrently") else "" - exists_sql = " IF NOT EXISTS" if expression.args.get("exists") else "" - no_schema_binding = ( - " WITH NO SCHEMA BINDING" - if expression.args.get("no_schema_binding") - else "" - ) - - clone = self.sql(expression, "clone") - clone = f" {clone}" if clone else "" - - if kind in self.EXPRESSION_PRECEDES_PROPERTIES_CREATABLES: - properties_expression = f"{expression_sql}{properties_sql}" - else: - properties_expression = f"{properties_sql}{expression_sql}" - - expression_sql = f"CREATE{modifiers} {kind}{concurrently}{exists_sql} {this}{properties_expression}{postexpression_props_sql}{index_sql}{no_schema_binding}{clone}" - return self.prepend_ctes(expression, expression_sql) - - def sequenceproperties_sql(self, expression: exp.SequenceProperties) -> str: - start = self.sql(expression, "start") - start = f"START WITH {start}" if start else "" - increment = self.sql(expression, "increment") - increment = f" INCREMENT BY {increment}" if increment else "" - minvalue = self.sql(expression, "minvalue") - minvalue = f" MINVALUE {minvalue}" if minvalue else "" - maxvalue = self.sql(expression, "maxvalue") - maxvalue = f" MAXVALUE {maxvalue}" if maxvalue else "" - owned = self.sql(expression, "owned") - owned = f" OWNED BY {owned}" if owned else "" - - cache = expression.args.get("cache") - if cache is None: - cache_str = "" - elif cache is True: - cache_str = " CACHE" - else: - cache_str = f" CACHE {cache}" - - options = self.expressions(expression, key="options", flat=True, sep=" ") - options = f" {options}" if options else "" - - return f"{start}{increment}{minvalue}{maxvalue}{cache_str}{options}{owned}".lstrip() - - def clone_sql(self, expression: exp.Clone) -> str: - this = self.sql(expression, "this") - shallow = "SHALLOW " if expression.args.get("shallow") else "" - keyword = ( - "COPY" - if expression.args.get("copy") and self.SUPPORTS_TABLE_COPY - else "CLONE" - ) - return f"{shallow}{keyword} {this}" - - def describe_sql(self, expression: exp.Describe) -> str: - style = expression.args.get("style") - style = f" {style}" if style else "" - partition = self.sql(expression, "partition") - partition = f" {partition}" if partition else "" - format = self.sql(expression, "format") - format = f" {format}" if format else "" - - return f"DESCRIBE{style}{format} {self.sql(expression, 'this')}{partition}" - - def heredoc_sql(self, expression: exp.Heredoc) -> str: - tag = self.sql(expression, "tag") - return f"${tag}${self.sql(expression, 'this')}${tag}$" - - def prepend_ctes(self, expression: exp.Expression, sql: str) -> str: - with_ = self.sql(expression, "with_") - if with_: - sql = f"{with_}{self.sep()}{sql}" - return sql - - def with_sql(self, expression: exp.With) -> str: - sql = self.expressions(expression, flat=True) - recursive = ( - "RECURSIVE " - if self.CTE_RECURSIVE_KEYWORD_REQUIRED and expression.args.get("recursive") - else "" - ) - search = self.sql(expression, "search") - search = f" {search}" if search else "" - - return f"WITH {recursive}{sql}{search}" - - def cte_sql(self, expression: exp.CTE) -> str: - alias = expression.args.get("alias") - if alias: - alias.add_comments(expression.pop_comments()) - - alias_sql = self.sql(expression, "alias") - - materialized = expression.args.get("materialized") - if materialized is False: - materialized = "NOT MATERIALIZED " - elif materialized: - materialized = "MATERIALIZED " - - key_expressions = self.expressions(expression, key="key_expressions", flat=True) - key_expressions = f" USING KEY ({key_expressions})" if key_expressions else "" - - return f"{alias_sql}{key_expressions} AS {materialized or ''}{self.wrap(expression)}" - - def tablealias_sql(self, expression: exp.TableAlias) -> str: - alias = self.sql(expression, "this") - columns = self.expressions(expression, key="columns", flat=True) - columns = f"({columns})" if columns else "" - - if columns and not self.SUPPORTS_TABLE_ALIAS_COLUMNS: - columns = "" - self.unsupported("Named columns are not supported in table alias.") - - if not alias and not self.dialect.UNNEST_COLUMN_ONLY: - alias = self._next_name() - - return f"{alias}{columns}" - - def bitstring_sql(self, expression: exp.BitString) -> str: - this = self.sql(expression, "this") - if self.dialect.BIT_START: - return f"{self.dialect.BIT_START}{this}{self.dialect.BIT_END}" - return f"{int(this, 2)}" - - def hexstring_sql( - self, expression: exp.HexString, binary_function_repr: t.Optional[str] = None - ) -> str: - this = self.sql(expression, "this") - is_integer_type = expression.args.get("is_integer") - - if (is_integer_type and not self.dialect.HEX_STRING_IS_INTEGER_TYPE) or ( - not self.dialect.HEX_START and not binary_function_repr - ): - # Integer representation will be returned if: - # - The read dialect treats the hex value as integer literal but not the write - # - The transpilation is not supported (write dialect hasn't set HEX_START or the param flag) - return f"{int(this, 16)}" - - if not is_integer_type: - # Read dialect treats the hex value as BINARY/BLOB - if binary_function_repr: - # The write dialect supports the transpilation to its equivalent BINARY/BLOB - return self.func(binary_function_repr, exp.Literal.string(this)) - if self.dialect.HEX_STRING_IS_INTEGER_TYPE: - # The write dialect does not support the transpilation, it'll treat the hex value as INTEGER - self.unsupported( - "Unsupported transpilation from BINARY/BLOB hex string" - ) - - return f"{self.dialect.HEX_START}{this}{self.dialect.HEX_END}" - - def bytestring_sql(self, expression: exp.ByteString) -> str: - this = self.sql(expression, "this") - if self.dialect.BYTE_START: - escaped_byte_string = self.escape_str( - this, - escape_backslash=False, - delimiter=self.dialect.BYTE_END, - escaped_delimiter=self._escaped_byte_quote_end, - ) - is_bytes = expression.args.get("is_bytes", False) - delimited_byte_string = ( - f"{self.dialect.BYTE_START}{escaped_byte_string}{self.dialect.BYTE_END}" - ) - if is_bytes and not self.dialect.BYTE_STRING_IS_BYTES_TYPE: - return self.sql( - exp.cast( - delimited_byte_string, - exp.DataType.Type.BINARY, - dialect=self.dialect, - ) - ) - if not is_bytes and self.dialect.BYTE_STRING_IS_BYTES_TYPE: - return self.sql( - exp.cast( - delimited_byte_string, - exp.DataType.Type.VARCHAR, - dialect=self.dialect, - ) - ) - - return delimited_byte_string - return this - - def unicodestring_sql(self, expression: exp.UnicodeString) -> str: - this = self.sql(expression, "this") - escape = expression.args.get("escape") - - if self.dialect.UNICODE_START: - escape_substitute = r"\\\1" - left_quote, right_quote = ( - self.dialect.UNICODE_START, - self.dialect.UNICODE_END, - ) - else: - escape_substitute = r"\\u\1" - left_quote, right_quote = self.dialect.QUOTE_START, self.dialect.QUOTE_END - - if escape: - escape_pattern = re.compile(rf"{escape.name}(\d+)") - escape_sql = f" UESCAPE {self.sql(escape)}" if self.SUPPORTS_UESCAPE else "" - else: - escape_pattern = ESCAPED_UNICODE_RE - escape_sql = "" - - if not self.dialect.UNICODE_START or (escape and not self.SUPPORTS_UESCAPE): - this = escape_pattern.sub( - self.UNICODE_SUBSTITUTE or escape_substitute, this - ) - - return f"{left_quote}{this}{right_quote}{escape_sql}" - - def rawstring_sql(self, expression: exp.RawString) -> str: - string = expression.this - if "\\" in self.dialect.tokenizer_class.STRING_ESCAPES: - string = string.replace("\\", "\\\\") - - string = self.escape_str(string, escape_backslash=False) - return f"{self.dialect.QUOTE_START}{string}{self.dialect.QUOTE_END}" - - def datatypeparam_sql(self, expression: exp.DataTypeParam) -> str: - this = self.sql(expression, "this") - specifier = self.sql(expression, "expression") - specifier = ( - f" {specifier}" if specifier and self.DATA_TYPE_SPECIFIERS_ALLOWED else "" - ) - return f"{this}{specifier}" - - def datatype_sql(self, expression: exp.DataType) -> str: - nested = "" - values = "" - interior = self.expressions(expression, flat=True) - - type_value = expression.this - if type_value in self.UNSUPPORTED_TYPES: - self.unsupported( - f"Data type {type_value.value} is not supported when targeting {self.dialect.__class__.__name__}" - ) - - if type_value == exp.DataType.Type.USERDEFINED and expression.args.get("kind"): - type_sql = self.sql(expression, "kind") - else: - type_sql = ( - self.TYPE_MAPPING.get(type_value, type_value.value) - if isinstance(type_value, exp.DataType.Type) - else type_value - ) - - if interior: - if expression.args.get("nested"): - nested = ( - f"{self.STRUCT_DELIMITER[0]}{interior}{self.STRUCT_DELIMITER[1]}" - ) - if expression.args.get("values") is not None: - delimiters = ( - ("[", "]") - if type_value == exp.DataType.Type.ARRAY - else ("(", ")") - ) - values = self.expressions(expression, key="values", flat=True) - values = f"{delimiters[0]}{values}{delimiters[1]}" - elif type_value == exp.DataType.Type.INTERVAL: - nested = f" {interior}" - else: - nested = f"({interior})" - - type_sql = f"{type_sql}{nested}{values}" - if self.TZ_TO_WITH_TIME_ZONE and type_value in ( - exp.DataType.Type.TIMETZ, - exp.DataType.Type.TIMESTAMPTZ, - ): - type_sql = f"{type_sql} WITH TIME ZONE" - - return type_sql - - def directory_sql(self, expression: exp.Directory) -> str: - local = "LOCAL " if expression.args.get("local") else "" - row_format = self.sql(expression, "row_format") - row_format = f" {row_format}" if row_format else "" - return f"{local}DIRECTORY {self.sql(expression, 'this')}{row_format}" - - def delete_sql(self, expression: exp.Delete) -> str: - this = self.sql(expression, "this") - this = f" FROM {this}" if this else "" - using = self.expressions(expression, key="using") - using = f" USING {using}" if using else "" - cluster = self.sql(expression, "cluster") - cluster = f" {cluster}" if cluster else "" - where = self.sql(expression, "where") - returning = self.sql(expression, "returning") - order = self.sql(expression, "order") - limit = self.sql(expression, "limit") - tables = self.expressions(expression, key="tables") - tables = f" {tables}" if tables else "" - if self.RETURNING_END: - expression_sql = f"{this}{using}{cluster}{where}{returning}{order}{limit}" - else: - expression_sql = f"{returning}{this}{using}{cluster}{where}{order}{limit}" - return self.prepend_ctes(expression, f"DELETE{tables}{expression_sql}") - - def drop_sql(self, expression: exp.Drop) -> str: - this = self.sql(expression, "this") - expressions = self.expressions(expression, flat=True) - expressions = f" ({expressions})" if expressions else "" - kind = expression.args["kind"] - kind = self.dialect.INVERSE_CREATABLE_KIND_MAPPING.get(kind) or kind - exists_sql = " IF EXISTS " if expression.args.get("exists") else " " - concurrently_sql = ( - " CONCURRENTLY" if expression.args.get("concurrently") else "" - ) - on_cluster = self.sql(expression, "cluster") - on_cluster = f" {on_cluster}" if on_cluster else "" - temporary = " TEMPORARY" if expression.args.get("temporary") else "" - materialized = " MATERIALIZED" if expression.args.get("materialized") else "" - cascade = " CASCADE" if expression.args.get("cascade") else "" - constraints = " CONSTRAINTS" if expression.args.get("constraints") else "" - purge = " PURGE" if expression.args.get("purge") else "" - return f"DROP{temporary}{materialized} {kind}{concurrently_sql}{exists_sql}{this}{on_cluster}{expressions}{cascade}{constraints}{purge}" - - def set_operation(self, expression: exp.SetOperation) -> str: - op_type = type(expression) - op_name = op_type.key.upper() - - distinct = expression.args.get("distinct") - if ( - distinct is False - and op_type in (exp.Except, exp.Intersect) - and not self.EXCEPT_INTERSECT_SUPPORT_ALL_CLAUSE - ): - self.unsupported(f"{op_name} ALL is not supported") - - default_distinct = self.dialect.SET_OP_DISTINCT_BY_DEFAULT[op_type] - - if distinct is None: - distinct = default_distinct - if distinct is None: - self.unsupported(f"{op_name} requires DISTINCT or ALL to be specified") - - if distinct is default_distinct: - distinct_or_all = "" - else: - distinct_or_all = " DISTINCT" if distinct else " ALL" - - side_kind = " ".join(filter(None, [expression.side, expression.kind])) - side_kind = f"{side_kind} " if side_kind else "" - - by_name = " BY NAME" if expression.args.get("by_name") else "" - on = self.expressions(expression, key="on", flat=True) - on = f" ON ({on})" if on else "" - - return f"{side_kind}{op_name}{distinct_or_all}{by_name}{on}" - - def set_operations(self, expression: exp.SetOperation) -> str: - if not self.SET_OP_MODIFIERS: - limit = expression.args.get("limit") - order = expression.args.get("order") - - if limit or order: - select = self._move_ctes_to_top_level( - exp.subquery(expression, "_l_0", copy=False).select("*", copy=False) - ) - - if limit: - select = select.limit(limit.pop(), copy=False) - if order: - select = select.order_by(order.pop(), copy=False) - return self.sql(select) - - sqls: t.List[str] = [] - stack: t.List[t.Union[str, exp.Expression]] = [expression] - - while stack: - node = stack.pop() - - if isinstance(node, exp.SetOperation): - stack.append(node.expression) - stack.append( - self.maybe_comment( - self.set_operation(node), comments=node.comments, separated=True - ) - ) - stack.append(node.this) - else: - sqls.append(self.sql(node)) - - this = self.sep().join(sqls) - this = self.query_modifiers(expression, this) - return self.prepend_ctes(expression, this) - - def fetch_sql(self, expression: exp.Fetch) -> str: - direction = expression.args.get("direction") - direction = f" {direction}" if direction else "" - count = self.sql(expression, "count") - count = f" {count}" if count else "" - limit_options = self.sql(expression, "limit_options") - limit_options = f"{limit_options}" if limit_options else " ROWS ONLY" - return f"{self.seg('FETCH')}{direction}{count}{limit_options}" - - def limitoptions_sql(self, expression: exp.LimitOptions) -> str: - percent = " PERCENT" if expression.args.get("percent") else "" - rows = " ROWS" if expression.args.get("rows") else "" - with_ties = " WITH TIES" if expression.args.get("with_ties") else "" - if not with_ties and rows: - with_ties = " ONLY" - return f"{percent}{rows}{with_ties}" - - def filter_sql(self, expression: exp.Filter) -> str: - if self.AGGREGATE_FILTER_SUPPORTED: - this = self.sql(expression, "this") - where = self.sql(expression, "expression").strip() - return f"{this} FILTER({where})" - - agg = expression.this - agg_arg = agg.this - cond = expression.expression.this - agg_arg.replace(exp.If(this=cond.copy(), true=agg_arg.copy())) - return self.sql(agg) - - def hint_sql(self, expression: exp.Hint) -> str: - if not self.QUERY_HINTS: - self.unsupported("Hints are not supported") - return "" - - return ( - f" /*+ {self.expressions(expression, sep=self.QUERY_HINT_SEP).strip()} */" - ) - - def indexparameters_sql(self, expression: exp.IndexParameters) -> str: - using = self.sql(expression, "using") - using = f" USING {using}" if using else "" - columns = self.expressions(expression, key="columns", flat=True) - columns = f"({columns})" if columns else "" - partition_by = self.expressions(expression, key="partition_by", flat=True) - partition_by = f" PARTITION BY {partition_by}" if partition_by else "" - where = self.sql(expression, "where") - include = self.expressions(expression, key="include", flat=True) - if include: - include = f" INCLUDE ({include})" - with_storage = self.expressions(expression, key="with_storage", flat=True) - with_storage = f" WITH ({with_storage})" if with_storage else "" - tablespace = self.sql(expression, "tablespace") - tablespace = f" USING INDEX TABLESPACE {tablespace}" if tablespace else "" - on = self.sql(expression, "on") - on = f" ON {on}" if on else "" - - return f"{using}{columns}{include}{with_storage}{tablespace}{partition_by}{where}{on}" - - def index_sql(self, expression: exp.Index) -> str: - unique = "UNIQUE " if expression.args.get("unique") else "" - primary = "PRIMARY " if expression.args.get("primary") else "" - amp = "AMP " if expression.args.get("amp") else "" - name = self.sql(expression, "this") - name = f"{name} " if name else "" - table = self.sql(expression, "table") - table = f"{self.INDEX_ON} {table}" if table else "" - - index = "INDEX " if not table else "" - - params = self.sql(expression, "params") - return f"{unique}{primary}{amp}{index}{name}{table}{params}" - - def identifier_sql(self, expression: exp.Identifier) -> str: - text = expression.name - lower = text.lower() - text = lower if self.normalize and not expression.quoted else text - text = text.replace(self._identifier_end, self._escaped_identifier_end) - if ( - expression.quoted - or self.dialect.can_quote(expression, self.identify) - or lower in self.RESERVED_KEYWORDS - or ( - not self.dialect.IDENTIFIERS_CAN_START_WITH_DIGIT and text[:1].isdigit() - ) - ): - text = f"{self._identifier_start}{text}{self._identifier_end}" - return text - - def hex_sql(self, expression: exp.Hex) -> str: - text = self.func(self.HEX_FUNC, self.sql(expression, "this")) - if self.dialect.HEX_LOWERCASE: - text = self.func("LOWER", text) - - return text - - def lowerhex_sql(self, expression: exp.LowerHex) -> str: - text = self.func(self.HEX_FUNC, self.sql(expression, "this")) - if not self.dialect.HEX_LOWERCASE: - text = self.func("LOWER", text) - return text - - def inputoutputformat_sql(self, expression: exp.InputOutputFormat) -> str: - input_format = self.sql(expression, "input_format") - input_format = f"INPUTFORMAT {input_format}" if input_format else "" - output_format = self.sql(expression, "output_format") - output_format = f"OUTPUTFORMAT {output_format}" if output_format else "" - return self.sep().join((input_format, output_format)) - - def national_sql(self, expression: exp.National, prefix: str = "N") -> str: - string = self.sql(exp.Literal.string(expression.name)) - return f"{prefix}{string}" - - def partition_sql(self, expression: exp.Partition) -> str: - partition_keyword = ( - "SUBPARTITION" if expression.args.get("subpartition") else "PARTITION" - ) - return f"{partition_keyword}({self.expressions(expression, flat=True)})" - - def properties_sql(self, expression: exp.Properties) -> str: - root_properties = [] - with_properties = [] - - for p in expression.expressions: - p_loc = self.PROPERTIES_LOCATION[p.__class__] - if p_loc == exp.Properties.Location.POST_WITH: - with_properties.append(p) - elif p_loc == exp.Properties.Location.POST_SCHEMA: - root_properties.append(p) - - root_props_ast = exp.Properties(expressions=root_properties) - root_props_ast.parent = expression.parent - - with_props_ast = exp.Properties(expressions=with_properties) - with_props_ast.parent = expression.parent - - root_props = self.root_properties(root_props_ast) - with_props = self.with_properties(with_props_ast) - - if root_props and with_props and not self.pretty: - with_props = " " + with_props - - return root_props + with_props - - def root_properties(self, properties: exp.Properties) -> str: - if properties.expressions: - return self.expressions(properties, indent=False, sep=" ") - return "" - - def properties( - self, - properties: exp.Properties, - prefix: str = "", - sep: str = ", ", - suffix: str = "", - wrapped: bool = True, - ) -> str: - if properties.expressions: - expressions = self.expressions(properties, sep=sep, indent=False) - if expressions: - expressions = self.wrap(expressions) if wrapped else expressions - return f"{prefix}{' ' if prefix.strip() else ''}{expressions}{suffix}" - return "" - - def with_properties(self, properties: exp.Properties) -> str: - return self.properties( - properties, prefix=self.seg(self.WITH_PROPERTIES_PREFIX, sep="") - ) - - def locate_properties(self, properties: exp.Properties) -> t.DefaultDict: - properties_locs = defaultdict(list) - for p in properties.expressions: - p_loc = self.PROPERTIES_LOCATION[p.__class__] - if p_loc != exp.Properties.Location.UNSUPPORTED: - properties_locs[p_loc].append(p) - else: - self.unsupported(f"Unsupported property {p.key}") - - return properties_locs - - def property_name(self, expression: exp.Property, string_key: bool = False) -> str: - if isinstance(expression.this, exp.Dot): - return self.sql(expression, "this") - return f"'{expression.name}'" if string_key else expression.name - - def property_sql(self, expression: exp.Property) -> str: - property_cls = expression.__class__ - if property_cls == exp.Property: - return f"{self.property_name(expression)}={self.sql(expression, 'value')}" - - property_name = exp.Properties.PROPERTY_TO_NAME.get(property_cls) - if not property_name: - self.unsupported(f"Unsupported property {expression.key}") - - return f"{property_name}={self.sql(expression, 'this')}" - - def likeproperty_sql(self, expression: exp.LikeProperty) -> str: - if self.SUPPORTS_CREATE_TABLE_LIKE: - options = " ".join( - f"{e.name} {self.sql(e, 'value')}" for e in expression.expressions - ) - options = f" {options}" if options else "" - - like = f"LIKE {self.sql(expression, 'this')}{options}" - if self.LIKE_PROPERTY_INSIDE_SCHEMA and not isinstance( - expression.parent, exp.Schema - ): - like = f"({like})" - - return like - - if expression.expressions: - self.unsupported("Transpilation of LIKE property options is unsupported") - - select = exp.select("*").from_(expression.this).limit(0) - return f"AS {self.sql(select)}" - - def fallbackproperty_sql(self, expression: exp.FallbackProperty) -> str: - no = "NO " if expression.args.get("no") else "" - protection = " PROTECTION" if expression.args.get("protection") else "" - return f"{no}FALLBACK{protection}" - - def journalproperty_sql(self, expression: exp.JournalProperty) -> str: - no = "NO " if expression.args.get("no") else "" - local = expression.args.get("local") - local = f"{local} " if local else "" - dual = "DUAL " if expression.args.get("dual") else "" - before = "BEFORE " if expression.args.get("before") else "" - after = "AFTER " if expression.args.get("after") else "" - return f"{no}{local}{dual}{before}{after}JOURNAL" - - def freespaceproperty_sql(self, expression: exp.FreespaceProperty) -> str: - freespace = self.sql(expression, "this") - percent = " PERCENT" if expression.args.get("percent") else "" - return f"FREESPACE={freespace}{percent}" - - def checksumproperty_sql(self, expression: exp.ChecksumProperty) -> str: - if expression.args.get("default"): - property = "DEFAULT" - elif expression.args.get("on"): - property = "ON" - else: - property = "OFF" - return f"CHECKSUM={property}" - - def mergeblockratioproperty_sql( - self, expression: exp.MergeBlockRatioProperty - ) -> str: - if expression.args.get("no"): - return "NO MERGEBLOCKRATIO" - if expression.args.get("default"): - return "DEFAULT MERGEBLOCKRATIO" - - percent = " PERCENT" if expression.args.get("percent") else "" - return f"MERGEBLOCKRATIO={self.sql(expression, 'this')}{percent}" - - def datablocksizeproperty_sql(self, expression: exp.DataBlocksizeProperty) -> str: - default = expression.args.get("default") - minimum = expression.args.get("minimum") - maximum = expression.args.get("maximum") - if default or minimum or maximum: - if default: - prop = "DEFAULT" - elif minimum: - prop = "MINIMUM" - else: - prop = "MAXIMUM" - return f"{prop} DATABLOCKSIZE" - units = expression.args.get("units") - units = f" {units}" if units else "" - return f"DATABLOCKSIZE={self.sql(expression, 'size')}{units}" - - def blockcompressionproperty_sql( - self, expression: exp.BlockCompressionProperty - ) -> str: - autotemp = expression.args.get("autotemp") - always = expression.args.get("always") - default = expression.args.get("default") - manual = expression.args.get("manual") - never = expression.args.get("never") - - if autotemp is not None: - prop = f"AUTOTEMP({self.expressions(autotemp)})" - elif always: - prop = "ALWAYS" - elif default: - prop = "DEFAULT" - elif manual: - prop = "MANUAL" - elif never: - prop = "NEVER" - return f"BLOCKCOMPRESSION={prop}" - - def isolatedloadingproperty_sql( - self, expression: exp.IsolatedLoadingProperty - ) -> str: - no = expression.args.get("no") - no = " NO" if no else "" - concurrent = expression.args.get("concurrent") - concurrent = " CONCURRENT" if concurrent else "" - target = self.sql(expression, "target") - target = f" {target}" if target else "" - return f"WITH{no}{concurrent} ISOLATED LOADING{target}" - - def partitionboundspec_sql(self, expression: exp.PartitionBoundSpec) -> str: - if isinstance(expression.this, list): - return f"IN ({self.expressions(expression, key='this', flat=True)})" - if expression.this: - modulus = self.sql(expression, "this") - remainder = self.sql(expression, "expression") - return f"WITH (MODULUS {modulus}, REMAINDER {remainder})" - - from_expressions = self.expressions( - expression, key="from_expressions", flat=True - ) - to_expressions = self.expressions(expression, key="to_expressions", flat=True) - return f"FROM ({from_expressions}) TO ({to_expressions})" - - def partitionedofproperty_sql(self, expression: exp.PartitionedOfProperty) -> str: - this = self.sql(expression, "this") - - for_values_or_default = expression.expression - if isinstance(for_values_or_default, exp.PartitionBoundSpec): - for_values_or_default = f" FOR VALUES {self.sql(for_values_or_default)}" - else: - for_values_or_default = " DEFAULT" - - return f"PARTITION OF {this}{for_values_or_default}" - - def lockingproperty_sql(self, expression: exp.LockingProperty) -> str: - kind = expression.args.get("kind") - this = f" {self.sql(expression, 'this')}" if expression.this else "" - for_or_in = expression.args.get("for_or_in") - for_or_in = f" {for_or_in}" if for_or_in else "" - lock_type = expression.args.get("lock_type") - override = " OVERRIDE" if expression.args.get("override") else "" - return f"LOCKING {kind}{this}{for_or_in} {lock_type}{override}" - - def withdataproperty_sql(self, expression: exp.WithDataProperty) -> str: - data_sql = f"WITH {'NO ' if expression.args.get('no') else ''}DATA" - statistics = expression.args.get("statistics") - statistics_sql = "" - if statistics is not None: - statistics_sql = f" AND {'NO ' if not statistics else ''}STATISTICS" - return f"{data_sql}{statistics_sql}" - - def withsystemversioningproperty_sql( - self, expression: exp.WithSystemVersioningProperty - ) -> str: - this = self.sql(expression, "this") - this = f"HISTORY_TABLE={this}" if this else "" - data_consistency: t.Optional[str] = self.sql(expression, "data_consistency") - data_consistency = ( - f"DATA_CONSISTENCY_CHECK={data_consistency}" if data_consistency else None - ) - retention_period: t.Optional[str] = self.sql(expression, "retention_period") - retention_period = ( - f"HISTORY_RETENTION_PERIOD={retention_period}" if retention_period else None - ) - - if this: - on_sql = self.func("ON", this, data_consistency, retention_period) - else: - on_sql = "ON" if expression.args.get("on") else "OFF" - - sql = f"SYSTEM_VERSIONING={on_sql}" - - return f"WITH({sql})" if expression.args.get("with_") else sql - - def insert_sql(self, expression: exp.Insert) -> str: - hint = self.sql(expression, "hint") - overwrite = expression.args.get("overwrite") - - if isinstance(expression.this, exp.Directory): - this = " OVERWRITE" if overwrite else " INTO" - else: - this = self.INSERT_OVERWRITE if overwrite else " INTO" - - stored = self.sql(expression, "stored") - stored = f" {stored}" if stored else "" - alternative = expression.args.get("alternative") - alternative = f" OR {alternative}" if alternative else "" - ignore = " IGNORE" if expression.args.get("ignore") else "" - is_function = expression.args.get("is_function") - if is_function: - this = f"{this} FUNCTION" - this = f"{this} {self.sql(expression, 'this')}" - - exists = " IF EXISTS" if expression.args.get("exists") else "" - where = self.sql(expression, "where") - where = f"{self.sep()}REPLACE WHERE {where}" if where else "" - expression_sql = f"{self.sep()}{self.sql(expression, 'expression')}" - on_conflict = self.sql(expression, "conflict") - on_conflict = f" {on_conflict}" if on_conflict else "" - by_name = " BY NAME" if expression.args.get("by_name") else "" - default_values = "DEFAULT VALUES" if expression.args.get("default") else "" - returning = self.sql(expression, "returning") - - if self.RETURNING_END: - expression_sql = f"{expression_sql}{on_conflict}{default_values}{returning}" - else: - expression_sql = f"{returning}{expression_sql}{on_conflict}" - - partition_by = self.sql(expression, "partition") - partition_by = f" {partition_by}" if partition_by else "" - settings = self.sql(expression, "settings") - settings = f" {settings}" if settings else "" - - source = self.sql(expression, "source") - source = f"TABLE {source}" if source else "" - - sql = f"INSERT{hint}{alternative}{ignore}{this}{stored}{by_name}{exists}{partition_by}{settings}{where}{expression_sql}{source}" - return self.prepend_ctes(expression, sql) - - def introducer_sql(self, expression: exp.Introducer) -> str: - return f"{self.sql(expression, 'this')} {self.sql(expression, 'expression')}" - - def kill_sql(self, expression: exp.Kill) -> str: - kind = self.sql(expression, "kind") - kind = f" {kind}" if kind else "" - this = self.sql(expression, "this") - this = f" {this}" if this else "" - return f"KILL{kind}{this}" - - def pseudotype_sql(self, expression: exp.PseudoType) -> str: - return expression.name - - def objectidentifier_sql(self, expression: exp.ObjectIdentifier) -> str: - return expression.name - - def onconflict_sql(self, expression: exp.OnConflict) -> str: - conflict = ( - "ON DUPLICATE KEY" if expression.args.get("duplicate") else "ON CONFLICT" - ) - - constraint = self.sql(expression, "constraint") - constraint = f" ON CONSTRAINT {constraint}" if constraint else "" - - conflict_keys = self.expressions(expression, key="conflict_keys", flat=True) - conflict_keys = f"({conflict_keys}) " if conflict_keys else " " - action = self.sql(expression, "action") - - expressions = self.expressions(expression, flat=True) - if expressions: - set_keyword = "SET " if self.DUPLICATE_KEY_UPDATE_WITH_SET else "" - expressions = f" {set_keyword}{expressions}" - - where = self.sql(expression, "where") - return f"{conflict}{constraint}{conflict_keys}{action}{expressions}{where}" - - def returning_sql(self, expression: exp.Returning) -> str: - return f"{self.seg('RETURNING')} {self.expressions(expression, flat=True)}" - - def rowformatdelimitedproperty_sql( - self, expression: exp.RowFormatDelimitedProperty - ) -> str: - fields = self.sql(expression, "fields") - fields = f" FIELDS TERMINATED BY {fields}" if fields else "" - escaped = self.sql(expression, "escaped") - escaped = f" ESCAPED BY {escaped}" if escaped else "" - items = self.sql(expression, "collection_items") - items = f" COLLECTION ITEMS TERMINATED BY {items}" if items else "" - keys = self.sql(expression, "map_keys") - keys = f" MAP KEYS TERMINATED BY {keys}" if keys else "" - lines = self.sql(expression, "lines") - lines = f" LINES TERMINATED BY {lines}" if lines else "" - null = self.sql(expression, "null") - null = f" NULL DEFINED AS {null}" if null else "" - return f"ROW FORMAT DELIMITED{fields}{escaped}{items}{keys}{lines}{null}" - - def withtablehint_sql(self, expression: exp.WithTableHint) -> str: - return f"WITH ({self.expressions(expression, flat=True)})" - - def indextablehint_sql(self, expression: exp.IndexTableHint) -> str: - this = f"{self.sql(expression, 'this')} INDEX" - target = self.sql(expression, "target") - target = f" FOR {target}" if target else "" - return f"{this}{target} ({self.expressions(expression, flat=True)})" - - def historicaldata_sql(self, expression: exp.HistoricalData) -> str: - this = self.sql(expression, "this") - kind = self.sql(expression, "kind") - expr = self.sql(expression, "expression") - return f"{this} ({kind} => {expr})" - - def table_parts(self, expression: exp.Table) -> str: - return ".".join( - self.sql(part) - for part in ( - expression.args.get("catalog"), - expression.args.get("db"), - expression.args.get("this"), - ) - if part is not None - ) - - def table_sql(self, expression: exp.Table, sep: str = " AS ") -> str: - table = self.table_parts(expression) - only = "ONLY " if expression.args.get("only") else "" - partition = self.sql(expression, "partition") - partition = f" {partition}" if partition else "" - version = self.sql(expression, "version") - version = f" {version}" if version else "" - alias = self.sql(expression, "alias") - alias = f"{sep}{alias}" if alias else "" - - sample = self.sql(expression, "sample") - if self.dialect.ALIAS_POST_TABLESAMPLE: - sample_pre_alias = sample - sample_post_alias = "" - else: - sample_pre_alias = "" - sample_post_alias = sample - - hints = self.expressions(expression, key="hints", sep=" ") - hints = f" {hints}" if hints and self.TABLE_HINTS else "" - pivots = self.expressions(expression, key="pivots", sep="", flat=True) - joins = self.indent( - self.expressions(expression, key="joins", sep="", flat=True), - skip_first=True, - ) - laterals = self.expressions(expression, key="laterals", sep="") - - file_format = self.sql(expression, "format") - if file_format: - pattern = self.sql(expression, "pattern") - pattern = f", PATTERN => {pattern}" if pattern else "" - file_format = f" (FILE_FORMAT => {file_format}{pattern})" - - ordinality = expression.args.get("ordinality") or "" - if ordinality: - ordinality = f" WITH ORDINALITY{alias}" - alias = "" - - when = self.sql(expression, "when") - if when: - table = f"{table} {when}" - - changes = self.sql(expression, "changes") - changes = f" {changes}" if changes else "" - - rows_from = self.expressions(expression, key="rows_from") - if rows_from: - table = f"ROWS FROM {self.wrap(rows_from)}" - - indexed = expression.args.get("indexed") - if indexed is not None: - indexed = f" INDEXED BY {self.sql(indexed)}" if indexed else " NOT INDEXED" - else: - indexed = "" - - # Workaround https://github.com/tobymao/sqlglot/issues/7073 - return f"{only}{table}{changes}{alias}{partition}{version}{file_format}{sample_pre_alias}{indexed}{hints}{pivots}{sample_post_alias}{joins}{laterals}{ordinality}" - - def tablefromrows_sql(self, expression: exp.TableFromRows) -> str: - table = self.func("TABLE", expression.this) - alias = self.sql(expression, "alias") - alias = f" AS {alias}" if alias else "" - sample = self.sql(expression, "sample") - pivots = self.expressions(expression, key="pivots", sep="", flat=True) - joins = self.indent( - self.expressions(expression, key="joins", sep="", flat=True), - skip_first=True, - ) - return f"{table}{alias}{pivots}{sample}{joins}" - - def tablesample_sql( - self, - expression: exp.TableSample, - tablesample_keyword: t.Optional[str] = None, - ) -> str: - method = self.sql(expression, "method") - method = f"{method} " if method and self.TABLESAMPLE_WITH_METHOD else "" - numerator = self.sql(expression, "bucket_numerator") - denominator = self.sql(expression, "bucket_denominator") - field = self.sql(expression, "bucket_field") - field = f" ON {field}" if field else "" - bucket = f"BUCKET {numerator} OUT OF {denominator}{field}" if numerator else "" - seed = self.sql(expression, "seed") - seed = f" {self.TABLESAMPLE_SEED_KEYWORD} ({seed})" if seed else "" - - size = self.sql(expression, "size") - if size and self.TABLESAMPLE_SIZE_IS_ROWS: - size = f"{size} ROWS" - - percent = self.sql(expression, "percent") - if percent and not self.dialect.TABLESAMPLE_SIZE_IS_PERCENT: - percent = f"{percent} PERCENT" - - expr = f"{bucket}{percent}{size}" - if self.TABLESAMPLE_REQUIRES_PARENS: - expr = f"({expr})" - - return ( - f" {tablesample_keyword or self.TABLESAMPLE_KEYWORDS} {method}{expr}{seed}" - ) - - def pivot_sql(self, expression: exp.Pivot) -> str: - expressions = self.expressions(expression, flat=True) - direction = "UNPIVOT" if expression.unpivot else "PIVOT" - - group = self.sql(expression, "group") - - if expression.this: - this = self.sql(expression, "this") - if not expressions: - sql = f"UNPIVOT {this}" - else: - on = f"{self.seg('ON')} {expressions}" - into = self.sql(expression, "into") - into = f"{self.seg('INTO')} {into}" if into else "" - using = self.expressions(expression, key="using", flat=True) - using = f"{self.seg('USING')} {using}" if using else "" - sql = f"{direction} {this}{on}{into}{using}{group}" - return self.prepend_ctes(expression, sql) - - alias = self.sql(expression, "alias") - alias = f" AS {alias}" if alias else "" - - fields = self.expressions( - expression, - "fields", - sep=" ", - dynamic=True, - new_line=True, - skip_first=True, - skip_last=True, - ) - - include_nulls = expression.args.get("include_nulls") - if include_nulls is not None: - nulls = " INCLUDE NULLS " if include_nulls else " EXCLUDE NULLS " - else: - nulls = "" - - default_on_null = self.sql(expression, "default_on_null") - default_on_null = ( - f" DEFAULT ON NULL ({default_on_null})" if default_on_null else "" - ) - sql = f"{self.seg(direction)}{nulls}({expressions} FOR {fields}{default_on_null}{group}){alias}" - return self.prepend_ctes(expression, sql) - - def version_sql(self, expression: exp.Version) -> str: - this = f"FOR {expression.name}" - kind = expression.text("kind") - expr = self.sql(expression, "expression") - return f"{this} {kind} {expr}" - - def tuple_sql(self, expression: exp.Tuple) -> str: - return f"({self.expressions(expression, dynamic=True, new_line=True, skip_first=True, skip_last=True)})" - - def update_sql(self, expression: exp.Update) -> str: - this = self.sql(expression, "this") - set_sql = self.expressions(expression, flat=True) - from_sql = self.sql(expression, "from_") - where_sql = self.sql(expression, "where") - returning = self.sql(expression, "returning") - order = self.sql(expression, "order") - limit = self.sql(expression, "limit") - if self.RETURNING_END: - expression_sql = f"{from_sql}{where_sql}{returning}" - else: - expression_sql = f"{returning}{from_sql}{where_sql}" - options = self.expressions(expression, key="options") - options = f" OPTION({options})" if options else "" - sql = f"UPDATE {this} SET {set_sql}{expression_sql}{order}{limit}{options}" - return self.prepend_ctes(expression, sql) - - def values_sql(self, expression: exp.Values, values_as_table: bool = True) -> str: - values_as_table = values_as_table and self.VALUES_AS_TABLE - - # The VALUES clause is still valid in an `INSERT INTO ..` statement, for example - if values_as_table or not expression.find_ancestor(exp.From, exp.Join): - args = self.expressions(expression) - alias = self.sql(expression, "alias") - values = f"VALUES{self.seg('')}{args}" - values = ( - f"({values})" - if self.WRAP_DERIVED_VALUES - and (alias or isinstance(expression.parent, (exp.From, exp.Table))) - else values - ) - values = self.query_modifiers(expression, values) - return f"{values} AS {alias}" if alias else values - - # Converts `VALUES...` expression into a series of select unions. - alias_node = expression.args.get("alias") - column_names = alias_node and alias_node.columns - - selects: t.List[exp.Query] = [] - - for i, tup in enumerate(expression.expressions): - row = tup.expressions - - if i == 0 and column_names: - row = [ - exp.alias_(value, column_name) - for value, column_name in zip(row, column_names) - ] - - selects.append(exp.Select(expressions=row)) - - if self.pretty: - # This may result in poor performance for large-cardinality `VALUES` tables, due to - # the deep nesting of the resulting exp.Unions. If this is a problem, either increase - # `sys.setrecursionlimit` to avoid RecursionErrors, or don't set `pretty`. - query = reduce( - lambda x, y: exp.union(x, y, distinct=False, copy=False), selects - ) - return self.subquery_sql( - query.subquery(alias_node and alias_node.this, copy=False) - ) - - alias = f" AS {self.sql(alias_node, 'this')}" if alias_node else "" - unions = " UNION ALL ".join(self.sql(select) for select in selects) - return f"({unions}){alias}" - - def var_sql(self, expression: exp.Var) -> str: - return self.sql(expression, "this") - - @unsupported_args("expressions") - def into_sql(self, expression: exp.Into) -> str: - temporary = " TEMPORARY" if expression.args.get("temporary") else "" - unlogged = " UNLOGGED" if expression.args.get("unlogged") else "" - return ( - f"{self.seg('INTO')}{temporary or unlogged} {self.sql(expression, 'this')}" - ) - - def from_sql(self, expression: exp.From) -> str: - return f"{self.seg('FROM')} {self.sql(expression, 'this')}" - - def groupingsets_sql(self, expression: exp.GroupingSets) -> str: - grouping_sets = self.expressions(expression, indent=False) - return f"GROUPING SETS {self.wrap(grouping_sets)}" - - def rollup_sql(self, expression: exp.Rollup) -> str: - expressions = self.expressions(expression, indent=False) - return f"ROLLUP {self.wrap(expressions)}" if expressions else "WITH ROLLUP" - - def cube_sql(self, expression: exp.Cube) -> str: - expressions = self.expressions(expression, indent=False) - return f"CUBE {self.wrap(expressions)}" if expressions else "WITH CUBE" - - def group_sql(self, expression: exp.Group) -> str: - group_by_all = expression.args.get("all") - if group_by_all is True: - modifier = " ALL" - elif group_by_all is False: - modifier = " DISTINCT" - else: - modifier = "" - - group_by = self.op_expressions(f"GROUP BY{modifier}", expression) - - grouping_sets = self.expressions(expression, key="grouping_sets") - cube = self.expressions(expression, key="cube") - rollup = self.expressions(expression, key="rollup") - - groupings = csv( - self.seg(grouping_sets) if grouping_sets else "", - self.seg(cube) if cube else "", - self.seg(rollup) if rollup else "", - self.seg("WITH TOTALS") if expression.args.get("totals") else "", - sep=self.GROUPINGS_SEP, - ) - - if ( - expression.expressions - and groupings - and groupings.strip() not in ("WITH CUBE", "WITH ROLLUP") - ): - group_by = f"{group_by}{self.GROUPINGS_SEP}" - - return f"{group_by}{groupings}" - - def having_sql(self, expression: exp.Having) -> str: - this = self.indent(self.sql(expression, "this")) - return f"{self.seg('HAVING')}{self.sep()}{this}" - - def connect_sql(self, expression: exp.Connect) -> str: - start = self.sql(expression, "start") - start = self.seg(f"START WITH {start}") if start else "" - nocycle = " NOCYCLE" if expression.args.get("nocycle") else "" - connect = self.sql(expression, "connect") - connect = self.seg(f"CONNECT BY{nocycle} {connect}") - return start + connect - - def prior_sql(self, expression: exp.Prior) -> str: - return f"PRIOR {self.sql(expression, 'this')}" - - def join_sql(self, expression: exp.Join) -> str: - if not self.SEMI_ANTI_JOIN_WITH_SIDE and expression.kind in ("SEMI", "ANTI"): - side = None - else: - side = expression.side - - op_sql = " ".join( - op - for op in ( - expression.method, - "GLOBAL" if expression.args.get("global_") else None, - side, - expression.kind, - expression.hint if self.JOIN_HINTS else None, - ) - if op - ) - match_cond = self.sql(expression, "match_condition") - match_cond = f" MATCH_CONDITION ({match_cond})" if match_cond else "" - on_sql = self.sql(expression, "on") - using = expression.args.get("using") - - if not on_sql and using: - on_sql = csv(*(self.sql(column) for column in using)) - - this = expression.this - this_sql = self.sql(this) - - exprs = self.expressions(expression) - if exprs: - this_sql = f"{this_sql},{self.seg(exprs)}" - - if on_sql: - on_sql = self.indent(on_sql, skip_first=True) - space = self.seg(" " * self.pad) if self.pretty else " " - if using: - on_sql = f"{space}USING ({on_sql})" - else: - on_sql = f"{space}ON {on_sql}" - elif not op_sql: - if ( - isinstance(this, exp.Lateral) - and this.args.get("cross_apply") is not None - ): - return f" {this_sql}" - - return f", {this_sql}" - - if op_sql != "STRAIGHT_JOIN": - op_sql = f"{op_sql} JOIN" if op_sql else "JOIN" - - pivots = self.expressions(expression, key="pivots", sep="", flat=True) - return f"{self.seg(op_sql)} {this_sql}{match_cond}{on_sql}{pivots}" - - def lambda_sql( - self, expression: exp.Lambda, arrow_sep: str = "->", wrap: bool = True - ) -> str: - args = self.expressions(expression, flat=True) - args = f"({args})" if wrap and len(args.split(",")) > 1 else args - return f"{args} {arrow_sep} {self.sql(expression, 'this')}" - - def lateral_op(self, expression: exp.Lateral) -> str: - cross_apply = expression.args.get("cross_apply") - - # https://www.mssqltips.com/sqlservertip/1958/sql-server-cross-apply-and-outer-apply/ - if cross_apply is True: - op = "INNER JOIN " - elif cross_apply is False: - op = "LEFT JOIN " - else: - op = "" - - return f"{op}LATERAL" - - def lateral_sql(self, expression: exp.Lateral) -> str: - this = self.sql(expression, "this") - - if expression.args.get("view"): - alias = expression.args["alias"] - columns = self.expressions(alias, key="columns", flat=True) - table = f" {alias.name}" if alias.name else "" - columns = f" AS {columns}" if columns else "" - op_sql = self.seg( - f"LATERAL VIEW{' OUTER' if expression.args.get('outer') else ''}" - ) - return f"{op_sql}{self.sep()}{this}{table}{columns}" - - alias = self.sql(expression, "alias") - alias = f" AS {alias}" if alias else "" - - ordinality = expression.args.get("ordinality") or "" - if ordinality: - ordinality = f" WITH ORDINALITY{alias}" - alias = "" - - return f"{self.lateral_op(expression)} {this}{alias}{ordinality}" - - def limit_sql(self, expression: exp.Limit, top: bool = False) -> str: - this = self.sql(expression, "this") - - args = [ - self._simplify_unless_literal(e) if self.LIMIT_ONLY_LITERALS else e - for e in (expression.args.get(k) for k in ("offset", "expression")) - if e - ] - - args_sql = ", ".join(self.sql(e) for e in args) - args_sql = ( - f"({args_sql})" if top and any(not e.is_number for e in args) else args_sql - ) - expressions = self.expressions(expression, flat=True) - limit_options = self.sql(expression, "limit_options") - expressions = f" BY {expressions}" if expressions else "" - - return f"{this}{self.seg('TOP' if top else 'LIMIT')} {args_sql}{limit_options}{expressions}" - - def offset_sql(self, expression: exp.Offset) -> str: - this = self.sql(expression, "this") - value = expression.expression - value = ( - self._simplify_unless_literal(value) if self.LIMIT_ONLY_LITERALS else value - ) - expressions = self.expressions(expression, flat=True) - expressions = f" BY {expressions}" if expressions else "" - return f"{this}{self.seg('OFFSET')} {self.sql(value)}{expressions}" - - def setitem_sql(self, expression: exp.SetItem) -> str: - kind = self.sql(expression, "kind") - if not self.SET_ASSIGNMENT_REQUIRES_VARIABLE_KEYWORD and kind == "VARIABLE": - kind = "" - else: - kind = f"{kind} " if kind else "" - this = self.sql(expression, "this") - expressions = self.expressions(expression) - collate = self.sql(expression, "collate") - collate = f" COLLATE {collate}" if collate else "" - global_ = "GLOBAL " if expression.args.get("global_") else "" - return f"{global_}{kind}{this}{expressions}{collate}" - - def set_sql(self, expression: exp.Set) -> str: - expressions = f" {self.expressions(expression, flat=True)}" - tag = " TAG" if expression.args.get("tag") else "" - return f"{'UNSET' if expression.args.get('unset') else 'SET'}{tag}{expressions}" - - def queryband_sql(self, expression: exp.QueryBand) -> str: - this = self.sql(expression, "this") - update = " UPDATE" if expression.args.get("update") else "" - scope = self.sql(expression, "scope") - scope = f" FOR {scope}" if scope else "" - - return f"QUERY_BAND = {this}{update}{scope}" - - def pragma_sql(self, expression: exp.Pragma) -> str: - return f"PRAGMA {self.sql(expression, 'this')}" - - def lock_sql(self, expression: exp.Lock) -> str: - if not self.LOCKING_READS_SUPPORTED: - self.unsupported("Locking reads using 'FOR UPDATE/SHARE' are not supported") - return "" - - update = expression.args["update"] - key = expression.args.get("key") - if update: - lock_type = "FOR NO KEY UPDATE" if key else "FOR UPDATE" - else: - lock_type = "FOR KEY SHARE" if key else "FOR SHARE" - expressions = self.expressions(expression, flat=True) - expressions = f" OF {expressions}" if expressions else "" - wait = expression.args.get("wait") - - if wait is not None: - if isinstance(wait, exp.Literal): - wait = f" WAIT {self.sql(wait)}" - else: - wait = " NOWAIT" if wait else " SKIP LOCKED" - - return f"{lock_type}{expressions}{wait or ''}" - - def literal_sql(self, expression: exp.Literal) -> str: - text = expression.this or "" - if expression.is_string: - text = f"{self.dialect.QUOTE_START}{self.escape_str(text)}{self.dialect.QUOTE_END}" - return text - - def escape_str( - self, - text: str, - escape_backslash: bool = True, - delimiter: t.Optional[str] = None, - escaped_delimiter: t.Optional[str] = None, - ) -> str: - if self.dialect.ESCAPED_SEQUENCES: - to_escaped = self.dialect.ESCAPED_SEQUENCES - text = "".join( - to_escaped.get(ch, ch) if escape_backslash or ch != "\\" else ch - for ch in text - ) - - delimiter = delimiter or self.dialect.QUOTE_END - escaped_delimiter = escaped_delimiter or self._escaped_quote_end - - return self._replace_line_breaks(text).replace(delimiter, escaped_delimiter) - - def loaddata_sql(self, expression: exp.LoadData) -> str: - local = " LOCAL" if expression.args.get("local") else "" - inpath = f" INPATH {self.sql(expression, 'inpath')}" - overwrite = " OVERWRITE" if expression.args.get("overwrite") else "" - this = f" INTO TABLE {self.sql(expression, 'this')}" - partition = self.sql(expression, "partition") - partition = f" {partition}" if partition else "" - input_format = self.sql(expression, "input_format") - input_format = f" INPUTFORMAT {input_format}" if input_format else "" - serde = self.sql(expression, "serde") - serde = f" SERDE {serde}" if serde else "" - return ( - f"LOAD DATA{local}{inpath}{overwrite}{this}{partition}{input_format}{serde}" - ) - - def null_sql(self, *_) -> str: - return "NULL" - - def boolean_sql(self, expression: exp.Boolean) -> str: - return "TRUE" if expression.this else "FALSE" - - def booland_sql(self, expression: exp.Booland) -> str: - return f"(({self.sql(expression, 'this')}) AND ({self.sql(expression, 'expression')}))" - - def boolor_sql(self, expression: exp.Boolor) -> str: - return f"(({self.sql(expression, 'this')}) OR ({self.sql(expression, 'expression')}))" - - def order_sql(self, expression: exp.Order, flat: bool = False) -> str: - this = self.sql(expression, "this") - this = f"{this} " if this else this - siblings = "SIBLINGS " if expression.args.get("siblings") else "" - return self.op_expressions( - f"{this}ORDER {siblings}BY", expression, flat=this or flat - ) # type: ignore - - def withfill_sql(self, expression: exp.WithFill) -> str: - from_sql = self.sql(expression, "from_") - from_sql = f" FROM {from_sql}" if from_sql else "" - to_sql = self.sql(expression, "to") - to_sql = f" TO {to_sql}" if to_sql else "" - step_sql = self.sql(expression, "step") - step_sql = f" STEP {step_sql}" if step_sql else "" - interpolated_values = [ - f"{self.sql(e, 'alias')} AS {self.sql(e, 'this')}" - if isinstance(e, exp.Alias) - else self.sql(e, "this") - for e in expression.args.get("interpolate") or [] - ] - interpolate = ( - f" INTERPOLATE ({', '.join(interpolated_values)})" - if interpolated_values - else "" - ) - return f"WITH FILL{from_sql}{to_sql}{step_sql}{interpolate}" - - def cluster_sql(self, expression: exp.Cluster) -> str: - return self.op_expressions("CLUSTER BY", expression) - - def distribute_sql(self, expression: exp.Distribute) -> str: - return self.op_expressions("DISTRIBUTE BY", expression) - - def sort_sql(self, expression: exp.Sort) -> str: - return self.op_expressions("SORT BY", expression) - - def ordered_sql(self, expression: exp.Ordered) -> str: - desc = expression.args.get("desc") - asc = not desc - - nulls_first = expression.args.get("nulls_first") - nulls_last = not nulls_first - nulls_are_large = self.dialect.NULL_ORDERING == "nulls_are_large" - nulls_are_small = self.dialect.NULL_ORDERING == "nulls_are_small" - nulls_are_last = self.dialect.NULL_ORDERING == "nulls_are_last" - - this = self.sql(expression, "this") - - sort_order = " DESC" if desc else (" ASC" if desc is False else "") - nulls_sort_change = "" - if nulls_first and ( - (asc and nulls_are_large) or (desc and nulls_are_small) or nulls_are_last - ): - nulls_sort_change = " NULLS FIRST" - elif ( - nulls_last - and ((asc and nulls_are_small) or (desc and nulls_are_large)) - and not nulls_are_last - ): - nulls_sort_change = " NULLS LAST" - - # If the NULLS FIRST/LAST clause is unsupported, we add another sort key to simulate it - if nulls_sort_change and not self.NULL_ORDERING_SUPPORTED: - window = expression.find_ancestor(exp.Window, exp.Select) - if isinstance(window, exp.Window) and window.args.get("spec"): - self.unsupported( - f"'{nulls_sort_change.strip()}' translation not supported in window functions" - ) - nulls_sort_change = "" - elif self.NULL_ORDERING_SUPPORTED is False and ( - (asc and nulls_sort_change == " NULLS LAST") - or (desc and nulls_sort_change == " NULLS FIRST") - ): - # BigQuery does not allow these ordering/nulls combinations when used under - # an aggregation func or under a window containing one - ancestor = expression.find_ancestor(exp.AggFunc, exp.Window, exp.Select) - - if isinstance(ancestor, exp.Window): - ancestor = ancestor.this - if isinstance(ancestor, exp.AggFunc): - self.unsupported( - f"'{nulls_sort_change.strip()}' translation not supported for aggregate functions with {sort_order} sort order" - ) - nulls_sort_change = "" - elif self.NULL_ORDERING_SUPPORTED is None: - if expression.this.is_int: - self.unsupported( - f"'{nulls_sort_change.strip()}' translation not supported with positional ordering" - ) - elif not isinstance(expression.this, exp.Rand): - null_sort_order = ( - " DESC" if nulls_sort_change == " NULLS FIRST" else "" - ) - this = f"CASE WHEN {this} IS NULL THEN 1 ELSE 0 END{null_sort_order}, {this}" - nulls_sort_change = "" - - with_fill = self.sql(expression, "with_fill") - with_fill = f" {with_fill}" if with_fill else "" - - return f"{this}{sort_order}{nulls_sort_change}{with_fill}" - - def matchrecognizemeasure_sql(self, expression: exp.MatchRecognizeMeasure) -> str: - window_frame = self.sql(expression, "window_frame") - window_frame = f"{window_frame} " if window_frame else "" - - this = self.sql(expression, "this") - - return f"{window_frame}{this}" - - def matchrecognize_sql(self, expression: exp.MatchRecognize) -> str: - partition = self.partition_by_sql(expression) - order = self.sql(expression, "order") - measures = self.expressions(expression, key="measures") - measures = self.seg(f"MEASURES{self.seg(measures)}") if measures else "" - rows = self.sql(expression, "rows") - rows = self.seg(rows) if rows else "" - after = self.sql(expression, "after") - after = self.seg(after) if after else "" - pattern = self.sql(expression, "pattern") - pattern = self.seg(f"PATTERN ({pattern})") if pattern else "" - definition_sqls = [ - f"{self.sql(definition, 'alias')} AS {self.sql(definition, 'this')}" - for definition in expression.args.get("define", []) - ] - definitions = self.expressions(sqls=definition_sqls) - define = self.seg(f"DEFINE{self.seg(definitions)}") if definitions else "" - body = "".join( - ( - partition, - order, - measures, - rows, - after, - pattern, - define, - ) - ) - alias = self.sql(expression, "alias") - alias = f" {alias}" if alias else "" - return f"{self.seg('MATCH_RECOGNIZE')} {self.wrap(body)}{alias}" - - def query_modifiers(self, expression: exp.Expression, *sqls: str) -> str: - limit = expression.args.get("limit") - - if self.LIMIT_FETCH == "LIMIT" and isinstance(limit, exp.Fetch): - limit = exp.Limit(expression=exp.maybe_copy(limit.args.get("count"))) - elif self.LIMIT_FETCH == "FETCH" and isinstance(limit, exp.Limit): - limit = exp.Fetch(direction="FIRST", count=exp.maybe_copy(limit.expression)) - - return csv( - *sqls, - *[self.sql(join) for join in expression.args.get("joins") or []], - self.sql(expression, "match"), - *[self.sql(lateral) for lateral in expression.args.get("laterals") or []], - self.sql(expression, "prewhere"), - self.sql(expression, "where"), - self.sql(expression, "connect"), - self.sql(expression, "group"), - self.sql(expression, "having"), - *[ - gen(self, expression) - for gen in self.AFTER_HAVING_MODIFIER_TRANSFORMS.values() - ], - self.sql(expression, "order"), - *self.offset_limit_modifiers( - expression, isinstance(limit, exp.Fetch), limit - ), - *self.after_limit_modifiers(expression), - self.options_modifier(expression), - self.for_modifiers(expression), - sep="", - ) - - def options_modifier(self, expression: exp.Expression) -> str: - options = self.expressions(expression, key="options") - return f" {options}" if options else "" - - def for_modifiers(self, expression: exp.Expression) -> str: - for_modifiers = self.expressions(expression, key="for_") - return f"{self.sep()}FOR XML{self.seg(for_modifiers)}" if for_modifiers else "" - - def queryoption_sql(self, expression: exp.QueryOption) -> str: - self.unsupported("Unsupported query option.") - return "" - - def offset_limit_modifiers( - self, - expression: exp.Expression, - fetch: bool, - limit: t.Optional[exp.Fetch | exp.Limit], - ) -> t.List[str]: - return [ - self.sql(expression, "offset") if fetch else self.sql(limit), - self.sql(limit) if fetch else self.sql(expression, "offset"), - ] - - def after_limit_modifiers(self, expression: exp.Expression) -> t.List[str]: - locks = self.expressions(expression, key="locks", sep=" ") - locks = f" {locks}" if locks else "" - return [locks, self.sql(expression, "sample")] - - def select_sql(self, expression: exp.Select) -> str: - into = expression.args.get("into") - if not self.SUPPORTS_SELECT_INTO and into: - into.pop() - - hint = self.sql(expression, "hint") - distinct = self.sql(expression, "distinct") - distinct = f" {distinct}" if distinct else "" - kind = self.sql(expression, "kind") - - limit = expression.args.get("limit") - if isinstance(limit, exp.Limit) and self.LIMIT_IS_TOP: - top = self.limit_sql(limit, top=True) - limit.pop() - else: - top = "" - - expressions = self.expressions(expression) - - if kind: - if kind in self.SELECT_KINDS: - kind = f" AS {kind}" - else: - if kind == "STRUCT": - expressions = self.expressions( - sqls=[ - self.sql( - exp.Struct( - expressions=[ - exp.PropertyEQ( - this=e.args.get("alias"), expression=e.this - ) - if isinstance(e, exp.Alias) - else e - for e in expression.expressions - ] - ) - ) - ] - ) - kind = "" - - operation_modifiers = self.expressions( - expression, key="operation_modifiers", sep=" " - ) - operation_modifiers = ( - f"{self.sep()}{operation_modifiers}" if operation_modifiers else "" - ) - - # We use LIMIT_IS_TOP as a proxy for whether DISTINCT should go first because tsql and Teradata - # are the only dialects that use LIMIT_IS_TOP and both place DISTINCT first. - top_distinct = ( - f"{distinct}{hint}{top}" if self.LIMIT_IS_TOP else f"{top}{hint}{distinct}" - ) - expressions = f"{self.sep()}{expressions}" if expressions else expressions - sql = self.query_modifiers( - expression, - f"SELECT{top_distinct}{operation_modifiers}{kind}{expressions}", - self.sql(expression, "into", comment=False), - self.sql(expression, "from_", comment=False), - ) - - # If both the CTE and SELECT clauses have comments, generate the latter earlier - if expression.args.get("with_"): - sql = self.maybe_comment(sql, expression) - expression.pop_comments() - - sql = self.prepend_ctes(expression, sql) - - if not self.SUPPORTS_SELECT_INTO and into: - if into.args.get("temporary"): - table_kind = " TEMPORARY" - elif self.SUPPORTS_UNLOGGED_TABLES and into.args.get("unlogged"): - table_kind = " UNLOGGED" - else: - table_kind = "" - sql = f"CREATE{table_kind} TABLE {self.sql(into.this)} AS {sql}" - - return sql - - def schema_sql(self, expression: exp.Schema) -> str: - this = self.sql(expression, "this") - sql = self.schema_columns_sql(expression) - return f"{this} {sql}" if this and sql else this or sql - - def schema_columns_sql(self, expression: exp.Schema) -> str: - if expression.expressions: - return ( - f"({self.sep('')}{self.expressions(expression)}{self.seg(')', sep='')}" - ) - return "" - - def star_sql(self, expression: exp.Star) -> str: - except_ = self.expressions(expression, key="except_", flat=True) - except_ = f"{self.seg(self.STAR_EXCEPT)} ({except_})" if except_ else "" - replace = self.expressions(expression, key="replace", flat=True) - replace = f"{self.seg('REPLACE')} ({replace})" if replace else "" - rename = self.expressions(expression, key="rename", flat=True) - rename = f"{self.seg('RENAME')} ({rename})" if rename else "" - return f"*{except_}{replace}{rename}" - - def parameter_sql(self, expression: exp.Parameter) -> str: - this = self.sql(expression, "this") - return f"{self.PARAMETER_TOKEN}{this}" - - def sessionparameter_sql(self, expression: exp.SessionParameter) -> str: - this = self.sql(expression, "this") - kind = expression.text("kind") - if kind: - kind = f"{kind}." - return f"@@{kind}{this}" - - def placeholder_sql(self, expression: exp.Placeholder) -> str: - return ( - f"{self.NAMED_PLACEHOLDER_TOKEN}{expression.name}" - if expression.this - else "?" - ) - - def subquery_sql(self, expression: exp.Subquery, sep: str = " AS ") -> str: - alias = self.sql(expression, "alias") - alias = f"{sep}{alias}" if alias else "" - sample = self.sql(expression, "sample") - if self.dialect.ALIAS_POST_TABLESAMPLE and sample: - alias = f"{sample}{alias}" - - # Set to None so it's not generated again by self.query_modifiers() - expression.set("sample", None) - - pivots = self.expressions(expression, key="pivots", sep="", flat=True) - sql = self.query_modifiers(expression, self.wrap(expression), alias, pivots) - return self.prepend_ctes(expression, sql) - - def qualify_sql(self, expression: exp.Qualify) -> str: - this = self.indent(self.sql(expression, "this")) - return f"{self.seg('QUALIFY')}{self.sep()}{this}" - - def unnest_sql(self, expression: exp.Unnest) -> str: - args = self.expressions(expression, flat=True) - - alias = expression.args.get("alias") - offset = expression.args.get("offset") - - if self.UNNEST_WITH_ORDINALITY: - if alias and isinstance(offset, exp.Expression): - alias.append("columns", offset) - - if alias and self.dialect.UNNEST_COLUMN_ONLY: - columns = alias.columns - alias = self.sql(columns[0]) if columns else "" - else: - alias = self.sql(alias) - - alias = f" AS {alias}" if alias else alias - if self.UNNEST_WITH_ORDINALITY: - suffix = f" WITH ORDINALITY{alias}" if offset else alias - else: - if isinstance(offset, exp.Expression): - suffix = f"{alias} WITH OFFSET AS {self.sql(offset)}" - elif offset: - suffix = f"{alias} WITH OFFSET" - else: - suffix = alias - - return f"UNNEST({args}){suffix}" - - def prewhere_sql(self, expression: exp.PreWhere) -> str: - return "" - - def where_sql(self, expression: exp.Where) -> str: - this = self.indent(self.sql(expression, "this")) - return f"{self.seg('WHERE')}{self.sep()}{this}" - - def window_sql(self, expression: exp.Window) -> str: - this = self.sql(expression, "this") - partition = self.partition_by_sql(expression) - order = expression.args.get("order") - order = self.order_sql(order, flat=True) if order else "" - spec = self.sql(expression, "spec") - alias = self.sql(expression, "alias") - over = self.sql(expression, "over") or "OVER" - - this = f"{this} {'AS' if expression.arg_key == 'windows' else over}" - - first = expression.args.get("first") - if first is None: - first = "" - else: - first = "FIRST" if first else "LAST" - - if not partition and not order and not spec and alias: - return f"{this} {alias}" - - args = self.format_args( - *[arg for arg in (alias, first, partition, order, spec) if arg], sep=" " - ) - return f"{this} ({args})" - - def partition_by_sql(self, expression: exp.Window | exp.MatchRecognize) -> str: - partition = self.expressions(expression, key="partition_by", flat=True) - return f"PARTITION BY {partition}" if partition else "" - - def windowspec_sql(self, expression: exp.WindowSpec) -> str: - kind = self.sql(expression, "kind") - start = csv( - self.sql(expression, "start"), self.sql(expression, "start_side"), sep=" " - ) - end = ( - csv(self.sql(expression, "end"), self.sql(expression, "end_side"), sep=" ") - or "CURRENT ROW" - ) - - window_spec = f"{kind} BETWEEN {start} AND {end}" - - exclude = self.sql(expression, "exclude") - if exclude: - if self.SUPPORTS_WINDOW_EXCLUDE: - window_spec += f" EXCLUDE {exclude}" - else: - self.unsupported("EXCLUDE clause is not supported in the WINDOW clause") - - return window_spec - - def withingroup_sql(self, expression: exp.WithinGroup) -> str: - this = self.sql(expression, "this") - expression_sql = self.sql(expression, "expression")[ - 1: - ] # order has a leading space - return f"{this} WITHIN GROUP ({expression_sql})" - - def between_sql(self, expression: exp.Between) -> str: - this = self.sql(expression, "this") - low = self.sql(expression, "low") - high = self.sql(expression, "high") - symmetric = expression.args.get("symmetric") - - if symmetric and not self.SUPPORTS_BETWEEN_FLAGS: - return ( - f"({this} BETWEEN {low} AND {high} OR {this} BETWEEN {high} AND {low})" - ) - - flag = ( - " SYMMETRIC" - if symmetric - else " ASYMMETRIC" - if symmetric is False and self.SUPPORTS_BETWEEN_FLAGS - else "" # silently drop ASYMMETRIC – semantics identical - ) - return f"{this} BETWEEN{flag} {low} AND {high}" - - def bracket_offset_expressions( - self, expression: exp.Bracket, index_offset: t.Optional[int] = None - ) -> t.List[exp.Expression]: - return apply_index_offset( - expression.this, - expression.expressions, - (index_offset or self.dialect.INDEX_OFFSET) - - expression.args.get("offset", 0), - dialect=self.dialect, - ) - - def bracket_sql(self, expression: exp.Bracket) -> str: - expressions = self.bracket_offset_expressions(expression) - expressions_sql = ", ".join(self.sql(e) for e in expressions) - return f"{self.sql(expression, 'this')}[{expressions_sql}]" - - def all_sql(self, expression: exp.All) -> str: - this = self.sql(expression, "this") - if not isinstance(expression.this, (exp.Tuple, exp.Paren)): - this = self.wrap(this) - return f"ALL {this}" - - def any_sql(self, expression: exp.Any) -> str: - this = self.sql(expression, "this") - if isinstance(expression.this, (*exp.UNWRAPPED_QUERIES, exp.Paren)): - if isinstance(expression.this, exp.UNWRAPPED_QUERIES): - this = self.wrap(this) - return f"ANY{this}" - return f"ANY {this}" - - def exists_sql(self, expression: exp.Exists) -> str: - return f"EXISTS{self.wrap(expression)}" - - def case_sql(self, expression: exp.Case) -> str: - this = self.sql(expression, "this") - statements = [f"CASE {this}" if this else "CASE"] - - for e in expression.args["ifs"]: - statements.append(f"WHEN {self.sql(e, 'this')}") - statements.append(f"THEN {self.sql(e, 'true')}") - - default = self.sql(expression, "default") - - if default: - statements.append(f"ELSE {default}") - - statements.append("END") - - if self.pretty and self.too_wide(statements): - return self.indent("\n".join(statements), skip_first=True, skip_last=True) - - return " ".join(statements) - - def constraint_sql(self, expression: exp.Constraint) -> str: - this = self.sql(expression, "this") - expressions = self.expressions(expression, flat=True) - return f"CONSTRAINT {this} {expressions}" - - def nextvaluefor_sql(self, expression: exp.NextValueFor) -> str: - order = expression.args.get("order") - order = f" OVER ({self.order_sql(order, flat=True)})" if order else "" - return f"NEXT VALUE FOR {self.sql(expression, 'this')}{order}" - - def extract_sql(self, expression: exp.Extract) -> str: - from bigframes_vendored.sqlglot.dialects.dialect import map_date_part - - this = ( - map_date_part(expression.this, self.dialect) - if self.NORMALIZE_EXTRACT_DATE_PARTS - else expression.this - ) - this_sql = self.sql(this) if self.EXTRACT_ALLOWS_QUOTES else this.name - expression_sql = self.sql(expression, "expression") - - return f"EXTRACT({this_sql} FROM {expression_sql})" - - def trim_sql(self, expression: exp.Trim) -> str: - trim_type = self.sql(expression, "position") - - if trim_type == "LEADING": - func_name = "LTRIM" - elif trim_type == "TRAILING": - func_name = "RTRIM" - else: - func_name = "TRIM" - - return self.func(func_name, expression.this, expression.expression) - - def convert_concat_args( - self, expression: exp.Concat | exp.ConcatWs - ) -> t.List[exp.Expression]: - args = expression.expressions - if isinstance(expression, exp.ConcatWs): - args = args[1:] # Skip the delimiter - - if self.dialect.STRICT_STRING_CONCAT and expression.args.get("safe"): - args = [exp.cast(e, exp.DataType.Type.TEXT) for e in args] - - if not self.dialect.CONCAT_COALESCE and expression.args.get("coalesce"): - - def _wrap_with_coalesce(e: exp.Expression) -> exp.Expression: - if not e.type: - from bigframes_vendored.sqlglot.optimizer.annotate_types import ( - annotate_types, - ) - - e = annotate_types(e, dialect=self.dialect) - - if e.is_string or e.is_type(exp.DataType.Type.ARRAY): - return e - - return exp.func("coalesce", e, exp.Literal.string("")) - - args = [_wrap_with_coalesce(e) for e in args] - - return args - - def concat_sql(self, expression: exp.Concat) -> str: - if self.dialect.CONCAT_COALESCE and not expression.args.get("coalesce"): - # Dialect's CONCAT function coalesces NULLs to empty strings, but the expression does not. - # Transpile to double pipe operators, which typically returns NULL if any args are NULL - # instead of coalescing them to empty string. - from bigframes_vendored.sqlglot.dialects.dialect import concat_to_dpipe_sql - - return concat_to_dpipe_sql(self, expression) - - expressions = self.convert_concat_args(expression) - - # Some dialects don't allow a single-argument CONCAT call - if not self.SUPPORTS_SINGLE_ARG_CONCAT and len(expressions) == 1: - return self.sql(expressions[0]) - - return self.func("CONCAT", *expressions) - - def concatws_sql(self, expression: exp.ConcatWs) -> str: - return self.func( - "CONCAT_WS", - seq_get(expression.expressions, 0), - *self.convert_concat_args(expression), - ) - - def check_sql(self, expression: exp.Check) -> str: - this = self.sql(expression, key="this") - return f"CHECK ({this})" - - def foreignkey_sql(self, expression: exp.ForeignKey) -> str: - expressions = self.expressions(expression, flat=True) - expressions = f" ({expressions})" if expressions else "" - reference = self.sql(expression, "reference") - reference = f" {reference}" if reference else "" - delete = self.sql(expression, "delete") - delete = f" ON DELETE {delete}" if delete else "" - update = self.sql(expression, "update") - update = f" ON UPDATE {update}" if update else "" - options = self.expressions(expression, key="options", flat=True, sep=" ") - options = f" {options}" if options else "" - return f"FOREIGN KEY{expressions}{reference}{delete}{update}{options}" - - def primarykey_sql(self, expression: exp.PrimaryKey) -> str: - this = self.sql(expression, "this") - this = f" {this}" if this else "" - expressions = self.expressions(expression, flat=True) - include = self.sql(expression, "include") - options = self.expressions(expression, key="options", flat=True, sep=" ") - options = f" {options}" if options else "" - return f"PRIMARY KEY{this} ({expressions}){include}{options}" - - def if_sql(self, expression: exp.If) -> str: - return self.case_sql( - exp.Case(ifs=[expression], default=expression.args.get("false")) - ) - - def matchagainst_sql(self, expression: exp.MatchAgainst) -> str: - if self.MATCH_AGAINST_TABLE_PREFIX: - expressions = [] - for expr in expression.expressions: - if isinstance(expr, exp.Table): - expressions.append(f"TABLE {self.sql(expr)}") - else: - expressions.append(expr) - else: - expressions = expression.expressions - - modifier = expression.args.get("modifier") - modifier = f" {modifier}" if modifier else "" - return f"{self.func('MATCH', *expressions)} AGAINST({self.sql(expression, 'this')}{modifier})" - - def jsonkeyvalue_sql(self, expression: exp.JSONKeyValue) -> str: - return f"{self.sql(expression, 'this')}{self.JSON_KEY_VALUE_PAIR_SEP} {self.sql(expression, 'expression')}" - - def jsonpath_sql(self, expression: exp.JSONPath) -> str: - path = self.expressions(expression, sep="", flat=True).lstrip(".") - - if expression.args.get("escape"): - path = self.escape_str(path) - - if self.QUOTE_JSON_PATH: - path = f"{self.dialect.QUOTE_START}{path}{self.dialect.QUOTE_END}" - - return path - - def json_path_part(self, expression: int | str | exp.JSONPathPart) -> str: - if isinstance(expression, exp.JSONPathPart): - transform = self.TRANSFORMS.get(expression.__class__) - if not callable(transform): - self.unsupported( - f"Unsupported JSONPathPart type {expression.__class__.__name__}" - ) - return "" - - return transform(self, expression) - - if isinstance(expression, int): - return str(expression) - - if ( - self._quote_json_path_key_using_brackets - and self.JSON_PATH_SINGLE_QUOTE_ESCAPE - ): - escaped = expression.replace("'", "\\'") - escaped = f"\\'{expression}\\'" - else: - escaped = expression.replace('"', '\\"') - escaped = f'"{escaped}"' - - return escaped - - def formatjson_sql(self, expression: exp.FormatJson) -> str: - return f"{self.sql(expression, 'this')} FORMAT JSON" - - def formatphrase_sql(self, expression: exp.FormatPhrase) -> str: - # Output the Teradata column FORMAT override. - # https://docs.teradata.com/r/Enterprise_IntelliFlex_VMware/SQL-Data-Types-and-Literals/Data-Type-Formats-and-Format-Phrases/FORMAT - this = self.sql(expression, "this") - fmt = self.sql(expression, "format") - return f"{this} (FORMAT {fmt})" - - def jsonobject_sql(self, expression: exp.JSONObject | exp.JSONObjectAgg) -> str: - null_handling = expression.args.get("null_handling") - null_handling = f" {null_handling}" if null_handling else "" - - unique_keys = expression.args.get("unique_keys") - if unique_keys is not None: - unique_keys = f" {'WITH' if unique_keys else 'WITHOUT'} UNIQUE KEYS" - else: - unique_keys = "" - - return_type = self.sql(expression, "return_type") - return_type = f" RETURNING {return_type}" if return_type else "" - encoding = self.sql(expression, "encoding") - encoding = f" ENCODING {encoding}" if encoding else "" - - return self.func( - "JSON_OBJECT" - if isinstance(expression, exp.JSONObject) - else "JSON_OBJECTAGG", - *expression.expressions, - suffix=f"{null_handling}{unique_keys}{return_type}{encoding})", - ) - - def jsonobjectagg_sql(self, expression: exp.JSONObjectAgg) -> str: - return self.jsonobject_sql(expression) - - def jsonarray_sql(self, expression: exp.JSONArray) -> str: - null_handling = expression.args.get("null_handling") - null_handling = f" {null_handling}" if null_handling else "" - return_type = self.sql(expression, "return_type") - return_type = f" RETURNING {return_type}" if return_type else "" - strict = " STRICT" if expression.args.get("strict") else "" - return self.func( - "JSON_ARRAY", - *expression.expressions, - suffix=f"{null_handling}{return_type}{strict})", - ) - - def jsonarrayagg_sql(self, expression: exp.JSONArrayAgg) -> str: - this = self.sql(expression, "this") - order = self.sql(expression, "order") - null_handling = expression.args.get("null_handling") - null_handling = f" {null_handling}" if null_handling else "" - return_type = self.sql(expression, "return_type") - return_type = f" RETURNING {return_type}" if return_type else "" - strict = " STRICT" if expression.args.get("strict") else "" - return self.func( - "JSON_ARRAYAGG", - this, - suffix=f"{order}{null_handling}{return_type}{strict})", - ) - - def jsoncolumndef_sql(self, expression: exp.JSONColumnDef) -> str: - path = self.sql(expression, "path") - path = f" PATH {path}" if path else "" - nested_schema = self.sql(expression, "nested_schema") - - if nested_schema: - return f"NESTED{path} {nested_schema}" - - this = self.sql(expression, "this") - kind = self.sql(expression, "kind") - kind = f" {kind}" if kind else "" - - ordinality = " FOR ORDINALITY" if expression.args.get("ordinality") else "" - return f"{this}{kind}{path}{ordinality}" - - def jsonschema_sql(self, expression: exp.JSONSchema) -> str: - return self.func("COLUMNS", *expression.expressions) - - def jsontable_sql(self, expression: exp.JSONTable) -> str: - this = self.sql(expression, "this") - path = self.sql(expression, "path") - path = f", {path}" if path else "" - error_handling = expression.args.get("error_handling") - error_handling = f" {error_handling}" if error_handling else "" - empty_handling = expression.args.get("empty_handling") - empty_handling = f" {empty_handling}" if empty_handling else "" - schema = self.sql(expression, "schema") - return self.func( - "JSON_TABLE", - this, - suffix=f"{path}{error_handling}{empty_handling} {schema})", - ) - - def openjsoncolumndef_sql(self, expression: exp.OpenJSONColumnDef) -> str: - this = self.sql(expression, "this") - kind = self.sql(expression, "kind") - path = self.sql(expression, "path") - path = f" {path}" if path else "" - as_json = " AS JSON" if expression.args.get("as_json") else "" - return f"{this} {kind}{path}{as_json}" - - def openjson_sql(self, expression: exp.OpenJSON) -> str: - this = self.sql(expression, "this") - path = self.sql(expression, "path") - path = f", {path}" if path else "" - expressions = self.expressions(expression) - with_ = ( - f" WITH ({self.seg(self.indent(expressions), sep='')}{self.seg(')', sep='')}" - if expressions - else "" - ) - return f"OPENJSON({this}{path}){with_}" - - def in_sql(self, expression: exp.In) -> str: - query = expression.args.get("query") - unnest = expression.args.get("unnest") - field = expression.args.get("field") - is_global = " GLOBAL" if expression.args.get("is_global") else "" - - if query: - in_sql = self.sql(query) - elif unnest: - in_sql = self.in_unnest_op(unnest) - elif field: - in_sql = self.sql(field) - else: - in_sql = f"({self.expressions(expression, dynamic=True, new_line=True, skip_first=True, skip_last=True)})" - - return f"{self.sql(expression, 'this')}{is_global} IN {in_sql}" - - def in_unnest_op(self, unnest: exp.Unnest) -> str: - return f"(SELECT {self.sql(unnest)})" - - def interval_sql(self, expression: exp.Interval) -> str: - unit_expression = expression.args.get("unit") - unit = self.sql(unit_expression) if unit_expression else "" - if not self.INTERVAL_ALLOWS_PLURAL_FORM: - unit = self.TIME_PART_SINGULARS.get(unit, unit) - unit = f" {unit}" if unit else "" - - if self.SINGLE_STRING_INTERVAL: - this = expression.this.name if expression.this else "" - if this: - if unit_expression and isinstance(unit_expression, exp.IntervalSpan): - return f"INTERVAL '{this}'{unit}" - return f"INTERVAL '{this}{unit}'" - return f"INTERVAL{unit}" - - this = self.sql(expression, "this") - if this: - unwrapped = isinstance(expression.this, self.UNWRAPPED_INTERVAL_VALUES) - this = f" {this}" if unwrapped else f" ({this})" - - return f"INTERVAL{this}{unit}" - - def return_sql(self, expression: exp.Return) -> str: - return f"RETURN {self.sql(expression, 'this')}" - - def reference_sql(self, expression: exp.Reference) -> str: - this = self.sql(expression, "this") - expressions = self.expressions(expression, flat=True) - expressions = f"({expressions})" if expressions else "" - options = self.expressions(expression, key="options", flat=True, sep=" ") - options = f" {options}" if options else "" - return f"REFERENCES {this}{expressions}{options}" - - def anonymous_sql(self, expression: exp.Anonymous) -> str: - # We don't normalize qualified functions such as a.b.foo(), because they can be case-sensitive - parent = expression.parent - is_qualified = isinstance(parent, exp.Dot) and expression is parent.expression - return self.func( - self.sql(expression, "this"), - *expression.expressions, - normalize=not is_qualified, - ) - - def paren_sql(self, expression: exp.Paren) -> str: - sql = self.seg(self.indent(self.sql(expression, "this")), sep="") - return f"({sql}{self.seg(')', sep='')}" - - def neg_sql(self, expression: exp.Neg) -> str: - # This makes sure we don't convert "- - 5" to "--5", which is a comment - this_sql = self.sql(expression, "this") - sep = " " if this_sql[0] == "-" else "" - return f"-{sep}{this_sql}" - - def not_sql(self, expression: exp.Not) -> str: - return f"NOT {self.sql(expression, 'this')}" - - def alias_sql(self, expression: exp.Alias) -> str: - alias = self.sql(expression, "alias") - alias = f" AS {alias}" if alias else "" - return f"{self.sql(expression, 'this')}{alias}" - - def pivotalias_sql(self, expression: exp.PivotAlias) -> str: - alias = expression.args["alias"] - - parent = expression.parent - pivot = parent and parent.parent - - if isinstance(pivot, exp.Pivot) and pivot.unpivot: - identifier_alias = isinstance(alias, exp.Identifier) - literal_alias = isinstance(alias, exp.Literal) - - if identifier_alias and not self.UNPIVOT_ALIASES_ARE_IDENTIFIERS: - alias.replace(exp.Literal.string(alias.output_name)) - elif ( - not identifier_alias - and literal_alias - and self.UNPIVOT_ALIASES_ARE_IDENTIFIERS - ): - alias.replace(exp.to_identifier(alias.output_name)) - - return self.alias_sql(expression) - - def aliases_sql(self, expression: exp.Aliases) -> str: - return f"{self.sql(expression, 'this')} AS ({self.expressions(expression, flat=True)})" - - def atindex_sql(self, expression: exp.AtTimeZone) -> str: - this = self.sql(expression, "this") - index = self.sql(expression, "expression") - return f"{this} AT {index}" - - def attimezone_sql(self, expression: exp.AtTimeZone) -> str: - this = self.sql(expression, "this") - zone = self.sql(expression, "zone") - return f"{this} AT TIME ZONE {zone}" - - def fromtimezone_sql(self, expression: exp.FromTimeZone) -> str: - this = self.sql(expression, "this") - zone = self.sql(expression, "zone") - return f"{this} AT TIME ZONE {zone} AT TIME ZONE 'UTC'" - - def add_sql(self, expression: exp.Add) -> str: - return self.binary(expression, "+") - - def and_sql( - self, - expression: exp.And, - stack: t.Optional[t.List[str | exp.Expression]] = None, - ) -> str: - return self.connector_sql(expression, "AND", stack) - - def or_sql( - self, expression: exp.Or, stack: t.Optional[t.List[str | exp.Expression]] = None - ) -> str: - return self.connector_sql(expression, "OR", stack) - - def xor_sql( - self, - expression: exp.Xor, - stack: t.Optional[t.List[str | exp.Expression]] = None, - ) -> str: - return self.connector_sql(expression, "XOR", stack) - - def connector_sql( - self, - expression: exp.Connector, - op: str, - stack: t.Optional[t.List[str | exp.Expression]] = None, - ) -> str: - if stack is not None: - if expression.expressions: - stack.append(self.expressions(expression, sep=f" {op} ")) - else: - stack.append(expression.right) - if expression.comments and self.comments: - for comment in expression.comments: - if comment: - op += f" /*{self.sanitize_comment(comment)}*/" - stack.extend((op, expression.left)) - return op - - stack = [expression] - sqls: t.List[str] = [] - ops = set() - - while stack: - node = stack.pop() - if isinstance(node, exp.Connector): - ops.add(getattr(self, f"{node.key}_sql")(node, stack)) - else: - sql = self.sql(node) - if sqls and sqls[-1] in ops: - sqls[-1] += f" {sql}" - else: - sqls.append(sql) - - sep = "\n" if self.pretty and self.too_wide(sqls) else " " - return sep.join(sqls) - - def bitwiseand_sql(self, expression: exp.BitwiseAnd) -> str: - return self.binary(expression, "&") - - def bitwiseleftshift_sql(self, expression: exp.BitwiseLeftShift) -> str: - return self.binary(expression, "<<") - - def bitwisenot_sql(self, expression: exp.BitwiseNot) -> str: - return f"~{self.sql(expression, 'this')}" - - def bitwiseor_sql(self, expression: exp.BitwiseOr) -> str: - return self.binary(expression, "|") - - def bitwiserightshift_sql(self, expression: exp.BitwiseRightShift) -> str: - return self.binary(expression, ">>") - - def bitwisexor_sql(self, expression: exp.BitwiseXor) -> str: - return self.binary(expression, "^") - - def cast_sql( - self, expression: exp.Cast, safe_prefix: t.Optional[str] = None - ) -> str: - format_sql = self.sql(expression, "format") - format_sql = f" FORMAT {format_sql}" if format_sql else "" - to_sql = self.sql(expression, "to") - to_sql = f" {to_sql}" if to_sql else "" - action = self.sql(expression, "action") - action = f" {action}" if action else "" - default = self.sql(expression, "default") - default = f" DEFAULT {default} ON CONVERSION ERROR" if default else "" - return f"{safe_prefix or ''}CAST({self.sql(expression, 'this')} AS{to_sql}{default}{format_sql}{action})" - - # Base implementation that excludes safe, zone, and target_type metadata args - def strtotime_sql(self, expression: exp.StrToTime) -> str: - return self.func("STR_TO_TIME", expression.this, expression.args.get("format")) - - def currentdate_sql(self, expression: exp.CurrentDate) -> str: - zone = self.sql(expression, "this") - return f"CURRENT_DATE({zone})" if zone else "CURRENT_DATE" - - def collate_sql(self, expression: exp.Collate) -> str: - if self.COLLATE_IS_FUNC: - return self.function_fallback_sql(expression) - return self.binary(expression, "COLLATE") - - def command_sql(self, expression: exp.Command) -> str: - return f"{self.sql(expression, 'this')} {expression.text('expression').strip()}" - - def comment_sql(self, expression: exp.Comment) -> str: - this = self.sql(expression, "this") - kind = expression.args["kind"] - materialized = " MATERIALIZED" if expression.args.get("materialized") else "" - exists_sql = " IF EXISTS " if expression.args.get("exists") else " " - expression_sql = self.sql(expression, "expression") - return f"COMMENT{exists_sql}ON{materialized} {kind} {this} IS {expression_sql}" - - def mergetreettlaction_sql(self, expression: exp.MergeTreeTTLAction) -> str: - this = self.sql(expression, "this") - delete = " DELETE" if expression.args.get("delete") else "" - recompress = self.sql(expression, "recompress") - recompress = f" RECOMPRESS {recompress}" if recompress else "" - to_disk = self.sql(expression, "to_disk") - to_disk = f" TO DISK {to_disk}" if to_disk else "" - to_volume = self.sql(expression, "to_volume") - to_volume = f" TO VOLUME {to_volume}" if to_volume else "" - return f"{this}{delete}{recompress}{to_disk}{to_volume}" - - def mergetreettl_sql(self, expression: exp.MergeTreeTTL) -> str: - where = self.sql(expression, "where") - group = self.sql(expression, "group") - aggregates = self.expressions(expression, key="aggregates") - aggregates = self.seg("SET") + self.seg(aggregates) if aggregates else "" - - if not (where or group or aggregates) and len(expression.expressions) == 1: - return f"TTL {self.expressions(expression, flat=True)}" - - return f"TTL{self.seg(self.expressions(expression))}{where}{group}{aggregates}" - - def transaction_sql(self, expression: exp.Transaction) -> str: - modes = self.expressions(expression, key="modes") - modes = f" {modes}" if modes else "" - return f"BEGIN{modes}" - - def commit_sql(self, expression: exp.Commit) -> str: - chain = expression.args.get("chain") - if chain is not None: - chain = " AND CHAIN" if chain else " AND NO CHAIN" - - return f"COMMIT{chain or ''}" - - def rollback_sql(self, expression: exp.Rollback) -> str: - savepoint = expression.args.get("savepoint") - savepoint = f" TO {savepoint}" if savepoint else "" - return f"ROLLBACK{savepoint}" - - def altercolumn_sql(self, expression: exp.AlterColumn) -> str: - this = self.sql(expression, "this") - - dtype = self.sql(expression, "dtype") - if dtype: - collate = self.sql(expression, "collate") - collate = f" COLLATE {collate}" if collate else "" - using = self.sql(expression, "using") - using = f" USING {using}" if using else "" - alter_set_type = self.ALTER_SET_TYPE + " " if self.ALTER_SET_TYPE else "" - return f"ALTER COLUMN {this} {alter_set_type}{dtype}{collate}{using}" - - default = self.sql(expression, "default") - if default: - return f"ALTER COLUMN {this} SET DEFAULT {default}" - - comment = self.sql(expression, "comment") - if comment: - return f"ALTER COLUMN {this} COMMENT {comment}" - - visible = expression.args.get("visible") - if visible: - return f"ALTER COLUMN {this} SET {visible}" - - allow_null = expression.args.get("allow_null") - drop = expression.args.get("drop") - - if not drop and not allow_null: - self.unsupported("Unsupported ALTER COLUMN syntax") - - if allow_null is not None: - keyword = "DROP" if drop else "SET" - return f"ALTER COLUMN {this} {keyword} NOT NULL" - - return f"ALTER COLUMN {this} DROP DEFAULT" - - def alterindex_sql(self, expression: exp.AlterIndex) -> str: - this = self.sql(expression, "this") - - visible = expression.args.get("visible") - visible_sql = "VISIBLE" if visible else "INVISIBLE" - - return f"ALTER INDEX {this} {visible_sql}" - - def alterdiststyle_sql(self, expression: exp.AlterDistStyle) -> str: - this = self.sql(expression, "this") - if not isinstance(expression.this, exp.Var): - this = f"KEY DISTKEY {this}" - return f"ALTER DISTSTYLE {this}" - - def altersortkey_sql(self, expression: exp.AlterSortKey) -> str: - compound = " COMPOUND" if expression.args.get("compound") else "" - this = self.sql(expression, "this") - expressions = self.expressions(expression, flat=True) - expressions = f"({expressions})" if expressions else "" - return f"ALTER{compound} SORTKEY {this or expressions}" - - def alterrename_sql( - self, expression: exp.AlterRename, include_to: bool = True - ) -> str: - if not self.RENAME_TABLE_WITH_DB: - # Remove db from tables - expression = expression.transform( - lambda n: exp.table_(n.this) if isinstance(n, exp.Table) else n - ).assert_is(exp.AlterRename) - this = self.sql(expression, "this") - to_kw = " TO" if include_to else "" - return f"RENAME{to_kw} {this}" - - def renamecolumn_sql(self, expression: exp.RenameColumn) -> str: - exists = " IF EXISTS" if expression.args.get("exists") else "" - old_column = self.sql(expression, "this") - new_column = self.sql(expression, "to") - return f"RENAME COLUMN{exists} {old_column} TO {new_column}" - - def alterset_sql(self, expression: exp.AlterSet) -> str: - exprs = self.expressions(expression, flat=True) - if self.ALTER_SET_WRAPPED: - exprs = f"({exprs})" - - return f"SET {exprs}" - - def alter_sql(self, expression: exp.Alter) -> str: - actions = expression.args["actions"] - - if not self.dialect.ALTER_TABLE_ADD_REQUIRED_FOR_EACH_COLUMN and isinstance( - actions[0], exp.ColumnDef - ): - actions_sql = self.expressions(expression, key="actions", flat=True) - actions_sql = f"ADD {actions_sql}" - else: - actions_list = [] - for action in actions: - if isinstance(action, (exp.ColumnDef, exp.Schema)): - action_sql = self.add_column_sql(action) - else: - action_sql = self.sql(action) - if isinstance(action, exp.Query): - action_sql = f"AS {action_sql}" - - actions_list.append(action_sql) - - actions_sql = self.format_args(*actions_list).lstrip("\n") - - exists = " IF EXISTS" if expression.args.get("exists") else "" - on_cluster = self.sql(expression, "cluster") - on_cluster = f" {on_cluster}" if on_cluster else "" - only = " ONLY" if expression.args.get("only") else "" - options = self.expressions(expression, key="options") - options = f", {options}" if options else "" - kind = self.sql(expression, "kind") - not_valid = " NOT VALID" if expression.args.get("not_valid") else "" - check = " WITH CHECK" if expression.args.get("check") else "" - cascade = ( - " CASCADE" - if expression.args.get("cascade") - and self.dialect.ALTER_TABLE_SUPPORTS_CASCADE - else "" - ) - this = self.sql(expression, "this") - this = f" {this}" if this else "" - - return f"ALTER {kind}{exists}{only}{this}{on_cluster}{check}{self.sep()}{actions_sql}{not_valid}{options}{cascade}" - - def altersession_sql(self, expression: exp.AlterSession) -> str: - items_sql = self.expressions(expression, flat=True) - keyword = "UNSET" if expression.args.get("unset") else "SET" - return f"{keyword} {items_sql}" - - def add_column_sql(self, expression: exp.Expression) -> str: - sql = self.sql(expression) - if isinstance(expression, exp.Schema): - column_text = " COLUMNS" - elif ( - isinstance(expression, exp.ColumnDef) - and self.ALTER_TABLE_INCLUDE_COLUMN_KEYWORD - ): - column_text = " COLUMN" - else: - column_text = "" - - return f"ADD{column_text} {sql}" - - def droppartition_sql(self, expression: exp.DropPartition) -> str: - expressions = self.expressions(expression) - exists = " IF EXISTS " if expression.args.get("exists") else " " - return f"DROP{exists}{expressions}" - - def addconstraint_sql(self, expression: exp.AddConstraint) -> str: - return f"ADD {self.expressions(expression, indent=False)}" - - def addpartition_sql(self, expression: exp.AddPartition) -> str: - exists = "IF NOT EXISTS " if expression.args.get("exists") else "" - location = self.sql(expression, "location") - location = f" {location}" if location else "" - return f"ADD {exists}{self.sql(expression.this)}{location}" - - def distinct_sql(self, expression: exp.Distinct) -> str: - this = self.expressions(expression, flat=True) - - if not self.MULTI_ARG_DISTINCT and len(expression.expressions) > 1: - case = exp.case() - for arg in expression.expressions: - case = case.when(arg.is_(exp.null()), exp.null()) - this = self.sql(case.else_(f"({this})")) - - this = f" {this}" if this else "" - - on = self.sql(expression, "on") - on = f" ON {on}" if on else "" - return f"DISTINCT{this}{on}" - - def ignorenulls_sql(self, expression: exp.IgnoreNulls) -> str: - return self._embed_ignore_nulls(expression, "IGNORE NULLS") - - def respectnulls_sql(self, expression: exp.RespectNulls) -> str: - return self._embed_ignore_nulls(expression, "RESPECT NULLS") - - def havingmax_sql(self, expression: exp.HavingMax) -> str: - this_sql = self.sql(expression, "this") - expression_sql = self.sql(expression, "expression") - kind = "MAX" if expression.args.get("max") else "MIN" - return f"{this_sql} HAVING {kind} {expression_sql}" - - def intdiv_sql(self, expression: exp.IntDiv) -> str: - return self.sql( - exp.Cast( - this=exp.Div(this=expression.this, expression=expression.expression), - to=exp.DataType(this=exp.DataType.Type.INT), - ) - ) - - def dpipe_sql(self, expression: exp.DPipe) -> str: - if self.dialect.STRICT_STRING_CONCAT and expression.args.get("safe"): - return self.func( - "CONCAT", - *(exp.cast(e, exp.DataType.Type.TEXT) for e in expression.flatten()), - ) - return self.binary(expression, "||") - - def div_sql(self, expression: exp.Div) -> str: - l, r = expression.left, expression.right - - if not self.dialect.SAFE_DIVISION and expression.args.get("safe"): - r.replace(exp.Nullif(this=r.copy(), expression=exp.Literal.number(0))) - - if self.dialect.TYPED_DIVISION and not expression.args.get("typed"): - if not l.is_type(*exp.DataType.REAL_TYPES) and not r.is_type( - *exp.DataType.REAL_TYPES - ): - l.replace(exp.cast(l.copy(), to=exp.DataType.Type.DOUBLE)) - - elif not self.dialect.TYPED_DIVISION and expression.args.get("typed"): - if l.is_type(*exp.DataType.INTEGER_TYPES) and r.is_type( - *exp.DataType.INTEGER_TYPES - ): - return self.sql( - exp.cast( - l / r, - to=exp.DataType.Type.BIGINT, - ) - ) - - return self.binary(expression, "/") - - def safedivide_sql(self, expression: exp.SafeDivide) -> str: - n = exp._wrap(expression.this, exp.Binary) - d = exp._wrap(expression.expression, exp.Binary) - return self.sql(exp.If(this=d.neq(0), true=n / d, false=exp.Null())) - - def overlaps_sql(self, expression: exp.Overlaps) -> str: - return self.binary(expression, "OVERLAPS") - - def distance_sql(self, expression: exp.Distance) -> str: - return self.binary(expression, "<->") - - def dot_sql(self, expression: exp.Dot) -> str: - return f"{self.sql(expression, 'this')}.{self.sql(expression, 'expression')}" - - def eq_sql(self, expression: exp.EQ) -> str: - return self.binary(expression, "=") - - def propertyeq_sql(self, expression: exp.PropertyEQ) -> str: - return self.binary(expression, ":=") - - def escape_sql(self, expression: exp.Escape) -> str: - return self.binary(expression, "ESCAPE") - - def glob_sql(self, expression: exp.Glob) -> str: - return self.binary(expression, "GLOB") - - def gt_sql(self, expression: exp.GT) -> str: - return self.binary(expression, ">") - - def gte_sql(self, expression: exp.GTE) -> str: - return self.binary(expression, ">=") - - def is_sql(self, expression: exp.Is) -> str: - if not self.IS_BOOL_ALLOWED and isinstance(expression.expression, exp.Boolean): - return self.sql( - expression.this - if expression.expression.this - else exp.not_(expression.this) - ) - return self.binary(expression, "IS") - - def _like_sql(self, expression: exp.Like | exp.ILike) -> str: - this = expression.this - rhs = expression.expression - - if isinstance(expression, exp.Like): - exp_class: t.Type[exp.Like | exp.ILike] = exp.Like - op = "LIKE" - else: - exp_class = exp.ILike - op = "ILIKE" - - if isinstance(rhs, (exp.All, exp.Any)) and not self.SUPPORTS_LIKE_QUANTIFIERS: - exprs = rhs.this.unnest() - - if isinstance(exprs, exp.Tuple): - exprs = exprs.expressions - - connective = exp.or_ if isinstance(rhs, exp.Any) else exp.and_ - - like_expr: exp.Expression = exp_class(this=this, expression=exprs[0]) - for expr in exprs[1:]: - like_expr = connective(like_expr, exp_class(this=this, expression=expr)) - - parent = expression.parent - if not isinstance(parent, type(like_expr)) and isinstance( - parent, exp.Condition - ): - like_expr = exp.paren(like_expr, copy=False) - - return self.sql(like_expr) - - return self.binary(expression, op) - - def like_sql(self, expression: exp.Like) -> str: - return self._like_sql(expression) - - def ilike_sql(self, expression: exp.ILike) -> str: - return self._like_sql(expression) - - def match_sql(self, expression: exp.Match) -> str: - return self.binary(expression, "MATCH") - - def similarto_sql(self, expression: exp.SimilarTo) -> str: - return self.binary(expression, "SIMILAR TO") - - def lt_sql(self, expression: exp.LT) -> str: - return self.binary(expression, "<") - - def lte_sql(self, expression: exp.LTE) -> str: - return self.binary(expression, "<=") - - def mod_sql(self, expression: exp.Mod) -> str: - return self.binary(expression, "%") - - def mul_sql(self, expression: exp.Mul) -> str: - return self.binary(expression, "*") - - def neq_sql(self, expression: exp.NEQ) -> str: - return self.binary(expression, "<>") - - def nullsafeeq_sql(self, expression: exp.NullSafeEQ) -> str: - return self.binary(expression, "IS NOT DISTINCT FROM") - - def nullsafeneq_sql(self, expression: exp.NullSafeNEQ) -> str: - return self.binary(expression, "IS DISTINCT FROM") - - def sub_sql(self, expression: exp.Sub) -> str: - return self.binary(expression, "-") - - def trycast_sql(self, expression: exp.TryCast) -> str: - return self.cast_sql(expression, safe_prefix="TRY_") - - def jsoncast_sql(self, expression: exp.JSONCast) -> str: - return self.cast_sql(expression) - - def try_sql(self, expression: exp.Try) -> str: - if not self.TRY_SUPPORTED: - self.unsupported("Unsupported TRY function") - return self.sql(expression, "this") - - return self.func("TRY", expression.this) - - def log_sql(self, expression: exp.Log) -> str: - this = expression.this - expr = expression.expression - - if self.dialect.LOG_BASE_FIRST is False: - this, expr = expr, this - elif self.dialect.LOG_BASE_FIRST is None and expr: - if this.name in ("2", "10"): - return self.func(f"LOG{this.name}", expr) - - self.unsupported(f"Unsupported logarithm with base {self.sql(this)}") - - return self.func("LOG", this, expr) - - def use_sql(self, expression: exp.Use) -> str: - kind = self.sql(expression, "kind") - kind = f" {kind}" if kind else "" - this = self.sql(expression, "this") or self.expressions(expression, flat=True) - this = f" {this}" if this else "" - return f"USE{kind}{this}" - - def binary(self, expression: exp.Binary, op: str) -> str: - sqls: t.List[str] = [] - stack: t.List[t.Union[str, exp.Expression]] = [expression] - binary_type = type(expression) - - while stack: - node = stack.pop() - - if type(node) is binary_type: - op_func = node.args.get("operator") - if op_func: - op = f"OPERATOR({self.sql(op_func)})" - - stack.append(node.right) - stack.append(f" {self.maybe_comment(op, comments=node.comments)} ") - stack.append(node.left) - else: - sqls.append(self.sql(node)) - - return "".join(sqls) - - def ceil_floor(self, expression: exp.Ceil | exp.Floor) -> str: - to_clause = self.sql(expression, "to") - if to_clause: - return f"{expression.sql_name()}({self.sql(expression, 'this')} TO {to_clause})" - - return self.function_fallback_sql(expression) - - def function_fallback_sql(self, expression: exp.Func) -> str: - args = [] - - for key in expression.arg_types: - arg_value = expression.args.get(key) - - if isinstance(arg_value, list): - for value in arg_value: - args.append(value) - elif arg_value is not None: - args.append(arg_value) - - if self.dialect.PRESERVE_ORIGINAL_NAMES: - name = ( - expression._meta and expression.meta.get("name") - ) or expression.sql_name() - else: - name = expression.sql_name() - - return self.func(name, *args) - - def func( - self, - name: str, - *args: t.Optional[exp.Expression | str], - prefix: str = "(", - suffix: str = ")", - normalize: bool = True, - ) -> str: - name = self.normalize_func(name) if normalize else name - return f"{name}{prefix}{self.format_args(*args)}{suffix}" - - def format_args( - self, *args: t.Optional[str | exp.Expression], sep: str = ", " - ) -> str: - arg_sqls = tuple( - self.sql(arg) - for arg in args - if arg is not None and not isinstance(arg, bool) - ) - if self.pretty and self.too_wide(arg_sqls): - return self.indent( - "\n" + f"{sep.strip()}\n".join(arg_sqls) + "\n", - skip_first=True, - skip_last=True, - ) - return sep.join(arg_sqls) - - def too_wide(self, args: t.Iterable) -> bool: - return sum(len(arg) for arg in args) > self.max_text_width - - def format_time( - self, - expression: exp.Expression, - inverse_time_mapping: t.Optional[t.Dict[str, str]] = None, - inverse_time_trie: t.Optional[t.Dict] = None, - ) -> t.Optional[str]: - return format_time( - self.sql(expression, "format"), - inverse_time_mapping or self.dialect.INVERSE_TIME_MAPPING, - inverse_time_trie or self.dialect.INVERSE_TIME_TRIE, - ) - - def expressions( - self, - expression: t.Optional[exp.Expression] = None, - key: t.Optional[str] = None, - sqls: t.Optional[t.Collection[str | exp.Expression]] = None, - flat: bool = False, - indent: bool = True, - skip_first: bool = False, - skip_last: bool = False, - sep: str = ", ", - prefix: str = "", - dynamic: bool = False, - new_line: bool = False, - ) -> str: - expressions = expression.args.get(key or "expressions") if expression else sqls - - if not expressions: - return "" - - if flat: - return sep.join(sql for sql in (self.sql(e) for e in expressions) if sql) - - num_sqls = len(expressions) - result_sqls = [] - - for i, e in enumerate(expressions): - sql = self.sql(e, comment=False) - if not sql: - continue - - comments = ( - self.maybe_comment("", e) if isinstance(e, exp.Expression) else "" - ) - - if self.pretty: - if self.leading_comma: - result_sqls.append(f"{sep if i > 0 else ''}{prefix}{sql}{comments}") - else: - result_sqls.append( - f"{prefix}{sql}{(sep.rstrip() if comments else sep) if i + 1 < num_sqls else ''}{comments}" - ) - else: - result_sqls.append( - f"{prefix}{sql}{comments}{sep if i + 1 < num_sqls else ''}" - ) - - if self.pretty and (not dynamic or self.too_wide(result_sqls)): - if new_line: - result_sqls.insert(0, "") - result_sqls.append("") - result_sql = "\n".join(s.rstrip() for s in result_sqls) - else: - result_sql = "".join(result_sqls) - - return ( - self.indent(result_sql, skip_first=skip_first, skip_last=skip_last) - if indent - else result_sql - ) - - def op_expressions( - self, op: str, expression: exp.Expression, flat: bool = False - ) -> str: - flat = flat or isinstance(expression.parent, exp.Properties) - expressions_sql = self.expressions(expression, flat=flat) - if flat: - return f"{op} {expressions_sql}" - return f"{self.seg(op)}{self.sep() if expressions_sql else ''}{expressions_sql}" - - def naked_property(self, expression: exp.Property) -> str: - property_name = exp.Properties.PROPERTY_TO_NAME.get(expression.__class__) - if not property_name: - self.unsupported(f"Unsupported property {expression.__class__.__name__}") - return f"{property_name} {self.sql(expression, 'this')}" - - def tag_sql(self, expression: exp.Tag) -> str: - return f"{expression.args.get('prefix')}{self.sql(expression.this)}{expression.args.get('postfix')}" - - def token_sql(self, token_type: TokenType) -> str: - return self.TOKEN_MAPPING.get(token_type, token_type.name) - - def userdefinedfunction_sql(self, expression: exp.UserDefinedFunction) -> str: - this = self.sql(expression, "this") - expressions = self.no_identify(self.expressions, expression) - expressions = ( - self.wrap(expressions) - if expression.args.get("wrapped") - else f" {expressions}" - ) - return f"{this}{expressions}" if expressions.strip() != "" else this - - def joinhint_sql(self, expression: exp.JoinHint) -> str: - this = self.sql(expression, "this") - expressions = self.expressions(expression, flat=True) - return f"{this}({expressions})" - - def kwarg_sql(self, expression: exp.Kwarg) -> str: - return self.binary(expression, "=>") - - def when_sql(self, expression: exp.When) -> str: - matched = "MATCHED" if expression.args["matched"] else "NOT MATCHED" - source = ( - " BY SOURCE" - if self.MATCHED_BY_SOURCE and expression.args.get("source") - else "" - ) - condition = self.sql(expression, "condition") - condition = f" AND {condition}" if condition else "" - - then_expression = expression.args.get("then") - if isinstance(then_expression, exp.Insert): - this = self.sql(then_expression, "this") - this = f"INSERT {this}" if this else "INSERT" - then = self.sql(then_expression, "expression") - then = f"{this} VALUES {then}" if then else this - elif isinstance(then_expression, exp.Update): - if isinstance(then_expression.args.get("expressions"), exp.Star): - then = f"UPDATE {self.sql(then_expression, 'expressions')}" - else: - expressions_sql = self.expressions(then_expression) - then = ( - f"UPDATE SET{self.sep()}{expressions_sql}" - if expressions_sql - else "UPDATE" - ) - - else: - then = self.sql(then_expression) - return f"WHEN {matched}{source}{condition} THEN {then}" - - def whens_sql(self, expression: exp.Whens) -> str: - return self.expressions(expression, sep=" ", indent=False) - - def merge_sql(self, expression: exp.Merge) -> str: - table = expression.this - table_alias = "" - - hints = table.args.get("hints") - if hints and table.alias and isinstance(hints[0], exp.WithTableHint): - # T-SQL syntax is MERGE ... [WITH ()] [[AS] table_alias] - table_alias = f" AS {self.sql(table.args['alias'].pop())}" - - this = self.sql(table) - using = f"USING {self.sql(expression, 'using')}" - whens = self.sql(expression, "whens") - - on = self.sql(expression, "on") - on = f"ON {on}" if on else "" - - if not on: - on = self.expressions(expression, key="using_cond") - on = f"USING ({on})" if on else "" - - returning = self.sql(expression, "returning") - if returning: - whens = f"{whens}{returning}" - - sep = self.sep() - - return self.prepend_ctes( - expression, - f"MERGE INTO {this}{table_alias}{sep}{using}{sep}{on}{sep}{whens}", - ) - - @unsupported_args("format") - def tochar_sql(self, expression: exp.ToChar) -> str: - return self.sql(exp.cast(expression.this, exp.DataType.Type.TEXT)) - - def tonumber_sql(self, expression: exp.ToNumber) -> str: - if not self.SUPPORTS_TO_NUMBER: - self.unsupported("Unsupported TO_NUMBER function") - return self.sql(exp.cast(expression.this, exp.DataType.Type.DOUBLE)) - - fmt = expression.args.get("format") - if not fmt: - self.unsupported("Conversion format is required for TO_NUMBER") - return self.sql(exp.cast(expression.this, exp.DataType.Type.DOUBLE)) - - return self.func("TO_NUMBER", expression.this, fmt) - - def dictproperty_sql(self, expression: exp.DictProperty) -> str: - this = self.sql(expression, "this") - kind = self.sql(expression, "kind") - settings_sql = self.expressions(expression, key="settings", sep=" ") - args = ( - f"({self.sep('')}{settings_sql}{self.seg(')', sep='')}" - if settings_sql - else "()" - ) - return f"{this}({kind}{args})" - - def dictrange_sql(self, expression: exp.DictRange) -> str: - this = self.sql(expression, "this") - max = self.sql(expression, "max") - min = self.sql(expression, "min") - return f"{this}(MIN {min} MAX {max})" - - def dictsubproperty_sql(self, expression: exp.DictSubProperty) -> str: - return f"{self.sql(expression, 'this')} {self.sql(expression, 'value')}" - - def duplicatekeyproperty_sql(self, expression: exp.DuplicateKeyProperty) -> str: - return f"DUPLICATE KEY ({self.expressions(expression, flat=True)})" - - # https://docs.starrocks.io/docs/sql-reference/sql-statements/table_bucket_part_index/CREATE_TABLE/ - def uniquekeyproperty_sql( - self, expression: exp.UniqueKeyProperty, prefix: str = "UNIQUE KEY" - ) -> str: - return f"{prefix} ({self.expressions(expression, flat=True)})" - - # https://docs.starrocks.io/docs/sql-reference/sql-statements/data-definition/CREATE_TABLE/#distribution_desc - def distributedbyproperty_sql(self, expression: exp.DistributedByProperty) -> str: - expressions = self.expressions(expression, flat=True) - expressions = f" {self.wrap(expressions)}" if expressions else "" - buckets = self.sql(expression, "buckets") - kind = self.sql(expression, "kind") - buckets = f" BUCKETS {buckets}" if buckets else "" - order = self.sql(expression, "order") - return f"DISTRIBUTED BY {kind}{expressions}{buckets}{order}" - - def oncluster_sql(self, expression: exp.OnCluster) -> str: - return "" - - def clusteredbyproperty_sql(self, expression: exp.ClusteredByProperty) -> str: - expressions = self.expressions(expression, key="expressions", flat=True) - sorted_by = self.expressions(expression, key="sorted_by", flat=True) - sorted_by = f" SORTED BY ({sorted_by})" if sorted_by else "" - buckets = self.sql(expression, "buckets") - return f"CLUSTERED BY ({expressions}){sorted_by} INTO {buckets} BUCKETS" - - def anyvalue_sql(self, expression: exp.AnyValue) -> str: - this = self.sql(expression, "this") - having = self.sql(expression, "having") - - if having: - this = f"{this} HAVING {'MAX' if expression.args.get('max') else 'MIN'} {having}" - - return self.func("ANY_VALUE", this) - - def querytransform_sql(self, expression: exp.QueryTransform) -> str: - transform = self.func("TRANSFORM", *expression.expressions) - row_format_before = self.sql(expression, "row_format_before") - row_format_before = f" {row_format_before}" if row_format_before else "" - record_writer = self.sql(expression, "record_writer") - record_writer = f" RECORDWRITER {record_writer}" if record_writer else "" - using = f" USING {self.sql(expression, 'command_script')}" - schema = self.sql(expression, "schema") - schema = f" AS {schema}" if schema else "" - row_format_after = self.sql(expression, "row_format_after") - row_format_after = f" {row_format_after}" if row_format_after else "" - record_reader = self.sql(expression, "record_reader") - record_reader = f" RECORDREADER {record_reader}" if record_reader else "" - return f"{transform}{row_format_before}{record_writer}{using}{schema}{row_format_after}{record_reader}" - - def indexconstraintoption_sql(self, expression: exp.IndexConstraintOption) -> str: - key_block_size = self.sql(expression, "key_block_size") - if key_block_size: - return f"KEY_BLOCK_SIZE = {key_block_size}" - - using = self.sql(expression, "using") - if using: - return f"USING {using}" - - parser = self.sql(expression, "parser") - if parser: - return f"WITH PARSER {parser}" - - comment = self.sql(expression, "comment") - if comment: - return f"COMMENT {comment}" - - visible = expression.args.get("visible") - if visible is not None: - return "VISIBLE" if visible else "INVISIBLE" - - engine_attr = self.sql(expression, "engine_attr") - if engine_attr: - return f"ENGINE_ATTRIBUTE = {engine_attr}" - - secondary_engine_attr = self.sql(expression, "secondary_engine_attr") - if secondary_engine_attr: - return f"SECONDARY_ENGINE_ATTRIBUTE = {secondary_engine_attr}" - - self.unsupported("Unsupported index constraint option.") - return "" - - def checkcolumnconstraint_sql(self, expression: exp.CheckColumnConstraint) -> str: - enforced = " ENFORCED" if expression.args.get("enforced") else "" - return f"CHECK ({self.sql(expression, 'this')}){enforced}" - - def indexcolumnconstraint_sql(self, expression: exp.IndexColumnConstraint) -> str: - kind = self.sql(expression, "kind") - kind = f"{kind} INDEX" if kind else "INDEX" - this = self.sql(expression, "this") - this = f" {this}" if this else "" - index_type = self.sql(expression, "index_type") - index_type = f" USING {index_type}" if index_type else "" - expressions = self.expressions(expression, flat=True) - expressions = f" ({expressions})" if expressions else "" - options = self.expressions(expression, key="options", sep=" ") - options = f" {options}" if options else "" - return f"{kind}{this}{index_type}{expressions}{options}" - - def nvl2_sql(self, expression: exp.Nvl2) -> str: - if self.NVL2_SUPPORTED: - return self.function_fallback_sql(expression) - - case = exp.Case().when( - expression.this.is_(exp.null()).not_(copy=False), - expression.args["true"], - copy=False, - ) - else_cond = expression.args.get("false") - if else_cond: - case.else_(else_cond, copy=False) - - return self.sql(case) - - def comprehension_sql(self, expression: exp.Comprehension) -> str: - this = self.sql(expression, "this") - expr = self.sql(expression, "expression") - position = self.sql(expression, "position") - position = f", {position}" if position else "" - iterator = self.sql(expression, "iterator") - condition = self.sql(expression, "condition") - condition = f" IF {condition}" if condition else "" - return f"{this} FOR {expr}{position} IN {iterator}{condition}" - - def columnprefix_sql(self, expression: exp.ColumnPrefix) -> str: - return f"{self.sql(expression, 'this')}({self.sql(expression, 'expression')})" - - def opclass_sql(self, expression: exp.Opclass) -> str: - return f"{self.sql(expression, 'this')} {self.sql(expression, 'expression')}" - - def _ml_sql(self, expression: exp.Func, name: str) -> str: - model = self.sql(expression, "this") - model = f"MODEL {model}" - expr = expression.expression - if expr: - expr_sql = self.sql(expression, "expression") - expr_sql = ( - f"TABLE {expr_sql}" if not isinstance(expr, exp.Subquery) else expr_sql - ) - else: - expr_sql = None - - parameters = self.sql(expression, "params_struct") or None - - return self.func(name, model, expr_sql, parameters) - - def predict_sql(self, expression: exp.Predict) -> str: - return self._ml_sql(expression, "PREDICT") - - def generateembedding_sql(self, expression: exp.GenerateEmbedding) -> str: - name = ( - "GENERATE_TEXT_EMBEDDING" - if expression.args.get("is_text") - else "GENERATE_EMBEDDING" - ) - return self._ml_sql(expression, name) - - def mltranslate_sql(self, expression: exp.MLTranslate) -> str: - return self._ml_sql(expression, "TRANSLATE") - - def mlforecast_sql(self, expression: exp.MLForecast) -> str: - return self._ml_sql(expression, "FORECAST") - - def featuresattime_sql(self, expression: exp.FeaturesAtTime) -> str: - this_sql = self.sql(expression, "this") - if isinstance(expression.this, exp.Table): - this_sql = f"TABLE {this_sql}" - - return self.func( - "FEATURES_AT_TIME", - this_sql, - expression.args.get("time"), - expression.args.get("num_rows"), - expression.args.get("ignore_feature_nulls"), - ) - - def vectorsearch_sql(self, expression: exp.VectorSearch) -> str: - this_sql = self.sql(expression, "this") - if isinstance(expression.this, exp.Table): - this_sql = f"TABLE {this_sql}" - - query_table = self.sql(expression, "query_table") - if isinstance(expression.args["query_table"], exp.Table): - query_table = f"TABLE {query_table}" - - return self.func( - "VECTOR_SEARCH", - this_sql, - expression.args.get("column_to_search"), - query_table, - expression.args.get("query_column_to_search"), - expression.args.get("top_k"), - expression.args.get("distance_type"), - expression.args.get("options"), - ) - - def forin_sql(self, expression: exp.ForIn) -> str: - this = self.sql(expression, "this") - expression_sql = self.sql(expression, "expression") - return f"FOR {this} DO {expression_sql}" - - def refresh_sql(self, expression: exp.Refresh) -> str: - this = self.sql(expression, "this") - kind = ( - "" - if isinstance(expression.this, exp.Literal) - else f"{expression.text('kind')} " - ) - return f"REFRESH {kind}{this}" - - def toarray_sql(self, expression: exp.ToArray) -> str: - arg = expression.this - if not arg.type: - from bigframes_vendored.sqlglot.optimizer.annotate_types import ( - annotate_types, - ) - - arg = annotate_types(arg, dialect=self.dialect) - - if arg.is_type(exp.DataType.Type.ARRAY): - return self.sql(arg) - - cond_for_null = arg.is_(exp.null()) - return self.sql( - exp.func("IF", cond_for_null, exp.null(), exp.array(arg, copy=False)) - ) - - def tsordstotime_sql(self, expression: exp.TsOrDsToTime) -> str: - this = expression.this - time_format = self.format_time(expression) - - if time_format: - return self.sql( - exp.cast( - exp.StrToTime(this=this, format=expression.args["format"]), - exp.DataType.Type.TIME, - ) - ) - - if isinstance(this, exp.TsOrDsToTime) or this.is_type(exp.DataType.Type.TIME): - return self.sql(this) - - return self.sql(exp.cast(this, exp.DataType.Type.TIME)) - - def tsordstotimestamp_sql(self, expression: exp.TsOrDsToTimestamp) -> str: - this = expression.this - if isinstance(this, exp.TsOrDsToTimestamp) or this.is_type( - exp.DataType.Type.TIMESTAMP - ): - return self.sql(this) - - return self.sql( - exp.cast(this, exp.DataType.Type.TIMESTAMP, dialect=self.dialect) - ) - - def tsordstodatetime_sql(self, expression: exp.TsOrDsToDatetime) -> str: - this = expression.this - if isinstance(this, exp.TsOrDsToDatetime) or this.is_type( - exp.DataType.Type.DATETIME - ): - return self.sql(this) - - return self.sql( - exp.cast(this, exp.DataType.Type.DATETIME, dialect=self.dialect) - ) - - def tsordstodate_sql(self, expression: exp.TsOrDsToDate) -> str: - this = expression.this - time_format = self.format_time(expression) - - if time_format and time_format not in ( - self.dialect.TIME_FORMAT, - self.dialect.DATE_FORMAT, - ): - return self.sql( - exp.cast( - exp.StrToTime(this=this, format=expression.args["format"]), - exp.DataType.Type.DATE, - ) - ) - - if isinstance(this, exp.TsOrDsToDate) or this.is_type(exp.DataType.Type.DATE): - return self.sql(this) - - return self.sql(exp.cast(this, exp.DataType.Type.DATE)) - - def unixdate_sql(self, expression: exp.UnixDate) -> str: - return self.sql( - exp.func( - "DATEDIFF", - expression.this, - exp.cast(exp.Literal.string("1970-01-01"), exp.DataType.Type.DATE), - "day", - ) - ) - - def lastday_sql(self, expression: exp.LastDay) -> str: - if self.LAST_DAY_SUPPORTS_DATE_PART: - return self.function_fallback_sql(expression) - - unit = expression.text("unit") - if unit and unit != "MONTH": - self.unsupported("Date parts are not supported in LAST_DAY.") - - return self.func("LAST_DAY", expression.this) - - def dateadd_sql(self, expression: exp.DateAdd) -> str: - from bigframes_vendored.sqlglot.dialects.dialect import unit_to_str - - return self.func( - "DATE_ADD", expression.this, expression.expression, unit_to_str(expression) - ) - - def arrayany_sql(self, expression: exp.ArrayAny) -> str: - if self.CAN_IMPLEMENT_ARRAY_ANY: - filtered = exp.ArrayFilter( - this=expression.this, expression=expression.expression - ) - filtered_not_empty = exp.ArraySize(this=filtered).neq(0) - original_is_empty = exp.ArraySize(this=expression.this).eq(0) - return self.sql(exp.paren(original_is_empty.or_(filtered_not_empty))) - - from bigframes_vendored.sqlglot.dialects import Dialect - - # SQLGlot's executor supports ARRAY_ANY, so we don't wanna warn for the SQLGlot dialect - if self.dialect.__class__ != Dialect: - self.unsupported("ARRAY_ANY is unsupported") - - return self.function_fallback_sql(expression) - - def struct_sql(self, expression: exp.Struct) -> str: - expression.set( - "expressions", - [ - exp.alias_(e.expression, e.name if e.this.is_string else e.this) - if isinstance(e, exp.PropertyEQ) - else e - for e in expression.expressions - ], - ) - - return self.function_fallback_sql(expression) - - def partitionrange_sql(self, expression: exp.PartitionRange) -> str: - low = self.sql(expression, "this") - high = self.sql(expression, "expression") - - return f"{low} TO {high}" - - def truncatetable_sql(self, expression: exp.TruncateTable) -> str: - target = "DATABASE" if expression.args.get("is_database") else "TABLE" - tables = f" {self.expressions(expression)}" - - exists = " IF EXISTS" if expression.args.get("exists") else "" - - on_cluster = self.sql(expression, "cluster") - on_cluster = f" {on_cluster}" if on_cluster else "" - - identity = self.sql(expression, "identity") - identity = f" {identity} IDENTITY" if identity else "" - - option = self.sql(expression, "option") - option = f" {option}" if option else "" - - partition = self.sql(expression, "partition") - partition = f" {partition}" if partition else "" - - return f"TRUNCATE {target}{exists}{tables}{on_cluster}{identity}{option}{partition}" - - # This transpiles T-SQL's CONVERT function - # https://learn.microsoft.com/en-us/sql/t-sql/functions/cast-and-convert-transact-sql?view=sql-server-ver16 - def convert_sql(self, expression: exp.Convert) -> str: - to = expression.this - value = expression.expression - style = expression.args.get("style") - safe = expression.args.get("safe") - strict = expression.args.get("strict") - - if not to or not value: - return "" - - # Retrieve length of datatype and override to default if not specified - if ( - not seq_get(to.expressions, 0) - and to.this in self.PARAMETERIZABLE_TEXT_TYPES - ): - to = exp.DataType.build( - to.this, expressions=[exp.Literal.number(30)], nested=False - ) - - transformed: t.Optional[exp.Expression] = None - cast = exp.Cast if strict else exp.TryCast - - # Check whether a conversion with format (T-SQL calls this 'style') is applicable - if isinstance(style, exp.Literal) and style.is_int: - from bigframes_vendored.sqlglot.dialects.tsql import TSQL - - style_value = style.name - converted_style = TSQL.CONVERT_FORMAT_MAPPING.get(style_value) - if not converted_style: - self.unsupported(f"Unsupported T-SQL 'style' value: {style_value}") - - fmt = exp.Literal.string(converted_style) - - if to.this == exp.DataType.Type.DATE: - transformed = exp.StrToDate(this=value, format=fmt) - elif to.this in (exp.DataType.Type.DATETIME, exp.DataType.Type.DATETIME2): - transformed = exp.StrToTime(this=value, format=fmt) - elif to.this in self.PARAMETERIZABLE_TEXT_TYPES: - transformed = cast( - this=exp.TimeToStr(this=value, format=fmt), to=to, safe=safe - ) - elif to.this == exp.DataType.Type.TEXT: - transformed = exp.TimeToStr(this=value, format=fmt) - - if not transformed: - transformed = cast(this=value, to=to, safe=safe) - - return self.sql(transformed) - - def _jsonpathkey_sql(self, expression: exp.JSONPathKey) -> str: - this = expression.this - if isinstance(this, exp.JSONPathWildcard): - this = self.json_path_part(this) - return f".{this}" if this else "" - - if self.SAFE_JSON_PATH_KEY_RE.match(this): - return f".{this}" - - this = self.json_path_part(this) - return ( - f"[{this}]" - if self._quote_json_path_key_using_brackets - and self.JSON_PATH_BRACKETED_KEY_SUPPORTED - else f".{this}" - ) - - def _jsonpathsubscript_sql(self, expression: exp.JSONPathSubscript) -> str: - this = self.json_path_part(expression.this) - return f"[{this}]" if this else "" - - def _simplify_unless_literal(self, expression: E) -> E: - if not isinstance(expression, exp.Literal): - from bigframes_vendored.sqlglot.optimizer.simplify import simplify - - expression = simplify(expression, dialect=self.dialect) - - return expression - - def _embed_ignore_nulls( - self, expression: exp.IgnoreNulls | exp.RespectNulls, text: str - ) -> str: - this = expression.this - if isinstance(this, self.RESPECT_IGNORE_NULLS_UNSUPPORTED_EXPRESSIONS): - self.unsupported( - f"RESPECT/IGNORE NULLS is not supported for {type(this).key} in {self.dialect.__class__.__name__}" - ) - return self.sql(this) - - if self.IGNORE_NULLS_IN_FUNC and not expression.meta.get("inline"): - # The first modifier here will be the one closest to the AggFunc's arg - mods = sorted( - expression.find_all(exp.HavingMax, exp.Order, exp.Limit), - key=lambda x: 0 - if isinstance(x, exp.HavingMax) - else (1 if isinstance(x, exp.Order) else 2), - ) - - if mods: - mod = mods[0] - this = expression.__class__(this=mod.this.copy()) - this.meta["inline"] = True - mod.this.replace(this) - return self.sql(expression.this) - - agg_func = expression.find(exp.AggFunc) - - if agg_func: - agg_func_sql = self.sql(agg_func, comment=False)[:-1] + f" {text})" - return self.maybe_comment(agg_func_sql, comments=agg_func.comments) - - return f"{self.sql(expression, 'this')} {text}" - - def _replace_line_breaks(self, string: str) -> str: - """We don't want to extra indent line breaks so we temporarily replace them with sentinels.""" - if self.pretty: - return string.replace("\n", self.SENTINEL_LINE_BREAK) - return string - - def copyparameter_sql(self, expression: exp.CopyParameter) -> str: - option = self.sql(expression, "this") - - if expression.expressions: - upper = option.upper() - - # Snowflake FILE_FORMAT options are separated by whitespace - sep = " " if upper == "FILE_FORMAT" else ", " - - # Databricks copy/format options do not set their list of values with EQ - op = " " if upper in ("COPY_OPTIONS", "FORMAT_OPTIONS") else " = " - values = self.expressions(expression, flat=True, sep=sep) - return f"{option}{op}({values})" - - value = self.sql(expression, "expression") - - if not value: - return option - - op = " = " if self.COPY_PARAMS_EQ_REQUIRED else " " - - return f"{option}{op}{value}" - - def credentials_sql(self, expression: exp.Credentials) -> str: - cred_expr = expression.args.get("credentials") - if isinstance(cred_expr, exp.Literal): - # Redshift case: CREDENTIALS - credentials = self.sql(expression, "credentials") - credentials = f"CREDENTIALS {credentials}" if credentials else "" - else: - # Snowflake case: CREDENTIALS = (...) - credentials = self.expressions( - expression, key="credentials", flat=True, sep=" " - ) - credentials = ( - f"CREDENTIALS = ({credentials})" if cred_expr is not None else "" - ) - - storage = self.sql(expression, "storage") - storage = f"STORAGE_INTEGRATION = {storage}" if storage else "" - - encryption = self.expressions(expression, key="encryption", flat=True, sep=" ") - encryption = f" ENCRYPTION = ({encryption})" if encryption else "" - - iam_role = self.sql(expression, "iam_role") - iam_role = f"IAM_ROLE {iam_role}" if iam_role else "" - - region = self.sql(expression, "region") - region = f" REGION {region}" if region else "" - - return f"{credentials}{storage}{encryption}{iam_role}{region}" - - def copy_sql(self, expression: exp.Copy) -> str: - this = self.sql(expression, "this") - this = f" INTO {this}" if self.COPY_HAS_INTO_KEYWORD else f" {this}" - - credentials = self.sql(expression, "credentials") - credentials = self.seg(credentials) if credentials else "" - files = self.expressions(expression, key="files", flat=True) - kind = ( - self.seg("FROM" if expression.args.get("kind") else "TO") if files else "" - ) - - sep = ", " if self.dialect.COPY_PARAMS_ARE_CSV else " " - params = self.expressions( - expression, - key="params", - sep=sep, - new_line=True, - skip_last=True, - skip_first=True, - indent=self.COPY_PARAMS_ARE_WRAPPED, - ) - - if params: - if self.COPY_PARAMS_ARE_WRAPPED: - params = f" WITH ({params})" - elif not self.pretty and (files or credentials): - params = f" {params}" - - return f"COPY{this}{kind} {files}{credentials}{params}" - - def semicolon_sql(self, expression: exp.Semicolon) -> str: - return "" - - def datadeletionproperty_sql(self, expression: exp.DataDeletionProperty) -> str: - on_sql = "ON" if expression.args.get("on") else "OFF" - filter_col: t.Optional[str] = self.sql(expression, "filter_column") - filter_col = f"FILTER_COLUMN={filter_col}" if filter_col else None - retention_period: t.Optional[str] = self.sql(expression, "retention_period") - retention_period = ( - f"RETENTION_PERIOD={retention_period}" if retention_period else None - ) - - if filter_col or retention_period: - on_sql = self.func("ON", filter_col, retention_period) - - return f"DATA_DELETION={on_sql}" - - def maskingpolicycolumnconstraint_sql( - self, expression: exp.MaskingPolicyColumnConstraint - ) -> str: - this = self.sql(expression, "this") - expressions = self.expressions(expression, flat=True) - expressions = f" USING ({expressions})" if expressions else "" - return f"MASKING POLICY {this}{expressions}" - - def gapfill_sql(self, expression: exp.GapFill) -> str: - this = self.sql(expression, "this") - this = f"TABLE {this}" - return self.func( - "GAP_FILL", this, *[v for k, v in expression.args.items() if k != "this"] - ) - - def scope_resolution(self, rhs: str, scope_name: str) -> str: - return self.func("SCOPE_RESOLUTION", scope_name or None, rhs) - - def scoperesolution_sql(self, expression: exp.ScopeResolution) -> str: - this = self.sql(expression, "this") - expr = expression.expression - - if isinstance(expr, exp.Func): - # T-SQL's CLR functions are case sensitive - expr = f"{self.sql(expr, 'this')}({self.format_args(*expr.expressions)})" - else: - expr = self.sql(expression, "expression") - - return self.scope_resolution(expr, this) - - def parsejson_sql(self, expression: exp.ParseJSON) -> str: - if self.PARSE_JSON_NAME is None: - return self.sql(expression.this) - - return self.func(self.PARSE_JSON_NAME, expression.this, expression.expression) - - def rand_sql(self, expression: exp.Rand) -> str: - lower = self.sql(expression, "lower") - upper = self.sql(expression, "upper") - - if lower and upper: - return ( - f"({upper} - {lower}) * {self.func('RAND', expression.this)} + {lower}" - ) - return self.func("RAND", expression.this) - - def changes_sql(self, expression: exp.Changes) -> str: - information = self.sql(expression, "information") - information = f"INFORMATION => {information}" - at_before = self.sql(expression, "at_before") - at_before = f"{self.seg('')}{at_before}" if at_before else "" - end = self.sql(expression, "end") - end = f"{self.seg('')}{end}" if end else "" - - return f"CHANGES ({information}){at_before}{end}" - - def pad_sql(self, expression: exp.Pad) -> str: - prefix = "L" if expression.args.get("is_left") else "R" - - fill_pattern = self.sql(expression, "fill_pattern") or None - if not fill_pattern and self.PAD_FILL_PATTERN_IS_REQUIRED: - fill_pattern = "' '" - - return self.func( - f"{prefix}PAD", expression.this, expression.expression, fill_pattern - ) - - def summarize_sql(self, expression: exp.Summarize) -> str: - table = " TABLE" if expression.args.get("table") else "" - return f"SUMMARIZE{table} {self.sql(expression.this)}" - - def explodinggenerateseries_sql( - self, expression: exp.ExplodingGenerateSeries - ) -> str: - generate_series = exp.GenerateSeries(**expression.args) - - parent = expression.parent - if isinstance(parent, (exp.Alias, exp.TableAlias)): - parent = parent.parent - - if self.SUPPORTS_EXPLODING_PROJECTIONS and not isinstance( - parent, (exp.Table, exp.Unnest) - ): - return self.sql(exp.Unnest(expressions=[generate_series])) - - if isinstance(parent, exp.Select): - self.unsupported("GenerateSeries projection unnesting is not supported.") - - return self.sql(generate_series) - - def arrayconcat_sql( - self, expression: exp.ArrayConcat, name: str = "ARRAY_CONCAT" - ) -> str: - exprs = expression.expressions - if not self.ARRAY_CONCAT_IS_VAR_LEN: - if len(exprs) == 0: - rhs: t.Union[str, exp.Expression] = exp.Array(expressions=[]) - else: - rhs = reduce( - lambda x, y: exp.ArrayConcat(this=x, expressions=[y]), exprs - ) - else: - rhs = self.expressions(expression) # type: ignore - - return self.func(name, expression.this, rhs or None) - - def converttimezone_sql(self, expression: exp.ConvertTimezone) -> str: - if self.SUPPORTS_CONVERT_TIMEZONE: - return self.function_fallback_sql(expression) - - source_tz = expression.args.get("source_tz") - target_tz = expression.args.get("target_tz") - timestamp = expression.args.get("timestamp") - - if source_tz and timestamp: - timestamp = exp.AtTimeZone( - this=exp.cast(timestamp, exp.DataType.Type.TIMESTAMPNTZ), zone=source_tz - ) - - expr = exp.AtTimeZone(this=timestamp, zone=target_tz) - - return self.sql(expr) - - def json_sql(self, expression: exp.JSON) -> str: - this = self.sql(expression, "this") - this = f" {this}" if this else "" - - _with = expression.args.get("with_") - - if _with is None: - with_sql = "" - elif not _with: - with_sql = " WITHOUT" - else: - with_sql = " WITH" - - unique_sql = " UNIQUE KEYS" if expression.args.get("unique") else "" - - return f"JSON{this}{with_sql}{unique_sql}" - - def jsonvalue_sql(self, expression: exp.JSONValue) -> str: - def _generate_on_options(arg: t.Any) -> str: - return arg if isinstance(arg, str) else f"DEFAULT {self.sql(arg)}" - - path = self.sql(expression, "path") - returning = self.sql(expression, "returning") - returning = f" RETURNING {returning}" if returning else "" - - on_condition = self.sql(expression, "on_condition") - on_condition = f" {on_condition}" if on_condition else "" - - return self.func( - "JSON_VALUE", expression.this, f"{path}{returning}{on_condition}" - ) - - def conditionalinsert_sql(self, expression: exp.ConditionalInsert) -> str: - else_ = "ELSE " if expression.args.get("else_") else "" - condition = self.sql(expression, "expression") - condition = f"WHEN {condition} THEN " if condition else else_ - insert = self.sql(expression, "this")[len("INSERT") :].strip() - return f"{condition}{insert}" - - def multitableinserts_sql(self, expression: exp.MultitableInserts) -> str: - kind = self.sql(expression, "kind") - expressions = self.seg(self.expressions(expression, sep=" ")) - res = f"INSERT {kind}{expressions}{self.seg(self.sql(expression, 'source'))}" - return res - - def oncondition_sql(self, expression: exp.OnCondition) -> str: - # Static options like "NULL ON ERROR" are stored as strings, in contrast to "DEFAULT ON ERROR" - empty = expression.args.get("empty") - empty = ( - f"DEFAULT {empty} ON EMPTY" - if isinstance(empty, exp.Expression) - else self.sql(expression, "empty") - ) - - error = expression.args.get("error") - error = ( - f"DEFAULT {error} ON ERROR" - if isinstance(error, exp.Expression) - else self.sql(expression, "error") - ) - - if error and empty: - error = ( - f"{empty} {error}" - if self.dialect.ON_CONDITION_EMPTY_BEFORE_ERROR - else f"{error} {empty}" - ) - empty = "" - - null = self.sql(expression, "null") - - return f"{empty}{error}{null}" - - def jsonextractquote_sql(self, expression: exp.JSONExtractQuote) -> str: - scalar = " ON SCALAR STRING" if expression.args.get("scalar") else "" - return f"{self.sql(expression, 'option')} QUOTES{scalar}" - - def jsonexists_sql(self, expression: exp.JSONExists) -> str: - this = self.sql(expression, "this") - path = self.sql(expression, "path") - - passing = self.expressions(expression, "passing") - passing = f" PASSING {passing}" if passing else "" - - on_condition = self.sql(expression, "on_condition") - on_condition = f" {on_condition}" if on_condition else "" - - path = f"{path}{passing}{on_condition}" - - return self.func("JSON_EXISTS", this, path) - - def arrayagg_sql(self, expression: exp.ArrayAgg) -> str: - array_agg = self.function_fallback_sql(expression) - - # Add a NULL FILTER on the column to mimic the results going from a dialect that excludes nulls - # on ARRAY_AGG (e.g Spark) to one that doesn't (e.g. DuckDB) - if self.dialect.ARRAY_AGG_INCLUDES_NULLS and expression.args.get( - "nulls_excluded" - ): - parent = expression.parent - if isinstance(parent, exp.Filter): - parent_cond = parent.expression.this - parent_cond.replace( - parent_cond.and_(expression.this.is_(exp.null()).not_()) - ) - else: - this = expression.this - # Do not add the filter if the input is not a column (e.g. literal, struct etc) - if this.find(exp.Column): - # DISTINCT is already present in the agg function, do not propagate it to FILTER as well - this_sql = ( - self.expressions(this) - if isinstance(this, exp.Distinct) - else self.sql(expression, "this") - ) - - array_agg = f"{array_agg} FILTER(WHERE {this_sql} IS NOT NULL)" - - return array_agg - - def slice_sql(self, expression: exp.Slice) -> str: - step = self.sql(expression, "step") - end = self.sql(expression.expression) - begin = self.sql(expression.this) - - sql = f"{end}:{step}" if step else end - return f"{begin}:{sql}" if sql else f"{begin}:" - - def apply_sql(self, expression: exp.Apply) -> str: - this = self.sql(expression, "this") - expr = self.sql(expression, "expression") - - return f"{this} APPLY({expr})" - - def _grant_or_revoke_sql( - self, - expression: exp.Grant | exp.Revoke, - keyword: str, - preposition: str, - grant_option_prefix: str = "", - grant_option_suffix: str = "", - ) -> str: - privileges_sql = self.expressions(expression, key="privileges", flat=True) - - kind = self.sql(expression, "kind") - kind = f" {kind}" if kind else "" - - securable = self.sql(expression, "securable") - securable = f" {securable}" if securable else "" - - principals = self.expressions(expression, key="principals", flat=True) - - if not expression.args.get("grant_option"): - grant_option_prefix = grant_option_suffix = "" - - # cascade for revoke only - cascade = self.sql(expression, "cascade") - cascade = f" {cascade}" if cascade else "" - - return f"{keyword} {grant_option_prefix}{privileges_sql} ON{kind}{securable} {preposition} {principals}{grant_option_suffix}{cascade}" - - def grant_sql(self, expression: exp.Grant) -> str: - return self._grant_or_revoke_sql( - expression, - keyword="GRANT", - preposition="TO", - grant_option_suffix=" WITH GRANT OPTION", - ) - - def revoke_sql(self, expression: exp.Revoke) -> str: - return self._grant_or_revoke_sql( - expression, - keyword="REVOKE", - preposition="FROM", - grant_option_prefix="GRANT OPTION FOR ", - ) - - def grantprivilege_sql(self, expression: exp.GrantPrivilege): - this = self.sql(expression, "this") - columns = self.expressions(expression, flat=True) - columns = f"({columns})" if columns else "" - - return f"{this}{columns}" - - def grantprincipal_sql(self, expression: exp.GrantPrincipal): - this = self.sql(expression, "this") - - kind = self.sql(expression, "kind") - kind = f"{kind} " if kind else "" - - return f"{kind}{this}" - - def columns_sql(self, expression: exp.Columns): - func = self.function_fallback_sql(expression) - if expression.args.get("unpack"): - func = f"*{func}" - - return func - - def overlay_sql(self, expression: exp.Overlay): - this = self.sql(expression, "this") - expr = self.sql(expression, "expression") - from_sql = self.sql(expression, "from_") - for_sql = self.sql(expression, "for_") - for_sql = f" FOR {for_sql}" if for_sql else "" - - return f"OVERLAY({this} PLACING {expr} FROM {from_sql}{for_sql})" - - @unsupported_args("format") - def todouble_sql(self, expression: exp.ToDouble) -> str: - return self.sql(exp.cast(expression.this, exp.DataType.Type.DOUBLE)) - - def string_sql(self, expression: exp.String) -> str: - this = expression.this - zone = expression.args.get("zone") - - if zone: - # This is a BigQuery specific argument for STRING(, ) - # BigQuery stores timestamps internally as UTC, so ConvertTimezone is used with UTC - # set for source_tz to transpile the time conversion before the STRING cast - this = exp.ConvertTimezone( - source_tz=exp.Literal.string("UTC"), target_tz=zone, timestamp=this - ) - - return self.sql(exp.cast(this, exp.DataType.Type.VARCHAR)) - - def median_sql(self, expression: exp.Median): - if not self.SUPPORTS_MEDIAN: - return self.sql( - exp.PercentileCont( - this=expression.this, expression=exp.Literal.number(0.5) - ) - ) - - return self.function_fallback_sql(expression) - - def overflowtruncatebehavior_sql( - self, expression: exp.OverflowTruncateBehavior - ) -> str: - filler = self.sql(expression, "this") - filler = f" {filler}" if filler else "" - with_count = ( - "WITH COUNT" if expression.args.get("with_count") else "WITHOUT COUNT" - ) - return f"TRUNCATE{filler} {with_count}" - - def unixseconds_sql(self, expression: exp.UnixSeconds) -> str: - if self.SUPPORTS_UNIX_SECONDS: - return self.function_fallback_sql(expression) - - start_ts = exp.cast( - exp.Literal.string("1970-01-01 00:00:00+00"), - to=exp.DataType.Type.TIMESTAMPTZ, - ) - - return self.sql( - exp.TimestampDiff( - this=expression.this, expression=start_ts, unit=exp.var("SECONDS") - ) - ) - - def arraysize_sql(self, expression: exp.ArraySize) -> str: - dim = expression.expression - - # For dialects that don't support the dimension arg, we can safely transpile it's default value (1st dimension) - if dim and self.ARRAY_SIZE_DIM_REQUIRED is None: - if not (dim.is_int and dim.name == "1"): - self.unsupported("Cannot transpile dimension argument for ARRAY_LENGTH") - dim = None - - # If dimension is required but not specified, default initialize it - if self.ARRAY_SIZE_DIM_REQUIRED and not dim: - dim = exp.Literal.number(1) - - return self.func(self.ARRAY_SIZE_NAME, expression.this, dim) - - def attach_sql(self, expression: exp.Attach) -> str: - this = self.sql(expression, "this") - exists_sql = " IF NOT EXISTS" if expression.args.get("exists") else "" - expressions = self.expressions(expression) - expressions = f" ({expressions})" if expressions else "" - - return f"ATTACH{exists_sql} {this}{expressions}" - - def detach_sql(self, expression: exp.Detach) -> str: - this = self.sql(expression, "this") - # the DATABASE keyword is required if IF EXISTS is set - # without it, DuckDB throws an error: Parser Error: syntax error at or near "exists" (Line Number: 1) - # ref: https://duckdb.org/docs/stable/sql/statements/attach.html#detach-syntax - exists_sql = " DATABASE IF EXISTS" if expression.args.get("exists") else "" - - return f"DETACH{exists_sql} {this}" - - def attachoption_sql(self, expression: exp.AttachOption) -> str: - this = self.sql(expression, "this") - value = self.sql(expression, "expression") - value = f" {value}" if value else "" - return f"{this}{value}" - - def watermarkcolumnconstraint_sql( - self, expression: exp.WatermarkColumnConstraint - ) -> str: - return f"WATERMARK FOR {self.sql(expression, 'this')} AS {self.sql(expression, 'expression')}" - - def encodeproperty_sql(self, expression: exp.EncodeProperty) -> str: - encode = "KEY ENCODE" if expression.args.get("key") else "ENCODE" - encode = f"{encode} {self.sql(expression, 'this')}" - - properties = expression.args.get("properties") - if properties: - encode = f"{encode} {self.properties(properties)}" - - return encode - - def includeproperty_sql(self, expression: exp.IncludeProperty) -> str: - this = self.sql(expression, "this") - include = f"INCLUDE {this}" - - column_def = self.sql(expression, "column_def") - if column_def: - include = f"{include} {column_def}" - - alias = self.sql(expression, "alias") - if alias: - include = f"{include} AS {alias}" - - return include - - def xmlelement_sql(self, expression: exp.XMLElement) -> str: - name = f"NAME {self.sql(expression, 'this')}" - return self.func("XMLELEMENT", name, *expression.expressions) - - def xmlkeyvalueoption_sql(self, expression: exp.XMLKeyValueOption) -> str: - this = self.sql(expression, "this") - expr = self.sql(expression, "expression") - expr = f"({expr})" if expr else "" - return f"{this}{expr}" - - def partitionbyrangeproperty_sql( - self, expression: exp.PartitionByRangeProperty - ) -> str: - partitions = self.expressions(expression, "partition_expressions") - create = self.expressions(expression, "create_expressions") - return f"PARTITION BY RANGE {self.wrap(partitions)} {self.wrap(create)}" - - def partitionbyrangepropertydynamic_sql( - self, expression: exp.PartitionByRangePropertyDynamic - ) -> str: - start = self.sql(expression, "start") - end = self.sql(expression, "end") - - every = expression.args["every"] - if isinstance(every, exp.Interval) and every.this.is_string: - every.this.replace(exp.Literal.number(every.name)) - - return f"START {self.wrap(start)} END {self.wrap(end)} EVERY {self.wrap(self.sql(every))}" - - def unpivotcolumns_sql(self, expression: exp.UnpivotColumns) -> str: - name = self.sql(expression, "this") - values = self.expressions(expression, flat=True) - - return f"NAME {name} VALUE {values}" - - def analyzesample_sql(self, expression: exp.AnalyzeSample) -> str: - kind = self.sql(expression, "kind") - sample = self.sql(expression, "sample") - return f"SAMPLE {sample} {kind}" - - def analyzestatistics_sql(self, expression: exp.AnalyzeStatistics) -> str: - kind = self.sql(expression, "kind") - option = self.sql(expression, "option") - option = f" {option}" if option else "" - this = self.sql(expression, "this") - this = f" {this}" if this else "" - columns = self.expressions(expression) - columns = f" {columns}" if columns else "" - return f"{kind}{option} STATISTICS{this}{columns}" - - def analyzehistogram_sql(self, expression: exp.AnalyzeHistogram) -> str: - this = self.sql(expression, "this") - columns = self.expressions(expression) - inner_expression = self.sql(expression, "expression") - inner_expression = f" {inner_expression}" if inner_expression else "" - update_options = self.sql(expression, "update_options") - update_options = f" {update_options} UPDATE" if update_options else "" - return f"{this} HISTOGRAM ON {columns}{inner_expression}{update_options}" - - def analyzedelete_sql(self, expression: exp.AnalyzeDelete) -> str: - kind = self.sql(expression, "kind") - kind = f" {kind}" if kind else "" - return f"DELETE{kind} STATISTICS" - - def analyzelistchainedrows_sql(self, expression: exp.AnalyzeListChainedRows) -> str: - inner_expression = self.sql(expression, "expression") - return f"LIST CHAINED ROWS{inner_expression}" - - def analyzevalidate_sql(self, expression: exp.AnalyzeValidate) -> str: - kind = self.sql(expression, "kind") - this = self.sql(expression, "this") - this = f" {this}" if this else "" - inner_expression = self.sql(expression, "expression") - return f"VALIDATE {kind}{this}{inner_expression}" - - def analyze_sql(self, expression: exp.Analyze) -> str: - options = self.expressions(expression, key="options", sep=" ") - options = f" {options}" if options else "" - kind = self.sql(expression, "kind") - kind = f" {kind}" if kind else "" - this = self.sql(expression, "this") - this = f" {this}" if this else "" - mode = self.sql(expression, "mode") - mode = f" {mode}" if mode else "" - properties = self.sql(expression, "properties") - properties = f" {properties}" if properties else "" - partition = self.sql(expression, "partition") - partition = f" {partition}" if partition else "" - inner_expression = self.sql(expression, "expression") - inner_expression = f" {inner_expression}" if inner_expression else "" - return f"ANALYZE{options}{kind}{this}{partition}{mode}{inner_expression}{properties}" - - def xmltable_sql(self, expression: exp.XMLTable) -> str: - this = self.sql(expression, "this") - namespaces = self.expressions(expression, key="namespaces") - namespaces = f"XMLNAMESPACES({namespaces}), " if namespaces else "" - passing = self.expressions(expression, key="passing") - passing = f"{self.sep()}PASSING{self.seg(passing)}" if passing else "" - columns = self.expressions(expression, key="columns") - columns = f"{self.sep()}COLUMNS{self.seg(columns)}" if columns else "" - by_ref = ( - f"{self.sep()}RETURNING SEQUENCE BY REF" - if expression.args.get("by_ref") - else "" - ) - return f"XMLTABLE({self.sep('')}{self.indent(namespaces + this + passing + by_ref + columns)}{self.seg(')', sep='')}" - - def xmlnamespace_sql(self, expression: exp.XMLNamespace) -> str: - this = self.sql(expression, "this") - return this if isinstance(expression.this, exp.Alias) else f"DEFAULT {this}" - - def export_sql(self, expression: exp.Export) -> str: - this = self.sql(expression, "this") - connection = self.sql(expression, "connection") - connection = f"WITH CONNECTION {connection} " if connection else "" - options = self.sql(expression, "options") - return f"EXPORT DATA {connection}{options} AS {this}" - - def declare_sql(self, expression: exp.Declare) -> str: - return f"DECLARE {self.expressions(expression, flat=True)}" - - def declareitem_sql(self, expression: exp.DeclareItem) -> str: - variable = self.sql(expression, "this") - default = self.sql(expression, "default") - default = f" = {default}" if default else "" - - kind = self.sql(expression, "kind") - if isinstance(expression.args.get("kind"), exp.Schema): - kind = f"TABLE {kind}" - - return f"{variable} AS {kind}{default}" - - def recursivewithsearch_sql(self, expression: exp.RecursiveWithSearch) -> str: - kind = self.sql(expression, "kind") - this = self.sql(expression, "this") - set = self.sql(expression, "expression") - using = self.sql(expression, "using") - using = f" USING {using}" if using else "" - - kind_sql = kind if kind == "CYCLE" else f"SEARCH {kind} FIRST BY" - - return f"{kind_sql} {this} SET {set}{using}" - - def parameterizedagg_sql(self, expression: exp.ParameterizedAgg) -> str: - params = self.expressions(expression, key="params", flat=True) - return self.func(expression.name, *expression.expressions) + f"({params})" - - def anonymousaggfunc_sql(self, expression: exp.AnonymousAggFunc) -> str: - return self.func(expression.name, *expression.expressions) - - def combinedaggfunc_sql(self, expression: exp.CombinedAggFunc) -> str: - return self.anonymousaggfunc_sql(expression) - - def combinedparameterizedagg_sql( - self, expression: exp.CombinedParameterizedAgg - ) -> str: - return self.parameterizedagg_sql(expression) - - def show_sql(self, expression: exp.Show) -> str: - self.unsupported("Unsupported SHOW statement") - return "" - - def install_sql(self, expression: exp.Install) -> str: - self.unsupported("Unsupported INSTALL statement") - return "" - - def get_put_sql(self, expression: exp.Put | exp.Get) -> str: - # Snowflake GET/PUT statements: - # PUT - # GET - props = expression.args.get("properties") - props_sql = ( - self.properties(props, prefix=" ", sep=" ", wrapped=False) if props else "" - ) - this = self.sql(expression, "this") - target = self.sql(expression, "target") - - if isinstance(expression, exp.Put): - return f"PUT {this} {target}{props_sql}" - else: - return f"GET {target} {this}{props_sql}" - - def translatecharacters_sql(self, expression: exp.TranslateCharacters): - this = self.sql(expression, "this") - expr = self.sql(expression, "expression") - with_error = " WITH ERROR" if expression.args.get("with_error") else "" - return f"TRANSLATE({this} USING {expr}{with_error})" - - def decodecase_sql(self, expression: exp.DecodeCase) -> str: - if self.SUPPORTS_DECODE_CASE: - return self.func("DECODE", *expression.expressions) - - expression, *expressions = expression.expressions - - ifs = [] - for search, result in zip(expressions[::2], expressions[1::2]): - if isinstance(search, exp.Literal): - ifs.append(exp.If(this=expression.eq(search), true=result)) - elif isinstance(search, exp.Null): - ifs.append(exp.If(this=expression.is_(exp.Null()), true=result)) - else: - if isinstance(search, exp.Binary): - search = exp.paren(search) - - cond = exp.or_( - expression.eq(search), - exp.and_( - expression.is_(exp.Null()), search.is_(exp.Null()), copy=False - ), - copy=False, - ) - ifs.append(exp.If(this=cond, true=result)) - - case = exp.Case( - ifs=ifs, default=expressions[-1] if len(expressions) % 2 == 1 else None - ) - return self.sql(case) - - def semanticview_sql(self, expression: exp.SemanticView) -> str: - this = self.sql(expression, "this") - this = self.seg(this, sep="") - dimensions = self.expressions( - expression, "dimensions", dynamic=True, skip_first=True, skip_last=True - ) - dimensions = self.seg(f"DIMENSIONS {dimensions}") if dimensions else "" - metrics = self.expressions( - expression, "metrics", dynamic=True, skip_first=True, skip_last=True - ) - metrics = self.seg(f"METRICS {metrics}") if metrics else "" - facts = self.expressions( - expression, "facts", dynamic=True, skip_first=True, skip_last=True - ) - facts = self.seg(f"FACTS {facts}") if facts else "" - where = self.sql(expression, "where") - where = self.seg(f"WHERE {where}") if where else "" - body = self.indent(this + metrics + dimensions + facts + where, skip_first=True) - return f"SEMANTIC_VIEW({body}{self.seg(')', sep='')}" - - def getextract_sql(self, expression: exp.GetExtract) -> str: - this = expression.this - expr = expression.expression - - if not this.type or not expression.type: - from bigframes_vendored.sqlglot.optimizer.annotate_types import ( - annotate_types, - ) - - this = annotate_types(this, dialect=self.dialect) - - if this.is_type(*(exp.DataType.Type.ARRAY, exp.DataType.Type.MAP)): - return self.sql(exp.Bracket(this=this, expressions=[expr])) - - return self.sql( - exp.JSONExtract(this=this, expression=self.dialect.to_json_path(expr)) - ) - - def datefromunixdate_sql(self, expression: exp.DateFromUnixDate) -> str: - return self.sql( - exp.DateAdd( - this=exp.cast(exp.Literal.string("1970-01-01"), exp.DataType.Type.DATE), - expression=expression.this, - unit=exp.var("DAY"), - ) - ) - - def space_sql(self: Generator, expression: exp.Space) -> str: - return self.sql(exp.Repeat(this=exp.Literal.string(" "), times=expression.this)) - - def buildproperty_sql(self, expression: exp.BuildProperty) -> str: - return f"BUILD {self.sql(expression, 'this')}" - - def refreshtriggerproperty_sql(self, expression: exp.RefreshTriggerProperty) -> str: - method = self.sql(expression, "method") - kind = expression.args.get("kind") - if not kind: - return f"REFRESH {method}" - - every = self.sql(expression, "every") - unit = self.sql(expression, "unit") - every = f" EVERY {every} {unit}" if every else "" - starts = self.sql(expression, "starts") - starts = f" STARTS {starts}" if starts else "" - - return f"REFRESH {method} ON {kind}{every}{starts}" - - def modelattribute_sql(self, expression: exp.ModelAttribute) -> str: - self.unsupported("The model!attribute syntax is not supported") - return "" - - def directorystage_sql(self, expression: exp.DirectoryStage) -> str: - return self.func("DIRECTORY", expression.this) - - def uuid_sql(self, expression: exp.Uuid) -> str: - is_string = expression.args.get("is_string", False) - uuid_func_sql = self.func("UUID") - - if is_string and not self.dialect.UUID_IS_STRING_TYPE: - return self.sql( - exp.cast(uuid_func_sql, exp.DataType.Type.VARCHAR, dialect=self.dialect) - ) - - return uuid_func_sql - - def initcap_sql(self, expression: exp.Initcap) -> str: - delimiters = expression.expression - - if delimiters: - # do not generate delimiters arg if we are round-tripping from default delimiters - if ( - delimiters.is_string - and delimiters.this == self.dialect.INITCAP_DEFAULT_DELIMITER_CHARS - ): - delimiters = None - elif not self.dialect.INITCAP_SUPPORTS_CUSTOM_DELIMITERS: - self.unsupported("INITCAP does not support custom delimiters") - delimiters = None - - return self.func("INITCAP", expression.this, delimiters) - - def localtime_sql(self, expression: exp.Localtime) -> str: - this = expression.this - return self.func("LOCALTIME", this) if this else "LOCALTIME" - - def localtimestamp_sql(self, expression: exp.Localtime) -> str: - this = expression.this - return self.func("LOCALTIMESTAMP", this) if this else "LOCALTIMESTAMP" - - def weekstart_sql(self, expression: exp.WeekStart) -> str: - this = expression.this.name.upper() - if self.dialect.WEEK_OFFSET == -1 and this == "SUNDAY": - # BigQuery specific optimization since WEEK(SUNDAY) == WEEK - return "WEEK" - - return self.func("WEEK", expression.this) diff --git a/third_party/bigframes_vendored/sqlglot/helper.py b/third_party/bigframes_vendored/sqlglot/helper.py deleted file mode 100644 index 5cd16e2c3cd..00000000000 --- a/third_party/bigframes_vendored/sqlglot/helper.py +++ /dev/null @@ -1,532 +0,0 @@ -# Contains code from https://github.com/tobymao/sqlglot/blob/v28.5.0/sqlglot/helper.py - -from __future__ import annotations - -import datetime -import inspect -import logging -import re -import sys -import typing as t -from collections.abc import Collection, Set -from copy import copy -from difflib import get_close_matches -from enum import Enum -from itertools import count - -if t.TYPE_CHECKING: - from bigframes_vendored.sqlglot import exp - from bigframes_vendored.sqlglot._typing import A, E, T - from bigframes_vendored.sqlglot.dialects.dialect import DialectType - from bigframes_vendored.sqlglot.expressions import Expression - - -CAMEL_CASE_PATTERN = re.compile("(? t.Any: - return classmethod(self.fget).__get__(None, owner)() # type: ignore - - -def suggest_closest_match_and_fail( - kind: str, - word: str, - possibilities: t.Iterable[str], -) -> None: - close_matches = get_close_matches(word, possibilities, n=1) - - similar = seq_get(close_matches, 0) or "" - if similar: - similar = f" Did you mean {similar}?" - - raise ValueError(f"Unknown {kind} '{word}'.{similar}") - - -def seq_get(seq: t.Sequence[T], index: int) -> t.Optional[T]: - """Returns the value in `seq` at position `index`, or `None` if `index` is out of bounds.""" - try: - return seq[index] - except IndexError: - return None - - -@t.overload -def ensure_list(value: t.Collection[T]) -> t.List[T]: ... - - -@t.overload -def ensure_list(value: None) -> t.List: ... - - -@t.overload -def ensure_list(value: T) -> t.List[T]: ... - - -def ensure_list(value): - """ - Ensures that a value is a list, otherwise casts or wraps it into one. - - Args: - value: The value of interest. - - Returns: - The value cast as a list if it's a list or a tuple, or else the value wrapped in a list. - """ - if value is None: - return [] - if isinstance(value, (list, tuple)): - return list(value) - - return [value] - - -@t.overload -def ensure_collection(value: t.Collection[T]) -> t.Collection[T]: ... - - -@t.overload -def ensure_collection(value: T) -> t.Collection[T]: ... - - -def ensure_collection(value): - """ - Ensures that a value is a collection (excluding `str` and `bytes`), otherwise wraps it into a list. - - Args: - value: The value of interest. - - Returns: - The value if it's a collection, or else the value wrapped in a list. - """ - if value is None: - return [] - return ( - value - if isinstance(value, Collection) and not isinstance(value, (str, bytes)) - else [value] - ) - - -def csv(*args: str, sep: str = ", ") -> str: - """ - Formats any number of string arguments as CSV. - - Args: - args: The string arguments to format. - sep: The argument separator. - - Returns: - The arguments formatted as a CSV string. - """ - return sep.join(arg for arg in args if arg) - - -def subclasses( - module_name: str, - classes: t.Type | t.Tuple[t.Type, ...], - exclude: t.Set[t.Type] = set(), -) -> t.List[t.Type]: - """ - Returns all subclasses for a collection of classes, possibly excluding some of them. - - Args: - module_name: The name of the module to search for subclasses in. - classes: Class(es) we want to find the subclasses of. - exclude: Classes we want to exclude from the returned list. - - Returns: - The target subclasses. - """ - return [ - obj - for _, obj in inspect.getmembers( - sys.modules[module_name], - lambda obj: inspect.isclass(obj) - and issubclass(obj, classes) - and obj not in exclude, - ) - ] - - -def apply_index_offset( - this: exp.Expression, - expressions: t.List[E], - offset: int, - dialect: DialectType = None, -) -> t.List[E]: - """ - Applies an offset to a given integer literal expression. - - Args: - this: The target of the index. - expressions: The expression the offset will be applied to, wrapped in a list. - offset: The offset that will be applied. - dialect: the dialect of interest. - - Returns: - The original expression with the offset applied to it, wrapped in a list. If the provided - `expressions` argument contains more than one expression, it's returned unaffected. - """ - if not offset or len(expressions) != 1: - return expressions - - expression = expressions[0] - - from bigframes_vendored.sqlglot import exp - from bigframes_vendored.sqlglot.optimizer.annotate_types import annotate_types - from bigframes_vendored.sqlglot.optimizer.simplify import simplify - - if not this.type: - annotate_types(this, dialect=dialect) - - if t.cast(exp.DataType, this.type).this not in ( - exp.DataType.Type.UNKNOWN, - exp.DataType.Type.ARRAY, - ): - return expressions - - if not expression.type: - annotate_types(expression, dialect=dialect) - - if t.cast(exp.DataType, expression.type).this in exp.DataType.INTEGER_TYPES: - logger.info("Applying array index offset (%s)", offset) - expression = simplify(expression + offset) - return [expression] - - return expressions - - -def camel_to_snake_case(name: str) -> str: - """Converts `name` from camelCase to snake_case and returns the result.""" - return CAMEL_CASE_PATTERN.sub("_", name).upper() - - -def while_changing(expression: Expression, func: t.Callable[[Expression], E]) -> E: - """ - Applies a transformation to a given expression until a fix point is reached. - - Args: - expression: The expression to be transformed. - func: The transformation to be applied. - - Returns: - The transformed expression. - """ - - while True: - start_hash = hash(expression) - expression = func(expression) - end_hash = hash(expression) - - if start_hash == end_hash: - break - - return expression - - -def tsort(dag: t.Dict[T, t.Set[T]]) -> t.List[T]: - """ - Sorts a given directed acyclic graph in topological order. - - Args: - dag: The graph to be sorted. - - Returns: - A list that contains all of the graph's nodes in topological order. - """ - result = [] - - for node, deps in tuple(dag.items()): - for dep in deps: - if dep not in dag: - dag[dep] = set() - - while dag: - current = {node for node, deps in dag.items() if not deps} - - if not current: - raise ValueError("Cycle error") - - for node in current: - dag.pop(node) - - for deps in dag.values(): - deps -= current - - result.extend(sorted(current)) # type: ignore - - return result - - -def find_new_name(taken: t.Collection[str], base: str) -> str: - """ - Searches for a new name. - - Args: - taken: A collection of taken names. - base: Base name to alter. - - Returns: - The new, available name. - """ - if base not in taken: - return base - - i = 2 - new = f"{base}_{i}" - while new in taken: - i += 1 - new = f"{base}_{i}" - - return new - - -def is_int(text: str) -> bool: - return is_type(text, int) - - -def is_float(text: str) -> bool: - return is_type(text, float) - - -def is_type(text: str, target_type: t.Type) -> bool: - try: - target_type(text) - return True - except ValueError: - return False - - -def name_sequence(prefix: str) -> t.Callable[[], str]: - """Returns a name generator given a prefix (e.g. a0, a1, a2, ... if the prefix is "a").""" - sequence = count() - return lambda: f"{prefix}{next(sequence)}" - - -def object_to_dict(obj: t.Any, **kwargs) -> t.Dict: - """Returns a dictionary created from an object's attributes.""" - return { - **{ - k: v.copy() if hasattr(v, "copy") else copy(v) for k, v in vars(obj).items() - }, - **kwargs, - } - - -def split_num_words( - value: str, sep: str, min_num_words: int, fill_from_start: bool = True -) -> t.List[t.Optional[str]]: - """ - Perform a split on a value and return N words as a result with `None` used for words that don't exist. - - Args: - value: The value to be split. - sep: The value to use to split on. - min_num_words: The minimum number of words that are going to be in the result. - fill_from_start: Indicates that if `None` values should be inserted at the start or end of the list. - - Examples: - >>> split_num_words("db.table", ".", 3) - [None, 'db', 'table'] - >>> split_num_words("db.table", ".", 3, fill_from_start=False) - ['db', 'table', None] - >>> split_num_words("db.table", ".", 1) - ['db', 'table'] - - Returns: - The list of words returned by `split`, possibly augmented by a number of `None` values. - """ - words = value.split(sep) - if fill_from_start: - return [None] * (min_num_words - len(words)) + words - return words + [None] * (min_num_words - len(words)) - - -def is_iterable(value: t.Any) -> bool: - """ - Checks if the value is an iterable, excluding the types `str` and `bytes`. - - Examples: - >>> is_iterable([1,2]) - True - >>> is_iterable("test") - False - - Args: - value: The value to check if it is an iterable. - - Returns: - A `bool` value indicating if it is an iterable. - """ - from bigframes_vendored.sqlglot import Expression - - return hasattr(value, "__iter__") and not isinstance( - value, (str, bytes, Expression) - ) - - -def flatten(values: t.Iterable[t.Iterable[t.Any] | t.Any]) -> t.Iterator[t.Any]: - """ - Flattens an iterable that can contain both iterable and non-iterable elements. Objects of - type `str` and `bytes` are not regarded as iterables. - - Examples: - >>> list(flatten([[1, 2], 3, {4}, (5, "bla")])) - [1, 2, 3, 4, 5, 'bla'] - >>> list(flatten([1, 2, 3])) - [1, 2, 3] - - Args: - values: The value to be flattened. - - Yields: - Non-iterable elements in `values`. - """ - for value in values: - if is_iterable(value): - yield from flatten(value) - else: - yield value - - -def dict_depth(d: t.Dict) -> int: - """ - Get the nesting depth of a dictionary. - - Example: - >>> dict_depth(None) - 0 - >>> dict_depth({}) - 1 - >>> dict_depth({"a": "b"}) - 1 - >>> dict_depth({"a": {}}) - 2 - >>> dict_depth({"a": {"b": {}}}) - 3 - """ - try: - return 1 + dict_depth(next(iter(d.values()))) - except AttributeError: - # d doesn't have attribute "values" - return 0 - except StopIteration: - # d.values() returns an empty sequence - return 1 - - -def first(it: t.Iterable[T]) -> T: - """Returns the first element from an iterable (useful for sets).""" - return next(i for i in it) - - -def to_bool(value: t.Optional[str | bool]) -> t.Optional[str | bool]: - if isinstance(value, bool) or value is None: - return value - - # Coerce the value to boolean if it matches to the truthy/falsy values below - value_lower = value.lower() - if value_lower in ("true", "1"): - return True - if value_lower in ("false", "0"): - return False - - return value - - -def merge_ranges(ranges: t.List[t.Tuple[A, A]]) -> t.List[t.Tuple[A, A]]: - """ - Merges a sequence of ranges, represented as tuples (low, high) whose values - belong to some totally-ordered set. - - Example: - >>> merge_ranges([(1, 3), (2, 6)]) - [(1, 6)] - """ - if not ranges: - return [] - - ranges = sorted(ranges) - - merged = [ranges[0]] - - for start, end in ranges[1:]: - last_start, last_end = merged[-1] - - if start <= last_end: - merged[-1] = (last_start, max(last_end, end)) - else: - merged.append((start, end)) - - return merged - - -def is_iso_date(text: str) -> bool: - try: - datetime.date.fromisoformat(text) - return True - except ValueError: - return False - - -def is_iso_datetime(text: str) -> bool: - try: - datetime.datetime.fromisoformat(text) - return True - except ValueError: - return False - - -# Interval units that operate on date components -DATE_UNITS = {"day", "week", "month", "quarter", "year", "year_month"} - - -def is_date_unit(expression: t.Optional[exp.Expression]) -> bool: - return expression is not None and expression.name.lower() in DATE_UNITS - - -K = t.TypeVar("K") -V = t.TypeVar("V") - - -class SingleValuedMapping(t.Mapping[K, V]): - """ - Mapping where all keys return the same value. - - This rigamarole is meant to avoid copying keys, which was originally intended - as an optimization while qualifying columns for tables with lots of columns. - """ - - def __init__(self, keys: t.Collection[K], value: V): - self._keys = keys if isinstance(keys, Set) else set(keys) - self._value = value - - def __getitem__(self, key: K) -> V: - if key in self._keys: - return self._value - raise KeyError(key) - - def __len__(self) -> int: - return len(self._keys) - - def __iter__(self) -> t.Iterator[K]: - return iter(self._keys) diff --git a/third_party/bigframes_vendored/sqlglot/jsonpath.py b/third_party/bigframes_vendored/sqlglot/jsonpath.py deleted file mode 100644 index cc5ac7edd3d..00000000000 --- a/third_party/bigframes_vendored/sqlglot/jsonpath.py +++ /dev/null @@ -1,238 +0,0 @@ -# Contains code from https://github.com/tobymao/sqlglot/blob/v28.5.0/sqlglot/jsonpath.py - -from __future__ import annotations - -import typing as t - -import bigframes_vendored.sqlglot.expressions as exp -from bigframes_vendored.sqlglot.errors import ParseError -from bigframes_vendored.sqlglot.tokens import Token, Tokenizer, TokenType - -if t.TYPE_CHECKING: - from bigframes_vendored.sqlglot._typing import Lit - from bigframes_vendored.sqlglot.dialects.dialect import DialectType - - -class JSONPathTokenizer(Tokenizer): - SINGLE_TOKENS = { - "(": TokenType.L_PAREN, - ")": TokenType.R_PAREN, - "[": TokenType.L_BRACKET, - "]": TokenType.R_BRACKET, - ":": TokenType.COLON, - ",": TokenType.COMMA, - "-": TokenType.DASH, - ".": TokenType.DOT, - "?": TokenType.PLACEHOLDER, - "@": TokenType.PARAMETER, - "'": TokenType.QUOTE, - '"': TokenType.QUOTE, - "$": TokenType.DOLLAR, - "*": TokenType.STAR, - } - - KEYWORDS = { - "..": TokenType.DOT, - } - - IDENTIFIER_ESCAPES = ["\\"] - STRING_ESCAPES = ["\\"] - - VAR_TOKENS = { - TokenType.VAR, - } - - -def parse(path: str, dialect: DialectType = None) -> exp.JSONPath: - """Takes in a JSON path string and parses it into a JSONPath expression.""" - from bigframes_vendored.sqlglot.dialects import Dialect - - jsonpath_tokenizer = Dialect.get_or_raise(dialect).jsonpath_tokenizer() - tokens = jsonpath_tokenizer.tokenize(path) - size = len(tokens) - - i = 0 - - def _curr() -> t.Optional[TokenType]: - return tokens[i].token_type if i < size else None - - def _prev() -> Token: - return tokens[i - 1] - - def _advance() -> Token: - nonlocal i - i += 1 - return _prev() - - def _error(msg: str) -> str: - return f"{msg} at index {i}: {path}" - - @t.overload - def _match(token_type: TokenType, raise_unmatched: Lit[True] = True) -> Token: - pass - - @t.overload - def _match( - token_type: TokenType, raise_unmatched: Lit[False] = False - ) -> t.Optional[Token]: - pass - - def _match(token_type, raise_unmatched=False): - if _curr() == token_type: - return _advance() - if raise_unmatched: - raise ParseError(_error(f"Expected {token_type}")) - return None - - def _match_set(types: t.Collection[TokenType]) -> t.Optional[Token]: - return _advance() if _curr() in types else None - - def _parse_literal() -> t.Any: - token = _match(TokenType.STRING) or _match(TokenType.IDENTIFIER) - if token: - return token.text - if _match(TokenType.STAR): - return exp.JSONPathWildcard() - if _match(TokenType.PLACEHOLDER) or _match(TokenType.L_PAREN): - script = _prev().text == "(" - start = i - - while True: - if _match(TokenType.L_BRACKET): - _parse_bracket() # nested call which we can throw away - if _curr() in (TokenType.R_BRACKET, None): - break - _advance() - - expr_type = exp.JSONPathScript if script else exp.JSONPathFilter - return expr_type(this=path[tokens[start].start : tokens[i].end]) - - number = "-" if _match(TokenType.DASH) else "" - - token = _match(TokenType.NUMBER) - if token: - number += token.text - - if number: - return int(number) - - return False - - def _parse_slice() -> t.Any: - start = _parse_literal() - end = _parse_literal() if _match(TokenType.COLON) else None - step = _parse_literal() if _match(TokenType.COLON) else None - - if end is None and step is None: - return start - - return exp.JSONPathSlice(start=start, end=end, step=step) - - def _parse_bracket() -> exp.JSONPathPart: - literal = _parse_slice() - - if isinstance(literal, str) or literal is not False: - indexes = [literal] - while _match(TokenType.COMMA): - literal = _parse_slice() - - if literal: - indexes.append(literal) - - if len(indexes) == 1: - if isinstance(literal, str): - node: exp.JSONPathPart = exp.JSONPathKey(this=indexes[0]) - elif isinstance(literal, exp.JSONPathPart) and isinstance( - literal, (exp.JSONPathScript, exp.JSONPathFilter) - ): - node = exp.JSONPathSelector(this=indexes[0]) - else: - node = exp.JSONPathSubscript(this=indexes[0]) - else: - node = exp.JSONPathUnion(expressions=indexes) - else: - raise ParseError(_error("Cannot have empty segment")) - - _match(TokenType.R_BRACKET, raise_unmatched=True) - - return node - - def _parse_var_text() -> str: - """ - Consumes & returns the text for a var. In BigQuery it's valid to have a key with spaces - in it, e.g JSON_QUERY(..., '$. a b c ') should produce a single JSONPathKey(' a b c '). - This is done by merging "consecutive" vars until a key separator is found (dot, colon etc) - or the path string is exhausted. - """ - prev_index = i - 2 - - while _match_set(jsonpath_tokenizer.VAR_TOKENS): - pass - - start = 0 if prev_index < 0 else tokens[prev_index].end + 1 - - if i >= len(tokens): - # This key is the last token for the path, so it's text is the remaining path - text = path[start:] - else: - text = path[start : tokens[i].start] - - return text - - # We canonicalize the JSON path AST so that it always starts with a - # "root" element, so paths like "field" will be generated as "$.field" - _match(TokenType.DOLLAR) - expressions: t.List[exp.JSONPathPart] = [exp.JSONPathRoot()] - - while _curr(): - if _match(TokenType.DOT) or _match(TokenType.COLON): - recursive = _prev().text == ".." - - if _match_set(jsonpath_tokenizer.VAR_TOKENS): - value: t.Optional[str | exp.JSONPathWildcard] = _parse_var_text() - elif _match(TokenType.IDENTIFIER): - value = _prev().text - elif _match(TokenType.STAR): - value = exp.JSONPathWildcard() - else: - value = None - - if recursive: - expressions.append(exp.JSONPathRecursive(this=value)) - elif value: - expressions.append(exp.JSONPathKey(this=value)) - else: - raise ParseError(_error("Expected key name or * after DOT")) - elif _match(TokenType.L_BRACKET): - expressions.append(_parse_bracket()) - elif _match_set(jsonpath_tokenizer.VAR_TOKENS): - expressions.append(exp.JSONPathKey(this=_parse_var_text())) - elif _match(TokenType.IDENTIFIER): - expressions.append(exp.JSONPathKey(this=_prev().text)) - elif _match(TokenType.STAR): - expressions.append(exp.JSONPathWildcard()) - else: - raise ParseError(_error(f"Unexpected {tokens[i].token_type}")) - - return exp.JSONPath(expressions=expressions) - - -JSON_PATH_PART_TRANSFORMS: t.Dict[t.Type[exp.Expression], t.Callable[..., str]] = { - exp.JSONPathFilter: lambda _, e: f"?{e.this}", - exp.JSONPathKey: lambda self, e: self._jsonpathkey_sql(e), - exp.JSONPathRecursive: lambda _, e: f"..{e.this or ''}", - exp.JSONPathRoot: lambda *_: "$", - exp.JSONPathScript: lambda _, e: f"({e.this}", - exp.JSONPathSelector: lambda self, e: f"[{self.json_path_part(e.this)}]", - exp.JSONPathSlice: lambda self, e: ":".join( - "" if p is False else self.json_path_part(p) - for p in [e.args.get("start"), e.args.get("end"), e.args.get("step")] - if p is not None - ), - exp.JSONPathSubscript: lambda self, e: self._jsonpathsubscript_sql(e), - exp.JSONPathUnion: lambda self, - e: f"[{','.join(self.json_path_part(p) for p in e.expressions)}]", - exp.JSONPathWildcard: lambda *_: "*", -} - -ALL_JSON_PATH_PARTS = set(JSON_PATH_PART_TRANSFORMS) diff --git a/third_party/bigframes_vendored/sqlglot/lineage.py b/third_party/bigframes_vendored/sqlglot/lineage.py deleted file mode 100644 index 826e64bfdc8..00000000000 --- a/third_party/bigframes_vendored/sqlglot/lineage.py +++ /dev/null @@ -1,455 +0,0 @@ -# Contains code from https://github.com/tobymao/sqlglot/blob/v28.5.0/sqlglot/lineage.py - -from __future__ import annotations - -import json -import logging -import typing as t -from dataclasses import dataclass, field - -from bigframes_vendored.sqlglot import Schema, exp, maybe_parse -from bigframes_vendored.sqlglot.errors import SqlglotError -from bigframes_vendored.sqlglot.optimizer import ( - Scope, - build_scope, - find_all_in_scope, - normalize_identifiers, - qualify, -) -from bigframes_vendored.sqlglot.optimizer.scope import ScopeType - -if t.TYPE_CHECKING: - from bigframes_vendored.sqlglot.dialects.dialect import DialectType - -logger = logging.getLogger("sqlglot") - - -@dataclass(frozen=True) -class Node: - name: str - expression: exp.Expression - source: exp.Expression - downstream: t.List[Node] = field(default_factory=list) - source_name: str = "" - reference_node_name: str = "" - - def walk(self) -> t.Iterator[Node]: - yield self - - for d in self.downstream: - yield from d.walk() - - def to_html(self, dialect: DialectType = None, **opts) -> GraphHTML: - nodes = {} - edges = [] - - for node in self.walk(): - if isinstance(node.expression, exp.Table): - label = f"FROM {node.expression.this}" - title = f"
SELECT {node.name} FROM {node.expression.this}
" - group = 1 - else: - label = node.expression.sql(pretty=True, dialect=dialect) - source = node.source.transform( - lambda n: ( - exp.Tag(this=n, prefix="", postfix="") - if n is node.expression - else n - ), - copy=False, - ).sql(pretty=True, dialect=dialect) - title = f"
{source}
" - group = 0 - - node_id = id(node) - - nodes[node_id] = { - "id": node_id, - "label": label, - "title": title, - "group": group, - } - - for d in node.downstream: - edges.append({"from": node_id, "to": id(d)}) - return GraphHTML(nodes, edges, **opts) - - -def lineage( - column: str | exp.Column, - sql: str | exp.Expression, - schema: t.Optional[t.Dict | Schema] = None, - sources: t.Optional[t.Mapping[str, str | exp.Query]] = None, - dialect: DialectType = None, - scope: t.Optional[Scope] = None, - trim_selects: bool = True, - copy: bool = True, - **kwargs, -) -> Node: - """Build the lineage graph for a column of a SQL query. - - Args: - column: The column to build the lineage for. - sql: The SQL string or expression. - schema: The schema of tables. - sources: A mapping of queries which will be used to continue building lineage. - dialect: The dialect of input SQL. - scope: A pre-created scope to use instead. - trim_selects: Whether to clean up selects by trimming to only relevant columns. - copy: Whether to copy the Expression arguments. - **kwargs: Qualification optimizer kwargs. - - Returns: - A lineage node. - """ - - expression = maybe_parse(sql, copy=copy, dialect=dialect) - column = normalize_identifiers.normalize_identifiers(column, dialect=dialect).name - - if sources: - expression = exp.expand( - expression, - { - k: t.cast(exp.Query, maybe_parse(v, copy=copy, dialect=dialect)) - for k, v in sources.items() - }, - dialect=dialect, - copy=copy, - ) - - if not scope: - expression = qualify.qualify( - expression, - dialect=dialect, - schema=schema, - **{"validate_qualify_columns": False, "identify": False, **kwargs}, # type: ignore - ) - - scope = build_scope(expression) - - if not scope: - raise SqlglotError("Cannot build lineage, sql must be SELECT") - - if not any(select.alias_or_name == column for select in scope.expression.selects): - raise SqlglotError(f"Cannot find column '{column}' in query.") - - return to_node(column, scope, dialect, trim_selects=trim_selects) - - -def to_node( - column: str | int, - scope: Scope, - dialect: DialectType, - scope_name: t.Optional[str] = None, - upstream: t.Optional[Node] = None, - source_name: t.Optional[str] = None, - reference_node_name: t.Optional[str] = None, - trim_selects: bool = True, -) -> Node: - # Find the specific select clause that is the source of the column we want. - # This can either be a specific, named select or a generic `*` clause. - select = ( - scope.expression.selects[column] - if isinstance(column, int) - else next( - ( - select - for select in scope.expression.selects - if select.alias_or_name == column - ), - exp.Star() if scope.expression.is_star else scope.expression, - ) - ) - - if isinstance(scope.expression, exp.Subquery): - for source in scope.subquery_scopes: - return to_node( - column, - scope=source, - dialect=dialect, - upstream=upstream, - source_name=source_name, - reference_node_name=reference_node_name, - trim_selects=trim_selects, - ) - if isinstance(scope.expression, exp.SetOperation): - name = type(scope.expression).__name__.upper() - upstream = upstream or Node( - name=name, source=scope.expression, expression=select - ) - - index = ( - column - if isinstance(column, int) - else next( - ( - i - for i, select in enumerate(scope.expression.selects) - if select.alias_or_name == column or select.is_star - ), - -1, # mypy will not allow a None here, but a negative index should never be returned - ) - ) - - if index == -1: - raise ValueError(f"Could not find {column} in {scope.expression}") - - for s in scope.union_scopes: - to_node( - index, - scope=s, - dialect=dialect, - upstream=upstream, - source_name=source_name, - reference_node_name=reference_node_name, - trim_selects=trim_selects, - ) - - return upstream - - if trim_selects and isinstance(scope.expression, exp.Select): - # For better ergonomics in our node labels, replace the full select with - # a version that has only the column we care about. - # "x", SELECT x, y FROM foo - # => "x", SELECT x FROM foo - source = t.cast(exp.Expression, scope.expression.select(select, append=False)) - else: - source = scope.expression - - # Create the node for this step in the lineage chain, and attach it to the previous one. - node = Node( - name=f"{scope_name}.{column}" if scope_name else str(column), - source=source, - expression=select, - source_name=source_name or "", - reference_node_name=reference_node_name or "", - ) - - if upstream: - upstream.downstream.append(node) - - subquery_scopes = { - id(subquery_scope.expression): subquery_scope - for subquery_scope in scope.subquery_scopes - } - - for subquery in find_all_in_scope(select, exp.UNWRAPPED_QUERIES): - subquery_scope = subquery_scopes.get(id(subquery)) - if not subquery_scope: - logger.warning(f"Unknown subquery scope: {subquery.sql(dialect=dialect)}") - continue - - for name in subquery.named_selects: - to_node( - name, - scope=subquery_scope, - dialect=dialect, - upstream=node, - trim_selects=trim_selects, - ) - - # if the select is a star add all scope sources as downstreams - if isinstance(select, exp.Star): - for source in scope.sources.values(): - if isinstance(source, Scope): - source = source.expression - node.downstream.append( - Node(name=select.sql(comments=False), source=source, expression=source) - ) - - # Find all columns that went into creating this one to list their lineage nodes. - source_columns = set(find_all_in_scope(select, exp.Column)) - - # If the source is a UDTF find columns used in the UDTF to generate the table - if isinstance(source, exp.UDTF): - source_columns |= set(source.find_all(exp.Column)) - derived_tables = [ - source.expression.parent - for source in scope.sources.values() - if isinstance(source, Scope) and source.is_derived_table - ] - else: - derived_tables = scope.derived_tables - - source_names = { - dt.alias: dt.comments[0].split()[1] - for dt in derived_tables - if dt.comments and dt.comments[0].startswith("source: ") - } - - pivots = scope.pivots - pivot = pivots[0] if len(pivots) == 1 and not pivots[0].unpivot else None - if pivot: - # For each aggregation function, the pivot creates a new column for each field in category - # combined with the aggfunc. So the columns parsed have this order: cat_a_value_sum, cat_a, - # b_value_sum, b. Because of this step wise manner the aggfunc 'sum(value) as value_sum' - # belongs to the column indices 0, 2, and the aggfunc 'max(price)' without an alias belongs - # to the column indices 1, 3. Here, only the columns used in the aggregations are of interest - # in the lineage, so lookup the pivot column name by index and map that with the columns used - # in the aggregation. - # - # Example: PIVOT (SUM(value) AS value_sum, MAX(price)) FOR category IN ('a' AS cat_a, 'b') - pivot_columns = pivot.args["columns"] - pivot_aggs_count = len(pivot.expressions) - - pivot_column_mapping = {} - for i, agg in enumerate(pivot.expressions): - agg_cols = list(agg.find_all(exp.Column)) - for col_index in range(i, len(pivot_columns), pivot_aggs_count): - pivot_column_mapping[pivot_columns[col_index].name] = agg_cols - - for c in source_columns: - table = c.table - source = scope.sources.get(table) - - if isinstance(source, Scope): - reference_node_name = None - if ( - source.scope_type == ScopeType.DERIVED_TABLE - and table not in source_names - ): - reference_node_name = table - elif source.scope_type == ScopeType.CTE: - selected_node, _ = scope.selected_sources.get(table, (None, None)) - reference_node_name = selected_node.name if selected_node else None - - # The table itself came from a more specific scope. Recurse into that one using the unaliased column name. - to_node( - c.name, - scope=source, - dialect=dialect, - scope_name=table, - upstream=node, - source_name=source_names.get(table) or source_name, - reference_node_name=reference_node_name, - trim_selects=trim_selects, - ) - elif pivot and pivot.alias_or_name == c.table: - downstream_columns = [] - - column_name = c.name - if any(column_name == pivot_column.name for pivot_column in pivot_columns): - downstream_columns.extend(pivot_column_mapping[column_name]) - else: - # The column is not in the pivot, so it must be an implicit column of the - # pivoted source -- adapt column to be from the implicit pivoted source. - downstream_columns.append( - exp.column(c.this, table=pivot.parent.alias_or_name) - ) - - for downstream_column in downstream_columns: - table = downstream_column.table - source = scope.sources.get(table) - if isinstance(source, Scope): - to_node( - downstream_column.name, - scope=source, - scope_name=table, - dialect=dialect, - upstream=node, - source_name=source_names.get(table) or source_name, - reference_node_name=reference_node_name, - trim_selects=trim_selects, - ) - else: - source = source or exp.Placeholder() - node.downstream.append( - Node( - name=downstream_column.sql(comments=False), - source=source, - expression=source, - ) - ) - else: - # The source is not a scope and the column is not in any pivot - we've reached the end - # of the line. At this point, if a source is not found it means this column's lineage - # is unknown. This can happen if the definition of a source used in a query is not - # passed into the `sources` map. - source = source or exp.Placeholder() - node.downstream.append( - Node(name=c.sql(comments=False), source=source, expression=source) - ) - - return node - - -class GraphHTML: - """Node to HTML generator using vis.js. - - https://visjs.github.io/vis-network/docs/network/ - """ - - def __init__( - self, - nodes: t.Dict, - edges: t.List, - imports: bool = True, - options: t.Optional[t.Dict] = None, - ): - self.imports = imports - - self.options = { - "height": "500px", - "width": "100%", - "layout": { - "hierarchical": { - "enabled": True, - "nodeSpacing": 200, - "sortMethod": "directed", - }, - }, - "interaction": { - "dragNodes": False, - "selectable": False, - }, - "physics": { - "enabled": False, - }, - "edges": { - "arrows": "to", - }, - "nodes": { - "font": "20px monaco", - "shape": "box", - "widthConstraint": { - "maximum": 300, - }, - }, - **(options or {}), - } - - self.nodes = nodes - self.edges = edges - - def __str__(self): - nodes = json.dumps(list(self.nodes.values())) - edges = json.dumps(self.edges) - options = json.dumps(self.options) - imports = ( - """ - - """ - if self.imports - else "" - ) - - return f"""
-
- {imports} - -
""" - - def _repr_html_(self) -> str: - return self.__str__() diff --git a/third_party/bigframes_vendored/sqlglot/optimizer/__init__.py b/third_party/bigframes_vendored/sqlglot/optimizer/__init__.py deleted file mode 100644 index 9cc759fbe23..00000000000 --- a/third_party/bigframes_vendored/sqlglot/optimizer/__init__.py +++ /dev/null @@ -1,24 +0,0 @@ -# Contains code from https://github.com/tobymao/sqlglot/blob/v28.5.0/sqlglot/optimizer/__init__.py - -# ruff: noqa: F401 - -from bigframes_vendored.sqlglot.optimizer.optimizer import RULES as RULES # noqa: F401 -from bigframes_vendored.sqlglot.optimizer.optimizer import ( # noqa: F401 - optimize as optimize, -) -from bigframes_vendored.sqlglot.optimizer.scope import Scope as Scope # noqa: F401 -from bigframes_vendored.sqlglot.optimizer.scope import ( # noqa: F401 - build_scope as build_scope, -) -from bigframes_vendored.sqlglot.optimizer.scope import ( # noqa: F401 - find_all_in_scope as find_all_in_scope, -) -from bigframes_vendored.sqlglot.optimizer.scope import ( # noqa: F401 - find_in_scope as find_in_scope, -) -from bigframes_vendored.sqlglot.optimizer.scope import ( # noqa: F401 - traverse_scope as traverse_scope, -) -from bigframes_vendored.sqlglot.optimizer.scope import ( # noqa: F401 - walk_in_scope as walk_in_scope, -) diff --git a/third_party/bigframes_vendored/sqlglot/optimizer/annotate_types.py b/third_party/bigframes_vendored/sqlglot/optimizer/annotate_types.py deleted file mode 100644 index cca95feee82..00000000000 --- a/third_party/bigframes_vendored/sqlglot/optimizer/annotate_types.py +++ /dev/null @@ -1,893 +0,0 @@ -# Contains code from https://github.com/tobymao/sqlglot/blob/v28.5.0/sqlglot/optimizer/annotate_types.py - -from __future__ import annotations - -import functools -import logging -import typing as t - -from bigframes_vendored.sqlglot import exp -from bigframes_vendored.sqlglot.dialects.dialect import Dialect -from bigframes_vendored.sqlglot.helper import ( - ensure_list, - is_date_unit, - is_iso_date, - is_iso_datetime, - seq_get, -) -from bigframes_vendored.sqlglot.optimizer.scope import Scope, traverse_scope -from bigframes_vendored.sqlglot.schema import MappingSchema, Schema, ensure_schema - -if t.TYPE_CHECKING: - from bigframes_vendored.sqlglot._typing import B, E - - BinaryCoercionFunc = t.Callable[[exp.Expression, exp.Expression], exp.DataType.Type] - BinaryCoercions = t.Dict[ - t.Tuple[exp.DataType.Type, exp.DataType.Type], - BinaryCoercionFunc, - ] - - from bigframes_vendored.sqlglot.dialects.dialect import DialectType - from bigframes_vendored.sqlglot.typing import ExpressionMetadataType - -logger = logging.getLogger("sqlglot") - - -def annotate_types( - expression: E, - schema: t.Optional[t.Dict | Schema] = None, - expression_metadata: t.Optional[ExpressionMetadataType] = None, - coerces_to: t.Optional[t.Dict[exp.DataType.Type, t.Set[exp.DataType.Type]]] = None, - dialect: DialectType = None, - overwrite_types: bool = True, -) -> E: - """ - Infers the types of an expression, annotating its AST accordingly. - - Example: - >>> import sqlglot - >>> schema = {"y": {"cola": "SMALLINT"}} - >>> sql = "SELECT x.cola + 2.5 AS cola FROM (SELECT y.cola AS cola FROM y AS y) AS x" - >>> annotated_expr = annotate_types(sqlglot.parse_one(sql), schema=schema) - >>> annotated_expr.expressions[0].type.this # Get the type of "x.cola + 2.5 AS cola" - - - Args: - expression: Expression to annotate. - schema: Database schema. - expression_metadata: Maps expression type to corresponding annotation function. - coerces_to: Maps expression type to set of types that it can be coerced into. - overwrite_types: Re-annotate the existing AST types. - - Returns: - The expression annotated with types. - """ - - schema = ensure_schema(schema, dialect=dialect) - - return TypeAnnotator( - schema=schema, - expression_metadata=expression_metadata, - coerces_to=coerces_to, - overwrite_types=overwrite_types, - ).annotate(expression) - - -def _coerce_date_literal( - l: exp.Expression, unit: t.Optional[exp.Expression] -) -> exp.DataType.Type: - date_text = l.name - is_iso_date_ = is_iso_date(date_text) - - if is_iso_date_ and is_date_unit(unit): - return exp.DataType.Type.DATE - - # An ISO date is also an ISO datetime, but not vice versa - if is_iso_date_ or is_iso_datetime(date_text): - return exp.DataType.Type.DATETIME - - return exp.DataType.Type.UNKNOWN - - -def _coerce_date( - l: exp.Expression, unit: t.Optional[exp.Expression] -) -> exp.DataType.Type: - if not is_date_unit(unit): - return exp.DataType.Type.DATETIME - return l.type.this if l.type else exp.DataType.Type.UNKNOWN - - -def swap_args(func: BinaryCoercionFunc) -> BinaryCoercionFunc: - @functools.wraps(func) - def _swapped(ll: exp.Expression, r: exp.Expression) -> exp.DataType.Type: - return func(r, ll) - - return _swapped - - -def swap_all(coercions: BinaryCoercions) -> BinaryCoercions: - return { - **coercions, - **{(b, a): swap_args(func) for (a, b), func in coercions.items()}, - } - - -class _TypeAnnotator(type): - def __new__(cls, clsname, bases, attrs): - klass = super().__new__(cls, clsname, bases, attrs) - - # Highest-to-lowest type precedence, as specified in Spark's docs (ANSI): - # https://spark.apache.org/docs/3.2.0/sql-ref-ansi-compliance.html - text_precedence = ( - exp.DataType.Type.TEXT, - exp.DataType.Type.NVARCHAR, - exp.DataType.Type.VARCHAR, - exp.DataType.Type.NCHAR, - exp.DataType.Type.CHAR, - ) - numeric_precedence = ( - exp.DataType.Type.DECFLOAT, - exp.DataType.Type.DOUBLE, - exp.DataType.Type.FLOAT, - exp.DataType.Type.BIGDECIMAL, - exp.DataType.Type.DECIMAL, - exp.DataType.Type.BIGINT, - exp.DataType.Type.INT, - exp.DataType.Type.SMALLINT, - exp.DataType.Type.TINYINT, - ) - timelike_precedence = ( - exp.DataType.Type.TIMESTAMPLTZ, - exp.DataType.Type.TIMESTAMPTZ, - exp.DataType.Type.TIMESTAMP, - exp.DataType.Type.DATETIME, - exp.DataType.Type.DATE, - ) - - for type_precedence in ( - text_precedence, - numeric_precedence, - timelike_precedence, - ): - coerces_to = set() - for data_type in type_precedence: - klass.COERCES_TO[data_type] = coerces_to.copy() - coerces_to |= {data_type} - return klass - - -class TypeAnnotator(metaclass=_TypeAnnotator): - NESTED_TYPES = { - exp.DataType.Type.ARRAY, - } - - # Specifies what types a given type can be coerced into (autofilled) - COERCES_TO: t.Dict[exp.DataType.Type, t.Set[exp.DataType.Type]] = {} - - # Coercion functions for binary operations. - # Map of type pairs to a callable that takes both sides of the binary operation and returns the resulting type. - BINARY_COERCIONS: BinaryCoercions = { - **swap_all( - { - (t, exp.DataType.Type.INTERVAL): lambda ll, r: _coerce_date_literal( - ll, r.args.get("unit") - ) - for t in exp.DataType.TEXT_TYPES - } - ), - **swap_all( - { - # text + numeric will yield the numeric type to match most dialects' semantics - (text, numeric): lambda ll, r: t.cast( - exp.DataType.Type, - ll.type if ll.type in exp.DataType.NUMERIC_TYPES else r.type, - ) - for text in exp.DataType.TEXT_TYPES - for numeric in exp.DataType.NUMERIC_TYPES - } - ), - **swap_all( - { - ( - exp.DataType.Type.DATE, - exp.DataType.Type.INTERVAL, - ): lambda ll, r: _coerce_date(ll, r.args.get("unit")), - } - ), - } - - def __init__( - self, - schema: Schema, - expression_metadata: t.Optional[ExpressionMetadataType] = None, - coerces_to: t.Optional[ - t.Dict[exp.DataType.Type, t.Set[exp.DataType.Type]] - ] = None, - binary_coercions: t.Optional[BinaryCoercions] = None, - overwrite_types: bool = True, - ) -> None: - self.schema = schema - dialect = schema.dialect or Dialect() - self.dialect = dialect - self.expression_metadata = expression_metadata or dialect.EXPRESSION_METADATA - self.coerces_to = coerces_to or dialect.COERCES_TO or self.COERCES_TO - self.binary_coercions = binary_coercions or self.BINARY_COERCIONS - - # Caches the ids of annotated sub-Expressions, to ensure we only visit them once - self._visited: t.Set[int] = set() - - # Caches NULL-annotated expressions to set them to UNKNOWN after type inference is completed - self._null_expressions: t.Dict[int, exp.Expression] = {} - - # Databricks and Spark ≥v3 actually support NULL (i.e., VOID) as a type - self._supports_null_type = dialect.SUPPORTS_NULL_TYPE - - # Maps an exp.SetOperation's id (e.g. UNION) to its projection types. This is computed if the - # exp.SetOperation is the expression of a scope source, as selecting from it multiple times - # would reprocess the entire subtree to coerce the types of its operands' projections - self._setop_column_types: t.Dict[ - int, t.Dict[str, exp.DataType | exp.DataType.Type] - ] = {} - - # When set to False, this enables partial annotation by skipping already-annotated nodes - self._overwrite_types = overwrite_types - - def clear(self) -> None: - self._visited.clear() - self._null_expressions.clear() - self._setop_column_types.clear() - - def _set_type( - self, expression: E, target_type: t.Optional[exp.DataType | exp.DataType.Type] - ) -> E: - prev_type = expression.type - expression_id = id(expression) - - expression.type = target_type or exp.DataType.Type.UNKNOWN # type: ignore - self._visited.add(expression_id) - - if ( - not self._supports_null_type - and t.cast(exp.DataType, expression.type).this == exp.DataType.Type.NULL - ): - self._null_expressions[expression_id] = expression - elif ( - prev_type and t.cast(exp.DataType, prev_type).this == exp.DataType.Type.NULL - ): - self._null_expressions.pop(expression_id, None) - - if ( - isinstance(expression, exp.Column) - and expression.is_type(exp.DataType.Type.JSON) - and (dot_parts := expression.meta.get("dot_parts")) - ): - # JSON dot access is case sensitive across all dialects, so we need to undo the normalization. - i = iter(dot_parts) - parent = expression.parent - while isinstance(parent, exp.Dot): - parent.expression.set("this", exp.to_identifier(next(i), quoted=True)) - parent = parent.parent - - expression.meta.pop("dot_parts", None) - - return expression - - def annotate(self, expression: E, annotate_scope: bool = True) -> E: - # This flag is used to avoid costly scope traversals when we only care about annotating - # non-column expressions (partial type inference), e.g., when simplifying in the optimizer - if annotate_scope: - for scope in traverse_scope(expression): - self.annotate_scope(scope) - - # This takes care of non-traversable expressions - self._annotate_expression(expression) - - # Replace NULL type with the default type of the targeted dialect, since the former is not an actual type; - # it is mostly used to aid type coercion, e.g. in query set operations. - for expr in self._null_expressions.values(): - expr.type = self.dialect.DEFAULT_NULL_TYPE - - return expression - - def annotate_scope(self, scope: Scope) -> None: - selects = {} - - for name, source in scope.sources.items(): - if not isinstance(source, Scope): - continue - - expression = source.expression - if isinstance(expression, exp.UDTF): - values = [] - - if isinstance(expression, exp.Lateral): - if isinstance(expression.this, exp.Explode): - values = [expression.this.this] - elif isinstance(expression, exp.Unnest): - values = [expression] - elif not isinstance(expression, exp.TableFromRows): - values = expression.expressions[0].expressions - - if not values: - continue - - alias_column_names = expression.alias_column_names - - if ( - isinstance(expression, exp.Unnest) - and not alias_column_names - and expression.type - and expression.type.is_type(exp.DataType.Type.STRUCT) - ): - selects[name] = { - col_def.name: t.cast( - t.Union[exp.DataType, exp.DataType.Type], col_def.kind - ) - for col_def in expression.type.expressions - if isinstance(col_def, exp.ColumnDef) and col_def.kind - } - else: - selects[name] = { - alias: column.type - for alias, column in zip(alias_column_names, values) - } - elif isinstance(expression, exp.SetOperation) and len( - expression.left.selects - ) == len(expression.right.selects): - selects[name] = self._get_setop_column_types(expression) - - else: - selects[name] = {s.alias_or_name: s.type for s in expression.selects} - - if isinstance(self.schema, MappingSchema): - for table_column in scope.table_columns: - source = scope.sources.get(table_column.name) - - if isinstance(source, exp.Table): - schema = self.schema.find( - source, raise_on_missing=False, ensure_data_types=True - ) - if not isinstance(schema, dict): - continue - - struct_type = exp.DataType( - this=exp.DataType.Type.STRUCT, - expressions=[ - exp.ColumnDef(this=exp.to_identifier(c), kind=kind) - for c, kind in schema.items() - ], - nested=True, - ) - self._set_type(table_column, struct_type) - elif ( - isinstance(source, Scope) - and isinstance(source.expression, exp.Query) - and ( - source.expression.meta.get("query_type") - or exp.DataType.build("UNKNOWN") - ).is_type(exp.DataType.Type.STRUCT) - ): - self._set_type(table_column, source.expression.meta["query_type"]) - - # Iterate through all the expressions of the current scope in post-order, and annotate - self._annotate_expression(scope.expression, scope, selects) - - if self.dialect.QUERY_RESULTS_ARE_STRUCTS and isinstance( - scope.expression, exp.Query - ): - struct_type = exp.DataType( - this=exp.DataType.Type.STRUCT, - expressions=[ - exp.ColumnDef( - this=exp.to_identifier(select.output_name), - kind=select.type.copy() if select.type else None, - ) - for select in scope.expression.selects - ], - nested=True, - ) - - if not any( - cd.kind.is_type(exp.DataType.Type.UNKNOWN) - for cd in struct_type.expressions - if cd.kind - ): - # We don't use `_set_type` on purpose here. If we annotated the query directly, then - # using it in other contexts (e.g., ARRAY()) could result in incorrect type - # annotations, i.e., it shouldn't be interpreted as a STRUCT value. - scope.expression.meta["query_type"] = struct_type - - def _annotate_expression( - self, - expression: exp.Expression, - scope: t.Optional[Scope] = None, - selects: t.Optional[t.Dict[str, t.Dict[str, t.Any]]] = None, - ) -> None: - stack = [(expression, False)] - selects = selects or {} - - while stack: - expr, children_annotated = stack.pop() - - if id(expr) in self._visited or ( - not self._overwrite_types - and expr.type - and not expr.is_type(exp.DataType.Type.UNKNOWN) - ): - continue # We've already inferred the expression's type - - if not children_annotated: - stack.append((expr, True)) - for child_expr in expr.iter_expressions(): - stack.append((child_expr, False)) - continue - - if scope and isinstance(expr, exp.Column) and expr.table: - source = scope.sources.get(expr.table) - if isinstance(source, exp.Table): - self._set_type(expr, self.schema.get_column_type(source, expr)) - elif source: - if expr.table in selects and expr.name in selects[expr.table]: - self._set_type(expr, selects[expr.table][expr.name]) - elif isinstance(source.expression, exp.Unnest): - self._set_type(expr, source.expression.type) - else: - self._set_type(expr, exp.DataType.Type.UNKNOWN) - else: - self._set_type(expr, exp.DataType.Type.UNKNOWN) - - if expr.type and expr.type.args.get("nullable") is False: - expr.meta["nonnull"] = True - continue - - spec = self.expression_metadata.get(expr.__class__) - - if spec and (annotator := spec.get("annotator")): - annotator(self, expr) - elif spec and (returns := spec.get("returns")): - self._set_type(expr, t.cast(exp.DataType.Type, returns)) - else: - self._set_type(expr, exp.DataType.Type.UNKNOWN) - - def _maybe_coerce( - self, - type1: exp.DataType | exp.DataType.Type, - type2: exp.DataType | exp.DataType.Type, - ) -> exp.DataType | exp.DataType.Type: - """ - Returns type2 if type1 can be coerced into it, otherwise type1. - - If either type is parameterized (e.g. DECIMAL(18, 2) contains two parameters), - we assume type1 does not coerce into type2, so we also return it in this case. - """ - if isinstance(type1, exp.DataType): - if type1.expressions: - return type1 - type1_value = type1.this - else: - type1_value = type1 - - if isinstance(type2, exp.DataType): - if type2.expressions: - return type2 - type2_value = type2.this - else: - type2_value = type2 - - # We propagate the UNKNOWN type upwards if found - if exp.DataType.Type.UNKNOWN in (type1_value, type2_value): - return exp.DataType.Type.UNKNOWN - - if type1_value == exp.DataType.Type.NULL: - return type2_value - if type2_value == exp.DataType.Type.NULL: - return type1_value - - return ( - type2_value - if type2_value in self.coerces_to.get(type1_value, {}) - else type1_value - ) - - def _get_setop_column_types( - self, setop: exp.SetOperation - ) -> t.Dict[str, exp.DataType | exp.DataType.Type]: - """ - Computes and returns the coerced column types for a SetOperation. - - This handles UNION, INTERSECT, EXCEPT, etc., coercing types across - left and right operands for all projections/columns. - - Args: - setop: The SetOperation expression to analyze - - Returns: - Dictionary mapping column names to their coerced types - """ - setop_id = id(setop) - if setop_id in self._setop_column_types: - return self._setop_column_types[setop_id] - - col_types: t.Dict[str, exp.DataType | exp.DataType.Type] = {} - - # Validate that left and right have same number of projections - if not ( - isinstance(setop, exp.SetOperation) - and setop.left.selects - and setop.right.selects - and len(setop.left.selects) == len(setop.right.selects) - ): - return col_types - - # Process a chain / sub-tree of set operations - for set_op in setop.walk( - prune=lambda n: not isinstance(n, (exp.SetOperation, exp.Subquery)) - ): - if not isinstance(set_op, exp.SetOperation): - continue - - if set_op.args.get("by_name"): - r_type_by_select = { - s.alias_or_name: s.type for s in set_op.right.selects - } - setop_cols = { - s.alias_or_name: self._maybe_coerce( - t.cast(exp.DataType, s.type), - r_type_by_select.get(s.alias_or_name) - or exp.DataType.Type.UNKNOWN, - ) - for s in set_op.left.selects - } - else: - setop_cols = { - ls.alias_or_name: self._maybe_coerce( - t.cast(exp.DataType, ls.type), t.cast(exp.DataType, rs.type) - ) - for ls, rs in zip(set_op.left.selects, set_op.right.selects) - } - - # Coerce intermediate results with the previously registered types, if they exist - for col_name, col_type in setop_cols.items(): - col_types[col_name] = self._maybe_coerce( - col_type, col_types.get(col_name, exp.DataType.Type.NULL) - ) - - self._setop_column_types[setop_id] = col_types - return col_types - - def _annotate_binary(self, expression: B) -> B: - left, right = expression.left, expression.right - if not left or not right: - expression_sql = expression.sql(self.dialect) - logger.warning( - f"Failed to annotate badly formed binary expression: {expression_sql}" - ) - self._set_type(expression, None) - return expression - - left_type, right_type = left.type.this, right.type.this # type: ignore - - if isinstance(expression, (exp.Connector, exp.Predicate)): - self._set_type(expression, exp.DataType.Type.BOOLEAN) - elif (left_type, right_type) in self.binary_coercions: - self._set_type( - expression, self.binary_coercions[(left_type, right_type)](left, right) - ) - else: - self._set_type(expression, self._maybe_coerce(left_type, right_type)) - - if isinstance(expression, exp.Is) or ( - left.meta.get("nonnull") is True and right.meta.get("nonnull") is True - ): - expression.meta["nonnull"] = True - - return expression - - def _annotate_unary(self, expression: E) -> E: - if isinstance(expression, exp.Not): - self._set_type(expression, exp.DataType.Type.BOOLEAN) - else: - self._set_type(expression, expression.this.type) - - if expression.this.meta.get("nonnull") is True: - expression.meta["nonnull"] = True - - return expression - - def _annotate_literal(self, expression: exp.Literal) -> exp.Literal: - if expression.is_string: - self._set_type(expression, exp.DataType.Type.VARCHAR) - elif expression.is_int: - self._set_type(expression, exp.DataType.Type.INT) - else: - self._set_type(expression, exp.DataType.Type.DOUBLE) - - expression.meta["nonnull"] = True - - return expression - - @t.no_type_check - def _annotate_by_args( - self, - expression: E, - *args: str | exp.Expression, - promote: bool = False, - array: bool = False, - ) -> E: - literal_type = None - non_literal_type = None - nested_type = None - - for arg in args: - if isinstance(arg, str): - expressions = expression.args.get(arg) - else: - expressions = arg - - for expr in ensure_list(expressions): - expr_type = expr.type - - # Stop at the first nested data type found - we don't want to _maybe_coerce nested types - if expr_type.args.get("nested"): - nested_type = expr_type - break - - if not expr_type.is_type(exp.DataType.Type.UNKNOWN): - if isinstance(expr, exp.Literal): - literal_type = self._maybe_coerce( - literal_type or expr_type, expr_type - ) - else: - non_literal_type = self._maybe_coerce( - non_literal_type or expr_type, expr_type - ) - - if nested_type: - break - - result_type = None - - if nested_type: - result_type = nested_type - elif literal_type and non_literal_type: - if self.dialect.PRIORITIZE_NON_LITERAL_TYPES: - literal_this_type = ( - literal_type.this - if isinstance(literal_type, exp.DataType) - else literal_type - ) - non_literal_this_type = ( - non_literal_type.this - if isinstance(non_literal_type, exp.DataType) - else non_literal_type - ) - if ( - literal_this_type in exp.DataType.INTEGER_TYPES - and non_literal_this_type in exp.DataType.INTEGER_TYPES - ) or ( - literal_this_type in exp.DataType.REAL_TYPES - and non_literal_this_type in exp.DataType.REAL_TYPES - ): - result_type = non_literal_type - else: - result_type = literal_type or non_literal_type or exp.DataType.Type.UNKNOWN - - self._set_type( - expression, - result_type or self._maybe_coerce(non_literal_type, literal_type), - ) - - if promote: - if expression.type.this in exp.DataType.INTEGER_TYPES: - self._set_type(expression, exp.DataType.Type.BIGINT) - elif expression.type.this in exp.DataType.FLOAT_TYPES: - self._set_type(expression, exp.DataType.Type.DOUBLE) - - if array: - self._set_type( - expression, - exp.DataType( - this=exp.DataType.Type.ARRAY, - expressions=[expression.type], - nested=True, - ), - ) - - return expression - - def _annotate_timeunit( - self, expression: exp.TimeUnit | exp.DateTrunc - ) -> exp.TimeUnit | exp.DateTrunc: - if expression.this.type.this in exp.DataType.TEXT_TYPES: - datatype = _coerce_date_literal(expression.this, expression.unit) - elif expression.this.type.this in exp.DataType.TEMPORAL_TYPES: - datatype = _coerce_date(expression.this, expression.unit) - else: - datatype = exp.DataType.Type.UNKNOWN - - self._set_type(expression, datatype) - return expression - - def _annotate_bracket(self, expression: exp.Bracket) -> exp.Bracket: - bracket_arg = expression.expressions[0] - this = expression.this - - if isinstance(bracket_arg, exp.Slice): - self._set_type(expression, this.type) - elif this.type.is_type(exp.DataType.Type.ARRAY): - self._set_type(expression, seq_get(this.type.expressions, 0)) - elif isinstance(this, (exp.Map, exp.VarMap)) and bracket_arg in this.keys: - index = this.keys.index(bracket_arg) - value = seq_get(this.values, index) - self._set_type(expression, value.type if value else None) - else: - self._set_type(expression, exp.DataType.Type.UNKNOWN) - - return expression - - def _annotate_div(self, expression: exp.Div) -> exp.Div: - left_type, right_type = expression.left.type.this, expression.right.type.this # type: ignore - - if ( - expression.args.get("typed") - and left_type in exp.DataType.INTEGER_TYPES - and right_type in exp.DataType.INTEGER_TYPES - ): - self._set_type(expression, exp.DataType.Type.BIGINT) - else: - self._set_type(expression, self._maybe_coerce(left_type, right_type)) - if expression.type and expression.type.this not in exp.DataType.REAL_TYPES: - self._set_type( - expression, - self._maybe_coerce(expression.type, exp.DataType.Type.DOUBLE), - ) - - return expression - - def _annotate_dot(self, expression: exp.Dot) -> exp.Dot: - self._set_type(expression, None) - this_type = expression.this.type - - if this_type and this_type.is_type(exp.DataType.Type.STRUCT): - for e in this_type.expressions: - if e.name == expression.expression.name: - self._set_type(expression, e.kind) - break - - return expression - - def _annotate_explode(self, expression: exp.Explode) -> exp.Explode: - self._set_type(expression, seq_get(expression.this.type.expressions, 0)) - return expression - - def _annotate_unnest(self, expression: exp.Unnest) -> exp.Unnest: - child = seq_get(expression.expressions, 0) - - if child and child.is_type(exp.DataType.Type.ARRAY): - expr_type = seq_get(child.type.expressions, 0) - else: - expr_type = None - - self._set_type(expression, expr_type) - return expression - - def _annotate_subquery(self, expression: exp.Subquery) -> exp.Subquery: - # For scalar subqueries (subqueries with a single projection), infer the type - # from that single projection. This allows type propagation in cases like: - # SELECT (SELECT 1 AS c) AS c - query = expression.unnest() - - if isinstance(query, exp.Query): - selects = query.selects - if len(selects) == 1: - self._set_type(expression, selects[0].type) - return expression - - self._set_type(expression, exp.DataType.Type.UNKNOWN) - return expression - - def _annotate_struct_value( - self, expression: exp.Expression - ) -> t.Optional[exp.DataType] | exp.ColumnDef: - # Case: STRUCT(key AS value) - this: t.Optional[exp.Expression] = None - kind = expression.type - - if alias := expression.args.get("alias"): - this = alias.copy() - elif expression.expression: - # Case: STRUCT(key = value) or STRUCT(key := value) - this = expression.this.copy() - kind = expression.expression.type - elif isinstance(expression, exp.Column): - # Case: STRUCT(c) - this = expression.this.copy() - - if kind and kind.is_type(exp.DataType.Type.UNKNOWN): - return None - - if this: - return exp.ColumnDef(this=this, kind=kind) - - return kind - - def _annotate_struct(self, expression: exp.Struct) -> exp.Struct: - expressions = [] - for expr in expression.expressions: - struct_field_type = self._annotate_struct_value(expr) - if struct_field_type is None: - self._set_type(expression, None) - return expression - - expressions.append(struct_field_type) - - self._set_type( - expression, - exp.DataType( - this=exp.DataType.Type.STRUCT, expressions=expressions, nested=True - ), - ) - return expression - - @t.overload - def _annotate_map(self, expression: exp.Map) -> exp.Map: ... - - @t.overload - def _annotate_map(self, expression: exp.VarMap) -> exp.VarMap: ... - - def _annotate_map(self, expression): - keys = expression.args.get("keys") - values = expression.args.get("values") - - map_type = exp.DataType(this=exp.DataType.Type.MAP) - if isinstance(keys, exp.Array) and isinstance(values, exp.Array): - key_type = seq_get(keys.type.expressions, 0) or exp.DataType.Type.UNKNOWN - value_type = ( - seq_get(values.type.expressions, 0) or exp.DataType.Type.UNKNOWN - ) - - if ( - key_type != exp.DataType.Type.UNKNOWN - and value_type != exp.DataType.Type.UNKNOWN - ): - map_type.set("expressions", [key_type, value_type]) - map_type.set("nested", True) - - self._set_type(expression, map_type) - return expression - - def _annotate_to_map(self, expression: exp.ToMap) -> exp.ToMap: - map_type = exp.DataType(this=exp.DataType.Type.MAP) - arg = expression.this - if arg.is_type(exp.DataType.Type.STRUCT): - for coldef in arg.type.expressions: - kind = coldef.kind - if kind != exp.DataType.Type.UNKNOWN: - map_type.set("expressions", [exp.DataType.build("varchar"), kind]) - map_type.set("nested", True) - break - - self._set_type(expression, map_type) - return expression - - def _annotate_extract(self, expression: exp.Extract) -> exp.Extract: - part = expression.name - if part == "TIME": - self._set_type(expression, exp.DataType.Type.TIME) - elif part == "DATE": - self._set_type(expression, exp.DataType.Type.DATE) - else: - self._set_type(expression, exp.DataType.Type.INT) - return expression - - def _annotate_by_array_element(self, expression: exp.Expression) -> exp.Expression: - array_arg = expression.this - if array_arg.type.is_type(exp.DataType.Type.ARRAY): - element_type = ( - seq_get(array_arg.type.expressions, 0) or exp.DataType.Type.UNKNOWN - ) - self._set_type(expression, element_type) - else: - self._set_type(expression, exp.DataType.Type.UNKNOWN) - - return expression diff --git a/third_party/bigframes_vendored/sqlglot/optimizer/canonicalize.py b/third_party/bigframes_vendored/sqlglot/optimizer/canonicalize.py deleted file mode 100644 index ec17916e137..00000000000 --- a/third_party/bigframes_vendored/sqlglot/optimizer/canonicalize.py +++ /dev/null @@ -1,243 +0,0 @@ -# Contains code from https://github.com/tobymao/sqlglot/blob/v28.5.0/sqlglot/optimizer/canonicalize.py - -from __future__ import annotations - -import itertools -import typing as t - -from bigframes_vendored.sqlglot import exp -from bigframes_vendored.sqlglot.dialects.dialect import Dialect, DialectType -from bigframes_vendored.sqlglot.helper import is_date_unit, is_iso_date, is_iso_datetime -from bigframes_vendored.sqlglot.optimizer.annotate_types import TypeAnnotator - - -def canonicalize( - expression: exp.Expression, dialect: DialectType = None -) -> exp.Expression: - """Converts a sql expression into a standard form. - - This method relies on annotate_types because many of the - conversions rely on type inference. - - Args: - expression: The expression to canonicalize. - """ - - dialect = Dialect.get_or_raise(dialect) - - def _canonicalize(expression: exp.Expression) -> exp.Expression: - expression = add_text_to_concat(expression) - expression = replace_date_funcs(expression, dialect=dialect) - expression = coerce_type(expression, dialect.PROMOTE_TO_INFERRED_DATETIME_TYPE) - expression = remove_redundant_casts(expression) - expression = ensure_bools(expression, _replace_int_predicate) - expression = remove_ascending_order(expression) - return expression - - return exp.replace_tree(expression, _canonicalize) - - -def add_text_to_concat(node: exp.Expression) -> exp.Expression: - if ( - isinstance(node, exp.Add) - and node.type - and node.type.this in exp.DataType.TEXT_TYPES - ): - node = exp.Concat( - expressions=[node.left, node.right], - # All known dialects, i.e. Redshift and T-SQL, that support - # concatenating strings with the + operator do not coalesce NULLs. - coalesce=False, - ) - return node - - -def replace_date_funcs(node: exp.Expression, dialect: DialectType) -> exp.Expression: - if ( - isinstance(node, (exp.Date, exp.TsOrDsToDate)) - and not node.expressions - and not node.args.get("zone") - and node.this.is_string - and is_iso_date(node.this.name) - ): - return exp.cast(node.this, to=exp.DataType.Type.DATE) - if isinstance(node, exp.Timestamp) and not node.args.get("zone"): - if not node.type: - from bigframes_vendored.sqlglot.optimizer.annotate_types import ( - annotate_types, - ) - - node = annotate_types(node, dialect=dialect) - return exp.cast(node.this, to=node.type or exp.DataType.Type.TIMESTAMP) - - return node - - -COERCIBLE_DATE_OPS = ( - exp.Add, - exp.Sub, - exp.EQ, - exp.NEQ, - exp.GT, - exp.GTE, - exp.LT, - exp.LTE, - exp.NullSafeEQ, - exp.NullSafeNEQ, -) - - -def coerce_type( - node: exp.Expression, promote_to_inferred_datetime_type: bool -) -> exp.Expression: - if isinstance(node, COERCIBLE_DATE_OPS): - _coerce_date(node.left, node.right, promote_to_inferred_datetime_type) - elif isinstance(node, exp.Between): - _coerce_date(node.this, node.args["low"], promote_to_inferred_datetime_type) - elif isinstance(node, exp.Extract) and not node.expression.is_type( - *exp.DataType.TEMPORAL_TYPES - ): - _replace_cast(node.expression, exp.DataType.Type.DATETIME) - elif isinstance(node, (exp.DateAdd, exp.DateSub, exp.DateTrunc)): - _coerce_timeunit_arg(node.this, node.unit) - elif isinstance(node, exp.DateDiff): - _coerce_datediff_args(node) - - return node - - -def remove_redundant_casts(expression: exp.Expression) -> exp.Expression: - if ( - isinstance(expression, exp.Cast) - and expression.this.type - and expression.to == expression.this.type - ): - return expression.this - - if ( - isinstance(expression, (exp.Date, exp.TsOrDsToDate)) - and expression.this.type - and expression.this.type.this == exp.DataType.Type.DATE - and not expression.this.type.expressions - ): - return expression.this - - return expression - - -def ensure_bools( - expression: exp.Expression, replace_func: t.Callable[[exp.Expression], None] -) -> exp.Expression: - if isinstance(expression, exp.Connector): - replace_func(expression.left) - replace_func(expression.right) - elif isinstance(expression, exp.Not): - replace_func(expression.this) - # We can't replace num in CASE x WHEN num ..., because it's not the full predicate - elif isinstance(expression, exp.If) and not ( - isinstance(expression.parent, exp.Case) and expression.parent.this - ): - replace_func(expression.this) - elif isinstance(expression, (exp.Where, exp.Having)): - replace_func(expression.this) - - return expression - - -def remove_ascending_order(expression: exp.Expression) -> exp.Expression: - if isinstance(expression, exp.Ordered) and expression.args.get("desc") is False: - # Convert ORDER BY a ASC to ORDER BY a - expression.set("desc", None) - - return expression - - -def _coerce_date( - a: exp.Expression, - b: exp.Expression, - promote_to_inferred_datetime_type: bool, -) -> None: - for a, b in itertools.permutations([a, b]): - if isinstance(b, exp.Interval): - a = _coerce_timeunit_arg(a, b.unit) - - a_type = a.type - if ( - not a_type - or a_type.this not in exp.DataType.TEMPORAL_TYPES - or not b.type - or b.type.this not in exp.DataType.TEXT_TYPES - ): - continue - - if promote_to_inferred_datetime_type: - if b.is_string: - date_text = b.name - if is_iso_date(date_text): - b_type = exp.DataType.Type.DATE - elif is_iso_datetime(date_text): - b_type = exp.DataType.Type.DATETIME - else: - b_type = a_type.this - else: - # If b is not a datetime string, we conservatively promote it to a DATETIME, - # in order to ensure there are no surprising truncations due to downcasting - b_type = exp.DataType.Type.DATETIME - - target_type = ( - b_type - if b_type in TypeAnnotator.COERCES_TO.get(a_type.this, {}) - else a_type - ) - else: - target_type = a_type - - if target_type != a_type: - _replace_cast(a, target_type) - - _replace_cast(b, target_type) - - -def _coerce_timeunit_arg( - arg: exp.Expression, unit: t.Optional[exp.Expression] -) -> exp.Expression: - if not arg.type: - return arg - - if arg.type.this in exp.DataType.TEXT_TYPES: - date_text = arg.name - is_iso_date_ = is_iso_date(date_text) - - if is_iso_date_ and is_date_unit(unit): - return arg.replace(exp.cast(arg.copy(), to=exp.DataType.Type.DATE)) - - # An ISO date is also an ISO datetime, but not vice versa - if is_iso_date_ or is_iso_datetime(date_text): - return arg.replace(exp.cast(arg.copy(), to=exp.DataType.Type.DATETIME)) - - elif arg.type.this == exp.DataType.Type.DATE and not is_date_unit(unit): - return arg.replace(exp.cast(arg.copy(), to=exp.DataType.Type.DATETIME)) - - return arg - - -def _coerce_datediff_args(node: exp.DateDiff) -> None: - for e in (node.this, node.expression): - if e.type.this not in exp.DataType.TEMPORAL_TYPES: - e.replace(exp.cast(e.copy(), to=exp.DataType.Type.DATETIME)) - - -def _replace_cast(node: exp.Expression, to: exp.DATA_TYPE) -> None: - node.replace(exp.cast(node.copy(), to=to)) - - -# this was originally designed for presto, there is a similar transform for tsql -# this is different in that it only operates on int types, this is because -# presto has a boolean type whereas tsql doesn't (people use bits) -# with y as (select true as x) select x = 0 FROM y -- illegal presto query -def _replace_int_predicate(expression: exp.Expression) -> None: - if isinstance(expression, exp.Coalesce): - for child in expression.iter_expressions(): - _replace_int_predicate(child) - elif expression.type and expression.type.this in exp.DataType.INTEGER_TYPES: - expression.replace(expression.neq(0)) diff --git a/third_party/bigframes_vendored/sqlglot/optimizer/eliminate_ctes.py b/third_party/bigframes_vendored/sqlglot/optimizer/eliminate_ctes.py deleted file mode 100644 index 8714c6bfa1b..00000000000 --- a/third_party/bigframes_vendored/sqlglot/optimizer/eliminate_ctes.py +++ /dev/null @@ -1,45 +0,0 @@ -# Contains code from https://github.com/tobymao/sqlglot/blob/v28.5.0/sqlglot/optimizer/eliminate_ctes.py - -from bigframes_vendored.sqlglot.optimizer.scope import Scope, build_scope - - -def eliminate_ctes(expression): - """ - Remove unused CTEs from an expression. - - Example: - >>> import sqlglot - >>> sql = "WITH y AS (SELECT a FROM x) SELECT a FROM z" - >>> expression = sqlglot.parse_one(sql) - >>> eliminate_ctes(expression).sql() - 'SELECT a FROM z' - - Args: - expression (sqlglot.Expression): expression to optimize - Returns: - sqlglot.Expression: optimized expression - """ - root = build_scope(expression) - - if root: - ref_count = root.ref_count() - - # Traverse the scope tree in reverse so we can remove chains of unused CTEs - for scope in reversed(list(root.traverse())): - if scope.is_cte: - count = ref_count[id(scope)] - if count <= 0: - cte_node = scope.expression.parent - with_node = cte_node.parent - cte_node.pop() - - # Pop the entire WITH clause if this is the last CTE - if with_node and len(with_node.expressions) <= 0: - with_node.pop() - - # Decrement the ref count for all sources this CTE selects from - for _, source in scope.selected_sources.values(): - if isinstance(source, Scope): - ref_count[id(source)] -= 1 - - return expression diff --git a/third_party/bigframes_vendored/sqlglot/optimizer/eliminate_joins.py b/third_party/bigframes_vendored/sqlglot/optimizer/eliminate_joins.py deleted file mode 100644 index db6621495cf..00000000000 --- a/third_party/bigframes_vendored/sqlglot/optimizer/eliminate_joins.py +++ /dev/null @@ -1,191 +0,0 @@ -# Contains code from https://github.com/tobymao/sqlglot/blob/v28.5.0/sqlglot/optimizer/eliminate_joins.py - -from bigframes_vendored.sqlglot import expressions as exp -from bigframes_vendored.sqlglot.optimizer.normalize import normalized -from bigframes_vendored.sqlglot.optimizer.scope import Scope, traverse_scope - - -def eliminate_joins(expression): - """ - Remove unused joins from an expression. - - This only removes joins when we know that the join condition doesn't produce duplicate rows. - - Example: - >>> import sqlglot - >>> sql = "SELECT x.a FROM x LEFT JOIN (SELECT DISTINCT y.b FROM y) AS y ON x.b = y.b" - >>> expression = sqlglot.parse_one(sql) - >>> eliminate_joins(expression).sql() - 'SELECT x.a FROM x' - - Args: - expression (sqlglot.Expression): expression to optimize - Returns: - sqlglot.Expression: optimized expression - """ - for scope in traverse_scope(expression): - # If any columns in this scope aren't qualified, it's hard to determine if a join isn't used. - # It's probably possible to infer this from the outputs of derived tables. - # But for now, let's just skip this rule. - if scope.unqualified_columns: - continue - - joins = scope.expression.args.get("joins", []) - - # Reverse the joins so we can remove chains of unused joins - for join in reversed(joins): - if join.is_semi_or_anti_join: - continue - - alias = join.alias_or_name - if _should_eliminate_join(scope, join, alias): - join.pop() - scope.remove_source(alias) - return expression - - -def _should_eliminate_join(scope, join, alias): - inner_source = scope.sources.get(alias) - return ( - isinstance(inner_source, Scope) - and not _join_is_used(scope, join, alias) - and ( - ( - join.side == "LEFT" - and _is_joined_on_all_unique_outputs(inner_source, join) - ) - or (not join.args.get("on") and _has_single_output_row(inner_source)) - ) - ) - - -def _join_is_used(scope, join, alias): - # We need to find all columns that reference this join. - # But columns in the ON clause shouldn't count. - on = join.args.get("on") - if on: - on_clause_columns = {id(column) for column in on.find_all(exp.Column)} - else: - on_clause_columns = set() - return any( - column - for column in scope.source_columns(alias) - if id(column) not in on_clause_columns - ) - - -def _is_joined_on_all_unique_outputs(scope, join): - unique_outputs = _unique_outputs(scope) - if not unique_outputs: - return False - - _, join_keys, _ = join_condition(join) - remaining_unique_outputs = unique_outputs - {c.name for c in join_keys} - return not remaining_unique_outputs - - -def _unique_outputs(scope): - """Determine output columns of `scope` that must have a unique combination per row""" - if scope.expression.args.get("distinct"): - return set(scope.expression.named_selects) - - group = scope.expression.args.get("group") - if group: - grouped_expressions = set(group.expressions) - grouped_outputs = set() - - unique_outputs = set() - for select in scope.expression.selects: - output = select.unalias() - if output in grouped_expressions: - grouped_outputs.add(output) - unique_outputs.add(select.alias_or_name) - - # All the grouped expressions must be in the output - if not grouped_expressions.difference(grouped_outputs): - return unique_outputs - else: - return set() - - if _has_single_output_row(scope): - return set(scope.expression.named_selects) - - return set() - - -def _has_single_output_row(scope): - return isinstance(scope.expression, exp.Select) and ( - all(isinstance(e.unalias(), exp.AggFunc) for e in scope.expression.selects) - or _is_limit_1(scope) - or not scope.expression.args.get("from_") - ) - - -def _is_limit_1(scope): - limit = scope.expression.args.get("limit") - return limit and limit.expression.this == "1" - - -def join_condition(join): - """ - Extract the join condition from a join expression. - - Args: - join (exp.Join) - Returns: - tuple[list[str], list[str], exp.Expression]: - Tuple of (source key, join key, remaining predicate) - """ - name = join.alias_or_name - on = (join.args.get("on") or exp.true()).copy() - source_key = [] - join_key = [] - - def extract_condition(condition): - left, right = condition.unnest_operands() - left_tables = exp.column_table_names(left) - right_tables = exp.column_table_names(right) - - if name in left_tables and name not in right_tables: - join_key.append(left) - source_key.append(right) - condition.replace(exp.true()) - elif name in right_tables and name not in left_tables: - join_key.append(right) - source_key.append(left) - condition.replace(exp.true()) - - # find the join keys - # SELECT - # FROM x - # JOIN y - # ON x.a = y.b AND y.b > 1 - # - # should pull y.b as the join key and x.a as the source key - if normalized(on): - on = on if isinstance(on, exp.And) else exp.and_(on, exp.true(), copy=False) - - for condition in on.flatten(): - if isinstance(condition, exp.EQ): - extract_condition(condition) - elif normalized(on, dnf=True): - conditions = None - - for condition in on.flatten(): - parts = [part for part in condition.flatten() if isinstance(part, exp.EQ)] - if conditions is None: - conditions = parts - else: - temp = [] - for p in parts: - cs = [c for c in conditions if p == c] - - if cs: - temp.append(p) - temp.extend(cs) - conditions = temp - - for condition in conditions: - extract_condition(condition) - - return source_key, join_key, on diff --git a/third_party/bigframes_vendored/sqlglot/optimizer/eliminate_subqueries.py b/third_party/bigframes_vendored/sqlglot/optimizer/eliminate_subqueries.py deleted file mode 100644 index 9deb0f65dc5..00000000000 --- a/third_party/bigframes_vendored/sqlglot/optimizer/eliminate_subqueries.py +++ /dev/null @@ -1,195 +0,0 @@ -# Contains code from https://github.com/tobymao/sqlglot/blob/v28.5.0/sqlglot/optimizer/eliminate_subqueries.py - -from __future__ import annotations - -import itertools -import typing as t - -from bigframes_vendored.sqlglot import expressions as exp -from bigframes_vendored.sqlglot.helper import find_new_name -from bigframes_vendored.sqlglot.optimizer.scope import Scope, build_scope - -if t.TYPE_CHECKING: - ExistingCTEsMapping = t.Dict[exp.Expression, str] - TakenNameMapping = t.Dict[str, t.Union[Scope, exp.Expression]] - - -def eliminate_subqueries(expression: exp.Expression) -> exp.Expression: - """ - Rewrite derived tables as CTES, deduplicating if possible. - - Example: - >>> import sqlglot - >>> expression = sqlglot.parse_one("SELECT a FROM (SELECT * FROM x) AS y") - >>> eliminate_subqueries(expression).sql() - 'WITH y AS (SELECT * FROM x) SELECT a FROM y AS y' - - This also deduplicates common subqueries: - >>> expression = sqlglot.parse_one("SELECT a FROM (SELECT * FROM x) AS y CROSS JOIN (SELECT * FROM x) AS z") - >>> eliminate_subqueries(expression).sql() - 'WITH y AS (SELECT * FROM x) SELECT a FROM y AS y CROSS JOIN y AS z' - - Args: - expression (sqlglot.Expression): expression - Returns: - sqlglot.Expression: expression - """ - if isinstance(expression, exp.Subquery): - # It's possible to have subqueries at the root, e.g. (SELECT * FROM x) LIMIT 1 - eliminate_subqueries(expression.this) - return expression - - root = build_scope(expression) - - if not root: - return expression - - # Map of alias->Scope|Table - # These are all aliases that are already used in the expression. - # We don't want to create new CTEs that conflict with these names. - taken: TakenNameMapping = {} - - # All CTE aliases in the root scope are taken - for scope in root.cte_scopes: - taken[scope.expression.parent.alias] = scope - - # All table names are taken - for scope in root.traverse(): - taken.update( - { - source.name: source - for _, source in scope.sources.items() - if isinstance(source, exp.Table) - } - ) - - # Map of Expression->alias - # Existing CTES in the root expression. We'll use this for deduplication. - existing_ctes: ExistingCTEsMapping = {} - - with_ = root.expression.args.get("with_") - recursive = False - if with_: - recursive = with_.args.get("recursive") - for cte in with_.expressions: - existing_ctes[cte.this] = cte.alias - new_ctes = [] - - # We're adding more CTEs, but we want to maintain the DAG order. - # Derived tables within an existing CTE need to come before the existing CTE. - for cte_scope in root.cte_scopes: - # Append all the new CTEs from this existing CTE - for scope in cte_scope.traverse(): - if scope is cte_scope: - # Don't try to eliminate this CTE itself - continue - new_cte = _eliminate(scope, existing_ctes, taken) - if new_cte: - new_ctes.append(new_cte) - - # Append the existing CTE itself - new_ctes.append(cte_scope.expression.parent) - - # Now append the rest - for scope in itertools.chain( - root.union_scopes, root.subquery_scopes, root.table_scopes - ): - for child_scope in scope.traverse(): - new_cte = _eliminate(child_scope, existing_ctes, taken) - if new_cte: - new_ctes.append(new_cte) - - if new_ctes: - query = expression.expression if isinstance(expression, exp.DDL) else expression - query.set("with_", exp.With(expressions=new_ctes, recursive=recursive)) - - return expression - - -def _eliminate( - scope: Scope, existing_ctes: ExistingCTEsMapping, taken: TakenNameMapping -) -> t.Optional[exp.Expression]: - if scope.is_derived_table: - return _eliminate_derived_table(scope, existing_ctes, taken) - - if scope.is_cte: - return _eliminate_cte(scope, existing_ctes, taken) - - return None - - -def _eliminate_derived_table( - scope: Scope, existing_ctes: ExistingCTEsMapping, taken: TakenNameMapping -) -> t.Optional[exp.Expression]: - # This makes sure that we don't: - # - drop the "pivot" arg from a pivoted subquery - # - eliminate a lateral correlated subquery - if scope.parent.pivots or isinstance(scope.parent.expression, exp.Lateral): - return None - - # Get rid of redundant exp.Subquery expressions, i.e. those that are just used as wrappers - to_replace = scope.expression.parent.unwrap() - name, cte = _new_cte(scope, existing_ctes, taken) - table = exp.alias_(exp.table_(name), alias=to_replace.alias or name) - table.set("joins", to_replace.args.get("joins")) - - to_replace.replace(table) - - return cte - - -def _eliminate_cte( - scope: Scope, existing_ctes: ExistingCTEsMapping, taken: TakenNameMapping -) -> t.Optional[exp.Expression]: - parent = scope.expression.parent - name, cte = _new_cte(scope, existing_ctes, taken) - - with_ = parent.parent - parent.pop() - if not with_.expressions: - with_.pop() - - # Rename references to this CTE - for child_scope in scope.parent.traverse(): - for table, source in child_scope.selected_sources.values(): - if source is scope: - new_table = exp.alias_( - exp.table_(name), alias=table.alias_or_name, copy=False - ) - table.replace(new_table) - - return cte - - -def _new_cte( - scope: Scope, existing_ctes: ExistingCTEsMapping, taken: TakenNameMapping -) -> t.Tuple[str, t.Optional[exp.Expression]]: - """ - Returns: - tuple of (name, cte) - where `name` is a new name for this CTE in the root scope and `cte` is a new CTE instance. - If this CTE duplicates an existing CTE, `cte` will be None. - """ - duplicate_cte_alias = existing_ctes.get(scope.expression) - parent = scope.expression.parent - name = parent.alias - - if not name: - name = find_new_name(taken=taken, base="cte") - - if duplicate_cte_alias: - name = duplicate_cte_alias - elif taken.get(name): - name = find_new_name(taken=taken, base=name) - - taken[name] = scope - - if not duplicate_cte_alias: - existing_ctes[scope.expression] = name - cte = exp.CTE( - this=scope.expression, - alias=exp.TableAlias(this=exp.to_identifier(name)), - ) - else: - cte = None - return name, cte diff --git a/third_party/bigframes_vendored/sqlglot/optimizer/isolate_table_selects.py b/third_party/bigframes_vendored/sqlglot/optimizer/isolate_table_selects.py deleted file mode 100644 index f2ebf8a1a8a..00000000000 --- a/third_party/bigframes_vendored/sqlglot/optimizer/isolate_table_selects.py +++ /dev/null @@ -1,54 +0,0 @@ -# Contains code from https://github.com/tobymao/sqlglot/blob/v28.5.0/sqlglot/optimizer/isolate_table_selects.py - -from __future__ import annotations - -import typing as t - -from bigframes_vendored.sqlglot import alias, exp -from bigframes_vendored.sqlglot.errors import OptimizeError -from bigframes_vendored.sqlglot.optimizer.scope import traverse_scope -from bigframes_vendored.sqlglot.schema import ensure_schema - -if t.TYPE_CHECKING: - from bigframes_vendored.sqlglot._typing import E - from bigframes_vendored.sqlglot.dialects.dialect import DialectType - from bigframes_vendored.sqlglot.schema import Schema - - -def isolate_table_selects( - expression: E, - schema: t.Optional[t.Dict | Schema] = None, - dialect: DialectType = None, -) -> E: - schema = ensure_schema(schema, dialect=dialect) - - for scope in traverse_scope(expression): - if len(scope.selected_sources) == 1: - continue - - for _, source in scope.selected_sources.values(): - assert source.parent - - if ( - not isinstance(source, exp.Table) - or not schema.column_names(source) - or isinstance(source.parent, exp.Subquery) - or isinstance(source.parent.parent, exp.Table) - ): - continue - - if not source.alias: - raise OptimizeError( - "Tables require an alias. Run qualify_tables optimization." - ) - - source.replace( - exp.select("*") - .from_( - alias(source, source.alias_or_name, table=True), - copy=False, - ) - .subquery(source.alias, copy=False) - ) - - return expression diff --git a/third_party/bigframes_vendored/sqlglot/optimizer/merge_subqueries.py b/third_party/bigframes_vendored/sqlglot/optimizer/merge_subqueries.py deleted file mode 100644 index 81e213ee814..00000000000 --- a/third_party/bigframes_vendored/sqlglot/optimizer/merge_subqueries.py +++ /dev/null @@ -1,446 +0,0 @@ -# Contains code from https://github.com/tobymao/sqlglot/blob/v28.5.0/sqlglot/optimizer/merge_subqueries.py - -from __future__ import annotations - -import typing as t -from collections import defaultdict - -from bigframes_vendored.sqlglot import expressions as exp -from bigframes_vendored.sqlglot.helper import find_new_name, seq_get -from bigframes_vendored.sqlglot.optimizer.scope import Scope, traverse_scope - -if t.TYPE_CHECKING: - from bigframes_vendored.sqlglot._typing import E - - FromOrJoin = t.Union[exp.From, exp.Join] - - -def merge_subqueries(expression: E, leave_tables_isolated: bool = False) -> E: - """ - Rewrite sqlglot AST to merge derived tables into the outer query. - - This also merges CTEs if they are selected from only once. - - Example: - >>> import sqlglot - >>> expression = sqlglot.parse_one("SELECT a FROM (SELECT x.a FROM x) CROSS JOIN y") - >>> merge_subqueries(expression).sql() - 'SELECT x.a FROM x CROSS JOIN y' - - If `leave_tables_isolated` is True, this will not merge inner queries into outer - queries if it would result in multiple table selects in a single query: - >>> expression = sqlglot.parse_one("SELECT a FROM (SELECT x.a FROM x) CROSS JOIN y") - >>> merge_subqueries(expression, leave_tables_isolated=True).sql() - 'SELECT a FROM (SELECT x.a FROM x) CROSS JOIN y' - - Inspired by https://dev.mysql.com/doc/refman/8.0/en/derived-table-optimization.html - - Args: - expression (sqlglot.Expression): expression to optimize - leave_tables_isolated (bool): - Returns: - sqlglot.Expression: optimized expression - """ - expression = merge_ctes(expression, leave_tables_isolated) - expression = merge_derived_tables(expression, leave_tables_isolated) - return expression - - -# If a derived table has these Select args, it can't be merged -UNMERGABLE_ARGS = set(exp.Select.arg_types) - { - "expressions", - "from_", - "joins", - "where", - "order", - "hint", -} - - -# Projections in the outer query that are instances of these types can be replaced -# without getting wrapped in parentheses, because the precedence won't be altered. -SAFE_TO_REPLACE_UNWRAPPED = ( - exp.Column, - exp.EQ, - exp.Func, - exp.NEQ, - exp.Paren, -) - - -def merge_ctes(expression: E, leave_tables_isolated: bool = False) -> E: - scopes = traverse_scope(expression) - - # All places where we select from CTEs. - # We key on the CTE scope so we can detect CTES that are selected from multiple times. - cte_selections = defaultdict(list) - for outer_scope in scopes: - for table, inner_scope in outer_scope.selected_sources.values(): - if isinstance(inner_scope, Scope) and inner_scope.is_cte: - cte_selections[id(inner_scope)].append( - ( - outer_scope, - inner_scope, - table, - ) - ) - - singular_cte_selections = [v[0] for k, v in cte_selections.items() if len(v) == 1] - for outer_scope, inner_scope, table in singular_cte_selections: - from_or_join = table.find_ancestor(exp.From, exp.Join) - if _mergeable(outer_scope, inner_scope, leave_tables_isolated, from_or_join): - alias = table.alias_or_name - _rename_inner_sources(outer_scope, inner_scope, alias) - _merge_from(outer_scope, inner_scope, table, alias) - _merge_expressions(outer_scope, inner_scope, alias) - _merge_order(outer_scope, inner_scope) - _merge_joins(outer_scope, inner_scope, from_or_join) - _merge_where(outer_scope, inner_scope, from_or_join) - _merge_hints(outer_scope, inner_scope) - _pop_cte(inner_scope) - outer_scope.clear_cache() - return expression - - -def merge_derived_tables(expression: E, leave_tables_isolated: bool = False) -> E: - for outer_scope in traverse_scope(expression): - for subquery in outer_scope.derived_tables: - from_or_join = subquery.find_ancestor(exp.From, exp.Join) - alias = subquery.alias_or_name - inner_scope = outer_scope.sources[alias] - if _mergeable( - outer_scope, inner_scope, leave_tables_isolated, from_or_join - ): - _rename_inner_sources(outer_scope, inner_scope, alias) - _merge_from(outer_scope, inner_scope, subquery, alias) - _merge_expressions(outer_scope, inner_scope, alias) - _merge_order(outer_scope, inner_scope) - _merge_joins(outer_scope, inner_scope, from_or_join) - _merge_where(outer_scope, inner_scope, from_or_join) - _merge_hints(outer_scope, inner_scope) - outer_scope.clear_cache() - - return expression - - -def _mergeable( - outer_scope: Scope, - inner_scope: Scope, - leave_tables_isolated: bool, - from_or_join: FromOrJoin, -) -> bool: - """ - Return True if `inner_select` can be merged into outer query. - """ - inner_select = inner_scope.expression.unnest() - - def _is_a_window_expression_in_unmergable_operation(): - window_aliases = { - s.alias_or_name for s in inner_select.selects if s.find(exp.Window) - } - inner_select_name = from_or_join.alias_or_name - unmergable_window_columns = [ - column - for column in outer_scope.columns - if column.find_ancestor( - exp.Where, exp.Group, exp.Order, exp.Join, exp.Having, exp.AggFunc - ) - ] - window_expressions_in_unmergable = [ - column - for column in unmergable_window_columns - if column.table == inner_select_name and column.name in window_aliases - ] - return any(window_expressions_in_unmergable) - - def _outer_select_joins_on_inner_select_join(): - """ - All columns from the inner select in the ON clause must be from the first FROM table. - - That is, this can be merged: - SELECT * FROM x JOIN (SELECT y.a AS a FROM y JOIN z) AS q ON x.a = q.a - ^^^ ^ - But this can't: - SELECT * FROM x JOIN (SELECT z.a AS a FROM y JOIN z) AS q ON x.a = q.a - ^^^ ^ - """ - if not isinstance(from_or_join, exp.Join): - return False - - alias = from_or_join.alias_or_name - - on = from_or_join.args.get("on") - if not on: - return False - selections = [c.name for c in on.find_all(exp.Column) if c.table == alias] - inner_from = inner_scope.expression.args.get("from_") - if not inner_from: - return False - inner_from_table = inner_from.alias_or_name - inner_projections = {s.alias_or_name: s for s in inner_scope.expression.selects} - return any( - col.table != inner_from_table - for selection in selections - for col in inner_projections[selection].find_all(exp.Column) - ) - - def _is_recursive(): - # Recursive CTEs look like this: - # WITH RECURSIVE cte AS ( - # SELECT * FROM x <-- inner scope - # UNION ALL - # SELECT * FROM cte <-- outer scope - # ) - cte = inner_scope.expression.parent - node = outer_scope.expression.parent - - while node: - if node is cte: - return True - node = node.parent - return False - - return ( - isinstance(outer_scope.expression, exp.Select) - and not outer_scope.expression.is_star - and isinstance(inner_select, exp.Select) - and not any(inner_select.args.get(arg) for arg in UNMERGABLE_ARGS) - and inner_select.args.get("from_") is not None - and not outer_scope.pivots - and not any( - e.find(exp.AggFunc, exp.Select, exp.Explode) - for e in inner_select.expressions - ) - and not (leave_tables_isolated and len(outer_scope.selected_sources) > 1) - and not (isinstance(from_or_join, exp.Join) and inner_select.args.get("joins")) - and not ( - isinstance(from_or_join, exp.Join) - and inner_select.args.get("where") - and from_or_join.side in ("FULL", "LEFT", "RIGHT") - ) - and not ( - isinstance(from_or_join, exp.From) - and inner_select.args.get("where") - and any( - j.side in ("FULL", "RIGHT") - for j in outer_scope.expression.args.get("joins", []) - ) - ) - and not _outer_select_joins_on_inner_select_join() - and not _is_a_window_expression_in_unmergable_operation() - and not _is_recursive() - and not (inner_select.args.get("order") and outer_scope.is_union) - and not isinstance(seq_get(inner_select.expressions, 0), exp.QueryTransform) - ) - - -def _rename_inner_sources(outer_scope: Scope, inner_scope: Scope, alias: str) -> None: - """ - Renames any sources in the inner query that conflict with names in the outer query. - """ - inner_taken = set(inner_scope.selected_sources) - outer_taken = set(outer_scope.selected_sources) - conflicts = outer_taken.intersection(inner_taken) - conflicts -= {alias} - - taken = outer_taken.union(inner_taken) - - for conflict in conflicts: - new_name = find_new_name(taken, conflict) - - source, _ = inner_scope.selected_sources[conflict] - new_alias = exp.to_identifier(new_name) - - if isinstance(source, exp.Table) and source.alias: - source.set("alias", new_alias) - elif isinstance(source, exp.Table): - source.replace(exp.alias_(source, new_alias)) - elif isinstance(source.parent, exp.Subquery): - source.parent.set("alias", exp.TableAlias(this=new_alias)) - - for column in inner_scope.source_columns(conflict): - column.set("table", exp.to_identifier(new_name)) - - inner_scope.rename_source(conflict, new_name) - - -def _merge_from( - outer_scope: Scope, - inner_scope: Scope, - node_to_replace: t.Union[exp.Subquery, exp.Table], - alias: str, -) -> None: - """ - Merge FROM clause of inner query into outer query. - """ - new_subquery = inner_scope.expression.args["from_"].this - new_subquery.set("joins", node_to_replace.args.get("joins")) - node_to_replace.replace(new_subquery) - for join_hint in outer_scope.join_hints: - tables = join_hint.find_all(exp.Table) - for table in tables: - if table.alias_or_name == node_to_replace.alias_or_name: - table.set("this", exp.to_identifier(new_subquery.alias_or_name)) - outer_scope.remove_source(alias) - outer_scope.add_source( - new_subquery.alias_or_name, inner_scope.sources[new_subquery.alias_or_name] - ) - - -def _merge_joins( - outer_scope: Scope, inner_scope: Scope, from_or_join: FromOrJoin -) -> None: - """ - Merge JOIN clauses of inner query into outer query. - """ - - new_joins = [] - - joins = inner_scope.expression.args.get("joins") or [] - - for join in joins: - new_joins.append(join) - outer_scope.add_source( - join.alias_or_name, inner_scope.sources[join.alias_or_name] - ) - - if new_joins: - outer_joins = outer_scope.expression.args.get("joins", []) - - # Maintain the join order - if isinstance(from_or_join, exp.From): - position = 0 - else: - position = outer_joins.index(from_or_join) + 1 - outer_joins[position:position] = new_joins - - outer_scope.expression.set("joins", outer_joins) - - -def _merge_expressions(outer_scope: Scope, inner_scope: Scope, alias: str) -> None: - """ - Merge projections of inner query into outer query. - - Args: - outer_scope (sqlglot.optimizer.scope.Scope) - inner_scope (sqlglot.optimizer.scope.Scope) - alias (str) - """ - # Collect all columns that reference the alias of the inner query - outer_columns = defaultdict(list) - for column in outer_scope.columns: - if column.table == alias: - outer_columns[column.name].append(column) - - # Replace columns with the projection expression in the inner query - for expression in inner_scope.expression.expressions: - projection_name = expression.alias_or_name - if not projection_name: - continue - columns_to_replace = outer_columns.get(projection_name, []) - - expression = expression.unalias() - must_wrap_expression = not isinstance(expression, SAFE_TO_REPLACE_UNWRAPPED) - - for column in columns_to_replace: - # Ensures we don't alter the intended operator precedence if there's additional - # context surrounding the outer expression (i.e. it's not a simple projection). - if ( - isinstance(column.parent, (exp.Unary, exp.Binary)) - and must_wrap_expression - ): - expression = exp.paren(expression, copy=False) - - # make sure we do not accidentally change the name of the column - if isinstance(column.parent, exp.Select) and column.name != expression.name: - expression = exp.alias_(expression, column.name) - - column.replace(expression.copy()) - - -def _merge_where( - outer_scope: Scope, inner_scope: Scope, from_or_join: FromOrJoin -) -> None: - """ - Merge WHERE clause of inner query into outer query. - - Args: - outer_scope (sqlglot.optimizer.scope.Scope) - inner_scope (sqlglot.optimizer.scope.Scope) - from_or_join (exp.From|exp.Join) - """ - where = inner_scope.expression.args.get("where") - if not where or not where.this: - return - - expression = outer_scope.expression - - if isinstance(from_or_join, exp.Join): - # Merge predicates from an outer join to the ON clause - # if it only has columns that are already joined - from_ = expression.args.get("from_") - sources = {from_.alias_or_name} if from_ else set() - - for join in expression.args["joins"]: - source = join.alias_or_name - sources.add(source) - if source == from_or_join.alias_or_name: - break - - if exp.column_table_names(where.this) <= sources: - from_or_join.on(where.this, copy=False) - from_or_join.set("on", from_or_join.args.get("on")) - return - - expression.where(where.this, copy=False) - - -def _merge_order(outer_scope: Scope, inner_scope: Scope) -> None: - """ - Merge ORDER clause of inner query into outer query. - - Args: - outer_scope (sqlglot.optimizer.scope.Scope) - inner_scope (sqlglot.optimizer.scope.Scope) - """ - if ( - any( - outer_scope.expression.args.get(arg) - for arg in ["group", "distinct", "having", "order"] - ) - or len(outer_scope.selected_sources) != 1 - or any( - expression.find(exp.AggFunc) - for expression in outer_scope.expression.expressions - ) - ): - return - - outer_scope.expression.set("order", inner_scope.expression.args.get("order")) - - -def _merge_hints(outer_scope: Scope, inner_scope: Scope) -> None: - inner_scope_hint = inner_scope.expression.args.get("hint") - if not inner_scope_hint: - return - outer_scope_hint = outer_scope.expression.args.get("hint") - if outer_scope_hint: - for hint_expression in inner_scope_hint.expressions: - outer_scope_hint.append("expressions", hint_expression) - else: - outer_scope.expression.set("hint", inner_scope_hint) - - -def _pop_cte(inner_scope: Scope) -> None: - """ - Remove CTE from the AST. - - Args: - inner_scope (sqlglot.optimizer.scope.Scope) - """ - cte = inner_scope.expression.parent - with_ = cte.parent - if len(with_.expressions) == 1: - with_.pop() - else: - cte.pop() diff --git a/third_party/bigframes_vendored/sqlglot/optimizer/normalize.py b/third_party/bigframes_vendored/sqlglot/optimizer/normalize.py deleted file mode 100644 index daa4bfb84d0..00000000000 --- a/third_party/bigframes_vendored/sqlglot/optimizer/normalize.py +++ /dev/null @@ -1,216 +0,0 @@ -# Contains code from https://github.com/tobymao/sqlglot/blob/v28.5.0/sqlglot/optimizer/normalize.py - -from __future__ import annotations - -import logging - -from bigframes_vendored.sqlglot import exp -from bigframes_vendored.sqlglot.errors import OptimizeError -from bigframes_vendored.sqlglot.helper import while_changing -from bigframes_vendored.sqlglot.optimizer.scope import find_all_in_scope -from bigframes_vendored.sqlglot.optimizer.simplify import Simplifier, flatten - -logger = logging.getLogger("sqlglot") - - -def normalize(expression: exp.Expression, dnf: bool = False, max_distance: int = 128): - """ - Rewrite sqlglot AST into conjunctive normal form or disjunctive normal form. - - Example: - >>> import sqlglot - >>> expression = sqlglot.parse_one("(x AND y) OR z") - >>> normalize(expression, dnf=False).sql() - '(x OR z) AND (y OR z)' - - Args: - expression: expression to normalize - dnf: rewrite in disjunctive normal form instead. - max_distance (int): the maximal estimated distance from cnf/dnf to attempt conversion - Returns: - sqlglot.Expression: normalized expression - """ - simplifier = Simplifier(annotate_new_expressions=False) - - for node in tuple(expression.walk(prune=lambda e: isinstance(e, exp.Connector))): - if isinstance(node, exp.Connector): - if normalized(node, dnf=dnf): - continue - root = node is expression - original = node.copy() - - node.transform(simplifier.rewrite_between, copy=False) - distance = normalization_distance(node, dnf=dnf, max_=max_distance) - - if distance > max_distance: - logger.info( - f"Skipping normalization because distance {distance} exceeds max {max_distance}" - ) - return expression - - try: - node = node.replace( - while_changing( - node, - lambda e: distributive_law( - e, dnf, max_distance, simplifier=simplifier - ), - ) - ) - except OptimizeError as e: - logger.info(e) - node.replace(original) - if root: - return original - return expression - - if root: - expression = node - - return expression - - -def normalized(expression: exp.Expression, dnf: bool = False) -> bool: - """ - Checks whether a given expression is in a normal form of interest. - - Example: - >>> from sqlglot import parse_one - >>> normalized(parse_one("(a AND b) OR c OR (d AND e)"), dnf=True) - True - >>> normalized(parse_one("(a OR b) AND c")) # Checks CNF by default - True - >>> normalized(parse_one("a AND (b OR c)"), dnf=True) - False - - Args: - expression: The expression to check if it's normalized. - dnf: Whether to check if the expression is in Disjunctive Normal Form (DNF). - Default: False, i.e. we check if it's in Conjunctive Normal Form (CNF). - """ - ancestor, root = (exp.And, exp.Or) if dnf else (exp.Or, exp.And) - return not any( - connector.find_ancestor(ancestor) - for connector in find_all_in_scope(expression, root) - ) - - -def normalization_distance( - expression: exp.Expression, dnf: bool = False, max_: float = float("inf") -) -> int: - """ - The difference in the number of predicates between a given expression and its normalized form. - - This is used as an estimate of the cost of the conversion which is exponential in complexity. - - Example: - >>> import sqlglot - >>> expression = sqlglot.parse_one("(a AND b) OR (c AND d)") - >>> normalization_distance(expression) - 4 - - Args: - expression: The expression to compute the normalization distance for. - dnf: Whether to check if the expression is in Disjunctive Normal Form (DNF). - Default: False, i.e. we check if it's in Conjunctive Normal Form (CNF). - max_: stop early if count exceeds this. - - Returns: - The normalization distance. - """ - total = -(sum(1 for _ in expression.find_all(exp.Connector)) + 1) - - for length in _predicate_lengths(expression, dnf, max_): - total += length - if total > max_: - return total - - return total - - -def _predicate_lengths(expression, dnf, max_=float("inf"), depth=0): - """ - Returns a list of predicate lengths when expanded to normalized form. - - (A AND B) OR C -> [2, 2] because len(A OR C), len(B OR C). - """ - if depth > max_: - yield depth - return - - expression = expression.unnest() - - if not isinstance(expression, exp.Connector): - yield 1 - return - - depth += 1 - left, right = expression.args.values() - - if isinstance(expression, exp.And if dnf else exp.Or): - for a in _predicate_lengths(left, dnf, max_, depth): - for b in _predicate_lengths(right, dnf, max_, depth): - yield a + b - else: - yield from _predicate_lengths(left, dnf, max_, depth) - yield from _predicate_lengths(right, dnf, max_, depth) - - -def distributive_law(expression, dnf, max_distance, simplifier=None): - """ - x OR (y AND z) -> (x OR y) AND (x OR z) - (x AND y) OR (y AND z) -> (x OR y) AND (x OR z) AND (y OR y) AND (y OR z) - """ - if normalized(expression, dnf=dnf): - return expression - - distance = normalization_distance(expression, dnf=dnf, max_=max_distance) - - if distance > max_distance: - raise OptimizeError( - f"Normalization distance {distance} exceeds max {max_distance}" - ) - - exp.replace_children(expression, lambda e: distributive_law(e, dnf, max_distance)) - to_exp, from_exp = (exp.Or, exp.And) if dnf else (exp.And, exp.Or) - - if isinstance(expression, from_exp): - a, b = expression.unnest_operands() - - from_func = exp.and_ if from_exp == exp.And else exp.or_ - to_func = exp.and_ if to_exp == exp.And else exp.or_ - - simplifier = simplifier or Simplifier(annotate_new_expressions=False) - - if isinstance(a, to_exp) and isinstance(b, to_exp): - if len(tuple(a.find_all(exp.Connector))) > len( - tuple(b.find_all(exp.Connector)) - ): - return _distribute(a, b, from_func, to_func, simplifier) - return _distribute(b, a, from_func, to_func, simplifier) - if isinstance(a, to_exp): - return _distribute(b, a, from_func, to_func, simplifier) - if isinstance(b, to_exp): - return _distribute(a, b, from_func, to_func, simplifier) - - return expression - - -def _distribute(a, b, from_func, to_func, simplifier): - if isinstance(a, exp.Connector): - exp.replace_children( - a, - lambda c: to_func( - simplifier.uniq_sort(flatten(from_func(c, b.left))), - simplifier.uniq_sort(flatten(from_func(c, b.right))), - copy=False, - ), - ) - else: - a = to_func( - simplifier.uniq_sort(flatten(from_func(a, b.left))), - simplifier.uniq_sort(flatten(from_func(a, b.right))), - copy=False, - ) - - return a diff --git a/third_party/bigframes_vendored/sqlglot/optimizer/normalize_identifiers.py b/third_party/bigframes_vendored/sqlglot/optimizer/normalize_identifiers.py deleted file mode 100644 index eacf4305c49..00000000000 --- a/third_party/bigframes_vendored/sqlglot/optimizer/normalize_identifiers.py +++ /dev/null @@ -1,86 +0,0 @@ -# Contains code from https://github.com/tobymao/sqlglot/blob/v28.5.0/sqlglot/optimizer/normalize_identifiers.py - -from __future__ import annotations - -import typing as t - -from bigframes_vendored.sqlglot import exp -from bigframes_vendored.sqlglot.dialects.dialect import Dialect, DialectType - -if t.TYPE_CHECKING: - from bigframes_vendored.sqlglot._typing import E - - -@t.overload -def normalize_identifiers( - expression: E, - dialect: DialectType = None, - store_original_column_identifiers: bool = False, -) -> E: ... - - -@t.overload -def normalize_identifiers( - expression: str, - dialect: DialectType = None, - store_original_column_identifiers: bool = False, -) -> exp.Identifier: ... - - -def normalize_identifiers( - expression, dialect=None, store_original_column_identifiers=False -): - """ - Normalize identifiers by converting them to either lower or upper case, - ensuring the semantics are preserved in each case (e.g. by respecting - case-sensitivity). - - This transformation reflects how identifiers would be resolved by the engine corresponding - to each SQL dialect, and plays a very important role in the standardization of the AST. - - It's possible to make this a no-op by adding a special comment next to the - identifier of interest: - - SELECT a /* sqlglot.meta case_sensitive */ FROM table - - In this example, the identifier `a` will not be normalized. - - Note: - Some dialects (e.g. DuckDB) treat all identifiers as case-insensitive even - when they're quoted, so in these cases all identifiers are normalized. - - Example: - >>> import sqlglot - >>> expression = sqlglot.parse_one('SELECT Bar.A AS A FROM "Foo".Bar') - >>> normalize_identifiers(expression).sql() - 'SELECT bar.a AS a FROM "Foo".bar' - >>> normalize_identifiers("foo", dialect="snowflake").sql(dialect="snowflake") - 'FOO' - - Args: - expression: The expression to transform. - dialect: The dialect to use in order to decide how to normalize identifiers. - store_original_column_identifiers: Whether to store the original column identifiers in - the meta data of the expression in case we want to undo the normalization at a later point. - - Returns: - The transformed expression. - """ - dialect = Dialect.get_or_raise(dialect) - - if isinstance(expression, str): - expression = exp.parse_identifier(expression, dialect=dialect) - - for node in expression.walk(prune=lambda n: n.meta.get("case_sensitive")): - if not node.meta.get("case_sensitive"): - if store_original_column_identifiers and isinstance(node, exp.Column): - # TODO: This does not handle non-column cases, e.g PARSE_JSON(...).key - parent = node - while parent and isinstance(parent.parent, exp.Dot): - parent = parent.parent - - node.meta["dot_parts"] = [p.name for p in parent.parts] - - dialect.normalize_identifier(node) - - return expression diff --git a/third_party/bigframes_vendored/sqlglot/optimizer/optimize_joins.py b/third_party/bigframes_vendored/sqlglot/optimizer/optimize_joins.py deleted file mode 100644 index d09d8cc6ce0..00000000000 --- a/third_party/bigframes_vendored/sqlglot/optimizer/optimize_joins.py +++ /dev/null @@ -1,128 +0,0 @@ -# Contains code from https://github.com/tobymao/sqlglot/blob/v28.5.0/sqlglot/optimizer/optimize_joins.py - -from __future__ import annotations - -import typing as t - -from bigframes_vendored.sqlglot import exp -from bigframes_vendored.sqlglot.helper import tsort - -JOIN_ATTRS = ("on", "side", "kind", "using", "method") - - -def optimize_joins(expression): - """ - Removes cross joins if possible and reorder joins based on predicate dependencies. - - Example: - >>> from sqlglot import parse_one - >>> optimize_joins(parse_one("SELECT * FROM x CROSS JOIN y JOIN z ON x.a = z.a AND y.a = z.a")).sql() - 'SELECT * FROM x JOIN z ON x.a = z.a AND TRUE JOIN y ON y.a = z.a' - """ - - for select in expression.find_all(exp.Select): - joins = select.args.get("joins", []) - - if not _is_reorderable(joins): - continue - - references = {} - cross_joins = [] - - for join in joins: - tables = other_table_names(join) - - if tables: - for table in tables: - references[table] = references.get(table, []) + [join] - else: - cross_joins.append((join.alias_or_name, join)) - - for name, join in cross_joins: - for dep in references.get(name, []): - on = dep.args["on"] - - if isinstance(on, exp.Connector): - if len(other_table_names(dep)) < 2: - continue - - operator = type(on) - for predicate in on.flatten(): - if name in exp.column_table_names(predicate): - predicate.replace(exp.true()) - predicate = exp._combine( - [join.args.get("on"), predicate], operator, copy=False - ) - join.on(predicate, append=False, copy=False) - - expression = reorder_joins(expression) - expression = normalize(expression) - return expression - - -def reorder_joins(expression): - """ - Reorder joins by topological sort order based on predicate references. - """ - for from_ in expression.find_all(exp.From): - parent = from_.parent - joins = parent.args.get("joins", []) - - if not _is_reorderable(joins): - continue - - joins_by_name = {join.alias_or_name: join for join in joins} - dag = {name: other_table_names(join) for name, join in joins_by_name.items()} - parent.set( - "joins", - [ - joins_by_name[name] - for name in tsort(dag) - if name != from_.alias_or_name and name in joins_by_name - ], - ) - return expression - - -def normalize(expression): - """ - Remove INNER and OUTER from joins as they are optional. - """ - for join in expression.find_all(exp.Join): - if not any(join.args.get(k) for k in JOIN_ATTRS): - join.set("kind", "CROSS") - - if join.kind == "CROSS": - join.set("on", None) - else: - if join.kind in ("INNER", "OUTER"): - join.set("kind", None) - - if not join.args.get("on") and not join.args.get("using"): - join.set("on", exp.true()) - return expression - - -def other_table_names(join: exp.Join) -> t.Set[str]: - on = join.args.get("on") - return exp.column_table_names(on, join.alias_or_name) if on else set() - - -def _is_reorderable(joins: t.List[exp.Join]) -> bool: - """ - Checks if joins can be reordered without changing query semantics. - - Joins with a side (LEFT, RIGHT, FULL) cannot be reordered easily, - the order affects which rows are included in the result. - - Example: - >>> from sqlglot import parse_one, exp - >>> from sqlglot.optimizer.optimize_joins import _is_reorderable - >>> ast = parse_one("SELECT * FROM x JOIN y ON x.id = y.id JOIN z ON y.id = z.id") - >>> _is_reorderable(ast.find(exp.Select).args.get("joins", [])) - True - >>> ast = parse_one("SELECT * FROM x LEFT JOIN y ON x.id = y.id JOIN z ON y.id = z.id") - >>> _is_reorderable(ast.find(exp.Select).args.get("joins", [])) - False - """ - return not any(join.side for join in joins) diff --git a/third_party/bigframes_vendored/sqlglot/optimizer/optimizer.py b/third_party/bigframes_vendored/sqlglot/optimizer/optimizer.py deleted file mode 100644 index ba13d17383e..00000000000 --- a/third_party/bigframes_vendored/sqlglot/optimizer/optimizer.py +++ /dev/null @@ -1,106 +0,0 @@ -# Contains code from https://github.com/tobymao/sqlglot/blob/v28.5.0/sqlglot/optimizer/optimizer.py - -from __future__ import annotations - -import inspect -import typing as t - -from bigframes_vendored.sqlglot import Schema, exp -from bigframes_vendored.sqlglot.dialects.dialect import DialectType -from bigframes_vendored.sqlglot.optimizer.annotate_types import annotate_types -from bigframes_vendored.sqlglot.optimizer.canonicalize import canonicalize -from bigframes_vendored.sqlglot.optimizer.eliminate_ctes import eliminate_ctes -from bigframes_vendored.sqlglot.optimizer.eliminate_joins import eliminate_joins -from bigframes_vendored.sqlglot.optimizer.eliminate_subqueries import ( - eliminate_subqueries, -) -from bigframes_vendored.sqlglot.optimizer.merge_subqueries import merge_subqueries -from bigframes_vendored.sqlglot.optimizer.normalize import normalize -from bigframes_vendored.sqlglot.optimizer.optimize_joins import optimize_joins -from bigframes_vendored.sqlglot.optimizer.pushdown_predicates import pushdown_predicates -from bigframes_vendored.sqlglot.optimizer.pushdown_projections import ( - pushdown_projections, -) -from bigframes_vendored.sqlglot.optimizer.qualify import qualify -from bigframes_vendored.sqlglot.optimizer.qualify_columns import quote_identifiers -from bigframes_vendored.sqlglot.optimizer.simplify import simplify -from bigframes_vendored.sqlglot.optimizer.unnest_subqueries import unnest_subqueries -from bigframes_vendored.sqlglot.schema import ensure_schema - -RULES = ( - qualify, - pushdown_projections, - normalize, - unnest_subqueries, - pushdown_predicates, - optimize_joins, - eliminate_subqueries, - merge_subqueries, - eliminate_joins, - eliminate_ctes, - quote_identifiers, - annotate_types, - canonicalize, - simplify, -) - - -def optimize( - expression: str | exp.Expression, - schema: t.Optional[dict | Schema] = None, - db: t.Optional[str | exp.Identifier] = None, - catalog: t.Optional[str | exp.Identifier] = None, - dialect: DialectType = None, - rules: t.Sequence[t.Callable] = RULES, - sql: t.Optional[str] = None, - **kwargs, -) -> exp.Expression: - """ - Rewrite a sqlglot AST into an optimized form. - - Args: - expression: expression to optimize - schema: database schema. - This can either be an instance of `sqlglot.optimizer.Schema` or a mapping in one of - the following forms: - 1. {table: {col: type}} - 2. {db: {table: {col: type}}} - 3. {catalog: {db: {table: {col: type}}}} - If no schema is provided then the default schema defined at `sqlgot.schema` will be used - db: specify the default database, as might be set by a `USE DATABASE db` statement - catalog: specify the default catalog, as might be set by a `USE CATALOG c` statement - dialect: The dialect to parse the sql string. - rules: sequence of optimizer rules to use. - Many of the rules require tables and columns to be qualified. - Do not remove `qualify` from the sequence of rules unless you know what you're doing! - sql: Original SQL string for error highlighting. If not provided, errors will not include - highlighting. Requires that the expression has position metadata from parsing. - **kwargs: If a rule has a keyword argument with a same name in **kwargs, it will be passed in. - - Returns: - The optimized expression. - """ - schema = ensure_schema(schema, dialect=dialect) - possible_kwargs = { - "db": db, - "catalog": catalog, - "schema": schema, - "dialect": dialect, - "sql": sql, - "isolate_tables": True, # needed for other optimizations to perform well - "quote_identifiers": False, - **kwargs, - } - - optimized = exp.maybe_parse(expression, dialect=dialect, copy=True) - for rule in rules: - # Find any additional rule parameters, beyond `expression` - rule_params = inspect.getfullargspec(rule).args - rule_kwargs = { - param: possible_kwargs[param] - for param in rule_params - if param in possible_kwargs - } - optimized = rule(optimized, **rule_kwargs) - - return optimized diff --git a/third_party/bigframes_vendored/sqlglot/optimizer/pushdown_predicates.py b/third_party/bigframes_vendored/sqlglot/optimizer/pushdown_predicates.py deleted file mode 100644 index 092d513ac7d..00000000000 --- a/third_party/bigframes_vendored/sqlglot/optimizer/pushdown_predicates.py +++ /dev/null @@ -1,237 +0,0 @@ -# Contains code from https://github.com/tobymao/sqlglot/blob/v28.5.0/sqlglot/optimizer/pushdown_predicates.py - -from bigframes_vendored.sqlglot import Dialect, exp -from bigframes_vendored.sqlglot.optimizer.normalize import normalized -from bigframes_vendored.sqlglot.optimizer.scope import build_scope, find_in_scope -from bigframes_vendored.sqlglot.optimizer.simplify import simplify - - -def pushdown_predicates(expression, dialect=None): - """ - Rewrite sqlglot AST to pushdown predicates in FROMS and JOINS - - Example: - >>> import sqlglot - >>> sql = "SELECT y.a AS a FROM (SELECT x.a AS a FROM x AS x) AS y WHERE y.a = 1" - >>> expression = sqlglot.parse_one(sql) - >>> pushdown_predicates(expression).sql() - 'SELECT y.a AS a FROM (SELECT x.a AS a FROM x AS x WHERE x.a = 1) AS y WHERE TRUE' - - Args: - expression (sqlglot.Expression): expression to optimize - Returns: - sqlglot.Expression: optimized expression - """ - from bigframes_vendored.sqlglot.dialects.athena import Athena - from bigframes_vendored.sqlglot.dialects.presto import Presto - - root = build_scope(expression) - - dialect = Dialect.get_or_raise(dialect) - unnest_requires_cross_join = isinstance(dialect, (Athena, Presto)) - - if root: - scope_ref_count = root.ref_count() - - for scope in reversed(list(root.traverse())): - select = scope.expression - where = select.args.get("where") - if where: - selected_sources = scope.selected_sources - join_index = { - join.alias_or_name: i - for i, join in enumerate(select.args.get("joins") or []) - } - - # a right join can only push down to itself and not the source FROM table - # presto, trino and athena don't support inner joins where the RHS is an UNNEST expression - pushdown_allowed = True - for k, (node, source) in selected_sources.items(): - parent = node.find_ancestor(exp.Join, exp.From) - if isinstance(parent, exp.Join): - if parent.side == "RIGHT": - selected_sources = {k: (node, source)} - break - if isinstance(node, exp.Unnest) and unnest_requires_cross_join: - pushdown_allowed = False - break - - if pushdown_allowed: - pushdown( - where.this, - selected_sources, - scope_ref_count, - dialect, - join_index, - ) - - # joins should only pushdown into itself, not to other joins - # so we limit the selected sources to only itself - for join in select.args.get("joins") or []: - name = join.alias_or_name - if name in scope.selected_sources: - pushdown( - join.args.get("on"), - {name: scope.selected_sources[name]}, - scope_ref_count, - dialect, - ) - - return expression - - -def pushdown(condition, sources, scope_ref_count, dialect, join_index=None): - if not condition: - return - - condition = condition.replace(simplify(condition, dialect=dialect)) - cnf_like = normalized(condition) or not normalized(condition, dnf=True) - - predicates = list( - condition.flatten() - if isinstance(condition, exp.And if cnf_like else exp.Or) - else [condition] - ) - - if cnf_like: - pushdown_cnf(predicates, sources, scope_ref_count, join_index=join_index) - else: - pushdown_dnf(predicates, sources, scope_ref_count) - - -def pushdown_cnf(predicates, sources, scope_ref_count, join_index=None): - """ - If the predicates are in CNF like form, we can simply replace each block in the parent. - """ - join_index = join_index or {} - for predicate in predicates: - for node in nodes_for_predicate(predicate, sources, scope_ref_count).values(): - if isinstance(node, exp.Join): - name = node.alias_or_name - predicate_tables = exp.column_table_names(predicate, name) - - # Don't push the predicate if it references tables that appear in later joins - this_index = join_index[name] - if all( - join_index.get(table, -1) < this_index for table in predicate_tables - ): - predicate.replace(exp.true()) - node.on(predicate, copy=False) - break - if isinstance(node, exp.Select): - predicate.replace(exp.true()) - inner_predicate = replace_aliases(node, predicate) - if find_in_scope(inner_predicate, exp.AggFunc): - node.having(inner_predicate, copy=False) - else: - node.where(inner_predicate, copy=False) - - -def pushdown_dnf(predicates, sources, scope_ref_count): - """ - If the predicates are in DNF form, we can only push down conditions that are in all blocks. - Additionally, we can't remove predicates from their original form. - """ - # find all the tables that can be pushdown too - # these are tables that are referenced in all blocks of a DNF - # (a.x AND b.x) OR (a.y AND c.y) - # only table a can be push down - pushdown_tables = set() - - for a in predicates: - a_tables = exp.column_table_names(a) - - for b in predicates: - a_tables &= exp.column_table_names(b) - - pushdown_tables.update(a_tables) - - conditions = {} - - # pushdown all predicates to their respective nodes - for table in sorted(pushdown_tables): - for predicate in predicates: - nodes = nodes_for_predicate(predicate, sources, scope_ref_count) - - if table not in nodes: - continue - - conditions[table] = ( - exp.or_(conditions[table], predicate) - if table in conditions - else predicate - ) - - for name, node in nodes.items(): - if name not in conditions: - continue - - predicate = conditions[name] - - if isinstance(node, exp.Join): - node.on(predicate, copy=False) - elif isinstance(node, exp.Select): - inner_predicate = replace_aliases(node, predicate) - if find_in_scope(inner_predicate, exp.AggFunc): - node.having(inner_predicate, copy=False) - else: - node.where(inner_predicate, copy=False) - - -def nodes_for_predicate(predicate, sources, scope_ref_count): - nodes = {} - tables = exp.column_table_names(predicate) - where_condition = isinstance( - predicate.find_ancestor(exp.Join, exp.Where), exp.Where - ) - - for table in sorted(tables): - node, source = sources.get(table) or (None, None) - - # if the predicate is in a where statement we can try to push it down - # we want to find the root join or from statement - if node and where_condition: - node = node.find_ancestor(exp.Join, exp.From) - - # a node can reference a CTE which should be pushed down - if isinstance(node, exp.From) and not isinstance(source, exp.Table): - with_ = source.parent.expression.args.get("with_") - if with_ and with_.recursive: - return {} - node = source.expression - - if isinstance(node, exp.Join): - if node.side and node.side != "RIGHT": - return {} - nodes[table] = node - elif isinstance(node, exp.Select) and len(tables) == 1: - # We can't push down window expressions - has_window_expression = any( - select for select in node.selects if select.find(exp.Window) - ) - # we can't push down predicates to select statements if they are referenced in - # multiple places. - if ( - not node.args.get("group") - and scope_ref_count[id(source)] < 2 - and not has_window_expression - ): - nodes[table] = node - return nodes - - -def replace_aliases(source, predicate): - aliases = {} - - for select in source.selects: - if isinstance(select, exp.Alias): - aliases[select.alias] = select.this - else: - aliases[select.name] = select - - def _replace_alias(column): - if isinstance(column, exp.Column) and column.name in aliases: - return aliases[column.name].copy() - return column - - return predicate.transform(_replace_alias) diff --git a/third_party/bigframes_vendored/sqlglot/optimizer/pushdown_projections.py b/third_party/bigframes_vendored/sqlglot/optimizer/pushdown_projections.py deleted file mode 100644 index b83dcb2c563..00000000000 --- a/third_party/bigframes_vendored/sqlglot/optimizer/pushdown_projections.py +++ /dev/null @@ -1,183 +0,0 @@ -# Contains code from https://github.com/tobymao/sqlglot/blob/v28.5.0/sqlglot/optimizer/pushdown_projections.py - -from __future__ import annotations - -import typing as t -from collections import defaultdict - -from bigframes_vendored.sqlglot import alias, exp -from bigframes_vendored.sqlglot.errors import OptimizeError -from bigframes_vendored.sqlglot.helper import seq_get -from bigframes_vendored.sqlglot.optimizer.qualify_columns import Resolver -from bigframes_vendored.sqlglot.optimizer.scope import Scope, traverse_scope -from bigframes_vendored.sqlglot.schema import ensure_schema - -if t.TYPE_CHECKING: - from bigframes_vendored.sqlglot._typing import E - from bigframes_vendored.sqlglot.dialects.dialect import DialectType - from bigframes_vendored.sqlglot.schema import Schema - -# Sentinel value that means an outer query selecting ALL columns -SELECT_ALL = object() - - -# Selection to use if selection list is empty -def default_selection(is_agg: bool) -> exp.Alias: - return alias(exp.Max(this=exp.Literal.number(1)) if is_agg else "1", "_") - - -def pushdown_projections( - expression: E, - schema: t.Optional[t.Dict | Schema] = None, - remove_unused_selections: bool = True, - dialect: DialectType = None, -) -> E: - """ - Rewrite sqlglot AST to remove unused columns projections. - - Example: - >>> import sqlglot - >>> sql = "SELECT y.a AS a FROM (SELECT x.a AS a, x.b AS b FROM x) AS y" - >>> expression = sqlglot.parse_one(sql) - >>> pushdown_projections(expression).sql() - 'SELECT y.a AS a FROM (SELECT x.a AS a FROM x) AS y' - - Args: - expression (sqlglot.Expression): expression to optimize - remove_unused_selections (bool): remove selects that are unused - Returns: - sqlglot.Expression: optimized expression - """ - # Map of Scope to all columns being selected by outer queries. - schema = ensure_schema(schema, dialect=dialect) - source_column_alias_count: t.Dict[exp.Expression | Scope, int] = {} - referenced_columns: t.DefaultDict[Scope, t.Set[str | object]] = defaultdict(set) - - # We build the scope tree (which is traversed in DFS postorder), then iterate - # over the result in reverse order. This should ensure that the set of selected - # columns for a particular scope are completely build by the time we get to it. - for scope in reversed(traverse_scope(expression)): - parent_selections = referenced_columns.get(scope, {SELECT_ALL}) - alias_count = source_column_alias_count.get(scope, 0) - - # We can't remove columns SELECT DISTINCT nor UNION DISTINCT. - if scope.expression.args.get("distinct"): - parent_selections = {SELECT_ALL} - - if isinstance(scope.expression, exp.SetOperation): - set_op = scope.expression - if not (set_op.kind or set_op.side): - # Do not optimize this set operation if it's using the BigQuery specific - # kind / side syntax (e.g INNER UNION ALL BY NAME) which changes the semantics of the operation - left, right = scope.union_scopes - if len(left.expression.selects) != len(right.expression.selects): - scope_sql = scope.expression.sql(dialect=dialect) - raise OptimizeError( - f"Invalid set operation due to column mismatch: {scope_sql}." - ) - - referenced_columns[left] = parent_selections - - if any(select.is_star for select in right.expression.selects): - referenced_columns[right] = parent_selections - elif not any(select.is_star for select in left.expression.selects): - if scope.expression.args.get("by_name"): - referenced_columns[right] = referenced_columns[left] - else: - referenced_columns[right] = { - right.expression.selects[i].alias_or_name - for i, select in enumerate(left.expression.selects) - if SELECT_ALL in parent_selections - or select.alias_or_name in parent_selections - } - - if isinstance(scope.expression, exp.Select): - if remove_unused_selections: - _remove_unused_selections(scope, parent_selections, schema, alias_count) - - if scope.expression.is_star: - continue - - # Group columns by source name - selects = defaultdict(set) - for col in scope.columns: - table_name = col.table - col_name = col.name - selects[table_name].add(col_name) - - # Push the selected columns down to the next scope - for name, (node, source) in scope.selected_sources.items(): - if isinstance(source, Scope): - select = seq_get(source.expression.selects, 0) - - if scope.pivots or isinstance(select, exp.QueryTransform): - columns = {SELECT_ALL} - else: - columns = selects.get(name) or set() - - referenced_columns[source].update(columns) - - column_aliases = node.alias_column_names - if column_aliases: - source_column_alias_count[source] = len(column_aliases) - - return expression - - -def _remove_unused_selections(scope, parent_selections, schema, alias_count): - order = scope.expression.args.get("order") - - if order: - # Assume columns without a qualified table are references to output columns - order_refs = {c.name for c in order.find_all(exp.Column) if not c.table} - else: - order_refs = set() - - new_selections = [] - removed = False - star = False - is_agg = False - - select_all = SELECT_ALL in parent_selections - - for selection in scope.expression.selects: - name = selection.alias_or_name - - if ( - select_all - or name in parent_selections - or name in order_refs - or alias_count > 0 - ): - new_selections.append(selection) - alias_count -= 1 - else: - if selection.is_star: - star = True - removed = True - - if not is_agg and selection.find(exp.AggFunc): - is_agg = True - - if star: - resolver = Resolver(scope, schema) - names = {s.alias_or_name for s in new_selections} - - for name in sorted(parent_selections): - if name not in names: - new_selections.append( - alias( - exp.column(name, table=resolver.get_table(name)), - name, - copy=False, - ) - ) - - # If there are no remaining selections, just select a single constant - if not new_selections: - new_selections.append(default_selection(is_agg)) - - scope.expression.select(*new_selections, append=False, copy=False) - - if removed: - scope.clear_cache() diff --git a/third_party/bigframes_vendored/sqlglot/optimizer/qualify.py b/third_party/bigframes_vendored/sqlglot/optimizer/qualify.py deleted file mode 100644 index cf518b06015..00000000000 --- a/third_party/bigframes_vendored/sqlglot/optimizer/qualify.py +++ /dev/null @@ -1,124 +0,0 @@ -# Contains code from https://github.com/tobymao/sqlglot/blob/v28.5.0/sqlglot/optimizer/qualify.py - -from __future__ import annotations - -import typing as t - -from bigframes_vendored.sqlglot import exp -from bigframes_vendored.sqlglot.dialects.dialect import Dialect, DialectType -from bigframes_vendored.sqlglot.optimizer.isolate_table_selects import ( - isolate_table_selects, -) -from bigframes_vendored.sqlglot.optimizer.normalize_identifiers import ( - normalize_identifiers, -) -from bigframes_vendored.sqlglot.optimizer.qualify_columns import ( - qualify_columns as qualify_columns_func, -) -from bigframes_vendored.sqlglot.optimizer.qualify_columns import ( - quote_identifiers as quote_identifiers_func, -) -from bigframes_vendored.sqlglot.optimizer.qualify_columns import ( - validate_qualify_columns as validate_qualify_columns_func, -) -from bigframes_vendored.sqlglot.optimizer.qualify_tables import qualify_tables -from bigframes_vendored.sqlglot.schema import Schema, ensure_schema - - -def qualify( - expression: exp.Expression, - dialect: DialectType = None, - db: t.Optional[str] = None, - catalog: t.Optional[str] = None, - schema: t.Optional[dict | Schema] = None, - expand_alias_refs: bool = True, - expand_stars: bool = True, - infer_schema: t.Optional[bool] = None, - isolate_tables: bool = False, - qualify_columns: bool = True, - allow_partial_qualification: bool = False, - validate_qualify_columns: bool = True, - quote_identifiers: bool = True, - identify: bool = True, - canonicalize_table_aliases: bool = False, - on_qualify: t.Optional[t.Callable[[exp.Expression], None]] = None, - sql: t.Optional[str] = None, -) -> exp.Expression: - """ - Rewrite sqlglot AST to have normalized and qualified tables and columns. - - This step is necessary for all further SQLGlot optimizations. - - Example: - >>> import sqlglot - >>> schema = {"tbl": {"col": "INT"}} - >>> expression = sqlglot.parse_one("SELECT col FROM tbl") - >>> qualify(expression, schema=schema).sql() - 'SELECT "tbl"."col" AS "col" FROM "tbl" AS "tbl"' - - Args: - expression: Expression to qualify. - db: Default database name for tables. - catalog: Default catalog name for tables. - schema: Schema to infer column names and types. - expand_alias_refs: Whether to expand references to aliases. - expand_stars: Whether to expand star queries. This is a necessary step - for most of the optimizer's rules to work; do not set to False unless you - know what you're doing! - infer_schema: Whether to infer the schema if missing. - isolate_tables: Whether to isolate table selects. - qualify_columns: Whether to qualify columns. - allow_partial_qualification: Whether to allow partial qualification. - validate_qualify_columns: Whether to validate columns. - quote_identifiers: Whether to run the quote_identifiers step. - This step is necessary to ensure correctness for case sensitive queries. - But this flag is provided in case this step is performed at a later time. - identify: If True, quote all identifiers, else only necessary ones. - canonicalize_table_aliases: Whether to use canonical aliases (_0, _1, ...) for all sources - instead of preserving table names. - on_qualify: Callback after a table has been qualified. - sql: Original SQL string for error highlighting. If not provided, errors will not include - highlighting. Requires that the expression has position metadata from parsing. - - Returns: - The qualified expression. - """ - schema = ensure_schema(schema, dialect=dialect) - dialect = Dialect.get_or_raise(dialect) - - expression = normalize_identifiers( - expression, - dialect=dialect, - store_original_column_identifiers=True, - ) - expression = qualify_tables( - expression, - db=db, - catalog=catalog, - dialect=dialect, - on_qualify=on_qualify, - canonicalize_table_aliases=canonicalize_table_aliases, - ) - - if isolate_tables: - expression = isolate_table_selects(expression, schema=schema) - - if qualify_columns: - expression = qualify_columns_func( - expression, - schema, - expand_alias_refs=expand_alias_refs, - expand_stars=expand_stars, - infer_schema=infer_schema, - allow_partial_qualification=allow_partial_qualification, - ) - - if quote_identifiers: - expression = quote_identifiers_func( - expression, dialect=dialect, identify=identify - ) - - if validate_qualify_columns: - validate_qualify_columns_func(expression, sql=sql) - - return expression diff --git a/third_party/bigframes_vendored/sqlglot/optimizer/qualify_columns.py b/third_party/bigframes_vendored/sqlglot/optimizer/qualify_columns.py deleted file mode 100644 index 51a0a6d4dc0..00000000000 --- a/third_party/bigframes_vendored/sqlglot/optimizer/qualify_columns.py +++ /dev/null @@ -1,1053 +0,0 @@ -# Contains code from https://github.com/tobymao/sqlglot/blob/v28.5.0/sqlglot/optimizer/qualify_columns.py - -from __future__ import annotations - -import itertools -import typing as t - -from bigframes_vendored.sqlglot import alias, exp -from bigframes_vendored.sqlglot.dialects.dialect import Dialect, DialectType -from bigframes_vendored.sqlglot.errors import OptimizeError, highlight_sql -from bigframes_vendored.sqlglot.helper import seq_get -from bigframes_vendored.sqlglot.optimizer.annotate_types import TypeAnnotator -from bigframes_vendored.sqlglot.optimizer.resolver import Resolver -from bigframes_vendored.sqlglot.optimizer.scope import ( - Scope, - build_scope, - traverse_scope, - walk_in_scope, -) -from bigframes_vendored.sqlglot.optimizer.simplify import simplify_parens -from bigframes_vendored.sqlglot.schema import Schema, ensure_schema - -if t.TYPE_CHECKING: - from bigframes_vendored.sqlglot._typing import E - - -def qualify_columns( - expression: exp.Expression, - schema: t.Dict | Schema, - expand_alias_refs: bool = True, - expand_stars: bool = True, - infer_schema: t.Optional[bool] = None, - allow_partial_qualification: bool = False, - dialect: DialectType = None, -) -> exp.Expression: - """ - Rewrite sqlglot AST to have fully qualified columns. - - Example: - >>> import sqlglot - >>> schema = {"tbl": {"col": "INT"}} - >>> expression = sqlglot.parse_one("SELECT col FROM tbl") - >>> qualify_columns(expression, schema).sql() - 'SELECT tbl.col AS col FROM tbl' - - Args: - expression: Expression to qualify. - schema: Database schema. - expand_alias_refs: Whether to expand references to aliases. - expand_stars: Whether to expand star queries. This is a necessary step - for most of the optimizer's rules to work; do not set to False unless you - know what you're doing! - infer_schema: Whether to infer the schema if missing. - allow_partial_qualification: Whether to allow partial qualification. - - Returns: - The qualified expression. - - Notes: - - Currently only handles a single PIVOT or UNPIVOT operator - """ - schema = ensure_schema(schema, dialect=dialect) - annotator = TypeAnnotator(schema) - infer_schema = schema.empty if infer_schema is None else infer_schema - dialect = schema.dialect or Dialect() - pseudocolumns = dialect.PSEUDOCOLUMNS - - for scope in traverse_scope(expression): - if dialect.PREFER_CTE_ALIAS_COLUMN: - pushdown_cte_alias_columns(scope) - - scope_expression = scope.expression - is_select = isinstance(scope_expression, exp.Select) - - _separate_pseudocolumns(scope, pseudocolumns) - - resolver = Resolver(scope, schema, infer_schema=infer_schema) - _pop_table_column_aliases(scope.ctes) - _pop_table_column_aliases(scope.derived_tables) - using_column_tables = _expand_using(scope, resolver) - - if ( - schema.empty or dialect.FORCE_EARLY_ALIAS_REF_EXPANSION - ) and expand_alias_refs: - _expand_alias_refs( - scope, - resolver, - dialect, - expand_only_groupby=dialect.EXPAND_ONLY_GROUP_ALIAS_REF, - ) - - _convert_columns_to_dots(scope, resolver) - _qualify_columns( - scope, - resolver, - allow_partial_qualification=allow_partial_qualification, - ) - - if not schema.empty and expand_alias_refs: - _expand_alias_refs(scope, resolver, dialect) - - if is_select: - if expand_stars: - _expand_stars( - scope, - resolver, - using_column_tables, - pseudocolumns, - annotator, - ) - qualify_outputs(scope) - - _expand_group_by(scope, dialect) - - # DISTINCT ON and ORDER BY follow the same rules (tested in DuckDB, Postgres, ClickHouse) - # https://www.postgresql.org/docs/current/sql-select.html#SQL-DISTINCT - _expand_order_by_and_distinct_on(scope, resolver) - - if dialect.ANNOTATE_ALL_SCOPES: - annotator.annotate_scope(scope) - - return expression - - -def validate_qualify_columns(expression: E, sql: t.Optional[str] = None) -> E: - """Raise an `OptimizeError` if any columns aren't qualified""" - all_unqualified_columns = [] - for scope in traverse_scope(expression): - if isinstance(scope.expression, exp.Select): - unqualified_columns = scope.unqualified_columns - - if ( - scope.external_columns - and not scope.is_correlated_subquery - and not scope.pivots - ): - column = scope.external_columns[0] - for_table = f" for table: '{column.table}'" if column.table else "" - line = column.this.meta.get("line") - col = column.this.meta.get("col") - start = column.this.meta.get("start") - end = column.this.meta.get("end") - - error_msg = f"Column '{column.name}' could not be resolved{for_table}." - if line and col: - error_msg += f" Line: {line}, Col: {col}" - if sql and start is not None and end is not None: - formatted_sql = highlight_sql(sql, [(start, end)])[0] - error_msg += f"\n {formatted_sql}" - - raise OptimizeError(error_msg) - - if unqualified_columns and scope.pivots and scope.pivots[0].unpivot: - # New columns produced by the UNPIVOT can't be qualified, but there may be columns - # under the UNPIVOT's IN clause that can and should be qualified. We recompute - # this list here to ensure those in the former category will be excluded. - unpivot_columns = set(_unpivot_columns(scope.pivots[0])) - unqualified_columns = [ - c for c in unqualified_columns if c not in unpivot_columns - ] - - all_unqualified_columns.extend(unqualified_columns) - - if all_unqualified_columns: - first_column = all_unqualified_columns[0] - line = first_column.this.meta.get("line") - col = first_column.this.meta.get("col") - start = first_column.this.meta.get("start") - end = first_column.this.meta.get("end") - - error_msg = f"Ambiguous column '{first_column.name}'" - if line and col: - error_msg += f" (Line: {line}, Col: {col})" - if sql and start is not None and end is not None: - formatted_sql = highlight_sql(sql, [(start, end)])[0] - error_msg += f"\n {formatted_sql}" - - raise OptimizeError(error_msg) - - return expression - - -def _separate_pseudocolumns(scope: Scope, pseudocolumns: t.Set[str]) -> None: - if not pseudocolumns: - return - - has_pseudocolumns = False - scope_expression = scope.expression - - for column in scope.columns: - name = column.name.upper() - if name not in pseudocolumns: - continue - - if name != "LEVEL" or ( - isinstance(scope_expression, exp.Select) - and scope_expression.args.get("connect") - ): - column.replace(exp.Pseudocolumn(**column.args)) - has_pseudocolumns = True - - if has_pseudocolumns: - scope.clear_cache() - - -def _unpivot_columns(unpivot: exp.Pivot) -> t.Iterator[exp.Column]: - name_columns = [ - field.this - for field in unpivot.fields - if isinstance(field, exp.In) and isinstance(field.this, exp.Column) - ] - value_columns = (c for e in unpivot.expressions for c in e.find_all(exp.Column)) - - return itertools.chain(name_columns, value_columns) - - -def _pop_table_column_aliases(derived_tables: t.List[exp.CTE | exp.Subquery]) -> None: - """ - Remove table column aliases. - - For example, `col1` and `col2` will be dropped in SELECT ... FROM (SELECT ...) AS foo(col1, col2) - """ - for derived_table in derived_tables: - if ( - isinstance(derived_table.parent, exp.With) - and derived_table.parent.recursive - ): - continue - table_alias = derived_table.args.get("alias") - if table_alias: - table_alias.set("columns", None) - - -def _expand_using(scope: Scope, resolver: Resolver) -> t.Dict[str, t.Any]: - columns = {} - - def _update_source_columns(source_name: str) -> None: - for column_name in resolver.get_source_columns(source_name): - if column_name not in columns: - columns[column_name] = source_name - - joins = list(scope.find_all(exp.Join)) - names = {join.alias_or_name for join in joins} - ordered = [key for key in scope.selected_sources if key not in names] - - if names and not ordered: - raise OptimizeError(f"Joins {names} missing source table {scope.expression}") - - # Mapping of automatically joined column names to an ordered set of source names (dict). - column_tables: t.Dict[str, t.Dict[str, t.Any]] = {} - - for source_name in ordered: - _update_source_columns(source_name) - - for i, join in enumerate(joins): - source_table = ordered[-1] - if source_table: - _update_source_columns(source_table) - - join_table = join.alias_or_name - ordered.append(join_table) - - using = join.args.get("using") - if not using: - continue - - join_columns = resolver.get_source_columns(join_table) - conditions = [] - using_identifier_count = len(using) - is_semi_or_anti_join = join.is_semi_or_anti_join - - for identifier in using: - identifier = identifier.name - table = columns.get(identifier) - - if not table or identifier not in join_columns: - if (columns and "*" not in columns) and join_columns: - raise OptimizeError(f"Cannot automatically join: {identifier}") - - table = table or source_table - - if i == 0 or using_identifier_count == 1: - lhs: exp.Expression = exp.column(identifier, table=table) - else: - coalesce_columns = [ - exp.column(identifier, table=t) - for t in ordered[:-1] - if identifier in resolver.get_source_columns(t) - ] - if len(coalesce_columns) > 1: - lhs = exp.func("coalesce", *coalesce_columns) - else: - lhs = exp.column(identifier, table=table) - - conditions.append(lhs.eq(exp.column(identifier, table=join_table))) - - # Set all values in the dict to None, because we only care about the key ordering - tables = column_tables.setdefault(identifier, {}) - - # Do not update the dict if this was a SEMI/ANTI join in - # order to avoid generating COALESCE columns for this join pair - if not is_semi_or_anti_join: - if table not in tables: - tables[table] = None - if join_table not in tables: - tables[join_table] = None - - join.set("using", None) - join.set("on", exp.and_(*conditions, copy=False)) - - if column_tables: - for column in scope.columns: - if not column.table and column.name in column_tables: - tables = column_tables[column.name] - coalesce_args = [ - exp.column(column.name, table=table) for table in tables - ] - replacement: exp.Expression = exp.func("coalesce", *coalesce_args) - - if isinstance(column.parent, exp.Select): - # Ensure the USING column keeps its name if it's projected - replacement = alias(replacement, alias=column.name, copy=False) - elif isinstance(column.parent, exp.Struct): - # Ensure the USING column keeps its name if it's an anonymous STRUCT field - replacement = exp.PropertyEQ( - this=exp.to_identifier(column.name), expression=replacement - ) - - scope.replace(column, replacement) - - return column_tables - - -def _expand_alias_refs( - scope: Scope, - resolver: Resolver, - dialect: Dialect, - expand_only_groupby: bool = False, -) -> None: - """ - Expand references to aliases. - Example: - SELECT y.foo AS bar, bar * 2 AS baz FROM y - => SELECT y.foo AS bar, y.foo * 2 AS baz FROM y - """ - expression = scope.expression - - if not isinstance(expression, exp.Select) or dialect.DISABLES_ALIAS_REF_EXPANSION: - return - - alias_to_expression: t.Dict[str, t.Tuple[exp.Expression, int]] = {} - projections = {s.alias_or_name for s in expression.selects} - replaced = False - - def replace_columns( - node: t.Optional[exp.Expression], - resolve_table: bool = False, - literal_index: bool = False, - ) -> None: - nonlocal replaced - is_group_by = isinstance(node, exp.Group) - is_having = isinstance(node, exp.Having) - if not node or (expand_only_groupby and not is_group_by): - return - - for column in walk_in_scope(node, prune=lambda node: node.is_star): - if not isinstance(column, exp.Column): - continue - - # BigQuery's GROUP BY allows alias expansion only for standalone names, e.g: - # SELECT FUNC(col) AS col FROM t GROUP BY col --> Can be expanded - # SELECT FUNC(col) AS col FROM t GROUP BY FUNC(col) --> Shouldn't be expanded, will result to FUNC(FUNC(col)) - # This not required for the HAVING clause as it can evaluate expressions using both the alias & the table columns - if expand_only_groupby and is_group_by and column.parent is not node: - continue - - skip_replace = False - table = ( - resolver.get_table(column.name) - if resolve_table and not column.table - else None - ) - alias_expr, i = alias_to_expression.get(column.name, (None, 1)) - - if alias_expr: - skip_replace = bool( - alias_expr.find(exp.AggFunc) - and column.find_ancestor(exp.AggFunc) - and not isinstance( - column.find_ancestor(exp.Window, exp.Select), exp.Window - ) - ) - - # BigQuery's having clause gets confused if an alias matches a source. - # SELECT x.a, max(x.b) as x FROM x GROUP BY 1 HAVING x > 1; - # If "HAVING x" is expanded to "HAVING max(x.b)", BQ would blindly replace the "x" reference with the projection MAX(x.b) - # i.e HAVING MAX(MAX(x.b).b), resulting in the error: "Aggregations of aggregations are not allowed" - if is_having and dialect.PROJECTION_ALIASES_SHADOW_SOURCE_NAMES: - skip_replace = skip_replace or any( - node.parts[0].name in projections - for node in alias_expr.find_all(exp.Column) - ) - elif dialect.PROJECTION_ALIASES_SHADOW_SOURCE_NAMES and ( - is_group_by or is_having - ): - column_table = table.name if table else column.table - if column_table in projections: - # BigQuery's GROUP BY and HAVING clauses get confused if the column name - # matches a source name and a projection. For instance: - # SELECT id, ARRAY_AGG(col) AS custom_fields FROM custom_fields GROUP BY id HAVING id >= 1 - # We should not qualify "id" with "custom_fields" in either clause, since the aggregation shadows the actual table - # and we'd get the error: "Column custom_fields contains an aggregation function, which is not allowed in GROUP BY clause" - column.replace(exp.to_identifier(column.name)) - replaced = True - return - - if table and (not alias_expr or skip_replace): - column.set("table", table) - elif not column.table and alias_expr and not skip_replace: - if (isinstance(alias_expr, exp.Literal) or alias_expr.is_number) and ( - literal_index or resolve_table - ): - if literal_index: - column.replace(exp.Literal.number(i)) - replaced = True - else: - replaced = True - column = column.replace(exp.paren(alias_expr)) - simplified = simplify_parens(column, dialect) - if simplified is not column: - column.replace(simplified) - - for i, projection in enumerate(expression.selects): - replace_columns(projection) - if isinstance(projection, exp.Alias): - alias_to_expression[projection.alias] = (projection.this, i + 1) - - parent_scope = scope - on_right_sub_tree = False - while parent_scope and not parent_scope.is_cte: - if parent_scope.is_union: - on_right_sub_tree = ( - parent_scope.parent.expression.right is parent_scope.expression - ) - parent_scope = parent_scope.parent - - # We shouldn't expand aliases if they match the recursive CTE's columns - # and we are in the recursive part (right sub tree) of the CTE - if parent_scope and on_right_sub_tree: - cte = parent_scope.expression.parent - if cte.find_ancestor(exp.With).recursive: - for recursive_cte_column in cte.args["alias"].columns or cte.this.selects: - alias_to_expression.pop(recursive_cte_column.output_name, None) - - replace_columns(expression.args.get("where")) - replace_columns(expression.args.get("group"), literal_index=True) - replace_columns(expression.args.get("having"), resolve_table=True) - replace_columns(expression.args.get("qualify"), resolve_table=True) - - if dialect.SUPPORTS_ALIAS_REFS_IN_JOIN_CONDITIONS: - for join in expression.args.get("joins") or []: - replace_columns(join) - - if replaced: - scope.clear_cache() - - -def _expand_group_by(scope: Scope, dialect: Dialect) -> None: - expression = scope.expression - group = expression.args.get("group") - if not group: - return - - group.set( - "expressions", _expand_positional_references(scope, group.expressions, dialect) - ) - expression.set("group", group) - - -def _expand_order_by_and_distinct_on(scope: Scope, resolver: Resolver) -> None: - for modifier_key in ("order", "distinct"): - modifier = scope.expression.args.get(modifier_key) - if isinstance(modifier, exp.Distinct): - modifier = modifier.args.get("on") - - if not isinstance(modifier, exp.Expression): - continue - - modifier_expressions = modifier.expressions - if modifier_key == "order": - modifier_expressions = [ordered.this for ordered in modifier_expressions] - - for original, expanded in zip( - modifier_expressions, - _expand_positional_references( - scope, modifier_expressions, resolver.dialect, alias=True - ), - ): - for agg in original.find_all(exp.AggFunc): - for col in agg.find_all(exp.Column): - if not col.table: - col.set("table", resolver.get_table(col.name)) - - original.replace(expanded) - - if scope.expression.args.get("group"): - selects = { - s.this: exp.column(s.alias_or_name) for s in scope.expression.selects - } - - for expression in modifier_expressions: - expression.replace( - exp.to_identifier(_select_by_pos(scope, expression).alias) - if expression.is_int - else selects.get(expression, expression) - ) - - -def _expand_positional_references( - scope: Scope, - expressions: t.Iterable[exp.Expression], - dialect: Dialect, - alias: bool = False, -) -> t.List[exp.Expression]: - new_nodes: t.List[exp.Expression] = [] - ambiguous_projections = None - - for node in expressions: - if node.is_int: - select = _select_by_pos(scope, t.cast(exp.Literal, node)) - - if alias: - new_nodes.append(exp.column(select.args["alias"].copy())) - else: - select = select.this - - if dialect.PROJECTION_ALIASES_SHADOW_SOURCE_NAMES: - if ambiguous_projections is None: - # When a projection name is also a source name and it is referenced in the - # GROUP BY clause, BQ can't understand what the identifier corresponds to - ambiguous_projections = { - s.alias_or_name - for s in scope.expression.selects - if s.alias_or_name in scope.selected_sources - } - - ambiguous = any( - column.parts[0].name in ambiguous_projections - for column in select.find_all(exp.Column) - ) - else: - ambiguous = False - - if ( - isinstance(select, exp.CONSTANTS) - or select.is_number - or select.find(exp.Explode, exp.Unnest) - or ambiguous - ): - new_nodes.append(node) - else: - new_nodes.append(select.copy()) - else: - new_nodes.append(node) - - return new_nodes - - -def _select_by_pos(scope: Scope, node: exp.Literal) -> exp.Alias: - try: - return scope.expression.selects[int(node.this) - 1].assert_is(exp.Alias) - except IndexError: - raise OptimizeError(f"Unknown output column: {node.name}") - - -def _convert_columns_to_dots(scope: Scope, resolver: Resolver) -> None: - """ - Converts `Column` instances that represent STRUCT or JSON field lookup into chained `Dots`. - - These lookups may be parsed as columns (e.g. "col"."field"."field2"), but they need to be - normalized to `Dot(Dot(...(
., field1), field2, ...))` to be qualified properly. - """ - converted = False - for column in itertools.chain(scope.columns, scope.stars): - if isinstance(column, exp.Dot): - continue - - column_table: t.Optional[str | exp.Identifier] = column.table - dot_parts = column.meta.pop("dot_parts", []) - if ( - column_table - and column_table not in scope.sources - and ( - not scope.parent - or column_table not in scope.parent.sources - or not scope.is_correlated_subquery - ) - ): - root, *parts = column.parts - - if root.name in scope.sources: - # The struct is already qualified, but we still need to change the AST - column_table = root - root, *parts = parts - was_qualified = True - else: - column_table = resolver.get_table(root.name) - was_qualified = False - - if column_table: - converted = True - new_column = exp.column(root, table=column_table) - - if dot_parts: - # Remove the actual column parts from the rest of dot parts - new_column.meta["dot_parts"] = dot_parts[ - 2 if was_qualified else 1 : - ] - - column.replace(exp.Dot.build([new_column, *parts])) - - if converted: - # We want to re-aggregate the converted columns, otherwise they'd be skipped in - # a `for column in scope.columns` iteration, even though they shouldn't be - scope.clear_cache() - - -def _qualify_columns( - scope: Scope, - resolver: Resolver, - allow_partial_qualification: bool, -) -> None: - """Disambiguate columns, ensuring each column specifies a source""" - for column in scope.columns: - column_table = column.table - column_name = column.name - - if column_table and column_table in scope.sources: - source_columns = resolver.get_source_columns(column_table) - if ( - not allow_partial_qualification - and source_columns - and column_name not in source_columns - and "*" not in source_columns - ): - raise OptimizeError(f"Unknown column: {column_name}") - - if not column_table: - if scope.pivots and not column.find_ancestor(exp.Pivot): - # If the column is under the Pivot expression, we need to qualify it - # using the name of the pivoted source instead of the pivot's alias - column.set("table", exp.to_identifier(scope.pivots[0].alias)) - continue - - # column_table can be a '' because bigquery unnest has no table alias - column_table = resolver.get_table(column) - - if column_table: - column.set("table", column_table) - elif ( - resolver.dialect.TABLES_REFERENCEABLE_AS_COLUMNS - and len(column.parts) == 1 - and column_name in scope.selected_sources - ): - # BigQuery and Postgres allow tables to be referenced as columns, treating them as structs/records - scope.replace(column, exp.TableColumn(this=column.this)) - - for pivot in scope.pivots: - for column in pivot.find_all(exp.Column): - if not column.table and column.name in resolver.all_columns: - column_table = resolver.get_table(column.name) - if column_table: - column.set("table", column_table) - - -def _expand_struct_stars_no_parens( - expression: exp.Dot, -) -> t.List[exp.Alias]: - """[BigQuery] Expand/Flatten foo.bar.* where bar is a struct column""" - - dot_column = expression.find(exp.Column) - if not isinstance(dot_column, exp.Column) or not dot_column.is_type( - exp.DataType.Type.STRUCT - ): - return [] - - # All nested struct values are ColumnDefs, so normalize the first exp.Column in one - dot_column = dot_column.copy() - starting_struct = exp.ColumnDef(this=dot_column.this, kind=dot_column.type) - - # First part is the table name and last part is the star so they can be dropped - dot_parts = expression.parts[1:-1] - - # If we're expanding a nested struct eg. t.c.f1.f2.* find the last struct (f2 in this case) - for part in dot_parts[1:]: - for field in t.cast(exp.DataType, starting_struct.kind).expressions: - # Unable to expand star unless all fields are named - if not isinstance(field.this, exp.Identifier): - return [] - - if field.name == part.name and field.kind.is_type(exp.DataType.Type.STRUCT): - starting_struct = field - break - else: - # There is no matching field in the struct - return [] - - taken_names = set() - new_selections = [] - - for field in t.cast(exp.DataType, starting_struct.kind).expressions: - name = field.name - - # Ambiguous or anonymous fields can't be expanded - if name in taken_names or not isinstance(field.this, exp.Identifier): - return [] - - taken_names.add(name) - - this = field.this.copy() - root, *parts = [part.copy() for part in itertools.chain(dot_parts, [this])] - new_column = exp.column( - t.cast(exp.Identifier, root), - table=dot_column.args.get("table"), - fields=t.cast(t.List[exp.Identifier], parts), - ) - new_selections.append(alias(new_column, this, copy=False)) - - return new_selections - - -def _expand_struct_stars_with_parens(expression: exp.Dot) -> t.List[exp.Alias]: - """[RisingWave] Expand/Flatten (.bar).*, where bar is a struct column""" - - # it is not ().* pattern, which means we can't expand - if not isinstance(expression.this, exp.Paren): - return [] - - # find column definition to get data-type - dot_column = expression.find(exp.Column) - if not isinstance(dot_column, exp.Column) or not dot_column.is_type( - exp.DataType.Type.STRUCT - ): - return [] - - parent = dot_column.parent - starting_struct = dot_column.type - - # walk up AST and down into struct definition in sync - while parent is not None: - if isinstance(parent, exp.Paren): - parent = parent.parent - continue - - # if parent is not a dot, then something is wrong - if not isinstance(parent, exp.Dot): - return [] - - # if the rhs of the dot is star we are done - rhs = parent.right - if isinstance(rhs, exp.Star): - break - - # if it is not identifier, then something is wrong - if not isinstance(rhs, exp.Identifier): - return [] - - # Check if current rhs identifier is in struct - matched = False - for struct_field_def in t.cast(exp.DataType, starting_struct).expressions: - if struct_field_def.name == rhs.name: - matched = True - starting_struct = struct_field_def.kind # update struct - break - - if not matched: - return [] - - parent = parent.parent - - # build new aliases to expand star - new_selections = [] - - # fetch the outermost parentheses for new aliaes - outer_paren = expression.this - - for struct_field_def in t.cast(exp.DataType, starting_struct).expressions: - new_identifier = struct_field_def.this.copy() - new_dot = exp.Dot.build([outer_paren.copy(), new_identifier]) - new_alias = alias(new_dot, new_identifier, copy=False) - new_selections.append(new_alias) - - return new_selections - - -def _expand_stars( - scope: Scope, - resolver: Resolver, - using_column_tables: t.Dict[str, t.Any], - pseudocolumns: t.Set[str], - annotator: TypeAnnotator, -) -> None: - """Expand stars to lists of column selections""" - - new_selections: t.List[exp.Expression] = [] - except_columns: t.Dict[int, t.Set[str]] = {} - replace_columns: t.Dict[int, t.Dict[str, exp.Alias]] = {} - rename_columns: t.Dict[int, t.Dict[str, str]] = {} - - coalesced_columns = set() - dialect = resolver.dialect - - pivot_output_columns = None - pivot_exclude_columns: t.Set[str] = set() - - pivot = t.cast(t.Optional[exp.Pivot], seq_get(scope.pivots, 0)) - if isinstance(pivot, exp.Pivot) and not pivot.alias_column_names: - if pivot.unpivot: - pivot_output_columns = [c.output_name for c in _unpivot_columns(pivot)] - - for field in pivot.fields: - if isinstance(field, exp.In): - pivot_exclude_columns.update( - c.output_name - for e in field.expressions - for c in e.find_all(exp.Column) - ) - - else: - pivot_exclude_columns = set( - c.output_name for c in pivot.find_all(exp.Column) - ) - - pivot_output_columns = [ - c.output_name for c in pivot.args.get("columns", []) - ] - if not pivot_output_columns: - pivot_output_columns = [c.alias_or_name for c in pivot.expressions] - - if dialect.SUPPORTS_STRUCT_STAR_EXPANSION and any( - isinstance(col, exp.Dot) for col in scope.stars - ): - # Found struct expansion, annotate scope ahead of time - annotator.annotate_scope(scope) - - for expression in scope.expression.selects: - tables = [] - if isinstance(expression, exp.Star): - tables.extend(scope.selected_sources) - _add_except_columns(expression, tables, except_columns) - _add_replace_columns(expression, tables, replace_columns) - _add_rename_columns(expression, tables, rename_columns) - elif expression.is_star: - if not isinstance(expression, exp.Dot): - tables.append(expression.table) - _add_except_columns(expression.this, tables, except_columns) - _add_replace_columns(expression.this, tables, replace_columns) - _add_rename_columns(expression.this, tables, rename_columns) - elif ( - dialect.SUPPORTS_STRUCT_STAR_EXPANSION - and not dialect.REQUIRES_PARENTHESIZED_STRUCT_ACCESS - ): - struct_fields = _expand_struct_stars_no_parens(expression) - if struct_fields: - new_selections.extend(struct_fields) - continue - elif dialect.REQUIRES_PARENTHESIZED_STRUCT_ACCESS: - struct_fields = _expand_struct_stars_with_parens(expression) - if struct_fields: - new_selections.extend(struct_fields) - continue - - if not tables: - new_selections.append(expression) - continue - - for table in tables: - if table not in scope.sources: - raise OptimizeError(f"Unknown table: {table}") - - columns = resolver.get_source_columns(table, only_visible=True) - columns = columns or scope.outer_columns - - if pseudocolumns and dialect.EXCLUDES_PSEUDOCOLUMNS_FROM_STAR: - columns = [ - name for name in columns if name.upper() not in pseudocolumns - ] - - if not columns or "*" in columns: - return - - table_id = id(table) - columns_to_exclude = except_columns.get(table_id) or set() - renamed_columns = rename_columns.get(table_id, {}) - replaced_columns = replace_columns.get(table_id, {}) - - if pivot: - if pivot_output_columns and pivot_exclude_columns: - pivot_columns = [ - c for c in columns if c not in pivot_exclude_columns - ] - pivot_columns.extend(pivot_output_columns) - else: - pivot_columns = pivot.alias_column_names - - if pivot_columns: - new_selections.extend( - alias(exp.column(name, table=pivot.alias), name, copy=False) - for name in pivot_columns - if name not in columns_to_exclude - ) - continue - - for name in columns: - if name in columns_to_exclude or name in coalesced_columns: - continue - if name in using_column_tables and table in using_column_tables[name]: - coalesced_columns.add(name) - tables = using_column_tables[name] - coalesce_args = [exp.column(name, table=table) for table in tables] - - new_selections.append( - alias( - exp.func("coalesce", *coalesce_args), alias=name, copy=False - ) - ) - else: - alias_ = renamed_columns.get(name, name) - selection_expr = replaced_columns.get(name) or exp.column( - name, table=table - ) - new_selections.append( - alias(selection_expr, alias_, copy=False) - if alias_ != name - else selection_expr - ) - - # Ensures we don't overwrite the initial selections with an empty list - if new_selections and isinstance(scope.expression, exp.Select): - scope.expression.set("expressions", new_selections) - - -def _add_except_columns( - expression: exp.Expression, tables, except_columns: t.Dict[int, t.Set[str]] -) -> None: - except_ = expression.args.get("except_") - - if not except_: - return - - columns = {e.name for e in except_} - - for table in tables: - except_columns[id(table)] = columns - - -def _add_rename_columns( - expression: exp.Expression, tables, rename_columns: t.Dict[int, t.Dict[str, str]] -) -> None: - rename = expression.args.get("rename") - - if not rename: - return - - columns = {e.this.name: e.alias for e in rename} - - for table in tables: - rename_columns[id(table)] = columns - - -def _add_replace_columns( - expression: exp.Expression, - tables, - replace_columns: t.Dict[int, t.Dict[str, exp.Alias]], -) -> None: - replace = expression.args.get("replace") - - if not replace: - return - - columns = {e.alias: e for e in replace} - - for table in tables: - replace_columns[id(table)] = columns - - -def qualify_outputs(scope_or_expression: Scope | exp.Expression) -> None: - """Ensure all output columns are aliased""" - if isinstance(scope_or_expression, exp.Expression): - scope = build_scope(scope_or_expression) - if not isinstance(scope, Scope): - return - else: - scope = scope_or_expression - - new_selections = [] - for i, (selection, aliased_column) in enumerate( - itertools.zip_longest(scope.expression.selects, scope.outer_columns) - ): - if selection is None or isinstance(selection, exp.QueryTransform): - break - - if isinstance(selection, exp.Subquery): - if not selection.output_name: - selection.set( - "alias", exp.TableAlias(this=exp.to_identifier(f"_col_{i}")) - ) - elif ( - not isinstance(selection, (exp.Alias, exp.Aliases)) - and not selection.is_star - ): - selection = alias( - selection, - alias=selection.output_name or f"_col_{i}", - copy=False, - ) - if aliased_column: - selection.set("alias", exp.to_identifier(aliased_column)) - - new_selections.append(selection) - - if new_selections and isinstance(scope.expression, exp.Select): - scope.expression.set("expressions", new_selections) - - -def quote_identifiers( - expression: E, dialect: DialectType = None, identify: bool = True -) -> E: - """Makes sure all identifiers that need to be quoted are quoted.""" - return expression.transform( - Dialect.get_or_raise(dialect).quote_identifier, identify=identify, copy=False - ) # type: ignore - - -def pushdown_cte_alias_columns(scope: Scope) -> None: - """ - Pushes down the CTE alias columns into the projection, - - This step is useful in Snowflake where the CTE alias columns can be referenced in the HAVING. - - Args: - scope: Scope to find ctes to pushdown aliases. - """ - for cte in scope.ctes: - if cte.alias_column_names and isinstance(cte.this, exp.Select): - new_expressions = [] - for _alias, projection in zip(cte.alias_column_names, cte.this.expressions): - if isinstance(projection, exp.Alias): - projection.set("alias", exp.to_identifier(_alias)) - else: - projection = alias(projection, alias=_alias) - new_expressions.append(projection) - cte.this.set("expressions", new_expressions) diff --git a/third_party/bigframes_vendored/sqlglot/optimizer/qualify_tables.py b/third_party/bigframes_vendored/sqlglot/optimizer/qualify_tables.py deleted file mode 100644 index 42e99f668e4..00000000000 --- a/third_party/bigframes_vendored/sqlglot/optimizer/qualify_tables.py +++ /dev/null @@ -1,227 +0,0 @@ -# Contains code from https://github.com/tobymao/sqlglot/blob/v28.5.0/sqlglot/optimizer/qualify_tables.py - -from __future__ import annotations - -import typing as t - -from bigframes_vendored.sqlglot import exp -from bigframes_vendored.sqlglot.dialects.dialect import Dialect, DialectType -from bigframes_vendored.sqlglot.helper import ensure_list, name_sequence, seq_get -from bigframes_vendored.sqlglot.optimizer.normalize_identifiers import ( - normalize_identifiers, -) -from bigframes_vendored.sqlglot.optimizer.scope import Scope, traverse_scope - -if t.TYPE_CHECKING: - from bigframes_vendored.sqlglot._typing import E - - -def qualify_tables( - expression: E, - db: t.Optional[str | exp.Identifier] = None, - catalog: t.Optional[str | exp.Identifier] = None, - on_qualify: t.Optional[t.Callable[[exp.Table], None]] = None, - dialect: DialectType = None, - canonicalize_table_aliases: bool = False, -) -> E: - """ - Rewrite sqlglot AST to have fully qualified tables. Join constructs such as - (t1 JOIN t2) AS t will be expanded into (SELECT * FROM t1 AS t1, t2 AS t2) AS t. - - Examples: - >>> import sqlglot - >>> expression = sqlglot.parse_one("SELECT 1 FROM tbl") - >>> qualify_tables(expression, db="db").sql() - 'SELECT 1 FROM db.tbl AS tbl' - >>> - >>> expression = sqlglot.parse_one("SELECT 1 FROM (t1 JOIN t2) AS t") - >>> qualify_tables(expression).sql() - 'SELECT 1 FROM (SELECT * FROM t1 AS t1, t2 AS t2) AS t' - - Args: - expression: Expression to qualify - db: Database name - catalog: Catalog name - on_qualify: Callback after a table has been qualified. - dialect: The dialect to parse catalog and schema into. - canonicalize_table_aliases: Whether to use canonical aliases (_0, _1, ...) for all sources - instead of preserving table names. Defaults to False. - - Returns: - The qualified expression. - """ - dialect = Dialect.get_or_raise(dialect) - next_alias_name = name_sequence("_") - - if db := db or None: - db = exp.parse_identifier(db, dialect=dialect) - db.meta["is_table"] = True - db = normalize_identifiers(db, dialect=dialect) - if catalog := catalog or None: - catalog = exp.parse_identifier(catalog, dialect=dialect) - catalog.meta["is_table"] = True - catalog = normalize_identifiers(catalog, dialect=dialect) - - def _qualify(table: exp.Table) -> None: - if isinstance(table.this, exp.Identifier): - if db and not table.args.get("db"): - table.set("db", db.copy()) - if catalog and not table.args.get("catalog") and table.args.get("db"): - table.set("catalog", catalog.copy()) - - if (db or catalog) and not isinstance(expression, exp.Query): - with_ = expression.args.get("with_") or exp.With() - cte_names = {cte.alias_or_name for cte in with_.expressions} - - for node in expression.walk(prune=lambda n: isinstance(n, exp.Query)): - if isinstance(node, exp.Table) and node.name not in cte_names: - _qualify(node) - - def _set_alias( - expression: exp.Expression, - canonical_aliases: t.Dict[str, str], - target_alias: t.Optional[str] = None, - scope: t.Optional[Scope] = None, - normalize: bool = False, - columns: t.Optional[t.List[t.Union[str, exp.Identifier]]] = None, - ) -> None: - alias = expression.args.get("alias") or exp.TableAlias() - - if canonicalize_table_aliases: - new_alias_name = next_alias_name() - canonical_aliases[alias.name or target_alias or ""] = new_alias_name - elif not alias.name: - new_alias_name = target_alias or next_alias_name() - if normalize and target_alias: - new_alias_name = normalize_identifiers( - new_alias_name, dialect=dialect - ).name - else: - return - - alias.set("this", exp.to_identifier(new_alias_name)) - - if columns: - alias.set("columns", [exp.to_identifier(c) for c in columns]) - - expression.set("alias", alias) - - if scope: - scope.rename_source(None, new_alias_name) - - for scope in traverse_scope(expression): - local_columns = scope.local_columns - canonical_aliases: t.Dict[str, str] = {} - - for query in scope.subqueries: - subquery = query.parent - if isinstance(subquery, exp.Subquery): - subquery.unwrap().replace(subquery) - - for derived_table in scope.derived_tables: - unnested = derived_table.unnest() - if isinstance(unnested, exp.Table): - joins = unnested.args.get("joins") - unnested.set("joins", None) - derived_table.this.replace( - exp.select("*").from_(unnested.copy(), copy=False) - ) - derived_table.this.set("joins", joins) - - _set_alias(derived_table, canonical_aliases, scope=scope) - if pivot := seq_get(derived_table.args.get("pivots") or [], 0): - _set_alias(pivot, canonical_aliases) - - table_aliases = {} - - for name, source in scope.sources.items(): - if isinstance(source, exp.Table): - # When the name is empty, it means that we have a non-table source, e.g. a pivoted cte - is_real_table_source = bool(name) - - if pivot := seq_get(source.args.get("pivots") or [], 0): - name = source.name - - table_this = source.this - table_alias = source.args.get("alias") - function_columns: t.List[t.Union[str, exp.Identifier]] = [] - if isinstance(table_this, exp.Func): - if not table_alias: - function_columns = ensure_list( - dialect.DEFAULT_FUNCTIONS_COLUMN_NAMES.get(type(table_this)) - ) - elif columns := table_alias.columns: - function_columns = columns - elif type(table_this) in dialect.DEFAULT_FUNCTIONS_COLUMN_NAMES: - function_columns = ensure_list(source.alias_or_name) - source.set("alias", None) - name = None - - _set_alias( - source, - canonical_aliases, - target_alias=name or source.name or None, - normalize=True, - columns=function_columns, - ) - - source_fqn = ".".join(p.name for p in source.parts) - table_aliases[source_fqn] = source.args["alias"].this.copy() - - if pivot: - target_alias = source.alias if pivot.unpivot else None - _set_alias( - pivot, - canonical_aliases, - target_alias=target_alias, - normalize=True, - ) - - # This case corresponds to a pivoted CTE, we don't want to qualify that - if isinstance(scope.sources.get(source.alias_or_name), Scope): - continue - - if is_real_table_source: - _qualify(source) - - if on_qualify: - on_qualify(source) - elif isinstance(source, Scope) and source.is_udtf: - _set_alias(udtf := source.expression, canonical_aliases) - - table_alias = udtf.args["alias"] - - if isinstance(udtf, exp.Values) and not table_alias.columns: - column_aliases = [ - normalize_identifiers(i, dialect=dialect) - for i in dialect.generate_values_aliases(udtf) - ] - table_alias.set("columns", column_aliases) - - for table in scope.tables: - if not table.alias and isinstance(table.parent, (exp.From, exp.Join)): - _set_alias(table, canonical_aliases, target_alias=table.name) - - for column in local_columns: - table = column.table - - if column.db: - table_alias = table_aliases.get( - ".".join(p.name for p in column.parts[0:-1]) - ) - - if table_alias: - for p in exp.COLUMN_PARTS[1:]: - column.set(p, None) - - column.set("table", table_alias.copy()) - elif ( - canonical_aliases - and table - and (canonical_table := canonical_aliases.get(table, "")) - != column.table - ): - # Amend existing aliases, e.g. t.c -> _0.c if t is aliased to _0 - column.set("table", exp.to_identifier(canonical_table)) - - return expression diff --git a/third_party/bigframes_vendored/sqlglot/optimizer/resolver.py b/third_party/bigframes_vendored/sqlglot/optimizer/resolver.py deleted file mode 100644 index 02b216ff0e6..00000000000 --- a/third_party/bigframes_vendored/sqlglot/optimizer/resolver.py +++ /dev/null @@ -1,399 +0,0 @@ -# Contains code from https://github.com/tobymao/sqlglot/blob/v28.5.0/sqlglot/optimizer/resolver.py - -from __future__ import annotations - -import itertools -import typing as t - -from bigframes_vendored.sqlglot import exp -from bigframes_vendored.sqlglot.dialects.dialect import Dialect -from bigframes_vendored.sqlglot.errors import OptimizeError -from bigframes_vendored.sqlglot.helper import SingleValuedMapping, seq_get -from bigframes_vendored.sqlglot.optimizer.scope import Scope - -if t.TYPE_CHECKING: - from bigframes_vendored.sqlglot.schema import Schema - - -class Resolver: - """ - Helper for resolving columns. - - This is a class so we can lazily load some things and easily share them across functions. - """ - - def __init__(self, scope: Scope, schema: Schema, infer_schema: bool = True): - self.scope = scope - self.schema = schema - self.dialect = schema.dialect or Dialect() - self._source_columns: t.Optional[t.Dict[str, t.Sequence[str]]] = None - self._unambiguous_columns: t.Optional[t.Mapping[str, str]] = None - self._all_columns: t.Optional[t.Set[str]] = None - self._infer_schema = infer_schema - self._get_source_columns_cache: t.Dict[t.Tuple[str, bool], t.Sequence[str]] = {} - - def get_table(self, column: str | exp.Column) -> t.Optional[exp.Identifier]: - """ - Get the table for a column name. - - Args: - column: The column expression (or column name) to find the table for. - Returns: - The table name if it can be found/inferred. - """ - column_name = column if isinstance(column, str) else column.name - - table_name = self._get_table_name_from_sources(column_name) - - if not table_name and isinstance(column, exp.Column): - # Fall-back case: If we couldn't find the `table_name` from ALL of the sources, - # attempt to disambiguate the column based on other characteristics e.g if this column is in a join condition, - # we may be able to disambiguate based on the source order. - if join_context := self._get_column_join_context(column): - # In this case, the return value will be the join that _may_ be able to disambiguate the column - # and we can use the source columns available at that join to get the table name - # catch OptimizeError if column is still ambiguous and try to resolve with schema inference below - try: - table_name = self._get_table_name_from_sources( - column_name, self._get_available_source_columns(join_context) - ) - except OptimizeError: - pass - - if not table_name and self._infer_schema: - sources_without_schema = tuple( - source - for source, columns in self._get_all_source_columns().items() - if not columns or "*" in columns - ) - if len(sources_without_schema) == 1: - table_name = sources_without_schema[0] - - if table_name not in self.scope.selected_sources: - return exp.to_identifier(table_name) - - node, _ = self.scope.selected_sources.get(table_name) - - if isinstance(node, exp.Query): - while node and node.alias != table_name: - node = node.parent - - node_alias = node.args.get("alias") - if node_alias: - return exp.to_identifier(node_alias.this) - - return exp.to_identifier(table_name) - - @property - def all_columns(self) -> t.Set[str]: - """All available columns of all sources in this scope""" - if self._all_columns is None: - self._all_columns = { - column - for columns in self._get_all_source_columns().values() - for column in columns - } - return self._all_columns - - def get_source_columns_from_set_op(self, expression: exp.Expression) -> t.List[str]: - if isinstance(expression, exp.Select): - return expression.named_selects - if isinstance(expression, exp.Subquery) and isinstance( - expression.this, exp.SetOperation - ): - # Different types of SET modifiers can be chained together if they're explicitly grouped by nesting - return self.get_source_columns_from_set_op(expression.this) - if not isinstance(expression, exp.SetOperation): - raise OptimizeError(f"Unknown set operation: {expression}") - - set_op = expression - - # BigQuery specific set operations modifiers, e.g INNER UNION ALL BY NAME - on_column_list = set_op.args.get("on") - - if on_column_list: - # The resulting columns are the columns in the ON clause: - # {INNER | LEFT | FULL} UNION ALL BY NAME ON (col1, col2, ...) - columns = [col.name for col in on_column_list] - elif set_op.side or set_op.kind: - side = set_op.side - kind = set_op.kind - - # Visit the children UNIONs (if any) in a post-order traversal - left = self.get_source_columns_from_set_op(set_op.left) - right = self.get_source_columns_from_set_op(set_op.right) - - # We use dict.fromkeys to deduplicate keys and maintain insertion order - if side == "LEFT": - columns = left - elif side == "FULL": - columns = list(dict.fromkeys(left + right)) - elif kind == "INNER": - columns = list(dict.fromkeys(left).keys() & dict.fromkeys(right).keys()) - else: - columns = set_op.named_selects - - return columns - - def get_source_columns( - self, name: str, only_visible: bool = False - ) -> t.Sequence[str]: - """Resolve the source columns for a given source `name`.""" - cache_key = (name, only_visible) - if cache_key not in self._get_source_columns_cache: - if name not in self.scope.sources: - raise OptimizeError(f"Unknown table: {name}") - - source = self.scope.sources[name] - - if isinstance(source, exp.Table): - columns = self.schema.column_names(source, only_visible) - elif isinstance(source, Scope) and isinstance( - source.expression, (exp.Values, exp.Unnest) - ): - columns = source.expression.named_selects - - # in bigquery, unnest structs are automatically scoped as tables, so you can - # directly select a struct field in a query. - # this handles the case where the unnest is statically defined. - if self.dialect.UNNEST_COLUMN_ONLY and isinstance( - source.expression, exp.Unnest - ): - unnest = source.expression - - # if type is not annotated yet, try to get it from the schema - if not unnest.type or unnest.type.is_type( - exp.DataType.Type.UNKNOWN - ): - unnest_expr = seq_get(unnest.expressions, 0) - if isinstance(unnest_expr, exp.Column) and self.scope.parent: - col_type = self._get_unnest_column_type(unnest_expr) - # extract element type if it's an ARRAY - if col_type and col_type.is_type(exp.DataType.Type.ARRAY): - element_types = col_type.expressions - if element_types: - unnest.type = element_types[0].copy() - else: - if col_type: - unnest.type = col_type.copy() - # check if the result type is a STRUCT - extract struct field names - if unnest.is_type(exp.DataType.Type.STRUCT): - for k in unnest.type.expressions: # type: ignore - columns.append(k.name) - elif isinstance(source, Scope) and isinstance( - source.expression, exp.SetOperation - ): - columns = self.get_source_columns_from_set_op(source.expression) - - else: - select = seq_get(source.expression.selects, 0) - - if isinstance(select, exp.QueryTransform): - # https://spark.apache.org/docs/3.5.1/sql-ref-syntax-qry-select-transform.html - schema = select.args.get("schema") - columns = ( - [c.name for c in schema.expressions] - if schema - else ["key", "value"] - ) - else: - columns = source.expression.named_selects - - node, _ = self.scope.selected_sources.get(name) or (None, None) - if isinstance(node, Scope): - column_aliases = node.expression.alias_column_names - elif isinstance(node, exp.Expression): - column_aliases = node.alias_column_names - else: - column_aliases = [] - - if column_aliases: - # If the source's columns are aliased, their aliases shadow the corresponding column names. - # This can be expensive if there are lots of columns, so only do this if column_aliases exist. - columns = [ - alias or name - for (name, alias) in itertools.zip_longest(columns, column_aliases) - ] - - self._get_source_columns_cache[cache_key] = columns - - return self._get_source_columns_cache[cache_key] - - def _get_all_source_columns(self) -> t.Dict[str, t.Sequence[str]]: - if self._source_columns is None: - self._source_columns = { - source_name: self.get_source_columns(source_name) - for source_name, source in itertools.chain( - self.scope.selected_sources.items(), - self.scope.lateral_sources.items(), - ) - } - return self._source_columns - - def _get_table_name_from_sources( - self, - column_name: str, - source_columns: t.Optional[t.Dict[str, t.Sequence[str]]] = None, - ) -> t.Optional[str]: - if not source_columns: - # If not supplied, get all sources to calculate unambiguous columns - if self._unambiguous_columns is None: - self._unambiguous_columns = self._get_unambiguous_columns( - self._get_all_source_columns() - ) - - unambiguous_columns = self._unambiguous_columns - else: - unambiguous_columns = self._get_unambiguous_columns(source_columns) - - return unambiguous_columns.get(column_name) - - def _get_column_join_context(self, column: exp.Column) -> t.Optional[exp.Join]: - """ - Check if a column participating in a join can be qualified based on the source order. - """ - args = self.scope.expression.args - joins = args.get("joins") - - if not joins or args.get("laterals") or args.get("pivots"): - # Feature gap: We currently don't try to disambiguate columns if other sources - # (e.g laterals, pivots) exist alongside joins - return None - - join_ancestor = column.find_ancestor(exp.Join, exp.Select) - - if ( - isinstance(join_ancestor, exp.Join) - and join_ancestor.alias_or_name in self.scope.selected_sources - ): - # Ensure that the found ancestor is a join that contains an actual source, - # e.g in Clickhouse `b` is an array expression in `a ARRAY JOIN b` - return join_ancestor - - return None - - def _get_available_source_columns( - self, join_ancestor: exp.Join - ) -> t.Dict[str, t.Sequence[str]]: - """ - Get the source columns that are available at the point where a column is referenced. - - For columns in JOIN conditions, this only includes tables that have been joined - up to that point. Example: - - ``` - SELECT * FROM t_1 INNER JOIN ... INNER JOIN t_n ON t_1.a = c INNER JOIN t_n+1 ON ... - ``` ^ - | - +----------------------------------+ - | - ⌄ - The unqualified column `c` is not ambiguous if no other sources up until that - join i.e t_1, ..., t_n, contain a column named `c`. - - """ - args = self.scope.expression.args - - # Collect tables in order: FROM clause tables + joined tables up to current join - from_name = args["from_"].alias_or_name - available_sources = {from_name: self.get_source_columns(from_name)} - - for join in args["joins"][: t.cast(int, join_ancestor.index) + 1]: - available_sources[join.alias_or_name] = self.get_source_columns( - join.alias_or_name - ) - - return available_sources - - def _get_unambiguous_columns( - self, source_columns: t.Dict[str, t.Sequence[str]] - ) -> t.Mapping[str, str]: - """ - Find all the unambiguous columns in sources. - - Args: - source_columns: Mapping of names to source columns. - - Returns: - Mapping of column name to source name. - """ - if not source_columns: - return {} - - source_columns_pairs = list(source_columns.items()) - - first_table, first_columns = source_columns_pairs[0] - - if len(source_columns_pairs) == 1: - # Performance optimization - avoid copying first_columns if there is only one table. - return SingleValuedMapping(first_columns, first_table) - - unambiguous_columns = {col: first_table for col in first_columns} - all_columns = set(unambiguous_columns) - - for table, columns in source_columns_pairs[1:]: - unique = set(columns) - ambiguous = all_columns.intersection(unique) - all_columns.update(columns) - - for column in ambiguous: - unambiguous_columns.pop(column, None) - for column in unique.difference(ambiguous): - unambiguous_columns[column] = table - - return unambiguous_columns - - def _get_unnest_column_type(self, column: exp.Column) -> t.Optional[exp.DataType]: - """ - Get the type of a column being unnested, tracing through CTEs/subqueries to find the base table. - - Args: - column: The column expression being unnested. - - Returns: - The DataType of the column, or None if not found. - """ - scope = self.scope.parent - - # if column is qualified, use that table, otherwise disambiguate using the resolver - if column.table: - table_name = column.table - else: - # use the parent scope's resolver to disambiguate the column - parent_resolver = Resolver(scope, self.schema, self._infer_schema) - table_identifier = parent_resolver.get_table(column) - if not table_identifier: - return None - table_name = table_identifier.name - - source = scope.sources.get(table_name) - return self._get_column_type_from_scope(source, column) if source else None - - def _get_column_type_from_scope( - self, source: t.Union[Scope, exp.Table], column: exp.Column - ) -> t.Optional[exp.DataType]: - """ - Get a column's type by tracing through scopes/tables to find the base table. - - Args: - source: The source to search - can be a Scope (to iterate its sources) or a Table. - column: The column to find the type for. - - Returns: - The DataType of the column, or None if not found. - """ - if isinstance(source, exp.Table): - # base table - get the column type from schema - col_type: t.Optional[exp.DataType] = self.schema.get_column_type( - source, column - ) - if col_type and not col_type.is_type(exp.DataType.Type.UNKNOWN): - return col_type - elif isinstance(source, Scope): - # iterate over all sources in the scope - for source_name, nested_source in source.sources.items(): - col_type = self._get_column_type_from_scope(nested_source, column) - if col_type and not col_type.is_type(exp.DataType.Type.UNKNOWN): - return col_type - - return None diff --git a/third_party/bigframes_vendored/sqlglot/optimizer/scope.py b/third_party/bigframes_vendored/sqlglot/optimizer/scope.py deleted file mode 100644 index 4256abc6173..00000000000 --- a/third_party/bigframes_vendored/sqlglot/optimizer/scope.py +++ /dev/null @@ -1,983 +0,0 @@ -# Contains code from https://github.com/tobymao/sqlglot/blob/v28.5.0/sqlglot/optimizer/scope.py - -from __future__ import annotations - -import itertools -import logging -import typing as t -from collections import defaultdict -from enum import Enum, auto - -from bigframes_vendored.sqlglot import exp -from bigframes_vendored.sqlglot.errors import OptimizeError -from bigframes_vendored.sqlglot.helper import ensure_collection, find_new_name, seq_get - -logger = logging.getLogger("sqlglot") - -TRAVERSABLES = (exp.Query, exp.DDL, exp.DML) - - -class ScopeType(Enum): - ROOT = auto() - SUBQUERY = auto() - DERIVED_TABLE = auto() - CTE = auto() - UNION = auto() - UDTF = auto() - - -class Scope: - """ - Selection scope. - - Attributes: - expression (exp.Select|exp.SetOperation): Root expression of this scope - sources (dict[str, exp.Table|Scope]): Mapping of source name to either - a Table expression or another Scope instance. For example: - SELECT * FROM x {"x": Table(this="x")} - SELECT * FROM x AS y {"y": Table(this="x")} - SELECT * FROM (SELECT ...) AS y {"y": Scope(...)} - lateral_sources (dict[str, exp.Table|Scope]): Sources from laterals - For example: - SELECT c FROM x LATERAL VIEW EXPLODE (a) AS c; - The LATERAL VIEW EXPLODE gets x as a source. - cte_sources (dict[str, Scope]): Sources from CTES - outer_columns (list[str]): If this is a derived table or CTE, and the outer query - defines a column list for the alias of this scope, this is that list of columns. - For example: - SELECT * FROM (SELECT ...) AS y(col1, col2) - The inner query would have `["col1", "col2"]` for its `outer_columns` - parent (Scope): Parent scope - scope_type (ScopeType): Type of this scope, relative to it's parent - subquery_scopes (list[Scope]): List of all child scopes for subqueries - cte_scopes (list[Scope]): List of all child scopes for CTEs - derived_table_scopes (list[Scope]): List of all child scopes for derived_tables - udtf_scopes (list[Scope]): List of all child scopes for user defined tabular functions - table_scopes (list[Scope]): derived_table_scopes + udtf_scopes, in the order that they're defined - union_scopes (list[Scope, Scope]): If this Scope is for a Union expression, this will be - a list of the left and right child scopes. - """ - - def __init__( - self, - expression, - sources=None, - outer_columns=None, - parent=None, - scope_type=ScopeType.ROOT, - lateral_sources=None, - cte_sources=None, - can_be_correlated=None, - ): - self.expression = expression - self.sources = sources or {} - self.lateral_sources = lateral_sources or {} - self.cte_sources = cte_sources or {} - self.sources.update(self.lateral_sources) - self.sources.update(self.cte_sources) - self.outer_columns = outer_columns or [] - self.parent = parent - self.scope_type = scope_type - self.subquery_scopes = [] - self.derived_table_scopes = [] - self.table_scopes = [] - self.cte_scopes = [] - self.union_scopes = [] - self.udtf_scopes = [] - self.can_be_correlated = can_be_correlated - self.clear_cache() - - def clear_cache(self): - self._collected = False - self._raw_columns = None - self._table_columns = None - self._stars = None - self._derived_tables = None - self._udtfs = None - self._tables = None - self._ctes = None - self._subqueries = None - self._selected_sources = None - self._columns = None - self._external_columns = None - self._local_columns = None - self._join_hints = None - self._pivots = None - self._references = None - self._semi_anti_join_tables = None - - def branch( - self, - expression, - scope_type, - sources=None, - cte_sources=None, - lateral_sources=None, - **kwargs, - ): - """Branch from the current scope to a new, inner scope""" - return Scope( - expression=expression.unnest(), - sources=sources.copy() if sources else None, - parent=self, - scope_type=scope_type, - cte_sources={**self.cte_sources, **(cte_sources or {})}, - lateral_sources=lateral_sources.copy() if lateral_sources else None, - can_be_correlated=self.can_be_correlated - or scope_type in (ScopeType.SUBQUERY, ScopeType.UDTF), - **kwargs, - ) - - def _collect(self): - self._tables = [] - self._ctes = [] - self._subqueries = [] - self._derived_tables = [] - self._udtfs = [] - self._raw_columns = [] - self._table_columns = [] - self._stars = [] - self._join_hints = [] - self._semi_anti_join_tables = set() - - for node in self.walk(bfs=False): - if node is self.expression: - continue - - if isinstance(node, exp.Dot) and node.is_star: - self._stars.append(node) - elif isinstance(node, exp.Column) and not isinstance( - node, exp.Pseudocolumn - ): - if isinstance(node.this, exp.Star): - self._stars.append(node) - else: - self._raw_columns.append(node) - elif isinstance(node, exp.Table) and not isinstance( - node.parent, exp.JoinHint - ): - parent = node.parent - if isinstance(parent, exp.Join) and parent.is_semi_or_anti_join: - self._semi_anti_join_tables.add(node.alias_or_name) - - self._tables.append(node) - elif isinstance(node, exp.JoinHint): - self._join_hints.append(node) - elif isinstance(node, exp.UDTF): - self._udtfs.append(node) - elif isinstance(node, exp.CTE): - self._ctes.append(node) - elif _is_derived_table(node) and _is_from_or_join(node): - self._derived_tables.append(node) - elif isinstance(node, exp.UNWRAPPED_QUERIES) and not _is_from_or_join(node): - self._subqueries.append(node) - elif isinstance(node, exp.TableColumn): - self._table_columns.append(node) - - self._collected = True - - def _ensure_collected(self): - if not self._collected: - self._collect() - - def walk(self, bfs=True, prune=None): - return walk_in_scope(self.expression, bfs=bfs, prune=None) - - def find(self, *expression_types, bfs=True): - return find_in_scope(self.expression, expression_types, bfs=bfs) - - def find_all(self, *expression_types, bfs=True): - return find_all_in_scope(self.expression, expression_types, bfs=bfs) - - def replace(self, old, new): - """ - Replace `old` with `new`. - - This can be used instead of `exp.Expression.replace` to ensure the `Scope` is kept up-to-date. - - Args: - old (exp.Expression): old node - new (exp.Expression): new node - """ - old.replace(new) - self.clear_cache() - - @property - def tables(self): - """ - List of tables in this scope. - - Returns: - list[exp.Table]: tables - """ - self._ensure_collected() - return self._tables - - @property - def ctes(self): - """ - List of CTEs in this scope. - - Returns: - list[exp.CTE]: ctes - """ - self._ensure_collected() - return self._ctes - - @property - def derived_tables(self): - """ - List of derived tables in this scope. - - For example: - SELECT * FROM (SELECT ...) <- that's a derived table - - Returns: - list[exp.Subquery]: derived tables - """ - self._ensure_collected() - return self._derived_tables - - @property - def udtfs(self): - """ - List of "User Defined Tabular Functions" in this scope. - - Returns: - list[exp.UDTF]: UDTFs - """ - self._ensure_collected() - return self._udtfs - - @property - def subqueries(self): - """ - List of subqueries in this scope. - - For example: - SELECT * FROM x WHERE a IN (SELECT ...) <- that's a subquery - - Returns: - list[exp.Select | exp.SetOperation]: subqueries - """ - self._ensure_collected() - return self._subqueries - - @property - def stars(self) -> t.List[exp.Column | exp.Dot]: - """ - List of star expressions (columns or dots) in this scope. - """ - self._ensure_collected() - return self._stars - - @property - def columns(self): - """ - List of columns in this scope. - - Returns: - list[exp.Column]: Column instances in this scope, plus any - Columns that reference this scope from correlated subqueries. - """ - if self._columns is None: - self._ensure_collected() - columns = self._raw_columns - - external_columns = [ - column - for scope in itertools.chain( - self.subquery_scopes, - self.udtf_scopes, - (dts for dts in self.derived_table_scopes if dts.can_be_correlated), - ) - for column in scope.external_columns - ] - - named_selects = set(self.expression.named_selects) - - self._columns = [] - for column in columns + external_columns: - ancestor = column.find_ancestor( - exp.Select, - exp.Qualify, - exp.Order, - exp.Having, - exp.Hint, - exp.Table, - exp.Star, - exp.Distinct, - ) - if ( - not ancestor - or column.table - or isinstance(ancestor, exp.Select) - or ( - isinstance(ancestor, exp.Table) - and not isinstance(ancestor.this, exp.Func) - ) - or ( - isinstance(ancestor, (exp.Order, exp.Distinct)) - and ( - isinstance(ancestor.parent, (exp.Window, exp.WithinGroup)) - or not isinstance(ancestor.parent, exp.Select) - or column.name not in named_selects - ) - ) - or ( - isinstance(ancestor, exp.Star) - and not column.arg_key == "except_" - ) - ): - self._columns.append(column) - - return self._columns - - @property - def table_columns(self): - if self._table_columns is None: - self._ensure_collected() - - return self._table_columns - - @property - def selected_sources(self): - """ - Mapping of nodes and sources that are actually selected from in this scope. - - That is, all tables in a schema are selectable at any point. But a - table only becomes a selected source if it's included in a FROM or JOIN clause. - - Returns: - dict[str, (exp.Table|exp.Select, exp.Table|Scope)]: selected sources and nodes - """ - if self._selected_sources is None: - result = {} - - for name, node in self.references: - if name in self._semi_anti_join_tables: - # The RHS table of SEMI/ANTI joins shouldn't be collected as a - # selected source - continue - - if name in result: - raise OptimizeError(f"Alias already used: {name}") - if name in self.sources: - result[name] = (node, self.sources[name]) - - self._selected_sources = result - return self._selected_sources - - @property - def references(self) -> t.List[t.Tuple[str, exp.Expression]]: - if self._references is None: - self._references = [] - - for table in self.tables: - self._references.append((table.alias_or_name, table)) - for expression in itertools.chain(self.derived_tables, self.udtfs): - self._references.append( - ( - _get_source_alias(expression), - expression - if expression.args.get("pivots") - else expression.unnest(), - ) - ) - - return self._references - - @property - def external_columns(self): - """ - Columns that appear to reference sources in outer scopes. - - Returns: - list[exp.Column]: Column instances that don't reference sources in the current scope. - """ - if self._external_columns is None: - if isinstance(self.expression, exp.SetOperation): - left, right = self.union_scopes - self._external_columns = left.external_columns + right.external_columns - else: - self._external_columns = [ - c - for c in self.columns - if c.table not in self.sources - and c.table not in self.semi_or_anti_join_tables - ] - - return self._external_columns - - @property - def local_columns(self): - """ - Columns in this scope that are not external. - - Returns: - list[exp.Column]: Column instances that reference sources in the current scope. - """ - if self._local_columns is None: - external_columns = set(self.external_columns) - self._local_columns = [c for c in self.columns if c not in external_columns] - - return self._local_columns - - @property - def unqualified_columns(self): - """ - Unqualified columns in the current scope. - - Returns: - list[exp.Column]: Unqualified columns - """ - return [c for c in self.columns if not c.table] - - @property - def join_hints(self): - """ - Hints that exist in the scope that reference tables - - Returns: - list[exp.JoinHint]: Join hints that are referenced within the scope - """ - if self._join_hints is None: - return [] - return self._join_hints - - @property - def pivots(self): - if not self._pivots: - self._pivots = [ - pivot - for _, node in self.references - for pivot in node.args.get("pivots") or [] - ] - - return self._pivots - - @property - def semi_or_anti_join_tables(self): - return self._semi_anti_join_tables or set() - - def source_columns(self, source_name): - """ - Get all columns in the current scope for a particular source. - - Args: - source_name (str): Name of the source - Returns: - list[exp.Column]: Column instances that reference `source_name` - """ - return [column for column in self.columns if column.table == source_name] - - @property - def is_subquery(self): - """Determine if this scope is a subquery""" - return self.scope_type == ScopeType.SUBQUERY - - @property - def is_derived_table(self): - """Determine if this scope is a derived table""" - return self.scope_type == ScopeType.DERIVED_TABLE - - @property - def is_union(self): - """Determine if this scope is a union""" - return self.scope_type == ScopeType.UNION - - @property - def is_cte(self): - """Determine if this scope is a common table expression""" - return self.scope_type == ScopeType.CTE - - @property - def is_root(self): - """Determine if this is the root scope""" - return self.scope_type == ScopeType.ROOT - - @property - def is_udtf(self): - """Determine if this scope is a UDTF (User Defined Table Function)""" - return self.scope_type == ScopeType.UDTF - - @property - def is_correlated_subquery(self): - """Determine if this scope is a correlated subquery""" - return bool(self.can_be_correlated and self.external_columns) - - def rename_source(self, old_name, new_name): - """Rename a source in this scope""" - old_name = old_name or "" - if old_name in self.sources: - self.sources[new_name] = self.sources.pop(old_name) - - def add_source(self, name, source): - """Add a source to this scope""" - self.sources[name] = source - self.clear_cache() - - def remove_source(self, name): - """Remove a source from this scope""" - self.sources.pop(name, None) - self.clear_cache() - - def __repr__(self): - return f"Scope<{self.expression.sql()}>" - - def traverse(self): - """ - Traverse the scope tree from this node. - - Yields: - Scope: scope instances in depth-first-search post-order - """ - stack = [self] - result = [] - while stack: - scope = stack.pop() - result.append(scope) - stack.extend( - itertools.chain( - scope.cte_scopes, - scope.union_scopes, - scope.table_scopes, - scope.subquery_scopes, - ) - ) - - yield from reversed(result) - - def ref_count(self): - """ - Count the number of times each scope in this tree is referenced. - - Returns: - dict[int, int]: Mapping of Scope instance ID to reference count - """ - scope_ref_count = defaultdict(lambda: 0) - - for scope in self.traverse(): - for _, source in scope.selected_sources.values(): - scope_ref_count[id(source)] += 1 - - for name in scope._semi_anti_join_tables: - # semi/anti join sources are not actually selected but we still need to - # increment their ref count to avoid them being optimized away - if name in scope.sources: - scope_ref_count[id(scope.sources[name])] += 1 - - return scope_ref_count - - -def traverse_scope(expression: exp.Expression) -> t.List[Scope]: - """ - Traverse an expression by its "scopes". - - "Scope" represents the current context of a Select statement. - - This is helpful for optimizing queries, where we need more information than - the expression tree itself. For example, we might care about the source - names within a subquery. Returns a list because a generator could result in - incomplete properties which is confusing. - - Examples: - >>> import sqlglot - >>> expression = sqlglot.parse_one("SELECT a FROM (SELECT a FROM x) AS y") - >>> scopes = traverse_scope(expression) - >>> scopes[0].expression.sql(), list(scopes[0].sources) - ('SELECT a FROM x', ['x']) - >>> scopes[1].expression.sql(), list(scopes[1].sources) - ('SELECT a FROM (SELECT a FROM x) AS y', ['y']) - - Args: - expression: Expression to traverse - - Returns: - A list of the created scope instances - """ - if isinstance(expression, TRAVERSABLES): - return list(_traverse_scope(Scope(expression))) - return [] - - -def build_scope(expression: exp.Expression) -> t.Optional[Scope]: - """ - Build a scope tree. - - Args: - expression: Expression to build the scope tree for. - - Returns: - The root scope - """ - return seq_get(traverse_scope(expression), -1) - - -def _traverse_scope(scope): - expression = scope.expression - - if isinstance(expression, exp.Select): - yield from _traverse_select(scope) - elif isinstance(expression, exp.SetOperation): - yield from _traverse_ctes(scope) - yield from _traverse_union(scope) - return - elif isinstance(expression, exp.Subquery): - if scope.is_root: - yield from _traverse_select(scope) - else: - yield from _traverse_subqueries(scope) - elif isinstance(expression, exp.Table): - yield from _traverse_tables(scope) - elif isinstance(expression, exp.UDTF): - yield from _traverse_udtfs(scope) - elif isinstance(expression, exp.DDL): - if isinstance(expression.expression, exp.Query): - yield from _traverse_ctes(scope) - yield from _traverse_scope( - Scope(expression.expression, cte_sources=scope.cte_sources) - ) - return - elif isinstance(expression, exp.DML): - yield from _traverse_ctes(scope) - for query in find_all_in_scope(expression, exp.Query): - # This check ensures we don't yield the CTE/nested queries twice - if not isinstance(query.parent, (exp.CTE, exp.Subquery)): - yield from _traverse_scope(Scope(query, cte_sources=scope.cte_sources)) - return - else: - logger.warning( - "Cannot traverse scope %s with type '%s'", expression, type(expression) - ) - return - - yield scope - - -def _traverse_select(scope): - yield from _traverse_ctes(scope) - yield from _traverse_tables(scope) - yield from _traverse_subqueries(scope) - - -def _traverse_union(scope): - prev_scope = None - union_scope_stack = [scope] - expression_stack = [scope.expression.right, scope.expression.left] - - while expression_stack: - expression = expression_stack.pop() - union_scope = union_scope_stack[-1] - - new_scope = union_scope.branch( - expression, - outer_columns=union_scope.outer_columns, - scope_type=ScopeType.UNION, - ) - - if isinstance(expression, exp.SetOperation): - yield from _traverse_ctes(new_scope) - - union_scope_stack.append(new_scope) - expression_stack.extend([expression.right, expression.left]) - continue - - for scope in _traverse_scope(new_scope): - yield scope - - if prev_scope: - union_scope_stack.pop() - union_scope.union_scopes = [prev_scope, scope] - prev_scope = union_scope - - yield union_scope - else: - prev_scope = scope - - -def _traverse_ctes(scope): - sources = {} - - for cte in scope.ctes: - cte_name = cte.alias - - # if the scope is a recursive cte, it must be in the form of base_case UNION recursive. - # thus the recursive scope is the first section of the union. - with_ = scope.expression.args.get("with_") - if with_ and with_.recursive: - union = cte.this - - if isinstance(union, exp.SetOperation): - sources[cte_name] = scope.branch(union.this, scope_type=ScopeType.CTE) - - child_scope = None - - for child_scope in _traverse_scope( - scope.branch( - cte.this, - cte_sources=sources, - outer_columns=cte.alias_column_names, - scope_type=ScopeType.CTE, - ) - ): - yield child_scope - - # append the final child_scope yielded - if child_scope: - sources[cte_name] = child_scope - scope.cte_scopes.append(child_scope) - - scope.sources.update(sources) - scope.cte_sources.update(sources) - - -def _is_derived_table(expression: exp.Subquery) -> bool: - """ - We represent (tbl1 JOIN tbl2) as a Subquery, but it's not really a "derived table", - as it doesn't introduce a new scope. If an alias is present, it shadows all names - under the Subquery, so that's one exception to this rule. - """ - return isinstance(expression, exp.Subquery) and bool( - expression.alias or isinstance(expression.this, exp.UNWRAPPED_QUERIES) - ) - - -def _is_from_or_join(expression: exp.Expression) -> bool: - """ - Determine if `expression` is the FROM or JOIN clause of a SELECT statement. - """ - parent = expression.parent - - # Subqueries can be arbitrarily nested - while isinstance(parent, exp.Subquery): - parent = parent.parent - - return isinstance(parent, (exp.From, exp.Join)) - - -def _traverse_tables(scope): - sources = {} - - # Traverse FROMs, JOINs, and LATERALs in the order they are defined - expressions = [] - from_ = scope.expression.args.get("from_") - if from_: - expressions.append(from_.this) - - for join in scope.expression.args.get("joins") or []: - expressions.append(join.this) - - if isinstance(scope.expression, exp.Table): - expressions.append(scope.expression) - - expressions.extend(scope.expression.args.get("laterals") or []) - - for expression in expressions: - if isinstance(expression, exp.Final): - expression = expression.this - if isinstance(expression, exp.Table): - table_name = expression.name - source_name = expression.alias_or_name - - if table_name in scope.sources and not expression.db: - # This is a reference to a parent source (e.g. a CTE), not an actual table, unless - # it is pivoted, because then we get back a new table and hence a new source. - pivots = expression.args.get("pivots") - if pivots: - sources[pivots[0].alias] = expression - else: - sources[source_name] = scope.sources[table_name] - elif source_name in sources: - sources[find_new_name(sources, table_name)] = expression - else: - sources[source_name] = expression - - # Make sure to not include the joins twice - if expression is not scope.expression: - expressions.extend( - join.this for join in expression.args.get("joins") or [] - ) - - continue - - if not isinstance(expression, exp.DerivedTable): - continue - - if isinstance(expression, exp.UDTF): - lateral_sources = sources - scope_type = ScopeType.UDTF - scopes = scope.udtf_scopes - elif _is_derived_table(expression): - lateral_sources = None - scope_type = ScopeType.DERIVED_TABLE - scopes = scope.derived_table_scopes - expressions.extend(join.this for join in expression.args.get("joins") or []) - else: - # Makes sure we check for possible sources in nested table constructs - expressions.append(expression.this) - expressions.extend(join.this for join in expression.args.get("joins") or []) - continue - - child_scope = None - - for child_scope in _traverse_scope( - scope.branch( - expression, - lateral_sources=lateral_sources, - outer_columns=expression.alias_column_names, - scope_type=scope_type, - ) - ): - yield child_scope - - # Tables without aliases will be set as "" - # This shouldn't be a problem once qualify_columns runs, as it adds aliases on everything. - # Until then, this means that only a single, unaliased derived table is allowed (rather, - # the latest one wins. - sources[_get_source_alias(expression)] = child_scope - - # append the final child_scope yielded - if child_scope: - scopes.append(child_scope) - scope.table_scopes.append(child_scope) - - scope.sources.update(sources) - - -def _traverse_subqueries(scope): - for subquery in scope.subqueries: - top = None - for child_scope in _traverse_scope( - scope.branch(subquery, scope_type=ScopeType.SUBQUERY) - ): - yield child_scope - top = child_scope - scope.subquery_scopes.append(top) - - -def _traverse_udtfs(scope): - if isinstance(scope.expression, exp.Unnest): - expressions = scope.expression.expressions - elif isinstance(scope.expression, exp.Lateral): - expressions = [scope.expression.this] - else: - expressions = [] - - sources = {} - for expression in expressions: - if isinstance(expression, exp.Subquery): - top = None - for child_scope in _traverse_scope( - scope.branch( - expression, - scope_type=ScopeType.SUBQUERY, - outer_columns=expression.alias_column_names, - ) - ): - yield child_scope - top = child_scope - sources[_get_source_alias(expression)] = child_scope - - scope.subquery_scopes.append(top) - - scope.sources.update(sources) - - -def walk_in_scope(expression, bfs=True, prune=None): - """ - Returns a generator object which visits all nodes in the syntrax tree, stopping at - nodes that start child scopes. - - Args: - expression (exp.Expression): - bfs (bool): if set to True the BFS traversal order will be applied, - otherwise the DFS traversal will be used instead. - prune ((node, parent, arg_key) -> bool): callable that returns True if - the generator should stop traversing this branch of the tree. - - Yields: - tuple[exp.Expression, Optional[exp.Expression], str]: node, parent, arg key - """ - # We'll use this variable to pass state into the dfs generator. - # Whenever we set it to True, we exclude a subtree from traversal. - crossed_scope_boundary = False - - for node in expression.walk( - bfs=bfs, prune=lambda n: crossed_scope_boundary or (prune and prune(n)) - ): - crossed_scope_boundary = False - - yield node - - if node is expression: - continue - - if ( - isinstance(node, exp.CTE) - or ( - isinstance(node.parent, (exp.From, exp.Join)) - and _is_derived_table(node) - ) - or (isinstance(node.parent, exp.UDTF) and isinstance(node, exp.Query)) - or isinstance(node, exp.UNWRAPPED_QUERIES) - ): - crossed_scope_boundary = True - - if isinstance(node, (exp.Subquery, exp.UDTF)): - # The following args are not actually in the inner scope, so we should visit them - for key in ("joins", "laterals", "pivots"): - for arg in node.args.get(key) or []: - yield from walk_in_scope(arg, bfs=bfs) - - -def find_all_in_scope(expression, expression_types, bfs=True): - """ - Returns a generator object which visits all nodes in this scope and only yields those that - match at least one of the specified expression types. - - This does NOT traverse into subscopes. - - Args: - expression (exp.Expression): - expression_types (tuple[type]|type): the expression type(s) to match. - bfs (bool): True to use breadth-first search, False to use depth-first. - - Yields: - exp.Expression: nodes - """ - for expression in walk_in_scope(expression, bfs=bfs): - if isinstance(expression, tuple(ensure_collection(expression_types))): - yield expression - - -def find_in_scope(expression, expression_types, bfs=True): - """ - Returns the first node in this scope which matches at least one of the specified types. - - This does NOT traverse into subscopes. - - Args: - expression (exp.Expression): - expression_types (tuple[type]|type): the expression type(s) to match. - bfs (bool): True to use breadth-first search, False to use depth-first. - - Returns: - exp.Expression: the node which matches the criteria or None if no node matching - the criteria was found. - """ - return next(find_all_in_scope(expression, expression_types, bfs=bfs), None) - - -def _get_source_alias(expression): - alias_arg = expression.args.get("alias") - alias_name = expression.alias - - if ( - not alias_name - and isinstance(alias_arg, exp.TableAlias) - and len(alias_arg.columns) == 1 - ): - alias_name = alias_arg.columns[0].name - - return alias_name diff --git a/third_party/bigframes_vendored/sqlglot/optimizer/simplify.py b/third_party/bigframes_vendored/sqlglot/optimizer/simplify.py deleted file mode 100644 index 573dc9e67d3..00000000000 --- a/third_party/bigframes_vendored/sqlglot/optimizer/simplify.py +++ /dev/null @@ -1,1796 +0,0 @@ -# Contains code from https://github.com/tobymao/sqlglot/blob/v28.5.0/sqlglot/optimizer/simplify.py - -from __future__ import annotations - -import datetime -import functools -import itertools -import logging -import typing as t -from collections import defaultdict, deque -from functools import reduce, wraps - -import bigframes_vendored.sqlglot -from bigframes_vendored.sqlglot import Dialect, exp -from bigframes_vendored.sqlglot.helper import first, merge_ranges, while_changing -from bigframes_vendored.sqlglot.optimizer.annotate_types import TypeAnnotator -from bigframes_vendored.sqlglot.optimizer.scope import find_all_in_scope, walk_in_scope -from bigframes_vendored.sqlglot.schema import ensure_schema - -if t.TYPE_CHECKING: - from bigframes_vendored.sqlglot.dialects.dialect import DialectType - - DateRange = t.Tuple[datetime.date, datetime.date] - DateTruncBinaryTransform = t.Callable[ - [exp.Expression, datetime.date, str, Dialect, exp.DataType], - t.Optional[exp.Expression], - ] - - -logger = logging.getLogger("sqlglot") - - -# Final means that an expression should not be simplified -FINAL = "final" - -SIMPLIFIABLE = ( - exp.Binary, - exp.Func, - exp.Lambda, - exp.Predicate, - exp.Unary, -) - - -def simplify( - expression: exp.Expression, - constant_propagation: bool = False, - coalesce_simplification: bool = False, - dialect: DialectType = None, -): - """ - Rewrite sqlglot AST to simplify expressions. - - Example: - >>> import sqlglot - >>> expression = sqlglot.parse_one("TRUE AND TRUE") - >>> simplify(expression).sql() - 'TRUE' - - Args: - expression: expression to simplify - constant_propagation: whether the constant propagation rule should be used - coalesce_simplification: whether the simplify coalesce rule should be used. - This rule tries to remove coalesce functions, which can be useful in certain analyses but - can leave the query more verbose. - Returns: - sqlglot.Expression: simplified expression - """ - return Simplifier(dialect=dialect).simplify( - expression, - constant_propagation=constant_propagation, - coalesce_simplification=coalesce_simplification, - ) - - -class UnsupportedUnit(Exception): - pass - - -def catch(*exceptions): - """Decorator that ignores a simplification function if any of `exceptions` are raised""" - - def decorator(func): - def wrapped(expression, *args, **kwargs): - try: - return func(expression, *args, **kwargs) - except exceptions: - return expression - - return wrapped - - return decorator - - -def annotate_types_on_change(func): - @wraps(func) - def _func( - self, expression: exp.Expression, *args, **kwargs - ) -> t.Optional[exp.Expression]: - new_expression = func(self, expression, *args, **kwargs) - - if new_expression is None: - return new_expression - - if self.annotate_new_expressions and expression != new_expression: - self._annotator.clear() - - # We annotate this to ensure new children nodes are also annotated - new_expression = self._annotator.annotate( - expression=new_expression, - annotate_scope=False, - ) - - # Whatever expression the original expression is transformed into needs to preserve - # the original type, otherwise the simplification could result in a different schema - new_expression.type = expression.type - - return new_expression - - return _func - - -def flatten(expression): - """ - A AND (B AND C) -> A AND B AND C - A OR (B OR C) -> A OR B OR C - """ - if isinstance(expression, exp.Connector): - for node in expression.args.values(): - child = node.unnest() - if isinstance(child, expression.__class__): - node.replace(child) - return expression - - -def simplify_parens(expression: exp.Expression, dialect: DialectType) -> exp.Expression: - if not isinstance(expression, exp.Paren): - return expression - - this = expression.this - parent = expression.parent - parent_is_predicate = isinstance(parent, exp.Predicate) - - if isinstance(this, exp.Select): - return expression - - if isinstance(parent, (exp.SubqueryPredicate, exp.Bracket)): - return expression - - if ( - Dialect.get_or_raise(dialect).REQUIRES_PARENTHESIZED_STRUCT_ACCESS - and isinstance(parent, exp.Dot) - and (isinstance(parent.right, (exp.Identifier, exp.Star))) - ): - return expression - - if ( - not isinstance(parent, (exp.Condition, exp.Binary)) - or isinstance(parent, exp.Paren) - or ( - not isinstance(this, exp.Binary) - and not (isinstance(this, (exp.Not, exp.Is)) and parent_is_predicate) - ) - or ( - isinstance(this, exp.Predicate) - and not (parent_is_predicate or isinstance(parent, exp.Neg)) - ) - or (isinstance(this, exp.Add) and isinstance(parent, exp.Add)) - or (isinstance(this, exp.Mul) and isinstance(parent, exp.Mul)) - or (isinstance(this, exp.Mul) and isinstance(parent, (exp.Add, exp.Sub))) - ): - return this - - return expression - - -def propagate_constants(expression, root=True): - """ - Propagate constants for conjunctions in DNF: - - SELECT * FROM t WHERE a = b AND b = 5 becomes - SELECT * FROM t WHERE a = 5 AND b = 5 - - Reference: https://www.sqlite.org/optoverview.html - """ - - if ( - isinstance(expression, exp.And) - and (root or not expression.same_parent) - and bigframes_vendored.sqlglot.optimizer.normalize.normalized( - expression, dnf=True - ) - ): - constant_mapping = {} - for expr in walk_in_scope( - expression, prune=lambda node: isinstance(node, exp.If) - ): - if isinstance(expr, exp.EQ): - l, r = expr.left, expr.right - - # TODO: create a helper that can be used to detect nested literal expressions such - # as CAST(123456 AS BIGINT), since we usually want to treat those as literals too - if isinstance(l, exp.Column) and isinstance(r, exp.Literal): - constant_mapping[l] = (id(l), r) - - if constant_mapping: - for column in find_all_in_scope(expression, exp.Column): - parent = column.parent - column_id, constant = constant_mapping.get(column) or (None, None) - if ( - column_id is not None - and id(column) != column_id - and not ( - isinstance(parent, exp.Is) - and isinstance(parent.expression, exp.Null) - ) - ): - column.replace(constant.copy()) - - return expression - - -def _is_number(expression: exp.Expression) -> bool: - return expression.is_number - - -def _is_interval(expression: exp.Expression) -> bool: - return ( - isinstance(expression, exp.Interval) - and extract_interval(expression) is not None - ) - - -def _is_nonnull_constant(expression: exp.Expression) -> bool: - return isinstance(expression, exp.NONNULL_CONSTANTS) or _is_date_literal(expression) - - -def _is_constant(expression: exp.Expression) -> bool: - return isinstance(expression, exp.CONSTANTS) or _is_date_literal(expression) - - -def _datetrunc_range( - date: datetime.date, unit: str, dialect: Dialect -) -> t.Optional[DateRange]: - """ - Get the date range for a DATE_TRUNC equality comparison: - - Example: - _datetrunc_range(date(2021-01-01), 'year') == (date(2021-01-01), date(2022-01-01)) - Returns: - tuple of [min, max) or None if a value can never be equal to `date` for `unit` - """ - floor = date_floor(date, unit, dialect) - - if date != floor: - # This will always be False, except for NULL values. - return None - - return floor, floor + interval(unit) - - -def _datetrunc_eq_expression( - left: exp.Expression, drange: DateRange, target_type: t.Optional[exp.DataType] -) -> exp.Expression: - """Get the logical expression for a date range""" - return exp.and_( - left >= date_literal(drange[0], target_type), - left < date_literal(drange[1], target_type), - copy=False, - ) - - -def _datetrunc_eq( - left: exp.Expression, - date: datetime.date, - unit: str, - dialect: Dialect, - target_type: t.Optional[exp.DataType], -) -> t.Optional[exp.Expression]: - drange = _datetrunc_range(date, unit, dialect) - if not drange: - return None - - return _datetrunc_eq_expression(left, drange, target_type) - - -def _datetrunc_neq( - left: exp.Expression, - date: datetime.date, - unit: str, - dialect: Dialect, - target_type: t.Optional[exp.DataType], -) -> t.Optional[exp.Expression]: - drange = _datetrunc_range(date, unit, dialect) - if not drange: - return None - - return exp.and_( - left < date_literal(drange[0], target_type), - left >= date_literal(drange[1], target_type), - copy=False, - ) - - -def always_true(expression): - return (isinstance(expression, exp.Boolean) and expression.this) or ( - isinstance(expression, exp.Literal) - and expression.is_number - and not is_zero(expression) - ) - - -def always_false(expression): - return is_false(expression) or is_null(expression) or is_zero(expression) - - -def is_zero(expression): - return isinstance(expression, exp.Literal) and expression.to_py() == 0 - - -def is_complement(a, b): - return isinstance(b, exp.Not) and b.this == a - - -def is_false(a: exp.Expression) -> bool: - return type(a) is exp.Boolean and not a.this - - -def is_null(a: exp.Expression) -> bool: - return type(a) is exp.Null - - -def eval_boolean(expression, a, b): - if isinstance(expression, (exp.EQ, exp.Is)): - return boolean_literal(a == b) - if isinstance(expression, exp.NEQ): - return boolean_literal(a != b) - if isinstance(expression, exp.GT): - return boolean_literal(a > b) - if isinstance(expression, exp.GTE): - return boolean_literal(a >= b) - if isinstance(expression, exp.LT): - return boolean_literal(a < b) - if isinstance(expression, exp.LTE): - return boolean_literal(a <= b) - return None - - -def cast_as_date(value: t.Any) -> t.Optional[datetime.date]: - if isinstance(value, datetime.datetime): - return value.date() - if isinstance(value, datetime.date): - return value - try: - return datetime.datetime.fromisoformat(value).date() - except ValueError: - return None - - -def cast_as_datetime(value: t.Any) -> t.Optional[datetime.datetime]: - if isinstance(value, datetime.datetime): - return value - if isinstance(value, datetime.date): - return datetime.datetime(year=value.year, month=value.month, day=value.day) - try: - return datetime.datetime.fromisoformat(value) - except ValueError: - return None - - -def cast_value( - value: t.Any, to: exp.DataType -) -> t.Optional[t.Union[datetime.date, datetime.date]]: - if not value: - return None - if to.is_type(exp.DataType.Type.DATE): - return cast_as_date(value) - if to.is_type(*exp.DataType.TEMPORAL_TYPES): - return cast_as_datetime(value) - return None - - -def extract_date( - cast: exp.Expression, -) -> t.Optional[t.Union[datetime.date, datetime.date]]: - if isinstance(cast, exp.Cast): - to = cast.to - elif isinstance(cast, exp.TsOrDsToDate) and not cast.args.get("format"): - to = exp.DataType.build(exp.DataType.Type.DATE) - else: - return None - - if isinstance(cast.this, exp.Literal): - value: t.Any = cast.this.name - elif isinstance(cast.this, (exp.Cast, exp.TsOrDsToDate)): - value = extract_date(cast.this) - else: - return None - return cast_value(value, to) - - -def _is_date_literal(expression: exp.Expression) -> bool: - return extract_date(expression) is not None - - -def extract_interval(expression): - try: - n = int(expression.this.to_py()) - unit = expression.text("unit").lower() - return interval(unit, n) - except (UnsupportedUnit, ModuleNotFoundError, ValueError): - return None - - -def extract_type(*expressions): - target_type = None - for expression in expressions: - target_type = ( - expression.to if isinstance(expression, exp.Cast) else expression.type - ) - if target_type: - break - - return target_type - - -def date_literal(date, target_type=None): - if not target_type or not target_type.is_type(*exp.DataType.TEMPORAL_TYPES): - target_type = ( - exp.DataType.Type.DATETIME - if isinstance(date, datetime.datetime) - else exp.DataType.Type.DATE - ) - - return exp.cast(exp.Literal.string(date), target_type) - - -def interval(unit: str, n: int = 1): - from dateutil.relativedelta import relativedelta - - if unit == "year": - return relativedelta(years=1 * n) - if unit == "quarter": - return relativedelta(months=3 * n) - if unit == "month": - return relativedelta(months=1 * n) - if unit == "week": - return relativedelta(weeks=1 * n) - if unit == "day": - return relativedelta(days=1 * n) - if unit == "hour": - return relativedelta(hours=1 * n) - if unit == "minute": - return relativedelta(minutes=1 * n) - if unit == "second": - return relativedelta(seconds=1 * n) - - raise UnsupportedUnit(f"Unsupported unit: {unit}") - - -def date_floor(d: datetime.date, unit: str, dialect: Dialect) -> datetime.date: - if unit == "year": - return d.replace(month=1, day=1) - if unit == "quarter": - if d.month <= 3: - return d.replace(month=1, day=1) - elif d.month <= 6: - return d.replace(month=4, day=1) - elif d.month <= 9: - return d.replace(month=7, day=1) - else: - return d.replace(month=10, day=1) - if unit == "month": - return d.replace(month=d.month, day=1) - if unit == "week": - # Assuming week starts on Monday (0) and ends on Sunday (6) - return d - datetime.timedelta(days=d.weekday() - dialect.WEEK_OFFSET) - if unit == "day": - return d - - raise UnsupportedUnit(f"Unsupported unit: {unit}") - - -def date_ceil(d: datetime.date, unit: str, dialect: Dialect) -> datetime.date: - floor = date_floor(d, unit, dialect) - - if floor == d: - return d - - return floor + interval(unit) - - -def boolean_literal(condition): - return exp.true() if condition else exp.false() - - -class Simplifier: - def __init__( - self, dialect: DialectType = None, annotate_new_expressions: bool = True - ): - self.dialect = Dialect.get_or_raise(dialect) - self.annotate_new_expressions = annotate_new_expressions - - self._annotator: TypeAnnotator = TypeAnnotator( - schema=ensure_schema(None, dialect=self.dialect), overwrite_types=False - ) - - # Value ranges for byte-sized signed/unsigned integers - TINYINT_MIN = -128 - TINYINT_MAX = 127 - UTINYINT_MIN = 0 - UTINYINT_MAX = 255 - - COMPLEMENT_COMPARISONS = { - exp.LT: exp.GTE, - exp.GT: exp.LTE, - exp.LTE: exp.GT, - exp.GTE: exp.LT, - exp.EQ: exp.NEQ, - exp.NEQ: exp.EQ, - } - - COMPLEMENT_SUBQUERY_PREDICATES = { - exp.All: exp.Any, - exp.Any: exp.All, - } - - LT_LTE = (exp.LT, exp.LTE) - GT_GTE = (exp.GT, exp.GTE) - - COMPARISONS = ( - *LT_LTE, - *GT_GTE, - exp.EQ, - exp.NEQ, - exp.Is, - ) - - INVERSE_COMPARISONS: t.Dict[t.Type[exp.Expression], t.Type[exp.Expression]] = { - exp.LT: exp.GT, - exp.GT: exp.LT, - exp.LTE: exp.GTE, - exp.GTE: exp.LTE, - } - - NONDETERMINISTIC = (exp.Rand, exp.Randn) - AND_OR = (exp.And, exp.Or) - - INVERSE_DATE_OPS: t.Dict[t.Type[exp.Expression], t.Type[exp.Expression]] = { - exp.DateAdd: exp.Sub, - exp.DateSub: exp.Add, - exp.DatetimeAdd: exp.Sub, - exp.DatetimeSub: exp.Add, - } - - INVERSE_OPS: t.Dict[t.Type[exp.Expression], t.Type[exp.Expression]] = { - **INVERSE_DATE_OPS, - exp.Add: exp.Sub, - exp.Sub: exp.Add, - } - - NULL_OK = (exp.NullSafeEQ, exp.NullSafeNEQ, exp.PropertyEQ) - - CONCATS = (exp.Concat, exp.DPipe) - - DATETRUNC_BINARY_COMPARISONS: t.Dict[ - t.Type[exp.Expression], DateTruncBinaryTransform - ] = { - exp.LT: lambda ll, dt, u, d, t: ll - < date_literal( - dt if dt == date_floor(dt, u, d) else date_floor(dt, u, d) + interval(u), t - ), - exp.GT: lambda ll, dt, u, d, t: ll - >= date_literal(date_floor(dt, u, d) + interval(u), t), - exp.LTE: lambda ll, dt, u, d, t: ll - < date_literal(date_floor(dt, u, d) + interval(u), t), - exp.GTE: lambda ll, dt, u, d, t: ll >= date_literal(date_ceil(dt, u, d), t), - exp.EQ: _datetrunc_eq, - exp.NEQ: _datetrunc_neq, - } - - DATETRUNC_COMPARISONS = {exp.In, *DATETRUNC_BINARY_COMPARISONS} - DATETRUNCS = (exp.DateTrunc, exp.TimestampTrunc) - - SAFE_CONNECTOR_ELIMINATION_RESULT = (exp.Connector, exp.Boolean) - - # CROSS joins result in an empty table if the right table is empty. - # So we can only simplify certain types of joins to CROSS. - # Or in other words, LEFT JOIN x ON TRUE != CROSS JOIN x - JOINS = { - ("", ""), - ("", "INNER"), - ("RIGHT", ""), - ("RIGHT", "OUTER"), - } - - def simplify( - self, - expression: exp.Expression, - constant_propagation: bool = False, - coalesce_simplification: bool = False, - ): - wheres = [] - joins = [] - - for node in expression.walk( - prune=lambda n: bool(isinstance(n, exp.Condition) or n.meta.get(FINAL)) - ): - if node.meta.get(FINAL): - continue - - # group by expressions cannot be simplified, for example - # select x + 1 + 1 FROM y GROUP BY x + 1 + 1 - # the projection must exactly match the group by key - group = node.args.get("group") - - if group and hasattr(node, "selects"): - groups = set(group.expressions) - group.meta[FINAL] = True - - for s in node.selects: - for n in s.walk(FINAL): - if n in groups: - s.meta[FINAL] = True - break - - having = node.args.get("having") - - if having: - for n in having.walk(): - if n in groups: - having.meta[FINAL] = True - break - - if isinstance(node, exp.Condition): - simplified = while_changing( - node, - lambda e: self._simplify( - e, constant_propagation, coalesce_simplification - ), - ) - - if node is expression: - expression = simplified - elif isinstance(node, exp.Where): - wheres.append(node) - elif isinstance(node, exp.Join): - # snowflake match_conditions have very strict ordering rules - if match := node.args.get("match_condition"): - match.meta[FINAL] = True - - joins.append(node) - - for where in wheres: - if always_true(where.this): - where.pop() - for join in joins: - if ( - always_true(join.args.get("on")) - and not join.args.get("using") - and not join.args.get("method") - and (join.side, join.kind) in self.JOINS - ): - join.args["on"].pop() - join.set("side", None) - join.set("kind", "CROSS") - - return expression - - def _simplify( - self, - expression: exp.Expression, - constant_propagation: bool, - coalesce_simplification: bool, - ): - pre_transformation_stack = [expression] - post_transformation_stack = [] - - while pre_transformation_stack: - original = pre_transformation_stack.pop() - node = original - - if not isinstance(node, SIMPLIFIABLE): - if isinstance(node, exp.Query): - self.simplify(node, constant_propagation, coalesce_simplification) - continue - - parent = node.parent - root = node is expression - - node = self.rewrite_between(node) - node = self.uniq_sort(node, root) - node = self.absorb_and_eliminate(node, root) - node = self.simplify_concat(node) - node = self.simplify_conditionals(node) - - if constant_propagation: - node = propagate_constants(node, root) - - if node is not original: - original.replace(node) - - for n in node.iter_expressions(reverse=True): - if n.meta.get(FINAL): - raise - pre_transformation_stack.extend( - n for n in node.iter_expressions(reverse=True) if not n.meta.get(FINAL) - ) - post_transformation_stack.append((node, parent)) - - while post_transformation_stack: - original, parent = post_transformation_stack.pop() - root = original is expression - - # Resets parent, arg_key, index pointers– this is needed because some of the - # previous transformations mutate the AST, leading to an inconsistent state - for k, v in tuple(original.args.items()): - original.set(k, v) - - # Post-order transformations - node = self.simplify_not(original) - node = flatten(node) - node = self.simplify_connectors(node, root) - node = self.remove_complements(node, root) - - if coalesce_simplification: - node = self.simplify_coalesce(node) - node.parent = parent - - node = self.simplify_literals(node, root) - node = self.simplify_equality(node) - node = simplify_parens(node, dialect=self.dialect) - node = self.simplify_datetrunc(node) - node = self.sort_comparison(node) - node = self.simplify_startswith(node) - - if node is not original: - original.replace(node) - - return node - - @annotate_types_on_change - def rewrite_between(self, expression: exp.Expression) -> exp.Expression: - """Rewrite x between y and z to x >= y AND x <= z. - - This is done because comparison simplification is only done on lt/lte/gt/gte. - """ - if isinstance(expression, exp.Between): - negate = isinstance(expression.parent, exp.Not) - - expression = exp.and_( - exp.GTE(this=expression.this.copy(), expression=expression.args["low"]), - exp.LTE( - this=expression.this.copy(), expression=expression.args["high"] - ), - copy=False, - ) - - if negate: - expression = exp.paren(expression, copy=False) - - return expression - - @annotate_types_on_change - def simplify_not(self, expression: exp.Expression) -> exp.Expression: - """ - Demorgan's Law - NOT (x OR y) -> NOT x AND NOT y - NOT (x AND y) -> NOT x OR NOT y - """ - if isinstance(expression, exp.Not): - this = expression.this - if is_null(this): - return exp.and_(exp.null(), exp.true(), copy=False) - if this.__class__ in self.COMPLEMENT_COMPARISONS: - right = this.expression - complement_subquery_predicate = self.COMPLEMENT_SUBQUERY_PREDICATES.get( - right.__class__ - ) - if complement_subquery_predicate: - right = complement_subquery_predicate(this=right.this) - - return self.COMPLEMENT_COMPARISONS[this.__class__]( - this=this.this, expression=right - ) - if isinstance(this, exp.Paren): - condition = this.unnest() - if isinstance(condition, exp.And): - return exp.paren( - exp.or_( - exp.not_(condition.left, copy=False), - exp.not_(condition.right, copy=False), - copy=False, - ), - copy=False, - ) - if isinstance(condition, exp.Or): - return exp.paren( - exp.and_( - exp.not_(condition.left, copy=False), - exp.not_(condition.right, copy=False), - copy=False, - ), - copy=False, - ) - if is_null(condition): - return exp.and_(exp.null(), exp.true(), copy=False) - if always_true(this): - return exp.false() - if is_false(this): - return exp.true() - if ( - isinstance(this, exp.Not) - and self.dialect.SAFE_TO_ELIMINATE_DOUBLE_NEGATION - ): - inner = this.this - if inner.is_type(exp.DataType.Type.BOOLEAN): - # double negation - # NOT NOT x -> x, if x is BOOLEAN type - return inner - return expression - - @annotate_types_on_change - def simplify_connectors(self, expression, root=True): - def _simplify_connectors(expression, left, right): - if isinstance(expression, exp.And): - if is_false(left) or is_false(right): - return exp.false() - if is_zero(left) or is_zero(right): - return exp.false() - if ( - (is_null(left) and is_null(right)) - or (is_null(left) and always_true(right)) - or (always_true(left) and is_null(right)) - ): - return exp.null() - if always_true(left) and always_true(right): - return exp.true() - if always_true(left): - return right - if always_true(right): - return left - return self._simplify_comparison(expression, left, right) - elif isinstance(expression, exp.Or): - if always_true(left) or always_true(right): - return exp.true() - if ( - (is_null(left) and is_null(right)) - or (is_null(left) and always_false(right)) - or (always_false(left) and is_null(right)) - ): - return exp.null() - if is_false(left): - return right - if is_false(right): - return left - return self._simplify_comparison(expression, left, right, or_=True) - - if isinstance(expression, exp.Connector): - original_parent = expression.parent - expression = self._flat_simplify(expression, _simplify_connectors, root) - - # If we reduced a connector to, e.g., a column (t1 AND ... AND tn -> Tk), then we need - # to ensure that the resulting type is boolean. We know this is true only for connectors, - # boolean values and columns that are essentially operands to a connector: - # - # A AND (((B))) - # ~ this is safe to keep because it will eventually be part of another connector - if not isinstance( - expression, self.SAFE_CONNECTOR_ELIMINATION_RESULT - ) and not expression.is_type(exp.DataType.Type.BOOLEAN): - while True: - if isinstance(original_parent, exp.Connector): - break - if not isinstance(original_parent, exp.Paren): - expression = expression.and_(exp.true(), copy=False) - break - - original_parent = original_parent.parent - - return expression - - @annotate_types_on_change - def _simplify_comparison(self, expression, left, right, or_=False): - if isinstance(left, self.COMPARISONS) and isinstance(right, self.COMPARISONS): - ll, lr = left.args.values() - rl, rr = right.args.values() - - largs = {ll, lr} - rargs = {rl, rr} - - matching = largs & rargs - columns = { - m - for m in matching - if not _is_constant(m) and not m.find(*self.NONDETERMINISTIC) - } - - if matching and columns: - try: - l0 = first(largs - columns) - r = first(rargs - columns) - except StopIteration: - return expression - - if l0.is_number and r.is_number: - l0 = l0.to_py() - r = r.to_py() - elif l0.is_string and r.is_string: - l0 = l0.name - r = r.name - else: - l0 = extract_date(l0) - if not l0: - return None - r = extract_date(r) - if not r: - return None - # python won't compare date and datetime, but many engines will upcast - l0, r = cast_as_datetime(l0), cast_as_datetime(r) - - for (a, av), (b, bv) in itertools.permutations( - ((left, l0), (right, r)) - ): - if isinstance(a, self.LT_LTE) and isinstance(b, self.LT_LTE): - return left if (av > bv if or_ else av <= bv) else right - if isinstance(a, self.GT_GTE) and isinstance(b, self.GT_GTE): - return left if (av < bv if or_ else av >= bv) else right - - # we can't ever shortcut to true because the column could be null - if not or_: - if isinstance(a, exp.LT) and isinstance(b, self.GT_GTE): - if av <= bv: - return exp.false() - elif isinstance(a, exp.GT) and isinstance(b, self.LT_LTE): - if av >= bv: - return exp.false() - elif isinstance(a, exp.EQ): - if isinstance(b, exp.LT): - return exp.false() if av >= bv else a - if isinstance(b, exp.LTE): - return exp.false() if av > bv else a - if isinstance(b, exp.GT): - return exp.false() if av <= bv else a - if isinstance(b, exp.GTE): - return exp.false() if av < bv else a - if isinstance(b, exp.NEQ): - return exp.false() if av == bv else a - return None - - @annotate_types_on_change - def remove_complements(self, expression, root=True): - """ - Removing complements. - - A AND NOT A -> FALSE (only for non-NULL A) - A OR NOT A -> TRUE (only for non-NULL A) - """ - if isinstance(expression, self.AND_OR) and (root or not expression.same_parent): - ops = set(expression.flatten()) - for op in ops: - if isinstance(op, exp.Not) and op.this in ops: - if expression.meta.get("nonnull") is True: - return ( - exp.false() - if isinstance(expression, exp.And) - else exp.true() - ) - - return expression - - @annotate_types_on_change - def uniq_sort(self, expression, root=True): - """ - Uniq and sort a connector. - - C AND A AND B AND B -> A AND B AND C - """ - if isinstance(expression, exp.Connector) and ( - root or not expression.same_parent - ): - flattened = tuple(expression.flatten()) - - if isinstance(expression, exp.Xor): - result_func = exp.xor - # Do not deduplicate XOR as A XOR A != A if A == True - deduped = None - arr = tuple((gen(e), e) for e in flattened) - else: - result_func = exp.and_ if isinstance(expression, exp.And) else exp.or_ - deduped = {gen(e): e for e in flattened} - arr = tuple(deduped.items()) - - # check if the operands are already sorted, if not sort them - # A AND C AND B -> A AND B AND C - for i, (sql, e) in enumerate(arr[1:]): - if sql < arr[i][0]: - expression = result_func(*(e for _, e in sorted(arr)), copy=False) - break - else: - # we didn't have to sort but maybe we need to dedup - if deduped and len(deduped) < len(flattened): - unique_operand = flattened[0] - if len(deduped) == 1: - expression = unique_operand.and_(exp.true(), copy=False) - else: - expression = result_func(*deduped.values(), copy=False) - - return expression - - @annotate_types_on_change - def absorb_and_eliminate(self, expression, root=True): - """ - absorption: - A AND (A OR B) -> A - A OR (A AND B) -> A - A AND (NOT A OR B) -> A AND B - A OR (NOT A AND B) -> A OR B - elimination: - (A AND B) OR (A AND NOT B) -> A - (A OR B) AND (A OR NOT B) -> A - """ - if isinstance(expression, self.AND_OR) and (root or not expression.same_parent): - kind = exp.Or if isinstance(expression, exp.And) else exp.And - - ops = tuple(expression.flatten()) - - # Initialize lookup tables: - # Set of all operands, used to find complements for absorption. - op_set = set() - # Sub-operands, used to find subsets for absorption. - subops = defaultdict(list) - # Pairs of complements, used for elimination. - pairs = defaultdict(list) - - # Populate the lookup tables - for op in ops: - op_set.add(op) - - if not isinstance(op, kind): - # In cases like: A OR (A AND B) - # Subop will be: ^ - subops[op].append({op}) - continue - - # In cases like: (A AND B) OR (A AND B AND C) - # Subops will be: ^ ^ - subset = set(op.flatten()) - for i in subset: - subops[i].append(subset) - - a, b = op.unnest_operands() - if isinstance(a, exp.Not): - pairs[frozenset((a.this, b))].append((op, b)) - if isinstance(b, exp.Not): - pairs[frozenset((a, b.this))].append((op, a)) - - for op in ops: - if not isinstance(op, kind): - continue - - a, b = op.unnest_operands() - - # Absorb - if isinstance(a, exp.Not) and a.this in op_set: - a.replace(exp.true() if kind == exp.And else exp.false()) - continue - if isinstance(b, exp.Not) and b.this in op_set: - b.replace(exp.true() if kind == exp.And else exp.false()) - continue - superset = set(op.flatten()) - if any( - any(subset < superset for subset in subops[i]) for i in superset - ): - op.replace(exp.false() if kind == exp.And else exp.true()) - continue - - # Eliminate - for other, complement in pairs[frozenset((a, b))]: - op.replace(complement) - other.replace(complement) - - return expression - - @annotate_types_on_change - @catch(ModuleNotFoundError, UnsupportedUnit) - def simplify_equality(self, expression: exp.Expression) -> exp.Expression: - """ - Use the subtraction and addition properties of equality to simplify expressions: - - x + 1 = 3 becomes x = 2 - - There are two binary operations in the above expression: + and = - Here's how we reference all the operands in the code below: - - l r - x + 1 = 3 - a b - """ - if isinstance(expression, self.COMPARISONS): - ll, r = expression.left, expression.right - - if ll.__class__ not in self.INVERSE_OPS: - return expression - - if r.is_number: - a_predicate = _is_number - b_predicate = _is_number - elif _is_date_literal(r): - a_predicate = _is_date_literal - b_predicate = _is_interval - else: - return expression - - if ll.__class__ in self.INVERSE_DATE_OPS: - ll = t.cast(exp.IntervalOp, ll) - a = ll.this - b = ll.interval() - else: - ll = t.cast(exp.Binary, ll) - a, b = ll.left, ll.right - - if not a_predicate(a) and b_predicate(b): - pass - elif not a_predicate(b) and b_predicate(a): - a, b = b, a - else: - return expression - - return expression.__class__( - this=a, expression=self.INVERSE_OPS[ll.__class__](this=r, expression=b) - ) - return expression - - @annotate_types_on_change - def simplify_literals(self, expression, root=True): - if isinstance(expression, exp.Binary) and not isinstance( - expression, exp.Connector - ): - return self._flat_simplify(expression, self._simplify_binary, root) - - if isinstance(expression, exp.Neg) and isinstance(expression.this, exp.Neg): - return expression.this.this - - if type(expression) in self.INVERSE_DATE_OPS: - return ( - self._simplify_binary( - expression, expression.this, expression.interval() - ) - or expression - ) - - return expression - - def _simplify_integer_cast(self, expr: exp.Expression) -> exp.Expression: - if isinstance(expr, exp.Cast) and isinstance(expr.this, exp.Cast): - this = self._simplify_integer_cast(expr.this) - else: - this = expr.this - - if isinstance(expr, exp.Cast) and this.is_int: - num = this.to_py() - - # Remove the (up)cast from small (byte-sized) integers in predicates which is side-effect free. Downcasts on any - # integer type might cause overflow, thus the cast cannot be eliminated and the behavior is - # engine-dependent - if ( - self.TINYINT_MIN <= num <= self.TINYINT_MAX - and expr.to.this in exp.DataType.SIGNED_INTEGER_TYPES - ) or ( - self.UTINYINT_MIN <= num <= self.UTINYINT_MAX - and expr.to.this in exp.DataType.UNSIGNED_INTEGER_TYPES - ): - return this - - return expr - - def _simplify_binary(self, expression, a, b): - if isinstance(expression, self.COMPARISONS): - a = self._simplify_integer_cast(a) - b = self._simplify_integer_cast(b) - - if isinstance(expression, exp.Is): - if isinstance(b, exp.Not): - c = b.this - not_ = True - else: - c = b - not_ = False - - if is_null(c): - if isinstance(a, exp.Literal): - return exp.true() if not_ else exp.false() - if is_null(a): - return exp.false() if not_ else exp.true() - elif isinstance(expression, self.NULL_OK): - return None - elif (is_null(a) or is_null(b)) and isinstance(expression.parent, exp.If): - return exp.null() - - if a.is_number and b.is_number: - num_a = a.to_py() - num_b = b.to_py() - - if isinstance(expression, exp.Add): - return exp.Literal.number(num_a + num_b) - if isinstance(expression, exp.Mul): - return exp.Literal.number(num_a * num_b) - - # We only simplify Sub, Div if a and b have the same parent because they're not associative - if isinstance(expression, exp.Sub): - return ( - exp.Literal.number(num_a - num_b) if a.parent is b.parent else None - ) - if isinstance(expression, exp.Div): - # engines have differing int div behavior so intdiv is not safe - if ( - isinstance(num_a, int) and isinstance(num_b, int) - ) or a.parent is not b.parent: - return None - return exp.Literal.number(num_a / num_b) - - boolean = eval_boolean(expression, num_a, num_b) - - if boolean: - return boolean - elif a.is_string and b.is_string: - boolean = eval_boolean(expression, a.this, b.this) - - if boolean: - return boolean - elif _is_date_literal(a) and isinstance(b, exp.Interval): - date, b = extract_date(a), extract_interval(b) - if date and b: - if isinstance(expression, (exp.Add, exp.DateAdd, exp.DatetimeAdd)): - return date_literal(date + b, extract_type(a)) - if isinstance(expression, (exp.Sub, exp.DateSub, exp.DatetimeSub)): - return date_literal(date - b, extract_type(a)) - elif isinstance(a, exp.Interval) and _is_date_literal(b): - a, date = extract_interval(a), extract_date(b) - # you cannot subtract a date from an interval - if a and b and isinstance(expression, exp.Add): - return date_literal(a + date, extract_type(b)) - elif _is_date_literal(a) and _is_date_literal(b): - if isinstance(expression, exp.Predicate): - a, b = extract_date(a), extract_date(b) - boolean = eval_boolean(expression, a, b) - if boolean: - return boolean - - return None - - @annotate_types_on_change - def simplify_coalesce(self, expression: exp.Expression) -> exp.Expression: - # COALESCE(x) -> x - if ( - isinstance(expression, exp.Coalesce) - and (not expression.expressions or _is_nonnull_constant(expression.this)) - # COALESCE is also used as a Spark partitioning hint - and not isinstance(expression.parent, exp.Hint) - ): - return expression.this - - if self.dialect.COALESCE_COMPARISON_NON_STANDARD: - return expression - - if not isinstance(expression, self.COMPARISONS): - return expression - - if isinstance(expression.left, exp.Coalesce): - coalesce = expression.left - other = expression.right - elif isinstance(expression.right, exp.Coalesce): - coalesce = expression.right - other = expression.left - else: - return expression - - # This transformation is valid for non-constants, - # but it really only does anything if they are both constants. - if not _is_constant(other): - return expression - - # Find the first constant arg - for arg_index, arg in enumerate(coalesce.expressions): - if _is_constant(arg): - break - else: - return expression - - coalesce.set("expressions", coalesce.expressions[:arg_index]) - - # Remove the COALESCE function. This is an optimization, skipping a simplify iteration, - # since we already remove COALESCE at the top of this function. - coalesce = coalesce if coalesce.expressions else coalesce.this - - # This expression is more complex than when we started, but it will get simplified further - return exp.paren( - exp.or_( - exp.and_( - coalesce.is_(exp.null()).not_(copy=False), - expression.copy(), - copy=False, - ), - exp.and_( - coalesce.is_(exp.null()), - type(expression)(this=arg.copy(), expression=other.copy()), - copy=False, - ), - copy=False, - ), - copy=False, - ) - - @annotate_types_on_change - def simplify_concat(self, expression): - """Reduces all groups that contain string literals by concatenating them.""" - if not isinstance(expression, self.CONCATS) or ( - # We can't reduce a CONCAT_WS call if we don't statically know the separator - isinstance(expression, exp.ConcatWs) - and not expression.expressions[0].is_string - ): - return expression - - if isinstance(expression, exp.ConcatWs): - sep_expr, *expressions = expression.expressions - sep = sep_expr.name - concat_type = exp.ConcatWs - args = {} - else: - expressions = expression.expressions - sep = "" - concat_type = exp.Concat - args = { - "safe": expression.args.get("safe"), - "coalesce": expression.args.get("coalesce"), - } - - new_args = [] - for is_string_group, group in itertools.groupby( - expressions or expression.flatten(), lambda e: e.is_string - ): - if is_string_group: - new_args.append( - exp.Literal.string(sep.join(string.name for string in group)) - ) - else: - new_args.extend(group) - - if len(new_args) == 1 and new_args[0].is_string: - return new_args[0] - - if concat_type is exp.ConcatWs: - new_args = [sep_expr] + new_args - elif isinstance(expression, exp.DPipe): - return reduce(lambda x, y: exp.DPipe(this=x, expression=y), new_args) - - return concat_type(expressions=new_args, **args) - - @annotate_types_on_change - def simplify_conditionals(self, expression): - """Simplifies expressions like IF, CASE if their condition is statically known.""" - if isinstance(expression, exp.Case): - this = expression.this - for case in expression.args["ifs"]: - cond = case.this - if this: - # Convert CASE x WHEN matching_value ... to CASE WHEN x = matching_value ... - cond = cond.replace(this.pop().eq(cond)) - - if always_true(cond): - return case.args["true"] - - if always_false(cond): - case.pop() - if not expression.args["ifs"]: - return expression.args.get("default") or exp.null() - elif isinstance(expression, exp.If) and not isinstance( - expression.parent, exp.Case - ): - if always_true(expression.this): - return expression.args["true"] - if always_false(expression.this): - return expression.args.get("false") or exp.null() - - return expression - - @annotate_types_on_change - def simplify_startswith(self, expression: exp.Expression) -> exp.Expression: - """ - Reduces a prefix check to either TRUE or FALSE if both the string and the - prefix are statically known. - - Example: - >>> from bigframes_vendored.sqlglot import parse_one - >>> Simplifier().simplify_startswith(parse_one("STARTSWITH('foo', 'f')")).sql() - 'TRUE' - """ - if ( - isinstance(expression, exp.StartsWith) - and expression.this.is_string - and expression.expression.is_string - ): - return exp.convert(expression.name.startswith(expression.expression.name)) - - return expression - - def _is_datetrunc_predicate( - self, left: exp.Expression, right: exp.Expression - ) -> bool: - return isinstance(left, self.DATETRUNCS) and _is_date_literal(right) - - @annotate_types_on_change - @catch(ModuleNotFoundError, UnsupportedUnit) - def simplify_datetrunc(self, expression: exp.Expression) -> exp.Expression: - """Simplify expressions like `DATE_TRUNC('year', x) >= CAST('2021-01-01' AS DATE)`""" - comparison = expression.__class__ - - if isinstance(expression, self.DATETRUNCS): - this = expression.this - trunc_type = extract_type(this) - date = extract_date(this) - if date and expression.unit: - return date_literal( - date_floor(date, expression.unit.name.lower(), self.dialect), - trunc_type, - ) - elif comparison not in self.DATETRUNC_COMPARISONS: - return expression - - if isinstance(expression, exp.Binary): - ll, r = expression.left, expression.right - - if not self._is_datetrunc_predicate(ll, r): - return expression - - ll = t.cast(exp.DateTrunc, ll) - trunc_arg = ll.this - unit = ll.unit.name.lower() - date = extract_date(r) - - if not date: - return expression - - return ( - self.DATETRUNC_BINARY_COMPARISONS[comparison]( - trunc_arg, date, unit, self.dialect, extract_type(r) - ) - or expression - ) - - if isinstance(expression, exp.In): - ll = expression.this - rs = expression.expressions - - if rs and all(self._is_datetrunc_predicate(ll, r) for r in rs): - ll = t.cast(exp.DateTrunc, ll) - unit = ll.unit.name.lower() - - ranges = [] - for r in rs: - date = extract_date(r) - if not date: - return expression - drange = _datetrunc_range(date, unit, self.dialect) - if drange: - ranges.append(drange) - - if not ranges: - return expression - - ranges = merge_ranges(ranges) - target_type = extract_type(*rs) - - return exp.or_( - *[ - _datetrunc_eq_expression(ll, drange, target_type) - for drange in ranges - ], - copy=False, - ) - - return expression - - @annotate_types_on_change - def sort_comparison(self, expression: exp.Expression) -> exp.Expression: - if expression.__class__ in self.COMPLEMENT_COMPARISONS: - l, r = expression.this, expression.expression - l_column = isinstance(l, exp.Column) - r_column = isinstance(r, exp.Column) - l_const = _is_constant(l) - r_const = _is_constant(r) - - if ( - (l_column and not r_column) - or (r_const and not l_const) - or isinstance(r, exp.SubqueryPredicate) - ): - return expression - if ( - (r_column and not l_column) - or (l_const and not r_const) - or (gen(l) > gen(r)) - ): - return self.INVERSE_COMPARISONS.get( - expression.__class__, expression.__class__ - )(this=r, expression=l) - return expression - - def _flat_simplify(self, expression, simplifier, root=True): - if root or not expression.same_parent: - operands = [] - queue = deque(expression.flatten(unnest=False)) - size = len(queue) - - while queue: - a = queue.popleft() - - for b in queue: - result = simplifier(expression, a, b) - - if result and result is not expression: - queue.remove(b) - queue.appendleft(result) - break - else: - operands.append(a) - - if len(operands) < size: - return functools.reduce( - lambda a, b: expression.__class__(this=a, expression=b), operands - ) - return expression - - -def gen(expression: t.Any, comments: bool = False) -> str: - """Simple pseudo sql generator for quickly generating sortable and uniq strings. - - Sorting and deduping sql is a necessary step for optimization. Calling the actual - generator is expensive so we have a bare minimum sql generator here. - - Args: - expression: the expression to convert into a SQL string. - comments: whether to include the expression's comments. - """ - return Gen().gen(expression, comments=comments) - - -class Gen: - def __init__(self): - self.stack = [] - self.sqls = [] - - def gen(self, expression: exp.Expression, comments: bool = False) -> str: - self.stack = [expression] - self.sqls.clear() - - while self.stack: - node = self.stack.pop() - - if isinstance(node, exp.Expression): - if comments and node.comments: - self.stack.append(f" /*{','.join(node.comments)}*/") - - exp_handler_name = f"{node.key}_sql" - - if hasattr(self, exp_handler_name): - getattr(self, exp_handler_name)(node) - elif isinstance(node, exp.Func): - self._function(node) - else: - key = node.key.upper() - self.stack.append(f"{key} " if self._args(node) else key) - elif type(node) is list: - for n in reversed(node): - if n is not None: - self.stack.extend((n, ",")) - if node: - self.stack.pop() - else: - if node is not None: - self.sqls.append(str(node)) - - return "".join(self.sqls) - - def add_sql(self, e: exp.Add) -> None: - self._binary(e, " + ") - - def alias_sql(self, e: exp.Alias) -> None: - self.stack.extend( - ( - e.args.get("alias"), - " AS ", - e.args.get("this"), - ) - ) - - def and_sql(self, e: exp.And) -> None: - self._binary(e, " AND ") - - def anonymous_sql(self, e: exp.Anonymous) -> None: - this = e.this - if isinstance(this, str): - name = this.upper() - elif isinstance(this, exp.Identifier): - name = this.this - name = f'"{name}"' if this.quoted else name.upper() - else: - raise ValueError( - f"Anonymous.this expects a str or an Identifier, got '{this.__class__.__name__}'." - ) - - self.stack.extend( - ( - ")", - e.expressions, - "(", - name, - ) - ) - - def between_sql(self, e: exp.Between) -> None: - self.stack.extend( - ( - e.args.get("high"), - " AND ", - e.args.get("low"), - " BETWEEN ", - e.this, - ) - ) - - def boolean_sql(self, e: exp.Boolean) -> None: - self.stack.append("TRUE" if e.this else "FALSE") - - def bracket_sql(self, e: exp.Bracket) -> None: - self.stack.extend( - ( - "]", - e.expressions, - "[", - e.this, - ) - ) - - def column_sql(self, e: exp.Column) -> None: - for p in reversed(e.parts): - self.stack.extend((p, ".")) - self.stack.pop() - - def datatype_sql(self, e: exp.DataType) -> None: - self._args(e, 1) - self.stack.append(f"{e.this.name} ") - - def div_sql(self, e: exp.Div) -> None: - self._binary(e, " / ") - - def dot_sql(self, e: exp.Dot) -> None: - self._binary(e, ".") - - def eq_sql(self, e: exp.EQ) -> None: - self._binary(e, " = ") - - def from_sql(self, e: exp.From) -> None: - self.stack.extend((e.this, "FROM ")) - - def gt_sql(self, e: exp.GT) -> None: - self._binary(e, " > ") - - def gte_sql(self, e: exp.GTE) -> None: - self._binary(e, " >= ") - - def identifier_sql(self, e: exp.Identifier) -> None: - self.stack.append(f'"{e.this}"' if e.quoted else e.this) - - def ilike_sql(self, e: exp.ILike) -> None: - self._binary(e, " ILIKE ") - - def in_sql(self, e: exp.In) -> None: - self.stack.append(")") - self._args(e, 1) - self.stack.extend( - ( - "(", - " IN ", - e.this, - ) - ) - - def intdiv_sql(self, e: exp.IntDiv) -> None: - self._binary(e, " DIV ") - - def is_sql(self, e: exp.Is) -> None: - self._binary(e, " IS ") - - def like_sql(self, e: exp.Like) -> None: - self._binary(e, " Like ") - - def literal_sql(self, e: exp.Literal) -> None: - self.stack.append(f"'{e.this}'" if e.is_string else e.this) - - def lt_sql(self, e: exp.LT) -> None: - self._binary(e, " < ") - - def lte_sql(self, e: exp.LTE) -> None: - self._binary(e, " <= ") - - def mod_sql(self, e: exp.Mod) -> None: - self._binary(e, " % ") - - def mul_sql(self, e: exp.Mul) -> None: - self._binary(e, " * ") - - def neg_sql(self, e: exp.Neg) -> None: - self._unary(e, "-") - - def neq_sql(self, e: exp.NEQ) -> None: - self._binary(e, " <> ") - - def not_sql(self, e: exp.Not) -> None: - self._unary(e, "NOT ") - - def null_sql(self, e: exp.Null) -> None: - self.stack.append("NULL") - - def or_sql(self, e: exp.Or) -> None: - self._binary(e, " OR ") - - def paren_sql(self, e: exp.Paren) -> None: - self.stack.extend( - ( - ")", - e.this, - "(", - ) - ) - - def sub_sql(self, e: exp.Sub) -> None: - self._binary(e, " - ") - - def subquery_sql(self, e: exp.Subquery) -> None: - self._args(e, 2) - alias = e.args.get("alias") - if alias: - self.stack.append(alias) - self.stack.extend((")", e.this, "(")) - - def table_sql(self, e: exp.Table) -> None: - self._args(e, 4) - alias = e.args.get("alias") - if alias: - self.stack.append(alias) - for p in reversed(e.parts): - self.stack.extend((p, ".")) - self.stack.pop() - - def tablealias_sql(self, e: exp.TableAlias) -> None: - columns = e.columns - - if columns: - self.stack.extend((")", columns, "(")) - - self.stack.extend((e.this, " AS ")) - - def var_sql(self, e: exp.Var) -> None: - self.stack.append(e.this) - - def _binary(self, e: exp.Binary, op: str) -> None: - self.stack.extend((e.expression, op, e.this)) - - def _unary(self, e: exp.Unary, op: str) -> None: - self.stack.extend((e.this, op)) - - def _function(self, e: exp.Func) -> None: - self.stack.extend( - ( - ")", - list(e.args.values()), - "(", - e.sql_name(), - ) - ) - - def _args(self, node: exp.Expression, arg_index: int = 0) -> bool: - kvs = [] - arg_types = list(node.arg_types)[arg_index:] if arg_index else node.arg_types - - for k in arg_types: - v = node.args.get(k) - - if v is not None: - kvs.append([f":{k}", v]) - if kvs: - self.stack.append(kvs) - return True - return False diff --git a/third_party/bigframes_vendored/sqlglot/optimizer/unnest_subqueries.py b/third_party/bigframes_vendored/sqlglot/optimizer/unnest_subqueries.py deleted file mode 100644 index 0e3431bfa9b..00000000000 --- a/third_party/bigframes_vendored/sqlglot/optimizer/unnest_subqueries.py +++ /dev/null @@ -1,331 +0,0 @@ -# Contains code from https://github.com/tobymao/sqlglot/blob/v28.5.0/sqlglot/optimizer/unnest_subqueries.py - -from bigframes_vendored.sqlglot import exp -from bigframes_vendored.sqlglot.helper import name_sequence -from bigframes_vendored.sqlglot.optimizer.scope import ( - ScopeType, - find_in_scope, - traverse_scope, -) - - -def unnest_subqueries(expression): - """ - Rewrite sqlglot AST to convert some predicates with subqueries into joins. - - Convert scalar subqueries into cross joins. - Convert correlated or vectorized subqueries into a group by so it is not a many to many left join. - - Example: - >>> import sqlglot - >>> expression = sqlglot.parse_one("SELECT * FROM x AS x WHERE (SELECT y.a AS a FROM y AS y WHERE x.a = y.a) = 1 ") - >>> unnest_subqueries(expression).sql() - 'SELECT * FROM x AS x LEFT JOIN (SELECT y.a AS a FROM y AS y WHERE TRUE GROUP BY y.a) AS _u_0 ON x.a = _u_0.a WHERE _u_0.a = 1' - - Args: - expression (sqlglot.Expression): expression to unnest - Returns: - sqlglot.Expression: unnested expression - """ - next_alias_name = name_sequence("_u_") - - for scope in traverse_scope(expression): - select = scope.expression - parent = select.parent_select - if not parent: - continue - if scope.external_columns: - decorrelate(select, parent, scope.external_columns, next_alias_name) - elif scope.scope_type == ScopeType.SUBQUERY: - unnest(select, parent, next_alias_name) - - return expression - - -def unnest(select, parent_select, next_alias_name): - if len(select.selects) > 1: - return - - predicate = select.find_ancestor(exp.Condition) - if ( - not predicate - or parent_select is not predicate.parent_select - or not parent_select.args.get("from_") - ): - return - - if isinstance(select, exp.SetOperation): - select = exp.select(*select.selects).from_(select.subquery(next_alias_name())) - - alias = next_alias_name() - clause = predicate.find_ancestor(exp.Having, exp.Where, exp.Join) - - # This subquery returns a scalar and can just be converted to a cross join - if not isinstance(predicate, (exp.In, exp.Any)): - column = exp.column(select.selects[0].alias_or_name, alias) - - clause_parent_select = clause.parent_select if clause else None - - if ( - isinstance(clause, exp.Having) and clause_parent_select is parent_select - ) or ( - (not clause or clause_parent_select is not parent_select) - and ( - parent_select.args.get("group") - or any( - find_in_scope(select, exp.AggFunc) - for select in parent_select.selects - ) - ) - ): - column = exp.Max(this=column) - elif not isinstance(select.parent, exp.Subquery): - return - - join_type = "CROSS" - on_clause = None - if isinstance(predicate, exp.Exists): - # If a subquery returns no rows, cross-joining against it incorrectly eliminates all rows - # from the parent query. Therefore, we use a LEFT JOIN that always matches (ON TRUE), then - # check for non-NULL column values to determine whether the subquery contained rows. - column = column.is_(exp.null()).not_() - join_type = "LEFT" - on_clause = exp.true() - - _replace(select.parent, column) - parent_select.join( - select, on=on_clause, join_type=join_type, join_alias=alias, copy=False - ) - return - - if select.find(exp.Limit, exp.Offset): - return - - if isinstance(predicate, exp.Any): - predicate = predicate.find_ancestor(exp.EQ) - - if not predicate or parent_select is not predicate.parent_select: - return - - column = _other_operand(predicate) - value = select.selects[0] - - join_key = exp.column(value.alias, alias) - join_key_not_null = join_key.is_(exp.null()).not_() - - if isinstance(clause, exp.Join): - _replace(predicate, exp.true()) - parent_select.where(join_key_not_null, copy=False) - else: - _replace(predicate, join_key_not_null) - - group = select.args.get("group") - - if group: - if {value.this} != set(group.expressions): - select = ( - exp.select(exp.alias_(exp.column(value.alias, "_q"), value.alias)) - .from_(select.subquery("_q", copy=False), copy=False) - .group_by(exp.column(value.alias, "_q"), copy=False) - ) - elif not find_in_scope(value.this, exp.AggFunc): - select = select.group_by(value.this, copy=False) - - parent_select.join( - select, - on=column.eq(join_key), - join_type="LEFT", - join_alias=alias, - copy=False, - ) - - -def decorrelate(select, parent_select, external_columns, next_alias_name): - where = select.args.get("where") - - if not where or where.find(exp.Or) or select.find(exp.Limit, exp.Offset): - return - - table_alias = next_alias_name() - keys = [] - - # for all external columns in the where statement, find the relevant predicate - # keys to convert it into a join - for column in external_columns: - if column.find_ancestor(exp.Where) is not where: - return - - predicate = column.find_ancestor(exp.Predicate) - - if not predicate or predicate.find_ancestor(exp.Where) is not where: - return - - if isinstance(predicate, exp.Binary): - key = ( - predicate.right - if any(node is column for node in predicate.left.walk()) - else predicate.left - ) - else: - return - - keys.append((key, column, predicate)) - - if not any(isinstance(predicate, exp.EQ) for *_, predicate in keys): - return - - is_subquery_projection = any( - node is select.parent - for node in map(lambda s: s.unalias(), parent_select.selects) - if isinstance(node, exp.Subquery) - ) - - value = select.selects[0] - key_aliases = {} - group_by = [] - - for key, _, predicate in keys: - # if we filter on the value of the subquery, it needs to be unique - if key == value.this: - key_aliases[key] = value.alias - group_by.append(key) - else: - if key not in key_aliases: - key_aliases[key] = next_alias_name() - # all predicates that are equalities must also be in the unique - # so that we don't do a many to many join - if isinstance(predicate, exp.EQ) and key not in group_by: - group_by.append(key) - - parent_predicate = select.find_ancestor(exp.Predicate) - - # if the value of the subquery is not an agg or a key, we need to collect it into an array - # so that it can be grouped. For subquery projections, we use a MAX aggregation instead. - agg_func = exp.Max if is_subquery_projection else exp.ArrayAgg - if not value.find(exp.AggFunc) and value.this not in group_by: - select.select( - exp.alias_(agg_func(this=value.this), value.alias, quoted=False), - append=False, - copy=False, - ) - - # exists queries should not have any selects as it only checks if there are any rows - # all selects will be added by the optimizer and only used for join keys - if isinstance(parent_predicate, exp.Exists): - select.set("expressions", []) - - for key, alias in key_aliases.items(): - if key in group_by: - # add all keys to the projections of the subquery - # so that we can use it as a join key - if isinstance(parent_predicate, exp.Exists) or key != value.this: - select.select(f"{key} AS {alias}", copy=False) - else: - select.select( - exp.alias_(agg_func(this=key.copy()), alias, quoted=False), copy=False - ) - - alias = exp.column(value.alias, table_alias) - other = _other_operand(parent_predicate) - op_type = type(parent_predicate.parent) if parent_predicate else None - - if isinstance(parent_predicate, exp.Exists): - alias = exp.column(list(key_aliases.values())[0], table_alias) - parent_predicate = _replace(parent_predicate, f"NOT {alias} IS NULL") - elif isinstance(parent_predicate, exp.All): - assert issubclass(op_type, exp.Binary) - predicate = op_type(this=other, expression=exp.column("_x")) - parent_predicate = _replace( - parent_predicate.parent, f"ARRAY_ALL({alias}, _x -> {predicate})" - ) - elif isinstance(parent_predicate, exp.Any): - assert issubclass(op_type, exp.Binary) - if value.this in group_by: - predicate = op_type(this=other, expression=alias) - parent_predicate = _replace(parent_predicate.parent, predicate) - else: - predicate = op_type(this=other, expression=exp.column("_x")) - parent_predicate = _replace( - parent_predicate, f"ARRAY_ANY({alias}, _x -> {predicate})" - ) - elif isinstance(parent_predicate, exp.In): - if value.this in group_by: - parent_predicate = _replace(parent_predicate, f"{other} = {alias}") - else: - parent_predicate = _replace( - parent_predicate, - f"ARRAY_ANY({alias}, _x -> _x = {parent_predicate.this})", - ) - else: - if is_subquery_projection and select.parent.alias: - alias = exp.alias_(alias, select.parent.alias) - - # COUNT always returns 0 on empty datasets, so we need take that into consideration here - # by transforming all counts into 0 and using that as the coalesced value - if value.find(exp.Count): - - def remove_aggs(node): - if isinstance(node, exp.Count): - return exp.Literal.number(0) - elif isinstance(node, exp.AggFunc): - return exp.null() - return node - - alias = exp.Coalesce( - this=alias, expressions=[value.this.transform(remove_aggs)] - ) - - select.parent.replace(alias) - - for key, column, predicate in keys: - predicate.replace(exp.true()) - nested = exp.column(key_aliases[key], table_alias) - - if is_subquery_projection: - key.replace(nested) - if not isinstance(predicate, exp.EQ): - parent_select.where(predicate, copy=False) - continue - - if key in group_by: - key.replace(nested) - elif isinstance(predicate, exp.EQ): - parent_predicate = _replace( - parent_predicate, - f"({parent_predicate} AND ARRAY_CONTAINS({nested}, {column}))", - ) - else: - key.replace(exp.to_identifier("_x")) - parent_predicate = _replace( - parent_predicate, - f"({parent_predicate} AND ARRAY_ANY({nested}, _x -> {predicate}))", - ) - - parent_select.join( - select.group_by(*group_by, copy=False), - on=[predicate for *_, predicate in keys if isinstance(predicate, exp.EQ)], - join_type="LEFT", - join_alias=table_alias, - copy=False, - ) - - -def _replace(expression, condition): - return expression.replace(exp.condition(condition)) - - -def _other_operand(expression): - if isinstance(expression, exp.In): - return expression.this - - if isinstance(expression, (exp.Any, exp.All)): - return _other_operand(expression.parent) - - if isinstance(expression, exp.Binary): - return ( - expression.right - if isinstance(expression.left, (exp.Subquery, exp.Any, exp.Exists, exp.All)) - else expression.left - ) - - return None diff --git a/third_party/bigframes_vendored/sqlglot/parser.py b/third_party/bigframes_vendored/sqlglot/parser.py deleted file mode 100644 index 706649f43fb..00000000000 --- a/third_party/bigframes_vendored/sqlglot/parser.py +++ /dev/null @@ -1,9719 +0,0 @@ -# Contains code from https://github.com/tobymao/sqlglot/blob/v28.5.0/sqlglot/parser.py - -from __future__ import annotations - -import itertools -import logging -import re -import typing as t -from collections import defaultdict - -from bigframes_vendored.sqlglot import exp -from bigframes_vendored.sqlglot.errors import ( - ErrorLevel, - ParseError, - TokenError, - concat_messages, - highlight_sql, - merge_errors, -) -from bigframes_vendored.sqlglot.helper import apply_index_offset, ensure_list, seq_get -from bigframes_vendored.sqlglot.time import format_time -from bigframes_vendored.sqlglot.tokens import Token, Tokenizer, TokenType -from bigframes_vendored.sqlglot.trie import TrieResult, in_trie, new_trie - -if t.TYPE_CHECKING: - from bigframes_vendored.sqlglot._typing import E, Lit - from bigframes_vendored.sqlglot.dialects.dialect import Dialect, DialectType - - T = t.TypeVar("T") - TCeilFloor = t.TypeVar("TCeilFloor", exp.Ceil, exp.Floor) - -logger = logging.getLogger("sqlglot") - -OPTIONS_TYPE = t.Dict[str, t.Sequence[t.Union[t.Sequence[str], str]]] - -# Used to detect alphabetical characters and +/- in timestamp literals -TIME_ZONE_RE: t.Pattern[str] = re.compile(r":.*?[a-zA-Z\+\-]") - - -def build_var_map(args: t.List) -> exp.StarMap | exp.VarMap: - if len(args) == 1 and args[0].is_star: - return exp.StarMap(this=args[0]) - - keys = [] - values = [] - for i in range(0, len(args), 2): - keys.append(args[i]) - values.append(args[i + 1]) - - return exp.VarMap( - keys=exp.array(*keys, copy=False), values=exp.array(*values, copy=False) - ) - - -def build_like(args: t.List) -> exp.Escape | exp.Like: - like = exp.Like(this=seq_get(args, 1), expression=seq_get(args, 0)) - return exp.Escape(this=like, expression=seq_get(args, 2)) if len(args) > 2 else like - - -def binary_range_parser( - expr_type: t.Type[exp.Expression], reverse_args: bool = False -) -> t.Callable[[Parser, t.Optional[exp.Expression]], t.Optional[exp.Expression]]: - def _parse_binary_range( - self: Parser, this: t.Optional[exp.Expression] - ) -> t.Optional[exp.Expression]: - expression = self._parse_bitwise() - if reverse_args: - this, expression = expression, this - return self._parse_escape( - self.expression(expr_type, this=this, expression=expression) - ) - - return _parse_binary_range - - -def build_logarithm(args: t.List, dialect: Dialect) -> exp.Func: - # Default argument order is base, expression - this = seq_get(args, 0) - expression = seq_get(args, 1) - - if expression: - if not dialect.LOG_BASE_FIRST: - this, expression = expression, this - return exp.Log(this=this, expression=expression) - - return (exp.Ln if dialect.parser_class.LOG_DEFAULTS_TO_LN else exp.Log)(this=this) - - -def build_hex(args: t.List, dialect: Dialect) -> exp.Hex | exp.LowerHex: - arg = seq_get(args, 0) - return exp.LowerHex(this=arg) if dialect.HEX_LOWERCASE else exp.Hex(this=arg) - - -def build_lower(args: t.List) -> exp.Lower | exp.Hex: - # LOWER(HEX(..)) can be simplified to LowerHex to simplify its transpilation - arg = seq_get(args, 0) - return ( - exp.LowerHex(this=arg.this) if isinstance(arg, exp.Hex) else exp.Lower(this=arg) - ) - - -def build_upper(args: t.List) -> exp.Upper | exp.Hex: - # UPPER(HEX(..)) can be simplified to Hex to simplify its transpilation - arg = seq_get(args, 0) - return exp.Hex(this=arg.this) if isinstance(arg, exp.Hex) else exp.Upper(this=arg) - - -def build_extract_json_with_path( - expr_type: t.Type[E], -) -> t.Callable[[t.List, Dialect], E]: - def _builder(args: t.List, dialect: Dialect) -> E: - expression = expr_type( - this=seq_get(args, 0), expression=dialect.to_json_path(seq_get(args, 1)) - ) - if len(args) > 2 and expr_type is exp.JSONExtract: - expression.set("expressions", args[2:]) - if expr_type is exp.JSONExtractScalar: - expression.set("scalar_only", dialect.JSON_EXTRACT_SCALAR_SCALAR_ONLY) - - return expression - - return _builder - - -def build_mod(args: t.List) -> exp.Mod: - this = seq_get(args, 0) - expression = seq_get(args, 1) - - # Wrap the operands if they are binary nodes, e.g. MOD(a + 1, 7) -> (a + 1) % 7 - this = exp.Paren(this=this) if isinstance(this, exp.Binary) else this - expression = ( - exp.Paren(this=expression) if isinstance(expression, exp.Binary) else expression - ) - - return exp.Mod(this=this, expression=expression) - - -def build_pad(args: t.List, is_left: bool = True): - return exp.Pad( - this=seq_get(args, 0), - expression=seq_get(args, 1), - fill_pattern=seq_get(args, 2), - is_left=is_left, - ) - - -def build_array_constructor( - exp_class: t.Type[E], args: t.List, bracket_kind: TokenType, dialect: Dialect -) -> exp.Expression: - array_exp = exp_class(expressions=args) - - if exp_class == exp.Array and dialect.HAS_DISTINCT_ARRAY_CONSTRUCTORS: - array_exp.set("bracket_notation", bracket_kind == TokenType.L_BRACKET) - - return array_exp - - -def build_convert_timezone( - args: t.List, default_source_tz: t.Optional[str] = None -) -> t.Union[exp.ConvertTimezone, exp.Anonymous]: - if len(args) == 2: - source_tz = exp.Literal.string(default_source_tz) if default_source_tz else None - return exp.ConvertTimezone( - source_tz=source_tz, target_tz=seq_get(args, 0), timestamp=seq_get(args, 1) - ) - - return exp.ConvertTimezone.from_arg_list(args) - - -def build_trim(args: t.List, is_left: bool = True): - return exp.Trim( - this=seq_get(args, 0), - expression=seq_get(args, 1), - position="LEADING" if is_left else "TRAILING", - ) - - -def build_coalesce( - args: t.List, is_nvl: t.Optional[bool] = None, is_null: t.Optional[bool] = None -) -> exp.Coalesce: - return exp.Coalesce( - this=seq_get(args, 0), expressions=args[1:], is_nvl=is_nvl, is_null=is_null - ) - - -def build_locate_strposition(args: t.List): - return exp.StrPosition( - this=seq_get(args, 1), - substr=seq_get(args, 0), - position=seq_get(args, 2), - ) - - -class _Parser(type): - def __new__(cls, clsname, bases, attrs): - klass = super().__new__(cls, clsname, bases, attrs) - - klass.SHOW_TRIE = new_trie(key.split(" ") for key in klass.SHOW_PARSERS) - klass.SET_TRIE = new_trie(key.split(" ") for key in klass.SET_PARSERS) - - return klass - - -class Parser(metaclass=_Parser): - """ - Parser consumes a list of tokens produced by the Tokenizer and produces a parsed syntax tree. - - Args: - error_level: The desired error level. - Default: ErrorLevel.IMMEDIATE - error_message_context: The amount of context to capture from a query string when displaying - the error message (in number of characters). - Default: 100 - max_errors: Maximum number of error messages to include in a raised ParseError. - This is only relevant if error_level is ErrorLevel.RAISE. - Default: 3 - """ - - FUNCTIONS: t.Dict[str, t.Callable] = { - **{name: func.from_arg_list for name, func in exp.FUNCTION_BY_NAME.items()}, - **dict.fromkeys(("COALESCE", "IFNULL", "NVL"), build_coalesce), - "ARRAY": lambda args, dialect: exp.Array(expressions=args), - "ARRAYAGG": lambda args, dialect: exp.ArrayAgg( - this=seq_get(args, 0), - nulls_excluded=dialect.ARRAY_AGG_INCLUDES_NULLS is None or None, - ), - "ARRAY_AGG": lambda args, dialect: exp.ArrayAgg( - this=seq_get(args, 0), - nulls_excluded=dialect.ARRAY_AGG_INCLUDES_NULLS is None or None, - ), - "CHAR": lambda args: exp.Chr(expressions=args), - "CHR": lambda args: exp.Chr(expressions=args), - "COUNT": lambda args: exp.Count( - this=seq_get(args, 0), expressions=args[1:], big_int=True - ), - "CONCAT": lambda args, dialect: exp.Concat( - expressions=args, - safe=not dialect.STRICT_STRING_CONCAT, - coalesce=dialect.CONCAT_COALESCE, - ), - "CONCAT_WS": lambda args, dialect: exp.ConcatWs( - expressions=args, - safe=not dialect.STRICT_STRING_CONCAT, - coalesce=dialect.CONCAT_COALESCE, - ), - "CONVERT_TIMEZONE": build_convert_timezone, - "DATE_TO_DATE_STR": lambda args: exp.Cast( - this=seq_get(args, 0), - to=exp.DataType(this=exp.DataType.Type.TEXT), - ), - "GENERATE_DATE_ARRAY": lambda args: exp.GenerateDateArray( - start=seq_get(args, 0), - end=seq_get(args, 1), - step=seq_get(args, 2) - or exp.Interval(this=exp.Literal.string(1), unit=exp.var("DAY")), - ), - "GENERATE_UUID": lambda args, dialect: exp.Uuid( - is_string=dialect.UUID_IS_STRING_TYPE or None - ), - "GLOB": lambda args: exp.Glob( - this=seq_get(args, 1), expression=seq_get(args, 0) - ), - "GREATEST": lambda args, dialect: exp.Greatest( - this=seq_get(args, 0), - expressions=args[1:], - ignore_nulls=dialect.LEAST_GREATEST_IGNORES_NULLS, - ), - "LEAST": lambda args, dialect: exp.Least( - this=seq_get(args, 0), - expressions=args[1:], - ignore_nulls=dialect.LEAST_GREATEST_IGNORES_NULLS, - ), - "HEX": build_hex, - "JSON_EXTRACT": build_extract_json_with_path(exp.JSONExtract), - "JSON_EXTRACT_SCALAR": build_extract_json_with_path(exp.JSONExtractScalar), - "JSON_EXTRACT_PATH_TEXT": build_extract_json_with_path(exp.JSONExtractScalar), - "LIKE": build_like, - "LOG": build_logarithm, - "LOG2": lambda args: exp.Log( - this=exp.Literal.number(2), expression=seq_get(args, 0) - ), - "LOG10": lambda args: exp.Log( - this=exp.Literal.number(10), expression=seq_get(args, 0) - ), - "LOWER": build_lower, - "LPAD": lambda args: build_pad(args), - "LEFTPAD": lambda args: build_pad(args), - "LTRIM": lambda args: build_trim(args), - "MOD": build_mod, - "RIGHTPAD": lambda args: build_pad(args, is_left=False), - "RPAD": lambda args: build_pad(args, is_left=False), - "RTRIM": lambda args: build_trim(args, is_left=False), - "SCOPE_RESOLUTION": lambda args: ( - exp.ScopeResolution(expression=seq_get(args, 0)) - if len(args) != 2 - else exp.ScopeResolution(this=seq_get(args, 0), expression=seq_get(args, 1)) - ), - "STRPOS": exp.StrPosition.from_arg_list, - "CHARINDEX": lambda args: build_locate_strposition(args), - "INSTR": exp.StrPosition.from_arg_list, - "LOCATE": lambda args: build_locate_strposition(args), - "TIME_TO_TIME_STR": lambda args: exp.Cast( - this=seq_get(args, 0), - to=exp.DataType(this=exp.DataType.Type.TEXT), - ), - "TO_HEX": build_hex, - "TS_OR_DS_TO_DATE_STR": lambda args: exp.Substring( - this=exp.Cast( - this=seq_get(args, 0), - to=exp.DataType(this=exp.DataType.Type.TEXT), - ), - start=exp.Literal.number(1), - length=exp.Literal.number(10), - ), - "UNNEST": lambda args: exp.Unnest(expressions=ensure_list(seq_get(args, 0))), - "UPPER": build_upper, - "UUID": lambda args, dialect: exp.Uuid( - is_string=dialect.UUID_IS_STRING_TYPE or None - ), - "VAR_MAP": build_var_map, - } - - NO_PAREN_FUNCTIONS = { - TokenType.CURRENT_DATE: exp.CurrentDate, - TokenType.CURRENT_DATETIME: exp.CurrentDate, - TokenType.CURRENT_TIME: exp.CurrentTime, - TokenType.CURRENT_TIMESTAMP: exp.CurrentTimestamp, - TokenType.CURRENT_USER: exp.CurrentUser, - TokenType.LOCALTIME: exp.Localtime, - TokenType.LOCALTIMESTAMP: exp.Localtimestamp, - TokenType.CURRENT_ROLE: exp.CurrentRole, - } - - STRUCT_TYPE_TOKENS = { - TokenType.FILE, - TokenType.NESTED, - TokenType.OBJECT, - TokenType.STRUCT, - TokenType.UNION, - } - - NESTED_TYPE_TOKENS = { - TokenType.ARRAY, - TokenType.LIST, - TokenType.LOWCARDINALITY, - TokenType.MAP, - TokenType.NULLABLE, - TokenType.RANGE, - *STRUCT_TYPE_TOKENS, - } - - ENUM_TYPE_TOKENS = { - TokenType.DYNAMIC, - TokenType.ENUM, - TokenType.ENUM8, - TokenType.ENUM16, - } - - AGGREGATE_TYPE_TOKENS = { - TokenType.AGGREGATEFUNCTION, - TokenType.SIMPLEAGGREGATEFUNCTION, - } - - TYPE_TOKENS = { - TokenType.BIT, - TokenType.BOOLEAN, - TokenType.TINYINT, - TokenType.UTINYINT, - TokenType.SMALLINT, - TokenType.USMALLINT, - TokenType.INT, - TokenType.UINT, - TokenType.BIGINT, - TokenType.UBIGINT, - TokenType.BIGNUM, - TokenType.INT128, - TokenType.UINT128, - TokenType.INT256, - TokenType.UINT256, - TokenType.MEDIUMINT, - TokenType.UMEDIUMINT, - TokenType.FIXEDSTRING, - TokenType.FLOAT, - TokenType.DOUBLE, - TokenType.UDOUBLE, - TokenType.CHAR, - TokenType.NCHAR, - TokenType.VARCHAR, - TokenType.NVARCHAR, - TokenType.BPCHAR, - TokenType.TEXT, - TokenType.MEDIUMTEXT, - TokenType.LONGTEXT, - TokenType.BLOB, - TokenType.MEDIUMBLOB, - TokenType.LONGBLOB, - TokenType.BINARY, - TokenType.VARBINARY, - TokenType.JSON, - TokenType.JSONB, - TokenType.INTERVAL, - TokenType.TINYBLOB, - TokenType.TINYTEXT, - TokenType.TIME, - TokenType.TIMETZ, - TokenType.TIME_NS, - TokenType.TIMESTAMP, - TokenType.TIMESTAMP_S, - TokenType.TIMESTAMP_MS, - TokenType.TIMESTAMP_NS, - TokenType.TIMESTAMPTZ, - TokenType.TIMESTAMPLTZ, - TokenType.TIMESTAMPNTZ, - TokenType.DATETIME, - TokenType.DATETIME2, - TokenType.DATETIME64, - TokenType.SMALLDATETIME, - TokenType.DATE, - TokenType.DATE32, - TokenType.INT4RANGE, - TokenType.INT4MULTIRANGE, - TokenType.INT8RANGE, - TokenType.INT8MULTIRANGE, - TokenType.NUMRANGE, - TokenType.NUMMULTIRANGE, - TokenType.TSRANGE, - TokenType.TSMULTIRANGE, - TokenType.TSTZRANGE, - TokenType.TSTZMULTIRANGE, - TokenType.DATERANGE, - TokenType.DATEMULTIRANGE, - TokenType.DECIMAL, - TokenType.DECIMAL32, - TokenType.DECIMAL64, - TokenType.DECIMAL128, - TokenType.DECIMAL256, - TokenType.DECFLOAT, - TokenType.UDECIMAL, - TokenType.BIGDECIMAL, - TokenType.UUID, - TokenType.GEOGRAPHY, - TokenType.GEOGRAPHYPOINT, - TokenType.GEOMETRY, - TokenType.POINT, - TokenType.RING, - TokenType.LINESTRING, - TokenType.MULTILINESTRING, - TokenType.POLYGON, - TokenType.MULTIPOLYGON, - TokenType.HLLSKETCH, - TokenType.HSTORE, - TokenType.PSEUDO_TYPE, - TokenType.SUPER, - TokenType.SERIAL, - TokenType.SMALLSERIAL, - TokenType.BIGSERIAL, - TokenType.XML, - TokenType.YEAR, - TokenType.USERDEFINED, - TokenType.MONEY, - TokenType.SMALLMONEY, - TokenType.ROWVERSION, - TokenType.IMAGE, - TokenType.VARIANT, - TokenType.VECTOR, - TokenType.VOID, - TokenType.OBJECT, - TokenType.OBJECT_IDENTIFIER, - TokenType.INET, - TokenType.IPADDRESS, - TokenType.IPPREFIX, - TokenType.IPV4, - TokenType.IPV6, - TokenType.UNKNOWN, - TokenType.NOTHING, - TokenType.NULL, - TokenType.NAME, - TokenType.TDIGEST, - TokenType.DYNAMIC, - *ENUM_TYPE_TOKENS, - *NESTED_TYPE_TOKENS, - *AGGREGATE_TYPE_TOKENS, - } - - SIGNED_TO_UNSIGNED_TYPE_TOKEN = { - TokenType.BIGINT: TokenType.UBIGINT, - TokenType.INT: TokenType.UINT, - TokenType.MEDIUMINT: TokenType.UMEDIUMINT, - TokenType.SMALLINT: TokenType.USMALLINT, - TokenType.TINYINT: TokenType.UTINYINT, - TokenType.DECIMAL: TokenType.UDECIMAL, - TokenType.DOUBLE: TokenType.UDOUBLE, - } - - SUBQUERY_PREDICATES = { - TokenType.ANY: exp.Any, - TokenType.ALL: exp.All, - TokenType.EXISTS: exp.Exists, - TokenType.SOME: exp.Any, - } - - RESERVED_TOKENS = { - *Tokenizer.SINGLE_TOKENS.values(), - TokenType.SELECT, - } - {TokenType.IDENTIFIER} - - DB_CREATABLES = { - TokenType.DATABASE, - TokenType.DICTIONARY, - TokenType.FILE_FORMAT, - TokenType.MODEL, - TokenType.NAMESPACE, - TokenType.SCHEMA, - TokenType.SEMANTIC_VIEW, - TokenType.SEQUENCE, - TokenType.SINK, - TokenType.SOURCE, - TokenType.STAGE, - TokenType.STORAGE_INTEGRATION, - TokenType.STREAMLIT, - TokenType.TABLE, - TokenType.TAG, - TokenType.VIEW, - TokenType.WAREHOUSE, - } - - CREATABLES = { - TokenType.COLUMN, - TokenType.CONSTRAINT, - TokenType.FOREIGN_KEY, - TokenType.FUNCTION, - TokenType.INDEX, - TokenType.PROCEDURE, - *DB_CREATABLES, - } - - ALTERABLES = { - TokenType.INDEX, - TokenType.TABLE, - TokenType.VIEW, - TokenType.SESSION, - } - - # Tokens that can represent identifiers - ID_VAR_TOKENS = { - TokenType.ALL, - TokenType.ANALYZE, - TokenType.ATTACH, - TokenType.VAR, - TokenType.ANTI, - TokenType.APPLY, - TokenType.ASC, - TokenType.ASOF, - TokenType.AUTO_INCREMENT, - TokenType.BEGIN, - TokenType.BPCHAR, - TokenType.CACHE, - TokenType.CASE, - TokenType.COLLATE, - TokenType.COMMAND, - TokenType.COMMENT, - TokenType.COMMIT, - TokenType.CONSTRAINT, - TokenType.COPY, - TokenType.CUBE, - TokenType.CURRENT_SCHEMA, - TokenType.DEFAULT, - TokenType.DELETE, - TokenType.DESC, - TokenType.DESCRIBE, - TokenType.DETACH, - TokenType.DICTIONARY, - TokenType.DIV, - TokenType.END, - TokenType.EXECUTE, - TokenType.EXPORT, - TokenType.ESCAPE, - TokenType.FALSE, - TokenType.FIRST, - TokenType.FILTER, - TokenType.FINAL, - TokenType.FORMAT, - TokenType.FULL, - TokenType.GET, - TokenType.IDENTIFIER, - TokenType.IS, - TokenType.ISNULL, - TokenType.INTERVAL, - TokenType.KEEP, - TokenType.KILL, - TokenType.LEFT, - TokenType.LIMIT, - TokenType.LOAD, - TokenType.LOCK, - TokenType.MATCH, - TokenType.MERGE, - TokenType.NATURAL, - TokenType.NEXT, - TokenType.OFFSET, - TokenType.OPERATOR, - TokenType.ORDINALITY, - TokenType.OVER, - TokenType.OVERLAPS, - TokenType.OVERWRITE, - TokenType.PARTITION, - TokenType.PERCENT, - TokenType.PIVOT, - TokenType.PRAGMA, - TokenType.PUT, - TokenType.RANGE, - TokenType.RECURSIVE, - TokenType.REFERENCES, - TokenType.REFRESH, - TokenType.RENAME, - TokenType.REPLACE, - TokenType.RIGHT, - TokenType.ROLLUP, - TokenType.ROW, - TokenType.ROWS, - TokenType.SEMI, - TokenType.SET, - TokenType.SETTINGS, - TokenType.SHOW, - TokenType.TEMPORARY, - TokenType.TOP, - TokenType.TRUE, - TokenType.TRUNCATE, - TokenType.UNIQUE, - TokenType.UNNEST, - TokenType.UNPIVOT, - TokenType.UPDATE, - TokenType.USE, - TokenType.VOLATILE, - TokenType.WINDOW, - *ALTERABLES, - *CREATABLES, - *SUBQUERY_PREDICATES, - *TYPE_TOKENS, - *NO_PAREN_FUNCTIONS, - } - ID_VAR_TOKENS.remove(TokenType.UNION) - - TABLE_ALIAS_TOKENS = ID_VAR_TOKENS - { - TokenType.ANTI, - TokenType.ASOF, - TokenType.FULL, - TokenType.LEFT, - TokenType.LOCK, - TokenType.NATURAL, - TokenType.RIGHT, - TokenType.SEMI, - TokenType.WINDOW, - } - - ALIAS_TOKENS = ID_VAR_TOKENS - - COLON_PLACEHOLDER_TOKENS = ID_VAR_TOKENS - - ARRAY_CONSTRUCTORS = { - "ARRAY": exp.Array, - "LIST": exp.List, - } - - COMMENT_TABLE_ALIAS_TOKENS = TABLE_ALIAS_TOKENS - {TokenType.IS} - - UPDATE_ALIAS_TOKENS = TABLE_ALIAS_TOKENS - {TokenType.SET} - - TRIM_TYPES = {"LEADING", "TRAILING", "BOTH"} - - FUNC_TOKENS = { - TokenType.COLLATE, - TokenType.COMMAND, - TokenType.CURRENT_DATE, - TokenType.CURRENT_DATETIME, - TokenType.CURRENT_SCHEMA, - TokenType.CURRENT_TIMESTAMP, - TokenType.CURRENT_TIME, - TokenType.CURRENT_USER, - TokenType.CURRENT_CATALOG, - TokenType.FILTER, - TokenType.FIRST, - TokenType.FORMAT, - TokenType.GET, - TokenType.GLOB, - TokenType.IDENTIFIER, - TokenType.INDEX, - TokenType.ISNULL, - TokenType.ILIKE, - TokenType.INSERT, - TokenType.LIKE, - TokenType.LOCALTIME, - TokenType.LOCALTIMESTAMP, - TokenType.MERGE, - TokenType.NEXT, - TokenType.OFFSET, - TokenType.PRIMARY_KEY, - TokenType.RANGE, - TokenType.REPLACE, - TokenType.RLIKE, - TokenType.ROW, - TokenType.SESSION_USER, - TokenType.UNNEST, - TokenType.VAR, - TokenType.LEFT, - TokenType.RIGHT, - TokenType.SEQUENCE, - TokenType.DATE, - TokenType.DATETIME, - TokenType.TABLE, - TokenType.TIMESTAMP, - TokenType.TIMESTAMPTZ, - TokenType.TRUNCATE, - TokenType.UTC_DATE, - TokenType.UTC_TIME, - TokenType.UTC_TIMESTAMP, - TokenType.WINDOW, - TokenType.XOR, - *TYPE_TOKENS, - *SUBQUERY_PREDICATES, - } - - CONJUNCTION: t.Dict[TokenType, t.Type[exp.Expression]] = { - TokenType.AND: exp.And, - } - - ASSIGNMENT: t.Dict[TokenType, t.Type[exp.Expression]] = { - TokenType.COLON_EQ: exp.PropertyEQ, - } - - DISJUNCTION: t.Dict[TokenType, t.Type[exp.Expression]] = { - TokenType.OR: exp.Or, - } - - EQUALITY = { - TokenType.EQ: exp.EQ, - TokenType.NEQ: exp.NEQ, - TokenType.NULLSAFE_EQ: exp.NullSafeEQ, - } - - COMPARISON = { - TokenType.GT: exp.GT, - TokenType.GTE: exp.GTE, - TokenType.LT: exp.LT, - TokenType.LTE: exp.LTE, - } - - BITWISE = { - TokenType.AMP: exp.BitwiseAnd, - TokenType.CARET: exp.BitwiseXor, - TokenType.PIPE: exp.BitwiseOr, - } - - TERM = { - TokenType.DASH: exp.Sub, - TokenType.PLUS: exp.Add, - TokenType.MOD: exp.Mod, - TokenType.COLLATE: exp.Collate, - } - - FACTOR = { - TokenType.DIV: exp.IntDiv, - TokenType.LR_ARROW: exp.Distance, - TokenType.SLASH: exp.Div, - TokenType.STAR: exp.Mul, - } - - EXPONENT: t.Dict[TokenType, t.Type[exp.Expression]] = {} - - TIMES = { - TokenType.TIME, - TokenType.TIMETZ, - } - - TIMESTAMPS = { - TokenType.TIMESTAMP, - TokenType.TIMESTAMPNTZ, - TokenType.TIMESTAMPTZ, - TokenType.TIMESTAMPLTZ, - *TIMES, - } - - SET_OPERATIONS = { - TokenType.UNION, - TokenType.INTERSECT, - TokenType.EXCEPT, - } - - JOIN_METHODS = { - TokenType.ASOF, - TokenType.NATURAL, - TokenType.POSITIONAL, - } - - JOIN_SIDES = { - TokenType.LEFT, - TokenType.RIGHT, - TokenType.FULL, - } - - JOIN_KINDS = { - TokenType.ANTI, - TokenType.CROSS, - TokenType.INNER, - TokenType.OUTER, - TokenType.SEMI, - TokenType.STRAIGHT_JOIN, - } - - JOIN_HINTS: t.Set[str] = set() - - LAMBDAS = { - TokenType.ARROW: lambda self, expressions: self.expression( - exp.Lambda, - this=self._replace_lambda( - self._parse_disjunction(), - expressions, - ), - expressions=expressions, - ), - TokenType.FARROW: lambda self, expressions: self.expression( - exp.Kwarg, - this=exp.var(expressions[0].name), - expression=self._parse_disjunction(), - ), - } - - COLUMN_OPERATORS = { - TokenType.DOT: None, - TokenType.DOTCOLON: lambda self, this, to: self.expression( - exp.JSONCast, - this=this, - to=to, - ), - TokenType.DCOLON: lambda self, this, to: self.build_cast( - strict=self.STRICT_CAST, this=this, to=to - ), - TokenType.ARROW: lambda self, this, path: self.expression( - exp.JSONExtract, - this=this, - expression=self.dialect.to_json_path(path), - only_json_types=self.JSON_ARROWS_REQUIRE_JSON_TYPE, - ), - TokenType.DARROW: lambda self, this, path: self.expression( - exp.JSONExtractScalar, - this=this, - expression=self.dialect.to_json_path(path), - only_json_types=self.JSON_ARROWS_REQUIRE_JSON_TYPE, - scalar_only=self.dialect.JSON_EXTRACT_SCALAR_SCALAR_ONLY, - ), - TokenType.HASH_ARROW: lambda self, this, path: self.expression( - exp.JSONBExtract, - this=this, - expression=path, - ), - TokenType.DHASH_ARROW: lambda self, this, path: self.expression( - exp.JSONBExtractScalar, - this=this, - expression=path, - ), - TokenType.PLACEHOLDER: lambda self, this, key: self.expression( - exp.JSONBContains, - this=this, - expression=key, - ), - } - - CAST_COLUMN_OPERATORS = { - TokenType.DOTCOLON, - TokenType.DCOLON, - } - - EXPRESSION_PARSERS = { - exp.Cluster: lambda self: self._parse_sort(exp.Cluster, TokenType.CLUSTER_BY), - exp.Column: lambda self: self._parse_column(), - exp.ColumnDef: lambda self: self._parse_column_def(self._parse_column()), - exp.Condition: lambda self: self._parse_disjunction(), - exp.DataType: lambda self: self._parse_types( - allow_identifiers=False, schema=True - ), - exp.Expression: lambda self: self._parse_expression(), - exp.From: lambda self: self._parse_from(joins=True), - exp.GrantPrincipal: lambda self: self._parse_grant_principal(), - exp.GrantPrivilege: lambda self: self._parse_grant_privilege(), - exp.Group: lambda self: self._parse_group(), - exp.Having: lambda self: self._parse_having(), - exp.Hint: lambda self: self._parse_hint_body(), - exp.Identifier: lambda self: self._parse_id_var(), - exp.Join: lambda self: self._parse_join(), - exp.Lambda: lambda self: self._parse_lambda(), - exp.Lateral: lambda self: self._parse_lateral(), - exp.Limit: lambda self: self._parse_limit(), - exp.Offset: lambda self: self._parse_offset(), - exp.Order: lambda self: self._parse_order(), - exp.Ordered: lambda self: self._parse_ordered(), - exp.Properties: lambda self: self._parse_properties(), - exp.PartitionedByProperty: lambda self: self._parse_partitioned_by(), - exp.Qualify: lambda self: self._parse_qualify(), - exp.Returning: lambda self: self._parse_returning(), - exp.Select: lambda self: self._parse_select(), - exp.Sort: lambda self: self._parse_sort(exp.Sort, TokenType.SORT_BY), - exp.Table: lambda self: self._parse_table_parts(), - exp.TableAlias: lambda self: self._parse_table_alias(), - exp.Tuple: lambda self: self._parse_value(values=False), - exp.Whens: lambda self: self._parse_when_matched(), - exp.Where: lambda self: self._parse_where(), - exp.Window: lambda self: self._parse_named_window(), - exp.With: lambda self: self._parse_with(), - "JOIN_TYPE": lambda self: self._parse_join_parts(), - } - - STATEMENT_PARSERS = { - TokenType.ALTER: lambda self: self._parse_alter(), - TokenType.ANALYZE: lambda self: self._parse_analyze(), - TokenType.BEGIN: lambda self: self._parse_transaction(), - TokenType.CACHE: lambda self: self._parse_cache(), - TokenType.COMMENT: lambda self: self._parse_comment(), - TokenType.COMMIT: lambda self: self._parse_commit_or_rollback(), - TokenType.COPY: lambda self: self._parse_copy(), - TokenType.CREATE: lambda self: self._parse_create(), - TokenType.DELETE: lambda self: self._parse_delete(), - TokenType.DESC: lambda self: self._parse_describe(), - TokenType.DESCRIBE: lambda self: self._parse_describe(), - TokenType.DROP: lambda self: self._parse_drop(), - TokenType.GRANT: lambda self: self._parse_grant(), - TokenType.REVOKE: lambda self: self._parse_revoke(), - TokenType.INSERT: lambda self: self._parse_insert(), - TokenType.KILL: lambda self: self._parse_kill(), - TokenType.LOAD: lambda self: self._parse_load(), - TokenType.MERGE: lambda self: self._parse_merge(), - TokenType.PIVOT: lambda self: self._parse_simplified_pivot(), - TokenType.PRAGMA: lambda self: self.expression( - exp.Pragma, this=self._parse_expression() - ), - TokenType.REFRESH: lambda self: self._parse_refresh(), - TokenType.ROLLBACK: lambda self: self._parse_commit_or_rollback(), - TokenType.SET: lambda self: self._parse_set(), - TokenType.TRUNCATE: lambda self: self._parse_truncate_table(), - TokenType.UNCACHE: lambda self: self._parse_uncache(), - TokenType.UNPIVOT: lambda self: self._parse_simplified_pivot(is_unpivot=True), - TokenType.UPDATE: lambda self: self._parse_update(), - TokenType.USE: lambda self: self._parse_use(), - TokenType.SEMICOLON: lambda self: exp.Semicolon(), - } - - UNARY_PARSERS = { - TokenType.PLUS: lambda self: ( - self._parse_unary() - ), # Unary + is handled as a no-op - TokenType.NOT: lambda self: self.expression( - exp.Not, this=self._parse_equality() - ), - TokenType.TILDA: lambda self: self.expression( - exp.BitwiseNot, this=self._parse_unary() - ), - TokenType.DASH: lambda self: self.expression(exp.Neg, this=self._parse_unary()), - TokenType.PIPE_SLASH: lambda self: self.expression( - exp.Sqrt, this=self._parse_unary() - ), - TokenType.DPIPE_SLASH: lambda self: self.expression( - exp.Cbrt, this=self._parse_unary() - ), - } - - STRING_PARSERS = { - TokenType.HEREDOC_STRING: lambda self, token: self.expression( - exp.RawString, token=token - ), - TokenType.NATIONAL_STRING: lambda self, token: self.expression( - exp.National, token=token - ), - TokenType.RAW_STRING: lambda self, token: self.expression( - exp.RawString, token=token - ), - TokenType.STRING: lambda self, token: self.expression( - exp.Literal, token=token, is_string=True - ), - TokenType.UNICODE_STRING: lambda self, token: self.expression( - exp.UnicodeString, - token=token, - escape=self._match_text_seq("UESCAPE") and self._parse_string(), - ), - } - - NUMERIC_PARSERS = { - TokenType.BIT_STRING: lambda self, token: self.expression( - exp.BitString, token=token - ), - TokenType.BYTE_STRING: lambda self, token: self.expression( - exp.ByteString, - token=token, - is_bytes=self.dialect.BYTE_STRING_IS_BYTES_TYPE or None, - ), - TokenType.HEX_STRING: lambda self, token: self.expression( - exp.HexString, - token=token, - is_integer=self.dialect.HEX_STRING_IS_INTEGER_TYPE or None, - ), - TokenType.NUMBER: lambda self, token: self.expression( - exp.Literal, token=token, is_string=False - ), - } - - PRIMARY_PARSERS = { - **STRING_PARSERS, - **NUMERIC_PARSERS, - TokenType.INTRODUCER: lambda self, token: self._parse_introducer(token), - TokenType.NULL: lambda self, _: self.expression(exp.Null), - TokenType.TRUE: lambda self, _: self.expression(exp.Boolean, this=True), - TokenType.FALSE: lambda self, _: self.expression(exp.Boolean, this=False), - TokenType.SESSION_PARAMETER: lambda self, _: self._parse_session_parameter(), - TokenType.STAR: lambda self, _: self._parse_star_ops(), - } - - PLACEHOLDER_PARSERS = { - TokenType.PLACEHOLDER: lambda self: self.expression(exp.Placeholder), - TokenType.PARAMETER: lambda self: self._parse_parameter(), - TokenType.COLON: lambda self: ( - self.expression(exp.Placeholder, this=self._prev.text) - if self._match_set(self.COLON_PLACEHOLDER_TOKENS) - else None - ), - } - - RANGE_PARSERS = { - TokenType.AT_GT: binary_range_parser(exp.ArrayContainsAll), - TokenType.BETWEEN: lambda self, this: self._parse_between(this), - TokenType.GLOB: binary_range_parser(exp.Glob), - TokenType.ILIKE: binary_range_parser(exp.ILike), - TokenType.IN: lambda self, this: self._parse_in(this), - TokenType.IRLIKE: binary_range_parser(exp.RegexpILike), - TokenType.IS: lambda self, this: self._parse_is(this), - TokenType.LIKE: binary_range_parser(exp.Like), - TokenType.LT_AT: binary_range_parser(exp.ArrayContainsAll, reverse_args=True), - TokenType.OVERLAPS: binary_range_parser(exp.Overlaps), - TokenType.RLIKE: binary_range_parser(exp.RegexpLike), - TokenType.SIMILAR_TO: binary_range_parser(exp.SimilarTo), - TokenType.FOR: lambda self, this: self._parse_comprehension(this), - TokenType.QMARK_AMP: binary_range_parser(exp.JSONBContainsAllTopKeys), - TokenType.QMARK_PIPE: binary_range_parser(exp.JSONBContainsAnyTopKeys), - TokenType.HASH_DASH: binary_range_parser(exp.JSONBDeleteAtPath), - TokenType.ADJACENT: binary_range_parser(exp.Adjacent), - TokenType.OPERATOR: lambda self, this: self._parse_operator(this), - TokenType.AMP_LT: binary_range_parser(exp.ExtendsLeft), - TokenType.AMP_GT: binary_range_parser(exp.ExtendsRight), - } - - PIPE_SYNTAX_TRANSFORM_PARSERS = { - "AGGREGATE": lambda self, query: self._parse_pipe_syntax_aggregate(query), - "AS": lambda self, query: self._build_pipe_cte( - query, [exp.Star()], self._parse_table_alias() - ), - "EXTEND": lambda self, query: self._parse_pipe_syntax_extend(query), - "LIMIT": lambda self, query: self._parse_pipe_syntax_limit(query), - "ORDER BY": lambda self, query: query.order_by( - self._parse_order(), append=False, copy=False - ), - "PIVOT": lambda self, query: self._parse_pipe_syntax_pivot(query), - "SELECT": lambda self, query: self._parse_pipe_syntax_select(query), - "TABLESAMPLE": lambda self, query: self._parse_pipe_syntax_tablesample(query), - "UNPIVOT": lambda self, query: self._parse_pipe_syntax_pivot(query), - "WHERE": lambda self, query: query.where(self._parse_where(), copy=False), - } - - PROPERTY_PARSERS: t.Dict[str, t.Callable] = { - "ALLOWED_VALUES": lambda self: self.expression( - exp.AllowedValuesProperty, expressions=self._parse_csv(self._parse_primary) - ), - "ALGORITHM": lambda self: self._parse_property_assignment( - exp.AlgorithmProperty - ), - "AUTO": lambda self: self._parse_auto_property(), - "AUTO_INCREMENT": lambda self: self._parse_property_assignment( - exp.AutoIncrementProperty - ), - "BACKUP": lambda self: self.expression( - exp.BackupProperty, this=self._parse_var(any_token=True) - ), - "BLOCKCOMPRESSION": lambda self: self._parse_blockcompression(), - "CHARSET": lambda self, **kwargs: self._parse_character_set(**kwargs), - "CHARACTER SET": lambda self, **kwargs: self._parse_character_set(**kwargs), - "CHECKSUM": lambda self: self._parse_checksum(), - "CLUSTER BY": lambda self: self._parse_cluster(), - "CLUSTERED": lambda self: self._parse_clustered_by(), - "COLLATE": lambda self, **kwargs: self._parse_property_assignment( - exp.CollateProperty, **kwargs - ), - "COMMENT": lambda self: self._parse_property_assignment( - exp.SchemaCommentProperty - ), - "CONTAINS": lambda self: self._parse_contains_property(), - "COPY": lambda self: self._parse_copy_property(), - "DATABLOCKSIZE": lambda self, **kwargs: self._parse_datablocksize(**kwargs), - "DATA_DELETION": lambda self: self._parse_data_deletion_property(), - "DEFINER": lambda self: self._parse_definer(), - "DETERMINISTIC": lambda self: self.expression( - exp.StabilityProperty, this=exp.Literal.string("IMMUTABLE") - ), - "DISTRIBUTED": lambda self: self._parse_distributed_property(), - "DUPLICATE": lambda self: self._parse_composite_key_property( - exp.DuplicateKeyProperty - ), - "DYNAMIC": lambda self: self.expression(exp.DynamicProperty), - "DISTKEY": lambda self: self._parse_distkey(), - "DISTSTYLE": lambda self: self._parse_property_assignment( - exp.DistStyleProperty - ), - "EMPTY": lambda self: self.expression(exp.EmptyProperty), - "ENGINE": lambda self: self._parse_property_assignment(exp.EngineProperty), - "ENVIRONMENT": lambda self: self.expression( - exp.EnviromentProperty, - expressions=self._parse_wrapped_csv(self._parse_assignment), - ), - "EXECUTE": lambda self: self._parse_property_assignment(exp.ExecuteAsProperty), - "EXTERNAL": lambda self: self.expression(exp.ExternalProperty), - "FALLBACK": lambda self, **kwargs: self._parse_fallback(**kwargs), - "FORMAT": lambda self: self._parse_property_assignment(exp.FileFormatProperty), - "FREESPACE": lambda self: self._parse_freespace(), - "GLOBAL": lambda self: self.expression(exp.GlobalProperty), - "HEAP": lambda self: self.expression(exp.HeapProperty), - "ICEBERG": lambda self: self.expression(exp.IcebergProperty), - "IMMUTABLE": lambda self: self.expression( - exp.StabilityProperty, this=exp.Literal.string("IMMUTABLE") - ), - "INHERITS": lambda self: self.expression( - exp.InheritsProperty, expressions=self._parse_wrapped_csv(self._parse_table) - ), - "INPUT": lambda self: self.expression( - exp.InputModelProperty, this=self._parse_schema() - ), - "JOURNAL": lambda self, **kwargs: self._parse_journal(**kwargs), - "LANGUAGE": lambda self: self._parse_property_assignment(exp.LanguageProperty), - "LAYOUT": lambda self: self._parse_dict_property(this="LAYOUT"), - "LIFETIME": lambda self: self._parse_dict_range(this="LIFETIME"), - "LIKE": lambda self: self._parse_create_like(), - "LOCATION": lambda self: self._parse_property_assignment(exp.LocationProperty), - "LOCK": lambda self: self._parse_locking(), - "LOCKING": lambda self: self._parse_locking(), - "LOG": lambda self, **kwargs: self._parse_log(**kwargs), - "MATERIALIZED": lambda self: self.expression(exp.MaterializedProperty), - "MERGEBLOCKRATIO": lambda self, **kwargs: self._parse_mergeblockratio(**kwargs), - "MODIFIES": lambda self: self._parse_modifies_property(), - "MULTISET": lambda self: self.expression(exp.SetProperty, multi=True), - "NO": lambda self: self._parse_no_property(), - "ON": lambda self: self._parse_on_property(), - "ORDER BY": lambda self: self._parse_order(skip_order_token=True), - "OUTPUT": lambda self: self.expression( - exp.OutputModelProperty, this=self._parse_schema() - ), - "PARTITION": lambda self: self._parse_partitioned_of(), - "PARTITION BY": lambda self: self._parse_partitioned_by(), - "PARTITIONED BY": lambda self: self._parse_partitioned_by(), - "PARTITIONED_BY": lambda self: self._parse_partitioned_by(), - "PRIMARY KEY": lambda self: self._parse_primary_key(in_props=True), - "RANGE": lambda self: self._parse_dict_range(this="RANGE"), - "READS": lambda self: self._parse_reads_property(), - "REMOTE": lambda self: self._parse_remote_with_connection(), - "RETURNS": lambda self: self._parse_returns(), - "STRICT": lambda self: self.expression(exp.StrictProperty), - "STREAMING": lambda self: self.expression(exp.StreamingTableProperty), - "ROW": lambda self: self._parse_row(), - "ROW_FORMAT": lambda self: self._parse_property_assignment( - exp.RowFormatProperty - ), - "SAMPLE": lambda self: self.expression( - exp.SampleProperty, - this=self._match_text_seq("BY") and self._parse_bitwise(), - ), - "SECURE": lambda self: self.expression(exp.SecureProperty), - "SECURITY": lambda self: self._parse_security(), - "SET": lambda self: self.expression(exp.SetProperty, multi=False), - "SETTINGS": lambda self: self._parse_settings_property(), - "SHARING": lambda self: self._parse_property_assignment(exp.SharingProperty), - "SORTKEY": lambda self: self._parse_sortkey(), - "SOURCE": lambda self: self._parse_dict_property(this="SOURCE"), - "STABLE": lambda self: self.expression( - exp.StabilityProperty, this=exp.Literal.string("STABLE") - ), - "STORED": lambda self: self._parse_stored(), - "SYSTEM_VERSIONING": lambda self: self._parse_system_versioning_property(), - "TBLPROPERTIES": lambda self: self._parse_wrapped_properties(), - "TEMP": lambda self: self.expression(exp.TemporaryProperty), - "TEMPORARY": lambda self: self.expression(exp.TemporaryProperty), - "TO": lambda self: self._parse_to_table(), - "TRANSIENT": lambda self: self.expression(exp.TransientProperty), - "TRANSFORM": lambda self: self.expression( - exp.TransformModelProperty, - expressions=self._parse_wrapped_csv(self._parse_expression), - ), - "TTL": lambda self: self._parse_ttl(), - "USING": lambda self: self._parse_property_assignment(exp.FileFormatProperty), - "UNLOGGED": lambda self: self.expression(exp.UnloggedProperty), - "VOLATILE": lambda self: self._parse_volatile_property(), - "WITH": lambda self: self._parse_with_property(), - } - - CONSTRAINT_PARSERS = { - "AUTOINCREMENT": lambda self: self._parse_auto_increment(), - "AUTO_INCREMENT": lambda self: self._parse_auto_increment(), - "CASESPECIFIC": lambda self: self.expression( - exp.CaseSpecificColumnConstraint, not_=False - ), - "CHARACTER SET": lambda self: self.expression( - exp.CharacterSetColumnConstraint, this=self._parse_var_or_string() - ), - "CHECK": lambda self: self.expression( - exp.CheckColumnConstraint, - this=self._parse_wrapped(self._parse_assignment), - enforced=self._match_text_seq("ENFORCED"), - ), - "COLLATE": lambda self: self.expression( - exp.CollateColumnConstraint, - this=self._parse_identifier() or self._parse_column(), - ), - "COMMENT": lambda self: self.expression( - exp.CommentColumnConstraint, this=self._parse_string() - ), - "COMPRESS": lambda self: self._parse_compress(), - "CLUSTERED": lambda self: self.expression( - exp.ClusteredColumnConstraint, - this=self._parse_wrapped_csv(self._parse_ordered), - ), - "NONCLUSTERED": lambda self: self.expression( - exp.NonClusteredColumnConstraint, - this=self._parse_wrapped_csv(self._parse_ordered), - ), - "DEFAULT": lambda self: self.expression( - exp.DefaultColumnConstraint, this=self._parse_bitwise() - ), - "ENCODE": lambda self: self.expression( - exp.EncodeColumnConstraint, this=self._parse_var() - ), - "EPHEMERAL": lambda self: self.expression( - exp.EphemeralColumnConstraint, this=self._parse_bitwise() - ), - "EXCLUDE": lambda self: self.expression( - exp.ExcludeColumnConstraint, this=self._parse_index_params() - ), - "FOREIGN KEY": lambda self: self._parse_foreign_key(), - "FORMAT": lambda self: self.expression( - exp.DateFormatColumnConstraint, this=self._parse_var_or_string() - ), - "GENERATED": lambda self: self._parse_generated_as_identity(), - "IDENTITY": lambda self: self._parse_auto_increment(), - "INLINE": lambda self: self._parse_inline(), - "LIKE": lambda self: self._parse_create_like(), - "NOT": lambda self: self._parse_not_constraint(), - "NULL": lambda self: self.expression( - exp.NotNullColumnConstraint, allow_null=True - ), - "ON": lambda self: ( - ( - self._match(TokenType.UPDATE) - and self.expression( - exp.OnUpdateColumnConstraint, this=self._parse_function() - ) - ) - or self.expression(exp.OnProperty, this=self._parse_id_var()) - ), - "PATH": lambda self: self.expression( - exp.PathColumnConstraint, this=self._parse_string() - ), - "PERIOD": lambda self: self._parse_period_for_system_time(), - "PRIMARY KEY": lambda self: self._parse_primary_key(), - "REFERENCES": lambda self: self._parse_references(match=False), - "TITLE": lambda self: self.expression( - exp.TitleColumnConstraint, this=self._parse_var_or_string() - ), - "TTL": lambda self: self.expression( - exp.MergeTreeTTL, expressions=[self._parse_bitwise()] - ), - "UNIQUE": lambda self: self._parse_unique(), - "UPPERCASE": lambda self: self.expression(exp.UppercaseColumnConstraint), - "WITH": lambda self: self.expression( - exp.Properties, expressions=self._parse_wrapped_properties() - ), - "BUCKET": lambda self: self._parse_partitioned_by_bucket_or_truncate(), - "TRUNCATE": lambda self: self._parse_partitioned_by_bucket_or_truncate(), - } - - def _parse_partitioned_by_bucket_or_truncate(self) -> t.Optional[exp.Expression]: - if not self._match(TokenType.L_PAREN, advance=False): - # Partitioning by bucket or truncate follows the syntax: - # PARTITION BY (BUCKET(..) | TRUNCATE(..)) - # If we don't have parenthesis after each keyword, we should instead parse this as an identifier - self._retreat(self._index - 1) - return None - - klass = ( - exp.PartitionedByBucket - if self._prev.text.upper() == "BUCKET" - else exp.PartitionByTruncate - ) - - args = self._parse_wrapped_csv( - lambda: self._parse_primary() or self._parse_column() - ) - this, expression = seq_get(args, 0), seq_get(args, 1) - - if isinstance(this, exp.Literal): - # Check for Iceberg partition transforms (bucket / truncate) and ensure their arguments are in the right order - # - For Hive, it's `bucket(, )` or `truncate(, )` - # - For Trino, it's reversed - `bucket(, )` or `truncate(, )` - # Both variants are canonicalized in the latter i.e `bucket(, )` - # - # Hive ref: https://docs.aws.amazon.com/athena/latest/ug/querying-iceberg-creating-tables.html#querying-iceberg-partitioning - # Trino ref: https://docs.aws.amazon.com/athena/latest/ug/create-table-as.html#ctas-table-properties - this, expression = expression, this - - return self.expression(klass, this=this, expression=expression) - - ALTER_PARSERS = { - "ADD": lambda self: self._parse_alter_table_add(), - "AS": lambda self: self._parse_select(), - "ALTER": lambda self: self._parse_alter_table_alter(), - "CLUSTER BY": lambda self: self._parse_cluster(wrapped=True), - "DELETE": lambda self: self.expression(exp.Delete, where=self._parse_where()), - "DROP": lambda self: self._parse_alter_table_drop(), - "RENAME": lambda self: self._parse_alter_table_rename(), - "SET": lambda self: self._parse_alter_table_set(), - "SWAP": lambda self: self.expression( - exp.SwapTable, - this=self._match(TokenType.WITH) and self._parse_table(schema=True), - ), - } - - ALTER_ALTER_PARSERS = { - "DISTKEY": lambda self: self._parse_alter_diststyle(), - "DISTSTYLE": lambda self: self._parse_alter_diststyle(), - "SORTKEY": lambda self: self._parse_alter_sortkey(), - "COMPOUND": lambda self: self._parse_alter_sortkey(compound=True), - } - - SCHEMA_UNNAMED_CONSTRAINTS = { - "CHECK", - "EXCLUDE", - "FOREIGN KEY", - "LIKE", - "PERIOD", - "PRIMARY KEY", - "UNIQUE", - "BUCKET", - "TRUNCATE", - } - - NO_PAREN_FUNCTION_PARSERS = { - "ANY": lambda self: self.expression(exp.Any, this=self._parse_bitwise()), - "CASE": lambda self: self._parse_case(), - "CONNECT_BY_ROOT": lambda self: self.expression( - exp.ConnectByRoot, this=self._parse_column() - ), - "IF": lambda self: self._parse_if(), - } - - INVALID_FUNC_NAME_TOKENS = { - TokenType.IDENTIFIER, - TokenType.STRING, - } - - FUNCTIONS_WITH_ALIASED_ARGS = {"STRUCT"} - - KEY_VALUE_DEFINITIONS = (exp.Alias, exp.EQ, exp.PropertyEQ, exp.Slice) - - FUNCTION_PARSERS = { - **{ - name: lambda self: self._parse_max_min_by(exp.ArgMax) - for name in exp.ArgMax.sql_names() - }, - **{ - name: lambda self: self._parse_max_min_by(exp.ArgMin) - for name in exp.ArgMin.sql_names() - }, - "CAST": lambda self: self._parse_cast(self.STRICT_CAST), - "CEIL": lambda self: self._parse_ceil_floor(exp.Ceil), - "CONVERT": lambda self: self._parse_convert(self.STRICT_CAST), - "DECODE": lambda self: self._parse_decode(), - "EXTRACT": lambda self: self._parse_extract(), - "FLOOR": lambda self: self._parse_ceil_floor(exp.Floor), - "GAP_FILL": lambda self: self._parse_gap_fill(), - "INITCAP": lambda self: self._parse_initcap(), - "JSON_OBJECT": lambda self: self._parse_json_object(), - "JSON_OBJECTAGG": lambda self: self._parse_json_object(agg=True), - "JSON_TABLE": lambda self: self._parse_json_table(), - "MATCH": lambda self: self._parse_match_against(), - "NORMALIZE": lambda self: self._parse_normalize(), - "OPENJSON": lambda self: self._parse_open_json(), - "OVERLAY": lambda self: self._parse_overlay(), - "POSITION": lambda self: self._parse_position(), - "SAFE_CAST": lambda self: self._parse_cast(False, safe=True), - "STRING_AGG": lambda self: self._parse_string_agg(), - "SUBSTRING": lambda self: self._parse_substring(), - "TRIM": lambda self: self._parse_trim(), - "TRY_CAST": lambda self: self._parse_cast(False, safe=True), - "TRY_CONVERT": lambda self: self._parse_convert(False, safe=True), - "XMLELEMENT": lambda self: self.expression( - exp.XMLElement, - this=self._match_text_seq("NAME") and self._parse_id_var(), - expressions=self._match(TokenType.COMMA) - and self._parse_csv(self._parse_expression), - ), - "XMLTABLE": lambda self: self._parse_xml_table(), - } - - QUERY_MODIFIER_PARSERS = { - TokenType.MATCH_RECOGNIZE: lambda self: ( - "match", - self._parse_match_recognize(), - ), - TokenType.PREWHERE: lambda self: ("prewhere", self._parse_prewhere()), - TokenType.WHERE: lambda self: ("where", self._parse_where()), - TokenType.GROUP_BY: lambda self: ("group", self._parse_group()), - TokenType.HAVING: lambda self: ("having", self._parse_having()), - TokenType.QUALIFY: lambda self: ("qualify", self._parse_qualify()), - TokenType.WINDOW: lambda self: ("windows", self._parse_window_clause()), - TokenType.ORDER_BY: lambda self: ("order", self._parse_order()), - TokenType.LIMIT: lambda self: ("limit", self._parse_limit()), - TokenType.FETCH: lambda self: ("limit", self._parse_limit()), - TokenType.OFFSET: lambda self: ("offset", self._parse_offset()), - TokenType.FOR: lambda self: ("locks", self._parse_locks()), - TokenType.LOCK: lambda self: ("locks", self._parse_locks()), - TokenType.TABLE_SAMPLE: lambda self: ( - "sample", - self._parse_table_sample(as_modifier=True), - ), - TokenType.USING: lambda self: ( - "sample", - self._parse_table_sample(as_modifier=True), - ), - TokenType.CLUSTER_BY: lambda self: ( - "cluster", - self._parse_sort(exp.Cluster, TokenType.CLUSTER_BY), - ), - TokenType.DISTRIBUTE_BY: lambda self: ( - "distribute", - self._parse_sort(exp.Distribute, TokenType.DISTRIBUTE_BY), - ), - TokenType.SORT_BY: lambda self: ( - "sort", - self._parse_sort(exp.Sort, TokenType.SORT_BY), - ), - TokenType.CONNECT_BY: lambda self: ( - "connect", - self._parse_connect(skip_start_token=True), - ), - TokenType.START_WITH: lambda self: ("connect", self._parse_connect()), - } - QUERY_MODIFIER_TOKENS = set(QUERY_MODIFIER_PARSERS) - - SET_PARSERS = { - "GLOBAL": lambda self: self._parse_set_item_assignment("GLOBAL"), - "LOCAL": lambda self: self._parse_set_item_assignment("LOCAL"), - "SESSION": lambda self: self._parse_set_item_assignment("SESSION"), - "TRANSACTION": lambda self: self._parse_set_transaction(), - } - - SHOW_PARSERS: t.Dict[str, t.Callable] = {} - - TYPE_LITERAL_PARSERS = { - exp.DataType.Type.JSON: lambda self, this, _: self.expression( - exp.ParseJSON, this=this - ), - } - - TYPE_CONVERTERS: t.Dict[ - exp.DataType.Type, t.Callable[[exp.DataType], exp.DataType] - ] = {} - - DDL_SELECT_TOKENS = {TokenType.SELECT, TokenType.WITH, TokenType.L_PAREN} - - PRE_VOLATILE_TOKENS = {TokenType.CREATE, TokenType.REPLACE, TokenType.UNIQUE} - - TRANSACTION_KIND = {"DEFERRED", "IMMEDIATE", "EXCLUSIVE"} - TRANSACTION_CHARACTERISTICS: OPTIONS_TYPE = { - "ISOLATION": ( - ("LEVEL", "REPEATABLE", "READ"), - ("LEVEL", "READ", "COMMITTED"), - ("LEVEL", "READ", "UNCOMITTED"), - ("LEVEL", "SERIALIZABLE"), - ), - "READ": ("WRITE", "ONLY"), - } - - CONFLICT_ACTIONS: OPTIONS_TYPE = dict.fromkeys( - ("ABORT", "FAIL", "IGNORE", "REPLACE", "ROLLBACK", "UPDATE"), tuple() - ) - CONFLICT_ACTIONS["DO"] = ("NOTHING", "UPDATE") - - CREATE_SEQUENCE: OPTIONS_TYPE = { - "SCALE": ("EXTEND", "NOEXTEND"), - "SHARD": ("EXTEND", "NOEXTEND"), - "NO": ("CYCLE", "CACHE", "MAXVALUE", "MINVALUE"), - **dict.fromkeys( - ( - "SESSION", - "GLOBAL", - "KEEP", - "NOKEEP", - "ORDER", - "NOORDER", - "NOCACHE", - "CYCLE", - "NOCYCLE", - "NOMINVALUE", - "NOMAXVALUE", - "NOSCALE", - "NOSHARD", - ), - tuple(), - ), - } - - ISOLATED_LOADING_OPTIONS: OPTIONS_TYPE = {"FOR": ("ALL", "INSERT", "NONE")} - - USABLES: OPTIONS_TYPE = dict.fromkeys( - ("ROLE", "WAREHOUSE", "DATABASE", "SCHEMA", "CATALOG"), tuple() - ) - - CAST_ACTIONS: OPTIONS_TYPE = dict.fromkeys(("RENAME", "ADD"), ("FIELDS",)) - - SCHEMA_BINDING_OPTIONS: OPTIONS_TYPE = { - "TYPE": ("EVOLUTION",), - **dict.fromkeys(("BINDING", "COMPENSATION", "EVOLUTION"), tuple()), - } - - PROCEDURE_OPTIONS: OPTIONS_TYPE = {} - - EXECUTE_AS_OPTIONS: OPTIONS_TYPE = dict.fromkeys( - ("CALLER", "SELF", "OWNER"), tuple() - ) - - KEY_CONSTRAINT_OPTIONS: OPTIONS_TYPE = { - "NOT": ("ENFORCED",), - "MATCH": ( - "FULL", - "PARTIAL", - "SIMPLE", - ), - "INITIALLY": ("DEFERRED", "IMMEDIATE"), - "USING": ( - "BTREE", - "HASH", - ), - **dict.fromkeys(("DEFERRABLE", "NORELY", "RELY"), tuple()), - } - - WINDOW_EXCLUDE_OPTIONS: OPTIONS_TYPE = { - "NO": ("OTHERS",), - "CURRENT": ("ROW",), - **dict.fromkeys(("GROUP", "TIES"), tuple()), - } - - INSERT_ALTERNATIVES = {"ABORT", "FAIL", "IGNORE", "REPLACE", "ROLLBACK"} - - CLONE_KEYWORDS = {"CLONE", "COPY"} - HISTORICAL_DATA_PREFIX = {"AT", "BEFORE", "END"} - HISTORICAL_DATA_KIND = {"OFFSET", "STATEMENT", "STREAM", "TIMESTAMP", "VERSION"} - - OPCLASS_FOLLOW_KEYWORDS = {"ASC", "DESC", "NULLS", "WITH"} - - OPTYPE_FOLLOW_TOKENS = {TokenType.COMMA, TokenType.R_PAREN} - - TABLE_INDEX_HINT_TOKENS = {TokenType.FORCE, TokenType.IGNORE, TokenType.USE} - - VIEW_ATTRIBUTES = {"ENCRYPTION", "SCHEMABINDING", "VIEW_METADATA"} - - WINDOW_ALIAS_TOKENS = ID_VAR_TOKENS - {TokenType.RANGE, TokenType.ROWS} - WINDOW_BEFORE_PAREN_TOKENS = {TokenType.OVER} - WINDOW_SIDES = {"FOLLOWING", "PRECEDING"} - - JSON_KEY_VALUE_SEPARATOR_TOKENS = {TokenType.COLON, TokenType.COMMA, TokenType.IS} - - FETCH_TOKENS = ID_VAR_TOKENS - {TokenType.ROW, TokenType.ROWS, TokenType.PERCENT} - - ADD_CONSTRAINT_TOKENS = { - TokenType.CONSTRAINT, - TokenType.FOREIGN_KEY, - TokenType.INDEX, - TokenType.KEY, - TokenType.PRIMARY_KEY, - TokenType.UNIQUE, - } - - DISTINCT_TOKENS = {TokenType.DISTINCT} - - UNNEST_OFFSET_ALIAS_TOKENS = TABLE_ALIAS_TOKENS - SET_OPERATIONS - - SELECT_START_TOKENS = {TokenType.L_PAREN, TokenType.WITH, TokenType.SELECT} - - COPY_INTO_VARLEN_OPTIONS = { - "FILE_FORMAT", - "COPY_OPTIONS", - "FORMAT_OPTIONS", - "CREDENTIAL", - } - - IS_JSON_PREDICATE_KIND = {"VALUE", "SCALAR", "ARRAY", "OBJECT"} - - ODBC_DATETIME_LITERALS: t.Dict[str, t.Type[exp.Expression]] = {} - - ON_CONDITION_TOKENS = {"ERROR", "NULL", "TRUE", "FALSE", "EMPTY"} - - PRIVILEGE_FOLLOW_TOKENS = {TokenType.ON, TokenType.COMMA, TokenType.L_PAREN} - - # The style options for the DESCRIBE statement - DESCRIBE_STYLES = {"ANALYZE", "EXTENDED", "FORMATTED", "HISTORY"} - - SET_ASSIGNMENT_DELIMITERS = {"=", ":=", "TO"} - - # The style options for the ANALYZE statement - ANALYZE_STYLES = { - "BUFFER_USAGE_LIMIT", - "FULL", - "LOCAL", - "NO_WRITE_TO_BINLOG", - "SAMPLE", - "SKIP_LOCKED", - "VERBOSE", - } - - ANALYZE_EXPRESSION_PARSERS = { - "ALL": lambda self: self._parse_analyze_columns(), - "COMPUTE": lambda self: self._parse_analyze_statistics(), - "DELETE": lambda self: self._parse_analyze_delete(), - "DROP": lambda self: self._parse_analyze_histogram(), - "ESTIMATE": lambda self: self._parse_analyze_statistics(), - "LIST": lambda self: self._parse_analyze_list(), - "PREDICATE": lambda self: self._parse_analyze_columns(), - "UPDATE": lambda self: self._parse_analyze_histogram(), - "VALIDATE": lambda self: self._parse_analyze_validate(), - } - - PARTITION_KEYWORDS = {"PARTITION", "SUBPARTITION"} - - AMBIGUOUS_ALIAS_TOKENS = (TokenType.LIMIT, TokenType.OFFSET) - - OPERATION_MODIFIERS: t.Set[str] = set() - - RECURSIVE_CTE_SEARCH_KIND = {"BREADTH", "DEPTH", "CYCLE"} - - MODIFIABLES = (exp.Query, exp.Table, exp.TableFromRows, exp.Values) - - STRICT_CAST = True - - PREFIXED_PIVOT_COLUMNS = False - IDENTIFY_PIVOT_STRINGS = False - - LOG_DEFAULTS_TO_LN = False - - # Whether the table sample clause expects CSV syntax - TABLESAMPLE_CSV = False - - # The default method used for table sampling - DEFAULT_SAMPLING_METHOD: t.Optional[str] = None - - # Whether the SET command needs a delimiter (e.g. "=") for assignments - SET_REQUIRES_ASSIGNMENT_DELIMITER = True - - # Whether the TRIM function expects the characters to trim as its first argument - TRIM_PATTERN_FIRST = False - - # Whether string aliases are supported `SELECT COUNT(*) 'count'` - STRING_ALIASES = False - - # Whether query modifiers such as LIMIT are attached to the UNION node (vs its right operand) - MODIFIERS_ATTACHED_TO_SET_OP = True - SET_OP_MODIFIERS = {"order", "limit", "offset"} - - # Whether to parse IF statements that aren't followed by a left parenthesis as commands - NO_PAREN_IF_COMMANDS = True - - # Whether the -> and ->> operators expect documents of type JSON (e.g. Postgres) - JSON_ARROWS_REQUIRE_JSON_TYPE = False - - # Whether the `:` operator is used to extract a value from a VARIANT column - COLON_IS_VARIANT_EXTRACT = False - - # Whether or not a VALUES keyword needs to be followed by '(' to form a VALUES clause. - # If this is True and '(' is not found, the keyword will be treated as an identifier - VALUES_FOLLOWED_BY_PAREN = True - - # Whether implicit unnesting is supported, e.g. SELECT 1 FROM y.z AS z, z.a (Redshift) - SUPPORTS_IMPLICIT_UNNEST = False - - # Whether or not interval spans are supported, INTERVAL 1 YEAR TO MONTHS - INTERVAL_SPANS = True - - # Whether a PARTITION clause can follow a table reference - SUPPORTS_PARTITION_SELECTION = False - - # Whether the `name AS expr` schema/column constraint requires parentheses around `expr` - WRAPPED_TRANSFORM_COLUMN_CONSTRAINT = True - - # Whether the 'AS' keyword is optional in the CTE definition syntax - OPTIONAL_ALIAS_TOKEN_CTE = True - - # Whether renaming a column with an ALTER statement requires the presence of the COLUMN keyword - ALTER_RENAME_REQUIRES_COLUMN = True - - # Whether Alter statements are allowed to contain Partition specifications - ALTER_TABLE_PARTITIONS = False - - # Whether all join types have the same precedence, i.e., they "naturally" produce a left-deep tree. - # In standard SQL, joins that use the JOIN keyword take higher precedence than comma-joins. That is - # to say, JOIN operators happen before comma operators. This is not the case in some dialects, such - # as BigQuery, where all joins have the same precedence. - JOINS_HAVE_EQUAL_PRECEDENCE = False - - # Whether TIMESTAMP can produce a zone-aware timestamp - ZONE_AWARE_TIMESTAMP_CONSTRUCTOR = False - - # Whether map literals support arbitrary expressions as keys. - # When True, allows complex keys like arrays or literals: {[1, 2]: 3}, {1: 2} (e.g. DuckDB). - # When False, keys are typically restricted to identifiers. - MAP_KEYS_ARE_ARBITRARY_EXPRESSIONS = False - - # Whether JSON_EXTRACT requires a JSON expression as the first argument, e.g this - # is true for Snowflake but not for BigQuery which can also process strings - JSON_EXTRACT_REQUIRES_JSON_EXPRESSION = False - - # Dialects like Databricks support JOINS without join criteria - # Adding an ON TRUE, makes transpilation semantically correct for other dialects - ADD_JOIN_ON_TRUE = False - - # Whether INTERVAL spans with literal format '\d+ hh:[mm:[ss[.ff]]]' - # can omit the span unit `DAY TO MINUTE` or `DAY TO SECOND` - SUPPORTS_OMITTED_INTERVAL_SPAN_UNIT = False - - __slots__ = ( - "error_level", - "error_message_context", - "max_errors", - "dialect", - "sql", - "errors", - "_tokens", - "_index", - "_curr", - "_next", - "_prev", - "_prev_comments", - "_pipe_cte_counter", - ) - - # Autofilled - SHOW_TRIE: t.Dict = {} - SET_TRIE: t.Dict = {} - - def __init__( - self, - error_level: t.Optional[ErrorLevel] = None, - error_message_context: int = 100, - max_errors: int = 3, - dialect: DialectType = None, - ): - from bigframes_vendored.sqlglot.dialects import Dialect - - self.error_level = error_level or ErrorLevel.IMMEDIATE - self.error_message_context = error_message_context - self.max_errors = max_errors - self.dialect = Dialect.get_or_raise(dialect) - self.reset() - - def reset(self): - self.sql = "" - self.errors = [] - self._tokens = [] - self._index = 0 - self._curr = None - self._next = None - self._prev = None - self._prev_comments = None - self._pipe_cte_counter = 0 - - def parse( - self, raw_tokens: t.List[Token], sql: t.Optional[str] = None - ) -> t.List[t.Optional[exp.Expression]]: - """ - Parses a list of tokens and returns a list of syntax trees, one tree - per parsed SQL statement. - - Args: - raw_tokens: The list of tokens. - sql: The original SQL string, used to produce helpful debug messages. - - Returns: - The list of the produced syntax trees. - """ - return self._parse( - parse_method=self.__class__._parse_statement, raw_tokens=raw_tokens, sql=sql - ) - - def parse_into( - self, - expression_types: exp.IntoType, - raw_tokens: t.List[Token], - sql: t.Optional[str] = None, - ) -> t.List[t.Optional[exp.Expression]]: - """ - Parses a list of tokens into a given Expression type. If a collection of Expression - types is given instead, this method will try to parse the token list into each one - of them, stopping at the first for which the parsing succeeds. - - Args: - expression_types: The expression type(s) to try and parse the token list into. - raw_tokens: The list of tokens. - sql: The original SQL string, used to produce helpful debug messages. - - Returns: - The target Expression. - """ - errors = [] - for expression_type in ensure_list(expression_types): - parser = self.EXPRESSION_PARSERS.get(expression_type) - if not parser: - raise TypeError(f"No parser registered for {expression_type}") - - try: - return self._parse(parser, raw_tokens, sql) - except ParseError as e: - e.errors[0]["into_expression"] = expression_type - errors.append(e) - - raise ParseError( - f"Failed to parse '{sql or raw_tokens}' into {expression_types}", - errors=merge_errors(errors), - ) from errors[-1] - - def _parse( - self, - parse_method: t.Callable[[Parser], t.Optional[exp.Expression]], - raw_tokens: t.List[Token], - sql: t.Optional[str] = None, - ) -> t.List[t.Optional[exp.Expression]]: - self.reset() - self.sql = sql or "" - - total = len(raw_tokens) - chunks: t.List[t.List[Token]] = [[]] - - for i, token in enumerate(raw_tokens): - if token.token_type == TokenType.SEMICOLON: - if token.comments: - chunks.append([token]) - - if i < total - 1: - chunks.append([]) - else: - chunks[-1].append(token) - - expressions = [] - - for tokens in chunks: - self._index = -1 - self._tokens = tokens - self._advance() - - expressions.append(parse_method(self)) - - if self._index < len(self._tokens): - self.raise_error("Invalid expression / Unexpected token") - - self.check_errors() - - return expressions - - def check_errors(self) -> None: - """Logs or raises any found errors, depending on the chosen error level setting.""" - if self.error_level == ErrorLevel.WARN: - for error in self.errors: - logger.error(str(error)) - elif self.error_level == ErrorLevel.RAISE and self.errors: - raise ParseError( - concat_messages(self.errors, self.max_errors), - errors=merge_errors(self.errors), - ) - - def raise_error(self, message: str, token: t.Optional[Token] = None) -> None: - """ - Appends an error in the list of recorded errors or raises it, depending on the chosen - error level setting. - """ - token = token or self._curr or self._prev or Token.string("") - formatted_sql, start_context, highlight, end_context = highlight_sql( - sql=self.sql, - positions=[(token.start, token.end)], - context_length=self.error_message_context, - ) - formatted_message = ( - f"{message}. Line {token.line}, Col: {token.col}.\n {formatted_sql}" - ) - - error = ParseError.new( - formatted_message, - description=message, - line=token.line, - col=token.col, - start_context=start_context, - highlight=highlight, - end_context=end_context, - ) - - if self.error_level == ErrorLevel.IMMEDIATE: - raise error - - self.errors.append(error) - - def expression( - self, - exp_class: t.Type[E], - token: t.Optional[Token] = None, - comments: t.Optional[t.List[str]] = None, - **kwargs, - ) -> E: - """ - Creates a new, validated Expression. - - Args: - exp_class: The expression class to instantiate. - comments: An optional list of comments to attach to the expression. - kwargs: The arguments to set for the expression along with their respective values. - - Returns: - The target expression. - """ - if token: - instance = exp_class(this=token.text, **kwargs) - instance.update_positions(token) - else: - instance = exp_class(**kwargs) - instance.add_comments(comments) if comments else self._add_comments(instance) - return self.validate_expression(instance) - - def _add_comments(self, expression: t.Optional[exp.Expression]) -> None: - if expression and self._prev_comments: - expression.add_comments(self._prev_comments) - self._prev_comments = None - - def validate_expression(self, expression: E, args: t.Optional[t.List] = None) -> E: - """ - Validates an Expression, making sure that all its mandatory arguments are set. - - Args: - expression: The expression to validate. - args: An optional list of items that was used to instantiate the expression, if it's a Func. - - Returns: - The validated expression. - """ - if self.error_level != ErrorLevel.IGNORE: - for error_message in expression.error_messages(args): - self.raise_error(error_message) - - return expression - - def _find_sql(self, start: Token, end: Token) -> str: - return self.sql[start.start : end.end + 1] - - def _is_connected(self) -> bool: - return self._prev and self._curr and self._prev.end + 1 == self._curr.start - - def _advance(self, times: int = 1) -> None: - self._index += times - self._curr = seq_get(self._tokens, self._index) - self._next = seq_get(self._tokens, self._index + 1) - - if self._index > 0: - self._prev = self._tokens[self._index - 1] - self._prev_comments = self._prev.comments - else: - self._prev = None - self._prev_comments = None - - def _retreat(self, index: int) -> None: - if index != self._index: - self._advance(index - self._index) - - def _warn_unsupported(self) -> None: - if len(self._tokens) <= 1: - return - - # We use _find_sql because self.sql may comprise multiple chunks, and we're only - # interested in emitting a warning for the one being currently processed. - sql = self._find_sql(self._tokens[0], self._tokens[-1])[ - : self.error_message_context - ] - - logger.warning( - f"'{sql}' contains unsupported syntax. Falling back to parsing as a 'Command'." - ) - - def _parse_command(self) -> exp.Command: - self._warn_unsupported() - return self.expression( - exp.Command, - comments=self._prev_comments, - this=self._prev.text.upper(), - expression=self._parse_string(), - ) - - def _try_parse( - self, parse_method: t.Callable[[], T], retreat: bool = False - ) -> t.Optional[T]: - """ - Attemps to backtrack if a parse function that contains a try/catch internally raises an error. - This behavior can be different depending on the uset-set ErrorLevel, so _try_parse aims to - solve this by setting & resetting the parser state accordingly - """ - index = self._index - error_level = self.error_level - - self.error_level = ErrorLevel.IMMEDIATE - try: - this = parse_method() - except ParseError: - this = None - finally: - if not this or retreat: - self._retreat(index) - self.error_level = error_level - - return this - - def _parse_comment(self, allow_exists: bool = True) -> exp.Expression: - start = self._prev - exists = self._parse_exists() if allow_exists else None - - self._match(TokenType.ON) - - materialized = self._match_text_seq("MATERIALIZED") - kind = self._match_set(self.CREATABLES) and self._prev - if not kind: - return self._parse_as_command(start) - - if kind.token_type in (TokenType.FUNCTION, TokenType.PROCEDURE): - this = self._parse_user_defined_function(kind=kind.token_type) - elif kind.token_type == TokenType.TABLE: - this = self._parse_table(alias_tokens=self.COMMENT_TABLE_ALIAS_TOKENS) - elif kind.token_type == TokenType.COLUMN: - this = self._parse_column() - else: - this = self._parse_id_var() - - self._match(TokenType.IS) - - return self.expression( - exp.Comment, - this=this, - kind=kind.text, - expression=self._parse_string(), - exists=exists, - materialized=materialized, - ) - - def _parse_to_table( - self, - ) -> exp.ToTableProperty: - table = self._parse_table_parts(schema=True) - return self.expression(exp.ToTableProperty, this=table) - - # https://clickhouse.com/docs/en/engines/table-engines/mergetree-family/mergetree#mergetree-table-ttl - def _parse_ttl(self) -> exp.Expression: - def _parse_ttl_action() -> t.Optional[exp.Expression]: - this = self._parse_bitwise() - - if self._match_text_seq("DELETE"): - return self.expression(exp.MergeTreeTTLAction, this=this, delete=True) - if self._match_text_seq("RECOMPRESS"): - return self.expression( - exp.MergeTreeTTLAction, this=this, recompress=self._parse_bitwise() - ) - if self._match_text_seq("TO", "DISK"): - return self.expression( - exp.MergeTreeTTLAction, this=this, to_disk=self._parse_string() - ) - if self._match_text_seq("TO", "VOLUME"): - return self.expression( - exp.MergeTreeTTLAction, this=this, to_volume=self._parse_string() - ) - - return this - - expressions = self._parse_csv(_parse_ttl_action) - where = self._parse_where() - group = self._parse_group() - - aggregates = None - if group and self._match(TokenType.SET): - aggregates = self._parse_csv(self._parse_set_item) - - return self.expression( - exp.MergeTreeTTL, - expressions=expressions, - where=where, - group=group, - aggregates=aggregates, - ) - - def _parse_statement(self) -> t.Optional[exp.Expression]: - if self._curr is None: - return None - - if self._match_set(self.STATEMENT_PARSERS): - comments = self._prev_comments - stmt = self.STATEMENT_PARSERS[self._prev.token_type](self) - stmt.add_comments(comments, prepend=True) - return stmt - - if self._match_set(self.dialect.tokenizer_class.COMMANDS): - return self._parse_command() - - expression = self._parse_expression() - expression = ( - self._parse_set_operations(expression) - if expression - else self._parse_select() - ) - return self._parse_query_modifiers(expression) - - def _parse_drop(self, exists: bool = False) -> exp.Drop | exp.Command: - start = self._prev - temporary = self._match(TokenType.TEMPORARY) - materialized = self._match_text_seq("MATERIALIZED") - - kind = self._match_set(self.CREATABLES) and self._prev.text.upper() - if not kind: - return self._parse_as_command(start) - - concurrently = self._match_text_seq("CONCURRENTLY") - if_exists = exists or self._parse_exists() - - if kind == "COLUMN": - this = self._parse_column() - else: - this = self._parse_table_parts( - schema=True, is_db_reference=self._prev.token_type == TokenType.SCHEMA - ) - - cluster = self._parse_on_property() if self._match(TokenType.ON) else None - - if self._match(TokenType.L_PAREN, advance=False): - expressions = self._parse_wrapped_csv(self._parse_types) - else: - expressions = None - - return self.expression( - exp.Drop, - exists=if_exists, - this=this, - expressions=expressions, - kind=self.dialect.CREATABLE_KIND_MAPPING.get(kind) or kind, - temporary=temporary, - materialized=materialized, - cascade=self._match_text_seq("CASCADE"), - constraints=self._match_text_seq("CONSTRAINTS"), - purge=self._match_text_seq("PURGE"), - cluster=cluster, - concurrently=concurrently, - ) - - def _parse_exists(self, not_: bool = False) -> t.Optional[bool]: - return ( - self._match_text_seq("IF") - and (not not_ or self._match(TokenType.NOT)) - and self._match(TokenType.EXISTS) - ) - - def _parse_create(self) -> exp.Create | exp.Command: - # Note: this can't be None because we've matched a statement parser - start = self._prev - - replace = ( - start.token_type == TokenType.REPLACE - or self._match_pair(TokenType.OR, TokenType.REPLACE) - or self._match_pair(TokenType.OR, TokenType.ALTER) - ) - refresh = self._match_pair(TokenType.OR, TokenType.REFRESH) - - unique = self._match(TokenType.UNIQUE) - - if self._match_text_seq("CLUSTERED", "COLUMNSTORE"): - clustered = True - elif self._match_text_seq( - "NONCLUSTERED", "COLUMNSTORE" - ) or self._match_text_seq("COLUMNSTORE"): - clustered = False - else: - clustered = None - - if self._match_pair(TokenType.TABLE, TokenType.FUNCTION, advance=False): - self._advance() - - properties = None - create_token = self._match_set(self.CREATABLES) and self._prev - - if not create_token: - # exp.Properties.Location.POST_CREATE - properties = self._parse_properties() - create_token = self._match_set(self.CREATABLES) and self._prev - - if not properties or not create_token: - return self._parse_as_command(start) - - concurrently = self._match_text_seq("CONCURRENTLY") - exists = self._parse_exists(not_=True) - this = None - expression: t.Optional[exp.Expression] = None - indexes = None - no_schema_binding = None - begin = None - end = None - clone = None - - def extend_props(temp_props: t.Optional[exp.Properties]) -> None: - nonlocal properties - if properties and temp_props: - properties.expressions.extend(temp_props.expressions) - elif temp_props: - properties = temp_props - - if create_token.token_type in (TokenType.FUNCTION, TokenType.PROCEDURE): - this = self._parse_user_defined_function(kind=create_token.token_type) - - # exp.Properties.Location.POST_SCHEMA ("schema" here is the UDF's type signature) - extend_props(self._parse_properties()) - - expression = self._match(TokenType.ALIAS) and self._parse_heredoc() - extend_props(self._parse_properties()) - - if not expression: - if self._match(TokenType.COMMAND): - expression = self._parse_as_command(self._prev) - else: - begin = self._match(TokenType.BEGIN) - return_ = self._match_text_seq("RETURN") - - if self._match(TokenType.STRING, advance=False): - # Takes care of BigQuery's JavaScript UDF definitions that end in an OPTIONS property - # # https://cloud.google.com/bigquery/docs/reference/standard-sql/data-definition-language#create_function_statement - expression = self._parse_string() - extend_props(self._parse_properties()) - else: - expression = self._parse_user_defined_function_expression() - - end = self._match_text_seq("END") - - if return_: - expression = self.expression(exp.Return, this=expression) - elif create_token.token_type == TokenType.INDEX: - # Postgres allows anonymous indexes, eg. CREATE INDEX IF NOT EXISTS ON t(c) - if not self._match(TokenType.ON): - index = self._parse_id_var() - anonymous = False - else: - index = None - anonymous = True - - this = self._parse_index(index=index, anonymous=anonymous) - elif create_token.token_type in self.DB_CREATABLES: - table_parts = self._parse_table_parts( - schema=True, is_db_reference=create_token.token_type == TokenType.SCHEMA - ) - - # exp.Properties.Location.POST_NAME - self._match(TokenType.COMMA) - extend_props(self._parse_properties(before=True)) - - this = self._parse_schema(this=table_parts) - - # exp.Properties.Location.POST_SCHEMA and POST_WITH - extend_props(self._parse_properties()) - - has_alias = self._match(TokenType.ALIAS) - if not self._match_set(self.DDL_SELECT_TOKENS, advance=False): - # exp.Properties.Location.POST_ALIAS - extend_props(self._parse_properties()) - - if create_token.token_type == TokenType.SEQUENCE: - expression = self._parse_types() - props = self._parse_properties() - if props: - sequence_props = exp.SequenceProperties() - options = [] - for prop in props: - if isinstance(prop, exp.SequenceProperties): - for arg, value in prop.args.items(): - if arg == "options": - options.extend(value) - else: - sequence_props.set(arg, value) - prop.pop() - - if options: - sequence_props.set("options", options) - - props.append("expressions", sequence_props) - extend_props(props) - else: - expression = self._parse_ddl_select() - - # Some dialects also support using a table as an alias instead of a SELECT. - # Here we fallback to this as an alternative. - if not expression and has_alias: - expression = self._try_parse(self._parse_table_parts) - - if create_token.token_type == TokenType.TABLE: - # exp.Properties.Location.POST_EXPRESSION - extend_props(self._parse_properties()) - - indexes = [] - while True: - index = self._parse_index() - - # exp.Properties.Location.POST_INDEX - extend_props(self._parse_properties()) - if not index: - break - else: - self._match(TokenType.COMMA) - indexes.append(index) - elif create_token.token_type == TokenType.VIEW: - if self._match_text_seq("WITH", "NO", "SCHEMA", "BINDING"): - no_schema_binding = True - elif create_token.token_type in (TokenType.SINK, TokenType.SOURCE): - extend_props(self._parse_properties()) - - shallow = self._match_text_seq("SHALLOW") - - if self._match_texts(self.CLONE_KEYWORDS): - copy = self._prev.text.lower() == "copy" - clone = self.expression( - exp.Clone, - this=self._parse_table(schema=True), - shallow=shallow, - copy=copy, - ) - - if self._curr and not self._match_set( - (TokenType.R_PAREN, TokenType.COMMA), advance=False - ): - return self._parse_as_command(start) - - create_kind_text = create_token.text.upper() - return self.expression( - exp.Create, - this=this, - kind=self.dialect.CREATABLE_KIND_MAPPING.get(create_kind_text) - or create_kind_text, - replace=replace, - refresh=refresh, - unique=unique, - expression=expression, - exists=exists, - properties=properties, - indexes=indexes, - no_schema_binding=no_schema_binding, - begin=begin, - end=end, - clone=clone, - concurrently=concurrently, - clustered=clustered, - ) - - def _parse_sequence_properties(self) -> t.Optional[exp.SequenceProperties]: - seq = exp.SequenceProperties() - - options = [] - index = self._index - - while self._curr: - self._match(TokenType.COMMA) - if self._match_text_seq("INCREMENT"): - self._match_text_seq("BY") - self._match_text_seq("=") - seq.set("increment", self._parse_term()) - elif self._match_text_seq("MINVALUE"): - seq.set("minvalue", self._parse_term()) - elif self._match_text_seq("MAXVALUE"): - seq.set("maxvalue", self._parse_term()) - elif self._match(TokenType.START_WITH) or self._match_text_seq("START"): - self._match_text_seq("=") - seq.set("start", self._parse_term()) - elif self._match_text_seq("CACHE"): - # T-SQL allows empty CACHE which is initialized dynamically - seq.set("cache", self._parse_number() or True) - elif self._match_text_seq("OWNED", "BY"): - # "OWNED BY NONE" is the default - seq.set( - "owned", - None if self._match_text_seq("NONE") else self._parse_column(), - ) - else: - opt = self._parse_var_from_options( - self.CREATE_SEQUENCE, raise_unmatched=False - ) - if opt: - options.append(opt) - else: - break - - seq.set("options", options if options else None) - return None if self._index == index else seq - - def _parse_property_before(self) -> t.Optional[exp.Expression]: - # only used for teradata currently - self._match(TokenType.COMMA) - - kwargs = { - "no": self._match_text_seq("NO"), - "dual": self._match_text_seq("DUAL"), - "before": self._match_text_seq("BEFORE"), - "default": self._match_text_seq("DEFAULT"), - "local": (self._match_text_seq("LOCAL") and "LOCAL") - or (self._match_text_seq("NOT", "LOCAL") and "NOT LOCAL"), - "after": self._match_text_seq("AFTER"), - "minimum": self._match_texts(("MIN", "MINIMUM")), - "maximum": self._match_texts(("MAX", "MAXIMUM")), - } - - if self._match_texts(self.PROPERTY_PARSERS): - parser = self.PROPERTY_PARSERS[self._prev.text.upper()] - try: - return parser(self, **{k: v for k, v in kwargs.items() if v}) - except TypeError: - self.raise_error(f"Cannot parse property '{self._prev.text}'") - - return None - - def _parse_wrapped_properties(self) -> t.List[exp.Expression]: - return self._parse_wrapped_csv(self._parse_property) - - def _parse_property(self) -> t.Optional[exp.Expression]: - if self._match_texts(self.PROPERTY_PARSERS): - return self.PROPERTY_PARSERS[self._prev.text.upper()](self) - - if self._match(TokenType.DEFAULT) and self._match_texts(self.PROPERTY_PARSERS): - return self.PROPERTY_PARSERS[self._prev.text.upper()](self, default=True) - - if self._match_text_seq("COMPOUND", "SORTKEY"): - return self._parse_sortkey(compound=True) - - if self._match_text_seq("SQL", "SECURITY"): - return self.expression( - exp.SqlSecurityProperty, - this=self._match_texts(("DEFINER", "INVOKER")) - and self._prev.text.upper(), - ) - - index = self._index - - seq_props = self._parse_sequence_properties() - if seq_props: - return seq_props - - self._retreat(index) - key = self._parse_column() - - if not self._match(TokenType.EQ): - self._retreat(index) - return None - - # Transform the key to exp.Dot if it's dotted identifiers wrapped in exp.Column or to exp.Var otherwise - if isinstance(key, exp.Column): - key = key.to_dot() if len(key.parts) > 1 else exp.var(key.name) - - value = self._parse_bitwise() or self._parse_var(any_token=True) - - # Transform the value to exp.Var if it was parsed as exp.Column(exp.Identifier()) - if isinstance(value, exp.Column): - value = exp.var(value.name) - - return self.expression(exp.Property, this=key, value=value) - - def _parse_stored( - self, - ) -> t.Union[exp.FileFormatProperty, exp.StorageHandlerProperty]: - if self._match_text_seq("BY"): - return self.expression( - exp.StorageHandlerProperty, this=self._parse_var_or_string() - ) - - self._match(TokenType.ALIAS) - input_format = ( - self._parse_string() if self._match_text_seq("INPUTFORMAT") else None - ) - output_format = ( - self._parse_string() if self._match_text_seq("OUTPUTFORMAT") else None - ) - - return self.expression( - exp.FileFormatProperty, - this=( - self.expression( - exp.InputOutputFormat, - input_format=input_format, - output_format=output_format, - ) - if input_format or output_format - else self._parse_var_or_string() - or self._parse_number() - or self._parse_id_var() - ), - hive_format=True, - ) - - def _parse_unquoted_field(self) -> t.Optional[exp.Expression]: - field = self._parse_field() - if isinstance(field, exp.Identifier) and not field.quoted: - field = exp.var(field) - - return field - - def _parse_property_assignment(self, exp_class: t.Type[E], **kwargs: t.Any) -> E: - self._match(TokenType.EQ) - self._match(TokenType.ALIAS) - - return self.expression(exp_class, this=self._parse_unquoted_field(), **kwargs) - - def _parse_properties( - self, before: t.Optional[bool] = None - ) -> t.Optional[exp.Properties]: - properties = [] - while True: - if before: - prop = self._parse_property_before() - else: - prop = self._parse_property() - if not prop: - break - for p in ensure_list(prop): - properties.append(p) - - if properties: - return self.expression(exp.Properties, expressions=properties) - - return None - - def _parse_fallback(self, no: bool = False) -> exp.FallbackProperty: - return self.expression( - exp.FallbackProperty, no=no, protection=self._match_text_seq("PROTECTION") - ) - - def _parse_security(self) -> t.Optional[exp.SecurityProperty]: - if self._match_texts(("NONE", "DEFINER", "INVOKER")): - security_specifier = self._prev.text.upper() - return self.expression(exp.SecurityProperty, this=security_specifier) - return None - - def _parse_settings_property(self) -> exp.SettingsProperty: - return self.expression( - exp.SettingsProperty, expressions=self._parse_csv(self._parse_assignment) - ) - - def _parse_volatile_property(self) -> exp.VolatileProperty | exp.StabilityProperty: - if self._index >= 2: - pre_volatile_token = self._tokens[self._index - 2] - else: - pre_volatile_token = None - - if ( - pre_volatile_token - and pre_volatile_token.token_type in self.PRE_VOLATILE_TOKENS - ): - return exp.VolatileProperty() - - return self.expression( - exp.StabilityProperty, this=exp.Literal.string("VOLATILE") - ) - - def _parse_retention_period(self) -> exp.Var: - # Parse TSQL's HISTORY_RETENTION_PERIOD: {INFINITE | DAY | DAYS | MONTH ...} - number = self._parse_number() - number_str = f"{number} " if number else "" - unit = self._parse_var(any_token=True) - return exp.var(f"{number_str}{unit}") - - def _parse_system_versioning_property( - self, with_: bool = False - ) -> exp.WithSystemVersioningProperty: - self._match(TokenType.EQ) - prop = self.expression( - exp.WithSystemVersioningProperty, - on=True, - with_=with_, - ) - - if self._match_text_seq("OFF"): - prop.set("on", False) - return prop - - self._match(TokenType.ON) - if self._match(TokenType.L_PAREN): - while self._curr and not self._match(TokenType.R_PAREN): - if self._match_text_seq("HISTORY_TABLE", "="): - prop.set("this", self._parse_table_parts()) - elif self._match_text_seq("DATA_CONSISTENCY_CHECK", "="): - prop.set( - "data_consistency", - self._advance_any() and self._prev.text.upper(), - ) - elif self._match_text_seq("HISTORY_RETENTION_PERIOD", "="): - prop.set("retention_period", self._parse_retention_period()) - - self._match(TokenType.COMMA) - - return prop - - def _parse_data_deletion_property(self) -> exp.DataDeletionProperty: - self._match(TokenType.EQ) - on = self._match_text_seq("ON") or not self._match_text_seq("OFF") - prop = self.expression(exp.DataDeletionProperty, on=on) - - if self._match(TokenType.L_PAREN): - while self._curr and not self._match(TokenType.R_PAREN): - if self._match_text_seq("FILTER_COLUMN", "="): - prop.set("filter_column", self._parse_column()) - elif self._match_text_seq("RETENTION_PERIOD", "="): - prop.set("retention_period", self._parse_retention_period()) - - self._match(TokenType.COMMA) - - return prop - - def _parse_distributed_property(self) -> exp.DistributedByProperty: - kind = "HASH" - expressions: t.Optional[t.List[exp.Expression]] = None - if self._match_text_seq("BY", "HASH"): - expressions = self._parse_wrapped_csv(self._parse_id_var) - elif self._match_text_seq("BY", "RANDOM"): - kind = "RANDOM" - - # If the BUCKETS keyword is not present, the number of buckets is AUTO - buckets: t.Optional[exp.Expression] = None - if self._match_text_seq("BUCKETS") and not self._match_text_seq("AUTO"): - buckets = self._parse_number() - - return self.expression( - exp.DistributedByProperty, - expressions=expressions, - kind=kind, - buckets=buckets, - order=self._parse_order(), - ) - - def _parse_composite_key_property(self, expr_type: t.Type[E]) -> E: - self._match_text_seq("KEY") - expressions = self._parse_wrapped_id_vars() - return self.expression(expr_type, expressions=expressions) - - def _parse_with_property( - self, - ) -> t.Optional[exp.Expression] | t.List[exp.Expression]: - if self._match_text_seq("(", "SYSTEM_VERSIONING"): - prop = self._parse_system_versioning_property(with_=True) - self._match_r_paren() - return prop - - if self._match(TokenType.L_PAREN, advance=False): - return self._parse_wrapped_properties() - - if self._match_text_seq("JOURNAL"): - return self._parse_withjournaltable() - - if self._match_texts(self.VIEW_ATTRIBUTES): - return self.expression( - exp.ViewAttributeProperty, this=self._prev.text.upper() - ) - - if self._match_text_seq("DATA"): - return self._parse_withdata(no=False) - elif self._match_text_seq("NO", "DATA"): - return self._parse_withdata(no=True) - - if self._match(TokenType.SERDE_PROPERTIES, advance=False): - return self._parse_serde_properties(with_=True) - - if self._match(TokenType.SCHEMA): - return self.expression( - exp.WithSchemaBindingProperty, - this=self._parse_var_from_options(self.SCHEMA_BINDING_OPTIONS), - ) - - if self._match_texts(self.PROCEDURE_OPTIONS, advance=False): - return self.expression( - exp.WithProcedureOptions, - expressions=self._parse_csv(self._parse_procedure_option), - ) - - if not self._next: - return None - - return self._parse_withisolatedloading() - - def _parse_procedure_option(self) -> exp.Expression | None: - if self._match_text_seq("EXECUTE", "AS"): - return self.expression( - exp.ExecuteAsProperty, - this=self._parse_var_from_options( - self.EXECUTE_AS_OPTIONS, raise_unmatched=False - ) - or self._parse_string(), - ) - - return self._parse_var_from_options(self.PROCEDURE_OPTIONS) - - # https://dev.mysql.com/doc/refman/8.0/en/create-view.html - def _parse_definer(self) -> t.Optional[exp.DefinerProperty]: - self._match(TokenType.EQ) - - user = self._parse_id_var() - self._match(TokenType.PARAMETER) - host = self._parse_id_var() or (self._match(TokenType.MOD) and self._prev.text) - - if not user or not host: - return None - - return exp.DefinerProperty(this=f"{user}@{host}") - - def _parse_withjournaltable(self) -> exp.WithJournalTableProperty: - self._match(TokenType.TABLE) - self._match(TokenType.EQ) - return self.expression( - exp.WithJournalTableProperty, this=self._parse_table_parts() - ) - - def _parse_log(self, no: bool = False) -> exp.LogProperty: - return self.expression(exp.LogProperty, no=no) - - def _parse_journal(self, **kwargs) -> exp.JournalProperty: - return self.expression(exp.JournalProperty, **kwargs) - - def _parse_checksum(self) -> exp.ChecksumProperty: - self._match(TokenType.EQ) - - on = None - if self._match(TokenType.ON): - on = True - elif self._match_text_seq("OFF"): - on = False - - return self.expression( - exp.ChecksumProperty, on=on, default=self._match(TokenType.DEFAULT) - ) - - def _parse_cluster(self, wrapped: bool = False) -> exp.Cluster: - return self.expression( - exp.Cluster, - expressions=( - self._parse_wrapped_csv(self._parse_ordered) - if wrapped - else self._parse_csv(self._parse_ordered) - ), - ) - - def _parse_clustered_by(self) -> exp.ClusteredByProperty: - self._match_text_seq("BY") - - self._match_l_paren() - expressions = self._parse_csv(self._parse_column) - self._match_r_paren() - - if self._match_text_seq("SORTED", "BY"): - self._match_l_paren() - sorted_by = self._parse_csv(self._parse_ordered) - self._match_r_paren() - else: - sorted_by = None - - self._match(TokenType.INTO) - buckets = self._parse_number() - self._match_text_seq("BUCKETS") - - return self.expression( - exp.ClusteredByProperty, - expressions=expressions, - sorted_by=sorted_by, - buckets=buckets, - ) - - def _parse_copy_property(self) -> t.Optional[exp.CopyGrantsProperty]: - if not self._match_text_seq("GRANTS"): - self._retreat(self._index - 1) - return None - - return self.expression(exp.CopyGrantsProperty) - - def _parse_freespace(self) -> exp.FreespaceProperty: - self._match(TokenType.EQ) - return self.expression( - exp.FreespaceProperty, - this=self._parse_number(), - percent=self._match(TokenType.PERCENT), - ) - - def _parse_mergeblockratio( - self, no: bool = False, default: bool = False - ) -> exp.MergeBlockRatioProperty: - if self._match(TokenType.EQ): - return self.expression( - exp.MergeBlockRatioProperty, - this=self._parse_number(), - percent=self._match(TokenType.PERCENT), - ) - - return self.expression(exp.MergeBlockRatioProperty, no=no, default=default) - - def _parse_datablocksize( - self, - default: t.Optional[bool] = None, - minimum: t.Optional[bool] = None, - maximum: t.Optional[bool] = None, - ) -> exp.DataBlocksizeProperty: - self._match(TokenType.EQ) - size = self._parse_number() - - units = None - if self._match_texts(("BYTES", "KBYTES", "KILOBYTES")): - units = self._prev.text - - return self.expression( - exp.DataBlocksizeProperty, - size=size, - units=units, - default=default, - minimum=minimum, - maximum=maximum, - ) - - def _parse_blockcompression(self) -> exp.BlockCompressionProperty: - self._match(TokenType.EQ) - always = self._match_text_seq("ALWAYS") - manual = self._match_text_seq("MANUAL") - never = self._match_text_seq("NEVER") - default = self._match_text_seq("DEFAULT") - - autotemp = None - if self._match_text_seq("AUTOTEMP"): - autotemp = self._parse_schema() - - return self.expression( - exp.BlockCompressionProperty, - always=always, - manual=manual, - never=never, - default=default, - autotemp=autotemp, - ) - - def _parse_withisolatedloading(self) -> t.Optional[exp.IsolatedLoadingProperty]: - index = self._index - no = self._match_text_seq("NO") - concurrent = self._match_text_seq("CONCURRENT") - - if not self._match_text_seq("ISOLATED", "LOADING"): - self._retreat(index) - return None - - target = self._parse_var_from_options( - self.ISOLATED_LOADING_OPTIONS, raise_unmatched=False - ) - return self.expression( - exp.IsolatedLoadingProperty, no=no, concurrent=concurrent, target=target - ) - - def _parse_locking(self) -> exp.LockingProperty: - if self._match(TokenType.TABLE): - kind = "TABLE" - elif self._match(TokenType.VIEW): - kind = "VIEW" - elif self._match(TokenType.ROW): - kind = "ROW" - elif self._match_text_seq("DATABASE"): - kind = "DATABASE" - else: - kind = None - - if kind in ("DATABASE", "TABLE", "VIEW"): - this = self._parse_table_parts() - else: - this = None - - if self._match(TokenType.FOR): - for_or_in = "FOR" - elif self._match(TokenType.IN): - for_or_in = "IN" - else: - for_or_in = None - - if self._match_text_seq("ACCESS"): - lock_type = "ACCESS" - elif self._match_texts(("EXCL", "EXCLUSIVE")): - lock_type = "EXCLUSIVE" - elif self._match_text_seq("SHARE"): - lock_type = "SHARE" - elif self._match_text_seq("READ"): - lock_type = "READ" - elif self._match_text_seq("WRITE"): - lock_type = "WRITE" - elif self._match_text_seq("CHECKSUM"): - lock_type = "CHECKSUM" - else: - lock_type = None - - override = self._match_text_seq("OVERRIDE") - - return self.expression( - exp.LockingProperty, - this=this, - kind=kind, - for_or_in=for_or_in, - lock_type=lock_type, - override=override, - ) - - def _parse_partition_by(self) -> t.List[exp.Expression]: - if self._match(TokenType.PARTITION_BY): - return self._parse_csv(self._parse_disjunction) - return [] - - def _parse_partition_bound_spec(self) -> exp.PartitionBoundSpec: - def _parse_partition_bound_expr() -> t.Optional[exp.Expression]: - if self._match_text_seq("MINVALUE"): - return exp.var("MINVALUE") - if self._match_text_seq("MAXVALUE"): - return exp.var("MAXVALUE") - return self._parse_bitwise() - - this: t.Optional[exp.Expression | t.List[exp.Expression]] = None - expression = None - from_expressions = None - to_expressions = None - - if self._match(TokenType.IN): - this = self._parse_wrapped_csv(self._parse_bitwise) - elif self._match(TokenType.FROM): - from_expressions = self._parse_wrapped_csv(_parse_partition_bound_expr) - self._match_text_seq("TO") - to_expressions = self._parse_wrapped_csv(_parse_partition_bound_expr) - elif self._match_text_seq("WITH", "(", "MODULUS"): - this = self._parse_number() - self._match_text_seq(",", "REMAINDER") - expression = self._parse_number() - self._match_r_paren() - else: - self.raise_error("Failed to parse partition bound spec.") - - return self.expression( - exp.PartitionBoundSpec, - this=this, - expression=expression, - from_expressions=from_expressions, - to_expressions=to_expressions, - ) - - # https://www.postgresql.org/docs/current/sql-createtable.html - def _parse_partitioned_of(self) -> t.Optional[exp.PartitionedOfProperty]: - if not self._match_text_seq("OF"): - self._retreat(self._index - 1) - return None - - this = self._parse_table(schema=True) - - if self._match(TokenType.DEFAULT): - expression: exp.Var | exp.PartitionBoundSpec = exp.var("DEFAULT") - elif self._match_text_seq("FOR", "VALUES"): - expression = self._parse_partition_bound_spec() - else: - self.raise_error("Expecting either DEFAULT or FOR VALUES clause.") - - return self.expression( - exp.PartitionedOfProperty, this=this, expression=expression - ) - - def _parse_partitioned_by(self) -> exp.PartitionedByProperty: - self._match(TokenType.EQ) - return self.expression( - exp.PartitionedByProperty, - this=self._parse_schema() or self._parse_bracket(self._parse_field()), - ) - - def _parse_withdata(self, no: bool = False) -> exp.WithDataProperty: - if self._match_text_seq("AND", "STATISTICS"): - statistics = True - elif self._match_text_seq("AND", "NO", "STATISTICS"): - statistics = False - else: - statistics = None - - return self.expression(exp.WithDataProperty, no=no, statistics=statistics) - - def _parse_contains_property(self) -> t.Optional[exp.SqlReadWriteProperty]: - if self._match_text_seq("SQL"): - return self.expression(exp.SqlReadWriteProperty, this="CONTAINS SQL") - return None - - def _parse_modifies_property(self) -> t.Optional[exp.SqlReadWriteProperty]: - if self._match_text_seq("SQL", "DATA"): - return self.expression(exp.SqlReadWriteProperty, this="MODIFIES SQL DATA") - return None - - def _parse_no_property(self) -> t.Optional[exp.Expression]: - if self._match_text_seq("PRIMARY", "INDEX"): - return exp.NoPrimaryIndexProperty() - if self._match_text_seq("SQL"): - return self.expression(exp.SqlReadWriteProperty, this="NO SQL") - return None - - def _parse_on_property(self) -> t.Optional[exp.Expression]: - if self._match_text_seq("COMMIT", "PRESERVE", "ROWS"): - return exp.OnCommitProperty() - if self._match_text_seq("COMMIT", "DELETE", "ROWS"): - return exp.OnCommitProperty(delete=True) - return self.expression( - exp.OnProperty, this=self._parse_schema(self._parse_id_var()) - ) - - def _parse_reads_property(self) -> t.Optional[exp.SqlReadWriteProperty]: - if self._match_text_seq("SQL", "DATA"): - return self.expression(exp.SqlReadWriteProperty, this="READS SQL DATA") - return None - - def _parse_distkey(self) -> exp.DistKeyProperty: - return self.expression( - exp.DistKeyProperty, this=self._parse_wrapped(self._parse_id_var) - ) - - def _parse_create_like(self) -> t.Optional[exp.LikeProperty]: - table = self._parse_table(schema=True) - - options = [] - while self._match_texts(("INCLUDING", "EXCLUDING")): - this = self._prev.text.upper() - - id_var = self._parse_id_var() - if not id_var: - return None - - options.append( - self.expression( - exp.Property, this=this, value=exp.var(id_var.this.upper()) - ) - ) - - return self.expression(exp.LikeProperty, this=table, expressions=options) - - def _parse_sortkey(self, compound: bool = False) -> exp.SortKeyProperty: - return self.expression( - exp.SortKeyProperty, this=self._parse_wrapped_id_vars(), compound=compound - ) - - def _parse_character_set(self, default: bool = False) -> exp.CharacterSetProperty: - self._match(TokenType.EQ) - return self.expression( - exp.CharacterSetProperty, this=self._parse_var_or_string(), default=default - ) - - def _parse_remote_with_connection(self) -> exp.RemoteWithConnectionModelProperty: - self._match_text_seq("WITH", "CONNECTION") - return self.expression( - exp.RemoteWithConnectionModelProperty, this=self._parse_table_parts() - ) - - def _parse_returns(self) -> exp.ReturnsProperty: - value: t.Optional[exp.Expression] - null = None - is_table = self._match(TokenType.TABLE) - - if is_table: - if self._match(TokenType.LT): - value = self.expression( - exp.Schema, - this="TABLE", - expressions=self._parse_csv(self._parse_struct_types), - ) - if not self._match(TokenType.GT): - self.raise_error("Expecting >") - else: - value = self._parse_schema(exp.var("TABLE")) - elif self._match_text_seq("NULL", "ON", "NULL", "INPUT"): - null = True - value = None - else: - value = self._parse_types() - - return self.expression( - exp.ReturnsProperty, this=value, is_table=is_table, null=null - ) - - def _parse_describe(self) -> exp.Describe: - kind = self._match_set(self.CREATABLES) and self._prev.text - style = self._match_texts(self.DESCRIBE_STYLES) and self._prev.text.upper() - if self._match(TokenType.DOT): - style = None - self._retreat(self._index - 2) - - format = ( - self._parse_property() - if self._match(TokenType.FORMAT, advance=False) - else None - ) - - if self._match_set(self.STATEMENT_PARSERS, advance=False): - this = self._parse_statement() - else: - this = self._parse_table(schema=True) - - properties = self._parse_properties() - expressions = properties.expressions if properties else None - partition = self._parse_partition() - return self.expression( - exp.Describe, - this=this, - style=style, - kind=kind, - expressions=expressions, - partition=partition, - format=format, - ) - - def _parse_multitable_inserts( - self, comments: t.Optional[t.List[str]] - ) -> exp.MultitableInserts: - kind = self._prev.text.upper() - expressions = [] - - def parse_conditional_insert() -> t.Optional[exp.ConditionalInsert]: - if self._match(TokenType.WHEN): - expression = self._parse_disjunction() - self._match(TokenType.THEN) - else: - expression = None - - else_ = self._match(TokenType.ELSE) - - if not self._match(TokenType.INTO): - return None - - return self.expression( - exp.ConditionalInsert, - this=self.expression( - exp.Insert, - this=self._parse_table(schema=True), - expression=self._parse_derived_table_values(), - ), - expression=expression, - else_=else_, - ) - - expression = parse_conditional_insert() - while expression is not None: - expressions.append(expression) - expression = parse_conditional_insert() - - return self.expression( - exp.MultitableInserts, - kind=kind, - comments=comments, - expressions=expressions, - source=self._parse_table(), - ) - - def _parse_insert(self) -> t.Union[exp.Insert, exp.MultitableInserts]: - comments = [] - hint = self._parse_hint() - overwrite = self._match(TokenType.OVERWRITE) - ignore = self._match(TokenType.IGNORE) - local = self._match_text_seq("LOCAL") - alternative = None - is_function = None - - if self._match_text_seq("DIRECTORY"): - this: t.Optional[exp.Expression] = self.expression( - exp.Directory, - this=self._parse_var_or_string(), - local=local, - row_format=self._parse_row_format(match_row=True), - ) - else: - if self._match_set((TokenType.FIRST, TokenType.ALL)): - comments += ensure_list(self._prev_comments) - return self._parse_multitable_inserts(comments) - - if self._match(TokenType.OR): - alternative = ( - self._match_texts(self.INSERT_ALTERNATIVES) and self._prev.text - ) - - self._match(TokenType.INTO) - comments += ensure_list(self._prev_comments) - self._match(TokenType.TABLE) - is_function = self._match(TokenType.FUNCTION) - - this = self._parse_function() if is_function else self._parse_insert_table() - - returning = self._parse_returning() # TSQL allows RETURNING before source - - return self.expression( - exp.Insert, - comments=comments, - hint=hint, - is_function=is_function, - this=this, - stored=self._match_text_seq("STORED") and self._parse_stored(), - by_name=self._match_text_seq("BY", "NAME"), - exists=self._parse_exists(), - where=self._match_pair(TokenType.REPLACE, TokenType.WHERE) - and self._parse_disjunction(), - partition=self._match(TokenType.PARTITION_BY) - and self._parse_partitioned_by(), - settings=self._match_text_seq("SETTINGS") - and self._parse_settings_property(), - default=self._match_text_seq("DEFAULT", "VALUES"), - expression=self._parse_derived_table_values() or self._parse_ddl_select(), - conflict=self._parse_on_conflict(), - returning=returning or self._parse_returning(), - overwrite=overwrite, - alternative=alternative, - ignore=ignore, - source=self._match(TokenType.TABLE) and self._parse_table(), - ) - - def _parse_insert_table(self) -> t.Optional[exp.Expression]: - this = self._parse_table(schema=True, parse_partition=True) - if isinstance(this, exp.Table) and self._match(TokenType.ALIAS, advance=False): - this.set("alias", self._parse_table_alias()) - return this - - def _parse_kill(self) -> exp.Kill: - kind = ( - exp.var(self._prev.text) - if self._match_texts(("CONNECTION", "QUERY")) - else None - ) - - return self.expression( - exp.Kill, - this=self._parse_primary(), - kind=kind, - ) - - def _parse_on_conflict(self) -> t.Optional[exp.OnConflict]: - conflict = self._match_text_seq("ON", "CONFLICT") - duplicate = self._match_text_seq("ON", "DUPLICATE", "KEY") - - if not conflict and not duplicate: - return None - - conflict_keys = None - constraint = None - - if conflict: - if self._match_text_seq("ON", "CONSTRAINT"): - constraint = self._parse_id_var() - elif self._match(TokenType.L_PAREN): - conflict_keys = self._parse_csv(self._parse_id_var) - self._match_r_paren() - - action = self._parse_var_from_options(self.CONFLICT_ACTIONS) - if self._prev.token_type == TokenType.UPDATE: - self._match(TokenType.SET) - expressions = self._parse_csv(self._parse_equality) - else: - expressions = None - - return self.expression( - exp.OnConflict, - duplicate=duplicate, - expressions=expressions, - action=action, - conflict_keys=conflict_keys, - constraint=constraint, - where=self._parse_where(), - ) - - def _parse_returning(self) -> t.Optional[exp.Returning]: - if not self._match(TokenType.RETURNING): - return None - return self.expression( - exp.Returning, - expressions=self._parse_csv(self._parse_expression), - into=self._match(TokenType.INTO) and self._parse_table_part(), - ) - - def _parse_row( - self, - ) -> t.Optional[exp.RowFormatSerdeProperty | exp.RowFormatDelimitedProperty]: - if not self._match(TokenType.FORMAT): - return None - return self._parse_row_format() - - def _parse_serde_properties( - self, with_: bool = False - ) -> t.Optional[exp.SerdeProperties]: - index = self._index - with_ = with_ or self._match_text_seq("WITH") - - if not self._match(TokenType.SERDE_PROPERTIES): - self._retreat(index) - return None - return self.expression( - exp.SerdeProperties, - expressions=self._parse_wrapped_properties(), - with_=with_, - ) - - def _parse_row_format( - self, match_row: bool = False - ) -> t.Optional[exp.RowFormatSerdeProperty | exp.RowFormatDelimitedProperty]: - if match_row and not self._match_pair(TokenType.ROW, TokenType.FORMAT): - return None - - if self._match_text_seq("SERDE"): - this = self._parse_string() - - serde_properties = self._parse_serde_properties() - - return self.expression( - exp.RowFormatSerdeProperty, this=this, serde_properties=serde_properties - ) - - self._match_text_seq("DELIMITED") - - kwargs = {} - - if self._match_text_seq("FIELDS", "TERMINATED", "BY"): - kwargs["fields"] = self._parse_string() - if self._match_text_seq("ESCAPED", "BY"): - kwargs["escaped"] = self._parse_string() - if self._match_text_seq("COLLECTION", "ITEMS", "TERMINATED", "BY"): - kwargs["collection_items"] = self._parse_string() - if self._match_text_seq("MAP", "KEYS", "TERMINATED", "BY"): - kwargs["map_keys"] = self._parse_string() - if self._match_text_seq("LINES", "TERMINATED", "BY"): - kwargs["lines"] = self._parse_string() - if self._match_text_seq("NULL", "DEFINED", "AS"): - kwargs["null"] = self._parse_string() - - return self.expression(exp.RowFormatDelimitedProperty, **kwargs) # type: ignore - - def _parse_load(self) -> exp.LoadData | exp.Command: - if self._match_text_seq("DATA"): - local = self._match_text_seq("LOCAL") - self._match_text_seq("INPATH") - inpath = self._parse_string() - overwrite = self._match(TokenType.OVERWRITE) - self._match_pair(TokenType.INTO, TokenType.TABLE) - - return self.expression( - exp.LoadData, - this=self._parse_table(schema=True), - local=local, - overwrite=overwrite, - inpath=inpath, - partition=self._parse_partition(), - input_format=self._match_text_seq("INPUTFORMAT") - and self._parse_string(), - serde=self._match_text_seq("SERDE") and self._parse_string(), - ) - return self._parse_as_command(self._prev) - - def _parse_delete(self) -> exp.Delete: - # This handles MySQL's "Multiple-Table Syntax" - # https://dev.mysql.com/doc/refman/8.0/en/delete.html - tables = None - if not self._match(TokenType.FROM, advance=False): - tables = self._parse_csv(self._parse_table) or None - - returning = self._parse_returning() - - return self.expression( - exp.Delete, - tables=tables, - this=self._match(TokenType.FROM) and self._parse_table(joins=True), - using=self._match(TokenType.USING) - and self._parse_csv(lambda: self._parse_table(joins=True)), - cluster=self._match(TokenType.ON) and self._parse_on_property(), - where=self._parse_where(), - returning=returning or self._parse_returning(), - order=self._parse_order(), - limit=self._parse_limit(), - ) - - def _parse_update(self) -> exp.Update: - kwargs: t.Dict[str, t.Any] = { - "this": self._parse_table( - joins=True, alias_tokens=self.UPDATE_ALIAS_TOKENS - ), - } - while self._curr: - if self._match(TokenType.SET): - kwargs["expressions"] = self._parse_csv(self._parse_equality) - elif self._match(TokenType.RETURNING, advance=False): - kwargs["returning"] = self._parse_returning() - elif self._match(TokenType.FROM, advance=False): - kwargs["from_"] = self._parse_from(joins=True) - elif self._match(TokenType.WHERE, advance=False): - kwargs["where"] = self._parse_where() - elif self._match(TokenType.ORDER_BY, advance=False): - kwargs["order"] = self._parse_order() - elif self._match(TokenType.LIMIT, advance=False): - kwargs["limit"] = self._parse_limit() - else: - break - - return self.expression(exp.Update, **kwargs) - - def _parse_use(self) -> exp.Use: - return self.expression( - exp.Use, - kind=self._parse_var_from_options(self.USABLES, raise_unmatched=False), - this=self._parse_table(schema=False), - ) - - def _parse_uncache(self) -> exp.Uncache: - if not self._match(TokenType.TABLE): - self.raise_error("Expecting TABLE after UNCACHE") - - return self.expression( - exp.Uncache, - exists=self._parse_exists(), - this=self._parse_table(schema=True), - ) - - def _parse_cache(self) -> exp.Cache: - lazy = self._match_text_seq("LAZY") - self._match(TokenType.TABLE) - table = self._parse_table(schema=True) - - options = [] - if self._match_text_seq("OPTIONS"): - self._match_l_paren() - k = self._parse_string() - self._match(TokenType.EQ) - v = self._parse_string() - options = [k, v] - self._match_r_paren() - - self._match(TokenType.ALIAS) - return self.expression( - exp.Cache, - this=table, - lazy=lazy, - options=options, - expression=self._parse_select(nested=True), - ) - - def _parse_partition(self) -> t.Optional[exp.Partition]: - if not self._match_texts(self.PARTITION_KEYWORDS): - return None - - return self.expression( - exp.Partition, - subpartition=self._prev.text.upper() == "SUBPARTITION", - expressions=self._parse_wrapped_csv(self._parse_disjunction), - ) - - def _parse_value(self, values: bool = True) -> t.Optional[exp.Tuple]: - def _parse_value_expression() -> t.Optional[exp.Expression]: - if self.dialect.SUPPORTS_VALUES_DEFAULT and self._match(TokenType.DEFAULT): - return exp.var(self._prev.text.upper()) - return self._parse_expression() - - if self._match(TokenType.L_PAREN): - expressions = self._parse_csv(_parse_value_expression) - self._match_r_paren() - return self.expression(exp.Tuple, expressions=expressions) - - # In some dialects we can have VALUES 1, 2 which results in 1 column & 2 rows. - expression = self._parse_expression() - if expression: - return self.expression(exp.Tuple, expressions=[expression]) - return None - - def _parse_projections(self) -> t.List[exp.Expression]: - return self._parse_expressions() - - def _parse_wrapped_select(self, table: bool = False) -> t.Optional[exp.Expression]: - if self._match_set((TokenType.PIVOT, TokenType.UNPIVOT)): - this: t.Optional[exp.Expression] = self._parse_simplified_pivot( - is_unpivot=self._prev.token_type == TokenType.UNPIVOT - ) - elif self._match(TokenType.FROM): - from_ = self._parse_from(skip_from_token=True, consume_pipe=True) - # Support parentheses for duckdb FROM-first syntax - select = self._parse_select(from_=from_) - if select: - if not select.args.get("from_"): - select.set("from_", from_) - this = select - else: - this = exp.select("*").from_(t.cast(exp.From, from_)) - this = self._parse_query_modifiers(self._parse_set_operations(this)) - else: - this = ( - self._parse_table(consume_pipe=True) - if table - else self._parse_select(nested=True, parse_set_operation=False) - ) - - # Transform exp.Values into a exp.Table to pass through parse_query_modifiers - # in case a modifier (e.g. join) is following - if table and isinstance(this, exp.Values) and this.alias: - alias = this.args["alias"].pop() - this = exp.Table(this=this, alias=alias) - - this = self._parse_query_modifiers(self._parse_set_operations(this)) - - return this - - def _parse_select( - self, - nested: bool = False, - table: bool = False, - parse_subquery_alias: bool = True, - parse_set_operation: bool = True, - consume_pipe: bool = True, - from_: t.Optional[exp.From] = None, - ) -> t.Optional[exp.Expression]: - query = self._parse_select_query( - nested=nested, - table=table, - parse_subquery_alias=parse_subquery_alias, - parse_set_operation=parse_set_operation, - ) - - if consume_pipe and self._match(TokenType.PIPE_GT, advance=False): - if not query and from_: - query = exp.select("*").from_(from_) - if isinstance(query, exp.Query): - query = self._parse_pipe_syntax_query(query) - query = query.subquery(copy=False) if query and table else query - - return query - - def _parse_select_query( - self, - nested: bool = False, - table: bool = False, - parse_subquery_alias: bool = True, - parse_set_operation: bool = True, - ) -> t.Optional[exp.Expression]: - cte = self._parse_with() - - if cte: - this = self._parse_statement() - - if not this: - self.raise_error("Failed to parse any statement following CTE") - return cte - - while isinstance(this, exp.Subquery) and this.is_wrapper: - this = this.this - - if "with_" in this.arg_types: - this.set("with_", cte) - else: - self.raise_error(f"{this.key} does not support CTE") - this = cte - - return this - - # duckdb supports leading with FROM x - from_ = ( - self._parse_from(joins=True, consume_pipe=True) - if self._match(TokenType.FROM, advance=False) - else None - ) - - if self._match(TokenType.SELECT): - comments = self._prev_comments - - hint = self._parse_hint() - - if self._next and not self._next.token_type == TokenType.DOT: - all_ = self._match(TokenType.ALL) - distinct = self._match_set(self.DISTINCT_TOKENS) - else: - all_, distinct = None, None - - kind = ( - self._match(TokenType.ALIAS) - and self._match_texts(("STRUCT", "VALUE")) - and self._prev.text.upper() - ) - - if distinct: - distinct = self.expression( - exp.Distinct, - on=self._parse_value(values=False) - if self._match(TokenType.ON) - else None, - ) - - if all_ and distinct: - self.raise_error("Cannot specify both ALL and DISTINCT after SELECT") - - operation_modifiers = [] - while self._curr and self._match_texts(self.OPERATION_MODIFIERS): - operation_modifiers.append(exp.var(self._prev.text.upper())) - - limit = self._parse_limit(top=True) - projections = self._parse_projections() - - this = self.expression( - exp.Select, - kind=kind, - hint=hint, - distinct=distinct, - expressions=projections, - limit=limit, - operation_modifiers=operation_modifiers or None, - ) - this.comments = comments - - into = self._parse_into() - if into: - this.set("into", into) - - if not from_: - from_ = self._parse_from() - - if from_: - this.set("from_", from_) - - this = self._parse_query_modifiers(this) - elif (table or nested) and self._match(TokenType.L_PAREN): - this = self._parse_wrapped_select(table=table) - - # We return early here so that the UNION isn't attached to the subquery by the - # following call to _parse_set_operations, but instead becomes the parent node - self._match_r_paren() - return self._parse_subquery(this, parse_alias=parse_subquery_alias) - elif self._match(TokenType.VALUES, advance=False): - this = self._parse_derived_table_values() - elif from_: - this = exp.select("*").from_(from_.this, copy=False) - elif self._match(TokenType.SUMMARIZE): - table = self._match(TokenType.TABLE) - this = self._parse_select() or self._parse_string() or self._parse_table() - return self.expression(exp.Summarize, this=this, table=table) - elif self._match(TokenType.DESCRIBE): - this = self._parse_describe() - else: - this = None - - return self._parse_set_operations(this) if parse_set_operation else this - - def _parse_recursive_with_search(self) -> t.Optional[exp.RecursiveWithSearch]: - self._match_text_seq("SEARCH") - - kind = ( - self._match_texts(self.RECURSIVE_CTE_SEARCH_KIND) - and self._prev.text.upper() - ) - - if not kind: - return None - - self._match_text_seq("FIRST", "BY") - - return self.expression( - exp.RecursiveWithSearch, - kind=kind, - this=self._parse_id_var(), - expression=self._match_text_seq("SET") and self._parse_id_var(), - using=self._match_text_seq("USING") and self._parse_id_var(), - ) - - def _parse_with(self, skip_with_token: bool = False) -> t.Optional[exp.With]: - if not skip_with_token and not self._match(TokenType.WITH): - return None - - comments = self._prev_comments - recursive = self._match(TokenType.RECURSIVE) - - last_comments = None - expressions = [] - while True: - cte = self._parse_cte() - if isinstance(cte, exp.CTE): - expressions.append(cte) - if last_comments: - cte.add_comments(last_comments) - - if not self._match(TokenType.COMMA) and not self._match(TokenType.WITH): - break - else: - self._match(TokenType.WITH) - - last_comments = self._prev_comments - - return self.expression( - exp.With, - comments=comments, - expressions=expressions, - recursive=recursive, - search=self._parse_recursive_with_search(), - ) - - def _parse_cte(self) -> t.Optional[exp.CTE]: - index = self._index - - alias = self._parse_table_alias(self.ID_VAR_TOKENS) - if not alias or not alias.this: - self.raise_error("Expected CTE to have alias") - - key_expressions = ( - self._parse_wrapped_id_vars() - if self._match_text_seq("USING", "KEY") - else None - ) - - if not self._match(TokenType.ALIAS) and not self.OPTIONAL_ALIAS_TOKEN_CTE: - self._retreat(index) - return None - - comments = self._prev_comments - - if self._match_text_seq("NOT", "MATERIALIZED"): - materialized = False - elif self._match_text_seq("MATERIALIZED"): - materialized = True - else: - materialized = None - - cte = self.expression( - exp.CTE, - this=self._parse_wrapped(self._parse_statement), - alias=alias, - materialized=materialized, - key_expressions=key_expressions, - comments=comments, - ) - - values = cte.this - if isinstance(values, exp.Values): - if values.alias: - cte.set("this", exp.select("*").from_(values)) - else: - cte.set( - "this", - exp.select("*").from_(exp.alias_(values, "_values", table=True)), - ) - - return cte - - def _parse_table_alias( - self, alias_tokens: t.Optional[t.Collection[TokenType]] = None - ) -> t.Optional[exp.TableAlias]: - # In some dialects, LIMIT and OFFSET can act as both identifiers and keywords (clauses) - # so this section tries to parse the clause version and if it fails, it treats the token - # as an identifier (alias) - if self._can_parse_limit_or_offset(): - return None - - any_token = self._match(TokenType.ALIAS) - alias = ( - self._parse_id_var( - any_token=any_token, tokens=alias_tokens or self.TABLE_ALIAS_TOKENS - ) - or self._parse_string_as_identifier() - ) - - index = self._index - if self._match(TokenType.L_PAREN): - columns = self._parse_csv(self._parse_function_parameter) - self._match_r_paren() if columns else self._retreat(index) - else: - columns = None - - if not alias and not columns: - return None - - table_alias = self.expression(exp.TableAlias, this=alias, columns=columns) - - # We bubble up comments from the Identifier to the TableAlias - if isinstance(alias, exp.Identifier): - table_alias.add_comments(alias.pop_comments()) - - return table_alias - - def _parse_subquery( - self, this: t.Optional[exp.Expression], parse_alias: bool = True - ) -> t.Optional[exp.Subquery]: - if not this: - return None - - return self.expression( - exp.Subquery, - this=this, - pivots=self._parse_pivots(), - alias=self._parse_table_alias() if parse_alias else None, - sample=self._parse_table_sample(), - ) - - def _implicit_unnests_to_explicit(self, this: E) -> E: - from bigframes_vendored.sqlglot.optimizer.normalize_identifiers import ( - normalize_identifiers as _norm, - ) - - refs = { - _norm(this.args["from_"].this.copy(), dialect=self.dialect).alias_or_name - } - for i, join in enumerate(this.args.get("joins") or []): - table = join.this - normalized_table = table.copy() - normalized_table.meta["maybe_column"] = True - normalized_table = _norm(normalized_table, dialect=self.dialect) - - if isinstance(table, exp.Table) and not join.args.get("on"): - if normalized_table.parts[0].name in refs: - table_as_column = table.to_column() - unnest = exp.Unnest(expressions=[table_as_column]) - - # Table.to_column creates a parent Alias node that we want to convert to - # a TableAlias and attach to the Unnest, so it matches the parser's output - if isinstance(table.args.get("alias"), exp.TableAlias): - table_as_column.replace(table_as_column.this) - exp.alias_( - unnest, None, table=[table.args["alias"].this], copy=False - ) - - table.replace(unnest) - - refs.add(normalized_table.alias_or_name) - - return this - - @t.overload - def _parse_query_modifiers(self, this: E) -> E: ... - - @t.overload - def _parse_query_modifiers(self, this: None) -> None: ... - - def _parse_query_modifiers(self, this): - if isinstance(this, self.MODIFIABLES): - for join in self._parse_joins(): - this.append("joins", join) - for lateral in iter(self._parse_lateral, None): - this.append("laterals", lateral) - - while True: - if self._match_set(self.QUERY_MODIFIER_PARSERS, advance=False): - modifier_token = self._curr - parser = self.QUERY_MODIFIER_PARSERS[modifier_token.token_type] - key, expression = parser(self) - - if expression: - if this.args.get(key): - self.raise_error( - f"Found multiple '{modifier_token.text.upper()}' clauses", - token=modifier_token, - ) - - this.set(key, expression) - if key == "limit": - offset = expression.args.get("offset") - expression.set("offset", None) - - if offset: - offset = exp.Offset(expression=offset) - this.set("offset", offset) - - limit_by_expressions = expression.expressions - expression.set("expressions", None) - offset.set("expressions", limit_by_expressions) - continue - break - - if self.SUPPORTS_IMPLICIT_UNNEST and this and this.args.get("from_"): - this = self._implicit_unnests_to_explicit(this) - - return this - - def _parse_hint_fallback_to_string(self) -> t.Optional[exp.Hint]: - start = self._curr - while self._curr: - self._advance() - - end = self._tokens[self._index - 1] - return exp.Hint(expressions=[self._find_sql(start, end)]) - - def _parse_hint_function_call(self) -> t.Optional[exp.Expression]: - return self._parse_function_call() - - def _parse_hint_body(self) -> t.Optional[exp.Hint]: - start_index = self._index - should_fallback_to_string = False - - hints = [] - try: - for hint in iter( - lambda: self._parse_csv( - lambda: ( - self._parse_hint_function_call() or self._parse_var(upper=True) - ), - ), - [], - ): - hints.extend(hint) - except ParseError: - should_fallback_to_string = True - - if should_fallback_to_string or self._curr: - self._retreat(start_index) - return self._parse_hint_fallback_to_string() - - return self.expression(exp.Hint, expressions=hints) - - def _parse_hint(self) -> t.Optional[exp.Hint]: - if self._match(TokenType.HINT) and self._prev_comments: - return exp.maybe_parse( - self._prev_comments[0], into=exp.Hint, dialect=self.dialect - ) - - return None - - def _parse_into(self) -> t.Optional[exp.Into]: - if not self._match(TokenType.INTO): - return None - - temp = self._match(TokenType.TEMPORARY) - unlogged = self._match_text_seq("UNLOGGED") - self._match(TokenType.TABLE) - - return self.expression( - exp.Into, - this=self._parse_table(schema=True), - temporary=temp, - unlogged=unlogged, - ) - - def _parse_from( - self, - joins: bool = False, - skip_from_token: bool = False, - consume_pipe: bool = False, - ) -> t.Optional[exp.From]: - if not skip_from_token and not self._match(TokenType.FROM): - return None - - return self.expression( - exp.From, - comments=self._prev_comments, - this=self._parse_table(joins=joins, consume_pipe=consume_pipe), - ) - - def _parse_match_recognize_measure(self) -> exp.MatchRecognizeMeasure: - return self.expression( - exp.MatchRecognizeMeasure, - window_frame=self._match_texts(("FINAL", "RUNNING")) - and self._prev.text.upper(), - this=self._parse_expression(), - ) - - def _parse_match_recognize(self) -> t.Optional[exp.MatchRecognize]: - if not self._match(TokenType.MATCH_RECOGNIZE): - return None - - self._match_l_paren() - - partition = self._parse_partition_by() - order = self._parse_order() - - measures = ( - self._parse_csv(self._parse_match_recognize_measure) - if self._match_text_seq("MEASURES") - else None - ) - - if self._match_text_seq("ONE", "ROW", "PER", "MATCH"): - rows = exp.var("ONE ROW PER MATCH") - elif self._match_text_seq("ALL", "ROWS", "PER", "MATCH"): - text = "ALL ROWS PER MATCH" - if self._match_text_seq("SHOW", "EMPTY", "MATCHES"): - text += " SHOW EMPTY MATCHES" - elif self._match_text_seq("OMIT", "EMPTY", "MATCHES"): - text += " OMIT EMPTY MATCHES" - elif self._match_text_seq("WITH", "UNMATCHED", "ROWS"): - text += " WITH UNMATCHED ROWS" - rows = exp.var(text) - else: - rows = None - - if self._match_text_seq("AFTER", "MATCH", "SKIP"): - text = "AFTER MATCH SKIP" - if self._match_text_seq("PAST", "LAST", "ROW"): - text += " PAST LAST ROW" - elif self._match_text_seq("TO", "NEXT", "ROW"): - text += " TO NEXT ROW" - elif self._match_text_seq("TO", "FIRST"): - text += f" TO FIRST {self._advance_any().text}" # type: ignore - elif self._match_text_seq("TO", "LAST"): - text += f" TO LAST {self._advance_any().text}" # type: ignore - after = exp.var(text) - else: - after = None - - if self._match_text_seq("PATTERN"): - self._match_l_paren() - - if not self._curr: - self.raise_error("Expecting )", self._curr) - - paren = 1 - start = self._curr - - while self._curr and paren > 0: - if self._curr.token_type == TokenType.L_PAREN: - paren += 1 - if self._curr.token_type == TokenType.R_PAREN: - paren -= 1 - - end = self._prev - self._advance() - - if paren > 0: - self.raise_error("Expecting )", self._curr) - - pattern = exp.var(self._find_sql(start, end)) - else: - pattern = None - - define = ( - self._parse_csv(self._parse_name_as_expression) - if self._match_text_seq("DEFINE") - else None - ) - - self._match_r_paren() - - return self.expression( - exp.MatchRecognize, - partition_by=partition, - order=order, - measures=measures, - rows=rows, - after=after, - pattern=pattern, - define=define, - alias=self._parse_table_alias(), - ) - - def _parse_lateral(self) -> t.Optional[exp.Lateral]: - cross_apply = self._match_pair(TokenType.CROSS, TokenType.APPLY) - if not cross_apply and self._match_pair(TokenType.OUTER, TokenType.APPLY): - cross_apply = False - - if cross_apply is not None: - this = self._parse_select(table=True) - view = None - outer = None - elif self._match(TokenType.LATERAL): - this = self._parse_select(table=True) - view = self._match(TokenType.VIEW) - outer = self._match(TokenType.OUTER) - else: - return None - - if not this: - this = ( - self._parse_unnest() - or self._parse_function() - or self._parse_id_var(any_token=False) - ) - - while self._match(TokenType.DOT): - this = exp.Dot( - this=this, - expression=self._parse_function() - or self._parse_id_var(any_token=False), - ) - - ordinality: t.Optional[bool] = None - - if view: - table = self._parse_id_var(any_token=False) - columns = ( - self._parse_csv(self._parse_id_var) - if self._match(TokenType.ALIAS) - else [] - ) - table_alias: t.Optional[exp.TableAlias] = self.expression( - exp.TableAlias, this=table, columns=columns - ) - elif isinstance(this, (exp.Subquery, exp.Unnest)) and this.alias: - # We move the alias from the lateral's child node to the lateral itself - table_alias = this.args["alias"].pop() - else: - ordinality = self._match_pair(TokenType.WITH, TokenType.ORDINALITY) - table_alias = self._parse_table_alias() - - return self.expression( - exp.Lateral, - this=this, - view=view, - outer=outer, - alias=table_alias, - cross_apply=cross_apply, - ordinality=ordinality, - ) - - def _parse_stream(self) -> t.Optional[exp.Stream]: - index = self._index - if self._match_text_seq("STREAM"): - this = self._try_parse(self._parse_table) - if this: - return self.expression(exp.Stream, this=this) - - self._retreat(index) - return None - - def _parse_join_parts( - self, - ) -> t.Tuple[t.Optional[Token], t.Optional[Token], t.Optional[Token]]: - return ( - self._match_set(self.JOIN_METHODS) and self._prev, - self._match_set(self.JOIN_SIDES) and self._prev, - self._match_set(self.JOIN_KINDS) and self._prev, - ) - - def _parse_using_identifiers(self) -> t.List[exp.Expression]: - def _parse_column_as_identifier() -> t.Optional[exp.Expression]: - this = self._parse_column() - if isinstance(this, exp.Column): - return this.this - return this - - return self._parse_wrapped_csv(_parse_column_as_identifier, optional=True) - - def _parse_join( - self, skip_join_token: bool = False, parse_bracket: bool = False - ) -> t.Optional[exp.Join]: - if self._match(TokenType.COMMA): - table = self._try_parse(self._parse_table) - cross_join = self.expression(exp.Join, this=table) if table else None - - if cross_join and self.JOINS_HAVE_EQUAL_PRECEDENCE: - cross_join.set("kind", "CROSS") - - return cross_join - - index = self._index - method, side, kind = self._parse_join_parts() - hint = self._prev.text if self._match_texts(self.JOIN_HINTS) else None - join = self._match(TokenType.JOIN) or ( - kind and kind.token_type == TokenType.STRAIGHT_JOIN - ) - join_comments = self._prev_comments - - if not skip_join_token and not join: - self._retreat(index) - kind = None - method = None - side = None - - outer_apply = self._match_pair(TokenType.OUTER, TokenType.APPLY, False) - cross_apply = self._match_pair(TokenType.CROSS, TokenType.APPLY, False) - - if not skip_join_token and not join and not outer_apply and not cross_apply: - return None - - kwargs: t.Dict[str, t.Any] = { - "this": self._parse_table(parse_bracket=parse_bracket) - } - if kind and kind.token_type == TokenType.ARRAY and self._match(TokenType.COMMA): - kwargs["expressions"] = self._parse_csv( - lambda: self._parse_table(parse_bracket=parse_bracket) - ) - - if method: - kwargs["method"] = method.text.upper() - if side: - kwargs["side"] = side.text.upper() - if kind: - kwargs["kind"] = kind.text.upper() - if hint: - kwargs["hint"] = hint - - if self._match(TokenType.MATCH_CONDITION): - kwargs["match_condition"] = self._parse_wrapped(self._parse_comparison) - - if self._match(TokenType.ON): - kwargs["on"] = self._parse_disjunction() - elif self._match(TokenType.USING): - kwargs["using"] = self._parse_using_identifiers() - elif ( - not method - and not (outer_apply or cross_apply) - and not isinstance(kwargs["this"], exp.Unnest) - and not (kind and kind.token_type in (TokenType.CROSS, TokenType.ARRAY)) - ): - index = self._index - joins: t.Optional[list] = list(self._parse_joins()) - - if joins and self._match(TokenType.ON): - kwargs["on"] = self._parse_disjunction() - elif joins and self._match(TokenType.USING): - kwargs["using"] = self._parse_using_identifiers() - else: - joins = None - self._retreat(index) - - kwargs["this"].set("joins", joins if joins else None) - - kwargs["pivots"] = self._parse_pivots() - - comments = [ - c for token in (method, side, kind) if token for c in token.comments - ] - comments = (join_comments or []) + comments - - if ( - self.ADD_JOIN_ON_TRUE - and not kwargs.get("on") - and not kwargs.get("using") - and not kwargs.get("method") - and kwargs.get("kind") in (None, "INNER", "OUTER") - ): - kwargs["on"] = exp.true() - - return self.expression(exp.Join, comments=comments, **kwargs) - - def _parse_opclass(self) -> t.Optional[exp.Expression]: - this = self._parse_disjunction() - - if self._match_texts(self.OPCLASS_FOLLOW_KEYWORDS, advance=False): - return this - - if not self._match_set(self.OPTYPE_FOLLOW_TOKENS, advance=False): - return self.expression( - exp.Opclass, this=this, expression=self._parse_table_parts() - ) - - return this - - def _parse_index_params(self) -> exp.IndexParameters: - using = ( - self._parse_var(any_token=True) if self._match(TokenType.USING) else None - ) - - if self._match(TokenType.L_PAREN, advance=False): - columns = self._parse_wrapped_csv(self._parse_with_operator) - else: - columns = None - - include = ( - self._parse_wrapped_id_vars() if self._match_text_seq("INCLUDE") else None - ) - partition_by = self._parse_partition_by() - with_storage = self._match(TokenType.WITH) and self._parse_wrapped_properties() - tablespace = ( - self._parse_var(any_token=True) - if self._match_text_seq("USING", "INDEX", "TABLESPACE") - else None - ) - where = self._parse_where() - - on = self._parse_field() if self._match(TokenType.ON) else None - - return self.expression( - exp.IndexParameters, - using=using, - columns=columns, - include=include, - partition_by=partition_by, - where=where, - with_storage=with_storage, - tablespace=tablespace, - on=on, - ) - - def _parse_index( - self, index: t.Optional[exp.Expression] = None, anonymous: bool = False - ) -> t.Optional[exp.Index]: - if index or anonymous: - unique = None - primary = None - amp = None - - self._match(TokenType.ON) - self._match(TokenType.TABLE) # hive - table = self._parse_table_parts(schema=True) - else: - unique = self._match(TokenType.UNIQUE) - primary = self._match_text_seq("PRIMARY") - amp = self._match_text_seq("AMP") - - if not self._match(TokenType.INDEX): - return None - - index = self._parse_id_var() - table = None - - params = self._parse_index_params() - - return self.expression( - exp.Index, - this=index, - table=table, - unique=unique, - primary=primary, - amp=amp, - params=params, - ) - - def _parse_table_hints(self) -> t.Optional[t.List[exp.Expression]]: - hints: t.List[exp.Expression] = [] - if self._match_pair(TokenType.WITH, TokenType.L_PAREN): - # https://learn.microsoft.com/en-us/sql/t-sql/queries/hints-transact-sql-table?view=sql-server-ver16 - hints.append( - self.expression( - exp.WithTableHint, - expressions=self._parse_csv( - lambda: ( - self._parse_function() or self._parse_var(any_token=True) - ) - ), - ) - ) - self._match_r_paren() - else: - # https://dev.mysql.com/doc/refman/8.0/en/index-hints.html - while self._match_set(self.TABLE_INDEX_HINT_TOKENS): - hint = exp.IndexTableHint(this=self._prev.text.upper()) - - self._match_set((TokenType.INDEX, TokenType.KEY)) - if self._match(TokenType.FOR): - hint.set("target", self._advance_any() and self._prev.text.upper()) - - hint.set("expressions", self._parse_wrapped_id_vars()) - hints.append(hint) - - return hints or None - - def _parse_table_part(self, schema: bool = False) -> t.Optional[exp.Expression]: - return ( - (not schema and self._parse_function(optional_parens=False)) - or self._parse_id_var(any_token=False) - or self._parse_string_as_identifier() - or self._parse_placeholder() - ) - - def _parse_table_parts( - self, - schema: bool = False, - is_db_reference: bool = False, - wildcard: bool = False, - ) -> exp.Table: - catalog = None - db = None - table: t.Optional[exp.Expression | str] = self._parse_table_part(schema=schema) - - while self._match(TokenType.DOT): - if catalog: - # This allows nesting the table in arbitrarily many dot expressions if needed - table = self.expression( - exp.Dot, - this=table, - expression=self._parse_table_part(schema=schema), - ) - else: - catalog = db - db = table - # "" used for tsql FROM a..b case - table = self._parse_table_part(schema=schema) or "" - - if ( - wildcard - and self._is_connected() - and (isinstance(table, exp.Identifier) or not table) - and self._match(TokenType.STAR) - ): - if isinstance(table, exp.Identifier): - table.args["this"] += "*" - else: - table = exp.Identifier(this="*") - - # We bubble up comments from the Identifier to the Table - comments = table.pop_comments() if isinstance(table, exp.Expression) else None - - if is_db_reference: - catalog = db - db = table - table = None - - if not table and not is_db_reference: - self.raise_error(f"Expected table name but got {self._curr}") - if not db and is_db_reference: - self.raise_error(f"Expected database name but got {self._curr}") - - table = self.expression( - exp.Table, - comments=comments, - this=table, - db=db, - catalog=catalog, - ) - - changes = self._parse_changes() - if changes: - table.set("changes", changes) - - at_before = self._parse_historical_data() - if at_before: - table.set("when", at_before) - - pivots = self._parse_pivots() - if pivots: - table.set("pivots", pivots) - - return table - - def _parse_table( - self, - schema: bool = False, - joins: bool = False, - alias_tokens: t.Optional[t.Collection[TokenType]] = None, - parse_bracket: bool = False, - is_db_reference: bool = False, - parse_partition: bool = False, - consume_pipe: bool = False, - ) -> t.Optional[exp.Expression]: - stream = self._parse_stream() - if stream: - return stream - - lateral = self._parse_lateral() - if lateral: - return lateral - - unnest = self._parse_unnest() - if unnest: - return unnest - - values = self._parse_derived_table_values() - if values: - return values - - subquery = self._parse_select(table=True, consume_pipe=consume_pipe) - if subquery: - if not subquery.args.get("pivots"): - subquery.set("pivots", self._parse_pivots()) - return subquery - - bracket = parse_bracket and self._parse_bracket(None) - bracket = self.expression(exp.Table, this=bracket) if bracket else None - - rows_from = self._match_text_seq("ROWS", "FROM") and self._parse_wrapped_csv( - self._parse_table - ) - rows_from = ( - self.expression(exp.Table, rows_from=rows_from) if rows_from else None - ) - - only = self._match(TokenType.ONLY) - - this = t.cast( - exp.Expression, - bracket - or rows_from - or self._parse_bracket( - self._parse_table_parts(schema=schema, is_db_reference=is_db_reference) - ), - ) - - if only: - this.set("only", only) - - # Postgres supports a wildcard (table) suffix operator, which is a no-op in this context - self._match_text_seq("*") - - parse_partition = parse_partition or self.SUPPORTS_PARTITION_SELECTION - if parse_partition and self._match(TokenType.PARTITION, advance=False): - this.set("partition", self._parse_partition()) - - if schema: - return self._parse_schema(this=this) - - # see: https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/query-syntax#from_clause - # from_item, then alias, then time travel, then sample. - alias = self._parse_table_alias( - alias_tokens=alias_tokens or self.TABLE_ALIAS_TOKENS - ) - if alias: - this.set("alias", alias) - - version = self._parse_version() - - if version: - this.set("version", version) - - if self.dialect.ALIAS_POST_TABLESAMPLE: - this.set("sample", self._parse_table_sample()) - - if self._match(TokenType.INDEXED_BY): - this.set("indexed", self._parse_table_parts()) - elif self._match_text_seq("NOT", "INDEXED"): - this.set("indexed", False) - - if isinstance(this, exp.Table) and self._match_text_seq("AT"): - return self.expression( - exp.AtIndex, - this=this.to_column(copy=False), - expression=self._parse_id_var(), - ) - - this.set("hints", self._parse_table_hints()) - - if not this.args.get("pivots"): - this.set("pivots", self._parse_pivots()) - - if not self.dialect.ALIAS_POST_TABLESAMPLE: - this.set("sample", self._parse_table_sample()) - - if joins: - for join in self._parse_joins(): - this.append("joins", join) - - if self._match_pair(TokenType.WITH, TokenType.ORDINALITY): - this.set("ordinality", True) - this.set("alias", self._parse_table_alias()) - - return this - - def _parse_version(self) -> t.Optional[exp.Version]: - if self._match(TokenType.TIMESTAMP_SNAPSHOT): - this = "TIMESTAMP" - elif self._match(TokenType.VERSION_SNAPSHOT): - this = "VERSION" - else: - return None - - if self._match_set((TokenType.FROM, TokenType.BETWEEN)): - kind = self._prev.text.upper() - start = self._parse_bitwise() - self._match_texts(("TO", "AND")) - end = self._parse_bitwise() - expression: t.Optional[exp.Expression] = self.expression( - exp.Tuple, expressions=[start, end] - ) - elif self._match_text_seq("CONTAINED", "IN"): - kind = "CONTAINED IN" - expression = self.expression( - exp.Tuple, expressions=self._parse_wrapped_csv(self._parse_bitwise) - ) - elif self._match(TokenType.ALL): - kind = "ALL" - expression = None - else: - self._match_text_seq("AS", "OF") - kind = "AS OF" - expression = self._parse_type() - - return self.expression(exp.Version, this=this, expression=expression, kind=kind) - - def _parse_historical_data(self) -> t.Optional[exp.HistoricalData]: - # https://docs.snowflake.com/en/sql-reference/constructs/at-before - index = self._index - historical_data = None - if self._match_texts(self.HISTORICAL_DATA_PREFIX): - this = self._prev.text.upper() - kind = ( - self._match(TokenType.L_PAREN) - and self._match_texts(self.HISTORICAL_DATA_KIND) - and self._prev.text.upper() - ) - expression = self._match(TokenType.FARROW) and self._parse_bitwise() - - if expression: - self._match_r_paren() - historical_data = self.expression( - exp.HistoricalData, this=this, kind=kind, expression=expression - ) - else: - self._retreat(index) - - return historical_data - - def _parse_changes(self) -> t.Optional[exp.Changes]: - if not self._match_text_seq("CHANGES", "(", "INFORMATION", "=>"): - return None - - information = self._parse_var(any_token=True) - self._match_r_paren() - - return self.expression( - exp.Changes, - information=information, - at_before=self._parse_historical_data(), - end=self._parse_historical_data(), - ) - - def _parse_unnest(self, with_alias: bool = True) -> t.Optional[exp.Unnest]: - if not self._match_pair(TokenType.UNNEST, TokenType.L_PAREN, advance=False): - return None - - self._advance() - - expressions = self._parse_wrapped_csv(self._parse_equality) - offset = self._match_pair(TokenType.WITH, TokenType.ORDINALITY) - - alias = self._parse_table_alias() if with_alias else None - - if alias: - if self.dialect.UNNEST_COLUMN_ONLY: - if alias.args.get("columns"): - self.raise_error("Unexpected extra column alias in unnest.") - - alias.set("columns", [alias.this]) - alias.set("this", None) - - columns = alias.args.get("columns") or [] - if offset and len(expressions) < len(columns): - offset = columns.pop() - - if not offset and self._match_pair(TokenType.WITH, TokenType.OFFSET): - self._match(TokenType.ALIAS) - offset = self._parse_id_var( - any_token=False, tokens=self.UNNEST_OFFSET_ALIAS_TOKENS - ) or exp.to_identifier("offset") - - return self.expression( - exp.Unnest, expressions=expressions, alias=alias, offset=offset - ) - - def _parse_derived_table_values(self) -> t.Optional[exp.Values]: - is_derived = self._match_pair(TokenType.L_PAREN, TokenType.VALUES) - if not is_derived and not ( - # ClickHouse's `FORMAT Values` is equivalent to `VALUES` - self._match_text_seq("VALUES") or self._match_text_seq("FORMAT", "VALUES") - ): - return None - - expressions = self._parse_csv(self._parse_value) - alias = self._parse_table_alias() - - if is_derived: - self._match_r_paren() - - return self.expression( - exp.Values, - expressions=expressions, - alias=alias or self._parse_table_alias(), - ) - - def _parse_table_sample( - self, as_modifier: bool = False - ) -> t.Optional[exp.TableSample]: - if not self._match(TokenType.TABLE_SAMPLE) and not ( - as_modifier and self._match_text_seq("USING", "SAMPLE") - ): - return None - - bucket_numerator = None - bucket_denominator = None - bucket_field = None - percent = None - size = None - seed = None - - method = self._parse_var(tokens=(TokenType.ROW,), upper=True) - matched_l_paren = self._match(TokenType.L_PAREN) - - if self.TABLESAMPLE_CSV: - num = None - expressions = self._parse_csv(self._parse_primary) - else: - expressions = None - num = ( - self._parse_factor() - if self._match(TokenType.NUMBER, advance=False) - else self._parse_primary() or self._parse_placeholder() - ) - - if self._match_text_seq("BUCKET"): - bucket_numerator = self._parse_number() - self._match_text_seq("OUT", "OF") - bucket_denominator = bucket_denominator = self._parse_number() - self._match(TokenType.ON) - bucket_field = self._parse_field() - elif self._match_set((TokenType.PERCENT, TokenType.MOD)): - percent = num - elif ( - self._match(TokenType.ROWS) or not self.dialect.TABLESAMPLE_SIZE_IS_PERCENT - ): - size = num - else: - percent = num - - if matched_l_paren: - self._match_r_paren() - - if self._match(TokenType.L_PAREN): - method = self._parse_var(upper=True) - seed = self._match(TokenType.COMMA) and self._parse_number() - self._match_r_paren() - elif self._match_texts(("SEED", "REPEATABLE")): - seed = self._parse_wrapped(self._parse_number) - - if not method and self.DEFAULT_SAMPLING_METHOD: - method = exp.var(self.DEFAULT_SAMPLING_METHOD) - - return self.expression( - exp.TableSample, - expressions=expressions, - method=method, - bucket_numerator=bucket_numerator, - bucket_denominator=bucket_denominator, - bucket_field=bucket_field, - percent=percent, - size=size, - seed=seed, - ) - - def _parse_pivots(self) -> t.Optional[t.List[exp.Pivot]]: - return list(iter(self._parse_pivot, None)) or None - - def _parse_joins(self) -> t.Iterator[exp.Join]: - return iter(self._parse_join, None) - - def _parse_unpivot_columns(self) -> t.Optional[exp.UnpivotColumns]: - if not self._match(TokenType.INTO): - return None - - return self.expression( - exp.UnpivotColumns, - this=self._match_text_seq("NAME") and self._parse_column(), - expressions=self._match_text_seq("VALUE") - and self._parse_csv(self._parse_column), - ) - - # https://duckdb.org/docs/sql/statements/pivot - def _parse_simplified_pivot(self, is_unpivot: t.Optional[bool] = None) -> exp.Pivot: - def _parse_on() -> t.Optional[exp.Expression]: - this = self._parse_bitwise() - - if self._match(TokenType.IN): - # PIVOT ... ON col IN (row_val1, row_val2) - return self._parse_in(this) - if self._match(TokenType.ALIAS, advance=False): - # UNPIVOT ... ON (col1, col2, col3) AS row_val - return self._parse_alias(this) - - return this - - this = self._parse_table() - expressions = self._match(TokenType.ON) and self._parse_csv(_parse_on) - into = self._parse_unpivot_columns() - using = self._match(TokenType.USING) and self._parse_csv( - lambda: self._parse_alias(self._parse_column()) - ) - group = self._parse_group() - - return self.expression( - exp.Pivot, - this=this, - expressions=expressions, - using=using, - group=group, - unpivot=is_unpivot, - into=into, - ) - - def _parse_pivot_in(self) -> exp.In: - def _parse_aliased_expression() -> t.Optional[exp.Expression]: - this = self._parse_select_or_expression() - - self._match(TokenType.ALIAS) - alias = self._parse_bitwise() - if alias: - if isinstance(alias, exp.Column) and not alias.db: - alias = alias.this - return self.expression(exp.PivotAlias, this=this, alias=alias) - - return this - - value = self._parse_column() - - if not self._match(TokenType.IN): - self.raise_error("Expecting IN") - - if self._match(TokenType.L_PAREN): - if self._match(TokenType.ANY): - exprs: t.List[exp.Expression] = ensure_list( - exp.PivotAny(this=self._parse_order()) - ) - else: - exprs = self._parse_csv(_parse_aliased_expression) - self._match_r_paren() - return self.expression(exp.In, this=value, expressions=exprs) - - return self.expression(exp.In, this=value, field=self._parse_id_var()) - - def _parse_pivot_aggregation(self) -> t.Optional[exp.Expression]: - func = self._parse_function() - if not func: - if self._prev and self._prev.token_type == TokenType.COMMA: - return None - self.raise_error("Expecting an aggregation function in PIVOT") - - return self._parse_alias(func) - - def _parse_pivot(self) -> t.Optional[exp.Pivot]: - index = self._index - include_nulls = None - - if self._match(TokenType.PIVOT): - unpivot = False - elif self._match(TokenType.UNPIVOT): - unpivot = True - - # https://docs.databricks.com/en/sql/language-manual/sql-ref-syntax-qry-select-unpivot.html#syntax - if self._match_text_seq("INCLUDE", "NULLS"): - include_nulls = True - elif self._match_text_seq("EXCLUDE", "NULLS"): - include_nulls = False - else: - return None - - expressions = [] - - if not self._match(TokenType.L_PAREN): - self._retreat(index) - return None - - if unpivot: - expressions = self._parse_csv(self._parse_column) - else: - expressions = self._parse_csv(self._parse_pivot_aggregation) - - if not expressions: - self.raise_error("Failed to parse PIVOT's aggregation list") - - if not self._match(TokenType.FOR): - self.raise_error("Expecting FOR") - - fields = [] - while True: - field = self._try_parse(self._parse_pivot_in) - if not field: - break - fields.append(field) - - default_on_null = self._match_text_seq( - "DEFAULT", "ON", "NULL" - ) and self._parse_wrapped(self._parse_bitwise) - - group = self._parse_group() - - self._match_r_paren() - - pivot = self.expression( - exp.Pivot, - expressions=expressions, - fields=fields, - unpivot=unpivot, - include_nulls=include_nulls, - default_on_null=default_on_null, - group=group, - ) - - if not self._match_set((TokenType.PIVOT, TokenType.UNPIVOT), advance=False): - pivot.set("alias", self._parse_table_alias()) - - if not unpivot: - names = self._pivot_column_names( - t.cast(t.List[exp.Expression], expressions) - ) - - columns: t.List[exp.Expression] = [] - all_fields = [] - for pivot_field in pivot.fields: - pivot_field_expressions = pivot_field.expressions - - # The `PivotAny` expression corresponds to `ANY ORDER BY `; we can't infer in this case. - if isinstance(seq_get(pivot_field_expressions, 0), exp.PivotAny): - continue - - all_fields.append( - [ - fld.sql() if self.IDENTIFY_PIVOT_STRINGS else fld.alias_or_name - for fld in pivot_field_expressions - ] - ) - - if all_fields: - if names: - all_fields.append(names) - - # Generate all possible combinations of the pivot columns - # e.g PIVOT(sum(...) as total FOR year IN (2000, 2010) FOR country IN ('NL', 'US')) - # generates the product between [[2000, 2010], ['NL', 'US'], ['total']] - for fld_parts_tuple in itertools.product(*all_fields): - fld_parts = list(fld_parts_tuple) - - if names and self.PREFIXED_PIVOT_COLUMNS: - # Move the "name" to the front of the list - fld_parts.insert(0, fld_parts.pop(-1)) - - columns.append(exp.to_identifier("_".join(fld_parts))) - - pivot.set("columns", columns) - - return pivot - - def _pivot_column_names(self, aggregations: t.List[exp.Expression]) -> t.List[str]: - return [agg.alias for agg in aggregations if agg.alias] - - def _parse_prewhere( - self, skip_where_token: bool = False - ) -> t.Optional[exp.PreWhere]: - if not skip_where_token and not self._match(TokenType.PREWHERE): - return None - - return self.expression( - exp.PreWhere, comments=self._prev_comments, this=self._parse_disjunction() - ) - - def _parse_where(self, skip_where_token: bool = False) -> t.Optional[exp.Where]: - if not skip_where_token and not self._match(TokenType.WHERE): - return None - - return self.expression( - exp.Where, comments=self._prev_comments, this=self._parse_disjunction() - ) - - def _parse_group(self, skip_group_by_token: bool = False) -> t.Optional[exp.Group]: - if not skip_group_by_token and not self._match(TokenType.GROUP_BY): - return None - comments = self._prev_comments - - elements: t.Dict[str, t.Any] = defaultdict(list) - - if self._match(TokenType.ALL): - elements["all"] = True - elif self._match(TokenType.DISTINCT): - elements["all"] = False - - if self._match_set(self.QUERY_MODIFIER_TOKENS, advance=False): - return self.expression(exp.Group, comments=comments, **elements) # type: ignore - - while True: - index = self._index - - elements["expressions"].extend( - self._parse_csv( - lambda: ( - None - if self._match_set( - (TokenType.CUBE, TokenType.ROLLUP), advance=False - ) - else self._parse_disjunction() - ) - ) - ) - - before_with_index = self._index - with_prefix = self._match(TokenType.WITH) - - if cube_or_rollup := self._parse_cube_or_rollup(with_prefix=with_prefix): - key = "rollup" if isinstance(cube_or_rollup, exp.Rollup) else "cube" - elements[key].append(cube_or_rollup) - elif grouping_sets := self._parse_grouping_sets(): - elements["grouping_sets"].append(grouping_sets) - elif self._match_text_seq("TOTALS"): - elements["totals"] = True # type: ignore - - if before_with_index <= self._index <= before_with_index + 1: - self._retreat(before_with_index) - break - - if index == self._index: - break - - return self.expression(exp.Group, comments=comments, **elements) # type: ignore - - def _parse_cube_or_rollup( - self, with_prefix: bool = False - ) -> t.Optional[exp.Cube | exp.Rollup]: - if self._match(TokenType.CUBE): - kind: t.Type[exp.Cube | exp.Rollup] = exp.Cube - elif self._match(TokenType.ROLLUP): - kind = exp.Rollup - else: - return None - - return self.expression( - kind, - expressions=[] - if with_prefix - else self._parse_wrapped_csv(self._parse_bitwise), - ) - - def _parse_grouping_sets(self) -> t.Optional[exp.GroupingSets]: - if self._match(TokenType.GROUPING_SETS): - return self.expression( - exp.GroupingSets, - expressions=self._parse_wrapped_csv(self._parse_grouping_set), - ) - return None - - def _parse_grouping_set(self) -> t.Optional[exp.Expression]: - return ( - self._parse_grouping_sets() - or self._parse_cube_or_rollup() - or self._parse_bitwise() - ) - - def _parse_having(self, skip_having_token: bool = False) -> t.Optional[exp.Having]: - if not skip_having_token and not self._match(TokenType.HAVING): - return None - return self.expression( - exp.Having, comments=self._prev_comments, this=self._parse_disjunction() - ) - - def _parse_qualify(self) -> t.Optional[exp.Qualify]: - if not self._match(TokenType.QUALIFY): - return None - return self.expression(exp.Qualify, this=self._parse_disjunction()) - - def _parse_connect_with_prior(self) -> t.Optional[exp.Expression]: - self.NO_PAREN_FUNCTION_PARSERS["PRIOR"] = lambda self: self.expression( - exp.Prior, this=self._parse_bitwise() - ) - connect = self._parse_disjunction() - self.NO_PAREN_FUNCTION_PARSERS.pop("PRIOR") - return connect - - def _parse_connect(self, skip_start_token: bool = False) -> t.Optional[exp.Connect]: - if skip_start_token: - start = None - elif self._match(TokenType.START_WITH): - start = self._parse_disjunction() - else: - return None - - self._match(TokenType.CONNECT_BY) - nocycle = self._match_text_seq("NOCYCLE") - connect = self._parse_connect_with_prior() - - if not start and self._match(TokenType.START_WITH): - start = self._parse_disjunction() - - return self.expression( - exp.Connect, start=start, connect=connect, nocycle=nocycle - ) - - def _parse_name_as_expression(self) -> t.Optional[exp.Expression]: - this = self._parse_id_var(any_token=True) - if self._match(TokenType.ALIAS): - this = self.expression( - exp.Alias, alias=this, this=self._parse_disjunction() - ) - return this - - def _parse_interpolate(self) -> t.Optional[t.List[exp.Expression]]: - if self._match_text_seq("INTERPOLATE"): - return self._parse_wrapped_csv(self._parse_name_as_expression) - return None - - def _parse_order( - self, this: t.Optional[exp.Expression] = None, skip_order_token: bool = False - ) -> t.Optional[exp.Expression]: - siblings = None - if not skip_order_token and not self._match(TokenType.ORDER_BY): - if not self._match(TokenType.ORDER_SIBLINGS_BY): - return this - - siblings = True - - return self.expression( - exp.Order, - comments=self._prev_comments, - this=this, - expressions=self._parse_csv(self._parse_ordered), - siblings=siblings, - ) - - def _parse_sort(self, exp_class: t.Type[E], token: TokenType) -> t.Optional[E]: - if not self._match(token): - return None - return self.expression( - exp_class, expressions=self._parse_csv(self._parse_ordered) - ) - - def _parse_ordered( - self, parse_method: t.Optional[t.Callable] = None - ) -> t.Optional[exp.Ordered]: - this = parse_method() if parse_method else self._parse_disjunction() - if not this: - return None - - if this.name.upper() == "ALL" and self.dialect.SUPPORTS_ORDER_BY_ALL: - this = exp.var("ALL") - - asc = self._match(TokenType.ASC) - desc = self._match(TokenType.DESC) or (asc and False) - - is_nulls_first = self._match_text_seq("NULLS", "FIRST") - is_nulls_last = self._match_text_seq("NULLS", "LAST") - - nulls_first = is_nulls_first or False - explicitly_null_ordered = is_nulls_first or is_nulls_last - - if ( - not explicitly_null_ordered - and ( - (not desc and self.dialect.NULL_ORDERING == "nulls_are_small") - or (desc and self.dialect.NULL_ORDERING != "nulls_are_small") - ) - and self.dialect.NULL_ORDERING != "nulls_are_last" - ): - nulls_first = True - - if self._match_text_seq("WITH", "FILL"): - with_fill = self.expression( - exp.WithFill, - from_=self._match(TokenType.FROM) and self._parse_bitwise(), - to=self._match_text_seq("TO") and self._parse_bitwise(), - step=self._match_text_seq("STEP") and self._parse_bitwise(), - interpolate=self._parse_interpolate(), - ) - else: - with_fill = None - - return self.expression( - exp.Ordered, - this=this, - desc=desc, - nulls_first=nulls_first, - with_fill=with_fill, - ) - - def _parse_limit_options(self) -> t.Optional[exp.LimitOptions]: - percent = self._match_set((TokenType.PERCENT, TokenType.MOD)) - rows = self._match_set((TokenType.ROW, TokenType.ROWS)) - self._match_text_seq("ONLY") - with_ties = self._match_text_seq("WITH", "TIES") - - if not (percent or rows or with_ties): - return None - - return self.expression( - exp.LimitOptions, percent=percent, rows=rows, with_ties=with_ties - ) - - def _parse_limit( - self, - this: t.Optional[exp.Expression] = None, - top: bool = False, - skip_limit_token: bool = False, - ) -> t.Optional[exp.Expression]: - if skip_limit_token or self._match(TokenType.TOP if top else TokenType.LIMIT): - comments = self._prev_comments - if top: - limit_paren = self._match(TokenType.L_PAREN) - expression = self._parse_term() if limit_paren else self._parse_number() - - if limit_paren: - self._match_r_paren() - - else: - # Parsing LIMIT x% (i.e x PERCENT) as a term leads to an error, since - # we try to build an exp.Mod expr. For that matter, we backtrack and instead - # consume the factor plus parse the percentage separately - index = self._index - expression = self._try_parse(self._parse_term) - if isinstance(expression, exp.Mod): - self._retreat(index) - expression = self._parse_factor() - elif not expression: - expression = self._parse_factor() - limit_options = self._parse_limit_options() - - if self._match(TokenType.COMMA): - offset = expression - expression = self._parse_term() - else: - offset = None - - limit_exp = self.expression( - exp.Limit, - this=this, - expression=expression, - offset=offset, - comments=comments, - limit_options=limit_options, - expressions=self._parse_limit_by(), - ) - - return limit_exp - - if self._match(TokenType.FETCH): - direction = self._match_set((TokenType.FIRST, TokenType.NEXT)) - direction = self._prev.text.upper() if direction else "FIRST" - - count = self._parse_field(tokens=self.FETCH_TOKENS) - - return self.expression( - exp.Fetch, - direction=direction, - count=count, - limit_options=self._parse_limit_options(), - ) - - return this - - def _parse_offset( - self, this: t.Optional[exp.Expression] = None - ) -> t.Optional[exp.Expression]: - if not self._match(TokenType.OFFSET): - return this - - count = self._parse_term() - self._match_set((TokenType.ROW, TokenType.ROWS)) - - return self.expression( - exp.Offset, this=this, expression=count, expressions=self._parse_limit_by() - ) - - def _can_parse_limit_or_offset(self) -> bool: - if not self._match_set(self.AMBIGUOUS_ALIAS_TOKENS, advance=False): - return False - - index = self._index - result = bool( - self._try_parse(self._parse_limit, retreat=True) - or self._try_parse(self._parse_offset, retreat=True) - ) - self._retreat(index) - return result - - def _parse_limit_by(self) -> t.Optional[t.List[exp.Expression]]: - return self._match_text_seq("BY") and self._parse_csv(self._parse_bitwise) - - def _parse_locks(self) -> t.List[exp.Lock]: - locks = [] - while True: - update, key = None, None - if self._match_text_seq("FOR", "UPDATE"): - update = True - elif self._match_text_seq("FOR", "SHARE") or self._match_text_seq( - "LOCK", "IN", "SHARE", "MODE" - ): - update = False - elif self._match_text_seq("FOR", "KEY", "SHARE"): - update, key = False, True - elif self._match_text_seq("FOR", "NO", "KEY", "UPDATE"): - update, key = True, True - else: - break - - expressions = None - if self._match_text_seq("OF"): - expressions = self._parse_csv(lambda: self._parse_table(schema=True)) - - wait: t.Optional[bool | exp.Expression] = None - if self._match_text_seq("NOWAIT"): - wait = True - elif self._match_text_seq("WAIT"): - wait = self._parse_primary() - elif self._match_text_seq("SKIP", "LOCKED"): - wait = False - - locks.append( - self.expression( - exp.Lock, update=update, expressions=expressions, wait=wait, key=key - ) - ) - - return locks - - def parse_set_operation( - self, this: t.Optional[exp.Expression], consume_pipe: bool = False - ) -> t.Optional[exp.Expression]: - start = self._index - _, side_token, kind_token = self._parse_join_parts() - - side = side_token.text if side_token else None - kind = kind_token.text if kind_token else None - - if not self._match_set(self.SET_OPERATIONS): - self._retreat(start) - return None - - token_type = self._prev.token_type - - if token_type == TokenType.UNION: - operation: t.Type[exp.SetOperation] = exp.Union - elif token_type == TokenType.EXCEPT: - operation = exp.Except - else: - operation = exp.Intersect - - comments = self._prev.comments - - if self._match(TokenType.DISTINCT): - distinct: t.Optional[bool] = True - elif self._match(TokenType.ALL): - distinct = False - else: - distinct = self.dialect.SET_OP_DISTINCT_BY_DEFAULT[operation] - if distinct is None: - self.raise_error(f"Expected DISTINCT or ALL for {operation.__name__}") - - by_name = self._match_text_seq("BY", "NAME") or self._match_text_seq( - "STRICT", "CORRESPONDING" - ) - if self._match_text_seq("CORRESPONDING"): - by_name = True - if not side and not kind: - kind = "INNER" - - on_column_list = None - if by_name and self._match_texts(("ON", "BY")): - on_column_list = self._parse_wrapped_csv(self._parse_column) - - expression = self._parse_select( - nested=True, parse_set_operation=False, consume_pipe=consume_pipe - ) - - return self.expression( - operation, - comments=comments, - this=this, - distinct=distinct, - by_name=by_name, - expression=expression, - side=side, - kind=kind, - on=on_column_list, - ) - - def _parse_set_operations( - self, this: t.Optional[exp.Expression] - ) -> t.Optional[exp.Expression]: - while this: - setop = self.parse_set_operation(this) - if not setop: - break - this = setop - - if isinstance(this, exp.SetOperation) and self.MODIFIERS_ATTACHED_TO_SET_OP: - expression = this.expression - - if expression: - for arg in self.SET_OP_MODIFIERS: - expr = expression.args.get(arg) - if expr: - this.set(arg, expr.pop()) - - return this - - def _parse_expression(self) -> t.Optional[exp.Expression]: - return self._parse_alias(self._parse_assignment()) - - def _parse_assignment(self) -> t.Optional[exp.Expression]: - this = self._parse_disjunction() - if not this and self._next and self._next.token_type in self.ASSIGNMENT: - # This allows us to parse := - this = exp.column( - t.cast(str, self._advance_any(ignore_reserved=True) and self._prev.text) - ) - - while self._match_set(self.ASSIGNMENT): - if isinstance(this, exp.Column) and len(this.parts) == 1: - this = this.this - - this = self.expression( - self.ASSIGNMENT[self._prev.token_type], - this=this, - comments=self._prev_comments, - expression=self._parse_assignment(), - ) - - return this - - def _parse_disjunction(self) -> t.Optional[exp.Expression]: - return self._parse_tokens(self._parse_conjunction, self.DISJUNCTION) - - def _parse_conjunction(self) -> t.Optional[exp.Expression]: - return self._parse_tokens(self._parse_equality, self.CONJUNCTION) - - def _parse_equality(self) -> t.Optional[exp.Expression]: - return self._parse_tokens(self._parse_comparison, self.EQUALITY) - - def _parse_comparison(self) -> t.Optional[exp.Expression]: - return self._parse_tokens(self._parse_range, self.COMPARISON) - - def _parse_range( - self, this: t.Optional[exp.Expression] = None - ) -> t.Optional[exp.Expression]: - this = this or self._parse_bitwise() - negate = self._match(TokenType.NOT) - - if self._match_set(self.RANGE_PARSERS): - expression = self.RANGE_PARSERS[self._prev.token_type](self, this) - if not expression: - return this - - this = expression - elif self._match(TokenType.ISNULL) or (negate and self._match(TokenType.NULL)): - this = self.expression(exp.Is, this=this, expression=exp.Null()) - - # Postgres supports ISNULL and NOTNULL for conditions. - # https://blog.andreiavram.ro/postgresql-null-composite-type/ - if self._match(TokenType.NOTNULL): - this = self.expression(exp.Is, this=this, expression=exp.Null()) - this = self.expression(exp.Not, this=this) - - if negate: - this = self._negate_range(this) - - if self._match(TokenType.IS): - this = self._parse_is(this) - - return this - - def _negate_range( - self, this: t.Optional[exp.Expression] = None - ) -> t.Optional[exp.Expression]: - if not this: - return this - - return self.expression(exp.Not, this=this) - - def _parse_is(self, this: t.Optional[exp.Expression]) -> t.Optional[exp.Expression]: - index = self._index - 1 - negate = self._match(TokenType.NOT) - - if self._match_text_seq("DISTINCT", "FROM"): - klass = exp.NullSafeEQ if negate else exp.NullSafeNEQ - return self.expression(klass, this=this, expression=self._parse_bitwise()) - - if self._match(TokenType.JSON): - kind = ( - self._match_texts(self.IS_JSON_PREDICATE_KIND) - and self._prev.text.upper() - ) - - if self._match_text_seq("WITH"): - _with = True - elif self._match_text_seq("WITHOUT"): - _with = False - else: - _with = None - - unique = self._match(TokenType.UNIQUE) - self._match_text_seq("KEYS") - expression: t.Optional[exp.Expression] = self.expression( - exp.JSON, - this=kind, - with_=_with, - unique=unique, - ) - else: - expression = self._parse_null() or self._parse_bitwise() - if not expression: - self._retreat(index) - return None - - this = self.expression(exp.Is, this=this, expression=expression) - this = self.expression(exp.Not, this=this) if negate else this - return self._parse_column_ops(this) - - def _parse_in( - self, this: t.Optional[exp.Expression], alias: bool = False - ) -> exp.In: - unnest = self._parse_unnest(with_alias=False) - if unnest: - this = self.expression(exp.In, this=this, unnest=unnest) - elif self._match_set((TokenType.L_PAREN, TokenType.L_BRACKET)): - matched_l_paren = self._prev.token_type == TokenType.L_PAREN - expressions = self._parse_csv( - lambda: self._parse_select_or_expression(alias=alias) - ) - - if len(expressions) == 1 and isinstance(query := expressions[0], exp.Query): - this = self.expression( - exp.In, - this=this, - query=self._parse_query_modifiers(query).subquery(copy=False), - ) - else: - this = self.expression(exp.In, this=this, expressions=expressions) - - if matched_l_paren: - self._match_r_paren(this) - elif not self._match(TokenType.R_BRACKET, expression=this): - self.raise_error("Expecting ]") - else: - this = self.expression(exp.In, this=this, field=self._parse_column()) - - return this - - def _parse_between(self, this: t.Optional[exp.Expression]) -> exp.Between: - symmetric = None - if self._match_text_seq("SYMMETRIC"): - symmetric = True - elif self._match_text_seq("ASYMMETRIC"): - symmetric = False - - low = self._parse_bitwise() - self._match(TokenType.AND) - high = self._parse_bitwise() - - return self.expression( - exp.Between, - this=this, - low=low, - high=high, - symmetric=symmetric, - ) - - def _parse_escape( - self, this: t.Optional[exp.Expression] - ) -> t.Optional[exp.Expression]: - if not self._match(TokenType.ESCAPE): - return this - return self.expression( - exp.Escape, this=this, expression=self._parse_string() or self._parse_null() - ) - - def _parse_interval( - self, match_interval: bool = True - ) -> t.Optional[exp.Add | exp.Interval]: - index = self._index - - if not self._match(TokenType.INTERVAL) and match_interval: - return None - - if self._match(TokenType.STRING, advance=False): - this = self._parse_primary() - else: - this = self._parse_term() - - if not this or ( - isinstance(this, exp.Column) - and not this.table - and not this.this.quoted - and self._curr - and self._curr.text.upper() not in self.dialect.VALID_INTERVAL_UNITS - ): - self._retreat(index) - return None - - # handle day-time format interval span with omitted units: - # INTERVAL ' hh[:][mm[:ss[.ff]]]' - interval_span_units_omitted = None - if ( - this - and this.is_string - and self.SUPPORTS_OMITTED_INTERVAL_SPAN_UNIT - and exp.INTERVAL_DAY_TIME_RE.match(this.name) - ): - index = self._index - - # Var "TO" Var - first_unit = self._parse_var(any_token=True, upper=True) - second_unit = None - if first_unit and self._match_text_seq("TO"): - second_unit = self._parse_var(any_token=True, upper=True) - - interval_span_units_omitted = not (first_unit and second_unit) - - self._retreat(index) - - unit = ( - None - if interval_span_units_omitted - else ( - self._parse_function() - or ( - not self._match(TokenType.ALIAS, advance=False) - and self._parse_var(any_token=True, upper=True) - ) - ) - ) - - # Most dialects support, e.g., the form INTERVAL '5' day, thus we try to parse - # each INTERVAL expression into this canonical form so it's easy to transpile - if this and this.is_number: - this = exp.Literal.string(this.to_py()) - elif this and this.is_string: - parts = exp.INTERVAL_STRING_RE.findall(this.name) - if parts and unit: - # Unconsume the eagerly-parsed unit, since the real unit was part of the string - unit = None - self._retreat(self._index - 1) - - if len(parts) == 1: - this = exp.Literal.string(parts[0][0]) - unit = self.expression(exp.Var, this=parts[0][1].upper()) - - if self.INTERVAL_SPANS and self._match_text_seq("TO"): - unit = self.expression( - exp.IntervalSpan, - this=unit, - expression=self._parse_var(any_token=True, upper=True), - ) - - interval = self.expression(exp.Interval, this=this, unit=unit) - - index = self._index - self._match(TokenType.PLUS) - - # Convert INTERVAL 'val_1' unit_1 [+] ... [+] 'val_n' unit_n into a sum of intervals - if self._match_set((TokenType.STRING, TokenType.NUMBER), advance=False): - return self.expression( - exp.Add, - this=interval, - expression=self._parse_interval(match_interval=False), - ) - - self._retreat(index) - return interval - - def _parse_bitwise(self) -> t.Optional[exp.Expression]: - this = self._parse_term() - - while True: - if self._match_set(self.BITWISE): - this = self.expression( - self.BITWISE[self._prev.token_type], - this=this, - expression=self._parse_term(), - ) - elif self.dialect.DPIPE_IS_STRING_CONCAT and self._match(TokenType.DPIPE): - this = self.expression( - exp.DPipe, - this=this, - expression=self._parse_term(), - safe=not self.dialect.STRICT_STRING_CONCAT, - ) - elif self._match(TokenType.DQMARK): - this = self.expression( - exp.Coalesce, this=this, expressions=ensure_list(self._parse_term()) - ) - elif self._match_pair(TokenType.LT, TokenType.LT): - this = self.expression( - exp.BitwiseLeftShift, this=this, expression=self._parse_term() - ) - elif self._match_pair(TokenType.GT, TokenType.GT): - this = self.expression( - exp.BitwiseRightShift, this=this, expression=self._parse_term() - ) - else: - break - - return this - - def _parse_term(self) -> t.Optional[exp.Expression]: - this = self._parse_factor() - - while self._match_set(self.TERM): - klass = self.TERM[self._prev.token_type] - comments = self._prev_comments - expression = self._parse_factor() - - this = self.expression( - klass, this=this, comments=comments, expression=expression - ) - - if isinstance(this, exp.Collate): - expr = this.expression - - # Preserve collations such as pg_catalog."default" (Postgres) as columns, otherwise - # fallback to Identifier / Var - if isinstance(expr, exp.Column) and len(expr.parts) == 1: - ident = expr.this - if isinstance(ident, exp.Identifier): - this.set( - "expression", ident if ident.quoted else exp.var(ident.name) - ) - - return this - - def _parse_factor(self) -> t.Optional[exp.Expression]: - parse_method = self._parse_exponent if self.EXPONENT else self._parse_unary - this = self._parse_at_time_zone(parse_method()) - - while self._match_set(self.FACTOR): - klass = self.FACTOR[self._prev.token_type] - comments = self._prev_comments - expression = parse_method() - - if not expression and klass is exp.IntDiv and self._prev.text.isalpha(): - self._retreat(self._index - 1) - return this - - this = self.expression( - klass, this=this, comments=comments, expression=expression - ) - - if isinstance(this, exp.Div): - this.set("typed", self.dialect.TYPED_DIVISION) - this.set("safe", self.dialect.SAFE_DIVISION) - - return this - - def _parse_exponent(self) -> t.Optional[exp.Expression]: - return self._parse_tokens(self._parse_unary, self.EXPONENT) - - def _parse_unary(self) -> t.Optional[exp.Expression]: - if self._match_set(self.UNARY_PARSERS): - return self.UNARY_PARSERS[self._prev.token_type](self) - return self._parse_type() - - def _parse_type( - self, parse_interval: bool = True, fallback_to_identifier: bool = False - ) -> t.Optional[exp.Expression]: - interval = parse_interval and self._parse_interval() - if interval: - return self._parse_column_ops(interval) - - index = self._index - data_type = self._parse_types(check_func=True, allow_identifiers=False) - - # parse_types() returns a Cast if we parsed BQ's inline constructor () e.g. - # STRUCT(1, 'foo'), which is canonicalized to CAST( AS ) - if isinstance(data_type, exp.Cast): - # This constructor can contain ops directly after it, for instance struct unnesting: - # STRUCT(1, 'foo').* --> CAST(STRUCT(1, 'foo') AS STRUCT 1: - self._retreat(index2) - return self._parse_column_ops(data_type) - - self._retreat(index) - - if fallback_to_identifier: - return self._parse_id_var() - - this = self._parse_column() - return this and self._parse_column_ops(this) - - def _parse_type_size(self) -> t.Optional[exp.DataTypeParam]: - this = self._parse_type() - if not this: - return None - - if isinstance(this, exp.Column) and not this.table: - this = exp.var(this.name.upper()) - - return self.expression( - exp.DataTypeParam, this=this, expression=self._parse_var(any_token=True) - ) - - def _parse_user_defined_type( - self, identifier: exp.Identifier - ) -> t.Optional[exp.Expression]: - type_name = identifier.name - - while self._match(TokenType.DOT): - type_name = f"{type_name}.{self._advance_any() and self._prev.text}" - - return exp.DataType.build(type_name, dialect=self.dialect, udt=True) - - def _parse_types( - self, - check_func: bool = False, - schema: bool = False, - allow_identifiers: bool = True, - ) -> t.Optional[exp.Expression]: - index = self._index - - this: t.Optional[exp.Expression] = None - prefix = self._match_text_seq("SYSUDTLIB", ".") - - if self._match_set(self.TYPE_TOKENS): - type_token = self._prev.token_type - else: - type_token = None - identifier = allow_identifiers and self._parse_id_var( - any_token=False, tokens=(TokenType.VAR,) - ) - if isinstance(identifier, exp.Identifier): - try: - tokens = self.dialect.tokenize(identifier.name) - except TokenError: - tokens = None - - if ( - tokens - and len(tokens) == 1 - and tokens[0].token_type in self.TYPE_TOKENS - ): - type_token = tokens[0].token_type - elif self.dialect.SUPPORTS_USER_DEFINED_TYPES: - this = self._parse_user_defined_type(identifier) - else: - self._retreat(self._index - 1) - return None - else: - return None - - if type_token == TokenType.PSEUDO_TYPE: - return self.expression(exp.PseudoType, this=self._prev.text.upper()) - - if type_token == TokenType.OBJECT_IDENTIFIER: - return self.expression(exp.ObjectIdentifier, this=self._prev.text.upper()) - - # https://materialize.com/docs/sql/types/map/ - if type_token == TokenType.MAP and self._match(TokenType.L_BRACKET): - key_type = self._parse_types( - check_func=check_func, - schema=schema, - allow_identifiers=allow_identifiers, - ) - if not self._match(TokenType.FARROW): - self._retreat(index) - return None - - value_type = self._parse_types( - check_func=check_func, - schema=schema, - allow_identifiers=allow_identifiers, - ) - if not self._match(TokenType.R_BRACKET): - self._retreat(index) - return None - - return exp.DataType( - this=exp.DataType.Type.MAP, - expressions=[key_type, value_type], - nested=True, - prefix=prefix, - ) - - nested = type_token in self.NESTED_TYPE_TOKENS - is_struct = type_token in self.STRUCT_TYPE_TOKENS - is_aggregate = type_token in self.AGGREGATE_TYPE_TOKENS - expressions = None - maybe_func = False - - if self._match(TokenType.L_PAREN): - if is_struct: - expressions = self._parse_csv( - lambda: self._parse_struct_types(type_required=True) - ) - elif nested: - expressions = self._parse_csv( - lambda: self._parse_types( - check_func=check_func, - schema=schema, - allow_identifiers=allow_identifiers, - ) - ) - if type_token == TokenType.NULLABLE and len(expressions) == 1: - this = expressions[0] - this.set("nullable", True) - self._match_r_paren() - return this - elif type_token in self.ENUM_TYPE_TOKENS: - expressions = self._parse_csv(self._parse_equality) - elif is_aggregate: - func_or_ident = self._parse_function( - anonymous=True - ) or self._parse_id_var( - any_token=False, tokens=(TokenType.VAR, TokenType.ANY) - ) - if not func_or_ident: - return None - expressions = [func_or_ident] - if self._match(TokenType.COMMA): - expressions.extend( - self._parse_csv( - lambda: self._parse_types( - check_func=check_func, - schema=schema, - allow_identifiers=allow_identifiers, - ) - ) - ) - else: - expressions = self._parse_csv(self._parse_type_size) - - # https://docs.snowflake.com/en/sql-reference/data-types-vector - if type_token == TokenType.VECTOR and len(expressions) == 2: - expressions = self._parse_vector_expressions(expressions) - - if not self._match(TokenType.R_PAREN): - self._retreat(index) - return None - - maybe_func = True - - values: t.Optional[t.List[exp.Expression]] = None - - if nested and self._match(TokenType.LT): - if is_struct: - expressions = self._parse_csv( - lambda: self._parse_struct_types(type_required=True) - ) - else: - expressions = self._parse_csv( - lambda: self._parse_types( - check_func=check_func, - schema=schema, - allow_identifiers=allow_identifiers, - ) - ) - - if not self._match(TokenType.GT): - self.raise_error("Expecting >") - - if self._match_set((TokenType.L_BRACKET, TokenType.L_PAREN)): - values = self._parse_csv(self._parse_disjunction) - if not values and is_struct: - values = None - self._retreat(self._index - 1) - else: - self._match_set((TokenType.R_BRACKET, TokenType.R_PAREN)) - - if type_token in self.TIMESTAMPS: - if self._match_text_seq("WITH", "TIME", "ZONE"): - maybe_func = False - tz_type = ( - exp.DataType.Type.TIMETZ - if type_token in self.TIMES - else exp.DataType.Type.TIMESTAMPTZ - ) - this = exp.DataType(this=tz_type, expressions=expressions) - elif self._match_text_seq("WITH", "LOCAL", "TIME", "ZONE"): - maybe_func = False - this = exp.DataType( - this=exp.DataType.Type.TIMESTAMPLTZ, expressions=expressions - ) - elif self._match_text_seq("WITHOUT", "TIME", "ZONE"): - maybe_func = False - elif type_token == TokenType.INTERVAL: - unit = self._parse_var(upper=True) - if unit: - if self._match_text_seq("TO"): - unit = exp.IntervalSpan( - this=unit, expression=self._parse_var(upper=True) - ) - - this = self.expression( - exp.DataType, this=self.expression(exp.Interval, unit=unit) - ) - else: - this = self.expression(exp.DataType, this=exp.DataType.Type.INTERVAL) - elif type_token == TokenType.VOID: - this = exp.DataType(this=exp.DataType.Type.NULL) - - if maybe_func and check_func: - index2 = self._index - peek = self._parse_string() - - if not peek: - self._retreat(index) - return None - - self._retreat(index2) - - if not this: - if self._match_text_seq("UNSIGNED"): - unsigned_type_token = self.SIGNED_TO_UNSIGNED_TYPE_TOKEN.get(type_token) - if not unsigned_type_token: - self.raise_error(f"Cannot convert {type_token.value} to unsigned.") - - type_token = unsigned_type_token or type_token - - # NULLABLE without parentheses can be a column (Presto/Trino) - if type_token == TokenType.NULLABLE and not expressions: - self._retreat(index) - return None - - this = exp.DataType( - this=exp.DataType.Type[type_token.value], - expressions=expressions, - nested=nested, - prefix=prefix, - ) - - # Empty arrays/structs are allowed - if values is not None: - cls = exp.Struct if is_struct else exp.Array - this = exp.cast(cls(expressions=values), this, copy=False) - - elif expressions: - this.set("expressions", expressions) - - # https://materialize.com/docs/sql/types/list/#type-name - while self._match(TokenType.LIST): - this = exp.DataType( - this=exp.DataType.Type.LIST, expressions=[this], nested=True - ) - - index = self._index - - # Postgres supports the INT ARRAY[3] syntax as a synonym for INT[3] - matched_array = self._match(TokenType.ARRAY) - - while self._curr: - datatype_token = self._prev.token_type - matched_l_bracket = self._match(TokenType.L_BRACKET) - - if (not matched_l_bracket and not matched_array) or ( - datatype_token == TokenType.ARRAY and self._match(TokenType.R_BRACKET) - ): - # Postgres allows casting empty arrays such as ARRAY[]::INT[], - # not to be confused with the fixed size array parsing - break - - matched_array = False - values = self._parse_csv(self._parse_disjunction) or None - if ( - values - and not schema - and ( - not self.dialect.SUPPORTS_FIXED_SIZE_ARRAYS - or datatype_token == TokenType.ARRAY - or not self._match(TokenType.R_BRACKET, advance=False) - ) - ): - # Retreating here means that we should not parse the following values as part of the data type, e.g. in DuckDB - # ARRAY[1] should retreat and instead be parsed into exp.Array in contrast to INT[x][y] which denotes a fixed-size array data type - self._retreat(index) - break - - this = exp.DataType( - this=exp.DataType.Type.ARRAY, - expressions=[this], - values=values, - nested=True, - ) - self._match(TokenType.R_BRACKET) - - if self.TYPE_CONVERTERS and isinstance(this.this, exp.DataType.Type): - converter = self.TYPE_CONVERTERS.get(this.this) - if converter: - this = converter(t.cast(exp.DataType, this)) - - return this - - def _parse_vector_expressions( - self, expressions: t.List[exp.Expression] - ) -> t.List[exp.Expression]: - return [ - exp.DataType.build(expressions[0].name, dialect=self.dialect), - *expressions[1:], - ] - - def _parse_struct_types( - self, type_required: bool = False - ) -> t.Optional[exp.Expression]: - index = self._index - - if ( - self._curr - and self._next - and self._curr.token_type in self.TYPE_TOKENS - and self._next.token_type in self.TYPE_TOKENS - ): - # Takes care of special cases like `STRUCT>` where the identifier is also a - # type token. Without this, the list will be parsed as a type and we'll eventually crash - this = self._parse_id_var() - else: - this = ( - self._parse_type(parse_interval=False, fallback_to_identifier=True) - or self._parse_id_var() - ) - - self._match(TokenType.COLON) - - if ( - type_required - and not isinstance(this, exp.DataType) - and not self._match_set(self.TYPE_TOKENS, advance=False) - ): - self._retreat(index) - return self._parse_types() - - return self._parse_column_def(this) - - def _parse_at_time_zone( - self, this: t.Optional[exp.Expression] - ) -> t.Optional[exp.Expression]: - if not self._match_text_seq("AT", "TIME", "ZONE"): - return this - return self._parse_at_time_zone( - self.expression(exp.AtTimeZone, this=this, zone=self._parse_unary()) - ) - - def _parse_column(self) -> t.Optional[exp.Expression]: - this = self._parse_column_reference() - column = self._parse_column_ops(this) if this else self._parse_bracket(this) - - if self.dialect.SUPPORTS_COLUMN_JOIN_MARKS and column: - column.set("join_mark", self._match(TokenType.JOIN_MARKER)) - - return column - - def _parse_column_reference(self) -> t.Optional[exp.Expression]: - this = self._parse_field() - if ( - not this - and self._match(TokenType.VALUES, advance=False) - and self.VALUES_FOLLOWED_BY_PAREN - and (not self._next or self._next.token_type != TokenType.L_PAREN) - ): - this = self._parse_id_var() - - if isinstance(this, exp.Identifier): - # We bubble up comments from the Identifier to the Column - this = self.expression(exp.Column, comments=this.pop_comments(), this=this) - - return this - - def _parse_colon_as_variant_extract( - self, this: t.Optional[exp.Expression] - ) -> t.Optional[exp.Expression]: - casts = [] - json_path = [] - escape = None - - while self._match(TokenType.COLON): - start_index = self._index - - # Snowflake allows reserved keywords as json keys but advance_any() excludes TokenType.SELECT from any_tokens=True - path = self._parse_column_ops( - self._parse_field(any_token=True, tokens=(TokenType.SELECT,)) - ) - - # The cast :: operator has a lower precedence than the extraction operator :, so - # we rearrange the AST appropriately to avoid casting the JSON path - while isinstance(path, exp.Cast): - casts.append(path.to) - path = path.this - - if casts: - dcolon_offset = next( - i - for i, t in enumerate(self._tokens[start_index:]) - if t.token_type == TokenType.DCOLON - ) - end_token = self._tokens[start_index + dcolon_offset - 1] - else: - end_token = self._prev - - if path: - # Escape single quotes from Snowflake's colon extraction (e.g. col:"a'b") as - # it'll roundtrip to a string literal in GET_PATH - if isinstance(path, exp.Identifier) and path.quoted: - escape = True - - json_path.append(self._find_sql(self._tokens[start_index], end_token)) - - # The VARIANT extract in Snowflake/Databricks is parsed as a JSONExtract; Snowflake uses the json_path in GET_PATH() while - # Databricks transforms it back to the colon/dot notation - if json_path: - json_path_expr = self.dialect.to_json_path( - exp.Literal.string(".".join(json_path)) - ) - - if json_path_expr: - json_path_expr.set("escape", escape) - - this = self.expression( - exp.JSONExtract, - this=this, - expression=json_path_expr, - variant_extract=True, - requires_json=self.JSON_EXTRACT_REQUIRES_JSON_EXPRESSION, - ) - - while casts: - this = self.expression(exp.Cast, this=this, to=casts.pop()) - - return this - - def _parse_dcolon(self) -> t.Optional[exp.Expression]: - return self._parse_types() - - def _parse_column_ops( - self, this: t.Optional[exp.Expression] - ) -> t.Optional[exp.Expression]: - this = self._parse_bracket(this) - - while self._match_set(self.COLUMN_OPERATORS): - op_token = self._prev.token_type - op = self.COLUMN_OPERATORS.get(op_token) - - if op_token in self.CAST_COLUMN_OPERATORS: - field = self._parse_dcolon() - if not field: - self.raise_error("Expected type") - elif op and self._curr: - field = self._parse_column_reference() or self._parse_bitwise() - if isinstance(field, exp.Column) and self._match( - TokenType.DOT, advance=False - ): - field = self._parse_column_ops(field) - else: - field = self._parse_field(any_token=True, anonymous_func=True) - - # Function calls can be qualified, e.g., x.y.FOO() - # This converts the final AST to a series of Dots leading to the function call - # https://cloud.google.com/bigquery/docs/reference/standard-sql/functions-reference#function_call_rules - if isinstance(field, (exp.Func, exp.Window)) and this: - this = this.transform( - lambda n: ( - n.to_dot(include_dots=False) if isinstance(n, exp.Column) else n - ) - ) - - if op: - this = op(self, this, field) - elif isinstance(this, exp.Column) and not this.args.get("catalog"): - this = self.expression( - exp.Column, - comments=this.comments, - this=field, - table=this.this, - db=this.args.get("table"), - catalog=this.args.get("db"), - ) - elif isinstance(field, exp.Window): - # Move the exp.Dot's to the window's function - window_func = self.expression(exp.Dot, this=this, expression=field.this) - field.set("this", window_func) - this = field - else: - this = self.expression(exp.Dot, this=this, expression=field) - - if field and field.comments: - t.cast(exp.Expression, this).add_comments(field.pop_comments()) - - this = self._parse_bracket(this) - - return ( - self._parse_colon_as_variant_extract(this) - if self.COLON_IS_VARIANT_EXTRACT - else this - ) - - def _parse_paren(self) -> t.Optional[exp.Expression]: - if not self._match(TokenType.L_PAREN): - return None - - comments = self._prev_comments - query = self._parse_select() - - if query: - expressions = [query] - else: - expressions = self._parse_expressions() - - this = seq_get(expressions, 0) - - if not this and self._match(TokenType.R_PAREN, advance=False): - this = self.expression(exp.Tuple) - elif isinstance(this, exp.UNWRAPPED_QUERIES): - this = self._parse_subquery(this=this, parse_alias=False) - elif isinstance(this, (exp.Subquery, exp.Values)): - this = self._parse_subquery( - this=self._parse_query_modifiers(self._parse_set_operations(this)), - parse_alias=False, - ) - elif len(expressions) > 1 or self._prev.token_type == TokenType.COMMA: - this = self.expression(exp.Tuple, expressions=expressions) - else: - this = self.expression(exp.Paren, this=this) - - if this: - this.add_comments(comments) - - self._match_r_paren(expression=this) - - if isinstance(this, exp.Paren) and isinstance(this.this, exp.AggFunc): - return self._parse_window(this) - - return this - - def _parse_primary(self) -> t.Optional[exp.Expression]: - if self._match_set(self.PRIMARY_PARSERS): - token_type = self._prev.token_type - primary = self.PRIMARY_PARSERS[token_type](self, self._prev) - - if token_type == TokenType.STRING: - expressions = [primary] - while self._match(TokenType.STRING): - expressions.append(exp.Literal.string(self._prev.text)) - - if len(expressions) > 1: - return self.expression( - exp.Concat, - expressions=expressions, - coalesce=self.dialect.CONCAT_COALESCE, - ) - - return primary - - if self._match_pair(TokenType.DOT, TokenType.NUMBER): - return exp.Literal.number(f"0.{self._prev.text}") - - return self._parse_paren() - - def _parse_field( - self, - any_token: bool = False, - tokens: t.Optional[t.Collection[TokenType]] = None, - anonymous_func: bool = False, - ) -> t.Optional[exp.Expression]: - if anonymous_func: - field = ( - self._parse_function(anonymous=anonymous_func, any_token=any_token) - or self._parse_primary() - ) - else: - field = self._parse_primary() or self._parse_function( - anonymous=anonymous_func, any_token=any_token - ) - return field or self._parse_id_var(any_token=any_token, tokens=tokens) - - def _parse_function( - self, - functions: t.Optional[t.Dict[str, t.Callable]] = None, - anonymous: bool = False, - optional_parens: bool = True, - any_token: bool = False, - ) -> t.Optional[exp.Expression]: - # This allows us to also parse {fn } syntax (Snowflake, MySQL support this) - # See: https://community.snowflake.com/s/article/SQL-Escape-Sequences - fn_syntax = False - if ( - self._match(TokenType.L_BRACE, advance=False) - and self._next - and self._next.text.upper() == "FN" - ): - self._advance(2) - fn_syntax = True - - func = self._parse_function_call( - functions=functions, - anonymous=anonymous, - optional_parens=optional_parens, - any_token=any_token, - ) - - if fn_syntax: - self._match(TokenType.R_BRACE) - - return func - - def _parse_function_args(self, alias: bool = False) -> t.List[exp.Expression]: - return self._parse_csv(lambda: self._parse_lambda(alias=alias)) - - def _parse_function_call( - self, - functions: t.Optional[t.Dict[str, t.Callable]] = None, - anonymous: bool = False, - optional_parens: bool = True, - any_token: bool = False, - ) -> t.Optional[exp.Expression]: - if not self._curr: - return None - - comments = self._curr.comments - prev = self._prev - token = self._curr - token_type = self._curr.token_type - this = self._curr.text - upper = this.upper() - - parser = self.NO_PAREN_FUNCTION_PARSERS.get(upper) - if ( - optional_parens - and parser - and token_type not in self.INVALID_FUNC_NAME_TOKENS - ): - self._advance() - return self._parse_window(parser(self)) - - if not self._next or self._next.token_type != TokenType.L_PAREN: - if optional_parens and token_type in self.NO_PAREN_FUNCTIONS: - self._advance() - return self.expression(self.NO_PAREN_FUNCTIONS[token_type]) - - return None - - if any_token: - if token_type in self.RESERVED_TOKENS: - return None - elif token_type not in self.FUNC_TOKENS: - return None - - self._advance(2) - - parser = self.FUNCTION_PARSERS.get(upper) - if parser and not anonymous: - this = parser(self) - else: - subquery_predicate = self.SUBQUERY_PREDICATES.get(token_type) - - if subquery_predicate: - expr = None - if self._curr.token_type in (TokenType.SELECT, TokenType.WITH): - expr = self._parse_select() - self._match_r_paren() - elif prev and prev.token_type in (TokenType.LIKE, TokenType.ILIKE): - # Backtrack one token since we've consumed the L_PAREN here. Instead, we'd like - # to parse "LIKE [ANY | ALL] (...)" as a whole into an exp.Tuple or exp.Paren - self._advance(-1) - expr = self._parse_bitwise() - - if expr: - return self.expression( - subquery_predicate, comments=comments, this=expr - ) - - if functions is None: - functions = self.FUNCTIONS - - function = functions.get(upper) - known_function = function and not anonymous - - alias = not known_function or upper in self.FUNCTIONS_WITH_ALIASED_ARGS - args = self._parse_function_args(alias) - - post_func_comments = self._curr and self._curr.comments - if known_function and post_func_comments: - # If the user-inputted comment "/* sqlglot.anonymous */" is following the function - # call we'll construct it as exp.Anonymous, even if it's "known" - if any( - comment.lstrip().startswith(exp.SQLGLOT_ANONYMOUS) - for comment in post_func_comments - ): - known_function = False - - if alias and known_function: - args = self._kv_to_prop_eq(args) - - if known_function: - func_builder = t.cast(t.Callable, function) - - if "dialect" in func_builder.__code__.co_varnames: - func = func_builder(args, dialect=self.dialect) - else: - func = func_builder(args) - - func = self.validate_expression(func, args) - if self.dialect.PRESERVE_ORIGINAL_NAMES: - func.meta["name"] = this - - this = func - else: - if token_type == TokenType.IDENTIFIER: - this = exp.Identifier(this=this, quoted=True).update_positions( - token - ) - - this = self.expression(exp.Anonymous, this=this, expressions=args) - - this = this.update_positions(token) - - if isinstance(this, exp.Expression): - this.add_comments(comments) - - self._match_r_paren(this) - return self._parse_window(this) - - def _to_prop_eq(self, expression: exp.Expression, index: int) -> exp.Expression: - return expression - - def _kv_to_prop_eq( - self, expressions: t.List[exp.Expression], parse_map: bool = False - ) -> t.List[exp.Expression]: - transformed = [] - - for index, e in enumerate(expressions): - if isinstance(e, self.KEY_VALUE_DEFINITIONS): - if isinstance(e, exp.Alias): - e = self.expression( - exp.PropertyEQ, this=e.args.get("alias"), expression=e.this - ) - - if not isinstance(e, exp.PropertyEQ): - e = self.expression( - exp.PropertyEQ, - this=e.this if parse_map else exp.to_identifier(e.this.name), - expression=e.expression, - ) - - if isinstance(e.this, exp.Column): - e.this.replace(e.this.this) - else: - e = self._to_prop_eq(e, index) - - transformed.append(e) - - return transformed - - def _parse_user_defined_function_expression(self) -> t.Optional[exp.Expression]: - return self._parse_statement() - - def _parse_function_parameter(self) -> t.Optional[exp.Expression]: - return self._parse_column_def(this=self._parse_id_var(), computed_column=False) - - def _parse_user_defined_function( - self, kind: t.Optional[TokenType] = None - ) -> t.Optional[exp.Expression]: - this = self._parse_table_parts(schema=True) - - if not self._match(TokenType.L_PAREN): - return this - - expressions = self._parse_csv(self._parse_function_parameter) - self._match_r_paren() - return self.expression( - exp.UserDefinedFunction, this=this, expressions=expressions, wrapped=True - ) - - def _parse_introducer(self, token: Token) -> exp.Introducer | exp.Identifier: - literal = self._parse_primary() - if literal: - return self.expression(exp.Introducer, token=token, expression=literal) - - return self._identifier_expression(token) - - def _parse_session_parameter(self) -> exp.SessionParameter: - kind = None - this = self._parse_id_var() or self._parse_primary() - - if this and self._match(TokenType.DOT): - kind = this.name - this = self._parse_var() or self._parse_primary() - - return self.expression(exp.SessionParameter, this=this, kind=kind) - - def _parse_lambda_arg(self) -> t.Optional[exp.Expression]: - return self._parse_id_var() - - def _parse_lambda(self, alias: bool = False) -> t.Optional[exp.Expression]: - index = self._index - - if self._match(TokenType.L_PAREN): - expressions = t.cast( - t.List[t.Optional[exp.Expression]], - self._parse_csv(self._parse_lambda_arg), - ) - - if not self._match(TokenType.R_PAREN): - self._retreat(index) - else: - expressions = [self._parse_lambda_arg()] - - if self._match_set(self.LAMBDAS): - return self.LAMBDAS[self._prev.token_type](self, expressions) - - self._retreat(index) - - this: t.Optional[exp.Expression] - - if self._match(TokenType.DISTINCT): - this = self.expression( - exp.Distinct, expressions=self._parse_csv(self._parse_disjunction) - ) - else: - this = self._parse_select_or_expression(alias=alias) - - return self._parse_limit( - self._parse_order( - self._parse_having_max(self._parse_respect_or_ignore_nulls(this)) - ) - ) - - def _parse_schema( - self, this: t.Optional[exp.Expression] = None - ) -> t.Optional[exp.Expression]: - index = self._index - if not self._match(TokenType.L_PAREN): - return this - - # Disambiguate between schema and subquery/CTE, e.g. in INSERT INTO table (), - # expr can be of both types - if self._match_set(self.SELECT_START_TOKENS): - self._retreat(index) - return this - args = self._parse_csv( - lambda: self._parse_constraint() or self._parse_field_def() - ) - self._match_r_paren() - return self.expression(exp.Schema, this=this, expressions=args) - - def _parse_field_def(self) -> t.Optional[exp.Expression]: - return self._parse_column_def(self._parse_field(any_token=True)) - - def _parse_column_def( - self, this: t.Optional[exp.Expression], computed_column: bool = True - ) -> t.Optional[exp.Expression]: - # column defs are not really columns, they're identifiers - if isinstance(this, exp.Column): - this = this.this - - if not computed_column: - self._match(TokenType.ALIAS) - - kind = self._parse_types(schema=True) - - if self._match_text_seq("FOR", "ORDINALITY"): - return self.expression(exp.ColumnDef, this=this, ordinality=True) - - constraints: t.List[exp.Expression] = [] - - if (not kind and self._match(TokenType.ALIAS)) or self._match_texts( - ("ALIAS", "MATERIALIZED") - ): - persisted = self._prev.text.upper() == "MATERIALIZED" - constraint_kind = exp.ComputedColumnConstraint( - this=self._parse_disjunction(), - persisted=persisted or self._match_text_seq("PERSISTED"), - data_type=exp.Var(this="AUTO") - if self._match_text_seq("AUTO") - else self._parse_types(), - not_null=self._match_pair(TokenType.NOT, TokenType.NULL), - ) - constraints.append( - self.expression(exp.ColumnConstraint, kind=constraint_kind) - ) - elif ( - kind - and self._match(TokenType.ALIAS, advance=False) - and ( - not self.WRAPPED_TRANSFORM_COLUMN_CONSTRAINT - or (self._next and self._next.token_type == TokenType.L_PAREN) - ) - ): - self._advance() - constraints.append( - self.expression( - exp.ColumnConstraint, - kind=exp.ComputedColumnConstraint( - this=self._parse_disjunction(), - persisted=self._match_texts(("STORED", "VIRTUAL")) - and self._prev.text.upper() == "STORED", - ), - ) - ) - - while True: - constraint = self._parse_column_constraint() - if not constraint: - break - constraints.append(constraint) - - if not kind and not constraints: - return this - - return self.expression( - exp.ColumnDef, this=this, kind=kind, constraints=constraints - ) - - def _parse_auto_increment( - self, - ) -> exp.GeneratedAsIdentityColumnConstraint | exp.AutoIncrementColumnConstraint: - start = None - increment = None - order = None - - if self._match(TokenType.L_PAREN, advance=False): - args = self._parse_wrapped_csv(self._parse_bitwise) - start = seq_get(args, 0) - increment = seq_get(args, 1) - elif self._match_text_seq("START"): - start = self._parse_bitwise() - self._match_text_seq("INCREMENT") - increment = self._parse_bitwise() - if self._match_text_seq("ORDER"): - order = True - elif self._match_text_seq("NOORDER"): - order = False - - if start and increment: - return exp.GeneratedAsIdentityColumnConstraint( - start=start, increment=increment, this=False, order=order - ) - - return exp.AutoIncrementColumnConstraint() - - def _parse_auto_property(self) -> t.Optional[exp.AutoRefreshProperty]: - if not self._match_text_seq("REFRESH"): - self._retreat(self._index - 1) - return None - return self.expression( - exp.AutoRefreshProperty, this=self._parse_var(upper=True) - ) - - def _parse_compress(self) -> exp.CompressColumnConstraint: - if self._match(TokenType.L_PAREN, advance=False): - return self.expression( - exp.CompressColumnConstraint, - this=self._parse_wrapped_csv(self._parse_bitwise), - ) - - return self.expression(exp.CompressColumnConstraint, this=self._parse_bitwise()) - - def _parse_generated_as_identity( - self, - ) -> ( - exp.GeneratedAsIdentityColumnConstraint - | exp.ComputedColumnConstraint - | exp.GeneratedAsRowColumnConstraint - ): - if self._match_text_seq("BY", "DEFAULT"): - on_null = self._match_pair(TokenType.ON, TokenType.NULL) - this = self.expression( - exp.GeneratedAsIdentityColumnConstraint, this=False, on_null=on_null - ) - else: - self._match_text_seq("ALWAYS") - this = self.expression(exp.GeneratedAsIdentityColumnConstraint, this=True) - - self._match(TokenType.ALIAS) - - if self._match_text_seq("ROW"): - start = self._match_text_seq("START") - if not start: - self._match(TokenType.END) - hidden = self._match_text_seq("HIDDEN") - return self.expression( - exp.GeneratedAsRowColumnConstraint, start=start, hidden=hidden - ) - - identity = self._match_text_seq("IDENTITY") - - if self._match(TokenType.L_PAREN): - if self._match(TokenType.START_WITH): - this.set("start", self._parse_bitwise()) - if self._match_text_seq("INCREMENT", "BY"): - this.set("increment", self._parse_bitwise()) - if self._match_text_seq("MINVALUE"): - this.set("minvalue", self._parse_bitwise()) - if self._match_text_seq("MAXVALUE"): - this.set("maxvalue", self._parse_bitwise()) - - if self._match_text_seq("CYCLE"): - this.set("cycle", True) - elif self._match_text_seq("NO", "CYCLE"): - this.set("cycle", False) - - if not identity: - this.set("expression", self._parse_range()) - elif not this.args.get("start") and self._match( - TokenType.NUMBER, advance=False - ): - args = self._parse_csv(self._parse_bitwise) - this.set("start", seq_get(args, 0)) - this.set("increment", seq_get(args, 1)) - - self._match_r_paren() - - return this - - def _parse_inline(self) -> exp.InlineLengthColumnConstraint: - self._match_text_seq("LENGTH") - return self.expression( - exp.InlineLengthColumnConstraint, this=self._parse_bitwise() - ) - - def _parse_not_constraint(self) -> t.Optional[exp.Expression]: - if self._match_text_seq("NULL"): - return self.expression(exp.NotNullColumnConstraint) - if self._match_text_seq("CASESPECIFIC"): - return self.expression(exp.CaseSpecificColumnConstraint, not_=True) - if self._match_text_seq("FOR", "REPLICATION"): - return self.expression(exp.NotForReplicationColumnConstraint) - - # Unconsume the `NOT` token - self._retreat(self._index - 1) - return None - - def _parse_column_constraint(self) -> t.Optional[exp.Expression]: - this = self._match(TokenType.CONSTRAINT) and self._parse_id_var() - - procedure_option_follows = ( - self._match(TokenType.WITH, advance=False) - and self._next - and self._next.text.upper() in self.PROCEDURE_OPTIONS - ) - - if not procedure_option_follows and self._match_texts(self.CONSTRAINT_PARSERS): - return self.expression( - exp.ColumnConstraint, - this=this, - kind=self.CONSTRAINT_PARSERS[self._prev.text.upper()](self), - ) - - return this - - def _parse_constraint(self) -> t.Optional[exp.Expression]: - if not self._match(TokenType.CONSTRAINT): - return self._parse_unnamed_constraint( - constraints=self.SCHEMA_UNNAMED_CONSTRAINTS - ) - - return self.expression( - exp.Constraint, - this=self._parse_id_var(), - expressions=self._parse_unnamed_constraints(), - ) - - def _parse_unnamed_constraints(self) -> t.List[exp.Expression]: - constraints = [] - while True: - constraint = self._parse_unnamed_constraint() or self._parse_function() - if not constraint: - break - constraints.append(constraint) - - return constraints - - def _parse_unnamed_constraint( - self, constraints: t.Optional[t.Collection[str]] = None - ) -> t.Optional[exp.Expression]: - if self._match(TokenType.IDENTIFIER, advance=False) or not self._match_texts( - constraints or self.CONSTRAINT_PARSERS - ): - return None - - constraint = self._prev.text.upper() - if constraint not in self.CONSTRAINT_PARSERS: - self.raise_error(f"No parser found for schema constraint {constraint}.") - - return self.CONSTRAINT_PARSERS[constraint](self) - - def _parse_unique_key(self) -> t.Optional[exp.Expression]: - return self._parse_id_var(any_token=False) - - def _parse_unique(self) -> exp.UniqueColumnConstraint: - self._match_texts(("KEY", "INDEX")) - return self.expression( - exp.UniqueColumnConstraint, - nulls=self._match_text_seq("NULLS", "NOT", "DISTINCT"), - this=self._parse_schema(self._parse_unique_key()), - index_type=self._match(TokenType.USING) - and self._advance_any() - and self._prev.text, - on_conflict=self._parse_on_conflict(), - options=self._parse_key_constraint_options(), - ) - - def _parse_key_constraint_options(self) -> t.List[str]: - options = [] - while True: - if not self._curr: - break - - if self._match(TokenType.ON): - action = None - on = self._advance_any() and self._prev.text - - if self._match_text_seq("NO", "ACTION"): - action = "NO ACTION" - elif self._match_text_seq("CASCADE"): - action = "CASCADE" - elif self._match_text_seq("RESTRICT"): - action = "RESTRICT" - elif self._match_pair(TokenType.SET, TokenType.NULL): - action = "SET NULL" - elif self._match_pair(TokenType.SET, TokenType.DEFAULT): - action = "SET DEFAULT" - else: - self.raise_error("Invalid key constraint") - - options.append(f"ON {on} {action}") - else: - var = self._parse_var_from_options( - self.KEY_CONSTRAINT_OPTIONS, raise_unmatched=False - ) - if not var: - break - options.append(var.name) - - return options - - def _parse_references(self, match: bool = True) -> t.Optional[exp.Reference]: - if match and not self._match(TokenType.REFERENCES): - return None - - expressions = None - this = self._parse_table(schema=True) - options = self._parse_key_constraint_options() - return self.expression( - exp.Reference, this=this, expressions=expressions, options=options - ) - - def _parse_foreign_key(self) -> exp.ForeignKey: - expressions = ( - self._parse_wrapped_id_vars() - if not self._match(TokenType.REFERENCES, advance=False) - else None - ) - reference = self._parse_references() - on_options = {} - - while self._match(TokenType.ON): - if not self._match_set((TokenType.DELETE, TokenType.UPDATE)): - self.raise_error("Expected DELETE or UPDATE") - - kind = self._prev.text.lower() - - if self._match_text_seq("NO", "ACTION"): - action = "NO ACTION" - elif self._match(TokenType.SET): - self._match_set((TokenType.NULL, TokenType.DEFAULT)) - action = "SET " + self._prev.text.upper() - else: - self._advance() - action = self._prev.text.upper() - - on_options[kind] = action - - return self.expression( - exp.ForeignKey, - expressions=expressions, - reference=reference, - options=self._parse_key_constraint_options(), - **on_options, # type: ignore - ) - - def _parse_primary_key_part(self) -> t.Optional[exp.Expression]: - return self._parse_field() - - def _parse_period_for_system_time( - self, - ) -> t.Optional[exp.PeriodForSystemTimeConstraint]: - if not self._match(TokenType.TIMESTAMP_SNAPSHOT): - self._retreat(self._index - 1) - return None - - id_vars = self._parse_wrapped_id_vars() - return self.expression( - exp.PeriodForSystemTimeConstraint, - this=seq_get(id_vars, 0), - expression=seq_get(id_vars, 1), - ) - - def _parse_primary_key( - self, wrapped_optional: bool = False, in_props: bool = False - ) -> exp.PrimaryKeyColumnConstraint | exp.PrimaryKey: - desc = ( - self._match_set((TokenType.ASC, TokenType.DESC)) - and self._prev.token_type == TokenType.DESC - ) - - this = None - if ( - self._curr.text.upper() not in self.CONSTRAINT_PARSERS - and self._next - and self._next.token_type == TokenType.L_PAREN - ): - this = self._parse_id_var() - - if not in_props and not self._match(TokenType.L_PAREN, advance=False): - return self.expression( - exp.PrimaryKeyColumnConstraint, - desc=desc, - options=self._parse_key_constraint_options(), - ) - - expressions = self._parse_wrapped_csv( - self._parse_primary_key_part, optional=wrapped_optional - ) - - return self.expression( - exp.PrimaryKey, - this=this, - expressions=expressions, - include=self._parse_index_params(), - options=self._parse_key_constraint_options(), - ) - - def _parse_bracket_key_value( - self, is_map: bool = False - ) -> t.Optional[exp.Expression]: - return self._parse_slice( - self._parse_alias(self._parse_disjunction(), explicit=True) - ) - - def _parse_odbc_datetime_literal(self) -> exp.Expression: - """ - Parses a datetime column in ODBC format. We parse the column into the corresponding - types, for example `{d'yyyy-mm-dd'}` will be parsed as a `Date` column, exactly the - same as we did for `DATE('yyyy-mm-dd')`. - - Reference: - https://learn.microsoft.com/en-us/sql/odbc/reference/develop-app/date-time-and-timestamp-literals - """ - self._match(TokenType.VAR) - exp_class = self.ODBC_DATETIME_LITERALS[self._prev.text.lower()] - expression = self.expression(exp_class=exp_class, this=self._parse_string()) - if not self._match(TokenType.R_BRACE): - self.raise_error("Expected }") - return expression - - def _parse_bracket( - self, this: t.Optional[exp.Expression] = None - ) -> t.Optional[exp.Expression]: - if not self._match_set((TokenType.L_BRACKET, TokenType.L_BRACE)): - return this - - if self.MAP_KEYS_ARE_ARBITRARY_EXPRESSIONS: - map_token = seq_get(self._tokens, self._index - 2) - parse_map = map_token is not None and map_token.text.upper() == "MAP" - else: - parse_map = False - - bracket_kind = self._prev.token_type - if ( - bracket_kind == TokenType.L_BRACE - and self._curr - and self._curr.token_type == TokenType.VAR - and self._curr.text.lower() in self.ODBC_DATETIME_LITERALS - ): - return self._parse_odbc_datetime_literal() - - expressions = self._parse_csv( - lambda: self._parse_bracket_key_value( - is_map=bracket_kind == TokenType.L_BRACE - ) - ) - - if bracket_kind == TokenType.L_BRACKET and not self._match(TokenType.R_BRACKET): - self.raise_error("Expected ]") - elif bracket_kind == TokenType.L_BRACE and not self._match(TokenType.R_BRACE): - self.raise_error("Expected }") - - # https://duckdb.org/docs/sql/data_types/struct.html#creating-structs - if bracket_kind == TokenType.L_BRACE: - this = self.expression( - exp.Struct, - expressions=self._kv_to_prop_eq( - expressions=expressions, parse_map=parse_map - ), - ) - elif not this: - this = build_array_constructor( - exp.Array, - args=expressions, - bracket_kind=bracket_kind, - dialect=self.dialect, - ) - else: - constructor_type = self.ARRAY_CONSTRUCTORS.get(this.name.upper()) - if constructor_type: - return build_array_constructor( - constructor_type, - args=expressions, - bracket_kind=bracket_kind, - dialect=self.dialect, - ) - - expressions = apply_index_offset( - this, expressions, -self.dialect.INDEX_OFFSET, dialect=self.dialect - ) - this = self.expression( - exp.Bracket, - this=this, - expressions=expressions, - comments=this.pop_comments(), - ) - - self._add_comments(this) - return self._parse_bracket(this) - - def _parse_slice( - self, this: t.Optional[exp.Expression] - ) -> t.Optional[exp.Expression]: - if not self._match(TokenType.COLON): - return this - - if self._match_pair(TokenType.DASH, TokenType.COLON, advance=False): - self._advance() - end: t.Optional[exp.Expression] = -exp.Literal.number("1") - else: - end = self._parse_unary() - step = self._parse_unary() if self._match(TokenType.COLON) else None - return self.expression(exp.Slice, this=this, expression=end, step=step) - - def _parse_case(self) -> t.Optional[exp.Expression]: - if self._match(TokenType.DOT, advance=False): - # Avoid raising on valid expressions like case.*, supported by, e.g., spark & snowflake - self._retreat(self._index - 1) - return None - - ifs = [] - default = None - - comments = self._prev_comments - expression = self._parse_disjunction() - - while self._match(TokenType.WHEN): - this = self._parse_disjunction() - self._match(TokenType.THEN) - then = self._parse_disjunction() - ifs.append(self.expression(exp.If, this=this, true=then)) - - if self._match(TokenType.ELSE): - default = self._parse_disjunction() - - if not self._match(TokenType.END): - if ( - isinstance(default, exp.Interval) - and default.this.sql().upper() == "END" - ): - default = exp.column("interval") - else: - self.raise_error("Expected END after CASE", self._prev) - - return self.expression( - exp.Case, comments=comments, this=expression, ifs=ifs, default=default - ) - - def _parse_if(self) -> t.Optional[exp.Expression]: - if self._match(TokenType.L_PAREN): - args = self._parse_csv( - lambda: self._parse_alias(self._parse_assignment(), explicit=True) - ) - this = self.validate_expression(exp.If.from_arg_list(args), args) - self._match_r_paren() - else: - index = self._index - 1 - - if self.NO_PAREN_IF_COMMANDS and index == 0: - return self._parse_as_command(self._prev) - - condition = self._parse_disjunction() - - if not condition: - self._retreat(index) - return None - - self._match(TokenType.THEN) - true = self._parse_disjunction() - false = self._parse_disjunction() if self._match(TokenType.ELSE) else None - self._match(TokenType.END) - this = self.expression(exp.If, this=condition, true=true, false=false) - - return this - - def _parse_next_value_for(self) -> t.Optional[exp.Expression]: - if not self._match_text_seq("VALUE", "FOR"): - self._retreat(self._index - 1) - return None - - return self.expression( - exp.NextValueFor, - this=self._parse_column(), - order=self._match(TokenType.OVER) - and self._parse_wrapped(self._parse_order), - ) - - def _parse_extract(self) -> exp.Extract | exp.Anonymous: - this = self._parse_function() or self._parse_var_or_string(upper=True) - - if self._match(TokenType.FROM): - return self.expression( - exp.Extract, this=this, expression=self._parse_bitwise() - ) - - if not self._match(TokenType.COMMA): - self.raise_error("Expected FROM or comma after EXTRACT", self._prev) - - return self.expression(exp.Extract, this=this, expression=self._parse_bitwise()) - - def _parse_gap_fill(self) -> exp.GapFill: - self._match(TokenType.TABLE) - this = self._parse_table() - - self._match(TokenType.COMMA) - args = [this, *self._parse_csv(self._parse_lambda)] - - gap_fill = exp.GapFill.from_arg_list(args) - return self.validate_expression(gap_fill, args) - - def _parse_cast( - self, strict: bool, safe: t.Optional[bool] = None - ) -> exp.Expression: - this = self._parse_disjunction() - - if not self._match(TokenType.ALIAS): - if self._match(TokenType.COMMA): - return self.expression( - exp.CastToStrType, this=this, to=self._parse_string() - ) - - self.raise_error("Expected AS after CAST") - - fmt = None - to = self._parse_types() - - default = self._match(TokenType.DEFAULT) - if default: - default = self._parse_bitwise() - self._match_text_seq("ON", "CONVERSION", "ERROR") - - if self._match_set((TokenType.FORMAT, TokenType.COMMA)): - fmt_string = self._parse_string() - fmt = self._parse_at_time_zone(fmt_string) - - if not to: - to = exp.DataType.build(exp.DataType.Type.UNKNOWN) - if to.this in exp.DataType.TEMPORAL_TYPES: - this = self.expression( - exp.StrToDate - if to.this == exp.DataType.Type.DATE - else exp.StrToTime, - this=this, - format=exp.Literal.string( - format_time( - fmt_string.this if fmt_string else "", - self.dialect.FORMAT_MAPPING or self.dialect.TIME_MAPPING, - self.dialect.FORMAT_TRIE or self.dialect.TIME_TRIE, - ) - ), - safe=safe, - ) - - if isinstance(fmt, exp.AtTimeZone) and isinstance(this, exp.StrToTime): - this.set("zone", fmt.args["zone"]) - return this - elif not to: - self.raise_error("Expected TYPE after CAST") - elif isinstance(to, exp.Identifier): - to = exp.DataType.build(to.name, dialect=self.dialect, udt=True) - elif to.this == exp.DataType.Type.CHAR: - if self._match(TokenType.CHARACTER_SET): - to = self.expression(exp.CharacterSet, this=self._parse_var_or_string()) - - return self.build_cast( - strict=strict, - this=this, - to=to, - format=fmt, - safe=safe, - action=self._parse_var_from_options( - self.CAST_ACTIONS, raise_unmatched=False - ), - default=default, - ) - - def _parse_string_agg(self) -> exp.GroupConcat: - if self._match(TokenType.DISTINCT): - args: t.List[t.Optional[exp.Expression]] = [ - self.expression(exp.Distinct, expressions=[self._parse_disjunction()]) - ] - if self._match(TokenType.COMMA): - args.extend(self._parse_csv(self._parse_disjunction)) - else: - args = self._parse_csv(self._parse_disjunction) # type: ignore - - if self._match_text_seq("ON", "OVERFLOW"): - # trino: LISTAGG(expression [, separator] [ON OVERFLOW overflow_behavior]) - if self._match_text_seq("ERROR"): - on_overflow: t.Optional[exp.Expression] = exp.var("ERROR") - else: - self._match_text_seq("TRUNCATE") - on_overflow = self.expression( - exp.OverflowTruncateBehavior, - this=self._parse_string(), - with_count=( - self._match_text_seq("WITH", "COUNT") - or not self._match_text_seq("WITHOUT", "COUNT") - ), - ) - else: - on_overflow = None - - index = self._index - if not self._match(TokenType.R_PAREN) and args: - # postgres: STRING_AGG([DISTINCT] expression, separator [ORDER BY expression1 {ASC | DESC} [, ...]]) - # bigquery: STRING_AGG([DISTINCT] expression [, separator] [ORDER BY key [{ASC | DESC}] [, ... ]] [LIMIT n]) - # The order is parsed through `this` as a canonicalization for WITHIN GROUPs - args[0] = self._parse_limit(this=self._parse_order(this=args[0])) - return self.expression( - exp.GroupConcat, this=args[0], separator=seq_get(args, 1) - ) - - # Checks if we can parse an order clause: WITHIN GROUP (ORDER BY [ASC | DESC]). - # This is done "manually", instead of letting _parse_window parse it into an exp.WithinGroup node, so that - # the STRING_AGG call is parsed like in MySQL / SQLite and can thus be transpiled more easily to them. - if not self._match_text_seq("WITHIN", "GROUP"): - self._retreat(index) - return self.validate_expression(exp.GroupConcat.from_arg_list(args), args) - - # The corresponding match_r_paren will be called in parse_function (caller) - self._match_l_paren() - - return self.expression( - exp.GroupConcat, - this=self._parse_order(this=seq_get(args, 0)), - separator=seq_get(args, 1), - on_overflow=on_overflow, - ) - - def _parse_convert( - self, strict: bool, safe: t.Optional[bool] = None - ) -> t.Optional[exp.Expression]: - this = self._parse_bitwise() - - if self._match(TokenType.USING): - to: t.Optional[exp.Expression] = self.expression( - exp.CharacterSet, this=self._parse_var() - ) - elif self._match(TokenType.COMMA): - to = self._parse_types() - else: - to = None - - return self.build_cast(strict=strict, this=this, to=to, safe=safe) - - def _parse_xml_table(self) -> exp.XMLTable: - namespaces = None - passing = None - columns = None - - if self._match_text_seq("XMLNAMESPACES", "("): - namespaces = self._parse_xml_namespace() - self._match_text_seq(")", ",") - - this = self._parse_string() - - if self._match_text_seq("PASSING"): - # The BY VALUE keywords are optional and are provided for semantic clarity - self._match_text_seq("BY", "VALUE") - passing = self._parse_csv(self._parse_column) - - by_ref = self._match_text_seq("RETURNING", "SEQUENCE", "BY", "REF") - - if self._match_text_seq("COLUMNS"): - columns = self._parse_csv(self._parse_field_def) - - return self.expression( - exp.XMLTable, - this=this, - namespaces=namespaces, - passing=passing, - columns=columns, - by_ref=by_ref, - ) - - def _parse_xml_namespace(self) -> t.List[exp.XMLNamespace]: - namespaces = [] - - while True: - if self._match(TokenType.DEFAULT): - uri = self._parse_string() - else: - uri = self._parse_alias(self._parse_string()) - namespaces.append(self.expression(exp.XMLNamespace, this=uri)) - if not self._match(TokenType.COMMA): - break - - return namespaces - - def _parse_decode(self) -> t.Optional[exp.Decode | exp.DecodeCase]: - args = self._parse_csv(self._parse_disjunction) - - if len(args) < 3: - return self.expression( - exp.Decode, this=seq_get(args, 0), charset=seq_get(args, 1) - ) - - return self.expression(exp.DecodeCase, expressions=args) - - def _parse_json_key_value(self) -> t.Optional[exp.JSONKeyValue]: - self._match_text_seq("KEY") - key = self._parse_column() - self._match_set(self.JSON_KEY_VALUE_SEPARATOR_TOKENS) - self._match_text_seq("VALUE") - value = self._parse_bitwise() - - if not key and not value: - return None - return self.expression(exp.JSONKeyValue, this=key, expression=value) - - def _parse_format_json( - self, this: t.Optional[exp.Expression] - ) -> t.Optional[exp.Expression]: - if not this or not self._match_text_seq("FORMAT", "JSON"): - return this - - return self.expression(exp.FormatJson, this=this) - - def _parse_on_condition(self) -> t.Optional[exp.OnCondition]: - # MySQL uses "X ON EMPTY Y ON ERROR" (e.g. JSON_VALUE) while Oracle uses the opposite (e.g. JSON_EXISTS) - if self.dialect.ON_CONDITION_EMPTY_BEFORE_ERROR: - empty = self._parse_on_handling("EMPTY", *self.ON_CONDITION_TOKENS) - error = self._parse_on_handling("ERROR", *self.ON_CONDITION_TOKENS) - else: - error = self._parse_on_handling("ERROR", *self.ON_CONDITION_TOKENS) - empty = self._parse_on_handling("EMPTY", *self.ON_CONDITION_TOKENS) - - null = self._parse_on_handling("NULL", *self.ON_CONDITION_TOKENS) - - if not empty and not error and not null: - return None - - return self.expression( - exp.OnCondition, - empty=empty, - error=error, - null=null, - ) - - def _parse_on_handling( - self, on: str, *values: str - ) -> t.Optional[str] | t.Optional[exp.Expression]: - # Parses the "X ON Y" or "DEFAULT ON Y syntax, e.g. NULL ON NULL (Oracle, T-SQL, MySQL) - for value in values: - if self._match_text_seq(value, "ON", on): - return f"{value} ON {on}" - - index = self._index - if self._match(TokenType.DEFAULT): - default_value = self._parse_bitwise() - if self._match_text_seq("ON", on): - return default_value - - self._retreat(index) - - return None - - @t.overload - def _parse_json_object(self, agg: Lit[False]) -> exp.JSONObject: ... - - @t.overload - def _parse_json_object(self, agg: Lit[True]) -> exp.JSONObjectAgg: ... - - def _parse_json_object(self, agg=False): - star = self._parse_star() - expressions = ( - [star] - if star - else self._parse_csv( - lambda: self._parse_format_json(self._parse_json_key_value()) - ) - ) - null_handling = self._parse_on_handling("NULL", "NULL", "ABSENT") - - unique_keys = None - if self._match_text_seq("WITH", "UNIQUE"): - unique_keys = True - elif self._match_text_seq("WITHOUT", "UNIQUE"): - unique_keys = False - - self._match_text_seq("KEYS") - - return_type = self._match_text_seq("RETURNING") and self._parse_format_json( - self._parse_type() - ) - encoding = self._match_text_seq("ENCODING") and self._parse_var() - - return self.expression( - exp.JSONObjectAgg if agg else exp.JSONObject, - expressions=expressions, - null_handling=null_handling, - unique_keys=unique_keys, - return_type=return_type, - encoding=encoding, - ) - - # Note: this is currently incomplete; it only implements the "JSON_value_column" part - def _parse_json_column_def(self) -> exp.JSONColumnDef: - if not self._match_text_seq("NESTED"): - this = self._parse_id_var() - ordinality = self._match_pair(TokenType.FOR, TokenType.ORDINALITY) - kind = self._parse_types(allow_identifiers=False) - nested = None - else: - this = None - ordinality = None - kind = None - nested = True - - path = self._match_text_seq("PATH") and self._parse_string() - nested_schema = nested and self._parse_json_schema() - - return self.expression( - exp.JSONColumnDef, - this=this, - kind=kind, - path=path, - nested_schema=nested_schema, - ordinality=ordinality, - ) - - def _parse_json_schema(self) -> exp.JSONSchema: - self._match_text_seq("COLUMNS") - return self.expression( - exp.JSONSchema, - expressions=self._parse_wrapped_csv( - self._parse_json_column_def, optional=True - ), - ) - - def _parse_json_table(self) -> exp.JSONTable: - this = self._parse_format_json(self._parse_bitwise()) - path = self._match(TokenType.COMMA) and self._parse_string() - error_handling = self._parse_on_handling("ERROR", "ERROR", "NULL") - empty_handling = self._parse_on_handling("EMPTY", "ERROR", "NULL") - schema = self._parse_json_schema() - - return exp.JSONTable( - this=this, - schema=schema, - path=path, - error_handling=error_handling, - empty_handling=empty_handling, - ) - - def _parse_match_against(self) -> exp.MatchAgainst: - if self._match_text_seq("TABLE"): - # parse SingleStore MATCH(TABLE ...) syntax - # https://docs.singlestore.com/cloud/reference/sql-reference/full-text-search-functions/match/ - expressions = [] - table = self._parse_table() - if table: - expressions = [table] - else: - expressions = self._parse_csv(self._parse_column) - - self._match_text_seq(")", "AGAINST", "(") - - this = self._parse_string() - - if self._match_text_seq("IN", "NATURAL", "LANGUAGE", "MODE"): - modifier = "IN NATURAL LANGUAGE MODE" - if self._match_text_seq("WITH", "QUERY", "EXPANSION"): - modifier = f"{modifier} WITH QUERY EXPANSION" - elif self._match_text_seq("IN", "BOOLEAN", "MODE"): - modifier = "IN BOOLEAN MODE" - elif self._match_text_seq("WITH", "QUERY", "EXPANSION"): - modifier = "WITH QUERY EXPANSION" - else: - modifier = None - - return self.expression( - exp.MatchAgainst, this=this, expressions=expressions, modifier=modifier - ) - - # https://learn.microsoft.com/en-us/sql/t-sql/functions/openjson-transact-sql?view=sql-server-ver16 - def _parse_open_json(self) -> exp.OpenJSON: - this = self._parse_bitwise() - path = self._match(TokenType.COMMA) and self._parse_string() - - def _parse_open_json_column_def() -> exp.OpenJSONColumnDef: - this = self._parse_field(any_token=True) - kind = self._parse_types() - path = self._parse_string() - as_json = self._match_pair(TokenType.ALIAS, TokenType.JSON) - - return self.expression( - exp.OpenJSONColumnDef, this=this, kind=kind, path=path, as_json=as_json - ) - - expressions = None - if self._match_pair(TokenType.R_PAREN, TokenType.WITH): - self._match_l_paren() - expressions = self._parse_csv(_parse_open_json_column_def) - - return self.expression( - exp.OpenJSON, this=this, path=path, expressions=expressions - ) - - def _parse_position(self, haystack_first: bool = False) -> exp.StrPosition: - args = self._parse_csv(self._parse_bitwise) - - if self._match(TokenType.IN): - return self.expression( - exp.StrPosition, this=self._parse_bitwise(), substr=seq_get(args, 0) - ) - - if haystack_first: - haystack = seq_get(args, 0) - needle = seq_get(args, 1) - else: - haystack = seq_get(args, 1) - needle = seq_get(args, 0) - - return self.expression( - exp.StrPosition, this=haystack, substr=needle, position=seq_get(args, 2) - ) - - def _parse_join_hint(self, func_name: str) -> exp.JoinHint: - args = self._parse_csv(self._parse_table) - return exp.JoinHint(this=func_name.upper(), expressions=args) - - def _parse_substring(self) -> exp.Substring: - # Postgres supports the form: substring(string [from int] [for int]) - # (despite being undocumented, the reverse order also works) - # https://www.postgresql.org/docs/9.1/functions-string.html @ Table 9-6 - - args = t.cast( - t.List[t.Optional[exp.Expression]], self._parse_csv(self._parse_bitwise) - ) - - start, length = None, None - - while self._curr: - if self._match(TokenType.FROM): - start = self._parse_bitwise() - elif self._match(TokenType.FOR): - if not start: - start = exp.Literal.number(1) - length = self._parse_bitwise() - else: - break - - if start: - args.append(start) - if length: - args.append(length) - - return self.validate_expression(exp.Substring.from_arg_list(args), args) - - def _parse_trim(self) -> exp.Trim: - # https://www.w3resource.com/sql/character-functions/trim.php - # https://docs.oracle.com/javadb/10.8.3.0/ref/rreftrimfunc.html - - position = None - collation = None - expression = None - - if self._match_texts(self.TRIM_TYPES): - position = self._prev.text.upper() - - this = self._parse_bitwise() - if self._match_set((TokenType.FROM, TokenType.COMMA)): - invert_order = ( - self._prev.token_type == TokenType.FROM or self.TRIM_PATTERN_FIRST - ) - expression = self._parse_bitwise() - - if invert_order: - this, expression = expression, this - - if self._match(TokenType.COLLATE): - collation = self._parse_bitwise() - - return self.expression( - exp.Trim, - this=this, - position=position, - expression=expression, - collation=collation, - ) - - def _parse_window_clause(self) -> t.Optional[t.List[exp.Expression]]: - return self._match(TokenType.WINDOW) and self._parse_csv( - self._parse_named_window - ) - - def _parse_named_window(self) -> t.Optional[exp.Expression]: - return self._parse_window(self._parse_id_var(), alias=True) - - def _parse_respect_or_ignore_nulls( - self, this: t.Optional[exp.Expression] - ) -> t.Optional[exp.Expression]: - if self._match_text_seq("IGNORE", "NULLS"): - return self.expression(exp.IgnoreNulls, this=this) - if self._match_text_seq("RESPECT", "NULLS"): - return self.expression(exp.RespectNulls, this=this) - return this - - def _parse_having_max( - self, this: t.Optional[exp.Expression] - ) -> t.Optional[exp.Expression]: - if self._match(TokenType.HAVING): - self._match_texts(("MAX", "MIN")) - max = self._prev.text.upper() != "MIN" - return self.expression( - exp.HavingMax, this=this, expression=self._parse_column(), max=max - ) - - return this - - def _parse_window( - self, this: t.Optional[exp.Expression], alias: bool = False - ) -> t.Optional[exp.Expression]: - func = this - comments = func.comments if isinstance(func, exp.Expression) else None - - # T-SQL allows the OVER (...) syntax after WITHIN GROUP. - # https://learn.microsoft.com/en-us/sql/t-sql/functions/percentile-disc-transact-sql?view=sql-server-ver16 - if self._match_text_seq("WITHIN", "GROUP"): - order = self._parse_wrapped(self._parse_order) - this = self.expression(exp.WithinGroup, this=this, expression=order) - - if self._match_pair(TokenType.FILTER, TokenType.L_PAREN): - self._match(TokenType.WHERE) - this = self.expression( - exp.Filter, - this=this, - expression=self._parse_where(skip_where_token=True), - ) - self._match_r_paren() - - # SQL spec defines an optional [ { IGNORE | RESPECT } NULLS ] OVER - # Some dialects choose to implement and some do not. - # https://dev.mysql.com/doc/refman/8.0/en/window-function-descriptions.html - - # There is some code above in _parse_lambda that handles - # SELECT FIRST_VALUE(TABLE.COLUMN IGNORE|RESPECT NULLS) OVER ... - - # The below changes handle - # SELECT FIRST_VALUE(TABLE.COLUMN) IGNORE|RESPECT NULLS OVER ... - - # Oracle allows both formats - # (https://docs.oracle.com/en/database/oracle/oracle-database/19/sqlrf/img_text/first_value.html) - # and Snowflake chose to do the same for familiarity - # https://docs.snowflake.com/en/sql-reference/functions/first_value.html#usage-notes - if isinstance(this, exp.AggFunc): - ignore_respect = this.find(exp.IgnoreNulls, exp.RespectNulls) - - if ignore_respect and ignore_respect is not this: - ignore_respect.replace(ignore_respect.this) - this = self.expression(ignore_respect.__class__, this=this) - - this = self._parse_respect_or_ignore_nulls(this) - - # bigquery select from window x AS (partition by ...) - if alias: - over = None - self._match(TokenType.ALIAS) - elif not self._match_set(self.WINDOW_BEFORE_PAREN_TOKENS): - return this - else: - over = self._prev.text.upper() - - if comments and isinstance(func, exp.Expression): - func.pop_comments() - - if not self._match(TokenType.L_PAREN): - return self.expression( - exp.Window, - comments=comments, - this=this, - alias=self._parse_id_var(False), - over=over, - ) - - window_alias = self._parse_id_var( - any_token=False, tokens=self.WINDOW_ALIAS_TOKENS - ) - - first = self._match(TokenType.FIRST) - if self._match_text_seq("LAST"): - first = False - - partition, order = self._parse_partition_and_order() - kind = self._match_set((TokenType.ROWS, TokenType.RANGE)) and self._prev.text - - if kind: - self._match(TokenType.BETWEEN) - start = self._parse_window_spec() - - end = self._parse_window_spec() if self._match(TokenType.AND) else {} - exclude = ( - self._parse_var_from_options(self.WINDOW_EXCLUDE_OPTIONS) - if self._match_text_seq("EXCLUDE") - else None - ) - - spec = self.expression( - exp.WindowSpec, - kind=kind, - start=start["value"], - start_side=start["side"], - end=end.get("value"), - end_side=end.get("side"), - exclude=exclude, - ) - else: - spec = None - - self._match_r_paren() - - window = self.expression( - exp.Window, - comments=comments, - this=this, - partition_by=partition, - order=order, - spec=spec, - alias=window_alias, - over=over, - first=first, - ) - - # This covers Oracle's FIRST/LAST syntax: aggregate KEEP (...) OVER (...) - if self._match_set(self.WINDOW_BEFORE_PAREN_TOKENS, advance=False): - return self._parse_window(window, alias=alias) - - return window - - def _parse_partition_and_order( - self, - ) -> t.Tuple[t.List[exp.Expression], t.Optional[exp.Expression]]: - return self._parse_partition_by(), self._parse_order() - - def _parse_window_spec(self) -> t.Dict[str, t.Optional[str | exp.Expression]]: - self._match(TokenType.BETWEEN) - - return { - "value": ( - (self._match_text_seq("UNBOUNDED") and "UNBOUNDED") - or (self._match_text_seq("CURRENT", "ROW") and "CURRENT ROW") - or self._parse_bitwise() - ), - "side": self._match_texts(self.WINDOW_SIDES) and self._prev.text, - } - - def _parse_alias( - self, this: t.Optional[exp.Expression], explicit: bool = False - ) -> t.Optional[exp.Expression]: - # In some dialects, LIMIT and OFFSET can act as both identifiers and keywords (clauses) - # so this section tries to parse the clause version and if it fails, it treats the token - # as an identifier (alias) - if self._can_parse_limit_or_offset(): - return this - - any_token = self._match(TokenType.ALIAS) - comments = self._prev_comments or [] - - if explicit and not any_token: - return this - - if self._match(TokenType.L_PAREN): - aliases = self.expression( - exp.Aliases, - comments=comments, - this=this, - expressions=self._parse_csv(lambda: self._parse_id_var(any_token)), - ) - self._match_r_paren(aliases) - return aliases - - alias = self._parse_id_var(any_token, tokens=self.ALIAS_TOKENS) or ( - self.STRING_ALIASES and self._parse_string_as_identifier() - ) - - if alias: - comments.extend(alias.pop_comments()) - this = self.expression(exp.Alias, comments=comments, this=this, alias=alias) - column = this.this - - # Moves the comment next to the alias in `expr /* comment */ AS alias` - if not this.comments and column and column.comments: - this.comments = column.pop_comments() - - return this - - def _parse_id_var( - self, - any_token: bool = True, - tokens: t.Optional[t.Collection[TokenType]] = None, - ) -> t.Optional[exp.Expression]: - expression = self._parse_identifier() - if not expression and ( - (any_token and self._advance_any()) - or self._match_set(tokens or self.ID_VAR_TOKENS) - ): - quoted = self._prev.token_type == TokenType.STRING - expression = self._identifier_expression(quoted=quoted) - - return expression - - def _parse_string(self) -> t.Optional[exp.Expression]: - if self._match_set(self.STRING_PARSERS): - return self.STRING_PARSERS[self._prev.token_type](self, self._prev) - return self._parse_placeholder() - - def _parse_string_as_identifier(self) -> t.Optional[exp.Identifier]: - output = exp.to_identifier( - self._match(TokenType.STRING) and self._prev.text, quoted=True - ) - if output: - output.update_positions(self._prev) - return output - - def _parse_number(self) -> t.Optional[exp.Expression]: - if self._match_set(self.NUMERIC_PARSERS): - return self.NUMERIC_PARSERS[self._prev.token_type](self, self._prev) - return self._parse_placeholder() - - def _parse_identifier(self) -> t.Optional[exp.Expression]: - if self._match(TokenType.IDENTIFIER): - return self._identifier_expression(quoted=True) - return self._parse_placeholder() - - def _parse_var( - self, - any_token: bool = False, - tokens: t.Optional[t.Collection[TokenType]] = None, - upper: bool = False, - ) -> t.Optional[exp.Expression]: - if ( - (any_token and self._advance_any()) - or self._match(TokenType.VAR) - or (self._match_set(tokens) if tokens else False) - ): - return self.expression( - exp.Var, this=self._prev.text.upper() if upper else self._prev.text - ) - return self._parse_placeholder() - - def _advance_any(self, ignore_reserved: bool = False) -> t.Optional[Token]: - if self._curr and ( - ignore_reserved or self._curr.token_type not in self.RESERVED_TOKENS - ): - self._advance() - return self._prev - return None - - def _parse_var_or_string(self, upper: bool = False) -> t.Optional[exp.Expression]: - return self._parse_string() or self._parse_var(any_token=True, upper=upper) - - def _parse_primary_or_var(self) -> t.Optional[exp.Expression]: - return self._parse_primary() or self._parse_var(any_token=True) - - def _parse_null(self) -> t.Optional[exp.Expression]: - if self._match_set((TokenType.NULL, TokenType.UNKNOWN)): - return self.PRIMARY_PARSERS[TokenType.NULL](self, self._prev) - return self._parse_placeholder() - - def _parse_boolean(self) -> t.Optional[exp.Expression]: - if self._match(TokenType.TRUE): - return self.PRIMARY_PARSERS[TokenType.TRUE](self, self._prev) - if self._match(TokenType.FALSE): - return self.PRIMARY_PARSERS[TokenType.FALSE](self, self._prev) - return self._parse_placeholder() - - def _parse_star(self) -> t.Optional[exp.Expression]: - if self._match(TokenType.STAR): - return self.PRIMARY_PARSERS[TokenType.STAR](self, self._prev) - return self._parse_placeholder() - - def _parse_parameter(self) -> exp.Parameter: - this = self._parse_identifier() or self._parse_primary_or_var() - return self.expression(exp.Parameter, this=this) - - def _parse_placeholder(self) -> t.Optional[exp.Expression]: - if self._match_set(self.PLACEHOLDER_PARSERS): - placeholder = self.PLACEHOLDER_PARSERS[self._prev.token_type](self) - if placeholder: - return placeholder - self._advance(-1) - return None - - def _parse_star_op(self, *keywords: str) -> t.Optional[t.List[exp.Expression]]: - if not self._match_texts(keywords): - return None - if self._match(TokenType.L_PAREN, advance=False): - return self._parse_wrapped_csv(self._parse_expression) - - expression = self._parse_alias(self._parse_disjunction(), explicit=True) - return [expression] if expression else None - - def _parse_csv( - self, parse_method: t.Callable, sep: TokenType = TokenType.COMMA - ) -> t.List[exp.Expression]: - parse_result = parse_method() - items = [parse_result] if parse_result is not None else [] - - while self._match(sep): - self._add_comments(parse_result) - parse_result = parse_method() - if parse_result is not None: - items.append(parse_result) - - return items - - def _parse_tokens( - self, parse_method: t.Callable, expressions: t.Dict - ) -> t.Optional[exp.Expression]: - this = parse_method() - - while self._match_set(expressions): - this = self.expression( - expressions[self._prev.token_type], - this=this, - comments=self._prev_comments, - expression=parse_method(), - ) - - return this - - def _parse_wrapped_id_vars(self, optional: bool = False) -> t.List[exp.Expression]: - return self._parse_wrapped_csv(self._parse_id_var, optional=optional) - - def _parse_wrapped_csv( - self, - parse_method: t.Callable, - sep: TokenType = TokenType.COMMA, - optional: bool = False, - ) -> t.List[exp.Expression]: - return self._parse_wrapped( - lambda: self._parse_csv(parse_method, sep=sep), optional=optional - ) - - def _parse_wrapped(self, parse_method: t.Callable, optional: bool = False) -> t.Any: - wrapped = self._match(TokenType.L_PAREN) - if not wrapped and not optional: - self.raise_error("Expecting (") - parse_result = parse_method() - if wrapped: - self._match_r_paren() - return parse_result - - def _parse_expressions(self) -> t.List[exp.Expression]: - return self._parse_csv(self._parse_expression) - - def _parse_select_or_expression( - self, alias: bool = False - ) -> t.Optional[exp.Expression]: - return ( - self._parse_set_operations( - self._parse_alias(self._parse_assignment(), explicit=True) - if alias - else self._parse_assignment() - ) - or self._parse_select() - ) - - def _parse_ddl_select(self) -> t.Optional[exp.Expression]: - return self._parse_query_modifiers( - self._parse_set_operations( - self._parse_select(nested=True, parse_subquery_alias=False) - ) - ) - - def _parse_transaction(self) -> exp.Transaction | exp.Command: - this = None - if self._match_texts(self.TRANSACTION_KIND): - this = self._prev.text - - self._match_texts(("TRANSACTION", "WORK")) - - modes = [] - while True: - mode = [] - while self._match(TokenType.VAR) or self._match(TokenType.NOT): - mode.append(self._prev.text) - - if mode: - modes.append(" ".join(mode)) - if not self._match(TokenType.COMMA): - break - - return self.expression(exp.Transaction, this=this, modes=modes) - - def _parse_commit_or_rollback(self) -> exp.Commit | exp.Rollback: - chain = None - savepoint = None - is_rollback = self._prev.token_type == TokenType.ROLLBACK - - self._match_texts(("TRANSACTION", "WORK")) - - if self._match_text_seq("TO"): - self._match_text_seq("SAVEPOINT") - savepoint = self._parse_id_var() - - if self._match(TokenType.AND): - chain = not self._match_text_seq("NO") - self._match_text_seq("CHAIN") - - if is_rollback: - return self.expression(exp.Rollback, savepoint=savepoint) - - return self.expression(exp.Commit, chain=chain) - - def _parse_refresh(self) -> exp.Refresh | exp.Command: - if self._match(TokenType.TABLE): - kind = "TABLE" - elif self._match_text_seq("MATERIALIZED", "VIEW"): - kind = "MATERIALIZED VIEW" - else: - kind = "" - - this = self._parse_string() or self._parse_table() - if not kind and not isinstance(this, exp.Literal): - return self._parse_as_command(self._prev) - - return self.expression(exp.Refresh, this=this, kind=kind) - - def _parse_column_def_with_exists(self): - start = self._index - self._match(TokenType.COLUMN) - - exists_column = self._parse_exists(not_=True) - expression = self._parse_field_def() - - if not isinstance(expression, exp.ColumnDef): - self._retreat(start) - return None - - expression.set("exists", exists_column) - - return expression - - def _parse_add_column(self) -> t.Optional[exp.ColumnDef]: - if not self._prev.text.upper() == "ADD": - return None - - expression = self._parse_column_def_with_exists() - if not expression: - return None - - # https://docs.databricks.com/delta/update-schema.html#explicitly-update-schema-to-add-columns - if self._match_texts(("FIRST", "AFTER")): - position = self._prev.text - column_position = self.expression( - exp.ColumnPosition, this=self._parse_column(), position=position - ) - expression.set("position", column_position) - - return expression - - def _parse_drop_column(self) -> t.Optional[exp.Drop | exp.Command]: - drop = self._match(TokenType.DROP) and self._parse_drop() - if drop and not isinstance(drop, exp.Command): - drop.set("kind", drop.args.get("kind", "COLUMN")) - return drop - - # https://docs.aws.amazon.com/athena/latest/ug/alter-table-drop-partition.html - def _parse_drop_partition( - self, exists: t.Optional[bool] = None - ) -> exp.DropPartition: - return self.expression( - exp.DropPartition, - expressions=self._parse_csv(self._parse_partition), - exists=exists, - ) - - def _parse_alter_table_add(self) -> t.List[exp.Expression]: - def _parse_add_alteration() -> t.Optional[exp.Expression]: - self._match_text_seq("ADD") - if self._match_set(self.ADD_CONSTRAINT_TOKENS, advance=False): - return self.expression( - exp.AddConstraint, - expressions=self._parse_csv(self._parse_constraint), - ) - - column_def = self._parse_add_column() - if isinstance(column_def, exp.ColumnDef): - return column_def - - exists = self._parse_exists(not_=True) - if self._match_pair(TokenType.PARTITION, TokenType.L_PAREN, advance=False): - return self.expression( - exp.AddPartition, - exists=exists, - this=self._parse_field(any_token=True), - location=self._match_text_seq("LOCATION", advance=False) - and self._parse_property(), - ) - - return None - - if not self._match_set(self.ADD_CONSTRAINT_TOKENS, advance=False) and ( - not self.dialect.ALTER_TABLE_ADD_REQUIRED_FOR_EACH_COLUMN - or self._match_text_seq("COLUMNS") - ): - schema = self._parse_schema() - - return ( - ensure_list(schema) - if schema - else self._parse_csv(self._parse_column_def_with_exists) - ) - - return self._parse_csv(_parse_add_alteration) - - def _parse_alter_table_alter(self) -> t.Optional[exp.Expression]: - if self._match_texts(self.ALTER_ALTER_PARSERS): - return self.ALTER_ALTER_PARSERS[self._prev.text.upper()](self) - - # Many dialects support the ALTER [COLUMN] syntax, so if there is no - # keyword after ALTER we default to parsing this statement - self._match(TokenType.COLUMN) - column = self._parse_field(any_token=True) - - if self._match_pair(TokenType.DROP, TokenType.DEFAULT): - return self.expression(exp.AlterColumn, this=column, drop=True) - if self._match_pair(TokenType.SET, TokenType.DEFAULT): - return self.expression( - exp.AlterColumn, this=column, default=self._parse_disjunction() - ) - if self._match(TokenType.COMMENT): - return self.expression( - exp.AlterColumn, this=column, comment=self._parse_string() - ) - if self._match_text_seq("DROP", "NOT", "NULL"): - return self.expression( - exp.AlterColumn, - this=column, - drop=True, - allow_null=True, - ) - if self._match_text_seq("SET", "NOT", "NULL"): - return self.expression( - exp.AlterColumn, - this=column, - allow_null=False, - ) - - if self._match_text_seq("SET", "VISIBLE"): - return self.expression(exp.AlterColumn, this=column, visible="VISIBLE") - if self._match_text_seq("SET", "INVISIBLE"): - return self.expression(exp.AlterColumn, this=column, visible="INVISIBLE") - - self._match_text_seq("SET", "DATA") - self._match_text_seq("TYPE") - return self.expression( - exp.AlterColumn, - this=column, - dtype=self._parse_types(), - collate=self._match(TokenType.COLLATE) and self._parse_term(), - using=self._match(TokenType.USING) and self._parse_disjunction(), - ) - - def _parse_alter_diststyle(self) -> exp.AlterDistStyle: - if self._match_texts(("ALL", "EVEN", "AUTO")): - return self.expression( - exp.AlterDistStyle, this=exp.var(self._prev.text.upper()) - ) - - self._match_text_seq("KEY", "DISTKEY") - return self.expression(exp.AlterDistStyle, this=self._parse_column()) - - def _parse_alter_sortkey( - self, compound: t.Optional[bool] = None - ) -> exp.AlterSortKey: - if compound: - self._match_text_seq("SORTKEY") - - if self._match(TokenType.L_PAREN, advance=False): - return self.expression( - exp.AlterSortKey, - expressions=self._parse_wrapped_id_vars(), - compound=compound, - ) - - self._match_texts(("AUTO", "NONE")) - return self.expression( - exp.AlterSortKey, this=exp.var(self._prev.text.upper()), compound=compound - ) - - def _parse_alter_table_drop(self) -> t.List[exp.Expression]: - index = self._index - 1 - - partition_exists = self._parse_exists() - if self._match(TokenType.PARTITION, advance=False): - return self._parse_csv( - lambda: self._parse_drop_partition(exists=partition_exists) - ) - - self._retreat(index) - return self._parse_csv(self._parse_drop_column) - - def _parse_alter_table_rename( - self, - ) -> t.Optional[exp.AlterRename | exp.RenameColumn]: - if self._match(TokenType.COLUMN) or not self.ALTER_RENAME_REQUIRES_COLUMN: - exists = self._parse_exists() - old_column = self._parse_column() - to = self._match_text_seq("TO") - new_column = self._parse_column() - - if old_column is None or to is None or new_column is None: - return None - - return self.expression( - exp.RenameColumn, this=old_column, to=new_column, exists=exists - ) - - self._match_text_seq("TO") - return self.expression(exp.AlterRename, this=self._parse_table(schema=True)) - - def _parse_alter_table_set(self) -> exp.AlterSet: - alter_set = self.expression(exp.AlterSet) - - if self._match(TokenType.L_PAREN, advance=False) or self._match_text_seq( - "TABLE", "PROPERTIES" - ): - alter_set.set( - "expressions", self._parse_wrapped_csv(self._parse_assignment) - ) - elif self._match_text_seq("FILESTREAM_ON", advance=False): - alter_set.set("expressions", [self._parse_assignment()]) - elif self._match_texts(("LOGGED", "UNLOGGED")): - alter_set.set("option", exp.var(self._prev.text.upper())) - elif self._match_text_seq("WITHOUT") and self._match_texts(("CLUSTER", "OIDS")): - alter_set.set("option", exp.var(f"WITHOUT {self._prev.text.upper()}")) - elif self._match_text_seq("LOCATION"): - alter_set.set("location", self._parse_field()) - elif self._match_text_seq("ACCESS", "METHOD"): - alter_set.set("access_method", self._parse_field()) - elif self._match_text_seq("TABLESPACE"): - alter_set.set("tablespace", self._parse_field()) - elif self._match_text_seq("FILE", "FORMAT") or self._match_text_seq( - "FILEFORMAT" - ): - alter_set.set("file_format", [self._parse_field()]) - elif self._match_text_seq("STAGE_FILE_FORMAT"): - alter_set.set("file_format", self._parse_wrapped_options()) - elif self._match_text_seq("STAGE_COPY_OPTIONS"): - alter_set.set("copy_options", self._parse_wrapped_options()) - elif self._match_text_seq("TAG") or self._match_text_seq("TAGS"): - alter_set.set("tag", self._parse_csv(self._parse_assignment)) - else: - if self._match_text_seq("SERDE"): - alter_set.set("serde", self._parse_field()) - - properties = self._parse_wrapped(self._parse_properties, optional=True) - alter_set.set("expressions", [properties]) - - return alter_set - - def _parse_alter_session(self) -> exp.AlterSession: - """Parse ALTER SESSION SET/UNSET statements.""" - if self._match(TokenType.SET): - expressions = self._parse_csv(lambda: self._parse_set_item_assignment()) - return self.expression( - exp.AlterSession, expressions=expressions, unset=False - ) - - self._match_text_seq("UNSET") - expressions = self._parse_csv( - lambda: self.expression( - exp.SetItem, this=self._parse_id_var(any_token=True) - ) - ) - return self.expression(exp.AlterSession, expressions=expressions, unset=True) - - def _parse_alter(self) -> exp.Alter | exp.Command: - start = self._prev - - alter_token = self._match_set(self.ALTERABLES) and self._prev - if not alter_token: - return self._parse_as_command(start) - - exists = self._parse_exists() - only = self._match_text_seq("ONLY") - - if alter_token.token_type == TokenType.SESSION: - this = None - check = None - cluster = None - else: - this = self._parse_table( - schema=True, parse_partition=self.ALTER_TABLE_PARTITIONS - ) - check = self._match_text_seq("WITH", "CHECK") - cluster = self._parse_on_property() if self._match(TokenType.ON) else None - - if self._next: - self._advance() - - parser = self.ALTER_PARSERS.get(self._prev.text.upper()) if self._prev else None - if parser: - actions = ensure_list(parser(self)) - not_valid = self._match_text_seq("NOT", "VALID") - options = self._parse_csv(self._parse_property) - cascade = ( - self.dialect.ALTER_TABLE_SUPPORTS_CASCADE - and self._match_text_seq("CASCADE") - ) - - if not self._curr and actions: - return self.expression( - exp.Alter, - this=this, - kind=alter_token.text.upper(), - exists=exists, - actions=actions, - only=only, - options=options, - cluster=cluster, - not_valid=not_valid, - check=check, - cascade=cascade, - ) - - return self._parse_as_command(start) - - def _parse_analyze(self) -> exp.Analyze | exp.Command: - start = self._prev - # https://duckdb.org/docs/sql/statements/analyze - if not self._curr: - return self.expression(exp.Analyze) - - options = [] - while self._match_texts(self.ANALYZE_STYLES): - if self._prev.text.upper() == "BUFFER_USAGE_LIMIT": - options.append(f"BUFFER_USAGE_LIMIT {self._parse_number()}") - else: - options.append(self._prev.text.upper()) - - this: t.Optional[exp.Expression] = None - inner_expression: t.Optional[exp.Expression] = None - - kind = self._curr and self._curr.text.upper() - - if self._match(TokenType.TABLE) or self._match(TokenType.INDEX): - this = self._parse_table_parts() - elif self._match_text_seq("TABLES"): - if self._match_set((TokenType.FROM, TokenType.IN)): - kind = f"{kind} {self._prev.text.upper()}" - this = self._parse_table(schema=True, is_db_reference=True) - elif self._match_text_seq("DATABASE"): - this = self._parse_table(schema=True, is_db_reference=True) - elif self._match_text_seq("CLUSTER"): - this = self._parse_table() - # Try matching inner expr keywords before fallback to parse table. - elif self._match_texts(self.ANALYZE_EXPRESSION_PARSERS): - kind = None - inner_expression = self.ANALYZE_EXPRESSION_PARSERS[self._prev.text.upper()]( - self - ) - else: - # Empty kind https://prestodb.io/docs/current/sql/analyze.html - kind = None - this = self._parse_table_parts() - - partition = self._try_parse(self._parse_partition) - if not partition and self._match_texts(self.PARTITION_KEYWORDS): - return self._parse_as_command(start) - - # https://docs.starrocks.io/docs/sql-reference/sql-statements/cbo_stats/ANALYZE_TABLE/ - if self._match_text_seq("WITH", "SYNC", "MODE") or self._match_text_seq( - "WITH", "ASYNC", "MODE" - ): - mode = f"WITH {self._tokens[self._index - 2].text.upper()} MODE" - else: - mode = None - - if self._match_texts(self.ANALYZE_EXPRESSION_PARSERS): - inner_expression = self.ANALYZE_EXPRESSION_PARSERS[self._prev.text.upper()]( - self - ) - - properties = self._parse_properties() - return self.expression( - exp.Analyze, - kind=kind, - this=this, - mode=mode, - partition=partition, - properties=properties, - expression=inner_expression, - options=options, - ) - - # https://spark.apache.org/docs/3.5.1/sql-ref-syntax-aux-analyze-table.html - def _parse_analyze_statistics(self) -> exp.AnalyzeStatistics: - this = None - kind = self._prev.text.upper() - option = self._prev.text.upper() if self._match_text_seq("DELTA") else None - expressions = [] - - if not self._match_text_seq("STATISTICS"): - self.raise_error("Expecting token STATISTICS") - - if self._match_text_seq("NOSCAN"): - this = "NOSCAN" - elif self._match(TokenType.FOR): - if self._match_text_seq("ALL", "COLUMNS"): - this = "FOR ALL COLUMNS" - if self._match_texts("COLUMNS"): - this = "FOR COLUMNS" - expressions = self._parse_csv(self._parse_column_reference) - elif self._match_text_seq("SAMPLE"): - sample = self._parse_number() - expressions = [ - self.expression( - exp.AnalyzeSample, - sample=sample, - kind=self._prev.text.upper() - if self._match(TokenType.PERCENT) - else None, - ) - ] - - return self.expression( - exp.AnalyzeStatistics, - kind=kind, - option=option, - this=this, - expressions=expressions, - ) - - # https://docs.oracle.com/en/database/oracle/oracle-database/21/sqlrf/ANALYZE.html - def _parse_analyze_validate(self) -> exp.AnalyzeValidate: - kind = None - this = None - expression: t.Optional[exp.Expression] = None - if self._match_text_seq("REF", "UPDATE"): - kind = "REF" - this = "UPDATE" - if self._match_text_seq("SET", "DANGLING", "TO", "NULL"): - this = "UPDATE SET DANGLING TO NULL" - elif self._match_text_seq("STRUCTURE"): - kind = "STRUCTURE" - if self._match_text_seq("CASCADE", "FAST"): - this = "CASCADE FAST" - elif self._match_text_seq("CASCADE", "COMPLETE") and self._match_texts( - ("ONLINE", "OFFLINE") - ): - this = f"CASCADE COMPLETE {self._prev.text.upper()}" - expression = self._parse_into() - - return self.expression( - exp.AnalyzeValidate, kind=kind, this=this, expression=expression - ) - - def _parse_analyze_columns(self) -> t.Optional[exp.AnalyzeColumns]: - this = self._prev.text.upper() - if self._match_text_seq("COLUMNS"): - return self.expression( - exp.AnalyzeColumns, this=f"{this} {self._prev.text.upper()}" - ) - return None - - def _parse_analyze_delete(self) -> t.Optional[exp.AnalyzeDelete]: - kind = self._prev.text.upper() if self._match_text_seq("SYSTEM") else None - if self._match_text_seq("STATISTICS"): - return self.expression(exp.AnalyzeDelete, kind=kind) - return None - - def _parse_analyze_list(self) -> t.Optional[exp.AnalyzeListChainedRows]: - if self._match_text_seq("CHAINED", "ROWS"): - return self.expression( - exp.AnalyzeListChainedRows, expression=self._parse_into() - ) - return None - - # https://dev.mysql.com/doc/refman/8.4/en/analyze-table.html - def _parse_analyze_histogram(self) -> exp.AnalyzeHistogram: - this = self._prev.text.upper() - expression: t.Optional[exp.Expression] = None - expressions = [] - update_options = None - - if self._match_text_seq("HISTOGRAM", "ON"): - expressions = self._parse_csv(self._parse_column_reference) - with_expressions = [] - while self._match(TokenType.WITH): - # https://docs.starrocks.io/docs/sql-reference/sql-statements/cbo_stats/ANALYZE_TABLE/ - if self._match_texts(("SYNC", "ASYNC")): - if self._match_text_seq("MODE", advance=False): - with_expressions.append(f"{self._prev.text.upper()} MODE") - self._advance() - else: - buckets = self._parse_number() - if self._match_text_seq("BUCKETS"): - with_expressions.append(f"{buckets} BUCKETS") - if with_expressions: - expression = self.expression( - exp.AnalyzeWith, expressions=with_expressions - ) - - if self._match_texts(("MANUAL", "AUTO")) and self._match( - TokenType.UPDATE, advance=False - ): - update_options = self._prev.text.upper() - self._advance() - elif self._match_text_seq("USING", "DATA"): - expression = self.expression(exp.UsingData, this=self._parse_string()) - - return self.expression( - exp.AnalyzeHistogram, - this=this, - expressions=expressions, - expression=expression, - update_options=update_options, - ) - - def _parse_merge(self) -> exp.Merge: - self._match(TokenType.INTO) - target = self._parse_table() - - if target and self._match(TokenType.ALIAS, advance=False): - target.set("alias", self._parse_table_alias()) - - self._match(TokenType.USING) - using = self._parse_table() - - return self.expression( - exp.Merge, - this=target, - using=using, - on=self._match(TokenType.ON) and self._parse_disjunction(), - using_cond=self._match(TokenType.USING) and self._parse_using_identifiers(), - whens=self._parse_when_matched(), - returning=self._parse_returning(), - ) - - def _parse_when_matched(self) -> exp.Whens: - whens = [] - - while self._match(TokenType.WHEN): - matched = not self._match(TokenType.NOT) - self._match_text_seq("MATCHED") - source = ( - False - if self._match_text_seq("BY", "TARGET") - else self._match_text_seq("BY", "SOURCE") - ) - condition = ( - self._parse_disjunction() if self._match(TokenType.AND) else None - ) - - self._match(TokenType.THEN) - - if self._match(TokenType.INSERT): - this = self._parse_star() - if this: - then: t.Optional[exp.Expression] = self.expression( - exp.Insert, this=this - ) - else: - then = self.expression( - exp.Insert, - this=exp.var("ROW") - if self._match_text_seq("ROW") - else self._parse_value(values=False), - expression=self._match_text_seq("VALUES") - and self._parse_value(), - ) - elif self._match(TokenType.UPDATE): - expressions = self._parse_star() - if expressions: - then = self.expression(exp.Update, expressions=expressions) - else: - then = self.expression( - exp.Update, - expressions=self._match(TokenType.SET) - and self._parse_csv(self._parse_equality), - ) - elif self._match(TokenType.DELETE): - then = self.expression(exp.Var, this=self._prev.text) - else: - then = self._parse_var_from_options(self.CONFLICT_ACTIONS) - - whens.append( - self.expression( - exp.When, - matched=matched, - source=source, - condition=condition, - then=then, - ) - ) - return self.expression(exp.Whens, expressions=whens) - - def _parse_show(self) -> t.Optional[exp.Expression]: - parser = self._find_parser(self.SHOW_PARSERS, self.SHOW_TRIE) - if parser: - return parser(self) - return self._parse_as_command(self._prev) - - def _parse_set_item_assignment( - self, kind: t.Optional[str] = None - ) -> t.Optional[exp.Expression]: - index = self._index - - if kind in ("GLOBAL", "SESSION") and self._match_text_seq("TRANSACTION"): - return self._parse_set_transaction(global_=kind == "GLOBAL") - - left = self._parse_primary() or self._parse_column() - assignment_delimiter = self._match_texts(self.SET_ASSIGNMENT_DELIMITERS) - - if not left or ( - self.SET_REQUIRES_ASSIGNMENT_DELIMITER and not assignment_delimiter - ): - self._retreat(index) - return None - - right = self._parse_statement() or self._parse_id_var() - if isinstance(right, (exp.Column, exp.Identifier)): - right = exp.var(right.name) - - this = self.expression(exp.EQ, this=left, expression=right) - return self.expression(exp.SetItem, this=this, kind=kind) - - def _parse_set_transaction(self, global_: bool = False) -> exp.Expression: - self._match_text_seq("TRANSACTION") - characteristics = self._parse_csv( - lambda: self._parse_var_from_options(self.TRANSACTION_CHARACTERISTICS) - ) - return self.expression( - exp.SetItem, - expressions=characteristics, - kind="TRANSACTION", - global_=global_, - ) - - def _parse_set_item(self) -> t.Optional[exp.Expression]: - parser = self._find_parser(self.SET_PARSERS, self.SET_TRIE) - return parser(self) if parser else self._parse_set_item_assignment(kind=None) - - def _parse_set( - self, unset: bool = False, tag: bool = False - ) -> exp.Set | exp.Command: - index = self._index - set_ = self.expression( - exp.Set, - expressions=self._parse_csv(self._parse_set_item), - unset=unset, - tag=tag, - ) - - if self._curr: - self._retreat(index) - return self._parse_as_command(self._prev) - - return set_ - - def _parse_var_from_options( - self, options: OPTIONS_TYPE, raise_unmatched: bool = True - ) -> t.Optional[exp.Var]: - start = self._curr - if not start: - return None - - option = start.text.upper() - continuations = options.get(option) - - index = self._index - self._advance() - for keywords in continuations or []: - if isinstance(keywords, str): - keywords = (keywords,) - - if self._match_text_seq(*keywords): - option = f"{option} {' '.join(keywords)}" - break - else: - if continuations or continuations is None: - if raise_unmatched: - self.raise_error(f"Unknown option {option}") - - self._retreat(index) - return None - - return exp.var(option) - - def _parse_as_command(self, start: Token) -> exp.Command: - while self._curr: - self._advance() - text = self._find_sql(start, self._prev) - size = len(start.text) - self._warn_unsupported() - return exp.Command(this=text[:size], expression=text[size:]) - - def _parse_dict_property(self, this: str) -> exp.DictProperty: - settings = [] - - self._match_l_paren() - kind = self._parse_id_var() - - if self._match(TokenType.L_PAREN): - while True: - key = self._parse_id_var() - value = self._parse_primary() - if not key and value is None: - break - settings.append( - self.expression(exp.DictSubProperty, this=key, value=value) - ) - self._match(TokenType.R_PAREN) - - self._match_r_paren() - - return self.expression( - exp.DictProperty, - this=this, - kind=kind.this if kind else None, - settings=settings, - ) - - def _parse_dict_range(self, this: str) -> exp.DictRange: - self._match_l_paren() - has_min = self._match_text_seq("MIN") - if has_min: - min = self._parse_var() or self._parse_primary() - self._match_text_seq("MAX") - max = self._parse_var() or self._parse_primary() - else: - max = self._parse_var() or self._parse_primary() - min = exp.Literal.number(0) - self._match_r_paren() - return self.expression(exp.DictRange, this=this, min=min, max=max) - - def _parse_comprehension( - self, this: t.Optional[exp.Expression] - ) -> t.Optional[exp.Comprehension]: - index = self._index - expression = self._parse_column() - position = self._match(TokenType.COMMA) and self._parse_column() - - if not self._match(TokenType.IN): - self._retreat(index - 1) - return None - iterator = self._parse_column() - condition = self._parse_disjunction() if self._match_text_seq("IF") else None - return self.expression( - exp.Comprehension, - this=this, - expression=expression, - position=position, - iterator=iterator, - condition=condition, - ) - - def _parse_heredoc(self) -> t.Optional[exp.Heredoc]: - if self._match(TokenType.HEREDOC_STRING): - return self.expression(exp.Heredoc, this=self._prev.text) - - if not self._match_text_seq("$"): - return None - - tags = ["$"] - tag_text = None - - if self._is_connected(): - self._advance() - tags.append(self._prev.text.upper()) - else: - self.raise_error("No closing $ found") - - if tags[-1] != "$": - if self._is_connected() and self._match_text_seq("$"): - tag_text = tags[-1] - tags.append("$") - else: - self.raise_error("No closing $ found") - - heredoc_start = self._curr - - while self._curr: - if self._match_text_seq(*tags, advance=False): - this = self._find_sql(heredoc_start, self._prev) - self._advance(len(tags)) - return self.expression(exp.Heredoc, this=this, tag=tag_text) - - self._advance() - - self.raise_error(f"No closing {''.join(tags)} found") - return None - - def _find_parser( - self, parsers: t.Dict[str, t.Callable], trie: t.Dict - ) -> t.Optional[t.Callable]: - if not self._curr: - return None - - index = self._index - this = [] - while True: - # The current token might be multiple words - curr = self._curr.text.upper() - key = curr.split(" ") - this.append(curr) - - self._advance() - result, trie = in_trie(trie, key) - if result == TrieResult.FAILED: - break - - if result == TrieResult.EXISTS: - subparser = parsers[" ".join(this)] - return subparser - - self._retreat(index) - return None - - def _match(self, token_type, advance=True, expression=None): - if not self._curr: - return None - - if self._curr.token_type == token_type: - if advance: - self._advance() - self._add_comments(expression) - return True - - return None - - def _match_set(self, types, advance=True): - if not self._curr: - return None - - if self._curr.token_type in types: - if advance: - self._advance() - return True - - return None - - def _match_pair(self, token_type_a, token_type_b, advance=True): - if not self._curr or not self._next: - return None - - if ( - self._curr.token_type == token_type_a - and self._next.token_type == token_type_b - ): - if advance: - self._advance(2) - return True - - return None - - def _match_l_paren(self, expression: t.Optional[exp.Expression] = None) -> None: - if not self._match(TokenType.L_PAREN, expression=expression): - self.raise_error("Expecting (") - - def _match_r_paren(self, expression: t.Optional[exp.Expression] = None) -> None: - if not self._match(TokenType.R_PAREN, expression=expression): - self.raise_error("Expecting )") - - def _match_texts(self, texts, advance=True): - if ( - self._curr - and self._curr.token_type != TokenType.STRING - and self._curr.text.upper() in texts - ): - if advance: - self._advance() - return True - return None - - def _match_text_seq(self, *texts, advance=True): - index = self._index - for text in texts: - if ( - self._curr - and self._curr.token_type != TokenType.STRING - and self._curr.text.upper() == text - ): - self._advance() - else: - self._retreat(index) - return None - - if not advance: - self._retreat(index) - - return True - - def _replace_lambda( - self, node: t.Optional[exp.Expression], expressions: t.List[exp.Expression] - ) -> t.Optional[exp.Expression]: - if not node: - return node - - lambda_types = {e.name: e.args.get("to") or False for e in expressions} - - for column in node.find_all(exp.Column): - typ = lambda_types.get(column.parts[0].name) - if typ is not None: - dot_or_id = column.to_dot() if column.table else column.this - - if typ: - dot_or_id = self.expression( - exp.Cast, - this=dot_or_id, - to=typ, - ) - - parent = column.parent - - while isinstance(parent, exp.Dot): - if not isinstance(parent.parent, exp.Dot): - parent.replace(dot_or_id) - break - parent = parent.parent - else: - if column is node: - node = dot_or_id - else: - column.replace(dot_or_id) - return node - - def _parse_truncate_table(self) -> t.Optional[exp.TruncateTable] | exp.Expression: - start = self._prev - - # Not to be confused with TRUNCATE(number, decimals) function call - if self._match(TokenType.L_PAREN): - self._retreat(self._index - 2) - return self._parse_function() - - # Clickhouse supports TRUNCATE DATABASE as well - is_database = self._match(TokenType.DATABASE) - - self._match(TokenType.TABLE) - - exists = self._parse_exists(not_=False) - - expressions = self._parse_csv( - lambda: self._parse_table(schema=True, is_db_reference=is_database) - ) - - cluster = self._parse_on_property() if self._match(TokenType.ON) else None - - if self._match_text_seq("RESTART", "IDENTITY"): - identity = "RESTART" - elif self._match_text_seq("CONTINUE", "IDENTITY"): - identity = "CONTINUE" - else: - identity = None - - if self._match_text_seq("CASCADE") or self._match_text_seq("RESTRICT"): - option = self._prev.text - else: - option = None - - partition = self._parse_partition() - - # Fallback case - if self._curr: - return self._parse_as_command(start) - - return self.expression( - exp.TruncateTable, - expressions=expressions, - is_database=is_database, - exists=exists, - cluster=cluster, - identity=identity, - option=option, - partition=partition, - ) - - def _parse_with_operator(self) -> t.Optional[exp.Expression]: - this = self._parse_ordered(self._parse_opclass) - - if not self._match(TokenType.WITH): - return this - - op = self._parse_var(any_token=True) - - return self.expression(exp.WithOperator, this=this, op=op) - - def _parse_wrapped_options(self) -> t.List[t.Optional[exp.Expression]]: - self._match(TokenType.EQ) - self._match(TokenType.L_PAREN) - - opts: t.List[t.Optional[exp.Expression]] = [] - option: exp.Expression | None - while self._curr and not self._match(TokenType.R_PAREN): - if self._match_text_seq("FORMAT_NAME", "="): - # The FORMAT_NAME can be set to an identifier for Snowflake and T-SQL - option = self._parse_format_name() - else: - option = self._parse_property() - - if option is None: - self.raise_error("Unable to parse option") - break - - opts.append(option) - - return opts - - def _parse_copy_parameters(self) -> t.List[exp.CopyParameter]: - sep = TokenType.COMMA if self.dialect.COPY_PARAMS_ARE_CSV else None - - options = [] - while self._curr and not self._match(TokenType.R_PAREN, advance=False): - option = self._parse_var(any_token=True) - prev = self._prev.text.upper() - - # Different dialects might separate options and values by white space, "=" and "AS" - self._match(TokenType.EQ) - self._match(TokenType.ALIAS) - - param = self.expression(exp.CopyParameter, this=option) - - if prev in self.COPY_INTO_VARLEN_OPTIONS and self._match( - TokenType.L_PAREN, advance=False - ): - # Snowflake FILE_FORMAT case, Databricks COPY & FORMAT options - param.set("expressions", self._parse_wrapped_options()) - elif prev == "FILE_FORMAT": - # T-SQL's external file format case - param.set("expression", self._parse_field()) - elif ( - prev == "FORMAT" - and self._prev.token_type == TokenType.ALIAS - and self._match_texts(("AVRO", "JSON")) - ): - param.set("this", exp.var(f"FORMAT AS {self._prev.text.upper()}")) - param.set("expression", self._parse_field()) - else: - param.set( - "expression", self._parse_unquoted_field() or self._parse_bracket() - ) - - options.append(param) - self._match(sep) - - return options - - def _parse_credentials(self) -> t.Optional[exp.Credentials]: - expr = self.expression(exp.Credentials) - - if self._match_text_seq("STORAGE_INTEGRATION", "="): - expr.set("storage", self._parse_field()) - if self._match_text_seq("CREDENTIALS"): - # Snowflake case: CREDENTIALS = (...), Redshift case: CREDENTIALS - creds = ( - self._parse_wrapped_options() - if self._match(TokenType.EQ) - else self._parse_field() - ) - expr.set("credentials", creds) - if self._match_text_seq("ENCRYPTION"): - expr.set("encryption", self._parse_wrapped_options()) - if self._match_text_seq("IAM_ROLE"): - expr.set( - "iam_role", - exp.var(self._prev.text) - if self._match(TokenType.DEFAULT) - else self._parse_field(), - ) - if self._match_text_seq("REGION"): - expr.set("region", self._parse_field()) - - return expr - - def _parse_file_location(self) -> t.Optional[exp.Expression]: - return self._parse_field() - - def _parse_copy(self) -> exp.Copy | exp.Command: - start = self._prev - - self._match(TokenType.INTO) - - this = ( - self._parse_select(nested=True, parse_subquery_alias=False) - if self._match(TokenType.L_PAREN, advance=False) - else self._parse_table(schema=True) - ) - - kind = self._match(TokenType.FROM) or not self._match_text_seq("TO") - - files = self._parse_csv(self._parse_file_location) - if self._match(TokenType.EQ, advance=False): - # Backtrack one token since we've consumed the lhs of a parameter assignment here. - # This can happen for Snowflake dialect. Instead, we'd like to parse the parameter - # list via `_parse_wrapped(..)` below. - self._advance(-1) - files = [] - - credentials = self._parse_credentials() - - self._match_text_seq("WITH") - - params = self._parse_wrapped(self._parse_copy_parameters, optional=True) - - # Fallback case - if self._curr: - return self._parse_as_command(start) - - return self.expression( - exp.Copy, - this=this, - kind=kind, - credentials=credentials, - files=files, - params=params, - ) - - def _parse_normalize(self) -> exp.Normalize: - return self.expression( - exp.Normalize, - this=self._parse_bitwise(), - form=self._match(TokenType.COMMA) and self._parse_var(), - ) - - def _parse_ceil_floor(self, expr_type: t.Type[TCeilFloor]) -> TCeilFloor: - args = self._parse_csv(lambda: self._parse_lambda()) - - this = seq_get(args, 0) - decimals = seq_get(args, 1) - - return expr_type( - this=this, - decimals=decimals, - to=self._match_text_seq("TO") and self._parse_var(), - ) - - def _parse_star_ops(self) -> t.Optional[exp.Expression]: - star_token = self._prev - - if self._match_text_seq("COLUMNS", "(", advance=False): - this = self._parse_function() - if isinstance(this, exp.Columns): - this.set("unpack", True) - return this - - return self.expression( - exp.Star, - except_=self._parse_star_op("EXCEPT", "EXCLUDE"), - replace=self._parse_star_op("REPLACE"), - rename=self._parse_star_op("RENAME"), - ).update_positions(star_token) - - def _parse_grant_privilege(self) -> t.Optional[exp.GrantPrivilege]: - privilege_parts = [] - - # Keep consuming consecutive keywords until comma (end of this privilege) or ON - # (end of privilege list) or L_PAREN (start of column list) are met - while self._curr and not self._match_set( - self.PRIVILEGE_FOLLOW_TOKENS, advance=False - ): - privilege_parts.append(self._curr.text.upper()) - self._advance() - - this = exp.var(" ".join(privilege_parts)) - expressions = ( - self._parse_wrapped_csv(self._parse_column) - if self._match(TokenType.L_PAREN, advance=False) - else None - ) - - return self.expression(exp.GrantPrivilege, this=this, expressions=expressions) - - def _parse_grant_principal(self) -> t.Optional[exp.GrantPrincipal]: - kind = self._match_texts(("ROLE", "GROUP")) and self._prev.text.upper() - principal = self._parse_id_var() - - if not principal: - return None - - return self.expression(exp.GrantPrincipal, this=principal, kind=kind) - - def _parse_grant_revoke_common( - self, - ) -> t.Tuple[t.Optional[t.List], t.Optional[str], t.Optional[exp.Expression]]: - privileges = self._parse_csv(self._parse_grant_privilege) - - self._match(TokenType.ON) - kind = self._match_set(self.CREATABLES) and self._prev.text.upper() - - # Attempt to parse the securable e.g. MySQL allows names - # such as "foo.*", "*.*" which are not easily parseable yet - securable = self._try_parse(self._parse_table_parts) - - return privileges, kind, securable - - def _parse_grant(self) -> exp.Grant | exp.Command: - start = self._prev - - privileges, kind, securable = self._parse_grant_revoke_common() - - if not securable or not self._match_text_seq("TO"): - return self._parse_as_command(start) - - principals = self._parse_csv(self._parse_grant_principal) - - grant_option = self._match_text_seq("WITH", "GRANT", "OPTION") - - if self._curr: - return self._parse_as_command(start) - - return self.expression( - exp.Grant, - privileges=privileges, - kind=kind, - securable=securable, - principals=principals, - grant_option=grant_option, - ) - - def _parse_revoke(self) -> exp.Revoke | exp.Command: - start = self._prev - - grant_option = self._match_text_seq("GRANT", "OPTION", "FOR") - - privileges, kind, securable = self._parse_grant_revoke_common() - - if not securable or not self._match_text_seq("FROM"): - return self._parse_as_command(start) - - principals = self._parse_csv(self._parse_grant_principal) - - cascade = None - if self._match_texts(("CASCADE", "RESTRICT")): - cascade = self._prev.text.upper() - - if self._curr: - return self._parse_as_command(start) - - return self.expression( - exp.Revoke, - privileges=privileges, - kind=kind, - securable=securable, - principals=principals, - grant_option=grant_option, - cascade=cascade, - ) - - def _parse_overlay(self) -> exp.Overlay: - def _parse_overlay_arg(text: str) -> t.Optional[exp.Expression]: - return ( - self._match(TokenType.COMMA) or self._match_text_seq(text) - ) and self._parse_bitwise() - - return self.expression( - exp.Overlay, - this=self._parse_bitwise(), - expression=_parse_overlay_arg("PLACING"), - from_=_parse_overlay_arg("FROM"), - for_=_parse_overlay_arg("FOR"), - ) - - def _parse_format_name(self) -> exp.Property: - # Note: Although not specified in the docs, Snowflake does accept a string/identifier - # for FILE_FORMAT = - return self.expression( - exp.Property, - this=exp.var("FORMAT_NAME"), - value=self._parse_string() or self._parse_table_parts(), - ) - - def _parse_max_min_by(self, expr_type: t.Type[exp.AggFunc]) -> exp.AggFunc: - args: t.List[exp.Expression] = [] - - if self._match(TokenType.DISTINCT): - args.append( - self.expression(exp.Distinct, expressions=[self._parse_lambda()]) - ) - self._match(TokenType.COMMA) - - args.extend(self._parse_function_args()) - - return self.expression( - expr_type, - this=seq_get(args, 0), - expression=seq_get(args, 1), - count=seq_get(args, 2), - ) - - def _identifier_expression( - self, token: t.Optional[Token] = None, **kwargs: t.Any - ) -> exp.Identifier: - return self.expression(exp.Identifier, token=token or self._prev, **kwargs) - - def _build_pipe_cte( - self, - query: exp.Query, - expressions: t.List[exp.Expression], - alias_cte: t.Optional[exp.TableAlias] = None, - ) -> exp.Select: - new_cte: t.Optional[t.Union[str, exp.TableAlias]] - if alias_cte: - new_cte = alias_cte - else: - self._pipe_cte_counter += 1 - new_cte = f"__tmp{self._pipe_cte_counter}" - - with_ = query.args.get("with_") - ctes = with_.pop() if with_ else None - - new_select = exp.select(*expressions, copy=False).from_(new_cte, copy=False) - if ctes: - new_select.set("with_", ctes) - - return new_select.with_(new_cte, as_=query, copy=False) - - def _parse_pipe_syntax_select(self, query: exp.Select) -> exp.Select: - select = self._parse_select(consume_pipe=False) - if not select: - return query - - return self._build_pipe_cte( - query=query.select(*select.expressions, append=False), - expressions=[exp.Star()], - ) - - def _parse_pipe_syntax_limit(self, query: exp.Select) -> exp.Select: - limit = self._parse_limit() - offset = self._parse_offset() - if limit: - curr_limit = query.args.get("limit", limit) - if curr_limit.expression.to_py() >= limit.expression.to_py(): - query.limit(limit, copy=False) - if offset: - curr_offset = query.args.get("offset") - curr_offset = curr_offset.expression.to_py() if curr_offset else 0 - query.offset( - exp.Literal.number(curr_offset + offset.expression.to_py()), copy=False - ) - - return query - - def _parse_pipe_syntax_aggregate_fields(self) -> t.Optional[exp.Expression]: - this = self._parse_disjunction() - if self._match_text_seq("GROUP", "AND", advance=False): - return this - - this = self._parse_alias(this) - - if self._match_set((TokenType.ASC, TokenType.DESC), advance=False): - return self._parse_ordered(lambda: this) - - return this - - def _parse_pipe_syntax_aggregate_group_order_by( - self, query: exp.Select, group_by_exists: bool = True - ) -> exp.Select: - expr = self._parse_csv(self._parse_pipe_syntax_aggregate_fields) - aggregates_or_groups, orders = [], [] - for element in expr: - if isinstance(element, exp.Ordered): - this = element.this - if isinstance(this, exp.Alias): - element.set("this", this.args["alias"]) - orders.append(element) - else: - this = element - aggregates_or_groups.append(this) - - if group_by_exists: - query.select(*aggregates_or_groups, copy=False).group_by( - *[ - projection.args.get("alias", projection) - for projection in aggregates_or_groups - ], - copy=False, - ) - else: - query.select(*aggregates_or_groups, append=False, copy=False) - - if orders: - return query.order_by(*orders, append=False, copy=False) - - return query - - def _parse_pipe_syntax_aggregate(self, query: exp.Select) -> exp.Select: - self._match_text_seq("AGGREGATE") - query = self._parse_pipe_syntax_aggregate_group_order_by( - query, group_by_exists=False - ) - - if self._match(TokenType.GROUP_BY) or ( - self._match_text_seq("GROUP", "AND") and self._match(TokenType.ORDER_BY) - ): - query = self._parse_pipe_syntax_aggregate_group_order_by(query) - - return self._build_pipe_cte(query=query, expressions=[exp.Star()]) - - def _parse_pipe_syntax_set_operator( - self, query: exp.Query - ) -> t.Optional[exp.Query]: - first_setop = self.parse_set_operation(this=query) - if not first_setop: - return None - - def _parse_and_unwrap_query() -> t.Optional[exp.Select]: - expr = self._parse_paren() - return expr.assert_is(exp.Subquery).unnest() if expr else None - - first_setop.this.pop() - - setops = [ - first_setop.expression.pop().assert_is(exp.Subquery).unnest(), - *self._parse_csv(_parse_and_unwrap_query), - ] - - query = self._build_pipe_cte(query=query, expressions=[exp.Star()]) - with_ = query.args.get("with_") - ctes = with_.pop() if with_ else None - - if isinstance(first_setop, exp.Union): - query = query.union(*setops, copy=False, **first_setop.args) - elif isinstance(first_setop, exp.Except): - query = query.except_(*setops, copy=False, **first_setop.args) - else: - query = query.intersect(*setops, copy=False, **first_setop.args) - - query.set("with_", ctes) - - return self._build_pipe_cte(query=query, expressions=[exp.Star()]) - - def _parse_pipe_syntax_join(self, query: exp.Query) -> t.Optional[exp.Query]: - join = self._parse_join() - if not join: - return None - - if isinstance(query, exp.Select): - return query.join(join, copy=False) - - return query - - def _parse_pipe_syntax_pivot(self, query: exp.Select) -> exp.Select: - pivots = self._parse_pivots() - if not pivots: - return query - - from_ = query.args.get("from_") - if from_: - from_.this.set("pivots", pivots) - - return self._build_pipe_cte(query=query, expressions=[exp.Star()]) - - def _parse_pipe_syntax_extend(self, query: exp.Select) -> exp.Select: - self._match_text_seq("EXTEND") - query.select( - *[exp.Star(), *self._parse_expressions()], append=False, copy=False - ) - return self._build_pipe_cte(query=query, expressions=[exp.Star()]) - - def _parse_pipe_syntax_tablesample(self, query: exp.Select) -> exp.Select: - sample = self._parse_table_sample() - - with_ = query.args.get("with_") - if with_: - with_.expressions[-1].this.set("sample", sample) - else: - query.set("sample", sample) - - return query - - def _parse_pipe_syntax_query(self, query: exp.Query) -> t.Optional[exp.Query]: - if isinstance(query, exp.Subquery): - query = exp.select("*").from_(query, copy=False) - - if not query.args.get("from_"): - query = exp.select("*").from_(query.subquery(copy=False), copy=False) - - while self._match(TokenType.PIPE_GT): - start = self._curr - parser = self.PIPE_SYNTAX_TRANSFORM_PARSERS.get(self._curr.text.upper()) - if not parser: - # The set operators (UNION, etc) and the JOIN operator have a few common starting - # keywords, making it tricky to disambiguate them without lookahead. The approach - # here is to try and parse a set operation and if that fails, then try to parse a - # join operator. If that fails as well, then the operator is not supported. - parsed_query = self._parse_pipe_syntax_set_operator(query) - parsed_query = parsed_query or self._parse_pipe_syntax_join(query) - if not parsed_query: - self._retreat(start) - self.raise_error( - f"Unsupported pipe syntax operator: '{start.text.upper()}'." - ) - break - query = parsed_query - else: - query = parser(self, query) - - return query - - def _parse_declareitem(self) -> t.Optional[exp.DeclareItem]: - vars = self._parse_csv(self._parse_id_var) - if not vars: - return None - - return self.expression( - exp.DeclareItem, - this=vars, - kind=self._parse_types(), - default=self._match(TokenType.DEFAULT) and self._parse_bitwise(), - ) - - def _parse_declare(self) -> exp.Declare | exp.Command: - start = self._prev - expressions = self._try_parse(lambda: self._parse_csv(self._parse_declareitem)) - - if not expressions or self._curr: - return self._parse_as_command(start) - - return self.expression(exp.Declare, expressions=expressions) - - def build_cast(self, strict: bool, **kwargs) -> exp.Cast: - exp_class = exp.Cast if strict else exp.TryCast - - if exp_class == exp.TryCast: - kwargs["requires_string"] = self.dialect.TRY_CAST_REQUIRES_STRING - - return self.expression(exp_class, **kwargs) - - def _parse_json_value(self) -> exp.JSONValue: - this = self._parse_bitwise() - self._match(TokenType.COMMA) - path = self._parse_bitwise() - - returning = self._match(TokenType.RETURNING) and self._parse_type() - - return self.expression( - exp.JSONValue, - this=this, - path=self.dialect.to_json_path(path), - returning=returning, - on_condition=self._parse_on_condition(), - ) - - def _parse_group_concat(self) -> t.Optional[exp.Expression]: - def concat_exprs( - node: t.Optional[exp.Expression], exprs: t.List[exp.Expression] - ) -> exp.Expression: - if isinstance(node, exp.Distinct) and len(node.expressions) > 1: - concat_exprs = [ - self.expression( - exp.Concat, - expressions=node.expressions, - safe=True, - coalesce=self.dialect.CONCAT_COALESCE, - ) - ] - node.set("expressions", concat_exprs) - return node - if len(exprs) == 1: - return exprs[0] - return self.expression( - exp.Concat, - expressions=args, - safe=True, - coalesce=self.dialect.CONCAT_COALESCE, - ) - - args = self._parse_csv(self._parse_lambda) - - if args: - order = args[-1] if isinstance(args[-1], exp.Order) else None - - if order: - # Order By is the last (or only) expression in the list and has consumed the 'expr' before it, - # remove 'expr' from exp.Order and add it back to args - args[-1] = order.this - order.set("this", concat_exprs(order.this, args)) - - this = order or concat_exprs(args[0], args) - else: - this = None - - separator = self._parse_field() if self._match(TokenType.SEPARATOR) else None - - return self.expression(exp.GroupConcat, this=this, separator=separator) - - def _parse_initcap(self) -> exp.Initcap: - expr = exp.Initcap.from_arg_list(self._parse_function_args()) - - # attach dialect's default delimiters - if expr.args.get("expression") is None: - expr.set( - "expression", - exp.Literal.string(self.dialect.INITCAP_DEFAULT_DELIMITER_CHARS), - ) - - return expr - - def _parse_operator( - self, this: t.Optional[exp.Expression] - ) -> t.Optional[exp.Expression]: - while True: - if not self._match(TokenType.L_PAREN): - break - - op = "" - while self._curr and not self._match(TokenType.R_PAREN): - op += self._curr.text - self._advance() - - this = self.expression( - exp.Operator, - comments=self._prev_comments, - this=this, - operator=op, - expression=self._parse_bitwise(), - ) - - if not self._match(TokenType.OPERATOR): - break - - return this diff --git a/third_party/bigframes_vendored/sqlglot/planner.py b/third_party/bigframes_vendored/sqlglot/planner.py deleted file mode 100644 index d564253e57b..00000000000 --- a/third_party/bigframes_vendored/sqlglot/planner.py +++ /dev/null @@ -1,473 +0,0 @@ -# Contains code from https://github.com/tobymao/sqlglot/blob/v28.5.0/sqlglot/planner.py - -from __future__ import annotations - -import math -import typing as t - -from bigframes_vendored.sqlglot import alias, exp -from bigframes_vendored.sqlglot.helper import name_sequence -from bigframes_vendored.sqlglot.optimizer.eliminate_joins import join_condition - - -class Plan: - def __init__(self, expression: exp.Expression) -> None: - self.expression = expression.copy() - self.root = Step.from_expression(self.expression) - self._dag: t.Dict[Step, t.Set[Step]] = {} - - @property - def dag(self) -> t.Dict[Step, t.Set[Step]]: - if not self._dag: - dag: t.Dict[Step, t.Set[Step]] = {} - nodes = {self.root} - - while nodes: - node = nodes.pop() - dag[node] = set() - - for dep in node.dependencies: - dag[node].add(dep) - nodes.add(dep) - - self._dag = dag - - return self._dag - - @property - def leaves(self) -> t.Iterator[Step]: - return (node for node, deps in self.dag.items() if not deps) - - def __repr__(self) -> str: - return f"Plan\n----\n{repr(self.root)}" - - -class Step: - @classmethod - def from_expression( - cls, expression: exp.Expression, ctes: t.Optional[t.Dict[str, Step]] = None - ) -> Step: - """ - Builds a DAG of Steps from a SQL expression so that it's easier to execute in an engine. - Note: the expression's tables and subqueries must be aliased for this method to work. For - example, given the following expression: - - SELECT - x.a, - SUM(x.b) - FROM x AS x - JOIN y AS y - ON x.a = y.a - GROUP BY x.a - - the following DAG is produced (the expression IDs might differ per execution): - - - Aggregate: x (4347984624) - Context: - Aggregations: - - SUM(x.b) - Group: - - x.a - Projections: - - x.a - - "x"."" - Dependencies: - - Join: x (4347985296) - Context: - y: - On: x.a = y.a - Projections: - Dependencies: - - Scan: x (4347983136) - Context: - Source: x AS x - Projections: - - Scan: y (4343416624) - Context: - Source: y AS y - Projections: - - Args: - expression: the expression to build the DAG from. - ctes: a dictionary that maps CTEs to their corresponding Step DAG by name. - - Returns: - A Step DAG corresponding to `expression`. - """ - ctes = ctes or {} - expression = expression.unnest() - with_ = expression.args.get("with_") - - # CTEs break the mold of scope and introduce themselves to all in the context. - if with_: - ctes = ctes.copy() - for cte in with_.expressions: - step = Step.from_expression(cte.this, ctes) - step.name = cte.alias - ctes[step.name] = step # type: ignore - - from_ = expression.args.get("from_") - - if isinstance(expression, exp.Select) and from_: - step = Scan.from_expression(from_.this, ctes) - elif isinstance(expression, exp.SetOperation): - step = SetOperation.from_expression(expression, ctes) - else: - step = Scan() - - joins = expression.args.get("joins") - - if joins: - join = Join.from_joins(joins, ctes) - join.name = step.name - join.source_name = step.name - join.add_dependency(step) - step = join - - projections = [] # final selects in this chain of steps representing a select - operands = {} # intermediate computations of agg funcs eg x + 1 in SUM(x + 1) - aggregations = {} - next_operand_name = name_sequence("_a_") - - def extract_agg_operands(expression): - agg_funcs = tuple(expression.find_all(exp.AggFunc)) - if agg_funcs: - aggregations[expression] = None - - for agg in agg_funcs: - for operand in agg.unnest_operands(): - if isinstance(operand, exp.Column): - continue - if operand not in operands: - operands[operand] = next_operand_name() - - operand.replace(exp.column(operands[operand], quoted=True)) - - return bool(agg_funcs) - - def set_ops_and_aggs(step): - step.operands = tuple( - alias(operand, alias_) for operand, alias_ in operands.items() - ) - step.aggregations = list(aggregations) - - for e in expression.expressions: - if e.find(exp.AggFunc): - projections.append(exp.column(e.alias_or_name, step.name, quoted=True)) - extract_agg_operands(e) - else: - projections.append(e) - - where = expression.args.get("where") - - if where: - step.condition = where.this - - group = expression.args.get("group") - - if group or aggregations: - aggregate = Aggregate() - aggregate.source = step.name - aggregate.name = step.name - - having = expression.args.get("having") - - if having: - if extract_agg_operands(exp.alias_(having.this, "_h", quoted=True)): - aggregate.condition = exp.column("_h", step.name, quoted=True) - else: - aggregate.condition = having.this - - set_ops_and_aggs(aggregate) - - # give aggregates names and replace projections with references to them - aggregate.group = { - f"_g{i}": e for i, e in enumerate(group.expressions if group else []) - } - - intermediate: t.Dict[str | exp.Expression, str] = {} - for k, v in aggregate.group.items(): - intermediate[v] = k - if isinstance(v, exp.Column): - intermediate[v.name] = k - - for projection in projections: - for node in projection.walk(): - name = intermediate.get(node) - if name: - node.replace(exp.column(name, step.name)) - - if aggregate.condition: - for node in aggregate.condition.walk(): - name = intermediate.get(node) or intermediate.get(node.name) - if name: - node.replace(exp.column(name, step.name)) - - aggregate.add_dependency(step) - step = aggregate - else: - aggregate = None - - order = expression.args.get("order") - - if order: - if aggregate and isinstance(step, Aggregate): - for i, ordered in enumerate(order.expressions): - if extract_agg_operands( - exp.alias_(ordered.this, f"_o_{i}", quoted=True) - ): - ordered.this.replace( - exp.column(f"_o_{i}", step.name, quoted=True) - ) - - set_ops_and_aggs(aggregate) - - sort = Sort() - sort.name = step.name - sort.key = order.expressions - sort.add_dependency(step) - step = sort - - step.projections = projections - - if isinstance(expression, exp.Select) and expression.args.get("distinct"): - distinct = Aggregate() - distinct.source = step.name - distinct.name = step.name - distinct.group = { - e.alias_or_name: exp.column(col=e.alias_or_name, table=step.name) - for e in projections or expression.expressions - } - distinct.add_dependency(step) - step = distinct - - limit = expression.args.get("limit") - - if limit: - step.limit = int(limit.text("expression")) - - return step - - def __init__(self) -> None: - self.name: t.Optional[str] = None - self.dependencies: t.Set[Step] = set() - self.dependents: t.Set[Step] = set() - self.projections: t.Sequence[exp.Expression] = [] - self.limit: float = math.inf - self.condition: t.Optional[exp.Expression] = None - - def add_dependency(self, dependency: Step) -> None: - self.dependencies.add(dependency) - dependency.dependents.add(self) - - def __repr__(self) -> str: - return self.to_s() - - def to_s(self, level: int = 0) -> str: - indent = " " * level - nested = f"{indent} " - - context = self._to_s(f"{nested} ") - - if context: - context = [f"{nested}Context:"] + context - - lines = [ - f"{indent}- {self.id}", - *context, - f"{nested}Projections:", - ] - - for expression in self.projections: - lines.append(f"{nested} - {expression.sql()}") - - if self.condition: - lines.append(f"{nested}Condition: {self.condition.sql()}") - - if self.limit is not math.inf: - lines.append(f"{nested}Limit: {self.limit}") - - if self.dependencies: - lines.append(f"{nested}Dependencies:") - for dependency in self.dependencies: - lines.append(" " + dependency.to_s(level + 1)) - - return "\n".join(lines) - - @property - def type_name(self) -> str: - return self.__class__.__name__ - - @property - def id(self) -> str: - name = self.name - name = f" {name}" if name else "" - return f"{self.type_name}:{name} ({id(self)})" - - def _to_s(self, _indent: str) -> t.List[str]: - return [] - - -class Scan(Step): - @classmethod - def from_expression( - cls, expression: exp.Expression, ctes: t.Optional[t.Dict[str, Step]] = None - ) -> Step: - table = expression - alias_ = expression.alias_or_name - - if isinstance(expression, exp.Subquery): - table = expression.this - step = Step.from_expression(table, ctes) - step.name = alias_ - return step - - step = Scan() - step.name = alias_ - step.source = expression - if ctes and table.name in ctes: - step.add_dependency(ctes[table.name]) - - return step - - def __init__(self) -> None: - super().__init__() - self.source: t.Optional[exp.Expression] = None - - def _to_s(self, indent: str) -> t.List[str]: - return [f"{indent}Source: {self.source.sql() if self.source else '-static-'}"] # type: ignore - - -class Join(Step): - @classmethod - def from_joins( - cls, joins: t.Iterable[exp.Join], ctes: t.Optional[t.Dict[str, Step]] = None - ) -> Join: - step = Join() - - for join in joins: - source_key, join_key, condition = join_condition(join) - step.joins[join.alias_or_name] = { - "side": join.side, # type: ignore - "join_key": join_key, - "source_key": source_key, - "condition": condition, - } - - step.add_dependency(Scan.from_expression(join.this, ctes)) - - return step - - def __init__(self) -> None: - super().__init__() - self.source_name: t.Optional[str] = None - self.joins: t.Dict[str, t.Dict[str, t.List[str] | exp.Expression]] = {} - - def _to_s(self, indent: str) -> t.List[str]: - lines = [f"{indent}Source: {self.source_name or self.name}"] - for name, join in self.joins.items(): - lines.append(f"{indent}{name}: {join['side'] or 'INNER'}") - join_key = ", ".join( - str(key) for key in t.cast(list, join.get("join_key") or []) - ) - if join_key: - lines.append(f"{indent}Key: {join_key}") - if join.get("condition"): - lines.append(f"{indent}On: {join['condition'].sql()}") # type: ignore - return lines - - -class Aggregate(Step): - def __init__(self) -> None: - super().__init__() - self.aggregations: t.List[exp.Expression] = [] - self.operands: t.Tuple[exp.Expression, ...] = () - self.group: t.Dict[str, exp.Expression] = {} - self.source: t.Optional[str] = None - - def _to_s(self, indent: str) -> t.List[str]: - lines = [f"{indent}Aggregations:"] - - for expression in self.aggregations: - lines.append(f"{indent} - {expression.sql()}") - - if self.group: - lines.append(f"{indent}Group:") - for expression in self.group.values(): - lines.append(f"{indent} - {expression.sql()}") - if self.condition: - lines.append(f"{indent}Having:") - lines.append(f"{indent} - {self.condition.sql()}") - if self.operands: - lines.append(f"{indent}Operands:") - for expression in self.operands: - lines.append(f"{indent} - {expression.sql()}") - - return lines - - -class Sort(Step): - def __init__(self) -> None: - super().__init__() - self.key = None - - def _to_s(self, indent: str) -> t.List[str]: - lines = [f"{indent}Key:"] - - for expression in self.key: # type: ignore - lines.append(f"{indent} - {expression.sql()}") - - return lines - - -class SetOperation(Step): - def __init__( - self, - op: t.Type[exp.Expression], - left: str | None, - right: str | None, - distinct: bool = False, - ) -> None: - super().__init__() - self.op = op - self.left = left - self.right = right - self.distinct = distinct - - @classmethod - def from_expression( - cls, expression: exp.Expression, ctes: t.Optional[t.Dict[str, Step]] = None - ) -> SetOperation: - assert isinstance(expression, exp.SetOperation) - - left = Step.from_expression(expression.left, ctes) - # SELECT 1 UNION SELECT 2 <-- these subqueries don't have names - left.name = left.name or "left" - right = Step.from_expression(expression.right, ctes) - right.name = right.name or "right" - step = cls( - op=expression.__class__, - left=left.name, - right=right.name, - distinct=bool(expression.args.get("distinct")), - ) - - step.add_dependency(left) - step.add_dependency(right) - - limit = expression.args.get("limit") - - if limit: - step.limit = int(limit.text("expression")) - - return step - - def _to_s(self, indent: str) -> t.List[str]: - lines = [] - if self.distinct: - lines.append(f"{indent}Distinct: {self.distinct}") - return lines - - @property - def type_name(self) -> str: - return self.op.__name__ diff --git a/third_party/bigframes_vendored/sqlglot/py.typed b/third_party/bigframes_vendored/sqlglot/py.typed deleted file mode 100644 index e69de29bb2d..00000000000 diff --git a/third_party/bigframes_vendored/sqlglot/schema.py b/third_party/bigframes_vendored/sqlglot/schema.py deleted file mode 100644 index 87928f2fc6f..00000000000 --- a/third_party/bigframes_vendored/sqlglot/schema.py +++ /dev/null @@ -1,641 +0,0 @@ -# Contains code from https://github.com/tobymao/sqlglot/blob/v28.5.0/sqlglot/schema.py - -from __future__ import annotations - -import abc -import typing as t - -from bigframes_vendored.sqlglot import expressions as exp -from bigframes_vendored.sqlglot.dialects.dialect import Dialect -from bigframes_vendored.sqlglot.errors import SchemaError -from bigframes_vendored.sqlglot.helper import dict_depth, first -from bigframes_vendored.sqlglot.trie import TrieResult, in_trie, new_trie - -if t.TYPE_CHECKING: - from bigframes_vendored.sqlglot.dialects.dialect import DialectType - - ColumnMapping = t.Union[t.Dict, str, t.List] - - -class Schema(abc.ABC): - """Abstract base class for database schemas""" - - @property - def dialect(self) -> t.Optional[Dialect]: - """ - Returns None by default. Subclasses that require dialect-specific - behavior should override this property. - """ - return None - - @abc.abstractmethod - def add_table( - self, - table: exp.Table | str, - column_mapping: t.Optional[ColumnMapping] = None, - dialect: DialectType = None, - normalize: t.Optional[bool] = None, - match_depth: bool = True, - ) -> None: - """ - Register or update a table. Some implementing classes may require column information to also be provided. - The added table must have the necessary number of qualifiers in its path to match the schema's nesting level. - - Args: - table: the `Table` expression instance or string representing the table. - column_mapping: a column mapping that describes the structure of the table. - dialect: the SQL dialect that will be used to parse `table` if it's a string. - normalize: whether to normalize identifiers according to the dialect of interest. - match_depth: whether to enforce that the table must match the schema's depth or not. - """ - - @abc.abstractmethod - def column_names( - self, - table: exp.Table | str, - only_visible: bool = False, - dialect: DialectType = None, - normalize: t.Optional[bool] = None, - ) -> t.Sequence[str]: - """ - Get the column names for a table. - - Args: - table: the `Table` expression instance. - only_visible: whether to include invisible columns. - dialect: the SQL dialect that will be used to parse `table` if it's a string. - normalize: whether to normalize identifiers according to the dialect of interest. - - Returns: - The sequence of column names. - """ - - @abc.abstractmethod - def get_column_type( - self, - table: exp.Table | str, - column: exp.Column | str, - dialect: DialectType = None, - normalize: t.Optional[bool] = None, - ) -> exp.DataType: - """ - Get the `sqlglot.exp.DataType` type of a column in the schema. - - Args: - table: the source table. - column: the target column. - dialect: the SQL dialect that will be used to parse `table` if it's a string. - normalize: whether to normalize identifiers according to the dialect of interest. - - Returns: - The resulting column type. - """ - - def has_column( - self, - table: exp.Table | str, - column: exp.Column | str, - dialect: DialectType = None, - normalize: t.Optional[bool] = None, - ) -> bool: - """ - Returns whether `column` appears in `table`'s schema. - - Args: - table: the source table. - column: the target column. - dialect: the SQL dialect that will be used to parse `table` if it's a string. - normalize: whether to normalize identifiers according to the dialect of interest. - - Returns: - True if the column appears in the schema, False otherwise. - """ - name = column if isinstance(column, str) else column.name - return name in self.column_names(table, dialect=dialect, normalize=normalize) - - @property - @abc.abstractmethod - def supported_table_args(self) -> t.Tuple[str, ...]: - """ - Table arguments this schema support, e.g. `("this", "db", "catalog")` - """ - - @property - def empty(self) -> bool: - """Returns whether the schema is empty.""" - return True - - -class AbstractMappingSchema: - def __init__( - self, - mapping: t.Optional[t.Dict] = None, - ) -> None: - self.mapping = mapping or {} - self.mapping_trie = new_trie( - tuple(reversed(t)) for t in flatten_schema(self.mapping, depth=self.depth()) - ) - self._supported_table_args: t.Tuple[str, ...] = tuple() - - @property - def empty(self) -> bool: - return not self.mapping - - def depth(self) -> int: - return dict_depth(self.mapping) - - @property - def supported_table_args(self) -> t.Tuple[str, ...]: - if not self._supported_table_args and self.mapping: - depth = self.depth() - - if not depth: # None - self._supported_table_args = tuple() - elif 1 <= depth <= 3: - self._supported_table_args = exp.TABLE_PARTS[:depth] - else: - raise SchemaError(f"Invalid mapping shape. Depth: {depth}") - - return self._supported_table_args - - def table_parts(self, table: exp.Table) -> t.List[str]: - return [part.name for part in reversed(table.parts)] - - def find( - self, - table: exp.Table, - raise_on_missing: bool = True, - ensure_data_types: bool = False, - ) -> t.Optional[t.Any]: - """ - Returns the schema of a given table. - - Args: - table: the target table. - raise_on_missing: whether to raise in case the schema is not found. - ensure_data_types: whether to convert `str` types to their `DataType` equivalents. - - Returns: - The schema of the target table. - """ - parts = self.table_parts(table)[0 : len(self.supported_table_args)] - value, trie = in_trie(self.mapping_trie, parts) - - if value == TrieResult.FAILED: - return None - - if value == TrieResult.PREFIX: - possibilities = flatten_schema(trie) - - if len(possibilities) == 1: - parts.extend(possibilities[0]) - else: - message = ", ".join(".".join(parts) for parts in possibilities) - if raise_on_missing: - raise SchemaError(f"Ambiguous mapping for {table}: {message}.") - return None - - return self.nested_get(parts, raise_on_missing=raise_on_missing) - - def nested_get( - self, - parts: t.Sequence[str], - d: t.Optional[t.Dict] = None, - raise_on_missing=True, - ) -> t.Optional[t.Any]: - return nested_get( - d or self.mapping, - *zip(self.supported_table_args, reversed(parts)), - raise_on_missing=raise_on_missing, - ) - - -class MappingSchema(AbstractMappingSchema, Schema): - """ - Schema based on a nested mapping. - - Args: - schema: Mapping in one of the following forms: - 1. {table: {col: type}} - 2. {db: {table: {col: type}}} - 3. {catalog: {db: {table: {col: type}}}} - 4. None - Tables will be added later - visible: Optional mapping of which columns in the schema are visible. If not provided, all columns - are assumed to be visible. The nesting should mirror that of the schema: - 1. {table: set(*cols)}} - 2. {db: {table: set(*cols)}}} - 3. {catalog: {db: {table: set(*cols)}}}} - dialect: The dialect to be used for custom type mappings & parsing string arguments. - normalize: Whether to normalize identifier names according to the given dialect or not. - """ - - def __init__( - self, - schema: t.Optional[t.Dict] = None, - visible: t.Optional[t.Dict] = None, - dialect: DialectType = None, - normalize: bool = True, - ) -> None: - self.visible = {} if visible is None else visible - self.normalize = normalize - self._dialect = Dialect.get_or_raise(dialect) - self._type_mapping_cache: t.Dict[str, exp.DataType] = {} - self._depth = 0 - schema = {} if schema is None else schema - - super().__init__(self._normalize(schema) if self.normalize else schema) - - @property - def dialect(self) -> Dialect: - """Returns the dialect for this mapping schema.""" - return self._dialect - - @classmethod - def from_mapping_schema(cls, mapping_schema: MappingSchema) -> MappingSchema: - return MappingSchema( - schema=mapping_schema.mapping, - visible=mapping_schema.visible, - dialect=mapping_schema.dialect, - normalize=mapping_schema.normalize, - ) - - def find( - self, - table: exp.Table, - raise_on_missing: bool = True, - ensure_data_types: bool = False, - ) -> t.Optional[t.Any]: - schema = super().find( - table, - raise_on_missing=raise_on_missing, - ensure_data_types=ensure_data_types, - ) - if ensure_data_types and isinstance(schema, dict): - schema = { - col: self._to_data_type(dtype) if isinstance(dtype, str) else dtype - for col, dtype in schema.items() - } - - return schema - - def copy(self, **kwargs) -> MappingSchema: - return MappingSchema( - **{ # type: ignore - "schema": self.mapping.copy(), - "visible": self.visible.copy(), - "dialect": self.dialect, - "normalize": self.normalize, - **kwargs, - } - ) - - def add_table( - self, - table: exp.Table | str, - column_mapping: t.Optional[ColumnMapping] = None, - dialect: DialectType = None, - normalize: t.Optional[bool] = None, - match_depth: bool = True, - ) -> None: - """ - Register or update a table. Updates are only performed if a new column mapping is provided. - The added table must have the necessary number of qualifiers in its path to match the schema's nesting level. - - Args: - table: the `Table` expression instance or string representing the table. - column_mapping: a column mapping that describes the structure of the table. - dialect: the SQL dialect that will be used to parse `table` if it's a string. - normalize: whether to normalize identifiers according to the dialect of interest. - match_depth: whether to enforce that the table must match the schema's depth or not. - """ - normalized_table = self._normalize_table( - table, dialect=dialect, normalize=normalize - ) - - if ( - match_depth - and not self.empty - and len(normalized_table.parts) != self.depth() - ): - raise SchemaError( - f"Table {normalized_table.sql(dialect=self.dialect)} must match the " - f"schema's nesting level: {self.depth()}." - ) - - normalized_column_mapping = { - self._normalize_name(key, dialect=dialect, normalize=normalize): value - for key, value in ensure_column_mapping(column_mapping).items() - } - - schema = self.find(normalized_table, raise_on_missing=False) - if schema and not normalized_column_mapping: - return - - parts = self.table_parts(normalized_table) - - nested_set(self.mapping, tuple(reversed(parts)), normalized_column_mapping) - new_trie([parts], self.mapping_trie) - - def column_names( - self, - table: exp.Table | str, - only_visible: bool = False, - dialect: DialectType = None, - normalize: t.Optional[bool] = None, - ) -> t.List[str]: - normalized_table = self._normalize_table( - table, dialect=dialect, normalize=normalize - ) - - schema = self.find(normalized_table) - if schema is None: - return [] - - if not only_visible or not self.visible: - return list(schema) - - visible = ( - self.nested_get(self.table_parts(normalized_table), self.visible) or [] - ) - return [col for col in schema if col in visible] - - def get_column_type( - self, - table: exp.Table | str, - column: exp.Column | str, - dialect: DialectType = None, - normalize: t.Optional[bool] = None, - ) -> exp.DataType: - normalized_table = self._normalize_table( - table, dialect=dialect, normalize=normalize - ) - - normalized_column_name = self._normalize_name( - column if isinstance(column, str) else column.this, - dialect=dialect, - normalize=normalize, - ) - - table_schema = self.find(normalized_table, raise_on_missing=False) - if table_schema: - column_type = table_schema.get(normalized_column_name) - - if isinstance(column_type, exp.DataType): - return column_type - elif isinstance(column_type, str): - return self._to_data_type(column_type, dialect=dialect) - - return exp.DataType.build("unknown") - - def has_column( - self, - table: exp.Table | str, - column: exp.Column | str, - dialect: DialectType = None, - normalize: t.Optional[bool] = None, - ) -> bool: - normalized_table = self._normalize_table( - table, dialect=dialect, normalize=normalize - ) - - normalized_column_name = self._normalize_name( - column if isinstance(column, str) else column.this, - dialect=dialect, - normalize=normalize, - ) - - table_schema = self.find(normalized_table, raise_on_missing=False) - return normalized_column_name in table_schema if table_schema else False - - def _normalize(self, schema: t.Dict) -> t.Dict: - """ - Normalizes all identifiers in the schema. - - Args: - schema: the schema to normalize. - - Returns: - The normalized schema mapping. - """ - normalized_mapping: t.Dict = {} - flattened_schema = flatten_schema(schema) - error_msg = "Table {} must match the schema's nesting level: {}." - - for keys in flattened_schema: - columns = nested_get(schema, *zip(keys, keys)) - - if not isinstance(columns, dict): - raise SchemaError( - error_msg.format(".".join(keys[:-1]), len(flattened_schema[0])) - ) - if not columns: - raise SchemaError( - f"Table {'.'.join(keys[:-1])} must have at least one column" - ) - if isinstance(first(columns.values()), dict): - raise SchemaError( - error_msg.format( - ".".join(keys + flatten_schema(columns)[0]), - len(flattened_schema[0]), - ), - ) - - normalized_keys = [self._normalize_name(key, is_table=True) for key in keys] - for column_name, column_type in columns.items(): - nested_set( - normalized_mapping, - normalized_keys + [self._normalize_name(column_name)], - column_type, - ) - - return normalized_mapping - - def _normalize_table( - self, - table: exp.Table | str, - dialect: DialectType = None, - normalize: t.Optional[bool] = None, - ) -> exp.Table: - dialect = dialect or self.dialect - normalize = self.normalize if normalize is None else normalize - - normalized_table = exp.maybe_parse( - table, into=exp.Table, dialect=dialect, copy=normalize - ) - - if normalize: - for part in normalized_table.parts: - if isinstance(part, exp.Identifier): - part.replace( - normalize_name( - part, dialect=dialect, is_table=True, normalize=normalize - ) - ) - - return normalized_table - - def _normalize_name( - self, - name: str | exp.Identifier, - dialect: DialectType = None, - is_table: bool = False, - normalize: t.Optional[bool] = None, - ) -> str: - return normalize_name( - name, - dialect=dialect or self.dialect, - is_table=is_table, - normalize=self.normalize if normalize is None else normalize, - ).name - - def depth(self) -> int: - if not self.empty and not self._depth: - # The columns themselves are a mapping, but we don't want to include those - self._depth = super().depth() - 1 - return self._depth - - def _to_data_type( - self, schema_type: str, dialect: DialectType = None - ) -> exp.DataType: - """ - Convert a type represented as a string to the corresponding `sqlglot.exp.DataType` object. - - Args: - schema_type: the type we want to convert. - dialect: the SQL dialect that will be used to parse `schema_type`, if needed. - - Returns: - The resulting expression type. - """ - if schema_type not in self._type_mapping_cache: - dialect = Dialect.get_or_raise(dialect) if dialect else self.dialect - udt = dialect.SUPPORTS_USER_DEFINED_TYPES - - try: - expression = exp.DataType.build(schema_type, dialect=dialect, udt=udt) - self._type_mapping_cache[schema_type] = expression - except AttributeError: - in_dialect = f" in dialect {dialect}" if dialect else "" - raise SchemaError(f"Failed to build type '{schema_type}'{in_dialect}.") - - return self._type_mapping_cache[schema_type] - - -def normalize_name( - identifier: str | exp.Identifier, - dialect: DialectType = None, - is_table: bool = False, - normalize: t.Optional[bool] = True, -) -> exp.Identifier: - if isinstance(identifier, str): - identifier = exp.parse_identifier(identifier, dialect=dialect) - - if not normalize: - return identifier - - # this is used for normalize_identifier, bigquery has special rules pertaining tables - identifier.meta["is_table"] = is_table - return Dialect.get_or_raise(dialect).normalize_identifier(identifier) - - -def ensure_schema(schema: Schema | t.Optional[t.Dict], **kwargs: t.Any) -> Schema: - if isinstance(schema, Schema): - return schema - - return MappingSchema(schema, **kwargs) - - -def ensure_column_mapping(mapping: t.Optional[ColumnMapping]) -> t.Dict: - if mapping is None: - return {} - elif isinstance(mapping, dict): - return mapping - elif isinstance(mapping, str): - col_name_type_strs = [x.strip() for x in mapping.split(",")] - return { - name_type_str.split(":")[0].strip(): name_type_str.split(":")[1].strip() - for name_type_str in col_name_type_strs - } - elif isinstance(mapping, list): - return {x.strip(): None for x in mapping} - - raise ValueError(f"Invalid mapping provided: {type(mapping)}") - - -def flatten_schema( - schema: t.Dict, depth: t.Optional[int] = None, keys: t.Optional[t.List[str]] = None -) -> t.List[t.List[str]]: - tables = [] - keys = keys or [] - depth = dict_depth(schema) - 1 if depth is None else depth - - for k, v in schema.items(): - if depth == 1 or not isinstance(v, dict): - tables.append(keys + [k]) - elif depth >= 2: - tables.extend(flatten_schema(v, depth - 1, keys + [k])) - - return tables - - -def nested_get( - d: t.Dict, *path: t.Tuple[str, str], raise_on_missing: bool = True -) -> t.Optional[t.Any]: - """ - Get a value for a nested dictionary. - - Args: - d: the dictionary to search. - *path: tuples of (name, key), where: - `key` is the key in the dictionary to get. - `name` is a string to use in the error if `key` isn't found. - - Returns: - The value or None if it doesn't exist. - """ - for name, key in path: - d = d.get(key) # type: ignore - if d is None: - if raise_on_missing: - name = "table" if name == "this" else name - raise ValueError(f"Unknown {name}: {key}") - return None - - return d - - -def nested_set(d: t.Dict, keys: t.Sequence[str], value: t.Any) -> t.Dict: - """ - In-place set a value for a nested dictionary - - Example: - >>> nested_set({}, ["top_key", "second_key"], "value") - {'top_key': {'second_key': 'value'}} - - >>> nested_set({"top_key": {"third_key": "third_value"}}, ["top_key", "second_key"], "value") - {'top_key': {'third_key': 'third_value', 'second_key': 'value'}} - - Args: - d: dictionary to update. - keys: the keys that makeup the path to `value`. - value: the value to set in the dictionary for the given key path. - - Returns: - The (possibly) updated dictionary. - """ - if not keys: - return d - - if len(keys) == 1: - d[keys[0]] = value - return d - - subd = d - for key in keys[:-1]: - if key not in subd: - subd = subd.setdefault(key, {}) - else: - subd = subd[key] - - subd[keys[-1]] = value - return d diff --git a/third_party/bigframes_vendored/sqlglot/serde.py b/third_party/bigframes_vendored/sqlglot/serde.py deleted file mode 100644 index dc21407e5f3..00000000000 --- a/third_party/bigframes_vendored/sqlglot/serde.py +++ /dev/null @@ -1,127 +0,0 @@ -# Contains code from https://github.com/tobymao/sqlglot/blob/v28.5.0/sqlglot/serde.py - -from __future__ import annotations - -import typing as t - -from bigframes_vendored.sqlglot import expressions as exp - -INDEX = "i" -ARG_KEY = "k" -IS_ARRAY = "a" -CLASS = "c" -TYPE = "t" -COMMENTS = "o" -META = "m" -VALUE = "v" -DATA_TYPE = "DataType.Type" - - -def dump(expression: exp.Expression) -> t.List[t.Dict[str, t.Any]]: - """ - Dump an Expression into a JSON serializable List. - """ - i = 0 - payloads = [] - stack: t.List[t.Tuple[t.Any, t.Optional[int], t.Optional[str], bool]] = [ - (expression, None, None, False) - ] - - while stack: - node, index, arg_key, is_array = stack.pop() - - payload: t.Dict[str, t.Any] = {} - - if index is not None: - payload[INDEX] = index - if arg_key is not None: - payload[ARG_KEY] = arg_key - if is_array: - payload[IS_ARRAY] = is_array - - payloads.append(payload) - - if hasattr(node, "parent"): - klass = node.__class__.__qualname__ - - if node.__class__.__module__ != exp.__name__: - klass = f"{node.__module__}.{klass}" - - payload[CLASS] = klass - - if node.type: - payload[TYPE] = dump(node.type) - if node.comments: - payload[COMMENTS] = node.comments - if node._meta is not None: - payload[META] = node._meta - if node.args: - for k, vs in reversed(node.args.items()): - if type(vs) is list: - for v in reversed(vs): - stack.append((v, i, k, True)) - elif vs is not None: - stack.append((vs, i, k, False)) - elif type(node) is exp.DataType.Type: - payload[CLASS] = DATA_TYPE - payload[VALUE] = node.value - else: - payload[VALUE] = node - - i += 1 - - return payloads - - -@t.overload -def load(payloads: None) -> None: ... - - -@t.overload -def load(payloads: t.List[t.Dict[str, t.Any]]) -> exp.Expression: ... - - -def load(payloads): - """ - Load a list of dicts generated by dump into an Expression. - """ - - if not payloads: - return None - - payload, *tail = payloads - root = _load(payload) - nodes = [root] - for payload in tail: - node = _load(payload) - nodes.append(node) - parent = nodes[payload[INDEX]] - arg_key = payload[ARG_KEY] - - if payload.get(IS_ARRAY): - parent.append(arg_key, node) - else: - parent.set(arg_key, node) - - return root - - -def _load(payload: t.Dict[str, t.Any]) -> exp.Expression | exp.DataType.Type: - class_name = payload.get(CLASS) - - if not class_name: - return payload[VALUE] - if class_name == DATA_TYPE: - return exp.DataType.Type(payload[VALUE]) - - if "." in class_name: - module_path, class_name = class_name.rsplit(".", maxsplit=1) - module = __import__(module_path, fromlist=[class_name]) - else: - module = exp - - expression = getattr(module, class_name)() - expression.type = load(payload.get(TYPE)) - expression.comments = payload.get(COMMENTS) - expression._meta = payload.get(META) - return expression diff --git a/third_party/bigframes_vendored/sqlglot/time.py b/third_party/bigframes_vendored/sqlglot/time.py deleted file mode 100644 index 05873b187a9..00000000000 --- a/third_party/bigframes_vendored/sqlglot/time.py +++ /dev/null @@ -1,689 +0,0 @@ -# Contains code from https://github.com/tobymao/sqlglot/blob/v28.5.0/sqlglot/time.py - -import datetime -import typing as t - -# The generic time format is based on python time.strftime. -# https://docs.python.org/3/library/time.html#time.strftime -from bigframes_vendored.sqlglot.trie import TrieResult, in_trie, new_trie - - -def format_time( - string: str, mapping: t.Dict[str, str], trie: t.Optional[t.Dict] = None -) -> t.Optional[str]: - """ - Converts a time string given a mapping. - - Examples: - >>> format_time("%Y", {"%Y": "YYYY"}) - 'YYYY' - - Args: - mapping: dictionary of time format to target time format. - trie: optional trie, can be passed in for performance. - - Returns: - The converted time string. - """ - if not string: - return None - - start = 0 - end = 1 - size = len(string) - trie = trie or new_trie(mapping) - current = trie - chunks = [] - sym = None - - while end <= size: - chars = string[start:end] - result, current = in_trie(current, chars[-1]) - - if result == TrieResult.FAILED: - if sym: - end -= 1 - chars = sym - sym = None - else: - chars = chars[0] - end = start + 1 - - start += len(chars) - chunks.append(chars) - current = trie - elif result == TrieResult.EXISTS: - sym = chars - - end += 1 - - if result != TrieResult.FAILED and end > size: - chunks.append(chars) - - return "".join(mapping.get(chars, chars) for chars in chunks) - - -TIMEZONES = { - tz.lower() - for tz in ( - "Africa/Abidjan", - "Africa/Accra", - "Africa/Addis_Ababa", - "Africa/Algiers", - "Africa/Asmara", - "Africa/Asmera", - "Africa/Bamako", - "Africa/Bangui", - "Africa/Banjul", - "Africa/Bissau", - "Africa/Blantyre", - "Africa/Brazzaville", - "Africa/Bujumbura", - "Africa/Cairo", - "Africa/Casablanca", - "Africa/Ceuta", - "Africa/Conakry", - "Africa/Dakar", - "Africa/Dar_es_Salaam", - "Africa/Djibouti", - "Africa/Douala", - "Africa/El_Aaiun", - "Africa/Freetown", - "Africa/Gaborone", - "Africa/Harare", - "Africa/Johannesburg", - "Africa/Juba", - "Africa/Kampala", - "Africa/Khartoum", - "Africa/Kigali", - "Africa/Kinshasa", - "Africa/Lagos", - "Africa/Libreville", - "Africa/Lome", - "Africa/Luanda", - "Africa/Lubumbashi", - "Africa/Lusaka", - "Africa/Malabo", - "Africa/Maputo", - "Africa/Maseru", - "Africa/Mbabane", - "Africa/Mogadishu", - "Africa/Monrovia", - "Africa/Nairobi", - "Africa/Ndjamena", - "Africa/Niamey", - "Africa/Nouakchott", - "Africa/Ouagadougou", - "Africa/Porto-Novo", - "Africa/Sao_Tome", - "Africa/Timbuktu", - "Africa/Tripoli", - "Africa/Tunis", - "Africa/Windhoek", - "America/Adak", - "America/Anchorage", - "America/Anguilla", - "America/Antigua", - "America/Araguaina", - "America/Argentina/Buenos_Aires", - "America/Argentina/Catamarca", - "America/Argentina/ComodRivadavia", - "America/Argentina/Cordoba", - "America/Argentina/Jujuy", - "America/Argentina/La_Rioja", - "America/Argentina/Mendoza", - "America/Argentina/Rio_Gallegos", - "America/Argentina/Salta", - "America/Argentina/San_Juan", - "America/Argentina/San_Luis", - "America/Argentina/Tucuman", - "America/Argentina/Ushuaia", - "America/Aruba", - "America/Asuncion", - "America/Atikokan", - "America/Atka", - "America/Bahia", - "America/Bahia_Banderas", - "America/Barbados", - "America/Belem", - "America/Belize", - "America/Blanc-Sablon", - "America/Boa_Vista", - "America/Bogota", - "America/Boise", - "America/Buenos_Aires", - "America/Cambridge_Bay", - "America/Campo_Grande", - "America/Cancun", - "America/Caracas", - "America/Catamarca", - "America/Cayenne", - "America/Cayman", - "America/Chicago", - "America/Chihuahua", - "America/Ciudad_Juarez", - "America/Coral_Harbour", - "America/Cordoba", - "America/Costa_Rica", - "America/Creston", - "America/Cuiaba", - "America/Curacao", - "America/Danmarkshavn", - "America/Dawson", - "America/Dawson_Creek", - "America/Denver", - "America/Detroit", - "America/Dominica", - "America/Edmonton", - "America/Eirunepe", - "America/El_Salvador", - "America/Ensenada", - "America/Fort_Nelson", - "America/Fort_Wayne", - "America/Fortaleza", - "America/Glace_Bay", - "America/Godthab", - "America/Goose_Bay", - "America/Grand_Turk", - "America/Grenada", - "America/Guadeloupe", - "America/Guatemala", - "America/Guayaquil", - "America/Guyana", - "America/Halifax", - "America/Havana", - "America/Hermosillo", - "America/Indiana/Indianapolis", - "America/Indiana/Knox", - "America/Indiana/Marengo", - "America/Indiana/Petersburg", - "America/Indiana/Tell_City", - "America/Indiana/Vevay", - "America/Indiana/Vincennes", - "America/Indiana/Winamac", - "America/Indianapolis", - "America/Inuvik", - "America/Iqaluit", - "America/Jamaica", - "America/Jujuy", - "America/Juneau", - "America/Kentucky/Louisville", - "America/Kentucky/Monticello", - "America/Knox_IN", - "America/Kralendijk", - "America/La_Paz", - "America/Lima", - "America/Los_Angeles", - "America/Louisville", - "America/Lower_Princes", - "America/Maceio", - "America/Managua", - "America/Manaus", - "America/Marigot", - "America/Martinique", - "America/Matamoros", - "America/Mazatlan", - "America/Mendoza", - "America/Menominee", - "America/Merida", - "America/Metlakatla", - "America/Mexico_City", - "America/Miquelon", - "America/Moncton", - "America/Monterrey", - "America/Montevideo", - "America/Montreal", - "America/Montserrat", - "America/Nassau", - "America/New_York", - "America/Nipigon", - "America/Nome", - "America/Noronha", - "America/North_Dakota/Beulah", - "America/North_Dakota/Center", - "America/North_Dakota/New_Salem", - "America/Nuuk", - "America/Ojinaga", - "America/Panama", - "America/Pangnirtung", - "America/Paramaribo", - "America/Phoenix", - "America/Port-au-Prince", - "America/Port_of_Spain", - "America/Porto_Acre", - "America/Porto_Velho", - "America/Puerto_Rico", - "America/Punta_Arenas", - "America/Rainy_River", - "America/Rankin_Inlet", - "America/Recife", - "America/Regina", - "America/Resolute", - "America/Rio_Branco", - "America/Rosario", - "America/Santa_Isabel", - "America/Santarem", - "America/Santiago", - "America/Santo_Domingo", - "America/Sao_Paulo", - "America/Scoresbysund", - "America/Shiprock", - "America/Sitka", - "America/St_Barthelemy", - "America/St_Johns", - "America/St_Kitts", - "America/St_Lucia", - "America/St_Thomas", - "America/St_Vincent", - "America/Swift_Current", - "America/Tegucigalpa", - "America/Thule", - "America/Thunder_Bay", - "America/Tijuana", - "America/Toronto", - "America/Tortola", - "America/Vancouver", - "America/Virgin", - "America/Whitehorse", - "America/Winnipeg", - "America/Yakutat", - "America/Yellowknife", - "Antarctica/Casey", - "Antarctica/Davis", - "Antarctica/DumontDUrville", - "Antarctica/Macquarie", - "Antarctica/Mawson", - "Antarctica/McMurdo", - "Antarctica/Palmer", - "Antarctica/Rothera", - "Antarctica/South_Pole", - "Antarctica/Syowa", - "Antarctica/Troll", - "Antarctica/Vostok", - "Arctic/Longyearbyen", - "Asia/Aden", - "Asia/Almaty", - "Asia/Amman", - "Asia/Anadyr", - "Asia/Aqtau", - "Asia/Aqtobe", - "Asia/Ashgabat", - "Asia/Ashkhabad", - "Asia/Atyrau", - "Asia/Baghdad", - "Asia/Bahrain", - "Asia/Baku", - "Asia/Bangkok", - "Asia/Barnaul", - "Asia/Beirut", - "Asia/Bishkek", - "Asia/Brunei", - "Asia/Calcutta", - "Asia/Chita", - "Asia/Choibalsan", - "Asia/Chongqing", - "Asia/Chungking", - "Asia/Colombo", - "Asia/Dacca", - "Asia/Damascus", - "Asia/Dhaka", - "Asia/Dili", - "Asia/Dubai", - "Asia/Dushanbe", - "Asia/Famagusta", - "Asia/Gaza", - "Asia/Harbin", - "Asia/Hebron", - "Asia/Ho_Chi_Minh", - "Asia/Hong_Kong", - "Asia/Hovd", - "Asia/Irkutsk", - "Asia/Istanbul", - "Asia/Jakarta", - "Asia/Jayapura", - "Asia/Jerusalem", - "Asia/Kabul", - "Asia/Kamchatka", - "Asia/Karachi", - "Asia/Kashgar", - "Asia/Kathmandu", - "Asia/Katmandu", - "Asia/Khandyga", - "Asia/Kolkata", - "Asia/Krasnoyarsk", - "Asia/Kuala_Lumpur", - "Asia/Kuching", - "Asia/Kuwait", - "Asia/Macao", - "Asia/Macau", - "Asia/Magadan", - "Asia/Makassar", - "Asia/Manila", - "Asia/Muscat", - "Asia/Nicosia", - "Asia/Novokuznetsk", - "Asia/Novosibirsk", - "Asia/Omsk", - "Asia/Oral", - "Asia/Phnom_Penh", - "Asia/Pontianak", - "Asia/Pyongyang", - "Asia/Qatar", - "Asia/Qostanay", - "Asia/Qyzylorda", - "Asia/Rangoon", - "Asia/Riyadh", - "Asia/Saigon", - "Asia/Sakhalin", - "Asia/Samarkand", - "Asia/Seoul", - "Asia/Shanghai", - "Asia/Singapore", - "Asia/Srednekolymsk", - "Asia/Taipei", - "Asia/Tashkent", - "Asia/Tbilisi", - "Asia/Tehran", - "Asia/Tel_Aviv", - "Asia/Thimbu", - "Asia/Thimphu", - "Asia/Tokyo", - "Asia/Tomsk", - "Asia/Ujung_Pandang", - "Asia/Ulaanbaatar", - "Asia/Ulan_Bator", - "Asia/Urumqi", - "Asia/Ust-Nera", - "Asia/Vientiane", - "Asia/Vladivostok", - "Asia/Yakutsk", - "Asia/Yangon", - "Asia/Yekaterinburg", - "Asia/Yerevan", - "Atlantic/Azores", - "Atlantic/Bermuda", - "Atlantic/Canary", - "Atlantic/Cape_Verde", - "Atlantic/Faeroe", - "Atlantic/Faroe", - "Atlantic/Jan_Mayen", - "Atlantic/Madeira", - "Atlantic/Reykjavik", - "Atlantic/South_Georgia", - "Atlantic/St_Helena", - "Atlantic/Stanley", - "Australia/ACT", - "Australia/Adelaide", - "Australia/Brisbane", - "Australia/Broken_Hill", - "Australia/Canberra", - "Australia/Currie", - "Australia/Darwin", - "Australia/Eucla", - "Australia/Hobart", - "Australia/LHI", - "Australia/Lindeman", - "Australia/Lord_Howe", - "Australia/Melbourne", - "Australia/NSW", - "Australia/North", - "Australia/Perth", - "Australia/Queensland", - "Australia/South", - "Australia/Sydney", - "Australia/Tasmania", - "Australia/Victoria", - "Australia/West", - "Australia/Yancowinna", - "Brazil/Acre", - "Brazil/DeNoronha", - "Brazil/East", - "Brazil/West", - "CET", - "CST6CDT", - "Canada/Atlantic", - "Canada/Central", - "Canada/Eastern", - "Canada/Mountain", - "Canada/Newfoundland", - "Canada/Pacific", - "Canada/Saskatchewan", - "Canada/Yukon", - "Chile/Continental", - "Chile/EasterIsland", - "Cuba", - "EET", - "EST", - "EST5EDT", - "Egypt", - "Eire", - "Etc/GMT", - "Etc/GMT+0", - "Etc/GMT+1", - "Etc/GMT+10", - "Etc/GMT+11", - "Etc/GMT+12", - "Etc/GMT+2", - "Etc/GMT+3", - "Etc/GMT+4", - "Etc/GMT+5", - "Etc/GMT+6", - "Etc/GMT+7", - "Etc/GMT+8", - "Etc/GMT+9", - "Etc/GMT-0", - "Etc/GMT-1", - "Etc/GMT-10", - "Etc/GMT-11", - "Etc/GMT-12", - "Etc/GMT-13", - "Etc/GMT-14", - "Etc/GMT-2", - "Etc/GMT-3", - "Etc/GMT-4", - "Etc/GMT-5", - "Etc/GMT-6", - "Etc/GMT-7", - "Etc/GMT-8", - "Etc/GMT-9", - "Etc/GMT0", - "Etc/Greenwich", - "Etc/UCT", - "Etc/UTC", - "Etc/Universal", - "Etc/Zulu", - "Europe/Amsterdam", - "Europe/Andorra", - "Europe/Astrakhan", - "Europe/Athens", - "Europe/Belfast", - "Europe/Belgrade", - "Europe/Berlin", - "Europe/Bratislava", - "Europe/Brussels", - "Europe/Bucharest", - "Europe/Budapest", - "Europe/Busingen", - "Europe/Chisinau", - "Europe/Copenhagen", - "Europe/Dublin", - "Europe/Gibraltar", - "Europe/Guernsey", - "Europe/Helsinki", - "Europe/Isle_of_Man", - "Europe/Istanbul", - "Europe/Jersey", - "Europe/Kaliningrad", - "Europe/Kiev", - "Europe/Kirov", - "Europe/Kyiv", - "Europe/Lisbon", - "Europe/Ljubljana", - "Europe/London", - "Europe/Luxembourg", - "Europe/Madrid", - "Europe/Malta", - "Europe/Mariehamn", - "Europe/Minsk", - "Europe/Monaco", - "Europe/Moscow", - "Europe/Nicosia", - "Europe/Oslo", - "Europe/Paris", - "Europe/Podgorica", - "Europe/Prague", - "Europe/Riga", - "Europe/Rome", - "Europe/Samara", - "Europe/San_Marino", - "Europe/Sarajevo", - "Europe/Saratov", - "Europe/Simferopol", - "Europe/Skopje", - "Europe/Sofia", - "Europe/Stockholm", - "Europe/Tallinn", - "Europe/Tirane", - "Europe/Tiraspol", - "Europe/Ulyanovsk", - "Europe/Uzhgorod", - "Europe/Vaduz", - "Europe/Vatican", - "Europe/Vienna", - "Europe/Vilnius", - "Europe/Volgograd", - "Europe/Warsaw", - "Europe/Zagreb", - "Europe/Zaporozhye", - "Europe/Zurich", - "GB", - "GB-Eire", - "GMT", - "GMT+0", - "GMT-0", - "GMT0", - "Greenwich", - "HST", - "Hongkong", - "Iceland", - "Indian/Antananarivo", - "Indian/Chagos", - "Indian/Christmas", - "Indian/Cocos", - "Indian/Comoro", - "Indian/Kerguelen", - "Indian/Mahe", - "Indian/Maldives", - "Indian/Mauritius", - "Indian/Mayotte", - "Indian/Reunion", - "Iran", - "Israel", - "Jamaica", - "Japan", - "Kwajalein", - "Libya", - "MET", - "MST", - "MST7MDT", - "Mexico/BajaNorte", - "Mexico/BajaSur", - "Mexico/General", - "NZ", - "NZ-CHAT", - "Navajo", - "PRC", - "PST8PDT", - "Pacific/Apia", - "Pacific/Auckland", - "Pacific/Bougainville", - "Pacific/Chatham", - "Pacific/Chuuk", - "Pacific/Easter", - "Pacific/Efate", - "Pacific/Enderbury", - "Pacific/Fakaofo", - "Pacific/Fiji", - "Pacific/Funafuti", - "Pacific/Galapagos", - "Pacific/Gambier", - "Pacific/Guadalcanal", - "Pacific/Guam", - "Pacific/Honolulu", - "Pacific/Johnston", - "Pacific/Kanton", - "Pacific/Kiritimati", - "Pacific/Kosrae", - "Pacific/Kwajalein", - "Pacific/Majuro", - "Pacific/Marquesas", - "Pacific/Midway", - "Pacific/Nauru", - "Pacific/Niue", - "Pacific/Norfolk", - "Pacific/Noumea", - "Pacific/Pago_Pago", - "Pacific/Palau", - "Pacific/Pitcairn", - "Pacific/Pohnpei", - "Pacific/Ponape", - "Pacific/Port_Moresby", - "Pacific/Rarotonga", - "Pacific/Saipan", - "Pacific/Samoa", - "Pacific/Tahiti", - "Pacific/Tarawa", - "Pacific/Tongatapu", - "Pacific/Truk", - "Pacific/Wake", - "Pacific/Wallis", - "Pacific/Yap", - "Poland", - "Portugal", - "ROC", - "ROK", - "Singapore", - "Turkey", - "UCT", - "US/Alaska", - "US/Aleutian", - "US/Arizona", - "US/Central", - "US/East-Indiana", - "US/Eastern", - "US/Hawaii", - "US/Indiana-Starke", - "US/Michigan", - "US/Mountain", - "US/Pacific", - "US/Samoa", - "UTC", - "Universal", - "W-SU", - "WET", - "Zulu", - ) -} - - -def subsecond_precision(timestamp_literal: str) -> int: - """ - Given an ISO-8601 timestamp literal, eg '2023-01-01 12:13:14.123456+00:00' - figure out its subsecond precision so we can construct types like DATETIME(6) - - Note that in practice, this is either 3 or 6 digits (3 = millisecond precision, 6 = microsecond precision) - - 6 is the maximum because strftime's '%f' formats to microseconds and almost every database supports microsecond precision in timestamps - - Except Presto/Trino which in most cases only supports millisecond precision but will still honour '%f' and format to microseconds (replacing the remaining 3 digits with 0's) - - Python prior to 3.11 only supports 0, 3 or 6 digits in a timestamp literal. Any other amounts will throw a 'ValueError: Invalid isoformat string:' error - """ - try: - parsed = datetime.datetime.fromisoformat(timestamp_literal) - subsecond_digit_count = len(str(parsed.microsecond).rstrip("0")) - precision = 0 - if subsecond_digit_count > 3: - precision = 6 - elif subsecond_digit_count > 0: - precision = 3 - return precision - except ValueError: - return 0 diff --git a/third_party/bigframes_vendored/sqlglot/tokens.py b/third_party/bigframes_vendored/sqlglot/tokens.py deleted file mode 100644 index 9a2c4f7a650..00000000000 --- a/third_party/bigframes_vendored/sqlglot/tokens.py +++ /dev/null @@ -1,1640 +0,0 @@ -# Contains code from https://github.com/tobymao/sqlglot/blob/v28.5.0/sqlglot/tokens.py - -from __future__ import annotations - -import os -import typing as t -from enum import auto - -from bigframes_vendored.sqlglot.errors import SqlglotError, TokenError -from bigframes_vendored.sqlglot.helper import AutoName -from bigframes_vendored.sqlglot.trie import TrieResult, in_trie, new_trie - -if t.TYPE_CHECKING: - from bigframes_vendored.sqlglot.dialects.dialect import DialectType - - -try: - from bigframes_vendored.sqlglotrs import Tokenizer as RsTokenizer # type: ignore - from bigframes_vendored.sqlglotrs import ( - TokenizerDialectSettings as RsTokenizerDialectSettings, - ) - from bigframes_vendored.sqlglotrs import TokenizerSettings as RsTokenizerSettings - from bigframes_vendored.sqlglotrs import TokenTypeSettings as RsTokenTypeSettings - - USE_RS_TOKENIZER = os.environ.get("SQLGLOTRS_TOKENIZER", "1") == "1" -except ImportError: - USE_RS_TOKENIZER = False - - -class TokenType(AutoName): - L_PAREN = auto() - R_PAREN = auto() - L_BRACKET = auto() - R_BRACKET = auto() - L_BRACE = auto() - R_BRACE = auto() - COMMA = auto() - DOT = auto() - DASH = auto() - PLUS = auto() - COLON = auto() - DOTCOLON = auto() - DCOLON = auto() - DCOLONDOLLAR = auto() - DCOLONPERCENT = auto() - DCOLONQMARK = auto() - DQMARK = auto() - SEMICOLON = auto() - STAR = auto() - BACKSLASH = auto() - SLASH = auto() - LT = auto() - LTE = auto() - GT = auto() - GTE = auto() - NOT = auto() - EQ = auto() - NEQ = auto() - NULLSAFE_EQ = auto() - COLON_EQ = auto() - COLON_GT = auto() - NCOLON_GT = auto() - AND = auto() - OR = auto() - AMP = auto() - DPIPE = auto() - PIPE_GT = auto() - PIPE = auto() - PIPE_SLASH = auto() - DPIPE_SLASH = auto() - CARET = auto() - CARET_AT = auto() - TILDA = auto() - ARROW = auto() - DARROW = auto() - FARROW = auto() - HASH = auto() - HASH_ARROW = auto() - DHASH_ARROW = auto() - LR_ARROW = auto() - DAT = auto() - LT_AT = auto() - AT_GT = auto() - DOLLAR = auto() - PARAMETER = auto() - SESSION = auto() - SESSION_PARAMETER = auto() - SESSION_USER = auto() - DAMP = auto() - AMP_LT = auto() - AMP_GT = auto() - ADJACENT = auto() - XOR = auto() - DSTAR = auto() - QMARK_AMP = auto() - QMARK_PIPE = auto() - HASH_DASH = auto() - EXCLAMATION = auto() - - URI_START = auto() - - BLOCK_START = auto() - BLOCK_END = auto() - - SPACE = auto() - BREAK = auto() - - STRING = auto() - NUMBER = auto() - IDENTIFIER = auto() - DATABASE = auto() - COLUMN = auto() - COLUMN_DEF = auto() - SCHEMA = auto() - TABLE = auto() - WAREHOUSE = auto() - STAGE = auto() - STREAMLIT = auto() - VAR = auto() - BIT_STRING = auto() - HEX_STRING = auto() - BYTE_STRING = auto() - NATIONAL_STRING = auto() - RAW_STRING = auto() - HEREDOC_STRING = auto() - UNICODE_STRING = auto() - - # types - BIT = auto() - BOOLEAN = auto() - TINYINT = auto() - UTINYINT = auto() - SMALLINT = auto() - USMALLINT = auto() - MEDIUMINT = auto() - UMEDIUMINT = auto() - INT = auto() - UINT = auto() - BIGINT = auto() - UBIGINT = auto() - BIGNUM = auto() # unlimited precision int - INT128 = auto() - UINT128 = auto() - INT256 = auto() - UINT256 = auto() - FLOAT = auto() - DOUBLE = auto() - UDOUBLE = auto() - DECIMAL = auto() - DECIMAL32 = auto() - DECIMAL64 = auto() - DECIMAL128 = auto() - DECIMAL256 = auto() - DECFLOAT = auto() - UDECIMAL = auto() - BIGDECIMAL = auto() - CHAR = auto() - NCHAR = auto() - VARCHAR = auto() - NVARCHAR = auto() - BPCHAR = auto() - TEXT = auto() - MEDIUMTEXT = auto() - LONGTEXT = auto() - BLOB = auto() - MEDIUMBLOB = auto() - LONGBLOB = auto() - TINYBLOB = auto() - TINYTEXT = auto() - NAME = auto() - BINARY = auto() - VARBINARY = auto() - JSON = auto() - JSONB = auto() - TIME = auto() - TIMETZ = auto() - TIME_NS = auto() - TIMESTAMP = auto() - TIMESTAMPTZ = auto() - TIMESTAMPLTZ = auto() - TIMESTAMPNTZ = auto() - TIMESTAMP_S = auto() - TIMESTAMP_MS = auto() - TIMESTAMP_NS = auto() - DATETIME = auto() - DATETIME2 = auto() - DATETIME64 = auto() - SMALLDATETIME = auto() - DATE = auto() - DATE32 = auto() - INT4RANGE = auto() - INT4MULTIRANGE = auto() - INT8RANGE = auto() - INT8MULTIRANGE = auto() - NUMRANGE = auto() - NUMMULTIRANGE = auto() - TSRANGE = auto() - TSMULTIRANGE = auto() - TSTZRANGE = auto() - TSTZMULTIRANGE = auto() - DATERANGE = auto() - DATEMULTIRANGE = auto() - UUID = auto() - GEOGRAPHY = auto() - GEOGRAPHYPOINT = auto() - NULLABLE = auto() - GEOMETRY = auto() - POINT = auto() - RING = auto() - LINESTRING = auto() - LOCALTIME = auto() - LOCALTIMESTAMP = auto() - MULTILINESTRING = auto() - POLYGON = auto() - MULTIPOLYGON = auto() - HLLSKETCH = auto() - HSTORE = auto() - SUPER = auto() - SERIAL = auto() - SMALLSERIAL = auto() - BIGSERIAL = auto() - XML = auto() - YEAR = auto() - USERDEFINED = auto() - MONEY = auto() - SMALLMONEY = auto() - ROWVERSION = auto() - IMAGE = auto() - VARIANT = auto() - OBJECT = auto() - INET = auto() - IPADDRESS = auto() - IPPREFIX = auto() - IPV4 = auto() - IPV6 = auto() - ENUM = auto() - ENUM8 = auto() - ENUM16 = auto() - FIXEDSTRING = auto() - LOWCARDINALITY = auto() - NESTED = auto() - AGGREGATEFUNCTION = auto() - SIMPLEAGGREGATEFUNCTION = auto() - TDIGEST = auto() - UNKNOWN = auto() - VECTOR = auto() - DYNAMIC = auto() - VOID = auto() - - # keywords - ALIAS = auto() - ALTER = auto() - ALL = auto() - ANTI = auto() - ANY = auto() - APPLY = auto() - ARRAY = auto() - ASC = auto() - ASOF = auto() - ATTACH = auto() - AUTO_INCREMENT = auto() - BEGIN = auto() - BETWEEN = auto() - BULK_COLLECT_INTO = auto() - CACHE = auto() - CASE = auto() - CHARACTER_SET = auto() - CLUSTER_BY = auto() - COLLATE = auto() - COMMAND = auto() - COMMENT = auto() - COMMIT = auto() - CONNECT_BY = auto() - CONSTRAINT = auto() - COPY = auto() - CREATE = auto() - CROSS = auto() - CUBE = auto() - CURRENT_DATE = auto() - CURRENT_DATETIME = auto() - CURRENT_SCHEMA = auto() - CURRENT_TIME = auto() - CURRENT_TIMESTAMP = auto() - CURRENT_USER = auto() - CURRENT_ROLE = auto() - CURRENT_CATALOG = auto() - DECLARE = auto() - DEFAULT = auto() - DELETE = auto() - DESC = auto() - DESCRIBE = auto() - DETACH = auto() - DICTIONARY = auto() - DISTINCT = auto() - DISTRIBUTE_BY = auto() - DIV = auto() - DROP = auto() - ELSE = auto() - END = auto() - ESCAPE = auto() - EXCEPT = auto() - EXECUTE = auto() - EXISTS = auto() - FALSE = auto() - FETCH = auto() - FILE = auto() - FILE_FORMAT = auto() - FILTER = auto() - FINAL = auto() - FIRST = auto() - FOR = auto() - FORCE = auto() - FOREIGN_KEY = auto() - FORMAT = auto() - FROM = auto() - FULL = auto() - FUNCTION = auto() - GET = auto() - GLOB = auto() - GLOBAL = auto() - GRANT = auto() - GROUP_BY = auto() - GROUPING_SETS = auto() - HAVING = auto() - HINT = auto() - IGNORE = auto() - ILIKE = auto() - IN = auto() - INDEX = auto() - INDEXED_BY = auto() - INNER = auto() - INSERT = auto() - INSTALL = auto() - INTERSECT = auto() - INTERVAL = auto() - INTO = auto() - INTRODUCER = auto() - IRLIKE = auto() - IS = auto() - ISNULL = auto() - JOIN = auto() - JOIN_MARKER = auto() - KEEP = auto() - KEY = auto() - KILL = auto() - LANGUAGE = auto() - LATERAL = auto() - LEFT = auto() - LIKE = auto() - LIMIT = auto() - LIST = auto() - LOAD = auto() - LOCK = auto() - MAP = auto() - MATCH = auto() - MATCH_CONDITION = auto() - MATCH_RECOGNIZE = auto() - MEMBER_OF = auto() - MERGE = auto() - MOD = auto() - MODEL = auto() - NATURAL = auto() - NEXT = auto() - NOTHING = auto() - NOTNULL = auto() - NULL = auto() - OBJECT_IDENTIFIER = auto() - OFFSET = auto() - ON = auto() - ONLY = auto() - OPERATOR = auto() - ORDER_BY = auto() - ORDER_SIBLINGS_BY = auto() - ORDERED = auto() - ORDINALITY = auto() - OUTER = auto() - OVER = auto() - OVERLAPS = auto() - OVERWRITE = auto() - PARTITION = auto() - PARTITION_BY = auto() - PERCENT = auto() - PIVOT = auto() - PLACEHOLDER = auto() - POSITIONAL = auto() - PRAGMA = auto() - PREWHERE = auto() - PRIMARY_KEY = auto() - PROCEDURE = auto() - PROPERTIES = auto() - PSEUDO_TYPE = auto() - PUT = auto() - QUALIFY = auto() - QUOTE = auto() - QDCOLON = auto() - RANGE = auto() - RECURSIVE = auto() - REFRESH = auto() - RENAME = auto() - REPLACE = auto() - RETURNING = auto() - REVOKE = auto() - REFERENCES = auto() - RIGHT = auto() - RLIKE = auto() - ROLLBACK = auto() - ROLLUP = auto() - ROW = auto() - ROWS = auto() - SELECT = auto() - SEMI = auto() - SEPARATOR = auto() - SEQUENCE = auto() - SERDE_PROPERTIES = auto() - SET = auto() - SETTINGS = auto() - SHOW = auto() - SIMILAR_TO = auto() - SOME = auto() - SORT_BY = auto() - SOUNDS_LIKE = auto() - START_WITH = auto() - STORAGE_INTEGRATION = auto() - STRAIGHT_JOIN = auto() - STRUCT = auto() - SUMMARIZE = auto() - TABLE_SAMPLE = auto() - TAG = auto() - TEMPORARY = auto() - TOP = auto() - THEN = auto() - TRUE = auto() - TRUNCATE = auto() - UNCACHE = auto() - UNION = auto() - UNNEST = auto() - UNPIVOT = auto() - UPDATE = auto() - USE = auto() - USING = auto() - VALUES = auto() - VIEW = auto() - SEMANTIC_VIEW = auto() - VOLATILE = auto() - WHEN = auto() - WHERE = auto() - WINDOW = auto() - WITH = auto() - UNIQUE = auto() - UTC_DATE = auto() - UTC_TIME = auto() - UTC_TIMESTAMP = auto() - VERSION_SNAPSHOT = auto() - TIMESTAMP_SNAPSHOT = auto() - OPTION = auto() - SINK = auto() - SOURCE = auto() - ANALYZE = auto() - NAMESPACE = auto() - EXPORT = auto() - - # sentinel - HIVE_TOKEN_STREAM = auto() - - -_ALL_TOKEN_TYPES = list(TokenType) -_TOKEN_TYPE_TO_INDEX = {token_type: i for i, token_type in enumerate(_ALL_TOKEN_TYPES)} - - -class Token: - __slots__ = ("token_type", "text", "line", "col", "start", "end", "comments") - - @classmethod - def number(cls, number: int) -> Token: - """Returns a NUMBER token with `number` as its text.""" - return cls(TokenType.NUMBER, str(number)) - - @classmethod - def string(cls, string: str) -> Token: - """Returns a STRING token with `string` as its text.""" - return cls(TokenType.STRING, string) - - @classmethod - def identifier(cls, identifier: str) -> Token: - """Returns an IDENTIFIER token with `identifier` as its text.""" - return cls(TokenType.IDENTIFIER, identifier) - - @classmethod - def var(cls, var: str) -> Token: - """Returns an VAR token with `var` as its text.""" - return cls(TokenType.VAR, var) - - def __init__( - self, - token_type: TokenType, - text: str, - line: int = 1, - col: int = 1, - start: int = 0, - end: int = 0, - comments: t.Optional[t.List[str]] = None, - ) -> None: - """Token initializer. - - Args: - token_type: The TokenType Enum. - text: The text of the token. - line: The line that the token ends on. - col: The column that the token ends on. - start: The start index of the token. - end: The ending index of the token. - comments: The comments to attach to the token. - """ - self.token_type = token_type - self.text = text - self.line = line - self.col = col - self.start = start - self.end = end - self.comments = [] if comments is None else comments - - def __repr__(self) -> str: - attributes = ", ".join(f"{k}: {getattr(self, k)}" for k in self.__slots__) - return f"" - - -class _Tokenizer(type): - def __new__(cls, clsname, bases, attrs): - klass = super().__new__(cls, clsname, bases, attrs) - - def _convert_quotes(arr: t.List[str | t.Tuple[str, str]]) -> t.Dict[str, str]: - return dict( - (item, item) if isinstance(item, str) else (item[0], item[1]) - for item in arr - ) - - def _quotes_to_format( - token_type: TokenType, arr: t.List[str | t.Tuple[str, str]] - ) -> t.Dict[str, t.Tuple[str, TokenType]]: - return {k: (v, token_type) for k, v in _convert_quotes(arr).items()} - - klass._QUOTES = _convert_quotes(klass.QUOTES) - klass._IDENTIFIERS = _convert_quotes(klass.IDENTIFIERS) - - klass._FORMAT_STRINGS = { - **{ - p + s: (e, TokenType.NATIONAL_STRING) - for s, e in klass._QUOTES.items() - for p in ("n", "N") - }, - **_quotes_to_format(TokenType.BIT_STRING, klass.BIT_STRINGS), - **_quotes_to_format(TokenType.BYTE_STRING, klass.BYTE_STRINGS), - **_quotes_to_format(TokenType.HEX_STRING, klass.HEX_STRINGS), - **_quotes_to_format(TokenType.RAW_STRING, klass.RAW_STRINGS), - **_quotes_to_format(TokenType.HEREDOC_STRING, klass.HEREDOC_STRINGS), - **_quotes_to_format(TokenType.UNICODE_STRING, klass.UNICODE_STRINGS), - } - - klass._STRING_ESCAPES = set(klass.STRING_ESCAPES) - klass._ESCAPE_FOLLOW_CHARS = set(klass.ESCAPE_FOLLOW_CHARS) - klass._IDENTIFIER_ESCAPES = set(klass.IDENTIFIER_ESCAPES) - klass._COMMENTS = { - **dict( - (comment, None) - if isinstance(comment, str) - else (comment[0], comment[1]) - for comment in klass.COMMENTS - ), - "{#": "#}", # Ensure Jinja comments are tokenized correctly in all dialects - } - if klass.HINT_START in klass.KEYWORDS: - klass._COMMENTS[klass.HINT_START] = "*/" - - klass._KEYWORD_TRIE = new_trie( - key.upper() - for key in ( - *klass.KEYWORDS, - *klass._COMMENTS, - *klass._QUOTES, - *klass._FORMAT_STRINGS, - ) - if " " in key or any(single in key for single in klass.SINGLE_TOKENS) - ) - - if USE_RS_TOKENIZER: - settings = RsTokenizerSettings( - white_space={ - k: _TOKEN_TYPE_TO_INDEX[v] for k, v in klass.WHITE_SPACE.items() - }, - single_tokens={ - k: _TOKEN_TYPE_TO_INDEX[v] for k, v in klass.SINGLE_TOKENS.items() - }, - keywords={ - k: _TOKEN_TYPE_TO_INDEX[v] for k, v in klass.KEYWORDS.items() - }, - numeric_literals=klass.NUMERIC_LITERALS, - identifiers=klass._IDENTIFIERS, - identifier_escapes=klass._IDENTIFIER_ESCAPES, - string_escapes=klass._STRING_ESCAPES, - quotes=klass._QUOTES, - format_strings={ - k: (v1, _TOKEN_TYPE_TO_INDEX[v2]) - for k, (v1, v2) in klass._FORMAT_STRINGS.items() - }, - has_bit_strings=bool(klass.BIT_STRINGS), - has_hex_strings=bool(klass.HEX_STRINGS), - comments=klass._COMMENTS, - var_single_tokens=klass.VAR_SINGLE_TOKENS, - commands={_TOKEN_TYPE_TO_INDEX[v] for v in klass.COMMANDS}, - command_prefix_tokens={ - _TOKEN_TYPE_TO_INDEX[v] for v in klass.COMMAND_PREFIX_TOKENS - }, - heredoc_tag_is_identifier=klass.HEREDOC_TAG_IS_IDENTIFIER, - string_escapes_allowed_in_raw_strings=klass.STRING_ESCAPES_ALLOWED_IN_RAW_STRINGS, - nested_comments=klass.NESTED_COMMENTS, - hint_start=klass.HINT_START, - tokens_preceding_hint={ - _TOKEN_TYPE_TO_INDEX[v] for v in klass.TOKENS_PRECEDING_HINT - }, - escape_follow_chars=klass._ESCAPE_FOLLOW_CHARS, - ) - token_types = RsTokenTypeSettings( - bit_string=_TOKEN_TYPE_TO_INDEX[TokenType.BIT_STRING], - break_=_TOKEN_TYPE_TO_INDEX[TokenType.BREAK], - dcolon=_TOKEN_TYPE_TO_INDEX[TokenType.DCOLON], - heredoc_string=_TOKEN_TYPE_TO_INDEX[TokenType.HEREDOC_STRING], - raw_string=_TOKEN_TYPE_TO_INDEX[TokenType.RAW_STRING], - hex_string=_TOKEN_TYPE_TO_INDEX[TokenType.HEX_STRING], - identifier=_TOKEN_TYPE_TO_INDEX[TokenType.IDENTIFIER], - number=_TOKEN_TYPE_TO_INDEX[TokenType.NUMBER], - parameter=_TOKEN_TYPE_TO_INDEX[TokenType.PARAMETER], - semicolon=_TOKEN_TYPE_TO_INDEX[TokenType.SEMICOLON], - string=_TOKEN_TYPE_TO_INDEX[TokenType.STRING], - var=_TOKEN_TYPE_TO_INDEX[TokenType.VAR], - heredoc_string_alternative=_TOKEN_TYPE_TO_INDEX[ - klass.HEREDOC_STRING_ALTERNATIVE - ], - hint=_TOKEN_TYPE_TO_INDEX[TokenType.HINT], - ) - klass._RS_TOKENIZER = RsTokenizer(settings, token_types) - else: - klass._RS_TOKENIZER = None - - return klass - - -class Tokenizer(metaclass=_Tokenizer): - SINGLE_TOKENS = { - "(": TokenType.L_PAREN, - ")": TokenType.R_PAREN, - "[": TokenType.L_BRACKET, - "]": TokenType.R_BRACKET, - "{": TokenType.L_BRACE, - "}": TokenType.R_BRACE, - "&": TokenType.AMP, - "^": TokenType.CARET, - ":": TokenType.COLON, - ",": TokenType.COMMA, - ".": TokenType.DOT, - "-": TokenType.DASH, - "=": TokenType.EQ, - ">": TokenType.GT, - "<": TokenType.LT, - "%": TokenType.MOD, - "!": TokenType.NOT, - "|": TokenType.PIPE, - "+": TokenType.PLUS, - ";": TokenType.SEMICOLON, - "/": TokenType.SLASH, - "\\": TokenType.BACKSLASH, - "*": TokenType.STAR, - "~": TokenType.TILDA, - "?": TokenType.PLACEHOLDER, - "@": TokenType.PARAMETER, - "#": TokenType.HASH, - # Used for breaking a var like x'y' but nothing else the token type doesn't matter - "'": TokenType.UNKNOWN, - "`": TokenType.UNKNOWN, - '"': TokenType.UNKNOWN, - } - - BIT_STRINGS: t.List[str | t.Tuple[str, str]] = [] - BYTE_STRINGS: t.List[str | t.Tuple[str, str]] = [] - HEX_STRINGS: t.List[str | t.Tuple[str, str]] = [] - RAW_STRINGS: t.List[str | t.Tuple[str, str]] = [] - HEREDOC_STRINGS: t.List[str | t.Tuple[str, str]] = [] - UNICODE_STRINGS: t.List[str | t.Tuple[str, str]] = [] - IDENTIFIERS: t.List[str | t.Tuple[str, str]] = ['"'] - QUOTES: t.List[t.Tuple[str, str] | str] = ["'"] - STRING_ESCAPES = ["'"] - VAR_SINGLE_TOKENS: t.Set[str] = set() - ESCAPE_FOLLOW_CHARS: t.List[str] = [] - - # The strings in this list can always be used as escapes, regardless of the surrounding - # identifier delimiters. By default, the closing delimiter is assumed to also act as an - # identifier escape, e.g. if we use double-quotes, then they also act as escapes: "x""" - IDENTIFIER_ESCAPES: t.List[str] = [] - - # Whether the heredoc tags follow the same lexical rules as unquoted identifiers - HEREDOC_TAG_IS_IDENTIFIER = False - - # Token that we'll generate as a fallback if the heredoc prefix doesn't correspond to a heredoc - HEREDOC_STRING_ALTERNATIVE = TokenType.VAR - - # Whether string escape characters function as such when placed within raw strings - STRING_ESCAPES_ALLOWED_IN_RAW_STRINGS = True - - NESTED_COMMENTS = True - - HINT_START = "/*+" - - TOKENS_PRECEDING_HINT = { - TokenType.SELECT, - TokenType.INSERT, - TokenType.UPDATE, - TokenType.DELETE, - } - - # Autofilled - _COMMENTS: t.Dict[str, str] = {} - _FORMAT_STRINGS: t.Dict[str, t.Tuple[str, TokenType]] = {} - _IDENTIFIERS: t.Dict[str, str] = {} - _IDENTIFIER_ESCAPES: t.Set[str] = set() - _QUOTES: t.Dict[str, str] = {} - _STRING_ESCAPES: t.Set[str] = set() - _KEYWORD_TRIE: t.Dict = {} - _RS_TOKENIZER: t.Optional[t.Any] = None - _ESCAPE_FOLLOW_CHARS: t.Set[str] = set() - - KEYWORDS: t.Dict[str, TokenType] = { - **{f"{{%{postfix}": TokenType.BLOCK_START for postfix in ("", "+", "-")}, - **{f"{prefix}%}}": TokenType.BLOCK_END for prefix in ("", "+", "-")}, - **{f"{{{{{postfix}": TokenType.BLOCK_START for postfix in ("+", "-")}, - **{f"{prefix}}}}}": TokenType.BLOCK_END for prefix in ("+", "-")}, - HINT_START: TokenType.HINT, - "&<": TokenType.AMP_LT, - "&>": TokenType.AMP_GT, - "==": TokenType.EQ, - "::": TokenType.DCOLON, - "?::": TokenType.QDCOLON, - "||": TokenType.DPIPE, - "|>": TokenType.PIPE_GT, - ">=": TokenType.GTE, - "<=": TokenType.LTE, - "<>": TokenType.NEQ, - "!=": TokenType.NEQ, - ":=": TokenType.COLON_EQ, - "<=>": TokenType.NULLSAFE_EQ, - "->": TokenType.ARROW, - "->>": TokenType.DARROW, - "=>": TokenType.FARROW, - "#>": TokenType.HASH_ARROW, - "#>>": TokenType.DHASH_ARROW, - "<->": TokenType.LR_ARROW, - "&&": TokenType.DAMP, - "??": TokenType.DQMARK, - "~~~": TokenType.GLOB, - "~~": TokenType.LIKE, - "~~*": TokenType.ILIKE, - "~*": TokenType.IRLIKE, - "-|-": TokenType.ADJACENT, - "ALL": TokenType.ALL, - "AND": TokenType.AND, - "ANTI": TokenType.ANTI, - "ANY": TokenType.ANY, - "ASC": TokenType.ASC, - "AS": TokenType.ALIAS, - "ASOF": TokenType.ASOF, - "AUTOINCREMENT": TokenType.AUTO_INCREMENT, - "AUTO_INCREMENT": TokenType.AUTO_INCREMENT, - "BEGIN": TokenType.BEGIN, - "BETWEEN": TokenType.BETWEEN, - "CACHE": TokenType.CACHE, - "UNCACHE": TokenType.UNCACHE, - "CASE": TokenType.CASE, - "CHARACTER SET": TokenType.CHARACTER_SET, - "CLUSTER BY": TokenType.CLUSTER_BY, - "COLLATE": TokenType.COLLATE, - "COLUMN": TokenType.COLUMN, - "COMMIT": TokenType.COMMIT, - "CONNECT BY": TokenType.CONNECT_BY, - "CONSTRAINT": TokenType.CONSTRAINT, - "COPY": TokenType.COPY, - "CREATE": TokenType.CREATE, - "CROSS": TokenType.CROSS, - "CUBE": TokenType.CUBE, - "CURRENT_DATE": TokenType.CURRENT_DATE, - "CURRENT_SCHEMA": TokenType.CURRENT_SCHEMA, - "CURRENT_TIME": TokenType.CURRENT_TIME, - "CURRENT_TIMESTAMP": TokenType.CURRENT_TIMESTAMP, - "CURRENT_USER": TokenType.CURRENT_USER, - "CURRENT_CATALOG": TokenType.CURRENT_CATALOG, - "DATABASE": TokenType.DATABASE, - "DEFAULT": TokenType.DEFAULT, - "DELETE": TokenType.DELETE, - "DESC": TokenType.DESC, - "DESCRIBE": TokenType.DESCRIBE, - "DISTINCT": TokenType.DISTINCT, - "DISTRIBUTE BY": TokenType.DISTRIBUTE_BY, - "DIV": TokenType.DIV, - "DROP": TokenType.DROP, - "ELSE": TokenType.ELSE, - "END": TokenType.END, - "ENUM": TokenType.ENUM, - "ESCAPE": TokenType.ESCAPE, - "EXCEPT": TokenType.EXCEPT, - "EXECUTE": TokenType.EXECUTE, - "EXISTS": TokenType.EXISTS, - "FALSE": TokenType.FALSE, - "FETCH": TokenType.FETCH, - "FILTER": TokenType.FILTER, - "FILE": TokenType.FILE, - "FIRST": TokenType.FIRST, - "FULL": TokenType.FULL, - "FUNCTION": TokenType.FUNCTION, - "FOR": TokenType.FOR, - "FOREIGN KEY": TokenType.FOREIGN_KEY, - "FORMAT": TokenType.FORMAT, - "FROM": TokenType.FROM, - "GEOGRAPHY": TokenType.GEOGRAPHY, - "GEOMETRY": TokenType.GEOMETRY, - "GLOB": TokenType.GLOB, - "GROUP BY": TokenType.GROUP_BY, - "GROUPING SETS": TokenType.GROUPING_SETS, - "HAVING": TokenType.HAVING, - "ILIKE": TokenType.ILIKE, - "IN": TokenType.IN, - "INDEX": TokenType.INDEX, - "INET": TokenType.INET, - "INNER": TokenType.INNER, - "INSERT": TokenType.INSERT, - "INTERVAL": TokenType.INTERVAL, - "INTERSECT": TokenType.INTERSECT, - "INTO": TokenType.INTO, - "IS": TokenType.IS, - "ISNULL": TokenType.ISNULL, - "JOIN": TokenType.JOIN, - "KEEP": TokenType.KEEP, - "KILL": TokenType.KILL, - "LATERAL": TokenType.LATERAL, - "LEFT": TokenType.LEFT, - "LIKE": TokenType.LIKE, - "LIMIT": TokenType.LIMIT, - "LOAD": TokenType.LOAD, - "LOCALTIME": TokenType.LOCALTIME, - "LOCALTIMESTAMP": TokenType.LOCALTIMESTAMP, - "LOCK": TokenType.LOCK, - "MERGE": TokenType.MERGE, - "NAMESPACE": TokenType.NAMESPACE, - "NATURAL": TokenType.NATURAL, - "NEXT": TokenType.NEXT, - "NOT": TokenType.NOT, - "NOTNULL": TokenType.NOTNULL, - "NULL": TokenType.NULL, - "OBJECT": TokenType.OBJECT, - "OFFSET": TokenType.OFFSET, - "ON": TokenType.ON, - "OR": TokenType.OR, - "XOR": TokenType.XOR, - "ORDER BY": TokenType.ORDER_BY, - "ORDINALITY": TokenType.ORDINALITY, - "OUTER": TokenType.OUTER, - "OVER": TokenType.OVER, - "OVERLAPS": TokenType.OVERLAPS, - "OVERWRITE": TokenType.OVERWRITE, - "PARTITION": TokenType.PARTITION, - "PARTITION BY": TokenType.PARTITION_BY, - "PARTITIONED BY": TokenType.PARTITION_BY, - "PARTITIONED_BY": TokenType.PARTITION_BY, - "PERCENT": TokenType.PERCENT, - "PIVOT": TokenType.PIVOT, - "PRAGMA": TokenType.PRAGMA, - "PRIMARY KEY": TokenType.PRIMARY_KEY, - "PROCEDURE": TokenType.PROCEDURE, - "OPERATOR": TokenType.OPERATOR, - "QUALIFY": TokenType.QUALIFY, - "RANGE": TokenType.RANGE, - "RECURSIVE": TokenType.RECURSIVE, - "REGEXP": TokenType.RLIKE, - "RENAME": TokenType.RENAME, - "REPLACE": TokenType.REPLACE, - "RETURNING": TokenType.RETURNING, - "REFERENCES": TokenType.REFERENCES, - "RIGHT": TokenType.RIGHT, - "RLIKE": TokenType.RLIKE, - "ROLLBACK": TokenType.ROLLBACK, - "ROLLUP": TokenType.ROLLUP, - "ROW": TokenType.ROW, - "ROWS": TokenType.ROWS, - "SCHEMA": TokenType.SCHEMA, - "SELECT": TokenType.SELECT, - "SEMI": TokenType.SEMI, - "SESSION": TokenType.SESSION, - "SESSION_USER": TokenType.SESSION_USER, - "SET": TokenType.SET, - "SETTINGS": TokenType.SETTINGS, - "SHOW": TokenType.SHOW, - "SIMILAR TO": TokenType.SIMILAR_TO, - "SOME": TokenType.SOME, - "SORT BY": TokenType.SORT_BY, - "START WITH": TokenType.START_WITH, - "STRAIGHT_JOIN": TokenType.STRAIGHT_JOIN, - "TABLE": TokenType.TABLE, - "TABLESAMPLE": TokenType.TABLE_SAMPLE, - "TEMP": TokenType.TEMPORARY, - "TEMPORARY": TokenType.TEMPORARY, - "THEN": TokenType.THEN, - "TRUE": TokenType.TRUE, - "TRUNCATE": TokenType.TRUNCATE, - "UNION": TokenType.UNION, - "UNKNOWN": TokenType.UNKNOWN, - "UNNEST": TokenType.UNNEST, - "UNPIVOT": TokenType.UNPIVOT, - "UPDATE": TokenType.UPDATE, - "USE": TokenType.USE, - "USING": TokenType.USING, - "UUID": TokenType.UUID, - "VALUES": TokenType.VALUES, - "VIEW": TokenType.VIEW, - "VOLATILE": TokenType.VOLATILE, - "WHEN": TokenType.WHEN, - "WHERE": TokenType.WHERE, - "WINDOW": TokenType.WINDOW, - "WITH": TokenType.WITH, - "APPLY": TokenType.APPLY, - "ARRAY": TokenType.ARRAY, - "BIT": TokenType.BIT, - "BOOL": TokenType.BOOLEAN, - "BOOLEAN": TokenType.BOOLEAN, - "BYTE": TokenType.TINYINT, - "MEDIUMINT": TokenType.MEDIUMINT, - "INT1": TokenType.TINYINT, - "TINYINT": TokenType.TINYINT, - "INT16": TokenType.SMALLINT, - "SHORT": TokenType.SMALLINT, - "SMALLINT": TokenType.SMALLINT, - "HUGEINT": TokenType.INT128, - "UHUGEINT": TokenType.UINT128, - "INT2": TokenType.SMALLINT, - "INTEGER": TokenType.INT, - "INT": TokenType.INT, - "INT4": TokenType.INT, - "INT32": TokenType.INT, - "INT64": TokenType.BIGINT, - "INT128": TokenType.INT128, - "INT256": TokenType.INT256, - "LONG": TokenType.BIGINT, - "BIGINT": TokenType.BIGINT, - "INT8": TokenType.TINYINT, - "UINT": TokenType.UINT, - "UINT128": TokenType.UINT128, - "UINT256": TokenType.UINT256, - "DEC": TokenType.DECIMAL, - "DECIMAL": TokenType.DECIMAL, - "DECIMAL32": TokenType.DECIMAL32, - "DECIMAL64": TokenType.DECIMAL64, - "DECIMAL128": TokenType.DECIMAL128, - "DECIMAL256": TokenType.DECIMAL256, - "DECFLOAT": TokenType.DECFLOAT, - "BIGDECIMAL": TokenType.BIGDECIMAL, - "BIGNUMERIC": TokenType.BIGDECIMAL, - "BIGNUM": TokenType.BIGNUM, - "LIST": TokenType.LIST, - "MAP": TokenType.MAP, - "NULLABLE": TokenType.NULLABLE, - "NUMBER": TokenType.DECIMAL, - "NUMERIC": TokenType.DECIMAL, - "FIXED": TokenType.DECIMAL, - "REAL": TokenType.FLOAT, - "FLOAT": TokenType.FLOAT, - "FLOAT4": TokenType.FLOAT, - "FLOAT8": TokenType.DOUBLE, - "DOUBLE": TokenType.DOUBLE, - "DOUBLE PRECISION": TokenType.DOUBLE, - "JSON": TokenType.JSON, - "JSONB": TokenType.JSONB, - "CHAR": TokenType.CHAR, - "CHARACTER": TokenType.CHAR, - "CHAR VARYING": TokenType.VARCHAR, - "CHARACTER VARYING": TokenType.VARCHAR, - "NCHAR": TokenType.NCHAR, - "VARCHAR": TokenType.VARCHAR, - "VARCHAR2": TokenType.VARCHAR, - "NVARCHAR": TokenType.NVARCHAR, - "NVARCHAR2": TokenType.NVARCHAR, - "BPCHAR": TokenType.BPCHAR, - "STR": TokenType.TEXT, - "STRING": TokenType.TEXT, - "TEXT": TokenType.TEXT, - "LONGTEXT": TokenType.LONGTEXT, - "MEDIUMTEXT": TokenType.MEDIUMTEXT, - "TINYTEXT": TokenType.TINYTEXT, - "CLOB": TokenType.TEXT, - "LONGVARCHAR": TokenType.TEXT, - "BINARY": TokenType.BINARY, - "BLOB": TokenType.VARBINARY, - "LONGBLOB": TokenType.LONGBLOB, - "MEDIUMBLOB": TokenType.MEDIUMBLOB, - "TINYBLOB": TokenType.TINYBLOB, - "BYTEA": TokenType.VARBINARY, - "VARBINARY": TokenType.VARBINARY, - "TIME": TokenType.TIME, - "TIMETZ": TokenType.TIMETZ, - "TIME_NS": TokenType.TIME_NS, - "TIMESTAMP": TokenType.TIMESTAMP, - "TIMESTAMPTZ": TokenType.TIMESTAMPTZ, - "TIMESTAMPLTZ": TokenType.TIMESTAMPLTZ, - "TIMESTAMP_LTZ": TokenType.TIMESTAMPLTZ, - "TIMESTAMPNTZ": TokenType.TIMESTAMPNTZ, - "TIMESTAMP_NTZ": TokenType.TIMESTAMPNTZ, - "DATE": TokenType.DATE, - "DATETIME": TokenType.DATETIME, - "INT4RANGE": TokenType.INT4RANGE, - "INT4MULTIRANGE": TokenType.INT4MULTIRANGE, - "INT8RANGE": TokenType.INT8RANGE, - "INT8MULTIRANGE": TokenType.INT8MULTIRANGE, - "NUMRANGE": TokenType.NUMRANGE, - "NUMMULTIRANGE": TokenType.NUMMULTIRANGE, - "TSRANGE": TokenType.TSRANGE, - "TSMULTIRANGE": TokenType.TSMULTIRANGE, - "TSTZRANGE": TokenType.TSTZRANGE, - "TSTZMULTIRANGE": TokenType.TSTZMULTIRANGE, - "DATERANGE": TokenType.DATERANGE, - "DATEMULTIRANGE": TokenType.DATEMULTIRANGE, - "UNIQUE": TokenType.UNIQUE, - "VECTOR": TokenType.VECTOR, - "STRUCT": TokenType.STRUCT, - "SEQUENCE": TokenType.SEQUENCE, - "VARIANT": TokenType.VARIANT, - "ALTER": TokenType.ALTER, - "ANALYZE": TokenType.ANALYZE, - "CALL": TokenType.COMMAND, - "COMMENT": TokenType.COMMENT, - "EXPLAIN": TokenType.COMMAND, - "GRANT": TokenType.GRANT, - "REVOKE": TokenType.REVOKE, - "OPTIMIZE": TokenType.COMMAND, - "PREPARE": TokenType.COMMAND, - "VACUUM": TokenType.COMMAND, - "USER-DEFINED": TokenType.USERDEFINED, - "FOR VERSION": TokenType.VERSION_SNAPSHOT, - "FOR TIMESTAMP": TokenType.TIMESTAMP_SNAPSHOT, - } - - WHITE_SPACE: t.Dict[t.Optional[str], TokenType] = { - " ": TokenType.SPACE, - "\t": TokenType.SPACE, - "\n": TokenType.BREAK, - "\r": TokenType.BREAK, - } - - COMMANDS = { - TokenType.COMMAND, - TokenType.EXECUTE, - TokenType.FETCH, - TokenType.SHOW, - TokenType.RENAME, - } - - COMMAND_PREFIX_TOKENS = {TokenType.SEMICOLON, TokenType.BEGIN} - - # Handle numeric literals like in hive (3L = BIGINT) - NUMERIC_LITERALS: t.Dict[str, str] = {} - - COMMENTS = ["--", ("/*", "*/")] - - __slots__ = ( - "sql", - "size", - "tokens", - "dialect", - "use_rs_tokenizer", - "_start", - "_current", - "_line", - "_col", - "_comments", - "_char", - "_end", - "_peek", - "_prev_token_line", - "_rs_dialect_settings", - ) - - def __init__( - self, - dialect: DialectType = None, - use_rs_tokenizer: t.Optional[bool] = None, - **opts: t.Any, - ) -> None: - from bigframes_vendored.sqlglot.dialects import Dialect - - self.dialect = Dialect.get_or_raise(dialect) - - # initialize `use_rs_tokenizer`, and allow it to be overwritten per Tokenizer instance - self.use_rs_tokenizer = ( - use_rs_tokenizer if use_rs_tokenizer is not None else USE_RS_TOKENIZER - ) - - if self.use_rs_tokenizer: - self._rs_dialect_settings = RsTokenizerDialectSettings( - unescaped_sequences=self.dialect.UNESCAPED_SEQUENCES, - identifiers_can_start_with_digit=self.dialect.IDENTIFIERS_CAN_START_WITH_DIGIT, - numbers_can_be_underscore_separated=self.dialect.NUMBERS_CAN_BE_UNDERSCORE_SEPARATED, - ) - - self.reset() - - def reset(self) -> None: - self.sql = "" - self.size = 0 - self.tokens: t.List[Token] = [] - self._start = 0 - self._current = 0 - self._line = 1 - self._col = 0 - self._comments: t.List[str] = [] - - self._char = "" - self._end = False - self._peek = "" - self._prev_token_line = -1 - - def tokenize(self, sql: str) -> t.List[Token]: - """Returns a list of tokens corresponding to the SQL string `sql`.""" - if self.use_rs_tokenizer: - return self.tokenize_rs(sql) - - self.reset() - self.sql = sql - self.size = len(sql) - - try: - self._scan() - except Exception as e: - start = max(self._current - 50, 0) - end = min(self._current + 50, self.size - 1) - context = self.sql[start:end] - raise TokenError(f"Error tokenizing '{context}'") from e - - return self.tokens - - def _scan(self, until: t.Optional[t.Callable] = None) -> None: - while self.size and not self._end: - current = self._current - - # Skip spaces here rather than iteratively calling advance() for performance reasons - while current < self.size: - char = self.sql[current] - - if char.isspace() and (char == " " or char == "\t"): - current += 1 - else: - break - - offset = current - self._current if current > self._current else 1 - - self._start = current - self._advance(offset) - - if not self._char.isspace(): - if self._char.isdigit(): - self._scan_number() - elif self._char in self._IDENTIFIERS: - self._scan_identifier(self._IDENTIFIERS[self._char]) - else: - self._scan_keywords() - - if until and until(): - break - - if self.tokens and self._comments: - self.tokens[-1].comments.extend(self._comments) - - def _chars(self, size: int) -> str: - if size == 1: - return self._char - - start = self._current - 1 - end = start + size - - return self.sql[start:end] if end <= self.size else "" - - def _advance(self, i: int = 1, alnum: bool = False) -> None: - if self.WHITE_SPACE.get(self._char) is TokenType.BREAK: - # Ensures we don't count an extra line if we get a \r\n line break sequence - if not (self._char == "\r" and self._peek == "\n"): - self._col = i - self._line += 1 - else: - self._col += i - - self._current += i - self._end = self._current >= self.size - self._char = self.sql[self._current - 1] - self._peek = "" if self._end else self.sql[self._current] - - if alnum and self._char.isalnum(): - # Here we use local variables instead of attributes for better performance - _col = self._col - _current = self._current - _end = self._end - _peek = self._peek - - while _peek.isalnum(): - _col += 1 - _current += 1 - _end = _current >= self.size - _peek = "" if _end else self.sql[_current] - - self._col = _col - self._current = _current - self._end = _end - self._peek = _peek - self._char = self.sql[_current - 1] - - @property - def _text(self) -> str: - return self.sql[self._start : self._current] - - def _add(self, token_type: TokenType, text: t.Optional[str] = None) -> None: - self._prev_token_line = self._line - - if self._comments and token_type == TokenType.SEMICOLON and self.tokens: - self.tokens[-1].comments.extend(self._comments) - self._comments = [] - - self.tokens.append( - Token( - token_type, - text=self._text if text is None else text, - line=self._line, - col=self._col, - start=self._start, - end=self._current - 1, - comments=self._comments, - ) - ) - self._comments = [] - - # If we have either a semicolon or a begin token before the command's token, we'll parse - # whatever follows the command's token as a string - if ( - token_type in self.COMMANDS - and self._peek != ";" - and ( - len(self.tokens) == 1 - or self.tokens[-2].token_type in self.COMMAND_PREFIX_TOKENS - ) - ): - start = self._current - tokens = len(self.tokens) - self._scan(lambda: self._peek == ";") - self.tokens = self.tokens[:tokens] - text = self.sql[start : self._current].strip() - if text: - self._add(TokenType.STRING, text) - - def _scan_keywords(self) -> None: - size = 0 - word = None - chars = self._text - char = chars - prev_space = False - skip = False - trie = self._KEYWORD_TRIE - single_token = char in self.SINGLE_TOKENS - - while chars: - if skip: - result = TrieResult.PREFIX - else: - result, trie = in_trie(trie, char.upper()) - - if result == TrieResult.FAILED: - break - if result == TrieResult.EXISTS: - word = chars - - end = self._current + size - size += 1 - - if end < self.size: - char = self.sql[end] - single_token = single_token or char in self.SINGLE_TOKENS - is_space = char.isspace() - - if not is_space or not prev_space: - if is_space: - char = " " - chars += char - prev_space = is_space - skip = False - else: - skip = True - else: - char = "" - break - - if word: - if self._scan_string(word): - return - if self._scan_comment(word): - return - if prev_space or single_token or not char: - self._advance(size - 1) - word = word.upper() - self._add(self.KEYWORDS[word], text=word) - return - - if self._char in self.SINGLE_TOKENS: - self._add(self.SINGLE_TOKENS[self._char], text=self._char) - return - - self._scan_var() - - def _scan_comment(self, comment_start: str) -> bool: - if comment_start not in self._COMMENTS: - return False - - comment_start_line = self._line - comment_start_size = len(comment_start) - comment_end = self._COMMENTS[comment_start] - - if comment_end: - # Skip the comment's start delimiter - self._advance(comment_start_size) - - comment_count = 1 - comment_end_size = len(comment_end) - - while not self._end: - if self._chars(comment_end_size) == comment_end: - comment_count -= 1 - if not comment_count: - break - - self._advance(alnum=True) - - # Nested comments are allowed by some dialects, e.g. databricks, duckdb, postgres - if ( - self.NESTED_COMMENTS - and not self._end - and self._chars(comment_end_size) == comment_start - ): - self._advance(comment_start_size) - comment_count += 1 - - self._comments.append( - self._text[comment_start_size : -comment_end_size + 1] - ) - self._advance(comment_end_size - 1) - else: - while ( - not self._end - and self.WHITE_SPACE.get(self._peek) is not TokenType.BREAK - ): - self._advance(alnum=True) - self._comments.append(self._text[comment_start_size:]) - - if ( - comment_start == self.HINT_START - and self.tokens - and self.tokens[-1].token_type in self.TOKENS_PRECEDING_HINT - ): - self._add(TokenType.HINT) - - # Leading comment is attached to the succeeding token, whilst trailing comment to the preceding. - # Multiple consecutive comments are preserved by appending them to the current comments list. - if comment_start_line == self._prev_token_line: - self.tokens[-1].comments.extend(self._comments) - self._comments = [] - self._prev_token_line = self._line - - return True - - def _scan_number(self) -> None: - if self._char == "0": - peek = self._peek.upper() - if peek == "B": - return ( - self._scan_bits() - if self.BIT_STRINGS - else self._add(TokenType.NUMBER) - ) - elif peek == "X": - return ( - self._scan_hex() - if self.HEX_STRINGS - else self._add(TokenType.NUMBER) - ) - - decimal = False - scientific = 0 - - while True: - if self._peek.isdigit(): - self._advance() - elif self._peek == "." and not decimal: - if self.tokens and self.tokens[-1].token_type == TokenType.PARAMETER: - return self._add(TokenType.NUMBER) - decimal = True - self._advance() - elif self._peek in ("-", "+") and scientific == 1: - # Only consume +/- if followed by a digit - if ( - self._current + 1 < self.size - and self.sql[self._current + 1].isdigit() - ): - scientific += 1 - self._advance() - else: - return self._add(TokenType.NUMBER) - elif self._peek.upper() == "E" and not scientific: - scientific += 1 - self._advance() - elif self._peek == "_" and self.dialect.NUMBERS_CAN_BE_UNDERSCORE_SEPARATED: - self._advance() - elif self._peek.isidentifier(): - number_text = self._text - literal = "" - - while self._peek.strip() and self._peek not in self.SINGLE_TOKENS: - literal += self._peek - self._advance() - - token_type = self.KEYWORDS.get( - self.NUMERIC_LITERALS.get(literal.upper(), "") - ) - - if token_type: - self._add(TokenType.NUMBER, number_text) - self._add(TokenType.DCOLON, "::") - return self._add(token_type, literal) - elif self.dialect.IDENTIFIERS_CAN_START_WITH_DIGIT: - return self._add(TokenType.VAR) - - self._advance(-len(literal)) - return self._add(TokenType.NUMBER, number_text) - else: - return self._add(TokenType.NUMBER) - - def _scan_bits(self) -> None: - self._advance() - value = self._extract_value() - try: - # If `value` can't be converted to a binary, fallback to tokenizing it as an identifier - int(value, 2) - self._add(TokenType.BIT_STRING, value[2:]) # Drop the 0b - except ValueError: - self._add(TokenType.IDENTIFIER) - - def _scan_hex(self) -> None: - self._advance() - value = self._extract_value() - try: - # If `value` can't be converted to a hex, fallback to tokenizing it as an identifier - int(value, 16) - self._add(TokenType.HEX_STRING, value[2:]) # Drop the 0x - except ValueError: - self._add(TokenType.IDENTIFIER) - - def _extract_value(self) -> str: - while True: - char = self._peek.strip() - if char and char not in self.SINGLE_TOKENS: - self._advance(alnum=True) - else: - break - - return self._text - - def _scan_string(self, start: str) -> bool: - base = None - token_type = TokenType.STRING - - if start in self._QUOTES: - end = self._QUOTES[start] - elif start in self._FORMAT_STRINGS: - end, token_type = self._FORMAT_STRINGS[start] - - if token_type == TokenType.HEX_STRING: - base = 16 - elif token_type == TokenType.BIT_STRING: - base = 2 - elif token_type == TokenType.HEREDOC_STRING: - self._advance() - - if self._char == end: - tag = "" - else: - tag = self._extract_string( - end, - raw_string=True, - raise_unmatched=not self.HEREDOC_TAG_IS_IDENTIFIER, - ) - - if ( - tag - and self.HEREDOC_TAG_IS_IDENTIFIER - and (self._end or tag.isdigit() or any(c.isspace() for c in tag)) - ): - if not self._end: - self._advance(-1) - - self._advance(-len(tag)) - self._add(self.HEREDOC_STRING_ALTERNATIVE) - return True - - end = f"{start}{tag}{end}" - else: - return False - - self._advance(len(start)) - text = self._extract_string(end, raw_string=token_type == TokenType.RAW_STRING) - - if base and text: - try: - int(text, base) - except Exception: - raise TokenError( - f"Numeric string contains invalid characters from {self._line}:{self._start}" - ) - - self._add(token_type, text) - return True - - def _scan_identifier(self, identifier_end: str) -> None: - self._advance() - text = self._extract_string( - identifier_end, escapes=self._IDENTIFIER_ESCAPES | {identifier_end} - ) - self._add(TokenType.IDENTIFIER, text) - - def _scan_var(self) -> None: - while True: - char = self._peek.strip() - if char and ( - char in self.VAR_SINGLE_TOKENS or char not in self.SINGLE_TOKENS - ): - self._advance(alnum=True) - else: - break - - self._add( - TokenType.VAR - if self.tokens and self.tokens[-1].token_type == TokenType.PARAMETER - else self.KEYWORDS.get(self._text.upper(), TokenType.VAR) - ) - - def _extract_string( - self, - delimiter: str, - escapes: t.Optional[t.Set[str]] = None, - raw_string: bool = False, - raise_unmatched: bool = True, - ) -> str: - text = "" - delim_size = len(delimiter) - escapes = self._STRING_ESCAPES if escapes is None else escapes - - while True: - if ( - not raw_string - and self.dialect.UNESCAPED_SEQUENCES - and self._peek - and self._char in self.STRING_ESCAPES - ): - unescaped_sequence = self.dialect.UNESCAPED_SEQUENCES.get( - self._char + self._peek - ) - if unescaped_sequence: - self._advance(2) - text += unescaped_sequence - continue - - is_valid_custom_escape = ( - self.ESCAPE_FOLLOW_CHARS - and self._char == "\\" - and self._peek not in self.ESCAPE_FOLLOW_CHARS - ) - - if ( - (self.STRING_ESCAPES_ALLOWED_IN_RAW_STRINGS or not raw_string) - and self._char in escapes - and ( - self._peek == delimiter - or self._peek in escapes - or is_valid_custom_escape - ) - and (self._char not in self._QUOTES or self._char == self._peek) - ): - if self._peek == delimiter: - text += self._peek - elif is_valid_custom_escape and self._char != self._peek: - text += self._peek - else: - text += self._char + self._peek - - if self._current + 1 < self.size: - self._advance(2) - else: - raise TokenError( - f"Missing {delimiter} from {self._line}:{self._current}" - ) - else: - if self._chars(delim_size) == delimiter: - if delim_size > 1: - self._advance(delim_size - 1) - break - - if self._end: - if not raise_unmatched: - return text + self._char - - raise TokenError( - f"Missing {delimiter} from {self._line}:{self._start}" - ) - - current = self._current - 1 - self._advance(alnum=True) - text += self.sql[current : self._current - 1] - - return text - - def tokenize_rs(self, sql: str) -> t.List[Token]: - if not self._RS_TOKENIZER: - raise SqlglotError("Rust tokenizer is not available") - - tokens, error_msg = self._RS_TOKENIZER.tokenize(sql, self._rs_dialect_settings) - for token in tokens: - token.token_type = _ALL_TOKEN_TYPES[token.token_type_index] - - # Setting this here so partial token lists can be inspected even if there is a failure - self.tokens = tokens - - if error_msg is not None: - raise TokenError(error_msg) - - return tokens diff --git a/third_party/bigframes_vendored/sqlglot/transforms.py b/third_party/bigframes_vendored/sqlglot/transforms.py deleted file mode 100644 index edb1a21d6cb..00000000000 --- a/third_party/bigframes_vendored/sqlglot/transforms.py +++ /dev/null @@ -1,1127 +0,0 @@ -# Contains code from https://github.com/tobymao/sqlglot/blob/v28.5.0/sqlglot/transforms.py - -from __future__ import annotations - -import typing as t - -from bigframes_vendored.sqlglot import expressions as exp -from bigframes_vendored.sqlglot.errors import UnsupportedError -from bigframes_vendored.sqlglot.helper import find_new_name, name_sequence, seq_get - -if t.TYPE_CHECKING: - from bigframes_vendored.sqlglot._typing import E - from bigframes_vendored.sqlglot.generator import Generator - - -def preprocess( - transforms: t.List[t.Callable[[exp.Expression], exp.Expression]], - generator: t.Optional[t.Callable[[Generator, exp.Expression], str]] = None, -) -> t.Callable[[Generator, exp.Expression], str]: - """ - Creates a new transform by chaining a sequence of transformations and converts the resulting - expression to SQL, using either the "_sql" method corresponding to the resulting expression, - or the appropriate `Generator.TRANSFORMS` function (when applicable -- see below). - - Args: - transforms: sequence of transform functions. These will be called in order. - - Returns: - Function that can be used as a generator transform. - """ - - def _to_sql(self, expression: exp.Expression) -> str: - expression_type = type(expression) - - try: - expression = transforms[0](expression) - for transform in transforms[1:]: - expression = transform(expression) - except UnsupportedError as unsupported_error: - self.unsupported(str(unsupported_error)) - - if generator: - return generator(self, expression) - - _sql_handler = getattr(self, expression.key + "_sql", None) - if _sql_handler: - return _sql_handler(expression) - - transforms_handler = self.TRANSFORMS.get(type(expression)) - if transforms_handler: - if expression_type is type(expression): - if isinstance(expression, exp.Func): - return self.function_fallback_sql(expression) - - # Ensures we don't enter an infinite loop. This can happen when the original expression - # has the same type as the final expression and there's no _sql method available for it, - # because then it'd re-enter _to_sql. - raise ValueError( - f"Expression type {expression.__class__.__name__} requires a _sql method in order to be transformed." - ) - - return transforms_handler(self, expression) - - raise ValueError( - f"Unsupported expression type {expression.__class__.__name__}." - ) - - return _to_sql - - -def unnest_generate_date_array_using_recursive_cte( - expression: exp.Expression, -) -> exp.Expression: - if isinstance(expression, exp.Select): - count = 0 - recursive_ctes = [] - - for unnest in expression.find_all(exp.Unnest): - if ( - not isinstance(unnest.parent, (exp.From, exp.Join)) - or len(unnest.expressions) != 1 - or not isinstance(unnest.expressions[0], exp.GenerateDateArray) - ): - continue - - generate_date_array = unnest.expressions[0] - start = generate_date_array.args.get("start") - end = generate_date_array.args.get("end") - step = generate_date_array.args.get("step") - - if not start or not end or not isinstance(step, exp.Interval): - continue - - alias = unnest.args.get("alias") - column_name = ( - alias.columns[0] if isinstance(alias, exp.TableAlias) else "date_value" - ) - - start = exp.cast(start, "date") - date_add = exp.func( - "date_add", - column_name, - exp.Literal.number(step.name), - step.args.get("unit"), - ) - cast_date_add = exp.cast(date_add, "date") - - cte_name = "_generated_dates" + (f"_{count}" if count else "") - - base_query = exp.select(start.as_(column_name)) - recursive_query = ( - exp.select(cast_date_add) - .from_(cte_name) - .where(cast_date_add <= exp.cast(end, "date")) - ) - cte_query = base_query.union(recursive_query, distinct=False) - - generate_dates_query = exp.select(column_name).from_(cte_name) - unnest.replace(generate_dates_query.subquery(cte_name)) - - recursive_ctes.append( - exp.alias_(exp.CTE(this=cte_query), cte_name, table=[column_name]) - ) - count += 1 - - if recursive_ctes: - with_expression = expression.args.get("with_") or exp.With() - with_expression.set("recursive", True) - with_expression.set( - "expressions", [*recursive_ctes, *with_expression.expressions] - ) - expression.set("with_", with_expression) - - return expression - - -def unnest_generate_series(expression: exp.Expression) -> exp.Expression: - """Unnests GENERATE_SERIES or SEQUENCE table references.""" - this = expression.this - if isinstance(expression, exp.Table) and isinstance(this, exp.GenerateSeries): - unnest = exp.Unnest(expressions=[this]) - if expression.alias: - return exp.alias_(unnest, alias="_u", table=[expression.alias], copy=False) - - return unnest - - return expression - - -def eliminate_distinct_on(expression: exp.Expression) -> exp.Expression: - """ - Convert SELECT DISTINCT ON statements to a subquery with a window function. - - This is useful for dialects that don't support SELECT DISTINCT ON but support window functions. - - Args: - expression: the expression that will be transformed. - - Returns: - The transformed expression. - """ - if ( - isinstance(expression, exp.Select) - and expression.args.get("distinct") - and isinstance(expression.args["distinct"].args.get("on"), exp.Tuple) - ): - row_number_window_alias = find_new_name(expression.named_selects, "_row_number") - - distinct_cols = expression.args["distinct"].pop().args["on"].expressions - window = exp.Window(this=exp.RowNumber(), partition_by=distinct_cols) - - order = expression.args.get("order") - if order: - window.set("order", order.pop()) - else: - window.set( - "order", exp.Order(expressions=[c.copy() for c in distinct_cols]) - ) - - window = exp.alias_(window, row_number_window_alias) - expression.select(window, copy=False) - - # We add aliases to the projections so that we can safely reference them in the outer query - new_selects = [] - taken_names = {row_number_window_alias} - for select in expression.selects[:-1]: - if select.is_star: - new_selects = [exp.Star()] - break - - if not isinstance(select, exp.Alias): - alias = find_new_name(taken_names, select.output_name or "_col") - quoted = ( - select.this.args.get("quoted") - if isinstance(select, exp.Column) - else None - ) - select = select.replace(exp.alias_(select, alias, quoted=quoted)) - - taken_names.add(select.output_name) - new_selects.append(select.args["alias"]) - - return ( - exp.select(*new_selects, copy=False) - .from_(expression.subquery("_t", copy=False), copy=False) - .where(exp.column(row_number_window_alias).eq(1), copy=False) - ) - - return expression - - -def eliminate_qualify(expression: exp.Expression) -> exp.Expression: - """ - Convert SELECT statements that contain the QUALIFY clause into subqueries, filtered equivalently. - - The idea behind this transformation can be seen in Snowflake's documentation for QUALIFY: - https://docs.snowflake.com/en/sql-reference/constructs/qualify - - Some dialects don't support window functions in the WHERE clause, so we need to include them as - projections in the subquery, in order to refer to them in the outer filter using aliases. Also, - if a column is referenced in the QUALIFY clause but is not selected, we need to include it too, - otherwise we won't be able to refer to it in the outer query's WHERE clause. Finally, if a - newly aliased projection is referenced in the QUALIFY clause, it will be replaced by the - corresponding expression to avoid creating invalid column references. - """ - if isinstance(expression, exp.Select) and expression.args.get("qualify"): - taken = set(expression.named_selects) - for select in expression.selects: - if not select.alias_or_name: - alias = find_new_name(taken, "_c") - select.replace(exp.alias_(select, alias)) - taken.add(alias) - - def _select_alias_or_name(select: exp.Expression) -> str | exp.Column: - alias_or_name = select.alias_or_name - identifier = select.args.get("alias") or select.this - if isinstance(identifier, exp.Identifier): - return exp.column(alias_or_name, quoted=identifier.args.get("quoted")) - return alias_or_name - - outer_selects = exp.select( - *list(map(_select_alias_or_name, expression.selects)) - ) - qualify_filters = expression.args["qualify"].pop().this - expression_by_alias = { - select.alias: select.this - for select in expression.selects - if isinstance(select, exp.Alias) - } - - select_candidates = ( - exp.Window if expression.is_star else (exp.Window, exp.Column) - ) - for select_candidate in list(qualify_filters.find_all(select_candidates)): - if isinstance(select_candidate, exp.Window): - if expression_by_alias: - for column in select_candidate.find_all(exp.Column): - expr = expression_by_alias.get(column.name) - if expr: - column.replace(expr) - - alias = find_new_name(expression.named_selects, "_w") - expression.select(exp.alias_(select_candidate, alias), copy=False) - column = exp.column(alias) - - if isinstance(select_candidate.parent, exp.Qualify): - qualify_filters = column - else: - select_candidate.replace(column) - elif select_candidate.name not in expression.named_selects: - expression.select(select_candidate.copy(), copy=False) - - return outer_selects.from_( - expression.subquery(alias="_t", copy=False), copy=False - ).where(qualify_filters, copy=False) - - return expression - - -def remove_precision_parameterized_types(expression: exp.Expression) -> exp.Expression: - """ - Some dialects only allow the precision for parameterized types to be defined in the DDL and not in - other expressions. This transforms removes the precision from parameterized types in expressions. - """ - for node in expression.find_all(exp.DataType): - node.set( - "expressions", - [e for e in node.expressions if not isinstance(e, exp.DataTypeParam)], - ) - - return expression - - -def unqualify_unnest(expression: exp.Expression) -> exp.Expression: - """Remove references to unnest table aliases, added by the optimizer's qualify_columns step.""" - from bigframes_vendored.sqlglot.optimizer.scope import find_all_in_scope - - if isinstance(expression, exp.Select): - unnest_aliases = { - unnest.alias - for unnest in find_all_in_scope(expression, exp.Unnest) - if isinstance(unnest.parent, (exp.From, exp.Join)) - } - if unnest_aliases: - for column in expression.find_all(exp.Column): - leftmost_part = column.parts[0] - if ( - leftmost_part.arg_key != "this" - and leftmost_part.this in unnest_aliases - ): - leftmost_part.pop() - - return expression - - -def unnest_to_explode( - expression: exp.Expression, - unnest_using_arrays_zip: bool = True, -) -> exp.Expression: - """Convert cross join unnest into lateral view explode.""" - - def _unnest_zip_exprs( - u: exp.Unnest, unnest_exprs: t.List[exp.Expression], has_multi_expr: bool - ) -> t.List[exp.Expression]: - if has_multi_expr: - if not unnest_using_arrays_zip: - raise UnsupportedError( - "Cannot transpile UNNEST with multiple input arrays" - ) - - # Use INLINE(ARRAYS_ZIP(...)) for multiple expressions - zip_exprs: t.List[exp.Expression] = [ - exp.Anonymous(this="ARRAYS_ZIP", expressions=unnest_exprs) - ] - u.set("expressions", zip_exprs) - return zip_exprs - return unnest_exprs - - def _udtf_type(u: exp.Unnest, has_multi_expr: bool) -> t.Type[exp.Func]: - if u.args.get("offset"): - return exp.Posexplode - return exp.Inline if has_multi_expr else exp.Explode - - if isinstance(expression, exp.Select): - from_ = expression.args.get("from_") - - if from_ and isinstance(from_.this, exp.Unnest): - unnest = from_.this - alias = unnest.args.get("alias") - exprs = unnest.expressions - has_multi_expr = len(exprs) > 1 - this, *_ = _unnest_zip_exprs(unnest, exprs, has_multi_expr) - - columns = alias.columns if alias else [] - offset = unnest.args.get("offset") - if offset: - columns.insert( - 0, - offset - if isinstance(offset, exp.Identifier) - else exp.to_identifier("pos"), - ) - - unnest.replace( - exp.Table( - this=_udtf_type(unnest, has_multi_expr)(this=this), - alias=exp.TableAlias(this=alias.this, columns=columns) - if alias - else None, - ) - ) - - joins = expression.args.get("joins") or [] - for join in list(joins): - join_expr = join.this - - is_lateral = isinstance(join_expr, exp.Lateral) - - unnest = join_expr.this if is_lateral else join_expr - - if isinstance(unnest, exp.Unnest): - if is_lateral: - alias = join_expr.args.get("alias") - else: - alias = unnest.args.get("alias") - exprs = unnest.expressions - # The number of unnest.expressions will be changed by _unnest_zip_exprs, we need to record it here - has_multi_expr = len(exprs) > 1 - exprs = _unnest_zip_exprs(unnest, exprs, has_multi_expr) - - joins.remove(join) - - alias_cols = alias.columns if alias else [] - - # # Handle UNNEST to LATERAL VIEW EXPLODE: Exception is raised when there are 0 or > 2 aliases - # Spark LATERAL VIEW EXPLODE requires single alias for array/struct and two for Map type column unlike unnest in trino/presto which can take an arbitrary amount. - # Refs: https://spark.apache.org/docs/latest/sql-ref-syntax-qry-select-lateral-view.html - - if not has_multi_expr and len(alias_cols) not in (1, 2): - raise UnsupportedError( - "CROSS JOIN UNNEST to LATERAL VIEW EXPLODE transformation requires explicit column aliases" - ) - - offset = unnest.args.get("offset") - if offset: - alias_cols.insert( - 0, - offset - if isinstance(offset, exp.Identifier) - else exp.to_identifier("pos"), - ) - - for e, column in zip(exprs, alias_cols): - expression.append( - "laterals", - exp.Lateral( - this=_udtf_type(unnest, has_multi_expr)(this=e), - view=True, - alias=exp.TableAlias( - this=alias.this, # type: ignore - columns=alias_cols, - ), - ), - ) - - return expression - - -def explode_projection_to_unnest( - index_offset: int = 0, -) -> t.Callable[[exp.Expression], exp.Expression]: - """Convert explode/posexplode projections into unnests.""" - - def _explode_projection_to_unnest(expression: exp.Expression) -> exp.Expression: - if isinstance(expression, exp.Select): - from bigframes_vendored.sqlglot.optimizer.scope import Scope - - taken_select_names = set(expression.named_selects) - taken_source_names = {name for name, _ in Scope(expression).references} - - def new_name(names: t.Set[str], name: str) -> str: - name = find_new_name(names, name) - names.add(name) - return name - - arrays: t.List[exp.Condition] = [] - series_alias = new_name(taken_select_names, "pos") - series = exp.alias_( - exp.Unnest( - expressions=[ - exp.GenerateSeries(start=exp.Literal.number(index_offset)) - ] - ), - new_name(taken_source_names, "_u"), - table=[series_alias], - ) - - # we use list here because expression.selects is mutated inside the loop - for select in list(expression.selects): - explode = select.find(exp.Explode) - - if explode: - pos_alias = "" - explode_alias = "" - - if isinstance(select, exp.Alias): - explode_alias = select.args["alias"] - alias = select - elif isinstance(select, exp.Aliases): - pos_alias = select.aliases[0] - explode_alias = select.aliases[1] - alias = select.replace(exp.alias_(select.this, "", copy=False)) - else: - alias = select.replace(exp.alias_(select, "")) - explode = alias.find(exp.Explode) - assert explode - - is_posexplode = isinstance(explode, exp.Posexplode) - explode_arg = explode.this - - if isinstance(explode, exp.ExplodeOuter): - bracket = explode_arg[0] - bracket.set("safe", True) - bracket.set("offset", True) - explode_arg = exp.func( - "IF", - exp.func( - "ARRAY_SIZE", - exp.func("COALESCE", explode_arg, exp.Array()), - ).eq(0), - exp.array(bracket, copy=False), - explode_arg, - ) - - # This ensures that we won't use [POS]EXPLODE's argument as a new selection - if isinstance(explode_arg, exp.Column): - taken_select_names.add(explode_arg.output_name) - - unnest_source_alias = new_name(taken_source_names, "_u") - - if not explode_alias: - explode_alias = new_name(taken_select_names, "col") - - if is_posexplode: - pos_alias = new_name(taken_select_names, "pos") - - if not pos_alias: - pos_alias = new_name(taken_select_names, "pos") - - alias.set("alias", exp.to_identifier(explode_alias)) - - series_table_alias = series.args["alias"].this - column = exp.If( - this=exp.column(series_alias, table=series_table_alias).eq( - exp.column(pos_alias, table=unnest_source_alias) - ), - true=exp.column(explode_alias, table=unnest_source_alias), - ) - - explode.replace(column) - - if is_posexplode: - expressions = expression.expressions - expressions.insert( - expressions.index(alias) + 1, - exp.If( - this=exp.column( - series_alias, table=series_table_alias - ).eq(exp.column(pos_alias, table=unnest_source_alias)), - true=exp.column(pos_alias, table=unnest_source_alias), - ).as_(pos_alias), - ) - expression.set("expressions", expressions) - - if not arrays: - if expression.args.get("from_"): - expression.join(series, copy=False, join_type="CROSS") - else: - expression.from_(series, copy=False) - - size: exp.Condition = exp.ArraySize(this=explode_arg.copy()) - arrays.append(size) - - # trino doesn't support left join unnest with on conditions - # if it did, this would be much simpler - expression.join( - exp.alias_( - exp.Unnest( - expressions=[explode_arg.copy()], - offset=exp.to_identifier(pos_alias), - ), - unnest_source_alias, - table=[explode_alias], - ), - join_type="CROSS", - copy=False, - ) - - if index_offset != 1: - size = size - 1 - - expression.where( - exp.column(series_alias, table=series_table_alias) - .eq(exp.column(pos_alias, table=unnest_source_alias)) - .or_( - ( - exp.column(series_alias, table=series_table_alias) - > size - ).and_( - exp.column(pos_alias, table=unnest_source_alias).eq( - size - ) - ) - ), - copy=False, - ) - - if arrays: - end: exp.Condition = exp.Greatest( - this=arrays[0], expressions=arrays[1:] - ) - - if index_offset != 1: - end = end - (1 - index_offset) - series.expressions[0].set("end", end) - - return expression - - return _explode_projection_to_unnest - - -def add_within_group_for_percentiles(expression: exp.Expression) -> exp.Expression: - """Transforms percentiles by adding a WITHIN GROUP clause to them.""" - if ( - isinstance(expression, exp.PERCENTILES) - and not isinstance(expression.parent, exp.WithinGroup) - and expression.expression - ): - column = expression.this.pop() - expression.set("this", expression.expression.pop()) - order = exp.Order(expressions=[exp.Ordered(this=column)]) - expression = exp.WithinGroup(this=expression, expression=order) - - return expression - - -def remove_within_group_for_percentiles(expression: exp.Expression) -> exp.Expression: - """Transforms percentiles by getting rid of their corresponding WITHIN GROUP clause.""" - if ( - isinstance(expression, exp.WithinGroup) - and isinstance(expression.this, exp.PERCENTILES) - and isinstance(expression.expression, exp.Order) - ): - quantile = expression.this.this - input_value = t.cast(exp.Ordered, expression.find(exp.Ordered)).this - return expression.replace( - exp.ApproxQuantile(this=input_value, quantile=quantile) - ) - - return expression - - -def add_recursive_cte_column_names(expression: exp.Expression) -> exp.Expression: - """Uses projection output names in recursive CTE definitions to define the CTEs' columns.""" - if isinstance(expression, exp.With) and expression.recursive: - next_name = name_sequence("_c_") - - for cte in expression.expressions: - if not cte.args["alias"].columns: - query = cte.this - if isinstance(query, exp.SetOperation): - query = query.this - - cte.args["alias"].set( - "columns", - [ - exp.to_identifier(s.alias_or_name or next_name()) - for s in query.selects - ], - ) - - return expression - - -def epoch_cast_to_ts(expression: exp.Expression) -> exp.Expression: - """Replace 'epoch' in casts by the equivalent date literal.""" - if ( - isinstance(expression, (exp.Cast, exp.TryCast)) - and expression.name.lower() == "epoch" - and expression.to.this in exp.DataType.TEMPORAL_TYPES - ): - expression.this.replace(exp.Literal.string("1970-01-01 00:00:00")) - - return expression - - -def eliminate_semi_and_anti_joins(expression: exp.Expression) -> exp.Expression: - """Convert SEMI and ANTI joins into equivalent forms that use EXIST instead.""" - if isinstance(expression, exp.Select): - for join in expression.args.get("joins") or []: - on = join.args.get("on") - if on and join.kind in ("SEMI", "ANTI"): - subquery = exp.select("1").from_(join.this).where(on) - exists = exp.Exists(this=subquery) - if join.kind == "ANTI": - exists = exists.not_(copy=False) - - join.pop() - expression.where(exists, copy=False) - - return expression - - -def eliminate_full_outer_join(expression: exp.Expression) -> exp.Expression: - """ - Converts a query with a FULL OUTER join to a union of identical queries that - use LEFT/RIGHT OUTER joins instead. This transformation currently only works - for queries that have a single FULL OUTER join. - """ - if isinstance(expression, exp.Select): - full_outer_joins = [ - (index, join) - for index, join in enumerate(expression.args.get("joins") or []) - if join.side == "FULL" - ] - - if len(full_outer_joins) == 1: - expression_copy = expression.copy() - expression.set("limit", None) - index, full_outer_join = full_outer_joins[0] - - tables = ( - expression.args["from_"].alias_or_name, - full_outer_join.alias_or_name, - ) - join_conditions = full_outer_join.args.get("on") or exp.and_( - *[ - exp.column(col, tables[0]).eq(exp.column(col, tables[1])) - for col in full_outer_join.args.get("using") - ] - ) - - full_outer_join.set("side", "left") - anti_join_clause = ( - exp.select("1").from_(expression.args["from_"]).where(join_conditions) - ) - expression_copy.args["joins"][index].set("side", "right") - expression_copy = expression_copy.where( - exp.Exists(this=anti_join_clause).not_() - ) - expression_copy.set("with_", None) # remove CTEs from RIGHT side - expression.set("order", None) # remove order by from LEFT side - - return exp.union(expression, expression_copy, copy=False, distinct=False) - - return expression - - -def move_ctes_to_top_level(expression: E) -> E: - """ - Some dialects (e.g. Hive, T-SQL, Spark prior to version 3) only allow CTEs to be - defined at the top-level, so for example queries like: - - SELECT * FROM (WITH t(c) AS (SELECT 1) SELECT * FROM t) AS subq - - are invalid in those dialects. This transformation can be used to ensure all CTEs are - moved to the top level so that the final SQL code is valid from a syntax standpoint. - - TODO: handle name clashes whilst moving CTEs (it can get quite tricky & costly). - """ - top_level_with = expression.args.get("with_") - for inner_with in expression.find_all(exp.With): - if inner_with.parent is expression: - continue - - if not top_level_with: - top_level_with = inner_with.pop() - expression.set("with_", top_level_with) - else: - if inner_with.recursive: - top_level_with.set("recursive", True) - - parent_cte = inner_with.find_ancestor(exp.CTE) - inner_with.pop() - - if parent_cte: - i = top_level_with.expressions.index(parent_cte) - top_level_with.expressions[i:i] = inner_with.expressions - top_level_with.set("expressions", top_level_with.expressions) - else: - top_level_with.set( - "expressions", top_level_with.expressions + inner_with.expressions - ) - - return expression - - -def ensure_bools(expression: exp.Expression) -> exp.Expression: - """Converts numeric values used in conditions into explicit boolean expressions.""" - from bigframes_vendored.sqlglot.optimizer.canonicalize import ensure_bools - - def _ensure_bool(node: exp.Expression) -> None: - if ( - node.is_number - or ( - not isinstance(node, exp.SubqueryPredicate) - and node.is_type(exp.DataType.Type.UNKNOWN, *exp.DataType.NUMERIC_TYPES) - ) - or (isinstance(node, exp.Column) and not node.type) - ): - node.replace(node.neq(0)) - - for node in expression.walk(): - ensure_bools(node, _ensure_bool) - - return expression - - -def unqualify_columns(expression: exp.Expression) -> exp.Expression: - for column in expression.find_all(exp.Column): - # We only wanna pop off the table, db, catalog args - for part in column.parts[:-1]: - part.pop() - - return expression - - -def remove_unique_constraints(expression: exp.Expression) -> exp.Expression: - assert isinstance(expression, exp.Create) - for constraint in expression.find_all(exp.UniqueColumnConstraint): - if constraint.parent: - constraint.parent.pop() - - return expression - - -def ctas_with_tmp_tables_to_create_tmp_view( - expression: exp.Expression, - tmp_storage_provider: t.Callable[[exp.Expression], exp.Expression] = lambda e: e, -) -> exp.Expression: - assert isinstance(expression, exp.Create) - properties = expression.args.get("properties") - temporary = any( - isinstance(prop, exp.TemporaryProperty) - for prop in (properties.expressions if properties else []) - ) - - # CTAS with temp tables map to CREATE TEMPORARY VIEW - if expression.kind == "TABLE" and temporary: - if expression.expression: - return exp.Create( - kind="TEMPORARY VIEW", - this=expression.this, - expression=expression.expression, - ) - return tmp_storage_provider(expression) - - return expression - - -def move_schema_columns_to_partitioned_by(expression: exp.Expression) -> exp.Expression: - """ - In Hive, the PARTITIONED BY property acts as an extension of a table's schema. When the - PARTITIONED BY value is an array of column names, they are transformed into a schema. - The corresponding columns are removed from the create statement. - """ - assert isinstance(expression, exp.Create) - has_schema = isinstance(expression.this, exp.Schema) - is_partitionable = expression.kind in {"TABLE", "VIEW"} - - if has_schema and is_partitionable: - prop = expression.find(exp.PartitionedByProperty) - if prop and prop.this and not isinstance(prop.this, exp.Schema): - schema = expression.this - columns = {v.name.upper() for v in prop.this.expressions} - partitions = [ - col for col in schema.expressions if col.name.upper() in columns - ] - schema.set( - "expressions", [e for e in schema.expressions if e not in partitions] - ) - prop.replace( - exp.PartitionedByProperty(this=exp.Schema(expressions=partitions)) - ) - expression.set("this", schema) - - return expression - - -def move_partitioned_by_to_schema_columns(expression: exp.Expression) -> exp.Expression: - """ - Spark 3 supports both "HIVEFORMAT" and "DATASOURCE" formats for CREATE TABLE. - - Currently, SQLGlot uses the DATASOURCE format for Spark 3. - """ - assert isinstance(expression, exp.Create) - prop = expression.find(exp.PartitionedByProperty) - if ( - prop - and prop.this - and isinstance(prop.this, exp.Schema) - and all(isinstance(e, exp.ColumnDef) and e.kind for e in prop.this.expressions) - ): - prop_this = exp.Tuple( - expressions=[exp.to_identifier(e.this) for e in prop.this.expressions] - ) - schema = expression.this - for e in prop.this.expressions: - schema.append("expressions", e) - prop.set("this", prop_this) - - return expression - - -def struct_kv_to_alias(expression: exp.Expression) -> exp.Expression: - """Converts struct arguments to aliases, e.g. STRUCT(1 AS y).""" - if isinstance(expression, exp.Struct): - expression.set( - "expressions", - [ - exp.alias_(e.expression, e.this) if isinstance(e, exp.PropertyEQ) else e - for e in expression.expressions - ], - ) - - return expression - - -def eliminate_join_marks(expression: exp.Expression) -> exp.Expression: - """https://docs.oracle.com/cd/B19306_01/server.102/b14200/queries006.htm#sthref3178 - - 1. You cannot specify the (+) operator in a query block that also contains FROM clause join syntax. - - 2. The (+) operator can appear only in the WHERE clause or, in the context of left-correlation (that is, when specifying the TABLE clause) in the FROM clause, and can be applied only to a column of a table or view. - - The (+) operator does not produce an outer join if you specify one table in the outer query and the other table in an inner query. - - You cannot use the (+) operator to outer-join a table to itself, although self joins are valid. - - The (+) operator can be applied only to a column, not to an arbitrary expression. However, an arbitrary expression can contain one or more columns marked with the (+) operator. - - A WHERE condition containing the (+) operator cannot be combined with another condition using the OR logical operator. - - A WHERE condition cannot use the IN comparison condition to compare a column marked with the (+) operator with an expression. - - A WHERE condition cannot compare any column marked with the (+) operator with a subquery. - - -- example with WHERE - SELECT d.department_name, sum(e.salary) as total_salary - FROM departments d, employees e - WHERE e.department_id(+) = d.department_id - group by department_name - - -- example of left correlation in select - SELECT d.department_name, ( - SELECT SUM(e.salary) - FROM employees e - WHERE e.department_id(+) = d.department_id) AS total_salary - FROM departments d; - - -- example of left correlation in from - SELECT d.department_name, t.total_salary - FROM departments d, ( - SELECT SUM(e.salary) AS total_salary - FROM employees e - WHERE e.department_id(+) = d.department_id - ) t - """ - - from collections import defaultdict - - from bigframes_vendored.sqlglot.optimizer.normalize import normalize, normalized - from bigframes_vendored.sqlglot.optimizer.scope import traverse_scope - - # we go in reverse to check the main query for left correlation - for scope in reversed(traverse_scope(expression)): - query = scope.expression - - where = query.args.get("where") - joins = query.args.get("joins", []) - - if not where or not any( - c.args.get("join_mark") for c in where.find_all(exp.Column) - ): - continue - - # knockout: we do not support left correlation (see point 2) - assert not scope.is_correlated_subquery, "Correlated queries are not supported" - - # make sure we have AND of ORs to have clear join terms - where = normalize(where.this) - assert normalized(where), "Cannot normalize JOIN predicates" - - joins_ons = defaultdict(list) # dict of {name: list of join AND conditions} - for cond in [where] if not isinstance(where, exp.And) else where.flatten(): - join_cols = [ - col for col in cond.find_all(exp.Column) if col.args.get("join_mark") - ] - - left_join_table = set(col.table for col in join_cols) - if not left_join_table: - continue - - assert not (len(left_join_table) > 1), ( - "Cannot combine JOIN predicates from different tables" - ) - - for col in join_cols: - col.set("join_mark", False) - - joins_ons[left_join_table.pop()].append(cond) - - old_joins = {join.alias_or_name: join for join in joins} - new_joins = {} - query_from = query.args["from_"] - - for table, predicates in joins_ons.items(): - join_what = old_joins.get(table, query_from).this.copy() - new_joins[join_what.alias_or_name] = exp.Join( - this=join_what, on=exp.and_(*predicates), kind="LEFT" - ) - - for p in predicates: - while isinstance(p.parent, exp.Paren): - p.parent.replace(p) - - parent = p.parent - p.pop() - if isinstance(parent, exp.Binary): - parent.replace(parent.right if parent.left is None else parent.left) - elif isinstance(parent, exp.Where): - parent.pop() - - if query_from.alias_or_name in new_joins: - only_old_joins = old_joins.keys() - new_joins.keys() - assert len(only_old_joins) >= 1, ( - "Cannot determine which table to use in the new FROM clause" - ) - - new_from_name = list(only_old_joins)[0] - query.set("from_", exp.From(this=old_joins[new_from_name].this)) - - if new_joins: - for n, j in old_joins.items(): # preserve any other joins - if n not in new_joins and n != query.args["from_"].name: - if not j.kind: - j.set("kind", "CROSS") - new_joins[n] = j - query.set("joins", list(new_joins.values())) - - return expression - - -def any_to_exists(expression: exp.Expression) -> exp.Expression: - """ - Transform ANY operator to Spark's EXISTS - - For example, - - Postgres: SELECT * FROM tbl WHERE 5 > ANY(tbl.col) - - Spark: SELECT * FROM tbl WHERE EXISTS(tbl.col, x -> x < 5) - - Both ANY and EXISTS accept queries but currently only array expressions are supported for this - transformation - """ - if isinstance(expression, exp.Select): - for any_expr in expression.find_all(exp.Any): - this = any_expr.this - if isinstance(this, exp.Query) or isinstance( - any_expr.parent, (exp.Like, exp.ILike) - ): - continue - - binop = any_expr.parent - if isinstance(binop, exp.Binary): - lambda_arg = exp.to_identifier("x") - any_expr.replace(lambda_arg) - lambda_expr = exp.Lambda(this=binop.copy(), expressions=[lambda_arg]) - binop.replace(exp.Exists(this=this.unnest(), expression=lambda_expr)) - - return expression - - -def eliminate_window_clause(expression: exp.Expression) -> exp.Expression: - """Eliminates the `WINDOW` query clause by inling each named window.""" - if isinstance(expression, exp.Select) and expression.args.get("windows"): - from bigframes_vendored.sqlglot.optimizer.scope import find_all_in_scope - - windows = expression.args["windows"] - expression.set("windows", None) - - window_expression: t.Dict[str, exp.Expression] = {} - - def _inline_inherited_window(window: exp.Expression) -> None: - inherited_window = window_expression.get(window.alias.lower()) - if not inherited_window: - return - - window.set("alias", None) - for key in ("partition_by", "order", "spec"): - arg = inherited_window.args.get(key) - if arg: - window.set(key, arg.copy()) - - for window in windows: - _inline_inherited_window(window) - window_expression[window.name.lower()] = window - - for window in find_all_in_scope(expression, exp.Window): - _inline_inherited_window(window) - - return expression - - -def inherit_struct_field_names(expression: exp.Expression) -> exp.Expression: - """ - Inherit field names from the first struct in an array. - - BigQuery supports implicitly inheriting names from the first STRUCT in an array: - - Example: - ARRAY[ - STRUCT('Alice' AS name, 85 AS score), -- defines names - STRUCT('Bob', 92), -- inherits names - STRUCT('Diana', 95) -- inherits names - ] - - This transformation makes the field names explicit on all structs by adding - PropertyEQ nodes, in order to facilitate transpilation to other dialects. - - Args: - expression: The expression tree to transform - - Returns: - The modified expression with field names inherited in all structs - """ - if ( - isinstance(expression, exp.Array) - and expression.args.get("struct_name_inheritance") - and isinstance(first_item := seq_get(expression.expressions, 0), exp.Struct) - and all(isinstance(fld, exp.PropertyEQ) for fld in first_item.expressions) - ): - field_names = [fld.this for fld in first_item.expressions] - - # Apply field names to subsequent structs that don't have them - for struct in expression.expressions[1:]: - if not isinstance(struct, exp.Struct) or len(struct.expressions) != len( - field_names - ): - continue - - # Convert unnamed expressions to PropertyEQ with inherited names - new_expressions = [] - for i, expr in enumerate(struct.expressions): - if not isinstance(expr, exp.PropertyEQ): - # Create PropertyEQ: field_name := value - new_expressions.append( - exp.PropertyEQ( - this=exp.Identifier(this=field_names[i].copy()), - expression=expr, - ) - ) - else: - new_expressions.append(expr) - - struct.set("expressions", new_expressions) - - return expression diff --git a/third_party/bigframes_vendored/sqlglot/trie.py b/third_party/bigframes_vendored/sqlglot/trie.py deleted file mode 100644 index 1475ea58774..00000000000 --- a/third_party/bigframes_vendored/sqlglot/trie.py +++ /dev/null @@ -1,83 +0,0 @@ -# Contains code from https://github.com/tobymao/sqlglot/blob/v28.5.0/sqlglot/trie.py - -import typing as t -from enum import Enum, auto - -key = t.Sequence[t.Hashable] - - -class TrieResult(Enum): - FAILED = auto() - PREFIX = auto() - EXISTS = auto() - - -def new_trie(keywords: t.Iterable[key], trie: t.Optional[t.Dict] = None) -> t.Dict: - """ - Creates a new trie out of a collection of keywords. - - The trie is represented as a sequence of nested dictionaries keyed by either single - character strings, or by 0, which is used to designate that a keyword is in the trie. - - Example: - >>> new_trie(["bla", "foo", "blab"]) - {'b': {'l': {'a': {0: True, 'b': {0: True}}}}, 'f': {'o': {'o': {0: True}}}} - - Args: - keywords: the keywords to create the trie from. - trie: a trie to mutate instead of creating a new one - - Returns: - The trie corresponding to `keywords`. - """ - trie = {} if trie is None else trie - - for key in keywords: - current = trie - for char in key: - current = current.setdefault(char, {}) - - current[0] = True - - return trie - - -def in_trie(trie: t.Dict, key: key) -> t.Tuple[TrieResult, t.Dict]: - """ - Checks whether a key is in a trie. - - Examples: - >>> in_trie(new_trie(["cat"]), "bob") - (, {'c': {'a': {'t': {0: True}}}}) - - >>> in_trie(new_trie(["cat"]), "ca") - (, {'t': {0: True}}) - - >>> in_trie(new_trie(["cat"]), "cat") - (, {0: True}) - - Args: - trie: The trie to be searched. - key: The target key. - - Returns: - A pair `(value, subtrie)`, where `subtrie` is the sub-trie we get at the point - where the search stops, and `value` is a TrieResult value that can be one of: - - - TrieResult.FAILED: the search was unsuccessful - - TrieResult.PREFIX: `value` is a prefix of a keyword in `trie` - - TrieResult.EXISTS: `key` exists in `trie` - """ - if not key: - return (TrieResult.FAILED, trie) - - current = trie - for char in key: - if char not in current: - return (TrieResult.FAILED, current) - current = current[char] - - if 0 in current: - return (TrieResult.EXISTS, current) - - return (TrieResult.PREFIX, current) diff --git a/third_party/bigframes_vendored/sqlglot/typing/__init__.py b/third_party/bigframes_vendored/sqlglot/typing/__init__.py deleted file mode 100644 index 0e666836196..00000000000 --- a/third_party/bigframes_vendored/sqlglot/typing/__init__.py +++ /dev/null @@ -1,360 +0,0 @@ -# Contains code from https://github.com/tobymao/sqlglot/blob/v28.5.0/sqlglot/typing/__init__.py - -import typing as t - -from bigframes_vendored.sqlglot import exp -from bigframes_vendored.sqlglot.helper import subclasses - -ExpressionMetadataType = t.Dict[type[exp.Expression], t.Dict[str, t.Any]] - -TIMESTAMP_EXPRESSIONS = { - exp.CurrentTimestamp, - exp.StrToTime, - exp.TimeStrToTime, - exp.TimestampAdd, - exp.TimestampSub, - exp.UnixToTime, -} - -EXPRESSION_METADATA: ExpressionMetadataType = { - **{ - expr_type: {"annotator": lambda self, e: self._annotate_binary(e)} - for expr_type in subclasses(exp.__name__, exp.Binary) - }, - **{ - expr_type: {"annotator": lambda self, e: self._annotate_unary(e)} - for expr_type in subclasses(exp.__name__, (exp.Unary, exp.Alias)) - }, - **{ - expr_type: {"returns": exp.DataType.Type.BIGINT} - for expr_type in { - exp.ApproxDistinct, - exp.ArraySize, - exp.CountIf, - exp.Int64, - exp.Length, - exp.UnixDate, - exp.UnixSeconds, - exp.UnixMicros, - exp.UnixMillis, - } - }, - **{ - expr_type: {"returns": exp.DataType.Type.BINARY} - for expr_type in { - exp.FromBase32, - exp.FromBase64, - } - }, - **{ - expr_type: {"returns": exp.DataType.Type.BOOLEAN} - for expr_type in { - exp.All, - exp.Any, - exp.Between, - exp.Boolean, - exp.Contains, - exp.EndsWith, - exp.Exists, - exp.In, - exp.LogicalAnd, - exp.LogicalOr, - exp.RegexpLike, - exp.StartsWith, - } - }, - **{ - expr_type: {"returns": exp.DataType.Type.DATE} - for expr_type in { - exp.CurrentDate, - exp.Date, - exp.DateFromParts, - exp.DateStrToDate, - exp.DiToDate, - exp.LastDay, - exp.StrToDate, - exp.TimeStrToDate, - exp.TsOrDsToDate, - } - }, - **{ - expr_type: {"returns": exp.DataType.Type.DATETIME} - for expr_type in { - exp.CurrentDatetime, - exp.Datetime, - exp.DatetimeAdd, - exp.DatetimeSub, - } - }, - **{ - expr_type: {"returns": exp.DataType.Type.DOUBLE} - for expr_type in { - exp.ApproxQuantile, - exp.Avg, - exp.Exp, - exp.Ln, - exp.Log, - exp.Pi, - exp.Pow, - exp.Quantile, - exp.Radians, - exp.Round, - exp.SafeDivide, - exp.Sqrt, - exp.Stddev, - exp.StddevPop, - exp.StddevSamp, - exp.ToDouble, - exp.Variance, - exp.VariancePop, - exp.Skewness, - } - }, - **{ - expr_type: {"returns": exp.DataType.Type.INT} - for expr_type in { - exp.Ascii, - exp.Ceil, - exp.DatetimeDiff, - exp.TimestampDiff, - exp.TimeDiff, - exp.Unicode, - exp.DateToDi, - exp.Levenshtein, - exp.Sign, - exp.StrPosition, - exp.TsOrDiToDi, - } - }, - **{ - expr_type: {"returns": exp.DataType.Type.INTERVAL} - for expr_type in { - exp.Interval, - exp.JustifyDays, - exp.JustifyHours, - exp.JustifyInterval, - exp.MakeInterval, - } - }, - **{ - expr_type: {"returns": exp.DataType.Type.JSON} - for expr_type in { - exp.ParseJSON, - } - }, - **{ - expr_type: {"returns": exp.DataType.Type.TIME} - for expr_type in { - exp.CurrentTime, - exp.Time, - exp.TimeAdd, - exp.TimeSub, - } - }, - **{ - expr_type: {"returns": exp.DataType.Type.TIMESTAMPLTZ} - for expr_type in { - exp.TimestampLtzFromParts, - } - }, - **{ - expr_type: {"returns": exp.DataType.Type.TIMESTAMPTZ} - for expr_type in { - exp.CurrentTimestampLTZ, - exp.TimestampTzFromParts, - } - }, - **{ - expr_type: {"returns": exp.DataType.Type.TIMESTAMP} - for expr_type in TIMESTAMP_EXPRESSIONS - }, - **{ - expr_type: {"returns": exp.DataType.Type.TINYINT} - for expr_type in { - exp.Day, - exp.DayOfMonth, - exp.DayOfWeek, - exp.DayOfWeekIso, - exp.DayOfYear, - exp.Month, - exp.Quarter, - exp.Week, - exp.WeekOfYear, - exp.Year, - exp.YearOfWeek, - exp.YearOfWeekIso, - } - }, - **{ - expr_type: {"returns": exp.DataType.Type.VARCHAR} - for expr_type in { - exp.ArrayToString, - exp.Concat, - exp.ConcatWs, - exp.Chr, - exp.DateToDateStr, - exp.DPipe, - exp.GroupConcat, - exp.Initcap, - exp.Lower, - exp.Substring, - exp.String, - exp.TimeToStr, - exp.TimeToTimeStr, - exp.Trim, - exp.ToBase32, - exp.ToBase64, - exp.TsOrDsToDateStr, - exp.UnixToStr, - exp.UnixToTimeStr, - exp.Upper, - } - }, - **{ - expr_type: {"annotator": lambda self, e: self._annotate_by_args(e, "this")} - for expr_type in { - exp.Abs, - exp.AnyValue, - exp.ArrayConcatAgg, - exp.ArrayReverse, - exp.ArraySlice, - exp.Filter, - exp.HavingMax, - exp.LastValue, - exp.Limit, - exp.Order, - exp.SortArray, - exp.Window, - } - }, - **{ - expr_type: { - "annotator": lambda self, e: self._annotate_by_args( - e, "this", "expressions" - ) - } - for expr_type in { - exp.ArrayConcat, - exp.Coalesce, - exp.Greatest, - exp.Least, - exp.Max, - exp.Min, - } - }, - **{ - expr_type: {"annotator": lambda self, e: self._annotate_by_array_element(e)} - for expr_type in { - exp.ArrayFirst, - exp.ArrayLast, - } - }, - **{ - expr_type: {"returns": exp.DataType.Type.UNKNOWN} - for expr_type in { - exp.Anonymous, - exp.Slice, - } - }, - **{ - expr_type: {"annotator": lambda self, e: self._annotate_timeunit(e)} - for expr_type in { - exp.DateAdd, - exp.DateSub, - exp.DateTrunc, - } - }, - **{ - expr_type: {"annotator": lambda self, e: self._set_type(e, e.args["to"])} - for expr_type in { - exp.Cast, - exp.TryCast, - } - }, - **{ - expr_type: {"annotator": lambda self, e: self._annotate_map(e)} - for expr_type in { - exp.Map, - exp.VarMap, - } - }, - exp.Array: { - "annotator": lambda self, e: self._annotate_by_args( - e, "expressions", array=True - ) - }, - exp.ArrayAgg: { - "annotator": lambda self, e: self._annotate_by_args(e, "this", array=True) - }, - exp.Bracket: {"annotator": lambda self, e: self._annotate_bracket(e)}, - exp.Case: { - "annotator": lambda self, e: self._annotate_by_args( - e, *[if_expr.args["true"] for if_expr in e.args["ifs"]], "default" - ) - }, - exp.Count: { - "annotator": lambda self, e: self._set_type( - e, - exp.DataType.Type.BIGINT - if e.args.get("big_int") - else exp.DataType.Type.INT, - ) - }, - exp.DateDiff: { - "annotator": lambda self, e: self._set_type( - e, - exp.DataType.Type.BIGINT - if e.args.get("big_int") - else exp.DataType.Type.INT, - ) - }, - exp.DataType: {"annotator": lambda self, e: self._set_type(e, e.copy())}, - exp.Div: {"annotator": lambda self, e: self._annotate_div(e)}, - exp.Distinct: { - "annotator": lambda self, e: self._annotate_by_args(e, "expressions") - }, - exp.Dot: {"annotator": lambda self, e: self._annotate_dot(e)}, - exp.Explode: {"annotator": lambda self, e: self._annotate_explode(e)}, - exp.Extract: {"annotator": lambda self, e: self._annotate_extract(e)}, - exp.GenerateSeries: { - "annotator": lambda self, e: self._annotate_by_args( - e, "start", "end", "step", array=True - ) - }, - exp.GenerateDateArray: { - "annotator": lambda self, e: self._set_type( - e, exp.DataType.build("ARRAY") - ) - }, - exp.GenerateTimestampArray: { - "annotator": lambda self, e: self._set_type( - e, exp.DataType.build("ARRAY") - ) - }, - exp.If: {"annotator": lambda self, e: self._annotate_by_args(e, "true", "false")}, - exp.Literal: {"annotator": lambda self, e: self._annotate_literal(e)}, - exp.Null: {"returns": exp.DataType.Type.NULL}, - exp.Nullif: { - "annotator": lambda self, e: self._annotate_by_args(e, "this", "expression") - }, - exp.PropertyEQ: { - "annotator": lambda self, e: self._annotate_by_args(e, "expression") - }, - exp.Struct: {"annotator": lambda self, e: self._annotate_struct(e)}, - exp.Sum: { - "annotator": lambda self, e: self._annotate_by_args( - e, "this", "expressions", promote=True - ) - }, - exp.Timestamp: { - "annotator": lambda self, e: self._set_type( - e, - exp.DataType.Type.TIMESTAMPTZ - if e.args.get("with_tz") - else exp.DataType.Type.TIMESTAMP, - ) - }, - exp.ToMap: {"annotator": lambda self, e: self._annotate_to_map(e)}, - exp.Unnest: {"annotator": lambda self, e: self._annotate_unnest(e)}, - exp.Subquery: {"annotator": lambda self, e: self._annotate_subquery(e)}, -} diff --git a/third_party/bigframes_vendored/sqlglot/typing/bigquery.py b/third_party/bigframes_vendored/sqlglot/typing/bigquery.py deleted file mode 100644 index 37304eef36c..00000000000 --- a/third_party/bigframes_vendored/sqlglot/typing/bigquery.py +++ /dev/null @@ -1,402 +0,0 @@ -# Contains code from https://github.com/tobymao/sqlglot/blob/v28.5.0/sqlglot/typing/bigquery.py - -from __future__ import annotations - -import typing as t - -from bigframes_vendored.sqlglot import exp -from bigframes_vendored.sqlglot.typing import EXPRESSION_METADATA, TIMESTAMP_EXPRESSIONS - -if t.TYPE_CHECKING: - from bigframes_vendored.sqlglot.optimizer.annotate_types import TypeAnnotator - - -def _annotate_math_functions( - self: TypeAnnotator, expression: exp.Expression -) -> exp.Expression: - """ - Many BigQuery math functions such as CEIL, FLOOR etc follow this return type convention: - +---------+---------+---------+------------+---------+ - | INPUT | INT64 | NUMERIC | BIGNUMERIC | FLOAT64 | - +---------+---------+---------+------------+---------+ - | OUTPUT | FLOAT64 | NUMERIC | BIGNUMERIC | FLOAT64 | - +---------+---------+---------+------------+---------+ - """ - this: exp.Expression = expression.this - - self._set_type( - expression, - exp.DataType.Type.DOUBLE - if this.is_type(*exp.DataType.INTEGER_TYPES) - else this.type, - ) - return expression - - -def _annotate_safe_divide( - self: TypeAnnotator, expression: exp.SafeDivide -) -> exp.Expression: - """ - +------------+------------+------------+-------------+---------+ - | INPUT | INT64 | NUMERIC | BIGNUMERIC | FLOAT64 | - +------------+------------+------------+-------------+---------+ - | INT64 | FLOAT64 | NUMERIC | BIGNUMERIC | FLOAT64 | - | NUMERIC | NUMERIC | NUMERIC | BIGNUMERIC | FLOAT64 | - | BIGNUMERIC | BIGNUMERIC | BIGNUMERIC | BIGNUMERIC | FLOAT64 | - | FLOAT64 | FLOAT64 | FLOAT64 | FLOAT64 | FLOAT64 | - +------------+------------+------------+-------------+---------+ - """ - if expression.this.is_type( - *exp.DataType.INTEGER_TYPES - ) and expression.expression.is_type(*exp.DataType.INTEGER_TYPES): - return self._set_type(expression, exp.DataType.Type.DOUBLE) - - return _annotate_by_args_with_coerce(self, expression) - - -def _annotate_by_args_with_coerce( - self: TypeAnnotator, expression: exp.Expression -) -> exp.Expression: - """ - +------------+------------+------------+-------------+---------+ - | INPUT | INT64 | NUMERIC | BIGNUMERIC | FLOAT64 | - +------------+------------+------------+-------------+---------+ - | INT64 | INT64 | NUMERIC | BIGNUMERIC | FLOAT64 | - | NUMERIC | NUMERIC | NUMERIC | BIGNUMERIC | FLOAT64 | - | BIGNUMERIC | BIGNUMERIC | BIGNUMERIC | BIGNUMERIC | FLOAT64 | - | FLOAT64 | FLOAT64 | FLOAT64 | FLOAT64 | FLOAT64 | - +------------+------------+------------+-------------+---------+ - """ - self._set_type( - expression, self._maybe_coerce(expression.this.type, expression.expression.type) - ) - return expression - - -def _annotate_by_args_approx_top( - self: TypeAnnotator, expression: exp.ApproxTopK -) -> exp.ApproxTopK: - struct_type = exp.DataType( - this=exp.DataType.Type.STRUCT, - expressions=[expression.this.type, exp.DataType(this=exp.DataType.Type.BIGINT)], - nested=True, - ) - self._set_type( - expression, - exp.DataType( - this=exp.DataType.Type.ARRAY, expressions=[struct_type], nested=True - ), - ) - - return expression - - -def _annotate_concat(self: TypeAnnotator, expression: exp.Concat) -> exp.Concat: - annotated = self._annotate_by_args(expression, "expressions") - - # Args must be BYTES or types that can be cast to STRING, return type is either BYTES or STRING - # https://cloud.google.com/bigquery/docs/reference/standard-sql/string_functions#concat - if not annotated.is_type(exp.DataType.Type.BINARY, exp.DataType.Type.UNKNOWN): - self._set_type(annotated, exp.DataType.Type.VARCHAR) - - return annotated - - -def _annotate_array(self: TypeAnnotator, expression: exp.Array) -> exp.Array: - array_args = expression.expressions - - # BigQuery behaves as follows: - # - # SELECT t, TYPEOF(t) FROM (SELECT 'foo') AS t -- foo, STRUCT - # SELECT ARRAY(SELECT 'foo'), TYPEOF(ARRAY(SELECT 'foo')) -- foo, ARRAY - # ARRAY(SELECT ... UNION ALL SELECT ...) -- ARRAY - if len(array_args) == 1: - unnested = array_args[0].unnest() - projection_type: t.Optional[exp.DataType | exp.DataType.Type] = None - - # Handle ARRAY(SELECT ...) - single SELECT query - if isinstance(unnested, exp.Select): - if ( - (query_type := unnested.meta.get("query_type")) is not None - and query_type.is_type(exp.DataType.Type.STRUCT) - and len(query_type.expressions) == 1 - and isinstance(col_def := query_type.expressions[0], exp.ColumnDef) - and (col_type := col_def.kind) is not None - and not col_type.is_type(exp.DataType.Type.UNKNOWN) - ): - projection_type = col_type - - # Handle ARRAY(SELECT ... UNION ALL SELECT ...) - set operations - elif isinstance(unnested, exp.SetOperation): - # Get all column types for the SetOperation - col_types = self._get_setop_column_types(unnested) - # For ARRAY constructor, there should only be one projection - # https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/array_functions#array - if col_types and unnested.left.selects: - first_col_name = unnested.left.selects[0].alias_or_name - projection_type = col_types.get(first_col_name) - - # If we successfully determine a projection type and it's not UNKNOWN, wrap it in ARRAY - if projection_type and not ( - ( - isinstance(projection_type, exp.DataType) - and projection_type.is_type(exp.DataType.Type.UNKNOWN) - ) - or projection_type == exp.DataType.Type.UNKNOWN - ): - element_type = ( - projection_type.copy() - if isinstance(projection_type, exp.DataType) - else exp.DataType(this=projection_type) - ) - array_type = exp.DataType( - this=exp.DataType.Type.ARRAY, - expressions=[element_type], - nested=True, - ) - return self._set_type(expression, array_type) - - return self._annotate_by_args(expression, "expressions", array=True) - - -EXPRESSION_METADATA = { - **EXPRESSION_METADATA, - **{ - expr_type: {"annotator": lambda self, e: _annotate_math_functions(self, e)} - for expr_type in { - exp.Avg, - exp.Ceil, - exp.Exp, - exp.Floor, - exp.Ln, - exp.Log, - exp.Round, - exp.Sqrt, - } - }, - **{ - expr_type: {"annotator": lambda self, e: self._annotate_by_args(e, "this")} - for expr_type in { - exp.Abs, - exp.ArgMax, - exp.ArgMin, - exp.DateTrunc, - exp.DatetimeTrunc, - exp.FirstValue, - exp.GroupConcat, - exp.IgnoreNulls, - exp.JSONExtract, - exp.Lead, - exp.Left, - exp.Lower, - exp.NthValue, - exp.Pad, - exp.PercentileDisc, - exp.RegexpExtract, - exp.RegexpReplace, - exp.Repeat, - exp.Replace, - exp.RespectNulls, - exp.Reverse, - exp.Right, - exp.SafeNegate, - exp.Sign, - exp.Substring, - exp.TimestampTrunc, - exp.Translate, - exp.Trim, - exp.Upper, - } - }, - **{ - expr_type: {"returns": exp.DataType.Type.BIGINT} - for expr_type in { - exp.Ascii, - exp.BitwiseAndAgg, - exp.BitwiseCount, - exp.BitwiseOrAgg, - exp.BitwiseXorAgg, - exp.ByteLength, - exp.DenseRank, - exp.FarmFingerprint, - exp.Grouping, - exp.LaxInt64, - exp.Length, - exp.Ntile, - exp.Rank, - exp.RangeBucket, - exp.RegexpInstr, - exp.RowNumber, - exp.Unicode, - } - }, - **{ - expr_type: {"returns": exp.DataType.Type.BINARY} - for expr_type in { - exp.ByteString, - exp.CodePointsToBytes, - exp.MD5Digest, - exp.SHA, - exp.SHA2, - exp.SHA1Digest, - exp.SHA2Digest, - exp.Unhex, - } - }, - **{ - expr_type: {"returns": exp.DataType.Type.BOOLEAN} - for expr_type in { - exp.IsInf, - exp.IsNan, - exp.JSONBool, - exp.LaxBool, - } - }, - **{ - expr_type: {"returns": exp.DataType.Type.DATETIME} - for expr_type in { - exp.ParseDatetime, - exp.TimestampFromParts, - } - }, - **{ - expr_type: {"returns": exp.DataType.Type.DOUBLE} - for expr_type in { - exp.Acos, - exp.Acosh, - exp.Asin, - exp.Asinh, - exp.Atan, - exp.Atan2, - exp.Atanh, - exp.Cbrt, - exp.Corr, - exp.CosineDistance, - exp.Cot, - exp.Coth, - exp.CovarPop, - exp.CovarSamp, - exp.Csc, - exp.Csch, - exp.CumeDist, - exp.EuclideanDistance, - exp.Float64, - exp.LaxFloat64, - exp.PercentRank, - exp.Rand, - exp.Sec, - exp.Sech, - exp.Sin, - exp.Sinh, - } - }, - **{ - expr_type: {"returns": exp.DataType.Type.JSON} - for expr_type in { - exp.JSONArray, - exp.JSONArrayAppend, - exp.JSONArrayInsert, - exp.JSONObject, - exp.JSONRemove, - exp.JSONSet, - exp.JSONStripNulls, - } - }, - **{ - expr_type: {"returns": exp.DataType.Type.TIME} - for expr_type in { - exp.ParseTime, - exp.TimeFromParts, - exp.TimeTrunc, - exp.TsOrDsToTime, - } - }, - **{ - expr_type: {"returns": exp.DataType.Type.VARCHAR} - for expr_type in { - exp.CodePointsToString, - exp.Format, - exp.JSONExtractScalar, - exp.JSONType, - exp.LaxString, - exp.LowerHex, - exp.MD5, - exp.NetHost, - exp.Normalize, - exp.SafeConvertBytesToString, - exp.Soundex, - exp.Uuid, - } - }, - **{ - expr_type: {"annotator": lambda self, e: _annotate_by_args_with_coerce(self, e)} - for expr_type in { - exp.PercentileCont, - exp.SafeAdd, - exp.SafeDivide, - exp.SafeMultiply, - exp.SafeSubtract, - } - }, - **{ - expr_type: { - "annotator": lambda self, e: self._annotate_by_args(e, "this", array=True) - } - for expr_type in { - exp.ApproxQuantiles, - exp.JSONExtractArray, - exp.RegexpExtractAll, - exp.Split, - } - }, - **{ - expr_type: {"returns": exp.DataType.Type.TIMESTAMPTZ} - for expr_type in TIMESTAMP_EXPRESSIONS - }, - exp.ApproxTopK: { - "annotator": lambda self, e: _annotate_by_args_approx_top(self, e) - }, - exp.ApproxTopSum: { - "annotator": lambda self, e: _annotate_by_args_approx_top(self, e) - }, - exp.Array: {"annotator": _annotate_array}, - exp.ArrayConcat: { - "annotator": lambda self, e: self._annotate_by_args(e, "this", "expressions") - }, - exp.Concat: {"annotator": _annotate_concat}, - exp.DateFromUnixDate: {"returns": exp.DataType.Type.DATE}, - exp.GenerateTimestampArray: { - "annotator": lambda self, e: self._set_type( - e, exp.DataType.build("ARRAY", dialect="bigquery") - ) - }, - exp.JSONFormat: { - "annotator": lambda self, e: self._set_type( - e, - exp.DataType.Type.JSON - if e.args.get("to_json") - else exp.DataType.Type.VARCHAR, - ) - }, - exp.JSONKeysAtDepth: { - "annotator": lambda self, e: self._set_type( - e, exp.DataType.build("ARRAY", dialect="bigquery") - ) - }, - exp.JSONValueArray: { - "annotator": lambda self, e: self._set_type( - e, exp.DataType.build("ARRAY", dialect="bigquery") - ) - }, - exp.Lag: { - "annotator": lambda self, e: self._annotate_by_args(e, "this", "default") - }, - exp.ParseBignumeric: {"returns": exp.DataType.Type.BIGDECIMAL}, - exp.ParseNumeric: {"returns": exp.DataType.Type.DECIMAL}, - exp.SafeDivide: {"annotator": lambda self, e: _annotate_safe_divide(self, e)}, - exp.ToCodePoints: { - "annotator": lambda self, e: self._set_type( - e, exp.DataType.build("ARRAY", dialect="bigquery") - ) - }, -} diff --git a/third_party/bigframes_vendored/tpch/LICENSE b/third_party/bigframes_vendored/tpch/LICENSE deleted file mode 100644 index 06d01f6abfb..00000000000 --- a/third_party/bigframes_vendored/tpch/LICENSE +++ /dev/null @@ -1,19 +0,0 @@ -Copyright (c) 2020 Ritchie Vink - -Permission is hereby granted, free of charge, to any person obtaining a copy -of this software and associated documentation files (the "Software"), to deal -in the Software without restriction, including without limitation the rights -to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -copies of the Software, and to permit persons to whom the Software is -furnished to do so, subject to the following conditions: - -The above copyright notice and this permission notice shall be included in all -copies or substantial portions of the Software. - -THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -SOFTWARE. diff --git a/third_party/bigframes_vendored/tpch/METADATA b/third_party/bigframes_vendored/tpch/METADATA deleted file mode 100644 index 65dd8cab935..00000000000 --- a/third_party/bigframes_vendored/tpch/METADATA +++ /dev/null @@ -1,19 +0,0 @@ -name: "polars-tpch" -description: - "This repository contains modified TPC-H benchmark queries that are " - "specifically adapted to evaluate the performance of the BigFrames library. " - "These benchmarks are designed to test complex data processing workflows " - "that are typical in decision support systems." - -third_party { - identifier { - type: "Git" - value: "https://github.com/pola-rs/tpch" - primary_source: true - version: "Latest Commit on Main Branch as of Access" - } - version: "Latest Commit on Main Branch as of Access" - last_upgrade_date { year: 2024 month: 7 day: 12 } - license_type: PERMISSIVE - local_modifications: "Modified the queries to test and benchmark the BigFrames project" -} diff --git a/third_party/bigframes_vendored/tpch/README.md b/third_party/bigframes_vendored/tpch/README.md deleted file mode 100644 index ef0b77d7d3c..00000000000 --- a/third_party/bigframes_vendored/tpch/README.md +++ /dev/null @@ -1,34 +0,0 @@ -polars-tpch -=========== - -This repo contains the code used for performance evaluation of polars. The benchmarks are TPC-standardised queries and data designed to test the performance of "real" workflows. - -From the [TPC website](https://www.tpc.org/tpch/): -> TPC-H is a decision support benchmark. It consists of a suite of business-oriented ad hoc queries and concurrent data modifications. The queries and the data populating the database have been chosen to have broad industry-wide relevance. This benchmark illustrates decision support systems that examine large volumes of data, execute queries with a high degree of complexity, and give answers to critical business questions. - -## Generating TPC-H Data - -### Project setup - -```shell -# clone this repository -git clone https://github.com/pola-rs/tpch.git -cd tpch/tpch-dbgen - -# build tpch-dbgen -make -``` - -### Execute - -```shell -# change directory to the root of the repository -cd ../ -./run.sh -``` - -This will do the following, - -- Create a new virtual environment with all required dependencies. -- Generate data for benchmarks. -- Run the benchmark suite. diff --git a/third_party/bigframes_vendored/tpch/TPC-EULA.txt b/third_party/bigframes_vendored/tpch/TPC-EULA.txt deleted file mode 100644 index feed8c4973b..00000000000 --- a/third_party/bigframes_vendored/tpch/TPC-EULA.txt +++ /dev/null @@ -1,320 +0,0 @@ -END USER LICENSE AGREEMENT -VERSION 2.2 - -READ THE TERMS AND CONDITIONS OF THIS AGREEMENT ("AGREEMENT") CAREFULLY -BEFORE INSTALLING OR USING THE ACCOMPANYING SOFTWARE. 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This Agreement is the entire agreement -between us with respect to the subject matter hereof and You agree that -TPC will not have any liability for any untrue statement or -representation made by it, its agents or anyone else (whether innocently -or negligently) upon which You relied upon entering this Agreement, -unless such untrue statement or representation was made fraudulently. diff --git a/third_party/bigframes_vendored/tpch/__init__.py b/third_party/bigframes_vendored/tpch/__init__.py deleted file mode 100644 index e69de29bb2d..00000000000 diff --git a/third_party/bigframes_vendored/tpch/queries/__init__.py b/third_party/bigframes_vendored/tpch/queries/__init__.py deleted file mode 100644 index e69de29bb2d..00000000000 diff --git a/third_party/bigframes_vendored/tpch/queries/q1.py b/third_party/bigframes_vendored/tpch/queries/q1.py deleted file mode 100644 index aa6289866c5..00000000000 --- a/third_party/bigframes_vendored/tpch/queries/q1.py +++ /dev/null @@ -1,42 +0,0 @@ -# Contains code from https://github.com/pola-rs/tpch/blob/main/queries/pandas/q1.py - -import typing -from datetime import datetime - -import bigframes -import bigframes.pandas as bpd - - -def q(project_id: str, dataset_id: str, session: bigframes.Session): - lineitem = session.read_gbq( - f"{project_id}.{dataset_id}.LINEITEM", - index_col=bigframes.enums.DefaultIndexKind.NULL, - ) - - var1 = datetime(1998, 9, 2) - lineitem = lineitem[lineitem["L_SHIPDATE"] <= var1.date()] - - lineitem["DISC_PRICE"] = lineitem["L_EXTENDEDPRICE"] * ( - 1.0 - lineitem["L_DISCOUNT"] - ) - lineitem["CHARGE_PRICE"] = ( - lineitem["L_EXTENDEDPRICE"] - * (1.0 - lineitem["L_DISCOUNT"]) - * (1.0 + lineitem["L_TAX"]) - ) - - result = lineitem.groupby(["L_RETURNFLAG", "L_LINESTATUS"], as_index=False).agg( - SUM_QTY=bpd.NamedAgg(column="L_QUANTITY", aggfunc="sum"), - SUM_BASE_PRICE=bpd.NamedAgg(column="L_EXTENDEDPRICE", aggfunc="sum"), - SUM_DISC_PRICE=bpd.NamedAgg(column="DISC_PRICE", aggfunc="sum"), - SUM_CHARGE=bpd.NamedAgg(column="CHARGE_PRICE", aggfunc="sum"), - AVG_QTY=bpd.NamedAgg(column="L_QUANTITY", aggfunc="mean"), - AVG_PRICE=bpd.NamedAgg(column="L_EXTENDEDPRICE", aggfunc="mean"), - AVG_DISC=bpd.NamedAgg(column="L_DISCOUNT", aggfunc="mean"), - COUNT_ORDER=bpd.NamedAgg(column="L_QUANTITY", aggfunc="count"), - ) - result = typing.cast(bpd.DataFrame, result).sort_values( - ["L_RETURNFLAG", "L_LINESTATUS"] - ) - - next(result.to_pandas_batches(max_results=1500)) diff --git a/third_party/bigframes_vendored/tpch/queries/q10.py b/third_party/bigframes_vendored/tpch/queries/q10.py deleted file mode 100644 index 19a33b07ec2..00000000000 --- a/third_party/bigframes_vendored/tpch/queries/q10.py +++ /dev/null @@ -1,79 +0,0 @@ -# Contains code from https://github.com/pola-rs/tpch/blob/main/queries/polars/q10.py - -import typing -from datetime import date - -import bigframes -import bigframes.pandas as bpd - - -def q(project_id: str, dataset_id: str, session: bigframes.Session): - customer = session.read_gbq( - f"{project_id}.{dataset_id}.CUSTOMER", - index_col=bigframes.enums.DefaultIndexKind.NULL, - ) - lineitem = session.read_gbq( - f"{project_id}.{dataset_id}.LINEITEM", - index_col=bigframes.enums.DefaultIndexKind.NULL, - ) - nation = session.read_gbq( - f"{project_id}.{dataset_id}.NATION", - index_col=bigframes.enums.DefaultIndexKind.NULL, - ) - orders = session.read_gbq( - f"{project_id}.{dataset_id}.ORDERS", - index_col=bigframes.enums.DefaultIndexKind.NULL, - ) - - var1 = date(1993, 10, 1) - var2 = date(1994, 1, 1) - - q_final = ( - customer.merge(orders, left_on="C_CUSTKEY", right_on="O_CUSTKEY") - .merge(lineitem, left_on="O_ORDERKEY", right_on="L_ORDERKEY") - .merge(nation, left_on="C_NATIONKEY", right_on="N_NATIONKEY") - ) - - q_final = typing.cast( - bpd.DataFrame, - q_final[ - (q_final["O_ORDERDATE"] >= var1) - & (q_final["O_ORDERDATE"] < var2) - & (q_final["L_RETURNFLAG"] == "R") - ], - ) - q_final["INTERMEDIATE_REVENUE"] = ( - q_final["L_EXTENDEDPRICE"] * (1 - q_final["L_DISCOUNT"]) - ).round(2) - - q_final = q_final.groupby( - [ - "C_CUSTKEY", - "C_NAME", - "C_ACCTBAL", - "C_PHONE", - "N_NAME", - "C_ADDRESS", - "C_COMMENT", - ], - as_index=False, - ).agg(REVENUE=bpd.NamedAgg(column="INTERMEDIATE_REVENUE", aggfunc="sum")) - - q_final = ( - q_final[ - [ - "C_CUSTKEY", - "C_NAME", - "REVENUE", - "C_ACCTBAL", - "N_NAME", - "C_ADDRESS", - "C_PHONE", - "C_COMMENT", - ] - ] - .sort_values(by="REVENUE", ascending=False) - .head(20) - ) - - next(q_final.to_pandas_batches(max_results=1500)) diff --git a/third_party/bigframes_vendored/tpch/queries/q11.py b/third_party/bigframes_vendored/tpch/queries/q11.py deleted file mode 100644 index 365aa12eb92..00000000000 --- a/third_party/bigframes_vendored/tpch/queries/q11.py +++ /dev/null @@ -1,46 +0,0 @@ -# Contains code from https://github.com/pola-rs/tpch/blob/main/queries/duckdb/q11.py - -import bigframes -import bigframes.pandas as bpd - - -def q(project_id: str, dataset_id: str, session: bigframes.Session): - supplier = session.read_gbq( - f"{project_id}.{dataset_id}.SUPPLIER", - index_col=bigframes.enums.DefaultIndexKind.NULL, - ) - partsupp = session.read_gbq( - f"{project_id}.{dataset_id}.PARTSUPP", - index_col=bigframes.enums.DefaultIndexKind.NULL, - ) - nation = session.read_gbq( - f"{project_id}.{dataset_id}.NATION", - index_col=bigframes.enums.DefaultIndexKind.NULL, - ) - - nation = nation[nation["N_NAME"] == "GERMANY"] - - merged_df = nation.merge(supplier, left_on="N_NATIONKEY", right_on="S_NATIONKEY") - merged_df = merged_df.merge(partsupp, left_on="S_SUPPKEY", right_on="PS_SUPPKEY") - - merged_df["VALUE"] = merged_df["PS_SUPPLYCOST"] * merged_df["PS_AVAILQTY"] - grouped = merged_df.groupby("PS_PARTKEY", as_index=False).agg( - VALUE=bpd.NamedAgg(column="VALUE", aggfunc="sum") - ) - - grouped["VALUE"] = grouped["VALUE"].round(2) - - total_value = ( - (merged_df["PS_SUPPLYCOST"] * merged_df["PS_AVAILQTY"]).to_frame().sum() - ) - threshold = (total_value * 0.0001).rename("THRESHOLD") - - grouped = grouped.merge(threshold, how="cross") - - result_df = grouped[grouped["VALUE"] > grouped["THRESHOLD"]].drop( - columns="THRESHOLD" - ) - - result_df = result_df.sort_values(by="VALUE", ascending=False) - - next(result_df.to_pandas_batches(max_results=1500)) diff --git a/third_party/bigframes_vendored/tpch/queries/q12.py b/third_party/bigframes_vendored/tpch/queries/q12.py deleted file mode 100644 index 20097f72ca1..00000000000 --- a/third_party/bigframes_vendored/tpch/queries/q12.py +++ /dev/null @@ -1,49 +0,0 @@ -# Contains code from https://github.com/pola-rs/tpch/blob/main/queries/polars/q12.py - -import typing -from datetime import date - -import bigframes -import bigframes.pandas as bpd - - -def q(project_id: str, dataset_id: str, session: bigframes.Session): - lineitem = session.read_gbq( - f"{project_id}.{dataset_id}.LINEITEM", - index_col=bigframes.enums.DefaultIndexKind.NULL, - ) - orders = session.read_gbq( - f"{project_id}.{dataset_id}.ORDERS", - index_col=bigframes.enums.DefaultIndexKind.NULL, - ) - - var1 = "MAIL" - var2 = "SHIP" - var3 = date(1994, 1, 1) - var4 = date(1995, 1, 1) - - q_final = orders.merge(lineitem, left_on="O_ORDERKEY", right_on="L_ORDERKEY") - - q_final = q_final[ - (q_final["L_SHIPMODE"].isin([var1, var2])) - & (q_final["L_COMMITDATE"] < q_final["L_RECEIPTDATE"]) - & (q_final["L_SHIPDATE"] < q_final["L_COMMITDATE"]) - & (q_final["L_RECEIPTDATE"] >= var3) - & (q_final["L_RECEIPTDATE"] < var4) - ] - - q_final["HIGH_LINE_COUNT"] = ( - q_final["O_ORDERPRIORITY"].isin(["1-URGENT", "2-HIGH"]) - ).astype("Int64") - q_final["LOW_LINE_COUNT"] = ( - ~q_final["O_ORDERPRIORITY"].isin(["1-URGENT", "2-HIGH"]) - ).astype("Int64") - - agg_results = q_final.groupby("L_SHIPMODE", as_index=False).agg( - HIGH_LINE_COUNT=bpd.NamedAgg(column="HIGH_LINE_COUNT", aggfunc="sum"), - LOW_LINE_COUNT=bpd.NamedAgg(column="LOW_LINE_COUNT", aggfunc="sum"), - ) - - agg_results = typing.cast(bpd.DataFrame, agg_results).sort_values("L_SHIPMODE") - - next(agg_results.to_pandas_batches(max_results=1500)) diff --git a/third_party/bigframes_vendored/tpch/queries/q13.py b/third_party/bigframes_vendored/tpch/queries/q13.py deleted file mode 100644 index 8201a1191dc..00000000000 --- a/third_party/bigframes_vendored/tpch/queries/q13.py +++ /dev/null @@ -1,37 +0,0 @@ -# Contains code from https://github.com/pola-rs/tpch/blob/main/queries/polars/q13.py - -import typing - -import bigframes -import bigframes.pandas as bpd - - -def q(project_id: str, dataset_id: str, session: bigframes.Session): - customer = session.read_gbq( - f"{project_id}.{dataset_id}.CUSTOMER", - index_col=bigframes.enums.DefaultIndexKind.NULL, - ) - orders = session.read_gbq( - f"{project_id}.{dataset_id}.ORDERS", - index_col=bigframes.enums.DefaultIndexKind.NULL, - ) - - var1 = "special" - var2 = "requests" - - regex_pattern = f"{var1}.*{var2}" - orders = orders[~orders["O_COMMENT"].str.contains(regex_pattern, regex=True)] - - q_final = ( - customer.merge(orders, left_on="C_CUSTKEY", right_on="O_CUSTKEY", how="left") - .groupby("C_CUSTKEY", as_index=False) - .agg(C_COUNT=bpd.NamedAgg(column="O_ORDERKEY", aggfunc="count")) - .groupby("C_COUNT", as_index=False) - .agg("size") - .rename(columns={"size": "CUSTDIST"}) - ) - q_final = typing.cast(bpd.DataFrame, q_final).sort_values( - ["CUSTDIST", "C_COUNT"], ascending=[False, False] - ) - - next(q_final.to_pandas_batches(max_results=1500)) diff --git a/third_party/bigframes_vendored/tpch/queries/q14.py b/third_party/bigframes_vendored/tpch/queries/q14.py deleted file mode 100644 index a0260394b9b..00000000000 --- a/third_party/bigframes_vendored/tpch/queries/q14.py +++ /dev/null @@ -1,45 +0,0 @@ -# Contains code from https://github.com/pola-rs/tpch/blob/main/queries/polars/q14.py - -from datetime import date - -import bigframes - - -def q(project_id: str, dataset_id: str, session: bigframes.Session): - lineitem = session.read_gbq( - f"{project_id}.{dataset_id}.LINEITEM", - index_col=bigframes.enums.DefaultIndexKind.NULL, - ) - part = session.read_gbq( - f"{project_id}.{dataset_id}.PART", - index_col=bigframes.enums.DefaultIndexKind.NULL, - ) - - var1 = date(1995, 9, 1) - var2 = date(1995, 10, 1) - - merged = part.merge(lineitem, left_on="P_PARTKEY", right_on="L_PARTKEY") - - filtered = merged[(merged["L_SHIPDATE"] >= var1) & (merged["L_SHIPDATE"] < var2)] - - filtered["CONDI_REVENUE"] = ( - filtered["L_EXTENDEDPRICE"] * (1 - filtered["L_DISCOUNT"]) - ) * filtered["P_TYPE"].str.contains("PROMO").astype("Int64") - - total_revenue = ( - (filtered["L_EXTENDEDPRICE"] * (1 - filtered["L_DISCOUNT"])) - .to_frame(name="TEMP") - .sum() - ) - - promo_revenue = filtered["CONDI_REVENUE"].to_frame(name="TEMP").sum() - - promo_revenue_percent = ( - (100.00 * promo_revenue / total_revenue) - .sort_index() - .reset_index(drop=True) - .round(2) - .to_frame(name="PROMO_REVENUE") - ) - - next(promo_revenue_percent.to_pandas_batches(max_results=1500)) diff --git a/third_party/bigframes_vendored/tpch/queries/q15.py b/third_party/bigframes_vendored/tpch/queries/q15.py deleted file mode 100644 index 0e3460189d1..00000000000 --- a/third_party/bigframes_vendored/tpch/queries/q15.py +++ /dev/null @@ -1,54 +0,0 @@ -# Contains code from https://github.com/pola-rs/tpch/blob/main/queries/polars/q15.py - -from datetime import date - -import bigframes -import bigframes.pandas as bpd - - -def q(project_id: str, dataset_id: str, session: bigframes.Session): - lineitem = session.read_gbq( - f"{project_id}.{dataset_id}.LINEITEM", - index_col=bigframes.enums.DefaultIndexKind.NULL, - ) - supplier = session.read_gbq( - f"{project_id}.{dataset_id}.SUPPLIER", - index_col=bigframes.enums.DefaultIndexKind.NULL, - ) - - var1 = date(1996, 1, 1) - var2 = date(1996, 4, 1) - - filtered_lineitem = lineitem[ - (lineitem["L_SHIPDATE"] >= var1) & (lineitem["L_SHIPDATE"] < var2) - ] - filtered_lineitem["REVENUE"] = filtered_lineitem["L_EXTENDEDPRICE"] * ( - 1 - filtered_lineitem["L_DISCOUNT"] - ) - - grouped_revenue = ( - filtered_lineitem.groupby("L_SUPPKEY", as_index=False) - .agg(TOTAL_REVENUE=bpd.NamedAgg(column="REVENUE", aggfunc="sum")) - .rename(columns={"L_SUPPKEY": "SUPPLIER_NO"}) - ) - # Round earlier to prevent non-determinism in the later join due to - # differences in distributed floating point operation sort order. - grouped_revenue = grouped_revenue.assign( - TOTAL_REVENUE=grouped_revenue["TOTAL_REVENUE"].round(2) - ) - - joined_data = bpd.merge( - supplier, grouped_revenue, left_on="S_SUPPKEY", right_on="SUPPLIER_NO" - ) - - max_revenue = joined_data[["TOTAL_REVENUE"]].max().rename("MAX_REVENUE") - - joined_data = joined_data.merge(max_revenue, how="cross") - - max_revenue_suppliers = joined_data[ - joined_data["TOTAL_REVENUE"] == joined_data["MAX_REVENUE"] - ] - q_final = max_revenue_suppliers[ - ["S_SUPPKEY", "S_NAME", "S_ADDRESS", "S_PHONE", "TOTAL_REVENUE"] - ].sort_values("S_SUPPKEY") - next(q_final.to_pandas_batches(max_results=1500)) diff --git a/third_party/bigframes_vendored/tpch/queries/q16.py b/third_party/bigframes_vendored/tpch/queries/q16.py deleted file mode 100644 index f55939b03c0..00000000000 --- a/third_party/bigframes_vendored/tpch/queries/q16.py +++ /dev/null @@ -1,44 +0,0 @@ -# Contains code from https://github.com/pola-rs/tpch/blob/main/queries/polars/q16.py - -import bigframes -import bigframes.pandas as bpd - - -def q(project_id: str, dataset_id: str, session: bigframes.Session): - part = session.read_gbq( - f"{project_id}.{dataset_id}.PART", - index_col=bigframes.enums.DefaultIndexKind.NULL, - ) - partsupp = session.read_gbq( - f"{project_id}.{dataset_id}.PARTSUPP", - index_col=bigframes.enums.DefaultIndexKind.NULL, - ) - supplier = session.read_gbq( - f"{project_id}.{dataset_id}.SUPPLIER", - index_col=bigframes.enums.DefaultIndexKind.NULL, - ) - - var1 = "Brand#45" - - supplier = supplier[ - ~supplier["S_COMMENT"].str.contains("Customer.*Complaints", regex=True) - ]["S_SUPPKEY"] - - q_filtered = part.merge(partsupp, left_on="P_PARTKEY", right_on="PS_PARTKEY") - q_filtered = q_filtered[q_filtered["P_BRAND"] != var1] - q_filtered = q_filtered[~q_filtered["P_TYPE"].str.contains("MEDIUM POLISHED")] - q_filtered = q_filtered[q_filtered["P_SIZE"].isin([49, 14, 23, 45, 19, 3, 36, 9])] - - final_df = q_filtered[q_filtered["PS_SUPPKEY"].isin(supplier)] - - grouped = final_df.groupby(["P_BRAND", "P_TYPE", "P_SIZE"], as_index=False) - result = grouped.agg( - SUPPLIER_CNT=bpd.NamedAgg(column="PS_SUPPKEY", aggfunc="nunique") - ) - - q_final = result.sort_values( - by=["SUPPLIER_CNT", "P_BRAND", "P_TYPE", "P_SIZE"], - ascending=[False, True, True, True], - ) - - next(q_final.to_pandas_batches(max_results=1500)) diff --git a/third_party/bigframes_vendored/tpch/queries/q17.py b/third_party/bigframes_vendored/tpch/queries/q17.py deleted file mode 100644 index aa7f7436021..00000000000 --- a/third_party/bigframes_vendored/tpch/queries/q17.py +++ /dev/null @@ -1,40 +0,0 @@ -# Contains code from https://github.com/pola-rs/tpch/blob/main/queries/polars/q17.py - -import bigframes -import bigframes.pandas as bpd - - -def q(project_id: str, dataset_id: str, session: bigframes.Session): - lineitem = session.read_gbq( - f"{project_id}.{dataset_id}.LINEITEM", - index_col=bigframes.enums.DefaultIndexKind.NULL, - ) - part = session.read_gbq( - f"{project_id}.{dataset_id}.PART", - index_col=bigframes.enums.DefaultIndexKind.NULL, - ) - - VAR1 = "Brand#23" - VAR2 = "MED BOX" - - filtered_part = part[(part["P_BRAND"] == VAR1) & (part["P_CONTAINER"] == VAR2)] - q1 = bpd.merge( - lineitem, filtered_part, how="right", left_on="L_PARTKEY", right_on="P_PARTKEY" - ) - - grouped = ( - q1.groupby("P_PARTKEY", as_index=False) - .agg(AVG_QUANTITY=bpd.NamedAgg(column="L_QUANTITY", aggfunc="mean")) - .rename(columns={"P_PARTKEY": "KEY"}) - ) - grouped["AVG_QUANTITY"] = grouped["AVG_QUANTITY"] * 0.2 - - q_final = bpd.merge(grouped, q1, left_on="KEY", right_on="P_PARTKEY") - - q_final = q_final[q_final["L_QUANTITY"] < q_final["AVG_QUANTITY"]] - - q_final = ( - (q_final[["L_EXTENDEDPRICE"]].sum() / 7.0).round(2).to_frame(name="AVG_YEARLY") - ) - - next(q_final.to_pandas_batches(max_results=1500)) diff --git a/third_party/bigframes_vendored/tpch/queries/q18.py b/third_party/bigframes_vendored/tpch/queries/q18.py deleted file mode 100644 index 576ce58d5ce..00000000000 --- a/third_party/bigframes_vendored/tpch/queries/q18.py +++ /dev/null @@ -1,50 +0,0 @@ -# Contains code from https://github.com/pola-rs/tpch/blob/main/queries/polars/q18.py - -import typing - -import bigframes -import bigframes.pandas as bpd - - -def q(project_id: str, dataset_id: str, session: bigframes.Session): - customer = session.read_gbq( - f"{project_id}.{dataset_id}.CUSTOMER", - index_col=bigframes.enums.DefaultIndexKind.NULL, - ) - lineitem = session.read_gbq( - f"{project_id}.{dataset_id}.LINEITEM", - index_col=bigframes.enums.DefaultIndexKind.NULL, - ) - orders = session.read_gbq( - f"{project_id}.{dataset_id}.ORDERS", - index_col=bigframes.enums.DefaultIndexKind.NULL, - ) - - var1 = 300 - - # order with over 300 items - q1 = lineitem.groupby("L_ORDERKEY", as_index=False).agg( - SUM_QUANTITY=bpd.NamedAgg(column="L_QUANTITY", aggfunc="sum") - ) - q1 = q1[q1["SUM_QUANTITY"] > var1] - - filtered_orders = orders[orders["O_ORDERKEY"].isin(q1["L_ORDERKEY"])] - - result = filtered_orders.merge( - lineitem, left_on="O_ORDERKEY", right_on="L_ORDERKEY" - ) - result = result.merge(customer, left_on="O_CUSTKEY", right_on="C_CUSTKEY") - - final_result = result.groupby( - ["C_NAME", "C_CUSTKEY", "O_ORDERKEY", "O_ORDERDATE", "O_TOTALPRICE"], - as_index=False, - ).agg(COL6=bpd.NamedAgg(column="L_QUANTITY", aggfunc="sum")) - - final_result = final_result.rename(columns={"O_ORDERDATE": "O_ORDERDAT"}) - - final_result = typing.cast(bpd.DataFrame, final_result).sort_values( - ["O_TOTALPRICE", "O_ORDERDAT"], ascending=[False, True] - ) - - q_final = final_result.head(100) - next(q_final.to_pandas_batches(max_results=1500)) diff --git a/third_party/bigframes_vendored/tpch/queries/q19.py b/third_party/bigframes_vendored/tpch/queries/q19.py deleted file mode 100644 index a217db3dc32..00000000000 --- a/third_party/bigframes_vendored/tpch/queries/q19.py +++ /dev/null @@ -1,63 +0,0 @@ -# Contains code from https://github.com/pola-rs/tpch/blob/main/queries/polars/q19.py - -import bigframes -import bigframes.pandas as bpd - - -def q(project_id: str, dataset_id: str, session: bigframes.Session): - lineitem = session.read_gbq( - f"{project_id}.{dataset_id}.LINEITEM", - index_col=bigframes.enums.DefaultIndexKind.NULL, - ) - part = session.read_gbq( - f"{project_id}.{dataset_id}.PART", - index_col=bigframes.enums.DefaultIndexKind.NULL, - ) - - merged = bpd.merge(part, lineitem, left_on="P_PARTKEY", right_on="L_PARTKEY") - - filtered = merged[ - (merged["L_SHIPMODE"].isin(["AIR", "AIR REG"])) - & (merged["L_SHIPINSTRUCT"] == "DELIVER IN PERSON") - & ( - ( - (merged["P_BRAND"] == "Brand#12") - & ( - merged["P_CONTAINER"].isin( - ["SM CASE", "SM BOX", "SM PACK", "SM PKG"] - ) - ) - & (merged["L_QUANTITY"].between(1, 11, inclusive="both")) - & (merged["P_SIZE"].between(1, 5, inclusive="both")) - ) - | ( - (merged["P_BRAND"] == "Brand#23") - & ( - merged["P_CONTAINER"].isin( - ["MED BAG", "MED BOX", "MED PKG", "MED PACK"] - ) - ) - & (merged["L_QUANTITY"].between(10, 20, inclusive="both")) - & (merged["P_SIZE"].between(1, 10, inclusive="both")) - ) - | ( - (merged["P_BRAND"] == "Brand#34") - & ( - merged["P_CONTAINER"].isin( - ["LG CASE", "LG BOX", "LG PACK", "LG PKG"] - ) - ) - & (merged["L_QUANTITY"].between(20, 30, inclusive="both")) - & (merged["P_SIZE"].between(1, 15, inclusive="both")) - ) - ) - ] - - result_df = ( - (filtered["L_EXTENDEDPRICE"] * (1 - filtered["L_DISCOUNT"])) - .agg(["sum"]) - .rename("REVENUE") - .to_frame() - ) - - next(result_df.to_pandas_batches(max_results=1500)) diff --git a/third_party/bigframes_vendored/tpch/queries/q2.py b/third_party/bigframes_vendored/tpch/queries/q2.py deleted file mode 100644 index e154e8ae983..00000000000 --- a/third_party/bigframes_vendored/tpch/queries/q2.py +++ /dev/null @@ -1,62 +0,0 @@ -# Contains code from https://github.com/pola-rs/tpch/blob/main/queries/pandas/q2.py - -import bigframes - - -def q(project_id: str, dataset_id: str, session: bigframes.Session): - region = session.read_gbq( - f"{project_id}.{dataset_id}.REGION", - index_col=bigframes.enums.DefaultIndexKind.NULL, - ) - nation = session.read_gbq( - f"{project_id}.{dataset_id}.NATION", - index_col=bigframes.enums.DefaultIndexKind.NULL, - ) - supplier = session.read_gbq( - f"{project_id}.{dataset_id}.SUPPLIER", - index_col=bigframes.enums.DefaultIndexKind.NULL, - ) - part = session.read_gbq( - f"{project_id}.{dataset_id}.PART", - index_col=bigframes.enums.DefaultIndexKind.NULL, - ) - partsupp = session.read_gbq( - f"{project_id}.{dataset_id}.PARTSUPP", - index_col=bigframes.enums.DefaultIndexKind.NULL, - ) - - jn = ( - part.merge(partsupp, left_on="P_PARTKEY", right_on="PS_PARTKEY") - .merge(supplier, left_on="PS_SUPPKEY", right_on="S_SUPPKEY") - .merge(nation, left_on="S_NATIONKEY", right_on="N_NATIONKEY") - .merge(region, left_on="N_REGIONKEY", right_on="R_REGIONKEY") - ) - - jn = jn[jn["P_SIZE"] == 15] - jn = jn[jn["P_TYPE"].str.endswith("BRASS")] - jn = jn[jn["R_NAME"] == "EUROPE"] - - gb = jn.groupby("P_PARTKEY", as_index=False) - agg = gb["PS_SUPPLYCOST"].min() - jn2 = agg.merge(jn, on=["P_PARTKEY", "PS_SUPPLYCOST"]) - - sel = jn2[ - [ - "S_ACCTBAL", - "S_NAME", - "N_NAME", - "P_PARTKEY", - "P_MFGR", - "S_ADDRESS", - "S_PHONE", - "S_COMMENT", - ] - ] - - sort = sel.sort_values( - by=["S_ACCTBAL", "N_NAME", "S_NAME", "P_PARTKEY"], - ascending=[False, True, True, True], - ) - - result_df = sort.head(100) - next(result_df.to_pandas_batches(max_results=1500)) diff --git a/third_party/bigframes_vendored/tpch/queries/q20.py b/third_party/bigframes_vendored/tpch/queries/q20.py deleted file mode 100644 index 7c434eba03b..00000000000 --- a/third_party/bigframes_vendored/tpch/queries/q20.py +++ /dev/null @@ -1,62 +0,0 @@ -# Contains code from https://github.com/pola-rs/tpch/blob/main/queries/polars/q20.py - -from datetime import date - -import bigframes -import bigframes.pandas as bpd - - -def q(project_id: str, dataset_id: str, session: bigframes.Session): - lineitem = session.read_gbq( - f"{project_id}.{dataset_id}.LINEITEM", - index_col=bigframes.enums.DefaultIndexKind.NULL, - ) - nation = session.read_gbq( - f"{project_id}.{dataset_id}.NATION", - index_col=bigframes.enums.DefaultIndexKind.NULL, - ) - part = session.read_gbq( - f"{project_id}.{dataset_id}.PART", - index_col=bigframes.enums.DefaultIndexKind.NULL, - ) - partsupp = session.read_gbq( - f"{project_id}.{dataset_id}.PARTSUPP", - index_col=bigframes.enums.DefaultIndexKind.NULL, - ) - supplier = session.read_gbq( - f"{project_id}.{dataset_id}.SUPPLIER", - index_col=bigframes.enums.DefaultIndexKind.NULL, - ) - - var1 = date(1994, 1, 1) - var2 = date(1995, 1, 1) - var3 = "CANADA" - var4 = "forest" - - q1 = lineitem[(lineitem["L_SHIPDATE"] >= var1) & (lineitem["L_SHIPDATE"] < var2)] - q1 = q1.groupby(["L_PARTKEY", "L_SUPPKEY"], as_index=False).agg( - SUM_QUANTITY=bpd.NamedAgg(column="L_QUANTITY", aggfunc="sum") - ) - q1["SUM_QUANTITY"] = q1["SUM_QUANTITY"] * 0.5 - q2 = nation[nation["N_NAME"] == var3] - - q3 = supplier.merge(q2, left_on="S_NATIONKEY", right_on="N_NATIONKEY") - - filtered_parts = part[part["P_NAME"].str.startswith(var4)] - - filtered_parts = filtered_parts["P_PARTKEY"] - joined_parts = partsupp[partsupp["PS_PARTKEY"].isin(filtered_parts)] - - final_join = q1.merge( - joined_parts, - left_on=["L_SUPPKEY", "L_PARTKEY"], - right_on=["PS_SUPPKEY", "PS_PARTKEY"], - ) - final_filtered = final_join[final_join["PS_AVAILQTY"] > final_join["SUM_QUANTITY"]][ - "PS_SUPPKEY" - ] - - final_result = q3[q3["S_SUPPKEY"].isin(final_filtered)] - final_result = final_result[["S_NAME", "S_ADDRESS"]].sort_values(by="S_NAME") - - next(final_result.to_pandas_batches(max_results=1500)) diff --git a/third_party/bigframes_vendored/tpch/queries/q21.py b/third_party/bigframes_vendored/tpch/queries/q21.py deleted file mode 100644 index c27aab0e69b..00000000000 --- a/third_party/bigframes_vendored/tpch/queries/q21.py +++ /dev/null @@ -1,59 +0,0 @@ -# Contains code from https://github.com/pola-rs/tpch/blob/main/queries/polars/q21.py - -import bigframes -import bigframes.pandas as bpd - - -def q(project_id: str, dataset_id: str, session: bigframes.Session): - lineitem = session.read_gbq( - f"{project_id}.{dataset_id}.LINEITEM", - index_col=bigframes.enums.DefaultIndexKind.NULL, - ) - nation = session.read_gbq( - f"{project_id}.{dataset_id}.NATION", - index_col=bigframes.enums.DefaultIndexKind.NULL, - ) - orders = session.read_gbq( - f"{project_id}.{dataset_id}.ORDERS", - index_col=bigframes.enums.DefaultIndexKind.NULL, - ) - supplier = session.read_gbq( - f"{project_id}.{dataset_id}.SUPPLIER", - index_col=bigframes.enums.DefaultIndexKind.NULL, - ) - - var1 = "SAUDI ARABIA" - - q1 = lineitem.groupby("L_ORDERKEY", as_index=False).agg( - N_SUPP_BY_ORDER=bpd.NamedAgg(column="L_SUPPKEY", aggfunc="size") - ) - q1 = q1[q1["N_SUPP_BY_ORDER"] > 1] - - lineitem_filtered = lineitem[lineitem["L_RECEIPTDATE"] > lineitem["L_COMMITDATE"]] - - q1 = q1.merge(lineitem_filtered, on="L_ORDERKEY") - - q_final = q1.groupby("L_ORDERKEY", as_index=False).agg( - N_SUPP_BY_ORDER_FINAL=bpd.NamedAgg(column="L_SUPPKEY", aggfunc="size") - ) - - q_final = q_final.merge(q1, on="L_ORDERKEY") - q_final = q_final.merge(supplier, left_on="L_SUPPKEY", right_on="S_SUPPKEY") - q_final = q_final.merge(nation, left_on="S_NATIONKEY", right_on="N_NATIONKEY") - q_final = q_final.merge(orders, left_on="L_ORDERKEY", right_on="O_ORDERKEY") - - q_final = q_final[ - (q_final["N_SUPP_BY_ORDER_FINAL"] == 1) - & (q_final["N_NAME"] == var1) - & (q_final["O_ORDERSTATUS"] == "F") - ] - - q_final = q_final.groupby("S_NAME", as_index=False).agg( - NUMWAIT=bpd.NamedAgg(column="L_SUPPKEY", aggfunc="size") - ) - - q_final = q_final.sort_values( - by=["NUMWAIT", "S_NAME"], ascending=[False, True] - ).head(100) - - next(q_final.to_pandas_batches(max_results=1500)) diff --git a/third_party/bigframes_vendored/tpch/queries/q22.py b/third_party/bigframes_vendored/tpch/queries/q22.py deleted file mode 100644 index a8d147eae42..00000000000 --- a/third_party/bigframes_vendored/tpch/queries/q22.py +++ /dev/null @@ -1,40 +0,0 @@ -# Contains code from https://github.com/pola-rs/tpch/blob/main/queries/polars/q22.py - -import bigframes -import bigframes.pandas as bpd - - -def q(project_id: str, dataset_id: str, session: bigframes.Session): - customer = session.read_gbq( - f"{project_id}.{dataset_id}.CUSTOMER", - index_col=bigframes.enums.DefaultIndexKind.NULL, - ) - orders = session.read_gbq( - f"{project_id}.{dataset_id}.ORDERS", - index_col=bigframes.enums.DefaultIndexKind.NULL, - ) - - country_codes = ["13", "31", "23", "29", "30", "18", "17"] - customer["CNTRYCODE"] = customer["C_PHONE"].str.slice(0, 2) - customer = customer[customer["CNTRYCODE"].isin(country_codes)] - - avg_acctbal = ( - customer[customer["C_ACCTBAL"] > 0.0][["C_ACCTBAL"]] - .mean() - .rename("AVG_ACCTBAL") - ) - customer = customer.merge(avg_acctbal, how="cross") - - filtered_customer = customer[customer["C_ACCTBAL"] > customer["AVG_ACCTBAL"]] - - filtered_customer = filtered_customer[ - ~filtered_customer["C_CUSTKEY"].isin(orders["O_CUSTKEY"]) - ] - result = filtered_customer.groupby("CNTRYCODE", as_index=False).agg( - NUMCUST=bpd.NamedAgg(column="C_CUSTKEY", aggfunc="count"), - TOTACCTBAL=bpd.NamedAgg(column="C_ACCTBAL", aggfunc="sum"), - ) - - result = result.sort_values(by="CNTRYCODE") - - next(result.to_pandas_batches(max_results=1500)) diff --git a/third_party/bigframes_vendored/tpch/queries/q3.py b/third_party/bigframes_vendored/tpch/queries/q3.py deleted file mode 100644 index 5a43f5fff73..00000000000 --- a/third_party/bigframes_vendored/tpch/queries/q3.py +++ /dev/null @@ -1,44 +0,0 @@ -# Contains code from https://github.com/pola-rs/tpch/blob/main/queries/pandas/q3.py - -from datetime import date - -import bigframes - - -def q(project_id: str, dataset_id: str, session: bigframes.Session): - customer = session.read_gbq( - f"{project_id}.{dataset_id}.CUSTOMER", - index_col=bigframes.enums.DefaultIndexKind.NULL, - ) - lineitem = session.read_gbq( - f"{project_id}.{dataset_id}.LINEITEM", - index_col=bigframes.enums.DefaultIndexKind.NULL, - ) - orders = session.read_gbq( - f"{project_id}.{dataset_id}.ORDERS", - index_col=bigframes.enums.DefaultIndexKind.NULL, - ) - - date_var = date(1995, 3, 15) - - fcustomer = customer[customer["C_MKTSEGMENT"] == "BUILDING"] - - filtered_orders = orders[orders["O_ORDERDATE"] < date_var] - filtered_lineitem = lineitem[lineitem["L_SHIPDATE"] > date_var] - - jn1 = filtered_lineitem.merge( - filtered_orders, left_on="L_ORDERKEY", right_on="O_ORDERKEY" - ) - jn2 = fcustomer.merge(jn1, left_on="C_CUSTKEY", right_on="O_CUSTKEY") - jn2["REVENUE"] = jn2["L_EXTENDEDPRICE"] * (1 - jn2["L_DISCOUNT"]) - - gb = jn2.groupby(["O_ORDERKEY", "O_ORDERDATE", "O_SHIPPRIORITY"], as_index=False) - agg = gb["REVENUE"].sum() - - sel = agg[["O_ORDERKEY", "REVENUE", "O_ORDERDATE", "O_SHIPPRIORITY"]] - sel = sel.rename(columns={"O_ORDERKEY": "L_ORDERKEY"}) - - sorted_sel = sel.sort_values(by=["REVENUE", "O_ORDERDATE"], ascending=[False, True]) - result_df = sorted_sel.head(10) - - next(result_df.to_pandas_batches(max_results=1500)) diff --git a/third_party/bigframes_vendored/tpch/queries/q4.py b/third_party/bigframes_vendored/tpch/queries/q4.py deleted file mode 100644 index 9c855704ab9..00000000000 --- a/third_party/bigframes_vendored/tpch/queries/q4.py +++ /dev/null @@ -1,35 +0,0 @@ -# Contains code from https://github.com/pola-rs/tpch/blob/main/queries/pandas/q4.py - - -import typing -from datetime import date - -import bigframes -import bigframes.pandas as bpd - - -def q(project_id: str, dataset_id: str, session: bigframes.Session): - lineitem = session.read_gbq( - f"{project_id}.{dataset_id}.LINEITEM", - index_col=bigframes.enums.DefaultIndexKind.NULL, - ) - orders = session.read_gbq( - f"{project_id}.{dataset_id}.ORDERS", - index_col=bigframes.enums.DefaultIndexKind.NULL, - ) - - var1 = date(1993, 7, 1) - var2 = date(1993, 10, 1) - - jn = lineitem.merge(orders, left_on="L_ORDERKEY", right_on="O_ORDERKEY") - - jn = jn[(jn["O_ORDERDATE"] >= var1) & (jn["O_ORDERDATE"] < var2)] - jn = jn[jn["L_COMMITDATE"] < jn["L_RECEIPTDATE"]] - - jn = jn.groupby(["O_ORDERPRIORITY", "L_ORDERKEY"], as_index=False).agg("size") - - gb = jn.groupby("O_ORDERPRIORITY", as_index=False) - agg = gb.agg(ORDER_COUNT=bpd.NamedAgg(column="L_ORDERKEY", aggfunc="count")) - - result_df = typing.cast(bpd.DataFrame, agg).sort_values(["O_ORDERPRIORITY"]) - next(result_df.to_pandas_batches(max_results=1500)) diff --git a/third_party/bigframes_vendored/tpch/queries/q5.py b/third_party/bigframes_vendored/tpch/queries/q5.py deleted file mode 100644 index 1361c40901b..00000000000 --- a/third_party/bigframes_vendored/tpch/queries/q5.py +++ /dev/null @@ -1,55 +0,0 @@ -# Contains code from https://github.com/pola-rs/tpch/blob/main/queries/pandas/q5.py - -from datetime import date - -import bigframes - - -def q(project_id: str, dataset_id: str, session: bigframes.Session): - region = session.read_gbq( - f"{project_id}.{dataset_id}.REGION", - index_col=bigframes.enums.DefaultIndexKind.NULL, - ) - nation = session.read_gbq( - f"{project_id}.{dataset_id}.NATION", - index_col=bigframes.enums.DefaultIndexKind.NULL, - ) - customer = session.read_gbq( - f"{project_id}.{dataset_id}.CUSTOMER", - index_col=bigframes.enums.DefaultIndexKind.NULL, - ) - lineitem = session.read_gbq( - f"{project_id}.{dataset_id}.LINEITEM", - index_col=bigframes.enums.DefaultIndexKind.NULL, - ) - orders = session.read_gbq( - f"{project_id}.{dataset_id}.ORDERS", - index_col=bigframes.enums.DefaultIndexKind.NULL, - ) - supplier = session.read_gbq( - f"{project_id}.{dataset_id}.SUPPLIER", - index_col=bigframes.enums.DefaultIndexKind.NULL, - ) - - var1 = "ASIA" - var2 = date(1994, 1, 1) - var3 = date(1995, 1, 1) - - region = region[region["R_NAME"] == var1] - orders = orders[(orders["O_ORDERDATE"] >= var2) & (orders["O_ORDERDATE"] < var3)] - lineitem["REVENUE"] = lineitem["L_EXTENDEDPRICE"] * (1.0 - lineitem["L_DISCOUNT"]) - - jn1 = region.merge(nation, left_on="R_REGIONKEY", right_on="N_REGIONKEY") - jn2 = jn1.merge(customer, left_on="N_NATIONKEY", right_on="C_NATIONKEY") - jn3 = orders.merge(jn2, left_on="O_CUSTKEY", right_on="C_CUSTKEY") - jn4 = lineitem.merge(jn3, left_on="L_ORDERKEY", right_on="O_ORDERKEY") - jn5 = jn4.merge( - supplier, - left_on=["L_SUPPKEY", "N_NATIONKEY"], - right_on=["S_SUPPKEY", "S_NATIONKEY"], - ) - - gb = jn5.groupby("N_NAME", as_index=False)["REVENUE"].sum() - result_df = gb.sort_values("REVENUE", ascending=False) - - next(result_df.to_pandas_batches(max_results=1500)) diff --git a/third_party/bigframes_vendored/tpch/queries/q6.py b/third_party/bigframes_vendored/tpch/queries/q6.py deleted file mode 100644 index 8fe067bafec..00000000000 --- a/third_party/bigframes_vendored/tpch/queries/q6.py +++ /dev/null @@ -1,30 +0,0 @@ -# Contains code from https://github.com/pola-rs/tpch/blob/main/queries/pandas/q6.py - -from datetime import date - -import bigframes - - -def q(project_id: str, dataset_id: str, session: bigframes.Session): - lineitem = session.read_gbq( - f"{project_id}.{dataset_id}.LINEITEM", - index_col=bigframes.enums.DefaultIndexKind.NULL, - ) - - var1 = date(1994, 1, 1) - var2 = date(1995, 1, 1) - var3 = 0.05 - var4 = 0.07 - var5 = 24 - - filt = lineitem[(lineitem["L_SHIPDATE"] >= var1) & (lineitem["L_SHIPDATE"] < var2)] - filt = filt[(filt["L_DISCOUNT"] >= var3) & (filt["L_DISCOUNT"] <= var4)] - filt = filt[filt["L_QUANTITY"] < var5] - result_df = ( - (filt["L_EXTENDEDPRICE"] * filt["L_DISCOUNT"]) - .agg(["sum"]) - .rename("REVENUE") - .to_frame() - ) - - next(result_df.to_pandas_batches(max_results=1500)) diff --git a/third_party/bigframes_vendored/tpch/queries/q7.py b/third_party/bigframes_vendored/tpch/queries/q7.py deleted file mode 100644 index 0756bcc6566..00000000000 --- a/third_party/bigframes_vendored/tpch/queries/q7.py +++ /dev/null @@ -1,63 +0,0 @@ -# Contains code from https://github.com/pola-rs/tpch/blob/main/queries/pandas/q7.py - -import typing -from datetime import date - -import bigframes -import bigframes.dataframe -import bigframes.pandas as bpd - - -def q(project_id: str, dataset_id: str, session: bigframes.Session): - nation = session.read_gbq( - f"{project_id}.{dataset_id}.NATION", - index_col=bigframes.enums.DefaultIndexKind.NULL, - ) - customer = session.read_gbq( - f"{project_id}.{dataset_id}.CUSTOMER", - index_col=bigframes.enums.DefaultIndexKind.NULL, - ) - lineitem = session.read_gbq( - f"{project_id}.{dataset_id}.LINEITEM", - index_col=bigframes.enums.DefaultIndexKind.NULL, - ) - orders = session.read_gbq( - f"{project_id}.{dataset_id}.ORDERS", - index_col=bigframes.enums.DefaultIndexKind.NULL, - ) - supplier = session.read_gbq( - f"{project_id}.{dataset_id}.SUPPLIER", - index_col=bigframes.enums.DefaultIndexKind.NULL, - ) - - var1 = "FRANCE" - var2 = "GERMANY" - var3 = date(1995, 1, 1) - var4 = date(1996, 12, 31) - - nation = nation[nation["N_NAME"].isin([var1, var2])] - lineitem = lineitem[ - (lineitem["L_SHIPDATE"] >= var3) & (lineitem["L_SHIPDATE"] <= var4) - ] - - jn1 = customer.merge(nation, left_on="C_NATIONKEY", right_on="N_NATIONKEY") - jn2 = jn1.merge(orders, left_on="C_CUSTKEY", right_on="O_CUSTKEY") - jn2 = jn2.rename(columns={"N_NAME": "CUST_NATION"}) - jn3 = jn2.merge(lineitem, left_on="O_ORDERKEY", right_on="L_ORDERKEY") - jn4 = jn3.merge(supplier, left_on="L_SUPPKEY", right_on="S_SUPPKEY") - jn5 = jn4.merge(nation, left_on="S_NATIONKEY", right_on="N_NATIONKEY") - df1 = jn5.rename(columns={"N_NAME": "SUPP_NATION"}) - total = df1[df1["CUST_NATION"] != df1["SUPP_NATION"]] - - total["VOLUME"] = total["L_EXTENDEDPRICE"] * (1.0 - total["L_DISCOUNT"]) - total["L_YEAR"] = total["L_SHIPDATE"].dt.year - - gb = typing.cast(bpd.DataFrame, total).groupby( - ["SUPP_NATION", "CUST_NATION", "L_YEAR"], as_index=False - ) - agg = gb.agg(REVENUE=bpd.NamedAgg(column="VOLUME", aggfunc="sum")) - - result_df = typing.cast(bpd.DataFrame, agg).sort_values( - ["SUPP_NATION", "CUST_NATION", "L_YEAR"] - ) - next(result_df.to_pandas_batches(max_results=1500)) diff --git a/third_party/bigframes_vendored/tpch/queries/q8.py b/third_party/bigframes_vendored/tpch/queries/q8.py deleted file mode 100644 index 67e1af12419..00000000000 --- a/third_party/bigframes_vendored/tpch/queries/q8.py +++ /dev/null @@ -1,72 +0,0 @@ -# Contains code from https://github.com/pola-rs/tpch/blob/main/queries/pandas/q8.py - -from datetime import date - -import bigframes - - -def q(project_id: str, dataset_id: str, session: bigframes.Session): - customer = session.read_gbq( - f"{project_id}.{dataset_id}.CUSTOMER", - index_col=bigframes.enums.DefaultIndexKind.NULL, - ) - lineitem = session.read_gbq( - f"{project_id}.{dataset_id}.LINEITEM", - index_col=bigframes.enums.DefaultIndexKind.NULL, - ) - nation = session.read_gbq( - f"{project_id}.{dataset_id}.NATION", - index_col=bigframes.enums.DefaultIndexKind.NULL, - ) - orders = session.read_gbq( - f"{project_id}.{dataset_id}.ORDERS", - index_col=bigframes.enums.DefaultIndexKind.NULL, - ) - part = session.read_gbq( - f"{project_id}.{dataset_id}.PART", - index_col=bigframes.enums.DefaultIndexKind.NULL, - ) - region = session.read_gbq( - f"{project_id}.{dataset_id}.REGION", - index_col=bigframes.enums.DefaultIndexKind.NULL, - ) - supplier = session.read_gbq( - f"{project_id}.{dataset_id}.SUPPLIER", - index_col=bigframes.enums.DefaultIndexKind.NULL, - ) - - var1 = "BRAZIL" - var2 = "AMERICA" - var3 = "ECONOMY ANODIZED STEEL" - var4 = date(1995, 1, 1) - var5 = date(1996, 12, 31) - - n1 = nation[["N_NATIONKEY", "N_REGIONKEY"]] - n2 = nation[["N_NATIONKEY", "N_NAME"]] - - jn1 = part.merge(lineitem, left_on="P_PARTKEY", right_on="L_PARTKEY") - jn2 = jn1.merge(supplier, left_on="L_SUPPKEY", right_on="S_SUPPKEY") - jn3 = jn2.merge(orders, left_on="L_ORDERKEY", right_on="O_ORDERKEY") - jn4 = jn3.merge(customer, left_on="O_CUSTKEY", right_on="C_CUSTKEY") - jn5 = jn4.merge(n1, left_on="C_NATIONKEY", right_on="N_NATIONKEY") - jn6 = jn5.merge(region, left_on="N_REGIONKEY", right_on="R_REGIONKEY") - - jn6 = jn6[(jn6["R_NAME"] == var2)] - - jn7 = jn6.merge(n2, left_on="S_NATIONKEY", right_on="N_NATIONKEY") - - jn7 = jn7[(jn7["O_ORDERDATE"] >= var4) & (jn7["O_ORDERDATE"] <= var5)] - jn7 = jn7[jn7["P_TYPE"] == var3] - - jn7["O_YEAR"] = jn7["O_ORDERDATE"].dt.year - jn7["VOLUME"] = jn7["L_EXTENDEDPRICE"] * (1.0 - jn7["L_DISCOUNT"]) - jn7 = jn7.rename(columns={"N_NAME": "NATION"}) - - jn7["numerator"] = jn7["VOLUME"].where(jn7["NATION"] == var1, 0) - jn7["denominator"] = jn7["VOLUME"] - - sums = jn7.groupby("O_YEAR")[["numerator", "denominator"]].sum() - sums["MKT_SHARE"] = (sums["numerator"] / sums["denominator"]).round(2) - - result_df = sums["MKT_SHARE"].sort_index().rename("MKT_SHARE").reset_index() - next(result_df.to_pandas_batches(max_results=1500)) diff --git a/third_party/bigframes_vendored/tpch/queries/q9.py b/third_party/bigframes_vendored/tpch/queries/q9.py deleted file mode 100644 index 5c9ca1e9c30..00000000000 --- a/third_party/bigframes_vendored/tpch/queries/q9.py +++ /dev/null @@ -1,72 +0,0 @@ -# Contains code from https://github.com/pola-rs/tpch/blob/main/queries/polars/q9.py - -import typing - -import bigframes -import bigframes.pandas as bpd - - -def q(project_id: str, dataset_id: str, session: bigframes.Session): - lineitem = session.read_gbq( - f"{project_id}.{dataset_id}.LINEITEM", - index_col=bigframes.enums.DefaultIndexKind.NULL, - ) - nation = session.read_gbq( - f"{project_id}.{dataset_id}.NATION", - index_col=bigframes.enums.DefaultIndexKind.NULL, - ) - orders = session.read_gbq( - f"{project_id}.{dataset_id}.ORDERS", - index_col=bigframes.enums.DefaultIndexKind.NULL, - ) - part = session.read_gbq( - f"{project_id}.{dataset_id}.PART", - index_col=bigframes.enums.DefaultIndexKind.NULL, - ) - partsupp = session.read_gbq( - f"{project_id}.{dataset_id}.PARTSUPP", - index_col=bigframes.enums.DefaultIndexKind.NULL, - ) - supplier = session.read_gbq( - f"{project_id}.{dataset_id}.SUPPLIER", - index_col=bigframes.enums.DefaultIndexKind.NULL, - ) - - q_final = ( - part.merge( - lineitem, - left_on="P_PARTKEY", - right_on="L_PARTKEY", - ) - .merge( - partsupp, - left_on=["L_SUPPKEY", "L_PARTKEY"], - right_on=["PS_SUPPKEY", "PS_PARTKEY"], - ) - .merge(supplier, left_on="L_SUPPKEY", right_on="S_SUPPKEY") - .merge(orders, left_on="L_ORDERKEY", right_on="O_ORDERKEY") - .merge(nation, left_on="S_NATIONKEY", right_on="N_NATIONKEY") - ) - - q_final = q_final[q_final["P_NAME"].str.contains("green")] - - q_final = q_final.rename(columns={"N_NAME": "NATION"}) - q_final["O_YEAR"] = q_final["O_ORDERDATE"].dt.year - q_final["AMOUNT"] = ( - q_final["L_EXTENDEDPRICE"] * (1 - q_final["L_DISCOUNT"]) - - q_final["PS_SUPPLYCOST"] * q_final["L_QUANTITY"] - ) - - q_final = q_final[["NATION", "O_YEAR", "AMOUNT"]] - - q_final = q_final.groupby(["NATION", "O_YEAR"], as_index=False).agg( - SUM_PROFIT=bpd.NamedAgg(column="AMOUNT", aggfunc="sum") - ) - - q_final["SUM_PROFIT"] = q_final["SUM_PROFIT"].round(2) - - q_final = typing.cast(bpd.DataFrame, q_final).sort_values( - ["NATION", "O_YEAR"], ascending=[True, False] - ) - - next(q_final.to_pandas_batches(max_results=1500)) diff --git a/third_party/bigframes_vendored/tpch/sql_queries/q1.sql b/third_party/bigframes_vendored/tpch/sql_queries/q1.sql deleted file mode 100644 index c359614583b..00000000000 --- a/third_party/bigframes_vendored/tpch/sql_queries/q1.sql +++ /dev/null @@ -1,21 +0,0 @@ -select - l_returnflag, - l_linestatus, - sum(l_quantity) as sum_qty, - sum(l_extendedprice) as sum_base_price, - sum(l_extendedprice * (1 - l_discount)) as sum_disc_price, - sum(l_extendedprice * (1 - l_discount) * (1 + l_tax)) as sum_charge, - avg(l_quantity) as avg_qty, - avg(l_extendedprice) as avg_price, - avg(l_discount) as avg_disc, - count(*) as count_order -from - {line_item_ds} -where - l_shipdate <= '1998-09-02' -group by - l_returnflag, - l_linestatus -order by - l_returnflag, - l_linestatus diff --git a/third_party/bigframes_vendored/tpch/sql_queries/q10.sql b/third_party/bigframes_vendored/tpch/sql_queries/q10.sql deleted file mode 100644 index c07aa0b4c9e..00000000000 --- a/third_party/bigframes_vendored/tpch/sql_queries/q10.sql +++ /dev/null @@ -1,32 +0,0 @@ -select - c_custkey, - c_name, - round(sum(l_extendedprice * (1 - l_discount)), 2) as revenue, - c_acctbal, - n_name, - c_address, - c_phone, - c_comment -from - {customer_ds}, - {orders_ds}, - {line_item_ds}, - {nation_ds} -where - c_custkey = o_custkey - and l_orderkey = o_orderkey - and o_orderdate >= date '1993-10-01' - and o_orderdate < date '1993-10-01' + interval '3' month - and l_returnflag = 'R' - and c_nationkey = n_nationkey -group by - c_custkey, - c_name, - c_acctbal, - c_phone, - n_name, - c_address, - c_comment -order by - revenue desc -limit 20 diff --git a/third_party/bigframes_vendored/tpch/sql_queries/q11.sql b/third_party/bigframes_vendored/tpch/sql_queries/q11.sql deleted file mode 100644 index 08c45604233..00000000000 --- a/third_party/bigframes_vendored/tpch/sql_queries/q11.sql +++ /dev/null @@ -1,27 +0,0 @@ -select - ps_partkey, - round(sum(ps_supplycost * ps_availqty), 2) as value -from - {part_supp_ds}, - {supplier_ds}, - {nation_ds} -where - ps_suppkey = s_suppkey - and s_nationkey = n_nationkey - and n_name = 'GERMANY' -group by - ps_partkey having - sum(ps_supplycost * ps_availqty) > ( - select - sum(ps_supplycost * ps_availqty) * 0.0001 - from - {part_supp_ds}, - {supplier_ds}, - {nation_ds} - where - ps_suppkey = s_suppkey - and s_nationkey = n_nationkey - and n_name = 'GERMANY' - ) - order by - value desc diff --git a/third_party/bigframes_vendored/tpch/sql_queries/q12.sql b/third_party/bigframes_vendored/tpch/sql_queries/q12.sql deleted file mode 100644 index cb97f1fb3c2..00000000000 --- a/third_party/bigframes_vendored/tpch/sql_queries/q12.sql +++ /dev/null @@ -1,28 +0,0 @@ -select - l_shipmode, - sum(case - when o_orderpriority = '1-URGENT' - or o_orderpriority = '2-HIGH' - then 1 - else 0 - end) as high_line_count, - sum(case - when o_orderpriority <> '1-URGENT' - and o_orderpriority <> '2-HIGH' - then 1 - else 0 - end) as low_line_count -from - {orders_ds}, - {line_item_ds} -where - o_orderkey = l_orderkey - and l_shipmode in ('MAIL', 'SHIP') - and l_commitdate < l_receiptdate - and l_shipdate < l_commitdate - and l_receiptdate >= date '1994-01-01' - and l_receiptdate < date '1994-01-01' + interval '1' year -group by - l_shipmode -order by - l_shipmode diff --git a/third_party/bigframes_vendored/tpch/sql_queries/q13.sql b/third_party/bigframes_vendored/tpch/sql_queries/q13.sql deleted file mode 100644 index d1616f53609..00000000000 --- a/third_party/bigframes_vendored/tpch/sql_queries/q13.sql +++ /dev/null @@ -1,18 +0,0 @@ -SELECT - c_count, COUNT(*) AS custdist -FROM ( - SELECT - c_custkey, - COUNT(o_orderkey) AS c_count - FROM - {customer_ds} LEFT OUTER JOIN {orders_ds} ON - c_custkey = o_custkey - AND o_comment NOT LIKE '%special%requests%' - GROUP BY - c_custkey -) AS c_orders -GROUP BY - c_count -ORDER BY - custdist DESC, - c_count DESC diff --git a/third_party/bigframes_vendored/tpch/sql_queries/q14.sql b/third_party/bigframes_vendored/tpch/sql_queries/q14.sql deleted file mode 100644 index 1620ab97620..00000000000 --- a/third_party/bigframes_vendored/tpch/sql_queries/q14.sql +++ /dev/null @@ -1,13 +0,0 @@ -select - round(100.00 * sum(case - when p_type like 'PROMO%' - then l_extendedprice * (1 - l_discount) - else 0 - end) / sum(l_extendedprice * (1 - l_discount)), 2) as promo_revenue -from - {line_item_ds}, - {part_ds} -where - l_partkey = p_partkey - and l_shipdate >= date '1995-09-01' - and l_shipdate < date '1995-09-01' + interval '1' month diff --git a/third_party/bigframes_vendored/tpch/sql_queries/q15.sql b/third_party/bigframes_vendored/tpch/sql_queries/q15.sql deleted file mode 100644 index cbf77827bca..00000000000 --- a/third_party/bigframes_vendored/tpch/sql_queries/q15.sql +++ /dev/null @@ -1,26 +0,0 @@ -WITH revenue AS ( - SELECT - l_suppkey AS supplier_no, - SUM(l_extendedprice * (1 - l_discount)) AS total_revenue - FROM - {line_item_ds} - WHERE - l_shipdate >= DATE '1996-01-01' - AND l_shipdate < DATE '1996-01-01' + INTERVAL '3' month - GROUP BY - l_suppkey -) -SELECT - s.s_suppkey, - s.s_name, - s.s_address, - s.s_phone, - r.total_revenue -FROM - {supplier_ds} s -JOIN - revenue r ON s.s_suppkey = r.supplier_no -WHERE - r.total_revenue = (SELECT MAX(total_revenue) FROM revenue) -ORDER BY - s.s_suppkey; diff --git a/third_party/bigframes_vendored/tpch/sql_queries/q16.sql b/third_party/bigframes_vendored/tpch/sql_queries/q16.sql deleted file mode 100644 index 193c8e462d8..00000000000 --- a/third_party/bigframes_vendored/tpch/sql_queries/q16.sql +++ /dev/null @@ -1,30 +0,0 @@ -select - p_brand, - p_type, - p_size, - count(distinct ps_suppkey) as supplier_cnt -from - {part_supp_ds}, - {part_ds} -where - p_partkey = ps_partkey - and p_brand <> 'Brand#45' - and p_type not like 'MEDIUM POLISHED%' - and p_size in (49, 14, 23, 45, 19, 3, 36, 9) - and ps_suppkey not in ( - select - s_suppkey - from - {supplier_ds} - where - s_comment like '%Customer%Complaints%' - ) -group by - p_brand, - p_type, - p_size -order by - supplier_cnt desc, - p_brand, - p_type, - p_size diff --git a/third_party/bigframes_vendored/tpch/sql_queries/q17.sql b/third_party/bigframes_vendored/tpch/sql_queries/q17.sql deleted file mode 100644 index 390ecdd33bc..00000000000 --- a/third_party/bigframes_vendored/tpch/sql_queries/q17.sql +++ /dev/null @@ -1,17 +0,0 @@ -select - round(sum(l_extendedprice) / 7.0, 2) as avg_yearly -from - {line_item_ds}, - {part_ds} -where - p_partkey = l_partkey - and p_brand = 'Brand#23' - and p_container = 'MED BOX' - and l_quantity < ( - select - 0.2 * avg(l_quantity) - from - {line_item_ds} - where - l_partkey = p_partkey - ) diff --git a/third_party/bigframes_vendored/tpch/sql_queries/q18.sql b/third_party/bigframes_vendored/tpch/sql_queries/q18.sql deleted file mode 100644 index 4a98abafb97..00000000000 --- a/third_party/bigframes_vendored/tpch/sql_queries/q18.sql +++ /dev/null @@ -1,33 +0,0 @@ -select - c_name, - c_custkey, - o_orderkey, - o_orderdate as o_orderdat, - o_totalprice, - sum(l_quantity) as col6 -from - {customer_ds}, - {orders_ds}, - {line_item_ds} -where - o_orderkey in ( - select - l_orderkey - from - {line_item_ds} - group by - l_orderkey having - sum(l_quantity) > 300 - ) - and c_custkey = o_custkey - and o_orderkey = l_orderkey -group by - c_name, - c_custkey, - o_orderkey, - o_orderdate, - o_totalprice -order by - o_totalprice desc, - o_orderdate -limit 100 diff --git a/third_party/bigframes_vendored/tpch/sql_queries/q19.sql b/third_party/bigframes_vendored/tpch/sql_queries/q19.sql deleted file mode 100644 index 30b41ff3fff..00000000000 --- a/third_party/bigframes_vendored/tpch/sql_queries/q19.sql +++ /dev/null @@ -1,35 +0,0 @@ -select - round(sum(l_extendedprice* (1 - l_discount)), 2) as revenue -from - {line_item_ds}, - {part_ds} -where - ( - p_partkey = l_partkey - and p_brand = 'Brand#12' - and p_container in ('SM CASE', 'SM BOX', 'SM PACK', 'SM PKG') - and l_quantity >= 1 and l_quantity <= 1 + 10 - and p_size between 1 and 5 - and l_shipmode in ('AIR', 'AIR REG') - and l_shipinstruct = 'DELIVER IN PERSON' - ) - or - ( - p_partkey = l_partkey - and p_brand = 'Brand#23' - and p_container in ('MED BAG', 'MED BOX', 'MED PKG', 'MED PACK') - and l_quantity >= 10 and l_quantity <= 20 - and p_size between 1 and 10 - and l_shipmode in ('AIR', 'AIR REG') - and l_shipinstruct = 'DELIVER IN PERSON' - ) - or - ( - p_partkey = l_partkey - and p_brand = 'Brand#34' - and p_container in ('LG CASE', 'LG BOX', 'LG PACK', 'LG PKG') - and l_quantity >= 20 and l_quantity <= 30 - and p_size between 1 and 15 - and l_shipmode in ('AIR', 'AIR REG') - and l_shipinstruct = 'DELIVER IN PERSON' - ) diff --git a/third_party/bigframes_vendored/tpch/sql_queries/q2.sql b/third_party/bigframes_vendored/tpch/sql_queries/q2.sql deleted file mode 100644 index 082e1e7f536..00000000000 --- a/third_party/bigframes_vendored/tpch/sql_queries/q2.sql +++ /dev/null @@ -1,44 +0,0 @@ -select - s_acctbal, - s_name, - n_name, - p_partkey, - p_mfgr, - s_address, - s_phone, - s_comment -from - {part_ds}, - {supplier_ds}, - {part_supp_ds}, - {nation_ds}, - {region_ds} -where - p_partkey = ps_partkey - and s_suppkey = ps_suppkey - and p_size = 15 - and p_type like '%BRASS' - and s_nationkey = n_nationkey - and n_regionkey = r_regionkey - and r_name = 'EUROPE' - and ps_supplycost = ( - select - min(ps_supplycost) - from - {part_supp_ds}, - {supplier_ds}, - {nation_ds}, - {region_ds} - where - p_partkey = ps_partkey - and s_suppkey = ps_suppkey - and s_nationkey = n_nationkey - and n_regionkey = r_regionkey - and r_name = 'EUROPE' - ) -order by - s_acctbal desc, - n_name, - s_name, - p_partkey -limit 100 diff --git a/third_party/bigframes_vendored/tpch/sql_queries/q20.sql b/third_party/bigframes_vendored/tpch/sql_queries/q20.sql deleted file mode 100644 index 03348e82b89..00000000000 --- a/third_party/bigframes_vendored/tpch/sql_queries/q20.sql +++ /dev/null @@ -1,37 +0,0 @@ -select - s_name, - s_address -from - {supplier_ds}, - {nation_ds} -where - s_suppkey in ( - select - ps_suppkey - from - {part_supp_ds} - where - ps_partkey in ( - select - p_partkey - from - {part_ds} - where - p_name like 'forest%' - ) - and ps_availqty > ( - select - 0.5 * sum(l_quantity) - from - {line_item_ds} - where - l_partkey = ps_partkey - and l_suppkey = ps_suppkey - and l_shipdate >= date '1994-01-01' - and l_shipdate < date '1994-01-01' + interval '1' year - ) - ) - and s_nationkey = n_nationkey - and n_name = 'CANADA' -order by - s_name diff --git a/third_party/bigframes_vendored/tpch/sql_queries/q21.sql b/third_party/bigframes_vendored/tpch/sql_queries/q21.sql deleted file mode 100644 index 444d1274696..00000000000 --- a/third_party/bigframes_vendored/tpch/sql_queries/q21.sql +++ /dev/null @@ -1,40 +0,0 @@ -select - s_name, - count(*) as numwait -from - {supplier_ds}, - {line_item_ds} l1, - {orders_ds}, - {nation_ds} -where - s_suppkey = l1.l_suppkey - and o_orderkey = l1.l_orderkey - and o_orderstatus = 'F' - and l1.l_receiptdate > l1.l_commitdate - and exists ( - select - * - from - {line_item_ds} l2 - where - l2.l_orderkey = l1.l_orderkey - and l2.l_suppkey <> l1.l_suppkey - ) - and not exists ( - select - * - from - {line_item_ds} l3 - where - l3.l_orderkey = l1.l_orderkey - and l3.l_suppkey <> l1.l_suppkey - and l3.l_receiptdate > l3.l_commitdate - ) - and s_nationkey = n_nationkey - and n_name = 'SAUDI ARABIA' -group by - s_name -order by - numwait desc, - s_name -limit 100 diff --git a/third_party/bigframes_vendored/tpch/sql_queries/q22.sql b/third_party/bigframes_vendored/tpch/sql_queries/q22.sql deleted file mode 100644 index a1e1b2a2533..00000000000 --- a/third_party/bigframes_vendored/tpch/sql_queries/q22.sql +++ /dev/null @@ -1,36 +0,0 @@ -select - cntrycode, - count(*) as numcust, - sum(c_acctbal) as totacctbal -from ( - select - SUBSTR(c_phone, 1, 2) AS cntrycode, - c_acctbal - from - {customer_ds} - where - SUBSTR(c_phone, 1, 2) in - ('13', '31', '23', '29', '30', '18', '17') - and c_acctbal > ( - select - avg(c_acctbal) - from - {customer_ds} - where - c_acctbal > 0.00 - and SUBSTR(c_phone, 1, 2) in - ('13', '31', '23', '29', '30', '18', '17') - ) - and not exists ( - select - * - from - {orders_ds} - where - o_custkey = c_custkey - ) - ) as custsale -group by - cntrycode -order by - cntrycode diff --git a/third_party/bigframes_vendored/tpch/sql_queries/q3.sql b/third_party/bigframes_vendored/tpch/sql_queries/q3.sql deleted file mode 100644 index 69a40b8ef79..00000000000 --- a/third_party/bigframes_vendored/tpch/sql_queries/q3.sql +++ /dev/null @@ -1,23 +0,0 @@ -select - l_orderkey, - sum(l_extendedprice * (1 - l_discount)) as revenue, - o_orderdate, - o_shippriority -from - {customer_ds}, - {orders_ds}, - {line_item_ds} -where - c_mktsegment = 'BUILDING' - and c_custkey = o_custkey - and l_orderkey = o_orderkey - and o_orderdate < '1995-03-15' - and l_shipdate > '1995-03-15' -group by - l_orderkey, - o_orderdate, - o_shippriority -order by - revenue desc, - o_orderdate -limit 10 diff --git a/third_party/bigframes_vendored/tpch/sql_queries/q4.sql b/third_party/bigframes_vendored/tpch/sql_queries/q4.sql deleted file mode 100644 index 57204e8d709..00000000000 --- a/third_party/bigframes_vendored/tpch/sql_queries/q4.sql +++ /dev/null @@ -1,21 +0,0 @@ -select - o_orderpriority, - count(*) as order_count -from - {orders_ds} -where - o_orderdate >= date '1993-07-01' - and o_orderdate < date '1993-10-01' - and exists ( - select - * - from - {line_item_ds} - where - l_orderkey = o_orderkey - and l_commitdate < l_receiptdate - ) -group by - o_orderpriority -order by - o_orderpriority diff --git a/third_party/bigframes_vendored/tpch/sql_queries/q5.sql b/third_party/bigframes_vendored/tpch/sql_queries/q5.sql deleted file mode 100644 index 78dbb96ffa6..00000000000 --- a/third_party/bigframes_vendored/tpch/sql_queries/q5.sql +++ /dev/null @@ -1,24 +0,0 @@ -select - n_name, - sum(l_extendedprice * (1 - l_discount)) as revenue -from - {customer_ds}, - {orders_ds}, - {line_item_ds}, - {supplier_ds}, - {nation_ds}, - {region_ds} -where - c_custkey = o_custkey - and l_orderkey = o_orderkey - and l_suppkey = s_suppkey - and c_nationkey = s_nationkey - and s_nationkey = n_nationkey - and n_regionkey = r_regionkey - and r_name = 'ASIA' - and o_orderdate >= date '1994-01-01' - and o_orderdate < date '1995-01-01' -group by - n_name -order by - revenue desc diff --git a/third_party/bigframes_vendored/tpch/sql_queries/q6.sql b/third_party/bigframes_vendored/tpch/sql_queries/q6.sql deleted file mode 100644 index 0ea158332e3..00000000000 --- a/third_party/bigframes_vendored/tpch/sql_queries/q6.sql +++ /dev/null @@ -1,9 +0,0 @@ -select - sum(l_extendedprice * l_discount) as revenue -from - {line_item_ds} -where - l_shipdate >= date '1994-01-01' - and l_shipdate < date '1994-01-01' + interval '1' year - and l_discount between .05 and .07 - and l_quantity < 24 diff --git a/third_party/bigframes_vendored/tpch/sql_queries/q7.sql b/third_party/bigframes_vendored/tpch/sql_queries/q7.sql deleted file mode 100644 index 002e89e4a09..00000000000 --- a/third_party/bigframes_vendored/tpch/sql_queries/q7.sql +++ /dev/null @@ -1,50 +0,0 @@ -select - supp_nation, - cust_nation, - l_year, - sum(volume) as revenue -from - ( - select - n1.n_name as supp_nation, - n2.n_name as cust_nation, - EXTRACT( - YEAR - FROM - l_shipdate - ) as l_year, - l_extendedprice * (1 - l_discount) as volume - from - {supplier_ds}, - {line_item_ds}, - {orders_ds}, - {customer_ds}, - {nation_ds} n1, - {nation_ds} n2 - where - s_suppkey = l_suppkey - and o_orderkey = l_orderkey - and c_custkey = o_custkey - and s_nationkey = n1.n_nationkey - and c_nationkey = n2.n_nationkey - and ( - ( - n1.n_name = 'FRANCE' - and n2.n_name = 'GERMANY' - ) - or ( - n1.n_name = 'GERMANY' - and n2.n_name = 'FRANCE' - ) - ) - and l_shipdate between date '1995-01-01' - and date '1996-12-31' - ) as shipping -group by - supp_nation, - cust_nation, - l_year -order by - supp_nation, - cust_nation, - l_year diff --git a/third_party/bigframes_vendored/tpch/sql_queries/q8.sql b/third_party/bigframes_vendored/tpch/sql_queries/q8.sql deleted file mode 100644 index d4d1ddd2759..00000000000 --- a/third_party/bigframes_vendored/tpch/sql_queries/q8.sql +++ /dev/null @@ -1,39 +0,0 @@ -select - o_year, - round( - sum(case - when nation = 'BRAZIL' then volume - else 0 - end) / sum(volume) - , 2) as mkt_share -from - ( - select - extract(year from o_orderdate) as o_year, - l_extendedprice * (1 - l_discount) as volume, - n2.n_name as nation - from - {part_ds}, - {supplier_ds}, - {line_item_ds}, - {orders_ds}, - {customer_ds}, - {nation_ds} n1, - {nation_ds} n2, - {region_ds} - where - p_partkey = l_partkey - and s_suppkey = l_suppkey - and l_orderkey = o_orderkey - and o_custkey = c_custkey - and c_nationkey = n1.n_nationkey - and n1.n_regionkey = r_regionkey - and r_name = 'AMERICA' - and s_nationkey = n2.n_nationkey - and o_orderdate between date '1995-01-01' and date '1996-12-31' - and p_type = 'ECONOMY ANODIZED STEEL' - ) as all_nations -group by - o_year -order by - o_year diff --git a/third_party/bigframes_vendored/tpch/sql_queries/q9.sql b/third_party/bigframes_vendored/tpch/sql_queries/q9.sql deleted file mode 100644 index fcc3e194008..00000000000 --- a/third_party/bigframes_vendored/tpch/sql_queries/q9.sql +++ /dev/null @@ -1,32 +0,0 @@ -select - nation, - o_year, - round(sum(amount), 2) as sum_profit -from - ( - select - n_name as nation, - EXTRACT(YEAR FROM o_orderdate) as o_year, - l_extendedprice * (1 - l_discount) - ps_supplycost * l_quantity as amount - from - {part_ds}, - {supplier_ds}, - {line_item_ds}, - {part_supp_ds}, - {orders_ds}, - {nation_ds} - where - s_suppkey = l_suppkey - and ps_suppkey = l_suppkey - and ps_partkey = l_partkey - and p_partkey = l_partkey - and o_orderkey = l_orderkey - and s_nationkey = n_nationkey - and p_name like '%green%' - ) as profit -group by - nation, - o_year -order by - nation, - o_year desc diff --git a/third_party/bigframes_vendored/version.py b/third_party/bigframes_vendored/version.py deleted file mode 100644 index db3e62ea976..00000000000 --- a/third_party/bigframes_vendored/version.py +++ /dev/null @@ -1,19 +0,0 @@ -# Copyright 2022 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -__version__ = "2.47.0" - -# {x-release-please-start-date} -__release_date__ = "2026-06-12" -# {x-release-please-end} diff --git a/third_party/bigframes_vendored/xgboost/sklearn.py b/third_party/bigframes_vendored/xgboost/sklearn.py index 60a22e83d07..b7b43b85a3e 100644 --- a/third_party/bigframes_vendored/xgboost/sklearn.py +++ b/third_party/bigframes_vendored/xgboost/sklearn.py @@ -1,4 +1,4 @@ -"""scikit-learn Wrapper interface for XGBoost.""" +"""Scikit-Learn Wrapper interface for XGBoost.""" from typing import Any @@ -18,7 +18,7 @@ def predict(self, X): Series or DataFrame of shape (n_samples, n_features). Samples. Returns: - bigframes.dataframe.DataFrame: DataFrame of shape (n_samples, n_input_columns + n_prediction_columns). Returns predicted values. + DataFrame of shape (n_samples,): Returns predicted values. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -37,15 +37,8 @@ def fit(self, X, y): DataFrame of shape (n_samples,) or (n_samples, n_targets). Target values. Will be cast to X's dtype if necessary. - X_eval (bigframes.dataframe.DataFrame or bigframes.series.Series): - Series or DataFrame of shape (n_samples, n_features). Evaluation data. - - y_eval (bigframes.dataframe.DataFrame or bigframes.series.Series): - DataFrame of shape (n_samples,) or (n_samples, n_targets). - Evaluation target values. Will be cast to X_eval's dtype if necessary. - Returns: - XGBModel: Fitted estimator. + XGBModel: Fitted Estimator. """ raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE) @@ -62,7 +55,7 @@ class XGBRegressor(XGBModel, XGBRegressorBase): XGBoost regression model. Args: - n_estimators (Optional[int]): + num_parallel_tree (Optional[int]): Number of parallel trees constructed during each iteration. Default to 1. booster (Optional[str]): Specify which booster to use: gbtree or dart. Default to "gbtree". @@ -70,7 +63,7 @@ class XGBRegressor(XGBModel, XGBRegressorBase): Type of normalization algorithm for DART booster. Possible values: "TREE", "FOREST". Default to "TREE". tree_method (Optional[str]): Specify which tree method to use. Default to "auto". If this parameter is set to - default, XGBoost will choose the most conservative option available. Possible values: "exact", "approx", + default, XGBoost will choose the most conservative option available. Possible values: ""exact", "approx", "hist". min_child_weight (Optional[float]): Minimum sum of instance weight(hessian) needed in a child. Default to 1. @@ -91,12 +84,14 @@ class XGBRegressor(XGBModel, XGBRegressorBase): L1 regularization term on weights (xgb's alpha). Default to 0.0. reg_lambda (Optional[float]): L2 regularization term on weights (xgb's lambda). Default to 1.0. + early_stop (Optional[bool]): + Whether training should stop after the first iteration. Default to True. learning_rate (Optional[float]): Boosting learning rate (xgb's "eta"). Default to 0.3. max_iterations (Optional[int]): Maximum number of rounds for boosting. Default to 20. - tol (Optional[float]): - Minimum relative loss improvement necessary to continue training. Default to 0.01. + min_rel_progress (Optional[float]): + Minimum relative loss improvement necessary to continue training when early_stop is set to True. Default to 0.01. enable_global_explain (Optional[bool]): Whether to compute global explanations using explainable AI to evaluate global feature importance to the model. Default to False. xgboost_version (Optional[str]): @@ -109,7 +104,7 @@ class XGBClassifier(XGBModel, XGBClassifierMixIn, XGBClassifierBase): XGBoost classifier model. Args: - n_estimators (Optional[int]): + num_parallel_tree (Optional[int]): Number of parallel trees constructed during each iteration. Default to 1. booster (Optional[str]): Specify which booster to use: gbtree or dart. Default to "gbtree". @@ -117,7 +112,7 @@ class XGBClassifier(XGBModel, XGBClassifierMixIn, XGBClassifierBase): Type of normalization algorithm for DART booster. Possible values: "TREE", "FOREST". Default to "TREE". tree_method (Optional[str]): Specify which tree method to use. Default to "auto". If this parameter is set to - default, XGBoost will choose the most conservative option available. Possible values: "exact", "approx", + default, XGBoost will choose the most conservative option available. Possible values: ""exact", "approx", "hist". min_child_weight (Optional[float]): Minimum sum of instance weight(hessian) needed in a child. Default to 1. @@ -138,12 +133,14 @@ class XGBClassifier(XGBModel, XGBClassifierMixIn, XGBClassifierBase): L1 regularization term on weights (xgb's alpha). Default to 0.0. reg_lambda (Optional[float]): L2 regularization term on weights (xgb's lambda). Default to 1.0. + early_stop (Optional[bool]): + Whether training should stop after the first iteration. Default to True. learning_rate (Optional[float]): Boosting learning rate (xgb's "eta"). Default to 0.3. max_iterations (Optional[int]): Maximum number of rounds for boosting. Default to 20. - tol (Optional[float]): - Minimum relative loss improvement necessary to continue training. Default to 0.01. + min_rel_progress (Optional[float]): + Minimum relative loss improvement necessary to continue training when early_stop is set to True. Default to 0.01. enable_global_explain (Optional[bool]): Whether to compute global explanations using explainable AI to evaluate global feature importance to the model. Default to False. xgboost_version (Optional[str]): diff --git a/third_party/logo/colab-logo.png b/third_party/logo/colab-logo.png deleted file mode 100644 index 75740a2b6ae..00000000000 Binary files a/third_party/logo/colab-logo.png and /dev/null differ diff --git a/third_party/logo/github-logo.png b/third_party/logo/github-logo.png deleted file mode 100644 index 8b25551a979..00000000000 Binary files a/third_party/logo/github-logo.png and /dev/null differ diff --git a/third_party/sphinx/LICENSE.rst b/third_party/sphinx/LICENSE.rst deleted file mode 100644 index de3688cd2c6..00000000000 --- a/third_party/sphinx/LICENSE.rst +++ /dev/null @@ -1,31 +0,0 @@ -License for Sphinx -================== - -Unless otherwise indicated, all code in the Sphinx project is licenced under the -two clause BSD licence below. - -Copyright (c) 2007-2025 by the Sphinx team (see AUTHORS file). -All rights reserved. - -Redistribution and use in source and binary forms, with or without -modification, are permitted provided that the following conditions are -met: - -* Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - -* Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - -THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS -"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT -LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR -A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT -HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, -SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT -LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, -DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY -THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT -(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE -OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. diff --git a/third_party/sphinx/ext/autosummary/templates/autosummary/class.rst b/third_party/sphinx/ext/autosummary/templates/autosummary/class.rst deleted file mode 100644 index 6651591be64..00000000000 --- a/third_party/sphinx/ext/autosummary/templates/autosummary/class.rst +++ /dev/null @@ -1,42 +0,0 @@ -{{ fullname | escape | underline}} - -.. currentmodule:: {{ module }} - -{% set is_pandas = module.startswith("bigframes.pandas") or module.startswith("bigframes.geopandas") %} -{% set skip_inherited = is_pandas and not module.startswith("bigframes.pandas.typing.api") %} - -{% if is_pandas %} -.. autoclass:: {{ objname }} - :no-members: - - {% block attributes %} - {% if attributes %} - .. rubric:: {{ _('Attributes') }} - - .. autosummary:: - :toctree: - {% for item in attributes %} - {%- if not skip_inherited or not item in inherited_members%} - ~{{ name }}.{{ item }} - {%- endif %} - {%- endfor %} - {% endif %} - {% endblock %} - - {% block methods %} - {% if methods %} - .. rubric:: {{ _('Methods') }} - - .. autosummary:: - :toctree: - - {% for item in methods %} - {%- if not skip_inherited or not item in inherited_members%} - ~{{ name }}.{{ item }} - {%- endif %} - {%- endfor %} - {% endif %} - {% endblock %} -{% else %} -.. autoclass:: {{ objname }} -{% endif %} diff --git a/third_party/sphinx/ext/autosummary/templates/autosummary/module.rst b/third_party/sphinx/ext/autosummary/templates/autosummary/module.rst deleted file mode 100644 index 98d86d15230..00000000000 --- a/third_party/sphinx/ext/autosummary/templates/autosummary/module.rst +++ /dev/null @@ -1,57 +0,0 @@ -{{ fullname | escape | underline}} - -.. - Originally at - https://github.com/sphinx-doc/sphinx/blob/master/sphinx/ext/autosummary/templates/autosummary/module.rst - with modifications to support recursive generation from - https://github.com/sphinx-doc/sphinx/issues/7912 - -.. automodule:: {{ fullname }} - :no-members: - - {% block functions %} - {%- if functions %} - .. rubric:: {{ _('Functions') }} - - .. autosummary:: - :toctree: - {% for item in functions %} - {{ item }} - {%- endfor %} - {% endif %} - {%- endblock %} - - {%- block classes %} - {%- if classes %} - .. rubric:: {{ _('Classes') }} - - .. autosummary:: - :toctree: - {% for item in classes %}{% if item not in attributes %} - {{ item }} - {% endif %}{%- endfor %} - {% endif %} - {%- endblock %} - - {%- block exceptions %} - {%- if exceptions %} - .. rubric:: {{ _('Exceptions') }} - - .. autosummary:: - :toctree: - {% for item in exceptions %} - {{ item }} - {%- endfor %} - {% endif %} - {%- endblock %} - -{%- block attributes %} -{%- if attributes %} -.. rubric:: {{ _('Module Attributes') }} - -{% for item in attributes %} -.. autoattribute:: {{ fullname }}.{{ item }} - :no-index: -{% endfor %} -{% endif %} -{%- endblock %}